ACCESS - 11090

• (2025). Editorial board. ACCESS, 13, xi-xix. (www)

  @article{ , abstract = {Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.}, archive = {J_ACCESS}, doi = {10.1109/ACCESS.2024.3525195}, journal = {IEEE Access}, pages = {xi-xix}, shortjournal = {IEEE Access}, title = {Editorial board}, volume = {13}, year = {2025}, }
• (2025). IEEE access™ editorial board. ACCESS, 13, i-x. (www)

  @article{ , abstract = {Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.}, archive = {J_ACCESS}, doi = {10.1109/ACCESS.2024.3525276}, journal = {IEEE Access}, pages = {i-x}, shortjournal = {IEEE Access}, title = {IEEE access™ editorial board}, volume = {13}, year = {2025}, }
• (2025). Self-supervised depth estimation and 3D reconstruction with layer-wise LoRA of foundation model in endoscopy. ACCESS, 13, 173334-173346. (www)

  @article{ , abstract = {Depth estimation is crucial for 3D reconstruction and surgical navigation, providing critical insights for endoscopic procedures. While foundation models excel in depth estimation for natural images, their performance in the medical domain remains limited, particularly under challenging conditions like brightness fluctuations. This study develops a robust self-supervised framework for monocular depth estimation to address these challenges. We introduce a layer-wise low-rank adaptation (LW-LoRA) of the Depth-Anything-V2 foundation model, tailored for endoscopic data. Unlike conventional fine-tuning, LW-LoRA assigns an empirically determined rank vector across the encoder layers for efficient training. The method integrates residual convolutional blocks (ResConv) to capture fine-grained details and a multi-head attention-based pose network to enhance camera pose estimation, ensuring accurate 3D reconstructions. A multi-scale SSIM (MS-SSIM) reprojection loss refines depth predictions, while a brightness calibration module ensures robustness against illumination inconsistencies. During training, the backbone encoder is frozen, optimizing only the LoRA layers for efficiency. Extensive evaluations on the SCARED dataset highlight the superior performance of our framework, offering faster inference and high-quality depth maps. Zero-shot testing on Hamlyn and clinical datasets confirms its generalization across diverse data types. Our framework efficiently adapts the foundation model for depth estimation in the medical domain, addressing challenges in endoscopic imaging, such as brightness variations and fine-detail preservation. It enables accurate, dense 3D point cloud reconstructions, ensuring reliable performance in clinical settings.}, archive = {J_ACCESS}, author = {Saad Khalil and Sol Kim and Bo-In Lee and Youngbae Hwang}, doi = {10.1109/ACCESS.2025.3617567}, journal = {IEEE Access}, pages = {173334-173346}, shortjournal = {IEEE Access}, title = {Self-supervised depth estimation and 3D reconstruction with layer-wise LoRA of foundation model in endoscopy}, volume = {13}, year = {2025}, }
• (2025). Evaluating smart building features for fire, electrical, and life safety: A rapid human-LLM framework for literature review and research mapping. ACCESS, 13, 173312-173333. (www)

  @article{ , abstract = {The rapid integration of smart technologies into modern buildings is fundamentally transforming fire, electrical, and life safety (FELS) systems. This paper introduces a hybrid Human–Large Language Model (LLM) framework designed to efficiently conduct large-scale literature reviews, systematically map existing research, and identify critical knowledge gaps in smart building safety. Leveraging advanced LLMs for high-throughput summarization, topic modeling, and gap analysis, combined with expert validation, this method ensures both scalability and domain-specific rigor. The study analyzes 1,409 publications retrieved from Scopus, culminating in a refined corpus of 83 high-quality articles categorized into nine thematic clusters, including advanced sensing technologies, automation, enhanced connectivity, digital twins, cybersecurity, standard compliance, sustainability, specialized applications, and decision-making in disaster response. Detailed gap analyses reveal significant challenges related to real-world validation of AI-based systems, interoperability among IoT devices, cybersecurity vulnerabilities, and the need for dynamic evacuation and hazard modeling. The resulting knowledge map and research roadmap provide actionable insights for researchers, practitioners, and policymakers aiming to advance safer, smarter, and more resilient built environments. The proposed framework demonstrates how AI-assisted methodologies can accelerate knowledge synthesis while preserving analytical depth, offering a scalable solution for rapidly evolving interdisciplinary research domains.}, archive = {J_ACCESS}, author = {Andrés Leiva-Araos and Vamsi Sai Kalasapudi and Aiyin Jiang and Hemani Kaushal}, doi = {10.1109/ACCESS.2025.3613246}, journal = {IEEE Access}, pages = {173312-173333}, shortjournal = {IEEE Access}, title = {Evaluating smart building features for fire, electrical, and life safety: A rapid human-LLM framework for literature review and research mapping}, volume = {13}, year = {2025}, }
• (2025). A hierarchical probabilistic deep learning approach for contextual anomaly detection in mixed-type tabular data. ACCESS, 13, 173298-173311. (www)

  @article{ , abstract = {A contextual anomaly is a subtype of anomaly that, when observed in isolation, may not have the characteristics of an anomaly but becomes one when observed within a given context. Contextual anomaly detection is applied in several areas, such as industrial production process control, computer network security, and financial fraud detection. Probabilistic approaches to solving this problem are often neglected or insufficiently researched compared to numerous other anomaly detection techniques, and efficient algorithms for mixed-type tabular data are generally lacking. In this paper, we present a Hierarchical Variational Autoencoder for contextual anomaly detection, a new promising approach based on probabilistic modeling with a hierarchy of two levels: contextual and common. The trained model predicts the conditional likelihood of an event with respect to the observed context, which is used to distinguish between anomalies and regular data. We experimentally confirm the improvement of the results compared to a benchmark model that does not consider contextual information.}, archive = {J_ACCESS}, author = {Lovre Mrčela and Zvonko Kostanjčar}, doi = {10.1109/ACCESS.2025.3617799}, journal = {IEEE Access}, pages = {173298-173311}, shortjournal = {IEEE Access}, title = {A hierarchical probabilistic deep learning approach for contextual anomaly detection in mixed-type tabular data}, volume = {13}, year = {2025}, }
• (2025). Compiler-like code generation for fUML: Reducing overhead in executable UML. ACCESS, 13, 173267-173297. (www)

  @article{ , abstract = {fUML is a specification developed by the Object Management Group (OMG) that defines precise execution semantics as well as a respective execution environment for a subset of the Unified Modeling Language (UML). As such, fUML lays the foundation for standardized realizations of executable UML (xUML), which itself has been a major objective within the domain of model-driven software development since the early 2000s and a foundation for OMG’s model-driven architecture. Due to the properties of fUML’s execution semantics—the platform-independent design of its metamodel on the one hand and the interpretative characteristics of its behavior on the other hand—much computational overhead is required at runtime when executing a model. By preprocessing the models to be executed and utilizing features inherent to a specific implementation platform, such computational overhead at runtime can be substantially reduced. This can result in much faster, memory-saving, and more efficient model executions. This paper offers a model-driven realization approach to the fUML specification based on automated source code generation aimed at optimizing the runtime performance of executable UML models. This paper covers techniques for substituting generic, model-independent aspects of fUML with automatically generated, model-specific plug-in components, as well as an approach to transform the inherently platform-independent fUML metamodel into a platform-specific implementation using the example of object-oriented programming languages. Furthermore, this paper presents an exemplary generation-based fUML implementation in C++.}, archive = {J_ACCESS}, author = {Maximilian Hammer and Ralph Maschotta and Armin Zimmermann}, doi = {10.1109/ACCESS.2025.3615249}, journal = {IEEE Access}, pages = {173267-173297}, shortjournal = {IEEE Access}, title = {Compiler-like code generation for fUML: Reducing overhead in executable UML}, volume = {13}, year = {2025}, }
• (2025). Low-cost infrared vision systems for improved safety of emergency vehicle operations under low-visibility conditions. ACCESS, 13, 173241-173253. (www)

  @article{ , abstract = {This study investigates the potential of infrared (IR) camera technology to enhance driver safety for emergency vehicles operating in low-visibility conditions, particularly at night and in dense fog. Such environments significantly increase the risk of collisions, especially for tow trucks and snowplows that must remain operational in challenging conditions. IR cameras, which detect the thermal signatures of obstacles, show promise as standalone advanced driver assistance systems (ADAS) or as complements to existing ADAS technologies in emergency vehicles. This evaluation combines controlled laboratory experiments, real-world field tests, and surveys of emergency vehicle operators. In addition to assessing obstacle detection performance under low visibility, the study examines the feasibility of retrofitting existing Department of Transportation (DoT) fleets with cost-effective IR-based driver assistance systems. Results underscore the utility of IR technology in enhancing driver awareness and provide data-driven recommendations for scalable deployment across legacy emergency vehicle fleets.}, archive = {J_ACCESS}, author = {M-Mahdi Naddaf-Sh and Andrew Lee and Kin Yen and Eemon Amini and Iman Soltani}, doi = {10.1109/ACCESS.2025.3615117}, journal = {IEEE Access}, pages = {173241-173253}, shortjournal = {IEEE Access}, title = {Low-cost infrared vision systems for improved safety of emergency vehicle operations under low-visibility conditions}, volume = {13}, year = {2025}, }
• (2025). Security bug report classification via cross-project similarity-based data augmentation and deep learning models. ACCESS, 13, 173222-173240. (www)

  @article{ , abstract = {Accurate classification of security bug reports is a persistent challenge in software engineering due to the limited availability of labeled security data within individual projects. This study investigates whether semantically similar bug reports from external projects can be used to augment training data and improve classification performance in cross-project scenarios. Twelve text similarity techniques are evaluated, including four lexical methods such as TF-IDF, Jaccard, Levenshtein, and BM25, as well as eight embedding-based approaches such as Word2Vec, GloVe, FastText, Doc2Vec, BERT, SBERT, USE, and BERTScore. These similarity methods are applied to identify relevant bug reports from four open-source projects. The selected reports are combined with the security-labeled reports from each target project to create augmented training datasets. Five deep learning models are trained and evaluated using these datasets: CNN, LSTM, GRU, Transformer, and BERT. The experimental results show that CNN, LSTM, and GRU consistently achieve strong performance, with F1-scores frequently exceeding 0.92 across multiple projects and similarity methods. Both lexical and embedding-based similarity techniques contribute positively to performance, although their effectiveness varies depending on the model and project. No single similarity method performs best in all settings, but specific combinations of similarity techniques and model architectures lead to significantly improved classification outcomes. These findings highlight the practical value of cross-project data augmentation for security bug report classification. They also underscore the importance of selecting similarity methods and model architectures that align with the characteristics of the target project and available training data.}, archive = {J_ACCESS}, author = {Murun Ganzorig and Jinfeng Ji and Geunseok Yang}, doi = {10.1109/ACCESS.2025.3614638}, journal = {IEEE Access}, pages = {173222-173240}, shortjournal = {IEEE Access}, title = {Security bug report classification via cross-project similarity-based data augmentation and deep learning models}, volume = {13}, year = {2025}, }
• (2025). Research on dense small target scrap steel type recognition algorithm based on YOLO-SSNP. ACCESS, 13, 173209-173221. (www)

  @article{ , abstract = {Scrap can lower production costs for metallurgical companies and enhance environmental sustainability. It is a recyclable resource and an essential substitute for iron ore as a raw material. The accuracy of classification is hampered by the limits of current scrap steel recognition techniques, which include limited category coverage, small-target detection capabilities, and resistance to background interference. This research suggests YOLO-SSNP, a dense, small-target scrap steel material detection algorithm created by improving the YOLOv5 framework, as a solution to these issues. To improve the model’s ability to extract fine-grained features, a small-target detection head is first included. Second, to maximize feature representation while preserving model compactness, GSConv and VoVGSCSP are used to build a Slim-Neck module, which replaces conventional Conv and C3 modules in parts of the Neck layer. Finally, Soft-NMS replaces traditional NMS to enhance recognition accuracy for occluded and overlapping targets. The model is trained and evaluated on a self-constructed scrap steel image dataset and compared against several mainstream detection algorithms. Experimental results demonstrate that YOLO-SSNP offers superior accuracy and model compactness, with a precision (P) value of 97.9% and an mAP of 89.7%. It highlights the model’s efficacy in precisely and efficiently identifying the different types of scrap steel material by improving the mAP value by 28.6% and 17.8%, respectively, over earlier methods.}, archive = {J_ACCESS}, author = {Jihu Yin and Pengcheng Xiao and Liguang Zhu and Chao Wang and Yuxin Jin}, doi = {10.1109/ACCESS.2025.3612731}, journal = {IEEE Access}, pages = {173209-173221}, shortjournal = {IEEE Access}, title = {Research on dense small target scrap steel type recognition algorithm based on YOLO-SSNP}, volume = {13}, year = {2025}, }
• (2025). Gaze data imbalance: An overlooked challenge in appearance-based gaze estimation. ACCESS, 13, 173189-173200. (www)

  @article{ , abstract = {Data imbalance exists in appearance-based gaze estimation datasets, hurting the model’s generalizability and fairness. Unfortunately, this aspect is usually overlooked, and researchers focus mainly on developing more and more sophisticated gaze estimation neural networks. In this work, we identify two types of imbalance in gaze estimation data. The first is related to the uneven distribution of ground truth gaze vectors, and the second one comes from the uneven ethnicity distribution of dataset participants. We prove the negative impact of both of them on the model’s generalizability. Therefore, we propose Uniform Gaze Sampling and Uniform Ethnicity Sampling, simple yet effective re-sampling techniques tailored for gaze estimation. Moreover, we introduce balanced metrics, i.e., Balanced Gaze Error and Balanced Ethnicity Error, for a fair performance evaluation. Finally, we demonstrate the usefulness of the proposed methods and metrics on four benchmarks. To the best of our knowledge, we are the first to address data imbalance in gaze estimation comprehensively.}, archive = {J_ACCESS}, author = {Jan Glinko}, doi = {10.1109/ACCESS.2025.3617076}, journal = {IEEE Access}, pages = {173189-173200}, shortjournal = {IEEE Access}, title = {Gaze data imbalance: An overlooked challenge in appearance-based gaze estimation}, volume = {13}, year = {2025}, }
• (2025). DSCP-UNet: A tunnel crack segmentation algorithm based on lightweight diminutive size and colossal perception. ACCESS, 13, 173176-173188. (www)

  @article{ , abstract = {To effectively enhance the ability to detect surface crack defects in tunnels under tunnel conditions, this paper proposes a new tunnel crack segmentation algorithm using light-weight diminutive size and colossal perception UNet (DSCP-UNet). This method integrates a diminutive group convolution (DGConv) module and a self-adaptive pooling (SAPool) module into the UNet architecture, resulting in a segmentation network that balances lightweight design with strong perceptual capability. In the constructed DSCP-UNet model, the conventional convolutional backbone is replaced by DGConv, significantly reducing the number of model parameters. A convolutional block attention module (CBAM) is introduced to enhance the model’s focus on crack features, forming an efficient crack recognition network. The pyramid attention with sampling group (PASG) module is also investigated to improve the model’s ability to discriminate multi-scale crack features. Using collected tunnel crack images, the proposed DSCP-UNet achieves a model size of only 3.85 MB and demonstrates superior performance in tunnel crack segmentation tasks. Based on the collected images of surface cracks in the tunnel, we verified the rationality of the proposed algorithm. Meanwhile, by comparing with other models, it was found that the algorithm proposed in this paper has high robustness.}, archive = {J_ACCESS}, author = {Weihua Shi}, doi = {10.1109/ACCESS.2025.3617337}, journal = {IEEE Access}, pages = {173176-173188}, shortjournal = {IEEE Access}, title = {DSCP-UNet: A tunnel crack segmentation algorithm based on lightweight diminutive size and colossal perception}, volume = {13}, year = {2025}, }
• (2025). Design and research of a novel wheel-track rescue robot. ACCESS, 13, 173165-173175. (www)

  @article{ , abstract = {To improve the ability of rescue robots to navigate complex rescue environments, a novel wheel-track rescue robot is proposed, featuring rapid switching capability between wheel and track modes. The wheel-track morphing mechanism of the rescue robot is designed and analyzed. Furthermore, the roundness error and the track-ground contact length are concurrently defined as objective functions, with corresponding mathematical models developed to assess robot performance. Multi-objective optimization is performed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). To enhance NSGA-II’s population diversity, search capability, and Pareto front distribution uniformity, modifications are implemented to its elitist strategy, crossover operator, and mutation operator. Moreover, simulation results demonstrate that the proposed enhancements improve algorithmic convergence, population diversity, and exploratory efficacy. Concomitantly, the uniformity of the Pareto front distribution is enhanced, potentially yielding a broader spectrum of viable design solutions. The best solution identified by the improved algorithm exhibits superior values for both objective functions compared to other algorithms. Finally, a functional prototype of the morphing mechanism was successfully fabricated, providing a platform for subsequent robotic development and investigation.}, archive = {J_ACCESS}, author = {Bo Song and Erdong Xia and Chunrong Wang and Long Wu and Hao Gao}, doi = {10.1109/ACCESS.2025.3614044}, journal = {IEEE Access}, pages = {173165-173175}, shortjournal = {IEEE Access}, title = {Design and research of a novel wheel-track rescue robot}, volume = {13}, year = {2025}, }
• (2025). Balloon pin-array gripper: Modeling of holding force generation mechanism and parametric grasping analysis. ACCESS, 13, 173146-173164. (www)

  @article{ , abstract = {This study presents both theoretical and experimental investigations of the shape-dependent grasping performance of the “balloon pin-array gripper,” which integrates a pin-array structure with flexible inflatable balloons. First, we develop a theoretical model that captures the nonlinear expansion behavior of the balloon membrane and formulates the mechanism of holding force generation when multiple balloons simultaneously contact an axisymmetric object. Each balloon is assumed to expand until it reaches a specified compression depth, with the difference between internal pressure and membrane tension modeled as the contact pressure, which generates normal and frictional forces. Subsequently, we introduce a parametric evaluation method using axisymmetric objects, such as cylinders, conical frustums, and spheres. By systematically varying geometrical parameters, such as the height, radius, and base angle, we experimentally measure the holding force and analyze its dependence on contact area, surface orientation, and pin-array discreteness. The experimental results show reasonable agreement with the theoretical predictions with an overall coefficient of determination of $R^{2} = 0.63$ . We also demonstrate that the gripper can establish contact and generate holding force even on challenging geometries, such as downward-facing surfaces. However, several simplifying assumptions in the model—such as the omission of balloon stress distribution and balloon-to-balloon interference—are presented and discussed as limitations. Future improvements are expected to involve pressure distribution measurements and finite-element analysis. These findings provide valuable insights into the grasping mechanisms of pneumatically actuated grippers and serve as a basis for optimizing design parameters and evaluating scaling limitations.}, archive = {J_ACCESS}, author = {Yuto Kemmotsu and Kazuki Abe and Masahiro Watanabe and Kenjiro Tadakuma}, doi = {10.1109/ACCESS.2025.3617151}, journal = {IEEE Access}, pages = {173146-173164}, shortjournal = {IEEE Access}, title = {Balloon pin-array gripper: Modeling of holding force generation mechanism and parametric grasping analysis}, volume = {13}, year = {2025}, }
• (2025). A component-centric perspective on hardware accelerators for LLMs. ACCESS, 13, 173116-173145. (www)

  @article{ , abstract = {The rapid scaling of large language models (LLMs), especially those based on the Transformer architecture, has intensified the demand for high-performance hardware accelerators capable of supporting massive parameter counts with minimal latency and energy consumption. However, challenges such as communication bottlenecks, memory inefficiencies, and computational overhead have emerged as key limiting factors to scalable and efficient deployment. This survey focuses on LLM accelerators, providing a comprehensive review of state-of-the-art optimization targets, including system-level architectures, self-attention acceleration, normalization techniques, and operator-level enhancements. This review highlights the importance of co-optimizing model architecture and hardware design and suggests that hardware accelerators, particularly those with configurable and scalable capabilities, are critical for sustainable LLM deployment. This work can provide foundational knowledge and valuable insights for LLM researchers and engineers as well as machine learning practitioners in the industry.}, archive = {J_ACCESS}, author = {Jia Ke and Wang Xiaohao and Chen Hailin and Zhong Wei and Li Xinxiong and Fang Zenan and An Fengwei}, doi = {10.1109/ACCESS.2025.3609769}, journal = {IEEE Access}, pages = {173116-173145}, shortjournal = {IEEE Access}, title = {A component-centric perspective on hardware accelerators for LLMs}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven analysis of user bandwidth allocation and performance in 5G network. ACCESS, 13, 173081-173095. (www)

  @article{ , abstract = {Mobile devices have been an integral part of society today, with increasing reliance on wireless communication and mobile networks. Researchers have developed various methods to increase network users while maintaining the Quality of Experience (QoE) under the 5G Network. Various resource allocation methods, such as Quality of Service (QoS) priorities and network slicing, were proposed to improve the users’ QoE. The network-centric resource allocation algorithms allocate resources to specific services to maintain QoE. However, these network-centric algorithms failed to deliver QoE when the network was overloaded. Therefore, a novel user-priority resource allocation (UPRA) algorithm is proposed in this work. UPRA is compared to the current Network Slicing 5G architecture. The UPRA algorithm divides users into three separate priorities depending on the network congestion. Resources are split among the priorities, as each priority will have slices for services. Furthermore, the Enhanced-UPRA (EUPRA) algorithm is proposed for the dynamic allocation of users in each priority. Finally, Machine Learning (ML) (Q-Learning (QL) and Deep Q-Network (DQN)) models are applied on the EUPRA algorithm to dynamically configure resource allocation at various network states. The extensive simulation results showed a 12.73% improvement in QoE from the network slicing 5G algorithm when applying the proposed EUPRA-DQN algorithm when the 5G network is overloaded.}, archive = {J_ACCESS}, author = {Raymond Chia and Wai Leong Pang and Swee King Phang and Hui Hwang Goh and Kah Yoong Chan}, doi = {10.1109/ACCESS.2025.3615398}, journal = {IEEE Access}, pages = {173081-173095}, shortjournal = {IEEE Access}, title = {Machine learning-driven analysis of user bandwidth allocation and performance in 5G network}, volume = {13}, year = {2025}, }
• (2025). Defining medical device ecosystems: A systematic literature review. ACCESS, 13, 173065-173080. (www)

  @article{ , abstract = {Medical devices are used across a wide range of healthcare environments, from inside the human body to home settings, outpatient clinics, and advanced clinical facilities. Although the term medical device ecosystem (MDE) is widely used by stakeholders in healthcare, industry, academia, and government, it remains undefined in the academic literature. This study conducted a systematic review of 162 articles drawn from six major academic databases to determine whether a formal definition exists. No explicit definitions were found in the literature; however, definitions of related ecosystems—such as health informatics, digital platforms, and industrial systems—were identified in ISO standards. These sources informed the development of a working definition of MDEs as complex, interconnected sociotechnical systems that deliver engineered medical devices for clinical use under the oversight of regulatory authorities. In a second line of inquiry, full-text articles were analyzed using text mining techniques, including lemmatization and bigram frequency analysis, to identify recurring thematic patterns. This analysis revealed four dominant themes across the literature: clinical use of devices, their realization through manufacturing, regulatory governance, and a pervasive need for data across all stages of the device lifecycle. Together, these findings underscore the growing relevance of MDEs and demonstrate the value of formalizing the concept to support improved research, system design, and regulatory coordination across the medical device landscape.}, archive = {J_ACCESS}, author = {Peter J. Coronado and Erika E. Gallegos}, doi = {10.1109/ACCESS.2025.3615110}, journal = {IEEE Access}, pages = {173065-173080}, shortjournal = {IEEE Access}, title = {Defining medical device ecosystems: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A hybrid priority-laxity-based scheduling algorithm for real-time aperiodic tasks under varying environmental conditions. ACCESS, 13, 173035-173051. (www)

  @article{ , abstract = {Efficient task scheduling is fundamental to real-time systems, where minimizing deadline misses and improving stability and determinism are the main goals. In this paper, we propose an algorithm for dynamic task scheduling specifically designed for real-time workloads on homogenous platforms by integrating hybrid laxity-priority scheduling to manage aperiodic tasks with varying priorities. The design includes limiting preemption overhead by blocking unnecessary preemptions and dynamically adapting to incoming sensor data. We extend our algorithm by taking environmental conditions into account, where the weight of laxity and priorities can change based on the circumstances. This also includes the effect of external conditions, namely weather conditions on the priority of tasks. The scheduler is designed for systems such as ADAS, where tasks have different priorities, but are not necessarily dependent on each other. Aperiodic tasks are harder to schedule due to the many unpredictable factors that make up the task. Focusing on aperiodic tasks, the proposed multi-core task scheduler design handles sensor-triggered events and adapts scheduling dynamically by prioritizing running tasks by blocking unnecessary preemption. Experimental results using software-based validation demonstrate improved deadline adherence, affirming the performance of the proposed scheduler compared to classical and real-time schedulers. This study evaluates the scheduler’s performance based on key metrics including deadline misses, preempting, makespan, and the proportion of high-priority deadline misses relative to total deadline misses. Our proposed algorithm demonstrated a miss rate 6% lower than existing state-of-the-art algorithms for similar tasksets.}, archive = {J_ACCESS}, author = {Mahdi Seyfipoor and Sayyed Muhammad Jaffry and Siamak Mohammadi}, doi = {10.1109/ACCESS.2025.3612340}, journal = {IEEE Access}, pages = {173035-173051}, shortjournal = {IEEE Access}, title = {A hybrid priority-laxity-based scheduling algorithm for real-time aperiodic tasks under varying environmental conditions}, volume = {13}, year = {2025}, }
• (2025). Study on the path optimization method of autonomous navigation of uncrewed aerial vehicles integrating multi-sensor data. ACCESS, 13, 173016-173034. (www)

  @article{ , abstract = {AI-driven and sensor-fusion methods for autonomous systems, our work addresses the critical challenge of enhancing UAV path planning through multi-sensor integration. Prior approaches—such as traditional GNSS-inertial fusion or single-camera vision—suffer from limitations in GPS-denied or dynamic environments, often lacking robustness, adaptability, or real-time performance amid obstacles and sensor noise citeturn0search1turn1search3. We propose a novel optimization framework that deeply integrates data from multiple onboard sensors—including LiDAR, vision, and inertial units—within a deep reinforcement learning architecture informed by our method section. By leveraging sensor fusion, our method dynamically adjusts flight trajectories with heightened spatial awareness, improving obstacle avoidance and significantly reducing path deviations. Experimental results demonstrate that our approach outperforms baseline single-sensor and non-fused systems, achieving smoother, safer routes with lower cumulative error and real-time responsiveness. This integration highlights the advantages of multi-sensor fusion in UAV navigation, aligning strongly with the special issue’s theme of advanced data-driven autonomous flight.}, archive = {J_ACCESS}, author = {Guochuang Yan and Jiang Du and Song Chen and Xiaogeng Tian}, doi = {10.1109/ACCESS.2025.3609022}, journal = {IEEE Access}, pages = {173016-173034}, shortjournal = {IEEE Access}, title = {Study on the path optimization method of autonomous navigation of uncrewed aerial vehicles integrating multi-sensor data}, volume = {13}, year = {2025}, }
• (2025). Bridging system development perspectives and quantitative analysis of user adoption in educational information systems. ACCESS, 13, 172987-173015. (www)

  @article{ , abstract = {Sustaining digital platform adoption in computer science requires both robust system design and quantitative evaluation of user behavior and system performance. System Quality (SQ), encompassing reliability, responsiveness, security, and usability, is a critical determinant of adoption. However, most studies grounded in the Technology Acceptance Model (TAM) and Task–Technology Fit (TTF) have overlooked objective SQ metrics and the moderating influence of Digital Skills (DS). This study introduces the Digital Educational Systems Adoption Framework (DESAF), which integrates TAM, TTF, and DS to analyze the adoption of the Graduate Academic Information System Services (GAISS). A mixed-method design combined three years of Microsoft IIS server log data (2022–2024) with 550 user survey responses. SQ was quantified through request volumes, response times, error rates, and downtime, and analyzed alongside behavioral constructs using structural equation modeling in SmartPLS 4. Results indicate that SQ strongly predicts DS ( $\beta = 0.416$ , p <0.001) and actual system use ( $\beta = 0.251$ , p <0.001), while DS significantly enhances perceived ease of use, usefulness, satisfaction, and actual use ( $\beta = 0.151$ , p <0.001), confirming its dual role as both predictor and moderator. System performance improved substantially, with average response times decreasing from 526 ms to 305 ms and downtime decreasing from 3.67% to 2.86%, while the number of annual requests increased from 155,000 to 966,000. Yet, feature expansion increased error rates to 14.88%, highlighting a trade-off between performance and complexity. The validated DESAF model advances computer science by bridging software performance engineering and behavioral modeling, providing an evidence-based framework for adaptive, user-centered platforms that are sustained through longitudinal performance monitoring.}, archive = {J_ACCESS}, author = {Paniti Netinant and Sorapak Pukdesree and Meennapa Rukhiran}, doi = {10.1109/ACCESS.2025.3616630}, journal = {IEEE Access}, pages = {172987-173015}, shortjournal = {IEEE Access}, title = {Bridging system development perspectives and quantitative analysis of user adoption in educational information systems}, volume = {13}, year = {2025}, }
• (2025). From systematic to intelligent: Assessing AI-empowered optimization techniques for analog building block sizing. ACCESS, 13, 172972-172986. (www)

  @article{ , abstract = {This paper presents a comprehensive, design-insight-based comparison between an artificial intelligence (AI)-empowered optimization-based analog building block sizing framework and the conventional manual design methodology. Although recent AI-empowered approaches are showing high performance, conventional systematic manual design methods such as the $g_{m}/I_{D}$ -based sizing are still the most widely used methods in the analog IC design community. This raises the necessity of the comprehensive comparative analyses between the two kinds of methods. To fill this gap, this paper compares the optimal designs obtained by a typical method of the former with those obtained by the latter method in the literature/industry. Four case studies, including a comparator, an amplifier (both standard and low power design), and an oscillator, using technology nodes ranging from $0.35~\mu $ m to 65 nm, are presented. Detailed performance evaluations and design insights are presented for each case study, with silicon validation provided for three designs. Our findings highlight that AI-empowered sizing not only meets but often surpasses conventional designs in key performance metrics, while still benefiting from designer interaction to align with design intents.}, archive = {J_ACCESS}, author = {Yijia Hao and Miguel Gandara and Srinjoy Mitra and Maarten Strackx and Sandy Cochran and Francisco V. Fernández and Shaolan Li and Bo Liu}, doi = {10.1109/ACCESS.2025.3616647}, journal = {IEEE Access}, pages = {172972-172986}, shortjournal = {IEEE Access}, title = {From systematic to intelligent: Assessing AI-empowered optimization techniques for analog building block sizing}, volume = {13}, year = {2025}, }
• (2025). Generalization of knowledge transfer with user reviews for cross-domain recommendation. ACCESS, 13, 172954-172971. (www)

  @article{ , abstract = {Cross-domain recommendation systems have demonstrated the potential to address data sparsity and cold-start problems. However, current approaches primarily rely on domain-shareable attributes, such as overlapping user bases or identical contexts, to facilitate knowledge transfer, limiting their generalizability without these elements. To overcome these limitations, we propose exploiting review texts, which are ubiquitous across most e-commerce platforms. Our model, termed SER, incorporates three distinct text analysis modules, guided by a single discriminator to achieve disentangled representation learning. We introduce a novel optimization strategy that not only improves domain disentanglement but also minimizes the transfer of adverse information from the source domain. Furthermore, we have expanded our model’s encoding network from a single to multiple domains, enhancing its efficacy for review-based recommendation systems. Through comprehensive experiments and ablation studies, we establish that our approach is more efficient, robust, and scalable than existing single and cross-domain recommendation methods.}, archive = {J_ACCESS}, author = {Yoonhyuk Choi and Chong-Kwon Kim}, doi = {10.1109/ACCESS.2025.3616497}, journal = {IEEE Access}, pages = {172954-172971}, shortjournal = {IEEE Access}, title = {Generalization of knowledge transfer with user reviews for cross-domain recommendation}, volume = {13}, year = {2025}, }
• (2025). A review on crop residue measurement and sensing. ACCESS, 13, 172938-172953. (www)

  @article{ , abstract = {Crop residue plays a crucial role in diverse areas of soil, biomass, crop yield, and the environment by impacting aspects such as soil organic matter, erosion, nutrient leaching, soil moisture and temperature, plant emergence, in turn influencing plant biomass and crop yields. Measuring crop residue coverage has been a long-term practice, primarily for monitoring purposes by agencies for various programs. More recently, increased research effort has been focused on improving the means and mechanisms of measuring and sensing crop residue efficiently and on large scales for both monitoring and improved agricultural decision-making insights. This review attempts to provide an overview of crop residue measurement and sensing, presenting a holistic review of the different measurement techniques available. It also highlights how research has shifted from field-based measurement to the use of diverse sensing approaches and future directions. Traditional methods of crop residue measurement are reviewed and the literature on more modern sensing approaches is summarized through categorization of the approaches based on sensing scale of satellite-, UAV-, and ground-based systems. Key findings highlight the growing shift toward high-resolution UAV sensing and the increasing use of machine learning models for improved classification. Identified critical challenges, particularly in satellite sensing, include spectral similarity between residue and soil, influence of moisture, and limited real-time application. The review aims to provide a summary of the current state of crop residue sensing with insights into potential areas for opportunities, especially through on-machine sensing systems, emphasizing the need for real-time, integrated, and adaptive residue coverage sensing solutions.}, archive = {J_ACCESS}, author = {Sagar Regmi and Fengqing Qiu and Cody Allen}, doi = {10.1109/ACCESS.2025.3616603}, journal = {IEEE Access}, pages = {172938-172953}, shortjournal = {IEEE Access}, title = {A review on crop residue measurement and sensing}, volume = {13}, year = {2025}, }
• (2025). Reasoning beyond length limits: Improving accuracy in long-context question answering with small-scale language models. ACCESS, 13, 172930-172937. (www)

  @article{ , abstract = {Long-context question answering (QA) remains a significant challenge, particularly when using small-scale language models (SLLMs) with limited computational capacity. Despite their efficiency, SLLMs often struggle to capture complex reasoning patterns and synthesize information from lengthy documents due to constraints in context size and inference depth. Traditional retrieval-augmented generation (RAG) approaches offer partial relief but typically fall short when precise reasoning across multiple passages is required. In this study, we present a novel, lightweight framework designed to improve long-context QA performance in SLLMs by combining two key strategies: 1) instruction-tuned embedding-based retrieval for extracting semantically aligned context, and 2) a question rephrasing mechanism that decomposes complex queries into stepwise subquestions. This dual strategy enables structured reasoning without the need for additional training or model fine-tuning. Experiments on the LongBench and LongBench v2 benchmarks demonstrate consistent performance improvements, with gains of up to 5% over strong baselines. Our method is model-agnostic, effective across diverse input lengths and task difficulties, and compatible with a wide range of SLLMs, including LLaMA and GLM. The proposed approach offers a practical, generalizable solution for deploying robust QA systems in resource-constrained environments.}, archive = {J_ACCESS}, author = {Minyoung Kyoung and Joon-Ho Lim and Youngsoo Kim}, doi = {10.1109/ACCESS.2025.3617449}, journal = {IEEE Access}, pages = {172930-172937}, shortjournal = {IEEE Access}, title = {Reasoning beyond length limits: Improving accuracy in long-context question answering with small-scale language models}, volume = {13}, year = {2025}, }
• (2025). Adaptability of identification algorithms while MWD application in various geological conditions. ACCESS, 13, 172917-172929. (www)

  @article{ , abstract = {Machine Learning (ML) has been extensively utilized in the Measurement While Drilling (MWD) technology in the coal industry. However, there existed uncertainty about whether mainstream algorithms can effectively and accurately identify rock strength across diverse geological conditions. Therefore, this study compared the effects of the three mainstream algorithms, K-means clustering, BP neural network, and SVM classification, to clarify the geological conditions suitable for each algorithm, and finally proposed the optimal selection and application methods of various algorithms under different geological conditions. The study revealed that the accuracy for K-means clustering, BP neural networks, and SVM multi-label classification algorithms were 85%~88%, 92%~96%, and 89%~92% respectively. Additionally, BP neural networks, SVM multi-label classification, and SVM single-label classification are suitable under conditions where strength differences are less than 6.3 MPa, between 6.3 MPa and 22.7 MPa, and greater than 22.7 MPa, and their generalization rates are 98.12%, 98.48%, and 94.71%, respectively. K-means clustering is appropriate for the preliminary identification of strata information in scenarios with few rock types and simple existing conditions. At the same time, BP neural networks and SVM multi-label classification are well-suited for strata characterized by “multiple rock types and low strength differences.” In contrast, SVM single-label classification is more suitable for strata featuring “few rock types and high strength differences”, which would hold significant practical value in enhancing the applicability of ML for MWD technology in coal mines.}, archive = {J_ACCESS}, author = {Jingyi Cheng and Xin Sun and Keke Xing and Kuo Bao and Xianxin Zhang and Zheng Zhen and Zhijun Wan}, doi = {10.1109/ACCESS.2025.3612468}, journal = {IEEE Access}, pages = {172917-172929}, shortjournal = {IEEE Access}, title = {Adaptability of identification algorithms while MWD application in various geological conditions}, volume = {13}, year = {2025}, }
• (2025). Sensitivity analysis and characteristics study of area-pressure-difference composite regulated flow control systems. ACCESS, 13, 172878-172894. (www)

  @article{ , abstract = {Industrial automation demands increasingly high performance from hydraulic control systems. However, the key nonlinear factors within these systems significantly affect their control characteristics throughout the operating cycle. Traditional bond graph analysis focuses on the system’s dynamic response, but lacks efficient and systematic methods to evaluate how parameter variations influence performance. Although sensitivity analysis can assess these influences, it relies heavily on state-space equations, which involve complex modeling, offer limited physical intuition, and are prone to derivation errors that are difficult to verify. Therefore, this study focuses on an area-pressure difference composite flow control system and proposes a novel integration of power bond graph-based state-space modeling with first-order trajectory sensitivity analysis. A comprehensive sensitivity equation system with 21 parameters was established, and a dual-sensitivity evaluation metric was introduced for quantitative analysis. Quantitative analysis confirmed that the flow force dominated the fluctuations in the main valve pressure difference, with negligible effects from the spring force. The hydraulic resistance of the non-spring chamber orifice, spool equivalent mass, and throttle equivalent flow area significantly affected the compensator spool momentum. Further research indicates that increasing the hydraulic resistance of the damping orifice effectively suppresses spool momentum fluctuations, although hysteresis effects must be considered. Experimentally, flow-force compensation reduced the flow error by 4%, validating its role as the primary cause of the pressure difference variations. This research holds significant engineering application value for optimizing control strategies and improving dynamic response performance to enhance the flow control accuracy of the system.}, archive = {J_ACCESS}, author = {Zhishen Jing and Huaide Geng and Zhijie Pan and Qi Sun and Xingyu Zhao and Bo Wang and Long Quan}, doi = {10.1109/ACCESS.2025.3613349}, journal = {IEEE Access}, pages = {172878-172894}, shortjournal = {IEEE Access}, title = {Sensitivity analysis and characteristics study of area-pressure-difference composite regulated flow control systems}, volume = {13}, year = {2025}, }
• (2025). Inhalation motion analysis and visualization of error areas using two inertial measurement unit sensors. ACCESS, 13, 172841-172853. (www)

  @article{ , abstract = {Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory diseases, and the proper use of inhalers is crucial for effective symptom management. However, many patients use their inhalers incorrectly. This study proposes a method for evaluating inhaler-use behavior by employing inertial sensors to monitor patients with bronchial asthma or COPD. By augmenting an ElliptaTM inhaler with inertial measurement units, this study evaluates patient inhalation motions using the acquired motion data. Compared with conventional methods, the proposed method is less affected by external factors such as ambient sound and temperature and can be applied outside clinical settings. The evaluation process uses a linear discriminant analysis algorithm to identify key characteristic variables associated with specific inhaler-use errors, and a judgment method using these variables is proposed. A dynamic programming matching algorithm is then applied to assess correctness. Experimental results indicate that the proposed method achieves high discriminant accuracy. The proposed system enables error visualization by considering waveform similarity, unlike contemporary methods. The proposed inhalation method and accompanying dataset demonstrates a novel method for objective inhaler-use evaluation.}, archive = {J_ACCESS}, author = {Shunya Takano and Atsushi Hasegawa and Tomoyuki Shimono and Katsunori Masaki and Hideo Nakada and Jun Hakamata and Hiroki Kabata and Jun Miyata and Koichi Fukunaga}, doi = {10.1109/ACCESS.2025.3617064}, journal = {IEEE Access}, pages = {172841-172853}, shortjournal = {IEEE Access}, title = {Inhalation motion analysis and visualization of error areas using two inertial measurement unit sensors}, volume = {13}, year = {2025}, }
• (2025). Continuously operating burst-mode control (COBC) of half-bridge LLC converter. ACCESS, 13, 172829-172840. (www)

  @article{ , abstract = {Conventional burst-mode control for an LLC resonant converter improves the light load efficiency, but it suffers from dynamic problems such as output voltage dip during load transients due to discontinuous operation. Although there have been several approaches improving the light load efficiency without regulation or dynamic problems, they still reveal limitations such as topology dependency or distortion on the resonant current waveform. This paper proposes a continuously operating burst-mode control (COBC) for a half-bridge LLC resonant converter, inserting multiple freewheeling intervals in light load conditions. By doing so, COBC maintains sinusoidal waveform, thereby achieving higher efficiency even in a very light condition while maintaining superior dynamic response. In addition, detailed time-domain analysis has been conducted to provide the design guideline for the proposed COBC with a wide range of resonant tank values. Therefore, the proposed COBC can be implemented to pre-designed half-bridge LLC converters implemented by engineers, preventing the side effects such as the saturation of magnetic components. The effectiveness of the COBC is verified with 400 – 300 V input and 50 V/5 A output prototype. The COBC improves the light load efficiency by up to 4.35 % compared with conventional pulse frequency modulation (PFM) control and reduces the voltage dip during a 10 % to 100 % load transient by 36.1 % compared with conventional burst-mode control.}, archive = {J_ACCESS}, author = {Yu-Jin Lee and Jong-Woo Kim}, doi = {10.1109/ACCESS.2025.3614433}, journal = {IEEE Access}, pages = {172829-172840}, shortjournal = {IEEE Access}, title = {Continuously operating burst-mode control (COBC) of half-bridge LLC converter}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of incremental cooperative NOMA via partial relay selection. ACCESS, 13, 172799-172814. (www)

  @article{ , abstract = {Improving spectrum efficiency (SE) and energy efficiency (EE) remains a fundamental requirement for future wireless communication systems. To address this, we present a novel cooperative relaying strategy built on Incremental Relaying with Partial Relay Selection (IR-RS) using a Decode-and-Forward (DF) protocol (IR-RS-DF), tailored for Cooperative Non-Orthogonal Multiple Access (C-NOMA) networks. In this approach, a near user (NU) is selected dynamically based on its instantaneous signal-to-noise ratio (SNR) to serve as a DF relay for assisting a far user (FU), especially when the FU suffers from poor direct transmission conditions. To enhance energy utilization, the system incorporates a feedback-aided incremental relaying mechanism, which selectively engages relays only when necessary, thereby avoiding unnecessary retransmissions and reducing power consumption. We conduct a comprehensive statistical evaluation of key performance metrics such as bit error rate (BER), ergodic capacity (EC), system throughput, outage probability (OP), SE, and average energy savings over Rayleigh fading channels. Simulation outcomes validate that the proposed IR-RS scheme, when integrated with C-NOMA, consistently outperforms conventional IR-DF approaches across all evaluated parameters, showcasing its effectiveness for high-efficiency and energy-aware next-generation wireless networks.}, archive = {J_ACCESS}, author = {Gandlapalli Nagesham and Suseela Vappangi}, doi = {10.1109/ACCESS.2025.3616386}, journal = {IEEE Access}, pages = {172799-172814}, shortjournal = {IEEE Access}, title = {Performance enhancement of incremental cooperative NOMA via partial relay selection}, volume = {13}, year = {2025}, }
• (2025). BoundMatch: Boundary detection applied to semi-supervised segmentation. ACCESS, 13, 172776-172798. (www)

  @article{ , abstract = {Semi-supervised semantic segmentation (SS-SS) aims to mitigate the heavy annotation burden of dense pixel labeling by leveraging abundant unlabeled images alongside a small labeled set. While current consistency regularization methods achieve strong results, most do not explicitly model boundaries as a separate learning objective. In this paper, we propose BoundMatch, a novel multi-task SS-SS framework that explicitly integrates semantic boundary detection into a teacher-student consistency regularization pipeline. Our core mechanism, Boundary Consistency Regularized Multi-Task Learning (BCRM), enforces prediction agreement between teacher and student models on both segmentation masks and detailed semantic boundaries, providing complementary supervision from two independent tasks. To further enhance performance and encourage sharper boundaries, BoundMatch incorporates two lightweight fusion modules: Boundary-Semantic Fusion (BSF) injects learned boundary cues into the segmentation decoder, while Spatial Gradient Fusion (SGF) refines boundary predictions using mask gradients, yielding more reliable boundary pseudo-labels. This framework is built upon SAMTH, a strong teacher-student baseline featuring a Harmonious Batch Normalization (HBN) update strategy for improved stability. Extensive experiments on diverse datasets including Cityscapes and Pascal VOC show that BoundMatch achieves competitive performance against current state-of-the-art methods. Our approach achieves state-of-the-art results on the new Cityscapes benchmark with DINOv2 foundation model. Ablation studies highlight BoundMatch’s ability to improve boundary-specific evaluation metrics, its effectiveness in realistic large-scale unlabeled data scenario, and applicability to lightweight architectures for mobile deployment.}, archive = {J_ACCESS}, author = {Haruya Ishikawa and Yoshimitsu Aoki}, doi = {10.1109/ACCESS.2025.3617037}, journal = {IEEE Access}, pages = {172776-172798}, shortjournal = {IEEE Access}, title = {BoundMatch: Boundary detection applied to semi-supervised segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhancing smart outdoor object navigation for the visually impaired via YOLOv10 with neighbor coordinates and C2FCIB attention mechanism. ACCESS, 13, 172748-172775. (www)

  @article{ , abstract = {This research introduces an AI-driven outdoor object detection system aimed at enhancing navigation for visually impaired individuals (VIIs). VIIs often face significant challenges in accessing and interpreting visual information. Recent advancements in computer hardware and deep learning techniques have led to notable progress in developing assistive technologies for VIIs. However, existing datasets often focus on single scenarios and lack sufficient annotations to represent the diverse obstacles encountered in real-world settings. This limitation hinders the development of comprehensive object detection systems tailored to the needs of VIIs. The system utilizes advanced models such as YOLOv8 (Nano, Small, Medium), YOLOv9c, and YOLOv10n, with neighbor coordinates and C2FCIB attention modules trained on the WOTR dataset, which includes 20 classes of common outdoor objects. A comparative study evaluated the performance of these models across key metrics. The YOLOv8m model demonstrated balanced performance with an accuracy of 85.53. YOLOv8n showed slightly lower performance, with an accuracy of 77.05%, and the YOLOv8s model recorded an accuracy of 84.99%, precision and recall of 0.99, 0.89, matching YOLOv8n with an F1 score of 0.74. Similarly, YOLOv9c achieved an accuracy of 79.82%, and the proposed YOLOv10n model with neighbor coordinates and C2FCIB attention modules led with the highest accuracy of 89.33%, precision and recall of 0.99, 0.92, with F1 score of 0.79. A comparative analysis revealed that the proposed YOLOv10n with neighbor coordinates and C2FCIB attention modules achieved the highest accuracy with precision and recall indicating its reliability for assistive applications. In the realm of assistive technologies, similar AI-powered devices have been developed to aid visually impaired individuals. These innovations, alongside the described object detection system, exemplify the potential of AI in creating inclusive solutions that empower visually impaired individuals to navigate their environments more safely and independently.}, archive = {J_ACCESS}, author = {Abdullah Khan and Dzati Athiar Ramli and Syed Muhammad Zubair Rehman Gillani and Javed Iqbal Bangash and Muhammad Nouman Atta}, doi = {10.1109/ACCESS.2025.3612482}, journal = {IEEE Access}, pages = {172748-172775}, shortjournal = {IEEE Access}, title = {Enhancing smart outdoor object navigation for the visually impaired via YOLOv10 with neighbor coordinates and C2FCIB attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Multi-agent Q-learning via best choice dynamics. ACCESS, 13, 172736-172747. (www)

  @article{ , abstract = {Motivated by multi-agent Q-learning scenarios, this paper introduces a distributed action selection algorithm that relies on individual agents interacting with local neighbors to learn a joint action. The algorithm, termed Best Choice Dynamics, has each agent communicate its current planned action to its neighbors, who in turn utilize this information to update their own actions. We characterize the convergence and robustness of the algorithm against message losses, showing that it converges to locally optimal joint actions in finite time. We also discuss its relative advantages with respect to message-passing algorithms and best response dynamics regarding convergence guarantees, lack of oscillations, and communication complexity. We illustrate the algorithm performance in various simulation scenarios, including both on-line training and offline training with distributed on-line roll-out.}, archive = {J_ACCESS}, author = {Scott Addams and Jorge Cortés}, doi = {10.1109/ACCESS.2025.3612969}, journal = {IEEE Access}, pages = {172736-172747}, shortjournal = {IEEE Access}, title = {Multi-agent Q-learning via best choice dynamics}, volume = {13}, year = {2025}, }
• (2025). A feedback-driven federated zero-shot learning framework for adaptive detection of evolving banking malware. ACCESS, 13, 172717-172735. (www)

  @article{ , abstract = {The rise of digital banking has increased the frequency and sophistication of banking malware attacks, highlighting the need for privacy-preserving and adaptive detection frameworks. While Federated Learning (FL) offers a promising alternative to centralized detection by enabling collaborative model training without raw data sharing, its performance declines in the presence of evolving, previously unseen malware behaviors, especially under non-IID (non-independent and identically distributed) conditions. To address these challenges, we propose a hybrid FL and Zero-Shot Learning (ZSL) framework enhanced with a feedback-driven continual learning loop for resilient malware detection. Our approach assigns three federated clients disjoint banking malware datasets comprising Zeus, Emotet, TrickBot, and benign samples under a non-IID setting, simulating real-world institutional threat exposure. Two additional datasets: one with malware variants and another synthetically generated to reflect evolved behavior are used exclusively for testing. Among multiple deep learning architectures evaluated, a Multilayer Perceptron (MLP) is selected as the best-performing model and personalized at each client. ZSL operates during inference to reclassify low-confidence samples using semantic embeddings, and those with high cosine similarity are selectively reintegrated into FL training, supporting continual adaptation through feedback, without compromising data privacy. Experimental results show that the proposed FL-ZSL-feedback pipeline achieves an average improvement of 8.49% in correctly classifying samples with high confidence over baseline FL model across all clients and datasets. These findings validate the effectiveness of our framework in delivering privacy-aware, adaptive banking malware detection in dynamic, distributed environments.}, archive = {J_ACCESS}, author = {Nahid Ferdous Aurna and Yuzo Taenaka and Youki Kadobayashi}, doi = {10.1109/ACCESS.2025.3617219}, journal = {IEEE Access}, pages = {172717-172735}, shortjournal = {IEEE Access}, title = {A feedback-driven federated zero-shot learning framework for adaptive detection of evolving banking malware}, volume = {13}, year = {2025}, }
• (2025). Enhanced fuzzy logic controller design for alternate arm converter in short and extended overlap modes. ACCESS, 13, 172704-172716. (www)

  @article{ , abstract = {Alternate arm converters (AAC) are emerging as one of the most efficient voltage source converter (VSC) topologies in high-voltage direct current (HVDC) systems due to their advantages over conventional modular multilevel converters (MMCs). Compared to conventional MMC, they benefit from fewer number of submodules (SMs), lower capacitance per submodule and DC fault ride-through capability. However, the AAC VSC controller must ensure appropriate regulation of AC currents in both overlap and non-overlap modes while maintaining minimal harmonic distortion. This paper presents an optimized control design method for the half-bridge (HB) AAC VSC using a Fuzzy Logic (FL) controller. The average modeling of multilevel converters is used to reduce computational burden, and the proposed FLC scheme is employed to regulate AC and circulating currents. The performance of the proposed control architecture is evaluated under short and extended overlap operating modes. Sensitivity analysis of the proposed controller against changes in VSC design parameter of overlap period is also investigated to examine the proposed scheme usefulness in different operating conditions. A two-terminal HVDC link using AAC VSC converters is modeled in MATLAB/Simulink and simulation results under different operating conditions are presented to demonstrate the effectiveness of the proposed scheme.}, archive = {J_ACCESS}, author = {Shima Karbasi and Javad Ebrahimi and and Masood Hajian}, doi = {10.1109/ACCESS.2025.3611351}, journal = {IEEE Access}, pages = {172704-172716}, shortjournal = {IEEE Access}, title = {Enhanced fuzzy logic controller design for alternate arm converter in short and extended overlap modes}, volume = {13}, year = {2025}, }
• (2025). Vision-based relative pose estimation of non-cooperative spacecraft using time-of-flight camera. ACCESS, 13, 172692-172703. (www)

  @article{ , abstract = {This paper deals with a vision-based pose estimation of a target using a time-of-flight (ToF) camera. In particular, a relative pose estimation algorithm for non-cooperative targets that lack markers or accessible sensor is proposed. The proposed method, called the CSAC-ICP algorithm, combines a feature-based coarse registration method, known as Corner Random Sample Consensus (CSAC), with the well-known Iterative Closest Point (ICP) algorithm to ensure both accuracy and robustness. Here, 3D corner points are adopted as primary features. The corner points are obtained by first converting 3D point clouds into 2D images from which line features are extracted and then re-projected into 3D lines, enabling 3D corner points computation. This reduces the computation on a large number of 3D points, ensuring real-time performance. Furthermore, an extended Kalman filter (EKF) is integrated with the proposed pose estimation algorithm in a loosely coupled manner, not only to continuously provide the target pose information for a moving target, but also to reduce measurement noises due to the motion. Because feature-based coarse registration takes advantage of the global optimization strategy, the proposed method can avoid the inherent pitfall of the standalone ICP method, in which the optimization process may fall into a local minimum and lead to incorrect pose calculation under certain conditions. Moreover, the proposed pose estimation scheme minimizes the dependency on the prior knowledge about the target object’s shape by taking advantage of the RANSAC-based registration. Numerical simulation is set up to validate the functionality of the algorithm using simulated 3D point clouds, confirming the accuracy performance under controlled target motion. In addition, the proposed algorithm is implemented and tested on an actual hardware platform, which demonstrates the feasibility of real-time relative pose estimation for non-cooperative targets within the space environment. Moreover, the proposed pose estimation scheme reduces reliance on prior knowledge of the target object’s geometry by using RANSAC-based registration. The simulation results show that the proposed CSAC-ICP algorithm achieves 30% faster computation and 50% higher accuracy compared to the standalone ICP method. Experimental validation further confirms that 3D corner features can be extracted at a rate of 30 frames per second. In addition, the pose estimation achieves an accuracy of up to 3 degrees for rotational motion and within 0.02 meters for translational motion along all three axes, demonstrating the feasibility and superior performance of the proposed algorithm in real-world scenarios.}, archive = {J_ACCESS}, author = {Sangdo Park and Minsik Oh and Heokjune You and Dongwon Jung}, doi = {10.1109/ACCESS.2025.3617328}, journal = {IEEE Access}, pages = {172692-172703}, shortjournal = {IEEE Access}, title = {Vision-based relative pose estimation of non-cooperative spacecraft using time-of-flight camera}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of the application of artificial image data for visual defect detection. ACCESS, 13, 172674-172691. (www)

  @article{ , abstract = {Artificial images for defect detection have gained growing importance in the recently developed defect detection architectures. This systematic literature review examines the use of artificial image data for visual defect detection. It also provides an overview of the methods used for this purpose. Following PRISMA guidelines, the review analyses existing literature to identify key areas where artificial image generation is being applied or could be implemented to enhance defect detection capabilities. It was outlined that data augmentation is currently the most commonly used image generation method to improve individual datasets for defect detection tasks. The results provide insights into the current state of the art and potential future directions for addressing data scarcity in visual defect detection. The knowledge gained in this study can be used by researchers and users to find suitable methods for the desired field of application in order to overcome the problem of data scarcity, and the findings also show where there is still a need for research.}, archive = {J_ACCESS}, author = {Merlin Schadt and Christopher Mai and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3615795}, journal = {IEEE Access}, pages = {172674-172691}, shortjournal = {IEEE Access}, title = {A systematic literature review of the application of artificial image data for visual defect detection}, volume = {13}, year = {2025}, }
• (2025). Predictive modeling of multi-label enzyme substrate using machine learning. ACCESS, 13, 172657-172673. (www)

  @article{ , abstract = {Enzymes play a critical role in catalyzing biochemical reactions, and accurately predicting their substrate specificity is essential for applications in drug discovery, metabolic engineering, and biotechnology. However, traditional experimental methods are time-consuming and resource-intensive, while existing computational approaches often rely on predicting Enzyme Commission (EC) numbers as coarse proxies for substrate specificity, failing to capture the many-to-many relationships between enzymes and their substrates. In this study, we present a hybrid deep learning framework for prediction of enzyme-substrate interactions. The proposed model integrates ProtT5 protein language model embeddings with extended connectivity fingerprints (ECFP4) and 3D molecular descriptors through a dedicated geometric attention mechanism that dynamically weights features based on interaction patterns. The key findings demonstrate that proposed approach achieves state-of-the-art performance, with an overall accuracy of 88.2% and F1-score of 0.823 on the BRENDA benchmark dataset. The model shows particular strength in predicting interactions for promiscuous enzymes (18.7% of the dataset), improving recall by 22% compared to EC-based prediction methods. The hybrid attention mechanism provides a 9.2% AUC improvement over static feature concatenation. The proposed methodology can be extended to other multi-label classification problems in biological systems, paving the way for future advancements in computational biology.}, archive = {J_ACCESS}, author = {Sana Tariq and Muhammad Umer Sarwar and Muhammad Kashif Hanif and Muhammad Irfan Khan}, doi = {10.1109/ACCESS.2025.3615796}, journal = {IEEE Access}, pages = {172657-172673}, shortjournal = {IEEE Access}, title = {Predictive modeling of multi-label enzyme substrate using machine learning}, volume = {13}, year = {2025}, }
• (2025). Airborne wind energy potential in türkiye: A study of wind resource, life cycle assessment, and techno-economic analysis. ACCESS, 13, 172638-172656. (www)

  @article{ , abstract = {This paper provides an integrated assessment of Airborne Wind Energy (AWE) systems in Türkiye, evaluating their potential as a future renewable energy source. The study addresses key questions of resource availability, environmental impact, and economic viability through a threefold methodology applied to ten regions: 1) probabilistic wind modeling to accurately characterize high-altitude resources, 2) a comparative life-cycle assessment (LCA) to quantify environmental impacts against conventional turbines, and 3) a techno-economic analysis (TEA) to determine financial viability. Our results indicate that the Gamma distribution generally provides the most accurate model for the high-altitude wind profiles studied ( $R^{2} \gt 0.997$ ). The LCA reveals a compelling environmental case for AWE, with a global warming potential as low as 7.2 gCO2-eq/kWh in prime locations. The TEA projects a highly competitive levelized cost of electricity (LCOE), ranging from ${\$}$ 0.042/kWh in wind-rich areas like Çanakkale to over ${\$}$ 0.16/kWh in less ideal sites. With an internal rate of return exceeding 18% in the most favorable regions, the study concludes that AWE systems appear to be a technically promising and economically viable option for Türkiye, capable of matching or even outperforming conventional wind power in select locations.}, archive = {J_ACCESS}, author = {Ahmet Emre Onay and Emrah Dokur}, doi = {10.1109/ACCESS.2025.3617247}, journal = {IEEE Access}, pages = {172638-172656}, shortjournal = {IEEE Access}, title = {Airborne wind energy potential in türkiye: A study of wind resource, life cycle assessment, and techno-economic analysis}, volume = {13}, year = {2025}, }
• (2025). Point cloud upsampling for accurate surface reconstruction via attention-guided generation. ACCESS, 13, 172626-172637. (www)

  @article{ , abstract = {Reconstructing accurate surfaces from sparse point clouds remains a fundamental challenge in 3D vision. In this paper, we propose a novel upsampling framework, called Surf-PU, that focuses on surface-aware point generation. Our method comprises two key components. First, we introduce an adaptive query generation module that analyzes local attention information to determine where new points should be placed. These query points guide the generation toward regions of geometric significance. Second, we incorporate the Hyper Chamfer Distance (HCD) loss, which accounts for the distribution of point-wise distances to better capture complex surface structures. Unlike existing methods, our approach effectively combines attention-based guidance and loss-driven precision, leading to more accurate surface reconstruction. Extensive experiments on PU-GAN and PU1K datasets demonstrate that our method consistently outperforms state-of-the-art techniques, especially in terms of the P2F metric. Moreover, the proposed framework maintains robust performance across arbitrary upsampling ratios and under random noise, confirming its reliability and generalizability.}, archive = {J_ACCESS}, author = {Jong-Su Won and Jong-Ki Han}, doi = {10.1109/ACCESS.2025.3616323}, journal = {IEEE Access}, pages = {172626-172637}, shortjournal = {IEEE Access}, title = {Point cloud upsampling for accurate surface reconstruction via attention-guided generation}, volume = {13}, year = {2025}, }
• (2025). Thermal analysis and switching performance of a 1.7 kV SiC power MOSFET under high-frequency operation and double pulse testing. ACCESS, 13, 172617-172625. (www)

  @article{ , abstract = {This study presents a comprehensive simulation analysis of 1.7 kV SiC Power MOSFETs (SiC) compared to conventional Si power MOSFETs (Si1 and Si2) focusing on thermal behavior, switching performance, and efficiency under various operating conditions. LTspice simulations over 5000 switching cycles revealed that the SiC MOSFET exhibited lower conduction losses and superior thermal stability, with higher thermal fluctuations at 50 kHz ( $0.32664~^{\circ }$ C per cycle) due to prolonged on-times, while higher frequencies at 200 kHz reduced fluctuations to $0.13397~^{\circ }$ C per cycle. Meanwhile, under the same conditions, Si MOSFETs experience higher power dissipation and poorer thermal management. Double pulse testing at varying temperatures demonstrated the SiC MOSFET has superior efficiency, peaking as high as 99.61 %, 99.55 %, and 99.44 % at 500 V, 1000 V, and 1500 V, respectively, at $150~^{\circ }$ C. A boost converter test using a 0.5 mH inductor and $200~\mu $ F capacitor further evaluated energy conversion performance, showing minimal voltage deviation in SiC MOSFETs, with outputs of 1359.32 V, 1360.57 V, and 1358.56 V at $25~^{\circ }$ C, $100~^{\circ }$ C, and $150~^{\circ }$ C, respectively, compared to the theoretical 1360 V, while Si MOSFETs displayed greater voltage drops and efficiency losses. These results confirm that SiC Power MOSFETs provide significantly higher efficiency, lower thermal resistance, and superior performance in high-voltage, high-power applications compared to conventional Si MOSFETs.}, archive = {J_ACCESS}, author = {Muhammad Najmi Seth and Nurul Amirah Zulkifle and Nur Syamimi Noorasid and Ghazali Omar and Ahmad Nizamuddin and Faiz Arith}, doi = {10.1109/ACCESS.2025.3615447}, journal = {IEEE Access}, pages = {172617-172625}, shortjournal = {IEEE Access}, title = {Thermal analysis and switching performance of a 1.7 kV SiC power MOSFET under high-frequency operation and double pulse testing}, volume = {13}, year = {2025}, }
• (2025). Toward devising a multi-objective traffic signal scheduling approach for non-lane-based heterogeneous traffic. ACCESS, 13, 172598-172616. (www)

  @article{ , abstract = {Even though traffic signal scheduling is one of the fundamental problems addressed in Intelligent Transportation Systems, most of the related existing studies solved this problem in the contexts of lane-based and homogeneous traffic. These studies ignored various distinctive contexts prevalent in developing countries, such as non-lane-based (i.e., designated lanes are not followed) and heterogeneous (i.e., motorized and non-motorized) traffic. Therefore, in this study, we propose a new methodology incorporating chronological implementations of multi-objective optimization and appropriate simulations to develop an efficient traffic signal scheduling system appropriate for non-lane-based and heterogeneous traffic. First, we explore NSGA-II: Non-dominated Sorting Genetic Algorithm with binary and real encoding-based populations. We propose two novel objective functions considering the non-lane-based heterogeneous nature of traffic. Among the two encoding-based implementations, we find that real-encoded NSGA-II performs the best in providing an optimal traffic signal schedule on a numerical simulator. Further, we integrate the real-encoded NSGA-II in DhakaSim, which is a microscopic traffic simulator that realizes distinctive contexts of developing countries. Afterward, we conduct extensive simulations over three road networks from Dhaka, Miami, and Riyadh. Our simulation results show that the proposed method can lead to a 163.1% increase in average speed and a 56.1% reduction in average waiting time across Dhaka city’s road network during peak traffic hours, compared to traditional fixed-time signal scheduling currently used in some parts of the city. Besides, our proposed methodology confirms improved average speed and average waiting time over the road networks of Miami and Riyadh, underscoring the approach’s generalizability. These results highlight the effectiveness of the proposed multi-objective signal optimization framework for non-lane-based heterogeneous traffic and its potential to improve urban traffic flow in diverse, congested settings significantly.}, archive = {J_ACCESS}, author = {Masfiqur Rahaman and Ahmed Mahir Sultan Rumi and Md Shihabul Islam and Tarik Reza Toha and Md Masum Mushfiq and M. Sohel Rahman and Muhammad Ali Nayeem and Najla Abdulrahman Al-Nabhan and A. B. M. Alim Al Islam}, doi = {10.1109/ACCESS.2025.3616266}, journal = {IEEE Access}, pages = {172598-172616}, shortjournal = {IEEE Access}, title = {Toward devising a multi-objective traffic signal scheduling approach for non-lane-based heterogeneous traffic}, volume = {13}, year = {2025}, }
• (2025). Preventing witness leakage in adaptor signatures. ACCESS, 13, 172584-172597. (www)

  @article{ , abstract = {Adaptor signature schemes allow two parties to trade fairly. When a valid signature is revealed, the secret witness can also be extracted. This is useful in blockchain settings such as atomic swaps and fair exchange. However, if an adversary obtains both the pre-signature and the full signature, they can extract the witness. To fix this, we introduce a secret value $\textsf {aux}$ into the extract algorithm. Only those who know $\textsf {aux}$ can extract the witness. We also find that the adaptor algorithm must use $\textsf {aux}$ to remain secure. In a fair exchange, the buyer extracts the witness, and the seller runs the adaptor algorithm. Since both parts need $\textsf {aux}$ , both parties must share it. The auxiliary secret $\textsf {aux}$ can be shared using various methods such as non-interactive key exchange (NIKE), interactive key exchange (IKE), and key encapsulation mechanism (KEM). We show that our scheme is aEUF-CMA secure, allows witness extraction only with the shared secret $\textsf {aux}$ , and introduces a new property called restricted extractability, which ensures the witness remains hidden without $\textsf {aux}$ . Our contributions are: 1) We show that adaptor signatures can leak the witness if both the pre-signature and full signature are seen, 2) We fix this by adding a shared secret $\textsf {aux}$ that only the right party knows, 3) We define and prove a new security idea called restricted extractability, and 4) We give an example using Schnorr signatures and show how to share $\textsf {aux}$ using simple methods like NIKE.}, archive = {J_ACCESS}, author = {Jen-Chieh Hsu and Raylin Tso}, doi = {10.1109/ACCESS.2025.3616813}, journal = {IEEE Access}, pages = {172584-172597}, shortjournal = {IEEE Access}, title = {Preventing witness leakage in adaptor signatures}, volume = {13}, year = {2025}, }
• (2025). A multi-seasonal SS-MSTL-DR approach with efficient training using deep learning and LLaMA: A case study of 6-element air quality prediction in a subtropical city. ACCESS, 13, 172542-172561. (www)

  @article{ , abstract = {Large models and deep learning models for real-time services are trained from time to time to refresh the new features and achieve better accuracy. Multivariate time-series decomposition and recombination (MTS-DR) models effectively improve training efficiency. In this research, an MTS method that is more stable and can handle finer decomposed components helps the training process of deep learning and Large Language Model Meta AI (LLaMA), and is crucial for our subsequent study on the capabilities of adaptive features engineering. Thus, this study introduces an innovative method developed using the Multiple Seasonal-Trend Decomposition using LOESS (MSTL) algorithm, termed “Multiseasonal Scalable Sub-model MSTL decomposition and recombining” (Multiseasonal SS-MSTL-DR) model. This method is designed to upgrade the decomposition and recombining of multiseasonal trends within a scalable sub-model framework. Its architecture is specifically tailored for seamless integration into deep learning systems to enhance the efficiency of model training. The objective is to establish a robust foundation for analyzing time-series data exhibiting distinctive and intricate seasonal patterns. Research methods include conducting a State-of-the-Art (SOTA) literature review, data preparation, model building, feasibility tests, comprehensive evaluation, and summary. Through a case of subtropical urban air quality prediction, it is found that the “Multiseasonal SS-MSTL-DR” model can further reduce the complexity of time-series and highlight its inherent characteristics. The multiple seasonality design and the SS training make the characteristics easier to mine in MTS models. Those experimental results of the cold/warm seasons show that the learning speed and accuracy of deep learning and LLaMA models have been improved, especially in the MTS deep learning model. Since we have extended Multiseasonal SS-MSTL-DR to six different air pollutant concentration data sets, the six of them have completely different spatial characteristics of physical and chemical, which is equivalent to proving the commonality of application, i.e. the proposed method is suitable for other multiseasonal time-series data.}, archive = {J_ACCESS}, author = {Benedito Chi Man Tam and Su-Kit Tang and Alberto Cardoso}, doi = {10.1109/ACCESS.2025.3615518}, journal = {IEEE Access}, pages = {172542-172561}, shortjournal = {IEEE Access}, title = {A multi-seasonal SS-MSTL-DR approach with efficient training using deep learning and LLaMA: A case study of 6-element air quality prediction in a subtropical city}, volume = {13}, year = {2025}, }
• (2025). The role of nature-based solutions in improving temperature and noise-related comfort in compact urban areas. ACCESS, 13, 172520-172541. (www)

  @article{ , abstract = {Urban areas are increasingly affected by environmental stressors such as elevated temperatures and excessive noise, which compromise the usability of public spaces. Nature-based solutions (NBS) are gaining recognition as context-sensitive interventions to mitigate such issues, yet their effective implementation, especially in compact urban areas, depends on specific spatial and morphological conditions. This study evaluates the spatial feasibility of four NBS types: green roofs, vertical greenery, and natural terrain with high or low vegetation across four pilot streets in Ljubljana, each representing a distinct urban typology. Using high-resolution environmental data and discomfort analysis (Ravnikar et al., in press), we developed spatial suitability criteria based on a targeted literature review and technical guidelines. These were operationalized in a GIS-based overlay analysis to identify feasible implementation sites. The results confirm that spatial constraints such as limited open space, unfavorable facade orientation, and underground infrastructure—often restrict the capacity of NBS to deliver meaningful improvements. While some interventions are feasible, their impact remains fragmentary, highlighting the need to consider spatial and technical requirements from the earliest planning stages. Proactive integration of NBS into spatial development is essential to move beyond isolated interventions and achieve systemic environmental benefits.}, archive = {J_ACCESS}, author = {Živa Ravnikar and Alfonso Bahillo and Barbara Goličnik Marušić}, doi = {10.1109/ACCESS.2025.3616791}, journal = {IEEE Access}, pages = {172520-172541}, shortjournal = {IEEE Access}, title = {The role of nature-based solutions in improving temperature and noise-related comfort in compact urban areas}, volume = {13}, year = {2025}, }
• (2025). Non-control-data attacks and defenses: A review. ACCESS, 13, 172507-172519. (www)

  @article{ , abstract = {In recent years, non-control-data attacks have become a research hotspot in the field of network security, driven by the increasing number of defense methods against control-flow hijacking attacks. These attacks exploit memory vulnerabilities to modify non-control data within a program, thereby altering its behavior without compromising control-flow integrity. Research has shown that non-control-data attacks can be just as damaging as control-flow hijacking attacks and are even Turing complete, making them a serious security threat. However, despite being discovered long ago, the threat of non-control-data attacks has not been adequately addressed. In this review, we first classify non-control-data attacks into two categories based on their evolution: security-sensitive function attacks and data-oriented programming (DOP) attacks. Subsequently, based on the non-control-data attack model, we categorize existing defense methods into three main strategies: memory safety, data confidentiality, and data integrity protection. We then analyze recent defense techniques specifically designed for DOP attacks. Finally, we identify the key challenges hindering the widespread adoption of defenses against non-control-data attacks and explore future research directions in this field.}, archive = {J_ACCESS}, author = {Chong Lei and Bahari Belaton}, doi = {10.1109/ACCESS.2025.3617245}, journal = {IEEE Access}, pages = {172507-172519}, shortjournal = {IEEE Access}, title = {Non-control-data attacks and defenses: A review}, volume = {13}, year = {2025}, }
• (2025). A real-time urban traffic congestion prediction framework based on dynamic risk field and multi-source data fusion. ACCESS, 13, 172471-172495. (www)

  @article{ , abstract = {This paper proposes a novel real-time traffic congestion prediction framework based on dynamic risk field modeling and multi-source data fusion, tailored for complex urban road networks. By integrating physically interpretable risk field theory with data-driven deep learning methods, we develop a hybrid system that quantifies spatiotemporal congestion risk in a transparent and computationally efficient manner. The proposed model introduces a dynamic field superposition structure driven by Gaussian kernel functions, enabling intuitive three-dimensional visualization of congestion propagation. To meet the demands of real-time urban mobility management, we design a spatial parallel processing architecture that significantly improves computational speed while maintaining high prediction accuracy. Empirical validation using large-scale real-world traffic data demonstrates that our model improves prediction accuracy by 26.2% over standard LSTM methods while reducing CPU usage by 75%. Moreover, the proposed framework supports a dual-driven decision paradigm by linking micro-level driver behavior with macro-level congestion evolution, offering interpretable insights for smart traffic control policies such as dynamic lane assignment and variable speed limits. This study contributes to the development of robust and scalable solutions for intelligent transportation systems and sustainable urban mobility.}, archive = {J_ACCESS}, author = {Yuman Xu and Yunxia Li}, doi = {10.1109/ACCESS.2025.3608954}, journal = {IEEE Access}, pages = {172471-172495}, shortjournal = {IEEE Access}, title = {A real-time urban traffic congestion prediction framework based on dynamic risk field and multi-source data fusion}, volume = {13}, year = {2025}, }
• (2025). SignForensics: A robust framework for forensic offline signature verification with enhanced detection, noise removal, and multi-stage authentication. ACCESS, 13, 172456-172470. (www)

  @article{ , abstract = {Reliable signature authentication is essential across fields where identity verification is critical. Despite advances in machine learning, validating handwritten signatures remains challenging due to inherent variability in individual writing styles. Most current methods focus exclusively on verification, assuming signatures are already isolated and noise-free—an assumption rarely met in real-world scenarios. In this paper, we present a comprehensive framework called SignForensics for robust signature authentication, directly extracting and refining signatures from real-world documents. Our system employs YOLOv10 for signature detection and extraction, and leverages CycleGAN to effectively remove residual visual noise, producing noise-free samples optimized for downstream analysis. To evaluate authenticity, we developed specialized SigNet and CapsNet neural networks, achieving strong generalization and accuracy. Our method outperforms current state-of-the-art techniques, reaching 100% accuracy on the CEDAR dataset, 94.79% on the HINDI dataset, and 96.52% on the BENGALI dataset, remaining robust across writer-independent settings and previously unseen samples.}, archive = {J_ACCESS}, author = {Giovanni Piccinini and Luca Guarnera and Sebastiano Battiato}, doi = {10.1109/ACCESS.2025.3617221}, journal = {IEEE Access}, pages = {172456-172470}, shortjournal = {IEEE Access}, title = {SignForensics: A robust framework for forensic offline signature verification with enhanced detection, noise removal, and multi-stage authentication}, volume = {13}, year = {2025}, }
• (2025). Optimizing test case prioritization with meta deep reinforcement learning in continuous integration. ACCESS, 13, 172435-172455. (www)

  @article{ , abstract = {Software developers use Continuous Integration (CI) environments to reduce integration issues and expedite development cycles. Regression testing is an important part of CI practice, as it includes reconducting all test cases to guarantee system stability after upgrades. However, as test suites grow, this process becomes increasingly resource-intensive and time-consuming. While many Test Case Prioritization (TCP) techniques have been proposed to address this challenge, previous approaches often rely on static configurations and lack the adaptability needed to handle the dynamic nature of CI environments and different dataset complexities. To address these gaps, this study presents a novel TCP framework based on Deep Reinforcement Learning (DRL), integrating a pairwise ranking model with state-of-the-art DRL algorithms, including A2C, DQN, PPO, and TRPO. The proposed framework improves prioritization accuracy and execution efficiency, particularly when combined with an optimal cycle count strategy. An adaptive training framework based on Meta-Deep Reinforcement Learning (meta-DRL) was introduced to further enhance the adaptability of the framework. This component allows the DRL agent to assess its performance during training and dynamically modify the essential hyperparameters, thereby enhancing its ability to develop successful prioritizing methods over time. Finally, the results of the proposed methodology demonstrate that Meta-Deep Reinforcement Learning (meta-DRL) significantly reduces the training time and achieves a 60% reduction compared to existing approaches. These findings show the efficiency of meta-DRL-based TCP in providing a scalable and adaptive solution for enhancing regression testing in CI environments.}, archive = {J_ACCESS}, author = {Nahlah A. Alrakban and Mubarak Alrashoud and M. Abdullah-Al-Wadud}, doi = {10.1109/ACCESS.2025.3617387}, journal = {IEEE Access}, pages = {172435-172455}, shortjournal = {IEEE Access}, title = {Optimizing test case prioritization with meta deep reinforcement learning in continuous integration}, volume = {13}, year = {2025}, }
• (2025). A high-speed power flow method based on equivalent resistance for realtime simulators of MVDC distribution networks. ACCESS, 13, 172426-172434. (www)

  @article{ , abstract = {Medium-voltage direct current (MVDC) distribution networks have garnered significant attention due to their ability to enhance power distribution in modern grids. To efficiently operate the networks, an energy management system (EMS) is required, and it is imperative to validate applications in the EMS under a realistic environment using real-time simulators with a high-speed network analysis engine for power flow. However, classical NR-based methods are not suitable as analytical engines for real-time simulators due to their prolonged computation times resulting from complex numerical calculations. For application to real-time simulators that require high-speed computational capabilities, this paper presents a fast power flow method based on equivalent resistance for real-time simulators of MVDC distribution networks. In the proposed method, the injected power at each node is transformed into an equivalent resistance enabling a direct circuit analysis for a simple resistive circuit based on Kirchhoff’s law, which reduces computation time significantly compared with the classical NR-based method. Case studies on various test systems demonstrate the effectiveness of the proposed method, which exhibits a high degree of accuracy and a high-speed computation capability when compared with the classical NR-based method.}, archive = {J_ACCESS}, author = {Ruihua Zhang and Seong-Il Lim and Yun-Sik Oh}, doi = {10.1109/ACCESS.2025.3617039}, journal = {IEEE Access}, pages = {172426-172434}, shortjournal = {IEEE Access}, title = {A high-speed power flow method based on equivalent resistance for realtime simulators of MVDC distribution networks}, volume = {13}, year = {2025}, }
• (2025). Space debris observation using star sensors based on scanning circle model. ACCESS, 13, 172416-172425. (www)

  @article{ , abstract = {With the large-scale deployment of mega-constellations, the number of on-orbit star sensors is rapidly increasing. Building on the advancements in star sensor detection capabilities, this paper proposes utilizing star sensors as dual-purpose sensors to observe space debris while fulfilling their primary task of attitude determination. A scanning circle (SC) model is established to analyze the observation performance of the space debris located in the Geostationary Earth Orbit (GEO) belt region using star sensors. Based on this model, the optimal optical axis installation orientation of star sensors is derived to ensure full coverage of the GEO belt. Taking a star sensor onboard a sun-synchronous orbit (SSO) satellite at an altitude of 700 km as an example, it is demonstrated that setting the installation pitch angle at 13.8° and azimuth angle at 21.2° enables approximately full coverage of the GEO belt through 10 days of long-term observation. Furthermore, by expanding the single star sensor SC model, this paper proposes using star sensors onboard distributed satellite platforms to enhance observation efficiency. The results indicate that deploying 5 star sensors on distributed platforms can achieve rapid full-coverage of the GEO belt within 24 hours. The proposed strategy of using star sensors for space debris observation and the SC model not only expand the application of star sensors into the field of space situational awareness but also open up broader commercial prospects.}, archive = {J_ACCESS}, author = {Fei Feng and Xia Wang and Wei Qi and Huisheng Yao and Zeyu Dong}, doi = {10.1109/ACCESS.2025.3610865}, journal = {IEEE Access}, pages = {172416-172425}, shortjournal = {IEEE Access}, title = {Space debris observation using star sensors based on scanning circle model}, volume = {13}, year = {2025}, }
• (2025). LENS: Lightweight and explainable LLM-based APT detection at the edge for 6G security. ACCESS, 13, 172402-172415. (www)

  @article{ , abstract = {Expected to be deployed in the early 2030s, sixth-generation (6G) wireless networks, with their high speed and integration with cutting-edge technology such as intelligent edge computing, expand the attack surface and face serious cyber threat risks such as Advanced Persistent Threats (APTs). This type of cyber attack can imitate benign network traffic and operate for long periods of time without being detected by traditional detection systems. This paper introduces LENS, a lightweight and explainable LLM-based network security framework designed to address this cybersecurity threat for 6G environments. LENS uses a fine-tuned DistilBERT model to convert raw network streams into natural language commands using contextual metadata and is trained on the CICAPT-IIoT (2024) dataset generated using real-time network traffic data. To evaluate the proposed model, adapted versions of DeepLog and EarlyCrow are compared using F1-score, false positive rate, and explainability metrics for binary APT classification on the CICAPT-IIoT dataset. All models are trained using a high-performance GPU (Nvidia A10) and validated by deploying on a real-world resource-constrained edge node (Raspberry Pi 4). The results confirm that LENS has higher performance in APT detection with 0.82 accuracy and 0.82 recall despite consuming higher energy compared to the other two baselines, and is applicable for edge-enabled 6G environments.}, archive = {J_ACCESS}, author = {Suhib Bani Melhem and Muhammed Golec and Abdulmalik Alwarafy and Yaser Khamayseh}, doi = {10.1109/ACCESS.2025.3616235}, journal = {IEEE Access}, pages = {172402-172415}, shortjournal = {IEEE Access}, title = {LENS: Lightweight and explainable LLM-based APT detection at the edge for 6G security}, volume = {13}, year = {2025}, }
• (2025). Fault detection isolation and localization (FDIL) scheme: A robust ML framework with novel architecture for fault localization. ACCESS, 13, 172371-172383. (www)

  @article{ , abstract = {Fault detection, classification, and location estimation are paramount in transmission lines to ensure uninterrupted power supply, enhance reliability, and safety. Faster and more accurate fault localization is critical for prompt repair and efficient power restoration, thus minimizing economic losses. This paper presents a novel Fault Detection Isolation and Localization (FDIL) scheme, a machine learning (ML)-based framework, to increase the accuracy of fault location (FL) prediction. The proposed framework utilizes the phasor voltage, current, and frequency data obtained from optimally placed Phasor Measurement Units (PMUs) for fault detection, isolation, and localization. This paper also proposes a hybrid deep learning-based approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks and an attention mechanism for FL prediction. Extensive simulations using the IEEE 9-bus system, with different types and locations of faults, are performed on the OPAL-RT simulator-based testbed to generate and train the ML models. The performance of the proposed scheme is evaluated by calculating the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared ( $R^{2}$ ) metric. The results demonstrate that the proposed approach achieves high accuracy in FL prediction, with an MAE of 0.0228 km, an MSE of 0.001, and an $R^{2}$ of 0.9894. Compared to the best-performing technique reported in the literature (CNN-LSTM, MAE = 0.211km), the proposed approach achieves an 89.2% reduction in localization error. The proposed scheme and model outperform the contemporary model, as reported in various literature, making it an effective solution for real-time fault monitoring in transmission systems.}, archive = {J_ACCESS}, author = {Rajendra Shrestha and Manohar Chamana and Mostafa Mohammadpourfard and Larissa Souto and Stephen Bayne}, doi = {10.1109/ACCESS.2025.3617464}, journal = {IEEE Access}, pages = {172371-172383}, shortjournal = {IEEE Access}, title = {Fault detection isolation and localization (FDIL) scheme: A robust ML framework with novel architecture for fault localization}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient buffer-based ternary SRAM cell with application to image processing. ACCESS, 13, 172359-172370. (www)

  @article{ , abstract = {This paper presents a design of a variation-resilient and energy-efficient ternary memory cell (TSRAM) suited for power-demanding internet-of-things (IoT) applications that run on batteries. The TSRAM cell utilizes a latch composed of an efficient ternary buffer (TBUF) with positive feedback, a single-bitline, and a transmission gate for switching access, with an overall area only about 39% more than binary 6T SRAM. The threshold voltage ( ${\mathrm {V}}_{\mathrm {th}}$ ) tuning of carbon nanotube field-effect transistor (CNTFET) devices has been explored to achieve the three storage levels. Simulations were conducted using the standard Stanford 32-nm CNTFET model file in the Synopsis HSPICE simulator. The projected design offers substantial reductions of 54.94% in read power, 67.06% in write power, and 21.59% in area compared to the best buffer-based TSRAM designs. These power savings are achieved by minimizing the transistor count and eliminating any direct current path between VDD and ground in the TBUF design for getting logic ‘1’. Furthermore, the proposed design demonstrates the highest logic ‘1’ static noise margin (SNM1) and shows resilience to process, voltage, and temperature (PVT) variations. The TSRAM electrical quality matrix (TEQM), a crucial figure of merit, indicates the superior performance of the proposed design for IoT applications. The study was further extended to conduct simulations and report the performance metrics of the proposed TSRAM array. Ultimately, to evaluate the real-world application of the triple memory structures, the pixel-by-pixel storage process of a grayscale image with three-value data content is performed based on a hardware algorithm. The obtained results demonstrate that the proposed TSRAM architecture has about a 26.3% improvement in hardware performance compared to its highest performing counterpart scheme.}, archive = {J_ACCESS}, author = {Shams Ul Haq and Erfan Abbasian and Abdolreza Darabi and Imran Ahmed Khan and Owais Ahmad Shah and Vakkalakula Bharath Sreenivasulu}, doi = {10.1109/ACCESS.2025.3616000}, journal = {IEEE Access}, pages = {172359-172370}, shortjournal = {IEEE Access}, title = {Energy-efficient buffer-based ternary SRAM cell with application to image processing}, volume = {13}, year = {2025}, }
• (2025). A wireless and machine learning-based electrochemical biosensor potentiostat system for CD63 protein detection in lung cancer biomarkers. ACCESS, 13, 172341-172358. (www)

  @article{ , abstract = {Lung cancer remains one of the leading causes of cancer-related deaths globally, with approximately 2.5 million new cases and 1.8 million deaths reported annually according to World Health Organization in 2022. Early and accurate detection is crucial for improving survival rates through timely treatment. However, current detection methods face challenges such as invasiveness, high costs, and delayed diagnosis. Electrochemical biosensors, which use potentiostats to detect biological analytes by controlling cell voltage and measuring current, voltage, or impedance, offer a rapid and sensitive alternative. Nevertheless, many existing potentiostats are limited by narrow voltage ranges, single current measurement capabilities, and inadequate support for advanced electrochemical techniques. This study presents a novel wireless electrochemical biosensor potentiostat system enhanced with machine learning and Internet of Things (IoT) integration for the detection of lung cancer biomarkers, specifically the CD63 protein. The system is designed to be portable, energy efficient, cost-effective, and high sensitive, supporting advanced electrochemical techniques such as cyclic voltammetry (CV) and square wave voltammetry (SWV). It achieves a limit of detection (LoD) of $2\times 10^{1}$ particles/mL and a sensitivity of $5.188~\mu \text {A}$ per log concentration unit, comparable to the commercial systems like uStat8000 potentiostat. Furthermore, the integrated random forest classifier enables fast and automated data interpretation, achieving accuracy rates of 83.5% and 87.5% for CV and SWV cases, respectively. Overall, the developed platform offers a smart, portable, and scalable solution for point-of-care diagnostics, contributing to early detection and improved prognosis in lung cancer management.}, archive = {J_ACCESS}, author = {Ahmed Faozi Rabea and Effariza Hanafi and Subhashini Raj Kumal and Anis Salwa Mohd Khairuddin and Leo Bey Fen}, doi = {10.1109/ACCESS.2025.3614293}, journal = {IEEE Access}, pages = {172341-172358}, shortjournal = {IEEE Access}, title = {A wireless and machine learning-based electrochemical biosensor potentiostat system for CD63 protein detection in lung cancer biomarkers}, volume = {13}, year = {2025}, }
• (2025). From opening to endgame: When does the game get engaging and exciting? exploring phase-based velocity dynamics using the MiM framework. ACCESS, 13, 172326-172340. (www)

  @article{ , abstract = {Understanding how engagement and excitement evolve throughout different phases of a sporting event is essential for advancing research in sports analytics, psychology, and game design. Previous models, like Game Refinement theory, have effectively quantified overall game sophistication but often overlook the dynamic, temporal nature of gameplay. This study addresses this gap by applying the physics-inspired Motion in Mind (MiM) framework to explicitly examine phase-specific gameplay dynamics in three globally popular sports: ODI cricket, T20 cricket, and soccer. Each match was segmented into opening, middle, and endgame phases, enabling detailed computation of key MiM parameters including velocity, acceleration, jerk, momentum, and potential reinforcement energy. Notably, ODI cricket showed initial high excitement that gradually declined toward the end, highlighting strategic shifts from aggressive to more calculated gameplay, whereas T20 cricket exhibited rising momentum and heightened excitement towards the endgame. In contrast, soccer unexpectedly revealed intense peaks during its middle phase. These findings provide novel theoretical insights into how temporal structures shape spectator and player engagement and offer practical strategies for optimizing match design, coaching tactics, and spectator enjoyment across competitive sports contexts.}, archive = {J_ACCESS}, author = {Muhammad Numan and Hiroyuki Iida and Mohd Nor Akmal Khalid}, doi = {10.1109/ACCESS.2025.3614624}, journal = {IEEE Access}, pages = {172326-172340}, shortjournal = {IEEE Access}, title = {From opening to endgame: When does the game get engaging and exciting? exploring phase-based velocity dynamics using the MiM framework}, volume = {13}, year = {2025}, }
• (2025). Designing intelligent sensor networks: A comprehensive survey of sensing, edge AI, and 5G+/6G connectivity. ACCESS, 13, 172306-172325. (www)

  @article{ , abstract = {With the advent of advanced transmission technologies, powerful neural-network architectures, and the availability of cheap sensing solutions, new paradigms for monitoring, controlling, and optimizing any automated system are appearing. While traditionally, sensors simply provided visual or acoustic feedback to human operators of such systems, nowadays, autonomous systems more and more rely on the automatic sensing and processing of such data to react and adapt their control mechanisms. In such complex control systems, sensor networks play a vital role. In this paper, we assess the state of the art in the three main components used within sensor networks: smart sensing elements, transmission technologies, and processing technologies. Concerning the latter, more and more artificial intelligence-based methods are developed and applied turning smart sensor networks into intelligent or even cognitive sensor networks. For the development of such sensing systems, a high variety of performance metrics could be important to the designer. This paper discusses the vast design space for intelligent sensor networks and provides guidance and directions on how to construct future intelligent sensor networks efficiently.}, archive = {J_ACCESS}, author = {Christian Herglotz and Ireneusz Jabłoński and Reinhardt Karnapke and Keyvan Shahin and Marc Reichenbach}, doi = {10.1109/ACCESS.2025.3615205}, journal = {IEEE Access}, pages = {172306-172325}, shortjournal = {IEEE Access}, title = {Designing intelligent sensor networks: A comprehensive survey of sensing, edge AI, and 5G+/6G connectivity}, volume = {13}, year = {2025}, }
• (2025). Implementation of deep reinforcement learning for model-free switching and control of a 23-level single DC source hybrid packed U-cell (HPUC) converter. ACCESS, 13, 172293-172305. (www)

  @article{ , abstract = {This paper proposes a novel Deep Reinforcement Learning (DRL) method for controlling a 23-level Single DC Source Hybrid Packed U-Cell (HPUC) converter. The HPUC topology generates a high number of voltage levels with minimal components but presents control challenges due to its numerous switching states and dynamic charging behavior. Unlike conventional control methods, which require accurate models and are sensitive to noise and parameter mismatches, DRL offers a model-free and resilient approach to the non-linear control of such complex systems. A Deep Q-Network (DQN) agent which is inherently model-free and suited for high-dimensional state spaces and discrete action spaces, is employed to address these issues. To validate the proposed method, simulations were conducted in the MATLAB/Simulink environment. The obtained results demonstrated the satisfactory performance of the proposed DRL method, achieving a Total Harmonic Distortion (THD) of 2.71% in the output current under steady-state, maintaining stable capacitor voltage balancing, and exhibiting rapid dynamic response (e.g., settling within approximately 40 ms for current step changes). Furthermore, its resilience was highlighted by its ability to maintain control despite a 25dB SNR noise condition and up to 15% variations in capacitor values.}, archive = {J_ACCESS}, author = {Pouria Qashqai and Mohammad Babaie and Rawad Zgheib and Kamal Al-Haddad}, doi = {10.1109/ACCESS.2025.3605874}, journal = {IEEE Access}, pages = {172293-172305}, shortjournal = {IEEE Access}, title = {Implementation of deep reinforcement learning for model-free switching and control of a 23-level single DC source hybrid packed U-cell (HPUC) converter}, volume = {13}, year = {2025}, }
• (2025). A novel machine learning workflow to classify mobile home parks at scale. ACCESS, 13, 172279-172292. (www)

  @article{ , abstract = {Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. We show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.}, archive = {J_ACCESS}, author = {Clinton Stipek and Sameer Nathawat and Taylor Hauser and Nagendra Singh}, doi = {10.1109/ACCESS.2025.3615118}, journal = {IEEE Access}, pages = {172279-172292}, shortjournal = {IEEE Access}, title = {A novel machine learning workflow to classify mobile home parks at scale}, volume = {13}, year = {2025}, }
• (2025). Imbalance-aware scheduling for PV-battery storage systems using deep reinforcement learning. ACCESS, 13, 172245-172258. (www)

  @article{ , abstract = {The growing penetration of distributed renewable energy resources necessitates more intelligent and adaptive energy management strategies. In this paper, we propose a novel imbalance-aware control framework for photovoltaic-battery storage systems (PV-BSS) participating in day-ahead electricity markets characterized by strict penalty mechanisms, such as those in Japan. The core of the framework is a Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) agent, which is explicitly trained to minimize imbalance penalties by embedding forecast deviations into the reward function. To enhance operational feasibility under real-world constraints, the PPO agent is complemented by a Model Predictive Control (MPC) layer that refines actions in real time based on updated forecasts and system constraints. The proposed framework integrates probabilistic PV forecasting using Lower-Upper Bound Estimation (LUBE) and electricity price prediction via multi-layer perceptron (MLP) models within a unified control loop. Through extensive simulations using actual Japanese market data, the method demonstrates a 47% reduction in imbalance penalties compared to the rule-based strategy and a 26% reduction compared to the DRL model without imbalance awareness. These results highlight the proposed method’s potential for economically efficient and regulation-compliant scheduling in dynamic and penalty-intensive electricity markets.}, archive = {J_ACCESS}, author = {Yuki Osone and Daisuke Kodaira}, doi = {10.1109/ACCESS.2025.3615960}, journal = {IEEE Access}, pages = {172245-172258}, shortjournal = {IEEE Access}, title = {Imbalance-aware scheduling for PV-battery storage systems using deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Comparative perspective of visual attention: From human focus to visual Transformers—An in-depth review. ACCESS, 13, 172230-172244. (www)

  @article{ , abstract = {Although neuroscience has made considerable progress in recent decades by proposing robust models that explain the mechanisms of attention and perception in humans, emulating this capability using computational techniques remains complex. It was not until the development of models such as Visual Transformers (ViT) that it became possible to partially replicate this uniquely human trait. The main objective of this study was to explore the extent to which attention models, such as ViT, can reproduce the manner in which people distribute their visual attention when exposed to various stimuli, particularly in the context of handcrafted objects. Human fixations (i.e., attention) were recorded using an eye tracker, while the ViT model processed the same images to generate attention maps to evaluate the degree of similarity between the two patterns. For this purpose, heatmaps were constructed, and quantitative metrics were applied to assess their similarity. The results revealed areas of convergence and significant differences, highlighting the current limitations of computational models in capturing the more subtle aspects of human perception. This comparison not only helps us better understand the capabilities of ViT but also provides a foundation for reflecting on future improvements in automated attention models and their potential applications in contexts where visual interpretation is crucial.}, archive = {J_ACCESS}, author = {Luis Guillermo Oliveros Piñero and Miguel Carrasco and José Aranda and César González-Martín}, doi = {10.1109/ACCESS.2025.3615404}, journal = {IEEE Access}, pages = {172230-172244}, shortjournal = {IEEE Access}, title = {Comparative perspective of visual attention: From human focus to visual Transformers—An in-depth review}, volume = {13}, year = {2025}, }
• (2025). A novel adversarial gray-box attack on DCT-based face deepfake detectors. ACCESS, 13, 172216-172229. (www)

  @article{ , abstract = {In recent years, several techniques have been developed to detect deepfake images, with particular success of approaches that exploit analytical traces (e.g. frequency domain features), such as those derived from the Discrete Cosine Transform (DCT). Despite their effectiveness, these detectors remain vulnerable to adversarial attacks. In this paper, we introduce a novel gray-box adversarial attack specifically designed to evade DCT-based deepfake detectors. Our method accurately tunes the AC coefficient statistics of synthetic images to closely match those of real ones, while preserving high visual quality. The attack assumes full knowledge of the DCT feature extraction process, but not access to the internal parameters of the classifiers. We evaluate the proposed method against a set of DCT-based detectors using deepfakes generated from both Generative Adversarial Networks (GANs) and Diffusion Models (DMs). Experimental results show significant degradation in detection performance, exposing critical weaknesses in systems traditionally considered interpretable and robust. This work raises important concerns about the reliability of frequency domain detectors in forensic and cybersecurity applications.}, archive = {J_ACCESS}, author = {Francesco Guarnera and Luca Guarnera and Alessandro Ortis and Sebastiano Battiato and Giovanni Puglisi}, doi = {10.1109/ACCESS.2025.3617766}, journal = {IEEE Access}, pages = {172216-172229}, shortjournal = {IEEE Access}, title = {A novel adversarial gray-box attack on DCT-based face deepfake detectors}, volume = {13}, year = {2025}, }
• (2025). A convolutional neural network to spiking neural network conversion framework for seismic denoising. ACCESS, 13, 172203-172215. (www)

  @article{ , abstract = {This study investigates the application of Spiking Neural Network (SNN) in seismic signal denoising by developing a Convolutional Neural Network (CNN) to SNN conversion framework. We focus on two challenges: optimal spike encoding strategy adaptation for seismic data; and denoising performance preservation during CNN-SNN conversion. Through systematic experiments on the public Marmousi 2 dataset and field data from Sichuan Basin, we demonstrate that SNN can feasibly serve as an alternative to traditional CNN for seismic denoising tasks. Our experiments show that both count-rate encoding (CR) and time-to-first-spike coding (TTFS) can effectively encode seismic data. However, when integrated with the proposed CNN-SNN conversion framework for seismic denoising, CR outperforms TTFS due to structural mismatches between TTFS and the framework in signal representation, temporal control, and weight mapping, which lead to information loss. Our neuron optimization strategy combines soft reset mechanisms with adaptive thresholding, demonstrating enhanced performance that narrows the gap between native CNN and converted SNN implementations. To enable rigorous evaluation, we introduce a baseline alignment scheme ensuring fair comparison between the native CNN and its CNN-SNN converted architecture. This work demonstrates the first successful application of SNN to seismic signal denoising, offering a bio-inspired alternative to conventional CNN while preserving comparable signal-to-noise ratio performance.}, archive = {J_ACCESS}, author = {Shuna Chen and Zhege Liu and Ziyu Qin and Xinyi Liu and Yajuan Xue and Junxing Cao}, doi = {10.1109/ACCESS.2025.3617570}, journal = {IEEE Access}, pages = {172203-172215}, shortjournal = {IEEE Access}, title = {A convolutional neural network to spiking neural network conversion framework for seismic denoising}, volume = {13}, year = {2025}, }
• (2025). Color the conflict: Enabling simultaneous traversal in AIM using graph coloring. ACCESS, 13, 172183-172202. (www)

  @article{ , abstract = {Conflict resolution, particularly through effective priority assignment and right-of-way negotiation, is a critical factor in maximizing intersection throughput and minimizing vehicle crossing time in Autonomous Intersection Management (AIM). This paper proposes a novel color-based round scheduling algorithm that enables simultaneous traversal of multiple vehicles on both conflicting and non-conflicting paths by segmenting the paths inside the intersection using a graph coloring technique. Unlike traditional approaches that resolve conflicts at each individual conflict point, this method eliminates conflict checking by globally coordinating vehicle movements based on color rounds. As a result, the algorithm significantly reduces computational complexity and the number of conflict comparisons, which otherwise scale exponentially with the number of vehicles and conflict points. Simulations in the CARLA simulator across varying traffic densities and movement scenarios demonstrate that the proposed algorithm improves intersection throughput by up to 343% compared to reservation-based methods using FCFS for priority resolution. It also reduces average crossing time by approximately 76% compared to traditional traffic signals and by 94% relative to standalone FCFS strategies. We have also demonstrated that the proposed algorithm is fair in terms of the number of vehicles crossing the intersection per route.}, archive = {J_ACCESS}, author = {Shraddha Dulera and RAM Narayan Yadav}, doi = {10.1109/ACCESS.2025.3616231}, journal = {IEEE Access}, pages = {172183-172202}, shortjournal = {IEEE Access}, title = {Color the conflict: Enabling simultaneous traversal in AIM using graph coloring}, volume = {13}, year = {2025}, }
• (2025). EEG-based prognostic prediction in moderate traumatic brain injury: A hybrid BiLSTM-AdaBoost approach. ACCESS, 13, 172157-172182. (www)

  @article{ , abstract = {An electroencephalography (EEG)-based prognostic model for moderate traumatic brain injury (moTBI) overcomes the limitations of traditional clinical assessment. Although machine learning (ML) algorithms promise better results, existing frameworks often struggle to capture the complex temporal patterns in EEG data, resulting in poor prediction accuracy. This study addresses this gap by presenting a novel hybrid deep learning (DL) architecture of a prediction model that uses resting-state EEG data as input. This architecture synergistically combines bidirectional long short-term memory (BiLSTM) with adaptive boosting (AdaBoost), which enables accurate discrimination between poor and good prognosis of moTBI. The BiLSTM component extracts complex features from the EEG signals, while AdaBoost serves as a classifier discriminator, replacing the conventional softmax function to optimize performance. Experimental results show that the BiLSTM-AdaBoost model achieves impressive under the curve (AUC) performance of $99.51~\pm ~1.36$ % and $98.91~\pm ~2.47$ % for preprocessed and $98.91~\pm ~2.47$ % for unprocessed EEG sequences. Notably, the model outperforms conventional DL architectures and other hybrid models, showing improvements of up to 14.14% over DL-only models and 24.51% over traditional ML approaches. This innovative architecture of the BiLSTM-AdaBoost model represents a significant advance in EEG-based decision support systems for moTBI and rapid assessment of a potentially overlooked poor prognosis.}, archive = {J_ACCESS}, author = {Nor Safira Elaina Mohd Noor and Haidi Ibrahim and Lai Chi Qin and Jafri Malin Abdullah}, doi = {10.1109/ACCESS.2025.3608067}, journal = {IEEE Access}, pages = {172157-172182}, shortjournal = {IEEE Access}, title = {EEG-based prognostic prediction in moderate traumatic brain injury: A hybrid BiLSTM-AdaBoost approach}, volume = {13}, year = {2025}, }
• (2025). Hybrid deep machine learning feature selection for high-dimensional cybersecurity data. ACCESS, 13, 172136-172156. (www)

  @article{ , abstract = {The rapid increase in cyber threats has heightened the demand for Intrusion Detection Systems (IDS) that are both accurate and efficient. While deep learning models outperform traditional machine learning models in identifying complex attack patterns, their effectiveness is often constrained by high-dimensional feature spaces, reduced interpretability, and increased computational cost. To address these, we propose a novel IDS framework: Hybrid Deep Machine Learning Feature Selection (HDMLFS), which leverages Integrated Gradients (IG) and SHapley Additive exPlanations (SHAP) sensitivity to feature perturbations and global consistency in feature importance, enabling a more robust selection process with high performance. First, a correlation-based algorithm removes redundant features by analyzing the upper triangular part of the correlation matrix and discarding the less informative feature from each highly correlated pair. Next, a voting-based algorithm combines IG and SHAP rankings to identify the most informative features, ensuring that at least half of the features are retained while maximizing relevance. The framework was evaluated using the NSL-KDD and CSE-CIC IDS2018 datasets, reducing the feature space by 48% and 65%, respectively. Models trained with the selected features demonstrated superior performance, with ResNet-SF achieving the best results: 98.23% weighted accuracy on CSE-CIC IDS2018, including 86.96% recall for rare Web attacks, and 99.77% accuracy on NSL-KDD, including 80.77% recall for the rare U2R attack. These results highlight the effectiveness of HDMLFS in improving detection capability while reducing complexity and supporting efficient and interpre table IDS solutions.}, archive = {J_ACCESS}, author = {Sesan F. Akintade and Kaushik Roy and Seong-Tae Kim}, doi = {10.1109/ACCESS.2025.3615582}, journal = {IEEE Access}, pages = {172136-172156}, shortjournal = {IEEE Access}, title = {Hybrid deep machine learning feature selection for high-dimensional cybersecurity data}, volume = {13}, year = {2025}, }
• (2025). TVIM: Thermoactive variable impedance module evaluating shear-mode capabilities of polycaprolactone. ACCESS, 13, 172123-172135. (www)

  @article{ , abstract = {This paper presents a significant advancement in the field of thermoactive variable impedance modules by building upon prior research focused on utilizing thermal-induced changes in impedance within actuation systems. The initial design leveraged the temperature-dependent properties of Polycaprolactone (PCL) alongside Peltier elements to modulate stiffness and damping. However, this reliance on compressive forces and the inherent stress relaxation of PCL contributed to prolonged response times in the system. The newly developed module addresses these limitations by operating in a ‘shear mode’, fundamentally enhancing both heat transfer and response speed, as validated through comprehensive shear rheology analysis of PCL. A standout feature of the revised design is its scalability, which effectively eliminates the necessity for additional mechanical actuators. The compact nature of this thermal actuation solution supports effective miniaturization, making it exceptionally suitable for scenarios where space and weight are at a premium. This development represents a valuable innovation in variable impedance actuators for applications in robotics and biomechanics, where constant adaptation of impedance is less critical, providing a pragmatic solution for environments that demand agile yet compact actuation capabilities.}, archive = {J_ACCESS}, author = {Trevor Exley and Rashmi Wijesundara and Shuopu Wang and Arian Moridani and Taha Nilforooshan and Amir Jafari}, doi = {10.1109/ACCESS.2025.3613465}, journal = {IEEE Access}, pages = {172123-172135}, shortjournal = {IEEE Access}, title = {TVIM: Thermoactive variable impedance module evaluating shear-mode capabilities of polycaprolactone}, volume = {13}, year = {2025}, }
• (2025). Quasi-resonance linear active disturbance rejection-based control method of linear-rotary PM motors for electric-driven agricultural equipment application. ACCESS, 13, 172111-172122. (www)

  @article{ , abstract = {Electric-driven agricultural machinery is one of the effective means to achieve precise and efficient agricultural farming and green sustainable agriculture. The Linear-rotary (LR) motion is a common mode of farming drive requirement in agricultural machinery equipment. Currently, the linear-rotary permanent magnet (LRPM) motors with linear, rotary, and helical motions feature the merits of integrated structure and high material utilization due to the absence of the intermediate mechanical devices which has broad application prospects in the field of two-degree-of-freedom drive for agricultural machinery preparation. Yet, the LR motors always suffer from the coupling effect, which causes the low driving accuracy with large speed, torque, and thrust ripple. This paper proposes a decoupling and ripple suppression control method with the quasi-resonant linear active disturbance rejection controller (QR-LADRC). A stator excited LRPM motor is selected as the research objective. Based on the analysis of the flux coupling effect, the variable combination modes of the QR-LADRC controller are designed and analyzed in detail which is compared with the traditional LADRC. The simulation and experiments are conducted, and the results show that the control method can not only suppress the periodic ripple and reduce the position error of the LRPM motor, but also does not worsen the dynamic response ability.}, archive = {J_ACCESS}, author = {Yao Zhang and Lei Xu and Chi Zhang and Chao Zhang and Li Quan}, doi = {10.1109/ACCESS.2025.3616245}, journal = {IEEE Access}, pages = {172111-172122}, shortjournal = {IEEE Access}, title = {Quasi-resonance linear active disturbance rejection-based control method of linear-rotary PM motors for electric-driven agricultural equipment application}, volume = {13}, year = {2025}, }
• (2025). Agentic reasoning for social event extrapolation: Integrating knowledge graphs and language models. ACCESS, 13, 172096-172110. (www)

  @article{ , abstract = {Accurate prediction of socio-political events is a longstanding challenge with profound implications for risk management, policy planning, and international relations. Traditional machine learning approaches, such as graph neural networks and recurrent neural networks, have achieved notable progress but often struggle to integrate rich textual context and provide interpretable reasoning. Recent advances in large language models (LLMs) have demonstrated unprecedented capabilities across diverse tasks, including text generation, code synthesis, and complex multimodal reasoning, making them promising candidates for event prediction in dynamic, data-rich environments. This research presents an agentic reasoning framework combining temporal knowledge graphs, large language models (LLMs), and a modular tool-based architecture to address event extrapolation in complex, real-world settings. The methodology integrates agent-based reasoning, iterative tool invocation, and explicit validation mechanisms to ensure logical consistency and transparency in predictions. Experiments on country-specific subsets of the POLECAT dataset employ multiple LLM architectures and multiple evaluation metrics, including Hit@k, MRR, F1-scores and Prediction Entropy. Comparative analyses demonstrate that the agentic framework achieves robust predictive performance and interpretability, complementing fine-tuned LLM baselines. Furthermore, the ethical implications of deploying AI in social computing are addressed, including bias, transparency, and accountability. This study advances event prediction by demonstrating how agentic LLMs, equipped with explicit reasoning and validation, provide scalable and ethically grounded solutions for complex social event extrapolation.}, archive = {J_ACCESS}, author = {Aditya Narasimhan Sampath and Avani Thakur and Siddharth Krishnan}, doi = {10.1109/ACCESS.2025.3612015}, journal = {IEEE Access}, pages = {172096-172110}, shortjournal = {IEEE Access}, title = {Agentic reasoning for social event extrapolation: Integrating knowledge graphs and language models}, volume = {13}, year = {2025}, }
• (2025). How prompt language structures influence perceived camera movement and usage intentions in AI-generated video: An S–O–R framework. ACCESS, 13, 172080-172095. (www)

  @article{ , abstract = {With the widespread application of generative artificial intelligence (AIGC) in video content creation, the linguistic structure of prompts has emerged as a key factor influencing both generation outcomes and user experience. Grounded in the Stimulus–Organism–Response (S–O–R) theoretical framework, this study investigates how the linguistic structure of prompts affects users’ willingness to use AI-based video generation tools by shaping their perception of camera movement. The research adopts two types of prompt structures—terminology-driven and context-guided—as independent variables. Using image-to-video generation as experimental stimuli, the study employs a within-subjects experimental design and structural equation modeling (SEM) for empirical analysis. The results indicate that five perceptual dimensions—visual coherence, sense of immersion, realism and naturalness, output professionalism, and trust—significantly and positively predict users’ behavioral intention to use the tools. However, the difference in user ratings across perceptual dimensions between the two prompt types did not reach statistical significance, suggesting that prompt language structures have not yet exerted a measurable impact on subjective user perception. Furthermore, mediation analysis did not reveal significant indirect effects through perceptual variables. This study extends the applicability of the S–O–R model within AIGC contexts and demonstrates that semantically clear and logically structured natural language prompts can be equally effective in guiding AI video generation. The findings offer empirical support for reducing creative barriers and expanding user participation in generative media environments.}, archive = {J_ACCESS}, author = {Jun Liu and Yue Sun}, doi = {10.1109/ACCESS.2025.3613754}, journal = {IEEE Access}, pages = {172080-172095}, shortjournal = {IEEE Access}, title = {How prompt language structures influence perceived camera movement and usage intentions in AI-generated video: An S–O–R framework}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning with clustering optimization for antenna parameter adjustment in HAPS networks. ACCESS, 13, 172062-172079. (www)

  @article{ , abstract = {High-Altitude Platform Stations (HAPS) promise to extend broadband coverage over wide areas by acting as quasi-stationary aerial base stations. However, wind-induced movements cause antenna misalignment and unbalanced user distributions, leading to throughput degradation. This work presents a low-complexity, equal clustering (EC) -aided deep Q-network (DQN) algorithm for dynamic antenna control in HAPS networks. The proposed EC algorithm partitions users into balanced clusters by identifying low-density sectors in a single pass, avoiding the iterative refinements of other clustering algorithms such as K-Means. A DQN then fine-tunes the antenna tilt angles and beam widths based on the EC results to maximize the throughput of low-throughput users. The proposed method’s performance is evaluated through simulations under rotation and shift scenarios and benchmarked against three Reinforcement Learning (RL)-based approaches and a K-Means clustering baseline. Compared with these methods, the EC-aided DQN delivers higher throughput, especially for users in the low-throughput tail, while reducing computational complexity. For instance, the percentage of low-throughput users was reduced by over 10% in the case of 5 km shift in the regions of Osaka and Sendai, compared to K-means. Similarly, in the case of 30° rotation, the percentage is reduced by 4.1% and 6.5% for the same regions. Statistical significance tests confirm that the improvements are not due to random simulation noise.}, archive = {J_ACCESS}, author = {Mondher Bouazizi and Siyuan Yang and Zhaojie Li and Yue Yin and Tomoaki Ohtsuki}, doi = {10.1109/ACCESS.2025.3612393}, journal = {IEEE Access}, pages = {172062-172079}, shortjournal = {IEEE Access}, title = {Reinforcement learning with clustering optimization for antenna parameter adjustment in HAPS networks}, volume = {13}, year = {2025}, }
• (2025). RecipeBuild: Learning to build recipe ideas using language model-based recipe contextual modeling. ACCESS, 13, 172051-172061. (www)

  @article{ , abstract = {We introduce RecipeBuild, a fine-tuned Pre-trained Language Model (PLM) specifically trained for enhanced contextual understanding in cooking recipes, including human-written recipe titles and user preferences such as cuisine style and cooking method. Our model excels in two key tasks: the Recipe Completion Task, where it recommends proper ingredients, and the Recipe Ideation Task, where it suggests innovative and diverse ingredient combinations. Experimental results demonstrate that RecipeBuild outperforms the baseline models in the Recipe Completion task, achieving an MRR of 0.0798 and a recall of 0.0794, as well as in the Recipe Ideation task, with a Macro MRR of 0.3606 and a Micro MRR of 0.0641, making it a valuable tool for generating personalized and realistic cooking ideas that cater to various dietary needs and culinary cultures. By addressing the generalized task of ingredient recommendation, RecipeBuild demonstrates the potential for improved recipe representation learning. Code and datasets are available at https://github.com/dongheechoi/recipebuild}, archive = {J_ACCESS}, author = {Donghee Choi and Jaesung Hwang and Mogan Gim and Donghyeon Park}, doi = {10.1109/ACCESS.2025.3610410}, journal = {IEEE Access}, pages = {172051-172061}, shortjournal = {IEEE Access}, title = {RecipeBuild: Learning to build recipe ideas using language model-based recipe contextual modeling}, volume = {13}, year = {2025}, }
• (2025). Attention-based convolutional neural network model for skin cancer classification. ACCESS, 13, 172027-172050. (www)

  @article{ , abstract = {Early detection of skin cancer (SC) is paramount for effective treatment. Although convolutional neural networks (CNN) have facilitated automated learning of high-level features from dermoscopic images, many prior studies relied on separate feature extraction and selection stages combined with machine learning classifiers, resulting in increased model complexity and a higher risk of overfitting. Furthermore, sensitivity of conventional CNNs to subtle lesion changes can be challenging without inherent mechanisms to emphasize salient image regions, ultimately hindering classification performance. Moreover, the ambiguous nature of these models often leaves dermatologists uncertain about the reasoning behind predictions, which limits their adoption in clinical settings. Therefore, we propose an efficient dual attention-based ResNet50 (EDA-ResNet50) model that effectively captures vital details in lesion areas. EDA-ResNet50 integrates a multi-scale feature representation block to effectively capture features across various spatial scales. Additionally, a unique efficient dual attention module is introduced, amalgamating a lightweight convolution-based recalibration unit with channel and spatial attention blocks to enhance information flow. Gradient-weighted class activation mapping is exploited to understand the EDA-ResNet50 model decisions. The proposed system classifies dermoscopic images into benign and malignant categories with accuracy of 93.18%, offering confidence for dermatologist. EDA-ResNet50 outperformed several CNN techniques. Unlike existing studies, EDA-ResNet50 streamlined architecture presents a compelling solution for automated SC classification, combining high diagnostic accuracy with low computational overhead. Moreover, EDA-ResNet50 holds significant potential for application to other image classification tasks. EDA-ResNet50 presents a new frontier in the intersection of deep learning and healthcare, empowering medical professionals with efficient diagnostic tools.}, archive = {J_ACCESS}, author = {Mohamed Hosny and Ibrahim A. Elgendy and Samia Allaoua Chelloug and Mousa Ahmad Albashrawi}, doi = {10.1109/ACCESS.2025.3616022}, journal = {IEEE Access}, pages = {172027-172050}, shortjournal = {IEEE Access}, title = {Attention-based convolutional neural network model for skin cancer classification}, volume = {13}, year = {2025}, }
• (2025). A sacrificial porous silicon layer-based resistive chemical vapor sensor. ACCESS, 13, 172017-172026. (www)

  @article{ , abstract = {In this study, a resistive gas sensor based on a sacrificial porous silicon layer was fabricated and investigated for its capability to detect various volatile organic compounds (VOCs) at room temperature. The porous structure was formed via electrochemical anodization of low-resistivity p-type crystalline silicon, and it was subsequently detached from the substrate to form the sensor structure. Interdigitated aluminum electrodes were deposited on the surface of borosilicate glass to enable electrical measurements, and then the porous silicon layer was transferred onto the aluminum IDE. The sensor exhibited repeatable and reversible responses to a range of VOCs, including acetone, methanol, ethanol, 2-propanol, 1-butanol, and n-heptane. Among these, acetone produced the highest response ( $I_{g}/I_{a} = 2.2$ ) at the highest concentration (7700 ppm), correlating with its high polarity. Notably, the sensor exhibited an atypical increase in current upon exposure to VOCs at room temperature, which was attributed to surface-state passivation and dielectric modulation effects within the electrically isolated porous matrix. However, this effect reversed at temperatures above approximately $60~\circ $ C, where the sensor displayed conventional p-type behavior, marked by a decrease in current under reducing gas exposure. These findings demonstrate that structurally isolated porous silicon layers can serve as effective platforms for low-power VOC detection at room temperature, with their sensing behavior strongly influenced by surface interactions, temperature, and analyte properties.}, archive = {J_ACCESS}, author = {T. Seven and O. Coban}, doi = {10.1109/ACCESS.2025.3616777}, journal = {IEEE Access}, pages = {172017-172026}, shortjournal = {IEEE Access}, title = {A sacrificial porous silicon layer-based resistive chemical vapor sensor}, volume = {13}, year = {2025}, }
• (2025). Adaptive grid selection training strategy for tiny object detection. ACCESS, 13, 171998-172016. (www)

  @article{ , abstract = {Detecting small objects and managing occlusions remain persistent challenges in object detection tasks, particularly in complex scenarios with diverse environments or densely packed scenes. These challenges often result in poor detection accuracy and limited model generalization across varying datasets. To address these challenges, we propose two novel methods: Object-Oriented Cutout (OOC) and Selective Grid Loss Function (SGLoss). OOC enhances training data diversity while preserving small object integrity by strategically applying object-aware augmentation. SGLoss optimizes grid cell allocation dynamically based on object size and aspect ratio, ensuring the generation of high-quality positive samples while reducing inconsistencies in grid-level assignments, and further enabling precise alignment between anchors and ground truth boxes for improved detection accuracy across scales. Our proposed methods achieve a 0.6% improvement in mean Average Precision (mAP) compared to the YOLOv5 baseline, with significant gains observed across small and occluded object categories in datasets such as VisDrone 2019, DOTA, MS COCO 2017, PASCAL VOC, and SODA10M. These contributions advance the field of object detection by addressing critical limitations and provide a foundation for future research in diverse domains, including autonomous driving, aerial imagery analysis, and real-time surveillance systems.}, archive = {J_ACCESS}, author = {Sunhyuk Yim and Myeongah Cho and Dongwoo Kang and Sangyoun Lee}, doi = {10.1109/ACCESS.2025.3613741}, journal = {IEEE Access}, pages = {171998-172016}, shortjournal = {IEEE Access}, title = {Adaptive grid selection training strategy for tiny object detection}, volume = {13}, year = {2025}, }
• (2025). In-presence, take-home, and online education: The state of equipment use and practices in robotics labs and other hands-on activities. ACCESS, 13, 171984-171997. (www)

  @article{ , abstract = {A study is presented that gives an overview of the state of the art in hands-on robotics education. It is based on an international survey among instructors on their use of different forms of equipment, i.e., robot hardware, simulation, and data-sets, in combination with different access forms, i.e., in-presence, online, and take-home, across different teaching activities with practical components, i.e., labs, projects, and the supervision of BSc/MSc theses. Furthermore, it is checked whether the COVID-19 pandemic, which broke out five years before the survey, had lasting effects. It turns out that simulation is the most popular form of regularly used equipment, i.e., almost all instructors report to use simulation in robotics hands-on activities. It is followed by robot hardware where it is noteworthy that in labs about a quarter, i.e., 24% of the instructors do not regularly use it. Data-sets are particular popular as a occasionally used element. With respect to access forms, in-presence dominates for hardware. For simulation and data-sets, in-presence, online, and take-home are all popular and often used in combination. Among the alternatives to in-presence use of hardware, take-home robotics hardware is clearly more popular than online, i.e., remote access to robots. While the effect is less pronounced for participants with more than 10 years of teaching experience, there is a clear sentiment that the COVID-19 pandemic has had lasting effects on hands-on teaching in robotics. Instructors experimented with new forms of hands-on teaching during the pandemic, and they kept (some of) the changes afterward.}, archive = {J_ACCESS}, author = {Andreas Birk}, doi = {10.1109/ACCESS.2025.3614055}, journal = {IEEE Access}, pages = {171984-171997}, shortjournal = {IEEE Access}, title = {In-presence, take-home, and online education: The state of equipment use and practices in robotics labs and other hands-on activities}, volume = {13}, year = {2025}, }
• (2025). Study on the spatiotemporal distribution analysis of nitrate concentration in surface water bodies in east asia based on remote sensing data. ACCESS, 13, 171969-171983. (www)

  @article{ , abstract = {Environmental monitoring and intelligent modeling have become pivotal components of computer science applications addressing sustainability and data-driven policy. In response to the increasing need for robust computational solutions in environmental informatics, this study advances remote sensing–based modeling techniques to analyze nutrient pollution with high spatial and temporal resolution. Traditional remote estimation methods for nutrient indicators struggle to generalize across varied geospatial contexts and dynamic climatic interactions due to reliance on rigid empirical assumptions and static input features. To overcome these challenges, we propose a physics-aware deep learning architecture—integrated with atmospheric transport theory and guided by region-specific data assimilation. This method, constructed on a transformer-enhanced encoder-decoder framework, extracts latent patterns from multi-source inputs, including meteorological data and emission inventories. Our domain-adaptive optimization strategy dynamically calibrates spatial biases using regionally embedded emission priors, enhancing generalization across heterogeneous land-water interfaces. Experimental validation demonstrates our model’s capacity to capture fine-grained concentration gradients while maintaining physical consistency, significantly outperforming traditional regression and neural baselines. This approach exemplifies how informatics and intelligent systems can be leveraged for environmental pattern discovery, advancing the journal’s mission in AI-powered remote sensing, environmental simulation, and computational sustainability.}, archive = {J_ACCESS}, author = {Yuxuan Chu}, doi = {10.1109/ACCESS.2025.3612564}, journal = {IEEE Access}, pages = {171969-171983}, shortjournal = {IEEE Access}, title = {Study on the spatiotemporal distribution analysis of nitrate concentration in surface water bodies in east asia based on remote sensing data}, volume = {13}, year = {2025}, }
• (2025). Enhancing nuclei segmentation in histopathology images via quantitative to qualitative transitions in supervised and weakly supervised deep-learning approaches. ACCESS, 13, 171957-171968. (www)

  @article{ , abstract = {Segmentation of nuclei in histopathology images stained with Hematoxylin and Eosin is essential for accurate disease diagnosis and tissue characterization. Traditional methods usually struggle with complicated tissue structures and overlapping nuclei. Deep learning-based approaches, particularly U-Net along with its variants, have demonstrated improved effectiveness but still face limitations, including restricted receptive fields, overfitting, boundary ambiguity, and uneven nuclei distribution. This paper presents the deep image segmentation network (DISegNet) customized for nuclei segmentation. DISegNet integrates enhanced lightweight convolutional modules with expanded receptive fields and introduces an Improved Global Context (IGC) block to capture long-range dependencies. A dedicated stem block further boosts feature extraction. These components improve boundary precision and model robustness in visually challenging conditions. We examine DISegNet on the MoNuSAC dataset, its subsets, and the MoNuSeg dataset. Experimental results show that DISegNet consistently outperforms preexisting fully supervised segmentation methods. Further, in a bid to mitigate the costly dense annotations, we introduce a weakly supervised approach using refined-point annotations. Point annotations are transformed to coarse labels via k-means clustering and Voronoi diagrams. This strategy significantly reduces annotation effort while maintaining high segmentation performance, yielding more efficient and accurate results than previous point annotation approaches. Future work will explore our models for broader medical imaging tasks involving intricate anatomical structures and overlapping regions.}, archive = {J_ACCESS}, author = {Ijaz Muhammad Umer and Haider Ali and Moon-Il Joo and Hee-Cheol Kim}, doi = {10.1109/ACCESS.2025.3600694}, journal = {IEEE Access}, pages = {171957-171968}, shortjournal = {IEEE Access}, title = {Enhancing nuclei segmentation in histopathology images via quantitative to qualitative transitions in supervised and weakly supervised deep-learning approaches}, volume = {13}, year = {2025}, }
• (2025). An event-triggered multi-AAV coordination scheme for simultaneous tracking and pursuit of multiple moving targets. ACCESS, 13, 171926-171941. (www)

  @article{ , abstract = {This paper proposes an event-triggered cooperative localization scheme and a pursuit control method for multiple autonomous aerial vehicles (AAVs) for the purpose of tracking and monitoring multiple moving target vessels in a maritime patrol scenario. In the proposed method, each AAV has an optical sensor that measures its distances to the target vessels moving on the water surface. In particular, each AAV combines its own measurements with those of other AAVs to be subsequently processed using an event-triggered distributed extended Kalman filter algorithm to obtain its estimates of target vessels’ positions. The AAVs then use the position estimates of the targets to construct dynamic convex hulls that connect all of the detected moving target vessels. The multi-AAV tracker system then generates spatio-temporal reference path curves using cycloid-type trajectories with a formation geometry that is adapted in such a way to cover/contain the constructed convex hulls of the target vessels. Based on the generated reference path, a distributed cooperative control law based on the moving path following approach is finally proposed to maintain tracker-target’s relative geometry that guarantee accurate tracking of the moving vessels. Extensive simulation results are presented to demonstrate the effective performance, computational efficiency, and scalability of the proposed scheme.}, archive = {J_ACCESS}, author = {Tua Agustinus Tamba and Immanuel R. Santjoko and Yul Y. Nazaruddin and Vebi Nadhira}, doi = {10.1109/ACCESS.2025.3611400}, journal = {IEEE Access}, pages = {171926-171941}, shortjournal = {IEEE Access}, title = {An event-triggered multi-AAV coordination scheme for simultaneous tracking and pursuit of multiple moving targets}, volume = {13}, year = {2025}, }
• (2025). A wideband high-gain millimeter-wave antenna array with inverted-feed stacked patch design and single-port compact corporate feed network. ACCESS, 13, 171916-171925. (www)

  @article{ , abstract = {This paper presents the design, development, and experimental validation of a novel wideband and high-gain antenna array for millimeter-wave 5G applications. The proposed structure introduces an inverted-feed stacked patch antenna element that achieves both wide impedance bandwidth and a symmetric E-plane radiation pattern. A $1\times 2$ subarray is then constructed using an inverted T-junction feed to preserve pattern symmetry in both E- and H-planes. To further enhance gain and suppress side lobes, a 12-element linear array operating at 28 GHz is designed with a non-uniform corporate feed network. Unlike conventional approaches that rely on dual-port configurations or complex feeding structures, the proposed design utilizes a compact single-port architecture, achieving amplitude tapering through control of the number of power-splitting junctions. The final array achieves a 19.7% measured fractional bandwidth (26–31.7 GHz), peak gain of 18.1 dBi, and side lobe level below −17.3 dB, all within a compact footprint of $1.15\lambda \times 8\lambda $ . Measurement results confirm strong agreement with simulations, validating the performance of the proposed antenna. The combination of wide bandwidth, low SLL, compact structure, and simplified feed network makes the design highly suitable for next-generation millimeter-wave communication systems.}, archive = {J_ACCESS}, author = {Mohammad Hadi Moradi Ardekani and Shima Pashangeh}, doi = {10.1109/ACCESS.2025.3615994}, journal = {IEEE Access}, pages = {171916-171925}, shortjournal = {IEEE Access}, title = {A wideband high-gain millimeter-wave antenna array with inverted-feed stacked patch design and single-port compact corporate feed network}, volume = {13}, year = {2025}, }
• (2025). ACSMPred-FRL: Accelerating the identification of anticancer small molecules using feature representation learning. ACCESS, 13, 171902-171915. (www)

  @article{ , abstract = {Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to their unique ability to selectively target cancer cells without directly affecting healthy cells, they have been extensively studied. Meanwhile, many small molecule drugs are being introduced and evaluated in preclinical and clinical trials for anticancer treatments. However, experimental identification and characterization of anticancer small molecules (ACSMs) remains laborious and time-consuming. In this study, we propose an innovative computational approach, termed ACSMPred-FRL, that can accurately and rapidly identify compounds with or without anticancer activity based on a feature representation learning scheme. In ACSMPred-FRL, we initially constructed 442 base-classifiers by employing 13 machine learning (ML) algorithms in conjunction with 14 molecular descriptors and 20 molecular embeddings. Next, probabilistic features based on these 442 base-classifiers were generated and combined to provide multi-view information embedded in ACSMs. Then, principal component analysis (PCA) was applied to process the 442 probabilistic features resulting in 42 principal components used as input vectors for developing the final meta-classifier. Finally, the optimal feature subset was determined and used to train the final meta-classifier. Both cross-validation and independent tests showed that ACSMPred-FRL not only surpassed several conventional ML-based classifiers but also outperformed the existing method, highlighting its effectiveness and predictive capability. Specifically, on the independent test dataset, ACSMPred-FRL achieved an accuracy of 0.834, a specificity of 0.88, and a Matthew’s correlation coefficient of 0.672, representing improvements of 4.45, 4.70, and 9.20%, respectively, over the existing method. Therefore, we anticipate that ACSMPred-FRL will serve as a useful and reliable tool for the large-scale identification of ACSMs, accelerating their application in cancer treatment. The source code and datasets used in this study are publicly available for reproducibility at https://github.com/lawankorn-m/ACSMPred-MVF}, archive = {J_ACCESS}, author = {Watshara Shoombuatong and Nalini Schaduangrat and Lawankorn Mookdarsanit and S. M. Hasan Mahmud and Kanthida Kusonmano and Pakpoom Mookdarsanit}, doi = {10.1109/ACCESS.2025.3613332}, journal = {IEEE Access}, pages = {171902-171915}, shortjournal = {IEEE Access}, title = {ACSMPred-FRL: Accelerating the identification of anticancer small molecules using feature representation learning}, volume = {13}, year = {2025}, }
• (2025). Quantitative analysis of reliability and cost function of 15-level inverter. ACCESS, 13, 171889-171901. (www)

  @article{ , abstract = {Multilevel inverters have gained significant attention in high-voltage and high-power applications due to their high quality and smoother output with reduced harmonic distortion and low dv/dt stress compared to traditional inverters. Despite the reduction in the switches that have been made, reliability remains a critical challenge. The reliability of these inverters tends to decrease due to the high failure rate of the switches and the capacitors involved in those inverters. This paper presents a novel approach to estimating the cost function and conducting a reliability analysis in a reduced-switch multilevel inverter. It compares it with real-time switches using the military handbook 217F. The failure rate of major components like diodes, capacitors, and switches is mathematically calculated and evaluated. Furthermore, the mean time between failures and recovery time is calculated in the reduced switch 15-level MLI, and further steps to improve reliability have been discussed. A performance comparison of silicon and silicon carbide material switches is made. All simulations are performed in the MATLAB software, and the results are validated in a hardware configuration. This work will benefit industry professionals and researchers in the advancement of multilevel inverters to the next level.}, archive = {J_ACCESS}, author = {N. Subramanian and Albert Alexander Stonier}, doi = {10.1109/ACCESS.2025.3616252}, journal = {IEEE Access}, pages = {171889-171901}, shortjournal = {IEEE Access}, title = {Quantitative analysis of reliability and cost function of 15-level inverter}, volume = {13}, year = {2025}, }
• (2025). Advancements in assistive robotics: A systematic review of inclusive technologies for people with disabilities. ACCESS, 13, 171871-171888. (www)

  @article{ , abstract = {According to the World Health Organization, approximately 16% of the global population lives with some form of disability, be it physical, sensory, intellectual, or psychosocial. Individuals with disabilities face numerous challenges related to mobility, communication, access to education, and other essential aspects of daily life. In this context, robotic technologies have emerged as innovative solutions aimed at improving autonomy, rehabilitation, and social inclusion. The aim of this systematic review was to identify and synthesize the scientific evidence on robotic technologies developed to support people with disabilities. More specifically, the review sought to analyze the temporal and geographical distribution of research, classify the types of robotic technologies and their applications, examine methodological characteristics and participant demographics, and highlight the key contributions and gaps reported in the included studies. Following PRISMA guidelines, a comprehensive search was conducted in Scopus, PubMed, and IEEE Xplore. From an initial pool of 6,290 articles, 89 studies met the inclusion criteria and were analyzed. The results were categorized into five main themes: publication trends, types of robotic technologies and applications, methodological characteristics, participant demographics, and key contributions. The findings reveal a strong concentration of research on articulated robots for physical disabilities, alongside limited exploration of intellectual, sensory, and psychosocial contexts. While robotic interventions demonstrate significant potential for rehabilitation and daily assistance, challenges remain regarding sample sizes, diversity of disabilities addressed, and long-term validation studies. This review contributes to a deeper understanding of the state of the art in assistive robotics and identifies future research directions to enhance inclusion, accessibility, and clinical integration.}, archive = {J_ACCESS}, author = {Mireya Zapata and Diego Guevara and Jennifer Obregón and Hugo Arias-Flores and Carlos Ramos-Galarza}, doi = {10.1109/ACCESS.2025.3608614}, journal = {IEEE Access}, pages = {171871-171888}, shortjournal = {IEEE Access}, title = {Advancements in assistive robotics: A systematic review of inclusive technologies for people with disabilities}, volume = {13}, year = {2025}, }
• (2025). Low ESL aluminum electrolytic capacitors for the application of transcranial magnetic stimulation. ACCESS, 13, 171863-171870. (www)

  @article{ , abstract = {The development of portable Transcranial Magnetic Stimulation (TMS) devices requires lightweight and low-loss capacitors. This paper presents a novel redesign of standard aluminum electrolytic capacitors, using an optimized tab foil layout. The approach emphasizes the reduction of Equivalent Series Inductance (ESL), ensuring compatibility with high switching frequencies and a variety of pulse patterns. The redesign achieved a 25 % reduction in ESL and paves the way for lightweight and more compact TMS systems for on-site therapy.}, archive = {J_ACCESS}, author = {Jonas Wittmaack and Florian Schwitzgebel and Thomas Ebel}, doi = {10.1109/ACCESS.2025.3614845}, journal = {IEEE Access}, pages = {171863-171870}, shortjournal = {IEEE Access}, title = {Low ESL aluminum electrolytic capacitors for the application of transcranial magnetic stimulation}, volume = {13}, year = {2025}, }
• (2025). Safe reactive control for autonomous navigation in unknown environments via probabilistic maps. ACCESS, 13, 171850-171862. (www)

  @article{ , abstract = {Achieving full autonomy in robotics requires safe navigation and reliable collision avoidance in unknown environments. This demands robust perception and mapping, as well as control strategies that can effectively utilize environmental information while enforcing safety constraints. However, to address noisy and sparse measurements from inexpensive onboard sensors commonly used on low-power mobile platforms, probabilistic and continuous map representations have emerged as more effective alternatives to traditional deterministic occupancy grids. Despite their advantages in perception and mapping, their integration into safety-critical control frameworks remains underexplored. In this work, we address this gap by proposing a reactive and safe control strategy that operates over probabilistic maps constructed using Gaussian Process-based continuous representations. Our method combines a reactive control framework with control barrier functions, and incorporates risk measures that consider both occupancy probabilities and their associated uncertainties. Furthermore, for practical implementation, we introduce an object-level probabilistic mapping approach suited for indoor environments and integrate it with the proposed control strategy. The resulting framework enables risk-aware, reactive navigation in previously unknown environments. We validate its effectiveness through extensive simulations and hardware experiments, demonstrating robust vision-based navigation under uncertainty.}, archive = {J_ACCESS}, author = {Dabin Kim and Youngsoo Han and H. Jin Kim}, doi = {10.1109/ACCESS.2025.3610851}, journal = {IEEE Access}, pages = {171850-171862}, shortjournal = {IEEE Access}, title = {Safe reactive control for autonomous navigation in unknown environments via probabilistic maps}, volume = {13}, year = {2025}, }
• (2025). Explainable AI with homomorphic encryption for secure cloud-based ECG analysis in heart disease diagnosis. ACCESS, 13, 171831-171849. (www)

  @article{ , abstract = {Electrocardiogram (ECG) analysis is widely used for early detection of cardiac abnormalities, yet the deployment of deep learning models in cloud environments raises concerns regarding data privacy and clinical interpretability. To address these challenges, this work presents a novel framework that integrates homomorphic encryption with explainable deep learning for secure and interpretable ECG classification in the cloud. The novel framework that integrates homomorphic encryption with explainable deep learning for secure ECG-based heart disease diagnosis in the cloud. Explainable AI (XAI) was employed to enhance clinician and patient trust, while homomorphic encryption (HE) ensures confidentiality of sensitive ECG signals during cloud-based processing. The originality of this lies in jointly addressing three critical requirements—data privacy, interpretability, and computational efficiency—within a single diagnostic pipeline. The proposed method employs a convolutional neural network (CNN) optimized for encrypted computation and applies SHapley Additive exPlanations (SHAP) to provide interpretable results aligned with clinical decision-making. Experimental validation on the MIT-BIH dataset demonstrates that the model achieves 94.2% accuracy in plaintext inference and 93.4% under encrypted inference, with an F1-score of 92.0% and 91% agreement with cardiologists, while maintaining an average encrypted inference latency of 420 ms, demonstrating its practicality for secure cloud deployment. These results confirm that the framework offers a practical and trustworthy solution for secure, cloud-based ECG diagnostics.}, archive = {J_ACCESS}, author = {D. Cenitta and G. K. Shwetha and K. P. Vyshali Rao and Srividya Ramisetty and N. Arul and R. Vijaya Arjunan}, doi = {10.1109/ACCESS.2025.3614655}, journal = {IEEE Access}, pages = {171831-171849}, shortjournal = {IEEE Access}, title = {Explainable AI with homomorphic encryption for secure cloud-based ECG analysis in heart disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Empirical study on multi-robot task allocation under communication range restrictions. ACCESS, 13, 171818-171830. (www)

  @article{ , abstract = {Efficient communication is vital for coordination in multi-robot systems and directly influences the overall performance of the system. However, many advanced approaches overlook the challenges of intermittent connectivity caused by communication range limitations. This paper addresses the assumption of global communication and instead assumes that robots have a predefined limited communication range, reflecting real-world conditions. This comparative empirical study evaluates the impact of limited communication range on three decentralized task allocation algorithms that typically assume continuous communication. Performance is analyzed by varying the communication model between—Bernoulli, Gilbert-Elliot, and Rayleigh Fading—and also the number of tasks in simulation. Each algorithm is assessed using four key metrics: 1) the total rejected tasks, 2) the cumulative penalty incurred by all robots, 3) the overall completion time, and 4) the cumulative energy consumption to execute all the accepted tasks. Finally, the study examines the impact of communication range constraints on the effectiveness of these approaches.}, archive = {J_ACCESS}, author = {Ashish Verma and Avinash Gautam and Virendra Singh Shekhawat and Sudeept Mohan}, doi = {10.1109/ACCESS.2025.3614892}, journal = {IEEE Access}, pages = {171818-171830}, shortjournal = {IEEE Access}, title = {Empirical study on multi-robot task allocation under communication range restrictions}, volume = {13}, year = {2025}, }
• (2025). Enhancing observability in distribution grids: A novel approach to mitigate cyberattack risks in smart grid environments. ACCESS, 13, 171807-171817. (www)

  @article{ , abstract = {This paper stresses the need for precise observability in distribution grids, especially with the integration of smart grid technologies, which introduces new cyberattack vulnerabilities. In response, this paper introduces an innovative approach designed to heighten observability within distribution systems and enhance their resilience against False Data Injection (FDI) cyberattacks. Central to this approach is the introduction of a novel index to quantify the reduction in observability caused by FDI attacks. Distinguishing itself from existing techniques, the proposed approach encompasses three vital components: optimizing monitoring device allocation to minimize costs and enhance observability, FDI detection through existing monitoring devices employing probability density functions (PDF) curves, and the strategic allocation of monitoring devices while considering the impact of an attack on observability. The approach’s validation is performed via tests on a typical distribution system, with varying numbers of monitoring devices and FDI scenarios, consistently delivering significantly improved observability and estimation accuracy. The research findings provide valuable insights into the proactive management of FDI threats, fortifying system integrity, resulting in cost savings, and operational efficiency. Simulation outcomes underscore that optimal allocation of nine monitoring devices in the system under study achieves observability losses and FDI impact index (FDI3) values below 10% and 5%, respectively.}, archive = {J_ACCESS}, author = {Mohamed Elyamani and Mostafa F. Shaaban and Mohamed H. Ahmed and Muhammad Ismail and Maher A. Azzouz and Abdelfatah Ali}, doi = {10.1109/ACCESS.2025.3613488}, journal = {IEEE Access}, pages = {171807-171817}, shortjournal = {IEEE Access}, title = {Enhancing observability in distribution grids: A novel approach to mitigate cyberattack risks in smart grid environments}, volume = {13}, year = {2025}, }
• (2025). KSSD2025: A new annotated dataset for automatic kidney stone segmentation and evaluation with modified U-net based deep learning models. ACCESS, 13, 171790-171806. (www)

  @article{ , abstract = {Kidney stone disease is a prevalent condition associated with an increased risk of chronic kidney failure. Non-contrast Computed Tomography (CT) remains the gold standard for diagnosis due to its high sensitivity and ability to evaluate clinically relevant stone parameters. However, manual segmentation of stones in CT images is time-consuming and subject to inter-observer variability, highlighting the need for automated solutions. This paper introduces the KSSD2025, a novel dataset comprising 838 manually annotated axial CT images for kidney stone segmentation. We conducted a comprehensive evaluation of four segmentation models based on modified U-Net architectures: U-Net, U-Net++, U-Net3+, and TransU-Net. We evaluated their performance through 5-fold cross-validation and external tests. All models achieved mean Dice scores above 95%, with modified U-Net reaching 97.06% during cross-validation and modified U-Net3+ showing better generalization to external data. Our results confirm that U-Net-based architectures, when trained on high-quality annotations, can achieve accurate and reliable kidney stone segmentation. Our findings suggest that the KSSD2025 dataset offers a valuable resource to advance research in kidney stone analysis and the deployment of AI-assisted diagnostic tools in clinical practice.}, archive = {J_ACCESS}, author = {Murillo F. Murillobouzon and Paulo H. S. de Santana and Gabriel N. Missima and Weverson S. Pereira and Fernando P. Rivera and Gilson A. Giraldi and Oscar E. H. Fugita and Paulo S. S. Silva}, doi = {10.1109/ACCESS.2025.3610027}, journal = {IEEE Access}, pages = {171790-171806}, shortjournal = {IEEE Access}, title = {KSSD2025: A new annotated dataset for automatic kidney stone segmentation and evaluation with modified U-net based deep learning models}, volume = {13}, year = {2025}, }
• (2025). Enhancing game strategy optimization using deep reinforcement learning. ACCESS, 13, 171772-171789. (www)

  @article{ , abstract = {The growing complexity and dynamism of modern computational environments demand more adaptive and intelligent methodologies for strategic decision-making, especially in multi-agent systems. These systems, consisting of multiple autonomous agents interacting in shared environments, pose challenges that traditional models struggle to address. Conventional approaches to strategy optimization in game-theoretic contexts typically rely on static models and predefined payoff matrices. While effective in controlled settings, these models lack the flexibility to reflect the adaptive and interactive behavior of agents in real-world scenarios. They often ignore bounded rationality, where agents have limited knowledge or computational resources, and thus fail to capture the full complexity of dynamic environments. In contrast, emerging techniques such as reinforcement learning, evolutionary computation, and stochastic games offer more dynamic and responsive alternatives. These approaches can adapt to changing conditions, learn from interactions, and generalize across different strategic contexts. They provide a more accurate reflection of real-world scenarios where agents must constantly revise strategies based on limited or evolving information. Such advanced methodologies are critical for practical applications in areas like autonomous systems, economic modeling, distributed control, and cybersecurity. They enable the development of systems capable of robust decision-making in uncertain, multi-agent environments. Embracing these innovations will be key to addressing the limitations of static models and realizing the full potential of intelligent computational strategies in dynamic and complex systems.}, archive = {J_ACCESS}, author = {Jinhan Meng}, doi = {10.1109/ACCESS.2025.3613207}, journal = {IEEE Access}, pages = {171772-171789}, shortjournal = {IEEE Access}, title = {Enhancing game strategy optimization using deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Prediction of the compressive strength of alkali-activated mortar for block-work binders using adaptive neuro-fuzzy inference system. ACCESS, 13, 171746-171771. (www)

  @article{ , abstract = {This study employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the compressive strength of alkali-activated mortars (AAMs) designed for blockwork applications. The model, configured with 225 fuzzy rules, evaluates key mix parameters including activator ratio, alkali concentration, and liquid-to-solid ratio, optimizing strength predictions. Experimental validation confirms that ANFIS achieves high accuracy, with an RMSE of 0.97 and an a20-index of 0.74, indicating strong predictive reliability. The research utilizes waste cellular lightweight concrete (CLC) as a precursor, demonstrating its feasibility for sustainable construction. By reducing the need for extensive laboratory testing, ANFIS improves mix design efficiency, making AAMs more viable for commercial applications. The findings highlight the effectiveness of ANFIS in optimizing AAM formulations, ensuring a balance between material efficiency, sustainability, and structural performance. This research supports the transition toward more eco-friendly, high-performance construction materials, facilitating the broader adoption of alkali-activated mortars in real-world applications.}, archive = {J_ACCESS}, author = {Suraphan Santiyanon and Paijit Pawan and Warisara Lertpaitoonpan}, doi = {10.1109/ACCESS.2025.3608437}, journal = {IEEE Access}, pages = {171746-171771}, shortjournal = {IEEE Access}, title = {Prediction of the compressive strength of alkali-activated mortar for block-work binders using adaptive neuro-fuzzy inference system}, volume = {13}, year = {2025}, }
• (2025). Multi-objective comprehensive structural optimization of the bed for a GPC fixed-beam gantry machining center. ACCESS, 13, 171721-171745. (www)

  @article{ , abstract = {The static and dynamic characteristics of the fixed-beam gantry machining center bed are critical for determining machining accuracy and efficiency in precision machine tools. To address the lightweight-stiffness trade-off limitation of traditional empirical methods, this study proposes a multi-objective comprehensive structural optimization approach for the GPC bed, integrating bionic design, topology optimization, and grey relational analysis based on static-dynamic characteristic analysis. A multi-objective topology optimization model is established using the variable density method. An optimized rib design inspired by the glass sponge’s thin-walled multi-layer structure is derived, and its performance advantages are validated by finite element simulations. Combining grey relational analysis and BP neural networks, a response surface model is constructed to identify core performance-influencing parameters. The optimal rib layout and cavity-to-rib plate ratio are determined through multi-configuration experiments. Experimental verification demonstrates significant performance enhancements over the prototype: a 3.1% mass reduction, a 24.3% increase in natural frequency (improved vibration resistance), and a 17.0% reduction in maximum displacement deformation (enhanced stiffness). These improvements lead to higher machining accuracy and efficiency.}, archive = {J_ACCESS}, author = {Bo Huang and Jun Liu and Jiawei Yan and Fengkui Xiong and Junqi Wu and Chongshan Zhou}, doi = {10.1109/ACCESS.2025.3615419}, journal = {IEEE Access}, pages = {171721-171745}, shortjournal = {IEEE Access}, title = {Multi-objective comprehensive structural optimization of the bed for a GPC fixed-beam gantry machining center}, volume = {13}, year = {2025}, }
• (2025). A review on vehicle-grid-integration development in south korea. ACCESS, 13, 171702-171720. (www)

  @article{ , abstract = {The uncertainty in demand and supply in the power grid due to the widespread adoption of renewable energy resources is a significant concern. Electric vehicles (EVs) can be utilized as resources to mitigate the concern. However, to apply these resources, it is necessary to verify their suitability, resource potential, and economic impact. Therefore, this study analyzes how EVs can be utilized as resources and proposes a pilot project for operating smart charging (V1G) of EVs in South Korea. This pilot project includes experiments performing V1G in response to Demand Response (DR) signals and surveys to determine EV users’ pReferences for V1G participation. Additionally, an analysis of charging characteristics was conducted because resource potential depends on EV users’ charging behaviors. The experimental results confirmed the technical value of EVs as resources. However, the economic analysis revealed that there is no economic benefit for V1G operators, making V1G implementation difficult under the current system. Finally, meaningful suggestions are provided for the application and activation of EVs as resources.}, archive = {J_ACCESS}, author = {Hwanmin Jeong and Eunjung Lee and Jinho Kim}, doi = {10.1109/ACCESS.2025.3615595}, journal = {IEEE Access}, pages = {171702-171720}, shortjournal = {IEEE Access}, title = {A review on vehicle-grid-integration development in south korea}, volume = {13}, year = {2025}, }
• (2025). Overcoming natural similarity: A DenseNet-based approach for reliable transform copy-move forgery identification. ACCESS, 13, 171684-171701. (www)

  @article{ , abstract = {Digital image forensics aims to verify the authenticity of an image by determining its origin and any processing it has undergone. Among passive approaches in copy move forgery detection, which analyze images without requiring prior embedded information, a region of interest in an image is duplicated and manipulated to conceal or alter content. This process often involves multiple transformations that significantly alter the image. These transformations consist of several operations, such as geometrical transformations, frequency domain transformations, noise addition, etc. In copy-move forgery detection, conventional approaches are generally effective at identifying simple copy-move forgeries. However, these approaches often fall short in detecting copy-move forgeries when frequency domain transformations are applied to the copied regions. Further, existing approaches have a high false positive rate in terms of detecting natural similarity. The proposed method introduces an advanced deep learning approach for detecting copy-move forgeries in frequency domains. During preprocessing, segmentation is utilized to extract the objects or regions of interest in the image. DenseNet121 is employed to extract high-level feature from these segmented regions with a detailed analysis of features from each block. By comparing the features extracted from block 4, the method identifies and maps regions with similar features, thereby detecting potential transformed copy-move manipulations. This proposed method demonstrates robustness in identifying forgeries that involve transformations and significantly enhances detection accuracy with natural similarity. However, the method shows reduced performance on highly textured images, where natural similarity and repetitive patterns make discrimination more difficult.}, archive = {J_ACCESS}, author = {Tanzeela Qazi and Mohsin Shah and Mushtaq Ali and Muhammad Nawaz Khan and Ahmad Ali Alzubi and Abeer Alnuaim and Tariq Hussain}, doi = {10.1109/ACCESS.2025.3609668}, journal = {IEEE Access}, pages = {171684-171701}, shortjournal = {IEEE Access}, title = {Overcoming natural similarity: A DenseNet-based approach for reliable transform copy-move forgery identification}, volume = {13}, year = {2025}, }
• (2025). ESRVA: Enhanced super-resolution and visual annotation model for object-level image interpretation using deep learning. ACCESS, 13, 171666-171683. (www)

  @article{ , abstract = {Computer vision allows machines to read and analyze visual information, driving applications ranging from medical imaging to autonomous systems. Such applications depend on the quality of input images; therefore, image enhancement is very important when working with low-resolution, noisy, or degraded inputs. The Enhanced Super-Resolution Annotation for Vision-based Systems (ESRVA) research presents an image enhancement and annotation framework based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). The goal is to enhance the sharpness, resolution, and perceptual accuracy of low-quality images to aid downstream applications like object detection and segmentation. ESRVA uses a Residual-in-Residual Dense Block (RRDB) structure and perceptual loss to produce detail-retaining high-fidelity outputs. After enhancement, the framework conducts automatic image annotation based on an instance segmentation pipeline, which uses deep feature maps from the enhanced images to localize objects with high accuracy and classify them. Experimental evaluation shows appreciable improvement in different performance metrics. Evaluated proposed method on standard benchmarks using bicubic downsampling to simulate low-resolution inputs. While this provides a consistent basis for comparison, it does not fully replicate real-world degradation. Experimental evaluation after 150 epochs shows appreciable improvement in different performance metrics. The model produced a Peak Signal-to-Noise Ratio (PSNR) of 36.66 dB, Mean Squared Error (MSE) of 0.000216, and Structural Similarity Index Measure (SSIM) of 0.9621, reflecting high similarity to ground truth images. The system also registered a perceptual loss of 0.018849, Mean Intersection over Union (mIoU) of 0.9746, and Dice Coefficient of 0.9924, confirming the accuracy of instance segmentation on the improved outputs. Among the social implications, the most applicable use of ESRVA is in smart surveillance systems. In restricted light or bandwidth conditions, surveillance footage is typically of low resolution. With ESRVA built into such systems, they can reconstruct high-resolution scenes that are clean, improving people, car, and event identification. This helps to enhance public safety, ease law enforcement, and enhance the utility of smart city infrastructure and autonomous systems. By combining enhancement and annotation within a single pipeline, ESRVA provides a streamlined solution for applications needing high-quality visual inspection from low-quality input data.}, archive = {J_ACCESS}, author = {Ramyashree and Ashmika and S. Raghavendra and B. N. Anoop and P. S. Venugopala}, doi = {10.1109/ACCESS.2025.3616216}, journal = {IEEE Access}, pages = {171666-171683}, shortjournal = {IEEE Access}, title = {ESRVA: Enhanced super-resolution and visual annotation model for object-level image interpretation using deep learning}, volume = {13}, year = {2025}, }
• (2025). A SAR-driven approach for winter catch crop classification in germany. ACCESS, 13, 171642-171665. (www)

  @article{ , abstract = {Growing winter catch crops is a key agricultural practice offering a number of advantages, particularly for soil fertility, soil health, nitrogen leaching and soil protection. The growing phase of these crops, spanning autumn and winter, coincides with dense cloud cover in Germany, leading to data gaps in optical remote sensing that hinder effective monitoring. To address this limitation, this study assesses the potential of Sentinel-1 SAR time series to identify and classify winter catch crops and their specific types. Here, we propose a two level hierarchical Random Forest (RF) classification framework and evaluate its spatio-temporal transferability. In level-1, the model distinguished between catch crops and non-catch crops using phenological contrasts and sowing window differences observed in the time series derived from radar parameters, including VV, VH, VH/VV, and dpRVI. In Level-2, the model classified six specific catch crop types using 22 defined crop specific descriptive features. The RF model, trained on 2021 data from Lower Saxony (LS), achieved overall accuracies of 94.9% in level-1 and 85.7% in level-2. The model maintained strong temporal transferability when applied to LS data from 2022 and 2023. Moreover, spatio-temporal transfer tests demonstrated consistently good performance in Brandenburg (BB), while North Rhine–Westphalia (NRW) showed satisfactory results with some misclassifications in non-catch crops likely due to phenological shifts. The results highlight the potential of SAR-based frameworks for identifying catch crop types and their applicability for scaling to other federal states of Germany and national-level mapping efforts.}, archive = {J_ACCESS}, author = {Shanmugapriya Selvaraj and Damian Bargiel and Abdelaziz Htitiou and Heike Gerighausen}, doi = {10.1109/ACCESS.2025.3615739}, journal = {IEEE Access}, pages = {171642-171665}, shortjournal = {IEEE Access}, title = {A SAR-driven approach for winter catch crop classification in germany}, volume = {13}, year = {2025}, }
• (2025). Stability and robust control of time-varying systems using linear matrix inequalities. ACCESS, 13, 171626-171641. (www)

  @article{ , abstract = {This article provides a theoretical contribution by presenting a detailed study on the application of Linear Matrix Inequalities (LMIs) in time-varying systems. Specifically, it addresses a set of LMI conditions that ensure the stability of such systems. To derive these conditions, Lyapunov’s Direct Method is employed, and the stability is characterized using class- $\mathcal {K}$ functions. The parallel distributed compensation technique is utilized to guarantee stability for both open-loop and closed-loop systems. Furthermore, the study establishes sufficient LMI conditions for global exponential stability in linear time-varying systems and presents exponential stability conditions for nonlinear time-varying systems. Finally, the robust control method is validated through the controller design of a parametrically excited pendulum. In addition, a comparative analysis between the robust method and the control method based on the Lyapunov–Floquet transformation is presented.}, archive = {J_ACCESS}, author = {Marco A. Travassos and Rodrigo Cardim and Marcelo C. M. Teixeira and Flávio A. Faria}, doi = {10.1109/ACCESS.2025.3616053}, journal = {IEEE Access}, pages = {171626-171641}, shortjournal = {IEEE Access}, title = {Stability and robust control of time-varying systems using linear matrix inequalities}, volume = {13}, year = {2025}, }
• (2025). Spatial annotation-free sound event localization and detection via spatial instance classification. ACCESS, 13, 171613-171625. (www)

  @article{ , abstract = {Sound Event Localization and Detection (SELD) typically relies on strongly labeled data that specify when, where, and which sounds occur, resulting in high annotation costs. To address this challenge, we present a framework that enables SELD training without spatial labels, termed spatial annotation-free SELD. The core idea is to reinterpret source localization—traditionally handled as a regression problem—as a classification task called spatial instance classification (SIC). SIC first partitions the observation region into sub-regions. For each sub-region, the acoustic signal capturing its sound events is extracted as a spatial instance. Classifying these instances reveals the sub-regions that contain sound events, thereby providing SELD functionality. By solving SIC under weak supervision, we realize spatial annotation-free SELD. SIC does not depend on a specific microphone configuration, and it is expected to be generally applicable as long as the sound in each divided region can be extracted as an instance. We validate the generality of SIC through two contrasting experiments: (i) application to SELD using first-order Ambisonics (FOA), and (ii) application to a newly introduced distributed SELD task, where events are localized on a 2D plane using widely spaced microphones. In both settings, SIC effectively trains SELD systems capable of localizing sound events without spatial annotations. The proposed approach, therefore, facilitates the development of practical, spatial-annotation-free SELD across diverse microphone setups.}, archive = {J_ACCESS}, author = {Masahiro Yasuda and Noboru Harada and Shoichiro Saito and Nobutaka Ono}, doi = {10.1109/ACCESS.2025.3609479}, journal = {IEEE Access}, pages = {171613-171625}, shortjournal = {IEEE Access}, title = {Spatial annotation-free sound event localization and detection via spatial instance classification}, volume = {13}, year = {2025}, }
• (2025). The impact of using intelligent reflecting surfaces (IRSs) on minimizing operational costs in UAV wireless networks. ACCESS, 13, 171598-171612. (www)

  @article{ , abstract = {Integrating Non-Orthogonal Multiple Access (NOMA) and Intelligent Reflecting Surfaces (IRSs) with Unmanned Aerial Vehicles (UAVs) presents a promising research area with significant potential to enhance the efficiency and performance of wireless communication systems. This work proposed a novel communication approach that leverages user-pairing-based NOMA to provide high data-rate wireless coverage for Internet of Things (IoT) devices using UAV-IRS systems. By combining the benefits of NOMA and IRS, we can achieve higher spectral utilization, superior energy efficiency, and a more reliable communication system. However, providing wireless coverage to IoT devices using a single UAV-IRS system can be challenging due to a UAV’s low transmit power. As a result, the optimization problem is formulated to minimize the number of required UAV-IRS systems while ensuring that each IoT device meets its Quality-of-Service (QoS) requirements. Specifically, we propose an efficient heuristic algorithm to determine the minimum number of UAV-IRS systems required to serve IoT devices. Our proposed approach reduces operational costs during the planning and operation of wireless network systems using UAV-IRS, while extending the wireless coverage for IoT devices. The proposed approach is evaluated through simulations, and the results show that integrating NOMA and IRS with UAVs improves the performance and efficiency of wireless communication systems. Additionally, the proposed approach reduces the number of required UAV-IRS systems. The proposed IRS-NOMA system uses only seven UAVs, whereas the NOMA system without IRS requires nine UAVs to ensure 100% user coverage at the same data rate.}, archive = {J_ACCESS}, author = {Ahmad Sawalmeh and Ali H. Alenezi and Hazim Shakhatreh and Muhammad Anan}, doi = {10.1109/ACCESS.2025.3616254}, journal = {IEEE Access}, pages = {171598-171612}, shortjournal = {IEEE Access}, title = {The impact of using intelligent reflecting surfaces (IRSs) on minimizing operational costs in UAV wireless networks}, volume = {13}, year = {2025}, }
• (2025). Energy management systems for maritime microgrids: A comprehensive review of intelligent optimization strategies. ACCESS, 13, 171563-171597. (www)

  @article{ , abstract = {In modern maritime operations, electric ship power systems (SPSs) play a crucial role as they integrate distributed energy resources to meet stringent environmental targets while ensuring reliability in dynamic and isolated marine environments. Energy management systems (EMS) are critical to optimizing fuel efficiency, reducing emissions, and maintaining power quality under variable load demands and the harsh conditions in which ships operate. This paper presents a comprehensive review of EMS methodologies for SPS, covering traditional methods such as evolutionary algorithms, model predictive control (MPC), fuzzy logic control, and more modern approaches such as machine learning (ML) and deep learning (DL). ML and DL for EMS offer predictive and adaptive capabilities for real-time optimization, but face limitations in data security and centralized computational demands. Federated learning (FL), which is a decentralized, privacy-preserving paradigm, is a viable solution to the disadvantages of the conventional centralized form of training in ML. FL enables collaborative model training across distributed systems without raw data sharing, addressing critical concerns in cybersecurity and communication overhead. However, FL does require individual devices or clients to have enough computational power to carry out distributed optimization locally. A detailed overview of FL, including the FL training process, categories, architectures and various applications of FL in ship and energy systems, is presented in this paper. To further establish the usefulness of conducting energy management (EM) in ship systems, the paper presents a case study on a notional four-zone DC SPS in which FL has been utilized to collaboratively train DL models across multiple generators without sharing sensitive local data. The results demonstrate better generator output power prediction and effective load management in comparison to the conventional centralized learning setup, indicating the potential of FL to enhance energy efficiency and reliability in ship systems. Finally, some challenges and possible research gaps in applying FL to ship systems are discussed.}, archive = {J_ACCESS}, author = {Asif Ahmed Khan and Laxman Timilsina and Grace Muriithi and Ali Arsalan and Ali Moghassemi and Behnaz Papari and Gokhan Ozkan and Christopher S. Edrington and Nafiseh Seifi Boghrabadi and Zhenbo Wang}, doi = {10.1109/ACCESS.2025.3613231}, journal = {IEEE Access}, pages = {171563-171597}, shortjournal = {IEEE Access}, title = {Energy management systems for maritime microgrids: A comprehensive review of intelligent optimization strategies}, volume = {13}, year = {2025}, }
• (2025). Wavelet-based multi-stream spatial-frequency fusion network for few-shot hyperspectral image classification. ACCESS, 13, 171546-171562. (www)

  @article{ , abstract = {In the research of hyperspectral image classification, deep learning-based methods have achieved promising results. However, due to the high cost and difficulty in obtaining labeled hyperspectral image samples, traditional deep learning methods experience a significant performance decline under few-shot conditions. This paper proposes a deep few-shot learning method, referred to as Wavelet-Based Multi-stream Spatial-Frequency Fusion Network, to address the issue of small sample size in hyperspectral image classification. Through a multi-stream structure, features at various scales are obtained, effectively alleviating the issue of insufficient feature representation caused by limited training samples. These features are then fed into a multi-level wavelet transformation module integrated with a frequency-domain attention module. By decomposing the input into high-frequency and low-frequency components via wavelet transformation, and subsequently inputting these components into the frequency-domain attention module, a richer representation of features in the frequency domain is achieved. Finally, a Spatial-Frequency Fusion module is employed to not only extract abundant feature information but also fuse features from different scales. This process results in feature maps that encompass more comprehensive global feature information, thereby significantly enhancing classification accuracy under small sample conditions. The proposed method has been compared with traditional machine learning approaches and recent deep learning methods on the Houston, Dioni, and University of Pavia datasets. Under training conditions using only 1 to 5, 10, 15, and 20 samples per class, the experimental results demonstrate the superiority of the method proposed in this paper.}, archive = {J_ACCESS}, author = {Hu Wang and Jun Liu and Zhihui Wang and Yingying Peng}, doi = {10.1109/ACCESS.2025.3616126}, journal = {IEEE Access}, pages = {171546-171562}, shortjournal = {IEEE Access}, title = {Wavelet-based multi-stream spatial-frequency fusion network for few-shot hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). EPUN: Efficient 2D phase unwrapping with a lightweight neural network. ACCESS, 13, 171537-171545. (www)

  @article{ , abstract = {Phase unwrapping is a fundamental task. In recent years, deep learning has been widely used for phase unwrapping in different fields. Nevertheless, along with the unwrapping performance improvement of deep networks, their structures become more and more complicated and diverse. As a result, these designed networks make it difficult to achieve an optimal balance between efficiency and effectiveness. For this purpose, we proposed a lightweight neural network for efficient 2d phase unwrapping (EPUN). EPUN first introduces a feature modulation block to efficiently aggregate long-range spatial context while executing channel mixing on the features. In addition, we design a feature supervision module exclusively used during training, which provides explicit unwrapping supervision for deep-level features, facilitating the network to converge to a better parameter set for optimized phase unwrapping performance. We evaluate EPUN qualitatively and quantitatively on both simulation and real data. The experimental results show that EPUN not only achieves optimal unwrapping performance but also has excellent unwrapping speed.}, archive = {J_ACCESS}, author = {Zhiliang Zhang and Fang Luo and Ying Liu and Yin Zhang}, doi = {10.1109/ACCESS.2025.3613182}, journal = {IEEE Access}, pages = {171537-171545}, shortjournal = {IEEE Access}, title = {EPUN: Efficient 2D phase unwrapping with a lightweight neural network}, volume = {13}, year = {2025}, }
• (2025). Optimized hybrid framework versus spark and hadoop: Performance analysis for big data applications in vehicular engine systems. ACCESS, 13, 171523-171536. (www)

  @article{ , abstract = {The rapid growth of data generated by vehicular engine systems has necessitated the development of efficient frameworks for real-time big data processing. Existing solutions such as Apache Hadoop and Apache Spark, have been widely adopted in various domains due to their respective strengths; Hadoop’s distributed storage capabilities and Spark’s in-memory processing efficiency. However, these standalone frameworks present limitations when applied to vehicular systems, particularly in handling real-time analytics, low-latency requirements, and efficient use of network and energy resources. Previous research, including approaches like the one by Shabestari and Navimipour (2023), which combines Spark with Hadoop YARN for remote sensing data, and Ahmed et al. (2020), which investigates parameter tuning for Hadoop and Spark, highlights the difficulties in achieving optimal performance in dynamic environments, particularly when managing varying data loads, noise levels, and non-numeric attributes. This paper introduces a novel Optimized Hybrid Framework that integrates Hadoop’s distributed storage and Spark’s in-memory processing while addressing the limitations of existing hybrid models. Unlike prior approaches, our framework incorporates a dynamic switching mechanism based on business logic, data urgency, and noise levels, providing an adaptive and efficient solution for real-time vehicular big data analytics. Moreover, the framework introduced controlled noise injection strategies and handling for non-numeric attributes such as decision and driver behavior data, ensuring data integrity in real-world vehicular datasets. Experimental results demonstrate that the proposed Hybrid Framework outperforms both Hadoop and Spark in key performance metrics, achieving an execution time of 0.030 seconds, latency of 1.67e-7 seconds/row, and a network load of 55.01%. These results highlight the framework’s ability to optimize throughput, minimize latency, and reduce energy consumption, making it particularly suitable for predictive maintenance, fault diagnosis, and adaptive vehicular control systems. This study contributes to the field by offering a practical, scalable, and low-latency solution for vehicular big data analytics, addressing the shortcomings of both standalone and hybrid approaches in the automotive manufacturers domain, fleet operators, and researchers for a more effective tool for vehicle health monitoring. The enhanced data processing capabilities facilitate better vehicle maintenance, increase safety, and reduce operational costs, delivering substantial benefits to the broader community through more dependable and efficient transportation solution.}, archive = {J_ACCESS}, author = {Isinka Joseph Chukwudi and Md. Arafatur Rahman and Ali H. Alenezi and Hai Tao and Prashant Pillai}, doi = {10.1109/ACCESS.2025.3607788}, journal = {IEEE Access}, pages = {171523-171536}, shortjournal = {IEEE Access}, title = {Optimized hybrid framework versus spark and hadoop: Performance analysis for big data applications in vehicular engine systems}, volume = {13}, year = {2025}, }
• (2025). A trustworthy model with uncertainty management for predicting vascular access dysfunction in hemodialysis patients. ACCESS, 13, 171511-171522. (www)

  @article{ , abstract = {Vascular access dysfunction is a prevalent and critical complication among hemodialysis patients, particularly in Taiwan, which has the highest proportion of dialysis patients globally. Early diagnosis and effective management are essential for improving patient outcomes. However, traditional diagnostic approaches, such as routine surveillance and fixed blood flow thresholds, often fail to reliably identify patients requiring timely surgical intervention. To address these challenges, this study proposes a trustworthy AI system utilizing an uncertainty-aware, tree-based machine learning framework to improve the assessment of vascular access dysfunction. The proposed framework incorporates advanced uncertainty management techniques to address both aleatoric uncertainty (data uncertainty) originating from inherent data variability, and epistemic uncertainty (model uncertainty) arising from model limitations. Aleatoric uncertainty is systematically quantified using a multipass perturbation strategy that simulates sample variability to capture the distribution of potential outcomes, while epistemic uncertainty is mitigated using ensemble methods. By leveraging the multipass perturbation strategy, the framework generates calibrated uncertainty estimations. To increase the reliability and trustworthiness of clinical decision-making, the framework assigns low-confidence (high-uncertainty) predictions to an ‘Uncertain’ category, thereby achieving a near-zero leakage rate, which is crucial in high-stakes clinical scenarios. Additionally, an extended confusion matrix and novel uncertainty metrics are introduced to comprehensively evaluate model performance. The system has been validated using real-world hospital datasets, demonstrating its deployable AI potential with superior predictive accuracy, sensitivity, and robustness compared to traditional methods such as the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines.}, archive = {J_ACCESS}, author = {Chia-Hsun Lin and Zheng Lin Chen and Chung-Kuan Wu and Tsen-Che Wu and Ching-Wen Ma}, doi = {10.1109/ACCESS.2025.3612957}, journal = {IEEE Access}, pages = {171511-171522}, shortjournal = {IEEE Access}, title = {A trustworthy model with uncertainty management for predicting vascular access dysfunction in hemodialysis patients}, volume = {13}, year = {2025}, }
• (2025). Alternating MSA and mo-QEA to design slow-time coded PRF-set in pulse-doppler radar. ACCESS, 13, 171493-171510. (www)

  @article{ , abstract = {In this paper, we propose a novel framework based on a multivariable hybrid optimization algorithm that integrates single-objective modified simulated annealing (MSA) and multi-objective quantum-inspired evolutionary algorithm (Mo-QEA) layers based on the impact of the slow-time phase coding for minimizing unwanted signals at the output of matching filters, on the frequency spectrum of pulsed-Doppler radars. Slow-time codes alter the spectrum’s structure and result in increasing the blind areas within the blind-zone map (BZM). The hybrid framework comprises two layers that identify the optimum pulse repetition frequency (PRF) set and slow-time code by considering spectrum disturbances. The first layer employs the MSA for PRF set optimization. Modifications to the SA include restructured loops based on the perturbation functions. The outer loop considers the overall PRF set structure (overall direction and step—ODS), while the inner loop conducts a sequential search based on the cost function’s standard deviation and small increments. The output of this layer serves as the foundation for the subsequent layer, where the phase sequence of each PRF is optimized to concurrently minimize blind areas due to the M of N criterion and grating lobes through Mo-QEA. Innovations are mutation-like operators and tournament selection to enhance the diversity of generated solutions, alongside rotation gates in quantum computing. Our research highlights a previously unexplored relationship between BZM and slow-time coding, demonstrating improved performance for each layer compared to existing studies.}, archive = {J_ACCESS}, author = {Aata Nokhostin Roohi and Hossein Soleimani and Mohammad Soleimani}, doi = {10.1109/ACCESS.2025.3616316}, journal = {IEEE Access}, pages = {171493-171510}, shortjournal = {IEEE Access}, title = {Alternating MSA and mo-QEA to design slow-time coded PRF-set in pulse-doppler radar}, volume = {13}, year = {2025}, }
• (2025). Is this the best prompt? scoring prompts for arabic NLP across LLMs. ACCESS, 13, 171468-171492. (www)

  @article{ , abstract = {Large language models (LLMs) demonstrate impressive capabilities across a range of natural language processing (NLP) tasks. However, they are highly sensitive to prompt design, which significantly affects their ability to align outputs with user intent. Poorly crafted prompts can result in misleading or irrelevant responses. Nevertheless, selecting the most effective prompt from several candidates remains an open challenge. Despite the growing importance of prompt engineering, there is no comprehensive framework to systematically evaluate prompts across multiple dimensions, such as similarity, performance, efficiency, and consistency, particularly in scenarios where performance can be traded off against computational cost or consistency. In this study, we propose a novel scoring framework to evaluate handcrafted prompts across four essential dimensions: Similarity, performance, efficiency (measured by latency, input tokens, and output tokens), and consistency. Considering Arabic, a relatively low-resource, morphologically rich language, as a case study, we evaluated this framework on six diverse text classification tasks: Dialect identification, sentiment analysis, offensive language detection, stance detection, emotion detection, and sarcasm detection. Our methodology assesses prompts across multiple LLMs (GPT-4o mini, LLaMA, ALLAM, and Claude 3.5 Haiku), providing valuable insights into model-specific and task-specific performance patterns. Results demonstrate that no single prompt universally excels across all dimensions; rather, optimal prompts vary based on specific task requirements and evaluation priorities. The proposed framework enables the identification of the most effective prompts for each application context while revealing important trade-offs between performance metrics. By addressing the unique challenges of Arabic NLP, this research not only advances prompt engineering for underrepresented languages but also provides a systematic and adaptable methodology for prompt evaluation that can enhance LLM performance across a range of linguistic contexts, diverse domains, tasks, and various model architectures.}, archive = {J_ACCESS}, author = {Dania Refai and Maged S. Al-Shaibani and Irfan Ahmad}, doi = {10.1109/ACCESS.2025.3616181}, journal = {IEEE Access}, pages = {171468-171492}, shortjournal = {IEEE Access}, title = {Is this the best prompt? scoring prompts for arabic NLP across LLMs}, volume = {13}, year = {2025}, }
• (2025). Summing modulo 2 of stationary binary stochastic processes. ACCESS, 13, 171457-171467. (www)

  @article{ , abstract = {In the paper, we analyze the properties of the stochastic process obtained as the result of summing modulo 2 without carry of a finite number of stationary binary stochastic processes, some of which do not satisfy the independence condition. The primary goal of the mathematical analysis is to determine the formula for the minimum number of independent stochastic processes that will ensure the mean and the covariance values between adjacent elements of the output process are acceptable to the user, regardless of the number of dependent stochastic processes at the input. We assume that we do not know which of the summed processes are dependent and which are independent. To the authors’ knowledge, this problem has not been addressed in the literature so far, and it may be helpful when using the modulo 2 summation of binary stationary independent random processes with binary dependent processes. Possible applications include random sequence generation, cryptography, simulations, coding theory, etc.}, archive = {J_ACCESS}, author = {Mieczysław Jessa and Jakub Nikonowicz}, doi = {10.1109/ACCESS.2025.3615472}, journal = {IEEE Access}, pages = {171457-171467}, shortjournal = {IEEE Access}, title = {Summing modulo 2 of stationary binary stochastic processes}, volume = {13}, year = {2025}, }
• (2025). LiteQSign: Lightweight and quantum-safe signatures for heterogeneous IoT applications. ACCESS, 13, 171442-171456. (www)

  @article{ , abstract = {The rapid proliferation of resource-constrained IoT devices across sectors like healthcare, industrial automation, and finance introduces major security challenges. Traditional digital signatures, though foundational for authentication, are often infeasible for low-end devices with limited computational, memory, and energy resources. Also, the rise of quantum computing necessitates post-quantum (PQ) secure alternatives. However, NIST-standardized PQ signatures impose substantial overhead, limiting their practicality in energy-sensitive applications such as wearables, where signer-side efficiency is critical. To address these challenges, we present LightQSign ( $\texttt {LiteQS} { }$ ), a novel lightweight PQ signature that achieves near-optimal signature generation efficiency with only a small, constant number of hash operations per signing. Its core innovation enables verifiers to obtain one-time hash-based public keys without interacting with signers or third parties through secure computation. We formally prove the security of $\texttt {LiteQS} { }$ in the random oracle model and evaluate its performance on commodity hardware and a resource-constrained 8-bit AtMega128A1 microcontroller. Experimental results show that $\texttt {LiteQS} { }$ outperforms NIST PQ standards with lower computational overhead, minimal memory usage, and compact signatures. On an 8-bit microcontroller, it achieves up to $1.5-24\times $ higher energy efficiency and $1.7-22\times $ shorter signatures than PQ counterparts, and $56-76\times $ better energy efficiency than conventional standards–enabling longer device lifespans and scalable, quantum-resilient authentication.}, archive = {J_ACCESS}, author = {Attila Altay Yavuz and Saleh Darzi and Saif Eddine Nouma}, doi = {10.1109/ACCESS.2025.3612735}, journal = {IEEE Access}, pages = {171442-171456}, shortjournal = {IEEE Access}, title = {LiteQSign: Lightweight and quantum-safe signatures for heterogeneous IoT applications}, volume = {13}, year = {2025}, }
• (2025). Design and application of an intelligent decision-making system for unmanned aerial vehicles based on deep reinforcement learning. ACCESS, 13, 171435-171441. (www)

  @article{ , abstract = {This paper uses deep reinforcement learning algorithms to achieve intelligent decision-making for unmanned aerial vehicles (UAVs) to realize autonomous obstacle avoidance and target tracking of UAVs. This research proposes an improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) algorithm combined with a multi-experience pool mechanism. The UAV obstacle avoidance task is modeled as a Partially Observable Markov Decision Process (POMDP) model. The experimental results show that the ITD3 algorithm demonstrates significant advantages in the simulated flight environment, with the task success rate increasing to 93.7%, and it also has excellent anti-noise interference ability and generalization.}, archive = {J_ACCESS}, author = {Xiaobo Song}, doi = {10.1109/ACCESS.2025.3615941}, journal = {IEEE Access}, pages = {171435-171441}, shortjournal = {IEEE Access}, title = {Design and application of an intelligent decision-making system for unmanned aerial vehicles based on deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Multi-STAR-RIS-assisted multi-user communication with symbiotic RIS-feedback channels and BS-user direct links. ACCESS, 13, 171422-171434. (www)

  @article{ , abstract = {Modern urban communication networks face challenges such as signal propagation blockages, which can be effectively mitigated using simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). This paper explores the integration of multiple STAR-RISs into a symbiotic radio framework to enhance overall network performance, supporting both primary users and STAR-RISs that transmit their own data over the shared spectrum. The system enables RIS-feedback links within the same spectrum while accounting for direct links between the base station (BS) and user equipments (UEs). Two optimization problems are formulated based on different symbiotic resource-sharing policies and are equivalently transformed into weighted minimum mean square error (WMMSE) problems, which are then solved using the block coordinate descent method. Furthermore, an iterative penalty dual decomposition is adopted to determine the optimal BS beamformer and the STAR-RIS transmitting and reflecting coefficients. The effectiveness of the proposed STAR-RIS optimization framework is demonstrated through in-depth evaluation results. The findings indicate that deploying multiple STAR-RISs significantly enhances overall communication performance. While direct BS-to-UE links enhance user performance, they negatively impact RIS feedback performance. Moreover, performance trends vary according to the selected symbiotic resource-sharing policy.}, archive = {J_ACCESS}, author = {Bakunda John Ngunga and Ji-Hoon Yun}, doi = {10.1109/ACCESS.2025.3616285}, journal = {IEEE Access}, pages = {171422-171434}, shortjournal = {IEEE Access}, title = {Multi-STAR-RIS-assisted multi-user communication with symbiotic RIS-feedback channels and BS-user direct links}, volume = {13}, year = {2025}, }
• (2025). Prediction of cardiovascular disease risk from retinal vasculature using a quantitative diagnostic approach with CVD-net in DR and HR patients. ACCESS, 13, 171406-171421. (www)

  @article{ , abstract = {The global incidence of diabetes is increasing significantly each year, posing serious health risks if not diagnosed at an early stage. Elevated blood glucose levels can lead to complications such as Diabetic Retinopathy (DR), Hypertensive Retinopathy (HR), Cardiovascular Diseases (CVD), and renal failure. Background The objective of this study is to establish an association between retinopathy and CVD, which is driven by shared pathogenic mechanisms including inflammation, microvascular damage, oxidative stress, and endothelial dysfunction. Diabetes and hypertension substantially increase the likelihood of DR and HR, which in turn elevate the risk of developing CVD. Objective This work is to forecast the risk of cardiovascular diseases by analyzing retinal vasculature and associated clinical risk factors in patients with DR and HR. Methods The method involves a quantitative diagnostic approach using morphological and physiological attributes of the retinal vascular system, such as the Arteriole-to-Venule ratio (AVR) and the Cup-to-Disc ratio (CDR), which serve as biomarkers. In addition, systemic risk indicators including age, gender, Body Mass Index (BMI), smoking habits, and alcohol consumption are incorporated. Deep Learning (DL) techniques are employed to detect DR, HR, and CVD and to quantify their characteristic features. Results The results indicate that early investigations based on the proposed approach can effectively identify patients at high risk, potentially preventing up to 90% of CVD cases when detected during the early stages. Implications This study highlight the potential of retinal imaging, when combined with deep learning, to serve as a reliable, non-invasive tool for early cardiovascular risk prediction in diabetic and hypertensive individuals, enabling timely clinical intervention.}, archive = {J_ACCESS}, author = {Sathyavani Addanki and D. Sumathi}, doi = {10.1109/ACCESS.2025.3610424}, journal = {IEEE Access}, pages = {171406-171421}, shortjournal = {IEEE Access}, title = {Prediction of cardiovascular disease risk from retinal vasculature using a quantitative diagnostic approach with CVD-net in DR and HR patients}, volume = {13}, year = {2025}, }
• (2025). An adaptive multi-metric fuzzy logic-based parent selection strategy for scalable RPL routing in heterogeneous WSNs. ACCESS, 13, 171386-171405. (www)

  @article{ , abstract = {In wireless sensor networks (WSNs), the Routing Protocol for Low-Power and Lossy Networks (RPL) is the de facto standard for routing. However, its performance degrades in large-scale, dynamic, or resource-constrained deployments owing to unbalanced load distribution, premature node failures, and limited scalability. This study proposes an adaptive parent selection framework that integrates three critical parameters - residual energy, queue length, and parent capacity into a unified decision model. A Mamdani-type fuzzy inference system processes these inputs to compute an optimal parent score, while an autoregressive moving average (ARMA) model monitors packet arrival intervals to regulate parent reselection. This ensures that route changes are triggered only by significant traffic deviations, avoiding unnecessary switching and improving stability. Additionally, by incorporating the MAC-layer feedback from IEEE 802.15.4, the method enhances route reliability, balances energy consumption, and reduces retransmissions. Simulation studies over a $500\times 500$ m2 region with 100–1000 nodes suggested significant improvements compared to benchmark protocols, achieving 83.91% throughput efficiency, a 46.7% reduction in energy consumption, and extended network lifetime.}, archive = {J_ACCESS}, author = {R. Radha and G. Rithvika and P. J. Sidharth and S. A. Sajidha}, doi = {10.1109/ACCESS.2025.3615956}, journal = {IEEE Access}, pages = {171386-171405}, shortjournal = {IEEE Access}, title = {An adaptive multi-metric fuzzy logic-based parent selection strategy for scalable RPL routing in heterogeneous WSNs}, volume = {13}, year = {2025}, }
• (2025). Indian traffic surveillance video summarization using YOLO and multi-level masking. ACCESS, 13, 171371-171385. (www)

  @article{ , abstract = {The rapid growth of urban environments and vehicle usage has made it more challenging to interpret and extract insights from surveillance videos. Surveillance traffic systems produce a huge amount of video, rendering manual screening time-consuming and inefficient. To meet this severe challenge, our work presents an efficient and scalable video summarization system tailored for the quick and precise analysis of Indian traffic surveillance video. The framework combines cutting-edge deep learning object detection models such as YOLOv8, YOLO11, Faster R-CNN, and RetinaNet, which were trained on the Indian Road User Vehicle Dataset (IRUVD) a detailed dataset reflecting varied road environments peculiar to India and tested on a custom video dataset captured in Bangalore, India. Following a thorough comparative study in terms of performance metrics such as mAP50, precision, recall, training stability, and inference speed, YOLO11 emerged as the best performing model for deployment in real-world applications due to its balance between accuracy and compute efficiency. In addition to object detection, the system uses sophisticated masking methods to filter and emphasize vehicle classes or objects of interest to support focused video summarization. Several masking approaches were created like black and white masking, blackout, Gaussian blur, telea inpainting, and selective blur to provide adaptability according to traffic enforcement, urban planning and anomaly detection analysis requirements for Indian traffic scenario. Experimental evaluation of the proposed system showed excellent object detection precision and recall scores of 0.9504, with a mAP@0.5 value of 0.9688 and an overall Fitness Score of 90.14%. Frame selection also achieved good results with an F1-score of 0.9705 and an accuracy of 0.9750, thus assuring the systems reliability in selecting relevant vehicle classes and frames for efficient video summarization. Visual masking was assessed using PSNR and MSE metrics keeping selective texture blurring masking technique as the base. It maintained contextual integrity with PSNR of 31.72 while on the other hand structural segmentation achieved higher concealment with PSNR of 5.59, indicating the framework’s flexibility across privacy and use-case driven approach.}, archive = {J_ACCESS}, author = {Abhishek Saraff and Aishwarya Gite and Ashutosh Joshi and Mohana and P. Ramakanth Kumar and K. Sreelakshmi and T. Shankar}, doi = {10.1109/ACCESS.2025.3616267}, journal = {IEEE Access}, pages = {171371-171385}, shortjournal = {IEEE Access}, title = {Indian traffic surveillance video summarization using YOLO and multi-level masking}, volume = {13}, year = {2025}, }
• (2025). STAR: Sea turtle basic activity recognizer network via efficient transformer. ACCESS, 13, 171356-171370. (www)

  @article{ , abstract = {Sea turtles are endemic to several islands and are crucial in maintaining ecosystem balance. However, they face significant threats from poaching, extreme weather conditions, and abnormal activities. Monitoring and protecting their habitats has become a key focus in blue economy research, a priority program in several developed nations. Observing sea turtle behavior not only aids in conservation efforts but also supports the development of behavioral analysis that can serve as metrics for further research. In this work, we propose STAR, a basic turtle activity recognition system, employing an efficient deep learning approach. This system categorizes simple activities such as swimming, eating, hiding, resting, and other distinct behaviors. An effective network is offered by utilizing enhanced modules to discriminate activity features involving sequential frames. In addition, a lightweight transformer is presented as a novel attentive module that improves the feature extraction ability. Another contribution offers a new video dataset of sea turtle activity recognition captured underwater from various turtle poses. Several extensive experiments analyzed the performances of essential modules and achieved satisfactory results compared to other lightweight models. The efficiency of the STAR model shows that it obtained fast data processing speed without abundant computational resources in single-frame analysis. Applying this recognition system requires sea turtle detection to capture the area of interest in the beginning process.}, archive = {J_ACCESS}, author = {Muhamad Dwisnanto Putro and Agung Sutrisno and Indri Shelovita Manembu and Il Yong Chun and Tae-Hyun Oh}, doi = {10.1109/ACCESS.2025.3615067}, journal = {IEEE Access}, pages = {171356-171370}, shortjournal = {IEEE Access}, title = {STAR: Sea turtle basic activity recognizer network via efficient transformer}, volume = {13}, year = {2025}, }
• (2025). Why RF fingerprinting needs better data, not bigger models. ACCESS, 13, 171348-171355. (www)

  @article{ , abstract = {Recent advances in Radio Frequency Fingerprint Identification have demonstrated impressive classification performance, often relying on deep neural networks. However, such approaches remain too computationally intensive for deployment on resource-constrained IoT devices. In this work, we revisit Fully Connected Neural Networks (FCNNs) and show that a simple one-hidden-layer FCNN can achieve competitive performance, requiring at least $5\times $ fewer FLOPs and inference time than state-of-the-art architectures, with only a marginal drop in F1-score (0.02% to 0.21% depending on the dataset, for 16 transmitters or fewer). More importantly, our results reveal that despite their simplicity, our FCNNs exhibit comparable resilience to channel and device variability comparable to more complex architectures. This suggests that the current emphasis on increasing model size may be misguided. Rather than continuously increasing model depth or sophistication, our results highlight the importance of reexamining how datasets are designed and used in evaluation.}, archive = {J_ACCESS}, author = {Emma Bothereau and Robin Gerzaguet and Matthieu Gautier and Alice Chillet and Olivier Berder}, doi = {10.1109/ACCESS.2025.3614459}, journal = {IEEE Access}, pages = {171348-171355}, shortjournal = {IEEE Access}, title = {Why RF fingerprinting needs better data, not bigger models}, volume = {13}, year = {2025}, }
• (2025). Optimized design of LCL filter for grid tied inverters with deployment of large-scale grid connected PV system for harmonics compensation and stable operation under sudden changes in weather conditions. ACCESS, 13, 171327-171347. (www)

  @article{ , abstract = {With the growing concerns about climate change and energy security, solar energy has been recognised as a green and reliable source of electrical energy. However, considering the variations of solar irradiance, particularly in Grid Connected Photovoltaic (GCPV) systems, the operating point of the PV panel is shifted, which ultimately cause several power quality issues in the power systems. When the operating conditions change, two major concerns arose regarding the power quality in GCPV systems: 1) voltage instability at the Point of Common Coupling (PCC) and 2) harmonic distortion for voltage and current waveforms at the PCC. Therefore, it is essential to maintain the power quality at varied operating points caused by variations of solar irradiance and weather conditions. In this paper, a Voltage Source Converter (VSC) based control strategy is developed for the voltage regulation, with a LCL filter design as the key component for harmonics elimination. Though, with the changes in the operating conditions, conventionally designed filters can deteriorate the control performance and risk the stability of the system. To overcome such issues, an optimized design method of LCL filter is also presented to incorporate changes in solar irradiance and to maintain the Total Harmonic Distortion (THD), in accordance with the IEEE standards 519. A MATLAB/Simulink based model was developed and analysed to exhibit the effectiveness of the proposed control and design method. Experimental work on an industrial scale was also caried out to validate the developed control scheme and the associate LCL filter.}, archive = {J_ACCESS}, author = {Arjmand Khaliq and Hussin Zahloul and Hamed Hamzehbahmani and Sana Salous}, doi = {10.1109/ACCESS.2025.3615892}, journal = {IEEE Access}, pages = {171327-171347}, shortjournal = {IEEE Access}, title = {Optimized design of LCL filter for grid tied inverters with deployment of large-scale grid connected PV system for harmonics compensation and stable operation under sudden changes in weather conditions}, volume = {13}, year = {2025}, }
• (2025). CPMTech: A technology-based model for construction progress monitoring. ACCESS, 13, 171302-171326. (www)

  @article{ , abstract = {The construction industry is a vital global economic sector that has evolved and expanded rapidly in recent years. However, despite this growth, the sector continues to face significant challenges worldwide, including labor shortages, safety risks, and low levels of process automation. Construction progress monitoring is a key process that requires automation enhancement. This study examines the current Construction Progress Monitoring (CPM) process, with a focus on the adoption of technologies for data acquisition, integration, inspection, and visualization in construction environments. The research objectives comprised the proposal of a technology-based model for construction progress monitoring, followed by its instantiation and validation under real-world and simulated conditions. A Systematic Literature Review (SLR) was conducted to identify existing studies, their findings and contributions, leading to the Construction Progress Monitoring using technologies (CPMTech) model. A case study was then performed on an active construction site to validate an instance of the proposed model, specifically for manual and controlled data acquisition processes. Additionally, a virtual environment simulation was used to validate an instance of the model for autonomous data acquisition. The results correspond to the CPMTech model and its instances validated in real and simulated scenarios. The contributions stand out for supporting computing, automation, and civil engineering professionals in integrating technologies to the CPM. Thus, CPMTech represents a significant step forward in the gradual and continuous adoption of technologies for monitoring construction progress, overcoming traditional methods that have limited the sector’s potential for decades.}, archive = {J_ACCESS}, author = {Gerson Lima Serejo and Viviane Almeida Dos Santos and Alexandre Francisco Barral Silva and Carlos Gustavo Resque Dos Santos}, doi = {10.1109/ACCESS.2025.3611937}, journal = {IEEE Access}, pages = {171302-171326}, shortjournal = {IEEE Access}, title = {CPMTech: A technology-based model for construction progress monitoring}, volume = {13}, year = {2025}, }
• (2025). A DETR-based evidential RGB-NIR data fusion framework for fire and smoke detection. ACCESS, 13, 171287-171301. (www)

  @article{ , abstract = {Timely and reliable detection of fire and smoke is critical, where even a single misjudgment can lead to major safety risks. While near-infrared (NIR) imagery can complement RGB data by capturing thermal signatures invisible to standard cameras, NIR data is often limited in practice, making effective fusion a challenge. In this work, we present a decision-level fusion method based on Dempster–Shafer Theory (DST), designed to integrate sparse NIR information into a pre-trained RGB detection framework. Our pipeline processes RGB images using DETR, while a Cluster Variational Autoencoder extracts soft evidence from NIR. To bridge the gap between the two modalities, we introduce a projection mechanism that maps NIR-derived beliefs to the object class space, enabling meaningful fusion even when NIR data is scarce. We also propose a KL-divergence-based loss to guide the model in learning not just accurate labels, but also spatially coherent and uncertainty-aware bounding boxes. Experiments show that our method improves mAP by around 20% over RGB-only baselines and outperforms other fusion strategies by over 30% under unbalanced settings, demonstrating its robustness and adaptability.}, archive = {J_ACCESS}, author = {Dongxing Yu and Bing Han and Xiaorui Guo and Wei Ding and Weikai Ren and Yuanlei Hou}, doi = {10.1109/ACCESS.2025.3616037}, journal = {IEEE Access}, pages = {171287-171301}, shortjournal = {IEEE Access}, title = {A DETR-based evidential RGB-NIR data fusion framework for fire and smoke detection}, volume = {13}, year = {2025}, }
• (2025). LLM-driven MDA pipeline for generating UML class diagrams and code. ACCESS, 13, 171266-171286. (www)

  @article{ , abstract = {The transformation of textual specifications into formal software models is a major challenge in software design automation. This study presents an integrated approach that combines the natural language interpretation capabilities of transformer models with explicit concept structuring using a domain specific language (DSL). This DSL, designed as a pivotal intermediate layer, ensures continuity between semantic extraction, UML modeling, and automatic Python code generation. By following the model-driven architecture (MDA) paradigm, the proposed pipeline follows a structured progression from text to model to code while maintaining a high level of traceability and controllability. The experimental evaluation conducted on a dedicated annotated corpus demonstrates the accuracy of the models on UML entities and highlights the importance of DSL for validation, editing, and exploitation of results. This approach paves the way for the development of intelligent modeling tools and structuring of automated transformation chains.}, archive = {J_ACCESS}, author = {Zakaria Babaalla and Abdeslam Jakimi and Mohamed Oualla}, doi = {10.1109/ACCESS.2025.3615828}, journal = {IEEE Access}, pages = {171266-171286}, shortjournal = {IEEE Access}, title = {LLM-driven MDA pipeline for generating UML class diagrams and code}, volume = {13}, year = {2025}, }
• (2025). Wavelet-guided hybrid attention neural network for identification of microseismic event. ACCESS, 13, 171255-171265. (www)

  @article{ , abstract = {Microseismic monitoring technology enables accurate and real-time identification of the spatial location and type of geological hazards, serving as a crucial component of early warning systems for geological disaster prevention and mitigation. Due to the complex and unpredictable physical conditions of highway slopes, effective identification of microseismic signals remains a challenging task. To address this issue, a wavelet-guided hybrid attention intelligent framework for microseismic event recognition is proposed. Initially, a wavelet domain attention-guided filtering module is designed, which maps time-domain signals to the wavelet domain and employs an attention mechanism to filter and extract features from the signals, thereby enhancing noise suppression and robustness. Subsequently, a physical-knowledge-guided loss function composed of Hilbert transform and $l2 / l4$ norm is designed to optimize the neural network model. Finally, experimental validation is conducted on a dataset of microseismic events from highway slopes. The experimental results show that the average recognition accuracy of the proposed method reached 96.31%, outperforming comparative methods and demonstrating the potential application of the proposed method in practical engineering.}, archive = {J_ACCESS}, author = {Qi Luo and Huifang Chen and Peng Zhang and Tingquan He and Siyu Dang and Rongyue Li and Jianhui Wu and Youdong Huang and Jiangcun Xie}, doi = {10.1109/ACCESS.2025.3610110}, journal = {IEEE Access}, pages = {171255-171265}, shortjournal = {IEEE Access}, title = {Wavelet-guided hybrid attention neural network for identification of microseismic event}, volume = {13}, year = {2025}, }
• (2025). ECG heartbeat classification using CNN autoencoder feature extraction and attention-augmented BiLSTM classifier. ACCESS, 13, 171241-171254. (www)

  @article{ , abstract = {The electrocardiogram (ECG) is a primary non-invasive technique for recording the heart’s electrical activity, but the complexity and subjectivity of manual interpretation have driven the growth of computer-aided diagnostic systems to improve accuracy and reliability in detecting cardiac abnormalities. This research proposes a deep learning (DL)-based framework that integrates a convolutional autoencoder (CAE) for dimensionality reduction and unsupervised feature extraction from raw ECG signals. The pipeline then incorporates a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced with a multi-head self-attention mechanism for robust and automated heartbeat classification. To validate this architecture, experiments were conducted using the MIT-BIH Arrhythmia dataset, focusing on five arrhythmia classes. Additionally, we benchmarked our system’s performance against state-of-the-art methods. Our results demonstrate the efficiency of this method, with an overall accuracy of 98.57%.}, archive = {J_ACCESS}, author = {Oumayma Degachi and Lilia El Amraoui and Kais Ouni}, doi = {10.1109/ACCESS.2025.3615111}, journal = {IEEE Access}, pages = {171241-171254}, shortjournal = {IEEE Access}, title = {ECG heartbeat classification using CNN autoencoder feature extraction and attention-augmented BiLSTM classifier}, volume = {13}, year = {2025}, }
• (2025). Multimodal metaphor detection based on affective embedding and deep transformer. ACCESS, 13, 171221-171240. (www)

  @article{ , abstract = {We propose to detect and interpret multimodal metaphors based on affective embeddings and deep transformer representations across diverse linguistic and visual contexts. Designed for large-scale figurative language analysis, our system uncovers coherent, metaphorically rich expressions through hierarchical multimodal learning. This pipeline extracts diverse semantic featuresałsuch as emotional valence and conceptual mappingsałenhanced via weakly supervised learning to ensure broad coverage of metaphorical phenomena. An affective-embedding-based feature selection strategy filters out less discriminative attributes, producing refined multimodal representations. These representations are projected into a transformer, where each expression is modeled as a spatial sequence of the abstract conceptual blends, supporting precise metaphor identification. We construct a weighted similarity graph from these multimodal embeddings, enabling large-scale metaphor clustering through advanced graph-based detection. The resulting metaphor communities reflect shared conceptual mappings-such as anger-as-heat or life-as-journey patternsałand reveal both conventional and novel metaphorical associations. To deliver accurate metaphor interpretation, a ranking module integrates individual expression features with community-level conceptual patterns to suggest relevant metaphorical meanings. Evaluations on the Multimodal Metaphor Dataset (MMD-1.3M), comprising 1.3 million instances spanning 50 conceptual categories, show that our model achieves an BER score of 0.487 on metaphor identification, outperforming strong baselines like ViLBERT and CLIP by more than 6 points. The system also demonstrates 0.792 clustering precision on novel metaphorical associations, confirming its scalability and accuracy across varied metaphor types and its effectiveness in large-scale figurative language processing.}, archive = {J_ACCESS}, author = {Sheng Guo and Narinderjit Singh Sawaran and Goh Khang Wen}, doi = {10.1109/ACCESS.2025.3613763}, journal = {IEEE Access}, pages = {171221-171240}, shortjournal = {IEEE Access}, title = {Multimodal metaphor detection based on affective embedding and deep transformer}, volume = {13}, year = {2025}, }
• (2025). Real-time human finger pointing recognition and estimation for robot directives using a single web-camera. ACCESS, 13, 171201-171220. (www)

  @article{ , abstract = {Gestures play a pivotal role in human communication, often serving as a preferred or complementary medium to verbal expression due to their superior spatial reference capabilities. A finger-pointing gesture conveys vital information regarding some point of interest in the environment. In Human-Robot Interaction (HRI), users can easily direct robots to target locations, facilitating tasks in diverse domains such as search and rescue or factory assistance. State-of-the-art approaches for visual pointing estimation often rely on depth cameras, are limited to indoor environments, and provide discrete predictions between limited targets. In this paper, we explore the development of models that enable robots to understand pointing directives from humans using a single web camera, even in diverse indoor and outdoor environments. A novel perception framework is proposed which includes a designated data-based model termed PointingNet. PointingNet recognizes the occurrence of pointing through classification followed by approximating the position and direction of the index finger with an advanced regression model. The model relies on a novel segmentation model for masking any lifted arm. While state-of-the-art human pose estimation models provide poor pointing angle estimation error of 28°, PointingNet exhibits a mean error of less than 2°. With the pointing information, the target location is computed, followed by robot motion planning and execution. The framework is evaluated on two robotic systems, demonstrating accurate target reaching.}, archive = {J_ACCESS}, author = {Eran Beeri Bamani and Eden Nissinman and Lisa Koenigsberg and Inbar Meir and Yoav Matalon and Avishai Sintov}, doi = {10.1109/ACCESS.2025.3616050}, journal = {IEEE Access}, pages = {171201-171220}, shortjournal = {IEEE Access}, title = {Real-time human finger pointing recognition and estimation for robot directives using a single web-camera}, volume = {13}, year = {2025}, }
• (2025). TransCalib: Automated extrinsic calibration of LiDAR–Camera fusion using convolutional transformer for targetless self-alignment. ACCESS, 13, 171185-171200. (www)

  @article{ , abstract = {In autonomous systems and robotic applications, accurate extrinsic calibration between light detection and ranging (LiDAR) sensors and cameras is crucial for reliable sensor fusion. Several techniques have been developed, including target-based and targetless calibration, but they are either impractical for real-world applications or limited in extracting complex and diverse features. This study presents TransCalib, an innovative deep-learning method for targetless and automatic extrinsic calibration. TransCalib predicts the misalignment between the camera and LiDAR by leveraging EfficientNetV2 to obtain features from the RGB camera image and LiDAR point cloud projection image (depth image), owing to its performance and parameter efficiency. We also developed an innovative feature-matching module that comprises a calibration convolutional feature aggregation block (Calib-CFAB) and a convolutional self-attention (CSA) transformer. Calib-CFAB enriches the combined feature map of the RGB and depth images, while the CSA transformer obtains the correlation in the feature maps. Trained and tested on the KITTI odometry dataset, TransCalib achieved a mean absolute rotation error of 0.14° and a mean translation error of 1.8 cm, outperforming existing methods. The proposed method allows for a robust fusion of LiDAR and camera data, improving the perception abilities of autonomous systems.}, archive = {J_ACCESS}, author = {Miftahul Khoir Shilahul Umam and Jaejun Yoo and Ida Bagus Krishna Yoga Utama and Muhammad Rangga Aziz Nasution and Muhammad Fairuz Mummtaz and Muhammad Alfi Aldolio and Su Mon Ko and Yeong Min Jang}, doi = {10.1109/ACCESS.2025.3615993}, journal = {IEEE Access}, pages = {171185-171200}, shortjournal = {IEEE Access}, title = {TransCalib: Automated extrinsic calibration of LiDAR–Camera fusion using convolutional transformer for targetless self-alignment}, volume = {13}, year = {2025}, }
• (2025). High-accuracy radar parameter estimation under low SNR environments. ACCESS, 13, 171170-171184. (www)

  @article{ , abstract = {This paper proposes a high-accuracy radar parameter estimation method for extremely low signal-to-noise ratio (SNR) environments, integrating deep learning and connected component analysis (CCA). Accurate estimation of parameters such as time of arrival, pulse width, pulse repetition interval, bandwidth, and carrier frequency are critical for various applications, but remains challenging under high noise conditions. The proposed method consists of three key steps. First, frequency-domain pulse detection isolates radar signals from noise-contaminated samples. Second, time-frequency analysis using the short-time Fourier transform is followed by UNet-based denoising and CCA to suppress noise and enhance signal features. Finally, edge-based parameter computation is applied to extract signal boundaries and estimate parameters precisely. Simulation results show that the method achieves approximately 3 dB improvement in time-domain estimation accuracy and lower root mean square error in frequency-domain parameters compared to conventional methods, across SNR levels from –20 dB to 10 dB. Test-bed experiments using Universal Software Radio Peripheral and GNURadio further validate its robustness, especially at –5 dB SNR. These findings confirm the method’s practical viability for reliable radar signal processing in challenging low-SNR environments.}, archive = {J_ACCESS}, author = {Jaehyeok Yoon and Siho Lee and Woojin Yun and Haewoon Nam}, doi = {10.1109/ACCESS.2025.3614172}, journal = {IEEE Access}, pages = {171170-171184}, shortjournal = {IEEE Access}, title = {High-accuracy radar parameter estimation under low SNR environments}, volume = {13}, year = {2025}, }
• (2025). IPFL: Interpretable federated learning for personalized healthcare. ACCESS, 13, 171156-171169. (www)

  @article{ , abstract = {Federated Learning (FL) enables decentralized training of neural networks across multiple hospitals or patients while preserving data privacy. However, FL schemes typically assume data is independent and identically distributed (IID) while healthcare data can be highly heterogeneous. To address this, we propose Interpretable Personalized Federated Learning (IPFL), a novel framework that allows patients to selectively collaborate with others based on both validation performance and historical collaboration success. By directly inferring patient similarities from data, IPFL enables personalized model training without requiring assumptions about cluster structures or interpolation with a global model. We validate IPFL on two real-world healthcare tasks: epileptic seizure detection and cardiac arrhythmia detection, and show that it achieves state-of-the-art performance. Moreover, our analysis demonstrates that IPFL naturally leads to interpretable collaboration graphs: patients with similar disease characteristics tend to collaborate more frequently.}, archive = {J_ACCESS}, author = {Arthur A. Nijdam and Amir Aminifar}, doi = {10.1109/ACCESS.2025.3608852}, journal = {IEEE Access}, pages = {171156-171169}, shortjournal = {IEEE Access}, title = {IPFL: Interpretable federated learning for personalized healthcare}, volume = {13}, year = {2025}, }
• (2025). HiT-RSNet: Enhancing remote sensing effectiveness using transformer-based super-resolution network and hierarchical modeling approach. ACCESS, 13, 171132-171155. (www)

  @article{ , abstract = {The accurate interpretation of remote sensing imagery in downstream geospatial tasks, such as urban monitoring, object detection, disaster management, and infrastructure mapping, heavily relies on effective data representation and analysis. In recent years, transformer-based methods have attracted significant interest in remote sensing super-resolution applications. However, these methods often struggle to reconstruct sharp boundaries, preserve fine textures, and maintain spectral-spatial consistency, particularly in complex scenes with dense urban layouts and smooth terrain transitions. These limitations include insufficient global context modeling, inadequate attention to geometric structures, and shallow multi-scale feature interactions. This paper proposes HiT-RSNet, a novel hybrid transformer-convolutional architectural design to address these challenges by jointly exploiting long-range dependencies and fine-grained local details. The model employs a dual-branch design that integrates Hierarchical Region Transformer Blocks (HRTB) for global contextual encoding with Residual Convolutional Attention Modules (RCAM) for local structure refinement. This design improves boundary sharpness and preserves fine textures. The HRTB comprises three specialized modules: a Channel-Wise Self-Attention (CWSAB) for spectral selectivity, Hierarchical Spatial Attention (HSAB) for structure-aware feature learning, and a Multi-Layer Feed-Forward Block (MLFFB) for efficient multi-scale information propagation. Extensive quantitative and qualitative experiments on four benchmarks (UCMerced, AID, RSSCN7, and WHU-RS19) across $\times 2$ , $\times 3$ , and $\times 4$ scales consistently demonstrate HiT-RSNet’s superior performance compared to state-of-the-art methods. HiT-RSNet provides an efficient and effective solution for enhancing the resolution of remote sensing data. The implementation code is available at https://github.com/CPEKMUTT/HiT-RSNet}, archive = {J_ACCESS}, author = {Naveed Sultan and Santitham Prom-On}, doi = {10.1109/ACCESS.2025.3615996}, journal = {IEEE Access}, pages = {171132-171155}, shortjournal = {IEEE Access}, title = {HiT-RSNet: Enhancing remote sensing effectiveness using transformer-based super-resolution network and hierarchical modeling approach}, volume = {13}, year = {2025}, }
• (2025). Analysis and numerical simulations of thermofluid dynamic characteristics in duct systems under radiation-convection coupling effects. ACCESS, 13, 171116-171131. (www)

  @article{ , abstract = {This paper presents a novel Focused Solar Chimney Generator (FSCG) that addresses the fundamental limitations of conventional Solar Chimney Power Plants through compact design, and enhanced thermal performance. The FSCG system integrates concave mirror concentration, molten salt thermal storage, and optimized heat-dissipating fins. A comprehensive theoretical model incorporating radiation-convection coupling effects, and temperature-dependent viscosity corrections was developed, and validated through three-dimensional CFD simulations using the k- $\varepsilon $ turbulence model. The FSCG demonstrates remarkable performance advantages: thermal efficiency of 7.5% compared to 0.1-0.5% in traditional systems, rapid wind generation within 2-5 minutes versus 1-3 hours for conventional designs, and 85-90% reduction in construction costs. The compact 1-meter height design achieves 500-4000 times better space efficiency than traditional solar chimney systems requiring 80-195-meter towers. Numerical simulations agree with theoretical predictions, with maximum deviations below 10%. At 500-1000 °C operating temperatures, radiative heat transfer contributes over 40% of total thermal flux, validating the importance of radiation-convection coupling. The optimized duct inlet velocity ranges from 2.07 to 2.37 m/s, demonstrating effective buoyancy-driven flow generation. This research provides a practical pathway for deploying high-efficiency, cost-effective solar thermal systems in space-constrained environments, offering significant potential for distributed renewable energy applications.}, archive = {J_ACCESS}, author = {Hao-Chin Chang and Hung-Chih Li and Chang-Hua Lien and Cheng-Ruei Yu and Chih-Yung Hsu and Sundarapandian Vaidyanathan}, doi = {10.1109/ACCESS.2025.3615638}, journal = {IEEE Access}, pages = {171116-171131}, shortjournal = {IEEE Access}, title = {Analysis and numerical simulations of thermofluid dynamic characteristics in duct systems under radiation-convection coupling effects}, volume = {13}, year = {2025}, }
• (2025). Matheuristic fixed set search applied to the two-stage capacitated facility location problem. ACCESS, 13, 171093-171115. (www)

  @article{ , abstract = {This paper addresses the Two-Stage Capacitated Facility Location Problem (TSCFLP), a challenging optimization problem with significant applications in supply chain network design. The need for effective solution methods arises from the problem’s large-scale complexity and the strong influence of spatial and capacity constraints on solution quality. We propose a twofold contribution: first, an adaptive greedy algorithm that generates high-quality initial solutions, achieving markedly better results than traditional constructive heuristics at comparable computational costs; and second, the adaptation of the Matheuristic Fixed Set Search (MFSS) to the TSCFLP. Computational experiments on standard benchmark instances show that MFSS is highly competitive with state-of-the-art methods, while demonstrating improved robustness by consistently reaching high-quality solutions across multiple runs. In addition, this work introduces geographically realistic instances—beyond the synthetic datasets used in previous research—and demonstrates that these experiments reveal regional cost dependencies and spatial utilization patterns, underscoring the practical value of MFSS in supporting real-world distribution network design. Overall, the findings emphasize that the main strength of MFSS lies in its flexible architecture, which combines solution quality, consistency, and ease of adaptation to related facility location variants.}, archive = {J_ACCESS}, author = {Denis Alicic and Nurettin Sezer and Miroslav Marić and Raka Jovanovic}, doi = {10.1109/ACCESS.2025.3616111}, journal = {IEEE Access}, pages = {171093-171115}, shortjournal = {IEEE Access}, title = {Matheuristic fixed set search applied to the two-stage capacitated facility location problem}, volume = {13}, year = {2025}, }
• (2025). Machine learning for fluid thermodynamics: State of the art and open challenges. ACCESS, 13, 171073-171092. (www)

  @article{ , abstract = {This paper presents a critical survey on adopting machine learning in solving complex real fluid thermodynamics problems. After reviewing the primary computational machine learning frameworks employed in thermodynamic modelling, we have analysed current research with a particular emphasis on properly estimating gas and liquid properties, vapour-liquid equilibrium, and supercritical fluids, focusing on pure gases. While ML offers a powerful paradigm for augmenting or even replacing traditional methods, its application faces significant open challenges. Key issues include the persistent trade-off between model accuracy and computational efficiency, the difficulty in capturing highly non-linear behaviour, especially near critical points or under extreme conditions, and the pervasive problem of data scarcity. We conclude the paper by introducing the main datasets available for thermodynamic property computation, such as the results of the GERG2008 project, and others relevant to turbomachinery applications. This survey provides a unified perspective on machine learning architectures used in thermodynamics and identifies open challenges and potential future advancements for enhancing predictive accuracy and efficiency while reducing execution time.}, archive = {J_ACCESS}, author = {Federico Succetti and Massimo Panella and Paolo Giannitrapani and Jean-Christophe Rigo and Stefania Colonnese}, doi = {10.1109/ACCESS.2025.3615454}, journal = {IEEE Access}, pages = {171073-171092}, shortjournal = {IEEE Access}, title = {Machine learning for fluid thermodynamics: State of the art and open challenges}, volume = {13}, year = {2025}, }
• (2025). LLM-based news recommendation system with multi-granularity news content fusion and dual-view user interest perception. ACCESS, 13, 171062-171072. (www)

  @article{ , abstract = {Owing to its formidable semantic comprehension capabilities, the Large Language Model (LLM) is extensively employed to enhance the accuracy and personalization of news recommendation. However, two issues restrict the performance of news recommendation systems based on LLM: 1) The single-prompt-based LLM fails to capture the multi-scale and multi-perspective perception of users when processing news content, leading to inaccurate and less diverse news representation; 2) The imbalanced distribution of user interests in historical news browsing leads to calibration issues in news recommendation scenarios, preventing the accurate identification of the full spectrum of user interests. To address these issues, we propose the LLM-Based News Recommendation System with Multi-granularity News Content Fusion and Dual-View User Interest Perception (LLMNR-MCFDIP). Specifically, LLMNR-MCFDIP constructs a unified representation of news content and dynamically adjusts user interest models through multi-granularity news content fusion and dual-view user interest perception. It employs a Multi-granularity Fusion News Encoder to generate intermediate representations of news and then fuses them into a unified representation in an adaptive manner. Meanwhile, by forming a weighted combination of user interests through dual views (autocorrelation among historically clicked news and correlation with candidate news), it avoids interest bias and enhances the accuracy and balance of recommendations. Extensive experimental results on MIND datasets demonstrate our LLMNR-MCFDIP significantly outperforms existing techniques.}, archive = {J_ACCESS}, author = {Jun Qin and Sitong Liu and Junjie Liang}, doi = {10.1109/ACCESS.2025.3616057}, journal = {IEEE Access}, pages = {171062-171072}, shortjournal = {IEEE Access}, title = {LLM-based news recommendation system with multi-granularity news content fusion and dual-view user interest perception}, volume = {13}, year = {2025}, }
• (2025). Distributed multimodal 2.4 GHz wi-fi received signal strength indicator prediction for wireless resilient robot operation. ACCESS, 13, 171048-171061. (www)

  @article{ , abstract = {This study aims to build a practical deep learning-based framework for resilient robot communication. We propose a proactive robot controller for each local robot to ensure a stable received signal by predicting future signal quality. The predictor is trained using multiple types of data experimentally obtained in robot operation up to the present, which include camera images, point clouds, and 2.4 GHz Wi-Fi channel state information using multimodal neural networks for local robots. The framework faces practical challenges in real-world deployments, such as synchronizing heterogeneous data modalities, aligning data with different spatial and temporal resolutions, and ensuring accuracy, performance, and robustness under dynamic environmental conditions typical of mobile robotics scenarios. The results show that the proposed multimodal predictor outperforms single-modality approaches in terms of root-mean-squared error (2.34 dB), accuracy (79%), correlation coefficient (0.88), and coefficient of determination (0.76). A comparison of the accuracy and performance of our proposed customized multimodal three-dimensional residual network (CM-3DResNet) and state-of-the-art architectures of 3DResNet-18, 3DResNet-50, and Vision Transformer shows feasibility in future link quality prediction tasks for robots. Aggregating the local results, a federated learning-based framework with a prediction result of 2.80 dB for two robots and 3.14 dB for three robots in root-mean-squared error compared to the ground truth is proposed as a candidate for cooperative robots operating in severe wireless environments at 2.4 GHz. The prediction results from the proposed federated learning paradigm surpassed those of Learning without Forgetting approach in terms of multitask prediction.}, archive = {J_ACCESS}, author = {Khanh N. Nguyen and Kenichi Takizawa and Takaaki Nara}, doi = {10.1109/ACCESS.2025.3615772}, journal = {IEEE Access}, pages = {171048-171061}, shortjournal = {IEEE Access}, title = {Distributed multimodal 2.4 GHz wi-fi received signal strength indicator prediction for wireless resilient robot operation}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches for EEG-based biometrics: A systematic review. ACCESS, 13, 171025-171047. (www)

  @article{ , abstract = {Biometics such as fingerprint, face, and iris are vulnerable to spoof attacks. The unique characteristics of Electroencephalography (EEG) make it a promising biometric modality especially because of its resistance to spoofing attacks. Many deep learning methods have been proposed for EEG-based biometric systems. This systematic review examines these methods in terms of their feature extraction ability and authentication performance. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search IEEE Xplore, PubMed, Web of Science, ScienceDirect, and Springer databases. Initially, we identified 285 relevant articles published between 2018 and 2024. After removing duplicates and applying predefined inclusion and exclusion criteria, 34 of them were selected and investigated thoroughly. This review highlights that deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers have been applied in EEG biometric systems. These architectures are capable of automatically extracting discriminative features from raw EEG signals without the need for feature engineering. They achieved a high identification or verification performance with over 90% accuracy and a low Equal Error Rate (EER). However, challenges persist with these models, such as generalizability and adaptability across diverse datasets and subjects, and the long-term stability of EEG signatures over time. These challenges underscore the need for more robust and generalizable solutions. This review consolidates EEG-based deep learning developments, identifies common trends and gaps, and provides insights into the current capabilities and limitations of EEG biometrics, thereby serving as a comprehensive reference for this field. Key future directions are proposed, including the design of hybrid deep learning architectures, leveraging transfer learning techniques, and conducting rigorous cross-subject evaluations to further improve the generalizability and real-world reliability of EEG-based biometric systems.}, archive = {J_ACCESS}, author = {Ali E. Albaiati and Muhammad Firdaus Akbar and Murtadha D. Hssayeni and Ashraf Khalil and Mohd Nadhir Ab Wahab and Sundus Sulaiman Weli and Enas A. Raheem}, doi = {10.1109/ACCESS.2025.3605614}, journal = {IEEE Access}, pages = {171025-171047}, shortjournal = {IEEE Access}, title = {Deep learning approaches for EEG-based biometrics: A systematic review}, volume = {13}, year = {2025}, }
• (2025). ViEMGT: A novel transformer architecture for enhanced hand gesture recognition from HD-sEMG in individuals with traumatic brain injury. ACCESS, 13, 171009-171024. (www)

  @article{ , abstract = {This paper introduces the Vision-EMG Transformer (ViEMGT), a novel deep learning architecture designed for hand gesture recognition using high-density surface electromyography (HD-sEMG) in individuals with traumatic brain injury (TBI). ViEMGT reformulates multichannel EMG signals into structured spatio-temporal patches, enabling attention mechanisms to capture intricate neuromuscular patterns. On a dataset of 22 gesture classes from 12 individuals with TBI using 56 electrodes, the model achieved a 95.2% accuracy and a macro-averaged F1-score of 95.1%, surpassing CNN-LSTM, 1D-CNN, LSTM, and SVM baselines by up to 12% in accuracy and F1-score. The approach demonstrated robust performance across gesture types, with F1-scores of 0.955 for grip patterns and 0.930 for finger movements. Attention-based modeling enhanced interpretability and provided valuable insights into neuromuscular control. Data augmentation improved robustness, supporting ViEMGT’s application in real-world rehabilitation, prosthetic systems, and adaptive human-computer interfaces.}, archive = {J_ACCESS}, author = {Saman Doostkam and Reza Boostani and Zohreh Azimifar}, doi = {10.1109/ACCESS.2025.3616066}, journal = {IEEE Access}, pages = {171009-171024}, shortjournal = {IEEE Access}, title = {ViEMGT: A novel transformer architecture for enhanced hand gesture recognition from HD-sEMG in individuals with traumatic brain injury}, volume = {13}, year = {2025}, }
• (2025). Sidewalk elevation barrier detection using a 2D LiDAR scanner. ACCESS, 13, 170993-171008. (www)

  @article{ , abstract = {While Pavement Management Systems are commonly used for effective roadway inspection and maintenance, similarly efficient solutions for sidewalk assessment remain underdeveloped. This study introduces a semi-automated method for sidewalk evaluation based on a mobile device equipped with a 2D LiDAR scanner, and image processing techniques. The proposed approach includes the design of a cost-effective sensor-equipped vehicle, data acquisition and processing workflows, elevation barrier detection methods, and a comprehensive validation framework. The system integrates data from a 2D LiDAR scanner, wheel encoders, and a localization unit to generate a local digital elevation model (DEM) via constrained spline interpolation. Barrier detection is performed using both a focal range thresholding method and a machine learning-based segmentation approach. Validation is conducted using high spatial resolution reference DEMs (1 mm and 2 cm) derived from Terrestrial Laser Scanning across two pilot sites. Detection quality is evaluated using three key metrics: Coefficient of Area Correspondence, Identification Accuracy, and Point Accuracy. The focal range method achieved overall accuracies of 91% and 92% at the two test sites, while the machine learning approach reached only 36% and 38%. Spatial coincidence indicators revealed reduced agreement, particularly due to shape distortions and segmentation inconsistencies; however, Point Accuracy proved to be the most robust validation metric. The results confirm that the proposed 2D LiDAR-based system and focal range method are effective in identifying significant elevation barriers on sidewalks. The compact design, low operational cost, and ease of use make the solution suitable for widespread deployment in municipal infrastructure assessment.}, archive = {J_ACCESS}, author = {Pavel Kukuliac and Jiri Horak and David Fojtik and Michal Kacmarik and Roman Kapica and Ondrej Krejcar and Petr Podesva and Rostislav Dandos and Petr Jadviščok and Milan Mihola}, doi = {10.1109/ACCESS.2025.3615420}, journal = {IEEE Access}, pages = {170993-171008}, shortjournal = {IEEE Access}, title = {Sidewalk elevation barrier detection using a 2D LiDAR scanner}, volume = {13}, year = {2025}, }
• (2025). A novel embedded convolution-NARX approach with an ensemble framework using multi-parameter seismic indicators for earthquake occurrence prediction. ACCESS, 13, 170973-170992. (www)

  @article{ , abstract = {The accurate prediction of earthquake frequency is currently a critical challenge. Several deep learning models have been developed but the sequential processing often limits the ability to capture complex spatiotemporal dynamics. Therefore, this study proposes a novel Ensemble Convolutional NARX (Ens-ConvNARX) framework where the convolutional feature extraction is embedded directly within the NARX feedback loop to enable a more integrated learning approach. The performance of the framework was validated across two tectonically distinct regions, including West Coast Sumatra, Indonesia, and Southern California, USA. It was also benchmarked against five baseline models which were Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, Random Forest (RF), and XGBoost. The results showed the superiority of the Ens-ConvNARX model which achieved multi-step predictive accuracies up to 88.5% in West Coast Sumatra and 90.2% in Southern California. An ablation study also confirmed the critical role of the Magnitude of Completeness (Mc) filter, as its removal reduced the one-month-ahead prediction accuracy by 50 percentage points. This study presents a robust and generalizable framework for seismic prediction and shows the important synergy between advanced model architecture and rigorous data preprocessing for improving disaster preparedness and response in seismically active regions.}, archive = {J_ACCESS}, author = {Hapsoro Agung Nugroho and Aries Subiantoro and Syafiie Syam and Benyamin Kusumoputro}, doi = {10.1109/ACCESS.2025.3611514}, journal = {IEEE Access}, pages = {170973-170992}, shortjournal = {IEEE Access}, title = {A novel embedded convolution-NARX approach with an ensemble framework using multi-parameter seismic indicators for earthquake occurrence prediction}, volume = {13}, year = {2025}, }
• (2025). Development of low-loss bandpass filters with multiple controllable transmission zeros by using an improved connecting-line coupling technique. ACCESS, 13, 170960-170972. (www)

  @article{ , abstract = {This paper presents a novel connecting-line coupling technique for the design of compact and low-loss bandpass filters (BPFs) with multiple controllable transmission zeros (TZs). Traditional gap-coupling methods are highly sensitive to fabrication errors and have limited design flexibility. By contrast, the proposed approach enables precise control of coupling strength without modifying resonator geometry. Such control is achieved through independent adjustments of the lengths, widths, and positions of connecting lines. Furthermore, the proposed approach facilitates the generation of up to n TZs for an nth-order BPF, thus considerably improving the BPF’s frequency selectivity and stopband attenuation. To validate the effectiveness of this method, four filters are designed and fabricated: a second-order stripline BPF, a fourth-order microstrip BPF, a fifth-order stripline BPF, and a sixth-order wideband microstrip BPF. The second-order BPF is based on asymmetric connecting-line coupling, whereas the fourth-, fifth-, and sixth-order BPFs are based on symmetric connecting-line coupling. Excellent agreement between simulation and measurement results is achieved for these designs, with low insertion loss, high roll-off rates, and high tolerance to fabrication variations being noted. The proposed technique is suitable for filters ranging from narrowband to ultra-wideband filters and across low to millimeter-wave frequency bands, making it a highly versatile and practical solution for modern RF and microwave communication systems.}, archive = {J_ACCESS}, author = {Po-Yi Ju and Chi-Feng Chen and Ruey-Beei Wu and Chin-Sheng Wang}, doi = {10.1109/ACCESS.2025.3615145}, journal = {IEEE Access}, pages = {170960-170972}, shortjournal = {IEEE Access}, title = {Development of low-loss bandpass filters with multiple controllable transmission zeros by using an improved connecting-line coupling technique}, volume = {13}, year = {2025}, }
• (2025). A dual-stage framework for behavior-enhanced automated code generation in industrial-scale meta-models. ACCESS, 13, 170943-170959. (www)

  @article{ , abstract = {Industrial model-driven engineering often struggles to generate functionally complete code, especially dynamic behaviors, from large-scale Meta-models, typically yielding only static structures. This paper introduces the S2D2 (Static-Structure & Dynamic-Behavior Dual-Stage) framework, a novel approach enhancing automated code generation through two key innovations. First, the Dual-Path Fusion stage constructs a semantically rich static code skeleton by integrating structural semantics from XMI with data constraints from XSD. Second, the dynamic behavior injection engine leverages model annotations and associations to inject operational capabilities into the static foundation. A language-independent Intermediate Code Model unifies semantic information, facilitating transformations via Query/View/Transformation-Relations and domain-specific languages. Validation using industrial Meta-models such as AUTOSAR (Automotive Open System Architecture) and OPC UA (Open Platform Communications Unified Architecture) demonstrates significant improvements in automation, code quality, and development efficiency. This work also discusses Large Language Model limitations for reliable Meta-model-based code generation and posits S2D2’s potential, particularly with Retrieval Augmented Generation, to improve their code synthesis fidelity. Key contributions include advanced model transformation, behavior-centric code enhancement, and a pathway for more dependable AI-assisted software development.}, archive = {J_ACCESS}, author = {Tong Ma and Shenlong Dai and Yongfan Gao and Fengjie Xu and Ling Fang}, doi = {10.1109/ACCESS.2025.3614174}, journal = {IEEE Access}, pages = {170943-170959}, shortjournal = {IEEE Access}, title = {A dual-stage framework for behavior-enhanced automated code generation in industrial-scale meta-models}, volume = {13}, year = {2025}, }
• (2025). Calculation of performance indicators in hybrid excitation synchronous machine as well as torque ripple minimization. ACCESS, 13, 170935-170942. (www)

  @article{ , abstract = {Smooth and distortion-free torque is one of the most important characteristics of instantaneous torque in the hybrid vehicle. In this paper, the instantaneous torque is firstly calculated by solving Maxwell’s equations which are partial differential equations. The general solution is determined by a separate variable method. They include two variables in radial and tangential directions. Consequently, they require two types of boundary and interface conditions in mentioned directions for assigning integral constants. Then, the impact of the pole arc to pole pitch ratio, slot-opening size, air gap radius and the integral and fractional slot structures are separately examined to reduce torque ripple. Additionally, magnetization pattern plays an important role in the magnetic field and instantaneous torque. Three magnetization patterns including radial, parallel and 9-segment magnetization patterns are introduced and assessed. Next, considering the effect of the aforementioned factors, an optimal structure that has the least distortion will be proposed so that the torque fluctuations are in acceptable range. After that, cogging, reluctance, electromagnetic and instantaneous torque are calculated. It is also important to remember that, Back-EMF (based on flux path in teeth for two types winding consist of alternate-teeth and all-teeth non-overlapping winding) and Torque-Speed characteristics are computed for the presented hybrid vehicle. Finally, the impact of the temperature on the all torque component, induced voltage magnitude as well as Torque-speed chart of hybrid vehicles based on the response of Maxwell’s equations are determined. The model computations have been performed with Matlab software.}, archive = {J_ACCESS}, author = {Abdulaziz Alkuhayli}, doi = {10.1109/ACCESS.2025.3613050}, journal = {IEEE Access}, pages = {170935-170942}, shortjournal = {IEEE Access}, title = {Calculation of performance indicators in hybrid excitation synchronous machine as well as torque ripple minimization}, volume = {13}, year = {2025}, }
• (2025). Enhancing remaining useful life prediction against adversarial attacks: An active learning approach. ACCESS, 13, 170921-170934. (www)

  @article{ , abstract = {Accurate assessment of the Remaining Useful Life (RUL) is critical for preventing catastrophic equipment failures, but the machine learning models used for these predictions are highly vulnerable to adversarial attacks that can corrupt sensor data and produce dangerously incorrect outputs. To address this critical security gap, this paper introduces a novel active learning framework designed to bolster the adversarial robustness of RUL models. By strategically selecting the most informative and uncertain data points for labeling, our method enhances defensive capabilities while significantly reducing the reliance on large, pre-labeled datasets. Experiments conducted on the NASA C-MAPSS FD001 dataset against CW, FGSM, and JSMA attacks demonstrate that this approach yields significant gains. Specifically, the Random Forest model paired with active learning achieves a superior accuracy of 89%. This robust performance is achieved while requiring 42% fewer labeled instances compared to a traditional supervised approach, at the cost of a 28% increase in iterative training time. This work provides a practical pathway toward developing more secure, efficient, and resilient predictive maintenance systems for safety-critical applications.}, archive = {J_ACCESS}, author = {Stephanie Ness}, doi = {10.1109/ACCESS.2025.3611453}, journal = {IEEE Access}, pages = {170921-170934}, shortjournal = {IEEE Access}, title = {Enhancing remaining useful life prediction against adversarial attacks: An active learning approach}, volume = {13}, year = {2025}, }
• (2025). A fully additively manufactured low-profile cost-effective antenna array based on tightly coupled monopole arrays. ACCESS, 13, 170912-170920. (www)

  @article{ , abstract = {This paper presents a cost-effective fully additively manufactured tightly coupled monopole array (TCMA) with a 6:1 operating bandwidth, covering 1.48 GHz to 8.90 GHz. The design achieves an active VSWR of less than 2.89 and demonstrates over 94% radiation efficiency when scanning to broadside. When scanning to 45° in the E-, D-, and H-planes, the array achieves a 5.57:1 bandwidth with an active VSWR of less than 3.9. By using advanced additive manufacturing techniques, the design overcomes limitations of conventional fabrication methods, offering a more cost-effective and flexible solution for broadband antenna arrays. To validate the design concept, a fully additively manufactured $4\times 4$ active antenna array prototype was fabricated using low-cost selective laser melting and fused deposition modeling technologies. The active VSWRs of a center element, far-field patterns, and realized gain of the antenna array are tested, with measurements aligning with numerical analyses.}, archive = {J_ACCESS}, author = {Hong Tang and Shiqi Li and S. M. Rakibul Hasan Shawon and Yunxi Dong and Yi Huang and Huan Zhao and Bowen Zheng and Hualiang Zhang}, doi = {10.1109/ACCESS.2025.3615131}, journal = {IEEE Access}, pages = {170912-170920}, shortjournal = {IEEE Access}, title = {A fully additively manufactured low-profile cost-effective antenna array based on tightly coupled monopole arrays}, volume = {13}, year = {2025}, }
• (2025). An adaptive modulation technique to improve the power quality of single-phase single-stage current source inverters. ACCESS, 13, 170895-170911. (www)

  @article{ , abstract = {Current source inverters (CSIs) are attractive options for battery energy storage systems (BESS) due to their inherent capabilities for voltage boosting and short-circuit protection. This article presents a single-phase, single-stage wide bandgap (WBG)-enabled CSI designed to serve as an interface between the battery and the ac load. By utilizing WBG anti-series connected pairs (ASCP) of MOSFETs, the proposed design provides reverse voltage blocking while reducing the losses compared to conventional CSI. However, incorporating ASCP of MOSFETs makes the proposed CSI vulnerable to inter-leg short circuit conditions when controlled using modulation techniques designed for conventional CSI. Additionally, like other single-phase inverters, the presence of double-frequency power ripples on the dc link adversely affects the output power quality of single-phase CSIs. To address the issue of inter-leg short circuit, as well as double-frequency power ripples simultaneously, this article introduces an adaptive modulation technique that ensures safe operation while effectively reducing the impact of low-frequency dc-link ripples on the ac side of the proposed CSI. However, the dc-link ripples are still present due to ac side power pulsations. By solving the latter issue, the proposed modulation technique significantly improves output power quality and allows for a smaller dc-link inductor. This article includes detailed derivations and analysis, along with a discussion on the challenges associated with the implementation of the proposed technique. The performance of the proposed adaptive modulation technique is evaluated through both MATLAB/Simulink simulations and experiments, with results validating the effectiveness of the proposed modulation technique.}, archive = {J_ACCESS}, author = {Umer Sohail and Trond Østrem and Bjarte Hoff}, doi = {10.1109/ACCESS.2025.3615466}, journal = {IEEE Access}, pages = {170895-170911}, shortjournal = {IEEE Access}, title = {An adaptive modulation technique to improve the power quality of single-phase single-stage current source inverters}, volume = {13}, year = {2025}, }
• (2025). Enhancing temporal coherence in image-to-video facial expression synthesis: A dual-loss framework for smoother generation. ACCESS, 13, 170876-170894. (www)

  @article{ , abstract = {Facial expression synthesis for video sequences presents significant challenges in terms of maintaining temporal coherence while preserving identity and expression accuracy. Most current approaches primarily focus on individual frame quality. This results in videos with unnatural transitions, flickering artifacts, and inconsistent expression dynamics. This paper presents a novel method for improving temporal coherence in facial expression image-to-video synthesis. The proposed approach uses a dual-focus framework that explicitly models both pixel-level transitions and geometric consistency between consecutive frames. Our method includes two specialized loss functions: a frame difference loss and a landmark consistency loss. We also implement a two-stage training strategy and a frame-blending post-processing technique. Extensive testing on the MUG Facial Expression Database demonstrates that the enhanced-coherence approach outperforms baseline methods and competes with other approaches from the literature. The proposed method has the lowest Average Content Distance score of all the approaches tested. This indicates smoother frame transitions while maintaining high visual quality. Subjective evaluation with 140 participants confirms that the synthesized expressions are more realistic and expressive. Our approach outperformed real videos in recognition rates for certain emotion expressions, particularly disgust and sadness. These findings demonstrate the effectiveness of explicitly modeling temporal coherence in generating realistic facial expression videos. This has promising applications in therapy, entertainment, and human-computer interaction.}, archive = {J_ACCESS}, author = {Rafael Luiz Testa and Ariane Machado-Lima and Fátima L. S. Nunes}, doi = {10.1109/ACCESS.2025.3612820}, journal = {IEEE Access}, pages = {170876-170894}, shortjournal = {IEEE Access}, title = {Enhancing temporal coherence in image-to-video facial expression synthesis: A dual-loss framework for smoother generation}, volume = {13}, year = {2025}, }
• (2025). Variable-guided attention for multichannel signal anomaly detection. ACCESS, 13, 170862-170875. (www)

  @article{ , abstract = {Anomaly detection in multichannel sensor data is essential for ensuring safety and reliability in domains such as industrial monitoring, automotive systems, and healthcare. Traditional time-series models often focus solely on temporal patterns, struggling to capture the complex inter-variable relationships among highly correlated sensors. This limitation hinders their effectiveness in detecting anomalies and identifying the key contributing variables. To address this issue, we propose a transformer-based framework that integrates variable encoding, a method for embedding sensor type information, and a variable-guided attention mechanism that emphasizes interactions among variables rather than focusing only on temporal patterns. In contrast to prior transformer-based models, which typically apply self-attention across time steps, the proposed approach models intervariable dependencies directly, enhancing interpretability and robustness. By explicitly modeling sensor types and focusing on inter-variable dependencies, the proposed framework improves both anomaly detection accuracy and interpretability. Experiments on synthetic datasets demonstrate the model’s ability to detect diverse anomaly patterns, including subtle and complex group anomalies. Validation on real-world automotive battery data demonstrates the model’s high performance and robustness, achieving an AUROC of 0.96. Our approach offers a practical and efficient solution for anomaly detection in high-dimensional sensor data, with significant potential for real-time monitoring applications.}, archive = {J_ACCESS}, author = {Chunghyup Mok and Seokho Moon and Eun Soo Choi and Hong Gi Shim and Seoung Bum Kim}, doi = {10.1109/ACCESS.2025.3615243}, journal = {IEEE Access}, pages = {170862-170875}, shortjournal = {IEEE Access}, title = {Variable-guided attention for multichannel signal anomaly detection}, volume = {13}, year = {2025}, }
• (2025). The research on demand-based regulation of mine airflow based on niche genetic algorithm. ACCESS, 13, 170844-170861. (www)

  @article{ , abstract = {As the core of coal mine safety production, the mine ventilation system plays a crucial role in ensuring the safety of the underground environment and reducing energy consumption. Demand-based regulation of volumetric airflow is essential in this context. To address the limitations of traditional methods in regulating optimal branch selection and multi-branch coordinated control, this paper proposes an intelligent volumetric airflow regulation method for mines based on the Niche Genetic Algorithm (NGA), focusing on solving the nonlinear optimization problem of multi-branch adjustment in complex ventilation networks. The study establishes a ventilation network optimization model with the objective of minimizing the total power consumption of the fans, incorporating the penalty function method and simulated annealing algorithm to handle constraint conditions. In terms of regulating branch selection, a method is proposed for determining the adjustable range by comprehensively considering sensitivity attenuation rate and volumetric airflow constraints. Additionally, the mutual disturbance effects during multi-branch regulation are quantified based on Taylor series expansion theory. By introducing an improved niche genetic algorithm and initializing the population using Latin hypercube sampling, a fitness sharing strategy is adopted to maintain population diversity, enabling the rapid acquisition of multiple sets of near-optimal solutions. Experimental results show that, for single-branch regulation, the error of fitting the resistance-airflow relationship using a power function is less than 0.6%. In multi-branch regulation, the airflow prediction accuracy of the second-order Taylor expansion is significantly better than that of the first-order. The optimization algorithm demonstrates effective convergence in both dual-branch and triple-branch regulation cases, providing multiple feasible regulation schemes with significant improvements in fan power optimization. The research results offer an effective solution for the dynamic optimization and energy-efficient operation of complex ventilation networks and can be further extended to application scenarios such as coordinated control of multiple fans and optimization of multiple demand airflow branches.}, archive = {J_ACCESS}, author = {Zhipeng Qi and Dariusz Obracaj and Ke Gao and Lianzeng Shi}, doi = {10.1109/ACCESS.2025.3614985}, journal = {IEEE Access}, pages = {170844-170861}, shortjournal = {IEEE Access}, title = {The research on demand-based regulation of mine airflow based on niche genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). High-accurate parameter identification of PEMFC using advanced multi-trial vector-based sine cosine meta-heuristic algorithm. ACCESS, 13, 170827-170843. (www)

  @article{ , abstract = {Development and modeling of proton exchange membrane fuel cells (PEMFCs) need accurate identification of unknown factors affecting mathematical models. The trigonometric function-based sine cosine algorithm (SCA) may solve such problems, but it traps in local optima, making it inappropriate for larger optimization tasks. This paper introduces a novel multi-trial vector-based sine cosine algorithm (MTV-SCA) for the identification of seven unknown parameters of PEMFCs. The proposed MTV-SCA incorporates MTV methodology utilizing three control parameters to achieve the desired optimization targets. A key contribution of this work is the development of four distinct search strategies, to mitigating early convergence issues. These strategies leverage various sinusoidal and cosinusoidal factors to improve the algorithm. The optimization goal is to minimize sum square error (SSE) between measured and simulated stack voltages. Five PEMFC stack mode:250W, BCS 500W, SR-12, H-12, and Temasek 1 kW, validate the MTV-SCA algorithm’s efficacy and robustness Compared to previously established optimization approaches, MTV-SCA extracts optimum PEMFC parameters more accurately and reliably. Statistical testing further demonstrates the method’s durability and consistency. Analyzing PEMFC performance at different pressures and temperatures helps verify the adjusted parameters. Simulations show that the MTV-SCA solves difficult PEMFC parameter identification issues better than SCA and other approaches.}, archive = {J_ACCESS}, author = {Badreddine Kanouni and Abdelbaset Laib and Salah Necaibia and Abdelbasset Krama and Ilyas Bennia and Sertac Bayhan}, doi = {10.1109/ACCESS.2025.3614048}, journal = {IEEE Access}, pages = {170827-170843}, shortjournal = {IEEE Access}, title = {High-accurate parameter identification of PEMFC using advanced multi-trial vector-based sine cosine meta-heuristic algorithm}, volume = {13}, year = {2025}, }
• (2025). Counterfactual medical images generation for lung disease diagnosis using probabilistic causal models and active learning. ACCESS, 13, 170817-170826. (www)

  @article{ , abstract = {Recent advancements in deep learning have shown promise in diagnosing lung diseases from medical images, but these methods often lack causal inference capabilities, limiting their applicability in clinical decision-making. This study focuses on leveraging causal generative modelling for counterfactual analysis to enhance the understanding and diagnosis of lung diseases. We developed a Structured Causal Model designed to generate clinically meaningful counterfactual images of lung diseases. Our framework integrates active learning with uncertainty measurement to address data quality issues in clinical datasets and refine the training set distribution. Inspired by the human-in-the-loop concept, expert feedback was incorporated into the training pipeline to ensure the generated images align with clinical expectations. We evaluated the generated counterfactuals using model accuracy and a specialized expert model that calculates disease probabilities based on the images. The proposed model achieved a 93.27% accuracy in generating counterfactual images representative of the corresponding clinical conditions, as confirmed by medical experts. Active learning with uncertainty measurement effectively enhanced the data distribution, maintaining a heavy-tailed structure to better reflect real-world clinical data. The integration of expert knowledge further ensured the clinical validity and relevance of the counterfactuals, supporting more informed diagnostic and prognostic decisions. Our study highlights the potential of causal generative modelling to improve lung disease diagnosis and prognosis by generating clinically meaningful counterfactual images, supported by active learning and expert feedback.}, archive = {J_ACCESS}, author = {Yifei Zhu and Lei Zhang and Chris Sainsbury and Feng Dong and John MacLay and David J. Lowe and Xujiong Ye}, doi = {10.1109/ACCESS.2025.3615683}, journal = {IEEE Access}, pages = {170817-170826}, shortjournal = {IEEE Access}, title = {Counterfactual medical images generation for lung disease diagnosis using probabilistic causal models and active learning}, volume = {13}, year = {2025}, }
• (2025). Smarter crowdsourcing with NLP and attention mechanisms for task complexity prediction. ACCESS, 13, 170802-170816. (www)

  @article{ , abstract = {Competitive Crowdsourcing Software Development (CCSD) has emerged as a powerful tool for developing software solutions, attracting researchers and the development market. Using crowdsourced collective intelligence, CCSD ensures the delivery of innovative, cost-effective, and high-quality solutions within specified time frames, making it an attractive approach for addressing complex challenges in software development. However, as the CCSD environment gains popularity, it also introduces challenges, particularly in predicting job complexity, which must be tackled to optimize the crowdsourcing process. In software platforms, client organizations register and upload jobs related to development projects. These jobs are manually reviewed by the copilots responsible for assessing the complexity of the job, a process that can lead to delays and an overburden of experts. To streamline this process, we propose an approach that automatically predicts job complexity and classifies it accordingly. We collect data from the TopCoder CCSD platform, focusing on projects related to software development. The collected data are pre-processed and tokenized using NLP techniques. We convert the text data into a word embedding vector matrix using a pre-trained GloVe model, which captures the semantic and contextual meaning of the text. The Sequence Attention (SA) mechanism is introduced in LSTM to identify the key parts of the input sequence that are most relevant for predicting the output, thereby improving job complexity classification. The proposed approach is then trained on these word embeddings using SA-LSTM and benchmarked against LSTM and other state-of-the-art techniques. The proposed approach, GloVe-based (GB) SA-LSTM, outperforms other approaches by achieving an accuracy improvement of 31.4%, 152.04%, 20.07%, 10.99%, 9.16%, and 17.64% over ZeroR, RP, LR, BERT, SVM, and DT. To verify the impact of SA, the proposed approach is compared with GB-LSTM. The GB-SA-LSTM outperforms GB-LSTM by 4.88%. This enhanced accuracy in task complexity prediction within software crowdsourcing platforms can significantly contribute to the efficient and effective development of crowdsourced projects.}, archive = {J_ACCESS}, author = {Yasir Munir and Qasim Umer and Muhammad Waheed Aslam and Muhammad Faheem and Hanadi Hakami}, doi = {10.1109/ACCESS.2025.3615745}, journal = {IEEE Access}, pages = {170802-170816}, shortjournal = {IEEE Access}, title = {Smarter crowdsourcing with NLP and attention mechanisms for task complexity prediction}, volume = {13}, year = {2025}, }
• (2025). Adaptive sparse basis learning: Optimizing compressed sensing transmission in large-scale WSNs via context-aware MOD algorithm. ACCESS, 13, 170782-170801. (www)

  @article{ , abstract = {With the rapid development of Internet of Things (IoT) technology, IoT devices are playing an increasingly important role in modern society. However, as IoT applications become more widespread and deeply integrated, the demand for higher information transmission rates is continuously increasing. This poses new challenges to the design and performance of IoT systems. Compressed Sensing (CS), an emerging signal processing technique, enables the reconstruction of signals at low sampling rates by performing random sub-sampling and compression. This technology has already achieved considerable success in traditional communication and has shown significant potential in various fields. The primary focus of this paper is to optimize the sparsity basis in the principles of Compressed Sensing. Most existing methods rely on predetermined sparsity bases, such as the Fourier matrix, wavelet transform matrix, or some sparsity basis that is effective for a specific application scenario. The focus of this paper is on using an adaptive dictionary learning algorithm, which, by training on historical data from a particular usage scenario, ultimately produces a sparsity basis that is tailored to the custom application scenario. This approach helps to some extent in overcoming the limitations associated with the selection of sparsity basis.}, archive = {J_ACCESS}, author = {Run Zhou and Shenwei Shou and Hui Wang and Han Wang}, doi = {10.1109/ACCESS.2025.3615777}, journal = {IEEE Access}, pages = {170782-170801}, shortjournal = {IEEE Access}, title = {Adaptive sparse basis learning: Optimizing compressed sensing transmission in large-scale WSNs via context-aware MOD algorithm}, volume = {13}, year = {2025}, }
• (2025). ViT-NeBLa: A hybrid vision transformer and neural Beer–Lambert framework for single-view 3D reconstruction of oral anatomy from panoramic radiographs. ACCESS, 13, 170761-170781. (www)

  @article{ , abstract = {Panoramic radiography (PX) is widely used in dentistry but provides only a flattened 2D view; Cone Beam Computed Tomography CT (CBCT) recovers 3D anatomy at higher dose and cost. We tackle full 3D reconstruction from a single real-world PX for varying patients. We propose ViT–NeBLa (Vision-Transformer Neural Beer–Lambert), a physics-guided framework that estimates a continuous 3D dentoalveolar density field directly from single PX. The design mirrors panoramic acquisition while simplifying the inverse problem: we parameterize projection-ray directions by tangency to a patient-adaptive elliptical path and independently restrict sampling to a jaw-focused horseshoe (focal trough) in which the projection rays do not intersect. This removes the intermediate density-aggregation step used by overlapping-ray methods and reduces per-ray samples by about 52%, lowering memory and compute. A hybrid ViT–CNN backbone extracts global anatomical context and local texture from the PX, and a learnable multi-resolution hash positional encoding maps 3D sample coordinates to expressive features that preserve fine dental and osseous detail. Per-point densities are predicted by a compact MLP and accumulated into a coarse 3D grid, which a lightweight 3D U-Net refines into the final volume. Training is end-to-end using synthetic PX rendered from CBCT via the Beer–Lambert law together with voxelwise, projection-consistency, and perceptual losses; at inference, a single PX is processed directly—without CBCT flattening or dental-arch priors. Experiments show that ViT–NeBLa outperforms contemporary PX-to-3D baselines both quantitatively and qualitatively, yielding sharper cortical boundaries, clearer trabecular patterns, and fewer artifacts. In sum, ViT–NeBLa provides a radiation-efficient route to clinically informative 3D visualization from routine panoramic radiographs while simplifying geometry, reducing sampling, and preserving high-frequency structure.}, archive = {J_ACCESS}, author = {Bikram Keshari Parida and Anusree P. Sunilkumar and Abhijit Sen and Wonsang You}, doi = {10.1109/ACCESS.2025.3613789}, journal = {IEEE Access}, pages = {170761-170781}, shortjournal = {IEEE Access}, title = {ViT-NeBLa: A hybrid vision transformer and neural Beer–Lambert framework for single-view 3D reconstruction of oral anatomy from panoramic radiographs}, volume = {13}, year = {2025}, }
• (2025). Improved signal detection in bistable systems through stochastic resonance across diverse noise environments. ACCESS, 13, 170748-170760. (www)

  @article{ , abstract = {This paper presents a simulation-based analysis of stochastic resonance (SR) in bistable systems under three distinct noise conditions: Gaussian white noise, pink noise, and impulse noise. A novel contribution of this work is the introduction of a sharpness metric, defined as the second derivative of the output SNR–noise intensity curve, which quantitatively characterizes the selectivity of resonance tuning. Using numerical solutions of the overdamped Langevin equation, we show that each noise type exhibits a unique optimal intensity for maximizing signal-to-noise ratio (SNR). Our results reveal a consistent trade-off between SNR gain and resonance sharpness: Gaussian noise yields the highest signal amplification, while pink noise induces the sharpest tuning response. The analysis includes time-domain trajectories, power spectral densities, and comparisons with monostable systems, confirming that bistability is essential for SR to occur. These findings provide a unified dual-metric framework for evaluating SR quality across noise types, with implications for weak signal detection, neuromorphic systems, and adaptive noise-enhanced devices.}, archive = {J_ACCESS}, author = {Raghav Krishna and Dilip Kumar Choudhary}, doi = {10.1109/ACCESS.2025.3615606}, journal = {IEEE Access}, pages = {170748-170760}, shortjournal = {IEEE Access}, title = {Improved signal detection in bistable systems through stochastic resonance across diverse noise environments}, volume = {13}, year = {2025}, }
• (2025). Online capacitance estimation for the DC link capacitor of PWM inverter. ACCESS, 13, 170735-170747. (www)

  @article{ , abstract = {In the inverter for driving an electric vehicle, a DC link capacitor is an important component. To ensure the reliability and stability of the inverter, research on a method capable of predicting the capacitor lifespan is becoming important. Therefore, research on methods capable of predicting the durability, heat generation management, and lifespan of capacitors is actively being conducted. The capacitance decreases according to the accumulated usage time of the inverter. Therefore, if the capacitance decrease amount can be estimated based on the initial capacitance of the capacitor in real time, then the remaining lifespan of the DC link capacitor may be predicted, and a failure may be diagnosed. The existing capacitance estimation method requires an additional circuit for relatively simple implementation and has the limitation that the real-time motor control is affected by additional signal injection into the capacitor. This paper proposes a method for estimating the capacitance of an additional circuit and a DC link capacitor online and offline without additional signal injection by analyzing the voltage and current characteristics of the capacitor during space vector pulse width modulation (SVPWM)-based control of a real-time motor. A capacitor current analysis equation considering the voltage sampling time of the capacitor and the dead time (DT) of the inverter is newly established to improve the capacitance estimation performance for the capacitor. The reliability of the capacitance estimation algorithm is confirmed by comparing the proposed capacitance estimation method and the manual measurement method under various motor driving and constraint conditions.}, archive = {J_ACCESS}, author = {Jesuk Park and Jaeyeob Hwang and Chanwoo Jeon and Heesun Lim and Geunho Lee}, doi = {10.1109/ACCESS.2025.3615584}, journal = {IEEE Access}, pages = {170735-170747}, shortjournal = {IEEE Access}, title = {Online capacitance estimation for the DC link capacitor of PWM inverter}, volume = {13}, year = {2025}, }
• (2025). B2MTGP: A double bayesian multi-task gaussian process method for wind turbine power forecasting under data uncertainty. ACCESS, 13, 170723-170734. (www)

  @article{ , abstract = {Wind power forecasting plays an increasingly important role on the safety, reliability, and stability of the power grid. Due to the fluctuating and intermittent nature of wind resources, it is very challenging to accurately forecast wind power under varying scenarios. This paper proposes a novel Double Bayesian Multi-Task Gaussian Process (B2MTGP) model tailored for wind turbine power forecasting with scarce data. The model leverages both wind speed and limited power data, employing a two-stage Bayesian inference framework: the first stage integrates discrete wavelet packet decomposition to denoise raw signals, enhancing prediction accuracy; the second stage optimizes the multi-task Gaussian process model structure and parameters to account for data uncertainty. A generalized procedure is developed to facilitate automatic implementation of the proposed method for small-sample forecasting scenarios. Comparative experiments using real-world wind turbine datasets demonstrate that B2MTGP significantly outperforms traditional Gaussian process regression and multi-task models, achieving up to 91% improvement in point prediction accuracy. These results highlight the potential of the proposed approach for robust and reliable wind power forecasting under data uncertainty.}, archive = {J_ACCESS}, author = {Huaiyu Hui and Xiaomo Jiang and Huize Chen and Kexin Zhang}, doi = {10.1109/ACCESS.2025.3614474}, journal = {IEEE Access}, pages = {170723-170734}, shortjournal = {IEEE Access}, title = {B2MTGP: A double bayesian multi-task gaussian process method for wind turbine power forecasting under data uncertainty}, volume = {13}, year = {2025}, }
• (2025). APAROS: A clustering-based hybrid approach for handling overlapped regions in imbalanced datasets. ACCESS, 13, 170705-170722. (www)

  @article{ , abstract = {In many real-world datasets, the class distribution is often highly imbalanced, and minority class samples are located within the majority regions, leading to significant overlap. These challenges produce a model of bias and misclassifications, particularly for minority classes. To address these issues, we introduced a novel technique that integrates Affinity Propagation with Adaptive Synthetic Sampling (ADASYN) and Random Oversampling (ROS), collectively termed as APAROS. Using Affinity Propagation Clustering (APC), our proposed method categorizes the data set into Overlapping Clusters (OLC), Pure minority Clusters (PmC) and Pure Majority Clusters (PMC). Further, we applied existing oversampling methods; ADASYN in OLC and ROS in PmC, effectively balancing the dataset while minimizing the risk of miss-classification in overlapped regions. The proposed method was evaluated on ten benchmark imbalanced datasets and compared against existing resampling techniques such as ADASYN, ROS and Kmeans-SMOTE. Seven machine learning classifiers were employed to evaluate the models performance considering the metrics such as accuracy, precision, recall, f1_score, Matthews Correlation Coefficient (MCC) and Cohen’s Kappa coefficient. Experimental results consistently demonstrate that APAROS performs significantly better than traditional resampling techniques and produced superior classification performance, particularly in highly overlapped and imbalanced scenarios.}, archive = {J_ACCESS}, author = {Annam Nandini and Tapas Kumar Mishra and Abinash Pujahari and Sanket Mishra}, doi = {10.1109/ACCESS.2025.3614993}, journal = {IEEE Access}, pages = {170705-170722}, shortjournal = {IEEE Access}, title = {APAROS: A clustering-based hybrid approach for handling overlapped regions in imbalanced datasets}, volume = {13}, year = {2025}, }
• (2025). Proposing a modified switchable schiffman structure to implement a wideband and low-loss 4-bit digital phase shifter. ACCESS, 13, 170693-170704. (www)

  @article{ , abstract = {This paper proposes a modified Schiffman structure to implement a wideband, low-loss 4-bit digital phase shifter. Here, low-cost positive intrinsic negative (PIN) diodes are used for electronically controlling the phase states of the phase shifter. For each bit of 22.5°, 45°, and 90°, a novel structure based on two edge-coupled microstrip transmission lines (MTLs) is utilized in which the PIN diodes of the main line are located along the coupled MTLs. To improve impedance matching and phase slope alignment, $\lambda _{\mathbf {0}}$ /8 parallel open/short-circuit stubs are used in the structure of all four bits. The operation of different parts of the phase shifter is elaborated through analyzing the relevant equations. The 4-bit phase shifter provides 16 phase shift states between 0° and 360°. The 4-bit digital phase shifter and its constructive bits have been designed, fabricated and measured. The measured results are in good agreement with full-wave simulations. The measurement average insertion loss (IL) of the 4-bit phase shifter is less than 4 dB within the wideband frequency range of 2.2-3.3 GHz equivalent to 40% of bandwidth. The input and output VSWR of each state of the 16 states doesn’t exceed 2.25 and the RMS phase error is less than 8.1° throughout the operational bandwidth. Simple structure, ease of fabrication, and low cost are the most important features of the proposed 4-bit digital phase shifter.}, archive = {J_ACCESS}, author = {Mostafa Khazaei and Seyyed Hossein Mohseni Armaki and Amir Saman Noor Amin}, doi = {10.1109/ACCESS.2025.3615716}, journal = {IEEE Access}, pages = {170693-170704}, shortjournal = {IEEE Access}, title = {Proposing a modified switchable schiffman structure to implement a wideband and low-loss 4-bit digital phase shifter}, volume = {13}, year = {2025}, }
• (2025). Traditional chinese painting completion via hierarchical optimal transport. ACCESS, 13, 170681-170692. (www)

  @article{ , abstract = {Traditional Chinese painting completion presents unique challenges where sparse ink distributions and intentional blank spaces serve aesthetic rather than functional purposes, contradicting assumptions in conventional inpainting methods. We propose Hierarchical Optimal Transport for Painting (HOT-Paint) that formulates completion as controlled texture redistribution in Wasserstein space. By interpreting texture synthesis through transport flow dynamics, we model brush stroke propagation using the static Kantorovich formulation while preserving characteristic sparsity through regularized transport plans. Our approach constructs multi-scale probability measures capturing artistic elements from fine strokes to global composition. The framework introduces adaptive cost functions integrating geometric distances, texture similarities via Gabor filters, and semantic relationships from pre-trained features. A modified Sinkhorn algorithm with soft-thresholding enforces sparsity constraints, while inter-scale consistency mechanisms ensure coherent information flow across resolutions. Experiments on CPDD and CelebA-HQ datasets demonstrate over 25.1% improvements in FID scores over state-of-the-art methods. Real-world restoration of authentic museum paintings validates practical utility. The proposed framework advances optimal transport theory for artistic image synthesis while addressing fundamental challenges in cultural heritage preservation.}, archive = {J_ACCESS}, author = {Wenmiao Jiang and Shunkai Zhang and Shengqi You and Pengbin Feng and Zhengyang Lu}, doi = {10.1109/ACCESS.2025.3615386}, journal = {IEEE Access}, pages = {170681-170692}, shortjournal = {IEEE Access}, title = {Traditional chinese painting completion via hierarchical optimal transport}, volume = {13}, year = {2025}, }
• (2025). Wideband 1-bit reconfigurable circularly polarized transmitarrays in ka-band. ACCESS, 13, 170670-170680. (www)

  @article{ , abstract = {This study details the design of an active Transmitarray (TA) operating in the Ka-band made of a reconfigurable linear-to-circular-polarization (LP-CP) unit-cell. The unit-cells incorporate a true-time-delay (TTD) polarization conversion mechanism and a stacked patch configuration to achieve wide transmission and axial ratio bandwidths. Each cell integrates a pair of PIN diodes for electronically controllable 180° phase shifts. This is the first demonstration of a wide-band reconfigurable and circularly polarized unit-cell in millimeter wave bands, without the use of an external polarizer. A $20 \times 20$ cells Ka-band circularly polarized TA was designed and fabricated. A random symmetric distribution of rotations applied to the inner patch antenna enhances CP purity and increases the scan range. Measurement results demonstrate a broadside gain of 20.55 dB, with both −3 dB gain bandwidth and 3 dB axial ratio bandwidth exceeding 22%, which surpasses all previous millimeter-wave reconfigurable design. In the case of the $20\times 20$ cell array manufactured the scan range extends to ±60° in both E and H planes, with a maximum scan loss of 4.2 dB and axial ratio values under 2 dB for elevation angles up to ±50°. Additional full wave simulations of a larger $60\times 60$ cell array demonstrate a better maximum scan loss of 2.4 dB for elevation angles reaching ±50°, 3.8 dB for an elevation angle of ±60° and 6 dB for an elevation angle of ±70° with axial ratio values consistently under 3 dB.}, archive = {J_ACCESS}, author = {Alessandro De Oliveira Cabral Junior and André Barka and Hamza Kaouach and Francesco Positano}, doi = {10.1109/ACCESS.2025.3615780}, journal = {IEEE Access}, pages = {170670-170680}, shortjournal = {IEEE Access}, title = {Wideband 1-bit reconfigurable circularly polarized transmitarrays in ka-band}, volume = {13}, year = {2025}, }
• (2025). A one-shot learning approach for fault classification of bearings via multi-autoencoder reconstruction. ACCESS, 13, 170660-170669. (www)

  @article{ , abstract = {In industrial scenarios, the scarcity of labeled data for fault diagnosis of rotating machinery poses a significant challenge to the development of reliable data-driven models. This paper proposes an interpretable one-shot fault diagnosis approach based on class-specific autoencoder, where a separate autoencoder is trained to reconstruct the vibration signal pattern of each fault class. The method operates effectively without requiring the extensive meta-training phases required by few-shot learning. During inference, the reconstruction similarity measured via Pearson correlation is used to identify the most likely fault class. To evaluate the method, experiments were conducted on the Case Western Reserve University (CWRU) dataset under two classification schemes: one with 4 classes representing fault types, and another with 10 classes including fault severities. In both cases, models were trained by using only one sample per class, and performance was averaged over 50-fold cross-validation using different samples in each fold. The proposed model achieved superior performance across all tested operating conditions (1797, 1772, 1750, and 1730 RPM), reaching average accuracies of 64% and 67% in the 10-class and 4-class scenarios, respectively. Comparisons with conventional and few-shot learning methods including Long Short-Term Memory (LSTM), Residual Network (ResNet18-1D), Model-Agnostic Meta-Learning (MAML), and Prototypical Networks. These results establish signal reconstruction with class-specific autoencoder as a simple yet powerful alternative to complex meta-learning strategies for fault diagnosis in real-world, data-scarce, industrial environments.}, archive = {J_ACCESS}, author = {Eduardo B. Gouveia and Amanda R. F. Jorge and Aldemir Ap. Cavallini and Márcio J. Cunha and Luiz C. G. Freitas and Marcus A. V. Duarte and Valder Steffen}, doi = {10.1109/ACCESS.2025.3615116}, journal = {IEEE Access}, pages = {170660-170669}, shortjournal = {IEEE Access}, title = {A one-shot learning approach for fault classification of bearings via multi-autoencoder reconstruction}, volume = {13}, year = {2025}, }
• (2025). Dynamic scenario transfer for motion planning in autonomous driving: A successor representation approach. ACCESS, 13, 170636-170659. (www)

  @article{ , abstract = {Adaptive motion planning systems are essential for autonomous vehicles to navigate complex and dynamic urban environments. Traditional motion planning approaches often struggle in environments lacking structured references or involving both static and dynamic obstacles, such as parking lots. This study proposes a semi-supervised deep transfer learning framework that enhances motion planning by enabling scenario-level knowledge transfer across diverse contexts. By leveraging successor representations, the framework captures transition relationships within specific scenarios, facilitating the adaptation of learned knowledge to new environments. This capability is incorporated through the Knowledge-based Scenario Transfer Learning and Artificial Potential Fields (KSTL-APF) approach. This study focuses on validating the proposed framework through simulation under static and semi-dynamic conditions, with real-world deployment identified as a future development phase. Experimental results indicate that KSTL-APF significantly improves planning efficiency, reduces computational time, and enhances trajectory robustness compared to conventional motion planning methods. These findings suggest that scenario-level knowledge transfer can significantly increase the adaptability and reliability of autonomous vehicle systems in unstructured or unfamiliar environments.}, archive = {J_ACCESS}, author = {Naghmeh Niroomand and Christian Bach}, doi = {10.1109/ACCESS.2025.3615723}, journal = {IEEE Access}, pages = {170636-170659}, shortjournal = {IEEE Access}, title = {Dynamic scenario transfer for motion planning in autonomous driving: A successor representation approach}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of machine learning and deep learning approaches for phonocardiogram classification using dataset integration. ACCESS, 13, 170619-170635. (www)

  @article{ , abstract = {The worldwide increase in cardiovascular diseases and related deaths necessitates advanced, non-invasive diagnostic methods that allow for timely detection and intervention to mitigate the risk of death. This study analyzes phonocardiogram (PCG) heart sound (HS) classification using Machine Learning (ML) and Deep Learning (DL) techniques. To improve accuracy and data diversity, two well-known datasets—PhysioNet CinC Challenge 2016 and CirCor 2022—were fused after resampling and label harmonization. ML algorithms like XGBoost and LightGBM, and DL algorithms like CNN+BiLSTM Attention and CBAM ResNet were incorporated to train the models leveraging the Mel-frequency ceptral coefficients as key acoustic features. Evaluation was performed using the stratified k-fold cross-validation, with bootstrapping, utilizing multiple orthogonal metrics like sensitivity, specificity, F1-score, ROC-AUC, alongside accuracy computed over all folds. The results analysis revealed 95.21% accuracy for the ResNet + CBAM model, the best performer, followed by CNN + BiLSTM at 94.01%. While ML models provided quicker inference and interpretability, DL models exhibited better generalization as well as greater sensitivity to intricate HS, particularly the quadruple rhythm. The findings reinforce the importance of dataset fusion and evaluation of hybrid models for the creation of clinically usable diagnostic systems. With this hybrid model approach, the proposed framework has advanced responsive capability for early detection of CVD even in constrained healthcare environments.}, archive = {J_ACCESS}, author = {M. Kalimuthu and C. Hemanth}, doi = {10.1109/ACCESS.2025.3612392}, journal = {IEEE Access}, pages = {170619-170635}, shortjournal = {IEEE Access}, title = {A comparative analysis of machine learning and deep learning approaches for phonocardiogram classification using dataset integration}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven framework for optimal split ratio determination in PMSM design. ACCESS, 13, 170601-170618. (www)

  @article{ , abstract = {The split ratio, which is the ratio of rotor diameter to stator diameter, plays a pivotal role in determining the performance of permanent magnet (PM) motors. This study presents a machine learning-driven framework to optimize the split ratio from a cost-efficiency perspective. Extensive datasets generated through a meta-heuristic optimization algorithm are analyzed using regression techniques to clarify the intricate relationships between the split ratio and key motor design variables. From this analysis, a novel logarithmic formula is derived, capturing these interdependencies with remarkable precision and offering predictive robustness across previously unseen design scenarios. The proposed framework provides two major advancements: 1) it enables rapid and accurate computation of the optimal split ratio across major six pole/slot families without extensive iterative simulations, and 2) it establishes a scalable, data-driven methodology applicable to broader motor design optimization tasks. The validity and effectiveness of the proposed approach are rigorously verified through finite element analysis (FEA) and experimental validation.}, archive = {J_ACCESS}, author = {Ju Hyung Lee and Dong Hoo Min and Ji Hoon Park and Seun Guy Min}, doi = {10.1109/ACCESS.2025.3615729}, journal = {IEEE Access}, pages = {170601-170618}, shortjournal = {IEEE Access}, title = {Machine learning-driven framework for optimal split ratio determination in PMSM design}, volume = {13}, year = {2025}, }
• (2025). Structure and control of biology-inspired networks. ACCESS, 13, 170587-170600. (www)

  @article{ , abstract = {There is growing interest in developing the theoretical foundations of networked control systems. These efforts aim to illuminate how brain networks support sensory perception, motor control, memory, and other functions essential for survival. The present paper proposes a biologically inspired network model featuring dynamic connections regulated by Hebbian learning. Drawing on tools from graph theory and classical control, we show that our novel nonlinear model exhibits several biologically plausible features, including bounded evolution, stability, and resilience. It also demonstrates a form of structural stability, meaning that perturbations to the model parameters do not alter its essential properties. The proposed network model involves generalized cactus graphs with multiple control input nodes, and it is shown that the properties of the network are resilient to various changes in network topology provided these changes preserve the generalized cactus structure. A particular example described in what follows is an idealized network model of the visual system of a Macaque monkey. The model remains resilient to disruptions that may occur in living organisms, such as those caused by disease or injury. A different model of the same type provides an example of a system that can perform data classification.}, archive = {J_ACCESS}, author = {Zexin Sun and John Baillieul}, doi = {10.1109/ACCESS.2025.3615831}, journal = {IEEE Access}, pages = {170587-170600}, shortjournal = {IEEE Access}, title = {Structure and control of biology-inspired networks}, volume = {13}, year = {2025}, }
• (2025). Mineral extraction scenarios from an asteroid via optimization considering rotational dynamics. ACCESS, 13, 170577-170586. (www)

  @article{ , abstract = {This study examines how removing surface materials affects the rotational dynamics of an asteroid to design efficient asteroid mining plans. The simulations solve Euler’s equations and track variations in the inertia tensor, angular momentum, and energy throughout the removal process. Two distinct methods (random and optimized) are employed to remove 30% and 70% of the total asteroid mass. Findings demonstrate that random removal results in large deviations from the initial angular velocity, most noticeably when a greater percentage of the asteroid’s mass is removed. It is also shown that random removal leads to irregular shapes as they gain angular velocity with higher percentages of mass removal. The optimized removal strategy achieves substantially lower dynamical error even when large portions of the surface are extracted and suggests excavation sites should be symmetrical, especially along the z-axis, while avoiding excessive polar areas, to preserve rotational stability. This paper shows the effectiveness of targeted material removal in maintaining dynamical stability and structural integrity and offers practical guidance for the safe and efficient design of future asteroid mining missions.}, archive = {J_ACCESS}, author = {Seyedmahdiar Ziaolhagh and Mahdi Jafari-Nadoushan}, doi = {10.1109/ACCESS.2025.3615449}, journal = {IEEE Access}, pages = {170577-170586}, shortjournal = {IEEE Access}, title = {Mineral extraction scenarios from an asteroid via optimization considering rotational dynamics}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral anomaly detection with enhanced spectral graph transformer network. ACCESS, 13, 170554-170576. (www)

  @article{ , abstract = {Hyperspectral imaging (HSI) plays a pivotal role across diverse sectors—including agriculture, environmental monitoring, and defense—by capturing rich spectral information that enables fine-grained material discrimination. Despite its advantages, the inherently high dimensionality and complexity of hyperspectral data present substantial challenges for reliable anomaly detection. Conventional approaches often fall short when confronted with subtle or context-dependent anomalies, underscoring the need for more advanced methodologies. In this study, we propose the Enhanced Spectral Graph Transformer Network (ESGTN), a novel framework that synergistically combines graph-based modeling, transformer architectures, and hyperbolic space embedding to improve the accuracy and efficiency of spectral anomaly detection. By representing HSIs as graphs, ESGTN effectively models both spatial and spectral relationships. The transformer component, equipped with self-attention mechanisms, adaptively emphasizes salient features, while the incorporation of hyperbolic embeddings provides a compact and distortion-minimized representation of the data’s hierarchical structure. Extensive experiments conducted on multiple benchmark hyperspectral datasets demonstrate that ESGTN consistently outperforms existing state-of-the-art methods, achieving superior precision, recall, and computational efficiency. These findings highlight the model’s robustness and practical applicability across a range of real-world scenarios. Moreover, this work contributes to the growing body of research at the intersection of deep learning and hyperspectral imaging, offering a scalable path forward for tackling the complex analytical demands inherent to high-dimensional remote sensing data.}, archive = {J_ACCESS}, author = {Safarov Furkat Eshpulatovich and Mukhiddin Toshpulatov and Misirov Komoliddin Mamasabirovich and Akmalbek Abdusalomov and Azizbek Khojamurotov and Wookey Lee}, doi = {10.1109/ACCESS.2025.3607472}, journal = {IEEE Access}, pages = {170554-170576}, shortjournal = {IEEE Access}, title = {Hyperspectral anomaly detection with enhanced spectral graph transformer network}, volume = {13}, year = {2025}, }
• (2025). A novel analog interface using 3D printed electrodes for mixed voltage-current mode EEG. ACCESS, 13, 170545-170553. (www)

  @article{ , abstract = {This work presents a comprehensive study on a novel analog interface for single- and dual-channel electroencephalographic (EEG) analysis, employing current-mode active building blocks (ABBs), specifically the second-generation current conveyor (CCII) and voltage conveyor (VCII). The proposed mixed voltage-current mode approach enables voltage signal acquisition at the input stage, followed by current-domain processing in subsequent stages. A discrete PCB prototype was developed using AD844 operational amplifiers to emulate both CCII and VCII behavior. Signal acquisition was performed using a range of custom-designed 3D-printed passive and active electrodes fabricated with copper-based conductive filament, whose contact impedance variability was also characterized. The performance of the proposed interface was experimentally evaluated and compared against commercial EEG analog front-end chips such as the ADS1298, ADS1299, AD630, and other microelectronic circuits. The current-mode implementation demonstrates significant advantages, including high input impedance (ranging from 300 M $\Omega $ to 6.49 T $\Omega $ ), excellent common-mode rejection ratio (CMRR >100 dB), and a tunable bandwidth (0.1–200 Hz). The system successfully acquired high-quality EEG signals and limb movement data, highlighting its potential for low-noise, high-impedance biosignal interfaces.}, archive = {J_ACCESS}, author = {Andrea Rotondo and Gianluca Barile and Giuseppe Ferri and Vincenzo Stornelli}, doi = {10.1109/ACCESS.2025.3614505}, journal = {IEEE Access}, pages = {170545-170553}, shortjournal = {IEEE Access}, title = {A novel analog interface using 3D printed electrodes for mixed voltage-current mode EEG}, volume = {13}, year = {2025}, }
• (2025). A levitating parallel robot. ACCESS, 13, 170535-170544. (www)

  @article{ , abstract = {Magnetic levitation may offer an alternative to traditional actuation in robotics, particularly for applications requiring minimal mechanical contact, reduced friction, and operation in sterile or sensitive environments. This paper presents a novel parallel Delta robot enhanced by magnetic levitation technology, utilizing “polarity-free magnetic repulsion” (PFR) method. Unlike conventional Delta robots that rely on mechanical joints and motors, our design integrates magnetic levitation to reduce the robot’s weight and mechanical wear. Levitation is controlled by manipulating the rotation speed of the base magnetic field, which adjusts the height of the robot’s legs using force-based dynamics rather than traditional kinematics. A mechanical low-pass-filter is used to stabilize the system. An extensive set of experiments demonstrates the feasibility of this technology in achieving high precision and stability in operations.}, archive = {J_ACCESS}, author = {Alon David and Roman Kalyna and Nir Shvalb}, doi = {10.1109/ACCESS.2025.3611279}, journal = {IEEE Access}, pages = {170535-170544}, shortjournal = {IEEE Access}, title = {A levitating parallel robot}, volume = {13}, year = {2025}, }
• (2025). Analysis of respiratory flow signals using poincaré plot descriptors for machine learning-based prediction of ventilator weaning success. ACCESS, 13, 170523-170534. (www)

  @article{ , abstract = {Acute respiratory distress syndrome (ARDS) is a severe pulmonary condition that often requires mechanical ventilation (MV) to ensure adequate gas exchange and minimize ventilator-induced lung injury. This study compares statistical descriptors derived from respiratory time series with nonlinear variability metrics obtained from Poincaré plots to predict the outcomes of the weaning process in mechanically ventilated patients. The database, which includes respiratory flow recordings from 243 patients who underwent a standardized 30-minute spontaneous breathing test (SBT), was used to validate the classification models. Patients were categorized into three clinical outcome groups: successful weaning (n = 132), failed weaning (n = 88), and reintubation within 48 hours after completion of the trial (n = 23). Features were selected using nonparametric statistical tests and correlation analysis, eliminating redundancy and retaining discriminative variables. Two machine learning (ML) classifiers, random forest and feedforward neural network, were designed to identify patients belonging to each of the three clinical outcome groups. Model performance was assessed using stratified hold-out cross-validation repeated over 150 iterations, with hyperparameters optimized using Bayesian methods. The random forest classifier using Poincaré descriptors achieved a mean accuracy of 90.4% and higher F1-scores across all groups, including those who required reintubation. These findings suggest that Poincaré-based variability metrics, in combination with ensemble learning, may enhance the accurate prediction of MV weaning outcomes in ARDS patients.}, archive = {J_ACCESS}, author = {Hernando González Acevedo and Carlos Julio Arizmendi and Beatriz F. Giraldo}, doi = {10.1109/ACCESS.2025.3615184}, journal = {IEEE Access}, pages = {170523-170534}, shortjournal = {IEEE Access}, title = {Analysis of respiratory flow signals using poincaré plot descriptors for machine learning-based prediction of ventilator weaning success}, volume = {13}, year = {2025}, }
• (2025). A comparative study of solidity and sui move: Advancing smart contract development. ACCESS, 13, 170504-170522. (www)

  @article{ , abstract = {The emergence of blockchain technology has resulted in the creation of various blockchain networks and their accompanying programming languages for creating smart contracts. Each smart contract language, with its unique characteristics, advantages, and drawbacks, leverages blockchain technology to create a secure, transparent, and efficient way to execute agreements without the need for intermediaries. In this study, we compared the Solidity language used in the Ethereum blockchain network and Sui Move, which is used for the Sui blockchain network, highlighting contrasting approaches to smart contract development. In this paper, we explore the aspects of both languages, such as their structure, operational models, and security components. By evaluating these factors, the study uncovers the strengths and limitations of each language, highlighting their security and performance features that developers must take into account when choosing a smart contract language to build robust, efficient, and scalable decentralized applications that are addressed at a wide audience. Additionally, the work entails a framework that helps developers transition existing applications built on Solidity to Sui Move and take advantage of Sui Move’s unique characteristics without sacrificing functionality. It is a step-by-step approach to modify data forms, refactor logic processes, and deploy modules as needed while ensuring that migration is smooth and the maximum benefits of the Sui-based blockchain network are achieved. The knowledge and resources offered are designed to enable developers to create more secure, scalable, and efficient decentralized systems.}, archive = {J_ACCESS}, author = {Antonios Giatzis and Stamatis Papangelou and Christos K. Georgiadis}, doi = {10.1109/ACCESS.2025.3615072}, journal = {IEEE Access}, pages = {170504-170522}, shortjournal = {IEEE Access}, title = {A comparative study of solidity and sui move: Advancing smart contract development}, volume = {13}, year = {2025}, }
• (2025). A novel VSG control strategy with droop compensation for improved oscillation damping and guaranteed steady-state performance in microgrids. ACCESS, 13, 170488-170503. (www)

  @article{ , abstract = {The Virtual Synchronous Generator (VSG) is a widely studied control strategy for Grid-Forming (GFM) converters, enabling frequency support by implementing virtual inertia and damping. In GFM converters, the VSG damping coefficient serves a dual purpose: damping the power and frequency oscillations and acting as the droop gain in frequency control. This duality restricts the ability to achieve the desired damping and steady-state performance simultaneously. Existing methods addressed this by adding transient damping loops, which are effective when the droop gain remains constant. However, in islanded microgrids, the droop gain may vary in real-time, possibly leading to poorly damped behavior. To contribute to this matter, this paper proposes a Droop-Compensation VSG control (DC-VSG), which incorporates an improved virtual governor, allowing the damping coefficient to be tuned independently from the steady-state response. A tuning procedure is also developed to enhance oscillation damping and speed up the transient response. With its improved governor and proposed tuning, the DC-VSG maintains adequate performance despite variations in droop gain. The DC-VSG is compared to two existing VSG techniques. Each strategy is tuned to maximize the oscillation damping. The performance of each strategy is assessed through time-domain simulations and eigenanalysis at various operating points and droop gain settings. The DC-VSG consistently outperformed existing strategies across all the investigated scenarios.}, archive = {J_ACCESS}, author = {Erico Gurski and Roman Kuiava and Gilney Damm}, doi = {10.1109/ACCESS.2025.3615391}, journal = {IEEE Access}, pages = {170488-170503}, shortjournal = {IEEE Access}, title = {A novel VSG control strategy with droop compensation for improved oscillation damping and guaranteed steady-state performance in microgrids}, volume = {13}, year = {2025}, }
• (2025). Detection of nonstationary events in motor signals using nonparametric statistical hypothesis testing. ACCESS, 13, 170475-170487. (www)

  @article{ , abstract = {This work presents a lightweight statistical method for detecting nonstationarities in motor current signals, aiming to improve the diagnosis of transient events such as sudden load changes or inverter disturbances. Unlike traditional MCSA techniques that rely on spectral analysis, the proposed framework segments the signals into non-overlapping windows and extracts global statistical features to capture distributional changes over time. These features are compared using nonparametric hypothesis tests with p-value fusion strategies, enhancing robustness, with an adaptive buffering scheme based on Wasserstein distance to allow the system to adjust to signal variability. Results indicates accuracy reaches values as high as 95.9% with the false positive and false negative rates go as low as 7.7% and 0% respectively for both Monte Carlo simulations and real-world tests (from 529 MCSA signals), demonstrating strong potential for real-time, embedded, and scalable motor health monitoring without requiring complex signal decomposition or machine learning models.}, archive = {J_ACCESS}, author = {Eduardo Vaz Fagundes Rech and Levy Ely De Lacerda De Oliveira and Wilson Cesar Sant’Ana}, doi = {10.1109/ACCESS.2025.3615553}, journal = {IEEE Access}, pages = {170475-170487}, shortjournal = {IEEE Access}, title = {Detection of nonstationary events in motor signals using nonparametric statistical hypothesis testing}, volume = {13}, year = {2025}, }
• (2025). A coupled unilateral teleoperation system using an omnidirectional mobile robot haptic interface. ACCESS, 13, 170454-170474. (www)

  @article{ , abstract = {This paper proposes a novel coupled unilateral teleoperation system that enables position and force interaction between two operators located at different sites via omnidirectional mobile robot haptic interfaces. It supports simultaneous translational and rotational motion and can switch seamlessly between unilateral and bilateral operating modes, which offers capabilities that extend beyond those of traditional haptic devices. The proposed approach establishes two unilateral subsystems that are dynamically and kinematically identical. The kinematic and dynamic models, the velocity cone constraints, and a linear time invariant simplification are derived to analyze and provide the basis for controller design and stability proofs. Position control is implemented with a PID controller, and closed-loop stability in the presence of communication time delays is proven using the small gain theorem. To validate the approach, two practical experimental modes, position tracking and force interaction, were conducted between Japan and Thailand, thereby demonstrating system performance under high-latency conditions. The results show that the position tracking root mean square error remains within the millimeter range, whereas force interactions are achieved without oscillation. At the modeling level, the study demonstrates that a symmetrically coupled unilateral teleoperation system can realize stable, high-fidelity bilateral teleoperation.}, archive = {J_ACCESS}, author = {Yongyut Pattanapong and Taisei Mikami and Ho Duc Tho and Takanori Miyoshi}, doi = {10.1109/ACCESS.2025.3615766}, journal = {IEEE Access}, pages = {170454-170474}, shortjournal = {IEEE Access}, title = {A coupled unilateral teleoperation system using an omnidirectional mobile robot haptic interface}, volume = {13}, year = {2025}, }
• (2025). UnifiedKP: A unified network knowledge plane for large model-enabled 6G networks. ACCESS, 13, 170441-170453. (www)

  @article{ , abstract = {The emergence of large language models (LLMs) and agentic systems is revolutionizing the landscape of 6G networks by enabling unprecedented levels of autonomous intelligence, including self-configuration, self-optimization, and self-healing capabilities. However, current implementations face significant challenges. Individual intelligence tasks require isolated knowledge retrieval pipelines. This isolation results in redundant data flows, inconsistent interpretations, and increased operational complexity. Inspired by the service model unification efforts in Open-RAN that promote interoperability and vendor diversity, we propose UnifiedKP: a unified Network Knowledge Plane specifically designed for large model-enabled autonomous 6G network intelligence. By decoupling network knowledge acquisition and management from intelligence logic, UnifiedKP streamlines development workflows and significantly reduces maintenance complexity for intelligence engineers. Through an intuitive and consistent knowledge interface, UnifiedKP enhances interoperability for network intelligence agents while maintaining semantic consistency across diverse intelligence tasks. We demonstrate the effectiveness of UnifiedKP through two representative intelligence applications: live network knowledge question-answering and edge AI service orchestration. Experimental results show that UnifiedKP reduces knowledge retrieval latency by 47%, improves knowledge consistency by 82%, and decreases development complexity by 65% compared to traditional isolated approaches. Our framework achieves 94.3% accuracy in network anomaly detection and reduces service orchestration time by 38% in dynamic edge computing environments. These findings establish UnifiedKP as a foundational architecture for realizing truly autonomous and intelligent 6G networks.}, archive = {J_ACCESS}, author = {Tiantian Wu and Chengjie Wei and Lingnan Xia}, doi = {10.1109/ACCESS.2025.3610890}, journal = {IEEE Access}, pages = {170441-170453}, shortjournal = {IEEE Access}, title = {UnifiedKP: A unified network knowledge plane for large model-enabled 6G networks}, volume = {13}, year = {2025}, }
• (2025). Common-source common-drain (CSCD)-based integrated RF power amplifier for wireless communication systems. ACCESS, 13, 170424-170440. (www)

  @article{ , abstract = {In wireless transceivers, the power amplifier (PA) is the most power-consuming and critical component, as it must meet stringent linearity and reliability requirements while maintaining high efficiency. Traditionally, PAs are implemented using a common-source (CS) configuration. However, recent studies have explored the common-drain amplifier (CDA) as a promising PA topology, offering advantages in stress handling and linearity. Since the CDA is inherently a current amplifier, it can be integrated with a CS amplifier to form a hybrid common-source common-drain (CSCD) configuration. Despite its potential, limited research has been conducted on CDA-based or CDA-inspired PAs. This work introduces a novel approach by employing the CDA as a foundation for developing CSCD PAs, thereby opening new avenues in CMOS PA design. Using a 130-nm RFCMOS process, a CSCD amplifier is fabricated and packaged in QFN-32. The amplifier achieves a saturated output power (P ${}_{sat}$ ) of 31 dBm and a power gain of 10 dB at a center frequency of 706 MHz, with a peak power-added efficiency (PAE) of 39%. To further validate the concept, wideband 4G LTE (Long-Term Evolution) signals are applied to evaluate linearity and output power. Under a 16-QAM, 20 MHz UTRA signal, the amplifier delivers 28.1 dBm output power with an Adjacent Channel Power Ratio (ACPR) of –30 dBc and an error vector magnitude (EVM) below –30 dBc, maintaining an average efficiency of 26%. The design meets LTE performance standards, demonstrating strong potential for robust, high-efficiency RF amplification in wireless communication systems.}, archive = {J_ACCESS}, author = {Muhammad Abdullah Khan and Muhammad Moazam Azeem and Widad Ismail}, doi = {10.1109/ACCESS.2025.3614176}, journal = {IEEE Access}, pages = {170424-170440}, shortjournal = {IEEE Access}, title = {Common-source common-drain (CSCD)-based integrated RF power amplifier for wireless communication systems}, volume = {13}, year = {2025}, }
• (2025). PTP over wide area networks with offset measurement outlier filtering. ACCESS, 13, 170410-170423. (www)

  @article{ , abstract = {The IEEE 1588 Precision Time Protocol (PTP) is capable of achieving nanosecond-level accuracy over standard Ethernet, making it widely used in industrial and scientific facilities. However, its time offset measurement process is highly sensitive to asymmetries and dynamic variation in the packet delays, hindering its applicability in wide-area use cases. In this paper, we demonstrate that PTP can be enhanced and used for Internet point-to-point time transfer with equal or better performance than the traditional Network Time Protocol (NTP). For this purpose, we propose a technique called offset measurement outlier filtering (OMOF), which leverages the short-term stability of the local clock to discard offset measurements affected by packet delay variation. We implemented OMOF in an open-source PTP client and validated it on a production network connection between two sites located more than 300 km apart. Both sites were fitted with high-performance reference clocks synchronized to less than a nanosecond of error using the GNSS-based common-view technique, enabling accurate measurement of our solution’s performance with state-of-the-art metrology techniques. Results show that our approach achieves a 95% improvement in peak-to-peak time error over the most popular open-source PTP implementation and 71% over NTP synchronization.}, archive = {J_ACCESS}, author = {Víctor Vázquez and Carlos Megías and Carmen Vélez and Héctor Esteban and Javier Díaz and Eduardo Ros}, doi = {10.1109/ACCESS.2025.3615674}, journal = {IEEE Access}, pages = {170410-170423}, shortjournal = {IEEE Access}, title = {PTP over wide area networks with offset measurement outlier filtering}, volume = {13}, year = {2025}, }
• (2025). Hybrid beamforming for multi-user joint communication and sensing with URA under partially connected architectures. ACCESS, 13, 170395-170409. (www)

  @article{ , abstract = {Joint Communication and Sensing (JCAS) is a key enabler for future 6G networks, allowing data transmission and environmental sensing through shared spectral and hardware resources. To address challenges such as the sensing-communication trade-off, hardware limitations, and multi-user interference, we propose a scalable Multiple Input Multiple Output (MIMO) framework based on Uniform Rectangular Arrays (URA) with partially connected hybrid beamforming (PC-HBF). On the transmitter side, a fairness-aware beamforming optimization maximizes sensing gain while ensuring communication quality, solved via an Interior-Point Method (IPM) with adaptive initialization for improved convergence. At the receiver, a two-stage Block Orthogonal Matching Pursuit (Block-OMP) with Adaptive Dictionary Selection and Refinement (ADSR) enhances combining accuracy under hardware constraints while maintaining low complexity. For radar sensing, we introduce a hybrid direction-of-arrival (DoA) estimation method combining Beamscan and MUSIC algorithms. This design enables accurate angle estimation despite PC-HBF limitations, providing a practical sensing solution for JCAS scenarios. Simulation results demonstrate the practical effectiveness of our design: IPM-based beamforming converges within 15 iterations, ADSR-enhanced combining improves spectral efficiency by up to 1 dB over baseline OMP, approaching the performance of fully digital beamforming while supporting joint sensing, and the DoA method resolves targets spaced as closely as 5°. These gains underline the framework’s potential in real-world applications such as smart mobility and autonomous driving, enabling scalable and high-performance JCAS systems.}, archive = {J_ACCESS}, author = {Mengyu Zhang and Yanpeng Su and Norman Franchi and Robert Weigel and Torsten Reissland}, doi = {10.1109/ACCESS.2025.3615453}, journal = {IEEE Access}, pages = {170395-170409}, shortjournal = {IEEE Access}, title = {Hybrid beamforming for multi-user joint communication and sensing with URA under partially connected architectures}, volume = {13}, year = {2025}, }
• (2025). Intelligent intrusion detection mechanism for cyber attacks in digital substations. ACCESS, 13, 170380-170394. (www)

  @article{ , abstract = {With the advent of communication infrastructure and automation, conventional substations have transformed into digital substations as cyber-physical systems. These substations lack matured and intelligent techniques to ensure cybersecurity, making them vulnerable to cyber-attacks. Several studies have been conducted in the literature on effective Intrusion Detection Systems (IDS) for digital substations based on supervised machine learning techniques. However, there are persistent challenges due to unlabeled datasets, and inaccurate detection with high false alarm rates. This paper proposes an intelligent intrusion detection mechanism (IIDM) with high accuracy to detect anomalies in substation Generix Object-Oriented Substation Event (GOOSE) packets caused by Data Manipulation, Man-in-the-middle attacks (MITM), replay, and Denial of Service (DoS). The IIDM is based on data-driven (statistical and ensemble unsupervised learning techniques) and model-based techniques considering the Single Line Diagram of the substation. The efficiency of the proposed IIDM for forensic analysis is discussed using standard GOOSE data sets. The accuracy of the proposed mechanism is tested in real-time on a substation testbed, demonstrating the capabilities of IIDM to provide suitable real-time intrusion detection for substations with high accuracy.}, archive = {J_ACCESS}, author = {Devika Jay and Tanushree Bhattacharjee and Umayal Manickam and S. Shashank}, doi = {10.1109/ACCESS.2025.3615247}, journal = {IEEE Access}, pages = {170380-170394}, shortjournal = {IEEE Access}, title = {Intelligent intrusion detection mechanism for cyber attacks in digital substations}, volume = {13}, year = {2025}, }
• (2025). Time-domain off-grid sparse bayesian learning for high-resolution time-delay estimation. ACCESS, 13, 170372-170379. (www)

  @article{ , abstract = {When estimating the acoustic channel impulse response (CIR) at low frequencies, matched filter (MF)-based methods suffer from limited resolution due to the narrow source bandwidth. To address this limitation, we propose a time-domain sparse Bayesian learning (SBL) framework that enables CIR estimation independently of the source frequency band. Unlike existing frequency-domain SBL approaches, the proposed method formulates the estimation problem as a time-domain linear system and incorporates off-grid modeling along with multiple measurements to achieve high-resolution results. The performance of the method is evaluated through simulations across varying signal-to-noise ratio (SNR) and number of measurements, and compared against conventional MF and on-grid SBL techniques. Its effectiveness is further validated using real-world experimental data collected in a shallow water environment. The results demonstrate that the proposed framework significantly improves temporal resolution, enhances denoising performance, and successfully resolves closely spaced arrivals that are otherwise indistinguishable.}, archive = {J_ACCESS}, author = {Haesang Yang and Youngmin Choo}, doi = {10.1109/ACCESS.2025.3614348}, journal = {IEEE Access}, pages = {170372-170379}, shortjournal = {IEEE Access}, title = {Time-domain off-grid sparse bayesian learning for high-resolution time-delay estimation}, volume = {13}, year = {2025}, }
• (2025). Dynamic advertisement pricing and bidding optimization: An integrated machine learning and auction framework. ACCESS, 13, 170360-170371. (www)

  @article{ , abstract = {Online advertising platforms rely on efficient pricing and bidding strategies to maximize returns for advertisers while maintaining fairness and competitiveness in auctions. This paper presents an integrated machine learning and auction-based framework that combines Click-Through Rate (CTR) prediction, convex optimization, fairness evaluation, and multi-agent auction simulation. Synthetic datasets mimicking real-world ad environments were generated, and multiple models, including Logistic Regression, Random Forest, and XGBoost, were evaluated. XGBoost achieved the highest ROC-AUC (0.8731) and lowest log loss (0.2187), improving F1-score by 12% over the baseline after applying SMOTE. Predicted CTRs were used in a convex optimization model, solved with CVXPY, to allocate budgets optimally, increasing ROI by up to 23.5% compared to uniform bidding. A multi-agent second-price auction simulation demonstrated that balanced bidding strategies improved clicks-per-dollar efficiency by 22% over aggressive bidding. Fairness analysis across gender groups revealed minimal disparity,with a prediction accuracy gap of only 1.3%. Comparative evaluation against LightGBM, CatBoost, and heuristic baselines confirmed the superiority of the proposed method in both prediction and ROI. The proposed framework is computationally efficient, scalable, and applicable to diverse online advertising scenarios.}, archive = {J_ACCESS}, author = {N. Najeetha Banu and Akilesh C. Harti and N. Naveeth Meeran and G. Poornalatha and K. B. Ajitha Shenoy}, doi = {10.1109/ACCESS.2025.3615136}, journal = {IEEE Access}, pages = {170360-170371}, shortjournal = {IEEE Access}, title = {Dynamic advertisement pricing and bidding optimization: An integrated machine learning and auction framework}, volume = {13}, year = {2025}, }
• (2025). Epileptic seizure detection using a small memory footprint convolutional neural network. ACCESS, 13, 170351-170359. (www)

  @article{ , abstract = {Automatically detecting epileptic seizures has been explored using machine learning techniques, with promising results in recent years. Epilepsy affects about 51 million people worldwide, and about 30% of them cannot control their symptoms, leading to unpredictable seizures. This means that approximately 15.3 million people continue to experience seizure episodes even after treatment (e.g., drugs or surgery). Many previous studies have focused on developing models for patient-specific tasks. This study explores the problem of seizure detection using a cross-patient approach. We present a technique for generating and preprocessing data from EEG signals to be used as input to a Convolutional Neural Network (CNN). The network architecture and performance are described, achieving results comparable to state of the art, with an average accuracy of around 95%. The proposed network has five convolutional layers, making it a good candidate for embedding in a wearable device. Details of the method and training hyperparameters are also provided.}, archive = {J_ACCESS}, author = {Levi Moreira De Albuquerque and Elias Teodoro Da Silva}, doi = {10.1109/ACCESS.2025.3615070}, journal = {IEEE Access}, pages = {170351-170359}, shortjournal = {IEEE Access}, title = {Epileptic seizure detection using a small memory footprint convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). More realistic audio-based drone detection and identification approaches with machine learning. ACCESS, 13, 170328-170350. (www)

  @article{ , abstract = {With advanced mobility and widespread accessibility, drones have attracted considerable attention, offering a wide range of applications in both military and civilian domains. Nonetheless, beyond their beneficial applications and potential, drones have given rise to serious security and privacy concerns as they are increasingly exploited for malicious activities such as drug smuggling or pose threats to individuals and public facilities. The emergence of security threats by drones has stimulated the development of countermeasure solutions including drone detection and identification. These solutions enable timely responses against security threats by facilitating the detection of drone presence and verifying their identities. In this paper, we propose more realistic audio-based drone detection and identification approaches using machine learning, which account for the presence of background environment noises, an often overlooked factor in current methods. By incorporating the interdependence between the drone’s audio and the surrounding environmental sounds, we simulate real-world scenarios where drones operate. We use drone datasets that capture these interactions to evaluate detection and identification performance. Mel-frequency cepstral coefficients (MFCC) features and support vector machine (SVM) classifiers with various kernels are employed to investigate the effectiveness of our approach across different environments. Our results demonstrate that our approach ensures reliable drone detection, achieving an accuracy of approximately 0.99 and an F1-score of 0.98 in closed-set experiments, and an accuracy and F1-score of about 0.94 in open-set experiments. For drone identification, we show the feasibility of our approach with an accuracy and F1-score of about 0.96 for only known drone classes and approximately 0.90 for both known and unknown drone classes.}, archive = {J_ACCESS}, author = {Shincheol Lee and Alimov Abdulboriy Abdulkhay Ugli and Ji Sun Shin}, doi = {10.1109/ACCESS.2025.3613683}, journal = {IEEE Access}, pages = {170328-170350}, shortjournal = {IEEE Access}, title = {More realistic audio-based drone detection and identification approaches with machine learning}, volume = {13}, year = {2025}, }
• (2025). Investigating data consistency in the ASHRAE dataset using clustering and label matching. ACCESS, 13, 170307-170327. (www)

  @article{ , abstract = {Data is a critical component in various fields, enabling researchers to perform analyses, improve decision-making, optimization, and scientific research. However, poor data quality can lead to flawed decisions and inefficiencies. In evaluating data quality, four primary aspects are typically considered: accuracy, completeness, consistency, and timeliness. The existence of human preferences in the dataset may present issues with data inconsistency. One of the popular datasets in the thermal comfort area is the ASHRAE Comfort Database II, which contains human preference features that may cause data inconsistency and affect the performance of machine learning models. In this paper, Clustering and Label Matching is proposed to clean the data and achieve consistency. Outlier detection techniques are employed as reference methods to evaluate how well different approaches can improve dataset consistency, with performance assessed using cluster analysis and validated through prediction model evaluation. The results show that the proposed method is capable of performing deep cleaning to enhance dataset consistency.}, archive = {J_ACCESS}, author = {Hui-Hui Tan and Yi-Fei Tan and Wooi-Haw Tan and Chee-Pun Ooi}, doi = {10.1109/ACCESS.2025.3615311}, journal = {IEEE Access}, pages = {170307-170327}, shortjournal = {IEEE Access}, title = {Investigating data consistency in the ASHRAE dataset using clustering and label matching}, volume = {13}, year = {2025}, }
• (2025). Wi-fi meets sound: A deep fusion leap beyond single-modality for device localization. ACCESS, 13, 170291-170306. (www)

  @article{ , abstract = {Wi-Fi-based indoor localization methods have gained significant attention due to the widespread availability of wireless infrastructure and the ability to collect high-resolution data, i.e., channel state information (CSI), at once. However, the Wi-Fi-based localization often suffers from accuracy limitations in complex indoor environments. Some studies have proposed multimodal localization methods using Wi-Fi and sound signals to address this issue. Although multimodal localization enables high accuracy and robust localization in indoor environments, existing systems face challenges due to infrastructure complexity and limited multimodal fusion capabilities. This paper proposes a novel single-anchor multimodal localization system that utilizes neural fusion techniques to address the challenges. More specifically, the proposed method extracts key features from the temporal and spatial domains using the temporal and element encoders, respectively, and fuses them to enable effective modeling of complex relationships between multimodal sensor data and the user’s location. Experimental results using the commercial Wi-Fi access point and microphone array demonstrate that the proposed system achieves a median localization error of less than 30 cm even in non-line-of-sight (NLoS) indoor environments.}, archive = {J_ACCESS}, author = {Takeru Fukushima and Takuma Matsukawa and Yusuke Ikemura and Hayato Terasoma and Sorachi Kato and Takuya Fujihashi and Tomoki Murakami and Yusuke Asai and Shunsuke Saruwatari}, doi = {10.1109/ACCESS.2025.3615632}, journal = {IEEE Access}, pages = {170291-170306}, shortjournal = {IEEE Access}, title = {Wi-fi meets sound: A deep fusion leap beyond single-modality for device localization}, volume = {13}, year = {2025}, }
• (2025). Sustainable smart energy hub design with V2H interfaces: Protecting EV and thermal load systems under disruption via deep Q-learning. ACCESS, 13, 170279-170290. (www)

  @article{ , abstract = {As smart energy hubs (SEHs) evolve with the integration of electric vehicles (EVs) and their dynamic charging and discharging processes, they become increasingly susceptible to advanced cyberattacks, especially those targeting electrical and thermal demands. These attacks pose significant risks to integrated systems, including charging stations, vehicle-to-home (V2H) interfaces, and energy storage systems (ESS) comprising batteries for EVs and thermal storage (TS). The analysis focuses on how SEHs respond when subjected to stealthy data manipulation threats that evade detection, which exploit weaknesses in electrical and thermal loads to bypass conventional detection mechanisms and disrupt operational stability. To mitigate these threats, a robust machine learning (ML) framework based on reinforcement learning (RL) with deep Q-learning (DQN) is proposed. This approach reconstructs compromised load data, restoring system integrity and enhancing resilience. Test scenarios demonstrate that tampered inputs may cause electrical consumption to vary by nearly 17% and thermal usage by approximately 25%, severely impacting SEH stability. Following the deployment of the defense mechanism, the Mean Absolute Percentage Error (MAPE) is significantly reduced to 11.96% for electrical demand and 2.33% for thermal demand. The proposed RL-based strategy effectively enhances energy storage systems’ resilience against cyber threats, addressing key vulnerabilities and ensuring stable operations in smart grid ecosystems.}, archive = {J_ACCESS}, author = {Daryoush Tavangar Rizi and Mohammad Hassan Nazari and Seyed Hossein Hosseinian}, doi = {10.1109/ACCESS.2025.3605062}, journal = {IEEE Access}, pages = {170279-170290}, shortjournal = {IEEE Access}, title = {Sustainable smart energy hub design with V2H interfaces: Protecting EV and thermal load systems under disruption via deep Q-learning}, volume = {13}, year = {2025}, }
• (2025). Iterative learning observer-based predefined-time sliding mode control for continuous stirred tank reactor with input and output constraints. ACCESS, 13, 170268-170278. (www)

  @article{ , abstract = {To address the challenge of rapid and precise control in the continuous stirred-tank reactor (CSTR), this paper proposes an iterative learning observer-based predefined-time sliding mode control (SMC) for CSTR with external disturbances and input and output constraints. The iterative learning observer is developed to estimate disturbances. The slide mode control can handle the input and output constraints by a novel sliding mode variable. The predefined-time control ensures that the system states can track their reference signals within the specified time. The stability of the closed-loop system is analyzed and demonstrated using Lyapunov stability theory. The effectiveness and feasibility of the proposed control method are validated through MATLAB/Simulink simulations. Compared to the existing controllers, the proposed controller demonstrates superior performance in both trajectory tracking accuracy and response speed, when the CSTR system was subject to external disturbances and input and output constraints. Under the iterative learning observer-based predefined-time SMC, the convergence time of closed-loop system can be independent of the initial state of the system, which can be designed by tuning the controller parameters.}, archive = {J_ACCESS}, author = {Xianghu Zhang and Xiaoxuan Song and Wenyue Shan and He Zhao and Li-Ping Xin}, doi = {10.1109/ACCESS.2025.3615715}, journal = {IEEE Access}, pages = {170268-170278}, shortjournal = {IEEE Access}, title = {Iterative learning observer-based predefined-time sliding mode control for continuous stirred tank reactor with input and output constraints}, volume = {13}, year = {2025}, }
• (2025). A multi-layered AI-driven cybersecurity architecture: Integrating entropy analytics, fuzzy reasoning, game theory, and multi-agent reinforcement learning for adaptive threat defense. ACCESS, 13, 170235-170257. (www)

  @article{ , abstract = {In the face of increasingly sophisticated cyberattacks, including adaptive adversaries and stealthy anomalies, key features of defense mechanisms should be effective, interpretable, and theoretically rooted. Conventional intrusion detection systems are typically based on a single-paradigm machine learning model which can be effective (because it is optimized for conditions), but fail in generalizability and falling back on an explanation of its prediction. This paper outlines a multi-layered AI-enabled cyber defense framework that integrates entropy analytics, fuzzy inference, game-theoretic defense, and multi-agent reinforcement learning (MARL) inside a closed-loop adaptive architecture. In its simplest form, the novelty of the paper is that, four functional paradigms - uncertainty quantification, interpretability, strategic adversarial thinking, and live policy adaptation - are placed into a single coherent system. The framework operates as sequential and feedback salients - entropy analytics quantify the uncertainty in are states, fuzzy inference end maps the uncertainty into qualitative decision rules, game theory shapes defender - attacker towards equilibrium strategies, and MARL dynamically updates those strategies for convergence and long term adaptation. The empirical work on appropriate benchmark intrusion detection datasets consistently outperformed baseline systems including the DDN, Fed-ID, AG-IDS, DL-FL systems producing a 6-12% increase in detection accuracy, lower false positive rates from non-intrusions, and a faster convergence, with adversarial examples across multiple epochs. Also, practical case studies reveal a level of improved explainability in threat classification and anomaly detection, which equates to practical interpretability for security analysts from the framework. The major contributions of the work are threefold: 1) an integrated multi-layered AI-based cybersecurity framework, 2) theoretical robustness results in bounded adversarial models, and 3) performance and interpretability form the systematic empirical evaluations over multiple datasets.}, archive = {J_ACCESS}, author = {Eram Fatima Siddiqui and Mohd Haleem and Sheikh Fahad Ahmad and Amina Salhi and Abu Taha Zamani and Naushad Varish}, doi = {10.1109/ACCESS.2025.3610526}, journal = {IEEE Access}, pages = {170235-170257}, shortjournal = {IEEE Access}, title = {A multi-layered AI-driven cybersecurity architecture: Integrating entropy analytics, fuzzy reasoning, game theory, and multi-agent reinforcement learning for adaptive threat defense}, volume = {13}, year = {2025}, }
• (2025). Low-similarity client sampling for decentralized federated learning. ACCESS, 13, 170225-170234. (www)

  @article{ , abstract = {Federated Learning (FL) often encounters challenges from statistical heterogeneity caused by non-identical client data distributions. A common approach to mitigate this issue is to cluster clients with similar data and train within each cluster, which reduces inter-client heterogeneity but also introduces communication overhead and redundant updates. While decentralized federated learning has made recent advances, decentralized client sampling for clustered environments remains unexplored, leaving an important gap between clustering strategies and communication-efficient training. To bridge this gap, we propose Low-Similarity Decentralized Client Sampling (LS-DCS), a framework that integrates decentralized decision-making with similarity-based filtering in clustered settings. Instead of relying on a central server, LS-DCS allows each client to independently determine whether to transmit an update by measuring cosine similarity with the previous cluster-level update, ensuring that only non-redundant and high-value updates are communicated. In addition, LS-DCS introduces a reset mechanism that preserves fairness and stability: if the number of active clients in a round falls below a threshold, all clients in the cluster are re-engaged, preventing long-term exclusion and preserving cluster completeness. Experimental results show that LS-DCS achieves faster convergence than prior decentralized sampling methods, even with fewer participating clients per round, while maintaining a strong balance between communication efficiency and training stability. These findings underline the importance of low-similarity sampling and adaptive reset strategies as key enablers of scalable, cluster-aware decentralized federated learning.}, archive = {J_ACCESS}, author = {Yunseok Kang and Jaeyoung Song}, doi = {10.1109/ACCESS.2025.3615459}, journal = {IEEE Access}, pages = {170225-170234}, shortjournal = {IEEE Access}, title = {Low-similarity client sampling for decentralized federated learning}, volume = {13}, year = {2025}, }
• (2025). Data-driven driver training via counterfactual and language-based guidance in racing scenarios. ACCESS, 13, 170181-170199. (www)

  @article{ , abstract = {Novice drivers in automobile racing face significant challenges in improving racing performance while maintaining safety. Traditional one-on-one human coaching faces practical limitations, such as subjectivity, inconsistency, high cost, and limited scalability. Recent alternatives, such as optimal racing line visualizations or predefined skill metrics, often lack personalization and fail to explain why or how drivers should improve. To address these gaps, we propose a fully data-driven training framework that delivers personalized, interpretable, and actionable feedback based on corner-specific performance. Our approach first clusters sequential driving behavior to generate pseudo skill labels in an unsupervised manner. A classification model is then trained on racing-critical features to assess performance levels for each corner. To provide targeted guidance, we introduce a novel counterfactual explanation method based on negative SHapley Additive exPlanations (SHAP) values. This identifies which feature changes would most effectively elevate performance to the expert level. Feedback is then translated into natural language using a customized large language model (LLM), enabling novice drivers to intuitively understand and apply the suggestions. We validate our system through both algorithmic evaluation and human-in-the-loop experiments in a simulated racing environment. Our clustering method effectively groups driver strategies, and the classification model achieves high skill-level prediction accuracy. Counterfactual feedback based on negative SHAP values drives actionable improvements, significantly enhancing both cornering performance and overall lap times. Furthermore, participants internalized the feedback and sustained performance gains even after feedback was removed, demonstrating the system’s strong potential for scalable driver training and broader educational applications.}, archive = {J_ACCESS}, author = {Jonghak Bae and Hyeongwoo Nam and Kunhee Ryu and Jihye Lee and Jinsung Kim and Hokyun Chun and Jongtaek Han and Jongeun Choi}, doi = {10.1109/ACCESS.2025.3615128}, journal = {IEEE Access}, pages = {170181-170199}, shortjournal = {IEEE Access}, title = {Data-driven driver training via counterfactual and language-based guidance in racing scenarios}, volume = {13}, year = {2025}, }
• (2025). Feasibility simulations of dual-layer flat panel detectors for bone mineral density estimation. ACCESS, 13, 170171-170180. (www)

  @article{ , abstract = {Bone mineral density (BMD) measurement plays a crucial role in the diagnosis and management of osteoporosis. The most widely used clinical method is dual-energy x-ray absorptiometry (DXA), where image scanning using fan-beam x-rays requires complex alignment mechanisms and switching of tube voltages and filters for dual-shot x-ray imaging. To simplify the BMD measurement system, a dual-layer flat panel detector (DFD) can be employed to acquire x-ray images with single-shot x-rays without scanning. In this paper, we study the feasibility of a single-shot approach using DFD in measuring BMD based on a second-order polynomial in comparison with conventional DXA scanning. We first synthesize x-ray spectra for different conditions of DFD and next conduct noise sensitivity analysis by adding log-uniform distributed noise to the input log intensities. Condition numbers of the BMD measurement functions and beam-hardening effect according to the soft-tissue thickness are also observed. Through extensive simulations, we observed that the noise sensitivity performance of the single-shot approach based on DFD could be better than the dual-shot approach based on a single flat panel detector if the switching filters are not employed. When the noise variance at the inputs was $10^{-9}$ , the single-shot BMD estimate showed a lower mean square error of $7.99\times 10^{-4}$ than the dual-shot case of $13.8\times 10^{-4}$ . This approach can be effectively utilized as a preliminary screening method prior to DXA scanning in clinical settings.}, archive = {J_ACCESS}, author = {Dayeon Lee and Dong Sik Kim}, doi = {10.1109/ACCESS.2025.3615668}, journal = {IEEE Access}, pages = {170171-170180}, shortjournal = {IEEE Access}, title = {Feasibility simulations of dual-layer flat panel detectors for bone mineral density estimation}, volume = {13}, year = {2025}, }
• (2025). Did:blox: A secure and efficient decentralized identifier method for scientific IoT ecosystems leveraging the bloxberg blockchain. ACCESS, 13, 170161-170170. (www)

  @article{ , abstract = {The proliferation of Internet of Things (IoT) devices in scientific research presents a critical challenge to data integrity and reproducibility. Ensuring the verifiable provenance of data from distributed, autonomous sensors is essential for academic trust. This paper introduces did:blox, a novel decentralized identity (DID) method engineered to create a secure and efficient identity layer for academic IoT data. The method utilizes a hybrid on/off-chain architecture, anchoring immutable cryptographic hashes on the permissioned, consortium-governed Bloxberg blockchain while storing full DID documents in off-chain storage. This approach is specifically designed to address the cost, performance, and governance requirements of the research community. We present a formal specification, system architecture, and a rigorous evaluation of a prototype implementation on the live Bloxberg mainnet. The performance of did:blox is benchmarked against a traditional, fully on-chain baseline did:eth. Empirical results demonstrate that did:blox reduces transaction gas costs for identity creation and updates by approximately 40%, improves throughput for concurrent device onboarding by 20%, and lowers latency for on-chain operations, at the cost of a minor, acceptable increase in resolution latency.}, archive = {J_ACCESS}, author = {Andrej Gono and František Dařena and David Procházka}, doi = {10.1109/ACCESS.2025.3615478}, journal = {IEEE Access}, pages = {170161-170170}, shortjournal = {IEEE Access}, title = {Did:blox: A secure and efficient decentralized identifier method for scientific IoT ecosystems leveraging the bloxberg blockchain}, volume = {13}, year = {2025}, }
• (2025). Tomato TransDeepLab: A robust framework for tomato leaf segmentation, disease severity prediction, and crop loss estimation. ACCESS, 13, 170147-170160. (www)

  @article{ , abstract = {This research aims to reduce crop loss in tomato crops by developing an effective disease detection and segmentation system. To meet this objective, the researchers prepared three annotated real-world datasets consisting of tomato leaf images, including samples with simple and complex backgrounds, and young plants aged two to four weeks. A transformer-based deep learning model, ‘Tomato TransDeepLab’, is proposed for accurately segmenting multiple and minute disease lesions on a single leaf. The model demonstrates high segmentation precision even with complex backgrounds and indistinct lesion boundaries. It achieves a maximum Intersection over Union (IoU) of 89.09% and accuracy of 98.17%. It also recorded the lowest training time of 989 minutes. Comparative analysis with state-of-the-art architectures including U-Net, ResUNet, DeepLabV3, and Tuned DeepLabV3 + across all three datasets confirms its superior performance. Furthermore, the model is employed to assess disease severity and estimate crop loss using a severity scale designed by plant pathologists. This scale links lesion area to severity level and crop loss. Results indicate a crop loss between 1 to 5% for Datasets 1 and 2. The proposed approach offers an automated solution for disease segmentation, severity assessment, and crop loss estimation in tomato crops.}, archive = {J_ACCESS}, author = {Ankita Gangwar and Geeta Rani and Vijaypal Singh Dhaka}, doi = {10.1109/ACCESS.2025.3611307}, journal = {IEEE Access}, pages = {170147-170160}, shortjournal = {IEEE Access}, title = {Tomato TransDeepLab: A robust framework for tomato leaf segmentation, disease severity prediction, and crop loss estimation}, volume = {13}, year = {2025}, }
• (2025). HFT-net: Hybrid fusion transformer network for multi-source breast cancer classification. ACCESS, 13, 170126-170146. (www)

  @article{ , abstract = {Automatic classification of breast cancer histopathological images presents significant challenges due to morphological ambiguities between disease subtypes, significant tissue heterogeneity, and limited availability of high-quality labeled datasets. We propose HFT-Net, a hybrid deep learning model that addresses the limitations of traditional single-architecture approaches in generalizing complex visual patterns. Unlike conventional feature fusion methods, our model employs a multi-head attention mechanism (MHA) to enrich information interaction and learn meaningful feature relationships, creating more discriminative representations. Despite the large data requirement of deep models, transfer learning and fine-tuning techniques enabled high success with few samples, and an efficient learning process was achieved by adapting pre-trained models. HFT-Net, which was developed as a solution to the problem of low generalization capacity of models optimized for a single dataset, aims for balanced performance and consistent results were obtained in three different datasets. As a result of extensive experimental evaluations, the proposed model shows competitive performance with 95.08% accuracy and 0.95 F1-score on the 8-class BreakHis dataset, 92.00% accuracy on the BACH secret test dataset, 92.50% accuracy and 0.92 F1-score on the BACH dataset, and 58.07% accuracy and 0.58 F1-score on the BRACS dataset.}, archive = {J_ACCESS}, author = {Kadir Guzel and Gokhan Bilgin}, doi = {10.1109/ACCESS.2025.3615654}, journal = {IEEE Access}, pages = {170126-170146}, shortjournal = {IEEE Access}, title = {HFT-net: Hybrid fusion transformer network for multi-source breast cancer classification}, volume = {13}, year = {2025}, }
• (2025). A pilot-free estimation method of fading channel for satellite communication based on limited intercept samples. ACCESS, 13, 170116-170125. (www)

  @article{ , abstract = {Satellite radio signal interception and processing systems demonstrate distinct advantages in battlefield situational awareness, spectrum resource management, and non-cooperative target recognition. However, addressing the challenges of channel modeling for non-cooperative emitters (where active probing/calibration is infeasible and training data is scarce), this paper proposes a pilot-free estimation method for time-varying fading channels based on non-uniform angles of arrival. The method characterizes the non-uniform spatial distribution of scatterers using a truncated Gaussian distribution, determines multipath fading parameters through limited empirical sample fitting, and introduces a time-varying update cycle for dynamic tracking. This constructs a channel model tailored to satellite communication environments (sparse scatterers, non-uniform distribution, and rapid time-varying characteristics). Tests confirm that the proposed model achieves high consistency with power spectrum envelopes of actual channel measurements (corr=0.7522, MSE=0.0068, MAE=0.0354). Its key error metrics (MSE/MAE) rival those of neural network-based modeling approaches, while autonomously generating differentiated Doppler spectra that align with empirical data. These results validate that the method effectively characterizes transmission properties of non-cooperative satellite fading channels using only limited intercepted samples.}, archive = {J_ACCESS}, author = {Mengsu Sun and Chundong Qi and Mengtian Qiu and Xinyu Li}, doi = {10.1109/ACCESS.2025.3615305}, journal = {IEEE Access}, pages = {170116-170125}, shortjournal = {IEEE Access}, title = {A pilot-free estimation method of fading channel for satellite communication based on limited intercept samples}, volume = {13}, year = {2025}, }
• (2025). LCRM: A linear-camera dataset for road marking segmentation and damage evaluation. ACCESS, 13, 170106-170115. (www)

  @article{ , abstract = {We introduce the Linear-Camera Road Marking (LCRM) dataset, which is the first to couple segmentation with marking damage evaluation on the same images, and the first captured with a linear (line-scan) camera. LCRM spans Russian cities, covers 12 marking types, and grades instance damage with a 3-level scheme aligned with maintenance thresholds. We release 3,966 labeled images (train/test) and 18,847 unlabeled images. We also benchmark representative models across four tasks (binary and multi-class semantic segmentation, instance segmentation, and damage evaluation) to establish baselines for this new sensing regime.}, archive = {J_ACCESS}, author = {Ilya Makarov and Nikita Sidelnikov and Daniil Storonkin and Eugene Kapustin and Alexander Karandeev and Alexey Maslov and Maksim Golyadkin}, doi = {10.1109/ACCESS.2025.3612491}, journal = {IEEE Access}, pages = {170106-170115}, shortjournal = {IEEE Access}, title = {LCRM: A linear-camera dataset for road marking segmentation and damage evaluation}, volume = {13}, year = {2025}, }
• (2025). AULoRA: Anomaly understanding with low-rank adaptation for zero-shot anomaly detection. ACCESS, 13, 170095-170105. (www)

  @article{ , abstract = {Zero-Shot Anomaly Detection (ZSAD) aims to identify anomalies in unseen categories or scenarios. Recently, Vision-Language Models (VLMs), most notably CLIP, have been utilized to enhance anomaly detection performance. However, CLIP struggles to capture local anomalies, which has led to the development of additional modules that significantly increase model complexity and computational overhead. To address this challenge, we propose AULoRA, a novel approach that enhances anomaly understanding by integrating Low Rank Adaptation (LoRA) into CLIP’s visual encoder and efficiently injecting visual context into the textual representation. While preserving CLIP’s general visual knowledge, we utilize Singular Value Decomposition (SVD) to selectively fine-tune only the most relevant singular components, enabling precise identification of semantic anomalies. Nevertheless, anomaly detection often requires capturing highly diverse and category-specific characteristics, which simple text prompts alone struggle to represent adequately. To overcome this, we adapt textual representations based on the visual context extracted from input images, allowing the model to achieve category-aware and anomaly sensitive alignment. AULoRA maintains the original architecture and inference efficiency of CLIP, while achieving state-of-the-art performance on both image-level and pixel-level anomaly detection benchmarks across diverse industrial datasets. Code is available at https://github.com/SeungHyun99/AULoRA}, archive = {J_ACCESS}, author = {Seunghyun Oh and Seongsu Lee and Seunghye Chae and Youngmin Ro}, doi = {10.1109/ACCESS.2025.3614713}, journal = {IEEE Access}, pages = {170095-170105}, shortjournal = {IEEE Access}, title = {AULoRA: Anomaly understanding with low-rank adaptation for zero-shot anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Genetic algorithm-based optimization framework for offshore wind farm layout design. ACCESS, 13, 170081-170094. (www)

  @article{ , abstract = {Offshore wind farms have emerged as a crucial component of renewable energy generation, offering higher energy production rates due to stronger and more consistent wind conditions. However, these advantages come with significant installation and maintenance costs, necessitating comprehensive optimization analyses of wind farms projects to ensure economic feasibility and maximize energy output. One of the most significant challenges in this context is the effective placement of wind turbines within an offshore wind farm, considering the historic wind intensity and direction of candidate areas, along with the impact of wake effects. This study presents a framework that integrates a genetic algorithm to optimize wind farms layouts with a simulation tool for evaluating configurations of wind farm projects. In this paper, we describe the optimization module implemented with genetic algorithms. Our approach aims to facilitate layouts optimization analysis for wind farms during the project phase, focusing on maximizing overall energy output by minimizing wake interference among turbines. The proposed approach demonstrates high modularity and achieves competitive results compared to established benchmarks, highlighting the effectiveness of our framework for optimization analysis in wind farm projects.}, archive = {J_ACCESS}, author = {Italo Firmino Da Silva and Telles Brunelli Lazzarin and Lenon Schmitz and Alison R. Panisson}, doi = {10.1109/ACCESS.2025.3614516}, journal = {IEEE Access}, pages = {170081-170094}, shortjournal = {IEEE Access}, title = {Genetic algorithm-based optimization framework for offshore wind farm layout design}, volume = {13}, year = {2025}, }
• (2025). Context-aware retrieval augmented generation using similarity validation to handle context inconsistencies in large language models. ACCESS, 13, 170065-170080. (www)

  @article{ , abstract = {Large Language Models (LLMs) have transformed natural language processing by offering human-like responses. However, issues such as incorrect information (hallucinations) and errors in specific subject areas remain, especially in Retrieval Augmented Generation (RAG) systems. This study introduces a Context-Aware Retrieval Augmented Generation (CA-RAG), which simplifies the process by removing the need to separately find relevant chunks of a document. Instead, after dividing the document into chunks, both the question and chunks are directly given to the LLMs to produce answers. The method then focuses on improving the answers through additional post-processing, aiming to reduce errors and make the answers more relevant to the question. To evaluate the effectiveness of CA-RAG, two scenarios have been designed. Scenario 1 involved experiments using widely adopted and recognized benchmark datasets, such as TriviaQA, Natural Questions, AmbigQA and Stanford Question Answering Dataset (SQuAD). In this context, the proposed CA-RAG method, combined with similarity measure (either cosine similarity or dot product) between generated answers and chunks, achieved the highest F1-score in TriviaQA and AmbigQA. Scenario 2 tested CA-RAG robustness using a custom dataset comprising domain-specific and unstructured documents. Results from automated and manual evaluations revealed that CA-RAG with post-processing consistently outperformed traditional RAG. These findings highlight the critical role of post-processing techniques and similarity measures in improving the accuracy and relevance of generated answers. CA-RAG demonstrates strong potential as a reliable and versatile solution for Retrieval Augmented Generation tasks across diverse datasets and domains.}, archive = {J_ACCESS}, author = {Enrico Collini and Felix Indra Kurniadi and Paolo Nesi and Gianni Pantaleo}, doi = {10.1109/ACCESS.2025.3614553}, journal = {IEEE Access}, pages = {170065-170080}, shortjournal = {IEEE Access}, title = {Context-aware retrieval augmented generation using similarity validation to handle context inconsistencies in large language models}, volume = {13}, year = {2025}, }
• (2025). Back and forward incremental learning through knowledge distillation for object detection unmanned aerial vehicles. ACCESS, 13, 170035-170046. (www)

  @article{ , abstract = {Incremental object detection is essential for real-world applications where the models must continuously learn new object categories without forgetting previously acquired knowledge. However, the conventional training methods often suffer from catastrophic forgetting, leading to performance degradation on early tasks when learning new ones. To address this, we proposed a novel framework that integrates an attention-based feature enhancement module within a knowledge distillation-based incremental learning pipeline. The AFEM combines self-attention and cross-attention mechanisms to strengthen feature representations by enhancing spatial localization and classification across tasks. Our approach employs a teacher-student architecture, where the teacher model preserves prior knowledge while the student model learns new classes through multi-level distillation losses applied to features, region proposals, and final predictions. Experiments conducted on the Pascal VOC dataset demonstrate the effectiveness of our method. Our method achieves 60.802% AP for the base class. It maintains 56.969% AP after one incremental phase, with a forgetting rate of just 3.833% using Huber loss and achieving improved detection accuracy with MSE loss. These results indicate that our method effectively retains performance on the old classes while adapting to new ones. The code is available at https://github.com/shahrul-amin/Incremental-Object-Detection.}, archive = {J_ACCESS}, author = {Qazi Mazhar Ul Haq and Shahrul Amin and Nandhagopal Chandrasekaran and Chen-Hao Liao and Yi-Jheng Huang}, doi = {10.1109/ACCESS.2025.3613768}, journal = {IEEE Access}, pages = {170035-170046}, shortjournal = {IEEE Access}, title = {Back and forward incremental learning through knowledge distillation for object detection unmanned aerial vehicles}, volume = {13}, year = {2025}, }
• (2025). Speed estimation in induction motor drive using long short-term memory-sensorless model reference adaptive system (LSTM-SMRAS) control. ACCESS, 13, 170017-170034. (www)

  @article{ , abstract = {Accurate rotor speed estimation is significant for achieving high-performance control in sensorless induction motor drives, especially under dynamic and low-speed operating conditions. This research proposes a new speed estimation method based on a Sensorless Model Reference Adaptive System (SMRAS) enhanced with Long Short-Term Memory (LSTM) neural networks. By integrating deep learning capabilities into the traditional MRAS approach, the proposed LSTM-SMRAS model significantly improves the robustness and accuracy of rotor speed prediction. Trained on historical motor performance data, the LSTM model captures nonlinear behaviors and transient dynamics more effectively than conventional estimators. The proposed method has been validated through extensive MATLAB/Simulink simulations across various operating scenarios, including step and ramp inputs at both high (1400 RPM) and low (200 RPM) speeds. Compared to existing MRAS techniques such as ANN-MRAS, FLC-MRAS, SMC-MRAS, and ANFIS-MRAS, the LSTM-MRAS method achieved the shortest delay time (0.47 s), rise time (0.85 s), and settling time (2.02 s) with a minimal peak overshoot of 0.21% at 1400 RPM. At 200 RPM, it maintained superior performance with a peak overshoot of only 3.70%. Under ramp input conditions, LSTM-MRAS exhibited the lowest steady-state error of 0.98%, significantly outperforming other methods. Furthermore, hardware implementation using a TMS320F28335 DSP controller confirmed the simulation results, with improved response time and reduced torque ripple. The FOC-SVPWM technique yielded a voltage total harmonic distortion (THD) of 2.2% and a current THD of 1.6%, meeting the IEEE 519 standards. These findings highlight the LSTM-MRAS approach as a reliable, sensorless control solution for precision-critical applications such as electric vehicles, robotics, and renewable energy systems.}, archive = {J_ACCESS}, author = {Gopal Lal Jat and Jagdish Kumar and Shiv Narayan}, doi = {10.1109/ACCESS.2025.3614728}, journal = {IEEE Access}, pages = {170017-170034}, shortjournal = {IEEE Access}, title = {Speed estimation in induction motor drive using long short-term memory-sensorless model reference adaptive system (LSTM-SMRAS) control}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of multilevel inverter topologies and control strategies for grid-connected photovoltaic battery energy storage systems integrating active power filter. ACCESS, 13, 169989-170016. (www)

  @article{ , abstract = {The growing integration of renewable energy sources (RESs), especially solar photovoltaic (PV) systems, has intensified the demand for high-quality and stable grid connections. This study reviewed shunt active power filter (SAPF) configurations and multilevel converters (MLCs), with a focus on improving power quality, scalability, and fault diagnostics in large-scale PV and battery energy storage systems (BESS) applications. A comprehensive methodology was employed to review and analyze multilevel SAPF topologies, inverter control strategies, modulation techniques, and hybrid semiconductor devices. The research discussed integration challenges, including current harmonics, voltage instability, and fault localization in MLCs using model-based and data-driven strategies. Review findings highlight the superior performance of five-level MLC-based SAPFs in reducing harmonic distortion, improving waveform quality, and enhancing control flexibility. Additionally, hybrid Si/SiC switch strategies and advanced modulation techniques like LS-PWM and predictive control demonstrate improved efficiency and fault tolerance in grid-connected applications. The study recommends further development of intelligent control schemes, modular SAPF design, and integration with energy storage for robust and adaptive grid systems. Advancements in semiconductor technologies and AI-based diagnostics are key to future reliability and performance enhancements.}, archive = {J_ACCESS}, author = {Julius Omorodion Uwagboe and Akshay Kumar Saha}, doi = {10.1109/ACCESS.2025.3615104}, journal = {IEEE Access}, pages = {169989-170016}, shortjournal = {IEEE Access}, title = {A comprehensive review of multilevel inverter topologies and control strategies for grid-connected photovoltaic battery energy storage systems integrating active power filter}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence as a tool for predicting the aesthetic appeal of abstract art: A deep learning approach. ACCESS, 13, 169979-169988. (www)

  @article{ , abstract = {This study proposes a novel deep learning framework for predicting the aesthetic appeal of abstract artworks, bridging the gap between subjective visual judgment and objective computational modeling. We introduce a Vision Transformer (ViT) architecture enhanced by contrastive learning, which jointly captures absolute and relative aesthetic cues. The model is trained on a hybrid dataset comprising over 5,000 abstract artworks, enriched with aesthetic scores derived from a fusion of crowdsourced evaluations, expert ratings, and predictive proxies. Optimization is performed using a Lookahead-Rectified Adam (RAdam) strategy to ensure fast convergence and generalization. Empirical results demonstrate that the proposed ViT model outperforms traditional CNN-based models such as ResNet, EfficientNet, and DenseNet by margins of up to 20% in $R^{2}$ score and 15% in MAE. Key performance metrics include an F1 score of 0.87, a Mean Absolute Error (MAE) of 10.0, and an $R^{2}$ score of 0.92. Additional analysis using scatter plots, Taylor diagrams, and violin plots confirms strong alignment with human judgment. The originality of this work lies in its mathematically grounded architecture for abstract aesthetic evaluation, its integration of contrastive loss for relational understanding, and its interpretability pipeline using attention-based visualization. The proposed model has applications in generative design, art recommendation, and creative AI interfaces.}, archive = {J_ACCESS}, author = {Haifeng Xie and Yuhong Dai}, doi = {10.1109/ACCESS.2025.3610240}, journal = {IEEE Access}, pages = {169979-169988}, shortjournal = {IEEE Access}, title = {Artificial intelligence as a tool for predicting the aesthetic appeal of abstract art: A deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Predicting economic quads through asset returns using ensemble machine learning. ACCESS, 13, 169968-169978. (www)

  @article{ , abstract = {This study explores the predictive capacity of financial asset returns in forecasting macroeconomic regime shifts, specifically the transition among Inflation, Expansion, Stagflation, and Deflation (IESD) quadrants. While traditional economic indicators such as GDP, CPI, and interest rates are commonly used to define these regimes, their delayed release limits real-time applicability. Motivated by the forward-looking nature of markets, we develop two Machine Learning (ML) models: a Neural Network and an Ensemble Learning classifier combining Random Forest and Gradient Boosting. These models are trained on a ten-year dataset comprising 22 financial instruments, including equity sector Exchange-Traded Fund (ETFs), bonds, commodities, and volatility indices. To enhance predictive robustness, the study incorporates extensive feature engineering, such as logarithmic returns, rolling volatilities, and moving averages, alongside dimensionality reduction via Principal Component Analysis (PCA). Evaluation is conducted through K-Fold cross-validation and performance metrics including accuracy, F1-score, and ROC AUC. Results indicate that the Neural Network achieves an accuracy of 57% on first predictions, rising to 78% when considering the top-two predicted quads. The Ensemble model outperforms with a 96% accuracy, exhibiting consistent classification across all quadrants. The findings highlight the practical potential of ML models to identify economic cycles using asset return behavior. By treating asset returns as early-warning indicators, the study bridges the gap between market signals and delayed macroeconomic statistics, contributing a novel approach to economic state prediction.}, archive = {J_ACCESS}, author = {Abbass Nasser and Jad Yammine and Joe Harouny and Danielle Khalife}, doi = {10.1109/ACCESS.2025.3610399}, journal = {IEEE Access}, pages = {169968-169978}, shortjournal = {IEEE Access}, title = {Predicting economic quads through asset returns using ensemble machine learning}, volume = {13}, year = {2025}, }
• (2025). Emulating reactive workloads for cyber-human systems: A data-driven methodology. ACCESS, 13, 169953-169967. (www)

  @article{ , abstract = {Wearable Cognitive Assistance (WCA) has the potential to revolutionize daily life with real-time, context-aware guidance, but current performance models often overlook the dynamic nature of human-system interactions, leading to inefficiencies in resource allocation and system responsiveness. In this work, we investigate the implications of the correlated nature of human-system interactions through the development of a novel data-driven methodology for the modeling of task execution times in WCA. We apply this methodology to a WCA application previously shown to exhibit second-order effects between system responsiveness and human performance. Our resulting model presents an improvement in up to 30% with respect to traditional first-order approaches, highlighting the importance of capturing complex behavioral dynamics. These findings raise important questions about the design and optimization of WCA systems and the tools that target them: What are the implications of this correlation for resource allocation and system design in real-world deployments? How can our methodology inform the development of more accurate and adaptive models for WCA applications? By exploring these questions, this research aims to contribute to the development of more efficient and effective WCA systems.}, archive = {J_ACCESS}, author = {Manuel Olguín Muñoz and Roberta Klatzky and Mahadev Satyanarayanan and James Gross}, doi = {10.1109/ACCESS.2025.3614639}, journal = {IEEE Access}, pages = {169953-169967}, shortjournal = {IEEE Access}, title = {Emulating reactive workloads for cyber-human systems: A data-driven methodology}, volume = {13}, year = {2025}, }
• (2025). Business-driven data model for consistent software robot monitoring in 24/7 environments. ACCESS, 13, 169929-169952. (www)

  @article{ , abstract = {Regular monitoring of software robots includes their health and performance monitoring so that they can run effectively and efficiently. In large-scale robotic process automation (RPA), managing quantities of tasks executed by software robots that run 24/7 is critical. It calls for an end-to-end solution comprising planned schedules, service level agreement (SLA) compliance, server capacities, and system outage avoidance strategies. This paper proposes a new business-driven data model for RPA monitoring to enhance the evaluation and maintenance of software robots in 24/7 environments. The literature review confirmed a lack of flexible data models for RPA monitoring with a business orientation, particularly in big environments. The business-driven data model contains three domains, 13 enumerations, seven entities, and 116 attributes for enabling seamless robot task execution. The quality is measured against five established criteria related to data models. Moreover, the Center of Excellence (CoE) applies the business-driven data model in a Shared Service Center (SSC). The CoE manages over 170 robotized tasks, ensuring efficient operations. This research increases the monitoring and assessment of Robotic Process Automation (RPA) to work effectively and efficiently with software robots. By embracing an enterprise-focused data model, organizations can align RPA initiatives more effectively with business objectives, enhance system visibility, and make more informed decisions.}, archive = {J_ACCESS}, author = {Eija Hartikainen and Virpi Hotti and Markku Tukiainen}, doi = {10.1109/ACCESS.2025.3611303}, journal = {IEEE Access}, pages = {169929-169952}, shortjournal = {IEEE Access}, title = {Business-driven data model for consistent software robot monitoring in 24/7 environments}, volume = {13}, year = {2025}, }
• (2025). Intelligent anomaly detection and localization with crowd density estimation for improved public safety. ACCESS, 13, 169912-169928. (www)

  @article{ , abstract = {The rapid growth of metropolitan populations and large public gatherings has increased the need for intelligent surveillance systems capable of ensuring public safety. This study proposes a novel framework, Intelligent Anomaly Detection and Localization with Crowd Density Estimation (IADL-CDE), which jointly addresses crowd density classification and anomaly detection in real-time. Leveraging the ShanghaiTech dataset for crowd density estimation and the UCSD Pedestrian Database for anomaly detection, the framework integrates a Robust Maximum Correntropy Kalman Filter (RMCKF) for effective image pre-processing, followed by a Self-Modulating Convolutional Neural Network (SMCNN) that accurately classifies crowd density levels into sparse, medium, and dense categories. Subsequently, a Pixel-Associated Auto Encoder (PAAE) is employed to detect and localize anomalies such as cycling, skateboarding, and unauthorized vehicles in pedestrian environments. Experimental results demonstrate that the proposed IADL-CDE model achieves significant improvements in accuracy, precision, recall, F1-Score and Cohen’s Kappa Coefficient compared to the baseline methods. These findings highlight the model’s effectiveness and robustness in enhancing public safety through intelligent crowd monitoring and anomaly detection.}, archive = {J_ACCESS}, author = {Deevesh Chaudhary and Sunil Kumar and Vijaypal Singh Dhaka}, doi = {10.1109/ACCESS.2025.3614146}, journal = {IEEE Access}, pages = {169912-169928}, shortjournal = {IEEE Access}, title = {Intelligent anomaly detection and localization with crowd density estimation for improved public safety}, volume = {13}, year = {2025}, }
• (2025). AIF_Pix2Pix: An ocean front airborne infrared image generation model. ACCESS, 13, 169899-169911. (www)

  @article{ , abstract = {We present an airborne infrared front Pix2Pix (AIF_Pix2Pix) model, which generates airborne infrared images of ocean fronts via inputs processed by a front region mask and region brightness perturbations. The improvements applied to this model include reducing the weight of the network, modifying its loss function, and adding data augmentation on the basis of the Pix2Pix framework. With a parameter reduction of 88.6%, the AIF_Pix2Pix model demonstrates superior performance to that of the existing approaches: the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) increase by 2.6% and 6.2%, respectively, and the root mean squared error (RMSE) decreases by 31.4%. In particular, the generated ocean fronts are highly consistent with real data in terms of their positions and intensities, with a reduction of 32.8% in the standard deviation of the gradient differences. Additionally, we provide the effectiveness results generated by AIF_Pix2Pix from actual airborne infrared imagery.}, archive = {J_ACCESS}, author = {Liming Yuan and Shuai Shan and Zonghao Chi and Kaiguo Fan and Zihao Wang and Mengyue Zhang and Yu Zhang and Biao Chen}, doi = {10.1109/ACCESS.2025.3610469}, journal = {IEEE Access}, pages = {169899-169911}, shortjournal = {IEEE Access}, title = {AIF_Pix2Pix: An ocean front airborne infrared image generation model}, volume = {13}, year = {2025}, }
• (2025). ISCC: Intelligent semantic caching and control for NDN-enabled industrial IoT networks. ACCESS, 13, 169881-169898. (www)

  @article{ , abstract = {Effective content caching plays a crucial role in meeting the latency and reliability requirements of Industrial Internet of Things (IIoT) networks. However, existing caching mechanisms in NDN-based IIoT networks often exhibit problems in adaptability, semantic awareness, and structural efficiency, resulting in limited performance in dynamic and resource-constrained environments. This paper introduces the Intelligent Semantic-Aware Caching and Control framework (ISCC), which integrates Cuckoo Hashing with Reinforcement Learning (RL) to enhance content replacement decisions in IIoT networks based on Named Data Networking (NDN). The framework presents a semantic scoring model that integrates content domain, source reliability, and criticality to enhance caching prioritisation. A reinforcement learning (RL) agent utilising Q-learning dynamically chooses actions by using unified caching scores that take into account frequency, time, and semantic fidelity. The results of the simulation indicate that ISCC outperforms conventional strategies, such as FIFO, LRU, and LRFU, in terms of cache hit ratio, semantic fidelity, and latency, achieving up to 23% fewer replacements and a 16% improvement in preserving semantic content. The results show ISCC’s capability to facilitate intelligent and adaptive caching in memory-constrained IIoT environments.}, archive = {J_ACCESS}, author = {Zupash Awais and Muzammil Hussain and Ayman Elshenawy and Ahmad Arsalan and Muhammad Anwar and Muhammad Asif Habib and Sohail Jabbar and Mudassar Ahmad}, doi = {10.1109/ACCESS.2025.3614984}, journal = {IEEE Access}, pages = {169881-169898}, shortjournal = {IEEE Access}, title = {ISCC: Intelligent semantic caching and control for NDN-enabled industrial IoT networks}, volume = {13}, year = {2025}, }
• (2025). Research on personalized recommendation based on matrix factorization, clustering, and deep reinforcement learning. ACCESS, 13, 169866-169880. (www)

  @article{ , abstract = {To address the challenges of data sparsity, cold start, and insufficient dynamic adaptability in traditional recommendation systems, this study proposes a personalized recommendation model named CDRL-MF, which integrates Matrix Factorization, multi-view clustering, and Deep Reinforcement Learning. The method achieves performance improvement through a three-stage collaborative optimization process: First, a dual-channel architecture employing Singular Value Decomposition and an Artificial Neural Network generates high-quality user and item embeddings. Second, multi-view K-means clustering is introduced to construct precise user interest clusters by synthesizing user rating patterns, statistical attributes, and content features. Finally, a Cluster-Guided Deep Reinforcement Recommendation framework is designed, where a DDPG-based agent integrates user states, item cluster features, and real-time feedback to achieve continuous dynamic optimization of recommendation policies. Experimental results on the MovieLens 1M dataset demonstrate that the CDRL-MF model significantly outperforms multiple baseline models across key evaluation metrics, including rating prediction (MAE, RMSE) and Top-N recommendation (Precision, Recall, F1, NDCG). Furthermore, the model exhibits excellent balancing capabilities in recommendation diversity, novelty, and user group fairness. By incorporating differential privacy and federated learning mechanisms, it maintains acceptable performance trade-offs while ensuring user privacy protection. Additional experiments on large-scale datasets such as Amazon Reviews and Netflix Prize further validate its robust generalization capability and practicality.}, archive = {J_ACCESS}, author = {Yuan Jiang and Jinshan Zhang and Lishan Qiao}, doi = {10.1109/ACCESS.2025.3614675}, journal = {IEEE Access}, pages = {169866-169880}, shortjournal = {IEEE Access}, title = {Research on personalized recommendation based on matrix factorization, clustering, and deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Clip, copy, and share: On-the-fly gaussian splatting-based virtual replicas for remote XR collaboration. ACCESS, 13, 169852-169865. (www)

  @article{ , abstract = {Effective remote collaboration in eXtended Reality (XR) requires realistic and interactive 3D spaces, but existing systems are often hindered by complex setups, such as specialized sensors or pre-made 3D models, limiting their accessibility. To address these limitations, this study proposes a novel remote XR collaboration system that leverages 3D Gaussian Splatting (GS) to reconstruct high-fidelity environments on-the-fly during collaboration, using only a standard RGB camera on a commercial XR device. The core of our system is an interaction technique we term ”Interactive Gaussian Clipping,” which enables users to instantly clip manipulable, photorealistic ”Gaussian Replicas” from any part of the newly generated scene. Then, the Gaussian Replicas are copied and shared between remote collaborators instantly. This approach eliminates the need for prior 3D modeling and specialized hardware, significantly lowering the barrier to remote collaboration. We validated the proposed approach through a within-subject user study (N=28) that compared the effectiveness of our Gaussian Replica (GR) method against a conventional 3D sketching (3DS) baseline in a collaborative assembly task. The results show that the GR method led to significantly faster task completion, a lower cognitive load, and a higher usability for local users. Furthermore, a majority of the participants (93%) preferred the GR method. Our work demonstrates a practical and accessible direction towards more effective and immersive remote collaboration, highlighting the potential of user-driven GS interaction in HCI.}, archive = {J_ACCESS}, author = {Seunghyeon Yu and Jae Yeol Lee}, doi = {10.1109/ACCESS.2025.3614220}, journal = {IEEE Access}, pages = {169852-169865}, shortjournal = {IEEE Access}, title = {Clip, copy, and share: On-the-fly gaussian splatting-based virtual replicas for remote XR collaboration}, volume = {13}, year = {2025}, }
• (2025). Energy-aware scientific real-time DAG scheduling using slack reclamation in heterogeneous DVFS cloud datacenters. ACCESS, 13, 169825-169840. (www)

  @article{ , abstract = {Provision of a high level of Quality of Services (QOS) to end users in a cost-effective way with minimum power consumption and minimum Service Level Agreement (SLA) violation is one of the main challenges facing cloud service providers. Accordingly, this paper presents an energy-efficient scheduling strategy in SLA-Aware and cost-effective manner for real-time scheduling of parallel tasks modeled as Directed Acyclic Graphs (DAG) in cloud environment. The proposed approach utilizes Dynamic Voltage and Frequency Scaling (DVFS) capability which has been incorporated into recent commodity multi-core processors to fill the gaps within schedule. The aim is to develop an energy-aware strategy which, besides yielding accurate results, maintains the high quality of the delivered services in a standard and financially rational manner. The proposed scheduling algorithm is compared with other basic strategies in terms of different service quality requirements. The performed experiments demonstrate that the proposed approach outperforms existing methods and can be vowed as a promising scheduling strategy. Thirdly, a novel algorithm to find the best combination of frequencies to result the optimal energy is presented. The proposed algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 3000 randomly generated task graphs, and 600 task graphs for two popular applications (Gauss–Jordan and Lower and Upper (LU) decomposition). The results show the superiority of the proposed algorithm in comparison with other techniques.}, archive = {J_ACCESS}, author = {Ruhullah Aliyan Nejadi and Homayun Motameni and Behnam Barzegar and Ebrahim Akbari and Ali Abbaszadeh Sori}, doi = {10.1109/ACCESS.2025.3614754}, journal = {IEEE Access}, pages = {169825-169840}, shortjournal = {IEEE Access}, title = {Energy-aware scientific real-time DAG scheduling using slack reclamation in heterogeneous DVFS cloud datacenters}, volume = {13}, year = {2025}, }
• (2025). Optimizing frequency switching pattern to reduce asynchronization effect in MCPD ranging systems. ACCESS, 13, 169791-169811. (www)

  @article{ , abstract = {Accurate distance estimation in two-way multi-carrier phase difference (MCPD) ranging systems is degraded by carrier frequency offset (CFO) and timing discrepancies caused by asynchronized clocks between nodes. In previous work, carrier frequencies were typically utilized in ascending order during the MCPD process, a method referred to as sequential switching (SS) frequency switching pattern (FSP). Under asynchronous conditions, the SS FSP induces a linear phase trend across the frequency spectrum, resulting in systematic biasing. Existing works address this problem by estimating and compensating for the phase shifts caused by the asynchronization, but introduce additional computational complexity, latency, and power consumption. In this paper, we present a new signal model accounting for the relevant hardware imperfections, characterizing the effects of asynchronization on the phase measurements, and highlighting the limitations of the SS FSP. Building on this analysis, we propose rearranging carrier frequencies in the FSP and introduce a set of novel FSPs that eliminate the linear phase trend across the frequency spectrum and reduce the estimation error without requiring estimation or correction procedure. The results of the simulation study show that the proposed FSPs outperform the SS FSP under different oscillator mismatches achieving sub-centimeter systematic error at high signal-to-noise ratios (SNR), with performance gain decreasing with decreasing SNR and performance variability occurring with different numbers of carriers. Real-world experimental validation confirms a strong agreement between simulated and measured channel impulse responses and that the proposed FSPs produce more robust distance estimates to CFO variations than the SS FSP.}, archive = {J_ACCESS}, author = {Aleš Simončič and Grega Morano and Andrej Hrovat and Tomaž Javornik}, doi = {10.1109/ACCESS.2025.3614753}, journal = {IEEE Access}, pages = {169791-169811}, shortjournal = {IEEE Access}, title = {Optimizing frequency switching pattern to reduce asynchronization effect in MCPD ranging systems}, volume = {13}, year = {2025}, }
• (2025). Design, fabrication, and validation of an integrated antenna for autonomous train control system: Supporting LTE-r, 5G, GPS, and V2X. ACCESS, 13, 169779-169790. (www)

  @article{ , abstract = {Autonomous Train Control Systems (ATCS) require highly reliable and low-latency wireless communication to support real-time decision-making and train coordination. This paper presents the design, fabrication, and validation of an integrated multi-band antenna system optimized for ATCS applications. The proposed antenna supports LTE-R, 5G sub-6 GHz, private 5G (e-Um), V2X, and GPS bands within a compact structure, enabling simultaneous connectivity to diverse wireless systems. The base-station antenna module utilizes miniaturized ultra-wideband monopole elements to achieve $4 \times 4$ MIMO operation with omnidirectional coverage, while the V2X module employs a $2 \times 8$ directional patch array to ensure high-gain train-to-train communication. A commercial GPS module with circular polarization is also integrated for precise positioning and synchronization. The antenna system was fabricated and tested through full-wave simulation, lab measurements, and full-scale field trials. Measured results demonstrate stable operation across all frequency bands, high isolation, and low envelope correlation coefficients, validating the system’s suitability for deployment in next-generation autonomous railway networks.}, archive = {J_ACCESS}, author = {Dong-Jin Lee and Youngju Kim and Rag-Gyo Jeong}, doi = {10.1109/ACCESS.2025.3611000}, journal = {IEEE Access}, pages = {169779-169790}, shortjournal = {IEEE Access}, title = {Design, fabrication, and validation of an integrated antenna for autonomous train control system: Supporting LTE-r, 5G, GPS, and V2X}, volume = {13}, year = {2025}, }
• (2025). A novel data augmentation framework for arabic multi-label text classification using AraBART, AraGPT2, and borderline-SMOTE. ACCESS, 13, 169769-169778. (www)

  @article{ , abstract = {Data Augmentation (DA) techniques present solutions for Natural Language Processing (NLP) to address class imbalance and data scarcity. The current solutions for class imbalance, either random oversampling or random undersampling techniques, suffer from several issues. For instance, oversampling leads to overfitting due to replication, whilst under-sampling leads to loss of information due to removals. Meanwhile, traditional DA techniques, including paraphrasing, rule-based, or noising approaches, require strong lexicons. These techniques are also either time-consuming or introduce noise, resulting in incorrect syntactical and semantic contexts. Hence, this paper aims to propose a novel DA framework for Arabic news articles to address the prevailing challenges in Arabic Multi-Label Text Classification (AMLTC). The proposed framework consists of three phases: abstractive summarization using the Arabic Bidirectional and Auto-Regressive Transformer (AraBART) model to introduce new features, data generation with Arabic Generative Pre-trained Transformer (AraGPT2) to create diverse and contextual texts, and data balancing using borderline Synthetic Minority Over-Sampling Technique (BorderlineSMOTE) to achieve an optimal balance. Each phase was evaluated to ensure the quality of the augmented data. Furthermore, a Bidirectional Long Short-Term Memory (BiLSTM) model was used to assess the performance of the augmented dataset (augDS) on a multi-label Arabic RTN news dataset. The results demonstrated that the proposed framework effectively addressed the class imbalance problem by preserving data integrity and significantly improving Multi-Label Text Classification (MLTC) performance compared to the non-augDS, as confirmed by independent and paired t-tests. Specifically, the F1-score increased from 0.54 on the original dataset to 0.90 after augmentation. Overall, this study demonstrates that the proposed framework successfully addresses the issues in Arabic datasets by generating diverse, novel augmented data. Additionally, it enhanced MLTC performance, showcasing its effectiveness.}, archive = {J_ACCESS}, author = {Samia F. Abdhood and Nazlia Omar and Sabrina Tiun}, doi = {10.1109/ACCESS.2025.3609462}, journal = {IEEE Access}, pages = {169769-169778}, shortjournal = {IEEE Access}, title = {A novel data augmentation framework for arabic multi-label text classification using AraBART, AraGPT2, and borderline-SMOTE}, volume = {13}, year = {2025}, }
• (2025). LiquidGAN: Integrating liquid neural networks and neural ODEs for high-fidelity thermal image synthesis under data scarcity. ACCESS, 13, 169758-169768. (www)

  @article{ , abstract = {The growing importance of thermal imaging in a variety of fields, including medical diagnostics, security, and environmental monitoring, emphasizes the critical need for efficient deep learning models capable of analysing thermal data. The training of these algorithms is much limited by the absence of suitably annotated thermal imaging datasets. We propose LiquidGAN, a novel Generative Adversarial Network (GAN) architecture which integrates the Neural Ordinary Differential Equations (ODEs) and Liquid Neural Networks (LNNs) to address the scarcity of annotated thermal imaging datasets. LiquidGAN employs a fixed-step Runge-Kutta (RK4) solver to refine the latent representations, enabling continuous image transformations through an encoder-ODE-decoder generator. The LNN-based discriminator adaptively models the subtle, and the dynamic temperature features, hence improving the ability to distinguish the real images from synthetic images. By integrating ODE-driven liquid dynamics across both the generator and discriminator, LiquidGAN effectively captures the dynamic temperature variations while stabilizing adversarial training under the limited data conditions. Moreover, Automatic Mixed Precision (AMP) and gradient scaling are used in our work to further improve the computational efficiency. Experimental results on multiple thermal imaging datasets, including those related to surveillance, healthcare, and precision agriculture, suggest that LiquidGAN offers a promising direction for generating high-fidelity synthetic thermal images under data-constrained conditions.}, archive = {J_ACCESS}, author = {S. Sarath and Jyothisha J. Nair}, doi = {10.1109/ACCESS.2025.3612038}, journal = {IEEE Access}, pages = {169758-169768}, shortjournal = {IEEE Access}, title = {LiquidGAN: Integrating liquid neural networks and neural ODEs for high-fidelity thermal image synthesis under data scarcity}, volume = {13}, year = {2025}, }
• (2025). Toward sustainable and clean energy futures: A techno-economic review of solar PV systems, challenges, and opportunities. ACCESS, 13, 169720-169757. (www)

  @article{ , abstract = {The integration of solar photovoltaic (PV) systems has gained significant attention from the global community, aiming to reduce dependency on fossil fuels and combat climate change. This paper provides a comprehensive review of the latest technical advancements and economic analyses of solar photovoltaic (PV) systems across various scales and applications, encompassing both grid-connected and off-grid configurations. The cost-effectiveness of important innovations, like enhanced inverter technologies, high-efficiency cell designs, and energy storage system integration, is summarized. Across a range of climatic and regional contexts, the review critically assesses economic parameters, including financial feasibility, the levelized cost of energy (LCOE), payback period, and return on investment (ROI). Along with examining intermittency issues, high startup costs, and new solutions, it also reviews the significant technical and financial challenges associated with voltage and frequency regulation in power systems with high PV penetration. To comprehend their role in promoting solar PV deployment worldwide, the environmental effects and policy framework, including government incentives and innovative financial models, are examined. Different case studies from both developing and developed countries are included to highlight the intersection among technologies, economics, and policies. This review identifies critical gaps in solar PV integration and provides comprehensive guidelines for researchers, investors, and policymakers with the ultimate goal of promoting clean energy systems to achieve sustainable development goals.}, archive = {J_ACCESS}, author = {Riad Mollik Babu and Md Shafiul Alam and Asif Islam and Md. Kamrul Islam}, doi = {10.1109/ACCESS.2025.3614771}, journal = {IEEE Access}, pages = {169720-169757}, shortjournal = {IEEE Access}, title = {Toward sustainable and clean energy futures: A techno-economic review of solar PV systems, challenges, and opportunities}, volume = {13}, year = {2025}, }
• (2025). A filtered gradient descent clustering method to recover communities in attributed networks. ACCESS, 13, 169698-169719. (www)

  @article{ , abstract = {An attributed network is a network in which in addition to the network structure each node is associated with a set of attributes. Community detection in such networks involves recovering the clusters by simultaneously using the network data and the node attributes. This research proposes a generic objective function and adopts the gradient descent (GD) approach to recover clusters in attributed networks. Our straightforward adoption of GD, even in its improved version, such as the adaptive moment estimation method, may encounter “bad sequences” of objects and converge to points that are far from optimal. To tackle this issue, we introduce a special “filter” mechanism, which culls potentially misleading objects. We established the theoretical foundation of the proposed filtering mechanism and the corresponding clustering method, and empirically evaluated and compared the performance of our proposed methods using synthetic and real-world datasets, including one new real-world dataset. Our results show that our proposed filter mechanism does improve results significantly and makes it competitive versus state-of-the-art algorithms.}, archive = {J_ACCESS}, author = {Soroosh Shalileh}, doi = {10.1109/ACCESS.2025.3614989}, journal = {IEEE Access}, pages = {169698-169719}, shortjournal = {IEEE Access}, title = {A filtered gradient descent clustering method to recover communities in attributed networks}, volume = {13}, year = {2025}, }
• (2025). A review of magnetic particle imaging: Comparative analysis of field free point and field free line methodologies. ACCESS, 13, 169678-169697. (www)

  @article{ , abstract = {This review provides an in-depth examination of the critical functions served by the Field Free Point (FFP) and Field Free Line (FFL) within Magnetic Particle Imaging (MPI), particularly high- lighting their importance for improving imaging accuracy and resolution. Basic FFP and FFL configurations and experimental conditions are presented first, followed by an evaluation of their impact on MPI protocol optimization. It delves deeper into the synthesis and functionalization of magnetic nanoparticles, highlighting their uses in targeted MPI and theragnostic. A comparison of FFP and FFL designs is presented to emphasize their benefits, limits, and possible contributions to trajectory optimization. The analysis continues with prospective future research areas that try to address existing challenges while improving MPI technology for both medical diagnostics and industrial applications.}, archive = {J_ACCESS}, author = {Ihteshamul Hayat and Timur Elebessov and Ali Kafash Hoshiar and Ton Duc do and Tri Thanh Pham}, doi = {10.1109/ACCESS.2025.3612847}, journal = {IEEE Access}, pages = {169678-169697}, shortjournal = {IEEE Access}, title = {A review of magnetic particle imaging: Comparative analysis of field free point and field free line methodologies}, volume = {13}, year = {2025}, }
• (2025). SwinGAN-CD: An end-to-end cross-domain change detection framework for global feature modeling and multi-dimensional feature alignment. ACCESS, 13, 169663-169677. (www)

  @article{ , abstract = {In remote sensing change detection tasks, significant cross-domain data distribution discrepancies severely weaken model adaptability, especially in scenarios where the target domain lacks labels. Existing methods struggle to effectively address domain shifts under such unsupervised conditions. To tackle this, this paper proposes SwinGAN-CD, an unsupervised cross-domain change detection method that uses the Swin Transformer as the feature extraction backbone combined with adversarial networks. By introducing the long-range dependency modeling capability of the Swin Transformer as the encoder and constructing a joint discriminator to align multi-dimensional feature spaces between the source and target domains, the method effectively enhances the model’s cross-domain recognition ability for change regions. Furthermore, a Self-Attention based Discriminator (SA-D) is proposed to enable more thorough and efficient feature alignment during adversarial training. Additionally, a Dual-space Feature Alignment (DFA) strategy tailored for change detection tasks is introduced to specifically align global features and change features across pre- and post-event phases, thereby improving detection accuracy. Cross-domain experiments conducted on the WHU-CD and LEVIR-CD datasets demonstrate that the proposed method achieves higher detection accuracy and stronger robustness under unsupervised conditions compared to existing approaches, attaining F1 scores of 82.71% and 79.04%, respectively, both of which are state-of-the-art. This study provides an effective unsupervised solution for cross-domain change detection in remote sensing.}, archive = {J_ACCESS}, author = {Zhen Xu and Fangrong Zhou and Guofang Wang and Shuangde Huang and Mingxian Liu}, doi = {10.1109/ACCESS.2025.3614224}, journal = {IEEE Access}, pages = {169663-169677}, shortjournal = {IEEE Access}, title = {SwinGAN-CD: An end-to-end cross-domain change detection framework for global feature modeling and multi-dimensional feature alignment}, volume = {13}, year = {2025}, }
• (2025). Hybrid RFID-GNSS circuit for robust vehicular navigation in signal-degraded environments. ACCESS, 13, 169636-169651. (www)

  @article{ , abstract = {This work presents the design and implementation of a novel hybrid GNSS–RFID circuit architecture aimed at enhancing positioning accuracy in environments where satellite navigation performance is degraded by signal obstruction and multipath effects. The RFID subsystem enhances global GNSS positioning by leveraging a network of local passive resonant circuits, i.e., UHF RFID tags, which act as local anchors. These RFID tags provide position refinement information that is used by the control circuit to improve localization performance. The theoretical model and the circuit-level implementation for the adaptive and dynamic power control over the RFID interface are presented, enabling the estimation of distance from local tags based on backscattered signal strength and controlled RF excitation levels. The circuit provides RFID-assisted dynamic reconfiguration of GNSS receiver parameters, including carrier-to-noise ratio thresholds (CN0), satellite angle-of-arrival filtering, and elevation-based 2D/3D mode switching. To validate the proposed circuit, experimental tests were conducted in three obstructed agricultural and forestry environments using U-blox Zed-F9P GNSS modules as the hardware platform. Positioning performance was compared between a standalone GNSS configuration and the same module augmented with the RFID-assisted circuit. A third Zed-F9P receiver, operating in real-time kinematic (RTK) fix mode, served as a ground-truth reference for accuracy evaluation. Positioning errors were evaluated using metrics such as mean error, median error, and error variance. Results demonstrate that the RFID-assisted architecture enables up to 43% reduction in positioning error and a significant decrease in error variance, confirming the robustness and adaptability of the proposed circuit under challenging conditions.}, archive = {J_ACCESS}, author = {Pierluigi Rossi and Luca Catarinucci and Massimo Cecchini and Giuseppe Grassi and Riccardo Colella}, doi = {10.1109/ACCESS.2025.3615074}, journal = {IEEE Access}, pages = {169636-169651}, shortjournal = {IEEE Access}, title = {Hybrid RFID-GNSS circuit for robust vehicular navigation in signal-degraded environments}, volume = {13}, year = {2025}, }
• (2025). Unraveling the dynamic characteristics of inertia fluctuations in modern power systems. ACCESS, 13, 169628-169635. (www)

  @article{ , abstract = {As an important index for the safe and stable operation of the power grid, inertia has traditionally been estimated and predicted. Significant progress has been made in this regard. Accurately predicting inertia is closely tied to effectively understanding and managing its dynamic characteristics. However, limited research has focused on how inertia fluctuates and the underlying principles governing these variations. Therefore, this paper employs statistical methods to unravel the seasonal, periodic, and heteroscedastic dynamic characteristics of inertia using data from the British power grid over the past five years. It integrates the principles of inertia generation and external environmental influences to demonstrate that power system inertia exhibits seasonal, heteroscedastic, and periodic fluctuation characteristics. Furthermore, this paper provides a reasonable explanation for the change mechanism of inertia by comparing and analyzing the inertia, load and renewable energy generation simultaneously. These findings provide a foundation for improving inertia forecasting and grid stability management in renewable-rich power systems.}, archive = {J_ACCESS}, author = {Xiaohua Nie and Hong Tang}, doi = {10.1109/ACCESS.2025.3611124}, journal = {IEEE Access}, pages = {169628-169635}, shortjournal = {IEEE Access}, title = {Unraveling the dynamic characteristics of inertia fluctuations in modern power systems}, volume = {13}, year = {2025}, }
• (2025). PhishOFE: A novel machine learning framework for real-time phishing URL detection with optimized feature engineering. ACCESS, 13, 169606-169627. (www)

  @article{ , abstract = {With the rapid expansion of the internet and the growing sophistication of cyber threats, phishing attacks have become a serious cybersecurity challenge for individuals and organizations. Phishing attacks, primarily executed through deceptive URLs, aim to mislead users into providing sensitive information, leading to financial loss, identity theft, and security breaches. The increasing complexity of phishing techniques necessitates the development of robust and intelligent detection frameworks. This paper introduces PhishOFE, a novel machine learning-based framework for phishing URL detection that utilizes Optimized Feature Engineering. The proposed framework extracts URL and HTML-based features and derives composite features to enhance phishing detection accuracy while minimizing dependence on third-party data. The PhishOFE dataset is constructed using diverse phishing and legitimate URLs sourced from various repositories, which ensures comprehensive feature representation. Experiments were conducted using ten different machine learning models, and the results show that CatBoost achieves the highest detection accuracy of 99.48%, with superior precision, recall, and F1-score. The framework reduces computational complexity by utilizing an existing machine learning model, making it suitable for real-time applications and its potential for integration into cybersecurity solutions to counter evolving phishing tactics.}, archive = {J_ACCESS}, author = {Yanche Ari Kustiawan and Khairil Imran Ghauth}, doi = {10.1109/ACCESS.2025.3614126}, journal = {IEEE Access}, pages = {169606-169627}, shortjournal = {IEEE Access}, title = {PhishOFE: A novel machine learning framework for real-time phishing URL detection with optimized feature engineering}, volume = {13}, year = {2025}, }
• (2025). Model-based gait recognition using multi-level graph neural networks with stochastic process and skip connections. ACCESS, 13, 169594-169605. (www)

  @article{ , abstract = {Gait recognition has become a vital tool in security, authentication and surveillance due to its abilities to perform at a distance and without the subject’s awareness, unlike traditional biometrics such as facial, iris or fingerprint recognition. In this work, we propose a new model-based gait recognition technique utilizing multi-level Graph Neural Networks (GNNs) that addresses two main challenges: shot walks and viewpoint issues. Human skeletal structures and movements are represented in the form of multi-level graphs. The proposed technique uses both frame-level graphs (postures) and subwalk-level (quick movements) as gait features to capture relationships among the entire body and characteristic of quick movements. The stochastic process and skip connections within GNN framework are employed to enhance flexibility, increase robustness, prevent overfitting and improve gradient flow. Experiments are conducted on a dataset of 2-second walks, where each sequence contains only 40 frames, to evaluate the performance of the proposed technique on very short walk sequences. The proposed technique outperforms existing techniques on both top-k accuracy test and the Cumulative Matching Characteristic (CMC) Curves. The experimental results show that the proposed technique achieves 85.54% on the top-1 accuracy test and surpass 95% accuracy in ranks below 10. These results suggest that unique posture and quick movement are sufficient to identify a person. The strong performance of the proposed technique on dataset that contains short walking sequences and viewpoint variations demonstrates its strong potential for real-world applications in security, surveillance and authentication where accurate identification from limited gait data obtained from various observation angles is crucial.}, archive = {J_ACCESS}, author = {Cholwich Nattee and Nirattaya Khamsemanan and Piya Limcharoen}, doi = {10.1109/ACCESS.2025.3611837}, journal = {IEEE Access}, pages = {169594-169605}, shortjournal = {IEEE Access}, title = {Model-based gait recognition using multi-level graph neural networks with stochastic process and skip connections}, volume = {13}, year = {2025}, }
• (2025). Groundwater resource prediction and management using comment feedback optimization algorithm for deep learning. ACCESS, 13, 169554-169593. (www)

  @article{ , abstract = {Water resource management remains a critical challenge globally, exacerbated by climate change, urbanization, and inefficient resource allocation. Predicting groundwater availability plays a crucial role in mitigating these challenges, especially in regions facing water scarcity. This study leverages the Comment Feedback Optimization Algorithm (CFOA) to enhance deep learning models for groundwater resource prediction. We propose a binary version of CFOA (bCFOA) for feature selection, which significantly improves the prediction performance of the LightGBM (Light Gradient Boosting Machine) model. The baseline LightGBM model achieved a mean squared error (MSE) of 0.045470041. After applying bCFOA for feature selection, the model’s performance improved with an average error of 0.41055. Furthermore, the integration of CFOA for hyperparameter optimization of LightGBM resulted in an impressive MSE of 6.11673E-06, demonstrating substantial improvements in accuracy. To further assess the robustness, efficiency, and interpretability of the proposed framework, an extensive ablation study was conducted. This included sensitivity analysis of key hyperparameters ( $Z_{0}$ , $K_{0}$ , $\lambda $ ), complexity evaluation of various metaheuristic variants, and cluster-based visual diagnostics. The study revealed not only the importance of parameter stability and search behavior, but also offered evidence of CFOA’s superior convergence and resource efficiency relative to competing approaches. These results highlight the potential of CFOA and bCFOA in optimizing machine learning models for groundwater resource prediction, offering valuable insights for sustainable water management. The added ablation insights strengthen the generalizability of the proposed method, illustrating its adaptability to broader environmental modeling scenarios. The implications of this work are far-reaching, suggesting that CFOA-based optimization can be applied to various environmental modeling tasks, enhancing predictive capabilities and facilitating more efficient resource management practices.}, archive = {J_ACCESS}, author = {Amel Ali Alhussan and El-Sayed M. El-Kenawy and Doaa Sami Khafaga and Amal H. Alharbi and Marwa M. Eid}, doi = {10.1109/ACCESS.2025.3614168}, journal = {IEEE Access}, pages = {169554-169593}, shortjournal = {IEEE Access}, title = {Groundwater resource prediction and management using comment feedback optimization algorithm for deep learning}, volume = {13}, year = {2025}, }
• (2025). From blurs to birds: Localization and classification of hard-to-see bird species in norwegian wilderness camera trap images. ACCESS, 13, 169534-169553. (www)

  @article{ , abstract = {Camera trap imagery datasets present unique challenges, including weather conditions, motion blur, occlusions from vegetation or other animals, and instances where the animal is partially or completely outside the frame due to the camera’s fixed position. This challenge is exacerbated when the target animal species are very small, highly similar in appearance, and fast-moving, such as different species of birds. Although deep learning has been extensively applied to classify and detect animal species, bird species are either captured in static, high-quality images under optimal conditions, with no motion blur or other disturbances, or they are grouped into a single class. Real camera trap images, however, are not optimal nor of high quality. Furthermore, recognizing bird species is more detailed and intricate than identifying larger animals such as deer, moose, or reindeer, making manual annotation more time-consuming and susceptible to errors. This study utilizes state-of-the-art deep-learning neural networks to localize and classify hard-to-see bird species in Norwegian wilderness camera trap images. Experimental results show that our fine-tuned YOLOv8x architecture achieved the highest performance for localization, outperforming YOLOv9, yielding a mAP@50 score of 0.92. For the classification of hard-to-distinguish bird species, EfficientNetB7 achieved the highest accuracy and F1-score of 0.87. Grad-CAM, an explainable AI technique, was employed to identify discriminative regions within the images.}, archive = {J_ACCESS}, author = {Håvard Teigen and Ammar Ahmed and Ali Shariq Imran and Mohib Ullah and R. Muhammad Atif Azad and Ahmet Soylu}, doi = {10.1109/ACCESS.2025.3613068}, journal = {IEEE Access}, pages = {169534-169553}, shortjournal = {IEEE Access}, title = {From blurs to birds: Localization and classification of hard-to-see bird species in norwegian wilderness camera trap images}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven hybrid Precopy/Postcopy VM migration for energy-efficient data centers. ACCESS, 13, 169521-169533. (www)

  @article{ , abstract = {This study proposes the use of a hybrid precopy/postcopy virtual machine (VM) migration framework to aid an autonomous agent when making migration decisions to continuously optimize the balance among migration time, downtime, and energy consumption. The data center state and the resource load, including the CPU, memory, and network, are represented in the agent’s state space using a two-layer graph neural network (GNN), and the asynchronous advantage actor–critic (A3C) algorithm is employed to dynamically determine whether to continue the precopy phase or switch to postcopy and optimize the trade-off among the total migration time, downtime, and energy consumption while adhering to the service-level agreement (SLA) constraints. An adaptive host selection policy ensures that VMs are migrated only to underloaded machines, preventing overload and ensuring system stability. A simulation evaluation that employed the VM workload from the GWA-Bitbrains dataset revealed that this framework achieved a total migration time of 45.5 s, with 30.1 s spent on the precopy phase and 15.4 s spent on the postcopy phase, resulting in a downtime of 15.4 s. Compared with previous approaches, this result represents an decrease in total migration time of 12.5% from 52 s to 45.5 s; a 23% decrease in downtime from 20 s to 15.4 s; and a 4.4% increase in energy efficiency from 87% to 91.4%. The SLA compliance remained stable at 92.8%, affirming that the service quality was preserved. This study demonstrates the effectiveness of integrating GNN-based embeddings and A3C scheduling in terms of reducing downtime and energy usage while maintaining reliable service delivery in data centers.}, archive = {J_ACCESS}, author = {Taufik Hidayat and Kalamullah Ramli and Ruki Harwahyu and Muhammad Salman and Teddy Surya Gunawan}, doi = {10.1109/ACCESS.2025.3613235}, journal = {IEEE Access}, pages = {169521-169533}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven hybrid Precopy/Postcopy VM migration for energy-efficient data centers}, volume = {13}, year = {2025}, }
• (2025). A time-fractional fischer-kolmogorov model for protein misfolding in neuro-degenerative disorders: A virtual element study. ACCESS, 13, 169504-169520. (www)

  @article{ , abstract = {We investigate the anomalous propagation of misfolded proteins implicated in neurodegenerative conditions, with emphasis on tau associated pathologies. To capture the sub-diffusive dynamics between misfolded and functional proteins, we formulate a two-dimensional time-fractional reaction-diffusion model governed by a Fisher-Kolmogorov type equation with a Riemann-Liouville derivative of order $\alpha \in (0,1$ ]. The spatial domain is discretized using the Virtual Element Method, enabling flexible polygonal meshing under a Galerkin framework. For temporal integration, we employ a convolution-based backward Euler scheme aligned with the Grünwald-Letnikov approximation. The resulting fully discrete system is rigorously analyzed: we establish existence, uniqueness, and derive optimal convergence bounds. Leveraging discrete maximal regularity, we demonstrate unconditional stability and consistency. Numerical experiments confirm convergence in both $L^{2}$ and $H^{1}$ norms across varying mesh topologies, underscoring the robustness and applicability of the proposed scheme.}, archive = {J_ACCESS}, author = {Zaffar Mehdi Dar and Chandru Muthusamy}, doi = {10.1109/ACCESS.2025.3610474}, journal = {IEEE Access}, pages = {169504-169520}, shortjournal = {IEEE Access}, title = {A time-fractional fischer-kolmogorov model for protein misfolding in neuro-degenerative disorders: A virtual element study}, volume = {13}, year = {2025}, }
• (2025). Robust model-predictive sliding mode control for disturbed linear systems with actuator constraint and measurement noise. ACCESS, 13, 169490-169503. (www)

  @article{ , abstract = {This manuscript presents a novel integrated model predictive sliding mode control (MPSMC) scheme for a class of distributed systems subjected to multiple simultaneous practical challenges, including noise and input/output constraints and incremental input limitations. While model predictive control (MPC) design, which predicts the performance of systems, can improve the control performance, it is not a robust control approach. To enhance the robustness of control performance, minimize the impact of constraints and perturbations, stabilize the closed-loop system, and achieve better trajectory tracking, a holistic control methodology that synergistically combines MPC and SMC is proposed for systems with input/output constraints, incremental input constraints, external disturbances, and measurement noise. The combination of MPC and SMC effectively stabilizes systems, achieving minimal overshoot, rapid rise time, and reduced settling time. The impact of external disturbances is mitigated using a filter. The closed-loop system stability analysis is subsequently conducted. Real-time implementation on a flexible-link manipulator and electric vehicle battery dynamic systems, subject to external disturbances, noise, saturation, incremental input constraints, and output constraints, highlights the efficiency and accuracy of the proposed approach.}, archive = {J_ACCESS}, author = {Hamede Karami and Farhad Bayat and Saleh Mobayen and Afef Fekih}, doi = {10.1109/ACCESS.2025.3614465}, journal = {IEEE Access}, pages = {169490-169503}, shortjournal = {IEEE Access}, title = {Robust model-predictive sliding mode control for disturbed linear systems with actuator constraint and measurement noise}, volume = {13}, year = {2025}, }
• (2025). Security of sensitive data in face recognition system applications: A novel encryption approach. ACCESS, 13, 169473-169489. (www)

  @article{ , abstract = {Background and Aim: The design of secure systems that can protect sensitive data is becoming a major area of research due to the growing number of applications that use the Internet. In mobile banking applications which use face recognition as unlock feature, the face data is encrypted and protected in a dedicated chip within the device called Secure Enclave that enhances the security of the application. This cutting-edge development is now being used by individuals in their personal devices like smartphones and tablets for accessing their own bank accounts and to perform online transactions. Since encrypted face data is stored locally on devices rather than in the bank’s database, it remains susceptible to security breaches, potentially allowing unauthorized access. While face recognition systems provide benefits like enhanced security, surveillance, and personalized marketing, they also raise privacy, security, ethical, and legal concerns. To address these challenges, this study proposes an encryption method for applications employing face recognition as an authentication feature.Methodology: The proposed method encrypts facial encodings generated by the face recognition model by utilizing Internet Protocol (IP) addresses as a dynamic key generation source. This approach ensures unique encryption for each communication, significantly reducing vulnerability to attacks. One of the widely used face recognition models in mobile applications is Dlib’s face recognition model which provides accurate facial embeddings.Results and Conclusion: The method is evaluated using the “pins_dataset” containing 10,000 images of 100 unique identities and an accuracy of 90.1% is achieved. The same accuracy is observed when testing face recognition without encrypting the facial encodings, demonstrating efficacy of the encryption algorithm. Additionally, the encryption algorithm’s time complexity is evaluated as $O(1)$ , confirming its ability to perform encryption with constant time complexity, making it suitable for real-time applications.}, archive = {J_ACCESS}, author = {S. Hemalatha and Aditya A. Kinjawadekar and Pranamya G. Kulal and G. Deepa and Prashanth Barla and Aakash B. Srinivasan and Shamathmika}, doi = {10.1109/ACCESS.2025.3614266}, journal = {IEEE Access}, pages = {169473-169489}, shortjournal = {IEEE Access}, title = {Security of sensitive data in face recognition system applications: A novel encryption approach}, volume = {13}, year = {2025}, }
• (2025). Lightweight multi-attention network for SMT dispensing electronic mount components identification. ACCESS, 13, 169460-169472. (www)

  @article{ , abstract = {During manufacturing process of electronic products, surface mounted technology component dispensing is a key technology in the process of chip production, which affects the quality of products. However, majority dispensing methods utilize rule-based methods, which are not robust to different styles of backgrounds and need to modify parameters when they face different templates. To address the above issues, a YOLOv5-based lightweight multi-attention detection network is proposed for SMT dispensing electronic mount components identification, in which cross and shuffle attention and ghost and multi-attention modules are designed to reduce computational complexity and locate dispensing components rapidly. Moreover, the SMT intelligent dispensing pipeline is realized and experiments on the self-constructed dispensing dataset and experiments show that YOLOv5-Light reaches the best frames 17 FPS on Jetson Nano and satisfied accuracy with mAP@.5 of 99.5% and mAP@.75 of 99.4%. Also, the inference speed is improved from 7.3 ms to 1.0 ms while the space complexity is improved from 10.2 MB to 6.2 MB, which indicates that the proposed dispensing system could implement fast detection under minimizing accuracy loss.}, archive = {J_ACCESS}, author = {Shuchen Yang and Zhenyi Xu and Zhonghao Wang and Yunbo Zhao and Shuoqiu Gan and Shumi Zhao and Jun Huang}, doi = {10.1109/ACCESS.2025.3612382}, journal = {IEEE Access}, pages = {169460-169472}, shortjournal = {IEEE Access}, title = {Lightweight multi-attention network for SMT dispensing electronic mount components identification}, volume = {13}, year = {2025}, }
• (2025). Data analytics for automated feature extraction and anomaly detection in a class of industrial manufacturing processes. ACCESS, 13, 169446-169459. (www)

  @article{ , abstract = {Industry 4.0/Smart Manufacturing is transforming the manufacturing industry through the integration of technologies such as the Internet of Things (IoT), big data, and cloud computing. These advancements have revolutionized the way production is carried out, leading to the generation of huge amounts of data in a more affordable and accessible manner, which makes it possible for anomalies to be detected. By identifying anomalies in this data, manufacturers can detect potential faults or issues in the production process early. This early detection can help prevent costly downtime, reduce waste, and improve product quality, ultimately leading to better customer satisfaction and profitability. Given the significant potential benefits, we have collected data from a local factory and launched a research initiative to explore how anomaly detection can help small/med-sized manufacturers to adopt Industry 4.0 technologies. This paper addresses this challenge for a key production operation in a local manufacturing plant. In particular, sensors were deployed to continuously measure a critical process variable of the equipment for this operation. Using the sensor data, we develop effective and robust algorithms that can effectively extract the normal pattern features and detect anomalies that occurred during the production process. Numerical experiments demonstrate that the proposed framework can detect the anomalies in high accuracy.}, archive = {J_ACCESS}, author = {Tianyu Zhu and Yishu Bai and Jiachen Tu and Liang Zhang}, doi = {10.1109/ACCESS.2025.3610576}, journal = {IEEE Access}, pages = {169446-169459}, shortjournal = {IEEE Access}, title = {Data analytics for automated feature extraction and anomaly detection in a class of industrial manufacturing processes}, volume = {13}, year = {2025}, }
• (2025). Building digital twins for elderly care: An end-to-end framework from data acquisition to modeling. ACCESS, 13, 169415-169445. (www)

  @article{ , abstract = {The rapid growth of the aging population, which is expected to reach 2.1 billion by 2050, poses profound challenges for healthcare systems and the quality of life of the elderly. Current research on digital healthcare has focused on incorporating Artificial Intelligence (AI) and wearable technology to deliver personalized care to the elderly population. Digital Twins (DTs) are virtual models that replicate real-world entities using real-time data and offer new possibilities for personalized healthcare. While prior studies have explored the adaptation of DTs for personalized healthcare, especially for older adults, the integration of generative AI (Gen AI) for real-time contextualized caregiver support remains underdeveloped. This study introduces an Elderly Digital Twin (EDT) framework that places Gen AI-driven personalized feedback and decision support at its core, supported by a comprehensive data pipeline, predictive modeling, and interactive user interaction. The framework integrates diverse physiological data (heart rate, SpO2, and sleep) and demonstrates feasibility and applicability by developing cardiac DT, sleep quality DT, and SpO2 monitoring DT models. Advanced predictive modeling achieved strong performance, with LSTM reaching 92% validation accuracy in sleep stage prediction, and Bi-LSTM yielding robust performance in real-time heart rate forecasting (MSE = 0.2944 and MAE = 0.3410). The EDT framework was deployed via a Node.js-based web application that offers real-time physiological monitoring, interactive 3D cardiac simulation, and GPT-4o-powered personalized feedback to enhance caregiver decision-making and elderly self-management. The primary contribution of this study is the incorporation of Gen AI into EDT to translate sensor data and predictive outputs into actionable recommendations for caregivers. The pipeline, predictive models, and ethical safeguards serve as enabling components, and this proof-of-concept study lays the groundwork for future multi-organ EDT and broader clinical use.}, archive = {J_ACCESS}, author = {Ziaullah Momand and Pornchai Mongkolnam and Jonathan H. Chan and Nipon Charoenkitkarn and Debajyoti Pal}, doi = {10.1109/ACCESS.2025.3607603}, journal = {IEEE Access}, pages = {169415-169445}, shortjournal = {IEEE Access}, title = {Building digital twins for elderly care: An end-to-end framework from data acquisition to modeling}, volume = {13}, year = {2025}, }
• (2025). Intelligent warehousing: A machine learning and IoT framework for precision inventory optimization. ACCESS, 13, 169381-169414. (www)

  @article{ , abstract = {The evolution of intelligent warehousing represents a paradigm shift in supply chain logistics, driven by the synergistic integration of machine learning (ML) algorithms, Internet of Things (IoT) networks, and robotic automation to address systemic inefficiencies in traditional inventory systems. This research establishes a holistic framework that leverages XGBoost, Random Forest, and synthetic data generation to optimize demand forecasting (reducing mean absolute error by 32% compared to ARIMA models), mitigate inventory discrepancies (achieving 99.9% accuracy via RFID-enabled IoT sensors), and minimize operational costs (35% reduction through predictive maintenance and energy-efficient automation). By synthesizing real-time data streams from IoT edge devices—including smart shelves, environmental sensors, and autonomous mobile robots (AMRs)—the proposed system enables dynamic decision-making, reducing order fulfillment times by 72% and carbon emissions by 25% through AI-optimized routing and renewable energy integration. A procedurally generated synthetic dataset (913,000 transactions across 10 simulated warehouses) was engineered to replicate real-world variables such as demand volatility, shipment delays, and SKU heterogeneity, enabling stress-testing of ML models without operational risks. The framework’s core innovation lies in its multi-echelon architecture, which unifies IoT-driven inventory tracking, blockchain-enhanced traceability, and reinforcement learning (RL)-optimized warehouse layouts. Case studies demonstrate that XGBoost-based demand forecasting aligns procurement with market trends (95% accuracy in retail trials), while Random Forest classifiers reduce stockouts by 45% through anomaly detection in IoT sensor data. The system’s edge computing infrastructure processes data with <10ms latency, enabling real-time adjustments to inventory buffers and robotic workflows. Sustainability metrics reveal a 20% reduction in material waste via ML-optimized packaging and a 12% decrease in fuel consumption through KNN-driven route optimization. Challenges such as IoT signal interference (20% error rates in metal-rich environments) and workforce resistance (60% employee apprehension) are addressed through hardware retrofitting and blockchain-auditable training programs. This research advances intelligent warehousing beyond incremental automation, positioning it as a cognitive supply chain ecosystem capable of self-optimization in response to demand shocks, geopolitical disruptions, and environmental constraints. By achieving 99% order accuracy and 40% lower carrying costs, the framework establishes a blueprint for Industry 4.0-compliant logistics, bridging the gap between predictive analytics and operational execution in global supply chains.}, archive = {J_ACCESS}, author = {V. Raju Arvind and R. M. Shrinidhi and T. Deepa and M. Maheedhar}, doi = {10.1109/ACCESS.2025.3614679}, journal = {IEEE Access}, pages = {169381-169414}, shortjournal = {IEEE Access}, title = {Intelligent warehousing: A machine learning and IoT framework for precision inventory optimization}, volume = {13}, year = {2025}, }
• (2025). EVDO: An enhanced framework for deepfake detection in videos through optical flow and temporal-spatial analysis. ACCESS, 13, 169367-169380. (www)

  @article{ , abstract = {As generative AI advances, the realism of synthetic media has sparked serious concerns in security, privacy, and misinformation. This issue is particularly concerning with the rise of deepfake technologies that manipulate facial imagery, undermining media authenticity. While existing research has largely focused on image-based deepfake detection with specialized feature extractors, detecting deepfakes in video, especially with broad generalization across varied manipulation techniques, remains a substantial challenge. This paper introduces EVDO, a novel framework designed to detect deepfake videos by integrating temporal and spatial analysis for enhanced detection accuracy. Our approach leverages optical flow techniques to capture subtle temporal manipulation artifacts between video frames overlooked in spatial analysis. Using a FlowFormer++ model for temporal analysis, frame pairs are sampled to produce cost volumes that highlight essential motion regions and capture manipulation-specific artifacts through optical flow images which encode temporal dependencies. A flow-finetuned detector then extracts flow-level features indicative of deepfake manipulation. Complementing this, Xception-based spatial detectors analyze each frame individually, generating high-dimensional embeddings that capture frame-specific anomalies. Fusing these temporal and spatial embeddings enables comprehensive binary classification of deepfakes. Validated on the FaceForensics++ benchmark, EVDO significantly improves generalization. Our proposed temporal path contributes correct classifications to around 12% of datapoints. EVDO achieves a video AUC of 99.13% (99.43% of end-to-end SOTA methods) while enabling forensically verifiable manipulation detection.}, archive = {J_ACCESS}, author = {Mohammed Mouad Melouk and Minghao Shao and Abdul Basit and Chaimae Abouzahir and Ruilin Zhou and Muhammad Shafique}, doi = {10.1109/ACCESS.2025.3614455}, journal = {IEEE Access}, pages = {169367-169380}, shortjournal = {IEEE Access}, title = {EVDO: An enhanced framework for deepfake detection in videos through optical flow and temporal-spatial analysis}, volume = {13}, year = {2025}, }
• (2025). Uncovering usability issues in mobile government applications: A user-centered approach. ACCESS, 13, 169352-169366. (www)

  @article{ , abstract = {Mobile government (M-Gov) applications have become essential platforms for delivering public services, with usability directly influencing user access, trust, and digital inclusion. However, most prior studies on M-Gov usability rely on top-down evaluation methods, such as heuristic assessments or standardized frameworks (e.g., ISO, SUS), which often miss the complex, context-sensitive nature of M-Gov user interaction. This study addresses this gap by adopting a bottom-up, user-centered approach to identify usability issues that arise during actual use of the M-Gov service. Usability data was collected through think-aloud protocols, post-task reflections, and post-study questionnaires. Inductive thematic analysis revealed eight interrelated usability themes, each composed of sub-themes that captured specific issues, resulting in a total of sixteen sub-themes. The core usability issues are: Task Flow Disruptions, Navigation Difficulties, Content Issues, Performance Issues, Support Limitations, Ineffective Error Feedback, Interface Design Issues, Accessibility Barriers. This approach shows how usability challenges emerge and escalate during service interactions. The findings reveal gaps in user behavior alignment, clear communication, and accessibility, especially for older users or people with limited digital skills. The study contributes to M-Gov usability research by offering a structured taxonomy of observed usability failures and extends current evaluation practices by offering a behaviorally grounded view of usability in M-Gov apps and informing more inclusive and reliable digital service design.}, archive = {J_ACCESS}, author = {Rawan Alabdulrahman and Fayez Alqahtani and Manal Binkhonain}, doi = {10.1109/ACCESS.2025.3614165}, journal = {IEEE Access}, pages = {169352-169366}, shortjournal = {IEEE Access}, title = {Uncovering usability issues in mobile government applications: A user-centered approach}, volume = {13}, year = {2025}, }
• (2025). LBSCA: Learning real-time power system state estimation under hidden adversarial attacks. ACCESS, 13, 169340-169351. (www)

  @article{ , abstract = {Accurate power system state estimation (PSSE) is crucial for effective control and monitoring of power grids. However, existing techniques face numerous challenges including compromised measurements from cyber-attacks and the need for real-time operations. Traditional PSSE methods based on weighted least squares are susceptible to outliers and convergence issues, while convex relaxation techniques like semi-definite programming struggle with computational complexity. Recent deep learning approaches for PSSE are data-driven but unaware of the underlying physical constraints. This paper proposes a novel framework called learned block successive convex approximation (LBSCA) to address PSSE amidst hidden adversarial attacks. LBSCA utilizes an efficient block successive convex approximation algorithm that provably converges to a stationary point and can be learned via algorithm unrolling. The resulting deep neural network achieves real-time, end-to-end PSSE in fractions of a millisecond, even when attacks are unobserved and the network topology is incomplete. Evaluated on the IEEE 118-bus system under realistic noise profiles and stealthy directional and sparse injection attacks, LBSCA achieves lower state estimation errors by up to a factor of three than state-of-the-art deep learning baselines and delivers inference speeds up to four orders of magnitude faster than classical optimization algorithms. Our results demonstrate that model-driven unrolling of robust estimation yields both theoretical rigor and practical efficiency, underscoring its potential in improving real-time power system operation’s efficiency and security.}, archive = {J_ACCESS}, author = {Kamal Basulaiman and Faisal Albeladi and Faisal M. Almutairi and Ahmed Saeed and Masoud Barati}, doi = {10.1109/ACCESS.2025.3610609}, journal = {IEEE Access}, pages = {169340-169351}, shortjournal = {IEEE Access}, title = {LBSCA: Learning real-time power system state estimation under hidden adversarial attacks}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven model predictive control of modular multilevel converters. ACCESS, 13, 169327-169339. (www)

  @article{ , abstract = {The Modular Multilevel Converter (MMC) has garnered significant interest recently due to its superior harmonic performance and improved efficiency in high-voltage direct current electrical grids. Model Predictive Control (MPC) is widely adopted for the MMC applications, as it provides a straightforward control design, facilitates the inclusion of multiple control objectives through a flexible cost function formulation, and offers excellent control performance. An emerging and promising solution involves integrating MPC with machine learning (ML)-based models, in which neural networks learn MPC behavior and predict the results as the traditional MPC does. In this paper, a multi-layered neural network is designed to approximate the control behavior of MPC correctly, enabling a substantial reduction in computational effort during real-time operation and replacing the complex optimization routines of MPC with lightweight neural network regression models that are both efficient and decoupled from the algorithmic complexity of traditional MPC. The performance of controllers is evaluated under both small and large disturbances in active power and reactive power.}, archive = {J_ACCESS}, author = {Sunny Singh and Dušan M. Stipanović and Aleksandra Lekić}, doi = {10.1109/ACCESS.2025.3614746}, journal = {IEEE Access}, pages = {169327-169339}, shortjournal = {IEEE Access}, title = {Machine learning-driven model predictive control of modular multilevel converters}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on cognitive cyber security analysis using machine learning approaches. ACCESS, 13, 169314-169326. (www)

  @article{ , abstract = {Cybersecurity threats have evolved in sophistication, necessitating the development of intelligent and adaptive defense mechanisms. Cognitive cybersecurity systems present the opportunity to provide answers to threat detection, vulnerability analysis, and anomaly detection since they combine the power of artificial intelligence (AI) with human knowledge. Ensemble machine learning is especially effective among AI methods because it will be able to enable prediction stability and accuracy through the aggregation of multiple classifiers. However, dynamic vulnerability assessments are difficult to perform on heterogeneous data volumes created in security operations centers, relying only on real-time data. The vulnerability scan is done manually, and it demands highly skilled analysts who need to go through a time-consuming process with cumulative results, and therefore the security repositories available still carry too much inconsistent information, and this leads to wastage of time in detecting the flaws. To address these issues, this paper proposes a cognitive cybersecurity framework powered by ensemble machine learning for real-time vulnerability detection and dynamic threat assessment. The framework aims to enhance accuracy, reduce redundancy in vulnerability reporting, and improve the efficiency of security analysis.}, archive = {J_ACCESS}, author = {Waseem Khan and K. Ashoka and M. S. Abdul Razak and M. V. Manoj Kumar and R. Naseer}, doi = {10.1109/ACCESS.2025.3614388}, journal = {IEEE Access}, pages = {169314-169326}, shortjournal = {IEEE Access}, title = {A comprehensive survey on cognitive cyber security analysis using machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). A novel fault location method based on VMD-MFE for distribution network. ACCESS, 13, 169304-169313. (www)

  @article{ , abstract = {Aiming at the problems existing in the distribution network, such as the difficulty in extracting fault signals and the difficulty in diagnosing various faults, a fault location method based on Variational Mode Decomposition (VMD) multi-scale fuzzy entropy is proposed. In order to solve the existing problems of confusing identification of traveling wave heads and inadequate fault feature extraction in complex distribution networks, the VMD algorithm is used to decompose the fault transient signals into a number of Intrinsic Mode Function (IMF) and quantify the complexity and regularity of different mode components. Meanwhile, the fuzzy entropy values of the signals at different coarse-grained scales are calculated separately to determine the appropriate mode components and accurately calibrate the transient signals, and the effect of the method on the calibration of the wave heads in different noise environments is compared. The simulation results show that the proposed protection scheme has strong reliability and high accuracy. It can accurately identify the traveling wave signal under different levels of noise in the distribution network and has a certain anti-noise ability.}, archive = {J_ACCESS}, author = {Hongwen Liu and Zihan Wang and Chunli Zhang and Shan Wang and Fan Feng}, doi = {10.1109/ACCESS.2025.3611522}, journal = {IEEE Access}, pages = {169304-169313}, shortjournal = {IEEE Access}, title = {A novel fault location method based on VMD-MFE for distribution network}, volume = {13}, year = {2025}, }
• (2025). The force of habit: Comparing graphical user interfaces of popular operating systems using eye-tracking analysis. ACCESS, 13, 169236-169261. (www)

  @article{ , abstract = {Background:. Usability plays an important role in user experience and directly influences users’ satisfaction and overall perception of the IT solution. A combination of subjective and objective evaluation methods can increase the overall usability assessment. Methods: This paper presents a comparative analysis of eye-tracking and survey data that provided a comparison of the graphical user interfaces (GUIs) of three popular operating systems (OSs), Linux, macOS, and Windows, in terms of usability, efficiency, and overall user experience. The study was conducted on a group of over sixty participants using eye-tracking technology and System Usability Scale (SUS) surveys. Several metrics were applied based on features related to eye tracking and task completion. Raw data, including fixations and saccades, was used to generate scan paths and heat maps, enabling objective measurement of respondents’ aggregated attention intensity and gaze patterns. Results: The results revealed particular usability issues that significantly confuse users or make the system difficult to use. Although Linux and macOS differ significantly in interface design and functionality, these objective distinctions were not reflected in users’ mean subjective assessments. Median score analysis suggests that Windows and Linux were rated similarly, while macOS consistently ranked lower. Conversely, in objective performance measures, Linux demonstrated the poorest outcomes, whereas Windows performed best in perceived usability. The results suggest that users rely on familiar interaction patterns and spatial memory, which may explain inconsistencies linked to prior experience with Windows. In detailed primary OS-specific analysis, Linux and macOS demonstrate relatively stable outcomes, whereas Windows shows greater variability.}, archive = {J_ACCESS}, author = {Karol Łazaruk and Piotr Tokarski and Karolina Rybak and Małgorzata Plechawska-Wójcik and Mariusz Dzieńkowski}, doi = {10.1109/ACCESS.2025.3614018}, journal = {IEEE Access}, pages = {169236-169261}, shortjournal = {IEEE Access}, title = {The force of habit: Comparing graphical user interfaces of popular operating systems using eye-tracking analysis}, volume = {13}, year = {2025}, }
• (2025). EBPF-based runtime detection of semantic DDoS attacks in linux containers. ACCESS, 13, 169178-169219. (www)

  @article{ , abstract = {Modern Distributed Denial-of-Service (DDoS)attacks increasingly target the application layer to exhaust CPU resources and disrupt service availability, particularly in containerized environments where isolation and diverse implementations complicate traditional detection mechanisms. Existing solutions like CODA(Containerized Denial-of-Service Attack detection) monitor CPU burst times between accept() and close() system calls but fail when attackers maintain persistent connections without triggering close() calls. To address this limitation, we propose CODAX (Container-aware DDoS Attack detection using eXtended Berkeley Packet Filter), a lightweight CPU-time-based detection method that identifies long-running malicious connections by monitoring system calls at the kernel level using extended Berkeley Packet Filter (eBPF) probes. Unlike prior methods such as CODA that rely on measuring CPU burst time between accept() and close() system calls, our approach tracks CPU usage thresholds from the moment of the accept() call using a global map of 64-bit Unix timestamps, enabling early detection of ongoing attacks before connection closure. The experimental evaluation demonstrates significant performance improvements over existing solutions. The proposed system achieves faster detection times, high attack detection accuracy (ADR: 0.92), and maintains low false positive rates (FPR: 0.02). Statistical validation using paired t-tests confirms that our eBPF-based approach reduces detection latency by an average of 1,772.3ms compared to CODA, representing a 94.2% relative reduction from CODA’s baseline performance (p<0.0001). The system operates efficiently with 30% CPU utilization and demonstrates high scalability for production environments. This research provides a timely and efficient mechanism for mitigating CPU exhaustion DDoS attacks in containerized applications, offering substantial improvements in responsiveness and reliability over existing detection frameworks.}, archive = {J_ACCESS}, author = {S. Remya and Manu J. Pillai and B. Niranjan and P. M. Ajith Kumar and K. Merin Shaju and K. Dinoy Raj and Somula Ramasubbareddy and Yongyun Cho}, doi = {10.1109/ACCESS.2025.3614389}, journal = {IEEE Access}, pages = {169178-169219}, shortjournal = {IEEE Access}, title = {EBPF-based runtime detection of semantic DDoS attacks in linux containers}, volume = {13}, year = {2025}, }
• (2025). Adaptive modified RISE control for quadrotors: Enhancing trajectory tracking through uncertainty compensation. ACCESS, 13, 169166-169177. (www)

  @article{ , abstract = {Autonomous quadrotors are increasingly deployed in environments with significant uncertainties and disturbances, where ensuring robust trajectory tracking is critical for safety and performance. This paper presents an adaptive modified robust inverse of signum error (AM-RISE) control method, which achieves reliable trajectory tracking control for a quadrotor autonomous aerial vehicle (AAV). The proposed method systematically accounts for gyroscopic effects, rotor dynamics, parametric uncertainties, and external disturbances, ensuring robust performance across varying trajectory speeds. Through novel mathematical manipulation in the error system development, the quadrotor dynamics are expressed in a control-oriented form, which explicitly incorporates the uncertainty in the gyroscopic term and control actuation term. An adaptive modified RISE law is then designed to stabilize both the position and attitude loops of the quadrotor system. A rigorous Lyapunov-based analysis is utilized to prove asymptotic trajectory tracking, where the region of convergence can be made arbitrarily large through judicious control gain selection. Moreover, the stability analysis formally addresses gyroscopic effects and actuator uncertainty. To illustrate the performance of the control law, comparative numerical simulation results are provided, which demonstrate the improved closed-loop performance achieved under varying levels of parametric uncertainty, disturbance magnitudes and trajectory speeds.}, archive = {J_ACCESS}, author = {Kevin Johnston and Musabbir Ahmed Arrafi and Krishna B. Kidambi and Madhur Tiwari}, doi = {10.1109/ACCESS.2025.3612215}, journal = {IEEE Access}, pages = {169166-169177}, shortjournal = {IEEE Access}, title = {Adaptive modified RISE control for quadrotors: Enhancing trajectory tracking through uncertainty compensation}, volume = {13}, year = {2025}, }
• (2025). Leveraging intrinsic components for few-shot neural radiance fields in unconstrained illumination. ACCESS, 13, 169150-169165. (www)

  @article{ , abstract = {Neural Radiance Fields (NeRF) have demonstrated strong performance in novel view synthesis under idealized conditions such as dense multi-view observations, consistent illumination, and known camera poses. However, these assumptions often do not hold in real-world scenarios, where inputs are sparse and lighting varies significantly. This paper presents a novel regularization framework for few-shot NeRF reconstruction under unconstrained illumination. By leveraging intrinsic, illumination-invariant representations (e.g., albedo), our method enforces cross-view appearance consistency, leading to more stable synthesis. To further improve applicability, we propose a lightweight variant that achieves comparable improvements with significantly reduced computational cost. We also establish new benchmarks that reflect diverse illumination and viewpoint conditions. Extensive experiments show that our method improves robustness and rendering quality across challenging real-world scenes, without relying on dense inputs or manual supervision.}, archive = {J_ACCESS}, author = {Seokyeong Lee and Junyong Choi and Seungryong Kim and Ig-Jae Kim and Junghyun Cho}, doi = {10.1109/ACCESS.2025.3610908}, journal = {IEEE Access}, pages = {169150-169165}, shortjournal = {IEEE Access}, title = {Leveraging intrinsic components for few-shot neural radiance fields in unconstrained illumination}, volume = {13}, year = {2025}, }
• (2025). A reconstruction-inversion method of atmospheric duct parameters using ADS-B signals based on autoencoder and ResNet18. ACCESS, 13, 169138-169149. (www)

  @article{ , abstract = {Low-altitude atmospheric ducts are anomalous atmospheric phenomena in the troposphere, significantly affecting the operation of microwave or ultrashort-wave radio systems. Therefore, an accurate operation crucially relies on the real-time acquisition of these atmospheric duct parameters. Based on Automatic Dependent Surveillance-Broadcast (ADS-B) signals and deep neural networks, this paper initiates a reconstruction-inversion method. ADS-B signals are for the first time applied to the inversion of atmospheric duct parameters. First, the PL field is reconstructed utilizing the Propagation Loss Field Reconstruction Network (PLFRN) with an autoencoder architecture. Then, the inversion of the duct parameters is conducted by means of the Duct Parameter Inversion Network (DPIN) based on ResNet18. According to experimental results in actual maritime scenarios, the Mean Absolute Percentage Error (MAPE) of less than 13% is obtained between the inverted duct parameters and the measured parameters. In addition, the MAPE between the reconstructed PL field and the results calculated by the traditional Parabolic Equation (PE) method is only 3%. The algorithm accuracy is well verified in its applications and points out greater future potentials.}, archive = {J_ACCESS}, author = {Mian Wu and Yihao Xu and Yuqiang Zhang and Chen Zhou}, doi = {10.1109/ACCESS.2025.3609857}, journal = {IEEE Access}, pages = {169138-169149}, shortjournal = {IEEE Access}, title = {A reconstruction-inversion method of atmospheric duct parameters using ADS-B signals based on autoencoder and ResNet18}, volume = {13}, year = {2025}, }
• (2025). Lightweight real-time segmentation for agricultural environments. ACCESS, 13, 169129-169137. (www)

  @article{ , abstract = {The application of autonomous driving in agriculture poses unique challenges compared to urban autonomous vehicles. Unlike structured roads with clear signals, agricultural environments feature unstructured, irregular terrains requiring precise perception and high-level decision-making. Current autonomous agricultural machines primarily rely on LiDAR, which detects object distance and shape with high accuracy. However, LiDAR systems are computationally expensive due to large data volumes and complex processing. This paper proposes dilated group convolution network (DG-Net), a lightweight deep learning model for real-time farmland segmentation. DG-Net incorporates DG-blocks, combining dilated and group convolutions, and an upsampling module using pixel shuffle. DG-blocks expand the receptive field to extract contextual features, which are connected to the decoder via skip connections. The decoder restores resolution using pixel shuffle by rearranging low-resolution feature maps into high-resolution outputs. DG-Net achieves a pixel accuracy of 75.1% and a mIoU of 77.7%, surpassing the state-of-the-art segment anything model – vision transformer base (SAM-ViT-B) by 2.5% in mean intersection over union (mIoU) while using fewer parameters and maintaining lightweight computational cost. These results demonstrate DG-Net’s applicability for efficient and accurate real-time farmland perception.}, archive = {J_ACCESS}, author = {Na Yeon Bae and Dong Seog Han}, doi = {10.1109/ACCESS.2025.3614053}, journal = {IEEE Access}, pages = {169129-169137}, shortjournal = {IEEE Access}, title = {Lightweight real-time segmentation for agricultural environments}, volume = {13}, year = {2025}, }
• (2025). Simplified approach for the grounding grid design of oil onshore site. ACCESS, 13, 169114-169128. (www)

  @article{ , abstract = {In most cases, the soil resistivity in oil onshore sites is low due to its structure with facilities to extract and process petroleum and natural gas that lie in rock formations beneath the sea or to use water in extracting oil and gas according to the technologies used in that. In this article, a simplified approach for the grounding grid design of an oil onshore site is proposed. The onshore site is divided into seven zones, each has its own grounding grid. The paper used a simplified formula in combination with finite element method (FEM) simulations using COMSOL and CYMGrd software that provides a comprehensive technical approach in the design of an oil onshore grounding system. Empirical data from an actual onshore site enhances the applicability of the research. Standards such as IEEE 80, NEC 70, and IEC-60364 are well-integrated in the design methodology. Key improvements include reducing the maximum values of the step and touch potentials to be in the ranges of 100 and 590 volts, respectively, while the surface potential is reduced to 600 volts, which are considered safe values according to the IEC-60364 standard, and achieving the grid resistance of each zone to be less than 0.5 ohm. The resultant resistance when connecting the ground grids of the seven zones reached less than 0.01ohm through optimized electrode arrangements. A 3-D model is used to study the impact of lightning strikes on the tank farm area due to its importance. The proposed approach helps the engineering applications for a well-designed grounding grid in oil onshore sites, which is crucial for ensuring safety and reliable operation by mitigating electrical hazards and providing a path for fault currents.}, archive = {J_ACCESS}, author = {Mohamed H. Elmashtoly and Ossama E. Gouda and Mohamed M. F. Darwish and Matti Lehtonen and Mohammed A. Shams}, doi = {10.1109/ACCESS.2025.3613227}, journal = {IEEE Access}, pages = {169114-169128}, shortjournal = {IEEE Access}, title = {Simplified approach for the grounding grid design of oil onshore site}, volume = {13}, year = {2025}, }
• (2025). A back-to-back converter-based electric spring with an improved control structure for unbalanced load operations. ACCESS, 13, 169098-169113. (www)

  @article{ , abstract = {This article proposes a back-to-back converter-based electric spring for the voltage and frequency regulation in a self-excited induction generator-based isolated generation system. The proposed electric spring topology gives the system seamless performance with linear, non-linear, and unbalanced load conditions. The electric spring in this work is equipped with a modified complex coefficient filter, which eliminates the harmonics and the unbalanced negative sequence component from the system currents caused by unbalanced loads. The performance of the proposed electric spring was evaluated with non-linear loads, unbalanced critical load, and unbalanced non-critical load. The proposed electric spring works effectively to regulate the system voltage and frequency within 2% tolerance limits. In the presence of non-linear loads, the THD of the generator current could be reduced to 1.97%, proving the electric spring’s efficacy as an active filter. The modified complex coefficient filter-based shunt side control strategy effectively eliminates the unbalanced current components from the system currents, which keeps the self-excited induction generator unaffected by load imbalances caused by both the critical load and non-critical load sides. The shunt side control strategy also effectively maintained the DC link voltage fluctuation within 16V, which is 6.4% of the rated value of 250V. The proposed minimum voltage injection control strategy to control the series converter was highly effective in maintaining a constant voltage of 230V during unbalanced non-critical load conditions. The real-time validation of the proposed work is done with OPAL-RT 4510 and 4520 platforms, and the detailed results and their analysis are presented in this work.}, archive = {J_ACCESS}, author = {Swagat Pati and Amar Bijay Nanda and Binod Kumar Sahu and Futa Osumanu and Abhijeet Choudhury}, doi = {10.1109/ACCESS.2025.3614175}, journal = {IEEE Access}, pages = {169098-169113}, shortjournal = {IEEE Access}, title = {A back-to-back converter-based electric spring with an improved control structure for unbalanced load operations}, volume = {13}, year = {2025}, }
• (2025). CyberGuard: An adaptive AI-driven backdoor attack detection. ACCESS, 13, 169083-169097. (www)

  @article{ , abstract = {Smart manufacturing, a core of Industry 4.0 revolution, leverages Artificial Intelligence (AI) to boost efficiency and productivity. However, this data-driven interconnectivity exposes manufacturing to sophisticated cyber-attacks, one of the well-known attack is a backdoor that silently impairs AI systems. Traditional defence mechanisms are often rigid and not tailored to the unique requirements of Industry 4.0. To address this, we introduce CyberGuard, a novel Adaptive Anomaly Detection (AAD) framework designed specifically for detecting backdoor attacks in smart manufacturing environments. Unlike methods focusing on direct attack recognition, proposed CyberGuard models normal system behavior and identifies deviations as potential threats. It utilizes a combination of Convolutional Neural Network (CNN) and Transformer AutoEncoder (TAE) to detect anomalies within feature maps. Further, extensive testing on multiple datasets confirms CyberGuard’s capability to detect subtle yet crucial irregularities. We have conducted a comprehensive experiment against multiple backdoor attacks and compared and evaluated CyberGuard with Neural Cleanse, STRIP, Active Clustering, and Spectral Signature. In addition, an additional experiment is also conducted by employing a T-test statistical analysis to demonstrate the significant differentiation between normal and compromised feature maps, thereby validating CyberGuard effectiveness without prior distribution knowledge of the attacks. The results demonstrate that CyberGuard significantly outperformed traditional defences by achieving approximate 100% detection with high F1-score compared to existing defences.}, archive = {J_ACCESS}, author = {Iram Arshad and Saeed Hamood Alsamhi and Yuansong Qiao and Brian Lee and Yuhang Ye}, doi = {10.1109/ACCESS.2025.3608343}, journal = {IEEE Access}, pages = {169083-169097}, shortjournal = {IEEE Access}, title = {CyberGuard: An adaptive AI-driven backdoor attack detection}, volume = {13}, year = {2025}, }
• (2025). On ensemble learning models for autoencoder-based identification of nonlinear dynamical systems. ACCESS, 13, 169071-169082. (www)

  @article{ , abstract = {Identifying the dynamic model of a system has been a subject of extensive research for decades. However, dealing with highly complex, possibly nonlinear, and noisy systems remains an open problem. In recent years, the use of data-driven techniques based on machine learning for system identification has represented a promising solution for addressing the challenges of complex system identification. Among these methods, auto-encoders have been successfully applied to compactly represent the system state in a latent space used for estimating the system dynamics. In this work, we propose leveraging the ensemble paradigm for system identification, and in particular to exploit an ensemble of autoencoders, to reduce the uncertainty compared to individual autoencoders. To this end, we propose an algorithm for selecting autoencoders based on a combination of an accuracy metric and a diversity index. We validate this approach using traditional benchmarks in the field of nonlinear system identification.}, archive = {J_ACCESS}, author = {Nicola Arridu and Martina Lippi and Mauro Franceschelli and Andrea Gasparri}, doi = {10.1109/ACCESS.2025.3611914}, journal = {IEEE Access}, pages = {169071-169082}, shortjournal = {IEEE Access}, title = {On ensemble learning models for autoencoder-based identification of nonlinear dynamical systems}, volume = {13}, year = {2025}, }
• (2025). A taxonomy-driven survey of AI for seizure detection: Thalamic signals, phase dynamics, and translational gaps. ACCESS, 13, 169049-169070. (www)

  @article{ , abstract = {Seizure prediction and postictal recovery remain critical challenges in epilepsy care, particularly in real-world, resource-constrained settings. This survey presents a taxonomy-driven synthesis of 150+ peer-reviewed studies spanning seizure phase modeling, thalamic EEG biomarkers, edge inference, and clinical AI integration. We introduce an eight-axis framework covering neurophysiological foundations, machine learning advances, wearable inference pipelines, large language model (LLM) assistants, and privacy-preserving architectures. A key focus is the emerging role of thalamic stereo-EEG (SEEG) as a high-fidelity substrate for modeling seizure transitions and informing closed-loop interventions. Unlike prior reviews, this work explicitly unifies preictal and postictal phase dynamics with modern AI tools—such as federated learning, explainable deep learning, and agentic reasoning. We highlight gaps in dataset diversity, clinical interpretability, and cross-center generalization, while proposing a translational roadmap toward ethical, explainable, and deployment-ready seizure care.}, archive = {J_ACCESS}, author = {Bhargava Ganti and Karthi Balasubramanian and Sandipan Pati}, doi = {10.1109/ACCESS.2025.3614148}, journal = {IEEE Access}, pages = {169049-169070}, shortjournal = {IEEE Access}, title = {A taxonomy-driven survey of AI for seizure detection: Thalamic signals, phase dynamics, and translational gaps}, volume = {13}, year = {2025}, }
• (2025). Aerial reconfigurable intelligent surface assisted vehicle-to-vehicle communication. ACCESS, 13, 169037-169048. (www)

  @article{ , abstract = {Vehicle-to-vehicle (V2V) communication enhances safety by allowing moving vehicles to exchange real-time information. However, due to dynamic environments, V2V links are vulnerable to blockage due to obstacles. We propose the use of aerial reconfigurable intelligent surface (ARIS) mounted on an unmanned aerial vehicle (UAV) to enhance V2V communication reliability by reflecting the signal between moving vehicles. Then we adopt the power consumption models for multirotor UAVs, where three flight statuses, i.e., forward flight, vertical ascent, and vertical descent, are included into the system model. The optimization problem is formulated to maximize energy efficiency (EE) by jointly optimizing the three-dimensional (3D) position of ARIS and the transmit power of the system, while ensuring a minimum data rate requirement to meet quality-of-service (QoS) constraints. Due to the non-convexity of the formulated problem, the EE optimization is solved using Dinkelbach’s iterative algorithm and interior point method. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, with optimized hybrid ARIS yielding the highest EE compared to passive ARIS scheme.}, archive = {J_ACCESS}, author = {Norlezah Hashim and Chee Yen Leow and Klaus Moessner and Chuan Heng Foh}, doi = {10.1109/ACCESS.2025.3606978}, journal = {IEEE Access}, pages = {169037-169048}, shortjournal = {IEEE Access}, title = {Aerial reconfigurable intelligent surface assisted vehicle-to-vehicle communication}, volume = {13}, year = {2025}, }
• (2025). 3D reconstruction of X-ray skeletal images. ACCESS, 13, 169026-169036. (www)

  @article{ , abstract = {X-ray image enhancement technology effectively improves the contrast between pathological regions and normal tissues, thereby enhancing image segmentation performance. High-quality segmentation results provide reliable sources for three-dimensional reconstruction. This study proposes a three-stage processing framework: 1) X-ray super-resolution enhancement based on the Cbc-SwinIR model (Coordinate-based Convolution - Image Restoration Using Swin Transformer); 2) the application of the MAXIM model (Multi-Axis MLP for Image Processing) to remove artifacts from X-ray images, followed by segmentation of target regions using the SAM model (Segment and Recognize Anything at Any Granularity); 3) monocular 3D reconstruction of X-ray images utilizing the One-2-3-45 model. Experimental results demonstrate outstanding performance in X-ray image enhancement and segmentation. A comprehensive series of tests was conducted on the X-ray image dataset collected in this study. By reconstructing image data corresponding to the angles of the original 2D images from multiple viewpoints of the generated 3D X-ray images, we obtained an average SSIM of 0.86, an average PSNR of 35.70, and an average MSE of 0.0155. The generation time for each 3D image was approximately 45 seconds, demonstrating both the effectiveness and real-time capability of the proposed algorithm.}, archive = {J_ACCESS}, author = {Yibo Zhai and Yuefeng Wang and Junfeng Xu and Weiguo Lin and Zehua Ji}, doi = {10.1109/ACCESS.2025.3613258}, journal = {IEEE Access}, pages = {169026-169036}, shortjournal = {IEEE Access}, title = {3D reconstruction of X-ray skeletal images}, volume = {13}, year = {2025}, }
• (2025). Improving weather predictions accuracy with a hybrid LSTM-transformer model: A study on indian climate. ACCESS, 13, 169014-169025. (www)

  @article{ , abstract = {Weather prediction is a challenging task, particularly in dealing with the diverse geography and weather patterns of various Indian cities throughout the year. This research presents an optimized hybrid model (OHM) that combines long-short-term memory (LSTM) and transformer models to increase the prediction performance for weather variables. The primary objective was to provide accurate weather forecasts for various Indian cities, which contributes to better disaster management, better agricultural sector, and daily lives of people. Weather forecasting was conducted for multiple Indian cities using advanced hybrid models and traditional machine learning algorithms. Data were collected, pre-processed, benchmarked against existing algorithms, and the results were evaluated. OHM was trained and tested using open source Kaggle weather data covering several Indian cities. The proposed hybrid model took advantage of the capabilities of LSTM and transformer to learn temporal dependencies, enhance feature extraction, and manage long-range dependencies more effectively. The OHM was compared with standard machine learning models, comprising random forest, XGBoost, support vector regression, linear regression, and an isolated LSTM model. The results showed an increase of $0.2~^{\circ }$ C in the average annual temperature over the next 10 years and a variation in rainfall of 8% during the monsoon season. An increase in cyclone occurrence 15% was also expected. The OHM outperformed traditional learning models by achieving lower root mean square error (RMSE) values when tested in various Indian cities and provided better forecasts for different weather parameters, such as rainfall, wind speed and temperature. This research proposes a novel hybrid deep learning model combining LSTM and transformer architectures to forecast key weather parameters such as temperature, humidity, precipitation, wind speed, and atmospheric pressure, for various Indian cities.}, archive = {J_ACCESS}, author = {P. Ranjan and V. Akshay and P. R. Girijesh Kumar and M. Jagannath}, doi = {10.1109/ACCESS.2025.3614473}, journal = {IEEE Access}, pages = {169014-169025}, shortjournal = {IEEE Access}, title = {Improving weather predictions accuracy with a hybrid LSTM-transformer model: A study on indian climate}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of LED driver with variable inductor-controlled quasi-resonant converter. ACCESS, 13, 168998-169013. (www)

  @article{ , abstract = {The resonant tank circuit of quasi-resonant converters, which operate based on the resonant switch concept, provides soft switching by shaping the voltage or current of the switch and conducts energy transfer from input to output. Thus, variable frequency switching emerges as a natural control technique. The disadvantage of this technique is that the operating frequency needs to be changed over a wide range to regulate the output power against changing input voltage and output load. As a result, the design of input and output filters becomes difficult, EMI noise increases, and drivers and switches capable of operating at very high frequencies are required. To reduce or eliminate the disadvantages, variable inductor control is proposed for power control of zero voltage switching quasi-resonant buck converter used as an LED driver. The 40W LED driver, with an input voltage ranging from 18V to 25V, is designed to operate at the switching frequency of 150kHz, and its prototype is built. The operating parameters of the variable inductor are determined through finite element analysis. The variable inductor-controlled driver is tested for dynamically changing input voltage and LED module. The test results show that the power switch of the closed-loop controlled driver is turned on under zero voltage conditions against a wide range of varying input voltage, and the LED module current follows the reference current. Test results confirming the validity of the theoretical analysis and the feasibility of the variable inductor-controlled LED driver are presented.}, archive = {J_ACCESS}, author = {Salih Nacar and Kenan Ünal}, doi = {10.1109/ACCESS.2025.3613066}, journal = {IEEE Access}, pages = {168998-169013}, shortjournal = {IEEE Access}, title = {Design and implementation of LED driver with variable inductor-controlled quasi-resonant converter}, volume = {13}, year = {2025}, }
• (2025). Turkish optical character recognition under the lens: A systematic review of language-specific challenges, dataset scarcity, and open-source limitations. ACCESS, 13, 168977-168997. (www)

  @article{ , abstract = {This systematic literature review explores the progress, challenges, and opportunities in the field of Optical Character Recognition (OCR) for the Turkish language. Despite significant advancements, the development of robust Turkish OCR systems faces several obstacles, such as a lack of publicly available datasets, limited open-source solutions, and the underutilization of cutting-edge deep learning techniques. These challenges hinder the creation of OCR systems that can match the capabilities of those developed for languages like English. Focusing on 38 peer-reviewed studies published between 2019 and 2023, this paper provides the first systematic review of Turkish OCR research, offering a comprehensive analysis of the current methods, datasets, and evaluation metrics across both modern Turkish (Latin script) and Ottoman Turkish (Arabic script) contexts. Our findings highlight that while Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Convolutional Recurrent Neural Networks (CRNN) architectures are frequently used, Transformer-based and end-to-end models remain underexplored in Turkish OCR. We also identify data scarcity and the lack of reproducible benchmark datasets as key barriers. By analyzing current research trends, pinpointing challenges, and emphasizing opportunities for future advancements, this review aims to be a valuable resource for researchers and Turkish language recognition. Our study contributes to the field by offering a structured overview of existing methods and proposes practical recommendations for improving dataset availability, encouraging open-source collaboration, and adopting more advanced model architectures.}, archive = {J_ACCESS}, author = {Míraç Göksu Öztürk and Durmus Özkan Sahin and Erdal Kiliç}, doi = {10.1109/ACCESS.2025.3614147}, journal = {IEEE Access}, pages = {168977-168997}, shortjournal = {IEEE Access}, title = {Turkish optical character recognition under the lens: A systematic review of language-specific challenges, dataset scarcity, and open-source limitations}, volume = {13}, year = {2025}, }
• (2025). Hybrid multimodal knowledge graph and meta-learning framework for accurate dynamic RP-VRC prediction in renewable-rich power systems. ACCESS, 13, 168966-168976. (www)

  @article{ , abstract = {Contemporary power grids with substantial renewable energy adoption face significant challenges in predicting dynamic reactive power-voltage response characteristics (RP-VRC) due to the inherent variability and complexity of renewable energy sources (RES). This paper proposes a hybrid multimodal knowledge graph and meta-learning framework that integrates diverse operational data to enable rapid adaptation to new RES installations. The framework constructs a “Turbine-Weather-Operating Regime” knowledge graph where multimodal RES data including text, images, and videos are unified into entity embeddings through deep learning techniques. These embeddings enable zero-shot adaptation by identifying pre-trained models based on graph similarity, reducing the need for extensive recalibration. Additionally, a conditional clustering algorithm analyzes temporal and visual data streams simultaneously to detect high-risk operational modes, enhancing situational awareness. The framework’s innovation lies in systematically combining knowledge graph-driven meta-learning with multimodal fusion to capture complex relationships between RES dynamics and environmental factors. Experimental evaluation on real-world datasets demonstrates superior prediction accuracy and robustness compared to conventional approaches, particularly under unfamiliar operational conditions. This work provides a scalable solution for renewable energy integration with practical benefits for grid reliability and sustainable power management.}, archive = {J_ACCESS}, author = {Nanjun Ye}, doi = {10.1109/ACCESS.2025.3614067}, journal = {IEEE Access}, pages = {168966-168976}, shortjournal = {IEEE Access}, title = {Hybrid multimodal knowledge graph and meta-learning framework for accurate dynamic RP-VRC prediction in renewable-rich power systems}, volume = {13}, year = {2025}, }
• (2025). Neural network coding layer (NNCL): Enhancing deep learning robustness against feature erasure. ACCESS, 13, 168951-168965. (www)

  @article{ , abstract = {This paper proposes a novel Neural Network Coding Layer (NNCL) that applies network coding theory to provide structured redundancy and enable reconstruction of lost features, thereby mitigating information loss problems in deep learning models. Unlike existing skip connections or attention mechanisms, NNCL embeds reconstructible redundancy into intermediate features through a learnable coding process, providing an explicit algebraic restoration mechanism for the original features. This design adapts the core principle of network coding—encoding information recoverably across a channel—for application within the computational graph of a neural network. Extensive experiments on the CIFAR-10 and CIFAR-100 datasets validate NNCL’s effectiveness. Even under a more conventional feature erasure rate of 20%, NNCL consistently improved classification accuracy by up to 8.3 percentage points. The model’s robustness becomes even more pronounced under extreme conditions. In a deliberate stress test involving 60% feature erasure—a scenario where baseline model performance collapses—NNCL dramatically boosted accuracy by up to 40.3 percentage points (e.g., from 19.8% to 60.1% on CIFAR-100). The proposed layer is designed to be modular and has been successfully integrated into various modern architectures, including ResNet and EfficientNet, and Vision Transformer, proving its broad applicability.}, archive = {J_ACCESS}, author = {Chae-Seok Lee and Adeer Khan and Seong-Ju Chang and Ho-Jong Chang}, doi = {10.1109/ACCESS.2025.3610080}, journal = {IEEE Access}, pages = {168951-168965}, shortjournal = {IEEE Access}, title = {Neural network coding layer (NNCL): Enhancing deep learning robustness against feature erasure}, volume = {13}, year = {2025}, }
• (2025). AI, ML, and LLM integration in 5G/6G networks: A comprehensive survey of architectures, challenges, and future directions. ACCESS, 13, 168914-168950. (www)

  @article{ , abstract = {The transition from 5G to 6G networks demands groundbreaking advances in intelligence, adaptability, and security to support emerging applications such as real-time telemedicine, immersive extended reality (XR), and autonomous systems. This article provides a comprehensive analysis of how artificial intelligence (AI), machine learning (ML), and large language models (LLM) are revolutionizing next-generation telecommunications. We present a structured roadmap for integrating these technologies into 6G infrastructure, emphasizing their transformative roles in intelligent network management, dynamic resource allocation, and proactive threat mitigation. By addressing key challenges such as ultralow latency, heterogeneous data handling, and ethical governance, this study bridges theoretical innovations with practical applications. Notable contributions include novel frameworks for AI-enhanced security, self-healing networks, and privacy-preserving techniques like federated learning. Furthermore, we explore critical ethical considerations, including bias mitigation and transparency in AI decision-making, while highlighting emerging research directions such as adaptive learning systems and hybrid AI architectures. This work underscores the synergistic potential of AI and 6G, equipping researchers and industry stakeholders with actionable insights to develop resilient, user-centric networks that will shape the future of global connectivity.}, archive = {J_ACCESS}, author = {Yusuf Usman and Habeeb Oladipupo and Adegboyega D. During and Robert Akl and Robin Chataut}, doi = {10.1109/ACCESS.2025.3608736}, journal = {IEEE Access}, pages = {168914-168950}, shortjournal = {IEEE Access}, title = {AI, ML, and LLM integration in 5G/6G networks: A comprehensive survey of architectures, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Predicting pipe failures: A machine learning approach to asset management in district heating. ACCESS, 13, 168893-168902. (www)

  @article{ , abstract = {As district heating networks (DHNs) across Europe near or surpass their intended service life, the need for proactive, data-driven maintenance and replacement planning is increasing. This paper presents a predictive asset management framework based on machine learning (ML) to assess the fault vulnerability of pipes. To train the ML algorithm, a combined dataset of both geospatial and operational data is implemented. While pressure is often estimated using complex hydraulic simulations, this study proposes and evaluates a lightweight linear pressure approximation method based on expert knowledge. The ML model is applied to a real DHN in Denmark through collaboration with the utility company HOFOR. Due to a significant class imbalance in the data (~1:215), multiple oversampling (OS) strategies are investigated. Model performance is evaluated using fault capture versus length capture (FCLC) curves and area under the curve (AUC) metrics. The inclusion of pressure data is found to improve the AUC by 4.5%, and the model captures over 40% of failures by inspecting only 10% of the network. These results demonstrate the feasibility of using ML-based fault vulnerability prediction as a tool for predictive maintenance and asset replacement planning in DHNs.}, archive = {J_ACCESS}, author = {Tobias Vilbrad Rishøj Jensen and Maryamsadat Tahavori and Hamid Reza Shaker and Hamid Mirshekali}, doi = {10.1109/ACCESS.2025.3612096}, journal = {IEEE Access}, pages = {168893-168902}, shortjournal = {IEEE Access}, title = {Predicting pipe failures: A machine learning approach to asset management in district heating}, volume = {13}, year = {2025}, }
• (2025). Investigation and analysis of solar and wind energy potential for green hydrogen production via alkaline water electrolysis. ACCESS, 13, 168878-168892. (www)

  @article{ , abstract = {Hydrogen is being explored in several industrial applications to produce various chemicals products, including ammonia and methanol. However, conventional hydrogen production is associated with harmful emissions and relies heavily on depleting fossil fuels. Renewable energy sources have been offered as alternative energy supplies to minimize fossil fuels and ensure lower emission levels, therefore, providing a sustainable energy transition. Renewable energy sources, such as solar photovoltaics (PV) and wind turbines (WT), are gaining wide attention for the production of green hydrogen. This article focuses on the storage of renewable energy generation to enhance the production process of green hydrogen. One of the main obstacles to the rapid development of renewable energy is the need for inexpensive, highly efficient energy storage. Integrating renewable energy sources into green hydrogen production stands out as a promising solution to this problem. This work aims to evaluate the potential of hydrogen production by electrolysis from solar PV and wind energies. The implemented methodology is a theoretical model of a caustic electrolyzer connected to a solar PV panel and a wind generator. The study assessed the input/output power of solar and wind energy and calculated the green hydrogen production rate to underline the efficiency of the MPPT controller and renewable-based hydrogen production systems. According to the simulation results obtained using the MATLAB/Simulink environment, the hydrogen production system performs effectively, especially in terms of energy efficiency. Taking into account the final results, the Wind energy demonstrates superior performance in green hydrogen production, achieving 90% efficiency and a rate of 1.2 Nm3/h, significantly outperforming solar photovoltaics’ 82% efficiency and 0.66 Nm3/h.}, archive = {J_ACCESS}, author = {Slah Farhani and Salah S. Alharbi and Saleh S. Alharbi and El Manaa Barhoumi and Faouzi Bacha}, doi = {10.1109/ACCESS.2025.3607973}, journal = {IEEE Access}, pages = {168878-168892}, shortjournal = {IEEE Access}, title = {Investigation and analysis of solar and wind energy potential for green hydrogen production via alkaline water electrolysis}, volume = {13}, year = {2025}, }
• (2025). Identifying key predictors of students’ competency achievement using machine learning models: A bioengineering case study. ACCESS, 13, 168855-168877. (www)

  @article{ , abstract = {Competency-based education (CBE) in higher education demands interpretable and scalable tools to monitor student progress. Current studies on CBE have used small samples in short evaluation periods or have not used machine learning or explainability of the results. This study introduces a robust analytical pipeline that integrates correlation analysis, Factor Analysis of Mixed Data, and explainable machine learning to predict competency achievement in bioengineering programs. Using over 300,000 evaluations from a private Mexican university, Random Forest model achieved outstanding predictive performance in a Stratified 10-fold Cross-validation experiment (AUC = 0.9604–0.9653), outperforming deep neural networks for One-Class Classification in highly imbalanced data. Model interpretability using SHAP highlighted academic and course-related variables, rather than demographic factors, as the strongest predictors, reinforcing the fairness of the evaluation process. This work advances the operationalization of explainable AI in CBE, contributing to the emerging vision of Data-Based Education by providing actionable insights for curriculum design, academic advising, and institutional policy.}, archive = {J_ACCESS}, author = {Julio César Quintana-Zaez and Patricia Vázquez-Villegas and Danilo Valdes-Ramirez}, doi = {10.1109/ACCESS.2025.3613250}, journal = {IEEE Access}, pages = {168855-168877}, shortjournal = {IEEE Access}, title = {Identifying key predictors of students’ competency achievement using machine learning models: A bioengineering case study}, volume = {13}, year = {2025}, }
• (2025). A multimodal deep learning model for cross-project issue classification. ACCESS, 13, 168839-168854. (www)

  @article{ , abstract = {Software continuously evolves through changes, and issue reports encapsulate these change requests. In the GitHub system, a labeling mechanism has been introduced for systematic issue management, but significant effort from developers is required to label and manage these issues. To address this, numerous attempts have been made in previous research to automate issue report classification. However, these attempts have shown limitations in classification accuracy. We experiment to determine if integrating heterogeneous information through a multimodal model that combines text, images, and code from issue reports can improve classification accuracy. Specifically, we investigate whether training the model on extensive issue data can enhance classification accuracy. Experimental results show that the multimodal approach outperforms single-modal models by 5.50-7.01% in terms of F1-Score, demonstrating superior performance. These findings indicate that leveraging heterogeneous data sources in issue reports is effective in improving classification performance.}, archive = {J_ACCESS}, author = {Changwon Kwak and Jueun Heo and Pilsu Jung and Seonah Lee}, doi = {10.1109/ACCESS.2025.3613404}, journal = {IEEE Access}, pages = {168839-168854}, shortjournal = {IEEE Access}, title = {A multimodal deep learning model for cross-project issue classification}, volume = {13}, year = {2025}, }
• (2025). Link availability prediction framework for sub-terahertz wireless LANs based on multi-band propagation information. ACCESS, 13, 168821-168838. (www)

  @article{ , abstract = {This study proposes a link availability prediction framework for sub-terahertz wireless local area networks (LANs). In the sub-terahertz band, to combat severe path loss between an access point (AP) and a station (STA), it is necessary to form a narrow beam. Exhaustive beam search due to a large number of candidate beams causes significant processing overhead in establishing wireless channels between an AP and a STA. To address this issue, we propose a novel framework to predict link availability that utilizes the propagation information of lower frequency bands. To incorporate realistic channel characteristics, we measured the propagation characteristics of signals in the millimeter wave and microwave bands using off-the-shelf wireless devices under various AP/STA positions and orientations. We analyzed the measured characteristics of each band by quantifying the fluctuation and separability of the features in both linear and nonlinear spaces. The analysis revealed complementary characteristics: channel state information (CSI) in the microwave band showed lower fluctuation and higher separability in linear space, while the received signal strength indicator (RSSI) in the millimeter wave band showed greater robustness in linear space and better separability in nonlinear space. Motivated by this complementarity, we designed a probabilistic neural network (PNN)-based architecture that integrates neural encoders to predict the link availability of the sub-terahertz band. Numerical experiments using the measured data demonstrate high performance, and show robustness to fluctuations and adaptability to variations in AP/STA positions and orientations. This study verifies the effectiveness of the proposed link prediction framework for sub-terahertz wireless LANs.}, archive = {J_ACCESS}, author = {Kazunobu Serizawa and Katsuhiro Temma and Kazuto Yano and Abolfazl Mehbodniya and Julian Webber and Toshikazu Sakano and Kazumune Hashimoto}, doi = {10.1109/ACCESS.2025.3611524}, journal = {IEEE Access}, pages = {168821-168838}, shortjournal = {IEEE Access}, title = {Link availability prediction framework for sub-terahertz wireless LANs based on multi-band propagation information}, volume = {13}, year = {2025}, }
• (2025). Link budget analysis for dual-steering-based ground-to-air and satellite-to-air communications in 6G networks. ACCESS, 13, 168804-168820. (www)

  @article{ , abstract = {With the advent of 6G systems, non-terrestrial networks (NTNs) are emerging as a cornerstone for global, uninterrupted connectivity. This study evaluates the downlink link budget in a dual-steering TN–NTN architecture, where an aerial user simultaneously connects to both a terrestrial network and a LEO satellite network with on-board processing (OBP) for seamless connectivity. The analysis considers Ground-to-Air (G2A) and Satellite-to-Air (S2A) downlink link budgets under realistic 6G conditions, examining the effects of altitude, elevation angle, and frequency. The results demonstrate that minimum link margins of 16.04 dB for G2A and 13.4 dB for S2A are achievable, confirming reliable link performance for both configurations. We thoroughly describe the methodology used to perform the link budget computations. Additionally, we observe that in all cases, the results for the S2A scenario are relatively lower than those for G2A. This is attributed to the longer distance and multi-beam interference from other satellites. For example, at an elevation angle of 80°, the link margin in the S2A scenario is approximately 18 dB, whereas in the G2A case, it is about 45 dB—showing that the S2A link margin is nearly 60% lower.}, archive = {J_ACCESS}, author = {Sara Sualiheen and Chaitali J. Pawase and Kyunghi Chang}, doi = {10.1109/ACCESS.2025.3613407}, journal = {IEEE Access}, pages = {168804-168820}, shortjournal = {IEEE Access}, title = {Link budget analysis for dual-steering-based ground-to-air and satellite-to-air communications in 6G networks}, volume = {13}, year = {2025}, }
• (2025). Multi-perspective RGB and infrared dataset for UAV detection. ACCESS, 13, 168792-168803. (www)

  @article{ , abstract = {The increasing presence of Unmanned Aerial Vehicles (UAVs) in both civilian and military domains highlights the need for reliable detection systems for monitoring and surveillance. Although RGB cameras provide high-resolution imagery for UAV detection, their performance degrades significantly under low-light conditions. Conversely, thermal sensors are effective in detecting heat signatures, but lack the spatial resolution required for accurate localization. To overcome these limitations, this study explores a multimodal UAV detection approach based on the fusion of RGB and infrared (IR) data. We introduce a novel dataset comprising synchronized RGB and IR imagery of UAVs, captured from different viewpoints. The dataset includes recordings of several UAV models operating under various lighting and background conditions, ensuring a comprehensive benchmark for detection tasks. To present some metrics, we trained state-of-the-art deep learning-based object detection models on our data. The results demonstrate that by using a specific dataset, the detection accuracy can be enhanced, particularly in challenging conditions such as sunshine, sunset, and darkness, including different and variable backgrounds. This study highlights the potential of multimodal sensing for UAV detection and provides a foundation for future research on robust aerial surveillance systems.}, archive = {J_ACCESS}, author = {Denis Tavaris and Alberto de Zan and Andrea Toma and Gian Luca Foresti and Ivan Scagnetto and Niki Martinel}, doi = {10.1109/ACCESS.2025.3614069}, journal = {IEEE Access}, pages = {168792-168803}, shortjournal = {IEEE Access}, title = {Multi-perspective RGB and infrared dataset for UAV detection}, volume = {13}, year = {2025}, }
• (2025). SilentWhisper: Enabling user-independent ultra-low volume whispered speech recognition. ACCESS, 13, 168782-168791. (www)

  @article{ , abstract = {Existing Silent Speech Interfaces (SSIs), which enable private voice input, are often hampered by user dependency—a critical barrier requiring extensive training data from each individual. To address this, we propose SilentWhisper, a system that achieves truly user-independent recognition of ultra-low volume (45-50dB) whispered speech using only standard microphones. By fine-tuning a pre-trained speech recognition model on a multi-user dataset, our method allows anyone to use the system immediately without per-user adaptation. We validate our user-independent claim through rigorous leave-one-out cross-validation with 13 participants, which confirmed a significant reduction in the average Character Error Rate (CER) from 17.0% to 7.3% (a 57.1% improvement). Crucially, this specialized fine-tuning is also robust; the model maintains performance comparable to the powerful baseline on unseen texts, thus avoiding catastrophic forgetting. Evaluated on a 1,002-word vocabulary, our system’s strong performance represents a significant step towards a practical, private conversational interface that is accessible to all.}, archive = {J_ACCESS}, author = {Hirotaka Hiraki and Jun Rekimoto}, doi = {10.1109/ACCESS.2025.3612004}, journal = {IEEE Access}, pages = {168782-168791}, shortjournal = {IEEE Access}, title = {SilentWhisper: Enabling user-independent ultra-low volume whispered speech recognition}, volume = {13}, year = {2025}, }
• (2025). Quantitative analysis of centralization in the bitcoin lightning network through centrality metrics. ACCESS, 13, 168761-168781. (www)

  @article{ , abstract = {The Bitcoin Lightning Network, a Layer-2 more scalable solution for the Bitcoin blockchain, has emerged to address the scalability challenges faced by the Bitcoin network. However, the centralization of the Lightning Network has been a growing concern, as the concentration of highly active nodes within it could compromise the decentralized nature of the Bitcoin ecosystem. In this research paper, we conduct a quantitative analysis of centralization in the Lightning Network using various centrality metrics, such as Gini coefficient, Nakamoto coefficient, Herfindahl-Hirschman Index, Theil Index and Shannon entropy. The proposed methodology includes data collection, clustering nodes into entities and setting up the experimental environment. The quantitative analysis of centralization in the BLN reveals complex results, with the Gini coefficient for node capacity distribution increasing from 0.85 to 0.97 over eight yearly timestamps, indicating growing inequality. Meanwhile, the Nakamoto coefficient fluctuated, suggesting that while control over network resources is uneven, it may still be more decentralized than previously thought.}, archive = {J_ACCESS}, author = {Laura Atmanavičiūtė and Tomas Vanagas and Saulius Masteika}, doi = {10.1109/ACCESS.2025.3614085}, journal = {IEEE Access}, pages = {168761-168781}, shortjournal = {IEEE Access}, title = {Quantitative analysis of centralization in the bitcoin lightning network through centrality metrics}, volume = {13}, year = {2025}, }
• (2025). A framework for data-driven simulation of electrical circuits based on discrete-continuous optimization. ACCESS, 13, 168743-168760. (www)

  @article{ , abstract = {In this work, we present and investigate a new framework for the data-driven simulation of electrical circuits based on discrete-continuous optimization. For such an end, we provide a formal derivation of the data-driven discrete forward-dynamics problem and for its recursive solution, we adopt a strategy relying on an alternating direction method. Even when we focus primarily on capacitive elements (i.e., the relation between charges and voltages in the capacitive elements is completely replaced by an experimental and probably incomplete data set) and circuits of extremely simple topology, the approach has great potential for its generalization. Finally, we study several examples of increasing complexity and show through their analysis, the very promising features of this novel approach. In particular, we investigate the precision of the resulting numerical method, the ability to deal with nonlinearity contained in the data sets, the ability to handle data sets of varying size and the robustness with respect to noisy data. Where the last two, represent key features to establish data-driven digital twins.}, archive = {J_ACCESS}, author = {Cristian G. Gebhardt and Bruno A. Roccia and José M. Bossio and Guillermo R. Bossio}, doi = {10.1109/ACCESS.2025.3613573}, journal = {IEEE Access}, pages = {168743-168760}, shortjournal = {IEEE Access}, title = {A framework for data-driven simulation of electrical circuits based on discrete-continuous optimization}, volume = {13}, year = {2025}, }
• (2025). Uncertainty-aware multi-modal learning for autonomous pallet handling in dynamic warehouse scenarios. ACCESS, 13, 168732-168742. (www)

  @article{ , abstract = {Autonomous pallet transport in dynamic warehouses, where human workers and automated guided vehicles (AGVs) coexist, remains highly challenging due to sensory heterogeneity, environmental uncertainty, and strict real-time requirements. To address these issues, we propose an adaptive learning framework that integrates multi-modal sensory inputs, an Interactive Multiple Model (IMM) Kalman filter for trajectory forecasting, and Proximal Policy Optimization (PPO) for policy learning. A hybrid fusion strategy—combining early latent projection and shrinkage-enhanced cross-attention—ensures temporal-spatial alignment and suppresses noisy modalities, while a multi-objective reward balances safety, efficiency, and task success. Experiments in a high-fidelity warehouse simulator show that our method achieves a 78.5 % success rate, reducing collisions by 29 % and improving trajectory stability by 38% compared to PPO, MPC, SAC, and Transformer-based baselines. Real-time evaluation further confirms an average cycle latency of 33.3 ms, meeting industrial deployment standards. These results demonstrate that the proposed IMM-PPO framework effectively unifies predictive filtering, uncertainty-aware fusion, and reinforcement learning, enabling robust and intelligent automation for real-world logistics scenarios.}, archive = {J_ACCESS}, author = {Xiaoming Zhao and Ye Yang and Yunhong Niu and Xinlei Li}, doi = {10.1109/ACCESS.2025.3613729}, journal = {IEEE Access}, pages = {168732-168742}, shortjournal = {IEEE Access}, title = {Uncertainty-aware multi-modal learning for autonomous pallet handling in dynamic warehouse scenarios}, volume = {13}, year = {2025}, }
• (2025). Streamlining plug-and-charge authorization for electric vehicles with OAuth 2 and OpenID federation. ACCESS, 13, 168711-168731. (www)

  @article{ , abstract = {The Plug-and-Charge (PnC) standard defined in ISO 15118 facilitates simple Electric Vehicle (EV) charging with modern Charge Points (CPs). When drivers plug in their EV into a CP, the EV authenticates itself to the CP with a so-called contract certificate. Then, the CP automatically initiates charging and billing. In this paper, we identify gaps in PnC regarding the installation process of the contract certificate in EVs. We propose an alternative approach based on modern extensions of OAuth 2, i.e., OpenID Federation with a Device Authorization Grant and a Rich Authorization Request. We perform a symbolic protocol verification using the Tamarin prover to prove security, provide an open-source implementation to demonstrate the feasibility of the concept, and evaluate the performance of the prototype. The new approach is more user-friendly, more secure, and features a less complex EV charging authorization than existing solutions. The proposed cross-device authorization flow can also be applied to use cases beyond EV charging, where users authorize IoT devices from a trusted smartphone.}, archive = {J_ACCESS}, author = {Jonas Primbs and Dustin Kern and Michael Menth and Christoph Krauß}, doi = {10.1109/ACCESS.2025.3613667}, journal = {IEEE Access}, pages = {168711-168731}, shortjournal = {IEEE Access}, title = {Streamlining plug-and-charge authorization for electric vehicles with OAuth 2 and OpenID federation}, volume = {13}, year = {2025}, }
• (2025). XAI driven software defect prediction using adaptive feature engineering coupled with autoencoder and multi-layer perceptron: An empirical study. ACCESS, 13, 168693-168710. (www)

  @article{ , abstract = {Software defect prediction is essential for ensuring the reliability and robustness of software prototypes during development. The current study has proposed a novel strategy that integrates explainable artificial intelligence (XAI) techniques with adaptive feature engineering and autoencoder neural networks to improve defect prediction accuracy and interpretability. Adaptive feature engineering processes input data to identify critical features, while autoencoders handle non-linear datasets, reduce noise, and generate meaningful latent representations by learning underlying data patterns. A Multi-Layer Perceptron (MLP) is employed to classify code snippets and localize defects, leveraging its ability to manage complex data patterns and diverse input features. To enhance transparency and trust in model predictions, the XAI component provides insights into feature significance and classification outcomes. Empirical evaluations were conducted on the PROMISE dataset, with performance assessed using metrics such as sensitivity, specificity, F1-score, and the Matthews correlation coefficient. The proposed approach has demonstrated superior accuracy compared to other defect prediction methods, highlighting its effectiveness in identifying defective code snippets and enhancing software quality.}, archive = {J_ACCESS}, author = {Parvathaneni Naga Srinivasu and M. Sailaja and Sujatha Canavoy Narahari and Paolo Barsocchi and Akash Kumar Bhoi}, doi = {10.1109/ACCESS.2025.3603451}, journal = {IEEE Access}, pages = {168693-168710}, shortjournal = {IEEE Access}, title = {XAI driven software defect prediction using adaptive feature engineering coupled with autoencoder and multi-layer perceptron: An empirical study}, volume = {13}, year = {2025}, }
• (2025). Recommendation of A-share stock-based graph dual-attention self-learning. ACCESS, 13, 168676-168692. (www)

  @article{ , abstract = {This paper presents an innovative stock recommendation system tailored for the A-share market. Our approach leverages graph learning to construct a concept-stock graph from financial documents. These documents serve as evidential data, enriching the understanding of relationships between stocks and investment themes. At the core of our approach is a dual-attention mechanism that adaptively weights informative edges, enhancing the quality of node embeddings. To eliminate reliance on expert annotations, we employ a self-supervised learning objective that autonomously identifies relevant stocks based on document-concept-stock interactions. Empirical results on two real-world datasets—Jinrongjie and Tonghuashun—demonstrate that our method substantially outperforms traditional and state-of-the-art baselines across multiple key metrics. On the Jinrongjie dataset, our model improves precision at 5 (P@5) by 15% (from 0.524 to 0.603), recall at 30 (R@30) by 10% (from 0.428 to 0.472), and Mean Average Precision (MAP) by 9% (from 0.398 to 0.434) over the best existing method. Similar gains are observed on the Tonghuashun dataset. These results highlight the robustness, scalability, and effectiveness of our graph-based self-supervised learning framework in navigating the dynamic and complex financial market landscape, offering actionable insights for strategic investment decisions.}, archive = {J_ACCESS}, author = {Hanyang Chen and Adisorn Sirikham and Jessada Konpang}, doi = {10.1109/ACCESS.2025.3614302}, journal = {IEEE Access}, pages = {168676-168692}, shortjournal = {IEEE Access}, title = {Recommendation of A-share stock-based graph dual-attention self-learning}, volume = {13}, year = {2025}, }
• (2025). PGTAD: Real-time and lightweight multivariate time-series anomaly detection for IoT using patch gate GRU autoencoder. ACCESS, 13, 168654-168675. (www)

  @article{ , abstract = {The escalating diversity of Internet of Things (IoT) devices has intensified security challenges, as signature-based and heavyweight machine learning (ML) defenses often fall short in resource-constrained edge environments, unable to meet real-time demands. To overcome these limitations, we introduce Patch Gate Time-series Anomaly Detection (PGTAD), a novel, lightweight, unsupervised multivariate time-series (MVTS) anomaly detection algorithm specifically tailored for IoT network traffic. PGTAD is a Gated recurrent units (GRU) autoencoder-based framework that intelligently integrates features from three perspectives—forward sequences, backward sequences, and local patch segments—using a Gate Mechanism (GM). It employs sinusoidal positional encoding for temporal awareness and a patch operation to capture localized patterns. Anomaly detection is achieved via Mahalanobis distance on reconstruction errors. Extensive experiments on publicly available IoT network traffic datasets: Edge-IIoTset, CICIoT2023, CICIoMT2024, and CICIoV2024, utilizing both packet-based and flow-based feature perspectives, demonstrated PGTAD’s superior performance. Evaluated using various F-score metrics (including point-wise, composite, and affiliation) and inference throughput, PGTAD achieved an average F-score of 0.92 and sustained over 480K packets per second on an NVIDIA Jetson Orin Nano 8GB development kit. This confirms its effectiveness and generalizability for real-time detection in IoT edge environments.}, archive = {J_ACCESS}, author = {Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin}, doi = {10.1109/ACCESS.2025.3610684}, journal = {IEEE Access}, pages = {168654-168675}, shortjournal = {IEEE Access}, title = {PGTAD: Real-time and lightweight multivariate time-series anomaly detection for IoT using patch gate GRU autoencoder}, volume = {13}, year = {2025}, }
• (2025). TriP-LLM: A tri-branch patch-wise large language model framework for time-series anomaly detection. ACCESS, 13, 168643-168653. (www)

  @article{ , abstract = {Time-series anomaly detection plays a central role across a wide range of application domains. With the increasing proliferation of the Internet of Things (IoT) and smart manufacturing, time-series data has dramatically increased in both scale and dimensionality. This growth has exposed the limitations of traditional statistical methods in handling the high heterogeneity and complexity of such data. Inspired by the recent success of large language models (LLMs) in multimodal tasks across language and vision domains, we propose a novel unsupervised anomaly detection framework: A Tri-Branch Patch-wise Large Language Model Framework for Time-Series Anomaly Detection (TriP-LLM). TriP-LLM integrates local and global temporal features through a triple-branch design comprising Patching, Selecting, and Global modules, to encode the input time-series into patch-wise representations, which are then processed by a frozen, pretrained LLM. A lightweight patch-wise decoder reconstructs the input, from which anomaly scores are derived. We evaluate TriP-LLM on several public benchmark datasets using PATE, a recently proposed threshold-free evaluation metric, and conduct all comparisons within a unified open-source framework to ensure fairness. Experimental results show that TriP-LLM consistently outperforms recent state-of-the-art (SOTA) methods across all datasets, demonstrating strong detection capabilities. Furthermore, through extensive ablation studies, we verify the substantial contribution of the LLM to the overall architecture. Compared to LLM-based approaches using Channel Independence (CI) patch processing, TriP-LLM achieves significantly lower memory consumption, making it more suitable for GPU memory-constrained environments. All code and model checkpoints of TriP-LLM are publicly available on https://github.com/YYZStart/TriP-LLM.git}, archive = {J_ACCESS}, author = {Yuan-Cheng Yu and Yen-Chieh Ouyang and Chun-An Lin}, doi = {10.1109/ACCESS.2025.3613663}, journal = {IEEE Access}, pages = {168643-168653}, shortjournal = {IEEE Access}, title = {TriP-LLM: A tri-branch patch-wise large language model framework for time-series anomaly detection}, volume = {13}, year = {2025}, }
• (2025). E-commerce user shopping preference ranking toward million-scale products: A hierarchical feature learning and huge purchase graph clustering framework. ACCESS, 13, 168628-168642. (www)

  @article{ , abstract = {Modern e-commerce platforms face significant challenges in understanding user preferences at scale, particularly when dealing with million-product catalogs and diverse shopping behaviors. Traditional approaches struggle to 1) extract meaningful patterns from sparse, high-dimensional behavioral data, 2) efficiently cluster users with similar preferences, and 3) generate personalized recommendations that balance individual tastes with broader community trends. We propose a scalable graph-based framework that addresses these challenges through three key innovations. First, our hierarchical feature learning system processes diverse behavioral signals - including preferred product categories, dress colors, and visual styles - using weakly supervised learning to ensure comprehensive preference coverage. Second, a geometric selection strategy filters noisy features, producing compact yet expressive user representations that are projected into a latent topic space for precise differentiation. Third, we construct a weighted user similarity graph enabling efficient community detection at scale, revealing preference patterns ranging from bold aesthetics to minimalist styles. The complete pipeline integrates these components with a ranking module that fuses individual user profiles with community embeddings, achieving personalized recommendations that reflect both specific user behaviors and broader preference trends. Extensive evaluations on a dataset of over one million users demonstrate our framework’s superiority in both clustering quality (28% higher NMI than baseline methods) and recommendation accuracy (15% improvement in recall@k), while maintaining computational efficiency suitable for production deployment.}, archive = {J_ACCESS}, author = {Mingyuan Li and Chun-Ming Yang}, doi = {10.1109/ACCESS.2025.3612925}, journal = {IEEE Access}, pages = {168628-168642}, shortjournal = {IEEE Access}, title = {E-commerce user shopping preference ranking toward million-scale products: A hierarchical feature learning and huge purchase graph clustering framework}, volume = {13}, year = {2025}, }
• (2025). A switching cabinet contact temperature anomaly classification warning method based on dual-stream residual network and YOLOv10 enhancement. ACCESS, 13, 168612-168627. (www)

  @article{ , abstract = {Abnormal contact temperature of high-voltage switchgear poses a threat to the safety of the power system, and traditional detection methods have limitations in accuracy and efficiency. This study proposes a dual-stream residual-YOLOv10 hierarchical warning method by integrating infrared thermal imaging and visible light image data. The method uses cross-attention mechanisms for spatiotemporal alignment and improves the C2f structure of YOLOv10, combining spatial refinement units and channel refinement units to enhance small target localization accuracy. A hybrid local channel attention module is introduced to improve feature capture. The method combines absolute temperature values and rate of change for comprehensive scoring to achieve hierarchical warnings, and optimizes the model through online learning and knowledge distillation. Experimental results show that in performance tests, the final training accuracy of the model approaches 98%, with loss values stabilizing around 0.3. Compared with other algorithms, the model performs well across various abnormal category recognition. Application analysis indicates that after applying this model, the average response time for abnormal detection has been reduced from over 15 minutes to 4-8 minutes, and the false alarm rate has decreased from 7%-8% to 2%-3%. In different scenarios, the contact detection accuracy exceeds 95%, and the false alarm rate is controlled at 1.23%-2.15%, with the three-level warning response time being less than 19ms. In summary, this method effectively addresses the challenge of hierarchical warning for abnormal contact temperatures in switchgear, offering high reliability and strong practicality, performing excellently across multiple scenarios, and demonstrating promising application prospects.}, archive = {J_ACCESS}, author = {Fuxiang Li and Xiaoliang Zeng and Yu Song and Xinsheng Lan and Mingwei Li and Fangqiang Wang}, doi = {10.1109/ACCESS.2025.3614015}, journal = {IEEE Access}, pages = {168612-168627}, shortjournal = {IEEE Access}, title = {A switching cabinet contact temperature anomaly classification warning method based on dual-stream residual network and YOLOv10 enhancement}, volume = {13}, year = {2025}, }
• (2025). Temperature and dye concentration effects on camera calibration in underwater environments. ACCESS, 13, 168592-168611. (www)

  @article{ , abstract = {Camera calibration methods in underwater environments are widely used for computer vision applications, such as 3D reconstruction. In the present study, the effects of temperature and dye concentration on the camera calibration matrix were analyzed for underwater environments. Experimental results showed that both effects changed the estimations of the principal point, reprojection error and focal length. Zhang estimations presented a more linear tendency of the focal length and a lower reprojection error than Bouguet estimations in diverse underwater conditions. According to the analysis results, it is highly recommended to consider these effects during camera calibration tasks in underwater environments for 3D reconstruction and other vision applications.}, archive = {J_ACCESS}, author = {Cristian H. Sánchez-Saquín and Leonardo A. Baldenegro-Pérez and Leonardo Barriga-Rodríguez and María I. Medina-Montes and Juan M. Barrera Fernández and Tomás Salgado-Jiménez}, doi = {10.1109/ACCESS.2025.3613461}, journal = {IEEE Access}, pages = {168592-168611}, shortjournal = {IEEE Access}, title = {Temperature and dye concentration effects on camera calibration in underwater environments}, volume = {13}, year = {2025}, }
• (2025). AI-based predictive modeling and NSGA-II optimization for eco-driving route planning in electric vehicles. ACCESS, 13, 168580-168591. (www)

  @article{ , abstract = {As EVs reshape the automotive industry, eco-driving and route optimization become essential in reducing energy inefficiencies, especially in urban settings where frequent acceleration and deceleration degrade battery performance. This study proposes a mathematical model for EV energy consumption and applies Multi-Objective Optimization (MOO) with NSGA-II (Non-dominated Sorting Genetic Algorithm II) to minimize energy loss while extending driving range through route selection, traffic avoidance, and eco-driving strategies. The framework integrates real-time traffic and weather data from the TomTom Application Programming Interface (API) along with historical traffic records from NYC OpenData. Vehicle-specific data from the Volkswagen eGolf further trains the model under real driving conditions, producing the energy consumption dataset $E_{Total}$ . Using the $E_{Total}$ dataset, a predictive algorithm improves energy forecasting accuracy by incorporating traffic flow, road topology, and vehicle dynamics. Numerical simulation results confirm the model’s effectiveness in balancing energy use and travel time through Pareto-optimal decision-making. The AI model generated 100 predictive route options between Times Square and the Holland Tunnel. Among these, route 2 (5.17 km) was identified as the most efficient, requiring only 1.03 kWh of energy $E_{Total}$ . These findings demonstrate the potential of AI in eco-routing to extend EV range and enhance driver-assistance and autonomous vehicle systems.}, archive = {J_ACCESS}, author = {Muhammad Arsalan Jalees Abro and Abdul Sattar Larik and Mohammad Alsulami and Irfan Ahmed Halepoto and Sultan Alyami and Samar M. Alqhtani and Ali Alqazzaz and Asadullah Shaikh}, doi = {10.1109/ACCESS.2025.3613479}, journal = {IEEE Access}, pages = {168580-168591}, shortjournal = {IEEE Access}, title = {AI-based predictive modeling and NSGA-II optimization for eco-driving route planning in electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Physics-informed neural network-based open set classification of neighboring vehicle motion for decision-making in autonomous driving. ACCESS, 13, 168561-168579. (www)

  @article{ , abstract = {In this paper, a method for inferring the motion intentions of a neighboring vehicle ahead of an ego vehicle using a physics-informed deep neural network-based open-set classification approach is proposed. Relative motion data from real-world driving were categorized into known and unknown scenarios, with key feature trajectories represented as spatiotemporal 3D input data. A convolutional long short-term memory architecture was designed, and a novel loss function was proposed to incorporate physics-informed perspective and constraints, with integrated loss function’s convergence demonstrated for training. The proposed method was evaluated against comparative five classifiers in terms: 1) classification accuracy for known classes in single scenarios; 2) open-set classification accuracy; 3) analysis by deep reduced feature visualization; 4) generalization performance to unknown data; and 5) classification robustness and in-path decision validity in continuous scenarios. Results showed a 23.5% average improvement in accuracy, the highest generalization performance, and superior robustness, enabling faster and more reliable in-path detection compared to conventional radar including in ambiguous situations.}, archive = {J_ACCESS}, author = {Jin Ho Yang and Woo Young Choi and Chung Choo Chung}, doi = {10.1109/ACCESS.2025.3613724}, journal = {IEEE Access}, pages = {168561-168579}, shortjournal = {IEEE Access}, title = {Physics-informed neural network-based open set classification of neighboring vehicle motion for decision-making in autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Efficient frequency control in microgrids using padula and visioli-tuned PID strategy based on FOPDT model. ACCESS, 13, 168546-168560. (www)

  @article{ , abstract = {Frequency instability of an isolated microgrid (IMG) often arises due to load variations and uncertain power outputs of renewable sources. This contribution addresses the frequency instability issue by designing a Padula and Visioli (PV) tuned proportional-integral-derivative (PID) controller for IMG. The considered IMG model is novel due to the integration of eco-friendly generation sources, including a biogas turbine generator (BTG) and a biodiesel engine generator (BEG), along with other distributed generation units (DGUs) and energy storage devices (ESDs). In this contribution, mathematical modeling of considered units is derived as first-order model. Further, an overall transfer function of the IMG system is obtained using first-order transfer functions of IMG components and system dynamics. Thus, the obtained transfer function is then approximated into a first-order plus time delay (FOPTD) model to facilitate a simpler PID control design. PID controller parameters are computed using PV method for set point (SP) and load disturbance (LD) modes, keeping maximum sensitivity as a constraint. Various PID controller i.e. PID- $PV_{SP1.4}$ , PID- $PV_{SP2.0}$ , PID- $PV_{LD1.4}$ , and PID- $PV_{LD2.0}$ is designed for SP and LD modes. The efficacy of PID control design is tested by analyzing its impact on frequency deviation and mitigation of error with acceptable transient response for SP and LD mode. A comparative study is also performed based on tabulated data featuring error indices, time domain indicators, control efforts, and frequency plots to validate PV method-based PID control designs for IMG. Moreover, the real-time simulator, i.e., OPAL-RT, is utilized to further validate the efficacy of the PID controller in frequency error mitigation of the considered IMG model.}, archive = {J_ACCESS}, author = {Tarun Kumar Bashishtha and Vinay Pratap Singh and Rishav Dubey}, doi = {10.1109/ACCESS.2025.3614178}, journal = {IEEE Access}, pages = {168546-168560}, shortjournal = {IEEE Access}, title = {Efficient frequency control in microgrids using padula and visioli-tuned PID strategy based on FOPDT model}, volume = {13}, year = {2025}, }
• (2025). The role of large language models in designing reliable networks for internet of things: A short review of most recent developments. ACCESS, 13, 168527-168545. (www)

  @article{ , abstract = {The rapid growth of Internet of Things (IoT) networks has increased the need for intelligent, flexible, and scalable networking solutions. This paper reviews the use of Large Language Models (LLMs) to improving network protocols, automate decision-making, and strengthen security in IoT networks. A detailed analysis was conducted to classify the existing research based on applications, network types, methodologies, and performance metrics. LLMs have been used in network configuration, security monitoring, cyber threat detection, federated learning, and for improving network performance. Their integration with edge computing, 6G networks, and AI-driven network control enables real-time network adjustment, automated troubleshooting, and efficient traffic management. However, challenges such as high computing demands, high energy consumption, security risks, and slow adaptation in dynamic networks still exist. This study identifies emerging trends, including LLM-based self-learning networks, privacy-aware AI training, and hybrid AI models that combine graph-based neural networks, reinforcement learning, and multimodal AI. By reviewing recent research from 2023 to early-2025, this study provides a clear understanding of how LLMs transform the IoT and network management. The discussion highlights future research directions, focusing on decentralized AI frameworks, optimized model training, and AI-driven network automation, with the aim of developing more efficient, secure, and reliable network infrastructures.}, archive = {J_ACCESS}, author = {Melchizedek Alipio and Miroslav Bures}, doi = {10.1109/ACCESS.2025.3614246}, journal = {IEEE Access}, pages = {168527-168545}, shortjournal = {IEEE Access}, title = {The role of large language models in designing reliable networks for internet of things: A short review of most recent developments}, volume = {13}, year = {2025}, }
• (2025). Efficient and robust graph coloring algorithm for graph coloring problem: Malatya vertex coloring algorithm. ACCESS, 13, 168512-168526. (www)

  @article{ , abstract = {The graph coloring problem involves coloring the nodes of a graph using the minimum number of colors such that no two adjacent nodes share the same color. This NP-hard problem has various real-world applications, including scheduling problems, map coloring, and resource allocation. Although numerous algorithms have been proposed in the literature to solve this problem, there is a lack of effective and robust algorithms that perform well across different types of graphs. In this study, we propose the Malatya Vertex Coloring Algorithm, an efficient and robust solution for the graph coloring problem. The proposed algorithm calculates the Malatya centrality value for each node by summing the ratios of the node’s degree to the degrees of its neighboring nodes. Starting from the node with the highest Malatya centrality value, the graph is colored such that each node is assigned a color different from its neighbors. The effectiveness of the proposed algorithm is demonstrated through extensive testing and analysis. First, experiments were conducted on benchmark graphs from the literature, varying in size and complexity based on the number of nodes and edges. Additionally, tests were performed on social network graphs (which model social connections and behaviors) as well as randomly generated graphs with different densities and sizes. The results show that the proposed algorithm outperforms well-known existing methods such as Welsh-Powell, Greedy Coloring, DSatur, and RLF in terms of efficiency, robustness, and success rate. Moreover, in some graph types, it yields better results than MSISCA, another efficient and robust algorithm. It is mathematically proven that the proposed algorithm provides a solution in polynomial time for any arbitrary graph and guarantees an optimal solution for specific graph classes. The analyses and experimental results demonstrate that the Malatya Vertex Coloring Algorithm delivers effective and robust solutions for the graph coloring problem across various graph types without any constraints or parameter dependencies.}, archive = {J_ACCESS}, author = {Selman Yakut}, doi = {10.1109/ACCESS.2025.3614296}, journal = {IEEE Access}, pages = {168512-168526}, shortjournal = {IEEE Access}, title = {Efficient and robust graph coloring algorithm for graph coloring problem: Malatya vertex coloring algorithm}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid robot for in-pipe maintenance and inspection. ACCESS, 13, 168498-168511. (www)

  @article{ , abstract = {Pipelines play a crucial role in the movement of key resources such as water, oil, and gas in today’s globe. The continued development of urbanization is resulting in a significant annual increase in the number of pipes. The prompt maintenance and inspection of pipeline infrastructure are becoming difficult due to the extensive length of the pipelines and restricted external accessibility. Therefore, certain sections of the pipeline require internal repairs. The novelty of this research letter lies in a hybrid robot with an implemented manipulator to conduct maintenance work via teleoperation control. This hybrid robot is capable of inspecting and performing pipeline renovation tasks, including debris removal and welding operations. A laboratory prototype was developed for experimental validation in a controlled environment. Experimental results demonstrate that the debris detection time during in-pipe cleaning was approximately 0.87 s, enabling responsive operator feedback. Moreover, the robot successfully completed a simulated welding task by tracing a rectangular path with mean positional errors of 0.0277 m (x-axis), 0.0030 m (y-axis), and 0.0094 m (z-axis), showcasing high accuracy and precise linear motion critical for in-pipe repair operations. This study article is structured as follows: robot design concept, robot control, anomaly detection and inspection, in-pipe maintenance, and conclusion.}, archive = {J_ACCESS}, author = {Dias Akimbay and Yersaiyn Bushanov and Manat Nursultan and Darkhan Zholtayev and Daniyar Dauletiya and Perizat Rakhmetova and Azamat Yeshmukhametov}, doi = {10.1109/ACCESS.2025.3609193}, journal = {IEEE Access}, pages = {168498-168511}, shortjournal = {IEEE Access}, title = {A novel hybrid robot for in-pipe maintenance and inspection}, volume = {13}, year = {2025}, }
• (2025). A dynamic optimization method of material distribution intervals and quantity for high-end equipment manufacturing. ACCESS, 13, 168478-168497. (www)

  @article{ , abstract = {To address the logistics problems faced by high-end equipment manufacturers in their two-stage supply chain, this study proposes an optimization strategy for material distribution intervals and quantities. The strategy is based on an improved Mayfly algorithm. These problems include difficulties in accurately responding to production demands, high logistics distribution costs, and low material quality. We have established a two-stage collaborative optimization model of material distribution. This model is adapted to production takt time and its intelligent algorithm. Firstly, we construct a two-stage material distribution interval and quantity optimization model. This model incorporates the total logistics cost of the two-stage supply chain as the optimization objective. It uses Just-in-time delivery and delivery time as constraints. It also considers the complexity of multi-supplier proportional supply. The model is built from multi-supplier to the manufacturer’s warehouse and from the manufacturer’s warehouse to the assembly line side, taking into account material quality. Then, based on this model, we propose an improved Mayfly algorithm. This improved algorithm integrates chaotic maps and adaptive inertia weight. These enhancements aim to improve the optimization ability and convergence speed of the Mayfly algorithm. Finally, we verify the feasibility and effectiveness of the model and algorithm. We conduct an example application under different production takt times. The results demonstrate that the developed model and algorithm have competitive performance in addressing the studied problems, achieving significant reductions in material distribution costs.}, archive = {J_ACCESS}, author = {Zhiwen Zhang and Liping Chang and Xiaoying Yang and Shuting Zhang}, doi = {10.1109/ACCESS.2025.3609881}, journal = {IEEE Access}, pages = {168478-168497}, shortjournal = {IEEE Access}, title = {A dynamic optimization method of material distribution intervals and quantity for high-end equipment manufacturing}, volume = {13}, year = {2025}, }
• (2025). Tourist attractions recommendation by using enhanced location knowledge graph. ACCESS, 13, 168463-168477. (www)

  @article{ , abstract = {Location prediction has advanced through recommendation systems based on knowledge graphs, which excel in semantic and relational modeling. However, traditional knowledge graphs are primarily built around urban points of interest (POIs). Given that tourist activities outside the city exhibit unique patterns compared to daily urban behaviors, applying location-based knowledge graphs to tourist attraction prediction remains an ongoing research challenge. Furthermore, most studies focus on spatiotemporal data, often sidelining valuable textual information due to issues such as data scarcity and challenges in extracting meaningful semantics. To address these limitations, in our previous work, we introduced KGTR (Knowledge Graph with Tourist Reviews), a novel framework for predicting tourist attractions. Our framework goes beyond the traditional focus on temporal and spatial data by incorporating descriptive information on POIs visited by tourists, enhancing the knowledge graph’s expressiveness and semantic richness. Leveraging embeddings obtained through matrix factorization, we augmented the embeddings of locations and users with textual data to improve prediction accuracy. In this work, we compared three review embedding strategies: (a) before matrix factorization, (b) after matrix factorization, and (c) a combined before-and-after approach. To evaluate the performance, we collected a real-world tourism dataset containing POI user reviews. Results on both dense and sparse data indicate that the after factorization embedding consistently delivers the best performance. KGTR outperforms state-of-the-art methods, achieving an approximately 62.0% improvement in prediction accuracy. We further explored the impact of user clustering and found that finer cluster granularity enhances recommendation accuracy by better capturing user group preferences. Notably, the proposed after factorization embedding strategy remains the most effective. Additionally, we also compare KGTR with LightGCN, a widely used model in recommendation systems. Our experiments indicate that KGTR achieves the best performance, and by using text-enhanced information after matrix factorization, KGTR improves performance by 79.9% compared to LightGCN.}, archive = {J_ACCESS}, author = {Jing Xu and Zhengyang Bai and Qiang Ma}, doi = {10.1109/ACCESS.2025.3613788}, journal = {IEEE Access}, pages = {168463-168477}, shortjournal = {IEEE Access}, title = {Tourist attractions recommendation by using enhanced location knowledge graph}, volume = {13}, year = {2025}, }
• (2025). Deepfake audio detection: A comparative study of advanced deep learning models. ACCESS, 13, 168447-168462. (www)

  @article{ , abstract = {The rapid advancements in artificial intelligence (AI) have significantly enhanced audio synthesis capabilities, enabling deepfake technology to replicate human speech with near-perfect accuracy. This poses severe security threats to various domains, including banking, customer service, and law enforcement, as malicious actors can exploit speech synthesis techniques such as voice conversion, replay attacks, and text-to-speech (TTS) to manipulate or impersonate individuals. In this study, both spectral and temporal features were extracted from audio recordings to explore state-of-the-art deepfake audio detection frameworks, highlighting the effectiveness of advanced deep learning methods. Specifically, the performance of the bidirectional long short-term memory (BLSTM) network, a custom convolutional neural network (CNN), a residual CNN integrated with an attention mechanism and bidirectional gated recurrent unit (ResCNN-Attention-BGRU), WIREnet (a variant of BLSTM), the residual network for fully connected (ResNet FC), and a squeeze-and-excitation-enhanced one-dimensional CNN (SE-Enhanced 1D-CNN) were compared. The proposed models achieved notable testing accuracies, with SE-Enhanced 1D-CNN reaching the highest at 97.64%, followed by ResNet FC (97.46%) and WIREnet (97.20%), demonstrating strong generalization across the evaluated architectures. Furthermore, we experimented with different numbers of mel-frequency cepstral coefficients (MFCCs), specifically 13, 26, and 39, in combination with other spectral and temporal features. The experimental results demonstrated that MFCC-39, together with spectral and temporal features, had a robust feature representation and achieved the best performance for deepfake detection.}, archive = {J_ACCESS}, author = {Kavya Verma and Divyanshi Mittal and Sagnik Samanta and Kabir Gulati and Ojas Kulkarni and Muzaffar Ahmad Dar and C. L. Biji}, doi = {10.1109/ACCESS.2025.3611839}, journal = {IEEE Access}, pages = {168447-168462}, shortjournal = {IEEE Access}, title = {Deepfake audio detection: A comparative study of advanced deep learning models}, volume = {13}, year = {2025}, }
• (2025). Weapon target assessment with dynamic hit probabilities and heterogeneous targets: A two-stage model and solving algorithms. ACCESS, 13, 168433-168446. (www)

  @article{ , abstract = {As a classic optimization problem, the weapon target assignment (WTA) problem is widely studied. However, existing WTA models are mostly established on single-hit destruction and static hit probabilities and take the destroyed target value expectation as the optimization objective, which limits their application to more complex scenarios. In response to the above shortcomings, this paper establishes a two-stage WTA model for a more complex scenario. In this model, a complex network-based assessment method is introduced and applied to better quantify the damage degree of targets, a method for calculating the probability of a target being destroyed when struck by multiple weapons is proposed, and the stage division significantly reduced the dimension of the optimization model. A Monte Carlo tree search enhanced by a metaheuristic algorithm and a memetic algorithm with a coevolution mechanism are used to solve two sub-models of the two-stage WTA model, respectively. The simulation results demonstrate that the two targeted heuristic algorithms can produce better solutions than similar heuristic algorithms, and the WTA model proposed in this paper is applicable to complex scenarios involving dynamic hit probabilities and heterogeneous targets.}, archive = {J_ACCESS}, author = {Ze Geng and Yanyan Huang}, doi = {10.1109/ACCESS.2025.3612705}, journal = {IEEE Access}, pages = {168433-168446}, shortjournal = {IEEE Access}, title = {Weapon target assessment with dynamic hit probabilities and heterogeneous targets: A two-stage model and solving algorithms}, volume = {13}, year = {2025}, }
• (2025). Research on intelligent collision avoidance decision-making algorithm for multi-unmanned surface vehicles (USVs) based on COLREGs. ACCESS, 13, 168420-168432. (www)

  @article{ , abstract = {With the rapid advancement of USV technology, autonomous maritime systems are increasingly employed in marine monitoring, resource exploration, and environmental protection. However, collision avoidance remains a critical challenge, exceptionally in complex and dynamic multi-vessel maritime scenarios that demand legal compliance and real-time responsiveness. This paper proposes an International Regulations for Preventing Collisions at Sea (COLREGs)-compliant intelligent collision avoidance decision-making algorithm for multi-USV systems. The framework integrates Deep Reinforcement Learning (DRL), game-theoretic reasoning, distributed coordination, and multi-sensor fusion. A Collision Risk Indicator (CRI) is introduced, based on Distance at Closest Point of Approach (DCPA) and Time to Closest Point of Approach (TCPA), enhanced by differences in relative position, velocity, and heading, to support real-time risk assessment. To ensure regulatory adherence, the system classifies vessel encounter types (head-on, crossing, overtaking) and determines corresponding COLREGs roles (give-way or stand-on) using game-theoretic logic. These symbolic roles and encounter types are embedded into the DRL state vector as discrete semantic features. This semantic embedding enables the learning agent to generate behavior strategies that are both adaptive and COLREGs-compliant. A dynamic path planning and heading adjustment strategy is proposed, continuously modifying course and speed in response to evolving collision risk and regulatory constraints. A distributed multi-agent decision-making mechanism allows USVs to cooperate through local information exchange and decentralized negotiation. Simulation results demonstrate that the proposed approach achieves superior collision avoidance performance, faster response times, and improved compliance with COLREGs in complex maritime scenarios.}, archive = {J_ACCESS}, author = {Jianyin Lu}, doi = {10.1109/ACCESS.2025.3613595}, journal = {IEEE Access}, pages = {168420-168432}, shortjournal = {IEEE Access}, title = {Research on intelligent collision avoidance decision-making algorithm for multi-unmanned surface vehicles (USVs) based on COLREGs}, volume = {13}, year = {2025}, }
• (2025). LLM-enhanced security framework for IoT network: Anomaly detection and malicious devices identification. ACCESS, 13, 168405-168419. (www)

  @article{ , abstract = {Due to resource constraints, Internet of Things (IoT) devices often lack built-in security systems, making them vulnerable to zero-day attacks. Consequently, there is a growing need for anomaly-based intrusion detection systems for IoT networks. However, traditional anomaly systems suffer from a high number of false positives, which wastes analysts’ time. Besides this, there is a semantic gap between the system outputs and the network operators. In this paper, we propose a machine learning-based framework with large language model (LLM) integration to address those challenges we face in traditional systems. The model not only detects potential threats but also bridges the semantic gap. The framework employs isolation forest for anomaly detection and random forest for device integrity assessment. To enhance anomaly evaluation and improve interpretability, the system’s insights are further processed by GPT-4o mini, an LLM. The LLM elucidates statistical summaries of IoT traffic, assigns risk scores, and provides human-readable explanations and thereby enhancing decision-making. This approach reduces the reliance on expert network operators. As a result, non-technical users can understand and act upon the system’s outputs.}, archive = {J_ACCESS}, author = {Mohammed Arif Iftakher Mahmood and Fahim Ashab and Md Saifuzzaman Sohan and Md Hedayetul Islam Chy and Md Fazlul Kader}, doi = {10.1109/ACCESS.2025.3613588}, journal = {IEEE Access}, pages = {168405-168419}, shortjournal = {IEEE Access}, title = {LLM-enhanced security framework for IoT network: Anomaly detection and malicious devices identification}, volume = {13}, year = {2025}, }
• (2025). A 95.43%-peak-efficiency buck converter with seamless PWM/PFM transition and 12.3-μA quiescent current for wide-load applications. ACCESS, 13, 168393-168404. (www)

  @article{ , abstract = {This study presents a high-efficiency synchronous buck converter IC featuring wide load-range capability and ultra-low power consumption. The design employs an integrated PWM/PFM control scheme where a clamp circuit integrated into the PWM control loop and a clamp-current-dependent frequency-reduction module enable PFM operation under light-load conditions. This architecture achieves seamless PWM-to-PFM transition with minimized output voltage ripple, enhances light-load efficiency, and significantly reduces circuit complexity. Implementation of peak/valley current detection modules elevates sensing accuracy and control precision, thereby improving dynamic response characteristics. A time-division module activation strategy further reduces the controller’s quiescent current, optimizing ultra-light-load efficiency. The power-enable control module establishes dual-stability mechanisms for power rails and reference voltages, ensuring controlled module activation only after stable initialization to guarantee proper power sequencing and operational reliability. Fabricated in $0.18\mu $ m BCD technology, measurement results demonstrate stable startup characteristics, smooth PWM/PFM transitions, 95.43% peak efficiency at 8V-to-5V conversion, >90% efficiency across the 5mA-6A load range, and $12.3\mu $ A no-load ground current.}, archive = {J_ACCESS}, author = {Xiao-Hui Hu and Wan-Yuan Qu and Yong Ding}, doi = {10.1109/ACCESS.2025.3612097}, journal = {IEEE Access}, pages = {168393-168404}, shortjournal = {IEEE Access}, title = {A 95.43%-peak-efficiency buck converter with seamless PWM/PFM transition and 12.3-μA quiescent current for wide-load applications}, volume = {13}, year = {2025}, }
• (2025). Multi-fault prediction of electromechanical actuators based on SCI-BiGRU. ACCESS, 13, 168374-168392. (www)

  @article{ , abstract = {This paper presents a fault prediction model for electromechanical actuators (EMA) based on the SCI-BiGRU architecture, with the aim of achieving precise prediction of multiple fault modes. Upon analyzing the monitoring signals of the electromechanical actuators, target signal with good distinguishability across various fault modes was selected. Based on the target signal, this study implements multi-fault prediction through a predict-then-classify approach. Firstly, an improved SCI temporal prediction module was introduced to forecast the future waveform of the target signal. Subsequently, effective fault features were extracted from the predicted signal and fed into an enhanced BiGRU model for accurate fault category diagnosis. This integrated process achieves precise prediction of multiple fault modes within a unified framework. Experimental validation was conducted on the FLEA dataset from NASA, where the proposed model demonstrated robust performance in both temporal prediction and fault classification stages, thereby achieving high-precision early prediction of multiple fault modes.}, archive = {J_ACCESS}, author = {Xiaojun Bai and Hanlin Liang and Haiyang Jia and Yanfang Fu and Zhaofeng Pan}, doi = {10.1109/ACCESS.2025.3612910}, journal = {IEEE Access}, pages = {168374-168392}, shortjournal = {IEEE Access}, title = {Multi-fault prediction of electromechanical actuators based on SCI-BiGRU}, volume = {13}, year = {2025}, }
• (2025). RECA: A pipeline for refinement of compressed artifacts in image super-resolution training. ACCESS, 13, 168361-168373. (www)

  @article{ , abstract = {Training datasets for image super-resolution (SR) are often constructed from web images. However, these images are typically stored in JPEG format, introducing compression artifacts that degrade SR performance. To ensure data quality, conventional approaches discard severely compressed images. While this preserves image quality, it reduces data diversity, which in turn limits model performance. To address this issue, we propose RECA (Refinement of Compressed Artifacts), a novel pipeline that enables the reuse of JPEG images that would otherwise be excluded. RECA first detects heavily compressed images using a blockiness measure, then restores visual quality using a real-world diffusion-based SR model, and finally applies bicubic downsampling to suppress artifacts introduced during enhancement. This process allows the effective inclusion of compressed images in training without sacrificing data quality. Experimental results demonstrate that RECA consistently outperforms filtering-based baselines in terms of PSNR and SSIM. These findings highlight the effectiveness of RECA in expanding usable training data and improving SR model performance. Code is available at: https://github.com/gohtanii/RECA}, archive = {J_ACCESS}, author = {Go Ohtani and Hirokatsu Kataoka and Yoshimitsu Aoki}, doi = {10.1109/ACCESS.2025.3613826}, journal = {IEEE Access}, pages = {168361-168373}, shortjournal = {IEEE Access}, title = {RECA: A pipeline for refinement of compressed artifacts in image super-resolution training}, volume = {13}, year = {2025}, }
• (2025). End-to-end process optimization in polymer extrusion lines using model predictive control and multi-task learning. ACCESS, 13, 168344-168360. (www)

  @article{ , abstract = {Due to the inherent complexity of modern polymer extrusion lines caused by nonlinear, dynamic behavior with numerous influential factors, disturbances lead to higher scrap rates and downtimes. Unfortunately, conventional approaches to address this challenge rely on independent models and local control loops, neglecting the multistage characteristic of extrusion lines and therefore, cannot obtain a global optimum. Consequently, we propose a predictive control design based on a nonlinear and autoregressive multi-task learning model covering the entire extrusion line including local control loops and essential quality measurements. Training on an extended prediction horizon successfully addresses accumulating prediction errors and measurement noise. Due to its capability to predict state trajectories for up to 60 minutes, the proposed methodology enables effective model predictive control. Additionally, using an efficient method for adaptive error compensation based on previous error trajectories increases robustness substantially while the hierarchical control architecture supports global optimization and efficient local control loops. The proposed control design is validated on two polymer extrusion lines. The results show that changing operating points and optimizing process states, such as melt temperature, can be reliably achieved in spite of process disturbances. In comparison to baseline production periods, dominant oscillations are successfully damped by 62 %, reference values are closely followed and process variations are reduced by 41 % to 63 % improving product quality notably. A comparative analysis of disturbance rejection capabilities shows superior results while empirical analyses of stability and runtime further improve its closed-loop applicability. As a consequence, the proposed methodology for predictive control allows to steer extrusion processes accurately, address upcoming issues in advance and generally leads to more efficient processes.}, archive = {J_ACCESS}, author = {Raphael Hartner and Martin Kozek and Stefan Jakubek}, doi = {10.1109/ACCESS.2025.3614061}, journal = {IEEE Access}, pages = {168344-168360}, shortjournal = {IEEE Access}, title = {End-to-end process optimization in polymer extrusion lines using model predictive control and multi-task learning}, volume = {13}, year = {2025}, }
• (2025). A novel FDFD-TL method for the coupling analysis of multi-conductor transmission lines. ACCESS, 13, 168335-168343. (www)

  @article{ , abstract = {A novel frequency domain field-to-line hybrid method consisting of the Finite Difference Frequency Domain (FDFD) method and transmission line (TL) equations (FDFD-TL) is proposed, which can address the coupling problems of multi-conductor transmission lines (MTLs) excited by long-duration electromagnetic signals rapidly. First, the coupling model of long-duration interference signals to the MTLs is constructed via the TL equations combined with the analytical solutions of the excitation fields of MTLs. Then, the FDFD method is applied to solve these TL equations, and new FDFD schemes are developed for calculating the currents on the MTLs and their loads. Additionally, a new FDFD-TL matrix equation is formulated. Finally, the conjugate gradient method, enhanced by the OpenMP parallel technique, is employed to rapidly solve this matrix equation, thereby obtaining the current responses on the MTLs and their terminal loads. The advantages of this method include avoiding late-time instability and lengthy computation times associated with time-domain simulations for long-duration signals, while effectively calculating current responses along MTLs without directly modeling the MTL structures. Numerous coupling simulations of ideal transmission lines and actual power lines excited by various long-duration electromagnetic pulses are presented and compared to the results obtained using the method of moments (MoM) and FDTD-TL method, demonstrating the accuracy and efficiency of the proposed method.}, archive = {J_ACCESS}, author = {Zhihong Ye and Yufan Zhai and Xin Xia}, doi = {10.1109/ACCESS.2025.3610629}, journal = {IEEE Access}, pages = {168335-168343}, shortjournal = {IEEE Access}, title = {A novel FDFD-TL method for the coupling analysis of multi-conductor transmission lines}, volume = {13}, year = {2025}, }
• (2025). A design-of-experiments-based approach for efficient estimation of bimodal gaussian mixture weights. ACCESS, 13, 168322-168334. (www)

  @article{ , abstract = {Normal mixture models are widely used to represent data arising from latent subpopulations. We propose a Design-of-Experiments (DOE) and Response Surface Methodology (RSM) framework to estimate the weights of a bimodal Gaussian mixture when component families are known. The procedure is non-iterative: rather than alternating Expectation Maximization (EM) steps, it performs a double-stage method - fit a quadratic response surface to the sample log-likelihood over the weight simplex and solve one constrained optimization - followed by a final Maximum Likelihood re-estimation of means and variances. This yields predictable runtime (driven by design size) and reduced sensitivity to initialization. The pipeline uses 1) k-medians to obtain preliminary component parameters and 99% confidence intervals (CIs) for component proportions; 2) builds a simplex-lattice mixture design within those CI bounds; 3) fits a quadratic response surface to log-likelihood; and 4) optimizes this surface under sum-to-one constraints. We validate the method in 27 Monte Carlo scenarios (n = 100, 500, 1000; low/medium/high differentiation and three weight settings). In medium/high separation, it attains comparable likelihoods to EM while achieving more favorable BIC in multiple scenarios and indistinguishable AIC in many, whereas EM is preferable under low separation. Two real data sets - Old Faithful (Waiting variable) and Photovoltaic Energy (Production variable) - further confirm applicability, with lower AIC/BIC in Old Faithful and lower BIC in PV; clustering agreement is high ( $\kappa \approx ~0.99$ - 1.00). Overall, DOE-RSM offers a simple, interpretable, and often more parsimonious method, and constitutes a non-iterative alternative for mixture-weight estimation.}, archive = {J_ACCESS}, author = {Gustavo S. Leal and Lupércio F. Bessegato and Yasmin S. M. Xavier and Farid Melgani and Pedro Paulo Balestrassi}, doi = {10.1109/ACCESS.2025.3614023}, journal = {IEEE Access}, pages = {168322-168334}, shortjournal = {IEEE Access}, title = {A design-of-experiments-based approach for efficient estimation of bimodal gaussian mixture weights}, volume = {13}, year = {2025}, }
• (2025). Optimal defense strategies against ransomware with quantum acceleration. ACCESS, 13, 168305-168321. (www)

  @article{ , abstract = {This paper presents a comprehensive mathematical framework for modeling ransomware propagation and control across networked systems. We develop an eight-compartment model that captures the sequential stages of ransomware attacks: initial infection, scanning, lateral movement, encryption, ransom demand, final impact, malware propagation, and recovery. The model uses ordinary differential equations to analyze key factors including infection spread rates, detection times, and system recovery. We derive the basic reproduction number $R_{0}$ and perform stability analysis to determine conditions for outbreak control ( $R_{0} \lt 1$ ) versus persistence ( $R_{0} \gt 1$ ). Sensitivity analysis reveals that the proportion of infected systems becoming new malware sources (parameter $m$ ) most strongly influences outbreak potential. We extend the model to incorporate quantum computing effects, introducing quantum-enhanced malware and quantum-resistant defenses. Optimal control strategies are developed using Pontryagin’s Maximum Principle to minimize system damage while balancing intervention costs. Numerical simulations demonstrate that quantum threats can increase the reproduction number by 281%, while quantum defenses can reduce this amplification by 29%. The results provide actionable insights for cybersecurity policy, emphasizing early intervention and quantum-aware defense strategies.}, archive = {J_ACCESS}, author = {Mohammad Ababneh and Yousef AbuHour and Ammar Elhassan}, doi = {10.1109/ACCESS.2025.3613668}, journal = {IEEE Access}, pages = {168305-168321}, shortjournal = {IEEE Access}, title = {Optimal defense strategies against ransomware with quantum acceleration}, volume = {13}, year = {2025}, }
• (2025). Connecting the patches: Exploring multi-scale relations in patches via graph neural networks for short-term electricity load forecasting. ACCESS, 13, 168287-168304. (www)

  @article{ , abstract = {Precise short-term forecasting of electricity load is essential to ensure the consistent and dependable functioning of power systems. Yet, the intricate and evolving relationships among load sequences and various external variables (e.g., temperature, electricity cost) present major difficulties in multivariate modeling. Existing graph-based forecasting models typically construct graphs based on variables or time points, but often neglect local spatio-temporal patterns, making it difficult to capture rapid feature variations within short time windows. Moreover, due to the multi-periodic nature of load sequences, their correlations with external features vary across time scales, further reducing forecasting accuracy. To address these challenges, we propose a novel Multi-scale Dynamic Patch Graph Neural Network (MDPGNN). In the local channel, the model constructs patch-level graphs and integrates dynamic graph learning with adaptive graph convolution to jointly model the internal structure of load sequences and their multi-scale dependencies with external features. In the global channel, a global correlation graph is designed to preserve long-range temporal patterns and inter-variable relationships. Furthermore, a Mixture-of-Experts (MoE) mechanism is employed to effectively fuse multi-scale dependencies and the outputs of both local and global channels. Experimental results on two real-world load datasets show that MDPGNN consistently outperforms state-of-the-art GNN-based models in terms of prediction accuracy and stability.}, archive = {J_ACCESS}, author = {Li Zhu and Lugema Mi and Chunqiang Zhu and Wanru Xu and Jingkai Gao and Jinqi Qu}, doi = {10.1109/ACCESS.2025.3613842}, journal = {IEEE Access}, pages = {168287-168304}, shortjournal = {IEEE Access}, title = {Connecting the patches: Exploring multi-scale relations in patches via graph neural networks for short-term electricity load forecasting}, volume = {13}, year = {2025}, }
• (2025). Emotional awareness in software development: Evaluating software project managers’ perspectives. ACCESS, 13, 168268-168286. (www)

  @article{ , abstract = {Developers’ emotions significantly influence job satisfaction, motivation, productivity, and work quality throughout the software development lifecycle. Previous research highlighted the impact of human factors in various professional domains, such as decision-making, job performance, and project success. However, systematic monitoring and integration into decision-making processes within software development remain underexplored. This study investigates how software project managers approach emotional management in their projects, focusing on identifying emotional variations and their actions in response. Besides, the managers experimented with an emotional polarity-monitoring dashboard tool designed to display developers’ emotional states over time. The findings reveal that emotional variations are currently identified in an ad-hoc manner, underscoring the need for a systematic approach to monitor and manage the impact of developers’ emotional fluctuations on the development process. Software project managers recognized that a systematic approach such as the one provided by the tool used in this study would have the potential to boost productivity, team cohesion, decision-making, and institutional well-being, emphasizing the value of understanding developers’ psychological aspects to enhance the software development process.}, archive = {J_ACCESS}, author = {Leo Silva and Margarida Lima and Henrique Madeira}, doi = {10.1109/ACCESS.2025.3613682}, journal = {IEEE Access}, pages = {168268-168286}, shortjournal = {IEEE Access}, title = {Emotional awareness in software development: Evaluating software project managers’ perspectives}, volume = {13}, year = {2025}, }
• (2025). OAS-XGB: An OptiFlect adaptive search optimization framework using XGBoost to predict length of stay for CAD patients. ACCESS, 13, 168246-168267. (www)

  @article{ , abstract = {Accurately predicting the length of hospital stay for patients with coronary artery disease is important for effective healthcare resource management and enhancing patient outcomes. However, to achieve a high predictive performance in machine learning, one of the key challenges is to balance exploration and exploitation during the hyperparameter tuning process. To address this challenge, this work proposes an OptiFlect Adaptive Search (OAS) optimization with XGBoost, an advanced framework designed to enhance the predictive performance of XGBoost models. Initially, data preparation and preprocessing are performed, followed by representation learning using Variational Autoencoders with t-SNE to capture complex data patterns through low-dimensional embeddings. With the transformed latent features, the proposed framework initiates a random search and then transitions to a Gaussian Process-based Bayesian optimization, employing an adaptive switching strategy to achieve an optimal balance between exploration and exploitation. The experiment is performed on three comprehensive datasets, i.e., MIMIC-III, MIMIC-IV, and eICU. Across these datasets, the OAS optimization algorithm achieved the best log-loss. Utilizing the optimal hyperparameters identified from OAS, the XGBoost model was retrained, resulting in superior performance with an accuracy of 97.92% on MIMIC-III, 98.99% on MIMIC-IV, and 96.58% on eICU. This model also outperformed other baseline models, achieving the highest overall and class-specific metrics, including precision, recall, F1-score, AUC-ROC, and AUPRC, while maintaining the lowest overall inference time as evaluated across nested five-fold cross-validation. The SHapley Additive exPlanations are also performed for the OAS-XGB model’s interpretability by identifying crucial predictive features, helping healthcare professionals optimize resource allocation and enhance care quality.}, archive = {J_ACCESS}, author = {Geetha Pratyusha Miriyala and Arun Kumar Sinha}, doi = {10.1109/ACCESS.2025.3613771}, journal = {IEEE Access}, pages = {168246-168267}, shortjournal = {IEEE Access}, title = {OAS-XGB: An OptiFlect adaptive search optimization framework using XGBoost to predict length of stay for CAD patients}, volume = {13}, year = {2025}, }
• (2025). Quantum amplitude hash function for quantum evidence integrity. ACCESS, 13, 168236-168245. (www)

  @article{ , abstract = {The integrity of evidence, a core principle of digital forensics, is essential for ensuring the reliability and admissibility of evidence in legal proceedings. Classical cryptographic hash functions have effectively ensured the integrity of digital evidence. However, they cannot be applied to quantum evidence emerging with the advent of the quantum computing era. This is due to fundamental principles of quantum mechanics such as superposition and entanglement. To address the challenge of ensuring quantum evidence integrity, this paper introduces a quantum amplitude hash function as a new paradigm. The function operates by directly hashing the entire amplitude, which represents the totality of information within the quantum evidence state, into a hash qubit. The proposed function is implemented as a quantum circuit of constant depth, ensuring excellent scalability. It is theoretically proven to satisfy key cryptographic properties such as preimage resistance, collision resistance, and sensitivity. Moreover, simulation experiments were conducted to validate the generation of hash qubits for representative quantum algorithms. This work is expected to lay a crucial technical foundation for advancing the field of quantum forensics and preparing for future investigative environments.}, archive = {J_ACCESS}, author = {Donggun Lee and Hanbeom Shin and Seokhie Hong}, doi = {10.1109/ACCESS.2025.3611342}, journal = {IEEE Access}, pages = {168236-168245}, shortjournal = {IEEE Access}, title = {Quantum amplitude hash function for quantum evidence integrity}, volume = {13}, year = {2025}, }
• (2025). Enhancing 5G network security: A deep learning framework for real-time DDoS detection and explainable threat analysis. ACCESS, 13, 168217-168235. (www)

  @article{ , abstract = {With the rapid expansion of the Internet and the increasing reliance on digital services, cyberattacks have become more prevalent and sophisticated. This rise in malicious activities necessitates the development of stronger security measures, particularly across the physical, network, and application layers of the 5G architecture. Given the complexity and scale of modern networks, ensuring robust security mechanisms to mitigate cyber threats is crucial to maintaining service reliability and user trust. This paper proposes a DDoS detection framework capable of self-automating the detection and mitigation of denial-of-service (DoS) attacks as well as distributed denial-of-service (DDoS) attacks in next wireless generations. The framework utilizes machine learning (ML) and deep learning (DL) techniques to increase precision and optimize resource use regarding the detection of attacks on the 5G network’s application and transport layers. To test the efficacy of the suggested framework, a series of tests was conducted using diverse ML and DL models. The findings illustrate advanced cyber threat detection capabilities among some deep learning architectures. In binary classification of an attack, Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) indeed achieved the undisputable highest recall rate of 99.98%, confirming the systems’ effectiveness in discerning between normal and attack traffic. Also, while being involved in multi-attack classification, the Multi-Layer Perceptron (MLP) model surpassed other models in recall, reporting an impressive 99.04% rate. It can be observed that the suggested deep learning architectures have high effectiveness for enhancing 5G networks security related to ongoing DoS and DDoS threats. The findings revealed by the proposed framework do unlock a new perspective on enhancing network resilience against sophisticated cyber threats.}, archive = {J_ACCESS}, author = {Amjad Albashayreh and Saleh Al-Sharaeh and Yahya Tashtoush and Plamen Zahariev}, doi = {10.1109/ACCESS.2025.3613386}, journal = {IEEE Access}, pages = {168217-168235}, shortjournal = {IEEE Access}, title = {Enhancing 5G network security: A deep learning framework for real-time DDoS detection and explainable threat analysis}, volume = {13}, year = {2025}, }
• (2025). Graph neural network-based task migration strategy for battery run time optimization of smart medical device cluster. ACCESS, 13, 168202-168216. (www)

  @article{ , abstract = {The interconnection of smart medical devices is becoming an important trend in healthcare. Due to the differences in task requirements and power reserves of smart medical device clusters, if some devices run out of energy prematurely, it may lead to a decrease in the performance and reliability of the whole system. Thus, it is particularly important to globally optimize the run time of smart medical device clusters. This paper proposes a task migration strategy for battery run time collaborative optimization of smart medical device clusters. Firstly, a feature factor of device run time is proposed to evaluate the difference in the distribution of remaining power and pending tasks in the device cluster. Secondly, each device regulates the local scheduling according to the feature factor, so the dominant device reserves more free time for task migration. Finally, a reinforcement learning-based task migration strategy is proposed, where a graph neural network embeds graph features on the feature factor and its associated device and sub-task features. The task migration actions are selected based on the graph features for collaborative optimization of run time for smart medical device clusters. Validated in the distributed battery-drive medical device hardware-in-the-loop experimental platform, the results show that in the cluster with balanced battery run time, the proposed method effectively extends the overall battery run time of the cluster by up to 4.26%. In the cluster with unbalanced battery run time/task requirements, the proposed method can effectively extend the cluster run time by more than 14.64%.}, archive = {J_ACCESS}, author = {Yuanping Hu and Yaoxin Xia and Yue Wu and Fu Jiang and Heng Li}, doi = {10.1109/ACCESS.2025.3608821}, journal = {IEEE Access}, pages = {168202-168216}, shortjournal = {IEEE Access}, title = {Graph neural network-based task migration strategy for battery run time optimization of smart medical device cluster}, volume = {13}, year = {2025}, }
• (2025). Trustworthiness evaluation of large language models using multi-criteria decision making. ACCESS, 13, 168183-168201. (www)

  @article{ , abstract = {As Large language models (LLMs) become increasingly integrated into high-stakes applications, ensuring their trustworthiness has emerged as a critical research concern. This study proposes a novel evaluation framework that applies a multi-criteria decision making (MCDM) methodology, specifically the hesitant fuzzy analytic hierarchy process (AHP), to assess and rank LLMs based on five key trust dimensions: fairness, robustness, integrity, explainability, and safety. Drawing from expert evaluations, the framework systematically determines the relative importance of each criterion and applies a weighted scoring approach to compare seven leading LLMs, including both proprietary models such as GPT-3.5, GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 and open-source models such as Llama 3.1, Mistral Large 2 and DeepSeek V3. Results reveal GPT-4o as the most trustworthy model, significantly outperforming its peers, particularly in robustness and fairness. Open-source models showed lower scores, especially in safety and explainability, highlighting persistent gaps in their alignment with trust expectations. The findings demonstrate the effectiveness of MCDM in capturing expert uncertainty and prioritizing trust criteria, offering a robust and adaptable framework for evaluating LLMs in dynamic and sensitive domains.}, archive = {J_ACCESS}, author = {Meltem Aksoy and Aylin Adem and Metin Dağdeviren}, doi = {10.1109/ACCESS.2025.3612568}, journal = {IEEE Access}, pages = {168183-168201}, shortjournal = {IEEE Access}, title = {Trustworthiness evaluation of large language models using multi-criteria decision making}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of agricultural ground automatic navigation systems based on multi-sensor fusion. ACCESS, 13, 168159-168182. (www)

  @article{ , abstract = {In a complex and changing agricultural environment, achieving autonomous navigation of agricultural robots and field machinery is a fundamental prerequisite for completing various farming tasks. However, because of the unstructured nature, high dynamics, and signal interference commonly found in agricultural scenarios, achieving high-precision autonomous navigation in such challenging environments remains a significant research challenge. In response to the unique complexity of agricultural settings, this study outlines the evolution of agricultural navigation technologies from single-sensor systems to advanced multi-sensor approaches and systematically evaluates the applicability of commonly used single-sensor methods in complex field environments. The findings indicate that, among single-sensor navigation applications, vision-based techniques are more widely adopted in agricultural scenarios. In contrast, for multi-sensor navigation, integrated systems combining inertial navigation systems and global navigation satellite systems are the most prevalent. Furthermore, in terms of multi-sensor fusion algorithms, kalman filters and their extended variants are extensively employed, demonstrating strong robustness and adaptability. These analyses can offer valuable theoretical foundations and practical guidance for the selection and optimization of fusion strategies in future agricultural navigation systems. This study also compared global and local path planning algorithms. Finally, practical applications of fusion technologies in various agricultural scenarios are discussed.}, archive = {J_ACCESS}, author = {Junjin Yang and Noraishikin Zulkarnain and Mohd Faisal Ibrahim and Ili Najaa Aimi Mohd Nordin and Seyed Abdollah Vaghefi}, doi = {10.1109/ACCESS.2025.3612811}, journal = {IEEE Access}, pages = {168159-168182}, shortjournal = {IEEE Access}, title = {A comprehensive review of agricultural ground automatic navigation systems based on multi-sensor fusion}, volume = {13}, year = {2025}, }
• (2025). Adjusted exponential scaling: An innovative approach for combining diverse multiclass classifications. ACCESS, 13, 168150-168158. (www)

  @article{ , abstract = {Ensemble learning plays a crucial role in deep learning (DL) by improving classification accuracy through the aggregation of multiple classifiers. However, traditional methods, such as bagging, boosting, and stacking, face limitations when combining heterogeneous classifiers trained on unrelated tasks, owing to their reliance on closely aligned objectives. To address these challenges, we propose Adjusted Exponential Scaling (AES), a novel approach that combines diverse classifiers using probabilistic outputs, such as those generated by the Softmax function. AES eliminates the need to modify DL architectures or interfere with their training processes, thereby significantly reducing computational costs while ensuring compatibility with existing systems. We demonstrate AES scalability in simulations, successfully combining the outputs of up to 40 classifiers while maintaining probabilistic integrity. A formal verification using the Z3 Satisfiability Modulo Theories (SMT) solver confirmed its accuracy and reliability. AES is designed for simplicity and adaptability, making it particularly well suited for dynamic applications such as deepfake detection and medical diagnostics, where real-time combination is critical. By providing a scalable and computationally efficient solution, AES expands the applicability of ensemble learning and establishes a foundation for future extensions to sequential and binary tasks, further enhancing its versatility in machine learning challenges.}, archive = {J_ACCESS}, author = {Amin Arab Najafabadi and Ng Keng Yap and Zahra Nazemi Ashani and Faranak Nejati and Mohamed Abdullahi Ali}, doi = {10.1109/ACCESS.2025.3606472}, journal = {IEEE Access}, pages = {168150-168158}, shortjournal = {IEEE Access}, title = {Adjusted exponential scaling: An innovative approach for combining diverse multiclass classifications}, volume = {13}, year = {2025}, }
• (2025). Thermal and lifetime analysis of inverters and optimisers in building-integrated photovoltaic applications: A study on placement and environmental sensitivity. ACCESS, 13, 168139-168149. (www)

  @article{ , abstract = {This paper presents a comprehensive analysis of how the placement of key power electronic components within building-integrated photovoltaic (BIPV) systems influences their thermal behavior and long-term reliability, focusing on lifetime estimations of the IGBT switches in both inverters and optimisers. A building physics model including the BIPV system was incorporated and integrated within the power electronics device models. This joint model assesses the impact of surrounding conditions on power inverters and optimisers and explores various practical placement scenarios. A sensitivity analysis was conducted under different environmental conditions to evaluate the effects of airflow rates, BIPV ventilation cavity opening dimensions, and radiation-convection ratios on the system’s thermal behavior. Results indicate that proper placement strategies with sufficient airflow can significantly enhance cooling, reduce thermal stress, and improve the lifetime of the IGBT. This is particularly evident in the case of the inverter, where the DC IGBT (IGBT in the DC-DC converter stage) located in an isolated ceiling has an increased lifetime consumption of 10.9 % compared to an inverter placed in a utility room. This work provides valuable insights for the design and optimisation of BIPV systems in real-world applications, aiming to improve the operational lifespan of the systems.}, archive = {J_ACCESS}, author = {Leander Van Cappellen and Abdella Alzade and Dirk Saelens and Michaël Daenen}, doi = {10.1109/ACCESS.2025.3608118}, journal = {IEEE Access}, pages = {168139-168149}, shortjournal = {IEEE Access}, title = {Thermal and lifetime analysis of inverters and optimisers in building-integrated photovoltaic applications: A study on placement and environmental sensitivity}, volume = {13}, year = {2025}, }
• (2025). Path planning method for unmanned surface vehicle under high temporal and spatial dynamic characteristics of marine environment. ACCESS, 13, 168126-168138. (www)

  @article{ , abstract = {To address the problem of collision avoidance for Unmanned Surface Vehicles (USVs) in highly dynamic spatiotemporal marine environments, this paper proposes a USV path planning method that integrates the Velocity Obstacle (VO) approach with an improved Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The method creates a velocity obstacle area by combining environmental disturbances, USV dynamics, encounter scenarios, and the International Regulations for Preventing Collisions at Sea (COLREGS), resulting in a set of feasible avoidance strategies. Based on this, a multi-objective path planning model is developed with the dual optimization goals of minimizing energy consumption and collision risk. To address the limitations of traditional MOPSO, such as poor adaptability and susceptibility to local optima, this paper introduces improvements to particle encoding, update strategy, and external archive mechanism to improve convergence and solution diversity. Simulation results demonstrate that the proposed method can provide safe and energy-efficient autonomous collision avoidance paths for USVs in complex dynamic marine environments, demonstrating its practicality and intelligence.}, archive = {J_ACCESS}, author = {Shoumin Wang and Hua Chen and Zhichao Wang and Ning Niu and Jing Zhang}, doi = {10.1109/ACCESS.2025.3611466}, journal = {IEEE Access}, pages = {168126-168138}, shortjournal = {IEEE Access}, title = {Path planning method for unmanned surface vehicle under high temporal and spatial dynamic characteristics of marine environment}, volume = {13}, year = {2025}, }
• (2025). Leveraging fusion methods of human pose and motion dynamics for accurate violence detection in video surveillance. ACCESS, 13, 168116-168125. (www)

  @article{ , abstract = {The growing demand for security is leading to an increase in the installation of surveillance cameras and there is an urgent need for automated systems. Despite large number number of surveillance cameras are installed to detect and prevent violent activities, the need for monitoring and analyzing the footage in real time is a challenge. To accurately detect violence, it is essential to consider human interactions and minimize noise in surveillance footage. This study explores violence detection using a dual-stream deep neural network with human skeletons and motion changes as inputs extracted from surveillance videos. Furthermore, a weighted fusion strategy is employed, which integrates fusion operation functions, activation functions, and weighted outputs to prioritize the most relevant features from multiple inputs for effective violence detection. Our proposed framework is both effective and accurate, achieving accuracy of 95.05%. The use of skeletal data with a black background significantly improved performance compared to traditional RGB frames, while the incorporation of motion information and fusion strategy improved the performance of violence detection.}, archive = {J_ACCESS}, author = {Sai Thu Ya Aung and Worapan Kusakunniran and Yew Kwang Hooi}, doi = {10.1109/ACCESS.2025.3613765}, journal = {IEEE Access}, pages = {168116-168125}, shortjournal = {IEEE Access}, title = {Leveraging fusion methods of human pose and motion dynamics for accurate violence detection in video surveillance}, volume = {13}, year = {2025}, }
• (2025). Efficient detection of targeted adversarial attacks in automatic speech recognition systems. ACCESS, 13, 168103-168115. (www)

  @article{ , abstract = {Automatic Speech Recognition (ASR) systems convert spoken words into text and play a critical role in voice assistants, transcription services, and other accessibility tools. However, their reliance on machine learning models makes them susceptible to adversarial attacks. By adding small, often imperceptible perturbations to the input audio, or by convolving it with a carefully designed room impulse response, attackers can cause ASR systems to produce incorrect or even malicious transcripts. In this work, we propose an ensemble detection method for the efficient identification of targeted adversarial attacks in ASR systems. Specifically, we introduce MELo-FEST, which calculates the minimum energy in the low-frequency band of an audio signal to detect convolutive adversarial attacks. We then combine two spectrogram-based detection methods with a noise-adding approach to form an ensemble detector capable of identifying both additive and convolutive targeted adversarial attacks. Through extensive experiments, we demonstrate that our proposed ensemble detector can accurately identify adversarial audio generated by both non-adaptive and adaptive CW, PGD, and AdvReverb attacks in Wav2Vec2 and Whisper ASR systems, achieving an F1 score of at least 0.97. Moreover, our method performs both speech recognition and adversarial detection for each input audio sample in an average of under 0.13 seconds for Wav2Vec2 and 0.29 seconds for Whisper, making it well-suited for real-time applications. Additionally, we find that Whisper is more vulnerable than Wav2Vec2 to both non-adaptive and adaptive targeted adversarial attacks.}, archive = {J_ACCESS}, author = {Daniyal Parveez and Zesheng Chen and Jack Li and Chao Chen}, doi = {10.1109/ACCESS.2025.3613140}, journal = {IEEE Access}, pages = {168103-168115}, shortjournal = {IEEE Access}, title = {Efficient detection of targeted adversarial attacks in automatic speech recognition systems}, volume = {13}, year = {2025}, }
• (2025). Subjective and objective quality evaluation of super-resolution enhanced broadcast images on a novel SR-IQA dataset. ACCESS, 13, 168087-168102. (www)

  @article{ , abstract = {Super-Resolution (SR) is essential for displaying low-quality broadcast content on high-resolution screens. Recently, SR methods have been developed that not only increase resolution while preserving the original image information but also enhance the perceived quality. However, evaluating the quality of SR images generated from low-quality sources, such as SR-enhanced broadcast content, is challenging due to the need to consider both distortions and improvements. Additionally, assessing SR image quality without original high-quality sources presents another significant challenge. Unfortunately, there has been a dearth of research specifically addressing the Image Quality Assessment (IQA) of SR images under these conditions. In this work, we introduce a new IQA dataset for SR broadcast images in both 2K and 4K resolutions. We conducted a subjective quality evaluation to obtain Mean Opinion Score (MOS) for these SR images and performed a comprehensive human study to identify key factors influencing perceived quality. Finally, we evaluated the performance of existing IQA metrics on our dataset. This study reveals the limitations of current metrics, highlighting the need for a more robust IQA metric that better correlates with the perceived quality of SR images. The proposed dataset and the subjective evaluation platform are publicly available at https://sites.google.com/hanyang.ac.kr/ivml/datasets/sreb}, archive = {J_ACCESS}, author = {Yongrok Kim and Junha Shin and Juhyun Lee and Hyunsuk Ko}, doi = {10.1109/ACCESS.2025.3613598}, journal = {IEEE Access}, pages = {168087-168102}, shortjournal = {IEEE Access}, title = {Subjective and objective quality evaluation of super-resolution enhanced broadcast images on a novel SR-IQA dataset}, volume = {13}, year = {2025}, }
• (2025). HL-CAIoT: Hybrid lightweight cipher for IoT with chaotic maps and cellular automata. ACCESS, 13, 168067-168086. (www)

  @article{ , abstract = {The growing use of sensory devices in the IoT industry has increased the demand for secure and efficient encryption. Most traditional algorithms are too complex to be applied in IoT systems due to the limited resources available. Based on this, the paper proposes a novel, lightweight hybrid cipher suitable for IoT applications. Chaotic maps and Cellular Automata (CA) are ued to enhance the security level of the proposed cipher while maintaining minimal processing demands. The new cipher integrates Tinkerbell and Tent maps into a complex encryption sequence. Additionally, the S-box is generated using a piecewise linear chaotic map (PWLCM), further strengthening the encryption. This approach achieves strong security, as evidenced by a high NPCR of 99.6053, indicating high sensitivity to small input changes. It also records a high MSE value of 7157.7306, suggesting strong diffusion and scrambling. These results confirm that the cipher effectively protects IoT data. This lightweight cipher, which offers strong security without excessive power consumption, is ideal for IoT environments. Future work will focus on improving the performance of smaller devices and testing resilience against a broader range of attacks.}, archive = {J_ACCESS}, author = {Biswarup Yogi and Ajoy Kumar Khan and Satyabrata Roy}, doi = {10.1109/ACCESS.2025.3611240}, journal = {IEEE Access}, pages = {168067-168086}, shortjournal = {IEEE Access}, title = {HL-CAIoT: Hybrid lightweight cipher for IoT with chaotic maps and cellular automata}, volume = {13}, year = {2025}, }
• (2025). Real-time personalized car-following control with online parameter adaptation for intelligent regenerative braking systems. ACCESS, 13, 168055-168066. (www)

  @article{ , abstract = {With the increasing integration of advanced driver assistance systems (ADAS) in modern vehicles, intelligent regenerative braking systems (IRBS) have emerged as an effective solution for energy-efficient car-following control. However, current IRBS implementations lack personalization and require drivers to select fixed braking levels, often causing discomfort by failing to reflect individual preferences. Existing personalization techniques rely on deep learning or offline-trained models, which are computationally intensive and unsuitable for real-time deployment on vehicle electronic control units (ECUs). To address these limitations, this study proposes a computationally efficient parameter adaptation algorithm based on the extended Kalman filter (EKF), enabling real-time personalized car-following control in IRBS. The proposed method adopts a dynamic relative speed (DRS) spacing policy to capture individual driving characteristics. A reference data generator generates real-time reference data during specific driver interventions, allowing the EKF algorithm to adapt control parameters accordingly. Simulation tests using a driver model demonstrated a significant reduction in driver interventions and maintained inter-vehicle distances aligned with the driver’s preferred distance. Vehicle tests further confirmed the algorithm’s real-time performance and low computational load, affirming its suitability for large-scale production. This study presents a practical and scalable method for personalizing IRBS-based car-following control without complex learning architectures, thereby enhancing driving comfort and expanding applicability to ADAS technologies such as adaptive cruise control (ACC).}, archive = {J_ACCESS}, author = {Seungyeon Oak and Daekyeong Lee and Gyubin Sim and Sooyoung Kim and Giseo Park}, doi = {10.1109/ACCESS.2025.3611825}, journal = {IEEE Access}, pages = {168055-168066}, shortjournal = {IEEE Access}, title = {Real-time personalized car-following control with online parameter adaptation for intelligent regenerative braking systems}, volume = {13}, year = {2025}, }
• (2025). Beekeeper: Accelerating honeypot analysis with LLM-driven feedback. ACCESS, 13, 168034-168054. (www)

  @article{ , abstract = {Honeypots are decoy resources intended to entice adversaries and collect threat intelligence in the process. The amount and quality of the collected insights strongly correlate with the honeypot’s credibility to the adversary. However, the development of medium to high interaction honeypots, so, environments that offer at minimum a shell to the attacker, is laborious and complex. Additionally, getting feedback on a honeypot is often expensive and time-consuming, slowing down development and discouraging investment into honeypots. Therefore, we propose Beekeeper: a modular framework that combines static tests, known attack sequences, and automated, large language model based querying to investigate medium to high interaction honeypots. Afterward, the results are analyzed to provide feedback on the current state of the system and recommendations on how to improve key characteristics of the honeypot. To demonstrate the framework’s functionalities, we deploy Beekeeper with two medium and one high interaction systems and highlight how feedback and recommendations change after an initial set of improvements is implemented for each honeypot.}, archive = {J_ACCESS}, author = {Niclas Ilg and Dominik Germek and Paul Duplys and Michael Menth}, doi = {10.1109/ACCESS.2025.3613118}, journal = {IEEE Access}, pages = {168034-168054}, shortjournal = {IEEE Access}, title = {Beekeeper: Accelerating honeypot analysis with LLM-driven feedback}, volume = {13}, year = {2025}, }
• (2025). Reservoir simulations: A comparative review of machine learning approaches. ACCESS, 13, 167999-168033. (www)

  @article{ , abstract = {Machine learning (ML) has rapidly emerged as a transformative technology in the oil and gas sector, particularly in enhancing the efficiency and accuracy of reservoir simulations, well-placement optimization, and underground gas storage. This paper provides a comparative review of state-of-the-art ML models used in these areas. The review systematically evaluates the performance, limitations, and future potential of various ML approaches in tackling critical challenges in reservoir engineering. By analyzing and comparing recent advances, the review highlights the role of ML in improving production forecasting, reservoir characterization, enhanced oil recovery, and optimizing well configurations. Moreover, it explores ML’s application in underground Carbon, Hydrogen, and natural gas storage. Furthermore, we identify critical research gaps and propose several future directions, such as integrating ML with traditional physics-based models. By offering insights into these state-of-the-art developments, this review aims to guide researchers and industry professionals in selecting and developing the most effective ML models for subsurface energy management.}, archive = {J_ACCESS}, author = {Amr Zeedan and Abdulsalam Abd and Ahmad Sami Abushaikha}, doi = {10.1109/ACCESS.2025.3614017}, journal = {IEEE Access}, pages = {167999-168033}, shortjournal = {IEEE Access}, title = {Reservoir simulations: A comparative review of machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). Attention-enhanced dual-path CNN for early alzheimer’s detection from multi-planar t1-weighted MRI. ACCESS, 13, 167984-167998. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, and early detection is essential for effective clinical intervention. Previous deep learning approaches have achieved promising results using MRI data; however, most rely on full 3D volumes or standard 2D axial and coronal slices, often overlooking the diagnostic value of parasagittal views. In addition, many existing models lack attention mechanisms and multi-branch architectures, limiting their ability to capture both localized and contextual features. To overcome these limitations, we propose an attention-guided dual-path CNN that integrates sagittal, coronal, and parasagittal slices extracted at a 6.257° off-midline angle. The architecture combines a focused SNeurodCNN branch with an Inception-v4 path enhanced by CBAM for multi-scale feature extraction. Using T1-weighted MRI scans from the ADNI database, the proposed model achieved an accuracy of 98.9% and an AUC of 0.992, highlighting its potential for accurate and early AD classification.}, archive = {J_ACCESS}, author = {Vyshnavi Ramineni and Jun-Hyung Kim and Chun-Su Park and Ji-In Kim and Goo-Rak Kwon}, doi = {10.1109/ACCESS.2025.3613762}, journal = {IEEE Access}, pages = {167984-167998}, shortjournal = {IEEE Access}, title = {Attention-enhanced dual-path CNN for early alzheimer’s detection from multi-planar t1-weighted MRI}, volume = {13}, year = {2025}, }
• (2025). Contrastive and attention-based multimodal fusion: Detecting negative memes through diverse fusion strategies. ACCESS, 13, 167970-167983. (www)

  @article{ , abstract = {With the revolution of social media, the proper analysis of sentiment expressed through both textual and visual media is one of the most challenging problems. While not all negatively charged memes are harmful, sentiment analysis helps distinguish between general negativity and content that may contribute to online harms, such as cyberbullying, misinformation, or hate speech. Conventional sentiment analysis approaches, which mainly concentrate on text-based content, may miss out on nuanced emotional cues present in images, emojis, or the overall context of a conversation. Subtle negative expressions are often misclassified because models do not effectively learn fine-grained differences between sentiment classes. The existing works having static fusion often lack ability to detect fine-grained or subtle negativity. In this work we propose Gated Contrastive Multimodal Fusion Model that processes the text and image streams in memes, through independent modality-specific neural networks, combined by means of an attention mechanism. The gated fusion block present in our architecture allows the model to pay closer attention to the most sentiment-rich elements. The contrastive block provides an accurate training mechanism to improve the classification of negative memes. Through experiments on benchmark datasets, we show that our methodology provides interpretable insights into the contribution of each modality. Thus, outperform SOTA models and open the door to better identification of online harmful memes.}, archive = {J_ACCESS}, author = {B. S. Narendiran and Srividya and N. Sumith}, doi = {10.1109/ACCESS.2025.3613694}, journal = {IEEE Access}, pages = {167970-167983}, shortjournal = {IEEE Access}, title = {Contrastive and attention-based multimodal fusion: Detecting negative memes through diverse fusion strategies}, volume = {13}, year = {2025}, }
• (2025). Optimized DenseNet121 and quantum PennyLane fusion for explainable skin disease recognition and classification. ACCESS, 13, 167957-167969. (www)

  @article{ , abstract = {The accurate diagnosis of skin diseases, particularly in distinguishing between visually similar conditions such as Measles, Monkeypox, Chickenpox, and Normal skin states, remains a critical challenge in clinical dermatology. To address this, we propose a novel hybrid framework that seamlessly integrates classical convolutional neural networks (CNNs) with quantum computing principles for enhanced multi-class classification. Our approach utilizes DenseNet121 as the backbone feature extractor, which is then coupled with a quantum-enhanced classifier implemented using the PennyLane framework. This fusion not only improves classification accuracy but also ensures interpretability, a key requirement in medical diagnostics. To further enhance transparency and trust, we incorporate multiple Explainable AI (XAI) techniques, including SHAP, LIME, Grad-CAM, and XRAI, to provide both pixel-level visualizations and region-based annotations through bounding boxes that highlight infection zones. An embedding-based image recognition module is also introduced to retrieve visually similar cases from a database, aiding clinicians in comparative analysis and decision-making. We employ Optuna-based hyperparameter optimization to fine-tune the CNN components, achieving a validation accuracy exceeding 91.5% on a dataset of 770 real-world skin lesion images. The robustness of our pipeline is validated through rigorous random sampling and multi-class evaluation. This work demonstrates the potential of combining quantum machine learning with interpretable CNNs to advance medical image analysis in dermatology, offering a system that balances performance, transparency, and clinical utility.}, archive = {J_ACCESS}, author = {C. V. Aravinda and Emerson Raja Joseph and Sultan Alasmari}, doi = {10.1109/ACCESS.2025.3608217}, journal = {IEEE Access}, pages = {167957-167969}, shortjournal = {IEEE Access}, title = {Optimized DenseNet121 and quantum PennyLane fusion for explainable skin disease recognition and classification}, volume = {13}, year = {2025}, }
• (2025). SLIP: A sanskrit linguistic intelligence pipeline for enhanced neural machine translation of classical texts. ACCESS, 13, 167942-167956. (www)

  @article{ , abstract = {The translation of classical Sanskrit texts presents unique challenges due to the language’s complex morpho-phonological structure, rich grammatical system, and cultural-philosophical depth. This paper introduces the Sanskrit Linguistic Intelligence Pipeline (SLIP), a comprehensive framework that integrates traditional Sanskrit grammatical analysis with modern neural machine translation architectures to enhance translation quality for classical texts. SLIP incorporates morphological feature extraction (verb tenses, case endings, prefixes), phonological analysis (vowel ratios, syllable patterns, sandhi rules), and structural processing (compound word detection, sentence analysis) specifically designed for Sanskrit linguistic characteristics. The paper evaluates SLIP across multiple neural architectures, including enhanced transformers, MBART, and retrieval-augmented generation (RAG) systems using diverse evaluation metrics (BLEU, ROUGE, METEOR, BERTScore) on Bhagavad Gita verses spanning different thematic categories. The results demonstrate substantial improvements: SLIP-enhanced transformers achieve a 10.20% relative improvement in BERT F1-score (57.63% to 63.51%) with statistical significance, while RAG integration yields remarkable gains of up to 41.8% in BLEU-4 scores. Comparative analysis reveals that domain-specific linguistic intelligence significantly outperforms generic large language models, with enhanced transformers showing + 5.88 BERT F1 improvement compared to MBART’s -7.12 decline. Thematic analysis indicates variable effectiveness across philosophical concepts, with austerity practices showing the highest improvement (+ 7.23%) compared to food classifications (+ 4.73%). These findings demonstrate that Sanskrit-specific morpho-phonological feature engineering provides superior translation quality compared to generic multilingual approaches, offering a paradigm for preserving semantic accuracy and cultural nuances in automated translation of ancient languages. The SLIP framework addresses critical limitations in existing approaches and provides a foundation for broader digitization efforts of classical Indian literature.}, archive = {J_ACCESS}, author = {N. Biraja Isac and Himansu Das}, doi = {10.1109/ACCESS.2025.3613597}, journal = {IEEE Access}, pages = {167942-167956}, shortjournal = {IEEE Access}, title = {SLIP: A sanskrit linguistic intelligence pipeline for enhanced neural machine translation of classical texts}, volume = {13}, year = {2025}, }
• (2025). Effort estimation in agile and DevOps: A systematic literature review of stakeholder dissatisfaction. ACCESS, 13, 167925-167941. (www)

  @article{ , abstract = {Effort estimation is a critical aspect of software project management, directly influencing the budget, schedule, and resource allocation throughout the software development life cycle. Accurate estimation plays a central role in aligning team capabilities with stakeholder expectations, ensuring timely delivery and efficient use of resources. Despite the adoption of Agile and DevOps methodologies, which emphasize flexibility, rapid iterations and continuous integration, estimation inaccuracies remain a problematic area. These inaccuracies often result in project delays, cost overruns, and scope creep, ultimately leading to stakeholder dissatisfaction and a breakdown of trust in the development process. To investigate these challenges, a Systematic Literature Review (SLR) was conducted, surrounding 1,088 publications retrieved from six major digital libraries: Scopus (201), ScienceDirect (155), Springer (212), Google Scholar (145), IEEE (185), and ACM (190). This research examines the main drivers of estimation challenges in Agile and DevOps environments. Key findings indicate that human-centric issues (cognitive biases, lack of experience), technical issues (fluctuating requirements, lack of adequate history), and process inefficiencies (communication breakdown, poor planning) exert considerable influences on estimation accuracy. To address these challenges, the study categorizes and analyzes a range of estimation techniques, including machine learning models, expert-calibrated hybrid approaches, analogical estimation methods, and process calibration practices such as retrospectives. Findings reveal that a combination of data-driven tools and stakeholder-aligned strategies offers the most promise in narrowing the expectation experience gap. This research provides practitioners with practical recommendations and lays the ground for further study in developing estimation techniques to mitigate the expectation-experience gap.}, archive = {J_ACCESS}, author = {Sadaquat Ali Ruk and Yasir Mahmood and Azri Azmi and Nurulhuda Firdaus Mohd Azmi and Sehar Gul}, doi = {10.1109/ACCESS.2025.3600321}, journal = {IEEE Access}, pages = {167925-167941}, shortjournal = {IEEE Access}, title = {Effort estimation in agile and DevOps: A systematic literature review of stakeholder dissatisfaction}, volume = {13}, year = {2025}, }
• (2025). Anomaly synthesis strategy with gabor convolutional kernel and vision transformer. ACCESS, 13, 167912-167924. (www)

  @article{ , abstract = {This paper proposes GLASS-GNT, an anomaly detection network that synthesizes Gabor-shaped additive perturbation and filter-based anomaly data on normal images, utilizing a discriminator with a Vision Transformer. The proposed model generates various abnormal images using Gabor-shaped additive perturbation and employs filters to synthesize abnormal images that resemble normal ones. This process aims to enhance the model’s ability to detect complex anomalies. The discriminator functions as a Vision Transformer with a multi-head contextual attention module to identify outlier positions within the input image. Extensive evaluations on five real-world anomaly datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving high image-level AUROC and proving its effectiveness in real-world applications. The code and dataset are available at: https://github.com/minho6988/GLASS-GNT}, archive = {J_ACCESS}, author = {Min-Ho Ha and Tae-Hyoung Park}, doi = {10.1109/ACCESS.2025.3613732}, journal = {IEEE Access}, pages = {167912-167924}, shortjournal = {IEEE Access}, title = {Anomaly synthesis strategy with gabor convolutional kernel and vision transformer}, volume = {13}, year = {2025}, }
• (2025). An attention-guided improved decomposition-reconstruction model for stock market prediction. ACCESS, 13, 167893-167911. (www)

  @article{ , abstract = {The stock market plays a pivotal role in global economy. Accurate stock market prediction not only assists governments in formulating macroeconomic policies but also helps investors to make informed decisions, thereby contributing to economic and market stability. However, the inherent high volatility, nonlinearity, and nonstationarity of stock data pose significant challenges to accurate forecasting. To address these challenges, this study proposes a novel hybrid model, named Attention-guided Improved Decomposition-Reconstruction Model (AIDRM), for accurate stock market prediction. First, the model decomposes the raw stock indices using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to capture their inherent complexity. The decomposed sub-series are then concatenated and adaptively weighted through an attention mechanism, guiding the model focus on key features that affect forecasting accuracy. The concatenation of sub-series prior to prediction can mitigate the typical error accumulation problem of the traditional decomposition-reconstruction frameworks. Finally, the attention-refined features are fed into the Informer model to generate predictions. Experiments on four major indices (SSEC, SZI, STI, and S&P 500) demonstrate that the proposed model outperforms state-of-the-art deep learning benchmarks, achieving RMSE reductions of 17.6 – 32.5% across all tested stock indices. In addition, the ablation study verified the effectiveness of each module.}, archive = {J_ACCESS}, author = {Yi Li and Haipeng Wu and Cuijing Liang and Caixia Bu and Huawang Jin and Lei Chen}, doi = {10.1109/ACCESS.2025.3613336}, journal = {IEEE Access}, pages = {167893-167911}, shortjournal = {IEEE Access}, title = {An attention-guided improved decomposition-reconstruction model for stock market prediction}, volume = {13}, year = {2025}, }
• (2025). Applications of robust statistics in autonomous driving. ACCESS, 13, 167848-167892. (www)

  @article{ , abstract = {Autonomous vehicles have to interact with their environment with the goal to fulfill their tasks while respecting all desired constraints such as not causing dangerous situations, driving comfortable maneuvers, enabling a smooth traffic flow, or avoiding overly polluting driving behavior. All steps require a suitable perception of the environment conditions, such as the estimation of the own position, a prediction of the trajectories of other traffic participants, or the assessment of parameters corresponding to vehicle dynamics. However, classical estimation algorithms are known to be easily distorted by outliers in the data. In addition, apart from rule-based systems, it becomes more convenient to train autonomous agents by machine learning algorithms. Again, such algorithms need to be robust in order to cope with model misspecification or outliers in the data. Robust Statistics is a discipline of statistics which exactly addresses these challenges. This paper provides an extensive and systematic overview of current applications of Robust Statistics in autonomous driving in a unified notation, discusses different notions of the term “robustness” and identifies directions for future work.}, archive = {J_ACCESS}, author = {Tino Werner}, doi = {10.1109/ACCESS.2025.3614064}, journal = {IEEE Access}, pages = {167848-167892}, shortjournal = {IEEE Access}, title = {Applications of robust statistics in autonomous driving}, volume = {13}, year = {2025}, }
• (2025). An investigation into photovoltaic system performance ratio installed in a semi-arid region. ACCESS, 13, 167837-167847. (www)

  @article{ , abstract = {Encouraging the use of renewable energy sources has led to an increase in the installed capacity of grid-connected photovoltaic systems all over the world. Evaluating the performance of such systems is important to identify whether they are working properly or whether there are losses in the system. In this sense, the performance ratio plays a fundamental role in determining the behavior of systems, considering the various sources of losses. It is also essential for helping to design and implement photovoltaic systems. Therefore, this study aims to analyze the performance ratio of 105 grid-connected photovoltaic systems and located in the Brazilian semi-arid. Once the analysis was complete, it was possible to determine the monthly and annual performance of each photovoltaic plant analyzed. This study highlights the importance of evaluating the systems implemented in the region and provides valuable information for implementing new systems.}, archive = {J_ACCESS}, author = {Francisco W. S. Fonseca and Romênia G. Vieira and Maria I. S. Guerra}, doi = {10.1109/ACCESS.2025.3613462}, journal = {IEEE Access}, pages = {167837-167847}, shortjournal = {IEEE Access}, title = {An investigation into photovoltaic system performance ratio installed in a semi-arid region}, volume = {13}, year = {2025}, }
• (2025). Proactive cable replacement planning using neural weibull proportional hazard modeling and bayesian nested monte carlo simulation under data deficiencies. ACCESS, 13, 167823-167836. (www)

  @article{ , abstract = {The increasing failure rate of underground cable systems due to aging poses challenges for the reliable operation of power grids. Without proper asset management methods, the grid may not adequately support the ongoing climate-centered electrification of society. This highlights the need for strategic asset maintenance decision-making. While the existing literature includes inventive approaches for reliability modeling and maintenance strategy selection, there is still a gap in integrating detailed predictive analytics-based reliability modeling that accounts for common data deficiencies, alongside maintenance strategies that consider both reliability and cost-based performance indicators. In this paper, we present a comprehensive proactive cable replacement method using the Neural Weibull Proportional Hazard model, specifically designed to handle truncation and censoring mechanisms. The method employs a Bayesian nested Monte Carlo simulation to address the dual uncertainties associated with the probabilistic reliability model. We apply this maintenance simulation method to a case study of a real distribution system in Denmark. Our results indicate that the optimal replacement strategy reduces outage costs by up to 6.16 million DKK and can decrease the System Average Interruption Duration Index (SAIDI) by more than 50% compared to alternative replacement strategies. Finally, a sensitivity analysis of the maintenance budget shows that operators can reduce their annual budget for cable replacements by up to 50%, incurring only minor additional costs and an increase in peak SAIDI of just 1.5 minutes. The innovation lies in combining the NWPH model with Bayesian Monte Carlo simulation to handle incomplete records, quantify uncertainty, and guide cost-effective maintenance decisions.}, archive = {J_ACCESS}, author = {Mohammad Reza Shadi and Lasse Kappel Mortensen and Hamid Mirshekali and Hamid Reza Shaker}, doi = {10.1109/ACCESS.2025.3613832}, journal = {IEEE Access}, pages = {167823-167836}, shortjournal = {IEEE Access}, title = {Proactive cable replacement planning using neural weibull proportional hazard modeling and bayesian nested monte carlo simulation under data deficiencies}, volume = {13}, year = {2025}, }
• (2025). An innovative hybrid machine learning approach for student survey analysis: Random tree with ordinal noise filtering and feature selection (RTONF). ACCESS, 13, 167798-167822. (www)

  @article{ , abstract = {Analyzing student survey data using machine learning has become increasingly important for educational institutions aiming to understand the student experience, identify areas for improvement in teaching and curriculum, and make informed decisions to enhance learning outcomes. A major challenge in this domain is the presence of noisy data, which can substantially reduce the performance of classification algorithms. Existing studies often ignore the ordinal nature of class labels during noise detection, which may lead to suboptimal data cleaning. To address this problem, we propose a new approach entitled Random Tree with Ordinal Noise Filtering and Feature Selection (RTONF). This method explicitly incorporates the inherent order of class labels (e.g., poor < fair < good < very good < excellent) during the noise identification process, before predicting student performance or satisfaction. The Random Tree (RT) algorithm serves as the base classifier, while Pearson Correlation is employed for feature selection due to its outstanding performance. Experimental results demonstrated that the proposed hybrid method with an average accuracy of 84.61% achieved a 5.23% improvement compared to the traditional RT classifier. Furthermore, comparative analysis indicated that our method outperformed the state-of-the-art techniques in terms of prediction accuracy on the same dataset.}, archive = {J_ACCESS}, author = {Goksu Tuysuzoglu and Yunus Dogan and Feristah Dalkilic and Elife Ozturk Kiyak and Bita Ghasemkhani and Kokten Ulas Birant and Derya Birant}, doi = {10.1109/ACCESS.2025.3614058}, journal = {IEEE Access}, pages = {167798-167822}, shortjournal = {IEEE Access}, title = {An innovative hybrid machine learning approach for student survey analysis: Random tree with ordinal noise filtering and feature selection (RTONF)}, volume = {13}, year = {2025}, }
• (2025). Classification of histopathology images by siamese and triplet network embeddings. ACCESS, 13, 167778-167797. (www)

  @article{ , abstract = {In computational pathology, one of the most investigated aspects concerns the analysis of patients’ tissue images. This type of image is often called a Whole Slide Image (WSI) and is characterised by a high resolution that makes impractical, if not impossible, the classical analysis methods that process the image in a single step. Automatic and Deep Learning methods fit into this context in an attempt to provide automatic medical decision support. The paper presents a representation/classification schema showing three main contributions, firstly that, thanks to deep metric learning, it is possible to move the embeddings of the tissue images by minimizing the distance between elements of the same class and maximizing the distance between elements of different classes, and this makes easier the classification task; secondly it offers an effective way to visualize the clustered embeddings justifying the classification results; finally it presents a confidence score that indicates the accuracy of the classification result. Using four different datasets, we show how the proposed approach achieves results in line with and sometimes better than state-of-the-art methods, despite being a much simpler model. A further strength lies in the possibility of having a visualisation that makes the classification mechanism explicit. In addition, we provide a classification confidence score that can add useful information to the medical decision support system.}, archive = {J_ACCESS}, author = {Domenico Amato and Salvatore Calderaro and Giosuè Lo Bosco and Riccardo Rizzo and Filippo Vella}, doi = {10.1109/ACCESS.2025.3613448}, journal = {IEEE Access}, pages = {167778-167797}, shortjournal = {IEEE Access}, title = {Classification of histopathology images by siamese and triplet network embeddings}, volume = {13}, year = {2025}, }
• (2025). Enhanced lesion localization and classification in ocular tumor detection using grad-CAM and transfer learning. ACCESS, 13, 167762-167777. (www)

  @article{ , abstract = {Ocular tumors pose significant diagnostic challenges due to their rarity and the subtle visual cues they present in fundus images. This paper introduces a novel deep learning framework, termed ELRC-GI (Enhanced Lesion Recognition and Classification with Grad-CAM Integration), and designed for accurate and interpretable ocular tumor detection. The proposed model integrates VGG19 and ResNet50 convolutional neural networks with Grad-CAM-based attention supervision, enabling both high classification accuracy and precise lesion localization. Unlike traditional CNN-based approaches, ELRC-GI incorporates a heatmap-guided loss function that aligns model predictions with interpretable visual explanations, thereby improving clinical trust and diagnostic reliability. We utilize transfer learning by initializing VGG19 and ResNet50 with pre-trained ImageNet weights, freezing the initial layers and fine-tuning the final layers using the RFMiD dataset to adapt the models to the ocular tumor detection task. The model maintains a true positive rate of 96% at a false positive rate of 0.3%, as evidenced by a robust ROC curve. Experimental results on the RFMiD dataset demonstrate the superiority of ELRC-GI, achieving 97% accuracy, 93% precision, 85% recall, and an AUC of 0.98, significantly outperforming baseline CNN models. Grad-CAM visualizations further validate the model’s capability to highlight tumor regions, even in the presence of overlapping ocular conditions. The ELRC-GI model thus offers a robust, explainable, and clinically viable solution for early ocular tumor detection, setting the stage for broader application in interpretable medical AI.}, archive = {J_ACCESS}, author = {Mohsin A. Farhad and Abdul Razaque and Samat B. Mukhanov and Dina S. M. Hassan and Hari Mohan Rai}, doi = {10.1109/ACCESS.2025.3610183}, journal = {IEEE Access}, pages = {167762-167777}, shortjournal = {IEEE Access}, title = {Enhanced lesion localization and classification in ocular tumor detection using grad-CAM and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Using non-invasive EEG signals to identify urinary urgency and voiding intentions. ACCESS, 13, 167756-167761. (www)

  @article{ , abstract = {Urinary incontinence is closely associated with mobility limitations. In understaffed nursing homes, timely toileting support is often lacking, making reliable monitoring of voiding intention essential for reducing dependence and improving the quality of life. This study explores the feasibility of using non-invasive electroencephalography (EEG) signals to identify urinary urgency and voiding intentions. Ten participants were recruited, and EEG signals were recorded with a 64-channel EEG device during both non-urgent and urgent states. The SEResNet34 deep learning model, which incorporates ResNet34 and Squeeze-and-Excitation (SE) components with temporal aggregated pooling (TAP), was proposed, and the performance of the model was evaluated on the test set. The result showed that the improved SEResNet34 model can effectively classify EEG signals for urgency, achieving an accuracy of 0.9208, a precision of 0.9130, a recall of 0.9292, and an F1-score of 0.9211. The model outperformed ResNet18; however, the additional increase in layers beyond ResNet34 was not significantly beneficial. The ablation study showed that integrating the SE mechanism and the TAP function into the ResNet architecture can improve its accuracy. The proposed model surpasses the other models, indicating that it is better suited for the task of urgency detection. EEG shows significant potential in predicting urinary urgency and voiding intention, offering a new direction for the development of non-invasive monitoring and intelligent rehabilitation devices, with the potential to drive technological advancement in related fields.}, archive = {J_ACCESS}, author = {Zhonghan Zhou and Gongyue Liu and Limin Liao}, doi = {10.1109/ACCESS.2025.3609979}, journal = {IEEE Access}, pages = {167756-167761}, shortjournal = {IEEE Access}, title = {Using non-invasive EEG signals to identify urinary urgency and voiding intentions}, volume = {13}, year = {2025}, }
• (2025). Analyzing microservice communication performance in industrial edge platform. ACCESS, 13, 167744-167755. (www)

  @article{ , abstract = {The integration of legacy manufacturing systems into Industry 4.0 ecosystems necessitates architectures that balance interoperability and performance across infrastructures. This paper addresses these challenges by proposing a hybrid system leveraging Docker containers, the Moleculer framework, and the Asset Administration Shell (AAS) to enable semantic interoperability while quantifying communication latencies in bidirectional industrial workflows, analyzing microservice communication performance in edge platforms through a hybrid architecture that combines legacy protocols with edge virtualization, and contextualizing it against fieldbus-based systems and fully virtualized containerized microservices. Experimental validation on a Siemens S7-1500 Programmable Logic Controller (PLC) evaluates latency distributions across architectures, revealing average round-trip times of 7.23 ms for hybrid sensor updates and 5.94 ms for virtualized command writes, with 75% of operations under 10 ms. Performance tradeoffs emerge between protocol translation overhead in hybrid systems and transporter-induced variability in virtual deployments. By correlating temporal metrics with architectural choices, this work provides actionable insights for modernizing brownfield installations, enabling manufacturers to select optimal strategies based on legacy constraints and real-time requirements. The results highlight the viability of gradual migration paths, preserving existing investments while adopting Industry 4.0 capabilities through performance-aware microservice orchestration.}, archive = {J_ACCESS}, author = {Ricardo P. Pontarolli and Massimiliano Gaffurini and Eduardo P. Godoy and Paolo Ferrari}, doi = {10.1109/ACCESS.2025.3612701}, journal = {IEEE Access}, pages = {167744-167755}, shortjournal = {IEEE Access}, title = {Analyzing microservice communication performance in industrial edge platform}, volume = {13}, year = {2025}, }
• (2025). Signal bandwidth estimation for jamming detection: Skip connection and LARS augmented mask R-CNN. ACCESS, 13, 167726-167743. (www)

  @article{ , abstract = {Accurate bandwidth estimation is crucial for achieving effective jamming localization and suppression in the domain of jamming detection. Traditional methods exhibit low accuracy and a limited bandwidth range when confronted with conditions characterized by large bandwidth and low signal-to-noise ratio (SNR) of jamming signals. To tackle these challenges, we propose a signal bandwidth estimation algorithm based on skip connection and layer-wise adaptive rate scaling (LARS) augmented mask region-convolutional neural network (SLA-Mask). The proposed SLA-Mask progressively implements skip connections in the backbone and employs a stochastic gradient descent (SGD) and LARS optimization strategy during training. The skip connection framework adopts a dual-path design: a feature rebuilding module with two-stage discrete wavelet transform (DWT) enhancement and a dynamic attention gated fusion (DAGF) based fusion module. This architecture can achieve accurate jamming signal detection, segmentation, and bandwidth estimation. Experimental results demonstrate that the proposed SLA-Mask achieves accurate bandwidth estimation and effectively mitigates feature degradation and oscillations during the period of large-scale training. When the SNR is as low as -10 dB, the proposed method enhances estimation accuracy by 98.7% compared to the traditional auto-correlation function (ACF) based method.}, archive = {J_ACCESS}, author = {Zhaoyu Xia and Yujie Lin}, doi = {10.1109/ACCESS.2025.3609279}, journal = {IEEE Access}, pages = {167726-167743}, shortjournal = {IEEE Access}, title = {Signal bandwidth estimation for jamming detection: Skip connection and LARS augmented mask R-CNN}, volume = {13}, year = {2025}, }
• (2025). Emohance: Enhancing emotional arousal in interactive content via real-time vibratory feedback. ACCESS, 13, 167716-167725. (www)

  @article{ , abstract = {Delivering mechanical vibratory stimuli to the upper body in synchronization with audiovisual content can enhance emotional experiences. Previous studies on emotion amplification have primarily focused on videos with predetermined emotional content and timing. This study extends the application to interactive content, where the emotional narrative dynamically changes based on user interaction. We propose a novel approach that provides real-time vibratory feedback to the thoracoabdominal region in response to increases in a user’s skin conductance response (SCR), thereby amplifying emotional arousal. A total of 27 participants were involved in the study, engaging in two distinct types of games: an action game and a suspense-puzzle game. Using a within-participants design, we compared conditions with and without vibratory feedback during gameplay. The results showed a significant increase in self-reported emotional arousal with vibratory feedback. In the action game, subjective frustration and excitement were significantly enhanced, while in the puzzle game, tension was amplified. Additionally, in the action game, the number of peaks and variability of SCR signals increased significantly under the vibratory feedback condition. These findings establish a foundation for technologies aimed at augmenting human emotional experiences during interactive content engagement.}, archive = {J_ACCESS}, author = {Yuki Kosuge and Shogo Okamoto and Tatsuya Kitajima}, doi = {10.1109/ACCESS.2025.3613517}, journal = {IEEE Access}, pages = {167716-167725}, shortjournal = {IEEE Access}, title = {Emohance: Enhancing emotional arousal in interactive content via real-time vibratory feedback}, volume = {13}, year = {2025}, }
• (2025). An explainable AI framework for AC optimal power flow. ACCESS, 13, 167702-167715. (www)

  @article{ , abstract = {Despite their success and impressive performance, deep learning models are still considered opaque models. Experts have minimal trust in adopting them when operating critical systems, such as power systems. Integrating renewable energy sources increases grid uncertainty, making it crucial to frequently solve the Alternating Current Optimal Power Flow (AC-OPF) problem to provide cost-effective and reliable grid operations. This paper proposes an interpretable deep learning framework to address the AC-OPF problem, offering explainability without sacrificing accuracy. Feature attribution techniques, including Expected Gradients and Shapley Additive Explanations (SHAP), are employed to identify the most influential input features on the outputs, both individually and collectively. Interpretability is essential for building trust for system operators and plays a key role in debugging, validation, and fine-tuning. The proposed approach is evaluated on four test grids, demonstrating high accuracy and interpretability. The results confirm that the model aligns with the physical principles of power systems, providing trustworthy and explainable decision-making to power system operators.}, archive = {J_ACCESS}, author = {Ronald Kfouri and Harag Margossian}, doi = {10.1109/ACCESS.2025.3614071}, journal = {IEEE Access}, pages = {167702-167715}, shortjournal = {IEEE Access}, title = {An explainable AI framework for AC optimal power flow}, volume = {13}, year = {2025}, }
• (2025). Multi-relay UAV-RIS digital twin-based framework for user fairness and load balancing using multi-tasking DRL. ACCESS, 13, 167684-167701. (www)

  @article{ , abstract = {As revolutionary enablers capable of dynamically influencing communication link quality, Reflecting Intelligent Surfaces (RIS), particularly when mounted on Unmanned Aerial Vehicles (UAV) have emerged as reliable, cost-effective, and flexible tools for enhancing wireless connectivity across a range of real-world scenarios. Similarly, Digital Twin (DT) technology is increasingly recognized as an enabler for future wireless networks. To mitigate the challenges posed by building obstructions or natural disasters that disrupt radio propagation, this work incorporates multiple UAV-RIS to enhance connectivity and improve link quality throughout the network. This paper proposes a Multi-Relay UAV-RIS-assisted DT (MR-UAV-RIS DT) framework, focused on intelligent network rate management to ensure User Fairness (UF) and Load Balancing (LB) in small cell (SC)-based wireless networks. In the proposed system, dual-connectivity user equipments (UEs) are simultaneously connected to both the macrocell (MC) via relay UAV-RIS, and to Small cell-Base stations (SC-BSs), all operating in the mmWave frequency band to deliver high-speed connectivity. To meet UF and LB goals, an optimization problem is formulated to jointly control the active beamforming for both SC-BSs and the MC, as well as the passive beamforming and 3D positions of the UAV-RISs associated with SC-BSs and relay UAV-RIS linked to the MC. Due to the complexity and high dimensionality of the problem, a DT-based multi-task deep reinforcement learning model (DT-MTDRL) is proposed, built upon the Deep Deterministic Policy Gradient (DDPG) algorithm. Simulation results confirm that the proposed model not only ensures a fair rate distribution among users and SCs but also delivers superior performance compared to existing benchmark schemes, while maintaining robust and adaptive connectivity in dynamic and challenging environments.}, archive = {J_ACCESS}, author = {Abdulmalik Alwarafy and Naveed Khan and Nasir Saeed and Mustaqeem Khan and Zahid Mehmood and Saeed Abdallah}, doi = {10.1109/ACCESS.2025.3611023}, journal = {IEEE Access}, pages = {167684-167701}, shortjournal = {IEEE Access}, title = {Multi-relay UAV-RIS digital twin-based framework for user fairness and load balancing using multi-tasking DRL}, volume = {13}, year = {2025}, }
• (2025). HMSA-net: A hierarchical multi-scale attention network for brain tumor segmentation from multi-modal MRI. ACCESS, 13, 167672-167683. (www)

  @article{ , abstract = {This study introduces HMSA-Net, a Hierarchical Multi-Scale Attention Network designed for accurate brain tumor segmentation from multi-modal MRI scans. HMSA-Net employs a hierarchical encoder to capture multi-resolution features, complemented by full-scale skip connections that preserve spatial detail across different scales. In the decoder, dynamic multi-scale attention modules recalibrate and fuse features, enhancing the network’s ability to differentiate complex tumor subregions. The proposed method is evaluated on the BraTS 2021 dataset and demonstrated consistent improvements over baseline models in segmentation accuracy and boundary localization. Additionally, a bagging-based ensemble strategy improves prediction stability and enhances robustness in the presence of tumor heterogeneity. By integrating hierarchical multi-scale feature extraction with attention-driven refinement, HMSA-Net provides a reliable and efficient solution for automated brain tumor segmentation, with significant potential to support quantitative neuro-oncological analysis and inform clinical decision-making.}, archive = {J_ACCESS}, author = {Gayathri Ramasamy and Tripty Singh and Xiaohui Yuan and Ganesh R. Naik}, doi = {10.1109/ACCESS.2025.3606560}, journal = {IEEE Access}, pages = {167672-167683}, shortjournal = {IEEE Access}, title = {HMSA-net: A hierarchical multi-scale attention network for brain tumor segmentation from multi-modal MRI}, volume = {13}, year = {2025}, }
• (2025). Large language model-based functional scenario generation for automated vehicle safety evaluation using vehicle and pedestrian traffic accident data. ACCESS, 13, 167660-167671. (www)

  @article{ , abstract = {Despite advancements in automated driving technology, traffic accidents remain a significant safety concern in urban areas because of complex traffic environments and current technical limitations of automated vehicles (AV). Structured, quantitative scenario-based evaluations are essential for assessing AV safety. This study aims to generate functional scenarios by accurately inferring vehicle and pedestrian behavior information from unstructured traffic accident descriptions. The dataset comprises 137,432 traffic accident cases collected by the Korean National Police Agency. A total of 1,774 training samples were extracted using term frequency–inverse document frequency keyword analysis. We then evaluated the prediction performance of three large language models (LLM): Claude 3, GPT-4o, and GPT-4o with fine-tuning, using 500 randomly selected traffic accident records that lacked keyword-extractable behavior information. GPT-4o with fine-tuning achieved the highest accuracy of 81.1%. Using this model, 26 functional scenarios were generated. The risk levels for each scenario were evaluated by integrating the equivalent property damage only index with risk predictions from logistic regression and XGBoost models. A weighted average of the normalized results was used to rank the scenarios. Scenarios such as “turning left / crossing,” “straight / crossing,” and “turning right / crossing” were consistently identified as high-risk. This study demonstrates the effectiveness of LLM-based contextual inference in converting real-world traffic accident data into functional scenarios, and contributes to the advancement of AV safety evaluation frameworks.}, archive = {J_ACCESS}, author = {Jihun Kang and Woori Ko and Youngtaek Lee and Kyeongjin Lee and Ilsoo Yun}, doi = {10.1109/ACCESS.2025.3612989}, journal = {IEEE Access}, pages = {167660-167671}, shortjournal = {IEEE Access}, title = {Large language model-based functional scenario generation for automated vehicle safety evaluation using vehicle and pedestrian traffic accident data}, volume = {13}, year = {2025}, }
• (2025). Exploring latent feature representations of image pixels via convolutional neural network to enhance food segmentation. ACCESS, 13, 167649-167659. (www)

  @article{ , abstract = {For open-vocabulary recognition of ingredients in food images, ingredient segmentation is a crucial step; however, it remains a challenging problem. This paper proposes a novel approach that explores latent feature representations of image pixels via a convolutional neural network (CNN) to enhance ingredient segmentation. An internal clustering metric based on the silhouette score is defined to evaluate the clustering quality of various pixel-level feature representations generated by different feature maps derived from various CNN backbones. Using this metric, the paper explores the most suitable feature representations and clustering methods for ingredient segmentation. Additionally, it is found that principle component (PC)-driven latent feature presentations of pixels derived from concatenations of backbone feature maps improve the clustering quality of them, resulting in stable segmentation outcomes. Notably, the number of selected eigenvalues can be used as the number of clusters to achieve good segmentation results. The proposed method is validated on widely used public datasets, achieving state-of-the-art performances. Importantly, the proposed segmentation method is unsupervised, and pixel-level feature representations from backbones are not fine-tuned on specific datasets. This demonstrates the flexibility, generalizability, and interpretability of the proposed method, while reducing the need for extensive labeled datasets.}, archive = {J_ACCESS}, author = {Ying Dai}, doi = {10.1109/ACCESS.2025.3612465}, journal = {IEEE Access}, pages = {167649-167659}, shortjournal = {IEEE Access}, title = {Exploring latent feature representations of image pixels via convolutional neural network to enhance food segmentation}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence-driven thermal management in electric vehicle traction inverters: Cooling system optimization and real-time temperature prediction. ACCESS, 13, 167632-167648. (www)

  @article{ , abstract = {The accelerated adoption of electric vehicles (EVs) has intensified technical demands on traction inverters, particularly regarding power density, energy efficiency, and thermal management. Silicon Carbide (SiC) MOSFET power modules, with their superior switching performance and thermal characteristics, offer significant advantages but introduce new thermal challenges due to their increased power density and compact packaging. The primary objective of this study is to enhance real-time thermal control and prediction in a SiC-based dual traction inverter through the design and experimental validation of a high-performance cooling system integrated into a modular electric drive platform. To achieve this, multiple cold plate configurations were designed and subsequently evaluated using computational fluid dynamics (CFD) simulations, focusing on junction temperature, thermal uniformity, and pressure drop. To enable real-time thermal predictions, an artificial neural network (ANN) was developed and trained on datasets generated from high-fidelity CFD simulations and a CFD-derived reduced-order model (ROM) to estimate junction temperatures dynamically. Integrated into the inverter control system and supported by real-time sensor feedback, the ANN achieved prediction errors below 3.5% within 1 second across all operating conditions. The proposed framework offers a scalable and intelligent thermal management solution, supporting predictive maintenance and enhancing the reliability and efficiency of next-generation EV powertrains.}, archive = {J_ACCESS}, author = {Gamze Egin Martin and Boud Verbrugge and Mohammed Mahedi Hasan and Mohamed El Baghdadi and Claudio Romano and Omar Hegazy}, doi = {10.1109/ACCESS.2025.3612315}, journal = {IEEE Access}, pages = {167632-167648}, shortjournal = {IEEE Access}, title = {Artificial intelligence-driven thermal management in electric vehicle traction inverters: Cooling system optimization and real-time temperature prediction}, volume = {13}, year = {2025}, }
• (2025). Silicone rubber-based flexible optical sensor for precise twist angle measurement. ACCESS, 13, 167614-167631. (www)

  @article{ , abstract = {A novel torsion or twist angle measurement sensor, utilizing a flexible silicone rubber material, has been designed, fabricated, and experimentally evaluated. The operation of this sensor relies on the principle of optical polarization, involving two linear optical polarizers placed sequentially with their polarization axes perpendicular to each other. These polarizers are separated by a transparent cylindrical duct structure made from rigid epoxy resin, which efficiently guides the light rays from the source-side polarizer to the detector-side polarizer. Under normal, untwisted conditions, the orthogonal alignment of the polarizers prevents any light transmission. However, when the sensor experiences torsion or twisting around the axis aligned with the direction of light propagation, the relative angle between the polarizers’ axes decreases from 90°, allowing a portion of the light to pass through. The intensity of this transmitted optical power, detected by a phototransistor, is directly proportional to the square of the sine of the twist angle, enabling accurate twist angle measurement by monitoring the photocurrent output. A comprehensive analytical model has been developed to precisely correlate the photocurrent with the twist angle, applicable for both narrow and wide spectrum sensing configurations. Validation of this model through experimental measurements across a twist angle range from – $90^{0}$ to $+ 90^{0}$ demonstrates strong agreement. Furthermore, repeated cyclic twist tests indicate minimal degradation in measurement accuracy, highlighting the sensor’s robustness. This sensor structure achieves an effective balance among accuracy, reliability, scalability, and long-term stability, making it particularly suitable for applications involving frequent cyclic twisting.}, archive = {J_ACCESS}, author = {Sneha Ray and Soumya Kanti Raj and Sandip Nandi and Angsuman Sarkar and Palash Das and Soumyadeep Paty and Aritra Acharyya and Arindam Biswas and Naim Ahmad and Salman Arafath Mohammed and Saurav Mallik}, doi = {10.1109/ACCESS.2025.3610486}, journal = {IEEE Access}, pages = {167614-167631}, shortjournal = {IEEE Access}, title = {Silicone rubber-based flexible optical sensor for precise twist angle measurement}, volume = {13}, year = {2025}, }
• (2025). Performance comparison of advanced control strategies based on optimization for active suspension under real vertical speed reducers excitation. ACCESS, 13, 167595-167613. (www)

  @article{ , abstract = {In this study, real vertical speed reducers (VSRs) were modeled according to the Institute of Transportation Engineers (ITE) standards and applied as road inputs to an active suspension system (ASS). Proportional-Integral-Derivative (PID), Fractional-Order-PID (FO-PID), Sigmoid-PID (S-PID), Sigmoid-Based Fractional-PID (SFO-PID), Exponential-PID (E-PID) and Linear Quadratic Regulator (LQR) controllers are designed for the ASS. The parameters of the controllers are optimized using methods such as Artificial Bee Colony (ABC), Golden Jackal Optimization (GJO), Mayfly Optimization (MFO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). The root mean square (RMS) values of vertical body acceleration (VBA), vertical tire acceleration (VTA), suspension travel (SM), and dynamic load coefficients (DLC), which are the key performance indices of ASS, as well as error-performance indices such as integral of absolute value of error (IAE), integral of squared error (ISE), time integral of absolute value of error (ITAE), and time integral of squared error (ITSE), are compared with the advanced control methods and Passive Suspension (PS). The proposed MFO-E-PID provides 56.07% improvement in RMS values for VBA, 26.71% for VTA, 10.17% for SM, and 29.37% for DLC compared to PS; and 7.97% improvement in VBA, 3.99% for VTA, 2.37% for SM, and 2.77% for DLC compared to the second best, MFO-LQR. The MFO-E-PID method provided 30.45%, 49.99%, 23.58%, and 46.27% improvement in error-performance indices for IAE, ISE, ITAE, and ITSE, respectively, compared to PS; and 6.03%, 16.14%, 4.71%, and 17.20% improvement compared to MFO-LQR. Real-time tests have shown that MFO-E-PID improves ASS performance, safety, and comfort, and holds promise for future vehicles.}, archive = {J_ACCESS}, author = {Tugrulhan Akgul and Ali Unluturk}, doi = {10.1109/ACCESS.2025.3613460}, journal = {IEEE Access}, pages = {167595-167613}, shortjournal = {IEEE Access}, title = {Performance comparison of advanced control strategies based on optimization for active suspension under real vertical speed reducers excitation}, volume = {13}, year = {2025}, }
• (2025). Twin-interaction network: A classification method based on open set recognition and periodic full retraining. ACCESS, 13, 167578-167594. (www)

  @article{ , abstract = {In the industrial field, traditional classification models are unable to recognize unknown targets in real-time and lack the capability to accept and learn new knowledge in real-time. To address this isFsue, we propose a detection method based on Open Set Recognition and a dual-branch switching mechanism for periodic full retraining, called Twin-Interaction Network. By adding a shallow model on top of the Vision Transformer network for target feature extraction, this approach performs spatial transformations on feature vectors to enhance the accuracy of unknown target detection. Furthermore, to endow the model with the capability of periodic full retraining, two open-set recognition modules are integrated into a twin architecture, simultaneously performing classification and learning tasks without interference. The open-set recognition method is incorporated into the offline full-model retraining framework for validation. Through an alternating learning mechanism, the Twin-Interaction Network can acquire more precise feature tensors of unknown category, thereby making the classification accuracy of the model for unknown categories consistent with that for known categories. The generalization performance of the proposed method is evaluated using multiple public datasets under standard dataset settings, and the experimental results demonstrate that our method outperforms existing approaches in terms of open-set recognition accuracy.}, archive = {J_ACCESS}, author = {Yang Shen and Danping Huang and Fangji Gan and Shaodong Yu and Xiang Gao}, doi = {10.1109/ACCESS.2025.3613401}, journal = {IEEE Access}, pages = {167578-167594}, shortjournal = {IEEE Access}, title = {Twin-interaction network: A classification method based on open set recognition and periodic full retraining}, volume = {13}, year = {2025}, }
• (2025). LLM-based persona-driven text data augmentation. ACCESS, 13, 167560-167577. (www)

  @article{ , abstract = {Illicit online communication, such as drug-dealing dialogues, is increasingly conducted through covert, context dependent language patterns that evade traditional detection techniques in South Korea. However, developing reliable AI based detection systems remains challenging due to the scarcity of real world training data in such sensitive domains. This paper proposes a novel persona-driven data augmentation framework using Large Language Model(LLM) to generate realistic synthetic drug-dealing dialogues. By encoding domain specific buyer and seller personas along with linguistic behaviour rules, the method produces contextually coherent and semantically diverse dialogues that reflect authentic communication styles. Evaluation results demonstrate that the augmented data preserves key stylistic features (high cosine similarity), maintains lexical diversity (TTR), improves fluency (perplexity), and enhances coherence and lexical richness (ROUGE-L), outperforming traditional augmentation method. Furthermore, statistical validation confirms the semantic consistency and stability of the generated data. These findings highlight the viability of LLM-based augmentation in low-resource, high-risk domains and suggest its potential transferability to other specialized NLP applications requiring context-preserving generation.}, archive = {J_ACCESS}, author = {Hyeon Seong Jeong and Han Kyeong Ko and Soo Yong Park and Taehoon Kim}, doi = {10.1109/ACCESS.2025.3611636}, journal = {IEEE Access}, pages = {167560-167577}, shortjournal = {IEEE Access}, title = {LLM-based persona-driven text data augmentation}, volume = {13}, year = {2025}, }
• (2025). Noor: A pipeline framework for classical arabic parsing. ACCESS, 13, 167538-167559. (www)

  @article{ , abstract = {Classical Arabic (CA) presents unique and significant challenges for computational syntactic analysis, primarily due to its complex morphosyntax, pervasive ellipsis, flexible word order, and the critical scarcity of comprehensive annotated resources. This paper introduces Noor, a novel multi-stage pipeline framework specifically engineered for robust, high-performance syntactic parsing of CA. The Noor framework integrates several key innovations: (1) a dedicated pre-parsing module (TAQDIR) employing a hybrid rule-based and heuristic approach for explicit ellipsis resolution; (2) parallel dependency (ARC-IIRAB) and constituency (ARC-MAHAL) parsers utilizing Bidirectional Long Short-Term Memory (BiLSTM)-based representations and tailored transition systems; and (3) a synthesis mechanism generating enriched hybrid syntactic representations. A primary contribution of this work is the creation and public release of the Quranic Treebank—the first complete, validated, and fully machine-readable hybrid syntactic resource for the CA, reconstructed and augmented using the Noor framework. Empirical evaluation demonstrates that Noor achieves state-of-the-art (SOTA) performance on CA parsing, with the hybrid parser significantly outperforming previous benchmarks by over 4.6 F1 points on the Extended Labeled Attachment Score (ELAS). The framework is complemented by IIRAB Vis, a novel visualization tool designed to render complex hybrid structures in alignment with traditional Arabic grammatical principles (I’rāb). By providing both a validated SOTA methodology and a foundational public dataset, this research substantially advances the computational analysis capabilities for CA across Natural Language Processing (NLP), digital humanities, and linguistic studies.}, archive = {J_ACCESS}, author = {Wadee A. Nashir and Abdulqader M. Mohsen and Asma A. Al-Shargabi and Mohamed K. Nour and Badriyya B. Al-Onazi}, doi = {10.1109/ACCESS.2025.3607915}, journal = {IEEE Access}, pages = {167538-167559}, shortjournal = {IEEE Access}, title = {Noor: A pipeline framework for classical arabic parsing}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation and rectification of prosthetic sockets: A machine learning approach using wearable sensors. ACCESS, 13, 167526-167537. (www)

  @article{ , abstract = {This study demonstrates a data-driven decision support system to aid in rectification of prosthetic sockets aimed at improving overall comfort perceived by amputees. Prosthetic technology, particularly in the realm of socket design, plays a pivotal role in rehabilitation for individuals with limb amputations. Prosthetic sockets, which serve as the critical interface between the residual limb and the artificial limb, enable amputees to walk without the need for invasive implants that connect directly to the bone of the residual limb. This study focuses on the role of intra-socket pressure in socket performance and its impact on optimal socket rectifications for improving comfort in transfemoral amputees. Employing thin Force Sensing Resistor (FSR) sensors, the research measures dynamic pressure variations across individual gait cycles. To explore the effects of altered pressure distribution on socket performance, a clinical trial was conducted consisting of four different socket configurations across several participants, one of which was with no pad inserted and three of which incorporated a silicone pad to modify the dynamic pressure profiles. With data from multiple participants including specific dynamic pressure features extracted from FSR sensors, and subjective feedback of comfort, a Multi-Layer Perceptron (MLP) model is trained to establish predictive relationships between intra-socket pressure and appropriate rectification action. The findings suggest that the MLP agent is more accurate at suggesting rectification actions to prosthetists when compared to simpler classification algorithms such as Random Forest, XGBoost and Logistic regression, laying the foundation for future advancements in prosthetic design.}, archive = {J_ACCESS}, author = {Suranjan Ram Ottikkutti and Pouyan Mehryar and Begum Zeybek and Michalis Karamousadakis and Zulfiqur Ali and Dejiu Chen}, doi = {10.1109/ACCESS.2025.3609566}, journal = {IEEE Access}, pages = {167526-167537}, shortjournal = {IEEE Access}, title = {Performance evaluation and rectification of prosthetic sockets: A machine learning approach using wearable sensors}, volume = {13}, year = {2025}, }
• (2025). Deception-based defense of poisoning attack in federated learning. ACCESS, 13, 167512-167525. (www)

  @article{ , abstract = {Federated Learning (FL) enables decentralized model training to protect data privacy but introduces new security and privacy challenges. One of the most severe threats to FL systems is poisoning attacks, which can disrupt model convergence or manipulate prediction outcomes. Addressing these attacks is particularly challenging due to the distributed and opaque nature of FL. To proactively strengthen defenses, we propose HoneyFED, a novel mitigation strategy based on deception. HoneyFED introduces a global decoy model that misleads and disrupts malicious actors by creating obstacles in their attack process. When a potential attacker is detected, the system engages them and captures their poisoned attack methods. This information is used to design a convincing decoy model that appears legitimate to the attacker. As a result, attackers are tricked into believing their attack has succeeded, discouraging further attempts. We evaluate the realism of our decoy using three criteria: indistinguishability from the real model, credibility over time, and viability in influencing attacker behavior. Experimental results demonstrate that HoneyFED effectively defends FL training against various poisoning strategies while improving overall model convergence.}, archive = {J_ACCESS}, author = {Grace Colette Tessa Masse and Abderrahim Benslimane and Vianney Kengne Tchendji and Ahmed Hemida Anwar}, doi = {10.1109/ACCESS.2025.3611806}, journal = {IEEE Access}, pages = {167512-167525}, shortjournal = {IEEE Access}, title = {Deception-based defense of poisoning attack in federated learning}, volume = {13}, year = {2025}, }
• (2025). LSD-HybridViT: A hybrid vision transformer with lightweight mixed-domain attention and frequency multi-scale dilated convolution feature fusion for diabetic retinopathy grading. ACCESS, 13, 167501-167511. (www)

  @article{ , abstract = {Diabetic Retinopathy (DR) grading plays a pivotal role in clinical decision-making, where precise staging directly impacts treatment planning and blindness prevention. Inspired by ophthalmologists’ diagnostic reasoning, which systematically integrates local lesion assessment with global pathological pattern analysis while incorporating edge and frequency-domain information for comprehensive image analysis, we propose an innovative hybrid framework. This framework emulates the clinical cognitive process through three synergistic components, constructing a clinically aligned feature hierarchy: 1) A Lightweight Mixed-domain Attention (LMA)-driven biomarker localization module enhances sensitivity to microaneurysms and exudates by learning spatial-channel dependencies while maintaining a lightweight architecture. 2) Frequency Multi-scale Dilated Convolutional Blocks integrate multi-scale spatial and frequency domain features, mimicking the human visual process in lesion identification. 3) Vision Transformers model long-range dependencies to analyze lesion distribution patterns and quantify pathological severity, analogous to clinicians’ holistic evaluation. The entire model contains only 10M parameters, effectively bridging the local modeling capacity of convolutional networks with the global reasoning ability of transformers. Experiments on the APTOS-2019 and DDR datasets demonstrate that our method achieves a grading accuracy of 89.6% and a Quadratic Weighted Kappa (QWK) of 93.1% on the APTOS-2019, as well as a grading accuracy of 85.2% and a QWK of 84.9% on the DDR dataset. Results show that the proposed method outperforms many recently popular DR diagnosis and classification approaches, validating its excellence in performance.}, archive = {J_ACCESS}, author = {Xiaofang Gou and Ye Wang and Wenman Li}, doi = {10.1109/ACCESS.2025.3610904}, journal = {IEEE Access}, pages = {167501-167511}, shortjournal = {IEEE Access}, title = {LSD-HybridViT: A hybrid vision transformer with lightweight mixed-domain attention and frequency multi-scale dilated convolution feature fusion for diabetic retinopathy grading}, volume = {13}, year = {2025}, }
• (2025). Guided wave-based inspection for pipe bends in marine seawater pipelines. ACCESS, 13, 167493-167500. (www)

  @article{ , abstract = {Marine seawater pipelines function in harsh environments prone to defects, particularly at pipe bends, which represent key inspection targets. Although ultrasonic guided waves can effectively inspect pipelines, the complex geometry of pipe bends poses distinct challenges to their propagation. This study rigorously examined guided-wave testing methods for pipe bends in marine pipelines. The semi-analytical finite element method was initially used to compute the dispersion curves and mode structures of the guided waves for a typical marine pipeline. Mode structure analysis at the pipe bends identified the torsional T(0,1) mode, focusing on the extrados. Finite element simulations then characterized the T(0,1) mode scattering at the pipe bends. Using the trigonometric circumferential distribution of guided wave modes, signals for specific circumferential orders were extracted from the simulated data to elucidate the modal composition and propagation paths of the primary T(0,1) scattered signals. This establishes a theoretical framework for interpreting guided wave signals in pipe-bend inspections. Experiments confirmed the heightened sensitivity of the T(0,1) mode to extrados defects, validating theoretical mode structure analyses.}, archive = {J_ACCESS}, author = {Yu Zhang and Wenjun Wu and Wentao Wu and Li Wang and Ben Zhang}, doi = {10.1109/ACCESS.2025.3612291}, journal = {IEEE Access}, pages = {167493-167500}, shortjournal = {IEEE Access}, title = {Guided wave-based inspection for pipe bends in marine seawater pipelines}, volume = {13}, year = {2025}, }
• (2025). Efficient AR object alignment using deep closest point and cylinder-angle interpolation. ACCESS, 13, 167475-167492. (www)

  @article{ , abstract = {Accurate alignment of virtual objects in Augmented Reality (AR) is essential for precision-critical applications such as surgery, infrastructure inspection, and digital twin systems. However, conventional markerless approaches relying on planar detection or external sensors often incur errors, high cost, and reduced portability. This study presents a lightweight AR framework that operates solely on mobile devices, minimizing reliance on external hardware. The framework integrates Deep Closest Point (DCP)-based point cloud registration with a novel Cylinder-Angle Interpolation strategy. The DCP network, trained on synthetic data, employs attention-based feature embedding and SVD-based soft matching to achieve robust registration under noise and partial overlaps. The interpolation method estimates alignment errors from sparse angular samples, reducing computational cost and data usage by approximately 85%. Comparative experiments against ICP, Go-ICP, FGR, TEASER++, and DCP-only show that the proposed method achieves superior accuracy (average error rate 0.029), higher success rates (96%), and stable performance across diverse environments, including low-light and GPS-degraded scenarios. These results demonstrate a practical and scalable solution for mobile AR, with strong potential for future extensions to non-rigid registration, multi-user synchronization, and SLAM-based mapping.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3613226}, journal = {IEEE Access}, pages = {167475-167492}, shortjournal = {IEEE Access}, title = {Efficient AR object alignment using deep closest point and cylinder-angle interpolation}, volume = {13}, year = {2025}, }
• (2025). From doctors to teams: Expanding lean ILP models for smarter outpatient capacity planning. ACCESS, 13, 167459-167474. (www)

  @article{ , abstract = {Effective capacity planning in outpatient departments faces several challenges, particularly long patient waiting times and insufficient working time due to the increasing workload of senior doctors. Lean thinking, a methodology that improves efficiency by eliminating waste, has been widely adopted in healthcare operations. A recent approach integrated lean thinking with integer linear programming to improve capacity planning by optimizing resource allocation. Particularly, it reduces workload by transferring patients from senior to associate senior doctors. However, the existing model involves only doctors while neglecting other medical staff, and lacks a guarantee of balanced workload distribution after patient transfers. Therefore, incorporating additional medical staff and rebalancing the workload are required to reduce maximum working time and achieve a balanced workload among staff, respectively. In this paper, we propose two enhanced ILP-based lean thinking-enabled models, OCPlean1 and OCPlean2, to further improve planning efficiency and workload balance. OCPlean1 incorporates nurses into the model to reduce the maximum working time for the doctors by delegating certain examination services from associate senior doctors to nurses. In addition to that, OCPlean2 introduces an adaptive workload balancing strategy aimed at achieving a more equitable distribution of workload after patient transfers. Experimental results demonstrate that OCPlean1 significantly reduces maximum workload under varying conditions, including different numbers of doctors and patients, and fluctuating diagnosis and examination times. On the other hand, OCPlean2 achieves a more balanced workload distribution. These findings contribute to more efficient and sustainable outpatient scheduling models and support better utilization of healthcare personnel.}, archive = {J_ACCESS}, author = {Mohammad J. Abdel-Rahman and Ashwaq Khalil and Dania Refai}, doi = {10.1109/ACCESS.2025.3612254}, journal = {IEEE Access}, pages = {167459-167474}, shortjournal = {IEEE Access}, title = {From doctors to teams: Expanding lean ILP models for smarter outpatient capacity planning}, volume = {13}, year = {2025}, }
• (2025). Optimizing english listening pedagogy through AIGC: Evidence from TTS-enhanced anxiety mitigation, metacognitive awareness, and performance gains. ACCESS, 13, 167449-167458. (www)

  @article{ , abstract = {This study examined the efficacy of Artificial Intelligence Generated Content (AIGC), specifically Text-to-Speech (TTS) technology, in addressing three persistent challenges in English listening pedagogy: high learner anxiety, underdeveloped metacognitive awareness, and suboptimal performance. Guided by the IDEE framework, an eight-week quasi-experiment was conducted with 100 Chinese senior high school students (50 control, 50 experimental). Post-intervention analyses with Bonferroni correction revealed significant between-group differences: anxiety reduction (t(98) = -2.76, $p =$ .021, $d =0.55$ ), enhanced metacognitive awareness (t(90.34) = -3.06, $p =$ .009, $d =0.61$ ), and improved listening performance (t(98) = 4.51, $p \lt $ .001, $d =0.90$ ). Within the experimental group, paired samples t-tests confirmed substantial anxiety decrease (t(49) = 5.07, $p \lt $ .001) and metacognitive gains (t(49) = -5.45, $p \lt $ .001). These results demonstrate AIGC’s capacity to alleviate affective barriers through adaptive input scaffolding while fostering cognitive regulation. The study establishes TTS technology as a pedagogically transformative tool that advances sustainable listening instruction through personalized, cost-efficient interventions.}, archive = {J_ACCESS}, author = {Chenghang He}, doi = {10.1109/ACCESS.2025.3613519}, journal = {IEEE Access}, pages = {167449-167458}, shortjournal = {IEEE Access}, title = {Optimizing english listening pedagogy through AIGC: Evidence from TTS-enhanced anxiety mitigation, metacognitive awareness, and performance gains}, volume = {13}, year = {2025}, }
• (2025). DBCON: Dual bias control in zero-shot video moment retrieval. ACCESS, 13, 167439-167448. (www)

  @article{ , abstract = {Zero-shot Video Moment Retrieval (ZVMR) is a task that localizes temporal moments within a video using only natural language queries, without requiring large-scale datasets or extensive training resources. While previous ZVMR studies have focused on aligning high-quality video and query representations, they have faced performance limitations by overlooking the ‘Dual Bias’ problem, which arises from the excessive specificity of queries and the generality of video captions. To address this challenge, we propose the DBCON (Dual Bias CONtrol) framework, which simultaneously controls caption specificity and query generalization. DBCON employs ‘Query-Aware Caption Refinement’ to induce captions that explicitly reflect the core information of the query, and ‘Query Debiasing’ to rephrase the user query into a more universal expression via an LLM. Our experiments show that DBCON effectively mitigates this dual bias, achieving up to 1.73% higher retrieval accuracy on mAP@avg compared to the state-of-the-art model on the QVHighlights test benchmark. This study is significant as it presents a new direction for developing scalable and efficient video-language retrieval systems.}, archive = {J_ACCESS}, author = {Mingyu Jeon and Minuk Ma and Junyeong Kim}, doi = {10.1109/ACCESS.2025.3613635}, journal = {IEEE Access}, pages = {167439-167448}, shortjournal = {IEEE Access}, title = {DBCON: Dual bias control in zero-shot video moment retrieval}, volume = {13}, year = {2025}, }
• (2025). Forecasting the global price of corn using neural network and hybrid approach. ACCESS, 13, 167424-167438. (www)

  @article{ , abstract = {The global price of corn is critical in agricultural markets, food security, and trade policies, making accurate forecasting essential for policymakers and stakeholders. Traditional econometric forecasting models have been widely used but often fail to capture market volatility, nonlinear trends, and external shocks, necessitating advanced machine-learning techniques. This study explored forecasting models, including traditional and advanced machine learning and hybrid approaches, to determine the most effective method for predicting corn prices. Using monthly corn price data from 2014 to 2024, the study evaluated model performance based on different evaluation metrics. The results indicated that the Neural Network Auto Regressive (NNETAR) model outperformed traditional econometric models, effectively capturing complex temporal structures and adapting to sudden market fluctuations. Additionally, hybrid models integrating NNETAR and Seasonal and Trend decomposition using LOESS Model (STLM) demonstrated further improvements in forecasting accuracy, suggesting that combining statistical and machine learning methods enhances predictive reliability. These findings underscore the potential of machine learning in agricultural price forecasting, providing valuable insights for policymakers to implement market stabilization strategies, adjust trade policies, and enhance risk management in the U.S. corn sector.}, archive = {J_ACCESS}, author = {Raksha Khadka and Rumita Limbu Sanwa and Nabin Khadka and Yeong Nain Chi}, doi = {10.1109/ACCESS.2025.3611320}, journal = {IEEE Access}, pages = {167424-167438}, shortjournal = {IEEE Access}, title = {Forecasting the global price of corn using neural network and hybrid approach}, volume = {13}, year = {2025}, }
• (2025). Waveform classification from TEMPEST attacks. ACCESS, 13, 167405-167423. (www)

  @article{ , abstract = {The paper addresses the classification of typical baseband signals to be found in computing devices picked up without any physical contact, just using RF probing. The automatic classification applies a convolutional neural network and alternatively, by tracing dedicated signal properties that are specific for certain signal formats. The neural network classification is based on a time-frequency transform preprocessing. We selected a low-resolution Short-Time Fourier Transform (STFT), but other options, such as the Wigner-Ville Distribution (WVD) and the Continuous Wavelet Transform (CWT) outputs are also presented. We analyzed simulated and measured signals. We found that classification is possible with very high reliability, even based on low-resolution representations that the human eye cannot distinguish, such as for Ethernet 100-BaseT and 1000-BaseT. Both studied approaches can be nicely combined for enhanced classification performance. We briefly discuss jamming as a possible countermeasure to protect against eavesdropping.}, archive = {J_ACCESS}, author = {Muhammad Ahmed Leghari and Sina Mei Pralle and Soeren F. Peik and Sebastian Luetje and Werner Henkel}, doi = {10.1109/ACCESS.2025.3611658}, journal = {IEEE Access}, pages = {167405-167423}, shortjournal = {IEEE Access}, title = {Waveform classification from TEMPEST attacks}, volume = {13}, year = {2025}, }
• (2025). An adaptive multi-agent LLM-based clinical decision support system integrating biomedical RAG and web intelligence. ACCESS, 13, 167390-167404. (www)

  @article{ , abstract = {Increasing data complexity in clinical decision-making processes hinders physicians’ ability to make rapid and accurate decisions. This study proposes an innovative solution to this problem by designing a multi-layered, adaptive Clinical Decision Support System (CDSS) comprising interacting large language model (LLM) agents. The proposed system performs semantic-level information retrieval using a BioBERT-based vector database, enhances information retrieval by accessing up-to-date medical resources via the web, and restructures outputs by activating an adaptive optimization loop in low-confidence situations. Through the structuring of clinical texts, cross-validation of symptom analyses with literature and internet sources, and collaborative data fusion among agents, the system integrates multi-source data and produces consistent decisions. In experiments conducted on the MedQA, PubMedQA, and MedBullets datasets, the system achieved accuracies of 94%, 88%, and 84%, respectively, representing substantial improvements over state-of-the-art methods and demonstrating the significance of the proposed architecture for clinical decision-making reliability. This framework is not merely an information retrieval engine; it is a clinical intelligence partner designed to learn, actively contribute to the decision process, and focus on reliability. In contrast to current CDSS protocols, which frequently depend on static modules or single-agent models, our architecture tackles some of the shortcomings in timeliness, multi-source evidence fusion, and confidence calibration. This originality enables the system to be a next-generation clinical intelligence partner by enabling an unprecedented level of transparency, customizability, and adaptability in real-world decision-making processes.}, archive = {J_ACCESS}, author = {Çağatay Umut Öğdü and Kübra Arslanoğlu and Mehmet Karaköse}, doi = {10.1109/ACCESS.2025.3613340}, journal = {IEEE Access}, pages = {167390-167404}, shortjournal = {IEEE Access}, title = {An adaptive multi-agent LLM-based clinical decision support system integrating biomedical RAG and web intelligence}, volume = {13}, year = {2025}, }
• (2025). AI-driven integrated circuit design: A survey of techniques, challenges, and opportunities. ACCESS, 13, 167364-167389. (www)

  @article{ , abstract = {Analog and radio-frequency integrated circuit (RFIC) design has traditionally been defined by inherent complexity, reliance on manual iterations, and dependence on domain-specific heuristics. Recently, however, this landscape has undergone a transformative shift driven by advances in Artificial Intelligence (AI) and Machine Learning (ML). This survey provides a systematic and forward-looking review of AI-enabled methodologies—including evolutionary algorithms, Bayesian optimization, reinforcement learning, deep learning, and large language models—applied to key design and measurement stages: circuit topology and structure synthesis, circuit optimization, layout automation, and post-silicon calibration and fault diagnosis. By mapping these techniques to each phase of the analog and RFIC development pipeline, we identify emerging trends, persistent challenges such as generalization and data efficiency, and the trajectory toward fully autonomous, scalable, and innovation-driven analog/RFIC design. Beyond circuit design, this evolving ecosystem is also expected to reshape the broader Electronic Design Automation (EDA) landscape. This article serves as a comprehensive reference for academic researchers and industry practitioners seeking to leverage AI in next-generation circuit and system design.}, archive = {J_ACCESS}, author = {Islam Guven and Mehmet Parlak and Dimitri Lederer and Christophe De Vleeschouwer}, doi = {10.1109/ACCESS.2025.3607865}, journal = {IEEE Access}, pages = {167364-167389}, shortjournal = {IEEE Access}, title = {AI-driven integrated circuit design: A survey of techniques, challenges, and opportunities}, volume = {13}, year = {2025}, }
• (2025). Design of new shielding materials for space reactor shielding structure optimization. ACCESS, 13, 167344-167363. (www)

  @article{ , abstract = {Currently, the high density and heavy weight of space radiation shielding materials limit their development and application in the aerospace field. To address this issue, this study proposes a novel fast optimization algorithm for shielding material combinations by integrating Particle Swarm Optimization - Differential Evolution (PSO-DE), Monte Carlo (MC) method, and Back Propagation (BP) prediction model. Among them, the PSO-DE algorithm can be used to search for potential optimal solutions in a vast material parameter space. The MC method can simulate the interaction process between radiation and shielding materials, providing a basis for evaluating shielding effectiveness. The BP model can quickly predict the shielding performance of different material combinations, accelerating the entire optimization process. It was found that under the condition of 15 individuals per generation, different algorithms performed differently. In terms of optimization results, the PSO-DE algorithm achieved the best performance with the shield design, converging to a minimum mass of 25 kg. The Elitist Genetic Algorithm (EGA) significantly improved the results, achieving a minimum mass of over 30 kg. After optimizing the shield design using these algorithms, the total mass of the final solution that met the radiation value constraints was 1,483 kg, a reduction of 17.4174% compared to the initial method. For the BP model constructed, the average deviation percentage (PBIAS) error in predicting sub-annular dose values was controlled within 20%, and the PBIAS error for photon predictions was below 30%. Although the photon error was relatively high due to the incomplete description of the secondary photon generation mechanism, the predicted values were generally higher, effectively describing the decay pattern of photons. This study proposes a novel optimization process to achieve precise and rapid optimization of shielding materials, resulting in lightweight and efficient shielding solutions, and provides new insights for the development of aerospace radiation shielding materials.}, archive = {J_ACCESS}, author = {Yi Han and Qinjian Cao and Xiaomiao Chi and Yansong Sun}, doi = {10.1109/ACCESS.2025.3610902}, journal = {IEEE Access}, pages = {167344-167363}, shortjournal = {IEEE Access}, title = {Design of new shielding materials for space reactor shielding structure optimization}, volume = {13}, year = {2025}, }
• (2025). Classification of cognitive load using deep learning based on eye movement indices. ACCESS, 13, 167324-167343. (www)

  @article{ , abstract = {The classification of cognitive load is crucial to evaluate mental effort in various tasks. Compared to physiological measures such as electroencephalography (EEG), electrocardiography (ECG), or galvanic skin response (GSR), eye tracking offers a less intrusive method for the classification of cognitive load. However, previous studies that used traditional machine learning faced limitations in the accuracy of multiclass classification and relied on proprietary feature extraction methods. Hence, it hinders reproducibility with noncommercial software. To address these challenges, we propose a novel approach by implementing three time series deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN)—to enhance cognitive load classification in multiclass scenarios. By introducing various temporal windows for novel features and optimizing hyperparameters during training, we improved the accuracy of cognitive load classification. We benchmarked these deep learning models against traditional machine learning techniques on the COLET dataset. The experimental results revealed that deep learning models significantly outperformed conventional machine learning methods, with BiLSTM achieving the highest accuracies of 0.8780 and 0.8836 for the classification of cognitive load and activity tasks in multiclass scenarios, respectively. This study highlights the potential of deep learning to revolutionize cognitive workload assessments. Using eye movement indices, our method facilitates accurate, scalable, and cost-effective solutions suitable for low-cost eye tracking devices, broadening accessibility for applications in education, healthcare, and beyond.}, archive = {J_ACCESS}, author = {Sunu Wibirama and Syukron Abu Ishaq Alfarozi and Ahmad Riznandi Suhari and Ayuningtyas Hari Fristiana and Hafzatin Nurlatifa and Paulus Insap Santosa}, doi = {10.1109/ACCESS.2025.3613292}, journal = {IEEE Access}, pages = {167324-167343}, shortjournal = {IEEE Access}, title = {Classification of cognitive load using deep learning based on eye movement indices}, volume = {13}, year = {2025}, }
• (2025). Evaluating time-series deep learning models for accurate and efficient reconstruction of clinical 12-lead ECG signals. ACCESS, 13, 167315-167323. (www)

  @article{ , abstract = {With the transformation of healthcare systems, the aging population, and the impact of the COVID-19 pandemic, interest in the healthcare industry has significantly increased. In particular, demand has grown for personal electrocardiogram (ECG) monitoring using wearable devices, such as smartwatches. However, these devices usually only record single-lead ECG signals, which are not as accurate for diagnosing cardiac conditions as the standard 12-lead ECGs used in clinical settings. Deep learning models have been actively explored to reconstruct standard 12-lead ECG signals from several leads to overcome this limitation. Nevertheless, comprehensive comparative studies on time-series-based deep learning models capable of generating clinically equivalent ECG signals are lacking. In this study, we designed and evaluated three recurrent neural network (RNN) architectures LSTM, Bi-LSTM, and GRU to reconstruct 12-lead ECG signals from a limited subset of leads and compared their reconstruction performance. Each model consists of three RNN layers, batch normalization, and dropout layers. Experimental results on the PTB-XL dataset show that the GRU model achieves the best reconstruction performance, with a PCC of 0.911, MAE of 0.033 and RMSE of 0.065, outperforming Bi-LSTM (PCC: 0.893, MAE: 0.035, RMSE: 0.072) and LSTM (PCC: 0.819, MAE: 0.039, RMSE: 0.088). This study systematically compares the performance of RNN-based models for ECG signal reconstruction and identifies GRU as the most effective architecture for reconstructing standard 12-lead ECGs from single-lead inputs. Due to its relatively lightweight structure, the proposed model shows strong potential for real-time implementation on wearable devices and has valuable implications for mobile healthcare and remote diagnostic systems.}, archive = {J_ACCESS}, author = {Min Gu Kim and Gengjia Zhang and Meina Li and Jaehyo Jung}, doi = {10.1109/ACCESS.2025.3612961}, journal = {IEEE Access}, pages = {167315-167323}, shortjournal = {IEEE Access}, title = {Evaluating time-series deep learning models for accurate and efficient reconstruction of clinical 12-lead ECG signals}, volume = {13}, year = {2025}, }
• (2025). A non-markovian Birth–Death process approach for dynamic multi-echelon inventory optimization of repairable spares under uncertain demand. ACCESS, 13, 167302-167314. (www)

  @article{ , abstract = {In an era of complex systems, dynamic multi-echelon inventory optimization for repairable spares under uncertain demand is vital to ensure operational resilience, minimize downtime, and balance cost-efficiency with reliability across critical sectors like defense and energy. Aiming at the problem of inaccurate results of VMETRIC model under low availability, an inventory optimization model of repairable parts of any level and any tier based on the process of generation and destruction is established. By predicting the demand rate and arrival rate of each level of station and each type of spare parts, it builds a model of the birth and death process of each part, and proposes a method of calculating the equipment availability based on the birth and death process. Taking the equipment availability of the whole security system as the constraint index and the lowest total purchase cost of spare parts as the goal, the optimal spare parts allocation plan is obtained by using the marginal algorithm, and a simulation model is built to evaluate and adjust the optimized plan. The simulation model is built to evaluate and adjust the optimization scheme. Combined with the example, the simulation results are used as the test standard, and the authoritative VMETRIC software is selected to compare and illustrate with the analytical model in terms of optimization performance, calculation accuracy and applicability. The results show that both the analytical model and the VMETRIC software have a certain scope of application, and the combination of analytical model and simulation is undoubtedly more adaptable.}, archive = {J_ACCESS}, author = {Dunxiang Zhu and Qiwei Hu and Shihan Tan and Fei Xiong and Zhen Li and Quan Shi}, doi = {10.1109/ACCESS.2025.3608452}, journal = {IEEE Access}, pages = {167302-167314}, shortjournal = {IEEE Access}, title = {A non-markovian Birth–Death process approach for dynamic multi-echelon inventory optimization of repairable spares under uncertain demand}, volume = {13}, year = {2025}, }
• (2025). DualDRNet: A unified deep learning framework for customer baseline load estimation and demand response potential forecasting for load aggregators. ACCESS, 13, 167280-167301. (www)

  @article{ , abstract = {Accurately valuing demand-side flexibility hinges on two tightly coupled tasks: reconstructing the customer baseline load (CBL) that would have occurred in the absence of demand-response (DR) events and, from that baseline, forecasting the flexibility of load that can be curtailed. Existing studies usually treat these problems in isolation and often depend on appliance-level datasets that are rarely available at scale. This paper proposes DualDRNet, a unified deep learning framework that converts one-dimensional smart-meter load data into frequency-aware 2-D tensors, then captures intra-period and inter-period dynamics through a CNN–Transformer vision backbone. CBL estimation is cast as a masked-imputation task, where DR intervals are treated as missing values and the model learns the missing patterns mimicking CBL. DR Potential forecasting is addressed with the same network, trained autoregressively for multi-horizon prediction. The model is evaluated on the Low Carbon London dataset comprising half-hourly readings from 10% households exposed to 77 peak shaving and 81 valley filling DR events. DualDRNet lowers CBL root-mean-square error (RMSE) to $0.715~kW$ , mean absolute percentage error (MAPE) to 6.9%, outperforming both averaging and deep learning based CBL estimation baselines. For aggregated DR Potential forecasting, it sustains accurate performance from five- to nine-day horizons, delivering lowest RMSE and MAPE relative to state-of-the-art temporal convolutional, hierarchical interpolation and Transformer variants. By coupling baseline reconstruction and flexibility forecasting in a single architecture with near-identical hyper-parameters, DualDRNet offers load aggregators a practical, end-to-end solution for day-ahead market bidding without the need for intrusive sub-metering. Future work will extend the approach to mixed commercial–industrial portfolios and explore parameter-efficient CNN-Transformer variants suitable for edge deployment where computational requirements are more stringent.}, archive = {J_ACCESS}, author = {Ali Muqtadir and Bin Li and Bing Qi and Songsong Chen and Kun Shi}, doi = {10.1109/ACCESS.2025.3613248}, journal = {IEEE Access}, pages = {167280-167301}, shortjournal = {IEEE Access}, title = {DualDRNet: A unified deep learning framework for customer baseline load estimation and demand response potential forecasting for load aggregators}, volume = {13}, year = {2025}, }
• (2025). LLMYOLOEdge: An edge-IoT aware novel framework for integration of YOLO with localized quantized large language models. ACCESS, 13, 167250-167279. (www)

  @article{ , abstract = {Deploying multimodal AI on severely resource-constrained hardware remains challenging due to tight latency, memory, and privacy requirements. We present LLMYOLOEdge, a fully on-device framework that integrates YOLO object detection (YOLOv8/11/12 variants) with quantized instruction-tuned LLMs—Qwen2.5:0.5b-instruct for reliable image-reference extraction and Granite3-MoE:1b-instruct for concise textual summarization—served locally via Ollama and orchestrated by a lightweight Flask API on Raspberry Pi 4B as a resource constrained egde-Internet of Things (IoT) setup. The system employs grammar-constrained, multi-shot prompting to guarantee structured JSON outputs and instruments every stage with fine-grained metrics for rigorous statistical analysis. On a Raspberry Pi 4B, LLMYOLOEdge sustains real-time operation while preserving data locality. Across extensive trials, we observe significant performance differences among YOLO backbones – yolo11n.pt yields the shortest inference latency ( $1013.241\ \mathrm {ms}$ ), whereas yolo12s.pt minimizes extractor prompt-evaluation time ( $19.242\times 10^{9}\ \mathrm {ns}$ ). The multi-shot extractor achieves perfect URL/path extraction accuracy (100%), outperforming a baseline Granite3-MoE approach (88.89%). One-way ANOVA with Tukey’s HSD and pairwise t-tests confirm these effects at $p\lt 0.001$ , establishing both efficiency and accuracy gains under strict resource budgets. Our contributions are threefold such as 1) an integrated, privacy-preserving multimodal pipeline that runs entirely on commodity edge hardware; 2) a principled prompting strategy that removes brittle parsing failure modes; and 3) a reproducible evaluation suite reporting per-stage latencies, throughput, and correctness. Code and assets are available at https://github.com/ParthaPRay/yolo_ollama_raspberrypi, offering a practical template for edge-native multimodal AI deployments.}, archive = {J_ACCESS}, author = {Partha Pratim Ray and Mohan Pratap Pradhan and Shuai Li}, doi = {10.1109/ACCESS.2025.3613423}, journal = {IEEE Access}, pages = {167250-167279}, shortjournal = {IEEE Access}, title = {LLMYOLOEdge: An edge-IoT aware novel framework for integration of YOLO with localized quantized large language models}, volume = {13}, year = {2025}, }
• (2025). ESIFCD: A dual-temporal cross-domain fusion network for remote sensing change detection. ACCESS, 13, 167227-167249. (www)

  @article{ , abstract = {Remote sensing change detection (RSCD) must remain reliable under illumination, seasonal, and viewpoint variability while handling objects that range from narrow roads to block-scale complexes. This paper presents Efficient Spectral Inception Former Change Detector (ESIFCD), an efficient dual-temporal network that couples domain-aligned differencing with multiple cross-resolution exchange and lightweight global context encoding. ESIFCD introduces: (i) an Adaptive Difference Fusion Unit (ADFU) that fuses absolute and learned differences over spatial, spectral, and texture features; (ii) a Cross-Resolution Aggregation Module (CRAM) that repeatedly exchanges evidence among four scales to mitigate scale disagreement; (iii) a Patch-Aware Context Encoder (PACE) that models long-range dependencies via tokenized patches with modest cost; and (iv) a fast GLCM-Stats Encoder that supplies texture semantics (ASM/Contrast/Correlation) without analytical histograms. A re-parameterized large-kernel block (RLKB) sharpens local detail, and a late MetaCNNGate learns pixel-wise weights over one primary and four auxiliary heads to consolidate their complementary expertise (small vs. large structures). Across six public benchmarks, ESIFCD attains the best F1 on each dataset: 93.19% (LEVIR-CD), 87.89% (LEVIR-CD+), 70.10% (S2Looking), 95.95% (CDD), 83.48% (SYSU-CD), and 95.30% (WHU-CD). The model is efficient and fast (12.33 GFLOPs; 82.07 images/s at $256^{2}$ ), and with automatic mixed precision reaches over 165 images/s while maintaining accuracy, surpassing recent strong baselines under the same setting. Ablations confirm that ADFU improves robustness to radiometric and registration noise, CRAM preserves thin structures and prevents fragmentation on large objects, PACE improves cluttered scenes, and MetaCNNGate consistently lifts F1 on all benchmarks. The code and model weights for each CD dataset can be found at https://github.com/russo-ashraf/ESIFCD}, archive = {J_ACCESS}, author = {Russo Ashraf and Adri Priadana and Xuan-Thuy Vo and Ge Cao and Tien-Dat Tran and Kang-Hyun Jo}, doi = {10.1109/ACCESS.2025.3612956}, journal = {IEEE Access}, pages = {167227-167249}, shortjournal = {IEEE Access}, title = {ESIFCD: A dual-temporal cross-domain fusion network for remote sensing change detection}, volume = {13}, year = {2025}, }
• (2025). Multi-view collaborative fusion and contrastive learning for recommendation. ACCESS, 13, 167213-167226. (www)

  @article{ , abstract = {In knowledge-enhanced recommendation algorithms, Graph Neural Network (GNN)-based methods have become the dominant paradigm due to their powerful capabilities in modeling higher-order structural information. However, they still face two key challenges: 1) The traditional GNN aggregation mechanism suffers from the over-smoothing phenomenon, which hinders deep networks from effectively capturing fine-grained collaborative signals; 2) The extreme sparsity of user-item interaction data in real-world scenarios limits the availability of supervised signals, which in turn degrades the overall recommendation performance. To address these challenges, we propose a multi-view collaborative fusion and contrastive learning framework (MCFCR) that effectively mines implicit higher-order collaborative signals. Specifically, it performs feature extraction from four types of heterogeneous views, applies cosine similarity to filter semantically similar nodes in each view’s feature space, and introduces a multi-view top-K nearest neighbor fusion mechanism to construct a collaborative neighbor network. Furthermore, during the optimization phase, we design a multi-layer contrastive learning strategy to refine both the commonalities and differences across different views, thereby enhancing multiple self-supervised signals. Extensive experiments and ablation studies on three real-world datasets demonstrate that the proposed model consistently outperforms state-of-the-art recommendation methods and validate the effectiveness of our approach.}, archive = {J_ACCESS}, author = {Shuqin Zhang and Hui Zhao}, doi = {10.1109/ACCESS.2025.3613301}, journal = {IEEE Access}, pages = {167213-167226}, shortjournal = {IEEE Access}, title = {Multi-view collaborative fusion and contrastive learning for recommendation}, volume = {13}, year = {2025}, }
• (2025). Facial landmark detection and head pose estimation of occluded faces based on stable diffusion image completion. ACCESS, 13, 167196-167212. (www)

  @article{ , abstract = {Face occlusion often makes it challenging to detect facial landmarks and head pose accurately. In this work, we present a pipeline to estimate facial landmarks and head pose when only a partial face is visible. For each input image, we first apply the Stable Diffusion Inpainting algorithm multiple times to generate 25, 50, or 100 inpainted images. We then apply face trackers and calculate the mean shape of the extracted landmarks in each image. Lastly, we estimate the head pose using the iterative closest point method. For facial landmarks, we performed experiments on 406 images from publicly available datasets. For the four face trackers investigated, our proposed pipeline was able to reduce the landmark detection error by at least 2-3 times, with a reduction in the average detection error from 14.0 to 3.9 pixels. For head pose estimation, we performed experiments on different views of a 3D head model with head pose ranges from -60° to 60° and 2D face images. Across more than 3,300 occluded test images, the proposed pipeline obtained a head pose within 10° of the ground truth in most cases. In conclusion, we present a system that enhances the accuracy of facial landmark detection and head pose estimation under large occlusions, without training any new machine learning models.}, archive = {J_ACCESS}, author = {Wenzhangzhi Guo and Long Vân Tran Ha and Joel C. Davies and Eitan Grinspun and Lueder A. Kahrs}, doi = {10.1109/ACCESS.2025.3612890}, journal = {IEEE Access}, pages = {167196-167212}, shortjournal = {IEEE Access}, title = {Facial landmark detection and head pose estimation of occluded faces based on stable diffusion image completion}, volume = {13}, year = {2025}, }
• (2025). From centralized data fusion to hard- and soft-decision fusion in multisensor distributed detection systems a new multiple-bit decision fusion approach. ACCESS, 13, 167178-167195. (www)

  @article{ , abstract = {We consider multisensor distributed detection systems with data fusion which consist of a number of distributed local sensors and a data fusion center. The data fusion center receives information from the local sensors and fuses them to obtain a final global decision on one of two hypotheses. In this paper, we first revisit and formulate the three major approaches of data fusion in multisensor distributed detection systems in the general case of $n$ distributed sensors; namely, centralized data fusion, hard-decision fusion, and soft-decision fusion. The formulation is based on the Neyman-Pearson criterion assuming that the local sensors are independent and observe the same phenomenon. Then, we propose a new multiple-bit decision fusion approach in multisensor distributed detection systems with data fusion. Unlike most of the published studies related to hard-decision fusion, the proposed approach is based on fusing multi-bit decisions instead of fusing one-bit decisions. Unlike most of the published studies related to soft-decision fusion, the proposed approach can be applied easily to identical and non-identical sensors and can be extended easily to any number of sensors and any number of bits per decision. Furthermore, it does not depend on the derivatives of the equations related the false alarm and detection probabilities of the individual sensors and does not require excessive computations as the number of bits per decisions increases. The proposed multiple-bit decision fusion approach is derived and its global performance is evaluated and compared to other fusion approaches in case of noisy Gaussian and Rayleigh distributed observations.}, archive = {J_ACCESS}, author = {Ashraf M. Aziz}, doi = {10.1109/ACCESS.2025.3611786}, journal = {IEEE Access}, pages = {167178-167195}, shortjournal = {IEEE Access}, title = {From centralized data fusion to hard- and soft-decision fusion in multisensor distributed detection systems a new multiple-bit decision fusion approach}, volume = {13}, year = {2025}, }
• (2025). A noise immune physics informed data-driven digital twin for inverter component degradation estimation. ACCESS, 13, 167165-167177. (www)

  @article{ , abstract = {Performance deviation of inverters in grid-connected distributed energy resource systems, due to component degradation from environmental stressors, is a major reliability concern. This work presents a data-driven digital twin (DT) of an H-bridge inverter with output filtering, developed using physics-informed machine learning (PIML). A ‘switching’ digital model is created using experimental data collected from the inverter prototype and optimized via particle swarm optimization. Later, continuous degradation is introduced to filter parameters, including parasitic resistances and the aging/reliability factor in filter components, generating a comprehensive dataset for training supervised ML models, for fast, accurate degradation estimation. To account for real-world conditions, measurement noise—such as ripple and ground loop effects—is introduced for capacitor parasitic resistance. In this scenario, the proposed PIML-DT approach, enhanced with a modified loss function and physical constraints, accurately estimates capacitor equivalent series resistance despite partial and noisy measurement data. It achieves an $R^{2}$ of 0.6918 and $RMSE$ of 0.0053, demonstrating strong resilience to noise and effective tracking of inverter degradation. This PIML-DT approach supports reliable, data-driven condition monitoring and long-term health assessment of power electronic converters.}, archive = {J_ACCESS}, author = {Sukanta Roy and Arif Sarwat}, doi = {10.1109/ACCESS.2025.3612737}, journal = {IEEE Access}, pages = {167165-167177}, shortjournal = {IEEE Access}, title = {A noise immune physics informed data-driven digital twin for inverter component degradation estimation}, volume = {13}, year = {2025}, }
• (2025). Binary code analysis for cybersecurity: A systematic review of forensic techniques in vulnerability detection and anti-evasion strategies. ACCESS, 13, 167139-167164. (www)

  @article{ , abstract = {Binary code analysis is essential in modern cybersecurity, examining compiled program outputs to identify vulnerabilities, detect malware, and ensure software security compliance. However, the field faces significant challenges due to the fragmented nature of existing research and the lack of unified analytical frameworks, which hinder comprehensive understanding and practical application. To address these gaps, we conducted a systematic review of binary code analysis techniques across six key areas, analyzing 236 research papers published between 2007 and 2025. This review provides: 1) a comprehensive overview of methods for binary code similarity; 2) a detailed examination of binary code fingerprinting techniques across various scenarios, from malware detection to digital forensics; 3) a systematic review of vulnerability analysis methods, including control flow graphs, taint analysis, and symbolic execution; 4) an assessment of clone detection strategies, such as text-based, token-based, structural, and behavioral approaches; 5) an in-depth study of authorship attribution techniques, with emphasis on malware attribution methods used in real-world cybersecurity cases; and 6) a thorough review of evasion and anti-analysis strategies, along with their countermeasures. In addition to highlighting the strengths and applications of these approaches, the study identifies limitations in current methods, including challenges in malware analysis, vulnerability analysis, and authorship attribution. Finally, it outlines future research directions, including the development of more robust analytical tools, enhancements to attribution models, and the creation of scalable solutions. Overall, this survey provides a foundation for advancing binary code analysis and fostering innovation to enhance software security and resilience by leveraging insights from previous research.}, archive = {J_ACCESS}, author = {Haseeb Javed and Farman Ali and Babar Shah and Daehan Kwak}, doi = {10.1109/ACCESS.2025.3610616}, journal = {IEEE Access}, pages = {167139-167164}, shortjournal = {IEEE Access}, title = {Binary code analysis for cybersecurity: A systematic review of forensic techniques in vulnerability detection and anti-evasion strategies}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of machine learning algorithms for BLE-based indoor localization system. ACCESS, 13, 167120-167138. (www)

  @article{ , abstract = {Indoor positioning systems (IPS) play a vital role in applications such as navigation and asset tracking. This study presents a comprehensive evaluation of machine learning algorithms on a newly introduced Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) dataset, specifically developed for localization tasks. The dataset undergoes systematic robustness testing, including RSSI masking, added noise, and missing anchor nodes, to assess model resilience in challenging conditions. Five machine learning algorithms—K-nearest neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Fully Connected Neural Network (FCNN)—are examined to benchmark their performance. The models are evaluated based on localization error (LE) and success rate (SR) across different accuracy thresholds, utilizing a custom dataset collected in diverse room environments. Key contributions include an extensive robustness analysis under realistic BLE signal conditions—such as RSSI noise, partial signal masking, and missing anchor nodes—as well as the incorporation of Kalman filtering as a standard preprocessing step to stabilize RSSI signals prior to model evaluation. Results demonstrate that XGBoost and RF achieve superior accuracy and scalability compared to other methods. RF delivers strong localization accuracy with low computational overhead, while XGBoost balances high accuracy with robustness to environmental noise and partial data loss. However, challenges such as performance inconsistencies in rooms with poor signal coverage and susceptibility to signal fluctuations are noted. These findings establish a benchmark for BLE RSSI-based localization research, offering key insights into the strengths and limitations of different machine learning models for various real-world applications. The key contributions include an extensive robustness analysis conducted under realistic BLE signal conditions, such as RSSI noise, partial signal masking, and missing anchor nodes, along with the application of Kalman filtering as an effective preprocessing step to enhance model performance.}, archive = {J_ACCESS}, author = {Ayesha Ayub and Zuhairiah Zainal Abidin and Abdulraqeb Alhammadi and Naglaa F. Soliman and Muhammad Asim Khan and Nurul Bashirah Ghazali}, doi = {10.1109/ACCESS.2025.3609464}, journal = {IEEE Access}, pages = {167120-167138}, shortjournal = {IEEE Access}, title = {Comparative analysis of machine learning algorithms for BLE-based indoor localization system}, volume = {13}, year = {2025}, }
• (2025). Task-oriented semantic communications for image boundaries recognition: Sensing matrix based on polar code. ACCESS, 13, 167107-167119. (www)

  @article{ , abstract = {This paper proposes a task-oriented semantic communication system dedicated to image data, designed to extract and transmit the information required by the receiver. The goal of the system is to recognize the semantic boundaries of the images. For this relevant challenges, a novel semantic encoder, based on compressed sensing (CS) is developed at the transmitter to extract semantic information. Additionally, a novel semantic decoder is proposed in the receiver, utilizing sparse reconstruction techniques to reconstruct semantic information. In contrast to prior studies that focused on conveying a broad spectrum of semantic information related to images along with all extracted features, this approach concentrates solely on isolating the semantic features relevant to the specific target edges. This method generates a sparse feature map, allowing for a reduction in compression rates by a novel compressed sensing technique, which implements a new sensing matrix based on polar code (SMPC). The proposed system can perform better across different signal-to-noise ratios (SNR) without requiring training data or being tied to a specific dataset. Furthermore, the study examines two distinct typical scenarios: noiseless measurements and noisy measurements. Our simulations show that with a lower compression rate, the classification accuracy, and exact recovery probability of 100% can be attained in both scenarios.}, archive = {J_ACCESS}, author = {Vahid Rezaei and Nader Mokari and Paeiz Azmi and Eduard A. Jorswieck and Hossein Pishro-Nik}, doi = {10.1109/ACCESS.2025.3613422}, journal = {IEEE Access}, pages = {167107-167119}, shortjournal = {IEEE Access}, title = {Task-oriented semantic communications for image boundaries recognition: Sensing matrix based on polar code}, volume = {13}, year = {2025}, }
• (2025). EDATRAF: An enhanced depth-aware transformer for monocular 3D object detection using feature fusion and cross-query attention. ACCESS, 13, 167094-167106. (www)

  @article{ , abstract = {Monocular 3D object detection remains a critical yet challenging task in autonomous driving due to depth ambiguities and occlusions inherent in single RGB images. While recent transformer-based methods leverage global context via self-attention, they still suffer from limited spatial precision and weak depth supervision. In this paper, we propose an enhanced depth-aware transformer framework (EDATRAF) for monocular 3D object detection. EDATRAF integrates several key components in a depth-aware feature enhancement (DFE) module that fuses RGB and predicted depth features via cross-attention to reduce depth ambiguity. It includes a depth target feature (DTF) volume with instance-aware selective refinement (ISR) to enforce object-level depth consistency through auxiliary volume supervision and foreground-weighted query embeddings combined with cross-query attention (CQA) to enhance query discrimination and representation. To improve training, we design a distance-aware loss weighting scheme and a Bird’s-Eye-View (BEV) loss that captures object extent and orientation in the ground plane. Additionally, EDATRAF adopts a multi-scale positional encoding and supports dynamic anchor boxes (DAB) style query initialization for enhanced spatial reasoning. Extensive experiments on the KITTI dataset show that EDATRAF significantly outperforms the baseline models in terms of 3D average precision (AP) and BEV intersection over union (IoU), particularly in scenes with occlusion, scale variation, and complex spatial layouts. These results highlight the robustness and effectiveness of our approach for real-world autonomous perception.}, archive = {J_ACCESS}, author = {Daewoong Cha and Samuel Kakuba and George Albert Bitwire and Dong Seog Han}, doi = {10.1109/ACCESS.2025.3613157}, journal = {IEEE Access}, pages = {167094-167106}, shortjournal = {IEEE Access}, title = {EDATRAF: An enhanced depth-aware transformer for monocular 3D object detection using feature fusion and cross-query attention}, volume = {13}, year = {2025}, }
• (2025). Sea ice concentration mapping based on sentinel-2 multispectral imagery using multiscale deep learning approach. ACCESS, 13, 167080-167093. (www)

  @article{ , abstract = {One of the important climate indicators is the condition of sea ice concentration in the Arctic and Antarctic regions. Measuring this concentration using remote sensing and automated artificial intelligence allows continuous monitoring at relatively low cost. The purpose of this study is to develop a robust and scalable deep learning model to accurately estimate sea ice concentration from satellite imagery, supporting climate monitoring efforts. Hence, Ice-SPP-net (ISPP-net) is introduced as an innovative approach to measuring sea ice concentration using Sentinel-2 imagery, leveraging a novel multiscale convolutional neural network (CNN) architecture. This model is specifically designed to address challenges posed by the complex and heterogeneous nature of sea ice, which conventional fixed-scale CNNs struggle to assess accurately. By incorporating Spatial Pyramid Pooling (SPP) modules, the ISPP-net effectively captures and integrates features across multiple scales, enhancing the model’s ability to discern both fine details and broader contextual information essential for precise measurements. The model was trained and evaluated using annotated Sentinel-2 datasets and validated through a comprehensive ablation study to determine the optimal architecture configuration. Results show that placing the SPP module at the third encoder layer, using three parallel paths, yields the best performance. This configuration outperforms models like U-Net, SegNet, DABNet, TernausNet, and FC-DenseNet, achieving a mean Intersection over Union of 49.20% and an accuracy of 89.30%. ISPP-net provides critical data to refine climate models and improve predictions of future climate scenarios. Future enhancements will integrate more data sources and refine the architecture, supporting SDG 13: Climate Action.}, archive = {J_ACCESS}, author = {Marzuraikah Mohd Stofa and Mohamad Aizuddin Ismail and Jaesung Lee and Mohd Asyraf Zulkifley}, doi = {10.1109/ACCESS.2025.3613398}, journal = {IEEE Access}, pages = {167080-167093}, shortjournal = {IEEE Access}, title = {Sea ice concentration mapping based on sentinel-2 multispectral imagery using multiscale deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Trust@Health: A trust-based multilayered network for scalable healthcare service management. ACCESS, 13, 167061-167079. (www)

  @article{ , abstract = {We study the intricate relationships within healthcare systems, focusing on interactions among doctors, departments, and hospitals. Leveraging an evolutionary graph framework, the proposed model emphasizes both intra-layer and inter-layer trust relationships to better understand and optimize healthcare services. The trust-based network facilitates the identification of key healthcare entities by integrating their social and professional interactions, culminating in a trust-based algorithm that quantifies the importance of these entities. Validation with a real-world dataset reveals a strong correlation (0.91) between the proposed trust measures and the ratings of hospitals and departments, though doctor ratings demonstrate skewed distributions due to potential biases. By modeling these relationships and trust dynamics, the framework supports scalable healthcare infrastructure, enabling effective patient referrals, personalized recommendations, and enhanced decision-making pathways.}, archive = {J_ACCESS}, author = {Avijit Gayen and Somyajit Chakraborty and Joydeep Chakraborty and Angshuman Jana}, doi = {10.1109/ACCESS.2025.3613326}, journal = {IEEE Access}, pages = {167061-167079}, shortjournal = {IEEE Access}, title = {Trust@Health: A trust-based multilayered network for scalable healthcare service management}, volume = {13}, year = {2025}, }
• (2025). Soft computing-based damping controllers with online parameters tuning for stability enhancement of power systems. ACCESS, 13, 167045-167060. (www)

  @article{ , abstract = {Power system stability continues to be a major challenge as modern grids grow more complex, uncertain, and increasingly reliant on renewable energy sources. This paper presents two new Neuro-Fuzzy controllers for Static Synchronous Compensators (STATCOMs): the Direct Chebyshev Wavelet-Based Neuro-Fuzzy Controller (DNF-CW), which adapts parameters online using fixed-structure rules, and the Indirect Chebyshev Wavelet-Based Neuro-Fuzzy Controller (IDNF-CW), which uses an online identifier to measure plant sensitivity. Chebyshev wavelet-based neural networks are utilized in the consequent part of both controllers to enable accurate local modeling and improved damping performance. The proposed methods are evaluated using a Single Machine Infinite Bus (SMIB) system and the IEEE 9-bus multi-machine system under a variety of fault and loading conditions. Benchmark comparisons include a conventional Indirect Adaptive Neuro-Fuzzy Takagi–Sugeno–Kang (IDNF-TSK) based controller, a configuration without STATCOM (No STATCOM), and a configuration with STATCOM but without auxiliary control (No Control). In the SMIB scenario, the IDNF-CW achieves a 40% reduction in settling time compared to the IDNF-TSK. In the more demanding multi-machine setup, the IDNF-CW restores stability within 3 seconds after a sequence of faults, outperforming DNF-CW and IDNF-TSK. Additionally, reductions of over 53% in the Integral of Time-weighted Absolute Error (ITAE) and 36% in the Integral of Absolute Error (IAE) are observed. These tests under multiple fault conditions and 10% measurement noise confirm stable operation, with overshoot limited to 3.57%–4.7% and minimal impact on settling time. These findings highlight the effectiveness of combining Chebyshev wavelets, adaptive control, and indirect architectures for enhancing power system stability.}, archive = {J_ACCESS}, author = {Farman Ullah Jan and Rabiah Badar and Ahmad Sami Al-Shamayleh and Akie Uehara and Tomonobu Senjyu and Adnan Akhunzada}, doi = {10.1109/ACCESS.2025.3612288}, journal = {IEEE Access}, pages = {167045-167060}, shortjournal = {IEEE Access}, title = {Soft computing-based damping controllers with online parameters tuning for stability enhancement of power systems}, volume = {13}, year = {2025}, }
• (2025). Action-inclusive multi-future prediction using a generative model in human-related scenes for mobile robots. ACCESS, 13, 167034-167044. (www)

  @article{ , abstract = {Mobility in daily unstructured environments, particularly in human-centered scenarios, remains a fundamental challenge for mobile robots. While traditional prediction-based approaches primarily estimate partial features for robot decision making, such as position and velocity, recent world models enable direct prediction of future sensory data. However, their potentials in human-inclusive environments remain underexplored. To assess the feasibility of world models in facilitating human-robot interactions, we propose a robot framework using a deep generative model that jointly predicts multiple future observations and actions. Our approach leverages first-person-view (FPV) raw sensor data, integrating both observations and actions to enhance predictive capabilities in dynamic human-populated settings. Experimental results demonstrate that our method is capable of generating a range of candidate futures for one condition and planning actions based on observation guidance. These findings highlight the potential of our approach for facilitating autonomous robots’ coexistence with human.}, archive = {J_ACCESS}, author = {Chenfei Xu and Huthaifa Ahmad and Yuya Okadome and Hiroshi Ishiguro and Yutaka Nakamura}, doi = {10.1109/ACCESS.2025.3611812}, journal = {IEEE Access}, pages = {167034-167044}, shortjournal = {IEEE Access}, title = {Action-inclusive multi-future prediction using a generative model in human-related scenes for mobile robots}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection and segmentation in carotid ultrasound images using hybrid stable AnoGAN. ACCESS, 13, 167014-167033. (www)

  @article{ , abstract = {Detecting and segmenting arterial plaques in ultrasound images is essential for the early diagnosis and prevention of cardiovascular diseases. This paper presents Hybrid Stable AnoGAN (HS-AnoGAN), an enhanced anomaly detection framework based on AnoGAN (Anomaly Generative Adversarial Network), which utilizes generative adversarial learning to model normal anatomical structures and identify abnormal regions indicative of pathology. The proposed approach introduces key improvements, including direct latent space encoding, hybrid reconstruction loss, feature matching in the discriminator, and adaptive thresholding, leading to more precise anomaly localization. Additionally, spectral normalization and Wasserstein loss with gradient penalty are incorporated to improve training stability and prevent mode collapse. To the best of our knowledge, this is the first attempt to apply anomaly detection techniques for arterial plaque detection and segmentation in ultrasound images. Comparative experiments demonstrate that HS-AnoGAN outperforms state-of-the-art methods, achieving a 9.8% increase in detection accuracy, and a 7.5% enhancement in Dice score for segmentation quality. These results highlight the effectiveness of HS-AnoGAN in improving both plaque detection and segmentation in ultrasound imaging, making it a promising tool for clinical applications.}, archive = {J_ACCESS}, author = {Arash Saboori and Fredrik Öhberg and Ulf Näslund and Christer Grönlund}, doi = {10.1109/ACCESS.2025.3611327}, journal = {IEEE Access}, pages = {167014-167033}, shortjournal = {IEEE Access}, title = {Anomaly detection and segmentation in carotid ultrasound images using hybrid stable AnoGAN}, volume = {13}, year = {2025}, }
• (2025). Construction of a higher vocational culinary education quality evaluation model for University–Industry integration based on adaptive BP and DDAE-SVR. ACCESS, 13, 167001-167013. (www)

  @article{ , abstract = {In the rapidly evolving landscape of computer science, the integration of advanced computational techniques into interdisciplinary domains has become paramount. This study addresses the critical need for robust evaluation models in vocational education, emphasizing the synergy between academic institutions and industry partnerships. Traditional assessment methods in culinary education often rely on static metrics such as student grades, attendance rates, and standardized test scores. These metrics fail to capture the dynamic and evolving nature of culinary skills, which require the integration of both practical hands-on experience and theoretical knowledge. The challenges in culinary education include continuously adapting to changing industry trends, varying student learning styles, and the demand for real-time industry-relevant skills, which traditional methods do not adequately assess. To overcome these limitations, we propose an innovative evaluation framework that leverages Adaptive Evaluation Strategy for Educational Quality (AESEQ) and An Adaptive Attribution Strategy (AQAN). This model is designed to dynamically adjust to the evolving educational landscape, effectively capturing both quantitative and qualitative indicators of educational quality. The integration of DDAE enhances the model’s ability to manage complex, high-dimensional data, while the SVR component ensures precise predictive analytics. By incorporating deep feature extraction with noise reduction mechanisms, the model achieves greater generalization capability across diverse educational scenarios. Empirical validation demonstrates that our approach significantly outperforms traditional models in accuracy and reliability, offering a comprehensive tool for stakeholders to assess and enhance educational outcomes. This research contributes to the broader discourse on digital education within computer science, showcasing the transformative potential of computational methods in educational assessment and informing future policy and practice in data-driven educational reform.}, archive = {J_ACCESS}, author = {Xu Zhang}, doi = {10.1109/ACCESS.2025.3603627}, journal = {IEEE Access}, pages = {167001-167013}, shortjournal = {IEEE Access}, title = {Construction of a higher vocational culinary education quality evaluation model for University–Industry integration based on adaptive BP and DDAE-SVR}, volume = {13}, year = {2025}, }
• (2025). Real-time snowfall recognition and object tracking system for autonomous driving in adverse snowy weather. ACCESS, 13, 166987-167000. (www)

  @article{ , abstract = {Human can drive safely by instinctively accounting for real-time environmental and weather conditions such as snow and rain. In contrast, current autonomous driving systems still face challenges in achieving full autonomy, as their path planning and control rely solely on environmental perception without considering weather conditions. However, to realize fully autonomous driving, it is essential to implement vehicle control technologies that can recognize not only the surrounding environment but also weather conditions, and use this information to adjust steering and longitudinal behaviors—such as acceleration and deceleration—in a human-like manner. To address this issue, this study proposes a perception system capable of recognizing both environmental and weather conditions in real time under snowy weather, enabling weather-aware vehicle control. This is the first perception system to simultaneously provide both object and weather information. It improves detection performance for snow-affected cameras through YOLOv4 and YOLOv8-based training, and enhances LiDAR accuracy by removing snow-induced noise. Robust radar data, resistant to environmental changes, are then fused using a Kalman filter to enable reliable object tracking. Finally, weather information is inferred from the number of real-time filtered LiDAR points, allowing the system to perform simultaneous object tracking and environmental perception in snowy conditions. Compared with existing tracking systems that are not specialized for extreme environments, the proposed system offers more than 50% improved tracking accuracy, accurate snowfall classification with a precision of 0.983, and more than 15% faster processing time. This enables precise path planning and allows for safe vehicle control during steering, acceleration and deceleration by utilizing real-time weather information. This study provides valuable real-time data that enable the seamless operation of all essential components of autonomous driving technology, such as sensors, perception, path planning, and vehicle control, even under snowy conditions, therefore enhancing the potential to achieve Level 5 autonomy.}, archive = {J_ACCESS}, author = {Ji-IL Park and Seunghyeon Jo and Hyung-Tae Seo and Jihyuk Park}, doi = {10.1109/ACCESS.2025.3613456}, journal = {IEEE Access}, pages = {166987-167000}, shortjournal = {IEEE Access}, title = {Real-time snowfall recognition and object tracking system for autonomous driving in adverse snowy weather}, volume = {13}, year = {2025}, }
• (2025). An efficient approach for code-mixed emotion classification applying machine learning. ACCESS, 13, 166973-166986. (www)

  @article{ , abstract = {Emotion classification aims to find and extract all possible emotions from a piece of text that best represent the author’s state of mind. The task of emotion classification is still considered challenging for under-resourced languages, especially in the case of code-mixing, which is not a standardized language on its own. The widespread use of social media has led to the emergence of code-mixed language, which later gained attention from researchers due to its extensive usage. Emotion classification is an important problem with a range of applications, from healthcare and e-learning to social media. While some work has been done on code-mixed emotion classification, very few studies have focused on code-mixed emotion classification for English and Roman Urdu. Previously, researchers attempted to solve the problem of code-mixed multi-label emotion classification using code-mixed English and Roman Urdu, but the results were relatively low (e.g., Micro F $1=0.67$ ), indicating that there is still a need for improvement in this area. In this study, we mainly aim to solve two complex tasks: i) code-mixed multi-label emotion classification and ii) code-mixed multi-class emotion classification. Our contribution lies in utilizing classical machine learning methods with three distinct multi-label and multi-class classification approaches: i) One-Versus-Rest (OvR), ii) Label Powerset (LP), and iii) Binary Relevance (BR), along with two distinct feature extraction techniques. First, we employ content-based methods using TF-IDF at the word unigram level and experiment with various feature sets ranging from 500 to 3000 features. Second, we use context-based methods by leveraging SBERT-based models for embeddings to capture semantic meanings. Finally, we apply a state-of-the-art Generative AI-based approach, utilizing a quantized version of LLaMa, which is fine-tuned for evaluation. We conducted over 2,000 experiments, and the best results were obtained using classical machine learning (Micro F $1=0.9142$ for multi-label classification and Micro F $1=0.9238$ for multi-class classification) with the combination of the Binary Relevance approach in a context-based setting for both tasks, which indicates that Binary Relevance is an optimized approach for breaking complex multi-label, multi-class tasks into easier ones, especially when the language is difficult enough in its own.}, archive = {J_ACCESS}, author = {Ahmad Mahmood and Miguel Torres-Ruiz and Zainab Ahmad and Humaira Farid and Iqra Ameer and Rolando Quintero}, doi = {10.1109/ACCESS.2025.3598754}, journal = {IEEE Access}, pages = {166973-166986}, shortjournal = {IEEE Access}, title = {An efficient approach for code-mixed emotion classification applying machine learning}, volume = {13}, year = {2025}, }
• (2025). Analysis, design, and implementation of an information retrieval approach for QoS optimization in information-centric networking using hybrid naming schemes. ACCESS, 13, 166959-166972. (www)

  @article{ , abstract = {Understanding how the naming scheme applies in Information-Centric Networking (ICN) is critical for enhancing the user’s Quality of Service (QoS) during information retrieval. However, until now, no study has comprehensively investigated the influence of naming schemes in various topologies to improve information retrieval in ICN. This paper systematically analyzes, designs, and implements various naming schemes to enhance and optimize information retrieval. Various known naming schemes are simulated under different challenging conditions and topologies, such as congestion, delays, and mobility environments, to validate the effectiveness of each of the naming schemes in ICN. The experiments are simulated multiple times for each naming scheme to understand their specific properties, features, advantages, and disadvantages. The simulation results confirm the merit of the proposed Intelligent Prefix Information Retrieval naming scheme in enduring various topologies and highlight ways to achieve better data rates, higher success rates, lower delays, and reduced total traffic in the network.}, archive = {J_ACCESS}, author = {Rila Mandala}, doi = {10.1109/ACCESS.2025.3610467}, journal = {IEEE Access}, pages = {166959-166972}, shortjournal = {IEEE Access}, title = {Analysis, design, and implementation of an information retrieval approach for QoS optimization in information-centric networking using hybrid naming schemes}, volume = {13}, year = {2025}, }
• (2025). It’s dangerous to prompt alone! exploring how fine-tuning GPT-4o affects novices’ programming error resolution. ACCESS, 13, 166943-166958. (www)

  @article{ , abstract = {Large language models (LLMs) like GPT-4o are increasingly being incorporated into computer science classrooms in tasks such as helping students resolve programming error messages. Prior work has found weak or insignificant support that LLM-generated error explanations help students resolve errors faster, especially compared to expert-handwritten error explanations. This work explores how GPT-4o can be influenced through fine-tuning to produce error explanations that resemble expert-handwritten explanations. We evaluate how three error message styles (traditional Python error messages, baseline GPT-4o explanations, fine-tuned GPT-4o explanations) affect novice programmers’ ability to resolve programming errors. Eighty ( $n = 80$ ) CS1 students completed six debugging tasks using a within-subjects study design that ensured each participant saw each of the three error message styles twice. We measured time-to-fix, number of code reruns, task failure rate, and then asked for students’ preferences. Participants were found to resolve errors significantly faster with the fine-tuned GPT-4o messages, but, like prior work, we could not find a statistically significant difference between the baseline GPT-4o and traditional Python error messages. Error message style had no effect on number of reruns and task failures; however, the particular debugging task had a significant difference on both. Despite solving problems faster with the fine-tuned GPT-4o model, participants preferred the baseline GPT-4o output (similar to unmodified ChatGPT). These findings strongly suggest that the nature of the debugging task has a greater effect on novice programmers’ error resolution than whether or not the students use a GenAI system to help them debug.}, archive = {J_ACCESS}, author = {Eddie Antonio Santos and Audrey Salmon and Katie Hammer}, doi = {10.1109/ACCESS.2025.3613500}, journal = {IEEE Access}, pages = {166943-166958}, shortjournal = {IEEE Access}, title = {It’s dangerous to prompt alone! exploring how fine-tuning GPT-4o affects novices’ programming error resolution}, volume = {13}, year = {2025}, }
• (2025). Hierarchical multi-agent reinforcement learning method using energy field in sports games. ACCESS, 13, 166926-166942. (www)

  @article{ , abstract = {This paper proposes an energy-field-based hierarchical multi-agent reinforcement learning method (HES-COMA) for evaluating individual agent contributions and learning efficient policies in dynamic and complex multi-agent environments such as sports games. The proposed method addresses the limitations of the conventional single-layer approach by using energy fields in a global layer to learn strategic positioning, and in a local layer to determine tactical actions (e.g., shooting, stealing, and blocking) from those positions. Specifically, the method assigns an energy value to represent the relative importance of key elements in the game space (ball, opponents, teammates, basket, and shooting probability spots), and builds a dynamically changing energy field depending on the state of play (offense, defense, free scenario, etc.). Experimental results in a commercialized 3vs3 basketball game environment show that HES-COMA achieves approximately 1.5 times faster learning speed than Counterfactual Multi-Agent Policy Gradients (COMA). It also improved the success rates of steals, rebounds, and blocks by factors of 1.38, 1.87, and 2.71, respectively. Moreover, by combining global strategic positioning information with local tactical decision-making, HES-COMA’s movement patterns more closely resemble those of users and FSM-based agents in terms of spatial utilization. Consequently, HES-COMA effectively addresses contribution evaluation and data diversity issue in dynamic multi-agent sports games, thereby boosting both learning efficiency and overall performance.}, archive = {J_ACCESS}, author = {Hoshin Lee and Junoh Kim and Jisun Park and Phuong Minh Chu and Kyungeun Cho}, doi = {10.1109/ACCESS.2025.3613359}, journal = {IEEE Access}, pages = {166926-166942}, shortjournal = {IEEE Access}, title = {Hierarchical multi-agent reinforcement learning method using energy field in sports games}, volume = {13}, year = {2025}, }
• (2025). ENCoRP: A tool to compute and visualize an electrical network’s conductive and radiative propagation. ACCESS, 13, 166905-166925. (www)

  @article{ , abstract = {We present ENCoRP, a tool for computing and visualizing the frequency-domain conductive and radiative propagation associated with an injected signal traversing a user-defined electrical network. Emulating real-world power grids, ENCoRP allows the user to construct an electrical network composed of cables and nodes, with cables containing phase wires and ground/neutral wires and nodes representing various electrical elements, including transmitters, receivers, junction boxes, terminating loads, and shunt loads. ENCoRP is composed of a MATLAB-based graphical user interface that works in conjunction with a Python conductive model and COMSOL Multiphysics® radiative model. The interface may be used to visualize and output the network parameters (ABCD, S, Y, or Z), spatially resolved voltages and currents, or electric and magnetic fields. The underlying models and equations are presented, the numerical limitations are discussed, and the results are verified against Personal Simulation Program with Integrated Circuit Emphasis (PSpice). We conclude with an application of the tool to a hybrid wired-wireless system wherein we examine the electromagnetic fields experienced by a standalone device in the presence of an electrical network.}, archive = {J_ACCESS}, author = {John Chilleri and David S. Ching and Christopher Katinas and Lee Rashkin and Monica Jaramillo and Mark Converse and Thomas A. Reichardt}, doi = {10.1109/ACCESS.2025.3612839}, journal = {IEEE Access}, pages = {166905-166925}, shortjournal = {IEEE Access}, title = {ENCoRP: A tool to compute and visualize an electrical network’s conductive and radiative propagation}, volume = {13}, year = {2025}, }
• (2025). The effects of video compression on simulated time events. ACCESS, 13, 166892-166904. (www)

  @article{ , abstract = {Event-based cameras provide many advantages over conventional frame-based cameras, both in terms of time resolution and motion blur, making them increasingly popular in research and industrial applications, particularly in automotive sensing. However, despite the recent uptake, event-based datasets are still rare. This lack of availability of time-event datasets has led to the development of tools such as v2e, which can simulate event streams from conventional video. Although these tools are incredibly useful for early stage development and testing, little attention has been paid to how the quality of the source video affects these simulated events. This paper presents the first comprehensive quantitative study of the impact of video compression on simulated event streams. We evaluate how lossy compression algorithms affect the accuracy and noise levels of simulated events, revealing that compression artifacts, particularly in darker regions of videos, can produce significant noise in these event streams. We introduce a novel event stream comparison method to determine the fidelity of events simulated from varying video codecs and parameters. Our findings show that compression can severely degrade simulated event quality, and that codec choice and parameters can influence event fidelity significantly. We further benchmark a range of corrective methods, including conventional filters and machine learning-based preprocessing, to mitigate the adverse effects of compression. Although some approaches show some improvements, none fully restore the fidelity of uncompressed video sources. We conclude with practical recommendations for dataset publishers and researchers using event simulation tools, emphasizing the importance of compression-aware practices in dataset preparation and usage.}, archive = {J_ACCESS}, author = {Timothy Hanley and Joseph Lemley and Peter Corcoran and Michael Schukat}, doi = {10.1109/ACCESS.2025.3609977}, journal = {IEEE Access}, pages = {166892-166904}, shortjournal = {IEEE Access}, title = {The effects of video compression on simulated time events}, volume = {13}, year = {2025}, }
• (2025). A developer’s guide to compressing pre-trained transformer neural networks across different domains. ACCESS, 13, 166873-166891. (www)

  @article{ , abstract = {Transformer neural networks have become the benchmark across a variety of domains in part thanks to their competence in comprehending the importance and dependencies between large input segments. Pre-trained transformer models typically contain millions of parameters that have been trained with comprehensive datasets improving their generalization, allowing them to be fine-tuned quicker and with less data. Therefore, starting with a pre-trained model provides a substantial advantage over developing a network from the ground up. However, the race to exceed the accuracy of previous model generations leads pre-trained model to increase in size and the required computational resources for inference, as well as training. This is where compression techniques are vital to maximize the impact and performance of the models, while minimizing the cost to deploy. This work covers six of the most successful compression techniques including pruning, knowledge distillation, quantization, adaptive inference (early exit), low-rank factorization and mixture of experts. The study presents a comprehensive overview of their methods, strengths, and weaknesses meant to serve as a guide for machine learning practitioners. The key areas explored are model speedup, model size, accuracy retention, and training requirements.}, archive = {J_ACCESS}, author = {Fernando A. Barrios and Ali Emadi}, doi = {10.1109/ACCESS.2025.3611390}, journal = {IEEE Access}, pages = {166873-166891}, shortjournal = {IEEE Access}, title = {A developer’s guide to compressing pre-trained transformer neural networks across different domains}, volume = {13}, year = {2025}, }
• (2025). BI-tech: An IoT-based behavioral intervention system for user-driven energy optimization in commercial spaces. ACCESS, 13, 166853-166872. (www)

  @article{ , abstract = {This study presents the Behavioral Insights and Technology (BI-Tech) IoT platform for closing the intention–action gap via real-time behavioral verification by integrating: 1) synchronized validation of user-reported actions against energy consumption traces, 2) a dual-track evaluation linking macro-scale metered savings with micro-scale 15-minute action windows, and 3) enhancement of Theory of Planned Behavior (TPB) predictive validity under a low-cost deployment (US ${\$}$ 75 per node) with off-the-shelf sensors. In a field deployment across 12 small-to-medium commercial offices (SMOs) in Fukuoka, Japan (September–November 2024; N = 57), the platform was evaluated using a dual-track design. Statistical analysis of hour-level observations across valid workdays (baseline: $n=511$ ; intervention: $n=421$ ) demonstrated substantial energy reduction with large effect size (Cohen’s $d=0.72$ , $t(930)=10.94$ , $p\lt 0.001$ ), representing a 23.3% decrease in non-HVAC consumption. Targeted evening analysis confirmed intervention effectiveness, with lighting consumption declining 21.4% during notification periods ( $t=2.98$ , $p\lt 0.01$ , $d=0.51$ ). The action-window analysis isolated user-behavior effects from automated prompts; peak savings of 70.9 Wh were observed within 15 minutes following air-conditioning alerts. The awareness–behavior correlation increased from $r=0.336$ (n.s.) to $r=0.634$ ( $p=0.020$ ), $\Delta r=+0.298$ . In addition, 69.2% of participants shifted toward more conservation-oriented profiles in a four-quadrant behavioral classification. These results demonstrate a validated, low-cost method for real-time verification of IoT-mediated behavioral interventions in offices, emphasizing human-centered strategies rather than infrastructure-intensive automation.}, archive = {J_ACCESS}, author = {Yutong Chen and Daisuke Sumiyoshi and Yuutarou Morita and Soichiro Ishibashi and Xiangyu Wang and Takahiro Yamamoto and Takahiro Ueno and Jewon Oh}, doi = {10.1109/ACCESS.2025.3607329}, journal = {IEEE Access}, pages = {166853-166872}, shortjournal = {IEEE Access}, title = {BI-tech: An IoT-based behavioral intervention system for user-driven energy optimization in commercial spaces}, volume = {13}, year = {2025}, }
• (2025). Modified neuro-fuzzy system for online classification of wind turbine blade defects. ACCESS, 13, 166841-166852. (www)

  @article{ , abstract = {This study considers the current problem of the automated detection and classification of defects in wind turbine blades in the context of the development of modern wind energy. A critical analysis of the application of existing diagnostic methods, particularly convolutional neural networks and LIDAR technologies, was conducted, emphasizing their advantages and limitations. As an alternative solution, an adaptive neuro-fuzzy system based on the modified architecture of the Wang–Mendel system is used, which is optimized for classification tasks under conditions of limited data and nonstationary information flows. A detailed description of the system structure, features of its online training, and the implementation of a modular architecture considering the effective processing of features and preservation of the interpretability of the results are presented. The experimental results proved the high classification accuracy (94.6%) of the proposed system, with its faster processing time (2.2 times) compared to classical approaches, making it a promising candidate for implementation in practical systems for the technical monitoring of wind turbines.}, archive = {J_ACCESS}, author = {L. Dubchak and Y. Bodyanskiy and A. Sachenko and C. Wolff and N. Vivchar and N. Vasylkiv}, doi = {10.1109/ACCESS.2025.3612267}, journal = {IEEE Access}, pages = {166841-166852}, shortjournal = {IEEE Access}, title = {Modified neuro-fuzzy system for online classification of wind turbine blade defects}, volume = {13}, year = {2025}, }
• (2025). User satisfaction-oriented task offloading and resource allocation in UAV-assisted network systems. ACCESS, 13, 166819-166840. (www)

  @article{ , abstract = {As demand grows for computation-intensive applications such as object detection, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) task offloading systems offer an effective solution due to their flexibility and ability to operate across diverse geographic and functional environments. However, while many studies on UAV-assisted MEC systems focus on optimizing factors such as delay and energy consumption, fewer consider user satisfaction, which is an important determinant of overall system efficiency. This paper aims to address this gap. Specifically, we first develop a novel user satisfaction model by jointly considering processing delay, desired delay, marginal delay, delay sensitivity, and time sensitivity of the tasks. Then, we formulate an optimization problem that considers both computational resource allocation and task offloading decisions to maximize total user satisfaction while minimizing total energy consumption. Finally, a SAC-based US (User Satisfaction)-oriented Task Offloading (SAC-USTO) algorithm is proposed to address the task offloading problem, and a Task Size-based Resource Allocation (TS-RA) algorithm is introduced to optimize resource allocation. Simulation results demonstrate that the proposed algorithms outperform other baseline methods.}, archive = {J_ACCESS}, author = {Le Thi Huyen and Dat van Anh Duong and Thi-Nga Dao and Seokhoon Yoon}, doi = {10.1109/ACCESS.2025.3612193}, journal = {IEEE Access}, pages = {166819-166840}, shortjournal = {IEEE Access}, title = {User satisfaction-oriented task offloading and resource allocation in UAV-assisted network systems}, volume = {13}, year = {2025}, }
• (2025). Radar jamming decision-making: A review. ACCESS, 13, 166800-166818. (www)

  @article{ , abstract = {The emergence of advanced radar systems, including multifunction radar (MFR) and cognitive radar, coupled with rapid advancements in anti-jamming technologies, has significantly reduced the effectiveness and response time of conventional radar jamming decision-making. To address these challenges, recent studies have integrated cognitive thinking into jamming decision-making, enabling the development of cognitive jamming systems. While prior research has explored jamming decision-making at various stages, a comprehensive, phase-based framework remains underdeveloped. This study bridges this gap by formalizing the mathematical logic of radar jamming and introducing a systematic decision-making framework encompassing four critical dimensions: 1) jamming resource allocation, 2) jamming pattern selection, 3) jamming waveform optimization, and 4) jamming timing determination. Leveraging the proposed framework, we conduct a detailed review and comparative analysis of the existing literature, categorizing approaches into traditional and cognitive jamming decision-making paradigms. We provide a focused review of reinforcement learning-based cognitive jamming decision-making with a systematic analysis of four critical aspects: problem modeling, radar state representation, reward function setting, and algorithmic implementation. Finally, we highlight emerging trends and provide forward-looking insights into the future evolution of radar jamming decision-making.}, archive = {J_ACCESS}, author = {Didi Xie and Yu Pan and Yurui Zhao and Xiang Wang and Zhitao Huang}, doi = {10.1109/ACCESS.2025.3607937}, journal = {IEEE Access}, pages = {166800-166818}, shortjournal = {IEEE Access}, title = {Radar jamming decision-making: A review}, volume = {13}, year = {2025}, }
• (2025). Scalable data acquisition system for real-time monitoring of photovoltaic plants. ACCESS, 13, 166787-166799. (www)

  @article{ , abstract = {The lack of scalable and economically viable photovoltaic (PV) monitoring systems, especially in remote locations or areas with limited infrastructure, hampers performance monitoring and the implementation of preventive maintenance strategies. To address this issue, this research proposes a real-time PV monitoring system using ESP32 microcontrollers equipped with Long-Range (LoRa) modules and precision sensors for the collection and transmission of environmental and operational data. The monitored variables include PV module and ambient temperature, solar irradiance, relative humidity, and wind speed. The system is capable of transmitting data over long distances, storing it in a Internet of Things (IoT) platform, enabling real-time monitoring and analysis. The proposed solution facilitates anomaly detection and maintenance optimization, offering a robust and cost-effective approach, ideal for applications in remote areas or locations with limited infrastructure. Additionally, the stored data enables detailed studies of PV performance combining operational and environmental information.}, archive = {J_ACCESS}, author = {Dionizio Porfírio De Assis and Leticia De Oliveira Santos and Renata Imaculada Soares Pereira and José Janiere S. De Souza and Paulo Cesar Marques De Carvalho}, doi = {10.1109/ACCESS.2025.3609654}, journal = {IEEE Access}, pages = {166787-166799}, shortjournal = {IEEE Access}, title = {Scalable data acquisition system for real-time monitoring of photovoltaic plants}, volume = {13}, year = {2025}, }
• (2025). Sustainability balance for ICT infrastructures: A time-scale analysis of the dynamic fourth lane on the a4 italian motorway. ACCESS, 13, 166778-166786. (www)

  @article{ , abstract = {This paper addresses the measurement of the environmental impact of Information and Communication Technology (ICT) infrastructures. The methodology used, aligned with ISO standards and guidelines for the expression of uncertainty in measurements (GUM), involves several steps. First, a checklist is prepared, encompassing all the elements necessary for the infrastructure’s implementation, operation, and end-of-life stages. Then, the environmental impact of each element is modeled in terms of probability distributions, which, in the metrology framework, indicate the lack of complete knowledge about the true amount of emissions associated with the related element. The obtained distributions are aggregated through Monte Carlo simulations to yield an overall distribution of the infrastructure’s environmental impact. Upon determining the resulting emissions, this paper presents the novel concept of the Sustainability Balance. This concept is introduced by comparing the emissions generated by the considered infrastructure with those mitigated through its use. This balance is critical for determining the actual environmental sustainability of the infrastructure. As a practical case study, this methodology is applied to a real and complex monitoring scenario, namely the dynamic fourth lane of the Italian A4 motorway, which represents a comprehensive example of an ICT infrastructure. The dynamic fourth lane allows the emergency lane to be used as an additional traffic lane based on traffic volume, thereby reducing congestion and emissions from idling vehicles. The obtained results highlight the potential for significant environmental benefits, demonstrating the effectiveness of the proposed methodology in evaluating and enhancing the sustainability of ICTs.}, archive = {J_ACCESS}, author = {Leopoldo Angrisani and Giovanni Bonini and Mauro D’Arco and Egidio De Benedetto and Luigi Duraccio and Immacolata Esposito and Annarita Tedesco}, doi = {10.1109/ACCESS.2025.3611801}, journal = {IEEE Access}, pages = {166778-166786}, shortjournal = {IEEE Access}, title = {Sustainability balance for ICT infrastructures: A time-scale analysis of the dynamic fourth lane on the a4 italian motorway}, volume = {13}, year = {2025}, }
• (2025). DC–DC multi-arm interleaved zeta converter with parallel switch modulation for renewable-powered electrolyzers. ACCESS, 13, 166760-166777. (www)

  @article{ , abstract = {This manuscript presents a DC-DC Multi-Arm Interleaved Zeta Converter with Parallel Switch Modulation for renewable-powered electrolyzers. The proposed converter features low current and voltage ripple, reduced power losses, and minimized magnetic volume. The use of parallel switches combined with an interleaved PWM technique simplifies the design and enhances performance without requiring inductors proportional to the number of arms. Key advantages include electrolyzer voltage and current ripple frequencies six times higher than the switching frequency, as well as output capacitor current ripple frequencies also six times higher. In addition, the inductor and coupling capacitor current ripple frequencies are twice the switching frequency. This behavior enables a significant reduction in the volume and mass of passive components, thereby improving power density. The paper presents the steady-state operating principles under continuous conduction mode, design guidelines, mathematical derivations, and a comparative analysis with state-of-the-art converters. A 6-kW proof-of-concept prototype was experimentally tested with a DC bus voltage of 480 V and an electrolyzer voltage of 250 V, demonstrating a peak efficiency of 98.16%, supported by experimental results and an accompanying demonstration video.}, archive = {J_ACCESS}, author = {Menaouar Berrehil El Kattel and Reuber Saraiva De Santiago and Robson Mayer and Edilson Mineiro Sa and Luiz Henrique Silva Colado Barreto and Fernando Luiz Marcelo Antunes}, doi = {10.1109/ACCESS.2025.3612331}, journal = {IEEE Access}, pages = {166760-166777}, shortjournal = {IEEE Access}, title = {DC–DC multi-arm interleaved zeta converter with parallel switch modulation for renewable-powered electrolyzers}, volume = {13}, year = {2025}, }
• (2025). Joint camera-LiDAR scene synthesis and perception for autonomous driving. ACCESS, 13, 166740-166759. (www)

  @article{ , abstract = {The advancement of autonomous driving and embedded AI systems has intensified the need for large-scale, richly annotated multimodal datasets encompassing RGB images, semantic labels, and 3D LiDAR data. Manual collection and annotation of such datasets remain costly and time-consuming, especially when temporal and cross-modal consistency is required. The proposed method introduces Joint Camera-LiDAR Scene Synthesis and Perception (JCLSP), a unified generative framework that simultaneously synthesizes photorealistic RGB images, semantic segmentation maps, and LiDAR range images through a compact and optimized diffusion process. Unlike prior approaches that employ separate diffusion branches, JCLSP fuses image and LiDAR modalities early in the pipeline and leverages a shared latent space for coherent multimodal generation. The architecture integrates three key elements: BKSDM, which streamlines the diffusion process by eliminating redundant blocks, a joint image-LiDAR diffusion module that applies the BKSDM framework to enable depth-aware synthesis with geometric fidelity, and modality-specific decoders that extract semantic masks, LiDAR range images, and image scenes from the shared latent representation. Experimental results on synthetic datasets indicate that JCLSP captures meaningful cross-modal correlations and preserves spatial features. By generating joint representations from camera and LiDAR views along with semantic segmentation annotations, the method demonstrates promising potential for cross-modal representation learning with labeled data.}, archive = {J_ACCESS}, author = {S. Raghavendra and S. K. Abhilash and Venu Madhav Nookala and P. V. Arun Kumar and B. N. Anoop and Ramyashree}, doi = {10.1109/ACCESS.2025.3613054}, journal = {IEEE Access}, pages = {166740-166759}, shortjournal = {IEEE Access}, title = {Joint camera-LiDAR scene synthesis and perception for autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Real-time CAN bus joystick interface with TMS320 32-bit microcontroller for defence applications. ACCESS, 13, 166729-166739. (www)

  @article{ , abstract = {This paper employs the TMS320F28335 microcontroller to establish a CAN bus communication system for interfacing with a joystick. The microcontroller’s ADC converts analog joystick signals into digital values, which are processed using linear scaling algorithms to derive motion (azimuth) and angle (elevation) measurements. These measurements are averaged for noise reduction and then transmitted via the CAN bus in structured byte-array packets. Implementation involves initializing system control, GPIO, ADC, CAN, and timer peripherals. The ADC continuously samples signals and stores them in lookup tables; the converted values are packaged using pre-configured CAN mailboxes and sent at regular intervals using a timer-based ISR. Additionally, the system integrates a secondary CAN node (Arduino) to demonstrate multi-node communication. Visualization and validation are achieved through an Integrated Guidance and Control Display Unit (IGCDU) and a CAN Bus Analyzer. The authors have developed and implemented the complete embedded system architecture, including real-time signal acquisition routines, ADC-to-angle conversion algorithms, multi-node CAN communication logic, and visualization through the IGCDU. This integration enables synchronized, real-time data capture, monitoring, and fault-tolerant transmission, making it ideal for embedded defense system applications.}, archive = {J_ACCESS}, author = {N. S. Lakshmi Priyaa and Aparna Balasubramanian and G. Tejeswar and R. Vidhyashankar and S. Mohana Sundari and Chandramauleshwar Roy}, doi = {10.1109/ACCESS.2025.3611988}, journal = {IEEE Access}, pages = {166729-166739}, shortjournal = {IEEE Access}, title = {Real-time CAN bus joystick interface with TMS320 32-bit microcontroller for defence applications}, volume = {13}, year = {2025}, }
• (2025). Eye tracking-based substitution of human feedback for image quality assessment in diffusion models. ACCESS, 13, 166704-166728. (www)

  @article{ , abstract = {As diffusion models rapidly evolve, the need to incorporate manual feedback into their retraining processes has become increasingly urgent. This paper proposes a novel framework, ED3PO, to address this limitation and improve the objectivity and efficiency of image quality assessment in diffusion model retraining. The framework integrates the D3PO diffusion model with eye-tracking-based deep learning to automate the evaluation of image quality. Eye-tracking experiments were conducted with 200 participants to collect visual attention metrics during paired comparisons of image quality. Seven machine learning models were subsequently trained, with the best-performing Transformer model used to capture the relationship between eye-tracking data and human image quality assessments. The effectiveness of ED3PO retraining was validated through computational metrics and eye-tracking experiments, and it was further applied to four additional domains. By incorporating eye-tracking into the diffusion model retraining process, this study enables the automated assessment of image quality and the generation of higher-quality images. Furthermore, it offers a reference framework and future research directions for applying eye-tracking technology in the development of diffusion models.}, archive = {J_ACCESS}, author = {Qiaohua Gu and Zhiyong Zhou and Jianming Qi and Hanfei Wang and Chang Xu}, doi = {10.1109/ACCESS.2025.3610543}, journal = {IEEE Access}, pages = {166704-166728}, shortjournal = {IEEE Access}, title = {Eye tracking-based substitution of human feedback for image quality assessment in diffusion models}, volume = {13}, year = {2025}, }
• (2025). Crossing boundaries: How technology and occupational therapy students create solutions together. ACCESS, 13, 166695-166703. (www)

  @article{ , abstract = {This paper describes an interdisciplinary collaboration of computer science, engineering technology, and occupational therapy faculty and students focused on improving the lives of children with disabilities. In the spring of 2023, an occupational therapy student was approached by a friend of a young boy who had lost both of his legs due to infection to see if she knew of anyone who could adapt his drivable car to hand controls. A group of students from the Innovators and Makers Club and occupational therapy program met together to adapt the car. Funded by monies from the College of Innovation and Technology, the car was adapted with hand controls and the young boy was able to have his car for the summer. The authors completed virtual interviews with the students involved in the process in Fall 2023. The students shared their views on this uncommon learning exercise and how they thought the experience may enhance their careers. The information provided by the initial group of students allowed the car to be brought back to campus in Winter 2024 and students from Digital Manufacturing Technology & Automation (DMT) and Occupational Therapy (OT) worked together in combined class times to further adapt the car. The collaboration between two distinct disciplines, DMT and OT, to enhance the design and functionality of a car represents a groundbreaking approach to problem-solving and innovation. By merging their diverse expertise, these classes are able to tackle challenges from multiple angles, incorporating both technical and human-centric perspectives. This interdisciplinary collaboration fosters creativity and encourages outside-the-box thinking, leading to more comprehensive solutions that address not only the technical aspects of car design but also considerations of usability, accessibility, and user experience. By bridging the gap between engineering technology and healthcare, this innovative partnership demonstrates the power of interdisciplinary collaboration in driving forward progress and creating products that better serve the needs of diverse populations. In summer 2024, students from Occupational Therapy and the Maker’s Club came together to modify four cars for children from Early On. Students completed survey in Fall 2024 that provided opportunities to further improve the project. Currently, students in DMT and OT are adapting 6 additional cars for children with disabilities. In Winter 2025, six cars were adapted by DMT and OT students through combined course activities. In concert, the professional development component of these activities helps validate several of the concepts presented in both courses. Just as importantly, employers are seeking candidates who possess these real world experiences and working knowledge.}, archive = {J_ACCESS}, author = {Donna Case and E. Shirl Donaldson and Thiago Ferreira}, doi = {10.1109/ACCESS.2025.3612836}, journal = {IEEE Access}, pages = {166695-166703}, shortjournal = {IEEE Access}, title = {Crossing boundaries: How technology and occupational therapy students create solutions together}, volume = {13}, year = {2025}, }
• (2025). AI-driven security evaluation of the ZUC stream cipher in 5G networks. ACCESS, 13, 166680-166694. (www)

  @article{ , abstract = {The ZUC stream cipher plays a critical role in safeguarding confidentiality and integrity in 5G AKA protocols. As machine learning (ML) and artificial intelligence (AI) emerge as new tools in cryptanalysis, assessing cipher resilience against such techniques has become essential. This paper presents a comprehensive evaluation of ZUC’s robustness against ML-Based attacks using keystream instances generated through real-world 5G testing infrastructure. Different attack strategies are implemented, including bitwise machine learning prediction, entropy and randomness analysis, Variational Autoencoder (VAE)-based reconstruction, protocol metadata fusion, reinforcement learning-based refinement, and statistical correlation modeling. Our results demonstrate that despite extensive AI-based cryptanalysis efforts, ZUC maintains a high security margin, prediction accuracies from baseline models remain below 58.03%, a metric that establishes the cipher’s resilience rather than indicating a practical weakness. Entropy analysis further confirms near-ideal randomness. Minor bit position variations and metadata correlations were observed, which did not significantly impact overall security. These findings establish ZUC’s strong resistance against contemporary AI-based threats and offer critical insights for designing future AI-resilient cryptographic standards in 5G and beyond.}, archive = {J_ACCESS}, author = {Veydant Katyal and Vrinda Bajaj and Aditya Parihar and Indrasen Singh}, doi = {10.1109/ACCESS.2025.3612453}, journal = {IEEE Access}, pages = {166680-166694}, shortjournal = {IEEE Access}, title = {AI-driven security evaluation of the ZUC stream cipher in 5G networks}, volume = {13}, year = {2025}, }
• (2025). A novel antibacterial susceptibility test from microscopic images by deep learning. ACCESS, 13, 166670-166679. (www)

  @article{ , abstract = {Antibacterial susceptibility testing is fundamental for evaluating the efficacy of antimicrobial agents and addressing the growing challenge of bacterial resistance. Traditional methods, such as the Kirby-Bauer disk-diffusion test, are conducted at the macroscopic level, which restricts accuracy and disregards essential microscopic bacterial dynamics. In this study, we introduce a novel methodology that integrates microscopic imaging with deep learning to measure inhibition zones of Staphylococcus aureus, providing enhanced precision and deeper insights into bacterial behavior. A YOLOv8-based neural network was optimized using 74 microscopic training images and validated with 78 independent cases. The proposed approach achieved an accuracy of 89.74% within a tolerance of ±0.2 mm, exhibiting a strong Pearson correlation with expert measurements (r = 0.94). This performance represents a 90% reduction in measurement error compared to conventional Petri dish evaluations, where variability often reaches 2.0 mm. Additionally, the system demonstrated robustness across different microscope models, including one not employed during training, highlighting its generalization capacity. Beyond accuracy, this methodology enables automated, reproducible, and rapid measurements, significantly reducing analysis time while eliminating operator subjectivity. Importantly, by capturing bacterial growth dynamics at the microscopic scale, the method allows early detection of bacterial susceptibility, potentially reducing clinical decision-making times from days to hours. This advancement holds considerable promise for antimicrobial nanomaterial evaluation, epidemiological studies, and clinical diagnostics. To encourage transparency and facilitate further research, all datasets, codes, and experimental setups developed in this work have been made publicly available, ensuring reproducibility and promoting the extension of this methodology to other bacterial species.}, archive = {J_ACCESS}, author = {Luis Fernando Parra-Sánchez and Solange I. Rivera-Manrique and Arturo González-Vega and Luis Carlos Padierna}, doi = {10.1109/ACCESS.2025.3612431}, journal = {IEEE Access}, pages = {166670-166679}, shortjournal = {IEEE Access}, title = {A novel antibacterial susceptibility test from microscopic images by deep learning}, volume = {13}, year = {2025}, }
• (2025). OptiLock: Automated optimization of learning-resilient logic locking. ACCESS, 13, 166649-166669. (www)

  @article{ , abstract = {Logic locking is a design-for-trust technique aiming to protect the intellectual property of integrated circuits from piracy throughout the global semiconductor supply chain. Logic locking inserts key-controlled elements (e.g., Boolean gates, referred to as key-gates) into the circuit. A secret locking key activates the functionality of the circuit, preventing unauthorized access to the design. However, the security of logic locking has been threatened by advanced machine learning-based attacks that rely solely on the structure of the locked circuit (i.e., gate-level netlist) to break logic locking and decipher the keys. Manually identifying the vulnerabilities in logic locking design, in terms of the key-gate type chosen and the location of key-gate insertion, has proven insufficient in safeguarding logic locking. Therefore, we propose OptiLock, a first-of-its-kind automated logic locking framework that supports different types of key-gates and automatically finds learning-resilient places to insert the key-gates to thwart a specific attack on logic locking, without requiring any prior knowledge of how to thwart the attack. It employs a simulated annealing algorithm in addition to customized and adjustable fitness evaluation functions that allow OptiLock to find learning-resilient logic locking solutions in terms of security and implementation overhead. OptiLock successfully thwarts four state-of-the-art attacks (MuxLink, OMLA, SCOPE, and SAAM) by optimizing the insertion of X(N)OR or multiplexer key-gates. Evaluations across ISCAS-85, ITC-99, EPFL, Ariane RISC-V benchmarks, and CVA6 RISC-V CPU design show that the proposed key-gate insertion is tailored to the original circuit structure, rather than relying on predefined locking strategies. Additionally, we demonstrate that OptiLock thwarts structural attacks such as SnapShot, Redundancy, and Resynthesis. Our evaluation further shows that OptiLock performs multi-objective optimization, achieving the desired security level while minimizing implementation costs, achieving an average delay reduction of 70.18% compared to random logic locking.}, archive = {J_ACCESS}, author = {Zeng Wang and Lilas Alrahis and Animesh Basak Chowdhury and Dominik Germek and Ramesh Karri and Ozgur Sinanoglu}, doi = {10.1109/ACCESS.2025.3612444}, journal = {IEEE Access}, pages = {166649-166669}, shortjournal = {IEEE Access}, title = {OptiLock: Automated optimization of learning-resilient logic locking}, volume = {13}, year = {2025}, }
• (2025). Valve-side single-phase-to-ground fault clearance in bipolar hybrid-MMC HVDC systems utilizing thyristor commutation branches and mechanical interrupter. ACCESS, 13, 166630-166648. (www)

  @article{ , abstract = {The Hybrid Modular Multilevel Converter (HMMC) topology—comprising full-bridge (FB) and half-bridge (HB) submodules—is effective for DC fault clearance in HVDC systems. However, to prevent overvoltage in lower-arm full-bridge submodules (FBSMs) during valve-side single-phase-to-ground (SPG) fault mitigation—an uncommon yet critical internal fault in bipolar HVDC systems—the proportion of FBSMs used must account for approximately 86.6% of the arm voltage, compared to 43.3% for DC-side fault blocking. This diminishes the economic advantage of HMMC over fully full-bridge MMC topology. This paper proposes an HMMC SPG fault mitigation method using thyristor-based current commutation branches on the lower arms and a ground-side mechanical interrupter, while maintaining the minimum number of FBSMs required for conventional DC fault clearance. The method mitigates SPG-resultant lower-arm overvoltage by commutating current into the thyristor branches, after which the mechanical interrupter clear lower arms current, and subsequently thyristor branches are turned-off to clear the SPG-valve side fault. Additionally, the pole current is cleared using a mechanical breaker or passive oscillation DC circuit breaker (CB), effectively suppressing upper-arm submodule (SM) overvoltage under SPG valve-side fault in both short- and long-distance HMMC bipolar links. Although additional equipment is required, it incurs no on-state losses during steady-state operation. The proposed method is compared with existing SPG fault clearance approaches for HMMC bipolar systems. Its effectiveness is validated through PSCAD/EMTDC simulations and benchmarked against prior SPG fault clearance methods for HMMC systems.}, archive = {J_ACCESS}, author = {Muhammad Usman and Il Kwon and Bang-Wook Lee}, doi = {10.1109/ACCESS.2025.3612191}, journal = {IEEE Access}, pages = {166630-166648}, shortjournal = {IEEE Access}, title = {Valve-side single-phase-to-ground fault clearance in bipolar hybrid-MMC HVDC systems utilizing thyristor commutation branches and mechanical interrupter}, volume = {13}, year = {2025}, }
• (2025). Research on automation detection assembly line of power meter communication module based on machine vision. ACCESS, 13, 166611-166629. (www)

  @article{ , abstract = {In the realm of industrial automation, enhancing the efficiency and accuracy of assembly lines remains a critical objective, particularly as computer vision applications become increasingly pervasive. Traditional assembly line operations often rely heavily on manual labor, resulting in inconsistent performance and elevated error rates. Although conventional automated systems can reduce human intervention to some extent, they frequently lack the flexibility and precision required for handling complex component assembly tasks. To address these limitations, this study introduces an innovative approach that integrates advanced image analysis techniques to optimize assembly line performance. By leveraging state-of-the-art image processing algorithms, the proposed method enables real-time detection and precise manipulation of components during the assembly process. The system employs a combination of deep learning models for robust object recognition and spatial analysis, ensuring high accuracy in component placement and effective defect detection. The method demonstrates strong adaptability, allowing it to adjust dynamically to various assembly scenarios and production requirements, thereby expanding its applicability across diverse manufacturing workflows. Experimental evaluations reveal a substantial increase in assembly line throughput, alongside a significant reduction in operational errors compared to traditional systems. Our system achieves a detection precision of 92.4% on MVTec AD and 94.1% on KolektorSDD, while reducing false positive rates by 25% relative to baseline models such as YOLOv5 and FastDefectNet. The system also exhibits excellent robustness under different environmental conditions, further reinforcing its industrial viability. These results underscore the transformative potential of integrating sophisticated image analysis technologies into industrial automation systems.}, archive = {J_ACCESS}, author = {Bo Chen and Tao Li and Zucai Xiao}, doi = {10.1109/ACCESS.2025.3605659}, journal = {IEEE Access}, pages = {166611-166629}, shortjournal = {IEEE Access}, title = {Research on automation detection assembly line of power meter communication module based on machine vision}, volume = {13}, year = {2025}, }
• (2025). EEG-driven machine learning for stroke detection in high-risk patients. ACCESS, 13, 166593-166610. (www)

  @article{ , abstract = {Stroke remains a leading cause of disability and mortality worldwide, highlighting the need for effective tools for early detection and intervention. Recent research has explored the use of bio-signals generated by the human body as indicators of stroke occurrence. Among these, Electroencephalography (EEG) has shown particular promise. EEG-based stroke detection offers a non-invasive, cost-effective, accurate, and portable solution. This paper investigates the use of Machine Learning (ML) techniques with EEG data to detect strokes. Four experimental setups were designed to evaluate different feature engineering methods: using all features, selecting features via a Decision Tree (DT) with varying thresholds, and applying Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for dimensionality reduction. Results indicate that the fourth setup—applying ICA with both AdaBoost and XGBoost—yielded the best performance, achieving an accuracy of 89%, precision of 86%, recall of 100%, F1-score of 92%, and a Matthews Correlation Coefficient (MCC) value of 0.76.}, archive = {J_ACCESS}, author = {Fatemah H. Alghamedy and May Issa Aldossary and Dina A. Alabbad and Reem A. Alshami and Maimonah S. Altaweel and Renad A. Alnuaim and Haya A. Alzahim and Shahad F. Alotaibi and Sumayh S. Aljameel and Sunday Olusanya Olatunji and Arwa H. Alghamdi}, doi = {10.1109/ACCESS.2025.3597908}, journal = {IEEE Access}, pages = {166593-166610}, shortjournal = {IEEE Access}, title = {EEG-driven machine learning for stroke detection in high-risk patients}, volume = {13}, year = {2025}, }
• (2025). Innovative methodology for determining basic wood density using multispectral images and MAPIR RGNIR camera. ACCESS, 13, 166581-166592. (www)

  @article{ , abstract = {Basic wood density (BWD) is a critical parameter in the forestry sector, directly influencing yield, operational costs, and final product quality. Conventional methods for BWD determination are often time-consuming, destructive, and costly, necessitating the development of faster and more economical alternatives. In this study, multispectral imaging was employed to estimate the BWD of Eucalyptus grandis and Pinus oocarpa. A MAPIR Survey3 (RGNIR) camera was used to capture images of 142 wood samples at four drying stages: saturated, naturally drying, equilibrium moisture content, and oven-dry. Reflectance data were extracted using ENVI Classic software, while statistical correlations were analyzed in Minitab 16. Machine learning models—Linear Regression, Multilayer Perceptron (MLP), Random Forest (RF), and Support Vector Machine for Regression (SMOreg)—were implemented in Weka 3.8.6. Reflectance in the cross-sectional plane, particularly in the red spectral band, exhibited the highest correlation with BWD. The Random Forest model outperformed other approaches, achieving correlation coefficients exceeding 71% (percentage split) and 65% (cross-validation), with root mean square errors (RMSE) below 14% and 15%, respectively. The MLP, SMOreg, and Linear Regression models also demonstrated satisfactory performance, particularly for saturated wood samples. These findings confirm the viability of multispectral imaging as a rapid, non-destructive, cost-effective, and easily applicable method for BWD estimation in forestry applications.}, archive = {J_ACCESS}, author = {Fernanda De Souza Cardoso and Antonio José Vinha Zanuncio and George Deroco Martins and Amélia Guimarães Carvalho and Thaynara Francisca Vilela and Olivia Pereira Lopes and José Geraldo Mageste Da Silva}, doi = {10.1109/ACCESS.2025.3612819}, journal = {IEEE Access}, pages = {166581-166592}, shortjournal = {IEEE Access}, title = {Innovative methodology for determining basic wood density using multispectral images and MAPIR RGNIR camera}, volume = {13}, year = {2025}, }
• (2025). Exploring the determinants of sustainable digital startups in a developing country: A qualitative analysis. ACCESS, 13, 166569-166580. (www)

  @article{ , abstract = {Digital startups in emerging economies experience high failure rates because of misaligned growth models and absence of localized sustainability frameworks. The study fills this gap by using a multi-round Delphi method involving domain experts from underrepresented industries like digital healthcare, agriculture, and digital transport. By stating expert consensus, we validate the 33 sustainability factors that are combined to formulate the Sustainable Startup Success Framework (S3F). The framework consists of six fundamental pillars of Founder Factors, Financial Factors, Organizational Factors, Product Factors, Team Factors, Customer Factors organized along four levels. These four levels of framework are: conditions for input, strategic pillars, outcomes, and impact use cases. S3F model transcends theoretical and practical gaps: it is a diagnostic and planning tool for founders, incubators and policymakers. The results highlight working with volatile environment and market requirements of startup world in under developed fields, product timing, recurring revenue, infrastructure skills in developing robust, scalable and SDG driven digital ventures.}, archive = {J_ACCESS}, author = {Javaria Umbreen and Shahbaz Abbas and Muhammad Zeeshan Mirza and Afshan Naseem and Yasir Ahmad}, doi = {10.1109/ACCESS.2025.3606537}, journal = {IEEE Access}, pages = {166569-166580}, shortjournal = {IEEE Access}, title = {Exploring the determinants of sustainable digital startups in a developing country: A qualitative analysis}, volume = {13}, year = {2025}, }
• (2025). ABCD: Algorithm for balanced component discovery in signed networks. ACCESS, 13, 166553-166568. (www)

  @article{ , abstract = {The largest balanced element in signed graphs plays a vital role in helping researchers understand the fundamental structure of the graph, as it reveals valuable information about the complex relationships between vertices in the network. The challenge is an NP-hard problem; there is no current baseline to evaluate state-of-the-art signed graphs derived from real networks. A scalable state-of-the-art approach for the maximum balanced subgraph detection in the network of any size is proposed. This approach finds the largest balanced subgraph by considering only the top K balanced states with the lowest frustration index. The ABCD method efficiently selects the largest possible subset from an extensive signed network with millions of vertices and edges, and the size of the discovered subset is double that of the state-of-the-art in a similar time frame.}, archive = {J_ACCESS}, author = {Muhieddine Shebaro and Jelena Tešić}, doi = {10.1109/ACCESS.2025.3612924}, journal = {IEEE Access}, pages = {166553-166568}, shortjournal = {IEEE Access}, title = {ABCD: Algorithm for balanced component discovery in signed networks}, volume = {13}, year = {2025}, }
• (2025). Evolutionary multitask optimization based on search behavior learning. ACCESS, 13, 166542-166552. (www)

  @article{ , abstract = {Evolutionary multitask optimization (EMTO) is an emerging topic in evolutionary computation to solve multitask optimization problems (MTOPs) with the help of knowledge transfer (KT). However, the current EMTO algorithms often ignore the information of the search behavior, which only exploit individual information or historical performance when estimating task similarity or transferring knowledge. In this paper, we propose a search behavior learning-based multitask optimization (SBLMTO) algorithm to address the challenge. The search behavior similarity (SBS) strategy is first designed to estimate the similarity of tasks based on the search behavior, so as to adaptively adjust the KT frequency. Then, the knowledge archive is introduced to store the useful transferred knowledge, further enhance the KT efficiency. Next, a two-level transferring knowledge learning (TTKL) strategy is proposed to achieve the effective learning from both the search behavior and the knowledge archive. SBLMTO is tested on the widely used multitask test suites CEC2017 and CEC2022, and the experimental results show that SBLMTO performs significantly better than other state-of-the-art EMTO algorithms, showing its superiority.}, archive = {J_ACCESS}, author = {Dan-Ting Duan and Shi-Yuan Zhou and Zi-Jia Wang}, doi = {10.1109/ACCESS.2025.3610313}, journal = {IEEE Access}, pages = {166542-166552}, shortjournal = {IEEE Access}, title = {Evolutionary multitask optimization based on search behavior learning}, volume = {13}, year = {2025}, }
• (2025). Queue dissipation-based max pressure signal control using vehicle re-identification data. ACCESS, 13, 166530-166541. (www)

  @article{ , abstract = {Traffic congestion is a persistent urban challenge, and signal control strategies play a crucial role in enhancing network efficiency. Traditional max pressure-based control systems prioritize optimizing throughput based on known queue lengths but often overlook the impact of intersection delays and real-world operational constraints. This study aims to develop a decentralized max-pressure-based traffic signal control system that integrates both queue length and delay effects within the max-pressure framework by introducing a novel performance metric called queue dissipation time into the control strategy. The proposed cyclic-based max pressure algorithm relies on sample travel time data obtained from probe data sources. It considers pre-defined phase sequences to enhance real-world applicability. When evaluated in a simulation environment with a realistic 20% vehicle re-identification penetration rate, the proposed approach achieved queue and delay performance comparable to control strategies relying on complete vehicle arrival data. Furthermore, implementing dynamic cycle length led to a 23% reduction in delay compared to the fixed-cycle max pressure method, inferring the benefits of adaptive timing adjustments. By incorporating queue dissipation time and considering the constraints of real-world signal operations, the proposed control system enhances the practicality of max pressure-based traffic signal strategies. This study provides a foundation for implementing decentralized adaptive traffic signal control in urban networks, particularly in environments with limited vehicle data availability. The algorithm exhibited reliable performance across various levels of data availability, delivering comparable results under both low (20%) and high (50%) penetration rate scenarios. This consistency highlights its adaptability and operational feasibility, without requiring excessive traffic data or extensive infrastructure.}, archive = {J_ACCESS}, author = {Himabindu Maripini and Tamojit Ghosh and Lelitha Vanajakshi}, doi = {10.1109/ACCESS.2025.3612125}, journal = {IEEE Access}, pages = {166530-166541}, shortjournal = {IEEE Access}, title = {Queue dissipation-based max pressure signal control using vehicle re-identification data}, volume = {13}, year = {2025}, }
• (2025). High-efficiency, high-fundamental frequency inverter for high-speed electrical drive in a micro gas turbine-based vehicle powertrain. ACCESS, 13, 166515-166529. (www)

  @article{ , abstract = {This paper presents the design, analysis, and validation of a high-efficiency inverter for high-speed electric drives in micro gas turbine (MGT)-based vehicle powertrains. A 30 kW, 700 VDC, two-level, three-phase voltage source inverter (VSI) using silicon carbide (SiC) MOSFETs is developed to operate at a 2 kHz fundamental and 40 kHz switching frequency. Unlike conventional inverters operating at 50–60 Hz, this design supports high-speed motors (above 100,000 rpm) by increasing the fundamental frequency to 2 kHz. A detailed comparison between two-level and three-level T-type topologies is conducted, with the selected topology maintaining over 99% efficiency across a wide frequency range. The design incorporates detailed loss modelling, a low-inductance PCB layout, and optimized DC-link capacitor selection to ensure robust high-frequency operation. The influence of modulation index and switching frequency ratio on inverter efficiency and current total harmonic distortion (THD) is analyzed, highlighting their impact on thermal performance and current quality. Thermal simulations using PLECS estimate a peak efficiency of 99.3%. The design is validated through simulation and experimental testing on a 30 kW prototype, achieving 14.3 kW/L power density and 25 kW/kg gravimetric density. Comparative analysis with recent literature confirms the proposed inverter’s advantages in power density, efficiency, and high-frequency capability.}, archive = {J_ACCESS}, author = {Deepak Upadhyay and Ching-Lon Huang and Aleksi Mattsson and Ilya Petrov and Pasi Peltoniemi}, doi = {10.1109/ACCESS.2025.3612330}, journal = {IEEE Access}, pages = {166515-166529}, shortjournal = {IEEE Access}, title = {High-efficiency, high-fundamental frequency inverter for high-speed electrical drive in a micro gas turbine-based vehicle powertrain}, volume = {13}, year = {2025}, }
• (2025). Neural network-based attention-guided residual interpolation for division of focal plane polarization imaging. ACCESS, 13, 166496-166514. (www)

  @article{ , abstract = {The increasing demand for high-resolution imaging across diverse applications has catalyzed the development of advanced image interpolation techniques. Traditional interpolation methods often compromise image quality by introducing artifacts and losing critical detail, especially in complex images generated by Division of Focal Plane (DoFP) sensors. These sensors face unique challenges due to their low-resolution outputs and their ability to capture polarized light at different angles, e.g., 0°, 45°, 90°, and 135°. To address these challenges, we propose the Attention-Guided Residual Interpolation Network (AGRIN), a novel deep learning framework designed specifically for the interpolation of DoFP images. AGRIN leverages residual learning and attention mechanisms to enhance interpolation performance by focusing on significant image regions while preserving high-frequency details often neglected by conventional techniques. Our results demonstrate that AGRIN significantly outperforms traditional methods, as measured by Root Mean Square Error (RMSE) and qualitative assessments, especially when interpolating liver cancer images captured at various polarization angles. The results indicate that AGRIN effectively captures essential features with minimal distortion, validating its efficiency and suitability for high-stakes imaging applications that require precise interpolation. This research contributes valuable insights to the field of image processing and lays a foundation for future advancements in DoFP imaging technologies.}, archive = {J_ACCESS}, author = {Sadia Gul and Shakeela Bibi and Ashfaq Ahmed and Sajid Hussain and Amine Bermak}, doi = {10.1109/ACCESS.2025.3612443}, journal = {IEEE Access}, pages = {166496-166514}, shortjournal = {IEEE Access}, title = {Neural network-based attention-guided residual interpolation for division of focal plane polarization imaging}, volume = {13}, year = {2025}, }
• (2025). Topology optimization of distribution network using an efficient linearized model considering load type. ACCESS, 13, 166482-166495. (www)

  @article{ , abstract = {Distribution network reconfiguration stands out as a potent approach for reducing power losses in radial distribution feeders, where losses notably are more those observed in transmission systems. The distribution power losses incurred not only lead to costly operations but also contribute to a compromised voltage profile in electric energy grids. Habitual reconfiguration involves adjusting the form of the distribution grid to minimize electric energy losses, with a focus on the electricity demanded by end-consumers. The existing models predominantly address distribution network reconfiguration without accounting for the different types of load demand linked to points of consumption, each exerting distinct power loss effects in contrast to single-type demands. Despite the imperative load type diversity role, most models neglect this aspect. The limited studies incorporating load type into reconfiguration strategies tend to employ non-linear formulations, introducing complexities. While models with non-linear characteristics may be tackled via metaheuristic techniques, they fall short of guaranteeing optimal solutions. Alternatively, solving such models using solvers for non-linear equations in conventional tools of optimization demands computations that require significant time. To address these challenges, the present article proposes an impressive model for reconfiguring distribution networks that consider different load types. Notably, this model is designed for seamless implementation with linear procedures. Our study’s results demonstrate the exactitude of the introduced model in yielding accurate results for targeted reconfiguration challenges, coupled with the advantage of swift execution suitable for real-time reconfigurations.}, archive = {J_ACCESS}, author = {Nahar Fehaid Alshammari and Meisam Mahdavi and Anna Pinnarelli and Alireza Soleimani and Francisco Jurado and Pasquale Vizza}, doi = {10.1109/ACCESS.2025.3610585}, journal = {IEEE Access}, pages = {166482-166495}, shortjournal = {IEEE Access}, title = {Topology optimization of distribution network using an efficient linearized model considering load type}, volume = {13}, year = {2025}, }
• (2025). Time to frequency domain conversion for transient electromagnetic data with neural networks. ACCESS, 13, 166474-166481. (www)

  @article{ , abstract = {To further improve the efficiency and accuracy of time-to-frequency domain conversion (TFC) for transient electromagnetic (TEM) data, a UNet++ framework was established to learn and model the complex nonlinear functional relationship between time-domain (t-d) and frequency-domain (FD) electromagnetic (EM) fields. The input and output of UNet++ were defined as the t-d and FD vertical magnetic field data derived from a one-dimensional (1-D) geoelectric model, respectively. The dataset consisted of 500,000 samples, encompassing 1-D models with 2 to 6 layers. Through this process, the hyperparameters characterizing the mapping relationship between t-d and FD magnetic fields were determined. To verify the validity of the proposed algorithm, 20 models were randomly selected from the test dataset. The experimental results demonstrate that the theoretical FD responses agree well with the recovered FD responses transformed from the t-d signals via TFC, with a relative error of less than 5% for all frequencies. In addition, the algorithm was tested on an H-type model to demonstrate its superiority over the regularization inversion method in achieving precise TFC. To further validate the effectiveness of TFC for TEM data using UNet++, the algorithm was applied to measured data collected from a mining area in western Qinghai Province, China. The results indicate that reliable FD EM responses can be obtained from t-d signals using UNet++ across all frequencies. The proposed algorithm offers robust technical support for TFC of TEM data and contributes to the advancement of geophysical exploration technology.}, archive = {J_ACCESS}, author = {Dajun Li and Yaoming Wang and Yuan Gao}, doi = {10.1109/ACCESS.2025.3612575}, journal = {IEEE Access}, pages = {166474-166481}, shortjournal = {IEEE Access}, title = {Time to frequency domain conversion for transient electromagnetic data with neural networks}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Enhancing knapsack-based financial portfolio optimization using quantum approximate optimization algorithm”. ACCESS, 13, 166473. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Enhancing Knapsack-Based Financial Portfolio Optimization Using Quantum Approximate Optimization Algorithm”).}, archive = {J_ACCESS}, author = {Chansreynich Huot and Kimleang Kea and Tae-Kyung Kim and Youngsun Han}, doi = {10.1109/ACCESS.2025.3611744}, journal = {IEEE Access}, pages = {166473}, shortjournal = {IEEE Access}, title = {Corrections to “Enhancing knapsack-based financial portfolio optimization using quantum approximate optimization algorithm”}, volume = {13}, year = {2025}, }
• (2025). STRIKE-MS: Synergistic texture and raw intensity knowledge extraction for active MS lesion detection on non-contrast MRI. ACCESS, 13, 166452-166472. (www)

  @article{ , abstract = {Multiple sclerosis (MS) is an autoimmune disease that affects the central nervous system. To detect active lesions in this disease, gadolinium-based contrast agents are injected before magnetic resonance imaging (MRI). These materials can have side effects for the patient after several injections. The purpose of this study is to use deep learning methods to detect active lesions in MRI images without using contrast agents. Our data included 9097 lesions from 130 patients from four different imaging centers. First, the lesions were identified, and each one was separated from the images as a patch. Texture features were also calculated for each lesion. Then, the study was conducted based on the input type in three different phases, including image intensity, texture features, and the combination of image intensity and texture features. In each phase, a proper CNN network was designed. In addition, 13 pre-trained CNN networks were also applied to predict lesions in the third phase. The ensemble technique was another method used in this phase to categorize lesions. Additionally, inter-center variability and cross-center generalizability assessment analyses were conducted to evaluate the model’s generalizability rigorously. The statistical results for the designed network of the first phase were obtained as an accuracy value of 85%, specificity of 95%, sensitivity of 74%, and AUC of 0.90. These values for the second phase were 77%, 90%, 63%, and 0.85, respectively, and for the third phase, 91%, 94%, 87%, and 0.94, respectively. Among the pre-trained networks in the third phase, the EfficientNetV2M showed the best performance with an average accuracy of 88%, specificity of 91%, sensitivity of 86%, and AUC of 0.92. This study indicates that while using image intensity and texture features separately effectively distinguishes active from inactive lesions, their combination yields even better results.}, archive = {J_ACCESS}, author = {Amirabbas Amini and Azin Shayganfar and Zahra Amini and Leila Ostovar and Somayeh HajiAhmadi and Navid Chitsaz and Masoud Rabbani and Rahele Kafieh}, doi = {10.1109/ACCESS.2025.3609133}, journal = {IEEE Access}, pages = {166452-166472}, shortjournal = {IEEE Access}, title = {STRIKE-MS: Synergistic texture and raw intensity knowledge extraction for active MS lesion detection on non-contrast MRI}, volume = {13}, year = {2025}, }
• (2025). A TOpographic mapping (ATOM) method to design magnetic cores. ACCESS, 13, 166444-166451. (www)

  @article{ , abstract = {Wireless power transfer offers safe, convenient, and efficient way of charging electric vehicles. Ongoing research is targeting wireless charging pad design optimization; designing the magnetic component is the most important part of the coupler design because the magnetic part determines the coupling factor and efficiency. Optimizing the coil layout and geometry as well as ferrite design requires finite elements analysis based modeling and simulation for minimized core losses, maximized magnetic coupling, and minimized material use for cost-effectiveness. Although parametric finite element analysis or emerging artificial intelligence methods can generate very accurate results, simulation times are extremely long. To address this issue, this study proposes a simple, effective core design called A TOpographic Mapping (ATOM). The proposed design is based on the design of magnetic core by using the magnetic flux distribution. The thickness of the core increases with increasing magnetic flux density, forming a variable thickness core design with less material and minimized core losses compared to conventional designs. A superimposing method is used to create an optimal design for a rotational magnetic field-based system. According to simulation results, the ATOM design reduces the required material volume by 13.19% and yields the lowest core loss and highest mutual inductance compared to other designs. In addition, misalignment, electromagnetic interference, and thermal performance were evaluated for the proposed design.}, archive = {J_ACCESS}, author = {Emrullah Aydin and Omer C. Onar and Burak Ozpineci}, doi = {10.1109/ACCESS.2025.3612845}, journal = {IEEE Access}, pages = {166444-166451}, shortjournal = {IEEE Access}, title = {A TOpographic mapping (ATOM) method to design magnetic cores}, volume = {13}, year = {2025}, }
• (2025). Optimizing driver position using ANOVA and ANNs. ACCESS, 13, 166436-166443. (www)

  @article{ , abstract = {This study investigates how the optimal driving position is shaped by the forces acting on the driver and the performance requirements of an FSAE vehicle. Using a comprehensive dataset based on a parametric model, key SAE-standardized input variables are parameterized: the horizontal and vertical coordinates of the H-point, the horizontal distance between the AHP and H-point, the seatback angle, and the steering wheel position. The objective is to identify the ideal driving setup that balances vehicle performance with driver comfort. ANOVA is utilized to determine which of these ergonomic factors most significantly affect acceleration performance, while artificial neural networks provide a comparative method for validating results and predicting outcomes for new configurations. The novelty of this study lies in integrating driver positioning dynamics into performance optimization, providing a systematic approach to vehicle design before the CAD prototyping phase.}, archive = {J_ACCESS}, author = {Marco Freddi and Curzio Pagliari and Leonardo Frizziero}, doi = {10.1109/ACCESS.2025.3612846}, journal = {IEEE Access}, pages = {166436-166443}, shortjournal = {IEEE Access}, title = {Optimizing driver position using ANOVA and ANNs}, volume = {13}, year = {2025}, }
• (2025). Deployment of acoustic reconfigurable intelligent surface in underwater acoustic communications under deep-sea long-range scenarios. ACCESS, 13, 166419-166435. (www)

  @article{ , abstract = {As the only technology that can support kilometer-level underwater communications, the UnderWater Acoustic Communications (UWAC) suffers from low data rate which is unable to support various emerging marine applications. The recently proposed acoustic reconfigurable intelligent surface (aRIS) has been considered to have the ability to improve the data rate and the coverage of UWAC systems, since it can control the direction of acoustic signal propagation and realize beamforming. The deployment of aRIS is critical to realize its data rate and coverage enhancement capabilities. However, how to optimize the aRIS deployment in deep-sea long-range scenarios has not been studied. To fill in this gap, a transmission loss (TL) model for aRIS-aided UWAC systems is established in this paper, where the beamforming gain and signal amplification gain of aRIS are characterized. Then, based on the TL model, a deep sound channel axis focusing (DSCAF) deployment architecture is proposed and analyzed for aRIS-aided UWAC systems in deep-sea long-range scenarios. In the proposed architecture, one aRIS each is deployed near the transmitter and receiver at the depth of the deep sound channel axis. By utilizing the beamforming capability of aRISs, the proposed DSCAF deployment architecture can guide acoustic signals into and out of the deep sound channel. This will ensure the transmission is fully concentrated in the deep sound channel, thus reducing the TL and enhancing the coverage in a distant receiving area. The analysis proves that the proposed scheme enjoys asymptotic optimality of TL as the number of aRIS elements increases compared to deploying aRISs at other depths. Simulation results have illustrated that the proposed architecture can reduce the TL and improve the spectrum efficiency in a long-distance receiving area.}, archive = {J_ACCESS}, author = {Jingbo Tan and Zhizhen Ren and Xu Shi and Zhi Sun and Jintao Wang}, doi = {10.1109/ACCESS.2025.3607323}, journal = {IEEE Access}, pages = {166419-166435}, shortjournal = {IEEE Access}, title = {Deployment of acoustic reconfigurable intelligent surface in underwater acoustic communications under deep-sea long-range scenarios}, volume = {13}, year = {2025}, }
• (2025). Bayesian reliability analysis of a sine model under hybrid censoring. ACCESS, 13, 166397-166418. (www)

  @article{ , abstract = {This work introduces the three-parameter sine Kumaraswamy Shanker distribution (SKSD), a novel extension of the Shanker distribution that seeks to further enhance the flexibility of statistical models in explaining many real-life patterns of data. Through extending the Shanker distribution, the SKSD overcomes the deficiencies of the Shanker distribution, particularly in the modeling of advanced behaviors hazard rate functions. The suggested distribution can represent numerous real-world phenomena due to its different increasing and decreasing hazard rate shapes. Crucial statistical properties such as the quantile function, expansion of the density function, ordinary and incomplete moments, and stress-strength reliability are considered. The model parameters are estimated using both classical and Bayesian methods under adaptive Type-II hybrid progressive censoring with random unit removal. Additionally, the Fisher information matrix is used to derive asymptotic confidence intervals, while Markov Chain Monte Carlo methods are applied to estimate Bayesian credible intervals under symmetric and asymmetric loss functions. It is impossible to compare theoretically estimating processes. In order to investigate the behavior of various estimators and enable comparison using some criteria, extensive simulation research is performed. According to empirical results, relative absolute biases and mean squared errors tend to decrease with increasing sample size. The Bayesian estimate approach typically yields more accurate and stable findings than the maximum likelihood estimate. The SKSD’s adaptable features make it competitive for fitting jute fiber data when compared to the other alternatives.}, archive = {J_ACCESS}, author = {Ehab M. Almetwally and Amal S. Hassan and Hisham M. Almongy}, doi = {10.1109/ACCESS.2025.3610723}, journal = {IEEE Access}, pages = {166397-166418}, shortjournal = {IEEE Access}, title = {Bayesian reliability analysis of a sine model under hybrid censoring}, volume = {13}, year = {2025}, }
• (2025). EMF-aware energy efficiency optimization in active RIS-assisted 5G networks. ACCESS, 13, 166385-166396. (www)

  @article{ , abstract = {To address electromagnetic field (EMF) exposure concerns and enhance network performance, this work proposes an energy efficiency (EE) optimization algorithm for 5G networks operating in active reconfigurable intelligent surface (RIS)-assisted environments, considering both power and EMF exposure constraints. The algorithm tackles the challenge of maximizing a constrained EE utility function in multi-active RIS-aided multiple-input multiple-output (MIMO) systems by introducing a joint transmit and reflect beamforming approach. Specifically, EE is maximized by jointly optimizing the transmit beamforming weights and RIS reflection coefficients under stringent power and EMF exposure limitations. To solve the resulting non-convex optimization problem, a novel algorithm is developed to decouple the optimization variables, with its convergence behavior thoroughly validated. A quadratic transform technique is utilized, introducing a coverage identifier vector to recast the non-convex problem into a series of convex subproblems solvable using the CVX toolbox. Simulation results demonstrate that integrating RIS significantly boosts energy efficiency, with the performance gap between RIS-assisted and non-RIS scenarios widening over iterations, ultimately achieving a 31.7% improvement compared to systems without RIS. Furthermore, the proposed algorithm outperforms benchmark techniques, achieving 3.3% and 4.8% higher EE than the Truncated and Boosted beamforming (TBBF) and Equalized beamforming (EBF) techniques, respectively.}, archive = {J_ACCESS}, author = {Mohammed Ahmed Salem and Heng Siong Lim and Khaled Abdulaziz Alaghbari and Charilaos Zarakovitis and Su Fong Chien and Kah Seng Diong}, doi = {10.1109/ACCESS.2025.3609043}, journal = {IEEE Access}, pages = {166385-166396}, shortjournal = {IEEE Access}, title = {EMF-aware energy efficiency optimization in active RIS-assisted 5G networks}, volume = {13}, year = {2025}, }
• (2025). Atmospheric pressure plasma jet effectiveness in food processing: A review of current trends and opportunities. ACCESS, 13, 166357-166384. (www)

  @article{ , abstract = {Rising foodborne illnesses drive the demand for safer, fresher food, prompting the exploration of non-thermal technologies as alternatives to traditional thermal treatments. Cold plasma, particularly atmospheric pressure plasma jets, offers rapid and low-temperature processing. Meanwhile, APPJ demonstrates advantages over dielectric-barrier discharge (DBD) in terms of flexibility and plasma stability, but a comprehensive understanding of its application in food microbial decontamination remains limited. This review addresses this gap by systematically analyzing the factors and techniques influencing APPJ’s effectiveness. It provides a critical discussion of the limitations associated with these factors and techniques, outlines future research opportunities, and highlights key challenges for APPJ technology in food treatment. The technical novelty of this review lies in its detailed analysis of APPJ’s operational parameters and their impact on decontamination efficacy, providing a rationale for optimized application and targeted research efforts.}, archive = {J_ACCESS}, author = {Azfar Satari Abdullah and Mohd Hafizi Ahmad and Norhafezaidi Mat Saman}, doi = {10.1109/ACCESS.2025.3610724}, journal = {IEEE Access}, pages = {166357-166384}, shortjournal = {IEEE Access}, title = {Atmospheric pressure plasma jet effectiveness in food processing: A review of current trends and opportunities}, volume = {13}, year = {2025}, }
• (2025). YOLOv8n-GSE: Efficient steel surface defect detection method. ACCESS, 13, 166343-166356. (www)

  @article{ , abstract = {At present, deep learning-based intelligent detection technologies are progressively being applied to the field of steel surface defect inspection. An efficient defect inspection algorithm, YOLOv8n-GSE, is proposed to overcome low detection accuracy. We integrate a Global Attention Mechanism (GAM) into the YOLOv8n-based model’s backbone network to construct GAM attention layers, specifically designed to enhance feature representation for steel surface defects. We implement a CSP-ABAN structure in both the backbone and neck networks to improve the YOLOv8n-GSE model’s capacity for extracting steel surface defect features, while the ESCD head is introduced to process multi-scale features and capture defect information across various dimensions. By replacing standard convolutions with GSConv, the model achieves a balance between complexity and detection efficiency. Furthermore, the PIoUv2 loss function is adopted to address the limitations of CIoU. The experimental results demonstrate that the YOLOv8n-GSE model reduces computational complexity by 31%, with its complexity reaching 5.6 GFLOPs compared with the baseline YOLOv8n model. Meanwhile, it achieves a mean average precision of 94.2%, which represents a 5.4 percentage point improvement. Compared with other object detection algorithms, YOLOv8n-GSE significantly enhances defect detection accuracy along with statistically significant reduction in miss rates.}, archive = {J_ACCESS}, author = {Hongyan Wang and Weiyong Li and Bo Zhang and Zhenfei Gu}, doi = {10.1109/ACCESS.2025.3602360}, journal = {IEEE Access}, pages = {166343-166356}, shortjournal = {IEEE Access}, title = {YOLOv8n-GSE: Efficient steel surface defect detection method}, volume = {13}, year = {2025}, }
• (2025). Federated ViT: A distributed deep learning framework for skin cancer classification. ACCESS, 13, 166326-166342. (www)

  @article{ , abstract = {Skin cancer remains one of the most prevalent and life-threatening diseases globally, where early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. Although deep learning and digital pathology have advanced lesion classification performance, significant challenges remain due to complex histopathological patterns, privacy concerns over centralized data storage, and inconsistencies across datasets in terms of annotation, quality, and structure. These disparities hinder model generalization and complicate performance benchmarking. To overcome these issues, this study proposes a novel skin lesion classification framework that integrates Federated Learning (FL) with Vision Transformers (ViTs). FL enables decentralized model training across multiple clinical sites without sharing sensitive data, inherently accommodating data heterogeneity while preserving privacy. ViTs, with their self-attention mechanisms, effectively capture global contextual features, making them robust to structural and visual variations in non-standardized datasets. The framework incorporates key components such as client-side preprocessing, data augmentation, federated transfer learning, model aggregation, and feature optimization. Comprehensive experiments were conducted on two publicly available skin lesion datasets—HAM10000 and ISIC2019. The proposed model achieved a global test accuracy of 90%, with a sensitivity of 88.2% and specificity of 91.4% on the HAM10000 dataset. On the ISIC2019 dataset, the model attained a test accuracy of 87.6%, with Area Under the Curve (AUC) values reaching 0.96 for the best-performing class, demonstrating strong discriminative ability. These results confirm the framework’s robustness in real-world scenarios with non indentical data and its potential to support privacy-preserving, scalable clinical applications in dermatology. This work contributes to advancing FL-based computer vision solutions for skin cancer detection and lays the groundwork for future exploration of ViTs in decentralized healthcare environments.}, archive = {J_ACCESS}, author = {Riaz H. Junejo and Qaisar Abbas and Muhammad Awais and Tallha Akram and Mutlaq B. Aldajani}, doi = {10.1109/ACCESS.2025.3612477}, journal = {IEEE Access}, pages = {166326-166342}, shortjournal = {IEEE Access}, title = {Federated ViT: A distributed deep learning framework for skin cancer classification}, volume = {13}, year = {2025}, }
• (2025). Distance-aware bit-mapping-based energy-efficient MAC protocol for LoRaWAN. ACCESS, 13, 166312-166325. (www)

  @article{ , abstract = {Low power consumption is the primary requirement for battery-operated devices during data transmission. Researchers have long sought energy-efficient solutions for LoRa-based network communications to extend the battery life of these devices, which ensures prolonged network operation without energy shortages. The proposed research introduces an energy-efficient MAC protocol designed to extend the battery life of the devices in LoRa communication networks. This protocol, known as the Distance-Aware Bit-Mapping based Energy-Efficient MAC, uses bit-mapping to achieve energy-efficient network communication. It differentiates between critical and normal nodes, prioritizes time-sensitive data transmission, and incorporates a security mechanism at the application layer to adjust transmission power based on the distance of end nodes from the gateways. A probability-based end-node arrangement approach is used to study the impact of distance-based transmission power tuning on total energy consumption. Performance comparisons with standard TDMA and Bit-Mapping-based LoRa approach show that the proposed Distance-Aware approach is more energy-efficient, and confirms its superiority over existing methods.}, archive = {J_ACCESS}, author = {Gaurav Suman and Manoj Tolani and Pankaj Kumar and Arun Balodi}, doi = {10.1109/ACCESS.2025.3612250}, journal = {IEEE Access}, pages = {166312-166325}, shortjournal = {IEEE Access}, title = {Distance-aware bit-mapping-based energy-efficient MAC protocol for LoRaWAN}, volume = {13}, year = {2025}, }
• (2025). Transfer impedance modeling method for HFCT considering current-carrying conductor eccentricity and air gap. ACCESS, 13, 166301-166311. (www)

  @article{ , abstract = {To enhance the accuracy and reliability of partial discharge monitoring devices for power transformers, this paper focuses on the verification of the transfer impedance of high-frequency current transformer (HFCT), which is a core parameter for assessing their performance. This article examines the influence of air gaps on the performance of the HFCT. Based on the Biot-Savart law, this paper derives the analytical solution for the induced electromotive force of the HFCT under eccentric conditions of the current-carrying conductor. A finite element model of the HFCT and current-carrying conductor was established using the Ansys Maxwell module to perform coupled magnetic circuit simulations. The conductor eccentricity was introduced to investigate the impact of conductor position on the transfer impedance of the HFCT. The research results indicate that the presence of the air gap leads to magnetic field leakage and uneven distribution, increasing measurement errors. The transfer impedance error is only related to the ratio of the inner and outer radii of the coil and the size of the air gap. The smaller the air gap, the less its impact on the transfer impedance verification results. This study provides a theoretical basis for the optimization of transfer impedance verification for the HFCT.}, archive = {J_ACCESS}, author = {Tian Tian and Xiu Zhou and Liangliang Pan and Jin Bai and Yukun Ma and Longcheng Dai and Jiaqi Wu}, doi = {10.1109/ACCESS.2025.3612743}, journal = {IEEE Access}, pages = {166301-166311}, shortjournal = {IEEE Access}, title = {Transfer impedance modeling method for HFCT considering current-carrying conductor eccentricity and air gap}, volume = {13}, year = {2025}, }
• (2025). Radar signal pulse train recognition with dual-branch LSTM-transformer networks. ACCESS, 13, 166289-166300. (www)

  @article{ , abstract = {Radar signal pulse train recognition plays a crucial role in modern electronic warfare, serving as the foundation for further analysis of radar signal sources and operating modes. Currently, most studies rely on PRI modulation features as the primary basis for recognition. However, with the continuous development of radar technology, the frequency parameters of radar pulses have become increasingly dynamic and complex; nevertheless, this aspect has not received adequate attention over the years. This paper proposes a signal pulse train recognition method based on a Dual-branch LSTM-Transformer architecture, introducing improvements in both input design and network structure. In terms of architecture, a dual-branch structure is constructed, with each branch employing an LSTM-Transformer network that effectively combines the strengths of both models. For the input, the two branches respectively receive the normalized PRI sequences, frequency sequences, and their corresponding difference sequences. Additionally, a gating mechanism is introduced during the feature fusion stage to adaptively allocate feature weights, enabling more accurate and efficient radar pulse train recognition. Simulation results show that this method achieves a recognition rate exceeding 90% even in high-noise environments, demonstrating strong robustness.}, archive = {J_ACCESS}, author = {Dongping Zhou and Yaobing Lu and Hang Ruan and Minghui Sha and Yifan Fu}, doi = {10.1109/ACCESS.2025.3612770}, journal = {IEEE Access}, pages = {166289-166300}, shortjournal = {IEEE Access}, title = {Radar signal pulse train recognition with dual-branch LSTM-transformer networks}, volume = {13}, year = {2025}, }
• (2025). A fault-tolerant distributed agent-based framework for Active/Reactive power optimization using sensitivity gradient methods in smart grids. ACCESS, 13, 166273-166288. (www)

  @article{ , abstract = {This paper presents a novel fault-tolerant distributed control framework for integrated active/reactive power optimization in microgrids. The architecture employs Distributed Agent Controllers (DACs) equipped with -sensitivity-based gradient methods, establishing a dual-module control structure with separate but coordinated Active Power Control (APC) and Reactive Power Control (RPC) components. The framework introduces four key advances: 1) an intelligent priority-based load-shedding mechanism within APC that maintains service quality, 2) an adaptive generation dispatch scheme coordinated with RPC, 3) decentralized optimization algorithms enabling local decision-making at each DAC, and 4) a resilient communication architecture with self-healing capabilities for continuous operation during contingencies. Implementing a multi-layer real-time approach for load management, the system dynamically evaluates consumption priorities, grid conditions, and generation constraints. Comparative simulations demonstrate superior voltage stability maintenance, reduced unnecessary load shedding, and enhanced power distribution efficiency versus conventional methods. The solution’s distinctive advantages include: 1) Grid resilience through reduced central communication dependence, 2) operational adaptability across diverse scenarios, and 3) scalable architecture for modern microgrid applications.}, archive = {J_ACCESS}, author = {Ali Karimi and Ali Reza Abbasi}, doi = {10.1109/ACCESS.2025.3611650}, journal = {IEEE Access}, pages = {166273-166288}, shortjournal = {IEEE Access}, title = {A fault-tolerant distributed agent-based framework for Active/Reactive power optimization using sensitivity gradient methods in smart grids}, volume = {13}, year = {2025}, }
• (2025). Digital revival of traditional batik patterns: Accurate extraction with mask attention AR-net and AIGC design application. ACCESS, 13, 166254-166272. (www)

  @article{ , abstract = {The growing need for digitally preserving the cultural heritage of ethnic minorities has positioned the accurate segmentation and innovative utilization of traditional patterns as a pivotal challenge in both technological and cultural preservation domains. To tackle this challenge, a novel method for segmenting low-resolution batik patterns (MAR-Net) is proposed, and its potential applications in cultural and creative product design are investigated. Specifically, an adaptive rectangular convolution (ARConv) mechanism is incorporated into the conventional U-Net architecture to effectively capture the intricate and diverse texture features of batik patterns, while the AR-Net network is developed to facilitate improved feature extraction and the integration of local and global information. To further enhance segmentation precision, a mask attention mechanism (MaskAttention) is utilized to strengthen the network’s focus on critical regions, thereby improving detail restoration and pattern segmentation through dynamic adjustment of feature weights. To improve the model’s robustness and discriminative capacity, a composite loss function is formulated, integrating pixel-level cross-entropy loss with instance-contrast-based attention loss to enhance the discriminative power of the feature space. In terms of application, accurately segmented batik patterns are converted into resources for cultural and creative product design, and a digital batik pattern resource library is created, with the potential for incorporating traditional patterns into the AIGC creation process being explored. Experimental results indicate that the proposed MAR-Net method not only markedly enhances precision in low-resolution pattern segmentation tasks but also exhibits substantial potential for application in cultural and creative product design, offering a novel solution for the digital preservation and innovative utilization of batik patterns, thereby significantly contributing to the study and preservation of traditional ethnic minority arts.}, archive = {J_ACCESS}, author = {Xiaochan Wang and Liyun Wang and Peifang Liu}, doi = {10.1109/ACCESS.2025.3607435}, journal = {IEEE Access}, pages = {166254-166272}, shortjournal = {IEEE Access}, title = {Digital revival of traditional batik patterns: Accurate extraction with mask attention AR-net and AIGC design application}, volume = {13}, year = {2025}, }
• (2025). Structured extraction and BPMN generation from chinese procedural texts via hybrid attention-based domain language models. ACCESS, 13, 166241-166253. (www)

  @article{ , abstract = {Understanding and modeling workflows is crucial for advancing digital transformation and intelligent process management in the electric power industry. While recent progress in large language models (LLMs) has significantly enhanced natural language processing capabilities, data in the power sector presents unique challenges—including complex formatting, limited structured annotations, and extensive use of domain-specific terminology—that hinder the direct application of general-purpose LLMs. Moreover, conventional attention mechanisms often struggle with long procedural texts due to high computational overhead, excessive resource consumption, and unstable global semantic representations, further limiting their effectiveness in power workflow modeling tasks. To address these challenges, this paper proposes a domain-adapted LLM framework tailored for the standardization of electric power workflows and introduces a specialized model, Lumina BPM, designed for this task. The model incorporates a hybrid sparse self-attention mechanism that retains attention on initial tokens and dynamically selects the most relevant historical tokens based on query context. This design reduces the need to compute full-sequence dense attention matrices, thereby lowering computational costs while improving the model’s ability to capture critical information from lengthy and complex process descriptions. Experimental results show that Lumina BPM accurately transforms standardized procedural texts into executable piperflow statements and generates corresponding BPMN 2.0 diagrams. The proposed approach significantly improves both the accuracy and interpretability of workflow modeling, offering a practical pathway toward intelligent process standardization in critical infrastructure domains.}, archive = {J_ACCESS}, author = {Shan Li and Zesan Liu and Xinyi Liu and Gang Chen and Yongjian Zhang and Haojie Ling and Xing Huang}, doi = {10.1109/ACCESS.2025.3611811}, journal = {IEEE Access}, pages = {166241-166253}, shortjournal = {IEEE Access}, title = {Structured extraction and BPMN generation from chinese procedural texts via hybrid attention-based domain language models}, volume = {13}, year = {2025}, }
• (2025). A CNTFET-based 10T static memory design immune to read and half-select disturbs for low-power wearable biomedical systems. ACCESS, 13, 166222-166240. (www)

  @article{ , abstract = {This paper introduces a novel 10T SRAM cell optimized for low-power, high-reliability biomedical wearable systems. The proposed design features a read-assist mechanism to suppress read disturbs, enhancing read static noise margin (RSNM), while a decoupled pull-down path for the ‘0’ storage node improves write SNM (WSNM). Leveraging Stanford CNTFET models, HSPICE simulations reveal a $1.12\times $ and $14.02\times $ enhancement in RSNM and WSNM, respectively, at 0.3V over the conventional 6T cell. Additionally, the design achieves substantial power savings— $3.02\times $ (read), $1.97\times $ (write), and $1.89\times $ (leakage)—compared to state-of-the-art 9T/10T CNTFET SRAMs. Monte Carlo analysis further demonstrates superior robustness, with $2.38\times $ and $6.62\times $ higher yield in RSNM and WSNM at 0.3V, validating its reliability for energy-constrained biomedical applications}, archive = {J_ACCESS}, author = {Shams Ul Haq and Erfan Abbasian and Imran Ahmed Khan and Owais Ahmad Shah and Arun Balodi and Manoj Tolani and Vakkalakula Bharath Sreenivasulu}, doi = {10.1109/ACCESS.2025.3611787}, journal = {IEEE Access}, pages = {166222-166240}, shortjournal = {IEEE Access}, title = {A CNTFET-based 10T static memory design immune to read and half-select disturbs for low-power wearable biomedical systems}, volume = {13}, year = {2025}, }
• (2025). A data-driven review of remote sensing-based data fusion in precision agriculture from foundational to transformer-based techniques. ACCESS, 13, 166188-166209. (www)

  @article{ , abstract = {This review explores recent advancements in data fusion techniques and Transformer-based remote sensing applications in precision agriculture. Using a systematic, data-driven approach, we analyse research trends from 1994 to 2024, identifying key developments in data fusion, remote sensing, and AI-driven agricultural monitoring. While traditional machine learning and deep learning approaches have demonstrated effectiveness in agricultural decision-making, challenges such as limited scalability, suboptimal feature extraction, and reliance on extensive labelled data persist. This study examines the comparative advantages of Transformer-based fusion methods, particularly their ability to model spatiotemporal dependencies and integrate heterogeneous datasets for applications in soil analysis, crop classification, yield prediction, and disease detection. A comparative analysis of multimodal data fusion approaches is conducted, evaluating data types, fusion techniques, and remote sensing platforms. We demonstrate how Transformers outperform conventional models by enhancing prediction accuracy, mitigating feature redundancy, and optimizing large-scale data integration. Furthermore, we propose a structured roadmap for implementing data fusion in agricultural remote sensing, outlining best practices for ground-truth data selection, platform integration, and fusion model design. By addressing key research gaps and providing a strategic framework, this review offers valuable insights for advancing precision agriculture through AI-driven data fusion techniques.}, archive = {J_ACCESS}, author = {Mahdi Saki and Rasool Keshavarz and Daniel Franklin and Mehran Abolhasan and Justin Lipman and Negin Shariati}, doi = {10.1109/ACCESS.2025.3610649}, journal = {IEEE Access}, pages = {166188-166209}, shortjournal = {IEEE Access}, title = {A data-driven review of remote sensing-based data fusion in precision agriculture from foundational to transformer-based techniques}, volume = {13}, year = {2025}, }
• (2025). Performance and testing of a helical winding for HTS electric machines for aircraft propulsion. ACCESS, 13, 166181-166187. (www)

  @article{ , abstract = {This paper presents a promising winding technique that enables higher power densities in high-temperature superconducting (HTS) electrical machines with a slotless stator, such as self-supporting windings. The concept of helical winding is first defined, followed by an explanation of the skew angle and how different winding configurations can be characterized as a function of this angle. A case study is then conducted to select the most suitable helical winding for a specific 2 MW HTS electric motor. Finally, the manufacturing and testing of the helical winding configuration for electric aircraft propulsion are evaluated, comparing the active part weight of the machine with its power density.}, archive = {J_ACCESS}, author = {Pablo Alvarez and Marco Satrústegui and Borja Lizarribar and Miguel Martinez-Iturralde}, doi = {10.1109/ACCESS.2025.3612567}, journal = {IEEE Access}, pages = {166181-166187}, shortjournal = {IEEE Access}, title = {Performance and testing of a helical winding for HTS electric machines for aircraft propulsion}, volume = {13}, year = {2025}, }
• (2025). Stepping toward zenoh protocol in automotive scenarios. ACCESS, 13, 166167-166180. (www)

  @article{ , abstract = {As automotive systems evolve toward zonal and software-defined architectures, identifying communication protocols that are efficient and easily adaptable in diverse scenarios has become increasingly important. In this context, case study based validation plays a crucial role in determining whether a middleware solution is suitable for real-world automotive integration. The rigidity and slow pace in the automotive related protocol evolution and acceptance cause large gaps comparing to other industries and few available solutions as technology enablers, although the stringent necessities are obvious in the context of growing interoperability requirements. The current work, as the first step in a broader research direction, investigates Zenoh, a lightweight and modern data centric protocol developed from the ground up using current networking paradigms. Zenoh is implemented and evaluated in a representative automotive scenario that involves distributed zone controllers and an in-vehicle server, scenario based research being essential for solution tailoring and proper validation. To provide meaningful context for the research, Data Distribution Service (DDS) is used as a benchmark, a protocol that has consistently demonstrated strong performance in previous automotive research. The experimental results highlight Zenoh protocol advantages in message delivery integrity and resource efficiency, especially in scenarios with high data transmission frequency. Unlike traditional middleware, Zenoh shows promising adaptability in distributed environments with limited computational resources. The findings confirm that Zenoh is a viable candidate for next generation in-vehicle communication systems.}, archive = {J_ACCESS}, author = {Andreea-Iasmina Chisăliţă and Adrian Korodi}, doi = {10.1109/ACCESS.2025.3612964}, journal = {IEEE Access}, pages = {166167-166180}, shortjournal = {IEEE Access}, title = {Stepping toward zenoh protocol in automotive scenarios}, volume = {13}, year = {2025}, }
• (2025). Hephaistos: A data flow analysis framework for identifying browser fingerprinting functions in JavaScript. ACCESS, 13, 166150-166166. (www)

  @article{ , abstract = {With the increasing sophistication of web technologies in recent years, browser fingerprinting techniques have emerged as a widely used mechanism for uniquely identifying users based on device and browser characteristics. As advertising companies and web services actively adopt these techniques to deliver personalized content, user tracking activities have grown more prevalent, often without proper consent, thereby intensifying privacy concerns. In response to this evolving landscape, recent studies have explored machine learning-based approaches to detect tracking behaviors by analyzing script characteristics. However, existing techniques face limitations in identifying previously unknown trackers due to their reliance on known filter lists and insufficient training datasets. To address these challenges, this study proposes a novel detection method based on data flow analysis using the Hermes JavaScript engine. By analyzing intermediate representation (IR) and applying a script deobfuscation process, the method effectively tracks data flow across stages such as variable assignment, propagation, function calls, and external leakage. To evaluate the proposed approach, experiments were conducted on large-scale web datasets, including high-traffic websites. The results demonstrate the method’s capability to uncover previously undetected tracking activities, offering greater transparency in how behavioral information is collected and shared. Ultimately, this study aims to contribute to the development of more reliable and comprehensive tracking detection frameworks, strengthening user privacy protection in the modern web ecosystem.}, archive = {J_ACCESS}, author = {Kyungmin Sim and Honyeong Heo and Haehyun Cho}, doi = {10.1109/ACCESS.2025.3606296}, journal = {IEEE Access}, pages = {166150-166166}, shortjournal = {IEEE Access}, title = {Hephaistos: A data flow analysis framework for identifying browser fingerprinting functions in JavaScript}, volume = {13}, year = {2025}, }
• (2025). Hybrid resonant DC–DC converters for DC transformers in DC distribution networks with limited reverse power flow range. ACCESS, 13, 166140-166149. (www)

  @article{ , abstract = {In DC distribution networks integrating distributed generation, DC transformers (DCT) exhibit asymmetric forward and reverse power flows, with reverse power flow range being limited. Based on this characteristic, this paper proposes a hybrid resonant (HR) DC-DC converter for DCTs. Distinguished from traditional dual active bridge (DAB) converters and Hybrid Non-resonant (HNR) DC-DC converters, the proposed HR converter features a low-voltage hybrid resonant bridge composed of a low-voltage-side active bridge (LAB) and a low-voltage-side diode bridge (LDB) in parallel, enabling efficient adaptation to such asymmetric power flows. It not only satisfies power transmission requirements but also simplifies system complexity and reduces costs. Key technical aspects, including converter topology, operation modes, suppression of circulating currents in reverse mode, and impact of leakage inductance in high-frequency transformers, are analyzed in detail. Comparative studies show that the HR converter reduces semiconductor costs by 33.43% and conversion losses by 16.22% compared to DAB and HNR converters. Experimental results confirm the feasibility and performance benefits of the proposed converter.}, archive = {J_ACCESS}, author = {Kangan Wang and Jianglin Xu and Ke Xu and Min Huang and Zhilei Yao and Weimin Wu}, doi = {10.1109/ACCESS.2025.3611385}, journal = {IEEE Access}, pages = {166140-166149}, shortjournal = {IEEE Access}, title = {Hybrid resonant DC–DC converters for DC transformers in DC distribution networks with limited reverse power flow range}, volume = {13}, year = {2025}, }
• (2025). Efficient neural architecture search for split computing via supernet fine-tuning. ACCESS, 13, 166127-166139. (www)

  @article{ , abstract = {Split Computing (SC) is a method that partitions a deep neural network into a head model running on resource-constrained edge devices and a tail model executing on powerful servers. The edge device computes intermediate feature maps, which are sent to the server, significantly lowering local computational demands and reducing communication compared to sending raw input data. This results in improved latency-accuracy trade-offs, enabling real-time inference of large models on constrained edge hardware. While previous research on SC primarily focused on improving training methods, model optimization through automated design approaches such as Neural Architecture Search (NAS) remains limited due to substantial supernet training time. This paper proposes a novel method that efficiently applies NAS to SC by fine-tuning a pre-trained supernet from a general Two-stage NAS framework into one tailored for SC. Using publicly available pre-trained supernets significantly reduces training time. Experiments on ImageNet-1K demonstrate that our proposed method achieves better latency-accuracy trade-offs compared to state-of-the-art NAS and manual SC designs, requiring only about 13 to 15% of original supernet training time. Additionally, for a practical case study on object detection using the COCO 2017 dataset, models optimized with our proposed method as backbones achieved a 69% latency reduction and 0.4% improved average precision over the EfficientDet baseline.}, archive = {J_ACCESS}, author = {Kohei Ogawa and Yuko Hara}, doi = {10.1109/ACCESS.2025.3610959}, journal = {IEEE Access}, pages = {166127-166139}, shortjournal = {IEEE Access}, title = {Efficient neural architecture search for split computing via supernet fine-tuning}, volume = {13}, year = {2025}, }
• (2025). Leveraging transfer learning and mobile-enabled convolutional neural networks for improved arabic handwritten character recognition. ACCESS, 13, 166104-166126. (www)

  @article{ , abstract = {The study explores the integration of transfer learning (TL) with mobile-enabled convolutional neural networks (MbNets) to enhance Arabic Handwritten Character Recognition (AHCR). Addressing challenges like extensive computational requirements and dataset scarcity, this research evaluates three TL strategies—full fine-tuning, partial fine-tuning, and training from scratch—using four lightweight MbNets: MobileNet, SqueezeNet, MnasNet, and ShuffleNet. Experiments were conducted on three benchmark datasets: AHCD, HIJJA, and IFHCDB. MobileNet emerged as the top-performing model, consistently achieving superior accuracy, robustness, and efficiency, with ShuffleNet excelling in generalization, particularly under full fine-tuning. The IFHCDB dataset yielded the highest results, with 99% accuracy using MnasNet under full fine-tuning, highlighting its suitability for robust character recognition. The AHCD dataset achieved competitive accuracy (97%) with ShuffleNet, while HIJJA posed significant challenges due to its variability, achieving a peak accuracy of 92% with ShuffleNet. Notably, full fine-tuning demonstrated the best overall performance, balancing accuracy and convergence speed, while partial fine-tuning underperformed across metrics. These findings underscore the potential of combining TL and MbNets for resource-efficient AHCR, paving the way for further optimizations and broader applications. Future work will explore architectural modifications, in-depth dataset features analysis, data augmentation, and advanced sensitivity analysis to enhance model robustness and generalizability.}, archive = {J_ACCESS}, author = {Mohsine El Khayati and Ayyad Maafiri and Yassine Himeur and Hamzah Ali Alkhazaleh and Shadi Atalla and Wathiq Mansoor}, doi = {10.1109/ACCESS.2025.3613265}, journal = {IEEE Access}, pages = {166104-166126}, shortjournal = {IEEE Access}, title = {Leveraging transfer learning and mobile-enabled convolutional neural networks for improved arabic handwritten character recognition}, volume = {13}, year = {2025}, }
• (2025). A framework for dynamic user modeling integrating data stream mining and process mining in educational contexts. ACCESS, 13, 166078-166103. (www)

  @article{ , abstract = {A user model (UM) is a representation of the characteristics, behaviors, and preferences of a user that a computer system utilizes to provide the user with personalized experiences. In educational settings, for instance, a system can use information about the Learning Styles (LSs) of a student to provide them with adequate study materials. Furthermore, with the growing use of Learning Management System (LMS) platforms, such as Moodle, continuous streams of user data highlight the need to compute student models dynamically to support adaptive decision-making in teaching and learning. This paper introduces a framework for Dynamic User Modeling (DUMING) to create evolving Dynamic User Models (DUMs) that capture and adapt to changes in student LSs over time, based on event log streams from an LMS. This framework leverages the Felder and Silverman Learning Styles Model (FSLSM) and integrates Data Stream Mining (DSM) and Process Mining (PM) techniques across three layers: collection, preprocessing, and mining. Collected event streams are processed within landmark windows and classified into High-Level Events (HLEs) based on LMS functionality. The STREAMKMeans algorithm clusters events, and the PM phase uses Alpha Miner (AM), Heuristic Miner (HM), and Inductive Miner (IM) algorithms to identify learning paths represented as Petri Nets (PNs), with IM showing the highest values in the quality metrics used. Implemented and evaluated in a course using Moodle at the Universidad Veracruzana in Mexico, the framework employs IF-THEN rules to relate HLE proportions to FSLSM-based LSs, visualizing changes through radar charts. The results demonstrate the effectiveness of the framework in generating adaptive DUMs, providing insights that enhance over time educational support and enable timely interventions.}, archive = {J_ACCESS}, author = {Maria Yesenia Zavaleta-Sanchez and Edgard Benitez-Guerrero and Guillermo Gilberto Molero-Castillo and Carmen Mezura-Godoy and Luis Gerardo Montane-Jimenez}, doi = {10.1109/ACCESS.2025.3611957}, journal = {IEEE Access}, pages = {166078-166103}, shortjournal = {IEEE Access}, title = {A framework for dynamic user modeling integrating data stream mining and process mining in educational contexts}, volume = {13}, year = {2025}, }
• (2025). A multi-factor authentication method for power grid terminals based on edge computing paradigm. ACCESS, 13, 166062-166077. (www)

  @article{ , abstract = {In modern power systems, the management of smart terminals is usually based on a hierarchical management architecture, in which each edge device is regarded as a node, and at the same time, there will be a superior node in a certain range to carry out the coordination in that range. However, with the rapid increase in the number of smart terminal devices, authentication generates a large amount of identity information, leading to storage and transmission problems, including large data transmission delays and strained computational capabilities at some nodes. Through the design of lightweight multi-factor authentication method and the terminal side migration of computing resources, this paper proposes a multi-factor authentication method for grid terminal based on the idea of edge computing. It is based on the target recognition technology of the YOLO algorithm and takes the target ‘category’ and the target’attitude’ in the detection box as ‘ownership-based’ and ‘knowledge-based’ authentication factors. Finally, the authentication security and the real-time performance of the mechanism are verified through experiments. Experimental results show that the method achieves 96.3% interception of predefined identity attacks (e.g., brute force, impersonation) and 5.1 times faster authentication compared to traditional multi-factor mechanisms with the same level of security. The method achieves an average authentication delay of 3.63 seconds, balancing real-time performance and security.}, archive = {J_ACCESS}, author = {Qingfeng Li and Hui Xiang and Wei Cui and Ke Liu and Donghe Li}, doi = {10.1109/ACCESS.2025.3609692}, journal = {IEEE Access}, pages = {166062-166077}, shortjournal = {IEEE Access}, title = {A multi-factor authentication method for power grid terminals based on edge computing paradigm}, volume = {13}, year = {2025}, }
• (2025). A survey of continuous variable quantum key distribution in quantum communication. ACCESS, 13, 166027-166061. (www)

  @article{ , abstract = {Continuous Variable Quantum Key Distribution (CV-QKD) is currently experiencing rapid and significant growth. This can be attributed to its capacity to increase resilience to noise and its ease of integration into current optical communication networks. In this article, we review and survey existing work on CV-QKD post-processing techniques, privacy amplification, and key reconciliation, with the aim of identifying research gaps and areas for further improvement. We also classify protocols based on transmission, preparation, and measurement, which allows for a more systematic and organized understanding of protocols, as well as identifying potential vulnerabilities or weaknesses in each component and allowing for targeted improvements to enhance the overall security and efficiency of protocols. Finally, we outline potential research directions and future challenges that require attention to advance the optimization of CV-QKD technology.}, archive = {J_ACCESS}, author = {Samuel Leyikun Birhanu and Milad Ghadimi and Yilun Hai and Patrick Seeling and Riccardo Bassoli and Frank H. P. Fitzek}, doi = {10.1109/ACCESS.2025.3610519}, journal = {IEEE Access}, pages = {166027-166061}, shortjournal = {IEEE Access}, title = {A survey of continuous variable quantum key distribution in quantum communication}, volume = {13}, year = {2025}, }
• (2025). A voronoi-based isotemporal task allocation system for autonomous tractor fleet. ACCESS, 13, 166012-166026. (www)

  @article{ , abstract = {This study proposes an isotemporal task allocation system for autonomous tractor vehicles to improve agricultural task efficiency. The proposed system integrates Voronoi-based workspace partitioning and isotemporal task allocation. The method performs isotemporal tasks by considering the performance and state (including distance, velocity, and fuel and battery capacity) of each tractor by adopting an optimal workspace partitioning method. Based on these factors, the system optimizes the sub-workspace allocation to minimize the task time deviation and ensure balanced workload distribution among heterogeneous robots. The proposed system is evaluated through numerical verification and field evaluation in an agricultural environment. The results of the field evaluation show that the task efficiency is significantly improved, such as a 25.88% reduction in total task time and a 92.89% reduction in task time deviation under optimized conditions. In addition, the similar results of the two evaluations indicate high consistency and performance maintenance of the proposed system performance. Through the proposed system, it can be easily applied to various tractor-based vehicle cooperative task models, and efficient task performance can be expected by reducing idle time and allowing tractors to perform the next task.}, archive = {J_ACCESS}, author = {Changjo Kim and Yuseung Jo and Hyoung Il Son}, doi = {10.1109/ACCESS.2025.3613016}, journal = {IEEE Access}, pages = {166012-166026}, shortjournal = {IEEE Access}, title = {A voronoi-based isotemporal task allocation system for autonomous tractor fleet}, volume = {13}, year = {2025}, }
• (2025). Optimization of a double-layer flared-shaped IPMSM for torque ripple reduction. ACCESS, 13, 165999-166011. (www)

  @article{ , abstract = {Interior permanent magnet synchronous motors (IPMSMs) offer high torque density. However, they are expensive owing to the rising prices of rare earth elements used in permanent magnets (PMs). This study proposes a double-layer flared-shaped IPMSM (DF-IPMSM) that achieves equivalent output torque and reduced torque ripple, while minimizing PM usage. It achieves flux concentration using C-type ferrite PMs arranged in a flared configuration and enhances reluctance torque by increasing the difference between $L_{d}$ and $L_{q}$ using a double-layer PM arrangement. Finite element analysis (FEA) under rated load and drive conditions demonstrates that the DF-IPMSM achieves output torque and efficiency equivalent to those of a single-layer flared-shaped IPMSM (SF-IPMSM) despite the reduced PM usage. To verify the structural advantages of the DF-IPMSM, it was compared with three alternatives, namely, SF-IPMSM, double-bar type IPMSM (DB-IPMSM), and double-spoke type IPMSM (DS-IPMSM), under rated load conditions. The FEA results indicated that the DF-IPMSM achieves the highest reluctance torque at equivalent output torque and efficiency. Although DF-IPMSM utilizes the reluctance torque more effectively than SF-IPMSM, it has the disadvantage of increasing torque ripple due to magnetic saturation and harmonic distortion. We performed an optimization rotor geometry design to improve this drawback. Sample points were generated using Latin Hypercube Sampling (LHS) and constructed a Kriging-based surrogate model. Subsequently, a genetic algorithm (GA) was employed to determine the optimal values for minimizing torque ripple. Finally, the FEA results of the optimized model were compared with the prototype measurements to verify the validity of the proposed DF-IPMSM. The proposed DF-IPMSM design offers a cost-effective alternative to conventional IPMSMs by significantly reducing reliance on rare earth PMs while maintaining high torque performance and efficiency.}, archive = {J_ACCESS}, author = {Jin-Hee Lee and Keun-Young Yoon and Byung-Il Kwon}, doi = {10.1109/ACCESS.2025.3609485}, journal = {IEEE Access}, pages = {165999-166011}, shortjournal = {IEEE Access}, title = {Optimization of a double-layer flared-shaped IPMSM for torque ripple reduction}, volume = {13}, year = {2025}, }
• (2025). Integrated task and motion planning for multi-robot manipulation in industry and service automation. ACCESS, 13, 165989-165998. (www)

  @article{ , abstract = {This paper introduces a fully integrated task-and-motion planning (TAMP) framework for robotic manipulation in dynamic and uncertain environments, particularly in multi-arm manipulator systems where reachability constraints change over time. Existing approaches, including probabilistic road maps and traditional TAMP frameworks, struggle with high-dimensionality, infeasible actions, and limited adaptability. To address these challenges, we propose a novel planning system that seamlessly integrates high-level symbolic task planning with low-level motion execution via a Dynamic Shared-Space Graph (D-SSG) and a Planning Domain Definition Language (PDDL)-based interface layer. We couple ROSPlan-based symbolic planning with a Dynamic Shared-Space Graph (D-SSG) to enable planning-level real-time adaptation: dynamic validation of actions, local plan repair, and execution updates without full re-planning. A PDDL-driven translation layer links predicates and effects to geometric feasibility checks and motion primitives. In ROS/Gazebo studies with multi-arm setups, the framework improves success rates and reduces planning time in semi-dynamic environments. These results verify the proposed framework as a promising solution for advancing multi-robot coordination in industrial and service automation applications.}, archive = {J_ACCESS}, author = {Ilknur Umay and William Melek and Barış Fidan}, doi = {10.1109/ACCESS.2025.3610811}, journal = {IEEE Access}, pages = {165989-165998}, shortjournal = {IEEE Access}, title = {Integrated task and motion planning for multi-robot manipulation in industry and service automation}, volume = {13}, year = {2025}, }
• (2025). A web-based platform for korean calligraphy learners using deep learning. ACCESS, 13, 165970-165988. (www)

  @article{ , abstract = {Calligraphy writing has a positive impact on multiple aspects of life. Learning to write in the calligraphic form helps in information acquisition, advancing language fluency, well-being development and unique personality disclosure. However, the trend of calligraphy writing is declining, and the younger generation tends to type with the keyboard instead of handwriting. This phenomenon has led to issues such as inappropriate handwriting. Therefore, a smart writing platform is required to provide writing practice to help the young generation with handwriting due to the inability to detect the correctness of handwriting. With the intense interest in deep learning, a writing platform is proposed which integrates deep learning technologies such as Convolutional Neural Network (CNN) and RetinaNet to resolve the issue of poor handwriting. The proposed platform is a beginner-friendly application that reduces the learning curve of Korean calligraphy writing, provides an interactive writing platform that allows users to practice, provides detection of handwriting correctness to train the users to get the correct writing and tracks the users’ learning progress. Deep learning model is employed for handwriting-detection module to accurately detect the correctness of a handwriting. Writing practice with varying levels of difficulty is delivered to the users. Users’ data and progress in writing practices are expected to be stored in the cloud service safely. The expected system outcome is a web-based writing platform that is able to display the correctness of handwriting input by the users with a mean accuracy, precision, recall and F1-score of 86.67%, 89.12%, 90.27% and 89.64%, respectively.}, archive = {J_ACCESS}, author = {Ch’ng Guo Yang and Mohd Azam Osman and Mohammad Ali Sarvghadi}, doi = {10.1109/ACCESS.2025.3612578}, journal = {IEEE Access}, pages = {165970-165988}, shortjournal = {IEEE Access}, title = {A web-based platform for korean calligraphy learners using deep learning}, volume = {13}, year = {2025}, }
• (2025). Triple-band notched and highly decoupled MIMO antenna using characteristic mode analysis. ACCESS, 13, 165956-165969. (www)

  @article{ , abstract = {This study presents a decoupled four-port multiple input multiple output (MIMO) antenna with three notched bands, which is systematically designed using characteristic mode analysis (CMA). A modified rectangular-shaped radiator with a ground plane integrated with a small open-ended slot is conceptualized as a quasi-monopole ultra-wideband (UWB) antenna with compact dimensions of 24 mm $\times 17$ mm. The ultra-wide range is achieved using exciting wideband modes where modal significance $\boldsymbol {\ge }~0.8$ . The three notched bands (NBs) are designed by introducing various slots in the radiator, which produce radiation-cancelling modes with opposite current directions at the corresponding slots. Then, a four-element MIMO antenna is created by arranging each antenna element (AE) in an orthogonal configuration. The multiply-shaped structure, combined with a square ring, provides orthogonal modes for high isolation between AEs. The MIMO antenna construction achieves $2-16.49$ GHz bandwidth, 6.72 dB peak gain, and more than 75% radiation efficiency with >16 dB isolation between the AEs. Diversity performances are observed to be within adequate limits.}, archive = {J_ACCESS}, author = {Shailesh and Garima Srivastava and Mohammad Al-Hamami and Sachin Kumar and Sunil M P and Om Prakash Kumar}, doi = {10.1109/ACCESS.2025.3611809}, journal = {IEEE Access}, pages = {165956-165969}, shortjournal = {IEEE Access}, title = {Triple-band notched and highly decoupled MIMO antenna using characteristic mode analysis}, volume = {13}, year = {2025}, }
• (2025). Long short-term memory-based approach for ensuring cyber-security in grid-forming inverters. ACCESS, 13, 165941-165955. (www)

  @article{ , abstract = {Grid-forming inverters (GFMIs) have significantly improved power system reliability and stability by integrating renewable energy sources as voltage sources. Unlike grid-following inverters, which require an external power source for synchronization, GFMIs operate independently by using internally generated reference signals to regulate voltage and frequency, enabling stable operation even in islanding mode. However, since they primarily depend on reference signals, manipulating these signals can lead to grid instability. While some studies have explored anomaly and cyber-attack detection in conventional power systems, the cybersecurity of GFMIs remains insufficiently addressed, leaving critical vulnerabilities that could disrupt their operation (e.g., power generation shutdown, frequency oscillation). As cyber-attack techniques advance, these gaps pose significant risks in the transition toward carbon neutrality. This work proposes a novel anomaly classification framework using long short-term memory (LSTM) to address the cybersecurity challenges of GFMIs, focusing on false data injection attacks, denial of service attacks, hijacking attacks, and short-circuit faults. The model classifies inverter operational states and detects potential anomalies based on measurement signals at the point of common coupling. Contrastive case studies conducted in MATLAB/Simulink (Registered trademark) demonstrate the effectiveness of the proposed method, achieving 90% accuracy in anomaly detection and classification, leading to precise deployment of countermeasures. Implementing this approach in GFMIs ensuresmaximum renewable energy integration with resilience and enhances grid security, supporting a safer transition to a carbon-neutral future.}, archive = {J_ACCESS}, author = {Pongsakorn Anantarativakorn and Thongchart Kerdphol}, doi = {10.1109/ACCESS.2025.3611803}, journal = {IEEE Access}, pages = {165941-165955}, shortjournal = {IEEE Access}, title = {Long short-term memory-based approach for ensuring cyber-security in grid-forming inverters}, volume = {13}, year = {2025}, }
• (2025). Development of a hybrid span-QA model with ontology integration for semantic enrichment of answers. ACCESS, 13, 165927-165940. (www)

  @article{ , abstract = {In the expanding frontier of natural language processing, the challenge of building accurate and semantically rich question answering (QA) systems for low-resource languages remains largely unresolved. This study presents a hybrid extractive QA model tailored for Kazakh, a morphologically complex and digitally underrepresented language, by integrating dense retrieval mechanisms with ontology-based semantic prefixing mechanism. Unlike conventional approaches that rely solely on retrieval and reading comprehension, our architecture injects dynamically constructed semantic definitions for domain-specific terms into the answer context, enabling more profound understanding and improved accuracy. Leveraging a novel dataset of Kazakh QA pairs generated through GPT-4 with expert validation, we introduce a dual-stream hybrid model (Hybrid B) that combines ontology-driven enrichment with fine-tuned transformer models for the retriever and reader components. The proposed system achieves a significant leap in performance, with 88% F1 and 76% exact match scores, substantially outperforming established baselines, including KazQAD. By anchoring the QA pipeline in a custom-built educational ontology relevant to the target domain, this research demonstrates how semantic structure can compensate for linguistic and data scarcity, paving the way for scalable QA systems in other low-resource languages such as Turkish. Beyond its empirical results, the study contributes a reproducible and modular framework for retrieval-augmented, ontology-aware QA, with implications for educational platforms, intelligent tutoring systems, and domain-specific information access. Through this work, we argue that the future of multilingual QA lies in hybrid architectures that combine symbolic structure with neural adaptability.}, archive = {J_ACCESS}, author = {Manas Yergesh and Banu Yergesh and Altynbek Sharipbay and Sadi Evren Seker and Kundyz Maxutova}, doi = {10.1109/ACCESS.2025.3608820}, journal = {IEEE Access}, pages = {165927-165940}, shortjournal = {IEEE Access}, title = {Development of a hybrid span-QA model with ontology integration for semantic enrichment of answers}, volume = {13}, year = {2025}, }
• (2025). SearchLight: Neural architecture search for lightweight spatio-temporal graph neural networks. ACCESS, 13, 165909-165926. (www)

  @article{ , abstract = {Spatio-Temporal Graph Neural Networks (STGNNs) are neural network models that integrate spatial information into time series processing, and have been successfully applied in various applications. Although these models have demonstrated strong prediction capabilities, most existing STGNN architectures require a significant memory size and long training times. Some lightweight versions of STGNNs have been proposed, but they still rely on expert-driven manual designs to improve performance, which require implicit domain-specific knowledge that varies across datasets. This design approach limits their adaptability to different application scenarios. To address this limitation, Neural Architecture Search (NAS) has been applied to automate the STGNN design process. However, existing NAS-based approaches prioritize prediction accuracy rather than resource efficiency. As a result, current approaches fail to provide compact model architectures or efficient training and limit their scalability. In this work, we introduce SearchLight, a novel NAS-based STGNN framework to automatically discover lightweight STGNN models while maintaining prediction performance. We set two cells for spatial and temporal operations into two distinct sets to capture spatial and temporal data features better for each cell type of the NAS method. We specialize in cell types for spatial and temporal information so that the model can better capture and combine the intrinsic features of spatial and temporal data. We employ a multi-objective search strategy that optimizes both model compactness and prediction accuracy to enable our method to discover lightweight and accurate STGNN models. Experimental results across several real-world datasets show that SearchLight reduces the model size by an average of $\times 103.0$ and training time by an average of $\times 74.0$ , while sacrificing a small amount of prediction performance, an average of 1.6%p, compared to manually designed and existing NAS-based STGNN models.}, archive = {J_ACCESS}, author = {Heeyong Yoon and Jinhong Jung and Kang-Wook Chon and Min-Soo Kim}, doi = {10.1109/ACCESS.2025.3611994}, journal = {IEEE Access}, pages = {165909-165926}, shortjournal = {IEEE Access}, title = {SearchLight: Neural architecture search for lightweight spatio-temporal graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Toward explainable AI in satellite imagery: A ResNet-50-based study on EuroSAT classification. ACCESS, 13, 165900-165908. (www)

  @article{ , abstract = {Deep learning has substantially advanced satellite image classification, yet the opaque nature of neural networks continues to impede interpretability in remote sensing applications. While convolutional neural networks, such as ResNet-50, consistently demonstrate high classification accuracy, understanding their internal decision-making processes, particularly across heterogeneous land use categories, remains a critical challenge. This paper examines the interpretability of deep learning-based classification models using two prominent Explainable AI techniques: Gradient-weighted Class Activation Mapping and Local Interpretable Model-agnostic Explanations. Leveraging a pre-trained ResNet-50 model on the EuroSAT dataset, we analyzed twenty representative satellite image samples, comprising both correctly and incorrectly classified instances across ten land use classes. Grad-CAM generated class-discriminative spatial attention maps that identified salient regions influencing the model’s predictions, while LIME provided super pixel-based visual explanations reflecting localized feature contributions. Comparative analysis of both techniques revealed consistent patterns of model attention and exposed spectral and spatial ambiguities that underlie misclassification. These insights not only enhance the interpretability of CNN-based remote sensing models but also inform the development of more transparent and reliable Earth observation systems.}, archive = {J_ACCESS}, author = {Karunasri Gundla and Sheshikala Martha and Asisa Kumar Panigrahy}, doi = {10.1109/ACCESS.2025.3612014}, journal = {IEEE Access}, pages = {165900-165908}, shortjournal = {IEEE Access}, title = {Toward explainable AI in satellite imagery: A ResNet-50-based study on EuroSAT classification}, volume = {13}, year = {2025}, }
• (2025). Critical demand density thresholds for public transit mode transition: A study on fixed-route and demand-responsive systems under financial subsidies. ACCESS, 13, 165887-165899. (www)

  @article{ , abstract = {As urban mobility trends shift toward greater customization and flexibility, selecting between Fixed-Route Transit (FRT) and Demand-Responsive connector (DRC) systems remains a key challenge, particularly in low-density areas. This study develops a dual-perspective service quality model that integrates passenger utility and operator cost to determine the Critical Demand Density (CDD)—the threshold at which switching between FRT and DRT becomes optimal. The model captures core components of travel time (walking, waiting, riding) and operational characteristics. Under non-subsidized conditions, FRT is more suitable in compact, high-demand regions, while DRT better serves decentralized, low-demand areas, with the CDD threshold at approximately 13 passengers/hour/km2. Incorporating fiscal subsidies, the model reformulates utility functions to include passenger-based marginal subsidies. Results show that subsidies significantly shift the mode transition threshold, increasing the CDD to 22 passengers/hour/km2 and improving DRC’s competitiveness. Sensitivity analyses reveal that factors such as service area geometry, walking time importance, and subsidy magnitude markedly affect CDD values. The model provides a practical, policy-relevant tool for optimizing transit mode decisions based on spatial demand and financial support. This work highlights the strategic role of subsidies in enhancing DRT viability and contributes to sustainable and inclusive urban mobility planning.}, archive = {J_ACCESS}, author = {Yucong Zu and Xianglong Sun and Li Li and Li Meng and Meng Zhang}, doi = {10.1109/ACCESS.2025.3608026}, journal = {IEEE Access}, pages = {165887-165899}, shortjournal = {IEEE Access}, title = {Critical demand density thresholds for public transit mode transition: A study on fixed-route and demand-responsive systems under financial subsidies}, volume = {13}, year = {2025}, }
• (2025). A self-imputing deep multitask sequence model for traffic disruption detection in extreme conditions. ACCESS, 13, 165873-165886. (www)

  @article{ , abstract = {Abnormal traffic patterns caused by extreme events can significantly disrupt traffic flow across large regions of urban road networks. Timely and reliable detection of such disruptions is crucial for effective traffic management. However, existing anomaly detection methods cannot simultaneously detect disruptions at both network and local levels, and they typically struggle with incomplete traffic data, which is a common issue during extreme events. To address these limitations, we propose a novel Self-Imputing Deep Multitask Sequence (SI-DMSeq) model for dual-level traffic monitoring and disruption detection. The model simultaneously performs two distinct tasks: 1) reconstructing sequences of traffic data, which enables anomaly detection through an autoencoder-based approach, and 2) making one-step-ahead predictions, which serves to impute missing data for the subsequent time step. To support robust training despite data gaps, we incorporate an iterative imputation strategy during the training phase. During inference, the reconstruction task is used to detect traffic disruptions at both network and local levels, while the prediction task provides reliable imputation of missing values under typical traffic conditions. We evaluate the SI-DMSeq model using historical traffic data from the Manhattan road network in New York City, focusing on disruptions caused by Hurricane Sandy in 2012 and the Christmas blizzard in 2010. The results demonstrate that the proposed method accurately identifies disruptions at both the network and local levels and efficiently imputes missing traffic data.}, archive = {J_ACCESS}, author = {Mina Nouri and Mohammad Ilbeigi}, doi = {10.1109/ACCESS.2025.3612407}, journal = {IEEE Access}, pages = {165873-165886}, shortjournal = {IEEE Access}, title = {A self-imputing deep multitask sequence model for traffic disruption detection in extreme conditions}, volume = {13}, year = {2025}, }
• (2025). Copper and aluminum scrap detection model based on improved YOLOv11n. ACCESS, 13, 165860-165872. (www)

  @article{ , abstract = {With the development of global industry, the recycling of waste copper and aluminium metals from end-of-life vehicles holds significant economic and environmental value. Machine vision technology plays a crucial role in scrap metal recycling, where accurate identification of waste metals has consistently been a key focus in this field. To address issues such as missed detections, false positives, and low detection accuracy caused by complex backgrounds in copper and aluminium scrap detection, along with the need for lightweight deployment, an improved YOLOv11n-based detection model for copper and aluminium scraps is proposed. The C3k2_Star_CAA module was designed to replace the original C3K2 module, improving feature extraction and target recognition in complex environments. The FPNSC module was introduced in place of the original SPPF module to capture finer features while keeping the model lightweight. A Feature Focus Diffusion Pyramid Network (FDPN) was adopted to substitute the original feature pyramid network, enhancing multi-scale feature fusion through FocusFeature modules and a diffusion mechanism. Additionally, a Dynamic Detection Head (DyHead) was incorporated to replace the original detection head, integrating multi-scale features to improve detection accuracy for objects of different sizes. Experimental results demonstrate that the improved model achieved a 9% increase in mAP@0.5, a 6.5% improvement in mAP@0.5:0.95, along with 3.9% higher Precision, 12.4% better Recall, and 8.2% greater F1-Score on the dataset, while also showing significant improvements in both parameter count and detection speed, making it more suitable for copper and aluminium scrap detection tasks. Furthermore, generalisation experiments using public datasets confirmed that the model exhibits strong generalisation capabilities.}, archive = {J_ACCESS}, author = {Bo Huang and Chongshan Zhou and Junqi Wu and Jun Liu and Yang Yan}, doi = {10.1109/ACCESS.2025.3612144}, journal = {IEEE Access}, pages = {165860-165872}, shortjournal = {IEEE Access}, title = {Copper and aluminum scrap detection model based on improved YOLOv11n}, volume = {13}, year = {2025}, }
• (2025). Minimizing energy consumption of satellite edge computing-capable LEO satellites. ACCESS, 13, 165844-165859. (www)

  @article{ , abstract = {Low Earth Orbit (LEO) satellite constellations have gained considerable attention from both academia and industry due to their potential to provide reliable global coverage. However, the continuous movement of satellites causes periodic transitions between the sunlit and shadowed regions of orbit, leading to fluctuations in available energy. Satellites on the sunlit side can harvest solar energy to recharge their batteries, while those in the shadow primarily rely on stored battery power. This paper presents a joint task and energy allocation framework that optimizes satellite energy consumption by considering three key factors: onboard processing of small tasks, relaying tasks to sunlit satellites for processing, and offloading larger tasks to ground stations when available. An Integer Linear Programming (ILP) approach is employed to determine the optimal energy distribution across these tasks, while a computationally efficient greedy algorithm is introduced as an alternative. Additionally, Dynamic Voltage and Frequency Scaling (DVFS) is incorporated to optimize the energy consumption during task processing by adjusting the processing frequency according to the available energy. The results indicate that ILP achieves optimal energy efficiency, while the greedy approach provides a near-optimal solution with just a 4.4% deviation.}, archive = {J_ACCESS}, author = {Amad Alnahdi and Abdulhalim Fayad and László Toka and Tibor Cinkler}, doi = {10.1109/ACCESS.2025.3612693}, journal = {IEEE Access}, pages = {165844-165859}, shortjournal = {IEEE Access}, title = {Minimizing energy consumption of satellite edge computing-capable LEO satellites}, volume = {13}, year = {2025}, }
• (2025). MBAF-MSCNet: Multi-branch fusion for multi-scale context in infrared wildlife detection. ACCESS, 13, 165830-165843. (www)

  @article{ , abstract = {This paper proposes MBAF-MSCNet, an attention-enhanced multi-branch fusion network for infrared wildlife detection in Unmanned Aerial Vehicle (UAV) imagery, addressing critical challenges, including small target ambiguity, complex background interference, and target occlusion. Based on YOLOv11-n, the model integrates three synergistic innovations: a Parallelized Block-Aware Attention Enhancement module (PPA_Enhancer) that extracts multi-scale features through local-global branches while optimizing channel dimensionality; a Dilated Residual Module (DWR) employing depth-separated dilated convolutions and a two-step residual mechanism (regional-semantic residualization) for efficient multi-scale context capture; and angle-sensitive SIoU loss incorporating geometric constraints (angle/distance/shape) to refine bounding box regression. Based on the BIRDSAI dataset (18,557 training images), MBAF-MSCNet achieved 93.6% mAP50 and 50.2% mAP50-95, surpassing YOLOv11-n by 2.1% in mAP50while maintaining real-time inference (256.4 FPS) with minimal parameters (3.279M). Cross-dataset validation of ISOD further demonstrated exceptional generalizability (79.6% AP50). The superiority of the model lies in its balanced architecture, which significantly enhances the detection accuracy under complex infrared conditions without compromising computational efficiency.}, archive = {J_ACCESS}, author = {Yuwen Wu and Xiaoqun Liu and Juan Hao and Zhaoye Xing and Hailin Xi and Weikai Sun and Qiyou Yao}, doi = {10.1109/ACCESS.2025.3605738}, journal = {IEEE Access}, pages = {165830-165843}, shortjournal = {IEEE Access}, title = {MBAF-MSCNet: Multi-branch fusion for multi-scale context in infrared wildlife detection}, volume = {13}, year = {2025}, }
• (2025). Testing bayes error rate estimators in difficult situations using monte carlo simulations. ACCESS, 13, 165810-165829. (www)

  @article{ , abstract = {The Bayes Error Rate (BER) is the fundamental limit on the achievable generalizable classification accuracy of any machine learning model due to inherent uncertainty within the data. BER estimators offer insight into the difficulty of any classification problem and set expectations for optimal classification performance. In order to be useful, the estimators must also be accurate with a limited number of samples on multivariate problems with unknown class distributions. To determine which estimators meet the minimum requirements for “usefulness”, an in-depth examination of their accuracy is conducted using Monte Carlo simulations with synthetic data in order to obtain their confidence bounds for binary classification. To examine the usability of the estimators for real-world applications, new non-linear multi-modal test scenarios are introduced. In each scenario, 2500 Monte Carlo simulations per scenario are run over a wide range of BER values. In a comparison of k-Nearest Neighbor (kNN), Generalized Henze-Penrose (GHP) divergence and Kernel Density Estimation (KDE) techniques, results show that kNN is overwhelmingly the more accurate non-parametric estimator. In order to reach the target of an under 5% range for the 95% confidence bounds, the minimum number of required samples per class is 1000. As more features are added, more samples are needed, so that 2500 samples per class are required at only 4 features. Other estimators do become more accurate than kNN as more features are added, but continuously fail to meet the target range.}, archive = {J_ACCESS}, author = {Lesley Wheat and Martin V. Mohrenschildt and Saeid Habibi}, doi = {10.1109/ACCESS.2025.3609630}, journal = {IEEE Access}, pages = {165810-165829}, shortjournal = {IEEE Access}, title = {Testing bayes error rate estimators in difficult situations using monte carlo simulations}, volume = {13}, year = {2025}, }
• (2025). Indoor localization using smartphone magnetic sensor data: A bi-LSTM neural network approach. ACCESS, 13, 165795-165809. (www)

  @article{ , abstract = {Nowadays, smartphone-based indoor localization has gained significant attention across various research fields, industrial sectors, and practical applications. However, accurate indoor localization using smartphones is a challenging issue due to the presence of noise and crowded localization environments. To overcome these problems, we introduce a new system for localization in indoor locations using real magnetic data collected from a smartphone. In this system, we first normalize the dataset. After that, the normalized magnetic data are filtered by using the Empirical Mode Decomposition (EMD) method. Finally, we present a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network for indoor localization using fingerprinting technique. We use three datasets in three different locations in the Faculty of Engineering. Results show that Bi-LSTM neural network has achieved the best localization performance among Feed Forward Neural (FFN) Network, Gated Recurrent Unit (GRU) neural network and LSTM neural network. Compared to the performance of the mentioned neural networks, the proposed system had the least mean and standard deviation (SD) root mean square error (RMSE) for indoor localization in the datasets. According to the results obtained with the proposed system, mean and SD RMSE for the network and embedded systems research laboratory location on the second floor of the Engineering Faculty is 0.1244 m and 0.1070 m, for the corridor location on the same floor is 0.0251 m and 0.0166 m, and for first floor location of this faculty is 0.0192 m and 0.0165 m, respectively. Therefore, the proposed method can be used practically in the real world.}, archive = {J_ACCESS}, author = {Mohabbat Zardkoohi and Yousef Seifi Kavian and Karim Ansari-Asl}, doi = {10.1109/ACCESS.2025.3612579}, journal = {IEEE Access}, pages = {165795-165809}, shortjournal = {IEEE Access}, title = {Indoor localization using smartphone magnetic sensor data: A bi-LSTM neural network approach}, volume = {13}, year = {2025}, }
• (2025). Dynamical stability analysis of grid-following and grid-forming inverters with blockchain integration for enhanced performance in modernized nested microgrids. ACCESS, 13, 165769-165794. (www)

  @article{ , abstract = {The integration of renewable energy sources and distributed energy resources (DERs) has driven the evolution of modernized nested microgrids, enhancing resilience and flexibility in power distribution systems. Grid-following (GFL) and grid-forming (GFM) inverters are central to these systems, with GFL units emulating current sources challenged by uncertain grid impedance, and GFM units emulating voltage sources required to adapt to dynamic load variations. Mode transitions introduce instability through multi-loop control interactions. This work presents a comprehensive dynamical stability analysis of GFL and GFM inverters in nested microgrids, supported by advanced control strategies addressing dynamic response limitations, sensor dependencies, filter fluctuations, and controller complexities. An eigenvalue-based framework identifies dominant oscillatory modes, while online adaptation mitigates disturbances to preserve closed-loop performance. Time-evolution modeling of observables enables enhanced real-time monitoring. A blockchain-enabled decentralized framework ensures secure, transparent, and automated stability actions. Hardware-in-the-loop (HIL) experiments on a modified IEEE 123-node test feeder demonstrate a total harmonic distortion (THD) of 1.75% under weak-grid conditions compared with 2.73%, 4.76%, 8.40%, and 2.2% for other approaches and 0.3% under grid-impedance variation and <0.3% under nonlinear loading. The proposed controller achieves 0.06% tracking error dynamics and 0.02% steady-state error, outperforming classical methods (0.32–0.87% and 0.17–0.38%, respectively), with a computational time of 29 ms. The blockchain layer, implemented on the Polygon network, achieved a measured throughput of 1,572 transactions/s, an average block time of 2.3 s, and transaction fees below 0.01 USD, enabling rapid, economical, and scalable peer-to-peer stability service execution.}, archive = {J_ACCESS}, author = {Rajdip Debnath and Abdullah Umar and Gauri Shanker Gupta and Deepak Kumar and Innocent Kamwa and Prashant K. Jamwal}, doi = {10.1109/ACCESS.2025.3610430}, journal = {IEEE Access}, pages = {165769-165794}, shortjournal = {IEEE Access}, title = {Dynamical stability analysis of grid-following and grid-forming inverters with blockchain integration for enhanced performance in modernized nested microgrids}, volume = {13}, year = {2025}, }
• (2025). Validation of an emotion recognition system for people with down syndrome. ACCESS, 13, 165751-165768. (www)

  @article{ , abstract = {This research presents the validation of a novel software application designed to recognize primary emotions in individuals with Down syndrome (DS), to support therapeutic interventions through artificial intelligence. The research addresses the need for innovative technological tools that assist therapists in real-time emotional assessment during therapy sessions. The study’s objective was to validate the application’s effectiveness, reliability, and therapeutic usefulness in recognizing five spontaneous emotions (happiness, anger, sadness, surprise, and neutrality) in individuals with DS attending a specialized care institution. The study followed ethical protocols approved by the Ethics Committees in Colombia and Ecuador to achieve this. Data was collected during therapy sessions, and three research hypotheses were formulated to evaluate the application’s performance. Structural Equation Modeling (SEM), using SmartPLS, was employed to analyze the relationships between observed emotional responses and the system’s feedback. The results demonstrated that the application accurately identified the targeted emotions in real time, and 94% of participating therapists positively assessed its usefulness in clinical settings. This validation confirms that the software can provide valuable input for therapists, enabling the design of tailored strategies that address the emotional needs of individuals with DS. The findings support the growing evidence supporting integrating machine learning and deep learning technologies into therapeutic tools for vulnerable populations.}, archive = {J_ACCESS}, author = {Nancy Ivett Paredes Terán and Gonzalo Fernando Olmedo Cifuentes and Bacca Bladimir and Caicedo Eduardo and Luna Andrea and Barrera Mateo}, doi = {10.1109/ACCESS.2025.3610846}, journal = {IEEE Access}, pages = {165751-165768}, shortjournal = {IEEE Access}, title = {Validation of an emotion recognition system for people with down syndrome}, volume = {13}, year = {2025}, }
• (2025). Dual-MAC: Hardware acceleration of DNN inferences with a customized lightweight brain floating-point format. ACCESS, 13, 165738-165750. (www)

  @article{ , abstract = {Due to data explosion of the deep neural networks (DNNs), recent advancements for DNNs have been focusing on reducing an amount of data while preserving the accuracy. In spite of the advent of low precision data formats such as brain floating point 16-bit (bfloat16), the amount of data processed in DNN models is still increasing due to an increasingly growing parameter size. To this end, data reduction with efficient processing is crucial for the real-world deployment of the DNN models. In this paper, we propose a lightweight customized brain floating-point (bfloat) format, which is derived from bfloat16 data format. Based on our observation from the pre-trained weights, we reduce the size of each bfloat16 weight element up to 9-bit by using the fixed exponents and truncating the mantissa bits. For efficient processing of our lightweight customized bfloat format, we also propose dual-MAC hardware architecture, which enables up to $2\times $ better throughput when processing our proposed format as compared to the case when processing the conventional bfloat16 format. We devise a novel processing element architecture for systolic arrays by utilizing the lookup table (LUT) for multiplication operations between the fixed exponent bits and a part of mantissa bits. Our PE architecture also enables a versatile processing for both the conventional bfloat16 and our customized bfloat formats. According to our evaluation results across the six widely used DNN models, our customized bfloat format results in 37.95% data reduction, on average, as compared to the conventional bfloat16 format with only small accuracy losses. In terms of performance, our dual-MAC hardware with the customized bfloat format leads to better performance by 56.2% as compared to using the conventional systolic array with the conventional bfloat16 format. Our dual-MAC hardware also leads to better performance-energy trade-offs due to better performance even with increased power consumption of our dual-MAC hardware.}, archive = {J_ACCESS}, author = {Seungah Shin and Joonho Kong and Young Seo Lee}, doi = {10.1109/ACCESS.2025.3612913}, journal = {IEEE Access}, pages = {165738-165750}, shortjournal = {IEEE Access}, title = {Dual-MAC: Hardware acceleration of DNN inferences with a customized lightweight brain floating-point format}, volume = {13}, year = {2025}, }
• (2025). A multi-layer hybrid deep learning model for toxic comment detection in assamese. ACCESS, 13, 165718-165737. (www)

  @article{ , abstract = {The present research proposes a highly precise technique for detecting toxic comments in Assamese, a low-resource language that is a member of the Eastern Indo-Aryan family. As a low-resource language, Assamese encounters specific challenges such dialectal differences, incorrect spellings, frequent code-mixing with English, and a lack of resources with annotations. A custom-built dataset of 100,000 Assamese social media comments was gathered and split equally between toxic and non-toxic categories in order to solve the lack of labelled data. The article presents a multi-layer hybrid deep learning model that integrates multi-scale Convolutional Neural Networks (CNN), bidirectional LSTM (BiLSTM), and bidirectional GRU (BiGRU). A thorough grid search utilising several optimisers and activation functions was used to create and optimise two hybrid architectures: CNN-BiLSTM-BiGRU and BiGRU-BiLSTM-CNN. Among them, the CNN-BiLSTM-BiGRU model outperformed present approaches for Assamese and other structurally related languages, reaching 94.25% accuracy and an F1- score of 0.93%. This work not only produces strong analytical findings, but it also makes a novel contribution in developing and evaluating models for Assamese toxic comment detection under real-world linguistic and resource constraints, providing a solid foundation and valuable insights for furthering NLP research in other under resourced languages.}, archive = {J_ACCESS}, author = {Mandira Neog and Nomi Baruah}, doi = {10.1109/ACCESS.2025.3612497}, journal = {IEEE Access}, pages = {165718-165737}, shortjournal = {IEEE Access}, title = {A multi-layer hybrid deep learning model for toxic comment detection in assamese}, volume = {13}, year = {2025}, }
• (2025). An intelligent framework for multi-agent system based on dynamic balancing of production 4.0. ACCESS, 13, 165695-165717. (www)

  @article{ , abstract = {Balancing assembly lines is one of the biggest problems facing industrial processes. Conventionally, this balancing consists of optimally allocating production tasks between different workstations or stations, taking into account precedence, capacity and cycle time constraints. However, in the face of today’s dynamic environment (fluctuations in demand, technical hazards, labor shortages, etc.), static balancing is no longer sufficient. Companies need dynamic rebalancing solutions, designed to adapt task distribution in real time as soon as a perturbation appears. Our study based on the use of multi-agent systems (MAS) offers a potentially attractive approach. MAS enable decision-making to be decentralized, with each agent representative of a workstation, a set of tasks, an operator or a piece of equipment. Each agent assesses its situation locally (workload, availability, etc.) and negotiates with the others to achieve a more flexible, proactive assignment of tasks, in line with the principles of Industry 4.0 (connectivity, distributed intelligence and adaptability). The approach proposed in our study is a dynamic framework based on the use of three agents, each of which executes and cooperates with the other agents in order to determine parameters with high variability, propose adjustments and implement them. The results found show that the use of the dynamic balancing environment facilitates monitoring and decision-making through a dynamic dashboard including a set of KPIs specific to balancing. The operational impact of implementing this environment shows a significant increase in terms of productivity and availability, implicitly the synthetic rate of return (‘OEE’) of the production line. As a result, the financial aspect of reconfiguring the line has been reduced thanks to the recommendation agent, which proposes future adjustments without impacting the production line. The line balancing no longer depends on a single a priori measurement, but is instead adapted on an ongoing process, giving greater strength in the face of the uncertainties of a VUCA world.}, archive = {J_ACCESS}, author = {Youness Hillali and Mourad Zegrari and Samir Chafik and Najlae Alfathi}, doi = {10.1109/ACCESS.2025.3611631}, journal = {IEEE Access}, pages = {165695-165717}, shortjournal = {IEEE Access}, title = {An intelligent framework for multi-agent system based on dynamic balancing of production 4.0}, volume = {13}, year = {2025}, }
• (2025). Flexible neoprene rubber-based patch antenna for 5.9 GHz V2X applications. ACCESS, 13, 165683-165694. (www)

  @article{ , abstract = {This paper presents a compact, high-performance, linearly polarized patch antenna personalized for 5.9 GHz Vehicle-to-Everything (V2X) communication. The antenna, designed on a flexible neoprene rubber substrate with a measured dielectric constant of 5.85 and a loss tangent of 0.016, achieves a 10% drop in patch size compared to standard designs. The dielectric properties of neoprene were characterized using the two-waveguide method, ensuring precise optimization of performance. Neoprene’s exceptional thermal stability, water resistance in harsh weather conditions, and durability at temperatures up to 200°C make it a highly reliable material for automotive applications. The proposed antenna’s compact size, mechanical flexibility, and cost-effectiveness allow continuous and easy integration on vehicle rooftops, enabling advanced communication capabilities. This work contributes to scalable, sustainable innovations in vehicular communication systems, laying the foundations for smarter and safer transportation networks.}, archive = {J_ACCESS}, author = {Parth Bhaiya and Alaqmar Karampurwala and Abhishek Tiwari and Rahul Manohar O and Paresh R. Sagar and Arpan Shah}, doi = {10.1109/ACCESS.2025.3611814}, journal = {IEEE Access}, pages = {165683-165694}, shortjournal = {IEEE Access}, title = {Flexible neoprene rubber-based patch antenna for 5.9 GHz V2X applications}, volume = {13}, year = {2025}, }
• (2025). Target parameter estimation for OTFS-integrated radar-communication systems. ACCESS, 13, 165671-165682. (www)

  @article{ , abstract = {In high-mobility scenarios envisioned for 6G communications, orthogonal time frequency space (OTFS) has emerged as a promising alternative to conventional orthogonal frequency division multiplexing (OFDM) schemes. To address the parameter estimation problem in OTFS-integrated radar and communication systems, we propose a two-stage parameter estimation framework based on sparse reconstruction and the Alternating Direction Method of Multipliers-Newton (ADMM-Newton), which exploits the sparsity of OTFS signals in the delay-Doppler (DD) domain to enable precise parameter estimation in high-mobility scenarios. In the first step, we construct an echo signal model for an OTFS-integrated radar-communication system and estimate the delay and Doppler integer grid values of the detected targets using a sparse reconstruction-based ADMM algorithm. In the second step, the estimates of the integer grid parameters are used as constraints for a more specific search. Fractional-precision estimations of the range and velocity parameters are obtained by iteratively solving for the Newton direction via the Newton algorithm. Finally, numerical simulations are executed to verify the proposed algorithm’s performance in parameter estimation in integrated sensing and communication (ISAC) systems. The results demonstrate that the proposed algorithm achieves millimeter-level accuracy in velocity estimation under single-target simulation scenarios. Particularly in situations involving multiple sensing targets, the proposed method can estimate target range and velocity with centimeter-level accuracy. The proposed algorithm effectively eliminates notable differences between estimated and real values due to three targets interfering with one another, demonstrating strong estimation accuracy. Furthermore, in target conditions that are closely spaced, the proposed algorithm maintains robust estimation accuracy.}, archive = {J_ACCESS}, author = {Jiayin Liang and Jinfang Wen and Zhiling Tang}, doi = {10.1109/ACCESS.2025.3612692}, journal = {IEEE Access}, pages = {165671-165682}, shortjournal = {IEEE Access}, title = {Target parameter estimation for OTFS-integrated radar-communication systems}, volume = {13}, year = {2025}, }
• (2025). Advanced receiver for 3GPP 5G-A intra-cell inter-user interference mitigation. ACCESS, 13, 165658-165670. (www)

  @article{ , abstract = {In the fifth-generation (5G) New Radio (NR) multi-user multiple-input multiple-output (MU-MIMO) system, addressing inter-user interference is a significant challenge for efficient network deployment. To this end, the 3rd Generation Partnership Project (3GPP) RAN4 working group is diligently working on the investigation and advancement of sophisticated receiver technologies with the primary aim of enhancing interference management and mitigation strategies. Based on the receiver architecture under the 3GPP standard specification, this study systematically investigated the network assistance signalling required for advanced receivers in interference scenarios. It focuses on analyzing the impact mechanism of scheduling parameters, such as demodulation reference signal (DMRS) sequence information and modulation order, on the receiver performance and evaluates the performance of the modulation order blind detection technique in depth. The simulation experimental results show that the reduced complexity maximum likelihood (R-ML) receiver exhibits significant advantages in terms of interference suppression performance and outperforms the traditional minimum mean square error interference suppression combining (MMSE-IRC) and enhanced minimum mean square error interference suppression combining (E-MMSE-IRC) receivers. With the advancement of the 3GPP Release 18 standard, R-ML receiver technology is expected to be widely used in actual network deployment, which will provide key technical support for system performance improvement in MU-MIMO scenarios.}, archive = {J_ACCESS}, author = {Kun Yao and Fei Xue and Chenchen Zhang and Kaibo Tian}, doi = {10.1109/ACCESS.2025.3612418}, journal = {IEEE Access}, pages = {165658-165670}, shortjournal = {IEEE Access}, title = {Advanced receiver for 3GPP 5G-A intra-cell inter-user interference mitigation}, volume = {13}, year = {2025}, }
• (2025). Trademark infringement detection in E-commerce with multimodality contrastive learning. ACCESS, 13, 165645-165657. (www)

  @article{ , abstract = {Trademark infringement detection in e-commerce presents unique challenges due to the subtle, often multimodal nature of brand imitation. Prior approaches have predominantly focused on textual similarity or structural metadata, overlooking the visual and semantic interplay critical to modern infringement tactics. In this paper, we propose a novel multimodal framework that integrates textual and visual signals for robust trademark infringement detection. Leveraging large language models (LLMs) and vision-language models (VLMs), we introduce targeted data augmentation techniques to simulate adversarial modifications in trademark names and logos. These augmented pairs are encoded through modality-specific encoders and aligned using cross-modal and infringement-specific contrastive losses, enabling the model to learn discriminative and generalizable embeddings. Experiments on the TMID dataset demonstrate that our framework significantly outperforms text-only, visual-only, and graph-based baselines. We further evaluate model performance across infringement types and under noisy conditions, showcasing strong resilience and interpretability. Our findings highlight the promise of multimodal learning for scalable, real-world trademark protection in dynamic digital marketplaces.}, archive = {J_ACCESS}, author = {Xiaowei Ma and Jingfei Wu and Weijia Jiao}, doi = {10.1109/ACCESS.2025.3610856}, journal = {IEEE Access}, pages = {165645-165657}, shortjournal = {IEEE Access}, title = {Trademark infringement detection in E-commerce with multimodality contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Development of an expert system for precision irrigation: Knowledge modeling approach. ACCESS, 13, 165623-165644. (www)

  @article{ , abstract = {Precision irrigation necessitates decision support systems that are both efficient and comprehensible to farmers and agronomists. Numerous current solutions depend on Internet of Things (IoT) frameworks or black-box artificial intelligence (AI) models, which frequently encounter challenges related to interoperability, transparency, and reproducibility. To overcome these constraints, this research developed an ontology-driven expert system that formalizes knowledge of crops, soil, and climate into a modular framework capable of generating explicable irrigation recommendations. The system was developed using the Web Ontology Language (OWL) and augmented with Semantic Web Rule Language (SWRL) rules, employing a hybrid development methodology that integrates the structured precision of the V-cycle with the data-driven focus of the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the semantic precision of the NeOn (Networked Ontologies) methodology. Competency questions guided the development of numerous agricultural concepts pertaining to irrigation. Scenario-based assessments validated that the ontology accurately deduced crop coefficient (Kc), integrated AI-predicted ET0 with sensor-derived assertions, and produced consistent irrigation or non-irrigation recommendations across diverse situations. All reasoning processes performed in under one second, showcasing computational efficiency. Thus, the research provides a replicable and adaptable foundation for ontology-based irrigation decision support, facilitating future implementation using real-time sensor data and field validation.}, archive = {J_ACCESS}, author = {Nisrine Lachgar and Moad Essabbar and Hajar Saikouk and Ahmed El Hilali Alaoui}, doi = {10.1109/ACCESS.2025.3612263}, journal = {IEEE Access}, pages = {165623-165644}, shortjournal = {IEEE Access}, title = {Development of an expert system for precision irrigation: Knowledge modeling approach}, volume = {13}, year = {2025}, }
• (2025). AI versus IoT security: Fingerprinting and defenses against TLS handshake-based IoT device classification. ACCESS, 13, 165607-165622. (www)

  @article{ , abstract = {The number of Internet of Things (IoT) devices in smart homes is steadily increasing, enhancing convenience but also raising security concerns. While secure communication protocols like Transport Layer Security (TLS) are commonly used, attackers can still exploit metadata to profile users and identify vulnerabilities. This research focuses on analyzing the TLS handshake, where encryption parameters are established. Although newer versions of TLS aim to encrypt the Server Name Indication (SNI), we observed that some devices in real-world environments still transmit SNI in plaintext, potentially exposing device identities. Given this practical variability in SNI transmission among diverse IoT devices, we conducted two parallel studies, one including the SNI and one without it, while avoiding Media Access Control (MAC) and Internet Protocol (IP) addresses due to their inherent variability and privacy implications. We used TLS handshake parameters as input for machine learning algorithms to fingerprint IoT devices, classify them by type, and identify manufacturers. Six machine learning models were evaluated: Support Vector Machine (SVM), a multi-layer perceptron (MLP), Random Forest (RF), Convolutional Neural Network (CNN), XGBoost, and CNN+RF. The results showed that CNN+RF achieved the highest accuracy, reaching 99% for device type classification. However, our proposed countermeasure, which enhances TLS handshake privacy by obfuscating specific parameters, significantly reduced fingerprinting accuracy to a maximum of 80% when SNI was excluded. These findings highlight the potential risks of TLS metadata exposure and demonstrate the effectiveness of privacy-enhancing countermeasures in mitigating IoT device fingerprinting attacks.}, archive = {J_ACCESS}, author = {Juan Diego Llano-Miraval and Celeste Campo and Carlos Garcia-Rubio and Marta Moure-Garrido}, doi = {10.1109/ACCESS.2025.3611160}, journal = {IEEE Access}, pages = {165607-165622}, shortjournal = {IEEE Access}, title = {AI versus IoT security: Fingerprinting and defenses against TLS handshake-based IoT device classification}, volume = {13}, year = {2025}, }
• (2025). Impact of LED current waveforms on FSK-based VLC performance and design of a flyback driver for square-wave modulation. ACCESS, 13, 165598-165606. (www)

  @article{ , abstract = {This paper analyzes the performance of Visible Light Communication (VLC) systems when different LED current waveforms are applied, aiming to guide the design of switched-mode converters using Frequency Shift Keying (FSK) modulation schemes. Theoretical calculations and experimental tests were carried out using sinusoidal, triangular, and square current waveforms. Results indicate that the square waveform offers superior noise immunity, enabling transmission over distances up to three times longer than the other types, while maintaining a satisfactory Bit Error Rate (BER). In addition, this work presents a method for designing an LED driver capable of producing square-shaped current signals, based on a flyback converter operating in Continuous Conduction Mode (CCM). This topology enables single-switch control of both the average current and the data modulation. A laboratory prototype rated at 35-W and powered by a 300-V dc supply with a 13.7-V ripple was built and tested, demonstrating reliable data transmission over distances of up to 3 meters, achieving satisfactory BER and reaching data rates of 234 kb/s.}, archive = {J_ACCESS}, author = {Lucas H. G. Resende and Ruan M. Ferraz and Pedro S. Almeida and Guilherme M. Soares and Henrique A. C. Braga}, doi = {10.1109/ACCESS.2025.3612328}, journal = {IEEE Access}, pages = {165598-165606}, shortjournal = {IEEE Access}, title = {Impact of LED current waveforms on FSK-based VLC performance and design of a flyback driver for square-wave modulation}, volume = {13}, year = {2025}, }
• (2025). New generalized multilevel inverter based on developed H-bridge with reduced circuit component. ACCESS, 13, 165587-165597. (www)

  @article{ , abstract = {Multilevel inverters have attracted much attention in recent years due to advantages such as reduced circuit component and simplicity of structure. Existing multilevel inverter topologies are usually associated with challenges such as reducing circuit component, etc. In this paper, two structures are presented, the first topology (topology I) is symmetric, and the second topology (topology II) is an improved topology I that can be implemented both symmetrically and asymmetrically. The basic topology I has a significant reduction in the number of switches compared to similar topologies. In addition, the basic topology I has low conduction losses compared to similar structures at the same voltage level due to the low number of ON-state switches. Topology I uses an H-bridge that has 4 high-stress switches, while topology II uses a developed H-bridge that only requires 2 high-stress switches. Topology II produces high voltage levels with high output power quality in the asymmetric mode. For topology I, phase-shift pulse width modulation (PWM) control is used, and for topology II, nearest level modulation (NLM) control is used. The efficiency of topology I with the values of losses and switch temperatures is also presented for each switch separately. A comparison is made with similar structures in terms of the number of switches and the number of drivers. The comparison results show that at equal levels, the proposed structures require fewer components. To verify the performance of the proposed design, the proposed topologies I and II are investigated from a hardware perspective. A laboratory implementation is used to evaluate the performance of the proposed configuration under steady-state and dynamic conditions.}, archive = {J_ACCESS}, author = {Hamid Radmanesh and Ali Seifi}, doi = {10.1109/ACCESS.2025.3610505}, journal = {IEEE Access}, pages = {165587-165597}, shortjournal = {IEEE Access}, title = {New generalized multilevel inverter based on developed H-bridge with reduced circuit component}, volume = {13}, year = {2025}, }
• (2025). Global saturation-value translation approach for haze removal in urban aerial imagery. ACCESS, 13, 165575-165586. (www)

  @article{ , abstract = {This paper proposes Global Saturation-Value Translation (GSVT), a novel single-image dehazing technique that extends the principles of the Regional Saturation-Value Translation (RSVT) algorithm to achieve comprehensive haze removal across an entire image. While RSVT effectively mitigates haze in specific bright regions by leveraging localized saturation and value shifts in the HSV color space, its regional focus limits its applicability to heterogeneous scenes with varying haze densities. The proposed GSVT framework addresses this limitation by proposing a globally adaptive translation mechanism that dynamically adjusts saturation and value components across all image regions, guided by a spatially-aware translation map. By preserving hue consistency and employing a global coordinate shift in the saturation-value plane, GSVT ensures uniform dehazing without the need for soft segmentation, reducing computational complexity while enhancing color fidelity and detail recovery. Experimental results on synthetic and real-world data demonstrate that GSVT performs competitively when compared with state-of-the-art methods in terms of quantitative metrics and visual quality.}, archive = {J_ACCESS}, author = {Truong-Dong do and Le-Anh Tran and Junyong Lee and Sung Kyung Hong}, doi = {10.1109/ACCESS.2025.3604124}, journal = {IEEE Access}, pages = {165575-165586}, shortjournal = {IEEE Access}, title = {Global saturation-value translation approach for haze removal in urban aerial imagery}, volume = {13}, year = {2025}, }
• (2025). Unpacking conspiracy mentality through cross-country predictors. ACCESS, 13, 165548-165574. (www)

  @article{ , abstract = {Conspiracy theories are beliefs about covert, influential organizations pursuing obscure, frequently illegal objectives. They are not merely cognitive distortions; they can have major political and societal repercussions, including eroding democratic standards, encouraging skepticism about science, and igniting radical ideologies. It is crucial to do cross-country research because the effects of conspiracy theories spread differ depending on the national and cultural setting. Through the identification of its primary individual and sociocultural predictors across nations, the current study provides a detailed understanding of conspiratorial thinking. In contrast to context-specific conspiracy beliefs, we analyze conspiracy mentality, a generic mindset, as a multifaceted disposition influenced by both individual characteristics and larger sociopolitical circumstances, utilizing data from the publicly accessible Comparative Conspiracy Research Survey (CCRS) released in 2023. Through descriptive and inferential analyses, we address a set of four research questions, exploring the socio-demographic, psychological, and political dimensions of conspiracy mentality. Our findings confirm and extend previous insights: while some predictors, such as populism, have a broad influence, others are shaped by specific political and cultural contexts. Importantly, our results are based on an analysis across eight countries, offering a broader perspective than much of the existing literature, which often focuses on single-country cases. Moreover, the present results are not limited to a specific conspiracy theory and rely on data from the general population, rather than from social media users, allowing for more generalizable conclusions about the drivers of conspiracy thinking worldwide.}, archive = {J_ACCESS}, author = {Caterina Senette and Victoria Popa}, doi = {10.1109/ACCESS.2025.3611158}, journal = {IEEE Access}, pages = {165548-165574}, shortjournal = {IEEE Access}, title = {Unpacking conspiracy mentality through cross-country predictors}, volume = {13}, year = {2025}, }
• (2025). SENADA: A stacked ensemble learning for native advertisement detection in electronic news. ACCESS, 13, 165527-165547. (www)

  @article{ , abstract = {Native advertisements are increasingly common as hybrid commercial content embedded within digital news platforms. Although these ads can enhance user engagement, they often elicit negative reactions due to their concealed commercial intent. A key challenge in detecting native advertisements lies in their lack of explicit labeling, which can mislead readers and compromise the perceived credibility of news content. Prior studies have struggled to accurately identify native ads because of their inability to capture the implicit characteristics embedded in editorial-like text, such as positive sentiment, persuasive language, one-sided perspectives, and references to specific products or companies. To address this issue, this study proposes a deep learning architecture called SENADA (Stacked Ensemble for Native Advertising Analysis). The model integrates BERT-based contextual representations, ensemble learning strategies, and a BiLSTM with an attention mechanism. This design enables the model to effectively identify subtle patterns and contextual cues associated with native advertisements. In addition, we introduce a newly constructed Indonesian-language dataset of news articles, annotated based on the four implicit characteristics of native ads. The dataset was collected from six major e-news portals and manually labeled by trained annotators. Experimental evaluations, based on confusion matrix analysis, demonstrate that SENADA achieves an accuracy of 0.93, along with high values for precision, recall, and F1-score. The model is compared against several well-established baselines, including CNN, BiLSTM, BiGRU, and transformer-based architectures, which were selected based on their widespread use and proven effectiveness in prior text classification studies. The proposed model consistently outperforms these baselines, particularly in minimizing misclassification of borderline content, defined as news articles that exhibit some but not all native ad characteristics, such as persuasive tone without explicit product mention or positive sentiment without clear promotional framing. These borderline cases were validated through manual annotation and cross-checking among annotators to ensure reliability. These results confirm the model’s effectiveness in detecting native advertisements that exhibit complex and implicit textual patterns. While the model shows promising results, future work is needed to reduce architectural complexity and training time. Further research could explore explainability methods and cross-lingual adaptation to extend the model’s applicability across languages and media contexts.}, archive = {J_ACCESS}, author = {Brian Rizqi Paradisiaca Darnoto and Daniel Oranova Siahaan and Diana Purwitasari}, doi = {10.1109/ACCESS.2025.3609713}, journal = {IEEE Access}, pages = {165527-165547}, shortjournal = {IEEE Access}, title = {SENADA: A stacked ensemble learning for native advertisement detection in electronic news}, volume = {13}, year = {2025}, }
• (2025). Improving efficiency of container inbound scheduling using dual-spreader cranes in container yards. ACCESS, 13, 165508-165526. (www)

  @article{ , abstract = {Container terminals are vital to global trade, and improving operational efficiency is key to competitiveness. Currently, container terminals primarily rely on single-spreader cranes for operations. Some ports have begun introducing dual-spreader cranes to enhance the efficiency of lifting operations. However, the operation of dual-spreader cranes spans multiple bays, significantly increasing the complexity of time-space operations. Research in this area remains scarce. Thus, this paper proposes an optimization method for container inbound scheduling based on dual-spreader cranes. First, a multi-bay time-space network flow model is constructed. Next, a bi-objective function is designed to synergistically optimize the number of obstructing containers and double-lift operations. Existing software has limitations in solving the dual-spreader crane scheduling problem, so this paper develops a novel two-level branch-and-bound algorithm to address it. Experiments show that the proposed algorithm achieves better solution quality for both optimization objectives in the double-spreader crane scenario compared to the greedy heuristic algorithm. Additionally, this paper verifies the effectiveness of various optimization strategies in the proposed algorithm through further experiments. Finally, it was validated that compared to single-spreader cranes, the average number of lifting operations for dual-spreader cranes is reduced by 35.67%. The proposed method provides new insights for improving the operational efficiency of container terminals and holds significant value for future research and practical applications.}, archive = {J_ACCESS}, author = {Changwang Zhu}, doi = {10.1109/ACCESS.2025.3610722}, journal = {IEEE Access}, pages = {165508-165526}, shortjournal = {IEEE Access}, title = {Improving efficiency of container inbound scheduling using dual-spreader cranes in container yards}, volume = {13}, year = {2025}, }
• (2025). A parallel GRU-transformer neural network approach for feedforward compensation in precision motion platform control. ACCESS, 13, 165496-165507. (www)

  @article{ , abstract = {This paper proposes a feedforward compensation strategy based on Parallel GRU-Transformer neural network to address the issues of large tracking errors and insufficient stability of multi degree of freedom precision motion platforms in complex dynamic environments. By accurately predicting the output of the feedback controller at the next moment and using the predicted results as feedforward compensation inputs, the overall performance of the control system is significantly improved. First, a Parallel GRU-Transformer neural network was constructed to compensate for the limitations of LSTM and GRU neural networks in parallel processing due to their recursive structure, enabling the network to accurately predict the control output at the next moment. Then, a control strategy integrating Parallel GRU-Transformer feedforward compensation was designed, and the predicted results were introduced into the feedforward compensation control strategy to compensate for the output of the feedback controller. The effectiveness of the proposed method was verified by comparing its prediction accuracy with the State-of-the-Art neural network model and the original control strategy. The experimental results show that the neural network based on Parallel GRU-Transformer has excellent prediction ability. Introducing its prediction results into the feedforward compensation control strategy significantly reduces tracking error and its Moving Average (MA) and Moving Standard Deviation (MSD), improving tracking performance. Meanwhile, under different reference trajectories and controller conditions, the control strategy exhibits a certain level of robustness, demonstrating notable potential for industrial applications.}, archive = {J_ACCESS}, author = {Yi-Min Wang and Jun-Jie Wang and Ji-Teng Yang and Bin Liu and Shi Bu and Xi Chen and Jian-Ping Xing}, doi = {10.1109/ACCESS.2025.3607870}, journal = {IEEE Access}, pages = {165496-165507}, shortjournal = {IEEE Access}, title = {A parallel GRU-transformer neural network approach for feedforward compensation in precision motion platform control}, volume = {13}, year = {2025}, }
• (2025). Fast-parallel singular value thresholding for low-rank analysis with small columns matrices. ACCESS, 13, 165481-165495. (www)

  @article{ , abstract = {This paper proposes a fast-parallel method for singular value thresholding, aimed at low-rank analysis of many small matrices. In low-rank analysis, the problem of regularizing the nuclear norm, defined as the sum of singular values, is typically solved by iteratively applying singular value thresholding. However, this process often requires a long run-time due to the computational cost of singular value decomposition. To address this issue, we provide a method that is computationally inexpensive and can be executed in parallel. The core of our approach lies in the discovery of geometric properties of the nuclear norm, leading to a computational process that is fundamentally different from conventional methods. Our method is fast because it does not require singular value decomposition, which has been a bottleneck in terms of computational complexity and parallelism. We design the computation method for two-column matrices as the main proposal, and also demonstrate an optional method for approximately solving the three-column matrices problem. Through experiments, the proposed method calculated singular value thresholding up to 65.81 times faster with higher calculation accuracy than conventional methods. We also applied our method to real data for noise removal in 3D models and confirmed the improvement in computational speed.}, archive = {J_ACCESS}, author = {Takayuki Sasaki and Ryuichi Tanida and Masaki Kitahara and Yusuke Hiwasaki and Hideaki Kimata and Yukihiro Bandoh}, doi = {10.1109/ACCESS.2025.3611952}, journal = {IEEE Access}, pages = {165481-165495}, shortjournal = {IEEE Access}, title = {Fast-parallel singular value thresholding for low-rank analysis with small columns matrices}, volume = {13}, year = {2025}, }
• (2025). Hierarchical energy planning and control of DGs, BESS, and D-STATCOMs in unbalanced non-interconnected distribution networks. ACCESS, 13, 165456-165480. (www)

  @article{ , abstract = {Access to reliable and economically viable electricity services remains a major challenge in non-interconnected zones (NIZs), particularly in developing countries. To address this issue, this study proposes a comprehensive methodology for the simultaneous planning and intelligent operation of distributed energy resources (DERs), specifically distributed generation (DG) units, battery energy storage systems (BESS), and Distribution Static Synchronous Compensators (D-STATCOMs), within unbalanced three-phase distribution systems. The proposed approach is formulated as a mixed-integer nonlinear programming (MINLP) model aimed at minimizing total system costs over a 20-year planning horizon, while accounting for technical and operational constraints under dynamic conditions in both renewable generation and electricity demand. To solve the model, three metaheuristic optimization techniques were implemented: the Chu–Beasley Genetic Algorithm (CBGA), the Vortex Search Algorithm (VSA), and the Black Widow Optimization Algorithm (BWOA). Each algorithm was independently executed 100 times in two representative test scenarios: a 25-bus system based on the distribution network of Leticia, and a 37-bus system derived from the San Andrés Island network. This evaluation enabled a statistical comparison of performance, convergence behavior, and repeatability. Results indicate that BWOA achieved the greatest cost reductions, with savings of 26.9304% and 35.6691% in Leticia and San Andrés, respectively. The selected configurations complied with all voltage and current constraints, while significantly reducing reliance on conventional diesel-based generation. These findings confirm the technical and economic viability of the proposed approach for the simultaneous integration and operation of DERs in isolated power systems.}, archive = {J_ACCESS}, author = {Jhony Andrés Guzmán-Henao and Rubén Iván Bolaños and Luis Fernando Grisales-Noreña and Oscar Danilo Montoya and Jesús C. Hernández}, doi = {10.1109/ACCESS.2025.3610528}, journal = {IEEE Access}, pages = {165456-165480}, shortjournal = {IEEE Access}, title = {Hierarchical energy planning and control of DGs, BESS, and D-STATCOMs in unbalanced non-interconnected distribution networks}, volume = {13}, year = {2025}, }
• (2025). Maverick++: Collaboration-free unlearning for medical privacy preservation in healthcare federated systems. ACCESS, 13, 165441-165455. (www)

  @article{ , abstract = {Federated Learning (FL) enables decentralized model training while preserving patient privacy, making it essential for medical AI applications. However, regulatory frameworks such as GDPR, CCPA, and LGPD mandate “the right to be forgotten”, requiring patient data removal from trained models upon request. This has driven growing interest in Federated Unlearning (FU), but existing methods require the collaborative participation of all clients, which is often impractical and raises privacy concerns, particularly in privacy sensitive medical domain. This paper introduces Maverick++, the first collaboration-free FU framework designed specifically for medical imaging applications. Inspired by Lipschitz continuity, Maverick++ quantifies memorization through a novel model sensitivity metric and frames the unlearning task as a localized optimization problem. When an unlearning request is issued, only the target client performs local unlearning by applying a locally bounded optimization procedure. This process enables effective unlearning without requiring collaboration from other clients. Theoretical analysis and extensive experiments on three diverse imaging benchmarks, Colorectal Cancer Histology, Pigmented Skin Lesions, and Blood Cells, demonstrate that Maverick++ sustains classification accuracy on retain data within 1–2% of retraining from scratch, reduces accuracy on unlearn data to below 1%, and lowers the attack success rate of Membership Inference Attack (MIA) to under 10%. Furthermore, Maverick++ achieves speedups of $8\times $ – $45\times $ over state-of-the-art baselines by eliminating the need for global client coordination. These results establish Maverick++ as a practical, efficient, and privacy-aware solution for enforcing data deletion requests for FL in medical imaging domain. The code is publicly available at https://github.com/OngWinKent/Maverick}, archive = {J_ACCESS}, author = {Win Kent Ong and Chee Seng Chan}, doi = {10.1109/ACCESS.2025.3611992}, journal = {IEEE Access}, pages = {165441-165455}, shortjournal = {IEEE Access}, title = {Maverick++: Collaboration-free unlearning for medical privacy preservation in healthcare federated systems}, volume = {13}, year = {2025}, }
• (2025). Actual gray-scale level of the invariant visual cryptography scheme. ACCESS, 13, 165429-165440. (www)

  @article{ , abstract = {The Visual Cryptography Scheme (VCS) is a cryptographic technique that relies on the human visual system rather than computational decryption to recover the concealed image. In VCS, the secret image is divided into two or more shares, and the reconstruction of the original image is achieved by superimposing these shares. There are two primary approaches within VCS: one that utilizes a codebook, and the other known as the random grid approach that operates without a codebook. In both methods, grayscale images are first converted into halftone representations using various techniques, and the resulting halftone images are then encrypted using VCS algorithms. Notably, neither the encryption nor the decryption process explicitly encodes grayscale values. This study introduces two novel algorithms: one designed for grayscale recovery within the general codebook-based (VCS) structure and the other tailored to the random grid VCS (codebook-free). Both algorithms are scale-invariant (without pixel expansion) and adhere to essential security requirements. Furthermore, the proposed methods were evaluated against the most recent state-of-the-art algorithms using two complementary approaches: quantitative analysis based on the Mean Structural Similarity Index Measure (MSSIM) and qualitative assessment through visual inspection.}, archive = {J_ACCESS}, author = {Maged H. Wafy}, doi = {10.1109/ACCESS.2025.3611527}, journal = {IEEE Access}, pages = {165429-165440}, shortjournal = {IEEE Access}, title = {Actual gray-scale level of the invariant visual cryptography scheme}, volume = {13}, year = {2025}, }
• (2025). PassStyles: Graphical authentication using mixed face styles. ACCESS, 13, 165411-165428. (www)

  @article{ , abstract = {We propose PassStyles, a graphical authentication system that utilizes images generated by StyleGAN. PassStyles authenticates users who successfully select one correct image from nine generated images multiple times in a row. The benefit of PassStyles is that the user can authenticate intuitively by choosing an image from nine images. The other benefit is its robustness to shoulder surfing because the different images generated by StyleGAN and the three-by-three combination of those images make it difficult for non-genuine users to identify the password. Moreover, PassStyles has a registration scheme that specifies which layers of StyleGAN contain the password in order to generate a wide variety of images for authentication from a few images. In addition, it uses password distilling, which removes inappropriate authentication images, to mitigate authentication errors that occur when the generated images do not adequately match the password. The evaluation experiments indicated that various image features can be passwords. In addition, we conducted shoulder surfing experiments on PassStyles and achieved a protection rate of over 90% for some passwords.}, archive = {J_ACCESS}, author = {Hikaru Matsuzaki and Tomoyuki Maekawa and Michita Imai and Kentaro Ishii}, doi = {10.1109/ACCESS.2025.3612089}, journal = {IEEE Access}, pages = {165411-165428}, shortjournal = {IEEE Access}, title = {PassStyles: Graphical authentication using mixed face styles}, volume = {13}, year = {2025}, }
• (2025). Random forests relay selector in buffer-aided cooperative networks. ACCESS, 13, 165402-165410. (www)

  @article{ , abstract = {This paper presents a data-driven Random Forest (RF) framework for the joint relay–link selection and buffer management problem in buffer-aided cooperative relay networks, with the goal of minimizing outage probability. Buffer-aided relaying exploits dynamic link selection to enhance throughput and reliability, but the underlying mixed-integer optimization is computationally intractable for even moderate network sizes. We recast relay selection as a multi-class classification task by generating “true” labels via a time window-based outage minimization procedure that captures long-term buffer–channel interactions. An RF classifier learns the mapping from system state features like channel gain, buffer length, link availability to optimal link labeling. A key benefit of the proposed framework is that the computationally intensive training phase is performed offline, and the resulting RF model can, in principle, be a strong candidate for real-time applications. Extensive simulations for networks with 3 and 6 relays and buffer sizes $\in $ [1, 50] demonstrate that the RF-based selector consistently achieves the lowest outage probability and outperforms the available conventional schemes especially in small-buffer networks, thereby making RF a promising tool for real-time cooperative network control.}, archive = {J_ACCESS}, author = {Mohammad Alkhawatrah}, doi = {10.1109/ACCESS.2025.3612192}, journal = {IEEE Access}, pages = {165402-165410}, shortjournal = {IEEE Access}, title = {Random forests relay selector in buffer-aided cooperative networks}, volume = {13}, year = {2025}, }
• (2025). Adversarial mask-guided generation for multi-temporal change detection in remote sensing. ACCESS, 13, 165386-165401. (www)

  @article{ , abstract = {Accurate change detection in multi-temporal remote sensing imagery is critical for applications such as urban development monitoring, disaster response, and environmental surveillance. However, the performance of deep learning-based models is often hindered by the scarcity of annotated image pairs. Although various semi-supervised and self-supervised methods have been proposed to alleviate annotation requirements, they often lack fine-grained control over the spatial and semantic consistency of changes. To address this limitation, we present GenCD-GAN, a novel generative adversarial framework tailored for synthesizing realistic and semantically consistent change pairs. Unlike conventional GAN-based methods, GenCD-GAN introduces an adversarial consistency loss to preserve structural integrity, an adaptive change masking mechanism to refine fine-grained changes, and a multi-modal augmentation strategy to enhance robustness under diverse conditions. Extensive experiments on the LEVIR-CD and WHU-CD datasets show that GenCD-GAN consistently improves the performance of state-of-the-art change detection models by generating high-fidelity synthetic training data. Compared to existing approaches, GenCD-GAN offers better interpretability, stronger semantic alignment, and controllable change synthesis. The proposed framework offers a scalable and effective solution to reduce manual annotation efforts and advance deep change detection systems in remote sensing.}, archive = {J_ACCESS}, author = {Ke Li and Huiying Wang and You He and Jingyun Li and Wei Du and Chengling Cui}, doi = {10.1109/ACCESS.2025.3610511}, journal = {IEEE Access}, pages = {165386-165401}, shortjournal = {IEEE Access}, title = {Adversarial mask-guided generation for multi-temporal change detection in remote sensing}, volume = {13}, year = {2025}, }
• (2025). An improved iterative approach for static output feedback control of discrete-time hidden markov jump linear systems. ACCESS, 13, 165372-165385. (www)

  @article{ , abstract = {This paper features new results for the synthesis of ${\mathcal {H}}_{2}$ , ${\mathcal {H}}_{\infty }$ , and mixed ${\mathcal {H}}_{2}$ / ${\mathcal {H}}_{\infty }$ static output feedback controllers for discrete-time Markov jump linear systems, in a scenario of partial information on the Markov chain. We assume that the mode of operation of the system is not available to the controller, but only an estimate provided by a detector, so that the joint process of the Markov chain and the detector is modeled as a hidden Markov model. In this context, we propose sufficient conditions in terms of bilinear matrix inequalities for the ${\mathcal {H}}_{2}$ , ${\mathcal {H}}_{\infty }$ , and mixed ${\mathcal {H}}_{2}$ / ${\mathcal {H}}_{\infty }$ control design cases. We also present an iterative approach, characterized by an LMI-based coordinate descent algorithm, which is employed to solve the proposed conditions and reduces the guaranteed cost from a feasible initial solution. Unlike existing methods, the proposed approach does not require the computation of stochastic stabilizing state-feedback gains for algorithm initialization. Three numerical examples are presented to illustrate the effectiveness of the proposed results and to demonstrate their advantages compared with existing methods in the literature.}, archive = {J_ACCESS}, author = {Marco A. Granato and Oswaldo L. V. Costa}, doi = {10.1109/ACCESS.2025.3612268}, journal = {IEEE Access}, pages = {165372-165385}, shortjournal = {IEEE Access}, title = {An improved iterative approach for static output feedback control of discrete-time hidden markov jump linear systems}, volume = {13}, year = {2025}, }
• (2025). Analysis and insights on regular polygon loop antennas with attenuating current: Axial ratio deterioration and recovery. ACCESS, 13, 165360-165371. (www)

  @article{ , abstract = {The current along a loop antenna is characterized by either a propagation constant ( $\alpha + j\beta $ ) or ( $\alpha - j\beta $ ). Understanding the impact of the propagation constant on the axial ratio is crucial for maintaining the performance of circularly polarized (CP) antennas. Based on this, this paper systematically investigates the axial ratio using a generalized loop antenna model: a regular polygon loop antenna (RPLA) with $N$ arm elements. The investigation reveals the following: 1) The axial ratio for a current with a positive phase constant ( $\beta \gt 0$ ) is identical to that of a current with a negative phase constant ( $-\beta \lt 0$ ), provided that both currents have the same attenuation constant $\alpha $ . 2) As $N$ decreases from infinity (corresponding to a round loop antenna) under a fixed attenuation constant $\alpha $ , axial ratio degradation increases. However, if the attenuation constant $\alpha $ is small, even an equilateral triangle loop antenna ( $N = 3$ ) can radiate a CP wave. 3) Among RPLAs, the round loop antenna provides the best axial ratio performance. 4) Axial ratio degradation is mitigated when a reactive current is generated on the loop.}, archive = {J_ACCESS}, author = {Hisamatsu Nakano and Tomoki Abe}, doi = {10.1109/ACCESS.2025.3611021}, journal = {IEEE Access}, pages = {165360-165371}, shortjournal = {IEEE Access}, title = {Analysis and insights on regular polygon loop antennas with attenuating current: Axial ratio deterioration and recovery}, volume = {13}, year = {2025}, }
• (2025). Deep learning-driven personalized course recommendations in MOOC platforms using domain-specific strategies. ACCESS, 13, 165342-165359. (www)

  @article{ , abstract = {Course recommendations on online platforms are full of uncertainty and ambiguities due to the involvement of several uncertain factors. The primary goal of this study is to investigate an intelligent fuzzy decision algorithm that can assess learners and enhance personalized instruction within Massive Open Online Courses (MOOC) platforms. Several frameworks model uncertain situations, but the T-spherical fuzzy set (TSFS) can deal with uncertainties in data within diverse contexts. The algorithm discussed in this study can assess multiple learning performance metrics to enhance learners’ abilities, recognize strengths, and identify areas for improvement. The instructors can quickly evaluate and select training modules customized to particular requirements using the designed algorithm. The algorithm assesses learner performance using a quantitative evaluation that includes Learning Proficiency Level (LPL), Engagement Metrics (EM), Conceptual Understanding (CU), Consistency in Learning (CL), Response Time (RT), Analytical and Critical Thinking (ACT), Adaptability to Course Content (AC), and Collaboration Skills (CS). The results inform the customization of training programs for learners. This research examines strategies to decrease dropout rates in MOOCs by tailoring learning experiences to align with learners’ motivational factors. The process discussed in this study proposes a novel methodology for profile construction in a MOOC recommendation system, incorporating external data to delineate a learner’s interests.}, archive = {J_ACCESS}, author = {Junyan Yin and Xusheng Zhou}, doi = {10.1109/ACCESS.2025.3608055}, journal = {IEEE Access}, pages = {165342-165359}, shortjournal = {IEEE Access}, title = {Deep learning-driven personalized course recommendations in MOOC platforms using domain-specific strategies}, volume = {13}, year = {2025}, }
• (2025). Optimizing retail inventory and sales through advanced time series forecasting using fine tuned PrGB regressor. ACCESS, 13, 165328-165341. (www)

  @article{ , abstract = {Retail transactions time series analysis helps the super market to analyze the customer behavior based on their purchases. It helps the super market to increase their sales based on the model components such as seasonality, impact of holidays and weather. This time series analysis helps the inventory management to analyze the trends, which in return helps the super market to get optimal stock and to have minimal loss. In existing approaches, time series analysis is performed using the pure statistical and anomaly detection approaches but statistical approaches assume the linear relation between the features due to which the model cannot extract the complex patterns. Without extracting the complex patterns, the parallel processing becomes difficult and also customer segmentation becomes difficult. In this research, the model fine tunes the prophet, gradient boost regressor and ensembles them to forecast the sales on daily basis. Boosting regressor can efficiently extract the relation between both internal and external factors to understand the complex patterns and trends on timely basis. Boosting algorithm has the capability to perform data analysis and feature engineering simultaneously. While capturing the patterns, the model needs long term analysis because of seasonal changes, festival offers, weather impacts and other parameters, these are analyzed by the prophet model along with shifts in the trends and component analysis. By combining XGBoost Regressor and Prophet for retail sales forecasting, model gain the ability to model both time-dependent patterns (seasonality, trends, holiday effects) and non-linear relationships (promotions, pricing, external factors), leading to improved forecast accuracy, robustness to real-world data challenges, and better decision-making capabilities in retail environments.}, archive = {J_ACCESS}, author = {Chintapanti Anusha and Sandipan Maiti}, doi = {10.1109/ACCESS.2025.3605229}, journal = {IEEE Access}, pages = {165328-165341}, shortjournal = {IEEE Access}, title = {Optimizing retail inventory and sales through advanced time series forecasting using fine tuned PrGB regressor}, volume = {13}, year = {2025}, }
• (2025). Fixed-frequency 3V-MPCC strategy for single-phase NPC inverters with multi-objective optimization. ACCESS, 13, 165317-165327. (www)

  @article{ , abstract = {Traditional finite control set model predictive control for single-phase three-level neutral-point-clamped inverters suffers from three main issues: non-constant switching frequency, high harmonic distortion, and neutral-point voltage fluctuations. In order to overcome these traditional problems, this paper explores the predictive control strategy of a three-vector model with fixed switching frequency. Taking the output voltage vector as the finite set, the objective function is reasonably constructed by optimizing the region division and vector synthesis, and finally the constant switching frequency and efficient harmonic suppression are realized under the condition of fast neutral point potential equilibrium. In view of the delay effect in predictive control, this paper proposes a two-step prediction delay compensation, which can effectively hedge the calculation delay and improve the control accuracy and dynamic response. Experimental results demonstrated that, compared to conventional finite control set model predictive control under a 10 kHz sampling frequency, the proposed method reduces the total harmonic distortion of the output current from 7.5% to 1.8%, stabilizes the switching frequency at 1.2 kHz, achieves neutral-point voltage balance, and realizes simultaneous optimization of steady-state accuracy and dynamic performance. This approach resolves the trade-off between dynamic performance and steady-state accuracy, offering a robust solution for energy integration applications under complex operating conditions.}, archive = {J_ACCESS}, author = {Tang Yong and Tang Huiying and Wang Bo}, doi = {10.1109/ACCESS.2025.3611961}, journal = {IEEE Access}, pages = {165317-165327}, shortjournal = {IEEE Access}, title = {Fixed-frequency 3V-MPCC strategy for single-phase NPC inverters with multi-objective optimization}, volume = {13}, year = {2025}, }
• (2025). A low-cost wireless sensor network to soil data acquisition using A-star algorithm. ACCESS, 13, 165303-165316. (www)

  @article{ , abstract = {Currently, the latest research on Wireless Sensor Networks (WSNs) focuses on several types of applications, given the progress in microelectronics and telecommunication. One of the most studied topics is energy efficiency, in which different approaches are evaluated to deal with energy consumption and extend network lifetime. Many algorithms can be used to deal with these problems, ranging from simple algorithms to complex machine-learning techniques. However, implementing these algorithms is not an often-explored topic, although essential, because hardware can have particularities that decrease performance compared to simulated situations. Therefore, this paper proposes an efficient routing protocol based on an A-star algorithm to improve the lifetime of a WSN. We simulate, implement, and embed into a prototype using ESP-32 to build a real WSN. The aim is to show how the proposed algorithm can improve network lifetime. In the experiments, it was possible to compare the proposed algorithm with LEACH, PEGASIS and others algorithms. The proposed algorithm is also embedded in a low-cost wireless sensor network based on ESP-32 to check if it is possible and whether the use of the proposed algorithm improves the network lifetime.}, archive = {J_ACCESS}, author = {Rayanne M. C. Silveira and Thalyson D. R. Dutra and Allyx Fontaine and Ewaldo E. C. Santana}, doi = {10.1109/ACCESS.2025.3610294}, journal = {IEEE Access}, pages = {165303-165316}, shortjournal = {IEEE Access}, title = {A low-cost wireless sensor network to soil data acquisition using A-star algorithm}, volume = {13}, year = {2025}, }
• (2025). An improved current control scheme for reduced switching frequency in a rooftop grid-interfaced PV system. ACCESS, 13, 165290-165302. (www)

  @article{ , abstract = {This paper presents a novel modified unipolar-toggle hysteresis current control (UTHCC) strategy for a single-phase grid-connected photovoltaic (GCPV) rooftop system. The proposed UTHCC is designed to minimise the switching frequency and hence the switching losses in the GCPV rooftop system, thus improving its efficiency, without compromising the THD. In this work, the performance of the proposed scheme is compared with conventional HCC and unipolar-toggle (UT) modulation schemes. The comparison study is done through simulation and using a real-time platform, along with experimental validation of the same. The proposed UTHCC scheme was found to be capable of reducing the switching frequency by nearly 46% (theoretically) and by up to 32% through real-time and experimental validation, along with a reduction in switching losses, which is about 35% of that with conventional HCC. The proposed UTHCC scheme was also found to have 3.4% lower THD as compared to the UT modulation scheme. The proposed work is modelled in the MATLAB/Simulink platform and validated using the OPAL-RT 4510 real-time platform as well as an experimental setup.}, archive = {J_ACCESS}, author = {Asim Kumar Dey and Swagat Pati and Tapas Kumar Mohapatra}, doi = {10.1109/ACCESS.2025.3611077}, journal = {IEEE Access}, pages = {165290-165302}, shortjournal = {IEEE Access}, title = {An improved current control scheme for reduced switching frequency in a rooftop grid-interfaced PV system}, volume = {13}, year = {2025}, }
• (2025). Fine-grained and thematic evaluation of LLMs in social deduction game. ACCESS, 13, 165276-165289. (www)

  @article{ , abstract = {Recent studies have investigated whether large language models (LLMs) can support obscured communication, which is characterized by core aspects such as inferring subtext and evading suspicions. To conduct the investigation, researchers have used social deduction games (SDGs) as their experimental environment, in which players conceal and infer specific information. However, prior work has often overlooked how LLMs should be evaluated in such settings. Specifically, we point out two limitations with the evaluation methods they employed. First, metrics used in prior studies are coarse-grained as they are based on overall game outcomes that often fail to capture event-level behaviors; Second, error analyses have lacked structured methodologies capable of producing insights that meaningfully support evaluation outcomes. To address these limitations, we propose a microscopic and systematic approach to the investigation. Specifically, we introduce six fine-grained metrics that resolve the first issue. To tackle the second issue, we conducted a thematic analysis and identified four major reasoning failures that undermine LLMs’ performance in obscured communication.}, archive = {J_ACCESS}, author = {Byungjun Kim and Dayeon Seo and Minju Kim and Bugeun Kim}, doi = {10.1109/ACCESS.2025.3611399}, journal = {IEEE Access}, pages = {165276-165289}, shortjournal = {IEEE Access}, title = {Fine-grained and thematic evaluation of LLMs in social deduction game}, volume = {13}, year = {2025}, }
• (2025). Fault tolerant CCS-MPC for asymmetrical DTP-PMSM drives assisted by fault detection algorithm. ACCESS, 13, 165263-165275. (www)

  @article{ , abstract = {Fault tolerance is a highly desirable feature for safety-critical electric drive applications. This paper proposes an open-circuit fault tolerant model predictive controller (FT-MPC) for asymmetrical dual three-phase (DTP) permanent magnet synchronous machine (PMSM) drives, which is formulated using the continuous control set (CCS) approach. As an alternative to conventional control methods, this predictive controller solves an unconstrained quadratic programming (QP) problem to obtain the voltage references. The FT-MPC is able to operate in a reduced form under healthy operation. To enable the transition to the post-fault control mode, a fault detection and diagnosis algorithm, based on the natural coupling concept, is proposed. The control solution is implemented in an OPAL-RT OP4510 real-time digital system and is experimentally validated. Thanks to the optimisation efforts carried out during implementation, the computational burden of the proposed algorithm is kept low: the developed software requires only about 7.6 % of the controller’s sampling time to compute during healthy operation (reduced form), and about 10.1 % during the post-fault mode, demonstrating its potential applicability in automotive grade microcontrollers. On average, open-phase fault detection times of the order of 15 ms are achieved, allowing for a fast and smooth transition between healthy and post-fault operating modes. Finally, dynamic performance is addressed under standardised driving cycle profiles. The ability of the FT-MPC to keep the torque-producing sub-space on a circular trajectory during the fault detection stage is confirmed, thus avoiding additional undesired torque ripple.}, archive = {J_ACCESS}, author = {Adriano Navarro-Temoche and Josu Jugo and Edorta Ibarra and Iñigo Kortabarria and Asier Matallana}, doi = {10.1109/ACCESS.2025.3612337}, journal = {IEEE Access}, pages = {165263-165275}, shortjournal = {IEEE Access}, title = {Fault tolerant CCS-MPC for asymmetrical DTP-PMSM drives assisted by fault detection algorithm}, volume = {13}, year = {2025}, }
• (2025). Research and implementation of secure dual algorithm-based transmission system for sensitive data of large models in an open environment. ACCESS, 13, 165247-165262. (www)

  @article{ , abstract = {With the rapid deployment of sensitive data of large models in open environments (SDLMIOE), ensuring secure and reliable transmission has become increasingly vital. This study suggests a secure transmission basis and framework that integrates Reinforcement Learning (RL) for dynamic encryption-parameter optimization and the Cubature Kalman Filter (CKF) for robust state estimation and anomaly detection in SDLMIOE. The RL module adaptively adjusts encryption levels, density strategies, and transmission priorities based on simulated network conditions, while the CKF well filters noisy signals and identifies potential interfering or injection attacks in real time. The complete system is modeled and evaluated using MATLAB simulations, focusing on scenarios linking large model file transfers (up to 1.2 GB) over variable network conditions. Simulation results show that the proposed method attains a 31.5% reduction in average latency, 27.8% increase in throughput, and 99.96% data integrity holding. The CKF-based anomaly detection module achieves a 98.9% detection accuracy with an incorrect alarm rate of 1.3%, and the RL-based optimization leads to a 22% improvement in encryption efficiency compared to fixed-policy systems. These results confirm the effectiveness of the proposed system in protecting sensitive model data during transmission in open and potentially combative environments.}, archive = {J_ACCESS}, author = {Shouyu Liang and Zhaojie Dong and Junquan Peng and Cheng Jiang and Xiangyu Zhao}, doi = {10.1109/ACCESS.2025.3605073}, journal = {IEEE Access}, pages = {165247-165262}, shortjournal = {IEEE Access}, title = {Research and implementation of secure dual algorithm-based transmission system for sensitive data of large models in an open environment}, volume = {13}, year = {2025}, }
• (2025). Algorithms based on dynamic minimum probabilistic support and utility thresholds for mining top-K high-utility itemsets from uncertain databases. ACCESS, 13, 165222-165246. (www)

  @article{ , abstract = {Mining high-utility itemsets (HUIs) from large and uncertain databases is challenging due to the vast data volume and extensive search space. Determining the minimum utility and probabilistic support for all items is also a time-consuming task. Setting these thresholds too high results in too few itemsets, whereas setting them too low leads to an excessive number of itemsets and high computational costs. To address these issues, we developed solutions for mining Top-K high-utility itemsets from uncertain databases. First, we formally define the problem of mining Top-K high-utility itemsets from uncertain databases. Based on this formulation, we propose four algorithms: FTKHUUIM+, ITUHUFP, TUHUFP, and TKUU, which effectively meet the problem requirements. These algorithms adopt automated threshold-raising strategies and specialized storage structures to ensure optimal performance. Experimental results on public benchmark datasets demonstrate the scalability and robust performance of the proposed algorithms across both dense and sparse datasets in terms of runtime and efficiency. All of them share the common feature of pruning candidate itemsets based on dynamic minimum probabilistic support and utility thresholds. Although their pruning strategies differ, all algorithms follow a recursive process to select Top-K HUIs by pairing items from the database and filtering patterns according to the thresholds. Among the proposed algorithms, the ITUHUFP algorithm shows the best performance and stability on various benchmark datasets.}, archive = {J_ACCESS}, author = {Khoi Nguyen and Thien Nguyen}, doi = {10.1109/ACCESS.2025.3612006}, journal = {IEEE Access}, pages = {165222-165246}, shortjournal = {IEEE Access}, title = {Algorithms based on dynamic minimum probabilistic support and utility thresholds for mining top-K high-utility itemsets from uncertain databases}, volume = {13}, year = {2025}, }
• (2025). A dictionary-based pursuit algorithm for magnetotelluric signal-noise separation. ACCESS, 13, 165213-165221. (www)

  @article{ , abstract = {A critical challenge in magnetotelluric (MT) studies is the effective suppression of noise in collected data prior to investigating deep geological structures and detecting deep-seated blind ore bodies. To address this issue, this study proposes a novel method for magnetotelluric signal-noise separation based on stagewise orthogonal matching pursuit. The approach involves constructing a redundant dictionary comprising wavelet packets, cosine atoms, and other basis atoms to sparsely represent segmented MT data. A Gaussian matrix is then employed to observe and sample the sparsely represented data. Subsequently, the stagewise orthogonal matching pursuit algorithm is applied to suppress strong interference and accurately reconstruct useful MT signals. The method is validated using both simulated typical strong interference and measured MT data from an ore concentration area. Comparisons with matching pursuit, orthogonal matching pursuit, and the remote reference method demonstrate that the proposed technique offers higher computational efficiency, fewer iterations, better convergence, and more effective noise suppression. Furthermore, the calculated apparent resistivity-phase curves exhibit improved continuity and smoothness, and the quality of low-frequency MT data is significantly enhanced.}, archive = {J_ACCESS}, author = {Jin Cai and Jianhua Cai}, doi = {10.1109/ACCESS.2025.3612256}, journal = {IEEE Access}, pages = {165213-165221}, shortjournal = {IEEE Access}, title = {A dictionary-based pursuit algorithm for magnetotelluric signal-noise separation}, volume = {13}, year = {2025}, }
• (2025). Modeling, analysis, and comparison of voltage and charge sampling techniques in sub-sampling PLL design. ACCESS, 13, 165203-165212. (www)

  @article{ , abstract = {This paper compares two different architectures for implementing a sub-sampling phase-locked loop (SS-PLL). First, a time-domain model is used to analyze the noise contributions of the sampling mechanisms: voltage sampling (VS) and charge sampling (CS). Then, based on the obtained noise results, the performance of complete SS-PLL systems is evaluated using a phase-domain model. This approach applies to PLLs utilized as frequency synthesizers with large divide ratios (e.g., 64 to 512) as well as to SS-PLLs operating without a divider in their loop. Although the CS technique can theoretically enhance the Signal-to-Noise Ratio (SNR) by 3 dB compared to VS, the proposed phase-domain model highlights that, within typical power budgets ranging from 1 to 20 mW, a VS-PLL achieves a lower level of integrated jitter than a CS-PLL. This is due to the intrinsic noise of the charge sampling phase detector (CSPD) that, unlike in the VS-PLL, cannot be suppressed and therefore becomes the dominant in-band noise source.}, archive = {J_ACCESS}, author = {Gabriele Ciarpi and Danilo Monda and Marco Mestice and Daniele Rossi and Sergio Saponara}, doi = {10.1109/ACCESS.2025.3611008}, journal = {IEEE Access}, pages = {165203-165212}, shortjournal = {IEEE Access}, title = {Modeling, analysis, and comparison of voltage and charge sampling techniques in sub-sampling PLL design}, volume = {13}, year = {2025}, }
• (2025). Reference point MCDM algorithm for spectral and energy efficiency trade-off in massive MIMO systems. ACCESS, 13, 165189-165202. (www)

  @article{ , abstract = {This paper proposes a new algorithm for choosing a solution in the Pareto Optimal Front of the multi-objective optimization problem of the spectral and energy efficiency trade-off in Massive MIMO (Multiple-Input, Multiple-Output) systems. The new method is based on the weights assigned to each objective, which are used to calculate a reference point. The solution chosen by the algorithm is the solution that has the shortest distance from the reference point. The algorithm is able to explore the whole Pareto Front in both its concave and convex parts, but it has a flaw, which is that it is highly impacted by the range of values of the objectives. This limitation of the algorithm can be solved by normalizing the Pareto Front, so that the values for all of the criteria are numbers from 0 to 1, or by using the cosine similarity to measure the distance of the solutions from the reference point. Simulations show that after implementing one of the two methods that make the algorithm independent of the range of the values of the objectives, it performs well by choosing solutions with an optimal distribution throughout the whole Pareto Front.}, archive = {J_ACCESS}, author = {Eni Haxhiraj and Ina Papadhopulli and Elson Agastra}, doi = {10.1109/ACCESS.2025.3612324}, journal = {IEEE Access}, pages = {165189-165202}, shortjournal = {IEEE Access}, title = {Reference point MCDM algorithm for spectral and energy efficiency trade-off in massive MIMO systems}, volume = {13}, year = {2025}, }
• (2025). A novel multi-scale feature attention network-based prediction model for the accurate forecasting of photovoltaic power generation. ACCESS, 13, 165173-165188. (www)

  @article{ , abstract = {The global rise in energy demand has driven the large-scale integration of photovoltaic systems into the power grids, increasing the need for accurate forecasting models to manage solar energy variability and uncertainty. This study proposes a novel 24-hour-ahead PV power generation prediction model based on the multi-scale feature attention network, which integrates convolutional neural networks, attention mechanisms, and gated recurrent units within a non-sequential architecture to enhance prediction accuracy. The model also incorporates a two-step feature selection technique, mutual information-interaction gain, to extract the most relevant input features whilst minimizing redundancies. The proposed model was validated using real-world PV power generation data from the Arvand power plant in Mahan, Iran. The results indicate that the the model outperformed traditional methods, including long short-term memory, multi layer perceptron, and Elman neural networks. It achieved a mean squared error (MSE) of 0.001, mean absolute error (MAE) of 0.02, mean absolute percentage error (MAPE) of 2.5%, and a coefficient of determination (R2) of 0.991—substantially surpassing the performance of the comparison models. These results underscore the model’s potential to improve PV forecasting accuracy and support more stable and efficient power grid operations.}, archive = {J_ACCESS}, author = {Faezeh Amirteimoury and Gholamreza Memarzadeh and Hossein Noori and Farshid Keynia and Azim Heydari and Afef Fekih}, doi = {10.1109/ACCESS.2025.3611168}, journal = {IEEE Access}, pages = {165173-165188}, shortjournal = {IEEE Access}, title = {A novel multi-scale feature attention network-based prediction model for the accurate forecasting of photovoltaic power generation}, volume = {13}, year = {2025}, }
• (2025). Robust μ- optimal control synthesis for modular multi-port DC-DC converter. ACCESS, 13, 165157-165172. (www)

  @article{ , abstract = {Owing to the intermittent characteristics of renewable energy sources (RESs) and the unpredictability of load demand, integrating multiple RESs and energy storage systems (ESSs) has become imperative. Modular Multi-port Converters (MMPC) have emerged as a viable solution to meet this need, offering superior performance, efficiency, and reliability compared to multiple SISO dc/dc converters. To this end, this paper presents a comprehensive model of an MMPC, which is bidirectional and capable of operating in both step-up and step-down modes. Following the derivation of the converter model, a robust ${\mu }-$ controller using the $D {-\mathcal {G}-\mathcal {K}} $ iterative procedure is designed. This controller addresses the cross-coupling challenges inherent in MIMO systems and effectively overcomes the parametric uncertainties associated with the converter. Finally, hardware-in-the-loop (HIL) test results derived from OPAL-RT 4610, and experimental results from a prototype are used to validate this control approach.}, archive = {J_ACCESS}, author = {Shahriar Farajdadian and Amin Hajizadeh and Mohsen Soltani and Pavol Bauer and Hani Vahedi}, doi = {10.1109/ACCESS.2025.3610203}, journal = {IEEE Access}, pages = {165157-165172}, shortjournal = {IEEE Access}, title = {Robust μ- optimal control synthesis for modular multi-port DC-DC converter}, volume = {13}, year = {2025}, }
• (2025). Arc erosion behaviors of pantograph of high-speed train during its rise and fall processes. ACCESS, 13, 165144-165156. (www)

  @article{ , abstract = {Arc melting pools are generated when the pantograph and catenary (PAC) are offline during the rise and fall of the pantograph. They can lead to the material damage, accelerated wear, increased splashing, and exacerbated PAC fatigue through the thermal cycling, shortening its service life. In this paper, the voltage and current are measured by a high-speed PAC experimental platform when the PAC occur the offline discharge. And the arc erosion images are obtained using a scanning electron microscope. Based on the principle of the magnetohydrodynamics, a simulation model of the PAC arc is established and its correctness is verified by comparing the experimental data. The distribution of the heat flow input density of the arc is obtained by adopting the moving mesh technique and coupling the actual auto-transformer (AT) traction grid circuit. Meanwhile, the melting solidification model and evaporation sublimation model are introduced to study the behavior of the material erosion by the arc when the pantograph rises and lowers. When the PAC offline discharge occurs during the rise of the pantograph, the heat flow density is concentrated and the temperatures of the contact line and pantograph are 3550 K and 4290 K, respectively. The melting pool is of a deep and narrow type. Its depth is 1.71 mm, which is 80% more than that during the fall of the pantograph. While its width decreases by 17.48% compared with that during the fall of the pantograph, which increases the risk of the localized overheating and thermal stresses. As the pantograph falls, the density of the heat flow decreases and the temperature of the contact point is lowered. The melting pool is shallow and wide, making it more susceptible to splashing and accelerating the wear. The increased temperature in the melting pool triggers the Marangoni effect, which leads to the eddy currents. They increase the instability and spattering on the surface of the pantograph, accelerating the material damage. The innovation of this paper lies in the integration of the arc model, erosion model, and AT traction circuits, and the introduction of the Marangoni force, evaporation-sublimation behaviors, and moving mesh technology, to systematically study the PAC erosion behaviors during the rise and fall of the pantograph. Its purpose is to provide theoretical support for the selection of the PAC materials, their structural design, and preventive maintenance.}, archive = {J_ACCESS}, author = {Like Pan and Xudong Yi and Tong Xing and Zhiqiang Yu and Caizhi Yang and Qun Yu and Xiaohan Zhu and Nan Zhuang and An Jin and Yuehua Cui and Tao Feng and Jianhua Chen and Wenxia Yan}, doi = {10.1109/ACCESS.2025.3612260}, journal = {IEEE Access}, pages = {165144-165156}, shortjournal = {IEEE Access}, title = {Arc erosion behaviors of pantograph of high-speed train during its rise and fall processes}, volume = {13}, year = {2025}, }
• (2025). A fuzzy framework for managing supply chain disruptions and uncertainties via aperiodic time-dependent nonfragile sampled-data control with communication delays. ACCESS, 13, 165126-165143. (www)

  @article{ , abstract = {This article investigates fuzzy frameworks for analyzing the stability and stabilization of nonlinear supply chain systems (SCSs) affected by external disturbances and uncertainties. First, the dynamics of nonlinear SCSs are transformed into linear subsystems through Takagi-Sugeno (T-S) fuzzy frameworks. Then, the time-dependent nonfragile sampled-data controller (NSDC) with communication delays is developed to mitigate parametric uncertainties and attenuate external disturbances via $H_{\infty }$ -control theory. In contrast to existing NSDC methods, the constructed NSDC signals vary over time within each sampling period, thereby enhancing robustness and control performance. Thereafter, a novel integral inequality is developed to estimate the integral quadratic terms, where free-weighting matrices are uniquely assigned for each sampling stage and can take distinct values for different aperiodic sampling periods. To further reduce conservatism, an augmented dual looped-type Lyapunov functional (ADLLF) is introduced to fully utilize the knowledge of the aperiodic sampling pattern and communication delay effects. Subsequently, by leveraging this ADLLF along with the proposed integral inequality, sufficient conditions are derived as linear matrix inequalities (LMIs) to ensure the robust asymptotic stability of the T-S fuzzy SCSs with $H_{\infty }$ -performance level $\bar {\gamma }$ . Finally, the performance of the considered SCSs is validated through numerical simulations, and a comparative example demonstrates the effectiveness and superiority of the proposed theoretical findings.}, archive = {J_ACCESS}, author = {Nivetha Thirumalairaj Kannadasan and Aruna Polisetty}, doi = {10.1109/ACCESS.2025.3611496}, journal = {IEEE Access}, pages = {165126-165143}, shortjournal = {IEEE Access}, title = {A fuzzy framework for managing supply chain disruptions and uncertainties via aperiodic time-dependent nonfragile sampled-data control with communication delays}, volume = {13}, year = {2025}, }
• (2025). Analytical model for calculating magnetic field of nonlinear spoke-type permanent-magnet motors considering rotor eccentricity and pole shape. ACCESS, 13, 165117-165125. (www)

  @article{ , abstract = {This paper proposes an analytical method to calculate magnetic field of nonlinear spoke-type permanent-magnet motors (STPMM) considering static, dynamic, mixed rotor eccentricities and pole shape. First of all, eccentricity problem within two circular boundaries is handled by using bilinear mapping to transform two eccentric circles in S-plane to two concentric circles in T-plane. Then magnetic field in the slotted STPMM with concentric rotor is predicted according to conformal mapping, double reaction theory, superposing principle complex relative air-gap permeance, and nonlinear magnetic network. After that, back electromotive force (BEMF) and torque are obtained according to the results of magnetic field. The calculating results show that open-circuit and on-load air-gap flux density, cogging torque, BEMF, and static torque of STPMM considering rotor eccentricity and pole shape by the proposed analytical method are in good agreement with those predicted by finite-element method. Analytical results of BEMF and static torque are also similar with experimental results. The finite-element and experimental results confirm that the developed analytical method has high accuracy for predicting magnetic field of nonlinear STPMM considering rotor eccentricities and pole shape.}, archive = {J_ACCESS}, author = {Yingdong Zou and Fulong Zheng and Xueqing Wang and Yu Zhou}, doi = {10.1109/ACCESS.2025.3611332}, journal = {IEEE Access}, pages = {165117-165125}, shortjournal = {IEEE Access}, title = {Analytical model for calculating magnetic field of nonlinear spoke-type permanent-magnet motors considering rotor eccentricity and pole shape}, volume = {13}, year = {2025}, }
• (2025). Enhancement of implicit emotion recognition in arabic text: Annotated dataset and baseline models. ACCESS, 13, 165096-165116. (www)

  @article{ , abstract = {Emotion recognition in textual data has emerged as a rapidly advancing task within the field of Natural Language Processing (NLP). Implicit Emotion Recognition (IER), which aims to identify emotions primarily through contextual cues rather than overt or explicit emotional expressions, remains in its early stages. Despite the significant progress in emotion recognition tasks, current research has largely ignored IER, particularly for low-resource languages such as Arabic. This study aims to comprehensively address this task for the Arabic language, including dataset construction, annotation, modeling, and evaluation. Specifically, the study 1) introduces the first annotated dataset for Arabic Implicit Emotion Recognition (AIER); 2) annotates the dataset with emotions, cues, and causes using a semi-automatic annotation tool validated by four native Arabic speakers and linguists; 3) investigates the potential of two categories of transformer-based models: masked language models, exemplified by pre-trained Bidirectional Encoder Representations from Transformers (BERT)-based architectures through a series of fine-tuning experiments, and causal-based models, such as generative Large Language Models (LLMs), through a zero-shot prompting approach; and 4) evaluates the performance of four distinct algorithmic approaches on the proposed dataset, namely classical Machine Learning (ML), Deep Learning (DL), BERT-based models, and generative LLMs. The experimental results indicate that BERT-based models outperform ML, DL, and generative LLMs. In particular, the fine-tuned MARBERTv2 model achieved the best performance among all pre-trained models, obtaining an F1-score of 79.83% on the AIER dataset.}, archive = {J_ACCESS}, author = {Hanane Boutouta and Abdelaziz Lakhfif and Ferial Senator and Chahrazed Mediani}, doi = {10.1109/ACCESS.2025.3611337}, journal = {IEEE Access}, pages = {165096-165116}, shortjournal = {IEEE Access}, title = {Enhancement of implicit emotion recognition in arabic text: Annotated dataset and baseline models}, volume = {13}, year = {2025}, }
• (2025). Margin-based supervised contrastive learning for robust malware detection. ACCESS, 13, 165082-165095. (www)

  @article{ , abstract = {The growth of network technologies has led to a rise in network traffic diversity, which has resulted in increased complexity within each class. However, distinguishing between malicious and benign actions has become more challenging as the two have started to overlap. Consequently, false alarms have become a common issue, which can cause problems in detection models. We proposed a novel approach to enhance intrusion detection based on supervised contrastive learning that combines a maximum-margin-based approach for malware detection in an end-to-end process. This approach differs from traditional methods, where feature selection and detection are considered separate stages. Our model guarantees robustness and correlation between these two processes. To handle the challenges posed by Variation within a class and similarities between different classes, we use the margin-based supervised contrastive learning (MbSCL) technique. This technique involves a two-stage learning process that enables the network to learn embeddings that bring closer the similar data points and separate dissimilar ones. We employ a specialized loss function that combines supervised contrastive loss and marginal loss to improve the accuracy of the classification. This process balances the effectiveness of embedding separation and classification to improve overall performance. Mathematical theorems and proofs support the optimization process, which provides insights into contrastive loss in similarity learning. This two-stage learning approach and loss function design are particularly useful for malware detection, as they enable the identification of subtle data patterns and anomalies in network traffic.}, archive = {J_ACCESS}, author = {Yawar Abbas Abid and Jinsong Wu and Said Jadid Abdulkadir and Ayed Alwadain and Muhammad Waqas and Muhammad Farhan}, doi = {10.1109/ACCESS.2025.3609670}, journal = {IEEE Access}, pages = {165082-165095}, shortjournal = {IEEE Access}, title = {Margin-based supervised contrastive learning for robust malware detection}, volume = {13}, year = {2025}, }
• (2025). Locomotion mode recognition based on inception-enhanced vision transformer method with kolmogorov-arnold network fusion. ACCESS, 13, 165061-165081. (www)

  @article{ , abstract = {Locomotion mode recognition plays a crucial role in enhancing the control and adaptability of lower limb exoskeletons. This paper proposes a novel Inception-ViT hybrid model for Locomotion mode recognition based on hip, knee, and ankle joint angle data collected from six IMU sensors. The study focuses on five motion patterns: level ground walking (LW), stair ascent (SA), stair descent (SD), ramp ascent (RA), and ramp descent (RD). we employ variational mode decomposition (VMD) to decompose raw joint angle signals into three intrinsic mode functions (IMFs) and generate enhanced features by subtracting different IMF combinations. In the feature extraction stage, Inception convolutional layers are first utilized to extract local features. Subsequently, global feature modeling is performed through an improved Vision Transformer (ViT), where traditional multilayer perceptron (MLP) layers are replaced by Kolmogorov-Arnold Networks (KAN) to strengthen the model’s nonlinear fitting capability. To validate the effectiveness of the model, comparative experiments were conducted with traditional convolutional neural networks (CNN), standard ViT, and Inception models. The results demonstrate that our method outperforms existing models in terms of accuracy, noise resistance, and generalization capability for Locomotion mode recognition. The experimental results demonstrate that the model successfully captures both local and global dynamic information of locomotion modes. It achieves recognition accuracy exceeding 98% across all motion patterns (average 98.92%), confirming its superior performance in motion pattern recognition.}, archive = {J_ACCESS}, author = {Jianbin Zheng and Xinyu Zhou and Yang Gao and Ziyao Chen and Yifan Gao and Chuyi Zhou and Yizhuo Zhang}, doi = {10.1109/ACCESS.2025.3608775}, journal = {IEEE Access}, pages = {165061-165081}, shortjournal = {IEEE Access}, title = {Locomotion mode recognition based on inception-enhanced vision transformer method with kolmogorov-arnold network fusion}, volume = {13}, year = {2025}, }
• (2025). EEG-based emotion recognition: A review and emerging paths. ACCESS, 13, 165037-165060. (www)

  @article{ , abstract = {Electroencephalography (EEG)-based emotion recognition (ER) is a growing research area in affective computing and human–computer interaction. EEG signals capture non-invasive measurements of brain activity and provide real-time access to emotional processes at the neural level. Compared to facial expressions or speech, EEG is less susceptible to voluntary masking and therefore holds promise for robust emotion decoding. However, traditional EEG-based ER methods are often limited by low classification accuracy, small sample sizes, and reliance on discrete or bipolar affective labels. Recent advances in deep learning, multimodal fusion, and semantic-space emotion models offer promising directions. This paper reviews state-of-the-art EEG-based ER approaches, focusing on signal preprocessing, feature extraction, and classification techniques. In addition, we identify emerging trends and persistent challenges, including dataset generalizability, inter-subject variability, and the need for fine-grained emotion annotation. By synthesizing current methodologies and outlining future paths, this review aims to guide both researchers and practitioners in the development of more accurate, scalable, and cognitively grounded EEG-based emotion recognition systems}, archive = {J_ACCESS}, author = {Phuong Huy-Tung and Im Eun-Tack and Oh Myeong-Seok and Gim Gwang-Yong}, doi = {10.1109/ACCESS.2025.3610918}, journal = {IEEE Access}, pages = {165037-165060}, shortjournal = {IEEE Access}, title = {EEG-based emotion recognition: A review and emerging paths}, volume = {13}, year = {2025}, }
• (2025). Digital twin modeling for the analysis of thermal and mechanical effects of mutual coupling in power transformer windings. ACCESS, 13, 165025-165036. (www)

  @article{ , abstract = {In this work, digital twins are proposed to analyze the thermal and mechanical effects of mutual coupling in power transformer windings. To achieve this, construction data from two transformers were used to create computational models in the Comsol Multiphysics platform, enabling four case studies: thermal performance analysis, mechanical performance analysis, investigation of thermal expansion forces, and evaluation of thermal behavior under winding deformations. The results reveal a strong correlation between thermal and mechanical performance, a topic that has been scarcely explored in literature. The coupled simulations show that thermal stresses generate radial displacements even in scenarios where axial displacements would be expected. Furthermore, the analysis demonstrates that deformations, particularly in the radial direction, can significantly alter the thermal performance of the windings, increasing the hot-spot temperature by up to 11.9%. These findings provide new insights into the interplay between thermal and mechanical phenomena in transformer windings, supporting the development of more reliable designs, advanced monitoring strategies, and improved identification of potential failure causes.}, archive = {J_ACCESS}, author = {Jalberth F. Araújo and Filipe L. M. Andrade and Edson G. Costa and Henrique D. Silva}, doi = {10.1109/ACCESS.2025.3611116}, journal = {IEEE Access}, pages = {165025-165036}, shortjournal = {IEEE Access}, title = {Digital twin modeling for the analysis of thermal and mechanical effects of mutual coupling in power transformer windings}, volume = {13}, year = {2025}, }
• (2025). Leveraging embedded accelerometers and machine learning for real-time sheep behavior classification in precision farming. ACCESS, 13, 165006-165024. (www)

  @article{ , abstract = {The development of efficient and real-time animal behavior monitoring systems is crucial to support precision livestock farming. This study proposes a behavior classification system for sheep using a triaxial accelerometer and embedded machine learning models, implemented on a low-power Internet of Things (IoT) device. Accelerometer signals were filtered using a Kalman Filter and segmented with a 2-second moving average window with 50% overlap. From each window, statistical features including mean, standard deviation, minimum, maximum, and signal energy were extracted for each axis (X, Y, Z), resulting in a 15-feature vector per segment. Four classification algorithms such as Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and XGBoost were evaluated for recognizing three sheep behaviors: standing, laying, and sleeping. Experimental results on a dataset of 6,000 labeled samples showed that Random Forest outperformed other models with an accuracy of 92.1% and an F1-score of 0.9272. The trained model was then converted and deployed on an ESP32 Mini C3 microcontroller, allowing real-time behavior classification directly on the device. Classified data was transmitted via Wi-Fi and visualized on a web dashboard with latency under 2 seconds. The system demonstrates that lightweight machine learning models can be effectively integrated into embedded platforms for autonomous animal behavior monitoring. This approach reduces the reliance on manual observation and enables scalable implementation in practical farm environments. Future enhancements include incorporating additional sensor modalities expanding behavior classes and adopting adaptive learning techniques to improve robustness in diverse and dynamic livestock settings.}, archive = {J_ACCESS}, author = {Al Fahri Suhaimi and Giva Andriana Mutiara and Muhammad Fahru Rizal}, doi = {10.1109/ACCESS.2025.3612098}, journal = {IEEE Access}, pages = {165006-165024}, shortjournal = {IEEE Access}, title = {Leveraging embedded accelerometers and machine learning for real-time sheep behavior classification in precision farming}, volume = {13}, year = {2025}, }
• (2025). Stacked-via metal wall suppressing global flying crosstalk validated in RF switch circuits in 45 nm SOI. ACCESS, 13, 164998-165005. (www)

  @article{ , abstract = {Evolution of 5G and 6G wireless communications brings in new challenges to RF front-end module (FEM) integrated circuit (IC) designs. Higher frequencies, wider frequency spectrum and more frequency bands necessitate increasingly complex RF FEM IC architectures, which are extremely sensitive to global flying crosstalk occurring in the back-end-of-line (BEOL) stack containing complex metal interconnects. This paper reports a new 3D in-BEOL stacked-via vertical metal wall structure in standard CMOS to efficiently suppress global flying noises within BEOL, which is validated in silicon ICs using 38GHz single-pole double-throw (SPDT) traveling wave switch circuits fabricated in a foundry 45nm silicon-on-insulator (SOI) CMOS process technology. Measurement shows significant inter-switch flying crosstalk reduction of ~17 dB at 38GHz.}, archive = {J_ACCESS}, author = {Weiquan Hao and Xunyu Li and Zijin Pan and Runyu Miao and Zijian Yue and Albert Wang}, doi = {10.1109/ACCESS.2025.3612283}, journal = {IEEE Access}, pages = {164998-165005}, shortjournal = {IEEE Access}, title = {Stacked-via metal wall suppressing global flying crosstalk validated in RF switch circuits in 45 nm SOI}, volume = {13}, year = {2025}, }
• (2025). Dynamic and experimental analysis of decoupling simultaneous vibration isolation and energy harvesting for suspension systems. ACCESS, 13, 164985-164997. (www)

  @article{ , abstract = {Simultaneous vibration isolation and energy harvesting (SVIEH) have become much attractive in suspension systems for driving safety and energy efficiency. However, vibration isolation and energy harvesting are always conflict in existing structures. In this paper, the idea of vibration absorber is introduced to address this issue from the perspective of energy transfer. Firstly, the limitation of traditional regenerative shock absorbers (RSAs) in suspensions is outlined and the underlying reason is revealed. Then a dual-plane spring is presented to support the Halbach array and form a dynamic vibration absorber. Particularly, the electromagnetic energy harvester replaces the traditional damper. By this way, a novel decoupling SVIEH (D-SVIEH) structure is proposed for suspension systems. A two-degrees-of-freedom (2-DOF) magneto-electromechanical coupling model of the D-SVIEH system is derived and the harmonic balance method is adopted to analytically solve the dimensionless model. Next, numerical simulations are done to investigate the effects of key parameters on the force transmissibility and the average harvested power. In the end, a D-SVIEH prototype is built and experimental tests are carried out. Simulation and experimental results show that the proposed D-SVIEH structure can achieve a peak output power in the vibration isolation zone, so it can effectively overcome the conflict between vibration isolation and energy harvesting. Also, the vibration isolation starting frequency of the D-SVIEH structure is less than those of linear and traditional quasi-zero stiffness-based SVIEH structures, so the D-SVIEH structure is more suitable for low-frequency vibrations than the others. The significance of this work is to provide new insights into designing RSAs in suspension systems.}, archive = {J_ACCESS}, author = {Zhongsheng Chen and Yangyi Zhang and Zhiwen Chen}, doi = {10.1109/ACCESS.2025.3612030}, journal = {IEEE Access}, pages = {164985-164997}, shortjournal = {IEEE Access}, title = {Dynamic and experimental analysis of decoupling simultaneous vibration isolation and energy harvesting for suspension systems}, volume = {13}, year = {2025}, }
• (2025). Multi-mode multi-source electrical power subsystem design for CubeSats-internet of things missions. ACCESS, 13, 164965-164984. (www)

  @article{ , abstract = {The need to extend the global coverage of mobile communication operators in 100 percent of the earth has accelerated the launches of 5G narrowband (NB)-internet of things (IoTs) non-terrestrial network (NTN) constellations of low-earth orbit (LEO) nanosatellites. Space-borne low-power IoTs sensors are power-hungry at highly variable rates during mission operations. The total energy reserve of the CubeSat-IoTs system is 24 hours premium resource constrained by the small satellite’s solar eclipse regime(s). With the conventional heavy and rigid silicon-based solar technology design reaching its efficiency, use cases, and application limits, perovskite photovoltaic (PPV) solar cells have emerged as a worthy alternative. PPV’s flexible and lightweight properties, cost-effective silicon panels’ efficiency-boosting capability, and comparably cheaper production cost have attracted substantial commercial and scientific interests worldwide. To eliminate this problem, we propose a hybrid multiband radio frequency-perovskite photovoltaic energy harvester to sustain the constellation and/or clusters of CubeSat-IoTs systems operations anytime, anywhere, and optimise power supply equipment sizing and thermal reliability evaluation. This paper presents a multi-source multimode radio frequency-perovskite photovoltaic energy harvester development at the conceptual design phase of a CubeSat mission. This is supported by an experimental measurement of the actual power consumption of a field programmable gates array (FPGA) device in a typical CubeSat mission’s digital design implementation. The power estimate is as close to the value obtained at the system integration phase. The results show that the FPGA device’s quiescent (static) power consumptions at 50 and 100 MHz are 78 mW and 79 mW, respectively. Hybrid adaptive beamformed massive multiple-input-multiple-output heterogeneous cellular network (mMIMO-HetNets) green energy transmission and harvesting promise to address the small form factor (size, weight, power and cost) requirements and/or constraints of future sustainable small satellite missions in the LEO. The energy efficiency savings of the proposed multimode multiband RF-PPV energy harvesting and transmission system for the METCubeSat-IoTs mission is 0.23 Gb/J. This has enabled an energy budget balance for the operational space-borne devices with a multi-source N-power mode of operation to be developed for green 5G/6G/Wi-Fi and future-generation satellite-cellular convergence ecosystem and CubeSat-IoTs missions.}, archive = {J_ACCESS}, author = {Sunday C. Ekpo and Fanuel Elias and Mfonobong C. Uko and Sunday Enahoro and Stephen Alabi and Muhammad Ijaz and Rahul Unnikrishnan and Nurudeen Olasunkanmi}, doi = {10.1109/ACCESS.2025.3612339}, journal = {IEEE Access}, pages = {164965-164984}, shortjournal = {IEEE Access}, title = {Multi-mode multi-source electrical power subsystem design for CubeSats-internet of things missions}, volume = {13}, year = {2025}, }
• (2025). Xades: Static taint analysis framework for c#-based multilingual applications. ACCESS, 13, 164952-164964. (www)

  @article{ , abstract = {The C# development ecosystem, centered around the.NET Framework, underpins various platforms for mobile, desktop, and web application development. These platforms support multilingual development, enabling developers to incorporate not only C# but also a variety of subsidiary programming languages. With the rise of multilingual applications, ensuring secure interaction between different programming languages has become a critical aspect of software security. However, prior analysis approaches for Android applications have primarily focused on Java, Python, and C, leaving the risks in C#-based multilingual applications largely unaddressed. We propose a static taint analysis approach tailored for C#-based multilingual applications, along with an implementation for the Xamarin platform, named Xades, to detect sensitive data leaks. Xades offers an overview of how C# code interfaces with other programming languages by constructing a comprehensive call graph that captures cross-language method invocations. To ensure accuracy, Xades analyzes C# code in detail, addressing accessor methods and method chaining to produce a precise call graph. It, then, performs context-, flow-, field-, object- sensitive taint analysis with the call graph. Following the analysis of the C# codebase, Xades produces a taint analysis summary and translates it into Jimple IR, to analyze Xamarin.Android applications through a modified FlowDroid. We evaluated Xades on our benchmarks and real-world apps. As a result, Xades successfully detected all data leaks with interactions between C# and Java in benchmarks. It, also, detected 395 out of 6,087 real-world Android applications leak sensitive data.}, archive = {J_ACCESS}, author = {O. Sehwan and Minho Kim and Younghoon Ban and Haehyun Cho}, doi = {10.1109/ACCESS.2025.3608237}, journal = {IEEE Access}, pages = {164952-164964}, shortjournal = {IEEE Access}, title = {Xades: Static taint analysis framework for c#-based multilingual applications}, volume = {13}, year = {2025}, }
• (2025). Effectiveness of government subsidy policies in green contract farming supply chain with altruistic preference. ACCESS, 13, 164922-164951. (www)

  @article{ , abstract = {An indispensable driving force behind parties entering into green contract farming agreements is government subsidy. This paper considers a green contract farming supply chain (GCFSC) consisting of a company and a farmer, and compares the effectiveness of two green subsidy policies on the GCFSC. We find that both green subsidy policy are an effective way to improve the economic, environmental, and social benefits of the GCFSC. However, the performance of these two subsidy policies varies depending on the government’s total subsidy expenditure. In general, the green production quantity subsidy policy is more beneficial to farmers and consumers, while the green investment cost subsidy policy is more beneficial to companies and the environment. Notably, when the subsidy coefficient is high, subsidizing the company based on green investment costs can maximize returns in terms of environmental benefits and total social welfare. Furthermore, we examined the impact of a company’s altruistic preference on GCFSC decision-making and benefits under the green investment cost subsidies. The results show that although the company’s altruistic preference contributes to the overall social welfare of the GCFSC, it reduces the company’s profits. Moreover, the company’s profit losses increase as the company’s altruistic preference or the government’s subsidy coefficient increases. Our findings provide insights for policymakers to optimize green subsidy policies, and for GCFSC members to enhance decision-making and cooperation mechanisms, thereby improving the economic and environmental performance of GCFSCs.}, archive = {J_ACCESS}, author = {Xueli Ma and Ziyang Wang and Na Wang}, doi = {10.1109/ACCESS.2025.3610044}, journal = {IEEE Access}, pages = {164922-164951}, shortjournal = {IEEE Access}, title = {Effectiveness of government subsidy policies in green contract farming supply chain with altruistic preference}, volume = {13}, year = {2025}, }
• (2025). Characteristics-based design of generalized- exponent bandpass filters. ACCESS, 13, 164905-164921. (www)

  @article{ , abstract = {We develop characteristics-based filter design methods for certain related classes of IIR bandpass filters that have a peak including Generalized-Exponent Filters (GEFs) which are represented as second-order filters raised to non-unitary exponents. Such filters are useful for seismic signal phase-picking, cochlear implants, and equalizers. Instead of requiring specification of a frequency response and tolerance, our characteristics-based methods for filter design accommodates direct specification of a trio of frequency-domain characteristics from amongst the peak frequency, convexity, ndB quality factors, equivalent rectangular bandwidth, maximum group delay, and phase accumulation. We achieve this by deriving filter parameterizations in terms of sets of filter characteristics which involves deriving closed-form expressions mapping sets of filter characteristics to the original filter constants by making sharp-filter approximations. This results in various parameterizations for GEFs - including parameterizations with simultaneous specification of magnitude-based characteristics and phase-based characteristics (e.g. bandwidths and group delays) which enable designing sharply-tuned filters without significant group delay, and parameterizations for simultaneous control over frequency selectivity and synchronization which is important in designing filterbanks. We demonstrate that the methods directly apply in designing related bandpass and multiband filters. Our methods with direct control over characteristics may also be utilized beyond static filter design for higher-order variable bandpass filter design and may be useful for characteristics-based adaptive filtering. The methods are inherently stable, highly accurate in meeting strict specifications on desired characteristics, simple, and computationally efficient. Furthermore, the general paradigm can guide the development of characteristics-based filter design methods for other classes of filters.}, archive = {J_ACCESS}, author = {Samiya A. Alkhairy}, doi = {10.1109/ACCESS.2025.3610619}, journal = {IEEE Access}, pages = {164905-164921}, shortjournal = {IEEE Access}, title = {Characteristics-based design of generalized- exponent bandpass filters}, volume = {13}, year = {2025}, }
• (2025). Graph neural networks for evaluating the reliability and resilience of infrastructure systems: A systematic review of models, applications, and future directions. ACCESS, 13, 164883-164904. (www)

  @article{ , abstract = {Interdependent infrastructure systems serve as the foundation of modern society, ensuring the delivery of essential services, economic stability, and public well-being. The increasing integration of these systems, driven by advancements in cyber-physical systems (CPS) and the Internet of Things (IoT), has improved operational efficiency and adaptability. However, this interconnectivity and interdependency have also introduced vulnerabilities, making infrastructure networks more susceptible to cascading failures, cyber attacks, and disruptions from natural disasters and human-induced events. Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges, offering data-driven solutions for reliability and resilience analysis across interconnected infrastructure networks. This paper provides a comprehensive review of GNN applications in interdependent infrastructure systems, including transportation networks, power distribution networks, water distribution networks, and communication networks. By leveraging spatial-temporal modeling, multi-modal data integration, and physics-informed learning, GNN-based approaches enhance predictive accuracy, system resilience, and decision-making efficiency. This review underscores the potential of GNNs in infrastructure management optimization and risk mitigation, and enabling the development of more sustainable, adaptive, and resilient infrastructure systems in the face of evolving challenges.}, archive = {J_ACCESS}, author = {Tong Liu and Fangyu Liu}, doi = {10.1109/ACCESS.2025.3611333}, journal = {IEEE Access}, pages = {164883-164904}, shortjournal = {IEEE Access}, title = {Graph neural networks for evaluating the reliability and resilience of infrastructure systems: A systematic review of models, applications, and future directions}, volume = {13}, year = {2025}, }
• (2025). Techno-economic, environmental, and social multi-objective optimization of a grid-connected hybrid renewable energy system using metaheuristic algorithms. ACCESS, 13, 164859-164882. (www)

  @article{ , abstract = {The transition from fossil fuels to renewable energy demands energy systems that are not only technically and economically sound but also environmentally and socially sustainable. This study proposed a novel multi-objective optimisation (MOO) framework for the design of a grid-connected hybrid renewable energy system (GC-HRES) that explicitly integrated Job Creation (JC) as a fourth objective along with traditional technical, economic, and environmental objectives, such as Loss of Power Supply Probability (LPSP), Cost of Energy (COE), and Renewable Energy Fraction (REF). Prior studies often treated JC as a post-optimisation metric, while this study incorporated JC into the MOO using employment technology-specific factors. Metaheuristic algorithms, Multi-Objective Particle Swarm Optimisation (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were applied to find the optimal number of solar panels, wind turbines, and battery banks under scenarios which excluded and included JC. The results demonstrated that including JC reshaped the Pareto front, revealed new objective compromises and led to diverse configurations. MOPSO favoured solutions with higher JC and REF at the expense of cost and reliability, while NSGA-II achieved more balanced, cost-effective and reliable solutions with competitive JC values. Additional constraint sensitivity analysis further demonstrated the influence of battery bank constraints on solution feasibility. The results highlighted that integrating JC into the optimisation framework enriched design possibilities and fostered a more inclusive transition to renewable energy with a focus on GC-HRESs. This paper provided a flexible and replicable MOO framework that sets the foundation for socially attuned GC-HRES designs that align with broader sustainability goals.}, archive = {J_ACCESS}, author = {Dhruti Dheda and Jandré Albertyn and Kayode Adetunji and Zhenqing Liu and Adnan M. Abu-Mahfouz and Ling Cheng}, doi = {10.1109/ACCESS.2025.3612294}, journal = {IEEE Access}, pages = {164859-164882}, shortjournal = {IEEE Access}, title = {Techno-economic, environmental, and social multi-objective optimization of a grid-connected hybrid renewable energy system using metaheuristic algorithms}, volume = {13}, year = {2025}, }
• (2025). Substrate integrated waveguide cavity resonator-based differential RF biosensor for glucose detection in blood serum. ACCESS, 13, 164846-164858. (www)

  @article{ , abstract = {The detection of low levels of analyte concentration in biomedical fluids such as whole blood, blood serum, platelets, etc., is quite challenging for medical diagnostics and sensing applications. The difficulty mainly arises due to the fact that these biofluids usually exhibit high permittivity with significantly high dielectric losses, thereby posing challenges for their accurate dielectric testing using microwave sensors. Additionally, environmental factors like temperature, humidity, etc., and internal noises can also introduce errors in the measurement limiting the use of these types of sensors for low analyte concentrations. To address these challenges, a novel differential microwave sensor based on a microstrip line-fed Wilkinson power divider and combiner, along with a pair of identical substrate integrated waveguide (SIW) resonant cavities is proposed. The proposed differential configuration achieves low limit of detection (LoD), high quality factor (Q), and high sensitivity by effectively nullifying common-mode errors caused by environmental fluctuations and internal noises. The parameters of the proposed differential RF sensor are first optimized using the CST microwave studio for a better performance. The sensor performance is verified utilizing the dielectric testing of industrial liquids and medical grade solvents such as acetone, acetonitrile, chloroform and deionized (DI) water, with a maximum error of ±2.69%. The designed sensor is then utilized for blood glucose concentration measurement with a minimum LoD of 5 mg/dL in the heat-inactivated fetal bovine serum (FBS) to emulate the dielectric characteristics of blood. The proposed RF sensor can be potentially developed as a standalone real-time glucose concentration monitoring system.}, archive = {J_ACCESS}, author = {Prakrati Azad and Ankita Kumari and Surabhi Jain and M. Jaleel Akhtar}, doi = {10.1109/ACCESS.2025.3611675}, journal = {IEEE Access}, pages = {164846-164858}, shortjournal = {IEEE Access}, title = {Substrate integrated waveguide cavity resonator-based differential RF biosensor for glucose detection in blood serum}, volume = {13}, year = {2025}, }
• (2025). P4THLS: A templated HLS framework to automate efficient mapping of p4 data-plane applications to FPGAs. ACCESS, 13, 164829-164845. (www)

  @article{ , abstract = {The rising demand for high-bandwidth, low-latency network processing has led to a significant shift towards programmable data planes. The P4 language enables network operators to define packet processing pipelines flexibly. However, efficiently deploying P4-defined applications onto Field-Programmable Gate Arrays (FPGAs) remains a complex task due to the low-level hardware design requirements. This paper introduces P4THLS, a templated high-level synthesis framework that converts P4 data-plane applications into synthesizable C++ code for FPGA deployment. Key contributions include an automatic design process, a templated data structure and bus width, and unified memory management techniques. The proposed P4THLS architecture is evaluated through experiments, showing significant improvements in throughput, latency, and resource utilization over existing FPGA-based packet processing methods. The experiments demonstrate that P4THLS achieves up to 143.3 Gbps throughput with a 512-bit bus and 76.5 Gbps with a 256-bit bus, supports match-action tables with up to 64K entries, and sustains sub-50-cycle processing latency at 250 MHz, with end-to-end latencies of around 8 $\mu $ s.}, archive = {J_ACCESS}, author = {Mostafa Abbasmollaei and Tarek Ould-Bachir and Yvon Savaria}, doi = {10.1109/ACCESS.2025.3610893}, journal = {IEEE Access}, pages = {164829-164845}, shortjournal = {IEEE Access}, title = {P4THLS: A templated HLS framework to automate efficient mapping of p4 data-plane applications to FPGAs}, volume = {13}, year = {2025}, }
• (2025). Soft adaptive feet for legged robots: An open-source model for locomotion simulation. ACCESS, 13, 164818-164828. (www)

  @article{ , abstract = {In recent years, artificial feet based on soft robotics and under-actuation principles emerged to improve mobility on challenging terrains. This paper presents the application of the MuJoCo physics engine to realize a digital twin of an adaptive soft foot developed for use with legged robots. We release the MuJoCo soft foot digital twin as open source to allow users and researchers to explore new approaches to locomotion. The work includes the system modeling techniques along with the kinematic and dynamic attributes involved. Validation is conducted through a rigorous comparison with bench tests on a physical prototype, replicating these experiments in simulation. Results are evaluated based on sole deformation and contact forces during foot-obstacle interaction. The model estimates the forces distribution on the foot sole with good accuracy, reaching a 10.5% error on the foot heel force estimation, 21.2% error on the foot modules, and 13.1% error on the foot metatarsus. The foot model is subsequently integrated into simulations of the humanoid robot COMAN+, replacing its original flat feet. Results show an improvement in the robot’s ability to negotiate small obstacles without altering its control strategy. Ultimately, this study offers a comprehensive modeling approach for adaptive soft feet, supported by qualitative comparisons of bipedal locomotion with state of the art robotic feet.}, archive = {J_ACCESS}, author = {Matteo Crotti and Luca Rossini and Balint K. Hodossy and Anna Pace and Giorgio Grioli and Antonio Bicchi and Manuel G. Catalano}, doi = {10.1109/ACCESS.2025.3608584}, journal = {IEEE Access}, pages = {164818-164828}, shortjournal = {IEEE Access}, title = {Soft adaptive feet for legged robots: An open-source model for locomotion simulation}, volume = {13}, year = {2025}, }
• (2025). Assessing the effectiveness of transfer learning in chest X-ray analysis with the BRAX dataset. ACCESS, 13, 164805-164817. (www)

  @article{ , abstract = {The integration of machine learning techniques in medicine has significantly enhanced medical diagnostics, making them more efficient, agile, and accurate. With the continuous evolution of medical datasets, adapting machine learning models to distinct clinical contexts – often characterized by domain shift and data scarcity – is crucial. In this scenario, transfer learning and fine-tuning strategies play a key role in optimizing the training by adapting pre-trained models to new data effectively. This study investigates the impact of different fine-tuning strategies on the classification of pathologies in chest X-ray images. Instead of focusing solely on model comparisons, we analyze how various fine-tuning approaches affect performance when transferring knowledge from the CheXpert dataset to BRAX (Brazilian Labeled Chest X-ray dataset). The architectures DenseNet-121, ResNet-101, and Swin Transformer are employed as tools to assess the effectiveness of these strategies. The fine-tuning techniques explored include full layer freezing, gradual unfreezing of layers, and L1-SP/L2-SP regularization. Model performance is evaluated using ROC AUC, precision, recall, and F1-score, with 5-fold cross-validation ensuring result validity and generalization. Our findings demonstrate that the choice of fine-tuning strategy significantly impacts performance, surpassing the baseline where models were pre-trained on ImageNet and trained exclusively on BRAX. These results emphasize the importance of tailored fine-tuning approaches for optimizing deep learning models in medical imaging.}, archive = {J_ACCESS}, author = {Elton Douglas Silva and Thiago B. Pereira and Carlos Eduardo Brandão and Francisco De Assis Boldt and Thiago M. Paixão}, doi = {10.1109/ACCESS.2025.3610282}, journal = {IEEE Access}, pages = {164805-164817}, shortjournal = {IEEE Access}, title = {Assessing the effectiveness of transfer learning in chest X-ray analysis with the BRAX dataset}, volume = {13}, year = {2025}, }
• (2025). Stagnation-free PSO: A random reinitialization PSO (R²-PSO) algorithm for parameter extraction of solar cells with improved speed, accuracy, and consistency. ACCESS, 13, 164787-164804. (www)

  @article{ , abstract = {Particle swarm optimization (PSO) often stagnates in photovoltaic (PV) parameter extraction due to premature convergence and velocity decay. This paper proposes Random Reinitialized PSO (R2-PSO), which periodically reinitializes particle positions and velocities to restore diversity and sustain exploration without disrupting convergence. Performance is evaluated using the run-length distribution (RLD), a probabilistic benchmark that jointly characterizes convergence dynamics and steady-state success. In PV parameter extraction, R2-PSO achieves RMSE $= 10^{-9}$ , 100% success over 100 runs, and convergence in $\approx 1.05$ M function evaluations. Experimental validation on multi-crystalline (SM55), monocrystalline (S75), and thin-film (ST40) modules shows close agreement with manufacturer datasheets. Generalization capability is confirmed through the CEC-2022 single-objective benchmark suite, where R2-PSO attains the lowest total rank and 1st overall position among five competitive PSO-based algorithms, securing best-mean performance on 8 out of 12 functions. Statistical comparisons across both application and benchmark domains indicate uniformly lower error metrics (SSE, MAE, MSE, AE, SE) relative to competing methods. Sensitivity analyses further demonstrate robustness, maintaining 100% success under variations in the VTR threshold, population size, maximum velocity, and epoch initialization frequency. These results establish R2-PSO as an accurate, reliable, and computationally efficient optimizer for PV parameter extraction, while highlighting RLD as a rigorous standard for evaluating stochastic search algorithms.}, archive = {J_ACCESS}, author = {Samrina Zamir and Kashif Ishaque and Saba Javed and Muhammad Affan Alim and Muhammad Mansoor and Mazliham Mohd Su’ud}, doi = {10.1109/ACCESS.2025.3609672}, journal = {IEEE Access}, pages = {164787-164804}, shortjournal = {IEEE Access}, title = {Stagnation-free PSO: A random reinitialization PSO (R²-PSO) algorithm for parameter extraction of solar cells with improved speed, accuracy, and consistency}, volume = {13}, year = {2025}, }
• (2025). MLcXR+: Multilevel semantic compression for 3D immersion over 5G networks. ACCESS, 13, 164771-164786. (www)

  @article{ , abstract = {Immersive mixed reality in industrial applications requires low latencies; however, systems are commonly limited by the large amounts of generated data and the subsequent high traffic loads on the 5G network. Semantic or goal-oriented compression has emerged as a solution to this problem. In this study, we combine the generated data from several cameras at the network edge through multilevel semantic coding, which we refer to as MLcXR+. We define the overall MLcXR+ approach, including computation load placements on eas, and demonstrate feasibility in a 5G system with an open-source prototype on commodity hardware. We develop and evaluate two MLcXR+ modes: independent MLcXR+ (iMLcXR+), where the EASs operate independently, and dependent MLcXR+ (dMLcXR+), where the EASs form a chain of successive compression levels. From our evaluations with a reproducible dataset and our open-source prototype, we find that compared to prior state-of-the-art, our MLcXR+ approach can achieve a 3.5 times higher compression ratio factor, substantially reducing the data amounts sent over the 5G Data Network (DN) and processed at the Head-Mounted Display (HMD). We also find that iMLcXR+ at a single EAS, or dMLcXR+ with one or an appropriately configured higher number of EASs achieve the highest compression. Load balancing of the MLcXR+ computations over multiple EASs substantially reduces the iMLcXR+ compression performance, and can slightly increase the network transport delays of dMLcXR+ due to the compression chain. However, even load-balanced dMLcXR+ still incurs slightly less total computation (compression) and network transport delays than prior approaches, mainly due to the reduced HMD processing.}, archive = {J_ACCESS}, author = {Sifat Rezwan and Huanzhuo Wu and Juan A. Cabrera and Patrick Seeling and Martin Reisslein and Frank H. P. Fitzek}, doi = {10.1109/ACCESS.2025.3610029}, journal = {IEEE Access}, pages = {164771-164786}, shortjournal = {IEEE Access}, title = {MLcXR+: Multilevel semantic compression for 3D immersion over 5G networks}, volume = {13}, year = {2025}, }
• (2025). A compound of deep-learning and feature selection for solar power forecasting applications. ACCESS, 13, 164742-164770. (www)

  @article{ , abstract = {The greenhouse gas effect escalates due to carbon emissions from traditional use of fossil fuel, particularly driven by the development and urbanization of the modern world. In this context, solar photovoltaic (Solar-PV) power generation offers a valuable opportunity to mitigate the impact of conventional fuels. However, accurate forecasting has consistently been a challenge in the context of Solar-PV systems. This paper presents an effort to address this issue by developing a new hybrid deep-learning method named as an improved WT-LSTM model. In first phase of the proposed methodology, the most relevant features that significantly impacted the power generation are selected using various feature selection methods, including Least absolute shrinkage and selection operator (LASSO) regression, coefficient of determination, forward selection, backward selection, and mutual information. Besides, in second phase, the Solar-PV power generation is predicted by applying the proposed improved WT-LSTM model. The proposed hybrid improved WT-LSTM model adopts a wavelet transform based decomposition of the historical input time series data of solar-PV power generation into various frequency components, from which statistical features are extracted. These statistical features are also combined with meteorological features and become the basis for Solar-PV generation forecasting using improved LSTM. Moreover, the proposed improved WT-LSTM model further incorporates batch normalization, dropout, and L2 regularization to forecast various time horizons. The algorithmic performance of the proposed model is compared with different other deep-learning models such as Feedforward Neural Network (FFNN), 1D Convolutional Neural Network (1D CNN), Bidirectional Long Short-Term Memory (Bi LSTM), and Long Short-Term Memory (LSTM), and a basic hybrid WT-LSTM model. All these deep-learning models are tested on two datasets of distinct sites i.e. 1A DKASC, and 1B DKASC Alice Springs Solar-PV system, from 01 January 2019 to 31 December 2019 with 5-minute resolution. The simulation results show a significant improvement in all the performance metrics obtained by the proposed improved WT-LSTM model compared to other competing deep-learning models. Specifically, the best performance values for a 15-minute forecasting horizon are: MAE (Mean Absolute Error) of 0.38617 and 0.29736, RMSE (Root Mean Square Error) of 0.78038 and 0.64868, and R2 values of 0.95423 and 0.95592 across two datasets. Furthermore, MAE, RMSE, and R2 values are also favorable for the 30-minute and 60-minute forecasting horizons, indicating that the proposed improved WT-LSTM model delivers superior forecasting accuracy. As these simulation results indicate that the proposed hybrid deep learning method increases the prediction precision significantly, it is useful for generation planning and reserve estimation in renewable-dominated power systems.}, archive = {J_ACCESS}, author = {Praveen Kumar Singh and Anu Prakash and Amit Saraswat and Yogesh Gupta and Jayalakshmi N. Sabhahit}, doi = {10.1109/ACCESS.2025.3610419}, journal = {IEEE Access}, pages = {164742-164770}, shortjournal = {IEEE Access}, title = {A compound of deep-learning and feature selection for solar power forecasting applications}, volume = {13}, year = {2025}, }
• (2025). Partial secrecy analysis in wireless systems: Diversity-enhanced PLS over generalized fading channels. ACCESS, 13, 164723-164741. (www)

  @article{ , abstract = {Securing information in future mobile networks is particularly challenging for devices with limited computational resources. In such contexts, physical layer security (PLS) offers a promising alternative by exploiting the inherent randomness of wireless channels. When full secrecy cannot be guaranteed, the partial secrecy regime provides a realistic and effective approach to system design. This study investigates the partial secrecy performance of wireless systems operating over the recently introduced generalized multicluster fluctuating two-ray (MFTR) fading model. This model is highly regarded for its adaptability in representing a broad range of propagation environments and includes various classical fading models as exceptional cases. We consider a secure communication setup involving a transmitter (A), an intended receiver (B), and an eavesdropper (E). Inspired by previous work showing the benefits and simplicity of diversity schemes for improving PLS, we assume that both B and E are equipped with antenna arrays and apply maximal ratio combining (MRC). The model also considers independently but not identically distributed (i.n.i.d.) fading conditions on the $\texttt {A} \rightarrow \texttt {B} $ and $\texttt {A} \rightarrow \texttt {E} $ links. We derive exact and closed-form approximate expressions for three central metrics in partial secrecy theory: generalized secrecy outage probability (GSOP), average fractional equivocation (AFE), and average information leakage rate (AILR). Unlike previous approaches based on simpler fading models, the proposed expressions maintain a constant computational complexity, irrespective of the number of diversity branches, and are compatible with standard mathematical tools. Validation via Monte Carlo simulations confirms the accuracy of the derived expressions and highlights the impact of key system parameters on secrecy performance. The flexible MFTR framework enables assessment across varied fading conditions. In particular, increasing the number of MRC branches at B improves the GSOP diversity order and alters PLS behavior based on the fading characteristics of the $\texttt {A} \rightarrow \texttt {E} $ link.}, archive = {J_ACCESS}, author = {Henry Ramiro Carvajal Mora and Nathaly Verónica Orozco Garzón and Fernando Darío Almeida García and José David Vega Sánchez and Felipe Grijalva and Edgar Eduardo Benitez Olivo}, doi = {10.1109/ACCESS.2025.3611833}, journal = {IEEE Access}, pages = {164723-164741}, shortjournal = {IEEE Access}, title = {Partial secrecy analysis in wireless systems: Diversity-enhanced PLS over generalized fading channels}, volume = {13}, year = {2025}, }
• (2025). Item cold start in E-commerce recommender systems: A survey. ACCESS, 13, 164702-164722. (www)

  @article{ , abstract = {Recommender systems in e-commerce face a persistent challenge known as the item cold-start problem, where systems struggle to make relevant suggestions about new items due to insufficient historical data. While early research primarily focused on effectiveness metrics like accuracy, recent years have seen a change toward incorporating fairness and explainability into the systems. The evolution of transformer architectures has significantly impacted data complexity. This also marks a shift enabling systems to power recommendations on complex patterns in data, but with a requirement of larger datasets. This survey categorizes existing approaches to the item cold-start problem using a three-tier classification system that distinguishes methodological foundation, data structure and implementation technique and presents them in a temporal framework that highlights both data complexity and underlying objectives. The survey outlines a practical framework tailored to different e-commerce business scales, guiding implementation strategies accordingly. Additionally, evaluation metrics are analyzed through the lens of effectiveness and fairness, providing practitioners with a comprehensive guide for implementing these systems in practice.}, archive = {J_ACCESS}, author = {Natalija Glisovic and Danica Kragic and Martin Tegner}, doi = {10.1109/ACCESS.2025.3611026}, journal = {IEEE Access}, pages = {164702-164722}, shortjournal = {IEEE Access}, title = {Item cold start in E-commerce recommender systems: A survey}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence (AI) in Patient–Healthcare relationships: Psychosocial perspective. ACCESS, 13, 164682-164701. (www)

  @article{ , abstract = {As artificial intelligence (AI) becomes increasingly integrated into healthcare–ranging from basic health monitoring tools such as heart rate monitors and sleep tracking apps to advanced systems such as AI–powered software and companion robots, it is vital to better understand how these technologies affect the dynamics of patient–healthcare relationships. This paper aims to provide a comprehensive perspective of existing research examining both the observable and potential effects of AI–enabled technologies on existing relationships and interactions among patients, healthcare providers, and caregivers. It also explores the emerging field of patient–AI engagement. Based on existing research and challenges, this paper proposes a conceptual model, a Healthcare–AI Triadic Relational Model, to facilitate analyze, discuss, and communicate the psychosocial impacts of AI–powered healthcare technologies on these patient–healthcare relationships. The discussion concludes by highlighting key considerations and practical recommendations for the implementation of AI technologies within these relational contexts, aiming to improve healthcare outcomes and uphold a patient-centered approach to care.}, archive = {J_ACCESS}, author = {Manob Jyoti Saikia and Debanjan Borthakur}, doi = {10.1109/ACCESS.2025.3611818}, journal = {IEEE Access}, pages = {164682-164701}, shortjournal = {IEEE Access}, title = {Artificial intelligence (AI) in Patient–Healthcare relationships: Psychosocial perspective}, volume = {13}, year = {2025}, }
• (2025). Single-layer compact circularly polarized antenna for unmanned aerial vehicle applications. ACCESS, 13, 164675-164681. (www)

  @article{ , abstract = {This paper presents a single-layer circularly polarized (CP) antenna with compact size for unmanned aerial vehicle (UAV) applications. The antenna consists of four truncated corner square patches, which are simultaneously excited by a microstrip line in the same layer. This configuration allows the antenna to realize CP radiation with better gain and front-to-back ratio (FBR) in comparison to other compact CP antennas, while maintaining a compact size of $0.49~\lambda \times 0.49~\lambda \times 0.03~\lambda $ . The measured performance of the proposed antenna shows good CP operation over a bandwidth of about 3.3% (5.34–5.52 GHz) with broadside gain of 7.0 dBi and an FBR greater than 20 dB. These performances demonstrate the effectiveness of the proposed antenna in the UAV communication systems. Additionally, only four such antennas, arranged in either 2-dimensional or circular configurations, are required to provide wide coverage for communication between a UAV and a ground station or between UAVs.}, archive = {J_ACCESS}, author = {Hung Nguyen-Manh and Giang Nguyen-Hoai and Duy-Phong Pham and Tu Chu-Anh and Nguyen Quoc Dinh}, doi = {10.1109/ACCESS.2025.3606538}, journal = {IEEE Access}, pages = {164675-164681}, shortjournal = {IEEE Access}, title = {Single-layer compact circularly polarized antenna for unmanned aerial vehicle applications}, volume = {13}, year = {2025}, }
• (2025). Automated and semi-automated frameworks for left ventricle volume and ejection fraction computation from CMR images. ACCESS, 13, 164659-164674. (www)

  @article{ , abstract = {Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. Cardiovascular Magnetic Resonance (CMR) imaging is widely recognized as the gold standard for the non-invasive assessment of cardiac structure and function. Among the key clinical biomarkers, left ventricular volume and ejection fraction are routinely estimated from CMR images to evaluate cardiac performance. This paper presents both fully automated and semi-automated frameworks for LVEF estimation at end-systole and end-diastole, with the primary distinction lying in their segmentation methodologies. The proposed pipeline consists of image enhancement, heart localization using Fourier transform techniques, segmentation, and volume estimation. In the semi-automated system, segmentation is achieved through a learning-free region growing algorithm, augmented by global thresholding and an optimized seed point selection strategy. The automated approach employs a deep convolutional neural network based on the U-Net architecture. Both systems incorporate inter-slice spatial continuity to enhance segmentation consistency across short-axis slices of the left ventricle. Experimental evaluation was conducted on a dataset comprising 4,400 short-axis CMR images. The proposed methods achieved Continuous Ranked Probability Score (CRPS) values of 0.053 and 0.015 for the automated and semi-automated systems, respectively. The results demonstrate the effectiveness and robustness of the proposed approaches, particularly the learning-free method, in handling anatomical and imaging variability. These findings underscore the potential of the proposed systems to support reliable and efficient LVEF quantification, thereby contributing to improved clinical workflows and decision-making in cardiovascular diagnostics.}, archive = {J_ACCESS}, author = {Aziz M. Qaroush and Mohammad Jubran and Riyad Yahya and Omar Muhtaseb and Qusai Safa and Ahmad Alsadeh}, doi = {10.1109/ACCESS.2025.3611512}, journal = {IEEE Access}, pages = {164659-164674}, shortjournal = {IEEE Access}, title = {Automated and semi-automated frameworks for left ventricle volume and ejection fraction computation from CMR images}, volume = {13}, year = {2025}, }
• (2025). Analyzing factors influencing temporary and permanent work-related accident disabilities: An interpretable machine learning approach. ACCESS, 13, 164641-164658. (www)

  @article{ , abstract = {This study investigates the use of machine learning (ML) to predict work-related accident injury outcomes in Brazil, specifically distinguishing between temporary and permanent disabilities. A dataset of 4,476 well-documented cases, collected between 2014 and 2022, was constructed from the Brazilian Notifiable Diseases Information System (‘Sistema de Informação de Agravos de Notificação’, SINAN). To identify the 10 most influential predictors, this study integrated the rankings from three distinct algorithms: Kruskal-Wallis, ANOVA, and MRMR. Several classifiers were then evaluated, including Random Forest (RF), Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), and K-Nearest Neighbors (KNN). Compared to a baseline model that achieved an accuracy of 66.62% and an F1-score of 53.28%, the RF model recorded the highest overall performance with an accuracy of 86.84% and F1-score of 86.59%. To enhance model interpretability, Shapley Additive Explanations (SHAP) analysis was employed, revealing that affected body part, accident type, and occupational role are critical predictors of disability outcome. These findings highlight the potential of ML, coupled with explainability tools, to facilitate early risk identification and inform targeted occupational health interventions, aligning with regulatory frameworks and established occupational health literature.}, archive = {J_ACCESS}, author = {André Luis Schneider and Érick Oliveira Rodrigues and Marcelo Gonçalves Trentin and Flávio Trojan and Sérgio Luiz Ribas Pessa}, doi = {10.1109/ACCESS.2025.3611324}, journal = {IEEE Access}, pages = {164641-164658}, shortjournal = {IEEE Access}, title = {Analyzing factors influencing temporary and permanent work-related accident disabilities: An interpretable machine learning approach}, volume = {13}, year = {2025}, }
• (2025). A multimodal AI-driven system for preoperative difficult airway assessment. ACCESS, 13, 164630-164640. (www)

  @article{ , abstract = {Preoperative assessment of the difficult airway is essential to ensure anesthetic safety. However, traditional assessment methods rely on subjective clinical evaluations and demonstrate limited predictive accuracy. To address this challenge, we propose a multimodal AI-driven airway assessment system. The hardware architecture consists of an integrated camera module, computation module, and display module, while the software is built on a three-stage intelligent framework that comprises image feature extraction, feature-based parameter quantification, and data fusion-based risk prediction. In the feature extraction stage, the framework employs deep learning (DL) models to process input images. Subsequently, the framework automatically measures five critical airway parameters. In the prediction stage, these parameters are fused with personal basic information. Finally, an eXtreme gradient boosting (XGBoost) model processes the fused data to generate a final risk prediction for airway assessment. We evaluated the system performance using a dataset including 1,207 patients undergoing general anesthesia. On an independent test set, the DL models for feature extraction demonstrate high accuracy, with the AI-driven system achieving an area under the curve (AUC) of 0.831. The proposed system offers an objective and accurate solution for preoperative airway assessment, outperforming traditional methods.}, archive = {J_ACCESS}, author = {Mannan Abdul and Weixiong Chen and Lili Feng and Jianhao Tang and Yuan Han and Wenxian Li and Yu Tian}, doi = {10.1109/ACCESS.2025.3610860}, journal = {IEEE Access}, pages = {164630-164640}, shortjournal = {IEEE Access}, title = {A multimodal AI-driven system for preoperative difficult airway assessment}, volume = {13}, year = {2025}, }
• (2025). Customer load data synthesis for privacy to provide demand response service. ACCESS, 13, 164621-164629. (www)

  @article{ , abstract = {With advancements in information and communication technology (ICT), the use of big data has become increasingly prevalent, particularly in the power industry. This study proposes a novel data synthesis methodology to enable the secure sharing and utilization of data containing personal information while retaining the utility of the original data. To model the key components of electricity consumption data, a segmented seasonal trend decomposition using loess (STL) synthesis methodology is proposed, focusing on capturing temporal patterns and characteristics. Segmented STL synthesizes electricity consumption data by segmenting into smaller units and using Gaussian process regression (GPR) and generalized extreme value (GEV) models to effectively capture temporal patterns while enhancing privacy protection. A comparative analysis was conducted on row-wise shuffling, segment-unit row-wise shuffling, and the proposed model. The effectiveness of the proposed method was evaluated using various metrics, including population stability index (PSI), principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). In addition, a case study was conducted to determine whether the proposed method properly estimates the potential of a demand response service. The experimental results demonstrate that the proposed methodology effectively reflects the time-series characteristics and electricity usage patterns of the actual data with an error margin within 0.1% while providing a high level of privacy protection.}, archive = {J_ACCESS}, author = {Min-Su Kim and Dae-Sung Lee and Sung-Yong Son}, doi = {10.1109/ACCESS.2025.3608739}, journal = {IEEE Access}, pages = {164621-164629}, shortjournal = {IEEE Access}, title = {Customer load data synthesis for privacy to provide demand response service}, volume = {13}, year = {2025}, }
• (2025). An undeniable signature scheme based on approximate trapdoors for lattices. ACCESS, 13, 164609-164620. (www)

  @article{ , abstract = {An undeniable signature is a type of digital signature in which the signer retains control over whether their signatures can be verified by others. Furthermore, it enables signatories to revoke their signatures in cases of significant compromise or disputes. Due to the great trend towards undeniable signatures based on lattices, the key and signature sizes are still large, which leads to concerns about efficiency. This work proposes a new undeniable signature scheme depending on approximate-ISIS problem, including signature verification, disavowal procedure, and key generation. We analyze the security of the proposed signature scheme against known cryptographic attacks on undeniable signatures. Also, we demonstrate that our scheme possesses completeness, soundness, and invisibility properties. Moreover, under the suppositions of the random oracle model, that propositioned signature is resistant to existential forgery. Our results demonstrate that it is possible to reduce both the public key and signature sizes by half compared to designs based on accurate trapdoors.}, archive = {J_ACCESS}, author = {Hussah Mohammad Almenderj and Eddie Shahril Ismail}, doi = {10.1109/ACCESS.2025.3611521}, journal = {IEEE Access}, pages = {164609-164620}, shortjournal = {IEEE Access}, title = {An undeniable signature scheme based on approximate trapdoors for lattices}, volume = {13}, year = {2025}, }
• (2025). Assessing power quality disturbances caused by urban EV fast charging stations in low voltage grids: A case from kavacik, istanbul. ACCESS, 13, 164593-164608. (www)

  @article{ , abstract = {The establishment of a large number of Electric Vehicle Charging Stations (EVCSs) increases the attractiveness of Electric Vehicle (EV) use. However, these types of charging stations are classified as mobile and non-linear loads in electrical power systems. This situation introduces new challenges of a scale and nature that electricity distribution networks are not accustomed to. Considering technological changes from the past to the present, it is predicted that Electric Vehicle Fast Charging Stations (EVFCS) will constitute a significant share of all types of EVCSs. Consequently, many electrical issues are likely to emerge in the grid with the integration of these charging stations into the distribution network. Considering the concerns about these potential problems and strategies for mitigating them, it is crucial to examine the possible electrical impacts of EVCSs on the distribution network. In this study, substations, transformer substations, transformer points, HV/LV distribution feeders, and loads belonging to the distribution company were analyzed in the pilot region of Istanbul/Kavacik. The locations of existing stations in the region were taken into account when determining the placement of EVCSs on the distribution network. The simulation of the system is modeled using MATLAB/Simulink software, incorporating real-time technical data such as the location of distribution transformers, power data, load density, and the number and length of feeders, along with data on system protection and control elements. When the simulation results are analyzed, it is noted that the current value drawn in the feeder to which the single-phase EVFCS is connected increases by approximately 32%, while this value rises by 75% in all phases of three-phase EVFCS systems. Although the total harmonic distortion value did not increase significantly in single-phase systems, it was found to exceed the limit values in three-phase systems. It was confirmed that the voltage drops did not surpass the permitted limits.}, archive = {J_ACCESS}, author = {Fikret Kaya and Onur Akar}, doi = {10.1109/ACCESS.2025.3610775}, journal = {IEEE Access}, pages = {164593-164608}, shortjournal = {IEEE Access}, title = {Assessing power quality disturbances caused by urban EV fast charging stations in low voltage grids: A case from kavacik, istanbul}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Enhancing metal surface defect recognition through image patching and synthetic defect generation”. ACCESS, 13, 164592. (www)

  @article{ , abstract = {Presents corrections to the paper, (“Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation”).}, archive = {J_ACCESS}, author = {Behkzod Mustafaev and Sungwon Kim and Eungsoo Kim}, doi = {10.1109/ACCESS.2025.3609895}, journal = {IEEE Access}, pages = {164592}, shortjournal = {IEEE Access}, title = {Corrections to “Enhancing metal surface defect recognition through image patching and synthetic defect generation”}, volume = {13}, year = {2025}, }
• (2025). New generation of higher-order controllers: Leveraging PID control for improved system response. ACCESS, 13, 164567-164591. (www)

  @article{ , abstract = {The article extends the performance that can be achieved with the setup for the design of higher-order proportional-integral-derivative (HO PID) controllers using ultra-local integrator-plus-dead-time (IPDT) models. The previously derived reference family of controllers, referred to as 1PID, is complemented by a new family of 2PIDs corresponding to the double integrator plus dead time (DIPDT) model. The individual members of the 1PID and 2PID families are derived using the multiple real dominant pole method (MRDP) and normalized by the parameters of the ultra-local models used. The degree of the considered controller derivatives $m \in [{1, 6}]$ significantly outperforms most of the known methods for PID control design, which mainly use $m \leq 1$ and only exceptionally consider the cases with $m \gt 1$ . Therefore, the obtained results are even comparable to the HO controllers created by approximating fractional-order PID controllers. To implement the m th-order derivative, binomial low-pass filters of order $n \geq m$ are used in the controllers, specified by an equivalent filter delay added to the process. The traditional controllers with two degrees of freedom, for the separate design of setpoint and disturbance responses, are extended by two additional degrees of freedom provided by m and n, allowing to modify the speed of the transients together with their shapes and the closed-loop robustness. The choice of 1PIDs and 2PIDs brings another degree of freedom related to the process model used. The multiple controller parameters are set by a new modification of the Performance Portrait Method (PPM), which makes it possible to shape the closed-loop responses by specifying tolerances for deviations of step responses from their ideal shape in the time domain. The illustrative examples, which deal with two stable higher-order processes and an unstable system, show the possibility of a multiple increase in performance compared to previously known methods. They show that HO-2PID controllers are inherently better suited for processes characterized by multiple dominant time constants. The novel design opens the door to wider use of HO controllers enabled by embedded control and programmable device technology, and allows the development of new HO controllers tuned by parallel computation.}, archive = {J_ACCESS}, author = {Mikulas Huba and Jarmila Skrinarova and Damir Vrancic and Pavol Bistak}, doi = {10.1109/ACCESS.2025.3610297}, journal = {IEEE Access}, pages = {164567-164591}, shortjournal = {IEEE Access}, title = {New generation of higher-order controllers: Leveraging PID control for improved system response}, volume = {13}, year = {2025}, }
• (2025). Intelligent battery management system for electric vehicles: AI-driven voltage cell prediction using GRU and K-means clustering. ACCESS, 13, 164552-164566. (www)

  @article{ , abstract = {The advancement of Electric Vehicles (EVs) requires intelligent Battery Management Systems (BMS) for accurate voltage prediction, ensuring battery reliability, longevity, and efficiency. Traditional BMS architectures rely on rule-based monitoring, which lacks predictive capabilities for early fault detection and proactive maintenance. This study presents an AI-driven BMS framework, integrating K-Means clustering and Gated Recurrent Unit (GRU) networks to enhance real-time voltage forecasting. Unlike conventional approaches that focus solely on clustering or deep learning, this research combines both methodologies to create a robust predictive system. K-Means clustering segments battery voltage data into operational groups, improving predictive accuracy by organizing similar voltage behaviors. GRU networks then capture sequential voltage dependencies, enabling precise voltage fluctuation predictions. Experimental validation was conducted using voltage data from 120 lithium-ion battery cells, recorded at 20 km/h under three load conditions (Load 0, Load 10, and Load 20) over a 5-minute duration. The findings demonstrate that K-Means clustering effectively categorizes battery voltage states, while GRU-based forecasting achieves high accuracy, reinforcing the practicality of AI-powered predictive maintenance. This study advances AI-driven BMS frameworks by demonstrating the integration of clustering and deep learning for voltage prediction, providing a foundation for real-time battery diagnostics and predictive analytics.}, archive = {J_ACCESS}, author = {Narawit Pahaisuk and Supavee Pourbunthidkul and Pattarapong Phasukkit and Nongluck Houngkamhang}, doi = {10.1109/ACCESS.2025.3611488}, journal = {IEEE Access}, pages = {164552-164566}, shortjournal = {IEEE Access}, title = {Intelligent battery management system for electric vehicles: AI-driven voltage cell prediction using GRU and K-means clustering}, volume = {13}, year = {2025}, }
• (2025). Rotor position estimation for a position-sensorless FOC PMSM Drive—A super-twisting-based sliding mode observer approach. ACCESS, 13, 164540-164551. (www)

  @article{ , abstract = {The fact that the back-EMF voltage on the d-axis is zero can be used to develop a rotor position estimation method for a surface-mounted PMSM (SPMSM). However, d-axis voltage modeling error may degrade the transient-state performance of a position-sensorless drive. Therefore, this paper presents a second-order sliding mode observer (SMO) based position-sensorless Field-Oriented Control (FOC) drive for SPMSMs. In order to alleviate the chattering phenomenon induced by estimated back-EMF as well as phase delay caused by a low-pass filter that are inherent in conventional SMOs, a super-twisting algorithm-based SMO (STA-SMO) with a modified phase-locked loop (PLL) is adopted to precisely estimate the angular position and angular speed of a SPMSM. Moreover, dynamic performance can be further improved due to a well-tracked estimated back-EMF on the d-axis. Finally, the proposed position-sensorless FOC based on STA-SMO is implemented and verified on a 0.75 kW SPMSM using MATLAB/Simulink. Simulation results verify the effectiveness of the proposed rotor position estimation method.}, archive = {J_ACCESS}, author = {Jun-Ping Chang and Ming-Yang Cheng}, doi = {10.1109/ACCESS.2025.3611005}, journal = {IEEE Access}, pages = {164540-164551}, shortjournal = {IEEE Access}, title = {Rotor position estimation for a position-sensorless FOC PMSM Drive—A super-twisting-based sliding mode observer approach}, volume = {13}, year = {2025}, }
• (2025). DOA estimation of completely polarized signals by one-bit cross-dipole arrays. ACCESS, 13, 164528-164539. (www)

  @article{ , abstract = {The one-bit cross-dipole array employs one-bit quantization to reduce the sampling system overhead while extracting polarization information from electromagnetic signals, thereby lowering the system complexity of the polarization array. However, the existing DOA estimation algorithms for this array utilize incomplete information from the received signals of the array. Cross-dipole array consists of two scalar arrays orthogonal to each other. With one-bit measurements, the array outputs two one-bit scalar array datum. By exploiting the two array datum, we can generate two covariance matrices and two cross-covariance matrices of the cross-dipole array signals. Existing direction-of-arrival (DOA) estimation algorithms based on one-bit measurements only realize DOA estimation by combining two covariance matrices, resulting in insufficient mining of information. In this paper, we propose a DOA estimation method for completely polarized signals that jointly utilizes auto-covariance and cross-covariance. To do this, we first study the reconstruction of two cross-covariance matrices from one-bit measurements. Based on the analyses of four covariance matrices, we then formulate virtual array datum consisting of four virtual snapshots by difference array technique, called parallel coarray signals. As the parallel coarray signals are coherent although the incoming sources are independent, we employ the spatial-smoothing technique on the parallel coarray signals to perform the DOA estimation. Numerical results show that the performance of the proposed method is significantly improved for completely polarized signals.}, archive = {J_ACCESS}, author = {Yu Wang and Qian Cao and Zhiyong Cheng and Di Song and Zhenyu Liu and Zheng Lu}, doi = {10.1109/ACCESS.2025.3610644}, journal = {IEEE Access}, pages = {164528-164539}, shortjournal = {IEEE Access}, title = {DOA estimation of completely polarized signals by one-bit cross-dipole arrays}, volume = {13}, year = {2025}, }
• (2025). Survey of lightweight hardware-based hash functions for security in constrained IoT devices. ACCESS, 13, 164508-164527. (www)

  @article{ , abstract = {Lightweight cryptographic primitives are commonly used to secure communication in resource-constrained IoT devices such as RFID tags, sensors, actuators, wireless sensor networks, and edge gateways. This paper surveys a selection of key hardware-based lightweight hash functions, classified according to their architectural construction and compared using hardware-specific performance metrics, including gate equivalency, throughput, and figure of merit. We map each hash function to a distinct IoT device category based on a novel correlation between throughput and gate equivalency and use this to assess their suitability for the specific resource-constrained applications. The security properties of the lightweight hash functions are systematically evaluated based on preimage resistance, second preimage resistance, and collision resistance, complemented by regression analysis and a comprehensive review of published cryptanalytic attacks and known vulnerabilities. We highlight structural weaknesses in commonly used lightweight hash-function construction approaches and expose key trade-offs between hardware performance, cost, and cryptographic strength.}, archive = {J_ACCESS}, author = {Mohsin Khan and Elisavet Kozyri and Dag Johansen and Håvard Dagenborg}, doi = {10.1109/ACCESS.2025.3611280}, journal = {IEEE Access}, pages = {164508-164527}, shortjournal = {IEEE Access}, title = {Survey of lightweight hardware-based hash functions for security in constrained IoT devices}, volume = {13}, year = {2025}, }
• (2025). Trend-fluctuation correlated attention unit for remaining useful life prediction. ACCESS, 13, 164486-164507. (www)

  @article{ , abstract = {Health indicators (HIs) are measures that consolidate complex machinery characteristics into a single dimension and have been extensively studied for predicting the remaining useful life (RUL). Recent research has proposed HI-prediction models based on recursive cell structures that demonstrate promising performance. However, recent cell structures have limitations, such as saturation in hidden states and difficulty learning interdependent patterns among various components. Accurate RUL predictions require minimizing error accumulation in the recursive HI-prediction process, which can be achieved using a cell structure capable of capturing interdependent patterns. This study proposes a new cell structure termed the trend-fluctuation correlated attention unit (TFCAU) that employs a gate mechanism to capture interactions between divided trends and fluctuations in HI and an attention mechanism for robust long-term predictions. The superiority of the TFCAU was validated using a bearing dataset from the PRONOSTIA platform and a real-world dataset from the Korean weapons system. Comparative experiments using six sub-datasets demonstrated that TFCAU outperformed the state-of-the-art model—the gated transient fluctuation dual-attention unit—achieving a 169.37% improvement in the score metric and a reduction of 49.93 units in the mean-absolute error. An extensive ablation study experimentally confirmed that the TFCAU effectively captured the interdependent characteristics between trends and fluctuations. TFCAU delivered excellent performance for both rotating and non-rotating systems, suggesting its potential applicability across multiple operational scenarios.}, archive = {J_ACCESS}, author = {Hanbyeol Park and Sunghyun Sim and Yunkyung Park and Hyerim Bae}, doi = {10.1109/ACCESS.2025.3607444}, journal = {IEEE Access}, pages = {164486-164507}, shortjournal = {IEEE Access}, title = {Trend-fluctuation correlated attention unit for remaining useful life prediction}, volume = {13}, year = {2025}, }
• (2025). Event-triggered resilient matrix-scaled consensus of heterogeneous multi-agent systems. ACCESS, 13, 164474-164485. (www)

  @article{ , abstract = {This paper investigates the resilient matrix-scaled consensus problem for continuous-time heterogeneous multi-agent systems in the presence of bounded malicious nodes. Each agent is assigned a predefined positive/negative matrix weight. Unlike existing research on consensus problems, this work adopts a generalized matrix-scaled approach to accommodate the independence of multi-dimensional states, extending the final convergence subspace to a broader open region within the state space rather than restricting it to a common value or specific ratio. An event-triggered communication-based matrix-scaled resilient algorithm is proposed to mitigate the influence of malicious nodes on normal agent convergence while significantly reducing communication overhead among agents. Furthermore, by employing system transformation, necessary and sufficient conditions for network topology are established to address the resilient matrix-scaled consensus problem. The effectiveness of theoretical result is further verified by a numerical simulation.}, archive = {J_ACCESS}, author = {Zheng Zhang and Junjie Guan and Xiujuan Zhao and Shiming Chen}, doi = {10.1109/ACCESS.2025.3606484}, journal = {IEEE Access}, pages = {164474-164485}, shortjournal = {IEEE Access}, title = {Event-triggered resilient matrix-scaled consensus of heterogeneous multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). Comparison of complexity of regular versus oblivious decision trees for decision tables with many-valued decisions from closed classes. ACCESS, 13, 164464-164473. (www)

  @article{ , abstract = {In this work, we examine decision table (DTA) classes that are closed in terms of decision modification (i.e., the sets of decisions) and attribute deletion (i.e., columns). We examine the relationship between the minimum complexity of regular decision trees (DTRs) and the minimum complexity of oblivious DTRs, where the order of queries on attribute values is preset, for tables belonging to these classes. We show (and this is the main novelty of the paper) that the function expressing this dependence either grows logarithmically, or develops almost linearly, or is bounded from above by a constant. The depth and weighted depth of DTRs are examples of so-called bounded complexity measures (BCMs) for which this result is valid. Furthermore, for a DTA, we show that the minimum complexity of a reduct for that table is equal to the minimum complexity of an oblivious DTR.}, archive = {J_ACCESS}, author = {Azimkhon Ostonov and Mikhail Moshkov}, doi = {10.1109/ACCESS.2025.3610956}, journal = {IEEE Access}, pages = {164464-164473}, shortjournal = {IEEE Access}, title = {Comparison of complexity of regular versus oblivious decision trees for decision tables with many-valued decisions from closed classes}, volume = {13}, year = {2025}, }
• (2025). Enhanced gated MLP-based lung disease classification using grey wolf optimized (GWO) multi-feature extraction with cumulative learning. ACCESS, 13, 164442-164463. (www)

  @article{ , abstract = {Lung diseases are prevalent in today’s world, exhibiting a wide range of disease variations that requires early diagnosis for preventing disease evolvement, diminishing long-term complications, and limiting the fatality rate. Conventional feature extraction methods such as scale-invariant feature transform (SIFT), discrete wavelet transform (DWT) and classification models like convolutional neural network (CNN), multi-layer perceptron (MLP), graph neural network (GNN), are associated with high computational complexity, redundancy in feature structure, and restricted adaptability due to rigid designs. Moreover, it demonstrates limitations in scalability, generalization over limited datasets, susceptible to noise or illumination and lack of optimized feature sets leading to weak predictive performance. In order to overcome that, a novel framework that enhances lung disease prediction using Grey wolf optimization (GWO) based optimized multi-feature extraction with cumulative, recursive learning and a gated multi-layer perceptron (GMLP) for robust classification. This approach provides robustness to noise or illumination i.e. invariant feature extraction, efficient dimensionality and complexity of the extracted features, captures multi-scale spatial-textural patterns through global and local feature fusion and it provides high scalability and generalization capability while maintaining computational efficiency. Also, the optimization approach leads to progressive feature enhancement, knowledge retention, adaptability and redundancy elimination for the extracted features which are further processed by a classifier that selectively integrates full and regional patterns to sharpen feature distinctiveness and improve classification performance. The results obtained from the proposed framework tested on benchmark datasets were encouraging, showcasing notable performance with 98% accuracy outperforming the evaluated baseline methods.}, archive = {J_ACCESS}, author = {Lakshman Shanmugam and Aishwarya Shaji and S. Abinaya and J. S. Sourish}, doi = {10.1109/ACCESS.2025.3611029}, journal = {IEEE Access}, pages = {164442-164463}, shortjournal = {IEEE Access}, title = {Enhanced gated MLP-based lung disease classification using grey wolf optimized (GWO) multi-feature extraction with cumulative learning}, volume = {13}, year = {2025}, }
• (2025). Enhancing GNSS localization in urban canyons with a hybrid CNN-autoencoder approach to LOS/NLOS classification. ACCESS, 13, 164426-164441. (www)

  @article{ , abstract = {Non-Line-of-Sight (NLOS) reception is acknowledged as a primary source of positioning error in Global Navigation Satellite System (GNSS) applications, particularly within dense urban settings. When the direct satellite path is obstructed by buildings and other structures, receivers must depend on the reflected signals. This reliance results in significant pseudorange biases and a diminished localization accuracy. Methods based on learning have been developed to classify signals as Line-of-Sight (LOS) or NLOS using signal features; however, many approaches depend on external sensors, heuristic thresholds, or hand-crafted features, restricting their scalability and adaptability. A hybrid deep learning (DL) framework is proposed to address these constraints, integrating a Convolutional Neural Network (CNN) with an Autoencoder to improve LOS/NLOS signal classification utilizing raw GNSS measurements. The model is trained and evaluated using the UrbanNav dataset, which offers dual-frequency multi-GNSS observations from urban canyons in Hong Kong and Tokyo. The training process encompasses feature normalization, class balance, and structured learning, with optimization conducted through the Adam optimizer and dynamic learning rate scheduling. It has been demonstrated through experimental results that the proposed approach achieves a global classification accuracy of 99.90%. For LOS signals, an accuracy of 99.95% is recorded, along with a precision, recall, and F1 score of 100%. In contrast, NLOS signals are accurately captured with 100% in all evaluation criteria. These outcomes highlight the robustness and precision of the proposed method, rendering it highly suitable for applications necessitating reliable urban positioning, such as autonomous driving, UAV localization, and pedestrian navigation.}, archive = {J_ACCESS}, author = {S. Titouni and I. Messaoudene and Y. Himeur and D. W. Dawoud and M. Belazzoug and B. Hammache and F. Chetouah and S. Atalla and W. Mansoor}, doi = {10.1109/ACCESS.2025.3611517}, journal = {IEEE Access}, pages = {164426-164441}, shortjournal = {IEEE Access}, title = {Enhancing GNSS localization in urban canyons with a hybrid CNN-autoencoder approach to LOS/NLOS classification}, volume = {13}, year = {2025}, }
• (2025). FLAGaTST: Fuzzy logic transformed adversarial GAN and time series transformer for robust MPPT under partial shading conditions. ACCESS, 13, 164409-164425. (www)

  @article{ , abstract = {Partial shading poses a significant challenge in photovoltaic systems by creating multiple peaks in the power-voltage curve, complicating the task of accurately tracking the Maximum Power Point. Traditional maximum power point tracking methods often struggle to identify the true Global Maximum Power Point, leading to suboptimal energy harvesting. This paper proposes a novel hybrid tracking framework that integrates fuzzy logic, synthetic data generation using Generative Adversarial Networks (GANs), and time-series modeling with Transformer architectures. Fuzzy logic improves resilience to input uncertainties by translating raw data into interpretable fuzzy values. GANs augment the dataset by generating realistic synthetic samples, thereby improving generalization. The Transformer model leverages self-attention mechanisms to capture long-term temporal patterns in solar irradiance and power profiles. By combining these strengths, the proposed method delivers a robust and accurate global maximum power point tracking solution, particularly under dynamic and partially shaded environments. Experimental results demonstrate its superior performance and scalability compared to conventional maximum power point tracking approaches.}, archive = {J_ACCESS}, author = {E. Elakkiya and S. Antony Raj and S. Priya}, doi = {10.1109/ACCESS.2025.3611707}, journal = {IEEE Access}, pages = {164409-164425}, shortjournal = {IEEE Access}, title = {FLAGaTST: Fuzzy logic transformed adversarial GAN and time series transformer for robust MPPT under partial shading conditions}, volume = {13}, year = {2025}, }
• (2025). Deep-learning-based determination of textile properties: A novel triplet architecture approach for classifying cotton content. ACCESS, 13, 164395-164408. (www)

  @article{ , abstract = {To reach sustainable production and consumption patterns, recycling is a key task. Automating the recycling process, especially in sorting tasks, is a strong hope to improve the efficiency and economic viability of the recycling industry. Especially the determination of textile properties such as cotton content is an important task in recycling and the textile industry as a whole. Approaches using NIR-spectrography are common, but can be costly and produce complex datasets. We therefore propose a visual approach to classify fabrics after their cotton content. For this, we apply a novel triplet-architecture approach with a variety of modifications. We combine ResNet50, EfficientNetB4, and DenseNet121 in this architecture in order to make use of their respective strengths while overcoming the weaknesses of the models. For a further accuracy enhancement, we modified all three architectures with adaptive feature pyramid networks, and we added a deformable convolution layer to ResNet50 and DenseNet121. Additionally, we use a second fully connected layer to enhance the model’s classification capacity. With this architecture approach, we achieve an average Root Mean Squared Error of 14.77%, setting a new benchmark for visual approaches to cotton-content classification using cross-validation. We further prove the effectiveness and enhanced accuracy of using a triplet model approach, as well as using all of our modifications. Visual approaches are not market-ready yet, but we show the potential of deep-learning methods for lowering labor-intensiveness and time needed in textile property determination. By that, the recycling industry can be made more efficient and economically viable, helping to come closer to circular economy practices in the textile industry.}, archive = {J_ACCESS}, author = {Max Wiedemann and Pascal Penava and Christopher Mai and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3610920}, journal = {IEEE Access}, pages = {164395-164408}, shortjournal = {IEEE Access}, title = {Deep-learning-based determination of textile properties: A novel triplet architecture approach for classifying cotton content}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of privacy related to sensing in smart buildings. ACCESS, 13, 164358-164394. (www)

  @article{ , abstract = {The concept of smart building is based on the deployment of Internet of Things (IoT) technologies to develop various building applications and services. Aided by the proliferation of smart devices, research in building automation has grown significantly. Nevertheless, these smart devices are integrated with sensors that can collect and share sensitive data and private information related to the building occupants, exposing them to a variety of privacy threats. Although research efforts to promote the development of privacy-aware solutions for smart buildings have been on the rise, a comprehensive review that summarizes these studies is lacking in the literature. This paper provides an extensive review of the studies related to sensing in smart buildings. It highlights privacy issues connected to sensing in smart buildings, provides mitigation strategies that can be deployed to minimize occupants’ privacy invasions, and discusses future research directions towards realising privacy-aware smart buildings. To fulfill the aim of this study, five research questions are formulated, which enable systematic navigation through existing studies related to the topic. These research questions are directed to providing answers to privacy related to data leakage, privacy connected to sensor types, privacy related to different applications, privacy concerns with sensor deployment locations and building types, privacy issues with data processing methods, and to highlight mitigation strategies for reducing privacy invasion. It further discusses the technical approaches, general principles, and design choices for privacy-aware applications which are relevant for guiding relevant stakeholders.}, archive = {J_ACCESS}, author = {Kayode Sakariyah Adewole and Jan A. Persson and Andreas Jacobsson and Erdal Akin and Azad Shokrollahi and Reza Malekian and Héctor Caltenco and Marcus Valtonen Örnhag}, doi = {10.1109/ACCESS.2025.3611344}, journal = {IEEE Access}, pages = {164358-164394}, shortjournal = {IEEE Access}, title = {A systematic literature review of privacy related to sensing in smart buildings}, volume = {13}, year = {2025}, }
• (2025). Application of drone images analysis to predict the damage of streets in HCMC. ACCESS, 13, 164338-164357. (www)

  @article{ , abstract = {This study introduces a novel framework leveraging drone-based image analysis to improve urban street infrastructure management. With urbanization accelerating, effective street maintenance is critical for sustainable development. The study focuses on streets 6–8 meters wide and 1–20 years old, as defined by 22TCVN 4054-2005 standards. This study aims to develop an integrated analytical framework for urban street infrastructure management, combining drone-based image analysis, ML predictive modeling, and financial assessment. The purpose is to enhance decision-making for street maintenance and sustainability through the application of advanced technologies. The methodology involves three key phases: 1) collecting drone images of urban streets and developing a deep learning classifier to assess street conditions; 2) constructing a Markov model to predict street longevity and usage based on classified data; and 3) creating a financial model to estimate maintenance and reconstruction costs. These three models are designed to work in unison, forming a comprehensive system that transitions seamlessly through condition assessment, prediction, and cost estimation. Using around 1,000 street surface images collected from Ho Chi Minh City (HCMC) and 1000 images from RSXD, deep learning classifiers were trained to evaluate road conditions. Outputs are then fed into a Markov model for usage and lifespan predictions, alongside financial projections for maintenance or reconstruction. The findings reveal a critical disparity in performance: while the models achieved moderate success in assessing the damage severity level, they failed to generalize for the more complex task of classifying the specific damage type, where validation accuracies were low despite high training accuracies. The framework features a Markov model that effectively predicts street longevity, and a cost model aiding financial planning. The framework demonstrates robust performance metrics, validating its applicability in urban street management. This study’s originality lies in its integration of advanced vision recognition, predictive modeling, and cost analysis for infrastructure planning. Limitations include a restricted geographical scope and reliance on specific street dimensions. Future research should extend the approach to broader contexts and refine the models for diverse urban conditions.}, archive = {J_ACCESS}, author = {Hieu Le Ngoc}, doi = {10.1109/ACCESS.2025.3610426}, journal = {IEEE Access}, pages = {164338-164357}, shortjournal = {IEEE Access}, title = {Application of drone images analysis to predict the damage of streets in HCMC}, volume = {13}, year = {2025}, }
• (2025). Analyzing lane visibility distance on urban and suburban roads in slovakia under various weather conditions using a single camera. ACCESS, 13, 164327-164337. (www)

  @article{ , abstract = {This article investigates lane visibility distance on urban and suburban roads in Slovakia using a single-camera system under various weather conditions. The study aims to understand how environmental factors and road quality influence lane visibility, employing a single-frame road distance estimation method that is affordable and scalable. The methodology involves rigorous data cleaning, including frame filtering and the application of empirically defined thresholds, to further improve the accuracy of lane detection. The analysis highlights variability in lane visibility influenced by weather conditions, with rain and snow typically reducing visibility; however, snow along roadsides can enhance lane detection on well-maintained highways. The type of road is also a significant factor, with highways offering the best visibility, while city roads and mountainous suburban roads pose challenges due to intersections, tight turns, and uneven terrain. Over 96 drives, we observed median lane visibility distances of 82.2 m on D2 highway and 78.7 m on D1 highway compared to just 51.6 m on urban roads, with analyzable frames representing 86.8% and 77.3% of highway data compared to 47.4% in urban environments, and down to 42.6% in rain. The results demonstrate both the feasibility and limitations of employing a single-camera setup for real-time AI-based lane visibility assessment, providing insights to improve road safety technologies and adapt them to diverse urban and suburban environments.}, archive = {J_ACCESS}, author = {Marek Galinski and Volodymyr Danylov and Peter Lehoczký and Rastislav Bencel and Matej Janeba and Ivan Kotuliak}, doi = {10.1109/ACCESS.2025.3611230}, journal = {IEEE Access}, pages = {164327-164337}, shortjournal = {IEEE Access}, title = {Analyzing lane visibility distance on urban and suburban roads in slovakia under various weather conditions using a single camera}, volume = {13}, year = {2025}, }
• (2025). Adaptive energy-efficient power allocation in downlink NOMA systems with imperfect CSI: A PSO-based approach for improving SIC accuracy and system performance. ACCESS, 13, 164313-164326. (www)

  @article{ , abstract = {Non-Orthogonal Multiple Access (NOMA) has emerged as a critical facilitator of downlink communications in next-generation wireless networks, where user connectivity and spectral efficiency are paramount. The present paper examines dynamic and energy-efficient power allocation in the NOMA systems with imperfect channel state information (CSI). To overcome the issues caused by CSI uncertainty and to ensure maximum performance of the system, an Adaptive Particle Swarm Optimization-Dynamic Power Allocation (APSO-DPA) algorithm is proposed. The algorithm has a multi-objective optimization framework that takes into account energy-efficiency (EE), user fairness and the reliability of Successive Interference Cancellation (SIC) decoding in a combined manner. Simulation results reveal that APSO-DPA has always shown better results in comparison with the conventional fixed and equal power allocation scheme at different signal-to-noise ratio (SNR) levels. Its maximum sum rate at 40 dB is 17.7367 bps/Hz, with an energy-efficiency of 0.00177 bits/joule and a Jain’s Fairness Index of 0.5971. Although EE is expected to decline with elevated SNR as more power is consumed, APSO-DPA keeps the balance between throughput and fairness very high. It also provides better SIC robustness and stable convergence to verify that it is resilient to CSI imperfections. In conclusion, the implementation of APSO-DPA in 5G and next-generation wireless communications is highly suitable as it provides a scalable, efficient, and robust power allocation approach.}, archive = {J_ACCESS}, author = {Ogenyi Fabian Chukwudi and Mohammed Dahiru Buhari and Bashir Olaniyi Sadiq and Enerst Edozie}, doi = {10.1109/ACCESS.2025.3611161}, journal = {IEEE Access}, pages = {164313-164326}, shortjournal = {IEEE Access}, title = {Adaptive energy-efficient power allocation in downlink NOMA systems with imperfect CSI: A PSO-based approach for improving SIC accuracy and system performance}, volume = {13}, year = {2025}, }
• (2025). Investigating the robustness of graph neural networks to data drift: A case study on financial transaction data. ACCESS, 13, 164302-164312. (www)

  @article{ , abstract = {The performance of machine learning models in real-world applications is often challenged by data drift, in which the statistical properties of the data evolve over time. This phenomenon is particularly acute in dynamic domains such as financial fraud detection, where adversarial behaviors constantly change. Graph Neural Networks (GNNs), valued for their ability to model complex relational data, are increasingly applied in this domain; however, their inherent robustness to temporal data shifts remains underexplored. This study systematically investigates the impact of natural data drift on the robustness of three foundational GNN architectures: Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Sample and Aggregate (GraphSAGE). Using the large-scale and highly imbalanced IEEE-CIS dataset as a challenging case study, we constructed homogeneous transaction graphs and employed a strict temporal data splitting methodology to simulate a realistic deployment scenario. Model performance was evaluated over 50 sequential, nonoverlapping monitoring windows, using AUC-PR and F2-Score as primary metrics due to their sensitivity in imbalanced contexts. Our findings confirm that all evaluated GNNs suffer from significant performance degradation over time, with GCN and GAT showing pronounced declines. A comparative analysis revealed that GraphSAGE exhibited substantially greater robustness, maintaining more stable and resilient performance. These results highlight that architectural choice is a critical factor for GNNs deployed in dynamic environments and underscore the need to develop adaptive strategies.}, archive = {J_ACCESS}, author = {Rener S. Menezes and Raimir H. Filho}, doi = {10.1109/ACCESS.2025.3611817}, journal = {IEEE Access}, pages = {164302-164312}, shortjournal = {IEEE Access}, title = {Investigating the robustness of graph neural networks to data drift: A case study on financial transaction data}, volume = {13}, year = {2025}, }
• (2025). Deep neighborhood-aware hashing via class-center guiding for multi-label image retrieval. ACCESS, 13, 164288-164301. (www)

  @article{ , abstract = {Learning to hash effectively addresses the challenges posed by massive data due to its low storage cost and fast search speed. Under multi-label scenarios, pair-wise hashing is typically determined by roughly counting the number of shared labels. However, this approach overlooks the intrinsic semantic information, leading to significant intra-class variations and ambiguity in neighborhood relationships. In this paper, by learning semantic class centers, the novel Deep Neighborhood-aware Hashing framework, termed DNaH, is designed to generate a highly discriminative Hamming space, thereby preserving binary codes with rich neighborhood information. Specifically, by building cross-block non-local information transmission, the simplified and effective hierarchical transformer encoder is introduced into the deep hash stream to capture distinguishing semantic descriptors, which achieves the distinctive representation capability of sophisticated networks while effectively promoting convergence efficiency. The binary representations from the Label Network are viewed as the semantic centers of categories, in which the class-guided loss is proposed to encourage the learned hashing codes to be close to the corresponding class centers. Moreover, by maximizing the semantic similarity between the relative position of binary-like representations and corresponding centroids, the position quantization strategy is developed to decrease projection errors and significantly enhance the distinguishability of the discrete codes. Comprehensive experiments on three benchmark datasets reveal that the DHaN framework delivers better performance in visual similarity search compared to several leading deep hashing approaches. The source code is available at: https://github.com/QinLab-WFU/DNaH}, archive = {J_ACCESS}, author = {Chunping Dong and Jiaqiang Zhao and Meiling Ge and Wenfeng Zhang and Congcong Zhu and Xin Wang}, doi = {10.1109/ACCESS.2025.3608019}, journal = {IEEE Access}, pages = {164288-164301}, shortjournal = {IEEE Access}, title = {Deep neighborhood-aware hashing via class-center guiding for multi-label image retrieval}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection in railway tracks using hybrid clustering and spectral analysis for predictive maintenance. ACCESS, 13, 164265-164287. (www)

  @article{ , abstract = {Efficient railway track maintenance is critical for safety, operational reliability, and cost-effective asset management. While traditional inspection methods are well-established and comply with safety regulations, they face challenges in providing continuous, real-time monitoring capabilities that could enhance preventive maintenance strategies. This study introduces a hybrid machine learning framework for real-time anomaly detection in railway tracks, leveraging unsupervised clustering and spectral analysis to improve defect identification. The proposed approach integrates Principal Component Analysis (PCA) and MiniBatch K-Means clustering with a novel distance-from-mean spectral analysis technique to detect deviations in accelerometer signals from in-service locomotives. Vertical and lateral axle-box accelerations undergo Short-Term Fourier Transform (STFT) processing, generating normalized spectrograms that ensure signal consistency. Clustering identifies major anomalies based on spectral pattern shifts, while the distance-based method enhances sensitivity to subtle defects. Experimental results demonstrate that the hybrid model effectively detects 1.5–3.5% of track segments as high-risk across different railway routes. The combination of clustering and spectral deviation analysis enhances anomaly detection sensitivity, improving the identification of track defects. The framework was validated against ground truth data from Metro Valencia’s track inspection records on Lines L3 and L9. Results revealed global recall scores ranging from 0.67 to 0.83, and weighted recall scores up to 0.97, depending on the method and track line. Clustering-based detection showed strong performance in identifying track width and lateral deviation issues on L3 (up to 1.00 recall), while the distance-based method excelled in detecting twist-related anomalies on L9. These findings confirm the complementary strengths of both methods in practical railway scenarios. The proposed framework complements existing inspection systems and can support enhanced maintenance planning and early defect detection. Future work will focus on expanding validation campaigns, integrating additional sensor modalities, and refining detection thresholds for broader deployment in transport operations.}, archive = {J_ACCESS}, author = {Juan Pineda-Jaramillo and Federico Bigi and Ignacio Villalba-Sanchis and Pablo Salvador-Zuriaga}, doi = {10.1109/ACCESS.2025.3611009}, journal = {IEEE Access}, pages = {164265-164287}, shortjournal = {IEEE Access}, title = {Anomaly detection in railway tracks using hybrid clustering and spectral analysis for predictive maintenance}, volume = {13}, year = {2025}, }
• (2025). Website phishing attack detection using innovative meta learning-based ensemble approach. ACCESS, 13, 164249-164264. (www)

  @article{ , abstract = {Phishing remains the most pervasive cybersecurity threat, exploiting human vulnerabilities through sophisticated social engineering. While traditional heuristic and blacklist-based solutions struggle against evolving attacks, this paper proposes a novel stacked ensemble model combining Artificial Neural Networks (ANN) and Bagging K-Nearest Neighbors (KNN) with Logistic Regression (LR) as a meta-learner classifier. Our hybrid framework leverages the strength of ANN in identifying global non-linear patterns and the proficiency of Bagging KNN in local pattern detection, achieving superior performance through optimal decision-level fusion. Experimental results on the 2019-2024 datasets demonstrate that the proposed ensemble model outperforms state-of-the-art techniques, achieving a 97.20% accuracy score, a precision of 97%, and a recall of 97%. The proposed model employs hyperparameter-optimised architectures including a 512-256-128-128 layer ANN with Relu activation and 30% dropout, along with a Bagging KNN configuration using 10 estimators with K=7 and Euclidean distance. Key advantages of our stacking approach include enhanced generalization with accuracy improvement over the best standalone model, adaptability to emerging attack patterns through weighted averaging, and scalability through the modular design that allows incremental model additions. This study introduces an innovative hybrid ensemble method that stacks ANN and Bagging KNN, incorporating a meta-learning strategy optimized for real-time phishing detection. The model also utilizes SMOTE oversampling to address the issue of class disproportion and employs k-fold cross-validation to ensure stable performance testing. This study highlights the significance of feature engineering, ensemble methods, and hybrid approaches in enhancing phishing detection.}, archive = {J_ACCESS}, author = {Saba Naseeb and Shabana Ramzan and Ali Raza and Muhammad Shadab Alam Hashmi and Yeonghyeon Gu and Muhammad Syafrudin and Norma Latif Fitriyani}, doi = {10.1109/ACCESS.2025.3610961}, journal = {IEEE Access}, pages = {164249-164264}, shortjournal = {IEEE Access}, title = {Website phishing attack detection using innovative meta learning-based ensemble approach}, volume = {13}, year = {2025}, }
• (2025). A hybrid network of CNN and transformer for 3D lane detection. ACCESS, 13, 164237-164248. (www)

  @article{ , abstract = {3D lane detection is a critical and challenging task in autonomous driving, yet accurately capturing the spatial structure and precise location of lane markings remains challenging in complex traffic environments. Existing methods typically rely on a single network architecture of Convolutional Neural Networks(CNNs) or Transformer for lane detection, CNNs captures local details but lacks long-range context, while Transformers extract global semantics at the expense of fine local detail, often resulting in imprecise localization. To overcome these limitations, we propose a 3D lane detection framework that integrates both CNNs and Transformer architectures to simultaneously harness local and global information. Specifically, we introduce a view transformation module based on Spatial Cross-Attention to optimize bird’s-eye view (BEV) feature representation. Then we designed a Dual-Branch network, where the CNN branch extracts fine textures and edges, and the Transformer branch captures global semantic relationships. Finally, a feature fusion module (FFM) combining spatial attention and channel attention mechanisms dynamically merges these features to enhance detection performance. Experiments on the OpenLane and Apollo datasets show that our approach significantly outperforms current advanced methods under various complex road conditions, demonstrating its effectiveness and generalizability.}, archive = {J_ACCESS}, author = {Yanyang Deng and Mingwei Wang and Mengli Zhang and Menglu Zhou}, doi = {10.1109/ACCESS.2025.3611412}, journal = {IEEE Access}, pages = {164237-164248}, shortjournal = {IEEE Access}, title = {A hybrid network of CNN and transformer for 3D lane detection}, volume = {13}, year = {2025}, }
• (2025). Design and control of a mechatronic system to apply gait synchronized warnings and perturbations to runners. ACCESS, 13, 164223-164236. (www)

  @article{ , abstract = {A mechatronic system is designed to track a runner on a treadmill and interact with them through precisely applied forces and warnings synchronized with their gait. Design, analysis, and control of a parallel wire robot is presented to apply forces in the horizontal plane of the runner to simulate the user being impacted during sports. Workspace analysis demonstrates attainable impact forces and resulting regions where the user must be located to produce omnidirectional impacts. Vibration analysis determines tether preloads that are required to minimize the effects of perturbations introduced by the runner’s motion. Predictive gait tracking algorithms and system control are designed to provide consistent impacts and warnings that are synchronized with the runner’s gait. Performance is validated to provide omni-directional forcing and interaction timed with the runner’s gait cycle. System automation is presented for applying randomized warnings and perturbations during running. A pilot study demonstrates the efficacy of the system to help runners use warnings to prepare for simulated impacts.}, archive = {J_ACCESS}, author = {Nathaniel G. Luttmer and Takara E. Truong and Alicia M. Boynton and Andrew S. Merryweather and David R. Carrier and Mark A. Minor}, doi = {10.1109/ACCESS.2025.3609249}, journal = {IEEE Access}, pages = {164223-164236}, shortjournal = {IEEE Access}, title = {Design and control of a mechatronic system to apply gait synchronized warnings and perturbations to runners}, volume = {13}, year = {2025}, }
• (2025). Neural network-based single-carrier joint communication and sensing: Loss design, constellation shaping and precoding. ACCESS, 13, 164208-164222. (www)

  @article{ , abstract = {We investigate the impact of higher-order modulation formats on the sensing performance of single-carrier joint communication and sensing (JCAS) systems. Several separate components such as a beamformer, a modulator, a target detector, an angle of arrival (AoA) estimator and a communication demapper are implemented as trainable neural networks (NNs). We compare geometrically shaped modulation formats to a classical quadrature amplitude modulation (QAM) scheme. We assess the influence of multi-snapshot sensing and varying signal-to-noise ratio (SNR) on the overall performance of the autoencoder-based system. To improve the training behavior of the system, we decouple the loss functions from the respective SNR values and the number of sensing snapshots, using upper bounds of the sensing and communication performance, namely the Cramér-Rao bound for AoA estimation and the mutual information for communication. The NN-based sensing outperforms classical algorithms, such as a Neyman-Pearson based power detector for object detection and ESPRIT for AoA estimation for both the trained constellations and QAM at low SNRs. We show that the gap in sensing performance between classical and shaped modulation formats can be significantly reduced through multi-snapshot sensing. Lastly, we demonstrate system extension to multi-user multiple-input multiple-output to address the improvement of spatial efficiency when servicing multiple user equipments. Our contribution emphasizes the importance of estimation bounds for training neural networks, especially when the trained solutions are deployed in varying SNR conditions.}, archive = {J_ACCESS}, author = {Charlotte Muth and Benedikt Geiger and Daniel Gil Gaviria and Laurent Schmalen}, doi = {10.1109/ACCESS.2025.3607561}, journal = {IEEE Access}, pages = {164208-164222}, shortjournal = {IEEE Access}, title = {Neural network-based single-carrier joint communication and sensing: Loss design, constellation shaping and precoding}, volume = {13}, year = {2025}, }
• (2025). Independent time transfer in 5G technology over IP core networks using PTN overlay and comparison with GPS/GNSS-based synchronization. ACCESS, 13, 164199-164207. (www)

  @article{ , abstract = {Time synchronization is critical for 5G networks, enabling ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC). While GPS/GNSS-based synchronization is widely used, it is susceptible to spoofing, jamming, and signal degradation in urban environments. This paper compares Precision Time Network (PTN-ePTS) with GPS/GNSS synchronization, presenting extensive PTN measurement results from a multi-city testbed in Turkey. The study evaluates synchronization accuracy, jitter, and resilience to network impairments, demonstrating that PTN achieves sub-100 ns accuracy, faster recovery times, and enhanced fault tolerance compared to GPS/GNSS. By leveraging existing IP infrastructure, PTN offers a cost-effective and secure alternative, validated through real-world deployments in Istanbul and Ankara.}, archive = {J_ACCESS}, author = {Mustafa Serdar Osmanca and Umut Keten and Yusuf İslam Demır and Kahraman Zaım}, doi = {10.1109/ACCESS.2025.3611321}, journal = {IEEE Access}, pages = {164199-164207}, shortjournal = {IEEE Access}, title = {Independent time transfer in 5G technology over IP core networks using PTN overlay and comparison with GPS/GNSS-based synchronization}, volume = {13}, year = {2025}, }
• (2025). Simple and effective simulation methodology to optimize obstacle-avoiding flocks. ACCESS, 13, 164186-164198. (www)

  @article{ , abstract = {This article presents a simple and effective methodology to optimize bio-inspired behavior of drone swarms when the search space of operational parameters has low dimensionality. We exemplify our methodology by optimizing the operational parameters of a drone swarm that traverses a region while swarming and avoiding obstacles. To this end, we devise a new fitness function that favors drone swarms navigating together the obstacle as fast as possible and producing stable swarms. Next, we optimize the swarm operational parameters using gradient descent methods in a three-dimensional search space. This enables us to produce robust desired behavior of our drone swarm in the presence of external and internal noise in the operation of our system.}, archive = {J_ACCESS}, author = {Mario Aguilera-Ruiz and Alejandro Galaviz-Mosqueda and Benjamín Jaramillo-Ávila and Salvador Villarreal-Reyes and Raúl Rivera-Rodríguez}, doi = {10.1109/ACCESS.2025.3608603}, journal = {IEEE Access}, pages = {164186-164198}, shortjournal = {IEEE Access}, title = {Simple and effective simulation methodology to optimize obstacle-avoiding flocks}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning for intelligent load balancing in smart power grids. ACCESS, 13, 164170-164185. (www)

  @article{ , abstract = {This study addresses the challenge of intelligent load balancing in modern power grids, which are increasingly characterized by renewable energy sources, electric vehicles, and decentralized generation. Traditional control mechanisms, often based on rule-based systems, fail to cope with the dynamic and stochastic nature of these grids. To overcome these limitations, we propose an innovative hierarchical reinforcement learning framework for load balancing, integrating Proximal Policy Optimization (PPO) within a dual-layer control architecture. This framework employs both local agent-based decision-making and a global critic network for system-wide optimization. The approach is designed to adapt to the temporal and spatial variability inherent in modern power grids, ensuring efficient load distribution and stability across various operating conditions. We introduce the Grid-aware Structured Embedding Network (GSEN), a novel model that enhances power grid state estimation by capturing multi-scale topological and temporal dependencies. GSEN integrates spectral graph convolutions and temporal attention mechanisms, providing robust, real-time predictions. The Stability-Aware Adaptive Inference Mechanism (SAIM) enhances the stability and adaptability of the model by dynamically adjusting inference pathways based on real-world grid conditions. Empirical evaluations demonstrate that the proposed framework outperforms traditional methods and state-of-the-art models, showing significant improvements in load balancing efficiency and energy dispatch precision. These findings underline the potential of reinforcement learning-based solutions to meet the growing complexity and demands of smart power grids, providing a scalable and adaptable solution for intelligent grid management.}, archive = {J_ACCESS}, author = {Boning Liu}, doi = {10.1109/ACCESS.2025.3606914}, journal = {IEEE Access}, pages = {164170-164185}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning for intelligent load balancing in smart power grids}, volume = {13}, year = {2025}, }
• (2025). Electromagnetic environment simulation software design and its application using DRFM techniques on software defined radio. ACCESS, 13, 164156-164169. (www)

  @article{ , abstract = {As electronic warfare (EW) becomes increasingly significant in modern battlefields, the demand for simulation environments in the development of EW systems rises. There is a strong focus on creating flexible and cost-effective systems to simplify the design process of electronic warfare systems. In this study, a generalized electromagnetic environment simulation software capable of simulating different types of Radars, Electronic Countermeasures (ECM) and Electronic Counter-Countermeasures (ECCM) is designed. The software can model radars and Digital Radio Frequency Memory (DRFM) based EW systems. Its open architecture structure allows users to input the parameters of radar and EW systems for modeling. A scenario-based interface is developed to provide users with flexible analysis capabilities. The performance of the software was tested using commercially available Software-Defined Radio (SDR) equipment to establish a Hardware-in-the-Loop (HIL) setup. Results from multiple scenarios demonstrated that the simulation software effectively implements EW techniques in accordance with the conditions and scenarios defined by the user. The key novelty of this work is the integration of radar and ECM simulation within a unified, scenario-based platform that supports real-time validation through Software-Defined Radio (SDR) hardware. This approach enables cost-effective and scalable Hardware-in-the-Loop (HIL) testing.}, archive = {J_ACCESS}, author = {Serdar Kaya and Hayrullah Yildiz}, doi = {10.1109/ACCESS.2025.3611877}, journal = {IEEE Access}, pages = {164156-164169}, shortjournal = {IEEE Access}, title = {Electromagnetic environment simulation software design and its application using DRFM techniques on software defined radio}, volume = {13}, year = {2025}, }
• (2025). Power demand forecasting in iraq using singular spectrum analysis and kalman filter-smoother. ACCESS, 13, 164130-164155. (www)

  @article{ , abstract = {The critical need for accurate electricity demand forecasting in a country like Iraq, where power generation is frequently disrupted, has driven the development of a novel hybrid framework. The methodology is designed to systematically compare multiple preprocessing strategies: it combines Singular Spectrum Analysis (SSA) to reconstruct the signal by emphasizing regularly occurring patterns, removing anomalies, and compensating for potential missing data, followed by various filtering techniques (Exponential Moving Average (EMA), Kalman Filter (KF), and Kalman Smoother (KS)) to further refine the signal, and then applies multiple deep learning algorithms such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Bidirectional GRU (BiGRU) to predict hourly electricity demand over a 72-hour horizon, providing a strategic buffer for operational planning and unexpected demand spikes. A comparative analysis of these combinations revealed that the SSA-KS-BiLSTM model achieves state-of-the-art performance, reducing MAE by 54% (from 1531.5 MW to 699.2 MW) and improving R2 by 19% (from 0.811 to 0.967) compared to BiLSTM on raw data. This robust, scalable approach is suitable for other regions aiming to improve power demand forecasting in noisy or uncertain conditions.}, archive = {J_ACCESS}, author = {Khalid Alhashemi and Okkes Tolga Altinoz}, doi = {10.1109/ACCESS.2025.3611790}, journal = {IEEE Access}, pages = {164130-164155}, shortjournal = {IEEE Access}, title = {Power demand forecasting in iraq using singular spectrum analysis and kalman filter-smoother}, volume = {13}, year = {2025}, }
• (2025). Carbon footprint management architecture in urban mobility systems. ACCESS, 13, 164107-164129. (www)

  @article{ , abstract = {The rapid urbanization and increasing reliance on private vehicles have led to significant carbon emissions and traffic congestion in urban areas. This study proposes a Multi-Layer Architecture for Carbon Emission Monitoring and Mitigation, integrating policy-driven interventions, AI-based route optimization, and public transport incentives to improve urban mobility sustainability. The proposed framework is validated through an iterative simulation-based approach using IDEF10, ensuring its effectiveness in reducing emissions, optimizing fuel consumption, and enhancing public transportation efficiency. Simulation results indicate a 49.22% reduction in carbon emissions, a 66.59% increase in public transport utilization, and a 17.47% reduction in traffic congestion. Fuel consumption decreased by 46.50%, while private vehicle dependency was reduced from 79.35% to 49.52%. These improvements demonstrate the effectiveness of integrating carbon taxation, electric vehicle subsidies, AI-based traffic management, and public transport incentives in achieving sustainable urban mobility. The findings emphasize the importance of iterative policy refinement, where targeted interventions are continuously evaluated and optimized to maximize their impact. The proposed framework provides a scalable and adaptable approach to managing carbon footprints in urban transportation systems. Future research should focus on real-world pilot implementations to further validate and refine this model across diverse metropolitan contexts.}, archive = {J_ACCESS}, author = {Asep Somantri and Yoanes Bandung and Kridanto Surendro}, doi = {10.1109/ACCESS.2025.3611423}, journal = {IEEE Access}, pages = {164107-164129}, shortjournal = {IEEE Access}, title = {Carbon footprint management architecture in urban mobility systems}, volume = {13}, year = {2025}, }
• (2025). GNSS spoofing discovery by using a type-based detector. ACCESS, 13, 164084-164106. (www)

  @article{ , abstract = {With the increase in the number of devices relying on Global Navigation Satellite Sustems (GNSS) and the wide use of such technology for critical applications, such as in aviation and autonomous vehicles, security is a main concern. In particular GNSS spoofing attacks and their mitigation attracted much attention in the research community. Availability of efficient methods for combating spoofing are a significant factor in the use of GNSS technology. This paper considers GNSS spoofing detection without the need for despreading the received signal. As a specific application of our method we consider the most commonly used civilian GPS C/A L1 signal. The proposed technique consists of two stages: signal preprocessing stage and the detection stage. We show theoretically that signal samples at the output of the preprocessing stage follow a mixture Gaussian distribution, rather than a Gaussian distribution as infered from other publications based on measurement results. Using computer simulations we demonstrate the adequacy of our mixture Gaussian model. The consequence of our models is that detection methods for such application that are based on the Gaussian model may not be adequate. Then the second stage consists of a type-based technique for spoofing detection. Type detection is a trainable data oriented technique with foundations in Information Theory, and hence it can be considered as a learning algorithm. In our specific application such detector requires training only on a signal without spoofing. In order to apply the type-based detector in our system, a linear quantizer is used and the effect of the number of quantization levels is studied. Simulation results for Receiver Operating Characteritics (ROC) indicate that reliable performance can be achieved when using a type-based detector following the preprocessing stage for GNSS spoofing detection. Furthermore, the shortcomings of energy detection in such applications are also studied. Both theoretical and simulation results in our paper indicate the advantages of type-based detection over energy detection in GNSS spoofing discovery.}, archive = {J_ACCESS}, author = {Runzhou Fan and Harry Leib}, doi = {10.1109/ACCESS.2025.3606353}, journal = {IEEE Access}, pages = {164084-164106}, shortjournal = {IEEE Access}, title = {GNSS spoofing discovery by using a type-based detector}, volume = {13}, year = {2025}, }
• (2025). Rethinking persistent scheduling in 5G new radio vehicle-to-everything sidelink communications. ACCESS, 13, 164065-164083. (www)

  @article{ , abstract = {5G New Radio (NR) Vehicle-to-Everything (V2X) Mode 2 relies on Semi-Persistent Scheduling (SPS) to manage sidelink resources, with persistence playing a key role in affecting communication integrity and Age of Information (AoI) for connected and autonomous vehicles. However, its impact remains underexplored, and whether to include SPS as is in future standards remains an open issue, calling for a deeper understanding of its effects. In this paper, we introduce a simpler method for implementing the persistence concept in 5G NR-V2X Mode 2, providing a broader range of persistence degrees than standard SPS. Through ns-3 simulations, we show that higher persistence improves communication stability, but it degrades the AoI by slowing sub-channel switching. Conversely, weaker persistence strikes a better balance between collision occurrence rate and collision duration, minimizing AoI. Crucially, our results reveal that Dynamic Scheduling (DS) can outperform SPS even for strictly periodic traffic flows, challenging the longstanding assumption that SPS is optimal in such scenarios. In fact, it turns out that persistent collisions boost message delivery burstiness, creating large gaps in the flow of delivered update messages. Our findings offer clearer guidance on designing simpler, more effective sidelink multiple access strategies to improve communication timeliness.}, archive = {J_ACCESS}, author = {Alexey Rolich and Mert Yildiz and Ion Turcanu and Alexey Vinel and Andrea Baiocchi}, doi = {10.1109/ACCESS.2025.3611486}, journal = {IEEE Access}, pages = {164065-164083}, shortjournal = {IEEE Access}, title = {Rethinking persistent scheduling in 5G new radio vehicle-to-everything sidelink communications}, volume = {13}, year = {2025}, }
• (2025). Cyber-attacks and countermeasures at the perception layer for electromedical devices in community-based care: A systematic review. ACCESS, 13, 164050-164064. (www)

  @article{ , abstract = {This review investigates cyber-attacks and countermeasures of the perception layer in electromedical devices used in decentralized healthcare contexts. Emphasis is placed on sensing and actuation functionalities performed outside hospital environments, where physical exposure and heterogeneous communication protocols increase threat complexity. A systematic search was performed across Scopus, Web of Science, IEEE Xplore, and PubMed using a structured query covering cybersecurity threats and countermeasures in perception-layer medical devices. Only peer-reviewed English articles were included. Screening and eligibility assessment followed PRISMA guidelines and Kitchenham’s methodology, excluding studies unrelated to perception-layer cybersecurity or lacking countermeasure discussion. A structured framework was developed to classify devices, attack types, and defensive strategies. Twenty-one studies were included, revealing a research focus on wearable devices and monitoring functionalities, while portable and implantable systems with actuation capabilities remain underexplored. Most documented attacks involve replay, DoS, eavesdropping, and Man-in-the-Middle attacks, whereas advanced threats like side-channel attacks are rarely addressed. Defensive strategies mainly rely on authentication schemes and transmission-level cryptography, while recovery capabilities are inadequately addressed. Research on perception-layer cybersecurity in electromedical devices is still fragmented, with uneven protection of critical security properties. Future work should expand to implantable and actuation-enabled devices, develop multi-layer defenses addressing all security objectives, and introduce resilience mechanisms to support robust cybersecurity in decentralized healthcare environments.}, archive = {J_ACCESS}, author = {Rosanna Manzo and Andrea Apicella and Pasquale Arpaia and Francesco Caputo and Antonella Cioffi and Antonio Esposito and Francesco Isgró and Nicola Moccaldi and Ettore Toscano}, doi = {10.1109/ACCESS.2025.3609777}, journal = {IEEE Access}, pages = {164050-164064}, shortjournal = {IEEE Access}, title = {Cyber-attacks and countermeasures at the perception layer for electromedical devices in community-based care: A systematic review}, volume = {13}, year = {2025}, }
• (2025). FPGA-implementation of lossless integer tchebichef transform with application for tampering detection in ROI medical images. ACCESS, 13, 164026-164049. (www)

  @article{ , abstract = {This research introduces efficient algorithms for the forward and inverse lossless Integer-to-Integer Tchebichef Transform (I2ITT), tailored for resource-constrained embedded systems. These algorithms are then implemented on FPGA using fixed-point arithmetic for lossless image reconstruction. The paper further explores an application of the proposed I2ITT algorithms for tampering detection in medical images, presenting a new scheme that detects tampered Regions of Interest (ROIs) in medical images transmitted over insecure channels. Detailed descriptions of the proposed algorithms, their FPGA implementation, simulations, and comparison results are provided, collectively demonstrating the efficiency of both the suggested FPGA design and the tampering detection scheme.}, archive = {J_ACCESS}, author = {Achraf Daoui and Mohamed Yamni and Chakir El-Kasri and May Almousa and Ahmed A. Abd El-Latif}, doi = {10.1109/ACCESS.2025.3610801}, journal = {IEEE Access}, pages = {164026-164049}, shortjournal = {IEEE Access}, title = {FPGA-implementation of lossless integer tchebichef transform with application for tampering detection in ROI medical images}, volume = {13}, year = {2025}, }
• (2025). What shapes learners’ trust in AI? a meta-analytic review of its antecedents and consequences. ACCESS, 13, 164008-164025. (www)

  @article{ , abstract = {With the growing integration of artificial intelligence (AI) into higher education, learners’ trust in AI systems has attracted increasing attention. This meta-analysis synthesized 78 independent effect sizes from 42 empirical studies (N = 28 970) to examine key antecedents and consequences of AI trust. Four categories of antecedents were assessed: technological performance, user experience, ethics and safety, and sociodemographic and individual differences. Trust in AI was also analyzed in relation to behavioral intention, learning outcomes, and AI acceptance. Results showed that technological performance was the strongest predictor of trust, followed by user experience and individual differences, while ethics and safety had a relatively weaker impact. Trust in AI significantly predicted all three outcomes, underscoring its mediating role. Moderator analyses revealed that high-level feedback enhanced the effects of technological performance and ethics on trust, and that trust was generally higher in learning-support tasks than in academic writing tasks. These findings offer theoretical and practical insights for optimizing educational AI design and promoting adaptive, trustworthy systems.}, archive = {J_ACCESS}, author = {Yu Lu Cui and Man Lin Zeng and Xing Ke Du and Wei Miao He}, doi = {10.1109/ACCESS.2025.3611367}, journal = {IEEE Access}, pages = {164008-164025}, shortjournal = {IEEE Access}, title = {What shapes learners’ trust in AI? a meta-analytic review of its antecedents and consequences}, volume = {13}, year = {2025}, }
• (2025). Efficient unsupervised semantic similarity through dual-masked prompting. ACCESS, 13, 163999-164007. (www)

  @article{ , abstract = {Unsupervised text semantic similarity is an important task in natural language processing that aims to learn robust text representations without labeled data. A major challenge in this domain, particularly for contrastive learning methods, is efficiently generating a sufficient number of high-quality positive and negative samples. Existing approaches like SimCSE and PromptBERT have limitations in sample generation efficiency and volume. To address these limitations, we propose a novel dual-mask prompt template strategy that greatly increases the quantity and efficiency of positive and negative sample generation. Our method uniquely allows each template to simultaneously produce a positive and a negative sample. Furthermore, to eliminate noise interference caused by prompt templates, we introduce a simple difference-based noise separation technique. Concurrently, we extend the InfoNCE loss function to optimize the learning of feature space distributions. We used the SentEval toolkit to evaluate our method on seven standard text similarity datasets. The results show that, without any external data augmentation, our method achieves an average Spearman correlation coefficient of 78.52, performing comparably to or surpassing some classical methods. Comprehensive ablation studies rigorously validate the individual effectiveness of our dual-mask prompting, noise separation strategy, and extended loss function.}, archive = {J_ACCESS}, author = {Huilai Zou and Minfeng Lu and Qifei Zhang}, doi = {10.1109/ACCESS.2025.3611338}, journal = {IEEE Access}, pages = {163999-164007}, shortjournal = {IEEE Access}, title = {Efficient unsupervised semantic similarity through dual-masked prompting}, volume = {13}, year = {2025}, }
• (2025). Age of information-based optimal scheduling with energy cost trade-off for smart warehouse: A deep reinforcement learning-based approach. ACCESS, 13, 163982-163998. (www)

  @article{ , abstract = {Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a smart warehouse scenario, we address the problem of minimization of the average weighted sum of AoI subject to the available energy constraint of each source node. We focus on joint optimization of packet transmission and energy may not be suitable for finding a solution in a reasonable time. To address the issue, we develop a deep Q-network (DQN) algorithm that enables us to a simultaneous reduction of the state-space dimension with learning of the age-optimal policy in unknown environmental dynamics. Taking account into the limited energy constraints of the user nodes, the proposed DQN algorithm aims to minimize the long-term average weighted sum-AoI of the user-transmitted data. Finally, we produce multifaceted simulation results in terms of convergence, learning process, and change of average sum-AoI under different energy constraints that shows the effectiveness of the proposed DQN algorithm.}, archive = {J_ACCESS}, author = {Sandip Roy and Abhishek Bisht and Ashok Kumar Das and Sachin Shetty}, doi = {10.1109/ACCESS.2025.3609200}, journal = {IEEE Access}, pages = {163982-163998}, shortjournal = {IEEE Access}, title = {Age of information-based optimal scheduling with energy cost trade-off for smart warehouse: A deep reinforcement learning-based approach}, volume = {13}, year = {2025}, }
• (2025). Brain age estimation: A multi-region approach using groupwise registration and 3D shape contexts derived from displacement vector fields. ACCESS, 13, 163966-163981. (www)

  @article{ , abstract = {Brain age estimation has emerged as a crucial biomarker for quantifying inter-individual variability in brain aging and identifying risks of cognitive decline. This is particularly relevant for the study of neurodegenerative conditions like Alzheimer’s disease, where deviations from normal brain aging patterns can serve as early indicators of pathology. We introduce an interpretable framework for brain age estimation that adopts a multi-region perspective to capture heterogeneous neuroanatomical changes. Groupwise registration was employed to construct sex- and age-stratified templates, enabling the computation of anatomically meaningful displacement vector fields (DVFs) that characterize voxel-wise structural deviations from the population average. From these DVFs, features were extracted in fifteen brain regions using a 3D shape context descriptor to encode displacement direction and magnitude. Validation was performed on 1,956 T1-weighted MRI scans from cognitively normal individuals in the ADNI dataset (ages 60–90). The combined multi-region model achieved a mean absolute error (MAE) of $1.66 \pm 0.15$ years ( $R^{2} = 0.84 \pm 0.05$ ) for males and $1.81 \pm 0.12$ years ( $R^{2} = 0.80 \pm 0.04$ ) for females, significantly outperforming many contemporary deep learning models. These findings demonstrate that biologically interpretable, registration-derived features—when analyzed from a multi-regional perspective—can yield robust and accurate estimates of brain age. Beyond predictive accuracy, the framework provides insights into the regional specificity of aging processes, thereby offering a foundation for early detection strategies and targeted clinical applications in neurodegenerative disease research.}, archive = {J_ACCESS}, author = {Leandro Prado de Andrade and Marco Antonio Bernardi Grivol and Mario Augusto de Souza Liziér and Roger Tam and Ricardo José Ferrari}, doi = {10.1109/ACCESS.2025.3609684}, journal = {IEEE Access}, pages = {163966-163981}, shortjournal = {IEEE Access}, title = {Brain age estimation: A multi-region approach using groupwise registration and 3D shape contexts derived from displacement vector fields}, volume = {13}, year = {2025}, }
• (2025). Understanding viewers’ attitudes toward virtual streamers: A systematic review. ACCESS, 13, 163949-163965. (www)

  @article{ , abstract = {Live streaming is a significant market for millions of users. Advancements in artificial intelligence (AI) have led to the emergence of virtual humans, which play an important role in live streaming. However, there is a lack of systematic reviews of viewers’ attitudes towards virtual streamers. To fill this gap, we conducted a systematic review according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines, obtaining 40 papers on viewers’ attitudes towards virtual streamers from the Web of Science Core Collection, Scopus, IEEE, and EBSCO Business Source Complete. Our results indicate that research on virtual streamers has shown an increasing trend. This topic has multidisciplinary theoretical foundations and is suitable for multiple qualitative and quantitative methods. Various attitudes towards virtual streamers were studied as dependent variables in the included studies. The most frequently investigated attitude is the purchasing intention of products endorsed by virtual streamers because e-commerce is the most common scenario in which virtual streamers are used. The use, type, and characteristics of virtual streamers can influence these attitudes. Based on the stereotype content model (SCM), the stimulus-organism-response (SOR), the affect-behavior-cognition (ABC) attitude model, and the elaboration likelihood model (ELM), this study established a comprehensive framework to integrate the main findings of existing research on virtual streamers and reveals the mechanisms through which the use, types, and characteristics of virtual streamers influence viewers’ attitudes. These findings contribute to the knowledge about virtual streamers and practical implications for practitioners in fields such as e-commerce and marketing.}, archive = {J_ACCESS}, author = {Yangxin Huang and Tianran Wang}, doi = {10.1109/ACCESS.2025.3611819}, journal = {IEEE Access}, pages = {163949-163965}, shortjournal = {IEEE Access}, title = {Understanding viewers’ attitudes toward virtual streamers: A systematic review}, volume = {13}, year = {2025}, }
• (2025). The impact of regulation threshold heterogeneity on the spread of negative emotions in social networks. ACCESS, 13, 163936-163948. (www)

  @article{ , abstract = {Incorporating regulation threshold heterogeneity into the modeling of emotion diffusion provides critical insights into the spread of negative emotions within complex social networks. This study develops a threshold-based diffusion framework to systematically examine how individual differences in emotional response thresholds influence the spatiotemporal dynamics of negative emotion propagation. The proposed behavioral model assumes that individual thresholds follow a Gaussian distribution, capturing variations in emotional susceptibility across the network. Using edge-based compartmental theory, the model analyzes the propagation process under the influence of threshold heterogeneity. Through numerical simulations and empirical validation, the study demonstrates that increasing the average regulation threshold significantly raises the outbreak threshold and reduces the risk of large-scale emotional contagion. At the same time, heterogeneity in threshold distribution leads to complex nonlinear propagation behaviors, including both continuous and discontinuous phase transitions. These findings emphasize the importance of threshold distribution characteristics in shaping propagation patterns, particularly in heterogeneous networks where multiphase dynamics emerge. This research reframes threshold heterogeneity as a controllable structural feature within the emotional diffusion system, offering a refined theoretical foundation and practical guidance for managing negative emotions in social networks and improving system resilience during public crises.}, archive = {J_ACCESS}, author = {Wei Yu and Congcong Li and Tiantian Wang}, doi = {10.1109/ACCESS.2025.3610903}, journal = {IEEE Access}, pages = {163936-163948}, shortjournal = {IEEE Access}, title = {The impact of regulation threshold heterogeneity on the spread of negative emotions in social networks}, volume = {13}, year = {2025}, }
• (2025). Acoustic sensing for contactless health monitoring: Technologies, algorithms, and emerging applications. ACCESS, 13, 163907-163935. (www)

  @article{ , abstract = {Contactless acoustic sensing is emerging as a potential solution for health monitoring due to its ubiquity, low cost, and non-invasive nature. This paper presents a unified framework for acoustic-based vital sign detection, encompassing signal transmission, reception, processing, and algorithmic inference. Firstly, a structured taxonomy is proposed that categorizes existing methods into active sensing, passive sensing, and multimodal fusion systems. Secondly, special attention is given to recent advancements in signal processing and machine learning, including deep learning models and time–frequency analysis techniques that enhance monitoring accuracy under real-world conditions. Thirdly, this paper explores practical deployment scenarios and highlight key challenges such as noise resilience, multi-user interference, and privacy concerns. By integrating insights across domains, this review uniquely positions acoustic sensing within the broader context of scalable, privacy-conscious, and robust health monitoring.}, archive = {J_ACCESS}, author = {Shuyu Li and Guangyao Liu and Peixuan Hu and Peisong Li and Dongyang Xu and Zhi Wang}, doi = {10.1109/ACCESS.2025.3609787}, journal = {IEEE Access}, pages = {163907-163935}, shortjournal = {IEEE Access}, title = {Acoustic sensing for contactless health monitoring: Technologies, algorithms, and emerging applications}, volume = {13}, year = {2025}, }
• (2025). Roof boundary points extraction from LiDAR point cloud data using adaptive neighbourhood-based α-shape algorithm. ACCESS, 13, 163896-163906. (www)

  @article{ , abstract = {Accurate building roof boundary extraction from airborne LiDAR point clouds remains a challenge due to abrupt density variations and complex roof structures. Existing $\alpha $ -shape-based methods are capable of detecting building boundary points. However, their reliance on fixed neighbourhood selection strategies can lead to inaccurate boundary point extraction in regions with abrupt variations in point density. This paper presents a boundary point extraction technique that selects optimal $\alpha $ values adaptively for individual points by analyzing local geometric point density using an adaptive variable point neighbourhood selection technique. The proposed method first constructs a Delaunay triangulation network from input building roof point cloud data, then iteratively selects optimal neighbouring points from multiple scanlines to compute point-specific $\alpha $ values that adapt to local density variations. This approach effectively mitigates the misclassification of densely clustered points as boundary points by adaptively adjusting to local point distributions. The proposed method is tested on sample buildings of five different datasets with various point densities. Experimental results confirm that this approach outperforms conventional $\alpha $ -Shape-based algorithms, geometric rule-based approaches, and recent adaptive methods by successfully preserving the actual boundary points of a roof while filtering out false positives caused by the fluctuating abrupt point density. The proposed method significantly reduces the Relative Area Error (RAE) and RCC distances, aligning with the expected performance of roof boundary extraction. Furthermore, an ablation study validates the contribution of the proposed adaptive $\alpha $ value and the adaptive neighbourhood selection.}, archive = {J_ACCESS}, author = {Emon Kumar Dey}, doi = {10.1109/ACCESS.2025.3611301}, journal = {IEEE Access}, pages = {163896-163906}, shortjournal = {IEEE Access}, title = {Roof boundary points extraction from LiDAR point cloud data using adaptive neighbourhood-based α-shape algorithm}, volume = {13}, year = {2025}, }
• (2025). Classification of psoriasis using vision transformer: A deep learning-based approach. ACCESS, 13, 163885-163895. (www)

  @article{ , abstract = {Psoriasis is a chronic autoimmune skin condition causing complex symptoms, often complicating accurate diagnosis. While convolutional neural networks (CNN) have potential in dermatological image classification, they struggle to adapt to the subtle variations in skin tones, lesion textures and lighting condition compromising the accuracy in psoriasis classification. In addition, traditional methods have difficulty modelling the various factors associated with psoriasis symptoms. This research provides an enhanced method based on the Vision Transformer (ViT) architecture, which includes data augmentation and transfer learning, to overcome the difficulties of limited data access and class imbalance in medical image classification. The dataset used comprises 1307 images categorized into two classes representing psoriatic and non-psoriatic images. The self-attention mechanism in the ViT effectively captures both local and global picture information, enabling precise identification of patterns unique to psoriatic lesions. The proposed model achieved 97.53% accuracy, a 0.98 F1-score, and a strong Pearson correlation of 0.962 (p<0.001), with high sensitivity (98.37%) and specificity (97.84%), outperforming traditional CNN-based methods. The model’s improved accuracy and reliability has the potential of early diagnosis of psoriasis and assisting dermatologists for making informed decisions. Furthermore, ViT’s ability to effectively capture lesion variation in different skin tones, textures and lighting conditions, highlighting its advantage over CNN-based approaches. These findings indicate that ViT can efficiently manage the complicated nature of dermatological image analysis for timely and improved personalized treatment plans.}, archive = {J_ACCESS}, author = {Mashal and Hafiz Ishfaq Ahmad and Moazam Ali and Erum Ashraf and Fares Alharbi and Ibrahim Tariq Javed and Entisar Alkayal}, doi = {10.1109/ACCESS.2025.3607989}, journal = {IEEE Access}, pages = {163885-163895}, shortjournal = {IEEE Access}, title = {Classification of psoriasis using vision transformer: A deep learning-based approach}, volume = {13}, year = {2025}, }
• (2025). Advanced steering control algorithm for semi-trailer trucks considering trailer dynamics and lateral motion delay. ACCESS, 13, 163869-163884. (www)

  @article{ , abstract = {In this paper, a novel steering control algorithm for semi-trailer trucks is proposed, which effectively incorporates the vehicle’s dynamic characteristics and compensates for lateral motion delays. To model the tractor and trailer, both kinematic and dynamic models are derived and utilized to design a state estimator and a lateral control system. To achieve accurate trailer motion estimation, a novel Velocity-Dependent Kalman Filter (VDKF) is developed. Unlike the standard Kalman Filter, the VDKF adaptively adjusts measurement confidence according to vehicle speed, thereby improving hitch angle estimation across a wide range of operating conditions. Furthermore, an advanced steering controller is proposed that integrates trailer dynamics and a prediction-based compensation mechanism for lateral motion delay. By predicting the future states of the tractor and trailer, the controller mitigates the delay effect by incorporating these future states into the control system. The overall objective of this work is to enhance lateral stability and robust path tracking of semi-trailer trucks under varying payloads and steering delays. Simulation results demonstrate that the proposed controller reduces lateral deviation by up to 86.4% compared to Pure Pursuit (PP) and 53.0% compared to the Linear Quadratic Regulator (LQR), while maintaining robustness under payload variation and a 500 ms steering delay.}, archive = {J_ACCESS}, author = {Geonyeong Park and Yoonyong Ahn and Sangwon Han}, doi = {10.1109/ACCESS.2025.3611300}, journal = {IEEE Access}, pages = {163869-163884}, shortjournal = {IEEE Access}, title = {Advanced steering control algorithm for semi-trailer trucks considering trailer dynamics and lateral motion delay}, volume = {13}, year = {2025}, }
• (2025). Toward intelligent ad breaks: A survey and taxonomy of AI-driven ad placement in streaming media. ACCESS, 13, 163844-163868. (www)

  @article{ , abstract = {Streaming media growth has increased demand for intelligent, non-intrusive placement of advertisements that balance monetization targets with viewer experience. Traditional rule-based heuristics such as scene change or silence discovery fail to capture contemporary video consumption diversity, complexity, and cognitive variability. Here, we present a comprehensive summary of artificial intelligence (AI) methods for optimizing ad break placement within streaming systems. We introduce a new three-phase taxonomy Data, Decision, and Delivery organizing state-of-the-art techniques within computer vision, natural language processing, affective computing, and reinforcement learning. AdBreakScore(t), a cognitively and emotionally guided model of appropriateness evaluation for advertisements, underpins this work with additions to model ethical and computational constraints. We analyze multimodal scene interpretation, viewer engagement prediction, and adaptable scheduling to illustrate prospects and trade-offs along technical, cognitive, and regulatory axes. We conclude with future directions neurophysiological engagement modeling, generative storyline alignment, and edge-compliant delivery aiming to advance development of viewer-centered, adaptive, and ethically principled systems for placing advertisements within an ever-changing digital media landscape.}, archive = {J_ACCESS}, author = {Waruna De Silva and Anil Fernando}, doi = {10.1109/ACCESS.2025.3610662}, journal = {IEEE Access}, pages = {163844-163868}, shortjournal = {IEEE Access}, title = {Toward intelligent ad breaks: A survey and taxonomy of AI-driven ad placement in streaming media}, volume = {13}, year = {2025}, }
• (2025). Dynamic differential protection of transformer considering CT saturation based on waveform distortion estimation. ACCESS, 13, 163832-163843. (www)

  @article{ , abstract = {The protection relay performs a significant role in maintaining the power system’s reliability and stability. However, Current Transformer (CT) saturation affects the overcurrent, differential and distance protection under various conditions. Under the CT saturation condition, the secondary current waveform is distorted depending upon the level of saturation. This paper describes an adaptive differential protection scheme for a transformer considering the effect of magnetizing inrush and the CT saturation. In the proposed methodology, the inrush conditions and CT saturation conditions are detected using the waveform distortion estimation and current polarity index technique by utilizing the current signal parameters. The power system is simulated in PSCAD software to create various fault conditions in the transformer. Different transformer internal and external faults have been generated, considering mild to heavy CT saturation phenomenon. The proposed dynamic differential relaying method is implemented in MATLAB software for CT saturation detection and transformer fault zone identification. A hardware prototype has been developed to simulate practical inrush and fault scenarios on a transformer to validate the proposed relaying technique. The proposed algorithm is implemented in the microcontroller, which efficiently detects the occurrence of different levels of CT saturation and successfully operates for all internal faults. On the other hand, the proposed relaying scheme remains inoperative for all inrush conditions and out-of-zone transformer faults by effectively detecting CT saturation.}, archive = {J_ACCESS}, author = {Nilesh Chothani and Swapnil Kumar}, doi = {10.1109/ACCESS.2025.3609541}, journal = {IEEE Access}, pages = {163832-163843}, shortjournal = {IEEE Access}, title = {Dynamic differential protection of transformer considering CT saturation based on waveform distortion estimation}, volume = {13}, year = {2025}, }
• (2025). Multimodal coherence-correlation fusion of EEG and ECoG signals for enhanced epileptic seizure detection using discrete lyapunov wavelet transform. ACCESS, 13, 163820-163831. (www)

  @article{ , abstract = {Epileptic seizure detection using multimodal neural signals has the potential to improve diagnostic reliability compared to unimodal approaches. In this work, we present a signal-processing-driven framework that integrates scalp EEG and intracranial ECoG signals using Dynamic Exponential Log Time Warping (DELTW) for temporal alignment and a robust artifact suppression method, Skewness Independent Percentile Kurtosis Analysis (SIPKA). The aligned multimodal signals are decomposed using a Discrete Lyapunov Exponents Wavelet Transform (DLEWT) to extract nonlinear dynamical features associated with seizure activity. The proposed method was evaluated on the CHB-MIT EEG and OpenNeuro ECoG datasets. Compared to baseline methods, the DELTW-aligned fusion achieved an accuracy of 96.1%, an AUC of 0.92, and an AUC-PR of 0.90 while reducing the false alarm rate to 0.17 h−1 and the mean detection latency to 4.6 s. Statistical significance testing (Wilcoxon signed-rank test) confirmed that these improvements were significant ( $p\lt 0.01$ ). These results demonstrate that combining DELTW-based alignment, SIPKA artifact rejection, and DLEWT-based nonlinear features can substantially enhance seizure detection accuracy and reduce false alarms without requiring deep learning models, offering a computationally efficient solution suitable for clinical deployment.}, archive = {J_ACCESS}, author = {Ajay Prinston Pinto and Dattathreya and Padma Prasada and S. Adarsh Rag}, doi = {10.1109/ACCESS.2025.3608806}, journal = {IEEE Access}, pages = {163820-163831}, shortjournal = {IEEE Access}, title = {Multimodal coherence-correlation fusion of EEG and ECoG signals for enhanced epileptic seizure detection using discrete lyapunov wavelet transform}, volume = {13}, year = {2025}, }
• (2025). Is my data in your AI? membership inference test (MINT) applied to face biometrics. ACCESS, 13, 163805-163819. (www)

  @article{ , abstract = {This article introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if given data was used during the training of AI/ML models. Specifically, we propose two MINT architectures designed to learn the distinct activation patterns that emerge when an Audited Model is exposed to data used during its training process. These architectures are based on Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The experimental framework focuses on the challenging task of Face Recognition, considering three state-of-the-art Face Recognition systems. Experiments are carried out using six publicly available databases, comprising over 22 million face images in total. Different experimental scenarios are considered depending on the context of the AI model to test. Our proposed MINT approach achieves promising results, with up to 90% accuracy, indicating the potential to recognize if an AI model has been trained with specific data. The proposed MINT approach can serve to enforce privacy and fairness in several AI applications, e.g., revealing if sensitive or private data was used for training or tuning Large Language Models (LLMs).}, archive = {J_ACCESS}, author = {Daniel DeAlcala and Aythami Morales and Julian Fierrez and Gonzalo Mancera and Ruben Tolosana and Javier Ortega-Garcia}, doi = {10.1109/ACCESS.2025.3608951}, journal = {IEEE Access}, pages = {163805-163819}, shortjournal = {IEEE Access}, title = {Is my data in your AI? membership inference test (MINT) applied to face biometrics}, volume = {13}, year = {2025}, }
• (2025). Crowd dynamics in online lending: Unveiling indicators of collective insight. ACCESS, 13, 163796-163804. (www)

  @article{ , abstract = {This paper investigates the presence of collective intelligence within online crowdfunding platforms, specifically focusing on lending scenarios. Utilizing data from a major lending platform comprising nearly 10 million loan contributions, the study explores how crowd behavior reveals indicators of collective insight. The findings suggest that opinion diversity and funding speed serve as significant indicators, particularly for assessing risk in projects. The research contributes to understanding crowd dynamics in online financial systems and offers implications for platform design and user behavior.}, archive = {J_ACCESS}, author = {Hetvi N. Desai}, doi = {10.1109/ACCESS.2025.3605316}, journal = {IEEE Access}, pages = {163796-163804}, shortjournal = {IEEE Access}, title = {Crowd dynamics in online lending: Unveiling indicators of collective insight}, volume = {13}, year = {2025}, }
• (2025). SMA-YOLO: A defect detection algorithm for self-explosion of insulators under complex backgrounds. ACCESS, 13, 163778-163795. (www)

  @article{ , abstract = {Insulators are essential components in power transmission systems, and identifying faults in them promptly through UAV imaging is vital for maintaining grid reliability and operational safety. In order to reduce the rate of missed and false detection of insulator self-explosion defects under the interference of complex background, we propose an advanced model named SMA-YOLO, based on YOLOv11. We propose the SALSK module. It improves the detection precision for objects of varying sizes. Furthermore, we redesign the core module C3k2 in YOLOv11 by incorporating a multi-scale feature extraction mechanism, which can aid in better distinguishing between defects and background noise and reduces the impact of complex backgrounds. Additionally, we propose the ASSPPF module. The module integrates detailed information across various scales, strengthening the detection capability for small objects. Experimental results demonstrate the superior performance of the proposed model across two distinct datasets. The model achieves mAP@0.5 values of 95.5% and 99.2%, and mAP@0.5:0.95 values of 84.3% and 84.4%, respectively. Compared to the baseline model, mAP@0.5:0.95 improved by 2.5% and 2.0%, validating the robust generalization and resilience of the model. These findings confirm that SMA-YOLO holds high practical value and application potential in the insulator inspection tasks.}, archive = {J_ACCESS}, author = {Kaiyue Yu and Lei Zhao and Xiaojun Xue and Heng Li and Hui Liu}, doi = {10.1109/ACCESS.2025.3609906}, journal = {IEEE Access}, pages = {163778-163795}, shortjournal = {IEEE Access}, title = {SMA-YOLO: A defect detection algorithm for self-explosion of insulators under complex backgrounds}, volume = {13}, year = {2025}, }
• (2025). Research on sub-aperture stitching interferometric testing technology for high-steepness mirrors based on instrument transfer function. ACCESS, 13, 163769-163777. (www)

  @article{ , abstract = {Precise measurement of high-steepness optical components is a critical challenge currently faced, which is being overcome through a groundbreaking high-steepness mirror sub-aperture stitching interferometric measurement method utilizing the instrument transfer function (ITF). A spherical step test plate was meticulously designed and manufactured based on ITF calibration principles. This essential tool enables the calibration of the ITF distribution at different spatial positions of the sub-aperture stitching instrument during the measurement of steep spherical mirrors. A comprehensive analysis then revealed a clear ITF distribution pattern, which made it possible to develop a data fusion algorithm model for the overlapping regions in the sub-aperture stitching process. In practical application, a highly successful stitching measurement was performed on a high-steepness spherical mirror with a diameter of 100 mm and a curvature radius of 103.314 mm. The experimental results demonstrate significant improvement in optical surface metrology. The full-aperture stitching achieved a peak-to-valley (PV) residual error of $0.073\lambda $ in the central region, with a root mean square (RMS) error of $0.002\lambda $ . Similarly, the edge region exhibited a PV error of $0.039\lambda $ and an RMS error of $0.002\lambda $ . These values represent a marked enhancement over conventional averaging-based fusion algorithms. These excellent results demonstrate the effectiveness of the method proposed in this study and provide a clear solution for high-precision measurement of steep optical components. This innovative method advances the field and sets a new benchmark for the precise measurement of optical component facets.}, archive = {J_ACCESS}, author = {Mengxue Cai and Xiaokun Wang and Zhengdong Yu and Zhongkai Liu and Wenhan Li and Zhiyu Zhang and Yukun Wang and Xuejun Zhang}, doi = {10.1109/ACCESS.2025.3609860}, journal = {IEEE Access}, pages = {163769-163777}, shortjournal = {IEEE Access}, title = {Research on sub-aperture stitching interferometric testing technology for high-steepness mirrors based on instrument transfer function}, volume = {13}, year = {2025}, }
• (2025). DBP-YOLO: A dynamic bifurcated-path fusion network for weld seam defect detection. ACCESS, 13, 163749-163768. (www)

  @article{ , abstract = {In order to tackle the issues related to identifying defects on weld seam surfaces, such as the concentrated distribution of defect targets, interference from complex background textures, and the lack of feature differentiation in conditions with low signal-to-noise ratios, this research proposes the Dynamic Bifurcated-Path Fusion Network (DBP-YOLO). This innovative network, grounded in the YOLOv8n architecture, incorporates the C2f-BiFusion (C2f-BiF). This unit greatly improves the ability to represent defect features by employing channel compression techniques, depthwise separable convolutions, and mechanisms for feature gating. Additionally, a Dynamic Perception Pyramid Network (DFFN) is introduced, which establish a cross-level two-way interaction mechanism to achieve multi-scale adaptive feature fusion. A lightweight detection module, low-rank and sparse cross-domain recommendation(LSCD), has been designed to successfully extract local characteristics of small defects while keeping the consumption of computational resources to a minimum. Moreover, we introduce the Focal PIoU loss function, which dynamically adjusts the weights for difficult and easy samples, integrating a joint optimization approach for accurate bounding box regression. This enhances both the convergence efficiency and the accuracy of the model. Experimental findings indicate that DBP-YOLO surpasses the benchmark model (YOLOv8n) with a 3.7% improvement in the mAP@0.5 metric and a reduction in model parameters by $1.21\times 10 ^{6}$ , achieving a detection speed of 62.4 FPS alongside an mAP@0.5 of 83.5% on the Jetson Xavier NX platform. These results satisfy the requirements for real-time detection and offer solid technical support for the advancement of intelligent weld seam detection technologies, the source codes are at https://github.com/xuchengniu/DBP-YOLO}, archive = {J_ACCESS}, author = {Chen Zhang and Cheng Xu and Wentao Shan and Zhenhua Han}, doi = {10.1109/ACCESS.2025.3609890}, journal = {IEEE Access}, pages = {163749-163768}, shortjournal = {IEEE Access}, title = {DBP-YOLO: A dynamic bifurcated-path fusion network for weld seam defect detection}, volume = {13}, year = {2025}, }
• (2025). AM-YOLO: An efficient object detection model for astragalus mongholicus authentication with dynamic gated feature fusion. ACCESS, 13, 163731-163748. (www)

  @article{ , abstract = {Astragalus membranaceus (AM) holds significant medicinal value in clinical applications. However, the market frequently encounters phenomena where morphologically similar counterfeits are used to impersonate AM, severely affecting the efficacy of medications and drug safety. To achieve rapid and accurate identification of genuine and counterfeit AM, this paper proposes an improved object detection model based on YOLOv8—AM-YOLO. The model achieves adaptive weighted integration of high- and low-level features by designing a gating fusion module L2Hblock. It proposes an improved spatial pyramid pooling structure ESPPF to enhance local saliency and global context awareness. Additionally, the lightweight Faster-Block is adopted to construct the Fc2f module, reducing the model’s computational complexity. Additionally, the introduction of a dynamic upsampling module, DySample, further improves the ability to restore edge details. To validate the effectiveness of AM-YOLO, we constructed the first AM detection dataset, covering 11 common fakes. The experimental results show that AM-YOLO achieves 95.7% in the mAP50-95 metric, an increase of 2.8% compared to YOLOv8, with an inference speed of 176 Frames Per Second (FPS). Compared with the state-of-the-art object detection algorithms, AM-YOLO reduces the average error rate by 30%. Moreover, the model can maintain leading inference speed when deployed on devices with different computing power. The datasets, code and test results used in this study are available https://github.com/feifeimao888/AM-YOLO}, archive = {J_ACCESS}, author = {Xingxin Kong and Jing Yu and Jiawei Guo and Qingqing Tian and Feiyan Li and Yingxin Liu}, doi = {10.1109/ACCESS.2025.3610491}, journal = {IEEE Access}, pages = {163731-163748}, shortjournal = {IEEE Access}, title = {AM-YOLO: An efficient object detection model for astragalus mongholicus authentication with dynamic gated feature fusion}, volume = {13}, year = {2025}, }
• (2025). Deep learning for glaucoma classification and grading: A comprehensive review on fundus imaging approaches. ACCESS, 13, 163699-163730. (www)

  @article{ , abstract = {Glaucoma is one of the leading causes of blindness worldwide and is characterized by progressive visual field loss due to optic nerve damage. Early detection is fundamental, yet it is often hindered by the asymptomatic nature of the disease in its initial stages. In response to this challenge, advanced techniques such as Deep Learning (DL) and computer vision are emerging as potential tools to revolutionize glaucoma diagnosis. This article aims to systematically evaluate the current state of artificial intelligence approaches for fundus image-based glaucoma detection and to identify trends, challenges, and opportunities. Following the PRISMA methodology, we conducted a comprehensive systematic review examining a total of 63 publications available in Scopus, ScienceDirect, IEEE, and Web of Science databases, available in English and published during the study period between 2020 and 2024. The review revealed key techniques in the critical stages of automated glaucoma detection. Convolutional neural networks dominated recent literature, with ResNet architectures achieving optimal performance (accuracy range: 82.37%-98.48%). For localization and segmentation, U-Net variants, attention-guided networks, and advanced ensemble approaches were prominent. In feature extraction, methods exploiting structural and textural metrics, wavelet-based transformations, and attention mechanisms showed considerable potential. Classification tasks benefited from Convolutional Neural Network (CNN) optimizations, attention-based architectures, hybrid models, and transformer frameworks, demonstrating high accuracy for both binary and multiclass glaucoma detection. Deep learning (DL) approaches have demonstrated significant potential for both binary and multiclass glaucoma classification from color fundus images. Key findings from this study include: 1) attention mechanisms and transformer architectures show superior performance in capturing subtle disease features, with accuracies exceeding 95%; 2) hybrid approaches combining multiple techniques achieve better generalization across datasets; 3) curriculum learning strategies improve multiclass severity grading accuracy; and 4) challenges persist in standardizing evaluation metrics and managing variations in data quality. Future research should focus on developing more robust architectures that can handle diverse image qualities and incorporate clinical knowledge into the learning process. Additionally, systematic analysis revealed critical implementation barriers: only 30%-40% of studies included external validation, with significant performance degradation on independent datasets; approximately 60%-70% relied on structure-only approaches, excluding essential visual field correlation for clinical decision-making.}, archive = {J_ACCESS}, author = {Eugenia Arrieta-Rodríguez and José Araque-Gallardo and Natalia Peñaloza Barrios and Oscar Luis Teherán-Forero and María Claudia Bonfante and Emiro de-la-Hoz-Franco and Margarita Gamarra and José Escorcia-Gutierrez}, doi = {10.1109/ACCESS.2025.3605819}, journal = {IEEE Access}, pages = {163699-163730}, shortjournal = {IEEE Access}, title = {Deep learning for glaucoma classification and grading: A comprehensive review on fundus imaging approaches}, volume = {13}, year = {2025}, }
• (2025). Range-doppler processing of high-speed target in nonlinear stepped-carrier OFDM radar for automotive applications. ACCESS, 13, 163687-163698. (www)

  @article{ , abstract = {Stepped-carrier orthogonal frequency division multiplexing (SC-OFDM) radar is an emerging low-cost alternative to traditional OFDM radar for automotive applications. It offers high-range resolution without increasing the sampling rate. However, it suffers from the Doppler intolerance in high-speed target detection, which arises from Doppler-induced inter-carrier interference (ICI), resulting in peak power loss and increased range sidelobes in the range-velocity map. In addition, the maximum unambiguous velocity is reduced by the number of frequency steps. This paper proposes a novel range-Doppler processing method for nonlinear stepped-carrier OFDM (NSC-OFDM) radar systems to mitigate the problems. The proposed method employs a two-stage Doppler compensation approach: initially applying all-cell Doppler correction under unambiguous velocity assumption, followed by ambiguous-cell Doppler correction for ambiguous targets identified through range sidelobes. The method can automatically resolve velocity ambiguity by selecting the hypothesis with optimal sidelobe suppression, achieving effective ICI mitigation. Finally, simulation results validate the effectiveness of the proposed method; meanwhile, the good performance of the proposed method on range sidelobe reduction, estimation accuracy, and computational complexity are shown by comparisons with three other methods.}, archive = {J_ACCESS}, author = {Xuanxuan Tian}, doi = {10.1109/ACCESS.2025.3611331}, journal = {IEEE Access}, pages = {163687-163698}, shortjournal = {IEEE Access}, title = {Range-doppler processing of high-speed target in nonlinear stepped-carrier OFDM radar for automotive applications}, volume = {13}, year = {2025}, }
• (2025). Self-supervised anomalous pose detection in multi-view human pose estimation for smart manufacturing with non-calibrated cameras. ACCESS, 13, 163672-163686. (www)

  @article{ , abstract = {In manufacturing environments, human pose estimation is important in monitoring workers’ safety in the workplace. Usually, a single camera struggles to capture a worker’s entire body clearly, which is necessary for precise movement analysis. To improve this, estimating human poses from multiple camera views significantly enhances worker safety by reducing the problem of not seeing all parts of the body. However, most multi-view methods require camera calibration, which is impractical in manufacturing environments. This study introduces a framework for detecting anomalous 3D poses using multi-view cameras without camera calibration. Our approach simplifies the process and reduces costs, involving four main steps: estimating 2D human poses, filling in occluded body parts with affine transformations, converting these to 3D human poses using a Lifting Network, and detecting anomalous poses using a graph convolutional network. By filling in occluded body parts using the affine transformation, we improve the accuracy of 3D pose detection by 1.54% in the term of the Percentage of Correct Keypoints. Furthermore, our graph convolutional network method achieves a 0.943 F1-score in detecting anomalous poses. This framework aims to boost the usefulness of safety systems that rely on accurate human pose data. Our findings confirm that this method is effective and practical for real-world applications.}, archive = {J_ACCESS}, author = {Tserenpurev Chuluunsaikhan and Jeong-Hun Kim and Md Azher Uddin and Young-Ho Park and Aziz Nasridinov}, doi = {10.1109/ACCESS.2025.3610229}, journal = {IEEE Access}, pages = {163672-163686}, shortjournal = {IEEE Access}, title = {Self-supervised anomalous pose detection in multi-view human pose estimation for smart manufacturing with non-calibrated cameras}, volume = {13}, year = {2025}, }
• (2025). Panoptic-guided scene description for visual place recognition of patrol robotic systems. ACCESS, 13, 163661-163671. (www)

  @article{ , abstract = {This paper addresses the challenge of visual place recognition (VPR) for patrol robots, aiming to estimate coarse location in GPS-denied environments. Place recognition is essential for autonomous navigation, particularly to avoid kidnapped robot scenarios. Since GPS signals are often unstable in cluttered environments such as urban areas and industrial complexes, many patrol robots rely on various sensors. Among them, VPR (i.e., image-based place recognition) is especially practical due to the versatility of camera sensors, which are lightweight, low-cost, and power-efficient. However, VPR performance can degrade significantly under large viewpoint variations between query and reference images. To address this, we propose a novel panoptic-guided VPR method that enhances robustness to such changes. The method first employs panoptic segmentation to extract pixel-level class information, then constructs a class frequency histogram while discarding predefined dynamic object classes to improve stability. In addition, a topological graph is generated to model objects and their relative spatial configuration, helping mitigate viewpoint-induced mismatches. The similarity between the query and reference candidates is computed using a radial basis function (RBF) for the histogram comparison and the Chebyshev distance for graph embedding comparison. Experimental results in multiple benchmark data sets demonstrate that our panoptic-based descriptor achieves an approximately 10% improvement in VPR performance over state-of-the-art baselines.}, archive = {J_ACCESS}, author = {Chaewon Song and Nak-Myoung Sung and Seokjun Lee and Chungjae Choe}, doi = {10.1109/ACCESS.2025.3602460}, journal = {IEEE Access}, pages = {163661-163671}, shortjournal = {IEEE Access}, title = {Panoptic-guided scene description for visual place recognition of patrol robotic systems}, volume = {13}, year = {2025}, }
• (2025). An RTL-based general synthesis methodology for device-independent ternary logic circuits. ACCESS, 13, 163648-163660. (www)

  @article{ , abstract = {Ternary logic circuits are considered a high-potential alternative that can continue the technological advance of binary logic. Current studies in ternary logic focus on two aspects: One focuses on designing specific ternary circuits (such as adders, multipliers, and CPUs), and the other proposes design methodologies that make circuit design possible on targeted ternary devices. However, the implementation of large-scale ternary logic requires hardware description language (HDL) and a synthesis methodology that can be applied to various ternary devices. Thus, this study proposes 1) a methodology of ternary RTL-to-gate-level netlist synthesis, 2) a syntax for ternary logic based on Verilog HDL, and 3) the first GT-LOGIC (generic ternary logic) library and mapping strategy for logic synthesis in our synthesis process. Our first RTL-level synthesis method reports that our synthesized netlist shows 63.39% reduced cell count on average compared to the previous MUX-based synthesis. In addition, we emphasize that our methodology successfully synthesizes ternary logic in various ternary devices such as Memristor-based CMOS, CNTFET, T-CMOS, and DEPFET.}, archive = {J_ACCESS}, author = {Hanmok Park and Seonghoon Kim and Sunmean Kim and Taigon Song}, doi = {10.1109/ACCESS.2025.3597293}, journal = {IEEE Access}, pages = {163648-163660}, shortjournal = {IEEE Access}, title = {An RTL-based general synthesis methodology for device-independent ternary logic circuits}, volume = {13}, year = {2025}, }
• (2025). Room temperature to 300°C SiC MOSFET oscillator under fixed bias. ACCESS, 13, 163640-163647. (www)

  @article{ , abstract = {Wireless monitoring systems in high-temperature, harsh industrial environments, including oscillator circuits to interface with sensing and transmission components, must operate over temperature ranges of several hundred degrees Celsius. An oscillator under these conditions must deliver sufficient power for wireless transmission while being designed with minimal components to reduce the impact of component temperature variation, increase circuit reliability, and reduce the size and complexity of circuits operating in high temperature harsh environment. A simple single supply biasing scheme is also essential to minimize the number of circuit components and to simplify implementation in harsh environments. In this work, for the first time, an oscillator that operates from room temperature to above 300°C using a single fixed voltage supply and a single SiC commercial transistor that outputs more than 12dBm to a $50\Omega $ load was designed, modelled, fabricated, and tested. Due to its few components, simplicity of operation, and robustness, this oscillator can be used for wireless sensing applications over a wide temperature range with the potential to be powered using energy harvesting. The impact of these fabrication and operational conditions is this circuit fulfilling the needs of a wireless sensing application, with potential for self-powered operation, in high-temperature harsh industrial environments.}, archive = {J_ACCESS}, author = {Jude A. Zanoni and Nuri W. Emanetoglu and Mauricio Pereira da Cunha}, doi = {10.1109/ACCESS.2025.3611143}, journal = {IEEE Access}, pages = {163640-163647}, shortjournal = {IEEE Access}, title = {Room temperature to 300°C SiC MOSFET oscillator under fixed bias}, volume = {13}, year = {2025}, }
• (2025). Accurate noise source separation in pulse-frequency modulation-controlled power converters using asynchronous near-field and far-field measurement analysis. ACCESS, 13, 163627-163639. (www)

  @article{ , abstract = {Aperiodic spike noise signals generated by pulse–frequency modulation (PFM)-controlled direct-current (DC)–DC converters introduce random jitter into the switching timing, rendering it challenging to isolate individual power supply noise sources. Conventional methods based on cross-correlation analysis suffer from inaccuracies owing to the random jitter and aperiodic characteristics inherent in PFM converters. This study proposes a method for accurately separating radiated electromagnetic noise sources from multiple PFM-controlled DC–DC converters. An asynchronous near-field and far-field measurement scheme is adopted owing to its considerably lower implementation cost than that of its synchronous counterpart. The proposed method extracts individual spike noise, and then identifies and associates each spike noise type with its corresponding source based on the spatial and temporal characteristics captured by the asynchronous measurements. The experimental results demonstrate improved estimation accuracy across a broad bandwidth, thereby enabling the identification of dominant noise sources. This enhanced accuracy supports more efficient mitigation of electromagnetic interference and reduces the complexity and cost of implementing noise suppression. As a result, the time to market can be shortened, facilitating the development of cost-effective electronic products.}, archive = {J_ACCESS}, author = {Hiroyasu Sano and Kenta Umebayashi and Satoshi Suzuki and Hidekatsu Sasaki and Yasuaki Kaneda}, doi = {10.1109/ACCESS.2025.3610518}, journal = {IEEE Access}, pages = {163627-163639}, shortjournal = {IEEE Access}, title = {Accurate noise source separation in pulse-frequency modulation-controlled power converters using asynchronous near-field and far-field measurement analysis}, volume = {13}, year = {2025}, }
• (2025). A predictive model for personalized course advising using data mining techniques. ACCESS, 13, 163618-163626. (www)

  @article{ , abstract = {The course selection process in higher education has grown more complex as the students face the growing volume of academic options that must align with their personal and career goals. The traditional advising methods often fail to provide the personalized recommendations, resulting in the delayed graduations and the higher dropout rates. Therefore, the automated and intelligent course selection models and systems are becoming more important. These AI-based systems nearly deliver the tailored guidance to the students by analyzing their academic performance. In addition, this guidance is also based on the students’ individual characteristics, helping to create the personalized course recommendations. This study presents a predictive model for the personalized course advising, in which it incorporates data from the academic, personal, and the social dimensions. Then, this model uses and employs the advanced machine learning algorithms and the optimization techniques and methods to maximize the accuracy of course recommendations. The feature selection methods like Information Gain and Chi-Square are used, in addition to dedicated classifiers such as the Decision Trees (DT) and the K-Nearest Neighbors (KNN). To balance the dataset, the Synthetic Minority Oversampling Technique (SMOTE) is applied. In the implementation phase, first, the Knapsack Problem algorithm is applied to optimize the students’ course loads in order to ensure the recommendations match their abilities and the institutional requirements. Next, the KNN is applied and reaches an accuracy of 91.57% and outperforms the DT, which provides a practical solution for the problem of adaptive course advising.}, archive = {J_ACCESS}, author = {Yasser Hachaichi and Ayman E. Khedr and Mohamed A. Belal and Ahmed Shawky Elbhrawy}, doi = {10.1109/ACCESS.2025.3600578}, journal = {IEEE Access}, pages = {163618-163626}, shortjournal = {IEEE Access}, title = {A predictive model for personalized course advising using data mining techniques}, volume = {13}, year = {2025}, }
• (2025). Lighting model-guided initialization for gradient descent-based projector compensation. ACCESS, 13, 163607-163617. (www)

  @article{ , abstract = {Projection mapping, which maps a projector input (i.e., an image) onto a physical surface, is widely used to display user-specified visual content. However, the projected content can become distorted when an ideal surface, such as a flat and white plane, is unavailable. As a result, adjusting the projector input using a projector compensation technique becomes necessary to ensure that the projected image matches the user-provided target image. While various techniques have been proposed for projector compensation, a recent approach models projection mapping as a simulatable process in virtual space, where a light transport algorithm simulates the projection mapping. In such rendering frameworks, projector compensation is typically achieved by iteratively adjusting the projector input via a gradient descent-based optimizer, starting from an initial guess (often chosen arbitrarily). In this paper, we investigate how to set the initial values more effectively rather than choosing them arbitrarily. As the main contribution of the paper, we propose a new initialization scheme that determines the starting values based on a lighting model. We then integrate this model-guided initialization into gradient descent-based optimization and demonstrate that it improves projection mapping results, particularly for non-planar and colored surfaces.}, archive = {J_ACCESS}, author = {Woochan Sun and Geonu Noh and Kwanghee Ko and Bochang Moon}, doi = {10.1109/ACCESS.2025.3611016}, journal = {IEEE Access}, pages = {163607-163617}, shortjournal = {IEEE Access}, title = {Lighting model-guided initialization for gradient descent-based projector compensation}, volume = {13}, year = {2025}, }
• (2025). Microservices design pattern in action: Improving modifiability in microservices-based software development. ACCESS, 13, 163588-163606. (www)

  @article{ , abstract = {Microservices architecture offers theoretical benefits for software maintainability, yet empirical validation of design patterns’ effectiveness for enhancing modifiability remains limited, with practitioners lacking evidence-based guidance for pattern selection and quantified improvement expectations. This study addresses this gap by presenting a quantitative assessment of microservices design patterns’ impact on modifiability through service-level analysis of 110 individual microservices across 15 open-source applications. Following Systematic Literature Review and Grey Literature analysis, ten modifiability-enhancing patterns were identified and evaluated using a novel context-sensitive selection framework derived from ISO 25002:2024 standards. The framework introduces three key theoretical contributions: the “Worst First” optimization principle for architectural refactoring prioritization, a service-level analysis methodology enabling granular pattern effectiveness assessment, and an evidence-based pattern selection model linking architectural characteristics to improvement potential. Quantitative evaluation using Change Impact Factor (CIF), Service Independence Metric (SIM), and Modifiability Index (MI) demonstrated significant improvements: CIF decreased by 49.7% (from 0.303 to 0.152), SIM increased by 14.2% (from 0.764 to 0.873), and MI improved by 17.6% (from 0.732 to 0.861). Statistical analysis confirmed significance for all metrics (CIF and MI: p<0.0001; SIM: p<0.05) with effect sizes ranging from small to large (d= 0.26-0.81). Strong correlations between initial architectural characteristics and improvement magnitude (r=-0.812) validated the “Worst First” principle, where services with poorest metrics benefit most from pattern application. The resulting evidence-based framework enables context-sensitive pattern selection and provides quantified improvement expectations, moving beyond universal approaches to establish scientific foundations for architectural decision-making in microservices development.}, archive = {J_ACCESS}, author = {Gintoro and Ford Lumban Gaol and Ahmad Nurul Fajar and Abba Suganda Girsang and Tokuro Matsuo}, doi = {10.1109/ACCESS.2025.3610272}, journal = {IEEE Access}, pages = {163588-163606}, shortjournal = {IEEE Access}, title = {Microservices design pattern in action: Improving modifiability in microservices-based software development}, volume = {13}, year = {2025}, }
• (2025). Spectrum management and interference mitigation between 5G and satellite services on extended C-band and mmWave: Enhancing national revenue. ACCESS, 13, 163571-163587. (www)

  @article{ , abstract = {The development of telecommunications technology has shown a dynamic trend, especially with the emergence of 5G mobile technology. Indonesia, a developing country with high connectivity needs, has allocated frequencies for 5G services, including the extended C-band (3.5 GHz) and the mmWave (26 GHz). However, the use of these frequencies can interfere with existing satellite services. Indonesia has gained access to these 5G frequencies at the World Radio Communication Conference. Implementing 5G in these bands can cause significant interference with satellite services, affecting operational sustainability and national revenue from the satellite sector. The sustainability of satellite services and the economic impact of interference require effective interference mitigation models. This study consisted of four phases. First, we present a review and analysis of published interference-mitigation models between 5G and satellite services. Second, data was collected from the Ministry of Communications and Digital, experts on satellites, mobile networks, and regulations through in-depth interviews. Third, the data collected and the interference mitigation models were integrated into the Spectrum Engineering Advanced Monte Carlo Analyzing Tool (SEAMCAT). Fourth, we analyzed the impact of interference mitigation to enhance non-tax state revenue through a spectrum license fee. The aim was to produce an effective model to mitigate interference, maintain satellite service continuity, and support the implementation of 5G. This research contributes to telecommunications technology in Indonesia and potentially increases national revenue by optimizing usage frequency.}, archive = {J_ACCESS}, author = {Alfin Hikmaturokhman and Eko Fajar Cahyadi and Gita Fadila Fitriana and Nachwan Mufti Adriansyah and Kasmad Ariansyah}, doi = {10.1109/ACCESS.2025.3610807}, journal = {IEEE Access}, pages = {163571-163587}, shortjournal = {IEEE Access}, title = {Spectrum management and interference mitigation between 5G and satellite services on extended C-band and mmWave: Enhancing national revenue}, volume = {13}, year = {2025}, }
• (2025). LLM-driven pareto-optimal multi-mode reinforcement learning for adaptive UAV navigation in urban wind environments. ACCESS, 13, 163550-163570. (www)

  @article{ , abstract = {Autonomous drones in complex urban wind environments must balance speed, safety, and energy efficiency under highly variable conditions. Traditional single-policy reinforcement learning controllers often perform poorly when exposed to scenarios beyond their training. We introduce a Pareto-optimal multi-mode framework that trains three specialized unmanned aerial vehicle (UAV) policies (aggressive, balanced, and cautious) via proximal policy optimization (PPO) with specific reward scalings, yielding controllers that collectively span the speed-safety-energy trade-off surface. To automate mode selection, we fine-tune a large language model (LLM) on 30,000 simulation-derived environment-performance tuples, allowing it to predict the optimal policy from building density, wind speed and orientation, battery state, and recent flight history. In a Unity-based Manhattan simulation with computational fluid dynamics (CFD) wind fields across four headings and 10 speed levels, the LLM-driven decision maker reduces average flight time by 16%, lowers the collision rate by 50%, and saves 18% energy compared to any single mode, while preserving nondominated trade-off performance. The decision maker also generalizes to unseen wind patterns and layouts without handcrafted heuristics, demonstrating the promise of combining Pareto-optimal reinforcement learning (RL) with LLM-based meta-decision making for UAV autonomy.}, archive = {J_ACCESS}, author = {Jiahao Wu and Hengxu You and Bowen Sun and Jing Du}, doi = {10.1109/ACCESS.2025.3611336}, journal = {IEEE Access}, pages = {163550-163570}, shortjournal = {IEEE Access}, title = {LLM-driven pareto-optimal multi-mode reinforcement learning for adaptive UAV navigation in urban wind environments}, volume = {13}, year = {2025}, }
• (2025). Reflexive thematic analysis of big data governance practices in public higher learning institutions. ACCESS, 13, 163521-163549. (www)

  @article{ , abstract = {Big Data Governance (BDG) has become essential for ensuring the secure, efficient, and strategic use of data in modern organizations. This study investigates the current state of BDG practices within Public Higher Learning Institutions (PHLIs) in Malaysia and Nigeria using a mixed-methods approach that integrates a review of existing literature, in-depth qualitative interviews, and a structured quantitative survey. The literature review identified core components of effective BDG roles and responsibilities, activities and processes, which informed the development of the study instruments. Qualitative data were gathered through interviews with ICT and MIS directors and analyzed using reflexive thematic analysis, while quantitative data were collected from 119 operational staff and analyzed using descriptive statistics. Findings from both data sources revealed fragmented BDG practices, low levels of tool deployment and integration, and inconsistencies in role definition and assignment, policy implementation, and institutional coordination. While institutions in Malaysia demonstrated relatively more mature and established BDG programs; responses from Nigerian institutions reflected early-stage development or complete absence of data governance structures. Notably, a large proportion of institutions lacks awareness or access to technical platforms necessary for effective BDG practices. The study highlights the need for tools and technologies, and the integration of BDG responsibilities across all institutional levels. This research contributes new empirical evidence on the challenges and disparities in BDG implementation. It offers actionable recommendations and proposes future research directions focused on BDG maturity models, multi-regional assessments, and the role of emerging technologies in strengthening big data governance frameworks in higher education.}, archive = {J_ACCESS}, author = {Yunusa Adamu Bena and Roliana Ibrahim and Jamilah Mahmood and Hitham Alhussian and Said Jadid Abdulkadir and Maged Nasser and Matthew O. Ayemowa}, doi = {10.1109/ACCESS.2025.3610092}, journal = {IEEE Access}, pages = {163521-163549}, shortjournal = {IEEE Access}, title = {Reflexive thematic analysis of big data governance practices in public higher learning institutions}, volume = {13}, year = {2025}, }
• (2025). Enhanced SAR image formation via a modified omega-K algorithm. ACCESS, 13, 163509-163520. (www)

  @article{ , abstract = {In this paper, a high-resolution synthetic aperture radar (SAR) focusing technique is proposed based on a modified omega-K algorithm in case of long synthetic aperture time. The proposed algorithm includes three stages. First, a modified reference function multiply is applied to two-dimensional frequency data to perform a coarse range cell migration. Second, the residual non-stationary phase error related to a range-azimuth coupling is compensated using a range block processing after implementing the interpolation process based on a newly devised kernel function. Finally, the high-precision SAR focusing is carried out via the azimuth focusing function, which enables us to realize a range variant motion compensation. The experimental results with simulated and real measured SAR data are presented to validate the feasibility and superiority of the proposed method.}, archive = {J_ACCESS}, author = {Min-Seok Kang}, doi = {10.1109/ACCESS.2025.3611350}, journal = {IEEE Access}, pages = {163509-163520}, shortjournal = {IEEE Access}, title = {Enhanced SAR image formation via a modified omega-K algorithm}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based SAR image generation under non-uniform vehicle speed. ACCESS, 13, 163494-163508. (www)

  @article{ , abstract = {In this paper, we propose a convolutional neural network (CNN)-based method to compensate for distortions that can occur when acquiring radar data from a synthetic aperture radar (SAR) system. The SAR system is widely used in various fields, including terrain observation, disaster monitoring, surveillance and reconnaissance. However, if the platform carrying the radar sensor moves at a non-uniform speed, distortions appear in the SAR image along the direction of the speed change. Therefore, we propose a CNN-based compensation method to address these distortions, and we evaluate its performance. Experiments are conducted in two distinct environments by mounting a radar sensor on a moving platform and varying the speed of the platform. The range-Doppler algorithm is then applied to the radar data to generate SAR images. Then, we use the CNN-based model to compensate for distortions in SAR images acquired while the platform was moving at varying speeds. The performance of the proposed method is evaluated by comparing SAR images captured at a constant platform speed with SAR images before and after compensation. The overall results obtained in three distinct environments revealed consistent gains. Using the proposed method, we achieved a 11.28%p increase in peak signal-to-noise ratio (PSNR) and a 1.92%p improvement in structural similarity index measure (SSIM) in Environment 1. In Environment 2, the method yielded a 20.46%p increase in PSNR and a 3.25%p increase in SSIM. For Environment 3, the results were a 5.88%p increase in PSNR and a 1.55%p increase in SSIM.}, archive = {J_ACCESS}, author = {Jungbin Lim and Chanul Park and Seongwook Lee}, doi = {10.1109/ACCESS.2025.3610356}, journal = {IEEE Access}, pages = {163494-163508}, shortjournal = {IEEE Access}, title = {Deep learning-based SAR image generation under non-uniform vehicle speed}, volume = {13}, year = {2025}, }
• (2025). Radio frequency fingerprint recognition method based on adaptive energy path integrated projection and trans-MAML method. ACCESS, 13, 163481-163493. (www)

  @article{ , abstract = {In wireless communications, radio frequency fingerprint identification (RFFI) leverages unique hardware characteristics for device recognition. This paper proposes an innovative few-shot RFFI method based on adaptive energy path integrated projection (AEP-IP) and an enhanced neural network, aiming to significantly improve recognition accuracy under various signal-to-noise ratio (SNR) conditions. The method integrates an enhanced densely connected temporal convolutional network (DenseTCN), transfer learning, and Meta-Learning (MAML). Key steps include extracting pulse sequences from received signals and constructing four features: ambiguity function (AF), time-frequency spectrogram (TFS), bispectrum analysis (BIS), and intrinsic function-phase (IFP). One-dimensional projection features are extracted through these features. The DenseTCN enables cross-layer feature fusion and serves as the backbone of the Trans-MAML scheme to enhance few-shot learning under low SNR conditions. Validated using real-world automatic dependent surveillance-broadcast (ADS-B) data, the results demonstrate: 1) Four-timestep feature improve accuracy by 7.9% (25 dB SNR) and 11% (0 dB SNR) compared to two-timestep feature. 2) DenseTCN outperforms TCN by 8.86% under the same conditions. 3) DenseTCN achieves comparable performance to TCN with 100% data using only 20% data (25 dB SNR). 4) With limited training data, the Trans-MAML method achieves higher recognition accuracy across all SNR levels compared to baseline methods.}, archive = {J_ACCESS}, author = {Jiawei Xin and Minjie Chen and Cong Hu}, doi = {10.1109/ACCESS.2025.3610669}, journal = {IEEE Access}, pages = {163481-163493}, shortjournal = {IEEE Access}, title = {Radio frequency fingerprint recognition method based on adaptive energy path integrated projection and trans-MAML method}, volume = {13}, year = {2025}, }
• (2025). Hypergraph neural networks based on enclosing subgraph extraction for temporal link prediction. ACCESS, 13, 163466-163480. (www)

  @article{ , abstract = {The Graph Neural Network (GNN) methods based on enclosing subgraph extraction have achieved excellent results in static graph link prediction tasks. However, most real-world networks are dynamic and evolve over time; the traditional models cannot capture temporal evolution. Moreover, the existing temporal link prediction methods mainly use simple GNNs to learn spatial embeddings, which not only ignore the higher-order relationships and local clustering structures between nodes, but also perform poorly in static graph link prediction tasks. In order to address the above problems, we propose Hypergraph Neural Networks for Temporal Link Prediction(HGTLP), which is based on enclosing subgraph extraction. The proposed HGTLP constructs a hypergraph through enclosing subgraph extraction and dual hypergraph transformation to learn the higher-order spatial and temporal embeddings of each link to be predicted, and then combines the global knowledge of the collapsed graph to improve the prediction accuracy. Experimental results demonstrate that HGTLP achieves significant performance improvement compared to the state-of-the-art models in both link prediction and new link prediction tasks.}, archive = {J_ACCESS}, author = {Ying Zhao and Xiangxu Meng and Liang Chen and Junjun Chen and Atul Sajjanhar}, doi = {10.1109/ACCESS.2025.3611308}, journal = {IEEE Access}, pages = {163466-163480}, shortjournal = {IEEE Access}, title = {Hypergraph neural networks based on enclosing subgraph extraction for temporal link prediction}, volume = {13}, year = {2025}, }
• (2025). Stall prediction in quadcopters with SHAP-based explainability and a novel flight dynamics dataset. ACCESS, 13, 163447-163465. (www)

  @article{ , abstract = {Uncrewed Aerial Vehicles (UAVs), particularly quadcopters, are increasingly deployed in high-stakes operations including defense, tactical surveillance, disaster response, and GPS-denied missions. However, aerodynamic stall—caused by conditions like vortex ring state or rotor stall—remains a critical failure mode that can lead to sudden lift loss and catastrophic crashes. This study presents a hybrid stall prediction framework that unifies physics-informed synthetic data generation, interpretable machine learning, and embedded rule-based logic for real-time, onboard risk assessment. A custom simulator was developed to generate 25,921 stall-prone flight states by modeling key parameters such as throttle, vertical speed, blade angle of attack (AoA), airspeed, disc loading, and thrust-to-weight ratio. Using this dataset, an XGBoost classifier was trained for binary classification with two output classes: stall and non-stall, and optimized via threshold tuning and SMOTE-based class balancing. Using this dataset, an XGBoost classifier achieved 0.97 precision, 0.98 recall, 0.98 F1-score, and 0.96 accuracy for stall prediction after integration with domain-specific rules. SHapley Additive exPlanations (SHAP) values were applied to provide transparent, instance-level justifications, revealing that throttle, VRS, and disc loading were consistently the top contributors to stall predictions. In parallel, domain-specific aerodynamic rules—such as critical AoA and high disc loading thresholds—were deployed to catch edge cases such as near-threshold predictions, ambiguous vortex ring state regions, or unseen aerodynamic configurations. This modular system is designed for compatibility with onboard processors like Raspberry Pi, enabling real-time deployment even in resource-constrained environments. It supports frugal innovation without compromising safety, making it suitable for stealth drones, ISR swarms, payload-carrying UAVs, and companion-computer-based quadcopters. By combining data-driven predictions with physics-aware rule triggers, the system reduces false negatives and false positives, improving reliability in mission-critical conditions. Ultimately, this work pushes drone autonomy toward proactive, explainable, and hardware-efficient safety mechanisms—where UAVs not only predict aerodynamic stall but also justify it in real time.}, archive = {J_ACCESS}, author = {Vatsal Siotia and Ruppikha Sree Shankar and Vishnu G. Nair}, doi = {10.1109/ACCESS.2025.3611165}, journal = {IEEE Access}, pages = {163447-163465}, shortjournal = {IEEE Access}, title = {Stall prediction in quadcopters with SHAP-based explainability and a novel flight dynamics dataset}, volume = {13}, year = {2025}, }
• (2025). Optimal design of key switching parameters for efficient CKKS bootstrapping. ACCESS, 13, 163431-163446. (www)

  @article{ , abstract = {Bootstrapping in fully homomorphic encryption depends on a wide range of parameters that significantly influence both performance and precision. Traditionally, these parameters have been selected through empirical methods and heuristic tuning. In this work, we present a comprehensive and rigorous analysis of the error components that accumulate throughout the bootstrapping process. Focusing specifically on the parameters related to key switching, we establish a theoretical framework that supports the systematic and optimal design of these parameters. Our optimized parameter selection enables the attainment of two additional levels in widely used parameter sets, improving the efficiency of bootstrapping without compromising security or precision.}, archive = {J_ACCESS}, author = {Sieun Seo and Chohong Min}, doi = {10.1109/ACCESS.2025.3610909}, journal = {IEEE Access}, pages = {163431-163446}, shortjournal = {IEEE Access}, title = {Optimal design of key switching parameters for efficient CKKS bootstrapping}, volume = {13}, year = {2025}, }
• (2025). Multiwavelength characterization of optical wireless communication in complex water-filled pipe environment. ACCESS, 13, 163418-163430. (www)

  @article{ , abstract = {This paper presents an in-depth investigation of optical wireless communication through water-filled PVC pipelines using high-brightness light-emitting diodes (HB-LEDs) operating at visible wavelengths: 475 nm (blue), 528 nm (green), 583 nm (yellow), and 625 nm (red). Simulations were conducted in Ansys Zemax OpticStudio using ray-tracing techniques and Bidirectional Scattering Distribution Function (BSDF) models to evaluate the effects of surface roughness, interface reflection, and wavelength-dependent absorption. A custom experimental setup was developed using a 375 mm long, 50 mm diameter PVC pipe and a Thorlabs S121C photodiode sensor to validate the simulation. Optical power was measured under five water fill conditions (0%, 25%, 50%, 75%, and 100%). Results show that the greatest transmission loss occurs at the 50% water level, where multiphase scattering dominates, with experimental power decreasing to −11.82 dBm at 583 nm (yellow). Full immersion improves transmission, with recovered power levels up to −2.3 dBm at 475 nm (blue). Absorption coefficients were calculated using the Beer–Lambert Law, with peak values exceeding 0.09 cm−1 at 50% fill. Simulation results aligned with experimental measurements within 1–2 dB, validating the model’s reliability. These findings support the development of adaptive gain control strategies and wavelength-optimized optical links for autonomous robotic inspection in submerged or semi-submerged pipeline environments.}, archive = {J_ACCESS}, author = {Nonchanutt Chudpooti and Kamol Boonlom and Suppat Rungraungsilp and Prayoot Akkaraekthalin and Weijia Zhang and Timothy J. Amsdon and Joachim Oberhammer and Nutapong Somjit}, doi = {10.1109/ACCESS.2025.3610711}, journal = {IEEE Access}, pages = {163418-163430}, shortjournal = {IEEE Access}, title = {Multiwavelength characterization of optical wireless communication in complex water-filled pipe environment}, volume = {13}, year = {2025}, }
• (2025). RRAC: Reciprocal robustness via asymmetric co-training for label noise resistance in deep learning. ACCESS, 13, 163390-163417. (www)

  @article{ , abstract = {Learning from noisy labels remains a significant challenge in deep learning. Although recent methods typically propose single-model strategies, practical implementations often employ dual-model training, particularly in realistic scenarios where no clean validation data is available for identifying noisy labels or selecting clean samples. Existing dual-model methods, including symmetric and asymmetric co-training, have limitations in handling label noise. Symmetric co-training suffers from model convergence in the later stage of training. In contrast, asymmetric co-training lacks explicit mutual updating, limiting its ability to mutually correct or reinforce each other’s predictions. To address these limitations, we propose Reciprocal Robustness via Asymmetric Co-training (RRAC). This novel framework leverages two robust models, the Temporal Weighting Model (TWM) and the Early Learning Regularization Model (ELRM), trained simultaneously and exchanging predictive information through cross-updating. TWM utilizes a dynamic instance weighting strategy (CSIW), while ELRM employs early learning-based regularization. Our experiments on synthetic and real-world noisy-label datasets demonstrate RRAC’s competitive performance compared to state-of-the-art methods, offering a promising solution for robust learning from noisy labels.}, archive = {J_ACCESS}, author = {Rana Althunyan and Abdel Monim Artoli}, doi = {10.1109/ACCESS.2025.3610572}, journal = {IEEE Access}, pages = {163390-163417}, shortjournal = {IEEE Access}, title = {RRAC: Reciprocal robustness via asymmetric co-training for label noise resistance in deep learning}, volume = {13}, year = {2025}, }
• (2025). MedFD: Personalized and communication-efficient federated distillation for heterogeneous medical image analysis. ACCESS, 13, 163372-163389. (www)

  @article{ , abstract = {Medical Image classification (MIC) in real-world clinical environments demands models that can operate across decentralized, privacy-sensitive institutions while remaining robust to statistical heterogeneity. Federated Learning (FL) offers a promising solution, yet conventional FL methods often suffer from performance degradation due to non-IID data distributions and incur high communication costs when deployed with complex deep models. We propose MedFD, a FL framework tailored for cross-institutional MIC under realistic constraints. MedFD enables each client to train a personalized model by incorporating both global and historical local knowledge in a unified distillation process, improving adaptation to heterogeneous data without accessing raw samples. To enhance communication efficiency, MedFD compresses model updates via low-rank decomposition guided by adaptive energy-retention constraints, substantially reducing transmitted parameter volume. We provide theoretical analysis on convergence and generalization under non-IID settings. Experiments on four public 2D and 3D medical imaging benchmarks demonstrate that MedFD consistently outperforms strong FL baselines in both accuracy and communication efficiency, making it well-suited for scalable and privacy-preserving clinical deployment.}, archive = {J_ACCESS}, author = {Yuan Sun and Shengfang Sun}, doi = {10.1109/ACCESS.2025.3608163}, journal = {IEEE Access}, pages = {163372-163389}, shortjournal = {IEEE Access}, title = {MedFD: Personalized and communication-efficient federated distillation for heterogeneous medical image analysis}, volume = {13}, year = {2025}, }
• (2025). Metamodel-based compaction of spacecraft operational scenario models for efficient review. ACCESS, 13, 163354-163371. (www)

  @article{ , abstract = {Owing to the difficulty in performing repairs during flight, spacecraft are operated according to the operational scenarios tested before launch. Operational models, which are a type of SysML activity diagram, can be used to depict these scenarios using model-based systems engineering techniques. Although it aids communication between engineers and stakeholders, the activity diagram can grow rapidly as development progresses. Owing to the extensive operational model, it is challenging to review the activity diagram, which can result in serious issues. Therefore, to make the operational models easier to evaluate, they must be concise. This study presents a metamodel that offers stereotypes that can successfully characterize spacecraft operational scenarios. First, the domain knowledge of spacecraft operations was visualized using a tree structure to organize common knowledge. Second, a stereotype metamodel was created by extracting common behaviors from common knowledge. Utilizing stereotypes can decrease the size of operational models; however, crucial review-related data can be lost. Therefore, tagged values ensure that crucial information is not lost. Several trials were conducted and showed that the number of elements of operational models with stereotypes, as well as their size, was reduced by almost half compared with the original models, allowing for a simplified review process and boosting trust in the accuracy of the operational scenarios.}, archive = {J_ACCESS}, author = {Kazunori Someya and Toshiaki Aoki and Naoki Ishihama}, doi = {10.1109/ACCESS.2025.3604529}, journal = {IEEE Access}, pages = {163354-163371}, shortjournal = {IEEE Access}, title = {Metamodel-based compaction of spacecraft operational scenario models for efficient review}, volume = {13}, year = {2025}, }
• (2025). Information-theoretic disturbance minimization for unified secure or robust distributed estimation in presence of impulsive FDI attack or impulsive noise. ACCESS, 13, 163342-163353. (www)

  @article{ , abstract = {In this paper, an information-theoretic and statistical viewpoint of distributed estimation problem is considered as the contribution of the work. So, a novel minimum information-theoretic disturbance diffusion least-mean-square (LMS) algorithm is presented for both robust distributed estimation against impulsive noise and secure distributed estimation in the presence of False Data Injection (FDI) attacks. Throughout the paper for the novelty of the paper, information-theoretic disturbance measure, specifically the Kullback-Leibler (KL) divergence, is formulated in a convenient and structured manner. It is considered as the generalization of Mean Square Error (MSE) disturbance measure. By some manipulations, it is seen that the proposed information-theoretic disturbance is equivalent to MSE disturbance plus an extra term that depends on the variance of the output variable. So, this KL divergence measure captures the variance information of the output variable. The optimal adaptation and combination coefficients in the sense of minimum KL disturbance is derived in closed-form formulas that provides the computational convenience of the proposed algorithm. Simulation results demonstrate the advantages of the proposed algorithm over some competing algorithms, while suggesting the impulsive FDI attacks shows its potential in lower detectability than Gaussian FDI attacks.}, archive = {J_ACCESS}, author = {Hadi Zayyani and Mohammad Salman and Mostafa Rashdan and Hasan Abuhilal}, doi = {10.1109/ACCESS.2025.3611147}, journal = {IEEE Access}, pages = {163342-163353}, shortjournal = {IEEE Access}, title = {Information-theoretic disturbance minimization for unified secure or robust distributed estimation in presence of impulsive FDI attack or impulsive noise}, volume = {13}, year = {2025}, }
• (2025). Single-view 3D face reconstruction method with multi-scale feature fusion encoder. ACCESS, 13, 163331-163341. (www)

  @article{ , abstract = {The enhancement of detailed facial features is a critical challenge in the application of inverse engineering within the medical field. However, existing methods: Position map Regression Network (PRNet), Detailed Expression Capture and Animation (DECA) exhibit insufficient accuracy in reconstructing mid-frequency details. To address this, we propose a single-view 3D face reconstruction method based on a multi-scale feature fusion encoder, which integrates local and global features to extract mid-frequency geometric details, thereby improving the fidelity of facial expression reconstruction. Furthermore, deformable convolutions are employed to optimize the generator network, enabling precise localization of mid-frequency detail regions associated with expressive geometry. This significantly enhances the accuracy of detail reconstruction, particularly for mid-frequency geometric features. Experimental validation on the FaceScape dataset demonstrates that, compared to the DECA algorithm, our network achieves a 9.1% improvement in Chamfer Distance and a 12.3% reduction in mean normal error. The evaluation on the REALY benchmark also achieved a significant 28.0% reduction in comprehensive frontal-view error compared to DECA, confirming its capability to accurately represent mid-frequency contour details of facial structures.}, archive = {J_ACCESS}, author = {Jin Sun and Yuheng Chen and Liang Dong}, doi = {10.1109/ACCESS.2025.3607040}, journal = {IEEE Access}, pages = {163331-163341}, shortjournal = {IEEE Access}, title = {Single-view 3D face reconstruction method with multi-scale feature fusion encoder}, volume = {13}, year = {2025}, }
• (2025). M2D-CLAP: Exploring general-purpose audio-language representations beyond CLAP. ACCESS, 13, 163313-163330. (www)

  @article{ , abstract = {Contrastive language-audio pre-training (CLAP), which learns audio-language representations by aligning audio and text in a common feature space, has become popular for solving audio tasks. However, CLAP’s audio features lack generalizability, whereas self-supervised learning (SSL) models offer general-purpose features that perform well across diverse audio tasks. We aim to develop a broadly applicable audio representation and hypothesize that a model that learns both general audio and CLAP features should achieve our goal, which we call a general-purpose audio-language representation. To implement our hypothesis, we propose M2D-CLAP, the first approach to jointly learn effective general audio and CLAP features. It extends an SSL masked modeling duo (M2D) by incorporating CLAP and utilizes LLM-based sentence embeddings. The training process consists of multiple stages. In the first stage, generalizable audio features are pre-trained via a multitask objective combining M2D and CLAP, with CLAP leveraging LLM-based semantic embeddings to distill semantic knowledge into them. In the following stages, CLAP features are pre-trained and refined with guidance from the learned audio features. Experiments demonstrated that M2D-CLAP learns high-performing general audio features (e.g., AudioSet mAP of 49.0, SOTA results in music tasks) and CLAP features, thereby enabling a general-purpose audio-language representation.}, archive = {J_ACCESS}, author = {Daisuke Niizumi and Daiki Takeuchi and Masahiro Yasuda and Binh Thien Nguyen and Yasunori Ohishi and Noboru Harada}, doi = {10.1109/ACCESS.2025.3611348}, journal = {IEEE Access}, pages = {163313-163330}, shortjournal = {IEEE Access}, title = {M2D-CLAP: Exploring general-purpose audio-language representations beyond CLAP}, volume = {13}, year = {2025}, }
• (2025). Efficient enumeration of d-minimal paths under capacity constraints: A zero-suppressed decision diagram approach. ACCESS, 13, 163302-163312. (www)

  @article{ , abstract = {We study the enumeration of all d-minimal paths in a multistate two-terminal network, where each arc has a specific capacity limit. Classical approaches based on generating functions or incremental enumeration tend to suffer from severe combinatorial explosion and require costly post-processing to recover feasible combinations. To address these limitations, we introduce a new construction method using Zero-Suppressed Binary Decision Diagrams (ZDDs) that integrates capacity constraints directly during the decision process. Our approach prunes infeasible branches on the fly and merges identical residual-capacity states to guarantee that each feasible combination is enumerated exactly once, without duplication. We show that the resulting structure is significantly more compact than conventional slot-based Binary Decision Diagrams (BDDs), especially when the feasible solution space is sparse. Experimental comparisons demonstrate that our capacity-aware ZDD achieves orders-of-magnitude improvements in both time and memory efficiency over BDD-based enumeration, even on networks with moderate size and demand values. Beyond performance, our framework provides a canonical and reusable representation of all feasible d-minimal-path combinations, which can be leveraged in reliability analysis, path planning, or network design. These findings support the relevance of ZDD-based models for exact enumeration tasks under resource constraints in real-world infrastructures.}, archive = {J_ACCESS}, author = {Yasser Lamalem and Soufiane Hamida and Khalid Housni}, doi = {10.1109/ACCESS.2025.3611227}, journal = {IEEE Access}, pages = {163302-163312}, shortjournal = {IEEE Access}, title = {Efficient enumeration of d-minimal paths under capacity constraints: A zero-suppressed decision diagram approach}, volume = {13}, year = {2025}, }
• (2025). A novel thermal image-based metal classification system using machine learning algorithms. ACCESS, 13, 163288-163301. (www)

  @article{ , abstract = {Classifying metals is an essential task in all industries to make sure the materials used in the processes are safe and meet the required standards all while enhancing operational and cost effectiveness. Metal classification is crucial in processes such as quality control, recycling, green manufacturing and any other processes that rely on metal. The metallurgical domain relies on techniques for metal classification such as X-ray Fluorescence, Magnetic Induction Spectroscopy and Eddy Current Separation with regards to their speed, accuracy and cost. This work proposes a new system for thermal image based metal classification which is expected to be better in terms of effectiveness, cost and time as compared to the traditional methods. In this work, heat conductivity and specific heat characteristics of metals are taken into account to generate a thermal image map. As different metals have different conductivity and specific heat, the heat absorption and radiation map generated by thermal image can be used to identify the type of metal using a machine learning based classification method. The results show that fused with machine learning Decision Tree and Random Forest algorithms in the metal identification system can classify metals with an accuracy of 96% and 98% respectively. These results highlight how machine learning and thermal imaging can offer more precise and reliable results than traditional machinery. This approach presents a practical solution that conserves cost and time.}, archive = {J_ACCESS}, author = {Siva Rajesh Chiluveru and Shuvendu Rana}, doi = {10.1109/ACCESS.2025.3610992}, journal = {IEEE Access}, pages = {163288-163301}, shortjournal = {IEEE Access}, title = {A novel thermal image-based metal classification system using machine learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Energy optimization in three-dimensional wireless sensor networks using hybrid K-means and mixed integer linear programming. ACCESS, 13, 163276-163287. (www)

  @article{ , abstract = {Energy optimization is a critical challenge in wireless sensor networks (WSNs) due to its direct impact on the network lifetime. This paper proposes the use of the K-means algorithm combined with linear programming to extend the lifetime of three-dimensional WSNs. First, the K-means algorithm is employed to obtain an effective initial clustering of sensor nodes. Then, mixed integer linear programming (MILP) is used to optimize the communication links, thereby reducing energy consumption and extending network lifetime. Linear programming is applied here to determine which sensor connections should remain active to minimize a formulated linear objective function while ensuring that a minimum number of sensor nodes remain connected. An inherent sleep/wake mechanism, guided by the optimization process, is employed to further minimize energy consumption. The proposed method is evaluated alongside two existing algorithms: the first one is based on the MILP algorithm, and the second one is the multi-hop low energy adaptive clustering hierarchy (MH-LEACH) algorithm. Simulation results demonstrate that the proposed technique consistently outperforms both algorithms in terms of network lifetime and energy consumption. Specifically, on average, the proposed algorithm extended network lifetime by 48% versus MILP and by 78% versus MH-LEACH, while reducing energy usage by 22% and 54%, respectively. Furthermore, our proposed approach uses only homogeneous sensors with finite energy while the MILP approach assumes that the sink nodes have infinite energy, which might be impractical. Consequently, the proposed approach is more practical, reduces the problem size for optimization, and eliminates the unrealistic requirement for sink nodes with infinite energy.}, archive = {J_ACCESS}, author = {Jehad I. Ababneh and Majid M. Khodier and Sari M. Khatalin}, doi = {10.1109/ACCESS.2025.3611305}, journal = {IEEE Access}, pages = {163276-163287}, shortjournal = {IEEE Access}, title = {Energy optimization in three-dimensional wireless sensor networks using hybrid K-means and mixed integer linear programming}, volume = {13}, year = {2025}, }
• (2025). Compact K-band phased array antenna with orthogonal calibration and error assessment. ACCESS, 13, 163258-163275. (www)

  @article{ , abstract = {This paper presents a 24-channel K-band beamforming phased array antenna incorporating orthogonal coding for calibration and calibration error assessment. The phased array antenna integrates a $1:24$ Wilkinson power divider, 24 elements of a modified antipodal Vivaldi antenna, and T/R modules onto a multi-layer PCB, achieving a compact size of $23.5 \times 9$ cm2 and enhanced portability, making it suitable for space-constrained platforms such as drones, vehicles, and satellite terminals. The array offers a high gain of 21.82 dBi and a wide bandwidth covering the entire 18–20 GHz band. Furthermore, it supports beam steering from −40° to + 40° while maintaining low sidelobe levels and minimal gain degradation during beamforming. An orthogonal coding-based calibration method is implemented to mitigate performance degradation due to mismatched line impedance and fabrication errors. By applying a set of orthogonal codes through onboard phase control circuitry, phase errors of each element are simultaneously measured. Furthermore, a new approach for assessing calibration error is introduced, which uses root mean square (RMS) amplitude and phase error metrics to evaluate post-calibration performance. This information can subsequently be used to correct residual amplitude and phase errors in the calibrated data. The paper covers the design specifications, theoretical framework, and measured results of the phased array antenna, including the calibration technique, its performance assessment, and several key findings from the experiments conducted in this work.}, archive = {J_ACCESS}, author = {Fathul Muin and Tommi Hariyadi and Jin Woo Park and Sunwoo Kong and Pansoo Kim and Seong-Ook Park}, doi = {10.1109/ACCESS.2025.3609771}, journal = {IEEE Access}, pages = {163258-163275}, shortjournal = {IEEE Access}, title = {Compact K-band phased array antenna with orthogonal calibration and error assessment}, volume = {13}, year = {2025}, }
• (2025). PatHME: Hierarchical multi-expert knowledge distillation for whole slide image analysis. ACCESS, 13, 163246-163257. (www)

  @article{ , abstract = {Foundation models have shown strong generalization across a variety of pathology whole slide image (WSI) analysis tasks, including classification, segmentation and report generation. However, most existing models are trained exclusively on single-scale, high-magnification patches, often overlooking the rich contextual information available at lower magnifications. Applying such models directly to low-resolution inputs can lead to degraded or misleading feature representations, as crucial spatial context may be lost. Moreover, fine-tuning foundation models for each task is both computationally intensive and inefficient. To address these challenges, we propose Hierarchical Multi-Expert Knowledge Distillation, PatHME, a unified framework that integrates multi-scale information and leverages multiple expert foundation models for more effective and efficient WSI analysis. First, the Prompt-guided Multi-Scale Distillation module bridges the gap between low- and high-resolution representations by distilling features across magnifications using scale-aware prompts, enabling the model to capture both global tissue architecture and fine-grained cellular detail. We further introduce a Multi-Expert Knowledge Distillation strategy that enables feature-level knowledge transfer from a teacher expert to a student expert without retraining, enhancing adaptability while reducing computational cost. Finally, we incorporate a Hierarchical Multi-Scale Attention mechanism to dynamically fuse multi-resolution features based on tissue context, enabling the model to effectively capture both fine-grained details and global structural patterns. Experimental results on multiple TCGA benchmarks demonstrate that our proposed method significantly enhances model performance and efficiency, providing a scalable and generalizable solution for advanced digital pathology applications.}, archive = {J_ACCESS}, author = {Jing Wei Tan and Gangsu Kim and Won-Ki Jeong}, doi = {10.1109/ACCESS.2025.3610117}, journal = {IEEE Access}, pages = {163246-163257}, shortjournal = {IEEE Access}, title = {PatHME: Hierarchical multi-expert knowledge distillation for whole slide image analysis}, volume = {13}, year = {2025}, }
• (2025). A novel unipolar pulse-width modulation scheme for single-phase H-bridge inverters with balanced switch loss distribution. ACCESS, 13, 163235-163245. (www)

  @article{ , abstract = {This paper introduces a novel unipolar pulse-width modulation (PWM) strategy for single-phase 4-switch H-bridge inverters, designed to enhance power efficiency, thermal balance, and device reliability without necessitating hardware modifications. The proposed strategy maintains the output voltage waveform while achieving total power losses significantly lower than the conventional unipolar PWM approach. Another key contribution of the proposed scheme is the even distribution of power losses across all four switches, resulting in balanced junction temperatures of 80°C. This thermal uniformity enhances device longevity and mitigates the need for dedicated cooling of overstressed switches, addressing a critical limitation in existing schemes. Furthermore, the proposed method preserves the favorable harmonic performance (low total harmonic distortion (THD)) of the modified conventional unipolar PWM scheme, avoiding the introduction of low-order harmonics common in alternative techniques.}, archive = {J_ACCESS}, author = {Majd Ghazi Batarseh and Yazan Samer Al-Jizawi and Fares S. El-Faouri and Laith Nidal Bulbul}, doi = {10.1109/ACCESS.2025.3608602}, journal = {IEEE Access}, pages = {163235-163245}, shortjournal = {IEEE Access}, title = {A novel unipolar pulse-width modulation scheme for single-phase H-bridge inverters with balanced switch loss distribution}, volume = {13}, year = {2025}, }
• (2025). BSM-DND: Bias and sensitivity-aware multilingual deepfake news detection using bloom filters and recurrent feature elimination. ACCESS, 13, 163218-163234. (www)

  @article{ , abstract = {The rapid spread of deepfake news across digital media platforms poses a growing threat to societal trust, public safety, and global security. With the increasing realism of AI-generated content, distinguishing between authentic and fabricated information has become increasingly challenging, especially in low-resource and multilingual content. In this work, we propose a novel bias and sensitivity-aware multilingual deepfake news detection framework, BSM-DND, which classifies news articles into three categories: Authentic (A), Fake but Not Dangerous (FND), and Fake and Dangerous (FD). Our approach incorporates language-specific preprocessing to extract features related to sensitive keywords, sentiment polarity, and structural complexity. To enhance efficiency in large-scale detection, the Bloom filter technique has been applied for rapid parallel keyword matching. By leveraging Support Vector Machines (SVMs), Random Forest (RF), and recursive feature elimination, we have optimized classification performance across multiple languages. Experimental results on English, Italian, and Bengali datasets demonstrate that our method has achieved high detection accuracy, with the most significant improvements observed for English language content, while also yielding substantial gains in the other two languages. In addition, the BSM-DND framework is scalable in low-resource environments, adaptable to multilingual scenarios with minimal overhead, and effective in quantifying the potential societal impact of deepfake news.}, archive = {J_ACCESS}, author = {Mohammed Al-Naeem and M. M. Hafizur Rahman and Anuradha Banerjee and Abu Sufian}, doi = {10.1109/ACCESS.2025.3609831}, journal = {IEEE Access}, pages = {163218-163234}, shortjournal = {IEEE Access}, title = {BSM-DND: Bias and sensitivity-aware multilingual deepfake news detection using bloom filters and recurrent feature elimination}, volume = {13}, year = {2025}, }
• (2025). Open-cash: An anonymous electronic cash scheme with open-source observers based on BBS+ signatures. ACCESS, 13, 163201-163217. (www)

  @article{ , abstract = {Electronic cash (e-cash) systems must provide users with anonymity while preventing criminal misuse and enabling selective tracing of malicious actors, such as double-spenders. One of the most ingenious solutions, introduced by Chaum and Brands, involves deploying observers on users’ devices to enforce legitimate payment behavior without compromising anonymity. In this paper, we propose a novel e-cash scheme based on BBS+ signatures and open-source observers running within Attested Execution Secure Processors (AESPs). Our construction employs zero-knowledge proofs to conceal private information, while the open-source observer program—installed within a tamper-proof secure element—acts as a proactive gatekeeper, preventing double-spend attempts before they succeed. The AESP-based attestation mechanism binds each output to the program’s hash, guaranteeing that the officially vetted observer is indeed installed and operating unmodified on the user’s device. This scheme provides three key properties. First, it achieves anonymity and one-more unforgeability using a technique inspired by Brickell et al.’s EPID. Second, it ensures robust double-spend prevention and traceability of double-spenders, even if the AESP assumption is compromised. Third, the scheme remains reversible: if the observer is compromised, it gracefully reverts to the basic scheme without the observer, which preserves anonymity and unforgeability. Overall, this work bridges cryptographic privacy and practical regulatory oversight, providing a transparent and extensible foundation for secure digital payments.}, archive = {J_ACCESS}, author = {Taishi Higuchi and Akira Otsuka}, doi = {10.1109/ACCESS.2025.3607958}, journal = {IEEE Access}, pages = {163201-163217}, shortjournal = {IEEE Access}, title = {Open-cash: An anonymous electronic cash scheme with open-source observers based on BBS+ signatures}, volume = {13}, year = {2025}, }
• (2025). Twitter-based OSINT for cyber event analytics. ACCESS, 13, 163189-163200. (www)

  @article{ , abstract = {Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system’s applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events affecting their systems. Furthermore, we propose a method to identify the network protocols associated with software vulnerabilities based on Twitter data, leveraging the aforementioned framework. This method can further aid operators in understanding the exploitability of vulnerabilities (e.g., whether exploitation is already blocked by firewall rules) and making mitigation measures (e.g., adding a firewall rule to block exploitation). Evaluations show the effectiveness of our solutions.}, archive = {J_ACCESS}, author = {Dakota S. Dale and Kylie Mcclanahan and W. Sky Elder and Qinghua Li}, doi = {10.1109/ACCESS.2025.3607382}, journal = {IEEE Access}, pages = {163189-163200}, shortjournal = {IEEE Access}, title = {Twitter-based OSINT for cyber event analytics}, volume = {13}, year = {2025}, }
• (2025). A review of large language models for energy systems: Applications, challenges, and future prospects. ACCESS, 13, 163162-163188. (www)

  @article{ , abstract = {Rapid developments in large language models (LLMs) have created new opportunities for their use in the energy sector, from forecasting renewable energy to power system operation and energy market analysis. These models help improve decision-making, anomaly detection, and optimization procedures in intricate energy systems by using vast amounts of structured and unstructured data. This study provides a comprehensive review of the LLM origins, evaluation, and fine-tuning techniques as well as their integration into energy systems, including their application in fault detection and diagnosis, energy forecasting, document automation, energy management, defect detection, and power system operation. Their performance in terms of explainability, generalization ability, and scalability for energy-related applications is critically examined in this paper. The report also emphasizes significant challenges to the adoption of LLMs, such as the need for computing power, the lack of data, and ethical issues like bias and false information. Power-efficient models, hybrid artificial intelligence (AI) platforms, and domain-specific fine-tuning are some of the solutions discussed. Future areas of interest include multi-modality to obtain maximal forecasting and operational intelligence, real-time adaptability, and explainable. This paper summarizes current developments and provides information on LLM-driven innovation in energy systems while maintaining transparency and dependability. Compared with prior LLM–energy surveys that either remain general-purpose or focus on a single subdomain, this review fills three concrete gaps: (i) a cross-domain synthesis of energy-specific LLM applications spanning power systems, buildings, and forecasting; (ii) a methods-oriented consolidation of evaluation and parameter-efficient fine-tuning practices tailored to energy tasks; and (iii) a deployment-centric analysis of real-time and edge constraints (energy, latency, hardware) with a practical reporting checklist for operational adoption.}, archive = {J_ACCESS}, author = {Hamid Mirshekali and Mohammad Reza Shadi and Fatemehsadat Ghanadi Ladani and Hamid Reza Shaker}, doi = {10.1109/ACCESS.2025.3610994}, journal = {IEEE Access}, pages = {163162-163188}, shortjournal = {IEEE Access}, title = {A review of large language models for energy systems: Applications, challenges, and future prospects}, volume = {13}, year = {2025}, }
• (2025). AeroYOLO: Efficient multiscale and attention-augmented YOLOv8s for robust aerial object detection. ACCESS, 13, 163143-163161. (www)

  @article{ , abstract = {Aerial object detection suffers from scale and spatial imbalance, significantly reducing detection accuracy in drone-based datasets. We propose progressively enhanced YOLOv8s-based models AeroYOLO-Fusion, AeroYOLO-Attn, and AeroYOLO-Lite addressing imbalance problems and efficiency challenges through multiscale fusion, attention mechanism, and lightweight architecture. To improve multiscale feature fusion, AeroYOLO-Fusion integrates bidirectional feature pyramid networks with multiscale depth-wise convolution. To enhance adaptive spatial attention, AeroYOLO-Attn introduces the receptive field attention convolution within the standard C2f module. AeroYOLO-Lite further reduces computational complexity with a lightweight shared group convolutional detection head. Extensive experiments on VisDrone, UAVDT, CARPK, and DIOR datasets demonstrate significant performance improvements over the baseline YOLOv8s, with AeroYOLO-Lite achieving AP increases of 2.80% on VisDrone, 4.3% on UAVDT, 4.1% on CARPK, and 1.0% on DIOR. The inference latency of 13.7ms demonstrates the model’s capability to meet real time detection requirements. Comparative analyses confirm AeroYOLO-Lite’s superior accuracy relative to state-of-the-art methods, while ablation studies validate the contributions of each proposed module, balancing computational efficiency and detection performance.}, archive = {J_ACCESS}, author = {Huiyao Zhang}, doi = {10.1109/ACCESS.2025.3610617}, journal = {IEEE Access}, pages = {163143-163161}, shortjournal = {IEEE Access}, title = {AeroYOLO: Efficient multiscale and attention-augmented YOLOv8s for robust aerial object detection}, volume = {13}, year = {2025}, }
• (2025). Evaluation of gait disorders using accelerometric and gyroscopic data for assessment of neurological diseases. ACCESS, 13, 163134-163142. (www)

  @article{ , abstract = {Computational intelligence and digital signal processing are essential mathematical tools widely applied in biomedical and engineering domains. Gait symmetry analysis is particularly important for detecting motion disorders in neurology, rehabilitation, and sports science. This study presents a methodology for motion analysis using time-synchronized accelerometric and gyrometric sensors to capture dynamic gait patterns. Data were collected from 14 healthy controls and 17 individuals with Parkinson’s disease-related gait impairments. The proposed approach integrates spectral analysis and digital filtering to remove noise and irrelevant frequency components during signal preprocessing. Motion classification is performed by analyzing energy distribution using discrete Fourier and wavelet transforms, enabling multilevel signal decomposition. Gait recognition—distinguishing between normal and abnormal patterns—is based on energy components in selected frequency bands and their ratios. Neural network classifiers achieved the highest performance, with a mean accuracy of 81.1% and a cross-validation error of 0.123, using data from sensors placed on the left and right sides of the body. Motion asymmetry detected by the model agreed with assessments of neurologists in 88% of cases. Results of this validation highlight the potential of frequency and scale domain analysis, digital signal processing, and artificial intelligence use in supporting the clinical diagnosis of Parkinson’s disease and further neurological disorders.}, archive = {J_ACCESS}, author = {David Matyáš and Libuše Smetanová and Oldřich Vyšata and Tereza Tumová and Lucie Gonsorčíková and Hana Charvátová and Aleš Procházka}, doi = {10.1109/ACCESS.2025.3610159}, journal = {IEEE Access}, pages = {163134-163142}, shortjournal = {IEEE Access}, title = {Evaluation of gait disorders using accelerometric and gyroscopic data for assessment of neurological diseases}, volume = {13}, year = {2025}, }
• (2025). Fused-AETNet: A variational transformer-based framework for diabetic retinopathy classification using OCT biomarkers. ACCESS, 13, 163120-163133. (www)

  @article{ , abstract = {Early detection and accurate diagnosis of Diabetic Retinopathy (DR) using Optical Coherence Tomography (OCT) images remain challenging due to subtle pathological alterations across multiple retinal layers. In this paper, we present Fused-AETNet (Fused AutoEncoder–Transformer Network), a novel deep-learning framework that uniquely combines clinically interpretable handcrafted OCT biomarkers with a robust variational autoencoder and a transformer-based classifier. Unlike prior OCT–DR approaches that directly utilize raw intensities or generic CNN-derived features, our method begins with precise segmentation of eleven retinal layers in the central OCT B-scan using an atlas-guided probabilistic approach. From each layer, we extract three clinically validated morphological descriptors—reflectivity, Laplacian-based thickness, and boundary tortuosity—and summarize them through percentile-based Cumulative Distribution Functions (CDFs) to capture regional variability in a compact, size-invariant form. These descriptors are fused into a smooth latent embedding via variational autoencoding, enabling robust modelling of cross-layer dependencies and uncertainty. The latent space is then refined using a lightweight transformer-inspired attention module that selectively amplifies diagnostically relevant embeddings while suppressing irrelevant variations. This biomarker-driven design enhances both the specificity and interpretability of the diagnostic decision process. Our training employs a composite loss function that jointly enforces accurate descriptor reconstruction, latent space regularization, and high classification accuracy. We validated the framework on OCT scans from 481 subjects, achieving superior performance compared to traditional machine learning classifiers and state-of-the-art deep networks, with an average accuracy of $93.08\% \pm 4.21$ , precision of $93.33\% \pm 4.84$ , recall of $96.00\% \pm 5.96$ , and F1 score of $94.48\% \pm 3.21$ . By explicitly modelling layer-wise biomarkers and integrating attention-guided latent feature selection, Fused-AETNet offers a compact, interpretable, and clinically relevant solution for OCT-based DR diagnosis.}, archive = {J_ACCESS}, author = {Mohamed Elsharkawy and Ibrahim Abdelhalim and Ali Mahmoud and Ahmed Gamal and Mohamed El-Said Abdel-Hady and Ashraf Sewelam and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3609175}, journal = {IEEE Access}, pages = {163120-163133}, shortjournal = {IEEE Access}, title = {Fused-AETNet: A variational transformer-based framework for diabetic retinopathy classification using OCT biomarkers}, volume = {13}, year = {2025}, }
• (2025). Application of semantic web techniques in DW/BI systems for strategic management. ACCESS, 13, 163103-163119. (www)

  @article{ , abstract = {Semantic Web techniques, such as ontologies, facilitate data and knowledge sharing in Information Systems due to their semantic formalization and inference qualities. Integrating knowledge-based artifacts into Decision Support Systems, such as Data Warehouse and Business Intelligence (DW/BI) systems, can provide new information sources, enable new analytical capabilities, and enhance decision-making. In previous work, the Balanced Scorecard Ontology (BSO) was developed to represent knowledge related to the Balanced Scorecard framework, including its concepts and relationships. The BSO was used to assess strategy formulation, implementation, and execution in a public sector organization, demonstrating its impact on strategy management. However, manual ontology population, especially related to performance indicator values, can lead to challenges in data availability, acquisition frequency, and quality. This paper proposes a semantic approach to integrating, aligning, and ensuring traceability between strategy and DW/BI systems. The Light Data Warehouse Ontology is introduced to represent the DW conceptual and logical models and semantically connect them with strategic information using the BSO. This integration enriches strategy analysis by providing strategic context to the BI environment and enabling automatic retrieval of performance indicator values. The proposed framework improves decision-making efficiency, reliability, and timeliness, providing managers with a data-driven environment aligned with organizational strategy.}, archive = {J_ACCESS}, author = {António Lorvão Antunes and José Barateiro and Elsa Cardoso}, doi = {10.1109/ACCESS.2025.3610577}, journal = {IEEE Access}, pages = {163103-163119}, shortjournal = {IEEE Access}, title = {Application of semantic web techniques in DW/BI systems for strategic management}, volume = {13}, year = {2025}, }
• (2025). MRSimEx-DSTC: A dynamic spanning tree coverage approach for multi-robot exploration and coverage path planning. ACCESS, 13, 163085-163102. (www)

  @article{ , abstract = {This paper presents MRSimEx-DSTC, a decentralized and adaptive framework for multi-robot coverage path planning in unknown and dynamic environments. The proposed method integrates frontier-based exploration with Dynamic Spanning Tree Coverage (DSTC), allowing each robot to incrementally map the environment while dynamically replanning its coverage path in response to both static and moving obstacles detected via onboard LiDAR. To enable decentralized execution and prevent task redundancy, the workspace is partitioned using Manhattan-distance-based Voronoi decomposition, ensuring disjoint task allocation and collision-free parallel operation without centralized coordination. The framework is validated through simulations in Python and Gazebo across varying obstacle densities and robot–obstacle speed scenarios. Experimental results show that MRSimEx-DSTC achieves high coverage efficiency (up to 99.5%), minimal overlap, and robust real-time adaptability. Compared to state-of-the-art methods such as MR-SimExCoverage and MAC-Planner, the proposed approach demonstrates superior performance, lower planning overhead, and greater resilience under real-world constraints.}, archive = {J_ACCESS}, author = {K. P. Jayalakshmi and Vishnu G. Nair and Dayakshini Sathish}, doi = {10.1109/ACCESS.2025.3610079}, journal = {IEEE Access}, pages = {163085-163102}, shortjournal = {IEEE Access}, title = {MRSimEx-DSTC: A dynamic spanning tree coverage approach for multi-robot exploration and coverage path planning}, volume = {13}, year = {2025}, }
• (2025). MDM-privacy: A derivative-driven framework for differential privacy. ACCESS, 13, 163071-163084. (www)

  @article{ , abstract = {Ensuring rigorous privacy protection while maintaining data utility remains a central challenge in privacy-preserving data analysis. Traditional Differential Privacy mechanisms, such as the Laplace and Gaussian mechanisms, rely on additive noise calibrated to sensitivity, which often leads to degraded utility, particularly in high-dimensional or low $\varepsilon $ regimes. Moreover, these mechanisms lack the flexibility to adapt to the local structure or the gradient behavior of the data. This paper introduces the MDM-Privacy framework, which leverages the $\varepsilon $ -MDM-Privacy mechanism, to calibrate privacy noise following sample-level sensitivity. Unlike traditional additive noise mechanisms, $\varepsilon $ -MDM-Privacy employs a non-additive, exponential noise model where the privatized output is computed as a function of the form $M(x) = C \cdot e^{\varepsilon \cdot L_{p}(x)}$ , enabling precise control over the privacy-utility trade-off through both the privacy budget $\varepsilon $ and the norm order p. The constant C is estimated via Monte Carlo simulations to ensure probabilistic utility guarantees under confidence bounds. Extensive experiments on the UCI Adult dataset demonstrate that $\varepsilon $ -MDM-Privacy achieves significantly lower Mean Absolute Error (MAE) and Mean Relative Error (MRE) compared to the classical Laplace mechanism, particularly under tight privacy regimes. The MDM-Privacy also exhibits robustness across different Minkowski norms, confirming its adaptability to various sensitivity geometries. Moreover, the $\varepsilon $ -MDM-Privacy is consistent under both parallel and sequential compositions, enabling the execution of an unlimited number of queries without exhausting the privacy budget ( $\varepsilon $ ). These results highlight the effectiveness of $\varepsilon $ -MDM-Privacy as a tunable and utility-preserving alternative for differentially private data release.}, archive = {J_ACCESS}, author = {Mohamed Mejri and Mahboobeh Dorafshanian and Djedjiga Mouheb}, doi = {10.1109/ACCESS.2025.3610100}, journal = {IEEE Access}, pages = {163071-163084}, shortjournal = {IEEE Access}, title = {MDM-privacy: A derivative-driven framework for differential privacy}, volume = {13}, year = {2025}, }
• (2025). A large language model-supported threat modeling framework for transportation cyber-physical systems. ACCESS, 13, 163046-163070. (www)

  @article{ , abstract = {Increased reliance on automation and connectivity exposes transportation cyber-physical systems (CPS) to many cyber vulnerabilities. Existing threat modeling frameworks are often narrow in scope, labor-intensive, and require substantial cybersecurity expertise. To this end, we introduce the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), a large language model (LLM)-based threat modeling framework for transportation CPS that requires limited cybersecurity expert intervention. TraCR-TMF identifies threats, potential attack techniques (i.e., methods to exploit vulnerabilities), and relevant countermeasures (e.g., attack detection and mitigation strategies) for transportation CPS. Three LLM-based approaches support these identifications: (i) a retrieval-augmented generation approach requiring no cybersecurity expert intervention, (ii) an in-context learning approach with low intervention from cybersecurity experts, and (iii) a supervised fine-tuning approach with moderate cybersecurity expert intervention. TraCR-TMF offers LLM-based attack path identification for critical assets based on vulnerabilities across different transportation CPS entities. Additionally, it incorporates the Common Vulnerability Scoring System (CVSS) scores of previously exploited vulnerabilities to prioritize threat mitigations. The framework was evaluated through two use cases. First, the framework identified relevant attack techniques for various transportation CPS applications, about 73% of which were validated by cybersecurity experts as correct. Second, the framework was used to identify attack paths for a target asset in a real-world cyberattack incident. TraCR-TMF successfully predicted exploitations, like lateral movement of adversaries, data exfiltration, and data encryption for ransomware, as reported in the incident. These findings demonstrate TraCR-TMF’s efficacy in transportation CPS threat modeling while reducing the need for extensive involvement of cybersecurity experts. To facilitate real-world adoption, all our codes are shared via an open-source repository.}, archive = {J_ACCESS}, author = {M Sabbir Salek and Mashrur Chowdhury and Muhaimin Bin Munir and Yuchen Cai and Mohammad Imtiaz Hasan and Jean-Michel Tine and Latifur Khan and Mizanur Rahman}, doi = {10.1109/ACCESS.2025.3603580}, journal = {IEEE Access}, pages = {163046-163070}, shortjournal = {IEEE Access}, title = {A large language model-supported threat modeling framework for transportation cyber-physical systems}, volume = {13}, year = {2025}, }
• (2025). Adaptive fault-tolerant control against actuator failures for a fast steering mirror system with a six-point driving structure. ACCESS, 13, 163035-163045. (www)

  @article{ , abstract = {In this paper, a novel adaptive fault-tolerant control (FTC) scheme for voice coil motor-driven fast steering mirror (FSM) systems is proposed. To improve the traditional four-point driving structure of voice coil motors, a six-point redundant driving architecture with hardware redundancy is firstly developed. By innovatively introducing the concept of drive groups, we establish a fault detection mechanism and a deactivation strategy for failed motors. Furthermore, a dual-mode adaptive FTC methodology that integrates compensation and switching techniques is proposed, ensuring both stability and transient performance of the FSM system throughout its operational lifecycle.}, archive = {J_ACCESS}, author = {Chuanqing Wang and Hupo Ouyang and Erbo Zou and Aidong Liu}, doi = {10.1109/ACCESS.2025.3609893}, journal = {IEEE Access}, pages = {163035-163045}, shortjournal = {IEEE Access}, title = {Adaptive fault-tolerant control against actuator failures for a fast steering mirror system with a six-point driving structure}, volume = {13}, year = {2025}, }
• (2025). Adaptive 2-DOF control for tracking sinusoidal signals with unknown frequency. ACCESS, 13, 163020-163034. (www)

  @article{ , abstract = {Tracking sinusoidal signals with unknown and time-varying frequencies is essential in many adaptive control applications. This paper presents a real-time method for tracking sinusoidal reference signals with unknown frequencies within a narrow bandwidth. The reference signals may include multiple harmonics and a DC bias. The proposed approach integrates a sinusoidal internal model with a Two-Degree-of-Freedom control structure. Unlike traditional methods that rely on offline tuning, this technique updates the controller coefficients online. A high-pass filter with notch characteristics ( $H_{f}$ ) is used to derive update equations for the Two-Degree-of-Freedom controller and the internal model parameters. These equations are obtained by matching the closed-loop transfer function of the algorithm to that of the desired filter ( $1-H_{f}$ ). The method is evaluated in MATLAB/Simulink using a second-order plant as an example. Two test cases are presented: the first involves a reference signal with a frequency change and a DC bias, while the second includes two additional harmonics. The algorithm is also tested without coefficient updating for comparison. Results show that the proposed method can accurately track signals with unknown and changing frequencies. It keeps the tracking error very small and quickly adjusts to frequency changes, making it suitable for real-time control in dynamic systems.}, archive = {J_ACCESS}, author = {Ibrahim Allafi and Lyndon J. Brown}, doi = {10.1109/ACCESS.2025.3610061}, journal = {IEEE Access}, pages = {163020-163034}, shortjournal = {IEEE Access}, title = {Adaptive 2-DOF control for tracking sinusoidal signals with unknown frequency}, volume = {13}, year = {2025}, }
• (2025). Accurate residential net load forecasting with hybrid statistical and machine learning techniques enhanced by advanced decomposition method. ACCESS, 13, 163006-163019. (www)

  @article{ , abstract = {The increased use of renewable energy (RE) in residential settings is driving the demand for smart energy management systems (EMS) to optimally coordinate the purchasing and selling of power to the grid. Given the uncertain and volatile nature of RE and electricity consumers’ changing load behavior, the EMSs require accurate net load forecasting, which has become a tough challenge. Unlike prior approaches that might employ a single, fixed Long Short-Term Memory (LSTM) architecture for all data components regardless of their underlying complexities, the model proposed in this paper introduces a specifically designed advanced hybrid machine-learning algorithm for highly accurate net load forecasting in residential areas equipped with solar panels. This involves applying more complex architectures to high-frequency data components and simpler architectures for lower-frequency components. This tailored approach is crucial for optimizing network complexity and computational burden while maintaining or enhancing forecasting performance, leading to comparable accuracy with significantly shorter training times compared to fixed architectures. This paper provides brief descriptions of powerful forecasting tools to accurately predict different variables in power systems, and presents a case study of these models in different combinations. This paper further compares them across three different time resolutions of data, and discusses their characteristics and different evaluation metrics. Compared to other methods that have similar computational efficiency, the proposed model demonstrates superior accuracy as the time resolution of the input data increases in order to accommodate more intricate details in the RE generation and consumers’ load behavior.}, archive = {J_ACCESS}, author = {Bibek Bimali and Daniela Wolter Ferreira Touma}, doi = {10.1109/ACCESS.2025.3610462}, journal = {IEEE Access}, pages = {163006-163019}, shortjournal = {IEEE Access}, title = {Accurate residential net load forecasting with hybrid statistical and machine learning techniques enhanced by advanced decomposition method}, volume = {13}, year = {2025}, }
• (2025). A new class of hybrid LSTM-VSMN for epileptic EEG signal generation and classification. ACCESS, 13, 162992-163005. (www)

  @article{ , abstract = {Epilepsy is a widespread neurological disorder affecting approximately 50 million people worldwide, significantly impacting quality of life and placing a heavy burden on healthcare systems. Early and reliable seizure detection remains a critical challenge, often hindered by limited availability of high-quality electroencephalogram (EEG) data and the suboptimal performance of existing classification methods. In this work, we propose a novel two-stage framework that addresses both data scarcity and classification accuracy. The first stage involves generating synthetic EEG signals that realistically mimic epileptic patterns using the Variable Structure Model Neuron (VSMN) with multidendrites, providing an effective means of data augmentation. In the second stage, we introduce a hybrid LSTM-VSMN model, where the VSMN activation function is integrated within the Long Short-Term Memory (LSTM) network gates, replacing conventional activations such as tanh. This integration improves the model’s ability to capture complex temporal dependencies in EEG sequences. To the best of our knowledge, this is the first study to leverage VSMN both for EEG signal synthesis and as an activation function within a deep recurrent neural network for seizure detection. The proposed model is rigorously evaluated against conventional activation functions, achieving an accuracy of 98.16% in single-fold validation and 97.59% under 3-fold cross-validation. Furthermore, it achieves a Mean Absolute Error (MAE) as low as 0.0241 and a Mean Absolute Percentage Error (MAPE) of 2.41%, substantially outperforming baseline approaches. These results demonstrate the effectiveness of the hybrid LSTM-VSMN architecture in enhancing automated seizure detection, offering a promising tool for clinical decision support and real-time monitoring applications.}, archive = {J_ACCESS}, author = {Souhaila Khalfallah and Borhen Louhichi and Sasan Sattarpanah Karganroudi and Kais Bouallegue}, doi = {10.1109/ACCESS.2025.3610411}, journal = {IEEE Access}, pages = {162992-163005}, shortjournal = {IEEE Access}, title = {A new class of hybrid LSTM-VSMN for epileptic EEG signal generation and classification}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of machine learning approaches for semiconductor device modeling and simulation. ACCESS, 13, 162969-162991. (www)

  @article{ , abstract = {Application of machine learning (ML) approaches has recently expanded within a wide range of domains, including semiconductor devices. Their strong potential resulted in increasing number of research in semiconductor device modeling and simulation. This article provides a comprehensive review of the recent research on ML-based semiconductor device modeling and simulations. We categorize the literature based on the device category, such as Planar Metal–Oxide–Semiconductor (MOS) devices, Field-Effect Transistor (FET) devices, High Electron Mobility Transistor (HEMT) devices, and other semiconductor devices. Within each device type, we further organize the review according to the implemented ML category, including traditional ML models, Artificial Neural Network (ANN) models, and emerging Physics-informed ML models. The implementation of the various ML techniques for each device type, as well as their primary findings, is discussed, and performance is compared based on the explored ML models using different evaluation metrics. These findings demonstrate the capability of ML models to capture complex, nonlinear patterns in device simulations. Furthermore, a comprehensive summary of all the reviewed ML approaches for semiconductor device simulation is presented in a table, highlighting the implemented models, devices, and features, along with key contributions and evaluation metrics. Finally, we identify key challenges associated with employed ML approaches in device simulations and suggest future directions for implementing ML models efficiently. Overall, this review serves as the most up-to-date resource for researchers, engineers, and industry experts seeking to leverage ML algorithms for semiconductor device modeling.}, archive = {J_ACCESS}, author = {Kazi Mohammad Mamun and Nezih Pala and Mst Shamim Ara Shawkat}, doi = {10.1109/ACCESS.2025.3605856}, journal = {IEEE Access}, pages = {162969-162991}, shortjournal = {IEEE Access}, title = {A comprehensive review of machine learning approaches for semiconductor device modeling and simulation}, volume = {13}, year = {2025}, }
• (2025). Advanced HOG research: Multi-scale partitioning HOG algorithm. ACCESS, 13, 162961-162968. (www)

  @article{ , abstract = {The Histogram of Oriented Gradients algorithm is a widely adopted technique in visual feature extraction, especially in object detection tasks. Traditionally, HOG descriptors rely on fixed-size cells and blocks to capture local gradient distributions. Although this technique has been proven effective in pedestrian detection, its single-scale structure limits its ability to represent features in changing spatial patterns. To address this limitation, this study introduces an improved feature extraction method that integrates multi-scale partitioning. Inspired by feature pyramid representation, the method captures image characteristics in different receptive fields by using multiple combinations of cell and block sizes. These features are aggregated to form a more discriminative and robust representation. Comprehensive experiments on multiple public datasets demonstrate that the proposed multi-scale HOG framework consistently outperforms standard HOG methods, providing remarkable improvements in both classification and detection accuracy. The results validate the effectiveness and adaptability of the method in diverse visual recognition scenarios.}, archive = {J_ACCESS}, author = {Wenjiao Liu and Anca Ralescu}, doi = {10.1109/ACCESS.2025.3609976}, journal = {IEEE Access}, pages = {162961-162968}, shortjournal = {IEEE Access}, title = {Advanced HOG research: Multi-scale partitioning HOG algorithm}, volume = {13}, year = {2025}, }
• (2025). Optimized Kolmogorov–Arnold networks-driven chronic obstructive pulmonary disease detection model. ACCESS, 13, 162947-162960. (www)

  @article{ , abstract = {Across the globe, chronic obstructive pulmonary disease (COPD) continues to be a critical healthcare burden due to its progressive and undetected decline in pulmonary function. In recent times, convolutional neural networks (CNNs) and vision transformers (ViTs) have been widely applied to lung sounds (LS) analysis, leading to the development of improved non-invasive, accessible, and scalable COPD screening methods. However, the shortcomings of CNNs and ViTs architectures restrict the COPD classifier’s discriminative power in distinguishing normal and COPD samples. In this study, a unique and interpretable COPD classification is proposed. This study introduces a hybrid feature extraction strategy integrating CNNs and ViTs architecture to capture the crucial COPD patterns from the LS’s Mel-spectrogram representation. The proposed CNNs-ViTs architecture addresses the limitations of standalone CNNs or ViTs architectures by capturing fine-grained acoustic features and long-range temporal dependencies while efficiently modeling non-linear relationships. A novel feature fusion is used to combine the heterogeneous features into a unified and highly discriminative feature representation space. A tailored hyperparameter optimization strategy is developed to fine-tune the Kolmogorov-Arnold Networks (KANs) for classifying the features into normal and COPD classes. By incorporating internal (552 Healthy and 1324 COPD LS recordings) and external (35 Healthy and 77 COPD LS recordings) datasets, the authors evaluate the model’s performance. The model achieves a generalization accuracy of 97.62% and specificity of 96.51% on the external dataset. Through Shapley Additive Explanations (SHAP) overlays, the model’s interpretability is enhanced. The experimental findings highlight the effectiveness of the proposed COPD classification architecture in the domain of biomedical acoustic signal processing. Operating with minimal reliance on specialized diagnostic infrastructure renders the proposed model ideal for primary care and resource-limited environments.}, archive = {J_ACCESS}, author = {Abdul Rahaman Wahab Sait and Ashit Kumar Dutta and Mujeeb Ahmed Shaikh}, doi = {10.1109/ACCESS.2025.3610633}, journal = {IEEE Access}, pages = {162947-162960}, shortjournal = {IEEE Access}, title = {Optimized Kolmogorov–Arnold networks-driven chronic obstructive pulmonary disease detection model}, volume = {13}, year = {2025}, }
• (2025). Exploiting human visual limitations for high capacity information hiding in digital images using alpha channel. ACCESS, 13, 162933-162946. (www)

  @article{ , abstract = {Information hiding in digital images is a critical technique for secure communication in the era of high-volume multimedia exchange. While many existing methods embed secret data by modifying image parameters in the spatial or frequency domains, they often overlook local pixel characteristics and the potential of the alpha channel for data embedding. This paper proposes a flexible Least Significant Bit (LSB) information hiding framework that adaptively adjusts the number of embedded bits based on pixel intensity ranges. By configuring the embedding function, the framework can support multiple strategies: i) allocating more bits to low-intensity regions and fewer to high-intensity ones, ii) leveraging Weber’s Just Noticeable Difference (JND) principle to embed more data in high-intensity pixels, and iii) combining both approaches to embed more in low- and high-intensity regions while reducing payload in mid-intensity regions. Additionally, the alpha channel is exploited to further enhance embedding capacity while preserving visual fidelity. Experimental results on standard test images demonstrate that the proposed adaptive framework significantly outperforms existing methods in embedding capacity, while maintaining high visual quality with PSNR values consistently above practical thresholds.}, archive = {J_ACCESS}, author = {Mohsin Shah and Sokjoon Lee and Muhammad Nawaz Khan and Hyeokchan Kwon}, doi = {10.1109/ACCESS.2025.3609789}, journal = {IEEE Access}, pages = {162933-162946}, shortjournal = {IEEE Access}, title = {Exploiting human visual limitations for high capacity information hiding in digital images using alpha channel}, volume = {13}, year = {2025}, }
• (2025). Context-aware gaussian process for physics-informed reinforcement learning with heterogeneous data. ACCESS, 13, 162915-162932. (www)

  @article{ , abstract = {Reinforcement learning (RL) with physics-informed dynamics models leverages known environments’ physics to improve data efficiency. Recently, we have introduced Gaussian Process Co-Adjustment (GPCA), a data-efficient model that conservatively refines simulator predictions, leading to the success of RL-controlled functional electrical stimulation (FES). However, GPCA’s behaviors and capabilities in broad settings remain unexplored. This study introduces context-aware GPCA (Ca-GPCA)—a novel extension of GPCA that incorporates a context encoder that learns latent representations (contexts) of transition histories. With context-augmented input, Ca-GPCA can generalize over heterogeneous datasets. Ca-GPCA consistently achieves 30% and 19% improvements in performance over GPCA in benchmark and FES control tasks, respectively, where the training data are collected from environments with different dynamics properties. These results demonstrate Ca-GPCA’s potential as a backbone for control systems that can operate instantly across diverse users and settings. In FES applications, for example, Ca-GPCA can utilize prior data from multiple individuals to perform in new users without requiring personalized calibration, paving the way for scalable, user-independent assistive technologies.}, archive = {J_ACCESS}, author = {Nat Wannawas and Jinpei Han and Jyotindra Narayan and Buntoeng Srikarun}, doi = {10.1109/ACCESS.2025.3608262}, journal = {IEEE Access}, pages = {162915-162932}, shortjournal = {IEEE Access}, title = {Context-aware gaussian process for physics-informed reinforcement learning with heterogeneous data}, volume = {13}, year = {2025}, }
• (2025). Federated learning with dual-end gradient correction and proxy-free self-distillation. ACCESS, 13, 162903-162914. (www)

  @article{ , abstract = {In the context of non-independent and identically distributed (non-IID) data and limited client participation, repeated iterations of local models in federated learning worsen model drift, resulting in directional bias during global aggregation. Existing single-end optimization approaches, such as client-side regularization or server-side control variables, have difficulty maintaining global-local consistency. To address this, we introduce the Federated Learning with Dual-end Gradient Correction and Proxy-free Self-Distillation (FedDGC-PSD) algorithm. On the client side, a self-distillation mechanism based on historical models constrains local prediction consistency via a Kullback-Leibler (KL) divergence loss, mitigating overfitting without the need for external data; concurrently, a gradient correction term aligns local updates with the global direction using historical gradients. On the server, dynamic cross-round gradient correction terms use weighted historical gradients to reduce variance accumulation. Extensive tests were run on five datasets against four benchmark algorithms. Results show FedDGC-PSD outperforms FedAvg, FedProx, SCAFFOLD, and FedDyn by 13.41%, 14.05%, 20.23%, and 3.41%, respectively, on CIFAR-100. In a large-scale scenario with 1,000 clients and 1.5% participation, FedDGC-PSD maintains stable convergence on CIFAR-10, outperforming FedDyn by 17.62%. Ablation studies show that combining the Dual-end Gradient Correction (DGC) and Proxy-free Self-Distillation (PSD) modules improves global model accuracy by about 0.71%, 2.26%, 4.50%, and 6.16% on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100, respectively, when compared to using only the DGC module.}, archive = {J_ACCESS}, author = {Haomin Wei and Yujiang Luo and Tao Xie and Yunong Yang}, doi = {10.1109/ACCESS.2025.3610089}, journal = {IEEE Access}, pages = {162903-162914}, shortjournal = {IEEE Access}, title = {Federated learning with dual-end gradient correction and proxy-free self-distillation}, volume = {13}, year = {2025}, }
• (2025). SearchSense: Haptic directional guidance for emergency response. ACCESS, 13, 162881-162902. (www)

  @article{ , abstract = {Urban Search and Rescue (USAR) teams lack portable, fast, and intuitive tools for locating victims in low-visibility disaster sites. Existing Direction-Finding (DF) systems are often too bulky, slow, or impractical because they require victims to have specialized transmitters. In this work, we present SearchSense, a portable device in two modalities: handheld and hands-free wearable, that provides left/right directional guidance via augmented reality and haptics to locate a victim’s cellphone. It overcomes the limitations of current systems by using an amplitude-comparison technique to detect unintentional radio frequency (RF) emissions from standard cellphones in the 2.45 GHz ISM and cellular bands, eliminating the need for any special equipment on the victim. In technical tests, SearchSense demonstrated high accuracy, achieving a 5.3° RMS Angle-of-Arrival (AOA) error in a controlled anechoic chamber and 6.8° RMS in a realistic outdoor environment with ground interference, comparable to existing AOA systems. In a user study with four USAR experts, participants found its DF vibrotactile feedback to be highly intuitive and praised its wearable design, confirming its potential to improve rescuer situational awareness and operational effectiveness. USAR feedback indicates that RF-based multimodal guidance could be a viable approach for passive DF in disaster response.}, archive = {J_ACCESS}, author = {Andrew L. Huard and Avinash Ajit Nargund and Alejandro Aponte and Megumi Ondo and Misha Sra}, doi = {10.1109/ACCESS.2025.3610656}, journal = {IEEE Access}, pages = {162881-162902}, shortjournal = {IEEE Access}, title = {SearchSense: Haptic directional guidance for emergency response}, volume = {13}, year = {2025}, }
• (2025). A multimodal transformer-based fusion model for enhanced relations extraction from texts. ACCESS, 13, 162855-162880. (www)

  @article{ , abstract = {Transformer models are significantly advancing the field of natural language processing (NLP), particularly in tasks such as the extraction of concepts and relationships from texts. However, while various studies employ single transformer models for one or both tasks and achieve performance improvements, there remain some limitations. A single model may struggle to optimize for both tasks simultaneously, resulting in poor performance in one or both areas. Furthermore, a single model may experience increased computational and memory demands when used for both tasks, as it must process and retain both entity-level and relation-level features. This can lead to inefficiencies and poor performance, especially on large or complex datasets. Moreover, when a single transformer model handles both tasks, errors in concept extraction can directly affect relation extraction, as the latter relies on accurate identification of entities. To address these limitations, this study introduces a novel Multimodal Transformer-based Fusion (MTF) model that integrates token-level representations and entity representations from different transformer models for enhanced relation classification. The proposed MTF model employs a fused cross-attention mechanism to effectively integrate these heterogeneous representations within the transformer encoder, leveraging complementary linguistic and entity-specific information. Through a structured tokenization process, we generate unified token and entity embeddings, enabling the model to learn a more distinctive representation space for improved relational understanding. The proposed MTF model is evaluated on five transformer-based architectures using the Conll04 and NYT benchmark datasets. The transformer-based architectures include three models for concept extration, namely, BERT, DistilBERT and RoBERTa, and two models for relation extraction, namely, LUKE and mLUKE. The comparison of individual model results reveal RoBERTa and mLuke as the best performing models for concept extraction and relation extraction, respectively. On both tasks, fusing the best two performing models results in the proposed MTF model outperforming the best individual models in terms of precision, recall, and F1 score. Furthermore, a comparative analysis of the results with related studies, shows the superior performance of the proposed MTF model with higher accuracy, precision, recall, and F1-score of 96.86%, 96.89%, 96.86%, 96.87%, respectively.}, archive = {J_ACCESS}, author = {Tsitsi Zengeya and Kenny Emmanuel Naidoo and Jean Vincent Fonou-Dombeu and Mandlenkosi Gwetu}, doi = {10.1109/ACCESS.2025.3608576}, journal = {IEEE Access}, pages = {162855-162880}, shortjournal = {IEEE Access}, title = {A multimodal transformer-based fusion model for enhanced relations extraction from texts}, volume = {13}, year = {2025}, }
• (2025). Neutral-type CGNNs with multiple delays: A frobenius norm approach to stability. ACCESS, 13, 162845-162854. (www)

  @article{ , abstract = {For neutral Cohen-Grossberg neural networks incorporating multiple time delays, we construct a tailored Lyapunov function to derive stability criteria independent of delay magnitudes. Despite the inherent challenge of avoiding vector-matrix representation due to delay multiplicity, this work introduces a pioneering sufficient condition based on parameter matrix Frobenius norms and network parameters. Owing to the method’s feasibility and simplicity, our stability finding not only decreases computational complexity but also lowers conservativeness when compared with several prior studies. The validity and superiority of the findings in this research are confirmed by two specific neural network models.}, archive = {J_ACCESS}, author = {Lin Wang and Binbin Gan and Hao Chen}, doi = {10.1109/ACCESS.2025.3610116}, journal = {IEEE Access}, pages = {162845-162854}, shortjournal = {IEEE Access}, title = {Neutral-type CGNNs with multiple delays: A frobenius norm approach to stability}, volume = {13}, year = {2025}, }
• (2025). Exploring deep learning and explainable AI for precise rice leaf disease detection and classification. ACCESS, 13, 162821-162844. (www)

  @article{ , abstract = {This study presents an innovative approach to detect and classify four types of rice leaf diseases, including bacterial leaf blight, brown spot, tungro, and blast, as well as healthy leaves, using deep learning techniques. By integrating explainable AI methods, such as Grad-CAM, Grad-CAM++, and LIME, our proposed method provides detailed visual explanations of model predictions, enhancing transparency and trustworthiness. Image preprocessing techniques, including Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE), were employed to enhance image contrast, while Canny and Prewitt edge detection algorithms were utilized for feature extraction. Experimental results demonstrate that models achieved remarkable accuracy of 99%, significantly outperforming previous methods. However, the study is limited by the insufficient number of rice disease samples. We anticipate that expanding the dataset and increasing the testing scale will further improve the model’s generalization capacity and better support agricultural development. The integration of visualization techniques not only improves the interpretability of deep learning models but also facilitates their practical application in precision agriculture.}, archive = {J_ACCESS}, author = {Tanjim Mahmud and Mst. Sharmin Akter and Tahmina Akter and Mohammad Shahadat Hossain and Karl Andersson}, doi = {10.1109/ACCESS.2025.3609671}, journal = {IEEE Access}, pages = {162821-162844}, shortjournal = {IEEE Access}, title = {Exploring deep learning and explainable AI for precise rice leaf disease detection and classification}, volume = {13}, year = {2025}, }
• (2025). Semantic object navigation with segmenting decision transformer. ACCESS, 13, 162807-162820. (www)

  @article{ , abstract = {Object navigation remains a fundamental challenge in robotics, particularly when agents must reach targets specified by semantic categories. While existing approaches often treat semantic understanding and navigation as separate components, we demonstrate that their tight coupling is crucial for robust performance. We present SegDT (Segmenting Decision Transformer), a novel architecture that jointly learns to predict semantic segmentation masks and navigation actions through a unified transformer-based model. Our key insight is that temporal information from sequential observations can simultaneously enhance both segmentation quality and navigation decisions. To address the inherent challenges of transformer-based navigation—notably poor sample efficiency and computational complexity—we introduce a two-phase training approach: offline pretraining on expert demonstrations followed by online policy refinement through knowledge transfer from a recurrent neural network. Extensive experiments in the Habitat simulator demonstrate that SegDT achieves higher results using predicted segmentation masks, outperforming a single-frame baseline with a pre-trained semantic segmentation model and approaching the performance of systems using ground truth semantic information. Our ablation studies reveal that SegDT’s temporal processing also improves segmentation quality, highlighting the synergistic benefits of joint optimization. When integrated into complete object navigation systems, SegDT enhances overall performance by 9.6% in path efficiency compared to the state-of-the-art method. The code of SegDT is made publicly available at https://github.com/CognitiveAISystems/SegDT}, archive = {J_ACCESS}, author = {Aleksei Staroverov and Tatiana Zemskova and Dmitry A. Yudin and Aleksandr I. Panov}, doi = {10.1109/ACCESS.2025.3607672}, journal = {IEEE Access}, pages = {162807-162820}, shortjournal = {IEEE Access}, title = {Semantic object navigation with segmenting decision transformer}, volume = {13}, year = {2025}, }
• (2025). CP-FN: A collaborative perception fusion network for infrared and visible image fusion. ACCESS, 13, 162793-162806. (www)

  @article{ , abstract = {Multimodal feature fusion aims to generate fusion results with prominent target features and rich detail information from data of different complementary modalities. To better address key issues in multimodal feature alignment, such as channel misalignment, scale mismatch, and insufficient robustness to large-sized images, we propose a novel Collaborative Perception Fusion Network (CP-FN). The network employs a dual-stream architecture design, using a hierarchical self-attention mechanism to extract deep hierarchical multi-scale feature representations from the base stream. Combined with dilated convolution technology to effectively expand the receptive field, the extracted multi-scale information is gradually integrated into the detailed information stream to achieve feature compensation. The collaborative perception of multi-scale features progresses layer-by-layer from coarse-grained to fine-grained, promoting more comprehensive feature representation and effective cross-modal information alignment. Subsequently, we design an innovative dynamic weight allocation fusion strategy, which adaptively adjusts the weight coefficients of each modality according to the statistical characteristics of features to ensure balanced fusion of multimodal information. Additionally, through deep optimization of the self-attention mechanism, the network’s robustness and stability in processing large-sized images are significantly improved. Extensive experiments conducted on multiple benchmark datasets show that the CP-FN significantly outperforms existing comparative methods, particularly in feature alignment accuracy and fusion quality.}, archive = {J_ACCESS}, author = {Wentao Jiang and Hanyan Li and Yongtao Li}, doi = {10.1109/ACCESS.2025.3609880}, journal = {IEEE Access}, pages = {162793-162806}, shortjournal = {IEEE Access}, title = {CP-FN: A collaborative perception fusion network for infrared and visible image fusion}, volume = {13}, year = {2025}, }
• (2025). Collaborative optimization of air compressor units based on MSI-BWO algorithm. ACCESS, 13, 162779-162792. (www)

  @article{ , abstract = {To improve the operational efficiency of compressor units and reduce waste, a Multi-Strategy Improved Beluga Whale Optimization (MSI-BWO) algorithm is proposed for the collaborative combination optimization of compressor units in an air compressor plant. Although the original Beluga Whale Optimization (BWO) algorithm, which simulates the social and predatory behaviors of beluga whales, shows certain potential in global optimization problems, there is still room for improvement in convergence speed and accuracy. The MSI-BWO algorithm integrates elite opposition-based learning, dynamic refraction opposition-based learning, and cyclone foraging strategies. Among these, the elite opposition-based learning strategy enhances the diversity and quality of the initial population by constructing an opposition-based search space for elite individuals, thereby accelerating the initial convergence speed of the algorithm. The dynamic refraction opposition-based learning strategy draws on the physical principle of light refraction to dynamically and adaptively adjust the algorithm’s search space, significantly improving its global search capability. The cyclone foraging strategy mimics the unique spiral foraging behavior of manta rays, skillfully balancing the algorithm’s performance between global exploration and local fine-grained search. Through rigorous validation on unit optimization problems, the MSI-BWO algorithm demonstrates superior performance in convergence speed, accuracy, and stability compared to the original algorithm and other popular intelligent optimization algorithms, and the algorithm achieved an energy-saving effect of about 8.31%, while significantly improving the imbalance problem.}, archive = {J_ACCESS}, author = {Lei Chen and Jiaxin Hou and Xiaolong Zheng and Tao Zhang and Chunhua Hu}, doi = {10.1109/ACCESS.2025.3609757}, journal = {IEEE Access}, pages = {162779-162792}, shortjournal = {IEEE Access}, title = {Collaborative optimization of air compressor units based on MSI-BWO algorithm}, volume = {13}, year = {2025}, }
• (2025). Toward automated quality control: An end-to-end image processing and machine learning pipeline for bullet case mouth defect detection. ACCESS, 13, 162764-162778. (www)

  @article{ , abstract = {The structural integrity of metallic bullet casings is critical not only for military applications—where a single defective casing can compromise ammunition performance and jeopardize personnel safety—but also for industrial manufacturing, where undetected flaws translate into costly rework and production delays. In this study, we present a comprehensive, automated pipeline that combines image processing, feature extraction and machine learning to detect defects in the mouth regions of bullet casings with high reliability and scalability. A dataset of 860 images (440 defective, 420 normal) was captured under varying lighting and camera angles. Each image undergoes adaptive thresholding and morphological opening/closing to isolate the rim contour, followed by Hough Transform–based circle detection and radius comparison to align and normalize the mouth region. Geometric features—including area, perimeter, circularity, aspect ratio, extent and solidity—are computed from the largest contour, while variance analysis of radial profiles captures subtle surface irregularities. Extracted features were fed into a suite of classifiers (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, XGBoost), alongside an Artificial Neural Network). The best-performing Artificial Neural Network model achieved 97.3% accuracy on held-out test data, substantially outperforming tree-based and linear baselines. In summary, effective results are obtained when classical image processing techniques are combined with modern learning algorithms for real-time quality control of bullet cartridges used in the military.}, archive = {J_ACCESS}, author = {Merve Ozkan}, doi = {10.1109/ACCESS.2025.3610358}, journal = {IEEE Access}, pages = {162764-162778}, shortjournal = {IEEE Access}, title = {Toward automated quality control: An end-to-end image processing and machine learning pipeline for bullet case mouth defect detection}, volume = {13}, year = {2025}, }
• (2025). Hierarchically guided residual-aware equalization for orthogonal time frequency space modulation. ACCESS, 13, 162747-162763. (www)

  @article{ , abstract = {With the growing demand for high-mobility next-generation wireless environment, Orthogonal Time Frequency Space (OTFS) has emerged as the best transformative modulation technique due to its unique potential to provide reliable next-generation communication amid doubly dispersive channel conditions resulting from high mobility and multipath effects, which are prevalent in vehicular communication and aerial communication networks. The reliability and robustness of the OTFS system rely heavily on the equalization techniques employed. To ensure robustness and to improve reliability, this work proposes a novel equalization technique called Hierarchically Guided Residual Aware Equalization (HGRAE). This framework of doubly iterative Minimum Mean Square Error (MMSE) equalization encompasses the Transpose-free Quasi Minimal Residual (TFQMR) solver along with an Algebraic Multigrid (AMG) preconditioner. HGRAE uses a two-stage iterative approach, in which the Soft-Input Soft-Output (SISO) MMSE equalizer exchanges extrinsic information with the SISO decoder through multiple turbo iterations. These two iterations, namely TFQMR and AMG, are used to suppress the residual errors, respectively. TFQMR is used as the outer solver, which iteratively refines the solution to the linear equation without the need for matrix transposition while ensuring the algorithm’s numerical stability. AMG is a multilevel preconditioner that accelerates the convergence via multigrid decomposition of the MMSE problem matrix into a hierarchy of progressively coarser problems. The proposed synergistic equalization design circumvents the need for direct matrix inversion by utilizing the residual-based efficient iteration technique of TFQMR and achieves convergence through AMG’s hierarchical estimation of the regularized channel system. Simulation results prove that the HGRAE approach achieves a superior Bit Error Rate (BER) performance level and enhanced convergence rate and reduced complexity when compared to the state-of-the-art equalizers, specifically in high mobility scenarios, and it proves to be a promising OTFS-based system to be deployed in the next generation of wireless communication networks.}, archive = {J_ACCESS}, author = {B. M. Bergyl Jeslin and T. Deepa}, doi = {10.1109/ACCESS.2025.3608324}, journal = {IEEE Access}, pages = {162747-162763}, shortjournal = {IEEE Access}, title = {Hierarchically guided residual-aware equalization for orthogonal time frequency space modulation}, volume = {13}, year = {2025}, }
• (2025). Exact beam synthesis with RIS-augmented receiver. ACCESS, 13, 162731-162746. (www)

  @article{ , abstract = {This article introduces an exact, first-principles method for beamforming with a receiver that is augmented by a reconfigurable intelligent surface (RIS). We demonstrate a way to synthesize a target beam from a finite set of elementary beams. The article also discusses a way to optimize the codebook of the augmented receiver. The goal is to optimize the average utility of the synthesized beam pattern. The analysis is based on full-wave electromagnetic field theory and its analytical solutions. In particular, we make use of the theory of multiple scattering to model the electromagnetic interactions between the RIS elements. We consider three geometries of the receiver array: (a) linear, (b) wedge-like, and (c) circular. The scattering elements in the arrays are taken to be circular cylinders of infinite length, so that the model is effectively two-dimensional. The authors show that the method of exact beam synthesis outperforms their foregoing beam synthesis methods and conventional beamforming.}, archive = {J_ACCESS}, author = {Tommi E. Rimpiläinen and Riku Jäntti}, doi = {10.1109/ACCESS.2025.3608937}, journal = {IEEE Access}, pages = {162731-162746}, shortjournal = {IEEE Access}, title = {Exact beam synthesis with RIS-augmented receiver}, volume = {13}, year = {2025}, }
• (2025). Intelligent fault detection in power grids using deep learning algorithms. ACCESS, 13, 162713-162730. (www)

  @article{ , abstract = {Modern power systems are increasingly complex and interconnected, demanding intelligent, adaptive solutions for fault detection and operational resilience. Traditional methods often depend on linear approximations or computationally demanding simulations, which struggle to capture the nonlinear, time-varying dynamics of real-world power networks. To address these challenges, this study proposes an advanced framework that integrates spectral graph theory with deep learning, offering a more efficient and accurate approach to fault detection. Central to the framework is the Spectral-Temporal Energy Flow Graph (STEF-Graph), which models spatial and temporal interactions within the grid. By applying spectral filters in the graph’s eigenbasis, the system learns high-level spatial features reflective of the network topology. Concurrently, Gated Recurrent Units (GRUs) process temporal sequences, enabling robust predictions of system states under dynamic conditions. To ensure practical viability, a projection operator is applied to enforce physical constraints such as voltage limits, line capacity, and stability requirements. This ensures all predictions remain operationally feasible. Based on these predictive insights, the Graph-Informed Energy Adjustment Mechanism (GEAM) is introduced to guide control decisions. GEAM balances localized efficiency with system-wide coordination using a graph-regularized penalty, ensuring both adaptability and coherence. STEF-Graph and GEAM jointly constitute an integrated two-module framework that boosts fault detection precision while strengthening the grid’s adaptability to disruptions and fluctuations. This research contributes to the development of scalable, data-driven methodologies aligned with the evolving demands of modern energy systems, and reflects the journal’s emphasis on innovative computational methods for complex infrastructures.}, archive = {J_ACCESS}, author = {Cencen Liu and Li Ding and He Yu}, doi = {10.1109/ACCESS.2025.3606856}, journal = {IEEE Access}, pages = {162713-162730}, shortjournal = {IEEE Access}, title = {Intelligent fault detection in power grids using deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Robust stabilization of a twin rotor system using event-driven observer-based fault-tolerant sliding mode control under actuator failure. ACCESS, 13, 162696-162712. (www)

  @article{ , abstract = {Twin rotorcrafts, such as helicopters with their capability for vertical take-off, hovering, and maneuverability, pose intricacies in missions like search and rescue, medical transport, and operations in challenging environments. Twin rotor MIMO systems (TRMS), which emulate helicopter dynamics, face significant challenges due to nonlinearities, rotor coupling, external disturbances, and limitations in sensors and actuators. These factors hinder controller design, particularly in ensuring stability and precise positioning. Furthermore, undetected faults, the need for real-time sensor data processing, and acoustic interference exacerbate control difficulties, necessitating robust solutions for effective TRMS operation. To address these challenges, this study proposes a fault-tolerant sliding mode controller (FT-SMC) as a robust control solution for TRMS. The controller is designed to manage parametric uncertainties, exogenous disturbances, and faults, ensuring reliable performance. To address the dearth of actual parameter values, a fault-tolerant sliding-mode observer (FT-SMO) was also set up to figure out uncertainties and sensor/actuator imperfections. Implementation of FT-SMC demonstrated significant improvements in the issues related to parametric uncertainties, external disturbances, and faults. A comprehensive Lyapunov stability analysis confirmed the stability and convergence of the proposed control strategy, ensuring reliable TRMS operation under challenging conditions.}, archive = {J_ACCESS}, author = {Atanu Panda and Subhasish Mahapatra and Koteswara Rao Palepogu and Rames C. Panda}, doi = {10.1109/ACCESS.2025.3610076}, journal = {IEEE Access}, pages = {162696-162712}, shortjournal = {IEEE Access}, title = {Robust stabilization of a twin rotor system using event-driven observer-based fault-tolerant sliding mode control under actuator failure}, volume = {13}, year = {2025}, }
• (2025). Overview of dynamic spectrum sharing between LEO satellites and mobile networks: Enabling seamless connectivity and coexistence in 6G. ACCESS, 13, 162681-162695. (www)

  @article{ , abstract = {In recent years, we have witnessed an acceleration in the growth of wireless communication technologies, particularly Low Earth Orbit (LEO) satellite constellations, with the direction towards integration with terrestrial networks in the domain of sixth-generation networks. This evolution promises to provide high-speed internet, low-latency communication, and extended global coverage. However, the availability of sufficient spectrum has become a critical challenge. The purpose of this paper is to review existing literature on Dynamic Spectrum Sharing (DSS) between LEO and geostationary satellites, as well as between LEO satellites and terrestrial networks. The contribution of this research lies in providing insights into key topics, including LEO constellation development, DSS techniques, spectrum regulatory compliance, current and future mobile satellite frequency allocations, and emerging trends related to providing direct connectivity to user equipment from space. The findings emphasize the importance of DSS as a vital technique to address spectrum scarcity and ensure coexistence between satellites and terrestrial networks. Based on the reviewed literature, we propose three approaches for future research directions that are crucial for providing additional spectrum resources and integration of next-generation satellites and terrestrial networks.}, archive = {J_ACCESS}, author = {Yasir Ahmed and David Grace}, doi = {10.1109/ACCESS.2025.3606375}, journal = {IEEE Access}, pages = {162681-162695}, shortjournal = {IEEE Access}, title = {Overview of dynamic spectrum sharing between LEO satellites and mobile networks: Enabling seamless connectivity and coexistence in 6G}, volume = {13}, year = {2025}, }
• (2025). A hybrid machine learning framework for daily dairy demand forecasting: Integrating SARIMAX and XGBoost for seasonal production optimization. ACCESS, 13, 162668-162680. (www)

  @article{ , abstract = {Accurate demand forecasting is a critical requirement in the dairy industry, where fluctuations in consumption, seasonal effects, and product perishability create significant operational challenges. This case study systematically compares Extreme Gradient Boosting (XGBoost) and Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) for dairy demand forecasting. The models were trained and validated using one year of production and distribution data from a major dairy manufacturing facility in Jordan, representing Middle Eastern market conditions, covering six products: Laban, Shanenah, Labneh, Jameed, Milk, and Zabadi. Novel integration of seasonal decomposition and breakpoint detection techniques identified structural demand shifts during cultural events, particularly Ramadan, providing a methodological framework for incorporating religious and cultural factors into demand forecasting. Forecasting accuracy was evaluated using the Diebold–Mariano test, which confirmed that SARIMAX significantly outperformed XGBoost in most cases (p < 0.05), achieving an average accuracy of 94.27% compared with 92.99%. SARIMAX showed superior performance for highly seasonal products such as Jameed (95.78% vs. 91.46%) and Shanenah (94.41% vs. 93.48%), whereas XGBoost offered marginal advantages for stable-demand products like Milk (94.63% vs. 94.48%). Economic analysis for this facility indicates potential inventory cost reductions of 15–20% and waste decreases of up to 25%, demonstrating the business value of enhanced forecasting accuracy. This case study illustrates the potential for integrating advanced forecasting methods in dairy manufacturing operations, contributing to Industry 4.0 applications in perishable goods supply chains within similar cultural and operational contexts.}, archive = {J_ACCESS}, author = {Mazin Obaidat and Hesham A. Almomani and Roxane Mallouhy and Abdalmageed AlMotari and Osama T. Al Meanazel}, doi = {10.1109/ACCESS.2025.3610316}, journal = {IEEE Access}, pages = {162668-162680}, shortjournal = {IEEE Access}, title = {A hybrid machine learning framework for daily dairy demand forecasting: Integrating SARIMAX and XGBoost for seasonal production optimization}, volume = {13}, year = {2025}, }
• (2025). Security analysis and performance evaluation of certificateless proxy re-encryption for blockchain applications. ACCESS, 13, 162639-162667. (www)

  @article{ , abstract = {Certificateless Proxy Re-Encryption (CL-PRE) eliminates certificate management and private key exposure risks for blockchain data sharing, but existing schemes have critical security vulnerabilities and performance limitations. This research work presents comprehensive security analysis and performance evaluation of CL-PRE schemes for blockchain applications. The primary contribution is discovering a critical public key replacement attack against Wang et al.’s CL-PRE scheme, where Type I adversaries completely compromise message confidentiality by substituting legitimate public keys with adversary-controlled keys, enabling ciphertext decryption without private keys and violating IND-CCA security. The systematic performance evaluation of pairing-free PRE schemes for blockchain environments is conducted through extensive benchmarking of three schemes implemented in Go. Results show self PRE achieves superior security but incurs 13.7% higher execution time than certificateless schemes. To address vulnerabilities, this work proposes a secure CL-PRE framework with enhanced validation mechanisms. The Ethereum implementation reduces on-chain storage by 40% while maintaining provable security. The framework achieves 14.1% better performance than existing secure schemes and reduces gas costs by 14.3%. These findings establish security benchmarks and practical guidelines for blockchain developers, emphasizing rigorous cryptographic analysis importance for decentralized access control advancement.}, archive = {J_ACCESS}, author = {S. Remya and Manu J. Pillai and Preethi Ann Jacob and Sruthi Suresh and S. Sharmila Deva Selvi}, doi = {10.1109/ACCESS.2025.3606635}, journal = {IEEE Access}, pages = {162639-162667}, shortjournal = {IEEE Access}, title = {Security analysis and performance evaluation of certificateless proxy re-encryption for blockchain applications}, volume = {13}, year = {2025}, }
• (2025). A price risk early warning model for agricultural products based on the integration of deep learning and knowledge graphs. ACCESS, 13, 162623-162638. (www)

  @article{ , abstract = {Agricultural price volatility poses significant challenges for food security, market regulation, and rural economic stability. To address these issues, this study proposes an integrated early warning model that combines the representational capabilities of deep learning with the interpretability of symbolic knowledge. The framework features two primary components: AgroFormer, a Transformer-based architecture specifically designed to capture hierarchical spatio-temporal dependencies across crops, regions, environmental factors, and time; and AgroScope, a strategic reasoning module that converts predictive insights into operational decisions through knowledge-aware constraints and risk-sensitive optimization. The model is evaluated on four benchmark datasets—Agmarknet, AGRIS, WorldCereal, and GAEZ—spanning structured tabular records, multilingual textual documents, and high-resolution remote sensing data. Experimental results show that the proposed approach achieves superior performance across all datasets, outperforming state-of-the-art baselines with an average improvement of 12.3% in prediction accuracy and a 12.1% reduction in RMSE. Ablation studies further validate the unique contributions of symbolic attribute alignment, differentiable scheduling graphs, and knowledge-aware constraints. Beyond technical contributions, the proposed system offers tangible value to agricultural practitioners. It enables proactive decision-making by delivering early warnings for price fluctuations, optimizing crop rotation schedules under policy and environmental constraints, and supporting adaptive planning in response to real-time market signals. The framework’s modular design facilitates deployment in diverse agro-ecological contexts, making it suitable for applications ranging from farm-level management to national food security planning. These results highlight the potential of combining deep learning with domain knowledge to create interpretable, scalable, and actionable tools for intelligent agricultural risk management.}, archive = {J_ACCESS}, author = {Da Pan}, doi = {10.1109/ACCESS.2025.3609817}, journal = {IEEE Access}, pages = {162623-162638}, shortjournal = {IEEE Access}, title = {A price risk early warning model for agricultural products based on the integration of deep learning and knowledge graphs}, volume = {13}, year = {2025}, }
• (2025). Robust adaptive neural network control for nonlinear CSTRs with prescribed control performance and event-triggered inputs. ACCESS, 13, 162611-162622. (www)

  @article{ , abstract = {This paper presents a robust adaptive neural network event-triggered controller (NN ETC) for nonlinear continuous stirred tank reactors (CSTRs) with prescribed performance. The proposed controller employs a globally invertible prescribed performance function to ensure the tracking error remains within predefined bounds. A radial basis function (RBF) NN approximates the unknown nonlinear system dynamics, reducing reliance on precise modeling. A switching threshold event-triggering mechanism (SWT-ETM) is adopted that dynamically switches between fixed and relative thresholds to reduce communication and actuation frequency while avoiding Zeno behavior. Stability analysis proved that all system signals are globally bounded and the tracking error exponentially converges to an adjustable neighborhood. Simulation results demonstrate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Kaiyue Liu and Xiangyu Yang and Shuke Lyu and Rui Wang and Chenkang Gao and Yongtao Liu}, doi = {10.1109/ACCESS.2025.3609753}, journal = {IEEE Access}, pages = {162611-162622}, shortjournal = {IEEE Access}, title = {Robust adaptive neural network control for nonlinear CSTRs with prescribed control performance and event-triggered inputs}, volume = {13}, year = {2025}, }
• (2025). Neuro-fuzzy ARX approach for dynamic RAW duration adaptation in event triggered large scale IEEE 802.11ah networks. ACCESS, 13, 162588-162610. (www)

  @article{ , abstract = {Effective resource management techniques for large heterogeneous IEEE 802.11ah networks with event-driven scenarios are demanded by the expansion of the Internet of Things (IoT). This paper introduces a new system that provides dynamic adaptation of Restricted Access Window (RAW) duration to enhance performance by combining an Auto-Regressive with Exogenous Input (ARX) model with an Adaptive Neuro-Fuzzy Inference System (ANFIS), called the Predictive ANFIS RAW Scheduler (PARS). The PARS method dynamically changes RAW group durations to meet different traffic need, including text, audio, and image. While the ARX model faithfully forecasts group duration, the ANFIS offers adaptive decision-making by using conditions including change, maintain, and alternate based on traffic patterns in real-time networks. Simulations carried out on event-driven scenarios of a large IoT environment comprising 2000 devices show that the suggested framework greatly increases channel usage by 14%, has a 24% decrease in delay, had an 18% reduction in energy consumption, and lowers collision rates by 28% compared to current static allocation strategies. This study provides a scalable, intelligent, and dependable solution for upcoming IEEE 802.11ah implementations, therefore addressing the collision issues in crowded networks. It opens the path for more efficient management of event-driven IoT networks, hence promoting developments in major IoT installations.}, archive = {J_ACCESS}, author = {R. Nandhini and R. Radha}, doi = {10.1109/ACCESS.2025.3610136}, journal = {IEEE Access}, pages = {162588-162610}, shortjournal = {IEEE Access}, title = {Neuro-fuzzy ARX approach for dynamic RAW duration adaptation in event triggered large scale IEEE 802.11ah networks}, volume = {13}, year = {2025}, }
• (2025). Autonomous mobile robot design and testing for data center monitoring mission. ACCESS, 13, 162571-162587. (www)

  @article{ , abstract = {This paper presents a cost-effective autonomous mobile robot (AMR) designed for remote data center monitoring, motivated by the need to enhance operational efficiency and reduce human intervention in critical infrastructure. The proposed model integrates a TurtleBot Kobuki base with a novel SRF10-based virtual bumper system, RPLIDAR scanner, and GoPro camera, enabling autonomous navigation, obstacle avoidance, and high-resolution photo capture in data center corridors. Developed through a university-industrial collaboration, the prototype leverages ROS-compatible subroutines to minimize development costs. Laboratory and real-world tests validate the AMR’s performance, achieving 92% navigation success and 94.2% gap navigation accuracy, while identifying limitations like positioning errors and low-clearance obstacle detection. Proposed upgrades, including a faster LIDAR and enhanced computing power, aim to meet industrial standards. This study establishes a foundation for scalable, automated monitoring solutions across data centers and similar sectors.}, archive = {J_ACCESS}, author = {Fatah Chetouane}, doi = {10.1109/ACCESS.2025.3610346}, journal = {IEEE Access}, pages = {162571-162587}, shortjournal = {IEEE Access}, title = {Autonomous mobile robot design and testing for data center monitoring mission}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Depth map reconstruction for scleral bleb models using structured light in light field imaging”. ACCESS, 13, 162570. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Depth Map Reconstruction for Scleral Bleb Models Using Structured Light in Light Field Imaging”).}, archive = {J_ACCESS}, author = {Octa Heriana and Andriyan Bayu Suksmono and Hasballah Zakaria and Andika Prahasta}, doi = {10.1109/ACCESS.2025.3609920}, journal = {IEEE Access}, pages = {162570}, shortjournal = {IEEE Access}, title = {Corrections to “Depth map reconstruction for scleral bleb models using structured light in light field imaging”}, volume = {13}, year = {2025}, }
• (2025). On the design of a hybrid framework for resilient indoor IoT connectivity via distributed 3-D voronoi deployment, mobile agents, and RIS-assisted communication. ACCESS, 13, 162552-162569. (www)

  @article{ , abstract = {In indoor smart spaces, ensuring reliable IoT connectivity is a significant challenge. Walls, furniture, and other obstacles often create coverage gaps that disrupt wireless links. This work presents a system-level integration and validation study of a hybrid networking framework that combines static, mobility-enabled, and reconfigurable components to sustain connectivity in complex indoor environments. First, a 3-D Voronoi-based deployment of static IoT nodes provides an optimized baseline coverage map. Next, a set of mobility-enabled platforms, ceiling-rail robots and floor Automated Guided Vehicles (AGVs), form an adaptive layer that can reposition as needed to restore links or extend coverage around obstacles. In parallel, Reconfigurable Intelligent Surfaces (RIS) are strategically placed on walls or ceilings to dynamically redirect wireless signals, creating virtual line-of-sight paths and mitigating shadowing. A multi-objective Genetic Algorithm (GA) coordinates these elements, optimizing coverage, connectivity, and latency simultaneously. Across hospital, office, and warehouse layouts, the integrated system achieves on average 95.6% volumetric coverage and 100% connectivity. Latency and energy-efficiency results are reported together with 95% confidence intervals to highlight statistical robustness. The study quantifies the gains of a unified controller versus sequential or disjoint orchestration and provides sensitivity analyses under RIS quantization and shadowing effects. The contribution is therefore positioned as a practical, unified framework that couples planning, mobility, and RIS operation under non-ideal hardware and channel conditions.}, archive = {J_ACCESS}, author = {Andreas Andreou and Constandinos X. Mavromoustakis and George Mastorakis and Athina Bourdena and Evangelos K. Markakis}, doi = {10.1109/ACCESS.2025.3610321}, journal = {IEEE Access}, pages = {162552-162569}, shortjournal = {IEEE Access}, title = {On the design of a hybrid framework for resilient indoor IoT connectivity via distributed 3-D voronoi deployment, mobile agents, and RIS-assisted communication}, volume = {13}, year = {2025}, }
• (2025). Hierarchical deep reinforcement learning for multi-objective integrated circuit physical layout optimization with congestion-aware reward shaping. ACCESS, 13, 162533-162551. (www)

  @article{ , abstract = {Physical layout optimization in integrated circuit (IC) design remains a critical challenge as semiconductor technology scales toward advanced nodes, where traditional electronic design automation (EDA) tools struggle to simultaneously optimize multiple conflicting objectives including routing congestion, power consumption, and timing performance. This paper presents a novel hierarchical deep reinforcement learning (HDRL) framework for multi-objective IC layout optimization that addresses the exponential complexity of modern chip design spaces. Our approach introduces a congestion-aware reward shaping mechanism that dynamically balances exploration and exploitation while incorporating domain-specific knowledge through a hierarchical policy decomposition. The framework employs a dual-level architecture where a high-level policy manages global placement strategies and a low-level policy handles detailed placement refinements, enabling efficient optimization across different granularities. We integrate adaptive action space pruning based on physical design rules and propose a novel state representation that captures both local congestion patterns and global routing accessibility. Extensive experiments on OpenROAD benchmarks demonstrate that our method achieves an average 23.7% reduction in routing congestion, 18.4% improvement in power efficiency, and 15.2% enhancement in timing closure compared to state-of-the-art commercial tools and recent learning-based approaches. The framework maintains computational efficiency with 40% faster convergence than baseline deep reinforcement learning methods while providing interpretable placement decisions through attention mechanism visualization. Our contributions include the hierarchical policy architecture, congestion-aware reward formulation, and comprehensive evaluation framework that advances the integration of artificial intelligence in physical design automation.}, archive = {J_ACCESS}, author = {Haijian Zhang and Yao Ge and Xiuyuan Zhao and Jiyuan Wang}, doi = {10.1109/ACCESS.2025.3610615}, journal = {IEEE Access}, pages = {162533-162551}, shortjournal = {IEEE Access}, title = {Hierarchical deep reinforcement learning for multi-objective integrated circuit physical layout optimization with congestion-aware reward shaping}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning based fault-alarmed hybrid control design for nonlinear cyber-physical systems via negative determination lemma. ACCESS, 13, 162519-162532. (www)

  @article{ , abstract = {This article delineates the stabilization problem of non-linear cyber physical systems by integrating the reinforcement learning based switched fault alarm controller and quadratic function negative determination lemma. To begin with, quadratic function based negative determination lemma is applied to handle the input delay occurred in the controller which derives the sufficient conditions for the quadratic polynomials arises in the system by partitioning the delay-intervals. Subsequently, a robust reinforcement learning control is designed in conjunction with a switched fault alarm. In particular, the alarm threshold value is used to determine the fault alarmed signal, which is intended to promptly notify the controller to jump from the robust learning controller to the fault-tolerant controller. Based on the Lyapunov stability theory and linear matrix inequality approach, the asymptotic stability criterion is developed to sustain skilfully in the presence of actuator-faults in the system as well as attains the accurate convergence with the aid of reinforced learning controller. Further, numerical simulations are performed to validate the proposed approach.}, archive = {J_ACCESS}, author = {D. Gomathi and S. Harshavarthini}, doi = {10.1109/ACCESS.2025.3610459}, journal = {IEEE Access}, pages = {162519-162532}, shortjournal = {IEEE Access}, title = {Reinforcement learning based fault-alarmed hybrid control design for nonlinear cyber-physical systems via negative determination lemma}, volume = {13}, year = {2025}, }
• (2025). High-resolution remote sensing image road extraction system: Data-model collaborative optimization. ACCESS, 13, 162505-162518. (www)

  @article{ , abstract = {Background: Accelerating global urbanization and increasing transportation demands highlight the critical need for precise and efficient road system planning in geospatial applications. Existing methods for extracting roads from high-resolution remote sensing images face challenges in balancing accuracy and computational efficiency, especially in complex urban environments. To address this, we introduce a data-model co-optimization framework that enhances both accuracy and efficiency. Methods: Our research focuses on three key aspects: 1) Multi-dimensional data quality optimization through a fourth-order image processing chain (“Gaussian noise reduction - non-local mean denoising - CLAHE contrast enhancement - edge enhancement”) combined with mask double median filtering and morphological closure operations to improve input data quality. 2) Development of a Lightweight Dynamic Attention Network Architecture (MACL-U-ResNet) based on ResNet50, integrating four attention modules (SE/CBAM/ECA/CA) to dynamically select channel-space-position perceptions via hyperparameter $\varphi $ . This architecture includes a depth-feature retention structure and cross-layer fusion decoder. 3) Proposal of an efficiency-accuracy balance coefficient $\eta $ ( $\eta = $ IoU/parameter count) to evaluate resource-constrained scenarios, revealing the gains of data preprocessing on system efficiency. Results: Experiments on the DeepGlobe dataset show that our system achieves 66.82% IoU and 79.32% F1-Score, surpassing the state-of-the-art LCMorph model by 0.52% in IoU while reducing the parameter count by 39.9%. The $\eta $ coefficient value of 2.33 validates the system’s balanced accuracy-efficiency, offering a significant advantage for edge computing applications. This study demonstrates a robust solution for road extraction, advancing the field towards more efficient and accurate geospatial applications.}, archive = {J_ACCESS}, author = {Suping Cui and Xiang Zhang and Xinyan Wang and Sa Du and Xu Gong}, doi = {10.1109/ACCESS.2025.3597152}, journal = {IEEE Access}, pages = {162505-162518}, shortjournal = {IEEE Access}, title = {High-resolution remote sensing image road extraction system: Data-model collaborative optimization}, volume = {13}, year = {2025}, }
• (2025). Vision-language-action models for robotics: A review towards real-world applications. ACCESS, 13, 162467-162504. (www)

  @article{ , abstract = {Amid growing efforts to leverage advances in large language models (LLMs) and vision-language models (VLMs) for robotics, Vision-Language-Action (VLA) models have recently gained significant attention. By unifying vision, language, and action data at scale, which have traditionally been studied separately, VLA models aim to learn policies that generalise across diverse tasks, objects, embodiments, and environments. This generalisation capability is expected to enable robots to solve novel downstream tasks with minimal or no additional task-specific data, facilitating more flexible and scalable real-world deployment. Unlike previous surveys that focus narrowly on action representations or high-level model architectures, this work offers a comprehensive, full-stack review, integrating both software and hardware components of VLA systems. In particular, this paper provides a systematic review of VLAs, covering their strategy and architectural transition, architectures and building blocks, modality-specific processing techniques, and learning paradigms. In addition, to support the deployment of VLAs in real-world robotic applications, we also review commonly used robot platforms, data collection strategies, publicly available datasets, data augmentation methods, and evaluation benchmarks. Throughout this comprehensive survey, this paper aims to offer practical guidance for the robotics community in applying VLAs to real-world robotic systems. All references categorized by training approach, evaluation method, modality, and dataset are available in the table on our project website: https://vla-survey.github.io}, archive = {J_ACCESS}, author = {Kento Kawaharazuka and Jihoon Oh and Jun Yamada and Ingmar Posner and Yuke Zhu}, doi = {10.1109/ACCESS.2025.3609980}, journal = {IEEE Access}, pages = {162467-162504}, shortjournal = {IEEE Access}, title = {Vision-language-action models for robotics: A review towards real-world applications}, volume = {13}, year = {2025}, }
• (2025). Evaluation of the electrical parameters of a piezoelectric transducer using a low-cost set-up and FFT analysis. ACCESS, 13, 162455-162466. (www)

  @article{ , abstract = {Ultrasonic piezoelectric technology is under study in fields like biomedical applications, data transmission, or non-destructive analysis. The behavior of the ultrasonic transducer is commonly described by using the Butterworth-Van Dyke model, and the determination of the lumped equivalent electrical parameters of the model is important in applications of ultrasonic devices. The model parameters are assessed by exploiting the values of the transducer admittance at different supply frequencies. In this paper the admittance was derived in the following way. A low-cost set-up designed to apply a periodic square-wave signal to the piezoelectric device under test was developed, the corresponding periodic voltage and current signals of the piezoelectric transducer were acquired by an oscilloscope and, after applying the FFT analysis, the first harmonic of each signal was taken and the admittance was calculated. In order to verify the method, two pairs of piezoelectric devices of two different manufactures were considered. The relevant measurements were carried out by both an LCR meter and by the proposed low-cost method. The determination of the Butterworth-Van Dyke model parameters was performed starting from a preliminary estimation based on analytical relationships, and then by using a MATLAB ® numerical identification. The electrical parameters derived from the data sets obtained by the developed prototype system resulted in very good agreement with those obtained by the LCR meter, demonstrating the effectiveness of the low-cost method presented in this paper.}, archive = {J_ACCESS}, author = {Manuele Bertoluzzo and Daniele Desideri and Federico Moro}, doi = {10.1109/ACCESS.2025.3609970}, journal = {IEEE Access}, pages = {162455-162466}, shortjournal = {IEEE Access}, title = {Evaluation of the electrical parameters of a piezoelectric transducer using a low-cost set-up and FFT analysis}, volume = {13}, year = {2025}, }
• (2025). Effects of domain, modality, and linguistic complexity on cognitive load and self-efficacy in learning academic content in l2. ACCESS, 13, 162436-162454. (www)

  @article{ , abstract = {Learning academic content in a second language (L2) depends on how content domain, presentation modality, and linguistic complexity jointly shape cognitive and motivational states. Using a repeated-measures design, we examined how task domain (science versus history), presentation modality (video versus text), and linguistic complexity (simple versus complex) jointly shaped cognitive load, self-efficacy, and performance accuracy in 36 Chinese-speaking learners of Japanese. Mixed-effects models revealed a key asymmetry: cognitive load was primarily driven by task features, whereas self-efficacy reflected both task features and disciplinary expertise. Key three-way interactions revealed that expertise moderated the effects of intrinsic load, with only science majors benefiting from low linguistic complexity on science tasks. This expertise also created a motivational buffer, as only science majors’ self-efficacy was sensitive to in-domain linguistic complexity. Furthermore, interactions showed that the effects of modality were conditional on the task’s combined conceptual and linguistic demands. Section-level analyses showed that momentary increases in load were associated with immediate drops in self-efficacy, especially for the science passages. Performance analyses showed a more negative load–accuracy slope for complex than simple syntax, and self-efficacy predicted accuracy chiefly for history and simple syntax. Bayesian mediation suggested a small negative indirect effect overall; the effect was credible for simple passages but not for complex passages. These findings highlight the need for adaptive instruction that manages cognitive demands and supports motivation, calibrated to domain, modality, linguistic complexity, and learner expertise.}, archive = {J_ACCESS}, author = {Jialiang Lu and Reiko Sato}, doi = {10.1109/ACCESS.2025.3610786}, journal = {IEEE Access}, pages = {162436-162454}, shortjournal = {IEEE Access}, title = {Effects of domain, modality, and linguistic complexity on cognitive load and self-efficacy in learning academic content in l2}, volume = {13}, year = {2025}, }
• (2025). Smart grids, super smart grids, and microgrids: A triple challenge for the future of energy landscape. ACCESS, 13, 162422-162435. (www)

  @article{ , abstract = {This paper explores the evolving landscape of smart grids and super smart grids (SSG) through a review of recent academic publications (2019-2024). An SSG expands the traditional smart grid concept by interconnecting power grids across vast regions, facilitating the efficient transfer of renewable energy sources (RES) across borders. While the potential of SSG lies in enhancing global energy security and reducing carbon emissions, significant challenges remain, including high infrastructure costs, geopolitical risks and cybersecurity concerns. Additionally, controversies surrounding energy control and the environmental impact of large-scale infrastructure development add complexity to the SSG debate. Our paper focuses on emerging research trends and key topics related to SSG, aiming to address several concerns: 1) SSG viability given the current global energy landscape and conflicts; 2) the prevailing academic sentiment towards SSG in recent publications; 3) the most prominent research topics and emergent themes in the field. By analyzing recent publications from the past five years, we aim to shed light on the future of SSG and their interactions with local energy systems such as microgrids. The first topic revolves around data-driven approaches within network contexts, highlighting load management, distribution systems and optimization techniques. The second topic explores performance analysis and comparative studies. The third topic concentrates on control methods in distributed networks. The fourth topic centers on voltage, current and control methods in direct current (DC) systems. Our research identifies seven new research directions for SSG development.}, archive = {J_ACCESS}, author = {Simona-Vasilica Oprea and Adela Bâra}, doi = {10.1109/ACCESS.2025.3609793}, journal = {IEEE Access}, pages = {162422-162435}, shortjournal = {IEEE Access}, title = {Smart grids, super smart grids, and microgrids: A triple challenge for the future of energy landscape}, volume = {13}, year = {2025}, }
• (2025). Digital twin-driven health management in microgrids. ACCESS, 13, 162406-162421. (www)

  @article{ , abstract = {The increasing shift towards decentralized energy systems have made microgrids (MGs) a key solution for improving energy reliability, resilience, and sustainability. However, managing the complexities of MG operations poses significant challenges. Traditional approaches often struggle to address these complexities, resulting in inefficiencies and compromised reliability. In response, this paper presents a digital twin (DT) framework designed for decision-making and real-time fault detection and diagnosis tested in a lab-scale DC microgrid. The DT model replicates the dynamics of the physical system (PS) under various fault conditions. An AI-driven Long Short-Term Memory (LSTM) model is integrated into the framework to improve health management. The performance of the system is assessed under both normal and emergency conditions using metrics such as Root Mean Square Error (RMSE) and confusion matrices. A comparative analysis between the DT and PS validates the DT’s ability to detect and diagnose faults accurately, demonstrating its potential to enhance microgrid operational resilience.}, archive = {J_ACCESS}, author = {Shayan Ebrahimi and Mohammad Seyedi and Kouhyar Sheida and Farzad Ferdowsi and Marc A. Carbone}, doi = {10.1109/ACCESS.2025.3610339}, journal = {IEEE Access}, pages = {162406-162421}, shortjournal = {IEEE Access}, title = {Digital twin-driven health management in microgrids}, volume = {13}, year = {2025}, }
• (2025). A proximal policy optimization-based controller for enhanced power sharing in microgrids. ACCESS, 13, 162391-162405. (www)

  @article{ , abstract = {This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By leveraging PPO, the controller improves upon traditional methods by reducing the need for manual tuning and offering better adaptability to varying operating conditions. The performance of the proposed controller is evaluated in both islanded and grid-connected modes, using batteries with capacities of 1 MW, 125 kW, and 100 kW. The results demonstrate that the PPO-based VI controller improves power-sharing accuracy and provides better response to disturbances across different scenarios compared to the conventional controller. To validate the performance of the proposed method, an assessment is conducted on the system frequency using key metrics, including Root Mean Square Error (RMSE), Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time-Weighted Squared Error (ITSE). The PPO controller consistently achieves the lowest errors across all scenarios compared to the conventional controller, with the IAE reduced by 27% in islanded mode and 36% in grid-connected mode.}, archive = {J_ACCESS}, author = {Seyedmohammad Hasheminasab and Armin Lotfy and Mohamad Alzayed and Hicham Chaoui}, doi = {10.1109/ACCESS.2025.3610451}, journal = {IEEE Access}, pages = {162391-162405}, shortjournal = {IEEE Access}, title = {A proximal policy optimization-based controller for enhanced power sharing in microgrids}, volume = {13}, year = {2025}, }
• (2025). Generalization assessment of palmprint verification models trained on synthetic data across diverse datasets. ACCESS, 13, 162378-162390. (www)

  @article{ , abstract = {Palmprint verification systems have shown great potential as a reliable biometric modality due to their distinctiveness and stability. However, developing robust palmprint verification models requires large-scale and diverse training data, which is often difficult to acquire. This paper investigates the effectiveness of leveraging synthetic palmprint data for enhancing the performance and generalization capabilities of palmprint verification systems. We utilize the PCE-Palm, a novel two-stage synthetic palmprint generation method that transforms parametric Bézier curves into realistic palmprint images via an intermediate Palm Crease Energy (PCE) domain, to generate 1.6 million synthetic palmprint samples representing 50,000 subjects. Our approach involves pretraining deep learning models on synthetic data followed by fine-tuning on real palmprint datasets. We propose a Region of Interest (ROI) extraction method based on circular fitting for accurate palmprint region localization and employ EfficientNetV2 with Circle Loss for feature extraction and classification. Extensive experiments across ten diverse public palmprint datasets demonstrate that models pretrained on synthetic data significantly outperform models initialized with ImageNet weights. Specifically, our synthetic data-driven approach achieves superior results with Equal Error Rates (EER) as low as 0.0026 on COEP, 0.0045 on IITD, 0.0046 on TCD, and 0.0622 on PolyU_V2 datasets. These results highlight the potential of synthetic data for improving the generalization and robustness of palmprint verification systems across various acquisition conditions and population demographics.}, archive = {J_ACCESS}, author = {Mahmoud Bahaa and Fahad A. Aloufi}, doi = {10.1109/ACCESS.2025.3608859}, journal = {IEEE Access}, pages = {162378-162390}, shortjournal = {IEEE Access}, title = {Generalization assessment of palmprint verification models trained on synthetic data across diverse datasets}, volume = {13}, year = {2025}, }
• (2025). 3D reconstruction: A feature fusion and transformer-based feature extraction network for MVSNet. ACCESS, 13, 162367-162377. (www)

  @article{ , abstract = {Three-dimensional (3D) reconstruction creates digital 3D models from multiple Two-dimensional images and is widely applied in robotics, cultural heritage preservation, industrial inspection, and immersive media. Deep learning has significantly improved the accuracy and robustness of 3D reconstruction methods by learning rich feature representations. However, challenges remain, including capturing features at multiple scales, handling low-textured or repetitive regions, and balancing accuracy with memory and speed, especially for high-resolution or large scenes. To address these issues, this paper introduces a novel Transformer-based Feature Fusion Pyramid Network (TF-FPN) for efficient and accurate 3D reconstruction. The proposed architecture integrates two main components: 1) a frequency-aware multi-scale fusion module that adaptively combines structural and textural information across different scales, and 2) a coarse-stage feature matching transformer that establishes reliable cross-view correspondences using lightweight self- and cross-attention mechanisms at the lowest resolution. Furthermore, a memory-efficient cascaded multi-view stereo network framework, termed TF-MVSNet, is introduced based on the TF-FPN module. This framework employs variance-based cost aggregation and channel reduction to minimize memory overhead without sacrificing accuracy. Extensive evaluation demonstrates that TF-MVSNet frame work achieving superior accuracy in DTU dataset ( $0.313mm$ error) and Tanks and Temples ( $F-score$ : 60.13%). We further show that replacing the baseline feature extractor with TF-FPN in MVSNeRF improves the rendering quality metrics (PSNR: 25.90 27.50 dB) while lowering memory usage. The lightweight design and efficiency make TF-FPN suitable for real-time or resource-constrained 3D reconstruction tasks. Code is available at https://github.com/jjunaid88/TF-MVSNet.git}, archive = {J_ACCESS}, author = {Junaid Jamshid and Zhang Zhen and Xuzhi Wang and Wan Wanggen and Gao Changyu and Xuetao Sun}, doi = {10.1109/ACCESS.2025.3610072}, journal = {IEEE Access}, pages = {162367-162377}, shortjournal = {IEEE Access}, title = {3D reconstruction: A feature fusion and transformer-based feature extraction network for MVSNet}, volume = {13}, year = {2025}, }
• (2025). HADP: Hybrid a*-diffusion planner for robust navigation in dynamic obstacle environments. ACCESS, 13, 162355-162366. (www)

  @article{ , abstract = {Autonomous driving in dynamic environments requires the ability to rapidly perceive changes and make safe, efficient decisions. While numerous studies have explored learning-based planning methods to address this challenge, diffusion-based planning methods have recently emerged as a promising approach in robotic path planning by modeling action sequences as probabilistic trajectories and capturing diverse planning behaviors under uncertainty. However, they still face significant challenges in generalizing to unseen situations and demand extensive data collection to achieve robust performance. In particular, efforts to ensure general robustness by learning from all possible scenarios can introduce inefficiencies, as many cases may be irrelevant during actual deployment. To address these challenges, this study proposes the Hybrid A*-Diffusion Planner (HADP), designed to achieve robust navigation performance even in unseen dynamic environments. In obstacle-free environments, HADP employs the A* algorithm for global path planning, whereas when a dynamic obstacle is detected within a predefined boundary, it records observations and leverages a conditional diffusion model to generate a local avoidance path. After avoidance, the path is re-planned using A*. To enable the diffusion model to generate effective avoidance paths, a semantic map is constructed by incorporating key environmental information within the predefined boundary. This map, along with the robot’s position and local goal information, serves as a conditional input to the diffusion model. HADP is trained on an expert trajectory dataset created by recording expert-driven navigation across various dynamic obstacle avoidance scenarios. Experiments in various simulations in dynamic obstacle environments demonstrated HADP’s superior success rate, trajectory smoothness, and robust generalization performance in unseen environments compared to existing approaches. Real-world tests further confirmed its practical applicability and highlighted its potential as a robust and scalable solution for dynamic obstacle environments, and the simulation code used in this study is publicly available at https://github.com/DSLab-MJU/HADP}, archive = {J_ACCESS}, author = {Mingyu Kim and Chanyeong Heo and Jaehee Jung}, doi = {10.1109/ACCESS.2025.3609454}, journal = {IEEE Access}, pages = {162355-162366}, shortjournal = {IEEE Access}, title = {HADP: Hybrid a*-diffusion planner for robust navigation in dynamic obstacle environments}, volume = {13}, year = {2025}, }
• (2025). Machine learning in prognostics and health management of cyber-physical systems: A review. ACCESS, 13, 162320-162354. (www)

  @article{ , abstract = {The digital transformation of industries within the framework of Industry 4.0 has accelerated the adoption of Cyber-Physical Systems (CPS), which integrate physical processes with computational and communication technologies. While these systems offer numerous advantages in monitoring and control, their complexity creates challenges in ensuring reliability and resilience. To address these challenges, Prognostics and Health Management (PHM) frameworks use modern sensor technologies and machine learning (ML) to anticipate faults, extend asset life, and improve operational efficiency. This paper presents a comprehensive review of ML methodologies applied to PHM within the domain of CPS, while exploring key advancements, existing challenges, and future prospects. A novel taxonomy is proposed to classify existing research based on hardware and software fault types, PHM stages, data characteristics, ML techniques, and performance metrics, with the aim of guiding researchers and practitioners in selecting appropriate ML methods for end-to-end PHM tasks spanning data collection, preprocessing, model development, and validation. The review also identifies emerging trends, including the growing adoption of deep learning techniques such as transformers and large language models. Additionally, it underscores the need for more holistic approaches that address the deeper integration of physical and cyber systems, the complexity of cascading fault scenarios, and the persistent gap between academic research and industrial applications. The findings of this study provide a valuable foundation for advancing PHM strategies and supporting their effective implementation within the evolving landscape of CPS.}, archive = {J_ACCESS}, author = {Pooya Sajjadi and Fateme Dinmohammadi and Mahmood Shafiee}, doi = {10.1109/ACCESS.2025.3605553}, journal = {IEEE Access}, pages = {162320-162354}, shortjournal = {IEEE Access}, title = {Machine learning in prognostics and health management of cyber-physical systems: A review}, volume = {13}, year = {2025}, }
• (2025). Development of a long short-term memory (LSTM)-based statistical model for earthquake forecasting in central asia. ACCESS, 13, 162304-162319. (www)

  @article{ , abstract = {Earthquake forecasting using traditional methods remains a complex task due to the inherent nonlinearity and stochastic nature of seismic activity. Therefore, this study examines the application of deep learning methods, particularly a hybrid model combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to create a robust prediction model that outperforms traditional methods. The study comprises six primary phases: data acquisition, pre-processing, exploratory analysis, seismicity characterization, model implementation, and effectiveness assessment. The dataset contained 17,565 earthquake events ( $M \geq 3.0$ ) in Central Asia, collected from 1973–2024 via the United States Geological Survey (USGS) Application Programming Interface (API), enriched with geological features such as distance to active faults (from Global Earthquake Model (GEM) datasets) and eight key seismicity indicators. Compared with baseline architectures (standard LSTM and fully connected neural network), the proposed CNN-LSTM hybrid achieved superior performance, particularly in recall (0.80 compared with 0.67) and F1-score (0.77 compared with 0.71), demonstrating its ability to capture both spatial and temporal dependencies. The results indicate that using a hybrid CNN-LSTM model leads to a significant improvement in forecasting strong earthquakes. This is validated by a Receiver Operating Characteristic–Area Under the Curve (ROC-AUC) of 0.78 and an F1-score of 0.77, supporting a good balance between model completeness and accuracy. These improvements confirm that the regional adaptation of CNN-LSTM, combined with seismic indicators and clustering-based subregionalization, provides a novel methodological contribution to earthquake forecasting in Central Asia. The model reliably distinguishes periods of elevated seismic risk from quiet intervals, supporting practical applications in regional forecasting and seismic risk visualization.}, archive = {J_ACCESS}, author = {Marat Nurtas and Aizhan Altaibek and Aizhan Ydyrys and Andrey Vilayev and Takhmina Nessipbay}, doi = {10.1109/ACCESS.2025.3610168}, journal = {IEEE Access}, pages = {162304-162319}, shortjournal = {IEEE Access}, title = {Development of a long short-term memory (LSTM)-based statistical model for earthquake forecasting in central asia}, volume = {13}, year = {2025}, }
• (2025). Structuring complex system for digital twin development: A systematic scoping review. ACCESS, 13, 162285-162303. (www)

  @article{ , abstract = {Complex systems, characterised by high interconnectedness and unpredictability, demand structured approaches to support decision-making, system integration, and operational efficiency. This study aims to develop a comprehensive understanding of these systems to facilitate the implementation of Digital Twins (DTs) in Complex Operations Environments (COEs). A Systematic Literature Review (SLR), following PRISMA methodology, is conducted and complemented by a grounded theory approach to identify, organise, and synthesise system characteristics. The result is a hierarchical conceptualisation comprising eight core categories that capture the essential dimensions of complexity, including: Process and System Interoperability, Human Relationships, Behaviour/Nature, Products and Services, Multi-processes, Performance, System Structure, and Management and Skill Requirements. This framework offers a structured foundation for designing and implementing DT-based decision support in COEs.}, archive = {J_ACCESS}, author = {Raziyeh Ghanbarifard and António H. Almeida and Américo Azevedo}, doi = {10.1109/ACCESS.2025.3610067}, journal = {IEEE Access}, pages = {162285-162303}, shortjournal = {IEEE Access}, title = {Structuring complex system for digital twin development: A systematic scoping review}, volume = {13}, year = {2025}, }
• (2025). Topological alternatives for precision and recall in generative models. ACCESS, 13, 162273-162284. (www)

  @article{ , abstract = {We introduce the Normalized Topological Divergence (NTD), a fully differentiable metric that simultaneously quantifies fidelity and diversity of generative models directly in raw pixel or spectrogram space, eliminating reliance on pretrained feature extractors. For two empirical distributions P (model) and Q (reference), NTD builds a Vietoris–Rips filtration over $P \cup Q$ where distance matrix within Q is equal to 0. Extensive experiments on six vision and audio benchmarks: ImageNet-1k, CIFAR-10, MNIST, AFHQv2, AFHQ-Cat, LJSpeech-1, and Gaussian mixtures covering ten generator families show that NTD exposes blur, mode collapse, variance inflation and other generation artifacts. As a result, our metrics are domain-agnostic, provide a precision-recall trade-off, not offered by FID. It represent difference in variance better than density-coverage, TopP&R and P-Precision (Recall) while indicating problems of VAE-like generation more effectively. Moreover, it can be directly optimized by gradient descent algorithms.}, archive = {J_ACCESS}, author = {Anton Dmitriev and Ilya Trofimov and Evgeny Burnaev and Serguei Barannikov}, doi = {10.1109/ACCESS.2025.3598704}, journal = {IEEE Access}, pages = {162273-162284}, shortjournal = {IEEE Access}, title = {Topological alternatives for precision and recall in generative models}, volume = {13}, year = {2025}, }
• (2025). Energy management solution for islanding based on a dynamic neuro-fuzzy-optical microscope algorithm. ACCESS, 13, 162256-162272. (www)

  @article{ , abstract = {Ensuring continuity of service is a primary objective in power systems. In grid-connected microgrids (MGs), islanding poses a significant threat to this continuity. Conventional approaches mitigate islanding by disconnecting the MG immediately after separation from the main grid to prevent overload and ensure safety, but this results in service interruption. This study proposes a dynamic islanding management strategy that maintains uninterrupted service using an optimal dynamic neuro-fuzzy–optical microscope algorithm (OMA). The method integrates a convolutional neural network (CNN), fuzzy logic (FL), and the novel OMA optimizer in a two-stage framework. In the first stage, the CNN detects islanding based on active current and voltage measurements at the point of common coupling (PCC) and their dominant harmonic components, obtained from a hybrid MG model. This model is comprising solar panels, wind turbines, a biomass generator, and a storage system. Signal and image processing techniques prepare the measurements for CNN implementation. Upon islanding detection, the second stage is activated, where FL predicts the penalty factor and OMA optimally manages economic power sharing between the grid and the MG. This integration enables safe load coverage without damaging MG components. Performance benchmarking against Quadratic Interpolation Optimization (QIO) and Hunger Games Optimizer (HGO) demonstrates that OMA achieves higher accuracy, faster convergence, and lower execution time. Validation across five scenarios under normal, islanding, and risky operating conditions confirms the method’s effectiveness, reliability, and economic benefits, achieving a 223.7% revenue improvement over the baseline with the shortest execution time. The proposed approach offers a robust and intelligent solution to the islanding problem, ensuring continuous and cost-effective microgrid operation.}, archive = {J_ACCESS}, author = {Ahmed Osama and Dalia Allam and Ahmed F. Zobaa and Magdy B. Eteiba}, doi = {10.1109/ACCESS.2025.3610524}, journal = {IEEE Access}, pages = {162256-162272}, shortjournal = {IEEE Access}, title = {Energy management solution for islanding based on a dynamic neuro-fuzzy-optical microscope algorithm}, volume = {13}, year = {2025}, }
• (2025). A slow-light mach-zehnder interferometric switch with one-dimensional grating waveguides. ACCESS, 13, 162249-162255. (www)

  @article{ , abstract = {Optical switches are essential components of all-optical switching networks, enabling optical path switching in the optical domain without the need for electronic conversion. However, the design of optical switches with low-power and ultra-compact footprints remains a significant challenge. In this work, a slow-light thermo-optic (TO) Mach-Zehnder interferometer (MZI) switch based on one-dimensional (1D) grating waveguides is proposed. The proposed structure, with a compact footprint of $72~\mu $ m, exhibited both strong and weak slow-light regions. In the strong-slow-light region, the structure achieved a high group index ( $n_{g}$ ) at a wavelength of 1539.8 nm, with a power consumption of 3.9 mW. Different from conventional designs that rely solely on strong-slow-light effects near the bandgap edge, our approach utilizes the weak-slow-light region centered around 1550 nm. This enables a broader operational bandwidth (~109.5 nm) while maintaining a low switching power of 8.73 mW. The integrated structure presented in this work is promising for building ultra-compact and broadband photonic networks.}, archive = {J_ACCESS}, author = {Hong Gao and Fujun Sun and Gang Yang and Tianyang Fu and Zaili Yang and Jing Xiao and Wei Tang and Yan Yang}, doi = {10.1109/ACCESS.2025.3608012}, journal = {IEEE Access}, pages = {162249-162255}, shortjournal = {IEEE Access}, title = {A slow-light mach-zehnder interferometric switch with one-dimensional grating waveguides}, volume = {13}, year = {2025}, }
• (2025). Accurate detection algorithm of diaphorina citri based on YOLOv10s-C2M. ACCESS, 13, 162238-162248. (www)

  @article{ , abstract = {Huanglong disease is a devastating disease that seriously threatens the safety of the citrus industry, and the accurate detection of Diaphorina Citri, as its main vector, is crucial for prevention and control. Aiming at the challenges of Diaphorina Citri, such as small size, easy background interference, complex orchard environment, and limited resources for deploying equipment, this paper proposes a novel lightweight composite attention module, MBCAM, and innovatively integrates it with the SE attention mechanism and the adaptive downsampling module (ADown), and introduces it into the YOLOv10s architecture, to construct a set of lightweight and highly efficient Diaphorina Citri detection model, YOLOv10s-C2M. Compared with the benchmark model, YOLOv10s-C2M has improved inference speed by 25.50%, reaching 3.8ms, while achieving a significant breakthrough in accuracy, mAP@0.5 improved by 4.53%, reaching 89.77%.This method balances detection accuracy and deployment efficiency, demonstrating good adaptability and practicality in actual citrus orchard scenarios. This study not only provides technical support for the intelligent monitoring of Diaphorina Citri, but also provides new ideas for the design of lightweight pest detection models, which has important agricultural intelligent application value and promotion prospects.}, archive = {J_ACCESS}, author = {Yang Chen}, doi = {10.1109/ACCESS.2025.3609839}, journal = {IEEE Access}, pages = {162238-162248}, shortjournal = {IEEE Access}, title = {Accurate detection algorithm of diaphorina citri based on YOLOv10s-C2M}, volume = {13}, year = {2025}, }
• (2025). Real-time multimodal defect detection and MES feedback on edge devices via bayesian fusion and causal adaptation. ACCESS, 13, 162226-162237. (www)

  @article{ , abstract = {Conventional single-modality or loosely coupled inspection systems often degrade under partial occlusion, transient sensor dropout, or illumination drift, and their feedback to Manufacturing Execution Systems (MES) can be delayed or non-deterministic. We present D3Lite-MES, a real-time multimodal defect detection framework that i) aligns heterogeneous sensors at both hardware and software levels, ii) fuses them via a Bayesian–Causal Weighted Fusion (BCWF) module, and iii) adapts model parameters on edge devices through a lightweight causal adaptation routine. Deployed on commodity edge hardware, the system sustains line-takt operation and writes verified defect events back to MES with bounded latency. On three production lines covering [product families anonymized], D3Lite-MES attains a mean F1 of 98.2% and mAP@0.5 of 97.6%, reducing false alarms by 98.2% compared with lightweight baselines, at an end-to-end latency of 34 ms and average power of 8.9 W. Ablations show that BCWF contributes + 1.7% F1 under partial observations, while causal adaptation yields + 1.7% under illumination drift. The MES feedback loop decreases manual triage time by 34% and shortens recovery from minor stops by 34%.}, archive = {J_ACCESS}, author = {Yong Lin and Wendi Lin and Yadi Lin}, doi = {10.1109/ACCESS.2025.3610341}, journal = {IEEE Access}, pages = {162226-162237}, shortjournal = {IEEE Access}, title = {Real-time multimodal defect detection and MES feedback on edge devices via bayesian fusion and causal adaptation}, volume = {13}, year = {2025}, }
• (2025). Enhancing marketing strategies through deep learning: A computational approach. ACCESS, 13, 162204-162225. (www)

  @article{ , abstract = {In an increasingly digitized marketplace, traditional marketing models struggle to capture the complexity of evolving consumer behaviors and multi-channel interactions. To address these limitations, this study introduces a novel computational framework that combines probabilistic modeling with dynamic decision-making to enhance marketing analytics. Central to this approach is the proposed Stochastic Market Graph Embedding Model (SMGEM), which captures both latent consumer states and their behavioral responses to marketing stimuli over time. By embedding consumer interactions within a dynamic graph structure, SMGEM effectively models short-term responsiveness and long-term engagement patterns, while accounting for exogenous market influences and peer effects. To operationalize this model in real-time environments, we implement the Adaptive Market Response Integration (AMRI) strategy. AMRI leverages reinforcement learning principles and streaming data assimilation to continuously update marketing policies, enabling context-aware and personalized interventions. The strategic framework integrates structural constraints such as budget limits, marketing fatigue, and coordinated targeting, ensuring that campaign actions are not only effective but also interpretable and aligned with organizational goals. Experimental results across four benchmark datasets—spanning segmentation, influence modeling, ROI attribution, and brand perception—demonstrate that the proposed framework outperforms several state-of-the-art deep learning models in both predictive accuracy and robustness. The modular design of the system also supports explainability and flexible deployment in real-world marketing infrastructures. Together, SMGEM and AMRI represent a significant step toward intelligent, scalable, and responsive marketing systems that align with the broader goals of computational and data-driven decision-making. Despite its architectural complexity, the framework maintains low-latency inference and high throughput, making it feasible for real-time deployment with measurable improvements in campaign effectiveness.}, archive = {J_ACCESS}, author = {Xiao Sun}, doi = {10.1109/ACCESS.2025.3606862}, journal = {IEEE Access}, pages = {162204-162225}, shortjournal = {IEEE Access}, title = {Enhancing marketing strategies through deep learning: A computational approach}, volume = {13}, year = {2025}, }
• (2025). A dual-path self-supervised framework for polyp segmentation using vision transformers and CNNs. ACCESS, 13, 162191-162203. (www)

  @article{ , abstract = {In the field of medical image analysis, accurate polyp segmentation remains a significant challenge due to the limited availability of labeled training data. This paper introduces a novel approach that addresses this issue by leveraging a self-supervised learning framework combined with deep supervision and a dual path architecture of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs). Our proposed model consists of two paths: Path 1 employs Vision Transformer blocks, with the initial stage utilizing a pre-trained backbone, while subsequent stages further refine these features. Path 2 incorporates self-supervised learning via the Barlow Twins method to capture global context, followed by ResNet blocks and attention-augmented convolution blocks to extract and enhance local features. The outputs from both paths are fused using a cross-attention module, concatenated, and processed through multiple MLP heads. To enhance the segmentation accuracy, deep supervision is applied at multiple decoder stages, utilizing skip connections from the MobileNetV2 backbone. Additionally, we leverage a modified Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic polyp images for Barlow Twins pre-training. Experimental results show that our approach outperforms existing methods in polyp segmentation, achieving higher accuracy and robustness in challenging scenarios. Specifically, our model achieves an average Dice score of 94% and an average Intersection over Union (IoU) of 89%, significantly surpassing state-of-the-art methods.}, archive = {J_ACCESS}, author = {P. Lijin and Madhu S. Nair}, doi = {10.1109/ACCESS.2025.3610453}, journal = {IEEE Access}, pages = {162191-162203}, shortjournal = {IEEE Access}, title = {A dual-path self-supervised framework for polyp segmentation using vision transformers and CNNs}, volume = {13}, year = {2025}, }
• (2025). Feasibility study of a thruster-based wearable robot for aquatic knee exercise. ACCESS, 13, 162176-162190. (www)

  @article{ , abstract = {Aquatic therapy exercises utilize buoyancy and hydrodynamic resistance forces, providing a safe and effective exercise environment for participants, particularly elderly individuals, those with obesity, and patients undergoing rehabilitation. However, conventional aquatic exercise tools, such as buoyant aids and rigid fins, offer limited capacity for providing active assistance or resistance. To address these challenges, this study proposes a wearable robotic device for aquatic lower-limb exercises, equipped with a bidirectional thruster. The robot measures knee joint angles in real time and controls the thrust moment to enable various exercise modes, including passive exercises such as stretching and continuous-passive-motion (CPM), as well as active exercises with assistance and resistance modes. To evaluate the performance of the proposed system, a dynamic model was derived, and its parameters and relationships were experimentally identified. A prototype was subsequently fabricated and attached to a motor-driven lower-leg mock-up. Control experiments were then conducted underwater across the proposed exercise modes to assess the feasibility of the proposed exercise mode. These results verify the feasibility of closed-loop, thruster-based assistance and resistance for lower-limb aquatic exercise, laying the groundwork for forthcoming human-subject trials aimed at elderly and post-operative populations.}, archive = {J_ACCESS}, author = {Jihoon Yoon and Jungho Kim and Chang-Hyung Lee and Junghan Kwon}, doi = {10.1109/ACCESS.2025.3610130}, journal = {IEEE Access}, pages = {162176-162190}, shortjournal = {IEEE Access}, title = {Feasibility study of a thruster-based wearable robot for aquatic knee exercise}, volume = {13}, year = {2025}, }
• (2025). Magnetocardiography detection and source localization from multiple sources using magnetoimpedance principle in controlled environment. ACCESS, 13, 162156-162175. (www)

  @article{ , abstract = {Recent advances in non-invasive cardiac imaging techniques have highlighted the potential of Magnetocardiography (MCG) as a powerful diagnostic tool. However, existing approaches face significant challenges in accurately localizing and characterizing cardiac electrical sources. This study presents an innovative methodology for cardiac source localization using magnetocardiography based on the magnetoimpedance principle, addressing these limitations through an experimental framework and sophisticated computational approaches. This work has established a novel lead field matrix computation methodology that shows the relationship between source currents and resulting magnetic fields. The forward problem was solved using known source configurations and validated against experimental measurements. For the inverse problem, three mathematical approaches were implemented: Minimum Norm Estimation (MNE), Empirical covariance, and Gaussian Process Variance methods. Results demonstrate that the overall experimental setup successfully replicates key aspects of cardiac electromagnetic activity, with the MI sensor array providing sufficient spatial and temporal resolution for accurate source localization. The novel lead field computation method significantly improved source reconstruction accuracy compared to conventional approaches. This work establishes a foundation for cost-effective high-sensitivity magnetocardiographic systems that could enhance clinical cardiac diagnostics without the need for magnetically shielded environments.}, archive = {J_ACCESS}, author = {K. V. Anu and Vikas R. Bhat and H. Reshma and H. Anitha}, doi = {10.1109/ACCESS.2025.3608853}, journal = {IEEE Access}, pages = {162156-162175}, shortjournal = {IEEE Access}, title = {Magnetocardiography detection and source localization from multiple sources using magnetoimpedance principle in controlled environment}, volume = {13}, year = {2025}, }
• (2025). Aesthetic evaluation of visual perception in AI-generated short films via integrated support vector machine and attention mechanism. ACCESS, 13, 162144-162155. (www)

  @article{ , abstract = {With the rise of AI-generated content in creative fields like film and animation, assessing its visual perception quality has become a major challenge. Traditional evaluation methods often fall short. This study presents ZCASNet, a new hybrid model for automated evaluation of AI-generated short films based on several significant indicators like visual coherence, scene rationality, detail reality, temporal consistency. In ZCASNet framework, Convolutional Neural Networks (CNN), ResNet50, is applied to extract spatial features from video frames. Multi-Head Attention (MHA) captures semantic and temporal relationships across frames. The Zebra Optimization Algorithm (ZOA) enhances the CNN-MHA architecture by refining hyperparameters to improve its performance. Subsequently, a Support Vector Machine (SVM) is employed to classify the optimized features. Our experiments show that ZCASNet outperforms baseline models, including CNN-only, CNN with MHA, Transformer models, and standard SVM. ZCASNet achieves an accuracy of 92.4%, demonstrating notable performance. Ablation studies confirm the importance of the RBF kernel function and each module in the model architecture. Overall, ZCASNet offers an effective and interpretable solution for evaluation in AI-generated video. It has potential uses in content recommendation, AI-assisted filmmaking, and quality control in multimedia applications.}, archive = {J_ACCESS}, author = {Yihuan Tian and Sunjung Lee}, doi = {10.1109/ACCESS.2025.3607486}, journal = {IEEE Access}, pages = {162144-162155}, shortjournal = {IEEE Access}, title = {Aesthetic evaluation of visual perception in AI-generated short films via integrated support vector machine and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). SDARL: Safe deep adaptive representation learning for high-DoF non-linear system manipulation in space. ACCESS, 13, 162129-162143. (www)

  @article{ , abstract = {Space robotic manipulation, particularly satellite servicing and repairing missions under uncertain and dynamically evolving orbital conditions has posed significant challenges in recent years due to inherent uncertainties and environmental disturbances. Precise and safe control of high degree-of-freedom (DoF) nonlinear robotic systems remains a significant challenge, particularly in uncertain and dynamic space environments. To address this, we introduce a novel deep adaptive control framework. Our approach integrates representation learning with a model predictive control (MPC) architecture to achieve robust and precise manipulation. The core contribution of our approach lies in the hierarchical architecture that incorporates deep representation learning with rigorous control-theoretic principles, enabling rapid online adaptation to unforeseen disturbances and environment-dependent nonlinearities. Motivated by the complexities encountered in robotic satellite manipulation tasks, our method learns shared representations that generalize across varying operational conditions to facilitate robust and efficient adaptation during manipulation. We rigorously integrate deep online representation learning methods with MPC, achieving a unified approach that provides both theoretical performance guarantees and practical control effectiveness to keep the whole system safe during the manipulation process. We provide rigorous theoretical analyses establishing explicit non-asymptotic convergence and stability guarantees, ensuring safe and reliable operation. Extensive simulations validate the effectiveness and superiority of our method, demonstrating significant improvements in trajectory tracking accuracy, disturbance rejection capability, and overall system stability compared to existing state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Ye Zhang and Rui Liu and Qi Chen and Kangtong Mo and Linyue Chu and Zhengjian Kang}, doi = {10.1109/ACCESS.2025.3602449}, journal = {IEEE Access}, pages = {162129-162143}, shortjournal = {IEEE Access}, title = {SDARL: Safe deep adaptive representation learning for high-DoF non-linear system manipulation in space}, volume = {13}, year = {2025}, }
• (2025). Enhanced flatfoot classification with transformer encoders and bayesian optimized SVM parameters. ACCESS, 13, 162106-162128. (www)

  @article{ , abstract = {Early detection of diseases allows for timely interventions that can reduce the risk of severe health complications; using Machine Learning (ML) tools to support medical specialists aids in early detection. Flatfoot is a musculoskeletal disorder that affects the biomechanical structure of the foot and the distribution of pressure while walking or running, often resulting in pain or injuries in the lower extremities. This work presents a methodology for classifying foot types using ML algorithms, requiring minimal information to be effective. Ten algorithms were trained on a dataset of 626 images and evaluated using a test set of 268 images. Hyper-parameter tuning of ten algorithms is achieved through Bayesian Optimization (BO), utilizing a descriptor based on the attention mechanism of a vision transformer. The results obtained by optimizing the Radial Basis Function Support Vector Machine (RBF-SVM) with BO are promising for flatfoot disease, achieving an accuracy greater than 95% in foot type classification. The methodology is also implemented in biomedical datasets to compare the results obtained with Flatfoot and define a methodology that provides good results for the type of flatfoot images analyzed.}, archive = {J_ACCESS}, author = {Jorge Ramos-Frutos and Oscar Ramos-Soto and Israel Miguel-Andrés and Diego Oliva and Omar Alvarez and Sandra E. Balderas-Mata and Marco Pérez-Cisneros and Francisco J. Alvarez-Padilla}, doi = {10.1109/ACCESS.2025.3609244}, journal = {IEEE Access}, pages = {162106-162128}, shortjournal = {IEEE Access}, title = {Enhanced flatfoot classification with transformer encoders and bayesian optimized SVM parameters}, volume = {13}, year = {2025}, }
• (2025). CoGait WalkAssist: Design and evaluation of a motor-free lower-limb rehabilitation robot for natural gait training on treadmills. ACCESS, 13, 162092-162105. (www)

  @article{ , abstract = {With the rising prevalence of lower-limb mobility impairments caused by stroke, neurological disorders, and accidents, there is an urgent need for innovative, cost-effective rehabilitation solutions. This paper presents the design, development, and experimental validation of CoGait, a novel gait rehabilitation robot that employs single-degree-of-freedom (DOF) multi-link mechanisms to enable natural walking patterns on commercial treadmills without independent actuators. CoGait transfers power from the treadmill to its six-link mechanisms, facilitating rehabilitation at low speeds (e.g., 0.4 m/s), which are critical for therapeutic efficacy. Key innovations include: reduced mechanical complexity (fewer linkages) compared to existing systems; ankle trajectory synthesis that accounts for both position and time, closely mimicking natural gait; self-synchronization with the treadmill, eliminating the need for external motor and control; enhanced safety and adjustability, with passive mechanisms for out-of-sagittal-plane motion and adaptability to patient anthropometrics. Functional experiments with healthy participants demonstrated that CoGait generates joint angle profiles closely resembling those of natural walking, achieved through low movement error metrics. The system recorded a root mean square error (RMSE) of 2.9° for the hip and 4.9° for the knee, alongside high correlation coefficients (CC) of 86.2% for the hip and 95.3% for the knee. These results indicate that CoGait is a promising, low-cost solution for lower-limb rehabilitation, particularly for elderly and mobility-impaired patients, and lays a strong foundation for future research in passive-assistive robotics.}, archive = {J_ACCESS}, author = {Mehdi Bakhtiari and Mohammad Reza Haghjoo and Borhan Beigzadeh and Mostafa Taghizadeh}, doi = {10.1109/ACCESS.2025.3610525}, journal = {IEEE Access}, pages = {162092-162105}, shortjournal = {IEEE Access}, title = {CoGait WalkAssist: Design and evaluation of a motor-free lower-limb rehabilitation robot for natural gait training on treadmills}, volume = {13}, year = {2025}, }
• (2025). A practical method for wideband matching of antennas based on filter design techniques. ACCESS, 13, 162072-162091. (www)

  @article{ , abstract = {This article shows several practical improvements on impedance matching methods based on modern filter network theory. New conclusions about matching have been obtained using the Bode-Fano limit, adding frequency-independent reactances (FIRs) to the formulation for the first time. Moreover, a matching limit can be obtained analytically before building the network, thus providing a performance limit in advance. A lumped network is obtained analytically as a first attempt with the reflection coefficient bounded between two limits with equiripple behavior for a maximum bandwidth. Then, this network model is modified by adding frequency-independent reactances (FIRs) to provide a better match even for asymmetric responses. The lumped network is converted into a distributed network where the dispersion of the lines is accounted for. The next improvement is the conversion into an electromagnetic model, where dispersion is also considered in the inverters modeled with irises. Three examples with increasing bandwidths are considered, and a manufactured prototype is measured to test the good behavior of the proposed matching method.}, archive = {J_ACCESS}, author = {Santiago Cogollos and Hector Esteban and Vicente E. Boria and Darío Herraiz and Angel Belenguer}, doi = {10.1109/ACCESS.2025.3610363}, journal = {IEEE Access}, pages = {162072-162091}, shortjournal = {IEEE Access}, title = {A practical method for wideband matching of antennas based on filter design techniques}, volume = {13}, year = {2025}, }
• (2025). Improving the lifetime of UWSN using hybrid PSO-EULC algorithm. ACCESS, 13, 162057-162071. (www)

  @article{ , abstract = {Underwater wireless sensor networks (UWSNs) are commonly used for data transmission underwater, detecting volcanic movements, and detecting water pollution. A UWSN is made up of acoustic sensors powered by batteries. Replacement of these batteries is complicated because of the complex underwater conditions of UWSNs. One strategy for fixing this issue is to extend the lifetime of UWSNs by decreasing the energy they spend and enhancing their energy usage. This research proposes a set of rules based on Energy-balanced unequal layering clustering (EULC) and PSO (Particle swarm optimization) that enhances the performance of the lifetime of UWSNs. The proposed hybrid PSO-EULC combination produces UWSNs with different layering based entirely on node depth, with Cluster head (CH) nodes chosen using the PSO cost function. The hybrid PSO-EULC combination successfully balances the energy used in UWSN nodes, thus extending sensor node lifetime, packet transmission cost, and network lifetime. The results are compared with Energy Efficient Unequal Multilevel Clustering (EEUMC), Energy-balanced reliable and effective clustering (EBREC), and LEACH with parameters such as residual energy, dead nodes, and total packets received at different initial power levels of 1J,0.5J for 6400 bits, and 4000 bits of data transmission. As per case 4 the residual energy of 44.4% improved, dead nodes 15% less, and 50.25% of received packets is achieved by comparing it with the EEUMC algorithm.}, archive = {J_ACCESS}, author = {J. Murali and T. Shankar}, doi = {10.1109/ACCESS.2025.3609455}, journal = {IEEE Access}, pages = {162057-162071}, shortjournal = {IEEE Access}, title = {Improving the lifetime of UWSN using hybrid PSO-EULC algorithm}, volume = {13}, year = {2025}, }
• (2025). Flexible dual-band ultrathin frequency selective surface with polarization independence, angular stability, and bending insensitivity. ACCESS, 13, 162048-162056. (www)

  @article{ , abstract = {This paper introduces a novel ultrathin frequency selective surface (FSS) based on interconnected logarithmic spiral geometries. The structure is fabricated on a flexible polyimide substrate with a thickness of 0.05 mm, a relative permittivity of 4.3, and a low-loss tangent of 0.004. The unit cell design follows a pursuit curve forming a logarithmic spiral, with four spirals symmetrically connected at the center. Our proposed FSS exhibits stopband resonances at 3.5 GHz and 9.2 GHz, with bandwidths of 680 MHz and 1.8 GHz, respectively. Owing to the ultrathin substrate, the overall thickness is just 0.05 mm ( $0.0005\lambda _{0}$ , where $\lambda _{0}$ is the wavelength at the lower resonance). Simulated results show excellent agreement with experimental measurements, confirming the accuracy of the design. Moreover, the FSS demonstrates polarization independence, angular stability up to 45° for both vertical and horizontal polarizations, and maintains performance under bending, highlighting its mechanical and electromagnetic robustness.}, archive = {J_ACCESS}, author = {Maciel Alves de Oliveira and Ruann Víctor de Andrade Lira and Alfredo Gomes Neto and Juliete da Silva Souza and Antonio Luiz Pereira de Siqueira Campos and Alexandre Jean René Serres}, doi = {10.1109/ACCESS.2025.3610353}, journal = {IEEE Access}, pages = {162048-162056}, shortjournal = {IEEE Access}, title = {Flexible dual-band ultrathin frequency selective surface with polarization independence, angular stability, and bending insensitivity}, volume = {13}, year = {2025}, }
• (2025). Usability evaluation methods and life skills for individuals with autism spectrum disorder: A systematic literature review. ACCESS, 13, 162027-162047. (www)

  @article{ , abstract = {Human Computer Interaction (HCI) technologies are increasingly used to support individuals with Autism Spectrum Disorder (ASD), yet their usability and effectiveness remain underexplored. Individuals diagnosed with autism frequently engage with software applications; however, many fail to address core challenges outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), particularly in communication, social interaction, and behavior. This systematic literature review (SLR) examines usability evaluation methods (UEMs) and their role in enhancing life skills for individuals with ASD, focusing on these three domains. This study employs the Tollgate approach to establish clear inclusion and exclusion criteria. A comprehensive search across five major databases (ACM Digital Library, Springer Link, IEEE Xplore, ScienceDirect, and Google Scholar) yielded 3,436 articles, of which 65 were selected for analysis. Findings reveal that questionnaires are the most commonly used UEMs (24.18%), with a stronger emphasis on social interaction and communication than on other ASD-related challenges. This SLR offers a systematic foundation for designing user-centered applications that improve social, communication, and behavioral life skills in individuals with ASD.}, archive = {J_ACCESS}, author = {Hamid Ullah and Sadaqat Jan and Jawad Khan and Fahad Alturise}, doi = {10.1109/ACCESS.2025.3607021}, journal = {IEEE Access}, pages = {162027-162047}, shortjournal = {IEEE Access}, title = {Usability evaluation methods and life skills for individuals with autism spectrum disorder: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Evaluation of machine learning and deep learning models for fake news detection in arabic headlines. ACCESS, 13, 162009-162026. (www)

  @article{ , abstract = {The headline plays a pivotal role in news content, designed to capture attention and provoke curiosity, thereby encouraging readers to engage with the full article or share it on social media. However, social media platforms have increasingly facilitated the rapid spread of misinformation, with misleading headlines often serving as vectors for fake news. Traditional methods, such as manual fact-checking, are inadequate in addressing the scale and speed of misinformation dissemination. This study investigates computational approaches for fake news detection in Arabic headlines using machine learning, deep learning, and transformer-based models specifically pre-trained for the Arabic language. We utilize two benchmark datasets: the AFND dataset—a large-scale collection of Arabic news headlines—and the Arabic News Stance (ANS) dataset, a smaller, unbalanced corpus of annotated claims. Transformer-based models demonstrated superior performance across both datasets. AraBERTv02 achieved the highest accuracy on the large-scale AFND dataset at 70.41%, while AraELECTRA reached 77% accuracy on the smaller ANS dataset. These results highlight the robustness and effectiveness of transformer architectures in capturing nuanced linguistic features relevant to fake news detection in Arabic. Additionally, our experiments reveal that preprocessing choices—such as the application of stemming—impact performance differently across model types, emphasizing the need for tailored preprocessing strategies. This study contributes to the growing body of research in Arabic natural language processing and offers practical insights to improve automated detection of fake news in Arabic media.}, archive = {J_ACCESS}, author = {Eman Salamah Albtoush and Keng Hoon Gan and Saif Addeen Ahmad ALrababah}, doi = {10.1109/ACCESS.2025.3606114}, journal = {IEEE Access}, pages = {162009-162026}, shortjournal = {IEEE Access}, title = {Evaluation of machine learning and deep learning models for fake news detection in arabic headlines}, volume = {13}, year = {2025}, }
• (2025). Multi-dataset training strategy for robust polyp detection with clinical validation insights. ACCESS, 13, 162000-162008. (www)

  @article{ , abstract = {Colorectal (CRC) represents one of the leading global causes of cancer-related deaths since polyps function as original precursors to cancer development. The identification of colorectal polyps at an early stage directly contributes to lowering CRC incidence while giving patients improved survival outcomes. The proposed research utilizes YOLOv8m through YOLOv11m state-of-the-art YOLO architectures as an advanced framework to enhance real-time detection and classification of polyps. The research used four different colorectal database sets including Kvasir-SEG and CVC-300, CVC-ClinicDB, CVC-ColonDB for training and validating the models across a wide spectrum of polyp image variations. Following 300 training epochs YOLOv8m achieved the best performance with precision at 92.4% and recall at 85.4% while reaching an F1-Score of 88.7% and mAP@0.5 at 91.1%. The testing of YOLOv8m’s generalization skills included an external validation on ETIS-LaribPolypDB where it demonstrated reliable detection performance and high accuracy levels. YOLOv8m demonstrates a strong capability for real-time polyp detection in endoscopic examinations through these performance results which showcase its potential to enhance diagnostic support.}, archive = {J_ACCESS}, author = {Alben Mert and Ali Hakan Büyüklü and Bülent Bayram and Semra Erpolat Taşabat}, doi = {10.1109/ACCESS.2025.3608668}, journal = {IEEE Access}, pages = {162000-162008}, shortjournal = {IEEE Access}, title = {Multi-dataset training strategy for robust polyp detection with clinical validation insights}, volume = {13}, year = {2025}, }
• (2025). Neural-semantic prediction of prenatal EPDS subscale 3A categories using socio-demographic features. ACCESS, 13, 161989-161999. (www)

  @article{ , abstract = {In this work we demonstrate the use of data-driven Machine learning approaches to predict prenatal anxiety. We rely on a large dataset of Edinburgh Postnatal Depression Scale (EPDS) scores compiled from women in 9 hospitals within Kampala, Uganda between January 2022 and April 2022. A subset of the EPDS scale, the EPDS subscale 3A, can be employed to measure anxiety. Respondents who score 5 and above on this subscale are flagged as possibly anxious whereas those below this cut-off are considered normal. We show that given enough data it may be possible to use socio-demographic markers to predict EPDS subscale 3A categories. In addition, we introduce a lexical-semantic method for handling socio-demographic features allowing us to reformulate the problem as a text classification problem and obtain markedly higher recall (sensitivity) performance with deep neural networks.}, archive = {J_ACCESS}, author = {Mark Abraham Magumba and Hasifah Kasujja and Tonny Justus Oyana}, doi = {10.1109/ACCESS.2025.3609552}, journal = {IEEE Access}, pages = {161989-161999}, shortjournal = {IEEE Access}, title = {Neural-semantic prediction of prenatal EPDS subscale 3A categories using socio-demographic features}, volume = {13}, year = {2025}, }
• (2025). A guaranteed global minimum mobile robots navigation based on khatib’s potential function. ACCESS, 13, 161973-161988. (www)

  @article{ , abstract = {The Artificial Potential Field (APF) methodology, first proposed by Khatib, has garnered significant attention as a prevalent technique for the navigation of mobile robots, attributed to its inherent simplicity and capacity for real-time application. This approach directs robots through the utilization of virtual attractive and repulsive forces; however, it is not without significant drawbacks, including the emergence of local minima, particularly the struggle of reaching the goal when confronted by close obstacles. Such challenges can result in the robot becoming ensnared or displaying oscillatory dynamics in proximity to obstacles. This manuscript introduces an amended potential function that mitigates these shortcomings while preserving computational efficiency. The proposed APF incorporates corrective terms that reformulate the potential landscape, thereby effectively abolishing undesirable local minima and guaranteeing convergence towards the goal, even when situated close to obstacles. Two control strategies, namely static and dynamic control laws, are proposed in conjunction with the revised APF, incorporating linear damping to enhance stability. A thorough mathematical analysis is provided, demonstrating the stability and efficacy of the suggested solution. Simulations conducted within complex environments reveal that the new function engenders smooth and successful trajectories, surpassing the original APF methodology in terms of dependability and robustness. The findings underscore the appropriateness of the proposed function for real-time navigation endeavors within dynamic and cluttered settings. This research contributes to the progression of autonomous navigation by improving the reliability of path planning based on potential fields.}, archive = {J_ACCESS}, author = {Adha Imam Cahyadi and Addy Wahyudie}, doi = {10.1109/ACCESS.2025.3609816}, journal = {IEEE Access}, pages = {161973-161988}, shortjournal = {IEEE Access}, title = {A guaranteed global minimum mobile robots navigation based on khatib’s potential function}, volume = {13}, year = {2025}, }
• (2025). Document re-ranking with evidential neural networks. ACCESS, 13, 161964-161972. (www)

  @article{ , abstract = {In multi-step Retrieval-Augmented Generation (RAG) systems for Question Answering (QA), the re-ranking phase following initial fast document retrieval is critical in determining the final answer quality by providing the most relevant context passages. This step typically relies on the ranking of candidate documents using deep neural network classification models that assign relevance scores based on their prediction probabilities. However, conventional Deep Neural Network (DNN) classifiers often generate poorly calibrated probability estimates when minimizing prediction loss. This miscalibration leads to inaccurate document rankings, undermining the reliability of the re-ranking stage. To address this issue, we propose the Evidential Document Re-Ranking (EDRR) model, which leverages Evidential Deep Learning (EDL) to better calibrate the prediction probabilities to true accuracies and thus provide better ranking performances. EDRR enables better calibration by simply modeling the full posterior distribution over class probabilities, which alleviates the overconfidence problem observed in DNN models. In addition, this approach facilitates active learning by identifying uncertain predictions, allowing for more effective sampling of diverse or underrepresented instances. Experimental results on the Wikipedia-NQ, MS MARCO, and HotpotQA datasets show that EDRR consistently outperforms Cross-Encoder, ColBERT, and Dense Passage Retrieval, achieving the highest mAP@10 scores across all three benchmarks. This demonstrates that incorporating well-calibrated uncertainty estimates can meaningfully enhance retrieval performance in the QA systems.}, archive = {J_ACCESS}, author = {Jeongnoh Yoon and Lee Sael}, doi = {10.1109/ACCESS.2025.3609194}, journal = {IEEE Access}, pages = {161964-161972}, shortjournal = {IEEE Access}, title = {Document re-ranking with evidential neural networks}, volume = {13}, year = {2025}, }
• (2025). Design of a novel active gate drive circuit to enhance current sharing in parallel-connected MOSFETs for low-voltage inverter applications. ACCESS, 13, 161947-161963. (www)

  @article{ , abstract = {Low-voltage systems offer advantages in terms of safety, cost-effectiveness, and ease of integration, making them a preferred choice for e-scooters, light electric vehicles (LEVs), and various small-scale and urban mobility applications. In such drive systems, low-voltage inverters powered by batteries operate under very high currents. To increase the output current capacity of the inverters and achieve higher power levels, the use of parallel-connected MOSFETs has become both important and widespread. Extensive investigations and studies have been conducted to examine the main causes of current imbalance in semiconductors, particularly the effects of parasitic factors originating from printed circuit boards (PCBs) and parameter variations due to the manufacturing processes of semiconductor switches on current distribution. Existing active and passive gate driver circuits in the literature were reviewed, and a new control method based on temperature and current feedback was developed to mitigate these effects. Within this scope, a hybrid active gate drive (AGD) circuit was proposed and designed to improve current sharing among parallel-connected MOSFETs. According to the modeling and analysis results, when the AGD is activated, current imbalance decreases from 25% to 5%. To experimentally test the proposed method, a PCB was designed and tested in a laboratory environment using the double pulse test (DPT) method. According to the DPT results, the current difference observed in the second switching event decreases from approximately 17% to 3% when the algorithm is activated. Based on the results obtained, the proposed gate driver has been shown to be effective in improving current sharing, reducing stress and power losses in MOSFETs, and enhancing thermal management and reliability.}, archive = {J_ACCESS}, author = {Berkay Keziban and Murat Yilmaz}, doi = {10.1109/ACCESS.2025.3610144}, journal = {IEEE Access}, pages = {161947-161963}, shortjournal = {IEEE Access}, title = {Design of a novel active gate drive circuit to enhance current sharing in parallel-connected MOSFETs for low-voltage inverter applications}, volume = {13}, year = {2025}, }
• (2025). On second-order fading statistics of multi-antenna 3-D vehicular communication channels. ACCESS, 13, 161923-161946. (www)

  @article{ , abstract = {Incorporating systems that have multiple antennas with an inherent capability of reconfigurable beam patterns can enhance vehicle-to-vehicle (V2V) communications in 6G networks by taking advantage of favorable channel conditions. To achieve the full potential of multiple antennas, it is of paramount importance to accurately express the relationship between the received signal envelope and the underlying 3-D spatio-temporal configuration of the channel. We propose an improved 3-D spatio-temporal configuration of V2V radio propagation channels and then provide detailed analytical insights into the fading characteristics of the received signal envelope. Although, first-order statistics describe the distribution of received signal amplitudes but do not capture their variation over time. In contrast, second-order statistics such as level crossing rate (LCR) and average fade duration (AFD) show how fades occur and persist over time. In this context, we analyze the impact of different system and channel parameters (such as mobility condition and composition of scattering region) on the second-order fading statistics of the received signal envelope by exploiting the theory of 3-D multipath shape factors. Further, we investigate the impact of notable physical and geometric channel parameters on angular, temporal, and envelop fading statistics. Finally, we validate the expressions derived by comparing the Monte Carlo simulation results with empirical datasets available in the literature and demonstrate the accuracy and applicability of our proposed model.}, archive = {J_ACCESS}, author = {Abrar Ahmed and Syed Junaid Nawaz and Babar Mansoor and Jaroslav Frnda and Muhammad Awais Javed and Sherali Zeadally}, doi = {10.1109/ACCESS.2025.3609883}, journal = {IEEE Access}, pages = {161923-161946}, shortjournal = {IEEE Access}, title = {On second-order fading statistics of multi-antenna 3-D vehicular communication channels}, volume = {13}, year = {2025}, }
• (2025). A lightweight three-phase induction motor fault detection method based on thermal imaging. ACCESS, 13, 161909-161922. (www)

  @article{ , abstract = {With the wide application of power electronic drive technology in industrial automation and smart grid, the fault monitoring of inverter-driven three-phase induction motors has become a key link to ensure the stable operation of the system and power quality. To this end, a lightweight three-phase induction motor fault detection method based on thermal imaging is proposed in this paper. For the model’s ability to adapt to irregular hot spots and complex edges, we optimize and introduce a dynamic adaptive group convolution (AKGConv) in the P3 layer of YOLOv11 to maintain the stability of the convolution structure. Meanwhile, to solve the problem of redundant parameters of multi-scale detection head, we design a lightweight shared convolutional detection head (LSCD-Detect) to achieve feature sharing and efficient prediction, which effectively improves the discrimination ability of the model. In addition, in order to improve the convergence speed and robustness of model training, generalized IoU (GIoU) is introduced to optimize the loss. The experimental results show that, compared with the baseline model YOLOv11 on our constructed induction motor fault thermography dataset, the method in this paper reduces the number of parameters and the computational volume by 11.15% and 13.6%, respectively, while maintaining the high accuracy, which fully verifies the superiority of the method in the field of industrial automation and intelligent manufacturing.}, archive = {J_ACCESS}, author = {Bo Jiang and Zhong Zheng and Cheng Pi and Jiang Long and Zheyu Yue}, doi = {10.1109/ACCESS.2025.3609311}, journal = {IEEE Access}, pages = {161909-161922}, shortjournal = {IEEE Access}, title = {A lightweight three-phase induction motor fault detection method based on thermal imaging}, volume = {13}, year = {2025}, }
• (2025). Identification and classification of ocular surface squamous neoplasia using YOLO and traditional feature extraction and fusion methods. ACCESS, 13, 161893-161908. (www)

  @article{ , abstract = {Ocular Surface Squamous Neoplasia (OSSN) is a potentially life-threatening eye condition that encompasses a range of abnormalities, from dysplasia to invasive carcinoma. Early detection and intervention are crucial for improving treatment outcomes and minimising complications. This study suggests a novel hybrid method for detecting and categorising OSSN that combines the advantages of conventional feature extraction techniques with deep learning-based object detection. The approach integrates the deep learning-based object detection framework, YOLO (You Only Look Once), with traditional feature extraction techniques, including texture descriptors. The YOLO algorithm detects OSSN lesions in ocular images, while conventional feature extraction methods derive relevant features from these identified lesions. A feature fusion strategy integrates features from the deep learning algorithm with handcrafted features, thus enhancing overall classification performance. Various classification algorithms, including K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest, and XGBoost, are trained using the fused feature set. The experiment results show that the proposed hybrid strategy can identify and categorize OSSN in ocular images. Performance metrics for the feature fusion model indicate accuracies of 99.04% for KNN, 98.1% for SVM, 97.14% for Random Forest, and 92.38% for XGBoost classifiers. These findings imply that the proposed hybrid method, which identifies and classifies OSSN in ocular images with excellent accuracy and resilience, is a viable tool for clinical applications.}, archive = {J_ACCESS}, author = {Iyyappan Sagadevan and Kunal Mandlik and Josephine S. Christy and Ruban Nersisson}, doi = {10.1109/ACCESS.2025.3609782}, journal = {IEEE Access}, pages = {161893-161908}, shortjournal = {IEEE Access}, title = {Identification and classification of ocular surface squamous neoplasia using YOLO and traditional feature extraction and fusion methods}, volume = {13}, year = {2025}, }
• (2025). A lightweight laser welding detection method for synchronized sound and photoelectric signals based on DC01 cold-rolled steel plate. ACCESS, 13, 161881-161892. (www)

  @article{ , abstract = {During laser welding, the penetration state (not penetration, moderate penetration, excessive penetration) is a key indicator affecting the welding quality. Traditional photoelectric signal detection has insufficient sensitivity to deep penetration information, while the fusion of the synchronized sound and photoelectric signals can complement each other’s advantages. This study puts forward a lightweight detection method relying on synchronized sound and photoelectric signals. Firstly, the preprocessing of synchronized sound and photoelectric signals are achieved through L2 norm, logarithmic normalization, and grayscale image conversion. Then, a lightweight model is formed by combining depth-wise separable convolution (DSC) and convolutional neural network (CNN) to efficiently recognize the fusion state. The experimental results indicate that the proposed model achieves high accuracy while maintaining only 50% of the original model in terms of computational complexity and parameter scale, providing an economical method to monitor laser welding in real time.}, archive = {J_ACCESS}, author = {Wenhao Cheng and Yanzu Huang and Binyu Huang and Nanfeng Zhang and Yanxi Zhang}, doi = {10.1109/ACCESS.2025.3608866}, journal = {IEEE Access}, pages = {161881-161892}, shortjournal = {IEEE Access}, title = {A lightweight laser welding detection method for synchronized sound and photoelectric signals based on DC01 cold-rolled steel plate}, volume = {13}, year = {2025}, }
• (2025). MLFI-net: A modified encoder-decoder network with multi-layer feature interwoven for liver tumor segmentation. ACCESS, 13, 161865-161880. (www)

  @article{ , abstract = {Timely screening of liver tumors plays a crucial role in minimizing the risk of tumor deterioration and improving the survival rate of patients. However, the locations of liver tumors in different patients vary greatly, and their morphological characteristics often show significant differences. Moreover, the boundary between tumor tissue and the surrounding healthy liver parenchyma is not clear, which makes the accurate segmentation of liver tumors in CT images a significant challenge. To handle these limitations, we present a modified encoder-decoder network with multi-layer feature interwoven for liver tumor segmentation, named MLFI-Net. In our approach, we build upon the widely used encoder-decoder architecture as the foundational framework. To enhance feature extraction capabilities, we replace the standard convolutional modules with multi-scale atrous residual block (MARB). Additionally, we optimize the skip connection pathways by integrating multi-layer feature interwoven module (MFIM), which enriches the flow of multi-level contextual information between the encoder and decoder stages. In the decoding stage, we further refine the output by employing a multi-branch attention fusion block (MAFB), which selectively emphasizes informative features while suppressing irrelevant or redundant responses. Finally, we conducted a large number of experiments on our self-built dual-phase liver lesion datasets (arterial phase and portal venous phase) and the widely recognized 3DIRCADb dataset, which demonstrated the effectiveness of MLFI-Net in improving the accuracy of liver tumor segmentation. Specifically, on the arterial phase dataset, MLFI-Net attained Dice of 0.8404, Mcc of 0.8367, and Jaccard of 0.7264. Meanwhile, on the portal venous phase dataset, the results further improved, with Dice of 0.8447, Mcc of 0.8414, and Jaccard of 0.7360. When applied to the 3DIRCADb dataset, MLFI-Net exhibited excellent generalizability, achieving Dice of 0.8844, Mcc of 0.8848, and Jaccard of 0.7972. Furthermore, a series of ablation studies were conducted to evaluate the effectiveness of multi-scale atrous residual block, multi-layer feature interwoven module and multi-branch attention fusion block.}, archive = {J_ACCESS}, author = {Meiqin Chen and Xiaoliang Jiang and Weili Lu and Chunxian Peng}, doi = {10.1109/ACCESS.2025.3610337}, journal = {IEEE Access}, pages = {161865-161880}, shortjournal = {IEEE Access}, title = {MLFI-net: A modified encoder-decoder network with multi-layer feature interwoven for liver tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). Pentagon-match (PMatch): Identification of view-invariant planar feature for local feature matching-based homography estimation. ACCESS, 13, 161854-161864. (www)

  @article{ , abstract = {In computer vision, finding correct point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research works focus on the matching of local features before a sampling method is employed, such as RANSAC, to verify initial matching results via repeated fitting of certain global transformation among the images. In this paper, a novel sampling scheme, Pentagon-Match (PMatch), is proposed for more efficient verification of the correctness of initially matched keypoints using pentagons randomly sampled from them. By ensuring shape and location of some sampled pentagons are view-invariant with various evaluations of cross-ratio (CR), incorrect matches of keypoint can then be identified easily with homography estimated from these pentagons. Experimental results show that highly accurate estimation of homography can be obtained efficiently and more accurately for planar scenes of the HPatches dataset, based on keypoint matching results provided by LoFTR. Besides, a new way of showing inliers (and outlier) for the above matching results and possible extension of the approach for multi-plane situation are also demonstrated.}, archive = {J_ACCESS}, author = {Yueh-Cheng Huang and Yi-Yu Hsieh and Chen-Tao Hsu and Jen-Hui Chuang}, doi = {10.1109/ACCESS.2025.3608487}, journal = {IEEE Access}, pages = {161854-161864}, shortjournal = {IEEE Access}, title = {Pentagon-match (PMatch): Identification of view-invariant planar feature for local feature matching-based homography estimation}, volume = {13}, year = {2025}, }
• (2025). Function-oriented programming attacks on ARM cortex-M processors. ACCESS, 13, 161844-161853. (www)

  @article{ , abstract = {In recent years, code-reuse attacks have been used to exploit software vulnerabilities and gain control of numerous software programs and embedded devices. Several measures have been put in place to prevent this type of attack, such as Control-Flow Integrity (CFI) systems, and some of these systems have already been integrated into hardware. Nevertheless, Function-Oriented Programming (FOP) attacks, a form of code-reuse that chains functions to carry out malicious actions, continue to persist. In this work, we present the first analysis of the implications and feasibility of FOP attacks on microcontrollers, focusing on ARM Cortex-M processors that support PACBTI, that is, a hardware feature designed for CFI system implementation. During this process, we identified multiple dispatch gadgets in two common Real-time Operating System (RTOS). Since these gadgets reside within core OS functionalities, they are inherently included in a broad range of embedded operating systems. Furthermore, we also present CortexMFopper - a tool specially built to identify FOP gadgets in embedded devices and to raise awareness of this technique.}, archive = {J_ACCESS}, author = {André Cirne and Patrícia R. Sousa and Luís Antunes and João S. Resende}, doi = {10.1109/ACCESS.2025.3610033}, journal = {IEEE Access}, pages = {161844-161853}, shortjournal = {IEEE Access}, title = {Function-oriented programming attacks on ARM cortex-M processors}, volume = {13}, year = {2025}, }
• (2025). Introducing differential privacy to recommender systems through a self-organizing feature map using a local distance-based methodology. ACCESS, 13, 161824-161843. (www)

  @article{ , abstract = {This paper introduces a robust, distance-based differential privacy (DP) framework called Privacy-Preserving Collaborative Filtering (PPCF) to protect user data in recommender systems (RSs) while maintaining high-quality recommendations. The proposed model integrates self-organizing map (SOM) clustering with collaborative filtering (CF) and differential privacy to enhance both privacy and personalization. A local adjustment strategy is applied to balance data availability with privacy, supported by aggregation, generalization, $\epsilon $ -budget management, and Laplace noise injection to mitigate inference attacks. Social network signals were also incorporated to further refine user preference modeling. The system was evaluated using the MovieLens dataset, in which the trade-off between privacy and accuracy was examined under various $\epsilon $ values. Key metrics such as the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used to assess the prediction performance. The experimental results demonstrate that PPCF consistently outperforms traditional non-private models and several existing differentially private methods. The model shows improved clustering stability and recommendation accuracy even at lower $\epsilon $ levels, though results confirm that small variations in $\epsilon $ can notably affect clustering quality. These findings underscore the importance of privacy-utility tuning for practical deployment. Overall, the proposed PPCF approach offers a privacy-resilient, accurate, and scalable solution for collaborative filtering in environments utilize sensitive data.}, archive = {J_ACCESS}, author = {Fatemeh Amiri and Gerald Quirchmayr and Edgar Weippl}, doi = {10.1109/ACCESS.2025.3605563}, journal = {IEEE Access}, pages = {161824-161843}, shortjournal = {IEEE Access}, title = {Introducing differential privacy to recommender systems through a self-organizing feature map using a local distance-based methodology}, volume = {13}, year = {2025}, }
• (2025). Physiologically informed sparse ICA for robust spatiotemporal source disentanglement. ACCESS, 13, 161806-161823. (www)

  @article{ , abstract = {Sparse independent component analysis (SICA) variants, including the recently developed SICA-L using Laplacian distribution, excel at extracting interpretable spatial maps from functional magnetic resonance imaging (fMRI) data. However, these methods exhibit poor temporal recovery performance as they can only model the mixing matrix instead of achieving full temporal recovery. To address this limitation, we propose physiologically informed sparse ICA (PSICA), a framework with a novel mathematical model $\mathbf {Y} = \mathbf {U}_{p}\mathbf {U}_{p}^{\dagger } \mathbf {U}_{q}\boldsymbol {\Psi }\boldsymbol {\Phi }\mathbf {Z}_{q}$ for decomposing the observed fMRI data matrix Y. This model incorporates physiologically informed basis functions in $\mathbf {U}_{p}$ across multiple temporal scales (hemodynamic response functions, discrete cosine transforms, splines, and polynomials) through the representation matrix $\mathbf {A} = \mathbf {U}_{p}^{\dagger } \mathbf {U}_{q}\boldsymbol {\Psi }$ and employs dissociation matrices $\boldsymbol {\Psi }$ and $\boldsymbol {\Phi }$ for systematic disentanglement of overlapping SICA-L components in $\mathbf {U}_{q}$ and $\mathbf {Z}_{q}$ . The algorithm employs efficient block coordinate descent optimization combined with adaptive soft thresholding for spatial component optimization, firm thresholding for background noise elimination, and correlation based greedy selection strategies for temporal basis selection. The proposed approach addresses the limitations of ICA variants by providing enhanced temporal and spatial recovery via representation and dissociation matrices. Comprehensive evaluation on synthetic and experimental datasets (block-design from three subjects and resting-state fMRI) demonstrates that the proposed method outperformed established state-of-the-art methods. It achieved 17% higher average temporal Pearson correlation coefficients compared to SICA-L across synthetic and experimental datasets, successfully addressing existing methods’ primary weakness while achieving superior spatial accuracy. Synthetic dataset and implementation code for all participating algorithms can be accessed at https://github.com/usmankhalid06/PSICA}, archive = {J_ACCESS}, author = {Muhammad Usman Khalid and Malik Muhammad Nauman and Shafiq Ur Rehman and Sheikh Naeem Shafqat}, doi = {10.1109/ACCESS.2025.3606236}, journal = {IEEE Access}, pages = {161806-161823}, shortjournal = {IEEE Access}, title = {Physiologically informed sparse ICA for robust spatiotemporal source disentanglement}, volume = {13}, year = {2025}, }
• (2025). A review on multi-step attack detection. ACCESS, 13, 161779-161805. (www)

  @article{ , abstract = {Multi-step Attacks (MSAs) pose a significant cybersecurity threat, aiming at valuable assets and essential infrastructure across diverse sectors. This study offers an in-depth investigation of methodologies for multi-step attack detection, prevention, and automation (DPA). It looks at terms such as ‘multi-stage’ and ‘composite’ attacks, highlighting shared characteristics, including their complexity, persistence, and evasion tactics. The document highlights the significant obstacles contemporary detection systems encounter, especially in managing high-dimensional data, comprehending the sequential characteristics of attacks, and sustaining real-time detection efficacy. The research employs comprehensive visualizations and statistics to demonstrate industry-specific behaviors associated with these challenges. The study assesses the pros and cons of machine/deep learning and graph-based approaches using performance metrics, execution time, and cross-dataset analyses. This review significantly identifies prevalent concerns across multiple research studies and their respective frequencies. This underscores the necessity for more dependable, robust, and scalable approaches capable of adapting to the expanding threat landscape, including zero-day vulnerabilities. This review offers scholars and practitioners novel insights into formulating multi-layered threat identification and response tactics to enhance cybersecurity frameworks. Furthermore, it examines how different methodologies used for threat detection, such as machine learning (ML), graph-based techniques, and large language models (LLMs), address particular challenges such as elevated false positives (FPs), dynamic threat patterns, and high-dimensional data processing, while emphasizing the limitations of existing solutions.}, archive = {J_ACCESS}, author = {Syed Usman Shaukat and Saad Khan and Simon Parkinson}, doi = {10.1109/ACCESS.2025.3607497}, journal = {IEEE Access}, pages = {161779-161805}, shortjournal = {IEEE Access}, title = {A review on multi-step attack detection}, volume = {13}, year = {2025}, }
• (2025). Model-free reinforcement learning in microgrid control: A review. ACCESS, 13, 161762-161778. (www)

  @article{ , abstract = {The global shift toward distributed energy resources (DERs) has accelerated the deployment of microgrids (MGs), introducing unprecedented control challenges that traditional strategies often struggle to address. Model-free reinforcement learning (MFRL) has emerged as a promising paradigm for adaptive, intelligent control without the need for explicit system modeling. This paper presents a comprehensive review of MFRL applications in MG control, proposing a systematic taxonomy that classifies existing approaches by control hierarchy, architectural configuration, operational modes, and action spaces. We analyze critical design considerations—including reward function shaping, exploration strategies, and computational requirements—that influence practical deployment. Furthermore, we systematically evaluate key MFRL algorithms and map their suitability across primary, secondary, and tertiary control levels. By examining recent applications, we highlight that MFRL has reached considerable maturity across all control hierarchies, revealing clear trends: continuous-action methods excel in real-time primary control, distributed schemes enhance scalability in secondary coordination, and multi-agent frameworks enable complex tertiary-level optimization. Finally, the review identifies persistent implementation challenges and offers practical guidance for algorithm selection and deployment strategies in modern MG systems. This review aims to serve both researchers and practitioners seeking to deploy MFRL in modern MG systems.}, archive = {J_ACCESS}, author = {Fethi Batincan Gurbuz and Ayman Karaki and Anas Karaki and Sevki Demirbas and Sertac Bayhan}, doi = {10.1109/ACCESS.2025.3609317}, journal = {IEEE Access}, pages = {161762-161778}, shortjournal = {IEEE Access}, title = {Model-free reinforcement learning in microgrid control: A review}, volume = {13}, year = {2025}, }
• (2025). Impact of non-gaussian noise on position accuracy provided by global navigation satellite systems (GNSS). ACCESS, 13, 161750-161761. (www)

  @article{ , abstract = {Accurate positioning obtained from the Global Navigation Satellite System (GNSS) is crucial for many applications; however, environmental and instrumental factors, such as satellite geometry, signal multipath, and atmospheric delays, often degrade its accuracy. Traditional uncertainty quantification and filtering methods, such as the Kalman Filter (KF), assume that the measurement noise follows a Gaussian distribution. In practice, real-world GNSS data often violate this assumption, showing skewness, heavy tails, and outliers that distort the estimates and reduce the accuracy. This study systematically evaluates the impact of non-Gaussian noise on GNSS positioning accuracy and introduces a unified framework that integrates robust statistical methods and nonlinear filtering. Specifically, we compared the performance of the KF, Median Absolute Deviation (MAD), and Particle Filter (PF) on stationary GNSS data. The evaluation used comprehensive accuracy and uncertainty diagnostics, including the root mean square error (RMSE), probable circular error (CEP), estimated position error at 95% (R95), covariance analysis, and nonparametric bootstrapping. The results demonstrate that Gaussian-based models fail under heavy-tailed noise, whereas MAD and PF achieve superior reliability by suppressing outlier influence and capturing complex error distributions. The proposed framework advances GNSS uncertainty quantification by bridging robust statistics and nonlinear filtering. It offers a statistically sound and practical approach for real-world environments in which non-Gaussian noise is prevalent.}, archive = {J_ACCESS}, author = {Abu Bantu and Józef Wiora}, doi = {10.1109/ACCESS.2025.3610048}, journal = {IEEE Access}, pages = {161750-161761}, shortjournal = {IEEE Access}, title = {Impact of non-gaussian noise on position accuracy provided by global navigation satellite systems (GNSS)}, volume = {13}, year = {2025}, }
• (2025). From timed automata to go: Formally verified code generation and runtime monitoring for cyber-physical systems. ACCESS, 13, 161729-161749. (www)

  @article{ , abstract = {The design of critical components in cyber-physical systems (CPS) demands rigorous guarantees of safety and correctness, particularly in safety-critical domains such as autonomous vehicles and industrial automation. While formal verification techniques, such as Timed Automata (TA) models analyzed with UPPAAL, provide strong offline assurances, transitioning these models into reliable executable systems remains a major challenge, especially when integrated with unverified legacy systems. This paper shows that combining formal verification with runtime monitoring can effectively bridge this gap and enhance system reliability. We introduce a novel framework that automatically translates UPPAAL-verified TA models into executable Go programs, leveraging a new intermediate formalism, Timed Automata with Disjoint Actions (TADA), to make time progression explicit and resolve semantic ambiguities in code generation. By integrating lightweight runtime monitors into the generated code, our approach ensures continuous enforcement of timing invariants even when verified components interact with legacy elements. Compared to prior work focused solely on offline verification, our hybrid approach offers a robust safety envelope in heterogeneous environments. We demonstrate the effectiveness of our framework through a case study on an industrial control system, showing that it successfully detects and mitigates timing violations introduced by legacy components, significantly improving overall system resilience.}, archive = {J_ACCESS}, author = {Soomin Cho and Inhye Kang and Jin Hyun Kim}, doi = {10.1109/ACCESS.2025.3608215}, journal = {IEEE Access}, pages = {161729-161749}, shortjournal = {IEEE Access}, title = {From timed automata to go: Formally verified code generation and runtime monitoring for cyber-physical systems}, volume = {13}, year = {2025}, }
• (2025). Optimal scheduling of a multi-energy hub with renewables, hydrogen vehicles, and storage systems. ACCESS, 13, 161709-161728. (www)

  @article{ , abstract = {Hydrogen energy is recognized as a prominent option of environmental sustainability, particularly through its production via electrolysis units, which makes it an essential part of the worldwide effort to reduce carbon footprints and confront climate change. The clean energy potential of hydrogen positions it at the forefront of these efforts by utilizing power-to-gas (P2G) technology. Nevertheless, the optimum functioning of multi-hub energy (MHE) systems relies on the complex interaction between different energy carriers and the increasing use of tri-generation (TG) technologies. Within this context, this research study offers a novel configuration of combined cooling, heat, and power systems (CCHPS) with highly penetrated renewable resources in the presence of hydrogen vehicles and storage units. In particular, multi-carrier markets under various uncertainties are considered which are modeled using stochastic programming methods. The main goal of this work is to develop an effective structure of multi-hub energy systems and underscore the potential of integrating hydrogen and renewable resources in achieving sustainable energy goals. The present study employs a multi-objective optimization approach (MOA) to assess the system’s operation in the context of a techno-environmental perspective. Furthermore, P2G technology has been adopted into the system to augment overall performance, reduce carbon dioxide emissions, and supply the cooling and heating demands under various uncertainties. The MOA emphasizes the main aim of decreasing operating costs, with the secondary target being the reduction of carbon dioxide emissions. The effectiveness of the MOA using the epsilon-constrained technique and Pareto solutions is examined, showing a 32.17% reduction in operating costs and a notable 79.21% reduction in CO2 emissions through the integration of Power-to-Gas (P2G) technology and the Demand Response Program (DRP).}, archive = {J_ACCESS}, author = {Dinmukhambet Baimbetov and Mohamed Salem and Samal Syrlybekkyzy and Nurmaganbet Ermek Talantovich and Yevgeniy Muralev and Mohammad Alhuyi Nazari}, doi = {10.1109/ACCESS.2025.3609439}, journal = {IEEE Access}, pages = {161709-161728}, shortjournal = {IEEE Access}, title = {Optimal scheduling of a multi-energy hub with renewables, hydrogen vehicles, and storage systems}, volume = {13}, year = {2025}, }
• (2025). Offshore hydrogen production via the PEM electrolyser coupled with the reverse osmosis system. ACCESS, 13, 161685-161708. (www)

  @article{ , abstract = {Offshore production of hydrogen using seawater reverse osmosis (SWRO) and proton exchange membrane (PEM) technologies offers a promising pathway for green hydrogen generation from renewable energy sources (RESs). Realising this potential requires the integration of the SWRO unit and PEM electrolyser into a single system, with precise modelling to address the variability of RESs. Nevertheless, there are limited studies in the literature that examine a coupled SWRO-PEM electrolyser model. Therefore, in this article, an integrated SWRO-PEM electrolyser model is developed, detailing the modelling steps for each component. The models are subsequently validated against experimental data and upscaled to reflect a realistic scenario. Finally, a straightforward control method for the SWRO unit based on the electrolyser operation is suggested. The PEM electrolyser model showed an average relative error of 0.66% in static operation and 3.93% in dynamic operation. Meanwhile, the SWRO unit demonstrated an average relative error of 3.64% for water quantity and 1.48% for water quality compared to experimental results at low total dissolved solids (TDS) values, and 2.74% and 0.13% for water quantity and quality, respectively, at seawater TDS levels. Also, the control method estimated the water demand of the electrolyser with an average error of 1.20%.}, archive = {J_ACCESS}, author = {Siavash Asiaban and Dimitar Bozalakov and Emile Cornelissen and Lieven Vandevelde}, doi = {10.1109/ACCESS.2025.3608297}, journal = {IEEE Access}, pages = {161685-161708}, shortjournal = {IEEE Access}, title = {Offshore hydrogen production via the PEM electrolyser coupled with the reverse osmosis system}, volume = {13}, year = {2025}, }
• (2025). Time series information visualization - A review of approaches and tools. ACCESS, 13, 161653-161684. (www)

  @article{ , abstract = {Time series data are prevalent across various domains and often encompass large datasets containing multiple time-dependent features in each sample. Exploring time-varying data is critical for data science practitioners aiming to understand dynamic behaviors and discover periodic patterns and trends. However, the analysis of such data often requires sophisticated procedures and tools. Information visualization is a communication channel that leverages human perceptual abilities to transform abstract data into visual representations. Visualization techniques have been successfully applied in the context of time series to enhance interpretability by graphically representing the temporal evolution of data. The challenge for information visualization developers lies in integrating a wide range of analytical tools into rich visualization systems that can summarize complex datasets while clearly describing the impacts of the temporal component. Such systems enable data scientists to turn raw data into understandable and potentially useful knowledge. This review examines techniques and approaches designed for handling time series data, guiding users through knowledge discovery processes based on visual analysis. We also provide readers with theoretical insights and design guidelines for considering when developing comprehensive information visualization approaches for time series, with a particular focus on time series with multiple features. As a result, we highlight the challenges and future research directions to address open questions in the visualization of time-dependent data.}, archive = {J_ACCESS}, author = {Evandro S. Ortigossa and Fábio F. Dias and Diego C. Nascimento and Luis Gustavo Nonato}, doi = {10.1109/ACCESS.2025.3609404}, journal = {IEEE Access}, pages = {161653-161684}, shortjournal = {IEEE Access}, title = {Time series information visualization - A review of approaches and tools}, volume = {13}, year = {2025}, }
• (2025). Channel sensing and vision-aided distributed beamforming for 6G ultra-dense networks. ACCESS, 13, 161633-161652. (www)

  @article{ , abstract = {This paper presents a beamforming strategy designed to optimize distributed beamforming (DSBF) in 6G ultra-dense networks. DSBF is a key enabler for high-frequency communications by spatially coordinating transmissions from geographically distributed base stations (BSs), but its practical deployment faces critical challenges, most notably accurate BS selection and precise beam alignment across distributed nodes. To address these limitations, we propose a two-phase solution that tightly integrates advanced radio signal processing with computer vision techniques. In the first phase, multi-BS angle-delay channel power matrices (ADCPMs) are constructed under multipath propagation and processed using a Vision Transformer to estimate user equipment (UE) positions. Leveraging observations from multiple BSs provides a broader spatial context compared to relying on a single BS, where the environmental visibility is limited and spatial cues are often insufficient for accurate positioning. These estimates are then used to identify the nearest BSs for DSBF node selection. In the second phase, RGB-depth cameras mounted on selected BSs leverage computer vision techniques to perform direct UE detection and line-of-sight path identification. These outputs guide accurate downlink beamforming while avoiding the quantization loss and search complexity of codebook-based methods. This vision-assisted approach avoids quantization loss and complexity from codebook-based beam searches. By bridging radio signal processing with computer vision, the proposed system significantly improves DSBF performance, addressing the limitations of conventional codebook-based approaches.}, archive = {J_ACCESS}, author = {Hyung Joon Cho and Byonghyo Shim}, doi = {10.1109/ACCESS.2025.3609826}, journal = {IEEE Access}, pages = {161633-161652}, shortjournal = {IEEE Access}, title = {Channel sensing and vision-aided distributed beamforming for 6G ultra-dense networks}, volume = {13}, year = {2025}, }
• (2025). Human pose analysis in smooth and continuous manifold space leveraging interpolated embedding variables. ACCESS, 13, 161619-161632. (www)

  @article{ , abstract = {Skeleton data, defined by the hierarchical structure of joints and bones, effectively capture the spatio-temporal features of human motion. While most studies simultaneously address spatial and temporal dynamics in skeleton datasets to solve specific tasks, such as classification, generation, or inpainting, this paper introduces a task-agnostic approach to pose representation. This method is designed to be versatile across various downstream applications. Within the proposed latent space, identical poses from different time sequences are mapped to the same or highly similar points, irrespective of external factors like temporal order or task-specific annotations. This ensures that the latent space remains generalizable and consistent, focusing purely on the geometric configuration of a single skeleton. To accurately capture the characteristics of individual poses across sequential frames, a self-attention-based autoencoder is employed. This autoencoder constructs a low-dimensional embedding space that preserves the original skeleton characteristics. Moreover, incorporating reconstruction loss from interpolated embedding variables facilitates the formation of a smooth and continuous manifold space. This smoothness enables the generation of realistic motion data through variable interpolation along the geometric path of latent variables. The potential of the method as a task-agnostic approach was validated through inpainting tests, arbitrary motion style generation, and motion recognition tasks. The effectiveness of the auxiliary loss function was demonstrated, achieving 9.5% higher accuracy in missing frame estimation compared to standard autoencoder in position accuracy (L2P) evaluation. Additionally, in a classification task utilizing pose representation, sarcopenia diagnosis performance improved by 11% compared to methods based on spatiotemporal graph convolutional networks.}, archive = {J_ACCESS}, author = {Jong-Hoon Kim and Chan-Su Lee}, doi = {10.1109/ACCESS.2025.3609836}, journal = {IEEE Access}, pages = {161619-161632}, shortjournal = {IEEE Access}, title = {Human pose analysis in smooth and continuous manifold space leveraging interpolated embedding variables}, volume = {13}, year = {2025}, }
• (2025). QuantEdge: A hybrid quantization approach for optimized AI deployment across edge devices. ACCESS, 13, 161605-161618. (www)

  @article{ , abstract = {Deploying artificial intelligence (AI) models on edge devices introduces significant challenges due to limited computational resources, strict latency requirements, and energy constraints. These limitations hinder the performance of traditional deep learning models in real-time applications. This study addresses the pressing problem of optimizing AI inference for heterogeneous and resource-constrained edge environments by introducing QuantEdge, a hybrid quantization approach that combines post-training quantization (PTQ) and quantization-aware training (QAT). The proposed method dynamically adapts model precision and computational load based on device-specific constraints, making it suitable for a wide spectrum of hardware from low-power IoT nodes to advanced embedded systems. Experiments conducted on devices such as Jetson AGX Xavier, Asus Tinker Edge T, Raspberry Pi, and AGX clusters show that QuantEdge reduces inference latency by up to 31.8% while maintaining high accuracy. Additionally, it significantly improves energy efficiency and memory usage. The research is motivated by the growing demand for efficient on-device AI in real-world domains such as autonomous vehicles, mobile health diagnostics, smart surveillance, and edge-enabled IoT. QuantEdge presents a robust solution to real-time AI deployment challenges by tailoring quantization dynamically to hardware capabilities, thus enhancing the practicality and scalability of edge AI systems.}, archive = {J_ACCESS}, author = {Rasim Mahmudov and Deok-Hwan Kim}, doi = {10.1109/ACCESS.2025.3609798}, journal = {IEEE Access}, pages = {161605-161618}, shortjournal = {IEEE Access}, title = {QuantEdge: A hybrid quantization approach for optimized AI deployment across edge devices}, volume = {13}, year = {2025}, }
• (2025). Data augmentation for text classification using autoencoders. ACCESS, 13, 161594-161604. (www)

  @article{ , abstract = {Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire. To mitigate this challenge, data augmentation techniques are employed to artificially expand the training data by generating synthetic samples. By enriching the dataset, data augmentation enhances model generalization, reduces overfitting, and improves model performance. This paper investigates the effectiveness of employing autoencoders for text data augmentation to enhance the performance of text classification models. The research compares four types of autoencoders which are Traditional Autoencoder (AE), Adversarial Autoencoder (AAE), Denoising Adversarial Autoencoder (DAAE), and Variational Autoencoder (VAE). Basic text preprocessing techniques, which are lowercasing, removal of non-alphanumeric characters and removal of stop words, are applied to all documents. Additionally, label-based filtering is applied, where the outputs of autoencoders that contradict the predictions of BERT are eliminated. The experiments are conducted using the SST-2 sentiment classification dataset, which consists of 7,791 training instances and 1,821 test instances. To better analyze the impact of data augmentation methods, experiments are also performed on smaller subsets of 100, 200, 400, and 1,000 instances. Data augmentation is applied at ratios of 1:1, 1:2 and 1:4 for these subsets. The results demonstrate that AE-based data augmentation methods, particularly at a 1:1 ratio, achieve better accuracy than the baseline models. This underscores the potential of autoencoders in improving text classification outcomes in NLP tasks.}, archive = {J_ACCESS}, author = {Mustafa Cataltas and Ilyas Cicekli and Nurdan Akhan Baykan}, doi = {10.1109/ACCESS.2025.3610157}, journal = {IEEE Access}, pages = {161594-161604}, shortjournal = {IEEE Access}, title = {Data augmentation for text classification using autoencoders}, volume = {13}, year = {2025}, }
• (2025). Optimization and feasibility assessment of hybrid mini-grid power systems for iraq’s residential rural electrification. ACCESS, 13, 161573-161593. (www)

  @article{ , abstract = {Iraq is struggling with a significant electricity crisis, especially in rural and remote areas where ensuring uninterrupted and reliable power access remains a long-standing problem. The electrical grid of the country is under growing pressure to meet expanding demand, as it continues to be heavily reliant on fossil fuel-based generation. Mini-grids with hybrid renewable energy sources may represent a promising sustainable solution, particularly for rural areas. This paper aims to explore the feasibility of hybrid mini-grid power systems for electrifying rural areas in Iraq. The focus is on identifying the most cost-effective and reliable system through size optimization as well as techno-economic and sensitivity analyses using HOMER software. Comprehensive assessments are conducted on multiple system types, such as technical, economic, electrical, and environmental analyses, reinforced by thorough sensitivity studies on key parameters such as fuel cost, system reliability, and the nominal discount rate. The hybrid system configuration, comprising a 666 kW of solar PV array, 47 wind turbines (WTs), a 2,000 kW large diesel generator, a 600 kW small diesel generator, 864 kWh of battery storage, and a 699 kW power converter, offers the optimal balance of cost, environmental impact, and reliability among all feasible options. It achieved a net present cost of ${\$}$ 9.13 million and a cost of energy of ${\$}$ 0.123/kWh. The results show that the integration of solar PV and WTs with diesel generators could provide cost-effective and reliable solutions to address the electricity shortage problem in Iraq. The analyses and insights presented in this paper may assist decision makers in Iraq by highlighting the strategic advantages of utilizing renewable energy sources, aiming to diversify power generation sources in the country.}, archive = {J_ACCESS}, author = {Ali M. S. Al-Bayati and Md Habib Ullah and Huda Hamza Abdulkhudhur}, doi = {10.1109/ACCESS.2025.3609628}, journal = {IEEE Access}, pages = {161573-161593}, shortjournal = {IEEE Access}, title = {Optimization and feasibility assessment of hybrid mini-grid power systems for iraq’s residential rural electrification}, volume = {13}, year = {2025}, }
• (2025). Recent advances in transparent reflectarray antennas. ACCESS, 13, 161541-161572. (www)

  @article{ , abstract = {Transparent Reflectarray Antennas (TRAs) have emerged as a promising solution for next-generation communication systems requiring both electromagnetic performance and optical transparency. This review focuses on a detailed latest work on unit cell design innovations, material engineering, and performance optimization across microwave, millimeter-wave, and terahertz frequency bands. Key developments include the use of fine metal lines (FML), frequency selective surfaces, indium tin oxides (ITO) films, and liquid crystal-based reconfigurable structures to achieve wide phase tuning, low reflection loss, and high transmittance. Various fabrication techniques, such as photolithography on quartz and soda-lime glass substrates, have enabled lightweight and high-efficiency RA structures suitable for integration into smart windows, satellite platforms, and 6G communication devices. Additionally, transparent beam-steering mechanisms using anisotropic liquid crystal (LC) materials and metallic mesh frameworks demonstrate the feasibility of dynamic control while maintaining optical clarity. The paper also discusses current challenges—such as optical loss, material limitations, and fabrication complexity—and outlines future directions aimed at enhancing bandwidth, reconfigurability, and multifunctional performance of TRAs.}, archive = {J_ACCESS}, author = {Altaf Ahmed Mugheri and Muhammad Inam Abbasi and Imran Mohd Ibrahim and Saad Hassan Kiani and Muhammad Hashim Dahri and Muhammad Ramlee Kamarudin and Zaid Ahmed Shamsan and Qammer Hussain Abbasi}, doi = {10.1109/ACCESS.2025.3607095}, journal = {IEEE Access}, pages = {161541-161572}, shortjournal = {IEEE Access}, title = {Recent advances in transparent reflectarray antennas}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence-based prediction of cognitive frailty: A clinical data approach. ACCESS, 13, 161529-161540. (www)

  @article{ , abstract = {Cognitive frailty (CF), defined as the co-existence of physical frailty and cognitive impairment without dementia, has two subtypes: reversible cognitive frailty (RCF) and potentially reversible cognitive frailty (PRCF). This study aimed to develop machine learning models for the early prediction of CF by incorporating both RCF and PRCF into a unified framework. Data from 2,173 participants were sourced from the AGELESS and MELoR cohort studies, including clinical, blood, urine, and health status information. Participants were grouped into six CF categories based on the FRAIL scale and MoCA scores from 2020 and 2022. Seven machine learning models, SVM, LR, KNN, RF, CART, LDA, and GNB, were trained using clinical, blood, and urine datasets. Clinical variables outperformed other data types, with SVM, LR, and CART models achieving 95% accuracy and AUC scores of 1.0, while blood- and urine-based models showed lower performance (AUCs of 0.8 and 0.89, respectively). F1 scores and ROC analysis confirmed the robustness of the clinical models, and k-fold and grid search cross-validations showed consistent performance on unseen data, indicating no overfitting. These findings highlight the superior predictive power of clinical variables for identifying both subtypes of CF, supporting their potential for use in early, accurate diagnosis. Nonetheless, external validation using independent cohorts is recommended to ensure broader applicability.}, archive = {J_ACCESS}, author = {Salami Safran Olugbenga and Mohamed Aashiq and Siti Anom Ahmad and Tan Maw Pin and Yuanyuan Zhao and Fakhrul Zaman Rokhani}, doi = {10.1109/ACCESS.2025.3595187}, journal = {IEEE Access}, pages = {161529-161540}, shortjournal = {IEEE Access}, title = {Artificial intelligence-based prediction of cognitive frailty: A clinical data approach}, volume = {13}, year = {2025}, }
• (2025). A survey of multi-agent reinforcement learning for cooperative control in multi-AUV systems. ACCESS, 13, 161505-161528. (www)

  @article{ , abstract = {The increasing demand for adaptive and autonomous smart ocean systems has accelerated the deployment of Autonomous Underwater Vehicles (AUVs) in various complex underwater missions, including collaborative navigation, target tracking, data collection, and energy management. To address the inherent challenges of the underwater environment, such as the limitations of acoustic communication, unpredictable environmental dynamics, and partial observability, Multi-Agent Reinforcement Learning (MARL) with the Centralized Training with Decentralized Execution (CTDE) paradigm has emerged as a promising solution. This paper presents a focused survey on MARL algorithms and their applications in multi-AUV systems. We classify the algorithms into value-based approaches (e.g., VDN and QMIX), policy-based approaches (e.g., Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Proximal Policy Optimization (MAPPO)), and hybrid approaches that support explicit communication (e.g., CommNet, DIAL, and ROMA). We further review several relevant simulation environments, such as PettingZoo, UWSim, and Gazebo, along with underwater acoustic channel modeling and key performance metrics used for system evaluation. Complementing the survey study, we present representative experiments in data collection and energy efficiency scenarios using MADDPG and MAPPO algorithms. The results include comparative analyses of average reward, overflow, Flight eXceedance (FX), and energy consumption, accompanied by trajectory visualizations in a grid-based simulation environment. Finally, we provide a synthesis of future research directions, including the integration of Meta-Reinforcement Learning (Meta-RL), graph-based role allocation, and 3D physics-based simulator training, to strengthen the adoption of MARL in autonomous ocean systems. This survey is expected to serve as a reference point for further research in developing AUVs that are smarter, more collaborative, and resilient to extreme conditions.}, archive = {J_ACCESS}, author = {Arif Wibisono and Hyoung-Kyu Song and Byung Moo Lee}, doi = {10.1109/ACCESS.2025.3609457}, journal = {IEEE Access}, pages = {161505-161528}, shortjournal = {IEEE Access}, title = {A survey of multi-agent reinforcement learning for cooperative control in multi-AUV systems}, volume = {13}, year = {2025}, }
• (2025). Interrupt caching: A hardware-assisted interrupt handling to enhance system responsiveness. ACCESS, 13, 161488-161504. (www)

  @article{ , abstract = {Interrupt request (IRQ) handling is a crucial capability of modern computing systems that greatly affects system functionality and responsiveness. The necessity of OS intervention for IRQ handling is a system performance bottleneck. Existing OS solutions have no direct improvement on interrupt latency, i.e., the interval between IRQ occurrence and handler invocation. In this paper, we propose the Immediate Response by Interrupt Caching (IRIC) mechanism to reduce the interrupt latency by eliminating OS intervention. We observe that, in many workloads, there is a strong correlation among interrupts, and the currently occurring interrupts are likely to come from the same sources as the recent interrupts. We name this phenomenon interrupt locality, which refers to the tendency of interrupts to originate from the same sources in a short period. Based on this discovery, we propose a caching mechanism that stores the required information for the next IRQs inside the processor for immediate interrupt handling on a match. IRIC monitors the sequence of all IRQs using a tiny history table and loads the program counter with the handler address on a match after saving the processor state. This mechanism eliminates the need for OS intervention in handling the interrupts by caching their required information. The system protection in our general solution is guaranteed by enabling the OS to dynamically designate the list of trustful handlers in advance to take advantage of IRIC. The proposed mechanism shortens the interrupt latency by an average of 76.9% compared to the OS mechanism and decreases the standard deviation by 57.6%, which results in a significantly lower jitter and up to 2.4x speedup in workload execution. The improvements come at low hardware overhead.}, archive = {J_ACCESS}, author = {Elham Cheshmikhani and Hamed Farbeh}, doi = {10.1109/ACCESS.2025.3609234}, journal = {IEEE Access}, pages = {161488-161504}, shortjournal = {IEEE Access}, title = {Interrupt caching: A hardware-assisted interrupt handling to enhance system responsiveness}, volume = {13}, year = {2025}, }
• (2025). PHIL testing of a battery integrated multilevel converter combining real and emulated modules. ACCESS, 13, 161472-161487. (www)

  @article{ , abstract = {This paper presents a Power Hardware-in-the-Loop (PHIL) testing methodology for emulating the different parts of a battery system with modular converters (a multilevel converter) developed in the SEABAT-project. Using PHIL offers significant advantages, such as reducing the risk of equipment damage and personal injury. In particular, the absence of a battery cell is a clear advantage regarding required safety measures. Additionally, it shortens development and testing cycles, as system testing can commence before the final hardware is available. The presented testing approach involves two physical battery modules, each with its real hardware module converters and control systems, but with emulated batteries. The remaining modules of the battery string are emulated using a wide-band power supply controlled by an OPAL-RT real-time simulator. The system also includes physical string controllers and a system master controller, with the string controller somewhat adapted to interface with the real-time simulator. During the PHIL testing phase, numerous minor and major software faults were identified and resolved early in the process. Furthermore, the control functionalities of a hybrid high-energy and high-power battery concept were tested and validated. The primary contribution of this work is the demonstration of integrating real and emulated battery modules with distributed converters within the same string of a multilevel converter in a PHIL test bench. It addresses the challenges and necessary adaptations for such a configuration.}, archive = {J_ACCESS}, author = {Tor Inge Reigstad and Olve Mo and Giuseppe Guidi and Kjell Ljøkelsøy}, doi = {10.1109/ACCESS.2025.3607042}, journal = {IEEE Access}, pages = {161472-161487}, shortjournal = {IEEE Access}, title = {PHIL testing of a battery integrated multilevel converter combining real and emulated modules}, volume = {13}, year = {2025}, }
• (2025). Real-time DDoS attack detection in SDN based on en-CNN and OptiLGBM. ACCESS, 13, 161453-161471. (www)

  @article{ , abstract = {Distributed Denial-of-Service (DDoS) attacks pose serious risks to Software-Defined Networks (SDN), where existing detection methods often suffer from low accuracy, high latency, and high false positives. To address these issues, this study applies an adaptive Borderline-SMOTE (BS) method to balance the CICDDoS2019 dataset, effectively reducing false positives. Enhanced Convolutional Neural Networks (En-CNN) is designed to perform efficient traffic feature extraction with minimal latency, while Optimized LightGBM (OptiLGBM) provides fast and accurate classification through tuned parameters for rapid convergence. The proposed hybrid model integrates En-CNN and OptiLGBM. Both models are deployed within the Ryu SDN controller. Together, they form a real-time framework for DDoS attack detection and mitigation. Experimental results demonstrate an accuracy of 99.98% and an average response time of less than 1 second, outperforming baseline models. Real-network tests confirm that the system can block attacks and restore normal traffic within 3 seconds, highlighting its practical value for SDN security.}, archive = {J_ACCESS}, author = {Guoqing Yang and Xiaoying Wang and Chunhui Li and Xinyu Li and Jinsha Zhang and Fangfang Cui and Ruize Gu}, doi = {10.1109/ACCESS.2025.3608205}, journal = {IEEE Access}, pages = {161453-161471}, shortjournal = {IEEE Access}, title = {Real-time DDoS attack detection in SDN based on en-CNN and OptiLGBM}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection based on frequent pattern mining in smart home devices. ACCESS, 13, 161436-161452. (www)

  @article{ , abstract = {As the rapid development of the Internet of Things (IoT) continues, smart homes, which install, connect, and monitor smart devices, are becoming increasingly prominent. However, the short life cycle, improper usage, security vulnerabilities, and potential manufacturing deficiencies of smart devices may lead to the generation of anomalous data that do not accurately reflect real-world conditions, thereby compromising the functionality and reliability of smart home systems. In the literature, several works focus on handling anomalies in smart homes. However, due to the intractably large amount of reported data, detecting abnormal devices and correcting abnormal data remain challenging. In this work, we propose a novel method for detecting device anomalies in smart homes. Specifically, we propose a novel concept of Frequent ACT group, which groups frequently triggered devices together to enhance the efficiency of anomaly detection. Then, we develop a learning model that learns features of frequent ACT groups and a detection algorithm that identifies and corrects abnormal devices and their corresponding abnormal data based on extracted features. Experimental results on five different real-world datasets from the CASAS smart home project illustrate the advantages of our method compared to four efficient baseline methods in the literature in terms of precision, recall, and detection time. Furthermore, we examine the performance of our method under various sets of hyperparameters.}, archive = {J_ACCESS}, author = {Minh-Hoang Ngo and Hong-Ha do and Hoang-Dung Pham and Quang-Huy do and Hoai-Son Nguyen}, doi = {10.1109/ACCESS.2025.3603558}, journal = {IEEE Access}, pages = {161436-161452}, shortjournal = {IEEE Access}, title = {Anomaly detection based on frequent pattern mining in smart home devices}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal forecasting of bus arrival times using context-aware deep learning models in urban transit systems. ACCESS, 13, 161423-161435. (www)

  @article{ , abstract = {Accurate forecasting of bus arrival times is critical for enhancing the reliability and efficiency of public transportation systems. However, complex factors such as traffic congestion, weather conditions, and temporal variability make this task challenging. In this study, we propose a context-aware hybrid deep learning framework that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer architectures to predict stop-level bus travel times. The model is trained on a comprehensive dataset collected from 500 bus routes in Istanbul, spanning six months and incorporating real-time GPS data, GTFS schedules, and hourly weather attributes. A hybrid trend component is selectively introduced for data groups with fewer than 1000 samples to mitigate overfitting under sparse data conditions. Experimental results show that the trend-augmented LSTM model outperforms baseline architectures, achieving up to 28% improvement in MAE. The best-performing model yields an MAE of 2.97 minutes, a MAPE of 14.79%, and an $R^{2}$ value of 0.9272 across all test routes. Furthermore, condition-based evaluations demonstrate that prediction accuracy varies significantly across different time blocks, weather conditions, and day types. The proposed approach is both scalable and adaptable, offering a robust solution for real-time transit forecasting in complex urban environments.}, archive = {J_ACCESS}, author = {Osman Kaya and Mustafa Utku Kalay}, doi = {10.1109/ACCESS.2025.3609530}, journal = {IEEE Access}, pages = {161423-161435}, shortjournal = {IEEE Access}, title = {Spatio-temporal forecasting of bus arrival times using context-aware deep learning models in urban transit systems}, volume = {13}, year = {2025}, }
• (2025). Enhancing coffee leaf disease classification via active learning and diverse sample selection. ACCESS, 13, 161410-161422. (www)

  @article{ , abstract = {Accurate classification of coffee leaf diseases is essential for early intervention and improved crop yield. However, training deep learning models for this task often requires large labeled datasets, which can be expensive and time-consuming to obtain. To address this challenge, we propose an iterative active learning framework with diverse expansion sampling for coffee leaf disease classification, aimed at the need to reduce annotation costs while maintaining high classification accuracy in agricultural disease detection. Our method iteratively selects the most informative and representative samples for labeling, reducing annotation costs while maintaining high classification performance. Diverse expansion sampling leverages a per-class diversity-based selection method inspired by Greedy Furthest-Point Selection, to effectively capture the data distribution. Experimental results demonstrate that our approach significantly improves classification accuracy with fewer labeled samples compared to traditional supervised learning. The proposed method is evaluated on two coffee leaf disease datasets from different countries across various data scenarios. The results indicate that our active learning strategy achieves an improvement of 1.7% and 0.9% in F1 score on VIECOLD and BRACOL, respectively, using the same amount of labeled data as in the supervised approach. The results also show that using only 35% of labeled data for VIECOLD and 50% for BRACOL surpasses the performance achieved with the full dataset when applied to the same baseline model. This emphasizes the advantages of selective data utilization for enhanced generalization and efficiency. This research highlights the potential of active learning in optimizing data annotation for agricultural applications, making deep learning-based plant disease detection more accessible and efficient.}, archive = {J_ACCESS}, author = {Tri-Cong Pham and Tien-Nam Nguyen and Van-Dung Hoang and Van-Duy Nguyen}, doi = {10.1109/ACCESS.2025.3609540}, journal = {IEEE Access}, pages = {161410-161422}, shortjournal = {IEEE Access}, title = {Enhancing coffee leaf disease classification via active learning and diverse sample selection}, volume = {13}, year = {2025}, }
• (2025). Genetic optimization of heat transfer coefficients for LPTN models. ACCESS, 13, 161398-161409. (www)

  @article{ , abstract = {Nowadays, the main focus of the design process of electrical machines is typically electromagnetic analysis. However, as requirements have increased for electrical machines and drives with specific uses and higher efficiency, thermal analysis has become a more significant part of the design process. The temperature rise within a machine influences its output power and can also lead to the thermal degradation of significant parts of the machine, in particular the winding insulation and permanent magnets, if these are used in the machine construction. A precise estimate of temperature is crucial for the protection of critical machine components, which in turn requires an accurate prediction of machine temperatures. This can be achieved through various research methods, such as finite element analysis or analytical approaches, along with appropriate analysis methodologies. In this work, Lumped Parameter Thermal Networks are used. This analytical method is very convenient due to its need for low computing time and fast optimization execution. This publication focuses on the optimization of heat transfer coefficients using a genetic algorithm, which is a key factor in achieving accurate thermal analysis. Various methods for the estimation of these coefficients are evaluated and incorporated into the optimization process. In addition, graphical outputs of the calculations, including comparisons of the calculated and measured temperatures, for different methods used to approximate the heat transfer coefficients, are also presented in this paper. The measured temperatures were obtained on a fully automated test bench under stable conditions. The paper concludes with a discussion of the deviations between individual results, highlighting their impact on overall optimization accuracy. Moreover, an improved methodology for thermal analysis is suggested, enabling real-time, sensor-less temperature predictions.}, archive = {J_ACCESS}, author = {Martin Svetlik and Marek Toman and Daniel Wockinger and Lassi Aarniovuori and Jan Barta}, doi = {10.1109/ACCESS.2025.3609403}, journal = {IEEE Access}, pages = {161398-161409}, shortjournal = {IEEE Access}, title = {Genetic optimization of heat transfer coefficients for LPTN models}, volume = {13}, year = {2025}, }
• (2025). Explaining automations in IoT settings. ACCESS, 13, 161370-161397. (www)

  @article{ , abstract = {Automation systems are increasingly present in our everyday environments, driven by the availability of a growing number of sensors and connected devices. However, users often struggle to understand or control these systems due to a lack of clear understanding of their behaviour, especially when multiple automations are active simultaneously. This paper explores effective strategies for delivering explanations in such settings by comparing two interaction modalities: visual and conversational. This paper presents an approach to addressing such issues and a mixed-method, within-subjects study involving 16 participants that has provided valuable insights for the design of more transparent and explainable automated systems.}, archive = {J_ACCESS}, author = {Simone Gallo and Sara Maenza and Andrea Mattioli and Fabio Paternò}, doi = {10.1109/ACCESS.2025.3609900}, journal = {IEEE Access}, pages = {161370-161397}, shortjournal = {IEEE Access}, title = {Explaining automations in IoT settings}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven CMOS 8T-SRAM design: Optimization with tunable trade-offs in power, delay, and area. ACCESS, 13, 161355-161369. (www)

  @article{ , abstract = {Driven by the continuous advancement of complementary metal-oxide semiconductor (CMOS) technology under Moore’s Law, modern microprocessors integrate over a billion transistors per chip, leading to unprecedented complexity at the transistor level. To balance power consumption, delay, and area, designers require more sophisticated optimization tools; however, traditional manual tuning and heuristic methods struggle to navigate high-dimensional process design spaces. This study presents the first reinforcement learning-driven framework based on the Soft Actor-Critic (SAC) algorithm that enables multi-objective optimization with tunable trade-offs among dynamic power, static power, delay, and area. We implemented an automated Python–LTspice closed-loop system, allowing a reinforcement learning (RL) agent trained at a 50 nm node to iteratively optimize transistor configurations for an eight-transistor static random-access memory (8T-SRAM) cell. Experimental results at the training node demonstrate reductions of up to 65% in dynamic power, 73% in static power, 39% in propagation delay, and 21% in silicon area, depending on weight settings. Additional evaluations across the 22 nm and 90 nm nodes indicate that the trained model generalizes effectively, maintaining robust optimization performance at different scales. The model significantly improves the exploration efficiency and flexibility of the optimization process compared with traditional static weighting methods, resulting in superior multi-objective balancing capabilities.}, archive = {J_ACCESS}, author = {Bisheng Zhu and Runhui Chen and Chonglin Wang and Runan Wang and Yuheng Li and Zikai Liao}, doi = {10.1109/ACCESS.2025.3609488}, journal = {IEEE Access}, pages = {161355-161369}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven CMOS 8T-SRAM design: Optimization with tunable trade-offs in power, delay, and area}, volume = {13}, year = {2025}, }
• (2025). SPP-l²: A PPO-enhanced large language model framework for student performance prediction on learner-sourced questions. ACCESS, 13, 161344-161354. (www)

  @article{ , abstract = {In response to the growing complexity and dynamism of learner-sourced education platforms, this paper presents SPP-L2, a unified student performance prediction framework that integrates semantic understanding with reinforcement learning. The framework leverages a signed bipartite graph to capture structured student–question interactions and employs a large language model to encode the nuanced semantics of natural language questions. These representations are fused and used as input to a Proximal Policy Optimization (PPO) agent, which dynamically learns to predict student responses while receiving feedback signals for policy refinement. A value-based feedback mechanism further enhances the system’s ability to adaptively recommend questions and personalize interventions. Extensive experiments conducted on five real-world PeerWise course datasets demonstrate that SPP-L2 outperforms existing methods in terms of prediction accuracy, robustness, and adaptability. The proposed framework provides a principled and scalable solution for intelligent learning platforms by bridging representation learning, policy optimization, and feedback-driven adaptation.}, archive = {J_ACCESS}, author = {Zhedong Zhang and Xi He and Yingjiao Zhang}, doi = {10.1109/ACCESS.2025.3605947}, journal = {IEEE Access}, pages = {161344-161354}, shortjournal = {IEEE Access}, title = {SPP-l²: A PPO-enhanced large language model framework for student performance prediction on learner-sourced questions}, volume = {13}, year = {2025}, }
• (2025). An evolutionary game approach to multi-technology synergy in logistics information sharing for smart supply chains. ACCESS, 13, 161320-161343. (www)

  @article{ , abstract = {Logistics information sharing plays a pivotal role in fostering efficient collaboration within the smart supply chain. With the deepening integration of digital technologies such as the Internet of Things (IoT), blockchain, and Artificial Intelligence (AI), inter-enterprise information sharing strategies exhibit novel and dynamic evolutionary patterns. From the perspective of multi-technology collaboration, this paper develops a novel evolutionary game model between manufacturers and logistics service providers. Digital maturity is employed as a comprehensive evaluation index for technology integration. Four technological dimensions—synergy amplification, cost reduction, risk mitigation, and capability enhancement—are introduced as parameters to characterize the synergy effects of technologies. Through dynamic analysis of the model, we obtain the following findings: First, there exists a critical threshold of digital maturity in the strategic evolution of both players. When the overall digital maturity surpasses this threshold, the system shifts from a non-cooperative monostable equilibrium to a bistable equilibrium, where both information sharing and non-sharing coexist. Second, the technological parameters exhibit differentiated synergy effects. Specifically, synergy amplification and capability enhancement demonstrate a multiplier effect that significantly lowers the threshold, whereas cost reduction and risk mitigation exert influence through linear synergy. Third, sensitivity analysis reveals that technological input positively influences cooperative strategies. However, inputs associated with synergy amplification and capability enhancement are subject to diminishing marginal returns. Finally, numerical simulations validate the analytical results and further suggest that phased technological investments and cross-technology collaboration are optimal strategies for promoting information sharing and fostering sustainable cooperation within the supply chain.}, archive = {J_ACCESS}, author = {Ying Zhao and Ya-Juan Yang and Bing Luo}, doi = {10.1109/ACCESS.2025.3610030}, journal = {IEEE Access}, pages = {161320-161343}, shortjournal = {IEEE Access}, title = {An evolutionary game approach to multi-technology synergy in logistics information sharing for smart supply chains}, volume = {13}, year = {2025}, }
• (2025). Optimized IRS-aided UAV communication for 6G networks with phase and user association control. ACCESS, 13, 161311-161319. (www)

  @article{ , abstract = {Industry 5.0 relies on advanced automation, high speed connectivity, improved real-time communication support and efficient collaboration to foster an industry ecosystem that is sustainable, innovative and competitive. To promote human-machine collaboration and to support massive machine type communication (mMTC) in Industry 5.0, advanced sixth generation (6G) and uncrewed aerial vehicles (UAVs) technology play an important role. They offer capabilities of enhanced coverage, improved connectivity, remote monitoring, efficient data collection and advanced automation. This paper presents a 6G powered framework for UAV network assisted with intelligent reflecting surfaces (IRSs). The IRS-aided UAV communication exploits the IRS beamforming capabilities with UAV mobility. An optimal phase control strategy of IRS is presented considering the total power consumption at the IRS and UAV. Further, an association scheme is proposed that smartly assigns users to the specific IRSs for maximum line-of-sight (LoS) channel power gain with optimal phase control. The impact of transmit power $p_{max}$ , number of time slots and number of IRS elements N on the average sum rate is evaluated under the proposed association and phase control. It is observed that the proposed association scheme promises an average sum rate of 12.8 bps/Hz with $N = 500$ . Also, the sum rate further improves by 6.49% with optimal phase control. colorblackIn the end, the system incorporating random association scheme is compared with conventional system with no association introduced.}, archive = {J_ACCESS}, author = {Ashu Taneja and Nayef Alqahtani and Ali Alqahtani}, doi = {10.1109/ACCESS.2025.3608235}, journal = {IEEE Access}, pages = {161311-161319}, shortjournal = {IEEE Access}, title = {Optimized IRS-aided UAV communication for 6G networks with phase and user association control}, volume = {13}, year = {2025}, }
• (2025). An optimized 2D ground area coverage using UAV-enabled sensor networks. ACCESS, 13, 161299-161310. (www)

  @article{ , abstract = {To meet the demands of terrestrial sensor network applications, unmanned aerial vehicles (UAVs) are increasingly used for efficient ground coverage. This paper presents a two-step methodology for 2D ground coverage using UAV-enabled sensor networks, integrating antenna tilt modeling with angle-aware path optimization based on ant colony optimization (ACO). This combination enables more efficient turning angle control while maintaining competitive path lengths. In the first step, an optimal set of waypoints is generated for the movement of the UAV. This is achieved through aerial decomposition, incorporating the antenna footprint with and without tilting. The ground area is divided into UAV footprints, with each footprint’s center serving as a waypoint for the UAV’s movement. The decomposition is optimized using a metaheuristic approach to balance the inner and outer sensing coverage. The proposed method is evaluated using three optimization algorithms: particle swarm optimization (PSO), genetic algorithm (GA), and cuckoo optimization algorithm (COA). The second step focuses on path planning to visit all generated waypoints while minimizing travel distance and turning angles. The turning angle is measured in terms of the yaw angle, which defines the rotation of the UAV around its vertical axis. We employ an ACO-based coverage path planning algorithm to minimize the path length and the turning angle. Performance is compared with two existing methods, showing an approximate improvement of 32.78% in the turning angle reduction.}, archive = {J_ACCESS}, author = {Sipra Swain and Rakesh Ranjan Swain and Biswa Ranjan Senapati and Pabitra Mohan Khilar}, doi = {10.1109/ACCESS.2025.3608044}, journal = {IEEE Access}, pages = {161299-161310}, shortjournal = {IEEE Access}, title = {An optimized 2D ground area coverage using UAV-enabled sensor networks}, volume = {13}, year = {2025}, }
• (2025). CLARiC: Contrastive learning and root-cause inference with causality for explainable smart manufacturing. ACCESS, 13, 161279-161298. (www)

  @article{ , abstract = {This paper introduces CLARiC (Contrastive Learning and Root-cause Inference with Causality), a next-generation AI framework that unifies anomaly detection, causal inference, and explainable AI (XAI) techniques to advance the state of smart manufacturing. Unlike existing methods limited to correlation-based insights or opaque “black-box” models, CLARiC stands out in four key ways. First, it employs multimodal contrastive learning—fusing sensor streams, text logs, and images—to robustly distinguish normal from anomalous data, even under severe class imbalance or shifting operational conditions. Second, the approach extends beyond correlation by integrating causal inference (e.g., propensity score matching, difference-in-differences), thereby identifying high-impact factors that genuinely drive process deviations. Third, through counterfactual simulations in the learned embedding space and controlled real-world interventions, CLARiC verifies whether modifying specific parameters actually enhances manufacturing outcomes—reducing defects, boosting yield, and mitigating equipment drifts. Finally, the framework’s built-in XAI components deliver transparent, fine-grained explanations, enabling domain experts to trace anomalies back to root causes with clear causal pathways rather than post-hoc guesswork. Rigorous theoretical analyses confirm robust representation learning and consistent causal predictions across heterogeneous environments. Empirically, CLARiC lowered real-line abnormal rates by 34% in a 30-day WBG semiconductor trial and outperformed the best state-of-the-art baseline by $\approx ~3$ percentage points in accuracy and F1-score, underscoring its effectiveness from wide-band-gap semiconductors to automotive assembly lines. By bridging anomaly detection, interpretability, and validated causal interventions, CLARiC offers a decisive step forward in industrial AI for mission-critical manufacturing.}, archive = {J_ACCESS}, author = {Chin-Yi Lin and Tzu-Liang Tseng and Tsung-Han Tsai}, doi = {10.1109/ACCESS.2025.3607848}, journal = {IEEE Access}, pages = {161279-161298}, shortjournal = {IEEE Access}, title = {CLARiC: Contrastive learning and root-cause inference with causality for explainable smart manufacturing}, volume = {13}, year = {2025}, }
• (2025). Global inequality in research: A quantitative analysis of 1628 institutions in THE world university rankings 2025. ACCESS, 13, 161262-161278. (www)

  @article{ , abstract = {The Research Quality metric in the Times Higher Education World University Rankings (THE WUR) plays a central role in evaluating global academic performance. However, concerns remain about its fairness and inclusivity. In THE WUR, “Research Quality” is a 30% composite metric based entirely on four citation-derived indicators—Citation Impact, Research Strength, Research Excellence, and Research Influence. This study investigates 1,628 institutions from 97 countries included in THE WUR 2025, categorized into six global regions: Anglophone, Asia, Europe, Latin America, Middle East and North Africa (MENA), and Sub-Saharan Africa (SSA). The study examines how institutional and regional factors influence citation-based research performance, highlighting disparities across six world regions. Using descriptive statistics, correlation analysis, multiple regression models with regional indicators and interaction effects, and residual analysis, we analyze how these institutional and regional factors shape differences in citation-based performance. The results reveal that Anglophone and European universities significantly outperform others in Research Quality, even after adjusting for Research Environment, Teaching (as defined in the THE WUR Teaching metric), and International Outlook. Notably, international collaboration shows a strong positive association with citation impact, particularly for Latin American institutions. In contrast, a negative correlation between Teaching and Research Quality highlights potential trade-offs in institutional strategy. The analysis also highlights persistent structural disadvantages for non-Western regions stemming from linguistic barriers, limited publishing access, and underrepresentation in global citation networks. These results underscore the need for more inclusive and regionally sensitive evaluation frameworks. Policy implications include enhancing international research networks, supporting open access publishing, and expanding recognition of locally relevant scholarship to reduce structural inequities in global rankings.}, archive = {J_ACCESS}, author = {Taewan Kim and Tae-Kook Kim}, doi = {10.1109/ACCESS.2025.3609563}, journal = {IEEE Access}, pages = {161262-161278}, shortjournal = {IEEE Access}, title = {Global inequality in research: A quantitative analysis of 1628 institutions in THE world university rankings 2025}, volume = {13}, year = {2025}, }
• (2025). Leveraging autocorrelation in a dilated CNN-LSTM framework for predicting the US supreme court decisions. ACCESS, 13, 161250-161261. (www)

  @article{ , abstract = {This paper introduces a hybrid Dilated Convolutional Neural Network and Long Short-Term Memory (H-DCNN-LSTM) architecture for predicting judicial decisions from the United States Supreme Court data. By examining autocorrelation patterns in textual inputs, we identify multiscale features, ranging from high-frequency to low-frequency, which guide the design of dilated convolutions for more effective feature extraction. These dilated convolutional representations are then integrated with an LSTM to retain long-term dependencies within the legal text. Experimental evaluations show that our H-DCNN-LSTM consistently surpasses standalone LSTM and non-dilated hybrid CNN-LSTM models’ prediction accuracy while mitigating overfitting issues. Moreover, the autocorrelation-informed dilation rates provide an interpretable way to target specific textual patterns, illustrating the benefit of aligning model architectures with inherent data properties. This study contributes a new perspective on fusing multi-resolution convolutional processing with recurrent sequence modeling, demonstrating tangible gains in the domain of legal decision prediction and offering broader implications for natural language processing tasks.}, archive = {J_ACCESS}, author = {M. Salman Abbasi and Bakht Munir and Vikram Jayaram and Pablo Rivas}, doi = {10.1109/ACCESS.2025.3609282}, journal = {IEEE Access}, pages = {161250-161261}, shortjournal = {IEEE Access}, title = {Leveraging autocorrelation in a dilated CNN-LSTM framework for predicting the US supreme court decisions}, volume = {13}, year = {2025}, }
• (2025). Fuzzy sliding mode control for dynamic walking assistance in lower limb exoskeletons. ACCESS, 13, 161235-161249. (www)

  @article{ , abstract = {Controlling lower limb exoskeletons presents substantial challenges due to the nonlinear dynamics of human gait and the need for adaptation to varying user requirements and environmental conditions. To address these challenges a fuzzy sliding mode Control (FSMC) strategy as an advanced hybrid control solution for a 2-DOF lower limb exoskeleton robot designed to assist dynamic walking has been proposed in this paper. The key novelty of the proposed method lies in the integration of fuzzy logic into the sliding mode control framework, where the fuzzy inference system dynamically adjusts the control gains based on real-time tracking errors and their rates of change. This adaptive mechanism enables FSMC to leverage the advantage of the high robustness of sliding mode control and the smooth response characteristics of fuzzy logic systems. The proposed FSMC was benchmarked against classical sliding mode control(SMC) and Quasi-SMC across multiple walking scenarios, including variations in walking speed and random stop-start motions. Unlike these SMC methods, which often struggle with nonlinearities, parameter uncertainties, and gait variability, the proposed FSMC approach offers superior improved tracking and robustness outperforming both the classical SMC and Quasi-SMC. Across all gait scenarios normal, increased, and random FSMC consistently outperforms both conventional SMC and Quasi-SMC in chattering suppression while maintaining superior tracking precision. Key performance metrics demonstrate FSMC’s dominance reduction in Chattering Index (CI), lower Total Variation (TV), and decrease in Chattering Energy Index (CEI), all while achieving better tracking accuracy with lower Integral Square error(ISE). The system’s ability to reduce chattering further supports its suitability for applications where energy efficiency and reduced mechanical wear are critical.}, archive = {J_ACCESS}, author = {Tsega Hailemariam Gebrecherkos and Abrahaley Gebrehiwot Berhe and Saravanakumar Gurusamy and Chala Merga Abdissa}, doi = {10.1109/ACCESS.2025.3607679}, journal = {IEEE Access}, pages = {161235-161249}, shortjournal = {IEEE Access}, title = {Fuzzy sliding mode control for dynamic walking assistance in lower limb exoskeletons}, volume = {13}, year = {2025}, }
• (2025). PL-FSOD: Pseudo-labeling for few-shot remote sensing object Detection—Mitigating annotation gaps and forgetting. ACCESS, 13, 161217-161234. (www)

  @article{ , abstract = {Few-shot object detection (FSOD) in remote sensing images (RSIs), unlike those in natural images, is highly challenging yet critical. Key hurdles include difficulties and incomplete annotations, drastic scale variations, and high inter-class similarity. Existing methods often underutilize K-shot support samples, perform poorly on novel categories, suffer from overfitting that degrades base-class performance, and lack efficacy for small objects. To address these, we propose Pseudo-Labeling FSOD (PL-FSOD) based on a teacher-student network. We design an Attention Path Aggregation Feature Pyramid Network (APAFPN) to boost small-object detection via multi-scale attention fusion. The Multi-Branch Region Proposal Network (MBRPN) and Pseudo-Labeling Bounding Box Head (PLBBH) enhance proposal quality and novel-object recall. Additionally, a metric-based loss function strengthens classifier discriminative power.Experiments on DIOR, NWPU VHR10.v2 show our method outperforms best FSOD approaches, validating the effectiveness of all designed modules.}, archive = {J_ACCESS}, author = {Chao Li and Enyue Ji and Bingying Yao and Shenzhi Li and Peng Wang}, doi = {10.1109/ACCESS.2025.3606032}, journal = {IEEE Access}, pages = {161217-161234}, shortjournal = {IEEE Access}, title = {PL-FSOD: Pseudo-labeling for few-shot remote sensing object Detection—Mitigating annotation gaps and forgetting}, volume = {13}, year = {2025}, }
• (2025). Self-supervised masked deep embeddings for dental caries detection. ACCESS, 13, 161206-161216. (www)

  @article{ , abstract = {This study introduces an automated system employing deep learning-based object detection to identify dental caries from dental radiographs, leveraging advancements in self-supervised learning, particularly masked image modeling (MIM). We emphasize the benefits of self-pre-training, particularly in scenarios where acquiring suitable pre-training data is challenging. Our proposed Masked Deep Embedding of Patches (MDEP) pre-training method, an extension of the masked autoencoder (MAE) transformer, significantly improves dental caries detection performance compared to traditional baselines. Key contributions include utilizing self-supervised learning for limited dental radiograph data, replacing MAE’s patch reconstruction with the reconstruction of patch embeddings, and achieving notable performance enhancements in caries detection. This research paves the way for AI-driven solutions in dentistry, offering valuable tools for dentists and patients in the early detection and management of dental caries. Additionally, we validate the robustness and generalizability of our proposed method using another dataset, CariesXrays. Our findings indicate that MDEP achieves higher precision and recall rates, providing a significant improvement in diagnostic accuracy. The implications of this study extend beyond dental caries detection, suggesting that self-supervised learning can be effectively applied to other areas of medical imaging where data scarcity is a significant challenge.}, archive = {J_ACCESS}, author = {Amani Almalki and Abdulrahman Almalki and Longin Jan Latecki}, doi = {10.1109/ACCESS.2025.3606811}, journal = {IEEE Access}, pages = {161206-161216}, shortjournal = {IEEE Access}, title = {Self-supervised masked deep embeddings for dental caries detection}, volume = {13}, year = {2025}, }
• (2025). A new trend in the warehousing: A review of Human–Robot collaboration. ACCESS, 13, 161194-161205. (www)

  @article{ , abstract = {The rapid advancement of IoT and AI technologies has accelerated the development of human–robot collaborative warehouses, enabling real-time monitoring and intelligent automation. By combining human adaptability with robotic precision, this hybrid model offers greater flexibility than fully automated systems. However, existing research predominantly concentrates on specific collaboration tasks such as order picking and storage, while important areas like human-robot coexistence scenarios and inbound/outbound logistics remain underexplored. This imbalance highlights gaps in understanding dynamic task allocation, safety considerations, and process integration across different warehouse stages. This paper presents a comprehensive literature review on human–robot collaboration in warehouse environments, analyzing 52 representative studies across multiple dimensions. The review identifies key research gaps, including limited exploration of human-centered factors, insufficient integration of physical and decision-making collaboration, and a lack of unified frameworks for multi-agent and cross-mode coordination. To address these gaps, future research directions are proposed, focusing on dynamic cross-modal collaboration, trust and fatigue-aware systems, and leveraging natural language interfaces to enhance human-centered interaction. By emphasizing these aspects, this review aims to guide the design of more efficient, adaptive, and human-friendly collaborative warehouse systems that better meet the challenges of real-world operations.}, archive = {J_ACCESS}, author = {Yu Zhang and Songdong Xue}, doi = {10.1109/ACCESS.2025.3606967}, journal = {IEEE Access}, pages = {161194-161205}, shortjournal = {IEEE Access}, title = {A new trend in the warehousing: A review of Human–Robot collaboration}, volume = {13}, year = {2025}, }
• (2025). Optimal sizing and location of electric vehicle charging stations considering carbon footprint of construction. ACCESS, 13, 161180-161193. (www)

  @article{ , abstract = {The growing adoption of Electric Vehicles (EVs) in urban areas aims to reduce fuel costs and Carbon dioxide (CO2) emissions from conventional transportation. To support this transition, the expansion of Electric Vehicle Charging Stations (EVCSs) has become essential to meet the increasing charging demand. However, although EV usage can reduce CO2 emissions in the transportation sector, existing planning approaches often overlook the CO2 emissions generated by the construction of EVCSs. This omission may undermine the environmental benefits of EV adoption, as the cost of mitigating construction-related emissions could offset the savings from reduced transportation emissions. To address this issue, this study proposes a novel EVCS expansion framework that integrates construction-related CO2 emission cost into the overall optimization model. The proposed approach jointly considers the costs incurred by Charging Station Owners (CSOs), EV users, and the environmental impact from EVCS construction. A realistic urban road network and EV charging behavior are modeled in detail, and Binary Differential Evolution (BDE) is employed to determine the optimal sizing and locations of EVCSs. The results demonstrate that incorporating construction-related CO2 emission costs leads to a total cost reduction of 2.06% compared to conventional methods that do not consider such CO2 emissions. These findings emphasize the importance of incorporating environmental externalities, such as construction-related CO2 emissions, into infrastructure planning to reduce long-term costs, mitigate potential carbon tax impacts, and support cost-effective and sustainable EVCS deployment.}, archive = {J_ACCESS}, author = {Thanida Moekchanthuek and Siripat Somchit and Niphon Kaewdornhan and Rongrit Chatthaworn}, doi = {10.1109/ACCESS.2025.3609874}, journal = {IEEE Access}, pages = {161180-161193}, shortjournal = {IEEE Access}, title = {Optimal sizing and location of electric vehicle charging stations considering carbon footprint of construction}, volume = {13}, year = {2025}, }
• (2025). Research on auxiliary power supply system of guideway rubber-tires system. ACCESS, 13, 161171-161179. (www)

  @article{ , abstract = {Auxiliary power supply system is an important component of the train, whose power supply capacity is closely related to the safety and stability of the train. Compared with high-capacity urban rail transit such as subways and maglev trains, the main characteristics of the auxiliary power supply system of guideway rubber-tires system are small size and light weight. In this case, how to ensure the power quality and reliability of the auxiliary power supply system while achieving lightweight has become an urgent problem to be solved in current research of auxiliary power supply systems. Therefore, based on the circuit topology and control logic of each component of the auxiliary power supply system of guideway rubber-tires system, this article first completes the development of GUI simulation platform for auxiliary power supply system in MATLAB, and compares the simulation results with the actual vehicle experimental results to verify the accuracy of the simulation modeling. At the same time, in response to the problem of zero sequence circulating current caused by the independent control strategy of the two inverters originally used in the auxiliary inverter, this article proposes a centralized control strategy for two inverters, and uses simulation models to complete the simulation comparison for two control strategies, proving the advantage of centralized control strategy.}, archive = {J_ACCESS}, author = {Haida Xu and Liwei Zhang and Jiaxi Wu}, doi = {10.1109/ACCESS.2025.3606697}, journal = {IEEE Access}, pages = {161171-161179}, shortjournal = {IEEE Access}, title = {Research on auxiliary power supply system of guideway rubber-tires system}, volume = {13}, year = {2025}, }
• (2025). HistoCLIP: Multilabel classification framework for histopathological images based on cross-modal matching learning. ACCESS, 13, 161150-161170. (www)

  @article{ , abstract = {Contrastive Language-Image Pre-training (CLIP) has demonstrated remarkable downstream classification capabilities by aligning image representations with corresponding text embeddings. However, its direct application to the complex domain of histopathological images—where multiple tissue and cellular features co-occur—remains underexplored. In this study, we propose HistoCLIP, a CLIP-driven framework for the multilabel classification of histopathological images. At its core is cross-modal matching learning that fuses class-specific text embeddings—generated from a zero-shot classifier built on a set of carefully designed prompt templates—with visual features extracted by a frozen CLIP encoder. Specifically, the text embeddings function as queries, whereas the image features serve as keys and values in a multihead cross-attention module, enabling the model to relearn and redefine the alignment between linguistic and visual modalities. We evaluated the proposed HistoCLIP on two tissue-level benchmarks (BCSS-WSSS and LUAD-HistoSeg) and a cell-level multilabel dataset (PanNuke). The experimental results show that the proposed HistoCLIP achieves category average accuracies of 91.21 % on BCSS-WSSS, 95.07 % on LUAD-HistoSeg, and 88.24 % on PanNuke, respectively, significantly surpassing existing CLIP-based shallow multilayer perceptron head fine-tuned methods and pathology-pretrained models. These findings underscore HistoCLIP’s cross-domain adaptability in histopathological image analysis and suggest new directions for leveraging vision language models in digital pathology.}, archive = {J_ACCESS}, author = {Bingyuan Bai and Kazunori Miyata}, doi = {10.1109/ACCESS.2025.3609221}, journal = {IEEE Access}, pages = {161150-161170}, shortjournal = {IEEE Access}, title = {HistoCLIP: Multilabel classification framework for histopathological images based on cross-modal matching learning}, volume = {13}, year = {2025}, }
• (2025). Enhancing entity resolution through graph-based data augmentation and label noise identification. ACCESS, 13, 161134-161149. (www)

  @article{ , abstract = {Entity resolution is an important task in data integration and data cleaning. This study explores how graph-based techniques can address the challenges of data availability and label quality. An approach is formalized that leverages graph theory to enhance entity-resolution performance by augmenting datasets and identifying mislabeled data points. To evaluate three methods of the graph-based approach, that is data augmentation, consistency check, and loss-outlier detection, a framework to generate entity resolution datasets with varying properties such as label noise, class distribution, or amount of entities is developed. The methods are then evaluated using this framework and ten different real-world datasets. The main findings are as follows: On average, the amount of usable data can be increased by a factor of 2.5 without introducing additional label noise. Among the methods aimed at identifying label noise, the consistency check is able to detect approximately 16% of incorrectly labeled data points. The loss-based outlier detection method removes about 26% of the false labels, although this entails discarding 10% of the overall data points. Further analyses are conducted to investigate the factors influencing these methods and to better understand their mechanisms of action. The contributions of this study include: 1) formalizing previous preliminary work on graph-based augmentation, 2) developing a framework for entity-resolution dataset generation, and 3) providing in-depth evaluation of the performance and influencing factors of graph-based augmentation and label-noise identification.}, archive = {J_ACCESS}, author = {Tobias Rettenmeier and Alexander Jesser}, doi = {10.1109/ACCESS.2025.3608236}, journal = {IEEE Access}, pages = {161134-161149}, shortjournal = {IEEE Access}, title = {Enhancing entity resolution through graph-based data augmentation and label noise identification}, volume = {13}, year = {2025}, }
• (2025). Systematic design of a compact lumped-element outphasing class-E power amplifier for HF band. ACCESS, 13, 161116-161133. (www)

  @article{ , abstract = {This paper presents a novel design methodology for an outphasing Class-E power amplifier operating at 27.12 MHz, specifically addressing limitations inherent to traditional outphasing combiners that utilize distributed microstrip transmission lines for harmonic termination. At MHz frequencies, conventional microstrip techniques become impractical due to excessively large physical dimensions. To overcome this, the proposed design employs an entirely lumped-element combiner network, eliminating the need for distributed microstrip harmonic terminations. A rigorous analytical framework is derived using two-port impedance matrices, ensuring simultaneous impedance matching at both peak and back-off power conditions. Parametric studies optimize component values, resulting in enhanced efficiency and stable output power across a broad range of outphasing angles. A prototype employing GaN-based transistors demonstrates high efficiency of 73.2% at peak output power and maintains above 50% efficiency at 6 dB output back-off, validating the effectiveness and practicality of the proposed lumped-element approach. This compact and efficient configuration offers significant advantages for MHz-band RF transmitters and wireless power transfer systems.}, archive = {J_ACCESS}, author = {Khairul Hilmi Yusof and Farid Zubir and Mohamad Kamal A. Rahim and Narendra Kumar and Jagadheswaran Rajendran and Noorlindawaty Md Jizat and Peter Gardner and Thomas Johnson}, doi = {10.1109/ACCESS.2025.3609773}, journal = {IEEE Access}, pages = {161116-161133}, shortjournal = {IEEE Access}, title = {Systematic design of a compact lumped-element outphasing class-E power amplifier for HF band}, volume = {13}, year = {2025}, }
• (2025). Unveiling hidden patterns in infant cry audio: A multi-feature vision transformer approach with explainable AI. ACCESS, 13, 161103-161115. (www)

  @article{ , abstract = {The early detection and diagnosis of neonatal problems are critical to ensuring that an infant receives timely medical attention, which greatly enhances health outcomes. In this study, we propose a novel deep learning framework that listens to an infant’s cry to identify and diagnose six separate conditions: one being healthy and the other five comprising sepsis, respiratory distress syndrome, jaundice, hyperbilirubinemia, and vomiting. The study utilizes a rich dataset of infant cry recordings from which key acoustic features such as spectrograms, Mel-spectrograms, and Gammatone Frequency Cepstral Coefficients (GFCCs) are extracted. A sophisticated Vision Transformer (ViT) model was developed and meticulously fine-tuned to achieve an impressive 99% classification accuracy through cross-validation. To enhance the model’s interpretability, powerful explainable artificial intelligence (XAI) methods such as LRP, LIME, and attention imaging were implemented to clarify the reasoning behind the model’s outputs. Through cross-validation tests, the model’s trustworthiness and extensive generalizability were assessed. The findings underscore the promising capabilities of employing transformer-based deep learning frameworks along with multimodal acoustic features and explanatory methods to improve cry analysis in infants and their usable scopes in pediatric medicine.}, archive = {J_ACCESS}, author = {Ahmad Hasasneh and Sari Masri and Chakib Tadj}, doi = {10.1109/ACCESS.2025.3609459}, journal = {IEEE Access}, pages = {161103-161115}, shortjournal = {IEEE Access}, title = {Unveiling hidden patterns in infant cry audio: A multi-feature vision transformer approach with explainable AI}, volume = {13}, year = {2025}, }
• (2025). Enhancing stock price forecasting accuracy through compositional learning of recurrent architectures: A multi-variant RNN approach. ACCESS, 13, 161073-161102. (www)

  @article{ , abstract = {Accurate stock price forecasting is essential for profitable and risk-aware decision-making in volatile financial markets. Accordingly, this study proposes a forecasting method that integrates a robust composition of deep learning algorithms applied to well-structured financial data to enhance the predictive accuracy. A compositional learning approach was developed to systematically design and assess Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Unit (SRU), for effective multivariate time-series prediction. A total of 54 models (18 for each type) were created and tested, with hyperparameter tuning using both Random Search (RS) and Grey Wolf Optimizer (GWO). The results show that LSTM-GWO (1-1-0-1) performed best, with R-squared = 99.2427%, MAPE = 1.1721%, RMSPE = 1.6221%, RMSE = 339.3902, WI = 0.9981, NSE = 0.9924, and PBIAS = 0.0523, demonstrating high accuracy, low error, excellent model agreement, efficiency, and minimal bias. The GRU-GWO (2-1-1-1) and SRU-GWO (2-2-0-0) also showed improved results over RS optimization, confirming that GWO consistently boosts model accuracy and stability across all RNN variants. These findings confirm that a systematic architecture design combined with metaheuristic optimization significantly enhances stock forecasting performance. The proposed compositional RNN framework offers a reliable foundation for developing robust and accurate stock prediction systems that aid strategic investment decisions in dynamic and nonlinear financial markets.}, archive = {J_ACCESS}, author = {Irwan Kasse and Jeffry Kusuma and Armin Lawi and Amran Rahim}, doi = {10.1109/ACCESS.2025.3602721}, journal = {IEEE Access}, pages = {161073-161102}, shortjournal = {IEEE Access}, title = {Enhancing stock price forecasting accuracy through compositional learning of recurrent architectures: A multi-variant RNN approach}, volume = {13}, year = {2025}, }
• (2025). Hybrid attention-enhanced regularized convolutional autoencoder for robust EEG feature extraction in seizure prediction. ACCESS, 13, 161053-161072. (www)

  @article{ , abstract = {Electroencephalography (EEG)-based seizure prediction has garnered significant attention in epilepsy management, with deep learning methods enhancing prediction performance. However, persistent challenges include insufficient robust low-dimensional representations and poor generalization across datasets. Consequently, this study proposes a novel hybrid attention-enhanced regularized convolutional autoencoder (HA-RCAE) feature extraction method that generates low-dimensional features, effectively preserving critical biological information while mitigating technical batch effects in EEG signals. The HA-RCAE is augmented with regularization techniques and a novel hybrid attention mechanism for enhanced feature extraction. Coupled with classic machine learning classifiers, the proposed method outperforms advanced techniques on two epilepsy EEG datasets. On the Kaggle dataset, the model achieves area under the curve (AUC), sensitivity (SS), specificity (SP), and false positive rate (FPR) values of 90.6%, 89.1%, 89.8%, and 0.186/h, respectively; on the CHB-MIT dataset, it achieves AUC, SS, SP, and FPR values of 92.7%, 85.4%, 91.7%, and 0.144/h, respectively. The proposed method exhibits excellent generalizability, characterized by minimal performance discrepancies across datasets. Furthermore, its applicability extends to EEG anomaly detection, demonstrating superior performance and generalizability across clinical tasks. This comprehensive approach offers a robust solution for seizure prediction and other clinical applications, distinguished by strong generalization and negligible batch effects, rendering it promising for clinical implementation.}, archive = {J_ACCESS}, author = {Yu-Hao Chen and Hsiang-Yu Chen and Yu-Chia Chen and Hsuan-Fu Chen and Hsiang-Han Chen}, doi = {10.1109/ACCESS.2025.3608302}, journal = {IEEE Access}, pages = {161053-161072}, shortjournal = {IEEE Access}, title = {Hybrid attention-enhanced regularized convolutional autoencoder for robust EEG feature extraction in seizure prediction}, volume = {13}, year = {2025}, }
• (2025). TelekineticDealer: Contactless card manipulation using airborne Ultrasound—Evaluation of card actuation performance. ACCESS, 13, 161038-161052. (www)

  @article{ , abstract = {We present TelekineticDealer, a method for acoustically manipulating cards placed on a flat platform. Strong airborne ultrasoud can apply a steady force on objects through the air. Using Phased arrays of ultrasonic transducers, which focuses ultrasonic waves, cards made of paper can be actuated without any optional parts. In addition, cards larger than the ultrasonic wavelength can be stably manipulated by introducing a support plane. Targeted card motions are divided into in-plane motions such as translation and rotation, and out-of-plane motions such as flip, stand, and vibration. The manipulation methods are described and evaluated separately for each motion. For example, the gradual standing up of the card was achieved at 10°/s and the translation of the card at 31 mm/s. At the same time, several limitations were found, including the shape and weight of the card and device aperture. Our method is expected to be applied to card game assistants using physical cards, displays for paper cutouts, and other tangible user interfaces.}, archive = {J_ACCESS}, author = {Gakuto Arakawa and Shun Suzuki and Takaaki Kamigaki and Yasutoshi Makino and Hiroyuki Shinoda}, doi = {10.1109/ACCESS.2025.3609774}, journal = {IEEE Access}, pages = {161038-161052}, shortjournal = {IEEE Access}, title = {TelekineticDealer: Contactless card manipulation using airborne Ultrasound—Evaluation of card actuation performance}, volume = {13}, year = {2025}, }
• (2025). Design of a narrow-beamwidth reflectarray antenna for K/Ka-band using a variable rotation technique and a dual-circularly polarized corrugated horn feed. ACCESS, 13, 161026-161037. (www)

  @article{ , abstract = {This paper presents a new design for a reflectarray antenna (RAA) that utilizes a variable rotation technique (VRT) and features a compact, single-layer substrate without the use of vias or air gaps. The proposed design achieves performance comparable to that of complex, multi-layer structures in terms of high gain, broad bandwidth, circular polarization, aperture efficiency, and low sidelobe and cross-polarization levels. Additionally, it offers the narrowest beamwidth among similar designs, making it a promising solution for point-to-point communication systems that require precise directionality. The resonance characteristics of the proposed RAA unit cell have been analyzed using characteristic mode analysis (CMA). Each element in the RAA is rotated to a specific angle, creating the required reflection phase distribution across the array and steering the main beam toward the desired direction. Furthermore, to enhance overall performance, the RAA is center-fed by a wideband, dual-circularly polarized corrugated horn antenna. The main beam direction is set at ( $\theta _{0} = -20^{\circ } $ , $\phi _{0} = 90^{\circ } $ ), with a focal ratio of $f/D = 1.1$ , which was selected to maximize aperture efficiency. A reflecting aperture measuring 143.43 mm $\times 143.43$ mm, consisting of $37\times 37$ elements, was fabricated and tested. The proposed RAA exhibits a measured 1.5-dB axial ratio bandwidth of 32% and a 3-dB gain bandwidth of 27.5%, with a measured peak gain of 28.6 dBic. The measured total aperture efficiency (TAE) reaches 40.5% at 25 GHz. The measured sidelobe and cross-polarization levels are less than −19.2 dB and −26.3 dB, respectively.}, archive = {J_ACCESS}, author = {Mohamed Elhefnawy and Kyoung-Hun Kim and Wang-Sang Lee}, doi = {10.1109/ACCESS.2025.3606535}, journal = {IEEE Access}, pages = {161026-161037}, shortjournal = {IEEE Access}, title = {Design of a narrow-beamwidth reflectarray antenna for K/Ka-band using a variable rotation technique and a dual-circularly polarized corrugated horn feed}, volume = {13}, year = {2025}, }
• (2025). Speech enhancement for electrolarynx devices using M-RLS: Intelligibility improvement and low-power hardware feasibility. ACCESS, 13, 161016-161025. (www)

  @article{ , abstract = {An electrolarynx, a widely used assistive device for voice restoration for individuals with laryngeal impairments, often suffers from mechanical buzz and poor intelligibility, limiting its everyday applicability. This work presents a modified recursive least squares (M-RLS) algorithm specifically designed to enhance electrolarynx speech by attenuating the excitation noise characteristic while preserving speech quality. The proposed work is evaluated on data across 44 speech samples and benchmarked against a baseline method using three objective metrics: the perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and the signal-to-noise ratio (SNR). The proposed M-RLS approach demonstrated notable improvements, with PESQ scores reaching 3.01, a median STOI value of 0.7, and an SNR performance comparable to that of the baseline. In addition, to evaluate its feasibility in embedded systems, the algorithm was synthesized in $15nm$ technology to form an application-specific integrated circuit (ASIC), confirming its suitability for real-time, low-power speech enhancement applications. The design occupies 0.289 mm2, operates at 1 GHz, and consumes only 16.9 mW, yielding a power-delay product of 15.41 pJ, making it well suited for energy-constrained environments such as electrolarynx-based speech enhancement systems. The proposed method is also quick prototyped via a DE10 field programmable gate array (FPGA) with a maximum operating frequency of 253.8 MHz.}, archive = {J_ACCESS}, author = {M. Madhushankara and Ribu Mathew and H. Muralikrishna and Suchitra N. Shenoy and B. S. Darshan and Rakshitha Lakshman Rao}, doi = {10.1109/ACCESS.2025.3605590}, journal = {IEEE Access}, pages = {161016-161025}, shortjournal = {IEEE Access}, title = {Speech enhancement for electrolarynx devices using M-RLS: Intelligibility improvement and low-power hardware feasibility}, volume = {13}, year = {2025}, }
• (2025). The research on a knowledge graph-based method for detecting version differences in metering foundations. ACCESS, 13, 161003-161015. (www)

  @article{ , abstract = {This paper addresses the version management challenges faced by metering automation systems in the context of smart grids by proposing a knowledge graph-based method for version difference detection and localization, named I-OVDL (Index-based Optimized Version Difference Location with Incremental Updates). As metering automation systems evolve toward distributed microservice architectures, independently updated subsystems have led to severe version fragmentation. Traditional text-based version control methods exhibit significant limitations in detecting semantic differences and providing real-time responses. The main contributions of this study are threefold: First, we construct a domain-specific ontology for the metering system version knowledge graph and apply the TransE algorithm for embedding representation, enabling the mapping from code changes to business semantics, with a 28.6% improvement in accuracy. Second, we design a weighted difference measurement model across four dimensions—functionality, interface, performance, and dependency—using the entropy weight method to determine the weighting coefficients for each dimension. Third, we introduce an indexing optimization technique based on locality-sensitive hashing, which reduces the complexity of version retrieval from O(n) to sublinear time. Experimental results on a dataset containing 685 real system versions demonstrate that the I-OVDL method achieves a 93.6% difference detection rate (a 30.1% improvement over traditional methods), 92.47% feature coverage, and reduces version localization time from 120 seconds to 6.8 seconds. In a provincial power grid pilot application, the method helped reduce regression test cases by 37%, significantly improving operation and maintenance efficiency.}, archive = {J_ACCESS}, author = {Lukun Zeng and Sheng Li and Jingxu Yang and Mingqi Wu and Guanyu Chen and Lin Lu and Hewen Zheng}, doi = {10.1109/ACCESS.2025.3609451}, journal = {IEEE Access}, pages = {161003-161015}, shortjournal = {IEEE Access}, title = {The research on a knowledge graph-based method for detecting version differences in metering foundations}, volume = {13}, year = {2025}, }
• (2025). AI-driven personalized privacy assistants: A systematic literature review. ACCESS, 13, 160982-161002. (www)

  @article{ , abstract = {In recent years, several personalized assistants based on AI have been researched and developed to help users make privacy-related decisions. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can provide significant benefits for users, who might otherwise struggle with making decisions about their personal data in online environments that often overload them with different privacy decision requests. So far, no studies have systematically investigated the emerging topic of AI-driven PPAs, classifying their underlying technologies, architecture and features, including decision types or the accuracy of their decisions. To fill this gap, we present a Systematic Literature Review (SLR) to map the existing solutions found in the scientific literature, which allows reasoning about existing approaches and open challenges for this research field. We screened several hundred unique research papers over the recent years (2013-2025), constructing a classification from 41 included papers. As a result, this SLR reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SLR, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.}, archive = {J_ACCESS}, author = {Victor Morel and Leonardo Horn Iwaya and Simone Fischer-Hübner}, doi = {10.1109/ACCESS.2025.3609188}, journal = {IEEE Access}, pages = {160982-161002}, shortjournal = {IEEE Access}, title = {AI-driven personalized privacy assistants: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Advances in intrusion detection approach in industry 4.0 systems using evolving continual deep learning. ACCESS, 13, 160962-160981. (www)

  @article{ , abstract = {Deep Learning (DL) detects intrusions in Industry 4.0 applications based on the Internet of Things (IoT) since it can grasp complex patterns from large datasets and identify irregularities. Current DL-based techniques for IoT-based Industry 4.0 suffer from catastrophic forgetting, computational inefficiency, security and privacy concerns, and false positive predictions. A new intrusion detection method for Industry 4.0 systems employing Evolving Continual DL (ECDL) addresses these issues. Model flexibility, accuracy, computational efficiency, security, and privacy are ECDL goals. ECDL includes local IoT model data gathering, pre-processing, feature engineering, and classification. In pre-processing, the problem of missing values and noisy data is addressed, and then it is normalized to facilitate the training process. In ECDL, each IoT model pre-processes and trains locally obtained data with advanced DL layers. The methodology is adaptable since the proposed DL layers are connected with Incremental Learning (IL), allowing for the integration of new IoT data and learning without the need for extensive re-training. The locally trained models are sent to the global model, where the Federated Learning (FL) technique is employed for aggregation. FL-based trained model disseminated to each local IoT model and SoftMax classifier for intrusion detection. In ECDL, FL with IL trains feature learning locally and adaptively in dynamic IoT contexts, resulting in complete efficiency. Implemented in real time using relevant IoT datasets, ECDL is assessed for accuracy, computational complexity, model flexibility, security, and privacy. Each IoT model uses Raspberry Pi, and the global server model uses Jetson Xavier. ECDL has 3.9% greater accuracy and 16.77% lower computing complexity than previous methods. The ECDL delivers effective security for Industry 4.0 design and implementation with model flexibility.}, archive = {J_ACCESS}, author = {Shubhangee Kishan Varma and Vibha Vyas}, doi = {10.1109/ACCESS.2025.3609003}, journal = {IEEE Access}, pages = {160962-160981}, shortjournal = {IEEE Access}, title = {Advances in intrusion detection approach in industry 4.0 systems using evolving continual deep learning}, volume = {13}, year = {2025}, }
• (2025). DriveSense: A noise-resilient framework for driving mode identification. ACCESS, 13, 160939-160961. (www)

  @article{ , abstract = {Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.}, archive = {J_ACCESS}, author = {Praveen Kumar and Wan Li and Burak Ozpineci}, doi = {10.1109/ACCESS.2025.3608615}, journal = {IEEE Access}, pages = {160939-160961}, shortjournal = {IEEE Access}, title = {DriveSense: A noise-resilient framework for driving mode identification}, volume = {13}, year = {2025}, }
• (2025). Integrating machine learning models with probability distribution methods for extreme flood risk assessment. ACCESS, 13, 160922-160938. (www)

  @article{ , abstract = {Climate change exacerbates flooding risks, a frequent and devastating natural calamity, particularly in flood-prone regions such as Pakistan. Constraints on computation and inadequate data might cause traditional flood prediction models to suffer. This paper proposes a novel hybrid framework for enhancing flood risk prediction and mapping by integrating statistical approaches with machine learning methods. Using 23 years of precipitation data, we evaluate six statistical distributions, including the Generalized Extreme Value (GEV) distribution, the Gumbel distribution, the normal distribution, the Log Pearson III (LP III) distribution, the Log-Normal distribution, and the Gamma distribution. Three Goodness-of-fit tests, along with the visualization technique, Quantile plots, were used to identify the most suitable distribution methods. The GEV was identified as the best model for predicting extreme events. Furthermore, by demonstrating its resilience in identifying flood-prone locations, with an AUC of 0.96 and PBIAS of −1.2% (training) and −1.4% (validation), the suggested ensemble demonstrated 98% accuracy in training and 98% accuracy in validation. This suggests a minor underestimation of flood risks with low bias and good model generalization. By combining return period estimates and flood susceptibility mapping, the hybrid model provides valuable insights for disaster management and infrastructure design. Not only in Pakistan but also in other flood-prone areas globally, thereby supporting climate adaptation and flood mitigation techniques.}, archive = {J_ACCESS}, author = {Narmeen Fatima and Haya Mesfer Alshahrani and Hatim Dafaalla and Randa Allafi and Changgyun Kim and Muhammad Syafrudin and Norma Latif Fitriyani}, doi = {10.1109/ACCESS.2025.3598121}, journal = {IEEE Access}, pages = {160922-160938}, shortjournal = {IEEE Access}, title = {Integrating machine learning models with probability distribution methods for extreme flood risk assessment}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of cogging torque reduction strategies in axial-field flux-switching permanent-magnet machines. ACCESS, 13, 160894-160921. (www)

  @article{ , abstract = {Axial-field flux-switching permanent-magnet machine (AFFSPMM) is an outstanding topology for sustainable applications. This structure encompasses several advantageous features, including compact volume, high power and torque density, worthy rigidity, and a separate assembly. These attributes have rendered this type of machine an optimal choice for the mentioned purpose as well as electric transportation. The use of a permanent magnet (PM) in this type of machine can lead to the production of cogging torque (COT), which is due to the saliency of the stator and rotor parts. This phenomenon has led to concerns among machine researchers and designers regarding the presence of noise, vibration, torque ripple, and speed fluctuations. This drawback can be mitigated either in design and structural considerations or in control and excitation methods. The complexity and commercial concerns are the main motives for focusing on the machine parts modification rather than the control aspects. In this paper, first, a brief statement of the design procedure, the essence of COT, and the problem statement are introduced. An in-depth review of various design strategies aimed at reducing COT is then presented. Consequently, a comprehensive comparison is presented, including an evaluation of the advantages, disadvantages, cost, complexity, and other relevant factors, to help researchers, designers, and manufacturers select the most appropriate strategies for each application considering the requirements. The review not only involves the key achievements of the topologies but also provides insight into the methodologies where the Finite Element Method (FEM) and experimental results authenticate the effectiveness. Indeed, the IEEE standard 1812-2023 considered to measure COT in both dynamic and static modes.}, archive = {J_ACCESS}, author = {Hamid Ebrahimi and Hossein Torkaman and Alireza Sohrabzadeh and Hamid Javadi}, doi = {10.1109/ACCESS.2025.3607371}, journal = {IEEE Access}, pages = {160894-160921}, shortjournal = {IEEE Access}, title = {A comprehensive review of cogging torque reduction strategies in axial-field flux-switching permanent-magnet machines}, volume = {13}, year = {2025}, }
• (2025). Cryptanalysis and modification of yang et al.’s certificateless multi-user matchmaking encryption scheme. ACCESS, 13, 160887-160893. (www)

  @article{ , abstract = {Matchmaking encryption, which is a cryptographic primitive that enables two-sided access control for both senders and receivers, has attracted significant attention from cryptography community due to its broad range of applications. Recently, Yang et al. proposed a certificateless multi-user matchmaking encryption scheme that was claimed to achieve IND-CCA2 security (IEEE Transactions on Information Forensics and Security, 2024). In this paper, we propose a plaintext recovery attack against their scheme under an adaptive chosen ciphertext attack model. We further identify critical flaws in both the scheme’s design and its accompanying security proof. To address these issues, we propose a revised construction that mitigates the identified vulnerabilities and restores the intended security guarantees. Our modification incurs only minimal overhead; hence, the computational and communication efficiency of the modified scheme remains almost equivalent to that of the original design.}, archive = {J_ACCESS}, author = {Jiseung Kim and Hyung Tae Lee}, doi = {10.1109/ACCESS.2025.3608453}, journal = {IEEE Access}, pages = {160887-160893}, shortjournal = {IEEE Access}, title = {Cryptanalysis and modification of yang et al.’s certificateless multi-user matchmaking encryption scheme}, volume = {13}, year = {2025}, }
• (2025). Comprehensive comparison of modular transverse and axial-modular flux-switching permanent magnet machine. ACCESS, 13, 160874-160886. (www)

  @article{ , abstract = {This paper proposes a novel modular transverse flux-switching permanent magnet (MTFSPM) machine, which is evolved from the axial-modular FSPM (AM-FSPM) machine and the transverse flux (TF) machine. Both the MTFSPM and AM-FSPM machines belong to the category of modular magnetic field modulation machines. A comparative analysis between the MTFSPM machine and the AM-FSPM machine is then conducted. Based on the magnetic field modulation theory, the similarities and differences between the magnetic field harmonics of the MTFSPM machine and the AM-FSPM machine are analyzed. From the perspective of harmonic contributions to torque, the reason for the high output torque capability of the MTFSPM machine is revealed. Furthermore, based on the torque-sizing equation, the output torque of the MTFSPM machine is compared with that of the AM-FSPM machine from the perspectives of magnetic load and electrical load, establishing the mapping relationship between output torques and machine parameters. It is found that the MTFSPM machine has higher output torque due to its transverse magnetic circuit and higher pole arc coefficient. Subsequently, a comparison of the electromagnetic performances of the MTFSPM and AM-FSPM machines is conducted, showing that the MTFSPM machine exhibits lower torque ripple and better overload capability. Finally, experimental validation is conducted.}, archive = {J_ACCESS}, author = {Yangyang Ma and Wenle Song and Junlei Zhao and Lei Wang and Shenghui Mu and Tianli Liu and Biao Zhang}, doi = {10.1109/ACCESS.2025.3602406}, journal = {IEEE Access}, pages = {160874-160886}, shortjournal = {IEEE Access}, title = {Comprehensive comparison of modular transverse and axial-modular flux-switching permanent magnet machine}, volume = {13}, year = {2025}, }
• (2025). KAN-MID: A kolmogorov-arnold networks-based framework for malicious URL and intrusion detection in IoT systems. ACCESS, 13, 160855-160873. (www)

  @article{ , abstract = {The growing sophistication of cyberattacks, especially with the ubiquity of the Internet of Things (IoT), highlights the urgent need for stronger detection and protection strategies. With millions of victims impacted by escalating threats and facing substantial financial repercussions, the importance of effective cyber-defense has reached unprecedented levels. This paper presents KAN-MID, a cybersecurity-aware framework that utilizes Kolmogorov-Arnold Networks (KANs) to detect malicious URLs and enhance intrusion detection systems (IDS). To the best of our knowledge, this is the first work to leverage KANs as a unified deep learning framework for cybersecurity tasks across three benchmark datasets CSE-CIC-IDS2018, ISCX-URL2016, and CIC-IoT-2023, demonstrating its effectiveness in both general network and IoT-specific threat detection. Experimental results on the CSE-CIC-IDS2018 and ISCX-URL2016 datasets show that the proposed framework achieves rapid and accurate predictions with inference times below 0.032 milliseconds in various scenarios. It achieved 100% testing accuracy in detecting botnet, DoS, DDoS, and brute force attacks, and 99.81% for spam URLs. The model demonstrates exceptional effectiveness in multi-class classification, achieving an accuracy rate of 98.21% for all attacks assessed in the ISCX-URL2016 dataset. Thorough evaluations conducted with benchmark datasets validate the outstanding model’s performance compared to existing deep learning techniques.}, archive = {J_ACCESS}, author = {Frederic Rizk and Rodrigue Rizk and Dominick Rizk and Patrick Rizk and Chee-Hung Henry Chu}, doi = {10.1109/ACCESS.2025.3605171}, journal = {IEEE Access}, pages = {160855-160873}, shortjournal = {IEEE Access}, title = {KAN-MID: A kolmogorov-arnold networks-based framework for malicious URL and intrusion detection in IoT systems}, volume = {13}, year = {2025}, }
• (2025). Model-free adaptive sliding mode control of parallel platform actuated by shape memory alloys. ACCESS, 13, 160845-160854. (www)

  @article{ , abstract = {This paper investigates the input coupling problem in a shape memory alloy (SMA) actuated parallel platform characterized by fully unknown nonlinear dynamics. In such a platform, the displacement of one actuator simultaneously affects the others owing to inherent coupling effects. The convergence of coupling phenomena and the nonlinear characteristics of SMA actuators renders bidirectional swing control exceptionally challenging, particularly under conditions of unknown dynamics. To address these limitations, a model-free adaptive sliding mode control (MFA-SMC) method is proposed to improve control accuracy for complex platform motions while avoiding the intricate modeling process typically required for SMA-actuated parallel platforms. Leveraging model-free adaptive control (MFAC) theory, a multiple-input, multiple-output (MIMO) dynamic linearized mathematical model of the parallel platform is developed using real-time input and output system data. Subsequently, a sliding mode controller is formulated based on this dynamic model. A rigorous theoretical analysis demonstrates that the proposed model-free adaptive sliding mode controller can effectively and stably manage the SMA-actuated parallel platform, with its tracking error being uniformly ultimately bounded (UUB). Lastly, experiments are performed on the developed SMA-actuated parallel platform. The results indicate that the proposed model-free controller is both feasible and effective, enabling precise control of the system to achieve complex bidirectional swing motion.}, archive = {J_ACCESS}, author = {Binwu Ma and Hongshuai Liu and Lina Hao}, doi = {10.1109/ACCESS.2025.3609314}, journal = {IEEE Access}, pages = {160845-160854}, shortjournal = {IEEE Access}, title = {Model-free adaptive sliding mode control of parallel platform actuated by shape memory alloys}, volume = {13}, year = {2025}, }
• (2025). Control strategy improvement of active power filters for harmonic elimination in heavy rail systems with installed PV arrays. ACCESS, 13, 160829-160844. (www)

  @article{ , abstract = {Electric train transportation requires a substantial amount of electrical energy to power trains’ propulsion systems. Consequently, photovoltaic (PV) arrays are considered for installation at traction substations in heavy rail systems to supply this energy. However, nonlinear loads in heavy rail systems cause power quality issues that affect the grid distribution system. To mitigate these issues, an active power filter (APF) is employed to eliminate harmonics and improve the power factor. This paper introduces an APF control strategy for heavy rail systems integrated with installed PV arrays. The proposed harmonic detection method for calculating the APF reference current has been improved to achieve fast computation and more accurate reference current value. In addition, the injection of compensating current by the APF is controlled using a model predictive current control strategy, which incorporates a current injection model through a transformer for high voltage system. The MPC controller effectively compensates for digital delays and minimizes the ripple in the compensating current. Real-time simulations conducted on OPAL RT-LAB hardware was used to verify the performance of the proposed control strategy. The experimental results confirm that the proposed APF control strategy effectively eliminates harmonics and complies with IEEE 519-2022 and MEA standards for power factor correction in heavy rail systems.}, archive = {J_ACCESS}, author = {Chakrit Panpean and Jeerawan Homjan and Sasiya Udomsuk and Kongpol Areerak}, doi = {10.1109/ACCESS.2025.3609518}, journal = {IEEE Access}, pages = {160829-160844}, shortjournal = {IEEE Access}, title = {Control strategy improvement of active power filters for harmonic elimination in heavy rail systems with installed PV arrays}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv8 architecture for object detection in sonar imagery with improved localization accuracy. ACCESS, 13, 160810-160828. (www)

  @article{ , abstract = {This paper introduces two enhanced YOLOv8-based models, SPD-LKA-YOLO4H and SPD-LKA-YOLO4H-CBAM, to address key challenges in sonar imagery object detection, such as low visibility and small object detection. SPD-LKA-YOLO4H integrates Space-to-Depth convolution (SPD-Conv) to preserve fine-grained details and Large Kernel Attention (LKA) to enhance global and local feature representations. A further refined variant, SPD-LKA-YOLO4H-CBAM, incorporates a Convolutional Block Attention Module (CBAM) to refine the feature selection and suppress noise. A high-resolution detection head is also included to improve small object detection. The extensive evaluation on a combination of controlled and challenging real-world sonar datasets demonstrated consistent performance improvements. Specifically, the SPD-LKA-YOLO4H model achieved superior performance on a real-world dataset, demonstrating its generalization ability with a strong balance of accuracy and speed. The SPD-LKA-YOLO4H-CBAM model, although slightly slower, achieved a 13.21% gain in mAP@50–95 on the complex UATD dataset, making it ideal for tasks requiring high precision. The study also highlighted that the efficacy of these enhancements is highly sensitive to module placement, underscoring the critical role of architectural optimization. Overall, the proposed models strike an excellent balance between detection precision and computational efficiency, positioning them as promising solutions for real-time autonomous underwater applications.}, archive = {J_ACCESS}, author = {Somying Thainimit and Kittitouch Kitiwouranon and Achiraya Buaphuan and Peerayot Sanposh}, doi = {10.1109/ACCESS.2025.3607955}, journal = {IEEE Access}, pages = {160810-160828}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv8 architecture for object detection in sonar imagery with improved localization accuracy}, volume = {13}, year = {2025}, }
• (2025). Explainable AI for reliable defect recognition and localization in non-destructive testing with millimeter-wave imaging. ACCESS, 13, 160799-160809. (www)

  @article{ , abstract = {Millimeter-wave imaging is a promising technique for non-destructive testing (NDT). It is a contactless sensing modality, it can penetrate many optically opaque materials, and millimeter-waves are not harmful to the operator. To enable automated defect recognition in millimeter-wave images, artificial intelligence (AI), such as deep learning, has been investigated in the research community. One problem associated with deep learning, however, is that the network’s decisions are not based on analytical criteria, but on a non-linear optimization process during training. Because of this, the bases for the network’s predictions are not transparent. This “black box” behavior limits confidence, which restricts the use of deep learning in NDT so far. To make deep learning networks more transparent, explainable AI (XAI) techniques have been introduced in the machine learning community. In this study, we introduce explainable AI to millimeter-wave based defect detection. It enables an understanding of how the deep-learning network classifies defects. At the same time, shortcomings of the deep learning network can be made visible. Since explainable AI provides visual maps of important image regions, it is possible to perform a basic defect localization without the need for a specific localization network. This approach is termed “weakly supervised object localization”. In this paper we discuss both defect classification and defect localization based on XAI techniques. Experimental demonstration is performed by means of a millimeter-wave imaging dataset of PVC objects with artificial defects.}, archive = {J_ACCESS}, author = {Ingrid Ullmann and Jan Schür}, doi = {10.1109/ACCESS.2025.3609437}, journal = {IEEE Access}, pages = {160799-160809}, shortjournal = {IEEE Access}, title = {Explainable AI for reliable defect recognition and localization in non-destructive testing with millimeter-wave imaging}, volume = {13}, year = {2025}, }
• (2025). Utilizing rPPG signal synchronization and deep learning techniques for deepfake video detection. ACCESS, 13, 160786-160798. (www)

  @article{ , abstract = {As deepfake technology advances, it poses increasing risks by blurring the line between reality and manipulated content. This challenge particularly threatens the integrity of information in contexts such as propaganda and misinformation. To address this, the paper introduces a novel approach for detecting deepfake videos using physiological signals, specifically remote photoplethysmography (rPPG) signals, obtained from the left and right cheeks. These physiological signals are complex to convincingly alter in tampered video frames, making them reliable indicators of authenticity; thus, we propose the novel method Sync_rPPG, which evaluates signal similarity by employing statistical measures such as Signal to Noise Ratio (SNR), Power Spectral Density (PSD), Mean Absolute Deviation (MAD), Standard Deviation (SD), and Pearson Correlation coefficient (PCC). To enhance signal analysis, we apply the Discrete Wavelet Transform (DWT) to decompose data into time and frequency components, enabling the detection of periodic pattern anomalies. Furthermore, the approach integrates spatial and temporal information, achieving an impressive average accuracy of 94% on CelebDF and 98% on FaceSwap Datasets, thus exceeding the performance of existing state-of-the-art (SOTA) methods. The research conducted makes a significant contribution to the field of media forensics but also opens up new avenues for deep learning applications in biometric analysis and signal processing.}, archive = {J_ACCESS}, author = {A. Susi and V. Akila and V. Govindasamy}, doi = {10.1109/ACCESS.2025.3604336}, journal = {IEEE Access}, pages = {160786-160798}, shortjournal = {IEEE Access}, title = {Utilizing rPPG signal synchronization and deep learning techniques for deepfake video detection}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of centralized and federated intrusion detection in IoT-enabled cyber-physical systems under data and label-skew. ACCESS, 13, 160767-160785. (www)

  @article{ , abstract = {Cyber-Physical Systems (CPS) increasingly leverage Internet of Things (IoT) technologies to enable seamless communication and control across distributed devices. However, the decentralized and heterogeneous nature of IoT-enabled CPS increases their susceptibility to cyberattacks, including unauthorized access and probing attacks. Intrusion Detection Systems (IDS) in CPS commonly employ machine learning (ML) and deep learning (DL) models, typically trained in centralized learning (CL) or federated learning (FL) settings. Both approaches, however, face significant challenges due to the non-independent and identically distributed (non-IID) nature of IoT data, particularly under data and label skew conditions, which adversely affect model convergence and detection performance. While existing studies have primarily focused on data skew, the combined impact of data and label skew remains underexplored. Moreover, prior evaluations have relied on limited performance metrics, providing an incomplete understanding of learning behavior and system robustness. This study addresses these gaps through a comprehensive evaluation of CL and FL for IDS in IoT-enabled CPS under both IID and non-IID conditions, with an emphasis on data and label skew scenarios. Performance is assessed using an extended set of metrics, including accuracy, precision, recall, F1-score, training loss, test loss, and client-level fairness indicators such as F1-score variance and fairness gap ( $\Delta $ F1-score) across clients. Experimental results show FL outperforms CL in IID settings with better accuracy and stability. In non-IID environments, FL is vulnerable, especially with label skew, leading to poor convergence, lower accuracy, and more client disparity, even with complex models. While FL handles data skew moderately, it struggles with label imbalance and client dropout. These findings highlight limitations of standard FL in heterogeneous IoT deployments and emphasize the need for fairness-aware, client-adaptive learning for robust intrusion detection in real-world CPS.}, archive = {J_ACCESS}, author = {Muhammad Ali Khan and Rao Naveed Bin Rais and Osman Khalid and Mohamed Deriche}, doi = {10.1109/ACCESS.2025.3608856}, journal = {IEEE Access}, pages = {160767-160785}, shortjournal = {IEEE Access}, title = {Comparative analysis of centralized and federated intrusion detection in IoT-enabled cyber-physical systems under data and label-skew}, volume = {13}, year = {2025}, }
• (2025). Real-time whole-body model predictive control for versatile 3D bipedal locomotion via novel kino-dynamic model. ACCESS, 13, 160755-160766. (www)

  @article{ , abstract = {Achieving versatile 3D bipedal locomotion in real-time is essential for humanoid robots navigating complex terrains such as stairs and uneven terrains. This paper extends our previous research on real-time WB-MPC by proposing a novel whole-body model predictive control (WB-MPC) framework that enables real-time 3D locomotion through an innovative kino-dynamic model integrating the virtual-mass-ellipsoid inverted pendulum model (VIPM) with the full kinematic model (FKM). Thanks to the dynamic properties of VIPM, the proposed kino-dynamic model accounts for the vertical center of mass motion, enabling stable locomotion on stair-like terrains. Furthermore, it also ensures real-time performance by maintaining continuity in the dynamics of the optimization problem. The modularized multi-layer perceptron based warm-start strategy and the ZMP-based whole-body controller, which were used in previous work to enhance the real-time performance and control effectiveness of the WB-MPC framework, have been modified and adapted in this study to suit the proposed model. Validated through simulations and real robot experiments on the humanoid TOCABI, our framework demonstrates stable stair ascent and descent with a WB-MPC latency below $18.25~ms$ and convergence in an average of 3.14 iterations. Moreover, the real-time capability of our proposed WB-MPC framework significantly improves balance recovery performance under external perturbations compared to existing algorithms.}, archive = {J_ACCESS}, author = {Junhyung Kim and Kwanwoo Lee and Sanghyuk Park and Jaeheung Park}, doi = {10.1109/ACCESS.2025.3602063}, journal = {IEEE Access}, pages = {160755-160766}, shortjournal = {IEEE Access}, title = {Real-time whole-body model predictive control for versatile 3D bipedal locomotion via novel kino-dynamic model}, volume = {13}, year = {2025}, }
• (2025). Hydrothermal air-conditioner optimal control scheduler using random forest-gradient boosting hybrid model and genetic algorithm. ACCESS, 13, 160737-160754. (www)

  @article{ , abstract = {This research aims to formulate an optimal control algorithm for a hydrothermal air-conditioning system, with the objective of minimizing energy consumption while simultaneously ensuring occupant comfort. Traditional air-conditioning systems often exhibit difficulties in effectively responding to nonlinear dynamic characteristics and variable environmental conditions. Although centralized control strategies can improve energy efficiency, they frequently do not adequately fulfill thermal comfort requirements. To mitigate these challenges, this study introduces an optimal control scheduler that amalgamates machine learning and optimization methodologies, specifically utilizing Random Forest, Gradient Boosting, and Genetic Algorithms. The proposed control scheduler consists of two principal phases. Initially, a forecasting model is constructed using a hybrid approach that integrates Random Forest for predicting water tank temperature and Gradient Boosting for forecasting indoor temperature. In the subsequent phase, an optimization framework is established employing a Genetic Algorithm, aimed at minimizing energy costs and occupant discomfort. This optimization model dynamically adjusts the comfort temperature range based on real-time occupancy estimations, thereby facilitating efficient and adaptive air-conditioning control. The efficacy of the optimal control scheduler was validated in a real-world testbed environment, where its energy efficiency and comfort maintenance were compared to manual control. The findings revealed an 11.41% reduction in energy consumption relative to manual control, alongside an improvement in the comfort maintenance rate from 56.2% to 91.7%, thereby affirming the effectiveness of the proposed approach in enhancing both energy efficiency and thermal comfort.}, archive = {J_ACCESS}, author = {Hyeongyu Son and Jeongju Park and Musu Kim and Younggyu Hwang and Sekyung Han}, doi = {10.1109/ACCESS.2025.3605843}, journal = {IEEE Access}, pages = {160737-160754}, shortjournal = {IEEE Access}, title = {Hydrothermal air-conditioner optimal control scheduler using random forest-gradient boosting hybrid model and genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). QARR-QGF: A dual-module data augmentation framework for enhanced few-shot question answering. ACCESS, 13, 160722-160736. (www)

  @article{ , abstract = {Few-shot question answering (QA) aims to train effective QA models using very limited annotated data, but existing approaches often struggle to generalize due to insufficient training examples. This paper proposes a two-stage data augmentation framework, called Question-Answer Replacement and Removal and Question Generation and Filtering (QARR-QGF), to address this challenge. The QARR module enhances pretraining data by systematically replacing and removing question-answer pairs to create more diverse examples. During fine-tuning, the QGF module generates paraphrased questions and applies semantic filtering to retain high-quality training samples. The framework is evaluated using three widely used generative models: Longformer-Encoder-Decoder (LED), BART, and T5, on the SQuAD, HotpotQA, and Natural Questions datasets. Experimental results show that QARR-QGF consistently improves performance across all datasets and few-shot settings. For example, the QARR-QGF-T5 model achieves F1 scores of 82.3% on SQuAD, 59.9% on HotpotQA, and 59.0% on Natural Questions in the 16-shot setting, outperforming previous state-of-the-art methods. These results demonstrate the effectiveness of QARR-QGF in improving few-shot QA performance by generating richer and more diverse training data.}, archive = {J_ACCESS}, author = {Siao Wah Tan and Chin Poo Lee and Kian Ming Lim and Ali Alqahtani}, doi = {10.1109/ACCESS.2025.3609472}, journal = {IEEE Access}, pages = {160722-160736}, shortjournal = {IEEE Access}, title = {QARR-QGF: A dual-module data augmentation framework for enhanced few-shot question answering}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based predictive control of vehicle-to-grid onboard charger back stage models. ACCESS, 13, 160707-160721. (www)

  @article{ , abstract = {To address the insufficient modeling accuracy and slow dynamic response of the Capacitor-Inductor-Inductor-Capacitor (CLLC) resonant converter in Vehicle-to-Grid (V2G) automotive onboard charger, a deep learning-based model predictive control method for the post-stage V2G onboard charger is proposed. This approach utilizes deep learning techniques to perform steady-state implicit modeling of the CLLC resonant converter, enabling accurate calculation of voltage gain across a diverse array of switching frequencies and varying load conditions. Based on this model, a deep learning model predictive control (DL-MPC) strategy is developed. The DL-MPC method leverages deep learning-based voltage gain predictions for direct multi-step control sequence prediction. Simulations demonstrate that the proposed DL-MPC strategy outperforms conventional proportional-integral (PI) and model predictive control (MPC) control, effectively suppressing voltage overshoot and undershoot, achieving reductions of up to 60.9%, and reducing recovery time by up to 55.6% under load transients and reference voltage step changes, thereby enhancing dynamic response and stability. Experimental validation that DL-MPC achieves a 65.21% reduction in voltage dips and a 31.25% faster recovery time compared to PI control under load transients. Simulation and experimental results consistently show that the deep-learning-based modeling attains high fidelity, while the proposed DL-MPC strategy delivers superior control performance for the CLLC resonant converter, yielding a markedly improved dynamic response.}, archive = {J_ACCESS}, author = {Yongquan Zhang and Weidong Xue and Shuaibing Li and Yongqiang Kang and Junmin Zhu and Haiying Dong}, doi = {10.1109/ACCESS.2025.3606020}, journal = {IEEE Access}, pages = {160707-160721}, shortjournal = {IEEE Access}, title = {Deep learning-based predictive control of vehicle-to-grid onboard charger back stage models}, volume = {13}, year = {2025}, }
• (2025). A hierarchical approach to trajectory planning for autonomous vehicles on curvy roads. ACCESS, 13, 160692-160706. (www)

  @article{ , abstract = {This paper introduces a three-layer trajectory planner to enhance the planning speed and kinematics of intelligent vehicles, navigating dynamic traffic environments on curvy roads. Within the coarse trajectory layer, we present the innovative jump node dynamic programming (DP) algorithm, designed to circumvent trajectory searches for overlapping nodes. In contrast to the conventional DP algorithm, the decision-making efficiency achieved with the jump node DP algorithm demonstrates a substantial improvement of up to 30.6% within simulated environments. Transitioning to the reference trajectory layer, we put forth an improved DP strategy. This strategy is forged through the integration of the jump node DP algorithm and the joint interpolation method, permitting the generation of a crafted reference trajectory. In the alternate trajectory layer, segments of the trajectory characterized by substantial curvature, as planned by the improved DP strategy, undergo initial replanning through the adaptive cruise control (ACC) strategy. Subsequently, these trajectory segments revert to the improved DP strategy after traversing specific waypoints within the receding horizon. Simulation cases illustrate that the minimum radius of curvature in the planned trajectory can be augmented by up to 12.7%. All proposed algorithms have undergone dynamic simulation experiments, affirming their effectiveness.}, archive = {J_ACCESS}, author = {Fuzhou Zhao and Heye Huang and Xuemei Wang and Ling Han and Fei Ye and Mingyang Cui}, doi = {10.1109/ACCESS.2025.3609394}, journal = {IEEE Access}, pages = {160692-160706}, shortjournal = {IEEE Access}, title = {A hierarchical approach to trajectory planning for autonomous vehicles on curvy roads}, volume = {13}, year = {2025}, }
• (2025). Influence of corona discharge plasma treatment on the antioxidant and neuroprotective applications of eclipta prostrata linn.. ACCESS, 13, 160674-160691. (www)

  @article{ , abstract = {This study investigates the impact of corona discharge plasma treatments on the phytochemical composition, antioxidant activity, and neuroprotective potential of Eclipta prostrata Linn. (Kameng) grown from plasma-treated seeds. Plasma treatments were applied at varying voltages ( $\pm \; 15$ , $\pm \; 22.5$ , and $\pm \; 30$ kV) and durations (30, 60, and 90 seconds) under both positive and negative polarities. Positive polarity treatments, particularly at + 30 kV for 90 seconds, significantly enhanced the levels of key bioactive compounds, including wedelolactone ( $6.75 \; \pm \; 0.01$ mg/g), total phenolic content ( $73.23 \; \pm \; 0.87$ mg GAE/g), and total flavonoid content ( $120.39 \; \pm \; 1.47$ mg QE/g). DPPH antioxidant activity assay, measured through IC50 values, showed remarkable improvement, with the lowest IC50 recorded at $2.76 \; \pm \; 0.01 \; \mu $ g/mL. Positive polarity also optimized germination rates (up to 77.67%) and promoted plant growth with enhanced root and straw lengths. Plasma-treated extracts demonstrated significant neuroprotective effects against A $\beta 42$ and MPP+-induced toxicity in SH-SY5Y cells, correlating with elevated wedelolactone, phenolic acid and flavonoid levels. The findings reveal the potential of plasma technology as a non-thermal, eco-friendly method to enhance the bioactive properties and therapeutic applications of E. prostrata. This study provides valuable insights into the scalability and broader implications of plasma treatments for agricultural and pharmaceutical industries.}, archive = {J_ACCESS}, author = {Weerasak Samee and Sarin Tadtong and Khanit Matra and Anusara Kueadet and Ameena Hawaekuelong and Ukrit Marung and Jiraporn Promping}, doi = {10.1109/ACCESS.2025.3590058}, journal = {IEEE Access}, pages = {160674-160691}, shortjournal = {IEEE Access}, title = {Influence of corona discharge plasma treatment on the antioxidant and neuroprotective applications of eclipta prostrata linn.}, volume = {13}, year = {2025}, }
• (2025). Variance-covariance regularization improves continual learning. ACCESS, 13, 160664-160673. (www)

  @article{ , abstract = {In this work, we explore the benefits of Variance-Covariance Regularization in Continual Learning (CL). Neural networks suffer from abrupt performance loss when updated with additional data. Numerous CL approaches try to mitigate this problem by preserving the already accumulated knowledge within the network. We propose to look at this problem from a different perspective and analyze how we can prepare the continually trained model for future changes. With a series of initial experiments, we show that increasing the diversity of extracted features can improve the classifier’s robustness, enhancing its immunity to future changes. Based on this observation, we introduce Variance-Covariance Regularization in Continual Learning. As we explore previously ignored aspects of CL, our method is orthogonal to most of the CL techniques. It can be combined with them, yielding a significant performance gain across different datasets, training scenarios, and architectures.}, archive = {J_ACCESS}, author = {Piotr Hondra and Daniel Marczak and Kamil Deja}, doi = {10.1109/ACCESS.2025.3603221}, journal = {IEEE Access}, pages = {160664-160673}, shortjournal = {IEEE Access}, title = {Variance-covariance regularization improves continual learning}, volume = {13}, year = {2025}, }
• (2025). Mitigating disruptions in transportation and logistics through adaptive neuro-fuzzy inference-based supply chain resilience. ACCESS, 13, 160646-160663. (www)

  @article{ , abstract = {To evaluate and improve supply chain resilience across various industries, including food manufacturing, this paper investigates the application of adaptive neuro-fuzzy inference systems (ANFIS) in assessing supply chain resilience, with a focus on transportation and logistics. ANFIS models were developed and validated using two real-world industrial cases: a local plastic manufacturer and a global food manufacturer, to capture and analyze relationships within supply chains across key attributes, including security, survivability, recovery, technological capabilities, and sustainability. This research highlights the effectiveness of the fuzzy inference system (FIS), including membership function types within the ANFIS framework, in achieving computational efficiency and precision for modeling dynamic supply chain systems. The models were validated using two standard metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE), ensuring accurate predictions. Results showed that the global food company had lower average errors, indicating a more stable and reliable supply chain with a testing error of 5%. In contrast, the local plastic company exhibited higher errors in some areas, with an average error of 8%, suggesting areas for improvement. Overall, this study underscores the potential of ANFIS as a powerful tool for providing valuable insights into the strengths and weaknesses of supply chains, thereby offering a robust approach to enhancing their resilience. The findings suggest that companies can significantly benefit from adopting this neuro-fuzzy model (i.e., ANFIS), particularly when combined with continuous data refinement and targeted improvements to address specific vulnerabilities in their supply chains.}, archive = {J_ACCESS}, author = {Murad Samhouri and Majdoleen Abualeenein and Maha Al-Sheikh}, doi = {10.1109/ACCESS.2025.3607194}, journal = {IEEE Access}, pages = {160646-160663}, shortjournal = {IEEE Access}, title = {Mitigating disruptions in transportation and logistics through adaptive neuro-fuzzy inference-based supply chain resilience}, volume = {13}, year = {2025}, }
• (2025). Thermal analysis of digital transceiving unit (DTU) for 400 kg-class communication satellite considering on-orbit environment. ACCESS, 13, 160638-160645. (www)

  @article{ , abstract = {Low Earth Orbit(LEO) communication satellites have emerged as a key technology driving the evolution of global communications technology, owing to their capacity for high data transmission rates and extensive coverage area. In the field of communication satellites, there is an increasing demand for enhancement in payloads, with the objective of performing real-time data processing capabilities. This necessity has given rise to the densification of the high heat generating Electrical, Electronic and Electromechanical(EEE) parts. This causes the junction temperature of the EEE parts to rise, which has devastating effects on the performance, lifetime, and reliability of the payloads. Ultimately, the entire satellite system can lead to mission failure, so thermal design with prior thermal analysis and testing is required. In this study, a steady-state thermal analysis of the digital transceiving unit(DTU) of a payload for a LEO communication satellite was performed in the on-orbit environment. DTU is highly integrated with high-performance EEE parts due to its mission to relay communication signals on communication satellites. During the mission, the temperature distribution of the EEE parts under the worst cold and hot conditions was verified to ensure that the temperature requirements of the DTU were met. Furthermore, derating analysis of temperature was conducted to assess the thermal stability and reliability of EEE parts.}, archive = {J_ACCESS}, author = {Jiye Bak and Hyun-Guk Kim and Jungil Cho and Seong-Ju Lee and Won-Seok Lee and Kyoung Youl Park and Ki Chul Kim}, doi = {10.1109/ACCESS.2025.3609380}, journal = {IEEE Access}, pages = {160638-160645}, shortjournal = {IEEE Access}, title = {Thermal analysis of digital transceiving unit (DTU) for 400 kg-class communication satellite considering on-orbit environment}, volume = {13}, year = {2025}, }
• (2025). Improved accelerometer-based calibration method for high-precision horizontal guidance. ACCESS, 13, 160627-160637. (www)

  @article{ , abstract = {The precision of attitude determination in horizontal directional drilling relies significantly on accelerometer-based inclinometers. Effective calibration is essential to mitigate sensor errors, including scale factor, bias, and misalignment. In this study, deterministic errors are modeled within a unified calibration matrix framework, enabling strategic selection of reference positions to improve accuracy. Each element of the $3\times 3$ calibration matrix is shown to depend on the spatial orientation of the sampling points, revealing a directional sensitivity in calibration accuracy. To address this, A novel calibration approach, termed equal angle interval balance correction (EABC), is proposed to minimize these orientation-dependent calibration matrix errors and to evenly distribute calibration errors across selected reference orientations, substantially reducing systematic deviations.}, archive = {J_ACCESS}, author = {Longhai Wei and Haitao Yan and Xialin Liu and Jiguo Liu and Feng Huang}, doi = {10.1109/ACCESS.2025.3607889}, journal = {IEEE Access}, pages = {160627-160637}, shortjournal = {IEEE Access}, title = {Improved accelerometer-based calibration method for high-precision horizontal guidance}, volume = {13}, year = {2025}, }
• (2025). Detection to framework for traffic signs using a hybrid approach. ACCESS, 13, 160619-160626. (www)

  @article{ , abstract = {Traffic sign detection plays a crucial role in autonomous driving systems, yet remains a challenging task due to factors such as small object sizes, distant placements, complex lighting conditions, and frequent occlusions. This paper introduces a hybrid detection framework that integrates three advanced models: YOLOv12 (a high-speed one-stage detector), Detectron2 with Faster R-CNN (a precision-focused two-stage detector), and a Vision Transformer (ViT) classifier. The proposed system is trained and evaluated on the Self-Driving Cars Dataset (SDCD) from Roboflow, featuring 15 traffic sign classes in complex urban environments. Detection outputs are merged using an overlapping box fusion strategy, and a ViT module further verifies predictions, resolves conflicting labels, and filters false positives. Experimental results demonstrate that the hybrid system achieves 97.7% mAP@50 and 88.3% mAP@50:95, outperforming individual detectors and previous hybrid approaches. Qualitative analysis shows substantial improvements in detecting small, occluded, and overlapping signs. The inclusion of ViT reduces both false positives and missed detections. While this ensemble increases computational complexity, it offers enhanced robustness, making it suitable for safety-critical applications. Future work will focus on improving inference efficiency and validating the system on larger and more diverse datasets such as TT100K to assess generalization performance across wider environments.}, archive = {J_ACCESS}, author = {Issam Dagher and Antonio Makary}, doi = {10.1109/ACCESS.2025.3606754}, journal = {IEEE Access}, pages = {160619-160626}, shortjournal = {IEEE Access}, title = {Detection to framework for traffic signs using a hybrid approach}, volume = {13}, year = {2025}, }
• (2025). Technical advancements of laterally constrained inversion for geophysical datasets. ACCESS, 13, 160602-160618. (www)

  @article{ , abstract = {Laterally Constrained Inversion (LCI) is presently a popular method for robust interpretation of geophysical data, particularly in defining subsurface structures with greater lateral continuity. The traditional standalone 1D inversion methods, while computationally efficient, often produce unrealistic discontinuities between adjacent soundings in the retrieved models. LCI overcomes this limitation by enforcing lateral constraints that enforce smooth variation across adjacent models, producing quasi-2D results that are more realistic in some geological settings. This paper presents a systematic review of the recent advancement in LCI and its methodological extensions like Weighted LCI (WLCI), Iterative LCI (ILCI), Minimum Gradient Support LCI (MGS-LCI), and Bayesian LCI. All these techniques offer more flexibility while handling heterogeneous subsurface conditions, hard contacts, and uncertainty estimation. A full methodology is given, with emphasis on mathematical formulae and regularization strategies for controlling lateral as well as vertical smoothing. Incorporation of a priori geological knowledge and constraint design, resolution versus smoothness trade-offs, and computational considerations are also covered. A case study from the Horn River Basin, Canada, demonstrates the application of LCI approaches to airborne TEM data and indicates improved model continuity, geological coherence, and interpretability. This review points out that while LCI methods have significantly enhanced the potential of geophysical inversion, future research has to be focused on hybrid methods, automatic tuning of constraints, large-scale algorithms, and multi-modal data fusion. These developments will be crucial to dealing with increasingly larger and more complex datasets and delivering more accurate and reliable subsurface models for a variety of geoscientific and engineering applications.}, archive = {J_ACCESS}, author = {Farhad Ali Memon and Hai Li and Mubashir Hussain and Peng Yuan and Ahmed M. Beshr}, doi = {10.1109/ACCESS.2025.3608240}, journal = {IEEE Access}, pages = {160602-160618}, shortjournal = {IEEE Access}, title = {Technical advancements of laterally constrained inversion for geophysical datasets}, volume = {13}, year = {2025}, }
• (2025). ViTRA: Vision transformer with relative position embedding attention for low-light image quality improvement. ACCESS, 13, 160588-160601. (www)

  @article{ , abstract = {Image enhancement is a crucial task in computer vision that aims to improve the quality of images and enhance visual elements, making them more informative and aesthetically pleasing. Generative Adversarial Networks (GANs) have emerged as a powerful tool in the field, showing promise in enhancing and restoring image quality. Despite their potential, GAN-based methods often face challenges such as instability, high computational costs, and the risk of generating unwanted visual artifacts that degrade the final image quality. To address these issues, this study introduces a novel architecture called ViTRA, which combines the capabilities of the Vision Transformer (ViT) with a Relative Positional Encoding (RPE)-based attention mechanism. The ViTRA architecture is integrated into the HVI-CIDNet framework, specifically within the Light Cross-Attention Block (LCAB). By incorporating RPE, the architecture is enhanced, leading to the creation of a new attention mechanism known as the Relative Light Cross-Attention Block (RLCAB). This innovation allows for more effective feature extraction and attention distribution across the image, resulting in better image enhancement and fewer artifacts. Additionally, ViTRA implements automatic loss function weight optimization using Comprehensive Learning Particle Swarm Optimization (CLPSO) which is designed to improve stability and efficiency during model training. Experimental results demonstrate that the ViTRA architecture significantly improves image quality in both qualitative and quantitative terms when compared to traditional GAN and transformer-based approaches. On various benchmark datasets, including the LOLv2-Syn dataset, ViTRA outperforms existing models across all test metrics. Specifically, it achieves a Peak Signal-to-Noise Ratio (PSNR) of 25.716, a Structural Similarity Index (SSIM) of 0.946, and a Learned Perceptual Image Patch Similarity (LPIPS) of 0.0446, highlighting its superior performance in producing high-quality images. This work establishes ViTRA as a promising solution for enhancing image quality, overcoming common limitations in GAN and transformer-based models, and providing a robust approach for future image enhancement applications.}, archive = {J_ACCESS}, author = {Irfan Darmawan and Alam Rahmatulloh and Rohmat Gunawan and R. Wahjoe Witjaksono and Ghatan Fauzi Nugraha}, doi = {10.1109/ACCESS.2025.3608457}, journal = {IEEE Access}, pages = {160588-160601}, shortjournal = {IEEE Access}, title = {ViTRA: Vision transformer with relative position embedding attention for low-light image quality improvement}, volume = {13}, year = {2025}, }
• (2025). Toward reliable knowledge graphs in virtual product development: Data strategies and reproducibility assessment. ACCESS, 13, 160576-160587. (www)

  @article{ , abstract = {The integration of knowledge graphs into industrial product development workflows offers a promising solution for managing complex and heterogeneous data to enhance development efficiency. However, concrete strategies for their deployment, particularly with respect to data quality, integration, and framework implementation, remain underexplored. This study addresses this gap by investigating the data-related requirements and strategies essential for constructing robust and reproducible knowledge graphs within the context of virtual product development. Building on a proposed framework within the context of the BMW Group, we identify critical factors including the use of ontologies, standardized data formats, the implementation of unique and versioned entity identifiers, as well as data quality and integration strategies. To validate the reproducibility and correctness of the framework, we conduct a comparative analysis using two independent example datasets. Both knowledge graphs were constructed under identical conditions and evaluated in terms of completeness and accuracy. Our results confirm that the framework reliably generates high-quality knowledge graphs across varying domains with minimal manual intervention.}, archive = {J_ACCESS}, author = {Carolin Lehmacher and Kristin Paetzold-Byhain}, doi = {10.1109/ACCESS.2025.3609662}, journal = {IEEE Access}, pages = {160576-160587}, shortjournal = {IEEE Access}, title = {Toward reliable knowledge graphs in virtual product development: Data strategies and reproducibility assessment}, volume = {13}, year = {2025}, }
• (2025). Adaptive federated learning with local large language models for modeling photonic and chemical systems. ACCESS, 13, 160559-160575. (www)

  @article{ , abstract = {We present AFL–L, an LLM-augmented federated learning (FL) framework for modeling photonic devices and chemical reactions under strict data-privacy constraints. Each client enriches its local data with a domain-specific prompt to a frozen large language model (LLM), then performs proximal-regularized updates that are aggregated on the server under the Gaussian mechanism with client-level $\ell _{2}$ clipping. Each update is clipped to a fixed radius C, yielding per-round sensitivity $S_{r}{=}2C/m$ for m participating clients, and noise scaled by $\sigma $ ensures rigorous $(\varepsilon,\delta)$ -DP. We provide theoretical guarantees, proving an $O(T^{-1/2})$ worst-client descent rate for non-convex objectives and a linear rate under $\gamma $ -strong convexity, consistent with the empirical decay of gradient norms. On two real-world benchmarks, AFL–L improves low-percentile client accuracy by $4{-}7$ points and reduces communication volume by $1.8\times $ compared to FedProx. Grounding prompts in cognitive semantics enables the LLM to bridge symbolic descriptors (reaction schemas, device layouts) and sub-symbolic representations, showing that meaning-aware conditioning—not mere token frequency—drives the observed gains. Finally, we sketch an extension toward a federation of heterogeneous expert models, where secure aggregation can combine updates from specialized LLMs or surrogate genetic-algorithm experts without compromising convergence or privacy guarantees.}, archive = {J_ACCESS}, author = {Koffka Khan}, doi = {10.1109/ACCESS.2025.3606855}, journal = {IEEE Access}, pages = {160559-160575}, shortjournal = {IEEE Access}, title = {Adaptive federated learning with local large language models for modeling photonic and chemical systems}, volume = {13}, year = {2025}, }
• (2025). AIvaluate: A multi-agent framework for automated answer script evaluation using large language models and semantic vector indexing. ACCESS, 13, 160549-160558. (www)

  @article{ , abstract = {In conventional educational environments, it is labor-intensive, subjective, and susceptible to human error to hand-mark descriptive answers. This article introduces AIvaluate, a modular, rubric-guided multi-agent system for theory-based student answer script marking automation. The system combines Optical Character Recognition (OCR) using Azure Document Intelligence with a Cleaning Agent powered by a Large Language Model (LLM) hosted on Azure AI Foundry that cleans the text obtained without losing semantic information and ensures anonymization. Answers are parsed using RegEx to de-sentence question identifiers and mapped into corresponding correct answers and faculty-defined marking schemes in a MongoDB database. The system supports structured input prompts, where rubrics are dynamically injected into the evaluation context for consistency and flexibility across subjects. An Evaluation Agent, also driven by the LLM, matches the sanitized student answer to the correct answer and provides both a score and one-line feedback. To supplement this generative scoring, a semantic similarity-based scoring is employed via FAISS and the all-MiniLM-L6-v2 transformer model; however, this vector-based pipeline is used purely for research benchmarking and does not contribute to the final marks. Experimental outcomes indicate strong correlation between AI-computed scores and human faculty ratings, particularly in the LLM-based pipeline. This system is designed particularly for technical and theory-based academic tests, and not for literary or creative questions where language usage is the prevailing criterion. The suggested approach reduces evaluation time by approximately 70–80% compared to manual correction, without compromising fairness and transparency.}, archive = {J_ACCESS}, author = {P. Suryakumar and A. Malini and S. K. Subasini and G. Priyanka and V. Sandhiya}, doi = {10.1109/ACCESS.2025.3608158}, journal = {IEEE Access}, pages = {160549-160558}, shortjournal = {IEEE Access}, title = {AIvaluate: A multi-agent framework for automated answer script evaluation using large language models and semantic vector indexing}, volume = {13}, year = {2025}, }
• (2025). The evaluation of nanosatellite missions: Statistical analysis of missions for payloads. ACCESS, 13, 160536-160548. (www)

  @article{ , abstract = {CubeSats have become a dominant platform for conducting cost-effective space missions in recent years. This study presents a comprehensive statistical analysis of 2806 CubeSat-class missions launched between 1998 and 2024. The missions are classified into five primary categories – communication, Earth observation, science, education, and technology demonstration-based on their payload functions. Each satellite was further evaluated by form factor and operational status to determine mission success rates. The results show that Earth observation accounts for 39% of all CubeSat missions, with the 3U form factor being the most commonly used and most successful in this domain. Educational missions are significantly underrepresented in launch frequency. Scientific missions, though more frequent, remain below Earth observation and communication in terms of mission volume. A matrix linking mission types, payload categories, form factors, and success outcomes is presented to guide future CubeSat design strategies. A detailed case study of Automatic Dependent Surveillance – Broadcast (ADS-B) payloads was conducted, revealing a 96.9% success rate across over 90 missions. Most of these satellites maintained operations for more than 1000 days, highlighting the reliability and efficiency of ADS-B as a cross-functional payload applicable to communication, Earth observation, and technology demonstration. The findings in this paper provide actionable insights for satellite developers, researchers, and mission planners. By correlating payload types with form factor performance and mission outcomes, this work offers a data-driven framework for optimizing CubeSat mission design and payload integration.}, archive = {J_ACCESS}, author = {Lazzat Abdizhalilova and Amirkhan A. Temirbayev and Peng Li and Nursultan Uzbekov and Sabyrzhan Orynbassar and Asset Beisen and Zhanzira Geroyeva and Darkhan Tokmyrzaev and Nursultan Meirambekuly}, doi = {10.1109/ACCESS.2025.3607970}, journal = {IEEE Access}, pages = {160536-160548}, shortjournal = {IEEE Access}, title = {The evaluation of nanosatellite missions: Statistical analysis of missions for payloads}, volume = {13}, year = {2025}, }
• (2025). AAPDEM: An intelligent model for automated attack path discovery and exploitation in modern networks. ACCESS, 13, 160490-160507. (www)

  @article{ , abstract = {Penetration testing plays a pivotal role in proactively identifying security vulnerabilities within modern networks. Traditional approaches, including Deep Reinforcement Learning (DRL)-based models such as Deep Q-Networks, often suffer from high computational cost and poor scalability, limiting their applicability in large and dynamic environments. This paper introduces AAPDEM, an Automated Attack Path Discovery and Exploitation Model, which integrates real-time network scanning, structured vulnerability analysis, and heuristic pathfinding to address these limitations. AAPDEM constructs attack graphs based on discovered vulnerabilities and employs a cost-aware A* search algorithm to identify optimal attack paths. This eliminates the need for model training while significantly reducing memory and processing overhead. The framework adapts efficiently to network changes by incrementally updating only the affected portions of the graph, maintaining high accuracy and responsiveness. Comprehensive experiments were conducted across various network scales using real-world vulnerability data. AAPDEM was benchmarked against state-of-the-art methods, including Random Walk, Greedy Search, Genetic Algorithms, and DRL-based approaches. Results show that AAPDEM consistently achieves higher path accuracy—improving up to 12%—while reducing execution time by 35% on average. Furthermore, it demonstrated strong resilience in dynamic environments by maintaining stability across fluctuating vulnerability conditions. Overall, AAPDEM offers a scalable, efficient, and accurate solution for automated penetration testing, equipping cybersecurity professionals with a practical tool for attack path discovery and risk assessment in evolving network infrastructures.}, archive = {J_ACCESS}, author = {Tamer Mohamed Abdellatif and Salih Rashid Majeed and Ahmed Hamed and Ahmed Ali Seyam}, doi = {10.1109/ACCESS.2025.3607976}, journal = {IEEE Access}, pages = {160490-160507}, shortjournal = {IEEE Access}, title = {AAPDEM: An intelligent model for automated attack path discovery and exploitation in modern networks}, volume = {13}, year = {2025}, }
• (2025). A hybrid neural-CRF framework for assamese part-of-speech tagging. ACCESS, 13, 160476-160489. (www)

  @article{ , abstract = {Natural Language Processing (NLP) involves computational techniques for analyzing and understanding human languages. Part-of-Speech (POS) tagging, a fundamental NLP task, assigns grammatical categories to words in text. The proposed research addresses the task of Part-of-Speech (POS) tagging for Assamese by integrating character-level embeddings into various neural architectures to handle the language’s complex morphology effectively. The study comparatively evaluates Conditional Random Fields (CRF), Hidden Markov Models (HMM), Long Short-Term Memory networks combined with CRF (LSTM-CRF), and Bidirectional LSTM-CRF (BiLSTM-CRF). Leveraging a comprehensive and manually annotated dataset consisting of 13,469 sentences with 213,087 tokens, the research employs the linguistically detailed LDCIL Assamese tagset developed by CIIL Mysore. Experimental results indicate that neural architectures substantially outperform traditional methods, with the BiLSTM-CRF model enhanced by character-level embeddings achieving the highest accuracy of 97.69%. These findings emphasize character-level embeddings’ critical role in elevating the performance of neural network-based POS tagging models compared to traditional CRF methods.}, archive = {J_ACCESS}, author = {Rituraj Phukan and Nomi Baruah and Mandira Neog and Shikhar KR. Sarma and Darpanjit Konwar}, doi = {10.1109/ACCESS.2025.3609572}, journal = {IEEE Access}, pages = {160476-160489}, shortjournal = {IEEE Access}, title = {A hybrid neural-CRF framework for assamese part-of-speech tagging}, volume = {13}, year = {2025}, }
• (2025). A compact flow regime algorithm for improving the efficiency of metaheuristics-based ontology matching system. ACCESS, 13, 160466-160475. (www)

  @article{ , abstract = {Ontology matching is a key technology for solving the problem of semantic heterogeneity between ontologies. In past years, scholars have proposed many approaches committed to improve the quality of ontology matching. In particular, the metaheuristics-based ontology meta-matching approaches have been proven to be able to achieve better results because it can adaptively combine multiple basic matchers according to application scenarios. However, beside quality, the execution efficiency of the system is also of great importance in dynamic applications. If the system’s response time is too long, it will lead to a decline for user experience and even abandonment. To address such issues, in this paper, a novel compact flow regime algorithm for improving the efficiency of metaheuristics-based ontology matching system is proposed, named CFRAOM. The proposed approach uses the probability representation of population behavior to avoid the algorithm consuming a lot of time and memory during the execution of matching, thereby improving the execution efficiency of the matching system. To demonstrate the efficiency and matching effectiveness of the proposed approach. The CFRAOM algorithm has been executed several large-scale ontology matching tasks and compared with other metaheuristic-based methods. The experimental results indicate that the proposed approach not only improves the execution efficiency of the metaheuristic-based system but also achieves better matching quality.}, archive = {J_ACCESS}, author = {Zhaoming Lv}, doi = {10.1109/ACCESS.2025.3607905}, journal = {IEEE Access}, pages = {160466-160475}, shortjournal = {IEEE Access}, title = {A compact flow regime algorithm for improving the efficiency of metaheuristics-based ontology matching system}, volume = {13}, year = {2025}, }
• (2025). LSTM-based online learning for real-time solar power forecasting. ACCESS, 13, 160456-160465. (www)

  @article{ , abstract = {Accurate solar power forecasting is essential to mitigate the variability and uncertainty of solar power generation and integrate renewable energy sources into power grids. A multi-step Long Short Term Memory (LSTM)-based model is proposed for solar power generation forecasting using online learning. To capture daily fluctuations in solar energy patterns, the model employs a dynamic sunrise–sunset data window operation, ensuring that the relevant transitions in solar irradiance are accurately represented in the training set. For seasonality analysis, the variance of the correlation of features with solar power output is determined across different months of the year. The simulation results show that the proposed LSTM-based online learning model achieves a mean absolute percentage error of less than 1%. The results demonstrate that incorporating solar transition period information significantly enhances the forecasting accuracy of the proposed model. Online learning exhibits higher performance than batch learning in terms of forecasting accuracy.}, archive = {J_ACCESS}, author = {Ziad Ullah and Syed Maaz Shahid and Jazzson Park and Byungseok Min and Sungoh Kwon}, doi = {10.1109/ACCESS.2025.3606910}, journal = {IEEE Access}, pages = {160456-160465}, shortjournal = {IEEE Access}, title = {LSTM-based online learning for real-time solar power forecasting}, volume = {13}, year = {2025}, }
• (2025). Multimodal SAM-adapter for semantic segmentation. ACCESS, 13, 160438-160455. (www)

  @article{ , abstract = {Semantic segmentation, a key task in computer vision with broad applications in autonomous driving, medical imaging, and robotics, has advanced substantially with deep learning. Nevertheless, current approaches remain vulnerable to challenging conditions such as poor lighting, occlusions, and adverse weather. To address these limitations, multimodal methods that integrate auxiliary sensor data (e.g., LiDAR, infrared) have recently emerged, providing complementary information that enhances robustness. In this work, we present MM SAM-adapter, a novel framework that extends the capabilities of the Segment Anything Model (SAM) for multimodal semantic segmentation. The proposed method employs an adapter network that injects fused multimodal features into SAM’s rich RGB features. This design enables the model to retain the strong generalization ability of RGB features while selectively incorporating auxiliary modalities only when they contribute additional cues. As a result, MM SAM-adapter achieves a balanced and efficient use of multimodal information. We evaluate our approach on three challenging benchmarks, DeLiVER, FMB, and MUSES, where MM SAM-adapter delivers state-of-the-art performance. To further analyze modality contributions, we partition DeLiVER and FMB into RGB-easy and RGB-hard subsets. Results consistently demonstrate that our framework outperforms competing methods in both favorable and adverse conditions, highlighting the effectiveness of multimodal adaptation for robust scene understanding.}, archive = {J_ACCESS}, author = {Iacopo Curti and Pierluigi Zama Ramirez and Alioscia Petrelli and Luigi Di Stefano}, doi = {10.1109/ACCESS.2025.3609640}, journal = {IEEE Access}, pages = {160438-160455}, shortjournal = {IEEE Access}, title = {Multimodal SAM-adapter for semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). (GRAVITY) graph-based reasoning with attention and visual information using transformers for yielding answers. ACCESS, 13, 160411-160437. (www)

  @article{ , abstract = {This paper introduces a novel framework for Visual Question Answering (VQA) that combines Graph Attention Networks (GATs) with Transformers to improve visual-semantic reasoning. The proposed method constructs question-conditioned visual graphs to capture fine-grained relationships between image regions, enabling accurate property extraction such as color, shape, and quantity. These structured representations are integrated with natural language questions through multi-head self-attention mechanisms within a Transformer-based reasoning module, enhancing contextual understanding and answer generation. The framework is evaluated on two widely-used VQA benchmarks: GQA and VQA 2.0. On GQA, the model achieves 60.2% accuracy in the zero-shot setting and 63.4% in the few-shot setting. On VQA 2.0, it achieves an overall accuracy of 71.4%, with strong performance in reasoning-heavy categories such as “Number” and “Color.” These results demonstrate the effectiveness of our graph-enhanced attention mechanism in improving response precision and robustness across both structured and naturalistic visual question answering tasks.}, archive = {J_ACCESS}, author = {Hassan Nazeer Chaudhry and Farzana Kulsoom and Zahid Ullah Khan}, doi = {10.1109/ACCESS.2025.3609656}, journal = {IEEE Access}, pages = {160411-160437}, shortjournal = {IEEE Access}, title = {(GRAVITY) graph-based reasoning with attention and visual information using transformers for yielding answers}, volume = {13}, year = {2025}, }
• (2025). Real-time detection and multi-class classification of DGAs with HybridBERT. ACCESS, 13, 160393-160410. (www)

  @article{ , abstract = {Domain fluxing, a technique employed by attackers to evade conventional Command and Control detection, presents a significant challenge for cybersecurity. This technique leverages Domain Generation Algorithms (DGAs) to dynamically generate domain names, often producing non-sensical sequences. The proposed framework presents a real-time DGA detection framework that analyzes Non-existent (NX) domain responses and applies a statistical anomaly detection approach to identify malicious activity. The detected DGAs are further classified into 56 families using a HybridBERT framework, integrating Bidirectional Encoder Representations from Transformer (BERT) with an attention mechanism and statistical characteristics. The dataset, comprising approximately 0.3 million samples from various online sources, was pre-processed to remove redundant data, approximately 25% of the total, and then divided into training, validation, and testing sets in a 60:20:20 ratio. The BERT model was fine-tuned by freezing the first five layers and trained over 20 epochs with early stopping, achieving an overall precision of 96%. Despite significant class imbalance, the framework demonstrated robust performance in both word-based and pseudorandom DGAs, with detailed metrics such as precision, recall, and F1-score providing a comprehensive evaluation. The proposed framework improves the ability of cybersecurity systems to detect zero-day DGAs and offers a scalable solution for real-time DGA classification.}, archive = {J_ACCESS}, author = {Aimen Mahmood and Haider Abbas and Muhammad Faisal Amjad and Waleed Bin Shahid and Sayed Qaiser Ali Shah}, doi = {10.1109/ACCESS.2025.3608450}, journal = {IEEE Access}, pages = {160393-160410}, shortjournal = {IEEE Access}, title = {Real-time detection and multi-class classification of DGAs with HybridBERT}, volume = {13}, year = {2025}, }
• (2025). Research on data security access detection of language technology resources using deep learning and long-range correlation analysis. ACCESS, 13, 160383-160392. (www)

  @article{ , abstract = {This paper collects sample data of language technology resource queries and access, preprocesses the data, and extracts feature values to construct feature vectors. It employs an improved deep learning method to detect malicious access behaviors of resources, and then conducts long-range correlation analysis (Detrended Fluctuation Analysis, DFA) on the detection results. Experimental results indicate that with the increase of experiment times, support vector machines (SVM), BP neural networks (BPNN), naive Bayes methods, and the improved deep learning method with attention mechanism proposed in this paper exhibit different trends in recall rate, precision rate, and accuracy rate of language technology resource access detection. The experimental method proposed in this paper demonstrates better practical application effects in language technology resource access detection with average recall rate is 89.2%, precision rate is 90.5% and accuracy rate is 87.6%, respectively, and the experimental results also exhibit long-range correlations. Thus, we can effectively monitor the data security access to language technology resources using the method proposed in this paper.}, archive = {J_ACCESS}, author = {Yanjun Zhang and Li Ran}, doi = {10.1109/ACCESS.2025.3602676}, journal = {IEEE Access}, pages = {160383-160392}, shortjournal = {IEEE Access}, title = {Research on data security access detection of language technology resources using deep learning and long-range correlation analysis}, volume = {13}, year = {2025}, }
• (2025). Enhanced erosion resistance of reduced graphene oxide-reinforced cu-W contacts for load current interruption in high-voltage circuit breakers. ACCESS, 13, 160372-160382. (www)

  @article{ , abstract = {This study investigates the arc erosion resistance of reduced graphene oxide-reinforced copper-tungsten (G-Cu-W) composites under normal load current conditions in high-voltage gas circuit breakers (HVCBs). Through systematic arc erosion testing and material characterization, G-Cu-W composites, which consisted of 0.02 wt.% graphene, 20 wt.% Cu, with the remainder being W, demonstrated superior performance compared to traditional Cu-W materials. This increased performance included lower mass loss, reduced surface roughness, and minimal droplet splashing. The enhanced erosion resistance is attributed to the refined tungsten grain structure and improved thermal conductivity facilitated by the graphene dispersion.}, archive = {J_ACCESS}, author = {Ruoyu Xu and Mingyu Zhou and Yannan Zhou and Andrew M. Connolly and Mark A. Bissett and Ian A. Kinloch and Shuhang Shen and Shanika Y. Matharage and Zhongdong Wang and James E. Humphries and Joseph W. Spencer and Joseph D. Yan}, doi = {10.1109/ACCESS.2025.3606267}, journal = {IEEE Access}, pages = {160372-160382}, shortjournal = {IEEE Access}, title = {Enhanced erosion resistance of reduced graphene oxide-reinforced cu-W contacts for load current interruption in high-voltage circuit breakers}, volume = {13}, year = {2025}, }
• (2025). Airborne laser target dynamic tracking system based on inertial reference unit. ACCESS, 13, 160358-160371. (www)

  @article{ , abstract = {A target fine tracking system based on inertial reference unit (IRU) is designed to improve the line of sight (LOS) stabilization performance of the photoelectric tracking and targeting system under the dynamic base platform. The IRU technology is introduced into the airborne photoelectric tracking and targeting systems for the first time, a combined measurement scheme of multiple inertial sensors is proposed, and an optimal data fusion algorithm based on Kalman filtering is developed. A high-precision kinematic model of the IRU system is established, and the IRU system loop controller is designed. After completing the system simulation and dynamic validation, the uncrewed aerial vehicle (UAV) tracking experiments are carried out, and the dual axis closed-loop tracking error of the dynamic target is within $12.5\mu $ rad (RMSE). The feasibility of the IRU LOS stabilization control technique, the accuracy of the system kinematic model, and the dynamic closed-loop tracking performance are verified. In engineering applications, the IRU-based fine tracking technology significantly improves the system’s anti-interference ability in complex disturbance environments, and the precision of LOS alignment is greatly improved, especially in high-speed motion target capture and precision tracking, showing unique advantages.}, archive = {J_ACCESS}, author = {Wei Wang and Xu Guo and Yan Dong and Tong Wang and Huilin Jiang}, doi = {10.1109/ACCESS.2025.3609515}, journal = {IEEE Access}, pages = {160358-160371}, shortjournal = {IEEE Access}, title = {Airborne laser target dynamic tracking system based on inertial reference unit}, volume = {13}, year = {2025}, }
• (2025). A fault-tolerant navigation framework for space vehicle coordination landing. ACCESS, 13, 160340-160357. (www)

  @article{ , abstract = {This paper proposes a multi-source data navigation framework designed for the formation landing of reusable launch vehicles (RLVs). The cornerstone of this framework is a centralized Dual Quaternion Unscented Kalman Filter (DQNUKF), which is specifically designed to correctly handle the Special Euclidean group, SE(3) manifold geometry of the six-degree-of-freedom (6-DoF) pose, ensuring a theoretically sound state estimation. In addition to conventional rocket sensor data, the framework incorporates relative observations between multiple rockets to enhance coordination and fault tolerance. An embedded Fault Detection and Isolation (FDI) algorithm further improves system reliability. Simulation results under various sensor failure and communication packet loss scenarios demonstrate that the proposed framework maintains high navigation accuracy and robust performance. Key advantages of this framework include high-precision state estimation on manifolds, dual robustness against both sensor and communication faults, and an innovative fault-tolerant cooperative design, making it a promising solution for RLV landing operations.}, archive = {J_ACCESS}, author = {Chenhui Li and Mai Bando and Shinji Hokamoto}, doi = {10.1109/ACCESS.2025.3609289}, journal = {IEEE Access}, pages = {160340-160357}, shortjournal = {IEEE Access}, title = {A fault-tolerant navigation framework for space vehicle coordination landing}, volume = {13}, year = {2025}, }
• (2025). A novel risk-based methodology for enhancing industrial control systems security: A systematic review and case study. ACCESS, 13, 160313-160339. (www)

  @article{ , abstract = {Critical infrastructures support essential services across various sectors, including finance, energy, healthcare, and public administration. However, their increasing exposure to the internet, combined with insecure communication protocols, weak authentication mechanisms, and misconfigurations, makes them prime targets for cyber threats. To address these risks, this paper presents a novel cybersecurity methodology tailored for Industrial Control Systems (ICS), leveraging the Shodan search engine for systematic vulnerability identification. The proposed approach integrates six key phases: IP discovery 1), DNS verification 2), port, protocol and service identification 3), operating system profiling 4), web application analysis 5), and comprehensive risk assessment using a customized risk matrix 6). This study contributes by 1) conducting a systematic literature review to identify gaps in existing ICS security frameworks, 2) developing a structured methodology for risk assessment across different ICS lifecycle phases, 3) applying the methodology to a real-world case study to validate its effectiveness, and 4) demonstrating its capability to systematically identify vulnerabilities, quantify risks, and provide actionable security insights. The results reveal critical security gaps, such as outdated software and unencrypted communication channels, emphasizing the need for improved security controls. By quantifying the attack surface and assessing risk levels, the proposed model enables stakeholders to prioritize remediation efforts effectively. The findings highlight the urgent need for proactive security measures in ICS environments, reinforcing the importance of continuous monitoring and risk mitigation strategies to enhance the resilience of critical infrastructures against evolving cyber threats.}, archive = {J_ACCESS}, author = {Aarón David Echeverría López and Mercedes Amor Pinilla and Henry Ramiro Carvajal Mora}, doi = {10.1109/ACCESS.2025.3609252}, journal = {IEEE Access}, pages = {160313-160339}, shortjournal = {IEEE Access}, title = {A novel risk-based methodology for enhancing industrial control systems security: A systematic review and case study}, volume = {13}, year = {2025}, }
• (2025). The effects of encountered-type haptics on immersion, presence, user experience, and task performance in virtual control panel environments. ACCESS, 13, 160288-160312. (www)

  @article{ , abstract = {Creating lifelike perception in virtual reality environments relies on the key elements of immersion, presence, user experience, and task performance. While these factors are routinely evaluated in virtual reality studies, there is a noticeable absence of assessment based on these criteria for encountered-type haptic device systems, which are designed to offer realistic feedback in virtual reality. We present the Fast ETHD system, a high-speed encountered-type haptic device capable of moving at speeds over 1m/s, that was used to determine how device speed affects these factors in virtual environments. Participants played a Space Invaders type virtual reality game and provided feedback, showing that increasing encountered-type haptic device speed had a positive effect on sentiment scores for most aspects of the Presence and User Experience Questionnaires, as well as game performance in various categories. A virtual reality version of the ISO/TS 9241-411 multidirectional tapping task evaluated task performance for different haptic conditions, and found that encountered-type haptics resulted in a lower throughput (2.23 bps) than minimal flat haptics with a table (3.53 bps) and no haptics at all (3.70 bps), being attributed to the latency in the Fast ETHD system. These results can be used to improve future encountered-type haptic device designs for more realistic virtual reality experiences.}, archive = {J_ACCESS}, author = {Kiren K. Padayachee and Stephen P. Levitt and Ken J. Nixon}, doi = {10.1109/ACCESS.2025.3609542}, journal = {IEEE Access}, pages = {160288-160312}, shortjournal = {IEEE Access}, title = {The effects of encountered-type haptics on immersion, presence, user experience, and task performance in virtual control panel environments}, volume = {13}, year = {2025}, }
• (2025). Prompt driven multimodal large language models for concrete defect identification. ACCESS, 13, 160278-160287. (www)

  @article{ , abstract = {Detecting surface defects in concrete structures is essential for ensuring structural safety; however, traditional methods have limitations, including subjectivity and heavy data dependency. This study proposes using pretrained multimodal large and small language models (LLM/SLM), specifically GPT-4o and Gemini 2.5-Flash, for concrete defect detection without additional fine-tuning. Structured prompts (S-Prompt) were employed to direct the task instructions and to elicit structured JSON outputs. The models’ performances were evaluated in both zero-shot and few-shot scenarios, the latter using a compact exemplar board. Performance was benchmarked against a fine-tuned YOLOv8 model, employing standard detection metrics alongside newly introduced metrics: Class Presence Accuracy (CPA), Relaxed Localization Recall (RLR), and Unmatched Prediction Ratio (UPR). Experimental results demonstrated that even in a zero-shot setting, the LLM/SLM models meaningfully identified defect presence, type, and approximate location (at an IoU threshold of 0.1) (GPT-4o: F1@ $0.1=0.468$ , CPA-F $1=0.667$ ). The few-shot scenario improved certain metrics (F1@0.1, CPA-F1, RLR@0.1, etc.) with reduced UPR@0.1, but exhibited a significant trade-off in reduced inference speed. Although the proposed method showed lower performance than YOLOv8 in precise localization tasks (higher IoU thresholds), it demonstrated significant potential as a training-free pipeline suitable for initial screening purposes without the burden of extensive data labeling and retraining.}, archive = {J_ACCESS}, author = {Seokjae Heo}, doi = {10.1109/ACCESS.2025.3605263}, journal = {IEEE Access}, pages = {160278-160287}, shortjournal = {IEEE Access}, title = {Prompt driven multimodal large language models for concrete defect identification}, volume = {13}, year = {2025}, }
• (2025). A simple and robust discretization of a higher-order serial differentiator with proven stability and accuracy. ACCESS, 13, 160266-160277. (www)

  @article{ , abstract = {This paper presents a systematic discretization of a continuous-time higher-order serial differentiator originally proposed in (Park and Lee, 2025). By applying the forward-Euler method and replacing the discontinuous signum function with a smooth hyperbolic tangent, a simple, modular, and computationally efficient discrete-time algorithm is developed. The proposed structure is shown to produce stable, chattering-free estimations while maintaining a well-damped transient response. A theoretical analysis demonstrates that the steady-state estimation error for the j-th derivative is $\mathcal {O}\left ({{T + \frac {\epsilon }{\lambda }}}\right)$ , where T is the sampling period, $\epsilon $ is the smoothing parameter, and $\lambda $ is a tunable gain. The effectiveness of the proposed differentiator is validated through extensive simulations. In noise-free scenarios, it achieves high-precision estimation up to the sixth derivative for various signals—a feat rarely demonstrated in the literature. Furthermore, its robustness is confirmed under measurement noise, where the algorithm maintains stability and accurately tracks the true derivatives.}, archive = {J_ACCESS}, author = {Jang-Hyun Park and Dong-Ho Lee and Soon-Youl So and Yoon-Seong Choi}, doi = {10.1109/ACCESS.2025.3608698}, journal = {IEEE Access}, pages = {160266-160277}, shortjournal = {IEEE Access}, title = {A simple and robust discretization of a higher-order serial differentiator with proven stability and accuracy}, volume = {13}, year = {2025}, }
• (2025). Fuzzy control for PVTOL stabilization based on nested saturation functions. ACCESS, 13, 160251-160265. (www)

  @article{ , abstract = {In this paper, a new approach for stabilization of the PVTOL is presented, considering the Nested Saturation Controller (NSC) algorithm that has been designed based on the Lyapunov theory for stabilization problem of this mechanical system. This NSC can be complemented with a Fuzzy Logic Control technique to improve the PVTOL features as the time of error convergence and performance in the feedback control system. As the NSC is based on saturation functions connected in recursive way, having as input the feedback states error, then the level of the bound limits for these saturation functions can be modified during the execution using a Fuzzy Logic Controller (FLC), which in base of the error calculates a new bound for the saturation function. The FLC operates in terms of the Mamdani inference Machine. Then the fuzzy nested saturation (FNSC) strategy allows a fast convergence of the states. Implementing numerical simulation the result are obtained, allowing a comparison of the F-NSC with respect to the classical NSC applied to the PVTOL dynamical model. To reinforce the results, the features of the F-NSC are established in terms of performance index criteria.}, archive = {J_ACCESS}, author = {David Lara Alabazares and Eduardo Tzitzihua García and Anaí Camacho Luna and Abdelhamid Rabhi}, doi = {10.1109/ACCESS.2025.3608948}, journal = {IEEE Access}, pages = {160251-160265}, shortjournal = {IEEE Access}, title = {Fuzzy control for PVTOL stabilization based on nested saturation functions}, volume = {13}, year = {2025}, }
• (2025). Implementing conversational AI into ERP software. ACCESS, 13, 160238-160250. (www)

  @article{ , abstract = {Enterprise Resource Planning (ERP) systems are pivotal in the digital transformation of organizations, offering an integrated platform to digitalize processes across various business functions, such as finance, supply chain, and human resources. Despite their expansive capabilities, these systems often present usability challenges due to their complexity and vastness. The integration of Conversational Artificial Intelligence (AI) presents a novel opportunity to enhance user interaction with ERP systems, streamlining processes and improving accessibility through natural language interfaces. Conversational AI refers to technology that enables machines to engage in human-like dialogue, understanding and responding to user queries in natural language. However, embedding Conversational AI into ERP systems is not without challenges due to the vast and intricate landscape of ERP systems, which are comprised of hundreds of thousands of business processes, involve hundreds of millions of lines of code and cater to diverse industry-specific requirements. This paper explores the critical research question: How to systematically develop Conversational AI in ERP systems? The paper conducts the business requirements and proposes a comprehensive solution framework. This framework is implemented and evaluated using real-world ERP use cases, demonstrating its potential to significantly enhance ERP user engagement and operational efficiency, while setting the stage for future innovations in enterprise software usability.}, archive = {J_ACCESS}, author = {Siar Sarferaz}, doi = {10.1109/ACCESS.2025.3608477}, journal = {IEEE Access}, pages = {160238-160250}, shortjournal = {IEEE Access}, title = {Implementing conversational AI into ERP software}, volume = {13}, year = {2025}, }
• (2025). Evolution and analysis of GaN GAA FET induced high-K spacer wrapped gate underlap for superfast circuitry and RF applications. ACCESS, 13, 160221-160237. (www)

  @article{ , abstract = {Gallium Nitride (GaN) Gate-All-Around (GAA) FETs are developed to be promising candidates for high-frequency, superfast circuitry and RF applications mainly due to their superior electron mobility and wide bandgap. A variety of GaN GAA FETs are developed incorporating gate underlap, spacer technology and source/drain engineering. A comprehensive analysis of DC and AC/RF performance parameters of high-k spacer-wrapped underlap-induced GaN GAA FETs with source and drain enlargements are attained. The impact of this spacer engineering on threshold voltage (Vth), drain-induced barrier lowering (DIBL), subthreshold swing (SS), ON-current (IOn), leakage current (IOff), and switching speed (IOn/IOff) are systematically evaluated. It is inevitably determined that integrating a high-K spacer substantially improves electrostatic control, thereby mitigates short-channel effects while achieve a near ideal SS of ~62 mV/decade. A refined underlap design with high-k wrapping effectively subdues fringing field effects by reducing parasitic capacitances and resistances acquiring cut-off frequency of ~7.2 THz, improves AC performances. The underlap region suppresses DIBL, maintaining excellent gate control while achieving a higher IOn/IOff ratio of $\sim 19 \times 10 ^{8}$ instigating development of the novel device for superfast circuitry applications. The analysis is calibrated and validated with experimentally fabricated devices while Vth is analytically modelled. Comparison with existing research and proposed IRDS 2028 of 1.5 nm technology node shows the underlap-induced GaN GAA FET is advantageous, highlighting its effectiveness in reducing power dissipation, and enhancing switching performance. These findings confirm the design is well-suited for next-generation low-power, high-performance, superfast circuitry and RF applications offering superior energy efficiency and reliability in ultra-scaled transistor architectures.}, archive = {J_ACCESS}, author = {Sneha Singh and Priya Devi and Rudra Sankar Dhar and Mousa I. Hussein and Falah Awwad}, doi = {10.1109/ACCESS.2025.3608819}, journal = {IEEE Access}, pages = {160221-160237}, shortjournal = {IEEE Access}, title = {Evolution and analysis of GaN GAA FET induced high-K spacer wrapped gate underlap for superfast circuitry and RF applications}, volume = {13}, year = {2025}, }
• (2025). A novel trusted framework for orthopedic disease detection with reliability against adversarial attacks. ACCESS, 13, 160193-160220. (www)

  @article{ , abstract = {This study introduces a novel explainable orthopaedic disease detection framework that is robust to both white-box and black-box adversarial attack examples based on hybrid fuzzy Multi-Criteria Decision-Making (MCDM). It addresses the lack of a resilient framework against adversarial attacks with complex evaluation matrices and the absence of a reliable method for selecting optimal orthopedic disease detection models using four-phase methodology. The dataset was augmented with edge-based techniques, improving generalizability and adversarial robustness. A comprehensive evaluation of nine Machine Learning (ML) models was conducted under Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Label Poisoning Method (LPM), and Model Extraction Method (MEM) adversarial attacks to increase the robustness of the developed models. The selection of the most robust developed model is established via a novel decision matrix framework and the optimized Weight Fuzzy Judgment Method (WFJM) and Z-Score Multi-Attribute-Based Aggregation (Z-MABAC) method. According to the WFJM, the fooling rate and prevalence obtained the highest average importance weights of 0.090 and 0.094, respectively, across the four attack scenarios. In contrast, specificity, precision, and Cohen’s kappa received the lowest weights, with values of 0.057, 0.058, and 0.069, respectively. The benchmarking results revealed the following top-performing models across adversarial scenarios: the LPM-Decision Tree, BIM-Support Vector Machin, FGSM-Gradient Boosting, and MEM-Random Forest with scores of 0.122, 0.180, 0.122 and 0.170, respectively. The proposed framework validated through sensitivity-correlation analysis and explainability analysis via Local Interpretable Model-agnostic Explanations (LIME). The results underscore the importance of adversarial robustness in AI-based orthopaedic detection systems, contributing to the broader field of secure and ethical AI adoption in healthcare.}, archive = {J_ACCESS}, author = {Thura J. Mohammed and Chew Xinying and A. S. Albahri and Alhamzah Alnoor and Khaw Khai Wah}, doi = {10.1109/ACCESS.2025.3609330}, journal = {IEEE Access}, pages = {160193-160220}, shortjournal = {IEEE Access}, title = {A novel trusted framework for orthopedic disease detection with reliability against adversarial attacks}, volume = {13}, year = {2025}, }
• (2025). Design of a generic fault diagnosis model for electrical distribution networks using a support vector machine (SVM) algorithm. ACCESS, 13, 160175-160192. (www)

  @article{ , abstract = {This article presents the design and implementation of a generic model for fault diagnosis in electrical distribution networks, based on the Support Vector Machine (SVM) algorithm. The proposal arises in response to the increasing complexity of modern distribution systems, which significantly heightens the need for rapid and accurate fault detection and localization. In this context, a computational model was developed within a Python programming environment and its performance was evaluated using the IEEE 34-bus standard test feeder. To facilitate the analysis, the network was segmented into seven zones, across which four common fault types observed in distribution systems were simulated: single line-to-ground fault, line-to-line fault, double line-to-ground fault, and three-phase fault. Simulations were performed using the specialized software DIgSILENT PowerFactory, generating a dataset representative of fault-related behavior. This dataset was subsequently preprocessed and balanced to ensure an adequate class distribution and then used in the training and validation phases of the SVM classifier. The results obtained from the simulations confirm that the proposed model achieves high levels of accuracy in both fault type identification and fault zone localization. These findings support the conclusion that the SVM-based approach constitutes a robust and effective tool for enhancing fault detection and diagnosis processes in real-world electrical distribution systems, thereby contributing to a safer and more reliable system operation.}, archive = {J_ACCESS}, author = {Jennifer Catucuamba and Alexander Aguila Téllez}, doi = {10.1109/ACCESS.2025.3603450}, journal = {IEEE Access}, pages = {160175-160192}, shortjournal = {IEEE Access}, title = {Design of a generic fault diagnosis model for electrical distribution networks using a support vector machine (SVM) algorithm}, volume = {13}, year = {2025}, }
• (2025). Unified performance analysis of wireless systems under mobility and generalized fading: Applications to diversity reception, UAV-to-UAV links, and IRS-enabled systems. ACCESS, 13, 160158-160174. (www)

  @article{ , abstract = {This paper introduces a comprehensive framework for assessing the performance of wireless communication systems under mobility conditions. Receiver mobility is modeled using the Random Waypoint (RWP) mobility model. The proposed analysis is applicable to most of the known fading models, provided that the moment generating function (MGF) of the underlying channel fading (static) distribution is readily available. Key performance metrics — such as outage probability, average bit error probability, and ergodic capacity — are represented through a unified integral expression that can be efficiently computed numerically. This framework is versatile and applicable to various practical scenarios, including diversity receivers with arbitrary antenna correlation, wireless communication involving uncrewed aerial vehicles (UAVs), and systems enhanced by intelligent reflecting surfaces (IRS). Numerical results are presented along with Monte Carlo simulations to substantiate the proposed analysis.}, archive = {J_ACCESS}, author = {Evangelos Xenos and Andrew C. M. Austin and Simon Armour and Mark A. Beach and Kostas P. Peppas}, doi = {10.1109/ACCESS.2025.3608093}, journal = {IEEE Access}, pages = {160158-160174}, shortjournal = {IEEE Access}, title = {Unified performance analysis of wireless systems under mobility and generalized fading: Applications to diversity reception, UAV-to-UAV links, and IRS-enabled systems}, volume = {13}, year = {2025}, }
• (2025). Automatic key detection in audio recordings using the signature of fifths. ACCESS, 13, 160149-160157. (www)

  @article{ , abstract = {The article presents new approaches to determining the key of a musical piece in audio format. These approaches build upon key detection algorithms that utilize the signature of fifths, which have previously been applied to musical pieces in symbolic form (MIDI). To adapt these algorithms for audio signal processing, short-term power spectral analysis was used to determine the signature of fifths corresponding to a given piece. The experiments were conducted on a set of 47 piano pieces by composers from various eras, recorded on a hybrid acoustic piano equipped with an add-on that enabled the production of synchronous symbolic notation. This setup made it possible to compare the accuracy of the proposed audio key detection algorithms with that of their symbolic precursors. Additionally, the comparative analysis was supplemented with experimental results obtained using a strictly correlational key-detection method implementing various tonal profiles.}, archive = {J_ACCESS}, author = {Tomasz Łukaszewicz and Dariusz Kania}, doi = {10.1109/ACCESS.2025.3609536}, journal = {IEEE Access}, pages = {160149-160157}, shortjournal = {IEEE Access}, title = {Automatic key detection in audio recordings using the signature of fifths}, volume = {13}, year = {2025}, }
• (2025). Fully integrated two-phase dual rails charge pump design in gate-all-around (GAA) ribbon FET process technology. ACCESS, 13, 160136-160148. (www)

  @article{ , abstract = {This paper presents fully integrated two-phase dual rails charge pump designs generating Vin_cp and Vss_cp, which are ~100 mV-150 mV above and below supply rail Vin and ground rail Vss, respectively. Designed for MOSFET channel leakage reduction in a voltage converter system, these charge pumps are fabricated in Intel’s latest 18A Gate-all-around (GAA) process technology where the channel leakage of MOSFETs is significant. With Vin set at 1.25 V, which is a SoC fixed input supply and a fixed input clock of 100 MHz from SoC, the designs have a nominal voltage ripple of around 25 mVpp. The design proposes a voltage feedback control with state-space averaging based stability analysis. As the electrical-overstress (EOS) limit for Ribbon FET, the type of MOSFET used in intel 18A, is smaller than 1.25 V, intermediate voltages are generated to tolerate Ribbon FETs’ EOS rules, and proper level shifters are applied to the switches and clock propagation circuits. The final design has a measured maximum current load of $\sim 300~\mu $ A and a voltage droop or voltage overshoot from no load to the maximum load of within 40 mV. It also achieves the highest output current density among previous works.}, archive = {J_ACCESS}, author = {Huanhuan Zhang and Keng Chen and Arvind Raghavan and Yura Kocharyan and Sivaraman Masilamani}, doi = {10.1109/ACCESS.2025.3605892}, journal = {IEEE Access}, pages = {160136-160148}, shortjournal = {IEEE Access}, title = {Fully integrated two-phase dual rails charge pump design in gate-all-around (GAA) ribbon FET process technology}, volume = {13}, year = {2025}, }
• (2025). Sub-THz non-contact probing system for on-chip microfluidic applications. ACCESS, 13, 160125-160135. (www)

  @article{ , abstract = {We present a novel sub-THz probe station enabling contact-free characterization of liquids within integrated microfluidic channels on quartz substrates. The chip substrate and the 3D-printed flow-cell are optically transparent, enabling microscopic visual alignment and compatibility with the optical inspection system. In the F-band (90–140 GHz), we demonstrate measurements of a coplanar waveguide combined with the microfluidic system, using ethanol and bovine serum albumin protein concentration gradients. The data are stable and repeatable: Traces for different concentrations remain clearly separated, and their standard-deviation envelopes do not overlap where the reflection coefficient $S_{11}$ and the transmission coefficient $S_{21}$ ’s mean standard deviation across all measurements for EtOH remains between 3.46-0.21 %, and BSA 3.89-0.18 %. This demonstrates the method’s high reliability, particularly for the transmission coefficient $S_{21}$ . Designed for flexibility and rapid prototyping, and the platform supports operation up to 1 THz and provides a versatile tool for high-frequency dielectric spectroscopy in fluidic environments and system-level lab-on-chip applications.}, archive = {J_ACCESS}, author = {Daniel Havelka and Petr Kůrka and Karel Chadt and Jaroslav Havlíček and Kubilay Sertel and Michal Cifra}, doi = {10.1109/ACCESS.2025.3607092}, journal = {IEEE Access}, pages = {160125-160135}, shortjournal = {IEEE Access}, title = {Sub-THz non-contact probing system for on-chip microfluidic applications}, volume = {13}, year = {2025}, }
• (2025). An auto bandwidth boost tuning method of current control for servo motor drives considering time delay. ACCESS, 13, 160103-160124. (www)

  @article{ , abstract = {This paper proposes an automatic bandwidth boost method for the current control loop in servo motor drives, specifically targeting the adverse effects of time delay. In digital control systems, time delays caused by PWM updates, computation latency, and current sensing significantly degrade the bandwidth and stability of current regulation. To address this issue, three main contributions are presented. First, the impact of observer-based current prediction on the effective control loop delay is thoroughly analyzed, showing that it reduces control latency and increases phase margin. Second, an FFT-based estimation method is proposed to estimate the compensated time delay by measuring the phase shift between the sensed and observer currents. Third, an autotuning method based on the Magnitude Optimum criterion is developed to automatically adjust controller gains using the estimated delay, thereby maximizing the control bandwidth while maintaining sufficient stability margin. Experimental validation on a 400 W servo motor drive demonstrates that the proposed method improves the –90° bandwidth from 1.48 to 2.17 kHz and from 4.67 to 6.71 kHz at 10 kHz and 30 kHz PWM frequencies, respectively. Phase margins remain above 69°, and dynamic stiffness increases from 8.4 to 14 dB and from 15.9 to 20.7 dB. Robustness is confirmed under motor parameter variations, where the proposed method maintains stable operation and outperforms the original controller design without gain retuning. These results fully support the effectiveness of the proposed techniques for servo motor drives.}, archive = {J_ACCESS}, author = {Chih-Jung Hsu and Yi-Cheng Zhuo and Zhao-Hong Qiu and Yen-Shin Lai}, doi = {10.1109/ACCESS.2025.3609303}, journal = {IEEE Access}, pages = {160103-160124}, shortjournal = {IEEE Access}, title = {An auto bandwidth boost tuning method of current control for servo motor drives considering time delay}, volume = {13}, year = {2025}, }
• (2025). APU-TrajGen+: GRU-based adaptive privacy and utility preserving trajectory generation. ACCESS, 13, 160081-160102. (www)

  @article{ , abstract = {Location-Based Service (LBS) are increasingly integrated into our daily life, to offer smart services and applications. However, ensuring user privacy while maintaining data utility remains a critical challenge. Trajectory data, vital for urban planning, mobility analytics or, is particularly sensitive to re-identification even when explicit identifiers are removed. This article introduces APU-TrajGen+, a novel method for real-time, privacy-preserving point-to-point trajectory generation. APU-TrajGen+ leverages a Gated Recurrent Unit-based model to synthesize trajectory points, followed by an adaptive perturbation mechanism that dynamically balances privacy and utility. Designed for local execution, APU-TrajGen+ protects location data at the device level, eliminating the need for centralized processing and preventing unprotected data transmission. Unlike many existing methods, APU-TrajGen+ does not rely on map data or points of interest to generate points in plausible locations. A configurable utility-privacy score guides an adaptive process, to allow data owners to set the protection levels to their specific needs. Experimental results show that APU-TrajGen+ produces realistic trajectories while achieving desired privacy and utility objectives. The method is particularly suitable for LBS and TMS scenarios that require continuous, point-to-point data release, supporting both high utility and strong privacy guarantees without compromising real-time performance.}, archive = {J_ACCESS}, author = {Adrian-Silviu Roman and Roland Bolboacă and Teri Lenard and Piroska Haller}, doi = {10.1109/ACCESS.2025.3609406}, journal = {IEEE Access}, pages = {160081-160102}, shortjournal = {IEEE Access}, title = {APU-TrajGen+: GRU-based adaptive privacy and utility preserving trajectory generation}, volume = {13}, year = {2025}, }
• (2025). Implementation of high reliability decision making processes: A systematic literature review and a roadmap for future research. ACCESS, 13, 160067-160080. (www)

  @article{ , abstract = {High Reliability Organizations (HRO) are those that operate in high-risk environments where there is a preoccupation with failure and a commitment to resilience. This research investigates the evolving landscape of HRO decision-making principles across industries, evaluating which sectors successfully implement these models, the key features of the models proposed, and the benefits and challenges of such implementations. The study divides industries into two groups: those that focus on practical implementation through case studies and process reviews, and those proposing theoretical models for future implementation. A systematic literature review process was conducted resulting in the inclusion and analysis of fifty studies. This analysis explored proposed models, highlighting recurring features such as decision-making processes, resilience, and multi-criteria decision-making techniques. Key benefits of HRO implementation, such as improved safety, decision-making, and organizational resilience, were identified, with organizational dynamics—particularly resistance to change and leadership challenges—were cited as significant barriers to implementation. The results of this study reveal a rich array of insights, highlighting a notable shift toward the digital platform sector as a leading area for both theoretical research and the practical application of HRO principles—particularly in cloud computing, data management, and automation. The findings also emphasize a persistent gap between theoretical models and their practical implementation in technology-driven industries, underscoring the need for the development of actionable frameworks to bridge this divide. Additionally, the study introduces potential new metrics designed to support emerging technologies, not only to enhance system resilience but also to navigate complex organizational dynamics that currently hinder broader adoption of HRO models. This SLR offers original insights by emphasizing the critical need to adapt HRO frameworks to incorporate emerging technologies and shifting organizational structures, thereby enhancing decision-making effectiveness in increasingly complex and dynamic operational environments.}, archive = {J_ACCESS}, author = {Christina Collura and Joanna F. DeFranco}, doi = {10.1109/ACCESS.2025.3609301}, journal = {IEEE Access}, pages = {160067-160080}, shortjournal = {IEEE Access}, title = {Implementation of high reliability decision making processes: A systematic literature review and a roadmap for future research}, volume = {13}, year = {2025}, }
• (2025). Electric field augmentation on surfaces of field aged composite insulators under varying contamination conditions. ACCESS, 13, 160048-160066. (www)

  @article{ , abstract = {The long-term reliability of high-voltage polymeric insulators is critically affected by surfacedegradation processes driven by environmental and electrical stressors. In our prior work, the role of hydrophobicity loss in intensifying surface electric field stress was established using finite element modeling. This study extends that investigation by experimentally assessing the severity and spatial variability of environmental pollution—specifically, soluble and non-soluble deposits—on field-aged 380-kV composite insulators that had been in service for over a decade. Using standardized procedures defined in IEC 60507, the Equivalent Salt Deposit Density (ESDD) and Non-Soluble Deposit Density (NSDD) were measured on individual sheds along the insulator profile to capture localized contamination patterns. Results reveal strong non-uniformity in pollution distribution, with significantly higher contamination levels on lower sheds due to gravitational runoff and environmental exposure asymmetry. Finite Element Method simulations were performed using COMSOL Multiphysics to evaluate the surface electric field distribution on degraded insulators. Material properties and boundary conditions were defined to reflect field-aged conditions and shed-level pollution gradients. The electric field maps showed intensified surface stress in regions with combined effects of hydrophobicity loss and high ESDD/NSDD values, particularly near the lower sheds. Microscopic observations further confirmed contaminant accumulation and physical erosion in these high-risk regions. The findings offer a comprehensive degradation model that integrates field-contamination data with electric field intensification mechanisms. This study provides actionable insights for predictive maintenance, condition assessment, and improved insulation design under polluted service environments.}, archive = {J_ACCESS}, author = {Mahmoud A. Badwey and Mansoor Asif and Umer Amir Khan}, doi = {10.1109/ACCESS.2025.3609529}, journal = {IEEE Access}, pages = {160048-160066}, shortjournal = {IEEE Access}, title = {Electric field augmentation on surfaces of field aged composite insulators under varying contamination conditions}, volume = {13}, year = {2025}, }
• (2025). Neighboring predictive gradient spatio-temporal sequencing algorithm: An object sequencing algorithm for logical ordering of sparse object detections. ACCESS, 13, 160034-160047. (www)

  @article{ , abstract = {When object detection is carried out in settings with sparse and irregular data acquisition, conventional sequencing techniques that depend on continuous tracking or dense observations frequently fall short of reconstructing the proper logical sequence of events. This paper introduces the Neighboring Predictive Gradient Spatio-Temporal Sequencing Algorithm (NPGSTSA), a novel framework for determining the sequential order of detected objects in sparse and scarce environments. NPGSTSA leverages the relative y-intercepts of detected objects and their neighboring relations as proxies for sequence position inference. By utilizing the properties of the gradient and its neighboring data object, the algorithm is capable of robustly estimating sequence flow even under severe data sparsity and insufficient data points. To evaluate its effectiveness, we constructed a simulated video-based object detection with varying data sparsity levels and downsampling factors. To assess the quantitative accuracy of the sequence, we modified a 1-Wasserstein distance as a measurement metric, demonstrating that NPGSTSA significantly outperforms conventional methods, such as First-In-First-Out (FIFO) and cluster-based sequencing. The results confirm the algorithm’s capacity to infer coherent object sequences even in data-constrained scenarios, highlighting its practical applicability, such as logistics environments where operational speed and efficiency are critical.}, archive = {J_ACCESS}, author = {Herrick Han Lin Yeap and Kok Seng Eu and Tee Hean Tan and Kian Meng Yap}, doi = {10.1109/ACCESS.2025.3604022}, journal = {IEEE Access}, pages = {160034-160047}, shortjournal = {IEEE Access}, title = {Neighboring predictive gradient spatio-temporal sequencing algorithm: An object sequencing algorithm for logical ordering of sparse object detections}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of large language model applications in industry. ACCESS, 13, 160010-160033. (www)

  @article{ , abstract = {Large Language Models are rapidly transforming processes across industries by enabling advanced capabilities in natural language understanding, code generation, diagnostics, and decision support. Despite the growing adoption of this technology, a systematic understanding of their application across the industrial value creation processes remains lacking. This paper addresses this gap by conducting a systematic literature review of 96 peer-reviewed studies, following the PRISMA guidelines. Based on this foundation, large language model use cases across industries were identified, categorized, and structured using the primary and secondary activities of Porter’s value chain as a classification framework. The analysis reveals that LLM adoption is heavily concentrated in technology-focused and internal operational activities across industries, where they offer immediate benefits at lower risk. In contrast, areas such as logistics, procurement, and customer-facing functions remain largely unexplored, mainly due to challenges related to integration, data governance, and regulatory requirements. The analysis shows that current deployments are primarily limited to isolated, manageable use cases, leaving substantial innovation potential unrealized in underexplored value chain activities. These findings provide a foundation for further research and for the strategic adoption of large language models throughout the industrial value chain.}, archive = {J_ACCESS}, author = {Norbert Moenks and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3608650}, journal = {IEEE Access}, pages = {160010-160033}, shortjournal = {IEEE Access}, title = {A systematic literature review of large language model applications in industry}, volume = {13}, year = {2025}, }
• (2025). Confidence-guided LiDAR depth completion for robust 3D object detection. ACCESS, 13, 159998-160009. (www)

  @article{ , abstract = {LiDAR depth completion aims to reconstruct dense depth images from sparse LiDAR scans. While prior methods claim to improve 3D scene understanding, their dense depth maps often introduce erroneous points around object boundaries when directly back-projected into 3D space, which degrades downstream recognition performance. Crucially, these methods focus solely on 2D reconstruction without mechanisms to control pseudo-LiDAR quality. To address this, we propose a novel depth-completion network that jointly predicts a dense depth map and a per-pixel confidence map. The confidence map enables selective back-projection by filtering out unreliable estimates during pseudo-LiDAR point cloud generation. Furthermore, as LiDAR returns exhibit increasing sparsity at longer ranges, our confidence map learns to prioritize reliable long-range estimates. This learned compensation mechanism mitigates sensor limitations while enhancing detection performance for distant objects. On the KITTI 3D benchmark, detectors trained on our pseudo-LiDAR outperform those using raw 32-channel data by 14.2% in 3D AP, narrowing the gap to full 64-channel sensors by 63%.}, archive = {J_ACCESS}, author = {Sangwon Hwang and Kyungjae Lee}, doi = {10.1109/ACCESS.2025.3609143}, journal = {IEEE Access}, pages = {159998-160009}, shortjournal = {IEEE Access}, title = {Confidence-guided LiDAR depth completion for robust 3D object detection}, volume = {13}, year = {2025}, }
• (2025). HybridATNet: Multi-scale attention and hybrid feature refinement network for remote sensing image super-resolution. ACCESS, 13, 159979-159997. (www)

  @article{ , abstract = {Remote sensing image super-resolution faces significant challenges, including detail loss, sensor noise, and atmospheric interference that compromise image quality for critical applications. Current deep learning approaches struggle with three key limitations: insufficient multi-scale feature extraction, inadequate attention to crucial image regions, and poor preservation of refined spatial details during reconstruction. We propose HybridATNet, a novel CNN-Transformer architecture that addresses these limitations through three primary innovations. First, our shallow feature extraction integrates Efficient Channel Attention (ECA), Multi-scale Spatial Attention (MSA), and Residual Atrous Spatial Pyramid Pooling (RASPP) to capture features across multiple scales in both channel and spatial domains. Second, we employ an Activated Sparsely Sub-Pixel Transformer (ASSPT) for deep feature extraction that efficiently captures long-range dependencies while maintaining computational efficiency through a novel sparse attention mechanism that reduces computational complexity from O(n2) to approximately O(n log n). Third, we introduce a dual-domain approach that combines multi-scale spatial refinement with frequency-domain processing to preserve structural details while simultaneously reducing high-frequency noise. Extensive experiments on the UCMerced and AID datasets demonstrate that our model consistently outperforms state-of-the-art methods across multiple scale factors ( $\times 2$ , $\times 3$ , $\times 4$ ), achieving PSNR improvements of up to 0.26dB on the UCMerced dataset and 0.13dB on the larger AID dataset. Visual comparisons confirm superior preservation of fine details and improved noise reduction, particularly in complex structural regions. HybridATNet delivers high-quality reconstructions while maintaining computational efficiency, requiring approximately 15% fewer parameters and 18% fewer floating-point operations (FLOPs) than other transformer-based models, making it well-suited for practical remote sensing applications.}, archive = {J_ACCESS}, author = {Naveed Sultan and Watchara Ruangsang and Supavadee Aramvith}, doi = {10.1109/ACCESS.2025.3608009}, journal = {IEEE Access}, pages = {159979-159997}, shortjournal = {IEEE Access}, title = {HybridATNet: Multi-scale attention and hybrid feature refinement network for remote sensing image super-resolution}, volume = {13}, year = {2025}, }
• (2025). MEDNER.DE: Medicinal product entity recognition in german-specific contexts. ACCESS, 13, 159961-159978. (www)

  @article{ , abstract = {Named entity recognition (NER) is an area of significant interest in the field of medicine, particularly for identifying specific terms such as medicinal products, medical events and diagnoses from doctors’ letters. However, training NER models in languages other than English presents challenges, including limited corpora and annotated training data, and existing models for extracting medicinal products often perform poorly when applied to real-life pharmacovigilance-related use cases. This study investigates the effectiveness of combining multiple techniques to achieve better performance compared to baseline models for medicinal product entity recognition in German-specific contexts. We propose a strategy that utilizes automatically annotated data that was generated using a rule-based approach, leveraging transfer learning along with further pre-training on language models (LMs) resulting in a set of models we named MedNER.de. Our models are designed for German-specific contexts and show a 3.3% improvement in F1 score compared to the best baseline models, GERNERMED++ and GPTNERMED. The best-performing MedNER.de model is publicly available at: https://huggingface.co/pei-germany/MEDNER-de-fp-gbert}, archive = {J_ACCESS}, author = {Farnaz Zeidi and Manuela Messelhäußer and Roman Christof and Xing David Wang and Ulf Leser and Dirk Mentzer and Renate König and Liam Childs}, doi = {10.1109/ACCESS.2025.3607266}, journal = {IEEE Access}, pages = {159961-159978}, shortjournal = {IEEE Access}, title = {MEDNER.DE: Medicinal product entity recognition in german-specific contexts}, volume = {13}, year = {2025}, }
• (2025). Augmentation-free longitudinal modeling through structuring whitened embeddings. ACCESS, 13, 159950-159960. (www)

  @article{ , abstract = {This paper introduces Structuring Whitened Embeddings, a modality- and encoder-agnostic framework designed to optimize encoders for extracting smooth, progression-aware feature representations by aligning real-world samples. Proportional inter-sample relationships are preserved, enabling the capture of subtle and continuous changes. By establishing relationships directly between samples and thereby avoiding the need for data augmentation, the approach is particularly well suited for transformation-sensitive data, such as medical time series, where even minor sample changes can lead to disproportionate shifts in interpretation. Experimental results on early atrial fibrillation prediction and timestamp imputation, modeling both inter- and intra-patient dynamics, demonstrate significant performance improvements using the optimized features. The framework’s augmentation-free design and generalizability across tasks and modalities position it as a practical solution for modeling evolution in complex datasets.}, archive = {J_ACCESS}, author = {Karel Fonteyn and Lennert Bontinck and Tom Dhaene and Dirk Deschrijver}, doi = {10.1109/ACCESS.2025.3608901}, journal = {IEEE Access}, pages = {159950-159960}, shortjournal = {IEEE Access}, title = {Augmentation-free longitudinal modeling through structuring whitened embeddings}, volume = {13}, year = {2025}, }
• (2025). Real-time vehicle tracking using magnetic field fingerprinting. ACCESS, 13, 159933-159949. (www)

  @article{ , abstract = {This paper focuses on developing a vehicle tracking algorithm for vehicle inspection systems. Tracking small vehicle movements in small enclosed systems can be challenging, but offers tremendous benefits to optimize the camera capture moment. Therefore, we focus on locating the vehicle position to ensure that pictures can be taken with equal spacing, limiting the likelihood of missed damages and making it easier to compare different inspections. We propose the usage of sensor fusion between Magnetic Field sensors and Time-of-Flight Lidar sensors and develop a fingerprinting method that accurately tracks vehicle movement through the system. Unlike previous research, we developed a unique approach that does not require offline data collection, prior knowledge of the vehicle, or any sensors to be attached to the vehicles. Using sensor fusion, we establish the fingerprint in real-time to ensure that the system does not require storing and collecting fingerprints in a database. We benchmark the proposed solution against varying vehicle brands, models, and driving behavior. The findings highlight the algorithm’s exceptional performance, which tracks vehicles with less than 0.32 sensor distances in accuracy, or almost 3 centimeters when 64 sensors are used. We show that the algorithm can scale in the number of sensors to improve tracking granularity, with almost no compromise on the algorithm’s accuracy. By evaluating different driving scenarios, we show that the proposed algorithm is robust against different driving speeds, speed changes, and complete stops of the vehicle during the drive-through process. Even when vehicles change driving direction, the proposed system demonstrates the capability to track them effectively.}, archive = {J_ACCESS}, author = {R. E. Van Ruitenbeek and S. Bhulai}, doi = {10.1109/ACCESS.2025.3604369}, journal = {IEEE Access}, pages = {159933-159949}, shortjournal = {IEEE Access}, title = {Real-time vehicle tracking using magnetic field fingerprinting}, volume = {13}, year = {2025}, }
• (2025). Hybrid model-based and data-driven sliding mode control for robust path tracking in intelligent vehicles. ACCESS, 13, 159920-159932. (www)

  @article{ , abstract = {Robust path tracking for intelligent vehicles represents a critical challenge due to model uncertainties and varying operational conditions. This paper proposes a novel model-data-driven sliding mode control (MDD-SMC) framework to address this issue. The core of this framework is a hybrid controller architecture. A model-based law, founded on SMC principles, governs the nominal vehicle dynamics to ensure baseline stability and robustness. This is synergistically integrated with a data-driven compensator, designed based on model-free adaptive control theory, which actively learns and rejects composite disturbances arising from unmodeled dynamics, parameter errors, and external interferences. Furthermore, a guidance law is employed to fuse lateral and heading errors into a single composite variable, simplifying the control problem to a provably stable single-input single-output system. Comparative simulations demonstrate that the proposed MDD-SMC consistently achieves superior tracking accuracy and stability over conventional control methods, particularly in challenging high-speed and low-adhesion scenarios. This framework therefore provides a highly adaptive and effective solution, significantly enhancing vehicle safety and control precision.}, archive = {J_ACCESS}, author = {Zhanguo Xu and Yunfei Yin and He Cai and Wanying Zhu and Chengyang Dai and Abaho G. Gershome}, doi = {10.1109/ACCESS.2025.3606861}, journal = {IEEE Access}, pages = {159920-159932}, shortjournal = {IEEE Access}, title = {Hybrid model-based and data-driven sliding mode control for robust path tracking in intelligent vehicles}, volume = {13}, year = {2025}, }
• (2025). Hybrid acoustic fault diagnosis in UAVs using wavelet scattering transform and deep learning. ACCESS, 13, 159909-159919. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) are increasingly employed in defense, agriculture, and logistics, where ensuring operational safety is critical. Propeller damages and rotor screw looseness represent two major fault types that can compromise flight reliability. Previous studies have typically addressed these faults separately, often relying on vibration analysis or conventional acoustic features. This paper introduces a hybrid model that combines Wavelet Scattering Transform (WST) and Long Short-Term Memory (LSTM) to simultaneously detect both fault types using microphone-recorded acoustic signals. Unlike traditional Fourier-based approaches or deep learning models utilizing Mel-Frequency Cepstral Coefficients (MFCCs) or spectrograms, the proposed framework leverages WST to extract deformation-stable, multi-resolution features, which are then modeled through an LSTM network. The model was trained and tested on a dataset comprising 750 one-second acoustic segments, approximately balanced between problem-free and faulty classes using stratified sampling and cross-validation. By integrating time–frequency-based multi-layered features into a time-sequential deep learning framework, the proposed model achieves reliable classification of both propeller faults and rotor screw looseness, reaching a high accuracy of 98.93%. These results highlight the potential of the WST-LSTM framework as a robust and innovative solution for UAV fault diagnosis, particularly in acoustic-based monitoring scenarios}, archive = {J_ACCESS}, author = {Temel Sonmezocak and Merıh Yildiz}, doi = {10.1109/ACCESS.2025.3607751}, journal = {IEEE Access}, pages = {159909-159919}, shortjournal = {IEEE Access}, title = {Hybrid acoustic fault diagnosis in UAVs using wavelet scattering transform and deep learning}, volume = {13}, year = {2025}, }
• (2025). Motion parameter optimization using modified epipolar geometric constraints for efficient object tracking. ACCESS, 13, 159894-159908. (www)

  @article{ , abstract = {As the practical implementation of autonomous driving technology accelerates, the ability to accurately recognize surrounding objects during driving and to stably track their movements have become essential. In particular, 3D multi-object tracking (3D MOT) is a core technology for safe path planning and collision avoidance in autonomous vehicles, because it is important to not only identify the current positions of objects but to accurately understand their trajectories. Recently, camera-based 3D MOT has been studied as a cost-effective alternative to expensive LiDAR sensors. However, this approach tends to rely heavily on deep learning detection models, resulting in a significant degradation of tracking continuity in environments with limited computational resources, or in situations where detection fails. To solve that degradation problem, this study proposes a geometry-based motion optimization method that can predict object movement to enable continuous tracking without deep learning inferences. The proposed system operates by optimizing object acceleration and yaw angle in the prediction stage of the extended Kalman filter (EKF) based on the 3D bounding box and the constant turn rate and acceleration (CTRA) model, which reflect the physical characteristics of vehicles. In particular, by introducing a line sampling technique that utilizes segments inside the bounding box and corresponding points in the image—based on an extended form of epipolar geometry—the geometric validity of motion prediction is secured. The optimization process is based on quadratic programming, ensuring both numerical stability and computational efficiency, and the generated pseudo-detection can be used as a substitute observation, even in time intervals where actual detection does not occur. Experiments using the nuScenes dataset evaluated the performance of the proposed method under various detection interval conditions. As a result, while fast, poly-based tracking systems show increased tracking fragmentation and error as the detection interval grows longer, the proposed method demonstrates more stable tracking performance in terms of six metrics. In particular, we quantitatively and qualitatively confirm that the proposed method predicts object motion in near real time, even in frames where deep learning detection is absent. This study presents a practical alternative that ensures both real-time performance and reliability from camera-based 3D MOT, and demonstrates its applicability in real-time autonomous driving systems where using high-performance GPUs or continuous deep learning inference is not feasible. Furthermore, it proves that high-performance tracking can be achieved without relying heavily on appearance features, but by using only geometric constraints and a physics-based prediction model. Future research plans will expand the proposed framework into a more generalized 3D MOT system by applying various motion models tailored to different object motion characteristics beyond the currently used CTRA model.}, archive = {J_ACCESS}, author = {Ji-Kyu Park and Deok-Hwan Kim}, doi = {10.1109/ACCESS.2025.3608472}, journal = {IEEE Access}, pages = {159894-159908}, shortjournal = {IEEE Access}, title = {Motion parameter optimization using modified epipolar geometric constraints for efficient object tracking}, volume = {13}, year = {2025}, }
• (2025). A multivariate nonlinear regression-based method for calculating lightning ablation mass of metal mesh in wind turbine blades. ACCESS, 13, 159883-159893. (www)

  @article{ , abstract = {Lightning strikes have posed a severe threat to the operational safety of wind turbines. As an additional lightning-protection measure, metal meshes have been deployed on a number of turbines. However, under high-amplitude lightning currents, the mesh undergoes erosion, potentially compromising its lightning-protection effectiveness. Currently, blade-mounted meshes directly follow aerospace mesh standards, and the relationship between structural parameters and erosion mass on curved blade geometries is seldom reported. Therefore, This paper develops a parametric grid structure-based lighting strike simulation model for wind turbine blade metal mesh. The model analyzes the influence weights of mesh thickness, wire width, and grid size on the ablation mass of the metal mesh. The validity of the simulation model was verified through A+C combined lightning current ablation tests. A multivariate nonlinear regression equation for the ablation mass of the metal mesh was established, providing a quantitative method to evaluate the lightning protection capability of metal meshes with different structural parameters. The results show that under a density of 800 g/m3, the test group’s metal mesh had an ablation mass of 800 mg after exposure to the combined waveform, while the calculated ablation mass using the regression equation was 748 mg, yielding an error of 6.5%. The proposed equation provides a method to quantify the lightning protection capability of metal meshes with varying grid structural parameters.}, archive = {J_ACCESS}, author = {Chaocheng Guo and Lingfeng Jiang and Pengkang Xie and Siyu Yan and Kai Ning and Ziqi He}, doi = {10.1109/ACCESS.2025.3608265}, journal = {IEEE Access}, pages = {159883-159893}, shortjournal = {IEEE Access}, title = {A multivariate nonlinear regression-based method for calculating lightning ablation mass of metal mesh in wind turbine blades}, volume = {13}, year = {2025}, }
• (2025). Bootstrap-driven adaptive resampling for enhanced solutions of second-order differential equations in RLC circuits using PINNs. ACCESS, 13, 159872-159882. (www)

  @article{ , abstract = {Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order Resistor–Inductor–Capacitor (RLC) circuit equations under underdamped conditions, where accurate modeling of small-amplitude oscillations is challenging. We introduce a bootstrap-driven adaptive resampling mechanism to enhance the effectiveness of the residual-based adaptive refinement (B-RAR) strategy. By integrating bootstrap perturbations and transfer initialization, our method improves convergence and robustness across both forward and inverse problems. Experiments show that the proposed method achieves precise voltage prediction and accurate parameter identification under noisy and limited data. Comparative evaluations across five sampling strategies—Grid, Latin hypercube, Halton, Residual-based adaptive distribution (RAD), and Residual-based adaptive refinement (RAR), highlight the critical role of bootstrap-enhanced resampling in improving the performance of PINN training for second-order differential equations.}, archive = {J_ACCESS}, author = {Jiani Zeng and Xianglong Li and Hanqi Dai and Lu Zhang and Weixian Wang and Shengxin Kong and Zihan Zhang and Liwen Xu}, doi = {10.1109/ACCESS.2025.3607315}, journal = {IEEE Access}, pages = {159872-159882}, shortjournal = {IEEE Access}, title = {Bootstrap-driven adaptive resampling for enhanced solutions of second-order differential equations in RLC circuits using PINNs}, volume = {13}, year = {2025}, }
• (2025). A scalable AI approach to bird species identification for conservation and ecological planning. ACCESS, 13, 159859-159871. (www)

  @article{ , abstract = {Birds represent an important component of ecosystems, holding substantial ecological and human interest, and significantly contributing to ecological equilibrium via their roles as pollinators, seed dispensers, and predators. If identified correctly, the wild life departments, farmers, ecologists, and various other experts can ensure saving endangered bird species, improving crop yield and ensure seed safety, and plan safe sanctuaries for birds, etc. Due to the vast array of bird species and the often subtle distinctions among them, human-driven manual classification poses a formidable challenge. The manual classification of bird species is a labor-intensive and error-prone process, predominantly reliant on domain experts. However recent algorithmic developments and progress in the deep-learning based object detection and classification models has made this difficult and time-consuming task easy and accurate. Nevertheless, numerous existing CNN models have been trained or fine-tuned on limited datasets, rendering them ill-equipped to handle the classification of a substantial volume of diverse bird species. To address this challenge, this study introduces a novel approach based on transfer learning and the ResNet-50 model to ensure accurate identification of endangered species. Our methodology involved fine-tuning the ResNet-50 model on the 100 Bird Species dataset, with the training data consisting of 85,000 images, representing 525 distinct bird species with each species comprising of 130 or more diverse pre-processed images, including 25 endangered and 12 critically endangered species. The transfer learning based mechanism applied on the large dataset achieved a state-of-the-art accuracy of 87% in bird species classification through deep learning. Our results showed that out of 12 critically endangered species like California condor, Ashy storm petrel, California quail and other quails, none is misclassified by our system. Similarly, none of the 25 endangered species is misclassified by our system, indicating a solid performance for use in identification of endangered and critically endangered bird species. This study shows that deep learning and transfer learning can be successfully deployed to identify birds, and ensure the safety of endangered and critically endangered species of birds, making it a powerful and adaptable approach for the future.}, archive = {J_ACCESS}, author = {Souad Hassanie and Aiman Gohar and Raja Hashim Ali and Talha Ali Khan and Iftikhar Ahmed and Shan Faiz}, doi = {10.1109/ACCESS.2025.3607597}, journal = {IEEE Access}, pages = {159859-159871}, shortjournal = {IEEE Access}, title = {A scalable AI approach to bird species identification for conservation and ecological planning}, volume = {13}, year = {2025}, }
• (2025). A comprehensive study on frequent pattern mining and clustering categories for topic detection in persian text stream. ACCESS, 13, 159843-159858. (www)

  @article{ , abstract = {Topic detection in social networks is extracting and summarizing frequent posts about an event or subject. Topic detection is a complex process and depends on language because it somehow needs to analyze text. There have been few studies on topic detection in Persian, and the existing algorithms are not remarkable. Therefore, we aimed to study topic detection in Persian. The objectives of this study are: 1) to conduct an extensive study on the best algorithms for topic detection; 2) to identify necessary adaptations to make these algorithms suitable for the Persian language; and 3) to evaluate their performance on Persian social network texts. To achieve these objectives, we have formulated two research questions: First, considering the lack of research in Persian, what modifications should be made to existing frameworks, especially those developed in English, to make them compatible with Persian? Second, how do these algorithms perform, and which one is superior? There are various topic detection methods that can be categorized into different categories. Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category. Then, ten methods from these three categories are selected. All of them are re-implemented from scratch, changed, and adapted with Persian. These ten methods encompass different types of topic detection methods and have shown good performance in English. The text of Persian social network posts is used as the dataset. Additionally, a new multiclass evaluation criterion, called FS, is used in this paper for the first time in the field of topic detection. Approximately 1.4 billion tokens are processed during experiments. The results indicate that if we are searching for keyword-topics that are easily understandable by humans, the hybrid category is better. However, if the aim is to cluster posts for further analysis, the frequent pattern category is more suitable.}, archive = {J_ACCESS}, author = {Elnaz Zafarani-Moattar and Mohammad Reza Kangavari and Amir Masoud Rahmani}, doi = {10.1109/ACCESS.2025.3607164}, journal = {IEEE Access}, pages = {159843-159858}, shortjournal = {IEEE Access}, title = {A comprehensive study on frequent pattern mining and clustering categories for topic detection in persian text stream}, volume = {13}, year = {2025}, }
• (2025). A review of machine learning and deep learning trends in EEG-based epileptic seizure prediction. ACCESS, 13, 159812-159842. (www)

  @article{ , abstract = {Epileptic seizures impair patients’ health and quality of life, and electroencephalography (EEG)-based prediction enables timely intervention. Early work on epileptic seizure prediction employed linear models, whereas recent studies employ more advanced machine learning and deep learning models. We review 149 studies (2018—May 2025), summarizing trends and gaps across the full pipeline in EEG-based epileptic seizure prediction. We find that four persistent challenges related to this domain remain. First, severe dataset imbalance, with overreliance on Children’s Hospital Boston–Massachusetts Institute of Technology EEG dataset (CHB-MIT), minimal use of intracranial electroencephalography (iEEG) datasets, and infrequent integration of public and private datasets. Second, preprocessing practices remain rigid, often involving segmentation into windows of 10 s or less, fixed preictal horizons of 30 to 60 min, basic filtering and undersampling procedures. Third, limited model diversity, as most studies use convolutional neural networks (CNNs), with scant exploration of Transformers or graph neural networks (GNNs), dimensionality reduction is predominantly implicit and few linear baselines. Fourth, validation and deployment are fragmented, with fixed warning horizons, single evaluation protocols, missing clinical metrics, static thresholds or k-of-n voting, minimal interpretability, and few real-world implementations. Future work should assemble heterogeneous EEG datasets, adopt adaptive multiscale preprocessing, and build unified benchmarks that pit CNN, Transformer, GNN, and related models against well-tuned linear baselines—advancing flexible non-convolutional designs beyond the time-frequency paradigm, mandating ablations that contrast implicit with explicit, task-aware dimensionality reduction, and pairing these evaluations with clinically aligned validation, dynamic risk-aware thresholds, embedded interpretability, and prospective testing on energy-efficient edge hardware in ward and home settings.}, archive = {J_ACCESS}, author = {Yitong Wu and Eileen Lee Ming Su and Mingyu Wu and Chia Yee Ooi and William Holderbaum}, doi = {10.1109/ACCESS.2025.3606966}, journal = {IEEE Access}, pages = {159812-159842}, shortjournal = {IEEE Access}, title = {A review of machine learning and deep learning trends in EEG-based epileptic seizure prediction}, volume = {13}, year = {2025}, }
• (2025). DigiPyramid: A multiresolution digital twin framework for reliable logistics management-conceptualization, implementation, and intelligentization. ACCESS, 13, 159796-159811. (www)

  @article{ , abstract = {This article presents DigiPyramid, a multiresolution Digital Twin framework that enhances reliable logistics management. DigiPyramid leverages Robotic Process Automation for low-cost, flexible data aggregation across heterogeneous systems like ERP, WMS, and TMS. The framework employs Bayesian Networks to infer and mitigate ambiguities such as “breakpoints,” “black boxes,” and “gray boxes,” ensuring data reliability. Additionally, it utilizes Analytic Hierarchy Process to tailor decision-support indicators for users at different organizational levels—managers, planners, schedulers, and operators—enabling context-specific resolutions. A case study of a Chinese national logistics enterprise demonstrates DigiPyramid’s effectiveness in improving visibility, adaptability, and coordination, particularly during disruptions like COVID-19. The integration of AI technologies, including Computer Vision and Bayesian Networks, further enhances real-time monitoring and predictive capabilities. By aligning digital transformation with sustainability goals, DigiPyramid offers a scalable solution for resilient and efficient logistics operations, contributing to both theoretical advancements and practical applications in logistics process management.}, archive = {J_ACCESS}, author = {Wei Shang and Yijun Wang and Anding Zhu and Yan Chen and Peihua Fu and Xiulin Li and Lingfei Zhu and Jiaqi Sheng and Bo Zhang and Jun Yue}, doi = {10.1109/ACCESS.2025.3606168}, journal = {IEEE Access}, pages = {159796-159811}, shortjournal = {IEEE Access}, title = {DigiPyramid: A multiresolution digital twin framework for reliable logistics management-conceptualization, implementation, and intelligentization}, volume = {13}, year = {2025}, }
• (2025). NOMA channel state estimation: Deep learning approaches. ACCESS, 13, 159774-159795. (www)

  @article{ , abstract = {The growing demand for ultra-reliable, high-capacity wireless networks necessitates efficient channel state estimation (CSE) in 6G-enabled non-orthogonal multiple access (NOMA) systems. Conventional estimators like least squares (LS) and minimum mean square error (MMSE) suffer from significant performance degradation under limited pilot symbols, creating a critical bottleneck for spectral efficiency. To address this, we propose a hybrid deep neural network (HyDNN) architecture that synergizes a 1D convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRU) to jointly exploit spatial-temporal channel correlations. The model is trained offline on multipath fading channel data and deployed online as a pilot-assisted estimator, requiring no prior channel knowledge. Extensive evaluations demonstrate that HyDNN reduces the symbol error rate (SER) by 34% (at 15 dB SNR) compared to MMSE with only 2 pilots, while achieving near-optimal performance with 64 pilots. The Adam-optimized HyDNN (CNN-GRU variant) outperforms standalone CNNs, LSTMs, and GRUs, showing robustness to reduced cyclic prefix lengths (e.g., CP= 12). Notably, with 2 pilots, HyDNN achieves an SER of 0.228 (vs. 0.465 for LS at 10 dB SNR), reducing pilot overhead by 75% without sacrificing accuracy. These advancements position HyDNN as a scalable, resource-efficient solution for next-generation NOMA systems, aligning with 6G goals of low-latency and high spectral efficiency.}, archive = {J_ACCESS}, author = {Sara Khaled and Doaa Abdelhameed and Mohamed Hassan Essai Ali and Hatim Ghazi Zaini and Shaima Elnazer and Samia Heshmat}, doi = {10.1109/ACCESS.2025.3608136}, journal = {IEEE Access}, pages = {159774-159795}, shortjournal = {IEEE Access}, title = {NOMA channel state estimation: Deep learning approaches}, volume = {13}, year = {2025}, }
• (2025). A new paradigm for scientific computing: Accelerated algorithm development with large reasoning models. ACCESS, 13, 159749-159773. (www)

  @article{ , abstract = {This study examines the application of Large Reasoning Model (LRM)-based artificial intelligence (AI) agents to accelerate scientific discovery, with a specific focus on the rapid prototyping of numerical algorithms. The research demonstrates that current-generation LRM-based AI agents, when collaborating with human experts, can significantly expedite the development of complex algorithms by formalizing a Human-in-the-Loop (HIL) plus Chain-of-Thought (CoT) workflow and introducing, to our knowledge, the first quantitative benchmark of LRMs on a CFD algorithm derivation task. We test the hypothesis that CoT prompting plus domain-expert oversight reduces the derivation error rate and development time of high-order numerical schemes relative to typical prompting, and we instantiate this hypothesis in a cross-model evaluation suite with end-to-end feasibility, from symbolic derivation (polynomials and smoothness indicators) to automated code generation and solver-level validation. The novelty lies in the use of advanced reasoning artificial intelligence (AI) models to assist in the algorithm development process. To this end, the derivation of key formulae within the widely utilized Weighted Essentially Non-Oscillatory (WENO) algorithm –a high-order algorithm applicable to various fields such as fluid dynamics, astrophysics, and medical imaging– serves as a case study. We employ the WENO algorithm as a test case to help evaluate and demonstrate the capabilities of several AI models in this context, thereby laying the foundation for future research and development in this field. The interaction between a human expert and an LRM was examined in the context of designing and deploying a WENO scheme for simulating vortical flows. Initial AI-generated responses, while generally accurate, required iterative refinement guided by expert knowledge and a CoT approach to correct minor errors and optimize performance. This iterative process demonstrated the importance of user involvement, fostering both deeper engagement and a clearer understanding of the algorithm’s intricacies. Optimal performance was achieved through a collaborative partnership that leverages the AI’s computational speed and the human’s ability to perform logical decomposition and error detection. This collaborative approach facilitates the rapid development of tailored solutions. This study highlights the transformative potential of AI copilots in scientific research, showing that their effectiveness is maximized through synergy with domain experts. The findings suggest that artificial intelligence is poised to significantly accelerate research and development, driving scientific innovation.}, archive = {J_ACCESS}, author = {Ioannis W. Kokkinakis and Dimitris Drikakis}, doi = {10.1109/ACCESS.2025.3607265}, journal = {IEEE Access}, pages = {159749-159773}, shortjournal = {IEEE Access}, title = {A new paradigm for scientific computing: Accelerated algorithm development with large reasoning models}, volume = {13}, year = {2025}, }
• (2025). Emotion analysis of children’s drawings. ACCESS, 13, 159730-159748. (www)

  @article{ , abstract = {This study investigates children’s emotional expression through the computational analysis of their drawings. Prior research has frequently relied on proprietary or small-scale datasets, often limited to digital drawings produced on tablets or smartphones, which may not fully capture the natural characteristics of hand-drawn expression. To address these gaps, we introduce KIDO, a novel publicly available dataset of scanned children’s drawings paired with reflections written by the children on whether their drawings represent happiness or sadness. The dataset contains contributions from 5,430 students, with balanced representation across primary and secondary education levels and additional metadata on gender and educational background. For our experiments, we employed CNN-based and Vision Transformer–based models for emotion recognition, obtaining F1-scores of up to 79.03% from drawings, while BERT-based models achieved accuracies of up to 97.47% in classifying children’s written reflections. We further trained a generative model on the KIDO dataset to synthesize emotionally expressive drawings, with subjective evaluations indicating that children often preferred the AI-generated images to those drawn by their peers. KIDO serves as a comprehensive benchmark for advancing research in the fields of computer vision, natural language processing, psychology, and education, and is publicly available at github.com/serdarciftci/KIDO}, archive = {J_ACCESS}, author = {Serdar Çiftçi and Hasarı Karcı and Neval Karaca and Büşra Söylemez and Halıl Koçakoğlu}, doi = {10.1109/ACCESS.2025.3606359}, journal = {IEEE Access}, pages = {159730-159748}, shortjournal = {IEEE Access}, title = {Emotion analysis of children’s drawings}, volume = {13}, year = {2025}, }
• (2025). Research on dual-channel transformer english translation model based on cross-layer semantic fusion. ACCESS, 13, 159714-159729. (www)

  @article{ , abstract = {The rapid advancement of computational sciences has created a pressing demand for innovative methodologies in the field of natural language processing (NLP), especially within the domain of machine translation. As a cornerstone of global communication, machine translation plays a crucial role in bridging linguistic barriers, facilitating intercultural exchange, and enabling access to information across languages. However, conventional translation models often encounter significant limitations when tasked with capturing the nuanced semantics and intricate contextual dependencies inherent in human language. These challenges are particularly evident in complex linguistic scenarios, where shallow representations and limited context modeling result in reduced translation fidelity. To address these persistent issues, we propose a novel translation architecture that integrates hierarchical function estimation with latent-variable encoding mechanisms. This approach enhances the model’s ability to comprehend and generate contextually and semantically rich translations. Central to our method is a multi-path encoder design, which conditions latent variables on both local word-level cues and global sentence-level structures, thereby enriching the contextual awareness of the system. We employ a deep Gaussian process framework to effectively model intricate and non-linear dependencies within language structures. To further refine the latent representations, we introduce a spectral regularization technique that aligns them with the underlying manifold structure of the data. This promotes geometric coherence and smoothness in the learned translations, improving both fluency and accuracy. Comprehensive experimental evaluations across multiple benchmarks demonstrate that our model consistently outperforms state-of-the-art baselines, particularly in preserving semantic integrity across a range of diverse linguistic settings. This contribution represents a meaningful step forward in the development of computational models that deepen machine understanding of natural language.}, archive = {J_ACCESS}, author = {Qian Cui}, doi = {10.1109/ACCESS.2025.3604031}, journal = {IEEE Access}, pages = {159714-159729}, shortjournal = {IEEE Access}, title = {Research on dual-channel transformer english translation model based on cross-layer semantic fusion}, volume = {13}, year = {2025}, }
• (2025). Exploring the feasibility of novel 3D polyhedrally-tiled computing arrays. ACCESS, 13, 159685-159713. (www)

  @article{ , abstract = {Polyhedral tiled computing arrays (PTCAs) are a largely unexplored paradigm in which the assembly of high performance computing (HPC) structures may be achieved by the multidimensional tiling of particular polyhedral modules, each housing computing devices. Typically, PTCAs form physical three-dimensional (3D) topologies which readily support logical three-dimensional topologies. PTCAs eliminate the need for traditional backplanes, racks and custom host circuit-board modules, while inherently composing coherent and scalable input/output (IO), power and cooling grids simply by abutting to neighboring modules in one or more dimensions. This highly novel concept offers unique possibilities in all three of those domains, and therefore quantifying PTCA capabilities and limitations is critical in establishing the desirability of such systems for future modular and heterogeneous HPC systems. The questions must then arise: is this a realistic paradigm?, and, can such a system be practically engineered? In this paper we contribute a number of insights, an analytical methodology for the evaluation of a specific class of PTCAs, based upon simple-cube and truncated octahedral modules arranged into cubic arrays, and we demonstrate the use of numerical methods and formulae to analyze the potential capabilities and limits of such novel systems with both existing reference points and future technology expectations. Whilst mapping out all aspects of this novel design-space is somewhat speculative, and beyond the scope of a single paper, we do conclude by identifying key challenges and ‘road-map’ goals aimed toward finally reaching that objective.}, archive = {J_ACCESS}, author = {Chris Crispin-Bailey and Pakon Thuphairo and Steven Wright and Anthony Moulds and Jim Austin}, doi = {10.1109/ACCESS.2025.3605303}, journal = {IEEE Access}, pages = {159685-159713}, shortjournal = {IEEE Access}, title = {Exploring the feasibility of novel 3D polyhedrally-tiled computing arrays}, volume = {13}, year = {2025}, }
• (2025). Vehicle speed estimation using modulated motion blur in diverse environments. ACCESS, 13, 159667-159684. (www)

  @article{ , abstract = {The relative velocity between a vehicle and the ground is a critical parameter for controlling the tire-ground reaction forces that directly influence vehicle dynamics. However, accurately measuring this relative velocity remains a challenge, as there are few practical solutions. In recent years, velocity estimation techniques such as optical image modulation (OIM) and modulated motion blur have emerged, but their limited measurement range has restricted their adoption in commercial vehicles. To overcome this limitation, a windshield-mounted remote sensing system based on OIM that uses warped directional projection with maximum variance angle (WDPMVA) to estimate vehicle velocity in real time was proposed in this study. To improve the cost efficiency of this sensor system, a novel hardware design was developed to modulate the optical system of the camera. The performance of the proposed system has been rigorously validated under diverse conditions, including highways, specialized test tracks, and rainy weather. Unlike camera- or LiDAR-based odometers that are dependent on surrounding objects, this system operates independently of external references and provides robust performance on various road surfaces. The effectiveness of the proposed system was benchmarked against a real-time kinematic global navigation satellite system. Compared to a baseline algorithm based on the Discrete Fourier Transform, the WDPMVA algorithm reduced the root-mean-square error (RMSE) of velocity estimation on asphalt surfaces by 40%. Furthermore, the newly developed windshield-mounted hardware achieved an additional RMSE reduction of approximately 17% while extending the measurement range to three times that of existing methods.}, archive = {J_ACCESS}, author = {Minyoung Lee and Mingeuk Kim and Chanseok Park and Moohyun Cha and Kyung-Soo Kim}, doi = {10.1109/ACCESS.2025.3601174}, journal = {IEEE Access}, pages = {159667-159684}, shortjournal = {IEEE Access}, title = {Vehicle speed estimation using modulated motion blur in diverse environments}, volume = {13}, year = {2025}, }
• (2025). Transformer and graph neural network-based federated learning for cellular traffic prediction with sustainability analysis. ACCESS, 13, 159649-159666. (www)

  @article{ , abstract = {Mobile network traffic prediction is critical for efficient network management in 5G and future 6G systems. Federated Learning (FL) enables multiple base stations (BSs) to collaboratively train prediction models without sharing raw data, preserving privacy. This paper presents a unified and extended study of FL for cellular traffic forecasting, integrating state-of-the-art deep learning models including recurrent networks, attention mechanisms, Transformers, and Graph Neural Networks (GNNs). We evaluate these models on real 4G LTE traffic datasets (including recent publicly available data from 2018–2023) and additional large-scale synthetic scenarios. Our results show that FL achieves prediction accuracy comparable to centralized training while avoiding data transmission, and that advanced architectures (e.g. Transformer and graph-based models) can improve forecasting accuracy by up to 5-10% over federated LSTM baselines. We introduce a comprehensive sustainability indicator to assess the trade-off between accuracy and energy efficiency of each approach. Through detailed experiments, we quantify the energy consumption and communication overhead of training and inference for each model. The Transformer-based FL model yields the highest accuracy but incurs increased training energy, whereas simpler models are more energy-efficient. We provide visualizations of the accuracy-energy trade-offs and discuss how attention and spatial graph modeling influence performance. The study concludes that FL is a promising solution for privacy-preserving and sustainable mobile traffic prediction. We offer insights into model aggregation strategies, personalization, and the benefits of incorporating external (exogenous) data features. This work uniquely integrates advanced Transformer and GNN architectures within a federated framework, explicitly highlighting improvements over previous recurrent-based methods and emphasizing sustainability metrics critical for practical deployments. Moreover, our evaluation shows that the federated LSTM model achieved up to 10% lower sustainability score (S) compared to centralized approaches, quantifying practical gains for real-world deployments.}, archive = {J_ACCESS}, author = {Muhammad Qasim and Muhammad Imran and Faisal Akram and Muhammad Ahmad Bilal and M. Jaleed Khan}, doi = {10.1109/ACCESS.2025.3605380}, journal = {IEEE Access}, pages = {159649-159666}, shortjournal = {IEEE Access}, title = {Transformer and graph neural network-based federated learning for cellular traffic prediction with sustainability analysis}, volume = {13}, year = {2025}, }
• (2025). Abnormal operation of 6-T SRAM based on nanosheet FET due to total ionizing dose. ACCESS, 13, 159639-159648. (www)

  @article{ , abstract = {This study investigates the total ionizing dose (TID) effect on the six-transistor static random access memory (6-T SRAM) consisting of a novel device, a nanosheet field effect transistor (NSFET). Via technology computer-aided design, we simulate the hold, read, and write static noise margin (HSNM, RSNM, and WSNM) degradations by the oxide trap, using stationary simulation. In the stationary simulation, the HSNM and RSNM decrease about 75% and bit flip occurs under the oxide trap conditions, respectively. The voltage stored at the $V_{CH}$ ( $V_{CL}$ ) node reduces owing to the leakage current of the pull-down (PD) transistors. The detail degradation mechanism for the SRAM operation (hold/read/write) problems due to the oxide trap is confirmed through the time-dependent simulations. In the read operations, the leakage current at PD transistors causes a bit flip of the CH node. Moreover, the CH node voltage decreases under hold and write operations ( $\Delta V_{CH} =15.7$ % and 14%). Significantly, when the adjacent cell (word-line (WL)2) of the target cell (WL1) operates to write data ( $V_{BL}$ down from $V_{DD}$ to 0), the voltage drop at the target cell (WL1) CH node (decreased by 23.3%) is enhanced under hold operation of the target cell. To mitigate the effects of oxide trap that cause electrical characteristic degradation in NSFET 6-T SRAM, we applied a partial dielectric isolation (PDI) structure that locally deposits an oxide (SiO2) on the bottom of the source/drain (S/D). SRAM cells incorporating the proposed PDI technique demonstrated that variations in SNM caused by oxide trap were suppressed to less than 1% across read, write, and hold operations. Additionally, through time-dependent electrical characteristic analysis, it was confirmed that the potential changes in $V_{ch}$ and $V_{cl}$ converge 1% under various operation modes and adjacent cell activation conditions, thereby demonstrating the effective blocking of the effects of oxide trap occurring in the shallow trench isolation (STI) region.}, archive = {J_ACCESS}, author = {Jonghyeon Ha and Minji Bang and Minki Suh and Dabok Lee and Minsang Ryu and Jin-Woo Han and Hyunchul Sagong and Hojoon Lee and Jungsik Kim}, doi = {10.1109/ACCESS.2025.3607964}, journal = {IEEE Access}, pages = {159639-159648}, shortjournal = {IEEE Access}, title = {Abnormal operation of 6-T SRAM based on nanosheet FET due to total ionizing dose}, volume = {13}, year = {2025}, }
• (2025). Hybrid deep learning model for scalogram-based ECG classification of cardiovascular diseases. ACCESS, 13, 159628-159638. (www)

  @article{ , abstract = {Cardiovascular diseases (CVDs), particularly arrhythmias, remain a leading global health challenge, underscoring the need for accurate and timely detection. Accurate and robust classification of electrocardiogram (ECG) signals for arrhythmia detection remains challenging due to noise, data imbalance, and the complexity of integrating spatial and temporal features. This study proposes a novel hybrid deep learning model for automated ECG-based heartbeat classification, achieving an outstanding classification accuracy of 99.20%. The architecture integrates Residual Network (ResNet)-50 with Squeeze-and-Excitation (SE) blocks and Long Short-Term Memory (LSTM) networks to effectively capture spatial and temporal features from ECG signals. Unlike most existing methods that treat spatial and temporal components separately, this hybrid integration represents a key novelty, enabling joint learning of hierarchical spatial features and sequential temporal dependencies. Raw ECG waveforms are transformed into two-dimensional scalograms using Continuous Wavelet Transform (CWT), enabling the extraction of rich time-frequency patterns. ResNet-50, enhanced with SE blocks, prioritizes diagnostically relevant spatial features, while LSTM models sequential dependencies critical for identifying cardiac irregularities. The model was trained and validated on a balanced dataset comprising three classes—Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR)—using preprocessing techniques such as denoising, segmentation, and data augmentation. With precision, recall, and F1-scores exceeding 0.99, the model demonstrates robust generalization and significant potential for integration into clinical diagnostic tools, offering a scalable solution for improved cardiovascular outcomes.}, archive = {J_ACCESS}, author = {Neeraj Singh Kathayat and A. Pravin Renold}, doi = {10.1109/ACCESS.2025.3605279}, journal = {IEEE Access}, pages = {159628-159638}, shortjournal = {IEEE Access}, title = {Hybrid deep learning model for scalogram-based ECG classification of cardiovascular diseases}, volume = {13}, year = {2025}, }
• (2025). Binary estimator selection methods for hearing aids with a remote microphone. ACCESS, 13, 159610-159627. (www)

  @article{ , abstract = {Using a high signal-to-noise ratio remote microphone (RM) with hearing aids (HAs) is advantageous for HA users. However, the benefit depends significantly on the properties of the wireless channel. While existing literature often assumes an error-free and instantaneous wireless transmission channel, in reality, wireless transmission of audio typically undergoes latency, which introduces a time difference of arrival (TDOA) between the local HA microphone signals and the wirelessly transmitted RM signal received at the HA. We first observe that, as the distance between the HAs and the RM increases, the TDOAs decrease, making the signals received from a distant RM more beneficial than those from a nearby RM. Next, we propose two methods to combine a RM signal with the HA, under realistic TDOAs. Acting in either the complex spectral domain or the magnitude spectral domain, the methods make a binary selection in each time-frequency component between minimum mean-square-error estimates determined from the HA microphone signals and RM signal, respectively. We evaluate the performance of the resulting estimate in terms of their signal quality and speech intelligibility using objective and subjective tests. We show that the proposed methods outperform their baselines that use only HA signals, for TDOAs in the range of 30-60 ms.}, archive = {J_ACCESS}, author = {Vasudha Sathyapriyan and Michael S. Pedersen and Mike Brookes and Jan Østergaard and Patrick A. Naylor and Jesper Jensen}, doi = {10.1109/ACCESS.2025.3609061}, journal = {IEEE Access}, pages = {159610-159627}, shortjournal = {IEEE Access}, title = {Binary estimator selection methods for hearing aids with a remote microphone}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-based position tracking control for proportional directional control valve-based electro-hydraulic system. ACCESS, 13, 159597-159609. (www)

  @article{ , abstract = {This paper presents a reinforcement learning-based discrete-time control strategy for nonlinear electro-hydraulic actuator systems equipped with a proportional directional control valve. The proposed method incorporates a Lyapunov-based stability framework, ensuring bounded tracking errors and neural network weights despite modeling uncertainties and external disturbances. A discrete-time dynamic model of the electro-hydraulic actuator system is derived, and unmeasured system states are estimated using an extended Kalman filter. The controller is validated through both MATLAB simulations and real-time hardware experiments involving sensors, a hydraulic cylinder, a proportional directional control valve, and a data acquisition system. The obtained results demonstrate accurate trajectory tracking in the presence of modeling uncertainties, disturbances, and real-time adaptability of neural network weights, highlighting the effectiveness of the proposed controller for industrial electro-hydraulic systems.}, archive = {J_ACCESS}, author = {Raja Rout and Ashok Kumar Kumawat}, doi = {10.1109/ACCESS.2025.3608253}, journal = {IEEE Access}, pages = {159597-159609}, shortjournal = {IEEE Access}, title = {Reinforcement learning-based position tracking control for proportional directional control valve-based electro-hydraulic system}, volume = {13}, year = {2025}, }
• (2025). Spatial nulling and MIMO pre-processing design for dual mmWave active array SDR platform with OTA effects. ACCESS, 13, 159582-159596. (www)

  @article{ , abstract = {This research studies transmit pre-processing designs using two millimeter wave (mmWave) active arrays with a multiple input multiple output (MIMO) software-defined radio (SDR) platform under an over-the-air (OTA) communication emulator. It covers the fast Doppler, line-of-sight (LOS) with small delay effect, and MIMO array interference effects. In this emulator, we propose spatial nulling and MIMO pre-processing cancellation techniques to eliminate the MIMO streams interference. First, differential training sequences are utilized to acquire and compensate for the LOS fast Doppler offset. Next, a maximum power beam scanning scheme via the phase shifters of mmWave arrays is employed to estimate the arrival angles between MIMO array transmitter and receiver stations. Subsequently, the steering vectors of the desired and interference signals via these estimated angles are adopted by the phase-only optimization algorithms to calculate the spatial beamforming weights of the two mmWave arrays, which can null the interference stream and retain the desired stream. Because we observed that the residual MIMO interference lingers in the MIMO receiver even after the spatial nulling processing with the interference mitigation, we further propose a MIMO pre-processing filters using the least square method to cancel the residual MIMO interference. Finally, the measurement results of the two mmWave arrays SDR platform with the fast Doppler and the OTA scenarios confirm that the proposed joint spatial nulling and MIMO pre-cancellation techniques can eliminate the MIMO interference and provide the high-quality error vector magnitude (EVM) performance.}, archive = {J_ACCESS}, author = {Juinn-Horng Deng and Keng-Hwa Liu and Yuanzhang Xiao and Wen-Yu Pan and Meng-Lin Ku and Soo Yong Lim}, doi = {10.1109/ACCESS.2025.3608569}, journal = {IEEE Access}, pages = {159582-159596}, shortjournal = {IEEE Access}, title = {Spatial nulling and MIMO pre-processing design for dual mmWave active array SDR platform with OTA effects}, volume = {13}, year = {2025}, }
• (2025). FocalVid: A platform for tracking visual attention to video via crowdsourcing validated against human gaze data. ACCESS, 13, 159566-159581. (www)

  @article{ , abstract = {Human attention to dynamic visual stimuli can be measured using mouse-contingent cursor movements as a proxy for tracking human gaze when eye tracking is not feasible, for example, in online studies. FocalVid is the first such platform for collecting mouse-contingent attention data for video as a proxy approximating eye tracking. The present study analyses in detail how data collected with FocalVid are related to human gaze data. Cursor movements of 225 participants watching a variety of videos are compared to eye movements for an established corpus on human gaze that used the same videos. Such a comparison is critical to justifying the use of FocalVid or similar platforms, and for choosing suitable user interface settings to produce results most similar to human gaze. Cursor movements in FocalVid are shown to have notable similarities with eye movements. For example, distributions of cursor velocities and fixation durations are qualitatively similar to those of eye movements. However, moment-to-moment cursor movements and eye movements were significantly different with most video stimuli. We discuss the implications for mouse-contingent attention measures to video stimuli in different contexts.}, archive = {J_ACCESS}, author = {Sahand Shaghaghi and Karissa B. Payne and Bryan Tripp and Kerstin Dautenhahn and Chrystopher L. Nehaniv}, doi = {10.1109/ACCESS.2025.3608621}, journal = {IEEE Access}, pages = {159566-159581}, shortjournal = {IEEE Access}, title = {FocalVid: A platform for tracking visual attention to video via crowdsourcing validated against human gaze data}, volume = {13}, year = {2025}, }
• (2025). Adaptive energy optimization in cloud computing through containerization. ACCESS, 13, 159548-159565. (www)

  @article{ , abstract = {Cloud computing has significantly transformed the way organizations utilize computing resources by offering scalability, flexibility, and cost-effectiveness. Energy consumption has become a major research concern with the growing demand for cloud services these days. Adaptive energy optimization techniques are very much essential to minimize energy usage across cloud servers. The main objective of this research is to conserve energy across heterogeneous cloud servers in datacentres. The proposed adaptive energy optimization framework uses container orchestration utilizing Kubernetes framework by sharing the host system kernel and reducing resource overhead to maximize energy efficiency. The work also incorporates smart algorithms, including Ant Colony Optimization (ACO), Swarm Intelligence, and Dynamic Voltage and Frequency Scaling (DVFS) to enable the real-time, energy cognizant distribution of workloads according to the current demand, avoiding unnecessary consumption of resources. Kubernetes container orchestration to optimize energy usage, facilitating effective scaling and thus simplifying the management of applications. Two novel components, the Adaptive Resource Adjustment Algorithm (ARAA) and the Automated Resource-Aware Kubernetes Lifecycle Management (ARKLM), work together to dynamically adjust resource allocation and deactivate idle containers within the Kubernetes environment. Experimental results confirm the framework’s effectiveness, demonstrating that the DVFS algorithm achieved a low 0.94% average CPU usage, while the ACO algorithm delivered the fastest execution time. This dual-focus on energy efficiency and speed makes the framework a scalable and sustainable solution for modern cloud operations, aligning with UN Sustainable Development Goals (7, 9, 12, 13, 17) by enabling energy-efficient, low-carbon, and collaborative cloud operations through intelligent automation.}, archive = {J_ACCESS}, author = {B. M. Beena and Prashanth Cheluvasai Ranga and Vijay Holimath and Swetha Sridhar and Samrudhi S. Kamble and Sanjivani P. Shendre and M. Yagnasri Priya}, doi = {10.1109/ACCESS.2025.3606932}, journal = {IEEE Access}, pages = {159548-159565}, shortjournal = {IEEE Access}, title = {Adaptive energy optimization in cloud computing through containerization}, volume = {13}, year = {2025}, }
• (2025). Genetic and epigenetic predispositions, shared mechanisms, and common biomarkers between cancer and CVD—Machine learning-based insights. ACCESS, 13, 159530-159547. (www)

  @article{ , abstract = {People affected by cancer are at an elevated risk of cardiovascular disease (CVD)-associated morbidity and mortality; the reverse association is also substantially evident. This bidirectional association can be characterized by their genetic susceptibility, common modifiable and non-modifiable risk factors, shared molecular mechanisms, and diagnostic biomarkers. The root of this intricate relationship between these two disempathetic phenotypes is not clearly understood yet, though their mechanistic correlations and underlying shared pathways are consistently investigated. Along with bioinformatics methods, machine learning capabilities are evolving and may enhance the understanding of this complex relationship through genetic variant and epigenetic modification detection, biological mechanism elucidation, common biomarker discovery, and predictive model development for cancer-related morbidity and mortality in CVD patients and CVD-related events in cancer patients or survivors. In this review, we critically analyze the advancements, contributions, potential, and challenges of machine learning applications. As a relatively new approach, we identify the necessity of optimized computational methods for important feature selection, efficient data imputation algorithms for handling missing data, effective data harmonization pipelines for multi-modal data, computational frameworks for the simultaneous cancer-CVD risk stratification, and explainable algorithms for comprehensive clinical translation. Finally, we recommend machine learning-based causal inference analysis of their interconnections in order to understand their mutual causal effects.}, archive = {J_ACCESS}, author = {Khalil Ahammad and Anahita Fouladzadeh and Hannah R. Wardill and Derek Abbott and Mohsen Dorraki}, doi = {10.1109/ACCESS.2025.3607285}, journal = {IEEE Access}, pages = {159530-159547}, shortjournal = {IEEE Access}, title = {Genetic and epigenetic predispositions, shared mechanisms, and common biomarkers between cancer and CVD—Machine learning-based insights}, volume = {13}, year = {2025}, }
• (2025). Communications performance analysis of wireless multiple access channel with specially correlated sources. ACCESS, 13, 159519-159529. (www)

  @article{ , abstract = {From both practical and theoretical viewpoints, performance analysis of communication systems using information-theoretic results is very important. In this study, first, we obtain a general achievable rate for a two-user wireless multiple access channel (MAC) with specially correlated sources as a more general version for continuous alphabet MACs, by extending the known discrete alphabet results to the wireless continuous alphabet version. Next, the impact of wireless channel coefficients correlation on the performance metrics using Copula theory, as the most convenient way for describing the dependence between several variables, is investigated. By applying the Farlie-Gumbel-Morgenstern (FGM) Copula function, we obtain closed-form expressions for the outage probability (OP) under positive/negative dependence conditions. It is shown that the fading correlation improves the OP for a negative dependence structure. Specifically, whenever the dependence structure tends to negative values, the OP decreases and the efficiency of the channel increases. Finally, the efficiency of the analytical results is illustrated numerically.}, archive = {J_ACCESS}, author = {Akram Entezami and Ghosheh Abed Hodtani}, doi = {10.1109/ACCESS.2025.3608724}, journal = {IEEE Access}, pages = {159519-159529}, shortjournal = {IEEE Access}, title = {Communications performance analysis of wireless multiple access channel with specially correlated sources}, volume = {13}, year = {2025}, }
• (2025). HiFused-depth: High-frequency fused depth estimation with knowledge guidance. ACCESS, 13, 159505-159518. (www)

  @article{ , abstract = {Monocular depth estimation (MDE) is a fundamental task in computer vision with a wide range of applications. However, achieving accurate depth prediction remains challenging in scenes with complex structures and low texture areas. To address this issue, we propose a new depth estimation network that integrates high-frequency information enhancement and knowledge extraction. Specifically, the Sobel operator is used to extract high-frequency features such as edges and textures from the input RGB image, and merge them into the feature representation to enrich geometric understanding. Establish a high-frequency perceptual loss function to guide the network to focus on texture defects and structurally blurry areas, improving depth prediction accuracy. In addition, adopting a knowledge distillation strategy from a pretrained external teacher network, which transfers both global semantic context and fine-grained geometric cues to the student model, enhances generalization ability in different environments. Extensive experiments conducted on the NYUv2 and KITTI datasets have shown that the proposed method consistently outperforms existing methods in multiple standard evaluation metrics, achieving up to 7.3% reduction in $\log _{10}$ error and 6.3% reduction in Abs Rel on NYUv2, and 6.9% reduction in Sq Rel on KITTI compared with the strong NeWCRFs baseline.}, archive = {J_ACCESS}, author = {Guangyi Ji and Xiaoxi Hu and Tao Chen and Wentao Zhang}, doi = {10.1109/ACCESS.2025.3608944}, journal = {IEEE Access}, pages = {159505-159518}, shortjournal = {IEEE Access}, title = {HiFused-depth: High-frequency fused depth estimation with knowledge guidance}, volume = {13}, year = {2025}, }
• (2025). Using machine learning to predict cognitive impairment from functional assessments: A study of 274 stroke patients. ACCESS, 13, 159499-159504. (www)

  @article{ , abstract = {Cognitive impairment, which commonly occurs after stroke, adversely impacts rehabilitation outcomes. The administration of traditional screening tools such as the Mini-Mental State Examination (MMSE) can be challenging for patients with communication or motor limitations. This study aimed to determine whether routinely collected functional assessments can effectively predict cognitive impairment in stroke patients undergoing robotic-assisted gait training with the Lokomat system. In this retrospective analysis, data from 274 stroke patients who received Lokomat-based rehabilitation were examined. Patients with complete data on the MMSE, Fugl-Meyer Assessment (FMA), Modified Barthel Index (MBI), and Timed Up and Go (TUG) test were included in the final analysis. Cognitive impairment was defined as an MMSE score <24. Five supervised machine learning models—logistic regression, decision tree, random forest, support vector machine (SVM), and XGBoost—were developed to classify cognitive status using FMA, MBI, and TUG as predictors. The performance of each model was assessed by the area under the curve (AUC), accuracy, sensitivity, and precision. Logistic regression demonstrated the highest predictive performance (AUC = 0.92, accuracy = 89.5%), followed by random forest (AUC = 0.88) and XGBoost (AUC = 0.87). FMA was identified as the most significant predictor in tree-based models. Patients classified as cognitively impaired exhibited significantly lower functional scores than their unimpaired counterparts (p <0.001). Functional assessments routinely performed during stroke rehabilitation can provide a practical and accurate method for identifying cognitive impairment, especially when cognitive screening is constrained. These results support the incorporation of machine learning–based tools into clinical decision-making during robotic rehabilitation.}, archive = {J_ACCESS}, author = {Ilyoung Moon and Chanhee Park}, doi = {10.1109/ACCESS.2025.3607979}, journal = {IEEE Access}, pages = {159499-159504}, shortjournal = {IEEE Access}, title = {Using machine learning to predict cognitive impairment from functional assessments: A study of 274 stroke patients}, volume = {13}, year = {2025}, }
• (2025). SQUAD: A scalable quantization accelerator toward energy-efficient on-device quantization-aware training. ACCESS, 13, 159487-159498. (www)

  @article{ , abstract = {Quantization-aware training (QAT) is a crucial technique for maintaining model accuracy at extremely low-bit precision (i.e., 4-bit or lower). However, the fake quantization operation of QAT introduces significant training time overhead compared to post-training quantization (PTQ). To address this challenge, we propose SQUAD, a scalable hardware accelerator designed to perform fake quantization operations in any precision at high-performance and energy efficiency. Our proposed SQUAD consists of multiple parallel and pipelined quantization accelerator cores (Agents), with peripherals such as buffer, control units, etc. According to our analysis on QAT of ResNet, MobileNetV2, and DeiT, SQUAD reduces the fake quantization latency by 93.1% and energy consumption by 97.6%, compared to a commercial edge GPU. When SQUAD is applied to QAT on an edge GPU, it significantly accelerates the forward pass, resulting in 48.8% lower latency and 51.2% lower energy consumption. Consequently, these improvements contribute to an overall training latency reduction and energy reduction of 14.6% and 15.3%, with only 0.43% area overhead.}, archive = {J_ACCESS}, author = {Ye-Bin Kwon and Young Seo Lee and Young-Ho Gong}, doi = {10.1109/ACCESS.2025.3608445}, journal = {IEEE Access}, pages = {159487-159498}, shortjournal = {IEEE Access}, title = {SQUAD: A scalable quantization accelerator toward energy-efficient on-device quantization-aware training}, volume = {13}, year = {2025}, }
• (2025). Enhancing smart contract security through DevSecOps: An adaptive approach for vulnerability detection. ACCESS, 13, 159454-159486. (www)

  @article{ , abstract = {Smart contracts are central to decentralized applications but are often vulnerable to security flaws, both during development and in deployment. Due to their immutable nature, any vulnerabilities introduced are permanent and exploitable, highlighting the need for a secure, structured development lifecycle. This study proposes an adaptive DevSecOps approach tailored to blockchain applications, drawing on established practices and gray literature. By embedding security into Continuous Integration and Continuous Delivery (CI/CD), the model ensures continuous protection from development through deployment. At its core is an adaptive cognitive security framework that automates vulnerability detection using techniques like static analysis, fuzzing, and symbolic execution. It also streamlines secure provisioning across various environments. To further enhance security, Agentic AI is integrated into the pipeline. These autonomous agents monitor code, infrastructure, and behavior in real-time, learning from past incidents, detecting threats, applying patches, and adapting CI/CD workflows dynamically. Experimental results validate the model’s effectiveness, showing significant improvements in the security and reliability of smart contracts. This approach offers a scalable, proactive solution for securing blockchain systems in a rapidly evolving threat landscape.}, archive = {J_ACCESS}, author = {Nghia Dinh and Vinh Truong Hoang and Bay Nguyen van and Thien Ho Huong and Ha Duong Thi Hong and Hau Nguyen Trung and Kiet Tran Trung}, doi = {10.1109/ACCESS.2025.3606572}, journal = {IEEE Access}, pages = {159454-159486}, shortjournal = {IEEE Access}, title = {Enhancing smart contract security through DevSecOps: An adaptive approach for vulnerability detection}, volume = {13}, year = {2025}, }
• (2025). On the effectiveness of zero-shot and few-shot pretrained language models for software requirement classification. ACCESS, 13, 159439-159453. (www)

  @article{ , abstract = {Accurate classification of software requirements, particularly distinguishing between fun- ctional and non-functional requirements, is essential for enabling traceability, guiding system design, and ensuring project success. Traditional supervised machine learning methods have shown promise in this domain but suffer from limited generalizability, high labeling costs, and overfitting. Recent advances in pretrained language models (PLMs) offer new opportunities for effective requirement classification through zero-shot (ZS) and few-shot (FS) learning. This study investigates the effectiveness of ZS and FS PLMs, including prompt-based generative large language models (e.g., GPT-4o and Llama3), in classifying functional vs. non-functional requirements and non-functional subtypes, such as usability, security, operational, and performance requirements. We conduct a comprehensive empirical evaluation across five classification tasks using two widely adopted datasets: PROMISE and SecReq. The study compares six PLMs, including two ZS, two FS transformer models, and two generative Large Language Models under both ZS and FS settings. We benchmark these models against state-of-the-art and domain-specific baselines, and complement the quantitative analysis with a detailed qualitative error analysis. FS models, such as All-MPNET, All-DistilRoBERTa, and GPT-4o in the FS setting, consistently outperform ZS models across all tasks, achieving superior performance in multi-class NFR and security requirement classification. While NoRBERT remains superior in binary FR/NFR classification, our FS models outperform or match its performance in other scenarios. ZS models, such as GPT-4o, also demonstrate strong results without requiring training data, highlighting their practical utility. Error analysis reveals that annotation issues and lack of context are major sources of misclassification. Our findings establish that ZS and FS PLMs, especially prompt-based generative large language models, offer viable and scalable alternatives to traditional supervised approaches. This study lays the foundation for the broader adoption of prompt-based PLMs in requirement engineering.}, archive = {J_ACCESS}, author = {Md Shafikuzzaman and Md Rakibul Islam and Shuaib Zaman and Andrew Ma and Anwarul Islam Sifat}, doi = {10.1109/ACCESS.2025.3607813}, journal = {IEEE Access}, pages = {159439-159453}, shortjournal = {IEEE Access}, title = {On the effectiveness of zero-shot and few-shot pretrained language models for software requirement classification}, volume = {13}, year = {2025}, }
• (2025). SITRUS: Simulation and analysis of irregular tessellated wireless sensor networks using cooja. ACCESS, 13, 159422-159438. (www)

  @article{ , abstract = {Efficient sensor deployment in Wireless Sensor Networks (WSNs) remains challenging in irregular environments due to the need for robust k-coverage and energy efficiency. This paper introduces SITRUS (Simulation of Irregular Tessellation in Realistic Unstructured Sensor Networks), a comprehensive simulation and analytical framework that leverages irregular tessellation for optimized node placement. A closed form density model estimates the minimal sensor count based on coverage degree, sensing range, and granularity. A max-min optimization strategy ensures uniform distribution within each tile, minimizing redundancy. The proposed design is validated through realistic simulations in Contiki-NG and Cooja using heterogeneous Sky and Z1 motes under varying node mobility and densities. Simulation results show up to 37.5% lower sensor density, 34.6% less control overhead, and over 40% increase in network lifetime compared to baseline methods. SITRUS provides a scalable, energy-aware deployment solution for real-world modern WSNs operating in complex and dynamic environments.}, archive = {J_ACCESS}, author = {Dakshanya Maddala and Haitham Y. Adarbah and Afzel Noore}, doi = {10.1109/ACCESS.2025.3609189}, journal = {IEEE Access}, pages = {159422-159438}, shortjournal = {IEEE Access}, title = {SITRUS: Simulation and analysis of irregular tessellated wireless sensor networks using cooja}, volume = {13}, year = {2025}, }
• (2025). A new lower bound for permutation codes using Reed–Solomon codes. ACCESS, 13, 159412-159421. (www)

  @article{ , abstract = {We present a constructive lower bound for permutation codes under Kendall’s $\tau $ -metric that improves upon the classical Gilbert-Varshamov estimate for many parameters. For any length $p \gt 5$ , we construct codes with minimum Kendall’s $\tau $ -distance 6 and size at least $p! / p^{3}$ . This result is achieved by proving that Reed-Solomon codes of length p and dimension $p-3$ have a minimum Lee distance of 6, and then applying a metric embedding from the Lee metric to the Kendall’s $\tau $ -metric.}, archive = {J_ACCESS}, author = {Rana T. Shwayyea and J. Bagherian and Reza Sobhani and Alireza Abdollahi}, doi = {10.1109/ACCESS.2025.3608654}, journal = {IEEE Access}, pages = {159412-159421}, shortjournal = {IEEE Access}, title = {A new lower bound for permutation codes using Reed–Solomon codes}, volume = {13}, year = {2025}, }
• (2025). Automated elastic regularized Savitzky–Golay filter for speckle noise reduction in ultrasound images. ACCESS, 13, 159400-159411. (www)

  @article{ , abstract = {Ultrasound image denoising is a promising approach to improving visualization quality and enhancing diagnostic accuracy, with speckle noise removal remaining a central focus in medical imaging research over recent decades. Among various denoising techniques, the Savitzky–Golay (SG) filter is especially appealing for ultrasound images since it provides an effective noise reduction while preserving edges and fine anatomical structures—critical for clinical interpretation. However, the classical SG filter and alternative approaches suffer from under-smoothing and over-smoothing issues due to in-optimal regularization coefficient. Then, the noise can be risks leaving unfiltered or the structural details may be blurred. In this paper, we propose Automated Elastic Regularized Savitzky–Golay (AERSG) filter which augments the conventional Savitzky–Golay (SG) framework with an adaptive Tikhonov regularization mechanism to determine the regularization coefficient automatically via the L-curve curvature maximization criterion. Then, the AERSG filter dynamically adjusts smoothing strength according to local image characteristics, effectively suppressing speckle noise while maintaining edges and fine anatomical features. Experimental results demonstrate that the proposed approach outperforms traditional SG and regularized SG methods in balancing noise suppression and edge preservation, making it particularly suitable for high-precision medical ultrasound imaging.}, archive = {J_ACCESS}, author = {Kanabadee Srisomboon and Vera Sa-Ing and Wilaiporn Lee}, doi = {10.1109/ACCESS.2025.3608304}, journal = {IEEE Access}, pages = {159400-159411}, shortjournal = {IEEE Access}, title = {Automated elastic regularized Savitzky–Golay filter for speckle noise reduction in ultrasound images}, volume = {13}, year = {2025}, }
• (2025). Method of eliminating interdependencies of essential services raising the risk of loss of business continuity. ACCESS, 13, 159380-159399. (www)

  @article{ , abstract = {The article examines the interdependencies of the essential services (ES) determined by the operators used to provide the ES. The research problem stems from the limited knowledge of ES operators on identifying ES interdependencies. Contemporary legislation and scientific discussion suggest the need to consider ES interdependence in the context of ES security management. More detailed scientific research and practical tools are needed to identify and analyze ES interdependencies in this area. The research gap mainly concerns the need for more transparent procedures to enable ES operators to understand the interdependencies between operators, hamper effective risk management. The article aims to develop a method for identifying and eliminating ES interdependencies. The method is intended to detect ES interdependencies that threaten the continuity of operation of more than one ES. The author applies Gorbatov Characterization Principle, which makes it possible to create graph models of interdependence and identify so-called forbidden figures or undesirable patterns of interdependence. By analyzing such patterns, it is possible to identify places where there are potential risks and propose structural changes that will eliminate them. In the context of the available research work on considering ES interdependencies and their impact on business continuity risk, the author’s proposed method fills the research gap on the analysis of the static structure of ES interdependencies, in terms of looking for structural defects.}, archive = {J_ACCESS}, author = {Michał Wiśniewski}, doi = {10.1109/ACCESS.2025.3608443}, journal = {IEEE Access}, pages = {159380-159399}, shortjournal = {IEEE Access}, title = {Method of eliminating interdependencies of essential services raising the risk of loss of business continuity}, volume = {13}, year = {2025}, }
• (2025). Green cybersecurity: Leveraging AI, ML, and LLMs to optimize energy, threat detection, and sustainability frameworks. ACCESS, 13, 159345-159379. (www)

  @article{ , abstract = {The rapid advancement of digital technologies has significantly increased energy consumption and carbon emissions, raising critical concerns about environmental sustainability. Green cybersecurity, which integrates environmental sustainability goals into security processes, is becoming increasingly vital as AI systems contribute to growing energy use and carbon dioxide emissions. This paper introduces the concept of green cybersecurity by examining how the integration of Artificial Intelligence (AI), Machine Learning (ML), and Large Language Models (LLMs) can simultaneously enhance cybersecurity practices while reducing their environmental impact. Through a systematic review of peer-reviewed literature, technical reports, and case studies from 2019 to 2025, we explore the potential of AI and ML to optimize energy usage, minimize electronic waste, and support the development of environmentally sustainable cybersecurity frameworks. Our findings indicate that AI and ML can drive advancements in resource management, predictive threat detection, automated response, and real-time monitoring. To assess these opportunities, we draw upon multiple theoretical frameworks, including the Systems Theory Model, Cybersecurity Maturity Model (CMM), and the UN Sustainable Development Goals (SDG) Framework, which collectively emphasize the socio-technical and ethical dimensions of AI-powered cybersecurity. We also address key challenges such as high computational demands, integration complexity, lifecycle environmental impacts, and ethical concerns related to data privacy and algorithmic transparency. This study underscores the need for interdisciplinary collaboration to develop security solutions that are both technologically robust and environmentally responsible.}, archive = {J_ACCESS}, author = {Yusuf Usman and Chidimma Judith Ihejirika and Stephen Nonye Offor and Robert Akl and Robin Chataut}, doi = {10.1109/ACCESS.2025.3602451}, journal = {IEEE Access}, pages = {159345-159379}, shortjournal = {IEEE Access}, title = {Green cybersecurity: Leveraging AI, ML, and LLMs to optimize energy, threat detection, and sustainability frameworks}, volume = {13}, year = {2025}, }
• (2025). Precision-controlled microwave hyperthermia preclinical system for small animal studies. ACCESS, 13, 159334-159344. (www)

  @article{ , abstract = {A small animal microwave hyperthermia (HT) system is reported for preclinical in vivo investigations on solid tumors. The portable system consists of a digitally controlled 2.45 GHz microwave source (1-30 W), miniaturized microwave applicator integrated with variable volume water bolus, water circulation module with flow (35-125 ml/minute) and temperature control (15-45°C), precision thermometry module to monitor subcutaneous and core body tissue temperatures with ±0.06 and $\pm ~0.08$ °C accuracy, respectively, and user interface for real time system control and monitoring. A water-coupled microstrip dipole applicator ( $10.5~mm \boldsymbol {\times } 10.5~mm$ ) resonating at 2.45 GHz was optimized for localized power deposition in superficial tissue with effective field size of ${149}~{mm}^{2}$ and 50% specific absorption rate coverage of ${973}~{mm}^{3}$ . Design, construction and characterization of the preclinical system is presented. System performance in tissue mimicking phantoms indicated thermal penetration depth of 6.1 mm and target coverage of ${1379}~{mm}^{3}$ . In vivo HT experiment confirmed steady state temperature rise of 4°C in subcutaneous tissue and core body rise less than 1°C. The preclinical system characteristics indicate its suitability for small animal HT research on solid tumors.}, archive = {J_ACCESS}, author = {Hemanth Kumar Dontiboina and Kavitha Arunachalam}, doi = {10.1109/ACCESS.2025.3606940}, journal = {IEEE Access}, pages = {159334-159344}, shortjournal = {IEEE Access}, title = {Precision-controlled microwave hyperthermia preclinical system for small animal studies}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on pre versus post quantum security schemes for 5G-enabled IoT applications. ACCESS, 13, 159305-159333. (www)

  @article{ , abstract = {The proliferation of 5th generation (5G) technology has catalyzed significant advancements in various application of Internet of Things (IoT) such as Intelligent Transportation Systems, Smart Energy, Industrial IoT (IIoT), Digital Twins, Autonomous Aerial Vehicles (UAVs) and Smart Health. However, with the upcoming advent of quantum computing, conventional security methods are encountering new and significant challenges, requiring the revaluation of pre and post-quantum security measures. This study provides an in-depth analysis of security measures before and after the advent of quantum computing for intelligent IoT applications enabled by 5G-connected network. The survey thoroughly analyzes the strengths and weaknesses of conventional public key cryptography methods such as RSA, Diffie-Hellman, Secret Sharing, and Elliptic Curve Cryptography (ECC), which are susceptible to quantum risks, and postquantum cryptographic methods such as lattice-based cryptography, code-based cryptography, and multivariate based techniques, which may provide protection against quantum attacks. Furthermore, we evaluate the suitability of these strategies in the context of 5G-enabled IoT deployments, taking into account the distinct requirements and obstacles presented by interconnected IoT devices. Although pre quantum public key cryptographic algorithms are still in use, but active research is going on to design and implement PQC algorithms to secure 5G IoT, while hybrid schemes have shown great promise being backward compatible with traditional cryptographic approaches at the same time bring quantum resistance. The survey offers significant insights into the latest pre and post-quantum security solutions for 5G-enabled IoT Applications. It helps in making educated decisions and implementing strong security measures in preparation for the quantum computing age.}, archive = {J_ACCESS}, author = {Bikramjit Choudhury and Ashlesha Hota and Mithun Karmakar and Sanchita Saha and Amitava Nag and Sukumar Nandi}, doi = {10.1109/ACCESS.2025.3608623}, journal = {IEEE Access}, pages = {159305-159333}, shortjournal = {IEEE Access}, title = {A comprehensive survey on pre versus post quantum security schemes for 5G-enabled IoT applications}, volume = {13}, year = {2025}, }
• (2025). Aerospace bricks: Modular and scalable 2D cellular automaton framework for delay and conflict analysis in en-route airspace. ACCESS, 13, 159286-159304. (www)

  @article{ , abstract = {Operational delays and air traffic complexity have increased with the growing global demand for air traffic, leading to irregular and less predictable traffic patterns. Although existing air traffic models have effectively addressed these challenges, they often trade off scalability, realism, and generalizability, particularly in en-route airspace, where both horizontal and vertical maneuvers matter. This paper proposes a two-dimensional cellular automaton model for simulating aircraft behavior in both the route direction and flight level. The model integrates realistic phase-specific aircraft dynamics and selectively applies separation controls (e.g., vectoring, holding, and speed control) only when operationally relevant. The proposed model is part of the Aerospace Bricks framework, which uses minimum simulation units (“bricks”) to represent specific airport and airspace features. Two case studies were conducted. The first used 10-day arrival traffic data from eight major Japanese airports to validate the model’s fidelity. The model successfully reproduced typical vector patterns and agreed closely with actual flight distances with spacing delays, averaging 95.3 s per flight within 80 NM of Tokyo International Airport. The second study simulated a full day of traffic in the Fukuoka Flight Information Region and identified high-complexity upper sectors at FL335 and above. The analysis detected several hotspots, such as flights with spacing adjustments and flights with traffic conflicts. These results demonstrate that the proposed model can efficiently reproduce realistic traffic dynamics and quantify delay and conflict risks. The proposed framework enables users to configure simulation environments that are easily tailored to specific operational scenarios.}, archive = {J_ACCESS}, author = {Katsuhiro Sekine and Tomoaki Tatsukawa and Yoshiaki Kawagoe and Eri Itoh}, doi = {10.1109/ACCESS.2025.3608241}, journal = {IEEE Access}, pages = {159286-159304}, shortjournal = {IEEE Access}, title = {Aerospace bricks: Modular and scalable 2D cellular automaton framework for delay and conflict analysis in en-route airspace}, volume = {13}, year = {2025}, }
• (2025). An enhanced method for objective QoE analysis in adaptive streaming services. ACCESS, 13, 159273-159285. (www)

  @article{ , abstract = {Evaluating Quality of Experience (QoE) is crucial for multimedia services, as it measures user satisfaction with content delivery. This paper presents an objective method for evaluating QoE in adaptive streaming services, using the commercial tool Video-MOS, which allows real-time monitoring and analysis of multimedia content quality across various platforms and networks. The method aims to provide a precise evaluation that incorporates both technical and subjective factors. This approach integrates multiple factors that influence the overall user perception of adaptive streaming quality, offering greater flexibility and performance, which could contribute to more comprehensive future assessment. The method is validated by a test plan that incorporates a variety of content and scenarios to simulate various network conditions. The results demonstrate the method’s effectiveness in predicting QoE, highlighting the rebuffering frequency as a significant factor. In optimal conditions, specific content types can achieve a QoE score as high as 3.36. Conversely, under unfavorable conditions, the QoE may decrease by up to 1.42 Mean Opinion Score (MOS) points, which represents an 80% reduction from its optimal level. Although the rebuffering frequency has a substantial influence, a long initial buffer can have an even more negative effect on QoE, particularly under adverse conditions. Furthermore, adaptive streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and Adaptive Bitrate Streaming (ABR) are integral to the assessment process.}, archive = {J_ACCESS}, author = {Sofía Ortiz-Arce and Álvaro Llorente and Alberto Del Rio and Federico Alvarez}, doi = {10.1109/ACCESS.2025.3606203}, journal = {IEEE Access}, pages = {159273-159285}, shortjournal = {IEEE Access}, title = {An enhanced method for objective QoE analysis in adaptive streaming services}, volume = {13}, year = {2025}, }
• (2025). Adaptive fuzzy tracking switching control for unknown nonstrict-feedback nonlinear systems. ACCESS, 13, 159261-159272. (www)

  @article{ , abstract = {This paper investigates an adaptive switching control strategy for output tracking in unknown n-dimensional nonstrict-feedback nonlinear systems (NFSs). To address the challenges posed by the unknown nonstrict terms (each involving all state variables), fuzzy systems are employed for function approximation. Specifically, a novel fuzzy basis function inequality is developed to enlarge the modulus of the fuzzy system in a way that restricts its dependence to only the first i state variables. This facilitates a smooth implementation of the backstepping design. Furthermore, by introducing switching functions, a switching controller is constructed to guarantee the convergence of the tracking error within a predetermined time. The controller ensures that the absolute tracking error eventually falls below a prescribed positive threshold, thereby achieving practical finite-time stability for the NFS. Simulation studies are conducted to validate the feasibility, effectiveness, and robustness of the proposed control scheme.}, archive = {J_ACCESS}, author = {Yaguang Li and Han Zhang and Fei Wu and Feifei He and Jiacheng Ma and Yingxin Song}, doi = {10.1109/ACCESS.2025.3608714}, journal = {IEEE Access}, pages = {159261-159272}, shortjournal = {IEEE Access}, title = {Adaptive fuzzy tracking switching control for unknown nonstrict-feedback nonlinear systems}, volume = {13}, year = {2025}, }
• (2025). Adaptive clustering and optimization with hardware-in-the-loop validation for enhanced overcurrent relay protection in power distribution networks. ACCESS, 13, 159240-159260. (www)

  @article{ , abstract = {This study presents an adaptive clustering and optimization approach to enhance overcurrent relay (OCR) protection in power distribution networks. Network topologies were initially clustered into four groups based on total operating times using both K-means and Particle Swarm Optimization (PSO) algorithms. The optimal number of clusters was determined using validation techniques, including the Elbow method, Silhouette Coefficient, and confusion matrix analysis, ensuring strong cluster cohesion and separation under non-standard operating conditions. The clustering process revealed four distinct clusters. Subsequently, the Cuckoo Search (CS) optimization technique was applied to fine-tune relay settings within representative network topologies from each cluster, optimizing the Time Multiplier Settings (TMS) to minimize relay tripping times while maintaining system reliability. Comparative analyses demonstrated that non-standard relay characteristics significantly improved fault detection and protection coordination compared to standard ones. To validate the proposed methodologies, both ETAP simulations and Hardware-in-the-Loop (HIL) testing were conducted, achieving effective Coordination Time Intervals (CTI) in real-time scenarios. The detailed setup, procedures, and validation results of the HIL experiments demonstrate the practical applicability of the proposed scheme under realistic operating conditions. The integration of advanced clustering, metaheuristic optimization, and HIL validation offers a comprehensive and reliable framework for adaptive OCR protection in modern power systems.}, archive = {J_ACCESS}, author = {Abdelsalam Elhaffar and Naser El-Naily and Majdi Mansouri}, doi = {10.1109/ACCESS.2025.3608815}, journal = {IEEE Access}, pages = {159240-159260}, shortjournal = {IEEE Access}, title = {Adaptive clustering and optimization with hardware-in-the-loop validation for enhanced overcurrent relay protection in power distribution networks}, volume = {13}, year = {2025}, }
• (2025). A deep reinforcement learning-based approach to resilient service placement for mobile edge computing. ACCESS, 13, 159231-159239. (www)

  @article{ , abstract = {In the Internet of Vehicles (IoV), Mobile Edge Computing (MEC) alleviates the computational and storage limitations of vehicle terminals by deploying cloud computing resources at the network edge, substantially reducing service access latency. However, the dynamic nature of vehicular networks introduces significant challenges to service placement strategies due to the tension between fluid network topologies and static resource allocations. Specifically, three critical issues arise: 1) the spatial mobility of vehicles causes pronounced spatiotemporal variability in edge service demands; 2) the fixed nature of edge services limits the elasticity of service provisioning; and 3) persistent risks of node failures, stemming from hardware faults or software errors, jeopardize service continuity. To address these challenges, this study proposes a fault-tolerant service placement model that jointly optimizes latency minimization and load balancing. We introduce an elastic service placement framework leveraging deep reinforcement learning (DRL). Simulation results demonstrate that our approach outperforms baseline algorithms, achieving robust single-point fault tolerance while maintaining optimal latency and load balancing.}, archive = {J_ACCESS}, author = {Yuan Zeng and Hengzhou Ye and Shiyu Wang}, doi = {10.1109/ACCESS.2025.3607811}, journal = {IEEE Access}, pages = {159231-159239}, shortjournal = {IEEE Access}, title = {A deep reinforcement learning-based approach to resilient service placement for mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). Joint use of time series and graph data for fake comment detection in CafeBazaar dataset. ACCESS, 13, 159217-159230. (www)

  @article{ , abstract = {Fake comments pose a significant challenge for e-commerce, as they manipulate customer perception and harm legitimate businesses. We propose a novel approach using recurrent neural networks to detect anomalies in user behavior patterns. We model user activity on Cafebazaar e-commerce as a multivariate time series and employ a long short-term memory autoencoder to determine periods of abnormal activity. By incorporating our model with existing methods, we observe significant improvement in the detection accuracy of fraudulent users, reaching 99 percent precision with the dataset. Our proposed model can enhance other fake comment detection methods, particularly those that struggle with the cold start problem, which often lacks information about the majority of users. The proposed system has been successfully implemented in a real-life application, removing 1.9 million fake comments from the Cafebazaar platform. We also provide a new dataset for fake comment detection that researchers can use to evaluate existing methods or develop new approaches.}, archive = {J_ACCESS}, author = {Mehrdad Salimi and Pasi Fränti}, doi = {10.1109/ACCESS.2025.3607849}, journal = {IEEE Access}, pages = {159217-159230}, shortjournal = {IEEE Access}, title = {Joint use of time series and graph data for fake comment detection in CafeBazaar dataset}, volume = {13}, year = {2025}, }
• (2025). An advanced detection framework for embedded system vulnerabilities. ACCESS, 13, 159207-159216. (www)

  @article{ , abstract = {Embedded systems serve as the foundation for modern computing in industrial, IoT, and defense applications. However, their increased adoption exposes them to security threats across multiple levels, from application software to operating systems and hardware. Traditional security mechanisms often focus on a single layer, missing cross-layer vulnerabilities that can be exploited by sophisticated attacks. This paper introduces a unified multi-layer vulnerability detection framework that integrates software, operating system, and hardware-level security analysis into a single LLAMA3-based deep learning model. The framework leverages a combined dataset consisting of source code vulnerabilities (C programming), Linux Kernel exploits (system calls), and Field-Programmable Gate Array (FPGA) hardware security risks in Hardware Description Languages (HDLs) such as Verilog and VHSIC Hardware Description Language (VHDL) and bitstream analysis. By merging these diverse vulnerability types into a single learning model, the framework is capable of detecting security threats across the entire embedded system stack. Applying static and dynamic analysis techniques across multiple layers, the proposed LLAMA3-based model achieves state-of-the-art detection accuracy across embedded system security domains. Experimental results demonstrate that the integrated framework outperforms existing layer-specific models, achieving 99.30% accuracy for software vulnerabilities, 99.16% accuracy for OS-level exploits, and 95.6% accuracy for hardware security threats.}, archive = {J_ACCESS}, author = {Mansour Alqarni and Akramul Azim}, doi = {10.1109/ACCESS.2025.3607595}, journal = {IEEE Access}, pages = {159207-159216}, shortjournal = {IEEE Access}, title = {An advanced detection framework for embedded system vulnerabilities}, volume = {13}, year = {2025}, }
• (2025). ARQ_HMAC: An automatic repeat reQuest strategy for hybrid MAC protocol in underwater acoustic sensor networks. ACCESS, 13, 159196-159206. (www)

  @article{ , abstract = {In underwater sensor networks, the use of acoustic waves with large propagation delays and narrow channel bandwidth poses significant challenges for Medium Access Control (MAC) protocols in reducing the number of retransmissions caused by packet collisions at the receiver node, thereby improving channel throughput. When packet collisions occur, the transmitting node enters a waiting state and will retransmit the packet at an appropriate time. However, if a node retransmits the packet at inappropriate times, it can lead to further collisions in subsequent transmission attempts. To reduce the number of retransmissions caused by collisions and improve channel throughput, in this study, we propose a Hybrid MAC protocol that combines asynchronous contention-free MAC with Carrier Sense Multiple Access (CSMA) and synchronous Time Division Multiple Access (TDMA) MAC protocols, along with an ARQ strategy applied to underwater communication channels, called the ARQ_HMAC (Automatic Repeat reQuest Strategy for Hybrid MAC) strategy. In the ARQ_HMAC protocol, when a node has a packet to transmit, it senses the channel state and operates according to the Contention-Free MAC protocol. If a packet collision occurs, the node switches to a waiting state and retransmits the packet using the TDMA protocol. The waiting time and the TDMA schedule are calculated based on the ARQ_HMAC strategy to avoid collisions in subsequent retransmission attempts. The simulation results will demonstrate that the ARQ_HMAC protocol helps reduce the number of packet retransmissions caused by collisions, thereby increasing and stabilizing the channel throughput as the arrival rate of new packets increases.}, archive = {J_ACCESS}, author = {van-Phuong Dao and Thu-Ha Tran}, doi = {10.1109/ACCESS.2025.3607746}, journal = {IEEE Access}, pages = {159196-159206}, shortjournal = {IEEE Access}, title = {ARQ_HMAC: An automatic repeat reQuest strategy for hybrid MAC protocol in underwater acoustic sensor networks}, volume = {13}, year = {2025}, }
• (2025). Improving inference time in diabetic Retinopathy—Recent trends and future directions. ACCESS, 13, 159178-159195. (www)

  @article{ , abstract = {Diabetic Retinopathy (DR), a leading cause of blindness worldwide, requires an early and accurate diagnosis. Deep learning models have advanced automated DR detection, but their reliance on high-dimensional features creates computational bottlenecks, limiting real-world deployment. This literature review (2019–2024) evaluates optimized Feature Selection (FS) techniques to address these challenges. We analyzed 70 studies focusing on FS methods integrated with deep learning models for DR classification. Our findings reveal that hybrid FS methods achieve higher accuracies, up to 98.9%, by combining filter-based dimensionality reduction with optimization-driven feature refinement. These methods reduce computational overhead by 40–60% compared to traditional techniques, enabling faster inference on resource-constrained devices. Classical techniques like LDA also show promising results but risk omitting non-linear biomarkers. Despite progress, challenges persist—class imbalance in public datasets skews models toward majority classes, while aggressive FS risks discarding critical features like microaneurysms. This survey provides a detailed analysis of FS techniques that reduce inference time and discusses accuracy-efficiency trade-offs, inference latency reporting, and future directions for real-time clinical deployment in the domain.}, archive = {J_ACCESS}, author = {Hafiz Muhammad Awais Riaz and Muhammad Nouman Noor and Asad Ali Safi and Ahmad Raza Shahid}, doi = {10.1109/ACCESS.2025.3607992}, journal = {IEEE Access}, pages = {159178-159195}, shortjournal = {IEEE Access}, title = {Improving inference time in diabetic Retinopathy—Recent trends and future directions}, volume = {13}, year = {2025}, }
• (2025). Dynamic environment adaptation in biped robots: A reinforcement learning approach. ACCESS, 13, 159157-159177. (www)

  @article{ , abstract = {This study explores the integration of rhythmic control mechanisms and latent visual encoding to enhance bipedal locomotion in complex environments. While model-free reinforcement learning has shown effectiveness in acquiring locomotion skills, the high dimensionality of visual inputs often leads to increased sample complexity and prolonged training times. To mitigate this issue, we propose a novel approach that combines rhythmic motor priors with latent space representations, thereby reducing the complexity of control inputs. Specifically, we introduce a semi-coupled Central Pattern Generator architecture to efficiently coordinate movement while leveraging compact visual embeddings. To address this challenge, we propose integrating rhythmic motor priors with latent-space perception to reduce the complexity of control inputs. This approach compresses high-dimensional observations into a lower-dimensional representation, thereby accelerating policy training and enhancing generalisation across varied environments. We validated our method in the Webots simulator using a four-level curriculum with progressively complex obstacles. In contrast to previous approaches that rely on 80 to 245 input variables, our framework reduces the observation space to just 40 dimensions. This results in stable bipedal walking in fewer than 10 million simulation steps and yields a 16% higher success rate in environments with dense obstacles. Moreover, the semi-coupled model achieves a walking speed of 21.08 cm/s, substantially outperforming the previously reported 1.79 cm/s on the same robot using reinforcement learning and inverse kinematics. An analysis of training time shows that the model converges more rapidly in simpler environments, while tasks with greater complexity require extended adaptation periods. This increased difficulty translates into longer overall training durations. Nevertheless, the improvements in success rate, walking speed, and generalisation justify the additional training time. These gains are statistically validated through formal t-tests, confirming significant improvements in success rate, stability, and velocity (p < 0.05). Collectively, the results demonstrate that integrating rhythmic motor priors with latent-space perception provides a scalable and efficient approach to vision-based bipedal locomotion.}, archive = {J_ACCESS}, author = {Matias Soto and Pablo Ormeño-Arriagada and Jorge Vásquez and Carla Taramasco and Juan Pablo Vásconez and Gustavo Gatica}, doi = {10.1109/ACCESS.2025.3604236}, journal = {IEEE Access}, pages = {159157-159177}, shortjournal = {IEEE Access}, title = {Dynamic environment adaptation in biped robots: A reinforcement learning approach}, volume = {13}, year = {2025}, }
• (2025). Frequency support for the decarbonization of an isolated low-inertia system with the integration of RES and BESS. ACCESS, 13, 159145-159156. (www)

  @article{ , abstract = {This paper proposes a technical methodology to decarbonize an isolated low-inertia system with the integration of a Renewable Energy Source (RES) and a Battery Energy Storage System (BESS). The proposal focuses on frequency support during contingencies, especially in terms of Synthetic Inertia (SI), for the Wind Power Plant (WPP), and Fast Frequency Response (FFR), for the BESS, allowing for a consistent decarbonization strategy throughout the operation. In particular, the BESS is analytically designed to provide the desired FFR for a contingency situation, allowing the Rate of Change of Frequency (RoCoF) of the system to be mitigated according to a design criterion that only requires the grid inertia and rated parameters, without relying on complex formulations or optimization methods. It is observed that the established design criterion is conservative and the enhancement in RoCoF values is above the expected. Furthermore, the synthetic inertia of the wind power plant can have an individual limited impact on the system’s RoCoF due to design and power ramping time, while the fast frequency response of the BESS has an additional impact due to the lack of power ramping requirements. Real-time simulation results validate the proposed methodology for an offshore industrial application within the Oil & Gas (O&G) industry.}, archive = {J_ACCESS}, author = {André G. P. Alves and João M. T. do Amaral and Giulia J. da Silva and Carolina C. M. de Souza and Pedro H. B. Cambruzzi and Gabriel Gentil and Emanuel L. van Emmerik and Annelys M. Schetinger and Hugo Bozelli and Robson F. S. Dias}, doi = {10.1109/ACCESS.2025.3608562}, journal = {IEEE Access}, pages = {159145-159156}, shortjournal = {IEEE Access}, title = {Frequency support for the decarbonization of an isolated low-inertia system with the integration of RES and BESS}, volume = {13}, year = {2025}, }
• (2025). Multicarrier spread spectrum communications with noncontiguous subcarrier bands for HF skywave links. ACCESS, 13, 159127-159144. (www)

  @article{ , abstract = {Existing high-frequency (HF) radio systems are becoming increasingly impaired by high levels of interference and traffic due to the growing use of the HF band. Although many HF systems exhibit robust performance against limited amounts of interference, their reliability degrades as more users occupy the same transmission band. To improve reliability in these congested channel conditions, we propose a filter-bank based multicarrier spread-spectrum waveform with noncontiguous subcarrier bands. Using noncontiguous subcarrier bands enables the use of a robust, wideband system while retaining the frequency agility of a narrowband system. In this study, we first modify a filter-bank transmitter structure to accommodate noncontiguous subcarrier bands and consider several impacts of this change, such as elevated peak-to-average-power ratios (PAPRs). We also introduce a receiver architecture to process the noncontiguous spread-spectrum signal, along with details regarding wideband channel estimation. Efficient transceiver structures are developed to support practical system implementations. We conclude by comparing the performance of contiguous and noncontiguous systems through both simulation and over-the-air testing. The results demonstrate that, in typical channel conditions, noncontiguous systems demonstrate comparable performance to contiguous; however, in congested conditions, noncontiguous systems significantly outperform contiguous.}, archive = {J_ACCESS}, author = {Brandon T. Hunt and Hussein Moradi and Behrouz Farhang-Boroujeny}, doi = {10.1109/ACCESS.2025.3607517}, journal = {IEEE Access}, pages = {159127-159144}, shortjournal = {IEEE Access}, title = {Multicarrier spread spectrum communications with noncontiguous subcarrier bands for HF skywave links}, volume = {13}, year = {2025}, }
• (2025). Network slice-enabled federated learning for collaborative traffic perception in vehicular edge computing. ACCESS, 13, 159109-159126. (www)

  @article{ , abstract = {This paper presents a novel network slice-enabled federated learning framework for collaborative traffic perception in vehicular edge computing environments. The proposed approach integrates channel-aware federated learning with intelligent resource allocation to enhance perception performance across heterogeneous vehicular networks. We establish a comprehensive channel model incorporating Doppler effects, multipath fading, and temporal-spatial variations to characterize vehicular communication dynamics. The framework employs three specialized perception slices for visual environment perception, radar environment perception, and semantic perception, each optimized through end-to-end neural network architectures. A reinforcement learning-based resource allocation mechanism dynamically manages computational, memory, and energy resources while considering real-time channel conditions. Extensive simulations across three resource environments demonstrate that the proposed PPO-based approach achieves superior performance with 94% task completion rate, 32ms communication-computation latency, and 3.35 energy efficiency ratio in high-resource scenarios. The results validate the framework’s effectiveness in managing federated learning convergence and resource optimization under varying vehicular network conditions, significantly outperforming traditional SAC and heuristic PSO approaches.}, archive = {J_ACCESS}, author = {Xu He and Peizhi Sheng and Bohao Tao and Wenhao Li}, doi = {10.1109/ACCESS.2025.3608561}, journal = {IEEE Access}, pages = {159109-159126}, shortjournal = {IEEE Access}, title = {Network slice-enabled federated learning for collaborative traffic perception in vehicular edge computing}, volume = {13}, year = {2025}, }
• (2025). Tree-based architecture for enabling live interaction in massive online events. ACCESS, 13, 159098-159108. (www)

  @article{ , abstract = {Massive online and hybrid events have become important during and after the COVID-19 pandemic. These events aim to replicate live experiences by maintaining interaction between the speakers and the audience. However, existing streaming and videoconferencing solutions fail to provide sufficient scalability and real-time interactions. Streaming technologies lack support for live interaction, whereas videoconferencing technologies scale enough. Events such as panel debates, conference presentations, and sessions with live interpretation require both the ability to support a large number of participants and real-time voice interactions. In this paper, we propose a new tree-based architecture that meets both requirements using videoconferencing technologies. This approach combines the scalability of streaming architectures with the low latency of videoconferencing technologies. The objective was to increase the maximum number of participants that videoconference providers can accept while maintaining live interactions. A prototype implementation of the proposed architecture was developed to test and validate it. Finally, this study provides valuable information for implementing and adapting the proposed architecture to various production environments.}, archive = {J_ACCESS}, author = {Carlos Arriaga and Alejandro Pozo and Alvaro Alonso}, doi = {10.1109/ACCESS.2025.3608475}, journal = {IEEE Access}, pages = {159098-159108}, shortjournal = {IEEE Access}, title = {Tree-based architecture for enabling live interaction in massive online events}, volume = {13}, year = {2025}, }
• (2025). Hybrid feature extraction and deep learning framework for power transformer fault classification: A real-world case study. ACCESS, 13, 159077-159097. (www)

  @article{ , abstract = {Reliable fault diagnosis in power transformers is paramount for ensuring grid stability and safeguarding critical assets. This paper proposes a novel deep learning-based diagnostic framework integrating hybrid feature extraction with advanced classification models. The framework is trained and evaluated on a comprehensive real-world dataset of 3,000 labeled samples, each characterized by 80 features. A dual-stage feature extraction methodology is employed, utilizing Discrete Wavelet Transform (DWT) for multiresolution time–frequency analysis and Matching Pursuit (MP) for adaptive sparse decomposition, followed by Principal Component Analysis (PCA) for dimensionality reduction to mitigate redundancy and enhance class separability. Four state-of-the-art deep learning architectures are comparatively evaluated: a Deep Ensemble of Multilayer Perceptrons (MLPs), a Time Series Transformer, a CNN–LSTM hybrid, and a Conv1D–BiLSTM network. Experimental results demonstrate that the ensemble MLP architecture achieves superior diagnostic performance, attaining peak accuracy of 99.50% coupled with a minimal false alarm rate of 0.33%, and consistently outperforms the comparative models across a comprehensive suite of evaluation metrics. These findings substantiate that ensemble methods and attention-based transformer models offer significant advantages over traditional recurrent-convolutional hybrid architectures for this task. The proposed framework demonstrates substantial potential for enabling robust, high-precision fault classification in real-time transformer condition monitoring systems.}, archive = {J_ACCESS}, author = {Mokhtar Shouran and Mohammed Alenezi and Saleh Almutairi and Mohammad Alajmi}, doi = {10.1109/ACCESS.2025.3608658}, journal = {IEEE Access}, pages = {159077-159097}, shortjournal = {IEEE Access}, title = {Hybrid feature extraction and deep learning framework for power transformer fault classification: A real-world case study}, volume = {13}, year = {2025}, }
• (2025). Ka-band link budget analysis for deep space communications. ACCESS, 13, 159064-159076. (www)

  @article{ , abstract = {The emerging demand for increased data rates between Earth, the lunar gateway, Mars, and various other deep space probes will stretch existing deep space network (DSN) antennas beyond capacity. The DSN antennas distributed across Canberra, Madrid, and Goldstone are capable of tracking the Mars Reconnaissance Orbiter (MRO) travelling tens of millions of kilometers from Earth, and life extension efforts continue to support such critical space infrastructure. However, these legacy systems still suffer failures and outages that have resulted in delays in data communication and this situation continues to be a significant challenge. This explains the current interest in this area. Thus it is timely that we present a fresh tutorial on a link budget to Mars, elucidating use of the latest ITU-R model recommendations for propagation through the Earth’s atmosphere. To facilitate communication to Mars, we target a downlink frequency in the K ${}_{\textrm {a}}$ band (31.8–32.3 GHz) as it allows for increased data transmission rate, increased effective isotropic radiated power (EIRP) and reduced antenna size. In this tutorial paper, we therefore focus on a link budget in the K ${}_{\textrm {a}}$ band and take into account all major sources of attenuation including free space path loss (FSPL), rain, fog, and system noise temperature ( $T_{\textrm {sys}}$ ), which are critical for ensuring a positive link margin. As a case study, the presented link budget uses parameters for actual MRO communications and the Canberra DSN antenna assuming communication from the Mars Reconnaissance Orbiter (MRO) to the Canberra DSN-DSS-35. For point of comparison, we compare this with the case of Lunar to Earth communications.}, archive = {J_ACCESS}, author = {Md. Saiful Islam and Jakeya Sultana and Nicholas P. Lawrence and Aaron T. Pereira and Stephen J. Salamon and Alex Dinovitser and Thien Duc Nguyen and Jose E. Velazco and Brian W.-H. Ng and Nelson Tansu and Frederick W. Menk and Ed Kruzins and Neil H. E. Weste and Said F. Al-Sarawi and Derek Abbott}, doi = {10.1109/ACCESS.2025.3603118}, journal = {IEEE Access}, pages = {159064-159076}, shortjournal = {IEEE Access}, title = {Ka-band link budget analysis for deep space communications}, volume = {13}, year = {2025}, }
• (2025). PRAMGCN-net: 3D human pose estimation with a parameterized routing adjacency modulation graph convolutional network. ACCESS, 13, 159050-159063. (www)

  @article{ , abstract = {Human pose estimation reconstructs 3D human joint positions from monocular images or videos, enabling applications in healthcare, sports, AR/VR, and animation. The challenge lies in depth ambiguity, temporal coherence, and complex motion patterns. Recent deep learning advancements, such as SMPLs, GCNs, and Transformers, have improved estimation precision. Most techniques evaluate poses in 2D before converting them to 3D, often ignoring temporal dynamics and encountering depth ambiguity, resulting in multiple 3D poses from a single 2D setup. While GCNs are effective for joint interactions, their dependence on pre-defined graph structures limits their capacity to capture global dependencies and adapt to varying joint configurations. To address these limitations, we proposed PRAMGraphConv, a novel graph convolution approach that dynamically models joint relationships. It is characterized by an expert routing block that dynamically assigns importance weights and alters joint relational patterns in an adaptable manner. Furthermore, the model enhances its ability to capture intricate, non-local linkages while maintaining processing efficiency, addressing depth ambiguity, and scaling issues, through the incorporation of an ANModGCN. We integrate these components into PRAMGCN-Net, a unified framework to achieve robust performance in challenging occluded scenarios. We evaluate our model through extensive experiments on standard benchmarks, including Human3.6M, HumanEva, and MPI-INF-3DHP. The results demonstrate that the proposed method outperforms several state-of-the-art approaches in 3D human pose estimation, while maintaining high accuracy with an efficient, lightweight architecture.}, archive = {J_ACCESS}, author = {Andy Pramono and I-Cheng Chang and Betty Dewi Puspasari}, doi = {10.1109/ACCESS.2025.3607702}, journal = {IEEE Access}, pages = {159050-159063}, shortjournal = {IEEE Access}, title = {PRAMGCN-net: 3D human pose estimation with a parameterized routing adjacency modulation graph convolutional network}, volume = {13}, year = {2025}, }
• (2025). The influence of robot descriptions and distraction on proxemics between humans and robots in virtual reality. ACCESS, 13, 159037-159049. (www)

  @article{ , abstract = {Proxemics, a key aspect of human-robot interaction, explores acceptable distances between humans and robots, which can vary depending on various factors. This study (n = 64) investigates the effects of negative attitudes and varying degrees of distraction on human interaction with a delivery robot in a virtual reality (VR) setting. Participants were divided into two groups exposed to video descriptions aimed at influencing their attitudes toward robots positively or negatively, or to a baseline group without an intervention. The experiment followed a $3\times 4$ mixed-design methodology with three groups (positive, negative, and baseline) and four distraction conditions: none, cognitive, visual, and combined. Participants’ acceptable distances, subjective discomfort, and perceived safety were measured. The results did not show significant differences in attitudes, distances, or discomfort between the three description groups, suggesting that the videos did not effectively alter attitudes. However, the baseline group reported significantly lower feelings of safety. Participants with high negative attitudes before the experiment showed a significant reduction in negativity regardless of group assignment, indicating that these individuals’ attitude benefited from the exposure. These individuals also maintained significantly greater distances and evaluated the interaction with the robot as less safe. Further analysis revealed a negative correlation between perceived robot weight and safety, with both video groups estimating the robot’s weight higher than the baseline group. Simultaneous distractions caused the highest discomfort, while no distraction resulted in the lowest. These findings highlight the importance of considering distraction, potential attitudes, and perceived threat when designing robot behavior for safe and satisfactory interactions.}, archive = {J_ACCESS}, author = {Olivia A. Herzog and Annika C. Boos and Tim S. Merkel and Klaus Bengler}, doi = {10.1109/ACCESS.2025.3608481}, journal = {IEEE Access}, pages = {159037-159049}, shortjournal = {IEEE Access}, title = {The influence of robot descriptions and distraction on proxemics between humans and robots in virtual reality}, volume = {13}, year = {2025}, }
• (2025). Model-tracking-based 3D vibration distribution measurement of a hovering multicopter using stereo HFR cameras. ACCESS, 13, 159028-159036. (www)

  @article{ , abstract = {A comprehensive understanding of structural vibration distributions during multicopter flight, along with their correlation with flight trajectory and operational status, which are influenced by load conditions, battery levels, and weather variations, is crucial for ensuring flight safety. However, discrete sensors such as accelerometers and strain gauges face limitations in observing vibration distributions in multicopters due to restricted attachment points and their potential impact on flight performance. To address these challenges, we propose a model-tracking-based 3D vibration measurement method for flying multicopters using ground-installed stereo high-frame-rate (HFR) cameras, avoiding the need for onboard sensor installation. Our method utilizes digital image correlation (DIC) on stereo HFR images to achieve precise 3D vibration displacement measurements in the audio frequency range, leveraging thousands of tracked measurement points on a 3D multicopter model for whole-body vibration analysis. To validate our model-tracking method, we analyze stereo $1920\times 1080$ images at 500 fps in the frequency domain, capturing a hovering multicopter with four rotors rotating at hundreds of rps. Along with estimating its 6-DoF trajectory and rotor rotation speeds as operational status indicators, the 3D vibration distribution at 3518 tracked points was visualized by referencing a pre-measured 3D model of the multicopter, including 3D frequency component images at peak frequencies ranging from 0 to 250 Hz, which correspond to structural properties, control errors, and rotor-induced forced vibrations.}, archive = {J_ACCESS}, author = {Takuto Ogata and Feiyue Wang and Kohei Shimasaki and Idaku Ishii}, doi = {10.1109/ACCESS.2025.3608190}, journal = {IEEE Access}, pages = {159028-159036}, shortjournal = {IEEE Access}, title = {Model-tracking-based 3D vibration distribution measurement of a hovering multicopter using stereo HFR cameras}, volume = {13}, year = {2025}, }
• (2025). Improving medical X-ray imaging diagnosis with attention mechanisms and robust transfer learning techniques. ACCESS, 13, 159002-159027. (www)

  @article{ , abstract = {X-ray imaging remains a cornerstone in medical diagnostics for conditions such as bone fractures, knee osteoarthritis, and lung diseases. However, variability in image quality and dataset diversity presents significant challenges for automated analysis using deep learning models. This study addresses these issues by proposing an EfficientNet B0 architecture enhanced with a Convolutional Block Attention Module (CBAM) to improve classification accuracy and interpretability across multiple X-ray datasets: FracAtlas, Knee, and Lung X-ray. A robust preprocessing pipeline comprising LAB color space conversion, morphological filtering, gamma correction, Non-Local Means denoising, resizing, and normalization was applied to optimize image quality, with each step’s effectiveness verified through established image quality metrics. Additionally, geometric augmentation techniques were performed to increase dataset variability and improve model generalization. Comparative experiments with transfer learning, transformer-based, and attention-based models identified the attention-based EfficientNet B0 as the best performer. An extensive ablation study on the Knee X-ray dataset refined hyperparameters to maximize performance. The optimized model achieved high test accuracies of 98.09%, 97.12%, and 99.51% on FracAtlas, Knee, and Lung datasets, respectively. Further analyses, including noise robustness testing, k-fold cross-validation, and Grad-CAM visualization, demonstrated the model’s resilience, consistency, and explainability. The findings highlight the critical role of attention mechanisms in enhancing feature representation and generalization across heterogeneous medical imaging tasks. This work lays a foundation for reliable, interpretable AI systems that can support clinical decision-making, with future efforts focusing on expanding dataset diversity and real-world clinical validation to accelerate adoption.}, archive = {J_ACCESS}, author = {Isha Das and Md. Alif Sheakh and Shahab Abdulla and Mst. Sazia Tahosin and Md. Mehedi Hassan and Sadika Zaman and Abhay Shukla}, doi = {10.1109/ACCESS.2025.3607639}, journal = {IEEE Access}, pages = {159002-159027}, shortjournal = {IEEE Access}, title = {Improving medical X-ray imaging diagnosis with attention mechanisms and robust transfer learning techniques}, volume = {13}, year = {2025}, }
• (2025). Bi-dimensional BSS using multi-statistical properties optimization. ACCESS, 13, 158984-159001. (www)

  @article{ , abstract = {Diversity measures employed in blind source separation methods typically exploit single statistical properties such as independence, sparsity, or temporal structure. This limits their effectiveness when underlying sources exhibit diverse statistical behaviors, which results in suboptimal separation performance. Although some recent studies have explored multiple statistical properties through single or multi-objective optimization, most methods remain constrained to exploiting at most two properties. To address this limitation, we propose a unified statistical properties framework named bi-dimensional and multi-statistical (BDMS) for source separation that simultaneously exploits spatial regularization and multiple spatial statistical characteristics through unified temporal optimization. The proposed method integrates spatial regularization through adaptive soft thresholding and a unified temporal optimization framework combining three statistical diversity measures (kurtosis, spectral entropy, Hoyer sparsity) with reconstruction fidelity constraints. Spatial properties are integrated through magnitude-matched gradient descent with performance-based optimized weighting factors that systematically balance the contribution of each statistical measure and reconstruction fidelity. Additionally, domain-specific post-processing enhances robustness by including discrete cosine transform basis expansion for functional magnetic resonance imaging (fMRI) temporal smoothness. The proposed framework demonstrates superior separation accuracy and robustness to noise and strong spatiotemporal dependencies compared to single and dual-property methods and maintains computational efficiency. Experimental validation on synthetic and real fMRI datasets demonstrates 19.5% improvement in source recovery accuracy and 2.6 times faster execution time compared to SICA-EBM (dual property multi-objective framework) across four different experimental conditions. To ensure reproducibility, we provide the synthetic dataset and complete source code at https://github.com/usmankhalid06/BDMS}, archive = {J_ACCESS}, author = {Muhammad Usman Khalid and Malik Muhammad Nauman}, doi = {10.1109/ACCESS.2025.3608581}, journal = {IEEE Access}, pages = {158984-159001}, shortjournal = {IEEE Access}, title = {Bi-dimensional BSS using multi-statistical properties optimization}, volume = {13}, year = {2025}, }
• (2025). Approximate relational games for green influencer marketing optimization. ACCESS, 13, 158967-158983. (www)

  @article{ , abstract = {The collaboration between celebrities, macro-influencers, and micro-influencers plays a pivotal role in amplifying the effectiveness and longevity of environmental sustainability messages. This study introduces the concept of a minimal coalition of influencers capable of efficiently promoting a sustainability message to the public. To address this, we propose a novel framework for Approximate Relational Games (ARG), which quantifies the degree of dependency of a follower on an influencer through a Dependency Decision Table (DDT). The DDT tracks follower reactions, categorized as “Like only” or “Like and Share”, across a series of posts by the influencer. By setting a threshold for the degree of dependency, we focus on followers with stronger relational dependencies, eliminating those with weaker associations. This method enables the identification of a minimal winning coalition of influencers who can effectively disseminate the sustainability message despite resource constraints, both financial and non-financial. Utilizing combinatorial tools such as closure operators and choice functions, we present a method for determining minimal winning coalitions with polynomial time complexity. This approach offers a cost-effective solution for green organizations and governments aiming to maximize the impact of their campaigns. To validate the proposed model and algorithms, we conduct Python-based simulations using data from five real-world Facebook influencers, both macro and micro-tier, complemented by manual calculations. The results demonstrate the efficacy and computational efficiency of the proposed framework in optimizing influencer marketing strategies for promoting environmental sustainability.}, archive = {J_ACCESS}, author = {Nghia D. Vu and Janos Demetrovics and Giang L. Nguyen and Thi D. Vu and Son H. Nguyen and Kuop G. Nguyen}, doi = {10.1109/ACCESS.2025.3605398}, journal = {IEEE Access}, pages = {158967-158983}, shortjournal = {IEEE Access}, title = {Approximate relational games for green influencer marketing optimization}, volume = {13}, year = {2025}, }
• (2025). Transformer-based semantic role labeling for crisis events using semi-supervised learning on low-resource language twitter texts. ACCESS, 13, 158938-158966. (www)

  @article{ , abstract = {Twitter texts related to crisis events often contain vital information about the impact of disasters. However, irregular language structures, slang, and character limitations pose challenges for automated information extraction related to disaster impact. The Semantic Role Labeling (SRL) task can assist in extracting this information by identifying semantic roles in a sentence, such as who was involved, what happened, when, and where. This task provides a more structured understanding of the information related to disaster management. However, traditional based SRL tends to use predicates as the main anchor, which is less flexible in capturing semantic role relationships in unstructured texts such as Twitter. In addition, SRL research in low-resource languages such as Indonesian is still limited due to the lack of labeled datasets. This study aims to develop a more flexible SRL without relying on predicates as the main anchor and applying a semi-supervised learning strategy with pseudo-labeling. This strategy lets the model gradually learn from unlabeled data, enhancing generalization without solely depending on labeled data. A filtering function is implemented to minimize noise from inaccurate pseudo-labels, eliminating low-confidence predictions and ensuring that only high-quality data is used for retraining. Transformer-based models were chosen because of their self-attention mechanisms that enable an adequate understanding of contextual semantic relationships. Experiments demonstrate that the transformer model with Bidirectional Encoder Representations from Transformers (BERT) based architecture adapted for a specific language achieves the highest performance, with an F1-score of 0.863 at a threshold of 0.9. This study contributes to the advancement of SRL for low-resource language (especially for Indonesian) and paves the way for using SRL results in disaster severity analysis to enhance social media-based emergency response.}, archive = {J_ACCESS}, author = {Amelia Devi Putri Ariyanto and Diana Purwitasari and Chastine Fatichah and Sri Devi Ravana and Andrian and Anak Agung Yatestha Parwata}, doi = {10.1109/ACCESS.2025.3604068}, journal = {IEEE Access}, pages = {158938-158966}, shortjournal = {IEEE Access}, title = {Transformer-based semantic role labeling for crisis events using semi-supervised learning on low-resource language twitter texts}, volume = {13}, year = {2025}, }
• (2025). Leveraging pedestrian detection and tracking in robotics navigation: A survey with practical illustrations. ACCESS, 13, 158926-158937. (www)

  @article{ , abstract = {Pedestrian Detection and Tracking (PDT) plays a pivotal role in enabling autonomous robots to navigate safely and efficiently in dynamic, human-populated environments. This paper presents a comprehensive survey of PDT methods, structured according to the sensing modalities employed: RGB cameras, LiDAR, thermal imaging, RGB-D sensors, and multi-modal fusion systems. For each category, we analyze representative techniques, synthesize their strengths and limitations, and discuss recent advancements including deep learning approaches and cross-modal fusion strategies. We highlight persistent challenges such as handling occlusions, achieving real-time performance, and ensuring robustness across diverse environments. In addition to this structured review, we provide two practical examples using the Ona autonomous robot platform (see Figure 1) to illustrate how PDT techniques can enhance robotic capabilities in real-world scenarios. These examples focus on improving SLAM consistency and enabling proxemic-aware navigation strategies. Through this survey, we aim to clarify the current state of the art, identify emerging trends, and suggest future research directions for robust and socially-aware robotic navigation.}, archive = {J_ACCESS}, author = {N. Picello and F. Herrero and S. Hernández and A. López and A. Santamaria-Navarro}, doi = {10.1109/ACCESS.2025.3607191}, journal = {IEEE Access}, pages = {158926-158937}, shortjournal = {IEEE Access}, title = {Leveraging pedestrian detection and tracking in robotics navigation: A survey with practical illustrations}, volume = {13}, year = {2025}, }
• (2025). Fire segmentation in complex environments: Learning representations for reflection, haze, and day-night adaptation. ACCESS, 13, 158906-158925. (www)

  @article{ , abstract = {This paper presents an efficient flame segmentation framework using RGB images and a lightweight U-Net architecture. The method incorporates color correction, haze removal, reflection elimination, and day–night adaptation to enhance flame features under complex conditions such as smoke and lighting variations. Unlike prior RGB-D or thermal-based approaches, our model operates on single RGB input and achieves real-time performance. Experimental results on real-world fire datasets show that the proposed method outperforms existing models, achieving up to 22.8% higher IoU and 24.2% higher Boundary IoU, demonstrating strong accuracy–speed balance for practical applications such as CCTV fire detection and drone-based monitoring.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3608509}, journal = {IEEE Access}, pages = {158906-158925}, shortjournal = {IEEE Access}, title = {Fire segmentation in complex environments: Learning representations for reflection, haze, and day-night adaptation}, volume = {13}, year = {2025}, }
• (2025). Game theoretic mixed experts for combinational adversarial machine learning. ACCESS, 13, 158887-158905. (www)

  @article{ , abstract = {Recent advances in adversarial machine learning have shown that defenses previously considered robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses. However, whether the adversarial examples generated by customized attacks, are effective on other defenses, is an open question. In this work we seek to explore three important security questions: First, do different defense strategies exhibit the same low transferability properties as different model architectures and, if so, how can this low transferability be utilized to improve robustness? Second, how can a white-box adversary design attacks to specifically thwart multi-defense based setups? Last, how can game theoretic analysis further improve the robustness against an adversary capable of implementing multiple state-of-the-art attacks? To this end we provide multiple contributions, including the first transferability study between multiple defense strategies, three new attack algorithms designed to break random transform and ensemble defenses, and two game theoretic frameworks for analyzing and optimizing robustness over a combination of adversarial attacks and defenses. Empirically, we show our framework is 18% more robust on CIFAR-10 and is 27% more robust on Tiny-ImageNet than the best single state-of-the-art defense that we analzye.}, archive = {J_ACCESS}, author = {Kaleel Mahmood and Ethan Rathbun and Ronak Sahu and Marten Van Dijk and Sohaib Ahmad and Caiwen Ding}, doi = {10.1109/ACCESS.2025.3608117}, journal = {IEEE Access}, pages = {158887-158905}, shortjournal = {IEEE Access}, title = {Game theoretic mixed experts for combinational adversarial machine learning}, volume = {13}, year = {2025}, }
• (2025). Knowledge-distilled multi-task model with enhanced transformer and bidirectional mamba2 for air quality forecasting. ACCESS, 13, 158870-158886. (www)

  @article{ , abstract = {Accurate air quality forecasting is crucial for public health and environmental policy, yet existing deep learning models often suffer from high computational costs and inadequate modeling of temporal dependencies and multi-pollutant interactions. This paper proposes a novel deep learning framework that integrates an Enhanced Transformer with Bidirectional Mamba2, optimized through multi-task learning and knowledge distillation. Using a teacher–student paradigm, the teacher model captures rich temporal semantics, while the lightweight student model retains predictive accuracy with reduced inference costs. Key innovations include a hybrid architecture combining multi-scale global-local attention and long-range dependency modeling, a regression-specific knowledge distillation approach with soft target smoothing and intermediate representation transfer, and an end-to-end multi-task design for joint forecasting of multiple pollutants. Extensive experiments on real-world datasets from Guangzhou, Chengdu, and Beijing (2018–2022) across four key pollutants (PM2.5, PM10, NO2, SO2) demonstrate that our model significantly outperforms baseline methods, with the student model maintaining accuracy within 5% of the teacher while requiring fewer parameters. These results highlight the framework’s potential for accurate, scalable, and efficient air quality forecasting in real-time and resource-constrained environments.}, archive = {J_ACCESS}, author = {Zi-Ang Xie and Chee-Onn Chow and Joon Huang Chuah and Wong Jee Keen Raymond}, doi = {10.1109/ACCESS.2025.3595679}, journal = {IEEE Access}, pages = {158870-158886}, shortjournal = {IEEE Access}, title = {Knowledge-distilled multi-task model with enhanced transformer and bidirectional mamba2 for air quality forecasting}, volume = {13}, year = {2025}, }
• (2025). SSTAF: Spatial-spectral-temporal attention fusion transformer for motor imagery classification. ACCESS, 13, 158855-158869. (www)

  @article{ , abstract = {Brain–computer interfaces (BCI) in electroencephalography (EEG)-based motor imagery classification offer promising solutions in neurorehabilitation and assistive technologies by enabling communication between the brain and external devices. However, the non-stationary nature of EEG signals and significant inter-subject variability cause substantial challenges for developing robust cross-subject classification models. This paper introduces a novel Spatial-Spectral-Temporal Attention Fusion (SSTAF) Transformer specifically designed for upper-limb motor imagery classification. Our architecture consists of a spectral transformer and a spatial transformer, followed by a transformer block and a classifier network. Each module is integrated with attention mechanisms that dynamically attend to the most discriminative patterns across multiple domains, such as spectral frequencies, spatial electrode locations, and temporal dynamics. The short-time Fourier transform is incorporated to extract features in the time-frequency domain to make it easier for the model to obtain a better feature distinction. We evaluated our SSTAF Transformer model on two publicly available datasets, the EEGMMIDB dataset and BCI Competition IV-2a. SSTAF Transformer achieves an accuracy of 76. 83% and 68. 30% in the data sets, respectively, outperforms traditional CNN-based architectures and a few existing transformer-based approaches.}, archive = {J_ACCESS}, author = {Ummay Maria Muna and Md Mehedi Hasan Shawon and Md Jobayer and Sumaiya Akter and Saifur Rahman Sabuj}, doi = {10.1109/ACCESS.2025.3605265}, journal = {IEEE Access}, pages = {158855-158869}, shortjournal = {IEEE Access}, title = {SSTAF: Spatial-spectral-temporal attention fusion transformer for motor imagery classification}, volume = {13}, year = {2025}, }
• (2025). Non-existence of perfect permutation codes under crown poset metric. ACCESS, 13, 158843-158854. (www)

  @article{ , abstract = {Permutation codes have been extensively investigated using various metrics such as Hamming metric, Ulam metric, Kendall $\tau $ metric, $l_{\infty }$ metric, Cayley metric etc., due to their potential applications in diverse fields such as rank modulation schemes for flash memories, block ciphers and powerline communications. In this article, we define permutation codes under the poset metric and established bounds for packing radius and covering radius. Moreover, we specifically addressed implementation of the results based on the crown poset metric. Additionally, we demonstrated the nonexistence of some perfect permutation codes such as alternating group, $C_{1}$ is the permutation code generated by the permutation $(1~2~3 \ldots ~2n)$ , $C_{2}$ is the permutation code generated by the two permutations such as $(1~2~3~\ldots ~2n)$ and $(1~2n)(2~2n-1)\ldots (n~n+1)$ , $C_{3}$ is the permutation code generated by the permutation $(1~2~3 \ldots ~n)$ under the crown poset metric.}, archive = {J_ACCESS}, author = {Rohini Baliram More and Venkatrajam Marka}, doi = {10.1109/ACCESS.2025.3602712}, journal = {IEEE Access}, pages = {158843-158854}, shortjournal = {IEEE Access}, title = {Non-existence of perfect permutation codes under crown poset metric}, volume = {13}, year = {2025}, }
• (2025). 2-bit electrically reconfigurable reflectarray with multi-polarization capability. ACCESS, 13, 158832-158842. (www)

  @article{ , abstract = {In this paper, a low-complexity multi-polarization reconfigurable reflectarray antenna (RRA) with flexible 1- or 2-bit phase quantization is proposed, based on a PIN-tuned electrically rotatable element. The RRA element features an octagonal reflective patch integrated with four phase delay lines, each controlled by PIN diodes. By adjusting the ON/OFF states of the diodes, 1-bit phase reconfigurability for linearly polarized (LP) waves and 2-bit phase control for circularly polarized (CP) waves are achieved. To validate the concept, a $16\times 16$ -element RRA prototype is fabricated and experimentally tested. An FPGA-based BCS is employed to enable dynamic electromagnetic functionality. Measurements confirm a ±60° beam-scanning capability. In the 1-bit LP-to-cross-LP mode, a 3-dB gain bandwidth of 10.6-12.8 GHz (~18.8%) is achieved, with a peak gain of 21.6 dBi and an aperture efficiency of 21.9%. For the 2-bit CP-to-co-CP mode, an AR <3 dB bandwidth of 21.3% and a 3-dB gain bandwidth of 18.4% are observed for both LHCP and RHCP. Peak gains of 23.8 dBic and 23.7 dBic are measured at 11.2 GHz, with aperture efficiencies of 37.1% and 36.3%, respectively. These results demonstrate the effectiveness of the proposed design, highlighting its strong potential for satellite communication and radar sensing systems where dual-mode reconfigurability is beneficial.}, archive = {J_ACCESS}, author = {Guowei Li and Yuehe Ge and Ziheng Zhou and Zhechen Zhang and Zhizhang Chen}, doi = {10.1109/ACCESS.2025.3607943}, journal = {IEEE Access}, pages = {158832-158842}, shortjournal = {IEEE Access}, title = {2-bit electrically reconfigurable reflectarray with multi-polarization capability}, volume = {13}, year = {2025}, }
• (2025). Diagnosability enforcement in discrete event systems using labeled petri nets. ACCESS, 13, 158818-158831. (www)

  @article{ , abstract = {This research solves the problem of diagnosability enforcement in discrete event systems modeled by labeled Petri nets. Since fault diagnosis relies on system observations, the detection of the occurrence of faults can be interfered with by deadlocks. Thus, most documented approaches in the literature assume that a plant is deadlock-free. In this paper, given a plant that may contain deadlocks, a revised topology is constructed to verify the diagnosability of the original plant. It is shown that a plant is diagnosable if there does not exist an elementary faulty path in the extended diagnoser derived from its simplified model. Further, it is shown that the diagnosability of a plant may remain unchanged if the initial tokens of certain places increase. An online control policy is designed to enforce the diagnosability without computing the (partial) state space of the plant. Finally, a manufacturing example is presented to illustrate the proposed method.}, archive = {J_ACCESS}, author = {Wei Wei and Abdulrahman Al-Ahmari and Zhiwu Li}, doi = {10.1109/ACCESS.2025.3603424}, journal = {IEEE Access}, pages = {158818-158831}, shortjournal = {IEEE Access}, title = {Diagnosability enforcement in discrete event systems using labeled petri nets}, volume = {13}, year = {2025}, }
• (2025). PSR: A dual-level learning framework for patching sequential recommendation to capture local preference transitions. ACCESS, 13, 158808-158817. (www)

  @article{ , abstract = {Sequential recommendation (SR) systems based on users’ past behavior sequences are widely used on online platforms to provide personalized suggestions effectively. Transformer-based SR models have demonstrated remarkable success by effectively learning the correlations between individual items in users’ item sequences. Although transformer-based SR models perform well overall, they often fail to capture changes in local trends within long interaction sequences, since they primarily deal with sequences on a global scale. Recently, various methods have been proposed to address this issue by emphasizing local preference information. However, these approaches focus solely on enhancing local preference information, often overlooking the transition information between local preferences. Consequently, they struggle to effectively represent dynamic user behavior sequences. To address this limitation, we propose a dual-level learning framework for sequential recommendation called Patching Sequential Recommendation (PSR), which introduces patch sequences derived from local groups of items within original item sequences. The PSR method uses both patch sequences and item sequences as training samples, allowing for simultaneous training at the individual item level and patch level. Moreover, the proposed framework employs a user representation alignment module to improve user representation by aligning users’ item and patch sequence embeddings. Extensive experiments conducted on four real-world datasets demonstrate that our framework outperforms other state-of-the-art baselines. Our code is available at https://github.com/kangvic0615/PSR}, archive = {J_ACCESS}, author = {Wooseung Kang and Minje Kim and Suwon Lee and Gun-Woo Kim and Sang-Min Choi}, doi = {10.1109/ACCESS.2025.3607806}, journal = {IEEE Access}, pages = {158808-158817}, shortjournal = {IEEE Access}, title = {PSR: A dual-level learning framework for patching sequential recommendation to capture local preference transitions}, volume = {13}, year = {2025}, }
• (2025). TANet: A multi-representational attention approach for change detection in very high-resolution remote sensing imagery. ACCESS, 13, 158789-158807. (www)

  @article{ , abstract = {The rapid progress of deep learning techniques has significantly enhanced various remote sensing tasks. However, detecting changes in very high-resolution (VHR) remote sensing images continues to be a challenge due to the complexity of the image details and limited amount of training samples. Currently, there is considerable research to achieve precise change detection (CD). This is particularly true for addressing issues such as improved detection of small targets, undulating borders and minimized noise in the output change features. To tackle these challenges, we introduce the triple-attention network (TANet), which incorporates three robust attention mechanisms; namely, channel, spatial, and frequency attentions, across both spatial and frequency domains. This is achieved by using dynamic channel attention (DCA), spatial support attention (SSA) and frequency refinement attention (FRA). In addition, we propose an advanced fusion process namely geographic matching module (GeoMM) that efficiently combines complementary information from the input bi-temporal features enabling high accuracy predictions. By integrating these powerful modules, TANet improves the model’s capacity to accurately represent changes and dynamically refine deep features at each layer by concentrating on the most critical information. Experiments on two prominent CD datasets (LEVIR-CD and WHU-CD) demonstrate that the proposed TANet outperforms previous notable approaches, with extensive ablation studies evaluating the effects of attention modules and frequency components optimization. These findings highlight TANet’s potential as a state-of-the-art solution for precise and reliable CD in VHR remote sensing imagery.}, archive = {J_ACCESS}, author = {Hazem Badawy and Seif Eldawlatly and Ahmed Rafea}, doi = {10.1109/ACCESS.2025.3605984}, journal = {IEEE Access}, pages = {158789-158807}, shortjournal = {IEEE Access}, title = {TANet: A multi-representational attention approach for change detection in very high-resolution remote sensing imagery}, volume = {13}, year = {2025}, }
• (2025). Localized gas equivalent model of electrical heat demand for urban LV distribution network impact analysis. ACCESS, 13, 158776-158788. (www)

  @article{ , abstract = {Understanding the impact of increased domestic heat pump penetration on power distribution networks is key to achieving decarbonization targets. Distribution networks connect domestic and light commercial loads to the wider grid, and operating outside of their design criteria will compromise reliability and may result in outright loss of supply. Owing to the extensive scale of distribution network assets in the form of transformers and underground cable networks, estimates of the remaining available headroom and its erosion through connection of new low carbon technology is unclear. The approach contributed in this paper enables appropriate scaling of electrical heat load, sensitive to local gas-equivalent use, facilitating the assessment of distribution network threats in the absence of monitoring data. Geospatially linked historical fiscal demand data is used as a proxy for detailed physical and behavioral model inputs and impacts are demonstrated experimentally via power flow calculations carried out on models of actual distribution feeders. Measurement of impact was assessed via voltage and thermal violations against statutory limits. This contribution can support distribution network operators in forestalling the localized impacts of various electric heat pump penetration scenarios, reducing potential of overloads, as well as informing optimal reinforcement investment decisions.}, archive = {J_ACCESS}, author = {Amy Anderson and Connor McGarry and Stuart Galloway and Bruce Stephen and Stephen McArthur}, doi = {10.1109/ACCESS.2025.3608478}, journal = {IEEE Access}, pages = {158776-158788}, shortjournal = {IEEE Access}, title = {Localized gas equivalent model of electrical heat demand for urban LV distribution network impact analysis}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of positioning by deep reinforcement learning in VLC-based empty office environment. ACCESS, 13, 158763-158775. (www)

  @article{ , abstract = {Recently, buildings in cities have been becoming increasingly large, resulting in more complex indoor environments. Consequently, there is a growing demand for indoor Location-Based Services (LBS). High-precision indoor positioning technology is required to provide high-quality LBS. While relatively precise positioning can be achieved outdoors using the Global Positioning System (GPS), indoor positioning accuracy deteriorates due to walls and obstacles. Therefore, many recent studies have proposed using Radio Frequency (RF)-based wireless communication technologies, such as WiFi, Bluetooth, and Ultra-Wide Band (UWB), for indoor positioning. Among these techniques, fingerprinting is commonly adopted because it is cost-effective and can achieve high accuracy. However, RF-based technologies are prone to significant fluctuations in the Received Signal Strength Indicator (RSSI) due to environmental uncertainty. Furthermore, distinguishing between floors in a multi-story building or precisely localizing on a single floor remains challenging. To address these issues, this paper proposes a method that integrates fingerprinting with Deep Reinforcement Learning (DRL), an Artificial Intelligence (AI) technique, in an indoor Visible Light Communication (VLC) scenario. VLC utilizes light for data transmission, enabling reliable floor separation. In contrast to RF, VLC can effectively mitigate signal interference and variations. Additionally, DRL can significantly reduce the matching time required for fingerprinting. To achieve this goal, we compare and analyze the performance of DRL algorithms, including Deep Q-Network (DQN), Double DQN and Dueling DQN. Experimental results demonstrate that the proposed approach is highly efficient in terms of both processing time and positioning accuracy.}, archive = {J_ACCESS}, author = {Sung Hyun Oh and Jeong Gon Kim}, doi = {10.1109/ACCESS.2025.3605950}, journal = {IEEE Access}, pages = {158763-158775}, shortjournal = {IEEE Access}, title = {Performance analysis of positioning by deep reinforcement learning in VLC-based empty office environment}, volume = {13}, year = {2025}, }
• (2025). Physics-based α-IGZO TFTs compact modeling and neural network application with 2T0C DRAM cell. ACCESS, 13, 158751-158762. (www)

  @article{ , abstract = {Advanced amorphous oxide devices such as amorphous InGaZnO ( $\alpha $ -IGZO) operate based on mechanisms that differ significantly from those of conventional Si-based devices, primarily due to structural differences. While both types of devices utilize field-effect mobility as the primary mode of charge transport, there is no consensus on the additional complexities involved in charge movement within $\alpha $ -IGZO devices, which arise from their unique material properties. The BSIM series model commonly used for silicon devices cannot fully explain the charge transport mechanism of $\alpha $ -IGZO devices. Unfortunately, physics-based compact models for $\alpha $ -IGZO, which reflect the intrinsic nature of charge transport involved in electrical conduction have not been completely proposed with a standard formula. This paper presents a compact model for $\alpha $ -IGZO TFTs that incorporates charge transport mechanisms such as percolation, Variable-Range Hopping (VRH), and Trap-Limited Conduction (TLC), along with a methodology for calculating surface potential using the Lambert W function. The model is implemented in Verilog-A for circuit-level simulation and provides high accuracy with fabricated devices measurement. The model’s performance is further evaluated using the MNIST dataset by comparing the classification accuracy across various shallow-layer neural network architectures, demonstrating the model’s potential in neuromorphic system applications.}, archive = {J_ACCESS}, author = {Hyoungsoo Kim and Eunchan Park and Been Kwak and Daewoong Kwon and Hyunwoo Kim}, doi = {10.1109/ACCESS.2025.3605345}, journal = {IEEE Access}, pages = {158751-158762}, shortjournal = {IEEE Access}, title = {Physics-based α-IGZO TFTs compact modeling and neural network application with 2T0C DRAM cell}, volume = {13}, year = {2025}, }
• (2025). Optimizing fault tolerance and latency of federated learning using edge servers and pre-trained model. ACCESS, 13, 158737-158750. (www)

  @article{ , abstract = {Federated learning (FL) presents a promising paradigm for decentralized machine learning, particularly well-suited for data-sensitive cyber-physical systems (CPS) where privacy preservation and low-latency inference are paramount. However, FL deployments at the edge face acute challenges in fault tolerance and communication efficiency due to the inherent unpredictability and heterogeneity of edge environments. This research addresses these issues through the development of a hierarchical federated learning framework that strategically combines clustered edge servers, the transfer-learning capabilities of pre-trained EfficientNet-B0 and TabNet models, and an enhanced aggregation approach based on FedProx to deliver superior robustness and efficiency. The proposed architecture introduces a multi-cluster aggregation mechanism, where edge servers within clusters coordinate local model updates before participating in higher-level aggregation, reducing inter-device communication overhead and overall latency. A distinctive contribution of this work is the explicit modeling and evaluation of system reliability by defining a “paralysis ratio” to quantify failed or unresponsive edge servers, and conducting real-time simulation experiments with incremental data delivery. Through this, the framework’s resilience is demonstrated, maintaining model accuracy above 93% even under up to 40% server paralysis. In comparative benchmarks against the established Multi-Cluster Hierarchical FL (MCHFL) baseline, the framework achieves a 4–6% increase in classification accuracy and a 25% reduction in average communication latency. Beyond quantitative improvements, the study analyzes hyperparameter trade-offs, notably the impact of the FedProx constraint ( $\mu $ ), and highlights the practical value of real-time transfer learning EfficientNet-B0 for images and TabNet for complex time-series accelerating convergence under non-IID conditions. Collectively, these innovations advance FL by delivering a fault-tolerant, latency-aware, and transferable aggregation strategy validated through real-time simulation, significantly broadening the applicability of FL in complex, real-world edge and CPS deployments.}, archive = {J_ACCESS}, author = {Rahul Haripriya and Amit Kumar and Manish Pandey and Nilay Khare and Jaytrilok Choudhary and Dhirendra Pratap Singh and Surendra Solanki and Akash Haripriya}, doi = {10.1109/ACCESS.2025.3607738}, journal = {IEEE Access}, pages = {158737-158750}, shortjournal = {IEEE Access}, title = {Optimizing fault tolerance and latency of federated learning using edge servers and pre-trained model}, volume = {13}, year = {2025}, }
• (2025). Intelligent extraction of remote sensing image change patches based on deep learning and Human–Computer collaboration in coal mine surface areas. ACCESS, 13, 158723-158736. (www)

  @article{ , abstract = {Deep learning-based change detection methods are sensitive to illumination and registration errors and require a massive amount of samples during training, which is challenging to acquire in tasks related to coal mining. Hence, this study develops intelligent extraction methods and technical processes for change patches of surface remote sensing images of a coal mine area using Cesium, a Web-based geographic information system (WebGIS), and deep learning theory. Specifically, the proposed scheme utilizes a deep learning model with improved fusion spatiotemporal feature matching capabilities and a large convolutional kernel spatiotemporal attention to extract remote sensing image change patches intelligently. Then, the extracted change detection patches are visualized, managed, and edited by the constructed WebGIS platform, supported by multi-user human-computer collaboration, to improve the correctness and accuracy of the detection results. Finally, the manually edited results are deposited into the sample library as new samples and input back to the deep learning model as a new sample for autonomous learning to improve the model’s accuracy further. The practical application of the developed method demonstrates that the proposed deep learning algorithm attains a better detection accuracy than similar algorithms, effectively utilizing the enhanced autonomous learning accuracy of the sample library to compensate for insufficient samples. The proposed method incorporates collaborative perception, interaction, and decision-making between humans and machines, providing a theoretical and practical reference for practical engineering applications of re-mote sensing image change detection.}, archive = {J_ACCESS}, author = {Huachao Yang and Sen Wang and Hefang Bian and Bin Li}, doi = {10.1109/ACCESS.2025.3602992}, journal = {IEEE Access}, pages = {158723-158736}, shortjournal = {IEEE Access}, title = {Intelligent extraction of remote sensing image change patches based on deep learning and Human–Computer collaboration in coal mine surface areas}, volume = {13}, year = {2025}, }
• (2025). A robust multi-granularity information fusion network for rotating machinery fault diagnosis. ACCESS, 13, 158709-158722. (www)

  @article{ , abstract = {Rotating machinery is crucial in industrial production, and its stable operation directly affects both efficiency and safety. However, diverse operating conditions, non-stationary signals, and complex environments pose significant challenges for fault diagnosis. To address these issues, we propose a multi-granularity information fusion network to improve fault classification in complex scenarios. FusionNet first applies Continuous Wavelet Transform to decompose time-series signals, enabling in-depth extraction of key time-frequency features. Next, the multi-granularity feature extraction module focuses on multi-scale and sparse features, dynamically capturing local variations in fault information to enhance and supplement the overall sequence while effectively suppressing noise interference. After that, the Swin-Transformer with window-based interaction adaptively fuses these multi-granularity features, progressively integrating both local details and global dependencies. Finally, the Multi-Granularity Feature Aggregation Module combines features from different scales layer-by-layer and incorporates channel attention to highlight critical fault patterns. Experiments conducted with two datasets, the benchmark CWRU, and a self-developed dataset, show that FusionNet achieves state-of-the-art performance for fault diagnosis. In CWRU, it achieves an average accuracy of 99.67% in four operating conditions. In the self-developed Paper Delivery Structure Coupling dataset, it reaches 99.44%, outperforming conventional models like LSTM by 30.90%.}, archive = {J_ACCESS}, author = {Fu Liu and Haopeng Chen and Yan Wang and Yaomiao Fan}, doi = {10.1109/ACCESS.2025.3606581}, journal = {IEEE Access}, pages = {158709-158722}, shortjournal = {IEEE Access}, title = {A robust multi-granularity information fusion network for rotating machinery fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Real-time high-speed mass transfer: A cross-scale precision positioning method. ACCESS, 13, 158695-158708. (www)

  @article{ , abstract = {In Mini LED mass transfer, accurate, efficient chip positioning is critical for productivity. Existing edge shape-based template matching algorithms have prolonged matching time, severely impacting efficiency. To address real-time positioning of coordinates and angles during flying die piercing, this paper proposes an algorithm based on edge feature reconstruction, ensuring robustness, accuracy and real-time performance to enhance transfer efficiency. Firstly, in template creation, a sparse edge point strategy balances accuracy and time. Relative gradient amplitude filters non-critical edge points to reduce computation, while keeping template reference points unchanged to ensure stability. Secondly, in image matching, the minimum region two-step search limits the target area, reducing matching points. The minimum similarity threshold restricts offset angle search, excluding incorrect templates in real time. Experiments used 40 single-chip images to verify optimization effectiveness, compare angle recognition effects, and evaluate performance against other algorithms. The results show that compared with NCC, SSDA and LGSC algorithms, the proposed real-time positioning algorithm can significantly improve the positioning efficiency(Avg time 3.85 ms) while ensuring the positioning accuracy(Avg precision 96.33%), and meet the robustness, accuracy and time requirements of the machine vision system for the positioning chip during the massive transfer process of Mini LED.}, archive = {J_ACCESS}, author = {Han Sun and Kele Liu and Hanwei Yang and Ning Yang and Peisen Wang and Zhijun Yang}, doi = {10.1109/ACCESS.2025.3608624}, journal = {IEEE Access}, pages = {158695-158708}, shortjournal = {IEEE Access}, title = {Real-time high-speed mass transfer: A cross-scale precision positioning method}, volume = {13}, year = {2025}, }
• (2025). Design of diode on-/Off-frequency-reconfigurable wearable antenna for sub-6 GHz, millimeter-wave, and sub-terahertz application. ACCESS, 13, 158679-158694. (www)

  @article{ , abstract = {This paper presents a reconfigurable antenna that can be applied to sub-6 GHz, millimeter-wave band and D-band for wearable mobile watches. Owing to the compact size of wearable devices, their close proximity to the human body, and the requirement for multi-band communication, the antenna design faces multiple challenges, including size miniaturization, frequency reconfigurability, flexible beam switching, and compliance with human safety regulations. In the sub-6 GHz antenna, the switch of the diode causes the sub-6 GHz antenna to form a short circuit and an open circuit to achieve the frequency switching effect. Millimeter-wave antennas use a DC voltage to allow the diode to change the direction of current flow to form a circuit switching effect to achieve beam switching for 6 antennas, and 11 modes of beams are realized through different diode switches. The signal is fed into the antenna through the GCPW with a center feed, which reduces signal transmission losses and allows the antenna to maintain good operating bandwidth when the diode is operating in the on/off function. Finally, the proposed D-band monopole antenna at the top layer of the wearable device has upward radiation and near-field communication performance. The sub-6 GHz antenna can be used for multi-frequency switching applications when the diode operates the switching function. The millimeter-wave antenna can keep the same operating range when the diode is turned on in any state. When the diode switch is turned on, 360° beam switching can be achieved. D-band monopole antennas have the ability to not interfere with other antennas by being located on the uppermost layer and radiating upwards. In order to ensure that the proposed antenna is in compliance with the International Organization on Radiation (IOR) regulations, the effects of the proposed antenna on the human body under various modes of electromagnetic waveforms are investigated by simulating the absorbed power ratios and power densities in the human body. The antenna can be used for wearable devices in sub-6 GHz, millimeter-wave band and D-band.}, archive = {J_ACCESS}, author = {Ming-An Chung and Chia-Wei Lin and Yu-Hsun Chen}, doi = {10.1109/ACCESS.2025.3608564}, journal = {IEEE Access}, pages = {158679-158694}, shortjournal = {IEEE Access}, title = {Design of diode on-/Off-frequency-reconfigurable wearable antenna for sub-6 GHz, millimeter-wave, and sub-terahertz application}, volume = {13}, year = {2025}, }
• (2025). Design and experimental validation of modular autonomous driving platform for off-road 8 × 4 trucks in agricultural operation. ACCESS, 13, 158660-158678. (www)

  @article{ , abstract = {This study introduces and experimentally validates an autonomous driving platform for off-road $8\times 4$ heavy-duty trucks, aimed at sugarcane harvesting operations. The system integrates a minimally invasive drive-by-wire interface for throttle, braking, and steering; a GNSS-based localization module; and a lightweight perception suite comprising a 2D LiDAR and a monocular camera. A modular ROS2-based architecture supports real-time perception, planning, and control. The platform was tested on an off-road track in Brazil across eight driving scenarios, including straight paths, curves, and obstacle avoidance at varying speeds. A spatial evaluation framework was implemented using arc-length reparameterization and KD-Tree nearest-neighbor matching to compare actual and reference trajectories. Metrics included lateral and heading RMSE, IAE, ITAE, jerk, and obstacle clearance. Results showed consistent high lateral tracking accuracy, with RMSE reaching 0.15 m in straight segments and remaining below 1.0 m in most complex scenarios. Scenario T8, involving curvature and obstacle deviation, surpassed this threshold due to increased control demands. Across 40 runs, heading RMSE remained below 10°, obstacle clearance above 1.5 m, and jerk below 0.5 m/s3. Geometrically modeled references yielded more stable heading control, while manually recorded paths resulted in smoother dynamic profiles. Statistical analysis confirmed that increased trajectory complexity degrades performance. Scenario T6 outperformed others in lateral stability, though results were statistically like T1 under simpler conditions. The platform demonstrated reliable tracking and real-time operation in scenarios representative of off-road agricultural tasks. Future work will focus on mitigating GNSS latency, improving post-obstacle recovery, and integrating sensor redundancy and adaptive planning to enhance deployment in agricultural logistics.}, archive = {J_ACCESS}, author = {Nouriandres Liborio Silva and André Mendes and Fabrizio Leonardi and Cirilo De Paula Silva and Edson Caoru Kitani and Leopoldo Rideki Yoshioka and Renato Camargo Giacomini}, doi = {10.1109/ACCESS.2025.3607715}, journal = {IEEE Access}, pages = {158660-158678}, shortjournal = {IEEE Access}, title = {Design and experimental validation of modular autonomous driving platform for off-road 8 × 4 trucks in agricultural operation}, volume = {13}, year = {2025}, }
• (2025). Optimal planning and operation of BESS Charging/Discharging under corporate PPA with BCP considerations. ACCESS, 13, 158645-158659. (www)

  @article{ , abstract = {The increasing adoption of photovoltaic (PV) systems has intensified the challenges in managing output fluctuations. Although corporate power purchase agreements (CPPAs) offer long-term renewable energy procurement, research on integrated on/off-site energy management, business continuity planning (BCP)-compliant frameworks, and optimal battery energy storage system (BESS) operations within CPPA models remains limited. This study proposes a CPPA business model for three customer types, each equipped with on-site PV systems, a BESS, and access to an off-site solar power plant with additional BESS. An algorithm is developed to generate optimal day-ahead planned power flows (PPFs) while considering BCP requirements and managing real-time BESS operations. The model comprises: 1) on-site CPPA for direct power supply and 2) off-site CPPA, enabling remote energy supply from a solar power plant and facilitating power sharing among customers. The algorithm employs multi-objective optimization to minimize grid dependence, maximize surplus electricity sales, and balance PV utilization with revenue maximization through a weighting coefficient. A lower coefficient increases PV utilization, whereas a higher value increases revenue. Simulation results demonstrate that optimal BESS operations effectively reduce PV generation prediction errors, reduce reverse power flow, fulfill PPF requirements, and ensure BCP compliance with approximately 97.5% backup power reliability, irrespective of weighting coefficient. The optimal coordination of on-site and off-site CPPAs enhances local consumption of PV-generated electricity while aligning power transactions with market prices to maximize revenue. These findings provide insight into the developing advanced business models and algorithms for CPPA projects and PV plant operators, particularly in post-feed-in tariff (FIT) agreements.}, archive = {J_ACCESS}, author = {Jindan Cui and Takayuki Sakakimoto and Yuzuru Ueda}, doi = {10.1109/ACCESS.2025.3608243}, journal = {IEEE Access}, pages = {158645-158659}, shortjournal = {IEEE Access}, title = {Optimal planning and operation of BESS Charging/Discharging under corporate PPA with BCP considerations}, volume = {13}, year = {2025}, }
• (2025). Research on traditional pattern generation based on adaptive fusion attention mechanism. ACCESS, 13, 158632-158644. (www)

  @article{ , abstract = {Traditional patterns, characterized by their intricate designs and vibrant colors, hold significant application value for generating cultural and artistic images that incorporate national characteristics while preserving the original pattern structure. To achieve high-quality traditional pattern image generation, this study introduces a novel method based on adaptive fusion attention. The AdaAttN module is integrated into the generator to enhance visual focus and improve image quality. A multi-scale feature fusion discriminator is employed to accurately distinguish between the global and local features of the patterns, thereby perfecting the pattern stylization. The training process is guided by a stylistic feature fusion loss function, which ensures that the generated images reflect more ethnic colors and styles. Experimental comparative analysis conducted on a real dataset demonstrates that the model enhances image generation quality, confirming its feasibility and artistic merit.}, archive = {J_ACCESS}, author = {Jie Sun and Xiaojun Ding and Yixin Lin and Fengyuan Zou}, doi = {10.1109/ACCESS.2025.3603129}, journal = {IEEE Access}, pages = {158632-158644}, shortjournal = {IEEE Access}, title = {Research on traditional pattern generation based on adaptive fusion attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Optimizing deep residual networks for short-term load forecasting with multidimensional weather data and principal component analysis. ACCESS, 13, 158614-158631. (www)

  @article{ , abstract = {Short-term load forecasting (STLF) is essential for power system operations, supporting efficient grid management and resource planning. Deep Residual Networks (DRNs) have emerged as a promising architecture for STLF, offering a balanced solution by combining training stability, deep feature extraction, and reduced gradient degradation compared to Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. This study focuses on optimizing DRN for STLF by evaluating different combinations of multidimensional weather variables and applying Principal Component Analysis (PCA) to address feature complexity. Using the Malaysia dataset, which includes historical load, time, and weather variables such as temperature, rainfall, and wind speed, the impact of different variable combinations on forecasting precision is evaluated. Experimental results show that the DRN model outperforms baseline models including CNNs, RNN-based models, and Transformers, achieving a Mean Absolute Percentage Error (MAPE) of 0.052514 and a coefficient of determination (R2) of 0.927993. Building upon this, the proposed PCA-DRN further improves forecasting performance, achieving a MAPE of 0.049994 and an R2 of 0.934473, representing a 4.80% reduction in MAPE and a 0.65% increase in R2 compared to the original DRN. These findings emphasize the importance of feature selection and dimensionality reduction in optimizing STLF models, particularly for tropical regions with relatively stable weather patterns.}, archive = {J_ACCESS}, author = {Junchen Liu and Faisul Arif Ahmad and Khairulmizam Samsudin and Fazirulhisyam Hashim and Mohd Zainal Abidin Ab Kadir}, doi = {10.1109/ACCESS.2025.3607330}, journal = {IEEE Access}, pages = {158614-158631}, shortjournal = {IEEE Access}, title = {Optimizing deep residual networks for short-term load forecasting with multidimensional weather data and principal component analysis}, volume = {13}, year = {2025}, }
• (2025). Optimal photovoltaic energy allocation in residential renewable energy communities. ACCESS, 13, 158603-158613. (www)

  @article{ , abstract = {Effective deployment of Renewable Energy Communities (RECs) is essential for the success of Europe’s energy transition. However, there are no clear criteria for sharing energy generation among community members, which conditions the rhythm of RECs penetration. The lack of transversal legislation gave rise to different approaches in EU countries for solving this issue, with the calculation of ‘a priori’ and ‘a posteriori’ energy sharing coefficients being the most extended ones. Nevertheless, it is still necessary to develop better optimization algorithms to make the most of RECs, to minimize the surplus of energy, and improve payback among end-users and their impact on the energy system. This paper gives an overview of the state-of-the-art and proposes improvements to the linear programming (LP) methodologies devoted to obtaining an optimal calculation of renewable sharing coefficients; able to provide satisfactory results, no matter the size of the REC, while consuming affordable computation resources. The performance of the proposed method has been validated using real generation and consumption data collected from a Spanish DSO, considering both ‘a priori’ and ‘a posteriori’ remuneration schemes. The results prove the good behaviour of the proposed solution for different REC sizes and operating conditions.}, archive = {J_ACCESS}, author = {Marc Girona-Badia and Pablo A. Moreno and Gerard Laguna and Jordi Cipriano and Alvaro Luna}, doi = {10.1109/ACCESS.2025.3607516}, journal = {IEEE Access}, pages = {158603-158613}, shortjournal = {IEEE Access}, title = {Optimal photovoltaic energy allocation in residential renewable energy communities}, volume = {13}, year = {2025}, }
• (2025). Model-oriented orchestration for federated learning: Personalization through intelligent client matching. ACCESS, 13, 158585-158602. (www)

  @article{ , abstract = {The proliferation of connected devices and the exponential growth of generated data have revealed several limitations of classical cloud-centric architectures, notably in latency, bandwidth saturation, and data privacy. In response to these challenges, fog and edge computing paradigms have emerged, promoting decentralized processing by bringing computation and storage capabilities closer to data sources. Federated learning is a distributed paradigm for training machine learning models, to preserve data privacy and reduce communication costs. Among the widely adopted strategies are federated stochastic gradient descent, which aggregates client-side gradients, and federated averaging, which computes an average of local model parameters. Nevertheless, these approaches often overlook disparities in data quality and the varying contributions of participating clients, which may degrade model performance, particularly in non-iid or heterogeneous settings. In this paper, we introduce a novel aggregation framework for federated learning, leveraging an orchestrator model to identify the client whose data distribution most closely aligns with the given input, rather than applying uniform aggregation across all client models, as done in FedAvg. In our methodology, each client employed a distinct machine learning algorithm trained on three categories of datasets: first related to human activity recognition, second the other comprising MRI data and MIMIC-III dataset. The proposed approach achieved high accuracies of 99.90% on the UCI-HAR dataset, 99.13% on the MRI dataset and 99.55% on the MIMIC-III dataset, and also, we compared the proposed approach with other well-known personalized federation methods such as FedPro, FedPer, pFedHN and Fed-Clustering.}, archive = {J_ACCESS}, author = {Chaimae Zaoui and Nabil Aharrane and Faouzia Benabbou and Amal Zaouch and Chaimaa Bouaine}, doi = {10.1109/ACCESS.2025.3606865}, journal = {IEEE Access}, pages = {158585-158602}, shortjournal = {IEEE Access}, title = {Model-oriented orchestration for federated learning: Personalization through intelligent client matching}, volume = {13}, year = {2025}, }
• (2025). Enhancing implicit neural representations with transfer learning. ACCESS, 13, 158572-158584. (www)

  @article{ , abstract = {Implicit Neural Representation (INR) has emerged as a powerful tool for encapsulating high-dimensional data within neural network parameters, providing continuous and differentiable representations. However, INR faces challenges such as prioritizing low-frequency components over high-frequency details (known as the spectral bias problem) and slow convergence during training. To mitigate these issues, we explore the application of transfer learning for INR models. We observe that transfer learning not only accelerates model convergence but also improves learning efficiency and enhances the representation of high-frequency details. Furthermore, we find that source images exhibiting higher edge density and contrast, along with reduced homogeneity, significantly enhance the learning performance on subsequent target images. This study provides new insights into the application of transfer learning in INR models and highlights its potential to enhance image reconstruction quality.}, archive = {J_ACCESS}, author = {Euijune Lee and Minseo Kim and Chaoning Zhang and Sung-Ho Bae}, doi = {10.1109/ACCESS.2025.3606540}, journal = {IEEE Access}, pages = {158572-158584}, shortjournal = {IEEE Access}, title = {Enhancing implicit neural representations with transfer learning}, volume = {13}, year = {2025}, }
• (2025). Platform for detecting illegal landfills using computer vision and satellite imagery from web map service. ACCESS, 13, 158551-158571. (www)

  @article{ , abstract = {This study addresses the environmental problems of landfills through an innovative solution that combines artificial vision, satellite image analysis and agent-based architecture. The detection of these sources of contamination is particularly complex due to the visual heterogeneity of the objects present in the aerial views. To this end, a proprietary dataset has been developed aimed at identifying irregular landfills from aerial and satellite images. The research focuses on the application and comparison of the latest generation algorithms YOLOv8, YOLOv11 and YOLOv12, deployed in a multi-agent system developed with PANGEA, which allows a distributed and efficient management of the detection process. In addition, a dynamic platform has been implemented that allows the user to select geographical areas within Spain and verify the existence of landfills in these areas. In the event of detection, the system cross-references the results with official public information to determine whether the landfill is legal or illegal. Experiments show that YOLOv8 achieves outstanding performance with an accuracy of 97.25%, recall of 89.59%, F1-score of 93.27%, mAP@50 of 94.74% and mAP@50-95 of 79.06%, evidencing its potential for automated landfill detection in a real operating environment.}, archive = {J_ACCESS}, author = {Sergio García González and David Cruz García and Arturo Álvarez Sánchez and Rubén Herrero Pérez and Gabriel Villarrubia González}, doi = {10.1109/ACCESS.2025.3607760}, journal = {IEEE Access}, pages = {158551-158571}, shortjournal = {IEEE Access}, title = {Platform for detecting illegal landfills using computer vision and satellite imagery from web map service}, volume = {13}, year = {2025}, }
• (2025). Low complexity and spectrally efficient photonic hybrid beamforming for 5G/6G millimeter-wave systems. ACCESS, 13, 158535-158550. (www)

  @article{ , abstract = {In the future of 5G/6G wireless systems, microwave photonic signal processing will outperform conventional electronic techniques owing to its ultra-wide bandwidth (GHz to THz), low-loss fiber transmission, and tolerance to electromagnetic interference. It reduces the size, weight, and power consumption (SWaP) of hardware and computational facilities while enabling high-speed tunability, reconfigurability, and low-latency computing for beamforming and spectrum sensing. Photonics is emerging as a key enabler for next-generation wireless innovation as 6G moves closer to THz and AI-driven networks. Therefore, this paper presents a novel low-complexity, partially connected photonic hybrid beamforming architecture designed to generate multiple analog beams. Unlike conventional radio frequency domain hybrid beamforming methods that utilize a single analog beam employing phase shifters, our proposed architecture relies on multiple photonic precoders for downlink transmission. This architecture utilizes programmable single-fed serially connected true-time delay lines (TDLs) that are capable of generating multiple independently tunable time delays, capable of avoiding the beam-squint effect inherent in phase shifter-based beamforming. This photonic-based configuration provides notable improvement in spectral efficiency with the same number of RF chains as the conventional architecture. For instance, at an SNR of 25 dB with 16 antenna elements and a partially connected architecture employing 8 RF chains, the proposed system with a single line-fed double precoder achieves nearly five times higher spectral efficiency compared to the conventional partially connected counterpart. Moreover, the use of independently tunable delays enabled by the single line-fed true time delay line significantly reduces hardware complexity and energy consumption, leveraging the low-loss inherent properties of photonic systems, in contrast to conventional RF-domain hybrid beamforming that relies on analog phase shifters.}, archive = {J_ACCESS}, author = {Fekadu Mihret Geremew and Fanuel Melak Asmare}, doi = {10.1109/ACCESS.2025.3607304}, journal = {IEEE Access}, pages = {158535-158550}, shortjournal = {IEEE Access}, title = {Low complexity and spectrally efficient photonic hybrid beamforming for 5G/6G millimeter-wave systems}, volume = {13}, year = {2025}, }
• (2025). G-SQL: A schema-aware and rule-guided approach for robust natural language to SQL translation. ACCESS, 13, 158520-158534. (www)

  @article{ , abstract = {This paper introduces G-SQL, a schema-aware and rule-guided framework for translating Natural Language Queries (NLQ) into SQL, designed to support users with limited technical expertise. G-SQL bridges the limitations of traditional rule-based and deep learning approaches by combining JSON-based schema serialization with Natural Language Processing (NLP) tools, such as GloVe and Stanford CoreNLP. The system encodes database schemas—including tables, columns, keys, and relationships—into a structured JSON format, enabling efficient and interpretable mapping between query tokens and schema elements. SQL statements are generated using a guided template-driven strategy. Evaluated across three benchmark datasets (IMDB, Yelp, MAS), For easy queries, it achieved 100% execution accuracy on all three datasets. On medium queries, G-SQL reached 95% on IMDB, 85% on Yelp, and 90% on MAS. For hard queries, the accuracy was 78% on IMDB, 65% on Yelp, and 72% on MAS. Finally, on extra-hard queries, G-SQL achieved 55% on IMDB, 45% on Yelp, and 50% on MAS., demonstrating robust performance across varying query complexities.}, archive = {J_ACCESS}, author = {Hassan S. Shalaan and Tayseer Hassan A. Soliman and Amr M. AbdelAziz}, doi = {10.1109/ACCESS.2025.3607879}, journal = {IEEE Access}, pages = {158520-158534}, shortjournal = {IEEE Access}, title = {G-SQL: A schema-aware and rule-guided approach for robust natural language to SQL translation}, volume = {13}, year = {2025}, }
• (2025). A multi-scale fusion GAN with stable field operator for image reconstruction. ACCESS, 13, 158504-158519. (www)

  @article{ , abstract = {Generative Adversarial Networks (GANs) hold significant potential in the field of image reconstruction, but existing methods suffer from deficiencies in semantic consistency, feature continuity, and multi-scale feature utilization in missing regions. These shortcomings limit their reconstruction accuracy in scenarios with large-scale missing areas. To address these issues, this paper proposes a multi-scale stable field generative adversarial network (MSSF-GAN) based on U-Net. This method introduces a stable field operator into the generator’s skip connections, enabling effective reconstruction of missing regions in the encoder’s feature maps, thereby improving semantic consistency and feature continuity. Additionally, a multi-scale fusion strategy is adopted to enhance information interaction between features of different scales, thereby improving the model’s ability to perceive high-level semantics. Experimental results demonstrate that MSSF-GAN outperforms mainstream convolutional methods and matches or surpasses recent transformer-based models in challenging large or irregular missing regions. Notably, in face recognition, the generated face images exhibit excellent performance, further validating the effectiveness of our method in precise image reconstruction tasks.}, archive = {J_ACCESS}, author = {Xueyi Ye and Jiaxin Wang and Deqi Jiang and Maosheng Zeng and Hao Wang}, doi = {10.1109/ACCESS.2025.3605644}, journal = {IEEE Access}, pages = {158504-158519}, shortjournal = {IEEE Access}, title = {A multi-scale fusion GAN with stable field operator for image reconstruction}, volume = {13}, year = {2025}, }
• (2025). IReWindColor v2: Cross-window enhanced transformer for point interactive image colorization. ACCESS, 13, 158494-158503. (www)

  @article{ , abstract = {Point-interactive image colorization is a task that enables users to add color to grayscale images by providing sparse color annotations (hints) at user-defined locations. These hints are then propagated across the image to generate a plausible colorization result with minimal user effort. While existing methods have demonstrated effectiveness in leveraging user input, they often suffer from excessive color diffusion and color bleeding beyond object boundaries, compromising visual fidelity. In this work, we present an extended version of our previous method, iReWindColor, which introduced a window-based Vision Transformer (ViT) framework for efficient and fine-grained colorization. The extended framework incorporates two key improvements. First, we enhance the Window Encoder by integrating Window-Connecting Convolution module, which enables information exchange across adjacent windows. This addition mitigates discontinuities at window boundaries and improves spatial coherence in the colorized output. Second, we provide a comprehensive ablation study to quantitatively assess the impact of each architectural component and design choice, offering deeper insights into the effectiveness of the proposed modifications. Our approach employs small patches to achieve detailed color propagation while maintaining computational efficiency through localized attention. The Residual Embedding mechanism remains integral to aggregating global context, whereas the proposed Encode Block, which combines Self-Attention and Convolutional Neural Networks (CNNs), strengthens inter-patch relationships and improves the utilization of user-provided hints. Experimental results on multiple benchmark datasets demonstrate that the extended model consistently outperforms the original iReWindColor as well as other state-of-the-art methods, producing visually coherent, user-aligned colorizations with improved computational efficiency.}, archive = {J_ACCESS}, author = {Hideyuki Ogura and Masaaki Ikehara}, doi = {10.1109/ACCESS.2025.3608008}, journal = {IEEE Access}, pages = {158494-158503}, shortjournal = {IEEE Access}, title = {IReWindColor v2: Cross-window enhanced transformer for point interactive image colorization}, volume = {13}, year = {2025}, }
• (2025). Modal hybrid analysis of a spherical resonator with a ferrite load. ACCESS, 13, 158483-158493. (www)

  @article{ , abstract = {The modal analysis of structures containing ferrite materials is one of the greatest challenges of computational electrodynamics and goes beyond the scope of commercial software’s capabilities. The reason for this is the strong dependence of the ferrite parameters on frequency. From a numerical point of view, the task boils down to a nonlinear eigenproblem (NLEVP), the solution of which is not trivial. In particular, if the problem involves more complex structures (modeled using discrete techniques), NLEVP contains matrices of enormous dimensions and its solution seems impossible. The method proposed in this paper is based on the projection of the NLEVP onto a smaller subspace obtained by hybridizing finite element method (FEM) and the mode matching (MM) technique. This approach reduces the size of the problem, which directly improves the efficiency of the algorithm, but also allows solving NLEVP using methods whose results can be verified with controlled accuracy (unlike previous attempts to use Beyn’s method). The effectiveness of the proposed technique has been validated through numerical simulations and experimental results demonstrating a complex mode structure of a microwave spherical cavity strongly coupled to a yttrium iron garnet sphere. The results pave the way for rigorous electrodynamic modeling of spin-to-electromagnetic coupling with emerging applications in magnonic and quantum technologies. Finally, it is worth mentioning that NLEVPs are not exclusively used for electromagnetic problems and an effective algorithm for solving them could also find application in other fields of technology.}, archive = {J_ACCESS}, author = {Malgorzata Warecka and Rafal Lech and Piotr Kowalczyk and Adam Pacewicz and Bartlomiej Salski and Jerzy Krupka}, doi = {10.1109/ACCESS.2025.3606583}, journal = {IEEE Access}, pages = {158483-158493}, shortjournal = {IEEE Access}, title = {Modal hybrid analysis of a spherical resonator with a ferrite load}, volume = {13}, year = {2025}, }
• (2025). A systems-theoretic approach to mental state estimation for theory-of-mind-aware social robots. ACCESS, 13, 158467-158482. (www)

  @article{ , abstract = {Social robots are increasingly deployed in fields such as health care and education to support users through social interactions. Nonetheless, these robots mostly rely on black-box machine learning methods that lack awareness of the mental states of their users, which often leads to unnatural behavior. To address this, we propose three model-based techniques for real-time estimation of invisible mental states of humans. Each method adapts the extended Kalman filter and incorporates a validated dynamic model of human mental states. These mental state estimators are designed for human-robot social interactions and personalize their parameters using initial user data. When tested with 10 human participants interacting with a NAO robot, the mental state estimators reduced the average error in estimation and prediction of mental states across all participants by 3% (i.e., from 12% to 9%), with improvements of up to 13% for individual participants. These results demonstrate the potential of integrating such state estimators into the behavioral control systems of social robots to enhance their awareness of the mental states of users.}, archive = {J_ACCESS}, author = {Maria L. Morão Patrício and Anahita Jamshidnejad}, doi = {10.1109/ACCESS.2025.3607165}, journal = {IEEE Access}, pages = {158467-158482}, shortjournal = {IEEE Access}, title = {A systems-theoretic approach to mental state estimation for theory-of-mind-aware social robots}, volume = {13}, year = {2025}, }
• (2025). Remote sensing image dehazing using content-driven state space modeling with scale-aware aggregation. ACCESS, 13, 158450-158466. (www)

  @article{ , abstract = {Haze leads to a substantial decrease in the quality of remote sensing images, thereby hindering high-quality earth observation and other vision-related applications. Recently, state space models have demonstrated great potential in image restoration tasks, offering advantages in global information modeling while maintaining low computational complexity. However, we observe that existing Mamba-based image dehazing methods rely on a fixed-scale spatial-aware scanning mechanism, which presents limitations when applied to complex remote sensing scenarios characterized by objects and features at varying spatial scales. In this paper, we propose an effective scale-aggregation content-driven state space model for remote sensing image dehazing, called SC-DehazeMamba. Specifically, the proposed framework integrates both coarse-to-fine and multi-patch strategies into a unified parallel multi-branch architecture, where each branch leverages multi-scale learning to enhance robustness against diverse degradation appearances. In addition, we design a content-driven dynamic adaptive scanning mechanism that can adaptively determine the scanning order and regions based on the input remote sensing image. Through a series of extensive experiments on different benchmark datasets, our proposed framework has shown its effectiveness and favorable performance compared to state-of-the-art solutions.}, archive = {J_ACCESS}, author = {Yunfeng Peng and Guowei Gao and Congming Shi}, doi = {10.1109/ACCESS.2025.3607857}, journal = {IEEE Access}, pages = {158450-158466}, shortjournal = {IEEE Access}, title = {Remote sensing image dehazing using content-driven state space modeling with scale-aware aggregation}, volume = {13}, year = {2025}, }
• (2025). Role-sensitive analysis of positional contributions and win prediction in multiplayer online battle arena esports. ACCESS, 13, 158440-158449. (www)

  @article{ , abstract = {This study developed a role-sensitive framework for evaluating player contributions and predicting match outcomes in League of Legends, a leading multiplayer online battle arena (MOBA) esports title. Unlike prior approaches that treat the team as a homogeneous unit, we introduced the Victory Contribution (VC) metric to quantify individual player impact across four strategic domains—offense, defense, laning, and gold acquisition—while accounting for positional roles. Using professional match data from 10,390 games across three global leagues in 2023 and an independent test set of 9,670 matches from 2024, we trained four classification models—logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), and XGBoost (XGB)—on 13 selected features, both with and without the VC metric. Across all classifiers, extended models incorporating VC consistently outperformed baseline models. On the test set, the XGB model achieved the highest performance with an accuracy of 0.8686, recall of 0.8084, F1-score of 0.8601, and AUC of 0.9512. VC also outperformed traditional metrics such as Damage Share (DS) and Kill Participation (KP), yielding higher test recall and F1-score than both. All observed performance improvements were statistically significant across evaluation metrics (p <0.0001). Cross-league analysis revealed no significant differences in positional VC ratios, suggesting structural consistency in role contributions across Legends Champions Korea (LCK), Legends European Championship (LEC), and Legends Championship Series (LCS). These findings demonstrate the practical value of incorporating role-aware metrics in predictive modeling and player evaluation in professional esports.}, archive = {J_ACCESS}, author = {Woongchang Yoon and Suyeong Jeong}, doi = {10.1109/ACCESS.2025.3607328}, journal = {IEEE Access}, pages = {158440-158449}, shortjournal = {IEEE Access}, title = {Role-sensitive analysis of positional contributions and win prediction in multiplayer online battle arena esports}, volume = {13}, year = {2025}, }
• (2025). Thematic and bibliometric analysis of game-based learning in project management higher education. ACCESS, 13, 158423-158439. (www)

  @article{ , abstract = {Game-Based Learning (GBL) is a well-recognized experiential learning practice for Project Management (PM) training. However, GBL suffers from a stagnant adoption rate in higher education despite its positive reputation in research. Hence, this systematic review presents key findings, trends and gaps in Project Management–Game-Based Learning (PM–GBL) research to facilitate and encourage widespread adoption. Using the PRISMA framework, ${50} ~{\mathrm{GBL}}$ case studies on Project Management Serious Games (PM-SGs) were selected. This review also presents a timeline of serious games in construction management, software project management, and business project management across undergraduate and graduate engineering courses. Then, the content analysis identified five key themes—pilot studies, case studies, comparative studies, student-made games, and game design trends. In this section, a synthesis of key takeaways is presented regarding knowledge gain, game feedback, and learning behavior. In addition, guidelines for GBL developers and adopters are discussed. Next, the bibliometric analysis sheds light on the chronological and geographical distribution of the selected literature. Lastly, this paper suggests pivotal directions for future research in serious game design, development, implementation, and evaluation.}, archive = {J_ACCESS}, author = {Vian Ahmed and Yara Elenany and Hamad Albinali and Sameh El-Sayegh and Chimay Anumba}, doi = {10.1109/ACCESS.2025.3603862}, journal = {IEEE Access}, pages = {158423-158439}, shortjournal = {IEEE Access}, title = {Thematic and bibliometric analysis of game-based learning in project management higher education}, volume = {13}, year = {2025}, }
• (2025). EyeChat: An eye-gaze-controlled web-browsing system for head-mounted augmented reality displays. ACCESS, 13, 158409-158422. (www)

  @article{ , abstract = {Accessing Web content using conventional input methods can be difficult for people with motor impairments. Current solutions often use webcams to enable eye gaze control, which may cause interaction delays and inefficient browsing. EyeChat is an eye gaze-controlled interface for efficient Web content delivery via Microsoft HoloLens 2. The system features a text entry interface optimized for augmented reality and a hybrid chatbot to deliver accurate content. By combining rule-based and large language models, EyeChat provides real-time, contextually relevant responses. Its frequency-based circular text-entry interface centralizes eye movement around head gaze to minimize effort. Predictive features, including current-word, next-word, and phrase suggestions, enhance typing speed. This paper presents an initial technical evaluation of EyeChat with 12 typically developed adults. Non-disabled participants reported that 83.83% of responses met expectations. The system achieved a text-entry rate of 9.88 words per minute and 0.53 keystrokes per character with minimal practice. Eye gaze analysis showed 95% of points fell within a 24° field of view.}, archive = {J_ACCESS}, author = {Mahya Mirbagheri and Tom Chau}, doi = {10.1109/ACCESS.2025.3606899}, journal = {IEEE Access}, pages = {158409-158422}, shortjournal = {IEEE Access}, title = {EyeChat: An eye-gaze-controlled web-browsing system for head-mounted augmented reality displays}, volume = {13}, year = {2025}, }
• (2025). Modified quadratic high-gain DC–DC converter for solar-powered EV charging systems: Simulation, hardware prototype, and CHIL-based real-time validation. ACCESS, 13, 158385-158408. (www)

  @article{ , abstract = {The paper presents a modified design of a quadratic boost DC-DC converter, specially designed for electric vehicle (EV) charging with high voltage gain, low component count, reduced switching stress, and enhanced overall efficiency. Further, a 2-stage constant current (CC) charging approach is developed for a solar-powered EV charging system. This work provides a detailed quadratic layout with boosting of the input voltage, steady-state operations, and inductor current conduction states. Additionally, it includes mathematical analysis considering non-ideal conditions, parameter design criteria, and performance comparison of the proposed converter with existing ones. The converter’s performance is initially validated through MATLAB/Simulink simulations for a 3-kW power level. Afterward, the proposed topology is integrated into a PV-powered EV battery charging architecture. A feedforward neural network-based MPPT algorithm is implemented to extract solar power effectively. To enhance battery health, a 2-stage CC charging approach is applied where the charging current is dynamically adjusted with battery state of charge (SOC). To investigate the behaviour of the system under varying irradiance, simulations and real-time implementation on the OPAL-RT (OP-4512) platform under Controller Hardware-in-the-Loop (CHIL) with Texas Instruments (TI-F28379D) controller are conducted. Also, a 100 W scaled-down converter prototype is built to test its feasibility and efficacy under diverse conditions. The experimental efficiency is obtained as 95.6%. The resemblance among the experimental and simulation results, together with the results achieved in the CHIL environment, confirms the practical feasibility of the converter and establishes the proposed system as a prospective solution to intelligent solar-integrated EV charging infrastructure.}, archive = {J_ACCESS}, author = {Farha Khan and Hari Om Bansal and Dheerendra Singh}, doi = {10.1109/ACCESS.2025.3606879}, journal = {IEEE Access}, pages = {158385-158408}, shortjournal = {IEEE Access}, title = {Modified quadratic high-gain DC–DC converter for solar-powered EV charging systems: Simulation, hardware prototype, and CHIL-based real-time validation}, volume = {13}, year = {2025}, }
• (2025). Event detection and classification for long range sensing of elephants using seismic signals. ACCESS, 13, 158366-158384. (www)

  @article{ , abstract = {Detecting elephants through seismic signals is an emerging research topic aimed at developing solutions for Human-Elephant Conflict (HEC). Despite the promising results, such solutions heavily rely on manual classification of elephant footfalls, which limits their applicability for real-time classification in natural settings. To address this limitation and build on our previous work, this study introduces a classification framework targeting resource-constrained implementations, prioritizing both accuracy and computational efficiency. As part of this framework, a novel event detection technique named Contextually Customized Windowing (CCW), tailored specifically for detecting elephant footfalls, was introduced, and evaluations were conducted by comparing it with the Short-Term Average/Long-Term Average (STA/LTA) method. The yielded results show that the maximum validated detection range was 155.6 m in controlled conditions and 140 m in natural environments. Elephant footfall classification using Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel demonstrated superior performance across multiple settings, achieving an accuracy of 99% in controlled environments, 73% in natural elephant habitats, and 70% in HEC-prone human habitats, the most challenging scenario. Furthermore, feature impact analysis using explainable AI identified the number of Zero Crossings and Dynamic Time Warping (DTW) Alignment Cost as the most influential factors in all experiments, while Predominant Frequency exhibited significant influence in controlled settings.}, archive = {J_ACCESS}, author = {Jaliya L. Wijayaraja and Janaka L. Wijekoon and Malitha Wijesundara}, doi = {10.1109/ACCESS.2025.3607383}, journal = {IEEE Access}, pages = {158366-158384}, shortjournal = {IEEE Access}, title = {Event detection and classification for long range sensing of elephants using seismic signals}, volume = {13}, year = {2025}, }
• (2025). Channel knowledge map assisted channel estimation for an extremely large antenna array system. ACCESS, 13, 158353-158365. (www)

  @article{ , abstract = {Sixth generation communication systems are expected to utilize extremely large antenna arrays (ELAAs) to significantly enhance the spectral efficiency by compensating for path loss at high operating frequencies. However, in ELAA systems, the required number of pilot signals can become excessive, especially in multi-user scenarios, making channel estimation for all antennas particularly challenging. In addition, ELAAs present challenges as the near-field region becomes more prominent, rendering traditional channel estimation methods inefficient. To address this, we propose to utilize a channel knowledge map for channel estimation in ELAA systems, enabling seamless near-field and far-field communication while reducing the pilot overhead. To enhance the practicality of the channel knowledge map in ELAA systems, we propose a channel angle distance map (CADM) database that provides the angles and distances of multipaths from the polar-domain for a given location. The estimated channel parameters are adopted as initial values and refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno quasi-Newton algorithm to minimize the channel estimation error. Extensive simulation results demonstrate that the CADM-based method outperforms benchmark schemes with reduced pilot overhead.}, archive = {J_ACCESS}, author = {Muhammad Awais and Yun Hee Kim}, doi = {10.1109/ACCESS.2025.3606947}, journal = {IEEE Access}, pages = {158353-158365}, shortjournal = {IEEE Access}, title = {Channel knowledge map assisted channel estimation for an extremely large antenna array system}, volume = {13}, year = {2025}, }
• (2025). Predictive maintenance for hydraulic turbine unit: A comparative deep learning approach using internet of things data in real-time. ACCESS, 13, 158340-158352. (www)

  @article{ , abstract = {Accurate prediction of the remaining useful life (RUL) of hydraulic turbines is crucial for optimizing maintenance, reducing downtime, and enhancing operational efficiency. This study evaluates advanced neural network approaches, including convolutional neural networks (CNNs), long short-term memory neural networks (LSTM NNs), and a hybrid CNNs-LSTM NNs framework to estimate RUL using a high-dimensional time-series dataset. Rigorous preprocessing techniques, such as square root and Yeo-Johnson transformations, were applied to normalize the data and improve model performance. The models were assessed with metrics like mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2 score). Experimental results highlight that the hybrid CNNs-LSTM NNs model outperforms standalone CNNs and LSTM NNs by effectively combining spatial and temporal data patterns and achieving an R2 score exceeding 70% despite dataset imbalances. A detailed benchmarking analysis offers a deeper understanding of the strengths and limitations of each architecture, guiding the selection of models tailored to specific operational needs. This research advances predictive maintenance frameworks in the renewable energy sector, bringing attention to the potential of hybrid deep learning approaches for precise RUL estimation in industrial applications.}, archive = {J_ACCESS}, author = {Blondelle Melina Atsafack and Charles Kabiri and Gerard Rushingabigwi}, doi = {10.1109/ACCESS.2025.3607733}, journal = {IEEE Access}, pages = {158340-158352}, shortjournal = {IEEE Access}, title = {Predictive maintenance for hydraulic turbine unit: A comparative deep learning approach using internet of things data in real-time}, volume = {13}, year = {2025}, }
• (2025). Privacy preserving cancellable template generation for crypto-biometric authentication system. ACCESS, 13, 158322-158339. (www)

  @article{ , abstract = {In the recent era, the cancellable approach of template protection is a widely applicable and secure method in fingerprint authentication systems. It has been proven that biometric traits offer promising security and enhanced confidentiality. Integrating cryptographic techniques with biometric modality has presented a dual-layered security mechanism, significantly mitigating the risk of biometric data breaches. In this paper, we propose a biometric template preservation scheme that employs minutiae transformation and encryption of biometric features for secure biometric template generation. The proposed methodology not only ensures the protection of biometric data in storage but also facilitates secure data retrieval for authentication purposes. Integrating cryptographic algorithms with biometric minutiae features effectively addresses the limitations of cancellable biometric templates, which can be susceptible to inversion attacks and template reconstruction. Our main contribution includes: (a) Secure template generation by Mobius transformation using user keyset (p, q), (b) Securing the user template using permutation and encryption operation. The analysis of security and matching performance of the proposed preservation scheme has been evaluated against publicly available fingerprint database FVC2000 DB1, FVC2000 DB2, FVC2002 DB1, FVC2002 DB3, FVC2004 DB1 and FVC2004 DB3 containing 800 images of 100 persons, 8 images per person in each database using FMR, FAR, GAR, ROC Curve, and EER parameters. The comparative performance has shown that the proposed technique performed favourably against the existing state-of-the-art techniques and can be used feasibly.}, archive = {J_ACCESS}, author = {Mohd Imran and Mohammad Sarosh Umar and Shivali Malhotra}, doi = {10.1109/ACCESS.2025.3602795}, journal = {IEEE Access}, pages = {158322-158339}, shortjournal = {IEEE Access}, title = {Privacy preserving cancellable template generation for crypto-biometric authentication system}, volume = {13}, year = {2025}, }
• (2025). Broadband circularly polarized antenna array via metasurface and partially emptied substrate. ACCESS, 13, 158316-158321. (www)

  @article{ , abstract = {A metasurface including an empty region constituted by a square-shaped aperture is proposed to improve the performance of a circularly polarized patch antenna array based on sequential-phase feeding. The metasurface is applied/stacked to a conventional antenna consisting of a loop feeding structure, which enables sequential incremental phase to four patches. Surface waves propagating on the metasurface are excited, affecting the resonance features of the overall radiating structure, thereby improving both the input impedance-matching and the axial ratio bandwidths of the antenna. The square-shaped aperture in the top layer is introduced to extend the range of frequency over which the sequential phase feeding remains effective. The antenna prototype and the stacked metasurface are fabricated. The experimental results, in good agreement with simulation, show that the antenna with the compact size $2.16 \lambda _{0}\times 2.16 \lambda _{0}\times 0.12 \lambda _{0}$ , at the central frequency, provides a broad impedance relative bandwidth of 43.8% from $11.6~GHz$ to $18.1~GHz$ and a 3-dB axial ratio bandwidth of 36.6% (from $11.6~GHz$ to $16.8~GHz$ ). In addition, the proposed antenna has a flat gain within the operating frequency band and a peak gain $G=13.9~dBic$ at frequency $f=14~GHz$ .}, archive = {J_ACCESS}, author = {Francesco Anelli and Antonella Maria Loconsole and Riccardo Losito and Francesco Prudenzano}, doi = {10.1109/ACCESS.2025.3604558}, journal = {IEEE Access}, pages = {158316-158321}, shortjournal = {IEEE Access}, title = {Broadband circularly polarized antenna array via metasurface and partially emptied substrate}, volume = {13}, year = {2025}, }
• (2025). Tech-CNN: Deep learning-based framework with stacked sequence encoding for predicting the growth potential of technologies. ACCESS, 13, 158302-158315. (www)

  @article{ , abstract = {This paper presents an enhanced deep learning-based prediction framework, Tech-CNN, designed to predict the future trajectory of emerging technologies based on academic publication data. The framework integrates textual, citation, and research-area metadata and converts them into an h-year stacked sequence encoding to capture long-term growth trends while maintaining a dense and fixed-dimensional representation. The proposed framework is evaluated on a large-scale dataset comprising publications from 2006 to 2021 and achieves a prediction accuracy of approximately 88.7%. Compared to the previous method, the proposed framework improves the accuracy by 0.9 percentage points and f1-score by 7.2 percentage points. When applied to a recent dataset, the accuracy and f1-score were measured at 0.887 and 0.854, respectively. Comparative experiments with existing approaches demonstrate that Tech-CNN outperforms traditional methods in forecasting technology growth. Additionally, the framework effectively tracks the evolution of technologies, including the emergence and transformation of AI-based chatbot technologies into personalized intelligent agents. The results indicate that the proposed framework is robust and scalable, with potential applications in data-driven technology forecasting, trend analysis, and strategic R&D planning.}, archive = {J_ACCESS}, author = {Juhyun Lee and Soyoung Kim}, doi = {10.1109/ACCESS.2025.3607395}, journal = {IEEE Access}, pages = {158302-158315}, shortjournal = {IEEE Access}, title = {Tech-CNN: Deep learning-based framework with stacked sequence encoding for predicting the growth potential of technologies}, volume = {13}, year = {2025}, }
• (2025). Lightweight and adaptive deep coding for wireless image transmission in semantic communication. ACCESS, 13, 158285-158301. (www)

  @article{ , abstract = {Currently, deep learning-based joint source channel coding (JSCC) methods have achieved significant progress in enabling semantic communication. However, existing methods of this type often fall short of meeting the new demands in terms of model parameter size and storage efficiency. To address these issues, we propose a lightweight wireless image transmission method based on a tiny ConvNeXt architecture (ConvNeXt-T), referred to as DeepJSCC-T. To reduce the system’s parameter size and storage requirements, we adopt the efficient ConvNeXt-T as the backbone network for JSCC, thereby achieving a lightweight design. To further ensure adaptability to varying bandwidth and signal-to-noise ratio (SNR) conditions, we design a lightweight SNR-adaptive module based on large convolutional kernels, which enhances both flexibility and transmission performance. The experimental results show that, compared with existing methods, the proposed DeepJSCC-T achieves significant reductions in both parameter size and storage overhead. Specifically, DeepJSCC-T reduces the number of parameters by 24.65% and 27.52% relative to the Attention-based DeepJSCC (ADJSCC) and the variable-length DeepJSCC (DeepJSCC-V), while lowering storage overhead by 30.98% and 33.47%, respectively. Notably, despite its more compact architecture, DeepJSCC-T maintains competitive PSNR performance, demonstrates clear advantages in high-resolution image transmission, and exhibits strong adaptability under different SNR and bandwidth conditions, making it suitable for resource-constrained semantic communication scenarios.}, archive = {J_ACCESS}, author = {Youming Sun and Jiafeng Wang and Lile Wei and Haiqiang Chen and Shuping Dang and Xiangcheng Li}, doi = {10.1109/ACCESS.2025.3607699}, journal = {IEEE Access}, pages = {158285-158301}, shortjournal = {IEEE Access}, title = {Lightweight and adaptive deep coding for wireless image transmission in semantic communication}, volume = {13}, year = {2025}, }
• (2025). A multimodal systematic review of drivers’ fatigue detection methodologies, datasets, and models. ACCESS, 13, 158266-158284. (www)

  @article{ , abstract = {Driver fatigue continues to be a leading contributor to global road accidents, posing significant risks particularly in long-distance, night-time, and commercial driving contexts. Recent advances in artificial intelligence (AI), sensor fusion, and signal processing have led to the development of automated fatigue detection systems capable of providing early warnings to prevent collisions. However, the field remains fragmented in terms of data modalities, modeling techniques, evaluation standards, and deployment readiness. This systematic review synthesizes findings from 78 peer-reviewed studies published between 2014 and 2025, critically analyzing the use of behavioral, physiological, vehicular, and multimodal approaches for fatigue detection. The review identifies a clear methodological shift from handcrafted features and traditional machine learning toward deep learning models, particularly CNNs and hybrid CNN-LSTM frameworks, enhancing performance in visual and EEG-based fatigue detection. Despite high accuracies reported under controlled conditions (often exceeding 95%), only a minority of models were validated in real-world settings or evaluated for real-time, in-vehicle deployability. Explainability and personalization, essential for trust and usability in human-centered AI, were substantially underrepresented which featured in only 19.2% and 12.8% of the studies respectively. Moreover, the widespread use of small sample sizes, unimodal data, and non-standardized metrics impedes reproducibility and comparative analysis. Nonetheless, emerging trends such as federated learning, edge deployment, and privacy-preserving AI indicate a growing emphasis on ethical, scalable, and context-aware systems. This review concludes by offering a roadmap for future research: emphasizing multimodal integration, standardized evaluation, on-road validation, and transparent model architectures, to bridge the gap between experimental prototypes and real-world fatigue monitoring systems.}, archive = {J_ACCESS}, author = {O. O. Ajayi and A. M. Kurien and K. Djouani and L. Dieng}, doi = {10.1109/ACCESS.2025.3606900}, journal = {IEEE Access}, pages = {158266-158284}, shortjournal = {IEEE Access}, title = {A multimodal systematic review of drivers’ fatigue detection methodologies, datasets, and models}, volume = {13}, year = {2025}, }
• (2025). CT-FSOD: CLIP-based text-guided few-shot object detection. ACCESS, 13, 158250-158265. (www)

  @article{ , abstract = {Few-Shot Object Detection (FSOD) has recently gained significant attention due to its potential to detect objects of novel classes with only a few annotated examples. Existing FSOD methods rely largely on visual features extracted from limited support images of novel classes, which are highly sensitive to background noise, pose variations, and intra-class diversity, thereby limiting generalization performance on object detection for the novel classes. To address these challenges, we propose a Contrastive Language-Image Pretraining (CLIP)-based text-guided FSOD framework, denoted as CT-FSOD, that leverages a CLIP-based vision-language pretraining model to enhance class prototypes (class representative vectors) learning and representation alignment between support and query images. Our framework consists of two core branches: 1) Prototype Distillation Branch that extracts semantically aligned features from support images using a frozen CLIP image encoder, producing robust and semantics-aware prototypes; 2) Text Matching Branch that generates initial prototypes for novel classes using CLIP text features constructed through CoOp-style learnable context tokens, providing pose-invariant and semantically consistent anchors. During training, query image features from a trainable CNN backbone are doubly aligned with CLIP-derived rich and semantic 1) image features via the Prototype Distillation Module and 2) text features via the CLIP Alignment Loss. These alignments encourage meaningful and generalizable discriminative feature learning, thus significantly improving object detection performance for novel classes under few-shot conditions. Extensive experiments on PASCAL VOC and MS COCO benchmarks demonstrate that our CT-FSOD achieves superior results compared to existing state-of-the-art methods for FSOD.}, archive = {J_ACCESS}, author = {Hyugjae Chang and Woojin Lee and Munchurl Kim}, doi = {10.1109/ACCESS.2025.3604521}, journal = {IEEE Access}, pages = {158250-158265}, shortjournal = {IEEE Access}, title = {CT-FSOD: CLIP-based text-guided few-shot object detection}, volume = {13}, year = {2025}, }
• (2025). Fine-grained underwater visual object tracking via long-term template refinement. ACCESS, 13, 158237-158249. (www)

  @article{ , abstract = {With the growing demand for underwater recognition systems, underwater visual object tracking has recently received wide attention. The visible light scene analysis in a liquid environment poses specific challenges for underwater visual object tracking, such as impairment of sharpness, blue tinge, and illumination refraction. These problems lead to a performance degradation of the conventional visual object tracking algorithms. In this paper, we propose to adapt the methodology for underwater tracking by performing a long-term template refinement to achieve fine-grained spatial template matching and long-range temporal dependency for use with a Siamese tracking network. The template refinement is based on an attention mechanism, which is designed to highlight the saliency and discriminatory information of the tracked content. This mechanism obtains an accurate target mask for the template and alleviates the impact of the underwater noise in the template feature embedding. The long-term template generator updates the dynamic appearance of the target adaptively by clustering historical templates using a Gaussian Mixture Model. This approach enhances the target representation capacity and alleviates challenges such as visual obstructions, complex background variations and prevalent noise in underwater environment. The experimental results obtained on the challenging underwater tracking dataset, UOT32, demonstrate the advantage of the proposed long-term template refinement tracker. It outperforms state-of-the-art approaches such as SiamMask by 5.03% in Distance Precision and 2.31% in Overlap Success, which provides a promising visual perception technology for underwater artificial intelligence systems. On the standard tracking benchmarking dataset VOT2018, the proposed method is 5.56% higher than SiamRPN++ on the Expected Average Overlap and demonstrates comparable performance, verifying its generalisation capacity in other tracking scenarios and environments.}, archive = {J_ACCESS}, author = {Shaochuan Zhao and Zhou Yu and Mingcong Chen and Hefeng Yin}, doi = {10.1109/ACCESS.2025.3604394}, journal = {IEEE Access}, pages = {158237-158249}, shortjournal = {IEEE Access}, title = {Fine-grained underwater visual object tracking via long-term template refinement}, volume = {13}, year = {2025}, }
• (2025). Adaptive fuzzy sliding mode controller of three link robot arm manipulator. ACCESS, 13, 158222-158236. (www)

  @article{ , abstract = {This paper presents the design of an adaptive fuzzy sliding mode controller (AFSMC) for robotic manipulators. Most of the dynamic parameters in robotic manipulators are uncertain. To deal with uncertainties, the paper designed a high-performance non-linear controller for a three-degree-of-freedom robot manipulator. The trajectory tracking error of the three-degree-of-freedom robot manipulating arm has been controlled and eliminated in this study using SMC with adaptive fuzzy control. In order to adjust the sliding surface gain parameter and help it converge and stay at zero, the adaptive fuzzy logic control system and the direct method of the Lyapunov stability of the robot manipulator has been applied. The study conducts a comprehensive performance comparison between AFSMC, FSMC and SMC. AFSMC demonstrates improvements in performance across various trajectory scenarios. Moreover, AFSMC demonstrates superior robustness in the presence of parametric uncertainty(payload variation) and disturbances compared to FSMC and SMC.}, archive = {J_ACCESS}, author = {Addisie Gietie Adane and Chala Merga Abdissa}, doi = {10.1109/ACCESS.2025.3607809}, journal = {IEEE Access}, pages = {158222-158236}, shortjournal = {IEEE Access}, title = {Adaptive fuzzy sliding mode controller of three link robot arm manipulator}, volume = {13}, year = {2025}, }
• (2025). Resilient ANFIS framework for robust boost converter stability in real world conditions. ACCESS, 13, 158210-158221. (www)

  @article{ , abstract = {The control of Boost converters under varying operating conditions poses a significant challenge due to nonlinear dynamics and external disturbances, often leading to poor regulation and stability issues with traditional controllers. This study introduces a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for Boost converters, evaluated across an unprecedented 27 diverse test scenarios, including load variations, parameter uncertainties, voltage fluctuations, noise, temperature drifts, switching frequency changes, and combined disturbances. Unlike prior studies with limited test scopes, this work establishes a comprehensive resilience framework, achieving up to 56% lower overshoot and 90% faster settling time compared to open-loop (OLBC) and PI-based closed-loop (CLBC) controllers in 25 of 27 cases (average overshoot: 29.82%, settling time: 0.0105 s). Limitations in extreme transients (210–225% overshoot) are transparently analyzed. Validated via MATLAB Simulink, the ANFIS controller offers robust control for applications like electric vehicles and renewable energy systems, setting a new benchmark for power electronics resilience.}, archive = {J_ACCESS}, author = {Farheen Bano and Muhammad Ayaz and Dur-E-Zehra Baig}, doi = {10.1109/ACCESS.2025.3606767}, journal = {IEEE Access}, pages = {158210-158221}, shortjournal = {IEEE Access}, title = {Resilient ANFIS framework for robust boost converter stability in real world conditions}, volume = {13}, year = {2025}, }
• (2025). Improving software fault prediction with a hybrid DE-WOA optimizer and ANFIS-enhanced ensemble learning. ACCESS, 13, 158194-158209. (www)

  @article{ , abstract = {Software Fault Prediction (SFP) plays a critical role in improving software quality by identifying defect-prone modules early in the development lifecycle. However, existing SFP approaches often struggle to balance predictive performance, interpretability, and robustness, particularly when faced with high-dimensional, imbalanced datasets and correlated features. This paper proposes a novel hybrid methodology that integrates a Differential Evolution–Whale Optimization Algorithm (DE-WOA) for efficient feature selection, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-enhanced ensemble for dynamic prediction, and explainability techniques (SHAP, permutation importance, PCA) for robust model interpretation. The proposed framework is evaluated on the PROMISE Ant 1.3 dataset where it reduces dimensionality (selecting 2 features for XGBoost and 4 for Logistic Regression), improves Logistic Regression ensemble accuracy to 0.84 (F1-score 0.817, AUC-ROC 0.714), and outperforms the XGBoost-based ensemble in both predictive performance and interpretability. Statistical analysis using the Wilcoxon test (p-value = 0.1408) suggests that the models are not significantly different in performance, yet practical improvements in non-defective recall and feature interpretability are notable. This unified framework addresses key challenges in SFP by combining metaheuristic feature optimization, ensemble learning, and explainable AI, offering a scalable and interpretable solution for software quality assurance.}, archive = {J_ACCESS}, author = {P. Jhansi Lakshmi and T. V. Sai Krishna and N. Bharath Kumar and D. S. Naga Malleswara Rao and Alireza Hosseinpour and Natesan Chokkalingam Lenin}, doi = {10.1109/ACCESS.2025.3603980}, journal = {IEEE Access}, pages = {158194-158209}, shortjournal = {IEEE Access}, title = {Improving software fault prediction with a hybrid DE-WOA optimizer and ANFIS-enhanced ensemble learning}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of a preload-controllable lead screw linear ultrasonic motor based on model-free adaptive control. ACCESS, 13, 158183-158193. (www)

  @article{ , abstract = {Conventional monolithic lead-screw linear ultrasonic motors exhibit poor output performance due to their lack of adjustable preload mechanisms and inadequate drive-control frameworks. To address these issues, this study proposes a novel split-rotor structure featuring adjustable preload capability and develops comprehensive friction models to characterize preload–interface–thrust and preload–thrust–velocity relationships. Moreover, integrating a model-free adaptive control (MFAC) approach, we design an advanced lead-screw linear ultrasonic motor system. An experimental platform was constructed to validate the accuracy of the proposed preload friction models and MFAC strategy. Experimental results indicate optimal performance at a resonance frequency of 30,890Hz, achieving a maximum velocity of approximately 7.5 mm/s and a peak thrust of around 25 N, with the optimal preload identified between 10 N and 40N. This research effectively resolves intrinsic limitations in thrust output and control precision, thereby laying a solid foundation for precision-driven applications in high-end manufacturing, aerospace, and related fields.}, archive = {J_ACCESS}, author = {Ranran Geng and Ming Yang and Junkai Li and Wufan Qian and Quanmin Liu}, doi = {10.1109/ACCESS.2025.3603625}, journal = {IEEE Access}, pages = {158183-158193}, shortjournal = {IEEE Access}, title = {Design and analysis of a preload-controllable lead screw linear ultrasonic motor based on model-free adaptive control}, volume = {13}, year = {2025}, }
• (2025). Type 2 fuzzy consensus control for leader-follower multiple euler-lagrange system under faults and deception attack. ACCESS, 13, 158170-158182. (www)

  @article{ , abstract = {In leader-follower (L-F) multi-robot systems deployed in dynamic environments, achieving task-space consensus is challenging due to the presence of actuator faults, deception attacks, and uncertainties. This paper presents a resilient control framework designed to ensure task-space consensus in multiple Euler-Lagrange system (ELS) while addressing these critical issues. The proposed approach combines an adaptive consensus controller to ensure coordinated control, an observer-based mechanism to detect and mitigate deception attacks, and an Interval Type-2 Fuzzy Logic Controller (IT2FLC) to effectively manage faults and uncertainties. To guarantee system stability and convergence, a Lyapunov-based analysis is conducted. Extensive simulations are performed in a network of four two-degree-of-freedom robotic manipulators to evaluate the proposed method compared with L-F consensus controller (LFCC) for multiple uncertain ELS and resilient consensus control (RCC) for network robotic manipulators under actuator faults and deception attack. The results show that in noiseless conditions, the proposed controller achieves up to 8.40% root mean square error (RMSE) reduction compared to baseline controllers. Under noisy conditions, it significantly outperforms LFCC and RCC, and improvements across all robots and axes with reductions ranging from 22.13% to 73.30%. That demonstrates the effectiveness of proposed method in maintaining consensus, enhancing resilience, and ensuring reliable operation under dynamic and uncertain conditions.}, archive = {J_ACCESS}, author = {Van-Truong Nguyen and Hai-Binh Giap and Mai The Vu and Tien-Dung Hoang and Phan Xuan Tan}, doi = {10.1109/ACCESS.2025.3606250}, journal = {IEEE Access}, pages = {158170-158182}, shortjournal = {IEEE Access}, title = {Type 2 fuzzy consensus control for leader-follower multiple euler-lagrange system under faults and deception attack}, volume = {13}, year = {2025}, }
• (2025). MLXOps4Medic: A service framework for machine learning and explainability operations in medical imaging AI development. ACCESS, 13, 158149-158169. (www)

  @article{ , abstract = {Integrating AI models with the medical domain is challenging as it involves more complex workflows compared to traditional machine learning operations (MLOps). Requirements such as model performance monitoring, calibration, and retraining on live medical data, as well as underdeveloped XAI and MLOps integration, have not yet been fully covered by existing works. This paper proposes the MLXOps4Medic, an initial machine learning and explainability operations (MLXOps) framework based on the microservices architecture for AI model development in the medical domain. The framework focuses on four types of tasks in the MLXOps: training, model evaluation, XAI explanation generation, and XAI evaluation. It can orchestrate tasks between different types of microservices, automatically transferring the necessary data and configurations for task execution to reduce operational overhead. Additionally, the framework collects the provenance of the workflow metadata and builds a provenance network, which facilitates workflow tracing and reproducibility throughout the complex MLXOps lifecycle. Three case studies illustrate the key findings concerning the framework: 1) a significant reduction in operational overhead by approximately 72.55% resulting from complex AI experiments; 2) facilitation of MLXOps customization for complex workflows such as data drift monitoring, integration of multimodal medical AI models, and reproduction of historical training data; and 3) provision for prediction and explanation generation for medical AI models through a web portal. Furthermore, the framework is compared with the well-established MLOps framework called MLflow on a general domain MLX pipeline execution, demonstrating observable efficiency in terms of zero-code configuration for the training task and utilizing 36.14% less time for training task execution. These key findings demonstrate that the MLXOps4Medic framework delivers an efficient and highly adaptable MLXOps solution for AI and medical integration. By addressing identified challenges in workflow orchestration, it serves as a foundational framework with far-reaching implications for future research and applications across diverse domains.}, archive = {J_ACCESS}, author = {Jun Huang and Yan Liu}, doi = {10.1109/ACCESS.2025.3606838}, journal = {IEEE Access}, pages = {158149-158169}, shortjournal = {IEEE Access}, title = {MLXOps4Medic: A service framework for machine learning and explainability operations in medical imaging AI development}, volume = {13}, year = {2025}, }
• (2025). RCM: A novel fire detection technique that effectively resists interference in complex scenarios. ACCESS, 13, 158137-158148. (www)

  @article{ , abstract = {Fire detection is critical to life. However, the reality of the scene is very complex. Interference from various scenarios makes it difficult to detect fires in a timely manner, e.g., there may be occluded fires in complex scenarios, which results in smaller flame targets that are difficult to detect and higher leakage and false detection rates. To address the above limitations, we develop a novel deep learning method, i.e., RCM, for complex scenarios. Specifically, it expands the model’s sensory field to capture the fire background information more extensively by designing a novel residual feature extraction network, i.e., RDNet, and combines it with the convolutional block attention module (CBAM) so that the model can pay more attention to the fire region, thus effectively reducing the interference of a complex background. Then, the multiscale feature fusion (MFF) module provides the model with a multilevel feature representation, which enhances the model’s ability to capture detailed features of smaller fires. Moreover, we introduce a new loss function, MPDIoU, to improve the convergence speed and detection accuracy of the model. Experiments on real fire scene datasets show that the precision and accuracy of the RCM model reach 94.77% and 97.99%, respectively, while the FPR and FNR are only 1.19% and 5.57%. Meanwhile, the detection performance of the method is optimized compared to more advanced algorithms in the field. The method demonstrates the effectiveness of fire detection in complex scenes and provides a novel and effective technique for fire detection.}, archive = {J_ACCESS}, author = {Zhongyi Fang and Xuefeng Jiang and Liuquan Xu and Xianjin Fang}, doi = {10.1109/ACCESS.2025.3603263}, journal = {IEEE Access}, pages = {158137-158148}, shortjournal = {IEEE Access}, title = {RCM: A novel fire detection technique that effectively resists interference in complex scenarios}, volume = {13}, year = {2025}, }
• (2025). Explainable AI-driven EEG channel selection for accurate and interpretable epilepsy diagnosis. ACCESS, 13, 158108-158122. (www)

  @article{ , abstract = {Epilepsy is a complex neurological disorder characterized by recurrent seizures, requiring accurate and timely diagnosis to ensure effective clinical management. This is particularly important in Brain-Computer Interfaces (BCIs), where neurological monitoring plays a key role in assistive and diagnostic applications. In this context, Electroencephalography (EEG) is the primary diagnostic modality; however, traditional manual analysis is labor-intensive, subjective, and prone to inter-expert variability. Automated machine learning techniques offer a promising alternative to manual EEG analysis. The opaque, “black-box” nature of many Machine Learning (ML) models often raise concerns about their reliability in clinical settings, which can hinder their practical implementation. To tackle this issue, Explainable Artificial Intelligence (XAI) has emerged as a transformative solution, providing greater transparency and shedding light on the critical features driving model predictions. In this study, we introduce Explainable LIMEbased Selection of EEG Electrodes (XLISEE), a novel approach that not only enhances EEG channel selection but also strengthens interpretability by leveraging XAI methodologies. The suggested approach is evaluated using the dataset provided by Guinea-Bissau Hospital. The most important EEG channels are identified by XLISEE to differentiate between epileptics and healthy subjects, reducing data complexity without sacrificing the precision of the diagnosis. With accuracy rates of 99.76% for both the Support Vector Machine (SVM) and k-Nearest Neighbor (kNN) classifiers, our experimental results show that the suggested method delivers remarkable classification performance. It also attains 100% accuracy, specificity, sensitivity, and F1-score, as well as a Matthews Correlation Coefficient (MCC) of 1.0. Moreover, lower computational demands improve efficiency, making real-time epilepsy detection more practical and feasible for clinical use. Beyond performance gains, integrating XAI enhances clinician confidence by offering interpretable, evidence-based insights, promoting wider adoption of AI-driven diagnostics.}, archive = {J_ACCESS}, author = {Afifa Jridi and Kais Belwafi and Ridha Djemal and Carlos Valderrama Sakuyama and Sami Bachir Mejri}, doi = {10.1109/ACCESS.2025.3608083}, journal = {IEEE Access}, pages = {158108-158122}, shortjournal = {IEEE Access}, title = {Explainable AI-driven EEG channel selection for accurate and interpretable epilepsy diagnosis}, volume = {13}, year = {2025}, }
• (2025). A graphene metasurface-based narrowband THz biosensor for non-invasive early cancer detection. ACCESS, 13, 158098-158107. (www)

  @article{ , abstract = {Terahertz spectral region, as a part of the electromagnetic spectrum, is an emerging field of technology for a future generation biomedical sensing applications thanks to the strong sensitivity to water, and its non-ionizing nature that guarantees safety for repeated uses on biological tissues. In this work, we propose a dual-band, tunable metasurface biosensor based on graphene that could be used for early in-vivo skin cancer detection. The sensor is designed to operate at two resonance frequencies: 1.092 THz and 3.18 THz. The double Debye framework was used to model optical properties of basal cell carcinoma (BCC). The sensor’s performance on the test sample showed a full width half maximum (FWHM) of 48 GHz and a resonance shift of 36 GHz. The sensitivity, figure of merit (FOM), detection limit and quality factor (Q) are found to be 0.2389 THz/RIU, $4.93~{RIU}^{-1}$ , and 21.7, 0.19 RIU respectively. Tunability of the sensor is obtained by varying graphene’s chemical potential or Fermi voltage from 0.8 to 1 eV. The design of biosensor was simulated in CST Microwave Studio, and its equivalent circuit model was developed. Thus, the numerically simulated results of the biosensor under sample testing confirm that the proposed design can be considered a label free promising candidate for THz biosensing applications.}, archive = {J_ACCESS}, author = {Abdul Jabbar and Somayyeh Asgari and Tapio Fabritius}, doi = {10.1109/ACCESS.2025.3606621}, journal = {IEEE Access}, pages = {158098-158107}, shortjournal = {IEEE Access}, title = {A graphene metasurface-based narrowband THz biosensor for non-invasive early cancer detection}, volume = {13}, year = {2025}, }
• (2025). BVGA-net: Lightweight network coupling BiLoop vector-mixed gradient aggregation for quayside crane driver state detection against complex cockpit interference. ACCESS, 13, 158083-158097. (www)

  @article{ , abstract = {Driver state detection in port quayside cranes faces challenges from complex cockpit backgrounds and lightweight deployment needs. We proposed a lightweight detection network BVGA-Net based on an improved receptive-field attention mechanism and multi-level feature gradient fusion strategy, which was validated on a proposed benchmark dataset using cross-scenario division for generalization. To address back-ground interference in the cockpit, we designed an enhanced feature extraction encoder, where the vector-mixed receptive-field adaptive attention uses unfold operations and spatial attention to address parameter-sharing limitations in traditional convolutions. The parameter-free SimAM is integrated to suppress noise based on local self-similarity of images, improving feature representation capability without additional parameters. To balance multi-scale detection accuracy and computational efficiency, we proposed the bidirectional cross-domain loop aggregation (BCLA) strategy with receptive-field spatial features and introduced multi-level feature gradient fusion (MFGF) module. The BCLA method fuses multi-scale features through bidirectional loop paths to enhance multi-scale objects detection, while the MFGF module combines a global branch with a local branch via gradient fusion to reduce computational complexity and improve feature diversity in complex backgrounds. Experiments demonstrate that the method achieves an mAP50 of 87.7% with 3.76M parameters and balances accuracy and edge-deployment efficiency for safety-critical port operations.}, archive = {J_ACCESS}, author = {Yong Zhang and Hengliang Ren and Jibo Pan and Lei Xian and Qianli Ma and Chunhao Han and Gao Liu and Xunbin Huang and Yang Liu and Yan Chen}, doi = {10.1109/ACCESS.2025.3604252}, journal = {IEEE Access}, pages = {158083-158097}, shortjournal = {IEEE Access}, title = {BVGA-net: Lightweight network coupling BiLoop vector-mixed gradient aggregation for quayside crane driver state detection against complex cockpit interference}, volume = {13}, year = {2025}, }
• (2025). A robust prioritization framework of data quality dimensions to improve ML-driven healthcare systems using AHP and sensitivity analysis. ACCESS, 13, 158057-158082. (www)

  @article{ , abstract = {The success of Machine Learning (ML) models in healthcare relies heavily on the quality of data used. High-quality data are crucial for improving the predictive capabilities and the overall performance of ML systems. Despite this, research on data quality in healthcare and ML remains limited, with varying definitions of issues and dimensions across contexts. This study introduces a structured, expert-driven framework for prioritizing data quality dimensions critical to ML performance in healthcare. In contrast to performance evaluation studies involving machine learning algorithms or classifiers, this research does not encompass the training or comparison of predictive models. It addresses a key gap in ML data control by integrating ISO/IEC 25012 with Priestley’s classification and using the Analytic Hierarchy Process (AHP) to evaluate 15 dimensions based on expert judgment. The findings identify Completeness (21.25%), Accuracy (15.53%), Consistency (14.31%), Currentness (14.82%), and Precision (13.82%) as the most influential dimensions for ML healthcare outcomes. A One-at-a-Time (OAT) sensitivity analysis with ±17.6% perturbation confirms the robustness of prioritization despite expert input variability. Key contributions include: 1) a tailored framework for ML healthcare data; 2) AHP-based dimension prioritization; 3) validation through sensitivity testing; 4) insights into data quality’s impact on ML fairness and transparency; and 5) practical guidance for data governance and resource allocation. Future work will apply this framework to clinical datasets to validate its effectiveness in enhancing ML model performance and generalizability.}, archive = {J_ACCESS}, author = {Areen Metib Al-Hgaish and Rodziah Binti Atan and Razali Yaakob and Mohd Hafeez Osman}, doi = {10.1109/ACCESS.2025.3601031}, journal = {IEEE Access}, pages = {158057-158082}, shortjournal = {IEEE Access}, title = {A robust prioritization framework of data quality dimensions to improve ML-driven healthcare systems using AHP and sensitivity analysis}, volume = {13}, year = {2025}, }
• (2025). Blockchain and chainlink oracle integration in EHR: Ensuring real-time data integrity and interoperability with ethereum, hyperledger fabric, and IPFS storage. ACCESS, 13, 158044-158056. (www)

  @article{ , abstract = {Healthcare systems face challenges in ensuring robust security, achieving scalable operations, and maintaining optimal efficiency, mainly attributable to the inherent constraints of conventional centralized architectural frameworks. These traditional systems have limitations that compromise healthcare delivery, affecting patient care quality, data integrity, and operational effectiveness. This research proposes a hybrid blockchain framework that integrates Hyperledger Fabric, Ethereum, and the Interplanetary File System (IPFS) to enable secure, scalable, and interoperable healthcare data management. Testing with simulated healthcare data across three institutional nodes demonstrated a significant 64% reduction in data retrieval time compared to traditional systems while maintaining exceptional 99.8% uptime reliability. Chainlink oracles serve as sophisticated bridges for cross-chain communication, securely transmitting data between blockchain networks while preserving complete data integrity. The framework ensures HIPAA and GDPR compliance through advanced AES-256 encryption protocols, immutable audit trails, and patient-controlled access mechanisms. The implementation results conclusively demonstrate that the hybrid model improves data security and interoperability while reducing operational costs by approximately 37% compared to current centralized solutions. This research establishes the transformative potential of hybrid blockchain frameworks in revolutionizing healthcare data environments by providing technically viable and economically efficient solutions to manage sensitive medical information.}, archive = {J_ACCESS}, author = {Jayapriya Jayabalan and N. Jeyanthi}, doi = {10.1109/ACCESS.2025.3606194}, journal = {IEEE Access}, pages = {158044-158056}, shortjournal = {IEEE Access}, title = {Blockchain and chainlink oracle integration in EHR: Ensuring real-time data integrity and interoperability with ethereum, hyperledger fabric, and IPFS storage}, volume = {13}, year = {2025}, }
• (2025). Green PON deployment: Enhancing performance and sustainability using SLA-aware DBWA algorithms for 25G/50G-EPON. ACCESS, 13, 158028-158043. (www)

  @article{ , abstract = {Emerging applications such as extreme virtual reality, enterprise access, 5G front-haul / back-haul, and future 6G immersive services demand exceptional bandwidth and low latency. Next-generation passive optical networks (PONs), including 25G/50G-EPON (Ethernet PON) and 50G-PON, address these requirements. However, the telecoms sector faces sustainability challenges, with networks consuming over 75% of the sector’s energy and contributing more than 25% of greenhouse gas emissions, which is increasing with bandwidth demand. Fiber-based PON networks offer high-speed, low-latency, and energy-efficient solutions. To further enhance energy efficiency, we developed a dynamic bandwidth wavelength allocation (DBWA) algorithm for 50G-EPON systems, adapted for single- and dual-wavelength configurations. For single-wavelength ( $1\times 50$ G) configurations, the sleep mode aware algorithm (SMA) activates the optical network unit (ONU) sleep mode when no data are transmitted, reducing energy consumption and ensuring minimal delay due to the absence of wavelength switching. For dual-wavelength ( $2\times 25$ G) systems, the online restricted wavelength switching (RWS) algorithm dynamically allocates wavelengths upon receiving user reports, regulating the switching to minimize delays while achieving energy savings at the ONU. Finally, leveraging a predictive approach, the offline restricted wavelength usage and switching (RWUS) algorithm evaluates bandwidth demands over 0.1-second intervals, enabling energy-efficient wavelength switching and optimizing both optical line terminal (OLT) and ONU energy usage. The simulation results highlight the superior energy efficiency of the offline RWUS algorithm and the extended ONU sleep times, especially under low load conditions, while maintaining the lowest delay among multi-wavelength algorithms. Conversely, despite being less energy-efficient, the SMA algorithm scores slightly better in delay for single-wavelength systems. All algorithms ensure compliance with service level agreements (SLAs), providing reliable bandwidth allocation for various profiles. These results support green PON deployments and recommend the RWUS algorithm for two-wavelength 50G-EPON systems ( $2\times 25$ G) and SMA for single-wavelength 50G-EPON ( $1\times 50$ G) to optimize energy efficiency, latency, and quality of service.}, archive = {J_ACCESS}, author = {Sukriti Garg and Noemi Merayo}, doi = {10.1109/ACCESS.2025.3606511}, journal = {IEEE Access}, pages = {158028-158043}, shortjournal = {IEEE Access}, title = {Green PON deployment: Enhancing performance and sustainability using SLA-aware DBWA algorithms for 25G/50G-EPON}, volume = {13}, year = {2025}, }
• (2025). TrajDD-GAN: A synthetic mobility trajectory generation solution based on diffusion models. ACCESS, 13, 158018-158027. (www)

  @article{ , abstract = {The pervasive integration of GPS-enabled devices and population growth, alongside diverse transportation options, has led to an exponential increase in the collection of trajectory data, driving advancement in spatiotemporal data mining and urban planning. However, sharing trajectory data that includes sensitive user information can significantly threaten personal privacy, as location-based services like mobile apps and vehicle tracking offer valuable insights into travel routes while simultaneously raising serious concerns about the exposure of personal geolocation information. To solve this dilemma, we propose a novel generative model based on a spatiotemporal diffusion probabilistic model: Trajectory Denoising Diffusion Generative Adversarial Network (TrajDD-GAN). TrajDD-GAN reconstructs and synthesizes geographic trajectories through a forward diffusion process that adds Gaussian noise to real trajectory data, followed by a multimodal conditional GAN for iterative denoising. This process significantly reduces the number of required diffusion steps. Our experiments demonstrate the potential of integrating probabilistic diffusion techniques with generative adversarial networks to achieve better sample quality, diversity, and efficiency for real-world applications. Extensive experimental results show that TrajDD-GAN generates synthetic trajectories that preserve the statistical properties of the original mobility data. One of TrajDD-GAN’s main advantages compared to other diffusion models is its cost-effective inference process: fewer steps mean faster generation. In particular, TrajDD-GAN achieves a 98.19% reduction in inference time compared to DiffTraj, while successfully generating up to 1,000,000 synthetic trajectories. This demonstrates its scalability and efficiency, enabling the large-scale generation of high-quality synthetic mobility data for real-world applications.}, archive = {J_ACCESS}, author = {Hamza Abbar and Sara Kassan and François Bidet and Aya Cherigui and Florent Guépin and Denis Renaud}, doi = {10.1109/ACCESS.2025.3602274}, journal = {IEEE Access}, pages = {158018-158027}, shortjournal = {IEEE Access}, title = {TrajDD-GAN: A synthetic mobility trajectory generation solution based on diffusion models}, volume = {13}, year = {2025}, }
• (2025). T-CPAD: A transformer-based approach for crowd flow prediction and anomaly detection. ACCESS, 13, 158001-158017. (www)

  @article{ , abstract = {Crowd flow prediction and anomaly are critical issues in urban planning and public safety. However, existing research lacks a comprehensive analysis of crowd flow-related information and fails to address the high variability of crowd flow effectively. Therefore, this study proposes T-CPAD, a Transformer-based approach for crowd flow prediction and anomaly detection, to improve the accuracy of both crowd flow prediction and anomaly detection. Specifically, the Transformer encoder is used to perform a deep and comprehensive analysis of the external features related to crowd flow, achieving precise flow predictions. On this basis, a regularized anomaly discrimination function is designed, the parameters of which are adaptively optimized using gradient descent, enabling accurate anomaly detection and alarms. In practical applications, using an electronic fence dataset provided by a Hi-Tech Park, T-CPAD achieves a mean squared error of 12.5 for crowd flow prediction, a recall and an accuracy of 0.99 and 0.98 in anomaly detection experiments, significantly outperforming traditional methods. These results demonstrate the superior performance and reliability of the T-CPAD approach for crowd flow prediction and anomaly detection.}, archive = {J_ACCESS}, author = {Junkai Yi and Ziyin Zhang and Fei Yang}, doi = {10.1109/ACCESS.2025.3598696}, journal = {IEEE Access}, pages = {158001-158017}, shortjournal = {IEEE Access}, title = {T-CPAD: A transformer-based approach for crowd flow prediction and anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Legal framework and data storage background of online collected data for predictive maintenance and repair purposes in the automotive sector in the european union. ACCESS, 13, 157986-158000. (www)

  @article{ , abstract = {This research begins with a simple but essential question: In predictive vehicle maintenance, where should raw data from a car be stored? Some argue that it makes sense to keep it within the vehicle, managed directly by its control units. Others prefer sending it to a backend system for evaluation by larger models. We found that this decision cannot be made based solely on technical reasoning. The legal conditions surrounding personal data, cybersecurity, product responsibility, and AI-based systems in the European Union carry equal weight in shaping feasibility. These regulations not only impose constraints but also actively influence the design and application of such technologies in real-world scenarios. The findings suggest that legal barriers frequently limit implementation more than current technical capabilities. Addressing these constraints early in the design process is crucial, as they define not only what types of data processing are permitted but also how predictive maintenance services can be delivered to users. To support responsible development, this study proposes a legal-technical framework tailored to the European regulatory landscape. It aligns predictive maintenance systems with key European Union regulations, including the GDPR, Cybersecurity Act, Cyber Resilience Act, and AI Act.}, archive = {J_ACCESS}, author = {József Nagy and Gergely Karácsony and Roland Kelemen and István Lakatos}, doi = {10.1109/ACCESS.2025.3594772}, journal = {IEEE Access}, pages = {157986-158000}, shortjournal = {IEEE Access}, title = {Legal framework and data storage background of online collected data for predictive maintenance and repair purposes in the automotive sector in the european union}, volume = {13}, year = {2025}, }
• (2025). Impact of voltage sensor faults on two-level hierarchical control required for grid-forming VSCs in isolated microgrids. ACCESS, 13, 157965-157985. (www)

  @article{ , abstract = {With the advancement of technology, most physical systems have become cyber-physical due to their level of connectivity; therefore, are not only the effects produced by non-malicious faults being analyzed but also the faults caused by malicious attacks. This paper presents a dynamic analysis of the impact of voltage sensor faults on the two-level Hierarchical Control designed for managing the one-phase Distributed Energy Resources (DER) based on Grid-Forming Control (GFC). The hierarchical control strategy implemented and further analyzed under the influence of sensor faults states the capability of regulating the voltage in the isolated Microgrid (MG). In addition, the proposed control system can compensate for the voltage unbalance produced at the point of common coupling or critical bus below a reference quality value of 2%. The hierarchical control design seeks simplicity, low processing effort, and fast response. Finally, a dynamic analysis is carried out about the controller’s capability to maintain the operating point in the event of different voltage sensor faults on the secondary control and the effect it imposes on the power quality generated in the MG. The Matlab/Simulink simulation platform has been used to test the performance of the proposed control strategy.}, archive = {J_ACCESS}, author = {Leony Ortiz-Matos and Jorge W. González-Sanchez and Luis B. Gutierrez-Zea and Orestes Llanes-Santiago and Alexander Aguila-Téllez}, doi = {10.1109/ACCESS.2025.3604415}, journal = {IEEE Access}, pages = {157965-157985}, shortjournal = {IEEE Access}, title = {Impact of voltage sensor faults on two-level hierarchical control required for grid-forming VSCs in isolated microgrids}, volume = {13}, year = {2025}, }
• (2025). Quantum computing in semantic communications: Overcoming optimization challenges with high-dimensional hilbert spaces. ACCESS, 13, 157942-157964. (www)

  @article{ , abstract = {Semantic communications prioritize transmitting meaningful information over raw data in communication systems. However, these systems face significant optimization challenges, particularly concerning resource efficiency and fidelity due to the costly and delicate nature of photonic resources. Quantum computing offers promising solutions to these challenges through its unique capabilities, such as superposition and entanglement, within high-dimensional Hilbert spaces. This survey reviews the integration of quantum computing into semantic communications, tracing developments from foundational concepts to current advancements. It explores how quantum embeddings and machine learning techniques enhance semantic representation and transmission, enabling the encoding and processing of only relevant information. This approach addresses issues of polysemy and contextual variations in large datasets more effectively than classical methods. Key topics include the utilization of quantum probability and cognition in semantic analysis, optimization of quantum protocols for faster information retrieval, and the role of variational quantum circuits in improving computation latency, communication bandwidth, data privacy, and transmission delays. It also examines the implications for future communication systems like next-generation networks. It highlights the shift towards intelligent computing-intensive architectures and the support for advanced applications such as extended reality and holographic communications. Potential pathways are identified to achieve more efficient, secure, and intelligent data transmission, aiming to provide researchers and practitioners with a thorough understanding of this emerging field, outlining open research challenges and future directions to inspire further exploration and innovation.}, archive = {J_ACCESS}, author = {Andreas Andreou and Constandinos X. Mavromoustakis and George Mastorakis and Athina Bourdena and Evangelos K. Markakis}, doi = {10.1109/ACCESS.2025.3603338}, journal = {IEEE Access}, pages = {157942-157964}, shortjournal = {IEEE Access}, title = {Quantum computing in semantic communications: Overcoming optimization challenges with high-dimensional hilbert spaces}, volume = {13}, year = {2025}, }
• (2025). WLS-based behind-the-meter PV generation disaggregation using partial AMI measurements with first-order time scaling. ACCESS, 13, 157933-157941. (www)

  @article{ , abstract = {The increasing penetration of distributed photovoltaic (PV) generation systems due to the expansion of renewable energy has posed challenges to power grid operators, particularly in managing the uncertainty caused by behind-the-meter (BTM) PV systems. These systems are unmeasured, complicating gross load estimation and grid management. To address this issue, a novel disaggregation method utilizing net load measurements and partially aggregated advanced metering infrastructure (AMI) data is proposed. The proposed method uses a weighted least squares (WLS) method by assigning higher weights to nighttime errors to mitigate daytime errors caused by PV generation. Additionally, a first-order time-scaling method is introduced to correct temporal discrepancies caused by partially aggregated AMI data. The proposed method is validated using data provided by the Korea Electric Power Research Institute (KEPRI).}, archive = {J_ACCESS}, author = {Dongho Hyun and Jaebeom Im and Jaepil Ban and Hyeonok Lim and Jongmin Jo and Seongsoo Cho and Young-Jin Kim}, doi = {10.1109/ACCESS.2025.3606526}, journal = {IEEE Access}, pages = {157933-157941}, shortjournal = {IEEE Access}, title = {WLS-based behind-the-meter PV generation disaggregation using partial AMI measurements with first-order time scaling}, volume = {13}, year = {2025}, }
• (2025). Optimization-based dynamic voltage support of microgrids using energy storage systems. ACCESS, 13, 157918-157932. (www)

  @article{ , abstract = {A microgrid network is characterized by a high $R/X$ ratio, making the voltage more sensitive to active power changes compared to bulk power systems, where the voltage is regulated primarily by reactive power. Due to its sensitivity, voltage control approaches for microgrids should also consider the active power input coupling, making it very different from conventional power systems. Additionally, as the energy costs associated with active and reactive powers are different and the operational conditions of microgrids connected to active distribution systems vary over time, the ideal controller to provide voltage support must be flexible enough to handle these technical and operational constraints. This paper proposes a model predictive control approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system to solve the model predictive control problem. By proper selection of model predictive control weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in MATLAB/Simulink validates the proposed approach for the Cordova, Alaska microgrid. Results show that the performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.}, archive = {J_ACCESS}, author = {Niranjan Bhujel and Nischal Guruwacharya and Ujjwol Tamrakar and Raymond H. Byrne and Timothy M. Hansen and Donald M. Hummels and Reinaldo Tonkoski}, doi = {10.1109/ACCESS.2025.3606815}, journal = {IEEE Access}, pages = {157918-157932}, shortjournal = {IEEE Access}, title = {Optimization-based dynamic voltage support of microgrids using energy storage systems}, volume = {13}, year = {2025}, }
• (2025). Reducing SAR-EO domain gap via semantic alignment for SAR segmentation. ACCESS, 13, 157909-157917. (www)

  @article{ , abstract = {Synthetic Aperture Radar (SAR) imagery is widely utilized in remote sensing due to its all-weather and day-night imaging capabilities, which offer advantages over Electro-Optical (EO) imagery. However, SAR image interpretation remains challenging, leading to significant interest in automated analysis using deep learning. A primary obstacle is the limited availability of labeled SAR datasets, due to their complex scattering and speckle noise. Typically, aligned EO data with abundant labels is often used for training; however, segmentation performance on SAR images remains suboptimal due to inherent differences in radiometric characteristics. This paper addresses the SAR-EO domain gap by leveraging semantic consistency between SAR and EO images. Despite visual differences, both modalities share highly consistent semantic label spaces. Exploiting this, we propose a semantic-level domain adaptation approach that transfers knowledge from EO to SAR images. Our model requires only SAR data at inference time and achieves state-of-the-art performance in SAR semantic segmentation, effectively bridging the domain gap between SAR and EO.}, archive = {J_ACCESS}, author = {Minseok Seo and Doyi Kim and Sohee Son}, doi = {10.1109/ACCESS.2025.3600615}, journal = {IEEE Access}, pages = {157909-157917}, shortjournal = {IEEE Access}, title = {Reducing SAR-EO domain gap via semantic alignment for SAR segmentation}, volume = {13}, year = {2025}, }
• (2025). A Human–Computer-interaction exploration of working memory using EEG. ACCESS, 13, 157877-157908. (www)

  @article{ , abstract = {In the context of Electroencephalography (EEG) research, how is Working Memory (WM) leveraged in Human-Computer Interaction (HCI)? To address this question, this paper explores how WM is represented in EEG-based HCI experiments, with the aim of informing interface and system design that more effectively aligns with users’ cognitive capacities and limitations. A total of 132 studies published between 2018 and 2024 were reviewed to identify HCI use-cases involving EEG to study WM, outline key WM concepts, and evaluate how these align with findings from other disciplines. The findings indicate that WM-related EEG studies in HCI aim to enhance user interaction through more efficient signal analysis and the development of adaptive systems and brain-computer interfaces (BCIs). However, the findings also highlight the lack of theoretical grounding for EEG-based WM research within HCI. Key cognitive theories are often overlooked, and the strong association between WM and Attention is rarely acknowledged. Although the neural basis of Working Memory (WM) is well represented, its conceptualization within HCI remains underdeveloped and often misaligned with advances in cognitive science and psychology—potentially limiting the development of safe, effective, and cognitively-aware user-centered technologies. Based on these findings, we recommend: (a) integrating alternative models of Working Memory (WM) into HCI system design; (b) incorporating Attention evaluation in EEG-based WM experiments; and (c) exploring the development of a custom WM foundation model to address conceptual limitations in HCI and variability across tasks, users, and environments.}, archive = {J_ACCESS}, author = {Konstantina Kyriaki and Christos A. Fidas}, doi = {10.1109/ACCESS.2025.3606802}, journal = {IEEE Access}, pages = {157877-157908}, shortjournal = {IEEE Access}, title = {A Human–Computer-interaction exploration of working memory using EEG}, volume = {13}, year = {2025}, }
• (2025). Evaluating cyber threat intelligence: Accuracy, completeness, relevance, and freshness. ACCESS, 13, 157863-157876. (www)

  @article{ , abstract = {In the realm of cybersecurity, the extraction of Cyber Threat Intelligence (CTI) is vital for acquiring accurate Tactics, Techniques, and Procedures (TTPs) and Indicators of Compromise (IoC) from reputable sources. The extracted CTI must be evaluated to ensure high quality, and different CTI platforms, such as VirusTotal, AlienVault, and MetaDefender, often yield varying evaluations. A robust method is required that automatically extracts CTI, evaluates it, and provides a verdict on IoCs, determining whether they are malicious, while also evaluating IoC information across multiple platforms. In this work, we propose an automated mechanism that first extracts TTPs and IoCs from reputable threat reports, submits the extracted CTI to multiple platforms, evaluates the platform responses using four key metrics —accuracy, freshness, completeness, and relevance, and provides weighted verdicts for IoCs. We tested 600 IoCs in February 2025, and the weighted verdict matched those of VirusTotal for 79.4%, AlienVault for 87.4%, and MetaDefender for 39.6% of the IoCs. The results also show that VirusTotal provides a consistent evaluation of various types of IoCs in terms of freshness, completeness, and relevance of information, whereas AlienVault shows inconsistencies across all IoC types, and MetaDefender shows inconsistency for some. VirusTotal also outperforms the other two when it comes to providing fresher and more complete intelligence, while AlienVault provides the most relevant information in terms of Structured Threat Information eXpression (STIX) 2.1 objects.}, archive = {J_ACCESS}, author = {Asad Ali and Ren-Hung Hwang and Ying-Dar Lin}, doi = {10.1109/ACCESS.2025.3606477}, journal = {IEEE Access}, pages = {157863-157876}, shortjournal = {IEEE Access}, title = {Evaluating cyber threat intelligence: Accuracy, completeness, relevance, and freshness}, volume = {13}, year = {2025}, }
• (2025). Multi-sensor wearable-based sleep stage classification using federated learning for enhanced privacy. ACCESS, 13, 157842-157862. (www)

  @article{ , abstract = {Sleep is an essential aspect of human health, and Obstructive Sleep Apnea (OSA) is a serious condition that is frequently unidentified because traditional Polysomnography (PSG) is costly and available only to a small number of patients. To address these limitations, this study develops and implements a Federated learning ensemble technique for privacy-preserving, sleep stage analysis with wearable devices. The framework adapts Random Forest classifiers for local client-side training and Logistic Regression for global aggregation of models, with a high level of classification accuracy and user data privacy. A principal approach in this methodology is the fusion of Blood Volume Pulse (BVP), Electrodermal Activity (EDA), heart rate, and triaxial accelerometry data for the assessment of sleep dynamics. The proposed approach highlights the possibility of developing low-cost, privacy-preserving health monitoring systems for multiple applications that often face obstacles such as data heterogeneity and resource limitations. This work provides support in progressing wearable sleep technologies and helps in the creation of effective, reliable applications in the healthcare industry.}, archive = {J_ACCESS}, author = {Asad Ali and Huang Jianjun and Ayesha Jabbar and Muhammad Kashif Jabbar}, doi = {10.1109/ACCESS.2025.3607720}, journal = {IEEE Access}, pages = {157842-157862}, shortjournal = {IEEE Access}, title = {Multi-sensor wearable-based sleep stage classification using federated learning for enhanced privacy}, volume = {13}, year = {2025}, }
• (2025). The use of artificial intelligence in supply chain management: Systematic literature review and future research directions. ACCESS, 13, 157828-157841. (www)

  @article{ , abstract = {This paper presents a systematic review of studies related to Artificial Intelligence (AI) and Machine Learning (ML) applications in Supply Chain Management (SCM). Our systematic search studied 109 journal articles published between 2007 and 2025. It was presented definitions for AI, ML, and SCM and a bibliometric analysis was carried out to examine trends and patterns in the existing literature. The 109 articles were grouped into five clusters based on keywords and abstracts, identifying promising research areas and proposes directions for future investigations. The current literature on AI and ML in SCM is expanding, with an increasing number of publications and growing diversity in applications, although significant gaps remain warranting attention. Various AI and ML techniques, including supervised learning algorithms, neural networks, and optimization techniques, have been studied in scenarios such as demand forecasting, inventory management, and risk analysis. Additionally, AI and ML methods are expected to gain broader acceptance in SCM scenarios, such as, application of Generative AI (GenAI) and Large Multimodal Models (LMMs); Quantum ML techniques to handle with large SCM data; to implement SHAP (SHapley Additive exPlanations) for demand forecasting and fraud detection; and to use Generative AI models (like, $\mathrm{GPT-4}$ ) to generate synthetic supply chain scenarios.}, archive = {J_ACCESS}, author = {Ana Claudia Andrade Ferreira and Matheus Brendon Francisco and Alexandre Ferreira De Pinho}, doi = {10.1109/ACCESS.2025.3603866}, journal = {IEEE Access}, pages = {157828-157841}, shortjournal = {IEEE Access}, title = {The use of artificial intelligence in supply chain management: Systematic literature review and future research directions}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv8 framework for precise small object detection in UAV imagery. ACCESS, 13, 157811-157827. (www)

  @article{ , abstract = {The rapid advancement of UAV technology is accelerating the intelligent transformation of object detection methods. UAV applications now span both civilian and military sectors, playing crucial roles in scenarios such as aerial surveying, disaster response, and security monitoring. However, UAV image detection faces numerous challenges: targets are affected by flight altitude and viewing angle, leading to significant scale variations; dynamic flight results in constantly changing spatial layouts and complex backgrounds—such as dense vegetation and building textures—often weaken or obscure object features, making detection more difficult. To tackle these issues, we present a novel YOLOv8-based model tailored for small object detection in UAV images. In response to the prevalence of small targets in UAV images, the model enhances the multi-scale network structure and introduces the Large Separable Kernel Attention-enhanced Spatial Pyramid Pooling Fast Layer (SPPF-LSKA), improving the model’s capability to extract features from and respond to small objects. Furthermore, the introduction of the Minimum Point Distance IoU (MPDIoU) and Soft Non-Maximum Suppression (Soft-NMS) enables effective detection of overlapping and occluded targets. Experimental results on the VisDrone2019 dataset show that, compared with YOLOv8, the improved model increases mAP@0.5 by 7.6%, reduces parameter count by 23%, and has a model size of just 5MB. Compared with other current algorithms, the enhanced model delivers the best detection performance, highlighting the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Juxing Di and Kaikai Xi and Huichang Niu and Xiawei Wu and Yang Yang}, doi = {10.1109/ACCESS.2025.3604772}, journal = {IEEE Access}, pages = {157811-157827}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv8 framework for precise small object detection in UAV imagery}, volume = {13}, year = {2025}, }
• (2025). Tracing components in e-supply chain at open marketplace using lightweight consensus and semi-fungible tokens. ACCESS, 13, 157789-157810. (www)

  @article{ , abstract = {In the present era, blockchain technology has been widely used to facilitate transparency and mitigate issues related to counterfeiting and piracy in the electronic supply chain (eSC). However, present state-of-the-art solutions do not provide sufficient information about tracking of the components in the eSC to users, who do not know about the technicalities of blockchain. We provide an approach to this using a token-based solution. Moreover, the existing models are expensive in terms of time, power, and energy, which we try to reduce by formulating a lightweight consensus algorithm. For this, we use a modified byzantine fault-tolerant mechanism that is more secure, less power-consuming than the conventional RAFT consensus strategy, which most of the existing works adhere to. Finally, we introduce an open marketplace having a public possession record. This provides a one-stop solution for all queries of the users, related to the e-supply chain and its products. Such type of informative open marketplace is also not focused on existing works. For enacting these, we adhere to semi-fungible tokens, which are unique identification codes that are stored in a blockchain, and transferred between users via the blockchain to confirm ownership and ensure traceability. Experimentation has been performed on Hyperledger Fabric. During experimentation and result analysis, we obtain higher stabilization and fault tolerance than existing strategies. Additionally, we observe that fewer communication bits are required in our strategy, which implies that fewer messages need to be transferred between the nodes and an increase in throughput and reduction of latency of the network is obtained. This is also the first provenance framework to combine on-chain tokenized lot management with off-chain regulatory audit trails and validated energy-performance trade-offs.}, archive = {J_ACCESS}, author = {Projat Banerjee and Krishnendu Guha}, doi = {10.1109/ACCESS.2025.3599793}, journal = {IEEE Access}, pages = {157789-157810}, shortjournal = {IEEE Access}, title = {Tracing components in e-supply chain at open marketplace using lightweight consensus and semi-fungible tokens}, volume = {13}, year = {2025}, }
• (2025). Dynamic delegation of behavior trees to enhance cooperation in robot teams. ACCESS, 13, 157780-157788. (www)

  @article{ , abstract = {This research introduces a novel approach to enhance cooperation among robots by collaboratively executing Behavior Trees (BTs). A new node, the DelegateActionNode, allows task delegation as subtrees, while a shared virtual memory (blackboard) enables efficient coordination. This method supports distributed behavior execution, even with potential connectivity loss. Experiments with real robots demonstrate effective virtual memory sharing, robust performance under stress, and 100% success in BT delegation. The approach shows significant potential for advancing multi-robot systems and their cooperative task capabilities.}, archive = {J_ACCESS}, author = {Rodrigo Pérez-Rodríguez and José Miguel Guerrero Hernández and Juan Diego Peña-Narvaez and Francisco Miguel Moreno and Francisco Martín-Rico}, doi = {10.1109/ACCESS.2025.3605797}, journal = {IEEE Access}, pages = {157780-157788}, shortjournal = {IEEE Access}, title = {Dynamic delegation of behavior trees to enhance cooperation in robot teams}, volume = {13}, year = {2025}, }
• (2025). Knowledge distillation-driven 3D convolutional neural networks for efficient and robust indoor localization. ACCESS, 13, 157764-157779. (www)

  @article{ , abstract = {Deep learning models have gained prominence in AI research due to their exceptional feature extraction capabilities. In fingerprint-based indoor localization, 1D and 2D convolutional neural networks (CNNs) are frequently utilized for their superior performance and minimal hardware requirements. However, these models primarily extract spatial features and often exhibit degraded real-time performance due to signal inconsistencies caused by shadowing, fading, and multipath effects. To address this, incorporating temporal variations through 3D CNNs can enhance outcomes, albeit with increased computational costs. This study proposes a knowledge distillation (KD) approach using 3D CNNs for indoor localization, to bridge the computational and performance gaps. The proposed KD framework employs a complex teacher model (TM) to train two simpler student models A (SM-A) and B (SM-B). We employ both response-based and feature-based KD methods to optimize the student models’ weights. The proposed techniques are evaluated using the UJILIB dataset collected from two floors of the University of Jaume I library. To enhance model performance, data augmentation is applied, yielding substantial improvements in test accuracy of 10.56% for TM, 16.19% for SM-A, and 16.93% for SM-B. The application of KD results in accuracy improvements of 5.45% for SM-A and 3.35% for SM-B, consequently reducing their average positioning errors (APEs) from 1.070 m and 1.030 m to 0.840 m and 0.944 m, respectively. Compared to KD-based SM-A, TM shows only 3.30% higher positioning accuracy, but with a 65.14% increase in computational complexity. Notably, the KD-enhanced SM-A also achieves a significantly lower APE of 0.84 m compared to the 2D CNN of 1.72 m.}, archive = {J_ACCESS}, author = {Muhammad Rizwan and Yin Hoe Ng and Hin-Yong Wong and Chee Keong Tan}, doi = {10.1109/ACCESS.2025.3602462}, journal = {IEEE Access}, pages = {157764-157779}, shortjournal = {IEEE Access}, title = {Knowledge distillation-driven 3D convolutional neural networks for efficient and robust indoor localization}, volume = {13}, year = {2025}, }
• (2025). A barrier certificate-based simplex architecture for systems with approximate and hybrid dynamics. ACCESS, 13, 157742-157763. (www)

  @article{ , abstract = {This paper presents Barrier-based Simplex (Bb-Simplex), a new, provably correct design for runtime assurance of continuous dynamic systems. Bb-Simplex is centered around the Simplex control architecture, which consists of a high-performance advanced controller that is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We also propose extensions to Bb-Simplex to enable its use in hybrid systems, which have multiple modes each with its own dynamics, and to support its use when only approximate dynamics (not exact dynamics) are available, for both continuous and hybrid dynamic systems. We consider significant applications of Bb-Simplex to microgrids featuring advanced controllers in the form of neural networks trained using reinforcement learning. These microgrids are modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex continuous and hybrid dynamic systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller when only approximate dynamics (with an error bound) are available. Our experiments to evaluate conservativeness of the switching conditions show that switches from an unsafe advanced controller to a baseline controller occur on average only a few control periods before a safety violation would have occurred.}, archive = {J_ACCESS}, author = {Amol Damare and Shouvik Roy and Roshan Sharma and Keith DSouza and Scott A. Smolka and Scott D. Stoller}, doi = {10.1109/ACCESS.2025.3599459}, journal = {IEEE Access}, pages = {157742-157763}, shortjournal = {IEEE Access}, title = {A barrier certificate-based simplex architecture for systems with approximate and hybrid dynamics}, volume = {13}, year = {2025}, }
• (2025). PoBiTag: Toward unobtrusive and customizable tag-based interaction with optical polarization and birefringence. ACCESS, 13, 157730-157741. (www)

  @article{ , abstract = {Traditional tag-based interaction systems often rely on visible codes such as QR codes or barcodes, which can disrupt the visual design of printed media, particularly in content-dense or space-limited environments. This work introduces PoBiTag, a visually unobtrusive tagging system that uses common transparent and birefringent materials (e.g., PET stickers or cellophane tape) as tags. This tag design allows users to embed tags on any surface of interest without compromising their visual aesthetics. To enable accurate and convenient tag scanning, PoBiTag enhances tag visibility based on optical phenomena including polarization and birefringence. It converts a mobile device’s camera and torch into polarized optical components by attaching polarizing films. This enables illumination and capture of tagged surfaces under polarized light, making birefringent regions visually distinguishable from others and thus allowing accurate tag segmentation and identification. Experiments with real-world users demonstrate that PoBiTag significantly reduces tag-scanning time, even on complex or glossy surfaces, while achieving a high tag identification accuracy of 96.5% on average. These results confirm PoBiTag’s practicality for diverse real-world tag-based interaction scenarios, such as interactive books.}, archive = {J_ACCESS}, author = {Seohyeon Park and Seunghun Chae and Jaemin Choi and Hyosu Kim}, doi = {10.1109/ACCESS.2025.3606941}, journal = {IEEE Access}, pages = {157730-157741}, shortjournal = {IEEE Access}, title = {PoBiTag: Toward unobtrusive and customizable tag-based interaction with optical polarization and birefringence}, volume = {13}, year = {2025}, }
• (2025). Creative agents: Simulating the systems model of creativity with generative agents. ACCESS, 13, 157712-157729. (www)

  @article{ , abstract = {With the growing popularity of generative AI for images, video, and music, we witnessed models rapidly improve in quality and performance. However, not much attention is paid towards enabling AI’s ability to “be creative”. We often attribute the quality of “being creative” to an individual or an object, but we believe that countless variables participate in determining what or who is creative, transcending a single entity or artifact. Csikszentmihalyi’s systems model of creativity suggests that creativity is a product of interactions among multiple parts of a society that create, evaluate, and record. In this study, we implemented and simulated Csikszentmihalyi’s systems model of creativity using virtual agents utilizing large language models (LLMs) and text prompts. We conducted experiments in virtual settings where creativity is achieved with the presence of specific characteristics in the artifact. For comparison, the simulations were conducted with two “virtual artists” being 1)in the system, which received feedback from the field, and 2)isolated, which did not. Both agents were compared by analyzing the novelty, which was measured via Creativity Implication Network, and value, quantified through the desired characteristics present in artifacts. Our results suggest that the agents that receive feedback from the field can generate artifacts that are more novel and more valuable, thus more creative, in the framework of the systems model of creativity. Furthermore, the difference becomes more evident when external factors enact changes to the domain.}, archive = {J_ACCESS}, author = {Naomi Imasato and Kazuki Miyazawa and Takayuki Nagai and Takato Horii}, doi = {10.1109/ACCESS.2025.3606498}, journal = {IEEE Access}, pages = {157712-157729}, shortjournal = {IEEE Access}, title = {Creative agents: Simulating the systems model of creativity with generative agents}, volume = {13}, year = {2025}, }
• (2025). Analyzing we-sentences in scientific writing to predict peer review outcomes. ACCESS, 13, 157700-157711. (www)

  @article{ , abstract = {Peer review is one of the mainstay tasks of scientific publishing. Nonetheless, the high volume of submissions, potential bias in peer review, and time dedicated to this process are problems related to publishing. Such challenges have inspired the development of computational methods aimed at automating publishing decisions in the peer review process. In this context, we studied the value of the information within phrases containing first-person plural pronouns (we-sentences) in English benchmark datasets of accepted and rejected scientific papers as a factor to differentiate them. Afterward, the information in these phrases was leveraged from a text classification perspective by applying two approaches: 1) training with we-sentences only and 2) using term weighting schemes to generate a text representation that emphasizes the value of the words in these sentences. Our results confirm that we-sentences express essential information about the content of the paper (e.g., findings, methods, contributions, and conclusions). Therefore, these sentences embed relevant lexical patterns that contribute to determining the final acceptance/rejection decision. Finally, our findings can be helpful in developing tools to assist the review process of research papers or as a source of feedback for authors.}, archive = {J_ACCESS}, author = {Gamaliel A. López-Reyes and Rosa María Ortega-Mendoza and Obed Pérez-Cortes and Ricardo Calderón-Suarez and Félix A. Castro-Espinoza}, doi = {10.1109/ACCESS.2025.3606415}, journal = {IEEE Access}, pages = {157700-157711}, shortjournal = {IEEE Access}, title = {Analyzing we-sentences in scientific writing to predict peer review outcomes}, volume = {13}, year = {2025}, }
• (2025). ISR-FABEL: A landmark-based dataset and multimodal emotion recognition framework for child-robot interaction. ACCESS, 13, 157687-157699. (www)

  @article{ , abstract = {Emotion recognition plays a crucial role in enhancing human-robot interaction (HRI), particularly in contexts involving children where understanding emotional states can improve engagement and therapeutic interventions. Current approaches primarily focus on facial expressions, overlooking the valuable emotional insights provided by body posture. The present study introduces a multimodal framework that integrates both facial and body posture landmarks to capture a more comprehensive representation of emotional expression. This work introduces ISR-FABEL, a new dataset collected from child-robot interactions involving 88 children, including both real-world sessions with the NAO robot and controlled emotion imitation tasks. The proposed architecture incorporates a Gated Recurrent Unit (GRU)-based model for facial emotion recognition, a Random Forest classifier leveraging engineered features from body posture, and a late-fusion strategy to combine both modalities. Results show that the multimodal approach achieves 72.53% classification accuracy, significantly outperforming unimodal models (face-only: 61.54%, body-only: 39.47%), underscoring the complementary nature of facial and bodily cues in emotion recognition. By relying on 2D and 3D landmark data rather than raw visual input, the framework also supports practical deployment in sensitive environments. The resulting dataset and baseline model aim to support future research in affective computing and child-robot interaction.}, archive = {J_ACCESS}, author = {Beatriz Ribeiro and Ana Lopes and Carlos Carona and Urbano J. Nunes}, doi = {10.1109/ACCESS.2025.3607148}, journal = {IEEE Access}, pages = {157687-157699}, shortjournal = {IEEE Access}, title = {ISR-FABEL: A landmark-based dataset and multimodal emotion recognition framework for child-robot interaction}, volume = {13}, year = {2025}, }
• (2025). Lightweight end-to-end patch-based self-attention network for robust image forgery detection. ACCESS, 13, 157674-157686. (www)

  @article{ , abstract = {Image tampering detection and localization have become critical tasks in digital forensics due to the widespread accessibility of powerful image editing tools. We came up with a new Vision Transformer (ViT)-based design that uses patch embedding and multi-head self-attention mechanisms to efficiently find fake documents. This is because we need lightweight but highly accurate detection systems that can model all of a document’s features. Our model uses a modular pipeline made up of a patch embedding layer, four transformer encoder blocks, and a streamlined classification head. The slimmed-down classification head consisted of only 69,793 trainable parameters to keep the computation costs as minimal as possible. Experimental evaluation conducted on four benchmark datasets—CASIA v1, DEFACTO (Splicing), MICC-F2000, and Columbia—demonstrates the superior performance of the proposed model. The model gets an AUC of 0.9712 and an F1 score of 0.9587 on CASIA v1, an almost perfect AUC of 0.9993 and an F1 score of 0.9914 on DEFACTO, a good AUC of 0.8491 and an F1 score of 0.8319 on Columbia, and an exeptional AUC of 0.9804 and an F1 score of 0.9097 on MICC-F2000. The proposed architecture was evaluated against 59 different models and demonstrated its effectiveness, showing its strong potential for practical applications such as detecting and localizing document forgeries.}, archive = {J_ACCESS}, author = {Police Aryan and Rama Muni Reddy Yanamala and Archana Pallakonda and Rayappa David Amar Raj and K. Krishna Prakasha}, doi = {10.1109/ACCESS.2025.3607206}, journal = {IEEE Access}, pages = {157674-157686}, shortjournal = {IEEE Access}, title = {Lightweight end-to-end patch-based self-attention network for robust image forgery detection}, volume = {13}, year = {2025}, }
• (2025). Chest X-ray report generation using abnormality guided vision language model. ACCESS, 13, 157651-157673. (www)

  @article{ , abstract = {Automated radiology report generation is essential for assisting radiologists in managing the growing volume of chest radiographs. Unlike conventional image captioning models, radiology report generation must prioritize abnormality-specific feature extraction over generic image features. However, existing models lack an inherent mechanism to extract clinically significant abnormality features directly from radiographs without relying on external knowledge bases or auxiliary models. Additionally, most existing approaches use a single vision encoder, limiting their ability to capture complementary visual cues essential for accurate abnormality identification. To address these limitations, we introduce META-CXR (Multimodal Expert Tokens-based VLM for Abnormality-Guided Chest X-ray Reporting), a vision-language model (VLM) designed to enhance both abnormality classification and radiology report generation. META-CXR employs a multi-encoder visual backbone that combines CNNs, ViTs, and Swin Transformers to extract diverse and complementary visual features. These multi-encoder features serve as a shared representation and are utilized in two key components of the architecture: the META-Former module, a modified Q-Former designed to fuse heterogeneous encoder outputs into a unified token space for effective report generation, and the Multi-Head Cross Attention Classification (MHCAC) module, which performs multi-class, multi-label abnormality classification, including an explicit “uncertain” category. The identified abnormalities are then integrated into the language modeling process as soft prompts to ensure clinically coherent and diagnosis-aware report generation using a large language model (LLM). META-CXR achieves state-of-the-art performance, demonstrating an F1-score of 0.699 for cross-domain multi-class, multi-label abnormality classification on the CheXpert dataset. Additionally, it achieves strong natural language generation results, with a BERTScore of 0.426 and a METEOR score of 0.173 on the MIMIC-CXR test set. Ablation studies validate the contributions of the multi-encoder backbone, hierarchical classification via MHCAC, the META-Former fusion strategy, and classification-aware report generation. Furthermore, attention map visualizations improve interpretability by highlighting clinically relevant image regions, offering radiologists transparent insights into model decisions. By addressing key challenges in abnormality-specific feature extraction, uncertainty-aware classification, and diagnosis-driven reporting, META-CXR establishes a new benchmark for vision-language models in radiology AI.}, archive = {J_ACCESS}, author = {Dasith Edirisinghe and Wimukthi Nimalsiri and Mahela Hennayake and Dulani Meedeniya and Gilbert Lim}, doi = {10.1109/ACCESS.2025.3606961}, journal = {IEEE Access}, pages = {157651-157673}, shortjournal = {IEEE Access}, title = {Chest X-ray report generation using abnormality guided vision language model}, volume = {13}, year = {2025}, }
• (2025). DySpect-net: Neural network predicting spectrum from non-uniformly sampled data. ACCESS, 13, 157634-157650. (www)

  @article{ , abstract = {Accurate spectral estimation from irregularly sampled signals is a long-standing challenge in signal processing, particularly in biomedical applications. Traditional methods, such as the Fast Fourier Transform (FFT), rely on resampling, which introduces artefacts and computational inefficiencies. To address this, we propose DySpect-Net (Dynamic Spectrum Network), a neural network-based model that directly predicts spectral representations from non-uniformly sampled data, eliminating the need for resampling. Our approach is validated using synthetic datasets that mimic real-world sampling irregularities. Unlike conventional spectral estimation methods, DySpect-Net learns to recognise and reconstruct dominant frequency components dynamically. We introduce FAVE (Frequency Absolute Variance Error), a novel evaluation metric designed to quantify spectral peak alignment accuracy, outperforming conventional metrics such as Mean Squared Error (MSE). Comparative analysis demonstrates that DySpect-Net performs superior spectral estimation, particularly in low-sampling-rate conditions, with fewer distortions than FFT-based approaches. The proposed model has broad applications in biomedical signal analysis, e.g. remote photoplethysmography (rPPG), and other domains where sampling irregularities compromise spectral fidelity. Future research will focus on integrating FAVE as a loss function to enhance spectral reconstruction accuracy.}, archive = {J_ACCESS}, author = {Jana Sturekova and Patrik Kamencay and Peter Sykora and Silvia Suchalova and Daniel Matthes and Mirco Fuchs}, doi = {10.1109/ACCESS.2025.3606956}, journal = {IEEE Access}, pages = {157634-157650}, shortjournal = {IEEE Access}, title = {DySpect-net: Neural network predicting spectrum from non-uniformly sampled data}, volume = {13}, year = {2025}, }
• (2025). Phaseper: A complex-valued transformer for automatic speech recognition. ACCESS, 13, 157621-157633. (www)

  @article{ , abstract = {Recent advances in automatic speech recognition (ASR) have largely focused on real-valued neural network models (e.g., Whisper) that use only the magnitude of the speech signal’s spectrogram, discarding phase information. In this paper, we propose Phaseper, a novel complex-valued Transformer architecture for ASR that explicitly incorporates both magnitude and phase information of the speech signal. By processing complex-valued spectrogram inputs, Phaseper can leverage phase information that is usually ignored in conventional pipelines. We evaluate Phaseper on several speech recognition benchmarks, including LibriSpeech, Common Voice, TED-LIUM 3, and CHiME-6. Our experimental results demonstrate that Phaseper achieves competitive or improved word error rates (WERs) using a smaller training dataset compared to a real-valued Transformer baseline, with notable gains on datasets with noisy or reverberant audio (e.g., CHiME-6). Specifically, on the LibriSpeech dataset, Phaseper achieved a Word Error Rate (WER) of 0.54%, compared to real-valued neural network model’s WER of 2.7%. These findings highlight the importance of complex-valued representations in speech recognition.}, archive = {J_ACCESS}, author = {Delany Ramirez-Delrio and Jose A. Jaramillo-Villegas}, doi = {10.1109/ACCESS.2025.3606844}, journal = {IEEE Access}, pages = {157621-157633}, shortjournal = {IEEE Access}, title = {Phaseper: A complex-valued transformer for automatic speech recognition}, volume = {13}, year = {2025}, }
• (2025). PublicPulse: A social media analysis framework for citizen reactions to government actions. ACCESS, 13, 157600-157620. (www)

  @article{ , abstract = {Social media has grown significantly, attracting more people who spend more time on these platforms and share vast amounts of information about their daily lives. Consequently, governments use such social media to disseminate information about their achievements, updated laws, and decisions. Simultaneously, citizens find social media platforms, such as Facebook, X (previously known as Twitter), and Instagram, appealing virtual spaces for expressing their thoughts and opinions. In brief, social media is becoming an increasingly vital channel of communication between governments and citizens. As a result, analyzing social media data to understand and predict public sentiment in the context of government-related events has become increasingly important. While sentiment analysis methods can be employed to mine sentiment from individual posts, estimating collective public sentiment about an event remains challenging. Specifically, sentiment analysis methods are associated with inaccuracies, and a group of individuals may express contradicting opinions about the same event. To address these challenges, we propose a novel framework named “PublicPulse”, designed to analyze social media posts and their associated metadata. PublicPulse incorporates a set of sentiment analysis methods along with statistical analysis to generate a public sentiment score for a set of posts related to a specific event. The framework was empirically evaluated using several events of interest to the government in Jordan from 2018 to 2022 and demonstrated accurate results when compared with the historical context of governmental actions.}, archive = {J_ACCESS}, author = {Amani Abu Jabal and Abdullah Alfarrarjeh and Reem Shtaiwi and Samer Nofal and Christina B. Class and Ismail Hababeh}, doi = {10.1109/ACCESS.2025.3606907}, journal = {IEEE Access}, pages = {157600-157620}, shortjournal = {IEEE Access}, title = {PublicPulse: A social media analysis framework for citizen reactions to government actions}, volume = {13}, year = {2025}, }
• (2025). EfficientSTNet: A deep learning approach for multi-class DDoS detection. ACCESS, 13, 157587-157599. (www)

  @article{ , abstract = {DDoS attacks are highly destructive, capable of targeting multiple devices simultaneously and posing significant threats to network systems. Therefore, it is increasingly essential to develop effective and reliable detection methods to ensure network security. Although deep learning techniques improve DDoS detection by autonomously learning feature representations, they still face challenges due to the high dimensionality and noise inherent in network traffic data, which impair computational efficiency and real-time responsiveness. Moreover, their limited adaptability to multi-class classification hinders accurate differentiation of specific attack types. This paper proposes a multi-class DDoS attack detection method based on multi-scale feature modeling—EfficientSTNet. The method employs a selective deep autoencoder for feature selection, then uses a convolutional neural network (CNN) to capture hidden spatial features in network data. Subsequently, a self-attention mechanism extracts temporal features and captures contextual dependencies among different features, improving DDoS attack detection accuracy. Finally, a fully connected layer and normalization compute the probability distribution across different classes, enabling multi-class DDoS detection. Additionally, the method integrates convolution decomposition techniques and residual network structures to accelerate model convergence and improve inference speed. The proposed EfficientSTNet model can identify different types of DDoS attacks, achieving an overall classification accuracy of 99.14% and F1-scores of 97.97% or higher across all categories, demonstrating strong practical application potential.}, archive = {J_ACCESS}, author = {Lei Zhang and Yujuan Bai and Tao Xue and Guanhua Feng and Haibin Zhang}, doi = {10.1109/ACCESS.2025.3606644}, journal = {IEEE Access}, pages = {157587-157599}, shortjournal = {IEEE Access}, title = {EfficientSTNet: A deep learning approach for multi-class DDoS detection}, volume = {13}, year = {2025}, }
• (2025). LightEdit: Textual image editing with lightweight stable diffusion. ACCESS, 13, 157570-157586. (www)

  @article{ , abstract = {Text-based image editing using diffusion models has shown remarkable progress, but most approaches rely on large-scale models (Stable Diffusion Models - SDMs) that require significant computational resources. In this work, we propose a lightweight diffusion-based model for textual image editing that balances efficiency and performance. We introduce a specialized U-Net architecture with channel-weight attention mechanisms. The model architecture is optimized for image editing quality (preserving the original image structure while editing the region specified by the prompt) while reducing parameter count. We leverage knowledge distillation with multiple stage-dependent teacher models to inherit the knowledge from a large-scale pre-trained editing model. Our model achieves better results than some other state-of-the-art methods for almost all evaluation metrics on the InstructPix2Pix dataset. Moreover, our proposed UNet is approximately $2\times $ , and $5\times $ (170M parameters) smaller than the model with BK-SDM and Stable Diffusion backbone, respectively. Human evaluations also confirm the superior visual quality of our edited outputs. Code is available at: https://github.com/thuyvuphuong/LightEdit-Textual-Image-Editing-with-Lightweight-Stable-Diffusion-Repository}, archive = {J_ACCESS}, author = {Thuy Phuong Vu and Minhhuy Le and Eiji Kamioka and Phan Xuan Tan}, doi = {10.1109/ACCESS.2025.3606427}, journal = {IEEE Access}, pages = {157570-157586}, shortjournal = {IEEE Access}, title = {LightEdit: Textual image editing with lightweight stable diffusion}, volume = {13}, year = {2025}, }
• (2025). Generalized ansatz for high-accuracy quantum sentiment classification using variational quantum algorithm scheme. ACCESS, 13, 157551-157569. (www)

  @article{ , abstract = {This paper investigates sentiment classification using QNLP within the Variational Quantum Algorithms (VQA) framework, which offers a practical approach for current hardware limitations by optimizing parameterized quantum circuits through classical computation. While the quantum computer is not ready yet for commercialization, the development and testing of quantum algorithms in classical simulations are useful for finding the quantum advantages. Moreover, designing an effective VQA ansatz is a key challenge because it directly influences the model’s expressiveness and trainability. To address this, a new ansatz called the GeneralQC ansatz is proposed by combining the preliminary SimpleSA ansatz with the circuit-centric quantum classifier, aiming to enhance performance by integrating the strengths of both methods. Three instantiations of the GeneralQC ansatz—H-CRx, Rx-CNOT, and Rx-CRz—are explored under the SimpleSA strategy of defining noun word boxes as fixed identity gates and are compared with both the baseline SimpleSA ansatz and versions with noun parameterization. The findings reveal that the Rx-CNOT and Rx-CRz models with non-parameterized nouns significantly outperform the SimpleSA ansatz, achieving an accuracy of 96.92%.}, archive = {J_ACCESS}, author = {Muhammad Rifat Abiwardani and Fariska Zakhralativa Ruskanda and Infall Syafalni and Rahmat Mulyawan and Akio Higo}, doi = {10.1109/ACCESS.2025.3596031}, journal = {IEEE Access}, pages = {157551-157569}, shortjournal = {IEEE Access}, title = {Generalized ansatz for high-accuracy quantum sentiment classification using variational quantum algorithm scheme}, volume = {13}, year = {2025}, }
• (2025). Reducing false positives in building fire detection systems via multiple metal oxide sensors. ACCESS, 13, 157537-157550. (www)

  @article{ , abstract = {Early and accurate fire detection in indoor environments is essential to ensuring public safety and minimising casualties and property loss. However, conventional fire alarm systems often generate false alarms due to non-fire aerosols such as water vapour and cigarette smoke. In this study, to address this issue, a sensor-based fire detection framework incorporating machine learning techniques has been proposed. A gas sensor matrix composed of multiple BME688 metal oxide sensors was used to collect air quality data representing five environmental conditions, including clean air, water vapour, e-cigarette aerosol, cigarette smoke, and real fire smoke. To enhance classification performance, the collected data have been pre-processed using low-pass filtering and wavelet transformation techniques. Machine learning approaches frequently used in the literature have been applied to classify sensor data. The results indicate that one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) approaches are superior to other models. These models have achieved perfect precision and recall scores (1.00) for the cigarette smoke and fire smoke classes, demonstrating their ability to reduce false alarms without compromising sensitivity to real fire events. These findings have confirmed the effectiveness of combining gas sensor arrays with deep learning in developing more reliable and context-aware fire detection systems.}, archive = {J_ACCESS}, author = {Musa Milli and Mehmet Milli}, doi = {10.1109/ACCESS.2025.3606584}, journal = {IEEE Access}, pages = {157537-157550}, shortjournal = {IEEE Access}, title = {Reducing false positives in building fire detection systems via multiple metal oxide sensors}, volume = {13}, year = {2025}, }
• (2025). FeatuReBeam: Efficient beamforming for high-frame ultrasound imaging using adaptive feature reuse with (m,k)-guarantee. ACCESS, 13, 157526-157536. (www)

  @article{ , abstract = {Existing tissue adaptive deep learning-based ultrasound image reconstruction models are computationally intensive, and achieving high frame rates particularly in the application of B-mode imaging on resource-constrained devices is challenging. Based on a traditional firm guarantee real-time model, i.e., the (m,k)-model, we propose a methodology that can take advantage of the similarity among feature maps generated by continuous frames to reduce the redundant computations and, thus, significantly improve the throughput and energy efficiency. A comprehensive evaluation on in-vitro and in-vivo data using U-Net and GoogLeNet-based beamformers demonstrated over a 50% reduction in computational complexity while preserving the original image quality. The implementation of the proposed methodology on the Xilinx ZU7EV FPGA achieved latency improvement of over 45% and 46% for the U-Net and GoogLeNet models, respectively. Additionally, when compared to state-of-the-art filter pruning approaches, including magnitude-based and feature map-based filter pruning, the proposed methodology outperformed both in terms of computational reduction and output image quality, achieving over a 14% improvement on the Xilinx ZU7EV FPGA.}, archive = {J_ACCESS}, author = {Abdul Rahoof and Vivek Chaturvedi and Mahesh Raveendranatha Panicker and Muhammad Shafique and Gang Quan}, doi = {10.1109/ACCESS.2025.3604479}, journal = {IEEE Access}, pages = {157526-157536}, shortjournal = {IEEE Access}, title = {FeatuReBeam: Efficient beamforming for high-frame ultrasound imaging using adaptive feature reuse with (m,k)-guarantee}, volume = {13}, year = {2025}, }
• (2025). Hybrid OCSSA-VMD and optimized deep learning networks for runoff forecasting. ACCESS, 13, 157511-157525. (www)

  @article{ , abstract = {To improve accuracy and address the non-linearity and non-stationarity in monthly runoff forecasting, this paper proposes a method that integrates intelligent optimization techniques with Deep Learning (DL) network. The Osprey-Cauchy-Sparrow Search Algorithm (OCSSA) is employed to fine-tune the parameters of Variational Mode Decomposition (VMD), which is utilized to break down the original runoff data into multiple Intrinsic Mode Functions (IMFs). A deep learning framework, combining Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Attention Mechanisms (AM), was developed, referred to as CNN-BiGRU-AM. Prior to feeding the IMFs into the network, the hyperparameters of models were optimized by using the Northern Goshawk Optimization (NGO) algorithm. Empirical analysis using 45 years of monthly runoff data from Hankou and Yichang Station in Hubei Province demonstrates that the proposed model achieves lower forecasting errors and higher accuracy compared to several commonly used models, offering a robust scientific foundation for runoff forecasting and water resource management.}, archive = {J_ACCESS}, author = {Hong Ma and Mohd Shareduwan Mohd Kasihmuddin and Mohd. Asyraf Mansor and Siti Zulaikha Mohd Jamaludin and Muhammad Fadhil Marsani and Farid Zamani Che Rose}, doi = {10.1109/ACCESS.2025.3602457}, journal = {IEEE Access}, pages = {157511-157525}, shortjournal = {IEEE Access}, title = {Hybrid OCSSA-VMD and optimized deep learning networks for runoff forecasting}, volume = {13}, year = {2025}, }
• (2025). Multimodal fake news classification in tamil using fact-checked social media content and cost-sensitive learning. ACCESS, 13, 157477-157510. (www)

  @article{ , abstract = {The exponential rise in user-generated social media content has contributed significantly to the spread of fake news. Regional language communities are particularly vulnerable because these fabricated narratives often integrate manipulated text, images, and audio, which we call multimodal fake news. Tamil language fake news detection presents unique challenges owing to the scarcity of multimodal datasets and the limited adaptation of state-of-the-art methods. This study proposes a transformer-based framework for multimodal fake news classification. It uses Bidirectional Encoder Representations from Transformers (BERT) because of its proven cross-lingual transfer capabilities and contextual understanding advantages in low-resource settings. We introduce TamilFacts, the first comprehensive multimodal dataset comprising 7,934 fact-checked samples, including text, images and speech, curated from Tamil fact-checking websites. The proposed approach integrates contextual embeddings for text, robust image feature extraction methods enhanced by error analysis, and denoised speech representations to capture multimodal context effectively. The experimental results demonstrate that transformer-based architectures efficiently classify fake news with an optimal feature representation and achieve an F1-score of 85%. Additionally, we integrated cost-sensitive learning to mitigate class imbalance by assigning higher weights to minority classes, thereby improving fairness and achieving an accuracy of 76.08%. This study establishes baseline benchmarks for Tamil multimodal fake news detection and provides methodological insights applicable to other regional Indian languages.}, archive = {J_ACCESS}, author = {Meclin A. Francis and Ayswarya R. Kurup and B. Premjith and Bharathi Raja Chakravarthi}, doi = {10.1109/ACCESS.2025.3606585}, journal = {IEEE Access}, pages = {157477-157510}, shortjournal = {IEEE Access}, title = {Multimodal fake news classification in tamil using fact-checked social media content and cost-sensitive learning}, volume = {13}, year = {2025}, }
• (2025). Fuzzy-DogNet: A lightweight and interpretable deep unstructured neuro-fuzzy system based on band-pass filters for traffic sign recognition. ACCESS, 13, 157463-157476. (www)

  @article{ , abstract = {Traffic Sign Recognition (TSR) plays a critical role in autonomous driving and advanced driver-assistance systems, necessitating accurate and efficient recognition under real-world constraints. This paper introduces Fuzzy-DoGNet, a novel lightweight architecture designed to enhance TSR performance while addressing challenges such as environmental variability, sensor noise, and resource limitations. The proposed model integrates a learnable Difference-of-Gaussian (DoG) module for adaptive, edge-enhanced feature extraction, a multi-scale feature pyramid to achieve scale invariance, and a feature-map attention mechanism for effective feature aggregation. These components feed into an Unstructured Neuro-Fuzzy Inference System (UNFIS), enabling robust decision-making under uncertainty. By emphasizing structural elements such as edges and line-based features, Fuzzy-DoGNet enhances recognition robustness in challenging conditions. Experimental evaluations demonstrate that the proposed architecture achieves competitive accuracy with only 1.9 million parameters, outperforming several state-of-the-art models in terms of efficiency and robustness, thereby making it suitable for real-time deployment on resource-constrained platforms.}, archive = {J_ACCESS}, author = {Sadra Berangi and Mohammad Mahdi Parchamijalal and Armin Salimi-Badr}, doi = {10.1109/ACCESS.2025.3606963}, journal = {IEEE Access}, pages = {157463-157476}, shortjournal = {IEEE Access}, title = {Fuzzy-DogNet: A lightweight and interpretable deep unstructured neuro-fuzzy system based on band-pass filters for traffic sign recognition}, volume = {13}, year = {2025}, }
• (2025). Polynomial function approximations with leading integer coefficients for efficient encrypted implementations. ACCESS, 13, 157455-157462. (www)

  @article{ , abstract = {Computations on encrypted data can, in principle, be performed using homomorphic encryption. However, due to certain limitations, only algorithms based on polynomial functions can be efficiently implemented in an encrypted setting. Consequently, polynomial approximations of non-polynomial functions are essential for efficient encrypted computations. In particular, low- to moderate-degree polynomial approximations of activation functions in neural networks are of special interest. We show that the accuracy of encryption-friendly approximations can be improved through a simple yet effective extension of state-of-the-art methods. Specifically, we show that enforcing a leading integer coefficient enables the use of polynomials of one degree higher than all existing approaches. Incorporating this novel integer constraint into classical regression problems initially leads to mixed-integer programs (MIPs). However, we develop tailored solution schemes that avoid MIP solving. Using these schemes, we compute new polynomial approximations for various test cases and demonstrate the effectiveness of our method compared to existing approaches.}, archive = {J_ACCESS}, author = {Dieter Teichrib and Janis Adamek and Philipp Binfet and Moritz Schulze Darup}, doi = {10.1109/ACCESS.2025.3606013}, journal = {IEEE Access}, pages = {157455-157462}, shortjournal = {IEEE Access}, title = {Polynomial function approximations with leading integer coefficients for efficient encrypted implementations}, volume = {13}, year = {2025}, }
• (2025). Toward few-shot multimodal voice pathology classification on an embedded edge device. ACCESS, 13, 157442-157454. (www)

  @article{ , abstract = {Voice pathologies hinder effective communication, often leading to social isolation and reduced quality of life. Automatic voice pathology classification enables identification of vocal disorders by analyzing acoustic signals from the affected individuals. However, existing methods primarily rely on machine learning approaches such as Support Vector Machines (SVMs), which often struggle to generalize effectively to unseen pathologies. Our study introduces PathoEAI, a deep learning-based multimodal voice pathology classification framework using few-shot learning designed for edge deployment, addressing the growing demand for accessible digital healthcare solutions. PathoEAI classifies five pathological voice categories alongside the healthy voice, resulting in a six-class classification task using voice and electroglottographic (EGG) signals, which are then fused using a temporal cross-attention block. It further employs a metric-based few-shot learning approach using prototypical networks, that mitigates overfitting by learning a robust representation space where each class is represented by a prototype, enabling distance-based classification and improved generalization to unseen categories with only a few examples. Evaluations on the publicly available Saarbrucken Voice Database (SVD) under varying shot settings achieve 72.41% accuracy in a 2-way classification task with 10 shots. Deployment on the NVIDIA Jetson Xavier using TensorRT optimizations results in approximately 3.61 times faster inference compared to a standard server. The proposed framework supports personalized and accessible healthcare through real-time, non-invasive, and early diagnosis of voice pathologies, thereby improving overall quality of life.}, archive = {J_ACCESS}, author = {Srinidhi Kanagachalam and Deok-Hwan Kim}, doi = {10.1109/ACCESS.2025.3606943}, journal = {IEEE Access}, pages = {157442-157454}, shortjournal = {IEEE Access}, title = {Toward few-shot multimodal voice pathology classification on an embedded edge device}, volume = {13}, year = {2025}, }
• (2025). QoE-driven adaptive video streaming: Architectures, techniques, and future research challenges toward 6G networks. ACCESS, 13, 157408-157441. (www)

  @article{ , abstract = {The paper provides a survey of architectures and techniques for QoE-driven adaptive video streaming services based on two (2) classifications: client-based video streaming, and delivery-based video rate adaptation. The paper presents in-depth review of QoE- driven network softwarization and virtualization approaches using SDN, NFV, and MEC, leveraging AI/ML techniques and cloud/edge computing architectures. Additionally, the paper provides a review of QoE-driven video streaming in various aspects including 6G-based Metaverse for Multi-User Extended Reality (MER), holographic telepresence, personalized media, Internet of Senses (IoS), Industrial Internet of Things (IIoT) and video coding compression standards. Moreover, the paper provide highlights on multimedia streaming in new verticals and next-generation mobile technologies by putting emphasis in 6G and beyond factories, education, social and entertainment, automotive and healthcare. Finally, the paper present concrete challenges and future research directions in emerging applications, video standards and new business cases towards 6G networks. This paper aims to guide and inspire the multimedia and networking research community both in academia and industry toward developing innovative solutions for monitoring, managing, and optimizing performance in future 6G networks.}, archive = {J_ACCESS}, author = {Moner Alsader and Alcardo Alex Barakabitze and Is-Haka Mkwawa}, doi = {10.1109/ACCESS.2025.3597058}, journal = {IEEE Access}, pages = {157408-157441}, shortjournal = {IEEE Access}, title = {QoE-driven adaptive video streaming: Architectures, techniques, and future research challenges toward 6G networks}, volume = {13}, year = {2025}, }
• (2025). Robust speaker recognition using perceptual stationary wavelet coefficients and prosodic feature in noisy conditions. ACCESS, 13, 157396-157407. (www)

  @article{ , abstract = {Wavelet-based front-ends have been extensively utilized in speech processing systems, particularly for recognizing speech and speakers, and have significantly improved their performance. This work proposes a Robust Speaker Recognition (RSR) system based on a novel Perceptual Stationary Wavelet Coefficients PSWC and prosodic pitch feature for noisy environments. The PSWC approach combines a stationary wavelet packet, characterized by frequency band spacing similar to psychoacoustics Equivalent Rectangular Bandwidth scale, with the Implicit Weiner filtering (IWF). The use of IWF aims to minimize residual noise in each of 24 stationary wavelet sub-bands obtained adopted packet decomposition. The proposed RSR system’s performance utilizing PSWC features and its combination with pitch feature, which is estimated using Pitch Estimation Filter with Amplitude Compression technique, are evaluated under stationary and non-stationary noise conditions with heigh noise levels using the TIMIT dataset and Aurora noises. It is compared to that obtained using five traditional features Human Factor cepstral coefficients (HFCC), Inverse Mel Frequency Cepstral Coefficients (IMFCC), Bark Frequency Cepstral Coefficients (BFCC), Mel Frequency Cepstral Coefficients (MFCC), and Perceptual Linear Prediction (PLP) in terms of RSR accuracy and precision values and two additional measurements F1-score and recall. The obtained results using Gaussian Mixture Model-Universal Background Model demonstrate the superiority of our RSR system using the proposed combination of PSWC with pitch in various noisy cases. For instance, under Exhibition noise at 9 dB SNR, our RSR system achieved recognition accuracies of 58.73% using the proposed feature fusion (PSWC + pitch) and 47.54% using PSWC alone, while using IMFCC, BFCC, MFCC, PLP, and HFCC yielded accuracies of 32.06%, 13.73%, 16.83%, 17.86%, and 15.40%, respectively.}, archive = {J_ACCESS}, author = {Ibrahim Missaoui and Zied Lachiri}, doi = {10.1109/ACCESS.2025.3607263}, journal = {IEEE Access}, pages = {157396-157407}, shortjournal = {IEEE Access}, title = {Robust speaker recognition using perceptual stationary wavelet coefficients and prosodic feature in noisy conditions}, volume = {13}, year = {2025}, }
• (2025). HiMSELF: A hierarchical misbehavior classification with sequence embedding by latent features in vehicular ad-hoc networks. ACCESS, 13, 157382-157395. (www)

  @article{ , abstract = {Vehicular ad hoc network (VANET) enables vehicles, infrastructure, and pedestrians to exchange information, enhancing safety, efficiency, and intelligent decision-making. VANETs operate by exchanging basic safety messages (BSMs) between vehicles and infrastructure. However, as network connectivity increases, so does the risk of malfunctions or malicious data injections that can cause severe disruptions. Accordingly, a misbehavior detection system (MDS) to detect and analyze abnormal data patterns and potential attack behaviors has become essential. In particular, identifying misbehavior classification based on specific abnormal types is essential for establishing a robust and comprehensive VANET security framework. To address these challenges, a hierarchical misbehavior classification method with sequence embedding by latent features, referred to as HiMSELF, is proposed. HiMSELF, defined as a hierarchical classification system (HCS), employs a trained deep learning (DL) model to classify various misbehavior types, embed their intrinsic representations, and perform hierarchical clustering. The resulting structure reflects the intrinsic relationships among the types and serves as the basis for constructing the HCS. The HiMSELF classification pipeline operates in two sequential stages. First, the system employs BSM sequence data as input and grouped into broader, higher-level categories associated with each sample. Subsequently, it identifies the specific misbehavior type within the corresponding category. In experiments classifying 19 misbehavior types, HiMSELF achieved an average F1-score of 0.9918, outperforming existing approaches and demonstrating its potential to underpin reliable security mechanisms in cooperative intelligent transportation systems (C-ITS).}, archive = {J_ACCESS}, author = {Mingyu Kim and Dae Hyun Yum and Jaehee Jung}, doi = {10.1109/ACCESS.2025.3606575}, journal = {IEEE Access}, pages = {157382-157395}, shortjournal = {IEEE Access}, title = {HiMSELF: A hierarchical misbehavior classification with sequence embedding by latent features in vehicular ad-hoc networks}, volume = {13}, year = {2025}, }
• (2025). Fairness-aware race and ethnicity detection from names. ACCESS, 13, 157365-157381. (www)

  @article{ , abstract = {Inferring race and ethnicity from personal names can provide crucial insights for research, policy, and business. Machine learning offers a powerful tool for this inference. However, the potential biases and unfairness inherent in predictive models used for this purpose have not been explored in the literature. This paper seeks to bridge this gap by introducing Fairness-aware Race and Ethnicity Detection (FRED). We propose a novel machine learning architecture to address the unfairness by incorporating a fairness regularization term in the loss function. Furthermore, we explore the application of large language models (LLMs) for race and ethnicity prediction and tackle fairness concerns through a data pre-processing approach. We conduct comprehensive experiments on two real-world datasets with baseline comparison. The results demonstrate the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Xiaoxiao Shang and Zhiyuan Peng and Subramaniam Vincent and Yi Fang}, doi = {10.1109/ACCESS.2025.3606490}, journal = {IEEE Access}, pages = {157365-157381}, shortjournal = {IEEE Access}, title = {Fairness-aware race and ethnicity detection from names}, volume = {13}, year = {2025}, }
• (2025). Optimal performance-preserving control of active suspension considering parameter uncertainty and genetic algorithm optimization. ACCESS, 13, 157352-157364. (www)

  @article{ , abstract = {Active suspension, as the main trend of future automobile suspension systems, is receiving more and more attention and research. For the control problem of active suspension, the most difficult one is the multi-objective optimization of ride comfort and handling stability. As the most important evaluation indexes of automobile suspension system design, they are conflicting and contradictory. The parameter changes of active suspension under bad road conditions are difficult to predict. This leads to a significant decline in vehicle performance and the inability to meet ride smoothness and handling stability requirements under different road conditions. To address this problem, this paper establishes a four-degree-of-freedom half-vehicle model considering the uncertain parameters of suspension stiffness and damping based on linear fractional transformation and norm boundedness principle. Then, the suspension hard constraint condition is defined as $H_{\infty }$ performance indicator, and the suspension control target is defined as $H_{2}$ performance indicator. An optimal performance-preserving robust controller is designed based on the Lyapunov stability theory. The linear matrix inequality (LMI) is used to transform the vehicle’s comprehensive performance problem into a convex optimization problem. Finally, the genetic algorithm is used to optimize the performance indicators under the constraints to obtain the global optimal control law. The simulation results show that the body smoothness indicator of the controller proposed has been greatly improved. Only the suspension dynamic load has deteriorated relatively, but it is still within the suspension ground contact constraint range. It proves that the proposed algorithm can improve the vehicle’s ride smoothness and handling stability and adapt well to suspension parameter perturbations.}, archive = {J_ACCESS}, author = {Jiankang Lu and Zhengdian Xu and Renrui Wang and Changdong Wan}, doi = {10.1109/ACCESS.2025.3606564}, journal = {IEEE Access}, pages = {157352-157364}, shortjournal = {IEEE Access}, title = {Optimal performance-preserving control of active suspension considering parameter uncertainty and genetic algorithm optimization}, volume = {13}, year = {2025}, }
• (2025). Entropy–Mean–Upper partial Deviation–Absolute deviation portfolio problem and its wasserstein distributionally robust counterpart. ACCESS, 13, 157336-157351. (www)

  @article{ , abstract = {This paper proposes an Entropy–Mean–Upper partial deviation–Absolute Deviation (EMUAD) portfolio problem, introducing entropy to reduce investment risk and enhance portfolio diversification while simultaneously considering metrics such as mean return, upper partial deviation, and absolute deviation. To overcome the instability of empirical distributions, EMUAD is extended into a distributionally robust optimization portfolio problem (DR-EMUAD), which constructs a Wasserstein ambiguity set centered on the empirical distribution using the Wasserstein distance. Moreover, by applying suitable approximation methods, we obtained a convex version of DR-EMUAD. The Robust Wasserstein Profile Inference (RWPI) method is utilized to effectively determine the radius of the Wasserstein ambiguity set. To ensure practical applicability, we embed a comprehensive set of real-world constraints–industry exposure limits, position size bounds, an explicit turnover cap, and a liquidity—driven transaction-cost budget. Numerical experiments demonstrate that both EMUAD and DR-EMUAD outperform traditional portfolio problems, with DR-EMUAD particularly effective at controlling return volatility, significantly enhancing robustness and practical utility in investment decision-making.}, archive = {J_ACCESS}, author = {Haonan Wang and Mingyang Fan and Bowen Liu}, doi = {10.1109/ACCESS.2025.3605364}, journal = {IEEE Access}, pages = {157336-157351}, shortjournal = {IEEE Access}, title = {Entropy–Mean–Upper partial Deviation–Absolute deviation portfolio problem and its wasserstein distributionally robust counterpart}, volume = {13}, year = {2025}, }
• (2025). GAM-attention enhanced DenseNet121 with bayesian optimization for accurate ovarian cancer diagnosis in MRI images. ACCESS, 13, 157317-157335. (www)

  @article{ , abstract = {Ovarian cancer is the most common cause of death among gynecological malignancies, due to its multiple subtypes that pose significant diagnostic challenges. Generally, improving classification accuracy leads to earlier diagnosis and more successful treatment outcomes. This study presents a new methodology based on the DenseNet121 model architecture with the integration of the GAM Attention module, which combines ductal and spatial attention mechanisms. This module aims to improve the model’s ability to focus on the most relevant regions and features in ovarian MRI images, thereby improving classification accuracy. Additionally, a Bayesian optimization algorithm is used to find the best set of hyperparameters to train the proposed model. The performance of the proposed model was tested on the KAUH-OCM ovarian cancer MRI dataset obtained from King Abdullah University Hospital (KAUH). The results demonstrated that the proposed model outperformed previously trained models (DenseNet121, VGG16, VGG19, ResNet50, and InceptionV3) with an accuracy of 91.84%, a specificity of 96.76%, and an F1 score of 91.78%. Furthermore, the proposed model was also tested on the KAUH-OCCTD ovarian cancer CT dataset and the polycystic ovary syndrome (PCOS) ultrasound dataset, achieving high accuracy rates of 97.27% and 100%, respectively. These results demonstrate the potential of the proposed model to revolutionize ovarian cancer diagnosis with high accuracy. These findings may inform gynecologists in developing more effective treatment strategies for these patients.}, archive = {J_ACCESS}, author = {Mohammad Amin and Salem Al-Hatamleh and Amer Mahmoud Sindiani and Hamad Yahia Abu Mhanna and Rola Madain and Duha Anakreh and Amal Alishwait and Hanan Fawaz Akhdar and Sitah Alanazi and Kholoud J. Sandougah}, doi = {10.1109/ACCESS.2025.3606260}, journal = {IEEE Access}, pages = {157317-157335}, shortjournal = {IEEE Access}, title = {GAM-attention enhanced DenseNet121 with bayesian optimization for accurate ovarian cancer diagnosis in MRI images}, volume = {13}, year = {2025}, }
• (2025). Deadline miss rate scheduling in DAG considering probabilistic execution time. ACCESS, 13, 157304-157316. (www)

  @article{ , abstract = {Autonomous driving systems have been extensively researched for safety. To ensure safety, a strict deadline is enforced before sensor data influences vehicle control. A common approach to this issue is Directed Acyclic Graph (DAG) scheduling. However, existing studies use fixed values, typically using Worst-case Execution Time (WCET). During actual vehicle operation, the task execution time varies due to factors such as input size (e.g., surrounding vehicles), and the actual execution time often takes on a value smaller than the WCET. The execution time variation causes the actual results of DAG scheduling to deviate from the intended strategy. To address this problem, this paper proposes a DAG scheduling algorithm that considers probabilistic execution time to reduce the number of deadline misses. For consecutive timer-driven nodes, a waiting time may occur, during which executing higher priority nodes does not affect end-to-end deadline misses. The proposed scheduling algorithm calculates this waiting time and executes high deadline miss rate nodes outside the end-to-end path during this time. In DAGs with high utilization, the evaluation results of the proposed scheduling algorithm demonstrate that the number of deadline misses decreases compared to existing methods. Specifically, our evaluation results indicate an approximate 8.3% improvement in deadline achievement rate compared to existing methods in high utilization scenarios.}, archive = {J_ACCESS}, author = {Hayate Toba and Daichi Yamazaki and Takuya Azumi}, doi = {10.1109/ACCESS.2025.3606480}, journal = {IEEE Access}, pages = {157304-157316}, shortjournal = {IEEE Access}, title = {Deadline miss rate scheduling in DAG considering probabilistic execution time}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning for constraint-aware knowledge base question answering. ACCESS, 13, 157294-157303. (www)

  @article{ , abstract = {Language models (LMs) have demonstrated promising results as sophisticated artificial intelligence assistants. Recently, researchers have explored integrating LMs using external tools; however, grounding LMs to knowledge bases (KBs), which are complex environments, remains unexplored. Existing KB question answering (KBQA) models heavily rely on handcrafted agents that generate programs or refine generated programs following predefined rules to inject knowledge related to faithfulness and grammaticality of programs. However, these handcrafted agents can hinder the potential use of KBQA systems in real-world scenarios. Inspired by the recent success of aligning LMs with reinforcement learning (RL), a constraint-aware KBQA that injected knowledge about the faithfulness and grammaticality of programs in the training phase, thereby eliminating handcrafted agents in the decoding phase, was proposed in this study. The proposed method improved the supervised fine-tuning baseline by 0.4 exact match points and 0.4 F1 score points and generated 2.0% more valid queries in GrailQA. Our findings demonstrated that the proposed method enhanced the executability and validity of the generated query; these improvements can significantly aid the challenging compositional and zero-shot generalization settings in GrailQA.}, archive = {J_ACCESS}, author = {Jonghyeon Yang and Kiyun Yu}, doi = {10.1109/ACCESS.2025.3606830}, journal = {IEEE Access}, pages = {157294-157303}, shortjournal = {IEEE Access}, title = {Reinforcement learning for constraint-aware knowledge base question answering}, volume = {13}, year = {2025}, }
• (2025). SHRED: An ensemble-based machine learning model to sift email messages for real-time spam detection. ACCESS, 13, 157276-157293. (www)

  @article{ , abstract = {The rapid expansion of email usage has been paralleled by a significant rise in spam, which not only clutters inboxes but also spreads malware and introduces serious security threats. While machine learning (ML) has proven to be a powerful approach for spam detection, single-classifier models often face issues such as poor generalization, overfitting, and elevated false positive rates. Key challenges in email spam detection include: 1) Improving accuracy while keeping false positives low to maintain usability; 2) Responding to threats in real-time; 3) Handling the ever-changing characteristics of email content; and 4) Automating model adaptation to these changes. To address these challenges, this study presents SHRED (Sifting Ham for Real-time Email Spam Detection), an ensemble-based ML model specifically built for real-time spam filtering. SHRED combines Naive Bayes, Decision Trees, AdaBoost, Random Forests, and Artificial Neural Networks using a hybrid approach of voting and stacking to enhance detection performance. It employs preprocessing methods such as text normalization and TF-IDF-driven feature selection to produce resilient input representations that automatically adapt to evolving email patterns. When tested on a diverse dataset of 83,448 emails, SHRED achieved a detection rate of 98.4%, a false positive rate of 1.6%, and an overall accuracy of 98.45%, with an average processing time of just 0.0522 seconds per email. This includes all stages: preprocessing, feature extraction and selection, training, and detection, underscoring the system’s capability to accurately detect spam in real-time.}, archive = {J_ACCESS}, author = {Shahid Alam and Amina Jameel and Zahida Parveen and Ehab Alnfrawy}, doi = {10.1109/ACCESS.2025.3605850}, journal = {IEEE Access}, pages = {157276-157293}, shortjournal = {IEEE Access}, title = {SHRED: An ensemble-based machine learning model to sift email messages for real-time spam detection}, volume = {13}, year = {2025}, }
• (2025). Efficient top-k content-based image retrieval exploiting matrix division toward SIFT. ACCESS, 13, 157264-157275. (www)

  @article{ , abstract = {With the growing demand for efficient content-based image retrieval (CBIR) systems, scalable and lightweight retrieval techniques have become essential. To obtain accurate retrieval results, several feature extraction techniques such as SIFT and deep feature vector exploiting CNN have been proposed. However, the computational cost for such techniques is enormous and the performance enhancement is still challenging. Thus, in this paper, we propose a novel method, Rank-based Feature on Grid-partitioning of SIFT Keypoints (RF-GSK), which enables effective top-k image retrieval without relying on deep learning models. RF-GSK utilizes handcrafted SIFT keypoints and transforms them into compact frequency and rank-based feature vectors through spatial grid partitioning which divides the spatial domain of an image into $m \times m$ cells. We also present how to determine the decompose parameter m by mathematical analysis adopting a quality assessment method for clustering and verify it empirically. To show effectiveness of our proposed method, we conduct the statistical analysis of RF-GSK and the state-of-the-art algorithms including memory requirement, feature size and the number of FLoating OPerations (FLOPs) as well as evaluate top-k accuracy of them. Our experimental results demonstrate that our proposed method show reasonable accuracy compared to the state-of-the-art algorithms but it is more efficient than the state-of-the-art algorithms in the aspects of memory usage, storage requirement and computational cost.}, archive = {J_ACCESS}, author = {Cheolhan Moon and Min Hyung Kim and Jun-Ki Min}, doi = {10.1109/ACCESS.2025.3606500}, journal = {IEEE Access}, pages = {157264-157275}, shortjournal = {IEEE Access}, title = {Efficient top-k content-based image retrieval exploiting matrix division toward SIFT}, volume = {13}, year = {2025}, }
• (2025). Compact dipolar patch UHF RFID tag antenna using interdigital capacitor for metal platforms. ACCESS, 13, 157249-157263. (www)

  @article{ , abstract = {A compact dipolar patch antenna is proposed for on-metal UHF RFID tag applications. The design employs two centrally located shorting stubs to establish a direct ground path, effectively increasing the antenna’s electrical length and lowering its resonant frequency. To enhance impedance matching and further reduce size, multiple inductive slots are etched into the top patch, introducing distributed inductance. The key innovation lies in the integration of dual interdigital capacitors (IDCs) within the radiating patch, which serve as compact, tunable capacitive elements. By carefully adjusting finger dimensions and spacing, the IDC capacitance is analytically modeled and optimized. These capacitive elements introduce additional reactance, improving impedance matching, increasing antenna gain, and shift the antenna’s operating band into the UHF RFID frequency range (860–960 MHz). Despite its compact dimensions of 50 mm $\times 25$ mm $\times 3.32$ mm ( $0.153\lambda $ $\times 0.077$ $\lambda $ $\times 0.010$ $\lambda $ ), the proposed tag achieves a maximum read range of 13 meters under 4W effective isotropic radiated power (EIRP), outperforming many existing on-metal RFID tags of similar size. The entire antenna structure is fabricated on a polyimide and foam substrate, optimizing both mechanical flexibility and performance on metal surfaces. The antenna demonstrates strong potential for space-constrained and metal-mountable RFID applications, offering a robust and efficient solution for industrial and logistics environments.}, archive = {J_ACCESS}, author = {Muthukannan Murugesh and Muhammad Firdaus Akbar and Nor Azlin Ghazali}, doi = {10.1109/ACCESS.2025.3606695}, journal = {IEEE Access}, pages = {157249-157263}, shortjournal = {IEEE Access}, title = {Compact dipolar patch UHF RFID tag antenna using interdigital capacitor for metal platforms}, volume = {13}, year = {2025}, }
• (2025). Efficient continuous control-set model-free predictive control of a grid-forming inverter with an LCL filter. ACCESS, 13, 157235-157248. (www)

  @article{ , abstract = {The application of finite control set model-free predictive control (FCS-MFPC) in power electronics eliminates the controller’s dependence on an explicit system model. However, high computational requirements hinder its practical implementation in power electronic systems. Furthermore, the inherent variable switching frequency of finite control set MFPC (FCS-MFPC) limits its suitability for high-performance applications, motivating the adoption of continuous control set MFPC (CCS-MFPC). This paper proposes a computationally efficient CCS-MFPC for regulating the output voltages of a grid-forming inverter (GFI) with an output LCL filter. The proposed controller incorporates constraints on the duty cycle and inverter-side filter current to ensure safe operation. The model-free strategy is based on an autoregressive structure with an exogenous input (ARX), enabling the estimation of GFI dynamics without the need for an explicit system model. Additionally, computational complexity is reduced by leveraging the structure of system constraints. Controller-hardware-in-the-loop (C-HIL) simulations are used to compare the proposed CCS-MFPC with existing CCS-MPC, FCS-MPC, and CCS-MFPC approaches. The results demonstrate the controller’s robustness to model inaccuracies, improved voltage tracking performance, and lower computational burden, paving the way for the practical application of MFPC in power electronic systems.}, archive = {J_ACCESS}, author = {M. Nauman and W. Shireen}, doi = {10.1109/ACCESS.2025.3605731}, journal = {IEEE Access}, pages = {157235-157248}, shortjournal = {IEEE Access}, title = {Efficient continuous control-set model-free predictive control of a grid-forming inverter with an LCL filter}, volume = {13}, year = {2025}, }
• (2025). An interactive multi-objective optimization for tactical revision of train timetables: A computationally-tractable method for planners. ACCESS, 13, 157220-157234. (www)

  @article{ , abstract = {Railway timetable planners often face the challenge of aligning ideal timetables with limited vehicles and fluctuating capacity needs during their tactical planning phases. To address this, we have developed a multi-objective timetable revision approach using mixed-integer linear programming (MILP) that optimizes trip cancellations and carriage type assignments. Unlike computationally intensive, fully integrated optimization methods that generate timetables from scratch, our method refines an existing draft timetable, offering a practical and efficient tool for planners. It utilizes a multi-product flow model to balance cost-service trade-offs while considering vehicle scheduling feasibility. Evaluation results from a case study on a commuter rail line in Japan demonstrate the method’s practicality. The revised timetable successfully reduced the number of trips by 5% and the total number of carriages by 4%, whereas satisfying user-defined goals for service quality, such as congestion rates. Interactive searches can be completed within 6 minutes, even when user-defined goals are changed. The optimization process is completed in a timeframe suitable for interactive planning, enabling planners to efficiently explore trade-offs. This approach offers a practical and computationally efficient solution for refining timetables under varying demand conditions, supporting the transition from draft planning to operational scheduling.}, archive = {J_ACCESS}, author = {Yuki Maekawa and Eric Tang Kui Shen and Yuuki Hashimoto and Satoru Hori}, doi = {10.1109/ACCESS.2025.3604912}, journal = {IEEE Access}, pages = {157220-157234}, shortjournal = {IEEE Access}, title = {An interactive multi-objective optimization for tactical revision of train timetables: A computationally-tractable method for planners}, volume = {13}, year = {2025}, }
• (2025). Power carrier-based simultaneous wireless power and data transfer module for photovoltaic systems. ACCESS, 13, 157207-157219. (www)

  @article{ , abstract = {This paper presents a power carrier-based simultaneous wireless power and data transfer (PC-SWPDT) module for photovoltaic (PV) systems. The proposed system addresses the challenge of power intermittency in small-scale PV applications that lack energy storage. The proposed PC-SWPDT module enables efficient wireless power and data transmission over a single channel by employing a series-parallel (SP) compensation topology combined with amplitude-shift keying (ASK) modulation. Simulation and experimental results demonstrate the system achieves stable energy transfer and reliable data communication under varying load conditions. The proposed PC-SWPDT module offers a practical solution for enhancing energy utilization and conversion efficiency in PV systems.}, archive = {J_ACCESS}, author = {Che-Yu Lu and Kai-Ying Qiu}, doi = {10.1109/ACCESS.2025.3606743}, journal = {IEEE Access}, pages = {157207-157219}, shortjournal = {IEEE Access}, title = {Power carrier-based simultaneous wireless power and data transfer module for photovoltaic systems}, volume = {13}, year = {2025}, }
• (2025). Small target building extraction using dual-branch enhanced U-net with multi-scale attention fusion. ACCESS, 13, 157191-157206. (www)

  @article{ , abstract = {This study addresses critical challenges in small target building extraction from high-resolution remote sensing imagery, particularly instance separation difficulties and boundary precision limitations in dense urban environments where adjacent building structures often appear as connected regions in conventional segmentation outputs. An Enhanced U-Net architecture with dual-branch processing and multi-scale attention fusion was developed. The architecture integrates a ResNet-34 encoder backbone with a novel boundary-aware loss function and specialized dual-branch outputs for semantic segmentation and instance separation. Experimental validation across Massachusetts, WHU, and INRIA datasets demonstrates consistent performance improvements, achieving IoU scores of 71.8%, 77.9%, and 67.3% respectively, representing improvements of 4.7%, 4.7%, and 5.7% over baseline U-Net architectures (67.1%, 73.2%, and 61.6%). The proposed method also achieves F1-scores of 71.2%, 77.4%, and 66.8% with accuracy rates of 91.7%, 94.2%, and 89.4% across the three datasets. Systematic ablation studies confirm progressive contributions of attention mechanisms (67.1% to 70.1% IoU) and loss function components (achieving final 71.8% IoU), while cross-dataset generalization analysis reveals realistic transferability with performance degradation ranging from 0.6 to 14.1 percentage points across different geographical contexts. The dual-branch design philosophy successfully decouples semantic understanding from geometric boundary detection, enabling superior optimization for both building classification and instance separation tasks while maintaining computational efficiency essential for practical deployment scenarios, with enhanced extraction capabilities supporting automated urban monitoring, disaster response mapping, and smart city development initiatives that provide sophisticated spatial analysis capabilities essential for evidence-based urban planning and infrastructure management applications.}, archive = {J_ACCESS}, author = {Yuzhe Zhao and Haizhong Qian}, doi = {10.1109/ACCESS.2025.3605678}, journal = {IEEE Access}, pages = {157191-157206}, shortjournal = {IEEE Access}, title = {Small target building extraction using dual-branch enhanced U-net with multi-scale attention fusion}, volume = {13}, year = {2025}, }
• (2025). An interpretable deep Actor–Critic framework for automated propofol dosing during general anesthesia. ACCESS, 13, 157175-157190. (www)

  @article{ , abstract = {Administering general anesthesia demands anesthesiologists to concurrently regulate various physiological functions, often under time-critical conditions. The automation of hypnotic drug delivery offers the potential to enhance precision in maintaining optimal sedation levels while allowing clinicians to focus on more complex intraoperative decisions. In this study, we propose a reinforcement learning (RL) framework to guide the real-time administration of propofol, a widely used anesthetic agent. Building upon earlier discrete-action approaches, our model adopts a continuous-action actor-critic architecture. It incorporates a policy network that generates a continuous probability distribution over infusion rates based on dynamic anesthetic state observations, along with a value network that evaluates the desirability of those states. The RL agent is trained on synthetic patient simulations derived from pharmacokinetic and pharmacodynamic (PK/PD) models with randomized parameter sets to ensure robustness across diverse patient profiles. We examine three distinct reward formulations tailored to different clinical objectives and evaluate agent performance in both simulated scenarios and retrospective clinical data from nine surgical cases. To enhance transparency and support clinical adoption, we apply Shapley Additive Explanations (SHAP) to interpret the agent’s dosing rationale. The analysis revealed that dosing decisions were primarily influenced by the level of unconsciousness (LoU) error and predicted effect-site concentration, with LoU target dominating steady-state control. These clinically consistent patterns demonstrate that the agent’s recommendations align with established pharmacological principles, thereby improving clinician trust and interpretability. Experimental results demonstrate that the proposed RL agent surpasses traditional PID controllers in maintaining target sedation levels and aligns closely with anesthesiologist-administered dosages in real cases. This work represents a novel advancement in automated anesthesia control, showcasing the adaptability of reward functions and the integration of interpretability tools to support the development of clinically relevant AI-based drug delivery system.}, archive = {J_ACCESS}, author = {Delin Zhang and Fei Wang}, doi = {10.1109/ACCESS.2025.3605643}, journal = {IEEE Access}, pages = {157175-157190}, shortjournal = {IEEE Access}, title = {An interpretable deep Actor–Critic framework for automated propofol dosing during general anesthesia}, volume = {13}, year = {2025}, }
• (2025). Real-time, high-fidelity face identity swapping with a vision foundation model. ACCESS, 13, 157160-157174. (www)

  @article{ , abstract = {Many recent face-swapping methods based on generative adversarial networks (GANs) or autoencoders achieve strong performance under constrained conditions but degrade significantly in high-resolution or extreme pose scenarios. Moreover, most existing models generate outputs at limited resolutions ( $128\times 128$ ), which fall short of modern visual standards. Diffusion-based approaches have shown promise in handling such challenges, but are computationally intensive and unsuitable for real-time applications. In this work, we propose FaceChanger, a real-time face identity swap framework designed to enhance robustness across various poses and outputs at $256\times 256$ (double the linear resolution of typical $128\times 128$ baselines). While maintaining compatibility with conventional GAN- and autoencoder-based pipelines, FaceChanger uniquely incorporates a vision foundation model (VFM) to extract richer semantic features, which can enhance identity preservation, attribute control, and robustness to variations. In this work, we employ the Contrastive Language-Image Pre-training (CLIP) model to obtain the features. These features guide identity preservation and attribute control through newly designed VFM-based visual and textual semantic contrastive losses. Extensive evaluations on benchmarks such as the FaceForensics++ (FF++) dataset, the Multiple Pose, Illumination, and Expression (MPIE) dataset, and the large-pose Flickr face (LPFF) dataset demonstrate that FaceChanger matches or exceeds state-of-the-art performance under standard conditions and significantly outperforms them in high-resolution, pose-intensive scenarios.}, archive = {J_ACCESS}, author = {Jongmin Yu and Hyeontaek Oh and Zhongtian Sun and Younkwan Lee and Jinhong Yang}, doi = {10.1109/ACCESS.2025.3606518}, journal = {IEEE Access}, pages = {157160-157174}, shortjournal = {IEEE Access}, title = {Real-time, high-fidelity face identity swapping with a vision foundation model}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection using generative language models and deep feature-based time series similarity. ACCESS, 13, 157147-157159. (www)

  @article{ , abstract = {Time series data play a critical role in decision-making across domains such as environmental monitoring, industrial equipment management, and financial market analysis. However, these data are highly susceptible to distortions caused by noise, anomalies, and sensor failures. Early detection and interpretability of abnormal patterns are essential to ensuring the reliability of data-driven systems. This paper proposes a novel anomaly detection framework that integrates large language models (LLMs) with time series retrieval-augmented generation. Traditional anomaly detection methods often rely on statistical models or single deep learning architectures, which struggle to capture irregular patterns and lack interpretability. Our method segments time series into interpretable subsequences, embeds them into high-dimensional vectors, and retrieves similar historical cases from a vector database. In addition, structural similarity across spatially adjacent sensors is quantified using Dynamic Time Warping (DTW) and Z-score metrics. These analytical results are then used to construct prompts, which are passed to an LLM to generate natural language decisions and explanations. The proposed framework enables both accurate detection and human-readable reasoning. Evaluated on real-world air quality monitoring data from South Korea, our approach reduced the false positive rate by over 50%, while maintaining recall, thereby validating its practical reliability and explainability compared to a segmentation-only approach.}, archive = {J_ACCESS}, author = {Junpyo Lee and Jungmu Choi and Jung Min Park and Yong-Jae Lim and Hae-Jin Jung and Soyoung Kang and Chanjung An and Jangwoo Kwon}, doi = {10.1109/ACCESS.2025.3604216}, journal = {IEEE Access}, pages = {157147-157159}, shortjournal = {IEEE Access}, title = {Anomaly detection using generative language models and deep feature-based time series similarity}, volume = {13}, year = {2025}, }
• (2025). Analysis of group III-V material-based symmetric high-k spacer on S/D underlap junctionless nanotube FET considering for cutting edge technology. ACCESS, 13, 157137-157146. (www)

  @article{ , abstract = {The usage of III-V group semiconductors has increased drastically in the last few years, owing to their superior characteristics that can be tailored by adjusting the composition and structure of the materials. In this paper, a junctionless GAA (gate all around) nanotube FET has been studied with a high-k spacer on the S/D (Source/Drain) underlap region. The paper examines the device performance by incorporating the group III-V semiconductors as a channel material, such as GaAs (Gallium Arsenide), GaN (Gallium Nitride), and alloys such as InGaAs and SiGe. For this, a comprehensive analysis has been done with the aim of finding the efficacy of these materials for a wide range of applications, mainly in terms of analog, static, and RF (Radio Frequency) linearity metrics. The results obtained are simultaneously compared with the silicon material. It is observed that each channel material has its own characteristics when amalgamated onto a junctionless Nanotube FET. It has been found that with GaN channel material, the device exhibits superior performance in terms of high current driving capability ( $\sim 1.6\mu $ A), improved transconductance, and higher derivative, making it suitable for linear amplifiers. Further, the InGaAs channel material device shows a low threshold voltage (below 0.1V), making it suitable for low-power applications. The GaAs achieves very low SS (18mV/decade), and thereby possesses a high switching ratio. Thus, the proposed analysis provides an in-depth insight into choosing channel material wisely based on the required applications.}, archive = {J_ACCESS}, author = {Neha Gupta and Ajay Kumar and Aditya Jain and Rajeev Gupta and Amit Kumar Goyal}, doi = {10.1109/ACCESS.2025.3607430}, journal = {IEEE Access}, pages = {157137-157146}, shortjournal = {IEEE Access}, title = {Analysis of group III-V material-based symmetric high-k spacer on S/D underlap junctionless nanotube FET considering for cutting edge technology}, volume = {13}, year = {2025}, }
• (2025). Integrating device-level flexibility in home energy management systems for prosumers. ACCESS, 13, 157126-157136. (www)

  @article{ , abstract = {The increasing integration of renewable energy sources (RES) introduces significant uncertainty in power supply, posing challenges to the stability of energy distribution systems. The demand for efficient energy utilization in smart grids has motivated many end-users to adopt home energy management system (HEMS), while power utilities seek to optimize grid operations. This study investigates the flexibility of devices within households to help prosumers realize the most flexible asset within their premises. By modeling and managing these flexibilities, we provide extensive predictive modeling and scheduling solutions to help the distribution system. A case study involving ten prosumers with flexible devices connected to a top-pole transformer is presented. Our findings demonstrate measurable time and energy flexibility, enabling the prediction of load and flexibility at the device level. This work contributes to the efficient and cost-effective integration of renewable energy sources into the power grid, promoting sustainable energy solutions.}, archive = {J_ACCESS}, author = {Sadam Hussain and Omar Alrumayh and Chunyan Lai}, doi = {10.1109/ACCESS.2025.3606499}, journal = {IEEE Access}, pages = {157126-157136}, shortjournal = {IEEE Access}, title = {Integrating device-level flexibility in home energy management systems for prosumers}, volume = {13}, year = {2025}, }
• (2025). A weakly supervised multi-stream feature enhancement network for image manipulation detection and localization. ACCESS, 13, 157111-157125. (www)

  @article{ , abstract = {Image manipulation detection plays an essential role in digital image processing. However, existing convolutional neural network (CNN)-based methods often rely on local perception, which makes it challenging to effectively capture long-range dependencies in images. This limitation results in degraded performance when detecting subtle forgery traces or handling complex backgrounds. To tackle this issue, this paper proposes a multi-stream feature-enhanced weakly-supervised image manipulation detection network, named WSMD-Net. First, we propose a stream module that leverages the global perception capability of Vision Transformers (ViT) to overcome the local perception limitations of CNN, enabling effective capture of long-range dependencies and subtle forgery traces. Second, we propose an SR-CA stream module that integrates the Steganalysis Rich Model (SRM) convolution to enhance the model’s ability to extract weak features in forgery regions, while improving stability and generalization performance. Finally, WSMD-Net enhances its capability in image manipulation detection across diverse feature dimensions by fusing multi-stream features, thereby improving detection accuracy and robustness. Experimental results demonstrate that WSMD-Net achieves superior accuracy and adaptability on four challenging public image manipulation datasets. Specifically, compared with other state-of-the-art weakly supervised methods, it improves the average image-level I-F1 by 7.4 %, and achieves consistent gains at the pixel level with 2.2 % and 1.5 % improvements in P-F1 and C-F1, respectively, highlighting its effectiveness and robustness.}, archive = {J_ACCESS}, author = {Gencheng Wang and Meiyan Yang and Rong Chen}, doi = {10.1109/ACCESS.2025.3607317}, journal = {IEEE Access}, pages = {157111-157125}, shortjournal = {IEEE Access}, title = {A weakly supervised multi-stream feature enhancement network for image manipulation detection and localization}, volume = {13}, year = {2025}, }
• (2025). Capacitance sensor-based board-level PUF for device identification. ACCESS, 13, 157104-157110. (www)

  @article{ , abstract = {The proliferation of counterfeit and refurbished semiconductor devices has become a critical concern. Device identification techniques based on Physically Unclonable Functions (PUFs) have therefore attracted significant attention. Conventional PUFs exploit only die-internal process variations and thus cannot achieve board-level identification. In this work, we propose a novel method that also harnesses external process variations on the printed circuit board (PCB)—including trace geometry variations—to realize robust board-level device identification. Our method employs an on-chip capacitance sensor to measure the capacitance between signal traces and ground. A histogram of the sensor output values is generated, from which distinctive feature points in the distribution are extracted. By selecting bits that exhibit both high reproducibility and uniqueness, we construct a 30-bit device identifier. This approach eliminates the need for the large-scale measurement systems required by traditional techniques, enabling a more convenient yet high-precision identification process. Evaluation on 20 microcontroller boards demonstrates that the proposed method achieves 97.14% identification accuracy with the 30-bit identifier. The average normalized intra-device Hamming distance (Intra-HD) is 3.7%, and the average normalized inter-device Hamming distance (Inter-HD) is 46.7%, closely approximating ideal values. These results indicate that our technique offers a promising approach for ensuring board-level authenticity.}, archive = {J_ACCESS}, author = {Rikuo Haga and Ayaki Tachikake and Shugo Kaji and Daisuke Fujimoto and Yuichi Hayashi}, doi = {10.1109/ACCESS.2025.3606858}, journal = {IEEE Access}, pages = {157104-157110}, shortjournal = {IEEE Access}, title = {Capacitance sensor-based board-level PUF for device identification}, volume = {13}, year = {2025}, }
• (2025). Hybrid KEM-QKD integration with parameter optimization for secure underwater networks via NetSquid simulations. ACCESS, 13, 157081-157103. (www)

  @article{ , abstract = {Quantum cryptography, especially quantum key distribution (QKD), is significant for shielding communications against the dangers of quantum computing by providing information-theoretic security established in quantum mechanics. To securely generate and distribute cryptographic keys between two parties, several quantum safe protocols were proposed. Even though the BB84 protocol is a more trusted, widely implemented and standardized protocol for QKD, it suffers from 50% key loss during sifting. The next version B92 protocol is the lighter version of BB84, has critical issues such as: less tolerance to noise and photon loss and is specifically applicable for short range, lower security scenarios and as B92 has only two non-orthogonal states, it makes it more vulnerable to certain Unambiguous State Discrimination (USD) attacks. Hence, this work has taken the most widely adopted protocol BB84 as it has more advantages for underwater networks by proposing a novel hybrid approach to mitigate the fundamental efficiency challenge of key losses during the sifting phase due to basis mismatches. As this inefficiency is a significant barrier to practical deployment, particularly in resource-constrained environments like underwater networks, we propose a novel hybrid KEM-QKD protocol that eliminates the sifting process of BB84 entirely. Our primary contributions are: (i) integration of a post-quantum key encapsulation mechanism (KEM), CRYSTALS-Kyber, to securely transmit basis information prior to measurement which allows for 100% basis alignment between communicating parties, preserving the entire raw key; (ii) benchmarked our modified protocol against traditional BB84 using NetSquid, a quantum network simulator and (iii) optimized the proposed protocol’s parameters using Bayesian Optimization (BO) and Genetic Algorithms (GA). Our results demonstrate two major performance enhancements: (i) a 100% raw key generation rate, a significant improvement over BB84’s 50% efficiency and (ii) an increase in the maximum secure distance of up to 42% in existing optic fiber cables, with BO proving superior for parameter optimization. Further, our rigorous experimentation results showcase the potential of the proposed hybrid quantum-classical framework to enhance the practical efficiency of QKD, advancing its viability for robust, high-performance underwater quantum networks.}, archive = {J_ACCESS}, author = {D. Jeya Mala and Achintya Kant Rastogi and Ayush Raj and Vaidik Manori}, doi = {10.1109/ACCESS.2025.3607474}, journal = {IEEE Access}, pages = {157081-157103}, shortjournal = {IEEE Access}, title = {Hybrid KEM-QKD integration with parameter optimization for secure underwater networks via NetSquid simulations}, volume = {13}, year = {2025}, }
• (2025). Optimized planning for electric vehicle charging stations: A systematic review. ACCESS, 13, 157059-157080. (www)

  @article{ , abstract = {The increasing integration of electric vehicles (EVs) into distribution systems is a global trend due to their advantages, such as reduced costs and decreased greenhouse gas emissions. To meet the increasing demand for EV charging, charging stations must be strategically incorporated into the power grid. This integration requires careful planning to minimize impacts on the electrical network while balancing costs for both system operators and EV users. This paper presents a systematic review of the state-of-the-art in the optimized planning of EV charging stations, aiming to guide future research and address emerging challenges associated with the rising energy demand from EVs. The review explores key methodologies for optimizing EV charging infrastructure, including the use of fast and slow charging options and their effects on the power system; the integration of distributed energy resources; the spatio-temporal distribution of EVs; the objective functions considered; and the optimization approaches adopted. It also examines the implementation of demand response strategies, such as time-of-use tariffs and smart charging. Finally, the review highlights the main research gaps, including the need to address environmental and economic factors, account for dynamic and uncertain variables, incorporate EV user behavior data, and integrate multiple objectives in practical applications.}, archive = {J_ACCESS}, author = {Rafael S. F. Ferraz and Renato S. F. Ferraz and Augusto C. Rueda-Medina}, doi = {10.1109/ACCESS.2025.3607112}, journal = {IEEE Access}, pages = {157059-157080}, shortjournal = {IEEE Access}, title = {Optimized planning for electric vehicle charging stations: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Designing cost-effective battery-less energy harvesting for intermittent wireless communication. ACCESS, 13, 157044-157058. (www)

  @article{ , abstract = {Conventional internet of things (IoT) nodes typically rely on batteries, posing challenges due to their periodic replacement. Harvesting energy purely from ambient sources like sunlight offers a solution without using a battery, but it raises considerable challenges for using wireless communication to transmit the collected data solely with limited harvesting power. In this paper, we propose a cost-effective design methodology for a battery-less energy harvesting IoT device, with a focus on enabling efficient wireless communication under intermittent power conditions. To this end, we first develop energy and timing models to optimize the sizes of energy harvesting and short-term energy storage devices, while optimizing the efficiency of data transmission. Then, we introduce a power management architecture tailored to support better quality of service (QoS) in terms of data transmission rate and communication frequency. Our extensive experiments demonstrate that the proposed models and design methodology effectively identify cost-effective energy harvesting IoT configuration, including optimal capacitor size for satisfying the desired wireless communication performance. Moreover, the proposed duty-based dynamic power management (DPM) scheme extends the operation time of IoT nodes by 9.1% to 20.0% and 48.5% to 90.0%, depending on weather conditions throughout the day, while ensuring the minimum required data transfer rate and frequency, respectively, compared to the state-of-the-art threshold-based DPM approach.}, archive = {J_ACCESS}, author = {Yongseok Kim and Yong Su Park and Hyung Gyu Lee and Donkyu Baek}, doi = {10.1109/ACCESS.2025.3606514}, journal = {IEEE Access}, pages = {157044-157058}, shortjournal = {IEEE Access}, title = {Designing cost-effective battery-less energy harvesting for intermittent wireless communication}, volume = {13}, year = {2025}, }
• (2025). LLM-based generative AI in medicine: Analysis of current research trends with BERTopic. ACCESS, 13, 157026-157043. (www)

  @article{ , abstract = {Recent advances in generative artificial intelligence (GenAI) have made large language models (LLMs) transformative tools in the healthcare industry. Powered by deep learning, these models show great potential in many medical applications such as clinical decision support systems, biomedical text mining and personalized patient care. However, while research on the use of LLMs in medicine is growing rapidly, there is a need to systematically analyze the key themes, emerging trends and challenges in this field. This study aims to identify dominant themes, track research trends and identify gaps in the literature by utilizing a large dataset of scientific publications in the medical field. A total of 3,941 academic publications related to the use of LLMs in medicine were retrieved from the Scopus database, covering the period from 2023 to 2024. Accordingly, the BERTopic method, an advanced topic modeling technique, is used to analyze and classify publications on LLMs applications in medicine. The findings show that LLMs are mostly concentrated in the fields of radiology, ophthalmology and mental health and that these models have the potential to support clinical processes. In particular, LLMs have been found to have a great impact on the analysis of medical imaging reports, are used for early diagnosis of ophthalmologic diseases, and are prominent in depression and suicide risk assessments in mental health. This study provides valuable insights for healthcare professionals, GenAI researchers, and policy makers, laying a solid foundation for future research and strategic applications for the use of LLMs in medicine. While emphasizing the transformative impact of LLMs in healthcare, it also highlights the necessity of responsible GenAI applications.}, archive = {J_ACCESS}, author = {Murat Kilinc and Fatih Gurcan and Ahmet Soylu}, doi = {10.1109/ACCESS.2025.3606335}, journal = {IEEE Access}, pages = {157026-157043}, shortjournal = {IEEE Access}, title = {LLM-based generative AI in medicine: Analysis of current research trends with BERTopic}, volume = {13}, year = {2025}, }
• (2025). MSAF-CF: A multimodal sentiment analysis framework based on feature enhancement and cross-fusion. ACCESS, 13, 157011-157025. (www)

  @article{ , abstract = {As the rapid dissemination of multimodal information continues, multimodal sentiment analysis (MSA) has increasingly become a pivotal focus in sentiment analysis research, widely applied in fields such as social media analysis and telemedicine. However, existing methods are confronted with significant challenges in practical applications, including modality absence, ineffective cross-modal interaction modeling, and inefficient feature fusion. A novel framework, MSAF-CF, is introduced in this work to address the identified challenges in multimodal sentiment analysis. More specifically, a Transformer-based modality enhancement and generation module (MEGM) is introduced to effectively generate missing modalities and maintain semantic consistency across multimodal inputs. Additionally, a tri-channel selective attention model (TriSelM) is designed to adaptively enhance cross-modal feature interactions through dynamic gating mechanisms, addressing varying modality availability and fusion requirements. Furthermore, a joint optimization strategy is employed to simultaneously optimize modality reconstruction and sentiment prediction tasks, significantly boosting model robustness and generalization. Based on extensive evaluations on two well-established benchmark datasets (CMU-MOSI and CMU-MOSEI), MSAF-CF achieves remarkable performance surpassing current state-of-the-art (SOTA) approaches, particularly in scenarios involving modality loss and misalignment, confirming its efficacy and practical feasibility in real-world multimodal sentiment analysis tasks.}, archive = {J_ACCESS}, author = {Zhongliang Wei and Ruofan Chen and Jing Sun}, doi = {10.1109/ACCESS.2025.3605712}, journal = {IEEE Access}, pages = {157011-157025}, shortjournal = {IEEE Access}, title = {MSAF-CF: A multimodal sentiment analysis framework based on feature enhancement and cross-fusion}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of heuristic and meta-heuristic algorithms on large-scale travelling salesman problem instances. ACCESS, 13, 156988-157010. (www)

  @article{ , abstract = {The Travelling Salesman Problem (TSP) is a well known method for the optimisation problem that asks you to find the shortest route that visits each city in a set exactly once and then goes back to the starting point. Owing to its classification as an NP-hard problem, generating optimal solutions for large instances remains computationally challenging. To address this, researchers have frequently applied heuristic and metaheuristic algorithms to obtain near-optimal solutions within reasonable time constraints. This study looks at how well a few heuristic methods work, like Nearest Neighbour, Farthest Insertion, and Minimum Spanning Tree (MST) Approximation. It also looks at how well some metaheuristic methods work, like Simulated Annealing (SA), Genetic Algorithm (GA), and Ant Colony Optimisation (ACO). Heuristic methods are generally faster and require fewer computational resources but may compromise solution quality. In contrast, metaheuristic algorithms tend to achieve better accuracy, particularly in large problem instances, albeit with higher computational demands. The comparative analysis was conducted on TSP datasets comprising 50, 100, 200, and 1000 cities. The results show that the MST Approximation algorithm works well for smaller problems and takes a reasonable amount of time to compute. But it doesn’t work as well for larger datasets when it comes to accuracy. On the other hand, ACO demonstrates strong performance across all test cases by consistently producing near-optimal solutions, especially in large-scale problems, but at the cost of increased computational effort. The standard benchmark dataset TSPLIB-eil51, kroA100, kroA200, and pr1002 has been taken into consideration to validate the results. This validation ensures consistency and comparability and offers practical insights into selecting appropriate solution strategies for the TSP based on the trade-offs between speed, accuracy, and available computational resources.}, archive = {J_ACCESS}, author = {Manya Goel and Juhi Singh and Amit Kumar Bairwa}, doi = {10.1109/ACCESS.2025.3606531}, journal = {IEEE Access}, pages = {156988-157010}, shortjournal = {IEEE Access}, title = {Performance evaluation of heuristic and meta-heuristic algorithms on large-scale travelling salesman problem instances}, volume = {13}, year = {2025}, }
• (2025). A compact and low-loss microstrip filtered power divider for millimeter-wave applications. ACCESS, 13, 156982-156987. (www)

  @article{ , abstract = {This paper describes the filtered power divider design using microstrip lines architecture. A transmission zero is generated through two resonant cavities to improve selectivity and isolation. The proposed filter is manufactured using a Rogers 5880 substrate with a thickness of 0.254 mm. The center frequency of the filter power divider is located at 20.71 GHz, the passband range is 19.5-22 GHz, the fractional bandwidth is about 12.04% and the minimum insertion loss is 3.7 dB. The filter power divider has an area of $20 \times 21.8 \; {\mathrm {mm}}^{2}$ and has a compact circuit integration area. Therefore, the proposed BPF designed as a power divider has the advantage of having low insertion loss and compact size.}, archive = {J_ACCESS}, author = {Ming-An Chung and Chia-Wei Lin and Yu-Hsun Chen}, doi = {10.1109/ACCESS.2025.3606458}, journal = {IEEE Access}, pages = {156982-156987}, shortjournal = {IEEE Access}, title = {A compact and low-loss microstrip filtered power divider for millimeter-wave applications}, volume = {13}, year = {2025}, }
• (2025). Exploiting performance scaling and predictive modeling for SA-HPL: From multicore to distributed systems. ACCESS, 13, 156967-156981. (www)

  @article{ , abstract = {High-Performance Computing (HPC) systems rely on computational benchmarks to evaluate and compare the performance of scientific applications across diverse architectures. However, the manual configuration required by traditional benchmarks, such as High-Performance Linpack (HPL), often leads to laborious and error-prone tuning processes. To overcome this, we introduce a self-adaptive HPL version (SA-HPL), now significantly enhanced with a Generalized Stochastic Petri Nets (GSPN)-based predictive modeling layer for autonomous parameter tuning. Building upon our foundational work focused on multicore systems, this study extends SA-HPL to support distributed environments through a hybrid MPI+OpenMP architecture, facilitating dynamic task parallelism and self-reconfiguration. A primary contribution of this research is the extensive experimental validation of SA-HPL, demonstrating its adaptability and scalability across diverse execution contexts, from multicore setups to distributed clusters, encompassing up to 32 processing cores. The integrated GSPN model efficiently simulates various workload scenarios, guiding performance-aware reconfigurations without the need for exhaustive empirical tuning. Our results confirm exceptional predictive accuracy in cluster environments, with Mean Absolute Percentage Error (MAPE) values as low as 0.12% for throughput and 0.03% for efficiency. These findings underscore the effectiveness of our approach in providing scalable, accurate, and cost-efficient performance evaluation and planning for high-performance computing systems.}, archive = {J_ACCESS}, author = {Cassiano Rista and Marcelo Teixeira and Mauro Fonseca}, doi = {10.1109/ACCESS.2025.3606742}, journal = {IEEE Access}, pages = {156967-156981}, shortjournal = {IEEE Access}, title = {Exploiting performance scaling and predictive modeling for SA-HPL: From multicore to distributed systems}, volume = {13}, year = {2025}, }
• (2025). ClusFed: A clustering-based defense for secure federated learning. ACCESS, 13, 156949-156966. (www)

  @article{ , abstract = {As IoT devices continue to proliferate, the demand for secure and efficient machine learning solutions becomes increasingly critical. Federated Learning (FL) offers a promising approach by enabling decentralized model training across distributed clients while preserving data privacy. However, FL systems are susceptible to adversarial threats, such as data and model poisoning attacks, where compromised clients send corrupted updates to undermine the global model’s performance. In this paper, we develop an innovative, lightweight Byzantine resistance strategy, called ClusFed, which uses adaptive client selection mechanisms. By leveraging clustering techniques to dynamically differentiate between honest and malicious clients, based on their local model updates. ClusFed effectively mitigates the impact of malicious nodes while sometimes exploiting their data, depending on the type of attack. This approach ensures robust performance even with up to 40% of clients being malicious. Extensive experiments on non independent, identically distributed (non-IID) partitions of MNIST, CIFAR-10, Shakespeare and a time series Water Leak datasets demonstrate that ClusFed consistently outperforms state-of-the-art Byzantine-resilient methods like FedMedian, Multi-Krum, and TrimmedMean. The results highlight ClusFed’s ability to maintain high accuracy, achieving up to 93% for MNIST and 76% for CIFAR-10, 86% for Water Leak and 40% for Shakespeare dataset, as well as stability in various attack scenarios while guaranteeing efficient learning times.}, archive = {J_ACCESS}, author = {Hibatallah Kabbaj and Rachid El-Azouzi and Abdellatif Kobbane}, doi = {10.1109/ACCESS.2025.3605870}, journal = {IEEE Access}, pages = {156949-156966}, shortjournal = {IEEE Access}, title = {ClusFed: A clustering-based defense for secure federated learning}, volume = {13}, year = {2025}, }
• (2025). Modeling and classifying parkinsonian tremors with nonlinear dynamics and kalman optimization. ACCESS, 13, 156930-156948. (www)

  @article{ , abstract = {Parkinson Disease (PD) is a chronic neurodegenerative disorder due to the degeneration of dopamine-producing neurons in a brain region, crucial for motor function regulation. Rest tremor is a vital parameter and is essential to diagnose the disease and assess its prognosis. The frequency of rest tremor is a key characteristic to differentiate it from other types of tremors such as Essential tremors. This highlights the need for reliable and accurate methods to estimate the rest tremor frequency as accurate as possible. This paper proposes a novel method, Extended Lagrangian combined with Kalman techniques for rest tremor frequency extraction. The Extended Lagrangian mechanism addresses the irregular oscillatory behavior of rest tremor by incorporating energy dissipation via Rayleigh’s dissipation function, nonlinear stiffness, as well as external forces. This mechanism serves as a foundation and helps to track the oscillatory nature of tremors. The video recordings of PD tremor taken in clinical settings may contain noise and have significant impact on the rest frequency estimate. The proposed approach addresses this issue, thus improving the accuracy of these estimates. This algorithm is validated on a dataset of 60 video recordings from PD patients, annotated by movement disorder specialists. The rest tremor frequency, along with other key features are then passed to a classifier to determine the severity of PD. The model achieved an accuracy of 98% with 1D CNN-LSTM classifier. This approach could be used in remote health assessment for PD patients, providing increased convenience to patients and caregivers.}, archive = {J_ACCESS}, author = {V. Vanitha and V. Dhilip Kumar and Oana Geman and Octavian Postolache and Sri Krishna Bellam and Roxana Toderean}, doi = {10.1109/ACCESS.2025.3603726}, journal = {IEEE Access}, pages = {156930-156948}, shortjournal = {IEEE Access}, title = {Modeling and classifying parkinsonian tremors with nonlinear dynamics and kalman optimization}, volume = {13}, year = {2025}, }
• (2025). Conditional GAN-based ECG signal denoising with skin-electrode impedance modeling. ACCESS, 13, 156917-156929. (www)

  @article{ , abstract = {ECG signals from dry electrodes, commonly used in wearable devices, are vulnerable to noise due to high skin-electrode impedance. In this work, we propose a denoising method based on conditional generative adversarial networks, where the noisy signal is treated as a condition for the generation of a clean ECG signal. To develop deep learning models with high generalization capability, it is important to ensure diversity in the training dataset. However, most existing denoising methods rely on ECG data collected by wet-contact electrodes. To remedy this, we adopt an input-referred voltage noise model at the circuit level of the measurement device, incorporating three types of internal noise: amplifier, thermal, and flicker noise. In addition, we include baseline wander, muscle artifact, and electrode motion noise to construct a comprehensive noise set. This allows us to synthesize a dataset with diversity in both high- and low- frequency components. The proposed method employs adversarial loss framework where the discriminator network determines whether the denoised signal follows the distribution of the clean ECG signals. Meanwhile, the generator network minimizes additional two loss components, namely $L_{1}$ and an identity type loss, whose role is to preserve noise-free segments during denoisng process. We observe robust denoising results in all the examples in the results section. In one example, we propose a new classification method derived as a byproduct of our work. By leveraging both the noisy and denoised signals, the proposed method achieves improved classification accuracy for detecting abnormal ECG signals.}, archive = {J_ACCESS}, author = {Bokyu Kim and Dohun Kim and Woonsang Choi and Tae-Heon Yang and Gwanghyun Jo and Young-Min Kim}, doi = {10.1109/ACCESS.2025.3607039}, journal = {IEEE Access}, pages = {156917-156929}, shortjournal = {IEEE Access}, title = {Conditional GAN-based ECG signal denoising with skin-electrode impedance modeling}, volume = {13}, year = {2025}, }
• (2025). AI-enhanced FES for foot drop: A novel biphasic charge-balanced stimulation approach. ACCESS, 13, 156906-156916. (www)

  @article{ , abstract = {Foot drop is a neuromuscular condition that impairs a patient’s ability to lift their foot while walking, leading to gait abnormalities and increased fall risk. Functional Electrical Stimulation (FES) is a rehabilitation technique that stimulates the peroneal nerve to restore normal movement. However, existing FES devices often lack precise charge balancing and timing accuracy, which can cause patient discomfort and potential tissue damage. To address these challenges, we developed a wearable, compact, and AI-driven FES system capable of generating biphasic, charge-balanced, trapezoidal pulses. The system integrates an ESP32 microcontroller, an OPA452-based stimulation circuit, and machine learning algorithms for foot lift detection. Unlike traditional threshold-based detection methods, our AI-enhanced approach adapts stimulation timing in real-time, improving synchronization with the patient’s gait. Initial testing demonstrated the system’s ability to generate precise charge-balanced stimulation pulses while accurately detecting foot-lift events. The AI-based detection method improved timing accuracy, reducing unwanted stimulation and enhancing patient comfort. The proposed system offers a technological advancement in neurorehabilitation, addressing key limitations of existing FES solutions. By integrating machine learning with FES, this work introduces a personalized, intelligent stimulation system that enhances rehabilitation outcomes for individuals with foot drop. The developed FES system achieved an average accuracy of 92.2%. Stimulation was found to be smooth between 120-220 microseconds of pulse width and 25-35 Hz of frequency.}, archive = {J_ACCESS}, author = {Bijit Basumatary and Rajat Suvra Halder and Adarsha Narayan Mallick and Arun Khokhar and Rajinder Bansal and Rajesh Kumar and Ashish Kumar Sahani}, doi = {10.1109/ACCESS.2025.3606694}, journal = {IEEE Access}, pages = {156906-156916}, shortjournal = {IEEE Access}, title = {AI-enhanced FES for foot drop: A novel biphasic charge-balanced stimulation approach}, volume = {13}, year = {2025}, }
• (2025). USE_WR 1.0: A new comprehensive instrument for subjective usability evaluation for wearable robots. ACCESS, 13, 156891-156905. (www)

  @article{ , abstract = {A successful transition from university laboratories to the commercial market requires structured and comprehensive evaluations of the subjective usability of wearable robots. To achieve this, we aimed to establish a new standard instrument for evaluating the subjective usability of wearable robots, based on a thorough understanding of both product characteristics and the human body. We conducted a systematic review of the literature and critically analyzed the existing scales and tools used in previous studies. From this, we developed a new comprehensive instrument for subjective usability evaluation, consisting of two distinct sections: a unified metric-based scale and a body map as a visual tool. We named the new instrument the “Usability Subjective Evaluation for Wearable Robots,” abbreviated as USE_WR 1.0. For the metric section, a total of 70 evaluation items were included across seven categories: usefulness, ease of use, satisfaction, ease of learning, safety, emotion, and wearability. We maintained consistency in these seven categories with existing metrics commonly found in the literature. Additionally, we proposed a new body map characterized by more detailed body segmentation and the inclusion of all body views—front, back, and side—enabling precise identification of localized body regions of discomfort when using wearable robots. USE_WR 1.0 was validated involving 11 human subjects using a commercially available lower-limb exoskeleton. The validation results demonstrated the practical relevance and effectiveness of the newly developed instrument, as well as future refinement, in assessing subjective usability within the context of wearable robots. We anticipate that this new and comprehensive instrument will facilitate a systematic approach to understanding user perspectives prior to market launch, enabling a more iterative and user-responsive development process for wearable robots.}, archive = {J_ACCESS}, author = {Juyeon Park and Soo-Min Lee and Minkyoung Kim}, doi = {10.1109/ACCESS.2025.3606883}, journal = {IEEE Access}, pages = {156891-156905}, shortjournal = {IEEE Access}, title = {USE_WR 1.0: A new comprehensive instrument for subjective usability evaluation for wearable robots}, volume = {13}, year = {2025}, }
• (2025). MOCQA: A multi-core optimizer for constrained quadratic assignment. ACCESS, 13, 156870-156890. (www)

  @article{ , abstract = {Despite advances in general processing hardware, optimization of NP-hard problems remains a time and compute-intensive task, with the end of Dennard Scaling leading to the increased development of domain-specific hardware in this area. Current research has focused on designing accelerators for solving problems in the NP-complete Quadratic Unconstrained Binary Optimization (QUBO) format due to its simplicity. We review the benefits and drawbacks of QUBO and its relevant hardware accelerators. We then contrast QUBO with the NP-complete Generalized Quadratic Assignment Problem (GQAP) format which, unlike QUBO, provides native support for integer variables and constraints. We propose a Boltzmann Machine Caching based Stochastic Local Search heuristic and use it to build MOCQA: A Multi-Core Optimizer for Constrained Quadratic Assignment problems. We present the system-level architecture of MOCQA, which uses Parallel Tempering in conjunction with multiple Replica Processing Cores to solve GQAP instances. We then detail mapping a MOCQA system to general-purpose hardware along with a circuit-level implementation of a sample MOCQA system on an Intel Stratix 10 FPGA. MOCQA FPGA comprises 32 domain-specific processing cores, supporting GQAPs with up to 255 integer variables, in combination with a Parallel Tempering controller, all operating at 240 MHz with a peak FPGA power draw of 37 W. We benchmark MOCQA FPGA’s performance across a wide range of GQAP benchmark instances and show that it is, on average, 4.4 times faster and 35 times more efficient than the next best solver.}, archive = {J_ACCESS}, author = {Mohammad Bagherbeik and Parastoo Ashtari and Kouichi Kanda and Hirotaka Tamura and Ali Sheikholeslami}, doi = {10.1109/ACCESS.2025.3603171}, journal = {IEEE Access}, pages = {156870-156890}, shortjournal = {IEEE Access}, title = {MOCQA: A multi-core optimizer for constrained quadratic assignment}, volume = {13}, year = {2025}, }
• (2025). Incremental learning frameworks for dynamic heart sound classification. ACCESS, 13, 156855-156869. (www)

  @article{ , abstract = {Heart sound classification plays a vital role in the early detection of cardiovascular diseases. Accurate and timely classification of these sounds can provide critical insights for patient care. However, deep learning models trained incrementally on new data are prone to catastrophic forgetting. To overcome this challenge, we employ Elastic Weight Consolidation (EWC) to facilitate continual learning while preserving previously acquired knowledge. Our study explores various deep learning architectures, including Convolutional Neural Networks (CNN), MobileNet, and Residual Network (ResNet), trained on Mel-Frequency Cepstral Coefficients (MFCC) and spectrogram features. Experimental results indicate that EWC effectively mitigates forgetting while maintaining high classification accuracy. Specifically, Bidirectional Long Short-Term Memory (BiLSTM) achieves 96.52% accuracy on MFCCs, and performs with 90% when a new label is introduced, while MobileNet attains 97.91% on spectrograms, followed by 92.77% after adding a new label. These findings underscore the effectiveness of EWC in heart sound classification, making it a promising approach for real-world applications that require continual learning.}, archive = {J_ACCESS}, author = {Gauri Santosh Pisharady and Kuppam Shireesha and Peddamallu Sai Gowthami and X. Anita}, doi = {10.1109/ACCESS.2025.3606999}, journal = {IEEE Access}, pages = {156855-156869}, shortjournal = {IEEE Access}, title = {Incremental learning frameworks for dynamic heart sound classification}, volume = {13}, year = {2025}, }
• (2025). Sanitizing diffusion-generated images via fingerprint removal and adversarial perturbation for forensic evasion. ACCESS, 13, 156843-156854. (www)

  @article{ , abstract = {Diffusion models have rapidly advanced the realism of synthetic image generation, posing new challenges for forensic detectors. This paper proposes a two-stage forensic evasion framework designed to undermine the detectability of diffusion-generated images. In the first stage, a spectrum-aware generative adversarial network (GAN) removes frequency-domain fingerprints that are commonly exploited by forensic models. In the second stage, adversarial perturbations are applied using the Iterative Fast Gradient Sign Method (I-FGSM) to further mislead detectors while preserving visual fidelity. Experiments conducted on COCO-based datasets demonstrate that our method significantly reduces detection accuracy across multiple state-of-the-art forensic models, including UniFD, DIGBD, and SSIP. Furthermore, we show that combining fingerprint removal with adversarial perturbation achieves stronger evasion than either method alone. Ablation studies also highlight the benefits of adaptive perturbation strengths and data augmentation for enhancing cross-model evasion. This work reveals critical vulnerabilities in current forensic approaches and underscores the need for more robust detection systems against adaptive evasion strategies.}, archive = {J_ACCESS}, author = {Meng-Luen Wu and Cheng-Pin Cheng}, doi = {10.1109/ACCESS.2025.3597641}, journal = {IEEE Access}, pages = {156843-156854}, shortjournal = {IEEE Access}, title = {Sanitizing diffusion-generated images via fingerprint removal and adversarial perturbation for forensic evasion}, volume = {13}, year = {2025}, }
• (2025). On the degenerate entanglement-assisted free-space optical communication with SLM-based beam steering using dual-pump lasers. ACCESS, 13, 156839-156842. (www)

  @article{ , abstract = {We propose a degenerate entanglement-assisted (EA) free-space optical (FSO) communication system employing spatial light modulator (SLM)-based beam steering and a novel dual-pump configuration. The system utilizes a type-0 periodically poled lithium niobate (PPLN) waveguide to generate signal and idler photon pairs at 1570 nm, using two independent low-cost C- and L-band pump lasers. To enable homodyne detection, unmodulated idler photons are transmitted alongside modulated signal photons thus avoiding the need for phase conjugation and quantum memories. We demonstrate 1 Gb/s degenerate entanglement assisted transmission over a 1.5 km terrestrial FSO link at the University of Arizona campus, operating in the L-band. Experimental results show that the proposed degenerate EA system significantly outperforms its classical counterpart, particularly under strong atmospheric turbulence conditions.}, archive = {J_ACCESS}, author = {Ivan B. Djordjevic and Ignacio A. Rojas}, doi = {10.1109/ACCESS.2025.3607303}, journal = {IEEE Access}, pages = {156839-156842}, shortjournal = {IEEE Access}, title = {On the degenerate entanglement-assisted free-space optical communication with SLM-based beam steering using dual-pump lasers}, volume = {13}, year = {2025}, }
• (2025). Predictive optimization of energy consumption in drinking water systems using parallel algorithms. ACCESS, 13, 156824-156838. (www)

  @article{ , abstract = {Optimising energy consumption has become a priority objective for companies and organisations, especially in essential services such as drinking water supply. Traditional strategies of maximising water storage generate energy inefficiency by forcing pumps to move large masses of water. These strategies also generate other negative effects, such as water loss in case of incidents and stress on aquifers. The problem addressed is the need to optimise the operation of drinking water production plants, seeking a balance between minimising stored water, guaranteeing supply and reducing energy consumption. Traditional optimisation systems have limitations in the number of variables they can handle and in the search for solutions in large search spaces. The optimisation problem is the search for solutions in an immense and computationally intractable data space. In this work, a new algorithm based on predictive artificial intelligence and the search for optimal values in distributed and parallel data spaces is proposed. This algorithm includes a consumption prediction module and a control module that calculates the optimal on/off sequence of the pumps. The algorithm uses a heuristic approach to define the search space and can be distributed and parallelised for computational feasibility. The prediction is based on a KNN algorithm that weights historical consumption, giving more weight to more recent data. To address the problem of large search spaces, the algorithm has been parallelised by distributing the computation of the optimisation matrix over several nodes. This allows the computation of local optima and the subsequent union of the results in a central node to obtain the global optimum. The proposed solution has been validated using real water infrastructure data in a city of approximately 5000 inhabitants in southeastern Spain. The validation shows that the new algorithm is more efficient than previous operational strategies, guaranteeing the service with a lower energy consumption of approximately 6%. The implementation of the algorithm has made it possible to maintain the water level at optimal levels, ensure the supply to the population, and adapt the operation to present and future needs.}, archive = {J_ACCESS}, author = {Carlos Calatayud Asensi and José Vicente Berná Martínez and Lucia Arnau Muñoz and Francisco Maciá Pérez}, doi = {10.1109/ACCESS.2025.3606573}, journal = {IEEE Access}, pages = {156824-156838}, shortjournal = {IEEE Access}, title = {Predictive optimization of energy consumption in drinking water systems using parallel algorithms}, volume = {13}, year = {2025}, }
• (2025). Specialized convolutional neural network models for echolocation-based perception. ACCESS, 13, 156811-156823. (www)

  @article{ , abstract = {Echolocating animals can rapidly learn echoic signatures of newly encountered objects from relatively few probing ultrasound pulses. Significant research has recently focused on replicating this ability in engineered systems, but the most promising methods rely on a single deep neural network classifier requiring very large training sets and posing significant challenges to learning additional objects. This work analyzes a perception framework in which multiple (but shallow) specialized convolutional neural networks acting in parallel identify and locate newly encountered objects using small training sets. In this approach, each recognized object has two associated networks, one specialized to identify the object and the other to locate it. Thus, learning an additional object only requires training two new networks without changing the previously trained ones. This paper shows that this architecture performs well even in cluttered environments containing closely spaced objects. More importantly, analysis of the trained neural networks provides insights into the echolocation process, such as salient echo features differentiating the echoes produced by various objects. The specialized neural network framework promises to bring the performance of artificial ultrasound perception closer to that of its biological counterparts.}, archive = {J_ACCESS}, author = {Hyung-Suk Kwon and Ganesh U. Patil and Bogdan I. Epureanu and Bogdan-Ioan Popa}, doi = {10.1109/ACCESS.2025.3607108}, journal = {IEEE Access}, pages = {156811-156823}, shortjournal = {IEEE Access}, title = {Specialized convolutional neural network models for echolocation-based perception}, volume = {13}, year = {2025}, }
• (2025). Dual-band second-order frequency selective surface with sharp roll-off using aperture-coupled techniques. ACCESS, 13, 156799-156810. (www)

  @article{ , abstract = {This research article presents the design and development of a dual-band, second-order bandpass frequency selective surface (FSS) characterized by sharp roll-off behaviour. The investigation begins by analyzing the dual-mode response using a circular patch resonator embedded with a diagonal triangular slot. To minimize cross-polarized reflections produced by these modes, four identical resonators are configured with a 90° rotational orientation. Thereafter, a FSS is developed to achieve dual-band bandpass performance using a multilayer aperture-coupled (AC) design. The designed FSS achieves good flexibility in realizing dual-band filtering characteristics when compared to conventional AC FSS. The effectiveness of the proposed design is confirmed through validation of electric and magnetic coupling within the patch resonators. Moreover, a lumped-element equivalent circuit model is developed, and performance is carried out using the even and odd mode analysis. The designed prototype of FSS undergo measurement verification, exhibiting polarization-insensitive performance and angular robustness over incident angles ranging from 0° to 40°. The structure consistently maintains stable resonant frequencies across both transmission bands, featuring pronounced transition with sharp roll-off characteristics.}, archive = {J_ACCESS}, author = {M. Neelavathy and Yogesh Kumar Choukiker}, doi = {10.1109/ACCESS.2025.3606634}, journal = {IEEE Access}, pages = {156799-156810}, shortjournal = {IEEE Access}, title = {Dual-band second-order frequency selective surface with sharp roll-off using aperture-coupled techniques}, volume = {13}, year = {2025}, }
• (2025). Extremely low-profile four-antenna module covering u6 GHz band and its extreme receive antennas-aided MIMO application for 6G mobile devices. ACCESS, 13, 156785-156798. (www)

  @article{ , abstract = {The upper 6 GHz band (U6 GHz, 6.425-7.125 GHz) has been identified as a new international mobile telecommunication (IMT) spectrum in the World Radiocommunication Conference 2023 (WRC-23). In the recent six generation (6G) workshop of the $3^{\mathrm {rd}}$ Generation Partnership Project (3GPP), the U6 GHz band is also anticipated as a potential 6G core spectrum. Additionally, the industry at the 6G workshop shares a common vision to obtain significant spectral efficiency gain for the user for not being at the expense of excess energy consumption or excess transmit power. To meet the 6G vision for the U6 GHz band, it is promising to apply the extreme receive antennas (ERA)-aided multi-input-multi-output (MIMO) with the receive antennas in the mobile device at least twice the spatial streams in the MIMO system. For example, by applying $12\times 4$ or $8\times 4$ MIMO (12 or 8 receive antennas in the mobile device for 4 spatial streams), user experienced spectral efficiency can reach up to 37 bps/Hz in this study, which is more than twice that of the current fifth generation (5G) $4\times 4$ MIMO with the same 4 spatial streams. Note that the significant spectral efficiency gain is obtained with the same transmit power as compared to $4\times 4$ MIMO at the base station. However, for the mobile device, to accommodate sufficiently many receive antennas for the ERA-aided MIMO application is a challenging task. For this application, we present an extremely low profile (ELP, 0.5 mm only) four-antenna module covering the U6 GHz band for MIMO communications. To our knowledge, the ELP four-antenna module has the smallest thickness (0.5 mm only) among related MIMO antenna modules reported to cover the U6 GHz band. The ELP feature allows the four-antenna module to be easily accommodated in the backcover region of modern mobile devices. By applying three ELP four-antenna modules, the mobile device can conveniently be equipped with 12 MIMO antennas. We present details of the ELP four-antenna module and fabricate three of them mounted in a smartphone-like device for $4\times 4$ , $8\times 4$ , and $12\times 4$ MIMO performance evaluation. Note that the $12\times 4$ MIMO has not yet been reported and is demonstrated for the first time in this study. The results obtained indicate that the ELP four-antenna module works for the ERA-aided MIMO with four spatial streams to achieve significant spectral efficiency gain in the U6 GHz band.}, archive = {J_ACCESS}, author = {Kin-Lu Wong and Chia-Ying Wu and Ping-Cheng Tsao and Shu-Wei Hsu and Wei-Yu Li}, doi = {10.1109/ACCESS.2025.3606909}, journal = {IEEE Access}, pages = {156785-156798}, shortjournal = {IEEE Access}, title = {Extremely low-profile four-antenna module covering u6 GHz band and its extreme receive antennas-aided MIMO application for 6G mobile devices}, volume = {13}, year = {2025}, }
• (2025). Prognostic modeling for PEM fuel cells: A comparative analysis among different types of approaches. ACCESS, 13, 156768-156784. (www)

  @article{ , abstract = {This paper presents a comparative analysis among different types of approaches for prognostics of proton exchange membrane fuel cells (PEMFCs). The approaches are broadly categorized into three groups: 1) model-based; 2) data-driven; and 3) hybrid approaches. Each type of approach is discussed in detail. Model-based approaches, particularly semi-empirical models, utilize electrochemical principles to characterize PEMFC degradation through interpretable parameters associated with activation, ohmic, and concentration losses. These parameters are extracted from polarization data and modeled over time using curve-fitting techniques. While computationally efficient and physically grounded, model-based methods show limited adaptability under nonlinear or highly dynamic operating conditions. Data-driven methods such as long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and support vector regression (SVR) offer high accuracy by learning degradation behavior directly from historical data. However, they typically require large training datasets, are sensitive to hyperparameter tuning, and lack physical interpretability. A hybrid prognostic approach is proposed by integrating a semi-empirical electrical equivalent circuit model with SVR. The hybrid model firstly predicts the voltage using the semi-empirical approach and then corrects residual errors using SVR. This structure combines the interpretability of physics-based models with the flexibility of machine learning. All approaches are evaluated on two publicly available aging datasets, covering both steady-state and quasi-dynamic conditions. Results demonstrate that the hybrid approach outperforms standalone models across varying training durations. The proposed hybrid approach offers a practical solution for real-time PEMFC health monitoring, providing a balanced trade-off between accuracy, computational efficiency, and interpretability.}, archive = {J_ACCESS}, author = {Purushottam Kumar and Sanjib Ganguly}, doi = {10.1109/ACCESS.2025.3606630}, journal = {IEEE Access}, pages = {156768-156784}, shortjournal = {IEEE Access}, title = {Prognostic modeling for PEM fuel cells: A comparative analysis among different types of approaches}, volume = {13}, year = {2025}, }
• (2025). A bayesian ensemble framework for financial event detection via reward-bayesian stochastic search and combinatorial thompson sampling. ACCESS, 13, 156755-156767. (www)

  @article{ , abstract = {In the financial market, economic players raise funds from various financial markets, such as the money market and capital market, according to their purposes and manage those funds. The direction of raising and operating these funds may change depending on financial market events. Financial market events include a variety of events, including stock market stress events related to stock market crashes, credit risk events related to bonds, inflation events related to commodities, and liquidity-related events. In order to accurately detect financial market events, key variables with high information power must be selected, and the variable selection method must reflect the dynamic characteristics of the time series. This study proposes a financial event detection framework that applies a variable selection process to macroeconomic variables and sector indices. To demonstrate that our methodology works well in various financial markets, we considered stock and bond markets as the test markets. For these test markets, we demonstrate that our framework outperforms other benchmarks across a variety of test periods.}, archive = {J_ACCESS}, author = {Jungyu Ahn and Myeongsu Choi and Hyoung-Goo Kang}, doi = {10.1109/ACCESS.2025.3605927}, journal = {IEEE Access}, pages = {156755-156767}, shortjournal = {IEEE Access}, title = {A bayesian ensemble framework for financial event detection via reward-bayesian stochastic search and combinatorial thompson sampling}, volume = {13}, year = {2025}, }
• (2025). Immersive learning model for university (ILMU): A novel VR-based distance learning in higher education. ACCESS, 13, 156734-156754. (www)

  @article{ , abstract = {The rapid adoption of Immersive Virtual Reality (IVR) distance learning in higher education necessitates cohesive frameworks to guide its effective implementation. However, existing models remain fragmented, lacking integration in pedagogy, technology, and institutions. This study addresses this gap by proposing the Immersive Learning Model for University (ILMU), a model tailored for higher education institutions adopting IVR-based distance learning, developed through the Design Research Methodology (DRM). The research systematically identifies critical success components via a scoping review of 227 studies, revealing six clusters: Learning Design, Technology, Immersion, Engagement, Interactivity, and Usability. These components were refined through Delphi verification with stakeholders (academics, developers, and users), resulting in 15 validated components. The ILMU model integrates these components into a layered structure, emphasizing institutional alignment (Standard, Policy & Curriculum), adaptive pedagogy, and immersive technological synergy. A quasi-experiment involving 80 students compared IVR-based learning (using Nusameta apps) with traditional Zoom instruction. Results demonstrated significant learning gains for the IVR group (mean post-test: 77.75 vs 72.00; p = 0.042), validated by non-parametric tests (Mann–Whitney U = 592.50). The study highlights ILMU’s capacity to increase learning effectiveness, reduce cognitive load, enhance engagement, and align with institutional policies while addressing hardware limitations and ergonomic design challenges. By bridging theoretical rigor with empirical validation, ILMU offers a scalable framework for universities transitioning to immersive, technology-enhanced education. This work contributes to the evolving discourse on IVR in academia, providing actionable insights for educators, policymakers, and developers to optimize immersive learning ecosystems.}, archive = {J_ACCESS}, author = {Sunardi and Meyliana and Spits Warnars Leslie Harco Hendric and Yusep Rosmansyah}, doi = {10.1109/ACCESS.2025.3606957}, journal = {IEEE Access}, pages = {156734-156754}, shortjournal = {IEEE Access}, title = {Immersive learning model for university (ILMU): A novel VR-based distance learning in higher education}, volume = {13}, year = {2025}, }
• (2025). Pareto-adaptive robust neural control with decision-making for multicopter. ACCESS, 13, 156714-156733. (www)

  @article{ , abstract = {A flight control system must balance tracking accuracy, control smoothness, and energy use under nonlinear dynamics, disturbances, actuator dynamics, and delays. Existing schemes (e.g., LQR, MPC) typically treat the problem as single-objective with fixed weights, provide limited guarantees for multi-objective trade-offs, and often neglect actuator dynamics, delays, or automated selection of weights—making consistent performance under uncertainty difficult. This paper presents an adaptive Neural-network Pareto-based Optimal Control (NPOC) framework that formalizes a weighted four-objective cost—energy usage, state deviation, control smoothness, and time-weighted tracking error. We prove the existence of optimal solutions within the weighted multi-objective formulation and derive a new state-feedback control law with closed-loop stability established via Lyapunov analysis. Actuator dynamics and signal delays are explicitly modeled within the control synthesis. A multilayer-perceptron decision maker, trained on simulation-augmented data, maps desired performance metrics (RMSE and energy) to weight selections, enabling automatic adaptation of the controller. Validation on a nonlinear quadcopter with wind disturbances, sensor noise, and 50% mass variation shows improved tracking accuracy, smoother inputs, and favorable energy usage compared with LQR and MPC. Monte Carlo trials over multiple initial conditions and ANOVA tests (p-values above significance thresholds) confirm robustness and statistical reliability. These results demonstrate a mathematically grounded and adaptive control methodology for multicopters operating under complex nonlinear dynamics and uncertainty.}, archive = {J_ACCESS}, author = {Navid Mohammadi and Morteza Tayefi and Javad Koushki}, doi = {10.1109/ACCESS.2025.3607096}, journal = {IEEE Access}, pages = {156714-156733}, shortjournal = {IEEE Access}, title = {Pareto-adaptive robust neural control with decision-making for multicopter}, volume = {13}, year = {2025}, }
• (2025). Numerical modeling method of turn-to-turn transient voltages taking into account the common mode interactions. ACCESS, 13, 156692-156713. (www)

  @article{ , abstract = {Electrifying future aircraft requires the development of advanced embedded electrical power systems. To achieve this objective, increasing on-board voltage levels are needed, beyond the conventional reference values of 230 VAC/540 VDC. In More-Electric Aircraft (MEA) and All-Electric Aircraft (AEA) applications, rotating electrical machines powered by Pulse Width Modulation (PWM) inverters are widely used. However, the use of these fast-switching PWM inverters combined with the presence of non-negligible cable lengths leads to significant overvoltages at the machine terminals (caused by waveform reflection phenomena) and non-uniform voltage distribution within stator windings. This leads to an increased risk of Partial Discharge (PD) inception, as turn-to-turn voltages may exceed the Partial Discharge Inception Voltage (PDIV). The risk is further exacerbated by the use of Wide Band Gap (WBG) inverters, which enable higher switching frequencies and faster voltage rise times. Combined with the harsh environmental conditions associated to high-altitude operations, these factors further increase the probability of PD inception and insulation breakdown in machine windings. This paper presents a time-domain validation of a High-Frequency (HF) model of an electrical machine stator windings. The method uses a two-time-scale approach, accounting for the electrical machine’s operating environment to predict turn-to-turn and turn-to-ground voltage distributions. In order to take into account the HF behavior of the different elements constituting the stator winding, an experimental validation is carried out using three systems of increasing complexity: an elementary air-core coil, an intermediate ferrite core coil, and a more representative coil with a laminated-iron core to simulate the materials of electrical machines.}, archive = {J_ACCESS}, author = {Héléna Gressinger and François Balavoine and Emmanuel Mateo and Loucif Benmamas and Robin Acheen and Stéphane Duchesne}, doi = {10.1109/ACCESS.2025.3603939}, journal = {IEEE Access}, pages = {156692-156713}, shortjournal = {IEEE Access}, title = {Numerical modeling method of turn-to-turn transient voltages taking into account the common mode interactions}, volume = {13}, year = {2025}, }
• (2025). Toward passive sensing large crowds through user-generated electromagnetic activity. ACCESS, 13, 156681-156691. (www)

  @article{ , abstract = {The emerging field of Joint Communication and Sensing (JCAS) will have a substantial impact on future wireless communication systems and how people interact with them. The investigation of sensing large crowds with these communication systems still lacks thorough exploration. Current studies are generally based on small-scale experiments with few participants. However, these studies are not representative in comparison to large crowds. By passively sensing an environment, it is possible to link the captured electromagnetic (EM) activity within that area to the crowd size without applying any demodulation or decoding of packets and preserving privacy. Measurements were conducted in an exclusive area at a large music festival, using ten Software-Defined Radios (SDRs) to capture EM wave energy across several Wi-Fi and LTE bands. The collected data were compared to a validation data set. The study shows that the short-time Fourier transform (STFT) and the magnitude extraction technique are two viable techniques to apply to the captured data sets. Additionally, the correlations found between the validation data set and the LTE data sets are significantly stronger than those in the Wi-Fi data sets. This indicates that further research is needed on specific frequency band selection, advanced digital signal processing methods and scalable data-capturing strategies.}, archive = {J_ACCESS}, author = {Dennis Joosens and Abdil Kaya and Ruben Nietvelt and Maarten Weyn and Rafael Berkvens}, doi = {10.1109/ACCESS.2025.3606439}, journal = {IEEE Access}, pages = {156681-156691}, shortjournal = {IEEE Access}, title = {Toward passive sensing large crowds through user-generated electromagnetic activity}, volume = {13}, year = {2025}, }
• (2025). Geometric design, mathematical modeling, and motion analysis of a novel 2T1R planar manipulator based on nested circular plates. ACCESS, 13, 156665-156680. (www)

  @article{ , abstract = {This paper presents a novel TTR mechanism, also known as a 2T1R (2-Translation-1-Rotation) three-degree-of-freedom planar mechanism, achieved through the arrangement of three nested circular plates. With its ample workspace and high stiffness, this mechanism is well-suited for a variety of industrial applications. The study begins with an exploration of the mechanism’s degree of freedom using screw theory, followed by mathematical modelling of its direct and inverse kinematics for optimal movement between points. Employing an algorithm to eliminate redundant responses, programming and analysis are conducted in LabVIEW. To ensure precise motion of the end effector through designated points, cubic spline interpolation is utilized, with coding implemented in LabVIEW following mathematical analysis of spline equations. Additionally, two distinct motion paths are considered. This examination reveals the mechanism’s ability to traverse linear and circular paths with minimal error. The mechanism demonstrates acceptable accuracy in executing circular trajectories, as confirmed through experimental validation. Verification of the real path’s conformity to the simulated path involves detecting the end effector’s motion using linear and circular sensors, with results recorded and compared to the simulated trajectory. The findings demonstrate consistency between the experimental and simulated results.}, archive = {J_ACCESS}, author = {Ahad Zare Jond and Siamak Pedrammehr and Kais I. Abdul-Lateef Al-Abdullah and Sajjad Pakzad}, doi = {10.1109/ACCESS.2025.3607166}, journal = {IEEE Access}, pages = {156665-156680}, shortjournal = {IEEE Access}, title = {Geometric design, mathematical modeling, and motion analysis of a novel 2T1R planar manipulator based on nested circular plates}, volume = {13}, year = {2025}, }
• (2025). Wavelength-selective coupling to vertical higher order modes in a 500-nm-thick silicon multimode waveguide using long- and short-period gratings. ACCESS, 13, 156656-156664. (www)

  @article{ , abstract = {This work explores thick silicon multimode waveguides (MMWs), focusing on vertical multimode operation. We present grating-assisted coupling structures to efficiently convert the fundamental mode to vertical higher-order modes in a 500-nm-thick silicon MMW. Both forward and backward coupling schemes are investigated. The forward coupling with a long-period grating exhibits broadband conversion over a coupling length less than $40\mu $ m. The backward coupling with a short-period grating demonstrates mode conversion with a 3-dB bandwidth ranging from 5.8 to 13.3 nm. These results open new avenues for applications such as on-chip spatial mode multiplexing with a higher capacity.}, archive = {J_ACCESS}, author = {Hyeongjung Kim and Jungwoo Lee and Jihoon Seo and Minjin Kim and Min-Suk Kwon}, doi = {10.1109/ACCESS.2025.3606842}, journal = {IEEE Access}, pages = {156656-156664}, shortjournal = {IEEE Access}, title = {Wavelength-selective coupling to vertical higher order modes in a 500-nm-thick silicon multimode waveguide using long- and short-period gratings}, volume = {13}, year = {2025}, }
• (2025). Homomorphic encryption-based facial recognition for dual-layer similarity analysis of modified images. ACCESS, 13, 156639-156655. (www)

  @article{ , abstract = {Recently, advanced closed-circuit televisions have been widely installed for security and crime prevention purposes, leading to the large-scale adoption of facial recognition systems. However, the indiscriminate use of such technologies has resulted in excessive intrusion into individuals’ private lives, prompting ongoing discussions around privacy infringement. In response, Homomorphic Encryption (HE)-based facial recognition technologies—which aim to ensure both privacy and security—have been actively researched and developed. Nevertheless, existing researches have limitations in that they did not seriously consider the possibility of original image leakage and failed to incorporate color information into the similarity measurement process. To address these issues, we propose a mechanism that considers not only facial landmarks but also color information in facial recognition, applying HE in a way that causes actual image modification in the facial area. This ensures that privacy can still be protected even in the event of facial data leakage. In the proposed mechanism, similarity values are independently derived from both facial landmarks and aligned color information. After decrypting each homomorphically encrypted value, a final Dual-Layer similarity is computed by combining both, enabling the identification of the facial image with the highest similarity. To achieve highly accurate and reliable final similarity computation, we propose a Dual-Layer similarity derivation formula based on the Softmax function, which is widely used as a core element in deep learning for multi-class classification. The effectiveness and necessity of the proposed method are demonstrated through comparisons with the arithmetic mean, weighted arithmetic mean, and harmonic mean. Furthermore, since most existing HE schemes are limited to supporting addition and multiplication operations, we analyze similarity derivation algorithms that comply with these constraints. Through a comparative analysis based on computational efficiency, we demonstrate that Euclidean Distance is well-suited for efficient HE-based facial recognition.}, archive = {J_ACCESS}, author = {Wooyoung Son and Soonhong Kwon and Jong-Hyouk Lee}, doi = {10.1109/ACCESS.2025.3605615}, journal = {IEEE Access}, pages = {156639-156655}, shortjournal = {IEEE Access}, title = {Homomorphic encryption-based facial recognition for dual-layer similarity analysis of modified images}, volume = {13}, year = {2025}, }
• (2025). Hybrid Quantum–Classical framework for urban traffic state classification. ACCESS, 13, 156628-156638. (www)

  @article{ , abstract = {This paper presents a hybrid quantum–classical approach for multiclass traffic state classification using a benchmark urban mobility dataset collected in Daejeon, South Korea. The proposed architecture, called the Multilayer Perceptron–Variational Quantum Classifier (MVQC), combines classical feature extraction via a multilayer perceptron (MLP) with quantum-based classification using a variational quantum classifier (VQC). On the medium-scale subset of the dataset, the MVQC model achieved a test accuracy of 91.4%, outperforming the standalone VQC model, which achieved 61.8%. Although MVQC did not reach the accuracy of the classical MLP model (98.6%), it consistently outperformed purely quantum models and demonstrated better scalability with increasing data complexity. All quantum operations were simulated under noise-free conditions using IBM Qiskit’s statevector simulator. These results demonstrate that classical feature extraction can effectively guide quantum circuits, enhance learning performance, and highlight the hybrid MVQC architecture as a viable solution for time-series classification in intelligent transportation systems.}, archive = {J_ACCESS}, author = {Hongsuk Yi}, doi = {10.1109/ACCESS.2025.3605175}, journal = {IEEE Access}, pages = {156628-156638}, shortjournal = {IEEE Access}, title = {Hybrid Quantum–Classical framework for urban traffic state classification}, volume = {13}, year = {2025}, }
• (2025). Exploring the concept and operations of sustainable cities: A text mining approach. ACCESS, 13, 156613-156627. (www)

  @article{ , abstract = {While there is a global trend towards making cities more sustainable, the concept and operations of sustainable cities may not be well-defined in the literature, as the number and diversity of studies on the topic make it challenging to integrate all perspectives. This study adopts a service system perspective to explore the concept and operational structure of sustainable cities. Text mining techniques and unsupervised machine learning algorithms were used to analyze 3,500 scientific articles on sustainable cities related to service systems. This analysis identified 24 research topics that represent key areas of inquiry, and classified them into three functional domains of control, infrastructure, and policy. The classification was used to develop a conceptual framework that interprets sustainable cities as large-scale service systems that are enabled by digital technology. The framework highlights the roles of various stakeholders, and provides insights into how urban operations can be designed to support sustainability. The findings offer a structured foundation for interdisciplinary understanding and future empirical research in sustainable urban development.}, archive = {J_ACCESS}, author = {Minjun Kim and Juram Kim and Junegak Joung and Chiehyeon Lim}, doi = {10.1109/ACCESS.2025.3605571}, journal = {IEEE Access}, pages = {156613-156627}, shortjournal = {IEEE Access}, title = {Exploring the concept and operations of sustainable cities: A text mining approach}, volume = {13}, year = {2025}, }
• (2025). HSIF: A transformer-based cross-attention framework for cryptocurrency trend forecasting via multimodal Sentiment–Market fusion. ACCESS, 13, 156600-156612. (www)

  @article{ , abstract = {Cryptocurrency markets are highly volatile and sentiment-driven, posing challenges to traditional forecasting methods. This paper presents Hard and Soft Information Fusion (HSIF), a novel Transformer-based dual-stream model that combines market data and social sentiment using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a financial sentiment analysis tool, and a bidirectional cross-attention mechanism. Evaluations on multi-year Bitcoin data show that HSIF achieves 97.48% accuracy and a 26.64% return, outperforming Long Short-Term Memory (LSTM)-based and other multimodal models. The results highlight the effectiveness of domain-specific sentiment embeddings and cross-modal attention in enhancing trend prediction accuracy for volatile cryptocurrency markets.}, archive = {J_ACCESS}, author = {Saeed Mohammadi Dashtaki and Mehdi Hosseini Chagahi and Aein Bahadori and Behzad Moshiri and Md. Jalil Piran and Allahyar Montazeri}, doi = {10.1109/ACCESS.2025.3605522}, journal = {IEEE Access}, pages = {156600-156612}, shortjournal = {IEEE Access}, title = {HSIF: A transformer-based cross-attention framework for cryptocurrency trend forecasting via multimodal Sentiment–Market fusion}, volume = {13}, year = {2025}, }
• (2025). An improved arithmetic optimization algorithm with multi-strategy variable step size for optimizing engineering problems. ACCESS, 13, 156566-156599. (www)

  @article{ , abstract = {Arithmetic Optimization Algorithm (AOA) has attracted considerable attention owing to its efficient search mechanism; however, it still exhibits deficiencies in optimization accuracy and convergence speed. To overcome these limitations, this paper proposes an improved arithmetic optimization algorithm based on multi-strategy variable step size, named MSVSAOA. The algorithm integrates four key strategies: a Lévy flight-based variable step size for the exploration phase, an update direction guidance-based variable step size for the exploitation phase, a doubly randomized variable step size for the boundary handling phase, and an information fusion strategy that combines the information of the best individual with that of the updated individuals. These improvements effectively enhance global exploration capability, refine local exploitation precision, and accelerate convergence. To evaluate its performance, MSVSAOA was tested on 29 functions from the CEC2017 test suite and compared with nine mainstream optimization algorithms. A comprehensive evaluation was conducted through statistical analysis, convergence analysis, Wilcoxon rank-sum tests, and Friedman tests. According to the Friedman ranking results, the average ranks of MSVSAOA, AOA, RCLSMAOA, HSMAAOA, AOAAO, GWOCS, GAT, tGSSA, ESO, and ISO were 1.48, 9.79, 5.55, 2.28, 7.10, 6.17, 5.03, 8.62, 3.55, and 5.41, respectively. Furthermore, the algorithm was successfully applied to four real-world engineering optimization problems. Experimental results demonstrate that MSVSAOA outperforms comparative algorithms in overall performance, with significant enhancements in convergence speed and optimization accuracy over AOA: objective function values are improved by 0.27%, 0.31%, 8.91%, and 99.9998% across four cases. This validation confirms the algorithm’s considerable practical value and application potential in engineering contexts.}, archive = {J_ACCESS}, author = {Jun Liu}, doi = {10.1109/ACCESS.2025.3601091}, journal = {IEEE Access}, pages = {156566-156599}, shortjournal = {IEEE Access}, title = {An improved arithmetic optimization algorithm with multi-strategy variable step size for optimizing engineering problems}, volume = {13}, year = {2025}, }
• (2025). AI-augmented literature reviews: Efficient clustering and summarization for researchers. ACCESS, 13, 156535-156565. (www)

  @article{ , abstract = {With the collaboration between agents and large language models (LLMs), this paper presents a framework for using retrieval-based relevance search on a large collection of academic papers to facilitate exploratory literature reviews, which can subsequently be expanded into comprehensive reviews. The goal of this paper is to help researchers provide a practical, automatic pipeline, supported by a case study, to integrate agents and LLMs into the literature review process. The proposed framework consists of three key steps: large-scale document preprocessing, relevant document retrieval, and in-category document clustering and summarization. Using this framework, researchers can efficiently review large-scale academic papers in a transparent, scalable, efficient, and reproducible manner.}, archive = {J_ACCESS}, author = {Shiyu Yuan and Carlo Lipizzi}, doi = {10.1109/ACCESS.2025.3603162}, journal = {IEEE Access}, pages = {156535-156565}, shortjournal = {IEEE Access}, title = {AI-augmented literature reviews: Efficient clustering and summarization for researchers}, volume = {13}, year = {2025}, }
• (2025). Functional interval observers-based active fault tolerant control for continuous-time linear parameter-varying systems. ACCESS, 13, 156522-156534. (www)

  @article{ , abstract = {This article addresses the problem of Functional Interval Observers (FIO) based on Active Fault Tolerant Control for continuous-time polytopic Linear Parameter-Varying systems in the presence of unknown but bounded uncertainties, external disturbances and actuator faults. To achieve this objective, a novel FIO is developed for an augmented descriptor LPV system to simultaneously estimate the functional system state and actuator faults. Then, an $\textrm {L}_{\infty }$ norm-based approach is employed to mitigate the impact of uncertainties and enhance the precision of the proposed observer. Based on the obtained estimation, an AFTC strategy is designed using the $\textrm {H}_{\infty }$ index to compensate the effects of faults and ensure the system’s performance and stability. All stability criteria are expressed in terms of Linear Matrix Inequalities (LMI), derived from a common Lyapunov function. Finally, the effectiveness and correctness of the proposed strategy are demonstrated in a simulation using a case study based on a three-tank system.}, archive = {J_ACCESS}, author = {Hanin Mizouri and Rihab Lamouchi and Messaoud Amairi}, doi = {10.1109/ACCESS.2025.3605821}, journal = {IEEE Access}, pages = {156522-156534}, shortjournal = {IEEE Access}, title = {Functional interval observers-based active fault tolerant control for continuous-time linear parameter-varying systems}, volume = {13}, year = {2025}, }
• (2025). Virtual museum experience design and user behavior analysis. ACCESS, 13, 156504-156521. (www)

  @article{ , abstract = {In the evolving landscape of digital cultural heritage, the integration of advanced computational models into immersive virtual environments has become essential for enriching user engagement and fostering more meaningful interpretative experiences. As digital heritage platforms increasingly adopt interactive technologies, there is a growing need to move beyond static presentations and introduce intelligent, responsive systems that adapt to individual users’ interests and behaviors. Traditional virtual museum systems often suffer from rigid content structures, limited adaptability, and a lack of personalized storytelling, all of which constrain user immersion and diminish the potential for impactful learning experiences. To overcome these challenges, our approach integrates semantic-topological modeling with adaptive interaction mechanisms, forming a dynamic and personalized digital museum experience. The proposed framework features a hybrid computational architecture that unites symbolic knowledge representations with algorithmic spatial layout generation. This integration enables the creation of flexible narrative trajectories that are contextually relevant and customized according to each user’s cognitive and behavioral profile. Furthermore, the system incorporates reinforcement-driven semantic planning to interpret and respond to user interactions in real time, ensuring a continuously evolving and coherent interpretative journey. Experimental results reveal substantial improvements in user satisfaction, engagement levels, and knowledge retention when compared to traditional virtual museum platforms. These outcomes highlight the scalability and transformative potential of the model, reinforcing its capacity to enhance accessibility, personalization, and educational value across diverse digital heritage applications.}, archive = {J_ACCESS}, author = {Wen Chen and Yingji Chen}, doi = {10.1109/ACCESS.2025.3600420}, journal = {IEEE Access}, pages = {156504-156521}, shortjournal = {IEEE Access}, title = {Virtual museum experience design and user behavior analysis}, volume = {13}, year = {2025}, }
• (2025). Analysis of bias temperature instability in peripheral CMOS devices for low-temperature memory applications. ACCESS, 13, 156497-156503. (www)

  @article{ , abstract = {In this study, the bias temperature instability (BTI) of peripheral complementary metal–oxide semiconductor devices in a cell-on-peri (COP) structure is investigated under cryogenic conditions. First, positive BTI (PBTI) in the nMOSFET is found to be more severe than negative BTI (NBTI) in the pMOSFET, despite the same overdrive voltage. This behavior is primarily attributed to the shorter carrier capture/emission time of the nMOSFET. Second, the threshold voltage shift ( $\Delta V_{\mathrm {th}}$ ) in the pMOSFET caused by NBTI increases as the temperature decreases from 300K to 77K. This trend differs from that of the nMOSFET and is reported here for the first time. It is attributed to increased Fowler–Nordheim (FN) tunneling and impact ionization, resulting from suppressed phonon scattering at low temperatures. Last of all, the nMOSFET exhibits hump characteristics due to PBTI.}, archive = {J_ACCESS}, author = {Jung Rae Cho and Seungwon Go and Jingyu Park and Tae Jun Yang and Seonhaeng Lee and Namhyun Lee and Dong Keun Lee and Yoon Kim and Myounggon Kang and Rock-Hyun Baek and Changhyun Kim and Sangwan Kim and Dae Hwan Kim}, doi = {10.1109/ACCESS.2025.3605829}, journal = {IEEE Access}, pages = {156497-156503}, shortjournal = {IEEE Access}, title = {Analysis of bias temperature instability in peripheral CMOS devices for low-temperature memory applications}, volume = {13}, year = {2025}, }
• (2025). A generalized multiple-valued FPGA architecture based on improved T-gate circuit. ACCESS, 13, 156486-156496. (www)

  @article{ , abstract = {Binary logic based FPGAs have been successfully used in a wide range of applications. Although these devices offer clear advantages, their programmable routing requires significant wiring resources between logic blocks. This overhead can be mitigated by employing multiple-valued logic instead of binary logic for signal transmission. Hence, multiple-valued FPGAs emerged. These devices are based on multiple-valued look-up tables and flip-flops within their configurable logic blocks. Although this is a valid strategy, it needs separate combinational and sequential elements which result in high power consumption. To overcome this problem, we propose a novel T-gate based architecture. Our architecture has the advantage of merging the look-up table and flip-flops within multiple-valued logic based configurable logic blocks. The proposed architecture is general such that it is applicable to any multiple-valued logic level and results in significant reduction in power consumption. Moreover, the proposed architecture significantly improves robustness against process variations. We compare the proposed architecture against recent studies to evaluate its performance. We show its strengths and weaknesses this way. All simulation files for the proposed architecture are made publicly available in our GitHub repository [https://github.com/ahmetunu/Nary-LUT/]. Hence, the readers can test and extend them in future studies.}, archive = {J_ACCESS}, author = {Ahmet Unutulmaz and Cem Ünsalan}, doi = {10.1109/ACCESS.2025.3605842}, journal = {IEEE Access}, pages = {156486-156496}, shortjournal = {IEEE Access}, title = {A generalized multiple-valued FPGA architecture based on improved T-gate circuit}, volume = {13}, year = {2025}, }
• (2025). The evolutions of reflectarray antennas for small satellites: Navigating to design, fabrication, and performance techniques. ACCESS, 13, 156465-156485. (www)

  @article{ , abstract = {Classical antennas in small satellites such as reflectors and phased arrays have encountered significant challenges, including restricted beam steering capabilities and complex deployment mechanisms. Reflectarray antennas have emerged as a promising alternative, offering a compact and lightweight solution. However, performance constraints related to high-gain, phase control, and multi-beam operation require continued innovation to meet the demands of long-distance communication. This article explores recent advancements in reflectarray antenna design, fabrication, and implementation techniques specifically tailored for small satellite platforms. The study traces the technological evolution of reflectarrays from the 1960s to the present, with a particular emphasis on deployment strategies including foldable and stowable configurations, as well as the integration of solar energy harvesting. A comparative analysis of various reflectarray architectures is presented, highlighting their electromagnetic performance characteristics and suitability for diverse mission profiles. The article also explores novel applications of reflectarrays in emerging domains such as low Earth orbit (LEO) satellite constellations, 6G communication networks, Earth observation, and defense systems. By addressing current challenges and identifying future research directions, this study aims to support the development of next-generation reflectarray systems for advanced small satellite applications.}, archive = {J_ACCESS}, author = {Jasim Uddin and Harvinder Sing Nagi and Benjamin Falkner and Reza Akhavan and Ginu Rajan and Jon Platts and Chaminda T. E. R. Hewage and Leshan Uggalla}, doi = {10.1109/ACCESS.2025.3597389}, journal = {IEEE Access}, pages = {156465-156485}, shortjournal = {IEEE Access}, title = {The evolutions of reflectarray antennas for small satellites: Navigating to design, fabrication, and performance techniques}, volume = {13}, year = {2025}, }
• (2025). Rethinking multimodality: Optimizing multimodal deep learning for biomedical signal classification. ACCESS, 13, 156436-156464. (www)

  @article{ , abstract = {This study presents a novel perspective on multimodal deep learning for biomedical signal classification, systematically analyzing the impact of complementary feature domains on model performance. While fusing multiple modalities often presumes enhanced accuracy, this research demonstrates that adding modalities can yield diminishing returns, as not all fusions are inherently advantageous. To validate this, five deep learning models were rigorously evaluated for electrocardiogram (ECG) classification: three unimodal models (1D-CNN for time, 2D-CNN for time-frequency, and a 1D-CNN-Transformer for frequency) and two multimodal models. Our findings, supported by Bayesian inference and p-values <0.05, show that the fusion of the time and time-frequency domains consistently outperformed the unimodal baselines. This confirmed the synergistic complementarity of these specific domains. Conversely, including the frequency domain offered no further improvement and, at times, led to a marginal decline in performance, a phenomenon we substantiated with an ablation study. This indicates that the frequency domain provided representational redundancy rather than new, useful information. Redefining the fundamental principles of multimodal design in biomedical signal analysis, we introduce the Complementary Domain Theory, which posits that optimal domain fusion is not about the number of modalities but the quality of their inherent complementarity. This paradigm shifts the focus from traditional, heuristic feature selection to a mathematically quantifiable framework for identifying ideal domain combinations. Our findings, while applied to ECG, have broader relevance to other biomedical domains involving multimodal time-series data. The core insight is that optimal multimodal performance arises from the intrinsic information-theoretic complementarity among fused domains.}, archive = {J_ACCESS}, author = {Timothy Oladunni and Alex Wong}, doi = {10.1109/ACCESS.2025.3605315}, journal = {IEEE Access}, pages = {156436-156464}, shortjournal = {IEEE Access}, title = {Rethinking multimodality: Optimizing multimodal deep learning for biomedical signal classification}, volume = {13}, year = {2025}, }
• (2025). Coordinated post-disaster restoration for coupled distribution-transportation system. ACCESS, 13, 156424-156435. (www)

  @article{ , abstract = {Effective coordination of diverse resources for rapid post-disaster recovery is crucial for ensuring power grid security. Urban power grids encompass varied load categories, and the recovery process is closely interconnected with transportation networks. To comprehensively address these challenges, this paper proposes a collaborative post-disaster recovery approach for urban coupled distribution-microgrid-transportation systems considering tiered load demands. First, differentiated electricity load demand characteristics are analyzed leveraging Long Short-Term Memory-AdaBoost (LSTM-AdaBoost) model, enabling precise identification of post-disaster priority user demand. Then the post-disaster model for coupled system is established incorporating the dispatch of repair crews within the transportation network and the support role of microgrids. The optimization problem is addressed by Benders decomposition technique. Numerical results demonstrate the effectiveness of the proposed post-disaster collaborative recovery and solution approach in accelerating power supply restoration.}, archive = {J_ACCESS}, author = {Leibao Wang and Jifeng Liang and Yu Xin and Tengkai Yu and Qi Zhang and Tongfei Cui and Wei Fu}, doi = {10.1109/ACCESS.2025.3605974}, journal = {IEEE Access}, pages = {156424-156435}, shortjournal = {IEEE Access}, title = {Coordinated post-disaster restoration for coupled distribution-transportation system}, volume = {13}, year = {2025}, }
• (2025). Optical mark recognition techniques for multiple-choice tests: A comprehensive review. ACCESS, 13, 156407-156423. (www)

  @article{ , abstract = {Optical Mark Recognition (OMR) still plays a vital role in the automation of grading multiple-choice tests, providing efficiency and reliability in educational assessment all over the world. A detailed review follows the progress of OMR systems from the earliest hardware-dominated solutions, which required very strict conditions to work, up to current ones that utilize the power of deep learning. Conventional image processing techniques—such as thresholding, projection profiles, and template matching–have typically supplied fast and accurate results; however, their performance significantly worsens due to the imperfections of the real world including misalignment, noise, and ambiguous markings. The rise of classical computer vision techniques, particularly edge detection, contour analysis, and geometric corrections, has considerably enhanced the capabilities of OMR systems to deal with such problems. Today, modern deep learning–especially object detection architectures such as YOLO, and advanced convolutional networks-has brought a new era of flexibility and accuracy, even though it requires more annotated data and computing power. Nonetheless, the new challenges remain the same, regarding ambiguous marks, variability in form layouts, lighting inconsistencies, and the lack of large, publicly available datasets for benchmarking. This article, which critically synthesizes 25 representative papers, gives a detailed insight into the current OMR methods and their strengths and weaknesses, as well as outlining research gaps. This paper concludes by indicating promise for future research, including hybrid pipelines, domain adaptation, and the creation of robust, diverse datasets to support fair and reproducible benchmarking.}, archive = {J_ACCESS}, author = {Mohammed Ali and Suhel Hammoud and Alida Esper}, doi = {10.1109/ACCESS.2025.3600106}, journal = {IEEE Access}, pages = {156407-156423}, shortjournal = {IEEE Access}, title = {Optical mark recognition techniques for multiple-choice tests: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Federated learning-based travel time prediction for adaptive congestion control using 5G-assisted roundabout traffic networks. ACCESS, 13, 156389-156406. (www)

  @article{ , abstract = {As urbanization accelerates and traffic volumes increase, managing congestion in unsignalized roundabouts becomes increasingly complex, particularly in mixed-traffic environments with human-driven vehicles (HVs), connected and automated vehicles (CAVs), and robotic vehicles (RVs). This study proposes a Roundabout Traffic Coordination Mechanism (RTCM), an integrated framework that combines Federated Learning-based Travel Time Prediction (FL–TTP) with hybrid reinforcement learning and model predictive control (RL–MPC). In this design, FL–TTP generates decentralized short-term travel time forecasts that serve as input to the RL–MPC controller within RTCM, where RL adapts long-term policies and MPC enforces short-horizon safety. The system operates on a 5G-assisted vehicle-to-everything (5G-V2X) network, ensuring low-latency synchronization between prediction and control. Simulations at the Nettakallappa Circle roundabout in Bangalore show that FL–TTP reduces waiting time by 26.5%, delay by 28.5%, and RMSE by 18.7% compared with centralized baselines, while the complete RTCM further decreases queue length by 41.2% and improves throughput by 22.3% over standalone RL and MPC. Supported by 5G communication validated using MATLAB 5G Toolbox (R2024b), the framework achieves a reduction in uplink latency from 40 ms to 4.8 ms, downlink latency from 30 ms to 1.7 ms, and a round-trip time of 12.3 ms, while improving packet delivery from 98.5% to 99.999%. These values, benchmarked against the 3GPP Ultra-Reliable Low-Latency Communication (URLLC)/V2X specifications (TS 22.261, TS 38.300, TR 22.886), confirm that the proposed RTCM is both numerically effective and compliant with 5G communication standards. The results establish a scalable, privacy-preserving, and communication-efficient framework for real-time traffic control in unsignalized roundabouts.}, archive = {J_ACCESS}, author = {V. Subbulakshmi and Shilpa Chaudhari and Sameena Pathan}, doi = {10.1109/ACCESS.2025.3605987}, journal = {IEEE Access}, pages = {156389-156406}, shortjournal = {IEEE Access}, title = {Federated learning-based travel time prediction for adaptive congestion control using 5G-assisted roundabout traffic networks}, volume = {13}, year = {2025}, }
• (2025). NDeepSMOTEc: An oversampling method for structured datasets. ACCESS, 13, 156375-156388. (www)

  @article{ , abstract = {The problem of imbalanced data is pervasive in the field of machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority class samples are crucial for accurate model predictions. To improve classifier performance on imbalanced datasets, this study proposes an enhanced oversampling method based on the DeepSMOTE framework and examines its effects across different classifiers. DeepSMOTE is specifically designed for image data. We modify DeepSMOTE to make it applicable to structured tabular data and compare its performance with other oversampling techniques using Support Vector Machines and Neural Networks. According to experiments on 26 public datasets, the results demonstrate that the proposed method, nDeepSMOTEc, consistently outperforms state-of-the-art oversampling approaches across both classification models, highlighting its practical utility for structured datasets. These findings provide valuable insights and a reference point for selecting appropriate oversampling strategies when addressing imbalanced data in future research.}, archive = {J_ACCESS}, author = {Wen-Lin Fan and Chun-Chi Chuang and Chia-Wen Hsu and Chieh-Chi Huang and Ting-Rui Guo and Chung-Chian Hsu}, doi = {10.1109/ACCESS.2025.3605975}, journal = {IEEE Access}, pages = {156375-156388}, shortjournal = {IEEE Access}, title = {NDeepSMOTEc: An oversampling method for structured datasets}, volume = {13}, year = {2025}, }
• (2025). Resilient event-triggered observer-based control of uncertain nonlinear fuzzy systems subject to unknown inputs. ACCESS, 13, 156359-156374. (www)

  @article{ , abstract = {This paper proposes a novel observer-based event-triggered integral sliding mode control (SMC) strategy for Takagi-Sugeno fuzzy systems (TSFM) subject to unknown inputs, external disturbances, and model uncertainties. To ensure robust state estimation, a newly formulated unknown input observer (UIO) is developed that accommodates nonlinear output functions, thereby overcoming the limitations of existing methods that assume linear outputs. Additionally, a disturbance observer is introduced to estimate unmatched external disturbances effectively. Two fuzzy integral sliding surfaces are designed, a periodic-time surface to enhance robustness and a time-triggered surface to reduce computational burden and control signal chattering. An event-triggered mechanism based on a time-decaying threshold is incorporated to optimize control update instances, ensuring Zeno-free behavior. Lyapunov-based stability analysis confirms the ultimate boundedness stability (UBS) of the closed-loop system, and controller gains are computed through linear matrix inequalities (LMIs) and steady-state output theory. Simulation studies, including a numerical and practical truck-trailer system, validate the superior performance of the proposed control strategy in terms of disturbance rejection, reduced computation, and robustness compared to conventional methods.}, archive = {J_ACCESS}, author = {Mohsen Farbood and Zeinab Echreshavi and Saleh Mobayen and Paweł Skruch}, doi = {10.1109/ACCESS.2025.3605847}, journal = {IEEE Access}, pages = {156359-156374}, shortjournal = {IEEE Access}, title = {Resilient event-triggered observer-based control of uncertain nonlinear fuzzy systems subject to unknown inputs}, volume = {13}, year = {2025}, }
• (2025). From a binary feature matrix to correlation analysis: A dual-paradigm classification of global robotics research objectives. ACCESS, 13, 156318-156358. (www)

  @article{ , abstract = {Robotics research encompasses a wide range of technical challenges and interdisciplinary approaches. This study introduces a dual-paradigm classification framework for organizing the stated objectives of 130 leading robotics laboratories across North America, Europe, Asia, and Australia. The framework includes a Problem-Based paradigm that addresses core technical challenges and a Solution-Based paradigm that focuses on methodological strategies. Each laboratory’s objectives are encoded into a $130\times 17$ binary matrix, enabling large-scale quantitative analysis. Spearman correlation analysis, corrected using the Benjamini–Hochberg False Discovery Rate (FDR) method, reveals statistically significant co-occurrence trends. Key findings include the synergy between artificial intelligence and cognition, a hardware–software divide, and underexplored intersections between AI and physical system innovation. In addition to correlation heatmaps, visualizations such as PCA biplots and radial similarity networks reveal research gaps and collaboration opportunities. These insights can guide agencies, industry partners, and academic institutions in shaping future robotics research. By identifying promising scientific directions, stakeholders can strategically combine these areas to develop more advanced, integrated, and capable robotic systems. All data and analysis tools have been deposited on IEEE DataPort and GitHub to ensure transparency and reproducibility.}, archive = {J_ACCESS}, author = {Elham Abdullah Alamoudi}, doi = {10.1109/ACCESS.2025.3604842}, journal = {IEEE Access}, pages = {156318-156358}, shortjournal = {IEEE Access}, title = {From a binary feature matrix to correlation analysis: A dual-paradigm classification of global robotics research objectives}, volume = {13}, year = {2025}, }
• (2025). Interpretable large-scale graph network model: A computational platform for predicting instructing achievement bias. ACCESS, 13, 156301-156317. (www)

  @article{ , abstract = {We introduce an interpretable and scalable graph-based system designed to predict biases in instructional achievement across diverse educational data. Aimed at large-scale learning platforms, the system identifies distinctive patterns of instructor-related biases by learning hierarchical features from a wide range of behaviorsąłincluding teaching styles, student interactions, and prior performanceąłusing weakly supervised methods to capture broad influencing factors. To refine these inputs, we apply a geometric feature selection process that removes less informative attributes, improving the quality of instructor and student representations. These refined features are embedded into a latent space where each instructor is characterized by a set of underlying bias-related behaviors. This facilitates a more nuanced understanding of disparities in achievement. Using these representations, we build a weighted similarity graph of instructors, connecting them based on shared bias-related patterns. Advanced clustering algorithms detect meaningful communities within this graph, grouping instructors who exhibit similar instructional biasesąłsuch as preferences for certain teaching methods or styles of student engagement. These communities reveal bias patterns at both broad and detailed levels within teaching environments. To enhance interpretability, the system links these community results with individual instructor profiles, offering personalized insights about instructional biases. By combining local instructional behaviors with broader educational trends, the system improves its ability to predict and address achievement disparities. We tested this approach on datasets containing millions of student and instructor interactions. The results demonstrate that the system scales efficiently and maintains high accuracy across different teaching contexts and instructor activity levels. Its ability to uncover hidden instructional biases and provide actionable feedback makes it a valuable tool for promoting fairness in education.}, archive = {J_ACCESS}, author = {Xiaoyu Sun and Zhangyan Jun}, doi = {10.1109/ACCESS.2025.3605056}, journal = {IEEE Access}, pages = {156301-156317}, shortjournal = {IEEE Access}, title = {Interpretable large-scale graph network model: A computational platform for predicting instructing achievement bias}, volume = {13}, year = {2025}, }
• (2025). A novel transformer-CNN hybrid deep learning architecture for robust broad-coverage diagnosis of eye diseases on color fundus images. ACCESS, 13, 156285-156300. (www)

  @article{ , abstract = {We present a novel and robust deep-learning architecture that takes into account the pathological characteristics of eye diseases on color fundus images. The proposed hybrid architecture is capable of accurately recognizing the local features on the one hand and detecting the peripheral abnormalities of the specific eye disease on the other, and diagnosing the respective eye disease on this basis. The hybrid architecture provides robust local feature extraction and enhances global contextual understanding via hierarchical self-attention mechanisms. In contrast to previous models, which were limited to binary or poorly differentiated classification tasks, our approach considers the most clinically relevant eye diseases (here 9 diseases), including central serous chorioretinopathy (CSCR), diabetic retinopathy (DR), disc edema, glaucoma, macular scar, myopia, pterygium, retinal detachment, and retinitis pigmentosa (RP). The architecture was validated using a peer-reviewed real-world dataset and stratified 5-fold cross-validation. It achieved an average balanced accuracy of 81.91%, an average accuracy and True Positive Rate (TPR) of 76.40%, and a True Negative Rate (TNR) of 95.60% across all classes. For individual diseases, the model reached an TPR of 88.93% for DR, 89.93% for RP, and 61.23% for Glaucoma. The results show that the architecture is powerful and can distinguish between the 10 different classes (9 disease classes + healthy class). This makes it particularly suitable for pre-screening applications in telemedicine, for screening programs at the population level, and for integration into clinical decision support systems.}, archive = {J_ACCESS}, author = {Celina Rieck and Christopher Mai and Luca Eisentraut and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3606334}, journal = {IEEE Access}, pages = {156285-156300}, shortjournal = {IEEE Access}, title = {A novel transformer-CNN hybrid deep learning architecture for robust broad-coverage diagnosis of eye diseases on color fundus images}, volume = {13}, year = {2025}, }
• (2025). Enhanced sign language translation between american sign language and indian sign language using LLMs. ACCESS, 13, 156270-156284. (www)

  @article{ , abstract = {This research introduces a foundational framework aimed at bridging the communication gap between American Sign Language (ASL) and Indian Sign Language (ISL) by translating alphabet-level gestures. The proposed system employs a hybrid deep learning model for ASL gesture recognition, integrating a random forest classifier (RFC) and a convolutional neural network (CNN) to enhance accuracy. Recognised gestures are converted into text, which is refined using a prompt-configured large language model (LLM) for contextual and grammatical accuracy. The corrected text is then synthesised into ISL gestures using RIFE-Net, a real-time intermediate flow estimation network, to generate smooth and natural gesture videos. The framework addresses key challenges such as gesture variability and linguistic differences between ASL and ISL. The hybrid model achieves a gesture recognition accuracy of 93.0%, measuring how accurately the system identifies ASL signs. Following recognition, the raw text output is refined using the Large Language Model (LLM), resulting in a text correction accuracy of 94.2%, which reflects improvements in grammatical correctness and contextual relevance. These metrics collectively demonstrate the system’s effectiveness in alphabet-level recognition and gesture synthesis, laying the groundwork for more advanced sentence-level translation. Initial experimental results demonstrate real-time processing capabilities, averaging one gesture per second, with video outputs at 60 FPS. This system not only facilitates seamless communication between ASL and ISL users but also lays the groundwork for scalability to other sign language pairs. The results highlight the potential to improve accessibility and inclusion of the global hard of hearing community, paving the way for future research in multi-modal sign language translation systems.}, archive = {J_ACCESS}, author = {Malay Kumar and S. Sarvajit Visagan and Tanish Mahajan and Anisha Natarajan and P. S. Sreeja}, doi = {10.1109/ACCESS.2025.3595943}, journal = {IEEE Access}, pages = {156270-156284}, shortjournal = {IEEE Access}, title = {Enhanced sign language translation between american sign language and indian sign language using LLMs}, volume = {13}, year = {2025}, }
• (2025). Higher-order statistics of the channel capacity in the presence of interference with applications to diversity receivers and intelligent reflecting surface-assisted systems. ACCESS, 13, 156258-156269. (www)

  @article{ , abstract = {This paper introduces a comprehensive analytical framework for the evaluation of the higher-order statistics of the channel capacity operating in the presence of fading and interference. The proposed generic analysis requires only the knowledge of the moment generating functions of the fading and interference signal powers for effectively providing accurate performance evaluation of important performance metrics, such as amount of dispersion, reliability of the signal throughput, skewness, kurtosis and q%-outage capacity, which reveal useful insights on the overall system performance. To illustrate the usefulness of the framework, the analysis is applied to two important applications, namely diversity and intelligent reflecting surface (IRS) receivers. For the former, we fist consider two case studies which examine the performance of wireless communication systems subject to shadowed $\kappa $ - $\mu $ fading. The receiver utilizes multiple antennas and employs maximal ratio combining (MRC) while the spatial node distributions are modeled using Poisson and binomial point processes. The third case study explores the performance of an MRC receiver in the presence of correlated $\kappa $ - $\mu $ fading with an arbitrary covariance matrix and multiple interfering sources. The fourth case analyzes the performance of a generalized selection diversity (GSC) receiver subjected to $\eta $ - $\mu $ fading. For the latter application, we have considered a wireless system enhanced with an intelligent reflecting surface (IRS), operating under Ricean fading and distinct interference conditions. For all cases, numerically evaluation results accompanied with Monte-Carlo simulations are provided to validate the accuracy of the theoretical analysis.}, archive = {J_ACCESS}, author = {Kostas P. Peppas and Kyriakos Manganaris and Jing Yang and P. Takis Mathiopoulos}, doi = {10.1109/ACCESS.2025.3603956}, journal = {IEEE Access}, pages = {156258-156269}, shortjournal = {IEEE Access}, title = {Higher-order statistics of the channel capacity in the presence of interference with applications to diversity receivers and intelligent reflecting surface-assisted systems}, volume = {13}, year = {2025}, }
• (2025). Chef-based optimization algorithm (CBOA)-based global peak power tracking scheme for PV system. ACCESS, 13, 156246-156257. (www)

  @article{ , abstract = {Environmental concerns are driving the demand for clean and renewable energy sources. Among renewable sources, the usage of Photovoltaic (PV) systems has been steeply growing in recent decades. Numerous factors, including temperature, shade percentage, and insolation, affect how well the PV system performs. The intermediate converter between solar panels and load adjusts the difference between PV output and load voltage due to variations in isolation and temperature levels. Shading on the solar panel, caused by natural or unnatural reasons, leads to technical issues by creating numerous peaks in the P-V characteristics. One of the several peaks is referred to as Global Peak Power (G ${}_{\mathrm {PP}}$ ), while the others are classified as Local Peak Power (L ${}_{\mathrm {PP}}$ ). The presence of several peaks and the possibility of trapping at LPP rather than GPP make it difficult to identify GPP throughout the optimization process. Therefore, this paper presents Chef based Optimization Algorithm (CBOA) to track GPP of the PV system. CBOA was developed based on the procedure of learning cooking skills in training courses and offers a much structured approach compared to Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The performance of the tracking algorithm is examined by simulation in MATLAB using a 5S5P configuration. In the simulation studies, the CBOA performance is better compared with PSO and GWO in terms of tracking time and maximum magnitude of power.}, archive = {J_ACCESS}, author = {Abhishake Jain and Asisa Kumar Panigrahy and Sumit Saini and Ajay Kumar Bansal and Muralidhar Nayak Bhukya}, doi = {10.1109/ACCESS.2025.3606338}, journal = {IEEE Access}, pages = {156246-156257}, shortjournal = {IEEE Access}, title = {Chef-based optimization algorithm (CBOA)-based global peak power tracking scheme for PV system}, volume = {13}, year = {2025}, }
• (2025). Chaotic dynamics in vector field characterized by spheres with constant divergence measure. ACCESS, 13, 156235-156245. (www)

  @article{ , abstract = {This paper contributes to discovering of continuous-time third-order autonomous deterministic chaotic system with predefined special property: homocentric spheres with constant vector field divergence measure. This kind of unique feature stands hidden within dynamics of system, i.e., it is not evident from a macroscopic point of view. Introduced mathematical model generates robust, topologically interesting, highly unpredictable, strange attractors. The way to new system was heuristic, based on nature-inspired optimization technique. Cyclical symmetricity of vector field selected at the beginning of system development process was followed by finding three free internal parameters that lead to chaotic behavior. Using both numerical analysis and experimental measurement, it is demonstrated that chaotic motion is robust and does not represent long transient or numerical artifact.}, archive = {J_ACCESS}, author = {Jiri Petrzela}, doi = {10.1109/ACCESS.2025.3606365}, journal = {IEEE Access}, pages = {156235-156245}, shortjournal = {IEEE Access}, title = {Chaotic dynamics in vector field characterized by spheres with constant divergence measure}, volume = {13}, year = {2025}, }
• (2025). Real-time activity recognition using minimal biomechanical features: A lightweight IMU-based classifier for older adults. ACCESS, 13, 156215-156234. (www)

  @article{ , abstract = {Real-time implementation of human activity recognition systems is often challenging due to high computational costs and the need for extensive datasets. Existing models rarely account for older adults, particularly those with mobility limitations. This study proposes a lightweight classifier for redhuman activity recognition that utilizes minimal biomechanical features based on thigh and foot angles measured by two IMUs to classify activities, including walking, stair ascent and descent, standing, and sitting. Data from 10 young adults were used to train the model, which was subsequently tested on separate cohorts of 5 young and 10 older adults. A KNN classifier was implemented in a smartphone application for real-time use. To optimize its performance, an adaptive segmentation method was developed to accurately detect toe-off in different activities and reduce computational overhead. The model was trained to recognize mobility limitations in older adults, non-alternating leg stair negotiation, where individuals rely on one leg. redRather than using deep learning architectures, the proposed physics-based framework uses simple and interpretable biomechanical features to enable real-time implementation on resource-constrained devices. The system achieved overall 99% detection accuracy for young and old adults with normal movement patterns and achieved 93% accuracy in distinguishing non-alternating leg stair negotiation from normal stair negotiation patterns in older adults. The results demonstrate the feasibility of a computationally efficient model with minimal features for real-time applications. By addressing the movement characteristics of older adults, this study contributes to the development of an accessible health monitoring system for real-world activity detection in aging populations.}, archive = {J_ACCESS}, author = {Ehsan M. Sharafian and Colby Ellis and Babak Hejrati}, doi = {10.1109/ACCESS.2025.3606339}, journal = {IEEE Access}, pages = {156215-156234}, shortjournal = {IEEE Access}, title = {Real-time activity recognition using minimal biomechanical features: A lightweight IMU-based classifier for older adults}, volume = {13}, year = {2025}, }
• (2025). Extracting supply chain information from news articles using large language models: A fully automatic approach. ACCESS, 13, 156203-156214. (www)

  @article{ , abstract = {Supply chain mapping is crucial for global companies to identify and mitigate potential risks. Although natural language processing techniques are analyzed to extract supply chain maps from textual sources to automate the process, they require extensive manual annotation of training data, limiting the scalability and efficiency of these approaches. This study addresses this limitation by exploring the use of large language models to automate supply chain mapping, specifically by investigating their potential for generating synthetic training data and improving relation classification. The performance of these synthetic datasets is compared with those of manually annotated datasets. Experimental results demonstrate that synthetic data can outperform manual data, particularly when manual data is scarce. To demonstrate its practical applicability, the developed method is applied to generate a supply chain map from mining-related news articles. The resulting visualization for mining-related industries highlights the method’s effectiveness in automated, real-time mapping using publicly available information, enabling the rapid identification of supply network changes. This study contributes to the supply chain management and natural language processing fields by advancing sophisticated, AI-driven supply chain analysis tools.}, archive = {J_ACCESS}, author = {Jaewon Kim and Eunbi Kim and Dongsoo Kim and Yoojoong Kim and Taesu Cheong}, doi = {10.1109/ACCESS.2025.3605837}, journal = {IEEE Access}, pages = {156203-156214}, shortjournal = {IEEE Access}, title = {Extracting supply chain information from news articles using large language models: A fully automatic approach}, volume = {13}, year = {2025}, }
• (2025). Phylogeny reconstruction using multi-agent surface area pressure features. ACCESS, 13, 156188-156202. (www)

  @article{ , abstract = {Phylogenetic analysis of species is essential for understanding the various biological activities of an organism, including gene editing, drug design, and vaccine development. Traditional alignment-based (AB) methods, however, face significant computational challenges, becoming NP-hard when applied to large or medium-sized sequences. As a result, alignment-free (AF) approaches have gained attraction. Despite their growing popularity, AF methods struggle to optimize the critical aspects of computational time, accuracy, and memory usage. Due to the time inefficiency of AF approaches, significant work hours are wasted in laboratories. Consequently, different studies have employed statistical approaches on entire sequences; however, these are less efficient because they lose positional information, for which relative information-based approaches, including entropy and joint probability, have achieved higher performance. However, entropy-based methods are not highly sensitive to small changes in DNA sequences. In response to these limitations, a novel multi-agent surface area pressure (MASAP) feature-based method is proposed, which is highly sensitive and efficient in representing the relative information of a DNA sequence. First, it selects the length of the k-mer and constructs a 2D list for the positional information of k-mers, where each row corresponds to a specific k-mer. Subsequently, the positional data is converted into relative data from which the MASAP feature is derived, and this is used for feature descriptor generation and phylogenetic tree reconstruction. Extensive experiments were carried out on benchmark datasets, and the proposed MASAP method consistently achieved top-ranked accuracy across all datasets. Furthermore, in terms of computational time, MASAP significantly outperforms existing methods, being $5,151$ to $8,162$ times faster than the MEGA tool. The source code and datasets are available at https://github.com/machbah/MASP}, archive = {J_ACCESS}, author = {Md Sayeed Iftekhar Yousuf and Machbah Uddin and Mohammad Khairul Islam and Md Rakib Hasan and Farah Jahan}, doi = {10.1109/ACCESS.2025.3605681}, journal = {IEEE Access}, pages = {156188-156202}, shortjournal = {IEEE Access}, title = {Phylogeny reconstruction using multi-agent surface area pressure features}, volume = {13}, year = {2025}, }
• (2025). A reinforcement learning-based intelligent duty cycle MAC protocol for internet of things. ACCESS, 13, 156170-156187. (www)

  @article{ , abstract = {The Wireless Sensor Networks (WSNs) enabled Internet of Things (IoT) applications face energy efficiency challenge due to the limited battery capacity of the sensor nodes. Hence, the network’s performance often involves a tradeoff with network lifetime. Traditional medium access control (MAC) protocols are less adaptable to the dynamic network conditions. While existing reinforcement learning (RL) based MACs are more adaptable, they still encounter challenges such as complexity and dimensionality. Therefore, this work aims to develop an RL based intelligent Duty cycle MAC (RiD-MAC) protocol that incorporates suitable network information to balance complexity and performance, effectively. The proposed RiD-MAC protocol is based on the Q-learning algorithm, meticulously designed with remaining energy as the state space and duty cycle as the action space. The reward is then formulated based on energy consumption and throughput. It is implemented on OMNeT++ platform-based Castalia simulator and the performance is compared with three state-of-the-art protocols, including AQSen-MAC, rlDC-MAC and QX-MAC under three simulation scenarios, stationary nodes with periodic traffic, hybrid traffic and node mobility. The simulation results demonstrate that RiD-MAC protocol significantly improves energy efficiency, with reduction in receiver energy consumption of up to 21%, and receiver energy consumption per bit of up to 26%, when compared to state-of-the-art protocols.}, archive = {J_ACCESS}, author = {Shah Abdul Latif and Micheal Drieberg and Sohail Sarang and Azrina Abd Aziz and Rizwan Ahmad and Goran M. Stojanović}, doi = {10.1109/ACCESS.2025.3606053}, journal = {IEEE Access}, pages = {156170-156187}, shortjournal = {IEEE Access}, title = {A reinforcement learning-based intelligent duty cycle MAC protocol for internet of things}, volume = {13}, year = {2025}, }
• (2025). S2-code: A resilient and lightweight self-synchronizing authentication protocol for unreliable IoT networks. ACCESS, 13, 156153-156169. (www)

  @article{ , abstract = {Reliable authentication in resource-constrained IoT remains challenging on lossy wireless links, which induce desynchronization and amplify denial-of-service (DoS) risk. We present S2-Code, a self-synchronizing symmetric protocol that couples a dual-window state machine with an AEAD-protected bidirectional token. We derive practical window-sizing bounds from packet loss/reordering, request rate, and offline duration, and validate the design via symbolic verification in ProVerif, network emulation in Mininet, and hardware experiments on Raspberry Pi and ESP32-C3. S2-Code achieves 100% session recovery after a catastrophic 50-count desynchronization where rolling codes fail. Under 30% packet loss, it sustains 60% success versus 20% for the rolling-code baseline (p $\lt 10^{-8}$ ). A layered DoS defense—kernel-level rate limiting (nftables) plus an adaptive protocol mechanism—raises legitimate success from $\approx 17$ % under flooding to $\approx 92.9$ % under protocol-aware attack. The protocol has a small footprint (<8 KB flash, <4 KB RAM), low overhead ( $\approx 114$ -byte packets, <17 ms latency), and scales to 100 concurrent devices in emulation. S2-Code offers a practical, robust middle ground between fragile rolling codes and heavyweight PKI for resource-constrained IoT.}, archive = {J_ACCESS}, author = {Yuning Cao and Menglei Kou and Yujia Lai and Ziqing Mei}, doi = {10.1109/ACCESS.2025.3605060}, journal = {IEEE Access}, pages = {156153-156169}, shortjournal = {IEEE Access}, title = {S2-code: A resilient and lightweight self-synchronizing authentication protocol for unreliable IoT networks}, volume = {13}, year = {2025}, }
• (2025). TeachAR—An interactive AR-based learning platform. ACCESS, 13, 156136-156152. (www)

  @article{ , abstract = {Virtual reality and augmented reality technologies have experienced a significant increase in popularity due to their potential to deliver a highly immersive user experience. However, there are still substantial difficulties in creating augmented environments with different purposes that can precisely localize users. In this context, TeachAR is an augmented reality solution designed to provide students with an interactive learning experience through an augmented reality quest system. The system comprises a desktop application and a mobile application, designed for three distinct user roles: building administrators, teachers, and students. Administrators use the desktop application to upload the building’s architectural plans, create a 3D replica of it, and define a navigation graph. Then, using the mobile application, the correspondence between the real building and its replica is created. Teachers employ a simple desktop user interface to design interactive quests. Students interact with the system through the mobile application in three different use cases: free exploration of the building, guided navigation to reach a destination, and individual or collaborative quests proposed by teachers. This paper provides a brief overview of the system’s main functionalities, implementation details, and evaluation of its technical capabilities. The pilot testing is also described in this paper, revealing the potential of using the TeachAR system in schools in the educational process.}, archive = {J_ACCESS}, author = {Maria Anca BĂluŢoiu and Anca Morar and Alin Moldoveanu and Alexandru Grădinaru and Victor Asavei and Magdalena Anghel and Cristian Lambru}, doi = {10.1109/ACCESS.2025.3604765}, journal = {IEEE Access}, pages = {156136-156152}, shortjournal = {IEEE Access}, title = {TeachAR—An interactive AR-based learning platform}, volume = {13}, year = {2025}, }
• (2025). Adaptive hyperbolic tangent wavelet threshold function for denoising in single-channel EEG attention classification. ACCESS, 13, 156125-156135. (www)

  @article{ , abstract = {Single-channel electroencephalography (EEG) signals have broad application prospects in attention state monitoring due to their portability and low cost. However, these signals are susceptible to noise interference, which degrades classification performance. This study proposes an Adaptive Hyperbolic Tangent Wavelet Threshold Function (AHTWTF), a novel denoising framework that dynamically adjusts thresholds according to the energy distribution of wavelet coefficients, combining the smoothness of hyperbolic tangent functions with adaptive level-specific thresholding to overcome the limitations of conventional hard/soft thresholding and mode decomposition methods. By integrating AHTWTF with an Extreme Learning Machine (ELM) classifier, we establish a joint denoising-classification pipeline. Experiments on Schulte Grid task data (12 subjects, 512 Hz sampling rate, containing physiological and environmental noise) demonstrate that AHTWTF achieves superior noise suppression by preserving critical EEG features (e.g., Alpha/Beta waves) through its energy-adaptive mechanism, while avoiding mode mixing (EMD) and over-decomposition (VMD). Under a noise intensity coefficient of 0.7, the signal-to-noise ratio (SNR) reached 6.2263, significantly higher than those of the hard thresholding (3.7064) and soft thresholding (1.4865) methods, with a 44.0%–70.2% reduction in mean squared error (MSE). For classification, AHTWTF achieved a mean AUC of 0.7911, a 14.7% improvement over the baseline, attributed to its balanced trade-off between denoising intensity and the preservation of important EEG features. These results validate AHTWTF’s robustness and highlight its potential for real-world EEG applications, particularly in portable attention monitoring systems and brain-computer interfaces.}, archive = {J_ACCESS}, author = {Yunde Li and Jin Feng and Chengliang Jiang and Yong Fan and Jing He}, doi = {10.1109/ACCESS.2025.3601576}, journal = {IEEE Access}, pages = {156125-156135}, shortjournal = {IEEE Access}, title = {Adaptive hyperbolic tangent wavelet threshold function for denoising in single-channel EEG attention classification}, volume = {13}, year = {2025}, }
• (2025). AI-powered robotic ultrasound: Electronic hardware, algorithms, and applications. ACCESS, 13, 156107-156124. (www)

  @article{ , abstract = {Robotic ultrasound powered by Artificial Intelligence (AI) represents a groundbreaking and transformative innovation in the field of medical imaging. By seamlessly automating probe movement and using sophisticated algorithms, these systems enhance overall diagnostic accuracy and reduce considerably operator variability. This paper provides a comprehensive review of AI-based solutions for the development of robotic ultrasound systems, with a special focus on both Machine Learning (ML) and Deep Learning (DL) techniques. In particular, key areas of investigation include probe management strategies and image quality optimization approaches. Applications across various anatomical regions and diverse clinical scenarios demonstrate improved image quality and reduced operator workload, thereby contributing to more consistent and reliable diagnostic outcomes. Main robotic arms employed in these systems are also reviewed, with detailed analysis highlighting technical advancements and engineering innovations. The review underscores the growing role of AI in enhancing diagnostic procedures and supports its potential to revolutionize ultrasound practices, discussing in depth technical and practical challenges related to the implementation of these systems. Overall, the findings emphasize the transformative impact of integrating AI with robotic platforms.}, archive = {J_ACCESS}, author = {Emanuele De Luca and Francesco Dell’Olio}, doi = {10.1109/ACCESS.2025.3605105}, journal = {IEEE Access}, pages = {156107-156124}, shortjournal = {IEEE Access}, title = {AI-powered robotic ultrasound: Electronic hardware, algorithms, and applications}, volume = {13}, year = {2025}, }
• (2025). Tackling heterogeneity in intelligent internet of medical things applications through federated transformer regularization. ACCESS, 13, 156091-156106. (www)

  @article{ , abstract = {In the conventional Internet of Medical Things (IoMT) system, patient data shared by the IoMT devices are susceptible and confidential and can be exposed to the antagonist. Hence, federated learning (FL) has become a promising solution for preserving the privacy of patient data. However, various complex variances in the IoMT context, such as device and data heterogeneity, model complexities, and others, may degrade the performance of the traditional FL model. This paper proposes a transformer-based federated learning framework for the IoMT network within a cloud-edge architecture. The proposed framework mitigates data and device heterogeneity in the IoMT framework by learning the IoMT devices through a multi-head self-attention (MHSA) mechanism. The transformer model is applied to each device’s local data to capture the complex pattern from each input data. Each device uses the transformer model as a part of its local model to minimize the local loss function. To reduce the risk of over-fitting in the transformer model, a regularization term is instigated to the local loss function of the learning process. Furthermore, it is shown that the proposed method augmented with the regularization factor converges to a stationary point $\theta ^{*}$ with a convergence rate of $\mathcal {O}(1/t)$ in some situations. In addition, the proposed method provides $\epsilon $ -privacy guarantees on a local data set of IoMT devices. The proposed framework is investigated on the Human Activity Recognition (HAR), Physical Activity Monitoring (PAMAP2), and Physikalisch-Technische Bundesanstalt (PTB)-XL datasets. It measures the performance of the proposed methods compared to the state-of-the-art on various parameters.}, archive = {J_ACCESS}, author = {Sanjoy Mondal and Taniya Ghosh and Abhishek Das}, doi = {10.1109/ACCESS.2025.3603596}, journal = {IEEE Access}, pages = {156091-156106}, shortjournal = {IEEE Access}, title = {Tackling heterogeneity in intelligent internet of medical things applications through federated transformer regularization}, volume = {13}, year = {2025}, }
• (2025). Semi-automatic BVI human-centered image conversational descriptions: Leveraging LLMs and expert refinements for inclusive visual accessibility. ACCESS, 13, 156072-156090. (www)

  @article{ , abstract = {Ensuring that blind and visually impaired (BVI) individuals can fully participate in today’s image-rich digital world remains a significant challenge. Current visual assistants often rely on annotations from sighted contributors, which may fail to capture BVI individuals’ preferences and expectations. Addressing this challenge, we explore how to develop optimal, conversational image descriptions that resonate with BVI individuals, and how to effectively scale their creation. We propose a semi-automatic approach that couples large language models (LLMs) with iterative, BVI-driven refinements. Starting with initial LLM-generated image descriptions, a small set of BVI experts refine them to better meet BVI individuals’ needs. These enhanced examples guide subsequent LLM outputs, which are then evaluated by BVI end-users to confirm improved quality and satisfaction. We contribute to constructing large-scale, BVI-centered training data, thereby advancing inclusive and conversational visual accessibility. Throughout our studies, the results show a significant improvement in BVI end-users’ satisfaction with image conversational descriptions when edited by BVI experts. Additionally, the results indicate promising improvements when the LLM re-generates descriptions using those edits as few-shot examples. Moreover, we got valuable insights from BVI participants’ focused mainly on optimizing clarity, relevance, and interaction patterns for image descriptions and conversational exchanges between AI and BVI individuals.}, archive = {J_ACCESS}, author = {Mazen Salous and Daniel Lange and Timo von Reeken and Maria K. Wolters and Wilko Heuten and Susanne Boll and Larbi Abdenebaoui}, doi = {10.1109/ACCESS.2025.3605490}, journal = {IEEE Access}, pages = {156072-156090}, shortjournal = {IEEE Access}, title = {Semi-automatic BVI human-centered image conversational descriptions: Leveraging LLMs and expert refinements for inclusive visual accessibility}, volume = {13}, year = {2025}, }
• (2025). A comprehensive evaluation of byzantine attacks and defenses under data heterogeneity. ACCESS, 13, 156054-156071. (www)

  @article{ , abstract = {In this work, we present an in-depth overview and evaluation of recent advances in Byzantine-resilient distributed learning, focusing on both defense strategies and threat modeling. While considerable efforts have been made to develop robust aggregation techniques, the underlying threat model, particularly under non-IID data distributions, remains underexplored. This imbalance can create a false sense of security regarding the effectiveness of current defenses. To address this gap, we recalibrate well-known Byzantine attacks to better reflect the challenges posed by heterogeneous data, enabling more realistic stress testing of robust aggregation methods. Through systematic evaluation on standard benchmark datasets and using diverse partitioning strategies, we show that data heterogeneity significantly increases the adversary’s leeway for model poisoning. We leverage this insight to critically evaluate existing defenses. Our findings underscore the need to assess robustness not only through defense design, but also via carefully constructed and calibrated threat models. Additionally, we provide actionable recommendations to improve evaluation practices and outline key research directions to advance the field.}, archive = {J_ACCESS}, author = {Latifa Errami and El Houcine Bergou and Hajar El Hammouti}, doi = {10.1109/ACCESS.2025.3602217}, journal = {IEEE Access}, pages = {156054-156071}, shortjournal = {IEEE Access}, title = {A comprehensive evaluation of byzantine attacks and defenses under data heterogeneity}, volume = {13}, year = {2025}, }
• (2025). A practical method for module-level capacitance estimation in automotive power module using datasheet-based modeling. ACCESS, 13, 156045-156053. (www)

  @article{ , abstract = {This paper proposes a practical method for efficiently estimating the module-level capacitance of high-voltage power modules used in automotive applications. Traditionally, acquiring the complete C–V characteristics of a module required expensive high-voltage measurement equipment such as curve tracers. In contrast, this study presents a low-cost alternative that combines low-voltage measurement data with vendor datasheets to estimate high-voltage characteristics. The proposed approach begins by measuring the total capacitance of the module under low-voltage conditions (below several tens of volts) using instruments such as an LCR meter. The measured data is then compared with device-level datasheet values to extract the package-induced parasitic capacitance. This parasitic component, obtained under low-voltage conditions, is added to the high-voltage C–V data from the datasheet to estimate the total capacitance in the high-voltage region. Additionally, for the collector-emitter capacitance, which shows voltage dependence due to the parallel diodes, a nonlinear model based on PN junction theory is applied. Using the capacitances estimated by the proposed method, noise source modeling is performed and compared against models based on direct curve tracer measurements. Verification using a double pulse test shows that the proposed method achieves high accuracy in predicting high-frequency ringing and switching noise. This study demonstrates that accurate module-level capacitance estimation is possible even without high-voltage measurement equipment, relying solely on simple low-voltage measurements and datasheet information. The proposed method is applicable to various practical scenarios such as custom module design, EMI analysis, and noise source modeling.}, archive = {J_ACCESS}, author = {Jaewon Rhee and Seungmin Ha and Jiseong Kim and Seungyoung Ahn}, doi = {10.1109/ACCESS.2025.3604358}, journal = {IEEE Access}, pages = {156045-156053}, shortjournal = {IEEE Access}, title = {A practical method for module-level capacitance estimation in automotive power module using datasheet-based modeling}, volume = {13}, year = {2025}, }
• (2025). An iterative systematic analytical review of machine learning techniques for blockchain optimization. ACCESS, 13, 156021-156044. (www)

  @article{ , abstract = {The exponential growth of blockchain and machine learning (ML) technologies has catalyzed innovations across domains; however, the lack of comprehensive reviews addressing their integration limits our understanding of their synergistic potential. Existing reviews focus on specific applications and neglect scalability, security, and performance metrics that are critical for deploying ML-blockchain frameworks in complex environments. To bridge these gaps, this study conducts a comprehensive review of the most recent state-of-the-art research that evaluates methodologies, performance metrics, and their applicability. The reviewed methods include random forests, federated learning (FL), explainable AI (XAI), reinforcement learning (RL), and federated/hybrid models such as federated reinforcement learning and learning chains. These methods have emerged with the potential to support the balance among accuracy, privacy, and scalability across a variety of domains, such as the Internet of Things (IoT), healthcare, smart grids, and decentralized finance scenarios. This review identifies optimal methods for blockchain optimization and scalability enhancement, while providing a roadmap for integrating advanced ML techniques with blockchains. The findings will assist in significantly advancing domain knowledge in blockchain and guide future research and real-world implementation operations.}, archive = {J_ACCESS}, author = {Nilesh Korde and Tausif Diwan and Mayuri Digalwar}, doi = {10.1109/ACCESS.2025.3604287}, journal = {IEEE Access}, pages = {156021-156044}, shortjournal = {IEEE Access}, title = {An iterative systematic analytical review of machine learning techniques for blockchain optimization}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient wind turbine operation via multi-class blade icing recognition using mutual-information feature selection and ensemble methods. ACCESS, 13, 156008-156020. (www)

  @article{ , abstract = {Icing on wind turbine blades not only degrades aerodynamic efficiency but also poses a significant risk to structural integrity. However, conventional sensor-based or image-based detection systems remain costly and prone to weather-related interference. This study introduces a lightweight, multi-class diagnostic framework designed to classify turbine states as normal, icing, or other anomalies using native SCADA (Supervisory Control and Data Acquisition) data. After standardizing and balancing the dataset, key features were selected using three complementary techniques: Support Vector Machine (SVM) feature importance, Random Forest feature importance, and Mutual Information ranking. Mutual Information is particularly utilized to identify the non-linear dependencies between features and turbine states, enhancing the feature selection process. Five ensemble learning models—XGBoost, LightGBM, Gradient Boosting, Extra Trees, and AdaBoost—were benchmarked. Among these, the Extra Trees model provided the optimal trade-off between accuracy and computational efficiency, achieving an F1 score of 99.54% while maintaining minimal training and inference time. Due to its strong generalization ability and low inference latency, this framework can be efficiently deployed on edge computing platforms or directly within SCADA systems. It offers a scalable, real-time solution for wind farm icing detection and maintenance decision-making, paving the way for cost-effective, rapid-response systems for proactive wind turbine management.}, archive = {J_ACCESS}, author = {Tao Chen and Caixia Yang and Yao Xiao and Chaoying Yan and Mingze Lei and Chonlatee Photong}, doi = {10.1109/ACCESS.2025.3605222}, journal = {IEEE Access}, pages = {156008-156020}, shortjournal = {IEEE Access}, title = {Energy-efficient wind turbine operation via multi-class blade icing recognition using mutual-information feature selection and ensemble methods}, volume = {13}, year = {2025}, }
• (2025). D-decomposition-based design of PIDF load frequency control of an integrated solar photo-voltaic system with thermal reheat plant. ACCESS, 13, 155987-156007. (www)

  @article{ , abstract = {Achieving a stable equilibrium between produced power and consumed power remains a critical challenge in present-day power systems, especially with the developing incorporation of renewable energy sources, such as solar photo-voltaic (SPV) systems. This study exhibits a graphical method-based D-Decomposition approach to design a controller with Proportional-Integral controller with Derivative Filter (PIDF) for load frequency control (LFC) single area. Design of LFC via the proposed approach also extends to a two-area power system, which includes both a SPV system and a thermal reheat power plant. Control Basins (CBs) are graphically depicted to ensure Hurwitz stability where all roots of LFC closed loop transfer function in the presence of PIDF are located in the open left half plane. Because of the drastically penetration of renewable energy sources in the electricity market and its sharply effect on the stability of the network, CBs based $\zeta $ -Hurwitz have been introduced in the paper to provide a rigid controller where all roots located at the left of the line inclined specific angle ensuring a certain damping coefficient. The controller’s performance was assessed through a series of scenarios to evaluate its effectiveness in improving frequency regulation and change in tie-line power, using time domain indices, ITAE, ISE, and IAE, and response indicators, settling time, under shooting, and over shooting, under step load perturbation (SLP) and uncertainties of power system parameters such as governor, turbine, and power system time constants. The simulation results are further supplemented with a comparative analysis against previous studies, demonstrating the proposed approach’s superior ability to reduce settling time and minimize overshooting. The proposed approach significantly improves system performance across all scenarios. Specifically, it reduces the settling time by approximately 35%, 31%, and 22% for frequency deviation in area 1, area 2, and tie-line power deviation, respectively, when compared to other methods. In addition, the maximum overshoot is decreased by at least 27%, 51%, and 73% for the same variables. Furthermore, the proposed strategy achieves a notable reduction in undershoot, demonstrating superior dynamic response and robustness over existing techniques. Finally, key nonlinearities-including Generation Rate Constraint (GRC), Governor Dead Band (GDB), and Time Delay (TD)-that significantly affect the performance of LFC are taken into account. Compared to recent related studies, the proposed method demonstrates superior performance, achieving faster settling times, reduced overshoot and undershoot, and minimized deviations in both frequency and tie-line power.}, archive = {J_ACCESS}, author = {Abdel-Rahman Sobieh and Mahmoud N. Ali and Mahmoud Soliman and Matti Lehtonen and Mohamed M. F. Darwish}, doi = {10.1109/ACCESS.2025.3605869}, journal = {IEEE Access}, pages = {155987-156007}, shortjournal = {IEEE Access}, title = {D-decomposition-based design of PIDF load frequency control of an integrated solar photo-voltaic system with thermal reheat plant}, volume = {13}, year = {2025}, }
• (2025). Autonomous electromagnetic simulation and modeling of QFN micro-electronic packages using LLM agents. ACCESS, 13, 155978-155986. (www)

  @article{ , abstract = {This paper presents a novel approach to automating the electromagnetic simulation workflow for packaging using Large Language Model (LLM) agents. We demonstrate the use of LLM-based agents to generate 3D models of Quad Flat No-Lead (QFN) packages in Ansys HFSS based on natural language prompts. The agent autonomously constructs the geometry, sets up the simulation environment, and extracts S-parameters following a full-wave electromagnetic analysis. Subsequently, it interprets the results to generate a compact equivalent circuit model and produces a SPICE-compatible netlist. This end-to-end pipeline enables rapid design exploration, reduced engineering time, and improved accessibility to high-fidelity electromagnetic modeling. The proposed framework lays the foundation for intelligent, prompt-driven design and simulation of systems, bridging the gap between high-level design intent and low-level simulation workflows.}, archive = {J_ACCESS}, author = {Hrvoje Molnar and Tvrtko Mandic and Vladimir Ceperic}, doi = {10.1109/ACCESS.2025.3605803}, journal = {IEEE Access}, pages = {155978-155986}, shortjournal = {IEEE Access}, title = {Autonomous electromagnetic simulation and modeling of QFN micro-electronic packages using LLM agents}, volume = {13}, year = {2025}, }
• (2025). Demonstration of remote powering a 1 × 2 thermo-optical switch with power-over-fiber technology. ACCESS, 13, 155969-155977. (www)

  @article{ , abstract = {A photonic system including the power-over-fiber powering of a silicon-on-insulator $1 \times 2$ thermo-optic switch based on a Mach-Zehnder structure was designed, and a prototype was developed and tested. The power section consists of an integrated photovoltaic cell, a dc-dc power converter and a NiMH battery. A standard communication fiber transmits light from a single-wavelength power laser to a fiber-pigtailed power converter photodiode, which is operated near its maximum power point through a dc-dc power converter. The converter stage was designed to set the output voltage to a level suitable for charging a battery and actuating the heater of the thermo-optic switch under test. The correct operation of the system is demonstrated. The results could be of interest for developing optical circuits capable of establishing reconfigurable optical paths among users in non-electrified optical networks.}, archive = {J_ACCESS}, author = {Alice Gelli and Maurizio Casalino and Teresa Crisci and Stefano Vergari and Pieter Dumon and Francesco G. Della Corte}, doi = {10.1109/ACCESS.2025.3605518}, journal = {IEEE Access}, pages = {155969-155977}, shortjournal = {IEEE Access}, title = {Demonstration of remote powering a 1 × 2 thermo-optical switch with power-over-fiber technology}, volume = {13}, year = {2025}, }
• (2025). Designing future aerospace systems: Integrating BIP formal verification into f prime framework. ACCESS, 13, 155952-155968. (www)

  @article{ , abstract = {Traditional component-based frameworks (CBFs), such as NASA’s F Prime, promote modularity and reuse but lack native support for formal verification, limiting their applicability in safety-critical and real-time domains. Ensuring deterministic behavior and correct synchronization remains a major challenge, especially on multicore embedded platforms. To address this gap, we propose a novel integration of the Behavior-Interaction-Priority (BIP) framework with F Prime, enabling correctness-by-construction through formal modeling and verification. Our methodology translates F Prime components into BIP models, allowing the use of priority rules and interaction semantics to detect race conditions and enforce synchronization determinism. We evaluate this hybrid framework on a Raspberry Pi 3 Model B platform, under both default Linux and PREEMPT_RT kernels, comparing two execution configurations: SYNC (synchronized mode updates via priority messages) and UNSYNC (sequential thread-level updates). Experimental results reveal that SYNC mode, when coupled with real-time kernel support, significantly reduces execution time variability and latency spikes, demonstrating improved synchronization and predictability. This integration enhances the reliability of modular embedded software while offering a practical pathway to adopt formal methods in industry-grade systems.}, archive = {J_ACCESS}, author = {Abdelrahman Metwally and Andrey Somov}, doi = {10.1109/ACCESS.2025.3606362}, journal = {IEEE Access}, pages = {155952-155968}, shortjournal = {IEEE Access}, title = {Designing future aerospace systems: Integrating BIP formal verification into f prime framework}, volume = {13}, year = {2025}, }
• (2025). Dynamic swin transformer with early exit for efficient quality defect detection in power distribution network. ACCESS, 13, 155940-155951. (www)

  @article{ , abstract = {Operational reliability of energy metering devices is paramount for modern smart grids, yet these devices face multifaceted degradation from concurrent environmental, electrical, and mechanical stresses. Conventional diagnostic methods prove inadequate in effectively integrating heterogeneous multi-modal sensor data for comprehensive fault prognostics. The “intelligent confidence-based early-exit mechanism” is defined as an adaptive decision system that dynamically adjusts the computational depth of a neural network by evaluating the confidence levels of intermediate layer features in real time, combined with KL-divergence verification, to allocate computational resources on demand while ensuring detection accuracy. To address this, we introduce a Hybrid Deep Network (HDN) featuring modality-specific feature extractors, an adaptive cross-modal attention fusion mechanism, and a dual-output diagnostic module. For industrial quality inspection, we further develop a Dynamic Swin Transformer Network (DTN) incorporating an intelligent confidence-based early-exit mechanism. The DTN dynamically selects optimal computational pathways conditioned on defect characteristics, enabling efficient inference via conditional computation. Extensive evaluations on the curated PowerDefect dataset (12,500 thermal/visible images across 15 defect categories) demonstrate that the DTN achieves state-of-the-art detection accuracy of 98.7% mAP@0.5, representing a +1.9% improvement over static Swin Transformers, while simultaneously reducing computational load by 31.4% (6.6 GFLOPs). Crucially, the early-exit mechanism facilitates real-time inference at 86 FPS—a $3.2\times $ speedup—with 79.3% of samples exiting early without accuracy degradation. This framework delivers a practical, high-efficiency solution for smart grid monitoring and industrial inspection, with significant reductions in unplanned maintenance validated in European utility deployments. The architecture’s adaptability also suggests promising extensions to diverse material defect detection scenarios. Future work will focus on adaptive threshold optimization and edge-computing implementations for large-scale distributed systems.}, archive = {J_ACCESS}, author = {Lijuan Yan and Ming Wang and Yan Zeng and Wensen Li and Yu Zou}, doi = {10.1109/ACCESS.2025.3605908}, journal = {IEEE Access}, pages = {155940-155951}, shortjournal = {IEEE Access}, title = {Dynamic swin transformer with early exit for efficient quality defect detection in power distribution network}, volume = {13}, year = {2025}, }
• (2025). Comprehensive performance evaluation of 60 kW PMSM, EESM, and SynRM for electric vehicle traction under steady-state and drive cycle conditions. ACCESS, 13, 155916-155939. (www)

  @article{ , abstract = {This paper presents a comprehensive performance evaluation of three 60 kW traction motor topologies: Permanent Magnet Synchronous Motor (PMSM), Electrically Excited Synchronous Motor (EESM), and Synchronous Reluctance Motor (SynRM), designed and simulated using Altair Flux under Field Oriented Control. The analysis covers both steady state and dynamic conditions using the Indian Highway Drive Cycle (IHDC) to capture realistic EV operation. The PMSM delivers the highest rated efficiency of 96.87% and the lowest reactive power, enabling compact inverter sizing. However, at high speeds, its efficiency declines, while SynRM and EESM maintain elevated efficiencies of 95.25% and 95.57%, respectively. Although the three motors differ in power factor and control complexity, their real-world energy consumption remains closely matched. PMSM consumes 93.70 Wh/km, EESM follows with 94.24 Wh/km, and SynRM with 94.86 Wh/km. These values result in IHDC-based driving ranges of 266.87 km for PMSM, 265.27 km for EESM, and 263.57 km for SynRM using a 25-kWh battery. The magnet-free SynRM offers a cost advantage through reduced material dependence, while EESM provides flexible torque control through field excitation. These findings confirm that both SynRM and EESM are competitive alternatives to PMSM in EV applications, delivering comparable performance, cost benefits, and sustainability.}, archive = {J_ACCESS}, author = {T. Paulraj and Yeddula Pedda Obulesu}, doi = {10.1109/ACCESS.2025.3606372}, journal = {IEEE Access}, pages = {155916-155939}, shortjournal = {IEEE Access}, title = {Comprehensive performance evaluation of 60 kW PMSM, EESM, and SynRM for electric vehicle traction under steady-state and drive cycle conditions}, volume = {13}, year = {2025}, }
• (2025). Fuzzy fitness distance balance gradient-based optimization algorithm (fFDBGBO): An application to design and performance optimization of PMSM. ACCESS, 13, 155898-155915. (www)

  @article{ , abstract = {The exceptional properties of Permanent Magnet Synchronous Motors (PMSMs), including their small construction, high power-torque density, and high efficiency, make them one of the most popular electrical machines. However, the motor structure needs to be optimized for circumstances like boosting the PMSM’s energy efficiency, optimizing output power, and lowering motor weight and cogging torque. This research aims to identify the optimal values for the parameters essential to achieving the most efficient design of a 15 kW PMSM. For this purpose, a new optimization algorithm is proposed. This proposed algorithm is an application of the fuzzy logic-based fitness distance balance design to the gradient-based optimization algorithm (GBO). The developed algorithm is called fuzzy Fitness Distance Balance Gradient-Based Optimization (fFDBGBO) algorithm. The proposed algorithm and various optimization algorithms with effective results in the literature were tested for the fitness function determined to find the optimal values of Embrace, Offset, Skew Width, Magnet Thickness, and slot bottom width (Bs1) of PMSM. The results indicate that the suggested algorithm achieves better performance than rival algorithms in terms of effectiveness. Compared to the GBO algorithm, the proposed fFDBGBO method achieves a 0.2979% increase in efficiency. Compared to other algorithms, the suggested algorithm’s objective function has the highest slot fill factor (49.9995%), yet it still complies with the specified limit value. Relative to the initial design, the motor efficiency achieved with the fFDBGBO algorithm improved from 91.7788% to 93.0366%, while the magnet weight decreased from 2.14837 to 2.09475, and the total motor weight remained approximately the same as 53.2286 kg and 54.0436 kg.}, archive = {J_ACCESS}, author = {Hasan Uzel and Yunus Hinislioğlu and Adem Dalcali and Uğur Güvenç}, doi = {10.1109/ACCESS.2025.3604649}, journal = {IEEE Access}, pages = {155898-155915}, shortjournal = {IEEE Access}, title = {Fuzzy fitness distance balance gradient-based optimization algorithm (fFDBGBO): An application to design and performance optimization of PMSM}, volume = {13}, year = {2025}, }
• (2025). Smart charging system in a bus depot: Cost-effective strategy. ACCESS, 13, 155883-155897. (www)

  @article{ , abstract = {This study presents a practical approach to quantify the economic benefits of dynamic price-based charging strategies for electric bus operators with overnight depot charging. The replication of the methodology allows for the precise estimation and charging schedule for the specific schedule. However, this work is also designed to target decision makers, allowing them to access the investment in this technology and its integration using the presented results. This paper defines a Mixed-Integer Linear Programming framework to optimize electric bus charging schedules using day-ahead electricity prices, incorporating real-world operational constraints and infrastructure limitations, and presents the potential savings achievable with the implementation of the proposed framework. The methodology includes a benchmark that schedules the charging as soon as possible, thereby replicating uncontrolled charging. Validated with actual operational data from a medium-sized Italian transit company, the model demonstrates substantial cost reductions by strategically shifting charging times to exploit lower-priced energy periods, especially aligning overnight charging with reduced electricity tariffs. The case study with real data from Italy shows a potential reduction of up to 1500 € (9% of the bill) for an 11-bus fleet. Scenario-based analyses highlight potential monthly savings with reductions in energy costs ranging between 7.8% and 15.1%, depending on operational contexts. Sensitivity analyses confirm that the operational variability further enhances cost-saving opportunities, while constrained grid power significantly limits these benefits, showing the impact for an adequately sized charging infrastructure. Also, a table is provided to easily extend the results to other European countries.}, archive = {J_ACCESS}, author = {Daniele Martini and Michela Longo and Luca Daniel}, doi = {10.1109/ACCESS.2025.3606410}, journal = {IEEE Access}, pages = {155883-155897}, shortjournal = {IEEE Access}, title = {Smart charging system in a bus depot: Cost-effective strategy}, volume = {13}, year = {2025}, }
• (2025). Integration of instance segmentation and three-dimensional point cloud analysis for pineapple pose estimation. ACCESS, 13, 155865-155882. (www)

  @article{ , abstract = {Fruit pose estimation technology is a critical component for enabling intelligent agricultural harvesting, holding significant application value for improving picking efficiency and reducing labor costs. To address the issues of high complexity in existing instance segmentation models and insufficient real-time performance in 3D pose estimation, this paper proposes a lightweight instance segmentation model, FLUG-YOLO, and an efficient method for pineapple pose estimation. Based on the YOLOv8-seg architecture, computational redundancy is reduced by replacing the backbone network with FasterNet. The feature representation capability is enhanced by embedding the Large-Separable-Kernel Attention (LSKA) mechanism within the feature pyramid. The neck convolutional layers are reconstructed using GhostConv, and a novel C2f_UIB structure is designed by incorporating the Universal Inverted Bottleneck (UIB) module. These modifications significantly reduce the number of parameters while improving multi-scale feature fusion performance. Furthermore, by integrating 3D point cloud analysis, a Principal Component Analysis (PCA)-based method for rapid pose estimation is proposed. This method calculates the pineapple’s roll and pitch angles by determining the principal axes of the point cloud. Experimental results demonstrate that FLUG-YOLO achieves an Average Precision (AP) of 96.1% (a 2.2% improvement over the baseline model) and an F1-score of 92.6% (a 3.1% improvement) on a custom pineapple dataset, with a model size of only 3.2 MB (a 49.2% reduction). For pose estimation, the average errors in unoccluded scenarios were 3.29° (roll) and 3.52° (pitch), increasing to 6.19° (roll) and 7.88° (pitch) under mild occlusion. The average pose estimation processing time was 301 ms. This approach achieves a synergistic optimization of accuracy, lightweight design, and speed, providing valuable technical support for intelligent pineapple harvesting.}, archive = {J_ACCESS}, author = {Rihong Zhang and Dezhao Chen and Hang Gao and Yi Wang and Xiaomin Li and Zhong Xue}, doi = {10.1109/ACCESS.2025.3602076}, journal = {IEEE Access}, pages = {155865-155882}, shortjournal = {IEEE Access}, title = {Integration of instance segmentation and three-dimensional point cloud analysis for pineapple pose estimation}, volume = {13}, year = {2025}, }
• (2025). Design and verification of a 53-gb/s PAM-4 cooled DFB-LD TOSA employing cost-effective TO-CAN package. ACCESS, 13, 155855-155864. (www)

  @article{ , abstract = {This article proposes a 53-Gb/s four-level pulse amplitude modulation (PAM-4) transmitter optical sub-assembly (TOSA) employing a cost-effective TO-CAN coaxial package. A distributed feedback (DFB) laser diode (LD), a monitoring photo-diode, a thermoelectric cooler (TEC), and a thermistor are integrated into a 7-pin commercial TO-56 TO-CAN package. The high-frequency characteristics of the entire TO-56 TO-CAN package, including the TO-56 header and LD submount, were simulated through a three-dimensional full-wave electromagnetic simulator; the resonance-free transmission bandwidth was over 30 GHz, and the reflection loss was below −10 dB within 19.55 GHz. The cooled O-band TO-56 TO-CAN DFB-LD TOSA had been realized, and its performance was measured using a self-designed evaluation board (EVB). The insertion loss of the EVB was simulated, and its bandwidth was 45.7 GHz. Using the TO-56 TO-CAN DFB-LD TOSA and EVB and setting the TEC temperature at 25 °C, the 53-Gb/s PAM-4 optical eye diagram was measured. Through a 15-tap equalizer, the extinction ratio of the 53-Gb/s PAM-4 optical eye diagram was 4.51 dB, the transmitter dispersion eye closure quaternary was 2.72 dB, and the optical modulation amplitude was 1.79 dBm. This cooled TO-56 TO-CAN DFB-LD TOSA with a TEC suits local area network - wavelength division multiplexing applications.}, archive = {J_ACCESS}, author = {Wahyu Hidayat and Pei-Hao Tseng and Jia-Hong Liu and Jau-Ji Jou}, doi = {10.1109/ACCESS.2025.3605604}, journal = {IEEE Access}, pages = {155855-155864}, shortjournal = {IEEE Access}, title = {Design and verification of a 53-gb/s PAM-4 cooled DFB-LD TOSA employing cost-effective TO-CAN package}, volume = {13}, year = {2025}, }
• (2025). Energy-aware MARL for coordinated data collection in multi-AUV systems. ACCESS, 13, 155835-155854. (www)

  @article{ , abstract = {As the demand for adaptive and autonomous smart ocean systems continues to grow, multi-agent control strategies based on reinforcement learning for Autonomous Underwater Vehicles (AUVs) play a vital role in supporting data collection in challenging deep-sea environments. Unlike previous surveys, this paper presents a comprehensive review of Multi-Agent Reinforcement Learning (MARL) approaches with a specific emphasis on energy efficiency and inter-AUV coordination. We examine various MARL algorithms and their applications in real-world scenarios such as buffer overflow prevention, avoidance of Flight eXceedance (FX) violations, and adaptive path planning. As a supporting illustration, we include a case study based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to demonstrate how coordinated policies can be formed under energy-constrained and partially observable scenarios. Additional experiments using MAPPO and MODDPG show that MODDPG excels in energy efficiency with low overflow, while MAPPO yields moderate rewards but lacks training stability. These results provide a conceptual foundation for validating energy-efficient reward strategies based on decentralized coordination. Scope note: the case study we included is a form of limited validation to complement the survey, not a new algorithmic contribution. Therefore, the experimental findings should be read as supporting the narrative (rather than as a claim of being the state-of-the-art method in this field). We also acknowledge that the simulation used is still limited to an idealistic 2D grid environment and does not fully represent real ocean dynamics. Therefore, we plan to extend it to a 3D particle-based or Computational Fluid Dynamics (CFD) framework, along with integration of ocean environmental data from historical sources such as NOAA, JAMSTEC, and Copernicus Marine. Major challenges such as non-stationarity in agent interactions, limitations of acoustic communication, and the simulation-to-reality gap are also discussed. Future research directions include Meta-Reinforcement Learning (Meta-RL), adaptive role assignment based on energy utility, large-scale decentralized MARL architectures, and training based on realistic ocean scenarios. This review and the supporting experiments are expected to serve as a strategic foundation for the development of efficient, robust, and scalable multi-agent AUV systems for future marine missions.}, archive = {J_ACCESS}, author = {Arif Wibisono and Hyoung-Kyu Song and Byung Moo Lee}, doi = {10.1109/ACCESS.2025.3606016}, journal = {IEEE Access}, pages = {155835-155854}, shortjournal = {IEEE Access}, title = {Energy-aware MARL for coordinated data collection in multi-AUV systems}, volume = {13}, year = {2025}, }
• (2025). AsaFG: A human-in-the-loop integration module for air combat simulations. ACCESS, 13, 155821-155834. (www)

  @article{ , abstract = {This work presents AsaFG, a human-in-the-loop integration module that connects the Aerospace Simulation Environment (ASA), a constructive simulation environment for military scenarios, with the FlightGear flight simulator. The module enables real-time interaction between human pilots and simulated entities in air combat scenarios, allowing pilots to assume roles within simulated missions and interact with autonomous agents. This integration enables direct interaction between constructive and virtual components, contributing to more realistic and immersive air combat simulations. The work also discusses key challenges during the integration process, including data exchange, control interfaces, and missile modeling differences between the platforms. A specific contribution involves adjusting the missile model in FlightGear to closely match ASA’s implementation, enabling consistent simulation behavior across systems. Validation includes performance metrics and experiments with Brazilian Air Force pilots across three operational scenarios. The results demonstrate the module’s applicability for training, operational analysis, and studies on human-autonomy teaming in simulated air combat scenarios.}, archive = {J_ACCESS}, author = {Samara R. Silva and Vitor C. F. Gomes and Alessandro O. Arantes and Andre F. M. Caetano and Victor L. D. B. Costa and Adrisson R. Samersla and Felipe L. L. Medeiros and Yuri D. Ferreira and Marcia R. C. Aquino and Joao P. A. Dantas}, doi = {10.1109/ACCESS.2025.3605878}, journal = {IEEE Access}, pages = {155821-155834}, shortjournal = {IEEE Access}, title = {AsaFG: A human-in-the-loop integration module for air combat simulations}, volume = {13}, year = {2025}, }
• (2025). Power amplifier distortion-aware resource allocation for multicarrier IoT devices. ACCESS, 13, 155808-155820. (www)

  @article{ , abstract = {One of the paradigms of the next-generation wireless networks is the Internet of Things, where many low-cost, low-rate Machine-Type Communication (MTC) devices exchange information with the Base Station (BS) along with the high-rate mobile broadband devices. It can be assumed that both device types utilize the same multicarrier resources grid. Typically resource allocation is a relatively simple task because of the subcarrier’s orthogonality. However, due to the requirement for high energy efficiency, the MTC devices should operate their transmit power amplifiers (PAs) in highly energy-efficient, yet nonlinear regions. As such, some nonlinear distortion can be introduced into the utilized subcarriers and the adjacent ones. In this paper, the nonlinear distortion produced by the MTC transmitter depending on subcarriers and power allocation is modeled and leveraged for the uplink resource allocation. The optimization problem is defined to maximize the minimal data rate of MTC devices. While it is a non-convex mixed binary problem, its solution is non-trivial. A suboptimal solution is proposed that, as confirmed by simulations, is very close to an optimal solution. Most importantly, in many configurations, significant performance gain can be obtained by utilizing a distortion-aware resource allocation.}, archive = {J_ACCESS}, author = {Pawel Sroka and Pawel Kryszkiewicz}, doi = {10.1109/ACCESS.2025.3606112}, journal = {IEEE Access}, pages = {155808-155820}, shortjournal = {IEEE Access}, title = {Power amplifier distortion-aware resource allocation for multicarrier IoT devices}, volume = {13}, year = {2025}, }
• (2025). AFMDS: Attention-based forecasting model against distribution shift for long-term non-stationary time series prediction. ACCESS, 13, 155797-155807. (www)

  @article{ , abstract = {Long-term time series forecasting has extensive applications across various critical domains. However, its modeling process encounters significant challenges, including difficulties in capturing long-range dependencies and issues arising from distribution shifts. As the prediction horizon extends, time series exhibit complex multiscale dynamic characteristics. Meanwhile, data non-stationarity causes trained models to struggle in adapting to future distributional changes, thereby substantially affecting forecasting performance. To alleviate the aforementioned challenges, this paper proposes an attention-based forecasting model against distribution shift (AFMDS). The proposed method aims to improve prediction accuracy and robustness under non-stationary environments. Specifically, this method employs a global-local decomposition framework to effectively separate shared long-term dynamics across variables from individual short-term fluctuations, thereby enhancing the model’s capability to capture both long-range dependencies and inter-variable correlations. To mitigate the negative effects of non-stationarity, a lightweight statistical normalization module reconstructs time series data based on core statistical characteristics. Additionally, a novel non-stationary attention mechanism is designed to dynamically restore essential temporal information. Experimental results on benchmark datasets demonstrate that this model outperforms existing SOAT method, showcasing superior forecasting accuracy and robustness in scenarios involving long-term prediction and distribution shifts.}, archive = {J_ACCESS}, author = {Haiyan Xu}, doi = {10.1109/ACCESS.2025.3601406}, journal = {IEEE Access}, pages = {155797-155807}, shortjournal = {IEEE Access}, title = {AFMDS: Attention-based forecasting model against distribution shift for long-term non-stationary time series prediction}, volume = {13}, year = {2025}, }
• (2025). One-leg half-bridge diode-clamped four-level PWM inverter based on MNRV PWM. ACCESS, 13, 155778-155796. (www)

  @article{ , abstract = {This paper introduces a one-leg half-bridge (HB) diode-clamped four-level PWM inverter incorporating the multi-neighboring reference vector pulse-width modulation (MNRV PWM) methodology. To mitigate the DC offset inherent in the one-leg HB inverter output, two approaches are proposed: the inclusion of a DC-blocking capacitor and the splitting of the central DC-link capacitor into two, with the resulting virtual neutral point connected to one end of the load. The variation in the DC-link voltage is rigorously analyzed by integrating the product of the duty cycles of the reference vectors and phase current. The proposed inverter was evaluated through simulations to assess its steady-state performance, total harmonic distortion (THD), average switching frequency, power losses per device, and voltage balancing capability, even in the presence of DC-link capacitor mismatches. Furthermore, prototype experiments were conducted to validate its fundamental operational characteristics, including its dynamic response to rapid changes in operating conditions and its voltage balancing ability under initial voltage imbalance conditions. The results demonstrated that the proposed method achieves performance comparable to a virtual-vector PWM (VVPWM) approach adapted from conventional multileg topologies, while reducing computational time by approximately 10%.}, archive = {J_ACCESS}, author = {Min-Sup Song and Hyungchul Kim}, doi = {10.1109/ACCESS.2025.3605879}, journal = {IEEE Access}, pages = {155778-155796}, shortjournal = {IEEE Access}, title = {One-leg half-bridge diode-clamped four-level PWM inverter based on MNRV PWM}, volume = {13}, year = {2025}, }
• (2025). Failures and resilience in the IP era: Navigating the fragility of modern telecommunications Networks—The sovereign functions. ACCESS, 13, 155759-155777. (www)

  @article{ , abstract = {Telecommunications networks have become critical infrastructure, with increasing reliance on their continuous availability driven by expectations of uninterrupted connectivity, real-time communication, and a surge in connected devices across both consumer and industrial domains. Over time, industrial systems and machine-to-machine applications have transitioned from isolated, purpose-built infrastructures to shared, general-purpose IP-based networks. While this evolution has enabled greater flexibility and integration, it has also introduced operational complexity and new classes of failure. Although physical layer redundancy and geographic isolation remain essential components of network resilience, modern networks are heavily dependent on a set of interdependent IP functions. These include routing, addressing & naming services, and time synchronization—each playing a sovereign role in end-to-end service continuity. Importantly, these functions are increasingly coupled with a range of supporting services—such as authentication & access control, license management, and network configuration & management—which, while not formally specified within the IP protocol suite, are essential to its operation and resilience. This paper presents a failure-centric survey of sovereign and supporting functions in modern telecommunications networks, illustrating how real-world outages often stem from failures in these interdependent layers, which form the organizing lens for this study. Through detailed case studies, we illustrate the severe consequences of such failures on network services, along with their broader economic and corporate ramifications. Furthermore, we explore potential mitigation strategies and emphasize the need for continued research to develop more resilient network architectures capable of withstanding inherent vulnerabilities in IP-based systems. This study contributes to enhancing service resilience amid the growing complexity and demands of modern mobile and fixed networks.}, archive = {J_ACCESS}, author = {Raymond Owen and Adam Bryant and Leigh Finch and Daniel Franklin and Mahrokh Abdollahi and Mehran Abolhasan}, doi = {10.1109/ACCESS.2025.3602054}, journal = {IEEE Access}, pages = {155759-155777}, shortjournal = {IEEE Access}, title = {Failures and resilience in the IP era: Navigating the fragility of modern telecommunications Networks—The sovereign functions}, volume = {13}, year = {2025}, }
• (2025). Advancing text-to-speech systems for low-resource languages: Challenges, innovations, and future directions. ACCESS, 13, 155729-155758. (www)

  @article{ , abstract = {Speech synthesis, the technology that converts text into spoken words, has advanced significantly for high-resource languages like English, Spanish, and Mandarin. However, many languages spoken by millions of people are still underserved by speech synthesis systems due to insufficient data to train accurate models. An analysis of current solutions and difficulties in low-resource language speech synthesis development is presented in this paper because these languages have restricted data and linguistic resources. The paper examines different research strategies for advancing speech synthesis techniques in these languages by borrowing data from rich-resource languages to enhance models used in poor-resource systems. The primary method involves the comparison of linguistic models that operate between highly supported languages to adapt them to languages that only have limited information available. Acoustic models requiring advancement represent a crucial need to process correctly the phonetic and prosodic attributes within these languages because their traits lack proper representation in small data collections. The paper investigates different data augmentation methods, which include synthetic data generation and multilingual corpus use to enhance available training resources. These methods enhance the quality of computer-generated speech by making it sound more natural while ensuring that the words remain clear and understandable, even in low-resource languages. Notably, data augmentation techniques expanded dataset sizes by up to 27 times, improving model robustness. Additionally, models like WaveNet achieved a Mean Opinion Score (MOS) of 4.21, while Tacotron-based systems reached MOS scores as high as 4.38, demonstrating high naturalness even with limited training data. Experiments using less than three hours of training data in low-resource settings initially showed Word Error Rates (WER) exceeding 50–80%, but data augmentation and transfer learning significantly reduced these error rates. By reviewing the present state of research, this paper offers an overview of the progress made in low-resource language speech synthesis. It identifies the potential future directions for this field. Ultimately, the goal is to make speech synthesis more accessible for underrepresented languages, helping with language preservation, assistive technologies, and broader accessibility.}, archive = {J_ACCESS}, author = {Shashi Bhushan and Ved Prakash Mishra and Vinay Rishiwal and Sharmila Arunkumar and Udit Agarwal}, doi = {10.1109/ACCESS.2025.3605236}, journal = {IEEE Access}, pages = {155729-155758}, shortjournal = {IEEE Access}, title = {Advancing text-to-speech systems for low-resource languages: Challenges, innovations, and future directions}, volume = {13}, year = {2025}, }
• (2025). Ensemble learning for wafer defect pattern classification in the semiconductor industry. ACCESS, 13, 155714-155728. (www)

  @article{ , abstract = {During semiconductor manufacturing, wafer defect patterns emerge in an uncontrolled environment, making immediate recognition challenging. To enhance the classification accuracy in pattern recognition, deep learning (DL) techniques are employed to address the issue of defective pattern identification. The inherent differences among various wafer defect types lead to imbalanced testing datasets, thereby reducing the efficiency of classification models in delivering accurate predictions. This study proposes a convolutional neural network (CNN) combined with a data transformation method at the feature extraction stage, incorporating Density-based features and Radon-based features to enhance the representation of key patterns. Subsequently, multiple classifiers are integrated using voting, bagging, and AdaBoost strategies to improve classification performance and model generalization. The primary objective is to mitigate data imbalance and selection bias by employing random sampling and assigning distinct weights to individual classifiers. The contributions of this study are threefold. First, we introduce a novel feature extraction strategy that leverages features extracted by the CNN model, Density-based features, and Radon-based features, effectively capturing structural and spatial characteristics in wafer defect patterns. Second, we propose an ensemble learning framework integrating multiple classifiers with optimized weighting mechanisms to enhance classification robustness. Third, we provide empirical evidence demonstrating that our weighted soft voting approach achieves superior performance, attaining a classification accuracy of 95.09% and an F1 score of 0.95. These results validate the effectiveness of our method in improving the reliability of wafer defect classification, which is crucial for advancing automated defect inspection in semiconductor manufacturing.}, archive = {J_ACCESS}, author = {Chia-Yun Lee and Matúš Pleva and Daniel Hládek and Chin-We Lee and Ming-Hsiang Su}, doi = {10.1109/ACCESS.2025.3604405}, journal = {IEEE Access}, pages = {155714-155728}, shortjournal = {IEEE Access}, title = {Ensemble learning for wafer defect pattern classification in the semiconductor industry}, volume = {13}, year = {2025}, }
• (2025). Research on detection optimization of the 3.0 base version of the metering automation system based on reinforcement learning algorithm. ACCESS, 13, 155697-155713. (www)

  @article{ , abstract = {In response to the problems of low detection efficiency and insufficient strategy optimization ability of the current base version detection tools in dynamic and complex scenarios, this paper proposes a detection optimization framework based on deep reinforcement learning. Firstly, by constructing a dual-mode dynamic testing model that integrates local detection and online detection, the base version quality assessment process is formalized as a Markov decision process, where the state space covers key indicators such as functional integrity, interface consistency, and storage throughput (refer to the GB/T 25000 standard). Secondly, an intelligent agent based on the Deep Q-Network (DQN) is designed to achieve adaptive optimization of detection dimension selection, feature index priority scheduling, and test case generation. A multi-objective hybrid reward mechanism (R =0.6 defect discovery rate +0.3 throughput gain - 0.1* time cost) is innovatively introduced to balance detection quality and resource consumption in dynamic interaction. Experiments show that this method improves detection efficiency by 37.2% compared to traditional schemes, with a feature index coverage rate of 98.5%, and its robustness in multi-unit detection scenarios is verified in third-party testing. The developed microservice architecture detection tool integrates a reinforcement learning decision interface, supporting real-time visualization of detection progress and automatic generation of electronic reports. This research provides an innovative solution for the full life cycle quality control of smart grid base versions.}, archive = {J_ACCESS}, author = {Lukun Zeng and Guoying Lin and Jingxu Yang and Guanyu Chen and Sheng Li and Guobo Liu and Jinghu Bi}, doi = {10.1109/ACCESS.2025.3606062}, journal = {IEEE Access}, pages = {155697-155713}, shortjournal = {IEEE Access}, title = {Research on detection optimization of the 3.0 base version of the metering automation system based on reinforcement learning algorithm}, volume = {13}, year = {2025}, }
• (2025). Adapting LLMs for satellite communications: Methodology, challenges, and impact. ACCESS, 13, 155675-155696. (www)

  @article{ , abstract = {The application of large language models (LLMs) to specialized fields, such as Satellite Communications (SatCom), presents unique challenges due to the extensive and cutting-edge knowledge required. SatCom encompasses a wide range of technical details, protocols, and operational guidelines that must be addressed to produce effective and accurate models for practical use. This paper presents a fine-tuning approach for adapting 7-billion-parameter instructed LLMs (Llama-3v and Mistral) to SatCom, using a proprietary corpus sourced from the European Space Agency (ESA) consisting of domain-specific PDF documents. The confidential nature of this corpus imposes constraints on both model training and evaluation, demanding a sensible text extraction pipeline capable of handling complex structures, such as tables, to preserve critical information. Our fine-tuning methodology employs a carefully configured process, followed by an automatic evaluation framework using a curated Q&A set tailored to SatCom. Models were created in both non-quantified and 8-bit quantized formats, ensuring feasibility for desktop-level inference. The fine-tuned models demonstrated a 6,6% improvement over the baseline LLM, as well as significant gains when compared to retrieval-augmented generation (RAG) methods. These results indicate a promising advancement in the development of LLMs for domain-specific applications within the SatCom field.}, archive = {J_ACCESS}, author = {Alejandro Mozo and Sergio Gálvez and Ioannis T. Christou and Dimitrios Vogiatzis and Tomás Navarro and Francisco L. Valverde}, doi = {10.1109/ACCESS.2025.3605022}, journal = {IEEE Access}, pages = {155675-155696}, shortjournal = {IEEE Access}, title = {Adapting LLMs for satellite communications: Methodology, challenges, and impact}, volume = {13}, year = {2025}, }
• (2025). UAV swarm trajectory design for wireless networks using genetic algorithm-driven repulsion forces. ACCESS, 13, 155657-155674. (www)

  @article{ , abstract = {Uncrewed Aerial Vehicle (UAV) swarms are increasingly recognized for their versatility and affordability. These swarms have enhanced various applications, including agriculture, surveillance, delivery services, and monitoring. However, fully utilizing the capabilities of UAV swarms requires addressing challenges related to trajectory design, particularly the Multiple Traveling Salesman Problem (MTSP). It involves optimizing the paths of multiple UAVs while avoiding collisions, minimizing overlap and interference, and managing the overall size of the swarm. These challenges highlight the complexities involved in developing high-performance, organized UAV swarm operations. We propose a novel approach based on repulsion force in UAV swarm trajectory design to tackle these issues. Our method utilizes a Genetic Algorithm (GA) to generate a dynamic Repulsion Force (RF) that optimizes the distance between UAVs and the size of the swarm. This approach reduces interference and overlap while effectively navigating the limitations posed by the MTSP. Our proposed solution aims to design efficient trajectories that enhance the overall performance of UAV swarms. We compared our proposed method to existing algorithms, including the MTSPGA, Particle Swarm Optimization (PSO), 2-OPT, Ant Colony (AC) Optimization, and Simulated Annealing (SA), using simulations and evaluations. The results indicate that our proposed method effectively optimizes travel distances and times, reduces interference levels and overlapping, prevents collisions between UAVs, and enhances the size of the UAV swarm. Overall, our method outperforms current approaches, demonstrating its effectiveness for UAV-based applications.}, archive = {J_ACCESS}, author = {Kaleem Arshid and Ali Krayani and Lucio Marcenaro and David Martín Gómez and Carlo Regazzoni}, doi = {10.1109/ACCESS.2025.3606121}, journal = {IEEE Access}, pages = {155657-155674}, shortjournal = {IEEE Access}, title = {UAV swarm trajectory design for wireless networks using genetic algorithm-driven repulsion forces}, volume = {13}, year = {2025}, }
• (2025). MHM-RTC: Multi-hop mobility-based real-time clustering algorithm for wide-area wireless sensor network. ACCESS, 13, 155642-155656. (www)

  @article{ , abstract = {Internet of Things (IoT) and wireless sensor networks (WSN) have significant potential for a wide range of applications. However, these methods are hindered by resource constraints, long-range connectivity and energy challenges. Existing clustering protocols attempt to address some of these problems but fall short when handling the demands of real-time mobility in large-scale WSNs. Many of these algorithms rely on high processing power and offline optimization and fail to account for the dynamic mobility of nodes within the network. This article introduces the Multi-hop Mobility-based Real-Time Clustering (MHM-RTC) algorithm, which utilizes long-range (LoRa) communication between mobile sensor nodes (SNs) and a mobile data sink (DS). Unlike traditional approaches, this algorithm enables dynamic, on-the-fly clustering through the active participation of both SNs and DS. It adapts to continuous topology changes in highly mobile environments, supporting real-time responsiveness and efficient wide-area WSN coverage. The algorithm includes a mathematical model to calculate the clustering time across various network sizes, focusing on energy optimization and extended coverage in mobile networks. The simulation results demonstrate significant improvements over existing mobility-supporting algorithms, such as the Energy Efficient Routing Algorithm (EERA), Energy-Efficient Mobility-Based Cluster Head Selection (EEMCS) and Lightweight Dynamic Clustering Algorithm (LDCA). Specifically, MHM-RTC reduced the clustering time by up to 20%, increased the sensor node coverage by 37-53%, and reduced the average residual energy consumption by 3.77–8.69% when the total number of sensor nodes varied from 100 to 250. These results highlight that MHM-RTC is an effective solution for real-time energy-efficient clustering in large-scale mobile sensor networks.}, archive = {J_ACCESS}, author = {Md Ridoy Ad Sumon and Mushran Siddiqui and Gazi M. Ehsan ur Rahman and Raqibul Mostafa}, doi = {10.1109/ACCESS.2025.3603021}, journal = {IEEE Access}, pages = {155642-155656}, shortjournal = {IEEE Access}, title = {MHM-RTC: Multi-hop mobility-based real-time clustering algorithm for wide-area wireless sensor network}, volume = {13}, year = {2025}, }
• (2025). A simplified disturbance detector approach based on self-adjusting thresholds. ACCESS, 13, 155632-155641. (www)

  @article{ , abstract = {The increasing prevalence of Inverter-Based Resources (IBRs) in modern power systems introduces new challenges to conventional fixed-threshold fault detection methods, which often lack adaptability and robustness under varying system topologies, noise levels, and signal resolutions. In this context, this paper presents a novel disturbance detection methodology that combines the simplicity of a low-cost signal processing technique with an adaptive thresholding-based detection logic. The method relies on the modulus of incremental current and employs self-adjusting thresholds derived from Mathematical Morphology. Extensive validation is conducted through simulations on a detailed real-world wind farm model comprising 120 full-converter turbines and 27,900 distinct fault scenarios. The simulation set includes variations in fault type, resistance, and inception angle, as well as the operating power factor and generation level of the wind farm. The proposed approach consistently achieves high accuracy, above 99%, across all scenarios, maintaining resilience under challenging signal-to-noise ratios as low as 40 dB and reduced sampling rates down to 16 points per cycle, without requiring system- or scenario-specific threshold tuning. Comparative analyses with conventional fixed-threshold techniques, including Sample-by-Sample, Cumulative Sum, and Current-Slope Based methods, highlight the superior robustness, accuracy, security, and deployment potential of the proposed method. Furthermore, processing benchmarks demonstrate a computational time reduction of approximately 99% compared to more complex techniques such as Wavelet Transform and entropy-based analysis, reinforcing its suitability for embedded and real-time protection systems.}, archive = {J_ACCESS}, author = {Sólon S. Otomura and Moisés J. B. B. Davi and Matheus do Val Oliveira and José C. M. Vieira and Mário Oleskovicz}, doi = {10.1109/ACCESS.2025.3606368}, journal = {IEEE Access}, pages = {155632-155641}, shortjournal = {IEEE Access}, title = {A simplified disturbance detector approach based on self-adjusting thresholds}, volume = {13}, year = {2025}, }
• (2025). Affect recognition in immersive room-scale environments: A large-scale VR study with custom facial sensing at the science museum in london. ACCESS, 13, 155616-155631. (www)

  @article{ , abstract = {Recent technological advances have provided the chance to conduct Virtual Reality (VR) experiments with increased ecological validity, which in turn can elicit more naturalistic responses in immersed users. However, many studies still prefer highly controlled setups and passive stimulation, often due to the practical complexities in effectively associating cause (stimulus) to response in highly interactive and dynamic VR experiences. Many of these studies also rely on subjective ratings from participants recorded either after the experience (relying on memory) or during the experience (interrupting immersion). In this paper, we advance this experimental protocol in a large-scale feasibility study by 1) investigating affective changes in terms of valence and arousal ratings in various interactive 3D room-scale VR environments with 2) continuous valence and arousal self-ratings from a controller and 3) a novel wireless physiological facial EMG and PPG sensor setup specifically designed to record affect, without relying on memory or interrupting immersion. In this study, n=291 participants experienced neutral, positive and negative virtual environments in ‘passive’ and ‘active’ conditions. Continuous self-ratings and physiological measures confirmed the feasibility of detecting affective states in room-scale VR conditions. To our knowledge, this is the highest n in a feasibility study in affect detection to date. Our study generated the most populated physiological data library collected in VR, which also compares passive and active VR settings. This setup can provide a solid experimental foundation for VR affective computing studies in more unconstrained, ecologically valid environments.}, archive = {J_ACCESS}, author = {Ifigeneia Mavridou and Emili Balaguer-Ballester and Theodoros Kostoulas and Ellen Seiss}, doi = {10.1109/ACCESS.2025.3597793}, journal = {IEEE Access}, pages = {155616-155631}, shortjournal = {IEEE Access}, title = {Affect recognition in immersive room-scale environments: A large-scale VR study with custom facial sensing at the science museum in london}, volume = {13}, year = {2025}, }
• (2025). A novel peer-to-peer electricity usage rights transfer framework for residential demand-side management. ACCESS, 13, 155605-155615. (www)

  @article{ , abstract = {This paper presents a quota-based peer-to-peer (P2P) electricity usage rights transfer mechanism tailored for apartment-style residential consumers who do not have access to on-site energy production or storage. The framework operates under Turkey’s two-tier residential electricity pricing structure, enabling users with monthly consumption below 240 kWh to transfer unused quota to over-consuming peers in exchange for financial compensation. The mechanism incorporates time-of-use (ToU) constraints, allocating transferable quota across daytime (T1), peak (T2), and nighttime (T3) periods, which encourages load shifting away from peak hours. A water fill algorithm ensures fair distribution of the limited quota among buyers, optimizing the allocation process. A case study is conducted using data from a 28-unit apartment block in Malatya, Türkiye. Real household demographic data and monthly bills are used to synthesize hourly profiles for analysis. Simulation results reveal that under the proposed mechanism, users can reduce their electricity bills by up to 9.5% without changing total consumption—merely by shifting usage to off-peak hours. Additionally, the system-level energy cost decreased by up to 4.2%, driven by a reduction in peak-hour consumption and improved alignment with market clearing prices. These findings suggest that the method not only benefits individual consumers but also provides value to distribution system operators by lowering grid stress and energy procurement costs. Overall, the study offers a regulation-compatible, low-cost alternative to traditional demand-side management solutions and demonstrates how behavioral incentives can unlock flexibility in urban residential settings through decentralized energy cooperation.}, archive = {J_ACCESS}, author = {Kadir Doğanşahin}, doi = {10.1109/ACCESS.2025.3606197}, journal = {IEEE Access}, pages = {155605-155615}, shortjournal = {IEEE Access}, title = {A novel peer-to-peer electricity usage rights transfer framework for residential demand-side management}, volume = {13}, year = {2025}, }
• (2025). An in-depth analysis of eBPF-based system security tools in cloud-native environments. ACCESS, 13, 155588-155604. (www)

  @article{ , abstract = {Kubernetes-based microservice architectures are central to modern cloud-native environments, offering flexibility, scalability, and diverse use cases. Given their large-scale and complex deployments, auditing is crucial for detecting potential threats. To address this need, various eBPF (extended Berkeley Packet Filter)-based security tools (ESSTs) have been developed, leveraging eBPF to monitor process and network activities in Kubernetes environments, log suspicious events, and enforce security policies. However, administrators often face challenges in selecting the most suitable ESST due to a lack of comprehensive comparative analyses. In this paper, we conduct an in-depth evaluation of four widely used ESSTs—KubeArmor, Falco, Tetragon, and Tracee—focusing on their internal architectures, auditing capabilities, overall performance, and real-world case studies in practice. Based on our findings, we provide key insights to guide administrators in selecting the appropriate ESST for their specific needs. We believe our research enhances the understanding of ESSTs, contributing to the development of more secure and high-performance cloud-native environments.}, archive = {J_ACCESS}, author = {Jin Her and Jongseop Kim and Jinwoo Kim and Seungsoo Lee}, doi = {10.1109/ACCESS.2025.3605432}, journal = {IEEE Access}, pages = {155588-155604}, shortjournal = {IEEE Access}, title = {An in-depth analysis of eBPF-based system security tools in cloud-native environments}, volume = {13}, year = {2025}, }
• (2025). Control analysis of a miniaturized worm-inspired robot using FOPID and fuzzy PID for bio-inspired locomotion. ACCESS, 13, 155560-155587. (www)

  @article{ , abstract = {In this paper, the design and control analysis of a lightweight, miniaturized worm-inspired robot actuated by 8 mini coreless DC motors are presented. The robot’s movement capabilities include straight-line motion, turning, and speed modulation, achieved through distinct motor configurations. Structural analysis played a vital role in assessing the forces, stresses and deformations the body experiences under various loads. Rigid body analysis is performed to analyze the contact forces and relative motion between the bodies. Authors explored two advanced control strategies—Fractional Order PID (FOPID) and Fuzzy PID—to compare their effectiveness in trajectory tracking and motor synchronization. The FOPID controller offers enhanced flexibility by fine-tuning the proportional, integral, and derivative gains using fractional calculus, ensuring smoother trajectory following. In contrast, the Fuzzy PID controller adapts its gains in real time, providing robust performance in dynamic environments. Simulation results over a 25-second interval show that the FOPID controller excels in smooth, precise motion, while the Fuzzy PID controller proves effective in handling external disturbances. This study contributes to soft robotics by showcasing effective control methodologies for bio-inspired locomotion, with potential applications in search and rescue missions, medical devices, and inspection tasks within confined spaces.}, archive = {J_ACCESS}, author = {Spoorthi Singh and Abhinav Pradeep Naidu and Vishnu G. Nair and Navya Thirumaleshwar Hegde and V. S. Dhananjai}, doi = {10.1109/ACCESS.2025.3605655}, journal = {IEEE Access}, pages = {155560-155587}, shortjournal = {IEEE Access}, title = {Control analysis of a miniaturized worm-inspired robot using FOPID and fuzzy PID for bio-inspired locomotion}, volume = {13}, year = {2025}, }
• (2025). PG-DETR: A lightweight and efficient detection transformer for early stage pomegranate fruit detection. ACCESS, 13, 155547-155559. (www)

  @article{ , abstract = {The detection of small, occluded, early-stage pomegranate fruits within complex orchard environments poses a significant challenge for efficient deployment on edge devices. To overcome this, we propose PG-DETR, a lightweight end-to-end Detection Transformer (DETR). Our model introduces two key innovations: a highly efficient Lite HGNetv2 backbone that drastically reduces model complexity, and a novel multi-path hierarchical context fusion encoder (MHCF Encoder) that enhances multi-scale feature fusion to improve robustness. Experimental results on the PG dataset show that PG-DETR achieves a mean average precision at 50% IoU (mAP50) of 93.37% with only 6.7 million parameters and a computational load of 12.4 gigaflops per second (GFLOPs). Notably, compared to the RT-DETR-R18 baseline, our model reduces parameters by 66.2% and GFLOPs by 78.2%, while increasing inference speed by a factor of 3.2, reaching 173.58 frames per second (FPS). PG-DETR demonstrates a superior balance of accuracy and efficiency, providing a practical solution for deploying high-performance transformer-based detectors in smart agriculture.}, archive = {J_ACCESS}, author = {Xiaorong Zhang}, doi = {10.1109/ACCESS.2025.3605887}, journal = {IEEE Access}, pages = {155547-155559}, shortjournal = {IEEE Access}, title = {PG-DETR: A lightweight and efficient detection transformer for early stage pomegranate fruit detection}, volume = {13}, year = {2025}, }
• (2025). Gender and academic indicators in first-year engineering dropout: A multi-model approach. ACCESS, 13, 155532-155546. (www)

  @article{ , abstract = {Student attrition during the first academic year remains a critical issue in engineering education, with implications for equity and institutional effectiveness. This study explores early dropout patterns across five engineering degree programs using data from 3889 first-year students spanning seven academic cohorts at one public and one private university in Spain. A multi-method analytical strategy—comprising logistic regression, artificial neural networks, and propensity score matching—was used to examine how gender and academic performance relate to early attrition. Findings challenge prevailing assumptions in the literature by showing that gender is not a statistically significant factor in first-year dropout, a result consistent across methods and after adjusting for confounders. In contrast, academic variables—such as entrance exam scores, failure rates, and exam absenteeism—exhibited strong associations with attrition. Institutional context also shaped dropout patterns: academic failure played a particularly salient role in the private university, while disengagement, measured through no-show rates, was more relevant in the public institution. Notably, an exception emerged in the Mathematical Engineering program, where gender moderated the link between academic failure and dropout, pointing to potential interaction effects in specific curricular settings. These results underscore the importance of understanding the conditional and context-dependent nature of early dropout, supporting targeted interventions grounded in academic, rather than demographic, indicators.}, archive = {J_ACCESS}, author = {Ana Aparicio Colino and José Luis Arroyo-Barrigüete and Adolfo Hernández and Carmen Sánchez-Ávila}, doi = {10.1109/ACCESS.2025.3605776}, journal = {IEEE Access}, pages = {155532-155546}, shortjournal = {IEEE Access}, title = {Gender and academic indicators in first-year engineering dropout: A multi-model approach}, volume = {13}, year = {2025}, }
• (2025). Measure-X: A platform for collecting network and energy metrics in 5G-based IoT applications. ACCESS, 13, 155520-155531. (www)

  @article{ , abstract = {Measure-X is a flexible measurement platform designed to collect both conventional network KPIs and IoT-centric metrics, such as energy and Age of Information (AoI), within 5G access networks. Measure-X features a central controller, distributed probes, and a RESTful API for experiment configuration and data retrieval. Its design enables the assessment of network performance, energy consumption, and AoI, as well as the analysis of interference from coexisting applications. We validate Measure-X through a case study conducted in a production environment at the Fraunhofer Institute for Production Technology (IPT), demonstrating its ability to identify optimal message rates for minimizing AoI and to quantify performance degradation caused by background traffic. Measure-X is released as open-source software to support further research and industrial experimentation (https://github.com/InternetMeasurements/measure-x).}, archive = {J_ACCESS}, author = {Francesco Bruno and Valerio Luconi and Alessio Vecchio}, doi = {10.1109/ACCESS.2025.3606153}, journal = {IEEE Access}, pages = {155520-155531}, shortjournal = {IEEE Access}, title = {Measure-X: A platform for collecting network and energy metrics in 5G-based IoT applications}, volume = {13}, year = {2025}, }
• (2025). LiDAR 2D and camera fusion for ADAS: A practical approach with YOLOv9 and ROS2 framework. ACCESS, 13, 155500-155519. (www)

  @article{ , abstract = {This study introduces a cost-effective sensor fusion system that integrates 2D LiDAR and camera data for Advanced Driver Assistance Systems (ADAS), implemented within the Robot Operating System 2 (ROS2) framework. The objective is to enable accurate and real-time obstacle detection using lightweight hardware. The system leverages the YOLOv9 object detection model, optimized with TensorRT for high-performance inference, and includes a projection pipeline that aligns LiDAR point clouds with camera images to enhance spatial localization. By combining geometric precision from LiDAR with visual semantics from the camera, the architecture achieves effective multi-modal perception. Unlike more expensive 3D LiDAR-based solutions, the use of 2D LiDAR reduces costs while maintaining competitive accuracy. Experimental evaluations in static real-world environments show robust performance, with a distance estimation error below 3% and an angular error under 0.3°. The proposed system supports modular deployment and real-time operation, making it suitable for low-cost embedded ADAS applications.}, archive = {J_ACCESS}, author = {Walid Benayed and Imen Mabrouk and Mohamed Slim Masmoudi and Wafa Ben Abdelaziz}, doi = {10.1109/ACCESS.2025.3604577}, journal = {IEEE Access}, pages = {155500-155519}, shortjournal = {IEEE Access}, title = {LiDAR 2D and camera fusion for ADAS: A practical approach with YOLOv9 and ROS2 framework}, volume = {13}, year = {2025}, }
• (2025). Genetic algorithm-based search space exploration to generate best convolutional neural network. ACCESS, 13, 155482-155499. (www)

  @article{ , abstract = {Despite the wide variety of applications and use cases that can be solved with the help of machine learning algorithms, researchers have yet to develop a general artificial intelligence solution that can solve different problems. A single machine learning model can provide at least or above-average human-level performance in multiple domains without re-training for each domain separately. Currently, machine learning solutions train multiple models for each domain and combine their outputs to generate high-performance in multi-domain results. While this multi-model approach is achievable, designing and training each model and fine-tuning hyperparameters is complex and time-consuming. However, automated machine learning approaches provide an algorithmic and probabilistic approach to training models with the best-performing hyperparameters. This paper, after taking inspiration from gene theory of adaptation, presents a Novel Inverse Shuffle crossover using the Genetic algorithm method that generates high-performance convolutional neural network architectures for search space exploration (GA-NISC). New integrations enable the proposed method to explore the hyperparameter search space more thoroughly before converging to the best-performing model architecture as compared to its peers. The experiments are performed on a benchmark German traffic signal classification dataset and the CIFAR-10 dataset. Experimental results revealed that the proposed method converged faster in contrast to other neural architectural search methods while reducing the training time and overall computations considerably. Besides, it also yield high fitness scores up to 92% and 98% respectively under the best generated architecture. Furthermore, a composite fitness metric was calculated by integrating Validation Accuracy, F1-Score, and Regularization into the cross-entropy loss metric to meaningfully improve model generalization, especially on imbalanced data.}, archive = {J_ACCESS}, author = {Dhananjay Bisen and Praneet Saurabh and Mayank Thakur and Gyanendra Chaubey and Upendra Singh and Aditya Dubey}, doi = {10.1109/ACCESS.2025.3606004}, journal = {IEEE Access}, pages = {155482-155499}, shortjournal = {IEEE Access}, title = {Genetic algorithm-based search space exploration to generate best convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Adaptive fusion of LiDAR and camera data for enhanced precision in 3D object detection for autonomous driving. ACCESS, 13, 155474-155481. (www)

  @article{ , abstract = {Reliable 3D object detection is critical for autonomous driving, yet static fusion strategies often fail under occlusion, sparse point clouds, or varying illumination. This paper proposes the Adaptive Multi-Modal Fusion (AMMF) framework, which dynamically integrates LiDAR and camera features through three mechanisms: modality-specific gating to filter input relevance, multi-head attention for cross-modal interaction, and uncertainty modeling for confidence-aware weighting. Experiments on the KITTI benchmark demonstrate that AMMF achieves state-of-the-art performance for Cars and Cyclists and competitive results for Pedestrians, highlighting robustness in cluttered and low-visibility scenarios. Runtime analysis shows that the attention block dominates computation, limiting throughput to ~6.3 FPS; however, optimization strategies such as sparse attention or head sharing can substantially reduce latency. These findings indicate that AMMF not only improves detection accuracy but also establishes a scalable foundation for deploying multimodal perception in real-world autonomous driving systems.}, archive = {J_ACCESS}, author = {Ha Thi Tran and Ic-Pyo Hong}, doi = {10.1109/ACCESS.2025.3605872}, journal = {IEEE Access}, pages = {155474-155481}, shortjournal = {IEEE Access}, title = {Adaptive fusion of LiDAR and camera data for enhanced precision in 3D object detection for autonomous driving}, volume = {13}, year = {2025}, }
• (2025). A new parametric yang-prentice regression model with applications to real-life survival medical data with crossing survival curves. ACCESS, 13, 155462-155473. (www)

  @article{ , abstract = {The Yang and Prentice (YP) regression models have attracted considerable attention in the scientific community due to their ability to handle survival data with crossing hazard functions. These models encompass both the proportional hazards (PH) and proportional odds (PO) models as special cases. A key feature of the YP framework is the inclusion of distinct short-term and long-term hazard ratio parameters, which allow it to accommodate intersecting survival curves. Notably, the original YP model leaves the baseline hazard function unspecified. In this study, a fully parametric method is introduced for fitting the YP model within a general regression context. The core idea involves modeling the baseline hazard using the exponentiated-Weibull distribution, which provides both the flexibility of parametric modeling and analytical tractability. To assess the effectiveness of the proposed approach, comprehensive simulation studies were performed. The results indicate that the model performs robustly even with moderate sample sizes and demonstrates improved accuracy compared to the original YP model, particularly in general regression scenarios beyond the traditional two-sample setup. Additionally, the utility and effectiveness of the proposed method are illustrated through applications to real-world datasets. The results underscore the model’s strengths in capturing complex survival patterns and enhancing the analysis of survival data.}, archive = {J_ACCESS}, author = {Mohamed A. S. Ishag and Ibrahim Elbatal and Anthony Kibira Wanjoya and Aggrey Adem and Ehab M. Almetwally and Ahmed Z. Afify}, doi = {10.1109/ACCESS.2025.3601730}, journal = {IEEE Access}, pages = {155462-155473}, shortjournal = {IEEE Access}, title = {A new parametric yang-prentice regression model with applications to real-life survival medical data with crossing survival curves}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of monolithic and microservice architectures for natural language processing in command and control applications. ACCESS, 13, 155447-155461. (www)

  @article{ , abstract = {The success of Command and Control (C2) operations relies heavily on clear communication among participants, often under stressful and time-sensitive conditions that demand accurate decision-making. Examples of such operations include military engagements, reconnaissance missions, search and rescue efforts, and peacekeeping activities. The design of systems to support C2 is therefore challenging, as they must support structured communication and interaction means among participants. To address this, we developed a method called Method to Support Semantic Interoperability in Command and Control (MAISC2) to support C2, in which we have applied Natural Language Processing (NLP) and semantic techniques to pinpoint specific elements in sentences, thereby enhancing the participants’ understanding of the C2 communications. Microservices Architecture (MSA) are known to offer potential benefits over MA, particularly in terms of scalability, independence, and maintenance. Furthermore, some NLP applications have already been developed using MSA, confirming these benefits. This paper presents a version of our system developed using MSA and compares it to its Monolithic Architecture (MA) counterpart. Our objective is to evaluate whether the benefits of adopting MSA, observed in other domains, also apply to C2 support systems based on NLP and semantic techniques, as exemplified by the MAISC2 method. Our results show that MA outperforms MSA in many scenarios; however, as the application load increases, MSA shows increasingly better performance. In particular, although MA had shorter processing and delivery times under normal conditions, MSA delivered better processing performance when handling significantly higher levels of concurrent activity.}, archive = {J_ACCESS}, author = {Flavio Ferreira Da Silva Mosafi and Mateus Sze Cosenza and Léo Victor Cruz Vasconcelos and Luís Ferreira Pires and Julio Cesar Duarte and Maria Claudia Reis Cavalcanti}, doi = {10.1109/ACCESS.2025.3604775}, journal = {IEEE Access}, pages = {155447-155461}, shortjournal = {IEEE Access}, title = {Performance evaluation of monolithic and microservice architectures for natural language processing in command and control applications}, volume = {13}, year = {2025}, }
• (2025). Attention-driven clustering of programming problem difficulty using statistical and machine learning techniques. ACCESS, 13, 155425-155446. (www)

  @article{ , abstract = {Programming is an important skill for advancement in computer science and information and communication technology (ICT). Online judge (OJ) systems are increasingly popular platforms in programming education to enhance users’ programming skills through practice. However, in most OJ systems, the problems are usually listed in a simple format without clear identification by difficulty levels. This research aims to cluster problem difficulty effectively and analyze user performance. We extract features from the Aizu OJ (AOJ) submission log data and estimate user ability and problem difficulty scores using logistic regression (LR). We propose an approach that combines an attention mechanism (AM) with K-means applied to statistically selected features via pairwise correlation to categorize the problems. Compared to standard K-means and Fuzzy C-means (FCM) with their AM integration on full and reduced feature sets by principal component analysis (PCA), autoencoder (AE), and variance inflation factor (VIF), the proposed method demonstrates the best clustering performance. It achieves the highest silhouette score (SS) of 0.507, Calinski-Harabasz index (CHI) of 6581.088, and a comparatively good Davis Bouldin index (DBI) of 0.625. We found a strong correlation between user ability and problem-solving rates; beginners and intermediate users consistently struggle with solving more complex problems. To support this, web-based clustering and group-based recommendations have been developed that visualize problem difficulty clusters, enabling users to understand difficulty levels and select problems based on their ability level. Moreover, the proposed approach is promising for effective clustering of more complex educational datasets and improving learning outcomes in programming education.}, archive = {J_ACCESS}, author = {Md. Shahajada Mia and Yutaka Watanobe and Md. Mostafizer Rahman and Md Faizul Ibne Amin and Daniel M. Muepu}, doi = {10.1109/ACCESS.2025.3606125}, journal = {IEEE Access}, pages = {155425-155446}, shortjournal = {IEEE Access}, title = {Attention-driven clustering of programming problem difficulty using statistical and machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). A single-input bipolar-output (SIBO) DC–DC boost converter for solar generators and on-chip power delivery: Modeling and experimental assessment of the so-called perilla converter. ACCESS, 13, 155411-155424. (www)

  @article{ , abstract = {Hybrid power converters that cascade switched-capacitor stages with conventional LC-based DC/DC switching converters have recently emerged as promising solutions for applications requiring a bipolar DC bus, such as photovoltaic systems and in-chip OLED drivers. However, despite clear advantages in efficiency, compactness, and implementation simplicity, their adoption has been limited due to the lack of accurate modeling and experimental validation. Traditional modeling techniques often fail to capture the static and dynamic behavior of these hybrid topologies. Some previous works either neglected capacitive losses or relied on empirical equivalent models without a clearly defined methodology. The Perilla SIBO (Single-Input Bipolar-Output) Boost Converter is a representative example of this new class of hybrid converters, integrating a conventional Single-Inductor Single-Output (SISO) boost stage with a switched-capacitor inverter. In earlier work, a static model for the Perilla Boost converter was derived using the small-ripple approximation and charge balance principles, assuming matched output capacitors and balanced load conditions—but without considering efficiency estimation. This paper extends the previous model to account for unbalanced loading and mismatched output capacitances and introduces a novel analytical expression to estimate converter efficiency. A comprehensive static modeling and experimental evaluation is presented under realistic operating conditions. The model is validated using a prototype operating across an input voltage range of 3 V to 10 V and delivering output power between 1 W and 20 W. The proposed model achieves a mean absolute percentage error of 5.76% and a standard deviation of 5.68%, offering a reliable tool for steady-state performance prediction. The dynamic modeling of the converter, as well as the development of a systematic design methodology, are beyond the scope of this work.}, archive = {J_ACCESS}, author = {Robert Urbina and Carlos Ivan Paez-Rueda and Germán Yamhure and Manuel R. Pérez and José Vuelvas and Manuel Fernando Párraga Meneses and Abdel Karim Hay Harb and Luis Fernando Melchior Ramirez and Gabriel Perilla Galindo and Arturo Fajardo}, doi = {10.1109/ACCESS.2025.3605109}, journal = {IEEE Access}, pages = {155411-155424}, shortjournal = {IEEE Access}, title = {A single-input bipolar-output (SIBO) DC–DC boost converter for solar generators and on-chip power delivery: Modeling and experimental assessment of the so-called perilla converter}, volume = {13}, year = {2025}, }
• (2025). A distributionally robust bi-level multi-objective decision making method under hybrid uncertainty. ACCESS, 13, 155399-155410. (www)

  @article{ , abstract = {This paper introduces a novel bi-level multi-objective optimization framework designed for decision-making under hybrid uncertainty, where system constraints account for both intuitionistic fuzzy sets and stochastic uncertainties. By combining interval programming and chance-constrained optimization, the proposed method relaxes the commonly adopted assumption of Gaussian distributions for stochastic uncertainties, thereby making it applicable to a wider range of distributional forms, including non-Gaussian scenarios. To effectively handle the interplay between fuzziness and stochasticity, we propose an acceptability index that quantifies uncertainty propagation, ensuring robust solutions that balance both sources of uncertainty. The approach provides a computationally efficient and theoretically sound decision-support tool for complex bi-level optimization problems. Numerical case studies demonstrate its ability to generate high-confidence solutions while offering flexibility in preference modeling.}, archive = {J_ACCESS}, author = {Xindi Wang and Jing Ma and Yong Qin}, doi = {10.1109/ACCESS.2025.3605948}, journal = {IEEE Access}, pages = {155399-155410}, shortjournal = {IEEE Access}, title = {A distributionally robust bi-level multi-objective decision making method under hybrid uncertainty}, volume = {13}, year = {2025}, }
• (2025). Real-time detection of skin-electrode adhesion based on embedded neural networks for bioimpedance spectroscopy. ACCESS, 13, 155385-155398. (www)

  @article{ , abstract = {A module based on embedded Multi-Layer Perceptrons (MLPs) was developed for real-time monitoring of skin-electrode adhesion quality. It was designed to integrate with Insulin-Meter, an established 4-wire bioimpedance spectroscopy system for measuring insulin absorption in diabetic patients, reported in previous studies. The MLPs address two classification tasks in cascade: (i) adhesion vs. partial detachment and (ii) identification of the partially detached electrode. The MLPs can be deployed on the same microcontroller used for insulin absorption assessment, leveraging the same impedance spectroscopy data. In literature, adhesion monitoring based on impedance measurement has been implemented in applications with unfavorable signal-to-noise ratio (SNR), such as electroencephalography (EEG), where contact quality is typically verified prior to signal acquisition using threshold-based approach. For other biosignal measurements, the higher signal-to-noise ratio and shorter acquisition durations have generally made real-time monitoring of electrode-skin adhesion unnecessary. However, Insulin-Meter requires extended acquisition sessions under unfavorable SNR conditions. MLPs were compared to other machine learning algorithms, including Logistic Regression, Support Vector Machines and Random Forest. Hyperparameter optimization was performed with consideration for the memory footprint of all classifiers. The MLPs outperformed the other algorithms and were deployed on a low-cost, general-purpose microcontroller, requiring significantly less than 50 % of its flash memory. The system achieved an accuracy of 98 % $\pm ~3$ % for discriminating between adhesion and partial detachment, and 97 % $\pm ~13$ % for identifying the partially detached electrode. The microcontroller requires an average inference time of 4.286 ms to implement the two-step classification task.}, archive = {J_ACCESS}, author = {Rosanna Manzo and Andrea Apicella and Pasquale Arpaia and Francesco Caputo and and Nicola Moccaldi}, doi = {10.1109/ACCESS.2025.3605928}, journal = {IEEE Access}, pages = {155385-155398}, shortjournal = {IEEE Access}, title = {Real-time detection of skin-electrode adhesion based on embedded neural networks for bioimpedance spectroscopy}, volume = {13}, year = {2025}, }
• (2025). Auto-configuring entity resolution pipelines. ACCESS, 13, 155367-155384. (www)

  @article{ , abstract = {The same real-world entity (e.g., a movie, a restaurant, a person) may be described in various ways on different datasets. Entity Resolution (ER) aims to find such different descriptions of the same entity, this way improving data quality and, therefore, data value. However, an ER pipeline typically involves several steps (e.g., blocking, similarity estimation, clustering), with each step requiring its own configurations and tuning. The choice of the best configuration, among a vast number of possible combinations, is a dataset-specific and labor-intensive task both for novice and expert users, while it often requires some ground truth knowledge of real matches. In this work, we examine ways of automatically configuring a state-of-the-art end-to-end ER pipeline based on pre-trained language models under two settings: i) When ground truth is available. In this case, sampling strategies that are typically used for hyperparameter optimization can significantly restrict the search of the configuration space. We experimentally compare their relative effectiveness and time efficiency, applying them to ER pipelines for the first time. ii) When no ground truth is available. In this case, labelled data extracted from other datasets with available ground truth can be used to train a regression model that predicts the relative effectiveness of parameter configurations. Experimenting with 11 ER benchmark datasets, we evaluate the relative performance of existing techniques that address each problem, but have not been applied to ER before.}, archive = {J_ACCESS}, author = {Konstantinos Nikoletos and Vasilis Efthymiou and George Papadakis and Kostas Stefanidis}, doi = {10.1109/ACCESS.2025.3600541}, journal = {IEEE Access}, pages = {155367-155384}, shortjournal = {IEEE Access}, title = {Auto-configuring entity resolution pipelines}, volume = {13}, year = {2025}, }
• (2025). The magnetic continuum robots for the treatment of atrial fibrillation. ACCESS, 13, 155355-155366. (www)

  @article{ , abstract = {Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, imposes a major global health burden. While antiarrhythmic drugs remain first-line therapy for rhythm control, their long-term efficacy is limited by variable response rates, adverse effects, and high recurrence rates in persistent AF. Radiofrequency catheter ablation (RFCA), primarily targeting pulmonary vein isolation (PVI), has emerged as a corner-stone intervention for AAD-refractory patients. However, conventional catheters face limitations in navigating complex anatomy and maintaining tissue contact, impacting efficacy and safety. To overcome these challenges and improve outcomes for drug-resistant AF, magnetic navigation systems (MNS) offer enhanced precision. Integrating MNS with robotic platforms and force sensing unlocks significant potential. This review examines Magnetic Continuum Robots (MCRs) with force feedback for AF ablation. We detail MNS principles, MCR design/actuation, and the role of force sensing in optimizing lesion formation—critical for durable PVI and reducing AF recurrence post-ablation. Pre-clinical and clinical data demonstrate advantages including navigation accuracy and reduced complications. MCRs show potential to address pharmacological limitations by offering a promising interventional approach for AAD-refractory AF, though further clinical validation is required.}, archive = {J_ACCESS}, author = {Yuqing Tang}, doi = {10.1109/ACCESS.2025.3605811}, journal = {IEEE Access}, pages = {155355-155366}, shortjournal = {IEEE Access}, title = {The magnetic continuum robots for the treatment of atrial fibrillation}, volume = {13}, year = {2025}, }
• (2025). A weighted two-hop raft consensus mechanism for large-scale agricultural products traceability. ACCESS, 13, 155340-155354. (www)

  @article{ , abstract = {In the face of escalating demands for food safety, blockchain technology has emerged as pivotal tool in enhancing traceability and accountability within the food supply chain. Raft is a consensus mechanism predicated upon voting and is pervasively utilized across diverse domains. Nevertheless, in the agri-food supply chain, numerous participants lead to a large number of consensus-involved nodes. The leader undertakes a huge amount of broadcast communication tasks, resulting in low consensus-reaching efficiency. Also, suppliers may misreport information for self-interest. To address these limitations, this paper proffers a Weighted Two-Hop Raft (WTH-Raft) consensus predicated upon weight apportionment. In this consensus, suppliers are first grouped to achieve two-hop consensuses from the leader to primary suppliers and then to secondary suppliers. Secondly, consensus weights are assigned to suppliers according to their reputation and product quality. This consensus is mainly for agri-food supply chains with numerous suppliers. Simulation experiments show that our proposed WTH-Raft is superior to the comparison methods in terms of delay and throughput. This enhanced approach ensures the stability and efficiency of the consensus among the groups.}, archive = {J_ACCESS}, author = {Junhong Yuan and Yang Li and Haoxiang Luo and Muhan He and Shijian Gao and Athanasios V. Vasilakos}, doi = {10.1109/ACCESS.2025.3603398}, journal = {IEEE Access}, pages = {155340-155354}, shortjournal = {IEEE Access}, title = {A weighted two-hop raft consensus mechanism for large-scale agricultural products traceability}, volume = {13}, year = {2025}, }
• (2025). AnnotationGym: A generic framework for automatic source code annotation. ACCESS, 13, 155321-155339. (www)

  @article{ , abstract = {A common approach to code optimization is to insert compiler hints in the source code using annotations. Two major challenges with using annotations effectively are their complexity and lack of portability. This means, first, that significant developer expertise is required, and, second, that the supported annotations, as well as their syntax and use, can vary substantially. Moreover, there is not currently any tool that can output performant annotation-inserted codes for different back-ends. To address these challenges, we present AnnotationGym, an easy-to-use, open-source, generic infrastructure that supplements or replaces the developer in annotating source code. It demonstrates a novel application of AI methods to code annotation. In addition to improving code performance, the flexibility of AnnotationGym enables easy comparisons of performance and optimization strategies among compilers and target architectures and thus provides an extensible platform to facilitate further progress in this field. AnnotationGym automatically extracts structured information about the target code and compiler to generate a list of possible annotations. AI-based optimization algorithms then traverse this space to determine the best set of annotations depending on the developer goals. To demonstrate its effectiveness, we run AnnotationGym on popular, representative workloads from the Polybench suite, as well as targeting various compilers (GCC, AMD HLS, Intel HLS), optimization algorithms (Reinforcement Learning, Bayesian Optimization), and architectures (CPU, FPGA). We also test our approach on FPGA codes derived, e.g., from the Rodinia and OpenDwarfs benchmarks and that are hand-optimized using standard best practices. An interesting finding is that the best overall performance obtained by AnnotationGym was generally with unoptimized codes.}, archive = {J_ACCESS}, author = {Hafsah Shahzad and Ahmed Sanaullah and Sanjay Arora and Ulrich Drepper and Martin C. Herbordt}, doi = {10.1109/ACCESS.2025.3605852}, journal = {IEEE Access}, pages = {155321-155339}, shortjournal = {IEEE Access}, title = {AnnotationGym: A generic framework for automatic source code annotation}, volume = {13}, year = {2025}, }
• (2025). Towards automated classification of adult attachment interviews in german language using the BERT language model. ACCESS, 13, 155305-155320. (www)

  @article{ , abstract = {Attachment theory, pioneered by John Bowlby, has become a prominent psychological framework that aids in our comprehension of human behavior within close relationships. The Adult Attachment Interview (AAI), a semi-structured interview, provides a standardized method for assessing adult attachment styles, offering insights into an individual’s attachment patterns, emotional regulation, and relational experiences. However, manual AAI classification is a labour-intensive and highly specialized task. Thus, automating this process can optimize the classification of individual attachment patterns and their psychological and relational implications, thereby increasing efficiency and accuracy in assessing attachment styles. In this work, we investigate the application of a BERT large language model and linguistic features for the automated classification of transcribed Adult Attachment Interviews conducted in German into the three categories: secure, dismissing, and preoccupied. The findings of our study indicate that using BERT embeddings alone can yield results comparable to, and in some cases better than, those achieved with traditional linguistic features. However, this effect should be interpreted with caution, as it does not hold consistently across all settings. Another key conclusion of our study is that augmenting the original AAI dataset with artificially generated interviews produced by state-of-the-art large language models generally improves predictive performance across a range of classification models.}, archive = {J_ACCESS}, author = {Teodor Stoev and Eva Flemming and Bernhard Strauss and Katja Petrowski and Carsten Spitzer and Kristina Yordanova}, doi = {10.1109/ACCESS.2025.3604573}, journal = {IEEE Access}, pages = {155305-155320}, shortjournal = {IEEE Access}, title = {Towards automated classification of adult attachment interviews in german language using the BERT language model}, volume = {13}, year = {2025}, }
• (2025). Complexity measures in biomedical signal analysis: A clinically-grounded survey across EEG, ECG, intracranial pressure, and photoplethysmogram modalities. ACCESS, 13, 155285-155304. (www)

  @article{ , abstract = {Biomedical signals such as electroencephalogram (EEG), electrocardiogram (ECG), intracranial pressure (ICP), and photoplethysmogram (PPG) encode complex dynamical patterns that reflect the adaptive control of physiological systems. Complexity analysis—through entropy, compression, and causality metrics—has emerged as a critical tool for early diagnosis, risk prediction, and continuous monitoring in clinical and wearable settings. This review presents a clinically grounded synthesis of key complexity measures, including Sample Entropy (SampEn), Permutation Entropy (PE), Lempel–Ziv Complexity (LZC), and Effort-to-Compress (ETC), as well as causal inference techniques like Granger Causality, Transfer Entropy, and Compression–Complexity Causality (CCC). Applications across neurological and cardiovascular pathologies—such as epilepsy and sudden unexpected death in epilepsy (SUDEP)—are examined alongside signal-specific preprocessing, interpretability, and statistical validation strategies. We further evaluate the feasibility of deploying these measures in real-time on constrained hardware platforms—microcontrollers, FPGAs, and RISC–V-based SoCs—highlighting their suitability for embedded health systems and remote diagnostics. The review also explores synergistic integration with explainable machine learning, showing how complexity-informed features can enhance both performance and interpretability. By bridging signal complexity with hardware-aware deployment, ML transparency, and clinical translation, this work aims to guide the development of scalable, interpretable, and secure biomedical analytics for next-generation digital health systems.}, archive = {J_ACCESS}, author = {Karthi Balasubramanian and Sree Ranjani Rajendran and Sandipan Pati}, doi = {10.1109/ACCESS.2025.3603848}, journal = {IEEE Access}, pages = {155285-155304}, shortjournal = {IEEE Access}, title = {Complexity measures in biomedical signal analysis: A clinically-grounded survey across EEG, ECG, intracranial pressure, and photoplethysmogram modalities}, volume = {13}, year = {2025}, }
• (2025). MonolayerFFF: An image dataset of MonolayerFFF 3D printed parts with different fabrication conditions. ACCESS, 13, 155277-155284. (www)

  @article{ , abstract = {This research presents the MonolayerFFF dataset, a novel addition to Fused Filament Fabrication (FFF) 3D printing image-based datasets, focusing on monolayer parts with unique geometric variations. Unlike existing multi-layered datasets, MonolayerFFF comprises three specific monolayer part conditions: Regular, Defect 1 (with reduced filament deposition), and Defect 2 (featuring filament retraction). This approach offers a different perspective on 3D printing analysis, capturing a range of typical and atypical scenarios. Utilizing the GoogLeNet architecture for Convolutional Neural Network (CNN) classification, the study achieved a notable validation accuracy of 95.81% on the MonolayerFFF dataset, demonstrating its quality and diversity without relying on data augmentation. The high accuracy, coupled with minimal misclassifications as evidenced in both test and validation confusion matrices, underscores the effectiveness of the proposed backbone for feature extraction and classification in identifying subtle differences in part conditions. The study’s outcomes, including the absence of overfitting and aligned loss curves, highlight the model’s capability to generalize from training to unseen data. The MonolayerFFF dataset, with its focus on monolayer parts and detailed variation in conditions, emerges as a significant contribution to advancing CNN analysis in the field of 3D printing. Its potential for real-time monitoring and quality assurance in additive manufacturing promises to enhance the FFF process efficiency and accuracy, making it a valuable tool for researchers in the field.}, archive = {J_ACCESS}, author = {Thiago G. Lopes and Paulo M. C. Monson and Paulo R. Aguiar and Doriana M. D'Addona and Pedro O. C. Júnior}, doi = {10.1109/ACCESS.2025.3603958}, journal = {IEEE Access}, pages = {155277-155284}, shortjournal = {IEEE Access}, title = {MonolayerFFF: An image dataset of MonolayerFFF 3D printed parts with different fabrication conditions}, volume = {13}, year = {2025}, }
• (2025). A wearable real-time 2D/3D eye-gaze interface to realize robot assistance for quadriplegics. ACCESS, 13, 155259-155276. (www)

  @article{ , abstract = {For people with severe sensory-based motor disorders or musculoskeletal disorders, robotic assistance offers a promising solution to improve their daily living standards. In this paper, we present a wearable real-time 2-Dimensional/3-Dimensional (2D/3D) eye-gaze control interface for people with quadriplegia to enable robot-assisted locomotion, sensing, and manipulation. Compared to other modalities in human-robot interaction, gaze point information in Cartesian space has the advantage of being directly feasible for robotic control. To achieve accurate 3D gaze point estimation, we propose a method that leverages a commercially available 2D wearable eye tracker and an off-the-shelf stereo camera. Unlike traditional stereoscopic depth computation or 3D eye reconstruction approaches, which often rely on user-specific eye model calibration, our method reformulates the 2D-to-3D mapping as an online Newton-Raphson search problem that does not depend on individual eye model parameters for gaze depth estimation, allowing the system to operate effectively across varied environments and depth ranges. This results in a solution that is easy to implement, robust to individual variability, and computationally efficient for accurate 3D gaze point estimation. At the same time, 2D gaze is utilized to interact with a screen displaying robotic sensing of specific environments that are not directly visible to the user, thereby enabling extended sensing through robotic assistance. The feasibility of the proposed method is verified through quantitative evaluations of the 3D gaze point estimation. On average, the 3D gaze point estimation yields a mean Euclidean distance error of approximately 1.88 cm across the 0.5–4.0 m distance (corresponding to a 0.9 % percentage error), outperforming comparable methods. A proof-of-concept study further demonstrates successful robot-assisted locomotion, sensing, and manipulation using the proposed gaze interface. The implementation of the core method is available at https://github.com/SavickTso/ros2-3d-gaze-mapping.git.}, archive = {J_ACCESS}, author = {Yongpeng Cao and Shouren Huang and Sune Lundø Sørensen and Yuji Yamakawa and Masatoshi Ishikawa}, doi = {10.1109/ACCESS.2025.3603617}, journal = {IEEE Access}, pages = {155259-155276}, shortjournal = {IEEE Access}, title = {A wearable real-time 2D/3D eye-gaze interface to realize robot assistance for quadriplegics}, volume = {13}, year = {2025}, }
• (2025). Positioning of mine loaders based on UWB and IMU. ACCESS, 13, 155247-155258. (www)

  @article{ , abstract = {Aiming at the difficulty of precise positioning of load-haul-dump (LHD) machines in complex mine environments, due to the constraints of satellite-denied environments on single positioning technologies and the severe impact of non-line-of-sight (NLOS) conditions, this paper proposes a positioning method suitable for LHD machines in mine environments. This method integrates ultra-wideband (UWB) and inertial measurement unit (IMU) measurement information through an improved error state Kalman filter (ESKF) algorithm. A Dynamic Covariance Robust Method (DCRM) is proposed for ESKF to improve it by constructing a residual covariance matrix and dynamically adjusting the measurement noise covariance matrix through covariance matching technology, achieving adaptive suppression of NLOS error. The experimental results demonstrate that in complex environments, compared to using UWB positioning alone, the improved ESKF positioning algorithm effectively reduces the ultra-wideband positioning error under NLOS conditions by providing prior information of the positioning target. Compared to LS-Taylor, the Extended Kalman Filter algorithm and the Motion Inertia Estimation Adaptive Kalman Filtering, the positioning accuracy is improved by 61%, 50% and 31%, respectively.}, archive = {J_ACCESS}, author = {Zhonghua Zhu and Ying Zhao and Wenxuan He and Zhang Peng and Haiying Fu and Yueyue Wu}, doi = {10.1109/ACCESS.2025.3605669}, journal = {IEEE Access}, pages = {155247-155258}, shortjournal = {IEEE Access}, title = {Positioning of mine loaders based on UWB and IMU}, volume = {13}, year = {2025}, }
• (2025). Recognizing multigenerational families from videos in the wild. ACCESS, 13, 155233-155246. (www)

  @article{ , abstract = {In the field of computer vision, current relative learning research has predominantly concentrated on identifying parent-child relationships from pairs of static facial images, neglecting the discriminative information inherent in relatives involving multiple subjects and disregarding the common noise in unconstrained settings. To tackle these limitations, we present a novel task of video-based multigenerational family recognition, aiming to recognize multigenerational families from videos captured in unconstrained environments. We propose a Support Vector Data Description (SVDD)-based family-shared multi-metric learning (SFM2L) method, where only purified samples are subjected to multi-metric learning to derive both family-shared and family-specific distance metrics. To further improve the recognition performance, we introduce a multi-view method named MSFM2L, which effectively integrates deep and shallow features. In addition, we have constructed a new video dataset consisting of 90 multigenerational families. Extensive experiments on both the newly collected dataset and the well-established KinFaceW kinship face dataset clearly demonstrate the superior performance of our proposed methods compared to existing metric learning approaches.}, archive = {J_ACCESS}, author = {Xiaoqian Qin and Bin Gui and Dong Wang}, doi = {10.1109/ACCESS.2025.3605630}, journal = {IEEE Access}, pages = {155233-155246}, shortjournal = {IEEE Access}, title = {Recognizing multigenerational families from videos in the wild}, volume = {13}, year = {2025}, }
• (2025). Trajectory and parameter optimization in robust tracking control of a quadrotor. ACCESS, 13, 155215-155232. (www)

  @article{ , abstract = {Research on combined control methods for quadrotors has focused on trajectory tracking, robust control, neural networks, parameter optimization, and path planning. While previous studies have not fully integrated all of these aspects, this study presents a comprehensive control framework that seamlessly combines robust control strategies, neural network-based uncertainty approximation, path planning, and optimization to achieve precise and reliable trajectory tracking of quadrotors operating under significant model uncertainties and external disturbances. At the heart of the framework is the Integrated sliding mode control (Intergrated SMC), a design that merges the inherent robustness of sliding mode control with the adaptive approximation capability of radial basis function (RBF) neural networks. The fusion of these two elements not only ensures stability but also strengthens the system’s resilience, delivering high-precision tracking even in the presence of unmodeled dynamics and external disturbances. The framework also incorporates the rapidly-exploring random tree star (RRT*) algorithm for trajectory planning, allowing the generation of collision-free and asymptotically optimal reference paths capable of navigating environments with complex obstacle distributions. In addition, particle swarm optimization (PSO) is employed to systematically tune the controller gains and neural network parameters, thereby enhancing overall control performance. Extensive simulations under varying conditions of model mismatch and disturbances confirm the superior performance of the proposed integrated approach, demonstrating significant improvements in tracking accuracy and disturbance rejection compared to conventional control methods. This unified architecture thus provides a robust and computationally efficient solution for quadrotor trajectory tracking, maintaining high performance even in the presence of model uncertainties and external disturbances.}, archive = {J_ACCESS}, author = {Ngoc-Hiep Tran and Quy-Thinh Dao and Thi-van-Anh Nguyen and Ngoc-Tam Bui}, doi = {10.1109/ACCESS.2025.3605761}, journal = {IEEE Access}, pages = {155215-155232}, shortjournal = {IEEE Access}, title = {Trajectory and parameter optimization in robust tracking control of a quadrotor}, volume = {13}, year = {2025}, }
• (2025). A contribution to the application of the phasor transform for solving electric circuits. ACCESS, 13, 155203-155214. (www)

  @article{ , abstract = {This paper presents an alternative analysis of the fundamental concepts underlying the use of the phasor transform in electric circuit analysis. Traditionally, the phasor transform is employed to determine the steady-state response of circuits composed of passive elements (resistors, capacitors, and inductors) and active sources (voltage and current sources) operating under sinusoidal excitation. Such circuits are essential for analyzing power systems, telecommunications networks, and similar applications. The primary objective of this study is to develop a comprehensive framework for solving electric circuits in the steady state by leveraging the phasor transform, without explicitly introducing the conventional phasor concept as a rotating vector nor using Euler’s identity. Instead, our approach mirrors the strategy of the Laplace transform, thereby unifying the analysis under a single, consistent methodology without the need for additional new concepts.}, archive = {J_ACCESS}, author = {Rubén Romero and César Reynoso and Leonardo H. Macedo and Natalia Alguacil}, doi = {10.1109/ACCESS.2025.3605059}, journal = {IEEE Access}, pages = {155203-155214}, shortjournal = {IEEE Access}, title = {A contribution to the application of the phasor transform for solving electric circuits}, volume = {13}, year = {2025}, }
• (2025). A threat-informed approach to malware evasion using DRM and TLS callbacks. ACCESS, 13, 155195-155202. (www)

  @article{ , abstract = {As adversaries refine their techniques to avoid detection, Advanced Persistent Threats (APTs) are increasingly exploring unconventional mechanisms to maintain stealth. This paper introduces a novel approach that leverages Digital Rights Management (DRM) as an anti-analysis shield to prevent reverse engineering and forensic investigation. By binding malware execution to a specific machine through DRM fingerprinting, sandbox, and virtual-machine analysis are effectively thwarted. To harden this mechanism, anti-debugging via Thread Local Storage (TLS) callbacks is implemented to detect debuggers before the main execution begins, rendering any debugging attempt futile by nullifying the malware’s main function. The paper presents a proof-of-concept in the form of a DLL injector, showcasing a stealthy persistence mechanism and anti-analysis measures. To further complicate analysis, Import Address Table (IAT) camouflage introduces whitelisted API noise, misleading defenders and evading static detection. While the current implementation is a minimal loader and persistence mechanism, the techniques outlined here provide a blueprint for more sophisticated operations by advanced threat actors. This paper aims to highlight a unique attack chain that could be utilized to essentially nullify modern defensive mechanisms.}, archive = {J_ACCESS}, author = {Swayam Tejas Padhy and P. B. Shanthi and Gururaj Bijur and S. Hemalatha}, doi = {10.1109/ACCESS.2025.3605020}, journal = {IEEE Access}, pages = {155195-155202}, shortjournal = {IEEE Access}, title = {A threat-informed approach to malware evasion using DRM and TLS callbacks}, volume = {13}, year = {2025}, }
• (2025). Automated identification of sheet metal parts using deep learning techniques. ACCESS, 13, 155181-155194. (www)

  @article{ , abstract = {Accurate identification and classification of sheet metal parts are crucial for optimizing production workflows, ensuring quality control, and reducing operational costs in manufacturing industries. Traditional identification methods, such as barcoding, labeling, marking, and inkjet printing, have several limitations, including label deterioration, misplacement, and errors caused by manual handling. This study presents a deep learning-based automated approach for identifying sheet metal parts, significantly improving efficiency, reliability, and scalability. The methodology involves converting 2D drawings into an image format, applying data augmentation techniques, and training a Convolutional Neural Network (CNN) using the InceptionV3 architecture. Furthermore, a semantic segmentation model based on U-Net is used to isolate sheet metal parts from their background, ensuring accurate classification. This study presents an integrated framework that combines classification and segmentation techniques specifically tailored for the automated identification of sheet metal parts in industrial environments. The proposed system achieves an outstanding classification accuracy with a Top-1 accuracy of 96%, Top-3 accuracy of 99.36%, and Top-5 accuracy of 100%. By automating the identification process, the proposed system eliminates the need for manual intervention, minimizes errors, and enhances traceability, making it a promising solution for smart manufacturing environments. The results demonstrate the potential of deep learning models in industrial automation and quality assurance processes in the sheet metal industry.}, archive = {J_ACCESS}, author = {Gül SIğIndI and Ali KIlIç and Sadettin Kapucu}, doi = {10.1109/ACCESS.2025.3604601}, journal = {IEEE Access}, pages = {155181-155194}, shortjournal = {IEEE Access}, title = {Automated identification of sheet metal parts using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). YOLOv8-ECA-SPD-lite for cantilever bolt defect detection in railway catenary systems. ACCESS, 13, 155167-155180. (www)

  @article{ , abstract = {To address the challenges of highly variable operating conditions in electrified railway catenary systems, the small target size of cantilever bolt defects, and the large parameter sizes of existing catenary inspection models, this paper proposes an improved lightweight algorithm named You Only Look Once version 8 Enhanced by Efficient Channel Attention, Space-to-Depth Convolution, and Lightweight Optimization via Pruning and Distillation (YOLOv8-ECA-SPD-Lite). The core innovations lie in: 1) Data augmentation on the original images to improve generalization capability; 2) Integration of the Efficient Channel Attention (ECA) module to enhance feature responses in bolt regions for small target detection; 3) The Space-to-Depth convolution (SPD-Conv) module was first applied to cantilever bolt defect detection to minimize feature loss for small targets; and 4) Replacement of the detection head with a decoupled head to enhance defect perception capabilities. Furthermore, applying model pruning and distillation techniques significantly improves model lightweighting, facilitating deployment on embedded onboard systems. Ablation studies validated the effectiveness of each proposed module. Comparative experiments demonstrated that the improved YOLOv8 model outperforms Faster R-CNN, SSD, YOLOv6s, YOLOX-s, YOLOv9s, and the original YOLOv8s across multiple metrics. Specifically, the proposed model achieved a higher mean Average Precision (mAP) than all comparison models. In terms of lightweight design, the improved YOLOv8s model achieves the lowest computational complexity among all compared methods, with only 17.2 GFLOPs. It demonstrates exceptional suitability for mobile deployment. This research holds significant theoretical and practical value, facilitating the transition of railway operation and maintenance to artificial intelligence, enhancing the efficiency of catenary inspection, and meeting the demands of high-density railway operations.}, archive = {J_ACCESS}, author = {Yapeng Wang and Mengru Chen and Lina Guo and Yi Ma and Min Wu}, doi = {10.1109/ACCESS.2025.3604831}, journal = {IEEE Access}, pages = {155167-155180}, shortjournal = {IEEE Access}, title = {YOLOv8-ECA-SPD-lite for cantilever bolt defect detection in railway catenary systems}, volume = {13}, year = {2025}, }
• (2025). Reducing information overload with machine learning in mobile pervasive augmented reality systems. ACCESS, 13, 155155-155166. (www)

  @article{ , abstract = {Augmented reality systems in dynamic environments still struggle with the challenge of what information should be displayed at which time. This work focuses on the case of Mobile Pervasive Augmented Reality Systems (MPARS) and their use in dynamic environments such as outdoor sports. An open-source proof-of-concept for a machine learning-based architecture to implement an MPARS on a specific use case of outdoor usage in a sports environment is presented. The new design for the system relies on heuristics that combine technology acceptance indicators, sensing, and information volume criteria to show the user a contextually meaningful subset of information. The information to the user is displayed in close-to-real-time, and the system can adjust and customise to prevent information overload. A first set of experiments was carried out based on end-user preferences to show the feasibility of the proposed system. To provide meaningful feedback, i.e., the right information when needed or wanted, to sports users on their MPARS experience, a predictive model was trained and shown to be able to estimate when information should be displayed to the user.}, archive = {J_ACCESS}, author = {Rui Pascoal and Ana Maria de Almeida and Rute C. Sofia}, doi = {10.1109/ACCESS.2025.3603917}, journal = {IEEE Access}, pages = {155155-155166}, shortjournal = {IEEE Access}, title = {Reducing information overload with machine learning in mobile pervasive augmented reality systems}, volume = {13}, year = {2025}, }
• (2025). Next-gen aviation: Who will rule the Skies—Hydrogen or Electric?—A review. ACCESS, 13, 155141-155154. (www)

  @article{ , abstract = {In pursuit of net-zero aviation by 2050, fully electric and hydrogen-fueled aircraft have emerged as two leading pathways to decarbonize the aviation sector. This study examines the pathways of fully electric and hydrogen-fueled aircraft as potential solutions for achieving net-zero aviation by 2050. Through a systematic literature review, we evaluate the energy efficiency, storage requirements, infrastructure needs, and environmental impacts associated with each technology. Our findings indicate that electric aircraft offer immediate advantages for short-haul operations, including reduced operating costs and compatibility with existing infrastructure; however, the low energy density of batteries currently limits their potential. Conversely, hydrogen propulsion holds promise for long-range flights due to its high energy content, but it necessitates advancements in liquid hydrogen storage, distribution, and green hydrogen production for practical implementation. This analysis offers valuable insights for industry stakeholders and policymakers aiming to advance sustainable aviation technologies.}, archive = {J_ACCESS}, author = {Saikat Chowdhury and Mona Ghassemi}, doi = {10.1109/ACCESS.2025.3605809}, journal = {IEEE Access}, pages = {155141-155154}, shortjournal = {IEEE Access}, title = {Next-gen aviation: Who will rule the Skies—Hydrogen or Electric?—A review}, volume = {13}, year = {2025}, }
• (2025). Modular lower limb 3D-printed orthoprosthesis for toddlers with fibular hemimelia. ACCESS, 13, 155124-155140. (www)

  @article{ , abstract = {Toddlers with congenital limb deficiencies, such as fibular hemimelia, face major barriers to independent mobility during a critical window of motor development. Despite the need for early intervention, existing orthoprosthetic solutions are often inaccessible and poorly suited to the anatomical and functional needs of children aged 12–36 months. This study presents a novel, user-centered approach to a modular pediatric lower limb orthoprosthesis dubbed +Limb developed in close collaboration with caregivers, physicians, and therapists. Using additive manufacturing, successive prototypes were rapidly produced and tested in a clinical setting. The final device met key requirements for modularity, light weight, postural support, and adjustability. Functional evaluations demonstrated significant improvements, including correction of a 4.5 cm leg length discrepancy, reduction of pelvic tilt from 15° to 0°, and initiation of unassisted gait with controlled knee flexion up to 45°. To study how the body and muscles responded, different sensors were used, including pressure sensors (FSRs), muscle activity sensors (EMG), and a pressure plate for foot contact analysis. This case study demonstrates the feasibility of a low-cost, customizable orthoprosthesis for toddlers with fibular hemimelia and emphasizes the advantages of combining additive manufacturing and sensor-based feedback to support early-stage rehabilitation in pediatric populations.}, archive = {J_ACCESS}, author = {Lilli Anders and Paula Agulheiro and Hugo Plácido Da Silva and Cláudia Quaresma}, doi = {10.1109/ACCESS.2025.3604278}, journal = {IEEE Access}, pages = {155124-155140}, shortjournal = {IEEE Access}, title = {Modular lower limb 3D-printed orthoprosthesis for toddlers with fibular hemimelia}, volume = {13}, year = {2025}, }
• (2025). Research and analysis on the topology of a two-stage electromagnetic launch circuit based on energy recovery. ACCESS, 13, 155110-155123. (www)

  @article{ , abstract = {This study investigates a two-stage electromagnetic launch circuit based on energy recovery, focusing on the excitation circuitry utilized in an electromagnetic net launcher for its operational efficacy. This study is aiming to improve the energy efficiency and launch frequency of electromagnetic launch systems. The basic principles of electromagnetic launch and the composition of electromagnetic launch systems are first introduced. Then, the working principle of the two-stage electromagnetic launch circuit topology and the selection of key parameters are elaborated in detail. Next, the effects of different parameters on circuit performance were analyzed through simulation. Finally, the feasibility of the two-stage electromagnetic launch circuit is verified experimentally. The results show that this circuit can effectively improve the energy utilization efficiency and launch frequency of electromagnetic launch systems, thereby providing a new direction for the development of electromagnetic launch technology.}, archive = {J_ACCESS}, author = {Haijun Lai and Qiang Li and Qing Lyu and Limin Li and Chunyan Liang and Huafeng Li and Yanfei Hou and Wei Zhang and Zhengyang Li}, doi = {10.1109/ACCESS.2025.3557813}, journal = {IEEE Access}, pages = {155110-155123}, shortjournal = {IEEE Access}, title = {Research and analysis on the topology of a two-stage electromagnetic launch circuit based on energy recovery}, volume = {13}, year = {2025}, }
• (2025). Generation of real world maintenance data of data center uninterruptible power supply systems and failure prediction. ACCESS, 13, 155096-155109. (www)

  @article{ , abstract = {Industry 4.0 positions real-world data as a transformative asset that drives the development of new knowledge, advanced solutions, and industrial growth. The exponential increase in data generation has accelerated the demand for data centers and consequently, uninterruptible power supply (UPS) systems, which are critical for ensuring continuous power delivery. Predicting failures in UPS systems using both structured and unstructured data requires a well-curated data preparation process to enable effective analysis and modeling of the data. This study introduces techniques for transforming these diverse data types to extract actionable insights using a GenAI model. The model leverages two feature sets: one sourced from the Customer Relationship Management (CRM) system and the other derived from service completion reports documented by field service representatives. A key innovation of the model is the use of instructional prompts and a rule set that includes keyword mappings and acronym references, which enables the accurate interpretation of domain-specific language. This study also captures global performance insights for UPS systems and integrates data visualization. These visualizations facilitate the identification of failure patterns, including symptomatic and asymptomatic service order categories, failure origin, failure types, and criticality, enabling proactive maintenance strategies and enhancing system reliability. Model validation demonstrated a weighted accuracy and precision exceeding 90 % and an F1 score of 0.91.}, archive = {J_ACCESS}, author = {L. Frederick and M. M. Manohara Pai}, doi = {10.1109/ACCESS.2025.3605758}, journal = {IEEE Access}, pages = {155096-155109}, shortjournal = {IEEE Access}, title = {Generation of real world maintenance data of data center uninterruptible power supply systems and failure prediction}, volume = {13}, year = {2025}, }
• (2025). Data reliability testing framework for biometric datasets using synthetic iris and fingerprint images generated via deep generative models. ACCESS, 13, 155084-155095. (www)

  @article{ , abstract = {This paper presents a comprehensive data reliability testing framework for evaluating synthetic biometric data, addressing privacy concerns in fingerprint and iris recognition systems. This unified and modality-independent methodology establishes six quantitative metrics: randomness, quality similarity, attribute similarity, non-duplication, ID-preservation, and geometric diversity. The framework is implemented through a novel RD-Net architecture consisting of a Random Network for privacy protection and a Deterministic Network for maintaining essential biometric characteristics. Experiments using public datasets (FVC 2002, IITDelhi-Iris, and CASIA-Iris-V4) demonstrate that synthetic samples maintain high dissimilarity from source datasets while preserving their structural properties. The synthetic biometric data generated through the proposed Random Network and Deterministic Network architectures are evaluated using the data reliability testing framework, confirming distribution similarity with real data across all proposed metrics and achieving scores over 80. This approach offers a method for generating and evaluating synthetic biometric data that balances privacy protection with functional validity in biometric system development and testing.}, archive = {J_ACCESS}, author = {Hyoungrae Kim and Hakil Kim}, doi = {10.1109/ACCESS.2025.3604894}, journal = {IEEE Access}, pages = {155084-155095}, shortjournal = {IEEE Access}, title = {Data reliability testing framework for biometric datasets using synthetic iris and fingerprint images generated via deep generative models}, volume = {13}, year = {2025}, }
• (2025). Exploring the role of information technology and digital innovation in socio-economic development: A case study of startup and home-based businesses. ACCESS, 13, 155064-155083. (www)

  @article{ , abstract = {Recent studies highlighted the growing importance of home-based businesses (HBBs) in fostering socio-economic development within developing countries. While prior literature pointed out enabling mechanisms of such HBBs including digital innovative platforms and IT infrastructure, the role and significance of HBBs within entrepreneurial ecosystem remained underexplored. This study offers a unique mix of longitudinal research over a period of ten years (2012–2021) and qualitative data to explain the process of development of HBBs. Furthermore, since majority of the HBBs are owned by women, the study offers valuable insight on socio-economic development that ensue the HBB development. This research investigates how IT infrastructure and digital innovative platforms facilitate entrepreneurial women’s startups and innovative women’s HBBs, subsequently influencing socio-economic development. Focusing on the Alborz province of Iran, we curated a primary panel dataset encompassing startups, academic IT programs, and women’s HBBs, supplemented with IT infrastructure data. Additionally, we extracted Night-time Light (NTL) data from high-resolution satellite imagery as a proxy for measuring socio-economic development. Our study confirms that IT-related innovation, startups, and HBBs significantly contribute to socio-economic development, emphasizing the critical role of technology in fostering economic progress in emerging economies. Our research clearly demonstrates how the creation of new home businesses, particularly those run by women, and the socio-economic development of the researched areas have been significantly supported by the advancement of IT infrastructures and digital innovative platforms during the previous years. Hence, the expansion of these businesses and the increase in the use of digital platforms, along with improvements in infrastructure, have been extremely remarkable.}, archive = {J_ACCESS}, author = {Mohammad Tondro and Mohammad Jahanbakth and Dervis Ozay}, doi = {10.1109/ACCESS.2025.3579806}, journal = {IEEE Access}, pages = {155064-155083}, shortjournal = {IEEE Access}, title = {Exploring the role of information technology and digital innovation in socio-economic development: A case study of startup and home-based businesses}, volume = {13}, year = {2025}, }
• (2025). CHULA: Custom heuristic uncertainty-guided loss for accurate land title deed segmentation. ACCESS, 13, 155047-155063. (www)

  @article{ , abstract = {Accurately segmenting land boundaries from Thai land title deeds is crucial for reliable land management and legal processes, but remains challenging due to low-quality scans, diverse layouts, and complex overlapping elements in documents. Existing methods often struggle with these difficulties, resulting in imprecise delineations that can cause disputes or inefficiencies. To address these issues, we propose CHULA, a novel Custom Heuristic Uncertainty-guided Loss tailored specifically for robust land title deed segmentation. CHULA uniquely combines domain-specific heuristic priors with uncertainty modeling in a unified loss function that effectively guides the model to focus on clearer regions while refining boundaries and suppressing noisy areas. Evaluated on a carefully curated Thai Land Title Deed Dataset, CHULA achieves an impressive 92.4% accuracy, significantly surpassing standard segmentation baselines. Our results highlight the promise of integrating uncertainty and heuristic knowledge to enhance segmentation accuracy in complex, real-world documents. The code is publicly available at https://github.com/kaopanboonyuen/CHULA}, archive = {J_ACCESS}, author = {Teerapong Panboonyuen and Chaiyut Charoenphon and Chalermchon Satirapod}, doi = {10.1109/ACCESS.2025.3605218}, journal = {IEEE Access}, pages = {155047-155063}, shortjournal = {IEEE Access}, title = {CHULA: Custom heuristic uncertainty-guided loss for accurate land title deed segmentation}, volume = {13}, year = {2025}, }
• (2025). Named entity recognition and news article classification: A lightweight approach. ACCESS, 13, 155031-155046. (www)

  @article{ , abstract = {This paper introduces TinyGreekNewsBERT, a 14.1 M-parameter distilled Transformer that performs both Named Entity Recognition (NER) and multiclass news-topic classification in Greek. We first compile and annotate a 20 000 article corpus with 32 IOB2 entity labels and 19 thematic categories, accompanied by a transparent, reproducible preprocessing pipeline. On this benchmark, TinyGreekNewsBERT reaches 81% micro F1 for NER and 78% classification accuracy, coming within five percentage points of GreekBERT (86% / 83%) while delivering comparable performance to mBERT (82% / 77%) and approaching XLM-RoBERTa (85% / 82%). Crucially, compared with GreekBERT, our model is $8\times $ smaller, requires $15\times $ fewer FLOPs (1.3 BFLOPs at 128 tokens), and yields a median CPU latency of 14.7 ms per article, a $10\times $ speed-up that makes it the first genuinely edge-deployable solution for Greek NER and news classification. Because the distillation and training pipeline is language-agnostic, the approach can be ported to other mid-resource languages and domains, offering a cost-effective path to multilingual, real-time NLP systems.}, archive = {J_ACCESS}, author = {Ioannis Katranis and Christos Troussas and Akrivi Krouska and Phivos Mylonas and Cleo Sgouropoulou}, doi = {10.1109/ACCESS.2025.3605709}, journal = {IEEE Access}, pages = {155031-155046}, shortjournal = {IEEE Access}, title = {Named entity recognition and news article classification: A lightweight approach}, volume = {13}, year = {2025}, }
• (2025). Post-quantum authentication framework based on iris recognition and homomorphic encryption. ACCESS, 13, 155015-155030. (www)

  @article{ , abstract = {This paper proposes an authentication protocol based on post-quantum cryptography and iris recognition. The process uses a modified version of the original McEliece cryptographic algorithm that provides post-quantum security. Additionally, iris recognition is included in the decryption process, ensuring that only authentic users have access to confidential information, or can operate critical infrastructures or systems that must be protected against malicious users or impostors. A distributed framework, in which several autonomous entities interact with each other, participates in the whole process to reduce the odds of a successful attack. Biometric templates are encrypted and safely stored in a database. These templates meet all requirements described in the ISO/IEC 24745 standard regarding data protection. Due to the homomorphic property of McEliece, the decryption process operates using the encrypted version of these biometric templates, ensuring that critical information is not revealed. The use of McEliece does not affect the overall accuracy of the recognition process, so that the same False Acceptance and False Rejection rates are obtained when compared with the non-protected scheme.}, archive = {J_ACCESS}, author = {Mariano López-García and Enrique Cantó-Navarro}, doi = {10.1109/ACCESS.2025.3605676}, journal = {IEEE Access}, pages = {155015-155030}, shortjournal = {IEEE Access}, title = {Post-quantum authentication framework based on iris recognition and homomorphic encryption}, volume = {13}, year = {2025}, }
• (2025). Structural design and real-world flight verification of a downwash-reducing UAV equipped with adaptive rotors. ACCESS, 13, 155003-155014. (www)

  @article{ , abstract = {This study addresses the challenge of excessive downwash generated by uncrewed aerial vehicles (UAVs) during pinpoint weeding in agriculture. We propose a novel UAV design incorporating adaptive rotors that can tilt during flight to reduce the downward airflow that interferes with close-proximity operations. A single actuator drives the rotor-tilting mechanism via bevel and worm gears, enabling synchronized rotor movement and stable self-locking to protect the actuator from external vibrations. Experimental results demonstrated that, even under significant vibration, the tilt mechanism maintained precise angle control and effectively reduced downwash. Indoor flight tests confirmed stable performance across a range of rotor angles from 0° to 30°. Notably, wind speed measurements taken approximately one meter below the UAV showed a reduction of up to 80% in downwash at the maximum tilt angle. In outdoor tests, the UAV maintained reliable hovering even in moderate wind conditions, with no significant degradation in positional accuracy when rotor angles were adjusted during flight. These results indicate that a UAV equipped with adaptive rotors can achieve a balance between aerodynamic efficiency and downwash reduction, making it well-suited for pinpoint weeding.}, archive = {J_ACCESS}, author = {Kenki Miyahara and Rin Watanabe and Shin Kawai and Hajime Nobuhara}, doi = {10.1109/ACCESS.2025.3588267}, journal = {IEEE Access}, pages = {155003-155014}, shortjournal = {IEEE Access}, title = {Structural design and real-world flight verification of a downwash-reducing UAV equipped with adaptive rotors}, volume = {13}, year = {2025}, }
• (2025). Data-driven learning of two-stage beamformers in passive IRS-assisted systems with inexact oracles. ACCESS, 13, 154984-155002. (www)

  @article{ , abstract = {The purpose of this work is the development of an efficient data-driven and model-free unsupervised learning framework for achieving fully passive intelligent reflective surface (IRS)-assisted optimal joint short/long-term beamforming in wireless communication networks. Under this challenging setting, our contribution amounts to the design of novel IRS training schemes —termed iZoSGA herein— relying on a zeroth-order stochastic gradient ascent methodology, suitable for tackling nonconvex two-stage stochastic optimization problems with continuous uncertainty and unknown (or “black-box”) terms present in the corresponding objective function, via utilization of inexact short-term evaluation oracles. Our findings are as follows: We showcase that iZoSGA can operate under realistic and general assumptions, and establish its (non-asymptotic) convergence rate close to some stationary point of the associated two-stage (i.e., short/long-term) problem, particularly in cases where the second-stage (i.e., short-term) beamforming problem (e.g., transmit precoding) is solved inexactly using an arbitrary (inexact) algorithm. iZoSGA is applicable on a wide variety of IRS-assisted optimal beamforming settings, while also being able to operate without (cascaded) channel model assumptions or knowledge of channel statistics, and over arbitrary IRS physical configurations; thus, no active sensing capability at the IRS(s) is needed. Additionally, our approach bypasses the need of imposing challenging nonconvex unit-modulus constraints, typically associated with IRS parameter optimization. Several algorithmic variants of iZoSGA are numerically demonstrated to be particularly effective in a range of experiments pertaining to a well-studied MISO downlink model, including scenarios demanding physical IRS tuning (e.g., directly through varactor capacitances), even in large-scale regimes. Overall we demonstrate that, by leveraging the developed theory and its insights on the propagation of oracle errors, we can create highly efficient and scalable algorithms for the solution of general (possibly large-scale) IRS-assisted optimal beamforming problems, under realistic assumptions.}, archive = {J_ACCESS}, author = {Spyridon Pougkakiotis and Hassaan Hashmi and Dionysis Kalogerias}, doi = {10.1109/ACCESS.2025.3605249}, journal = {IEEE Access}, pages = {154984-155002}, shortjournal = {IEEE Access}, title = {Data-driven learning of two-stage beamformers in passive IRS-assisted systems with inexact oracles}, volume = {13}, year = {2025}, }
• (2025). Observer-based induced l∞ control for nonlinear systems with deception attacks. ACCESS, 13, 154976-154983. (www)

  @article{ , abstract = {In this paper, the observer-based induced ${\mathcal {L}}_{\infty }$ control problem for nonlinear systems with deception attacks is studied. Construct the T–S fuzzy model to linearly approximate the nonlinearity of the system. Randomly occurring deception attack is assumed obey the Bernoulli distribution and the norm of the attack function satisfies certain conditions. Furthermore, the closed-loop control system (CLCS) is constructed in the descriptor form to directly separate the system matrices and the controller matrix in order to reduce the design complexity. The proposed design conditions enable the observer and controller to meet the requirements of stochastic stability and induced ${\mathcal {L}}_{\infty }$ performance. Finally, the algorithm was verified through a one-link manipulator.}, archive = {J_ACCESS}, author = {Ying Tian and Ming-Yang Qiao}, doi = {10.1109/ACCESS.2025.3605687}, journal = {IEEE Access}, pages = {154976-154983}, shortjournal = {IEEE Access}, title = {Observer-based induced l∞ control for nonlinear systems with deception attacks}, volume = {13}, year = {2025}, }
• (2025). Machine learning framework for industrial machine sound classification in predictive maintenance. ACCESS, 13, 154960-154975. (www)

  @article{ , abstract = {Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery. This research proposes a machine learning-based framework for classifying anomalous sounds in industrial machines, with a particular focus on CT scan machines and fan units. The study utilises both real-world data from CT scan machine sound and the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset. It offers a comprehensive analysis of sound signal processing techniques, synthetic data generation methods, feature extraction processes, and classification using machine learning models to support predictive maintenance applications. In this research, sound data from a CT scan machine was collected using an Internet of Things (IoT) connected microphone located on the machine in a Klang Valley hospital. Due to the limited availability of faulty condition data, synthetic anomalous data for both operational and non-operational conditions were generated using a noise injection method. Features derived from Mel Frequency Cepstral Coefficients (MFCCs) and Mel Spectrogram representations were employed to analyse the sound data. The dataset for CT scan machine sounds is categorised into four distinct classes: anomalous operational sound (Aop), anomalous non-operational sound (Anop), normal operational sound (Nop), and normal non-operational sound (Nnop). In contrast, the MIMII dataset is classified into two categories: normal and abnormal. A Convolutional Neural Network (CNN) model was used for a sound classification system, achieving training accuracies of 98.22% with Mel spectrogram features and 98.12% with MFCC features. The results emphasise the possibility of using CNN-based sound classification to effectively anticipate and maintain CT scan machines. This finding also has the potential to be applied to predictive maintenance applications by detecting both normal and anomalous operating sounds in industrial machinery.}, archive = {J_ACCESS}, author = {Nur Fatinah Md Hafiz and Syamsiah Mashohor and Mohammad Habib Shah Ershad Mohd Azrul Shazril and Azizi Mohd Ali and Mohd Fadlee A. Rasid}, doi = {10.1109/ACCESS.2025.3601999}, journal = {IEEE Access}, pages = {154960-154975}, shortjournal = {IEEE Access}, title = {Machine learning framework for industrial machine sound classification in predictive maintenance}, volume = {13}, year = {2025}, }
• (2025). Spectrum anomaly detection using deep neural networks: A wireless signal perspective. ACCESS, 13, 154948-154959. (www)

  @article{ , abstract = {In modern society, LTE and 5G communications serve as essential infrastructures, making the stability of communication networks a critical challenge. However, advancements in wireless communication devices and technologies can lead to the transmission of illegal signals, device malfunctions, and radio interference, potentially causing degradation of communication performance. This study proposes a wideband spectrum monitoring method for LTE and 5G communication bands used in cellular networks by utilizing object detection techniques, thereby ensuring stable network performance and security. The proposed network accounts for the temporal and frequency characteristics present in signals on the spectrogram and is designed to exhibit high detection performance even for low-power signals. It achieved an mean Average Precision(mAP)50 performance of 80% at an Interference Signal Ratio(ISR) of –10dB corresponding to low power and an overall mAP50 performance of 93%.}, archive = {J_ACCESS}, author = {Jinha Kim and Sunji Oh and Hyeongwoo Kim and Byungkwan Kim}, doi = {10.1109/ACCESS.2025.3603216}, journal = {IEEE Access}, pages = {154948-154959}, shortjournal = {IEEE Access}, title = {Spectrum anomaly detection using deep neural networks: A wireless signal perspective}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of NLP techniques for military terminologies and information operations on social media. ACCESS, 13, 154930-154947. (www)

  @article{ , abstract = {This paper presents a comprehensive study aimed at systematically analyzing and evaluating natural language processing (NLP) techniques for military information operations, with a special focus on social media intelligence. Among an ever-growing complicated information environment, NLP methods like sentiment analysis, named entity recognition, and topic modeling have been essential in tracking online propaganda efforts, discovering emerging issues and threats globally with dialogues on military operations. These techniques make an impact on available decision making via situational awareness and getting the added extraction from volumes of unstructured data outputs thus increasing the overall strategic benefits to military organizations. There are technical and operational challenges concerning the use of NLP in a military context such as requirements for real-time data processing; language diversity; and maintaining data privacy while preserving ethical standards. To address these challenges, the study conducts an exhaustive survey of NLP methods, reviewing their range of applications, and highlights the relevance of several approaches for military information operations, with special emphasis on social media intelligence. The work further provides discussion on the comprehensive adoption of artificial intelligence (AI), edge computing, and multilingual NLP models for enhancing adaptability, efficiency, and transparency of the systems. It also extols the need for explainable AI (XAI) to improve accountability and trust by making term or even whole early warning systems derived from NLP analyses, transparent and interpretable for these military research applications with significant financial consequences. The paper also emphasizes the strategic importance of multilingual and multimodal analysis and the integration of specialized military lexicons to improve the contextual understanding of military discourse in social media environments. We also elucidate the important capabilities of NLP in enabling military operations to be responsive, rapid and data-driven while also adapting to the evolving nature of warfare. Key conclusions suggest that applying advanced NLP tools enhances situational awareness, enables timely threat detection, and supports more agile, data-informed decision-making within modern military operations. The paper shows a perspective to optimize NLP and AI technologies, leveraging various perspectives to benefit the operational needs of military and defense sectors in more data-rich environments.}, archive = {J_ACCESS}, author = {Tamara Zhukabayeva and Zulfiqar Ahmad and Aigerim Yerimbetova and Madina Sambetbayeva and Duman Telman and Abdygalym Bayangali and Elmira Daiyrbayeva}, doi = {10.1109/ACCESS.2025.3605354}, journal = {IEEE Access}, pages = {154930-154947}, shortjournal = {IEEE Access}, title = {A comprehensive review of NLP techniques for military terminologies and information operations on social media}, volume = {13}, year = {2025}, }
• (2025). Reimagining unit test generation with AI: A journey from evolutionary models to transformers. ACCESS, 13, 154908-154929. (www)

  @article{ , abstract = {The rapid evolution of software development necessitates efficient unit testing to ensure reliability, yet manual test case generation is labor-intensive and often inadequate for agile workflows. Despite advancements, a comprehensive review of AI-driven unit test case generation, particularly for Java, is lacking, motivating this study to address this gap. The paper examines AI algorithms for unit test case generation, focusing on Java-specific challenges like class hierarchies and dependency injection. We propose a novel taxonomy categorizing methods into traditional machine learning (e.g., genetic algorithms, SVMs), deep learning (e.g., RNNs, GNNs), and transformer-based approaches (e.g., PLBART, enhanced by LoRA and QLoRA). Key contributions include: 1) a structured taxonomy for comparing AI methods based on effectiveness, usability, and maintainability; 2) a Java-specific focus addressing enterprise system complexities; and 3) identification of research gaps, such as scalability and assertion accuracy. Findings reveal that transformer-based models like A3Test and ChatUniTest achieve up to 59% test case correctness and 77% focal method coverage, outperforming traditional methods, though challenges in computational cost and assertion accuracy persist. Tools are evaluated for integration with CI/CD pipelines and advanced capabilities like parameter-efficient fine-tuning (PEFT) using LoRA and QLoRA. This review provides a roadmap for researchers and practitioners to advance automated, high-quality unit testing for Java software quality.}, archive = {J_ACCESS}, author = {Sintayehu Zekarias Esubalew and Beakal Gizachew Assefa}, doi = {10.1109/ACCESS.2025.3597049}, journal = {IEEE Access}, pages = {154908-154929}, shortjournal = {IEEE Access}, title = {Reimagining unit test generation with AI: A journey from evolutionary models to transformers}, volume = {13}, year = {2025}, }
• (2025). Highly sensitive optic fabry-perot ultrasonic sensor for power transformer partial discharge detection. ACCESS, 13, 154898-154907. (www)

  @article{ , abstract = {Optical sensing has been widely applied in the condition monitoring of power equipment due to its advantages, such as high insulation, anti-interference capability, and high sensitivity. However, for partial discharge detection, the sensitivity and applicability of optical sensors still need to be further improved to ensure their practical application. This paper introduces a silicon-grooved diaphragm-based fiber-optic Fabry-Perot (F-P) ultrasonic sensor that was designed and fabricated for partial discharge detection in power equipment such as power transformers. The groove parameters of the sensing diaphragm were optimized using finite element software. Compared to traditional circular diaphragms, the static sensitivity of the silicon-grooved diaphragm was improved by 4.09 times, while the resonant frequency remained essentially unchanged. The influence of the F-P cavity length on the contrast of the sensor’s interference spectrum was investigated by coupling efficiency to modify the traditional dual-beam interference model, thereby enhancing the sensor’s acoustic pressure sensitivity. The silicon grooved diaphragm was fabricated using micro-electro-mechanical system (MEMS) technology, with a groove diameter of $829.44~\mu $ m, a thickness of $2.09~\mu $ m, and an F-P cavity length of $163.600~\mu $ m. At the resonant frequency of 61.5 kHz, the sensor achieved an acoustic pressure sensitivity of 357.78 mV/Pa. The performance of the sensor was validated by testing in a power transformer with three types of partial discharge defect models. Experimental results demonstrate that the fabricated fiber-optic F-P ultrasonic sensor offers high acoustic pressure sensitivity, good real-time performance, and capabilities in detecting ultrasonic signals. In addition, the developed sensor maintains structural integrity and can function after long-term usage in a transformer environment.}, archive = {J_ACCESS}, author = {Jinggang Yang and Qun Li and Jiabi Liang and Jian Shao and Peng Wu and Tonglei Wang and Yuncai Lu and Xiaohan Li}, doi = {10.1109/ACCESS.2025.3605613}, journal = {IEEE Access}, pages = {154898-154907}, shortjournal = {IEEE Access}, title = {Highly sensitive optic fabry-perot ultrasonic sensor for power transformer partial discharge detection}, volume = {13}, year = {2025}, }
• (2025). PEGAT: Prediction error-guided adversarial training to enhance robustness of deep learning models in autonomous vehicles. ACCESS, 13, 154885-154897. (www)

  @article{ , abstract = {Adversarial training is a widely used method to improve the robustness of deep learning models in various applications. Although adversarial training enhances the robustness of the target model, it also suffers from an accuracy versus robustness trade-off, meaning that while improving robustness, the accuracy of the target model decreases. Moreover, they also suffer from catastrophic overfitting, where these methods offer reasonable robustness against specific adversarial attacks, but they become ineffective against others. Thus, to solve these issues, we proposed Prediction Error-Guided Adversarial Training (PEGAT). In this method, we first evaluate the normally trained target models against adversarial samples and then calculate class-wise prediction error in terms of loss in the F1-score, which we refer to as PEGAT scores. In the second phase, use the PEGAT scores as guidance to assign weights during the adversarial training process. In this phase, PEGAT adaptively assigns higher weights to classes with high PEGAT scores and lower weights to classes with low PEGAT scores. The PEGAT guides the models to focus on those classes where they struggle to maintain a higher F1 score in the first phase. By doing so, not only is the adversarial robustness of the target model increased, but also the issues such as the accuracy versus robustness tradeoff, catastrophic overfitting, and generalizability are addressed. Evaluating the PEGAT on classification models used in intelligent autonomous vehicle perception systems, we found that the technique could improve the robustness of WideResNet18, WideResNet34, and WideResNet50 by 16%, 6%, and 5%, respectively, without suffering from the aforementioned issues.}, archive = {J_ACCESS}, author = {Manzoor Hussain and Zhengyu Shang and Ahmed Dawod Mohammed Ibrahum and Jang-Eui Hong}, doi = {10.1109/ACCESS.2025.3603337}, journal = {IEEE Access}, pages = {154885-154897}, shortjournal = {IEEE Access}, title = {PEGAT: Prediction error-guided adversarial training to enhance robustness of deep learning models in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). CASCAFE approach with real-time data in vehicle maintenance. ACCESS, 13, 154854-154884. (www)

  @article{ , abstract = {Service to the vehicle is one of the important factors to be considered as part of vehicle maintenance. The service cost incurred represents how the vehicle has been maintained, making it essential to predict future service costs based on past service records. Vehicle maintenance is crucial for cost management and operational efficiency, as predicting service costs helps optimize resource allocation and long-term planning. Existing methods rely on fixed-interval maintenance schedules or predictive models based on historical usage. However, these techniques often lack adaptability and fail to integrate component aging effectively. Traditional systems are inefficient and costly, leading to inaccurate service predictions, while high variability in repair costs further complicates maintenance planning. This paper introduces CASCAFE (Clustering Analysis for Service Cost and Fuel Efficiency), a clustering-based approach leveraging historical service data for cost estimation. It analyzes kilometers traveled and service cost per kilometer to derive optimal maintenance strategies. Techniques such as Fuzzy C-Means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Models (GMM), and Ordering Points To Identify the Clustering Structure (OPTICS) are utilized to predict future service costs by examining past service data patterns. Among these, Fuzzy C-Means clustering reveals optimal service cost estimation for vehicles with a higher number of kilometers traveled. By integrating component aging and clustering-based analysis, CASCAFE supports informed maintenance decisions and improves cost efficiency in fleet management. Future work will extend CASCAFE to multi-vehicle datasets and incorporate environmental and driver-behavioral factors.}, archive = {J_ACCESS}, author = {Raparthi Yaswanth and M. Rajasekhara Babu}, doi = {10.1109/ACCESS.2025.3602820}, journal = {IEEE Access}, pages = {154854-154884}, shortjournal = {IEEE Access}, title = {CASCAFE approach with real-time data in vehicle maintenance}, volume = {13}, year = {2025}, }
• (2025). Rapid fault-tolerant MPC strategy for six-phase PMSMs: Optimizing torque stability, current constraint management during phase transition. ACCESS, 13, 154833-154853. (www)

  @article{ , abstract = {Six-phase permanent magnet synchronous machines (PMSMs) provide improved fault tolerance and reliability, making them well-suited for critical applications like aerospace and hybrid electric vehicle systems. Nonetheless, guaranteeing torque stability while keeping phase currents within safe limits during fault scenarios poses considerable challenges. Conventional control strategies struggle with current redistribution when phase faults occur, leading to torque oscillations and potentially damaging current levels in remaining healthy phases. This paper proposes a Fault-Tolerant Model Predictive Control (FT-MPC) strategy that optimizes current distribution in six-phase PMSMs to maintain smooth torque output while strictly adhering to peak current constraints. The proposed approach employs a predictive model to calculate optimal current references during the transition from six-phase to three-phase operation, implementing a cost function that balances torque maintenance with current limitation requirements. Simulation analysis and experimental testing on a 3 kW six-phase PMSM setup are conducted to validate the effectiveness of the proposed FT-MPC under various fault scenarios, comparing it with the conventional controllers. Compared to conventional controllers, the proposed method prevents current spikes during phase-switching transients while maintaining torque within reference values. Additionally, the controller successfully limits currents in healthy phases to remain below predetermined thresholds, preventing thermal damage while maximizing available torque. The comprehensive experimental results confirm that the FT-MPC approach significantly enhances system reliability and performance during fault conditions. It is particularly suitable for electric vehicle propulsion systems, aerospace applications, and other safety-critical industrial drives requiring fault-tolerant operation.}, archive = {J_ACCESS}, author = {Peter Nkwocha Harmony and Ahmed H. Okilly and Cheolgyu Kim and Do-Wan Kim and Seungdeog Choi and Jeihoon Baek}, doi = {10.1109/ACCESS.2025.3605650}, journal = {IEEE Access}, pages = {154833-154853}, shortjournal = {IEEE Access}, title = {Rapid fault-tolerant MPC strategy for six-phase PMSMs: Optimizing torque stability, current constraint management during phase transition}, volume = {13}, year = {2025}, }
• (2025). Context-aware directed acyclic graph network for conversational aspect-based sentiment quadruple analysis. ACCESS, 13, 154823-154832. (www)

  @article{ , abstract = {Conversational Aspect-based Sentiment Quadruple Analysis (DiaASQ) is a fine-grained sentiment analysis task that aims at extracting targets, aspects, opinions, and sentiments from multi-turn dialogues. Existing methods focus on token-level interaction modeling and neglect complex cross-utterance dependencies. To address this, we propose a context-aware directed acyclic graph network (CA-DAGNet). This model integrates syntax-aware context encoding and directed acyclic graph (DAG) modeling to capture intra-utterance syntactic structures and cross-utterance long-range dependencies. For global modeling, we construct the dialogue as a DAG and combine it with an information propagation mechanism, precisely capturing syntactic dependencies and semantic interactions while dynamically adjusting the scope of information propagation to avoid fixed-window limitations. In addition, we adopt a context filter to retain highly relevant information for the target utterance, suppress redundant noise, and improve the modeling of cross-utterance dependencies. Experiments conducted on Chinese and English datasets demonstrate that the proposed model achieves superior performance.}, archive = {J_ACCESS}, author = {Qiang Zhang and Jie Zeng and Runze Zhang and Dong Cui}, doi = {10.1109/ACCESS.2025.3605729}, journal = {IEEE Access}, pages = {154823-154832}, shortjournal = {IEEE Access}, title = {Context-aware directed acyclic graph network for conversational aspect-based sentiment quadruple analysis}, volume = {13}, year = {2025}, }
• (2025). Scale-free and phase-agnostic detection of interturn short-circuit faults in permanent magnet synchronous motors. ACCESS, 13, 154812-154822. (www)

  @article{ , abstract = {This study presents a scale-free and phase-agnostic method for detecting interturn short-circuit faults (ISCFs) in the stator windings of permanent magnet synchronous motors (PMSMs). The proposed method integrates denoising and feature extraction with a crest-factor-based health indicator. A wavelet-based denoising method with average peak extraction is devised to enhance the sensitivity to fault-induced current deviations while suppressing external noise. The crest factor is modified to enable phase-agnostic detection across stator windings. To eliminate scale dependency associated with motor capacity, the modified crest factor is further calibrated using baseline data obtained under healthy conditions. The resulting health indicator, namely max-min crest factor, enables consistent and reliable ISCF detection without requiring motor-specific parameters or prior knowledge of the faulty phase. The effectiveness of the proposed method is validated through multiple case studies, including 1) numerical simulations, 2) laboratory-scale testbed experiments on small-capacity PMSMs (100 W and 1 kW), and 3) full-scale testing on an 82.5 kW PMSM under realistic operating conditions. The experimental results demonstrate that the method reliably detects ISCFs regardless of fault location or motor scale.}, archive = {J_ACCESS}, author = {Mikyung Hwang and Jonghyeok Kim and Hyuntak Lee and Hyunseok Oh}, doi = {10.1109/ACCESS.2025.3605534}, journal = {IEEE Access}, pages = {154812-154822}, shortjournal = {IEEE Access}, title = {Scale-free and phase-agnostic detection of interturn short-circuit faults in permanent magnet synchronous motors}, volume = {13}, year = {2025}, }
• (2025). Integrating environmental costs into power plant bidding: A pathway to sustainable energy transition. ACCESS, 13, 154803-154811. (www)

  @article{ , abstract = {The current structures of the electricity market are criticized in this article for failing to internalize the external costs of carbon emissions from thermal power plants. It highlights the shortcomings of the two main policy instruments—carbon taxes and carbon trading. While carbon taxes are frequently too low to bring about meaningful change, particularly in capital-intensive industries like power generation, emissions trading schemes set overall caps but do not encourage reductions beyond the targets. By recalculating marginal costs using heat rates and input-output curves, the author suggests bridging this gap by incorporating environmental costs into power plant bidding prices. This method changes dispatch priorities according to actual societal costs and openly accounts for externalities as shown using data from the U.S. Department of Energy on five fossil fuel generation technologies. Moreover, screening curve analysis is carried out to show the overall economics of these technologies when the carbon costs are added. Using IEEE-118 fossil fuel data, the study shows more than ${\$}$ 20/MWh cost gap between the average and marginal costs for some plants when CO2 is priced at ${\$}$ 50/ton. It calls for policies mandating emission disclosure and integrating environmental costs into decisions to penalize polluters and drive a lasting shift toward cleaner energy and climate alignment.}, archive = {J_ACCESS}, author = {Arif S. Malik}, doi = {10.1109/ACCESS.2025.3605576}, journal = {IEEE Access}, pages = {154803-154811}, shortjournal = {IEEE Access}, title = {Integrating environmental costs into power plant bidding: A pathway to sustainable energy transition}, volume = {13}, year = {2025}, }
• (2025). A cyber resilient framework for V2X enabled roundabouts in intelligent transportation systems. ACCESS, 13, 154775-154802. (www)

  @article{ , abstract = {Vehicle-to-everything (V2X) communication systems are increasingly susceptible to cyber-physical threats that exploit trust assumptions, coordination latency, and semantic inconsistencies across agents. These vulnerabilities, particularly in dense or adversarial environments, undermine the reliability of cooperative perception, anomaly detection, and safety-critical maneuver execution. This paper presents CR-V2XR, a cross-layer, federated, and trust-aware coordination framework designed to enhance resilience in connected and autonomous vehicular networks. CR-V2XR integrates multi-modal anomaly detection with delay-sensitive trust estimation using features extracted from basic safety messages (BSMs), behavioral deviations, entropy shifts, and inter-vehicle trust validation. The architecture employs federated learning for distributed anomaly detection without centralized aggregation and uses a control layer that supports delay-aware trajectory selection. A multi-objective NSGA-III optimizer enables online trade-off adaptation across detection accuracy (DA), collision probability, and communication overhead. Simulations across eleven adversarial scenarios, including Sybil, wormhole, falsification, and replay attacks, demonstrate that CR-V2XR achieves 95% detection accuracy under worst-case attacks, reduces collision probability from 0.61 to 0.27 at 300 vehicles, maintains bounded delay, typically 45–60 ms under nominal load, and remains resilient under high-stress conditions with delays up to 180 ms and communication overhead ( $\leq 6.1$ MB/s). Compared to centralized IDS and stateless baselines, CR-V2XR improves detection fidelity, scalability, and robustness under non-IID data and partial synchronization. These results establish CR-V2XR as a viable architecture for delay-constrained, trust-centric coordination in federated V2X environments subject to persistent adversarial threats.}, archive = {J_ACCESS}, author = {Waseem Abbass and Nasim Abbas and Uzma Majeed and Waqas Nawaz and Qaiser Abbas and Ashfaq Hussain Farooqi}, doi = {10.1109/ACCESS.2025.3604095}, journal = {IEEE Access}, pages = {154775-154802}, shortjournal = {IEEE Access}, title = {A cyber resilient framework for V2X enabled roundabouts in intelligent transportation systems}, volume = {13}, year = {2025}, }
• (2025). Systematic review of deep learning techniques for gynecological cancer diagnosis. ACCESS, 13, 154754-154774. (www)

  @article{ , abstract = {Cancer remains a major global health concern, with estimates suggesting that nearly one in five women will be diagnosed with the disease during their lifetime. In recent years, deep learning (DL) has emerged as a transformative tool in medical diagnostics, showing particular promise in the detection and management of gynecological cancers, including cervical, ovarian, and endometrial types. This review critically examines the application of advanced DL techniques in gynecologic oncology, offering a detailed overview of the disease landscape and highlighting the vital role of open-access benchmark datasets in driving research progress. It explores the latest developments in DL-based medical image analysis, evaluating the performance, advantages, and limitations of various models. The discussion encompasses key stages such as image acquisition, feature extraction, segmentation, selection, and classification, shedding light on the technical complexities of DL workflows. Additionally, the review addresses clinical and computational challenges faced in real-world implementation and identifies future directions to enhance the accuracy and clinical utility of DL in gynecological cancer care.}, archive = {J_ACCESS}, author = {Bhawna Swarnkar and Nilay Khare and Manasi Gyanchandani}, doi = {10.1109/ACCESS.2025.3603458}, journal = {IEEE Access}, pages = {154754-154774}, shortjournal = {IEEE Access}, title = {Systematic review of deep learning techniques for gynecological cancer diagnosis}, volume = {13}, year = {2025}, }
• (2025). Cooperative multi-UAV edge computing: Delay minimization in emergency communications. ACCESS, 13, 154739-154753. (www)

  @article{ , abstract = {Uncrewed aerial vehicles (UAVs) have emerged as an effective solution for emergency disaster-relief missions, providing communication services in place of damaged ground base stations (BSs) and offloading computational tasks for applications such as target recognition. Serving as mobile edge computing (MEC) platforms, UAVs enable near-source data processing, which is crucial when conventional communication infrastructures are unavailable. To maximize the system’s potential, we introduce a cooperative multi-UAV edge computing framework with inter-UAV offloading, specifically designed to support delay-sensitive computing tasks for mobile terminals in emergency communication networks. In this framework, we formulate a delay minimization problem through on a dynamic slot allocation, which jointly optimizes computing task scheduling, UAV computation resource allocation, and 3D UAV trajectory planning. This complex mixed integer nonlinear problem is decomposed into three manageable subproblems, which are solved sequentially using tailored algorithms, including a penalty-based task scheduling algorithm, a Karush-Kuhn-Tucker (KKT)-based computation resource allocation algorithm, and a successive convex approximation (SCA) method for 3D trajectory planning. We then employ the block coordinate descent method to develop a low-complexity edge computing offloading optimization algorithm. Simulation results show that the proposed algorithm efficiently utilizes the cooperative computing capabilities of multi-UAV systems. Compared with the benchmark algorithms, it achieves up to 18.3% and 44,1% reductions in total task delay, respectively, under different data input levels.}, archive = {J_ACCESS}, author = {Hong Shen and Chaobin Chen and Tianyi Shi and Tiankui Zhang and Yapeng Wang}, doi = {10.1109/ACCESS.2025.3602985}, journal = {IEEE Access}, pages = {154739-154753}, shortjournal = {IEEE Access}, title = {Cooperative multi-UAV edge computing: Delay minimization in emergency communications}, volume = {13}, year = {2025}, }
• (2025). A highly miniaturized and wide angularly stable antenna-filter-antenna-based polarization rotating frequency selective surface for radar systems. ACCESS, 13, 154729-154738. (www)

  @article{ , abstract = {This paper presents a novel polarization rotating frequency selective surface (PR-FSS) design based on the antenna-filter-antenna (AFA) configuration. The proposed design efficiently overcomes the limitations related to miniaturization and wide angular stability in polarization converters currently used in advanced radar systems. The proposed metasurface (MS) features an aperture-coupled design using U- and 90° U-shaped patch elements separated by a 45° slotted ground plane. The incorporation of the particular shape in the proposed design enabled high-efficiency linear-to-linear transmissive cross-polarization (LTL-TCP) conversion and bandpass response, simultaneously. The design concept is initially verified using full-wave electromagnetic (EM) simulations and then validated experimentally. The results showed that the design has stable performance with a wide bandwidth and robust angular response up to 80°. In summary, the prototype offered a consistent polarization conversion ratio (PCR) above 0.95 over the frequency range from 5.18 – 5.72 GHz with a low insertion loss of around 0.4 dB. A good agreement between the simulation and measured results demonstrated the applicability of such miniaturized structures in polarization conversion applications, such as radar systems, to improve target detection and classification.}, archive = {J_ACCESS}, author = {Muhammad Nasir and Slawomir Koziel and Adnan Iftikhar}, doi = {10.1109/ACCESS.2025.3605845}, journal = {IEEE Access}, pages = {154729-154738}, shortjournal = {IEEE Access}, title = {A highly miniaturized and wide angularly stable antenna-filter-antenna-based polarization rotating frequency selective surface for radar systems}, volume = {13}, year = {2025}, }
• (2025). Detecting community evolution by modeling individual traits. ACCESS, 13, 154712-154728. (www)

  @article{ , abstract = {The detection of community structures in social networks has become an increasingly popular topic of research. A particular challenge lies in tracking the evolution of these structures, along with the accompanying community semantics, over time. Addressing this challenge requires a comprehensive understanding of the underlying factors that drive community evolution. This study contributes to that understanding by emphasizing the critical role of individual traits in influencing community dynamics. Building on this insight, we propose an innovative model for dynamic community detection that incorporates inherent evolutionary features. The model segments the social network into temporal snapshots and identifies community structures by leveraging both individual traits and information derived from preceding snapshots. Simultaneously, it tracks evolving discussion topics within communities, enabling the modeling of community semantics. Additionally, the model provides a robust framework for user trait profiling. Its effectiveness is validated through extensive experiments on three real-world datasets. Compared with seven state-of-the-art baselines, our model consistently demonstrates superior performance. Case studies further confirm its capability to uncover community semantics and accurately characterize user traits.}, archive = {J_ACCESS}, author = {Wei Jiang and Qingbin Liu}, doi = {10.1109/ACCESS.2025.3605651}, journal = {IEEE Access}, pages = {154712-154728}, shortjournal = {IEEE Access}, title = {Detecting community evolution by modeling individual traits}, volume = {13}, year = {2025}, }
• (2025). Microwave-based non-invasive blood glucose sensors: Key design parameters and case-informed evaluation. ACCESS, 13, 154695-154711. (www)

  @article{ , abstract = {Microwave-based non-invasive blood glucose monitoring (NIBGM) has emerged as a promising alternative to traditional finger-prick and enzymatic methods, offering painless, continuous, and real-time glucose tracking. This paper presents a comprehensive technical review and analytical framework for the design, modeling, and evaluation of microwave NIBGM sensors. Critical sensor architectures such as split-ring resonators (SRRs), complementary SRRs (CSRRs), dielectric resonator antennas (DRAs), and microfluidic electric-LC (ELC) structures are reviewed in terms of sensitivity, operating frequency, coupling efficiency, and material integration. We develop analytical and empirical models to simulate dielectric behavior, resonant frequency shifts, and permittivity changes across physiological glucose concentrations. Safety compliance is addressed using specific absorption rate (SAR) metrics aligned with IEEE C95.1 and ICNIRP standards. To enhance prediction accuracy, we apply machine learning models artificial neural networks (ANN), support vector regression (SVR), and long short-term memory (LSTM) to sensor outputs. These models are evaluated using mean absolute relative difference (MARD), coefficient of determination ( $R^{2}$ ), and Clarke Error Grid zones, benchmarked against commercial glucometer data from phantom and preliminary in vivo measurements. The validation strategy includes cross-validation and repeatability assessments, with plans for future deployment on standardized clinical datasets. Structured visual workflows and comparative performance graphs are presented to aid interpretability. Overall, the proposed framework synthesizes electromagnetic theory, safety constraints, and data-driven intelligence into a cohesive architecture for next-generation wearable glucose monitors that are accurate, safe, adaptive, and clinically aligned.}, archive = {J_ACCESS}, author = {Ahmed A. Zakaria and Ahmed Allam and Adel B. Abdel-Rahman}, doi = {10.1109/ACCESS.2025.3598618}, journal = {IEEE Access}, pages = {154695-154711}, shortjournal = {IEEE Access}, title = {Microwave-based non-invasive blood glucose sensors: Key design parameters and case-informed evaluation}, volume = {13}, year = {2025}, }
• (2025). Unveiling illusionary robust features: A novel approach for adversarial defenses in deep neural networks. ACCESS, 13, 154678-154694. (www)

  @article{ , abstract = {Deep Neural Networks (DNNs) are vulnerable to visually imperceptible perturbations, known as Adversarial Examples (AEs). The leading hypothesis attributes this susceptibility to “non-robust features,” which are highly predictive but fragile. Recent studies have challenged the robustness of models trained on robust features. One study demonstrates that models trained on robust features are vulnerable to AutoAttack in cross-paradigm settings. Another study showing the susceptibility of robust models to attacks based on Projected Gradient Descent (PGD) when attackers have complete knowledge of the robust model suggests the presence of “illusionary robust features”—robust features highly correlated with incorrect labels—as the root cause of this vulnerability. These findings complicate the analysis of DNNs’ robustness and reveal limitations, without offering concrete solutions. This paper extends previous works by reevaluating the susceptibility of the “robust model” to AutoAttack. Considering “illusionary robust features” as the root cause of this susceptibility, we propose a novel robustification algorithm that generates a “purified robust dataset”. This robustification method not only nullifies the effect of features weakly correlated with correct labels (non-robust features) but also features highly correlated with incorrect labels (illusionary robust features). We evaluated the robustness of the models trained on “standard”, “robust”, and “purified robust” datasets against various strategies based on state-of-the-art AutoAttack and PGD attacks. These evaluations resulted in a better understanding of how the presence of “non-robust” and “illusionary robust” features in datasets and classifiers and their entanglements can result in the susceptibility of DNNs. Our experiment also shows that employing our robustification method, which filters out the effect of “non-robust” and “illusionary robust” features in both train and test sets, effectively addresses the vulnerabilities of DNNs, regardless of the mentioned entanglements. The contributions of this paper advance the understanding of DNN vulnerabilities and provide a more robust solution against sophisticated adversarial attacks.}, archive = {J_ACCESS}, author = {Alireza Aghabagherloo and Rafa Gálvez and Davy Preuveneers and Bart Preneel}, doi = {10.1109/ACCESS.2025.3604636}, journal = {IEEE Access}, pages = {154678-154694}, shortjournal = {IEEE Access}, title = {Unveiling illusionary robust features: A novel approach for adversarial defenses in deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Optimized ensemble learning for semantic segmentation of satellite imagery using DeepLabV3+ and UNet with PSO and cross-dataset evaluation. ACCESS, 13, 154647-154677. (www)

  @article{ , abstract = {Semantic segmentation is critical in remote sensing applications such as urban planning, disaster management, and environmental monitoring. However, segmenting complex satellite images remains challenging due to varied textures and overlapping boundaries. This study proposes a PSO-optimized ensemble framework integrating DeepLabV3+ and UNet to enhance segmentation precision and reliability. DeepLabV3+ captures global contextual information, while UNet preserves fine spatial details, enabling the ensemble to achieve superior segmentation outcomes. The model is trained using the Semantic Segmentation of Aerial Imagery dataset and optimized with Particle Swarm Optimization (PSO) for adaptive learning rate tuning, ensuring convergence stability and performance maximization. Experimental evaluations demonstrate the ensemble’s ability to outperform individual models, achieving a Dice score of 0.9201 compared to DeepLabV3+ (0.8538) and UNet (0.7877). Further, cross-dataset validation on the Bhuvan Satellite Data confirms its generalization capability with an unprecedented Dice score of 0.9999, indicating robust segmentation across diverse landscapes. These findings highlight the effectiveness of PSO-driven ensemble learning for high-precision satellite image analysis, offering a scalable solution for real-world geospatial applications. Our approach sets a new benchmark in segmentation accuracy and cross-domain adaptability, enhancing the practical utility of semantic segmentation in remote sensing tasks. In this context, PSO is introduced as a metaheuristic strategy to optimize the learning rate of the ensemble model adaptively. Unlike static or heuristically chosen learning rates, PSO dynamically explores the search space based on segmentation loss feedback, enabling improved convergence and reduced training instability. This adaptive mechanism is particularly valuable in cross-dataset semantic segmentation tasks, where variability in resolution, scene complexity, and geographic distribution demands robust generalization and precise learning rate adjustment.}, archive = {J_ACCESS}, author = {Gurjot Kaur and Salil Bharany and Dalia H. Elkamchouchi and Seongki Kim}, doi = {10.1109/ACCESS.2025.3602922}, journal = {IEEE Access}, pages = {154647-154677}, shortjournal = {IEEE Access}, title = {Optimized ensemble learning for semantic segmentation of satellite imagery using DeepLabV3+ and UNet with PSO and cross-dataset evaluation}, volume = {13}, year = {2025}, }
• (2025). Hybrid framework combining diffusion-based image augmentation and feature level SMOTE for addressing extreme class imbalance. ACCESS, 13, 154623-154646. (www)

  @article{ , abstract = {Imbalanced datasets pose significant challenges to the reliability and robustness of visual classification systems, particularly in critical applications like solar panel dust detection. Conventional data augmentation techniques, such as Synthetic Minority Over-Sampling Technique (SMOTE) or generative models like Stable Diffusion, independently demonstrate limitations, either by lacking structural diversity or failing to sufficiently address feature-space imbalance. To overcome these constraints, this paper proposes DiSMHA (Diffusion-SMOTE Hybrid Augmentation), an innovative framework integrating pixel-level image synthesis using Stable Diffusion and feature-level oversampling via SMOTE. DiSMHA systematically balances and enriches minority class representation by producing high-quality synthetic images coupled with robust feature interpolation in the latent space. Experiments conducted on four imbalanced solar panel image dataset cases demonstrate substantial improvements across multiple performance metrics, particularly accuracy, recall and F1-score, emphasizing effective minority class detection. The proposed DiSMHA approach was evaluated using several classification models, including Support Vector Machine (SVM), Multilayer Perceptron (MLP), XGBoost, Vision Transformer (ViT-Head), and TabNet. These models achieved notable accuracies ranging from 94% to 98%, significantly outperforming baseline methods. Particularly, the MLP (with accuracy 96.67%) and SVM (with accuracy 94.67%) models exhibited substantial performance gains, confirming DiSMHA’s capacity to enhance traditionally imbalance-sensitive classifiers. Comparative analyses further underscored the superiority of DiSMHA, highlighting its consistent accuracy, enhanced recall, and improved F1-scores over individual augmentation approaches. Moreover, statistical validation using the Wilcoxon signed rank test confirmed the significant superiority of our approach for minority class detection in highly imbalanced scenarios. Thus, DiSMHA represents a scalable, robust solution applicable across diverse domains facing imbalanced classification challenges, including industrial inspection, medical imaging, and rare event detection.}, archive = {J_ACCESS}, author = {Raj Kumar and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3600622}, journal = {IEEE Access}, pages = {154623-154646}, shortjournal = {IEEE Access}, title = {Hybrid framework combining diffusion-based image augmentation and feature level SMOTE for addressing extreme class imbalance}, volume = {13}, year = {2025}, }
• (2025). Topology and robust power flow control strategy for grid-forming energy storage accessing low-voltage distribution station. ACCESS, 13, 154610-154622. (www)

  @article{ , abstract = {This study presents a novel high-power density flexible interconnection topology and a robust power flow control strategy for the grid-forming-control (GFC)-based energy storage accessing low-voltage interconnection distribution station. The proposed topology consists of the three-phase parallel converter, three single-phase series converters, and a multiple-active-bridge (MAB) converter. All the dc ports of converters, energy storage, and PV panels are coupled through the MAB converter, which avoids the use of series transformers and helps improve the power density. By modeling and analyzing the power flow in the interconnection station, the proposed series converter and line impedance system is reconstructed first. Through adding a state variable with integral action to the reconstructed system, a robust control law could be designed for the new augmented to suppress the unknown disturbance. Simulation results verify the effectiveness and correctness of proposed robust power flow control in the GFC-based energy storage accessing low-voltage interconnection distribution station.}, archive = {J_ACCESS}, author = {Shuang Zheng and Runbing Yang and Wenshuang Yu and Anping Hu}, doi = {10.1109/ACCESS.2025.3605233}, journal = {IEEE Access}, pages = {154610-154622}, shortjournal = {IEEE Access}, title = {Topology and robust power flow control strategy for grid-forming energy storage accessing low-voltage distribution station}, volume = {13}, year = {2025}, }
• (2025). Slow-slope reset scheme for highly-sensitive CMOS integrate-and-dump receiver OEIC. ACCESS, 13, 154599-154609. (www)

  @article{ , abstract = {This paper presents a slow-slope reset scheme that reduces charge injection for highly-sensitive integrate-and-dump direct detection receivers. The monolithic receiver OEIC utilizes a source-follower front-end and low-capacitance PIN photodiode, to achieve high sensitivity at higher data rates (250Mbit/s) than previous ultra-sensitive PIN receivers. Both, the slow-slope and classical rectangular reset scheme, are fabricated with the same front-end, on the same wafer, in 180nm high-voltage CMOS. The measured transient voltages are in agreement with theory and suggest effective mitigation of charge injection by the slow-slope reset. Using correlated double sampling (CDS), our improved receiver achieves a sensitivity of −47.0 dBm at 250 Mbit/s with 50% return-to-zero (RZ) on-off keying (OOK) modulation and −53.5 dBm at 100 Mbit/s with 80% RZ OOK modulation, both for the reference bit error probability (BER) of 0.002 and wavelength 642 nm. The difference to the shot noise quantum limit at 250 Mbit/s (100 Mbit/s) is 19.7dB (17.2 dB). In addition, we show that low charge injection enables single sampling, with sensitivities around 1dB worse than CDS.}, archive = {J_ACCESS}, author = {Simon Michael Laube and Christoph Gasser and Kerstin Schneider-Hornstein and Horst Zimmermann}, doi = {10.1109/ACCESS.2025.3602093}, journal = {IEEE Access}, pages = {154599-154609}, shortjournal = {IEEE Access}, title = {Slow-slope reset scheme for highly-sensitive CMOS integrate-and-dump receiver OEIC}, volume = {13}, year = {2025}, }
• (2025). Deployment and performance evaluation of 5G private networks, enabling use cases in rural remote areas. ACCESS, 13, 154581-154598. (www)

  @article{ , abstract = {Rural remote areas have unique challenges for providing reliable and high-speed cellular connectivity, which makes them unattractive for mobile network operators. The Horizon Europe COMMECT project has addressed connectivity in rural remote areas by 5G private network deployments, which so far have mainly been studied and deployed in industrial environment. The evaluation of the 5G private network deployments is conducted through field trials in four different environment, each of them characterized by different challenges and aiming to support different agro-forestry use cases. In particular, the evaluations relate to 1) indoor environments (e.g. hospitals and educational centers), 2) forestry, 3) livestock transportation, and 4) mines. The obtained results showcase that 5G private networks can support the proposed use cases. For example, in indoor environments, a downlink and uplink throughput of 700 Mbps and 50 Mbps, respectively, were measured, which are sufficient to enable e.g. remote monitoring of hospital patients. Moreover, it was measured that at least 10 Mbps in the uplink can be achieved for up to 300 m deep in the forest. The 10 Mbps target is also met in the farm location, including a satellite backhaul, which can enable e.g. the use case of (un)loading livestock from/to the truck. Additionally, throughput of more than 100 Mbps was measured at the mine location, which is sufficient to enhance safety by e.g. operating autonomous vehicles.}, archive = {J_ACCESS}, author = {Maria Raftopoulou and Muhammad Faheem Awan and Alejandro Ramírez-Arroyo and Mehmet Izzet Sağlam and Ljupco Jorguseski and Floris Drijver and Paweł Maćkowiak and Maria Rita Palattella}, doi = {10.1109/ACCESS.2025.3603591}, journal = {IEEE Access}, pages = {154581-154598}, shortjournal = {IEEE Access}, title = {Deployment and performance evaluation of 5G private networks, enabling use cases in rural remote areas}, volume = {13}, year = {2025}, }
• (2025). Aircraft-cargo separation rate-driven ground Risk–Cost optimization for urban logistics UAVs. ACCESS, 13, 154568-154580. (www)

  @article{ , abstract = {The surge in demand for urban logistics has positioned unmanned aerial vehicles (UAVs) as a vital solution for improving last mile delivery efficiency in complex urban environments. To overcome the ground safety bottleneck in the large-scale application of logistics UAVs, this paper establishes a full-chain framework of “risk characteristics–quantitative model–risk cost” that proposes an innovative dual-state risk–cost optimization model based on aircraft-cargo separation probability. The study quantifies the separation probability using the Weibull distribution, corrects the risk value assessment deviation under the traditional single fall assumption, and establishes a risk probability model for the separation state by combining the dynamic characteristic differences between the UAV body and cargo, enabling the generation of rasterized risk maps by integrating spatial parameters, including population density and shelter coefficients, to implement risk–cost conversion and support airspace management. Case verification reveals that in areas with different land uses, there are significant differences in the ground risk values and risk costs of logistics UAVs. Additionally, introducing the aircraft-cargo separation rate improves risk assessment accuracy by 57%, and the ground risk in the aircraft-cargo integrated state is 4.31 times higher than in the separated state. This study provides core theoretical support for the safe operation and route decision-making involving logistics UAVs in complex urban scenarios.}, archive = {J_ACCESS}, author = {Tao Yang and Baoli Zheng and Mingli Mo and Jie Shang and Dan Huang}, doi = {10.1109/ACCESS.2025.3603185}, journal = {IEEE Access}, pages = {154568-154580}, shortjournal = {IEEE Access}, title = {Aircraft-cargo separation rate-driven ground Risk–Cost optimization for urban logistics UAVs}, volume = {13}, year = {2025}, }
• (2025). Resilience-oriented planning for distribution systems combining distributed generation allocation and dynamic operational strategies. ACCESS, 13, 154556-154567. (www)

  @article{ , abstract = {A specialized strategy to enhance the resilience of distribution systems against high-impact and low-probability events is developed by incorporating investment decisions and emergency operational actions. Investment decisions involve the optimal distributed generation (DG) allocation, while operational actions include dynamic restoration plans that consider microgrid formation, network reconfiguration, and the pre-positioning and displacement of mobile energy storage systems. The resilience problem is formulated as a mixed-integer second-order cone programming (MISOCP) model, where the investment cost of installing new DG units in the system and the expected value of energy shedding in the system are simultaneously minimized. The MISOCP model is extended to a two-stage stochastic scenario-based approach to address the uncertainties associated with solar irradiance, demand, and fault events. In addition, this formulation captures load changes in response to voltage fluctuations through a voltage-dependent ZIP model, which accurately represents the composite load characteristics as a combination of constant impedance (Z), constant current (I), and constant power (P) components. The effectiveness of the proposed model is demonstrated through several experiments using the 33-node and the 69-node distribution systems under different study cases. Numerical results show the benefits of simultaneously addressing the planning and restoration problems, especially when considering emergency scenarios.}, archive = {J_ACCESS}, author = {Juan M. Home-Ortiz and Lucas C. Yamaguti and Jairo Yumbla and Ozy D. Melgar-Dominguez and Renato Machado Monaro and José R. S. Mantovani}, doi = {10.1109/ACCESS.2025.3603962}, journal = {IEEE Access}, pages = {154556-154567}, shortjournal = {IEEE Access}, title = {Resilience-oriented planning for distribution systems combining distributed generation allocation and dynamic operational strategies}, volume = {13}, year = {2025}, }
• (2025). Fault protection method for low-current grounding systems in active distribution networks based on an improved clustering algorithm. ACCESS, 13, 154545-154555. (www)

  @article{ , abstract = {With the increasing global focus on carbon neutrality and the development of renewable energy, a large number of distributed generation units have been integrated into the distribution network to enhance sustainability and reduce carbon emissions. As a result, fault signals often exhibit high-frequency, nonlinear, and weak characteristics, rendering traditional low-current grounding fault line selection methods ineffective. In this paper, by analyzing the differences between the transient zero-sequence currents of low-current grounding faults in active distribution networks, a fault protection method for low-current grounding systems based on an improved clustering algorithm with optimized initial cluster center selection is proposed. The proposed method involves filtering the transient zero-sequence currents, characterizing their phase differences using the cosine distance between each pair of zero-sequence currents, and performing clustering analysis with an improved k-means algorithm. By defining a range for the number of clusters k and determining the optimal value based on the within-cluster sum of squares (WCSS), the fault line can be accurately identified. Simulation results demonstrate that the method is applicable to both conventional low-current systems and low-current systems in active distribution networks, offering good practicality and anti-interference performance.}, archive = {J_ACCESS}, author = {Chen Xue and Jian Zhu and Shengmin Tan and Guojing Zhang and Chen Liu}, doi = {10.1109/ACCESS.2025.3605355}, journal = {IEEE Access}, pages = {154545-154555}, shortjournal = {IEEE Access}, title = {Fault protection method for low-current grounding systems in active distribution networks based on an improved clustering algorithm}, volume = {13}, year = {2025}, }
• (2025). A multi-criteria framework for selecting machine learning techniques for industrial fault prognosis. ACCESS, 13, 154508-154544. (www)

  @article{ , abstract = {Selecting appropriate Machine Learning (ML) techniques for fault prognosis remains a critical yet often understructured step in developing predictive maintenance strategies for industrial systems. While numerous studies apply ML to estimate Remaining Useful Life (RUL) or forecast failure probabilities, model selection is frequently guided by ad hoc practices or narrow performance metrics, overlooking contextual factors such as data availability, interpretability, automation level, and deployment feasibility. This paper presents a parameterized, multi-criteria decision-making framework to support ML techniques selection in fault prognosis, particularly within energy-related applications. Derived from a structured literature survey, the framework introduces twelve selection parameters, divided into primary requirements and secondary evaluation criteria. These parameters, such as label requirements, model complexity, and transferability, allow users to eliminate unsuitable techniques and rank viable candidates according to application-specific constraints. The framework is applied to a real-world use case involving failure prediction in electrical substations, illustrating how it supports transparent, replicable, and operationally grounded model selection. Results demonstrate the framework’s adaptability to different industrial contexts and its relevance for decision-making in energy systems. By bridging empirical insights with implementation demands, the proposed approach offers a practical tool for aligning ML technique selection with the goals of energy-sector prognostics and maintenance planning.}, archive = {J_ACCESS}, author = {Miguel A. De C. Michalski and Carlos A. Murad and Fabio N. Kashiwagi and Gilberto F. M. De Souza and Halley J. B. Da Silva and Hyghor M. Côrtes}, doi = {10.1109/ACCESS.2025.3604754}, journal = {IEEE Access}, pages = {154508-154544}, shortjournal = {IEEE Access}, title = {A multi-criteria framework for selecting machine learning techniques for industrial fault prognosis}, volume = {13}, year = {2025}, }
• (2025). Quantifying and mitigating cascading impacts in HVdc-interconnected power grids. ACCESS, 13, 154491-154507. (www)

  @article{ , abstract = {With the increasing penetration of HVdc technologies in today’s power systems, especially for interconnecting neighboring grids—including those that are renewable-rich—there is a need to explore their contribution to system reliability and resilience in the context of cascading failures. This paper introduces a holistic framework for quantifying and mitigating cascading risks in HVdc-interconnected systems. It considers extreme events in addition to credible or expected events that threaten the resilience of interconnected systems, as evidenced by recent cascading blackouts with cross-border propagation impacts in Europe and worldwide. To achieve this, advanced dynamic cascading failure modelling is leveraged to simulate and quantify the cascading effects in HVdc-interconnected systems, particularly focusing on frequency stability and large-scale disturbances. This sheds light on the influence of HVdc on mitigating the propagation of non-local cascading events with cross-border impacts in interconnected systems, attributed to its “firewall” property. It also seamlessly integrates the dynamic cascading simulator with operational strategies, specifically controlled islanding, to further mitigate both local and non-local cascading impacts, especially addressing cross-border propagation, in interconnected systems. The simulation results on HVdc-interconnected test systems demonstrate the efficiency of the proposed work in significantly reducing cascade metrics, including Expected Demand-Not-Served (EDNS) and, notably, Conditional Value-at-Risk (CVaR) which captures tail risk events.}, archive = {J_ACCESS}, author = {Sina Hashemi and Markos Asprou and Lenos Hadjidemetriou and Mathaios Panteli}, doi = {10.1109/ACCESS.2025.3603695}, journal = {IEEE Access}, pages = {154491-154507}, shortjournal = {IEEE Access}, title = {Quantifying and mitigating cascading impacts in HVdc-interconnected power grids}, volume = {13}, year = {2025}, }
• (2025). Make large language models efficient: A review. ACCESS, 13, 154466-154490. (www)

  @article{ , abstract = {Large Language Models (LLMs) have achieved remarkable success across a variety of natural language processing tasks, with larger architectures often exhibiting superior performance. This scaling behavior has fueled intense competition in generative AI, supported by projected investments that exceed ${\$} $ 1 trillion to develop increasingly sophisticated LLMs. This competition has in turn nurtured a vibrant ecosystem, inspiring new open-source models such as DeepSeek, and motivating application developers to harness state-of-the-art LLMs for real-world deployments. However, the extensive memory and computational requirements of large models present serious obstacles for small-medium organizations, leading to significant scalability concerns. This paper offers a comprehensive review of recent techniques to improve LLM efficiency through four categories: parameter-centric, architecture-centric, training-centric and data-centric. For a better understanding of the newcomer’s perspective, it covers the entire lifecycle when developing and deploying LLMs. Thus, this paper is organized around five core tasks: model compression for local deployment, accelerated pre-training to reduce time-to-train, efficient fine-tuning on custom data, optimized inference under resource constraints, and streamlined data preparation. Rather than focusing on broad strategies, we emphasize specialized techniques tailored to each stage of development. By applying targeted optimizations at each phase, the computational overhead can be reduced by 50–95% without compromising the quality of the model, making LLMs more accessible to researchers and practitioners with limited computational resources.}, archive = {J_ACCESS}, author = {Aman Mussa and Zhanseit Tuimebayev and Madina Mansurova}, doi = {10.1109/ACCESS.2025.3605110}, journal = {IEEE Access}, pages = {154466-154490}, shortjournal = {IEEE Access}, title = {Make large language models efficient: A review}, volume = {13}, year = {2025}, }
• (2025). A fuzzy logic-based adaptive backoff algorithm for OFDMA-based next-gen WLANs. ACCESS, 13, 154450-154465. (www)

  @article{ , abstract = {The Medium Access Control (MAC) layer of Wireless Local Area Networks (WLANs) undergoes several amendments since the birth of the legacy standard IEEE 802.11. The legacy standard IEEE 802.11 uses Distributed Coordination Function (DCF) for the MAC layer protocol. The DCF algorithm performs better in low dense WLAN networks where only one station (STA) can send data at one time. The selection of Contention Window (CW) plays a vital role in getting optimum WLANs performance. The legacy IEEE 802.11 and most of its amendments use Binary Exponential Backoff (BEB) algorithm to resolve collision in WLANs. In a conventional BEB algorithm, the size of CW is adjusted forward and backward i.e., the CW is initialized at minimum CW (CWmin) and for each collision the CW is exponentially incriminated to an optimal CW size where the packets can be transferred successfully. Hence the BEB mechanism is not suitable for dense environment as after every successful transmission the CW size is resumed to CWmin value where collision is obvious due to many contending stations. Finding the optimum CW is the key factor to maximize throughput and minimize delay. Moreover, Orthogonal Frequency Division Multiple Access (OFDMA) technology is used in next-generation IEEE 802.11ax WLANs to accommodate more active stations in a dense environment. To solve these issues, we proposed adaptive algorithm. In the proposed algorithm, First, we calculate the number of estimated active stations using a probabilistic approach. Second, we use Markov chain model to find the throughput ratio of the previous Contention Window (CW). We will consider the aforementioned parameters as input to the Fuzzy system, which adjust the optimum CW size to enhance throughput and minimize delay. The proposed approach enhances the throughput performances of IEEE 802.11ax and IEEE 802.11 medium access protocols up to 10% and 40% respectively.}, archive = {J_ACCESS}, author = {Sheraz Babar and Pervez Khan and Nasir Rashid and Haleem Farman and Sohaib Bin Altaf Khattak and Moustafa M. Nasralla}, doi = {10.1109/ACCESS.2025.3603858}, journal = {IEEE Access}, pages = {154450-154465}, shortjournal = {IEEE Access}, title = {A fuzzy logic-based adaptive backoff algorithm for OFDMA-based next-gen WLANs}, volume = {13}, year = {2025}, }
• (2025). Enhanced predictive modeling for anomaly detection in financial transactions using machine learning. ACCESS, 13, 154438-154449. (www)

  @article{ , abstract = {Credit card fraud detection presents a significant challenge due to the extreme class imbalance in transaction datasets. Traditional machine learning models struggle to achieve high recall while maintaining precision, limiting their effectiveness in real-world applications. To address this issue, a systematic approach integrating IQR-based outlier removal, PowerTransformer normalization, Principal Component Analysis (PCA)-based feature selection, and advanced resampling techniques is proposed. Performance comparisons across multiple resampling methods identify Adaptive Synthetic Sampling (ADASYN) as the optimal choice, balancing precision and recall effectively. Ensemble models, including LightGBM, XGBoost, and CatBoost, are trained and optimized, achieving substantial performance gains over baseline models (ROC-AUC: $0.9132~\to ~0.9916$ ). Further improvements are observed with Voting and Stacking ensemble strategies, leading to ROC-AUC 0.9947. Additionally, Stochastic Weight Averaging (SWA) is applied, enhancing the final performance to ROC-AUC 0.9970. The proposed framework demonstrates superior fraud detection capabilities by effectively handling data imbalance, feature selection, and model generalization, ensuring high detection accuracy while maintaining computational efficiency. These results contribute to the advancement of robust fraud prevention mechanisms in financial transactions.}, archive = {J_ACCESS}, author = {Youngjin Han and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3602236}, journal = {IEEE Access}, pages = {154438-154449}, shortjournal = {IEEE Access}, title = {Enhanced predictive modeling for anomaly detection in financial transactions using machine learning}, volume = {13}, year = {2025}, }
• (2025). Customer experience in E-commerce: A systematic review of metrics, models, and the role of AI. ACCESS, 13, 154422-154437. (www)

  @article{ , abstract = {As digital commerce platforms grow increasingly intelligent and engaging, delivering a superior customer experience (CX) has become a strategic compulsion. Despite expanding interdisciplinary interest, clarity remains limited regarding how CX is conceptualized, measured, and transformed by emerging technologies. This systematic literature review synthesizes findings from 82 peer-reviewed studies published between 1980 and 2025 using the PRISMA 2020 framework to explore four key questions: how CX is defined in e-commerce, which dimensions influence it, what methods are used to evaluate it, and how technologies such as Artificial Intelligence (AI), agentic AI, and automation are used. The review reveals a shift from traditional, transactional, and usability-based interpretations towards dynamic, emotional, and co-created experiences. Trust, system-driven personalization, user autonomy, and customer engagement emerge as the four central influencing factors due to technological advances in AI and Agentic AI. Methodologically, CX research has evolved from survey-based models to structural equation modeling, sentiment analysis, and AI-integrated frameworks. Notably, intelligent systems are increasingly acting as co-creators of customer journeys, shaping real-time personalization and emotional resonance. This review offers a consolidated framework for understanding CX in the modern e-commerce context, provides actionable insights for platform designers, and proposes a future research agenda centered on factors and methods describing AI, agency, and emotional intelligence.}, archive = {J_ACCESS}, author = {Srinivas Kumar Mittameedi and Varun Dogra and Anu Sayal}, doi = {10.1109/ACCESS.2025.3600729}, journal = {IEEE Access}, pages = {154422-154437}, shortjournal = {IEEE Access}, title = {Customer experience in E-commerce: A systematic review of metrics, models, and the role of AI}, volume = {13}, year = {2025}, }
• (2025). Signal-to-noise ratio in scanning electron microscopy: A comprehensive review. ACCESS, 13, 154395-154421. (www)

  @article{ , abstract = {Scanning Electron Microscopy (SEM) is critical in nanotechnology, materials science, and biological imaging due to its high spatial resolution and depth of focus. Signal-to-noise ratio (SNR) is an essential parameter in SEM because it directly impacts the quality and interpretability of the images. SEM is widely used in various scientific disciplines, but its utility can be compromised by noise, which degrades image clarity. This review explores multiple aspects of the SEM imaging process, from the principal operation of SEM, sources of noise in SEM, methods for SNR measurement and estimations, to various aspects that affect the SNR measurement and approaches to enhance SNR, both from a hardware and software standpoint. We review traditional and emerging techniques, focusing on their applications, advantages, and limitations. The paper aims to provide a comprehensive understanding of SNR optimization in SEM for researchers and practitioners and to encourage further research in the field.}, archive = {J_ACCESS}, author = {Kok Swee Sim and Iksan Bukhori and Dominic Chee Yong Ong and Kok Beng Gan}, doi = {10.1109/ACCESS.2025.3603013}, journal = {IEEE Access}, pages = {154395-154421}, shortjournal = {IEEE Access}, title = {Signal-to-noise ratio in scanning electron microscopy: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Design and computational modeling of an AI-based automated cybersecurity incident response system. ACCESS, 13, 154383-154394. (www)

  @article{ , abstract = {Modern cybersecurity operations face unsustainable alert volumes, averaging 22000 weekly alerts with 68% false positives, overwhelming defenses and delaying incident response due to limitations in conventional SOAR platforms. To address this, an AI-driven Automated Incident Response (AIR) system is proposed, integrating STIX/TAXII multimodal fusion for unified data ingestion, attention-LSTM networks for adaptive threat recognition across temporal sequences, Bayesian game-theoretic decision layers for strategic response planning, and DRL validation for real-time optimization. This architecture reduces false negatives by 42% in C2 tunneling detection and achieves Nash equilibrium in 97.3% of adversarial engagements. Rigorous testing on hybrid infrastructure datasets (100 K normal events, 20K DDoS, 5K C2 attacks) demonstrates a 93% mean F1-score across attack scenarios, end-to-end latency of 58.3 ms, and $12.5\times $ higher strategy updates/sec versus baselines. Compared to existing models, the system improves detection F1 by 10.7%, reduces false positives by 39%, and enhances energy efficiency to 1850 events/Joule ( $2.98\times $ Snort). The framework establishes a new paradigm for agile, auditable incident response validated by STIX action chains.}, archive = {J_ACCESS}, author = {Jiehao Zhang and Simin Li and Weiwei Huang and Haoxin Jing and Qin Zhang and Xing Xia}, doi = {10.1109/ACCESS.2025.3603975}, journal = {IEEE Access}, pages = {154383-154394}, shortjournal = {IEEE Access}, title = {Design and computational modeling of an AI-based automated cybersecurity incident response system}, volume = {13}, year = {2025}, }
• (2025). A new multi objective video summarization approach for video surveillance analytics applications on smart cities. ACCESS, 13, 154353-154382. (www)

  @article{ , abstract = {Summarizing surveillance videos used in smart city applications is very important in terms of transaction costs and sustainability. Although there is a considerable amount of literature on video summarization, the methods in the literature for summarizing surveillance videos used in smart cities are few and inadequate. Because, both object and event features of the mentioned video data must be preserved. In this study, we integrated an object-centric and an event-centric summarization method with Apache Kafka for effective summarization of such videos. With the object-centric summarization module of our proposed method, we focused on preserving the statistical and motion features of the objects in the videos. With the event-centric summarization module, we ensured the preservation of abnormal events in the videos. We presented the performance results of both modules and the integrated system in detail with different metrics. Finally, we compared the performances of both modules with the video summarization approaches in the literature based on different metrics. The developed object-centric summarization method preserves the statistical features of the video with a success rate of over 90% and shortens the videos with a rate of over 95%. The developed event-centric summarization approach provides summaries that include abnormal situations found in videos with a success rate of over 95%. The presented comparative results prove that this original method we developed is superior to the studies in the literature in terms of performance and many different evaluations.}, archive = {J_ACCESS}, author = {Turan Goktug Altundogan and Mehmet Karaköse and Fatih Mert}, doi = {10.1109/ACCESS.2025.3605259}, journal = {IEEE Access}, pages = {154353-154382}, shortjournal = {IEEE Access}, title = {A new multi objective video summarization approach for video surveillance analytics applications on smart cities}, volume = {13}, year = {2025}, }
• (2025). Image recognition with image enhancement and swin transformer improved YOLOXs algorithm. ACCESS, 13, 154341-154352. (www)

  @article{ , abstract = {Aiming at the problem of insufficient performance of existing object detection models under small-object recognition, complex background interference, and lighting changes, this study proposes an improved YOLOXs model that integrates multi-stage image enhancement, coordinated attention mechanism, and hierarchical vision Transformer. This study enhances the diversity of training data through a multi-stage data enhancement strategy, introduces coordinate attention mechanism to strengthen the key feature expression. Moreover, the global modeling capability of Swin Transformer is utilized to enhance the long-distance target association. According to experimental results, the enhanced model outperformed the original YOLOXs by 5.6%, achieving a mAP@0.5 of 46.8% on the COCO 2017 test set. In the small-object detection task, mAP@0.5 for small-objects improved to 36.7%, an increase of 8.2%. On the self-built Small-Object Dataset, the detection accuracy of the improved model reached 39.4%, an increase of 9.3% compared to the baseline model. In addition, in real-world scenarios such as traffic surveillance, unreturned aerial vehicle aerial photography, medical imaging, and industrial inspection, the average IoU of the improved model increased by up to 7.2%. The miss rate and false alarm rate were reduced by 4.4% and 2.5%, respectively. Overall, the proposed method can significantly enhance the robustness and accuracy of the object detection model in complex scenes. It also provides an effective solution for recognizing small-objects and performing vision tasks in low-contrast environments.}, archive = {J_ACCESS}, author = {Shuangli An and Jiawang Li}, doi = {10.1109/ACCESS.2025.3602053}, journal = {IEEE Access}, pages = {154341-154352}, shortjournal = {IEEE Access}, title = {Image recognition with image enhancement and swin transformer improved YOLOXs algorithm}, volume = {13}, year = {2025}, }
• (2025). A two-switch step-up topology with coupled inductor for high voltage conversion. ACCESS, 13, 154326-154340. (www)

  @article{ , abstract = {This paper introduces a new non–isolated boost–type dc–dc converter that is applicable to PV/fuel cell sources and other low–voltage dc systems requiring high voltage gain and a compact structure. The proposed architecture incorporates two synchronized switches, a coupled magnetic element, a passive energy recovery unit, and a multi–stage voltage elevating network to achieve a very high output voltage while minimizing component stress and maintaining a simple structure. A smooth input current profile with minimal ripple is ensured by the input inductor, and conduction losses are lowered through current distribution across the switching devices. The passive clamp design enables the recovery of leakage energy and limits overvoltage across power devices, permitting the employment of low–voltage–rated MOSFETs to enhance the overall efficiency. A staged voltage boosting technique further increases the output without significantly increasing circuit complexity. Compared to existing topologies, the proposed converter demonstrates advantages in voltage boosting capability, stress reduction on switches, and reduction in the overall part count. Theoretical analysis, design procedures, and performance comparisons are thoroughly discussed to validate the proposed design. A 200 W prototype operating from a 24 V input to a 400 V output was simulated and built. Simulation and test results match theoretical predictions, with peak efficiencies of 95.2% and 94.1% at full load, confirming the converter’s suitability for compact, high–efficiency power systems including PV microinverters, battery storage, EV auxiliary modules, marine power, medical devices, and high–voltage loads such as plasma generators and AMOLED displays.}, archive = {J_ACCESS}, author = {Kim-Anh Nguyen and Thai Anh Au Tran and Xuan Khanh Ho and Duong Thach Pham}, doi = {10.1109/ACCESS.2025.3605292}, journal = {IEEE Access}, pages = {154326-154340}, shortjournal = {IEEE Access}, title = {A two-switch step-up topology with coupled inductor for high voltage conversion}, volume = {13}, year = {2025}, }
• (2025). “Think before you Click”—Malicious URL detection in cybersecurity: A systematic review and research roadmap. ACCESS, 13, 154305-154325. (www)

  @article{ , abstract = {Introduction: Thanks to the world wide web (www) revolutions and COVID-19 pandemic, the dependency on digital platforms has tremendously increased and making them essential for day-to-day information access. Although, technological advancements boosted digital platform usage, but also increased user’s vulnerability to cyberattacks. Background: For instance, In India, the reported cyber crime complaints increased by 15.3% in Quarter-2 of 2022, totaling 237,658 complaints. Further, Phishing attacks, are responsible for over 90% of data breaches, which are the most common and severe cyber threats. Despite the diversity of cyber-threats, URLs serve as the primary gateway in most attacks, making their detection crucial for protecting users. Methodology: This article provides a comprehensive literature review on malicious URL detection techniques, including Listing, Heuristics, Machine Learning, Feature Engineering and Emerging Methods, by highlighting limitations, feature types, and datasets and thereby presents the broader perspective compared to contemporary single-methodology-based review studies. Further, this article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model for a thorough, transparent review process. Results: Based on the research study, this article highlights unexplored research challenges associated with each category of detection techniques, feature types, which play a crucial role in effective malicious URL detection. It also emphasizes on Theoretical, Managerial implications of this study, Real-world deployment constraints, Research Roadmap and thereby encourages the future researchers to address these challenges and develop innovative solutions.}, archive = {J_ACCESS}, author = {Saurabh Kailas and R. Roopalakshmi}, doi = {10.1109/ACCESS.2025.3601387}, journal = {IEEE Access}, pages = {154305-154325}, shortjournal = {IEEE Access}, title = {“Think before you Click”—Malicious URL detection in cybersecurity: A systematic review and research roadmap}, volume = {13}, year = {2025}, }
• (2025). Responsible gaussian model: Matrix-based approximation of gaussian mixture model. ACCESS, 13, 154295-154304. (www)

  @article{ , abstract = {Mechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles. In contrast, statistics shares similar theoretical foundations and application areas with machine learning but offers more interpretable models and mathematically rigorous frameworks. However, statistical methods face limitations when dealing with analytically intractable problems. The Gaussian mixture model (GMM) is a distribution obtained by adding normal distributions. In addition, it is a flexible probability distribution. It is highly practical but difficult to solve analytically; however, iterative methods, such as EM algorithm, can be applied to solve it. In this study, we propose the model with a new model, we propose The Responsible Gaussian Model a novel alternative to the Gaussian Mixture Model to avoid iterative solutions and obtain analytical approximation. To evaluate its performance, we applied the model to clustering on Iris plants dataset, Wine recognition dataset, Optical recognition of Handwritten digits dataset, and CIFAR-10.}, archive = {J_ACCESS}, author = {Wataru Obayashi and Takeru Aoki and Tomoaki Tatsukawa}, doi = {10.1109/ACCESS.2025.3604006}, journal = {IEEE Access}, pages = {154295-154304}, shortjournal = {IEEE Access}, title = {Responsible gaussian model: Matrix-based approximation of gaussian mixture model}, volume = {13}, year = {2025}, }
• (2025). Audio-based neural network to classify patients with early parkinson’s disease. ACCESS, 13, 154283-154294. (www)

  @article{ , abstract = {Parkinson’s disease is a progressive neurodegenerative disorder, and identifying patients at the premotor stage enables early intervention and improved treatment outcomes. Dysarthria affects over 90% of Parkinson’s patients, making speech a valuable biomarker. In this study, we propose an end-to-end deep learning model to detect early-stage Parkinson’s disease based on speech signals. The model was trained and evaluated using recordings from 131 early-stage PD patients and 42 healthy controls, including sustained vowels (e.g., /A/, /O/) and repetitive syllables (e.g., /pa/, /ta/). Experimental results demonstrate that our model outperforms various deep learning and ensemble learning classifiers in terms of detection accuracy and F1-score, Among indicators, ACC reached 0.78, and F1-score reached 0.831. Furthermore, we explore the temporal dynamics of speech sequences to reveal their correlation with disease progression.}, archive = {J_ACCESS}, author = {Weikang Hou and Changqin Quan and Zhonglue Chen and Sheng Cao and Kang Ren and Wen Su and Zhiwei Luo}, doi = {10.1109/ACCESS.2025.3604877}, journal = {IEEE Access}, pages = {154283-154294}, shortjournal = {IEEE Access}, title = {Audio-based neural network to classify patients with early parkinson’s disease}, volume = {13}, year = {2025}, }
• (2025). Dynamic programming alignments with skips. ACCESS, 13, 154267-154282. (www)

  @article{ , abstract = {The outcome of a Multiple Sequence Alignment (MSA) can be compactly represented by means of Elastic-Degenerate Strings (ED-strings) by collapsing conserved fragments into standard linear strings, while representing gaps and variants as sets of alternative strings. These alternative variants can differ in size and can possibly include the empty string. In 2022, Lee et al. introduced Partial Order Alignment (POA) to enable the alignment of a string against a graph-like structure derived from an MSA. However, the POA edit transcript (the sequence of edit operations that describe the alignment) does not reflect the possible elasticity of the MSA (such as different gaps sizes in the aligned string), leaving room for possible misalignment and its propagation in progressive MSA strategies. In this paper, we propose a dynamic programming based method that optimally aligns a string to an ED-string, the latter compactly representing an MSA, overcoming the ambiguity in the POA edit transcript while maintaining its time and space complexity. Moreover, since pangenomes ca also be represented using ED-strings, our algorithm paves the way to a new class of sequence to graph alignment methods capable of taking into account possible gaps in the pangenome representation, thus offering a richer and more flexible model for pangenomic analysis.}, archive = {J_ACCESS}, author = {Njagi Moses Mwaniki and Nadia Pisanti}, doi = {10.1109/ACCESS.2025.3597547}, journal = {IEEE Access}, pages = {154267-154282}, shortjournal = {IEEE Access}, title = {Dynamic programming alignments with skips}, volume = {13}, year = {2025}, }
• (2025). Machine learning-enabled trajectory optimization for AUV relaying in underwater communications. ACCESS, 13, 154251-154266. (www)

  @article{ , abstract = {Underwater communication (UWC) faces significant challenges due to signal attenuation, multipath effects, and complex underwater topography. Deploying an Autonomous Underwater Vehicle (AUV) as a relay node can improve link reliability; however, optimal positioning and trajectory planning remain inadequately explored. Given the dynamic nature of transmission loss with AUV movement, determining an efficient trajectory is crucial for enhancing end-to-end signal quality. While Deep Q-Networks (DQNs) have been applied to this task, their performance degrades in large and complex environments, especially due to difficulty in handling a long-term trajectory optimization. To overcome this, we propose a multi-staged DQN framework that divides the overall path into shorter segments, applying individual DQNs sequentially to identify optimal local paths. These are then concatenated to form a complete trajectory. A reward threshold mechanism guides exploration toward globally optimal solutions. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of average cumulative signal-to-noise ratio (SNR) gain, achieving rapid convergence, strong generalization across scenarios, and minimal performance loss in challenging conditions.}, archive = {J_ACCESS}, author = {Hyeon Woo Jeon and Duk Kyung Kim}, doi = {10.1109/ACCESS.2025.3604805}, journal = {IEEE Access}, pages = {154251-154266}, shortjournal = {IEEE Access}, title = {Machine learning-enabled trajectory optimization for AUV relaying in underwater communications}, volume = {13}, year = {2025}, }
• (2025). Leveraging OSINT for advanced proactive cybersecurity: Strategies and solutions. ACCESS, 13, 154229-154250. (www)

  @article{ , abstract = {The growing complexity of the digital environment has increased the need for proactive and intelligence-based approaches to cybersecurity. This study examines the role of open-source intelligence (OSINT) as a strategic tool in proactive cybersecurity operations. Drawing on a wide range of peer-reviewed literature and professional sources, it reviews definitions, operational processes, areas of application, benefits, and challenges associated with OSINT. The analysis highlights OSINT’s contribution to situational awareness, early threat detection, and cyber threat intelligence (CTI) capabilities. By using publicly accessible data from the internet and social platforms, organizations can strengthen their defensive posture against diverse cyber threats. The study outlines workflows for collecting and analyzing OSINT, with attention to its integration into organizational intelligence frameworks and cybersecurity strategies. Examples from both the public and private sectors demonstrate how OSINT supports decision-making, incident response, and preparedness for emerging threats. The review also considers OSINT’s main advantages, including cost effectiveness, accessibility, and real-time relevance, alongside its main challenges such as data reliability, legal concerns, and information overload. The findings suggest that, despite these limitations, OSINT has significant potential to enhance proactive cybersecurity measures, support compliance with standards, assist law enforcement, prevent terrorism, and contribute to business decision-making. The paper concludes with a call for further research on integration with advanced technologies, real-time data analysis, and effective intelligence collaboration.}, archive = {J_ACCESS}, author = {Zafrir Avrahami and Moti Zwilling and Chen Hajaj}, doi = {10.1109/ACCESS.2025.3603868}, journal = {IEEE Access}, pages = {154229-154250}, shortjournal = {IEEE Access}, title = {Leveraging OSINT for advanced proactive cybersecurity: Strategies and solutions}, volume = {13}, year = {2025}, }
• (2025). An entropy-driven evolutionary game framework for quantifying privacy-service tradeoffs. ACCESS, 13, 154219-154228. (www)

  @article{ , abstract = {With the rapid advancement of networked applications, privacy protection has emerged as a paramount concern for billions of users and service providers worldwide. While users have grown accustomed to submitting personal information through registration forms to access digital services, and service providers depend on such data disclosures to deliver tailored experiences, there remains a significant gap in protective frameworks. Current practices lack systematic mechanisms to empower users with optimal privacy control while disclosing only the minimally necessary information required for service acquisition. This imbalance between data disclosure and privacy preservation underscores a fundamental challenge in contemporary digital ecosystems. To address this issue, this paper proposes an evolutionary game theory approach combined with information entropy to explore how to provide more accurate and personalized services for users while minimizing privacy exposure. Focusing on real-world interactions in social networks, the game is exclusively played between users and service providers, aiming to strike a balance between user privacy and service precision. The theory of entropy is employed to quantify the information disclosed by users, and a privacy calculation formula is derived using a sensitivity coefficient and a modified information entropy formula. Building on this foundation, we present the time-varying dynamic replication equation, the equilibrium solution, and the evolutionary stable strategy (ESS). To validate our approach, we collected 200 real-world questionnaire responses and conducted simulation experiments. The results demonstrate that our strategy can provide users with an optimal solution, effectively balancing privacy preservation with the need for personalized services.}, archive = {J_ACCESS}, author = {Tingyu Zhang and Ruichen Xu and Jian Wang and Xinyu Yang and Zhigang Liang and Xinyu Guo and Yanzhao Yang}, doi = {10.1109/ACCESS.2025.3602140}, journal = {IEEE Access}, pages = {154219-154228}, shortjournal = {IEEE Access}, title = {An entropy-driven evolutionary game framework for quantifying privacy-service tradeoffs}, volume = {13}, year = {2025}, }
• (2025). Air quality prediction via embedded ML/DL and quantized models. ACCESS, 13, 154203-154218. (www)

  @article{ , abstract = {In the people’s everyday life, one of the most significant (and unfortunately well-known) environmental problems with substantial impact and effects is air pollution. To this end, extensive research (e.g., conducted by the World Health Organization, WHO) has uncovered a non-negligible correlation between hazardous environments and airborne Particulate Matters (PMs), emphasizing the importance of monitoring pollution to mitigate its adverse effects. In fact, since PMs are microscopic particles of solid or liquid materials suspended in the atmosphere and have negative effects on both weather and precipitations, it has been highlighted that the diffusion of diseases through the inhalation or absorption of contaminants is a concerning outcome of polluted environments. Hence, focusing on the critical need to monitor air quality in common (livable and work) environments (e.g., airports, public transports, or any closed environment) using affordable, compact IoT devices capable of collecting and processing environmental data, as well as forecasting future air quality trends, in this paper we present an experimental evaluation of several Machine Learning (ML), Deep Learning (DL), and quantized DL models designed to accurately predict air pollution (in terms of PM2.5 concentration). Furthermore, we conduct a comparative analysis of state-of-the-art DL models, unveiling the trade-offs associated with each ML/DL model, and discussing their practical applications and potential deployment on tiny IoT devices for on-site computing. Our results show that Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is the best performing model. According to the obtained results, using 8-bit quantization on CNN-BiGRU allows to achieve a 66% model size’s reduction with only a 1% drop in average prediction accuracy, making it a balanced and efficient choice for several applications.}, archive = {J_ACCESS}, author = {Armin Mazinani and Daniele Antonucci and Danilo Pietro Pau and Luca Davoli and Gianluigi Ferrari}, doi = {10.1109/ACCESS.2025.3603920}, journal = {IEEE Access}, pages = {154203-154218}, shortjournal = {IEEE Access}, title = {Air quality prediction via embedded ML/DL and quantized models}, volume = {13}, year = {2025}, }
• (2025). Brain tumor classification in MRI: Insights from LIME and grad-CAM explainable AI techniques. ACCESS, 13, 154172-154202. (www)

  @article{ , abstract = {Artificial intelligence (AI) has become increasingly vital in brain tumour diagnosis using magnetic resonance imaging (MRI). This study serves as a reproduction and interpretability case study, evaluating the behaviour of established convolutional neural network (CNN) architectures: VGG16, ResNet50, and EfficientNet through the implementation of Explainable AI (XAI) to interpret their black-box predictions of tumour classes. All models were trained with early stopping, achieving high classification accuracy: VGG16 and ResNet50 exceeded 98%, while EfficientNetB0 achieved 96%; however, misclassification between Glioma and Meningioma remains a challenge, suggesting overlapping features. VGG16 exhibited more stable validation accuracy; ResNet50 demonstrated fluctuations due to its deeper architecture and broader feature activations, while EfficientNet maintained a more gradual learning curve, attributed to its compound scaling. To enhance interpretability, Local Interpretable Model-agnostic Explanations (LIME) and Gradient-Weighted Class Activation Mapping (Grad-CAM) were applied. LIME segments images into superpixels and analyses their contributions through repeated perturbations. LIME revealed that CNNs sometimes relied on irrelevant background features, including brain edges and anatomical distractions, leading to misclassification. Meanwhile, Grad-CAM, which highlights class-discriminative regions through gradient-based heatmaps, demonstrated key architectural differences: ResNet50 activated broader spatial areas, VGG16 focused on more localized tumour features, while EfficientNet often considered wider anatomical contexts that extended to structures such as eye sockets and the nasal cavity. These results underscore how architectural depth and feature aggregation influence classification performance and reveal potential limitations in CNN-based classification. This study contributes a comparative interpretability perspective, demonstrating how architecture-specific behaviours influence feature attribution and reliability in CNN-based brain tumour classification.}, archive = {J_ACCESS}, author = {Han Chel Yoon and Lih Poh Lin}, doi = {10.1109/ACCESS.2025.3603272}, journal = {IEEE Access}, pages = {154172-154202}, shortjournal = {IEEE Access}, title = {Brain tumor classification in MRI: Insights from LIME and grad-CAM explainable AI techniques}, volume = {13}, year = {2025}, }
• (2025). Lightweight load identification based on fusion of electrical signal image features. ACCESS, 13, 154153-154171. (www)

  @article{ , abstract = {Load identification is a fundamental aspect of non-intrusive load monitoring (NILM), which seeks to discern the operational condition of individual electrical devices from aggregated power signals. In recent years, deep learning-based image classification techniques have demonstrated considerable promise in enhancing the precision and efficacy of load identification tasks. This research presents a unique strategy that combines electrical-signal image encoding with lightweight network optimization to improve image-driven recognition performance. Voltage, total current, and reactive current signals are initially normalized to create $U-I$ greyscale maps, while the periodic active power is represented using the Gramian Angular Field (GAF) approach. The features are subsequently integrated into a multimodal RGB image, offering abundant and complementary information for categorization. MobileNetV3 serves as the backbone network because of its equilibrium between performance and efficiency, while the Efficient Channel Attention (ECA) mechanism is integrated into bottleneck layers to improve feature discrimination. The classification head is reconfigured by removing superfluous upsampling layers and implementing pruning techniques, which substantially decrease model parameters and computing demands while maintaining recognition accuracy. Experimental results confirm the method’s advantages in both performance and efficiency.}, archive = {J_ACCESS}, author = {Linghang Ma and Xiaodong Xiang and Youlei Weng}, doi = {10.1109/ACCESS.2025.3603230}, journal = {IEEE Access}, pages = {154153-154171}, shortjournal = {IEEE Access}, title = {Lightweight load identification based on fusion of electrical signal image features}, volume = {13}, year = {2025}, }
• (2025). Smarter root cause analysis: Enhancing BARO with outlier filtering and ranking refinement. ACCESS, 13, 154142-154152. (www)

  @article{ , abstract = {Root Cause Analysis (RCA) is essential for diagnosing failures in modern software systems. BARO, which integrates Multivariate Bayesian Online Change Point Detection with a Robust Scorer, has demonstrated strong performance on RCAEval, a leading RCA benchmark for microservices. This method follows a two-stage process: it first detects failure onset and then ranks potential root causes by analyzing pre-failure and post-failure metric distributions. However, its sensitivity to extreme metric spikes can lead to unstable rankings and misdiagnoses. To address this, we introduce two key enhancements: (1) outlier filtering mechanisms to remove transient anomalies and (2) robust aggregation-based scoring to reduce the influence of fleeting spikes while emphasizing sustained deviations. Empirical evaluations on RCAEval show the enhanced BARO raises Avg@5 accuracy from 0.77 to 0.92 while adding only 0.21 s of extra processing time per failure case.}, archive = {J_ACCESS}, author = {Qiudan Wang and Jianan Tang and Nihar Dandekar and Shubha Pant and Shaja Arul Selvamani}, doi = {10.1109/ACCESS.2025.3602962}, journal = {IEEE Access}, pages = {154142-154152}, shortjournal = {IEEE Access}, title = {Smarter root cause analysis: Enhancing BARO with outlier filtering and ranking refinement}, volume = {13}, year = {2025}, }
• (2025). Accelerated low-rank tensor completion via projected tensor block coordinate descent. ACCESS, 13, 154129-154141. (www)

  @article{ , abstract = {The low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis. Among various low-rank tensor approximation methods, Tucker decomposition with nuclear norm minimization is widely employed due to its ability to clear represent low-rank structure and its compatibility to existing matrix completion algorithms. However, it demands significant computational resources to perform singular value decomposition along each matricized tensor. In this study, we propose a novel Tucker decomposition based low-rank tensor completion algorithm with nuclear norm minimization. To mitigate the computational burden, we introduce a projected tensor block coordinate descent for tensor completion, which computes the factor matrix from the projected tensor obtained in the previous iteration rather than recalculating it from the full-sized tensor at every iteration. Experimental results from practical applications, such as color image inpainting and traffic data analysis, demonstrate that the proposed algorithm produces an excellent balance between the prediction accuracy and execution speed compared to the other reference algorithms.}, archive = {J_ACCESS}, author = {Geunseop Lee}, doi = {10.1109/ACCESS.2025.3601557}, journal = {IEEE Access}, pages = {154129-154141}, shortjournal = {IEEE Access}, title = {Accelerated low-rank tensor completion via projected tensor block coordinate descent}, volume = {13}, year = {2025}, }
• (2025). Enhanced radio frequency system integration in optical networks using adaptive compensation techniques. ACCESS, 13, 154103-154128. (www)

  @article{ , abstract = {The combination of radio frequency (RF) systems with optical communication networks has made adaptive optical compensation methods an ideal means to improve communication. By reducing problems like chromatic dispersion (CD), polarization mode dispersion (PMD), and nonlinear effects, this study measures how efficiently RF systems work in optical networks. Researchers are investigating advanced algorithms such as Neural Networks (NN), Reinforcement Learning (RL), and Digital Signal Processing (DSP) methods, among other adaptable techniques. All of these helps optimize network parameters in real time. These flexible methods significantly improve key performance measures such as Bit Error Rate (BER), Signal-to-Noise Ratio (SNR), Optical Signal-to-Noise Ratio (OSNR), Error Vector Magnitude (EVM), lag, and speed. Because they can keep data quality and dependability high, these methods are necessary for next-generation networks like 5G. The study includes information about possible effects on network efficiency and proposes areas for future research, such as integrating machine learning (ML) methods deeper and developing cost-effective ways for networks to respond.}, archive = {J_ACCESS}, author = {Zhongtian Ma and Shengchun Liu and Fugang Liu and Qingjiang Yang}, doi = {10.1109/ACCESS.2025.3602110}, journal = {IEEE Access}, pages = {154103-154128}, shortjournal = {IEEE Access}, title = {Enhanced radio frequency system integration in optical networks using adaptive compensation techniques}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based drowsiness detection system for driver’s safety. ACCESS, 13, 154080-154102. (www)

  @article{ , abstract = {Driver drowsiness is a leading contributor to road accidents, accounting for over 100,000 crashes and approximately ~1000 fatalities each year in the United States alone, as per National Safety Council (NSC). To mitigate this urgent public-safety risk, we propose a real-time Driver Drowsiness Detection System that achieves both high accuracy and fast inference on standard hardware. Our key idea is to combine two complementary deep-learning strategies: 1) a custom Convolutional Neural Network (CNN) paired with a Support Vector Machine (SVM) classifier, and 2) a lightweight transfer-learning model built upon a pre-trained convolutional backbone. We evaluate these approaches on two datasets: a four-class Kaggle collection of open/closed eyes and yawn/no-yawn images, and the 37-subject MRL eye dataset. For the custom CNN+SVM pipeline, we optimized the split ratios, dropout rates, and L2 regularization to achieve 100% training accuracy and 99.7% validation accuracy. For the transfer-learning model, we leveraged an existing network to accelerate training, achieving 99.4% training accuracy and 99.1% validation accuracy. Finally, we compare both models using metrics including loss curves, confusion matrices, precision, recall, and F1-score. Our system demonstrates that real-time, highly accurate drowsiness detection is achievable without specialized hardware, paving the way for broader deployment in road-safety applications.}, archive = {J_ACCESS}, author = {Sindhu Vidyanathan Dixith and Shrikant Jadhav and Youngsoo Kim and Naveenkumar Jayakumar}, doi = {10.1109/ACCESS.2025.3604728}, journal = {IEEE Access}, pages = {154080-154102}, shortjournal = {IEEE Access}, title = {Deep learning-based drowsiness detection system for driver’s safety}, volume = {13}, year = {2025}, }
• (2025). Guidelines and feasibility of base station deployment for hyperloop communications. ACCESS, 13, 154069-154079. (www)

  @article{ , abstract = {This paper presents an analytical framework for base station (BS) deployment in Hyperloop communication systems. Recognizing the challenges of the systems, the study proposes three guidelines for BS deployment: 1) synchronized pod passage at BS locations, 2) exclusive spectrum access for each pod, and 3) symmetric deployment around the tube mid-point. The framework classifies all possible BS deployments and derives explicit existence conditions based on key system parameters: tube length as well as pod’s acceleration, maximum speed, and departure interval. The investigation reveals that only specific pod acceleration completion scenarios, where pods finish accelerating within the coverage of certain BSs, permit the guidelines to be satisfied, while other cases are shown to be infeasible due to conflicts with the guidelines. This framework enables a Hyperloop communication system designer to determine the location of BSs required for a set of given system parameters. Furthermore, the results demonstrate that BS deployments determined based on 30-second departure interval remain feasible for any departure interval which is an integer multiple of 30 seconds. This ensures scalability and robustness for real-world scheduling.}, archive = {J_ACCESS}, author = {J. Kim and H. Kim}, doi = {10.1109/ACCESS.2025.3603965}, journal = {IEEE Access}, pages = {154069-154079}, shortjournal = {IEEE Access}, title = {Guidelines and feasibility of base station deployment for hyperloop communications}, volume = {13}, year = {2025}, }
• (2025). A predictive stacking framework for energy output in photovoltaic system with uncertainty estimates. ACCESS, 13, 154046-154068. (www)

  @article{ , abstract = {The integration of grid-connected photovoltaic (PV) systems has transformed the global energy landscape by offering sustainable and efficient alternatives to conventional energy sources. However, the inherent variability and intermittency of solar energy pose significant challenges for grid operators, complicating reliable energy forecasting and management. Consequently, it is imperative to develop a predictive model that not only delivers accurate forecasts but also incorporates uncertainty analysis to enhance the reliability and decision-making processes. To address these issues, this study proposes a new stacking ensemble model with uncertainty analysis to accurately forecast the energy output in grid-connected PV systems. The model leverages an Extreme Learning Machine (ELM) and Convolutional Neural Network (CNN) as base learners, with a Bayesian Ridge regression model serving as a meta-learner to refine and aggregate the predictions. The ELM regressor was enhanced through bootstrapping, whereas the CNN employed a Monte Carlo Dropout to estimate the prediction uncertainty. The model’s incorporation of uncertainty analysis allows for the computation of the standard deviation and 99% confidence intervals for the predictions. Feature engineering using Shapley additive explanation (SHAP) analysis was employed to enhance predictive accuracy. The performance of the model was simulated using real-time meteorological data from Dammam, Saudi Arabia, for Amorphous Silicon (a-Si) modules. The results showed R2 values of 0.9999 (train), 0.9997 (test), and 0.9997 (forecast), with corresponding MAE values of 4.29, 7.75, and 6.71 W, respectively, indicating high accuracy for both seen and unseen data. The uncertainty analysis achieved a standard deviation of 7.77 W, PICP of 91%, and PIW of 46.56 W, demonstrating the model’s strong probabilistic reliability, narrow prediction intervals, and effective coverage of the true output values. The proposed model offers a robust and reliable framework for forecasting the energy output of grid-connected PV systems, supporting solar integration, and reducing greenhouse gas emissions.}, archive = {J_ACCESS}, author = {Imad Hassan and Ibrahim Alhamrouni and Nurul Hanis Azhan and Saad Mekhilef and Mehdi Seyedmahmoudian and Saad Ijaz Majid and Alex Stojcevski}, doi = {10.1109/ACCESS.2025.3604038}, journal = {IEEE Access}, pages = {154046-154068}, shortjournal = {IEEE Access}, title = {A predictive stacking framework for energy output in photovoltaic system with uncertainty estimates}, volume = {13}, year = {2025}, }
• (2025). Comparative study on the generalization ability of machine learning and deep learning algorithms for quality assessment of wearable PPG recordings. ACCESS, 13, 154031-154045. (www)

  @article{ , abstract = {One of the major challenges in using photoplethysmography (PPG) sensors for heart rate monitoring in real-world settings is ensuring signal quality. This work evaluates and compares quality assessment methods using generic machine learning (ML) and deep learning (DL) pipelines, on a unique and comprehensive framework that includes different sensors, wavelengths, measurement locations, and recording environments. The PPG signals from one proprietary and five publicly available datasets were labeled in terms of quality by comparing the PPG-derived heart rate to a reference heart rate estimated from simultaneous electrocardiograms. Diverse techniques based on common ML classifiers and one- and two-dimensional convolutional neural networks (CNN) were trained on a dataset and tested on the remaining ones. The results showed that several generated models performed comparably to previous studies when they were tested on datasets with similar measurement positions and sensors to the training database. Specifically, reductions in sensitivity, specificity, and F1-score of less than 3% from training to testing were observed on some methods. Contrarily, they reported a notably poorer performance when tested on datasets presenting conditions different from the training. Even the best-performing model, based on the well-known, pre-trained CNN AlexNet, experienced a performance drop of over 20% in that situation. These findings show that the analyzed ML and DL methods lack the ability to generalize across PPG signals captured from diverse environments, sensors, wavelengths, and measurement locations. This suggests that developing case-specific methods might be the shortest path towards reliable PPG quality assessment.}, archive = {J_ACCESS}, author = {Santiago Mula Muñoz and Roberto Zangróniz and Óscar Ayo-Martín and José Joaquín Rieta and Raúl Alcaraz}, doi = {10.1109/ACCESS.2025.3604652}, journal = {IEEE Access}, pages = {154031-154045}, shortjournal = {IEEE Access}, title = {Comparative study on the generalization ability of machine learning and deep learning algorithms for quality assessment of wearable PPG recordings}, volume = {13}, year = {2025}, }
• (2025). Numerical calculation of whole-body exposure to beams from mmWave base stations using superposition of spherical waves. ACCESS, 13, 154021-154030. (www)

  @article{ , abstract = {Whole-body exposure to beams from fifth-generation (5G) base stations is numerically studied. Since the base stations have the capability for beamforming, the assessment of exposure to all possible beams from such stations must be performed to find the worst-case value. However, numerical evaluations using anatomically realistic computational human models for various beams are computationally costly, and an exhaustive evaluation can be challenging. Therefore, a reduction in computational burden is required. In this paper, a computational method consisting of two stages is presented. This method is based on the principle that radiation from a source can be decomposed into a superposition of vector spherical harmonics. First, a computational human model is exposed to each decomposed wave, and the results for each wave are stored. Then, exposure to a beam is evaluated using the weighted sum of pre-exposure data with complex weights, which represent the beam. The whole-body-average (WBA) specific absorption rate (SAR) for various beams and antennas operating at 28 GHz was synthesized. The synthesized results were compared with the results of the conventional method. The comparisons for $8\times 8$ array antennas positioned at horizontal distances of 1.0, 2.0, 3.0, and 4.0 m and heights of 1.0 m and 2.5 m from a three-year-old pediatric model yielded less than 1.5% relative error. The worst-case WBA-SARs for exposure to $8\times 8$ and $4\times 4$ dipole arrays with various polarization angles and $8\times 8$ patch arrays were computed using the precalculation data.}, archive = {J_ACCESS}, author = {Yujiro Kushiyama and Tomoaki Nagaoka}, doi = {10.1109/ACCESS.2025.3601980}, journal = {IEEE Access}, pages = {154021-154030}, shortjournal = {IEEE Access}, title = {Numerical calculation of whole-body exposure to beams from mmWave base stations using superposition of spherical waves}, volume = {13}, year = {2025}, }
• (2025). Robust brain tumor diagnosis in clinical MRI scenarios: A non-negative matrix tri-factorization approach for missing modality imputation. ACCESS, 13, 154008-154020. (www)

  @article{ , abstract = {Brain tumors are among the most common and aggressive diseases, often resulting in significantly reduced life expectancy at advanced stages. Consequently, effective treatment planning becomes crucial for improving patient quality of life. However, in real-world clinical settings, MRI data often suffer from missing modalities i.e., certain MRI sequences (such as T1, T2, or FLAIR) are unavailable due to patient movement, time constraints, or equipment failure. This partial data availability leads to reduced performance of deep learning-based diagnostic systems, which typically rely on input. To address this gap, we propose a novel framework based on Non-negative Matrix Tri-Factorization (NMTF) that reconstructs missing portions of MRI data and enables reliable classification even when input is incomplete. Unlike previous studies that either ignore missing data or assume fixed missing patterns, our model adaptively factorizes available information into three low-rank non-negative matrices, enabling the recovery of missing features. To evaluate the effectiveness of proposed framework, six deep neural network classifiers are initially trained and tested on complete MRI scans (both 2D and 3D). Subsequently, varying amounts of data (from 10% to 50%) from sagittal slice are deliberately removed, and the classifiers are re-applied, resulting in a noticeable drop in performance due to the missing information. To address this, the proposed NMTF method is used to reconstruct the missing portions of the MRI data. The classifiers are then re-evaluated on the NMTF-recovered scans. This approach is validated across two distinct datasets. Results indicate that the average classification accuracy improves by approximately 10% $\pm ~2$ when comparing performance on incomplete MRI scans versus those reconstructed via NMTF.}, archive = {J_ACCESS}, author = {Waqas Mehmood and Aqsa Sajjad and Muhammad Akmal and Fatima Kainat}, doi = {10.1109/ACCESS.2025.3601623}, journal = {IEEE Access}, pages = {154008-154020}, shortjournal = {IEEE Access}, title = {Robust brain tumor diagnosis in clinical MRI scenarios: A non-negative matrix tri-factorization approach for missing modality imputation}, volume = {13}, year = {2025}, }
• (2025). Securing 5G and beyond-enabled UAV links: Resilience through multiagent learning and transformers detection. ACCESS, 13, 153993-154007. (www)

  @article{ , abstract = {Achieving resilience remains a significant challenge for Unmanned Aerial Vehicle (UAV) communications in 5G and 6G networks. Although UAVs benefit from superior positioning capabilities, rate optimization techniques, and extensive line-of-sight (LoS) range, these advantages alone cannot guarantee high reliability across diverse UAV use cases. This limitation becomes particularly evident in urban environments, where UAVs face vulnerability to jamming attacks and where LoS connectivity is frequently compromised by buildings and other physical obstructions. This paper introduces DET-FAIR-WINGS (Detection-Enhanced Transformer Framework for AI-Resilient Wireless Networks in Ground UAV Systems), a novel solution designed to enhance reliability in UAV communications under attacks. Our system leverages multi-agent reinforcement learning (MARL) and transformer-based detection algorithms to identify attack patterns within the network and subsequently select the most appropriate mechanisms to strengthen reliability in authenticated UAV-Base Station links. The DET-FAIR-WINGS approach integrates both discrete and continuous parameters. Discrete parameters include retransmission attempts, bandwidth partitioning, and notching mechanisms, while continuous parameters encompass beam angles and elevations from both the Base Station (BS) and user devices. The detection part integrates a transformer in the agents to speed up training. Our findings demonstrate that replacing fixed retransmission counts with AI-integrated flexible approaches in 5G networks significantly reduces latency by optimizing decision-making processes within 5G layers. The proposed algorithm effectively manages the diverse discrete and continuous parameters available in 5G and beyond to enhance resilience. Our results show that DET-FAIR-WINGS achieves faster convergence, reaching high performance in 400 epochs versus 800 for other approaches. The framework approach maintains lower packet loss rates, with 80% of measurements below 0.2 compared to 70% for alternatives and delivers lowest latency (mean: 14.61ms, median: 10.62ms) with less variance than Independent Proximal Policy Optimization (IPPO) and Multi-Agent Proximal Policy Optimization (MAPPO).}, archive = {J_ACCESS}, author = {Joseanne Viana and Hamed Farkhari and Víctor P. Gil Jiménez}, doi = {10.1109/ACCESS.2025.3602220}, journal = {IEEE Access}, pages = {153993-154007}, shortjournal = {IEEE Access}, title = {Securing 5G and beyond-enabled UAV links: Resilience through multiagent learning and transformers detection}, volume = {13}, year = {2025}, }
• (2025). A method and criterion for repetitive surge current in silicon carbide schottky diodes. ACCESS, 13, 153987-153992. (www)

  @article{ , abstract = {The need for efficient power-conversion systems in renewable energy, electric vehicles, and industrial power applications has motivated the development of wide-bandgap power semiconductor devices, such as the SiC Schottky diode. An important parameter that establishes the reliability of these high-power devices is their repetitive forward surge current, which ensures robust circuit designs that can withstand high current conditions without damage or failure. However, there is no consistent measurement method to determine this parameter for SiC Schottky diodes, and manufacturers that provide this parameter have no clear indication of the methodology used to obtain the presented value. In this work, we propose a measurement method and a user-focused criterion for the repetitive peak forward surge current, which also ensures the junction temperature does not exceed the maximum device rating. We demonstrate the need for this criterion by comparing measured surge-current performance of commercially available diodes with two different structures—merged PN Schottky diodes and homogenous Schottky-barrier diodes—designed with three different blocking voltages: 650 V, 1200 V, and 1700 V.}, archive = {J_ACCESS}, author = {Jenny Damcevska and Sima Dimitrijev and Daniel Haasmann and Philip Tanner}, doi = {10.1109/ACCESS.2025.3604871}, journal = {IEEE Access}, pages = {153987-153992}, shortjournal = {IEEE Access}, title = {A method and criterion for repetitive surge current in silicon carbide schottky diodes}, volume = {13}, year = {2025}, }
• (2025). Use of impedance for condition assessment of HV silicone-coated insulators. ACCESS, 13, 153979-153986. (www)

  @article{ , abstract = {Flashover due to contamination of glass insulators is common in electrical systems. An alternative to prevent such failures is the use of silicone-coated glass insulators. Silicone is hydrophobic, which prevents humidity from being deposited on the surface; therefore, the adhesion of environmental contaminants on the insulator is reduced. This prevents breakdowns and enables less frequent maintenance. However, in certain areas, silicone can lose its hydrophobic property, which causes contaminants to accumulate on the insulator surface. Consequently, evaluating the condition of silicone-coated insulators is essential to ascertaining their need for replacement. This requires a method that distinguishes between contamination and aging of the insulators. This paper presents the design of a machine learning classifier using a support vector machine (SVM) model that uses leakage current parameters to classify the condition of the silicone. As a distinguishing feature, the SVM classifier employs the root mean square (RMS) impedance magnitude and harmonic impedances as input parameters. This achieves an accuracy of 99% compared to 65% with data that lacked impedance measurements. The tests were conducted with insulators removed from a 500 kV line and evaluated within an artificial fog chamber.}, archive = {J_ACCESS}, author = {Rodrigo J. Villalobos and Eduardo Richard and Luis A. Moran and G. Cristian Pesce}, doi = {10.1109/ACCESS.2025.3602003}, journal = {IEEE Access}, pages = {153979-153986}, shortjournal = {IEEE Access}, title = {Use of impedance for condition assessment of HV silicone-coated insulators}, volume = {13}, year = {2025}, }
• (2025). A survey of challenges and opportunities in vertical federated unlearning. ACCESS, 13, 153959-153978. (www)

  @article{ , abstract = {Vertical Federated Unlearning (VFU) is an emerging research area focused on removing specific data contributions from models trained under Vertical Federated Learning (VFL), a setting where different organizations collaboratively train models using distinct feature sets of the same users. This paper presents the first comprehensive survey of VFU methods. We begin by defining VFU and outlining its goals. We then categorize existing methods into four unlearning targets: feature-level, instance-level, class-level, and party-level. Each category is analyzed in terms of model types, privacy guarantees, computational cost, and evaluation strategies. Our survey identifies key challenges in the field, including high resource demands, limited support for complex unlearning tasks, and inconsistent benchmarks. To support future research, we introduce a public benchmark suite and propose a practical roadmap for developing more efficient and privacy-preserving VFU systems. The main contributions of this paper are: i) the first in-depth review of VFU; ii) a structured taxonomy of unlearning methods; and iii) a standardized benchmark to guide evaluation. The public benchmark suite is available at: https://github.com/bryanhx/Vertical-Federated-Unlearning-Benchmark to support community efforts and future research.}, archive = {J_ACCESS}, author = {Hong Xi Tae and Chee Seng Chan}, doi = {10.1109/ACCESS.2025.3600884}, journal = {IEEE Access}, pages = {153959-153978}, shortjournal = {IEEE Access}, title = {A survey of challenges and opportunities in vertical federated unlearning}, volume = {13}, year = {2025}, }
• (2025). Enhancing dynamic malware behavior analysis through novel windows events with machine learning. ACCESS, 13, 153937-153958. (www)

  @article{ , abstract = {Malware analysis involves studying harmful software to understand its behavior and find ways to detect and prevent it. As cyberattacks become more advanced, this process becomes increasingly important for safeguarding systems and data. Traditional methods in malware analysis often rely on examining the code itself, which can miss malicious actions that only occur during execution. This study addresses this limitation by combining the dynamic observation of malware behavior with an innovative use of Windows Event Logs as input, a detailed system data source. During the study, a secure environment was created to safely execute malware, collect input, and provide valuable information on how malicious software interacts with systems. New methods were developed to extract meaningful information from the logs, then used to train machine-learning models capable of accurately distinguishing malware from legitimate programs. By demonstrating the untapped potential of Windows Event Logs, this study offers new tools to improve real-time malware detection and enhance cybersecurity. On a dataset of approximate 7000 Windows executable file, roughly sixty percent benign and forty percent malware, the log-feature MLP reached 91.2 % accuracy with a 1.6-point standard deviation across five folds, achieved a ROC-AUC of $0.962~\pm ~0.009$ on an unseen hold out set.}, archive = {J_ACCESS}, author = {Göksun Önal and Mesut Güven}, doi = {10.1109/ACCESS.2025.3604979}, journal = {IEEE Access}, pages = {153937-153958}, shortjournal = {IEEE Access}, title = {Enhancing dynamic malware behavior analysis through novel windows events with machine learning}, volume = {13}, year = {2025}, }
• (2025). Optimized transformer and GRU models for forecasting lost circulation volume in drilling operations. ACCESS, 13, 153918-153936. (www)

  @article{ , abstract = {Lost circulation remains a persistent challenge in drilling operations, often resulting in increased non-productive time (NPT), elevated operational costs, and heightened well control risks. While prior research has primarily focused on the detection or classification of mud losses, this study addresses the short-term forecasting of the lost circulation volume (LCV) using advanced deep learning techniques.We investigated two modeling approaches. First, a series of deep recurrent neural network (RNN) models based on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and hybrid configurations were developed to predict the LCV (in barrels) for the next short time interval. Second, a Transformer-based approach was introduced, with nine models exploring various architectural configurations. In total, twenty-two models were developed, including thirteen RNN-based models and 9 Transformer-based models.The dataset was divided into training, testing, and validation sets. A final validation set was held out from the initial split to simulate unseen conditions. The models were evaluated using standard regression metrics, with a particular focus on the coefficient of determination ( $R^{2}$ ) and the Mean Absolute Error (MAE).Our Transformer-based model outperforms traditional ML benchmarks, achieving up to 3.21% higher $R^{2}$ and reducing MAE by approximately 36.3%. Specifically, for the 15-minute aggregated testing data, our model achieves a 2.69% improvement in $R^{2}$ and a 33.5% reduction in MAE compared to the Random Forest model. For the 30-minute aggregated testing data, our model achieves a 2.99% improvement in $R^{2}$ and a 35.1% reduction in MAE compared to the Gradient Boosting model. For the 60-minute aggregated testing data, our model achieves a 3.21% improvement in $R^{2}$ and a 36.3% reduction in MAE compared to the MLP model. The best-performing model achieved $R^{2}$ scores of 0.9882, 0.9910, and 0.9932 on the 15-minute, 30-minute, and 60-minute aggregated testing data, respectively. Corresponding MAE scores were 0.0243, 0.0459, and 0.0837 barrels. These findings highlight the effectiveness of deep learning, especially Transformer architectures, for accurate and timely forecasting of LCV. This provides a practical tool for early loss detection, proactive fluid management, and improved operational efficiency in drilling workflows.}, archive = {J_ACCESS}, author = {Bayan Zumarah and Basirudin Rachman and Mohamed Mohandes and Ali A. Al-Shaikhi}, doi = {10.1109/ACCESS.2025.3603070}, journal = {IEEE Access}, pages = {153918-153936}, shortjournal = {IEEE Access}, title = {Optimized transformer and GRU models for forecasting lost circulation volume in drilling operations}, volume = {13}, year = {2025}, }
• (2025). Predicting vulnerable code changes using technical debt indicators. ACCESS, 13, 153896-153917. (www)

  @article{ , abstract = {Technical debt refers to the cost of delayed technical work and poor design choices in software systems. Software security focuses on protecting systems against vulnerabilities and exploits. In this study, we explore the relationship between technical debt and software vulnerabilities to understand how technical debt impacts software security. We collected real world data from six software projects, including code changes and corresponding vulnerabilities. We investigate various technical debt indicators, such as code smells, bugs, and software metrics, along with technical debt mitigation methods like refactoring activities, to predict vulnerabilities. The findings reveal a significant association between technical debt and vulnerabilities, highlighting the impact of technical debt on software security. We also identify specific artifacts, such as code smells, that contribute to the emergence of vulnerabilities. By understanding this relationship, software practitioners and project managers can gain insights into the risks associated with accumulated technical debt and take appropriate measures to enhance software security and quality.}, archive = {J_ACCESS}, author = {Rusen Akkus Halepmollasi and Ayse Tosun}, doi = {10.1109/ACCESS.2025.3602480}, journal = {IEEE Access}, pages = {153896-153917}, shortjournal = {IEEE Access}, title = {Predicting vulnerable code changes using technical debt indicators}, volume = {13}, year = {2025}, }
• (2025). Joint analysis of optical and SAR vegetation indices for vineyard monitoring: Assessing biomass dynamics and phenological stages over po valley, italy. ACCESS, 13, 153886-153895. (www)

  @article{ , abstract = {Multi-polarized Synthetic Aperture Radar (SAR) technology has gained increasing attention in agricultural applications. It offers unique capabilities for monitoring vegetation dynamics thanks to its all-weather, day-and-night operation and high revisit frequency. This study presents, for the first time, a comprehensive analysis combining dual-polarimetric radar vegetation index (DpRVI) with optical indices to characterize vineyard crops. Vineyards exhibit distinct non-isotropic scattering behavior due to their pronounced row orientation, making them particularly challenging and interesting targets for remote sensing. The study further explores the relationship between DpRVI and optical vegetation indices, revealing the complementary nature of their information. The low correlation between DpRVI and optical indices ( $r \leq 0.24$ ) suggests they capture distinct vegetation structure and health aspects. Notably, DpRVI exhibits a parabolic trend across the growing season, which may reflect biomass dynamics as inferred from the Winkler Index. Unlike optical indices reflecting vegetation greenness, DpRVI appears more directly related to biomass growth, aligning with specific phenological phases. This research aligns with the objectives of the PNRR-NODES project, which promotes nature-based solutions (NbS) for sustainable vineyard management. The application of DpRVI for monitoring vineyards is part of integrating remote sensing techniques into the broader field of strategies for climate-related change adaptation and risk reduction, emphasizing the role of innovative SAR-based monitoring in sustainable agriculture.}, archive = {J_ACCESS}, author = {Andrea Bergamaschi and Abhinav Verma and Avik Bhattacharya and Fabio Dell’Acqua}, doi = {10.1109/ACCESS.2025.3604663}, journal = {IEEE Access}, pages = {153886-153895}, shortjournal = {IEEE Access}, title = {Joint analysis of optical and SAR vegetation indices for vineyard monitoring: Assessing biomass dynamics and phenological stages over po valley, italy}, volume = {13}, year = {2025}, }
• (2025). FedChallenger: A robust challenge-response and aggregation strategy to defend poisoning attacks in federated learning. ACCESS, 13, 153866-153885. (www)

  @article{ , abstract = {Growing data privacy concerns in smart applications have spurred the development of Federated Learning (FL), a novel approach enabling heterogeneous clients to jointly train a global model without exchanging private data. However, FL faces significant challenges in aggregating model updates from different client devices, as malicious participants can poison the data and model updates to corrupt the global model. To enhance the global model’s accuracy, many state-of-the-art defence strategies in FL rely on aggregation-based security mechanisms. However, the global model can be more accurate if an attacker is excluded from the training. Therefore, this research proposes a dual-layer defence mechanism called FedChallenger to detect and prevent malicious client participation in the FL training process. The defence mechanism incorporates zero-trust challenge-response-based trusted exchange in the first layer, whereas, in the second layer, it uses a variant of the Trimmed-Mean aggregation strategy that uses pairwise cosine similarity along with Median Absolute Deviation (MAD) for aggregation to mitigate the malicious model parameters.Extensive evaluation using MNIST, FMNIST, EMNIST, and CIFAR-10 datasets demonstrates that the proposed FedChallenger outperforms state-of-the-art approaches, including Stake, Shap, Cluster, Trimmed-Mean, Krum, FedAvg, and DUEL, across both attack and non-attack scenarios. Under adversarial conditions with model and data poisoning attacks, FedChallenger achieves a 3-10% improvement in global model accuracy over the closest contender, along with 1.1-2.2 times faster convergence. Additionally, it attains a 2-3% higher F1-Score than the best-competing technique while maintaining robustness against varying attack intensities across different dataset complexities.}, archive = {J_ACCESS}, author = {M. A. Moyeen and Kuljeet Kaur and Anjali Agarwal and S. Ricardo Manzano and Marzia Zaman and Nishith Goel}, doi = {10.1109/ACCESS.2025.3592207}, journal = {IEEE Access}, pages = {153866-153885}, shortjournal = {IEEE Access}, title = {FedChallenger: A robust challenge-response and aggregation strategy to defend poisoning attacks in federated learning}, volume = {13}, year = {2025}, }
• (2025). High step-up DC–DC converter with galvanic isolation for distributed power generation. ACCESS, 13, 153855-153865. (www)

  @article{ , abstract = {This paper presents a new isolated DC-DC converter that integrates a coupled inductor, a transformer, and the voltage multiplier technique to achieve a high voltage gain. The proposed topology offers three degrees of freedom for voltage gain adjustment: the turns ratio of the coupled inductor ( $N_{a}$ ), the turns ratio of the transformer ( $N_{b}$ ), and the duty cycle (D). Using these parameters, the converter can achieve the desired voltage gain with flexibility. In addition, it ensures high efficiency while minimizing voltage and current stress on the components. Theoretical evaluations of the operating principle are presented, demonstrating that some semiconductors achieve ZCS during turn-ON or turn-OFF. Modeling and closed-loop control are also evaluated, demonstrating good performance. Additionally, a comparative evaluation with similar topologies is presented. Finally, a prototype was built in the laboratory, achieving a maximum efficiency of 97.53%.}, archive = {J_ACCESS}, author = {Tiago Miguel Klein Faistel and Ademir Toebe and António Manuel Santos Spencer Andrade and Ronaldo Antonio Guisso}, doi = {10.1109/ACCESS.2025.3603542}, journal = {IEEE Access}, pages = {153855-153865}, shortjournal = {IEEE Access}, title = {High step-up DC–DC converter with galvanic isolation for distributed power generation}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based ultra-fast surrogate model for AC loss estimation in superconductors using COMSOL. ACCESS, 13, 153845-153854. (www)

  @article{ , abstract = {High-temperature superconductors (HTS) demonstrate the potential to significantly benefit the energy and transport sectors by enabling high-current densities in cryogenic environments with minimal losses and exceptional efficiency. Finite element methods are widely used to model the electromagnetic behaviour of HTS, but are computationally intensive and time-consuming. The artificial intelligence (AI) method and its related techniques, such as machine learning, are advanced techniques to be used for the modelling of HTS in different large-scale power applications. AI-driven models are accurate and can be used for real-time simulation, together with correlating complex relations between inputs and outputs in a short computation time, in a fraction of one second or less. In this paper, we propose the use of built-in surrogate models in COMSOL Multiphysics and develop a deep learning surrogate model based on simulation data natively in COMSOL to estimate AC losses in HTS tapes. The training data was generated using the 2D FEM model using H formulation. This approach improves the speed of prediction to almost immediate prediction and maintains a high level of accuracy with a final root mean square error (RMSE) of 0.9990. In addition, the results of the built-in surrogate model have been compared with other AI-based models. The potential of using surrogate modelling to accurately predict AC losses for future superconducting power applications is demonstrated and discussed by analysing the deep learning model and results.}, archive = {J_ACCESS}, author = {Yizhao Huang and Mohammad Yazdani-Asrami and Wenjuan Song}, doi = {10.1109/ACCESS.2025.3603953}, journal = {IEEE Access}, pages = {153845-153854}, shortjournal = {IEEE Access}, title = {A deep learning-based ultra-fast surrogate model for AC loss estimation in superconductors using COMSOL}, volume = {13}, year = {2025}, }
• (2025). Advancing federated learning: A systematic literature review of methods, challenges, and applications. ACCESS, 13, 153817-153844. (www)

  @article{ , abstract = {Federated Learning (FL) has emerged as a cutting-edge paradigm in machine learning, showcasing remarkable advancements in recent years. This research paper delves into the dynamic landscape of FL by addressing four pivotal research questions. The study investigates the most recent advancements in implementing FL and explores additional applications that could benefit from this decentralized learning paradigm. This inquiry aims to provide an up-to-date overview of the evolving FL field and its potential cross-industry impact. The paper explores the integration of FL with various machine learning approaches to ensure optimal performance, privacy preservation, and scalability. By unraveling the collaborative aspects of FL with other machine learning paradigms, the research seeks to unveil novel strategies for enhancing efficiency in FL scenarios. The third research question focuses on the repercussions of scalability challenges and resource constraints in federated learning. This investigation aims to uncover the practical difficulties of implementing FL across diverse sectors, shedding light on potential barriers to its widespread adoption. The research probes into the future of federated learning by examining how it will be utilized in upcoming technological advancements and industries. This exploration aims to provide insights into the long-term viability and applicability of FL, anticipating its role in shaping the technological landscape across various sectors. Through a comprehensive analysis of these research questions, this paper contributes to the understanding of FL, providing valuable insights for researchers, practitioners, and decision-makers navigating the intricate intersection of FL, machine learning, and emerging technologies. This research paper aspires to provide a holistic overview of the advancements, integration possibilities, challenges, and prospects associated with federated learning, contributing to the ongoing discourse on the intersection of FL and machine learning in contemporary technological landscapes.}, archive = {J_ACCESS}, author = {Tamanna Zubairi Sana and Shahab Abdulla and Anindya Nag and Ayontika Das and Md. Mehedi Hassan and Zoya Zubairi Fiza and Asif Karim and Sheikh Ridwan Raihan Kabir}, doi = {10.1109/ACCESS.2025.3605165}, journal = {IEEE Access}, pages = {153817-153844}, shortjournal = {IEEE Access}, title = {Advancing federated learning: A systematic literature review of methods, challenges, and applications}, volume = {13}, year = {2025}, }
• (2025). Integrated multi-sensor device for police firearm real-time monitoring. ACCESS, 13, 153798-153816. (www)

  @article{ , abstract = {Police firearm use is complex and potentially life-threatening. Real-time monitoring enhances operational effectiveness, officer safety, accountability, and use-of-force auditing. However, firearm mechanics and user interactions are intricate, and integrating sensors may encounter practical and legal constraints. This paper presents a non-invasive solution that combines a photoelectric infrared sensor with a piezoelectric vibration sensor to detect key events in semiautomatic firearm use, including firing, reloading, impacts, and possible malfunctions, while also tracking the number of remaining rounds in the magazine. The sensors are designed as single transducers, integrated electrically and mechanically into a small, portable, easy-to-install, and ergonomic device. A preprocessing algorithm and state machine optimize signal detection, while noise reduction and linearization techniques enhance the signal from the photoelectric sensor. Experimental data demonstrate that fusing the temporal signals from both sensors provides an effective method to identify the mechanical behavior of the firearm and indicates promising robustness under typical operational conditions. Power consumption analysis validates the low-power design, supporting battery life between annual officer training sessions. The proposed design offers a low-cost, low-power solution that enables public administrations to effectively monitor and record firearm use, while also serving as a tool to enhance officer safety.}, archive = {J_ACCESS}, author = {Pau Gairí and Marcel Tresanchez and Tomàs Pallejà}, doi = {10.1109/ACCESS.2025.3603293}, journal = {IEEE Access}, pages = {153798-153816}, shortjournal = {IEEE Access}, title = {Integrated multi-sensor device for police firearm real-time monitoring}, volume = {13}, year = {2025}, }
• (2025). KC-UNet: Enhancing U-net with KAN and CBAM for medical image segmentation. ACCESS, 13, 153788-153797. (www)

  @article{ , abstract = {Medical image segmentation is a critical task in medical image analysis. However, traditional convolutional neural network (CNN) based methods are limited in modeling long-range dependencies, while Transformer-based segmentation models, though effective, suffer from high computational complexity due to quadratic attention operations. To address these challenges, this paper proposes an innovative U-Net variant, KC-UNet, which integrates Kolmogorov-Arnold Networks (KAN) and the Channel-Spatial Attention Module (CBAM). KC-UNet leverages the KAN representation theorem to represent features more efficiently, while CBAM enhances the model’s ability to adaptively capture both spatial and channel-wise dependencies, striking a balance between accuracy and computational efficiency. To validate the effectiveness of CBAM, this paper conducts comprehensive ablation experiments by replacing CBAM with Squeeze-and-Excitation (SE), and Efficient Channel Attention (ECA), as well as removing the attention module entirely. Results demonstrate that CBAM provides the most significant performance improvements in terms of segmentation accuracy, confirming its superior capability in enhancing feature representation. This study evaluates KC-UNet on four widely used benchmark datasets (BUSI, GLAS, CVC, and ISIC2017) and compare it against recent state-of-the-art models such as TransUNet, Swin-unet, and U-KAN. KC-UNet consistently achieves the best performance, with an IoU of 66.60% on BUSI, outperforming Swin-unet by 1.28%, and a Dice score of 80.46%, which improves upon the baseline U-Net by 7.44%. Similar advantages are observed on GLAS and ISIC2017, demonstrating the effectiveness and generalizability of our approach across different modalities. To the best of our knowledge, KC-UNet is the first framework to integrate KAN and CBAM for medical image segmentation.}, archive = {J_ACCESS}, author = {Jie Xu and Haoqing Gao and Zhifeng Wang}, doi = {10.1109/ACCESS.2025.3605148}, journal = {IEEE Access}, pages = {153788-153797}, shortjournal = {IEEE Access}, title = {KC-UNet: Enhancing U-net with KAN and CBAM for medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). Distributed MIMO with 5G NR signals: A standard-compliant evaluation. ACCESS, 13, 153773-153787. (www)

  @article{ , abstract = {This manuscript investigates the implementation of a distributed multiple-input multiple-output (D-MIMO) system within the New Radio (NR) architecture framework, emphasizing its feasibility and performance using standard-compliant signals. The study presents a comprehensive evaluation of D-MIMO in comparison to co-located MIMO, focusing on key aspects such as fronthaul requirements, precoding, pilot signaling, OFDM modulation and channel modeling under practical deployment scenarios. Leveraging numerical simulations aligned with the NR physical layer, the results indicate substantial gains in system performance: the distributed configuration achieves a notable increase in sum rates and one order of magnitude improvement in the data rates experienced by users in poor channel conditions. We assume a Urban Macrocell (UMa) non-line-of-sight model (NLoS), from 3GPP, and simulate cell areas of $0.5 \times 0.5$ , $1 \times 1$ , and $2 \times 2$ square kilometers. The outer loop link adaptation (OLLA) algorithm is applied to dynamically change the modulation and coding scheme (MCS) according to the channel conditions. Unlike previous studies, which often overlook NR compliance, this work ensures alignment with New Radio standards in the comparison between D-MIMO and co-located MIMO.}, archive = {J_ACCESS}, author = {Pedro Rendeiro and Igor Almeida and Eduardo Medeiros and Aldebaro Klautau and Leonardo Ramalho}, doi = {10.1109/ACCESS.2025.3602729}, journal = {IEEE Access}, pages = {153773-153787}, shortjournal = {IEEE Access}, title = {Distributed MIMO with 5G NR signals: A standard-compliant evaluation}, volume = {13}, year = {2025}, }
• (2025). A fully capacitive transformer device for galvanic isolation applications. ACCESS, 13, 153764-153772. (www)

  @article{ , abstract = {Galvanic isolation is commonly utilized in power transmission and data communication. Studies have explored inductive and electromagnetic coupling for isolation between nearby circuits, while optocoupler devices employ modulated light for data transmission. However, isolation devices have inherent limitations, such as restricted bandwidth, susceptibility to temperature variations, and long-term degradation. These challenges underscore the need for alternative methods to enable reliable and efficient bidirectional data exchange in isolated circuits. We developed a fully capacitive transformer device, based on mutual capacitance, which provides good linear properties across a wide range of frequencies, as well as stability with temperature variations. A model transformer was characterized using circuit analysis and experiments, confirming the physical properties of the device. Then, a functional device was built and applied to provide galvanic isolation between circuits with two different voltage levels. Finally, the device was explored in the configuration with maximum power transfer, using inductors to improve the coupling.}, archive = {J_ACCESS}, author = {João Francisco Justo and Wesley Beccaro and Antonio Mendes de Oliveira Neto and Alexandre Maniçoba de Oliveira}, doi = {10.1109/ACCESS.2025.3604635}, journal = {IEEE Access}, pages = {153764-153772}, shortjournal = {IEEE Access}, title = {A fully capacitive transformer device for galvanic isolation applications}, volume = {13}, year = {2025}, }
• (2025). Assessment of greenhouse gas emissions and mitigation strategies for a university in bangladesh. ACCESS, 13, 153741-153763. (www)

  @article{ , abstract = {Climate change is an urgent global challenge. Higher education institutions (HEIs) have a crucial role in addressing this issue by reducing their environmental impact. This study presents the first comprehensive assessment of greenhouse gas (GHG) emissions at a university in Bangladesh using the GHG Protocol Corporate Standard to categorize emissions into scopes 1, 2, and 3. The total GHG emissions amount to 2498.80 tons of carbon dioxide equivalent (tCO2e) annually, with a per capita carbon footprint of 0.4671 tCO2e. Scope 1 emissions, primarily from university vehicles, generators, cafeterias, and air conditioners, account for 29.73% (742.55 tCO2e). Scope 2 emissions, related to purchased electricity from power grid, are at 18.18% (454.14 tCO2e). The largest contributor is the indirect emissions from commuting in scope 3, accounting for 52.10% (1302.14 tCO2e) of the total. Major emission sources include air conditioners, purchased electricity and commuting of university personnel by cars. To reduce these emissions, the study presents a scenario based mitigation framework consisting of Conventional, Incremental, and Comprehensive Scenarios with targeted interventions for each scope. These include addressing high fugitive emissions from airconditioner by replacing current coolant gases with more efficient alternatives such as R32, transitioning to hybrid or solar-powered electric vehicles, expanding shared transport options, and integrating rooftop and floating solar systems. Although the study is based on one university, the proposed strategies are designed to be broadly applicable to universities across Bangladesh. The study also emphasizes energy efficiency, eco-friendly mobility, and integrating sustainability into the curriculum. This study underscores the need for localized assessments and tailored strategies to effectively address the unique challenges of reducing GHG emissions in HEIs, providing a robust framework for creating a more sustainable university community.}, archive = {J_ACCESS}, author = {Mirza Rasheduzzaman and Md. Sajid Hasan and Nayma Akther Jahan and Atik Jawad and M. Mofazzal Hossain}, doi = {10.1109/ACCESS.2025.3605517}, journal = {IEEE Access}, pages = {153741-153763}, shortjournal = {IEEE Access}, title = {Assessment of greenhouse gas emissions and mitigation strategies for a university in bangladesh}, volume = {13}, year = {2025}, }
• (2025). The coordinated control strategy of wheel-tracked driving force allocation based on hierarchical theory. ACCESS, 13, 153728-153740. (www)

  @article{ , abstract = {The wheel-tracked hybrid vehicle, as an innovative configuration integrating both wheels and tracks, exhibits significant advantages in driving adaptability and terrain handling compared to conventional single-mode wheeled or tracked vehicles. However, the multi-drive component architecture introduces substantial challenges in control coordination. This study proposes a novel hierarchical control-based coordinated driving force distribution strategy to address the coupling and coordination problem among diverse drive components in wheel-tracked hybrid vehicles. First, a compound speed controller combining feedforward and feedback mechanisms is designed to accurately compute the total driving force based on desired speed objectives. Then, an energy-optimal allocation layer is constructed, in which a cost function incorporating both slip ratio deviation and energy loss is formulated. The optimal wheel-track slip ratios are derived using the Lagrange multiplier method, enabling energy-efficient force distribution. Finally, a sliding mode control (SMC)-based optimal slip ratio controller is developed to precisely regulate driving torque and ensure tracking of the optimal slip ratio. The proposed method distinguishes itself from existing approaches by integrating multi-level control with energy optimization and robustness against nonlinearities and uncertainties. Simulation results show that, compared to conventional equal distribution strategies, the proposed approach reduces energy loss by 15%, demonstrating its superior energy efficiency and coordinated control performance. These findings highlight the effectiveness and novelty of the presented strategy in advancing intelligent control for complex hybrid drive systems.}, archive = {J_ACCESS}, author = {Xiaoyuan Zhang and Cong Li and Huanqin Feng and Hui Jing and Junhua Liu and Handong Lin and Xiangyu Li}, doi = {10.1109/ACCESS.2025.3601720}, journal = {IEEE Access}, pages = {153728-153740}, shortjournal = {IEEE Access}, title = {The coordinated control strategy of wheel-tracked driving force allocation based on hierarchical theory}, volume = {13}, year = {2025}, }
• (2025). Entropy-based data selection for language models. ACCESS, 13, 153713-153727. (www)

  @article{ , abstract = {Modern language models (LMs) increasingly require two critical resources: computational resources and data resources. Data selection techniques can effectively reduce the amount of training data required for fine-tuning LMs. However, their effectiveness is closely related to computational resources, which always require a high compute budget. Owing to the resource limitations in practical fine-tuning scenario, we systematically reveal the relationship between data selection and uncertainty estimation of selected data. Although large language models (LLMs) exhibit exceptional capabilities in language understanding and generation, which provide new ways to alleviate data scarcity, evaluating data usability remains a challenging task. This makes efficient data selection indispensable. To mitigate these issues, we propose Entropy-Based Unsupervised Data Selection (EUDS) framework. Empirical experiments on sentiment analysis (SA), topic classification (Topic-CLS), and question answering (Q&A) tasks validate its effectiveness. EUDS establishes a computationally efficient data-filtering mechanism. Theoretical analysis and experimental results confirm the effectiveness of our approach. EUDS significantly reduces computational costs and improves training time efficiency with less data requirement. This provides an innovative solution for the efficient fine-tuning of LMs in the compute-constrained scenarios.}, archive = {J_ACCESS}, author = {Hongming Li and Yang Liu and Chao Huang}, doi = {10.1109/ACCESS.2025.3605290}, journal = {IEEE Access}, pages = {153713-153727}, shortjournal = {IEEE Access}, title = {Entropy-based data selection for language models}, volume = {13}, year = {2025}, }
• (2025). A review of time series prediction models based on deep learning. ACCESS, 13, 153696-153712. (www)

  @article{ , abstract = {Time series data, capturing the evolution of specific variables over time, underpins critical forecasting tasks across diverse domains. While traditional models struggle with the inherent complexity (nonlinearity, non-stationarity, seasonality) of real-world time series, deep learning (DL) has emerged as a transformative solution, leveraging its prowess in nonlinear modeling and automated feature extraction. This paper provides a comprehensive review of recent advancements in DL-based time series prediction models. We systematically analyze the characteristics, strengths, and limitations of prominent model families, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) (e.g., LSTM, GRU), Transformer variants (e.g., Informer, Autoformer, iTransformer), Graph Neural Networks (GNNs), and hybrid models. On this basis, we establish a structured, task-oriented framework for model selection and cross-comparison, evaluating these models across essential dimensions such as sequence length handling, multivariate support, interpretability, computational efficiency, and real-time performance. Finally, addressing the challenges and issues faced by deep learning in time series prediction, this paper prospects future research directions, including enhancing model interpretability, optimizing model structures, and integrating multi-source data and domain knowledge.}, archive = {J_ACCESS}, author = {Xiqin Ao and Yujie Gong and Aixiang He}, doi = {10.1109/ACCESS.2025.3602791}, journal = {IEEE Access}, pages = {153696-153712}, shortjournal = {IEEE Access}, title = {A review of time series prediction models based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Orimura: A novel soft pneumatic actuator. ACCESS, 13, 153681-153695. (www)

  @article{ , abstract = {This paper introduces Orimura, a novel soft pneumatic actuator inspired by a modified Yoshimura Origami cylinder, designed explicitly for substantial axial elongation. To identify an optimal configuration, eight variations of modified Yoshimura paper models were precisely evaluated. The actuator was fabricated by folding watercolor paper into an origami pattern and coating it with Ecoflex 00-50 elastomer, forming a flexible yet robust paper–elastomer composite. To ensure air-tightness, the structure was sealed with additional paper–elastomer layers. Actuation was achieved using a syringe pump: the actuator extended under positive pressure and contracted under vacuum. A mathematical model was developed to describe the relationship between the geometric parameters of the origami structure, specifically the varying row heights, and the resulting height ratio. This model was validated through a rigorous experimental testing process. Three Orimura configurations were fabricated and tested, demonstrating substantial axial elongation ranging from 262% to 638%. Notably, the largest actuator configuration with a cross-sectional area of 3375 mm2 achieved a maximum extension ratio of 638% while sustaining loads up to 15 times its weight without significant performance degradation. Even under a load 22 times its weight, the actuator maintained a considerable extension of 585%, indicating high load-bearing capacity. Significantly, the incorporation of paper reinforcement effectively suppressed lateral expansion during operation. Furthermore, the mathematical model suggests that even greater elongation may be achieved by increasing the actuator’s dimensions. These results underscore Orimura’s innovative structural design and its potential contribution to the development of customizable, high-performance actuators in the field of soft robotics.}, archive = {J_ACCESS}, author = {Zinat Tasneem and Koichi Oka}, doi = {10.1109/ACCESS.2025.3603981}, journal = {IEEE Access}, pages = {153681-153695}, shortjournal = {IEEE Access}, title = {Orimura: A novel soft pneumatic actuator}, volume = {13}, year = {2025}, }
• (2025). Design of PM linear synchronous motor with chamfered teeth for reduced detent force considering modular connection. ACCESS, 13, 153671-153680. (www)

  @article{ , abstract = {This paper presents the design of the detent force reduction in the chamfered tooth permanent magnet linear synchronous motor (PMLSM). In PMLSMs, in addition to the cogging force characteristic, there is an additional end force at the longitudinal end. The sum of these cogging and end forces is called the detent force, and in a typical PMLSM, auxiliary teeth are designed to reduce this detent force. However, the design of the auxiliary teeth can increase the weight of the motor, degrade the acceleration and deceleration performance, and cause additional iron losses at the end. The detent force reduction with the tooth chamfering method was verified by PMLSM with 8 pole and 8 slot. The characteristics of the cogging force are analyzed for tooth chamfering shape per slot, including the left, right, and both chamfering. The cogging force per slot was analyzed according to the chamfer geometry, and the results showed that there is a phase shift in the cogging force of individual slots depending on the chamfer geometry. Since the phase shift of tooth chamfering shape increases the total cogging force and the phase of the end force and the total cogging force is opposite each other, the detent force can be reduced by this method. Under certain conditions, the detent force was reduced by 81% compared to unchamfered model. In addition, in order to improve the user’s design flexibility and reduce the cost of mass production, the detent force characteristics considering modularization were investigated and verified through prototype manufacturing and experiments.}, archive = {J_ACCESS}, author = {Seong-Hwi Kim and Hyunwoo Kim and Sung Gu Lee}, doi = {10.1109/ACCESS.2025.3604024}, journal = {IEEE Access}, pages = {153671-153680}, shortjournal = {IEEE Access}, title = {Design of PM linear synchronous motor with chamfered teeth for reduced detent force considering modular connection}, volume = {13}, year = {2025}, }
• (2025). Class separation dynamics in vision transformers: An empirical study. ACCESS, 13, 153651-153670. (www)

  @article{ , abstract = {Vision Transformers (ViTs) have emerged in recent years as a powerful architecture for image classification tasks and beyond. However, due to their black-box nature, our understanding of their learning dynamics is minimal. In this work, we study ViT learning dynamics through the lens of class separation, tracking inter- and intra-class structure, propagating from the patch-embedding layer to the classifier head. Across four datasets and extensive hyper-parameter configurations, we characterize two primary regularities. Firstly, we observe a layer-wise consistency, where separation fuzziness spikes between the input projection and the first transformer block, then decays exponentially with depth. Secondly, provided that the first law holds, fuzziness at the final layer also decays exponentially as a function of the training epochs. The persistence of these laws across datasets and hyper-parameters suggests an intrinsic relationship to ViT training. Our study supplies a quantitative foundation for interpreting the design of transformer-based vision models. Code and reproducibility scripts are available through https://github.com/DaraVaram/vit-data-separation}, archive = {J_ACCESS}, author = {Dara Varam and Lujain Khalil and Mahmoud Darwish and Mohamed I. Alhajri}, doi = {10.1109/ACCESS.2025.3602733}, journal = {IEEE Access}, pages = {153651-153670}, shortjournal = {IEEE Access}, title = {Class separation dynamics in vision transformers: An empirical study}, volume = {13}, year = {2025}, }
• (2025). Quantification of task-dependent leg muscle synergy patterns during isometric force generation tasks in a seated position among healthy older individuals. ACCESS, 13, 153638-153650. (www)

  @article{ , abstract = {The generation of isometric forces in the legs, which require the activation of combined muscle groups as muscle synergy, is essential during various daily activities. However, there is a lack of information on whether task-dependent leg muscle synergy patterns exist in a seated position. Thus, this study aimed to investigate whether task-dependent leg muscle synergy patterns exist in simplified direction-specific force generation tasks (FGT) on the sagittal and frontal planes in healthy older individuals. Thirteen healthy old individuals were seated with their hips and knees flexed at 90° on a customized lower limb force-measuring device. They generated isometric force in response to visual cues presented in anterior, posterior, lateral, and medial directions. Simultaneously, muscle activities from nine muscles in the same leg were obtained. Peak forces significantly differed across the four directions (p<0.05). Three muscle synergies accounted for 95% of the variance in electromyographic (EMG) data during the FGTs. Muscle synergies exhibited high reliability across subjects, with an average intraclass correlation coefficient (ICC) of 0.91 and a 95% confidence interval of 0.8-0.98 (p<0.001). Each muscle synergy had a significantly different preferred direction (PD) angle in degrees (p<0.01). Peak EMG activity of the quadriceps, hamstrings, and adductor muscles also exhibited significant differences across directions (p<0.05, p<0.01, and p<0.001, respectively). Our findings and approach may provide the foundation for developing an evaluation tool to plan targeted rehabilitation for individuals with neuromuscular disorders during the early stage of their rehabilitation or for older individuals when walking is not feasible.}, archive = {J_ACCESS}, author = {Duguma Teshome Gemechu and Eunyoung Seo and Olga V. Kim and Song Joo Lee}, doi = {10.1109/ACCESS.2025.3604416}, journal = {IEEE Access}, pages = {153638-153650}, shortjournal = {IEEE Access}, title = {Quantification of task-dependent leg muscle synergy patterns during isometric force generation tasks in a seated position among healthy older individuals}, volume = {13}, year = {2025}, }
• (2025). Cable fault double-ended ranging algorithm based on improved wavelet threshold denoising and TT transform. ACCESS, 13, 153627-153637. (www)

  @article{ , abstract = {To address the problem of large errors in single-end fault ranging for distribution cables, this paper proposes an offline double-end ranging method based on improved wavelet threshold denoising combined with the TT transform. First, the propagation characteristics of current and voltage traveling waves are theoretically analyzed. A traveling wave measurement method utilizing a secondary current coupler and a differential capacitive voltage divider is proposed, and evaluate sensor transmission performance within the concentrated frequency band of fault traveling wave signals. Second, signals containing noise are denoised using the improved wavelet threshold denoising algorithm. Subsequently, the TT transform is utilized to calibrate the initial arrival moment of the faulty traveling wave, thus realizing double-ended fault ranging. Finally, an ATP-EMTP flashover discharge model was established, and field tests were carried out for validation. The simulation and test results indicate that under various operating conditions, the double-ended distance measurement method is not influenced by the reflected pulse at the fault point and achieves higher measurement accuracy compared to the single-ended distance measurement method.}, archive = {J_ACCESS}, author = {Zhiwei Zhang and Tao Wang and Haojun Liu and Zhi Li and Jinfeng Zhang and Yiwen Wu}, doi = {10.1109/ACCESS.2025.3603735}, journal = {IEEE Access}, pages = {153627-153637}, shortjournal = {IEEE Access}, title = {Cable fault double-ended ranging algorithm based on improved wavelet threshold denoising and TT transform}, volume = {13}, year = {2025}, }
• (2025). Enhancing student retention in introductory programming courses: Leveraging advanced learning validation tools and educational data mining. ACCESS, 13, 153614-153626. (www)

  @article{ , abstract = {Student retention in introductory programming courses remains a persistent challenge in higher education, with high failure and dropout rates impacting both learners and institutions. This article presents a novel, behavior-based approach to addressing this issue through Educational Data Mining (EDM) and a custom-built learning validation framework called AssessMe. Developed by the authors in collaboration with SmoothSoft Ltd., AssessMe is an advanced software tool that monitors the real-time development of programming assignments. It captures detailed behavioral data–including active coding time, code changes (added, modified, removed lines), and submission timelines–to generate learning indicators reflecting how students approach problem-solving tasks. Unlike traditional assessment methods that focus solely on final code correctness, AssessMe emphasizes the coding process, offering deeper insights into student engagement, effort, and learning strategies. This study focuses on Programming I and II courses within the Web & Mobile Computing program at RIT Croatia. The dataset includes 3,537 student submissions from the 2024/2025 academic year, covering homework, practicals, and in-class activities, all enriched with AssessMe indicators. We apply traditional machine learning models combined with the TSFRESH library to extract meaningful time-series features from students’ coding activity. This enables the identification of temporal learning patterns and supports early prediction of academic outcomes. Our models achieve over 93% accuracy in forecasting pass/fail status by the fifth week of a 15-week semester, demonstrating a strong correlation between AssessMe indicators and final grades. This behavior-based assessment approach enhances early intervention strategies and provides actionable insights for improving student retention and learning outcomes.}, archive = {J_ACCESS}, author = {Alan Mutka and Fatima Živković Mutka and Martin Žagar and Domagoj Tolić}, doi = {10.1109/ACCESS.2025.3604563}, journal = {IEEE Access}, pages = {153614-153626}, shortjournal = {IEEE Access}, title = {Enhancing student retention in introductory programming courses: Leveraging advanced learning validation tools and educational data mining}, volume = {13}, year = {2025}, }
• (2025). An edge-deployed real-time adaptive traffic light control system using YOLO-based vehicle detection and PCE-aware density estimation. ACCESS, 13, 153586-153613. (www)

  @article{ , abstract = {This paper presents an edge-deployable, real-time adaptive traffic light control system designed for complex and undisciplined urban traffic environments. The system integrates computer vision and Internet of Things (IoT) technologies to enable smart traffic management. A robust dataset was developed to capture diverse traffic conditions, including occlusions and unstructured vehicle movement. A lightweight vehicle detection model, based on (You Only Look Once) YOLO and deployed on a Jetson Xavier NX-powered roadside edge node (RSEN), enables real-time traffic monitoring with a mean Average Precision (mAP) of 90% and 74 frames per second (FPS). To improve traffic flow representation, we introduce a traffic density estimation method that incorporates Passenger Car Equivalent (PCE) factors, considering the varying impact of different vehicle types. The estimated density is used by an adaptive traffic light control (ATLC) algorithm, deployed on a resource-constrained Portenta H7 board, which dynamically adjusts signal timings using real-time data, starvation prevention, and tie-breaking mechanisms. The proposed system reduces traffic congestion at intersections by up to 33% and waiting times by 23% compared to traditional fixed-time controls. The proposed framework is cost-effective, scalable, and fully operational on edge devices, offering a practical solution for real-world smart city deployments.}, archive = {J_ACCESS}, author = {Mehwish Raza and Majida Kazmi and Hamza Munir Kidwai and Hashim Raza Khan and Saad Ahmed Qazi and Kamran Arshad and Khaled Assaleh}, doi = {10.1109/ACCESS.2025.3602844}, journal = {IEEE Access}, pages = {153586-153613}, shortjournal = {IEEE Access}, title = {An edge-deployed real-time adaptive traffic light control system using YOLO-based vehicle detection and PCE-aware density estimation}, volume = {13}, year = {2025}, }
• (2025). BAM-net: Hardware friendly associative memory using binary dominant weights and NAND logic. ACCESS, 13, 153577-153585. (www)

  @article{ , abstract = {This paper presents BAM-Net, a hardware-efficient binarization algorithm designed for associative memory (AM) implementation. BAM-Net aims to reduce memory overhead, power consumption, and implementation complexity. Motivated by the observation that the largest positive weight in each row of a Hebbian-trained weight matrix dominates the recall process, the proposed approach retains only this maximum weight per row. This yields a highly sparse binary matrix containing only 0 and +1 values. The resulting simplification enables the exclusive use of standard NAND gates in hardware implementation, thereby eliminating the need for multi-level weight storage or complex arithmetic operations. Experimental results on synthetic $7\times 5$ pattern recall and the binarized MNIST dataset demonstrate that BAM-Net achieves superior recall accuracy, up to 96.2% and 92.7% respectively, while reducing memory usage by over 50% compared to state-of-the-art quantum, photonic, and quantized AM models. The proposed method converges in fewer recall attempts and exhibits excellent scalability, making it well suited for low-power, real-time, and resource-constrained hardware applications.}, archive = {J_ACCESS}, author = {Abdolah Amirany and Belal Jahannia and Abdulaziz S. Bazammul and Hamed Dalir and Elham Heidari}, doi = {10.1109/ACCESS.2025.3600466}, journal = {IEEE Access}, pages = {153577-153585}, shortjournal = {IEEE Access}, title = {BAM-net: Hardware friendly associative memory using binary dominant weights and NAND logic}, volume = {13}, year = {2025}, }
• (2025). A resilience evaluation framework for smart construction sites using the 4M1E-STAMP approach and cloud model. ACCESS, 13, 153557-153576. (www)

  @article{ , abstract = {In response to the global increase in construction safety incidents and the intertwined challenges of dynamism, complexity, and uncertainty in smart site safety management, this study proposes a resilience evaluation framework that integrates system-theoretic accident model and processes (STAMP), the man, machine, material, method, environment methodology (4M1E), and the cloud model. A Delphi-based approach is employed to construct a 26-indicator system across four dimensions: personnel, machinery, construction process, and environment. Decision making trial and evaluation laboratory-adversarial interpretive structural modeling (DEMATEL-AISM) method is then applied to identify the key driving factors and hierarchical causal paths. Finally, a cloud model is employed to quantitatively characterize the resilience levels of individual subsystems. The findings identify key indicators and their interaction pathways that are crucial for improving safety resilience in smart construction sites. The proposed evaluation framework is validated for effectiveness and offers an optimized path for resilience system development. This study provides a systematic and adaptive methodological foundation, contributing both theoretical innovation and practical guidance for the standardized advancement of smart site safety management.}, archive = {J_ACCESS}, author = {Yinglin Wang and Hong Xu and Yuxin Zhao}, doi = {10.1109/ACCESS.2025.3601709}, journal = {IEEE Access}, pages = {153557-153576}, shortjournal = {IEEE Access}, title = {A resilience evaluation framework for smart construction sites using the 4M1E-STAMP approach and cloud model}, volume = {13}, year = {2025}, }
• (2025). A practical gateway-based interoperability framework for blockchains in web3. ACCESS, 13, 153542-153556. (www)

  @article{ , abstract = {As blockchain technology evolves to support a wide range of Web3 services, seamless interoperability between heterogeneous blockchain platforms has emerged as a key technical challenge. Existing studies mainly address interoperability in homogeneous environments, often neglecting critical platform-specific factors such as consensus mechanisms, asset models, and block generation parameters. Additionally, current ISO and ITU-T standards define architectural frameworks but fall short of offering practical guidance for real-world implementation. This paper introduces a practical gateway-based interoperability framework designed to support dynamic and policy-driven interactions across diverse blockchain networks. The proposed architecture decouples interoperability logic from blockchain-specific interfaces, enabling modular scalability and flexible integration. Interoperability policies are defined with respect to consensus trust levels, transaction types, and asset compatibility to ensure atomicity and consistency in cross-chain operations. To validate the approach, we implemented a cross-chain asset-transfer scenario between Hyperledger Fabric and Hyperledger Besu (Private Ethereum). The case study demonstrates high success rates, low latency, and consistent data integrity, confirming the framework’s practical applicability in Web3 environments. Future work will explore enhanced support for asynchronous execution, trust-based transaction control, and regulatory compliance in cross-jurisdiction blockchain interactions.}, archive = {J_ACCESS}, author = {Migyeong Kim and Youngjin Kim}, doi = {10.1109/ACCESS.2025.3604816}, journal = {IEEE Access}, pages = {153542-153556}, shortjournal = {IEEE Access}, title = {A practical gateway-based interoperability framework for blockchains in web3}, volume = {13}, year = {2025}, }
• (2025). Vision-based time series crowd forecasting using semi-supervised learning. ACCESS, 13, 153523-153541. (www)

  @article{ , abstract = {Crowd forecasting is a crucial component of public safety, urban planning, and event management, enabling proactive decision-making based on anticipated crowd dynamics. Traditional sensor-based approaches, such as WiFi-based methods, suffer from accuracy issues due to device penetration limitations. On the other hand, vision-based approaches, while more precise, typically require fully extensive labeled data and high computational resources. These demands restrict their application to forecasting often limited to predicting the next frame or a few seconds ahead. To overcome these challenges, this research presents a vision-based time series forecasting framework that exploits a semi-supervised deep learning approach. A semi-supervised crowd counting model, trained on just 5% of labeled images from a single day, is used to extract time series crowd counts from images captured over 16 days at 5-minute intervals. These extracted time series data are then used for training multiple Long Short-Term Memory (LSTM) variants to analyze the dynamics of crowd forecasting. Experimental results demonstrate that the proposed framework enables accurate crowd forecasting while reducing annotation costs. Unlike existing vision-based approaches, which are constrained to forecasting seconds ahead, our approach can forecast a horizon of one hour ahead. Notably, the CNN Autoencoder LSTM and ConvLSTM models achieved an RMSE of 61.93 and a MAPE of 26.13%. These findings highlight the effectiveness of semi-supervised learning with minimal labeled data in vision-based crowd forecasting. Future work will focus on improving generalizability and robustness across different urban environments.}, archive = {J_ACCESS}, author = {Salma Saud Alghamdi and Lama Al Khuzayem and Ohoud Alzamzami}, doi = {10.1109/ACCESS.2025.3604713}, journal = {IEEE Access}, pages = {153523-153541}, shortjournal = {IEEE Access}, title = {Vision-based time series crowd forecasting using semi-supervised learning}, volume = {13}, year = {2025}, }
• (2025). Research on offshore vessel speed prediction based on TCN-transformer-CrossAttention. ACCESS, 13, 153510-153522. (www)

  @article{ , abstract = {Accurate speed prediction of maritime vessels is critical for navigation safety, traffic management, and collision avoidance, particularly in busy nearshore areas where complex spatial-temporal dependencies challenge traditional prediction models. To overcome the limitations of conventional single neural networks in feature extraction, this paper proposes a novel hybrid deep learning model that integrates Temporal Convolutional Network (TCN), Transformer, and Cross-Attention mechanisms for enhanced vessel speed prediction using Automatic Identification System (AIS) data. The TCN module captures local temporal patterns, while the Transformer models long-range dependencies, and the Cross-Attention mechanism effectively fuses spatial and temporal features to improve prediction robustness. Furthermore, we introduce an optimized feature fusion strategy to dynamically weigh multi-scale features, ensuring better representation learning. Through extensive experiments and hyperparameter tuning, our model achieves significant improvements over baseline methods in key metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The proposed framework not only advances the state-of-the-art in maritime speed prediction but also provides a practical solution for enhancing maritime traffic efficiency and safety management.}, archive = {J_ACCESS}, author = {Xin Liu and Zhaona Chen and Yu Cao and Yang Yang}, doi = {10.1109/ACCESS.2025.3602875}, journal = {IEEE Access}, pages = {153510-153522}, shortjournal = {IEEE Access}, title = {Research on offshore vessel speed prediction based on TCN-transformer-CrossAttention}, volume = {13}, year = {2025}, }
• (2025). Non-levitative electromagnet robot propulsion method for 2-D material handling. ACCESS, 13, 153498-153509. (www)

  @article{ , abstract = {Smart factories are increasingly adopting two-dimensional (2D) motionsystems due to their flexible layouts and support for independently routedtransport. However, current technologies face major scaling challenges. Autonomous mobile robots (AMRs) require onboard power, sensing, and computation, raising cost and design complexity. Large fleets also rely on wireless communication and, in many cases, decentralized coordination, introducing overhead that can limit throughput in large-scale deployments. Maglev planar motors, while precise, rely on dense electromagnet arrays, resulting in high material and installation costs that scale poorly with area. This paper presents a non-levitative electromagnetic propulsion method for planar robots that uses a sparse grid of floor-mounted stator coils to actuate fully passive mobile units equipped only with permanent magnets. The system performs centralized control using a force-to-current model derived from planar dynamics, and continuously adjusts motion based on real-time feedback from a grid of Hall-effect sensors. This combination of sparse actuation, passive mobility, and closed-loop control enables scalable, cost-effective 2D transport without onboard computation or power. A prototype system achieves stable 3-DOF planar motion with a mean absolute tracking error of $3.2~\pm ~0.77$ mm. These results demonstrate the feasibility of the approach for high-throughput material handling. Target applications include parcel sorting, modular conveyors, and reconfigurable manufacturing lines where scalable deployment and adaptable layouts are key.}, archive = {J_ACCESS}, author = {Shangqiu Li}, doi = {10.1109/ACCESS.2025.3596137}, journal = {IEEE Access}, pages = {153498-153509}, shortjournal = {IEEE Access}, title = {Non-levitative electromagnet robot propulsion method for 2-D material handling}, volume = {13}, year = {2025}, }
• (2025). A dynamic attention-guided deep learning framework (D-PAN) with reduced complexity for fine grained severity-aware classification of micronutrient deficiency and disease detection in banana and coffee leaves. ACCESS, 13, 153474-153497. (www)

  @article{ , abstract = {Nutrition is a key factor in enabling crops to reach their optimal growth level. Farmers and agronomists often mistake nutrient deficiencies for diseases, leading to incorrect crop treatments. This study proposes a deep learning model for classifying micronutrient deficiencies (m-ND), evaluating their severity, and distinguishing them from diseases in banana and coffee crops. To address common classification challenges, the model is initially trained and validated on images of both nutritional deficiencies and visually similar disease symptoms. To assess its robustness, we also evaluated it on a coffee crop dataset from Mendeley. The proposed method, D-PAN - a modified Pyramid Attention Network (PAN) uses a dynamic attention mechanism to process multi-scale input images. Model performance was compared with various vision transformer (ViT) based channel attention methods and a weighted ensemble of Transformer models combined with a modified convolution neural network (CNN). The model achieved top accuracies of 98.80% for banana and 94.60% for coffee, with reduced misclassification errors. Experimental results show that the framework maintains low complexity, improves accuracy by an average of 2.9%, and reduces parameters by 1.64 to 3.83 times compared to other attention methods. Overall, the proposed model surpasses all evaluated state-of-the-art attention mechanisms, achieving strong performance across recall, accuracy, precision, and F1-score.}, archive = {J_ACCESS}, author = {Sudhakar Muthusamy and Swarna Priya Ramu}, doi = {10.1109/ACCESS.2025.3599359}, journal = {IEEE Access}, pages = {153474-153497}, shortjournal = {IEEE Access}, title = {A dynamic attention-guided deep learning framework (D-PAN) with reduced complexity for fine grained severity-aware classification of micronutrient deficiency and disease detection in banana and coffee leaves}, volume = {13}, year = {2025}, }
• (2025). Predicting suspicious arabic x accounts using stylometric features. ACCESS, 13, 153464-153473. (www)

  @article{ , abstract = {Some users use the X platform to spread negativity, violence, and hatred. These users may initially conceal their true beliefs in order to gain users’ trust, making traditional content-based detection methods not helpful in identifying these accounts as suspicious (i.e., indicate whether an X account belongs to a terrorist). Stylometric features, which analyze writing styles, can reveal behavioral traits and hidden thoughts. In this paper, we propose a novel model that predicts whether an Arabic X account is suspicious (owned by a terrorist) using 85 stylometric features extracted from 1,500 accounts (750 suspicious, 750 non-suspicious). We utilized a variety of AI techniques, such as machine learning and deep learning, to evaluate our approach. We also used NLP techniques for preprocessing and feature extraction. Our results showed that the Random Forest (RF) model achieved the highest accuracy, reaching 98%. This approach can aid cybersecurity efforts by detecting suspicious accounts without relying on content analysis.}, archive = {J_ACCESS}, author = {Taghreed Bagies and Rahaf Alsuhaimi and Miada Almasre and Alaa Bafail}, doi = {10.1109/ACCESS.2025.3605126}, journal = {IEEE Access}, pages = {153464-153473}, shortjournal = {IEEE Access}, title = {Predicting suspicious arabic x accounts using stylometric features}, volume = {13}, year = {2025}, }
• (2025). An end to end wearable device and system for indefinite, continuous, real time gesture recognition of directional and shape-based arm gestures. ACCESS, 13, 153436-153463. (www)

  @article{ , abstract = {Human–Computer Interfaces designate how individuals interact with digital devices and systems. Often these interfaces heavily involve fine motor control via keyboards, controllers, and touch screens. These modalities tend to exclude users with disabilities, such as amputees or those affected by carpal tunnel syndrome. This paper proposes the creation of a wearable gesture recognition system that enables hands-free, device interaction through the detection of user movements and gestures. The system comprises of a wearable device with an onboard inertial measurement unit (IMU), a complementary filter for orientation estimation, and a gesture classification pipeline that recognizes a set of directional, rotational, and shape based gestures. We evaluate two models: an XGBoost classifier using IMU data preprocessed with a Mahony filter, and Wavelet Transform feature extraction, and a Convolutional Neural Network (CNN) directly using raw IMU data and Mahony-based orientation estimation. On the benchmark 6DMG dataset, our models achieve an accuracy of 99.55% and 99.18% respectively, while our custom-collected dataset yields 94.81% and 96.28%. In real-time, continuous, gesture tracking, the system achieves an average recognition accuracy of 92.33% with an average sensor to prediction latency of 20 milliseconds. Our system is actualized through a modular application programming interface (API) and graphical user interface (GUI), enabling real-time software interaction through movements and recognized gestures. Compared to existing methods, our approach achieves high accuracy, and continuous real-time performance with low-latency, scalable integration with external software.}, archive = {J_ACCESS}, author = {Abdurrahman Aliyu Gambo and Emmanuel Yahi Ali and David Adeshina Arungbemi and Mehwish Hanif and Praise Ngbede Anefu and Nyangwarimam Obadiah Ali and Sadiq Thomas and Francis Emmanuel Chinda and Zazilah May and Saima Qureshi and Muhammad Kashif}, doi = {10.1109/ACCESS.2025.3602871}, journal = {IEEE Access}, pages = {153436-153463}, shortjournal = {IEEE Access}, title = {An end to end wearable device and system for indefinite, continuous, real time gesture recognition of directional and shape-based arm gestures}, volume = {13}, year = {2025}, }
• (2025). Regression models in QSPR analysis for blood cancer treatment: A connection number approach. ACCESS, 13, 153420-153435. (www)

  @article{ , abstract = {Topological indices (TIs) are valuable tools in the study of blood cancer drugs, offering insights into molecular structure and associated properties critical for assessing efficacy and toxicity. This study introduces Connection Number-Based Topological Indices (CNTIs), designed to model and predict important physiochemical characteristics of anti-cancer compounds. Using edge-partitioning techniques, we analyze five clinically relevant blood cancer drugs—Gilteritinib, Ivosidenib, Daunorubicin, Glasdegib, and Palbociclib—by calculating CNTIs and several standard topological indices. A quantitative structure–property relationship (QSPR) model is developed through linear regression to predict properties such as boiling point, flash point, molar volume, molecular weight, and complexity. The results demonstrate that CNTIs are reliable and effective in capturing structure–property relationships, underscoring their potential application in drug design and cancer treatment research.}, archive = {J_ACCESS}, author = {Nasreen Almohanna and Abdulaziz Mutlaq Alotaibi and Ali Ahmad and Khawlah Alhulwah and Muhammad Azeem}, doi = {10.1109/ACCESS.2025.3602919}, journal = {IEEE Access}, pages = {153420-153435}, shortjournal = {IEEE Access}, title = {Regression models in QSPR analysis for blood cancer treatment: A connection number approach}, volume = {13}, year = {2025}, }
• (2025). GuideCAD: A lightweight multimodal framework for 3D CAD model generation via prefix embedding. ACCESS, 13, 153406-153419. (www)

  @article{ , abstract = {Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we propose GuideCAD, which leverages semantically rich visual-textual representations having only a small number of trainable parameters to generate 3D CAD models. Specifically, GuideCAD uses a mapping network that converts image embeddings into prefix embeddings, enabling a pretrained large language model to integrate visual and textual information. As a result, a transformer-based decoder predicts the construction sequence using the visual-textual embeddings in order to generate the 3D CAD model. For experimental evaluation, we construct a new dataset, referred to as GuideCAD, which consists of text-image pairs. Each pair includes a text prompt that represents a 3D CAD construction sequence and its corresponding 3D CAD image. Our experimental results show that GuideCAD generates comparably high-quality 3D CAD models while using approximately four times fewer parameters and achieving twice the training efficiency compared to fine-tuning approaches. We have released the source code and dataset for our method at: https://github.com/mskimS2/GuideCAD}, archive = {J_ACCESS}, author = {Minseong Kim and Jinyeong Park and Sungho Park and Jibum Kim}, doi = {10.1109/ACCESS.2025.3604810}, journal = {IEEE Access}, pages = {153406-153419}, shortjournal = {IEEE Access}, title = {GuideCAD: A lightweight multimodal framework for 3D CAD model generation via prefix embedding}, volume = {13}, year = {2025}, }
• (2025). Synthetic network packet data generator with protocol state and arrival timing awareness. ACCESS, 13, 153398-153405. (www)

  @article{ , abstract = {Recent advances in network traffic analysis use machine learning (ML) on packet-level data, but obtaining large amount of real data for training ML is becoming a challenge. ML-based generators like Generative Adversarial Networks and Variational Autoencoders are promising candidates for generation the packet-level data but often fail to preserve TCP protocol states and packet arrival time. Without accurately reproducing these characteristics, the synthetic data may fail to reflect real network behavior, potentially leading to erroneous conclusions in the training and evaluation of machine learning models. This paper proposes a new method to synthesize realistic packet-level data with accurate protocol transitions and timing. By separating IP address and port number generation and aligning timing with handshake completion, the model avoids overfitting. Using the CIC-IDS 2017 dataset, our method better replicates headers and timing, producing synthetic data that closely resembles real data features distribution. Visual and statistical comparisons reveal that the synthetic data closely resembles real packet traces, providing a reliable alternative for training and evaluating network analysis.}, archive = {J_ACCESS}, author = {Yukito Onodera and Erina Takeshita and Tomoya Kosugi and Takashi Nakanishi and Tatsuya Shimada}, doi = {10.1109/ACCESS.2025.3604773}, journal = {IEEE Access}, pages = {153398-153405}, shortjournal = {IEEE Access}, title = {Synthetic network packet data generator with protocol state and arrival timing awareness}, volume = {13}, year = {2025}, }
• (2025). Experimental validation of translational single LiDAR 2D mechanism into autonomous navigation and 3D SLAM of mobile robots. ACCESS, 13, 153386-153397. (www)

  @article{ , abstract = {Most autonomous navigation approaches for mobile robots rely either on 3D LiDAR sensors, which are often costly, or on horizontally mounted 2D LiDAR sensors, which do not support three-dimensional mapping. This paper presents the implementation of a 3D Simultaneous Localization and Mapping system integrated with autonomous navigation in a mobile robot using a Translational Single LiDAR 2D (TSL-2D) configuration: a three-dimensional mapping mechanism based on a single vertically mounted 2D LiDAR sensor. To verify the feasibility of the proposed approach, the methodology combines the mathematical foundations, implementation and evaluation of global planning algorithms–Dijkstra, A-star (A*), and Rapidly-exploring Random Tree Star (RRT*)–within the Robot Operating System 2 (ROS 2) framework, integrating point cloud generation from vertical LiDAR sweeps. Experiments are conducted both in simulated environments using Gazebo and in real-world scenarios. The qualitative results demonstrate that the system is reliable for evaluating autonomous navigation algorithms, including local and motion planners. Quantitatively, as a specific outcome of the proposed system, RRT* produces smoother and shorter trajectories compared to A* and Dijkstra, achieving a 7.62% reduction in path length. Furthermore, in comparison to RGB-D camera-based systems reported in the literature, the TSL-2D mechanism delivers superior precision in generating 2D projected maps from the acquired point clouds.}, archive = {J_ACCESS}, author = {Marjorie A. León Ramos and Raul Paolo Rivera Vera and Daniel D. Yanyachi and Alfredo Mamani Saico and German Alberto Echaiz Espinoza and Juan Carlos Cutipa Luque and Pablo R. Yanyachi}, doi = {10.1109/ACCESS.2025.3602283}, journal = {IEEE Access}, pages = {153386-153397}, shortjournal = {IEEE Access}, title = {Experimental validation of translational single LiDAR 2D mechanism into autonomous navigation and 3D SLAM of mobile robots}, volume = {13}, year = {2025}, }
• (2025). Predicting patient ICU readmission using recurrent neural networks with long short-term memory. ACCESS, 13, 153375-153385. (www)

  @article{ , abstract = {Intensive care is quite expensive, so patients’ transfer to lower-level wards must be carefully considered. A patient who is discharged from the ICU too soon carries the danger of receiving insufficient care, which results in readmission. Readmission prediction tasks are less extensively researched, and the techniques employed have not produced satisfactory results. Conventional machine learning methods with non-sequential data have been employed in the majority of the studies. The goal of this research is to propose a solution that can accurately predict the likelihood of an unplanned ICU readmission within 30 days. The proposed method addresses the issues of missing values and data imbalance, in particular. Our method combines low-dimensional embeddings of medical concepts, such as diseases labeled using the ICD-9 code, with temporal features extracted from chart events data. Three alternative models are trained using Long-Short-Term Memory (LSTM), which employ ICU stay data from the last 24 hours, 48 hours, and 72 hours. The Medical Information Mart for Intensive Care (MIMIC-III) dataset is used to train and validate models. We tested our proposed methods on the unseen data of the MIMIC-III dataset to assess their efficacy. The model that was trained using the last 48-hour ICU data performed better than other models, reaching an 84.2% area under the receiver operating characteristic curve (AUC-ROC). The ROC scores of the other models are 82.6%, 78.9%, 77.4%, 75.6%, and 71.4% for Gated Recurrent Unit (GRU), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Baise (NB), and K-Nearest Neighbor (KNN), respectively. The findings indicate that the suggested approach can outperform the state-of-the-art methods.}, archive = {J_ACCESS}, author = {Nadia Rasheed and Usman Khaliq and Ashfaq Ahmed and Amine Bermak}, doi = {10.1109/ACCESS.2025.3601117}, journal = {IEEE Access}, pages = {153375-153385}, shortjournal = {IEEE Access}, title = {Predicting patient ICU readmission using recurrent neural networks with long short-term memory}, volume = {13}, year = {2025}, }
• (2025). Enhancing vertical handover provisioning of heterogeneous network in marine pasture. ACCESS, 13, 153359-153374. (www)

  @article{ , abstract = {In marine ranching, intelligent operations, as a new economic development model, are increasingly demonstrating their importance. With the widespread adoption of intelligent autonomous devices and the diversification of demand, deploying dense heterogeneous networks has become one of the key challenges. The dynamic nature and uncertainty of these networks present significant difficulties in the vertical handoff between autonomous devices. To address these challenges, this paper proposes a vertical handoff algorithm based on network classification and multi-attribute decision-making. The algorithm standardizes network parameters, classifies networks using cosine similarity, and selects the optimal network through multi-attribute decision-making and hierarchical sub-network selection strategies. This approach helps balance the load, improve resource utilization efficiency, and optimize the user experience of intelligent autonomous devices, thereby contributing to the enhancement of intelligent management in marine ranching.}, archive = {J_ACCESS}, author = {Xiaoxuan Wang and Yuecheng Hong and Yang Li and Tao Jing and Yong Zhang and Haoyu Zhang and Yan Huo and Qinghe Gao}, doi = {10.1109/ACCESS.2025.3598566}, journal = {IEEE Access}, pages = {153359-153374}, shortjournal = {IEEE Access}, title = {Enhancing vertical handover provisioning of heterogeneous network in marine pasture}, volume = {13}, year = {2025}, }
• (2025). Systematic literature review on effort estimation by software development life cycle phases. ACCESS, 13, 153340-153358. (www)

  @article{ , abstract = {Several techniques have been proposed to estimate the total effort of the Software Development Life Cycle (SDLC) rather than by SDLC phase to be performed for an independent team. An accurate effort estimation is needed for software managers to create realistic plans and allocate resources appropriately to independent teams. Therefore, this Systematic Literature Review (SLR), unlike other secondary studies, examines the current state of effort estimation by SDLC phase instead of estimating it across the entire SDLC. In addition, this SLR identifies metaheuristics used for optimizing the parameters of effort estimation models. We searched for studies whose objective has been to propose models for estimating the effort of specific SDLC phases, rather than total SDLC effort. We firstly identified 216 studies, and finally we selected 31 of them published between 2014 and march 2025 in journals and conferences. The majority of the studies investigated effort estimation in the testing and maintenance phases, mainly using Machine Learning (ML) techniques. Functional size and project characteristics were the most common explanatory variables. International Software Benchmarking Standards Group was the predominant dataset for training models and the mean of Absolute Residual was the recommended precision measure. Cross-validation was the most used model validation method. We can conclude that more research is needed on effort estimation by SDLC phases such as requirements specification, design, and construction, as well as to further explore ML and metaheuristics to improve the prediction accuracies of models.}, archive = {J_ACCESS}, author = {Sulem Martínez-Aguilar and Ángel J. Sánchez-García and Cuauhtémoc López-Martín and Jorge Octavio Ocharán-Hernández}, doi = {10.1109/ACCESS.2025.3605102}, journal = {IEEE Access}, pages = {153340-153358}, shortjournal = {IEEE Access}, title = {Systematic literature review on effort estimation by software development life cycle phases}, volume = {13}, year = {2025}, }
• (2025). TForMIX: A method that combines LLM and multidimensional modeling for technological foresight. ACCESS, 13, 153320-153339. (www)

  @article{ , abstract = {Technical documents, such as scientific papers and patents, are widely used as a basis for Technological Foresight (TF) processes. Typically, these analyses require identifying elements (e.g., terms) in the textual contents of these documents, which are relevant to the scientific-technological domain under investigation. Information Extraction (IE) and Natural Language Processing (NLP) techniques are useful tools to automate the identification of these elements, which is essential in TF processes that usually involve the analysis of a corpus of hundreds (and sometimes thousands) of documents. An analytical view over this corpus, based on the occurrence of those relevant elements, helps prioritize document analysis and, consequently, accelerates the whole TF process. However, building a system that provides such analytical insight is expensive. Moreover, for each domain-specific TF process, a new system would have to be built. Thus, there is a need for viable solutions to analytically explore a corpus, according to the specific requirements of each domain. This work presents Technological Foresight with Multidimensional Information eXtraction (TForMIX), a novel method for building Decision Support Systems (DSSs) that applies Named Entity Recognition (NER) and Relation Extraction (RE) while allowing multidimensional analytical exploration of entities and relations together with bibliometric data from documents. TForMIX is a flexible method that can be applied to different domains, and speeds up building DSSs for each domain. Additionally, we evaluate the applicability of the produced DSSs in TF processes by conducting a practical experiment that demonstrates that applying the method to generate DSSs, supported by IE techniques, can significantly contribute to the conduction of TF analyses. The combination of the used theories, innovative methods, and proposed practical validation highlighted the high-quality nature of the analysis in this study while offering the potential for valuable insights and contributions to the TF process.}, archive = {J_ACCESS}, author = {Giselle F. Rosa and Jones O. Avelino and Maria Claudia Cavalcanti and Julio Cesar Duarte}, doi = {10.1109/ACCESS.2025.3605116}, journal = {IEEE Access}, pages = {153320-153339}, shortjournal = {IEEE Access}, title = {TForMIX: A method that combines LLM and multidimensional modeling for technological foresight}, volume = {13}, year = {2025}, }
• (2025). Dynamic thermal digital twin of liquid-immersed power transformer. ACCESS, 13, 153308-153319. (www)

  @article{ , abstract = {This paper presents the application of a detailed dynamic thermal-hydraulic network model (DTHNM) to real liquid-immersed power transformers (LIPTs). Unlike existing simple models based on ordinary differential equations (ODEs), such as those described in the standards, the DTHNM is deeply rooted in physics and resolves many key issues, such as distributed heat accumulation, the dependency of losses on tap position, load, and conductor temperature, as well as the influence of ambient temperature and the various cooling equipment operating stages. The DTHNM provides a time-varying global distribution of liquid flow inside the transformer tank and the distribution of flows and temperatures across each transformer part. It is validated here using field data from a three-phase 66-MVA transformer (summer and winter operating periods) and a single-phase 370-MVA transformer (60-hour extended temperature rise test). The DTHNM closely matches the measured temperatures, whereas standard models show larger errors, especially under high-load and/or low-ambient conditions. The main contributions of this paper are: 1) extension of the detailed steady-state thermal-hydraulic network model (steady-state THNM) to a detailed dynamic model for digital twin applications; 2) application of the model to large LIPTs with validation against measurements; 3) comparison of DTHNM accuracy with that of IEC and IEEE loading guide models; 4) analysis of practical issues including data requirements, computation time, real-time feasibility and limitations.}, archive = {J_ACCESS}, author = {Zoran Radakovic and Patrick Picher and Marko Novkovic and Federico Torriano}, doi = {10.1109/ACCESS.2025.3604720}, journal = {IEEE Access}, pages = {153308-153319}, shortjournal = {IEEE Access}, title = {Dynamic thermal digital twin of liquid-immersed power transformer}, volume = {13}, year = {2025}, }
• (2025). ST-DGCN: A novel spatial-temporal dynamic graph convolutional network for cardiovascular diseases diagnosis. ACCESS, 13, 153296-153307. (www)

  @article{ , abstract = {Recently, Deep Learning (DL) technology has made significant progress in the field of automatic diagnosis of cardiovascular diseases (CVDs) based on electrocardiograms (ECGs). Multi-lead ECG signals are physiological signals obtained through a series of lead systems based on potential differences between electrodes placed on the limbs and chest. However, most DL models treat them as one-dimensional signals distributed in Euclidean space, often focusing only on features along the temporal dimension and neglecting the spatial relationships between different leads. Different studies indicate that these spatial relationships are physiologically significant for the diagnosis of CVDs, as they represent the activity of different regions of the heart. Given the advantages of Graph Convolutional Networks (GCNs) in analyzing non-Euclidean data, this study proposes a novel method for CVD diagnosis. The method begins by segmenting ECG signals into multiple single-lead segments and converting them into the nodes of a graph. Subsequently, these nodes are interconnected through spatial-temporal connections based on their relationships of physiological structures. The proposed model utilizes a dynamic graph convolutional network to capture the spatial-temporal features of the ECG signals and employs hierarchical pooling techniques to mitigate issues of oversmoothing and overfitting. Compared to other state-of-the-art models (SOTAs), this model achieved at least a 6.5% and 9.3% increase in F1 scores on the Chapman and PTB-XL databases, respectively, such significant performance advantages highlight the effectiveness and reliability of the model in classifying ECG signals, providing a powerful tool for the diagnosis of cardiovascular diseases.}, archive = {J_ACCESS}, author = {Qi Xu and Yi Xia}, doi = {10.1109/ACCESS.2025.3605241}, journal = {IEEE Access}, pages = {153296-153307}, shortjournal = {IEEE Access}, title = {ST-DGCN: A novel spatial-temporal dynamic graph convolutional network for cardiovascular diseases diagnosis}, volume = {13}, year = {2025}, }
• (2025). A bilevel optimization framework for hybrid DSO-VPP coordination: Enhancing congestion management and market efficiency in decentralized energy systems. ACCESS, 13, 153281-153295. (www)

  @article{ , abstract = {This paper presents a bilevel optimization framework that improves congestion management and economic efficiency in decentralized energy markets by coordinating hybrid Distribution System Operators (DSOs) and Virtual Power Plants (VPPs). The framework utilizes Distributed Locational Marginal Pricing (DLMP) to align technical constraints with market incentives and addresses challenges arising from high-penetration Distributed Energy Resources (DERs). Simulations on the modified KEPCO Active Distribution Test System (KADTS) show performance improvements compared to conventional methods, including profit increases of up to 3.35%, effective congestion mitigation, and enhanced operational flexibility enabled by adaptive DLMP mechanisms and strategic bidding. The framework is scalable, adaptable, and compatible with existing market structures. It offers a practical approach to modern energy systems and supports ongoing developments in smart grids and sustainable grid modernization.}, archive = {J_ACCESS}, author = {Ilham Ramadhan Maulana and Rehman Zafar and Il-Yop Chung}, doi = {10.1109/ACCESS.2025.3604172}, journal = {IEEE Access}, pages = {153281-153295}, shortjournal = {IEEE Access}, title = {A bilevel optimization framework for hybrid DSO-VPP coordination: Enhancing congestion management and market efficiency in decentralized energy systems}, volume = {13}, year = {2025}, }
• (2025). CLEVER: A novel approach for improving EV charging duration and load predictions using curriculum learning approach. ACCESS, 13, 153263-153280. (www)

  @article{ , abstract = {Despite advancements in electric vehicle (EV) charging prediction models, existing approaches are suffering from complex charging patterns. The curriculum learning (CL) is a training approach which resembles the natural human learning progression by introducing training samples through different patterns, hence efficiently structuring the learning process. While the CL has been successfully used in other domains, its application in EV charging prediction remains unexploited. In this work, the CL is to be leveraged for the first time to improve the EV charging behavior predictions in both EV charging duration and charging load prediction. A feature-based curriculum learning approach, named CLEVER (Curriculum Learning EV chargER), is proposed for predicting charging session load and duration. CLEVER employs an advanced data stratification mechanism that introduces training samples progressively according to complexity metrics computed from temperature variations, state of charge variations, and temporal patterns. The CLEVER method integrates a CL strategy with a staged schedule mechanism over four neural network architectures: ANN, DNN, LSTM, and GRU. The performances obtained exhibit notable gains, where CL scores 20.9% reduction of Mean Absolute Error for GRU-based forecasting of EV charging duration and 2.2% improvement for DNN-based charging load forecasting. The CLEVER methodology shows considerable improvements in predicting the duration of EV charging, with, as many as 23.0% reductions in Mean Absolute Error with GRU models on Level 1 chargers and a near 20.7% improvement with DNN models on DC Fast Chargers. For EV charging load forecasts, curriculum learning produces consistent, but modest, gains, with improved up to 2.4% with ANN models on DC Fast Chargers and 1.6-2.1% improvements across different neural network architectures. This comprehensive analysis across different charger types, user groups, vehicle models, temperatures, and temporal patterns makes CL a superior approach to enhancing EV charging infrastructure management and grid stability.}, archive = {J_ACCESS}, author = {Esraa Eldesouky and Ahmed Fathalla and Mahmoud Bekhit and Ahmad Salah}, doi = {10.1109/ACCESS.2025.3604342}, journal = {IEEE Access}, pages = {153263-153280}, shortjournal = {IEEE Access}, title = {CLEVER: A novel approach for improving EV charging duration and load predictions using curriculum learning approach}, volume = {13}, year = {2025}, }
• (2025). Scalable intrusion detection in IoT networks via property testing and federated edge AI. ACCESS, 13, 153244-153262. (www)

  @article{ , abstract = {The rapid proliferation of Internet of Things (IoT) devices has expanded the attack surface for cyber threats, necessitating real-time and scalable security solutions. Traditional Intrusion Detection Systems (IDS) require extensive data processing, which is computationally infeasible for resource-constrained IoT environments. This paper proposes a novel property testing-based anomaly detection framework that efficiently monitors IoT networks while reducing computational overhead. By leveraging sublinear-time statistical sampling and probabilistic validation, the proposed framework detects network anomalies, unauthorized access, and malware propagation without requiring full dataset analysis. A hybrid Edge AI and Federated Learning model is integrated to enhance security across distributed IoT nodes while ensuring data privacy. The system is evaluated against real-world IoT cybersecurity threats, demonstrating up to 70% reduction in processing time and a 92% accuracy rate in detecting intrusions with only 10% of the dataset analyzed. Compared to recent lightweight federated and sketch-based IDS models, the proposed framework improves F1-score by up to 11.3%, while reducing inference time and energy consumption by over 60%, highlighting its practical efficiency and scalability. These findings underscore the potential of property testing as an efficient alternative for real-time cybersecurity monitoring in IoT ecosystems.}, archive = {J_ACCESS}, author = {Manuel J. Cabral S. Reis}, doi = {10.1109/ACCESS.2025.3603937}, journal = {IEEE Access}, pages = {153244-153262}, shortjournal = {IEEE Access}, title = {Scalable intrusion detection in IoT networks via property testing and federated edge AI}, volume = {13}, year = {2025}, }
• (2025). 3D CT slice image-based algorithm for non-wet defect inspection in solder joints. ACCESS, 13, 153234-153243. (www)

  @article{ , abstract = {This paper presents a robust inspection framework for detecting non-wet defects in semiconductor solder joints using 3D CT slice imaging and supervised learning. The proposed method leverages a slice-level ResNet18 classifier combined with a tunable classification confidence parameter to predict defective slices. These slice-level predictions are then aggregated to determine the volume-level defect status through a slice-counting strategy. To accommodate varying defect characteristics across semiconductor packages, we introduce an adjustable defect count threshold and validate its impact on detection performance. Experiments show that the method achieves perfect recall with zero false positives under optimal settings and maintains a stable range across thresholds, outperforming traditional unsupervised and feature-based baselines. The proposed approach is lightweight, adaptable, and requires no retraining to adjust sensitivity, making it well-suited for deployment in real-world inspection pipelines. This work demonstrates the practical synergy of 3D imaging and machine learning in enhancing reliability and efficiency in semiconductor manufacturing. Our codes and data are released at here.}, archive = {J_ACCESS}, author = {Sung Ju Lee and Sang Hwa Lee and Nam Ik Cho}, doi = {10.1109/ACCESS.2025.3604431}, journal = {IEEE Access}, pages = {153234-153243}, shortjournal = {IEEE Access}, title = {3D CT slice image-based algorithm for non-wet defect inspection in solder joints}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of universal fast-charging techniques for 400-V and 800-V electric vehicles. ACCESS, 13, 153212-153233. (www)

  @article{ , abstract = {Recently, several automotive manufacturers have shifted to 800-V battery electric vehicle (BEV) architectures, rather than the more conventional 400-V architectures. The main advantage is that fast-charging power levels can increase while staying within cable and battery current limits; thus, drivers can charge in a much shorter time, which increases the convenience and practicality of driving BEVs for longer distances. Hence, it is essential to develop fast-charging stations that are compatible with the conventional 400-V and the emerging 800-V vehicles. Therefore, dc/dc converter modules within these chargers should be capable of operating at high efficiency within an ultra-wide output voltage range (200 to 1000 V), which is a well-known power electronic challenge. This paper is the first to perform a comprehensive review of the universal fast-charger topic, including general methods to increase output voltage range and efficiency across this range, specific circuit topologies and control schemes proposed in the literature for universal fast-chargers, commercially available universal fast-chargers, and an on-board solution used in industry. Furthermore, this paper performs a detailed comparison of the reviewed universal fast-charging solutions and presents future trends towards the widespread use of universal fast-chargers.}, archive = {J_ACCESS}, author = {Mohammadhesam Hassani Sadi and Zahra Sadeghi and Jennifer Bauman}, doi = {10.1109/ACCESS.2025.3603845}, journal = {IEEE Access}, pages = {153212-153233}, shortjournal = {IEEE Access}, title = {A comprehensive review of universal fast-charging techniques for 400-V and 800-V electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Estimation of 3D ionospheric electron-density distribution specialized for the korean peninsula using a U-net super-resolution CNN. ACCESS, 13, 153199-153211. (www)

  @article{ , abstract = {Accurate knowledge of the three-dimensional ionospheric electron-density distribution is essential for reliable radio-wave propagation modeling, yet global empirical models (e.g., IRI-2020, NeQuick2) fail to capture local and short-term variability. In this work, we propose a U-Net–based super-resolution CNN (SRCNN) that reconstructs a regionally specialized 3D electron-density distribution over the Korean Peninsula from sparse, high-fidelity input profiles. These input profiles are generated for two sites (Icheon and Jeju) by combining direct ionosonde measurements for the bottomside with an ionosonde-corrected IRI-2020 model for the topside. The AI model was trained on electron-density distributions produced by the IRI-2020 model. The proposed model demonstrates significant improvements over the standard IRI model, showcasing its stability across all solar activity levels. Most notably, under solar-maximum conditions, the root mean square relative error (RMSRE) was drastically reduced at Icheon (from 367.23% to 16.04%) and Jeju (from 538.12% to 9.68%). The model also consistently improved other key metrics, such as the F2-peak altitude error and the Pearson correlation coefficient ( $r \gt 0.99$ ), proving its robust performance. The proposed approach can contribute to improving ionospheric error correction and signal quality in precise GNSS positioning, space surveillance radar, and satellite communication systems.}, archive = {J_ACCESS}, author = {Do Hyeon Lee and Junmo Yang and Hyosang Moon and Jaehoon Jung and Myungsik Lee and Yong Bae Park}, doi = {10.1109/ACCESS.2025.3603785}, journal = {IEEE Access}, pages = {153199-153211}, shortjournal = {IEEE Access}, title = {Estimation of 3D ionospheric electron-density distribution specialized for the korean peninsula using a U-net super-resolution CNN}, volume = {13}, year = {2025}, }
• (2025). AI-powered adaptive english language learning systems: Leveraging deep learning algorithms and natural language processing for personalized teaching approaches. ACCESS, 13, 153189-153198. (www)

  @article{ , abstract = {This study explores the development of AI-powered adaptive systems for English language learning, focusing on leveraging Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP) to create personalized learning experiences. The goal is to address the challenges of traditional, one-size-fits-all teaching methods by using AI to tailor content to individual learner needs, improving engagement and proficiency. Through RNNs, the system is able to model sequential language data, such as grammar and speech patterns, while NLP techniques process and interpret text and spoken input, allowing for real-time error detection and feedback. The key findings indicate that learners using the AI-powered system show significantly higher improvements in language proficiency—particularly in grammar accuracy and speech clarity—compared to those using traditional methods. The system’s ability to adapt content based on performance leads to greater user engagement, demonstrating the effectiveness of personalized learning paths. This study highlights the potential of AI to revolutionize language education, offering scalable solutions that cater to diverse learning styles and proficiency levels. The findings suggest that such systems could play a crucial role in enhancing global language education, particularly for non-native English learners, by offering a more flexible and individualized approach to learning.}, archive = {J_ACCESS}, author = {Ying Ma and Xiao-Jian Tang and Xin Huang}, doi = {10.1109/ACCESS.2025.3603602}, journal = {IEEE Access}, pages = {153189-153198}, shortjournal = {IEEE Access}, title = {AI-powered adaptive english language learning systems: Leveraging deep learning algorithms and natural language processing for personalized teaching approaches}, volume = {13}, year = {2025}, }
• (2025). Optimization of multi-agent scheduling based on MA-ID3QN for the riveting and welding work cell. ACCESS, 13, 153171-153188. (www)

  @article{ , abstract = {In industrial manufacturing, multi-agent scheduling is one of the key technologies for improving production efficiency. Due to the complexity of multi-agent systems and the interference between tasks, achieving efficient task scheduling is faced with significant challenges. To solve this problem, this paper introduces the dueling double deep Q-network (D3QN) into the multi-robot scheduling scenario of a riveting and welding work cell for the first time. Considering the characteristics of this scenario, an improved D3QN is proposed, which is designed as a multi-agent independent dueling double deep Q-network algorithm (MA-ID3QN) based on a multi-agent cooperation mechanism. In this approach, robots in the work cell are treated as independent agents, with decentralized training and decentralized execution to accommodate varying robot numbers. Meanwhile, several mechanisms are employed to enhance the algorithm’s performance. Furthermore, a digital twin-based riveting and welding work cell platform is constructed for validation. First, the MA-ID3QN algorithm generates a scheduling strategy based on the state of the physical space of the riveting and welding work cell. Then, the scheduling strategy is verified on the digital twin platform. Finally, comparative experiments are conducted to validate the effectiveness of the proposed method. The experimental results demonstrate that the MA-ID3QN-based agent scheduling method exhibits better reliability, higher efficiency, and stronger generalization capability in multi-agent task scheduling. This approach improves the efficiency of the riveting and welding work cell and reduces the time required for welding tasks in mass production scenarios. Moreover, it has promising application prospects in industrial robot scheduling.}, archive = {J_ACCESS}, author = {Jianbin Zheng and Yizhuo Zhang and Yang Gao and Ziyao Chen and Yifan Gao and Chuyi Zhou and Xinyu Zhou}, doi = {10.1109/ACCESS.2025.3604143}, journal = {IEEE Access}, pages = {153171-153188}, shortjournal = {IEEE Access}, title = {Optimization of multi-agent scheduling based on MA-ID3QN for the riveting and welding work cell}, volume = {13}, year = {2025}, }
• (2025). Natural scene text detection algorithm via internal feature enhancement and adaptive cross fusion. ACCESS, 13, 153159-153170. (www)

  @article{ , abstract = {In recent years, convolutional neural networks (CNNs) widely used in text detection tasks can effectively predict high-frequency details. However, most CNNs exhibit limited capability in jointly processing low-frequency semantic information and high-frequency localization information, which may reduce the naturalness and fidelity of the reconstructed text to some extent, thereby affecting the performance of downstream tasks. To address these issues, we propose a natural scene text detection algorithm via internal feature enhancement and adaptive cross fusion (IFEACF). Specifically, by splitting and classifying feature maps in the channel dimension, the Internal Feature Enhancement Residual Network (IFE-ResNet) is constructed to improve the internal correlation of features and enable the network to focus on information-rich regions. In feature fusion, the adaptive cross fusion (ACF) module is proposed to fully integrate the detailed information of shallow features with the semantic information of deep features, enhancing the fusion effect of features from non-adjacent layers. Furthermore, the spatial perception fusion (SPF) module is designed to adaptively fuse feature maps of different scales, which generates accurate RoI feature to improve the text detection performance of the model. The superiority of the presented method has been demonstrated through experiments conducted on the ICDAR2015 dataset, showing that precision, recall, and F-measure surpass the baseline PSENet by 4.01%, 0.91%, and 2.34% respectively. Moreover, both precision and F-measure of the proposed algorithm outperform those of the state-of-the-art methods.}, archive = {J_ACCESS}, author = {Guogang Wang and Shen Wei and Dan Yang and Aiying Guo}, doi = {10.1109/ACCESS.2025.3602805}, journal = {IEEE Access}, pages = {153159-153170}, shortjournal = {IEEE Access}, title = {Natural scene text detection algorithm via internal feature enhancement and adaptive cross fusion}, volume = {13}, year = {2025}, }
• (2025). Estimate crowd flow including side-trip behavior when exiting from large-scale event venues. ACCESS, 13, 153149-153158. (www)

  @article{ , abstract = {This study introduces a novel extended crowd simulation model for the task of estimating crowd flows in large-scale environments under unforced situations, where individuals freely select paths to their destination, such as exiting an event venue after participating in activities. The crowd flows in unforced situations, including side-trip behavior, such as stopping at roadside vending machines, stores, restrooms, and trash cans before arriving at their primary destination. The traditional models in a large-scale environment often focus on simulating forced situations such as evacuation. To accurately estimate unforced situations, we propose a minimal extended model to represent a stochastic side-trip. The proposed model contributes to evaluating crowd behavior in an entire building based on partial measurement data and reducing the high costs of deploying extensive measurement equipment and personnel. To evaluate the model, data on spectators exiting the Tokyo Dome were measured using LiDAR and 8K cameras. Evaluation results using measured data indicate that the proposed model achieves a 9.25% improvement in simulation accuracy over previously established models when evaluated on the same-day data. Furthermore, the model maintains superior performance, with a 3.15% improvement, even when calibrated parameters are applied to data from different day’s scenarios, demonstrating its robustness and generalizability.}, archive = {J_ACCESS}, author = {Ryo Niwa and Shunki Takami and Shusuke Shigenaka and Masaki Onishi and Wataru Naito and Tetsuo Yasutaka}, doi = {10.1109/ACCESS.2025.3598750}, journal = {IEEE Access}, pages = {153149-153158}, shortjournal = {IEEE Access}, title = {Estimate crowd flow including side-trip behavior when exiting from large-scale event venues}, volume = {13}, year = {2025}, }
• (2025). Parametrizations of a 77 GHz OFDM joint radar communication. ACCESS, 13, 153140-153148. (www)

  @article{ , abstract = {The parametrization of Orthogonal Frequency-Division Multiplexing (OFDM) radar is fundamental when designing a Joint Radar and Communication (JRC) system. The parameters of the OFDM frame simultaneously influence both communication and detection performance. While the radar component requires accurate range and speed estimation, the communication subsystem must transmit a robust signal over the transmission channel. Careful optimization of these parameters in the 77 GHz frequency band enables the design of a communicating radar (RadCom) suitable for automotive applications. In this article, we propose two configurations for OFDM radar and present their effects on both radar and communication performance. The first configuration is designed to provide range and Doppler coverage and resolution as close as possible to those of a typical automotive Frequency-Modulated Continuous Wave (FMCW) corner radar, yielding a subcarrier spacing (SCS) greater than 1 MHz. The second configuration employs a 480 kHz subcarrier spacing, which is compatible with current communication standards, while maintaining acceptable speed and distance resolutions. Simulations show that the second configuration offers advantages in terms of radar detection and communication data rate compared to both the first configuration and FMCW radar. On the other hand, the first configuration delivers better communication performance in terms of Bit Error Rate (BER) under Doppler shift and in noisy environments. Measurement results confirm the good radar performance of OFDM configuration 2 compared with FMCW radar and OFDM in configuration 1 in terms of Signal-to-Noise Ratio (SNR). Therefore, it can be concluded that in configuration 2, the OFDM waveform-based Radcom system is capable of achieving higher radar performance than current automotive radar, while simultaneously enabling fast and reliable V2X communication.}, archive = {J_ACCESS}, author = {K. B. Serge Angelo Dapa and Fouzia Elbahhar Boukour and Guillaume Point and Saleh Bensator}, doi = {10.1109/ACCESS.2025.3596870}, journal = {IEEE Access}, pages = {153140-153148}, shortjournal = {IEEE Access}, title = {Parametrizations of a 77 GHz OFDM joint radar communication}, volume = {13}, year = {2025}, }
• (2025). Field interactions and power conservation at lossy interfaces under normal incidence. ACCESS, 13, 153123-153139. (www)

  @article{ , abstract = {The conservation of local complex power density and global complex power flow at interfaces between lossy media is not inherently guaranteed within classical plane-wave treatments, which often neglect the contribution of cross-field interaction terms. An analytical investigation is conducted for a normally incident time-harmonic plane wave impinging on a planar half-space interface, wherein the omission of such interaction terms is shown to preserve conservation in the lossless case but to induce significant violations in lossy configurations. The precise parametric conditions under which these violations occur are derived. A complete formulation is then constructed, incorporating the requisite cross terms to restore both local and global conservation laws. Verification is achieved through consistency between global complex power flow—obtained via surface integrals of the corrected local power density—and volumetric field–material interaction integrals. A parametric analysis over a broad frequency range (300 MHz–300 GHz) and varying permittivity and conductivity reveals that the error resulting from neglecting interaction terms is highly localized, typically confined within a quarter-wavelength of the interface. The results delineate the exact scenarios necessitating inclusion of interaction terms and establish a rigorously balanced power framework for modeling wave behavior at lossy media boundaries.}, archive = {J_ACCESS}, author = {Ata Zadehgol}, doi = {10.1109/ACCESS.2025.3603546}, journal = {IEEE Access}, pages = {153123-153139}, shortjournal = {IEEE Access}, title = {Field interactions and power conservation at lossy interfaces under normal incidence}, volume = {13}, year = {2025}, }
• (2025). Soundy: A multimodal audio interface for educational music production. ACCESS, 13, 153105-153122. (www)

  @article{ , abstract = {Multimodal interaction has emerged as a promising approach to enrich user experience and foster engagement in Educational Music Production (EMP) environments. Traditional audio interfaces and Digital Audio Workstations (DAWs), while powerful, often present engagement challenges, particularly for beginners, and tend to limit opportunities for spontaneous creativity and hands-on exploration during the learning process. To overcome these limitations, this paper introduces Soundy, a multimodal audio interface that integrates an on-board, lightweight web-based DAW. The system is accessible via a standard browser through a Local Area Network (LAN) and allows users to control audio production tasks using predefined and custom voice and facial commands. A comparative within-subject study was conducted involving 20 participants, each of whom tested both Soundy and a standard configuration composed of a Behringer UMC404HD audio interface and the Reaper DAW. The evaluation focused on two key dimensions, usability and engagement, measured using two standardized and validated instruments: The Post-Study System Usability Questionnaire (PSSUQ) and the User Engagement Scale - Short Form (UES-SF), complemented by open-ended participant feedback. Results revealed a trade-off between configurations: the standard setup ensured higher usability and task efficiency, while Soundy promoted greater engagement, creativity, and exploratory behavior. These findings suggest that embedded multimodal solutions based on voice and facial interaction hold strong potential for enhancing student experience in EMP and support future developments in adaptive command recognition and integrated hardware design.}, archive = {J_ACCESS}, author = {Pietro Buccellato and Andrea Bianco and Cristina Rottondi}, doi = {10.1109/ACCESS.2025.3604631}, journal = {IEEE Access}, pages = {153105-153122}, shortjournal = {IEEE Access}, title = {Soundy: A multimodal audio interface for educational music production}, volume = {13}, year = {2025}, }
• (2025). Channel-robust specific emitter identification based on domain-adversarial training of neural networks and multi-feature fusion. ACCESS, 13, 153093-153104. (www)

  @article{ , abstract = {To address the significant decline in the accuracy of Specific Emitter Identification(SEI) under wireless channel, we propose a novel method that combines a domain adversarial network with multi-feature fusion(MFF) to extract domain-invariant features of the signal and leverage the complementary nature of signal features extracted from different views. Initially, we employ the IQ Convolutional Neural Network (IQCNN), the Gate Recurrent Unit (GRU), and the stacked Fourier Analysis Networks (SFAN) to directly extract and fuse correlation, temporal, and periodic features from the raw I/Q data. Subsequently, we integrate a Domain-Adversarial Training of Neural Networks (DANN) to eliminate channel features, ultimately enabling SEI under channel interference. The experimental results on the WiFi dataset demonstrate that the MFF network designed in this study achieves an identification accuracy of 97% under Additive White Gaussian Noise(AWGN) channel interference with a signal-to-noise ratio(SNR) of 10dB. Furthermore, the proposed method achieves identification accuracy of 93.8%, 90.3%, and 78.2% under three complex real-world channel interference scenarios, respectively. These findings indicate that the proposed method effectively mitigates channel interference and significantly enhances the robustness of SEI.}, archive = {J_ACCESS}, author = {Jialong He and Yuelei Xie and Xiangguo Liu}, doi = {10.1109/ACCESS.2025.3604428}, journal = {IEEE Access}, pages = {153093-153104}, shortjournal = {IEEE Access}, title = {Channel-robust specific emitter identification based on domain-adversarial training of neural networks and multi-feature fusion}, volume = {13}, year = {2025}, }
• (2025). A high-performance hybrid MPPT method for rapid tracking and steady power operation under varying weather conditions and load changes in photovoltaic systems. ACCESS, 13, 153072-153092. (www)

  @article{ , abstract = {Maximum power point tracking (MPPT) plays a crucial role in the performance of the photovoltaic (PV) output, which decreases dramatically under partial shading conditions (PSCs). The water cycle optimization (WCO) algorithm is presented as a promising solution to address the MPPT problem. However, conventional WCO suffers from a complex parameter tuning process, high oscillations, and low convergence speed when tracking the maximum power point under PSCs and uniform irradiance conditions (UICs), leading to low efficiency and significant power loss. Therefore, an enhanced WCO hybridized with a modified perturb and observe (MP&O) algorithm is introduced as an efficient real-time MPPT method. The enhanced WCO with one dynamic parameter is designed to rapidly identify the global maximum power point (GMPP) under PSCs. The MP&O algorithm, with an adaptive perturbation step size, is used to track the maximum power point under UICs, resulting in minimal oscillations. Moreover, our method employs a load line strategy to address the issue of restarting the tracking process in the MPPT application at load variations, ensuring reliable and rapid GMPP tracking. Extensive experimental tests were conducted to validate the performance of the proposed hybrid method under various UIC, PSC, and complex PSC patterns, as well as during load variations. The results reveal that the method achieves an average tracking time of 0.275 s under UICs and 0.45 s under PSCs, and an average efficiency of 99.90% for all irradiance patterns. A comparative study with other MPPT methods demonstrates the superior performance of the proposed hybrid MPPT method in terms of eleven tracking performance criteria.}, archive = {J_ACCESS}, author = {Rafah Ibraheem Jabbar and Saad Mekhilef and Marizan Mubin and Addy Wahyudie}, doi = {10.1109/ACCESS.2025.3603641}, journal = {IEEE Access}, pages = {153072-153092}, shortjournal = {IEEE Access}, title = {A high-performance hybrid MPPT method for rapid tracking and steady power operation under varying weather conditions and load changes in photovoltaic systems}, volume = {13}, year = {2025}, }
• (2025). Power quality improvement of MV networks using PV-MLI-based DVR with battery energy storage system. ACCESS, 13, 153056-153071. (www)

  @article{ , abstract = {This paper presents the design and analysis of a dynamic voltage restorer (DVR) based on a transistor-clamped H-bridge (TCHB) multilevel inverter with reduced number of switches. The DVR uses a renewable energy source—specifically, a solar PV array integrated with a battery energy storage system—as the DC voltage supply. A recently developed multilevel inverter (MLI) topology called the TCHB inverter has fewer components and relatively lower power losses than other conventional topologies with the same output quality. In this work, five level inverter-based DVR is used for 11 kV line. The proposed quick-propagation (Q-prop) control algorithm provides improved estimation accuracy, a faster rate of weight convergence, simplicity and an increase in the system’s robustness to changing parameters. The performance of the proposed system is assessed in the face of voltage-based power quality (PQ) problems like voltage sag, swell, harmonics, voltage imbalance, and their combinations. The performance of the suggested system is then compared with traditional methods. A digital simulator is used to experimentally validate the results in real time. The system is simulated in MATLAB/Simulink.}, archive = {J_ACCESS}, author = {Humeera Altaf and Abdul Hamid Bhat and Masood Ibni Nazir and Fahad S. Alshammari}, doi = {10.1109/ACCESS.2025.3599682}, journal = {IEEE Access}, pages = {153056-153071}, shortjournal = {IEEE Access}, title = {Power quality improvement of MV networks using PV-MLI-based DVR with battery energy storage system}, volume = {13}, year = {2025}, }
• (2025). Hashgraph over the edge: Achieving byzantine fault tolerance in decentralized P2P frameworks. ACCESS, 13, 153035-153055. (www)

  @article{ , abstract = {This paper explores the integration of the Hashgraph consensus protocol into two widely used peer-to-peer (P2P) communication frameworks: Secure Scuttlebutt (SSB) and libp2p. SSB operates within a local-first architecture, prioritizing offline-first data synchronization through append-only logs and gossip-based replication, while libp2p employs a modular, pub-sub-driven approach to facilitate real-time message dissemination across decentralized networks. We propose a dual integration approach, embedding an asynchronous Byzantine fault-tolerant (aBFT) layer into both frameworks to evaluate their complementary strengths in diverse networking scenarios. This dual-path strategy allows us to investigate how Hashgraph’s aBFT properties interact with fundamentally different P2P architectures—append-only, local-first versus modular, pub-sub—thus offering a broader understanding of its applicability. Our goal is to enhance their ability to achieve a globally consistent state despite the challenges of intermittent connectivity and network partitions. To assess the feasibility and performance implications of this integration, we conduct a series of controlled experiments using Docker-based network simulations. Our evaluation focuses on key trade-offs involving consistency, latency, and bandwidth utilization. The results show that SSB+Hashgraph achieves strong eventual consistency, ensuring robust data integrity even under adversarial conditions. However, this comes at the cost of increased message propagation latency, due to the bulk gossip overhead inherent in SSB’s replication model. In contrast, libp2p+Hashgraph enables significantly faster message propagation through its pub-sub mechanism, but incurs higher bandwidth consumption as more frequent state updates are required to maintain consensus. Notably, our experiments confirm that the proposed approach successfully maintains consensus even in the presence of up to 25% malicious nodes, highlighting its resilience and strong Byzantine fault tolerance. These findings demonstrate the practical viability of combining gossip-based data replication with aBFT consensus to improve the reliability and scalability of decentralized applications. Moreover, our study offers valuable insights into the trade-offs involved in designing resilient P2P communication systems capable of operating efficiently across heterogeneous network environments.}, archive = {J_ACCESS}, author = {Wanli Zhao and Satoshi Fujita}, doi = {10.1109/ACCESS.2025.3604512}, journal = {IEEE Access}, pages = {153035-153055}, shortjournal = {IEEE Access}, title = {Hashgraph over the edge: Achieving byzantine fault tolerance in decentralized P2P frameworks}, volume = {13}, year = {2025}, }
• (2025). Leveraging digital twins for integrated energy and transportation management in smart cities. ACCESS, 13, 153024-153034. (www)

  @article{ , abstract = {This paper presents an integrated framework for smart city management that combines digital twin systems, deep learning (DL) forecasting, and Particle Swarm Optimization (PSO) for multi-objective optimization. Leveraging real-time data from smart meters, transportation sensors, and urban infrastructure, the framework constructs an accurate digital twin representation to enable dynamic prediction and control. Long Short-Term Memory (LSTM) networks are employed to forecast energy consumption, grid load, and traffic congestion with high accuracy (e.g., energy prediction R ${}^{2} =0.92$ , traffic prediction R ${}^{2} =0.88$ ). These forecasts inform the PSO module, which optimizes energy usage and travel time simultaneously through a multi-objective cost function under operational constraints. The proposed model demonstrates a 15% reduction in energy consumption and a 12% decrease in average travel time in simulation studies. Compared to traditional methods such as Genetic Algorithms (GA) and Linear Programming (LP), the PSO-based approach achieves faster convergence and more robust optimization results, reflecting its superior performance. The novelty lies in the seamless integration of digital twin predictive capabilities with PSO-driven real-time optimization, providing a scalable, efficient solution for complex urban systems. This work advances smart city technology by enabling data-driven, adaptive management of energy and transportation resources.}, archive = {J_ACCESS}, author = {Zichu Zhou and Yonghan Li and Yaobei He}, doi = {10.1109/ACCESS.2025.3603883}, journal = {IEEE Access}, pages = {153024-153034}, shortjournal = {IEEE Access}, title = {Leveraging digital twins for integrated energy and transportation management in smart cities}, volume = {13}, year = {2025}, }
• (2025). PASTEL: An aerial multi-LiDAR dataset for research in SLAM tuning and robustness. ACCESS, 13, 153010-153023. (www)

  @article{ , abstract = {LiDAR-based SLAM algorithms are critically dependent on the configuration of the environment and the LiDAR characteristics. Existing LiDAR-based datasets are not devised for research in SLAM parameter tuning due to limited diversity in types of environments and difficulties in comparing results from LiDARs with different characteristics. This paper presents PASTEL, a dataset for research in LiDAR-based SLAM tuning performance and robustness for low-altitude navigation of uncrewed aerial vehicles. PASTEL provides high diversity in the main factors that affect LiDAR-based SLAM tuning: type of environment, 3D LiDAR characteristics, and aerial robot velocity. The aerial robot mounts three different 3D LiDAR models (with different resolutions, fields of view, and accuracies) that are recorded in parallel. PASTEL includes 17 sequences recorded in flights with two velocity profiles at distinct types of environments: wide-open spaces with distant obstacles, horizontally confined spaces with open sky, confined GNSS-denied spaces, and also includes sequences where the aerial robot transitions between two or more of these distinct environments. The dataset includes measurements from the 3 LiDARS and Inertial Measurement Units (IMUs), as well as the ground-truth robot trajectories and ground-truth maps. PASTEL, available at https://sites.google.com/view/pastel-lidar-slam-dataset and at https://zenodo.org/records/14796964, has been validated in terms of trajectory and map accuracies with well-known SLAM methods.}, archive = {J_ACCESS}, author = {Robert Milijas and Jose Ramiro Martínez-de Dios and Stjepan Bogdan}, doi = {10.1109/ACCESS.2025.3603733}, journal = {IEEE Access}, pages = {153010-153023}, shortjournal = {IEEE Access}, title = {PASTEL: An aerial multi-LiDAR dataset for research in SLAM tuning and robustness}, volume = {13}, year = {2025}, }
• (2025). A novel method for estimating the state of charge of lithium batteries adopts a bidirectional gated recurrent unit with attention mechanism and piecewise estimation. ACCESS, 13, 152997-153009. (www)

  @article{ , abstract = {Accurate estimation of the state of charge (SOC) in lithium-ion batteries is fundamental to ensuring their safe and reliable operation. however, the influence of factors such as battery state, discharge conditions, and environmental temperature poses significant challenges to achieving precise SOC estimation. To tackle these challenges, this paper is proposed a hybrid deep learning framework for SOC estimation that integrates the bidirectional information extraction capabilities of Bidirectional Gated Recurrent Units (BiGRU) with the adaptive feature weighting mechanism of the attention mechanism, forming the BiGRU-Attention model. To further enhance the model’s performance under varying temperatures and operational conditions, the Whale Optimization Algorithm (WOA) is employed to optimize its hyperparameters. Additionally, a redefined SOC calculation method to mitigate the impact of data disturbances caused by incomplete discharge cycles and battery aging. Furthermore, piecewise estimation addresses the problem of increasing root mean square error (RMSE) in low-temperature environments. Experimental results demonstrate that the proposed method achieves highly accurate and robust SOC estimation, outperforming existing approaches. The RMSE is maintained below 0.8%, while the maximum absolute error is constrained to within 4.3%, highlighting the superiority of this approach and offering a novel perspective for SOC estimation of lithium-ion batteries.}, archive = {J_ACCESS}, author = {Weida Cheng and Shuo Sun and Hang Yin and Zongliang Wang and Wei Cai}, doi = {10.1109/ACCESS.2025.3600641}, journal = {IEEE Access}, pages = {152997-153009}, shortjournal = {IEEE Access}, title = {A novel method for estimating the state of charge of lithium batteries adopts a bidirectional gated recurrent unit with attention mechanism and piecewise estimation}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based models for paddy disease classification and segmentation: An experimental review. ACCESS, 13, 152962-152996. (www)

  @article{ , abstract = {Early-stage diagnosis of paddy diseases, based on initial visible symptoms, is crucial for minimizing chemical use and preventing the spread of disease. Automated paddy disease diagnosis can facilitate this and contribute to improved crop production. Accurate segmentation of diseased regions is essential for precise severity analysis, and segmentation models can be used to address the challenges related to the complex disease patterns. This study analyses the performance and applicability of seven classification models (DenseNet, InceptionV3, Xception, MobileNet, Vision Transformer (ViT), parallel-ViT, and a custom ensemble model of DenseNet121 and Xception) and eight segmentation models (DeepLabv3+, UNet, VGG16 UNet, Inception-ResNetV2 UNet, TransUNet, Attention UNet, MultiResUNet, and Deep Residual UNet) in this domain. A comparative analysis of these models, highlighting their structural differences, advantages, and limitations, is also provided. Given the high computational demands and high data dependency of ViTs, we evaluated whether traditional models, their ensembles, or memory-efficient models could match ViT performance, and found this to be feasible. Additionally, we explored the effect of augmentation intensity on the classification models. We also identified significant research gaps within this domain. To address the lack of open-access paddy disease segmentation datasets, we created a novel dataset using image-processing techniques. The applicability of the above-mentioned segmentation models was evaluated for paddy diseases using this newly created dataset and we found that the Deep Residual UNet is the most suitable model to be used in resource-constraint applications, considering its quantitative and qualitative performance, model size, and structural advantages and limitations. Furthermore, we investigated the impact of training these segmentation models with a dataset containing more than double the number of augmented images compared to the original dataset. We observed that while the quantitative performance increased with increased data, the qualitative performance degraded in a few cases. Overall, our results can assist researchers in developing effective autonomous systems for paddy disease management.}, archive = {J_ACCESS}, author = {Mahrin Tasfe and Akm Nivrito and Fadi Al Machot and Mohib Ullah and Faouzi Alaya Cheikh and Habib Ullah}, doi = {10.1109/ACCESS.2025.3599355}, journal = {IEEE Access}, pages = {152962-152996}, shortjournal = {IEEE Access}, title = {Deep learning-based models for paddy disease classification and segmentation: An experimental review}, volume = {13}, year = {2025}, }
• (2025). Cloud-enabled predictive modeling of mental health using ensemble machine learning models and AES-256 security. ACCESS, 13, 152948-152961. (www)

  @article{ , abstract = {Mental health disorders are becoming a common concern among adults and children alike. With the growth of cloud computing and the rising concerns over data privacy, integrating machine learning (ML) with secure cloud infrastructure for mental health prediction helps in early detection and timely intervention. In this paper, we propose a novel approach of integrating a voting-based ensemble learning with AES-256-encrypted cloud infrastructure on AWS for a secure and scalable mental health prediction. The ensemble model combines five popular machine learning classifiers: Logistic Regression, Naïve Bayes, Support Vector Machine (SVM), XGBoost, and Random Forest. The proposed model was constructed using these classifiers as its base learners. The system was trained on a dataset of 7,731 Reddit posts related to mental health. We have employed AWS S3 storage and AES-256 encryption for data security. To evaluate the effectiveness of the proposed model, we compared its performance against individual classifiers using a range of evaluation metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that the ensemble model consistently outperforms the individual classifiers, achieving superior performance across all metrics. The model’s overall generalizability may be limited because the dataset, which consisted of Reddit postings, might not accurately represent the language or tone used on other platforms, such as clinical notes or journals. Computational overhead is another consequence of using an ensemble model. However, the model was able to demonstrate high accuracy, scalability, and security, making it a viable choice for deployment in real-world clinical settings for early mental health screening.}, archive = {J_ACCESS}, author = {Aparna Saju and Shivaprasad Gundibail and Ujjwal}, doi = {10.1109/ACCESS.2025.3601896}, journal = {IEEE Access}, pages = {152948-152961}, shortjournal = {IEEE Access}, title = {Cloud-enabled predictive modeling of mental health using ensemble machine learning models and AES-256 security}, volume = {13}, year = {2025}, }
• (2025). Application of complex wavelet transform in OCT image compressive sensing reconstruction. ACCESS, 13, 152934-152947. (www)

  @article{ , abstract = {This paper presents a method for reconstructing Optical Coherence Tomography (OCT) images from incomplete data, a crucial advancement for mitigating acquisition time and motion artifacts. For this reason, the cross-sectional OCT data is sampled using different sampling strategies for the acquisition data and different algorithms are employed to reconstruct the high SNR and high-resolution OCT data in two and three dimensions. The reconstruction problem is solved by using a two-phase approach, including sparse coding and dictionary updating. At first, the sparsity of data is investigated by Discrete Cosine Transform (DCT), wavelet and complex wavelet transforms as initial dictionary. Then, the missing data is reconstructed by a compressive sensing (CS) reconstruction approach. Simulation results demonstrate that the dictionary learned from the complex wavelet transform yields the sparsest image representation, leading to improved CS reconstruction. The proposed CS reconstruction algorithm offers the potential to significantly accelerate OCT data acquisition while reducing the impact of motion-induced distortions, representing a valuable contribution to the field of OCT imaging.}, archive = {J_ACCESS}, author = {Marzieh Mokhtari and Ehsan Yazdian and Gerlind Plonka and Hossein Rabbani}, doi = {10.1109/ACCESS.2025.3602356}, journal = {IEEE Access}, pages = {152934-152947}, shortjournal = {IEEE Access}, title = {Application of complex wavelet transform in OCT image compressive sensing reconstruction}, volume = {13}, year = {2025}, }
• (2025). Benchmark algorithm for asynchronous detection of M-ASPM packets combined with measuring carrier frequency offset. ACCESS, 13, 152914-152933. (www)

  @article{ , abstract = {This paper demonstrates that in M-ary Aggregate Spread Pulse Modulation (M-ASPM) a relatively short, low-gain front portion of the transmitted packet can be used for robust asynchronous detection of the packet at low computational cost. With low processing gain, this detection is insensitive to large carrier frequency offsets (CFO) between the transmitter and the receiver, and it can be combined with measuring the CFO within the desired range and with desired precision. The subsequent costly processing of the high-gain, CFO-sensitive segments of the packet (e.g., those allocated to the synchronization and the payload) is implemented only after the detection, and the measured CFO is used for modifying the processing to prevent signal deterioration. In the receiver, only low-order matched filtering is continuously performed, and a single detection channel can be shared by multiple quasi-orthogonal channels used for payloads. This greatly economizes M-ASPM networks, especially their multi-channel, high-throughput configurations. However, since the contrast in the processing gains between different portions of the packet can exceed two orders of magnitude, matching the detection sensitivity with that of the payload, while maintaining the portion of the packet dedicated to the detection relatively small, poses a significant challenge. This paper presents the detection algorithm that overcomes this challenge, together with detailed explanation of the procedures and the tools employed in implementation of its steps. In particular, the properties, scope, and limitations of such essential algorithm components as the modulo exponential averaging (MEA) and adaptive quantile tracking filters (QTFs) for troughs are analyzed.}, archive = {J_ACCESS}, author = {Alexei V. Nikitin and Ruslan L. Davidchack}, doi = {10.1109/ACCESS.2025.3604244}, journal = {IEEE Access}, pages = {152914-152933}, shortjournal = {IEEE Access}, title = {Benchmark algorithm for asynchronous detection of M-ASPM packets combined with measuring carrier frequency offset}, volume = {13}, year = {2025}, }
• (2025). A survey on application of AI on reverse engineering for software analysis and security. ACCESS, 13, 152903-152913. (www)

  @article{ , abstract = {Reverse engineering process serves essential functions in software analysis and security auditing and malware detection but requires significant time and effort. Researchers and practitioners now investigate how Artificial Intelligence (AI) technology can automate and improve different reverse engineering procedures. This survey provides an extensive evaluation of recent AI-based reverse engineering techniques which focus on software decompilation and function identification as well as control flow recovery and vulnerability analysis. The paper presents a classification system for existing methods while comparing them through an analysis of their development from traditional rule-based systems to contemporary deep learning frameworks. The research examines fundamental datasets together with field tools and evaluation metrics. This paper establishes a fundamental understanding of AI integration in reverse engineering for software security while discussing future development directions.}, archive = {J_ACCESS}, author = {Ashutosh Ghimire and Sahasra Rao Lingala and Junjie Zhang and Faris Alsulami and Fathi Amsaad}, doi = {10.1109/ACCESS.2025.3593456}, journal = {IEEE Access}, pages = {152903-152913}, shortjournal = {IEEE Access}, title = {A survey on application of AI on reverse engineering for software analysis and security}, volume = {13}, year = {2025}, }
• (2025). Multimodal analysis of emotional and visual responses to urban green spaces in virtual environments. ACCESS, 13, 152890-152902. (www)

  @article{ , abstract = {Urban green spaces (UGSs) play a vital role in promoting emotional well-being and supporting public health. Emotional responses to landscapes are closely linked to visual behavior, reflecting how individuals engage with environmental features. This study proposes and applies a multimodal sensing framework to examine how the types and proportions of landscape elements influence emotional and visual responses in immersive virtual environments. Sixty participants experienced 360° panoramic simulations of three UGS types—urban parks, community gardens, and ecological wetlands—featuring seven landscape elements: trees, shrubs, flowering trees, grass, herb plants, water landscapes, and artificial objects. Data were collected using electrodermal activity (EDA), pulse rate (PR), eye-tracking metrics, and self-reported emotional ratings. Visual behavior indices were analyzed in relation to landscape composition. Results show that both visual behavior and emotional responses were significantly influenced by landscape elements. Herb plants enhanced attention and positive emotions across compositions. Flowering trees exhibited a dual effect—eliciting physiological arousal while promoting relaxation. Water landscapes captured immediate attention and evoked positive emotional experiences, whereas artificial objects required greater visual effort but yielded limited emotional benefits. These findings provide actionable, data-driven insights for the design of emotionally supportive UGSs in high-density environments.}, archive = {J_ACCESS}, author = {Chong Liu and Tzu-Yang Wang and Takaya Yuizono}, doi = {10.1109/ACCESS.2025.3604420}, journal = {IEEE Access}, pages = {152890-152902}, shortjournal = {IEEE Access}, title = {Multimodal analysis of emotional and visual responses to urban green spaces in virtual environments}, volume = {13}, year = {2025}, }
• (2025). A novel approach for mitigating class imbalance in arabic text classification. ACCESS, 13, 152870-152889. (www)

  @article{ , abstract = {Natural language processing (NLP) has become somewhat well-known because of its many uses; deep neural networks have driven major developments. Still, there are difficulties, especially in Arabic NLP, where the language’s large vocabulary of over 12 million words and several dialects cause special issues. Arabic has a large speaker base; however, NLP studies in this language find challenges, particularly with class imbalance. Many times, standard class balancing methods overlook intra-class similarity, a crucial element influencing model training. We present a new approach for computing intra-class similarity using cosine similarity and embedding models to find ideal class weights for model training, hence bridging this difference. On two benchmark datasets—the Arabic Semantic Question Similarity dataset (NSURL) and the Microsoft Research Paragraph Corpus (MRPC)—we assessed the proposed approach. With an accuracy of state-of-the-art 83.25% on the MRPC dataset and 96.931% on the NSURL dataset, the proposed approach proved successful in improving model performance in Arabic text classification.}, archive = {J_ACCESS}, author = {Emad Nabil and Abdelrahman Ezzeldin Nagib and Mena Hany and Safiullah Faizullah and Wael Hassan Gomaa}, doi = {10.1109/ACCESS.2025.3604427}, journal = {IEEE Access}, pages = {152870-152889}, shortjournal = {IEEE Access}, title = {A novel approach for mitigating class imbalance in arabic text classification}, volume = {13}, year = {2025}, }
• (2025). Technologies, applications, and challenges of digital twin across industries: A systematic review of the state-of-the-art literature. ACCESS, 13, 152843-152869. (www)

  @article{ , abstract = {Digital twins can be defined as a virtual representation of a physical object that can replicate its behavior in a virtual environment. This concept involves the integration of three distinct components: the physical space, the virtual space, and the communication between these two spaces. Digital twins are recognized as one of the key driving forces in various industries, and a comprehensive understanding is necessary to utilize them. A thorough review of the advantages and disadvantages of digital twins, including their value, characteristics, applications, and challenges, is essential to analyze their growth potential and investment value. This paper comprehensively reviews the digital twin applications being studied across industries. To this end, we propose three research questions and address them through a structured literature review. First, we examine the core technologies of digital twins and find that they form an integrated framework combining modeling, sensing, and real-time data processing. Second, we analyze their adoption across industries and observe widespread use in aerospace and aeronautics, manufacturing, healthcare, energy, and urban systems with domain-specific objectives. Third, we explore life-cycle applications and identify digital twins as enablers of continuity from design to operation and evolution. These findings offer a broad perspective on the current state and future potential of digital twin applications.}, archive = {J_ACCESS}, author = {Changhui Bae and Euteum Choi and Seongjin Lee}, doi = {10.1109/ACCESS.2025.3601615}, journal = {IEEE Access}, pages = {152843-152869}, shortjournal = {IEEE Access}, title = {Technologies, applications, and challenges of digital twin across industries: A systematic review of the state-of-the-art literature}, volume = {13}, year = {2025}, }
• (2025). Fast and scalable intelligent reflecting surface beamforming using hybrid learning approaches. ACCESS, 13, 152826-152842. (www)

  @article{ , abstract = {This study presents a novel hybrid optimization framework for intelligent reflecting surface (IRS)-assisted beamforming, that synergistically integrates reinforcement learning (RL) and cross-entropy (CE) methods. The proposed approach addresses the key limitations of conventional techniques, such as the high computational complexity and slow convergence in large-scale IRS deployments. By combining the global exploration capability of RL with the precise local refinement of CE, the framework efficiently optimizes the IRS phase shifts to maximize the signal-to-interference-plus-noise ratio (SINR). The bidirectional interaction between the RL and CE components enables adaptive beamforming in dynamic environments, whereas the joint optimization of transmit and reflective beamforming eliminates the need for alternating optimization. Simulation results demonstrate that the proposed method outperforms state-of-the-art techniques, achieving an SINR of 35 dB with 512 IRS elements, a sum rate of 42 bps/Hz, and 40% faster convergence than deep RL approaches. Furthermore, the framework exhibits robust performance with less than 2.5 dB SINR degradation under 30% channel state information errors and supports user mobility up to 10 m/s. These advancements, combined with an energy efficiency of 25 bits/Joule/Hz, establish the practical viability of the proposed solution for future 6G wireless networks.}, archive = {J_ACCESS}, author = {Sivasankar S and Markkandan S}, doi = {10.1109/ACCESS.2025.3603206}, journal = {IEEE Access}, pages = {152826-152842}, shortjournal = {IEEE Access}, title = {Fast and scalable intelligent reflecting surface beamforming using hybrid learning approaches}, volume = {13}, year = {2025}, }
• (2025). A new 19-level switched capacitor multilevel inverter with self-managing voltage balance. ACCESS, 13, 152812-152825. (www)

  @article{ , abstract = {This paper introduces a novel single-phase multilevel inverter utilizing a switched-capacitor technique that generates 19-levels while minimizing component count to 12 switches, three diodes, two DC sources, and three capacitors. For the gate driver, a compact gate driver designed and all gate drivers powered with only one DC source. A significant challenge in switched capacitor multilevel inverters (SC-MLIs) is charge balancing across capacitors, especially at low values of the modulation index; however, the proposed design effectively self-manages this balance, eliminating the need for complex control circuits. Increasing the output levels significantly reduces harmonic distortion, particularly at lower modulation indices. Performance evaluation against existing SC-MLI configurations shows that the proposed 19-level inverter experiences lower total stress voltage (TSV) on its switches, enhancing reliability. The staggered application of voltage sources allows switches in each stage to operate at lower duty-cycle fractions, enabling the use of less expensive, lower-rated switches. Validation through laboratory prototyping and PLECS simulations confirmed an efficiency of 96.3% at a power output of 500 W. Additionally, high-frequency modulation techniques can effectively shift undesirable harmonics to higher frequencies, which can be filtered out with smaller component filters. The inverter meets stringent IEEE standards for total harmonic distortion (THD), confirming its suitability for dynamic voltage restorers and distributed generation systems, thereby enhancing power conversion in renewable energy applications.}, archive = {J_ACCESS}, author = {Seyed Hadi Shafiei and Yousef Seifi Kavian and Karim Ansari-Asl}, doi = {10.1109/ACCESS.2025.3603781}, journal = {IEEE Access}, pages = {152812-152825}, shortjournal = {IEEE Access}, title = {A new 19-level switched capacitor multilevel inverter with self-managing voltage balance}, volume = {13}, year = {2025}, }
• (2025). Maximizing PV hosting capacity in unbalanced and active distribution systems with EVs and demand response. ACCESS, 13, 152800-152811. (www)

  @article{ , abstract = {In this paper is presented a mixed-integer linear programming (MILP) model that maximizes the Photovoltaic-based (PV-based) hosting capacity (HC) in unbalanced and active distribution networks. The model takes into account the controlled charge of electric vehicles (EVs) and incorporates a demand-response program (DRP), for demand-side load shifting. The model’s solution determines the optimal operation of distributed generators (DGs), switched capacitor banks (SCBs), energy storage devices (ESDs), coordination of the EVs charging, and DRP. Linear formulation is obtained from a mixed-integer non-linear programming (MINLP) model, ensuring tractability and guarantee convergence, since it can be efficiently solved using commercial optimization solvers of convex optimization. The model’s effectiveness is demonstrated through tests on a 123-bus, three-phase unbalanced distribution system. Four case studies are conducted to assess the effect of different distributed energy resources (DERs). Results show that the simultaneous optimization of DERs, EVs charging and DR scheduling can significantly increase the PV-based HC -reaching up more than the substation capacity- while reducing total power losses. These findings demonstrate the technical potential of integrated DER coordination in enhancing PV penetration and improving the operational efficiency of active distribution systems.}, archive = {J_ACCESS}, author = {Jairo Yumbla and Juan M. Home-Ortiz and Tiago Pinto and José Roberto Sanches Mantovani}, doi = {10.1109/ACCESS.2025.3603855}, journal = {IEEE Access}, pages = {152800-152811}, shortjournal = {IEEE Access}, title = {Maximizing PV hosting capacity in unbalanced and active distribution systems with EVs and demand response}, volume = {13}, year = {2025}, }
• (2025). Radio network planning for 5G FWA at 3.5 GHz and 26 GHz: A link- and flow-level approach. ACCESS, 13, 152782-152799. (www)

  @article{ , abstract = {Fixed Wireless Access (FWA) offers a practical broadband solution in areas where fiber deployment is challenging, especially in rural regions. This study introduces a 3D point-cloud-based radio network planning framework for 5G FWA using both sub-6 GHz and 26 GHz bands. By incorporating detailed environmental features, the method classifies links as line-of-sight (LoS) or non-line-of-sight (NLoS) and evaluates the effect of antenna elevation on LoS availability. Users in LoS are assigned to 26 GHz for higher throughput, while NLoS users fallback to 3.5 GHz. Network performance is analyzed at both link and flow levels under no interference and maximum interference conditions. The link-level results show that up to 7% of the 4,999 households fallback to 3.5GHz under maximum interference, compared to 6.2% under no interference. At the flow level, various load balancing strategies are evaluated to assess their impact on flow-level throughput and overall system stability. The framework is validated using real data from a rural area in Pornainen, Finland. Findings demonstrate effective spectrum use and highlight the importance of proactive load balancing and infrastructure planning.}, archive = {J_ACCESS}, author = {Norshahida Saba and Pasi Lassila and Kalle Ruttik and Riku Jäntti and Jari Salo}, doi = {10.1109/ACCESS.2025.3603906}, journal = {IEEE Access}, pages = {152782-152799}, shortjournal = {IEEE Access}, title = {Radio network planning for 5G FWA at 3.5 GHz and 26 GHz: A link- and flow-level approach}, volume = {13}, year = {2025}, }
• (2025). Experimental implementation of a 3-level TNPC-IGBT inverter for uniform stress distribution and THD mitigation: MPC-driven switching optimization, LCL filter integration, and real-time stress monitoring. ACCESS, 13, 152762-152781. (www)

  @article{ , abstract = {The increasing demand for high-quality power conversion in industrial applications has led to advancements in multilevel inverter design and control. This paper presents a design and experimental implementation of a 3-level T-type neutral-point clamped (TNPC) inverter utilizing space vector pulse width modulation (SVPWM) and model predictive control (MPC) for optimized switching state selection. The proposed approach ensures DC-link voltage balance, symmetrical load voltage and current, reduced voltage harmonics, and uniform stress distribution among the inverter’s three legs. An LCL filter is integrated based on phase margin optimization criteria to maintain total harmonic distortion (THD) of the current within acceptable limits. Real-time stress monitoring circuits are developed to assess key parameters including on-state voltage, case temperature, and collector current, which are essential for the reliability analysis of the IGBT modules. The configuration is validated through laboratory experimentation and the use of a highly inductive load with currents of up to 100 A. Findings indicate uniform voltage and current distribution, reduced harmonics of less than 0.1% for current and 5% for voltage, under full load conditions, and enhanced dynamic performance and system reliability, making the proposed method suitable for high-quality industrial applications. Furthermore, the developed experimental setup with uniform stress distribution simplifies the TNPC-IGBT module reliability assessment using a one-leg equivalent circuit to estimate the lifespan and conduct reliability analysis, rather than analyzing the module’s three legs.}, archive = {J_ACCESS}, author = {Ahmed H. Okilly and Cheolgyu Kim and Jeihoon Baek}, doi = {10.1109/ACCESS.2025.3604037}, journal = {IEEE Access}, pages = {152762-152781}, shortjournal = {IEEE Access}, title = {Experimental implementation of a 3-level TNPC-IGBT inverter for uniform stress distribution and THD mitigation: MPC-driven switching optimization, LCL filter integration, and real-time stress monitoring}, volume = {13}, year = {2025}, }
• (2025). Optimization strategies for triple-phase-shift modulation in dual-active-bridge converters: A comprehensive review. ACCESS, 13, 152748-152761. (www)

  @article{ , abstract = {Triple-phase-shift (TPS) modulation endows dual-active-bridge (DAB) DC-DC converters with three independent degrees of freedom, enabling simultaneous control of power flow, device stress, and soft-switching conditions. Exploiting this flexibility requires the development of carefully crafted duty-cycle schedules. These schedules must jointly minimize conduction and transformer losses, extend the zero-voltage-switching (ZVS) range, and accommodate wide input/output envelopes. This paper delivers a unified review of TPS duty-cycle optimization strategies. A systematic taxonomy is proposed, separating offline analytical and lookup-table solutions from online adaptive methods based on mathematical programming, metaheuristics and artificial-intelligence (AI) frameworks. Representative techniques are benchmarked against four key performance indices, RMS/peak current, efficiency, ZVS coverage, and reactive-power circulation, to expose inherent trade-offs among current stress, switching loss, and control complexity. Reported case studies indicate that segmented analytical schemes reduce worst-case RMS current while reinforcement-learning controllers sustain full-range ZVS achieving real-time efficiency improvements over static lookup tables. Emerging trends highlight hybrid AI-analytical controllers, physics-informed neural surrogates, and FPGA-accelerated digital twins that promise sub-100 $\mu $ s optimization latencies. Open challenges, such as holistic electro-thermal objectives and data-efficient training, are distilled into a roadmap for future research.}, archive = {J_ACCESS}, author = {Tarek Younis and Fahad Saleh Al-Ismail and Syed Muhammad Amrr and S. M. Suhail Hussain}, doi = {10.1109/ACCESS.2025.3603104}, journal = {IEEE Access}, pages = {152748-152761}, shortjournal = {IEEE Access}, title = {Optimization strategies for triple-phase-shift modulation in dual-active-bridge converters: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). AI fusion of vision and management for 6G millimeter-wave aerial networks. ACCESS, 13, 152729-152747. (www)

  @article{ , abstract = {This paper aims to enhance beam management for 6G aerial communications by fusing real-time vision data with intelligent beam selection in dynamic urban environments where buildings and mobility-induced blockages frequently obstruct line-of-sight (LoS) connectivity. We propose an artificial intelligence-driven fusion of vision and beam selection (AI-FVBS) framework that integrates YOLOv8-based object detection, reconfigurable intelligent surfaces (RIS), and contextual multiarmed bandit (CMAB) learning to optimize beam selection for aerial platforms such as high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). Aerial platforms improve 6G wireless network throughput, reduces blockages, and improve LoS transmission. Unlike conventional channel state information (CSI)-based methods, the proposed system leverages real-time visual context to dynamically balance beam exploration and exploitation, enhancing robustness in dense urban environments with frequent blockages. Vision-assisted RIS further extends the communication range when direct visibility is lost. Mounted vision-assisted RIS on buildings enhance signal propagation and quality by intelligently reflecting signals, and assist in beam selection when aerial platforms cannot directly detect mobile IoT systems. Experimental validation using aerial datasets, including DOTA, demonstrates a vehicle detection precision of 89%, and spectral efficiencies of 28 bps/Hz for HAPS and 35 bps/Hz for UAVs. These findings confirm significant gains in throughput and localization accuracy over traditional CSI-dependent schemes, establishing AI-FVBS as an efficient and scalable solution for 6G aerial networks.}, archive = {J_ACCESS}, author = {Mohammad Shah Alamgir and Brian Kelley}, doi = {10.1109/ACCESS.2025.3604288}, journal = {IEEE Access}, pages = {152729-152747}, shortjournal = {IEEE Access}, title = {AI fusion of vision and management for 6G millimeter-wave aerial networks}, volume = {13}, year = {2025}, }
• (2025). Permutation entropy for time series over n-distance path graphs. ACCESS, 13, 152716-152728. (www)

  @article{ , abstract = {Permutation entropy (PE) is a widely used metric for quantifying the complexity of time series data. Recent efforts have extended PE to graph signals, resulting in the graph permutation entropy (GPE), which accounts for the interdependencies encoded in the graph structure underlying the signal. Despite its potential, GPE remains a relatively recent development, lacking systematic formulations and practical validations. In this paper, we revisit the definition of GPE by introducing a formulation based on the graph shift operator (GSO), which enables a more natural interpretation and facilitates the construction of graph-based versions of other PE variants. As an example, we define the fractional graph permutation entropy (FrGPE), a generalization of GPE that incorporates a tunable fractional order parameter. We then investigate the use of GPE and FrGPE for analyzing time series defined over n-distance path graphs. Through computational experiments, we demonstrate the effectiveness of the proposed entropies in two scenarios: 1) detecting stability islands and distinguishing chaotic sequences generated with close parameters and 2) classifying faults in rolling bearings, where classification accuracies above 99% were achieved. These results highlight the potential of the proposed entropies as powerful tools for analyzing structured time series data.}, archive = {J_ACCESS}, author = {Isabela B. V. Nogueira and Juliano B. Lima}, doi = {10.1109/ACCESS.2025.3604350}, journal = {IEEE Access}, pages = {152716-152728}, shortjournal = {IEEE Access}, title = {Permutation entropy for time series over n-distance path graphs}, volume = {13}, year = {2025}, }
• (2025). Benchmarking large language models for biomedical literature summarization: Abstractive versus extractive paradigms. ACCESS, 13, 152682-152715. (www)

  @article{ , abstract = {Summarizing biomedical literature has become an important tool for researchers and healthcare professionals by increasing the speed of access to information. In this context, abstractive and extractive summarization methods are the main techniques used to make texts concise without distorting their meaning. While abstractive summarization restructures the text to create more meaningful and contextually consistent summaries, extractive summarization summarizes by directly quoting from the source text. Analyses conducted with a dataset created using scientific articles written in different biomedical fields analyzed how accurately and consistently the models could summarize biomedical texts through the metrics used (SBERT, BLEU, ROUGE, etc.). In the evaluations, while abstractive summarization methods generally provide higher semantic consistency, extractive summarization offers higher word-matching accuracy. While abstractive summarization exhibits superior performance in metrics such as SBERT and preserves semantic integrity better, extractive summarization achieves higher results in metrics such as BLEU and ROUGE. In addition, it has been found that large models are generally more successful in extractive summarization, while medium-sized models show balanced performance in both methods. In the future, with the increasing use of LLMs in the biomedical field, it will be possible to make more accurate, faster, and more meaningful summaries. In addition, the integrated use of summarization methods can make biomedical information systems more efficient. Future studies can focus on making LLMs more effective with wider datasets and contextual suitability.}, archive = {J_ACCESS}, author = {Tugba Celikten and Aytug Onan}, doi = {10.1109/ACCESS.2025.3604351}, journal = {IEEE Access}, pages = {152682-152715}, shortjournal = {IEEE Access}, title = {Benchmarking large language models for biomedical literature summarization: Abstractive versus extractive paradigms}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization of on-grid park and ride EV parking station with renewable energy. ACCESS, 13, 152669-152681. (www)

  @article{ , abstract = {In response to the growing need to reduce greenhouse gas emissions and achieve a decarbonized society, this study focuses on the optimal design of electric vehicle (EV) charging stations powered by renewable energy sources. Battery electric vehicles (BEVs), which emit no CO2 during operation, are particularly effective when combined with Park and Ride (P&R) systems that mitigate urban traffic congestion. This paper investigates the optimal operation and configuration of P&R BEV charging stations in Okinawa, Japan, comparing two power supply methods: on-grid and off-grid systems using photovoltaic (PV), wind power generation (WG), and battery energy storage systems (BESS). A multi-objective optimization model is developed to minimize the annual operating cost while meeting EV charging demand. To identify the optimal configuration, an exhaustive search of equipment unit combinations is conducted. The resulting pareto front reveals trade-offs between total annual EV charging energy and parking operator profit. In addition, three representative charging levels are analyzed in detail. Results show that on-grid systems achieve higher profit margins under moderate charging demand, leveraging grid power to maintain stable operations. In contrast, off-grid systems, while requiring higher initial investment, exhibit greater self-sufficiency and perform better in high demand scenarios or in remote areas with limited grid access. Thus, the optimal power supply method should be selected based on site-specific conditions, such as EV charging scale, regional infrastructure, and resilience requirements. Among the options, the on-grid system is identified as the most practical and economically viable solution under current technological and institutional conditions.}, archive = {J_ACCESS}, author = {Soichiro Ueda and Masahiro Furukakoi and Akie Uehara and Hiroshi Takahashi and Ashraf Mohamed Hemeida and Mitsunaga Kinjo and Tomonobu Senjyu}, doi = {10.1109/ACCESS.2025.3604097}, journal = {IEEE Access}, pages = {152669-152681}, shortjournal = {IEEE Access}, title = {Multi-objective optimization of on-grid park and ride EV parking station with renewable energy}, volume = {13}, year = {2025}, }
• (2025). Diffusion models with implicit conditions driven by latent shifts. ACCESS, 13, 152651-152668. (www)

  @article{ , abstract = {This paper introduces a diffusion model that replaces the target prior distribution from a standard Gaussian to non-zero-mean Gaussian priors, with shifted latent trajectories determined by non-zero-mean Gaussian noise, thereby maintaining the closed-form of conventional diffusion models. Unlike conventional conditional models such as conditional denoising diffusion probabilistic models or classifier guidance, the proposed model implicitly aligns each class with a unique component in the Gaussian priors. Carefully devised positioning strategies uniformly distribute Gaussian components without introducing additional learnable parameters, which is essential to implicit-only conditioning on our model. Qualitative and quantitative experiments demonstrate that, even without additional conditioning layers or a classifier, the proposed framework achieves performance comparable to explicit methods in terms of Fréchet Inception Distance, precision, and recall. The framework is further validated under unsupervised settings by replacing class labels with pseudo-labels generated from K-means clustering on feature representations.}, archive = {J_ACCESS}, author = {Da Eun Lee and Kensuke Nakamura and Byung-Woo Hong}, doi = {10.1109/ACCESS.2025.3603215}, journal = {IEEE Access}, pages = {152651-152668}, shortjournal = {IEEE Access}, title = {Diffusion models with implicit conditions driven by latent shifts}, volume = {13}, year = {2025}, }
• (2025). Partial discharge assessment for high-voltage electric machines for aircraft propulsion applications. ACCESS, 13, 152637-152650. (www)

  @article{ , abstract = {This paper presents the proceedings related to the assessment of the insulation of high voltage electric machines and the arcing effects that may occur within the system. In this work, an overview of the state of the art of partial discharges (PDs) on the insulation of electric machines for aircraft applications is developed introducing PDs and the existing work regarding their detection, with a focus on aeronautical propulsion. A classification of the PDs is presented, as a function of the topology of the discharge and the medium surrounding the test unit. Moreover, different aspects of this phenomena are assessed such as the detection methods of Phase Resolved Partial Discharge (PRPD) patterns, acceptance limits and the time-frequency map. Additionally, the state of the art regarding the ageing of different insulation materials under electric stress is examined. On the other hand, dielectric strength tests have been performed on insulating materials to assess the influence of climatic conditions on this parameter. Finally, different partial discharge inception voltage (PDIV) prediction methods are evaluated and compared.}, archive = {J_ACCESS}, author = {Pablo Alvarez and Marco Satrústegui and Ibón Elósegui and Miguel Martinez-Iturralde}, doi = {10.1109/ACCESS.2025.3603645}, journal = {IEEE Access}, pages = {152637-152650}, shortjournal = {IEEE Access}, title = {Partial discharge assessment for high-voltage electric machines for aircraft propulsion applications}, volume = {13}, year = {2025}, }
• (2025). MC-NCLWO: Newton’s cooling law-based oversampling algorithm for multi-class imbalanced datasets with feature noise. ACCESS, 13, 152620-152636. (www)

  @article{ , abstract = {Imbalanced data classification remains a fundamental challenge in machine learning, especially in multi-class scenarios where feature noise, class overlap, and small disjunct sub-concepts are prevalent. While various oversampling techniques have been proposed, most existing methods are primarily designed for binary classification and often struggle with noise sensitivity and structural preservation in complex multi-class settings. To address these gaps, this paper proposes a Newton’s Cooling Law-Based Weighted Oversampling strategy (MC-NCLWO) specifically tailored for imbalanced multi-class datasets. First, a dynamic noise-cleaning mechanism inspired by Newton’s Cooling Law is introduced to adaptively identify and eliminate noisy majority class samples based on the classification difficulty of each instance. Second, a difficulty-weighted sample generation strategy is employed to maintain local class balance and preserve the intrinsic structure of minority class sub-concepts. Furthermore, a sampling-priority strategy is designed to coordinate the oversampling process among multiple classes, thereby reducing mutual interference and ensuring the structural integrity of each minority class. Comprehensive experiments on 23 benchmark datasets demonstrate that MC-NCLWO consistently outperforms 10 state-of-the-art methods across multiple performance metrics, including $F_{\beta M}$ , AvAcc, CBA, and CEN. Additionally, the proposed method exhibits strong robustness in high-noise environments, significantly mitigating performance degradation caused by feature-level noise.}, archive = {J_ACCESS}, author = {Liangliang Tao and Yage Liang and Shiqian Wu and Jiangkun Qian and Furong Zhan}, doi = {10.1109/ACCESS.2025.3604051}, journal = {IEEE Access}, pages = {152620-152636}, shortjournal = {IEEE Access}, title = {MC-NCLWO: Newton’s cooling law-based oversampling algorithm for multi-class imbalanced datasets with feature noise}, volume = {13}, year = {2025}, }
• (2025). A quantization-aware DL-based channel estimation algorithm for OFDM systems. ACCESS, 13, 152608-152619. (www)

  @article{ , abstract = {This paper proposes a quantization-aware deep learning (DL)-based channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) systems under varying effective number of bits (ENOB) configurations. The algorithm addresses two key aspects: generalization in both channel conditions and analog-to-digital converter (ADC) resolutions, and mitigation of quantization noise. Generalization is achieved during the training phase by utilizing a dataset that includes multiple channel realizations across different ADC resolutions. Regarding quantization noise mitigation, the algorithm uses medium- to high- resolution data as target labels during offline training to learn the corresponding ENOBs indirect mapping. This approach enables improved channel estimation accuracy and enhances end-to-end system performance in terms of mean square error (MSE) and bit error rate (BER). Test results demonstrate consistent improvements both locally and globally across ENOBs configurations, with validation conducted in an indoor over-the-air (OTA) scenario to confirm real-world applicability. To the best of the authors’ knowledge, this is the first work to address the mitigation of ADC quantization noise in the channel estimation process without relying on medium- to high- resolution data during inference, while ensuring algorithm generalization across multiple channel realizations and varying ENOB configurations.}, archive = {J_ACCESS}, author = {Fábio D. L. Coutinho and Samuel S. Pereira and Hugerles S. Silva and Petia Georgieva and Arnaldo S. R. Oliveira}, doi = {10.1109/ACCESS.2025.3604259}, journal = {IEEE Access}, pages = {152608-152619}, shortjournal = {IEEE Access}, title = {A quantization-aware DL-based channel estimation algorithm for OFDM systems}, volume = {13}, year = {2025}, }
• (2025). A robust throughput estimation in edge-assisted adaptive bitrate streaming networks. ACCESS, 13, 152598-152607. (www)

  @article{ , abstract = {Dynamic Adaptive Streaming over HTTP (DASH) ensures continuous video transmission via Adaptive Bitrate (ABR) algorithms reliant on accurate throughput estimation. Existing methods often falter in unstable network conditions, impacting Quality of Experience (QoE). We address two intertwined issues in mobile HTTP video delivery. First, we present Weighted Harmonic–Exponential Averaging (WHEA), a lightweight hybrid estimator that reduces the next segment throughput prediction error compared to state-of-the-art baselines. Second, we embed WHEA within a new edge-executed adaptation framework, called Extended-EQAH, which leverages the cross-client view available at the edge to coordinate bitrate choices. Experiments on realistic LTE traces show that Extended-EQAH raises the mean quality by 10.74 % and Jain’s Fairness Index (JFI) by 10.98 % compared to its baseline schemes. In higher-variability settings using 5G traces, Extended-EQAH with WHEA yields substantially larger average relative gains about 27.73% in mean quality and 11.11% in JFI over representative baselines. These results demonstrate that combining accurate edge-side forecasting with joint buffer and throughput control is the key to robust video streaming in dynamic wireless networks.}, archive = {J_ACCESS}, author = {Shuaibu Yau and Suphakit Awiphan and Jakramate Bootkrajang and Jiro Katto}, doi = {10.1109/ACCESS.2025.3602651}, journal = {IEEE Access}, pages = {152598-152607}, shortjournal = {IEEE Access}, title = {A robust throughput estimation in edge-assisted adaptive bitrate streaming networks}, volume = {13}, year = {2025}, }
• (2025). Estimation method of metallic sheath current of high-voltage power cables based on field data. ACCESS, 13, 152582-152597. (www)

  @article{ , abstract = {Sheath current is a critical indicator for defect diagnosis in the metallic sheath grounding system of high-voltage single-core shielded power cables. In practical engineering, the absence of precise cable electric and structural parameters and layout details makes it difficult to accurately calculate defect-free sheath current values using theoretical formulas, potentially leading to misjudgment. This paper proposes a data-driven functional methodology for estimating the normal sheath current. The approach estimates the normal sheath current of a target cable section using field-measured sheath current data from a reference cable section, based on the similarity between the two sections and the relationship between sheath current and key factors. A sheath current calculation model is established using multi-conductor transmission line theory and two-port electrical network theory to analyze the influence factors on sheath current. Through simulations under trefoil, flat, and right-angle layout formation, fitting functions are derived to describe the relationship between sheath current and key factors, including ground resistance, length unbalance rate, average minor section length, and load current. Based on these fitting functions, an estimation formula is proposed to account for differences between the target and reference sections in terms of influencing factors. This method enables the determination of normal sheath current without prior knowledge of inductance, induced potential, or other electrical parameters of the power cable. As a result, it improves the accuracy of defect diagnosis in cable sheath systems.}, archive = {J_ACCESS}, author = {Junping Cao and Yanqing Xuan and Yuntu Jiang and Yangchun Cheng and Bowen Wang and Xubo Zhang and Haoliang Ye and Zijian Huang}, doi = {10.1109/ACCESS.2025.3604645}, journal = {IEEE Access}, pages = {152582-152597}, shortjournal = {IEEE Access}, title = {Estimation method of metallic sheath current of high-voltage power cables based on field data}, volume = {13}, year = {2025}, }
• (2025). CAE neural network and reverse-time localization imaging method for picking of downhole fracturing microseismic signal. ACCESS, 13, 152566-152581. (www)

  @article{ , abstract = {Effective surface-based monitoring of microseismic signals generated by hydraulic fracturing in drilling is of significant importance to increase oil and gas production. Microseismic monitoring methods based on travel time analysis are widely used, but real-world observational data often contain substantial noise, which limits accurate picking of microseismic signal travel times and precise localization of seismic sources. To address this, this paper integrates the continuous wavelet transform (CWT), the convolutional autoencoder (CAE), and the K-means clustering algorithm to establish a CAE neural network model for picking microseismic waveforms. Building on the obtained high-precision microseismic signals, a preliminary test of microseismic localization imaging based on the reverse-time localization principle was conducted. Simulation experiments demonstrate that the proposed method achieves localization accuracy under noise conditions as severe as–8.28 dB, with results highly consistent with noise-free scenarios. Furthermore, real-world validation using coal mine transmission channel wave data shows that the method can effectively extract channel wave events from noisy records and achieve accurate source imaging, highlighting its practical applicability and robustness in complex environments.}, archive = {J_ACCESS}, author = {Wei Wang and Tingting Lin and Chaoqun Shi}, doi = {10.1109/ACCESS.2025.3582855}, journal = {IEEE Access}, pages = {152566-152581}, shortjournal = {IEEE Access}, title = {CAE neural network and reverse-time localization imaging method for picking of downhole fracturing microseismic signal}, volume = {13}, year = {2025}, }
• (2025). Toward cost-effective hardware-in-the-loop simulations: Implementation of a generic co-simulation interface in a stability study for orthogonal cutting. ACCESS, 13, 152546-152565. (www)

  @article{ , abstract = {The high cost associated with hardware platforms for implementing Hardware-in-the-Loop (HIL) applications is often prohibitive for many research groups and companies. Therefore, there is an increasing demand to develop cost-effective solutions that can still deliver accurate and reliable results. In this paper, we showcase the practical application of our previously developed co-simulation interface as a cost-effective alternative to replace these expensive hardware setups. Our proposal is composed of a hardware part, consisting of a ZedBoard equipped with commercial ADC and DAC modules, and a software part comprising the developed co-simulation interface. The ZedBoard serves as the primary computation platform and acts as a communication medium with the physical world. On the other hand, the interface allows real-time communication between the ZedBoard and various simulation environments and hardware components via Ethernet. To demonstrate this, we apply the interface in a study of chatter in industrial machining processes. Here, we connect the ZedBoard with Simulink via the interface, while the ADC and DAC modules are wired to the acquisition card of a real-time computing platform. As result, we demonstrate that our co-simulation interface offers an accessible and affordable solution for high-performance real-time simulations.}, archive = {J_ACCESS}, author = {Mikel Segura and Rafael Barcena and Tomaso Poggi and Alejandro J. Calderón}, doi = {10.1109/ACCESS.2025.3603262}, journal = {IEEE Access}, pages = {152546-152565}, shortjournal = {IEEE Access}, title = {Toward cost-effective hardware-in-the-loop simulations: Implementation of a generic co-simulation interface in a stability study for orthogonal cutting}, volume = {13}, year = {2025}, }
• (2025). Enhanced voice phishing detection using an LLM-based framework for data augmentation and classification. ACCESS, 13, 152530-152545. (www)

  @article{ , abstract = {Existing voice phishing detection models based on call transcripts often suffer from limited generalizability due to insufficient scenario diversity and the absence of ambiguous samples in data. To address these challenges, we propose an integrated framework that leverages GPT-4o, a large language model (LLM), to generate realistic call transcripts from actual fraud cases and to build an expert-guided phishing detection model. Using case reports from the Financial Supervisory Service (FSS) and Korean call transcripts from the KorCCVi dataset, we generate Korean phishing call transcripts that capture underrepresented fraud tactics. In addition, we generate non-phishing call transcripts that retain phishing-like linguistic patterns by removing or attenuating core fraudulent cues, thereby enabling training on ambiguous cases. The generated data are quantitatively evaluated for linguistic naturalness, scenario diversity, and detection difficulty. To assess the sample-level detection difficulty in semantic space, we introduce the metric Class Centroid Distance Variability (CCDV). We also propose the Domain Expert LLM, implemented using GPT-4o, a prompt-engineered detection model that incorporates six analytical criteria validated by a domain expert. The model not only improves detection performance but also produces structured analytical reports that enhance interpretability. In experiments, the Domain Expert LLM achieves an F1 score of 0.9686 on previously unseen and ambiguous transcripts, substantially outperforming conventional models such as RandomForest and KoBERT, which yield an average F1 score of approximately 0.70.}, archive = {J_ACCESS}, author = {Hyunghee Park and Jiwon Lee and Sanghyun Han and Haewon Byun}, doi = {10.1109/ACCESS.2025.3603007}, journal = {IEEE Access}, pages = {152530-152545}, shortjournal = {IEEE Access}, title = {Enhanced voice phishing detection using an LLM-based framework for data augmentation and classification}, volume = {13}, year = {2025}, }
• (2025). Advanced spectrum sensing techniques for robust train-to-train wireless communication systems: A comprehensive evaluation. ACCESS, 13, 152519-152529. (www)

  @article{ , abstract = {In the context of full automation of train operation, flexible train management, and improved traffic capacity, the Next Generation Train Control and Monitoring System (NG-TCMS) represents a key technology. The system will be based on wireless technologies at both the backbone and consist levels, thereby creating Wireless Train Backbone (WLTB) and Wireless Consist Network (WLCN) solutions. These solutions are being developed within the framework of several European projects. The chosen technology will have to guarantee robust train-to-train (T2T) communications between consists in the case of virtual coupling of two trains. In order to be able to detect any type of unwanted signal that may disturb wireless transmission, a specific mechanism based on spectrum sensing should be developed. To this end, this paper presents a comprehensive evaluation of advanced spectrum sensing techniques tailored for robust T2T wireless communication systems. Performance evaluation was carried out using real LTE and Wi-Fi 802.11ax signals in a doubly selective channel. By implementing spectrum sensing techniques on Software-Defined Radio (SDR) platforms, this study investigates the efficacy of eigenvalue-based methods under dynamic vehicular propagation conditions, including variable train speeds and distances between trains. The research identifies the most effective spectrum sensing technique among Maximum-Minimum Eigenvalue (MME), Energy to Maximum Eigenvalue (EME), Simplified Predicted Eigenvalue Threshold (SPET), Mean-to-Square Extreme Eigenvalue (MSEE), Maximum Eigenvalue to Geometric Mean (MEGM), and Covariance Absolute Value (CAV). The findings indicate that SPET demonstrates superior performance in various scenarios, making it a promising candidate for real-world railway communication systems.}, archive = {J_ACCESS}, author = {Chadli Hadji and Yamen Alsaba and Marion Berbineau and Fouzia Elbahhar and Rodrigue Fargeon}, doi = {10.1109/ACCESS.2025.3603626}, journal = {IEEE Access}, pages = {152519-152529}, shortjournal = {IEEE Access}, title = {Advanced spectrum sensing techniques for robust train-to-train wireless communication systems: A comprehensive evaluation}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of thermal conductivity of stator windings in electric machines. ACCESS, 13, 152493-152518. (www)

  @article{ , abstract = {Electric machines are crucial in modern industries, playing a substantial role in power generation, manufacturing, transportation, and renewable energy. Consequently, extensive research is focused on enhancing their performance, making it essential to understand their thermal behavior for effective modifications and improvements. While existing literature covers thermal effects in electric machines, the specific analysis of thermal conductivity in stator windings, a major contributor to energy losses, has remained significantly underexplored due to the complicated geometrical, material, and manufacturing factors. This paper presents a comprehensive review of methods and challenges related to characterizing the equivalent thermal conductivity of stator windings, categorizing them into analytical, numerical, and experimental approaches, with three sample levels (domain, motorette, and prototype). Furthermore, the paper introduces concepts of anisotropy, components, and available winding technologies to establish a solid foundation before discussing these methods. This review highlights the strengths and limitations of each method, emphasizing their needs and constraints to advance high-performance electric machines, and ultimately contributing to the evolution of more reliable and efficient electric machines.}, archive = {J_ACCESS}, author = {Nur Sarma and Mehmet C. Kulan and Nick J. Baker}, doi = {10.1109/ACCESS.2025.3604580}, journal = {IEEE Access}, pages = {152493-152518}, shortjournal = {IEEE Access}, title = {A comprehensive review of thermal conductivity of stator windings in electric machines}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal graph spectral network for personalized itinerary recommendation. ACCESS, 13, 152479-152492. (www)

  @article{ , abstract = {The goal of personalized itinerary recommendation is to generate travel routes that closely match each user’s unique preferences and spatiotemporal constraints. This task, however, is complicated by the prevalence of incidental and random “noise” interactions embedded within users’ behavioral histories. Most existing methods process these noisy records directly in the node domain, struggling to reliably separate stable interests from fleeting actions. To address this fundamental challenge, we propose a novel Spatio-Temporal Graph Spectral Network (ST-GSN). Rather than analyzing user behaviors solely in the node space, our approach shifts the perspective into the graph spectral domain. Specifically, we construct for each user a dynamic graph enriched with spatial, temporal, and semantic information, then project their behavioral signals into the spectral domain via the Graph Fourier Transform (GFT). We hypothesize that stable user preferences manifest as low-frequency, energy-concentrated signals, while noise emerges as high-frequency components. Leveraging this property, we design a learnable adaptive filter that precisely isolates and suppresses noise in the spectral space, enabling the extraction of a user’s core intent. The model further incorporates Time2Vec for fine-grained modeling of dwell and travel times, and employs a multi-task learning framework to enhance the robustness of its representations. Extensive experiments on the public Foursquare and Gowalla datasets show that ST-GSN consistently outperforms a suite of strong baselines across all key metrics. Most notably, in the full-corpus ranking scenario that best simulates real-world deployment, the advantage of ST-GSN becomes even more pronounced, demonstrating outstanding performance and resilience in the face of complex, noisy environments.}, archive = {J_ACCESS}, author = {Teng Wang and Rui Cheng and Yiheng Wang}, doi = {10.1109/ACCESS.2025.3604019}, journal = {IEEE Access}, pages = {152479-152492}, shortjournal = {IEEE Access}, title = {Spatio-temporal graph spectral network for personalized itinerary recommendation}, volume = {13}, year = {2025}, }
• (2025). Multi-agent deep reinforcement learning for dynamic routing in MANETs using graph neural networks. ACCESS, 13, 152469-152478. (www)

  @article{ , abstract = {Mobile Ad Hoc Networks (MANETs) consist of decentralized wireless networks with dynamic topologies and frequent link failures which make achieving efficient routing extremely difficult. Unlike prior ML-based MANET routing approaches that rely on static heuristics or offline training, our method performs real-time GNN embedding updates and decentralized execution to adapt immediately to topology changes. The paper introduces a new routing protocol for MANETs that combines Multi-Agent Deep Reinforcement Learning (MADRL) with Graph Neural Networks (GNNs) to improve routing decision-making processes. Network nodes function independently as autonomous agents which apply GNN-based state representations for capturing both local and wide network topology data. Nodes use a centralized training and decentralized execution model to collectively optimize their routing strategies through real-time network condition assessment. The protocol underwent evaluation through comprehensive NS-3 simulations within different mobility settings together with varying network densities and traffic patterns. The proposed approach shows superior performance compared to standard routing protocols like AODV and DSR in essential metrics such as packet delivery ratio (90.1%–97.8%), end-to-end delay (42ms–80ms), as well as routing overhead (12–24 packets/s). Merging MADRL with GNNs gives MANETs the ability to learn and adapt dynamically while providing a scalable and efficient solution for real-world applications such as disaster response and military communication environments.}, archive = {J_ACCESS}, author = {Faisal Alanazi and Mahdi Zareei}, doi = {10.1109/ACCESS.2025.3601994}, journal = {IEEE Access}, pages = {152469-152478}, shortjournal = {IEEE Access}, title = {Multi-agent deep reinforcement learning for dynamic routing in MANETs using graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Development of a hybrid control algorithm based on a combination of robust nonlinear techniques for electric power steering. ACCESS, 13, 152452-152468. (www)

  @article{ , abstract = {This work proposes a hybrid nonlinear control algorithm for electric power steering (EPS) systems, designed by integrating backstepping control (BSC), sliding mode control (SMC), and proportional-integral (PI) control. The robustness of SMC against external disturbances and the flexible trajectory handling of BSC are combined to achieve high-performance control. Meanwhile, PI controllers compensate for phase differences and tracking errors. Their gains are optimally tuned using a dual-loop optimization framework, which is later extended with fuzzy logic to enhance adaptability under varying operating conditions. System stability is rigorously analyzed and proven via Lyapunov theory. A dynamic EPS model is developed to account for disturbances (road reaction torques and external disturbances). Numerical simulations validate the proposed method, showing that the maximum tracking error remains below 0.01 rad, with root mean square error (RMSE) approaching zero. The proposed controller effectively suppresses chattering and eliminates phase lag, ensuring robust and precise steering assistance under various driving conditions.}, archive = {J_ACCESS}, author = {Tuan Anh Nguyen}, doi = {10.1109/ACCESS.2025.3603623}, journal = {IEEE Access}, pages = {152452-152468}, shortjournal = {IEEE Access}, title = {Development of a hybrid control algorithm based on a combination of robust nonlinear techniques for electric power steering}, volume = {13}, year = {2025}, }
• (2025). IMU-based systems for skiing sports: A scoping review. ACCESS, 13, 152430-152451. (www)

  @article{ , abstract = {Inertial sensor (IMU)-based wearable systems are the most employed technology to acquire in-field information about athletic gesture in skiing sports since they are portable, easy to setup, not bulky and they are compliant with this sport’s extreme conditions, i.e. on-snow measurements, borderless working area. This scoping review wants to map the current scientific works on IMU-based wearable systems in the skiing contexts with the peculiarity of being used in the field: what type of information can be obtained, taking into account number and body position of the inertial sensors, whether other kind of sensors are involved and which type of data analyses techniques applied. A search on various databases identified 134 publications from 2013 to 2025, of which 39 met the inclusion criteria. Three main application areas emerged: 1) skier kinematics characterization, particularly the reconstruction of center of mass and joint trajectories; 2) classification tasks, such as automatic detection of skiing techniques, movement phases, and skier performance levels; and 3) injury prevention and rehabilitation monitoring, including applications in Paralympic skiing. While the reported findings are promising, research in this domain would benefit from deeper investigations, larger participant samples, and standardized protocols, to allow generalization and support the development of robust, field-deployable systems.}, archive = {J_ACCESS}, author = {Claudia Presicci and Igor Ingegnosi and Paolo Dondero and Maura Casadio and Camilla Pierella}, doi = {10.1109/ACCESS.2025.3604571}, journal = {IEEE Access}, pages = {152430-152451}, shortjournal = {IEEE Access}, title = {IMU-based systems for skiing sports: A scoping review}, volume = {13}, year = {2025}, }
• (2025). Analysis of ansätze expressibility and complexity and their impact on classification accuracy using QNN and QLSTM. ACCESS, 13, 152412-152429. (www)

  @article{ , abstract = {Quantum Neural Networks (QNN) and Quantum Long Short-Term Memory (QLSTM) models are emerging as powerful tools in quantum machine learning. The effectiveness of these models is largely governed by the structure of their parameterized quantum circuits, also known as Ansätze. The Expressibility (measure of how well an ansätz can explore Hilbert space), Entanglement (which governs the correlation between states of multiple qubits), and Depth (a measure of how many layers of quantum gates a circuit has) are fundamental to a model’s capacity to learn complex patterns. In this paper, we analyze how varying these attributes for various ansätze influences classification cost and accuracy. We evaluated QNN and QLSTM models across three cybersecurity datasets using ten ansätze with varying expressibility and complexity. Results showed that ansätze with lower expressibility and complexity achieved accuracy and training stability comparable to the more complex ones. Additionally, we found that QNN models consistently offered a better trade-off between accuracy, training time, and computational efficiency compared to QLSTM models.}, archive = {J_ACCESS}, author = {Sarvapriya M. Tripathi and Himanshu Upadhyay and Jayesh Soni}, doi = {10.1109/ACCESS.2025.3604721}, journal = {IEEE Access}, pages = {152412-152429}, shortjournal = {IEEE Access}, title = {Analysis of ansätze expressibility and complexity and their impact on classification accuracy using QNN and QLSTM}, volume = {13}, year = {2025}, }
• (2025). High-isolation twin-cluster fan-blade metasurface-embedded wideband CP MIMO antenna with shorting-pin vias for IoT applications. ACCESS, 13, 152392-152411. (www)

  @article{ , abstract = {This study proposes a twin-cluster fan-blade metasurface (MTS)-embedded wideband circularly polarized (CP) multiple-input multiple-output (MIMO) antenna incorporating shorting-pin vias for Internet of Things (IoT) applications. The antenna is implemented on a double-layered substrate. The upper layer comprises two identical clusters of fan-blade MTS elemental arrays, while the lower layer integrates a dual-element coplanar waveguide feeding structure. Centrally placed shorting-pin vias are introduced to mitigate inter-cluster mutual coupling. Characteristic mode analysis is employed to investigate the CP behavior of each cluster of the twin-cluster antenna, with circular polarization achieved through two orthogonal modes (Modes 3 and 4). For coupling suppression, the optimal placement of the shorting-pin vias is determined based on minimizing the normalized electric field magnitude between clusters. The measured return loss bandwidth, axial ratio bandwidth, and maximum gain at the center frequency of 5.5 GHz are 46.72% (4.2–6.77 GHz), 11% (5.29–5.9 GHz), and 5.82 dBic, respectively. The antenna scheme achieves high isolation exceeding 25 dB, an envelope correlation coefficient below 0.002, a diversity gain above 9.99 dB, a mean effective gain below -3 dB, a total active reflection coefficient below -10 dB, and a channel capacity loss below 0.2 bits/s/Hz. This study is the first to utilize a fan-blade MTS-embedded structure for CP generation and to integrate shorting-pin vias within a CP MIMO antenna for mutual coupling suppression. Essentially, the proposed twin-cluster antenna scheme is well-suited for advanced IoT applications.}, archive = {J_ACCESS}, author = {Nathapat Supreeyatitikul and Pongsathorn Chomtong and Jessada Konpang and Prayoot Akkaraekthalin}, doi = {10.1109/ACCESS.2025.3604009}, journal = {IEEE Access}, pages = {152392-152411}, shortjournal = {IEEE Access}, title = {High-isolation twin-cluster fan-blade metasurface-embedded wideband CP MIMO antenna with shorting-pin vias for IoT applications}, volume = {13}, year = {2025}, }
• (2025). Systematic approach to the design of reconnection-less reconfigurable filters using the method of unknown nodal voltages. ACCESS, 13, 152377-152391. (www)

  @article{ , abstract = {The paper introduces a systematic approach to the design of reconnection-less reconfigurable filters based on the Method of Unknown Nodal Voltages (MUNV). The used approach of previously proposed filters is based on the trial and error (personal experience) selection of parameters of the factorization of the algebraic complements for Laplace expansion of the sub-determinants while the newly proposed approach consists of the gradual placement of individual parameters into the specific matrix element(s) based on the knowledge of which position in the matrix is reflected in particular terms of the transfer function. The proposed approach provides following advantages in comparison to the standard MUNV: a) a more systematic design of MUNV based reconnection-less reconfigurable filters, b) it only requires returning to the selection of signs of individual parameters when the resulting transfer function does not meet its desired form, c) it can be adjusted for different circuits other than reconnection-less reconfigurable filters. The introduced approach is tested and verified for several previously proposed reconnection-less reconfigurable filters (based on MUNV method) and newly presented solution. The possibility to design other circuit types (shown for the design of an oscillator) is also presented. The behavior of the proposed filter is supported by PSpice simulations and experimental measurements in order to verify the design approach.}, archive = {J_ACCESS}, author = {Lukas Langhammer and Roman Sotner}, doi = {10.1109/ACCESS.2025.3603673}, journal = {IEEE Access}, pages = {152377-152391}, shortjournal = {IEEE Access}, title = {Systematic approach to the design of reconnection-less reconfigurable filters using the method of unknown nodal voltages}, volume = {13}, year = {2025}, }
• (2025). Measurement of medial elbow joint space using landmark detection. ACCESS, 13, 152365-152376. (www)

  @article{ , abstract = {Ultrasound imaging of the medial elbow is crucial for early detection of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space on ultrasound images enables the assessment of valgus instability. Automating this measurement requires a precisely annotated dataset to leverage recent advances in deep neural networks; however, no publicly available dataset has been proposed to date. In this work, we introduce a novel medial elbow ultrasound dataset with landmark annotations, facilitating the development of deep neural networks for joint space measurement. The dataset comprises 4,109 medial elbow ultrasound images from 22 participants, with precise landmark annotations on the humerus and ulna, performed under the supervision of three orthopedic surgeons. We evaluate several landmark detection models on our dataset and demonstrate its effectiveness in training models. Additionally, we propose Shape Subspace Refinement (SSR) to refine landmark heatmap peaks, improving the accuracy of heatmap-based landmark detection models. Furthermore, we show that the detected landmarks can be used for point-based segmentation of the humerus and ulna. The dataset is publicly available at https://github.com/Akahori000/Ultrasound-Medial-Elbow-Dataset}, archive = {J_ACCESS}, author = {Shizuka Akahori and Shotaro Teruya and Pragyan Shrestha and Yuichi Yoshii and Ryuhei Michinobu and Satoshi Iizuka and Akira Ikumi and Hiromitsu Tsuge and Itaru Kitahara}, doi = {10.1109/ACCESS.2025.3602503}, journal = {IEEE Access}, pages = {152365-152376}, shortjournal = {IEEE Access}, title = {Measurement of medial elbow joint space using landmark detection}, volume = {13}, year = {2025}, }
• (2025). A novel virtual reality cycling system using controllers for pedal motion-based tracking: Movement techniques, motion sickness, and user experience. ACCESS, 13, 152346-152364. (www)

  @article{ , abstract = {This study proposes a novel, low-cost Virtual Reality (VR) cycling system that leverages VR controllers for pedal motion-based tracking, eliminating the need for additional sensors or complex setups. The system introduces two movement techniques: continuous and teleportation movement. These techniques allow users to control their movement in VR cycling by tilting their upper body for lateral movement and using leg-mounted controllers with pedal tracking for forward movement. This study investigated the system’s performance, motion sickness, and user experience within a virtual environment (VE) designed to simulate an urban landscape with a straight road, obstacles, and collectible items. A tunneling vignette was incorporated to mitigate visual-vestibular mismatches in the investigation of motion sickness. Results showed no significant differences in collected items or obstacle avoidance performance between the continuous and teleportation movement techniques. The tunneling vignette did not significantly affect performance or reduce motion sickness symptoms, as indicated by the SSQ scores. However, participants reported that the teleportation technique provided higher realism and internal-external correspondence (RJPQ: $p =0.007$ and $p =0.001$ , respectively). Intrinsic motivation scores were comparable for both techniques, though teleport technique showed slightly lower perceived tension ( $p =0.005$ ). Our low-cost VR cycling system provides an immersive and engaging experience, offering intuitive control mechanisms and realistic cycling dynamics. These findings contribute to the development of accessible VR cycling systems, ensuring movement design and user experience for immersive VEs.}, archive = {J_ACCESS}, author = {Chaowanan Khundam and Pornpon Thamrongrat and Chinnapat Panwisawas}, doi = {10.1109/ACCESS.2025.3604557}, journal = {IEEE Access}, pages = {152346-152364}, shortjournal = {IEEE Access}, title = {A novel virtual reality cycling system using controllers for pedal motion-based tracking: Movement techniques, motion sickness, and user experience}, volume = {13}, year = {2025}, }
• (2025). Optimal beamforming for full-duplex integrated sensing, communication, and computation systems. ACCESS, 13, 152335-152345. (www)

  @article{ , abstract = {This paper investigates optimal beamforming design for full-duplex (FD) Integrated Sensing, Communication, and Computation (ISCC) systems, which are envisioned as key enablers for next-generation wireless networks. To address the challenge of cross-domain interference from uplink transmissions, we employ successive interference cancellation (SIC) approach to enhance the radar signal quality. However, due to imperfections in interference cancellation algorithms, residual interference persists after SIC and is modeled as additive noise. Additionally, the portion of radar signal embedded in the computation data is modeled as additive noise. We derive closed-form expressions for the uplink receive combiners used in both sensing and computation, along with the corresponding power allocation strategy. We also propose an optimal beamforming algorithm to jointly maximize aggregated downlink communication and radar information rate (RIR) under total power constraint. Given the conflicting nature of these objectives, a multi-objective optimization (MOO) framework is adopted. To handle the non-convexities arising from inter-user and residual self-interference, we utilize semidefinite relaxation (SDR) in conjunction with sequential convex approximation (SCA). An alternating maximization approach is used to iteratively optimize the beamforming vectors and uplink power allocation. We further demonstrate that rank-one solutions can be reconstructed from the relaxed problem without loss of optimality. Extensive Monte Carlo simulation results confirm that the proposed algorithm significantly outperforms baseline schemes, yielding substantial improvements in communication rate, radar information rate (RIR), and computation mean squared error (MSE). These findings underscore the potential of full-duplex ISCC systems to deliver high-performance interference-resilient solutions for future wireless networks.}, archive = {J_ACCESS}, author = {Constant Akama and Solomon Nunoo and Kyoung-Jae Lee and John Kojo Annan}, doi = {10.1109/ACCESS.2025.3591679}, journal = {IEEE Access}, pages = {152335-152345}, shortjournal = {IEEE Access}, title = {Optimal beamforming for full-duplex integrated sensing, communication, and computation systems}, volume = {13}, year = {2025}, }
• (2025). A kalman filter-based sliding mode frequency regulator for power systems under multiple communication delays. ACCESS, 13, 152323-152334. (www)

  @article{ , abstract = {This paper proposes a novel load frequency control (LFC) scheme for multi-area thermal-hydro power systems (MATHPS) subject to multiple communication delays. The scheme integrates an integral sliding mode controller (ISMC) with a Kalman filter (KF) to enhance system robustness and dynamic performance. By formulating a novel state-space model for MATHPS that explicitly accounts for multiple time delays in thermal and hydro units, the approach ensures greater system fidelity and control accuracy. A KF is developed to handle delayed measurements and disturbances while effectively reducing network resource utilization from sensors to the controller, thereby enhancing system efficiency. By providing delay-resilient state estimation, the filter enables timely and reliable control actions, improving overall system stability under uncertain operating conditions. Leveraging these estimated states, a ISMC law is developed to drive the system trajectory toward a predefined sliding surface, thereby effectively reducing steady-state error and mitigating control chattering. Furthermore, a Lyapunov-based stability criterion with linear matrix inequality formulation is employed to ensure asymptotic stability of the closed-loop system, even in the presence of time delays. Simulation studies on the LFC models show that the proposed method achieves faster frequency recovery, reduced overshoot, and improved stability compared to existing techniques, particularly under severe disturbances and time-delay conditions.}, archive = {J_ACCESS}, author = {Anh-Tuan Tran and van van Huynh and Bang Le-Huy Nguyen and Jae Woong Shim and Ton Duc do}, doi = {10.1109/ACCESS.2025.3604658}, journal = {IEEE Access}, pages = {152323-152334}, shortjournal = {IEEE Access}, title = {A kalman filter-based sliding mode frequency regulator for power systems under multiple communication delays}, volume = {13}, year = {2025}, }
• (2025). Stable and fast geometric exaggeration via saliency-based bilateral filtering on GPU. ACCESS, 13, 152305-152322. (www)

  @article{ , abstract = {In this paper, we propose a GPU-based framework for reliably exaggerating the shape of a triangular mesh using mesh saliency and a boost filter. This framework is based on the high-boost mesh filter from digital signal processing, which affects the normal vectors of the triangular mesh and updates vertex positions to adapt to the exaggerated normal vectors. However, this process introduces noise at the vertices, and previous methods attempted to minimize this noise using an averaging filter. To address this issue, we apply a Bilateral filter to the high-boost filtering algorithm to effectively remove noise while exaggerating the mesh in the saliency direction, accelerating the process using GPU computation. Previous methods often resulted in noise, holes, or mesh shrinkage during the mesh enhancement process, whereas our method mitigates these issues. The effectiveness of our approach is evident not only in 3D objects but also in 3D-printed results. Through various experiments, we demonstrate that our method is an effective technique for exaggerating the shape of 3D meshes.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3604061}, journal = {IEEE Access}, pages = {152305-152322}, shortjournal = {IEEE Access}, title = {Stable and fast geometric exaggeration via saliency-based bilateral filtering on GPU}, volume = {13}, year = {2025}, }
• (2025). From patch to graph: Dual graph transformer network for few-shot metal surface defect semantic segmentation. ACCESS, 13, 152293-152304. (www)

  @article{ , abstract = {Recently few-shot segmentation (FSS) of metal surface defects has been extensively developed. Most previous methods strive to achieve generalization through the pixel-based prototypes directly derived from support-query pairs. However, these methods are biased towards the global context without the intrinsic object details, thus hindering the recognition of minute defects. To this end, we propose a dual graph transformer network (DGTNet) to fully capture the aforementioned intrinsic details for accurate metal surface defect FSS. Specifically, in DGTNet, a patch-to-graph (P2G) encoder is firstly proposed to generate patch-based local and global graphs from dual perspective of support-query pairs, while simultaneously guiding global context and intrinsic details. Moreover, we equip DGTNet with a P2G transformer and a graph-to-mask (G2M) decoder to reason the relations among different nodes of the two graphs and generate the pixel-level mask of the query images, respectively. Our DGTNet also manifests remarkable adaptability within the K-shot FSS setting. Extensive experiments on metal surface defect datasets show that DGTNet yields superior performance under both 1-shot and 5-shot settings. Our source code is available at: https://github.com/DingH2E/DGTNet.}, archive = {J_ACCESS}, author = {Jiyan Zhang and Hanze Ding and Ming Peng and Yingjie Liao and Yanfang Liu}, doi = {10.1109/ACCESS.2025.3604553}, journal = {IEEE Access}, pages = {152293-152304}, shortjournal = {IEEE Access}, title = {From patch to graph: Dual graph transformer network for few-shot metal surface defect semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Perceptual quality assessment for NeRF-generated scenes: A training reference metric. ACCESS, 13, 152277-152292. (www)

  @article{ , abstract = {Perceptual quality assessment is a key challenge in traditional image processing as well as emerging AI-based novel view synthesis methods such as neural radiation fields (NeRF). NeRF has revolutionized 3D scene reconstruction by leveraging neural networks for volumetric rendering, achieving unprecedented photorealistic results. Currently, the perceptual quality assessment of NeRF still relies heavily on full-reference (FR) metrics, such as PSNR and SSIM, which require external reference images from predefined camera positions and suffer from significant limitations. In this paper, we propose a new quality metric that directly leverages training views to quantify the perceptual quality of NeRF-generated scenes, eliminating the need for external predefined reference images and camera position metadata. In the proposed approach, we first extracted hierarchical and abstract features from training views using pretrained deep convolutional neural networks (CNNs) and then constructed a reference representation for perceptual quality evaluation through feature space interpolation. We evaluated the effectiveness of the proposed approach with two options: one with pretrained CNNs only (without calibration) and another with calibration applied to learn the importance of hierarchical feature stages. The experimental results demonstrate the effectiveness of the proposed method, which outperforms traditional no-reference (NR) metrics while being comparable to popular FR metrics. We found that deep features trained for high-level classification tasks have a strong potential to quantify perceptual quality across different viewpoints of the same object in NeRF. The code is released at https://github.com/WemoLuo/NVQS}, archive = {J_ACCESS}, author = {Shihao Luo and Nguyen Tien Phong and Chibuike Onuoha and Truong Cong Thang}, doi = {10.1109/ACCESS.2025.3603970}, journal = {IEEE Access}, pages = {152277-152292}, shortjournal = {IEEE Access}, title = {Perceptual quality assessment for NeRF-generated scenes: A training reference metric}, volume = {13}, year = {2025}, }
• (2025). Simulation-based benchmarking of RL algorithms for adaptive thermal control in IoT-enabled smart umbrella systems. ACCESS, 13, 152266-152276. (www)

  @article{ , abstract = {This paper presents a simulation-based benchmarking analysis of three reinforcement learning (RL) algorithms—Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO)—for real-time thermal regulation in IoT-enabled smart umbrella systems. Motivated by the environmental challenges faced during the Hajj pilgrimage, where pilgrims are exposed to extreme heat and humidity, a custom simulation environment was developed to emulate realistic Hajj conditions. Each RL agent was trained and evaluated across multiple training horizons. Experimental results show that SAC consistently outperforms DQN and PPO in achieving stable thermal comfort while minimizing energy consumption. Compared to traditional on-policy and value-based methods, SAC demonstrated faster convergence, better adaptability to dynamic environmental shifts, and suitability for deployment on resource-constrained microcontroller platforms. These findings highlight the potential of entropy-regularized reinforcement learning (RL) in climate-adaptive, embedded AI systems for human-centric applications.}, archive = {J_ACCESS}, author = {Afnan M. Al-Harbi and Salma O. Elhag and Hanan S. Alghamdi}, doi = {10.1109/ACCESS.2025.3602907}, journal = {IEEE Access}, pages = {152266-152276}, shortjournal = {IEEE Access}, title = {Simulation-based benchmarking of RL algorithms for adaptive thermal control in IoT-enabled smart umbrella systems}, volume = {13}, year = {2025}, }
• (2025). A transmission axle fault diagnosis system for massive rapid transit system with enhanced attention-based shuffle networks. ACCESS, 13, 152253-152265. (www)

  @article{ , abstract = {The transmission axle faults can cause severe damage to gearbox components and jeopardize motor power transmission. A transmission axle fault (TAF) diagnosis method for train propulsion systems in mass rapid transit (MRT) networks is proposed, utilizing an Enhanced Attention-based Shuffle Network (EASN). Time-domain vibration signals, directly acquired from sensors mounted on the transmission axles, are employed as input to the EASN model. The proposed model is specifically designed for deployment on lightweight AI edge devices, with an emphasis on computational efficiency and real-time performance for onboard diagnostic applications. The EASN architecture is composed of multiple building blocks, referred to as Split-Attention Shuffle Units (SASUs). Each SASU integrates a Shuffling Processing Module (SPM) and a Split Attention Module (SAM) in a cascaded configuration. While the SPM is based on ShuffleNet, which is known for its computational efficiency but relatively lower classification accuracy, the proposed SASU mitigates this limitation through the introduction of an even channel shuffling mechanism combined with a hybrid attention strategy. The hybrid attention scheme leverages both Spatial Excitation (SPE) and Squeeze-and-Excitation (SnE) mechanisms, significantly enhancing the network’s diagnostic accuracy without compromising its lightweight design. Experimental results demonstrate that the proposed EASN achieves a Top-1 classification accuracy of 93.9%, representing a 5.6% improvement over ResNet-50 while reducing the model size by 98.8%. Compared with lightweight models such as MobileNet V2, EASN improves accuracy by 19.4% with only a 28.6% increase in parameter size. These findings indicate that EASN offers an effective balance between diagnostic accuracy and model compactness, making it well-suited for real-time, edge-based fault detection in mass rapid transit systems.}, archive = {J_ACCESS}, author = {Leehter Yao and Hsuan Su and Matthew Cheng}, doi = {10.1109/ACCESS.2025.3604605}, journal = {IEEE Access}, pages = {152253-152265}, shortjournal = {IEEE Access}, title = {A transmission axle fault diagnosis system for massive rapid transit system with enhanced attention-based shuffle networks}, volume = {13}, year = {2025}, }
• (2025). A compact tree-shaped dual-band wearable antenna with enhanced far-field performance for WBAN applications. ACCESS, 13, 152241-152252. (www)

  @article{ , abstract = {This paper presents a compact, dual-band wearable antenna for wireless body area network (WBAN) applications. The proposed wearable antenna is designed and fabricated using a semi-flexible RT/duroid 5870 substrate enabling dual-band operation resonating at 2.45 GHz and 5.8 GHz ISM bands. The antenna prototype, comprises a rectangular patch that has a hexagonal inlay and a centrally positioned tree-shaped slot, with overall dimensions of $35\times 25\times 0.787$ mm3 ( $0.28~\lambda _{0} \times 0.20~\lambda _{0} \times 0.006~\lambda _{0}$ ) achieving an impedance bandwidth of 25% (2.21-2.82 GHz) at 2.45 GHz and 7.11% (5.48-5.78 GHz) at 5.8 GHz. The designed antenna exhibits omnidirectional and bidirectional radiation characteristics with an estimated gain of 2.30 dBi and 4.95 dBi and radiation efficiency of 97.5% and 98.3% at 2.45 GHz and 5.8 GHz respectively. Additionally, validation of both the bending and Specific Absorption Rate (SAR) analyses are carried out and the respective simulated and measured values strongly match providing effective and robust results. Therefore, the proposed design is recommended for Wireless Body Area Network (WBAN) applications including body-worn wearable devices.}, archive = {J_ACCESS}, author = {R Sandhya and Avinash Chandra}, doi = {10.1109/ACCESS.2025.3604551}, journal = {IEEE Access}, pages = {152241-152252}, shortjournal = {IEEE Access}, title = {A compact tree-shaped dual-band wearable antenna with enhanced far-field performance for WBAN applications}, volume = {13}, year = {2025}, }
• (2025). An adaptive Human–Robot interaction framework using real-time emotion recognition and context-aware task planning. ACCESS, 13, 152219-152240. (www)

  @article{ , abstract = {Existing approaches often operate poorly in noisy or occluded conditions, rely on cloud-based inference subject to latency and privacy issues, and do not take emotional context into consideration while planning for a task. This research introduces the Emotion–Context Reinforced Planner, a novel human–robot interaction framework designed to deliver emotionally and contextually adaptive behaviors in real-time. The objective is to enhance social coherence and responsiveness while enabling intelligent action sequencing on low-power robotic platforms. The framework utilizes a multimodal emotion recognition system that relies on a confidence-weighted estimate of how facial and vocal features should be fused, a context encoder, and a reinforcement learning policy based on a Double Deep Q-Network. The framework’s architecture was engineered to be readily deployed on low-power embedded systems (Jetson Nano, Raspberry Pi 4) and evaluated via TensorRT quantization and kernel fusion. Experimental evaluations reveal a 92.1% F1-score in emotion classification, 94.2% task-switching accuracy, and 34.8 ms response latency outperforming state-of-the-art models including CLEF and Pepper-based multimodal planners. Performance in real-time was achieved with up to 30 FPS on edge devices. A user satisfaction rating of 4.4/5 indicates strong perceived coherence and empathy in social interaction scenarios. ECRP offers a robust, scalable, and socially coherent solution for real-time human–robot interaction, outperforming existing methods both in accuracy and latency. Its fusion of emotional understanding and adaptive task planning significantly improves human engagement, making it suitable for use in elderly care, education, and customer service domains.}, archive = {J_ACCESS}, author = {Udit Mamodiya and Indra Kishor and Abrar Ahmed Syed and Pranati Sankalkar and Nithesh Naik}, doi = {10.1109/ACCESS.2025.3603738}, journal = {IEEE Access}, pages = {152219-152240}, shortjournal = {IEEE Access}, title = {An adaptive Human–Robot interaction framework using real-time emotion recognition and context-aware task planning}, volume = {13}, year = {2025}, }
• (2025). Online public opinion situation assessment by deep sparse autoencoder and time-series modeling convolutional neural network. ACCESS, 13, 152198-152218. (www)

  @article{ , abstract = {We have developed an innovative framework for analyzing online public sentiment, referred to as the Online Public Opinion Situation Assessment (OPOSA) model. This model employs Deep Sparse Autoencoder (DSAE) and Time-Series Modeling Convolutional Neural Networks (TBSMA-CNN) to effectively segment vast populations of online usersąłsuch as those on social media platforms or e-commerce websitesąłinto distinct groups based on shared preferences and evolving sentiment trends. Central to the model’s design is a geometry-based feature selection approach that enables the identification of key aspects of public opinion over time. These aspects, which are influenced by a range of temporal and contextual factors, including weakly supervised learning, are captured and iteratively refined to provide a clearer picture of online opinion dynamics. Unlike traditional methods that rely on probabilistic multi-topic distributions, our model uses DSAE to uncover hierarchical representations of opinion data and leverages TBSMA-CNN to track how public sentiment evolves across time. The synergy of DSAE and TBSMA-CNN enhances the model’s capacity to represent users’ opinions in a hidden feature space, revealing deeper insights into how individual preferences shift over time. To uncover connections between users with similar opinion trajectories, we introduce a graph-based framework. This framework enables the identification of opinion communitiesąłgroups of users bound by shared opinion patterns or trends, such as shifts in stance or collective movements toward particular issues. To ensure that the model’s insights are highly personalized, we incorporate a ranking mechanism that recommends content, discussions, or articles that align with the opinion dynamics of each identified community. This helps ensure that recommendations not only resonate with users’ current interests but also stay relevant to their changing preferences. We validated the performance of this approach using a large-scale dataset, which included millions of users and their engagement histories. The results highlighted the model’s ability to effectively detect and classify distinct opinion communities, offering large-scale, highly personalized content recommendations that are both timely and contextually relevant to each user’s evolving preferences and opinions.}, archive = {J_ACCESS}, author = {Wei Sun and Ran Xiao and Xuanmo Li}, doi = {10.1109/ACCESS.2025.3602491}, journal = {IEEE Access}, pages = {152198-152218}, shortjournal = {IEEE Access}, title = {Online public opinion situation assessment by deep sparse autoencoder and time-series modeling convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). An extensive overview and analysis of recent advancements in multiport converter topologies and control techniques. ACCESS, 13, 152161-152197. (www)

  @article{ , abstract = {The evolution of renewable energy-based power systems and smart grids has posed challenges in the development of innovative power conversion systems. The fluctuating load demands coupled with the intermittent nature of RES necessitate the combination of two or more energy sources along with auxiliary storage systems. This integration is crucial to meet load demands, enhance the system’s dynamic, steady-state characteristics, consistency, and availability. MPC dc-dc converters are required to facilitate the integration of RES with an ESS. These converters enable effective power flow control between RES, battery ports, and load ports in distributed power generation systems. This article comprehensively examines several topologies of MPCs associated with RES, ESS, EVs, and dc microgrids. This paper also provides an in-depth investigation, comparisons, and evaluations of non-isolated, isolated, and partially isolated MPC. It examines the diverse topologies relevant to each type of isolation, emphasizing the recent advancement of MPCs. A unique, extensive comparative analysis is conducted on the converter architectures and features, which includes operating conditions, device count, efficiency, control strategy, prototype rating, power loss analysis, switching voltage stress, voltage gain, and number of ports. Additionally, an investigation of the performance on different MPCs is carried out, thereby identifying the benefits and shortcomings of the different topologies, which are recommended for future research.}, archive = {J_ACCESS}, author = {Laishram Nikita Chanu and Balaji Subramanian}, doi = {10.1109/ACCESS.2025.3603687}, journal = {IEEE Access}, pages = {152161-152197}, shortjournal = {IEEE Access}, title = {An extensive overview and analysis of recent advancements in multiport converter topologies and control techniques}, volume = {13}, year = {2025}, }
• (2025). An ising machine approach to the personalized course selection problem. ACCESS, 13, 152148-152160. (www)

  @article{ , abstract = {A combinatorial optimization problem is a problem finding an optimal combination of variables that maximizes or minimizes an objective function while satisfying given constraints. Such problems arise in various fields, including transportation and communication. Many combinatorial optimization problems are NP-hard, meaning that the number of possible combinations grows exponentially as the number of variables increases. As a result, it is often difficult to solve large-scale combinatorial optimization problems for conventional classical computers. Recently, Ising machines, including quantum annealers, have gained attention as a promising architecture for efficiently solving combinatorial optimization problems. One real-world example of a combinatorial optimization problem is university course selection. Many students manually choose their courses, but this process is time consuming and labor intensive due to the large number of available options. To address this, in this paper, we formulate course selection as a combinatorial optimization problem, which we define as a Personalized Course Selection Problem (PCSP). To solve it efficiently, we use an Ising machine, a specialized computer designed for combinatorial optimization. We propose a Quadratic Unconstrained Binary Optimization (QUBO) formulation for solving PCSP and solve the problem with an Ising machine. Experimental evaluations demonstrate that, for the largest instance tested, the proposed method solves the problem 20X times faster than the conventional simulated annealing while achieving the same total course cost.}, archive = {J_ACCESS}, author = {Takeru Ota and Keisuke Fukada and Nozomu Togawa}, doi = {10.1109/ACCESS.2025.3603606}, journal = {IEEE Access}, pages = {152148-152160}, shortjournal = {IEEE Access}, title = {An ising machine approach to the personalized course selection problem}, volume = {13}, year = {2025}, }
• (2025). Estimating invisible passenger count using CCTV footage: An approach combining object detection models and machine learning. ACCESS, 13, 152131-152147. (www)

  @article{ , abstract = {Accurate passenger counting is essential for managing congestion in railway vehicles. Although onboard CCTV footage can be used for this purpose, limited camera views often cause occlusion, making some passengers invisible. Thus, even with precise detection of visible individuals using object detection algorithms, estimating the total count remains a challenge. To address this, we propose a two-stage approach. In the first stage, visible passengers are detected using models such as YOLOv8-L, Faster Region-Based Convolutional Neural Network (Faster R-CNN), and Single Shot Detector variants (SSD-VGG16, SSD-ResNet50). In the second stage, machine learning models—including Random Forest, Gradient Boosting, Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)—are used to predict total passenger numbers. Features based on spatial distribution and object size, extracted via region-wise segmentation, are used to train prediction models. These are evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). First-stage performance is assessed with metrics including Frames Per Second (FPS), Precision, Recall, F1-Score, and Intersection over Union (IoU). Experimental results show that combining YOLO-based detection with Random Forest or XGBoost achieves the best performance. Using a $4\times 4$ region division, models reached over 96% accuracy. Moreover, the second-stage algorithm improved the detection rate from 52% to 96%. These findings suggest that the proposed method enhances congestion monitoring and can support more efficient railway operations.}, archive = {J_ACCESS}, author = {Kyung-Hee Kim and Tae-Ki An and Sunhee Kim}, doi = {10.1109/ACCESS.2025.3597708}, journal = {IEEE Access}, pages = {152131-152147}, shortjournal = {IEEE Access}, title = {Estimating invisible passenger count using CCTV footage: An approach combining object detection models and machine learning}, volume = {13}, year = {2025}, }
• (2025). Frequency tunable millimeter-wave patch antenna integrating ferroelectric capacitors. ACCESS, 13, 152115-152130. (www)

  @article{ , abstract = {We present the design, realization and performance of a millimeter-wave patch antenna with frequency reconfiguration capabilities generated by integrating ferroelectric interdigitated tunable capacitors (FIDC). The tunable capacitors are based on Ba ${}_{\mathbf {0.8}} {\mathbf { Sr}} _{\mathbf {0.2}} {\mathbf { TiO}} _{\mathbf {3}} {\mathbf {(BST)}}$ ferroelectric layers and exhibit very good and stable performances over the microwave and millimeter-wave bands, with a maximum tunability of 40% for a bias voltage of 100 V. The ferroelectric layers have been integrated in the slot-loop excitation circuit of the antenna and their Direct Current (DC) polarization allows a continuous frequency reconfiguration of the antenna around 30 GHz. An electrical model of the whole radiating device has been developed allowing extracting the range of capacitance values to be integrated into the excitation slot for an optimum frequency tuning. The fabricated antenna shows a variation of the operation frequency from 28.5 GHz to 34.95 GHz, corresponding to a frequency tuning of 22.6%. The antenna is very well matched all over the operating frequency band, with total efficiencies between 20% (0 V bias on FIDCs) and 65% (100 V bias).}, archive = {J_ACCESS}, author = {Vincent Muzzupapa and Aurelian Crunteanu and Eric Arnaud and Damien Passerieux and Caroline Borderon and Laure Huitema}, doi = {10.1109/ACCESS.2025.3600868}, journal = {IEEE Access}, pages = {152115-152130}, shortjournal = {IEEE Access}, title = {Frequency tunable millimeter-wave patch antenna integrating ferroelectric capacitors}, volume = {13}, year = {2025}, }
• (2025). Reliability-driven HLS binding: A simulated annealing approach to minimize aging and process variation in digital circuits. ACCESS, 13, 152103-152114. (www)

  @article{ , abstract = {The aggressive scaling of CMOS technology into the nanoscale regime has driven remarkable performance improvements. However, it has introduced significant reliability challenges due to stochastic aging and process variation (APV) effects, including Bias Temperature Instability (BTI) and manufacturing variations. These effects degrade circuit performance, increase timing violations, and reduce operational lifetimes, posing critical obstacles to reliable Very Large Scale Integration (VLSI) design. This study proposes a novel High-Level Synthesis (HLS) framework that integrates stochastic APV considerations into functional unit (FU) binding and port assignment. Utilizing a Random Forest (RF) machine learning model with 98.7–98.9% accuracy to predict workload-induced APV effects, the framework employs Simulated Annealing (SA) optimization to concurrently minimize APV-induced degradation and interconnection costs while ensuring uniform degradation across FUs. Experimental results across benchmarks (MIPS, IDCT, FFT, and JPEG) show that the proposed method reduces APV intensity by up to 9.96% compared to APV-unaware methods (e.g., LegUp) and by up to 3.19% compared to APV aware weighted bipartite matching methods. Additionally, it improves circuit lifetime by an average of 34.22% and 5.9% compared with APV-unaware and aware methods, respectively, without area overhead. These advancements come with increased execution time overhead due to extensive design space exploration. This approach sets a new benchmark for reliability-aware HLS, offering a scalable and robust solution for designing resilient VLSI circuits in nanoscale technologies.}, archive = {J_ACCESS}, author = {Mohammad Bazli and Siavash Es’Haghi and Mahmoud Shahi and Majid Reza Naseh}, doi = {10.1109/ACCESS.2025.3600983}, journal = {IEEE Access}, pages = {152103-152114}, shortjournal = {IEEE Access}, title = {Reliability-driven HLS binding: A simulated annealing approach to minimize aging and process variation in digital circuits}, volume = {13}, year = {2025}, }
• (2025). Analytical solution of electromagnetic fields in a huygens bianisotropic metasurface energy harvester under vertical dipole illumination. ACCESS, 13, 152090-152102. (www)

  @article{ , abstract = {This article presents an analytical method for determining the electromagnetic fields of a bianisotropic energy harvester illuminated by a vertical electric dipole (VED). The process is also derived for vertical magnetic dipoles, horizontal electric dipoles, and horizontal magnetic dipoles. The Hertzian vector potentials at the top and bottom of the metasurface are formulated in the spectral domain. By applying the generalized sheet transition conditions (GSTCs) to the Hertzian magnetic vector potential equations, two unknown coefficients are expressed in terms of the metasurface susceptibilities. Consequently, the electromagnetic fields are derived as functions of these susceptibilities. A novel ultra-thin energy harvester is designed and analyzed in the simulation section using CST. The reflection coefficient of the harvester remains below −10 dB across the frequency band of 2.37 GHz to 2.48 GHz, with an efficiency exceeding 90% throughout the entire bandwidth. The unit cell dimensions are 30 mm $\times 30$ mm $\times 2$ mm $({\lambda }/ \,4\times {\lambda }/ \,4\times {\lambda }/63)$ . The reflection characteristics of the proposed structure are examined under varying polarization and oblique incidence angles. Additionally, the electromagnetic fields of the structure are extracted through CST simulations. A comparison between the analytical method and CST results for the bianisotropic energy harvester illuminated by a VED shows strong agreement. Finally, an ac-to-dc conversion circuit is designed using both ADS and CST, achieving an energy harvesting efficiency of 90% and a conversion efficiency of 81% across the operational bandwidth. The compact, ultra-thin energy harvester developed in this study holds significant potential for diverse practical applications.}, archive = {J_ACCESS}, author = {Pegah Nochian and Zahra Atlasbaf and Keyvan Forooraghi}, doi = {10.1109/ACCESS.2025.3601897}, journal = {IEEE Access}, pages = {152090-152102}, shortjournal = {IEEE Access}, title = {Analytical solution of electromagnetic fields in a huygens bianisotropic metasurface energy harvester under vertical dipole illumination}, volume = {13}, year = {2025}, }
• (2025). High-fidelity virtual model for industrial robot control under uncertain and disturbed scenarios: A comparative study on the UR5e. ACCESS, 13, 152063-152089. (www)

  @article{ , abstract = {Robust control of industrial manipulators under real-world uncertainties is critical for reliable automation. This work presents a comprehensive framework for modeling, control, and performance evaluation of the UR5e robotic manipulator. High-fidelity kinematic and dynamic models are developed and validated against experimental data to create a realistic virtual environment. Four control strategies, including Computed Torque Control, Proportional Integral Derivative, Sliding Mode Control, and Nonlinear Model Predictive Control are implemented and systematically compared. The comparison considers tracking accuracy, robustness, energy efficiency, and computational demand under nominal conditions as well as in the presence of external disturbances, sensor noise, and model uncertainties. Sliding Mode Control demonstrates consistent tracking under disturbances, Nonlinear Model Predictive Control achieves reduced energy consumption with smooth motion profiles, Computed Torque Control provides balanced accuracy and response, and Proportional Integral Derivative performs effectively under low-disturbance conditions. The methodology provides a validated simulation platform for benchmarking robotic control strategies and supports data-driven selection of controllers for industrial applications.}, archive = {J_ACCESS}, author = {Heni Belgacem and Mohammad Abuabiah and Inès Chihi}, doi = {10.1109/ACCESS.2025.3604653}, journal = {IEEE Access}, pages = {152063-152089}, shortjournal = {IEEE Access}, title = {High-fidelity virtual model for industrial robot control under uncertain and disturbed scenarios: A comparative study on the UR5e}, volume = {13}, year = {2025}, }
• (2025). A novel contactless approach to continuous blood pressure monitoring using complex CEEMDAN and neural networks. ACCESS, 13, 152046-152062. (www)

  @article{ , abstract = {Contactless blood pressure (BP) monitoring is essential for daily healthcare. Typically, existing methods based on radar or remote photoplethysmography (rPPG) estimate only systolic blood pressure (SBP) and diastolic blood pressure (DBP). However, continuous monitoring of the complete blood pressure waveform provides deeper insights into cardiovascular health. This paper proposes a novel contactless continuous blood pressure monitoring algorithm termed CCNN-BP (Complex CEEMDAN and neural networks for blood pressure monitoring). Radar signals reflected from the chest are decomposed using Complex CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), to extract heartbeat-related intrinsic mode functions (IMFs). These IMFs are fed into a specialized neural network trained to estimate continuous blood pressure waveforms with accurate morphology. The final blood pressure waveform is obtained by adjusting the amplitude through calibration factors in combination with waveform characteristics. The proposed algorithm was trained and validated using an open-source dataset from Hamburg University of Technology. Experimental results show that the method achieves a mean absolute error (MAE) of 4.79 mmHg and a root mean square error (RMSE) of 7.13 mmHg for continuous blood pressure amplitude estimation, with a Temporal Pearson correlation coefficient of 0.86. Furthermore, the mean normalized dynamic time warping (DTW) distance was $0.94~\pm ~0.54$ mmHg, confirming high morphological similarity. For peak delay evaluation, the method attains a MAE of 25.98 ms and RMSE of 34.79 ms, while achieving a Spectral Pearson correlation coefficient of 0.94, indicating robust preservation of key frequency components. These results demonstrate the feasibility of CCNN-BP for accurate, continuous, and contactless blood pressure waveform monitoring.}, archive = {J_ACCESS}, author = {Ketao Ma and Yiyan Zhang and Baohua Shan}, doi = {10.1109/ACCESS.2025.3594570}, journal = {IEEE Access}, pages = {152046-152062}, shortjournal = {IEEE Access}, title = {A novel contactless approach to continuous blood pressure monitoring using complex CEEMDAN and neural networks}, volume = {13}, year = {2025}, }
• (2025). A multitask deep learning model for food scene recognition and portion estimation—the food portion benchmark (FPB) dataset. ACCESS, 13, 152033-152045. (www)

  @article{ , abstract = {Deep learning-based approaches have achieved remarkable success in various image-based dietary assessment applications, including food detection and estimating portion sizes. However, most existing methods address these tasks separately, limiting their practical applicability in real-world scenarios where simultaneous processing is required. This study presents an end-to-end multi-task method that integrates food detection and portion size estimation within a single model, leveraging the state-of-the-art YOLOv12 architecture. To support this approach, this paper introduces the Food Portion Benchmark (FPB) dataset – a comprehensive and diverse collection comprising 14,083 images across 138 food classes, each with manually annotated bounding boxes and laboratory-measured component weights. To promote reproducibility and standardization, a public Food AI leaderboard has been deployed on Hugging Face, enabling researchers to benchmark their models for detection and weight estimation on the FPB dataset. In addition, this work provides pre-trained model weights and model-generated annotations, eliminating the need for researchers to perform their own resource-intensive measurements and annotations of food items. The proposed method achieves state-of-the-art performance, with a mean average precision (mAP50) of 0.978 for food detection and a mean absolute error (MAE) of only 90.95 grams for portion size estimation on the FPB test set. These results highlight the effectiveness and practicality of an integrated model for accurate and comprehensive dietary analysis. It is anticipated that this FPB dataset and its model will serve as a foundational reference for dietary assessment, particularly for culturally relevant foods in Central Asia.}, archive = {J_ACCESS}, author = {Aibota Sanatbyek and Tomiris Rakhimzhanova and Bibinur Nurmanova and Zhuldyz Omarova and Aidana Rakhmankulova and Rustem Orazbayev and Huseyin Atakan Varol and Mei Yen Chan}, doi = {10.1109/ACCESS.2025.3603287}, journal = {IEEE Access}, pages = {152033-152045}, shortjournal = {IEEE Access}, title = {A multitask deep learning model for food scene recognition and portion estimation—the food portion benchmark (FPB) dataset}, volume = {13}, year = {2025}, }
• (2025). A modified min-max method with adaptive distance adjustment for RSSI-based indoor localization. ACCESS, 13, 152010-152032. (www)

  @article{ , abstract = {In range-based localization systems based on received signal strength indicator (RSSI), position estimates are determined by measuring RSSI levels between all reference nodes and an unknown target. The RSSI level indicates the distance between the target and references. Because the RSSI signal is time-varying and fluctuates due to multipath effects, particularly in indoor contexts, this variation can cause distance calculation and localization inaccuracies. Inadequate estimation findings can lead to poor judgments throughout the system. In this paper, we present a modified min-max method to reduce RSSI-to-distance error and to improve localization precision. The novelty of this study is that an autonomous reference node identification, area separation, area selection, and adaptive distance adjustment solution are proposed and integrated with the traditional min-max method. The distances between the target and references are automatically measured and compensated. Experiments in real-world scenarios using 2.4 GHz ZigBee/IEEE 802.15.4 wireless networks have been conducted in different indoor environments, including an office room, an electrical machine laboratory, and a second-floor walking corridor. Experimental results show that the proposed method has estimation errors lower than the traditional min-max method: 0.460 m (proposed) and 0.715 m (traditional) for the office room, 0.735 m and 1.503 m for the machine laboratory, and 0.661 m and 1.340 m for the corridor. The proposed method significantly outperforms the min-max method by 35.623%, 51.063%, and 50.627%, respectively. For in mobile target scenarios, the proposed method also provides a more estimated precision of tracking results. Finally, the computational cost analysis of the proposed method in terms of mathematical operations is discussed. The worst-case scenario and the results obtained from the experiments are also reported.}, archive = {J_ACCESS}, author = {Apidet Booranawong and Naruesorn Prakobboon and Hiroshi Saito}, doi = {10.1109/ACCESS.2025.3603986}, journal = {IEEE Access}, pages = {152010-152032}, shortjournal = {IEEE Access}, title = {A modified min-max method with adaptive distance adjustment for RSSI-based indoor localization}, volume = {13}, year = {2025}, }
• (2025). The impact of combining datasets on the robustness of deep learning architectures: A cross-dataset analysis. ACCESS, 13, 151993-152009. (www)

  @article{ , abstract = {Convolutional Neural Networks have become a widely used technology. Typically, the performance of CNN models is measured using an accuracy score obtained on a single dataset. This often results in systems that perform significantly worse in real-world applications. The robustness of the model to unseen environments is still underresearched. Specifically, a research gap remains on how training data affects the robustness of deep learning systems. This research article investigates the impact of combining data in training datasets on the robustness and performance of deep learning models through a cross-dataset analysis. We employ a transfer learning approach to train deep learning models based on four popular architectures and two different datasets, as well as a combination of both datasets. Our results demonstrate that combining two datasets can improve robustness, but the specific effects on performance can vary between architectures, leading to a slight decrease in accuracy in most observed cases, or even an accuracy gain. Furthermore, we find that training on more complex datasets tends to outperform training on simpler datasets in cross-evaluation settings, indicating that models trained on more complex training datasets are more robust. However, we also observe that a simpler architecture fails to generalize when trained on the combined training data, indicating the need for caution and extensive evaluation when combining datasets during the development cycle of deep learning systems.}, archive = {J_ACCESS}, author = {Ricardo Buettner and Susan Bertram and Leopold Fischer-Brandies}, doi = {10.1109/ACCESS.2025.3604689}, journal = {IEEE Access}, pages = {151993-152009}, shortjournal = {IEEE Access}, title = {The impact of combining datasets on the robustness of deep learning architectures: A cross-dataset analysis}, volume = {13}, year = {2025}, }
• (2025). Multimodal learning techniques for time series forecasting in renewable energy systems: A comprehensive survey. ACCESS, 13, 151970-151992. (www)

  @article{ , abstract = {Renewable energy systems, such as solar, wind, and hybrid sources, present complex forecasting challenges due to their stochastic and weather-dependent nature. The growing availability of heterogeneous and complementary data modalities including meteorological forecasts, satellite imagery, numerical sensor streams, and grid interaction logs has motivated the application of multimodal learning techniques to improve time series forecasting accuracy. Unlike previous surveys that either focused narrowly on single-modality forecasting or provided only high-level overviews, this paper offers a focused and technically grounded review of multimodal learning strategies specific to renewable energy forecasting. We categorize and compare key fusion strategies (early, late, hybrid), analyze deep model architectures (e.g., co-attention and transformer-based fusion), and discuss deployment-related challenges such as data alignment, modality missingness, and interpretability. Then, this paper reviews multimodal learning techniques based on four main aspects, i.e., classify fusion techniques, examine model architectures, showcase practical application scenarios, and summarize benchmark datasets. Finally, the paper identifies existing research gaps and outlines promising future directions in this rapidly evolving field, such as self-supervised multimodal learning and adaptive fusion under uncertainty.}, archive = {J_ACCESS}, author = {Majdi Mansouri and Khadija Attouri and Shady S. Refaat}, doi = {10.1109/ACCESS.2025.3602914}, journal = {IEEE Access}, pages = {151970-151992}, shortjournal = {IEEE Access}, title = {Multimodal learning techniques for time series forecasting in renewable energy systems: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Design of a multi-band antireflector and RCS reduction metasurface based on the polarization conversion method. ACCESS, 13, 151954-151969. (www)

  @article{ , abstract = {This study introduces a metasurface with antireflective and Radar Cross-Section Reduction (RCSR) properties in the frequency range of 3-33 GHz. The design and validation stages of the structure include four stages, in which Linear-to-cross (LX) and Linear-to-Circular (LC) polarization conversion for unit cells are implemented. The highest performance in the selected frequency band is attained. To increase the operational bandwidth and other alignment in aspect of absorption, different incident waves and structural considerations, optimal combination of the first and second cells was implemented which improves polarization conversion criteria and Its synchronization with its Mirror-Symmetrical Array was intended to logically optimize the configuration when encountering various incident wave scenarios especially in the three frequency bands of 3–9 GHz, 18–22 GHz, and 31-33 GHz. An overall 60% absorption for Combined Array was obtained. Therefore, the $8\times 8$ array is devised from repeating Combined Arrays as a mirror symmetry to ensure the desired properties in a wide bandwidth by converting reflection polarization and phase cancellation. As a result, an RCS reduction greater than 10 dB has been achieved in nearly a quarter of the 3-33 GHz frequency band. Furthermore, the mentioned performance is relatively incident angle independent and is polarization independent. The structure was fabricated and the simulation and measurement results showed to be in good agreement.}, archive = {J_ACCESS}, author = {Sasan Kianjo and Amir Saman Nooramin}, doi = {10.1109/ACCESS.2025.3602800}, journal = {IEEE Access}, pages = {151954-151969}, shortjournal = {IEEE Access}, title = {Design of a multi-band antireflector and RCS reduction metasurface based on the polarization conversion method}, volume = {13}, year = {2025}, }
• (2025). A review of computer vision for industrial-grade waste classification. ACCESS, 13, 151934-151953. (www)

  @article{ , abstract = {The automation of municipal solid waste sorting is a rapidly advancing field, driven by the need to improve efficiency, accuracy, and recycling outcomes in material recovery facilities. A key component of this automation is accurate waste classification, where computer vision holds great potential, but most prior work has been limited to controlled and simplified laboratory setups or in-the-wild scenarios. The recently published industrial-grade waste datasets, recorded in real-world sorting facilities showing waste streams on conveyor belts with high level of clutter and occlusion, present a severe challenge for state-of-the-art computer vision models. Despite their significance, these datasets have not yet been comprehensively reviewed. This paper addresses that gap by systematically analyzing and comparing existing industrial-grade waste datasets. We further summarize well-reported model performances and investigate model architectures and methods evaluated on the presented datasets to support future model development. We identify both dataset-specific and general challenges in this domain and conclude by outlining future research directions to advance CV-based waste sorting in industrial environments.}, archive = {J_ACCESS}, author = {Pelle Thielmann and Yu Zhou and Bruno Mirbach and Didier Stricker and Jason Rambach}, doi = {10.1109/ACCESS.2025.3603903}, journal = {IEEE Access}, pages = {151934-151953}, shortjournal = {IEEE Access}, title = {A review of computer vision for industrial-grade waste classification}, volume = {13}, year = {2025}, }
• (2025). Multilingual relation extraction: A survey. ACCESS, 13, 151907-151933. (www)

  @article{ , abstract = {Relation extraction plays a fundamental role in applications of various research fields such as knowledge graph construction, event extraction, and question answering over knowledge graphs, as they often rely on extracting relationships between named entities. Relation extraction has been extensively studied in high-resource languages like English. However, there remains a significant gap in supporting languages with limited resources, defined as those lacking comprehensive annotated corpora, linguistic tools, or pre-trained models, limiting the completeness and accuracy of applications that rely on multilingual data. This paper provides a comprehensive survey of recent advances in relation extraction, focusing on multilingual approaches. We systematically review state-of-the-art methods, datasets used for evaluation, and key features leveraged in these approaches. Additionally, we perform a detailed comparative analysis of the surveyed methods, examining their methodologies, target domains, levels of extraction, explored languages, and effectiveness. Finally, we identify promising directions for future research, with an emphasis on enhancing multilingual relation extraction.}, archive = {J_ACCESS}, author = {Manzoor Ali and René Speck and Hamada M. Zahera and Muhammad Saleem and Diego Moussallem and Axel-Cyrille Ngonga Ngomo}, doi = {10.1109/ACCESS.2025.3604258}, journal = {IEEE Access}, pages = {151907-151933}, shortjournal = {IEEE Access}, title = {Multilingual relation extraction: A survey}, volume = {13}, year = {2025}, }
• (2025). Subgraph-aware joint modeling for graph classification via efficient frequent pattern mining and fusion. ACCESS, 13, 151894-151906. (www)

  @article{ , abstract = {This paper proposes a subgraph-aware classification framework that integrates efficient frequent subgraph mining with graph neural networks (GNNs) to address the limitations of existing GNNs in capturing explicit local structures. Specifically,we first introduce an improved mining strategy to extract representative and low-redundancy structural patterns from large-scale graph data, significantly reducing computational complexity and pattern duplication. These patterns are then dynamically injected into a subgraph-aware GNN module via a subgraph-corrected attention mechanism and gated fusion strategy, enabling deep collaborative modeling of local and global semantics. An end-to-end joint training mechanism is also introduced to coordinate subgraph selection with representation learning. Experimental results on the IMDB-MULTI and REDDIT-MULTI-12K datasets demonstrate that our method outperforms several state-of-the-art baselines. On IMDB-MULTI, our model achieves an Accuracy of 78.6% and a Macro-F1 score of 77.4%, showing notable gains over competing approaches. Further ablation and visualization analyses confirm the importance of both the frequent subgraph mining component and the subgraph-aware fusion mechanism in enhancing representation quality and model interpretability. This work presents a unified and interpretable framework for graph classification, offering new insights into structural perception and feature fusion in GNN-based learning.}, archive = {J_ACCESS}, author = {Weiyao Xu and Yumeng Sun and Yumeng MA and Yu Cheng and Jie Liu and Yuan Sheng and Yiwei Zhang and Junliang Du}, doi = {10.1109/ACCESS.2025.3602411}, journal = {IEEE Access}, pages = {151894-151906}, shortjournal = {IEEE Access}, title = {Subgraph-aware joint modeling for graph classification via efficient frequent pattern mining and fusion}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches for EEG-motor imagery-based BCIs: Current models, generalization challenges, and emerging trends. ACCESS, 13, 151866-151893. (www)

  @article{ , abstract = {This study critically examines the evolution of deep learning (DL) for electroencephalogram (EEG) based motor imagery (MI) decoding with a focus on real-time Brain Computer Interfaces (BCIs) development. Prior studies often prioritize accuracy in isolation, neglecting computational efficiency, interpretability, noise robustness, and neurophysiological variability across subjects and tasks, while recent DL advancements have introduced novel architectures to address these issues. This work systematically evaluates those novel architectures and emerging trends through addressing 4 research questions (RQs) based on an extensive review. Initially, over 188 papers from 3 databases were retrieved with a focus on publications from 2024 to 2025. Later, through multi-stage filtering based on strict inclusion criteria, a refined corpus of 68 high-quality studies was selected. This analysis reveals that state-of-the-art models achieve competitive accuracy, varying 85-100% on public datasets, but still face challenges in computational demands, noise resilience, generalization and BCI deployment. Additionally, preprocessing and integrated hybrid feature extraction paired with explainable AI (XAI) techniques are discussed. Emerging trends such as neuromorphic computing, federated learning (FL), and closed-loop adaptive systems offering solutions to current deployment barriers have been included in the discussion. Ethical and ecological considerations, such as data privacy, algorithmic bias, and energy efficiency, are notably represented in the literature. This review contributes a holistic framework for evaluating DL models, emphasizing the need to balance accuracy, efficiency, and adaptability. By synthesizing insights from large-scale datasets and explainability tools, this study exposes the limitations of current DL studies reliant on homogenous data, unavailability of codes to reproduce models and proposes strategies to mitigate neurophysiological variability. The finding underscores the urgency of prioritizing clinical relevance, ethical validation, and ecological robustness to bridge the lab to real-world divide, offering actionable directions for future research in low-power, generalizable, and user-centric BCI design.}, archive = {J_ACCESS}, author = {Aaqib Raza and Mohd Zuki Yusoff}, doi = {10.1109/ACCESS.2025.3604528}, journal = {IEEE Access}, pages = {151866-151893}, shortjournal = {IEEE Access}, title = {Deep learning approaches for EEG-motor imagery-based BCIs: Current models, generalization challenges, and emerging trends}, volume = {13}, year = {2025}, }
• (2025). Dynamic issues in power system restoration with synchronous generators and grid-forming inverters. ACCESS, 13, 151850-151865. (www)

  @article{ , abstract = {The integration of renewable energy sources with synchronous generators (SGs) and inverter-based resources (IBRs) is essential for enabling a decarbonized power grid. However, as renewable penetration increases and conventional SGs are displaced, the system becomes more susceptible to large-scale disturbances, as evidenced by the April 2025 blackout in Spain. This underscores the need for improved coordination between SGs and IBRs during power system restoration, particularly in its early stages. This study investigates the dynamic behavior of generator startup during restoration in mixed SG–IBR systems, with a focus on grid-forming (GFM) IBRs. Using electromagnetic transient (EMT) simulations, interaction challenges between SGs and GFM-IBRs are analyzed to identify solutions that enhance restoration reliability. A multi-loop droop (MLD) control framework is designed to examine the effects of droop gain, current-limiting strategies, system loading, and GFM-IBR placement in the cranking path. In addition, a comparative analysis of three GFM control strategies, MLD, dispatchable virtual oscillator control (dVOC), and virtual synchronous machine (VSM), is performed to evaluate their performance during IBR startup in a multi-machine scenario. Results reveal that voltage loop bandwidth, droop gain, and current-limiting techniques significantly impact SG–IBR dynamic interactions. The findings emphasize the critical role of GFM controller selection and tuning in achieving reliable restoration in mixed-generation power systems.}, archive = {J_ACCESS}, author = {Mohsen Alizadeh and Wei Sun}, doi = {10.1109/ACCESS.2025.3603566}, journal = {IEEE Access}, pages = {151850-151865}, shortjournal = {IEEE Access}, title = {Dynamic issues in power system restoration with synchronous generators and grid-forming inverters}, volume = {13}, year = {2025}, }
• (2025). Combining model-based and data-driven observer designs for sideslip angle estimation. ACCESS, 13, 151838-151849. (www)

  @article{ , abstract = {The vehicle side slip angle represents a key indicator of dynamic stability. Elevated values of the side slip angle may indicate a loss of stability or undesired vehicle behaviors such as understeering or oversteering. With the increased use of advanced driver assistance systems (ADAS), the need for accurate estimation of the side slip angle has become increasingly critical. This quantity in general needs to be indirectly measured or estimated, with the latter often representing a more cost-effective and more reliable approach. This is usually done by simple observer design, e.g., Kalman filter, which requires a well-parameterized system dynamics model. In this work we explore Machine Learning techniques in combination with a budget hardware inertial measurement unit to estimate the sideslip angle. This is done independently of the actual vehicle configuration, i.e., vehicle load and tires used. We model the system dynamics with a traditional Luenberger Observer, Long-short-term memory, Gated recurrent unit neural networks, and their combination, and investigate possible performance benefits when incorporating well-known physical relations. The results demonstrate that a well-designed combination of model-based and data-driven approaches can achieve high estimation accuracy even without the need for large datasets, which are typically required when employing purely data-driven methods. The performance of the proposed sideslip angle estimator under different driving conditions and tire configurations is validated with real-world measurement data.}, archive = {J_ACCESS}, author = {Martin Repka and Alexander L. Gratzer and Jan Fojtasek and Tomas Straka and Petr Portes and Alexander Schirrer}, doi = {10.1109/ACCESS.2025.3595282}, journal = {IEEE Access}, pages = {151838-151849}, shortjournal = {IEEE Access}, title = {Combining model-based and data-driven observer designs for sideslip angle estimation}, volume = {13}, year = {2025}, }
• (2025). Agentic AI: A comprehensive survey of technologies, applications, and societal implications. ACCESS, 13, 151824-151837. (www)

  @article{ , abstract = {Agentic AI brings a new level of advancement in artificial intelligence (AI), as it is capable of goal-directed behaviour, dynamic adaptation, and self-improvement. It influences various significant fields, such as robotics, healthcare, autonomous vehicles, and labor automation. This paper explores the defining features of agentic AI, highlights its differences from traditional AI, and discusses how autonomy, memory, goal-directed behavior, and adaptive reasoning contribute to increasingly general capabilities. Rather than proposing a new architecture, we offer a conceptual analysis of the trajectory from current agentic frameworks to future agentic AI systems. Examining applications in significant fields, including precision medicine, industrial robotics, and self-driving technologies, and discussing societal impacts, particularly concerning workforce disruption, augmentation, and ethical issues arising from agentic AI systems. The paper identifies key research directions for ensuring beneficial and controllable agentic AI development. This work serves as a primary source for exploring agentic AI by compiling existing research and identifying open questions.}, archive = {J_ACCESS}, author = {Ashis Kumar Pati}, doi = {10.1109/ACCESS.2025.3585609}, journal = {IEEE Access}, pages = {151824-151837}, shortjournal = {IEEE Access}, title = {Agentic AI: A comprehensive survey of technologies, applications, and societal implications}, volume = {13}, year = {2025}, }
• (2025). Nonlinear system control based on an output recurrent TS fuzzy controller. ACCESS, 13, 151803-151823. (www)

  @article{ , abstract = {The Takagi–Sugeno (TS) fuzzy control design method has been widely utilized for nonlinear system control due to its ability to approximate complex system behaviors. Traditionally, TS fuzzy controllers are designed by computing controller gains offline using the MATLAB linear matrix inequality (LMI) control toolbox, where system parameters are obtained through the linearization of each subsystem. While this simplification reduces computational complexity, it also compromises the nonlinear characteristics of the system, leading to a discrepancy between the mathematical model and the real-world implementation. Moreover, system uncertainties and time-varying dynamics introduce further challenges, as the offline-derived controller gains may become suboptimal during actual operation, limiting the controller’s adaptability and robustness. To address these issues, this study proposes an output recurrent Takagi–Sugeno fuzzy controller (ORTSFC), designed to enhance system adaptability and disturbance rejection. The novelty of this research lies in the integration of a recurrent structure within the TS fuzzy framework, transforming it from a static system into a dynamic one. This approach allows the controller to adapt continuously to real-time system dynamics, enhancing responsiveness and performance without requiring complex recalculations. Consequently, the proposed ORTSFC achieves robust control with significantly reduced computational demands. In addition, system stability is rigorously ensured through Lyapunov-based analysis. Overall, the ORTSFC provides a theoretically grounded and practically efficient solution for controlling nonlinear systems, especially under uncertainty and time-varying conditions. The main contributions of this study include: 1) Development of an ORTSFC, which introduces recurrence into the TS fuzzy control framework, improving its ability to capture dynamic system behaviors and adapt to uncertainties. 2) Application of the ORTSFC to a nonlinear control problem, specifically for the omnidirectional inverted pendulum (OIP), demonstrating its superior performance in stabilizing complex systems under uncertain conditions. Extensive simulation results validate the effectiveness of the proposed ORTSFC, showing its advantages over conventional TS fuzzy controllers in terms of adaptability, robustness, and computational efficiency.}, archive = {J_ACCESS}, author = {Chih-Hui Chiu}, doi = {10.1109/ACCESS.2025.3603228}, journal = {IEEE Access}, pages = {151803-151823}, shortjournal = {IEEE Access}, title = {Nonlinear system control based on an output recurrent TS fuzzy controller}, volume = {13}, year = {2025}, }
• (2025). Enhancing worker safety at heights: A deep learning model for detecting helmets and harnesses using DETR architecture. ACCESS, 13, 151788-151802. (www)

  @article{ , abstract = {We propose an approach for monitoring work-at-heights activities by ensuring the presence of safety helmets and harnesses in construction environments using the DEtection TRansformer (DETR) model. The DETR-based model is trained and tested on a custom dataset to accurately detect these essential safety items. The model’s performance was evaluated using standard metrics. For helmet detection, it achieved an average precision of 0.99, a recall of 0.92, an F1 score of 0.95, and a confidence rate exceeding 95%. Similarly, for harness detection, the model attained an average precision of 0.971, a recall of 0.91, an F1 score of 0.94, and a confidence rate above 93%. To evaluate its reliability, the model was tested under diverse conditions, including RGB, grayscale, blurred, and dusty images, as well as scenarios involving multiple helmets and harnesses. It was also assessed using drone-captured images to ensure robust performance across varying weather and lighting conditions. The results were then compared with a previously trained YOLOv3 model developed for the same detection task, as well as two newly trained models based on the YOLOv8 and Deformable DETR architectures. Our analysis demonstrates that the DETR model not only improves detection accuracy but also overcomes certain limitations inherent in other models. The comparative study highlights key architectural and performance differences, providing insights into the advantages of transformer-based models for object detection tasks in industrial safety applications.}, archive = {J_ACCESS}, author = {Mohammad Z. Shanti and Boohyun An and Chan Yeob Yeun and Chung-Suk Cho and Ernesto Damiani and Tae-Yeon Kim}, doi = {10.1109/ACCESS.2025.3603202}, journal = {IEEE Access}, pages = {151788-151802}, shortjournal = {IEEE Access}, title = {Enhancing worker safety at heights: A deep learning model for detecting helmets and harnesses using DETR architecture}, volume = {13}, year = {2025}, }
• (2025). Hybrid vector auto regression and tree-based ensembles for energy load forecasting: A case study. ACCESS, 13, 151773-151787. (www)

  @article{ , abstract = {The increasing depletion of non-renewable energy resources due to population growth, urbanization, and technological progress has heightened the need for accurate energy consumption forecasting to facilitate sustainable infrastructure and efficient resource management. Numerous conventional unimodal models have difficulty capturing non-linear trends in energy consumption patterns. We propose a two-stage hybrid forecasting framework that synergistically integrates Vector Autoregression (VAR) for capturing linear data and tree-based ensembles (Random Forest and XGBoost) trained on the residuals to capture nonlinearities. Results suggest that the VAR model is able to captures most of the energy patterns, while the hybrid extensions capture non-linearity. Across all evaluation metrics, the base VAR model and its hybrid extensions achieved strong predictive performance, with normalized root mean square errors (NRMSE) as low as 0.6977 and R2 values exceeding 0.5 in the most predictable settings. Although the base VAR model handled most temporal dynamics well, the hybrid model incorporating tree-based ensemble offered extra versatility and marginal gain, especially with more intricate circumstances. Most importantly, our hybrid framework performed better than or closely resembled top-performing deep learning and ensemble-based strategies published in the recent literature by achieving competitive accuracy levels with reduced error rates.}, archive = {J_ACCESS}, author = {Shagnik Guha and Sankalp Katyayanan and Gowrish Mallya and Rani Oomman Panicker}, doi = {10.1109/ACCESS.2025.3603620}, journal = {IEEE Access}, pages = {151773-151787}, shortjournal = {IEEE Access}, title = {Hybrid vector auto regression and tree-based ensembles for energy load forecasting: A case study}, volume = {13}, year = {2025}, }
• (2025). Improving CPU energy efficiency for low latency use cases with software-defined core labeling. ACCESS, 13, 151758-151772. (www)

  @article{ , abstract = {In this paper, we address the energy efficiency problems in general-purpose servers used for low-latency use cases. In use cases with strict latency requirements, power-saving features of Central Processing Unit (CPU) such as CPU idle are typically disabled to achieve the strict latency requirements, sacrificing energy efficiency. We determine that this problem arises since the task scheduler of Operating System (OS) and CPU idle control functions focus on generality and fairness. As a result, latency-critical tasks cannot avoid high wake-up latency caused by deep CPU idle states or being delayed by best-effort tasks. To mitigate this, we propose several policy changes. First, the task scheduler should separate cores into those handling latency-critical tasks and those handling best-effort tasks. Second, instead of predicting appropriate CPU idle states on the basis of assigned tasks, the system should assign tasks to cores with predetermined CPU idle states. To implement this policy, we introduce a method called software-defined core labeling, where cores are categorized as performance-oriented or energy-efficient. Performance-oriented cores execute latency-critical tasks with CPU idle disabled, while energy-efficient cores execute best-effort tasks with CPU idle enabled. Evaluations using virtualized Radio Access Network (vRAN) as a low-latency use case demonstrate that our method avoids latency violations that occur with default CPU idle enabled methods and has lower power consumption than CPU idle disabled methods, which are typically used in low-latency use cases. The contributions of this paper are a design of a new task scheduling method that improves energy efficiency in low-latency use cases with software-defined core labeling, and an implementation of this method to demonstrate its effectiveness in low-latency use cases.}, archive = {J_ACCESS}, author = {Ko Natori and Ikuo Otani and Kei Fujimoto}, doi = {10.1109/ACCESS.2025.3603713}, journal = {IEEE Access}, pages = {151758-151772}, shortjournal = {IEEE Access}, title = {Improving CPU energy efficiency for low latency use cases with software-defined core labeling}, volume = {13}, year = {2025}, }
• (2025). Mission profile-based reliability assessment for modular MVAC-LVDC solid-state transformers. ACCESS, 13, 151736-151757. (www)

  @article{ , abstract = {This paper introduces a comprehensive framework for evaluating the reliability of modular MVAC-LVDC Solid-State Transformers (SSTs) based on their mission profiles. The proposed approach emphasizes the importance of accurately assessing the reliability of Medium-Frequency Transformers (MFT), which play a critical role in SST performance. The study presents: 1) a mission profile-based methodology for analyzing MFT reliability; 2) a detailed process for estimating SST lifetime that accounts for both random and wear-out failure mechanisms; and 3) an exploration of how MFT reliability influences overall SST lifespan. Additionally, the paper outlines the design methodology for MVAC-LVDC SSTs and details the component selection process used in the reliability assessment. Two case studies—one involving an electric vehicle load and the other a data center load—are applied to a 6.32 MVA, 11 kV AC / 1 kV DC SST design. Results indicate that incorporating MFT reliability into the analysis may reduce the estimated SST lifespan by over 15%, highlighting the value of integrated reliability assessments in SST design. This assessment identifies components prone to failure, allowing for redesigns that improve resilience against mission profile stresses and extend the overall converter lifetime.}, archive = {J_ACCESS}, author = {Ahmed Meligy and Rafael Coelho-Medeiros and Ilknur Colak and Seddik Bacha}, doi = {10.1109/ACCESS.2025.3603095}, journal = {IEEE Access}, pages = {151736-151757}, shortjournal = {IEEE Access}, title = {Mission profile-based reliability assessment for modular MVAC-LVDC solid-state transformers}, volume = {13}, year = {2025}, }
• (2025). Robust fetal ECG extraction from abdominal surface potentials using a variational autoencoder. ACCESS, 13, 151726-151735. (www)

  @article{ , abstract = {The accurate extraction of fetal electrocardiogram (fECG) signals from noninvasive maternal abdominal recordings is essential for reliable and continuous fetal health monitoring. However, due to the dominance of maternal signals and the presence of various noise sources, this task remains technically challenging. In this study, we propose a deep learning-based approach that leverages a variational autoencoder (VAE) to achieve robust separation of fetal components and accurate detection of QRS complexes. This capability is crucial for estimating fetal heart rate and detecting potential abnormalities. The proposed method was evaluated on two publicly available datasets: the PhysioNet Challenge 2013 dataset and the Non-Invasive Fetal ECG Database (NIFECGDB). To validate its effectiveness, we compared the VAE-based approach against several baseline methods, including CycleGAN, W-NET, R2W-NET, and a combination of principal component analysis with discrete wavelet transform (PCA+DWT). Evaluation metrics included F1 score, precision, recall, and accuracy. Our method achieved superior performance, with F1 scores of 98.69% on the PhysioNet dataset and 99.61% on NIFECGDB. These results demonstrate the potential of the proposed VAE-based approach for practical use in noninvasive fetal monitoring systems, offering advantages in accuracy, robustness, and generalizability across different recording conditions.}, archive = {J_ACCESS}, author = {Yu Honda and Hiroshi Kawaguchi and Shintaro Izumi}, doi = {10.1109/ACCESS.2025.3602388}, journal = {IEEE Access}, pages = {151726-151735}, shortjournal = {IEEE Access}, title = {Robust fetal ECG extraction from abdominal surface potentials using a variational autoencoder}, volume = {13}, year = {2025}, }
• (2025). Research on fast calculation method for relay protection setting mismatch points of lines based on deep learning. ACCESS, 13, 151714-151725. (www)

  @article{ , abstract = {In complex ring networks, a significant amount of time is typically required to determine the coordination relationships of backup protection settings during online adjustment, which affects the efficiency of the online setting process. To address this, the paper introduces a data-driven approach by applying artificial intelligence technology—specifically, for the first time—into the field of online setting. A fast computation scheme for online backup protection setting based on Generative Adversarial Networks (GANs) is proposed. Firstly, a conditional GAN based on the Wasserstein distance is constructed, using system operating modes as conditional labels. The coordination pairs of backup protection settings are transformed into matrix indices, forming a backup protection coordination matrix, which serves as real sample data for GAN training. The generator and discriminator networks of the GAN are primarily composed of Convolutional Neural Networks (CNNs), and batch normalization is applied to the outputs of the neural networks to improve training stability. Next, the specific implementation of the GAN-based online setting scheme for backup protection is presented. Parallel computing techniques are employed to accelerate the computation of both the CNN and the setting values, further enhancing the efficiency of the online adjustment process. Finally, the IEEE 39-bus system is used as an example to compute backup protection coordination matrices under various operating conditions, thus constructing the dataset required for GAN training and validating the proposed scheme. Simulation results show that the proposed method can learn the complex coordination relationships among various backup protection settings from real sample data via GANs. It can generate the corresponding coordination matrix according to different operating modes, thereby achieving fast computation for online backup protection setting.}, archive = {J_ACCESS}, author = {Yongzhi Gong and Zhongyan Yang and Zhengya Luo and Zequ Zhang and Jijun Zhang}, doi = {10.1109/ACCESS.2025.3603252}, journal = {IEEE Access}, pages = {151714-151725}, shortjournal = {IEEE Access}, title = {Research on fast calculation method for relay protection setting mismatch points of lines based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Patch-shuffled superpixel synthetic anomaly insertion in autoencoder foreground extracted regions for improved visual anomaly detection. ACCESS, 13, 151696-151713. (www)

  @article{ , abstract = {We propose a self-supervised framework that combines an autoencoder and segmentation model to enhance anomaly detection and segmentation by inserting synthetic anomalies into the foreground regions of normal images. Synthetic anomalies were generated through a novel Patch Shuffle Superpixel Anomaly Insertion Method (PSSAIM), involving two stages. First, the normal images were divided into disjoint patches that were shuffled and rotated to produce distorted versions of the original images. Second, a Simple Linear Iterative Clustering (SLIC) superpixel extraction method is applied to distorted images to create abnormal segments that simulate real anomalies and cover a diverse range of synthetic anomalies. We developed a lightweight autoencoder (AE) model for each dataset class to identify regions of interest (RoI) where real defects typically occur. AE computes patch-wise similarities using the Structural Similarity Index Measure (SSIM) to map background areas, which are inverted to foreground regions using dissimilarity maps (1-SSIM). Threshold-based cleaning and region-based analysis refined the masks by removing noise and spurious blobs. For real test anomalies, we designed an enhanced segmentation model (EnSeg) featuring an encoder-decoder network with attention blocks and multiscale context aggregation. When trained on PSSAIM-augmented images, EnSeg achieved promising results: 98.9% area under the receiver operating characteristic curve (AUROC) for image-level detection, 77.1% average precision (AP) for pixel-level localization on the MVTec anomaly detection (MVTec AD) dataset, 98.1% AUROC with 68.4% AP on the beanTech Anomaly Detection (BTAD) dataset and 94.9% AUROC with 67.8% AP on the Kolektor SDD2 benchmark dataset. EnSeg also ensures rapid inference at 200 frames per second (fps), offering both accuracy and efficiency.}, archive = {J_ACCESS}, author = {Zain Ul Arifeen and Rizwan Ali Shah and Hyungwon Kim and Jae-Won Suh}, doi = {10.1109/ACCESS.2025.3603201}, journal = {IEEE Access}, pages = {151696-151713}, shortjournal = {IEEE Access}, title = {Patch-shuffled superpixel synthetic anomaly insertion in autoencoder foreground extracted regions for improved visual anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient resource allocation for mission-critical applications in underwater acoustic networks. ACCESS, 13, 151681-151695. (www)

  @article{ , abstract = {The Internet of Underwater Things (IoUT) has emerged as a vital technological domain, significantly advancing underwater exploration and communication capabilities. At the core of this paradigm is Underwater Acoustic Communication (UAC), which acts as the primary medium for data transmission in underwater environments, including environmental monitoring, security and surveillance, and underwater exploration. Despite its significance, UAC systems face inherent challenges such as limited bandwidth, high signal attenuation, and long propagation delays. These limitations become particularly significant in mission-critical maritime operations, such as warning systems, underwater navigation and control, and diver safety or emergency communications, where timely and reliable data transmission is crucial. Existing resource allocation solutions often fail to adequately optimize limited UAC bandwidth to address the distinct needs of mission-critical applications, making it difficult to ensure reliability and low delays for critical tasks without compromising non-critical performance. This paper introduces a novel resource management scheme to support mission-critical applications in UAC systems, optimizing the acoustic spectrum using Resource Blocks (RBs) with frequency and time division multiplexing. This approach prioritizes mission-critical applications to ensure high reliability and low latency, while also accommodating non-critical applications. We formulate a resource allocation optimization problem based on application type and present a solution. We also introduce a Meta-Heuristic approach designed to efficiently achieve near-optimal solutions while minimizing computational complexity. Simulation results obtained with the DESERT underwater network simulator show that, compared to existing resource management schemes, our approach significantly reduces end-to-end delay and improves throughput for mission critical applications while optimizes transmission power, thus validating the effectiveness of the proposed solution.}, archive = {J_ACCESS}, author = {Walid K. Hasan and Iftekhar Ahmad and Quoc Viet Phung and Yue Rong and Haitham Khaled and Daryoush Habibi}, doi = {10.1109/ACCESS.2025.3603642}, journal = {IEEE Access}, pages = {151681-151695}, shortjournal = {IEEE Access}, title = {Energy-efficient resource allocation for mission-critical applications in underwater acoustic networks}, volume = {13}, year = {2025}, }
• (2025). High-performance tuning for model predictive control for a renewable energy grid-interface converter with LCL filter. ACCESS, 13, 151671-151680. (www)

  @article{ , abstract = {Model predictive control (MPC) has emerged as a highly regarded control strategy in power electronics for renewable energy applications. While it minimizes tracking errors and control effort, a significant challenge is the lack of systematic tuning strategies to meet these systems’ energy quality performance requirements. This paper proposes a comprehensive MPC tuning methodology for grid-integrating converters with LCL filters, incorporating modulation and delay compensation. We conduct a stability analysis to define precise constraints for cost function weights. The fine-tuning strategy systematically maps a Figure of Merit (FoM) for system performance using an advanced computational model, revealing that optimal tunings reside in narrow parameter regions. Experimental validation on a 2 kW workbench confirmed that all proposed MPC tunings met IEEE Std. 519-2014 power quality criteria and consistently outperformed a two-sample deadbeat controller, exhibiting enhanced dynamic response and power quality.}, archive = {J_ACCESS}, author = {Jefferson S. Costa and Angelo Lunardi and Alessio Iovine and Darlan Alexandria Fernandes and Daniel Albiero and Alfeu J. Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3603336}, journal = {IEEE Access}, pages = {151671-151680}, shortjournal = {IEEE Access}, title = {High-performance tuning for model predictive control for a renewable energy grid-interface converter with LCL filter}, volume = {13}, year = {2025}, }
• (2025). Cost-efficiency and cost-effectiveness of XAI in predictive maintenance. ACCESS, 13, 151664-151670. (www)

  @article{ , abstract = {Predictive maintenance aims to reduce operational costs by anticipating and preventing system failures or inefficiencies. While high-performance AI models such as neural networks offer accurate predictions, their lack of transparency limits their usefulness for guiding interventions. Conversely, explainable AI (XAI) models provide insight but often at the expense of accuracy. This paper proposes a framework for cost-based evaluation of interpretable AI models in predictive maintenance, using both classification and regression contexts. We establish criteria to determine when the benefit of interpretability outweighs any reduction in accuracy and show that the utility of XAI is bounded by the relative cost of maintenance versus failure. These findings offer practical tools for assessing the business case for interpretable models in predictive maintenance and related domains. In research, the criteria enable cost-based evaluation and comparison of alternative machine learning methods for regression and classification.}, archive = {J_ACCESS}, author = {Peter Hughes and Suresh Perinpanayagam and Peter Ball}, doi = {10.1109/ACCESS.2025.3601385}, journal = {IEEE Access}, pages = {151664-151670}, shortjournal = {IEEE Access}, title = {Cost-efficiency and cost-effectiveness of XAI in predictive maintenance}, volume = {13}, year = {2025}, }
• (2025). Real-time sign language-to-speech translation system with AI-powered wearable technology. ACCESS, 13, 151652-151663. (www)

  @article{ , abstract = {Globally, over 430 million people live with disabling hearing loss, a number projected to rise to 2.5 billion by 2050 according to the World Health Organization. Among them, individuals with mutism or profound hearing impairments often rely on sign language as their primary mode of communication. However, a communication gap persists, as most people in the general population are not proficient in sign language. This barrier hampers inclusion, access to services, and full societal participation for individuals with hearing and speech disabilities. To address this challenge, we present SignSpeak, a real-time cyber-physical system that translates sign language gestures into audible speech, enabling interactions between sign language users and non-signers. The system features a wearable glove embedded with flex sensors on each finger and a spatial sensor for wrist tracking. Data from these sensors is collected by an Arduino microcontroller and processed using machine learning models implemented in Python and TensorFlow to recognize and classify gestures accurately. The recognized gestures are then converted into synthesized speech output. By combining low-cost hardware with scalable software, SignSpeak offers an affordable, portable, and user-friendly solution to bridge the communication divide. We evaluate the system in terms of recognition accuracy, responsiveness, and practicality, demonstrating its potential to support real-time communication in educational, professional, and social contexts. Experimental validation confirmed that SignSpeak achieved an average recognition accuracy of 94.7% across 29 gesture classes, including alphabet letters and basic expressions, with an end-to-end response time of 112 ms. These results demonstrate both reliability and real-time usability, reinforcing the potential of the proposed solution as an accessible communication aid.}, archive = {J_ACCESS}, author = {Iuliana Marin and Volodymyr Babalian and Serhii Slutu}, doi = {10.1109/ACCESS.2025.3602794}, journal = {IEEE Access}, pages = {151652-151663}, shortjournal = {IEEE Access}, title = {Real-time sign language-to-speech translation system with AI-powered wearable technology}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of SAC-OCDMA and physical layer security utilizing block shifted circulant matrix code. ACCESS, 13, 151641-151651. (www)

  @article{ , abstract = {In this study, we propose a zero correlation code block shifted circulant matrix (BSCM) using the circular shifting of identity matrix mapping technique for spectral amplitude coding optical code division multiple access. The design addresses cardinality limitations of zero cross-correlation (ZCC) codes by enabling flexible configurations between the number of users and code weight without sacrificing ZCC properties. We employ a direct detection technique using an avalanche photodiode (APD). Our simulation result, using OptiSystem 15, achieved a transmission distance of 130 km within a bit error rate (BER) $\le 10^{-9}$ for three users, closely aligning with the theoretical 133 km limit. The theoretical analysis shows that increasing transmission distance also increases the BER while degrading the signal-to-noise ratio (SNR). Our BSCM model outperformed random diagonal and diagonal eigenvalue unity codes in terms of transmission distance, SNR, and channel capacity. Furthermore, the BSCM model achieved an information-theoretic channel capacity of $\sim 10^{13}$ bps, indicating its potential for establishing secure and high-bandwidth optical communication. Our study further explores the physical layer security using the wiretap channel model. Our analysis shows that a low extraction ratio reduces the security leakage. Also, increasing code length improves the robustness of the system channel against eavesdropping by ensuring the eavesdropper’s channel remains in a noisy state.}, archive = {J_ACCESS}, author = {Precious Ogbonda Amadi and Syed Mohammad Ammar and Syed Alwee Aljunid Syed Junid and Norshamsuri Ali and Nursalasawati Rusli and Rosdisham Bin Endut}, doi = {10.1109/ACCESS.2025.3603260}, journal = {IEEE Access}, pages = {151641-151651}, shortjournal = {IEEE Access}, title = {Performance analysis of SAC-OCDMA and physical layer security utilizing block shifted circulant matrix code}, volume = {13}, year = {2025}, }
• (2025). A compact AMC-backed meander line antenna with low SAR and high gain for wearable ISM band applications. ACCESS, 13, 151624-151640. (www)

  @article{ , abstract = {This paper introduces an innovative meander line antenna structure supported by a compact Artificial Magnetic Conductor (AMC) for wearable applications operating in the 2.4 GHz Industrial, Scientific, and Medical (ISM) band. The proposed antenna features a low profile, high directivity, low specific absorption rate (SAR), and robustness to body loading. An AMC design achieving 59 % size reduction compared to previously reported configurations is proposed. Despite the AMC’s compact size of only $43\times 43\times 2.4$ mm3, the antenna’s performance is significantly improved. The integrated design maintains effective performance under deformation and body loading, offering a wide bandwidth that mitigates frequency detuning issues. The integrated design achieves a gain of 7.81 dBi, a bandwidth of 13.8% (2.341 GHz- 2.671GHz), an efficiency of 85.4%, and a front-to-back ratio (FBR) of 16.2 dB. The SAR analysis shows over 95% reduction as compared to the meander line alone, even at high input power levels of 1 W with direct skin contact. Furthermore, the proposed design outperforms all existing designs listed in Table 1 in terms of compactness, front to back ration and efficiency, while yielding competitive gain and bandwidth, making it as a leading solution and strong candidate for wearable ISM band applications.}, archive = {J_ACCESS}, author = {Adel Ashyap and Raad Raad and Faisel Tubbal and Wajid Ali Khan and Suhila Abulgasem}, doi = {10.1109/ACCESS.2025.3603226}, journal = {IEEE Access}, pages = {151624-151640}, shortjournal = {IEEE Access}, title = {A compact AMC-backed meander line antenna with low SAR and high gain for wearable ISM band applications}, volume = {13}, year = {2025}, }
• (2025). Sudden concept drift detection and adaptation in virtual metrology for semiconductor manufacturing. ACCESS, 13, 151614-151623. (www)

  @article{ , abstract = {Virtual metrology (VM) has emerged as a promising technology for semiconductor manufacturing; however, abrupt changes in production conditions can significantly degrade prediction accuracy. Therefore, detecting sudden concept drift becomes crucial. Micro Cluster Nearest Neighbor (MC-NN), a method that offers a potential solution for sudden concept drift detection, was originally designed for supervised classification tasks. As a result, it faces challenges when applied directly to high-dimensional, semi-supervised regression problems. To address these issues, this work proposes a novel Model Rebuilding Scheme (MRS) to not only detect but also adapt to sudden concept drift in high-dimensional, semi-supervised scenarios. Experimental results show that Advanced Automatic Virtual Metrology with Convolutional Neural Networks ( ${\mathrm {AVM}}_{\mathrm {CNN}}$ ), enhanced by MRS, outperforms both MC-NN and a previously established VM architecture. Furthermore, Advanced AVMCNN with MRS can achieve a 24.4% average reduction in mean absolute error compared to Advanced AVMCNN without MRS. The proposed framework is promising to provide a robust method for detecting and adapting to sudden concept drift in next-generation VM applications, ensuring sustained prediction accuracy.}, archive = {J_ACCESS}, author = {Chi Li and Yu-Ming Hsieh}, doi = {10.1109/ACCESS.2025.3602848}, journal = {IEEE Access}, pages = {151614-151623}, shortjournal = {IEEE Access}, title = {Sudden concept drift detection and adaptation in virtual metrology for semiconductor manufacturing}, volume = {13}, year = {2025}, }
• (2025). CC–CV wireless EV charging with power balance control in primary- and secondary-side converters. ACCESS, 13, 151597-151613. (www)

  @article{ , abstract = {This paper presents the design, simulation, and experimental validation of a constant current–constant voltage (CC–CV) wireless electric vehicle (EV) charging system utilizing Power Balance Control (PBC) in both primary-side (PS) and secondary-side (SS) converter configurations. The system was tested using a step-resistance load and a typical 72 V, 50 Ah lithium-ion NMC battery to assess control precision, dynamic behavior, and charging efficiency of the system. Experimental results demonstrate that both PS and SS converters employing PBC successfully achieve stable CC–CV operation. The SS converter achieved a peak efficiency of 82%, outperforming 77% from the PS converter. Regarding transient performance, the PBC significantly enhanced settling times over the conventional proportional–integral (PI) controller. Specifically, the settling time was reduced from 600ms to 100ms in the PS converter and from 400ms to 20ms in the SS converter, with minimal voltage overshoot. Power loss analysis indicated that the highest losses occurred in CC mode, particularly in the inductive power transfer (IPT) coils and high-frequency inverters. The SS control effectively minimized these losses by dynamically adjusting its input resistance to match the optimal load resistance. Overall, the proposed PBC approach, especially in the SS converter, demonstrates efficiency, faster dynamic response, and robust power regulation for next-generation wireless EV charging systems.}, archive = {J_ACCESS}, author = {Uthen Kamnarn and Surasak Yousawat and Tatapong Phondee and Ekkachai Chaidee and Burin Yodwong and Ehsan Jamshidpour and Nicu Bizon and Anon Namin and Phatiphat Thounthong}, doi = {10.1109/ACCESS.2025.3602478}, journal = {IEEE Access}, pages = {151597-151613}, shortjournal = {IEEE Access}, title = {CC–CV wireless EV charging with power balance control in primary- and secondary-side converters}, volume = {13}, year = {2025}, }
• (2025). Environment friendly ride-sharing route planning via deep learning approaches. ACCESS, 13, 151581-151596. (www)

  @article{ , abstract = {Ride-sharing platforms face significant supply-demand imbalances, resulting in unsustainable road planning caused by substantial energy waste and environmental pollution. Prevailing repositioning methods based on deep reinforcement learning (DRL) typically assume homogeneous drivers who unconditionally obey dispatch instructions, overlooking their diverse and dynamic cruising preferences, which leads to low instruction acceptance rates, reduced driver satisfaction, and suboptimal matching. Incorporating these personalized preferences is crucial for minimizing idle cruising, enhancing driver retention, and achieving better equilibrium. To address this, we propose E-Plan, a novel driver-centric repositioning framework employing a sequential dual-agent DRL strategy: a Grid Agent optimizes the dispatch sequence of idle vehicles to balance global supply-demand, while a Vehicle Agent generates personalized recommendations for each driver based on their predicted preferences and real-time demand within this sequence. This design efficiently navigates the complex action space while respecting driver autonomy. Evaluated on real-world trajectory data, E-Plan significantly increases driver acceptance rates by 38.07% and boosts overall driver income by 9.97%, demonstrating its effectiveness in promoting sustainable and efficient ride-hailing.}, archive = {J_ACCESS}, author = {Zikai Hu and Xiaoping Miao and Cuicui Zheng and Hao Wu}, doi = {10.1109/ACCESS.2025.3599795}, journal = {IEEE Access}, pages = {151581-151596}, shortjournal = {IEEE Access}, title = {Environment friendly ride-sharing route planning via deep learning approaches}, volume = {13}, year = {2025}, }
• (2025). Machine learning for predicting user satisfaction in Human–Robot interaction (HRI) teleoperation tasks. ACCESS, 13, 151567-151580. (www)

  @article{ , abstract = {Predicting user satisfaction in Human–Robot Interaction (HRI) tasks is essential to enhancing system adaptability and user experience. This study addresses this challenge in the context of teleoperated Autonomous Ground Vehicles (AGVs) by introducing the Robot Motion Dataset (RMD). This multimodal dataset integrates motion, control, and haptic feedback signals with user satisfaction scores. Data were collected from 30 participants performing navigation tasks with and without a haptic-actuated glove interface. A set of statistical features was extracted from inertial measurements, control commands, and haptic feedback signals, and the most informative features were selected through a sequential forward selection process. Several machine learning algorithms were trained to classify satisfaction levels, with evaluation performed on both internal and participant-independent external test sets. The best performance was achieved by a Weighted k-Nearest Neighbors classifier, reaching an accuracy above 80% in both experimental conditions. The results demonstrate the feasibility of predicting user satisfaction from multimodal sensor data in real time, highlighting the potential of the proposed framework for adaptive HRI systems.}, archive = {J_ACCESS}, author = {Antonio Tecco and Antonio Frisoli and Claudio Loconsole}, doi = {10.1109/ACCESS.2025.3597994}, journal = {IEEE Access}, pages = {151567-151580}, shortjournal = {IEEE Access}, title = {Machine learning for predicting user satisfaction in Human–Robot interaction (HRI) teleoperation tasks}, volume = {13}, year = {2025}, }
• (2025). Impact of rising number of electric vehicle charging points on electrical utility. ACCESS, 13, 151551-151566. (www)

  @article{ , abstract = {This paper presents an investigation of the impact of a rising number of electric-vehicle charging points on an interconnected distribution system. Using the PSCAD 4.6 software, this study simulates an electric-vehicle charging station consisting of a charging unit with 2 DC charging and a maximum power of 100 kW. The station receives power from a 23 kV substation through a step-down transformer at 400 V and provides power through the charging unit. The unit regulates the voltage level at 500 V and has a maximum charging capacity of 50 kW each. In addition, the study replicates an actual distribution system of the Provincial Electricity Authority (PEA) in the section spanning the Si Samrong to Sawankhalok substations in Sukhothai Province, Thailand. The effects of the following study conditions on the power quality are observed in the simulation: 1) variable location of charging panels, 2) variable number of charging panels, and 3) additional sets of charging panels in the distribution system. The results show that the charging-panel installation location and number of charging panels are significant factors that affect the power quality, including the voltage level. Therefore, optimizing the design of EV charging stations should consider the location and number of charging panels as important factors.}, archive = {J_ACCESS}, author = {Pathomthat Chiradeja and Praikanok Lertwanitrot and Panapong Songsukthawan and Suntiti Yoomak and Atthapol Ngaopitakkul and Chaichan Pothisarn}, doi = {10.1109/ACCESS.2025.3602821}, journal = {IEEE Access}, pages = {151551-151566}, shortjournal = {IEEE Access}, title = {Impact of rising number of electric vehicle charging points on electrical utility}, volume = {13}, year = {2025}, }
• (2025). Large language model-driven dynamic assessment of grammatical accuracy in english language learner writing. ACCESS, 13, 151538-151550. (www)

  @article{ , abstract = {This study investigates the potential for Large Language Models (LLMs) to scale-up Dynamic Assessment (DA). To facilitate such an investigation, we first developed DynaWrite—a modular, microservices-based grammatical tutoring application which supports multiple LLMs to generate dynamic feedback to learners of English. Initial testing of 21 LLMs, revealed GPT-4o and neural-chat to have the most potential to scale-up DA in the language learning classroom. Further testing of these two candidates found both models performed similarly in their ability to accurately identify grammatical errors in user sentences. However, GPT-4o consistently outperformed neural-chat in the quality of its DA by generating clear, consistent, and progressively explicit hints. Real-time responsiveness and system stability were also confirmed through detailed performance testing, with GPT-4o exhibiting sufficient speed and stability. This study shows that LLMs can be used to scale-up dynamic assessment and thus enable dynamic assessment to be delivered to larger groups than possible in traditional teacher-learner settings.}, archive = {J_ACCESS}, author = {Timur Jaganov and John Blake and Julián Villegas and Nicholas Carr}, doi = {10.1109/ACCESS.2025.3603191}, journal = {IEEE Access}, pages = {151538-151550}, shortjournal = {IEEE Access}, title = {Large language model-driven dynamic assessment of grammatical accuracy in english language learner writing}, volume = {13}, year = {2025}, }
• (2025). Robust representation learning for privacy-preserving machine learning: A multi-objective autoencoder approach. ACCESS, 13, 151527-151537. (www)

  @article{ , abstract = {Several domains increasingly rely on machine learning in their applications. The resulting heavy dependence on data has led to the emergence of various laws and regulations around data ethics and privacy and growing awareness of the need for privacy-preserving machine learning (ppML). Current ppML techniques utilize methods that are either purely based on cryptography, such as homomorphic encryption, or that introduce noise into the input, such as differential privacy. The main criticism given to those techniques is the fact that they either are too slow or they trade off a model’s performance for improved confidentiality. To address this performance reduction, we aim to leverage robust representation learning as a way of encoding our data while optimising the privacy-utility trade-off. Our method centers on training autoencoders in a multi-objective manner and then concatenating the latent and learned features from the encoding part as the encoded form of our data. Such a deep learning-powered encoding can then safely be sent to a third party for intensive training and hyperparameter tuning. With our proposed framework, we can share our data and use third party tools without being under the threat of revealing its original form. We empirically validate our results on unimodal and multimodal settings, the latter following a vertical splitting system and show improved performance over state-of-the-art.}, archive = {J_ACCESS}, author = {Sofiane Ouaari and Ali Burak Ünal and Mete Akgün and Nico Pfeifer}, doi = {10.1109/ACCESS.2025.3603429}, journal = {IEEE Access}, pages = {151527-151537}, shortjournal = {IEEE Access}, title = {Robust representation learning for privacy-preserving machine learning: A multi-objective autoencoder approach}, volume = {13}, year = {2025}, }
• (2025). A hybrid graph neural network model for predicting cyber attacks from heterogeneous and dynamic network data. ACCESS, 13, 151512-151526. (www)

  @article{ , abstract = {With valuable data constantly under attack, reactive security measures are no longer sufficient. Predicting cyber threats before they emerge is crucial. Cyberattacks do not occur randomly; they have a systematic underlying pattern. By discovering these patterns, it is possible to predict cyberattacks in advance. Unraveling the mysteries of these evolutionary patterns is quite challenging. Considering the potential of Graph Neural Networks to strengthen cybersecurity defenses, this paper proposes a new hybrid model, DyMHAG (Dynamic Meta-Path Heterogeneous Attention Graph). We propose a novel hybrid GNN model that integrates meta-path-based graph attention networks with the Gated Recurrent Unit (GRU) mechanism for temporal data processing. Our method consists of three layers: node-level attention-based graph embedding, meta-path-level attention-based graph embedding, and evolutionary pattern learning. This model aims to effectively capture complex relational structures and temporal dependencies in heterogeneous and temporally dynamic network data and provide a proactive solution for cyberthreat prediction. Preliminary evaluations indicate that our hybrid model not only improves prediction accuracy but also reduces the false positive rate, providing a more reliable defense against emerging cyber threats.}, archive = {J_ACCESS}, author = {Mücahit Soylu and Resul Das}, doi = {10.1109/ACCESS.2025.3603403}, journal = {IEEE Access}, pages = {151512-151526}, shortjournal = {IEEE Access}, title = {A hybrid graph neural network model for predicting cyber attacks from heterogeneous and dynamic network data}, volume = {13}, year = {2025}, }
• (2025). A secure and scalable blockchain framework for data sharing and cost distribution in railway condition monitoring. ACCESS, 13, 151491-151511. (www)

  @article{ , abstract = {Remote condition monitoring (RCM) systems, supported by advancements in sensors, verification tools, and high-speed data transmission, are becoming central to the Internet of Things (IoT) in railway infrastructure. However, the data generated by RCM systems often remains siloed, restricting effective sharing and limiting cross-industry applications. To address this challenge, this paper proposes a blockchain-based framework that employs smart contracts to automate and secure agreements for RCM data sharing. The research adopts a design-and-evaluation methodology. The proposed framework integrates blockchain and smart contracts to replace traditional paper-based agreements with auditable, tamper-resistant, and decentralized mechanisms. A prototype system was developed and tested through simulations and controlled experiments reflecting railway operational conditions. Performance was evaluated using key metrics, including throughput, latency, and reliability. The results demonstrate that the system can efficiently manage large-scale data exchange with low latency and high reliability, while ensuring transparency and equitable participation without reliance on a trusted third party. These findings indicate the feasibility of deploying blockchain-enabled data-sharing platforms in the railway sector. The study concludes that the proposed framework has strong potential to transform data management practices, foster collaboration, and support innovation across the industry.}, archive = {J_ACCESS}, author = {Rahma A. Alzahrani and John M. Easton}, doi = {10.1109/ACCESS.2025.3603413}, journal = {IEEE Access}, pages = {151491-151511}, shortjournal = {IEEE Access}, title = {A secure and scalable blockchain framework for data sharing and cost distribution in railway condition monitoring}, volume = {13}, year = {2025}, }
• (2025). GMM selector-based anomaly detection model for networks. ACCESS, 13, 151479-151490. (www)

  @article{ , abstract = {Ensemble models for network anomaly detection are often challenged by class imbalance, a condition known to substantially degrade predictive accuracy. To address this limitation, a novel ensemble learning framework, termed the GMM Selector for Anomaly Detection (GMM-SAD), is proposed. The GMM-SAD framework utilizes Gaussian Mixture Models (GMM) to first estimate the prevalence of anomalies and then dynamically routes data to the most suitable specialized detection model within its architecture. To validate its effectiveness and generalization capabilities, extensive empirical evaluations were conducted on three widely-recognized benchmark datasets: CICIDS2017, UNSW-NB15, and NSL-KDD, under varying degrees of class imbalance. The proposed GMM-SAD model demonstrates robust and consistent performance, achieving an impressive average Area Under the Curve (AUC) of 98.40%, F1-score of 94.16%, and a high Recall of 95.32% across all datasets and imbalance scenarios. These findings confirm that the model significantly enhances network anomaly detection accuracy and robustness, particularly its ability to identify rare instances under challenging imbalanced conditions. This research introduces a highly adaptive technical approach for mitigating class imbalance, demonstrating strong potential for practical deployment in real-time network security monitoring.}, archive = {J_ACCESS}, author = {Xinyu Guo and Jun He and Tao Jiang and Sunyan Hong and Jing Yu}, doi = {10.1109/ACCESS.2025.3599916}, journal = {IEEE Access}, pages = {151479-151490}, shortjournal = {IEEE Access}, title = {GMM selector-based anomaly detection model for networks}, volume = {13}, year = {2025}, }
• (2025). STRACK: Robust tracking of small objects in low-light conditions. ACCESS, 13, 151466-151478. (www)

  @article{ , abstract = {Tracking small objects in low-light conditions presents significant challenges for multi-object tracking (MOT) methods due to diminished image quality, motion blur, poor contrast, and increased noise levels. This research presents STRACK, a robust MOT framework specifically designed to address these challenges through the effective integration of advanced detection, motion modeling, and tracklet association methods. STRACK uses a fine-tuned YOLOv11 detector, ensuring reliable detection across varying object sizes and illumination conditions. The framework proposes an adaptive noise-aware Enhanced Kalman Filter (EKF) for reliable motion prediction, transformer-based Dynamic Motion Compensation (DMC) for reducing camera motion effects, and a Spatio-Temporal Relational Network (STRN), which combines LSTM and graph convolutional modules to strengthen associations under appearance degradation. Additionally, STRACK uses Adaptive Motion Smoothing (AMS) with Gaussian process regression to address missed detections, alongside IoU-ReID fusion to combine motion and appearance signals. Extensive experiments on the MOT17 and MOT20 benchmarks indicate that STRACK significantly surpasses leading methods, achieving HOTA scores of 66.71 and 66.44, respectively, while maintaining real-time performance at 25 FPS. These results confirm STRACK as a reliable and scalable platform for robust MOT in challenging low-light environments for applications in autonomous driving, surveillance, and ecological monitoring.}, archive = {J_ACCESS}, author = {Said Baz Jahfar Khan and Zhang Peng and Mian Muhammad Kamal and Heba G. Mohamed and Qasem M. Kharma and Muhammad Sheraz and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3602953}, journal = {IEEE Access}, pages = {151466-151478}, shortjournal = {IEEE Access}, title = {STRACK: Robust tracking of small objects in low-light conditions}, volume = {13}, year = {2025}, }
• (2025). An RF-TabPFN-based framework for few-shot IoT network attack recognition using lasso-RFE feature selection. ACCESS, 13, 151452-151465. (www)

  @article{ , abstract = {With the exponential growth of Internet of Things devices projected to exceed 75 billion by 2025, network security vulnerabilities including eavesdropping, distributed denial-of-service attacks, and firmware hijacking pose significant threats to IoT infrastructure. While existing machine learning approaches show promise in attack detection, they typically suffer from limited interpretability and poor performance on imbalanced datasets with minority attack classes. This study proposes a novel IoT network attack detection framework that integrates Lasso regression and Recursive Feature Elimination with the Random Forest-Tabular Prior-data Fitted Networks model to address these challenges. The methodology employs a three-stage approach: preprocessing and feature engineering using Lasso-Recursive Feature Elimination to reduce dimensionality while maintaining predictive capability, classification using Random Forest-Tabular Prior-data Fitted Networks (a Transformer-based foundation model designed for heterogeneous tabular data and few-shot learning scenarios), and model interpretation through Shapley Additive Explanations analysis to quantify feature contributions. Experimental validation on the Bot-IoT dataset containing 2,668,522 samples with severe class imbalance achieved exceptional performance with 99.89% accuracy and perfect precision and recall for most attack categories. The framework demonstrates superior handling of minority classes compared to traditional approaches, while Shapley analysis identified critical discriminative features such as connection timestamps and destination IP patterns, providing actionable insights for security analysts and representing a significant advancement in IoT security. The source code and data processing scripts are publicly available on GitHub(https://github.com/1558xxx/A-RF-TabPFN-Model-Based-on-Lasso-RFE-for-Feature-Selection.git) to support reproducibility and future extensions.}, archive = {J_ACCESS}, author = {Zongzheng Li and Chunru Xiong and Kai Zheng and Qiang Li}, doi = {10.1109/ACCESS.2025.3602840}, journal = {IEEE Access}, pages = {151452-151465}, shortjournal = {IEEE Access}, title = {An RF-TabPFN-based framework for few-shot IoT network attack recognition using lasso-RFE feature selection}, volume = {13}, year = {2025}, }
• (2025). An array-type segmented coil coupling structure for dynamic wireless charging of EV. ACCESS, 13, 151435-151451. (www)

  @article{ , abstract = {For the dynamic wireless charging (DWC) process of electric vehicles (EVs), this paper addresses the issue of output power fluctuations caused by dynamic changes in the coupling coefficient and the equivalent resistance of the battery load. An array-type segmented coil coupling structure is proposed, which reduces power fluctuations by activating multiple rows of transmitting coils when the receiving coil undergoes significant lateral misalignment. The layout of the transmitting coils was optimized using Maxwell finite element analysis (FEA) to enhance the system’s magnetic coupling performance. Based on the relative location between the receiving coil and the transmitting coil, the system’s operating conditions are categorized into two categories: without lateral misalignment and with lateral misalignment. Considering the differences in cross-coupling between transmitting coils under the two categories, a dual-sided LCC compensation network with topological transformation and hierarchical compensation capacitors was designed. This ensures that the system remains in resonance and exhibits zero-phase angle (ZPA) characteristics. To further suppress output power fluctuations during the dynamic wireless charging process, the optimal coil switching position was analyzed, and a power fluctuation suppression strategy based on real-time adjustment of the Buck-Boost circuit duty cycle was proposed. Finally, via MATLAB/Simulink simulations and experimental prototype validation, the proposed coupling structure and control strategy are verified to significantly enhance the stability of output power and operational reliability of the system.}, archive = {J_ACCESS}, author = {Yanming Cheng and Shuairan Yu and Pengfei Liu and Shaojie Yu and Ruiyang Zhang and Xiaodan Zhang and Yulian Zhao and Chao Ding}, doi = {10.1109/ACCESS.2025.3603146}, journal = {IEEE Access}, pages = {151435-151451}, shortjournal = {IEEE Access}, title = {An array-type segmented coil coupling structure for dynamic wireless charging of EV}, volume = {13}, year = {2025}, }
• (2025). Text location-aware framework for chinese meme generation. ACCESS, 13, 151421-151434. (www)

  @article{ , abstract = {Internet memes have become an essential tool for online communication and interaction on social media platforms. They reflect a wide range of emotional and cultural elements, thereby serving as effective vectors for rapid information exchange. In this digital age, the popularity and influence of memes highlight the need for innovative communication methods. This need has resulted in the exploration of technologies that are capable of capturing and expressing this unique cultural phenomenon. This research presents an innovative Chinese meme generation system that is capable of generating contextually relevant Chinese meme-style text using input images and dynamically predicting the best location of the text within the meme images. Our approach combines the tasks of object detection and image captioning, thereby drawing on the strengths of the fully convolutional one-stage object detection (FCOS) and multimodal pretrained models, ChineseCLIP and GPT-2, respectively. To facilitate this task, we constructed a dataset comprising meme images, textual content, and location information of the text within the images. Through comprehensive experiments, we validated the proficiency of our model by not only generating contextually relevant text that corresponds with the image but also locating it to improve the aesthetic allure and communicative efficacy of the memes. This study propels the automation of Chinese meme generation and provides new perspectives on the interplay between image understanding and multimodal learning.}, archive = {J_ACCESS}, author = {Lin Wang and Ruizheng Wu and Pengchu Luo and Chang-Hun Kim}, doi = {10.1109/ACCESS.2024.3401174}, journal = {IEEE Access}, pages = {151421-151434}, shortjournal = {IEEE Access}, title = {Text location-aware framework for chinese meme generation}, volume = {13}, year = {2025}, }
• (2025). Robot-manual forward-reserve allocation problem. ACCESS, 13, 151411-151420. (www)

  @article{ , abstract = {In this paper, we consider the order-picking operations in which orders are picked either by humans or robots. A warehouse is configured with a manual-picking area and a robot-picking area. The robot-picking area is used for the forward-picking area to perform inexpensive picking operations, whereas the manual-picking area is used for the reserve-picking area to replenish the robot-area. We consider the problem to seek which SKUs are picked by human or robot, which SKUs are located by the manual-picking area or the robot-picking area, and how much volume is stored for each SKU in the robot area. The objective is to minimize the sum of the robot-picking cost, the manual-picking cost, and the replenishment cost. We formulate the decision problem as mixed-integer non-linear programming (MINLP). The problem is difficult to solve because it is nonlinear and has a large number of items. We proposed an efficient algorithm to solve the problem based on Lagrange relaxation to exploit the special structure of the problem. We theoretically prove convexity in the decomposed subproblem and derive an analytical solution, allowing efficient evaluation of the lower bound. The algorithm is applied to real-world data from a fashion e-commerce fulfillment center, involving over 15000 SKUs. The proposed method achieves substantial cost savings compared to benchmark policies. In addition, to assess scalability and robustness, the proposed method was evaluated on larger, artificially generated datasets and benchmarked against state-of-the-art metaheuristics. The results demonstrate that our method consistently outperforms these alternatives in both solution quality and computation time.}, archive = {J_ACCESS}, author = {Shunichi Ohmori and Kanetaka M. Maki}, doi = {10.1109/ACCESS.2025.3601581}, journal = {IEEE Access}, pages = {151411-151420}, shortjournal = {IEEE Access}, title = {Robot-manual forward-reserve allocation problem}, volume = {13}, year = {2025}, }
• (2025). A novel fault diagnosis method for ZPW-2000A track circuit applied to small samples. ACCESS, 13, 151395-151410. (www)

  @article{ , abstract = {The occurrence of faults in the ZPW-2000A track circuit shows diverse types and sporadic distribution. Diversity means there are many different and complex fault patterns, while sporadicity leads to limited fault data, showing high-dimensional and small-sample characteristics. To enhance diagnostic performance in multi-task small-sample scenarios, this paper proposes a novel fault diagnosis approach. First, the Convolutional Neural Networks and Long Short-Term Memory Network with an attention mechanism are used to extract information from time-series data. Then, Linear Discriminant Analysis is applied to reduce feature dimensions and remove redundancy. Finally, a multi-task classification model is built to learn data distribution and manifold structures. Experiments show that our method achieves higher accuracy and robustness than traditional models, including Logistic Regression, Convolutional Neural Networks, K-Nearest Neighbors, Decision Trees, Random Forests, and Naive Bayes. It improves the average accuracy by 0.27%, 0.21%, 2.20%, 0.44%, and 0.50% on five small-sample datasets, while also exhibiting significant advantages in computational performance.}, archive = {J_ACCESS}, author = {Xuechun Ge and Peng Wang and Yi Shi and Chaoping Zhu and Ting Ke}, doi = {10.1109/ACCESS.2025.3602726}, journal = {IEEE Access}, pages = {151395-151410}, shortjournal = {IEEE Access}, title = {A novel fault diagnosis method for ZPW-2000A track circuit applied to small samples}, volume = {13}, year = {2025}, }
• (2025). A voice cloning approach utilizing dual attention mechanism and mixed logistic distribution. ACCESS, 13, 151384-151394. (www)

  @article{ , abstract = {Although current voice cloning technologies have achieved significant advancements in speech clarity and naturalness, they still exhibit insufficient robustness when processing low-quality data and when operating in noisy environments, thereby limiting their application scope. To address existing challenges, this paper presents a novel framework combining a foundational TTS system with a voice conversion module. The proposed method synergistically integrates a conditional variational autoencoder (cVAE) with a Flow-based model. It features a dual attention mechanism structurally tailored for speech, refining focus on both channel and spatial dimensions of time-frequency features. Moreover, it supplants the conventional Gaussian assumption with a Mixture of Logistic (MoL) distribution, significantly enhancing the expressivity for modeling complex asymmetric and multi-modal spectrograms. Experiments conducted on the Multilingual LibriSpeech and CommonVoice datasets demonstrate superior performance over baseline models in terms of timbre similarity, speech clarity, and robustness in noisy conditions. Specifically, on Multilingual LibriSpeech, the proposed model achieves a WER of 6.50%, an MCD of 4.94 dB, and a MOS of 4.68, marking improvements of 1.92%, 0.71 dB, and 0.42 over OpenVoice, respectively.}, archive = {J_ACCESS}, author = {Yan Zhu and Ying Kang and Gang Chen}, doi = {10.1109/ACCESS.2025.3603099}, journal = {IEEE Access}, pages = {151384-151394}, shortjournal = {IEEE Access}, title = {A voice cloning approach utilizing dual attention mechanism and mixed logistic distribution}, volume = {13}, year = {2025}, }
• (2025). Deep learning-driven garden design: Integrating AI into the creative process. ACCESS, 13, 151365-151383. (www)

  @article{ , abstract = {The integration of artificial intelligence into landscape architecture has opened new frontiers for computational design, enabling the generation of garden and outdoor spatial layouts that blend ecological logic with aesthetic innovation. This study presents a novel AI-driven framework tailored for garden design, synergizing symbolic formalism with neural generative modeling to support the synthesis of complex, context-aware landscape artifacts. Traditional garden design often relies on fixed templates or heuristic rules that limit adaptability to environmental constraints and stylistic variations. These methods struggle with semantic coherence and lack responsiveness to evolving cultural and ecological aesthetics. To overcome these challenges, we propose a dual-component system comprising StyleMorphNet and the Semantic Stylization Protocol (SSP). StyleMorphNet is a multi-branch neural architecture that decomposes the generative process into latent subspaces covering spatial structure, material composition, and stylistic innovation. This modular design supports fine-grained control over layout generation while preserving landscape semantics. Complementing it, SSP embeds domain-specific knowledge—including seasonal dynamics, functional zoning, and regional styles—into the generative loop via rule-conditioned modulation and symbolic-visual reasoning. Empirical evaluations show that our framework produces garden designs with superior spatial harmony, stylistic diversity, and ecological relevance compared to existing models. This work contributes to the field of computational landscape design by providing an adaptive and semantically grounded framework that supports the co-creation of expressive, sustainable outdoor environments.}, archive = {J_ACCESS}, author = {Han Yang and Dong Zheng and Lianyu Wang}, doi = {10.1109/ACCESS.2025.3602874}, journal = {IEEE Access}, pages = {151365-151383}, shortjournal = {IEEE Access}, title = {Deep learning-driven garden design: Integrating AI into the creative process}, volume = {13}, year = {2025}, }
• (2025). Adaptive RIS optimization for secure underwater optical communications. ACCESS, 13, 151347-151364. (www)

  @article{ , abstract = {Underwater Optical Wireless Communication (UOWC) systems are subject to significant degradation due to dynamic salinity gradients, temperature variations, and turbulence-induced noise, which distort light propagation and compromise data integrity. To mitigate these challenges, we propose an adaptive framework integrating: Long Short-Term Memory (LSTM)-based environmental prediction, Deep Reinforcement Learning (DRL)-based control, and a Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization for real-time reconfiguration of Reconfigurable Intelligent Surface (RIS). The LSTM model forecasts underwater conditions with a mean absolute error (MAE) of 0.008 PSU, facilitating proactive system adaptation. Experimental evaluations demonstrate that the system achieves a 41.4% reduction in BER, reaching a minimum of $2.3 \,\, {\times }{10}^{-5}$ compared to the static RIS baseline, a secrecy rate of 8.2 bps/Hz, 22.2% energy savings, and low reconfiguration latency of 47.3 ms, ensuring operation within the channel coherence time. These results set a new performance benchmark for secure and efficient UOWC systems, with applications in underwater surveillance, environmental monitoring, and offshore communication networks.}, archive = {J_ACCESS}, author = {Oliger Veronica Mendoza Betancourt and Delgi Peraza}, doi = {10.1109/ACCESS.2025.3602057}, journal = {IEEE Access}, pages = {151347-151364}, shortjournal = {IEEE Access}, title = {Adaptive RIS optimization for secure underwater optical communications}, volume = {13}, year = {2025}, }
• (2025). Grid-connected modeling and dynamic characteristics analysis of gravity energy storage system based on permanent magnet synchronous generator-motor. ACCESS, 13, 151338-151346. (www)

  @article{ , abstract = {In this paper, a modeling and simulation method of grid-connected system including gravity energy storage mechanical part, permanent magnet synchronous motor and back-to-back converter is proposed. Firstly, based on the principle of gravitational potential energy and electric energy conversion, the energy flow model and power model of gravity energy storage system are constructed to quantify the energy loss and power dynamic characteristics under charging and discharging conditions. Furthermore, the mathematical model of permanent magnet synchronous motor in d-q coordinate system is established, and the zero d-axis current control scheme is designed for the machine-side converter, and the vector control scheme based on grid voltage orientation is designed for the grid-side converter, so as to achieve accurate regulation of motor torque and stable transmission of grid-connected power. Finally, the simulation model of gravity energy storage grid-connected system is built by using MATLAB/SIMULINK platform, and the dynamic performance under different working conditions is analyzed. The results show that the output power of the gravity energy storage is accurately matched with the grid-connected power, the dc side voltage fluctuation is small, the grid-connected voltage and current waveform has high sinusoidality and low harmonic content, and the motor speed and torque response are fast and stable. The research results can provide technical reference for the engineering application of gravity energy storage grid-connected system.}, archive = {J_ACCESS}, author = {Yan Li and Huan Zhu and Qingshan Wang and Darui He and Jiaqi Zheng and Yifei Fan}, doi = {10.1109/ACCESS.2025.3603212}, journal = {IEEE Access}, pages = {151338-151346}, shortjournal = {IEEE Access}, title = {Grid-connected modeling and dynamic characteristics analysis of gravity energy storage system based on permanent magnet synchronous generator-motor}, volume = {13}, year = {2025}, }
• (2025). LAVP: A latency-aware virtual network function placement strategy for service function chain in network function virtualization. ACCESS, 13, 151327-151337. (www)

  @article{ , abstract = {Nowadays, businesses have benefited greatly from Network Function Virtualization (NFV), while users have gained new experiences. By transforming network functions into Virtual Network Functions (VNFs) running on High-Volume Servers (HVSs) instead of using dedicated hardware devices, NFV enables the reduction of Capital Expenditure (CAPEX) and Operation Expenditure (OPEX). However, NFV is now facing numerous issues, including performance, stability, and security. In this article, we examine the influence of VNF placement on the end-to-end latency. To reduce latency, the Latency-Aware VNF Placement (LAVP) algorithm, a heuristic technique, is offered to deal with the VNF placement problem by finding the best path for the Service Function Chain (SFC) while ensuring VNF ordering. The VNFs can then be resourced and deployed on the nodes along the path. Our algorithm is implemented on two different topologies and compared with other algorithms, namely First Fit, Best Fit, Worst Fit, ASR, AvReStrategy and an ant colony algorithm. The experimental results show that our proposed solution can significantly reduce delays for the service chain with less execution time compared to other strategies.}, archive = {J_ACCESS}, author = {Tung Thanh Hoang and Linh Manh Pham and Hoai-Son Nguyen}, doi = {10.1109/ACCESS.2025.3603213}, journal = {IEEE Access}, pages = {151327-151337}, shortjournal = {IEEE Access}, title = {LAVP: A latency-aware virtual network function placement strategy for service function chain in network function virtualization}, volume = {13}, year = {2025}, }
• (2025). Structural analysis of digital transceiver unit (DTU) for 400kg-class communication satellite considering launch environment. ACCESS, 13, 151316-151326. (www)

  @article{ , abstract = {Since Starlink’s introduction, global social and technical demands for satellite-based telecommunication systems have rapidly increased. In South Korea as well, research on communication satellites began several years ago. To realize satellite communication, both long-distance communication with ground station terminals and inter-satellite communication are essential. The digital transceiver unit (DTU), which provides beam-steering functionality to control signal directionality and concentrate emissions along specific trajectories, is one of the most critical payloads in satellite systems. This paper conducts a comprehensive structural analysis of the DTU to evaluate its stability under launch conditions. First, modal analysis was performed to verify compliance with stiffness requirements and ensure sufficient separation between the DTU’s vibrational modes and those of the satellite. Quasi-static analysis then assessed structural stability under static acceleration corresponding to design loads. Finally, random vibration and shock analyses were conducted to evaluate the reliability of EEE (electrical, electronic, and electromechanical) components on the PCB and fastener integrity. The analysis configuration modeled the DTU as integrated into the satellite system. Since environmental testing for the structural-thermal model of the satellite system remains ongoing, vibration/shock inputs followed ESA standards. Results demonstrate that the proposed DTU design meets all structural stability requirements for launch environments.}, archive = {J_ACCESS}, author = {Hyun-Guk Kim and Jiye Bak and Jungil Cho and Seong-Ju Lee and Kyoung Youl Park and Won-Seok Lee and Ki Chul Kim}, doi = {10.1109/ACCESS.2025.3603156}, journal = {IEEE Access}, pages = {151316-151326}, shortjournal = {IEEE Access}, title = {Structural analysis of digital transceiver unit (DTU) for 400kg-class communication satellite considering launch environment}, volume = {13}, year = {2025}, }
• (2025). Size-controllable tumor synthesis for improved detection of small bowel carcinoid tumors in CT scans. ACCESS, 13, 151304-151315. (www)

  @article{ , abstract = {Carcinoid tumors in the small bowel are rare, making it challenging to collect a sufficiently large dataset of lesions with diverse sizes for training detection models using computed tomography (CT) scans. This scarcity particularly affects the detection performance on relatively small or large tumors. To address this limitation, we propose a novel image synthesis method that can selectively augment underrepresented tumor sizes to enhance detection performance. Our method enables size-controllable tumor generation by integrating a tumor segmentation model and a size-aware loss into the training process. Specifically, one dimension of the input noise vector is designated to control the size of the synthesized tumors. These tumors, generated at desired sizes, are implanted into CT scans to enrich the training data for a tumor detection network. Our method produces tumors with clearer size distinctions, while maintaining comparable visual realism compared to a baseline synthesis method. In a visual Turing test, human observers could not reliably distinguish synthetic tumors from real ones. Lesion-level evaluation using free-response receiver operating characteristic (FROC) curves demonstrated that detection performance improved when synthetic tumors were included during training (P=0.04). This method offers a promising direction for improving the detection of rare tumors such as small bowel carcinoid tumors.}, archive = {J_ACCESS}, author = {Seung Yeon Shin and Stephen A. Wank and Ronald M. Summers}, doi = {10.1109/ACCESS.2025.3600672}, journal = {IEEE Access}, pages = {151304-151315}, shortjournal = {IEEE Access}, title = {Size-controllable tumor synthesis for improved detection of small bowel carcinoid tumors in CT scans}, volume = {13}, year = {2025}, }
• (2025). Overlapping community detection in statistical keyword co-occurrence network by mixed-SCORE. ACCESS, 13, 151289-151303. (www)

  @article{ , abstract = {Keyword co-occurrence networks, constructed by the co-occurrence relationships of keywords in academic publications, have become essential tools for exploring and understanding research topics within specific academic fields. However, traditional community detection methods often assume disjoint partitions and fail to capture the inter-topic nature of research fields. To the best of our knowledge, we are the first to analyze the research topics in statistics by constructing a keyword co-occurrence network using papers published in 42 statistical journals from 1992 to 2021. To correct for degree heterogeneity, we apply the Mixed-SCORE method on the 40-core network, resulting in ten overlapping communities, such as the time series analysis, high-dimensional analysis, and hypothesis testing communities. For the first time, the presence of overlapping communities within the keyword co-occurrence network is uncovered, indicating the interconnections between different research topics. In addition, to evaluate the effectiveness of the Mixed-SCORE method, we compare the results with other overlapping community detection methods and demonstrate its good performance. Our study contributes to the understanding of research topics and interconnections between multiple topics in statistics. The innovative use of overlapping community detection on keyword co-occurrence networks provides a new perspective for analyzing research patterns in various disciplines.}, archive = {J_ACCESS}, author = {Tianang Deng and Tianchen Gao and Chuanxin Xia and Rui Pan}, doi = {10.1109/ACCESS.2025.3603565}, journal = {IEEE Access}, pages = {151289-151303}, shortjournal = {IEEE Access}, title = {Overlapping community detection in statistical keyword co-occurrence network by mixed-SCORE}, volume = {13}, year = {2025}, }
• (2025). Probabilistic transmission expansion planning under renewable and load uncertainty. ACCESS, 13, 151275-151288. (www)

  @article{ , abstract = {This paper presents a novel probabilistic transmission expansion planning framework that leverages the unscented transformation (UT) to address uncertainties in renewable generation and load while significantly reducing computational effort compared to traditional Monte Carlo simulation. It represents the first integrated application of UT for modeling multiple uncertainty sources simultaneously in transmission expansion planning. The proposed probabilistic framework was validated using two test systems: the Garver system and the IEEE 24-bus system. For the Garver system, the unscented transformation approach produced results identical to those of Monte Carlo simulation while requiring 237 times less computational effort (49,500 vs. 11,250,000 load flow evaluations). For the IEEE 24-bus system, the framework effectively modeled load and generation uncertainties, achieving confidence levels of 91.56% and 100.00% for target thresholds of 90% and 100%, respectively, demonstrating consistency with Monte Carlo simulation results. Statistical analysis of seven metaheuristic algorithms revealed that Harmony Search and Differential Evolution performed best for the Garver system, while Grey Wolf Optimizer excelled for the IEEE 24-bus system. The efficiency improvement enables more complex analyses, such as multi-stage planning and N-1 contingency evaluation, that are often prohibitive with traditional Monte Carlo approaches, while maintaining solution accuracy and reliability requirements.}, archive = {J_ACCESS}, author = {Raphael Paulo Braga Poubel and Mariana Christina Silva Perpétuo and Wesley Peres}, doi = {10.1109/ACCESS.2025.3600866}, journal = {IEEE Access}, pages = {151275-151288}, shortjournal = {IEEE Access}, title = {Probabilistic transmission expansion planning under renewable and load uncertainty}, volume = {13}, year = {2025}, }
• (2025). Optimizing resource allocation for QoS and stability in dynamic VLC-NOMA networks via MARL. ACCESS, 13, 151258-151274. (www)

  @article{ , abstract = {Visible Light Communication (VLC) combined with Non-Orthogonal Multiple Access (NOMA) offers a promising solution for dense indoor wireless networks. Yet, managing resources effectively is challenged by VLC network dynamic conditions involving user mobility and light dimming, in addition to satisfying Quality of Service (QoS) and network stability requirements. Traditional resource allocation methods and simpler RL approaches struggle to jointly optimize QoS and stability under the dynamic conditions of mobile VLC-NOMA networks. This paper presents MARL frameworks tailored to perform complex joint optimization of resource allocation (NOMA power, user scheduling) and network stability (interference, handovers), considering heterogeneous QoS, user mobility, and dimming in VLC-NOMA systems. Our MARL frameworks capture dynamic channel conditions and diverse user QoS, enabling effective joint optimization. In these frameworks, VLC access points (APs) act as intelligent agents, learning to allocate power and schedule users to satisfy diverse requirements while maintaining network stability by managing interference and minimizing disruptive handovers. We conduct a comparative analysis of two key MARL paradigms: 1) Centralized Training with Decentralized Execution (CTDE) and 2) Centralized Training with Centralized Execution (CTCE). Comprehensive simulations validate the effectiveness of both tailored MARL frameworks and demonstrate an ability to handle complex optimization. The results show key trade-offs, as the CTDE approach achieved approximately 16% higher for High priority (HP) user QoS satisfaction, while the CTCE approach yielded nearly 7 dB higher average SINR and 12% lower ping-pong handover ratio, offering valuable insights into the performance differences between these paradigms in complex VLC-NOMA network scenarios.}, archive = {J_ACCESS}, author = {Aubida A. Al-Hameed and Safwan Hafeedh Younus and Mohamad A. Ahmed and Abdullah Baz}, doi = {10.1109/ACCESS.2025.3599664}, journal = {IEEE Access}, pages = {151258-151274}, shortjournal = {IEEE Access}, title = {Optimizing resource allocation for QoS and stability in dynamic VLC-NOMA networks via MARL}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning graphical neural network driven prediction model for path-planning and navigation in smart sustainable agriculture. ACCESS, 13, 151235-151257. (www)

  @article{ , abstract = {Recently, autonomous systems in agriculture have garnered increased attention among research communities and industries due to their greater significance. With rapid technological advances, numerous autonomous systems have been actively deployed in agriculture to accomplish complex tasks, such as surveying agricultural fields, managing the water cycle, resolving soil compaction issues, etc. Swarm robotics, path planning, and task assignment systems play a crucial role in agricultural automation and pave the way for smart, sustainable agriculture. However, the major problem here is that this system depends on dynamic path planning activities, which are the most crucial task for autonomous robots. This is because in smart agriculture, multiple agents are associated with the system to perform various activities on their way to the destination, making the path planning actions more complicated than ever. To address these constraints, this paper presents a cost-efficient, reliable, and safe navigable swarm approach, called the Self-Supervised Learning Graphical Neural Network Driven Prediction Model (SGNN), for smart sustainable agriculture. The proposed SGNN approach enables swarm robots to select the most optimized paths, thereby reducing the distance travelled by the robots. The work’s prime focus is on developing a forecasting model for the efficient navigation and landing of swarm robots in innovative agricultural systems. The Semantic Drone Dataset is used for experimentation purposes. It is observed from the analysis that the proposed approach offers more than 85% percent accurate results in comparison to standard ResNet architectures. Additionally, this approach yields a score of around 0.8, indicating a good balance between precision and recall measures. Further, the result suggests a correlation coefficient factor of around 0.685, representing the effective classification of the target classes. Thus, the proposed SGNN approach has been experimentally proven to be more robust and efficient than existing approaches.}, archive = {J_ACCESS}, author = {R. Nivetha and K. C. Sriharipriya and Balamurugan Balusamy}, doi = {10.1109/ACCESS.2025.3602476}, journal = {IEEE Access}, pages = {151235-151257}, shortjournal = {IEEE Access}, title = {Self-supervised learning graphical neural network driven prediction model for path-planning and navigation in smart sustainable agriculture}, volume = {13}, year = {2025}, }
• (2025). A comparative study of OpenMP scheduling algorithm selection strategies. ACCESS, 13, 151216-151234. (www)

  @article{ , abstract = {Scientific and data science applications are becoming more complex, with increasingly demanding computational and memory requirements during execution. Modern high performance computing (HPC) systems offer increased parallelism and heterogeneity across nodes, devices, and cores. Thus, effective scheduling and load balancing techniques are crucial to maximize applications performance on such systems. Commonly used node-level parallelization frameworks, such as OpenMP, employ an increasing number of advanced scheduling algorithms to support various applications and HPC platforms. For arbitrary application-system pairs, this results in an instance of the scheduling algorithm selection problem, which requires fast and accurate selection methods for the diversity of applications’ workloads and computing systems’ characteristics. In this work, we study the problem of learning to select scheduling algorithms in OpenMP. Specifically, we propose and use expert-based and reinforcement learning (RL) based selection approaches. We assess the effectiveness of each approach through an extensive performance analysis campaign with six applications and three systems, exposing capabilities and limits. We found that learning the highest-performing scheduling algorithm using RL-based methods is effective, but at a high exploration cost, and that the most relevant factor is the type of reward used by the RL methods. We also found that expert-based selection indeed leverages expert knowledge with fewer exploration needs, albeit at the risk of not selecting the highest-performing scheduling algorithm for a given application-system pair. We combine expert knowledge with RL-based approaches and show improved performance. Overall, this work shows that the selection of scheduling algorithms during execution is possible and beneficial for OpenMP applications. We anticipate that this work can be extended and combined with the selection of scheduling algorithms for MPI-based applications (provided that there is a portfolio of scheduling algorithms on distributed memory nodes), thereby optimizing scheduling decisions across parallelism levels.}, archive = {J_ACCESS}, author = {Jonas H. Müller Korndörfer and Ali Mohammed and Ahmed Eleliemy and Quentin Guilloteau and Reto Krummenacher and Florina M. Ciorba}, doi = {10.1109/ACCESS.2025.3602234}, journal = {IEEE Access}, pages = {151216-151234}, shortjournal = {IEEE Access}, title = {A comparative study of OpenMP scheduling algorithm selection strategies}, volume = {13}, year = {2025}, }
• (2025). Online calibration method for high-precision on-state voltage measurement of power semiconductors. ACCESS, 13, 151202-151215. (www)

  @article{ , abstract = {This paper presents an online high-precision calibration method for on-state voltage measurement of power semiconductors using active temperature control. Unlike other calibration techniques, this method operates continuously during converter operation, enabling monitoring of long-term changes in component characteristics. The method is implemented in a diode-based measurement circuit, selected for its fast response and simplicity. Despite these advantages, diode-based circuits have limited accuracy due to forward voltage variability caused by different junction temperatures of diodes and their manufacturing tolerances. By actively controlling the temperature of a diode and exploiting the fact that the forward voltage is temperature dependent, this method compensates for diode forward voltage variations and other sources of measurement errors, such as resistance tolerances and voltage drop across the relay contacts. The resulting high-accuracy voltage measurements are critical for applications like monitoring transistor degradation, precise conduction loss calculations, and determining the dynamic on-resistance of wide-bandgap devices. However, achieving a high common-mode rejection ratio, essential for reliable measurements, remains a challenge for the practical implementation of this method.}, archive = {J_ACCESS}, author = {Karla Draženović and Luka Matić and Viktor Šunde and Željko Ban}, doi = {10.1109/ACCESS.2025.3602135}, journal = {IEEE Access}, pages = {151202-151215}, shortjournal = {IEEE Access}, title = {Online calibration method for high-precision on-state voltage measurement of power semiconductors}, volume = {13}, year = {2025}, }
• (2025). LLTQ+: A hardware-friendly quantization framework for modern YOLO architectures. ACCESS, 13, 151189-151201. (www)

  @article{ , abstract = {YOLO-based object detection models are widely used in real-time applications due to their high accuracy and fast inference speed. However, their complex architectures and computational demands present challenges for deployment on low-power edge devices. To address this, we propose LLTQ+, an enhanced version of the hardware-friendly quantization method LLTQ. The proposed approach preserves batch normalization layers during Quantization-Aware Training (QAT) to maintain training stability and accuracy, and introduces a quantization strategy that preserves the representational capacity of RepConv, a key structural component of YOLO networks. Experimental results on the PASCAL VOC dataset demonstrate the effectiveness of LLTQ+. On YOLOv10-s, LLTQ+ achieved 80.6% mAP(0.5) and 61.8% mAP(0.5:0.95) under integer-only inference, surpassing LLTQ by 0.9 and 1.7 percentage points, respectively. On YOLOv9-t, LLTQ+ reached 52.9% mAP(0.5:0.95), an improvement of 0.5 points over LLTQ. Consistent performance gains were also observed across other architectures such as YOLOv7 and YOLOv7-tiny. These results confirm that LLTQ+ effectively supports integer quantization for even the latest and more complex YOLO networks, providing a practical quantization solution that balances accuracy and computational efficiency.}, archive = {J_ACCESS}, author = {Yugwon Seo and Jaemyung Kim and Jin-Ku Kang and Yongwoo Kim}, doi = {10.1109/ACCESS.2025.3603536}, journal = {IEEE Access}, pages = {151189-151201}, shortjournal = {IEEE Access}, title = {LLTQ+: A hardware-friendly quantization framework for modern YOLO architectures}, volume = {13}, year = {2025}, }
• (2025). E-PSOGA: An enhanced hybrid metaheuristic for optimal edge-to-cloud placement of services with multi-version components. ACCESS, 13, 151170-151188. (www)

  @article{ , abstract = {The evolution of edge-to-cloud networks has significantly increased the complexity of determining optimal service placement across these infrastructures, a challenge identified as an NP-complete problem. To address such problems, exact algorithms are impractical at larger scales owing to their computational demands. Heuristics exhibit faster runtimes but lower solution quality, whereas metaheuristics provide high-quality solutions at the cost of increased runtime. In this study, service placement in edge-to-cloud systems is investigated and formulated as an optimisation problem, where each service component is provided by different vendors and is available in multiple versions. The inclusion of multi-version components adds an additional layer of complexity, making the placement problem even more challenging. Specifically, this study addresses the service placement problem in Augmented Reality (AR)-and Virtual Reality (VR)-based remote repair and maintenance use cases, where service response time and system reliability are critical performance metrics. To optimise both metrics, we propose a novel hybrid metaheuristic algorithm (E-PSOGA) which combines the fast convergence of Particle Swarm Optimisation (PSO) with the global search capabilities of Genetic Algorithms (GA). A custom healing operator is also introduced to further enhance the solution quality and reduce the algorithm runtime. A comprehensive performance assessment shows that E-PSOGA reduces the response time by 37% compared with the other implemented baseline algorithms. E-PSOGA achieved 98% platform and 97% service reliability while maintaining a reasonable algorithm runtime. These results indicate that the proposed approach is well-suited for large-scale and time-sensitive scenarios requiring both computational efficiency and high solution quality.}, archive = {J_ACCESS}, author = {Mohammadsadeq Garshasbi Herabad and Javid Taheri and Bestoun S. Ahmed and Calin Curescu}, doi = {10.1109/ACCESS.2025.3603329}, journal = {IEEE Access}, pages = {151170-151188}, shortjournal = {IEEE Access}, title = {E-PSOGA: An enhanced hybrid metaheuristic for optimal edge-to-cloud placement of services with multi-version components}, volume = {13}, year = {2025}, }
• (2025). On security risk management for teleoperated driving systems. ACCESS, 13, 151153-151169. (www)

  @article{ , abstract = {Teleoperated driving technology enables human operators to control vehicles remotely, thereby reducing the need for the driver’s physical presence within the vehicle. Although this innovation enhances operational flexibility, it introduces security challenges that, if unaddressed, could undermine the reliability and safety of Teleoperated Driving Systems (TDS). This paper presents a comprehensive security risk analysis and architectural framework for TDS, emphasizing the system architecture. Our study combines an in-depth review of the existing literature with a structured risk management assessment to analyze TDS assets and data flows. By detailing the system and business assets, their interconnections, and the unique vulnerabilities inherent to TDS, we define critical security threats – ranging from unauthorized control and data manipulation to vulnerabilities in session management protocols and network configurations. The selected security risk management approach guides our risk analysis, maps security threats and vulnerabilities to the corresponding security requirements and controls, and provides a prioritized strategy for risk treatment. Validation results show that our risk analysis achieves strong coverage, with a third-party threat analysis tool confirming eighteen (18) of the thirty-two (32) identified security threats and highlighting three (3) additional scenarios, while missing fourteen (14) threats mainly due to tool limitations. Our results offer practitioners a practical blueprint for identifying and managing security risks in TDS.}, archive = {J_ACCESS}, author = {Abasi-Amefon O. Affia-Jomants and Raimundas Matulevičius and Nataliia Pabat and Lukaš Malina}, doi = {10.1109/ACCESS.2025.3602202}, journal = {IEEE Access}, pages = {151153-151169}, shortjournal = {IEEE Access}, title = {On security risk management for teleoperated driving systems}, volume = {13}, year = {2025}, }
• (2025). Advanced encryption using generative adversarial network for enhancing security of non-fungible tokens (NFTs). ACCESS, 13, 151137-151152. (www)

  @article{ , abstract = {Non-fungible tokens (NFTs) represent a unique form of digital asset stored on a blockchain, encompassing a wide array of assets from digital art to real-world commodities. While NFTs offer robust security through decentralization and smart contract enforcement, they are not impervious to cyber threats. Traditionally, NFTs are transferred over blockchain platforms via smart contracts. The primary objective of this study is to introduce a cryptographic framework that integrates an advanced encryption algorithm layer to fortify NFT image transfers, reinforcing content protection in an evolving digital ecosystem. Various pixel-based encryption algorithms have been implemented using image processing techniques and compared considering their levels of encryption and execution times. Based on the comparison, an advanced encryption algorithm has been devised with an added level of encryption and fast execution time. The proposed encryption algorithm leverages alpha composition with a randomly generated image obtained using a Generative Adversarial Network (GAN). The GAN-generated image is infused with Gaussian noise to deter decryption by unauthorized algorithms, thereby enhancing resilience against cryptanalysis. The encrypted image metadata would be transferred through both on-chain (i.e., blockchain) and off-chain methods to facilitate efficiency and security during NFT transfers. The image decryption protocol mandates the new owner/receiver of NFT to possess the correct private identification, transaction key, and off-chain data, ensuring exclusive access while automatically revoking ownership from the previous holder upon successful transfer. This approach not only secures the transfer process but also minimizes the risk of data leaks, as intermediaries should not have access to the complete metadata. This cryptographic approach not only safeguards digital assets but also aligns with the futuristic vision of secure transactions in the metaverse and Web 3.0. The experimental analysis based on the security level and encryption time based on multiple encryption algorithms justifies the benefits of the proposed method.}, archive = {J_ACCESS}, author = {Arnav Gujarathi and Pratham Oza and Asish Bera}, doi = {10.1109/ACCESS.2025.3603088}, journal = {IEEE Access}, pages = {151137-151152}, shortjournal = {IEEE Access}, title = {Advanced encryption using generative adversarial network for enhancing security of non-fungible tokens (NFTs)}, volume = {13}, year = {2025}, }
• (2025). Wrist-worn devices for remote monitoring of cardiovascular disease: A survey. ACCESS, 13, 151111-151136. (www)

  @article{ , abstract = {According to the World Health Organization, cardiovascular disease adversely impacts the quality of life, can lead to death and imposes a substantial burden on global healthcare systems. Addressing modifiable risk factors for cardiovascular disease through healthy lifestyle changes and early screening via continuous monitoring can help reduce both the disease burden and associated treatment costs. Smartphones and smartwatches have gained popularity due to their non-intrusive nature, making them suitable for daily health tracking. Modern smartwatches integrate sensors like accelerometer, electrocardiogram and photoplethysmogram, enabling continuous monitoring of lifestyle habits and cardiovascular health indicators. This survey reviews studies from January 2019 to December 2024 that utilized wrist-worn sensors for assessing cardiovascular health aspects, including hypertension, abnormal cardiac rhythms, sleep tracking, activity monitoring and post-stroke rehabilitation. Each study was evaluated based on the United Kingdom Research and Innovation’s Technology Readiness Level (TRL) 9-point scale to determine its applicability for real-world clinical use. Studies were categorized by TRL level, disease focus and sensing modalities used in clinical settings. This structured analysis helps identify mature technologies that are closer to clinical deployment, while highlighting gaps in other technologies that require further research. Additionally, the survey provides insights for addressing existing limitations and advancing wearable health technologies toward practical medical applications. By analysing the progress in the capabilities of wrist-worn sensors for cardiovascular health monitoring, this survey aims to bridge the gap between research and real-world application, thereby supporting the development of effective and clinically viable solutions.}, archive = {J_ACCESS}, author = {Ismail Sadiq and Aneeqa Ijaz and Ali Imran}, doi = {10.1109/ACCESS.2025.3602893}, journal = {IEEE Access}, pages = {151111-151136}, shortjournal = {IEEE Access}, title = {Wrist-worn devices for remote monitoring of cardiovascular disease: A survey}, volume = {13}, year = {2025}, }
• (2025). Synchronized tracking control design for autonomous vehicle formation based on multi-source error fusion. ACCESS, 13, 151097-151110. (www)

  @article{ , abstract = {To solve the problem of low efficiency of single autonomous vehicle individuals in complex environments, this paper proposes a synchronized tracking control method of autonomous vehicle formation with multi-source error fusion. Firstly, taking an individual autonomous vehicle as the research object, a normalized double integrator vehicle mathematical model is established to obtain the control objective of the formation. Secondly, by combining the synchronization error and the tracking error to form a multi-source fusion error with a compensation mechanism, the synchronization control problem of the formation is transformed into a stability problem of each fusion error variable. Then, on the basis of the multi-source fusion error, the formation synchronized tracking controller is designed to make the vehicle track the trajectory with relative deviation by introducing the position deviation and auxiliary variables, so as to transform the formation control problem into the synchronization tracking problem, and to solve the problem of formation geometry configuration that cannot be achieved by controlling a single state variable such as speed or position alone. Finally, the feasibility of the proposed scheme is verified by numerical simulation experiments as well as practical tests.}, archive = {J_ACCESS}, author = {Kang Wang and Limin Zheng and Zilu Wang}, doi = {10.1109/ACCESS.2025.3599507}, journal = {IEEE Access}, pages = {151097-151110}, shortjournal = {IEEE Access}, title = {Synchronized tracking control design for autonomous vehicle formation based on multi-source error fusion}, volume = {13}, year = {2025}, }
• (2025). Efficient and interpretable otoscopic image classification via distilled CNN with adaptive channel attention. ACCESS, 13, 151082-151096. (www)

  @article{ , abstract = {Accurate classification of otoscopic ear images is crucial for early diagnosis of ear pathologies such as Chronic Otitis Media, Earwax Plug, and Myringosclerosis. In this study, we propose a novel deep learning framework that employs a knowledge distillation strategy, wherein a high-capacity pre-trained teacher model (Vision Transformer or ResNet101) transfers learned representations to a lightweight student CNN model (EfficientNet-B0). The student network is further enhanced through the integration of an Adaptive Channel Attention (ACA) module, which selectively emphasizes informative features via channel-wise recalibration. The multi-scale feature distillation from the teacher improves generalization while the ACA block boosts sensitivity to clinically relevant regions. We validated our method on a publicly available otoscopic image dataset comprising four balanced classes. Our approach achieved an overall accuracy of 98.75%, with class-wise AUC values of 1.000 (CSOM), 1.000 (Earwax Plug), 0.994 (Myringosclerosis), and 0.992 (Normal), and a micro-average AUC of 0.997. Additionally, Grad-CAM analysis confirmed the model’s focus on diagnostically meaningful areas, supporting the interpretability of the predictions. These results demonstrate the effectiveness of our distillation-based ACA-enhanced architecture in otoscopic image classification, with potential to assist clinical decision-making in primary care and telemedicine applications.}, archive = {J_ACCESS}, author = {Zaka Ur Rehman and Mohammad Faizal Ahmad Fauzi and Farhana Nur Iman Lokman and Meriem Touhami and Lokman Saim}, doi = {10.1109/ACCESS.2025.3597769}, journal = {IEEE Access}, pages = {151082-151096}, shortjournal = {IEEE Access}, title = {Efficient and interpretable otoscopic image classification via distilled CNN with adaptive channel attention}, volume = {13}, year = {2025}, }
• (2025). Multi-scale attentional style transfer model with imbalanced contrastive loss. ACCESS, 13, 151072-151081. (www)

  @article{ , abstract = {To address the limitations of existing image style transfer models in style-content fidelity, training stability, and computational efficiency, we propose a Multi-Scale Attentional Style Transfer Model with Imbalanced Contrastive Loss. First, we design a multi-scale self-attention discriminator that combines Transformer-based global features with multi-scale convolutional local features to enhance the discrimination of global style consistency and local detail authenticity. Second, we introduce a Gram matrix style loss to constrain the style transfer process through Gram matrix differences across feature layers at various scales, thereby improving the accuracy of style expression. Furthermore, to address the quality imbalance between positive and negative samples in contrastive learning, we propose a dynamic temperature coefficient adjustment strategy and a hard sample prioritized mining mechanism to optimize the contrastive loss function and enhance model robustness. Experiments demonstrate that on Cat $\to $ Dog, Horse $\to $ Zebra, and Cityscapes datasets, our achieves FID reductions of 4.7, 4.2, and 4.0 respectively compared to the CUT baseline model, while maintaining real-time inference efficiency at 4.2 FPS. Ablation experiments validate the synergistic effects of the multi-scale discriminator, Gram loss, and balanced loss components. Additional style transfer experiments on Monet paintings and landscape datasets show our method achieves the most visually authentic style transformation, providing efficient and reliable technical support for digital art creation.}, archive = {J_ACCESS}, author = {Chanhe Yang}, doi = {10.1109/ACCESS.2025.3564902}, journal = {IEEE Access}, pages = {151072-151081}, shortjournal = {IEEE Access}, title = {Multi-scale attentional style transfer model with imbalanced contrastive loss}, volume = {13}, year = {2025}, }
• (2025). A cloud-integrated virtual framework for LSTM-driven solar forecasting and residential energy management. ACCESS, 13, 151055-151071. (www)

  @article{ , abstract = {With the increasing dependence on renewable energy–particularly solar power–accurate forecasting and intelligent energy management, it has become essential for reducing grid dependency and optimizing energy usage. This paper presents an AI-powered renewable energy forecasting and scheduling system designed for smart homes. A Virtual platform is developed to predict next-day solar energy generation using real-time weather data obtained via Application Programming Interfaces (API) (such as., Open-Meteo), enabling hourly forecasts without the need for physical sensors. Various machine learning algorithms, including Lasso, Ridge, Support Vector Machine(SVM), Decision Tree, Random Forest, and Long Short-Term Memory (LSTM) networks, were trained and evaluated using historical data. The LSTM-based algorithm demonstrated the highest prediction accuracy and was selected as the core forecasting model. The forecasted solar energy values, at one-hour intervals, are dynamically used to schedule household appliances through a priority-based greedy algorithm. The algorithm prioritizes appliance loads based on criticality and energy availability, ensuring optimal solar utilization and minimizing reliance on grid power. The system is deployed via a user-friendly website with two interactive tabs, namely Forecast and Schedule, where users can select a date, view predicted solar generation, and receive an optimized appliance schedule. Experimental results indicate that intelligent scheduling using LSTM-based forecasting achieves up to 2.81% cost savings compared to conventional non-forecast-based methods. This scalable, sensor-free solution opens avenues for future enhancements, including multi-day forecasting and real-time optimization for broader smart energy applications.}, archive = {J_ACCESS}, author = {B. Devanathan and A. Suyampulingam and P. Selvaraj and Ilango Karuppasamy and T. Ilamparithi}, doi = {10.1109/ACCESS.2025.3601722}, journal = {IEEE Access}, pages = {151055-151071}, shortjournal = {IEEE Access}, title = {A cloud-integrated virtual framework for LSTM-driven solar forecasting and residential energy management}, volume = {13}, year = {2025}, }
• (2025). Federated learning with AutoAlbum for kidney disease detection: A privacy-preserving approach to medical image analysis. ACCESS, 13, 151033-151054. (www)

  @article{ , abstract = {The increasing prevalence of kidney-related diseases necessitates efficient and accurate automated diagnostic tools to overcome the limitations of time-consuming and subjective manual image analysis. This study introduces a federated learning framework for decentralized kidney disease detection from CT scans, using AutoAlbum, an automated data augmentation technique, to introduce realistic variations through a diverse set of 11 augmentations. AutoAlbum enhances data variability and simulates real-world conditions, improving model robustness. The original Kidney Dataset (KD) is augmented globally using AutoAlbum to create a balanced dataset (AKD) for pre-trained model selection. KD is also partitioned into distributed subsets (KD-D) to mimic decentralized data in federated learning. Furthermore, augmentations are applied to these distributed KD subsets, resulting in the augmented and distributed dataset (AD-D), designed to evaluate the federated model’s performance under Non-Identical and Independent Distribution (Non-IID) conditions. KD represents the original data, AKD the globally augmented data, KD-D the distributed original data, and AD-D the distributed and augmented data. The federated learning framework enables privacy-preserving collaborative training across KD-D and AD-D. Evaluations showed steady performance improvements on KD-D, reaching 0.9663 accuracy by round five. Notably, the model demonstrated remarkable robustness on the more challenging AD-D, achieving an exceptional accuracy of 0.9916 by the fifth round, highlighting the framework’s potential for handling data heterogeneity in real-world clinical settings. To support model interpretability and clinical relevance, Grad-CAM visualizations were employed to reveal class-specific activation regions, enhancing transparency in model decision-making.}, archive = {J_ACCESS}, author = {Armaan Batra and Pratyush Chatterjee and Jyotismita Chaki}, doi = {10.1109/ACCESS.2025.3602224}, journal = {IEEE Access}, pages = {151033-151054}, shortjournal = {IEEE Access}, title = {Federated learning with AutoAlbum for kidney disease detection: A privacy-preserving approach to medical image analysis}, volume = {13}, year = {2025}, }
• (2025). Diagonal factorization subspace in I-vector extraction for fast computation and memory efficiency: A case study on frog sound identification. ACCESS, 13, 151016-151032. (www)

  @article{ , abstract = {In this study, we investigate the performance of low-dimensional identity vectors, called I-vectors, in automatically identifying frog species on the basis of their bio-acoustics. I-vector is one of the state-of-the-art techniques and has greatly improved speaker recognition performance. However, the I-vector method has some limitations, such as slow computation and high memory consumption. To address these issues, this study improves the I-vector algorithm using diagonal techniques. Although the proposed method reduces computational cost, it leads to some accuracy degradation. Therefore, an enhanced version, called Diagonal Factorization Subspace in I-vector (DFS I-vector), is developed. Additionally, channel compensation fusion is implemented to further improve the accuracy of the I-vector approach. Frog species identification are employed as a case study for this research and experiment data for this study are employed from five databases, i.e. Frog Identification Expert System Database, Frogs of Australia Database, Frog Watch Database, British Library Amphibian Database and Intelligent Biometric Group Universiti Sains Malaysia Database, which consist of 2,656 samples of bio-acoustic syllables. Experimental results prove the promising output of the proposed DFS I-vector-32, achieving 90.67% accuracy, which is five times faster, and reducing memory consumption by one-fifth the memory of standard I-vector-16. Experimental results also demonstrate the advantage of the fusion approach, DFS I-vector-32 with WCCN(LDA), achieving 91.56% accuracy. Both performances are better than that of standard I-vector 16, which achieves 88.00% accuracy.}, archive = {J_ACCESS}, author = {Dzati Athiar Ramli and Noor Salwani Ibrahim}, doi = {10.1109/ACCESS.2025.3602437}, journal = {IEEE Access}, pages = {151016-151032}, shortjournal = {IEEE Access}, title = {Diagonal factorization subspace in I-vector extraction for fast computation and memory efficiency: A case study on frog sound identification}, volume = {13}, year = {2025}, }
• (2025). An in-depth investigation of genetic programming under physical time and directional change frameworks for algorithmic trading. ACCESS, 13, 151001-151015. (www)

  @article{ , abstract = {The majority of algorithmic trading studies rely on datasets with fixed physical time intervals, such as hourly or daily data, resulting in a discontinuous representation of time. Directional Changes (DC) is an alternative method, which transforms these datasets from physical time (PT) intervals into event-driven sequences, allowing for unique price analysis. We introduce two novel algorithms based on Genetic Programming (GP): GP-DC, which only uses DC-based indicators as features, and GP-DC-PT, which combines DC-based indicators with physical-time indicators from technical analysis to create trading strategies. We extensively evaluate the return and risk of DC-based trading strategies in 220 datasets from ten international financial markets for periods up to ten years. We compare the performance of these trading algorithms against a non-DC-based GP, technical analysis derived strategies, as well as the well-known benchmark of buy-and-hold. Our results demonstrate that the proposed DC-based algorithm GP-DC creates profitable trading strategies with low risk, and statistically and significantly outperforms the other benchmarks. We also find that the feature set that includes both DC and physical time indicators (GP-DC-PT) leads to further improvements in algorithmic trading performance and is very competitive, as it achieves an average total return of over 18%. These findings suggest that including directional changes in trading strategies significantly enhances their return and risk performance.}, archive = {J_ACCESS}, author = {Xinpeng Long and Michael Kampouridis and Panagiotis Kanellopoulos}, doi = {10.1109/ACCESS.2025.3599677}, journal = {IEEE Access}, pages = {151001-151015}, shortjournal = {IEEE Access}, title = {An in-depth investigation of genetic programming under physical time and directional change frameworks for algorithmic trading}, volume = {13}, year = {2025}, }
• (2025). Transformer-based detection of hidden hazards in power transmission lines for UAV inspection. ACCESS, 13, 150992-151000. (www)

  @article{ , abstract = {Accurate and real-time detection of hidden hazards in power transmission lines is critical for ensuring the safety and reliability of modern power grids, particularly in Unmanned Aerial Vehicle (UAV)-based inspection scenarios. In this paper, we propose STDISNet, a novel Transformer-based object detection framework tailored for complex transmission environments. The model integrates a Swin Transformer backbone to enhance multi-scale semantic representation through hierarchical self-attention, a Dual-Branch Feature Fusion Convolution (DFFConv) module to enhance contextual feature extraction while maintaining low computational overhead compared to traditional convolutional methods, and a SIoU-based loss function to optimize bounding box regression by incorporating geometric constraints. Extensive experiments on a publicly available transmission hazard dataset demonstrate that STDISNet achieves state-of-the-art performance in both detection accuracy and inference speed. Qualitative visualizations further confirm its robustness in challenging scenarios such as occlusion, background clutter, and low-contrast objects. The proposed approach provides a reliable and efficient solution for UAV-assisted intelligent inspection of power transmission systems.}, archive = {J_ACCESS}, author = {Xiantao Yang and Keping Liu and Rongqiang Guan and Jing Yu and Yuanling Liu}, doi = {10.1109/ACCESS.2025.3603163}, journal = {IEEE Access}, pages = {150992-151000}, shortjournal = {IEEE Access}, title = {Transformer-based detection of hidden hazards in power transmission lines for UAV inspection}, volume = {13}, year = {2025}, }
• (2025). XceptMPX: A robust deep learning-powered web system for mpox detection and classification. ACCESS, 13, 150970-150991. (www)

  @article{ , abstract = {The rise of mpox as a global health concern highlights the necessity of effective early detection strategies to manage its propagation. Though less fatal than COVID-19, mpox poses some diagnostic challenges, especially via its characteristic skin rashes. Advances in deep learning provide a hopeful solution to enabling early diagnosis via medical imaging. Yet available datasets are limited and lack diversity, making it difficult to achieve practical AI-based solutions. To fill this gap, we present the Multi-Class Viral Skin Lesion Dataset (MCVSLD), a large publicly accessible dataset collected from varied and real-world sources. From this dataset, we present XceptMPX, a modified Xception deep-learning algorithm for mpox detection. Our algorithm attains state-of-the-art accuracy metrics, which are 96.36% and 97.01% in the original and augmented sets, respectively. In addition, we create an easy-to-use, web-based application for clinicians to identify mpox in real-time from skin lesion images. The study contributes to the global effort to control mpox by providing a high-quality, publicly available dataset, a highly effective deep-learning model for early detection, and a simple, web-based tool for clinicians. These have the potential to support early detection, pending clinical validation, decrease transmission, and mitigate the public health burden of mpox outbreaks.}, archive = {J_ACCESS}, author = {Kazi Rifat Ahmed and Suprove Chandra Sarkar and Abdul Kadar Muhammad Masum and Md Fokrul Islam Khan and Zakir Hossain and Isahaque Miah and Farhad Uddin Mahmud}, doi = {10.1109/ACCESS.2025.3600282}, journal = {IEEE Access}, pages = {150970-150991}, shortjournal = {IEEE Access}, title = {XceptMPX: A robust deep learning-powered web system for mpox detection and classification}, volume = {13}, year = {2025}, }
• (2025). Review of LLMs applications in electrical power and energy systems. ACCESS, 13, 150951-150969. (www)

  @article{ , abstract = {This paper presents a comprehensive review of the applications, challenges, and future directions of Large Language Models (LLMs) in the Electrical Power Domain (EPD). Leveraging transformer-based architectures such as GPT, BERT, and LLaMA, LLMs have shown transformative potential across power system applications including load forecasting, fault diagnosis, regulatory compliance, question answering, risk assessment, and intelligent data analysis. Through a systematic analysis of over 45 studies, the review highlights measurable benefits such as up to 20% improvement in load forecasting accuracy, 30% reduction in operational response time, and 40% decrease in manual workload for regulatory tasks. LLMs have demonstrated strong adaptability through zero-shot and few-shot learning and are capable of processing multimodal inputs for real-time decision-making. However, limitations such as high computational costs, lack of domain-specific datasets, limited explainability, and concerns around regulatory alignment hinder widespread deployment. To address these gaps, the paper outlines research opportunities including domain-specific fine-tuning, scalable deployment strategies, multimodal integration, and the development of unified benchmarks such as ElecBench. Overall, the integration of LLMs in power systems represents a significant step toward more intelligent, reliable, and sustainable energy management.}, archive = {J_ACCESS}, author = {Furqan Amjad and Tarmo Korõtko and Argo Rosin}, doi = {10.1109/ACCESS.2025.3599922}, journal = {IEEE Access}, pages = {150951-150969}, shortjournal = {IEEE Access}, title = {Review of LLMs applications in electrical power and energy systems}, volume = {13}, year = {2025}, }
• (2025). Balancing power and performance with task dependencies in multi-core systems. ACCESS, 13, 150933-150950. (www)

  @article{ , abstract = {The increasing use of FPGAs necessitates energy-efficient solutions, particularly for battery-powered applications. Although power dissipation is often perceived as a hardware issue, it can be mitigated through power-saving techniques such as dynamic voltage and frequency scaling and clock gating. In real-time systems, these strategies must reduce the power consumption and meet strict timing requirements to avoid deadline violations. However, hardware constraints and variability in execution times complicate their implementation, particularly in multi-core systems where task dependencies and inter-processor communication introduce delays and unpredictability. Real-time Operating Systems (RTOSs) manage task execution using scheduling algorithms, periodically checking task queues during context switches. Incoming messages trigger sporadic tasks that the RTOS must prioritize immediately, while regular tasks are executed, or power-saving strategies are applied during idle phases. Handling these diverse tasks in multi-core systems adds complexity, making it challenging to balance between predictability, energy efficiency, and system performance. This work introduces a heterogeneous multi-core architecture that integrates power-aware task scheduling algorithms, such as the Look-Ahead algorithm or Race-to-Idle strategy, to optimize power consumption while addressing task dependencies and inter-core communication. A hardware-based task scheduler improves scheduling performance and predictability, while tasks leverage the reconfigurable capabilities of FPGAs and are executed as hardware accelerators to further enhance energy efficiency. The experimental results demonstrate an improvement in scheduling performance of 64.91% and energy efficiency of 92% compared to a baseline without power optimization, highlighting the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Gökhan Akgün and Diana Göhringer}, doi = {10.1109/ACCESS.2025.3603030}, journal = {IEEE Access}, pages = {150933-150950}, shortjournal = {IEEE Access}, title = {Balancing power and performance with task dependencies in multi-core systems}, volume = {13}, year = {2025}, }
• (2025). Quality data extractor (QDE): Elevating synthetic data augmentation through post-generation filtration. ACCESS, 13, 150901-150932. (www)

  @article{ , abstract = {Synthetic data plays a crucial role in augmenting datasets for machine learning, particularly when real data is limited or sensitive. However, not all synthetic samples contribute positively to model performance; low-quality instances can degrade both predictive accuracy and data fidelity. To address this challenge, we propose the Quality Data Extractor (QDE), a novel framework for filtering synthetic data before augmentation. QDE offers two complementary techniques. The Comprehensive Extraction Strategy (CES) retains synthetic samples that improve classification performance. At the same time, the Optimal Extraction Strategy (OES) combines classifier accuracy with feature-space distance to identify and remove redundant or harmful instances. Together, these strategies enable flexible and robust data filtration across different synthetic data generators and learning tasks. We evaluate QDE on three benchmark datasets—Epileptic Seizure Recognition (ESR), WUSTL Human Activity, and German Credit Card (GCC) dataset using synthetic data generated by CTGAN, Copula GAN, TVAE, and Gaussian Copula. The performance of filtered datasets is evaluated across various statistical and predictive metrics, including the KL divergence, Anderson-Darling statistic, mean difference, propensity score range, and standard classification metrics. Experiments are conducted using Gaussian Naive Bayes (GaussianNB) and XGBoost (XGB) classifiers. Results show that both CES and OES consistently outperform naive augmentation. OES is most effective in low-quality synthetic scenarios, improving classification accuracy by up to 12.4% and reducing KL divergence by over 95% in low-quality synthetic data scenarios. CES excels when synthetic data already exhibits strong predictive performance, offering reliable gains with lower computational overhead. These findings underscore the need for post-generation filtering and position QDE as a practical and extensible solution for integrating high-quality synthetic data. QDE achieves this without requiring changes to generation models, making it a drop-in filtration step in existing pipelines.}, archive = {J_ACCESS}, author = {Pragati Sachdeva and Amarjit Malhotra and Karan Gupta}, doi = {10.1109/ACCESS.2025.3603435}, journal = {IEEE Access}, pages = {150901-150932}, shortjournal = {IEEE Access}, title = {Quality data extractor (QDE): Elevating synthetic data augmentation through post-generation filtration}, volume = {13}, year = {2025}, }
• (2025). Semantic and technical noise modeling in semantic image communication over wireless channels. ACCESS, 13, 150872-150900. (www)

  @article{ , abstract = {As wireless networks approach their Shannon capacity limit amid exponentially increasing data demands, semantic communication, which transmits the meaning of data rather than its exact form, has emerged as a compelling alternative. However, a key challenge lies in effectively modeling and mitigating both semantic and technical noise in such systems. We analyze a framework for semantic image transmission that independently and jointly models these two types of noise while employing Turbo codes for robust error detection and correction. We systematically examine their individual and combined impacts through seven experimental scenarios and benchmark the results against state-of-the-art image codecs. Our findings reveal that joint noise modeling not only improves semantic fidelity, but also enhances overall system robustness. Turbo coding proves effective in mitigating both noise types, enabling accurate meaning preservation even under adverse channel conditions. The simulation results demonstrate a reduction of approximately 33% in the channel bit rate when using joint modeling compared to alternative approaches, without compromising visual quality. Comparative evaluations against conventional codecs such as JPEG, JPEG2000, HEIF, and Turbo-coded image transmission systems highlight the superior performance of our semantic-aware strategy in noisy, bandwidth-constrained environments. This research underscores the value of integrating semantic intelligence with classical coding techniques, paving the way for next-generation wireless communication systems that are more efficient, resilient, and customized for emerging applications such as 6G, autonomous systems, and AI-enhanced networks.}, archive = {J_ACCESS}, author = {Prabhath Samarathunga and Nimesh Pollwaththage and Thanuj Fernando and Udara Jayasinghe and Nimesha Ihalagamage and Yasith Ganearachchi and Anil Fernando}, doi = {10.1109/ACCESS.2025.3600320}, journal = {IEEE Access}, pages = {150872-150900}, shortjournal = {IEEE Access}, title = {Semantic and technical noise modeling in semantic image communication over wireless channels}, volume = {13}, year = {2025}, }
• (2025). Unsupervised learning-based joint beamforming and phase-shift optimization for RIS-assisted DeepMIMO with large-scale arrays. ACCESS, 13, 150852-150871. (www)

  @article{ , abstract = {This paper considers a reconfigurable intelligent surface (RIS)-aided network, where discrete phase-shift RIS is investigated in large-scale arrays with hundreds of antennas at the source and thousands of passive elements at the RIS. We propose unsupervised learning (UnSL) approaches that eliminate the need for labeled data to address the joint beamforming and phase-shift (JBPS) optimization problem with high degrees of freedom (DoF), i.e., thousands of optimization variables, for efficient transmission design in RIS-DeepMIMO networks. Performance is analyzed by comparing the signal-to-noise ratio (SNR) with that of end-to-end SNR-based exhaustive search (ESES) and particle swarm optimization (PSO) algorithms under maximum ratio transmission (MRT) beamforming. We show that MRT-PSO, MRT-UnSL, and JBPS-UnSL with multitask neural network suffer performance degradation due to the high-dimensional input. On the other hand, numerical results reveal that the GPU-MRT-CCES method outperforms the other solutions and exhibits high scalability, owing to its novel combination of a predefined MRT solution, theoretical cascaded channel-based RIS configuration, and GPU-parallelized computation.}, archive = {J_ACCESS}, author = {Yosefine Triwidyastuti and Tri Nhu do and Ridho Hendra Yoga Perdana and Kyusung Shim and Beongku An}, doi = {10.1109/ACCESS.2025.3603198}, journal = {IEEE Access}, pages = {150852-150871}, shortjournal = {IEEE Access}, title = {Unsupervised learning-based joint beamforming and phase-shift optimization for RIS-assisted DeepMIMO with large-scale arrays}, volume = {13}, year = {2025}, }
• (2025). Adaptive network selection for latency-aware V2X systems under varying network and vehicle densities. ACCESS, 13, 150838-150851. (www)

  @article{ , abstract = {This paper presents ANS-V2X, an Adaptive Network Selection framework tailored for latency-aware V2X systems operating under varying vehicle densities and heterogeneous network conditions. Modern vehicular environments demand low-latency and high-throughput communication, yet achieving real-time network selection is challenging due to diverse application requirements and the coexistence of multiple Radio Access Technologies (RATs), such as 4G, 5G, and ad hoc links. ANS-V2X employs a heuristic-driven approach to assign vehicles to networks by considering application sensitivity, latency, computational load, and directionality constraints. The framework is benchmarked against a Mixed-Integer Linear Programming (MILP) formulation for optimal solutions and a Q-learning-based method representing reinforcement learning. Simulation results demonstrate that ANS-V2X achieves near-optimal performance, typically within 5 to 10% of the utility achieved by MILP-V2X, while reducing execution time by more than 85%. Although MILP-V2X offers globally optimal results, its computation time often exceeds 100 milliseconds, making it unsuitable for real-time applications. The Q-learning-based method is more adaptable but requires extensive training and converges slowly in dynamic scenarios. In contrast, ANS-V2X completes decisions in under 15 milliseconds and consistently delivers lower latency than both alternatives. This confirms its suitability for real-time, edge-level deployment in latency-critical V2X systems.}, archive = {J_ACCESS}, author = {Muhammad Z. Haq and Nadia N. Qadri and Omer Chughtai and Sadiq Ahmad and Waqas Khalid and Heejung Yu}, doi = {10.1109/ACCESS.2025.3602367}, journal = {IEEE Access}, pages = {150838-150851}, shortjournal = {IEEE Access}, title = {Adaptive network selection for latency-aware V2X systems under varying network and vehicle densities}, volume = {13}, year = {2025}, }
• (2025). BAAIoV: A blockchain-based authentication and authorization framework for secure and reliable internet of vehicles communication. ACCESS, 13, 150821-150837. (www)

  @article{ , abstract = {The Internet of Vehicles (IoV) is an emerging area within intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and pedestrians. While this connectivity enhances traffic management and safety, it also introduces significant security challenges—particularly in authentication, authorization, and data integrity. To address these concerns, blockchain technology is explored to eliminate centralized points of failure and support secure, decentralized communication in IoV environments. A solution titled Blockchain-based Authentication and Authorization in IoV (BAAIoV) is proposed, utilizing smart contracts and a distributed ledger to manage vehicle identities and securely control access to network resources. A simulation environment was established using Simulation of Urban Mobility (SUMO) for traffic modeling and Hyperledger Fabric for blockchain implementation. Python-based middleware facilitated real-time interaction between the simulation and the blockchain system. The results indicate that the BAAIoV model significantly reduces authentication delays, enhances emergency vehicle recognition, and mitigates threats like spoofing and replay attacks. These improvements demonstrate the potential of blockchain to enhance the security, efficiency, and reliability of vehicular networks. This research contributes to the development of secure communication frameworks for IoV. It highlights the potential for future blockchain integration with edge computing, artificial intelligence, and post-quantum cryptography technologies. Such integrations are expected to further strengthen smart transportation infrastructures and advance the capabilities of next-generation intelligent mobility systems.}, archive = {J_ACCESS}, author = {Alaa Alsaeed and Saleh Almowuena and Aasem N. Alyahya}, doi = {10.1109/ACCESS.2025.3601003}, journal = {IEEE Access}, pages = {150821-150837}, shortjournal = {IEEE Access}, title = {BAAIoV: A blockchain-based authentication and authorization framework for secure and reliable internet of vehicles communication}, volume = {13}, year = {2025}, }
• (2025). Parallel FEM for 3D electromagnetic scattering via sender-based FETI-DP and optimized subdomain decomposition. ACCESS, 13, 150806-150820. (www)

  @article{ , abstract = {This paper proposes a parallel finite element analysis framework for solving large-scale three-dimensional electromagnetic scattering problems in the high-frequency regime. The proposed method is based on the dual-primal finite element tearing and interconnecting (FETI-DP) algorithm and adopts a sender-based communication scheme in which each subdomain autonomously transmits its computed values, as opposed to the conventional receiver-based communication scheme. This reduces communication bottlenecks and ensures stable convergence of the biconjugate gradient stabilized method (BiCG-STAB) iterative solver. In addition, a one-to-many subdomain mapping strategy is implemented, allowing a single message passing interface (MPI) processor to manage multiple subdomains. This enables superior scalability and memory efficiency even in environments with limited parallel resources and allows for the derivation of the optimal number of subdomains based on total floating-point operations (total FLOPs). Simulations are conducted on dielectric only and dielectric-coated structures, validating numerical accuracy through comparison with HFSS and confirming the scalability and efficiency of the proposed framework across various problem sizes.}, archive = {J_ACCESS}, author = {Woobin Park and Sunghan Lee and Moonseong Kim and Woochan Lee}, doi = {10.1109/ACCESS.2025.3598091}, journal = {IEEE Access}, pages = {150806-150820}, shortjournal = {IEEE Access}, title = {Parallel FEM for 3D electromagnetic scattering via sender-based FETI-DP and optimized subdomain decomposition}, volume = {13}, year = {2025}, }
• (2025). Generating synthetic data using 2D multifractal detrended fluctuation analysis generative adversarial networks. ACCESS, 13, 150794-150805. (www)

  @article{ , abstract = {A novel 2-dimensional Multifractal Detrended Fluctuation Analysis (2D-MFDFA) as a discriminator for Generative Adversarial Networks (GAN) is presented in this work. The proposed approach demonstrates improved generated data quality at early iterations on the MNIST dataset. This algorithm produces synthetic images rapidly while maintaining lower computational complexity compared to existing GAN-based methods. The effectiveness and performance of this methodology are evaluated using the Fréchet Inception Distance (FID) score. The proposed 2D-MFDFA GAN is deployed on the AMD Xilinx Kintex7 FPGA KC705 using the NNGen framework, with validation conducted via a hardware experimental setup for power efficiency and performance. The experimental results reveal that the proposed method achieves an 85% improvement in FID score, reduces power consumption by 81%, and operates 94% faster, significantly outperforming comparable GAN models reported in the literature.}, archive = {J_ACCESS}, author = {Charudatta Gurudas Korde and K. G. Shreeharsha and R. K. Siddharth and M. H. Vasantha and Y. B. Nithin Kumar}, doi = {10.1109/ACCESS.2025.3603332}, journal = {IEEE Access}, pages = {150794-150805}, shortjournal = {IEEE Access}, title = {Generating synthetic data using 2D multifractal detrended fluctuation analysis generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). Advanced computational and experimental techniques for characterizing flow regimes in complex fluid systems using non-intrusive measurement methods. ACCESS, 13, 150772-150793. (www)

  @article{ , abstract = {This study presents a hybrid approach combining advanced Computational Fluid Dynamics (CFD) simulations with non-intrusive experimental techniques to characterize flow regimes in complex fluid systems. Targeting laminar, transitional, and turbulent flows, the research achieves real-time classification with high accuracy, critical for industrial applications where flow behavior impacts efficiency and safety. Using Reynolds number-based flow regime categorization and solving Navier-Stokes and continuity equations, the CFD models were validated with experimental data obtained via ultrasonic Doppler and eddy current flow meters. These non-intrusive methods provide real-time measurements without disturbing the flow, achieving an error margin below 5% across all flow types. The findings confirm the feasibility of this dual-method framework for dynamic flow regime identification and anomaly detection. This integrated approach enhances real-time monitoring, system performance optimization, and operational safety, setting a benchmark for accuracy and scalability in industrial fluid management.}, archive = {J_ACCESS}, author = {Nasir Muhammad and Xisheng Li}, doi = {10.1109/ACCESS.2025.3593390}, journal = {IEEE Access}, pages = {150772-150793}, shortjournal = {IEEE Access}, title = {Advanced computational and experimental techniques for characterizing flow regimes in complex fluid systems using non-intrusive measurement methods}, volume = {13}, year = {2025}, }
• (2025). Evaluating time series classification models for nocturnal hypoglycemia: From predictive performance to environmental impact. ACCESS, 13, 150756-150771. (www)

  @article{ , abstract = {Type 1 Diabetes (T1D) is an autoimmune condition that results in an insulin deficiency. People with T1D require the administration of exogenous insulin to maintain target glucose levels. However, insulin therapy can cause hypoglycemic episodes, which occur when blood glucose levels fall below 70 mg/dL. Nocturnal Hypoglycemia (NH) occurs while the individual is asleep and can lead to different clinical complications. Developing predictive approaches to predict NH before sleep could reduce these episodes and mitigate acute complications. While numerous models exist for Time Series Classification (TSC), their use for NH prediction remains limited. This study evaluates 14 different TSC models for NH prediction, assessing their performance by evaluating classification metrics, computational time, and environmental impact (measured by energy consumption and $CO_{2}$ emissions). The approaches include distance-based, convolutional-based, deep learning, dictionary-based, feature-based, shapelet-based, and interval-based methods. We employed glucose data from 52 individuals with T1D. Experimental results showed that interval-based and feature-based approaches achieved the best predictive performance, obtaining the highest Area Under the Curve Operator (AUCROC) of 0.703. Additionally, both demonstrated low environmental impact due to their short computational time. However, substantial differences in environmental impact were observed depending on the approach. Distance-based methods and deep learning approaches exhibited the highest environmental impact. This paper provides key insights into the effectiveness of TSC models for NH prediction, highlighting the trade-off between model performance and environmental impact.}, archive = {J_ACCESS}, author = {Francisco J. Lara-Abelenda and David Chushig-Muzo and Carmelo Betancort Acosta and Ana M. Wägner and Conceição Granja and Cristina Soguero-Ruiz}, doi = {10.1109/ACCESS.2025.3600917}, journal = {IEEE Access}, pages = {150756-150771}, shortjournal = {IEEE Access}, title = {Evaluating time series classification models for nocturnal hypoglycemia: From predictive performance to environmental impact}, volume = {13}, year = {2025}, }
• (2025). Modulation techniques for underwater acoustic communication: A comprehensive survey. ACCESS, 13, 150715-150755. (www)

  @article{ , abstract = {The underwater acoustic channel poses a significant challenge among communication mediums due to its time-varying dynamics, long multipath propagation, and severe Doppler shifts. Ensuring reliable data transmission under these conditions demands advanced modulation techniques, sophisticated signal processing algorithms, and high energy consumption. Moreover, underwater communication nodes typically have limited battery reserves, making them difficult and costly to replace or recharge. Additionally, as underwater communication networks expand, the risk of security threats that could compromise the system has increased, necessitating robust security measures that add further computational overhead. This comprehensive survey reviews the state-of-the-art modulation techniques for underwater acoustic communication, concentrating on energy efficiency and security measures while also exploring how Artificial Intelligence enhances these modulation techniques. We first cover the background of various modulation methods, including single-carrier modulation, multicarrier modulation, spread spectrum, spatial modulation, and hybrid-carrier modulation, highlighting their advantages and limitations. Then, we discuss strategies for minimizing power consumption and extending the operational life of battery-powered underwater nodes. Next, the paper addresses potential security risks and examines solutions to counteract security threats and attacks in underwater environments. Furthermore, this survey explores the benefits of incorporating AI to enhance underwater acoustic communication by optimizing synchronization, channel estimation, equalization, and detection, as well as adapting transmission parameters to changing conditions. Finally, we conclude the survey by suggesting future research directions and emphasizing the potential impact of integrating AI-based secure and energy-efficient receivers.}, archive = {J_ACCESS}, author = {Muhammad Haziq and Quoc Viet Phung and Stefan Lachowicz and Daryoush Habibi and Iftekhar Ahmad}, doi = {10.1109/ACCESS.2025.3601799}, journal = {IEEE Access}, pages = {150715-150755}, shortjournal = {IEEE Access}, title = {Modulation techniques for underwater acoustic communication: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). High-efficiency design and control of a single-stage 400 v/800 v EV charging station using a dual-output LF transformer with an ATS. ACCESS, 13, 150696-150714. (www)

  @article{ , abstract = {The escalating global demand for high-power electric vehicle (EV) charging has driven manufacturers to adopt 800 V battery systems. However, the prevalent 400 V-centric existing EV charging infrastructure requires substantial modifications or additional conversion stages for 800 V EVs, leading to increased complexity, cost, and efficiency losses. This paper proposes a novel EV charging system architecture comprising a dual-output low-frequency (LF) transformer, an automatic transfer switch (ATS), an LCL filter, and a Vienna rectifier, operating under three-phase 380 V/60 Hz grid input. The system offers a unified, single-stage solution, efficiently supporting both 400 V and 800 V EVs without additional DC/DC conversion. Within this architecture, the LF transformer provides dual 380 V and 180 V outputs; the ATS selects the appropriate output, fed via the LCL filter into the Vienna rectifier for wide-range DC outputs. The Vienna rectifier strategically controls DC output current/power and AC input current/DC output voltage. Experimental results from a 100 kW prototype demonstrate a peak efficiency of about 97.8%, with total harmonic distortion (THD) performance maintained below 3.9% (current THD). Compared to conventional multi-stage systems, this proposed system offers demonstrably superior efficiency, stability, and flexibility, making it ideally suited for comprehensive EV battery systems due to its simplified structure and optimized control algorithm.}, archive = {J_ACCESS}, author = {Jin-Su Jang and Elysee Malon Harerimana and Myeong-Jun Cha and Rae-Young Kim}, doi = {10.1109/ACCESS.2025.3598033}, journal = {IEEE Access}, pages = {150696-150714}, shortjournal = {IEEE Access}, title = {High-efficiency design and control of a single-stage 400 v/800 v EV charging station using a dual-output LF transformer with an ATS}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of UAV trajectory and resource allocation in multi-UAV-enabled wireless powered communication networks under Max–Min criterion. ACCESS, 13, 150684-150695. (www)

  @article{ , abstract = {We consider a wireless powered communication network (WPCN) where several unmanned aerial vehicles (UAVs) serve as energy transmitters and data collectors for low-powered ground devices (GDs). During the downlink phase, UAVs wirelessly transfer energy to the GDs, allowing them to harvest power for future data transmission. In the subsequent uplink phase, the GDs use the harvested energy to send information back to their corresponding UAVs. Our goal is to optimize system performance by maximizing the minimum throughput achieved among the GDs. We reformulate the original problem as a combination of three interrelated subproblems using a block coordinate descent approach: transmit power optimization of GDs, scheduling, and UAV trajectory optimization. These subproblems are solved iteratively to achieve an optimal solution. By adaptively adjusting UAV trajectories and optimizing scheduling and power distribution, our method ensures efficient energy delivery and reliable data collection. Numerical results confirm that the proposed approach significantly outperforms conventional methods, improving minimum throughput by up to 32.7% and 59.6% compared to fixed scheduling and fixed trajectory approaches, respectively. Furthermore, the adopted Lambert W-based energy harvesting model achieves up to 41% higher harvested energy compared to existing nonlinear energy harvesting models. Our findings underscore the potential of UAV-assisted wireless power transfer in boosting the efficiency and reliability of future wireless communication networks.}, archive = {J_ACCESS}, author = {Tabassum Alam Meem and Hyemin Yu and Minchul Ju}, doi = {10.1109/ACCESS.2025.3599579}, journal = {IEEE Access}, pages = {150684-150695}, shortjournal = {IEEE Access}, title = {Joint optimization of UAV trajectory and resource allocation in multi-UAV-enabled wireless powered communication networks under Max–Min criterion}, volume = {13}, year = {2025}, }
• (2025). TokenCutSeg: Label-efficient skin lesion segmentation by powering supervised refinement with self-learned visual vocabularies. ACCESS, 13, 150671-150683. (www)

  @article{ , abstract = {Accurate skin lesion segmentation is crucial for early melanoma diagnosis, yet deep learning models are often hindered by two key challenges: the scarcity of expert-annotated data and the difficulty of precisely delineating complex lesion boundaries. Existing methods either require extensive annotated data or, in the case of graph-based refinement techniques, suffer from a feature representation bottleneck, where performance is limited by the quality of initial, often supervised, features. This paper introduces TokenCutSeg, a novel hybrid framework designed to overcome these limitations by strategically separating representation learning from supervised refinement. Our approach first leverages all available images, both labelled and unlabeled, in a self-supervised stage. A Vector Quantised Generative Adversarial Network (VQGAN) learns a rich, discrete visual vocabulary, tokenising images into a compact and meaningful representation. Subsequently, a Transformer, trained with a masked token prediction task, models the deep contextual relationships between these visual tokens. This self-supervised pipeline produces powerful, context-aware features. Finally, these high-quality features are fed into a lightweight, supervised graph-based module that performs the final segmentation refinement, effectively solving the traditional feature bottleneck. Evaluated on the ISIC 2016, 2017, and 2018 datasets, TokenCutSeg establishes a new state-of-the-art, achieving mIoU scores of 86.18%, 81.37%, and 82.76%, respectively, demonstrating superior boundary accuracy and generalization. Our results validate that this label-efficient, hybrid approach leads to more robust and practical segmentation models. Our code is available at GitHub.}, archive = {J_ACCESS}, author = {Aryan Singh and Pepijn van de Ven and Ciarán Eising and Patrick Denny}, doi = {10.1109/ACCESS.2025.3603401}, journal = {IEEE Access}, pages = {150671-150683}, shortjournal = {IEEE Access}, title = {TokenCutSeg: Label-efficient skin lesion segmentation by powering supervised refinement with self-learned visual vocabularies}, volume = {13}, year = {2025}, }
• (2025). Low-power wide-range time-to-digital converter for time-of-flight range finders. ACCESS, 13, 150661-150670. (www)

  @article{ , abstract = {Time-to-digital Converters (TDCs) are increasingly considered to offer low complexity and scaling-friendly implementations in embedded applications, replacing voltage-domain analog-to-digital converters (ADCs). Hence, TDCs have been popular in embedded and typically resource-constrained sensing applications such as Time-of-Flight (ToF) or Lidar range finders, to convert time-domain data (travel time) into digital data. Yet, TDCs can suffer from relatively high power dissipation and a large footprint when a wide dynamic range and high resolution are required, which is the case for ToF applications. To tackle these challenges, this paper presents a power and area-efficient TDC architecture, with direct application in ToF measurements. The proposed architecture is based on a Nutt topology, offering a wide dynamic range up to 1.6 ms. In addition, the architecture is made event-based by utilizing gated ring oscillator GRO based TDCs, dissipating power only for a short duration in specific measurement windows. As a result, power dissipation is significantly reduced compared to state-of-the-art implementations (up to 55x). The proposed TDC has been implemented in 65-nm CMOS technology. It occupies only 100 $\mu $ m340 $\mu $ m including integrated serializers. It features a scaling-friendly implementation with all-digital control and readout circuits, opening the path for easier integration in embedded sensing scenarios.}, archive = {J_ACCESS}, author = {Ahmed M. Mohey and Marko Kosunen and Jussi Ryynänen and Martin Andraud}, doi = {10.1109/ACCESS.2025.3603422}, journal = {IEEE Access}, pages = {150661-150670}, shortjournal = {IEEE Access}, title = {Low-power wide-range time-to-digital converter for time-of-flight range finders}, volume = {13}, year = {2025}, }
• (2025). Preset-time synchronization for memristive neural networks under intermittent event-triggered control. ACCESS, 13, 150649-150660. (www)

  @article{ , abstract = {This paper investigates the preset-time synchronization (PTS) problem for memristive neural networks (MNNs) by introducing a preset-time gain function and designing an intermittent dynamic event-triggered strategy (IE-TCS). First, based on a nominal system, an IE-TCS is developed, and the exponential synchronization of this nominal system is rigorously proved using the proposed intermittent dynamic event-triggered control scheme combined with Lyapunov function methods. Subsequently, several key lemmas are established to eliminate Zeno behavior. Then, a preset-time gain function is designed, and a novel proof methodology for preset-time synchronization is proposed to achieve PTS for MNNs. This method involves designing a perturbed dynamical system and reconstructing the controller through time transformation. Compared with existing PTS approaches, the proposed time-scale transformation-based controller design method not only simplifies the synchronization process with improved effectiveness, but also maintains generality. Finally, numerical simulations validate the effectiveness of the theoretical results.}, archive = {J_ACCESS}, author = {Jian Xiao and Peilin He and Jiaxu Zhang}, doi = {10.1109/ACCESS.2025.3602046}, journal = {IEEE Access}, pages = {150649-150660}, shortjournal = {IEEE Access}, title = {Preset-time synchronization for memristive neural networks under intermittent event-triggered control}, volume = {13}, year = {2025}, }
• (2025). A new software-defined vehicular radio communications system based on DS-CDMA technology using orthogonal walsh-hadamard codes. ACCESS, 13, 150623-150648. (www)

  @article{ , abstract = {This paper presents a new software-defined vehicular radio communication system based on the DS-CDMA technology using orthogonal Walsh-Hadamard codes. The proposed radio communication system can simultaneously receive an audio stereo signal and a data signal with DS-CDMA modulation within standard FM radio broadcasting (87.5 – 108 MHz). This study also investigated the transmission/reception of the same data signal with frequency modulation in different ISM radio bands. The data transmission rate is significantly increased to 32 kbit/s using DS-CDMA technology, in which the input data signal is divided into 16 parallel sequences that are spread with orthogonal Walsh-Hadamard sequences. This reduces multi-access interference, jamming, and their contribution to the system self-noise. To perform symbol synchronization at reception, the Walsh-Hadamard codes of length 16 were reshaped by inserting synchronization pulses at the transmitter and, correspondingly by appending padding zeros at the receiver. The paper presents a new software-defined radio receiver capable to receive at the same time the data signal with DS-CDMA modulation, as well as the audio stereo signal. The proposed radio receiver includes a carrier synchronization loop operating at the baseband and a novel symbol timing synchronization loop based on synchronization pulses. The proposed radio communication system was implemented in MATLAB/Simulink and simulated under the conditions of channel impairments, considering the carrier frequency offset and timing offset on AWGN, Rayleigh, and Rician communication channels. To validate the simulation results, the bit error rate, depending on the signal-to-noise ratio of the received signal, was plotted for different communication channel models. The experimental results were obtained under laboratory conditions using USRP-type devices.}, archive = {J_ACCESS}, author = {Radu Gabriel Bozomitu and Marian Alexandru Corban and Ştefan Corneliu Stoica}, doi = {10.1109/ACCESS.2025.3602910}, journal = {IEEE Access}, pages = {150623-150648}, shortjournal = {IEEE Access}, title = {A new software-defined vehicular radio communications system based on DS-CDMA technology using orthogonal walsh-hadamard codes}, volume = {13}, year = {2025}, }
• (2025). PRCollector: Facilitating on-demand collection of pull request data from GitHub. ACCESS, 13, 150608-150622. (www)

  @article{ , abstract = {As pull-based software development has become popular, many researchers have analyzed pull request (PR) data in empirical studies. Consequently, they are keen to easily acquire and curate many PRs from software repositories. Although GitHub hosts numerous software projects, researchers have found online acquisition through the GitHub API troublesome. To overcome this problem, several offline datasets mirroring GitHub’s persistent data have been proposed. Unfortunately, these datasets incur high costs to maintain the latest data. Our proposed PRCollector, a PR data collection platform, enables users to write a simple Java program that automatically collects the desired PR data on demand and stores them in local storage. The collected data are organized according to our proposed metamodel, which illustrates the relationships between elements in PR activities. It is designed to bypass further analysis of Markdown descriptions in PR conversations and cumbersome tracking of source code elements corresponding to PR comments. The PR models stored locally reduce the time required to conduct investigations of PR data. Experimental results with 64,361 PRs across 23 repositories demonstrate that PRCollector is a superior choice for researchers seeking more efficient analyses of PRs.}, archive = {J_ACCESS}, author = {Bowen Tang and Katsuhisa Maruyama}, doi = {10.1109/ACCESS.2025.3603148}, journal = {IEEE Access}, pages = {150608-150622}, shortjournal = {IEEE Access}, title = {PRCollector: Facilitating on-demand collection of pull request data from GitHub}, volume = {13}, year = {2025}, }
• (2025). Wireless communication technology requirements for V2V-based merging assistance systems on highways. ACCESS, 13, 150594-150607. (www)

  @article{ , abstract = {In recent years, merging assistance systems that dynamically control the speed of vehicles on highway on-ramps while sharing real-time traffic information through wireless communication technologies have garnered considerable attention. The effectiveness of these systems critically depends on the performance of the employed wireless communication technology, particularly its ability to support high-speed and long-range communication. However, wireless communication inherently involves resource contention with other applications, requiring proper spectrum allocation in accordance with national radio regulations. Therefore, it is essential to clearly define the minimum communication requirements needed to deploy such systems, on the premise that safe and automated driving is ensured. This study aims to clarify the communication requirements for wireless technologies applied in merging assistance systems. To this end, simulation evaluations are conducted on a representative highway merging scenario by varying communication parameters such as transmission intervals and communication range. This approach enables the identification of conditions required for smooth vehicle merging and the derivation of the corresponding wireless communication technology requirements.}, archive = {J_ACCESS}, author = {Noriyasu Kikuchi}, doi = {10.1109/ACCESS.2025.3603233}, journal = {IEEE Access}, pages = {150594-150607}, shortjournal = {IEEE Access}, title = {Wireless communication technology requirements for V2V-based merging assistance systems on highways}, volume = {13}, year = {2025}, }
• (2025). An innovative prefabricated microsystem for industrial IoT: Addressing remote onboarding and OS provisioning challenges for edge nodes. ACCESS, 13, 150583-150593. (www)

  @article{ , abstract = {Chinese industrial IoT devices frequently encounter challenges during the cloud-based remote onboarding and OS provisioning: BIOS system of many industrial IoT devices does not support the HTTPS Boot protocol, which limits its ability to securely receive configuration information and increases security risks. Although there are alternative options such as PXE boot or manual configuration, both have security risks. For this reason, this paper has designed an innovative prefabricated micro-system with agent mechanism that enables Edge Nodes to securely access the cloud servers for management and control. Specifically, once the operator installs the uOS image on the Edge Node for initial deployment, bidirectional secure communication is established between the agent and the cloud server, enabling the Edge Node to successfully complete onboarding. Subsequently, the agent retrieves the latest configuration data from the cloud server to finalize provisioning. The pre-fabricated microsystem also supports fault recovery and process monitoring. Furthermore, this study conducts performance and maximum concurrency tests under both mTLS and TLS + JWT protocols. The innovative agent solution proposed herein will provide robust technical support and enhanced security guarantees for China’s Industrial Internet of Things (IIoT).}, archive = {J_ACCESS}, author = {Cunying Chen and Tingjie Chen and Zhenggang Jiang and Limin Wang}, doi = {10.1109/ACCESS.2025.3600962}, journal = {IEEE Access}, pages = {150583-150593}, shortjournal = {IEEE Access}, title = {An innovative prefabricated microsystem for industrial IoT: Addressing remote onboarding and OS provisioning challenges for edge nodes}, volume = {13}, year = {2025}, }
• (2025). DIA-YOLO: Diffusion-based involution attention YOLO for overhead imagery. ACCESS, 13, 150570-150582. (www)

  @article{ , abstract = {In this paper, a Diffusion-based Involution Attention YOLO network and a Diffusion-based Four-channel Fusion module are proposed, both specifically for oriented bounding box in overhead imagery. The propoased fusion module uses the Marigold depth-estimation network to infer depth information from a single RGB image, producing a four-channel RGBD input. In the proposed network backbone, involution captures fine-grained spatial relationships across color and depth patterns, and the Convolutional Block Attention Module injects the most salient features and locations into the detection head. Experimental results demonstrate significant improvements across multiple datasets. On PCB data, DIA-YOLO achieves 91.6% improvement in mAP50-95 (32.0% to 61.3%) with 16.5% faster inference time (0.71s). Cross-dataset evaluation shows consistent improvements: 21.7% in $PCB_{self}$ , 11.8% in $PCB_{open}$ , 31.3% in DOTA, and 7.6% in AITOD. Comparative analysis confirms superiority with 79.32% mAP50 on DOTA and 89.9% mAP50 on AITOD. As the inference time is 0.71s—well below 1 second—this method satisfies real-time requirements for practical overhead imagery applications, providing an effective solution for PCB inspection, satellite analysis, and autonomous systems.}, archive = {J_ACCESS}, author = {Tae-Jung Kim and Tae-Hyoung Park}, doi = {10.1109/ACCESS.2025.3603214}, journal = {IEEE Access}, pages = {150570-150582}, shortjournal = {IEEE Access}, title = {DIA-YOLO: Diffusion-based involution attention YOLO for overhead imagery}, volume = {13}, year = {2025}, }
• (2025). Learning representation of turbulent vector fields via efficient moving least squares based on monte carlo method. ACCESS, 13, 150555-150569. (www)

  @article{ , abstract = {In this study, we develop a numerical method to represent turbulent flow in various 2D vector fields using the Monte Carlo method-based MLS (Moving Least Squares) from a density field and express it as a learning representation through a neural network. Conventional MLS performs high-order interpolation solely based on vector-based constraints, making it difficult to effectively reflect the characteristics of a density field, which limits its applicability in various fields. Additionally, equations of higher degree require significant computational effort, making them costly in terms of computation time and resources when applied to simulation tasks that require per-frame calculations. To address these issues, this study integrates the Monte Carlo method-based weighting into MLS to efficiently consider the characteristics of the density field in the input data and design an algorithm that represents it as various forms of vector fields. Furthermore, we extend the solver to express this approach as a learning representation through a neural network. To validate the applicability of our method, we conducted experiments extracting turbulent vector fields from various density fields. Since conventional MLS does not guarantee temporal continuity, directly applying it to simulations results in noise. To resolve this, our method generates turbulent flow by analyzing the angular variation between the generated velocity and the underlying fluid velocity, ensuring stable advection of the density. As a result, our method efficiently and accurately extracts turbulent flow from a density field and integrates it into an underlying fluid solver, enabling the practical use of high-order interpolation in physics-based simulations. Experimental results across various scenarios demonstrate that our method improves both computation time and quality compared to previous methods, yielding enhanced turbulent flow fields.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim and Jung Lee}, doi = {10.1109/ACCESS.2025.3601241}, journal = {IEEE Access}, pages = {150555-150569}, shortjournal = {IEEE Access}, title = {Learning representation of turbulent vector fields via efficient moving least squares based on monte carlo method}, volume = {13}, year = {2025}, }
• (2025). Advanced fault diagnosis in photovoltaic arrays using a metropolis-hastings convolutional variational autoencoder. ACCESS, 13, 150537-150554. (www)

  @article{ , abstract = {Effective fault diagnosis is critical for maintaining the operational efficiency and reliability of photovoltaic (PV) arrays. While a multitude of artificial intelligence techniques have been applied to detect and diagnose faults in solar panel systems, this research presents a novel fault diagnosis strategy. Our approach leverages a Variational Autoencoder (VAE), further enhanced by Metropolis-Hastings Monte Carlo sampling and a robust residual convolutional neural network architecture. The proposed Metropolis-Hastings Convolutional Variational Autoencoder (MH-CVAE) demonstrates precise identification and categorization of diverse PV faults—including arc faults, MPPT failures, line-to-line, open circuits, degradation, and partial shading—under varied operational influences. This methodology is benchmarked against established machine learning techniques and existing autoencoder models reported in the literature. Comprehensive simulations highlight the MH-CVAE model’s superior performance, attaining a remarkable 99.86% accuracy on simulated test data and surpassing conventional diagnostic approaches. This novel approach significantly expands diagnostic capabilities for PV arrays, thereby offering potential improvements in system dependability and overall operational efficacy. Consequently, the MH-CVAE method shows considerable promise for advancing PV array fault diagnosis, ultimately contributing to the development of more resilient and efficient solar energy infrastructures.}, archive = {J_ACCESS}, author = {Ahmed Mesai Belgacem and Mounir Hadef and Kheira Kahili and Muhannad J. Alshareef and Mohammad Ghatasheh and Aymen Flah and Ayman Hoballah}, doi = {10.1109/ACCESS.2025.3601530}, journal = {IEEE Access}, pages = {150537-150554}, shortjournal = {IEEE Access}, title = {Advanced fault diagnosis in photovoltaic arrays using a metropolis-hastings convolutional variational autoencoder}, volume = {13}, year = {2025}, }
• (2025). Analysis of CMOS-based mm-wave rectifiers: A comprehensive survey. ACCESS, 13, 150523-150536. (www)

  @article{ , abstract = {Millimeter-wave (mm-wave) frequencies are attractive for energy harvesting in IoT devices because of their high energy density, which allows for more power to be captured in a smaller space. These frequencies enable the use of smaller, high-gain antennas, for compact devices, and offer wider bandwidths, thus supporting both energy-harvesting and high-data-rate communications simultaneously. The demand for IoT devices with increasingly smaller form factors has necessitated the use of mm-wave frequencies, enabling on-chip antenna integration and further reducing device size. With the expansion of the deployment of millimeter waves in 5G networks, the increasing availability of ambient millimeter wave signals presents new opportunities for IoT devices to harvest energy. This review paper explores the application of various CMOS based rectifier topologies, including Dickson, cross-coupled, inductor-peaked, and Colpitts, for efficient energy conversion at mm-wave frequencies. The transistor model and the effects of parasitic capacitances at mm-wave frequencies are analyzed and presented as well. The merits and demerits of each topology concerning key rectifier parameters such as sensitivity, efficiency, and overall performance are also presented.}, archive = {J_ACCESS}, author = {Syed Zahid Abbas and Lutfi Albasha and Nasir Abdul Quadir and Hasan Saeed Mir}, doi = {10.1109/ACCESS.2025.3602501}, journal = {IEEE Access}, pages = {150523-150536}, shortjournal = {IEEE Access}, title = {Analysis of CMOS-based mm-wave rectifiers: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Inner gate length modulation of MFMIS nanosheet FET memory for advanced technology nodes. ACCESS, 13, 150517-150522. (www)

  @article{ , abstract = {This work proposes a new way of lowering the area ratio (AR) between the ferroelectric and metal-oxide-semiconductor (MOS) regions of metal-ferroelectric-metal-insulator-semiconductor (MFMIS) ferroelectric field-effect transistors (FeFETs): the inner gate length ( $L_{\mathrm {G}}$ ) modulation. By leveraging the structural flexibility of nanosheet field-effect transistors (NSFETs), the areas of the MOS region were successfully increased by modulating the inner $L_{\mathrm {G}}$ independently from the outer $L_{\mathrm {G}}$ , achieving low AR without increasing the footprint area of the NSFETs. To validate the proposed inner $L_{\mathrm {G}}$ modulated structure, three-dimensional (3D) technology computer-aided design (TCAD) simulations were conducted. The results show that reducing the outer $L_{\mathrm {G}}$ by 2 nm while increasing the inner $L_{\mathrm {G}}$ by 2 nm effectively reduces the AR by 18 %, enhancing the memory window (MW) by 22 %. Additionally, the inner $L_{\mathrm {G}}$ modulation method enhances system-level performance, demonstrating an effective strategy for highly reliable FeFET-based embedded weight cells in advanced technology nodes.}, archive = {J_ACCESS}, author = {Kyoung Min Choi and Jae Seung Woo and Woo Young Choi}, doi = {10.1109/ACCESS.2025.3602436}, journal = {IEEE Access}, pages = {150517-150522}, shortjournal = {IEEE Access}, title = {Inner gate length modulation of MFMIS nanosheet FET memory for advanced technology nodes}, volume = {13}, year = {2025}, }
• (2025). Clarke-park transformation-based input configuration for CNN diagnosis of stator faults in three phase induction motors using motor current signature analysis. ACCESS, 13, 150505-150516. (www)

  @article{ , abstract = {Motor current signature analysis (MCSA) with convolutional neural networks (CNNs) is widely applied to three-phase induction motor fault diagnosis. Conventional input configurations, such as direct three-phase RGB mapping or discrete wavelet transform (DWT), primarily depend on inter-phase amplitude and phase differences, limiting their ability to capture subtle physical responses, especially for inter-turn short circuits (ITSC). This study proposes a Clarke–Park transformation (CPT)-based input configuration that extracts $I_{\alpha }$ , $I_{\beta }$ and $I_{d}$ components and assigns them to the red, green, and blue channels, respectively. This design simulatneously represents spatial current distribution ( $I_{\alpha }$ , $I_{\beta }$ ) and dynamic response ( $I_{d}$ ) in the rotating reference frame, enhancing physical interpretability and discriminative capability. Comparative experiments using identical raw data and the ResNet50 model evaluated CPT against RGB mapping and DWT inputs. CPT achieved the highest test accuracy (99.51%), with notable consistency in the challenging ITSC class (F1-score: 0.993) and perfect classification of the 4Turn class. Grad-CAM visualizations confirmed that CPT produces more localized, fault-focused activations than RGB mapping, comparable to DWT, without requiring complex frequency transforms. These results demonstrate that CPT effectively organizes current signal features, enabling faster convergence, improved classification stability, and high diagnostic accuracy, offering a physically meaningful input design for reliable motor fault diagnosis.}, archive = {J_ACCESS}, author = {Yeong-Jin Goh and Kyoung-Min Kim}, doi = {10.1109/ACCESS.2025.3602975}, journal = {IEEE Access}, pages = {150505-150516}, shortjournal = {IEEE Access}, title = {Clarke-park transformation-based input configuration for CNN diagnosis of stator faults in three phase induction motors using motor current signature analysis}, volume = {13}, year = {2025}, }
• (2025). A novel SiGe nanosheet transistor with channel release stopping layer structure for process variations suppression and circuit performance improvement. ACCESS, 13, 150498-150504. (www)

  @article{ , abstract = {In this study, SiGe channel nanosheet field-effect transistor (NSFET) with a novel channel release stopping layer (CRSL), namely CRSL-SiGe NSFET, is proposed to addresses the issue of sub-fin recess during channel release in conventional SiGe (C-SiGe) NSFET. Through a three-dimensional technology computer-aided design (TCAD) simulations, we systematically compare the DC and AC electrical characteristics of C-SiGe and CRSL-SiGe NSFETs. The results show that the proposed CRSL-SiGe NSFET achieves a 16% reduction in gate capacitance ( ${\mathrm {C}}_{\mathrm {GG}}$ ) while maintaining a comparable DC performance to C-SiGe NSFET. Additionally, an obvious improvement in gain of the inverter is achieved under various power. Moreover, the oscillation frequency of ring oscillators is increased and their delay is reduced under various power. These results indicate that CRSL-SiGe NSFET is a promising candidate for suppressing process variations and improving circuit performance in the advanced technology node.}, archive = {J_ACCESS}, author = {Yan Li and Jiayi Zhang and Haoyan Liu and Yongliang Li}, doi = {10.1109/ACCESS.2025.3602811}, journal = {IEEE Access}, pages = {150498-150504}, shortjournal = {IEEE Access}, title = {A novel SiGe nanosheet transistor with channel release stopping layer structure for process variations suppression and circuit performance improvement}, volume = {13}, year = {2025}, }
• (2025). Assessment of 5G RF-EMF exposure during large-scale public events via field measurements. ACCESS, 13, 150489-150497. (www)

  @article{ , abstract = {The development of Fifth-Generation (5G) mobile networks has raised public concern regarding human exposure to radiofrequency electromagnetic fields (RF-EMF). Key 5G features such as higher operating frequencies, broader bandwidths, beamforming, and traffic-dependent signal variability significantly influence RF-EMF exposure dynamics. Although many studies have assessed EMF levels induced by specific base stations under controlled user traffic, little empirical data are available on population-wide 5G environmental exposure across diverse scenarios. This study addresses this gap through field measurements conducted during a large public festival in Valencia, Spain and a baseline day prior to the event. Downlink signals from the 700 MHz and 3500 MHz 5G bands were measured across Spain’s three main network operators. Five locations surrounding the event venue were selected to capture the influence of the measurement location, and two measurement durations (6 and 30 min) were compared to assess temporal dynamics. The results showed that during the event, the power density in the 3500 MHz band increased by up to a factor of eight compared to the baseline conditions. However, even the highest recorded exposure levels remained well below the international safety limits. Additionally, a 30-minute continuous measurement revealed an approximately 30% increase in average power density compared to a 6-minute snapshot. These findings confirm that 5G environmental RF-EMF exposure remains within safe limits, even under high-demand conditions, and underscore the relevance of considering measurement location and temporal variability in exposure assessments.}, archive = {J_ACCESS}, author = {Alvaro Villaescusa-Tebar and Concepcion Garcia-Pardo}, doi = {10.1109/ACCESS.2025.3602263}, journal = {IEEE Access}, pages = {150489-150497}, shortjournal = {IEEE Access}, title = {Assessment of 5G RF-EMF exposure during large-scale public events via field measurements}, volume = {13}, year = {2025}, }
• (2025). Mitigating the bias in the model for continual test-time adaptation. ACCESS, 13, 150473-150488. (www)

  @article{ , abstract = {Modern machine-learning models often encounter streams of new, unlabeled data whose appearance gradually changes over time after deployment—for example, vision models on autonomous vehicles facing day-to-night transitions, surveillance cameras experiencing seasonal lighting shifts, or medical scanners with hardware ageing effects. Continual Test-Time Adaptation (CTA) refers to methods that, once a model is deployed, adapt it online during test time without any labeled examples or prior notice of distribution shifts to maintain accuracy in changing environments. However, as the model adapts, it can become over-confident in its mistakes and steadily biased toward recent but unreliable patterns. In this work, we introduce a simple prototype-based correction mechanism that stabilizes adaptation and reduces prediction bias. We maintain a running, class-wise exponential moving average (EMA) of target-domain feature centroids (prototypes) only from samples on which the model is confident. During adaptation, we softly re-cluster incoming features around these target prototypes and at the same time align them with the source-domain prototypes learned offline. This dual clustering alignment step can be seamlessly integrated into existing CTA methods with almost no extra computation. We validate our approach on standard corruption benchmarks, ImageNet-C and CIFAR100-C. When integrated into a strong CTA baseline, our method raises classification accuracy by 1.51% points on ImageNet-C and by 1.47% points on CIFAR100-C in the most challenging corruption streams, demonstrating that prototype-guided clustering significantly curbs drift without slowing down adaptation.}, archive = {J_ACCESS}, author = {Inseop Chung and Kyomin Hwang and Jayeon Yoo and Nojun Kwak}, doi = {10.1109/ACCESS.2025.3602672}, journal = {IEEE Access}, pages = {150473-150488}, shortjournal = {IEEE Access}, title = {Mitigating the bias in the model for continual test-time adaptation}, volume = {13}, year = {2025}, }
• (2025). Multipolar stimulator for deep brain stimulation with suppression of stimulation-induced common-mode artifacts. ACCESS, 13, 150458-150472. (www)

  @article{ , abstract = {A crucial part of treating neurological disorders like Parkinson’s and epilepsy involves using Deep Brain Stimulation (DBS) systems. This paper describes a system that provides stimulation through a multipolar biphasic current source. It consists of two identical instances, one for each cerebral hemisphere, each containing five stimulators: four of them are locally connected, while the fifth is connected to an implantable pulse generator for monopolar stimulation. Each stimulator can deliver a maximum current of 12 mA with 9-bit resolution and offers passive charge balancing. Safety features include a DC blocking capacitor for electrical isolation, ensuring minimal DC current flow in case of system failure. The proposed design compensates for up to ±50% mismatch in stimulation current sinks by utilizing current sources, thereby distributing current errors in ideal proportions. The stimulator’s voltage can be adjusted up to 30V. Based on post-layout Monte Carlo simulations, the common-mode voltage variates from its nominal value only by a standard deviation $\sigma $ equal to 2 mV. Thus, referring to an overall $3\sigma $ variation, the proposed topology shows a $4\times $ improvement in anchoring the brain voltage compared to the state-of-the-art. This architecture enables concurrent recording and stimulation, minimizing the artifacts generated by stimulation.}, archive = {J_ACCESS}, author = {Reza Shokri and Yarallah Koolivand and Omid Shoaei and Daniele D. Caviglia and Orazio Aiello}, doi = {10.1109/ACCESS.2025.3602824}, journal = {IEEE Access}, pages = {150458-150472}, shortjournal = {IEEE Access}, title = {Multipolar stimulator for deep brain stimulation with suppression of stimulation-induced common-mode artifacts}, volume = {13}, year = {2025}, }
• (2025). Triple model framework for medical diagnosis: Assist in the decentralization of medical resources. ACCESS, 13, 150438-150457. (www)

  @article{ , abstract = {The extreme imbalance of medical resources in the world today makes it crucial to achieve the decentralization of medical resources. However, whether it is the quality of doctors or medical equipment, small hospitals in towns and villages are unable to meet the medical needs of the vast rural population. Therefore, under the premise of existing township medical resources, it is crucial to effectively improve the diagnostic level of doctors. For this purpose, we use inexpensive X-rays as the diagnostic basis and use osteoarthritis as the pathological sample to train an AI diagnostic model to assist township doctors in diagnosis. However, there are three issues with existing AI diagnostics: 1). The disconnect between AI diagnostics and real-world use. 2). The data between various hospitals has not been effectively utilized. 3). The data of the entire society has not been effectively utilized. For this purpose, we have designed Triple Model Framework for Medical Diagnosis with Federated Learning Based on Blockchain (TMMD-FLchain) to address the above three issues. In addition, we have designed Reward mechanism based on expected value (RM-EV) for anonymous federated learning.}, archive = {J_ACCESS}, author = {Chenghan Wang and Hongjun Wang and Wei Li and Shanshan Wang and Jingyan Wang and Kegang Ji}, doi = {10.1109/ACCESS.2025.3601142}, journal = {IEEE Access}, pages = {150438-150457}, shortjournal = {IEEE Access}, title = {Triple model framework for medical diagnosis: Assist in the decentralization of medical resources}, volume = {13}, year = {2025}, }
• (2025). Distributed anomaly detection using federated learning. ACCESS, 13, 150426-150437. (www)

  @article{ , abstract = {One of the major challenges video surveillance systems usually face is high communication costs and computational bottlenecks, leading to latency in the transfer of high-definition video data. Such issues generally arise when large-scale projects are implemented in real-time capabilities. The paper explores Federated Learning (FL) algorithms for an efficient and scalable suspicious activity detection system to address these limitations. Federated Learning allows a decentralized approach that allows model training on participating devices (clients), and only the model updates are shared with the central server instead of sharing a huge amount of data. The approach reduces bandwidth consumption and minimizes latency without compromising the performance of the model. This study is among the first to explore federated learning (FL) for video-based anomaly detection using the UCF-Crime dataset. We propose and comparatively evaluate three FL aggregation strategies—FedAvg, Unweighted FedAvg, and FedSGD—under realistic, non-IID client data distributions. Unlike existing centralized methods, our approach offers enhanced privacy and reduced communication overhead while maintaining competitive performance. The results obtained in the experiments conducted on a varying number of clients for each approach suggest Unweighted FedAvg as the most reliable approach, which consistently achieves the highest accuracy and lowest loss for any given number of clients. The Accuracy for FedAvg ranges from 0.73 for 5 clients to 0.82 for 30 clients, and the loss decreases from 0.81 for 5 clients to 0.64 for 30 clients. Similarly, Unweighted FedAvg shows a consistent increase in accuracy from 0.74 for 5 clients to 0.85 for 25 clients and then drops slightly to 0.84 for 30 clients, and similarly for loss also it decreases from 0.78 for 5 clients to 0.53 for 25 clients and increases slightly to 0.56 for 30 clients. Despite the slight decrease in accuracy and increase in loss, it always performs better than FeDAvg. Whereas FedSGD has shown a very erratic pattern in change of accuracy and loss as the number of clients increases, with the lowest accuracy being 0.29 and the highest being 0.41, and for loss highest was 1.91 and the lowest was 1.59. The study successfully uncovers the potential of federated learning to address the limitations, such as computational bottlenecks, latency in data transfer, and model performance over non-iid data in real-time implementations of the public security systems.}, archive = {J_ACCESS}, author = {Pratyush Kumar Singh and Abishi Chowdhury and Amrit Pal}, doi = {10.1109/ACCESS.2025.3600450}, journal = {IEEE Access}, pages = {150426-150437}, shortjournal = {IEEE Access}, title = {Distributed anomaly detection using federated learning}, volume = {13}, year = {2025}, }
• (2025). Balancing security and usability in enterprise FIDO passkey recovery with peer assistance. ACCESS, 13, 150415-150425. (www)

  @article{ , abstract = {With the adoption of FIDO2 (Fast IDentity Online 2) and passkeys for passwordless authentication, balancing security and usability in account recovery and credential migration has become critical for enterprises. Traditional FIDO2 recovery solutions—which often depend on third-party cloud providers (e.g., Google Cloud backups, Apple iCloud Keychain synchronization)—introduce security risks, such as downgrade attacks, or impose usability and cost burdens, making them less suitable for corporate environments. This paper presents a peer-assisted FIDO2 passkey recovery approach that enhances security while maintaining user-friendly deployment. By leveraging peer-assisted key splitting and encrypted storage, the method eliminates reliance on external cloud providers, ensures compliance with FIDO2 standards, and provides resilience against eventual server compromise and unauthorized recovery attempts. The proposed solution is evaluated using the usability-deployability-security framework, demonstrating that it offers a scalable, secure, and user-friendly alternative to existing recovery mechanisms. This approach strengthens enterprise adoption of FIDO2 by improving both security resilience and usability in account recovery.}, archive = {J_ACCESS}, author = {Kemal Bicakci and Murat Sekmen and Yusuf Uzunay}, doi = {10.1109/ACCESS.2025.3602362}, journal = {IEEE Access}, pages = {150415-150425}, shortjournal = {IEEE Access}, title = {Balancing security and usability in enterprise FIDO passkey recovery with peer assistance}, volume = {13}, year = {2025}, }
• (2025). A 0.3 v ultra-low power hysteresis comparator with SRAM-based output stage and its application in asynchronous sigma-delta modulators. ACCESS, 13, 150400-150414. (www)

  @article{ , abstract = {This work presents a novel hysteresis comparator for ultra-low voltage asynchronous sigma-delta modulators, featuring a regenerative bulk-based SRAM output stage that enables rail-to-rail operation and significantly improves speed through positive feedback. Nominal simulations on TSMC 180 nm technology show a delay of 35.91 $\mu $ s, power dissipation of 7.27nW, and a hysteresis threshold of 4.24mV. The comparator demonstrates strong robustness under PVT and mismatch variations, with limited degradation in speed and hysteresis control. Analytical models for the hysteresis behavior and the comparator’s delay, based on small-signal approximation, have been developed and validated through simulations, showing close agreement. Compared to state-of-the-art design, the proposed solution achieves up to $2\times $ faster delay, lower power, and more stable hysteresis across operating conditions. Monte Carlo analysis further confirms its resilience to mismatch, with minimal variations regarding the hysteresis threshold. Performance improvements of the proposed comparator have been also tested in a real-case application scenario, integrating it in an asynchronous sigma-delta modulator from the literature. The results of the post-layout simulations show an improvement in the bandwidth and in the effective number of bits, with a consequent improvement in the value of the figure of merit, which goes from 0.76 pJ/step for the referenced literature solution to 0.31 pJ/step for the proposed solution. These results confirm the proposed comparator as a competitive topology for low-power, low-voltage analog systems, especially in biomedical and IoT applications.}, archive = {J_ACCESS}, author = {Riccardo Della Sala and Giovanni Nicolini and Giuseppe Scotti}, doi = {10.1109/ACCESS.2025.3600960}, journal = {IEEE Access}, pages = {150400-150414}, shortjournal = {IEEE Access}, title = {A 0.3 v ultra-low power hysteresis comparator with SRAM-based output stage and its application in asynchronous sigma-delta modulators}, volume = {13}, year = {2025}, }
• (2025). A review on dual-arm manipulation in agriculture. ACCESS, 13, 150379-150399. (www)

  @article{ , abstract = {This article systematically reviews the literature on major components (application, platform, and control mechanism) of dual-arm robots in agriculture. To achieve an insightful and transparent review, four research perspectives (RPs) were defined from the perspective of the dual-arm component: tasks, platforms, controls, and future directions. Following the RPs, the structure and flow of this review are derived. By highlighting the RPs, this review article aims to identify the current research state and gaps in dual-arm agricultural robots. In this phase, the characteristics and challenges of agricultural tasks, meaningful platform design in terms of mobility, adaptability, and performance, cutting-edge sensing technologies, and advanced control strategies are also indexed. Based on these, promising future directions have been suggested for advances such as tactile guidance and scene understanding. This study will help related researchers gain scalable and improvable insights into dual-arm agricultural robots in dealing with practical challenges. The insights generated from this analysis could be utilized in the agricultural automation industry, helping to respond promptly to the growing needs of sustainable and innovative agriculture.}, archive = {J_ACCESS}, author = {Yuseung Jo and Yonghyun Park and Jaehwi Seol and Jeonghyeon Pak and Bosung Kim and Changjo Kim and Hyoung Il Son}, doi = {10.1109/ACCESS.2025.3602396}, journal = {IEEE Access}, pages = {150379-150399}, shortjournal = {IEEE Access}, title = {A review on dual-arm manipulation in agriculture}, volume = {13}, year = {2025}, }
• (2025). A feasibility study with in-the-wild data in human interaction settings: Acoustic-visual fusion for predictive sound source positioning. ACCESS, 13, 150365-150378. (www)

  @article{ , abstract = {Acoustic data inherently contains spatial information regarding sounds. Most robotic systems rely primarily on speech recognition results for control; however, the utilization of rich directional cues could enhance their functionality. Especially in human-robot interaction, both verbal content and spatial context in acoustic data are crucial. To capture such auditory context, we employ acoustic maps in this research. These maps represent soundscapes as 2D projections, allowing robots to integrate auditory spatial cues with visual perception. Using a custom acoustic-visual module, we collect egocentric data from four daily scenarios and generate corresponding acoustic maps. By demonstrating a predictive sound source positioning task, we confirm the potential of acoustic maps to capture dialogue-relevant information. Based on these findings, we anticipate potential applications for robots, such as identifying individuals attempting to engage with the robot in crowded environments or predicting turn-taking in multi-party conversations.}, archive = {J_ACCESS}, author = {Zhichao Chen and Chenfei Xu and Huthaifa Ahmad and Yuya Okadome and Hiroshi Ishiguro and Yutaka Nakamura}, doi = {10.1109/ACCESS.2025.3602885}, journal = {IEEE Access}, pages = {150365-150378}, shortjournal = {IEEE Access}, title = {A feasibility study with in-the-wild data in human interaction settings: Acoustic-visual fusion for predictive sound source positioning}, volume = {13}, year = {2025}, }
• (2025). JFSP-ViT: A joint frequency-spatial perception vision transformer for diabetic retinopathy grading. ACCESS, 13, 150348-150364. (www)

  @article{ , abstract = {Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Early and accurate diagnosis is essential to prevent irreversible vision loss. However, current manual grading methods are inefficient, underscoring the need for automated computer-aided diagnosis (CAD) systems. Although deep learning has advanced DR grading, most approaches focus solely on spatial features, overlooking complementary frequency-domain information and facing challenges in detecting subtle lesions and modeling inter-lesion dependencies. This study addresses these limitations by proposing a novel architecture, the Joint Frequency-Spatial Perception Vision Transformer (JFSP-ViT), which integrates spatial and frequency-domain learning to improve DR severity assessment. JFSP-ViT employs a hierarchical design with Inverted Residual Blocks (IRB) and Inverted Residual Attention Blocks (IRAB) to enhance spatial feature representation progressively. Furthermore, the Dual-Domain Perception Transformer (DDPT) module enables joint-domain representation learning through spatial and frequency self-attention mechanisms, while a cross-domain attention fusion module facilitates interaction between the two domains. Experimental results on public DR datasets demonstrate that JFSP-ViT achieves superior performance in multi-level DR grading, surpassing state-of-the-art methods in both accuracy and robustness, particularly in detecting subtle lesions and modeling inter-lesion dependencies. These findings emphasize the significance of dual-domain learning in automated DR diagnosis and suggest that JFSP-ViT can serve as a reliable tool for early and precise DR assessment.}, archive = {J_ACCESS}, author = {Ye Wang and Sumin Shen and Nanwei Tong}, doi = {10.1109/ACCESS.2025.3600453}, journal = {IEEE Access}, pages = {150348-150364}, shortjournal = {IEEE Access}, title = {JFSP-ViT: A joint frequency-spatial perception vision transformer for diabetic retinopathy grading}, volume = {13}, year = {2025}, }
• (2025). Opti-route: A machine learning-based scheme for optimal placement and selection of controllers with an optimal route in software-defined vehicular network. ACCESS, 13, 150333-150347. (www)

  @article{ , abstract = {The controller placement problem and controller selection problem are two major issues that, if not considered seriously, lead to high network costs, load unbalancing, and high latency. To ensure stable connection in software-defined vehicular network (SDVN) with low cost, balanced load, and less latency, an optimal solution must be found that tackles all issues simultaneously. The aim of this article is to propose a solution called Opti-Route. Opti-Route focuses on solving three major problems simultaneously for SDVN. The cost of installing controllers at almost every roadside unit (RSU) to ensure stable connections is high. The second problem lies in the imbalance of load caused by the presence of a single main controller (MC) and multiple local controllers (LCs), and the third problem is the high latency involved in creating an end-to-end path from source to destination in a highly dynamic network. Opti-Route tackles these challenges by first analyzing the optimal placement of controllers from among all the RSUs in the network using a support vector machine (SVM) algorithm. From these selected LCs, it then selects the optimal controller (OC) to share the burden of the MC at the location where traffic density is high. And then, finally, taking instructions from optimal and local controllers, Opti-Route selects an optimal network route that ensures a high delivery ratio with minimum delay and overhead. To demonstrate the effectiveness of SVM in a dynamic SDVN architecture, we tested our Opti-Route in various case scenarios and compared it with different learning algorithms. Our result shows a minimum delay of 0.04 ms, a high delivery ratio of 97%, and a minimum overhead of 4%.}, archive = {J_ACCESS}, author = {Muhammad Rehan Basharat and Ayesha Khanam and Mastora Alizada and Huma Ghafoor and Sana Qadir and Seung Yeob Nam and Insoo Koo}, doi = {10.1109/ACCESS.2025.3602967}, journal = {IEEE Access}, pages = {150333-150347}, shortjournal = {IEEE Access}, title = {Opti-route: A machine learning-based scheme for optimal placement and selection of controllers with an optimal route in software-defined vehicular network}, volume = {13}, year = {2025}, }
• (2025). Innovative autonomous design of multi-band reconfigurable antenna using genetic algorithms. ACCESS, 13, 150321-150332. (www)

  @article{ , abstract = {This paper presents a novel approach to designing reconfigurable antenna using genetic algorithms (GAs) and accurate PIN diode modeling. The method integrates TRL calibration for precise diode characterization, equivalent circuit modeling, and a multi-objective GA implemented in CST Microwave Studio. A pixelization-based optimization technique is employed to determine optimal diode positions in a Defected Ground Structure (DGS). The proposed antenna operates in four distinct modes, achieved through strategic placement of two PIN diodes. Capacitive slot integration eliminates the need for separate bias circuits, making the design simpler and less complex. The results demonstrate good agreement between simulated and measured performance metrics, with maximum gains ranging from 3 to 4.34 dBi and Half-Power Beamwidths (HPBW) exceeding 100 degrees across all frequencies. This automated design methodology accelerates optimization, improves the antenna adaptability and performance, and ensures its compatibility with modern wireless communication systems.}, archive = {J_ACCESS}, author = {Mohammed Cherif Derbal and Mourad Nedil}, doi = {10.1109/ACCESS.2025.3603003}, journal = {IEEE Access}, pages = {150321-150332}, shortjournal = {IEEE Access}, title = {Innovative autonomous design of multi-band reconfigurable antenna using genetic algorithms}, volume = {13}, year = {2025}, }
• (2025). An adaptive clustering-based active learning kriging method combining with importance sampling for structural reliability analysis with multiple failure regions. ACCESS, 13, 150308-150320. (www)

  @article{ , abstract = {Reliability analysis of structures characterized by multiple failure regions remains a challenging task, especially when the computationally intensive numerical models are used to predict the system response. In order to address this problem, an adaptive clustering-based active learning Kriging method combining with importance sampling (abbreviated as AC-AK-IS) is proposed. The basic idea lies in the replacement of FORM stage of active learning and Kriging-based importance sampling (AK-IS) with a first stage adaptive Kriging to approximate the failure boundary. Then an adaptive clustering algorithm is proposed to automatically identify the failure regions, based on which a Gaussian mixture model (GMM) is fitted as the importance sampling density (ISD). With the fitted ISD, the second stage of AK-IS can be readily performed to estimate the failure probability. AC-AK-IS provides an effective way to identify the a-priori unknown multiple failure regions. It extends the application of AK-IS to cases with multiple failure regions while retaining the advantages of high efficiency and accuracy. Three examples are provided to demonstrate the application and efficiency of AC-AK-IS.}, archive = {J_ACCESS}, author = {Guofeng Xue and Xuetao Lyu}, doi = {10.1109/ACCESS.2025.3602203}, journal = {IEEE Access}, pages = {150308-150320}, shortjournal = {IEEE Access}, title = {An adaptive clustering-based active learning kriging method combining with importance sampling for structural reliability analysis with multiple failure regions}, volume = {13}, year = {2025}, }
• (2025). Optimization of power-to-gas and carbon capture coupling with a tiered carbon trading mechanism in hydrogen-blended gas integrated energy systems. ACCESS, 13, 150289-150307. (www)

  @article{ , abstract = {In the context of an increasingly integrated global economy, environmental hazards associated with fossil fuel consumption have become a significant concern, making the energy transition an urgent issue. This paper proposes an Hydrogen-Blended Gas Integrated System (HBGIS) model that combines a tiered carbon trading mechanism with Power-to-Gas (P2G), Carbon Capture and Storage (CCS) technologies, along with hydrogen blending in natural gas networks. First, a low-carbon system integrating multiple forms of energy is developed. The system converts excess renewable energy into hydrogen via P2G technology, with hydrogen blending in natural gas networks improving energy efficiency. A tiered carbon trading mechanism dynamically regulates carbon emission costs, optimizing both emission reduction and economic benefits. By employing a comprehensive optimal scheduling model, the proposed system flexibly adapts to energy demand fluctuations, thereby achieving dual objectives: improved energy efficiency and reduced carbon emissions. Furthermore, the effectiveness of the scheduling model is validated by comparing its performance across six distinct scenarios. The results indicate that the proposed system significantly enhances renewable energy utilization, reduces carbon emissions by 10.71%, and decreases total operational costs by CNY 343,600((USD 47,393.10) compared to conventional models. Furthermore, the synergistic integration of multi-mode hydrogen utilization and the tiered carbon trading mechanism notably improves both economic efficiency and environmental performance.}, archive = {J_ACCESS}, author = {Zhiyong Lei and Zhongsheng Li and Jianchao Huang and Tao Chen}, doi = {10.1109/ACCESS.2025.3602972}, journal = {IEEE Access}, pages = {150289-150307}, shortjournal = {IEEE Access}, title = {Optimization of power-to-gas and carbon capture coupling with a tiered carbon trading mechanism in hydrogen-blended gas integrated energy systems}, volume = {13}, year = {2025}, }
• (2025). Review of open-source AI tools for healthcare IT infrastructures. ACCESS, 13, 150275-150288. (www)

  @article{ , abstract = {Open-source artificial intelligence (AI) in the healthcare sector has the potential to revolutionize the industry by improving accessibility, efficiency, and accuracy of medical services. This review examines how open-source AI reshapes diagnostics, treatment planning, patient monitoring, and healthcare administration. By using the collaborative and transparent nature of open-source platforms, healthcare providers can adopt cutting-edge AI solutions without the financial or proprietary constraints typically associated with commercial software. This study reviews the current landscape of open-source AI tools in healthcare, evaluates their performance and reliability, and identifies key challenges and opportunities for widespread adoption. Through case studies and empirical analysis, the research demonstrates how open-source AI can improve patient outcomes, streamline healthcare operations, and foster innovation. The findings suggest that open-source AI holds significant promise in democratizing access to advanced healthcare technologies and promoting a more equitable and efficient healthcare system.}, archive = {J_ACCESS}, author = {Younis Mohammed Al-Kharusi and Ajmal Khan and Muhammad Rizwan Mughal and Mohammed M. Bait-Suwailam}, doi = {10.1109/ACCESS.2025.3599912}, journal = {IEEE Access}, pages = {150275-150288}, shortjournal = {IEEE Access}, title = {Review of open-source AI tools for healthcare IT infrastructures}, volume = {13}, year = {2025}, }
• (2025). Automated hardware design methodology for digital filters with high-level synthesis. ACCESS, 13, 150258-150274. (www)

  @article{ , abstract = {The increasing complexity of digital hardware systems and the demand for faster time-to-market for the semiconductor industry require a rapid and flexible design strategy at an increased abstraction level. To this end, we propose a fully automated hardware design methodology for digital signal processing (DSP) applications such as digital filters, using high-level synthesis (HLS) integrated with MATLAB. This approach enables a one-click workflow from concept design to a synthesized and functionally verified netlist, with the requirement of minimal hardware expertise. After comparison with industry standard register transfer level (RTL) designs of multiple filter architectures, the area of HLS-generated implementations exhibited a variance ranging from a 54.4 % reduction to a 35.8 % overhead. This indicates a notable quality of results (QoR), especially when weighed against the significant gains in design time reduction and productivity. Beyond digital filters, the work demonstrates that modern HLS tools, when paired with automation and parameterized code, can deliver rapid industrial grade ASIC results, bridging the gap between algorithm development and hardware deployment in digital signal processing and beyond.}, archive = {J_ACCESS}, author = {Shehryar Akbar and Luciano Lavagno and Mihai T. Lazarescu and Davide Mariz}, doi = {10.1109/ACCESS.2025.3602232}, journal = {IEEE Access}, pages = {150258-150274}, shortjournal = {IEEE Access}, title = {Automated hardware design methodology for digital filters with high-level synthesis}, volume = {13}, year = {2025}, }
• (2025). TDJSCC: Low complexity and bandwidth efficient deep joint source-channel coding with OFDM. ACCESS, 13, 150244-150257. (www)

  @article{ , abstract = {The practical deployment of deep joint source-channel coding (DJSCC) in edge devices faces two critical limitations. First, the prohibitive computational complexity of deep neural networks hinders efficiency. Second, existing OFDM-based systems suffer from bandwidth inefficiency due to dedicated pilot symbol allocation. To address these challenges, we propose tensorized deep joint source-channel coding (TDJSCC), a novel DJSCC framework that integrates a tensorized convolutional neural network (TCNN) with in-band pilot-augmented orthogonal frequency-division multiplexing (OFDM). The TCNN decomposes high-dimensional convolution kernels into cascaded low-rank tensor operations through singular value decomposition (SVD). Simultaneously, our in-band pilot design eliminates dedicated pilot symbols by strategically replacing data subcarriers with pilot tones. This approach achieves 100% bandwidth efficiency while maintaining channel estimation accuracy through optimized discrete Fourier transform (DFT) interpolation. The simulation results demonstrate that the proposed TDJSCC model outperforms the existing DJSCC model on low-resolution datasets and achieves comparable performance for high-resolution datasets, with 87% fewer parameters and $3.1\times $ floating-point operations (FLOPs) reduction. Furthermore, the proposed TDJSCC achieves improved performance, significantly lower computational complexity, and full bandwidth efficiency.}, archive = {J_ACCESS}, author = {Man Xu and Chan-Tong Lam and Yuanhui Liang and Benjamin Ng and Xiaochen Yuan and Sio-Kei Im}, doi = {10.1109/ACCESS.2025.3602059}, journal = {IEEE Access}, pages = {150244-150257}, shortjournal = {IEEE Access}, title = {TDJSCC: Low complexity and bandwidth efficient deep joint source-channel coding with OFDM}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-based recommender systems enhanced with graph neural networks. ACCESS, 13, 150228-150243. (www)

  @article{ , abstract = {Graph Neural Networks (GNNs) have emerged as powerful tools in recommender systems, enabling the modeling of complex user-item interactions by leveraging graph-structured representations. However, conventional GNN-based recommendation models often struggle to adapt to dynamically evolving user preferences and newly introduced items, as they predominantly rely on static supervised learning frameworks. These limitations hinder their ability to provide accurate and personalized recommendations over time, particularly in scenarios where user behavior and item availability change frequently. To address this challenge, we propose a novel recommender system that integrates GNNs with Reinforcement Learning (RL), combining the structural modeling capabilities of GNNs with the sequential decision-making strengths of RL. Our approach enables continuous learning and adaptation to shifting user preferences while optimizing long-term user engagement. Specifically, we introduce three key components. First, a self-attention-based state encoder adaptively captures both long-term and short-term user preferences from historical interactions. Second, a residual connection is added to the actor network to align the action space with the user embedding space, thereby stabilizing training and improving representation consistency. Third, we design a hybrid reward function that combines user feedback types (e.g., clicks, cart, purchase) with embedding-based similarity, enabling the model to balance accuracy, diversity, and adaptability. We evaluate our method on two real-world datasets: Taobao and Amazon-CD, using Recall, Hit Ratio, NDCG, Item Coverage, and Entropy-based Diversity as core evaluation metrics. Experimental results show that our method consistently outperforms baselines: it improves Recall by 21.4% and 50.4%, Hit Ratio by 8.3% and 33.9%, and NDCG by 11.0% and 22.2% on Taobao and Amazon-CD, respectively, while maintaining competitive Coverage and Entropy scores. These results demonstrate the effectiveness and robustness of our approach in dynamic recommendation scenarios.}, archive = {J_ACCESS}, author = {Chenxi Fan and Satoshi Fujita}, doi = {10.1109/ACCESS.2025.3598092}, journal = {IEEE Access}, pages = {150228-150243}, shortjournal = {IEEE Access}, title = {Reinforcement learning-based recommender systems enhanced with graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Finding device driver bugs with fuzzing PCIe configuration input. ACCESS, 13, 150216-150227. (www)

  @article{ , abstract = {Device drivers play a vital role in enabling communication between the operating system and hardware, yet their elevated privileges and interaction with untrusted inputs make them a frequent source of security vulnerabilities. While numerous studies have explored fuzzing and static analysis techniques to identify such flaws in device drivers, relatively less attention has been given to PCIe configuration space inputs, which are often treated as constant values, more focusing on post-initialization interactions with MMIO and DMA. This paper introduces PCIconfuzz, a device input fuzzer designed to uncover security vulnerabilities in device drivers by targeting the initialization phase, with particular emphasis on PCIe configuration space inputs. Leveraging the insight that drivers predominantly interact with configuration space during initialization, we developed a virtual PCI device capable of supplying fuzzed configuration space values to exercise a broader range of driver setup behaviors. Our evaluation on PCIe device drivers in the Linux kernel(v6.9-rc3) uncovered seven previously unknown bugs, two of which have since been acknowledged and patched by upstream maintainers. To the best of our knowledge, this is the first work to dynamically test PCIe configuration space inputs for security vulnerabilities in device drivers.}, archive = {J_ACCESS}, author = {Kyungwook Boo and Byoungyoung Lee}, doi = {10.1109/ACCESS.2025.3596641}, journal = {IEEE Access}, pages = {150216-150227}, shortjournal = {IEEE Access}, title = {Finding device driver bugs with fuzzing PCIe configuration input}, volume = {13}, year = {2025}, }
• (2025). A multi-agent system for cybersecurity threat detection and correlation using large language models. ACCESS, 13, 150199-150215. (www)

  @article{ , abstract = {As cyber-attacks rapidly evolve across communication, infrastructure and data layers, traditional security solutions such as rule-based intrusion detection systems (IDS) or signature-based antivirus programs are effective at detecting known threats, but they often lack the contextual understanding and semantic interpretation necessary to detect complex or evolving attacks. For example, spear-phishing campaigns, advanced persistent threats (APTs), and multi-stage attacks often escape detection due to their subtle and context-dependent nature. This limitation creates a critical gap in detecting coordinated or subtle attack patterns that span multiple systems and domains. The need for semantic understanding, cross-domain visibility, and adaptive detection is increasingly urgent, particularly as threat actors employ polymorphic and AI-driven strategies that traditional systems cannot interpret or correlate effectively. This paper presents a modular multi-agent architecture that integrates established cybersecurity analysis tools with large language models (LLMs) to achieve intelligent, explicable and highly accurate detection of threats across diverse data types. Three specialized agents: 1) email verification, 2) log analysis, and 3) IP address scanning each operate independently with tailored detection pipelines that combine domain-specific tools and LLM-powered semantic analysis components to identify, characterize, and report threats specific to their domain. At the core of the system lies a contextual recommendation system that processes and cross-analyzes the outputs of all specialized agents to detect complex threat patterns such as multi-vector, time-based, or stealth attacks that would otherwise evade isolated detection mechanisms. The evaluation on benchmark datasets, including CIC-IDS 2017, SpamAssassin, and custom simulated network environments, demonstrates threat detection accuracy of 93.6%, multi-agent correlation accuracy of 87%, and false positive reduction of 41.3% compared to traditional approaches. The use of LLMs for both structured explanations and chain-of-thought reporting further enhances analyst confidence and reduces triage time.}, archive = {J_ACCESS}, author = {Yasser Hmimou and Mohamed Tabaa and Azeddine Khiat and Zineb Hidila}, doi = {10.1109/ACCESS.2025.3602681}, journal = {IEEE Access}, pages = {150199-150215}, shortjournal = {IEEE Access}, title = {A multi-agent system for cybersecurity threat detection and correlation using large language models}, volume = {13}, year = {2025}, }
• (2025). A regionally adaptable nutrition centric food recommendation system (FR-RANC). ACCESS, 13, 150179-150198. (www)

  @article{ , abstract = {The regionally adaptable nutrition-centric food recommendation system (FR-RANC) introduces a holistic approach to personalized dietary recommendations that addresses the limitations of existing nutrition systems that often overlook regional food availability, economic conditions, and comprehensive nutrient requirements. Utilizing a decision support system (DSS) and an optimization algorithm, the system integrates user-specific data, including age, gender, health conditions, and socioeconomic status, with region-specific food databases to generate personalized food lists that meet macro- and micronutrient needs. Unlike traditional systems, which focus on a few nutrients or specific medical conditions, FR-RANC provides a holistic solution by considering a broader range of nutrients and adapting them to various regional and economic contexts. The initial evaluation, based on systematically generated test cases, demonstrated the system’s capability to achieve approximately 90% compliance with macronutrient targets. At the same time, micronutrient results exhibit a narrow distribution around the mean, indicating strong alignment with recommended requirements, even across varying health conditions, such as diabetes, cardiovascular issues, and pregnancy. Furthermore, FR-RANC effectively addresses cold-start and scalability issues, as observed in traditional systems. These results underscore its potential as a proactive tool for promoting balanced nutrition, supporting healthcare professionals, and addressing global nutritional disparities.}, archive = {J_ACCESS}, author = {Mushran Siddiqui and Farhana Akther and Gazi M. Ehsan Ur Rahman and Raqibul Mostafa}, doi = {10.1109/ACCESS.2025.3592210}, journal = {IEEE Access}, pages = {150179-150198}, shortjournal = {IEEE Access}, title = {A regionally adaptable nutrition centric food recommendation system (FR-RANC)}, volume = {13}, year = {2025}, }
• (2025). Multimodal classification of social media disaster posts with graph neural networks and few-shot learning. ACCESS, 13, 150168-150178. (www)

  @article{ , abstract = {Social media has emerged during the last decade as a potential information source in crisis scenarios, providing data in real-time or just after the occurrence of the event. Nevertheless, current social media data acquisition procedures result in datasets, presenting myriad non-informative content, which hinders posterior analyses. While previous studies have used machine learning to address this issue, they typically require many labeled examples, hardening their use in a real-world scenario. Moreover, social media posts tend to be multimodal, which adds complexity to how these data should be represented. This paper extends upon our previous work and presents a new method for identifying the most informative content related to an event in textual and visual data through few-shot learning. The results show that this method outperforms existing approaches in both performance and efficiency, offering a valuable solution for a timely analysis of crisis-related social media data and advancing research in this area.}, archive = {J_ACCESS}, author = {José Nascimento and Paolo Bestagini and Anderson Rocha}, doi = {10.1109/ACCESS.2025.3602364}, journal = {IEEE Access}, pages = {150168-150178}, shortjournal = {IEEE Access}, title = {Multimodal classification of social media disaster posts with graph neural networks and few-shot learning}, volume = {13}, year = {2025}, }
• (2025). Improving semantic parsing and text generation through multi-faceted data augmentation. ACCESS, 13, 150145-150167. (www)

  @article{ , abstract = {The increasing use of large language models has heightened the demand for more extensive datasets in natural language processing (NLP). While various augmentation techniques are being employed to enhance data quantity, many introduce noise or struggle with structurally complex inputs like Discourse Representation Structures (DRS). This study introduces novel data augmentation techniques for both semantic parsing (Text-to-DRS) and text generation (DRS-to-Text), emphasizing enhancements such as named entity augmentation, lexical substitutions utilizing WordNet, and grammatical transformations through changes in tense. The proposed methods led to a considerable expansion of the Parallel Meaning Bank (PMB) dataset, ensuring semantic accuracy and contextual relevance. The augmentation increased both gold and silver instances by a factor of 9, resulting in over 1.3 million new examples. We evaluated four transformer models (byT5, mT5, T5, and mBART) using this augmented dataset. Experimental evaluations revealed substantial improvements across multiple performance metrics. Notably, for semantic parsing, we observed a 17.65% increase in SMATCH (F1) score, and among different evaluation measures for text generation, we have improvements of 14.38% in BLEU score and 6.43% in METEOR score. The observed improvements highlight the effectiveness of our proposed augmentation methodologies in boosting model capabilities for complex neural semantic parsing and generation tasks.}, archive = {J_ACCESS}, author = {Muhammad Saad Amin and Luca Anselma and Alessandro Mazzei}, doi = {10.1109/ACCESS.2025.3593857}, journal = {IEEE Access}, pages = {150145-150167}, shortjournal = {IEEE Access}, title = {Improving semantic parsing and text generation through multi-faceted data augmentation}, volume = {13}, year = {2025}, }
• (2025). Private information retrieval from AI chatbots via proxy-prompt masking. ACCESS, 13, 150123-150144. (www)

  @article{ , abstract = {This paper proposes a privacy-preserving scheme, PromptMasking, for querying AI chatbots without disclosing users’ sensitive prompts. This scheme enables users to query AI systems without revealing their true intent by transforming sensitive prompts into non-sensitive proxy prompts, selected from a semantically constructed proxy dictionary. The scheme then supplements these with cover prompts, ensuring the original prompt remains indistinguishable during processing. Unlike prior techniques, PromptMasking does not rely on encryption or prompt rewriting, making it compatible with existing large language model APIs. To evaluate its effectiveness, we constructed a dataset comprising 240 topics (40 sensitive and 200 non-sensitive) and generated over 1.2 million high-quality natural language prompts. Experimental results demonstrate that PromptMasking significantly outperforms state-of-the-art baselines in preserving prompt privacy. In particular, it achieves substantial improvements in obfuscating related prompts across both single and multi-session search settings.}, archive = {J_ACCESS}, author = {Shariq Bashir}, doi = {10.1109/ACCESS.2025.3602921}, journal = {IEEE Access}, pages = {150123-150144}, shortjournal = {IEEE Access}, title = {Private information retrieval from AI chatbots via proxy-prompt masking}, volume = {13}, year = {2025}, }
• (2025). Multilingual low-latency emergency VoIP system using LLM for speech reconstruction and blockchain for secure data archiving. ACCESS, 13, 150101-150122. (www)

  @article{ , abstract = {Emergency VoIP communications frequently suffer from packet loss, jitter, and background noise, which degrade speech quality, delay dispatcher response, and threaten the reliability of emergency services. This paper presents a blockchain-governed, low-latency communication response framework powered by a Large Language Model (LLM), leveraging GPT-4o-mini for semantic speech reconstruction and dynamic emergency prioritization. The proposed architecture integrates multilingual speech-to-text and translation via Google APIs, a Facebook AI Similarity Search (FAISS)-indexed retrieval system for context-driven LLM inference, and severity classification for dispatcher triaging. To ensure tamper-proof data governance, the system incorporates AES-256-CBC encryption, Shamir’s Secret Sharing, and decentralized transcript storage on Arweave, with access mediated by Solana-based multi-signature wallets and program-derived addresses. Quantitative evaluations confirm that the proposed framework meets low-latency standards with sub-1.5 second one-way latency—significantly outperforming prior VoIP-based multilingual emergency systems, which typically report 3–5 second delays. Unlike conventional pipelines, it leverages asynchronous blockchain storage and optimized LLM-driven reconstruction to achieve high responsiveness without sacrificing Conceptual Precision, BLEU, or ROUGE accuracy.}, archive = {J_ACCESS}, author = {Rahman A. Rafi and Shakil Ahmed and Danush Venkateshperumal and Ashfaq Khokhar and Md Arifuzzaman and Akm Azad and Salem A. Alyami}, doi = {10.1109/ACCESS.2025.3600364}, journal = {IEEE Access}, pages = {150101-150122}, shortjournal = {IEEE Access}, title = {Multilingual low-latency emergency VoIP system using LLM for speech reconstruction and blockchain for secure data archiving}, volume = {13}, year = {2025}, }
• (2025). Reliable real-time communication for coach-assisted feedback systems in swimming. ACCESS, 13, 150091-150100. (www)

  @article{ , abstract = {Wireless communication in aquatic environments faces major challenges as the radio channel is temporarily unavailable due to submersion and the movement of swimmers. These interruptions can lead to sudden and prolonged packet loss, making real-time biofeedback difficult to achieve with standard communication protocols. This paper presents a real-time swimmer monitoring system built around a novel Block Selective Repeat (BSR) communication protocol specifically designed for environments with cyclic channel interruptions. BSR extends the traditional selective repeat model by introducing dynamic packet composition, cumulative acknowledgements with loss indicators and buffered retransmission. These features enable efficient and reliable transmission of sensor data, even when the connection is temporarily interrupted. The system was implemented using low-cost, commercially available Wi-Fi enabled microcontrollers and tested under real swimming conditions in four swimming styles. Experimental results show that BSR achieves 100% data recovery, with consistent low-latency performance across all swimming styles, including high-loss scenarios such as breaststroke and butterfly. The protocol also preserves the temporal structure of the movement data, enabling accurate recognition of movement patterns for real-time coaching and performance analysis. These findings demonstrate the suitability of BSR for biofeedback systems in swimming and other applications where intermittent wireless connectivity is a constraint.}, archive = {J_ACCESS}, author = {Anton Kos and Andrea Hernández Casillas and Sašo Tomažič and Anton Umek}, doi = {10.1109/ACCESS.2025.3602904}, journal = {IEEE Access}, pages = {150091-150100}, shortjournal = {IEEE Access}, title = {Reliable real-time communication for coach-assisted feedback systems in swimming}, volume = {13}, year = {2025}, }
• (2025). Learning software overtime estimation from experts‘ annotations: A greedy cross-validation-based machine learning approach. ACCESS, 13, 150068-150090. (www)

  @article{ , abstract = {Effective overtime planning in software projects remains a critical challenge due to the complex interplay between project constraints and human-centric decision-making. This study introduces a novel machine learning-based approach to software overtime planning that learns from project managers’ annotations. Unique datasets were constructed from six real-world software projects, annotated by 20 experienced project managers, and refined using a clustering-based outlier removal technique to ensure consistency and reliability. The study evaluates multiple regression models and proposes a Greedy Halving Grid Search-optimized Random Forest Regressor (gHGS-EFR) to estimate project managers’ satisfaction with overtime allocation plans. Empirical results demonstrate that the baseline RFR model achieves strong predictive performance (average R2 = 0.81), outperforming linear and kernel-based models and rivaling neural networks and gradient-boosting methods. The greedy halving grid search optimization method significantly enhances model accuracy and scalability, reducing MAE and RMSE by 14.5% and 16.4%, respectively, and improving R2 to 0.89. Compared to traditional hyperparameter tuning methods, gHGS offers superior computational efficiency, reducing wall-clock time by up to 70%. Moreover, the gHGS-RFR model maintains stable performance across increasing project complexity, highlighting its robustness and practical applicability. The SHAP interpretability analysis identified which project features most influence satisfaction scores and how their characteristics affect overtime estimation, offering actionable insights for project managers. This work represents the first application of machine learning to software overtime planning using expert-annotated data, bridging the gap between algorithmic planning and human judgment.}, archive = {J_ACCESS}, author = {Hammed A. Mojeed and Rafal Szlapczynski}, doi = {10.1109/ACCESS.2025.3601820}, journal = {IEEE Access}, pages = {150068-150090}, shortjournal = {IEEE Access}, title = {Learning software overtime estimation from experts‘ annotations: A greedy cross-validation-based machine learning approach}, volume = {13}, year = {2025}, }
• (2025). A CUDA-accelerated hybrid CNN-DNN approach for multi-class malware detection in IoT networks. ACCESS, 13, 150054-150067. (www)

  @article{ , abstract = {The Internet of Things (IoT) ecosystem has attracted widespread attention worldwide because of its profound commercial, economic, and social impact on the daily lives of human beings. Moreover, IoT devices generate large volumes of heterogeneous data, but participating nodes in the network are usually resource-constrained, which makes them attractive targets for cyber threats and attacks. With the rising complexity of modern attacks, it has become essential to develop scalable and efficient solutions that can handle large volumes of data with both speed and accuracy. In this context, GPU-accelerated computing provides a viable solution by offering massive parallel processing power. Using these capabilities, In this work, a CUDA-accelerated hybrid deep learning model is proposed that efficiently detects network attacks in IoT environments. The model incorporates an adaptive preprocessing and feature selection mechanism, distinguishing it from existing approaches. By integrating the strengths of convolutional neural networks for feature extraction and deep neural networks for classification, the proposed framework enhances detection accuracy, scalability, and overall performance. Malware samples are collected from the Kitsune dataset for experiments. The proposed scheme achieves performance with 98.41% precision and 98.56% recall. Compared to CPU-only training, the CUDA-enabled model achieved up to a 62% reduction in total training time. These results demonstrate the effectiveness and practical applicability of the CUDA-empowered hybrid model for real-time IoT malware detection.}, archive = {J_ACCESS}, author = {Hadi Nazari and Ashfaq Hussain Farooqi and Basit Raza and Shaharyar Kamal and Waqas Nawaz and Waseem Abbass}, doi = {10.1109/ACCESS.2025.3602723}, journal = {IEEE Access}, pages = {150054-150067}, shortjournal = {IEEE Access}, title = {A CUDA-accelerated hybrid CNN-DNN approach for multi-class malware detection in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Echo-based SAR imaging simulation for typical target assisted by ray tracing. ACCESS, 13, 150037-150053. (www)

  @article{ , abstract = {To achieve high-fidelity synthetic aperture radar (SAR) simulated images of typical target, we propose an echo-based SAR imaging simulation method assisted by ray tracing. The method incorporates multiple scattering mechanisms and establishes a backscattering model under the far-field plane wave assumption. A ray tracing algorithm is employed to capture the amplitude and phase of multiple backscattering components from numerous microfacets distributed across the target surface. The total backscattering field is then computed through vector superposition. Based on the principle of azimuthal synthetic aperture, we account for the real-time relative motion between the sensor and the target. A global phase correction is performed by leveraging the slant range history. This process transforms the local far-field scattering phase, which is referenced to the target modeling center, into a globally coherent scattering field. Then, the target’s global backscattering field is accurately reconstructed and serves as the raw echo signal. The range-Doppler (RD) imaging algorithm is subsequently applied to focus the simulated echo signal and generate the final SAR image. We select a typical aircraft carrier target for testing evaluation. The target is placed in the beam radiation scene and subjected to multi-angle rotation to produce corresponding multi-angle simulated SAR images. Qualitative and quantitative comparisons are conducted against both the RaySAR commercial software and real SAR image to verify the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Junjie Li and Ke Wu and Chuanxiang Cheng and Gaohao Zhu}, doi = {10.1109/ACCESS.2025.3603052}, journal = {IEEE Access}, pages = {150037-150053}, shortjournal = {IEEE Access}, title = {Echo-based SAR imaging simulation for typical target assisted by ray tracing}, volume = {13}, year = {2025}, }
• (2025). Estimation of the root mean square of the residuals of sine fitting in the presence of phase noise or jitter. ACCESS, 13, 150028-150036. (www)

  @article{ , abstract = {Fitting a sinusoidal model to sampled data is a fundamental operation in many engineering disciplines. Typical parameters estimated include amplitude, initial phase, and offset (three-parameter sine fitting), or additionally frequency (four-parameter sine fitting). The uncertainty in these estimates depends on the number of samples and the influence of non-ideal effects such as additive noise, oscillator phase noise, sampling jitter, and frequency errors. This work focuses on proposing an heuristically derived analytical expression for the standard deviation of the root mean square (RMS) of the residuals from three-parameter sine fitting in the presence of phase noise or sampling jitter. The expression is a function of the phase noise or jitter standard deviation, the number of samples, and the signal amplitude. This result aids engineers in selecting appropriate sample sizes and in reporting RMS values with confidence intervals. The formulation also applies directly to RMS estimation using the Discrete Fourier Transform. The analytical result is validated using Monte Carlo simulations.}, archive = {J_ACCESS}, author = {Francisco A. C. Alegria}, doi = {10.1109/ACCESS.2025.3601499}, journal = {IEEE Access}, pages = {150028-150036}, shortjournal = {IEEE Access}, title = {Estimation of the root mean square of the residuals of sine fitting in the presence of phase noise or jitter}, volume = {13}, year = {2025}, }
• (2025). Causal representation learning for predicting autoimmune disease progression from longitudinal multimodal clinical data. ACCESS, 13, 150010-150027. (www)

  @article{ , abstract = {Autoimmune diseases pose a persistent challenge to clinical decision-making due to their heterogeneous presentations, episodic flare-ups, and complex pathophysiological mechanisms. Existing deep learning models frequently rely on correlation-driven patterns, which often lack robustness and interpretability in real-world clinical settings. This study proposes a novel causal deep learning framework designed to model and predict the progression of autoimmune diseases using longitudinal multimodal clinical data. The proposed approach integrates a temporal transformer architecture with a structural causal model (SCM)-guided counterfactual inference module to capture latent disease mechanisms while mitigating spurious correlations introduced by observational confounders. The model is trained and evaluated on a real-world cohort consisting of patients diagnosed with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), incorporating electronic health records (EHRs), laboratory biomarkers, and immunological profiles. Empirical results demonstrate that the proposed framework outperforms conventional deep learning baselines in predicting disease transitions and flare-up events. In addition, interventional and counterfactual evaluation protocols are employed to validate the model’s causal consistency and interpretability. This work contributes to the advancement of clinically trustworthy AI systems by enabling robust, explainable, and data-efficient disease trajectory modeling in the autoimmune domain.}, archive = {J_ACCESS}, author = {Harshit Sharma and Simran Kaur}, doi = {10.1109/ACCESS.2025.3598276}, journal = {IEEE Access}, pages = {150010-150027}, shortjournal = {IEEE Access}, title = {Causal representation learning for predicting autoimmune disease progression from longitudinal multimodal clinical data}, volume = {13}, year = {2025}, }
• (2025). Measurement of selective attention in schoolchildren with cerebral palsy. ACCESS, 13, 150003-150009. (www)

  @article{ , abstract = {Deficits in executive function in children with cerebral palsy are accompanied by cognitive, behavioral, and communication disorders. Neuropsychological evaluations have been limited in this population because of the difficulty in communication and the lack of digitized tests that integrate augmented alternative communication (AAC) systems due to motor problems in children with cerebral palsy. This article describes the design and application of an eye tracking system to measure the level of selective attention (low, medium, or high) in school children with cerebral palsy. The software consists of three programs: eye tracking technology training, training evaluation, and selective attention testing. Twenty children with cerebral palsy participated in the study, all of whom completed the assessment (M = 8.75 years, SD = 1.80). Despite the additional cognitive load generated by the use of eye tracking, the results showed that 55% of the students had a low level of attention and 45% had normal attention. This demonstrates the potential of the system for monitoring attention levels in individuals with cerebral palsy.}, archive = {J_ACCESS}, author = {Omar Alvarado-Cando and Diana Pacheco and Diego Almeida-Galárraga and Johanna Pozo-Neira and Karen Cáceres-Benítez and Jose Javier Serrano Olmedo}, doi = {10.1109/ACCESS.2025.3602688}, journal = {IEEE Access}, pages = {150003-150009}, shortjournal = {IEEE Access}, title = {Measurement of selective attention in schoolchildren with cerebral palsy}, volume = {13}, year = {2025}, }
• (2025). ECSANet: Enhancement cross-aggregation statistical attention network for image style transfer. ACCESS, 13, 149991-150002. (www)

  @article{ , abstract = {Attention-based style transfer methods have made remarkable advancements in generating high-quality stylized images. However, they struggle to produce stylized images with consistent style distribution with real-style images while preserving the content structure. To this end, we propose a novel Enhancement Cross-Aggregation Statistical Attention Network (ECSANet) for image style transfer. Specifically, we design an enhancement cross-aggregation statistical attention (ECSA) module. It achieve can refine content feature and enhance style semantics by learning channel relevance and global dependency in addition to depict the captured style sematics into the content structure by a cross-aggregation statistical attention approach. Furthermore, we construct a statistical-aware skip connection (SSC) between the encoder-decoder. It can dynamically inject shallow information into the decoder, improving content retention and overall stylized quality. Extensive experiments have verified the effectiveness of our proposed ECSANet, surpassing previous state-of-the-art attention-based style transfer methods.}, archive = {J_ACCESS}, author = {Li Fang and Shenghao Huang and Jiawei Li}, doi = {10.1109/ACCESS.2025.3602281}, journal = {IEEE Access}, pages = {149991-150002}, shortjournal = {IEEE Access}, title = {ECSANet: Enhancement cross-aggregation statistical attention network for image style transfer}, volume = {13}, year = {2025}, }
• (2025). Optimized sequential agentic AI-guided trainable hybrid activation functions for solar irradiance forecasting. ACCESS, 13, 149976-149990. (www)

  @article{ , abstract = {Accurate solar irradiance forecasting is essential for seamlessly integrating renewable energy into the power grid. This study proposes a novel agentic AI framework for solar irradiance prediction, addressing the limitation of conventional static activation functions focusing on restrict adaptability and limited expressiveness when modelling non-linear relationships in time series data. To overcome these challenges, we designed a trainable hybrid activation function whose weights are dynamically optimised by the Adaptive Moment Estimation (Adam) controlled by the agentic AI based on the prior performance feedback. The methodology is rigorously validated using real-world datasets from Singapore and France, offering critical insights into how agentic AI optimally adjusts the weights of hybrid activation functions. For the Singapore dataset, the GRU with attention using ReLU activation achieved a MAE of 23.56, whereas the proposed agentic activation function attained a lower MAE of 22.38 reflecting an improvement of approximately 5%. Similarly, in the France dataset, the GRU with attention paired with Leaky-ReLU yielded an MAE of 26.47, while the agentic activation function achieved 25.41, indicating a performance gain of nearly 4%. In isolated scenarios, traditional activation functions demonstrated marginally superior performance (within 1%), underscoring the nuanced dependency between activation dynamics and model architecture in specific contexts. To ensure a thorough evaluation, three distinct error metrics were employed across four state-of-the-art forecasting models, providing a robust framework for assessing the effectiveness of the guided hybrid activation function. The results underscore the function’s adaptability to varying model architectures and diverse dataset characteristics while maintaining superior prediction accuracy and reliability. This research contributes a promising agentic AI-guided hybrid activation function, advancing the field of solar irradiance forecasting by demonstrating its impact on learning efficiency and accuracy across diverse forecasting models. The findings pave the way for more intelligent, adaptive approaches to renewable energy integration.}, archive = {J_ACCESS}, author = {Ngiap Tiam Koh and Anurag Sharma and Jianfang Xiao and Cheng Siong Chin and Chin Jun Xing and Wai Lok Woo}, doi = {10.1109/ACCESS.2025.3602978}, journal = {IEEE Access}, pages = {149976-149990}, shortjournal = {IEEE Access}, title = {Optimized sequential agentic AI-guided trainable hybrid activation functions for solar irradiance forecasting}, volume = {13}, year = {2025}, }
• (2025). Semi-static template matching for sports field estimation. ACCESS, 13, 149965-149975. (www)

  @article{ , abstract = {Unmanned air vehicle imaging enables a new way of collecting data for sports events. Spatio-temporal tracking data is one of the most used data formats for sports analytics and is substantially more compressed than video data. To extract spatio-temporal tracking data in videos from unmanned air vehicles, we need to track the players, the ball and need to transform the tracked players from the video into the physical world. We propose a novel method to address the challenge of field registration from aerial imaging with fields of variable size, such as football fields. In addition, we extend field registration for a video setting based on unmanned air vehicle imaging. First, we detect keypoints for different fields like tennis, football, or ultimate frisbee in a stand-alone keypoint regression network. After that, we compile different methods of keypoint matching and homography estimation. For football fields, we introduce a novel way of estimating the sizes of the fields with a gradient-based alternating optimization approach, where we alternate between matching keypoints and updating field-size parameters constrained by a Frobenius norm. The algorithm enables data collection of football fields without physically measuring the field dimensions with a ground truth tool, as the length and width of the fields is variable. To efficiently evaluate our method, we compile a new dataset that contains images from 60 different video clips of field sports games. We empirically show that our algorithms return time-consistent transformations by analyzing error graphs over different videos.}, archive = {J_ACCESS}, author = {Marc Schmid}, doi = {10.1109/ACCESS.2025.3601185}, journal = {IEEE Access}, pages = {149965-149975}, shortjournal = {IEEE Access}, title = {Semi-static template matching for sports field estimation}, volume = {13}, year = {2025}, }
• (2025). A MILP model for simultaneous optimal placement, sizing, and operation of PV distributed generation and D-STATCOMs in distribution systems. ACCESS, 13, 149947-149964. (www)

  @article{ , abstract = {The increasing penetration of photovoltaic distributed generation (PV-DG) and the growing demand for reactive power compensation in power distribution systems (PDS) require coordinated planning and operational strategies to improve system efficiency and stability. This paper presents a novel Mixed-Integer Linear Programming (MILP) model for the simultaneous optimal placement, sizing, and operation of both PV-DG units and Distribution Static Synchronous Compensators (D-STATCOMs) in radial PDS. The proposed model incorporates multi-period optimization, enabling the dynamic dispatch of active and reactive power from PV-DG units and D-STATCOMs based on hourly load profiles. In addition, it includes detailed modeling of network constraints, such as voltage limits, branch flow limits, and nodal power balance. The optimization problem is formulated in AMPL and solved using the CPLEX solver to ensure global optimality and computational efficiency. The model is validated on the IEEE 33-bus, 69-bus, and 136-bus test systems. The results show that the proposed coordinated planning and operation strategy achieves significant improvements, including reductions in active power losses of up to 68.63%, increases in voltage levels of up to 7.69%, and investment cost savings of up to 41.75%. The MILP-based model guarantees reproducibility and scalability, making it a robust and practical tool for modern distribution system planning.}, archive = {J_ACCESS}, author = {Luis A. Gallego Pareja and Oscar Gómez Carmona and Jesús M. López-Lezama}, doi = {10.1109/ACCESS.2025.3602002}, journal = {IEEE Access}, pages = {149947-149964}, shortjournal = {IEEE Access}, title = {A MILP model for simultaneous optimal placement, sizing, and operation of PV distributed generation and D-STATCOMs in distribution systems}, volume = {13}, year = {2025}, }
• (2025). YOLOv8-SCS: Improved object detection for autonomous driving under adverse weather conditions. ACCESS, 13, 149933-149946. (www)

  @article{ , abstract = {Adverse weather conditions significantly impact the performance of autonomous driving object detection systems, leading to reduced detection accuracy and an increased false detection rate. Limited annotated data further restricts performance improvement. Hence, improving detection performance under adverse weather conditions is a challenge that remains to be solved. In this paper, we develop an improved object detection model, named YOLOv8-SCS, by incorporating the Swin Transformer, Convolutional Block Attention (CBAM), and Spatial-Channel Decoupled Downsampling (SCDown) modules. YOLOv8-SCS aims to enhance feature extraction from objects and emphasize important features in the feature map, both of which are affected by adverse weather conditions, without compromising model efficiency for real-time applications. Experimental results verify that on the DAWN dataset, YOLOv8-SCS achieves performance improvements of 3.22%, 2.22%, 2.63%, 2.90%, and 0.72% in terms of Precision, Recall, F1-Score, mAP50, and mAP50-95 compared to the original YOLOv8. Furthermore, its lightweight variant, YOLOv8-SCS-light, also shows gains of 1.72%, 1.48%, 1.60%, 2.18%, and 0.4% in the same metrics, while enhancing model efficiency by reducing the number of parameters and GFLOPs, and increasing FPS. In addition, the generalization ability of the proposed model under clear weather conditions is verified using the BDD100K dataset.}, archive = {J_ACCESS}, author = {Younggyu Lee and Jinho Kang}, doi = {10.1109/ACCESS.2025.3602211}, journal = {IEEE Access}, pages = {149933-149946}, shortjournal = {IEEE Access}, title = {YOLOv8-SCS: Improved object detection for autonomous driving under adverse weather conditions}, volume = {13}, year = {2025}, }
• (2025). NM-TD3: A hybrid noise-driven TD3 algorithm with long-term reward propagation for mobile robot path planning. ACCESS, 13, 149921-149932. (www)

  @article{ , abstract = {Path planning is one of the core technologies enabling intelligent decision-making in autonomous navigation for mobile robots. However, existing reinforcement learning methods still face multiple challenges in complex environments, such as sparse rewards, insufficient exploration, slow convergence, and limited generalization capabilities. To address these issues, this paper proposes an improved version of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The algorithm incorporates an n-step return mechanism to enhance the modeling of long-term dependencies, and introduces a dynamic hybrid exploration strategy that combines Gaussian noise with Ornstein–Uhlenbeck (OU) noise to improve exploration stability and training efficiency. In addition, a shaped reward function is constructed by integrating several key factors, including distance to the target, heading alignment, obstacle avoidance, and time efficiency, to provide continuous feedback during the learning process. The proposed method is validated on the Gazebo simulation platform. Experimental simulation results show that, compared to the original algorithm, the improved version increases the success rate by 37% in complex static environments and by 28% in dynamic obstacle scenarios. The approach significantly accelerates training convergence and enhances policy generalization, effectively demonstrating its practicality for robotic path planning tasks.}, archive = {J_ACCESS}, author = {Xuqi Yang and Qinghui Wang and Jinfeng Li and Xiongwen Jiang}, doi = {10.1109/ACCESS.2025.3602286}, journal = {IEEE Access}, pages = {149921-149932}, shortjournal = {IEEE Access}, title = {NM-TD3: A hybrid noise-driven TD3 algorithm with long-term reward propagation for mobile robot path planning}, volume = {13}, year = {2025}, }
• (2025). An improved method for zero-shot semantic segmentation. ACCESS, 13, 149911-149920. (www)

  @article{ , abstract = {Zero-shot semantic segmentation continues to face challenges in effectively handling unseen object classes, despite its critical applications in medical imaging, autonomous driving, and aerial imagery. This paper introduces a hybrid approach that combines three key components: a Swin Transformer backbone, a Flash Masked Attention Transformer Decoder, and a Deformable Pixel Decoder, to enhance zero-shot semantic segmentation accuracy. The Swin Transformer backbone extracts hierarchical image features, the Flash Masked Attention Transformer Decoder refines feature extraction by focusing cross-attention on foreground regions, and the Deformable Pixel Decoder specializes in capturing small object features. Extensive evaluations on benchmark datasets demonstrate the method’s performance, achieving scores of 31.1 mIOU and 71.8 pAcc on ADE20K, 9.3 mIOU and 54.4 pAcc on ADE20K-Full, and 94.7 mIOU and 97.5 pAcc on Pascal VOC, significantly outperforming existing approaches. These results establish the effectiveness of the proposed method in advancing zero-shot semantic segmentation, particularly in scenarios involving diverse and previously unseen object classes.}, archive = {J_ACCESS}, author = {Kong Kuok Yong and Tan Fong Ang and Chin Soon Ku and Firdaus Sahran and Lip Yee Por}, doi = {10.1109/ACCESS.2025.3602481}, journal = {IEEE Access}, pages = {149911-149920}, shortjournal = {IEEE Access}, title = {An improved method for zero-shot semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Optimization of additive fibonacci generators based on primitive polynomials over GF(p). ACCESS, 13, 149901-149910. (www)

  @article{ , abstract = {This paper presents an approach to the modification of the additive Fibonacci generator by implementing it based on primitive polynomials over the field GF(p). The proposed modification addresses the inherent cryptographic instability of the classical additive Fibonacci generator and aims at its optimization. The modified additive Fibonacci generator (MAFG) introduced in this work achieves the maximum possible repetition period for the generated sequence. A critical drawback of generators constructed using primitive polynomials with odd values of p–namely, noncompliance with statistical requirements of the output sequence–has been eliminated. To address this issue, the original MAFG sequence with an odd p is combined via a logical XOR operation with a pseudorandom bit sequence exhibiting a uniform distribution of 0s and 1s. Statistical testing based on the NIST SP 800-22 methodology has demonstrated that this combination ensures maximum periodicity across all initial states and eliminates the occurrence of so-called “weak keys”. Extensive testing results confirm the high randomness quality of the output sequence and its suitability for cryptographic applications. Further optimization of such generators may focus on the selection and refinement of primitive polynomials for their implementation.}, archive = {J_ACCESS}, author = {Pawel Sawicki and Volodymyr Maksymovych and Oleh Harasymchuk and Serhii Lupenko and Mariia Shabatura and Katarzyna Marczak and Ruslan Shevchuk and Mikolaj Karpinski}, doi = {10.1109/ACCESS.2025.3600582}, journal = {IEEE Access}, pages = {149901-149910}, shortjournal = {IEEE Access}, title = {Optimization of additive fibonacci generators based on primitive polynomials over GF(p)}, volume = {13}, year = {2025}, }
• (2025). Convergence of iterative descent algorithms for LEO-PNT. ACCESS, 13, 149891-149900. (www)

  @article{ , abstract = {Positioning Navigation and Timing (PNT) from Low Earth Orbit (LEO) has been gaining momentum in the last couple of years. LEO-PNT can serve as an alternative to current Global Navigation Satellite Systems (GNSS) or as an addition to the current system using a multi-layer approach. The aim of launching satellites into a lower orbit is to mitigate the shortcomings of current GNSS. When moving towards a lower orbit, algorithmic challenges that relate to the initial point estimate for Iterative Descent (ID) algorithms arise. In this paper, we analyze the convergence behavior of ID algorithms, focusing on Steepest Descent, Gauss-Newton, Trust Region, and Levenberg-Marquardt, when performing positioning using only LEO satellites, starting from an initial estimate at the center of the Earth to emulate a cold start scenario. We employ a Monte-Carlo (MC) simulation and multiple proposed LEO-PNT constellations to verify the convergence behavior of the algorithms. Among the evaluated methods, Trust-Region converges most reliably. The commonly used Gauss-Newton method often fails. Moreover, Levenberg–Marquardt is more robust but does not reach a perfect convergence rate. While simple, Steepest Descent requires the most iterations.}, archive = {J_ACCESS}, author = {Wout Van Uytsel and Andres Maria Majorana and Thomas Janssen and Maarten Weyn}, doi = {10.1109/ACCESS.2025.3602499}, journal = {IEEE Access}, pages = {149891-149900}, shortjournal = {IEEE Access}, title = {Convergence of iterative descent algorithms for LEO-PNT}, volume = {13}, year = {2025}, }
• (2025). What’s going on in dark web question and answer forums: Topic diversity and linguistic characteristics. ACCESS, 13, 149880-149890. (www)

  @article{ , abstract = {The Dark Web hosts a variety of Q&A forums that facilitate discussions on topics ranging from technical guidance to illicit activities. This study investigates the linguistic and structural characteristics of three prominent Dark Web Q&A forum onion services—Onion1 (Deepweb Questions and Answers), Onion2 (Deep Answers), and Onion3 (Repostas Ocultas)—using a combination of topic modeling and quantitative text analysis. We employ Sklearn (TF-IDF) and Gensim (LDA) to extract and compare dominant topics, alongside metrics of lexical diversity, semantic diversity and syntactic complexity. Our findings reveal significant differences in topic diversity and linguistic patterns across the forums, with Onion1 exhibiting a focus on technical and financial discussions, Onion2 emphasizing community-driven and security-related topics, and Onion3 showing a narrower thematic scope influenced by its structured Q&A format and language-specific content. Statistical analyses, including Kruskal-Wallis tests and Dunn’s post-hoc comparisons, confirm these differences, highlighting the distinct communicative norms and user behaviors of each forum. The results underscore the importance of considering forum-specific characteristics when analyzing Dark Web communities and provide insights into the thematic and linguistic diversity of these platforms.}, archive = {J_ACCESS}, author = {Luis De-Marcos and Adrián Domínguez-Díaz and Zlatko Stapic}, doi = {10.1109/ACCESS.2025.3597975}, journal = {IEEE Access}, pages = {149880-149890}, shortjournal = {IEEE Access}, title = {What’s going on in dark web question and answer forums: Topic diversity and linguistic characteristics}, volume = {13}, year = {2025}, }
• (2025). Density detection method of three-dimensional tubular woven fabric based on fourier transform. ACCESS, 13, 149867-149879. (www)

  @article{ , abstract = {In the production of three - dimensional tubular woven fabric, online and real - time density detection is crucial. A low density can compromise the straightness rate, while a high density impacts the efficiency of the circular weaving machine and escalates business costs. In order to solve the problem of large image noise and high real-time requirement in the detection of the density of a hollow 3-dimensional tubular fabric, the frequency domain-based image density feature analysis algorithm is studied and a Fourier transform-based frequency domain feature enhancement algorithm is proposed. Initially, the Fourier transform is employed to convert the fabric image into the frequency domain. Subsequently, the autocorrelation algorithm is utilized to intensify the weft characteristics of the tubular woven fabric. Finally, the inverse Fourier transform constructed the weft spacing d in the spatial domain. Through experimental verification, it is determined that the optimal cut-off frequency scaling factor for low-pass filtering to eliminate noise is 0.02. When the number of detection iterations reaches four, the system can meet the accuracy requirements, with a detection period of approximately 420 ms. Through experimental comparisons with the boundary curve extreme point method and similar Fourier transform algorithms, the algorithm proposed in this paper demonstrates higher accuracy. Moreover, when the images are contaminated by noise, the method in this paper exhibits excellent robustness and stable output performance. This method has been verified and applied on actual production lines, where the detection efficiency and accuracy are greatly improved, enabling the automation of the detection process.}, archive = {J_ACCESS}, author = {Yiguan Shi and Xin Jin and Bolun Jing and Cong Li and Peng Qian and Chaojiang Li}, doi = {10.1109/ACCESS.2025.3601812}, journal = {IEEE Access}, pages = {149867-149879}, shortjournal = {IEEE Access}, title = {Density detection method of three-dimensional tubular woven fabric based on fourier transform}, volume = {13}, year = {2025}, }
• (2025). Power prediction in ground mobile agricultural robots: A context-aware deep learning framework. ACCESS, 13, 149853-149866. (www)

  @article{ , abstract = {Accurate power consumption forecasting is essential for energy-aware navigation and mission planning in mobile robots operating in agricultural environments. In this study, we present a comprehensive data-driven framework for predicting power usage, based on a dataset collected from 72 real-world trials performed in an orchard. These trials cover three distinct navigation patterns: rectangular paths, capsule-shaped circuits, and unstructured trajectories reflecting real-world field operations. The proposed forecasting approach integrates recent telemetry data with static mission parameters through an extended ensemble multi-head deep learning architecture. Several models are evaluated, including linear networks, convolutional neural networks, and recurrent architectures such as gated recurrent units and long short-term memory networks. Among these, the linear model consistently demonstrated the most stable cross-route performance, whereas the convolutional neural network achieved the lowest prediction error on structured routes. The results show that short-horizon forecasts maintained prediction errors below 20 percent for structured paths, whereas long-horizon predictions based solely on prior power measurements remained within 40 percent on average. These findings demonstrate the potential of lightweight, interpretable models for practical energy forecasting in agricultural mobile robotics. This work highlights the importance of combining static and dynamic features to improve power estimation and enhance robot autonomy in off-road environments.}, archive = {J_ACCESS}, author = {Ali Abedi and Ricardo Pinto de Castro and Reza Ehsani}, doi = {10.1109/ACCESS.2025.3602442}, journal = {IEEE Access}, pages = {149853-149866}, shortjournal = {IEEE Access}, title = {Power prediction in ground mobile agricultural robots: A context-aware deep learning framework}, volume = {13}, year = {2025}, }
• (2025). ActionSync video transformation: Automated object removal and responsive effects in motion videos using hybrid CNN and GRU. ACCESS, 13, 149834-149852. (www)

  @article{ , abstract = {Large data volumes, dynamic scenes, and intricate object motions make video analysis extremely difficult. Traditional methods depend on human-crafted features that are not scalable. This work develops a deep neural network-powered automated and controllable video transformation system. The main technique consists of an automated video transformation pipeline driven by spatial-temporal neural network components that are systematically connected. A 16-layer Convolutional Neural Network (CNN) encoder first extracts hierarchical visual features from individual frames to achieve spatial understanding. The CNN encodings are fed into a 512-unit Gated Recurrent Unit (GRU) sequencer, which models long-range sequential dynamics of object motions and interactions spanning thousands of frames to gain temporal context. Subsequently, an attentional transformer integrates the individual strengths of the CNN and GRU into unified space-time representations that reflect video content, object relationships, and interactions over both spatial and temporal dimensions simultaneously. The context-aware representations inform a specialized controller module, which deliberately adjusts backgrounds and object foreground layers based on their modeled connections. Finally, a flexible effects renderer composites the transformed backgrounds and adjusted objects into novel video sequences with effects synchronized to original timelines. Evaluations on a large 51-category video benchmark demonstrate responsive object removal and background substitution effects with strong system accuracy. The framework achieves high precision, recall, and performance on error metrics. The system demonstrates notable computational efficiency, processing 720p video clips at an average of 0.48 seconds per frame on an NVIDIA RTX 2070 GPU, highlighting its potential for near real-time applications. Ablations confirm that the fused CNN, GRU, and transformer components enable effective context modeling for deliberate video manipulations aligned to the original footage. Both quantitative and qualitative outcomes evidence the system’s capacity for automated, adaptive effects generation via joint spatio-temporal reasoning.}, archive = {J_ACCESS}, author = {Muhammad Asif Habib and Umar Raza and Sohail Jabbar and Muhammad Farhan and Farhan Ullah}, doi = {10.1109/ACCESS.2025.3600178}, journal = {IEEE Access}, pages = {149834-149852}, shortjournal = {IEEE Access}, title = {ActionSync video transformation: Automated object removal and responsive effects in motion videos using hybrid CNN and GRU}, volume = {13}, year = {2025}, }
• (2025). A knowledge-driven approach to threat validation and security reasoning in modular systems. ACCESS, 13, 149817-149833. (www)

  @article{ , abstract = {Modular systems are increasingly employed in critical application domains such as healthcare, smart cities, and Industry 4.0 platforms, where dynamic integration of components poses substantial challenges in ensuring consistent and secure operation. Validating system configurations against known vulnerabilities and threats requires formal, scalable, and explainable approaches. This paper presents a knowledge-driven framework designed to support the validation of modular systems with respect to cybersecurity threats and consistency constraints. Our approach leverages domain-specific ontologies built from well-established threat and vulnerability taxonomies and encodes inference rules to automatically detect potential threats and recommend mitigation strategies. The framework, which includes a reasoning engine and a user-friendly graphical interface providing transparent and traceable explanations for each identified threat, is applied to a modular platform for privacy-preserving, decentralized processing of health data across European institutions. While this composable architecture enables multiple stakeholders to develop, deploy, and maintain specialised components fostering scalability and flexibility, it also introduces critical risks related to architectural coherence and security enforcement. In this context, our framework ensures a human-interpretable assessment of the system’s security posture, even in the presence of heterogeneous technologies and policies.}, archive = {J_ACCESS}, author = {Laura Pandolfo and Giorgio Corona and Dario Guidotti and Luca Pulina}, doi = {10.1109/ACCESS.2025.3602292}, journal = {IEEE Access}, pages = {149817-149833}, shortjournal = {IEEE Access}, title = {A knowledge-driven approach to threat validation and security reasoning in modular systems}, volume = {13}, year = {2025}, }
• (2025). An explainable deep learning for data-driven turbulence model feature discovery. ACCESS, 13, 149801-149816. (www)

  @article{ , abstract = {This work presents an explainable deep learning for turbulence modelling feature discovery in several flow configurations where conventional approaches perform poorly. The proposed neural network-based turbulence model predicts the Reynolds stress based on the mean flow quantities. Furthermore, the neural network model is enriched with SHAPley Additive Explanations (SHAP) to complement the predictive power of the neural network with explainability. Our study demonstrates that the proposed model surpasses the benchmark performance of the conventional turbulence model and tensor-based neural network on the flow in a square duct and the flow over periodic hills. Most importantly, we studied the effect of input feature selection on the model’s accuracy and conducted an importance analysis using SHAP, which corroborated the findings regarding the effect of input features in our study. The findings from explainability also underscore the significance of variables less commonly utilized in traditional turbulence modeling, such as pressure gradient and Reynolds number. These findings underscore the potential of combining the proposed network with explainability to glean insights for the development of future turbulence models. Furthermore, this study demonstrates the neural network capability that can deliver exceptional accuracy, combined with SHAP as an explainable artificial intelligence method, serving as an efficient tool for feature analysis on specific flow cases that can be implemented in various fluid dynamics problems. While the model shows excellent predictive performance, it is important to note that its reliability is limited to the specific flow cases it was trained on.}, archive = {J_ACCESS}, author = {Muhammad Ridho Alhafiz and Lavi Rizki Zuhal and Duong Viet Dung and Pramudita Satria Palar}, doi = {10.1109/ACCESS.2025.3602021}, journal = {IEEE Access}, pages = {149801-149816}, shortjournal = {IEEE Access}, title = {An explainable deep learning for data-driven turbulence model feature discovery}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven framework for robust underwater image enhancement. ACCESS, 13, 149789-149800. (www)

  @article{ , abstract = {Underwater imaging poses persistent challenges such as color distortion, low contrast, and image noise, primarily due to light absorption and scattering in aquatic environments. This paper proposes a robust image enhancement pipeline composed of traditional image processing techniques—white balance, CLAHE, wavelet denoising, guided filtering, and Multi-Scale Retinex. To improve adaptability under diverse underwater conditions, reinforcement learning (RL) is employed to automatically optimize critical enhancement parameters, guided by no-reference image quality metrics (UIQM and UCIQE). This solution balances perceptual performance and computational efficiency while remaining interpretable and lightweight. Experimental evaluation on the UIEB dataset demonstrates that the RL-optimized pipeline outperforms fixed-parameter baselines and achieves consistent improvements in perceptual quality. The framework is well-suited for real-time and resource-constrained underwater imaging applications.}, archive = {J_ACCESS}, author = {P. Shobha}, doi = {10.1109/ACCESS.2025.3600666}, journal = {IEEE Access}, pages = {149789-149800}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven framework for robust underwater image enhancement}, volume = {13}, year = {2025}, }
• (2025). Improving adaptability in optimization-based decision support systems through large language models. ACCESS, 13, 149777-149788. (www)

  @article{ , abstract = {Since the 1950s, optimization-based Decision Support Systems have emerged as one of the most impactful applications of Operations Research, replacing human-based planning across a wide range of industries. These systems have greatly improved decision-making in terms of efficiency, accuracy, and scalability. However, they also come with a significant limitation: a lack of flexibility and adaptability. Typically, the algorithms used in these systems are predefined during the design phase, following a thorough-and often time-consuming-requirements analysis. They are then hard-coded into the system, making them well-suited for specific, well-understood scenarios. However, this rigidity makes it difficult for such systems to adapt to unforeseen changes or evolving operational needs. As a result, expert intervention, re-analysis, or even a complete system redesign may be required-often at considerable time and cost. This paper explores how Large Language Models (LLMs) can be integrated with traditional optimization algorithms to address this key limitation-lack of flexibility-while preserving their core strengths: speed and solution quality. The core idea is to leverage LLMs to interpret natural language instructions, reconfigure algorithmic components, support preference-based decision-making, and explain the rationale behind their choices. Computational results on a “rich” Vehicle Routing Problem (VRP) setting-a class of VRPs that incorporate multiple real-world constraints and complexities commonly encountered in last-mile distribution-demonstrate the potential of this hybrid approach.}, archive = {J_ACCESS}, author = {Gianpaolo Ghiani and Emanuele Manni and Sandro Zacchino}, doi = {10.1109/ACCESS.2025.3601518}, journal = {IEEE Access}, pages = {149777-149788}, shortjournal = {IEEE Access}, title = {Improving adaptability in optimization-based decision support systems through large language models}, volume = {13}, year = {2025}, }
• (2025). DCSCSA U-net: A deeper and compact spatial-channel synergistic attention U-net for scar segmentation and detection. ACCESS, 13, 149765-149776. (www)

  @article{ , abstract = {Scars are common outcomes of the skin wound healing process and have significant impacts on patients’ appearance and physiological functions; thus, accurate segmentation and detection are of great significance. Currently, scar assessment is mainly performed by visual inspection, which lacks precision. In this study, a segmentation network based on an encoder-decoder architecture, namely the Deeper and Compact Spatial-Channel Synergistic Attention U-Net (DCSCSA U-Net), is proposed for automatic segmentation and detection of scars. The proposed model introduces a Spatial-Channel Synergistic Attention (SCSA) module based on the DCSAU-Net architecture. By parallelly modeling spatial and channel dependencies and integrating them cooperatively, the module enhances the representation of critical lesion regions and achieves efficient multi-scale feature integration. Five-fold cross-validation experiments were conducted on both the ISIC 2018 and locally collected HPSH datasets, and superior IoU and DSC scores were achieved compared to existing benchmark models, reaching $0.832 \pm 0.29$ and $0.879 \pm 0.28$ (ISIC 2018), and $0.541 \pm 0.23$ and $0.662 \pm 0.25$ (HPSH), respectively. Furthermore, due to the limited sample size of the HPSH dataset, a transfer learning strategy was adopted. After pre-training on ISIC 2018 and transferring to HPSH, IoU and DSC were further improved by 9.4% and 6.6%, reaching $0.635 \pm 0.18$ and $0.728 \pm 0.24$ , respectively. In summary, DCSCSA U-Net demonstrates excellent generalization and segmentation performance, providing an effective technical solution for automatic scar and medical image segmentation and detection.}, archive = {J_ACCESS}, author = {Jun Shen and Jianhua Qin and Guibin Chen and Yuhuan Cui and Gang Li and Ying Miu}, doi = {10.1109/ACCESS.2025.3602064}, journal = {IEEE Access}, pages = {149765-149776}, shortjournal = {IEEE Access}, title = {DCSCSA U-net: A deeper and compact spatial-channel synergistic attention U-net for scar segmentation and detection}, volume = {13}, year = {2025}, }
• (2025). Explainable AI framework using XGBoost with SHAP and LIME for multi-scale household energy forecasting. ACCESS, 13, 149750-149764. (www)

  @article{ , abstract = {Forecasting energy consumption is critical for maximizing resource utilization, lowering operational costs, and increasing sustainability. Traditional machine learning models often function as black boxes, which complicates the understanding of decision-making processes. This research utilizes Explainable Artificial Intelligence (XAI) methodologies to improve the interpretability of Extreme Gradient Boosting (XGBoost) to effectively predict energy consumption patterns. Shapley additive explanations (SHAP) is applied to reveal the significance of both global and local features, thereby clarifying how various factors of energy consumption influence the forecasts. Furthermore, Local Interpretable Model-Agnostic Explanations (LIME) contribute to model interpretability by providing a detailed examination of individual predictions. The proposed model identifies key determinants affecting energy usage through Permutation Feature Importance (PFI), while Partial Dependence Plots (PDPs) illustrate the relationships between input variables and the model’s predictions. Additionally, Holt-Winters Exponential Smoothing is incorporated as a time-series decomposition method to investigate both short and long-term trends to predict energy consumption across various time intervals: hourly, daily, weekly, monthly, quarterly, as well as at the appliance level. This approach achieves a high R2 value of 0.9999 and low RMSE values of 0.00344. Comparisons with baseline models demonstrate the superior performance of the proposed method. By integrating Holt-Winters + XGBoost with XAI (HW+XGB+XAI) techniques, this study enables AI-driven energy management and home energy optimization, ensuring both interpretability and trust.}, archive = {J_ACCESS}, author = {B. Devanathan and K. Jnana Varshitha and L. Pavan Kumar and S. A. Lakshmanan and N. Krishna Prakash}, doi = {10.1109/ACCESS.2025.3602673}, journal = {IEEE Access}, pages = {149750-149764}, shortjournal = {IEEE Access}, title = {Explainable AI framework using XGBoost with SHAP and LIME for multi-scale household energy forecasting}, volume = {13}, year = {2025}, }
• (2025). Bi-linear homogeneity enforced calibration for pipelined ADCs. ACCESS, 13, 149734-149749. (www)

  @article{ , abstract = {Pipelined analog-to-digital converters (ADCs) are key enablers in many state-of-the-art signal processing systems with high sampling rates. In addition to high sampling rates, such systems often demand a high linearity. To meet these challenging linearity requirements, ADC calibration techniques were heavily investigated throughout the past decades. One limitation in ADC calibration is the need for a precisely known test signal. In our previous work, we proposed the homogeneity enforced calibration (HEC) approach, which circumvents this need by consecutively feeding a test signal and a scaled version of it into the ADC. The calibration itself is performed using only the corresponding output samples, such that the test signal can remain unknown. On the downside, the HEC approach requires to accurately scale the test signal, impeding an on-chip implementation. In this work, we provide a thorough analysis of the HEC approach, including limitations such as the effects of an inaccurately scaled test signal. Furthermore, the bi-linear homogeneity enforced calibration (BL-HEC) approach is introduced and suggested to account for an inaccurate scaling and, therefore, to facilitate an on-chip implementation. In addition, a comprehensive stability analysis of the BL-HEC approach is carried out. Finally, we verify our concept with behavioral Matlab simulations and measurements conducted on 24 integrated ADCs.}, archive = {J_ACCESS}, author = {Matthias Wagner and Oliver Lang and Esmaeil Kavousi Ghafi and Arianit Preniqi and Andreas Schwarz and Mario Huemer}, doi = {10.1109/ACCESS.2025.3602293}, journal = {IEEE Access}, pages = {149734-149749}, shortjournal = {IEEE Access}, title = {Bi-linear homogeneity enforced calibration for pipelined ADCs}, volume = {13}, year = {2025}, }
• (2025). QFS-MSGRU: Quantum feature selection approach integrate with multi-scale GRU networks for robust parkinson’s disease prediction. ACCESS, 13, 149719-149733. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a neurodegenerative disorder, and early diagnosis is a critical challenge due to the complexity of symptoms and heterogeneity of speech-based biomarkers. Traditional machine learning (ML) models are not always effective in dealing with high-dimensional feature spaces, causing poor quality classification accuracy. Overcoming such challenges, we proposed the QFS-MSGRU Model, a deep learning framework that combines Quantum-inspired Feature Selection (QFS) and Multi-Scale Gated Recurrent Units (MSGRU) with self-attention to facilitate PD prediction. The method starts with data preprocessing stages of feature normalization and class balancing with SMOTE. A quantum-inspired feature selection technique employs the most discriminative features while maintaining fundamental information without a significant loss of dimension. The classification model employs a multi-scale GRU to learn temporal relationships at multiple time scales, and a self-attention mechanism facilitates long-range feature interactions. The experimental results show that QFS-MSGRU achieves 96.46% accuracy, 97.30% precision, 95.58% recall, F1-Score 96.43% and an AUC score of 99.06%, significantly outperforming baseline techniques. This work introduces a novel and efficient Deep Learning (DL) model for PD prediction, showing enhanced classification performance through quantum inspired feature selection, multi-scale recurrent modeling, and attention mechanisms. The results confirm its real-world potential for clinical applications, facilitating improved early diagnosis and automated screening systems for neurodegenerative diseases.}, archive = {J_ACCESS}, author = {Naveed Ahmad and Mariam Akbar and Eman H. Alkhammash and Mona M. Jamjoom}, doi = {10.1109/ACCESS.2025.3600685}, journal = {IEEE Access}, pages = {149719-149733}, shortjournal = {IEEE Access}, title = {QFS-MSGRU: Quantum feature selection approach integrate with multi-scale GRU networks for robust parkinson’s disease prediction}, volume = {13}, year = {2025}, }
• (2025). Review on millimeter wave propagation through sand and dust Storm—Impact of humidity. ACCESS, 13, 149695-149718. (www)

  @article{ , abstract = {In this paper, sand and dust storm characteristics and attenuation prediction methods were reviewed and summarized comprehensively. The impact of humidity on the attenuation prediction process is focally discussed while highlighting the need to incorporate it in the modeling process for accurate attenuation prediction. This review is driven by the need to develop precise prediction models that account for the impact of meteorological factors, particularly humidity, on millimeter-wave attenuation during dust storms. By addressing these shortcomings, the review seeks to improve the design of resilient communication systems to support the ultra-reliable 5G and 6G networks in challenging environments. A critique of the previous studies that highlight the contributions and drawbacks is presented in the paper. This is followed by a taxonomy of models based on their prediction concepts and the timeline of developments. Furthermore, open issues and challenges for research were presented. The review study found that existing theoretical models have failed to predict measured attenuation accurately, often deviating significantly. Most of the theoretical and analytical approaches for estimating dust storm attenuation are found to neglect the critical influence of humidity. Few empirical and machine-learning models proposed recently, have demonstrated closer alignment with measurements after considering humidity and other metrological parameters.}, archive = {J_ACCESS}, author = {E. I. Eltahir and Md. Rafiqul Islam and Elfatih A. A. Elsheikh and Alhareth Zyoud and Aisha H. Abdalla and Mohamed Hadi Habaebi and Sharul Kamal Abdul Rahim and Mosab Hamdan}, doi = {10.1109/ACCESS.2025.3599995}, journal = {IEEE Access}, pages = {149695-149718}, shortjournal = {IEEE Access}, title = {Review on millimeter wave propagation through sand and dust Storm—Impact of humidity}, volume = {13}, year = {2025}, }
• (2025). Transforming applied behavior analysis therapy: An internet of things-guided, retrieval-augmented large language model framework. ACCESS, 13, 149679-149694. (www)

  @article{ , abstract = {We propose ABA-RAG, a retrieval-augmented generation (RAG) framework specifically tailored for applied behavior analysis (ABA) interventions, which integrates real-time emotional and behavioral data from Internet-of-Things (IoT) wearable devices. In ABA-RAG, we systematically retrieve semantically relevant prompts from a structured ABA task repository using embedding-based semantic search and dynamically construct context-aware prompts by incorporating IoT-derived predictions of learners’ emotional and behavioral states. To efficiently adapt large language model (LLM) to ABA contexts, we evaluate and compare several low-rank fine-tuning methods—including Low-Rank Adaptation (LoRA), Butterfly Orthogonal Fine-Tuning (BOFT), and LoRA with Gradient Approximation (LoRA-GA). Among these techniques, LoRA-GA demonstrates the best balance between computational efficiency and generation quality, making it particularly suitable for resource-constrained environments. Comprehensive experiments, validated through rigorous quantitative metrics and expert ABA practitioner evaluations, demonstrate that ABA-RAG significantly reduces manual workload while enhancing the precision, contextual relevance, and clinical utility of generated ABA tasks. Integrated into a practical web-based system, ABA-RAG provides ABA professionals with a scalable and real-time platform to generate individualized interventions.}, archive = {J_ACCESS}, author = {Haomin Qi and Sin Chung-Ho and Rosanna Yuen-Yan Chan and Chun Man Victor Wong}, doi = {10.1109/ACCESS.2025.3600316}, journal = {IEEE Access}, pages = {149679-149694}, shortjournal = {IEEE Access}, title = {Transforming applied behavior analysis therapy: An internet of things-guided, retrieval-augmented large language model framework}, volume = {13}, year = {2025}, }
• (2025). Explainable document classification via concept whitening and stable graph patterns. ACCESS, 13, 149657-149678. (www)

  @article{ , abstract = {This paper proposes a novel explainable document classification framework that integrates Concept Whitening (CW) with graph concepts that are derived from stable graph patterns, and extracted via methods based on Formal Concept Analysis (FCA) and pattern structures. Document graphs are constructed using Abstract Meaning Representation (AMR) graphs, from which graph concepts are extracted and aligned with the latent space axes of Graph Neural Networks (GNNs) using CW. We investigate four types of graph concepts for their effect on concept alignment: frequent subgraphs, graph pattern concepts, filtered equivalence classes, and closed subgraphs. A novel filtration mechanism based on support, along with a custom penalty metric, is proposed to refine graph concepts for maximizing concept alignment. Experiments on the 10 Newsgroups and BBC Sport datasets show that our document graphs effectively capture both structural and semantic information, thereby supporting competitive classification performance across multiple GNN model architectures and configurations. For the 10 Newsgroups dataset, GNN models equipped with a CW module show an average increase of 0.7599 in the macro-averaged F1 score of the Concept Alignment Performance (CAP) metric, with an average drop of only 0.0025 in the document classification macro-averaged F1 score. Similarly, on the BBC Sport dataset, the average CAP improvement is 0.6998, with an average drop of 0.0894 in document classification performance. Additionally, concept gradient importance analyses and concept similarity heatmaps provide insights into the interpretability and structural separability of the GNN’s latent representations, achieved using CW.}, archive = {J_ACCESS}, author = {Eric George Parakal and Sergei O. Kuznetsov and Ilya Makarov and Nikita Severin}, doi = {10.1109/ACCESS.2025.3602004}, journal = {IEEE Access}, pages = {149657-149678}, shortjournal = {IEEE Access}, title = {Explainable document classification via concept whitening and stable graph patterns}, volume = {13}, year = {2025}, }
• (2025). UResNet-based enhancement of underwater images through variational contrast and saturation. ACCESS, 13, 149637-149656. (www)

  @article{ , abstract = {Underwater imaging is an ever trending and evolving field in which the images obtained are subjected to enhancement to identify the critical features of the underwater world. Various enhancement approaches have evolved and certain issues have been identified in these techniques leading to a new technique. Generally, underwater images suffer from blurriness, color distortion, light absorption and scattering in water, low contrast issues which affect the overall clarity and visual appeal of the image. Our research demonstrates that hybrid preprocessing when combined with advanced deep learning models such as Underwater ResNet (UResNet) give high quality and visually appealing images. Three model variants have been explored in this work, the standard UResNet, UResNet augmented with a Sobel filter, and UResNet enhanced with a squeeze-and-excitation (SE) block. The models have employed on the EUVP (Enhancing Underwater Visual Perception) dataset. The core challenges such as color distortion, low contrast, and poor saturation have been overcome by the hybrid preprocessing pipeline, which combines Underwater White Balance (UWB) and Variational Contrast and Saturation Enhancement (VCSE) techniques that corrects the color imbalances, enhances the contrast, and amplifies the saturation before deep learning interference. The pre-processed images were given to all modified UResNet models and it was found that all models outperform the baseline model in both qualitative and quantitative evaluations. Among the three models that were modified, the SE-integrated model consistently delivered superior performance with all the metrics, highlighting significant improvements in color fidelity, contrast, edge definition, and overall image clarity. These results show the effectiveness of combining hybrid preprocessing with structure-aware deep learning architectures to improve underwater image quality and adaptability across diverse marine environments.}, archive = {J_ACCESS}, author = {Aditi Jain and Shivam and S. Angelin Beulah and M. Sivagami}, doi = {10.1109/ACCESS.2025.3591362}, journal = {IEEE Access}, pages = {149637-149656}, shortjournal = {IEEE Access}, title = {UResNet-based enhancement of underwater images through variational contrast and saturation}, volume = {13}, year = {2025}, }
• (2025). A machine learning framework for fire risk prediction with response and proximity insights. ACCESS, 13, 149620-149636. (www)

  @article{ , abstract = {Advances in artificial intelligence (AI) and machine learning (ML) have significantly enhanced fire risk assessment by enabling predictive analytics, real-time decision support, and optimized emergency response. Accurate fire risk assessments are crucial for prioritizing high-risk zones and optimizing resource deployment to minimize damage and enhance safety. In this work, we introduce novel fire risk models and propose a comprehensive ML-based framework for fire risk prediction that supports data-driven decision-making for fire and emergency response services. Our models incorporate response performance and service proximity to assess the impact of incidents more effectively within a city. The proposed framework provides an end-to-end ML pipeline that integrates diverse data sources to construct a dataset, compute risk scores, analyze key features, and formulate fire risk prediction as a regression problem. Additionally, it evaluates multiple regression models to analyze risk variations at both the incident and neighborhood levels. Experimental results demonstrate that our proposed models achieve a high degree of alignment between predicted and actual risk scores with minimal error. This framework captures valuable spatial risk patterns and can be used as a reliable tool for fire risk assessment, resource allocation, response strategy improvement, and urban safety planning.}, archive = {J_ACCESS}, author = {Dilli Prasad Sharma and Nasim Beigi-Mohammadi and Praveen Soni and Rob Madro and Phil Emmenegger and Carlos Tobar and Jeff Li and Alberto Leon-Garcia}, doi = {10.1109/ACCESS.2025.3602259}, journal = {IEEE Access}, pages = {149620-149636}, shortjournal = {IEEE Access}, title = {A machine learning framework for fire risk prediction with response and proximity insights}, volume = {13}, year = {2025}, }
• (2025). Leveraging hyperspectral data in cognitive environments. ACCESS, 13, 149604-149619. (www)

  @article{ , abstract = {Hyperspectral Imaging (HSI) captures data across multiple wavelengths of light, including visible and non-visible spectra (e.g., infrared or ultraviolet). It is usually used to analyze the composition, structure, and condition of objects of interest in a given environment through the spectral signature that, for a given material, represents its unique reflectance pattern of electromagnetic radiation across different wavelengths. Cognitive Environments (CEs) are intelligent systems designed to sense, interpret, and respond to context information within a physical space, enhancing user interactions through personalized and adaptive services. A key component in these environments is the ability to acquire and process rich and multidimensional data about dwellers and the environment itself, which is crucial for understanding and optimizing important aspects like comfort, safety, and users’ overall experience. HSI can be effectively exploited for retrieving rich contextual information from the environment in a non-invasive way. In particular, the ability to detect human skin and identify textile fabrics provides valuable insight into the clothing and physical state of a person, which in turn can be used to infer their activities. For example, distinguishing between exposed skin and covered areas can help estimate the type of clothing worn, offering clues about whether a person is exercising, resting, or engaged in specific tasks. In this paper, we propose an HSI-based framework for extracting representative spectral signatures of materials, with a focus on the recognition of human skin and textile fabric. A key strength of the approach lies in its use of standard and reproducible algorithms that operate directly on the observed scene, without requiring spectral references from controlled environments. The framework is validated using some publicly available hyperspectral datasets acquired with different cameras under various environmental conditions. Experimental results demonstrate that the extracted spectral signatures are robust and suitable for reliable matching and target detection despite the heterogeneity of the considered datasets. We also discuss key insights gained from the application of the framework on real data.}, archive = {J_ACCESS}, author = {Massimo Micieli and Franco Cicirelli and Antonio Guerrieri and Luigi Rizzo and Andrea Vinci and Paolo Zicari}, doi = {10.1109/ACCESS.2025.3601840}, journal = {IEEE Access}, pages = {149604-149619}, shortjournal = {IEEE Access}, title = {Leveraging hyperspectral data in cognitive environments}, volume = {13}, year = {2025}, }
• (2025). Modeling and simulation of pedestrian behavior in traffic environment: A systematic review of approaches and software. ACCESS, 13, 149576-149603. (www)

  @article{ , abstract = {Pedestrian behavior modeling and simulation play a critical role in traffic safety, urban planning, and transportation system optimization. In traffic environments, these models are used to predict and analyze pedestrian movements and interactions with surrounding infrastructure. Such simulations are essential for improving safety, guiding infrastructure design, and supporting crowd management strategies. This review systematically examines the major approaches and software tools for modeling and simulating pedestrian dynamics in traffic environments. Pedestrian behavior models are classified into theoretical, data-driven, and hybrid approaches, with hybrid models integrating the strengths of both for more comprehensive analysis. The review further evaluates the capabilities and limitations of current simulation platforms. Findings highlight the growing significance of agent-based modeling, along with the integration of real-time simulation, machine learning, and big data analytics. Overall, the review provides insights into the current state of pedestrian behavior simulation and offers guidance on model selection and software choice for both research and applied contexts.}, archive = {J_ACCESS}, author = {Pallavi Zambare and Ying Liu}, doi = {10.1109/ACCESS.2025.3602471}, journal = {IEEE Access}, pages = {149576-149603}, shortjournal = {IEEE Access}, title = {Modeling and simulation of pedestrian behavior in traffic environment: A systematic review of approaches and software}, volume = {13}, year = {2025}, }
• (2025). Gap waveguide-based MMIC packaging solutions for compact RF front-end modules at 100 GHz. ACCESS, 13, 149567-149575. (www)

  @article{ , abstract = {This paper presents two innovative packaging techniques for Monolithic Microwave Integrated Circuits (MMICs) designed for multilayer waveguide-based antennas operating near 100 GHz. The first technique involves a transition from a high-permittivity Gallium Arsenide (GaAs) MMIC to a rectangular waveguide using conventional bondwires as the coupling structure within the W-band. This approach enables seamless integration of any off-the-shelf RF chip into waveguide antenna modules without modifying the ground-signal-ground (GSG) pads or adding passive transition substrates. An electromagnetic bandgap (EBG) structure, implemented using metallic pins, effectively suppresses unwanted field propagation. Measurements of a back-to-back (B2B) prototype indicate a reflection coefficient below -10 dB and an average insertion loss of 0.12 dB for a single transition across a bandwidth from 90 to 97 GHz (10.5% relative bandwidth). The second technique introduces a contactless vertical transition from a high-permittivity Alumina (Al2O3) based microstrip line to a waveguide, targeting F-band applications. This proposed concept employs a patch-shaped probe placed on the substrate to couple electromagnetic waves to an H-shaped waveguide aperture positioned above the probe. Experimental results demonstrate a reflection coefficient below -10 dB and an average insertion loss of 0.17 dB over a frequency range of 102 to 120 GHz (15.3% relative bandwidth). In addition, the impact of manufacturing and assembly tolerances on the performance of both transitions is analyzed.}, archive = {J_ACCESS}, author = {Juan-Luis Albadalejo Lijarcio and Abbas Vosoogh and Thomas Emanuelsson and Jian Yang and Ashraf Uz Zaman}, doi = {10.1109/ACCESS.2025.3602517}, journal = {IEEE Access}, pages = {149567-149575}, shortjournal = {IEEE Access}, title = {Gap waveguide-based MMIC packaging solutions for compact RF front-end modules at 100 GHz}, volume = {13}, year = {2025}, }
• (2025). Enhancing sleep stage classification with single channel EEG: Feature extraction and random forest-XGBoost model. ACCESS, 13, 149554-149566. (www)

  @article{ , abstract = {Sleep stage classification, essential for diagnosing sleep disorders, monitoring neurological health, and advancing cognitive neuroscience. Traditionally involves manual scoring of polysomnographic (PSG) recordings, which is time-consuming and requires expert knowledge. However, recent machine learning advancements have enabled automated sleep stage classification using single channel electroencephalographic (EEG) signals. This study focuses on classifying sleep stages with single Channel EEG using an ensemble model combining Random Forest (RF) and XGBoost, achieving high accuracy across five sleep stages: Wake (W), N1, N2, N3, and REM (R). The RFXGBoost model demonstrated robust performance with accuracies of 97.3% for W, 98.6% for N2, 97.5% for N3, and 97.5% for R. However, the model showed lower accuracy for stage N1, achieving a recall of 83.5% and a precision of 57.4%, reflecting challenges in detecting this transitional stage. Receiver Operating Characteristic (ROC) analysis revealed excellent classification capability, with AUC values of 1.00 for W, N2, and N3, 0.99 for R, and 0.97 for N1. Compared to other models, including LSTM, Neural Network (NN), and Support Vector Machine (SVM), the RFXGBoost ensemble achieved the highest overall accuracy of 96.11% and a Cohen’s Kappa of 0.9388. By implementing the RFXGBoost model, this study contributes to Sustainable Development Goal (SDG) 3 by supports early diagnosis and monitoring of sleep-related disorders, promoting better health outcomes and contributing to improved well-being. Additionally, it also supports SDG 9. Through data-driven insights, this research contributes to the development of intelligent health monitoring systems, ultimately supporting sustainable healthcare practices.}, archive = {J_ACCESS}, author = {Kurniawan Eka Permana and Keiji Iramina}, doi = {10.1109/ACCESS.2025.3599828}, journal = {IEEE Access}, pages = {149554-149566}, shortjournal = {IEEE Access}, title = {Enhancing sleep stage classification with single channel EEG: Feature extraction and random forest-XGBoost model}, volume = {13}, year = {2025}, }
• (2025). A diffusion-based probabilistic ultra-short-term solar power prediction using the sky image sequences. ACCESS, 13, 149532-149553. (www)

  @article{ , abstract = {The inherently unpredictable nature of solar power generation, primarily due to rapidly changing cloud cover, poses a significant challenge to the operation of solar-integrated energy systems. Accurate ultra-short-term PhotoVoltaic (PV) power forecasting is crucial for the efficient and reliable operation of these energy systems. Recently, cloud images, providing more direct and comprehensive information about cloud patterns than on-site or off-site numerical weather prediction data, have become increasingly accessible for analyzing cloud dynamics, enabling more precise and efficient cloud change predictions. To enhance PV power probabilistic forecasting, this paper proposes a novel diffusion-based framework that leverages cross-attention on multi-resolution sky image patches. Central to this framework is the Cross Branch Visual Informer (CB-ViInf), the first model to integrate multi-resolution patches into the Vision Informer architecture, improving feature extraction and temporal modeling for solar forecasting. Furthermore, we propose an innovative Temporal Encoder with a dual-block architecture to refine temporal dependencies. The first block leverages Spatiotemporal Ridgelet Transform (STRT) and Multi-Frame Adaptive Singular Value Decomposition (MF-ASVD) as a dynamic feature refinement process, reducing noise and computational complexity while preserving critical features. The second block, a Spatio-Temporal Attention mechanism, simultaneously incorporates local and global attentions to capture fine-grained short-term variations and broader trends, enhancing adaptability to rapid cloud movements and irradiance fluctuations. Additionally, we propose a novel loss function based on Variational Inference and the Evidence Lower Bound (ELBO) to improve uncertainty quantification and prediction accuracy. Extensive evaluations on a real-world dataset against 64 baseline models using four evaluation metrics demonstrate the superiority of the proposed framework, establishing it as a state-of-the-art approach in ultra-short-term PV power forecasting. The proposed model offers an accurate, robust, and uncertainty-aware solar power forecasting methodology for improved power system operation and management.}, archive = {J_ACCESS}, author = {Razieh Rastgoo and Nima Amjady and Rakibuzzaman Shah and S. M. Muyeen}, doi = {10.1109/ACCESS.2025.3600713}, journal = {IEEE Access}, pages = {149532-149553}, shortjournal = {IEEE Access}, title = {A diffusion-based probabilistic ultra-short-term solar power prediction using the sky image sequences}, volume = {13}, year = {2025}, }
• (2025). A mobile health approach for glaucoma detection. ACCESS, 13, 149523-149531. (www)

  @article{ , abstract = {Glaucoma is a progressive eye disease that ranks as the second leading cause of blindness and the foremost cause of irreversible blindness globally. According to the National Institutes of Health’s National Eye Institute (NIH-NEI), glaucoma accounts for 3 million cases of irreversible blindness and low vision in the United States. Many individuals with glaucoma may exhibit no symptoms, making early detection essential for preventing blindness and preserving vision. This paper presents a novel smartphone-based method for the early detection and prediction of glaucoma. Our approach employs an innovative CIE LAB color-based enhancement technique for preprocessing, as well as a 3D stereo depth mapping method to extract the optic disc and optic cup from retinal fundus images. We calculate the areas of the optic disc and optic cup to determine the cup-to-disc ratios. For classification, we utilized a support vector machine (SVM). Our proposed method demonstrated a remarkable ability to differentiate glaucomatous eyes from healthy ones, achieving a sensitivity of 96.6%, specificity of 98.2%, and an overall accuracy of 97.1%.}, archive = {J_ACCESS}, author = {Fatemehsadat Tabei and Behnam Askarian}, doi = {10.1109/ACCESS.2025.3596547}, journal = {IEEE Access}, pages = {149523-149531}, shortjournal = {IEEE Access}, title = {A mobile health approach for glaucoma detection}, volume = {13}, year = {2025}, }
• (2025). Research on segmented duty cycle control strategy of motor operating mechanism of high voltage circuit breaker. ACCESS, 13, 149506-149522. (www)

  @article{ , abstract = {In order to improve the breaking performance of high-voltage circuit breakers and effectively suppress the bounce and mechanical impact caused by contact collision during closing, this study proposes a PWM control strategy based on segmented duty cycle for the motor operating mechanism of 126kV high-voltage circuit breakers. By analyzing the kinematic and mechanical characteristics of the transmission system, a simulation model under 200V operating voltage was established on the MATLAB/Simulink (R2022a) simulation platform to verify the driving performance of the permanent magnet brushless DC motor under load conditions. The control system with TMS320F28335 as the core processor is constructed. The HEDSS Hyde photoelectric encoder, CHF-400B current sensor and LK-H022 laser displacement sensor are used to realize the real-time control and monitoring of motor current, rotor position and contact bounce. The rebound displacement of the moving contact before the end of the closing process is detected to reflect the contact bounce. The switched capacitor circuit and segmented duty cycle PWM control strategy are developed. The rigid closing signal is defined as the moment of contact between the moving and static contacts. Based on the starting characteristics of the motor, the opening distance and over-range motion range of the moving contact of the circuit breaker and the characteristics of the rigid closing signal, the motor angle interval is divided into three stages: starting (0-10°), opening distance ( $10^{\circ } - 36^{\circ }$ ) and over-range ( $36^{\circ } - 64^{\circ }$ ). The genetic algorithm is used to establish a multi-objective function with the closing speed, closing time and contact bounce at the rigid closing point as the optimization objectives. The optimal duty cycle value of each corner interval is determined by optimization calculation. Under the condition that the weight coefficient is set to 1, a variety of control schemes are formulated, and the influence of each scheme on the closing characteristics of the circuit breaker is compared and analyzed by simulation and experiment. The experimental results show that using the switched capacitor circuit scheme, the closing time and the maximum closing speed are 54ms and 2.74m/s respectively, the closing time is shortened by 35.9%, the maximum closing speed is increased by 33.7%, and the contact bounce is 0.39mm. In Scheme 4, the duty ratios of the starting, opening and over-travel stages are 1,0.4 and 0.8, respectively. The closing time and the maximum closing speed are 51ms and 2.7m/s, respectively. The closing time is shortened by 37.1%, the maximum closing speed is increased by 25.9%, and the contact bounce is only 0.02mm, which is much lower than the IEC 62271-1 standard, and the balance is achieved in improving the operating speed and suppressing the contact bounce. This research provides a theoretical basis for the performance optimization of the motor operating mechanism of the high voltage circuit breaker, and provides a new idea for the stable operation of the power system protection.}, archive = {J_ACCESS}, author = {Dongpeng Han and Hebin Wang and Lei Huang and Feng Zuo}, doi = {10.1109/ACCESS.2025.3602458}, journal = {IEEE Access}, pages = {149506-149522}, shortjournal = {IEEE Access}, title = {Research on segmented duty cycle control strategy of motor operating mechanism of high voltage circuit breaker}, volume = {13}, year = {2025}, }
• (2025). SiamSpecNet: One-shot bearing fault diagnosis using siamese networks and gabor spectrograms. ACCESS, 13, 149490-149505. (www)

  @article{ , abstract = {Bearing faults are a leading cause of failure in mechanical systems, underscoring the need for efficient and reliable diagnostic methods. Although deep learning has shown promise in this domain, its dependence on large volumes of labeled, machine-specific data limits its industrial applicability. This study introduces SiamSpecNet, a one-shot learning framework that integrates Gabor spectrograms with a lightweight convolutional Siamese Neural Network (SNN) to address data scarcity in bearing fault diagnosis. The twin subnetworks of the SNN employ a previously developed CNN architecture as a precursory feature extractor. Raw vibration signals are converted into two-dimensional time–frequency spectrograms, and a comparative analysis of three transformation techniques—Short-Time Fourier Transform (STFT), Morlet wavelet, and Gabor transform—demonstrates that Gabor spectrograms achieve superior classification precision and faster processing. Evaluations on datasets from a laboratory test rig, the MFPT database, and real industrial equipment show that with only 11–13 training samples per class, the model achieves 97–100% in-domain accuracy and strong cross-domain generalization, with cross-domain accuracies ranging from 85.96% to 100%, and MFPT being the most challenging (85.96%).These results demonstrate that SiamSpecNet—combining one-shot learning, optimized spectrogram generation, and a robust CNN backbone—offers a scalable and data-efficient solution for bearing fault diagnosis in industrial environments.}, archive = {J_ACCESS}, author = {N. Fathiah Waziralilah and Aminudin Abu and M. H. Lim and Lee Kee Quen and Mohd Syahril Ramadhan Mohd Saufi}, doi = {10.1109/ACCESS.2025.3602227}, journal = {IEEE Access}, pages = {149490-149505}, shortjournal = {IEEE Access}, title = {SiamSpecNet: One-shot bearing fault diagnosis using siamese networks and gabor spectrograms}, volume = {13}, year = {2025}, }
• (2025). Detecting deepfake audio using spectrogram-based machine learning approaches. ACCESS, 13, 149478-149489. (www)

  @article{ , abstract = {The increasing ability of deep learning models to produce realistic-sounding synthetic speech poses serious problems for privacy, public trust, and digital security. To counter this danger, we offer a methodology for identifying deepfake audio that is based on machine learning. We use three ensemble learning models (Random Forest, Gradient Boosting, and XGBoost) for classification after transforming speech samples into mel-spectrograms to extract time-frequency information. These models, which were trained on the Deep Voice dataset, which included a variety of actual and synthetic samples, were assessed using common metrics such as F1-score, accuracy, precision, and recall. At 99.32% accuracy, XGBoost performed better than the others. These findings show the promise of lightweight, interpretable machine learning methods for identifying fake audio and protecting media authentication, digital forensics, and cybersecurity applications.}, archive = {J_ACCESS}, author = {Ritushree Bohara and Amit Kumar Bairwa}, doi = {10.1109/ACCESS.2025.3602531}, journal = {IEEE Access}, pages = {149478-149489}, shortjournal = {IEEE Access}, title = {Detecting deepfake audio using spectrogram-based machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). GSPO-LAD: A graph neural network-based sensor placement and anomaly detection in water distribution systems. ACCESS, 13, 149462-149477. (www)

  @article{ , abstract = {AI-based sensor placement for anomaly detection in water distribution systems (WDS) enhances system reliability and efficiency. Advanced WDS are attractive targets for malicious actors who may gain control over these systems to disrupt or manipulate water services for personal gain. Monitoring various points in the network is crucial for effective anomaly detection. However, increasing the number of sensors or placing them inaccurately can raise computational costs, increase management overhead, and degrade detection performance. In this study, we address sensor placement optimization (SPO) and anomaly detection (AD) in WDS to improve performance while reducing computational and management overhead by focusing on critical sensor locations. We use the BATADAL dataset, which includes key WDS components such as pump units, pressure junctions, and tanks. Our implementation identifies the most critical pump unit locations for monitoring flow rates, allowing us to focus only on essential points to lower computational costs and management overhead in anomaly detection. For SPO, we implement Graph Neural Networks (GNN) to extract relationships between pump units and other components, identifying the most significant pump units for anomaly prediction. GNN-based SPO (GSPO) selects critical locations, and we then use flow rate data from these selected sensors to train the AD model. We employ a Long Short-Term Memory (LSTM)-based AD approach (LAD) to capture sequential patterns in the sensor data. The LAD approach achieved significant results with an accuracy of 0.925 and an F1-score of 0.75. To further enhance model performance over time, we adopt a continuous learning approach. We fine-tune the LSTM model monthly with new incoming data while retaining previous knowledge using a weight interpolation approach, ensuring the model remains up-to-date and continues to improve in detecting anomalies.}, archive = {J_ACCESS}, author = {Furqan Rustam and Anca Delia Jurcut and Md Salauddin}, doi = {10.1109/ACCESS.2025.3598996}, journal = {IEEE Access}, pages = {149462-149477}, shortjournal = {IEEE Access}, title = {GSPO-LAD: A graph neural network-based sensor placement and anomaly detection in water distribution systems}, volume = {13}, year = {2025}, }
• (2025). Improving structure-from-motion and rendering quality for thermal image-based 3D gaussian splatting. ACCESS, 13, 149450-149461. (www)

  @article{ , abstract = {Novel-view synthesis, which generates new views from images captured at multiple viewpoints, is widely applied in fields such as autonomous driving, robotic vision, architecture, and augmented reality. Recent advances in Neural Radiance Fields and 3D Gaussian Splatting have enabled high-quality novel-view synthesis; however, these methods suffer from significant performance degradation in structure from motion and rendering under challenging conditions such as low light, illumination changes, and visual occlusions. To address these limitations, we propose a thermal image-based 3D Gaussian Splatting framework. By leveraging thermal cameras that visualize the surface temperature distribution of objects, our approach enables imaging in complete darkness and robust target identification even in low-visibility conditions such as smoke or fog. The proposed method offers 1) reliable structure from motion and novel-view synthesis using only thermal images, 2) robust feature matching under thermal inconsistency, low contrast, and low-texture environments, 3) improved camera pose estimation through a keyframe filtering and image registration strategy that considers Non-Uniformity Correction intervals and inter-frame baselines, and 4) enhanced rendering quality via an edge-aware loss function that preserves object boundaries. Quantitative and qualitative evaluations on public datasets demonstrate that our method outperforms existing approaches such as 3D Gaussian Splatting, Instant-NGP, and Plenoxels.}, archive = {J_ACCESS}, author = {Jinhoon Wang and Jusang Jeong and Junghyun Oh}, doi = {10.1109/ACCESS.2025.3600914}, journal = {IEEE Access}, pages = {149450-149461}, shortjournal = {IEEE Access}, title = {Improving structure-from-motion and rendering quality for thermal image-based 3D gaussian splatting}, volume = {13}, year = {2025}, }
• (2025). Minimum information trees for high dimensional data visualization in clustering. ACCESS, 13, 149430-149449. (www)

  @article{ , abstract = {Visualizing the qualitative outcomes of clustering algorithms in high-dimensional spaces remains a persistent challenge in data analysis and machine learning. Traditional dimensionality reduction techniques often distort the underlying structure of clusters or fail to provide interpretable representations of inter-cluster relationships. In this paper, we introduce Minimum Information Trees (MINFO Trees), an information-theoretic, graph-based method for visualizing high-dimensional data with an emphasis on preserving clustering structure. By leveraging pairwise information measures and constructing information-theoretic based k-NN graphs, MINFO Trees generate data visualizations that reflect both local cohesion and global separation among clusters. Our method provides interpretable and faithful representations of clustering results, enabling qualitative evaluation of cluster quality and relationships. Experimental results on real-world datasets highlight the differences between MINFO Trees over state-of-the-art dimensionality reduction methods such as t-SNE, UMAP and PaCMAP in terms of preserving cluster topology and enhancing visual interpretability.}, archive = {J_ACCESS}, author = {Alexandre L. M. Levada}, doi = {10.1109/ACCESS.2025.3602730}, journal = {IEEE Access}, pages = {149430-149449}, shortjournal = {IEEE Access}, title = {Minimum information trees for high dimensional data visualization in clustering}, volume = {13}, year = {2025}, }
• (2025). Design of a CNN–Swin transformer model for alzheimer’s disease prediction using MRI images. ACCESS, 13, 149409-149429. (www)

  @article{ , abstract = {Alzheimer’s Disease (AD) is a progressive neurological condition that deteriorates memory, cognition, and behavior, especially in older adults. Timely identification is essential to enhance patient outcomes and inform therapy choices. This research introduces an extensive deep learning model for multiclass Alzheimer’s disease stage classification using structural MRI images from the publicly accessible OASIS dataset. The procedure begins with thorough preprocessing, including skull stripping, axial slice extraction, and intensity normalization to guarantee uniform input quality. Deep Convolution Generative Adversarial Network (DCGAN) is used to produce authentic synthetic MRI slices, therefore addressing data imbalance and enhancing class representation and training stability. The EffSwin-XNet model introduces a novel hybrid deep learning framework that strategically fuses EfficientNet-B0 and the Swin Transformer, enabling both local and global feature extraction from MRI brain images. This represents a significant advancement over conventional convolutional neural networks and a feature fusion attention mechanism that adaptively emphasizes discriminative features. Grad-CAM is used for explainability to view the brain areas influencing each categorization judgment, hence increasing therapeutic confidence. The model attains a classification accuracy of 95.3%, surpassing traditional CNN and hybrid benchmarks. This study presents an enhanced, interpretable, and efficient method for stage-wise categorization of Alzheimer’s Disease, demonstrating significant potential for use in clinical decision-support systems for early classification and intervention.}, archive = {J_ACCESS}, author = {K. Velu and N. Jaisankar}, doi = {10.1109/ACCESS.2025.3602316}, journal = {IEEE Access}, pages = {149409-149429}, shortjournal = {IEEE Access}, title = {Design of a CNN–Swin transformer model for alzheimer’s disease prediction using MRI images}, volume = {13}, year = {2025}, }
• (2025). Domain generalization and domain adaptation approaches for face anti-spoofing: A survey. ACCESS, 13, 149390-149408. (www)

  @article{ , abstract = {Face Anti-Spoofing (FAS), also referred to as Face Liveness, has emerged as a rapidly evolving field of research in Computer Vision. Closely tied to Face Recognition, its primary objective is to authenticate an identity by verifying its authenticity. However, safeguarding against diverse types of spoof attacks poses significant challenges due to the vast range of spoofing methods, capture devices, and environments. To mitigate this issue, researchers frequently employ Domain Generalization and Domain Adaptation approaches. This paper presents a comprehensive review of the latest deep learning-based FAS techniques that have achieved state-of-the-art results using the leave-one-domain-out protocol and the OCIM evaluation method, a common benchmark in both Domain Generalization and Domain Adaptation FAS. Finally, we conclude this survey by exploring potential new research directions in FAS.}, archive = {J_ACCESS}, author = {Hanry Ham and Ari Saptawijaya and Aniati Murni Arymurthy}, doi = {10.1109/ACCESS.2025.3592034}, journal = {IEEE Access}, pages = {149390-149408}, shortjournal = {IEEE Access}, title = {Domain generalization and domain adaptation approaches for face anti-spoofing: A survey}, volume = {13}, year = {2025}, }
• (2025). GSPNet: A crowded pedestrian detection network integrating global context, spatial, and pose information. ACCESS, 13, 149373-149389. (www)

  @article{ , abstract = {To address pedestrian occlusion and multi-scale issues in crowded scenes, a crowded pedestrian detection algorithm that integrates global context information, spatial information, and pose information is proposed. Firstly, the feature pyramid network (FPN) is enhanced by integrating a dynamic sampling module and a weighted fusion branch, enabling more effective multi-scale feature aggregation and better handling of pedestrian scale variations. Secondly, a global context information module is designed to capture additional potential pedestrian features by fusing global context information and spatial information, which mitigates feature loss due to occlusion. Additionally, an enhanced Transformer-based pose module is constructed to extract pose information as supplementary resources, tackling the challenges of occlusion in crowded pedestrian detection. Since pose estimation is limited by pose estimation algorithms, an improved CA attention is incorporated into the visual information to complement the pose information. Finally, a novel data augmentation method is proposed to simulate occlusion, thereby improving the model’s generalization ability. Extensive experimental evaluations on the CrowdHuman and CityPersons benchmarks corroborate the robustness and superior detection capabilities of the proposed algorithm, which consistently surpasses current state-of-the-art approaches in crowded pedestrian detection scenarios.}, archive = {J_ACCESS}, author = {Hao Chang and Mingxing Li and Shucheng Huang and Tian Li}, doi = {10.1109/ACCESS.2025.3599213}, journal = {IEEE Access}, pages = {149373-149389}, shortjournal = {IEEE Access}, title = {GSPNet: A crowded pedestrian detection network integrating global context, spatial, and pose information}, volume = {13}, year = {2025}, }
• (2025). A new class of miniaturized balanced couplers: Low-loss design for wireless network and RF scavenging systems. ACCESS, 13, 149361-149372. (www)

  @article{ , abstract = {A new class of miniaturized balanced 0° microstrip filtering couplers is presented, characterized by an ultra-compact footprint of only $0.006~\lambda _{\mathrm {g}}^{2}$ achieved through a unique configuration of coupled thin microstrip lines integrated with solid stubs. The proposed design exhibits a built-in filtering response with balanced output magnitudes and phase (0° phase difference) across its passband, while maintaining exceptionally low group delays (0.84 ns at S21 and 0.83 ns at S31). Operating at 1.2 GHz, the coupler achieves outstanding performance metrics, including a return loss of 31.1 dB, an insertion loss of 3.11 dB, and a coupling factor of 2.98 dB. These metrics surpass those of prior coupler designs by concurrently achieving extreme miniaturization, low loss, and minimal group delay in a single device. Close agreement between full-wave electromagnetic simulations and measurements from a fabricated prototype validates the design’s effectiveness and underscores its suitability for wireless network and RF energy scavenging systems, where extreme compactness, low loss, and phase coherence are critical.}, archive = {J_ACCESS}, author = {Salah I. Yahya and Farid Zubir and Leila Nouri and Noorlindawaty Md Jizat}, doi = {10.1109/ACCESS.2025.3602051}, journal = {IEEE Access}, pages = {149361-149372}, shortjournal = {IEEE Access}, title = {A new class of miniaturized balanced couplers: Low-loss design for wireless network and RF scavenging systems}, volume = {13}, year = {2025}, }
• (2025). Optimized maximum power point tracking for hybrid PV-TEG systems using an improved water cycle algorithm. ACCESS, 13, 149343-149360. (www)

  @article{ , abstract = {Hybridizing a photovoltaic (PV) system with a thermoelectric generator (TEG) enhances overall energy density and input utilization. PV efficiency decreases, whereas TEG efficiency improves with increasing temperatures, making them complementary for boosting system efficiency. However, controlling the hybrid PV-TEG system is challenging due to the difficulty of maintaining operation at the maximum power point (MPP) under partial shading conditions (PSC), non-uniform temperature distribution (NUTD) and varying environmental conditions. PSC in PV arrays arises from uneven irradiance, while NUTD occurs within TEG arrays due to uneven temperature. Both issues cause multiple local maxima on the power-voltage curve, often trapping conventional maximum power point tracking (MPPT) methods in local maxima and suboptimal energy harvesting. To address this, an Improved Water Cycle Algorithm (IWCA) has been implemented for MPPT in series connected hybrid PV-TEG systems. An improvement to the conventional WCA algorithm’s mathematical model enhances exploitation and accelerates particle convergence. This is achieved by dynamically adjusting the stream movement equations, boosting performance without increasing computational complexity. Its performance is compared with the conventional WCA (CWCA), Particle Swarm Optimization (PSO), Dragon Fly Optimization (DFO), Flying Squirrel Search Optimization (FSSO), Grey Wolf Optimization (GWO), and the classical Perturb and Observe (P&O) techniques. Results indicate that IWCA successfully achieved the global maxima under all operating scenarios, with an average efficiency of 99.41%, tracked and settled at the global maxima within 80 to 120 ms. The algorithm exhibits high MPPT stability, maintaining consistent performance across varying conditions while avoiding power oscillations once achieve steady state. Furthermore, the results demonstrate that IWCA outperforms other techniques in speed, accuracy, and efficiency, demonstrating its capability to reliably track and operate at the global maxima of the hybrid system’s power-voltage curve.}, archive = {J_ACCESS}, author = {Ali Hasan and Muhammad Yaqoob Javed and Kamal Shahid and Thomas Mussenbrock}, doi = {10.1109/ACCESS.2025.3601440}, journal = {IEEE Access}, pages = {149343-149360}, shortjournal = {IEEE Access}, title = {Optimized maximum power point tracking for hybrid PV-TEG systems using an improved water cycle algorithm}, volume = {13}, year = {2025}, }
• (2025). Accurate and fast convergence technique for enhanced efficiency and performance of photovoltaic systems. ACCESS, 13, 149326-149342. (www)

  @article{ , abstract = {Global maximum power point tracking (GMPPT) techniques applied in photovoltaic (PV) systems under partial shading conditions (PSCs) require significant variations in the duty cycle of power electronic converters. Combined with energy storage elements in the converter, these variations lead to substantial power oscillations, complicating accurate sampling. Furthermore, a time interval is needed to ensure reliable power measurement, which affects tracking efficiency. Although various techniques have been proposed to reduce the number of interactions required to reach the maximum power point (MPP), strategies for shortening the tracking time remain underexplored. In this context, this work proposes a low-complexity acceleration strategy to reduce the tracking time in maximum power point tracking (MPPT) algorithms for PV systems. The method can be readily integrated into any MPPT technique with minimal code modification. It operates by identifying local maxima and minima in the instantaneous power curve and computing the mean value when a minimum follows a maximum. Five MPPT techniques based on different operating principles were experimentally evaluated under both uniform and non-uniform irradiance conditions. Results show that the proposed strategy reduces tracking time by 22.5% to 81.67%, improves dynamic efficiency, and does not compromise MPP accuracy. In some cases, efficiency gains increased up to 102.67%, indicating a substantial improvement in overall system performance. In all evaluated scenarios, the algorithms could reach the MPP more quickly, resulting in a significant positive impact on the overall efficiency of the PV system over its lifespan.}, archive = {J_ACCESS}, author = {Caio Meira Amaral Da Luz and Fábio Inácio Da Silva and Fernando Lessa Tofoli}, doi = {10.1109/ACCESS.2025.3602732}, journal = {IEEE Access}, pages = {149326-149342}, shortjournal = {IEEE Access}, title = {Accurate and fast convergence technique for enhanced efficiency and performance of photovoltaic systems}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of photonic-assisted distributed MIMO radar deployed on a swarm of drones. ACCESS, 13, 149310-149325. (www)

  @article{ , abstract = {Distributed multiple-input multiple-output (MIMO) radar systems deployed on mobile platforms are gaining strategic importance across a wide range of civil and military applications. These systems offer high sensitivity, robustness to shadowing, and improved target localization capabilities. In recent years, photonic technologies have demonstrated the capability to coherently distribute the radar signals via optical fiber or free-space optical links, thereby enabling the development of coherent distributed radar configurations. This coherence enables effective data fusion and centralized processing from the spatially distributed radars. Thereby, coherent MIMO processing can be achieved across widely separated radars, as in the case of radar mounted on a swarm of drones, where photonic-assisted signal distribution enables precise phase coherence across drones, overcoming RF synchronization limitations in mobile platforms. The design of a distributed MIMO radar system is inherently challenging due to the large number of design parameters involved, especially for mobile sensor platforms. Stepping from our recent experimental demonstrations of coherent photonics-assisted distributed MIMO radar, this paper analyzes the impact of the main system design parameters, i.e., radar signal bandwidth, number of radar peripherals, and baseline geometry and dimension, on the key performance indicators, i.e., spatial resolution and sidelobe levels of the MIMO ambiguity function. It also proposes an innovative approach to improve system performance. This approach aims to minimize hardware complexity by exploiting a combination of meta-heuristic algorithms for architecture optimization and signal post-processing. The former step allows optimization of spatial resolution, while the latter reduces the detrimental effects of sidelobes, which are typical in distributed MIMO radars. The proposed analysis focuses on a specific application of a photonics-assisted radar constellation mounted on a swarm of drones operating in the X and Ka bands. In particular, the proposed optimization method is applied to a 3-transmit/3-receive ( $ 3 \times 3~$ ) MIMO configuration using 3 radar peripherals (RPs), demonstrating the capability to achieve 2 cm resolution in both range and cross-range. The results are validated through MATLAB-based numerical simulations, demonstrating the system’s capability under realistic conditions. The detection capability of the enhanced system is validated when the target is located 50 m away from baseline, using cell-averaging constant false alarm rate detection criterion. A target detection probability of $\textbf {0.996}$ is achieved at false alarm probability of $10^{-6}$ . These results confirm the effectiveness of the proposed approach in enabling high-performance, low-resource drone-based MIMO radar surveillance systems.}, archive = {J_ACCESS}, author = {Fawad Ahmad and Malik Muhammad Haris Amir and Salvatore Maresca and Antonio Malacarne and Mirco Scaffardi and Antonella Bogoni}, doi = {10.1109/ACCESS.2025.3601004}, journal = {IEEE Access}, pages = {149310-149325}, shortjournal = {IEEE Access}, title = {Performance enhancement of photonic-assisted distributed MIMO radar deployed on a swarm of drones}, volume = {13}, year = {2025}, }
• (2025). Innovative spatial-temporal attention network (STAN) for skeleton-based timed-up-and-go analysis to stratify lower back pain severity with monocular RGB camera. ACCESS, 13, 149296-149309. (www)

  @article{ , abstract = {Lower back pain (LBP) significantly affects individuals’ quality of life and represents a considerable burden on society. Precise stratification of lower back pain (LBP) severity aids in the assessment and management of LBP, facilitating tailored treatment plans and enabling healthcare providers to track intervention effectiveness over time. In our research, we aim to introduce a novel human motion analysis technique focused on stratifying the severity of lower back pain (LBP) through the Timed-Up-and-Go (TUG) test. We present a novel spatial-temporal attention network (STAN) designed to extract intrinsic spatial-temporal features from skeletal data obtained from video recordings of TUG trials, leveraging the affordability of RGB cameras. Results: Our STAN model demonstrates remarkable efficacy in accurately (96.95% $\pm ~1.15$ %) stratifying the severity of lower back pain from extensive evaluation results, which validate the superiority of our STAN-based approach over state-of-the-art machine learning models when analyzing video recordings for LBP stratification, from statistical evaluations of multiple metrics. The proposed study holds promise in improving the LBP assessment, which can further facilitate the related care providers’ monitoring of the progress of LBP to set up effective treatment and rehabilitation plans.}, archive = {J_ACCESS}, author = {Liyun Gong and Pandey Shourya Prasad and Chan Chi Leung and Gautam Siddharth Kashyap and Saeid Pourroostaei Ardakani and Ross Clifford and Philip Williams and Miao Yu}, doi = {10.1109/ACCESS.2025.3602153}, journal = {IEEE Access}, pages = {149296-149309}, shortjournal = {IEEE Access}, title = {Innovative spatial-temporal attention network (STAN) for skeleton-based timed-up-and-go analysis to stratify lower back pain severity with monocular RGB camera}, volume = {13}, year = {2025}, }
• (2025). Multimodal spatiotemporal collaborative approach for network traffic anomaly detection. ACCESS, 13, 149286-149295. (www)

  @article{ , abstract = {Network traffic, generated by network entities, carries information related to system status, network environment, business secrets, and user privacy. Network traffic analysis is a crucial tool for network management, planning, and security. Traditional network traffic anomaly detection methods often rely on statistical features derived from DPI (Deep Packet Inspection). However, with the increasing sophistication and diversification of network attacks, single-data-source-based methods are no longer sufficient for effective security protection. To address the challenges posed by emerging network attacks, this paper proposes a multimodal fusion anomaly detection method based on wavelet analysis features. The proposed method utilizes the time-frequency features derived from wavelet analysis as a shared characteristic across multiple modalities. Distinct feature extraction approaches are developed for each modality, tailored to their specific attributes. By integrating feature fusion attention mechanisms with time-frequency feature guidance strategies, the method facilitates interaction between time-frequency and multimodal features. Experimental validation on the CIC-IDS-2017 dataset demonstrates that the model achieves an accuracy of 99.9% and an F1 score of 99.8%.}, archive = {J_ACCESS}, author = {Wei Sun and Zhiqi Li and Jie Cui and Lei Sun and Xiaokai Huang}, doi = {10.1109/ACCESS.2025.3598294}, journal = {IEEE Access}, pages = {149286-149295}, shortjournal = {IEEE Access}, title = {Multimodal spatiotemporal collaborative approach for network traffic anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Flexible DC grid protection scheme based on fitting slope magnitude. ACCESS, 13, 149273-149285. (www)

  @article{ , abstract = {In view of the current problems of insufficient ability to withstand high resistance in the single-terminal protection of multi-terminal flexible DC grids, a flexible DC grid protection scheme based on the fitting slope is proposed. First, the expression of the current change rate during faults inside and outside the zone is theoretically derived. And it is found that there are obvious differences in the magnitude and steepness of the current during faults inside and outside the zone. Then a protection scheme is proposed based on this difference. The slope of the fitted current is used to identify faults inside and outside the zone. In view of the limitation that the fitted current may malfunction under high resistance conditions, it is proposed to use the fitted power to construct a fitted power assist criterion. Finally, a four-terminal flexible DC grid model is built to verify the protection scheme. The simulation results show that the proposed protection method can accurately identify faults and fault poles inside and outside the zone, and still has good sensitivity under high-resistance faults.}, archive = {J_ACCESS}, author = {Min Zhang and Bin Zhou and Jingwei Liu and Huanwen Wang and Shuyi Fan and Minghui Hu}, doi = {10.1109/ACCESS.2025.3598050}, journal = {IEEE Access}, pages = {149273-149285}, shortjournal = {IEEE Access}, title = {Flexible DC grid protection scheme based on fitting slope magnitude}, volume = {13}, year = {2025}, }
• (2025). Low-complexity energy antenna allocation for wireless-powered IoT systems. ACCESS, 13, 149260-149272. (www)

  @article{ , abstract = {To improve the performance of a wireless-powered Internet of Things (IoT) system, a multi-source wireless power transfer technique based on a multi-antenna beamforming architecture has emerged as an attractive solution. However, the distance or phase diversity among power beams from various antennas to a wireless device may degrade the energy transfer efficiency. Thus, this paper considers the energy antenna resource allocation problem in a wireless-powered IoT system with multiple wireless devices and multi-antenna energy transmitters. To maximize the system objective function such as the energy transfer efficiency, spectral efficiency, or energy spectral efficiency, we designed a greedy-based antenna allocation algorithm with polynomial time complexity by taking the distance diversity and phase differences among various energy antennas into account. According to numerous experimental tests, the proposed greedy with fine-tuning algorithm achieves a near-optimal solution while preserving very low computation complexity relative to the backtracking and dynamic programming-based algorithms. Our design makes the real-time energy antenna resource allocation in a wireless-powered IoT system feasible.}, archive = {J_ACCESS}, author = {Mingfu Li and Guan-Yu Lu and Cheng-Bin Yao and Huei-Wen Ferng}, doi = {10.1109/ACCESS.2025.3601512}, journal = {IEEE Access}, pages = {149260-149272}, shortjournal = {IEEE Access}, title = {Low-complexity energy antenna allocation for wireless-powered IoT systems}, volume = {13}, year = {2025}, }
• (2025). Multimodal social relationship recognition based on LLM. ACCESS, 13, 149247-149259. (www)

  @article{ , abstract = {In recent years, multimodal social relation recognition has become a critical task in the fields of computer vision and natural language processing. However, existing research still faces key gaps, particularly in effectively aligning image features with linguistic features to improve recognition accuracy. This paper proposes a social relation recognition method based on multimodal feature fusion and validates the crucial role of cross-modal alignment mechanisms in enhancing recognition accuracy. Our approach leverages large language models to meticulously extract event structures from textual descriptions, capturing key elements such as emotional states, scenarios, and relationships, while employing convolutional neural networks to extract deep features from images. Subsequently, we introduce a cross-modal alignment mechanism to semantically align textual event structures with visual features, ensuring high semantic consistency between the two modalities. Extensive experiments on multiple public datasets demonstrate that our method significantly outperforms existing unimodal and basic multimodal approaches, confirming its effectiveness and innovative contributions to social relation recognition.}, archive = {J_ACCESS}, author = {Haopeng Wang and Zhitian Zhang and Menglei Xia and Dejiao Huang and Ruyi Chang and Shuai Guo}, doi = {10.1109/ACCESS.2025.3598186}, journal = {IEEE Access}, pages = {149247-149259}, shortjournal = {IEEE Access}, title = {Multimodal social relationship recognition based on LLM}, volume = {13}, year = {2025}, }
• (2025). Quantifying knock-on effects and total impact of design choices via combined sensitivity analysis and structural equation modeling techniques. ACCESS, 13, 149230-149246. (www)

  @article{ , abstract = {Vehicles are complex systems composed of interdependent subsystems, where a change in one component can propagate through others, causing knock-on effects and impacting the overall performance. This interconnected nature makes early stage design challenging due to limited knowledge of the consequences of design choices. Existing tools address different aspects of this gap in isolation and typically do not quantify cascading changes, that is, knock-on effects, or the total impact of an input on the output, thereby highlighting the need for an integrated approach. This study addresses these limitations by proposing a framework to quantify the magnitude and direction of knock-on effects and consequently calculate the total impact by integrating curve fitting, sensitivity analysis, and structural equation modeling. This framework derives representative functions, quantifies the influence of each interacting factor pair, and calculates the total impact. To demonstrate this framework, a rail traction system was used as a case study. The results indicate that while rated power had comparatively less impact on the output than frequency, it had six times as many paths and 1.86 times as many vertices as frequency, indicating higher knock-on effects. To validate the structural model, the total impact value was compared with the direct sensitivity coefficient for each input and output, yielding a maximum error of 3%. In addition, a comparison with the results of Global Sensitivity Analysis confirmed consistent input rankings and influence directions. This demonstrates the framework’s capability to quantify knock-on effects and total impact while preserving true input-output relationships.}, archive = {J_ACCESS}, author = {Sai Kausik Abburu and Ciarán J. O’Reilly and Carlos Casanueva}, doi = {10.1109/ACCESS.2025.3602720}, journal = {IEEE Access}, pages = {149230-149246}, shortjournal = {IEEE Access}, title = {Quantifying knock-on effects and total impact of design choices via combined sensitivity analysis and structural equation modeling techniques}, volume = {13}, year = {2025}, }
• (2025). PanSAM-HAT: A hybrid attention transformer with panchromatic spectral attention module for compression artifacts removal in satellite imagery. ACCESS, 13, 149216-149229. (www)

  @article{ , abstract = {Multispectral (MS) satellite images are often subject to spatial–spectral variable compression (SSVC) because of constraints in transmission bandwidth and sensor scanning capabilities. This compression process, which varies across spatial locations and spectral bands, can introduce severe distortions and block artifacts, particularly under unknown compression ratios. To address these challenges, we propose a hybrid attention transformer (PanSAM-HAT) that incorporates a novel panchromatic spectral attention module (PanSAM) for effective artifact removal in SSVC-compressed MS imagery. PanSAM adaptively integrates spectral information from MS and spatial structure from high-resolution panchromatic (PAN) images through a pixel-wise attention mechanism, enabling robust restoration of spatial and spectral fidelity. Additionally, we present JPEG LAM, a novel attribution mapping technique designed to analyze and interpret the performance of compression artifact removal (CAR) networks. JPEG LAM introduces a compression-aware interpolation path and gradient-based block boundary analysis to highlight the network’s attention to JPEG artifacts. Extensive experiments on both synthetic and real-world remote sensing datasets demonstrate that our proposed PanSAM-HAT consistently outperforms existing convolutional neural network-based and transformer-based methods in reducing compression artifacts and enhancing image quality in SSVC scenarios.}, archive = {J_ACCESS}, author = {Hyun-Ho Kim and Jaemyung Kim and Doo-Chun Seo and Jae-Heon Jeong and Yongwoo Kim}, doi = {10.1109/ACCESS.2025.3602150}, journal = {IEEE Access}, pages = {149216-149229}, shortjournal = {IEEE Access}, title = {PanSAM-HAT: A hybrid attention transformer with panchromatic spectral attention module for compression artifacts removal in satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Reliability-driven label enhancement in crowdsourcing: Integrating confidence filtering and collaborative correction. ACCESS, 13, 149203-149215. (www)

  @article{ , abstract = {Crowdsourcing has been widely used for labeling data in artificial intelligence and machine learning due to its cost-saving benefits. However, inconsistencies in worker-provided labels degrade the quality of the integrated labels. Most noise correction methods proposed by researchers in recent years focus on training a corrector using subsets of data that are filtered to be clean. These methods depend on limited data to train the corrector model, which inevitably compromises the corrector’s reliability. To address this problem, this paper aims to enhance the reliability of integrated labels by applying a novel noise correction method that uses correctors trained on both clean and noisy subsets. This method comprises two stages: confidence filtering and an improved co-teaching+ algorithm. In the first stage, this method calculates the confidence levels of the integrated labels for each instance in the crowdsourced dataset, and then divides the dataset into clean and noisy subsets according to the confidence level. In the second stage, our method initially trains a corrector, denoted as $C_{\text {clean}}$ , on the clean dataset. Subsequently, $C_{\text {clean}}$ guides the co-teaching+ algorithm to adaptively train two correctors on the complete crowdsourced dataset, which includes both clean and noisy subsets. Finally, the instances in the crowdsourced dataset are corrected by these three correctors. Within this study, comparative experiments have been carried out on fifteen simulated datasets and four real-world datasets. The proposed noise correction method attains the best outcomes on the majority of the datasets evaluated.}, archive = {J_ACCESS}, author = {Fu Yanming and Honghong Xie and Weigeng Han and Ruikang Guo and Xiao Liu and Xiaoping Zhang}, doi = {10.1109/ACCESS.2025.3602231}, journal = {IEEE Access}, pages = {149203-149215}, shortjournal = {IEEE Access}, title = {Reliability-driven label enhancement in crowdsourcing: Integrating confidence filtering and collaborative correction}, volume = {13}, year = {2025}, }
• (2025). ChemReservoir—An open-source framework for chemically-inspired reservoir computing. ACCESS, 13, 149197-149202. (www)

  @article{ , abstract = {Reservoir computing is a type of a recurrent neural network, mapping the input into higher-dimensional space using fixed and nonlinear dynamical systems, called reservoirs. In the literature, there are various types of reservoirs ranging from in-silico to in-vitro. In cheminformatics, previous studies contributed to the field by developing simulation-based chemically inspired in-silico reservoir models. Yahiro used a DNA-based chemical reaction network as its reservoir, and Nguyen developed a DNA chemistry-inspired tool based on the Gillespie algorithm. However, these software tools were designed mainly with the focus on DNA chemistry, and their maintenance status has limited their current usability. Due to these limitations, there was a need for a proper open-source tool. This study introduces ChemReservoir, an open-source framework for chemically-inspired reservoir computing. In contrast to the former studies focused on DNA chemistry, ChemReservoir is a general framework for the construction and analysis of chemically-inspired reservoirs, which also addresses the limitations in these previous studies by ensuring enhanced testing, evaluation, and reproducibility. The tool was evaluated using various cycle-based reservoir topologies and demonstrated stable performance across a range of configurations in memory capacity tasks. A moderate improvement was observed as the normalized root mean square errors decreased from 0.52 to 0.31 for the long-term memory task. A similar trend was also observed in the other memory tasks.}, archive = {J_ACCESS}, author = {Mehmet Aziz Yirik and Jakob Lykke Andersen and Rolf Fagerberg and Daniel Merkle}, doi = {10.1109/ACCESS.2025.3602358}, journal = {IEEE Access}, pages = {149197-149202}, shortjournal = {IEEE Access}, title = {ChemReservoir—An open-source framework for chemically-inspired reservoir computing}, volume = {13}, year = {2025}, }
• (2025). Design and functionality assessment of crutch-integrated variable grounding flexible limb for sit-to-stand and stand-to-sit transfer assistance. ACCESS, 13, 149187-149196. (www)

  @article{ , abstract = {Our previous work introduced motor-driven Variable Grounding Flexible Limbs (VGFLs) that assist users’ sit-to-stand (STS) and stand-to-sit (SIT) transfers by dynamically adjusting their ground contact positions. Building on this concept, this study proposes crutch-integrated VGFLs actuated by a fully passive mechanism, eliminating the need for powered actuators or onboard batteries, and assesses the functionality of the VGFLs through verification experiments involving healthy subjects simulating a forward-leaning posture. The proposed VGFLs incorporated a passive power unit consisting of constant-force spiral springs that transmitted mechanical energy to the VGFLs via the braided polymer lines. This configuration produces bidirectional coiling and uncoiling motions, generating a vertical assistive force during STS and SIT transfers. A functionality assessment of the VGFLs was conducted by comparing the users’ STS and SIT transfers under the VGFL-assisted condition with those under three other conditions (no support, short crutches, and long crutches), based on the root mean square (RMS) values of center of mass (CoM) acceleration and the peak-to-peak amplitudes of surface electromyography (sEMG) signals. The STS and SIT transfer assistance provided by the VGFLs resulted in statistically significant reductions in CoM acceleration RMS and quadriceps sEMG activity, specifically in one or more of the rectus femoris, vastus medialis, and vastus lateralis muscles, in several subjects compared with the control conditions. These findings demonstrate that crutch-integrated VGFLs can enhance postural stability and reduce lower-limb muscle loading during STS and SIT transfers, potentially improving transfer performance in daily living.}, archive = {J_ACCESS}, author = {Atsushi Tsukahara and Taisei Urata and Yahiro Ito and Jayant Unde and Yasuhisa Hasegawa}, doi = {10.1109/ACCESS.2025.3602438}, journal = {IEEE Access}, pages = {149187-149196}, shortjournal = {IEEE Access}, title = {Design and functionality assessment of crutch-integrated variable grounding flexible limb for sit-to-stand and stand-to-sit transfer assistance}, volume = {13}, year = {2025}, }
• (2025). CL-PINN: A physics-informed neural networks framework for equatorial tsunami wave propagation in geophysical ocean. ACCESS, 13, 149174-149186. (www)

  @article{ , abstract = {Wave dynamics are governed by linear and nonlinear partial differential equations, where prior physical knowledge in the form of differential equations plays a crucial role in simulating dynamical models. Traditional analytical models often drift over time due to noise in real-world data, while data-driven methods struggle to uncover underlying patterns when labeled data are scarce and frequently fail to yield meaningful scientific insights. Nonetheless, physics-informed neural networks bridge this gap by embedding governing physical laws directly into the learning process. This paper presents the CL-PINN model, a novel curriculum learning-based physics-informed neural networks to study travelling wave solutions of a geophysical Boussinesq-type equation modeling equatorial tsunami wave propagation. The analyzed wave structure exhibits explicit travelling wave solutions with linear propagation speed and small-amplitude near field variations. Our approach achieves at least a 74% improvement over the baseline models, reduces computational cost by 40%, and attains an error as low as 10-4 compared to the ground truth. This demonstrates the potential of physics-informed neural networks for modeling oceanic wave phenomena.}, archive = {J_ACCESS}, author = {Arup Kumar Sahoo and Swarup Kumar Sahoo and S. Chakraverty}, doi = {10.1109/ACCESS.2025.3602179}, journal = {IEEE Access}, pages = {149174-149186}, shortjournal = {IEEE Access}, title = {CL-PINN: A physics-informed neural networks framework for equatorial tsunami wave propagation in geophysical ocean}, volume = {13}, year = {2025}, }
• (2025). An enhanced peak cancellation algorithm for PAPR reduction with variable coefficient length and recoverable leak peak. ACCESS, 13, 149158-149173. (www)

  @article{ , abstract = {One of the primary drawbacks of orthogonal frequency division multiplexing (OFDM) systems is that the peak-to-average power ratio (PAPR) increase as the number of subcarriers increases. Peak cancellation crest factor reduction (PC-CFR) is a simple and efficient reduction algorithm, where high power signals can be reduced in advance and the occurrence of nonlinear distortion in the power amplifier (PA) is prevented. Although the PAPR can be effectively reduced by this method, some challenges are also encountered. An enhanced peak cancellation crest factor reduction (ePC-CFR) based on a global clock is presented in this study. First, the detection delay is shortened, and the peak density is decreased during the peak search. Second, the pulse length is determined based on the magnitude of the over-threshold peak. Additionally, a leak recovery mechanism is introduced to prioritize the order of peak cancellation. Meanwhile, the coefficient storage area is reduced by half through the folded storage structure and the sacrifice of a few logical resources. Ultimately, a lower resource is exhibited, the convergence speed is accelerated, and the robustness against a high peak density is enhanced. According to the experimental results, the PAPR reduction, error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) of the first test signal are 3.75 dB, 7.623% and 86.64 dBc respectively. These three metrics are further improved by approximately 1.3 dB, 0.015% and 1.48 dBc, respectively, compared with PC-CFR.}, archive = {J_ACCESS}, author = {Jing Wenhao and Jin Jie and Fei Yingying}, doi = {10.1109/ACCESS.2025.3600489}, journal = {IEEE Access}, pages = {149158-149173}, shortjournal = {IEEE Access}, title = {An enhanced peak cancellation algorithm for PAPR reduction with variable coefficient length and recoverable leak peak}, volume = {13}, year = {2025}, }
• (2025). Soundness correction of data petri nets. ACCESS, 13, 149142-149157. (www)

  @article{ , abstract = {A process model is called sound if it always terminates properly and each model activity can occur in a process instance. Conducting soundness verification right after process design enables the detection and elimination of design errors in a process to be implemented. The process of eliminating such errors is called soundness repair. In many repair scenarios, the resulting model should retain only the correct behavior of the source model, especially if a model is created manually. In this paper, we consider this type of soundness repair applied to data-aware process models represented as data Petri nets (DPNs). We investigate the capabilities to repair soundness of DPNs by restricting the transition guards and disprove some earlier statements regarding it. We propose a new repair algorithm that follows this approach, with a key distinction that it does not require an input DPN to have a sound control flow. The algorithm is implemented, and the results of its preliminary evaluation justify its practical applicability in realistic application domains.}, archive = {J_ACCESS}, author = {Nikolai M. Suvorov and Irina A. Lomazova}, doi = {10.1109/ACCESS.2025.3602229}, journal = {IEEE Access}, pages = {149142-149157}, shortjournal = {IEEE Access}, title = {Soundness correction of data petri nets}, volume = {13}, year = {2025}, }
• (2025). Dynamic spanning tree coverage with Q-learning: A hybrid framework for efficient robot navigation in environments with moving obstacles. ACCESS, 13, 149121-149141. (www)

  @article{ , abstract = {This paper presents a novel hybrid framework that combines Dynamic Spanning Tree Coverage (D-STC) with Q-learning to address the challenges of mobile robot navigation and coverage in dynamic environments. The proposed method enables efficient exploration and complete area coverage while actively avoiding both static and moving obstacles using LIDAR-based real-time sensing and adaptive path planning. To evaluate robustness under varying levels of environmental dynamics, simulations were conducted in three obstacle speed scenarios: 1) obstacles slower than the robot, 2) equal-speed obstacles, and 3) faster obstacles. While D-STC provides structured traversal and Q-learning offers real-time adaptability, their integration significantly improves overall performance. In a $10 \times 10$ grid, the hybrid method required only 104, 109, and 114 steps across the respective scenarios, achieving the highest coverage efficiencies (99.0%, 97.0%, and 94.5%) and the lowest overlap rates (4.81%, 11.01%, and 17.11%). These results demonstrate that the proposed hybrid approach effectively reduces redundant traversal, enhances path efficiency, and dynamically responds to changing obstacle configurations—making it highly suitable for real-world, unpredictable, and obstacle-dense environments.}, archive = {J_ACCESS}, author = {K. P. Jayalakshmi and Vishnu G. Nair and Dayakshini Sathish}, doi = {10.1109/ACCESS.2025.3602149}, journal = {IEEE Access}, pages = {149121-149141}, shortjournal = {IEEE Access}, title = {Dynamic spanning tree coverage with Q-learning: A hybrid framework for efficient robot navigation in environments with moving obstacles}, volume = {13}, year = {2025}, }
• (2025). Enhanced intrusion detection via hybrid data resampling and feature optimization. ACCESS, 13, 149100-149120. (www)

  @article{ , abstract = {Network security is increasingly challenged by sophisticated and evolving cyber threats, underscoring the need for efficient and robust intrusion detection systems (IDS). Although researchers have extensively studied intrusion detection, they have made limited progress in effectively detecting zero-day attacks, particularly those caused by subtle variations. Existing approaches often tackle class imbalance and feature selection independently, with minimal exploration of their combined impact as a unified preprocessing strategy. Moreover, researchers have shown that inconsistencies and artifacts in the widely used CICIDS2017 dataset impair model generalization and stability. This study proposes a systematic pipeline that employs a unified framework, integrating hybrid data balancing with advanced feature selection to enhance anomaly detection performance. By leveraging Extra Trees-based feature selection alongside a strategic combination of SMOTE and targeted undersampling, the approach effectively addresses critical challenges such as class imbalance and feature redundancy. Evaluated on the CICIDS2017 dataset, the framework demonstrates strong generalization across both machine learning and deep learning models. Notably, XGBoost and Decision Tree achieved balanced accuracy and F1-scores of up to 99.99% and 99.96%, respectively, in multiclass classification. The evaluation framework reports comprehensive metrics beyond accuracy, including Precision, Recall, F1-score, Matthews Correlation Coefficient (MCC), and ROC-AUC, ensuring robust assessment under class imbalance conditions. These results highlight the effectiveness of the approach for real-time anomaly detection. The study contributes a robust and efficient pipeline for improving the accuracy and reliability of anomaly-based intrusion detection systems.}, archive = {J_ACCESS}, author = {Nahida Nigar and Rashed Mustafa}, doi = {10.1109/ACCESS.2025.3602562}, journal = {IEEE Access}, pages = {149100-149120}, shortjournal = {IEEE Access}, title = {Enhanced intrusion detection via hybrid data resampling and feature optimization}, volume = {13}, year = {2025}, }
• (2025). Airborne underwater vehicle recovery system: Eagle-inspired trajectory generation and control for UAV-assisted recovery of AUVs. ACCESS, 13, 149087-149099. (www)

  @article{ , abstract = {Retrieving Autonomous Underwater Vehicles (AUVs) using Uncrewed Aerial Vehicles (UAVs) presents significant challenges due to wave-induced motion and wind disturbances during recovery. While prior research has primarily addressed air-to-air refueling and ground-to-air deployments, this work introduces an eagle-inspired UAV trajectory generation and control system specifically designed for AUV retrieval. Drawing inspiration from avian flight dynamics, the proposed approach mitigates the effects of downward airflow and environmental disturbances, enabling a stable and efficient recovery process. We propose a novel trajectory planning method that minimizes snap, integrates a cost function to account for the UAV’s airflow effects on the target, and adapts dynamically to wave-induced movements. A specialized connection mechanism, consisting of a hook-equipped UAV and a buoy-rope assembly attached to the AUV, is developed and validated through reduced-scale in-water field experiments. Post-connection, a suspended load controller ensures stability by compensating for shifts in inertia and external forces. Additionally, we present a Unity-based simulation environment that allows customization of vehicle parameters and mission scenarios. This work bridges the gap in maritime operations, offering a reliable and flexible solution for AUV recovery in dynamic marine environments. Our code is available at video https://youtu.be/w-ZA1QxNfQQ}, archive = {J_ACCESS}, author = {Li-Fan Wu and Jonathan Lane and Nils Kiefer and Aryan Dolas and Özer Özkahraman and John Folkesson and Ivan Stenius and Mo Rastgaar and Nina Mahmoudian}, doi = {10.1109/ACCESS.2025.3597902}, journal = {IEEE Access}, pages = {149087-149099}, shortjournal = {IEEE Access}, title = {Airborne underwater vehicle recovery system: Eagle-inspired trajectory generation and control for UAV-assisted recovery of AUVs}, volume = {13}, year = {2025}, }
• (2025). MJNMF-GAT: Multi-task joint non-negative matrix factorization graph attention network for understanding adolescent neurodevelopment. ACCESS, 13, 149071-149086. (www)

  @article{ , abstract = {Integration of multi-task brain imaging data is crucial for furthering our understanding of neurodevelopment. By utilizing functional magnetic resonance imaging (fMRI) data and machine learning methods such as graph neural networks to measure brain activity, one can efficiently capture complex brain network interactions. In this work, we introduce the Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network (MJNMF-GAT) to investigate fMRI data of multiple tasks with the goal of determining age-related differences in adolescence. Our framework integrates joint non-negative matrix factorization for extracting shared latent features across tasks with graph attention networks to model brain connectivity patterns. The model is capable of integrating data from different fMRI tasks to enhance both predictive performance and network interpretation. We applied GNNExplainer for model interpretability and identified key subnetworks that contribute to prediction tasks. The proposed MJNMF-GAT model demonstrates superior results over existing methods, achieving a root mean square error (RMSE) of $1.9212~\pm ~0.1742$ , mean absolute error (MAE) of $1.5368~\pm ~0.1469$ , and correlation of $0.8032~\pm ~0.0469$ in an age prediction task on the Philadelphia Neurodevelomental Cohort (PNC). The model also identifies the critical functional connections that change with the different stages of adolescent neurodevelopment. Our approach improves the predictive accuracy and, more importantly, interprets informative brain connectivity patterns, especially for detecting those important sub-networks responsible in age prediction. The MJNMF-GAT framework opens a new avenue for analyzing multi-task neuroimaging studies, which helps identify significant brain networks and develop a deeper understanding of functional connectivity changes in adolescence.}, archive = {J_ACCESS}, author = {Binish Patel and Tony W. Wilson and Julia M. Stephen and Vince D. Calhoun and Yu-Ping Wang}, doi = {10.1109/ACCESS.2025.3601649}, journal = {IEEE Access}, pages = {149071-149086}, shortjournal = {IEEE Access}, title = {MJNMF-GAT: Multi-task joint non-negative matrix factorization graph attention network for understanding adolescent neurodevelopment}, volume = {13}, year = {2025}, }
• (2025). LRT-DDPM: A diffusion model-based approach for network traffic data generation in intrusion detection. ACCESS, 13, 149054-149070. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDS) are fundamental to network security. However, traditional approaches often struggle with high-dimensional and imbalanced network traffic data. To address these challenges, we propose LRT-DDPM, a novel network traffic data generation model based on the Denoising Diffusion Probabilistic Model (DDPM). Our model employs a stepwise denoising mechanism to generate high-fidelity synthetic data, enhances feature extraction through a convolutional neural network with residual connections and introduces the Time-Adaptive Control Block (TACBlock) strategy for dynamic time step adjustment during diffusion. LRT-DDPM demonstrates superior performance on two widely-used benchmark datasets. It achieves an accuracy of 99.40% on NSL-KDD and 99.53% on CICIDS2017. Furthermore, the model attains high precision, recall, and F1-score metrics, consistently outperforms existing methods by enhancing detection accuracy and reducing false positive rates.}, archive = {J_ACCESS}, author = {Chaoling Wang and Jiang Wu and Liang Wang}, doi = {10.1109/ACCESS.2025.3598777}, journal = {IEEE Access}, pages = {149054-149070}, shortjournal = {IEEE Access}, title = {LRT-DDPM: A diffusion model-based approach for network traffic data generation in intrusion detection}, volume = {13}, year = {2025}, }
• (2025). On the adoption of distributed intelligence toward 6G networks. ACCESS, 13, 149035-149053. (www)

  @article{ , abstract = {Distributed intelligence (DI) is becoming increasingly important in mobile networks due to privacy and regulatory restrictions on data movement. This trend is fueled by an increase in data volume for training ML models, advancements in device computing/storage capabilities, and edge network nodes near data collection points. Techniques such as Federated and Split Learning, applied in horizontal and vertical feature spaces, enable efficient training of machine learning models while maintaining data privacy. Despite the adoption of DI techniques in mobile networks, there are still gaps and areas in mobile network architecture where DI is either not applied or not taking advantage of its full potential. To that end, this paper provides a state-of-the-art overview of the adoption of DI in mobile networks from the perspective of standardization. We identify the importance of machine learning model lifecycle management (ML LCM) and position DI as an extension of that. From this overview, gaps and limitations are identified and presented. To overcome these limitations, we perform a thought experiment which extends ML LCM with DI capability from Core Network (CN) to Radio Access Networks (RAN) which are distributed by nature. In that setting a set of Key Performance Indicators (KPIs) to quantify the performance of different DI techniques are introduced. These KPIs aim at quantifying computational overhead, memory/storage requirements, communication footprint, model efficiency and robustness, flexibility and privacy. We evaluate these KPIs on a RAN use case, that of secondary carrier prediction and show the interplay between model efficiency and computational footprint which are important metrics to consider when migrating from a centralized to a distributed setting. Finally, we provide additional enhancements to improve these KPIs and thus further promote the adoption of DI techniques.}, archive = {J_ACCESS}, author = {Konstantinos Vandikas and Hannes Larsson and Selim Ickin and Hassam Riaz and Dagnachew Azene Temesgene and Hossein Shokri Ghadikolaei and Klaus Raizer and Peter Öhlen and Dinand Roeland and Alexandros Palaios}, doi = {10.1109/ACCESS.2025.3600070}, journal = {IEEE Access}, pages = {149035-149053}, shortjournal = {IEEE Access}, title = {On the adoption of distributed intelligence toward 6G networks}, volume = {13}, year = {2025}, }
• (2025). Investigation into the fuel economy of plug-in hybrid electric vehicles with optimized gear-shifting strategies at different altitudes. ACCESS, 13, 149020-149034. (www)

  @article{ , abstract = {To investigate the fuel economy of plug-in hybrid electric vehicles (PHEVs) across varying altitudes, a forward model integrating internal combustion engine (ICE) performance characteristics at different altitudes is developed. This model served as the basis for optimizing gear-shifting velocity thresholds using a genetic algorithm, tailored to altitude-specific conditions. Moreover, dynamic programming (DP) is leveraged to refine the energy management strategy (EMS) across altitudes spanning 0 to 4000 meters, evaluating its fuel-saving potential. Results demonstrate that increasing altitude adversely impacts fuel consumption for both ICE vehicles (ICEVs) and PHEVs. At 4000 meters, ICEVs exhibit a 6.30% increase in fuel consumption, while PHEVs optimized via DP show a 4.67% increase. The study highlights that altitude-dependent adjustments to gear-shifting thresholds are critical, particularly for upshifting velocity, which increases by 30.23% at 4000 meters. These optimized thresholds evolve at varying rates proportional to altitude. PHEVs consistently outperform ICEVs in fuel efficiency across all tested altitudes, achieving a maximum fuel-saving rate exceeding 26%. This superiority stems from the PHEV’s ability to harness the power of electric propulsion and optimize energy distribution to counteract the ICE’s performance degradation experienced in low-efficiency zones at elevated altitudes. The research concludes that PHEVs, particularly when equipped with an altitude-aware gear-shifting strategy, represent a robust solution for reducing fuel consumption in plateau regions.}, archive = {J_ACCESS}, author = {Tingjie Ba and Liangzhao He and Yongnian Liu and Pingyang Xie and Guisheng Chen and Renxin Xiao}, doi = {10.1109/ACCESS.2025.3602137}, journal = {IEEE Access}, pages = {149020-149034}, shortjournal = {IEEE Access}, title = {Investigation into the fuel economy of plug-in hybrid electric vehicles with optimized gear-shifting strategies at different altitudes}, volume = {13}, year = {2025}, }
• (2025). On the feasibility and optimization of providing delay sensitive services based on bluetooth low energy advertising technology. ACCESS, 13, 149010-149019. (www)

  @article{ , abstract = {Low-cost, short-range, distributed communication technologies have been widely used to provide wireless-related applications in our daily life. Specifically, Bluetooth Low Energy (BLE) is one of the most popular wireless interfaces available in almost all end-user devices. Considering service popularity for unknown users (the general public), the connectionless mode based on BLE advertising technology is highly desirable, because it does not require the device address for connection setup. Among connectionless applications, some services, such as proximity warning, collision warning and entrance detection, are delay sensitive relying on the reception latency of a message. However, the reception performance of BLE connectionless transmissions is highly environmentally dependent, and is influenced by various factors. Hence, the reception latency is a random variable depending on the propagation environment and system parameters. To investigate the feasibility of providing delay sensitive services based on BLE advertising technology, we study the outage performance of connectionless transmissions under a message delivery latency constraint. We model the outage events and derive the outage probability of a delay sensitive service based on BLE advertising technology, taking into consideration the related BLE system parameters and the packet detection probability of the environment. In addition, some outage probability lower bounds and the essential conditions for ensuring good outage performance are presented for performance investigation. From the viewpoint of system optimization, BLE parameter setting and the tradeoff between energy consumption and outage performance are studied based on the numerical results.}, archive = {J_ACCESS}, author = {Yuh-Ren Tsai and Hsin-Yi Liu}, doi = {10.1109/ACCESS.2025.3601116}, journal = {IEEE Access}, pages = {149010-149019}, shortjournal = {IEEE Access}, title = {On the feasibility and optimization of providing delay sensitive services based on bluetooth low energy advertising technology}, volume = {13}, year = {2025}, }
• (2025). Motion tracking for automatically controlled functional electrical stimulation system in mirror therapy configuration: The enhanced lazarus solution. ACCESS, 13, 148992-149009. (www)

  @article{ , abstract = {In this work, we present the potential application of an automatic control system for adjusting the parameter values of Functional Electrical Stimulation (FES). FES is a key enabling technology in the functional recovery of patients undergoing neuro-muscular rehabilitation. In this application, successful stimulation outcomes depend on the accurate setting of patient-specific parameters – such as pulse width and stimulation frequency – which are typically determined by physiotherapists based on their clinical experience. The proposed system is implemented in a Mirror Therapy setup, where an injured limb is controlled via FES to replicate the movement of a healthy limb – either belonging to the same patient or to a physiotherapist. The FES activation signal is derived from surface Electromyography (sEMG) recordings of the healthy limb and transmitted to the FES unit after compression using an Average Threshold Crossing (ATC) algorithm. Optimal FES parameters are adjusted through a closed-loop feedback system based on motion tracking of the injured limb, which is continuously compared to the movement of the healthy limb. Additionally, we explore the possibility of replacing the ATC algorithm used for compressing the sEMG signal from the healthy limb with a Compressed Sensing-based approach. This alternative would enable full reconstruction of the sEMG signal–unlike ATC–at the cost of only a minimal increase in computational effort. A prototype was tested on 22 healthy subjects under five different parameter configurations, confirming the feasibility and adaptability of the proposed system.}, archive = {J_ACCESS}, author = {Elisabetta Spinazzola and Andrea Mongardi and Fabio Rossi and Andrea Prestia and Sara Becchi and Rosanna Cavazzana and Federica Savi and Jacopo Secco and Danilo Demarchi and Fabio Pareschi and Gianluca Setti}, doi = {10.1109/ACCESS.2025.3600583}, journal = {IEEE Access}, pages = {148992-149009}, shortjournal = {IEEE Access}, title = {Motion tracking for automatically controlled functional electrical stimulation system in mirror therapy configuration: The enhanced lazarus solution}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of active elements at main lobe region on intelligent reflecting surface: Deploy it at BS-side or user-side?. ACCESS, 13, 148983-148991. (www)

  @article{ , abstract = {In this paper, the placement of active reflecting elements on an intelligent reflecting surface (IRS) is studied. With a hybrid IRS, the placement problem of finite active elements is vital to performance. To meet the realistic environment, we incorporate the mutual coupling effect into the reflection coefficient calculation. The reflection coefficient of each element is determined by considering the influence of neighbor elements. However, there has been little investigation into which region of the IRS should be illuminated by the main lobe and whether placing active elements in the main lobe region or outside leads to better average user SNR. This important but previously overlooked question is the main focus of this study. To the best of our knowledge, this paper is the first work that analyzes the performance of placing active elements at main lobe or side lobe regions and verifies it through comprehensive simulations. We conduct a series of simulations to validate which main lobe region on IRS yields the highest signal-to-noise ratio (SNR). Additionally, we explore IRS placement strategies to determine the optimal deployment of the hybrid IRS. Simulation results showed that active reflecting elements should be placed in the main lobe region at the center of the IRS, and deploying the hybrid IRS at the user-side achieves the highest SNR value.}, archive = {J_ACCESS}, author = {Fan-Hsun Tseng and Pei-de Guo and Chi-Yuan Chen and Hsin-Hung Cho}, doi = {10.1109/ACCESS.2025.3600488}, journal = {IEEE Access}, pages = {148983-148991}, shortjournal = {IEEE Access}, title = {Performance analysis of active elements at main lobe region on intelligent reflecting surface: Deploy it at BS-side or user-side?}, volume = {13}, year = {2025}, }
• (2025). Accurate center of gravity compensation for improved translational control performance of two-wheeled robot with arm. ACCESS, 13, 148972-148982. (www)

  @article{ , abstract = {Owing to their high center of gravity (CoG) and small footprint, two-wheeled robots (TWRs) are expected to be used in collaborative tasks with humans. When a TWR uses its mounted arm to transport a load, its CoG shifts, deteriorating translational control performance. Conventionally, CoG shift is compensated using the torque applied to the arm by the load. However, because the load-weight information is not considered, gravity compensation is insufficient. This study aims to improve translational control performance through precise gravity compensation. The proposed method is a gravity compensator comprising a model-based compensator, which clarifies the physical characteristics, and a model-error compensator, which addresses errors caused by the load weight. A virtual point is also introduced to improve body stabilization and translational control performance. The proposed method enables precise gravity compensation for a TWR and improves its translational control performance. The experimental results demonstrate the robustness of the proposed method in maintaining translational control performance regardless of the load conditions. This compensation method significantly affects the practical deployment of TWRs in human collaborative environments, such as factories and warehouses.}, archive = {J_ACCESS}, author = {Hirotaka Kanazawa and Yasuhiro Miyata and Masamichi Nawa and Norihiko Kato and Toshiyuki Murakami}, doi = {10.1109/ACCESS.2025.3601563}, journal = {IEEE Access}, pages = {148972-148982}, shortjournal = {IEEE Access}, title = {Accurate center of gravity compensation for improved translational control performance of two-wheeled robot with arm}, volume = {13}, year = {2025}, }
• (2025). DATC-STP: Towards accurate yet efficient spatiotemporal prediction with transformer-style CNN. ACCESS, 13, 148961-148971. (www)

  @article{ , abstract = {Recently, convolutional neural networks (CNNs) or vision transformers (ViTs) based Multi-In-Multi-Out (MIMO) architectures have been proposed to overcome the limitations of recurrent neural networks (RNNs) based Single-In-Single-Out (SISO) architectures. These architectures prevent the inherent limitations of RNNs, such as performance degradation and inefficient parallelization due to sequential properties. However, there are still some challenges. CNN-based MIMO architectures struggle to capture global spatiotemporal information due to the local properties of their convolution kernels. Meanwhile, ViT-based MIMO architectures are less effective at capturing local spatiotemporal information and require a large amount of computational resources due to the self-attention mechanism. To address these limitations and improve MIMO architectures, we propose a novel accurate yet efficient Dual-Attention Transformer-style CNN for Spatiotemporal Prediction (DATC-STP). DATC-STP captures both local and global spatiotemporal information through 3D patch embedding and Transformer-style CNN based attention blocks. Specifically, 3D patch embedding extracts local spatiotemporal features and reduces the size of input data including temporal, height, and width dimensions. Two Transformer-style CNN-based attention blocks treat spatiotemporal data similarly to images and capture global information with CNNs. This structure makes DATC-STP accurate yet efficient. To demonstrate the effectiveness of DATC-STP, we conduct comprehensive experiments on three widely used benchmark datasets, MovingMNIST, TaxiBJ, and KTH. We evaluated that the proposed DATC-STP achieves both competitive accuracy and efficiency. Furthermore, results of the ablation study demonstrate the effectiveness of each component of DATC-STP and highlight the potential of the proposed method.}, archive = {J_ACCESS}, author = {Hyeonseok Jin and Kyungbaek Kim}, doi = {10.1109/ACCESS.2025.3573639}, journal = {IEEE Access}, pages = {148961-148971}, shortjournal = {IEEE Access}, title = {DATC-STP: Towards accurate yet efficient spatiotemporal prediction with transformer-style CNN}, volume = {13}, year = {2025}, }
• (2025). Multi-robot cooperative inspection planning of substation based on genetic algorithm and deep reinforcement learning. ACCESS, 13, 148949-148960. (www)

  @article{ , abstract = {The safe and reliable operation of power systems relies on the intelligent operation and maintenance (O&M) of substations. However, multi-robot collaborative inspection, an essential component of intelligent O&M, faces challenges such as uneven task distribution, inefficient path planning, and underutilized resources. This paper proposes a multi-robot collaborative inspection planning framework that fuses an improved genetic algorithm with deep reinforcement learning. The goal is to achieve a global optimization of substation inspection resources and high-efficiency coordination among the robot systems. First, a three-dimensional intelligent inspection information-sharing system based on the Internet of Things and edge computing is constructed, enabling data interoperability and state perception among heterogeneous inspection devices. Next, a mathematical model for task allocation is established under multiple constraints, such as energy limitations, time windows, and equipment specialization, with a weighted single-objective optimization function integrating energy consumption, time, and reliability. An improved genetic algorithm featuring an adaptive crossover operator and an elite retention strategy is designed to solve the global optimal task allocation scheme. Finally, based on the allocation results, a two-layer deep Q network collaborative path planning method is proposed. Through the design of a reward-penalty mechanism and prioritized experience replay, dynamic obstacle avoidance and cooperative navigation among the robots are achieved. Validation in an actual 500kV intelligent substation environment shows that, compared with traditional single-robot inspection and simple partition-based inspection, the proposed method improves the total inspection time, energy consumption, and equipment utilization by 32.7%, 28.4%, and 41.6%, respectively. The research provides both a theoretical foundation and technical support for the intelligent O&M of power systems and is of great significance for promoting intelligent collaboration among substation inspection robot teams.}, archive = {J_ACCESS}, author = {Jie Liu and Jingsheng Wang and Jiajun Peng and Yongxue Hong and Tianci Shao}, doi = {10.1109/ACCESS.2025.3598345}, journal = {IEEE Access}, pages = {148949-148960}, shortjournal = {IEEE Access}, title = {Multi-robot cooperative inspection planning of substation based on genetic algorithm and deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). DC-RRT*: Dubins-guided curvature RRT* for 3D path planning of unmanned aerial vehicles. ACCESS, 13, 148936-148948. (www)

  @article{ , abstract = {In this paper, we propose a Dubins-guided curvature rapidly exploring random tree* (DC-RRT*), a novel path planning method for unmanned aerial vehicles (UAVs). Different from conventional sampling-based path planning methods, DC-RRT* explicitly considers UAV-specific physical constraints, such as turning radius and vehicle size. Particularly, it employs a curvature-based node expansion strategy that explicitly accounts for both the UAV’s heading direction and minimum turning radius. The method enhances path convergence by attempting direct Dubins path connections between newly generated nodes and the target, while ensuring feasibility through comprehensive full-body collision checking. Our simulation study conducted on four obstacle scenarios, demonstrates that the proposed DC-RRT* generates paths with significantly fewer nodes than existing benchmarking methods, while maintaining computational efficiency.}, archive = {J_ACCESS}, author = {Yekyung Jung and Beom-Seok Oh}, doi = {10.1109/ACCESS.2025.3600283}, journal = {IEEE Access}, pages = {148936-148948}, shortjournal = {IEEE Access}, title = {DC-RRT*: Dubins-guided curvature RRT* for 3D path planning of unmanned aerial vehicles}, volume = {13}, year = {2025}, }
• (2025). Enhancing industrial PCB and PCBA defect detection: An efficient and accurate SEConv-YOLO approach. ACCESS, 13, 148917-148935. (www)

  @article{ , abstract = {Real-time accurate defect detection presents a critical bottleneck in high-throughput Printed Circuit Board Assembly (PCBA) manufacturing. Although contemporary state-of-the-art (SOTA) object detection models excel on diverse general-purpose datasets, their inherent architectural complexity and over-parameterization often render them ill-suited for the highly standardized visual conditions and stringent real-time performance requirements of industrial Printed Circuit Board (PCB) defect inspection. This study addresses this significant performance gap by introducing SEConv-YOLO, a streamlined and powerful object detection model that is meticulously tailored for PCBA Wire defect detection (broken, sweeping, and missing wires). Our approach pioneers a trifecta of architectural innovations: (1) a lightweight Squeeze Excite Convolution (SEConv) feature extraction module, designed to efficiently capture salient defect characteristics while minimizing computational overhead; (2) an advanced Weighted Residual Spatial Pyramid Pooling (WRSPP) feature fusion neck, engineered to enrich contextual information without introducing significant computational bloat; and (3) the novel application of the Normalized Complete Intersection over Union (N-CIoU) loss function, strategically implemented to accelerate model convergence and significantly enhance localization accuracy, particularly for small defects. Rigorous experimentation demonstrates that SEConv-YOLO not only substantially surpasses the baseline YOLOv8n in both speed and accuracy but also consistently outperforms other leading SOTA detectors. This study culminates in a robust, production-ready solution that establishes a new benchmark for automated quality control in PCBA manufacturing, thereby enabling enhanced product reliability and increased operational efficiency.}, archive = {J_ACCESS}, author = {Sze Kit Ong and Chee Keong Tan and Vishnu Monn Baskaran and Boon Keat Puah and Kok Hoong Liew}, doi = {10.1109/ACCESS.2025.3601151}, journal = {IEEE Access}, pages = {148917-148935}, shortjournal = {IEEE Access}, title = {Enhancing industrial PCB and PCBA defect detection: An efficient and accurate SEConv-YOLO approach}, volume = {13}, year = {2025}, }
• (2025). Cross-spectral attention for unsupervised RGB-IR face verification and person re-identification. ACCESS, 13, 148902-148916. (www)

  @article{ , abstract = {Cross-spectral biometrics, such as matching imagery of faces or persons from visible (RGB) and thermal infrared (IR) bands, have rapidly advanced over the last decade because of the increasing sensitivity, size, quality, and ubiquity of IR focal plane arrays and enhanced analytics beyond the visible spectrum. Current techniques for mitigating large spectral disparities between RGB and IR imagery often include learning a discriminative common subspace by exploiting precisely curated data acquired from multiple spectra. Although there are challenges with determining robust architectures for extracting common information, a critical limitation for supervised methods is poor scalability in terms of acquiring labeled data. Therefore, we propose a novel unsupervised cross-spectral framework that combines: 1) a new pseudo triplet loss with cross-spectral voting; 2) a new cross-spectral attention network leveraging multiple subspaces; and 3) structured sparsity to perform more discriminative cross-spectral clustering. We extensively compare our proposed RGB-IR biometric learning framework (and its individual components) with recent and previous state-of-the-art models on two challenging benchmark datasets: DEVCOM Army Research Laboratory Visible-Thermal Face Dataset (ARL-VTF) and RegDB person re-identification dataset, and, in some cases, achieve performance superior to completely supervised methods.}, archive = {J_ACCESS}, author = {Kshitij Nikhal and Cedric Nimpa Fondje and Benjamin S. Riggan}, doi = {10.1109/ACCESS.2025.3601601}, journal = {IEEE Access}, pages = {148902-148916}, shortjournal = {IEEE Access}, title = {Cross-spectral attention for unsupervised RGB-IR face verification and person re-identification}, volume = {13}, year = {2025}, }
• (2025). A LEO satellite mounted 256-element 19 GHz CMOS phased-array transmitter with on-chip amplitude and phase monitor. ACCESS, 13, 148893-148901. (www)

  @article{ , abstract = {This article presents a 256-element CMOS active phased-array transmitter (TX) that works in Ka-band downlink for low earth orbit (LEO) small satellite communication (SATCOM) applications. The phased array adapts an on-chip monitor circuit which can be used to detect the amplitude and phase for calibration. The TX chip’s output 1 dB compression point (OP1dB) is 11dBm and saturated output power (Psat) is 12dBm for every path. The phased array can support single circular polarization (CP) and dual circular polarization modes. The maximum effective isotropic radiated power (EIRP) for the single CP wave can reach 64.6 dBm under a power consumption of 37.4W. The error vector magnitude (EVM) of this work can support 256APSK modulation in both single CP mode (at 2 Gbaud symbol rate) and dual CP modes (at 250Mbaud symbol rate). As for beam steering ability, the phased array can cover a beam angle of ±66°. Compared to the conventional TX, this work has a larger beam angle and EIRP without sacrificing too much power consumption.}, archive = {J_ACCESS}, author = {Xiaolin Wang and Dongwon You and Xi Fu and Gao Yuan and Takeshi OTA and Jill Mayeda and Jun Sakamaki and Ashbir Aviat Fadila and Makoto Higaki and Jumpei Sudo and Hiroshi Takizawa and Masashi Shirakura and Takashi Tomura and Hiroyuki Sakai and Kazuaki Kunihiro and Kenichi Okada and Atsushi Shirane}, doi = {10.1109/ACCESS.2025.3597989}, journal = {IEEE Access}, pages = {148893-148901}, shortjournal = {IEEE Access}, title = {A LEO satellite mounted 256-element 19 GHz CMOS phased-array transmitter with on-chip amplitude and phase monitor}, volume = {13}, year = {2025}, }
• (2025). A 42.5 nW, 0.5 v differential difference transconductance amplifier and its application in low-power universal shadow filter. ACCESS, 13, 148878-148892. (www)

  @article{ , abstract = {This paper presents new low-power universal shadow filters based on an enhanced CMOS structure utilizing multiple-input differential difference current transconductance amplifiers (MI-DDTAs). The multiple inputs of the DDTA are achieved through a differential pair with multiple-input MOS transistors driven simultaneously from both the gate and bulk terminals using DTMOS technique, which increases the total transconductance of the structure. Furthermore, a self-cascode configuration combining regular and low-threshold voltage (LVT) transistors is employed to achieve high output resistance comparable to a standard cascode structure, while maintaining operation in a low-voltage environment. The DDTA operates in the subthreshold region, and simulation results with a supply voltage of 0.5 V show power consumption in the nanowatt range while offering near rail-to-rail operation. The proposed universal filter offers five standard filtering functions such as low-pass filter, high-pass filter, band-pass filter, band-stop filter, and all-pass filter when an input is applied to the input. Thanks to multiple inputs of DDTA, the proposed universal filter is resistor less and it offers both non-inverting and inverting transfer functions of five standard filtering functions. The natural frequency and the quality factor can be electronically controlled by internal parameters. The proposed universal filter can be transferred to work as universal shadow filter, which can control the natural frequency and the quantity factor using external parameters. The proposed shadow filter provides both non-inverting and inverting transfer functions of five standard filtering functions, thus providing 10 filter responses from a single circuit. The natural frequency and the quality factor of all filtering functions can be electronically controlled using external amplifiers. The proposed DDTA and active filters were designed and simulated using the Cadence Virtuoso Analog Design Environment, based on TSMC’s 65-nm 1P9M CMOS technology. The MI-DDTA occupies a chip area of $171~\mu $ m $\times 119~\mu $ m.}, archive = {J_ACCESS}, author = {Montree Kumngern and Fabian Khateb and Tomasz Kulej and Daniel Arbet}, doi = {10.1109/ACCESS.2025.3601372}, journal = {IEEE Access}, pages = {148878-148892}, shortjournal = {IEEE Access}, title = {A 42.5 nW, 0.5 v differential difference transconductance amplifier and its application in low-power universal shadow filter}, volume = {13}, year = {2025}, }
• (2025). Source verification of printed logos for anti-counterfeiting applications. ACCESS, 13, 148859-148877. (www)

  @article{ , abstract = {With the advent of the digital era, the growing prevalence of counterfeit goods poses serious threats to consumer safety and brand integrity, necessitating the development of novel techniques to combat forgery and counterfeiting. This paper focuses on the authentication of printed logos. The problem is formulated within a verification framework and is addressed using three distinct Siamese Neural Network (SNN) architectures: two shallow-and-wide networks and one based on the Xception model. Specifically, we investigate three SNN variants: Multiple Convolutions Summation (MCS-SNN), Multiple Convolutions Concatenation (MCC-SNN), and Mini-Xception SNN. To enhance authentication effectiveness, we adopt a two-step authentication process, beginning with an initial coarse analysis based on chromatic features, followed by a detailed verification that leverages micro-geometric, printer-specific artifacts, with each step focusing on distinct authentication patterns. The results obtained in both closed and open-set conditions indicate promising authentication accuracies, demonstrating the effectiveness of our approach for robust identity verification in real-world scenarios. Furthermore, the lightweight design of these models underscores their practical suitability for deployment on consumer-grade devices, highlighting their potential for real-world anticounterfeiting applications.}, archive = {J_ACCESS}, author = {Nischay Purnekar and Giacomo Cancelli and Anselmo Ferreira and Mauro Barni}, doi = {10.1109/ACCESS.2025.3601854}, journal = {IEEE Access}, pages = {148859-148877}, shortjournal = {IEEE Access}, title = {Source verification of printed logos for anti-counterfeiting applications}, volume = {13}, year = {2025}, }
• (2025). A study of federated deep learning for building indoor climate forecasting. ACCESS, 13, 148838-148858. (www)

  @article{ , abstract = {This study investigates federated deep learning for multi-horizon indoor climate forecasting in historic buildings. Unlike traditional centralized or isolated local learning approaches, this work explores federated learning (FL) as a solution that balances prediction accuracy with data privacy. By combining FL with deep learning, the study addresses challenges in distributed environments, including data heterogeneity and communication efficiency. Eight deep learning architectures and six FL algorithms have been evaluated across three learning cases: local learning, centralized learning, and FL. The case study has used several years of hourly data on indoor temperature, relative humidity, and CO2 concentration from three public historic buildings, along with outdoor weather conditions. It has been shown that time-series dense encoder (TiDE) and gated recurrent units (GRU) consistently outperformed other architectures, capturing temporal dependencies effectively across all cases. Among FL algorithms, federated averaging (FedAvg) and federated median (FedMedian) demonstrated the best overall performance, with fast convergence and low validation loss. A comparison of the three learning cases revealed distinct trade-offs. Centralized learning typically achieved the highest predictive accuracy but required full data aggregation, raising privacy concerns. Local learning ensured data privacy but performed worse than centralized learning and FL due to limited amount of data. FL offered a balance, enabling collaborative model training without sharing raw data. TiDE combined with FedAvg or FedMedian achieved accuracy within 5–10% of the centralized models, measured by the coefficient of variation of the root mean squared error (CV-RMSE), making it a promising approach for privacy-sensitive scenarios. These results highlight the potential of FL for accurate and privacy-preserving indoor climate forecasting. The proposed approach supports scalable, institution-level collaboration without compromising data confidentiality. It is especially relevant for facility managers and heritage conservationists who are seeking data-driven indoor climate control in sensitive heritage buildings.}, archive = {J_ACCESS}, author = {Zhongjun Ni and Magnus Karlsson and Shaofang Gong}, doi = {10.1109/ACCESS.2025.3601973}, journal = {IEEE Access}, pages = {148838-148858}, shortjournal = {IEEE Access}, title = {A study of federated deep learning for building indoor climate forecasting}, volume = {13}, year = {2025}, }
• (2025). Relative output feedback-based dynamic event-triggered secure consensus of multi-agent system with DoS attack. ACCESS, 13, 148827-148837. (www)

  @article{ , abstract = {This paper investigates the consensus problem of linear multi-agent systems (MASs) with relative output feedback under denial-of-service (DoS) attacks. A distributed control protocol based on relative output feedback is proposed, incorporating a dynamic event-triggered sampling strategy. By using the piecewise Lyapunov method, the convergence and divergence rates during attack-free periods and attack periods are obtained, thereby deriving the conditions for the system to achieve consensus, i.e., the attack frequency and duration of the attack model. Theoretical analysis proves that the event-triggering scheme excludes the Zeno behavior in the closed-loop system, and reconstructs the continuous-time relative information required for sampling errors, avoiding the need for continuous communication in triggering. Finally, numerical simulations are conducted to verify the effectiveness of the proposed scheme.}, archive = {J_ACCESS}, author = {Yi-Neng Xiong and Ke-Xin Cai and Yu-Cheng Yang}, doi = {10.1109/ACCESS.2025.3594720}, journal = {IEEE Access}, pages = {148827-148837}, shortjournal = {IEEE Access}, title = {Relative output feedback-based dynamic event-triggered secure consensus of multi-agent system with DoS attack}, volume = {13}, year = {2025}, }
• (2025). Calibrating sentiment analysis: A unimodal-weighted label distribution learning approach. ACCESS, 13, 148816-148826. (www)

  @article{ , abstract = {Label distribution learning (LDL) provides a robust framework for classification tasks exhibiting intrinsic label ordinality and ambiguity, yet its application to sentiment analysis remains underexplored. This work bridges this research gap through a comprehensive investigation of LDL for sentiment classification, revealing both strengths (predictive accuracy) and limitations (calibration reliability). To address these limitations, we propose Unimodal-Weighted LDL (UW-LDL), a novel framework that: 1) enforces unimodal label distributions via ordinality-grounded regularization, and 2) implements instance-specific calibration through adaptive weighted loss functions. Extensive experiments across five diverse benchmarks—including financial-domain datasets (AFS, FP, TFNS) and other sentiment corpora (SST-5, TSE)—demonstrate that UW-LDL outperforms state-of-the-art LDL methods and calibration techniques by significant margins in both predictive accuracy and uncertainty quantification. Furthermore, UW-LDL achieves these gains with single-pass inference, offering faster computation than ensemble-based calibration methods. Visualization analyses confirm that UW-LDL produces more discriminative and linearly separable text representations than conventional LDL approaches.}, archive = {J_ACCESS}, author = {Zhenze Liu and Shijing Si and Jiawen Gu}, doi = {10.1109/ACCESS.2025.3601610}, journal = {IEEE Access}, pages = {148816-148826}, shortjournal = {IEEE Access}, title = {Calibrating sentiment analysis: A unimodal-weighted label distribution learning approach}, volume = {13}, year = {2025}, }
• (2025). Traffic patterns in networks-on-chip: A survey. ACCESS, 13, 148803-148815. (www)

  @article{ , abstract = {Networks-on-chip (NoCs) widespread in computing system design are used in many applications. A critical NoC aspect is the interaction between computing cores and in particular the traffic patterns. The relevance of this work stems from the fact that traffic patterns in NoCs are poorly understood and have no general classification; and there are no universal generators and benchmarks for synthetic traffic patterns. The goal of the work is to investigate and classify the known data traffic patterns and find new possible patterns in communication between NoC cores. To achieve our goals, we compiled a detailed classification of synthetic and realistic traffic patterns in the form of a classification tree. The analysis of the existing benchmarks is done, and new specific benchmarks and traffic generation approaches for NoCs are provided. An analysis of existing benchmarks for NoCs and related areas was conducted. Based on the results of the analysis, we proposed two new synthetic benchmarks, as well as possible improvements and innovations for realistic benchmarks.}, archive = {J_ACCESS}, author = {Mikhail A. Malikov and Aleksandr Y. Romanov}, doi = {10.1109/ACCESS.2025.3601971}, journal = {IEEE Access}, pages = {148803-148815}, shortjournal = {IEEE Access}, title = {Traffic patterns in networks-on-chip: A survey}, volume = {13}, year = {2025}, }
• (2025). Attention-enhanced temporal LSTM network as a sonar position predictor for side-scanning sonar waterfall image. ACCESS, 13, 148792-148802. (www)

  @article{ , abstract = {Analysis of the river bottom using side-scan sonar allows for obtaining the so-called “waterfall” image - a scrolling visualization of sonar reflections from the bottom and underwater objects. This type of image allows for a detailed characterization of the bottom structure and detection of various objects. This image is created based on the measurement of the return time of the sound wave reflected from various surfaces and the position of the sonar. Unfortunately, quite often, objects (such as bridges) contribute to the loss of GPS signals and, consequently, the generation of distortions in this area. In this paper, we present a recurrent neural network model with attention modules to predict the lost GPS signal during measurements. The proposed predictor architecture combines local processing with a deep tower of alternating unidirectional and bidirectional LSTM (Long Short-Term Memory) layers with residual connections. Furthermore, we implemented multihead self-attention to capture global temporal dependencies and RevIN (Reversible Instance Normalization), which allows for adaptation to changing input data distributions. The evaluation of the methodology was tested on data collected in real conditions on the Odra River in Szczecin, Poland. The results indicate a very good adaptation of the predictor to GPS signal correction during its loss under bridges.}, archive = {J_ACCESS}, author = {Piotr Żerdziński and Dawid Połap and Natalia Wawrzyniak and Grzegorz Zaniewicz and Adam Zielonka}, doi = {10.1109/ACCESS.2025.3601188}, journal = {IEEE Access}, pages = {148792-148802}, shortjournal = {IEEE Access}, title = {Attention-enhanced temporal LSTM network as a sonar position predictor for side-scanning sonar waterfall image}, volume = {13}, year = {2025}, }
• (2025). A feedback-enhanced graph generative model with tunable features. ACCESS, 13, 148782-148791. (www)

  @article{ , abstract = {Graphs are data structures that represent abstract relationships between elements and are widely used across various domains. The ability to generate graphs with specific features is crucial for many applications. Recent advances in deep learning have led to progress in graph generation, enabling models to reproduce structural features observed in real-world graphs. A significant recent focus in this area is conditional graph generation, which allows models to tune desired features during the generation process. Among recent efforts in this direction, GraphTune was developed to support continuous tuning of graph features. Building on this approach, this paper introduces F-GraphTune, a model designed to improve the accuracy of feature specification in generated graphs. F-GraphTune incorporates a Feature Estimator (FE) into the GraphTune framework, which provides feedback on the feature values of the generated graphs. Experiments using a real-world graph dataset demonstrate that F-GraphTune outperforms existing models, including GraphTune, with up to a 53.7% improvement in accuracy of feature specification.}, archive = {J_ACCESS}, author = {Takahiro Yokoyama and Yoshiki Sato and Sho Tsugawa and Kohei Watabe}, doi = {10.1109/ACCESS.2025.3599442}, journal = {IEEE Access}, pages = {148782-148791}, shortjournal = {IEEE Access}, title = {A feedback-enhanced graph generative model with tunable features}, volume = {13}, year = {2025}, }
• (2025). Ozone generation reaction rate estimation from needle-plate partial discharges using experimental data and a finite element model. ACCESS, 13, 148768-148781. (www)

  @article{ , abstract = {In this work, an ozone reaction chamber was used to provide experimental data to numerically evaluate the ozone generation reaction rate in a high-voltage dielectric-barrier needle-plate setup operating at 60 Hz. Ozone is thus produced by partial discharges. Ozone concentrations were measured under controlled environmental conditions, with experiments conducted at four voltage levels in the range from 5.0 $\text {kV}_{\text {RMS}}$ to 6.5 $\text {kV}_{\text {RMS}}$ and under four relative humidity levels in the interval from 30% to 70%. The results demonstrate that increasing relative humidity along this range causes a reduction of up to 73% in the ozone production reaction rate and a 67% decrease in ozone concentration at $6.5~\text {kV}_{\text {RMS}}$ . In addition to ozone concentration, the average energy of partial discharge pulses was also recorded for each test condition. A finite element fluid dynamics model was developed and employed, providing a framework and a formula for calculating ozone production reaction rates based on experimental results of ozone concentrations.}, archive = {J_ACCESS}, author = {Rodrigo M. S. De Oliveira and Licinius D. S. De Alcantara and Caio B. O. De Oliveira and Nathan M. Lopes and Ronaldo F. Zampolo and Victor Dmitriev}, doi = {10.1109/ACCESS.2025.3599689}, journal = {IEEE Access}, pages = {148768-148781}, shortjournal = {IEEE Access}, title = {Ozone generation reaction rate estimation from needle-plate partial discharges using experimental data and a finite element model}, volume = {13}, year = {2025}, }
• (2025). A lossless layered-based non-encoding plain text compression and encryption algorithm utilizing binary fragmentation and defragmentation technique. ACCESS, 13, 148753-148767. (www)

  @article{ , abstract = {This paper proposes an innovative fusion of encryption and compression techniques that optimizes data handling and security. The approach employs a layered non-encoding encryption-compression algorithm DQRc (Data in Quick Response Code) that attempts to redefine data compression with layer-by-layer key-based encryption. This paper marks a transformative milestone in data optimization and protection, it offers a solution that not only efficiently compresses data but also safeguards it with robust encryption. In comparative analyses with convectional encryption-compression algorithms, the proposed method demonstrates significant improvements across several key performance indicators. The DQRc algorithm notably reduces processing times, achieving faster encryption and decryption cycles which is essential for time-sensitive applications. Furthermore, its design ensures completely lossless decompression, thereby delivering 100% data accuracy—a critical enhancement over existing methods that sometimes incur minor data losses. In terms of compression efficiency, the algorithm excels by achieving a higher compression percentage, measured by the absolute difference between the actual file size and the compressed size. This superior compression capability enables more effective storage utilization and faster data transmission without compromising the integrity of the original information. Overall, the DQRc algorithm represents a significant advancement in secure data management, providing an optimal blend of speed, accuracy, and compression efficiency.}, archive = {J_ACCESS}, author = {Pratham Mishra and Sunil Pathak and Bhupesh Kumar Singh and Mayuree Katara and Gireesh Kumar}, doi = {10.1109/ACCESS.2025.3600813}, journal = {IEEE Access}, pages = {148753-148767}, shortjournal = {IEEE Access}, title = {A lossless layered-based non-encoding plain text compression and encryption algorithm utilizing binary fragmentation and defragmentation technique}, volume = {13}, year = {2025}, }
• (2025). Dense stack meta ensemble classifier (DSMEC): An advanced stacking ensemble approach for early cardiovascular disease prediction with robust feature engineering. ACCESS, 13, 148735-148752. (www)

  @article{ , abstract = {Cardiovascular disease remains a major global health issue requiring accurate early risk prediction to enable timely intervention. Traditional approaches often fail to capture complex feature interactions, limiting diagnostic accuracy. Advanced statistical methods, domain-specific feature engineering, and modern machine learning (ML) methods can significantly enhance prediction performance. This study proposes Dense Stack Meta-Ensemble Classifier (DSMEC), a two-tier advanced stacking ensemble model that leverages robust feature engineering on 70,000 demographic, clinical, and lifestyle Kaggle dataset to improve CVD risk prediction. DSMEC integrates eight optimized base classifiers, Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), LightGBM, XGBoost, CatBoost, Multilayer Perceptron (MLP), and Artificial Neural Network (ANN), fine-tuned using RandomizedSearchCV with StratifiedKFold cross-validation. A Deep Neural Network (DNN) meta-model aggregates their predictions to capture complex and non-linear data interactions. Missing value imputation, Kolmogorov–Smirnov test for distribution analysis, SMOTE for class balancing, and RobustScaler normalization are used for rigorous data preprocessing. Domain-specific features, including pulse pressure, mean arterial pressure, body mass index (BMI), and systolic-to-diastolic pressure ratio are generated, and the Mann–Whitney U test is utilized to assess their statistical significance. Further interaction-based features are generated using Chi-Squared tests ( $\chi ^{2}$ ), followed by dimensionality reduction using PCA to retain 95% of the data variance. DSMEC outperformed traditional and state-of-the-art methods, achieving 96.80% accuracy and a 95.66% ROC-AUC score, a 24.92% improvement in accuracy. Strategic feature engineering and hierarchical ensemble learning enables DSMEC to capture complex clinical data interdependencies, making it effective for early CVD risk prediction.}, archive = {J_ACCESS}, author = {Dewan Ahmed Muhtasim and Rishad Amin Pulok and Md. Fahmidur Rahman Sakib and Ruhul Amin and Bushra Azmat Hussain and Muhammad Muzammil and Md. Mahfujul Hasan and Siok Yee Tan}, doi = {10.1109/ACCESS.2025.3598285}, journal = {IEEE Access}, pages = {148735-148752}, shortjournal = {IEEE Access}, title = {Dense stack meta ensemble classifier (DSMEC): An advanced stacking ensemble approach for early cardiovascular disease prediction with robust feature engineering}, volume = {13}, year = {2025}, }
• (2025). Long range CCTV-based context-aware petroglyphs surface change classification using dual attention-guided pyramid siamese network. ACCESS, 13, 148717-148734. (www)

  @article{ , abstract = {Advancements in deep learning and image acquisition technologies have significantly enhanced intelligent remote monitoring capabilities across various applications. Despite these advancements, there is a notable lack of longitudinal studies utilizing long range CCTV images to analyze surface changes in cultural heritage sites. This paper introduces a novel context-aware method for classifying petroglyph surface changes, leveraging deep learning to address this gap. Our proposed method comprises two main components: the context-aware sample selector module (CSSM) and the dual attention-guided pyramid siamese network (DAP-SiamNet). The CSSM enhances data quality by eliminating poor-quality images and detecting anomalies based on environmental context, followed by image normalization and registration. DAP-SiamNet focuses on extracting and integrating critical global features related to surface changes through the sequential feature amplification module and the deep feature consolidation module. We evaluated our methodology using two newly created datasets: a petroglyph anomaly detection dataset and a petroglyph change classification dataset. The experimental results demonstrate that the proposed model achieves outstanding performance with an accuracy of 0.863, an F1 score of 0.875, and an AUC of 0.932, verifying its effectiveness through rigorous field testing. This work provides a reliable solution for long-term monitoring of surface changes in cultural heritage.}, archive = {J_ACCESS}, author = {Dohyung Kwon and Jeonghwan Gwak and Jeongmin Yu}, doi = {10.1109/ACCESS.2025.3598378}, journal = {IEEE Access}, pages = {148717-148734}, shortjournal = {IEEE Access}, title = {Long range CCTV-based context-aware petroglyphs surface change classification using dual attention-guided pyramid siamese network}, volume = {13}, year = {2025}, }
• (2025). A smart fuzzy risk assessment system for epidemic prevention on cruise ships. ACCESS, 13, 148699-148716. (www)

  @article{ , abstract = {Cruise ships are a popular vacation choice, but their usually crowded conditions and enclosed spaces make them vulnerable to outbreaks of airborne infectious diseases. Risk assessment (RA) of epidemic outbreaks on cruise ships has traditionally relied on knowledge-based and probabilistic models, focusing on long-term predictions, with only a few studies considering the effect of short-term disease transmission. However, these methods face the following limitations: a) they do not utilize sensors for real-time monitoring, b) they require information that is unavailable in real-time conditions, c) they consider a limited number of risk factors in the RA process, and d) they lack comprehensive control measures. However, these methods face the following limitations: a) they do not utilize sensors for real-time monitoring, b) they require information that is unavailable in real-time conditions, c) they consider a limited number of risk factors in the RA process, and d) they lack comprehensive control measures. To address these challenges, a novel sensor-based RA system for real-time monitoring and disease control is proposed. The proposed RA system, named Healthy Ship Risk Assessment (HSRA), includes the following components: 1) a network of smart sensors for continuous monitoring of indoor ship areas and detection of symptomatic passengers; 2) a data analysis module intended to utilize deep learning methods for symptom detection; 3) a fuzzy rule-based decision module, developed using domain knowledge to compensate for data limitations; 4) a risk index derived from the inference process that corresponds to the spread of the disease onboard; and 5) adaptive safety guidelines for the ship personnel based on the risk estimated by the system. The system is validated using an agent-based model (ABM) and achieves an accuracy of 87% for experiments conducted across areas of a representative cruise ship with different characteristics, demonstrating its effectiveness in real-world conditions.}, archive = {J_ACCESS}, author = {Georgios Triantafyllou and Georgia Sovatzidi and George Dimas and Panagiotis G. Kalozoumis and Dimitris K. Iakovidis}, doi = {10.1109/ACCESS.2025.3600859}, journal = {IEEE Access}, pages = {148699-148716}, shortjournal = {IEEE Access}, title = {A smart fuzzy risk assessment system for epidemic prevention on cruise ships}, volume = {13}, year = {2025}, }
• (2025). Drug repositioning for childhood acute lymphoblastic leukemia using an explainable regularized bi-LSTM ensemble and molecular docking validation. ACCESS, 13, 148684-148698. (www)

  @article{ , abstract = {Childhood acute lymphoblastic leukemia is a fast-progressing blood cancer marked by uncontrolled proliferation of immature lymphoid cells. Although modern treatment options such as chemotherapy and immunotherapy have increased survival rates, recurrence and drug resistance remain critical challenges. In this study, we present a novel explainable deep learning framework for predicting protein-ligand interactions and identifying repositionable drugs for this disease. Among the five tested architectures, a regularized Bi-LSTM + Bi-LSTM ensemble achieved the best performance, with a test mean absolute error (MAE) of 0.041 and a test loss of 0.210, outperforming other models. Explainability techniques such as Integrated Gradients and LIME revealed key sequence features in both SMILES and protein data contributing to predictions. Molecular docking validation using PandaDock confirmed strong binding affinities for selected ligands, such as Copanlisib (-207.11 kcal/mol) and Dutasteride (-171.17 kcal/mol), in alignment with model outputs. Literature-based evidence further supported the therapeutic relevance of identified compounds. This study demonstrates that explainable deep learning combined with molecular docking provides a robust strategy for drug repositioning in childhood acute lymphoblastic leukemia.}, archive = {J_ACCESS}, author = {Utku Kose and Ilhan Uysal}, doi = {10.1109/ACCESS.2025.3599025}, journal = {IEEE Access}, pages = {148684-148698}, shortjournal = {IEEE Access}, title = {Drug repositioning for childhood acute lymphoblastic leukemia using an explainable regularized bi-LSTM ensemble and molecular docking validation}, volume = {13}, year = {2025}, }
• (2025). Sensing and characterizing the radio frequency field scattered by a human target in an indoor environment. ACCESS, 13, 148668-148683. (www)

  @article{ , abstract = {In this article, we present a comprehensive exploration of human-induced radio frequency (RF) scattering in indoor environments using a distributed antenna system, supported by the Physical Theory of Diffraction (PTD). Our methodology involves extracting the diffracted field induced by a human target from the forward transmission coefficients of the scattering-parameter (S-parameter) multiport channel matrix. This extraction is performed using two signal processing approaches: the Matrix Pencil Method (MPM) and time-domain gating. To improve efficiency, we further propose a finite rate of innovation (FRI)-based time-domain gating technique that reduces the computational burden while maintaining high resolution, enabling reliable extraction of scattering components. The results are cross-validated with PTD predictions based on a rounded-edge wedge model, which serves as a physically grounded reference for interpreting the spectral content of the scattered field. By comparing the strengths and limitations of each method, we demonstrate how our framework enables the identification of key scattering features, such as shadow radiation and creeping waves, from experimental RF measurements. Furthermore, we leverage the Para-Hermitian Eigenvalue Decomposition (PhEVD) to capture variations in the spatial and spectral structure of the scattered field caused by human presence. This study provides valuable insights into the nuanced interplay between human-induced scattering and RF propagation in indoor environments.}, archive = {J_ACCESS}, author = {Frank E. Ebong and Andrea M. Tonello}, doi = {10.1109/ACCESS.2025.3601364}, journal = {IEEE Access}, pages = {148668-148683}, shortjournal = {IEEE Access}, title = {Sensing and characterizing the radio frequency field scattered by a human target in an indoor environment}, volume = {13}, year = {2025}, }
• (2025). Advancing generalization in deepfake detection: Supervised contrastive representation learning with dual stream spatio-temporal features. ACCESS, 13, 148646-148667. (www)

  @article{ , abstract = {Deepfake technologies have rapidly evolved, enabling highly realistic facial manipulations that are increasingly difficult to detect. However, existing detection models remain limited in their ability to generalize beyond the manipulation techniques used during training. As deepfake generation methods continue to diversify, enhancing generalization has become critical for deployment in real-world scenarios. To address this challenge, this paper proposes a robust and generalizable deepfake detection framework based on supervised contrastive learning. Rather than overfitting to generation-specific artifacts, the proposed method learns discriminative representations by integrating domain- and similarity-aware contrastive loss with distributional regularization. The framework adopts a dual-stream architecture consisting of a 3D CNN (I3D with Non-Local Blocks) to capture temporal dynamics and a 2D CNN (ResNet) for spatial features. The extracted features are fused and passed to a support vector machine (SVM) classifier to refine decision boundaries. Extensive experiments on FaceForensics++, Celeb-DF, and DFDC datasets demonstrate that the proposed model achieves superior generalization performance across diverse and unseen deepfake generation techniques, outperforming existing methods in cross-dataset settings. Additionally, explainability analyses validate the model’s focus on meaningful facial regions. Appendix experiments also highlight its potential for efficient deployment with minimal performance loss.}, archive = {J_ACCESS}, author = {Sangho Son and Wooju Kim}, doi = {10.1109/ACCESS.2025.3598782}, journal = {IEEE Access}, pages = {148646-148667}, shortjournal = {IEEE Access}, title = {Advancing generalization in deepfake detection: Supervised contrastive representation learning with dual stream spatio-temporal features}, volume = {13}, year = {2025}, }
• (2025). Adversarial challenges in network intrusion detection systems: Research insights and future prospects. ACCESS, 13, 148613-148645. (www)

  @article{ , abstract = {Machine learning has brought significant advances in cybersecurity, particularly in the development of Intrusion Detection Systems (IDS). These improvements are mainly attributed to the ability of machine learning algorithms to identify complex relationships between features and effectively generalize to unseen data. Deep neural networks, in particular, contributed to this progress by enabling the analysis of large amounts of training data, significantly enhancing detection performance. However, machine learning models remain vulnerable to adversarial attacks, where carefully crafted input data can mislead the model into making incorrect predictions. While adversarial threats in unstructured data, such as images and text, have been extensively studied, their impact on structured data like network traffic is less explored. This survey aims to address this gap by providing a comprehensive review of machine learning-based Network Intrusion Detection Systems (NIDS) and analyzing their susceptibility to adversarial attacks, with a specific focus on evasion attacks due to their prevalence and real-world relevance. We critically examine existing research in NIDS, highlighting key trends, strengths, and limitations, while identifying areas that require further exploration. We also provide practical recommendations for adapting adversarial strategies to network-specific constraints. Additionally, we discuss emerging challenges in the field and offer insights for the development of more robust and resilient NIDS. In summary, this paper not only enhances the understanding of adversarial attacks and defenses in NIDS but also connects theoretical and practical perspectives, providing actionable guidance for future research to improve the robustness of machine learning models in cybersecurity applications.}, archive = {J_ACCESS}, author = {Sabrine Ennaji and Fabio de Gaspari and Dorjan Hitaj and Alicia Kbidi and Luigi Vincenzo Mancini}, doi = {10.1109/ACCESS.2025.3600984}, journal = {IEEE Access}, pages = {148613-148645}, shortjournal = {IEEE Access}, title = {Adversarial challenges in network intrusion detection systems: Research insights and future prospects}, volume = {13}, year = {2025}, }
• (2025). ICDRF: Indian coin denomination recognition framework. ACCESS, 13, 148595-148612. (www)

  @article{ , abstract = {Coins are commonly used in everyday transactions at supermarkets, retail shops, metro stations, and other locations. However, recognizing Indian coin denominations poses a challenge for visually impaired persons due to the lack of a robust coin denomination recognition system. To address this issue, we introduce the Indian Coin Denomination Recognition Network (ICDRNet), a deep convolutional neural network (CNN) tailored for the visual recognition of Indian coin denominations. ICDRNet combines densely connected convolutional layers with depthwise separable dense blocks, incorporating the Convolutional Block Attention Module (CBAM) and a novel Dilation Enabled Inverse Bottleneck (DEIB) block. The integration of CBAM enhances the model’s ability to highlight critical features across different coins by prioritizing essential spatial and channel information, thereby improving recognition accuracy. The DEIB block employs dilated convolutions to efficiently capture detailed features across various scales, focusing on subtle distinctions among coin denominations with minimal computational overhead. Furthermore, we present the Indian Metal Currency Dataset, a comprehensive collection of images representing Indian coins under diverse lighting, background, and environmental conditions, facilitating a robust evaluation of coin recognition performance in realistic scenarios. Experimental results show that the proposed method shows improved performance in identifying coin denominations compared to prior methods on the proposed Indian Metal Currency Dataset (IMCD), Indian Coin Currency Dataset (ICCD), Indian Coin Denomination Dataset (ICDD) & Comprehensive Image Dataset of Contemporary Indian Coins (CIDCIC) datasets. ICDRNet achieves an impressive performance with F1-score of 97.79%, 98.38%, 96.7% and 99.7% on IMCD, ICCD, ICDD & CIDCIC datasets, respectively.}, archive = {J_ACCESS}, author = {Muhammad Suhaib Kanroo and Hadia Showkat Kawoosa and Kapil Rana and Puneet Goyal}, doi = {10.1109/ACCESS.2025.3599594}, journal = {IEEE Access}, pages = {148595-148612}, shortjournal = {IEEE Access}, title = {ICDRF: Indian coin denomination recognition framework}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on scaling machine learning workflows using cloud technologies and DevOps. ACCESS, 13, 148559-148594. (www)

  @article{ , abstract = {Scaling Machine Learning (ML) workflows in cloud environments presents critical challenges in ensuring reproducibility, low-latency inference, infrastructure reliability, and regulatory compliance. This review addresses the lack of a comprehensive synthesis of how integrated DevOps practices and cloud-native technologies enable scalable, production-grade ML systems. We analyze the convergence of MLOps with tools such as Kubernetes, Jenkins, and Terraform, detailing their role in automating CI/CD pipelines, infrastructure provisioning, and model lifecycle management. The main highlights strategies for optimizing resource utilization, minimizing inference latency, and managing data versioning across hybrid and multi-cloud architectures (AWS, Azure, GCP). We also examine serverless computing, container orchestration, and monitoring practices to enhance scalability and governance. By categorizing challenges chronologically and evaluating emerging practices such as federated learning and security-by-design, this work bridges a key gap in existing literature. It offers a unified perspective on building reliable, reproducible, and compliant ML workflows, thereby advancing the state of scalable AI system engineering.}, archive = {J_ACCESS}, author = {G. Ramesh and T. Vaikunta Pai and Ramona Birau and Karthik K. Poojary and Abhay and Aishwarya R. Shingad and N. Sowjanya and Virgil Popescu and Adrian T. Mitroi and Roxana Mihaela Nioata and K. M. Kiran Raj}, doi = {10.1109/ACCESS.2025.3599281}, journal = {IEEE Access}, pages = {148559-148594}, shortjournal = {IEEE Access}, title = {A comprehensive review on scaling machine learning workflows using cloud technologies and DevOps}, volume = {13}, year = {2025}, }
• (2025). An adaptive multi-objective decision system with reinforcement learning for PV-ESS sizing and operation for grid-connected industrial PV-ESS systems in distribution networks. ACCESS, 13, 148547-148558. (www)

  @article{ , abstract = {The integration of photovoltaic (PV) systems with energy storage systems (ESS) introduces complex design and operational challenges due to the stochastic nature of solar generation and the dynamic behavior of energy markets. This paper presents a reinforcement learning -enhanced Decision Support System (DSS). The system jointly addresses the optimal sizing and real-time control of PV-ESS configurations. The proposed framework combines a multi-objective optimization core with a policy-gradient RL agent. This agent jointly optimizes operational decisions and dynamically adjusts the weights in the multi-objective function—based on real-time feedback and system state—to reflect the relative importance of competing objectives such as cost, emissions, curtailment and battery degradation. These adjustments reflect evolving environmental, market and load conditions. Historical and real-time data from utility-scale PV parks and industrial consumers inform the learning process, ensuring policy relevance and robustness. The system is designed for grid-connected industrial sites embedded in distribution networks, where node-level optimization can indirectly support peak reduction, load smoothing and demand-side flexibility at the network level. The system utilizes a policy-gradient reinforcement learning agent with adaptive weight tuning to balance conflicting objectives. Simulation results over a two-year horizon demonstrate that the proposed RL-DSS reduces average energy costs by up to 35%, increases PV self-consumption above 70% and lowers curtailed energy below 8%, outperforming static and rule-based strategies. The results validate the potential of RL-based decision systems to enable scalable, flexible and economically viable energy management under real-world uncertainty. Future work includes integration with probabilistic forecasting models and distributed multi-agent coordination, where each industrial node may act as an autonomous RL agent contributing to cooperative grid-level objectives.}, archive = {J_ACCESS}, author = {Athanasios C. Zournatzis and Eleni P. Nicolopoulou and Vassiliki T. Kontargyri and Christos A. Christodoulou}, doi = {10.1109/ACCESS.2025.3600985}, journal = {IEEE Access}, pages = {148547-148558}, shortjournal = {IEEE Access}, title = {An adaptive multi-objective decision system with reinforcement learning for PV-ESS sizing and operation for grid-connected industrial PV-ESS systems in distribution networks}, volume = {13}, year = {2025}, }
• (2025). Graph databases for group decision making in industry: A comprehensive literature review. ACCESS, 13, 148533-148546. (www)

  @article{ , abstract = {Virtual manufacturing, simulation, and optimization provide a wealth of knowledge about the possibilities of future production systems to support decision makers. However, this knowledge usually remains with a handful of domain experts, is hard to share even within the same team and cannot be easily used to support further optimization activities. At the same time, simulations can benefit from the incorporation of linked data from real factories once a process is running, to improve the accuracy of the simulation and hence the accuracy of optimization results. Graph databases provide a possible approach to storing and managing this form of interrelated heterogeneous data, with powerful querying capabilities that can identify important or interesting patterns that might otherwise remain hidden. This paper provides a structured literature review of the current directions of research within this broad but interconnected set of topics, with a special focus on how graphs can support finding knowledge within Virtual Factories, used by larger teams for industrial planning and optimization. Papers have been selected from a wide number of research databases and reviewed from the perspectives of the industries they focus on, the technologies they focus on and the approach they have taken. We conclude that current research focuses on one or two aspects of this problem but does not address all at once, despite the potential benefits of the combination.}, archive = {J_ACCESS}, author = {Richard Senington and Amos H. C. Ng and Ludwig Mittermeier and Sunith Bandaru}, doi = {10.1109/ACCESS.2025.3596632}, journal = {IEEE Access}, pages = {148533-148546}, shortjournal = {IEEE Access}, title = {Graph databases for group decision making in industry: A comprehensive literature review}, volume = {13}, year = {2025}, }
• (2025). Robust adaptive filtering with the hyperbolic tangent exponential kernel M-estimator function for active noise control. ACCESS, 13, 148522-148532. (www)

  @article{ , abstract = {In this work, we propose a robust adaptive filtering approach for active noise control applications in the presence of impulsive noise. In particular, we develop the filtered-x hyperbolic tangent exponential generalized Kernel M-estimate function (FXHEKM) robust adaptive algorithm. A statistical analysis of the proposed FXHEKM algorithm is carried out along with a study of its computational cost. In order to evaluate the proposed FXHEKM algorithm, the mean-square error (MSE) and the average noise reduction (ANR) performance metrics have been adopted. Numerical results show the efficiency of the proposed FXHEKM algorithm to cancel the presence of the additive spurious signals, such as $\alpha $ -stable noises against competing algorithms.}, archive = {J_ACCESS}, author = {Iam Kim De S. Hermont and André R. Flores and Rodrigo C. De Lamare}, doi = {10.1109/ACCESS.2025.3601570}, journal = {IEEE Access}, pages = {148522-148532}, shortjournal = {IEEE Access}, title = {Robust adaptive filtering with the hyperbolic tangent exponential kernel M-estimator function for active noise control}, volume = {13}, year = {2025}, }
• (2025). 2.4-GHz CMOS power amplifier with common-mode self-rejection technique to suppress even harmonics. ACCESS, 13, 148510-148521. (www)

  @article{ , abstract = {In this study, we propose a common-mode self-rejection (CMSR) technique to improve the linearity of CMOS differential power amplifiers. The proposed CMSR technique is implemented through a matching network consisting of a Guanella transformer between the driver and power stages of the differential power amplifier. The CMSR structure using the Guanella transformer serves as a matching network for the fundamental frequency and at the same time rejects even harmonics, thereby improving the linearity of the power amplifier. We design a 2.4-GHz differential power amplifier using a 130-nm PD-SOI CMOS process to verify the feasibility of the proposed CMSR technique. When a continuous wave signal is used, the P1dB and peak power-added efficiency (PAE) are measured as 25.51 dBm and 35.39% at 2.4 GHz, respectively. The second harmonic component in the low output power region and P1dB are −42.55 dBc and −55.4 dBc, respectively. When the 802.11n WLAN modulated signal of 64-QAM is used, the measured PAVG and PAE are 18.57 dBm and 15.97% under conditions where EVM is less than 3.98%, respectively. Additionally, when a 64-QAM LTE signal with a bandwidth of 20 MHz is used, the PAVG and PAE measured under the condition that the ACLR is less than −30 dBc are 20.33 dBm and 16.93%, respectively. The fabricated chip size is $2.6\times 1.3$ mm2, including test pads.}, archive = {J_ACCESS}, author = {Donghwan Seo and Jaeyong Lee and Jinho Yoo and Seongjin Jang and Changkun Park}, doi = {10.1109/ACCESS.2025.3601446}, journal = {IEEE Access}, pages = {148510-148521}, shortjournal = {IEEE Access}, title = {2.4-GHz CMOS power amplifier with common-mode self-rejection technique to suppress even harmonics}, volume = {13}, year = {2025}, }
• (2025). Precise analysis of secrecy outage probability and eavesdropping strategies in secure, energy-efficient wireless networks for copyright protection of digital contents. ACCESS, 13, 148495-148509. (www)

  @article{ , abstract = {Nowadays, the emergence of digital applications such as online gaming, extended reality (XR), and the Metaverse, causes the increasing demand of copyright data protection. The copyright data may be encrypted and transferred over communication media like wireless channels. In wireless communication networks, the need for robust content security and data protection is becoming increasingly critical. In addition, this communication also requires ultrareliable, low latency, and high-bandwidth connectivity. This paper proposes a secure and energy-efficient communication framework that leverages the synergies of Physical Layer Security (PLS) and Energy Harvesting (EH) techniques to address these demands. Specifically, a source node, energized by harvested energy, securely transmits confidential data to a multi-antenna destination node, while mitigating potential interception by an eavesdropper. The proposed framework incorporates advanced PLS strategies, including the use of jamming signals, Selection Combining (SC), and Maximal Ratio Combining (MRC) to enhance the confidentiality of the transmitted information. We derive exact analytical expressions for the Secrecy Outage Probability (SOP) and validate the theoretical results through Monte Carlo simulations. The simulation results demonstrate a significant performance improvement, revealing the intricate trade-offs between energy efficiency and security. In future work, we intend to explore the integration of AI-driven adaptive security protocols and blockchain-based authentication mechanisms to further fortify content security in next-generation wireless networks.}, archive = {J_ACCESS}, author = {Bui Vu Minh and Hye-Young Kim and Tan N. Nguyen and Phuong T. Tran and Quang-Sang Nguyen and Byung-Seo Kim and Miroslav Voznak}, doi = {10.1109/ACCESS.2025.3601007}, journal = {IEEE Access}, pages = {148495-148509}, shortjournal = {IEEE Access}, title = {Precise analysis of secrecy outage probability and eavesdropping strategies in secure, energy-efficient wireless networks for copyright protection of digital contents}, volume = {13}, year = {2025}, }
• (2025). An improved dual-vector-based deadbeat MPCC for SPMSM with quasi-resonant extended state observer. ACCESS, 13, 148483-148494. (www)

  @article{ , abstract = {Compared to the finite control set model predictive current control (MPCC) for surface-mounted permanent magnet synchronous motor (SPMSM), dual-vector deadbeat MPCC can improve the current tracking accuracy with rarely increasing switching frequency, which can improve the efficiency and performance of SPMSM drive system. However, the voltage error between the reference and synthesizing vectors cannot be eliminated, leading to current harmonic and torque ripple. To improve the control performance for SPMSM, the MPCC scheme with quasi-resonant extended state observer (ESO) is proposed in this paper. First, the mathematical expression of voltage error caused by dual-vector modulation is derived, and its harmonic characteristic is obtained. Then, the transfer functions of MPCC are derived, and the impact of DC and periodic voltage disturbances on speed and current is analyzed. To reduce the current harmonic caused by periodic voltage disturbance, a quasi-resonant ESO is introduced into the MPCC system, its mathematical model and the influence of parameters on stability and dynamic performance are presented. Finally, the feasibility and effectiveness of the proposed dual-vector deadbeat MPCC scheme with quasi-resonant ESO are verified by the experimental results, compared with two existing improved dual-vector MPCC schemes, the best steady-state performance can be obtained with minimum computation burden and no affection of the dynamic performance.}, archive = {J_ACCESS}, author = {Weiyang Hou and Yiru Miao and Ruizhi He and Wu Liao and Lingyan Luo and Shoudao Huang and Yuxiaoying Tu}, doi = {10.1109/ACCESS.2025.3601182}, journal = {IEEE Access}, pages = {148483-148494}, shortjournal = {IEEE Access}, title = {An improved dual-vector-based deadbeat MPCC for SPMSM with quasi-resonant extended state observer}, volume = {13}, year = {2025}, }
• (2025). Operational hazard analysis of nuclear power plant based on MFM functional modeling and reasoning. ACCESS, 13, 148467-148482. (www)

  @article{ , abstract = {The reliability and operability of operator intervention is critical in ensuring the safe operations of nuclear power plants, especially in the case of sudden emergency accident conditions. In such situations, the operators are more prone to errors or mistakes due to the time-limited tasks and high phycological stress. In this paper, we present a Multilevel Flow Models (MFM) based functional framework for operational hazard analysis by integrating human factors in emergency prevention and mitigation. The operational hazard analysis involves proactive risk assessment by evaluating the operability of operator action sequences with consequence reasoning. The forward induction reasoning is adopted in MFM for predicting the potential risk impacts and consequences that are resulted from the execution of action sequence. The framework method is demonstrated with a case study of manual boration makeup under inadvertent boric dilution accident in nuclear power plants, where the MFM models of reactor coolant system (RCS), the chemical and volume control system (RCV), and the reactor water and boron injection system are established for domain knowledge representation and reasoning with a specific focus on the safety critical functional objectives of reactivity control and integrity of reactor coolant inventory. The demonstration results show that the paradigm framework is effective for enhancing operational safety through proactive hazardous effects and risk identification and preventive control capabilities.}, archive = {J_ACCESS}, author = {Shu-Xin Bi and Jun Yang and Yipeng Fan}, doi = {10.1109/ACCESS.2025.3598861}, journal = {IEEE Access}, pages = {148467-148482}, shortjournal = {IEEE Access}, title = {Operational hazard analysis of nuclear power plant based on MFM functional modeling and reasoning}, volume = {13}, year = {2025}, }
• (2025). A semantics-aware head-driven approach for multilingual dependency parsing. ACCESS, 13, 148456-148466. (www)

  @article{ , abstract = {Dependency parsing is essential for language modeling because it offers a structured understanding of the syntactic relationships between words in a sentence. While recent advancements in large language models have greatly advanced the field of natural language processing, dependency parsing remains highly relevant for several key reasons. This paper introduces an effective method for improving dependency parsing which is based on a semantics-aware token embedding model. We propose to incorporate the ConceptNet embeddings which are trained by a retrofitting algorithm into a bidirectional recurrent neural network. The new model outperforms a strong baseline that employs a state-of-the-art method across three dependency treebanks, covering both low-resource and high-resource languages—Indonesian, Vietnamese, and English—achieving an improvement of approximately 1.21% in labeled attachment score. We also show that this method outperforms the popular transformer-based BERT model in capturing syntactic dependency between tokens. The new parser together with all trained models are made available under an open-source license, facilitating community engagement and advancement of natural language processing research for two low-resource languages with around 300 million users worldwide.}, archive = {J_ACCESS}, author = {Thi Minh Huyen Nguyen and Phuong Le-Hong}, doi = {10.1109/ACCESS.2025.3601225}, journal = {IEEE Access}, pages = {148456-148466}, shortjournal = {IEEE Access}, title = {A semantics-aware head-driven approach for multilingual dependency parsing}, volume = {13}, year = {2025}, }
• (2025). Personalized insulin adjustment with reinforcement learning: An in-silico validation for people with diabetes on intensive insulin treatment. ACCESS, 13, 148436-148455. (www)

  @article{ , abstract = {Despite recent advances in insulin preparations and technology, adjusting insulin remains an ongoing challenge for the majority of people with type 1 diabetes (T1D) and longstanding type 2 diabetes (T2D). In this study, we propose an enhanced version of the Adaptive Basal-Bolus Advisor (ABBA), a personalized insulin treatment recommendation system based on an actor-critic, model-free reinforcement learning approach. ABBA is designed for individuals with T1D and T2D, performing self-monitoring blood glucose measurements and multiple daily insulin injection therapy. We developed and evaluated the effectiveness of the enhanced version of ABBA to achieve better time-in-range (TIR) for individuals with T1D and T2D, compared to the use of a standard basal-bolus advisor (BBA). The in-silico test was performed using an FDA-accepted population, including 101 simulated adults with T1D and 101 with T2D. The in-silico evaluation shows that the updated version of ABBA significantly improved TIR by $9.54 \pm 7.76\%$ and $11.80 \pm 10.76\%$ in individuals with T1D and T2D, respectively, and significantly reduced both times below- and above-range, compared to BBA. After two months, TIR increased by $11.94 \pm 8.39\%$ and $7.74 \pm 5.53\%$ in T1D and T2D, respectively, on ABBA, while BBA showed only modest changes over time with variations of $1.32 \pm 1.41\%$ and $1.45 \pm 1.47\%$ , respectively. On a subgroup of people with T1D, the old version of ABBA was outperformed by $6.4 \pm 4.9\%$ , $5.8\pm 2.1\%$ , and $0.6 \pm 5.1\%$ in TIR, TBR, and TAR, accordingly. This personalized method for adjusting insulin has the potential to further optimize glycemic control and support people with T1D and T2D in their daily self-management. Our results warrant ABBA to be trialed for the first time in humans.}, archive = {J_ACCESS}, author = {Maria Panagiotou and Lorenzo Brigato and Vivien Streit and Amanda Hayoz and Stephan Proennecke and Stavros Athanasopoulos and Mikkel T. Olsen and Elizabeth J. den Brok and Cecilie H. Svensson and Konstantinos Makrilakis and Maria Xatzipsalti and Andriani Vazeou and Peter R. Mertens and Ulrik Pedersen-Bjergaard and Bastiaan E. de Galan and Stavroula Mougiakakou}, doi = {10.1109/ACCESS.2025.3600738}, journal = {IEEE Access}, pages = {148436-148455}, shortjournal = {IEEE Access}, title = {Personalized insulin adjustment with reinforcement learning: An in-silico validation for people with diabetes on intensive insulin treatment}, volume = {13}, year = {2025}, }
• (2025). UTAM: A unified thematic mapping approach for cross-domain personalization and cold start alleviation. ACCESS, 13, 148418-148435. (www)

  @article{ , abstract = {Cross-domain recommendation systems are essential for delivering personalized experiences across diverse content areas. However, aligning user preferences across distinct domains, especially in cold-start scenarios where users have no interaction history in the target domain remains a significant challenge. To bridge this gap, we propose UTAM (Unified Thematic Alignment and Mapping), a novel framework that leverages thematic clustering of user preferences in a source domain to enable recommendations in a target domain. By capturing semantically aligned user-topic engagement patterns, UTAM effectively transfers user intent across domains. We evaluate UTAM on Amazon Movies and Books datasets using ten different experimental configurations that explore various dimensionality reductions, clustering thresholds, and engagement metrics. In cold-start scenarios, UTAM outperforms its single-domain variants. Specifically, UTAM achieves a precision of 0.770, recall of 0.854, and F1-score of 0.783. In comparison, the single-domain Movies model records precision 0.7005, recall 0.2506, F1-score 0.3174, while the single-domain Books model achieves precision 0.6309, recall 0.3142, F1-score 0.3649. These results highlight UTAM’s robustness and effectiveness in alleviating the cold-start problem while delivering accurate, scalable, and interpretable cross-domain recommendations.}, archive = {J_ACCESS}, author = {Awais Azam and Muhammad Shahzad Sarfraz and Qamar Uz Zaman and Adeel Ashraf Cheema and Aitizaz Ali}, doi = {10.1109/ACCESS.2025.3601992}, journal = {IEEE Access}, pages = {148418-148435}, shortjournal = {IEEE Access}, title = {UTAM: A unified thematic mapping approach for cross-domain personalization and cold start alleviation}, volume = {13}, year = {2025}, }
• (2025). Vibration analysis of three-phase squirrel-cage induction motor with different winding configurations. ACCESS, 13, 148405-148417. (www)

  @article{ , abstract = {This paper presents a novel investigation into the vibration behavior of squirrel-cage induction motors, considering the influence of stator winding configurations and pole numbers. The primary contribution of this work is providing the first comprehensive comparative analysis of winding types across multiple pole numbers from a vibration perspective, offering specific design guidance for vibration mitigation. The analysis of the 48-slot induction motors is conducted with 4, 8, and 16 poles, and compares three winding types: distributed windings (DW), pseudo-concentrated windings (P-CW), and concentrated windings (CW) across various layer configurations. The comparison is performed at constant speed under steady-state conditions with a fixed supply frequency of 50 Hz for all motors, resulting in synchronous speeds of 1500 rpm for 4-pole, 750 rpm for 8-pole, and 375 rpm for 16-pole motors. The magnetic flux density harmonics and radial force density distributions are analyzed using a combination of analytical approaches and finite element analysis. This study also performs modal analysis for mode shape and natural frequency up to eighth circumferential order. It is shown that the configuration of winding plays an important role in vibration characteristics and that each pole number has an optimal configuration. Results indicate that increasing pole numbers reduces vibration amplitudes, whereas a four-layer P-CW presents better performance under low slot-per-pole-per-phase values and performance comparable to DW under high values. The experimental validation on the 4-pole baseline motor with a 2-layer DW under steady-state operation confirms the accuracy of the numerical findings.}, archive = {J_ACCESS}, author = {Hamid Reza Akbari and Ghasem Rezazadeh and Jawad Faiz and Moein Abedini}, doi = {10.1109/ACCESS.2025.3601342}, journal = {IEEE Access}, pages = {148405-148417}, shortjournal = {IEEE Access}, title = {Vibration analysis of three-phase squirrel-cage induction motor with different winding configurations}, volume = {13}, year = {2025}, }
• (2025). Bowel sound feature-based Stimulus/Response plots to differentiate healthy controls from functional constipation and functional diarrhea: A cross-sectional study. ACCESS, 13, 148396-148404. (www)

  @article{ , abstract = {Functional constipation (FC) and functional diarrhea (FD), which belong to the functional gastrointestinal disorders group, are primarily associated with abnormalities in intestinal motility and can result in chronic abdominal symptoms. However, because diagnoses are largely based on subjective evaluations, objective diagnostic indicators need to be established. In this study, a novel method is proposed for quantitatively evaluating characteristic intestinal motility patterns in FC and FD patients by using bowel sounds (BSs) to create stimulus–response plots. BSs were recorded before and after carbonated water ingestion in healthy individuals and patients diagnosed with FC or FD according to the Rome IV criteria, and these recordings were used to construct stimulus–response plots. For healthy individuals, there was a strong negative correlation (R = –0.8676, p <0.001) between the number of BS segments before and after ingestion, whereas no statistically significant correlation was observed in the FC/FD group. Furthermore, approximately 25% of measurements for the healthy group deviated from the region defined by a distance of 0.8217 from the healthy group regression line, compared with approximately 55% for those in the FC/FD group, suggesting a substantial difference in BS patterns. These findings indicate that some FC/FD patients may exhibit abnormal intestinal motility patterns upon ingesting carbonated water. In conclusion, the results of this study suggest that the proposed method has potential as a new objective evaluation tool to complement current diagnostic approaches, which rely heavily on subjective assessment.}, archive = {J_ACCESS}, author = {Takeyuki Haraguchi and Takahiro Emoto and Kazuhiro Kozuka and Hideki Kobara}, doi = {10.1109/ACCESS.2025.3600419}, journal = {IEEE Access}, pages = {148396-148404}, shortjournal = {IEEE Access}, title = {Bowel sound feature-based Stimulus/Response plots to differentiate healthy controls from functional constipation and functional diarrhea: A cross-sectional study}, volume = {13}, year = {2025}, }
• (2025). A digital twin approach to smart monitoring and fault diagnosis. ACCESS, 13, 148384-148395. (www)

  @article{ , abstract = {The continuous monitoring of induction motors, combined with early fault detection, is an essential tool for reducing maintenance costs and preventing unexpected downtime. This paper presents a Digital Twin-based methodology for the real-time diagnosis of incipient inter-turn short-circuit faults in induction motors. This diagnostic strategy capitalizes on the digital twin’s real-time integration of a physical asset and its virtual counterpart. It enables the continuous comparison of measured and simulated currents through residual analysis. The Evolving Similarity-Based Modeling Plus (eSBM+) approach facilitates the identification of the motor’s current operating state by comparing it with previously recorded conditions, supporting the precise detection of the onset and evolution of anomalies. The Reconstruction-Based Contributions Online (RBC-Online) system provides real-time insights into the root cause of the fault. The deviation identification automatically triggers a notification to the responsible technician, ensuring timely and well-informed decision-making. Experimental results validated the effectiveness of the proposed methodology, demonstrating high sensitivity and robustness under varying load conditions.}, archive = {J_ACCESS}, author = {Ana C. S. Araújo and Lane M. Rabelo and Paulo C. M. Lamim Filho and Walmir M. Caminhas}, doi = {10.1109/ACCESS.2025.3601349}, journal = {IEEE Access}, pages = {148384-148395}, shortjournal = {IEEE Access}, title = {A digital twin approach to smart monitoring and fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Analysis of a multimodal ocular ultrasound image classification algorithm based on YOLO and VanillaNet. ACCESS, 13, 148374-148383. (www)

  @article{ , abstract = {The generation of traditional ophthalmic ultrasound examination reports primarily relies on clinicians manually entering information during procedures. This process is not only inefficient and highly subjective but also heavily dependent on the clinician’s expertise. Training skilled ophthalmologists is time-consuming and costly, and with limited medical resources at the primary care level, diagnosing and treating ocular conditions faces significant challenges. To address these issues, this study proposes a multimodal AI-based method for automating report generation from ocular Doppler ultrasound images. The method integrates image features and textual information to facilitate unified disease diagnosis and report generation. The proposed approach consists of three collaboratively functioning modules:1) Multi-class Object Detection Module: Based on the YOLOv11s model, this module accurately localizes the orbital region, achieving a detection accuracy of 97.96%. 2) Text Extraction Module: By combining YOLOv11s and PaddleOCR, this module identifies key information within ultrasound images, achieving a text recognition accuracy of up to 99.9%. 3) Eye Disease Classification Module: Utilizing an improved VanillaNet architecture, this module can identify seven common ophthalmic conditions with a classification accuracy of 95.5%. This approach effectively integrates visual image features with semantic text information through a multimodal information fusion mechanism. It enables intelligent analysis and automated report generation for ocular ultrasound imaging, providing strong support in alleviating the shortage of professional ophthalmologists and holding significant potential for clinical applications.}, archive = {J_ACCESS}, author = {Yilu Zhang and Yanzhu Zhang and Tingxue Li and Mingyu Shi and Yue Huang}, doi = {10.1109/ACCESS.2025.3598747}, journal = {IEEE Access}, pages = {148374-148383}, shortjournal = {IEEE Access}, title = {Analysis of a multimodal ocular ultrasound image classification algorithm based on YOLO and VanillaNet}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of second-life EV batteries for off-grid solar energy storage system. ACCESS, 13, 148365-148373. (www)

  @article{ , abstract = {The increasing adoption of electric vehicles (EVs) has led to a growing volume of retired lithium-ion batteries that retain significant residual capacity, prompting interest in their repurposing for stationary energy storage systems (ESS). This study presents a comprehensive performance evaluation of second-life EV batteries sourced from three platforms: Nissan Leaf Gen 1 (LMO-LNO chemistry), Citroën C0 (Mitsubishi i-MiEV) (LMO), and China Aviation Lithium Battery Co., Ltd. (CALB) (LFP). Laboratory tests were conducted using Arbin battery testers to assess capacity retention, internal resistance, and state-of-health (SOH) across up to 5000 charge-discharge cycles. In parallel, real-world field tests were conducted using a solar-powered 120 W LED streetlight ESS to validate operational performance under ambient Malaysian climate conditions. Results from laboratory analysis showed that the CALB LFP battery exhibited the highest mid-life stability, retaining 70.9% of its capacity at 2500 cycles and 57.4% at 3000 cycles, with slower thermal-induced degradation. The Nissan Leaf battery maintained 72.6% capacity at 2500 cycles but experienced accelerated decay post-3000 cycles, dropping to 4.8% at 5000 cycles. The Citroën C0 battery showed comparable early-cycle behavior but degraded more sharply under thermal stress, retaining 63.6% at 3000 cycles and just 7.2% at 5000 cycles. Field test data revealed that actual SOH decline rates were 15–20% faster than in lab conditions, attributed to variable temperatures (30–38°C), inconsistent load patterns, and limited thermal management. The study confirms that second-life batteries are viable for low- to medium-demand ESS applications within a 2500–3000 cycle operating window, provided that chemistry-specific integration strategies and intelligent battery management systems are employed. The findings contribute to lifecycle extension modeling and support the deployment of second-life batteries as cost-effective, sustainable components in decentralized energy systems.}, archive = {J_ACCESS}, author = {Md Tanjil Sarker and Marran Al Qwaid and Gobbi Ramasamy and Mohammed Hussein Saleh Mohammed Haram}, doi = {10.1109/ACCESS.2025.3596991}, journal = {IEEE Access}, pages = {148365-148373}, shortjournal = {IEEE Access}, title = {Performance evaluation of second-life EV batteries for off-grid solar energy storage system}, volume = {13}, year = {2025}, }
• (2025). How to use your brain for cryptography without trustworthy machines. ACCESS, 13, 148353-148364. (www)

  @article{ , abstract = {In this work, we study cryptosystems that can be executed securely without fully trusting all machines, but only trusting the user’s brain. This paper focuses on the signature scheme. We first introduce a new concept called “server-aided in-brain signature,” a cryptographic protocol between a human brain and multiple servers to sign a message securely even if the servers and devices used to access the servers are not completely trusted. Second, we propose a concrete scheme that is secure against mobile hackers in servers and malware infecting users’ devices.}, archive = {J_ACCESS}, author = {Wakaha Ogata and Toi Tomita and Kenta Takahashi and Masakatsu Nishigaki}, doi = {10.1109/ACCESS.2025.3601597}, journal = {IEEE Access}, pages = {148353-148364}, shortjournal = {IEEE Access}, title = {How to use your brain for cryptography without trustworthy machines}, volume = {13}, year = {2025}, }
• (2025). Feasibility of a hybrid ANFIS-PSO model to predict medical waste: Case study for istanbul. ACCESS, 13, 148330-148352. (www)

  @article{ , abstract = {Accurate prediction of medical waste (MW) is critical for sustainable urban management. This study develops and validates a robust and reliable hybrid intelligent model for prediction of MW quantity. To reveal the effectiveness of the proposed model, a real case study focused on the city of Istanbul for MW is taken. First, a systematic variable selection process, incorporating Spearman Correlation and Variance Inflation Factor (VIF) analysis was employed to identify the most influential predictor variables. This process resulted in a final set of three key input variables including population density, literacy rate, and water consumption rate. Thus, to predict the MW amount in this study, a hybrid Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO) is proposed. The performance of this model was rigorously evaluated and benchmarked against four other machine learning (ML) models: a standard ANFIS, Support Vector Machine (SVM), Random Forest (RF), and an Artificial Neural Network (ANN). The results demonstrate that the proposed ANFIS-PSO model provides superior performance achieving the lowest error rates across all performance metrics. Accordingly, it yielded a Root Mean Square Error (RMSE) of 1837.75, a Mean Absolute Percentage Error (MAPE) of 5.60%, a Mean Absolute Error (MAE) of 1558.19 and Percent Bias (%PBIAS) of 2.04% on the test data. The findings confirm that the ANFIS-PSO hybrid model is a highly effective and useful tool for MW prediction offering a valuable resource for municipal authorities in sustainable waste management.}, archive = {J_ACCESS}, author = {Betul Yenisari and Sukran Seker}, doi = {10.1109/ACCESS.2025.3598629}, journal = {IEEE Access}, pages = {148330-148352}, shortjournal = {IEEE Access}, title = {Feasibility of a hybrid ANFIS-PSO model to predict medical waste: Case study for istanbul}, volume = {13}, year = {2025}, }
• (2025). Research on line loss calculation method of distribution network based on gaussian mixture modeling. ACCESS, 13, 148318-148329. (www)

  @article{ , abstract = {Line loss is a critical technical indicator for evaluating the energy-saving performance and operational efficiency of distribution systems. With the ongoing advancement of the “dual carbon” goals, the integration of a large number of renewable energy sources—characterized by randomness and intermittency—into distribution networks poses significant challenges to the accuracy and adaptability of traditional line loss calculation methods. This paper proposes a probabilistic line loss calculation method for distribution networks based on the Gaussian Mixture Model (GMM). First, a GMM-based model of node injection current is constructed. Then, by combining the generalized non-central chi-squared distribution, an analytical expression for the theoretical probability distribution of line losses under arbitrary current distributions is derived. For the first time, exact expressions for the mean, variance, and moment-generating function of theoretical line loss are provided. Considering the complexity of direct calculation, a numerical method based on Raphaeli series expansion is developed to compute the probability density function and cumulative distribution function of line loss. Finally, a case study on the IEEE 33-bus distribution system verifies the effectiveness and high accuracy of the proposed method. The results demonstrate that the method enables fast and precise evaluation of the mean and probabilistic characteristics of line losses, offering theoretical support for energy efficiency optimization in distribution networks with high renewable energy penetration.}, archive = {J_ACCESS}, author = {Biwei Shi and Qian Xu and Tao Liu and Yanlong Li}, doi = {10.1109/ACCESS.2025.3601651}, journal = {IEEE Access}, pages = {148318-148329}, shortjournal = {IEEE Access}, title = {Research on line loss calculation method of distribution network based on gaussian mixture modeling}, volume = {13}, year = {2025}, }
• (2025). Enhancing quantum cryptanalysis of binary elliptic curves through optimized out-of-place point addition and ECPM integration. ACCESS, 13, 148304-148317. (www)

  @article{ , abstract = {Shor’s algorithm remains a cornerstone of quantum cryptanalysis, driving active research into reducing quantum resource overheads in both spatial and temporal dimensions. This work presents a dual-optimization framework targeting resource-efficient Shor-based quantum cryptanalysis on binary elliptic curves. Our approach conducts a comprehensive resource assessment across multiple abstraction levels—ranging from finite field arithmetic to elliptic curve group operations. At the low-level layer, we benchmark various quantum arithmetic constructor designs—including GCD-based, FLT-based, FLT-Basic or extended variants, in-place, and out-of-place execution models—analyzing Toffoli gate counts, qubit requirements, and circuit depths. At the group operation layer, we examine point addition and doubling circuits, employing complete iterations of Shor’s algorithm with elliptic curve point multiplication (ECPM) that require point addition (PA) only (with ( $2n + 2$ ) PA) and point doubling (PD) utilization ( $2n$ PD + 2 PA). We further simulate selected ( $2n$ PD +2 PAoutplace) circuit designs, highlighting the advantages of optimized out-of-place PA circuits and integrated ECPM techniques from prior work. Additionally, we explore decompositions of multi-controlled gates to achieve a practical trade-off between qubit count and overall depth.}, archive = {J_ACCESS}, author = {Dedy Septono Catur Putranto and Rini Wisnu Wardhani and Howon Kim}, doi = {10.1109/ACCESS.2025.3601565}, journal = {IEEE Access}, pages = {148304-148317}, shortjournal = {IEEE Access}, title = {Enhancing quantum cryptanalysis of binary elliptic curves through optimized out-of-place point addition and ECPM integration}, volume = {13}, year = {2025}, }
• (2025). Extending secure Local5G networking over transcontinental network slicing. ACCESS, 13, 148297-148303. (www)

  @article{ , abstract = {5G technology introduces network slicing, which enables the creation of multiple virtual networks on a single physical infrastructure, each tailored to specific service requirements. However, deploying secure and efficient end-to-end slices, particularly across transcontinental networks, presents a significant challenge. Conventional solutions, such as public Wi-Fi with insufficient security and VPNs, often fail to address the complexity of connection management and stringent security, scalability, and performance demands of global operations. To overcome these challenges, this study proposes a novel approach that integrates a dynamic core configuration with SIM-based authentication, securely embeds user credentials into network operations and minimizes reliance on external software. The proposed solution, implemented on the FABRIC infrastructure that spans across the globe, supports real-time slice management and orchestration between self-managed wireless networks such as Local5G in Tokyo and Dallas, simulating business and industrial Internet of Things(IoT) scenarios. Performance evaluations, including Round-Trip Time (RTT) and bandwidth analysis, demonstrate significant latency, throughput, and operational efficiency improvements. These results validate the effectiveness of the proposed method in delivering secure, scalable, and high-performance global communications, offering a promising pathway for future Local5G deployments.}, archive = {J_ACCESS}, author = {Fatou S. Secka and Akihiro Nakao}, doi = {10.1109/ACCESS.2025.3564437}, journal = {IEEE Access}, pages = {148297-148303}, shortjournal = {IEEE Access}, title = {Extending secure Local5G networking over transcontinental network slicing}, volume = {13}, year = {2025}, }
• (2025). Improving gait balance of the blind during robot-assisted guidance by providing static external reference. ACCESS, 13, 148285-148296. (www)

  @article{ , abstract = {The gait of blind individuals is characterized by reduced gait stability and postural balance due to their limited sensory information. However, existing systems focus on guidance capability rather than improving gait stability and postural balance. Since static external references (SER), such as fixed handrails, can improve the gait balance of the blind, their functionality should be implemented during robot-assisted guidance. In this study, we focused on the proof-of-concept of whether the provision of SER during robot-assisted guidance based on a 3-degrees-of-freedom mobile manipulator, called the robotic haptic cane (RHC), enhances gait stability and postural balance in blind individuals. Accordingly, we conducted gait experiments with 20 blindfolded sighted participants and one blind participant. The experimental results from the blindfolded sighted participants indicate that gait stability (step width variability) and postural balance (mediolateral trunk tilt) are further decreased by walking faster with a blindfold. However, both gait stability and postural balance improved, especially at a faster speed, by using RHC. According to the experimental results of the blind participant, the preferred speed increased with the use of the RHC. In addition, gait stability and postural balance improved with RHC use regardless of speed. Thus, the RHC can enable blind individuals to walk more safely with enhanced gait stability and postural balance by providing SER during robot-assisted guidance.}, archive = {J_ACCESS}, author = {Junyeong Lee and Hosu Lee and Minkyung Kim and Jiho Park and Jungwon Yoon}, doi = {10.1109/ACCESS.2025.3599181}, journal = {IEEE Access}, pages = {148285-148296}, shortjournal = {IEEE Access}, title = {Improving gait balance of the blind during robot-assisted guidance by providing static external reference}, volume = {13}, year = {2025}, }
• (2025). Improving suicide ideation detection through feature engineering and machine learning. ACCESS, 13, 148269-148284. (www)

  @article{ , abstract = {Suicidal ideation detection from social media text is a growing area of research, yet many existing approaches suffer from limited contextual understanding, low interpretability, and inadequate scalability. This study addresses these challenges by proposing a comprehensive machine learning framework that balances performance, interpretability, and real-world applicability. Using a curated Reddit dataset (comparing “SuicideWatch” and “nonsuicide” posts), we evaluate both traditional feature extraction methods (TF-IDF, Bag-of-Words, and N-Grams) and contextual embeddings generated by BERT. These features are tested across five established classifiers: Random Forest, SVM, Logistic Regression, XGBoost, and LightGBM. Our results show that BERT combined with Logistic Regression achieves the highest F1-score (0.9392), outperforming both traditional models and ensemble methods. Additionally, we assess model robustness under varying dataset sizes and address class imbalance through targeted preprocessing and evaluation strategies. While our framework demonstrates strong performance, it is currently limited to English-language Reddit data and requires validation across platforms and demographics. This work contributes a reproducible and scalable pipeline to support mental health monitoring in online environments.}, archive = {J_ACCESS}, author = {Shadi Banitaan and Hebah Alquran}, doi = {10.1109/ACCESS.2025.3601853}, journal = {IEEE Access}, pages = {148269-148284}, shortjournal = {IEEE Access}, title = {Improving suicide ideation detection through feature engineering and machine learning}, volume = {13}, year = {2025}, }
• (2025). Design of a two-level controller for load frequency control in the presence of wind farms and battery energy storage system based on VIC. ACCESS, 13, 148254-148268. (www)

  @article{ , abstract = {Power generation from renewable energy sources (RES) due to its uncertain nature and load variations in electrical power grids can lead to inertia reduction and, consequently, frequency deviation. In this study, to solve this problem and regulate the frequency, an automatic generation control (AGC) system along with a battery energy storage system (BESS) based on virtual inertia control (VIC) is used. In order to reduce the effects of inter-area oscillations (IAO) in power systems, the approach used is that a linear quadratic controller (LQC) is used to optimize the parameters of the PI controller. This control configuration is designed as a dual structure: in the first layer, the state errors of the PI controller are improved by minimizing the integral square error (ISE) criterion. Then, in the second layer, a LQC controller, acting as a supervisor, adjusts the optimal values of the PI parameters in the lower layer. Due to the presence of noise and the lack of direct measurement of all state variables, the Kalman Filter (KF) is used as an observer to estimate inaccessible states. The objective functions for frequency tuning are also optimized using the Firefly Algorithm (FA) in the MATLAB software platform. The data obtained from the simulations, which have been evaluated under various conditions, indicate that the proposed solution, in addition to improving the efficiency of frequency tuning, has a higher accuracy in reducing the size of the overshoot, subsidence, and settling time compared to other solutions.}, archive = {J_ACCESS}, author = {Libing Chen and Mohamed Salem and Khlid Ben Hamad and Natarajan Prabaharan and Soichiro Ueda and Tomonobu Senjyu}, doi = {10.1109/ACCESS.2025.3596853}, journal = {IEEE Access}, pages = {148254-148268}, shortjournal = {IEEE Access}, title = {Design of a two-level controller for load frequency control in the presence of wind farms and battery energy storage system based on VIC}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of hallucinations in large language models. ACCESS, 13, 148231-148253. (www)

  @article{ , abstract = {This review systematically maps research on hallucinations in large language models using a descriptive scheme that links model outputs to four system architectures: unaugmented generation, post-hoc reactive validation, proactive detection-and-mitigation, and fully integrated detection-and-mitigation designs. Our methodology for this systematic review follows the PRISMA guidelines to ensure transparency and reproducibility. We searched IEEE Xplore, ACM Digital Library, and ScienceDirect for studies published between 2015 and January 2025 and extracted 125 peer-reviewed papers across nine application domains. Quantitative analysis shows that question answering and multimodal tasks account for 48% of all papers, whereas software engineering, educational technology, and autonomous systems are underexplored. Although 87.5% of the studies rely on additional reactive or proactive defenses, only 8.8% implement integrated architecture-level safeguards, revealing a critical gap in unified and dynamic architectures. The resulting classification matrix and domain map provide a diagnostic tool for locating blind spots and comparing architectural maturity. Three actionable priorities emerge: develop integrated reasoning-and-verification loops that pre-empt hallucinations; transfer proven causal-intervention and multi-agent validation pipelines to high-stakes, under-represented domains and benchmark them under real conditions; and build modular, cross-domain evaluation frameworks that isolate the contribution of individual mitigation components and support ablation studies. By consolidating fragmented evidence and quantifying architecture-domain imbalances, this review establishes a traceable foundation for engineering reliable, explainable, and domain-adaptable countermeasures to hallucinations in generative language technology.}, archive = {J_ACCESS}, author = {Christian Woesle and Leopold Fischer-Brandies and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3601206}, journal = {IEEE Access}, pages = {148231-148253}, shortjournal = {IEEE Access}, title = {A systematic literature review of hallucinations in large language models}, volume = {13}, year = {2025}, }
• (2025). Experimental validation of a naphthalene-fueled electrothermal plasma thruster. ACCESS, 13, 148219-148230. (www)

  @article{ , abstract = {A direct thrust measurement of an electrothermal radio-frequency (RF) plasma thruster, also known as a Pocket-Rocket, using naphthalene as a propellant, is experimentally performed. Naphthalene plasma is successfully ignited with only 5 W of RF power, resulting in a 32% increase in total thrust compared to cold-gas operation, which increased further to 71% as the power increased to 30 W. The total thrust is shown to increase proportionally with mass flow rate, achieving 0.84 mN at 4 mg/s. Several explanations for the thrust gain from RF power are discussed, with the most plausible mechanisms identified as neutral gas heating via ion-neutral charge exchange (I-N CEX) collisions and electron-impact dissociation. Although the total thrust measured using naphthalene remains lower than that achieved with argon, the calculated effective specific impulse, which accounts for propellant storage density, indicates that naphthalene is 1.3 to 1.7 times more efficient than argon. Moreover, at RF powers exceeding 26 W, 2 mg/s naphthalene demonstrates a better thrust-to-power ratio. These results demonstrate that sublimating solid propellants like naphthalene are a promising alternative propellant for CubeSat propulsion.}, archive = {J_ACCESS}, author = {Thimthana Lee and Mahdi Davoodianidalik and Dimitrios Tsifakis and Roderick W. Boswell and Christine Charles}, doi = {10.1109/ACCESS.2025.3601162}, journal = {IEEE Access}, pages = {148219-148230}, shortjournal = {IEEE Access}, title = {Experimental validation of a naphthalene-fueled electrothermal plasma thruster}, volume = {13}, year = {2025}, }
• (2025). Adaptive real-time degradation modeling for lithium-ion batteries in grid energy storage systems. ACCESS, 13, 148203-148218. (www)

  @article{ , abstract = {The increasing integration of renewable energy sources into smart grids has intensified the demand for efficient and reliable energy storage systems (ESSs). Predicting LIB degradation in real time remains a critical challenge, directly affecting the economics and reliability of grid-scale applications. Existing degradation models typically depend on costly and time-consuming laboratory-based cycling tests, limiting their scalability and real-world applicability. This paper proposes a novel Adaptive Real-Time Degradation Model (ARDM) that addresses these limitations by eliminating the need for experimental pre-cycling and instead relying solely on real-time operational data and manufacturer datasheet parameters. The ARDM uniquely and continuously adapts its modeling parameters during live operation, accounting for key stress factors such as depth of discharge, charge/discharge rates, and temperature. Unlike traditional static models, the ARDM is chemistry-agnostic, requiring no prior calibration and achieving degradation prediction errors of less than 0.0005%, representing a substantial improvement over state-of-the-art approaches. Experimental validation on two commercial LIBs under diverse stress conditions confirms the model’s accuracy, adaptability, and robustness. Comparative evaluations demonstrate that ARDM not only outperforms benchmark models but also offers a scalable and cost-effective solution for degradation-aware energy management in grid and electric vehicle applications. This work presents a significant advancement toward practical, real-time LIB health monitoring, empowering energy storage service providers and grid planners with a reliable tool for optimizing battery life, cost-efficiency, and operational decisions.}, archive = {J_ACCESS}, author = {Ali M. Eltamaly and Zeyad A. Almutairi}, doi = {10.1109/ACCESS.2025.3599496}, journal = {IEEE Access}, pages = {148203-148218}, shortjournal = {IEEE Access}, title = {Adaptive real-time degradation modeling for lithium-ion batteries in grid energy storage systems}, volume = {13}, year = {2025}, }
• (2025). Layer pruning with consensus: A triple-win solution. ACCESS, 13, 148192-148202. (www)

  @article{ , abstract = {Layer pruning offers a promising alternative to standard structured pruning, effectively reducing computational costs, latency, and memory footprint. While notable layer-pruning approaches aim to detect unimportant layers for removal, they often rely on single criteria that may not fully capture the complex, underlying properties of layers. We propose a novel approach that combines multiple similarity metrics for neural network internal representation. Our criterion, called Consensus, leverages shape and stochastic metrics, such as adaptations of the Bures and Procrustes distances, to create a single expressive measure of low-importance layers. Our technique delivers a triple-win solution: low accuracy drop, high performance improvement, and increased robustness to adversarial attacks. With up to 78.80% Floating-Point Operations (FLOPs) reduction and performance on par with state-of-the-art methods across different benchmarks, our approach reduces energy consumption and carbon emissions by up to 66.99% and 68.75%, respectively. Additionally, it avoids shortcut learning and improves robustness by up to 4 percentage points under various adversarial attacks. Overall, the Consensus criterion demonstrates its effectiveness in creating robust, efficient, and environmentally friendly pruned models.}, archive = {J_ACCESS}, author = {Leandro Giusti Mugnaini and Carolina Tavares Duarte and Anna Helena Reali Costa and Artur Jordao}, doi = {10.1109/ACCESS.2025.3601042}, journal = {IEEE Access}, pages = {148192-148202}, shortjournal = {IEEE Access}, title = {Layer pruning with consensus: A triple-win solution}, volume = {13}, year = {2025}, }
• (2025). M³-SLR: Self-supervised pretraining with MaxFlow MaskFeat for improved multi-view sign language representation. ACCESS, 13, 148170-148191. (www)

  @article{ , abstract = {Sign Language Recognition (SLR) is crucial for facilitating communication with the hard-of-hearing community, yet remains significantly challenged by factors such as inter-signer variations, self-occlusion, and the critical issue of Visually Indistinguishable Signs (VISigns) – signs appearing identical from a single viewpoint. While multi-view systems offer potential solutions by providing richer spatial information, effectively leveraging this data, particularly through pretraining tailored for fine-grained sign dynamics, remains an open problem. Furthermore, the computational demands of multi-view models hinder practical single-camera deployment. To address these limitations, we introduce M3-SLR, a comprehensive framework for Multi-View Isolated Sign Language Recognition (MultiView-ISLR). M3-SLR integrates three key proposals: 1) A novel MaxFlow Cube Masking strategy for MaskFeat, a self-supervised pretraining method, which utilizes optical flow to guide spatiotemporal cube masking, compelling the model to learn representations focused on dynamically critical regions essential for distinguishing signs. 2) The UF3V multi-view architecture, built upon effective UniFormer backbones and incorporating a Dual Co-Attention mechanism for the fusion of features from three adjacent views, significantly enhances the ability to resolve ambiguities like VISigns. 3) An efficient knowledge distillation process called 3-view to 1-view, which transfers the learned multi-view knowledge into a high-performance single-view model, bridging the gap to practical application while maintaining the ability to distinguish VISigns. Extensive experiments conducted on challenging public MultiView-ISLR benchmarks, Multi-VSL200 and MM-WLAuslan, demonstrate that M3-SLR achieves state-of-the-art performance using only RGB data, validating the effectiveness of our proposed framework for robust and practical SLR. Our code is available at https://github.com/fossbk/M3-SLR.git}, archive = {J_ACCESS}, author = {Tran Tri Dung Nguyen and Thi Thuy Nga Do and Quang Huy Hoang and Phi Le Nguyen and Anh Vu Tran}, doi = {10.1109/ACCESS.2025.3601235}, journal = {IEEE Access}, pages = {148170-148191}, shortjournal = {IEEE Access}, title = {M³-SLR: Self-supervised pretraining with MaxFlow MaskFeat for improved multi-view sign language representation}, volume = {13}, year = {2025}, }
• (2025). Self-attention-based masked spectrogram generation and self-supervised learning method for improving speech emotion recognition. ACCESS, 13, 148159-148169. (www)

  @article{ , abstract = {In this paper, we propose the Self-Attention-based Masked Spectrogram Generation (SAMSG) method to address the problem of model overfitting and improve generalization performance in speech emotion recognition under limited data conditions. A key challenge in many emotional speech datasets is that a small set of fixed sentences is repeatedly uttered with different emotional expressions, which can cause models to overfit to sentence-specific acoustic patterns rather than learn generalizable emotion-related features. To overcome this limitation, the proposed SAMSG method utilizes a pure self-attention-based model (DeiT) to obtain attention maps and applies the attention rollout technique to extract regions of high importance from time-frequency spectrograms. It then selectively masks only the regions that are important for emotion recognition, encouraging the model to learn complementary emotional information from less attended areas. This approach addresses the learning bias commonly seen in self-attention models, which tend to over-focus on localized regions of the input. The originality of the SAMSG method lies in its use of self-attention-driven masking, which—unlike conventional random masking—removes regions the model itself considers important, thereby promoting the learning of more diverse and robust emotional features. Our method alleviates overfitting without requiring external data or large-scale datasets, and achieves strong generalization even in data-constrained environments. Experiments conducted on the SAVEE, EmoDB, and CREMA-D datasets show that the proposed SAMSG method outperforms existing self-attention-based models, achieving accuracies of 94.44%, 96.30%, and 85.94%, respectively. It also attains macro-averaged F1-scores of 0.9401, 0.9692, and 0.8595, demonstrating consistent robustness across diverse emotional speech corpora.}, archive = {J_ACCESS}, author = {Jeong-Yoon Kim and Seung-Ho Lee}, doi = {10.1109/ACCESS.2025.3599218}, journal = {IEEE Access}, pages = {148159-148169}, shortjournal = {IEEE Access}, title = {Self-attention-based masked spectrogram generation and self-supervised learning method for improving speech emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Extended-range constant current WPT for dual-drone charging using a bifurcation approach. ACCESS, 13, 148145-148158. (www)

  @article{ , abstract = {Despite the increasing popularity, autonomous aerial vehicles (AAVs) are often hindered by limited battery endurance and complexities of in-flight recharging. This paper presents an extended-range wireless power transfer (WPT) system designed for simultaneous in-flight charging of dual drones. By employing a bifurcation approach, the system uses bifurcation-induced zero-phase angle (ZPA) frequencies to achieve coupling-independent current gain. This allows the system to maintain a constant battery charging current across dynamically varying coupling conditions, without the need for sensors or communication modules on the receiver drones. Finite element simulation study confirms the magnetic flux density characteristics, desired for practical operation constraints. An experimental study on a 500 W prototype demonstrates the proposed system’s effectiveness. The dual-receiver configuration successfully maintains a constant 5 A charging current of each drone while extending the operational air gap range by 16.7% (from 150 mm to 175 mm) compared to single-receiver scenarios. This work presents a practical solution that mitigates misalignment and air gap variation issues to enhance AAV flight endurance, an important step for drone operations during prolonged missions.}, archive = {J_ACCESS}, author = {Kasan Sukvanachaikul and Nattapong Hatchavanich and Supapong Nutwong and Sumate Naetiladdanon and Ekkachai Mujjalinvimut and Anawach Sangswang}, doi = {10.1109/ACCESS.2025.3601215}, journal = {IEEE Access}, pages = {148145-148158}, shortjournal = {IEEE Access}, title = {Extended-range constant current WPT for dual-drone charging using a bifurcation approach}, volume = {13}, year = {2025}, }
• (2025). Optimized quantum CNN with improvised iHow algorithm for MRI-based dementia diagnosis. ACCESS, 13, 148128-148144. (www)

  @article{ , abstract = {Dementia, a neurodegenerative disorder, requires early prediction and effective diagnosis for providing better treatment to avert the loss. The detection and classification of disease is intricate and needs optimized solutions using deep learning techniques. In context with this, the proposed work presents a novel approach with the inclusion of an Optimized Quantum Convolutional Neural Network (Q-CNN). The optimization algorithm utilized is the Improved iHow Optimization Algorithm (IiHOA), which fine-tunes the hyper-parameters in the proposed Q-CNN. Moreover, the MRI images are pre-processed with the combined act of B-spline and Lanczos Interpolation by removing the noises and preserving the sensitive details. This enhances the image quality and can be used to extract features. For feature extraction, U-Net is used, and the decomposition of MRI images into multi-scale frequency components is done for extracting the spatial and spectral information related to brain abnormalities. For further refining the extracted features, the Adaptive Hiking Optimization algorithm (AHOA) is used, which selects the appropriate features and therein significantly mitigates the computational complexities and enhances the classification accuracy. The feature representation and non-linear patterns from the MRI images are utilized by the proposed QCNN for the classification. The benchmark OASIS dementia dataset attained an accuracy of 98%, and various parameters were analysed with the proposed QCNN. This ensures a highly accurate dementia classification with the proposed work.}, archive = {J_ACCESS}, author = {Dhulipalla Nagajyothi and Venkata Rami Reddy Chirra}, doi = {10.1109/ACCESS.2025.3598935}, journal = {IEEE Access}, pages = {148128-148144}, shortjournal = {IEEE Access}, title = {Optimized quantum CNN with improvised iHow algorithm for MRI-based dementia diagnosis}, volume = {13}, year = {2025}, }
• (2025). Deep clustering with self-supervision using pairwise similarities. ACCESS, 13, 148104-148127. (www)

  @article{ , abstract = {Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework with self-supervision using pairwise data similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder’s latent space. In the second phase, we propose to employ pairwise data similarities to create a K-dimensional space that is capable of accommodating more complex cluster distributions, hence providing more accurate clustering performance. K is the number of clusters. The autoencoder’s latent space obtained in the first phase is used as the input of the second phase. The effectiveness of both phases is demonstrated on seven benchmark datasets by conducting a rigorous set of experiments. The DCSS code is available at https://github.com/Armanfard-Lab/DCSS}, archive = {J_ACCESS}, author = {Mohammadreza Sadeghi and Sareh Soleimani and Narges Armanfard}, doi = {10.1109/ACCESS.2025.3600986}, journal = {IEEE Access}, pages = {148104-148127}, shortjournal = {IEEE Access}, title = {Deep clustering with self-supervision using pairwise similarities}, volume = {13}, year = {2025}, }
• (2025). GAN-based data augmentation for fault diagnosis and prognosis of rolling bearings: A literature review. ACCESS, 13, 148083-148103. (www)

  @article{ , abstract = {In 2014, Goodfellow et al. introduced Generative Adversarial Networks (GANs), an adversarial learning framework designed to generate synthetic data. In rolling bearing fault diagnosis and prognosis, specific GAN variants such as Conditional GANs (cGANs), Wasserstein GANs (WGANs), and their derivatives have been developed to address data scarcity and class imbalance challenges. In this work, we conduct a literature review to systematically examine GAN-based data augmentation techniques for fault diagnosis and prognosis of rolling bearings. Through a rigorous selection process, we identified 229 primary studies that employed GAN-based data augmentation, underscoring the widespread use of GANs to generate synthetic data in this field. Our review shows that GANs were first applied to rolling bearing fault diagnosis in 2018, and their use has grown significantly since then. Among GAN variants, Wasserstein GANs (WGANs) and Conditional GANs (cGANs) have proven highly effective in generating realistic synthetic data, particularly when integrated with Convolutional Neural Networks (CNNs). The review further reveals that CNN models have been widely used, achieving accuracy rates exceeding 95% in fault diagnosis and prognosis. We also report that 90% of studies employ accuracy as the primary evaluation metric, while 15% use F1-score, as detailed in our metric analysis for bearing fault diagnosis. For fault prognosis, RMSE and MAE are the most commonly used metrics, appearing in 11% and 9% of studies, respectively. Our analysis reveals standardized hyperparameter configurations with learning rate 0.0001, Adam optimizer, and batch size 32 being most effective. The review identifies critical challenges including data imbalance (19.7%), training instability (11.0%), and data scarcity (10.7%) as primary bottlenecks for industrial adoption. This review establishes a comprehensive foundation for understanding the current state and future directions of GAN-based approaches for rolling bearing fault diagnosis and prognosis, offering researchers and practitioners a valuable resource in industrial predictive maintenance.}, archive = {J_ACCESS}, author = {Md. Sulyman Islam Sifat and Md Alamgir Kabir and M. M. Manjurul Islam and Atiq Ur Rehman and Amine Bermak}, doi = {10.1109/ACCESS.2025.3600235}, journal = {IEEE Access}, pages = {148083-148103}, shortjournal = {IEEE Access}, title = {GAN-based data augmentation for fault diagnosis and prognosis of rolling bearings: A literature review}, volume = {13}, year = {2025}, }
• (2025). FS-SMOTE: An improved SMOTE method based on feature space scoring mechanism for solving class-imbalanced problems. ACCESS, 13, 148074-148082. (www)

  @article{ , abstract = {Class-imbalance problems have become a key challenge in machine learning, often results in training too many majority samples and learning too few minority samples. The Synthetic Minority Oversampling Technique (SMOTE) has been proven to be one of the most effective methods for dealing with class-imbalanced problems, specifically by synthesizing new minority samples to achieve approximately equal numbers of majority and minority samples. However, traditional SMOTE and some of its improved methods have their own limitations, such as the risk of synthesizing noisy samples and the problems caused by ignoring data distribution. To overcome these shortcomings, we propose an improved SMOTE algorithm based on feature space scoring mechanism (FS-SMOTE). FS-SMOTE establishes a scoring mechanism based on feature space and select minority samples based on probability accounting to the scores to participate in the subsequent synthesis process. Experiments on several benchmark datasets have shown that the FS-SMOTE method is significantly superior to the traditional SMOTE and three improved SMOTE methods in terms of evaluation metrics.}, archive = {J_ACCESS}, author = {Yongjie Huang}, doi = {10.1109/ACCESS.2025.3597794}, journal = {IEEE Access}, pages = {148074-148082}, shortjournal = {IEEE Access}, title = {FS-SMOTE: An improved SMOTE method based on feature space scoring mechanism for solving class-imbalanced problems}, volume = {13}, year = {2025}, }
• (2025). Closed-loop control of droplet quality based on curriculum deep deterministic policy gradient algorithm. ACCESS, 13, 148060-148073. (www)

  @article{ , abstract = {Piezoelectric inkjet printing has important applications in high-precision manufacturing, but precise control of droplet quality faces challenges such as the nonlinear coupling between drive waveform parameters and droplet characteristics, as well as environmental disturbances. To address these issues, this paper proposes a closed-loop droplet control method based on a curriculum deep deterministic policy gradient (DDPG) algorithm, with the standard deviation of droplet volume and velocity as the main optimization objectives. To tackle the complex mapping relationships and boost training efficiency, we first built a high-fidelity simulation environment using a four-layer deep residual network trained on real-world industrial ink droplet data. Following that, we designed a dynamic weight-adaptive composite reward mechanism and integrated a curriculum learning strategy with the Deep Deterministic Policy Gradient (DDPG) algorithm. Relying on our self-developed droplet observation platform, we achieve closed-loop control of the standard deviation of droplet volume and velocity. Experimental results show that this method can precisely control the droplet volume within ±0.1 pl of the target value, while keeping the velocity standard deviation below 40 mm/s. The adjustment process is stable without severe oscillations, and the method demonstrates strong robustness against environmental disturbances such as ink supply pressure. This provides an efficient and practical solution for the automated control of droplet quality in piezoelectric inkjet printing.}, archive = {J_ACCESS}, author = {Yunyun Shen and Hongwu Zhan and Yinwei Zhang and Yankang Zhang and Shaojie Tang and Zhanfeng Li}, doi = {10.1109/ACCESS.2025.3595873}, journal = {IEEE Access}, pages = {148060-148073}, shortjournal = {IEEE Access}, title = {Closed-loop control of droplet quality based on curriculum deep deterministic policy gradient algorithm}, volume = {13}, year = {2025}, }
• (2025). A modular solid-state transformer for 3 kV DC grids: Design, topology, and performance evaluation. ACCESS, 13, 148047-148059. (www)

  @article{ , abstract = {The growing emergence of Distributed Energy Resources (DER) that operate natively in dc challenges the traditional logic of unidirectional power flow and the exclusive use of ac in distribution systems due to inefficiency and control problems. dc microgrids are seen as a promising alternative to address these challenges, and Solid-State Transformers (SSTs) could be a feasible solution to enable the development of such controllable hybrid dc/ac grids. This paper presents the design, modular topology, and experimental validation of a 150 kVA SiC-based SST. This SST follows a four-module input-parallel, output-series configuration, enabling the creation of a 3 kV MV dc microgrid from the LV ac electrical grid, allowing intrinsic voltage balancing among modules, straightforward scalability in both voltage and power, and easier maintenance. Each two-stage module, in turn, consists of an active front-end and an isolated dc/dc, which is composed of a dual full-bridge driving a series CLLC resonant tank at 88-90 kHz. Laboratory and field tests demonstrate continuous dc voltage regulation in a range from 1.2 kV to 3 kV, black-start capability and support of the ac grid through reactive power. The prototype achieves a power conversion efficiency of 96.4 % under nominal conditions while transferring 151.6 kW, with line current THD remaining below 1.8 %. The SST, along with the constructed MV dc microgrids, addresses the lack of real demonstrators that can validate the benefits of dc integration.}, archive = {J_ACCESS}, author = {David Cervero and Eduardo García-Martínez and J. Ignacio Abad and Jorge Pinilla-Torremocha and José F. Sanz-Osorio and Julio J. Melero}, doi = {10.1109/ACCESS.2025.3601207}, journal = {IEEE Access}, pages = {148047-148059}, shortjournal = {IEEE Access}, title = {A modular solid-state transformer for 3 kV DC grids: Design, topology, and performance evaluation}, volume = {13}, year = {2025}, }
• (2025). Blockchain-based governance for autonomous weapon systems. ACCESS, 13, 148026-148046. (www)

  @article{ , abstract = {Autonomous Weapon Lifecycle Management (AWLM) is a process that governs the entire lifecycle of autonomous weapons, including their production, deployment, resale, and disposal. However, the systems and technologies currently used for governing the AWLM fall short of ensuring traceability, selective transparency, auditability, privacy, trust, security, and compliance with international norms. Additionally, most existing AWLM systems are centralized, which makes them vulnerable to the single point of failure problem and creates the potential for misuse, as control is limited to a few entities within the coalition. In this paper, we propose a consortium blockchain-based AWLM governance system that manages all autonomous weapon data in a decentralized, traceable, private, trustworthy, and secure manner while ensuring compliance with international regulations and controlled transparency to authorized entities across all stages of the AWLM. We design and implement seven smart contracts to automate critical functionalities, ensure adherence to international public law, and promote accountability in autonomous weapon deployment. The system architecture and workflow are detailed through sequence diagrams, and the underlying algorithms are presented alongside testing and validation procedures. We evaluate the proposed system through a security analysis, addressing key research questions, and comparing it with existing solutions. The security analysis reveals that the solution is highly secure and has a strong audit score with no critical vulnerabilities. Unlike existing approaches, the proposed solution integrates international regulations, covers all stages of AWLM, and ensures adaptability for diverse applications. We also discuss the limitations and challenges of the proposed solution. We make the smart contract code publicly available on GitHub.}, archive = {J_ACCESS}, author = {Muhammad Mohsan Sheeraz and Anna Bohdanets and Abdullah Yousafzai and Immaculate Dadiso Motsi-Omoijiade and Ganna Pogrebna and Ibrar Yaqoob}, doi = {10.1109/ACCESS.2025.3601362}, journal = {IEEE Access}, pages = {148026-148046}, shortjournal = {IEEE Access}, title = {Blockchain-based governance for autonomous weapon systems}, volume = {13}, year = {2025}, }
• (2025). Autoencoder-based approaches for upper limb use detection. ACCESS, 13, 148016-148025. (www)

  @article{ , abstract = {Feature extraction is an essential step in traditional machine learning approaches for detecting upper limb use employing movement data from inertial measurement units. However, manual feature extraction requires domain expertise, and intra- and inter-subject movement pattern variations within and across activities could affect model performance. The use of autoencoders for automatic feature extraction for upper limb use detection has not yet been investigated. To this end, this study investigated the performance of three different autoencoder-based approaches for upper limb use detection using data from 10 healthy individuals (right and left limbs) and 5 hemiparetic individuals (affected and unaffected limbs) doing a set of activities while wearing two wrist-worn inertial measurement units. A supervised autoencoder using extracted latent features for a random forest classifier resulted in performance gains over previous studies based on manual feature extraction for affected and unaffected limbs in hemiparetic patients (stroke or TBI) and the right limb in healthy participants. Given the limited dataset, further validation with a larger sample with more tasks is necessary to confirm the generalizability of these findings. Using advanced machine learning models can lead to improved quantification of upper limb use and can improve the assessment of rehabilitation outcomes with wearable technologies.}, archive = {J_ACCESS}, author = {Parvathy Neelakandan and Varadhan SKM and Sivakumar Balasubramanian}, doi = {10.1109/ACCESS.2025.3598806}, journal = {IEEE Access}, pages = {148016-148025}, shortjournal = {IEEE Access}, title = {Autoencoder-based approaches for upper limb use detection}, volume = {13}, year = {2025}, }
• (2025). Frequency regulation analysis of the coca codo sinclair hydropower plant (Ecuador) in isolated operation with high wind power penetration. ACCESS, 13, 148003-148015. (www)

  @article{ , abstract = {The increasing integration of wind energy into isolated power systems presents significant challenges for frequency regulation, particularly in regions where renewable penetration is expected to rise. This paper analyzes the frequency control capabilities of the Coca Codo Sinclair Hydropower Plant under such conditions, proposing a systematic methodology for tuning the gains of its speed governor. A dynamic model of the plant is developed in Matlab Simulink, explicitly incorporating long penstocks and hydraulic coupling between generating units, a configuration rarely addressed in conventional tuning approaches. The tuning strategy is based on minimizing a performance penalty index that reflects the plant’s response to power disturbances. Two gain-setting approaches are evaluated: one that adapts to the operational configuration of the generation units, and another that uses fixed gains regardless of operating mode. Simulation results, aligned with national renewable expansion plans, show that both strategies improve frequency stability compared to conventional tuning. The mode-dependent tuning yields better performance in reducing frequency deviations, while the fixed-gain approach simplifies implementation but may increase mechanical stress on turbine components. The results demonstrate that a suitable methodology for tuning speed controller gains in isolated systems with complex hydraulic dynamics enhances system performance, even under high wind power penetration levels. Moreover, these results highlight the importance of realistic modeling and adaptive tuning in the reliable integration of renewable energy in remote and decarbonization-priority areas.}, archive = {J_ACCESS}, author = {Guillermo Martínez-Lucas and Ana Fernández-Guillamón and Victor A. Gallo Mier and José Ignacio Sarasua}, doi = {10.1109/ACCESS.2025.3601378}, journal = {IEEE Access}, pages = {148003-148015}, shortjournal = {IEEE Access}, title = {Frequency regulation analysis of the coca codo sinclair hydropower plant (Ecuador) in isolated operation with high wind power penetration}, volume = {13}, year = {2025}, }
• (2025). An integrated approach for criteria evaluation and software maintenance process management: Insights from global software development perspective. ACCESS, 13, 147983-148002. (www)

  @article{ , abstract = {The widespread adoption of Global Software Development (GSD) paradigm has completely changed the information technology industry. Offshore software maintenance has emerged as a prominent strategy due to its significant cost savings, resource optimization and gaining competitive advantages. However, the complex interrelationships among the software maintenance criteria, coupled with social and technical factors, have presented substantial challenges for the decision makers to select effective sourcing strategies. This study proposes an integrated approach for identifying the interrelationships among the criteria, and subsequently ranks the sourcing alternatives. To explore the interrelationships among the key factors, the Interpretive Structural Modeling (ISM) technique is applied using the experts’ responses. The factors are classified into three hierarchical levels and categorized based on their importance and interdependencies. Additionally, for optimizing the decision-making process the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are employed to prioritize and rank the offshoring alternatives. Finally, a case study is conducted in the software industry that demonstrates the usefulness and applicability of the proposed integrated approach. The findings reveal that the proposed approach helps the clients and vendors in adopting the effective sourcing strategies for software maintenance process.}, archive = {J_ACCESS}, author = {Asaad Alzayed and Hanif Ur Rahman}, doi = {10.1109/ACCESS.2025.3601346}, journal = {IEEE Access}, pages = {147983-148002}, shortjournal = {IEEE Access}, title = {An integrated approach for criteria evaluation and software maintenance process management: Insights from global software development perspective}, volume = {13}, year = {2025}, }
• (2025). Processing of MST radar data using the covariance fitting algorithm. ACCESS, 13, 147972-147982. (www)

  @article{ , abstract = {This paper introduces a high-resolution spectral estimation algorithm that minimizes the Frobenius norm between the estimated covariance matrix (derived from data) and the true covariance matrix (based on the assumed data model). The algorithm is iterative and employs a gradient descent method. Unlike the Sparse Iterative Covariance-based Estimation (SPICE) algorithm, it does not require calculating the inverse of the covariance matrix. It performs well in estimating the spectrum of simulated data, even at low Signal-to-Noise Ratios (SNR), demonstrating reliable performance. The results of this Proposed Method (PM) are compared with those from the periodogram and SPICE algorithms. Subsequently, the algorithm is applied to MST radar data to estimate the Doppler spectrum and, thus, the wind speed. These results are also compared with the periodogram and SPICE outputs. The Proposed Method demonstrates superior performance compared to the periodogram and yields results nearly identical to those of SPICE. To validate these findings, the estimated wind speed is compared with data from Global Positioning System (GPS) radiosonde. Satisfactory results are observed for practical MST radar measurement data.}, archive = {J_ACCESS}, author = {S. Sanjeeva Reddy and G. Ramachandra Reddy}, doi = {10.1109/ACCESS.2025.3601379}, journal = {IEEE Access}, pages = {147972-147982}, shortjournal = {IEEE Access}, title = {Processing of MST radar data using the covariance fitting algorithm}, volume = {13}, year = {2025}, }
• (2025). Leveraging machine learning approaches to decode hive sounds for stress prediction. ACCESS, 13, 147953-147971. (www)

  @article{ , abstract = {Beekeeping plays a vital role in preserving ecosystems through pollination and increasing biodiversity. Effective monitoring of honeybee health and hive conditions is essential to balance bee populations and their environment. This study addresses the challenges of data scarcity and generalization in beehive health monitoring by introducing a semi-supervised learning model that employs a Transformer-based encoder-classifier for acoustic analysis of hive sounds. This research demonstrates the application of a Transformer-based architecture specifically tailored for bee bioacoustics and stress detection, integrating advanced feature extraction and fine-tuning techniques for this application. The main objective is to identify stress-related indicators from audio data collected via smart beehives. The proposed method utilizes a dataset of 5,336 labelled audio clips from diverse sources, including the NU-hive project and YouTube audio, to aid the learning process and enhance the classification accuracy for both labeled and unlabeled data. The audio features used in the analysis include Mel-frequency cepstral coefficients (MFCCs) and their delta and delta-delta variants, root mean square (RMS) energy, spectral centroid, and dominant frequency from Short-Time Fourier Transform (STFT). The Transformer-based encoder-classifier is implemented to classify bee behaviour within the hive as Normal, NoQueen, or Swarm, and to distinguish stressed from not stressed states. Evaluations indicate that the semi-supervised Transformer encoder-classifier achieves 99% accuracy on labeled data, with precision and recall values of 0.99 or higher for the Normal and NoQueen classes, and 0.96 for the Swarm class. Cluster validation produced a silhouette score of 0.47 and a Davies-Bouldin index of 0.57, indicating moderate cluster separability and compactness. The model was able to pseudo-label 94.7% of unlabeled data, validated against the nearest labelled neighbours. These results show the effectiveness of AI-driven beehive monitoring in supporting sustainable beekeeping practices and ecosystem conservation efforts.}, archive = {J_ACCESS}, author = {Saba Mustafa and Mahsa Mohaghegh and Iman Ardekani and Abdolhossein Sarrafzadeh}, doi = {10.1109/ACCESS.2025.3599330}, journal = {IEEE Access}, pages = {147953-147971}, shortjournal = {IEEE Access}, title = {Leveraging machine learning approaches to decode hive sounds for stress prediction}, volume = {13}, year = {2025}, }
• (2025). A low-profile dual-polarized frequency-selective rasorber based on one-eighth wavelength spacer and interdigital resonator. ACCESS, 13, 147943-147952. (www)

  @article{ , abstract = {This paper presents a low-profile dual-polarized frequency-selective rasorber (FSR) that combines the design of a resistive absorber frequency-selective surface (FSS) with a bandpass FSS to achieve two absorption bands located on both sides of a transmission band. The main contribution of this research is the optimization of the thickness of the air spacer between the two dielectric substrates, achieved with the aid of the Advanced Design System software. This optimization was performed to achieve a thickness equivalent to one-eighth of the wavelength at the central frequency of the passband ( $\lambda _{2}$ ), which is half the standard value typically used, in order to obtain a low-profile structure with a wide absorption band. A theoretical analysis, based on transmission line theory, validated the $\lambda _{2}/8$ choice. Both FSSs feature new geometries, in which the lossy FSS elements are split rings with lumped resistors and interdigital resonators inserted to ensure a parallel resonance. The proposed FSR exhibits an operating bandwidth of $120~\%$ (fractional bandwidth) from 4.03 to $16.12~GHz$ ( $|S_{11}| \lt -10~dB$ ) and a passband centered at $9.7~GHz$ with a minimum insertion loss of $1.0~dB$ , under normal incidence. Due to the low-profile characteristic and the reduced physical dimensions of the unit cell, the presented FSR has good angular stability up to a 40° angle of incidence. An equivalent circuit model is presented to provide physical insight of the structure’s behavior. A prototype was fabricated and tested to validate the design approach and prove the proposed concept. Good agreement was observed between the simulated and experimental results.}, archive = {J_ACCESS}, author = {Reilian Da S. Macilon and Adaildo Gomes D’Assuncao and Valdemir P. Da Silva Neto and Adaildo Gomes D’Assunção and Custódio J. O. Peixeiro}, doi = {10.1109/ACCESS.2025.3596533}, journal = {IEEE Access}, pages = {147943-147952}, shortjournal = {IEEE Access}, title = {A low-profile dual-polarized frequency-selective rasorber based on one-eighth wavelength spacer and interdigital resonator}, volume = {13}, year = {2025}, }
• (2025). Multi-parameter prediction of inkjet droplet characteristics based on dual-pulse waveform and ensemble learning. ACCESS, 13, 147925-147942. (www)

  @article{ , abstract = {Inkjet printing technology has emerged as an efficient solution for large-scale manufacturing of electronic devices, sensors, and light-emitting diodes (LEDs) due to its ability to directly deposit functional materials onto flexible substrates according to predetermined patterns, while offering advantages of simple processing, low cost, and high adaptability. In the field of inkjet printing, piezoelectric waveform parameters directly influence droplet characteristics and quality, which in turn affect the final print quality. Therefore, accurate prediction of droplet parameters is crucial for optimizing printing quality. This research addresses the challenge of droplet parameter prediction in piezoelectric inkjet printing by proposing a multi-parameter collaborative prediction method for jetting performance based on dual-pulse waveforms. By constructing a six-dimensional parameter space including primary/secondary pulse voltages, dwell times, and interval times, and employing Sobol sequences to generate initial samples alongside an adaptive sampling strategy based on global sensitivity analysis, we innovatively designed an ensemble learning model based on stacking strategy to predict droplet parameters. Experimental results demonstrate that the adaptive sampling strategy based on global sensitivity analysis significantly enhances data acquisition efficiency (1408 valid datasets) and greatly increases data density in sensitive regions. The heterogeneous ensemble model achieves precise mapping between waveform parameters and droplet velocity (R2=0.91), volume (R2=0.94), and quality score (R2=0.82), enabling accurate prediction of droplet velocity, volume, and quality score.}, archive = {J_ACCESS}, author = {Shaojie Tang and Hongwu Zhan and Yankang Zhang and Yinwei Zhang and Yunyun Shen}, doi = {10.1109/ACCESS.2025.3594274}, journal = {IEEE Access}, pages = {147925-147942}, shortjournal = {IEEE Access}, title = {Multi-parameter prediction of inkjet droplet characteristics based on dual-pulse waveform and ensemble learning}, volume = {13}, year = {2025}, }
• (2025). Research on an optimization model for water resource management based on deep learning. ACCESS, 13, 147906-147924. (www)

  @article{ , abstract = {This study proposes HydroCortex, a unified optimization framework for water resource management that explicitly integrates deep learning-based hydrological forecasting with constraint-aware decision-making. It addresses a critical gap in existing methods, which often separate predictive modeling from operational control and rely on static, rule-based policies that underperform in dynamic environments. The central research question investigates how machine learning techniques—such as LSTM-based inflow prediction and graph-structured allocation modules—can support real-time, interpretable, and adaptive water distribution. HydroCortex combines spatiotemporal data processing, scenario-based uncertainty modeling, and multi-agent preference balancing in a modular decision structure. This integration aligns with the growing demand for intelligent, policy-compliant, and data-driven water systems. Experiments across four benchmark datasets and a real-world case in China’s Yellow River Basin show up to 30% improvement in allocation efficiency and notable gains in system robustness. The results demonstrate the model’s scalability, generalizability, and practical relevance under climate variability and diverse stakeholder needs.}, archive = {J_ACCESS}, author = {Tianyi Lu and Tao Jiang and Yong Liu}, doi = {10.1109/ACCESS.2025.3600664}, journal = {IEEE Access}, pages = {147906-147924}, shortjournal = {IEEE Access}, title = {Research on an optimization model for water resource management based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Nonlinear MPC with enhanced state estimation using UKF for interacting variable area hybrid process. ACCESS, 13, 147892-147905. (www)

  @article{ , abstract = {Most of the process industries use variable area tank processes such as conical and spherical tank processes. However, their nonlinear dynamics and interactions between such processes along with measurement noise, make controller design challenging. In spite of their widespread use, the development of a unified control strategy that ensures reliable performance across the entire operating range remains an open research problem. Hence, this paper proposes Non-Linear Model Predictive Control (NLMPC) with Unscented Kalman Filter (UKF) for a nonlinear lab scale interacting two tank hybrid process, which provides dynamic model predicting capability and better compensation towards nonlinearities in the presence of continuous measurement noise. To enhance the performance, NLMPC is designed using the estimated states with the help of UKF. Initial simulation is performed by the proposed controller with/without estimator under servo and regulatory operations in the presence of disturbance and noise. Further, NLMPC with UKF is implemented in a lab scale setup and results are compared with existing controllers. The robustness of the controllers is assessed by computing various performance indices such as integral absolute error, integral square error and control effort.}, archive = {J_ACCESS}, author = {Sathish Kumar Samy and S. Kanagalakshmi and N. Selvaganesan and Rishikeshwar Kumaresan}, doi = {10.1109/ACCESS.2025.3601352}, journal = {IEEE Access}, pages = {147892-147905}, shortjournal = {IEEE Access}, title = {Nonlinear MPC with enhanced state estimation using UKF for interacting variable area hybrid process}, volume = {13}, year = {2025}, }
• (2025). ISDC-YOLO: A lightweight small object detection algorithm based on inverted bottleneck and semantic detail fusion network for UAV aerial image. ACCESS, 13, 147874-147891. (www)

  @article{ , abstract = {Autonomous aerial vehicle (AAV) object detection in aerial images presents substantial value but also considerable challenges. UAV images often exhibit characteristics such as a high proportion of small objects, dense distributions, and significant scale variations, which present considerable challenges for accurate detection. To address these issues, we propose the ISDC-YOLO algorithm, which is based on inverted bottleneck structures and Semantic Detail Fusion techniques. First, the P5 layer, which has the highest parameter count and is primarily designed for detecting large objects, is removed from the backbone. Meanwhile, the P2, P3, and P4 layers, which specialize in detecting medium and small objects, are merged, with P2 and P3 serving as detection heads to enhance the model’s focus on small objects. Subsequently, a lightweight C2F-DOB module, based on the Inverted Bottleneck architecture, is designed to capture richer contextual information while maintaining a lightweight design. Next, a Semantic and Detail Fusion (SDFM) module is designed to fuse fine-grained information from shallow feature maps with semantic information from deep feature maps. Finally, the detection head is replaced with a Dynamic Head, allowing the integration of scale, spatial, and task awareness within a single module. Extensive experiments on the VisDrone dataset demonstrate that compared with YOLOv8s, ISDC-YOLOv8(128) achieves improvements of 9.8% in Recall and 9.2% in mAP@0.5, while reducing parameter count by 47.8%. On the AI-TOD dataset, Recall and mAP@0.5 increase by 11.7% and 9.3%, respectively, further validating its effectiveness and generalization on Solar Panel Defect datasets (SPDs). The improved model achieves enhanced detection accuracy while maintaining a lightweight architecture, validating the effectiveness of the proposed optimization strategies and outperforming other classical models.}, archive = {J_ACCESS}, author = {Chen Wang and Fen Liu}, doi = {10.1109/ACCESS.2025.3600840}, journal = {IEEE Access}, pages = {147874-147891}, shortjournal = {IEEE Access}, title = {ISDC-YOLO: A lightweight small object detection algorithm based on inverted bottleneck and semantic detail fusion network for UAV aerial image}, volume = {13}, year = {2025}, }
• (2025). Empowering sentiment analysis in african low-resource languages through transformer models and strategic language selection. ACCESS, 13, 147859-147873. (www)

  @article{ , abstract = {This research addresses the significant challenges of sentiment analysis in low-resource African languages by utilizing advanced transformer-based models to bridge gaps in natural language processing (NLP) for underrepresented linguistic communities. Covering 12 diverse languages: Hausa, Yoruba, Igbo, Nigerian Pidgin, Amharic, Algerian Arabic, Moroccan Arabic Darija, Swahili, Kinyarwanda, Twi, Mozambican Portuguese, and Xitsonga. Our work includes pre-trained language models (PLMs) such as XLM-R, AfroXLMR, AfriBERTa, and mDeBERTaV3, fine-tuning them for the specific task of sentiment classification. In particular, using datasets from the AfriSenti SemEval 2023 Shared Task 12, this process involves tailoring multilingual transformer models to low-resource African languages, enabling them to capture complex linguistic nuances and sentiment polarities effectively. The results demonstrate significant improvements across key metrics, including accuracy, precision, recall, and weighted F1 score. Notably, our work using AfroXLMR achieves the top 1 rank, with a weighted F1 score of 75.8%, outperforming all other submissions. In addition to multilingual transformer models, we evaluated traditional machine learning models, including CNN, Naïve Bayes, and SVM, to compare their performance. The evaluation focused on zero-shot sentiment classification for Tigrinya, a low-resource African language not seen during model training. The results indicate that while traditional models produced meaningful results, multilingual transformer models achieved higher performance in the zero-shot setting. This highlights both the challenges and potential of multilingual transfer approaches for sentiment analysis in African low-resource languages. Moreover, this research highlights the transformative role of Artificial Intelligence (AI) in addressing linguistic diversity, fostering digital inclusion, and enabling reliable sentiment analysis. Furthermore, it lays the groundwork for future advancements in processing low-resource languages and emphasizes the importance of promoting cultural inclusivity through AI technologies.}, archive = {J_ACCESS}, author = {Nilanjana Raychawdhary and Sutanu Bhattacharya and Cheryl Seals and Gerry V. Dozier}, doi = {10.1109/ACCESS.2025.3599480}, journal = {IEEE Access}, pages = {147859-147873}, shortjournal = {IEEE Access}, title = {Empowering sentiment analysis in african low-resource languages through transformer models and strategic language selection}, volume = {13}, year = {2025}, }
• (2025). Knee meniscus tear during exercise identification using capsule neural network optimized by adapted seagull optimizer. ACCESS, 13, 147842-147858. (www)

  @article{ , abstract = {Accurate, timely diagnosis of knee meniscus tear remains a clinical challenge due to the limitations of traditional diagnostic methods and the suboptimal performance of conventional deep learning models in capturing spatial and pose information from MRI images. To address these gaps, this study proposes a novel diagnostic model that combines the Capsule Neural Network (CapsNet) with an Adapted Seagull Optimization (ASO) algorithm, forming an integrated framework for enhanced feature extraction and automated hyperparameter tuning. Unlike existing methods—such as classic deep learning and convolutional neural networks—which often rely on manual parameter selection and are prone to local minima, our approach leverages the global search capability of ASO to optimize CapsNet configurations more effectively. The model is rigorously evaluated on a curated clinical MRI dataset and benchmarked against state-of-the-art techniques to demonstrate its superior accuracy and generalization. Results indicate that the proposed CapsNet/ASO model offers a robust decision-support tool for clinicians, substantially improving diagnostic performance over existing approaches.}, archive = {J_ACCESS}, author = {Xiaoman Liu and Xiaohui Wang and Jianfang Liu and Jingli Gao and Hasan Hosseinzadeh}, doi = {10.1109/ACCESS.2025.3600304}, journal = {IEEE Access}, pages = {147842-147858}, shortjournal = {IEEE Access}, title = {Knee meniscus tear during exercise identification using capsule neural network optimized by adapted seagull optimizer}, volume = {13}, year = {2025}, }
• (2025). Multitask deep learning for predicting parkinson’s progression and depression from multimodal time series data. ACCESS, 13, 147818-147841. (www)

  @article{ , abstract = {The high cases of Parkinson’s disease (PD) in the senior generation require reliable PD progression prediction. PD can be associated with depression as a common non-motor symptom. Depression in Parkinson’s disease (PD) is frequently underdiagnosed, despite its significant impact on patients’ quality of life and caregiver burden. Early detection and identification of predictive factors are essential for timely intervention. This study proposes a novel multitask hybrid deep learning framework that leverages multimodal time-series data—including demographic, motor, and non-motor features—to enhance the detection and understanding of depressive symptoms in PD patients. These three modalities are early fused, and the relevant features of the resulting feature set were extracted using a deep variational autoencoder model. Because MRI data are critical in neurodegenerative disease diagnosis, a 3D CNN module has been used to learn deep features from MRI images, and these deep representations were lately fused with the extracted features of the variational autoencoder. The extracted deep representations were used to build a multitask classifier based on a novel Bidirectional LSTM model. This model has been optimized to predict the three tasks of 1) a four-class NHY scale that defines the severity of Parkinson’s disease, 2) a binary class that determines whether or not a patient is depressed, and 3) a four-class depression scale that indicates the levels of depression. The proposed model has been evaluated on single-task and multitask problems with various time steps. Using a 2-time step in the NHY prediction task, our model obtains the maximum cross-validation and testing accuracies of 96.78% and 94.37%, respectively. The multitask model produced steady increases in testing accuracy of 86.29%, 87.12%, and 95.81% with 2-time step, 4-time step, and 6-time step, respectively. The proposed model investigated 1059 PD patients over six-time steps and 850 MRI images of single-time steps from the Parkinson’s Progression Markers Initiative study (PPMI) dataset.}, archive = {J_ACCESS}, author = {Muhammad Junaid and Mitra Ghergherehchi and Seunghyun Lee}, doi = {10.1109/ACCESS.2025.3593254}, journal = {IEEE Access}, pages = {147818-147841}, shortjournal = {IEEE Access}, title = {Multitask deep learning for predicting parkinson’s progression and depression from multimodal time series data}, volume = {13}, year = {2025}, }
• (2025). Reliability and synergy analysis of PDN integrated with wind generation and EV-FCS coupled traffic network. ACCESS, 13, 147798-147817. (www)

  @article{ , abstract = {Deployment of EV Fast Charging Stations (FCSs) is increasing widely to tackle pollution, limited energy and range anxiety. Their presence in power distribution networks (PDNs) might lead to additional stress on system components, resulting in increased losses and voltage drops. Wind energy, as a green alternative, can mitigate these impacts but may also introduce operational issues such as reverse power flow and voltage rise. This study performs a synergistic analysis of FCS and wind-integrated PDN. Wind power variability is modelled using a Weibull distribution function, while estimates of spatial-temporal FCS charging demand modelled as exponential load are integrated into PDN via a queuing model and transportation surveys. The PDN’s performance is determined by quantifying operation parameters and reliability indices. Further, the computation of reliability metrics is done by both analytical and Monte Carlo simulation methods to capture the deterministic and stochastic characteristics of the system’s performance. A case study involving a customized IEEE 123-bus PDN linked with a 25-node traffic network is presented. Simulation results show that system behavior is significantly influenced by EV load levels, the degree of wind integration, PDN topology, and the time of day. Wind integration improves system reliability, while increased EV penetration degrades it. Notably, even with wind present, 60% of the annual energy demand remains unserved at a 100 % EV load level. To address this, solar energy is integrated using a kernel distribution function, which reduces the unserved energy to 55%. However, this improvement leads to 5.8% voltage deviations at certain buses, indicating the need for control measures by decision makers to strategically intervene and ensure voltage stability in scenarios with high renewable and EV loads.}, archive = {J_ACCESS}, author = {Akanksha Shukla and Kusum Verma and Rajesh Kumar and Sachin Sharma}, doi = {10.1109/ACCESS.2025.3601186}, journal = {IEEE Access}, pages = {147798-147817}, shortjournal = {IEEE Access}, title = {Reliability and synergy analysis of PDN integrated with wind generation and EV-FCS coupled traffic network}, volume = {13}, year = {2025}, }
• (2025). Dual-port grid-forming control of double-stage interlinking converters in islanded hybrid AC/DC microgrids. ACCESS, 13, 147784-147797. (www)

  @article{ , abstract = {Hybrid AC/DC microgrids (MGs) are comprised of AC and DC MGs interconnected through an interlinking converter (ILC). This paper presents a novel control strategy for double-stage ILCs (consisting of bidirectional DC-DC and DC-AC converters) in islanded hybrid AC/DC microgrids. To provide global power sharing among AC and DC DERs, a PI controller obtains a single relative power reference for the AC and DC DERs based on the energy content of the DC link capacitor. This relative power is then translated into references for the output voltage angular frequency at the AC side of ILC and the output voltage at its DC side, in accordance with the AC and DC DERs’ droop characteristics. Then the DC-AC and DC-DC converters realize these references through a voltage-controlled method (VCM) in their inner control loops. Accordingly, the proposed method features grid-forming capability at both AC and DC terminals of ILC, which provides it with some unique advantages. First is the improved resiliency; the system remains stable even if all DC DERs are disconnected or all AC DERs are disconnected. Secondly, stability margin and voltage transients are improved thanks to the PLL-less design and adaptation of VCM. Small-signal stability analysis and hardware-in-the-loop test results demonstrate the stability and dynamic performance of the proposed method not only in normal operation but also under DER disconnection. The results show that the steady-state AC and DC voltage deviations are less than 5% and the AC voltage undershoot is less than 1V.}, archive = {J_ACCESS}, author = {Mehdi Baharizadeh and Mohammad Sadegh Golsorkhi and Ali Arzani and Thomas Ebel}, doi = {10.1109/ACCESS.2025.3601398}, journal = {IEEE Access}, pages = {147784-147797}, shortjournal = {IEEE Access}, title = {Dual-port grid-forming control of double-stage interlinking converters in islanded hybrid AC/DC microgrids}, volume = {13}, year = {2025}, }
• (2025). A single-ended fault location method for DC transmission lines based on inverse distance weighted interpolation. ACCESS, 13, 147772-147783. (www)

  @article{ , abstract = {The accuracy and reliability of single-ended fault location methods are often affected by boundary elements and transition resistances. This paper proposes a single-ended fault location method for DC transmission lines based on the initial traveling wave dominant frequency (ITWDF) attenuation characteristics. Firstly, the attenuation characteristics of the initial traveling wave frequency component are analyzed, and the attenuation law and distribution differences between the frequency component and fault distance are revealed. Secondly, to accurately extract and describe the trend of the initial traveling wave frequency component with the change of fault distance, the energy of the initial traveling wave is constructed, and the mathematical expression of the ITWDF related to the fault distance and unit length distortion coefficient is obtained. Theoretical analysis verifies that the ITWDF exhibits a monotonic attenuation mapping relationship with increasing fault distance. In addition, the attenuation characteristics of the ITWDF are not affected by fault impedance. Finally, a fault sample set containing the ITWDF and reference unit length distortion coefficients at different positions is constructed. The fault location is calculated using the ITWDF and the reference unit length distortion coefficient, with continuous wavelet transform and inverse distance weighting interpolation algorithms applied, respectively. Simulation results show that the proposed method can reliably locate faults under high-impedance conditions of up to $800\Omega $ , and the absolute error is less than 200m. Moreover, the proposed method can be applied to DC lines with various boundary structures, improving the reliability of single-ended fault location.}, archive = {J_ACCESS}, author = {Dongli Jia and Xiaoyu Yang and Kaiyuan He and Feng Deng and Zhentong Wang and Tianle Li}, doi = {10.1109/ACCESS.2025.3591637}, journal = {IEEE Access}, pages = {147772-147783}, shortjournal = {IEEE Access}, title = {A single-ended fault location method for DC transmission lines based on inverse distance weighted interpolation}, volume = {13}, year = {2025}, }
• (2025). Unleashing AI-empowered slices on mobile networks for natively cognitive service delivery. ACCESS, 13, 147757-147771. (www)

  @article{ , abstract = {Integrating Artificial Intelligence (AI) into network slice orchestration remains a challenge due to data availability, model generalization, and the need for real-time inference. The Network Data Analytics Function (NWDAF) systematically incorporates AI into the modern mobile network core to enhance network operation management. Although research fronts have explored AI applications in mobile networks, primarily to improve performance or connectivity management, orchestrating and delivering AI-driven slices for end users still requires resolving issues of scalability, interoperability, and latency. The literature categorizes these challenges into applying AI for autonomous network management, which involves a mature and evolving orchestration to support stringent AI-based slices for end-users. This work addresses the latter, proposing enhancements to two core network components, the User Plane Function (UPF) and NWDAF, to enable seamless deployment of AI slices for both training and inference targeted at mobile users. We introduce, for the first time, distinct training and inference slices orchestrated through an enhanced UPF (e-UPF) and NWDAF within the 5G core, where the UPF performs slice-aware forwarding of AI traffic to Multi-access Edge Computing (MEC) servers. All AI training and inference workloads are executed at the network edge (MEC). With this approach, the core network can deliver cognitive services directly to User Equipments (UEs). A disaster management case study demonstrates this, where Convolutional Neural Networks (CNNs) deployed on MEC servers classify disaster scenarios via AI slices. Results show that AI slices can be efficiently deployed and differentiated via data plane filters, enabling private cognitive services and paving the way for AI-based applications in mobile networks.}, archive = {J_ACCESS}, author = {Rodrigo Moreira and Larissa Ferreira Rodrigues Moreira and Flávio de Oliveira Silva}, doi = {10.1109/ACCESS.2025.3599356}, journal = {IEEE Access}, pages = {147757-147771}, shortjournal = {IEEE Access}, title = {Unleashing AI-empowered slices on mobile networks for natively cognitive service delivery}, volume = {13}, year = {2025}, }
• (2025). Automatic evaluation of programming tasks supported by language models. ACCESS, 13, 147741-147756. (www)

  @article{ , abstract = {Large language models are also increasingly used in education, both by students and teachers. Newly introduced LLM-based tools, such as Codex, Code Llama, and Microsoft’s Copilot, show that LLMs are effective in code generation. In this paper, we evaluate the effectiveness of using LLMs in code evaluation. In programming courses, traditional manual code evaluation is very slow, and the participation of multiple instructors reduces consistency. Test-based code evaluation does not help novice programmers; for low-achieving students, the score attributed by such automatic tests is usually lower than what would have been gained after manual correction. Using LLMs might combine the benefits of manual correction and automatic testing. In order to apply LLMs to the task, detailed scoring guidelines were provided for the language models. The experiment involved 40 students in the first exam and 45 in the second exam in a C++ language course, with six teachers manually assessing the code fragments. The exams consisted of several different programming exercises, and the number of code snippets evaluated exceeded 350. We found a strong correlation between manual scoring and the scores given by the language models. Depending on the language model and the exam set examined, we measured correlation values between 0.6 and 0.9. The results of the experiment were significantly influenced by inconsistent teacher evaluations, so during the research phase, a single teacher was assigned to manual evaluation, which significantly improved the results. Based on these results, we repeated the experiment with an increased number of students (160) learning the Java programming language. The results again show a strong correlation (between 0.6 and 0.7). To clarify the results, we compared the models based on rubric specificity. Still, more research and testing are needed to explore the differences between manual evaluation and evaluation by large language models.}, archive = {J_ACCESS}, author = {Zoltán István Karsa and Balázs Goldschmidt}, doi = {10.1109/ACCESS.2025.3601448}, journal = {IEEE Access}, pages = {147741-147756}, shortjournal = {IEEE Access}, title = {Automatic evaluation of programming tasks supported by language models}, volume = {13}, year = {2025}, }
• (2025). Fast point cloud ground segmentation using frequency analysis and linear plane estimation. ACCESS, 13, 147729-147740. (www)

  @article{ , abstract = {Deploying fully autonomous vehicles requires a reliable and secure Advanced Driving Assistance System (ADAS) which, crucially, must also offer real-time performance. This study proposes fast ground segmentation for LiDAR point clouds, utilizing frequency analysis and plane estimation. The process consists of four main steps: data sampling, intensity filtering, height filtering, and ground plane estimation. Data sampling selects partial data based on the vehicle’s LiDAR installation. Intensity filtering extracts points within the most frequent intensity range, while height filtering utilizes unimodal thresholding to extract points in the dominant frequent height range, and together these filtered points estimate the plane of the segmentation boundary. Our experimental results, utilizing the SemanticKITTI dataset, have collected sampling width ratios ( $R_{w}$ ), standard deviation multipliers (K), and tolerance coefficients ( $\varDelta T$ ), which were evaluated using ANOVA. The optimal parameter combination yielded 91% accuracy, 84% precision, 94% recall, and an F1 score of 89%, all taking 2.7 ms of processing time.}, archive = {J_ACCESS}, author = {Warintorn Rotjanakarin and Watcharin Kaewapichai}, doi = {10.1109/ACCESS.2025.3593664}, journal = {IEEE Access}, pages = {147729-147740}, shortjournal = {IEEE Access}, title = {Fast point cloud ground segmentation using frequency analysis and linear plane estimation}, volume = {13}, year = {2025}, }
• (2025). Analysis of seismic elastic steel structures using a new hybrid optimization deep learning model. ACCESS, 13, 147714-147728. (www)

  @article{ , abstract = {The traditional seismic elastic analysis model for steel structures suffers from insufficient performance. This study proposes a deep feature learning network model. The model combines a cross-attention mechanism, a graph neural network, and a random sampling consistency algorithm to optimize the point set metric space. Experiments showed that the average F1 score of this model in cloud matching tasks was 0.919. This score was 16.5%-22.6% higher than traditional methods. The matching success rate reached 55.43% and remained more than 15% higher than comparison methods at a 50% occlusion rate. For semantic segmentation tasks, the intersection to union ratio (0.798) and Dice coefficient (0.879) of steel pipe components were higher than those of traditional methods. In actual scenario testing, the model achieved sub-millimeter level deformation detection. The minimum deformation amounts for steel pipes and welded balls were 0.44 mm and 0.31 mm, respectively. The generalization error (0.06 mm) and memory peak value (11.3 GB at 10K points) were reduced by 76.9% and 53.9% respectively, compared to the comparison method. This makes the method suitable for the real-time monitoring needs of edge devices. In summary, the proposed model addresses the shortcomings of traditional methods in dynamic interaction, noise suppression, and deformation sensitivity. It does so through multimodal feature fusion and hybrid optimization strategies. This provides a high-precision and low-energy solution for seismic analysis of complex steel structures.}, archive = {J_ACCESS}, author = {Ping Li and Wanfeng Liu and Tiecheng Yan}, doi = {10.1109/ACCESS.2025.3601409}, journal = {IEEE Access}, pages = {147714-147728}, shortjournal = {IEEE Access}, title = {Analysis of seismic elastic steel structures using a new hybrid optimization deep learning model}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization of a 1P7S LFP prismatic battery pack with phase change material as a battery thermal management system in high-power applications. ACCESS, 13, 147703-147713. (www)

  @article{ , abstract = {Battery Thermal Management Systems (BTMS) are crucial for regulating the temperature of lithium-ion battery packs, particularly in high-power applications such as electric vehicles. This study presents a multi-objective optimization of a 1P7S LFP prismatic battery pack using Phase Change Material (PCM) as the BTMS. Although previous research has primarily focused on reducing the maximum temperature rise, this study also addresses the challenge of minimizing inter-cell temperature differences. The thermal behavior of the pack is modeled under various operating conditions using advanced multiphysics simulations and efficient optimization algorithms. The proposed approach considers multiple objectives: minimizing temperature rise, reducing temperature gradients, and optimizing PCM utilization. The results demonstrate the effectiveness of the optimized PCM-based BTMS in maintaining a uniform temperature distribution across prismatic cells during high-power discharge. A Pareto front of optimal solutions is presented to balance thermal performance, system complexity, and cost. This research contributes to the development of safer and more reliable battery systems for high-power applications.}, archive = {J_ACCESS}, author = {Samuel Henrique Werlich and Joelton Deonei Gotz and Emilson Ribeiro Viana and Milton Borsato}, doi = {10.1109/ACCESS.2025.3600326}, journal = {IEEE Access}, pages = {147703-147713}, shortjournal = {IEEE Access}, title = {Multi-objective optimization of a 1P7S LFP prismatic battery pack with phase change material as a battery thermal management system in high-power applications}, volume = {13}, year = {2025}, }
• (2025). Optimizing multi-layer LSTM based on non-local spatiotemporal interactions for vehicle trajectory prediction. ACCESS, 13, 147690-147702. (www)

  @article{ , abstract = {Accurate trajectory prediction is critical to improve the planning and control capabilities of autonomous vehicles. In complex traffic scenarios, the influence of social interactions among vehicles plays an important role in shaping their future trajectories. Vehicle trajectory models based on recurrent neural networks (RNNs) or convolution neural networks (CNNs) often struggle to perform well in long prediction horizons. The majority of existing approaches limit themselves to a fixed spatial neighborhood or a short temporal window for interaction modeling, which accumulate errors and overlook long-range dependencies. To overcome these limitations, a non-local spatio-temporal interaction-based optimized long-short-term memory (NST-LSTM) model is introduced to predict future trajectories. The proposed model effectively captures high- order interactions among vehicles without being constrained to a fixed spatial neighborhood or a limited temporal window. A series of experiments are carried out using real-world High-D dataset. The experimental results reveal that our model outperforms several baseline models and achieves 80% RMSE reduction relative to the next-best LS-LSTM across the five-second prediction horizon. A detailed ablation study is also conducted to choose optimal hyper-parameters for the LSTM model including depth, learning rate, and batch size.}, archive = {J_ACCESS}, author = {Sidra Rashid and Muazzam A. Khan Khattak and Muhammad Usman Akram and Awais Ahmad and Hatoon S. Alsagri and Haya Abdullah Abdulaziz Alhakbani}, doi = {10.1109/ACCESS.2025.3599769}, journal = {IEEE Access}, pages = {147690-147702}, shortjournal = {IEEE Access}, title = {Optimizing multi-layer LSTM based on non-local spatiotemporal interactions for vehicle trajectory prediction}, volume = {13}, year = {2025}, }
• (2025). Reliability and scalability of pedestrian monitoring practices: A systematic review. ACCESS, 13, 147679-147689. (www)

  @article{ , abstract = {Effective pedestrian monitoring is crucial for promoting sustainable, safe, and livable urban environments, yet current practices lack a rigorous, evidence-based evaluation of their reliability, scalability, and contextual relevance. This systematic review offers the first foundational assessment of pedestrian monitoring practices, synthesizing academic literature to identify trends and expose critical knowledge gaps. A significant contribution is our novel qualitative framework, which synthesizes literature findings to classify monitoring methods into high (e.g., Bluetooth, IoT), medium (e.g., GPS, LiDAR), and low quality (e.g., surveys, video surveillance), based on their characteristics related to reliability and scalability. This synthesis reveals persistent challenges: the absence of standardized evaluation protocols, limited capacity to address complex urban environments, the underuse of widely available technologies, lack of long-term performance reporting, and unresolved tensions between data privacy and utility. To address these issues, the review outlines a strategic research agenda, including the establishment of benchmarking standards, the development of integrated, context-aware systems (supported by a proposed machine learning data driven framework), and investment in long-term and privacy-conscious approaches. The findings offer actionable insights for researchers, planners, and policymakers, supporting the design of walkable cities and more resilient, pedestrian-friendly urban environments.}, archive = {J_ACCESS}, author = {Mohamed Shamroukh and Asya Natapov and Taimaz Larimian}, doi = {10.1109/ACCESS.2025.3599283}, journal = {IEEE Access}, pages = {147679-147689}, shortjournal = {IEEE Access}, title = {Reliability and scalability of pedestrian monitoring practices: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Geometric optimal transport for cross-modal medical manifold alignment: A differential approach to multimodal diagnosis. ACCESS, 13, 147666-147678. (www)

  @article{ , abstract = {We present a novel theoretical framework for multimodal medical diagnosis that formulates heterogeneous data integration as a problem of aligning Riemannian manifolds through differential geometric principles. Medical data modalities—ranging from imaging to clinical parameters—naturally reside on distinct manifolds with inherent topological structures that conventional fusion approaches fail to preserve. Our method, Twin-Topology Heteromodal Contrastive Alignment (TTHCA), establishes rigorous mathematical foundations for this alignment through three key innovations: 1) formulation of $\varepsilon $ -isometric embeddings that provably preserve geodesic distances during cross-modal mapping with theoretical error bounds; 2) development of a curvature-aware attention mechanism that adapts to local manifold geometry and approximates diffusion processes with convergence guarantees; and 3) introduction of a structure-preserving optimal transport framework that minimizes cross-modal distributional discrepancies while maintaining topological consistency. Empirically, we validate TTHCA on Parkinsonian neuroimaging and dermatological datasets, demonstrating significant improvements over state-of-the-art approaches. Through ablation studies and theoretical analyses, we show that our framework’s superiority stems from its ability to preserve intrinsic geometric structures during cross-modal learning. Our approach establishes a mathematically rigorous bridge between differential geometry and transfer learning in medical diagnostics, offering both theoretical contributions to representation learning and practical advances in early disease detection capabilities.}, archive = {J_ACCESS}, author = {Yuan Shen}, doi = {10.1109/ACCESS.2025.3587298}, journal = {IEEE Access}, pages = {147666-147678}, shortjournal = {IEEE Access}, title = {Geometric optimal transport for cross-modal medical manifold alignment: A differential approach to multimodal diagnosis}, volume = {13}, year = {2025}, }
• (2025). Hybrid quantum-safe cryptographic scheme with secure key exchange and signature scheme. ACCESS, 13, 147650-147665. (www)

  @article{ , abstract = {Hybrid cryptographic protocol was proposed by integrating quantum-safe algorithms (Kyber and Dilithium) together with a classical ECDSA-based signature scheme to verify secure proof on blockchain. Since quantum computers pose some loopholes in existing cryptography algorithms, the proposed mechanism addresses those vulnerabilities by maintaining compatibility with the existing infrastructure. The protocol integrates post-quantum key exchange to derive shared secrets securely. The shared secrets are used to enable hybrid encryption for efficient data protection. Additionally, the implementation uses a dual-signature approach, using quantum safe and classical signatures. These signatures are combined to ensure robust and tamper-resistant proof verification. The deployed solution validates its feasibility and efficiency for smart contract-based verification. The protocol maintains backward compatibility with existing systems while providing strong resistance to quantum attacks. Benchmark results demonstrate the system’s scalability and robustness under high transaction loads, with efficient gas usage for on-chain proof validation. This approach paves the way for post-quantum secure applications, including verifiable proofs, secure key exchanges, and decentralized systems resilient to quantum adversaries.}, archive = {J_ACCESS}, author = {Perera K. Maduni and Ilmu Byun and Jeongil Seo and Kyeongjun Ko}, doi = {10.1109/ACCESS.2025.3600068}, journal = {IEEE Access}, pages = {147650-147665}, shortjournal = {IEEE Access}, title = {Hybrid quantum-safe cryptographic scheme with secure key exchange and signature scheme}, volume = {13}, year = {2025}, }
• (2025). Construction of a numerical model for SLM based on smoothed particle hydrodynamics. ACCESS, 13, 147633-147649. (www)

  @article{ , abstract = {Selective laser melting technology has significant advantages in the manufacturing of complex components, but the strong coupling of multiple physical fields in the dynamic behavior of the molten pool poses severe challenges to numerical simulation. However, the traditional mesh method is difficult to accurately track the large deformation of the free surface of the molten pool, and it is hard to balance the simulation efficiency and accuracy of multi-particle systems. Therefore, a numerical mathematical model for selective laser melting based on smooth particle fluid dynamics was studied and constructed. Through the dynamic adaptive particle distribution strategy and linked list search method, combined with the parallel acceleration of the graphics processor, a large-scale simulation of 20 million particles was achieved, reducing the computing cost by 30% while ensuring the details of the temperature field and flow field of the molten pool. The results show that the relative error between the improved model and the analytical solution does not exceed 2%, and the calculation error of surface tension is reduced to within 5%. The prediction error of the molten pool morphology is less than 8.9%, and the convergence rate of the velocity field is superior to that of the traditional methods. By establishing a closed-loop mechanism of defect prediction and process optimization, the regulation laws of laser power and scanning speed on the size of the molten pool were quantified. This research has broken through the limitations of the traditional grid method, achieving efficient and accurate simulation of the dynamic behavior of the molten pool under multi-physical field coupling, and filling the application gap of existing models in large-scale powder layer melting scenarios.}, archive = {J_ACCESS}, author = {Shanmin Zhang}, doi = {10.1109/ACCESS.2025.3601199}, journal = {IEEE Access}, pages = {147633-147649}, shortjournal = {IEEE Access}, title = {Construction of a numerical model for SLM based on smoothed particle hydrodynamics}, volume = {13}, year = {2025}, }
• (2025). Exploring the impact mechanism of AIGC-driven social media marketing content on consumer decision-making behavior: A two-stage hybrid approach. ACCESS, 13, 147615-147632. (www)

  @article{ , abstract = {The swift rise of ChatGPT and the growing integration of AI-generated content (AIGC) technologies are reshaping the digital marketing landscape in profound ways. These advancements are not only changing how social media marketing content is produced, but also altering its underlying characteristics. Despite this shift, there remains a noticeable gap in empirical research on how businesses and brands can effectively harness AIGC to drive consumer engagement. This study draws on the Stimulus–Organism–Response (SOR) theoretical framework to examine how AI-generated content influences consumer cognition, builds trust, and ultimately shapes decision-making behavior. Based on 348 valid survey responses, we employed a mixed-methods approach that integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) with Artificial Neural Network (ANN) analysis. The findings indicate that entertainment, interactivity, trend relevance, electronic word-of-mouth, and visual appeal all positively influence both perceived value and trust. In contrast, customization was found to enhance perceived value only. Both perceived value and trust were shown to significantly increase consumers’ purchase intentions. The ANN model further supported the PLS-SEM results, confirming consistency across methods. Among the predictors, entertainment ( $\beta =0.176$ , ni =86.28%) was most influential for perceived value, EWOM ( $\beta =0.192$ , ni =85.94%) played a key role in shaping trust, and perceived value ( $\beta =0.344$ , ni =99.19%) had the strongest impact on purchase intention. These insights contribute to a deeper theoretical understanding of social media marketing and consumer behavior, while also offering actionable recommendations for businesses aiming to refine their content strategies in an AIGC-driven environment. Additionally, the study highlights promising directions for the evolution of digital consumption and the long-term sustainability of brand development.}, archive = {J_ACCESS}, author = {Jiacheng Luo and Kewei Zhang and Jiang Du}, doi = {10.1109/ACCESS.2025.3600564}, journal = {IEEE Access}, pages = {147615-147632}, shortjournal = {IEEE Access}, title = {Exploring the impact mechanism of AIGC-driven social media marketing content on consumer decision-making behavior: A two-stage hybrid approach}, volume = {13}, year = {2025}, }
• (2025). TinyML prototype of infant cry classification system. ACCESS, 13, 147604-147614. (www)

  @article{ , abstract = {Quick classification of infant cries is vital to determine the reason for a baby’s cry, especially in the first months of the baby’s life. This study aims to develop a lightweight TinyML model for rapid classification of infant cries that can be used for resource-constrained, low-power devices. Our contribution lies in achieving good accuracy with a minimal model size and suitable performance for tiny devices. We explored architectures including Convolutional Neural Networks (CNNs), Depthwise Separable CNNs (DS-CNNs), and a self-defined Residual Network (ResNet) tailored to be lightweight, unlike standard ResNet models, for resource efficiency. Mel Frequency Cepstral Coefficients (MFCCs) were extracted from cry signals and optimized by varying coefficients and frame lengths. We used two datasets, each with five categories, in our experiments. The datasets used were Baby Chillanto (DB1) and Donate a Cry (DB2). Each model was independently trained on each dataset in separate experiments. Then, each model was converted to TensorFlow Lite and quantized, with the unquantized self-defined ResNet achieving 96.26% and 93.7% accuracies on DB1 and DB2, respectively. After quantization, it maintains an accuracy of 94.71% on DB1 and 89% on DB2 with RAM usage under 30 KB and a model size of 93 KB. When deploying the quantized model trained on Baby Chillanto on a Raspberry Pi, it demonstrated an execution time of 1 s and an inference time of 3.44 ms, balancing accuracy, efficiency, and compactness for embedded systems.}, archive = {J_ACCESS}, author = {Omnia Badr Eldine and Mohammed Selim and Nagwa El-Makky and Nagia M. Ghanem}, doi = {10.1109/ACCESS.2025.3601441}, journal = {IEEE Access}, pages = {147604-147614}, shortjournal = {IEEE Access}, title = {TinyML prototype of infant cry classification system}, volume = {13}, year = {2025}, }
• (2025). FS-SVM: A synergistic approach to high-dimensional feature selection. ACCESS, 13, 147593-147603. (www)

  @article{ , abstract = {Supervised classification in high-dimensional gene expression datasets is frequently used in bio-informatics studies. Feature selection plays an important role in such type of classification problems to avoid over-fitting and to develop a more reliable classifier for the problem at hand. This paper proposes a novel feature / gene selection method, called Weighted Fisher Score ( $W_{FS}$ ) to identify the most informative features of gene expression values. The proposed method is based on the Support Vector Machine (SVM) and Weighted Fisher Score to select deferentially expressed features with good classification performance. The performance of the proposed method Weighted Fisher Score ( $W_{FS}$ ) is tested on seven openly accessible gene expression datasets, using Random Forest, k-Nearest Neighbor and Support Vector Machine classifiers compared with four well-known feature selection methods, i.e. Significant Feature Selection (SigF), Wilcoxon Rank Sum (Wilcox), Maximum relevance, and minimum redundancy (mRmR) and Proportional Overlapping Score (POS). Moreover, the performance has also been assessed by constructing Box-plots, Bar charts and Line charts of the results. The results show that the proposed method Weighted Fisher Score ( $W_{FS}$ ) stands apart in terms of classification performance.}, archive = {J_ACCESS}, author = {Inzamam Ul Haq and Muhammad Hamraz and Salman Jan and Dost Muhammad Khan and Nadeem Iqbal and Zardad Khan}, doi = {10.1109/ACCESS.2025.3593817}, journal = {IEEE Access}, pages = {147593-147603}, shortjournal = {IEEE Access}, title = {FS-SVM: A synergistic approach to high-dimensional feature selection}, volume = {13}, year = {2025}, }
• (2025). Joint-temporal heterogeneous human motion decomposition. ACCESS, 13, 147581-147592. (www)

  @article{ , abstract = {Human body motion contains various information and meanings, including individual’s emotions, habits, and health conditions. Performing a detailed analysis of human body motion may reveal rich and systematic information that benefits many computer vision tasks such as action recognition, motion style transfer, and action generation. Action segmentation considers temporal separation or a combination of body motion. In reality, actions do not always involve the movement of whole body joints. For example, punching action involves one or both arms movement regardless of any movement in the lower body parts. Frequently, more than one actions is performed simultaneously in different parts of the human body (punching while walking). Furthermore, human motions include other small movements such as habitual gestures, which are unrelated to the main action. To this end, we propose to decompose a human body motion into hierarchical subcategories in joint-temporal space. In this work, we focus on machine learning-based approach, specifically in motion segmentation and style transfer to apply our proposed motion categories. Our goal is to utilize applicable motion categories that enhance the understanding and detailed analysis of body motions. Based on the expanded understanding of human body motions, we perform three computer vision tasks that present new perspective for future research in the field. Experimental results on Northwestern-UCLA (N-UCLA) and Xia datasets demonstrate that our proposed motion categories are applicable in machine learning approaches, enables detailed understanding and analysis of human body motion.}, archive = {J_ACCESS}, author = {Usfita Kiftiyani and Dayeon Lee and Seungkyu Lee}, doi = {10.1109/ACCESS.2025.3596341}, journal = {IEEE Access}, pages = {147581-147592}, shortjournal = {IEEE Access}, title = {Joint-temporal heterogeneous human motion decomposition}, volume = {13}, year = {2025}, }
• (2025). CariesAttNet: An attention-enhanced encoder-decoder network for automated caries segmentation in CBCT images. ACCESS, 13, 147569-147580. (www)

  @article{ , abstract = {Dental caries is a prevalent bacterial infection, and its early and accurate detection is essential for preventing irreversible damage to dental structures. Cone-beam computed tomography (CBCT) offers high spatial resolution and clear anatomical visualization, enabling precise assessment of caries lesions. However, the manual layer-by-layer annotation of caries regions in CBCT images is not only time-consuming and inefficient but also heavily reliant on professional expertise, which can easily lead to inconsistencies in segmentation results. To address this challenge, we propose CariesAttNet, a novel attention-guided convolutional neural network designed for automatic caries segmentation. The network adopts an encoder–decoder architecture with skip connections, featuring an attention ConvNeXt block that enhances local feature extraction, a large kernel attention module that captures long-range contextual information through convolutional attention, and a multi-scale attention gate that effectively aggregates features across multiple semantic levels. Experimental results on a private CBCT dataset demonstrate that CariesAttNet outperforms existing mainstream methods across all evaluation metrics, achieving dice similarity coefficient and intersection over union scores of 80.85% and 72.01%, respectively, on the test set. Further ablation experiments and interpretability analyses validate the effectiveness and synergistic benefits of each module.}, archive = {J_ACCESS}, author = {Zihan Zhang and Lin Li and Jiawei Wu and Liwen Tang and Doudou Xu and Zongxiang Liu and Ran Zhang and Peng Ren}, doi = {10.1109/ACCESS.2025.3601329}, journal = {IEEE Access}, pages = {147569-147580}, shortjournal = {IEEE Access}, title = {CariesAttNet: An attention-enhanced encoder-decoder network for automated caries segmentation in CBCT images}, volume = {13}, year = {2025}, }
• (2025). Advances in receiver and detection systems for low earth orbit nanosatellite quantum communications. ACCESS, 13, 147545-147568. (www)

  @article{ , abstract = {Satellite quantum communications have received a surge of interest in recent years, encouraged by the success of the QUESS experiments and as a reaction to advances in quantum computing. This work focuses on low Earth orbit nanosatellite missions, which offer several benefits (e.g. lower costs, greater launch availability, easier to replenish) over satellites at different orbit heights (medium Earth orbit, geosynchronous Earth orbit) and satellite size classes (e.g. small-sats, etc). Recent work on low Earth orbit satellite Quantum Key Distribution (QKD) has focused on transmitters and quantum sources, with less attention paid to the receiver side of the link. This review addresses that imbalance by focusing on the receiver and detection systems of satellite quantum communications. This work has found that two-way beacon acquisition and tracking systems are both a common element of state-of-the-art missions, and likely to remain a critical subsystem to ensure high-quality satellite-to-ground station links; however, improvements to detection and tracking algorithms could be made. For discrete variable QKD, receivers and detection systems in nanosatellites at low Earth orbit are likely to remain using silicon single photon avalanche diodes, while receivers and detection systems in optical ground stations may opt for superconducting nanowire single photon detectors, as cryogenic cooling is feasible. For continuous variable QKD, the receivers and detection systems, using coherent detection systems, share many hardware elements with classical optical satellite communications. Combined with coherent detection systems’ inherent resilience to operating during daytime conditions, this makes continuous variable QKD an attractive prospect for future missions, if its fundamental challenges can be overcome.}, archive = {J_ACCESS}, author = {James J. Shawe and Jerry Horgan and Deirdre Kilbane}, doi = {10.1109/ACCESS.2025.3599744}, journal = {IEEE Access}, pages = {147545-147568}, shortjournal = {IEEE Access}, title = {Advances in receiver and detection systems for low earth orbit nanosatellite quantum communications}, volume = {13}, year = {2025}, }
• (2025). Advancements in thermal management for electric vehicles: Strategies, architectures, and power electronics cooling. ACCESS, 13, 147511-147544. (www)

  @article{ , abstract = {Electric vehicles (EVs) represent a key technology for achieving sustainable transportation. However, they face several persistent challenges, including limited driving range, system integration, and energy efficiency constraints. Among these, thermal management is critical for maintaining system reliability, enhancing energy efficiency, and supporting high-performance operation across essential subsystems. This article provides a comprehensive review of thermal management strategies in EVs, focusing on cooling concepts, current limitations, recent research advances, and emerging trends. The review begins by outlining fundamental thermal principles, with emphasis on thermal management in the cabin, battery, and electric motor. Special attention is devoted to power electronics, where the integration of wide-bandgap semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN), has introduced new thermal challenges due to higher power densities, greater heat flux, and elevated operating temperatures. In addressing these issues, the review explores the evolution of cooling architectures and identifies critical research gaps. The goal is to show the development of more efficient, compact, and robust thermal management systems for next-generation electric mobility.}, archive = {J_ACCESS}, author = {Gamze Egin Martin and Mohamed El Baghdadi and Omar Hegazy}, doi = {10.1109/ACCESS.2025.3599627}, journal = {IEEE Access}, pages = {147511-147544}, shortjournal = {IEEE Access}, title = {Advancements in thermal management for electric vehicles: Strategies, architectures, and power electronics cooling}, volume = {13}, year = {2025}, }
• (2025). Efficient bearing fault diagnosis for edge computing using grayscale spectrograms and hybrid neural model compression. ACCESS, 13, 147494-147510. (www)

  @article{ , abstract = {Bearing elements play important roles in mechanical machining. Efficient monitoring and detection of bearing faults are critical for ensuring motor reliability. Deep learning techniques, particularly those that use convolutional neural networks, have emerged as prominent solutions for achieving high accuracy in bearing fault classification. However, the computational complexities of these models render them unsuitable for use in resource-constrained devices. In this study, we explored the application of a combination of multiple techniques, including knowledge distillation, layer fusion, and quantization-aware training, to develop lightweight models that accelerate inference speed while maintaining high accuracy. In addition, acoustic emission signals containing bearing health information were preprocessed using a short-time Fourier transform and converted into grayscale spectrograms before being fed into a neural network. The experimental results indicate that the proposed method can significantly reduce the inference latency and memory footprint while achieving high accuracy for different bearing fault types at variable rotational speeds.}, archive = {J_ACCESS}, author = {Huynh-Anh-Huy Nguyen and Cheol Hong Kim}, doi = {10.1109/ACCESS.2025.3600678}, journal = {IEEE Access}, pages = {147494-147510}, shortjournal = {IEEE Access}, title = {Efficient bearing fault diagnosis for edge computing using grayscale spectrograms and hybrid neural model compression}, volume = {13}, year = {2025}, }
• (2025). ICAS: IP-adapter and ControlNet-based attention structure for multi-subject style transfer optimization. ACCESS, 13, 147482-147493. (www)

  @article{ , abstract = {Generating multi-subject stylized images remains a significant challenge due to the ambiguity in defining style attributes (e.g., color, texture, atmosphere, and structure) and the difficulty in consistently applying them across multiple subjects. Although recent diffusion-based text-to-image models have achieved remarkable progress, existing methods typically rely on computationally expensive inversion procedures or large-scale stylized datasets. Moreover, these methods often struggle with maintaining multi-subject semantic fidelity and are limited by high inference costs. To address these limitations, this paper proposes ICAS (IP-Adapter and ControlNet-based Attention Structure), a novel framework for efficient and controllable multi-subject style transfer. Instead of full-model tuning, ICAS adaptively fine-tunes only the content injection branch of a pre-trained diffusion model, thereby preserving identity-specific semantics while enhancing style controllability. By combining IP-Adapter for adaptive style injection with ControlNet for structural conditioning, our framework ensures faithful global layout preservation alongside accurate local style synthesis. Furthermore, ICAS introduces a cyclic multi-subject content embedding mechanism, which enables effective style transfer under limited-data settings without the need for extensive stylized corpora. Extensive experiments show that ICAS achieves superior performance in structure preservation, style consistency, and inference efficiency. In particular, in a comprehensive user study, the proposed method was rated superior to baselines, achieving an average score of 4.32 out of 5.0 for Style Fidelity and 4.28 for Subject Clarity. This performance establishes a new paradigm for multi-subject style transfer in real-world applications.}, archive = {J_ACCESS}, author = {Fuwei Liu}, doi = {10.1109/ACCESS.2025.3600621}, journal = {IEEE Access}, pages = {147482-147493}, shortjournal = {IEEE Access}, title = {ICAS: IP-adapter and ControlNet-based attention structure for multi-subject style transfer optimization}, volume = {13}, year = {2025}, }
• (2025). Enhanced short-term photovoltaic forecasting using PatchTST and metaheuristic optimization. ACCESS, 13, 147467-147481. (www)

  @article{ , abstract = {With the growing significance of renewable energy in the global energy structure, accurate photovoltaic power prediction is crucial for efficient power grid management. However, real-world photo- voltaic data is inherently nonlinear and non-stationary, posing challenges for precise prediction. This paper proposes a hybrid model, PatchTST-DFT-SO, which integrates discrete fourier transform decomposition with the novel patch time series transformer model, where the parameters are optimized using the snake optimization algorithm. Discrete Fourier transform is applied to fixed-length, three-day input segments to separate seasonal and trend-cyclical components, reducing the complexity of photovoltaic data modeling. Patch time series transformer model is employed for further predictions of these components, while the snake optimization algorithm optimizes the parameters in embedding module to adapt to diverse datasets. Extensive experiments are conducted on the Desert Knowledge Australia Solar Centre Dataset, comparing the results with those of previously proposed methods. The results demonstrate that the proposed model achieves a 40.4% reduction in RMSE compared with previous methods. According to the ablation study, PatchTST-DFT-SO achieves up to a 17.4% reduction in RMSE relative to the original patch time series transformer model. The proposed model achieves an inference speed of 21.4 ms per prediction on an NVIDIA Jetson TX2 edge device, satisfying the industry-standard real-time threshold of 1min for grid-tied PV systems.}, archive = {J_ACCESS}, author = {Hongying Wang and Hongtao Shen and Yaqi Song and Bo Feng and Yidi Wu and Yangrui Zhang and Peng Tao and Junpeng Zhao and Rujie Wan}, doi = {10.1109/ACCESS.2025.3599642}, journal = {IEEE Access}, pages = {147467-147481}, shortjournal = {IEEE Access}, title = {Enhanced short-term photovoltaic forecasting using PatchTST and metaheuristic optimization}, volume = {13}, year = {2025}, }
• (2025). Telematics in insurance: Challenges and limitations. ACCESS, 13, 147449-147466. (www)

  @article{ , abstract = {The integration of telematics in the car insurance sector has attracted significant interest due to its potential to disrupt the industry and improve road safety. By capturing actual driving behaviour, telematics offers insurers a data-driven approach to assess risk and price premiums and encourage safer driving practices. A substantial body of literature has explored the applications of telematics in insurance, aiming at enhancing the analysis and understanding of driving behaviour by using more advanced techniques, such as machine learning and deep learning, or introducing new data sources. The results obtained so far are promising. However, few studies have comprehensively addressed the practical challenges and limitations of applying such results in real-world telematics settings. Adopting the perspective of a motor insurance provider, this paper focuses on practical applicability by critically reviewing key contributions in the field. Our aim is to give a high-level overview that not only uncovers the limitations of existing studies but also highlights the potential of using telematics in insurance.}, archive = {J_ACCESS}, author = {Jing Qi and Matthew Woolway and Maria Kyropoulou and Panagiotis Kanellopoulos and David Steele}, doi = {10.1109/ACCESS.2025.3600095}, journal = {IEEE Access}, pages = {147449-147466}, shortjournal = {IEEE Access}, title = {Telematics in insurance: Challenges and limitations}, volume = {13}, year = {2025}, }
• (2025). Deep learning for dynamic wildlife monitoring: A real-time approach. ACCESS, 13, 147422-147448. (www)

  @article{ , abstract = {With the rapid amount of deforestation around the world, wild animals lose their habitat and move closer to human settlements, leading to increased human-wildlife conflict and the loss of important human lives, livestock, and rare wild animal species. Wildlife monitoring is an essential task in computer vision for maintaining biodiversity, ecosystem balance and stability. It plays a vital role where traditional systems, such as camera traps and manual methods, fall short, and helps to avoid human-wildlife conflict. However, due to the challenging forest terrains, dynamic backgrounds, diverse animal poses, and low lighting conditions, existing methods struggle to efficiently extract features in real-time. These models suffer from limitations, including slow speed and low accuracy. To address these challenges, a novel real-time wildlife monitoring model has been introduced that leverages YOLOv11, designed to perform effectively in complex backgrounds, diverse animal poses and low-light conditions. A new wild animal dataset is developed, comprising 17 different wild animal species, captured under low-light conditions and in challenging backgrounds. The Grad-CAM technique is integrated with YOLOv11 to enhance the model’s interpretability to generate heatmaps and attention maps. These maps help identify the important regions of the animal’s body on which the model focuses more in real-time monitoring applications. The YOLOv11n model achieves an mAP of 97.4%, precision of 96%, and an F1-score of 95% on self-created wild animal dataset. The parameters of the fine-tuned YOLOv11 decreased by 30% relative to the previous version, resulting in a very small model size of 5.5 MB and reduced processing time. The model was evaluated on two other datasets: Animal2 and Camera Trap, with an mAP of 92% on the Animal2 dataset and 90% on the Camera Trap dataset.}, archive = {J_ACCESS}, author = {Abdul Basit Mughal and Rafi Ullah Khan and Atiq Ur Rehman and and Amine Bermak}, doi = {10.1109/ACCESS.2025.3600625}, journal = {IEEE Access}, pages = {147422-147448}, shortjournal = {IEEE Access}, title = {Deep learning for dynamic wildlife monitoring: A real-time approach}, volume = {13}, year = {2025}, }
• (2025). Discovering domain-agnostic fake news detectors through deep self-supervised learning. ACCESS, 13, 147408-147421. (www)

  @article{ , abstract = {The rapid spread of misinformation across online platforms poses a major threat to societal trust, public health, and democratic processes. While recent advances in machine learning have improved the accuracy of fake news detection, most existing approaches remain limited to single-domain settings and struggle to generalize across diverse domains or platforms. To address this challenge, we propose DAFNE (Domain-Agnostic Fake NEws detector), a deep learning approach designed to capture cross-domain high-level features for fake news detection. By combining feature-level adversarial learning with self-supervised learning, DAFNE effectively learns domain-invariant representations that enable reliable detection across heterogeneous sources. The proposed approach is evaluated on five real-world benchmark datasets spanning multiple domains, and the results demonstrate superior generalization capabilities compared to state-of-the-art baselines. Specifically, DAFNE outperforms the competitors, with average micro-F1 improvements ranging from 11.3% to 39.9%. In comparison to the second-best model, our approach shows an average improvement of 18% across all domains in terms of the F-Score, reaching up to 25% on the Politifact dataset. These results highlight the capability of DAFNE to mitigate the domain shift problem, enabling more reliable and adaptive misinformation detection in dynamic online environments.}, archive = {J_ACCESS}, author = {Carmela Comito and Massimo Guarascio and Angelica Liguori and Giuseppe Manco and Francesco Sergio Pisani}, doi = {10.1109/ACCESS.2025.3608790}, journal = {IEEE Access}, pages = {147408-147421}, shortjournal = {IEEE Access}, title = {Discovering domain-agnostic fake news detectors through deep self-supervised learning}, volume = {13}, year = {2025}, }
• (2025). Investigation of a multi-tube ESP: CFD-based electric field and efficiency decrease during long-term operation. ACCESS, 13, 147397-147407. (www)

  @article{ , abstract = {This study focuses on the development, simulation, and experimental validation of a multi-tube electrostatic precipitator (ESP) designed for small-scale heat source applications. Using computational fluid dynamics (CFD), the particle mass concentration and electric field distribution were simulated to determine key operational parameters, including the onset voltage for corona discharge. ESP efficiency was evaluated through gravimetric measurements of particulate matter (PM) concentrations during short-term and long-term operations. Short-term tests at a supply voltage of 8.7 kV demonstrated an average PM capture efficiency of around 84%. In long-term operation, with an increased voltage of 12.5 kV, the ESP initially achieved efficiencies close to 90%. However, a gradual decline in performance was observed, with efficiency dropping to approximately 82% after 33 hours of continuous operation. The efficiency decrease was attributed to the accumulation of PM on the collection surfaces, which led to reduced electrostatic field strength and diminished particle capture capability. These results emphasize the necessity of regular cleaning to maintain high-efficiency operation, especially in extended-use scenarios. When properly maintained, the multi-tube ESP used in this work has the potential to reach high efficiency similar to existing small-scale ESP systems.}, archive = {J_ACCESS}, author = {Michal Holubcik and Patrik Nemec and Andrej Kapjor and Nikola Cajova Kantova}, doi = {10.1109/ACCESS.2025.3599211}, journal = {IEEE Access}, pages = {147397-147407}, shortjournal = {IEEE Access}, title = {Investigation of a multi-tube ESP: CFD-based electric field and efficiency decrease during long-term operation}, volume = {13}, year = {2025}, }
• (2025). GAT-APG: Graph attention network-based attack path generation for security simulation. ACCESS, 13, 147383-147396. (www)

  @article{ , abstract = {Existing attack path generation methods face limitations in dynamic simulation environments due to their reliance on static network models and computational inefficiencies when network configurations change frequently. This study proposes GAT-APG, a reinforcement learning framework that combines Graph Attention Networks with policy gradient methods to generate adaptive attack paths for security simulation. The approach employs node-centric vulnerability assessment that transforms network traffic data into vulnerability metrics, enabling adaptation to network changes without complete graph recalculation. Experimental validation on controlled graphs and the Kyoto dataset demonstrates competitive performance, achieving 93% accuracy against brute-force methods and showing 69.1% exact matches with Dijkstra’s algorithm on real-world topologies. The framework provides a simulation-ready environment for vulnerability assessment and defensive planning in dynamic networks.}, archive = {J_ACCESS}, author = {Min Geun Song and Jaewoong Choi and Huy Kang Kim}, doi = {10.1109/ACCESS.2025.3600669}, journal = {IEEE Access}, pages = {147383-147396}, shortjournal = {IEEE Access}, title = {GAT-APG: Graph attention network-based attack path generation for security simulation}, volume = {13}, year = {2025}, }
• (2025). Feature-based fault localization in evolving software: Leveraging regression testing insights. ACCESS, 13, 147369-147382. (www)

  @article{ , abstract = {Fault localization remains a vital yet resource-intensive task, particularly within software evolution, where swift and accurate fault localization is crucial. Whereas substantial research has improved fault localization techniques, challenges persist specifically in evolving software systems. Simultaneously, regression testing has been investigated to enhance testing efficiency while maintaining fault localization accuracy. Despite these advancements, further methodological improvements are necessary to refine fault localization accuracy in evolving software environments. This study proposes a new combination of features extracted from the source code and the associated test suite. The features include coverage, data and control dependencies, and code changes, with a new feature, “Test Case Weight,” derived from the test suite. “Test Case Weight” incorporates the effects of regression testing, which is influenced by two regression testing techniques: test case reduction and selection. Consequently, this feature is entirely influenced by regression testing. The results show that the proposed method achieves over 81% fault localization in the Top-3 rankings. It also improvesEXAM score, with over 68% of programs scoring 10 or less via test case selection and 63% via test case reduction. Compared to spectrum-based techniques, the proposed method located 54% more faults than Tarantula and Ochiai, and 45% more thanJaccard in theTop-3 rankings. Additionally, it outperforms existing methods in EXAM score while using only 40% to 80% of the test suite. These findings highlight the importance of incorporating historical data, code structure, runtime behavior, and test case to improve fault localization efficiency in evolving software.}, archive = {J_ACCESS}, author = {Faeze Aghazade-Par and Mojtaba Vahidi-Asl}, doi = {10.1109/ACCESS.2025.3599554}, journal = {IEEE Access}, pages = {147369-147382}, shortjournal = {IEEE Access}, title = {Feature-based fault localization in evolving software: Leveraging regression testing insights}, volume = {13}, year = {2025}, }
• (2025). A machine learning-based approach for multi-objective, multi-product, and multi-period supply chain optimization via demand forecasting. ACCESS, 13, 147343-147368. (www)

  @article{ , abstract = {The main aim of this research study is to provide a complex machine learning framework for Multi-Objective, Multi-Product, and Multi-Period Supply Chain Optimization (M3 SCO). We used a hybrid approach combining machine learning-based demand forecasting and time series decomposition methods to predict customer’s future demand. We consider multiple objective functions, including total cost minimization, delivery time reduction, service level maximization, and inventory instability minimization, along with practical constraints such as inventory balance at stores, factory production capacity, store capacity, customer demand satisfaction, and service level, per-period budget, and transportation capacity to address real-world supply chain challenges. The supply chain can be dynamically adjusted over multiple periods based on updated demand forecasts. We applied the proposed method to a real dataset, which includes the demand of three customers for two products over four years. The proposed multi-objective supply chain optimization model was solved using the weighted sum method and the open-source CBC solver for all three types of demand data, including historical, predicted, and actual, based on a real dataset comprising the demand of three customers for two products over four years, enabling a comprehensive comparison of the model’s performance under different demand scenarios. The results demonstrate the applicability and effectiveness of the proposed approach compared to traditional methods relying solely on historical data.}, archive = {J_ACCESS}, author = {Farshid Abdi and Shaghayegh Abolmakarem and Amir Karbassi Yazdi and Virgil Popescu and Ramona Birau and Adrian T. Mitroi}, doi = {10.1109/ACCESS.2025.3600092}, journal = {IEEE Access}, pages = {147343-147368}, shortjournal = {IEEE Access}, title = {A machine learning-based approach for multi-objective, multi-product, and multi-period supply chain optimization via demand forecasting}, volume = {13}, year = {2025}, }
• (2025). Validation of the calibration coefficient of the ZY1-02E VNIC sensor using different calibration reference. ACCESS, 13, 147330-147342. (www)

  @article{ , abstract = {ZY1-02E is a medium-resolution hyperspectral operational satellite equipped with a Visible-Near Infrared Camera (VNIC), an Advanced Hyperspectral Imager (AHSI), and a Thermal Infrared Sensor (IRS). These instruments collectively provide detailed geo-morphological data that enhance environmental and resource monitoring applications. Achieving high-precision radiometric calibration is essential to fully leverage the data from ZY1-02E. This study evaluates the on-orbit radiometric performance of the ZY1-02E VNIC sensor through two calibration methods: vicarious radiometric calibration and cross-calibration. For vicarious calibration, synchronized satellite-ground observation experiments were carried out at the Dunhuang radiometric calibration site (DRCS). The reflectance-based approach was employed to validate the absolute radiometric accuracy of the VNIC. For cross-calibration, data from ZY1-02E VNIC obtained during synchronized overpasses of the DRCS were calibrated against reference sensors including Landsat-8 OLI, Landsat-9 OLI2, and Sentinel-2 MSI. Calibration coefficients were derived, and their accuracy was quantitatively evaluated. The results demonstrate that the vicarious radiometric calibration achieves an average accuracy better than 6% in the visible and near-infrared (NIR) bands, with the calibration from April showing superior performance to that of October. The average relative difference among calibration outcomes derived from various reference sensors is maintained within 2.89%, with Landsat-9 OLI2 exhibiting the closest alignment to the vicarious calibration results, showing an average relative difference of only 1.96%. Overall, the ZY1-02E VNIC sensor displays stable and excellent on-orbit radiometric performance, which is pivotal for supporting quantitative applications in environmental monitoring and resource management.}, archive = {J_ACCESS}, author = {Xining Liu and Qilong Zhou and Chao Yang and Hao Niu and Wei Chen and Xiang Tang and Xueying Liang and Xianhui Dou}, doi = {10.1109/ACCESS.2025.3599676}, journal = {IEEE Access}, pages = {147330-147342}, shortjournal = {IEEE Access}, title = {Validation of the calibration coefficient of the ZY1-02E VNIC sensor using different calibration reference}, volume = {13}, year = {2025}, }
• (2025). The power i know: Zero-knowledge proofs and their transformative role in the future of cryptography. ACCESS, 13, 147317-147329. (www)

  @article{ , abstract = {Zero-Knowledge Proofs (ZKPs) are public-key cryptosystem that enables to demonstrate that a statement which is known by them is correct without revealing the same to the verifier. ZKPs have moved in modern cryptographic systems, blockchain applications, Decentralized Finance (DeFi) and identity authentication systems. This paper explores the evolution of ZKPs and their significance as in secure and privacy-preserving. We classify ZKPs into two groups, namely interactive and non-interactive, discussing prominent protocols such as zk-SNARKs, zk-STARKs, Bulletproofs, PLONK, and Halo2. Each approach has advantages such as efficiency, proof size, and computational overhead. The study further examines the multitude of applications of ZKPs, such as privacy-enhanced blockchain transactions, zero-knowledge rollups for scalability, decentralized identity management, secure voting mechanisms, and regulatory-compliant financial systems. With advantages, possible limitations in scalability, lack of standardization, and vulnerabilities to emerging quantum computing threats. Due to restrictions, hardware acceleration through GPUs and others presents promising solutions, while new protocols such as PLONK and Halo2 seek to optimize performance to earlier developed solutions. Finally, we discuss the future trajectory of ZKPs. This review aims to provide an understanding of the current state of ZKP research, its applications, and the key challenges that need to be addressed to facilitate broader adoption.}, archive = {J_ACCESS}, author = {Eshan Sud and Shirish Agarwal and Lav Upadhyay}, doi = {10.1109/ACCESS.2025.3599555}, journal = {IEEE Access}, pages = {147317-147329}, shortjournal = {IEEE Access}, title = {The power i know: Zero-knowledge proofs and their transformative role in the future of cryptography}, volume = {13}, year = {2025}, }
• (2025). A composite bi-level optimization model for integrated energy systems suitable for PEDF buildings. ACCESS, 13, 147301-147316. (www)

  @article{ , abstract = {Although the integrated energy system (IES) technology is developing rapidly along with energy transition and technological advancements, their configuration optimization still remains suboptimal. The capacity configuration of IES often relies on engineering experience for design. Existing optimization methods predominantly focus on the energy storage system. To address these issues, this paper proposes a composite bi-level optimization model. The proposed modeling framework integrates three interconnected sub-models to simulate daily system operations through building load demand forecasting coupled with local solar irradiance data. A simulation was conducted using a photovoltaic, energy storage, DC, flexible (PEDF) building-based IES as a case study. Through simulation, it was found that the system can achieve the lowest daily cost of 42.10 under the optimal configuration of 34kW photovoltaic and 69kWh energy storage. Sensitivity analysis shows that the load has a greater impact on the system compared to solar energy, and the deployable area of photovoltaics has varying degrees of influence on system configuration and cost depending on the degree of change. This study provides a reference for IES configurations that require consideration of pre design configuration optimization and optimization of both photovoltaic and energy storage configurations.}, archive = {J_ACCESS}, author = {Li Zhongwei and Zhou Yuhao and Liu WeiWei and Chen Yongxu and Li Qingyang}, doi = {10.1109/ACCESS.2025.3599875}, journal = {IEEE Access}, pages = {147301-147316}, shortjournal = {IEEE Access}, title = {A composite bi-level optimization model for integrated energy systems suitable for PEDF buildings}, volume = {13}, year = {2025}, }
• (2025). Experimental evaluation of TDD T-sync accuracy in A-RoF-based DAS using different delay measurement methods. ACCESS, 13, 147291-147300. (www)

  @article{ , abstract = {In order to realize a large capacity mobile fronthaul, analog radio over fiber (A-RoF) is highly attractive from the perspective of simplification, especially for the high frequency band distributed antenna system (DAS). In recent years, mobile communication systems have adopted time division multiplexing, especially in high frequency bands, and each distributed antenna section (or remote radio unit (RRU)) must also synchronize its time. Generally, digital-RoF-based functions are required such as time synchronization (T-Sync) using precision time protocol, buffering, and digital-to-analog conversion in order to output radio waves. As a result, the RRU is becoming complicated. In this paper, we propose a T-Sync method for an A-RoF-based DAS that enables simplified RRUs by implementing transmission timing adjustment at the central station (CS) to compensate for A-RoF link delay. This approach decouples T-Sync implementation from RRU hardware, making it possible to achieve synchronization accuracy solely through delay measurement and compensation at the CS. Additionally, we experimentally compare and validate three different delay measurement methods, which form the core of this technology. Achieving high-precision T-Sync typically requires compensation for the internal processing delay of the delay measurement equipment, which varies depending on the device. Therefore, we need a delay measurement method that considers compatibility when modifying an RRU with equipment from different vendors or flexibility when operating multi-vendor RRUs in parallel within the same network. We find that only the optical direct return (ODR) method, which measures the round-trip time of the optical signal while excluding internal processing delays on the RRU side, thus avoiding delay fluctuations, offers acceptable reliability. By utilizing this ODR method, it is possible to achieve stable and highly accurate T-Sync with an error of less than 10 nanoseconds. According to third-generation partnership project requirements, this level of accuracy is sufficient for application in MIMO systems.}, archive = {J_ACCESS}, author = {Yuta Takahashi and Yasuyoshi Yamamoto and Mizuki Suga and Hiroto Yorioka and Hideyuki Tsuboi and Takafumi Fujita and Fumihiro Yamashita}, doi = {10.1109/ACCESS.2025.3594848}, journal = {IEEE Access}, pages = {147291-147300}, shortjournal = {IEEE Access}, title = {Experimental evaluation of TDD T-sync accuracy in A-RoF-based DAS using different delay measurement methods}, volume = {13}, year = {2025}, }
• (2025). Novel predictive factors to boost short-term electricity demand forecasting using machine learning models. ACCESS, 13, 147265-147290. (www)

  @article{ , abstract = {Accurate Short-Term Load Forecasting (STLF) is essential for effective operational planning, particularly for optimizing maintenance schedules, managing power generation capacity, and ensuring grid stability. This paper presents a robust data-driven framework for STLF, focusing specifically on forecasting daily morning and evening peak electricity demand in Jordan. Multiple Machine Learning (ML) models are investigated, including Random Forest (RF), XGBoost, CatBoost, Support Vector Regression (SVR), Gradient Boosting Machines (GBM), Gaussian Process Regression (GPR), and a hybrid STL decomposition approach. Each model is accurately evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) across training and testing data sets. A key contribution of this research is the integration of diversity of features that significantly enhance prediction accuracy. In addition to temperature, the feature set includes load growth rates and comparison with last year loads, daily usage data, population growth trends, and yearly day order. Using a five-year dataset (2015–2019), GPR emerges as the most accurate model, achieving a testing MAE of 4.71 MW and MAPE of 0.20% for morning peaks, and an MAE of 4.31 MW with a MAPE of 0.15% for evening peaks. To enhance model transparency, SHAP (SHapley Additive exPlanations) analysis is applied to tree-based models (RF and CatBoost), while permutation importance is used for kernel-based models such as GPR and SVR. This enables interpretable insights into feature contributions. In summary, the proposed methodology offers a robust and reliable tool for STLF. By accounting for both daily fluctuations and trends.}, archive = {J_ACCESS}, author = {Ahmad Abu Sleem and Mujahid N. Syed}, doi = {10.1109/ACCESS.2025.3600864}, journal = {IEEE Access}, pages = {147265-147290}, shortjournal = {IEEE Access}, title = {Novel predictive factors to boost short-term electricity demand forecasting using machine learning models}, volume = {13}, year = {2025}, }
• (2025). An enhanced density peak clustering algorithm with dimensionality reduction and relative density normalization for high-dimensional duplicate data. ACCESS, 13, 147242-147264. (www)

  @article{ , abstract = {Cluster analysis is a fundamental method for studying big data problems, as it groups samples based on shared features. In cluster analysis, a particular class of big data problems is defined by large sample sizes, high dimensionality, and a substantial presence of duplicate samples. Traditional clustering algorithms often fail to adequately address such challenges, particularly in detecting low-density clusters. To tackle these challenges, this paper presents an enhanced variant of the Density Peaks clustering(DPC) algorithm, referred to as the Dimensionality Reduction and Relative Density Normalization Density Peaks Clustering(DRDN-DPC) algorithm. The DRDN-DPC algorithm incorporates sample count as a weighting factor during the clustering process, thereby refining the density estimation. Additionally, dimensionality reduction techniques are employed to alleviate the adverse effects of high-dimensional data. Moreover, relative density normalization is introduced to improve the detection of low-density clusters, thereby enhancing the overall clustering performance. Following a comprehensive analysis of the computational complexity in both temporal and spatial aspects, the DRDN-DPC is systematically evaluated using 12 standard benchmark datasets and high-dimensional benchmarks with added noise. Its performance is rigorously compared to several clustering algorithms, including the K-means, the DBSCAN, the original DPC algorithm and two modified version DPC, which are KNN-DPC and SNN-DPC. Furthermore, the DRDN-DPC is applied to real-world clustering problems, including molecular clustering in molecular dynamics (MD) simulations and clustering of Electronic Health Records (EHR). The results substantiate the practical efficacy of DRDN-DPC in both domains, highlighting its capability to address complex clustering tasks in diverse application contexts.}, archive = {J_ACCESS}, author = {Xuan Sun and Xin Liu and Chunli Deng and Huiying Chu and Guiyan Wang and Hui Zhao}, doi = {10.1109/ACCESS.2025.3596983}, journal = {IEEE Access}, pages = {147242-147264}, shortjournal = {IEEE Access}, title = {An enhanced density peak clustering algorithm with dimensionality reduction and relative density normalization for high-dimensional duplicate data}, volume = {13}, year = {2025}, }
• (2025). Mitigating bias in AI model using eXplainable AI in terms of hiring process in the industry. ACCESS, 13, 147218-147241. (www)

  @article{ , abstract = {The increasing adoption of Artificial Intelligence (AI) in recruitment has introduced substantial ethical concerns, particularly regarding the propagation of unintended biases that may reinforce or exacerbate existing employment disparities. In response to these challenges, this study proposes an Explainable Artificial Intelligence (XAI) approach that transitions from opaque “black-box” algorithms to transparent “glass-box” frameworks, enabling greater interpretability and accountability. Specifically, we employ SHapley Additive exPlanations (SHAP) to examine and interpret decisions made by widely used machine learning models—namely Random Forest, XGBoost, and LightGBM—within the context of AI-driven hiring systems. Through extensive empirical analysis, SHAP values were utilized to uncover key features, such as gender, nationality, and recreational preferences, that contributed disproportionately to biased outcomes. These features were iteratively reviewed, modified, or excluded to construct fairer models. Following refinement, the updated models demonstrated a notable increase in classification accuracy, rising from approximately 78% to 85%. In addition, precision, recall, and F1-score metrics improved across all model variants. Most significantly, fairness metrics, measured through demographic parity, showed an approximate 20% improvement, increasing from 0.70 to 0.90, indicating a substantial reduction in discriminatory bias. These results provide strong empirical support for integrating XAI techniques in recruitment pipelines, demonstrating that fairness and accuracy need not be mutually exclusive. This study underscores the practical viability of explainable methods in bias-sensitive domains and offers a replicable framework for ethically aligned model development. Ultimately, our findings advocate for adopting XAI methodologies as an essential safeguard in ensuring equitable, transparent, and socially responsible deployment of AI technologies in employment decision-making.}, archive = {J_ACCESS}, author = {Noir Alsubaie and Noura Aleisa}, doi = {10.1109/ACCESS.2025.3599947}, journal = {IEEE Access}, pages = {147218-147241}, shortjournal = {IEEE Access}, title = {Mitigating bias in AI model using eXplainable AI in terms of hiring process in the industry}, volume = {13}, year = {2025}, }
• (2025). DDR-YOLO: An efficient detection model based on YOLO11s for steel surface defect. ACCESS, 13, 147202-147217. (www)

  @article{ , abstract = {In the process of steel production, surface defects on the product are inevitable. Due to challenges such as high surface reflectivity and significant variation in defect scales on steel strips, this paper proposes an improved YOLO11-based algorithm to enhance the accuracy of steel surface defect detection. First, we propose a Dual-Branch Dynamic Structure Convolution module (DualSPDG), featuring a dual-channel architecture comprising a backbone and an auxiliary branch. This module preserves texture details and structural information during downsampling and enhances the backbone network’s feature extraction capability. Second, to improve the detection of small-scale defects, we propose a Dynamic Attention Multi-Scale Fusion (DAMF) module, which imposes dynamic weights on the fusion features after upsampling and summing fusion, and realizes more discriminative cross-scale feature fusion. Finally, steel surface defects often exhibit complex shapes, such as elongated strips and arcs. To extract such large-structure information, we propose the restructurable parameter fusion large-kernel convolution attention aggregation block (RepLK-AA). This module combines large-kernel depthwise separable convolution with attention mechanisms to parallelize multiple receptive fields, thereby enhancing feature representation capability. Ablation and comparative experiments demonstrate that DDR-YOLO outperforms existing detection algorithms. Compared with YOLO11s, our method improves mAP50 by 3.6% on NEU-DET dataset and 3.9% on GC10-DET dataset. The proposed method significantly enhances detection accuracy without compromising speed, meeting the industrial requirements for real-time steel surface defect detection.}, archive = {J_ACCESS}, author = {Xiaoguang Ren and Zhendong Liu and Hui Cheng and Lidong Chen}, doi = {10.1109/ACCESS.2025.3599978}, journal = {IEEE Access}, pages = {147202-147217}, shortjournal = {IEEE Access}, title = {DDR-YOLO: An efficient detection model based on YOLO11s for steel surface defect}, volume = {13}, year = {2025}, }
• (2025). Efficient anchor-free unified framework for panoptic part segmentation in real-time and resource-constrained environments. ACCESS, 13, 147185-147201. (www)

  @article{ , abstract = {Part segmentation is one of the most important problems in computer vision. In part segmentation objects are simultaneously detected and their parts segmented. While the issue is yet to be resolved, the proposed work introduces a connected, more efficient and anchor-free pathway for object and path segmentation. The proposed solution addresses the prevalent issues of current segmentation models with high computational cost and long inference times. As a result, it is more suitable for real-time applications and resource-constrained settings. The proposed architecture reduces computational complexity and eliminates anchors, a major cost driver in traditional models, to achieve faster segmentation with comparable panoptic and part-level accuracy. Experimental analysis shows that while the method maintains high accuracy compared to other state-of-the-art models and significantly reduces processing time, further evaluation across broader scenarios is needed to fully validate its effectiveness for real-time applications. The proposed architecture, built on a ResNet50 backbone, offers a lightweight design with only 35.6M parameters and a computational cost of 187 GFLOPs, while handling competitive input sizes ( $800\times 1300$ ). It achieves strong results on both the Cityscapes Panoptic Parts dataset (PQ: 71.3%, PartPQ: 66.9%) and the Pascal Panoptic Parts dataset (PQ: 64.8%, PartPQ: 55.3%), outperforming existing methods such as Panoptic-PartFormer++ and UPSNet+DeepLabv3+. In general, the contributions include a one-pass segmentation pipeline that is time efficient, higher accuracy, and reduced computational demands, positioning this method as a practical solution for real-world panoptic part segmentation tasks.}, archive = {J_ACCESS}, author = {S. Raghavendra and Shambhavi Jha and S. K. Abhilash and Venu Madhav Nookala and Ramyashree and B. N. Anoop}, doi = {10.1109/ACCESS.2025.3599184}, journal = {IEEE Access}, pages = {147185-147201}, shortjournal = {IEEE Access}, title = {Efficient anchor-free unified framework for panoptic part segmentation in real-time and resource-constrained environments}, volume = {13}, year = {2025}, }
• (2025). A reconfigurable transient antenna based on shaped charge jet. ACCESS, 13, 147173-147184. (www)

  @article{ , abstract = {In response to the demands for communication encryption and electromagnetic protection in modern military scenarios, a novel reconfigurable transient antenna has been designed using shaped charge. This antenna utilizes the shaped charge jet as the radiating element and a metal plate as the equivalent ground, with excitation provided by a coaxial line. A combined simulation approach (CST for electromagnetic analysis, Autodyn for explosive dynamics) validates its feasibility: the jet exhibits radiation characteristics similar to a planar monopole, and the explosive process confirms structural stability. Additionally, modal analysis reveals that the jet excites radiation modes where the jet acts as the primary radiating body. The reconfigurability is achieved through dynamic variation of jet length by stretching and adjusting the charge configuration, which modulates the impedance bandwidth and resonant frequency, with longer jets corresponding to lower frequencies. Equivalent physical models for the jets formed by charges with diameters of Ø30mm and Ø56mm were created, followed by measurement of return loss and radiation pattern The results indicate that the Ø30mm shaped charge jet antenna achieves a gain exceeding 2.2 dBi within the frequency range of 1.17-1.81 GHz, while the Ø56mm jet antenna offers a -10 dB impedance bandwidth covering 0.52-0.68 GHz with a peak gain of 4.5 dBi, exhibiting an omnidirectional radiation pattern. The findings of this study can address the miniaturization issue of low-frequency antennas used in EMP weapons.}, archive = {J_ACCESS}, author = {Jia-Hui Guo and Bin Ma and Zheng-Xiang Huang and Yi-Bo Sun}, doi = {10.1109/ACCESS.2025.3599878}, journal = {IEEE Access}, pages = {147173-147184}, shortjournal = {IEEE Access}, title = {A reconfigurable transient antenna based on shaped charge jet}, volume = {13}, year = {2025}, }
• (2025). Enhancing parking spot detection in extreme weather conditions: A deep learning approach with simulated weather augmentation. ACCESS, 13, 147163-147172. (www)

  @article{ , abstract = {This study tackles the challenge of parking spot recognition in extreme adverse weather by developing an automated scripting tool that enhances real parking images with simulated rain, snow, and fog effects. Experiments using pre-configured network models optimized for specific climatic conditions show that recognition is more effective under single weather conditions than mixed ones. Starting with 30 real foggy images and progressively adding simulated foggy images, the study enriches the dataset without needing large-scale data, improving the model’s generalization and adaptability to complex environments. This approach conserves resources and demonstrates efficient learning potential with limited data. Addressing fog’s severe visual safety hazards, the study explores the impact of fog density on recognition and assesses deep learning performance in extremely foggy conditions. Experiments establish a critical density threshold for fog, offering decision support for parking in such environments. Despite the human difficulty in recognizing parking spots under heavy fog, deep learning networks show relatively good performance, proving effective when human vision is limited. Supporting code is available on GitHub: https://github.com/Wzxzz/parking-plot.git}, archive = {J_ACCESS}, author = {Zixuan Wang and Penghua Zhou and Shuo Zhang and Wei Du and Xin Chen}, doi = {10.1109/ACCESS.2025.3600276}, journal = {IEEE Access}, pages = {147163-147172}, shortjournal = {IEEE Access}, title = {Enhancing parking spot detection in extreme weather conditions: A deep learning approach with simulated weather augmentation}, volume = {13}, year = {2025}, }
• (2025). Geometric optimization frameworks in mobile robot path planning. ACCESS, 13, 147127-147162. (www)

  @article{ , abstract = {Global and local path planning plays a crucial role in mobile robot navigation systems, ensuring efficient and collision-free movement through complex environments. However, many challenges such as high computational cost, path and curvature discontinuities, local minima entrapment, real-time computational constraints, and the need to account for dynamic obstacles and motion constraints, make it difficult to determine optimal paths. To overcome these problems, various geometric optimization frameworks have been proposed, leveraging a wide range of techniques from Delaunay triangulation to Model Predictive Control and Artificial Potential Field. This paper aims to provide a comprehensive survey of the optimization frameworks developed for path planning in mobile robots. Particularly, we first provide a fundamental background of the path planning problem and the prominent geometric optimization techniques. We then discuss diverse geometric optimization frameworks proposed to address challenges in different application areas, including global pathfinding, local path planning, and applications that combine both. Finally, we highlight important challenges, open issues, and future research directions of geometric optimization approaches for future path planning systems.}, archive = {J_ACCESS}, author = {Cong T. Nguyen and Hoang-Anh Pham}, doi = {10.1109/ACCESS.2025.3600708}, journal = {IEEE Access}, pages = {147127-147162}, shortjournal = {IEEE Access}, title = {Geometric optimization frameworks in mobile robot path planning}, volume = {13}, year = {2025}, }
• (2025). Research on prediction and discrimination method of compressor internal stall level based on time series feature extraction. ACCESS, 13, 147112-147126. (www)

  @article{ , abstract = {The accurate identification of internal stall onset in ultra-high load compressors is critical to ensuring the reliability and performance efficiency of next-generation aero-propulsion systems. Aligning with the thematic focus of Frontiers in Computer Science on intelligent modeling, predictive analytics, and complex systems computation, this work introduces a novel framework that leverages high-dimensional time-series feature extraction to address long-standing challenges in early stall detection. Traditional methods, primarily rooted in global stability analysis and empirical thresholds, often fall short in sensitivity and interpretability, particularly under transient or localized flow disturbances. They tend to overlook weakly amplified precursors due to insufficient resolution or overly generalized models, thereby compromising early warning capabilities. To overcome these limitations, we propose an integrated strategy consisting of the Selective Modal Growth Characterization Network (SMGCN) and the Incremental Margin Mapping Protocol (IMMP). SMGCN is a physics-informed neural architecture that decodes spatial and temporal growth patterns of unstable flow modes using sparse modal activation and orthogonal decomposition, ensuring both physical relevance and model compactness. IMMP then transforms these extracted features into actionable stall indicators through spatio-temporal risk localization, ferential phase tracking, and multi-resolution decomposition, culminating in accurate predictions of both stall initiation time and its spatial origin. Experimental validation demonstrates the framework’s superior capability to identify incipient instability with minimal data latency, offering robust, interpretable predictions compatible with real-time engine monitoring systems. By embedding domain-specific knowledge into deep learning pipelines, this method exemplifies a convergence of computational intelligence and aerothermal diagnostics, reinforcing the scope of the journal’s emphasis on cyber-physical systems, predictive modeling, and high-dimensional signal interpretation.}, archive = {J_ACCESS}, author = {Weiguang Zhao and Jia Yu and Tao Liu}, doi = {10.1109/ACCESS.2025.3599564}, journal = {IEEE Access}, pages = {147112-147126}, shortjournal = {IEEE Access}, title = {Research on prediction and discrimination method of compressor internal stall level based on time series feature extraction}, volume = {13}, year = {2025}, }
• (2025). Lightweight IDS for IoT based on counter-propagation networks. ACCESS, 13, 147086-147111. (www)

  @article{ , abstract = {The ubiquitous integration of Internet of Things (IoT) networks into almost every aspect of modern life is leading to a multitude of innovative services and applications. However, this massive proliferation of IoT networks exposes them to increasingly complex and massive cyberattacks. Therefore, the development of robust intrusion detection systems (IDS) that can protect these networks from malicious attacks is one of the cornerstones to ensure the sustainable proliferation of these networks. While deep learning-based IDS models have shown remarkable performance, their resource-intensive requirements stand in the way of their deployment on the tiny resources of IoT devices. To bridge this gap, this work proposes a novel IDS solution that synergistically integrates the unsupervised learning paradigm in the form of Growing Hierarchical Self-Organizing Maps (GHSOM) with the supervised learning paradigm in the form of Grossberg-outstar layer to form an improved variant of Counter Propagation Neural Networks. This integration provides the model with a versatile hypothesis space that can be adapted according to the intrinsic patterns and relationships of the IDS dataset in an autonomous and dynamic manner. Moreover, the inherent resilience of the model’s components to noise, outliers, concept drift, and catastrophic forgetting enhances its learning robustness and generalization ability. The simplicity and high interpretability of the underlying structures of the datasets are other important advantages of the proposed model. The proposed model has been thoroughly evaluated on several contemporary IoT-IDS datasets from various perspectives. These evaluations demonstrate the ability of the proposed model to achieve near-unity prediction accuracy across all benchmark datasets while consuming only a fraction of the resources required by comparable state-of-the-art models.}, archive = {J_ACCESS}, author = {Mohammed Baz}, doi = {10.1109/ACCESS.2025.3600620}, journal = {IEEE Access}, pages = {147086-147111}, shortjournal = {IEEE Access}, title = {Lightweight IDS for IoT based on counter-propagation networks}, volume = {13}, year = {2025}, }
• (2025). Automatic modulation recognition based on multi-source heterogeneous networks. ACCESS, 13, 147074-147085. (www)

  @article{ , abstract = {Automatic Modulation Recognition (AMR) is crucial to modern communications systems because it identifies the modulation scheme of incoming signals, serving as a key method to address spectrum resource constraints and enhance communication quality. Recently, multi-source models have achieved impressive results in a range of application domains. To improve AMR performance, we propose a multi-source heterogeneous network (MSHNet) that jointly processes time-series signals, images, and graph representations. Transforming the raw signals into images and graphs enables the model to capture complementary features from multiple dimensions, which markedly boosts recognition accuracy. In the time-series branch, we integrate a lifting scheme that extracts features in the frequency domain. For the image branch, the signal is converted into a Gramian Angular Field (GAF) image, supplying the network with two-dimensional spatial information while retaining temporal context. In the graph branch, we construct a graph from feature vector correlations, allowing the model to capture long range dependencies and to view the signal from a broader perspective. Features from the three branches are concatenated and then fused using fully connected layers to integrate the multi-source information. Experiments show that MSHNet outperforms state-of-the-art baselines on the RML2016.10a dataset and maintains strong performance even with a limited number of training samples.}, archive = {J_ACCESS}, author = {Shuyin Wang and Xin Wang and Zhuoran Cai and Chuan Wang and Cong Xu}, doi = {10.1109/ACCESS.2025.3600401}, journal = {IEEE Access}, pages = {147074-147085}, shortjournal = {IEEE Access}, title = {Automatic modulation recognition based on multi-source heterogeneous networks}, volume = {13}, year = {2025}, }
• (2025). Forecasting future corporate value including information on firm environmental activities: Using machine learning techniques. ACCESS, 13, 147054-147073. (www)

  @article{ , abstract = {This study aims to develop a predictive model of future corporate value by incorporating environmental activity data disclosed in sustainability management reports. Specifically, it examines whether including environmental indicators such as carbon emissions, water usage, and waste recycling capacity enhances the accuracy of corporate value prediction compared to models that exclude such information. To this end, we applied three boosting-based machine learning classifiers (CatBoost, LGBM, and GradientBoost) using both classification and regression techniques. The key contribution of this study lies in integrating firm-level environmental activity information into machine learning-based prediction models an approach that has been rarely explored in accounting and finance research. The empirical results show that models incorporating corporate environmental data consistently outperform the baseline model across all metrics. First, adding carbon emissions data significantly improved prediction performance. Second, incorporating water usage data led to even greater accuracy. Third, the inclusion of waste recycling capacity further enhanced the predictive power of the model. These findings suggest that environmental performance indicators play a critical role in accurately forecasting long-term firm value and offer meaningful insights for investors, managers, and policymakers.}, archive = {J_ACCESS}, author = {Jiyoung Park and Hyungjoon Kim and Hyung Jong Na}, doi = {10.1109/ACCESS.2025.3595456}, journal = {IEEE Access}, pages = {147054-147073}, shortjournal = {IEEE Access}, title = {Forecasting future corporate value including information on firm environmental activities: Using machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). Using Kolmogorov–Arnold networks in transformer model: A study on low-resource neural machine translation. ACCESS, 13, 147034-147053. (www)

  @article{ , abstract = {Neural machine translation is one of the most significant research area with the widespread use of deep learning. However, unlike other problems, machine translation includes at least two languages. Due to this situation, the amount of data between the languages to be translated is an important factor for translation success. On the other hand, low-resource languages have problems with the amount of data, which poses a significant challenge to the success of machine translation. Transformer models have achieved great success by modeling long-term dependencies with the self-attention mechanism. However, the feed forward layers (FFN) that follow each self-attention layer constitute almost all the non-embedding parameters of the model. On the other hand, studies in the literature have been conducted on the necessity of these FFN layers in the Transformer model and on different alternatives that can be used. Kolmogorov-Arnold networks (KAN) have recently come to the forefront as a new neural network architecture that has achieved success on many problems. The KAN structure can better learn patterns in complex data using learnable activation functions instead of fixed ones. Accordingly, this study proposes using KAN layers instead of FFN layers in the Transformer model for the low-resource translation problem. It is aimed to overcome the low-resource problem and to present a new alternative within the Transformer model employing the adaptive activation functions of KANs. In traditional Transformer models, FFN layers consist of two linear transformations and ReLU activation functions. In the proposed structure, firstly, the KAN structure is used instead of FFN layers in the Transformer model without any changes in the model dimensions. Then, experiments are conducted with lower-dimensional KAN layers and various parameter sets. The study is carried out using Turkish-English and Kazakh-English language pairs. Obtained findings reveal that using KAN layers instead of FFN layers in the Transformer model has a positive effect on the translation success and that KAN layers used in similar or lower dimensions significantly increase the success of the Transformer model.}, archive = {J_ACCESS}, author = {Bilge Kagan Yazar and Erdal Kiliç}, doi = {10.1109/ACCESS.2025.3601069}, journal = {IEEE Access}, pages = {147034-147053}, shortjournal = {IEEE Access}, title = {Using Kolmogorov–Arnold networks in transformer model: A study on low-resource neural machine translation}, volume = {13}, year = {2025}, }
• (2025). AI-generated messaging for life events using structured prompts: A comparative study of GPT with human experts and machine learning. ACCESS, 13, 147002-147033. (www)

  @article{ , abstract = {Large Language Models (LLMs) are increasingly used to generate social media posts, emails, narratives, and other forms of communication, yet few studies systematically assess how well these outputs align with their original prompt intent. This study evaluates the effectiveness of a zero-shot structured narrative prompt for generating 24 000 life event messages (birth, death, hiring, and firing events) using OpenAI’s GPT-4. A manually tagged sample of 2880 messages shows that 87.43% align with the intended life event when framed as X (formerly Twitter) posts. To scale this evaluation, we train nine machine learning (ML) models, including BERT, Keras-based architectures, eXtreme Gradient Boosting, Random Forest, and Support Vector Machines, per life event type, resulting in 36 binary classifiers. We apply an ensemble approach based on simple majority agreement to classify the remaining 21 120 messages. Manual validation of a 1% sample (n = 212) confirms a 90.57% match rate with human reviewers, with binomial tests confirming statistical significance above a 75% threshold across all event types (P-values ranging from 0.00025 to 0.0115). While valid messages are reliably identified, classifying invalid cases remains more challenging. This work offers a reproducible framework for validating AI-generated messaging and provides practical guidance for prompt-based applications. Limitations include the narrow event scope, exclusive use of English text, and reliance on structured prompts, which may not generalize to open-ended tasks.}, archive = {J_ACCESS}, author = {Christopher J. Lynch and Erik Jensen and Ross Gore and Virginia Zamponi and Kevin O’Brien and Brandon Feldhaus and Katherine Smith and Joseph Martínez and Madison H. Munro and Timur E. Ozkose and Tugce B. Gundogdu and Ann Marie Reinhold and Hamdi Kavak and Barry Ezell}, doi = {10.1109/ACCESS.2025.3600146}, journal = {IEEE Access}, pages = {147002-147033}, shortjournal = {IEEE Access}, title = {AI-generated messaging for life events using structured prompts: A comparative study of GPT with human experts and machine learning}, volume = {13}, year = {2025}, }
• (2025). Accurate rural road network with improved dataset for strengthen wildfire emergency response. ACCESS, 13, 146983-147001. (www)

  @article{ , abstract = {Wildfires pose significant threats to ecosystems worldwide, making rapid firefighting responses essential. Firefighters face major challenges in advanced-stage fires, highlighting the need for early detection and improved operational support. However, current rural road mapping methods often fail under occlusions from vegetation and shadows, lack adaptability to wildfire-prone regions, and offer limited automation for large-scale applications. This study introduces a system that integrates U-Net-based deep learning architectures with post-processing to detect rural roads from aerial imagery. A manually annotated dataset of 1,044 orthorectified aerial images from wildfire-prone areas was augmented to 4,700 samples to improve segmentation robustness under occlusion. The system follows a two-step process: rural road segmentation and post-processing to remove small connected components and reconnect occluded road segments. Four architectures: VGG16 U-Net, VGG19 U-Net, ResNet50 U-Net, and MobileNet U-Net were tested with extensive hyperparameter tuning, both before and after augmentation. Data augmentation significantly improved performance, with post-processing providing modest yet consistent gains. The best results reached 85.1% Precision, 81.9% Recall, and a 71.7% Jaccard index. MobileNet U-Net showed the largest post-processing improvement. Overall, the approach enables automated, accurate rural road mapping to support operational wildfire response, offering a practical and adaptable tool for crisis management.}, archive = {J_ACCESS}, author = {Miguel Lourenço and Duarte Caetano and André Mora and Luís B. Oliveira and Henrique Oliveira}, doi = {10.1109/ACCESS.2025.3600192}, journal = {IEEE Access}, pages = {146983-147001}, shortjournal = {IEEE Access}, title = {Accurate rural road network with improved dataset for strengthen wildfire emergency response}, volume = {13}, year = {2025}, }
• (2025). Unified framework for evaluating numerical integration methods through characteristic root distortion in linear ordinary differential equations. ACCESS, 13, 146973-146982. (www)

  @article{ , abstract = {Analytically solving complex or large-scale differential equations is often difficult or even impossible, making numerical integration methods indispensable. However, as all numerical integration methods approximate a continuous-time equation using a discrete-time model, they inevitably introduce errors into the numerical solution. This study presents a unified framework for evaluating the numerical errors introduced by various integration methods. The integration formulae are expressed as equations involving differential and difference operators applied to the solution of the equation. This study applies integration methods to constant-coefficient linear ordinary differential equations (ODEs) and adopt an approach in which these expressions are transformed using Laplace and z-transforms, deriving characteristic equations that capture the essential properties of widely used integration methods. Based on the characteristic equations, distortions of the characteristic root —real and conjugate complex values that represent the fundamental properties of linear ODEs due to integration methods have been evaluated, and regions where stable roots become numerically unstable have also been identified. Additionally, transfer functions were estimated from the numerical solutions, and the distorted characteristic roots were corrected using the inverse of the approximation formula. The proximity of the restored roots to the original roots served as a quantitative indicator of the accuracy and reliability of each integration method. This unified evaluation approach revealed the distinct characteristics of widely used integration methods and offered a generalizable basis for interpreting errors in numerical solutions.}, archive = {J_ACCESS}, author = {Takuya Yamakawa and Yosuke Kurihara and Kazuyuki Kobayashi and Kajiro Watanabe}, doi = {10.1109/ACCESS.2025.3598377}, journal = {IEEE Access}, pages = {146973-146982}, shortjournal = {IEEE Access}, title = {Unified framework for evaluating numerical integration methods through characteristic root distortion in linear ordinary differential equations}, volume = {13}, year = {2025}, }
• (2025). Crowd density estimation using enhanced multi-column convolutional neural network and adaptive collation. ACCESS, 13, 146956-146972. (www)

  @article{ , abstract = {Accurate crowd density estimation is essential for public safety operations, urban and transportation design and multiple intelligent systems. The research presents an improved Multi-Column Convolutional Neural Network (MC-CNN) structure to predict crowd density. The model uses MobileNet transfer learning capabilities and implements three parallel convolutional columns with different kernel sizes to extract spatial information at different receptive field levels. The design enhances the ability to extract and combine multi-scale features, resulting in better spatial crowd density representation. The model incorporates the output of its columns to improve its understanding of crowd movement patterns. During training, a dynamic collation and zero-padding strategy is introduced to handle variable-sized input images. This approach’s uniform batch shape improves training stability and allows flexible real-world deployment with inconsistent input resolutions. The experimental results show that the proposed architecture performs better, as revealed by its low MAE scores of 12.93 in Part A and 28.56 in Part B of the ShanghaiTech data, outperforming TransCrowd, CSRNet, and SANet. The research provides a functional solution that accurately measures crowd density in different scenarios while overcoming the existing approaches’ restrictions. It fills the academic research gaps in multi-scale feature extraction, efficient data collation and offers new solutions for crowd analysis.}, archive = {J_ACCESS}, author = {Azamat Serek and Beibut Amirgaliyev and Rita Yi Man Li and Ainur Zhumadillayeva and Didar Yedilkhan}, doi = {10.1109/ACCESS.2025.3597393}, journal = {IEEE Access}, pages = {146956-146972}, shortjournal = {IEEE Access}, title = {Crowd density estimation using enhanced multi-column convolutional neural network and adaptive collation}, volume = {13}, year = {2025}, }
• (2025). Estimation of essential battery state parameters for battery management systems (BMS) in electric vehicles using long short term memory (LSTM) and time series analysis combined with extended kalman filter. ACCESS, 13, 146944-146955. (www)

  @article{ , abstract = {In a world actively moving towards sustainable growth, the efficient management of Battery Management Systems (BMS) in Electric Vehicles is critical. The precise estimation of essential parameters such as State of Charge (SoC), State of Health (SoH), and State of Power (SoP) play a pivotal role in the effective management of batteries through timely decision making by battery management systems (BMS). There are many machine learning (ML) base models to predict health, energy, power and other battery parameters. The proposed work combines predictive modeling and time series analysis with an extended Kalman filter to develop a framework for SoH, SoC, and SoP prediction. This includes long short term memory (LSTM) based machine learning (ML) algorithms. In this article it is demonstrated that the suggested approach has better accuracy and resilience against modeling errors. The main finding of this study is that combining predictive modeling and time series analysis with extended Kalman filter can increase the accuracy of estimation of state variables and other essential parameters to more than 88% for various temperatures, such as 0°C, 25°C, and 45°C that would suit most of the tropical countries.}, archive = {J_ACCESS}, author = {N. Sradha and G. Sruthi and J. Florence Gnana Poovathy}, doi = {10.1109/ACCESS.2025.3600442}, journal = {IEEE Access}, pages = {146944-146955}, shortjournal = {IEEE Access}, title = {Estimation of essential battery state parameters for battery management systems (BMS) in electric vehicles using long short term memory (LSTM) and time series analysis combined with extended kalman filter}, volume = {13}, year = {2025}, }
• (2025). Optimization-based flux linkage control for torque ripple reduction in switched reluctance machines. ACCESS, 13, 146932-146943. (www)

  @article{ , abstract = {This work presents an optimization-based control strategy for reducing torque ripple in switched reluctance motors (SRMs), which are often affected by high ripple and acoustic noise. The proposed approach consists of two stages. First, optimal flux waveforms are generated offline as outer flux linkage references using mixed-integer quadratically constrained programming, explicitly incorporating machine constraints. Second, an inner flux control loop tracks these references using an optimal switching sequence model predictive control algorithm. This method mitigates the inherent limitations of conventional inner-loop strategies, which often exhibit poor trade-offs between tracking accuracy and low switching frequency in the presence of nonlinear SRM behavior. The strategy is validated through simulations and experimental tests on an 8/12 three-phase 2.32kW SRM, whose nonlinear flux characteristics are identified via locked-rotor tests using a radial basis function approach. The proposed reference generation and tracking algorithm are benchmarked against state-of-the-art methods. Experimental results demonstrate that the proposed strategy outperforms existing techniques, achieving a 4.3% torque root-mean-square error while reducing switching frequency by up to 60%.}, archive = {J_ACCESS}, author = {Andrés Carvajal and Alejandro Angulo and Jorge Juliet}, doi = {10.1109/ACCESS.2025.3600550}, journal = {IEEE Access}, pages = {146932-146943}, shortjournal = {IEEE Access}, title = {Optimization-based flux linkage control for torque ripple reduction in switched reluctance machines}, volume = {13}, year = {2025}, }
• (2025). From transformers to ChatGPT: An analysis of large language models research. ACCESS, 13, 146889-146931. (www)

  @article{ , abstract = {In recent years, the development and rapid adoption of the Large Language Models (LLMs) has revolutionized the Natural Language Processing (NLP) and Artificial Intelligence (AI) fields, influencing various research areas, as well as the everyday life of people, thanks to their wide use in a variety of areas, including decision and policy making. In this context, the present study focuses on the LLMs field from a bibliometric perspective, having the aim to discuss the global trends, applications and challenges associated with this type of language models. Starting from Transformer architecture – which serves as the foundation for the state-of-the-art models – and ending with ChatGPT, the paper analyzes the articles in the field of LLMs included in the Clarivate Analytics’ Web of Science Core Collection in terms of the most prolific authors, the dominant scientific outlets, the most prominent universities and countries based on the number of publications and characterizes the collaborative trends and interdisciplinary linkages. The exponential growth in the interest of the scientific community for LLMs is further highlighted through various indicators, such as an annual growth rate for the scientific production of 220.74%, which underscores the rapid development of this research domain. In order to complete the analysis, most cited papers are discussed in depth. Further, an n-gram analysis is conducted, accompanied by a trend and a factorial analysis, highlighting the most prominent themes, as well as key research areas of innovation and ongoing challenges. The insights derived from this study could provide valuable information for the interested parties and unlock potential future research directions.}, archive = {J_ACCESS}, author = {Liviu-Adrian Cotfas and Andra Sandu and Camelia Delcea and Paul Diaconu and Corina Frăsineanu and Aurelia Stănescu}, doi = {10.1109/ACCESS.2025.3600739}, journal = {IEEE Access}, pages = {146889-146931}, shortjournal = {IEEE Access}, title = {From transformers to ChatGPT: An analysis of large language models research}, volume = {13}, year = {2025}, }
• (2025). Pedi-medi-instructor: Pediatric medicine information and instruction system. ACCESS, 13, 146874-146888. (www)

  @article{ , abstract = {This study proposes a two-part system to enhance medication safety and user experience, especially for pediatric patients. The Data Acquisition System for Medicine Identification (DAMI) trains an AI model with comprehensive images of medications captured from various angles. The Medicine Identification and Instruction System (MIIS) leverages this model to identify medications and prescriptions, even with imperfect placement, and delivers audio instructions on usage. Designing the structured prescription provides highly accurate medicine text extraction, medicine type classification, and proper consumption unit extraction. While currently limited to pediatrics and challenged by handwriting variations, the system’s robustness to user errors, digital twin monitoring, and planned improvements for broader use and recognition accuracy position it as a promising tool for medication management.}, archive = {J_ACCESS}, author = {Ganesan Kaliyaperumal and N. S. Manikandan}, doi = {10.1109/ACCESS.2025.3591803}, journal = {IEEE Access}, pages = {146874-146888}, shortjournal = {IEEE Access}, title = {Pedi-medi-instructor: Pediatric medicine information and instruction system}, volume = {13}, year = {2025}, }
• (2025). Generation of critical information and sharing mechanism for multi-robot mission success. ACCESS, 13, 146855-146873. (www)

  @article{ , abstract = {This paper shows a real-time information-sharing mechanism and custom task allocation strategy for cooperative multi-robot systems operating in uncertain and dynamic environments. The proposed method is based on a decentralized, asynchronous architecture utilizing the Quality of Service (QoS) features of the Robot Operating System 2 (ROS2) framework and Data Distribution Service (DDS) middleware. To validate its practical applicability, extensive simulation experiments and real-world robot tests were conducted using mobile robot platforms under varying network conditions. Key performance metrics including message delay, reaction time, packet loss, and mission success rate were evaluated across multi-QoS configurations. Results show that Reliable and Transient Local settings provide the most consistent performance, ensuring stable obstacle information transmission and robust map updates even in environments with significant communication constraints. Additionally, comparisons between simulation and physical experiments reveal the impact of system-level variables such as synchronization error, network behavior, and sensor noise. The findings confirm the robustness and adaptability of the proposed approach and demonstrate its potential to serve as a scalable framework for future heterogeneous multi-robot deployments.}, archive = {J_ACCESS}, author = {Daeil Jo and Yongjin Kwon}, doi = {10.1109/ACCESS.2025.3600319}, journal = {IEEE Access}, pages = {146855-146873}, shortjournal = {IEEE Access}, title = {Generation of critical information and sharing mechanism for multi-robot mission success}, volume = {13}, year = {2025}, }
• (2025). Physics-aware hybrid estimator for dynamic power system dataset generation and cyber attack simulation. ACCESS, 13, 146830-146854. (www)

  @article{ , abstract = {The increasing vulnerability of power systems to cyber-attacks has underscored the need for robust data-driven anomaly detection methods. However, existing datasets used to evaluate these methods have several limitations, including restricted availability, limited attack scenarios, and a lack of fidelity assessment. To address these challenges, we first propose a hybrid framework that combines model-based and data-driven methods to generate a dataset under normal operation based on IEEE bus systems (4-, 14-, 39-, and 57-bus systems). The framework solves the direct current (DC) power flow problem to obtain initial measurements. A public load profile is scaled and distributed across these values to generate time-series data. A Kalman filter is integrated for state estimation and filtering. A neural network adaptively fuses the Kalman-filtered data with the original time-series data, generating accurate, temporally correlated measurements that reflect system dynamics. It is trained using physics-informed ground truth derived from DC power flow equations and Kirchhoff’s principles to ensure physical consistency. Second, we simulate two attack scenarios: 1) unpoisoned data, where cyberattacks evade traditional Bad Data Detection (BDD), and 2) data poisoning, where adversarial examples are generated using a Projected Gradient Descent (PGD) attack to improve the robustness of data-driven detection models through adversarial training. Finally, we evaluate supervised and unsupervised machine learning and deep learning models on the proposed dataset under both scenarios. Evaluation results show that the proposed dataset is physically consistent and temporally coherent, offering higher fidelity compared to the benchmark dataset. Moreover, the results reveal that deep neural models remain vulnerable to adversarial threats; however, the proposed poisoned data enhances their robustness through adversarial training.}, archive = {J_ACCESS}, author = {Belkacem Kadri and Layth Sliman and Abdullah Alsaedi and Juhar Abdella and Zoubeyr Farah}, doi = {10.1109/ACCESS.2025.3599318}, journal = {IEEE Access}, pages = {146830-146854}, shortjournal = {IEEE Access}, title = {Physics-aware hybrid estimator for dynamic power system dataset generation and cyber attack simulation}, volume = {13}, year = {2025}, }
• (2025). Off-eccentricity in a multi-ring planar Array—Improving the Cramér–Rao bound for azimuth-elevation direction finding. ACCESS, 13, 146820-146829. (www)

  @article{ , abstract = {This paper proposes an off-centric variation of the concentric circular array, which constitutes the most general planar array grid that offers azimuthal rotational symmetry for direction finding (with the “azimuth” defined on the circular plane). This off-centric variation improves direction-finding precision without adding more sensors and without enlarging the aperture size. These advantages are predicated on an astonishing insight uncovered in this paper that off-centering the rings’ origins will lower (i.e., improve) the overall array’s direction-finding Cramér-Rao bounds, which are analytically derived in this paper in mathematically closed forms.}, archive = {J_ACCESS}, author = {Dominic Makaa Kitavi and Kainam Thomas Wong and Yue Ivan Wu and Charles Hung Lee}, doi = {10.1109/ACCESS.2025.3597782}, journal = {IEEE Access}, pages = {146820-146829}, shortjournal = {IEEE Access}, title = {Off-eccentricity in a multi-ring planar Array—Improving the Cramér–Rao bound for azimuth-elevation direction finding}, volume = {13}, year = {2025}, }
• (2025). Network selection in B5G heterogeneous networks using AHP-TOPSIS. ACCESS, 13, 146810-146819. (www)

  @article{ , abstract = {The implementation of ultra-dense heterogeneous networks (HetNets) has become crucial to meet the increasing demands of 5G and beyond networks. These networks face complex challenges due to the proliferation of mobile devices, network overlay, and high mobility. In this context, it is essential to identify the most suitable network for a given user, taking into consideration the user’s specific requirements. This article proposes a multi-criteria method for dynamic and personalized network selection in the context of different technologies, highlighting the difficulty involved in deciding which access technology the user should connect to. The proposed model showed better performance for high QoS (Quality of Service) profiles and low energy consumption, and it facilitated a more efficient distribution of network resources considering the network load criterion. A comparison with another current method in the literature highlighted the efficiency of the proposed model in achieving improvements for all user profiles considered.}, archive = {J_ACCESS}, author = {Albert Einstein Coutinho Dos Santos and Matheus Gabriel Gomes Pantoja and Ermínio Augusto Ramos da Paixão and Reyso Cunha Teixeira and Marcos César da Rocha Seruffo and Diego Lisboa Cardoso}, doi = {10.1109/ACCESS.2025.3597791}, journal = {IEEE Access}, pages = {146810-146819}, shortjournal = {IEEE Access}, title = {Network selection in B5G heterogeneous networks using AHP-TOPSIS}, volume = {13}, year = {2025}, }
• (2025). Remote sensing dissolved organic matter in freshwater aquaculture ponds by the integration of UAV and satellite multispectral images. ACCESS, 13, 146794-146809. (www)

  @article{ , abstract = {Dissolved organic matter (DOM) is a pivotal indicator for assessing aquatic health and ecological functions. Monitoring DOM in aquaculture ponds using satellite requires validation through field measured samples. However, due to the inherent spatial variability of DOM in aquaculture ponds, individual samples are insufficient to represent the entire pond. Consequently, directly applying field measurements to satellite remote sensing can compromise the accuracy of estimation models. A spatial mapping approach was proposed in the study, which integrated UAV multispectral data with Sentinel-2 images to address scale mismatches between satellite images and ground-based measurements. Then a self-optimizing model was used to estimate and map DOM concentration at county scale. Firstly, high-resolution spatial distribution of DOM in some aquaculture ponds were obtained through field samples and UAV multispectral images. Secondly, a spatial mapping relationship was established between the UAV-derived DOM distribution and the corresponding satellite image pixels, thereby providing high-quality samples for large-scale monitoring of DOM in aquaculture. Results showed that: 1) Among the four models constructed using UAV data, the simulated annealing-optimized random forest (SA-RF) achieved the highest performance, with the R2 of 0.84, RMSE of 2.66mg/L, and MAE of 2.21mg/L. 2) The spatial mapping method improved the accuracy of DOM concentration estimation based on satellite images. Specifically, the accuracy of SA-RF model increased by 10% compared with the model constructed directly using satellites and ground measurements, achieving an R2 of 0.78. This study demonstrates that the spatial mapping method provides a novel method for UAV-satellite collaborative inversion of DOM concentration in aquaculture ponds.}, archive = {J_ACCESS}, author = {Guangxin Chen and Yancang Wang and Xiaohe Gu and Tianen Chen}, doi = {10.1109/ACCESS.2025.3596148}, journal = {IEEE Access}, pages = {146794-146809}, shortjournal = {IEEE Access}, title = {Remote sensing dissolved organic matter in freshwater aquaculture ponds by the integration of UAV and satellite multispectral images}, volume = {13}, year = {2025}, }
• (2025). Design of AI-CGF for analysis and training in warship combat M&S. ACCESS, 13, 146786-146793. (www)

  @article{ , abstract = {This study proposes a solution to the resource-intensive issue and environmental pollution associated with military training. By utilizing evolutionary and deep learning techniques within naval analyzing and training Modeling and Simulation (M&S) environments, this study shows significant progress in both the Analyzing and Training M&S. In Analyzing M&S, optimal tactical scenarios tailored to specific situations were automatically generated through evolutionary, supervised, and reinforcement learning. The traditional role of human operators was replaced with AI-based models. In Training M&S, reinforcement learning was used with optimization functions such as A3C, PPO, SAC, and DAC. AI-CGF (Artificial Intelligence-based Computer-Generated Force) was developed to replace human controllers in both Analyzing and Training M&S, which significantly reduces the requirement for human operators and empowers the generation of new Red Force scenarios. The research team achieved a 95% efficiency level in red-force scenarios effectively countering blue-force scenarios. These results are expected to enhance military training effectiveness and alleviate environmental impact, with potential real-world applications in the realm of naval M&S.}, archive = {J_ACCESS}, author = {Hyun-Geun Kim and Jae-Woo Kim and Jin-Won Kim and Bum-Joon Park and Kang-Moon Park and Sung-do Chi}, doi = {10.1109/ACCESS.2025.3600862}, journal = {IEEE Access}, pages = {146786-146793}, shortjournal = {IEEE Access}, title = {Design of AI-CGF for analysis and training in warship combat M&S}, volume = {13}, year = {2025}, }
• (2025). Phase formation in vacuum arc melted high entropy alloys: Insights from machine learning and experimental validation. ACCESS, 13, 146772-146785. (www)

  @article{ , abstract = {In recent years, High Entropy Alloys (HEAs) have gained significant interest due to their unique properties such as high strength, wear resistance, and high temperature stability. However, accurately predicting the phases of HEAs remains a challenge due to multiple elements within the alloy. This study proposes a Machine Learning (ML) framework for phase prediction of HEAs fabricated using the Vacuum Arc Melting (VAM) process. For this, a dataset containing 316 unique experimentally validated HEA combinations were used to train and test seven different classifiers including Support Vector Machine (SVC), K-Nearest Neighbours (KNN), XGBoost (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), and Gradient Boost (GB). Each alloy’s elemental composition and five different thermodynamic parameters were used as input features. Grid search Cross Validation (CV) method was used for hyperparameter tuning, and all the models were evaluated using various parameters such as 5-fold CV, F1, and Area Under the Curve (AUC) scores. KNN and ANN models achieved the best overall performance with F1 score of 0.83 and 0.81 respectively and AUC score of 0.92 each. XGBoost also performed well with F1 and AUC scores of 0.81 and 0.96 respectively. KNN and ANN have the best overall phase prediction accuracy of 82.81% each. In addition, KNN, ANN, and XGBoost predicted 9, 9, and 8 phases correctly. The model validation uses 10 novel HEA combinations not utilized during training and testing. These results illustrate the potential of ML techniques in design and phase prediction of arc melted HEAs.}, archive = {J_ACCESS}, author = {S. Arun and N. Radhika}, doi = {10.1109/ACCESS.2025.3600279}, journal = {IEEE Access}, pages = {146772-146785}, shortjournal = {IEEE Access}, title = {Phase formation in vacuum arc melted high entropy alloys: Insights from machine learning and experimental validation}, volume = {13}, year = {2025}, }
• (2025). Advancing anomaly detection: Non-semantic financial data encoding with large language models. ACCESS, 13, 146757-146771. (www)

  @article{ , abstract = {Detecting anomalies in general ledger data is of utmost importance to ensure the trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in feature dimensions adds significant complexity to data analysis. In this paper, we introduce a novel approach to anomaly detection in financial data using Large Language Model (LLM) embeddings. To encode non-semantic categorical data (i.e., attributes lacking inherent linguistic meaning) from real-world financial records, we tested 3 pretrained general-purpose sentence-transformer models. For the downstream classification task, we implemented and evaluated 5 optimized ML models, including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines, in selected settings by a large margin. The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity. We discuss a promising perspective on using SBERT embeddings for non-semantic data in the financial context and beyond.}, archive = {J_ACCESS}, author = {Alexander Bakumenko and Kateřina Hlaváčková-Schindler and Claudia Plant and Nina C. Hubig}, doi = {10.1109/ACCESS.2025.3600967}, journal = {IEEE Access}, pages = {146757-146771}, shortjournal = {IEEE Access}, title = {Advancing anomaly detection: Non-semantic financial data encoding with large language models}, volume = {13}, year = {2025}, }
• (2025). Practical nonlinear model predictive control with Kolmogorov–Arnold network models. ACCESS, 13, 146745-146756. (www)

  @article{ , abstract = {This paper presents a novel approach to practical nonlinear model predictive control (PNMPC) using Kolmogorov–Arnold networks (KANs) as prediction models. KANs are based on the Kolmogorov–Arnold superposition theorem, enabling efficient nonlinear system modeling with fewer parameters compared to traditional neural networks. This characteristic makes KANs particularly suitable for nonlinear model predictive control (NMPC), where computational efficiency is critical. This paper presents a method to efficiently identify nonlinear models from data, proposing a time-delay KAN (TDKAN) architecture, which consists of a KAN associated with tap delays in its input. Furthermore, it proposes the integration of this novel model architecture into a PNMPC algorithm, with the aim of improving control accuracy while reducing computational burden. The method is validated using a pH neutralization process, a benchmark for nonlinear control problems. The results show that the TDKAN-based PNMPC outperforms other PNMPC approaches, such as those that use echo-state networks and long short-term memory networks, providing better setpoint tracking, lower mean squared error, and faster computation times, which demonstrate the potential of KANs for prediction in practical NMPC applications.}, archive = {J_ACCESS}, author = {Bernardo Barancelli Schwedersky and Rodolfo César Costa Flesch}, doi = {10.1109/ACCESS.2025.3600308}, journal = {IEEE Access}, pages = {146745-146756}, shortjournal = {IEEE Access}, title = {Practical nonlinear model predictive control with Kolmogorov–Arnold network models}, volume = {13}, year = {2025}, }
• (2025). Barriers to adoption industry 4.0 in textile sector: Analyzing challenges in transitioning to smart manufacturing with ISM and MICMAC. ACCESS, 13, 146722-146744. (www)

  @article{ , abstract = {In the contemporary competitive landscape, industrial organisations are compelled to augment their innovation capacities and productivity levels through Industry 4.0 technologies. However, the adoption of these disruptive technologies is challenging for large manufacturing organisations, such as the textile industry in developing countries like Pakistan, where the Industry 4.0 paradigm is still in its early stages. This study examines the potential barriers that may hinder Pakistan’s textile industry from experiencing the Industry 4.0 revolution. Twenty-six barriers have been recognized through a systematic literature review and validated through surveys and interviews with senior-level automation engineers from the textile industry and academia. The Interpretive Structural Modeling (ISM) approach was employed to identify the linkages among inhibiting barriers, and Matrix of Cross-Impact Multiplication Applied to a Classification (MICMAC) analysis was used to determine the driving and dependent powers of these identified barriers concerning the adoption of Industry 4.0. The study reveals that overcoming the barriers to implementing Industry 4.0 strategically and systematically is crucial for the Pakistani textile industry to achieve optimal performance and stay competitive in the market during the digital revolution. The research identifies key driving factors such as technological infrastructure limitations and inadequate government support, while financial constraints and organizational resistance emerge as significant dependent factors. These findings offer valuable insights for policymakers and industry leaders to facilitate the digital transformation of Pakistan’s textile sector, highlighting the importance of developing technological infrastructure, enhancing workforce skills, and implementing supportive government policies.}, archive = {J_ACCESS}, author = {Arshad Mahmood and Muhammad Asif Habib and Mudassar Ahmad and Sajjad Ahmad Baig}, doi = {10.1109/ACCESS.2025.3600580}, journal = {IEEE Access}, pages = {146722-146744}, shortjournal = {IEEE Access}, title = {Barriers to adoption industry 4.0 in textile sector: Analyzing challenges in transitioning to smart manufacturing with ISM and MICMAC}, volume = {13}, year = {2025}, }
• (2025). Enhancing time synchronization in smart grid with white rabbit: Theory, architecture, and challenges. ACCESS, 13, 146707-146721. (www)

  @article{ , abstract = {The smart grid aims to provide economically efficient and sustainable power to consumers with high quality and security. However, the increasing integration of distributed renewable energy sources presents challenges for smart grid protection and control systems. To enhance smart grid operations, this article introduces White Rabbit (WR) as a more precise and accurate time synchronization technique. To explore White Rabbit’s potential applications in the smart grid, this study first explains existing time synchronization techniques and their limitations, followed by an analysis of the role and importance of time synchronization in smart grids. A comprehensive survey is then conducted, covering the theories, principles, implementations, performances, and existing application cases of White Rabbit. The findings suggest that White Rabbit is a promising technique for smart grid deployment. However, several challenges remain on the path to large-scale implementation. These challenges are analyzed in detail, highlighting key areas for future research.}, archive = {J_ACCESS}, author = {Yu Liu and Kazi Ishrak Ahmed and Naga Lakshmi Thotakura and Jiahui Yang and Yuru Wu and Haozong Wang and Biao Sun and Wen Wang and Yongxin Zhang and Yilu Liu}, doi = {10.1109/ACCESS.2025.3599486}, journal = {IEEE Access}, pages = {146707-146721}, shortjournal = {IEEE Access}, title = {Enhancing time synchronization in smart grid with white rabbit: Theory, architecture, and challenges}, volume = {13}, year = {2025}, }
• (2025). OPG-SPELL: Visual and textual explanations that influence understanding of the AI model for sex prediction from orthopantomograms. ACCESS, 13, 146690-146706. (www)

  @article{ , abstract = {As AI models become increasingly powerful, the field of Explainable AI (XAI) has gained significant attention. However, the effectiveness of explanations often varies depending on the audience’s domain knowledge. This paper introduces OPG-SPELL (understanding of OrthoPantomoGram’s artificial intelligence prediction through ShaPlEy value and LLms text generation), an approach combining visual and textual explanations to enhance understanding and trust in AI models. OPG-SPELL focuses on an AI model trained to classify sex based on orthopantomograms (panoramic radiographs of human jaw structure). Our method integrates visual explanations using the OPG-SHAP technique with textual explanations generated through Large Language Models’ Retrieval-Augmented Generation. This dual-modality approach aims to bridge the gap between expert and non-expert users, making AI decisions more interpretable across diverse audiences. We conducted a user study with 24 participants to evaluate the influence of both visual and textual explanations on understanding, trust, and willingness to use the XAI tool. Our findings demonstrate the potential of OPG-SPELL to enhance user comprehension and confidence in AI-driven sex prediction from orthopantomograms, contributing to the broader field of intelligent user interfaces and Explainable AI.}, archive = {J_ACCESS}, author = {Lapatrada Dangsungnoen and Kwansawan Thongprasant and Kannika Wiratchawa and Natthanich Hirunchavarod and Natnicha Sributsayakarn and Suchaya Pornprasertsuk-Damrongsri and Varangkanar Jirarattanasopha and Thammathip Piumsomboon and Thanapong Intharah}, doi = {10.1109/ACCESS.2025.3600956}, journal = {IEEE Access}, pages = {146690-146706}, shortjournal = {IEEE Access}, title = {OPG-SPELL: Visual and textual explanations that influence understanding of the AI model for sex prediction from orthopantomograms}, volume = {13}, year = {2025}, }
• (2025). Geo-intelligent solutions for urban heat island (UHI) detection and mitigation framework. ACCESS, 13, 146676-146689. (www)

  @article{ , abstract = {Urban areas often experience elevated temperatures compared to their rural surroundings, leading to the phenomenon of the Urban Heat Island (UHI) effect. This is a growing environmental concern driven by rapid urbanisation, turning cities into heat traps. In response to this pressing concern, this paper presents a globally scalable, remote sensing-based framework for UHI detection. Leveraging satellite-derived data and integrating multidimensional spatial, environmental, and socio-urban features, the system enables extensive UHI analysis across any region on the globe. The framework computes the Urban Heat Island Index, identifies the cause of UHI as anthropogenic or natural, and performs a 12-month heat trend analysis. It incorporates a modular machine learning pipeline comprising U-Net-based semantic segmentation with a 73.80% accuracy, autoencoder-based feature extraction, ensemble regression, and Random Forest classification, achieving a 94.44% accuracy. It also provides targeted UHI mitigation strategies using a fine-tuned Large Language Model Meta AI. The entire system is deployed as an interactive web application, enabling users to query any region of interest and receive a comprehensive overview of its geo-environmental and socio-spatial characteristics. This platform equips urban planners, researchers, and policymakers with the tools to evaluate urban heat intensity, analyse future heat trends, and implement localised, data-driven mitigation strategies tailored to the specific dynamics of each region.}, archive = {J_ACCESS}, author = {Krupa P. Nadgir and M. Nithyashree and Prithiviraj N and Manasvini G. Padmasali and Deeksha Hegde and Jyoti Shetty and Vinod A. R}, doi = {10.1109/ACCESS.2025.3599723}, journal = {IEEE Access}, pages = {146676-146689}, shortjournal = {IEEE Access}, title = {Geo-intelligent solutions for urban heat island (UHI) detection and mitigation framework}, volume = {13}, year = {2025}, }
• (2025). Various faults classification of industrial application of induction motors using supervised machine learning: A comprehensive review. ACCESS, 13, 146649-146675. (www)

  @article{ , abstract = {Induction motors are important to the industrial sector, acting as the backbone to various processes and machinery in several fields. In fact, with the huge consumption of electrical energy by the industry, approximately 90% of industrial processes rely on these motors. However, these devices are susceptible to various faults such as electrical and mechanical faults, which can negatively impact their operation and significantly affect motor performance. The main occurrences of these faults are outer and inner race faults in the bearing, eccentricity misalignment due to broken rotor bars, and short-circuit severity issues in the stator winding. Recently, the growth of artificial intelligence has infiltrated a variety of fields, including fault detection in induction motors. In current literature, there are a number of papers that address all these faults using different methods, and this paper compiles the information from the written works for ease of access. Machine learning algorithms are a set of data-driven rules that are able to classify specific faults in induction motors, which will be explained further in this review paper. This paper presents a comprehensive review of recent techniques proposed in the literature for bearing, stator winding and broken rotor bar faults with machine learning algorithms focused on fault detection for numerous faults. The review thoroughly examines the advantages and disadvantages of these online methods and provides a detailed comparison across various aspects. Finally, the study identifies the major challenges and research gaps in these techniques.}, archive = {J_ACCESS}, author = {Rehaan Hussain and Mohammad Alshaikh Saleh and Shady S. Refaat}, doi = {10.1109/ACCESS.2025.3600570}, journal = {IEEE Access}, pages = {146649-146675}, shortjournal = {IEEE Access}, title = {Various faults classification of industrial application of induction motors using supervised machine learning: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). CBS-HT: Prioritized safe interval path-planning algorithm for heterogeneous agricultural robot team. ACCESS, 13, 146630-146648. (www)

  @article{ , abstract = {Existing multi-agent path-finding (MAPF) methods either assume homogeneous robots or require inter-robot communication, limiting their deployment in large-scale agricultural settings. We present conflict-based search for heterogeneous tasks (CBS-HT), a fixed-priority path planner for unpiloted ground vehicles (UGVs) performing monitoring, harvesting, and transportation. CBS-HT assigns initial priorities according to robot type and inter-workspace movement (e.g., field, sorting, warehouse) and integrates this priority scheme directly into a CBS-inspired high-level search, constraining only the lower-priority agent at each conflict. By design, CBS-HT relaxes strict completeness and optimality guarantees in exchange for practical real-time performance on large robot teams, reducing search complexity while preserving collision-free coordination. Three evaluation tiers–simulation, lab-scale, and orchard—test scalability, mission-level efficiency, and real-world robustness. Compared with an uncoordinated baseline, CBS-HT shortens high-priority robots’ travel distance by up to 19.1 m (13.5 %) and mission time by 28.4 s (13.2 %), while maintaining a 100 % collision-free success rate for tested instances. These results demonstrate that priority-aware safe-interval planning can deliver practical, communication-free coordination of heterogeneous UGV fleets in commercial orchards and is adaptable to diverse multi-robot agricultural operations.}, archive = {J_ACCESS}, author = {Yuseung Jo and Hyoung Il Son}, doi = {10.1109/ACCESS.2025.3599858}, journal = {IEEE Access}, pages = {146630-146648}, shortjournal = {IEEE Access}, title = {CBS-HT: Prioritized safe interval path-planning algorithm for heterogeneous agricultural robot team}, volume = {13}, year = {2025}, }
• (2025). Wind turbine condition monitoring based on intra- and inter-farm federated learning. ACCESS, 13, 146617-146629. (www)

  @article{ , abstract = {As wind energy adoption is growing, ensuring the efficient operation and maintenance of wind turbines becomes essential for maximizing energy production and minimizing costs and downtime. Many AI applications in wind energy, such as in condition monitoring and power forecasting, may benefit from using operational data not only from individual wind turbines but from multiple turbines and multiple wind farms. Collaborative distributed AI which preserves data privacy holds a strong potential for these applications. Federated learning has emerged as a privacy-preserving distributed machine learning approach in this context. We explore federated learning in wind turbine condition monitoring, specifically for fault detection using normal behaviour models. We investigate various federated learning strategies, including collaboration across different wind farms and turbine models, as well as collaboration restricted to the same wind farm and turbine model. Our case study results indicate that federated learning across multiple wind turbines consistently outperforms models trained on a single turbine, especially when training data is scarce. Moreover, the amount of historical data necessary to train an effective model can be significantly reduced by employing a collaborative federated learning strategy. Finally, our findings show that extending the collaboration to multiple wind farms may result in inferior performance compared to restricting learning within a farm, specifically when faced with statistical heterogeneity and imbalanced datasets.}, archive = {J_ACCESS}, author = {Albin Grataloup and Stefan Jonas and Angela Meyer}, doi = {10.1109/ACCESS.2025.3599360}, journal = {IEEE Access}, pages = {146617-146629}, shortjournal = {IEEE Access}, title = {Wind turbine condition monitoring based on intra- and inter-farm federated learning}, volume = {13}, year = {2025}, }
• (2025). P-CORE: Exploring RISC-V packed-SIMD extension for CNNs. ACCESS, 13, 146603-146616. (www)

  @article{ , abstract = {In today’s technological landscape, embedded and IoT devices face escalating demands for performance and power efficiency in inference tasks employing Convolution Neural Networks (CNNs). Various methods have emerged to improve performance, including quantization and the adoption of fixed-point formats instead of floating-point formats. RISC-V Instruction Set Architecture (ISA) being open-source and with a modular specification poses as a suitable platform for targeting such applications for embedded processing. The RISC-V’s standard P-extension represents a significant advancement in integer-based computation, offering new ways for achieving higher speedups through integer-packed SIMD instructions. This work presents the first RISC-V processor based on RV32IM base ISA with an optional support of P-extension Version 0.9.11-draft-20211209 called the P-CORE. The P-CORE implements P-extension ISA for RV32 specification. The implemented processor is evaluated for Xilinx Zynq Ultrascale+ FPGA using different test case functions; matrix multiplication, convolution, max-pooling, and fully connected. A CNN used for detecting handwritten digits called Lenet-5 is also used as a test case to evaluate the P-CORE. The evaluation also presents Design Space Exploration (DSE) for P-extension by exploiting the level of data-level-parallelism; SIMD8 (8-bit (4-way)) and SIMD16 (16-bit (2-way)). Our investigation demonstrates that with the P-extension activated, CNN algorithm computations can achieve speedups of up to 17x for max-pooling, 7x for matrix multiplication, and 4.8x for fully connected networks, accompanied by similar enhancements in power efficiency. The processor achieved a maximum performance of 335.1 MIOPS and 490.6 MIOPS/W efficiency for matrix multiplication using -O3 compiler optimization.}, archive = {J_ACCESS}, author = {Muhammad Ali and Ensieh Aliagha and Mahmoud Elnashar and Diana Göhringer}, doi = {10.1109/ACCESS.2025.3600360}, journal = {IEEE Access}, pages = {146603-146616}, shortjournal = {IEEE Access}, title = {P-CORE: Exploring RISC-V packed-SIMD extension for CNNs}, volume = {13}, year = {2025}, }
• (2025). Exploring the landscape of end user energy flexibility: A systematic review of technologies, challenges, and opportunities. ACCESS, 13, 146579-146602. (www)

  @article{ , abstract = {The accelerating integration of variable renewable energy sources into power systems has intensified the need for end-user energy flexibility to maintain system stability, optimize resource utilization, and support decarbonization goals. This review systematically examines a wide range of flexibility technologies and their deployment across different sectors. Findings are synthesized from over 100 research papers, focusing on case studies, to identify current trends, challenges, and opportunities in the implementation of these initiatives. A multi-criteria scoring framework is introduced to evaluate flexibility sources based on five key dimensions: flexibility potential, cost reduction, control complexity, user acceptance, and scalability. The analysis identifies electric vehicles (EVs), battery energy storage systems (BESS), and heating, ventilation, and air conditioning (HVAC) systems as the most frequently studied and promising technologies. The review also maps implementation challenges according to their prevalence and impact, highlighting regulatory, technical, and behavioral barriers that may impede progress. The findings contribute to a more structured understanding of the current state and future direction of end-user flexibility technologies and provide actionable insights for researchers, policymakers, and system operators seeking to enhance grid resilience and facilitate the energy transition.}, archive = {J_ACCESS}, author = {Vahur Maask and Hannes Agabus and Hans Tiismus and Victor Astapov and Roya AhmadiAhangar and Tarmo Korõtko and Argo Rosin}, doi = {10.1109/ACCESS.2025.3599989}, journal = {IEEE Access}, pages = {146579-146602}, shortjournal = {IEEE Access}, title = {Exploring the landscape of end user energy flexibility: A systematic review of technologies, challenges, and opportunities}, volume = {13}, year = {2025}, }
• (2025). Dynamic event-triggered synchronization control for hidden markov jump neural networks subject to time-varying delay. ACCESS, 13, 146569-146578. (www)

  @article{ , abstract = {This paper investigates the synchronization control problem of continuous hidden Markov jump neural networks (MJNNs) with time-varying delay (Td). Due to the challenge of accurately obtaining the system’s mode information in real-world environments, the mode transitions of the original system are detected using a hidden Markov model. Subsequently, a controller is designed based on the detected mode information. To optimize the utilization of network resources and reduce the redundant data transmission, a dynamic event-triggered mechanism (DETM) is adopted. Then, through the application of Lyapunov stability theory, the delay-dependent Lyapunov functional is constructed and some sufficient conditions which ensure the system achieves stochastic stability and meet the $H_{\infty }$ performance index $\eta $ are established. Lastly, the feasibility of the method is validated through an example.}, archive = {J_ACCESS}, author = {Weibing Xu and Weichen Wang and Lei Su}, doi = {10.1109/ACCESS.2025.3599142}, journal = {IEEE Access}, pages = {146569-146578}, shortjournal = {IEEE Access}, title = {Dynamic event-triggered synchronization control for hidden markov jump neural networks subject to time-varying delay}, volume = {13}, year = {2025}, }
• (2025). A digital twin-empowered framework for interactive consumers in manufacturing using wearable device. ACCESS, 13, 146557-146568. (www)

  @article{ , abstract = {In the cyber-physical world, human-robot interaction (HRI) plays an increasingly important role in digital twin (DT)-enabled development, particularly in smart manufacturing. This investigation introduces a novel DT-based robotic framework for HRI (DTbRF-HRI), aiming to further productivity, automation, and safety. Our framework includes major elements such as real-time data communication, a common digital model, and a simulation environment for obstacle avoidance and autonomous manipulation. One contribution of our works is the application of an enhanced A-star algorithm for motion planning to support fast generation of paths and avoiding collisions. The data exchange between the physical and virtual agent is supported through system architecture and communication protocols, which meet with being very fast. The resulting framework is validated using numerical simulation and physical experiments in a common electronic consumer manufacturing scenario. Findings demonstrate the effectiveness, robustness, and practicability of DTbRF-HRI in improving intelligent robotic process in a cyber-physical system.}, archive = {J_ACCESS}, author = {Thanh Phuong Nguyen and Hung Nguyen and Hoang-Long Cao and The Tri Bui and Ha Quang Thinh Ngo}, doi = {10.1109/ACCESS.2025.3598864}, journal = {IEEE Access}, pages = {146557-146568}, shortjournal = {IEEE Access}, title = {A digital twin-empowered framework for interactive consumers in manufacturing using wearable device}, volume = {13}, year = {2025}, }
• (2025). An extended voltage gain three-level DC/DC bidirectional converter in an electric vehicle traction system. ACCESS, 13, 146527-146556. (www)

  @article{ , abstract = {Electric vehicle transportation is a constantly advancing technology. In electric vehicles, battery charging is a critical necessity. The demand for DC/DC bidirectional converters in electric vehicle electrical systems is significantly higher than that for DC/DC unidirectional converters. Electric vehicles necessitate an increased voltage gain in DC/DC converters to coordinate the voltage levels of the battery and DC-link. This article proposes an extended voltage gain DC/DC three-level bidirectional converter. This proposed DC/DC extended voltage gain converter provides substantial voltage gain in two directions. This converter structure is simple, comprising a fair total of 13 components. This converter minimizes voltage stress on switches in both directions. The charging and discharging procedure of a lithium-ion battery (48V, 75Ah) occurs via the constant voltage mode. The proposed extended voltage gain non-isolated DC/DC bidirectional converter is validated with MATLAB simulation results for 2000W power in both directions. The proposed extended voltage gain non-isolated DC/DC bidirectional converter is validated with OPAL-RT (OP5700) results for 2000W power in both directions.}, archive = {J_ACCESS}, author = {Nalluri Ramya Sri and M. Praveen Kumar}, doi = {10.1109/ACCESS.2025.3600441}, journal = {IEEE Access}, pages = {146527-146556}, shortjournal = {IEEE Access}, title = {An extended voltage gain three-level DC/DC bidirectional converter in an electric vehicle traction system}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning for urban air quality management: Multi-objective optimization of pollution mitigation booth placement in metropolitan environments. ACCESS, 13, 146503-146526. (www)

  @article{ , abstract = {Urban air pollution remains a significant public health concern in densely populated cities such as Delhi, where high pollutant concentrations stem from vehicular emissions, industrial activity, and construction dust. Conventional mitigation strategies often suffer from suboptimal placement and lack adaptability to complex urban dynamics. This study introduces a deep reinforcement learning (DRL) framework for optimizing the spatial deployment of air purification booths to improve air quality. The proposed method models a $50\times 50$ spatial grid using real-world data layers, including air quality indices (AQI), population density, traffic volume, green spaces, and industrial zones. A Proximal Policy Optimization (PPO) agent is trained to maximize pollution reduction while accounting for spatial fairness and deployment constraints. The framework is evaluated against random and greedy placement baselines using metrics such as population-weighted AQI improvement, spatial entropy, traffic impact, and coverage efficiency. Results indicate that the DRL-based approach achieves a balanced trade-off between pollution reduction and equitable distribution across high-impact zones. The findings demonstrate the potential of reinforcement learning in guiding data-driven, adaptive environmental interventions within smart city infrastructures.}, archive = {J_ACCESS}, author = {Kirtan Rajesh and Suvidha Rupesh Kumar}, doi = {10.1109/ACCESS.2025.3599541}, journal = {IEEE Access}, pages = {146503-146526}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning for urban air quality management: Multi-objective optimization of pollution mitigation booth placement in metropolitan environments}, volume = {13}, year = {2025}, }
• (2025). Pedestrian age recognition using contrastive learning by automatically generating descriptions with high inter-class separability. ACCESS, 13, 146492-146502. (www)

  @article{ , abstract = {There is a need to accurately recognize pedestrian age classes from whole-body images with minimal manual effort. Recent studies have improved age recognition accuracy by exploiting vision–language models (VLMs). However, the age-description texts provided to the VLM are not designed to be sufficiently discriminative across age classes. Moreover, preparing these texts entails considerable manual work. We address these issues by automatically generating detailed, class-specific age-description texts that are highly separable among age classes and using them in a contrastive learning framework. Our method expands the vocabulary of physical and adhered-human characteristics that represent each age class, thereby sharpening inter-class separability. Leveraging a VLM-powered visual question answering (VQA) module, our approach produces detailed age-description texts from concise question texts without the need for trial and error. The age-description texts yield strongly discriminative textual features, which also align discriminative visual features by contrastive learning. Experimental results on public datasets (RAPv2.0, PETA, MSP60k, and PA100k) demonstrated that our method achieves an average age recognition accuracy of 84.1%, outperforming the best baseline by 4.6 percentage points. We also confirmed that our method eliminates the trial and error of manually crafting age-description texts.}, archive = {J_ACCESS}, author = {Takumi Ozaki and Hidenori Kuribayashi and Michiko Inoue and Masashi Nishiyama}, doi = {10.1109/ACCESS.2025.3600575}, journal = {IEEE Access}, pages = {146492-146502}, shortjournal = {IEEE Access}, title = {Pedestrian age recognition using contrastive learning by automatically generating descriptions with high inter-class separability}, volume = {13}, year = {2025}, }
• (2025). Securing the internet of things: Deep learning driven intrusion detection with missing data imputation. ACCESS, 13, 146476-146491. (www)

  @article{ , abstract = {The Internet of Things (IoT), characterized by extensive connectivity, constantly generates and exchanges data among various devices. Ensuring the security of this data presents a critical challenge for IoT systems. Due to the inherent low-power nature of IoT devices and their minimal computing requirements, sensor usage patterns are heavily influenced by daily routines, leading to misclassification of normal patterns as intrusion attacks. This results in a high False Positive Rate (FPR) in the existing Intrusion Detection System (IDS). The reliance of these systems on weak features exacerbates the issue. Additionally, prevalent benchmark datasets lack missing values (removed in the pre-processing stage), thus reducing model robustness during training, which results in an elevated FPR. In response to these challenges, we propose a feature robustness framework that addresses missing values and enhances the feature set of the University of New South Wales-Network-Based 2015 (UNSW-NB15) dataset. This paper presents an improved framework for predicting missing values and estimating their gaps in real-time communication systems. The framework predicts and reconstructs missing values in real-time communication using an Imputed Missing Value-Multi Convolutional Neural Network (IMV-MCNN) for intrusion detection. The proposed system effectively detects and eliminates malicious devices and packets attempting to breach the network using the Grey Relational Analysis (GRA) for context security. Moreover, the proposed model illustrates that augmenting feature clusters in UNSW-NB15 improves accuracy and reduces the FPR. Experimental results demonstrate that the proposed model significantly improves detection performance, achieving an accuracy of 91.33%, increasing the detection rate by 6.33%, and reducing the FPR to 8.45%, thereby validating its effectiveness in securing IoT systems.}, archive = {J_ACCESS}, author = {Hussain Shah and Haleem Farman and Bilal Jan and Abizar Khalil and Moustafa M. Nasralla}, doi = {10.1109/ACCESS.2025.3599931}, journal = {IEEE Access}, pages = {146476-146491}, shortjournal = {IEEE Access}, title = {Securing the internet of things: Deep learning driven intrusion detection with missing data imputation}, volume = {13}, year = {2025}, }
• (2025). Investigating EEG-based neurofeedback for cognitive enhancement in remote learning. ACCESS, 13, 146461-146475. (www)

  @article{ , abstract = {In this paper, we examine the effectiveness of neurofeedback in improving attention in remote learning environments using a passive brain computer interface (BCI). Participants had three EEG-based neurofeedback training sessions, receiving immediate feedback on their cognitive states to improve attention and diminish inattentiveness by self-regulating brain activity. The results indicated substantial enhancements in attention during the sessions, as shown by elevated beta and gamma activity, which are essential for attention regulation and cognitive processing. Power spectral density (PSD) measurement demonstrated sustained increased brainwave power over time, corroborating individuals’ capacity to modify brain activity successfully. An analysis of variance (ANOVA) with a single factor revealed a significant increase in attention levels throughout sessions $(F_{(2, 27)} = 95.06$ , p > 0.0001), with post-hoc Tukey’s tests demonstrating considerable improvements between Sessions 1 and 3 (mean difference = 5.261, p = 0.001). The beta and gamma activity study corroborated the participants’ cognitive engagement enhancement, evidenced by less inattentiveness and improved higher-order processing. The negligible latency in processing real-time feedback guaranteed that the feedback stayed pertinent to the task. This study underscores the efficacy of neurofeedback as a valuable instrument for improving attention and cognitive engagement in online learning settings. Future studies should investigate long-term impacts, individualized feedback systems, and the incorporation of additional cognitive training methodologies to optimize the advantages of neurofeedback in educational settings.}, archive = {J_ACCESS}, author = {Nuraini Jamil and Abdelkader Nasreddine Belkacem}, doi = {10.1109/ACCESS.2025.3600223}, journal = {IEEE Access}, pages = {146461-146475}, shortjournal = {IEEE Access}, title = {Investigating EEG-based neurofeedback for cognitive enhancement in remote learning}, volume = {13}, year = {2025}, }
• (2025). Physical layer secure communication for active dual-RIS assisted MISO system. ACCESS, 13, 146449-146460. (www)

  @article{ , abstract = {This paper investigates physical layer security issues in a multiple-input single-output (MISO) network assisted by dual reconfigurable intelligent surfaces (RIS). Addressing the mutual cascading effects between the two RISs and the double fading phenomenon occurring in the communication reflection channel links, we propose an active dual-RIS MISO system model. To further enhance the system’s secrecy, we introduce artificial noise (AN) technology at the base station (BS) and derive the signal-to-interference-plus-noise ratio expressions for both the user (User) and the eavesdropper (Eve). To tackle the non-convex optimization problem of maximizing the secrecy transmission rate in the dual-RIS assisted MISO scenario, we employ an efficient alternating optimization algorithm combined with the Charnes-Cooper transformation. The simulation results demonstrate that the dual-RIS setup outperforms the single RIS configuration in terms of secrecy performance. Moreover, the integration of active RISs effectively mitigates the double fading phenomenon, and the introduced AN yields superior secrecy performance. Additionally, we analyze the impact of key parameters, including the BS’s transmit power, antenna configuration, the number of reflecting elements on the RISs, the amplification factor, and the distances between the RISs and both the User and Eve, on the secrecy rate. Our findings indicate that, compared to other schemes, the proposed system model achieves significant performance improvements.}, archive = {J_ACCESS}, author = {Jie Wen and Jianbo Ji and Dong Xie}, doi = {10.1109/ACCESS.2025.3599578}, journal = {IEEE Access}, pages = {146449-146460}, shortjournal = {IEEE Access}, title = {Physical layer secure communication for active dual-RIS assisted MISO system}, volume = {13}, year = {2025}, }
• (2025). Shape reconstruction and tip force estimation in tendon-driven soft robots using physics-informed neural networks. ACCESS, 13, 146433-146448. (www)

  @article{ , abstract = {This paper presents a theoretical model for the shape reconstruction of soft robots under a tip force, a tip moment, and a distributed load along the body, akin to blood flow forces acting on ablation catheters in the left atrium. The model is based on the Euler-Bernoulli beam theory and cubic Bézier curve approximation. Verification against a finite element model shows a maximum relative error below 5%. However, the average refresh rate (~1.1 Hz) falls short for clinical use, presenting a limitation. To address this, an artificial neural network (ANN) model is introduced and trained using data from the theoretical model over a range of tip forces, moments, and distributed loads. The ANN model exhibited an average refresh rate of 50 Hz, making it suitable for clinical applications. Given the importance of tip force estimation in minimally invasive surgery, an additional ANN model was developed to estimate the tip force, based on the deformed shape, tip moment, and distributed load. The model is trained and validated using rearranged data from the theoretical model and compared with in-vitro experimental results from a custom apparatus simulating blood flow interactions with ablation catheters. A strong agreement between the theoretical and experimental results was found, which confirms the model’s validity. This study underscores the potential of neural network models for accurate shape reconstruction and force estimation in soft robots, particularly in minimally invasive surgeries.}, archive = {J_ACCESS}, author = {Reza Khoshbakht and Mojtaba Kheiri and Javad Dargahi and Amir Hooshiar}, doi = {10.1109/ACCESS.2025.3599443}, journal = {IEEE Access}, pages = {146433-146448}, shortjournal = {IEEE Access}, title = {Shape reconstruction and tip force estimation in tendon-driven soft robots using physics-informed neural networks}, volume = {13}, year = {2025}, }
• (2025). A survey of indoor localization systems for multi-floor environments. ACCESS, 13, 146396-146432. (www)

  @article{ , abstract = {Most indoor localization systems determine only the 2D location of a user on a single floor, often overlooking multi-floor environments. This limitation reduces their effectiveness in scenarios such as emergency response in multi-floor buildings. As a result, multi-floor localization systems capable of identifying the user’s floor as well have recently gained significant attention. However, designing and deploying accurate multi-floor localization systems presents unique challenges due to the increased scale and complexity of the environment. These challenges include ensuring sensor reliability, addressing signal space ambiguity caused by complex environmental structures and infrastructure configurations, and managing deployment overhead and resource requirements. Despite growing interest in this area, a comprehensive, up-to-date survey on the challenges and recent advances in multi-floor localization remains lacking. In this paper, we present the first exhaustive survey of indoor localization systems designed for multi-floor environments. We begin by highlighting the importance of floor identification through various applications and provide background on extending localization techniques from 2D to multi-floor scenarios. We then categorize and analyze the key challenges unique to multi-floor localization, including both input-level and system-level challenges. Next, we review existing solutions aimed at mitigating these challenges, with a focus on recent advancements. Finally, we discuss open problems and outline future research directions. Although significant progress has been made in indoor localization, multi-floor localization remains an active and evolving research area with numerous opportunities to bridge existing gaps and drive further innovation.}, archive = {J_ACCESS}, author = {Sherif Mostafa and Khaled A. Harras and Moustafa Youssef}, doi = {10.1109/ACCESS.2025.3599785}, journal = {IEEE Access}, pages = {146396-146432}, shortjournal = {IEEE Access}, title = {A survey of indoor localization systems for multi-floor environments}, volume = {13}, year = {2025}, }
• (2025). Innovative gas control system for PEM electrolyzers using periodic polarity switching with bifunctional electrodes for enhanced efficiency and lifetime. ACCESS, 13, 146390-146395. (www)

  @article{ , abstract = {Efficient production of hydrogen via water electrolysis is one of the promising approaches to enable a complete fossil-free energy transition. Hydrogen can be utilized alongside renewable energy sources like wind and solar to smooth out their power production. It can also serve as a standalone solution to stabilize electrical grids and support various industrial processes. The most promising electrolyzer to be used for grid applications and alongside renewable energy sources is a proton exchange membrane electrolyzer, due to its fast dynamics. Due to their high cost and relatively short lifetime, they are not widely used in electric grids or industry today. With particularly designed electrodes, the proton exchange membrane electrolyzers can operate under cyclic polarity switching, which makes the electrodes self-regenerate. This increases the lifetime of the proton exchange membrane electrolyzers, as well as minimizes their efficiency drop over time. In order for polarity switching to be implemented on an industrial level, a novel solution to ensure that the hydrogen always goes to the right storage unit and is kept ultra-pure is of great importance. This paper presents an innovative modular gas routing system that enables safe polarity switching and prevents cross-contamination of gases through automatic valve control and a self-cleaning mechanism. The system was also implemented in a proof-of-concept prototype.}, archive = {J_ACCESS}, author = {Björn O. Åkermark and Biswanath Das and Björn E. Åkermark}, doi = {10.1109/ACCESS.2025.3600239}, journal = {IEEE Access}, pages = {146390-146395}, shortjournal = {IEEE Access}, title = {Innovative gas control system for PEM electrolyzers using periodic polarity switching with bifunctional electrodes for enhanced efficiency and lifetime}, volume = {13}, year = {2025}, }
• (2025). Design of a self-built bidirectional multi-slope integrating analog-to-digital converter. ACCESS, 13, 146380-146389. (www)

  @article{ , abstract = {In order to meet the requirements of high-precision and high-resolution measurement environments, this paper proposes a bidirectional multi-slope integrating ADC based on the Multi-Slope III technique. The paper details the design methodology and operational principles centered on the ‘multiple integration and high-bit sampling integration with low-bit residual detection’ mode, and rigorously derives the theoretical integration waveform along with the dependence of resolution on signal integration time settings. When the signal integration time is set to one power-line cycle (PLC) with a 10 V full-scale DC input, the integrated voltage waveform exhibits stable settling with negligible distortion, confirming effective suppression of nonlinear errors. Combined with the designed optimization schemes such as the “balanced circuit” and “power frequency cycle-matched integration time”, zero-adjustment errors and power frequency interference are effectively suppressed, further improving the measurement resolution to 7.5 bits. The bidirectional integration mechanism adopted expands the input range to −10 V to +10 V, eliminating the need for excessive reliance on external signal conditioning circuits. This architecture demonstrates significant utility in low-frequency, high-resolution measurement systems.}, archive = {J_ACCESS}, author = {Yuan Cui and Zhilong Zhu and Cheng Huang and Yingying Ding and Haohua Zhao and Xucong Chen}, doi = {10.1109/ACCESS.2025.3599577}, journal = {IEEE Access}, pages = {146380-146389}, shortjournal = {IEEE Access}, title = {Design of a self-built bidirectional multi-slope integrating analog-to-digital converter}, volume = {13}, year = {2025}, }
• (2025). ISAR image generation of ships by inpainting using SinGAN. ACCESS, 13, 146371-146379. (www)

  @article{ , abstract = {Inverse Synthetic Aperture Radar (ISAR) images are important in civilian and military applications. This is particularly true in military applications, where they are used for surveillance. One of the applications that we focus on is the Automatic Target Recognition (ATR) system for military ships that processes ISAR data. Extensive ISAR datasets are required to develop robust target recognition systems for ships. In this paper, we propose the use of SinGAN, a deep learning model which is architecturally a Generative Adversarial Network (GAN), to generate ISAR images of ships using an inpainting technique. The methodology begins with a single ISAR image of a ship being trained with the SinGAN model. Then, clip-arts, outlines, or even photographs of ships are preprocessed to ensure optimal inpainting results. This paper also evaluates noise reproducibility and demonstrates dataset creation using the generated ISAR images. Finally, this dataset is used to train a custom Convolutional Neural Network (CNN) model.}, archive = {J_ACCESS}, author = {Hari Prasad Nandakumar and K. A. Nethravathi and Srikanth Vadada}, doi = {10.1109/ACCESS.2025.3600459}, journal = {IEEE Access}, pages = {146371-146379}, shortjournal = {IEEE Access}, title = {ISAR image generation of ships by inpainting using SinGAN}, volume = {13}, year = {2025}, }
• (2025). System architecture design for low-complexity downlink receivers in MU-OFDM systems. ACCESS, 13, 146355-146370. (www)

  @article{ , abstract = {This study introduces a novel resource allocation approach and a simplified receiver design tailored for power- and processing-constrained devices within an orthogonal frequency division multiplexing (OFDM) system accommodating both mobile and static users. Specifically, pilot symbols are selectively allocated to high-mobility users to facilitate frequent updates of channel state information (CSI). In contrast, static users are excluded from regular pilot updates to enhance resource utilization and minimize unnecessary overhead. To further reduce computational load and make the receiver design simple, we implement $N/2$ -point fast Fourier transform (FFT) for processing a large number of subcarriers. This approach effectively addresses the computational limitations associated with traditional OFDM systems. The proposed design significantly reduces processing overhead while maintaining system reliability. Comprehensive simulations demonstrate that the proposed system achieves bit error rate (BER) performance, and spectral efficiency comparable to conventional methods while significantly reducing computational complexity.}, archive = {J_ACCESS}, author = {Tuncay Eren and Muhammad Bilal Janjua and Aydin Akan}, doi = {10.1109/ACCESS.2025.3600403}, journal = {IEEE Access}, pages = {146355-146370}, shortjournal = {IEEE Access}, title = {System architecture design for low-complexity downlink receivers in MU-OFDM systems}, volume = {13}, year = {2025}, }
• (2025). Mitigating cascading failures by enhancing N-2 security using a unified preventive-corrective generation rescheduling approach. ACCESS, 13, 146340-146354. (www)

  @article{ , abstract = {Although the N-1 criterion is widely applied, cascading failures and large-scale blackouts still occur in power systems. Thus, incorporating N-k (k >1) security constraints in the Security-Constrained Optimal Power Flow (SCOPF) problem is essential, with N-2 contingencies being the most probable cause of cascading failures. However, modeling all N-2 contingencies increases the number of constraints, resulting in excessive computational burden. To address this, we propose a statistical constraint screening method that reduces the number of N-2 constraints by 75%, from 19,108 to 4,744, significantly decreasing problem size. Additionally, a closed-form LODF-based formulation is developed to evaluate N-2 post-contingency flows without network topology reconstruction. Two optimization models-separate and unified-are proposed and compared for generation rescheduling in SCOPF. Simulation results on the IEEE 39-bus system demonstrate that the unified model reduces the total preventive action cost (PAC) by up to 7,417 /h and decreases computational time by 32%, while ensuring complete N-2 contingency security.}, archive = {J_ACCESS}, author = {Amirhossein Ramezani and Mohammad Reza Aghamohammadi and Alireza Sobbouhi}, doi = {10.1109/ACCESS.2025.3596142}, journal = {IEEE Access}, pages = {146340-146354}, shortjournal = {IEEE Access}, title = {Mitigating cascading failures by enhancing N-2 security using a unified preventive-corrective generation rescheduling approach}, volume = {13}, year = {2025}, }
• (2025). Two-stage neural network pipeline for kidney and tumor segmentation. ACCESS, 13, 146326-146339. (www)

  @article{ , abstract = {Located in the posterior abdominal region, the kidneys are an essential part of the urinary and endocrine systems, with a great role in not only maintaining the balance of substances in bodily fluids through filtration, reabsorption, secretion, and excretion, but also in the secretion of several important hormones. Globally, kidney cancer incidence is on the rise. Several models have been explored in the literature for automatic kidney and lesion segmentation in CT images, most based on various U-Net architecture variants, with CNN or Transformer encoders. One of the most used datasets is the one proposed for the KiTS23 competition. While recent state-of-the-art approaches primarily focus on 3D segmentation, we propose a faster and more computationally efficient alternative through a refined 2D segmentation pipeline. Specifically, this paper proposes a slice-based two-stage pipeline, which begins with region detection using a YOLO model to localize the renal area. Once this initial localization simplifies the task, the pipeline employs two independent U-Net models, one to segment the kidneys and lesions, and the other to segment the tumors themselves. Pretrained EfficientNet networks are tested as backbones for kidney segmentation, and Mix Transformer for tumors. To improve the results, we apply 3D reconstruction and postprocessing. On our randomly selected test set, the proposed pipeline achieves competitive results, particularly for the segmentation of kidneys and masses, while maintaining low training and inference time. Specifically, we report a volume-level mean Dice score of 0.9582 and an IoU of 0.9216 for kidney and mass segmentation. For tumor segmentation, the volume-level Dice score is 0.6261, and the IoU is 0.5148. While the kidney and mass segmentation results are highly competitive, the tumor segmentation performance remains suboptimal. These results highlight the need for further improvements in the model’s ability to capture fine-grained tumor details and to distinguish tumors from cysts effectively. Incorporating a 2.5D or 3D segmentation strategy may offer a promising direction for future work, along with more refined postprocessing and class balancing techniques.}, archive = {J_ACCESS}, author = {Bunica-Mihai Alexandru and Dan Popescu}, doi = {10.1109/ACCESS.2025.3600090}, journal = {IEEE Access}, pages = {146326-146339}, shortjournal = {IEEE Access}, title = {Two-stage neural network pipeline for kidney and tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). K-band MMIC active inductor-based bandpass filter suitable for industrial applications. ACCESS, 13, 146319-146325. (www)

  @article{ , abstract = {The availability of integrated electronic filters for industrial applications is a missing block at microwave frequencies, in spite of the fact that filters have a crucial role in modern transceivers. This work aims to fill this gap by presenting the monolithic implementation of a high-Q active filter operating in K-band and realized by means of active inductors. The active inductor is the core of the filter and allows a fine tuning of both quality factor and frequency, while reducing the losses and increasing the selectivity; a three-cell bandpass filter is presented as a first example of application. All circuits have been designed and fabricated with a $0.1~\mu $ m GaAs WIN technology. Measurements are also provided showing promising results. The inductors are stable and reproducible even when a relatively high equivalent negative resistance is obtained for loss reduction. The filter provides almost zero and constant in-band losses, a 1 dB relative bandwidth of around 7%, input and output matching better of 15 dB and a 30dB/3dB shape factor of 3.}, archive = {J_ACCESS}, author = {L. Pantoli and P. E. Longhi and F. Bolli and G. Leuzzi}, doi = {10.1109/ACCESS.2025.3600479}, journal = {IEEE Access}, pages = {146319-146325}, shortjournal = {IEEE Access}, title = {K-band MMIC active inductor-based bandpass filter suitable for industrial applications}, volume = {13}, year = {2025}, }
• (2025). LFCNet: Deep learning-based LiDAR-fisheye camera online automatic targetless extrinsic calibration. ACCESS, 13, 146305-146318. (www)

  @article{ , abstract = {This paper presents LFCNet, an end-to-end deep-learning framework for targetless extrinsic calibration between LiDAR and fisheye cameras. The method is specifically designed for autonomous driving systems, where accurate alignment of sensor data is critical. LFCNet performs calibration without physical targets and operates online, which makes it practical for real-time applications involving fisheye cameras and LiDAR. Sensor fusion has become a key strategy to improve the perception capabilities of autonomous vehicles. Among the various sensors used, LiDAR and cameras are commonly employed due to their complementary strengths. Fisheye cameras, in particular, provide a wide field of view and capture more comprehensive visual information. As a result, precise calibration between these sensors is essential to align their outputs within a unified coordinate system and maintain consistent environmental understanding. Conventional calibration methods often rely on statistical techniques, which struggle with high-dimensional multivariate data. Deep learning-based approaches have recently shown promise, particularly for LiDAR–camera systems. However, calibration involving fisheye cameras remains underexplored. LFCNet addresses this gap through a lightweight model with 12.1 million parameters, and a novel two-stage feature-extraction architecture that captures both local and global features while compensating for fisheye distortion. Experimental results demonstrate that LFCNet achieves high accuracy, with a mean calibration error of 0.509 cm in translation and 0.063° in rotation—setting a new benchmark for real-time, learning-based LiDAR–fisheye calibration. The code for LFCNet is available at https://github.com/rangganast/LFCNet}, archive = {J_ACCESS}, author = {Muhammad Rangga Aziz Nasution and Jaejun Yoo and Miftahul Khoir Shilahul Umam and Ida Bagus Krishna Yoga Utama and Muhammad Fairuz Mummtaz and Muhammad Alfi Aldolio and Su Mon Ko and Yeong Min Jang}, doi = {10.1109/ACCESS.2025.3599979}, journal = {IEEE Access}, pages = {146305-146318}, shortjournal = {IEEE Access}, title = {LFCNet: Deep learning-based LiDAR-fisheye camera online automatic targetless extrinsic calibration}, volume = {13}, year = {2025}, }
• (2025). Intelligent SWIPT-assisted UAV-IRS architecture. ACCESS, 13, 146295-146304. (www)

  @article{ , abstract = {The use of drones (UAVs) is rapidly increasing in areas like surveillance, communication, and monitoring the environment. But one major problem is that their batteries don’t last long. To solve this, drones are now being combined with a smart technology called Intelligent reflecting surfaces (IRS). This setup allows them to collect both energy and data from wireless signals at the same time, using a method known as Simultaneous Wireless Information and Power Transfer (SWIPT). To make this system work better in changing wireless conditions, researchers used an advanced AI method called Deep Reinforcement Learning (DRL). Simulation results showed that this AI-powered system was very efficient at harvesting energy—almost as good as trying every possible option manually providing high energy efficiency. Among the different AI techniques tested, one called Proximal Policy Optimization (PPO) performed the best, beating others like Softmax Deep Double Deterministic Policy Gradient (SD3). This research supports the United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) by improving energy efficiency, and SDG 9 (Industry, Innovation, and Infrastructure) through intelligent aerial systems.}, archive = {J_ACCESS}, author = {Yogesh Kumar Rathore and Pragya Swami and Deepak Kumar Dewangan and Aditya Trivedi and Rishav Dubey}, doi = {10.1109/ACCESS.2025.3600172}, journal = {IEEE Access}, pages = {146295-146304}, shortjournal = {IEEE Access}, title = {Intelligent SWIPT-assisted UAV-IRS architecture}, volume = {13}, year = {2025}, }
• (2025). Domain adaptation for retail demand prediction. ACCESS, 13, 146267-146294. (www)

  @article{ , abstract = {Predicting the demand of products in the retail industry is a complex task, especially when there are changes in the market. This paper examines three such market shifts in the retail industry: the COVID-19 pandemic, opening a new store, and introducing a new product. Our study found that the accuracy of demand prediction models decreases after these market shifts. To address this problem, we propose the use of domain adaptation methods, such as Frustratingly Easy and Kernel Mean Matching, to improve the accuracy of predictions by utilizing data from before the changes and adapting to the data after the changes. We show that using a pairing technique can further enhance prediction accuracy. Two retail demand forecasting models, XGBoost and Transformers, were assessed and XGBoost was found to be more effective. We demonstrate the effectiveness of the domain adaptation methods on a real-world case using point-of-sale data from 89 locations of Alimentation Couche-Tard convenience stores in Montreal between 2019-07 and 2021-02 focusing on the two best-selling product categories of coffee and energy drinks.}, archive = {J_ACCESS}, author = {Niloofar Tarighat and Maxime C. Cohen and James J. Clark}, doi = {10.1109/ACCESS.2025.3600468}, journal = {IEEE Access}, pages = {146267-146294}, shortjournal = {IEEE Access}, title = {Domain adaptation for retail demand prediction}, volume = {13}, year = {2025}, }
• (2025). Modeling and design of a frequency controller for full-bridge active rectifier applied to wireless power transfer system. ACCESS, 13, 146252-146266. (www)

  @article{ , abstract = {The dual-phase shift (DPS) controlled full-bridge active rectifier (FBAR) can facilitate a wider operating range and improve transfer performance in wireless power transfer (WPT) systems. The operating frequency of FBAR could be regulated by a frequency control loop on the secondary side to synchronize the FBAR and inverter without any physical connection. This paper proposes a modeling and design method for the frequency control loop for DPS-controlled FBAR, which consists of a frequency controller and signal conditioning. Based on the state space of the WPT system, a loop discrete small-signal model is developed to enable the frequency controller design. This model considers harmonics, DPS control, and synchronization cycle. Since frequency control depends on zero-cross detection, this paper also provides a method for determining the multiple zero-crosses of the FBAR input current and a reference design for a signal conditioning filter to systematize loop design further. Experiments and simulations verify the accuracy of the derived model. The experimental results indicate that the proposed design method can achieve stable and robust frequency control for FBAR.}, archive = {J_ACCESS}, author = {Siyuan Lu and Nejila Parspour}, doi = {10.1109/ACCESS.2025.3600437}, journal = {IEEE Access}, pages = {146252-146266}, shortjournal = {IEEE Access}, title = {Modeling and design of a frequency controller for full-bridge active rectifier applied to wireless power transfer system}, volume = {13}, year = {2025}, }
• (2025). Efficient resource allocation in RIS-assisted UAVs-enabled B5G/6G networks using digital twins. ACCESS, 13, 146237-146251. (www)

  @article{ , abstract = {Rapid technological advancements have revolutionized how people live, work, and communicate. Given the rapidly growing need for data and bandwidth, replacing existing communication systems with agile and modern alternatives is crucial. This work investigates a novel communication system that combines Uncrewed Aerial Vehicles (UAVs), Digital Twins (DTs), and Reconfigurable Intelligent Surfaces (RIS). RIS, composed of passive metasurfaces, can intelligently manipulate wireless channels with minimal power consumption, enhancing signal quality within its coverage area. UAVs, acting as flying base stations, further augment coverage and capacity. Leveraging the capabilities of UAVs and RIS, a DT of the network is created to address resource allocation challenges proactively. This paper explores efficient resource allocation in this DT-assisted, UAV-aided RIS system using combinatorial optimization techniques. The problem formulation leads to a Mixed-Integer Non-Linear Programming (MINLP) problem, which is inherently non-convex and computationally challenging due to its Nondeterministic Polynomial-time Hardness (NP-hard) classification. An $\epsilon $ -optimal branch-and-bound approach is proposed to address this complexity, optimizing resource allocation and maximizing system throughput. The results of simulations validate that the proposed algorithm significantly enhances resource allocation performance in terms of network throughput and admitted users for various QoS requirements. The result section also presents simulation outcomes comparing the proposed algorithm against other leading methods.}, archive = {J_ACCESS}, author = {Aasma Shafi and Mudassar Ali and Muhammad Naeem}, doi = {10.1109/ACCESS.2025.3594366}, journal = {IEEE Access}, pages = {146237-146251}, shortjournal = {IEEE Access}, title = {Efficient resource allocation in RIS-assisted UAVs-enabled B5G/6G networks using digital twins}, volume = {13}, year = {2025}, }
• (2025). A scalable approach to historical data management via optimized transaction logs. ACCESS, 13, 146218-146236. (www)

  @article{ , abstract = {Temporal databases play a crucial role in tracking the evolution of data over time by preserving historical states through valid time intervals instead of overwriting data. This approach enables robust data analysis, forecasting, and auditing while enforcing consistency and preventing conflicting entries. Traditional transactional systems complement this by recording the time context of changes in transaction logs, which are essential for ensuring data integrity, recovery, and auditability. However, conventional log management, relying on sequential scanning, imposes performance and scalability limitations. This paper introduces a novel, efficient solution for managing transaction logs through an integrated data mapping layer. The proposed structure stores direct references to logged data within the database, supported by five optimized indexes—including object definitions and four temporal B+trees—to track validity, load time, and transaction lifecycle. This block-oriented design enables dynamic access, supports prohibited history handling, and eliminates the need for separate temporal layers or data migrations. Performance evaluations demonstrate significant gains in processing speed and system scalability compared to traditional approaches. However, limitations persist, including the growing footprint of non-indexed log metadata, challenges in data recoverability, incompatibility with standard table-optimization techniques, static log block sizes, and potential bottlenecks during high-frequency change periods.}, archive = {J_ACCESS}, author = {Michal Kvet and Jarmila Škrinárová}, doi = {10.1109/ACCESS.2025.3599972}, journal = {IEEE Access}, pages = {146218-146236}, shortjournal = {IEEE Access}, title = {A scalable approach to historical data management via optimized transaction logs}, volume = {13}, year = {2025}, }
• (2025). SetFitQuad: A few-shot framework for aspect sentiment quad prediction with sampling strategies. ACCESS, 13, 146204-146217. (www)

  @article{ , abstract = {Aspect Sentiment Quad Prediction (ASQP) aims to extract structured sentiment quadruples, comprising aspect category, aspect term, opinion expression, and sentiment polarity from text to enable fine-grained sentiment analysis. Although generative models, such as T5, achieve strong results in this task, they require extensive labeled data and computational resources, limiting their practicality in low-resource scenarios. This study introduces SetFitQuad, a lightweight few-shot learning framework that decomposes ASQP into modular subtasks using sentence-embedding classification and syntactic-rule-based opinion extraction. To further enhance the performance on small and imbalanced datasets, SetFitQuad integrates a sampling-aware fine-tuning strategy based on entropy, density, and representational distance heuristics. Experiments on benchmark datasets show that SetFitQuad improves the F1-score for aspect category detection from 0.379 to 0.575 and sentiment polarity classification from 0.514 to 0.826, while reducing the training time by over 65% compared with the T5-based baseline. Furthermore, SetFitQuad achieves an F1-score of 0.532 for partial matches and 0.083 for exact matches on the ASQP task, demonstrating a competitive structured prediction performance under resource-conscious conditions.}, archive = {J_ACCESS}, author = {Hsiu-Min Chuang and Jhe-Kuan Lin}, doi = {10.1109/ACCESS.2025.3600351}, journal = {IEEE Access}, pages = {146204-146217}, shortjournal = {IEEE Access}, title = {SetFitQuad: A few-shot framework for aspect sentiment quad prediction with sampling strategies}, volume = {13}, year = {2025}, }
• (2025). Fusion-based additive manufacturing of hastelloy C-series: A comparative study on microstructure, mechanical properties, and residual stress. ACCESS, 13, 146189-146203. (www)

  @article{ , abstract = {The fusion-based single pulsed gas metal arc welding (SP-GMAW) additive manufacturing (AM) process has attracted considerable attention because of its high production efficiency, elevated deposition rates, and near-net-shape capabilities. The present study presents a comparative investigation of Hastelloy C-276 and Hastelloy C-22 thin-walled components fabricated using a SP-GMAW based AM process. This study thoroughly investigates the microstructure, material properties, and residual stress of the components. The microstructures in various regions comprise dendrite structure in Hastelloy C-276 and C-22 superalloys. The Scanning Electron Microscopy and Energy Dispersive Spectroscopy (SEM/EDS) analysis revealed a discrepancy in elemental composition between C-276 and C-22 materials. Additionally, the average grain size in the top, middle, and bottom portions of C-22 are $67.8~\mu $ m, $78.6~\mu $ m, and $87.6~\mu $ m while C-276 has $72.5~\mu $ m, $80.2~\mu $ m, and $96.8~\mu $ m, respectively. Compared to the build direction, the travel direction has a higher mean microhardness. Hastelloy C-22 achieves a maximum hardness of 320 HV, while Hastelloy C-276 has a hardness of 286 HV. The highest recorded tensile strength for Hastelloy C-22 was $772~\pm ~5.1$ MPa, whereas Hastelloy C-276 displayed a tensile strength of $758~\pm ~4.1$ MPa in the upper regions along the travel direction. According to the stress distribution, the as-fabricated specimens of Hastelloy C-276 and C-22 are mostly impacted by tensile residual stress. Research on Hastelloy C series alloy comparisons and single pulsed GMAW-based WAAM technologies is limited and progressing. The comparative results of this research will be significant in the chemical-based, nuclear energy, maritime, and manufacturing industries.}, archive = {J_ACCESS}, author = {M. D. Barath Kumar and R. Madesh and M. Sathishkumar and D. Sakthimurugan and Keerthipalli Trinath and S. Sathiyamurthy and V. Praveen Kumar and M. Vignesh}, doi = {10.1109/ACCESS.2025.3598731}, journal = {IEEE Access}, pages = {146189-146203}, shortjournal = {IEEE Access}, title = {Fusion-based additive manufacturing of hastelloy C-series: A comparative study on microstructure, mechanical properties, and residual stress}, volume = {13}, year = {2025}, }
• (2025). From fusion to adaptation: Investigation on enhancing multimodal biometric authentication systems. ACCESS, 13, 146169-146188. (www)

  @article{ , abstract = {Multimodal biometric authentication system has been proposed to mitigate issues inherent in systems that rely on a single biometric trait. Studies on multimodal biometric authentication systems have compared different fusion methods to find an optimal fusion level to combine different biometric traits; however the integration of facial and keystroke biometric samples have not been fully investigated. In addition, some recent studies on biometric authentication systems have shown the importance of adaption where systems can mitigate issues of performance degradation over time in dynamic environments. The application of heterogeneous multimodal contexts remains under explored - this study, therefore first investigates the fusion of facial and keystroke biometric traits by comparing feature and score level fusion methods utilising three virtual datasets: Face with CMU, Face with GREYC and Face with Mobile. The findings demonstrate that score-level fusion performs better in terms of accuracy compared to feature-level fusion. Leveraging this result, the study further applies an adaptation mechanism together with score-level fusion. The study updates thresholds and templates by comparing different strategies with the proposal of a hybrid strategy - Dual Path Adaptation and Positive Gallery Protection Adaptation. Instead of relying only on the threshold or using genuine biometric samples alone to determine adaptation, the hybrid strategy considers both imposter and genuine biometric references. Using information from both sources, the hybrid strategy strengthens adaptation decision making to improve recognition accuracy. The results demonstrate the effectiveness of different adaptation methods across three virtual datasets. Dual Path Adaptation achieved the highest performance with recognition accuracy scores of 0.7136 (Face with CMU), 0.6925 (Face with GREYC), and 0.7862 (Face with Mobile), where scores represent the proportion of correctly identified instances (e.g., 0.7136=71.36%). Positive Gallery Protection Adaptation followed with 0.6852, 0.7281, and 0.7422, respectively. Adaptive Dual Thresholds yielded 0.7050, 0.6506, and 0.7289, while the baseline without adaptation scored 0.7123, 0.7157, and 0.7499. These findings highlight the importance of selecting an appropriate adaptation strategy, as an unsuitable approach can degrade performance and compromise security. Therefore, it is crucial to identify a well-balanced adaptation method that enhances robustness without sacrificing security.}, archive = {J_ACCESS}, author = {Riseul Ryu and Soonja Yeom and David Herbert and Julian Dermoudy}, doi = {10.1109/ACCESS.2025.3599907}, journal = {IEEE Access}, pages = {146169-146188}, shortjournal = {IEEE Access}, title = {From fusion to adaptation: Investigation on enhancing multimodal biometric authentication systems}, volume = {13}, year = {2025}, }
• (2025). LLM-based automated generation and tri-modal representation of cyber attack scenario. ACCESS, 13, 146150-146168. (www)

  @article{ , abstract = {The rapid advancement of information technology, the proliferation of artificial intelligence (AI), and the increasing reliance on digital infrastructure have significantly intensified cyber threats faced by organizations. In this evolving landscape, timely identification of threats and coordinated strategic responses have become essential. However, traditional human-centric approaches to cyber-attack scenario generation are limited in scalability, timeliness, and semantic coherence–especially across stakeholders with diverse technical backgrounds. To address these challenges, this study proposes an automated, LLM-driven framework for generating cyber-attack scenarios and converting them into three semantically aligned modalities: natural language narratives, attack graphs, and formal mathematical representations. This tri-modal approach enhances cross-role interpretability and enables consistent understanding among technical experts and non-technical decision-makers alike. The core objective of this research is to propose an automated pipeline framework that spans from scenario generation to tri-modal representation, designed to support decision-makers by ensuring semantic consistency and enhancing interpretability across stakeholder roles. The framework incorporates real-world threat intelligence, structured input parameters, and prompt engineering techniques to ensure realism and fidelity. Evaluation results using state-of-the-art LLMs—including both open-source and proprietary models—demonstrate the system’s ability to generate coherent, context-aware, and logically consistent outputs. The findings validate the feasibility of the proposed approach and underscore its potential to improve cybersecurity preparedness, training efficacy, and governance communication through structured, intelligible scenario representations.}, archive = {J_ACCESS}, author = {Seonghyun Roh and Tae-Sung Kim}, doi = {10.1109/ACCESS.2025.3597850}, journal = {IEEE Access}, pages = {146150-146168}, shortjournal = {IEEE Access}, title = {LLM-based automated generation and tri-modal representation of cyber attack scenario}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of ultraviolet lamp configurations in large chambers for residual contaminant removal. ACCESS, 13, 146138-146149. (www)

  @article{ , abstract = {This study investigated optimal ultraviolet (UV) lamp configurations for the decontamination of large environmental test chambers using validated simulation and optimization. Using DIALux software, we simulated UV intensity distributions and validated the approach through physical measurements in an 8 m3 chamber (R ${}^{2} =0.777$ ). The initial analysis of ceiling, wall, and mixed configurations revealed the superiority of the ceiling placement in coverage uniformity. To enable systematic optimization beyond predefined configurations, we introduced the effective germicidal coverage ratio (EGCR)—a three-dimensional metric that quantifies the percentage of chamber volume receiving germicidal UV doses ( $\ge 30$ mJ/cm2). Unlike simple coverage calculations, EGCR integrates spatial dose distribution with biological inactivation thresholds, serving as an objective function for optimization algorithms. The use of maximal coverage problem and genetic algorithm optimization revealed scale-dependent phenomena: In 8 m3 chambers, simple ceiling placement (three lamps) achieved 99.0% EGCR, nearly matching genetic algorithm (GA)-optimized configurations (four lamps, 99.5%). However, in 24 m3 chambers, optimized mixed configurations (10 lamps, 97.3% EGCR) significantly outperformed uniform ceiling placement (seven lamps, 95.1% EGCR). Pareto analysis showed decreasing lamp efficiency with chamber size (0.25–0.33 vs. 0.10–0.14 EGCR/lamp). These findings demonstrate that UV system design cannot rely on proportional scaling: optimal strategies fundamentally differ between small and large chambers. While ceiling-mounted configurations are sufficient for compact chambers, larger volumes benefit from computationally optimized mixed placements. This simulation-based approach, which was validated through physical measurements, provides practical guidelines for the design of effective UV decontamination systems at different chamber scales.}, archive = {J_ACCESS}, author = {Dongha Kim and Seonghyun Park and Bokyung Jung and Janghoo Seo}, doi = {10.1109/ACCESS.2025.3598957}, journal = {IEEE Access}, pages = {146138-146149}, shortjournal = {IEEE Access}, title = {Comparative analysis of ultraviolet lamp configurations in large chambers for residual contaminant removal}, volume = {13}, year = {2025}, }
• (2025). WU-net: An automatic and lightweight deep learning method for water body extraction of multispectral remote sensing images. ACCESS, 13, 146129-146137. (www)

  @article{ , abstract = {Identification of Earth’s surface water bodies is crucial in flood monitoring, urban planning, and other applications, and is a key study in the science of remote sensing. However, in the actual remote sensing photos, the com-plex water body features and geological surroundings clearly impede water body identification. Meanwhile, traditional water body identification methods are incapable of automatically and properly identifying water bodies with significant variances. This research offers a lightweight WU-Net model to increase the accuracy and efficiency of identifying the earth’s surface water bodies. The WU-Net model uses the inverse residual structure and attention mechanism to increase the extraction of complicated water feature information while compressing the model size. It employs space pyramid pooling to acquire global features. The proposed approach was evaluated on the ESWKB (Earth Surface Water Knowledge Base) dataset and compared against several state-of-the-art models, including DeepLabV3+, DeepWaterMapV2, SegFormer, and DlinkNet50. Our WU-Net achieved the best overall performance among all compared models, with an OA of 98.88%, MIoU of 96.92%, Recall of 97.24%, and F1-score of 97.50%. Furthermore, it also achieves competitive performance on the Sen1Floods11 dataset, validating its generalization capability across different water body segmentation tasks.}, archive = {J_ACCESS}, author = {Hongcui Wang and Ouxiang Chen and Yihong Zheng}, doi = {10.1109/ACCESS.2025.3599768}, journal = {IEEE Access}, pages = {146129-146137}, shortjournal = {IEEE Access}, title = {WU-net: An automatic and lightweight deep learning method for water body extraction of multispectral remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Unified passivity-based control for turning and walking in asymptotically stable biped robots. ACCESS, 13, 146116-146128. (www)

  @article{ , abstract = {Dynamically stable biped robots that mimic human movement have seen significant progress in straight walking, but turning and steering remain challenging. This paper presents a novel passivity-based method for turning control and steering in 3D asymptotically stable biped robots. We replicate energy-efficient periodic gaits of passive dynamic turning—natural motions observed in passive walkers—on various surfaces using potential energy shaping control, which adjusts the robot’s potential energy to simulate gravitational effects from passive system. We introduce a unified steering framework that encompasses both straight walking and turning, viewing straight walking as a specific instance of turning with zero speed (or infinite radius). Steering is achieved by continuously adjusting the virtual gravity vector’s magnitude and direction to guide motion. Using a compliant 3D compass gait biped model and numerical simulations, we validate the efficacy of our approach. The results demonstrate smooth, continuous transitions between stable orbital gaits, contrasting with the discrete shifts reported in previous studies. This approach eliminates the need for extra stability setups and aligns with human turning studies, which observe gradual changes in walking parameters along curved paths. This framework enhances our understanding of bipedal locomotion and establishes a foundation for versatile, efficient gait planning in dynamically stable biped robots.}, archive = {J_ACCESS}, author = {Mohammad Reza Haghjoo}, doi = {10.1109/ACCESS.2025.3600313}, journal = {IEEE Access}, pages = {146116-146128}, shortjournal = {IEEE Access}, title = {Unified passivity-based control for turning and walking in asymptotically stable biped robots}, volume = {13}, year = {2025}, }
• (2025). NLJM: A simplified jitter model of digital standard cells for rapid automatic design of frequency synthesizers. ACCESS, 13, 146104-146115. (www)

  @article{ , abstract = {Synthesizable frequency synthesizers implemented with digital standard cells and automatic layout synthesis have demonstrated significant potential in recent studies. However, synthesizing analog mixed-signal circuits still involves manual pre- and post-layout simulation iterations, which consumes tens of hours and prolong the overall design cycle. To tackle this challenge, we propose the Non-Linear Jitter Model (NLJM), an extension of the Non-Linear Delay Model (NLDM), which incorporates phase noise information into digital standard cell models. This approach enables the application of digital design automation techniques to synthesizable analog mixed-signal timing circuits, facilitating faster design iterations. An experiment on single-stage logic gates shows that the phase noise prediction exhibits approximately 1.5dB error in the phase noise floor, and another experiment on ring oscillator planning demonstrates a maximum root mean squared (RMS) error of 1.56dB in the figure of merit (FoM). Critically, the experiments also reveal that NLJM reduces simulation time by at least 99.61% compared to the analog simulator for ring oscillator evaluations.}, archive = {J_ACCESS}, author = {Hóngyè Huáng and Ashbir Aviat Fadila and Dingxin Xu and Waleed Madany and Wenqian Wang and Zezheng Liu and Yuang Xiong and Tianshi Hu and Bangan Liu and Yuncheng Zhang and Atsushi Shirane and Kenichi Okada}, doi = {10.1109/ACCESS.2025.3596595}, journal = {IEEE Access}, pages = {146104-146115}, shortjournal = {IEEE Access}, title = {NLJM: A simplified jitter model of digital standard cells for rapid automatic design of frequency synthesizers}, volume = {13}, year = {2025}, }
• (2025). Research on online anomaly detection of business processes based on digital twin-intelligent body collaborative architecture. ACCESS, 13, 146088-146103. (www)

  @article{ , abstract = {The increasing complexity of business processes makes traditional rigid structure business process management methods difficult to apply in complex scenarios. This study establishes a collaborative integration framework of twin-intelligent bodies, combining digital twin and intelligent body technologies, based on dynamic declarative constraints. The digital twin enhances agents’ perception and decision-making, allowing real-time constraint adjustments. An association rule algorithm mines multi-perspective business process data to detect anomalies. Experiments on the DCGAN platform show an F1 score of 0.8–1 and a false alarm rate under 13%. The method outperforms LSTM and Transformer in comparative tests. The case study validated the effectiveness of the proposed model in real-world scenarios.}, archive = {J_ACCESS}, author = {Juan Li and Wenpeng Lv and Yan Wang}, doi = {10.1109/ACCESS.2025.3598321}, journal = {IEEE Access}, pages = {146088-146103}, shortjournal = {IEEE Access}, title = {Research on online anomaly detection of business processes based on digital twin-intelligent body collaborative architecture}, volume = {13}, year = {2025}, }
• (2025). KANB: A family of parametric activation functions with trainable parameters. ACCESS, 13, 146081-146087. (www)

  @article{ , abstract = {In this paper, a new family of four-parameter activation functions, referred to as KANB, is introduced to improve the performance of artificial neural networks (ANNs). The activation functions have been designed and tested under various problem settings –covering various ANN architectures, loss functions and datasets– and have succeeded in enhancing performance compared to the well-established ReLU, Sigmoid and Tanh. Initially, the parameters of the activation functions were considered fixed and preset. Later, they were treated as trainable parameters. The comparison of the outcomes revealed better results in the latter case. More specifically, KANB3 with trainable parameters shows strong potential on complex tasks, occasionally surpassing standard baselines by notable margins.}, archive = {J_ACCESS}, author = {George Kanakoudis and Petros Mandalis and Nikolaos Pelekis}, doi = {10.1109/ACCESS.2025.3600484}, journal = {IEEE Access}, pages = {146081-146087}, shortjournal = {IEEE Access}, title = {KANB: A family of parametric activation functions with trainable parameters}, volume = {13}, year = {2025}, }
• (2025). Intelligent handover management in ultra-dense 5G networks: A deep Q-learning-based prediction model. ACCESS, 13, 146068-146080. (www)

  @article{ , abstract = {Handover optimization is problematic in ultra-dense 5G networks because of the extremely high number of users and base stations. Unlike other methods, classical optimization approaches are not suitable for such technologies due to their complexity. In this context, a Machine Learning (ML)-based architecture using Deep Neural Networks (DNN) and Reinforcement Learning is proposed for handover and network optimization. The DNN model considers the temporal changes in the state of the network, and reinforcement learning is responsible for policy-based decision-making for proactive intervention, adaptive-driven interference suppression, and spectral efficiency improvement. These findings support the feasibility of the proposed resource allocation technique using ML in wireless communication systems.}, archive = {J_ACCESS}, author = {Nirant Dekate and Aklesh Anubhab and Siddarth Menon and Sudhanshu Arya and Abhijit Bhowmick and Yogesh Kumar Choukiker and Saranya Karattupalayam Chidambaram}, doi = {10.1109/ACCESS.2025.3600330}, journal = {IEEE Access}, pages = {146068-146080}, shortjournal = {IEEE Access}, title = {Intelligent handover management in ultra-dense 5G networks: A deep Q-learning-based prediction model}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based hyperparameter optimization for enhanced segmentation of RGB images in oil spill detection within port environments. ACCESS, 13, 146052-146067. (www)

  @article{ , abstract = {Accurate detection of oil spills in dynamic port environments remains critical for timely response and effective environmental protection. Recent studies have explored the application of deep learning models to high-resolution RGB imagery; however, challenges persist due to the complex visual characteristics of ports, including dynamic water surfaces, infrastructural occlusions, and heterogeneous backgrounds. Traditional detection approaches have demonstrated limited robustness under such conditions, often resulting in elevated rates of false positives and missed detections. In this study, a semantic segmentation model based on the DeepLabV3 architecture was developed and optimized for oil spill detection using drone-acquired RGB imagery. Model performance was enhanced through the integration of convolutional neural networks, transfer learning, and systematic hyperparameter tuning involving batch size, learning rate, and training epochs. Evaluation was conducted through five-fold cross-validation to ensure generalization capability, and the results were benchmarked against established segmentation models. The optimized model achieved a validation accuracy of 96.53%, with an F1-score of 0.9557 and an Intersection over Union (IoU) of 0.8643. Cross-validation yielded a mean accuracy of 89.35% (±0.0103), precision of 88.52% (±0.0081), recall of 85.26% (±0.0138), F1-score of 86.45% (±0.0122), and IoU of 76.97% (±0.0177), indicating consistent performance across all partitions. Per-class performance further supported model reliability, with F1-scores of 0.92 for oil, 0.91 for other objects, and 0.97 for water, and corresponding IoU values of 0.8569, 0.8273, and 0.9469, respectively. When compared to baseline models such as Fully Convolutional Networks (FCN, IoU: 83.48%), U-Net (IoU: 78.80%), and MFSCNet (IoU: 86.24%), the proposed DeepLabV3-based architecture demonstrated superior segmentation accuracy. These findings indicate the model’s potential for real-time deployment in high-risk maritime zones, offering significant contributions to environmental monitoring, rapid incident response, and operational decision-making.}, archive = {J_ACCESS}, author = {Ibrahim Muhammad Kurah and Shamsuddeen Adamu and Hitham Alhussian and Ayed Alwadain and Dahiru Adamu Aliyu and Hussaini Mamman and Aliyu Garba}, doi = {10.1109/ACCESS.2025.3599593}, journal = {IEEE Access}, pages = {146052-146067}, shortjournal = {IEEE Access}, title = {Deep learning-based hyperparameter optimization for enhanced segmentation of RGB images in oil spill detection within port environments}, volume = {13}, year = {2025}, }
• (2025). Possibilities and challenges for a phased array antenna system in ISAC: A hardware perspective. ACCESS, 13, 146032-146051. (www)

  @article{ , abstract = {The upcoming generation of wireless systems is expected to integrate both communication and sensing capabilities by flexibly allocating resources, including hardware. Phased array antenna (PAA) systems are already vital in communication systems and radars and, therefore, have been intensively studied for several decades. Considering the availability of multiple review assessments for PAA implementation and signal processing, this article fills an existing gap by evaluating the hardware challenges and possibilities for sharing a PAA towards deployable integrated sensing and communication (ISAC) applications. Both types of applications impose tight and often divergent antenna-performance requirements, such as control over beam shape, scanning resolution, and side lobe tolerance. The inherent performance trade-offs, such as pencil beam for refined sensing versus broader beam for instantaneous coverage, highlight the complex design priorities that must be tailored to the application to achieve optimal joint system performance. This paper evaluates state-of-the-art antenna array structures and beamforming techniques in the context of ISAC systems. Although less popular due to their inherent limitations, passive beamforming networks are particularly highlighted for several proposed solutions, e.g., reciprocity and multibeam concept, and elaborated through a design and measurement by the authors. This article addresses the theoretical challenges that emerge while realizing a phased array out of a single element, their impact on sensing and communication performances, and finally, a summary of their mitigation techniques from the state-of-the-art. Selected simulation and measurement results from the authors support the discussions. The paper presents a set of PAA performance matrices for the ISAC system and their related joint benefits or trade-offs. The key enablers towards PAA sharing for ISAC systems, including quasi-circulator, gain-boosting lens, and electromagnetic bandgap structures, are presented, highlighting contemporary technological developments and related theoretical aspects. With the in-depth review, the authors also demonstrate proof of concept for the selected technologies through design steps, simulations, and measurements.}, archive = {J_ACCESS}, author = {Muhammad Umar and Mehrab Ramzan and Shahanawaz Kamal and Muhammad Sajjad Ahmad and Padmanava Sen}, doi = {10.1109/ACCESS.2025.3599680}, journal = {IEEE Access}, pages = {146032-146051}, shortjournal = {IEEE Access}, title = {Possibilities and challenges for a phased array antenna system in ISAC: A hardware perspective}, volume = {13}, year = {2025}, }
• (2025). End-to-end occluded person re-identification with artificial occlusion generation. ACCESS, 13, 146020-146031. (www)

  @article{ , abstract = {In recent years, the task of person re-identification (Re-ID) has improved considerably with the advances in deep learning methodologies. However, occluded person Re-ID remains a challenging task, as parts of the body of the individual are frequently hidden by various objects, obstacles, or other people, making the identification process more difficult. To address these issues, we introduce a novel data augmentation strategy using artificial occlusions, consisting of random shapes and objects from a small image dataset that was created. We also propose an end-to-end methodology for occluded person Re-ID, which consists of three branches: a global branch, a feature dropping branch, and an occlusion detection branch. Experimental results show that the use of random shape occlusions is superior to random erasing using our architecture. Results on six datasets consisting of three tasks (holistic, partial and occluded person Re-ID) demonstrate that our method performs favourably against state-of-the-art methodologies.}, archive = {J_ACCESS}, author = {Leonardo Capozzi and Jaime S. Cardoso and Ana Rebelo}, doi = {10.1109/ACCESS.2025.3600385}, journal = {IEEE Access}, pages = {146020-146031}, shortjournal = {IEEE Access}, title = {End-to-end occluded person re-identification with artificial occlusion generation}, volume = {13}, year = {2025}, }
• (2025). Beamforming feedback-based respiration and heart rate estimation toward firmware-agnostic WiFi sensing. ACCESS, 13, 146008-146019. (www)

  @article{ , abstract = {WiFi-based vital sign monitoring has attracted growing attention for its potential applications in contactless healthcare. However, most existing techniques rely on channel state information (CSI), which typically requires custom firmware and specific chipsets. To address this issue, this study explores firmware-agnostic respiration and heart rate estimation using beamforming feedback (BFF), compressed representation of CSI. This eliminates the need for custom firmware or chipset support, enabling broader applicability using off-the-shelf devices. However, it is not trivial to apply CSI-based estimation techniques to BFF-based estimation because the information content and data structure of BFF differ from those of CSI. The proposed BFF-based estimation algorithm addresses this issue by adapting the CSI-based estimation techniques to work with BFF. The algorithm consists of four key components: subcarrier selection, data calibration, signal extraction, and respiration and heart rate estimation. The performance of the BFF-based estimation algorithm is experimentally validated in several indoor environments using commodity IEEE 802.11ac devices. Results show that respiration rate and heart rate can be estimated with average errors below 1 breaths/min and 10 beats/min, respectively. Furthermore, accuracy comparisons between BFF-based and CSI-based estimations are provided to investigate the impact of lossy compression from CSI to BFF, specifically singular value decomposition (SVD) calculation and quantization. Comparisons reveal that the accuracy degradation of the BFF-based estimation compared to CSI-based estimation is primarily caused by the quantization rather than the SVD calculation.}, archive = {J_ACCESS}, author = {Takamochi Kanda and Sota Kondo and Hiroki Shimomura and Takashi Sato and Hiromitsu Awano and Koji Yamamoto}, doi = {10.1109/ACCESS.2025.3600278}, journal = {IEEE Access}, pages = {146008-146019}, shortjournal = {IEEE Access}, title = {Beamforming feedback-based respiration and heart rate estimation toward firmware-agnostic WiFi sensing}, volume = {13}, year = {2025}, }
• (2025). Modeling railway express freight train operation plans in road-rail multimodal transport. ACCESS, 13, 145996-146007. (www)

  @article{ , abstract = {Within the road-rail multimodal transport service network, optimally designed railway operation plans contribute to reducing overall logistics costs while guaranteeing the freight delivery timeliness. This research focuses on the design of a railway express freight train operation plan for the road-rail multimodal transport service network. The goals include designing the operation sections, departure and stopping times, marshalling content, and loading/unloading plans for railway express freight trains within the multimodal transport network by constructing a service network model, and validating these elements through an empirical case. This study innovatively investigates the operation plan for railway express freight trains within this multimodal transport service network and introduces timeliness requirements as time-window constraints in the proposed optimization model, which aims to minimize the total multimodal transport costs. The results show that adopting the proposed plan effectively reduce the overall logistics costs by 19.1%, and achieves an 88.89% timeliness fulfillment rate. Furthermore, the proposed method enriches the relevant theoretical frameworks for road-rail multimodal transport research, providing strategies for the further improvement of service network.}, archive = {J_ACCESS}, author = {Yuxuan Fang and Xiaodong Zhang and Pei Wang and Yanming Tao}, doi = {10.1109/ACCESS.2025.3600470}, journal = {IEEE Access}, pages = {145996-146007}, shortjournal = {IEEE Access}, title = {Modeling railway express freight train operation plans in road-rail multimodal transport}, volume = {13}, year = {2025}, }
• (2025). Real-time adaptive target tracking via inverse kinematics optimization in stabilized systems. ACCESS, 13, 145977-145995. (www)

  @article{ , abstract = {To keep a target item inside their field of view, two-axis gimbal systems are designed to dynamically compensate for any movements that may occur, whether such movements originate from the target itself or from the platform on which the device is mounted. This study addresses the development and optimization of inverse kinematics algorithms to enable precise, real-time target tracking in Inertial Stabilization Platforms (ISPs), also known as gimbal systems. These systems are critical for applications demanding high stability and accuracy, such as surveillance, navigation, and scientific research. The research explores the mathematical principles of inverse kinematics, highlighting the complexities of real-time processing constraints. To overcome these challenges, advanced optimization techniques are applied, focusing on minimizing computational delays and enhancing tracking precision. The proposed algorithms are implemented within a Simscape Multibody simulation framework, allowing for rigorous testing under diverse operational conditions. The validation process combines simulated environments with real-world experiments to ensure the algorithms’ robustness and practical utility. The findings reveal notable advancements in both tracking accuracy and system responsiveness, offering a pathway to more efficient and dependable gimbal systems in highly dynamic settings.}, archive = {J_ACCESS}, author = {Abderahman Kriouile and Soufiane Hamida and Oussama El Gannour and Abdoul Latif Abdou Moussa}, doi = {10.1109/ACCESS.2025.3600197}, journal = {IEEE Access}, pages = {145977-145995}, shortjournal = {IEEE Access}, title = {Real-time adaptive target tracking via inverse kinematics optimization in stabilized systems}, volume = {13}, year = {2025}, }
• (2025). Improved dual-capacitor CMOS oscillator. ACCESS, 13, 145968-145976. (www)

  @article{ , abstract = {This paper presents an improved fully integrated CMOS oscillator and compares it with a conventional topology, a version with current steering, and latest research. The oscillator is based on alternately charging two ground-referenced capacitors to limit transition times between periodic cycles. The topologies were compared in XFAB 350 nm technology node for a clock period of $2.5~\mu $ s to ensure consistency. The proposed topology was also scaled down to 22 nm to demonstrate its excellent performance in modern nodes. In this paper, we describe a method for benchmarking the oscillator period for different segments. This enabled us to better understand the root cause of distortion and compensate for it. Our proposed topology uses fewer components than a common dual-capacitor topology while performing better and with fewer transitional distortions. The clock errors of the conventional topologies were −16 ns and +12 ns, whereas that of the proposed topology was only +4 ns. Finally, we discovered that the clock error is predominantly caused by the comparator, which encourages us to conduct further research on improving the comparator topology.}, archive = {J_ACCESS}, author = {Žiga Šmelcer and Janez Trontelj}, doi = {10.1109/ACCESS.2025.3599034}, journal = {IEEE Access}, pages = {145968-145976}, shortjournal = {IEEE Access}, title = {Improved dual-capacitor CMOS oscillator}, volume = {13}, year = {2025}, }
• (2025). ADAM-1: An AI reasoning and bioinformatics model for alzheimer’s disease detection and microbiome-clinical data integration. ACCESS, 13, 145953-145967. (www)

  @article{ , abstract = {Alzheimer’s Disease Analysis Model Generation 1 (ADAM-1) is a multi-agent reasoning large language model (LLM) framework designed to integrate and analyze multimodal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and classification of Alzheimer’s disease (AD). By leveraging the agentic system with LLM, ADAM-1 produces insights from diverse data sources and contextualizes the findings with literature-driven evidence. A comparative evaluation with XGBoost revealed a significantly improved mean F1 score and significantly reduced variance for ADAM-1, highlighting its robustness and consistency, particularly when utilizing human biological data. Although currently tailored for binary classification tasks with two data modalities, future iterations will aim to incorporate additional data types, such as neuroimaging and peripheral biomarkers, and expand them to predict disease progression, thereby broadening ADAM-1’s scalability and applicability in AD research and diagnostic applications.}, archive = {J_ACCESS}, author = {Ziyuan Huang and Vishaldeep Kaur Sekhon and Roozbeh Sadeghian and Maria L. Vaida and Cynthia Jo and Beth A. McCormick and Doyle V. Ward and Vanni Bucci and John P. Haran}, doi = {10.1109/ACCESS.2025.3599857}, journal = {IEEE Access}, pages = {145953-145967}, shortjournal = {IEEE Access}, title = {ADAM-1: An AI reasoning and bioinformatics model for alzheimer’s disease detection and microbiome-clinical data integration}, volume = {13}, year = {2025}, }
• (2025). The role of VR in education: A two-phase review of umbrella reviews and recent primary studies. ACCESS, 13, 145938-145952. (www)

  @article{ , abstract = {The use of virtual reality (VR) in research and development has increased dramatically across various fields over the past decade, and education is no exception. To study the status and recent trends of VR research in learning, we present a novel two-phase systematic review: First, in a meta-umbrella review, we drew together the general essence of VR studies by summarizing 11 umbrella reviews in the field from 2017 to present. Second, we assessed more recent research trends by constructing a topic model of over 6530 primary studies published in 2023–2024. We found that VR has become increasingly popular, offering various benefits across different fields, including healthcare and education. In specific learning domains, VR has been used for chemistry and engineering laboratory training, gamified experiences, aviation training, and various school subjects such as history. VR in education was typically characterized by personalized immersive experiences that could adapt to the learner. However, the definitions of VR, especially concerning the amount of immersion, were varied. VR can motivate, control the environment, and help with difficult concepts. Long-term evidence of the effectiveness of VR training was still scarce. Learning standards or frameworks for the use and design of VR in education have not yet been used or formed and important aspects like immersion have not been fully utilized. The results indicate that, more thorough research with long-term data, standards, and clear operationalization of VR features are needed to truly establish the benefits and improvements VR can provide within the field of education and beyond.}, archive = {J_ACCESS}, author = {Jeri Varjosalo and Paavo Räty and Ville Heilala and Raija Hämäläinen and Tommi Kärkkäinen}, doi = {10.1109/ACCESS.2025.3598952}, journal = {IEEE Access}, pages = {145938-145952}, shortjournal = {IEEE Access}, title = {The role of VR in education: A two-phase review of umbrella reviews and recent primary studies}, volume = {13}, year = {2025}, }
• (2025). Label-aware hierarchical ranking model for multi-label text classification. ACCESS, 13, 145925-145937. (www)

  @article{ , abstract = {Multi-label text classification involves assigning multiple relevant categories to a single text, enabling applications in academic indexing, medical diagnostics, and e-commerce. However, existing models often fail to capture complex text-label relationships and lack robust mechanisms for ranking label relevance, limiting their effectiveness. This paper proposes the Label-Aware Hierarchical Ranking Model, a novel approach that combines contextual embeddings, custom attention mechanisms, and gradient boosting to enhance label ranking. The proposed model integrates text and label embeddings with a contextual similarity attention module to capture text-label relationships and inter-label dependencies, followed by a hierarchical ranking mechanism for refining label prioritization. Experiments conducted on both the ArXiv Academic Paper dataset and the Reuters-21578 dataset demonstrate that the proposed model surpasses state-of-the-art models, achieving Precision@1 scores of 86.90 % and 94.47 %, respectively. These results advance multi-label text classification methodologies, offering more accurate, context-aware, and practically applicable classifications.}, archive = {J_ACCESS}, author = {Lama Ayash and Abdulmohsen Algarni and Omar Alqahtani}, doi = {10.1109/ACCESS.2025.3599944}, journal = {IEEE Access}, pages = {145925-145937}, shortjournal = {IEEE Access}, title = {Label-aware hierarchical ranking model for multi-label text classification}, volume = {13}, year = {2025}, }
• (2025). Based on fast non-singular dynamic terminal sliding mode control for four-wheel independently driven electric vehicle direct yaw moment control. ACCESS, 13, 145905-145924. (www)

  @article{ , abstract = {With the rapid development of new energy vehicles, electric vehicles have increasingly demonstrated their advantages in enhancing energy efficiency and reducing carbon emissions; however, challenges in handling and stability still persist. This paper proposes a direct yaw moment control (DYC) system architecture for four-wheel independently actuated (FWIA) electric vehicles. The system employs a fast non-singular dynamic terminal sliding mode control (FND-TSMC) strategy for the upper layer and a multi-level dynamic weighted axle load coordination control method (MDW-ALCCM) for lower-layer torque distribution. Initially, 7-degree-of-freedom (7-DOF) and 2-degree-of-freedom (2-DOF) vehicle dynamic models are established, and a composite stability analysis method is developed by integrating yaw rate limit boundaries with dynamic adaptive dual-line boundaries to formulate stability criteria under extreme conditions. The upper controller incorporates adaptive weighting coefficients and a feedforward compensation strategy, significantly enhancing response speed and robustness, while the MDW-ALCCM enables optimal torque distribution across normal, warning, and emergency modes. Simulation results demonstrate that the proposed DYC system markedly improves lateral stability, tracking accuracy, and convergence speed in complex driving scenarios such as double lane-change and serpentine maneuvers, providing an efficient and robust solution for electric vehicle stability control with significant implications for practical engineering applications.}, archive = {J_ACCESS}, author = {Zhengyong Tao and Zhongzhi Tong and Mingming Wu and Hui Wu and Min Qu and Deqiang Xie}, doi = {10.1109/ACCESS.2025.3598590}, journal = {IEEE Access}, pages = {145905-145924}, shortjournal = {IEEE Access}, title = {Based on fast non-singular dynamic terminal sliding mode control for four-wheel independently driven electric vehicle direct yaw moment control}, volume = {13}, year = {2025}, }
• (2025). Generating authentic grounded synthetic maintenance work orders. ACCESS, 13, 145888-145904. (www)

  @article{ , abstract = {Large language models (LLMs) are promising for generating synthetic technical data, particularly for industrial maintenance where real datasets are often limited and unbalanced. This study generates synthetic maintenance work orders (MWOs) that are grounded to accurately represent engineering knowledge and authentic–reflecting technician language, jargon, and abbreviations. First, we extracted valid engineering paths from a knowledge graph constructed using the MaintIE gold-annotated industrial MWO dataset. Each path encodes engineering knowledge as a triple. These paths are used to constrain the output of an LLM (GPT-4o mini) to generate grounded synthetic MWOs using few-shot prompting. The synthetic MWOs are made authentic by incorporating human-like elements, such as contractions, abbreviations, and typos. Evaluation results show that the synthetic data is 86% as natural and 95% as correct as real MWOs. Turing test experiments reveal that subject matter experts could distinguish real from synthetic data only 51% of the time while exhibiting near-zero agreement, indicating random guessing. Statistical hypothesis testing confirms the results from the Turing Test. This research offers a generic approach to extracting legitimate paths from a knowledge graph to ensure that synthetic data generated are grounded in engineering knowledge while reflecting the style and language of the technicians who write them. To enable replication and reuse, code, data and documentation are at https://github.com/nlp-tlp/LLM-KG-Synthetic-MWO}, archive = {J_ACCESS}, author = {Allison Lau and Jadeyn Feng and Melinda Hodkiewicz and Caitlin Woods and Michael Stewart and Adriano Polpo}, doi = {10.1109/ACCESS.2025.3598751}, journal = {IEEE Access}, pages = {145888-145904}, shortjournal = {IEEE Access}, title = {Generating authentic grounded synthetic maintenance work orders}, volume = {13}, year = {2025}, }
• (2025). GRVIO: Semantic-aware visual-inertial odometry for ground robot platforms. ACCESS, 13, 145875-145887. (www)

  @article{ , abstract = {Visual-inertial odometry (VIO) has emerged as a key technology for state estimation of mobile robots by complementarily fusing data from a lightweight, low-cost inertial measurement unit and a camera. However, ground robots face significant challenges that degrade VIO performance, including textureless image regions from ground surfaces, dynamic environments, and motion-induced vibrations. To address these issues, this paper proposes ground robot visual-inertial odometry (GRVIO), a semantic-aware VIO framework specifically designed for ground robots. The proposed approach introduces a novel camera-plane distance measurement model, a zero-motion update model, and a dynamic object removal strategy. Our measurement formulation process leverages deep neural network-based semantic information to effectively remove dynamic point features and enhance the accuracy of plane features through high-level scene understanding. All measurement models are systematically integrated into a multi-state constraint Kalman filter (MSCKF) framework for accurate state estimation with a lightweight filter design. The effectiveness of the proposed method is validated through Monte Carlo simulations and real-world experiments using a publicly available dataset collected from a quadruped robot operating in diverse indoor and outdoor environments. Simulation results demonstrate that each proposed measurement model enhances localization accuracy and show the robustness of the proposed method against semantic segmentation errors and extrinsic calibration uncertainties. Furthermore, experimental results validate that GRVIO outperforms state-of-the-art VIO methods relying solely on point features, achieving an average reduction of approximately 40% in position root mean squared error compared to MSCKF, thus highlighting its capability to impose effective measurements and enhance both robustness and accuracy for ground robots.}, archive = {J_ACCESS}, author = {Sangbum Lee and Hanyeol Lee and Chan Gook Park}, doi = {10.1109/ACCESS.2025.3598771}, journal = {IEEE Access}, pages = {145875-145887}, shortjournal = {IEEE Access}, title = {GRVIO: Semantic-aware visual-inertial odometry for ground robot platforms}, volume = {13}, year = {2025}, }
• (2025). Magnetic modeling of magnetic levitation systems based on a fast finite difference method. ACCESS, 13, 145865-145874. (www)

  @article{ , abstract = {An accurate magnetic force model is crucial for the high-precision control of magnetic levitation systems. Soft magnetic materials, renowned for their excellent magnetic properties, are commonly employed in these systems. However, traditional computational methods either failing to yield accurate magnetic force models estimates in the presence of soft magnetic cores or demanding excessive computational resources, resulting in significant increased processing times. This paper proposes an accurate, efficient, and robust fast finite difference method. The method leverages the finite difference method to solve Maxwell’s equations and derive the spatial distribution of the magnetic field. The computations are performed on a non-uniform grid using an iterative multigrid solver, which enhances both algorithmic efficiency and numerical stability. Following this, the Maxwell stress tensor is applied to compute the spatial distribution of the Lorentz forces. To validate the method, we computed magnetic force models across various magnetic levitation systems and compared its performance with the finite element method software COMSOL and the boundary element method software Radia. Comparative results confirm that the proposed approach is an accurate, versatile, and highly efficient solution.}, archive = {J_ACCESS}, author = {Yibo Wang and Wei Pang and Xianze Xu and Fengqiu Xu}, doi = {10.1109/ACCESS.2025.3600058}, journal = {IEEE Access}, pages = {145865-145874}, shortjournal = {IEEE Access}, title = {Magnetic modeling of magnetic levitation systems based on a fast finite difference method}, volume = {13}, year = {2025}, }
• (2025). Long-term analysis of temperature and current-dependent degradation in green high-power light-emitting diodes. ACCESS, 13, 145849-145864. (www)

  @article{ , abstract = {We report on the degradation dynamics and mechanisms of commercially available green high-power light-emitting diodes (LEDs) with a peak wavelength of 522 nm. The stress tests were carried out for up to 8800 hours with forward currents ranging from 350 mA to 1000 mA at junction temperatures between 86 C and 155 C. Two complementary test designs were used to isolate temperature- and current-driven effects. The results of the accelerated tests reveal the following key findings: 1.) A square-root–time-dependent loss in the quantum wells caused by the generation of point defects, leading to up to 90 % flux reduction within the first 500 hours at low forward currents. 2.) A logarithmic decay governed by defect-induced carrier-injection loss, evident above $I_{\mathrm {EQE,max}}$ and accompanied by a spectral red shift. 3.) A temperature-activated blue shift with an activation energy of $E_{\mathrm {a}}$ =0.23 eV, indicating the coexistence of competing degradation mechanisms. The interplay between different mechanisms results in an enhanced device lifetime at higher stress temperatures and stands in contrast to previous findings reported in the literature. 4.) The isothermal stress test indicates a cubic acceleration of degradation with carrier density, implicating Auger-Meitner-generated hot electrons in defect formation. These insights provide guidance for mitigating reliability issues of green high-power LEDs in future devices.}, archive = {J_ACCESS}, author = {Alexander Herzog and Matteo Buffolo and Francesco Piva and Simon Benkner and Babak Zandi and Jens Balasus and Paul Myland and Felix Wirth and Willem D. van Driel and Matteo Meneghini and Tran Quoc Khanh}, doi = {10.1109/ACCESS.2025.3599797}, journal = {IEEE Access}, pages = {145849-145864}, shortjournal = {IEEE Access}, title = {Long-term analysis of temperature and current-dependent degradation in green high-power light-emitting diodes}, volume = {13}, year = {2025}, }
• (2025). DenseNet-FPA: Integrating DenseNet and flower pollination algorithm for breast cancer histopathology image classification. ACCESS, 13, 145828-145848. (www)

  @article{ , abstract = {Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. While histopathological image analysis plays a key role in breast cancer diagnosis, the complexity and heterogeneity of these images present significant challenges. To address this, we propose DenseNet-FPA, a novel method that combines Dense Convolutional Networks (DenseNet) with the Flower Pollination Algorithm (FPA) to enhance feature selection and classification in breast cancer histopathology images. DenseNet effectively extracts hierarchical features, while FPA optimizes the selection of the most discriminative features, improving classification accuracy and reducing computational overhead. We evaluated our approach on two publicly available datasets, i.e. BreakHis and BACH, achieving accuracies of 99.32% and 96%, respectively. These results significantly outperform state-of-the-art methods, which achieved <99% on BreakHis and <93% on BACH. Our findings demonstrate significant improvements in binary classification performance, offering a more efficient and accurate approach for breast cancer diagnosis. The proposed method has potential applications in other histopathological analyses and clinical settings.}, archive = {J_ACCESS}, author = {Musa Adamu Wakili and Harisu Abdullahi Shehu and Mahdi Abdollahi and Badamasi Imam Ya'u and Md Haidar Sharif and Huseyin Kusetogullari}, doi = {10.1109/ACCESS.2025.3599319}, journal = {IEEE Access}, pages = {145828-145848}, shortjournal = {IEEE Access}, title = {DenseNet-FPA: Integrating DenseNet and flower pollination algorithm for breast cancer histopathology image classification}, volume = {13}, year = {2025}, }
• (2025). Enhanced indoor pedestrian tracking using UWB/PDR fusion and NLOS error mitigation. ACCESS, 13, 145812-145827. (www)

  @article{ , abstract = {Pedestrian Dead Reckoning (PDR) utilizing Inertial Navigation System (INS) technology often encounters substantial cumulative errors and random sensor-induced noise, while Ultra-Wideband (UWB) positioning can degrade severely under Non-Line-of-Sight (NLOS) conditions due to environmental factors. To overcome the above challenges, this research proposes an enhanced PDR/UWB fusion framework that significantly improves positioning accuracy. First, a novel NLOS ranging error mitigation approach is developed by integrating an Extreme Learning Machine (ELM) in conjunction with Gaussian Process Regression (GPR), with the initial parameters of the ELM optimized via the Nutcracker Optimizer. Channel Impulse Response (CIR) data collected from the UWB module serve as features, which are first processed by the ELM, and subsequently refined by the GPR, resulting in more accurate distance estimates under challenging NLOS conditions. Second, to accommodate scenarios where UWB signals are intermittently unavailable, a fusion strategy is proposed that employs a Multilayer Perceptron (MLP) neural network to predict pseudo-UWB observations from PDR data. Specifically, PDR-derived acceleration, angular velocity, and current position are fed into the MLP. When UWB measurements are absent, the MLP-predicted observations are integrated with PDR outputs using an Extended Kalman Filter (EKF), ensuring continuous and reliable localization. Experimental results demonstrate that the proposed integrated system substantially outperforms standalone UWB or EKF-based methods, achieving marked improvements in positioning accuracy and underscoring its effectiveness in delivering robust and precise indoor localization.}, archive = {J_ACCESS}, author = {Shoude Wang and Nur Syazreen Ahmad}, doi = {10.1109/ACCESS.2025.3599837}, journal = {IEEE Access}, pages = {145812-145827}, shortjournal = {IEEE Access}, title = {Enhanced indoor pedestrian tracking using UWB/PDR fusion and NLOS error mitigation}, volume = {13}, year = {2025}, }
• (2025). Spatiotemporal semantic modeling and cross-modal collaboration-based gait recognition under multiple views and various walking conditions. ACCESS, 13, 145799-145811. (www)

  @article{ , abstract = {With the increasing demand for non-intrusive and long-distance identity recognition in fields such as public safety, medical rehabilitation, and industrial energy, the importance of gait recognition technology has become increasingly prominent. However, existing gait recognition methods predominantly rely on unimodal data and lack sufficient integration of multimodal information, which hinders the ability to fully leverage the complementary advantages of different modalities. Furthermore, variations in viewing angles, walking states, and gait characteristics significantly affect the accuracy and robustness of recognition. In this paper, a spatiotemporal semantic modeling and cross-modal collaboration approach for multiple views and various walking conditions gait recognition is proposed by integrating temporal and spatial dimensions with silhouette, skeleton, Gait Energy Image (GEI), and Skeleton Energy Image (SEI). The method includes a Dual-Branch Attention Module (DBAM), a Multi-Branch Spatio-Temporal Modeling Module (MBSTM), and a Multi-modal Feature Fusion Module (MFFM). This approach effectively captures key features across various viewpoints and walking conditions while preserving local channel integrity. By modeling spatiotemporal information from multiple branches, it extracts high-level semantic features and fuses multi-modal data to enhance gait recognition performance. Experiments demonstrate that the proposed method achieves Rank-1 accuracies of 96.78%, 92.63%, and 76.28% under multiple views and various walking conditions (normal, carrying bags, wearing coats) on the CASIA-B dataset, and an average accuracy of 99.3% on the CASIA-C dataset, indicating strong generalization capability.}, archive = {J_ACCESS}, author = {Qianqing Duan and Ming Xu and Jun Liu and Yingying Peng}, doi = {10.1109/ACCESS.2025.3599550}, journal = {IEEE Access}, pages = {145799-145811}, shortjournal = {IEEE Access}, title = {Spatiotemporal semantic modeling and cross-modal collaboration-based gait recognition under multiple views and various walking conditions}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on LLM-powered recommender systems: From discriminative, generative to multi-modal paradigms. ACCESS, 13, 145772-145798. (www)

  @article{ , abstract = {Large Language Models (LLMs) have become transformative tools in Natural Language Processing (NLP). They are increasingly being integrated into recommendation systems to address existing limitations such as data sparsity, novelty, cold start, and long-tail challenges. Unlike traditional recommendation techniques that rely on user-item interaction matrices, LLMs provide context-aware reasoning and multi-modal processing capabilities. However, existing research mainly focuses on fine-tuning and prompt engineering strategies without fully exploring hybrid models, retrieval-augmented generation (RAG), graph-enhanced recommendations, and evaluation methodologies. This survey offers a comprehensive and structured examination of LLM-based recommendation systems, categorizing them into discriminative, generative, hybrid, graph-enhanced, and multimodal paradigms. Additionally, we explore adaptive fine-tuning techniques, prompt engineering strategies, and retrieval-augmented generation (RAG) approaches that improve LLM performance in personalized recommendations. We also examine evaluation methodologies, including LLM-as-a-Judge frameworks, benchmark limitations, and fairness considerations. Finally, we present a detailed discussion of open challenges, such as hallucination, scalability, bias, and privacy, highlighting critical research gaps and opportunities for future exploration. This survey aims to guide researchers in navigating the evolving landscape of LLM-driven recommendation systems.}, archive = {J_ACCESS}, author = {Dina Nawara and Rasha Kashef}, doi = {10.1109/ACCESS.2025.3599832}, journal = {IEEE Access}, pages = {145772-145798}, shortjournal = {IEEE Access}, title = {A comprehensive survey on LLM-powered recommender systems: From discriminative, generative to multi-modal paradigms}, volume = {13}, year = {2025}, }
• (2025). Temporal heterogeneous graph contrastive learning for fraud detection in credit card transactions. ACCESS, 13, 145754-145771. (www)

  @article{ , abstract = {Credit card fraud detection remains a critical challenge in financial security, characterized by evolving fraud patterns, sparse labeled data, and severe class imbalance. Traditional methods often fail to capture the complex temporal dynamics and heterogeneous relationships inherent in financial transaction networks. To address these limitations, we propose TH-GCL (Temporal Heterogeneous Graph Contrastive Learning), a novel framework that integrates heterogeneous graph modeling, temporal pattern recognition, and contrastive learning for enhanced fraud detection. Our approach constructs a temporal heterogeneous graph incorporating multiple entity types including users, transactions, merchants, and devices, with time-aware edge weights to capture evolving behavioral patterns. We design a temporal-aware graph neural network architecture that learns hierarchical representations by jointly modeling structural dependencies and temporal evolution patterns. Furthermore, we introduce a dual-view contrastive learning mechanism that creates augmented graph views through both structural perturbation and temporal masking, enabling the model to learn robust representations under different perspectives. The contrastive objective encourages the model to distinguish between normal and fraudulent transaction patterns while maintaining consistency across augmented views. Extensive experiments on the IEEE-CIS Fraud Detection dataset demonstrate that TH-GCL achieves superior performance compared to state-of-the-art baselines, with improvements of 5.2% in AUC-ROC and 8.7% in AUC-PR. Ablation studies confirm the effectiveness of each component, while analysis of learned representations reveals meaningful fraud pattern discovery. Our framework exhibits strong generalization capability across different time periods and transaction volumes, making it practical for real-world deployment in dynamic fraud detection scenarios.}, archive = {J_ACCESS}, author = {Jiyuan Wang and Jingyi Liu and Wenxia Zheng and Yao Ge}, doi = {10.1109/ACCESS.2025.3599787}, journal = {IEEE Access}, pages = {145754-145771}, shortjournal = {IEEE Access}, title = {Temporal heterogeneous graph contrastive learning for fraud detection in credit card transactions}, volume = {13}, year = {2025}, }
• (2025). Machine learning for early detection of phishing URLs in parked domains: An approach applied to a financial institution. ACCESS, 13, 145736-145753. (www)

  @article{ , abstract = {Phishing attacks remain a critical threat in the digital era, exploiting social engineering tactics to compromise user trust and sensitive information, often resulting in financial loss and identity theft. These attacks typically exploit multiple communication channels to impersonate trusted entities, inducing users to click on and access malicious Resource Locations (URLs). Early detection of these URLs can considerably reduce the volume and impact of these attacks. This paper proposes a machine learning-based framework for early detection of phishing URLs, specifically addressing newly registered and parked domains. Using a curated dataset comprising 211,659 URLs obtained from real-time SSL (Secure Sockets Layer) certificate monitoring, popular domain listings, and phishing incident reports, the methods encompass data pre-processing, feature engineering, and model optimization. A Light Gradient Boosting Machine classifier achieved recall of 96.02% and accuracy of 97.28% on a balanced dataset, validated through 10-fold cross-validation. Additionally, feature selection techniques reduced model complexity while maintaining detection effectiveness, facilitating practical deployment. The proposed frameworks provide a versatile tool for phishing prevention and brand protection, demonstrating potential applicability in sectors such as banking and e-commerce. This work contributes to proactive cybersecurity practices addressing evolving threats within digital ecosystems.}, archive = {J_ACCESS}, author = {Jaqueline D. Duarte and Pedro Chagas Junior and João Paulo Javidi da Costa and Elena J. da Costa and Laerte Peotta de Melo and Rafael Rabelo Nunes and Carlos V. N. Gabriel Soares and Thiago Erivan da Cunha Silva}, doi = {10.1109/ACCESS.2025.3599454}, journal = {IEEE Access}, pages = {145736-145753}, shortjournal = {IEEE Access}, title = {Machine learning for early detection of phishing URLs in parked domains: An approach applied to a financial institution}, volume = {13}, year = {2025}, }
• (2025). YOLO in healthcare: A comprehensive review of detection architectures, domain applications, and future innovations. ACCESS, 13, 145714-145735. (www)

  @article{ , abstract = {The growing adoption of deep learning in medical imaging has propelled object detection frameworks into clinical relevance, with YOLO (You Only Look Once) standing out for its real-time inference and high accuracy. This survey offers a comprehensive review of YOLO-based medical object detection, synthesizing findings from 123 peer-reviewed papers published between 2018 and 2024. Across oncology, radiology, pathology, surgery, and PPE detection, YOLO models have demonstrated strong performance in tumor localization, lesion segmentation, tool tracking, and anomaly detection. YOLOv3 and YOLOv5 remain the most frequently deployed variants, while YOLOv8 and YOLO-NAS are emerging in recent studies with improved mAP and lower latency. Findings reveal that mAP scores exceeding 85% were commonly achieved in breast cancer and pulmonary nodule detection tasks, while lightweight versions such as YOLOv5s maintained detection speed above 50 FPS in surgical environments. Despite its clinical promise, persistent challenges include poor sensitivity to small or rare lesions, absence of benchmark datasets across modalities, and variability in annotation practices. This survey introduces a structured taxonomy of YOLO architectures, benchmarks their comparative performance, highlights key limitations, and proposes forward-looking trends involving multimodal fusion, transformer-enhanced backbones, and privacy-preserving learning. The work consolidates YOLO’s growing role in healthcare AI and serves as a foundational reference for researchers and practitioners alike.}, archive = {J_ACCESS}, author = {Damodharan Palaniappan and Rituraj Jain and T. Premavathi and Kumar Parmar and Wade Ghribi and Abdelmoty M. Ahmed and Naim Ahmad}, doi = {10.1109/ACCESS.2025.3599358}, journal = {IEEE Access}, pages = {145714-145735}, shortjournal = {IEEE Access}, title = {YOLO in healthcare: A comprehensive review of detection architectures, domain applications, and future innovations}, volume = {13}, year = {2025}, }
• (2025). Simplifying two-stage object detectors for on-board remote sensing. ACCESS, 13, 145703-145713. (www)

  @article{ , abstract = {Deep learning has been applied to object detection in remotely sensed images. Typically, remote sensing object detection is performed on the ground rather than on-board due to the limited computational resources available on embedded systems. This offloading introduces delays in acquiring mission-critical information, restricting its applicability to real-time scenarios. To enable on-device object detection, research has focused on designing efficient detectors and applying model compression techniques to reduce inference latency. Nonetheless, there is an absence of relevant research that maintains the accuracy of remote sensing detectors. Currently, the majority of remote sensing detectors operate as two-stage detectors. Therefore, there is a need for efficient research into two-stage detectors. In this paper, we propose a model simplification method for two-stage object detectors. Instead of utilizing a conventional feature pyramid network (FPN), our approach employs a single feature extraction process within the two-stage detector. However, relying solely on a single feature degrades detection accuracy. To compensate for the accuracy drop, we modify the method of selecting positive anchors used in training. We apply a method to determine the threshold for positive anchors based on the intersection over union (IoU), with the k anchors closest to the object. To further enhance accuracy, we apply a high-pass filter to increase the objectness score of hard-to-detect objects on the region proposal network (RPN) score map. The proposed method is compatible with any two-stage detector that integrates an FPN. Experiments conducted on state-of-the-art two-stage detectors, such as Oriented-RCNN and LSKNet, demonstrate that our method reduced computation costs up to 58.1% while achieving a 1.6%p increase in average accuracy on the DOTAv2.0 dataset. With the fewer RoIs, our approach in the Oriented-RCNN achieves a 31.8% improvement in throughput while maintaining the same average accuracy.}, archive = {J_ACCESS}, author = {Jaemin Kang and Hoeseok Yang and Hyungshin Kim}, doi = {10.1109/ACCESS.2025.3599526}, journal = {IEEE Access}, pages = {145703-145713}, shortjournal = {IEEE Access}, title = {Simplifying two-stage object detectors for on-board remote sensing}, volume = {13}, year = {2025}, }
• (2025). Multi-object tracking with memory fusion in UAV videos. ACCESS, 13, 145690-145702. (www)

  @article{ , abstract = {Multi-object tracking (MOT) plays a pivotal role in numerous UAV-related tasks. Nevertheless, conventional approaches often encounter limitations when facing challenges such as motion blur and target deformation, primarily due to their dependence on local features and static spatial representations. To overcome these constraints, we propose AMF-MOT, an innovative framework featuring an Adaptive Memory Fusion module that exploits rich spatio-temporal information. Our method centers around a specialized short-term memory structure that adaptively retrieves relevant information through an attention mechanism and efficiently fuses multi-frame features via a dedicated fusion module. This design enables robust multi-frame dependency modeling and efficient memory propagation, thereby improving object association and re-identification performance. The AMF module surpasses existing methods by offering key advantages: lightweight, plug-and-play, and features fixed computational complexity without requiring a predefined number of input frames. we achieved an Identification F1 Score (IDF1) of 52.8% and a Multiple Object Tracking Accuracy (MOTA) of 41.2% on the VisDrone2019 dataset, and achieved an IDF1 of 69.2% and a MOTA of 48.8% on the UAVDT dataset. The model operates in real-time, making it suitable for time-critical UAV applications. In-depth ablation studies further validate the effectiveness of the AMF module particularly in challenging scenarios involving occlusions and motion blur. In this paper, we contribute a novel memory fusion mechanism, a lightweight MOT architecture, and improved ID association performance by using the AMF module. The source code will be publicly available at: https://github.com/keacifer/AMF-MOT}, archive = {J_ACCESS}, author = {Yibo Cui and Shangsheng Li and Xin Yang and Gang Wang}, doi = {10.1109/ACCESS.2025.3596684}, journal = {IEEE Access}, pages = {145690-145702}, shortjournal = {IEEE Access}, title = {Multi-object tracking with memory fusion in UAV videos}, volume = {13}, year = {2025}, }
• (2025). Star classifier head on deformable attention vision transformer for small datasets. ACCESS, 13, 145680-145689. (www)

  @article{ , abstract = {This study presents a dual approach to enhance the performance of Vision Transformers (ViT) on small datasets by implementing deformable attention mechanisms and a novel Multi-Layer Perceptron (MLP) head with star operations. Deformable attention dynamically focuses on key regions of input images, improving the model’s adaptability tspatial. The star operation captures high-dimensional and non-linear feature spaces from low-dimensional inputs, enhancing the model’s ability to learn complex patterns. We evaluated our proposed method on small datasets such as CIFAR-10, CIFAR-100, MNIST, and Fashion MNIST, demonstrating its superior performance over state-of-the-art transformer-based models like ViT-B, Swin-T, CvT-13, and TSD-T. Our model achieved a 94.63% accuracy on CIFAR-10, outperforming ViT-B by 17.15%, Swin-T by 15.06%, CvT-13 by 2.61%, and TSD-T by 1.36%. Efficiency-wise, our model significantly reduces parameters and FLOPs compared to ViT-B and Swin-T, while maintaining competitive performance against CvT-13 and TSD-T. This study provides valuable insights into the implementation of advanced MLP structures and attention mechanisms in ViT, offering a viable solution for image classification tasks with limited data.}, archive = {J_ACCESS}, author = {Muhammad Izzah Alfatih and Suryo Adhi Wibowo}, doi = {10.1109/ACCESS.2025.3599111}, journal = {IEEE Access}, pages = {145680-145689}, shortjournal = {IEEE Access}, title = {Star classifier head on deformable attention vision transformer for small datasets}, volume = {13}, year = {2025}, }
• (2025). Multi-scale contextual coding for human-machine vision of volumetric medical images. ACCESS, 13, 145663-145679. (www)

  @article{ , abstract = {In recent years, the continuous advancement of digital technologies such as telemedicine and medical cloud computing has promoted collaborative research and diagnosis across multiple medical centers. However, the timely remote transmission and analysis of large volumetric medical images still pose significant challenges. While classical methods predominantly employing lossless compression are increasingly constrained by the limits of compression ratios, lossy 3D medical image compression methods are emerging as a promising alternative. Different from the existing 3D convolutional compression algorithms oriented only for human vision, this paper proposes a Multi-scale Contextual Autoencoder (MCAE) architecture that recurrently incorporates anatomical inter-slice context to optimize the compression of the current slice for both human and machine vision. Our decoded intermediate features, with sufficiently preserved semantic information, enable high-quality visualization and allow downstream machine vision tasks (e.g., segmentation and classification) to be performed directly without pixel-level recovery. To reduce the compression bit cost, we create a Multi-Dimensional Entropy Model that integrates inter-slice latent context with spatial-channel context and hierarchical hypercontext. Experimental results demonstrate that our framework obtains an average 9% BD-Rate reduction over the Versatile Video Coding (VVC) anchor on MRNet datasets, while achieving superior recognition performance for downstream segmentation and classification tasks than inputting reconstructed lossy images.}, archive = {J_ACCESS}, author = {Jietao Chen and Weijie Chen and Qianjian Xing and Feng Yu}, doi = {10.1109/ACCESS.2025.3597008}, journal = {IEEE Access}, pages = {145663-145679}, shortjournal = {IEEE Access}, title = {Multi-scale contextual coding for human-machine vision of volumetric medical images}, volume = {13}, year = {2025}, }
• (2025). Joint friction dynamic estimation for robotic finger using novel fixed-time adaptive model free algorithm with ZNN-based approximator. ACCESS, 13, 145649-145662. (www)

  @article{ , abstract = {When performing delicate operations, robot fingers are often faced with the interference of unknown uncertainties and strong nonlinear friction dynamics, and advanced control strategies are needed to compensate for the uncertainties and nonlinear dynamics in the system. Therefore, this paper proposes a new fixed-time adaptive estimation algorithm (FXT-ZNN) based on zeroing neural network to estimate the joint friction force during grasping. The estimated friction force is useful for the compensation controller to effectively improve the control accuracy and system response speed in diverse grasping application. The algorithm does not rely on the friction model and has the characteristics of fast convergence speed and high estimation accuracy. To verify its performance, FXT-ZNN is compared with gradient descent (GDA), least squares (LSA) and fixed-time radial basis neural network (FXT-RBFNN) algorithms. Under standard conditions, disturbances and noise environments, the IAE of our proposed FXT-ZNN is 0.00074, 0.00098 and 0.00075 respectively, which is significantly better than GDA (0.0607, 0.1396, 0.619), LSA (0.0284, 0.0940, 0.0293) and FXT-RBFNN (0.0023, 0.0033, 0.0024). The results confirm the strong robustness, anti-interference and anti-noise of FXT-ZNN, and the method does not rely on the friction model, making it a powerful method for friction compensation in fine robot finger grasping tasks. The friction estimation of robot fingers during grasping based on this control strategy will lay the foundation for grasping applications that require fine manipulation.}, archive = {J_ACCESS}, author = {Wang Yuanyang and Muhammad Nasiruddin Mahyuddin}, doi = {10.1109/ACCESS.2025.3599835}, journal = {IEEE Access}, pages = {145649-145662}, shortjournal = {IEEE Access}, title = {Joint friction dynamic estimation for robotic finger using novel fixed-time adaptive model free algorithm with ZNN-based approximator}, volume = {13}, year = {2025}, }
• (2025). Modeling practical capacitive structures for electronic and dielectric property extraction and analysis. ACCESS, 13, 145627-145648. (www)

  @article{ , abstract = {A method for modeling the full steady-state and small-signal behavior of practical capacitive structures, such as metal-insulator-metal capacitors, diodes, and transistors, is presented. Simple lumped element models fail to properly represent the behavior of most practical structures, resulting in incorrect electronic material property extraction and evaluation. The methodology and model presented in this paper properly characterizes practical structures and can be employed to more accurately understand and evaluate experimental results, extract various electronic and dielectric properties of the constituent materials, and predict possible behavior of new structures and materials. The impact of dielectric properties (e.g., real versus complex permittivity, conductance, Cole-Cole dielectric response) and geometry on observed behavior of practical structures, especially with regard to extracting correct property values, is discussed.}, archive = {J_ACCESS}, author = {James G. Champlain and Vikrant J. Gokhale and Matthew T. Hardy and Eric N. Jin}, doi = {10.1109/ACCESS.2025.3597894}, journal = {IEEE Access}, pages = {145627-145648}, shortjournal = {IEEE Access}, title = {Modeling practical capacitive structures for electronic and dielectric property extraction and analysis}, volume = {13}, year = {2025}, }
• (2025). Resilient control strategy for a grid interactive PV-UAPC system under non sinusoidal grid and load disturbances. ACCESS, 13, 145611-145626. (www)

  @article{ , abstract = {The widespread deployment of power electronics based nonlinear loads, coupled with the increasing prevalence of highly distorted grid voltages, presents critical power quality challenges in grid interactive distributed networks. To tackle these challenges, this study employs a band pass filter based third order generalized integrator (BTOGI) structure to effectively synchronize the grid with a photovoltaic fed universal active power converter (PV-UAPC) system. The proposed filtering technique alleviates both grid side and load side abnormalities, suppresses dc offset, compensates for unbalance, and minimizes steady state error while improving dynamic response and enhancing harmonic elimination. To verify the efficacy of the proposed approach, the grid interactive PV-UAPC system is simulated under diverse operating conditions in MATLAB/Simulink. Furthermore, to ensure practical feasibility and resilience under non ideal grid and load conditions, the simulation findings are validated using a real time OP5700 based test bench. A comparative performance analysis between the second order and third order generalized integrators highlights the superior performance of the proposed BTOGI filter, particularly under distorted grid voltage and unbalanced load conditions. Furthermore, across all operating modes, the proposed control approach ensures that the total harmonic distortion (THD) of utility grid currents and load voltages maintains significantly below thresholds set by the IEEE 519 guideline, thereby enhancing overall power quality and system reliability.}, archive = {J_ACCESS}, author = {K. M. Sanjeeva Kumar and Satyajit Das}, doi = {10.1109/ACCESS.2025.3599734}, journal = {IEEE Access}, pages = {145611-145626}, shortjournal = {IEEE Access}, title = {Resilient control strategy for a grid interactive PV-UAPC system under non sinusoidal grid and load disturbances}, volume = {13}, year = {2025}, }
• (2025). Bounded individualized treatment effect: A novel approach for uplift modeling. ACCESS, 13, 145599-145610. (www)

  @article{ , abstract = {The advent of artificial intelligence has greatly facilitated the prediction of individualized treatment effects, expanding the field of causal inference beyond the traditional realm of econometric studies. This shift has enabled prescription of personalized treatments via predictive analytics, allowing businesses to deliver personalized and relevant interactions with customers across various touchpoints. In this paper, we propose a novel approach for uplift model estimation called Bounded Individualized Treatment Effect (BITE), which is designed for business analytics tasks. The goal is to prioritize customers who will be persuaded by the treatment, by filtering individuals based on their baseline probabilities. Our experiments show the virtues of the BITE approach on seven benchmark datasets and 21 uplift classifiers, achieving best average performance compared to traditional uplift modeling and standard predictive classification.}, archive = {J_ACCESS}, author = {Catalina Sánchez and Carla Vairetti and Kristof Coussement and Sebastián Maldonado}, doi = {10.1109/ACCESS.2025.3599482}, journal = {IEEE Access}, pages = {145599-145610}, shortjournal = {IEEE Access}, title = {Bounded individualized treatment effect: A novel approach for uplift modeling}, volume = {13}, year = {2025}, }
• (2025). Static output feedback control for linear systems via multiple lyapunov functions. ACCESS, 13, 145590-145598. (www)

  @article{ , abstract = {This paper is concerned with the problem of synthesizing static output feedback (SOF) controllers for linear systems via multiple Lyapunov functions (LFs). By appending a zero block to the matrix related to the control input and incorporating a real matrix into the control gain accordingly, the closed-loop system is extended, thereby introducing additional degrees of freedom into the proposed synthesis conditions. Based on the proposed multiple LFs, which are constituted by the summation of LFs dependent on system states at different instants, sufficient conditions in terms of linear matrix inequalities (LMIs) are derived to ensure the stability and desired performance of the closed-loop system. Compared with the existing methods, the proposed scheme achieves decoupling without requiring the correlation matrix to possess a specific structure, and yields superior results. Finally, the effectiveness and superiority of the proposed technique are validated through simulation example.}, archive = {J_ACCESS}, author = {Guokai Liang and Xin Xie and Wei Pan and Yanzhang Gu and Zeming Chen and Zhe Li and Weijie Zeng}, doi = {10.1109/ACCESS.2025.3597363}, journal = {IEEE Access}, pages = {145590-145598}, shortjournal = {IEEE Access}, title = {Static output feedback control for linear systems via multiple lyapunov functions}, volume = {13}, year = {2025}, }
• (2025). 5G-MobiCare: A secure 5G assisted remote mobile health monitoring platform. ACCESS, 13, 145572-145589. (www)

  @article{ , abstract = {E-Healthcare utilizes IoT-enabled medical devices equipped with sensors to gather and transmit patient health data to a centralized electronic health record (EHR) system via mobile networks, enabling remote health management. This setup generates vast amounts of real-time data, making secure device authentication essential to ensure data privacy and security. In critical scenarios like ambulance care, quick and reliable authentication becomes crucial as devices remain in motion. To address this, a remote registration and group authentication scheme is proposed for eSIM-enabled medical devices using the 5G mobile network for transmitting sensitive health data. eSIM-enabled sensors provide a flexible, secure means of connecting to healthcare networks, while group authentication further enhances efficient and reliable communication. Lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), one-way hash functions, and XOR operations, address the resource constraints of medical sensors. A group secret key enables collective device authentication, reducing computational and communication overhead. The scheme’s security is validated using BAN logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA). The proposed scheme enhances security and efficiency for e-healthcare systems. The group secret key reduces resource usage, making the approach suitable for constrained devices. Lightweight cryptographic functions ensure data security without overburdening sensors. Security validations confirm robustness against threats. The proposed framework for eSIM-enabled medical devices leverages the 5G network and lightweight cryptographic techniques to provide secure, efficient communication of health data. This approach enhances patient management, particularly in critical scenarios like ambulance care, where reliable authentication is vital. Future work can explore scalability and broader IoT applications.}, archive = {J_ACCESS}, author = {Hemangi Goswami and Tarun Pandey and Hiten Choudhury}, doi = {10.1109/ACCESS.2025.3593197}, journal = {IEEE Access}, pages = {145572-145589}, shortjournal = {IEEE Access}, title = {5G-MobiCare: A secure 5G assisted remote mobile health monitoring platform}, volume = {13}, year = {2025}, }
• (2025). Thermodynamic modeling of hashtag dynamics for social media clustering: A maxwell-boltzmann approach. ACCESS, 13, 145556-145571. (www)

  @article{ , abstract = {Social media hashtags function as critical organizational markers in digital discourse, yet traditional weighting methods fail to capture their dynamic significance across temporal and contextual dimensions. This paper presents a novel thermodynamic framework that conceptualizes social network activity as system “temperature”, applying statistical mechanics principles to model hashtag importance as process innovation. We establish mathematical foundations based on the Maxwell-Boltzmann distribution, providing an information-theoretic justification for dynamic hashtag weighting. Our approach incorporates activation thresholds and power-law scaling behaviors through a temperature-dependent function, with Simple Moving Average techniques implemented to stabilize temperature estimation, mathematically reducing variance by a factor of 1/N. Empirical evaluation using Twitter discourse from the US Presidential Election demonstrates unprecedented improvements in clustering performance: Silhouette Scores increased from 0.0126 to 0.9070 for Trump-related content and from 0.0105 to 0.8220 for Biden-related content, while Calinski-Harabasz Scores improved from 65.51 to nearly 98 million. These findings establish a rigorous mathematical bridge between thermodynamic systems and social media behavior, contributing to computational social science by providing a theoretical framework that significantly enhances discourse community detection in politically polarized environments. The approach enables more accurate identification of topic clusters, revealing distinct discourse patterns that conventional methods fail to capture.}, archive = {J_ACCESS}, author = {Krishnan Batri and Rajermani Thinakaran and Bhuvana Jayabalan and L. Karthikeyan and S. Lakshmi and R. Sowrirajan and Sivaram Murugan}, doi = {10.1109/ACCESS.2025.3598260}, journal = {IEEE Access}, pages = {145556-145571}, shortjournal = {IEEE Access}, title = {Thermodynamic modeling of hashtag dynamics for social media clustering: A maxwell-boltzmann approach}, volume = {13}, year = {2025}, }
• (2025). Performance analysis and design optimization of wearable RFID sensor-antenna system for healthcare applications. ACCESS, 13, 145540-145555. (www)

  @article{ , abstract = {Real-time tracking, together with monitoring and data collection of human body signals, happens through RFID technology amalgamated with wearable electronics called wearable RFID sensors. Modern sensors find placement in clothing items as well as accessories, and they also operate beneath the skin. The sensors serve several applications across healthcare monitoring, together with asset tracking and security system functions. This article delivers an extensive examination of body-area RFID antenna design issues regarding operating frequencies with biological tissue effects and antenna performance, together with safety compliance aspects. A multicomponent antenna system works as an RFID shield and functions for Bluetooth along with Wi-Fi (2.4 GHz), and includes temperature and sweat sensors for healthcare distance monitoring. The designed antenna spans 600 MHz (2.2–2.8 GHz) within its $25\times 40\times 1.5$ mm3 dimensions. This antenna finds optimal application in wearable systems because it utilizes semi-flexible Rogers RT5880 substrate material. The system obtains miniaturized dimensions through the implementation of two E-shaped slots, which allow the device to operate in the desired frequency range. Stability characteristics allow the antenna to operate properly near human bodies and meet the SAR safety requirements of 1.6 W/kg at its operating frequency. Realistic performance tests produced efficient results close agreement between simulated and measured data, thus validating the design potential as a remote healthcare monitoring solution.}, archive = {J_ACCESS}, author = {Waqas Ali and Nizam-Uddin and Muhammad Zahid and Sultan Shoaib}, doi = {10.1109/ACCESS.2025.3596519}, journal = {IEEE Access}, pages = {145540-145555}, shortjournal = {IEEE Access}, title = {Performance analysis and design optimization of wearable RFID sensor-antenna system for healthcare applications}, volume = {13}, year = {2025}, }
• (2025). Variable-gain state-of-charge balancing method for smart battery packs. ACCESS, 13, 145528-145539. (www)

  @article{ , abstract = {This paper investigates state-of-charge (SOC) balancing in battery energy storage systems (BESS) using integrated dc-dc converters, which commonly referred to as smart batteries. Mathematical models are developed for both conventional fixed-gain and proposed variable-gain balancing strategies. In the variable-gain method, the control gain is adapted in real-time based on the instantaneous SOC deviation, enabling faster convergence without compromising dc-link voltage stability. Analytical results demonstrate that the theoretical variable-gain approach achieves balancing up to five times faster than the fixed-gain method. The models are validated through simulations and Controller Hardware-in-the-Loop (C-HIL) experiments. The proposed strategy requires only a firmware update, making it suitable for retrofitting existing systems and applicable across different battery chemistries, including second-life applications.}, archive = {J_ACCESS}, author = {Jonathan Silva de Oliveira and Bruno de Araujo Coutinho and Pedro Ivo de Oliveira Tironi and Allan Fagner Cupertino and Erick Matheus da Silveira Brito and Heverton Augusto Pereira}, doi = {10.1109/ACCESS.2025.3597229}, journal = {IEEE Access}, pages = {145528-145539}, shortjournal = {IEEE Access}, title = {Variable-gain state-of-charge balancing method for smart battery packs}, volume = {13}, year = {2025}, }
• (2025). JSVE: JSON schema variant extraction based on clustering and formal concept analysis. ACCESS, 13, 145517-145527. (www)

  @article{ , abstract = {The JSON data format is widely used in a variety of data representation and exchange scenarios due to its flexibility. JSON data is usually schemaless, which ensures the lightweight and flexible advantages of JSON. However, the lack of schema information brings some problems in effective data processing, data management, and data integration. Currently, most research focuses on identifying the global schema of JSON data sets, unable to describe the detailed structure of data, which increases the implementation and maintenance costs. To solve this problem, this paper proposes an improved method called JSON Schema Variant Extraction (JSVE), which extracts precise JSON schema variants from datasets based on clustering and formal concept analysis. As complex structures of large, heterogeneous JSON data cannot be analyzed directly, JSVE solves this limitation by flattening field names and clustering similar documents. It then applies an algorithm based on the idea of formal concept analysis to identify schema variants from each cluster. Experimental evaluations in both stand-alone and distributed environments conducted on 4 real-world datasets—DBLP, Tweets, TV Series, and BestBuy—demonstrate that the proposed method JSVE is more fine-grained and efficient in extracting schema variants from a collection of JSON documents than the current state-of-the-art method ClustVariants.}, archive = {J_ACCESS}, author = {Ziyi Zhang and Teng Lv}, doi = {10.1109/ACCESS.2025.3599650}, journal = {IEEE Access}, pages = {145517-145527}, shortjournal = {IEEE Access}, title = {JSVE: JSON schema variant extraction based on clustering and formal concept analysis}, volume = {13}, year = {2025}, }
• (2025). RTK + 4G combination positioning method under sheltered environment for high-accuracy of MMS. ACCESS, 13, 145507-145516. (www)

  @article{ , abstract = {The accuracy of the existing positioning methods is not sufficiently ideal in mobile mapping system (MMS) when GPS signals are sheltered by such as tall buildings, mountain peaks. To solve this problem, this paper presents a new positioning method through RTK (Real-time kinematic) +4G (4th generation mobile communication technology) combination. This method is divided into two positioning modes: GNSS positioning mode and 4G positioning mode. Under normal conditions, the system operates in the GNSS positioning mode with the GNSS satellite signals and performs differential correction to obtain the precise position coordinates. When the system is under the sheltered environment, or the number of satellite signals receivable are less than 4, it switches to the 4G positioning mode. This means that the proposed positioning mode obtains the raw position coordinates by receiving the radio signals from the 4G base station and iteratively compute the coordinates using the UKF (Unscented Kalman Filter). Finally, a system, consisting of both software and hardware, was developed and constructed, and accuracy verification experiments are conducted under a variety of the sheltered environments. With the experimental results, it can be demonstrated that the developed system runs in a good working condition and the error of positioning is less than 0.1m when GNSS signal are sheltered.}, archive = {J_ACCESS}, author = {Guoqing Zhou and Wei Liu and Jianwu Liu and Zongyin Xie and Yi Tang and Jiasheng Xu and Ertao Gao}, doi = {10.1109/ACCESS.2025.3596116}, journal = {IEEE Access}, pages = {145507-145516}, shortjournal = {IEEE Access}, title = {RTK + 4G combination positioning method under sheltered environment for high-accuracy of MMS}, volume = {13}, year = {2025}, }
• (2025). Leveraging gradient noise for detection and filtering of byzantine clients. ACCESS, 13, 145494-145506. (www)

  @article{ , abstract = {Distributed Learning enables multiple clients to collaboratively train large models on private, decentralized data. However, this setting faces a significant challenge: real-world datasets are inherently heterogeneous, and the distributed nature of the system makes it vulnerable to Byzantine attacks. This combination makes it difficult for standard aggregations to reliably distinguish between honest clients with atypical data and malicious participants attempting to disrupt the training. To address this problem, we propose a novel Byzantine defense that leverages the statistical behavior of stochastic gradient noise (GN) in deep learning to identify malicious clients. Unlike pre-processing techniques that attempt to reduce inter-client gradient variance, our method directly exploits inherent gradient properties to filter malicious updates. Our aggregation can be deployed as a standalone defense or in combination with existing robust aggregation rules. We provide theoretical guarantees on convergence under standard assumptions. Our empirical evaluations on different benchmark datasets further demonstrates that our approach achieves high detection accuracy across a range of attack scenarios, significantly improving robustness without sacrificing model performance.}, archive = {J_ACCESS}, author = {Latifa Errami and Vyacheslav Kungurtsev and El Houcine Bergou}, doi = {10.1109/ACCESS.2025.3600189}, journal = {IEEE Access}, pages = {145494-145506}, shortjournal = {IEEE Access}, title = {Leveraging gradient noise for detection and filtering of byzantine clients}, volume = {13}, year = {2025}, }
• (2025). PPG-based accurate insomnia detection system using convolutional neural networks with self-attention mechanism and gated recurrent units. ACCESS, 13, 145474-145493. (www)

  @article{ , abstract = {Adequate sleep is essential for physical and mental well-being. The quality of sleep is significantly impacted by sleep disorders like narcolepsy, sleep apnea, nocturnal frontal lobe epilepsy, and insomnia. Insomnia is a prevalent sleep disorder characterized by difficulty initiating or maintaining sleep, leading to daytime fatigue and restlessness. An accurate and automated detection system is essential for effective diagnosis and management. Usually, polysomnogram (PSG) signals are utilized to identify sleep disorders, but these signals are challenging to handle, computationally intensive to process, and affect the convenience of patients. The proposed method provides a non-invasive approach for insomnia detection. To the best of our knowledge, this is the first study that utilizes single-channel photoplethysmography (PPG) to automatically detect insomnia. In the proposed study, we performed experiments on two publicly accessible datasets: the Cyclic Alternating Pattern (CAP) sleep database and the MESA Sleep Dataset, considering a diverse set of subjects. This study introduces a novel approach for PPG-based insomnia detection, utilizing Convolutional Neural Network (CNN) with self-attention, CNN with Gated Recurrent Unit (GRU), and transformer-based models. The CNN with GRU-based approach demonstrated notable performance, achieving impressive classification accuracy of 96.00%, alongside robust precision, recall, and F1-score. Importantly, we further explored model explainability using Grad-CAM, providing visual insights into the learned model’s decision-making from PPG signals. The results emphasize the effectiveness and interpretability of these advanced models in capturing complex patterns in PPG signals, establishing PPG-based sleep monitoring as a practical and efficient solution for home use with wearable devices.}, archive = {J_ACCESS}, author = {Hardik Telangore and Heneel Makwana and Prithviraj Verma and Manish Sharma and Hasan S. Mir and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3598863}, journal = {IEEE Access}, pages = {145474-145493}, shortjournal = {IEEE Access}, title = {PPG-based accurate insomnia detection system using convolutional neural networks with self-attention mechanism and gated recurrent units}, volume = {13}, year = {2025}, }
• (2025). Predict unmatching compositions for compositional zero-shot learning. ACCESS, 13, 145464-145473. (www)

  @article{ , abstract = {Compositional Zero-Shot Learning (CZSL) poses the challenge of predicting unseen attribute-object combinations in images. In this study, we focus on the open-world CZSL task, which presents a more realistic and comprehensive challenge by expanding the search space to include unmatching pairs. Through t-SNE visualization and convergence analysis, we observe that existing methods struggle to capture the interdependencies between labels, leading to the Plausible Unmatching Pair (PUP) problem, where models are prone to confusing matching and unmatching pairs. Inspired by label dependency modeling in multi-label classification, we propose a novel approach called Absence Modeling to address the PUP problem. Absence Modeling aims to predict unmatching compositions, allowing the model to learn irrelevant information between attributes and objects, thereby improving its ability to capture interdependencies. By applying Absence Modeling, we observe significant improvements in zero-shot performance and achieve state-of-the-art results. Our experimental results validate that our approach effectively addresses the PUP problem.}, archive = {J_ACCESS}, author = {Soohyeong Kim and Yong Suk Choi}, doi = {10.1109/ACCESS.2025.3596387}, journal = {IEEE Access}, pages = {145464-145473}, shortjournal = {IEEE Access}, title = {Predict unmatching compositions for compositional zero-shot learning}, volume = {13}, year = {2025}, }
• (2025). AMoRE: Additive manufacturing of RIS elements. ACCESS, 13, 145451-145463. (www)

  @article{ , abstract = {A deep investigation of Reconfigurable Intelligent Surfaces (RIS) and Holographic MIMO (H-MIMO) surfaces in 5G and beyond systems requires numerous tests with various prototypes. Traditional prototyping of these surfaces relies on expensive or time-consuming printed circuit board technologies. Alternatively, additive manufacturing, particularly 3D-printing, offers rapid prototyping of radio frequency components with sophisticated geometry and low cost. This paper presents a novel methodology for additive manufacturing of RIS elements (AMoRE). AMoRE provides a detailed description of the production process of 3D-printed elements of the surface, including dielectric material characterization and waveguide design for measurement. An experimental and simulation comparison of traditional and 3D-printed elements of the surface demonstrates that the latter increases the bandwidth by 40% and reduces losses, being five times cheaper and having the same thickness. The results highlight the potential of additive manufacturing to enable rapid and cost-effective prototyping of the surfaces compared with traditional methods.}, archive = {J_ACCESS}, author = {Andrey Tyarin and Kirill Glinskiy and Roman Zlobin and Aleksey Kureev and Evgeny Khorov}, doi = {10.1109/ACCESS.2025.3598596}, journal = {IEEE Access}, pages = {145451-145463}, shortjournal = {IEEE Access}, title = {AMoRE: Additive manufacturing of RIS elements}, volume = {13}, year = {2025}, }
• (2025). Vibration control of piezoelectric cantilever beam with physics-informed neural networks. ACCESS, 13, 145442-145450. (www)

  @article{ , abstract = {Intelligent actuators, particularly piezoelectric actuators, are widely used for vibration control of engineering structures like beams, plates, and shells due to their advantages of good linearity, high precision, and simple configuration. However, traditional control methods often suffer from limited adaptability to complex dynamic environments. This paper proposes a Physics-Informed Neural Networks (PINNs) enhanced Deep Reinforcement Learning (DRL) framework for high-precision vibration control of piezoelectric cantilever beams. Unlike conventional model-based methods, our approach integrates the Euler-Bernoulli beam dynamics directly into the DRL training process, generating voltage control strategies under physical constraints through joint optimization of data-driven loss and partial differential equation (PDE) residuals. A Double Deep Q-Network (DDQN) agent observes real-time tip displacement and velocity, then outputs voltage actions. In the paper, the fundamental electromechanical coupling mechanism is established based on the cantilever beam’s governing equations and sensor equations of general shell structures. Employing modal expansion methods, we derive both the modal voltage expression and the modal force formulation for the piezoelectric actuator. The architecture of the Deep Reinforcement Learning (DRL) controller—specifically a Physics-Informed Double Deep Q-Network and its underlying neural network structure are subsequently detailed. Evaluations across the first three vibration modes under free decay, sinusoidal excitation and white noise loads demonstrate that the PINNs-DRL controller significantly outperforms conventional negative velocity feedback (NVF) in suppressing transient oscillations and residual vibrations.}, archive = {J_ACCESS}, author = {Jiafeng Lao and Yu Chen and Huiyu Li}, doi = {10.1109/ACCESS.2025.3598501}, journal = {IEEE Access}, pages = {145442-145450}, shortjournal = {IEEE Access}, title = {Vibration control of piezoelectric cantilever beam with physics-informed neural networks}, volume = {13}, year = {2025}, }
• (2025). OPTNet: Optimized pixel-transformer model for adaptive retinal fundus image enhancement. ACCESS, 13, 145416-145441. (www)

  @article{ , abstract = {Retinal low-quality images present significant challenges for accurate diagnosis and monitoring of eye diseases by obscuring critical anatomical features and reducing analytical precision. This study introduces OPTNet, an optimized pixel-wise transformer model designed to efficiently enhance degraded or low-quality retinal images. The proposed approach consists of three main stages: 1) pre-processing to standardize image dimensions and balance color channels, 2) model development, in which a lightweight ANN-based feature extractor learns retinal structures and generates self-measured quality labels, and 3) pixel-level transformation guided by these predicted labels to perform localized enhancement. The performance of OPTNet was evaluated using statistical metrics across various architectures during training and testing, and benchmarked on six public retinal datasets: DRIVE, CHASE-DB1, HRF, DRHAGIS, FIRE, and FIVES. A comprehensive evaluation was conducted using both full-reference and no-reference quality assessment (QA) metrics, supported by qualitative analysis. OPTNet achieved competitive results including a 21.3% improvement in NIQE and 17.8% reduction in BRISQUE compared with existing methods. The final scores included SSIM (0.1925), VIF (0.1911), BIF (1.3018), EME (11.1704), NIQE (4.0730), and BRISQUE (30.3003), indicating perceptual and structural enhancement. Additionally, it effectively preserved brightness and anatomical fidelity while minimizing distortion (CD = 0.4214), blur (0.0889), and artifacts (0.2903). In conclusion, OPTNet outperforms state-of-the-art enhancement techniques by striking a robust balance between quality improvement and artifact suppression, demonstrating its strong potential for integration into clinical ophthalmic diagnostic pipelines.}, archive = {J_ACCESS}, author = {Faisal Majed Alqahtani and Somaya Adwan and Mohd Yazed Ahmad and Salmah Binti Karman}, doi = {10.1109/ACCESS.2025.3596045}, journal = {IEEE Access}, pages = {145416-145441}, shortjournal = {IEEE Access}, title = {OPTNet: Optimized pixel-transformer model for adaptive retinal fundus image enhancement}, volume = {13}, year = {2025}, }
• (2025). Automatic license plate recognition in in-the-wild scenarios: A comprehensive review, open issues, and future directions. ACCESS, 13, 145387-145415. (www)

  @article{ , abstract = {Over the past few years, breakthroughs in deep learning applied to computer vision has driven the development of novel techniques and various solutions for automatic license plate recognition (ALPR). Albeit this technology has been successfully used in constrained conditions, in-the-wild scenarios have emerged as the new challenge for these systems. In-the-wild scenarios, also referred to as complex natural scenes or open scenarios, involve using regular video surveillance in various settings, including but not limited to drones and security robots. This survey is devoted to reviewing the state-of-the-art associated with in-the-wild ALPR. From a literature-based perspective, we (i) highlight specific approaches used for license plate (LP) detection, LP rectification, and LP recognition; (ii) present some of the most widely used datasets for benchmarking and summarized the results reported in the reviewed studies; and (iii) outline key research directions for advancing in-the-wild ALPR. This paper aims to provide practitioners, researchers, and experts with a comprehensive understanding of the current state of ALPR systems and the associated challenges, particularly in-the-wild scenarios.}, archive = {J_ACCESS}, author = {Amir Ismail and Maroua Mehri and Anis Sahbani and Najoua Essoukri Ben Amara}, doi = {10.1109/ACCESS.2025.3598971}, journal = {IEEE Access}, pages = {145387-145415}, shortjournal = {IEEE Access}, title = {Automatic license plate recognition in in-the-wild scenarios: A comprehensive review, open issues, and future directions}, volume = {13}, year = {2025}, }
• (2025). Cost-efficient fall risk assessment with attention augmented vision machine learning on sit-to-stand test videos. ACCESS, 13, 145373-145386. (www)

  @article{ , abstract = {Falls among the elderly and other at-risk populations pose a significant public health challenge, necessitating innovative methods to detect and intervene early. While automated fall risk assessment methods using wearable sensors or depth cameras have been proposed, the physical and psychological burden of wearing extra sensors, and the high cost of specialized equipment like Kinect, limits their practical adoption. To tackle this challenge, this paper presents a novel machine learning-based fall risk assessment approach called FRAVM, which operates on Five times Sit-To-Stand (FSTS) test videos captured with standard, widely available cameras to identify individuals requiring fall prevention interventions. To enhance the practicality of FRAVM, 3D pose estimation is applied to generate vision-rich 3D body keypoints, mitigating the challenges posed by restricted camera angles. Median-average filtering is used to reduce noise caused by video shaking and pose estimation inaccuracies, while a new Dynamic Time Warping (DTW)-based matching algorithm is designed to handle interference from irrelevant individuals appearing in the video. Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. Experimental evaluations on a dataset of 450 FSTS test videos demonstrate the high performance of FRAVM, with detection accuracy, F1 score, and ROC-AUC achieving 88.64%, 88.58%, and 98.86%, respectively. The ablation analysis confirms that the components in FRAVM are essential for achieving optimal results.}, archive = {J_ACCESS}, author = {Chunhua Pan and Boting Qu and Rui Miao and Xin Wang}, doi = {10.1109/ACCESS.2025.3598002}, journal = {IEEE Access}, pages = {145373-145386}, shortjournal = {IEEE Access}, title = {Cost-efficient fall risk assessment with attention augmented vision machine learning on sit-to-stand test videos}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey of super-resolution remote sensing image datasets: Evolution, challenges, and future directions. ACCESS, 13, 145350-145372. (www)

  @article{ , abstract = {High-resolution (HR) imagery plays a crucial role in remote sensing applications such as land-cover classification, environmental monitoring, urban planning, and disaster assessment, yet acquiring such imagery remains challenging due to sensor limitations, atmospheric disturbances, and high data costs. Super-resolution (SR) has emerged as a promising computational alternative, enhancing spatial detail from low-resolution data without requiring hardware upgrades. Despite significant progress in SR algorithms—particularly those driven by deep learning—the supporting datasets have received comparatively little attention. This survey presents a comprehensive review of over 50 publicly available SR datasets, including satellite multispectral, panchromatic, hyperspectral, UAV, and airborne imagery. We systematically analyze them by image quantity, spatial resolution, sensor diversity, acquisition platform, and land-cover representation. We categorize these datasets based on SR application type (single-image vs. multi-image, pan-involve image, multi-spectral image, and hyperspectral image) and explore how dataset characteristics influence algorithm development and evaluation. Several challenges are identified, including the prevalence of unrealistic degradation models, geometric misalignments, land-class imbalances, and trade-offs between resolution and geographic coverage. In response, we propose a roadmap for next-generation SR datasets that prioritizes multi-source, multi-angle, and multi-temporal observations, standardized preprocessing pipelines, and balanced representation of land types. This work aims to inform dataset selection, support fair benchmarking, and guide the design of robust and scalable SR models for real-world remote sensing scenarios.}, archive = {J_ACCESS}, author = {Anh Vu-Duc and Khang Nguyen-Vi and Bao Bui-Quoc and Nidal Kamel}, doi = {10.1109/ACCESS.2025.3599535}, journal = {IEEE Access}, pages = {145350-145372}, shortjournal = {IEEE Access}, title = {A comprehensive survey of super-resolution remote sensing image datasets: Evolution, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Event-triggered adaptive neural control for pure feedback stochastic nonlinear systems with input saturation and output state constraints. ACCESS, 13, 145334-145349. (www)

  @article{ , abstract = {This paper proposes a novel event-triggered adaptive neural control scheme for pure-feedback stochastic nonlinear systems subject to input saturation and partial state constraints. By employing the implicit function theorem and mean value theorem, the pure-feedback system is systematically transformed into a strict-feedback form with bounded approximation errors. A key innovation is the introduction of a continuously differentiable asymmetric saturation model based on hyperbolic tangent functions, which accurately characterizes input constraints. The control design integrates backstepping with barrier Lyapunov functions (BLFs) and adaptive neural network, ensuring robust handling of state constraints and stochastic uncertainties at each design step. Theoretical analysis proves that all closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) in probability (fourth-moment sense), while the tracking error converges asymptotically to zero. An event-triggered mechanism further improves computational efficiency. Comparative simulations validate the superiority of the proposed method over conventional approaches.}, archive = {J_ACCESS}, author = {Daogen Jiang and Longyang Wang and Jiahao Li and Jiacheng Pan}, doi = {10.1109/ACCESS.2025.3598376}, journal = {IEEE Access}, pages = {145334-145349}, shortjournal = {IEEE Access}, title = {Event-triggered adaptive neural control for pure feedback stochastic nonlinear systems with input saturation and output state constraints}, volume = {13}, year = {2025}, }
• (2025). Weighted feature fusion network based on large kernel convolution and transformer for multi-modal remote sensing image segmentation. ACCESS, 13, 145319-145333. (www)

  @article{ , abstract = {The heterogeneity and complexity of multi-modal data in high-resolution remote sensing images posed a severe challenge to existing cross-modal networks that aim to fuse complementary information of high-resolution optical and elevation data information (DSM) to achieve accurate semantic segmentation. To solve this problem, a weighted feature fusion network based on large kernel convolution and Transformer (LTFCNet) was proposed. The model uses two parallel encoders to extract the features of different modalities, an improved cross-fusion module to enhance the encoder’s feature extraction capability, and a gate module based on large kernel and Transformer to achieve multi-modal fusion. Finally, a Difference information Feature Fusion Module (DFFM) leveraging attention to differential regions is used to achieve cross-level feature fusion and enhance small object detection. To evaluate the network, we compare it with several state-of-the-art models (SOTA), using the Potsdam and Vaihingen datasets. The experimental results demonstrate that the proposed model outperforms other SOTA models by approximately 2% in the mIoU metric, validating its effectiveness in multi-modal feature fusion.}, archive = {J_ACCESS}, author = {Jianxia Wang and Shaozu Qiu and Jia Cai and Xiaoming Zhang}, doi = {10.1109/ACCESS.2025.3598116}, journal = {IEEE Access}, pages = {145319-145333}, shortjournal = {IEEE Access}, title = {Weighted feature fusion network based on large kernel convolution and transformer for multi-modal remote sensing image segmentation}, volume = {13}, year = {2025}, }
• (2025). On block size value in proof-of-work blockchains with zero block reward. ACCESS, 13, 145303-145318. (www)

  @article{ , abstract = {According to the Bitcoin blockchain protocol, the block reward will decrease to zero over time, leaving miners to rely solely on transaction fees for their income. Consequently, elevating the block size translates into augmented rewards for the successful miner. However, counterbalancing this, network latency reduces the miner’s probability of success when the block size increases. Choosing the suitable block size is crucial for maximizing profit and can significantly influence the network’s stability and robustness. This paper focuses on the problem of selecting the most optimal block size under the given circumstances. Through the utilization of win probability and the computation of miners’ expected profits, we demonstrate that each miner possesses an optimal and distinct block size considering the block size and mining power of fellow miners. Furthermore, we determine and elucidate the Nash equilibrium in the miners’ game. In this Nash equilibrium, miners with higher mining power will choose larger block sizes, and the miner with the greatest mining power can independently determine their block size without considering other miners. Also, when all miners have equal mining power, they will unanimously select the same block size. We have additionally analyzed the scenario where the block size is restricted. In this case, we have identified the best response and Nash equilibrium, demonstrating that miners choose the maximum feasible block size under specific conditions. Moreover, it has been established that if the block size surpasses a certain threshold, none of the miners will select the maximum block size. Conversely, if the block size falls below a threshold, all miners will choose the maximum block size value.}, archive = {J_ACCESS}, author = {Mojtaba Shahrab and Hamed Narimani and Mohammad-Hossein Manshaei}, doi = {10.1109/ACCESS.2025.3598324}, journal = {IEEE Access}, pages = {145303-145318}, shortjournal = {IEEE Access}, title = {On block size value in proof-of-work blockchains with zero block reward}, volume = {13}, year = {2025}, }
• (2025). A practical multimodal fusion system with uncertainty modeling for robust visual and affective applications. ACCESS, 13, 145289-145302. (www)

  @article{ , abstract = {Multimodal fusion models are widely applied in practical tasks such as image classification and sentiment analysis. However, real-world data often contain noisy or imbalanced modality inputs, significantly affecting the performance of traditional fusion methods. To address this challenge, we propose an Uncertainty-Aware Multimodal Fusion (UAMF) framework designed to enhance robustness under noisy conditions. UAMF jointly models aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations. It integrates a Dynamic Direct Computation (DDC) module for adaptive uncertainty estimation and an Uncertainty Fusion Mechanism (UFM) that fuses predictions using Bayesian updates. We evaluate the effectiveness of our method on two practical tasks: image classification (FOOD101, CUB-200) and multimodal sentiment analysis (MOSI, MOSEI). Compared with the baseline model QMF, UAMF achieves an average accuracy improvement of 2.3% across tasks (e.g., +3.1% on CUB-200 and +1.2% on MVSA-Single under Gaussian noise). In addition, it reduces inference time by 52%, from 187.2ms to 89.5ms, compared to the recent state-of-the-art model EAU. The results demonstrate that UAMF outperforms existing methods across both application domains, particularly under noisy or modality-impaired conditions. These findings highlight the robustness and generalizability of the proposed method in real-world multimodal learning scenarios.}, archive = {J_ACCESS}, author = {Lei Pan and Xingyu Han and Xiaoming Liu and Yang Liu}, doi = {10.1109/ACCESS.2025.3599563}, journal = {IEEE Access}, pages = {145289-145302}, shortjournal = {IEEE Access}, title = {A practical multimodal fusion system with uncertainty modeling for robust visual and affective applications}, volume = {13}, year = {2025}, }
• (2025). LLM-driven APT detection for 6G wireless networks: A systematic review and taxonomy. ACCESS, 13, 145271-145288. (www)

  @article{ , abstract = {Sixth Generation (6G) wireless networks, which are expected to be deployed in the 2030s, have already created great excitement in academia and the private sector with their extremely high communication speed and low latency rates. However, despite the ultra-low latency, high throughput, and AI-assisted orchestration capabilities they promise, they are vulnerable to stealthy and long-term Advanced Persistent Threats (APTs). Large Language Models (LLMs) stand out as an ideal candidate to fill this gap with their high success in semantic reasoning and threat intelligence. This paper presents the first systematic review and taxonomy for LLM-assisted APT detection in 6G networks. It also provides insights by reviewing recent research on the intersection of LLMs, APTs, and 6G. Key challenges such as limitations in edge deployment, data scarcities, and explainability gaps are identified and a multidimensional taxonomy is provided in line with the APT lifecycle and 6G contexts. The paper is based on 142 studies from 2018 to 2025, searching leading databases such as IEEE Xplore, ACM Digital Library, SpringerLink, and Elsevier ScienceDirect.}, archive = {J_ACCESS}, author = {Muhammed Golec and Yaser Khamayseh and Suhib Bani Melhem and Abdulmalik Alwarafy}, doi = {10.1109/ACCESS.2025.3595665}, journal = {IEEE Access}, pages = {145271-145288}, shortjournal = {IEEE Access}, title = {LLM-driven APT detection for 6G wireless networks: A systematic review and taxonomy}, volume = {13}, year = {2025}, }
• (2025). Wind power prediction based on a hybrid model of ICEEMDAN and ModernTCN-informer. ACCESS, 13, 145256-145270. (www)

  @article{ , abstract = {Reliable and accurate wind power forecasting serves as one of the effective measures to enhance grid peak regulation capacity while improving the safety and stability of power systems. However, wind power generation exhibits strong randomness and volatility, which pose significant challenges to achieving precise predictions. This paper proposes a hybrid forecasting model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with ModernTCN-Informer. At first, the original wind power data undergoes ICEEMDAN decomposition to obtain several relatively stable subsequences, thereby mitigating data fluctuations. This process facilitates feature extraction from the sequences and enables the construction of a highly adaptive forecasting model. Secondly, the ModernTCN model is used to sequentially extract correlations among univariate patch sequences across multiple time steps in the dataset, long-term dependencies within univariate patch sequences, and latent correlations across variables. This effectively captures potential interrelationships in wind power data from both temporal and spatial dimensions, followed by accurate and efficient predictions using the Informer model. Finally, validation is conducted on real wind farm data, and the results show that: compared with Informer, the MAE of 12-step prediction is reduced by 3.1%, and compared with LSTM, it is reduced by 29.5%; after incorporating ICEEMDAN, the MAE is further reduced by 64.1%, and the R2 reaches 0.969. The multi-step prediction accuracy is superior to that of the comparison models, verifying the effectiveness of the proposed model.}, archive = {J_ACCESS}, author = {Jun He and Zijian Cheng and Zijie Zhong and Lizhuo Liang and Jianhui Ye}, doi = {10.1109/ACCESS.2025.3599609}, journal = {IEEE Access}, pages = {145256-145270}, shortjournal = {IEEE Access}, title = {Wind power prediction based on a hybrid model of ICEEMDAN and ModernTCN-informer}, volume = {13}, year = {2025}, }
• (2025). Empowering individuals with visual impairment: A digital braille solution for learning the urdu language. ACCESS, 13, 145243-145255. (www)

  @article{ , abstract = {In today’s digital era, smartphones have become an integral part of education and learning. The unification of smart devices with assistive technologies such as screen readers, voice-based navigation, and object detection, including the use of wearable devices, has significantly enhanced the abilities of individuals with disabilities. Despite these advancements, there is a notable gap in the realm of comprehensive and indigenous digital learning apps tailored for individuals with visual impairments. Traditional education for visually impaired students heavily relies on the Braille system, which required extensive training and continuous support from educators and professionals. This dependency poses a significant barrier to the availability of dedicated learning environments for this specific demographic. Addressing this challenge, our research proposes a digital solution named “Chasham” designed around the Braille system, specifically tailored for the Urdu language. This innovative solution involves mapping Braille codes to their corresponding Urdu alphabets and letters. Incorporating voice output further facilitates users in identifying typed letters and words. The Chasham includes various quizzes and exercises to assess and enhance student learning skills. To ensure the effectiveness and usability of the proposed solution, a 3-week user study was conducted involving 50 visually impaired students to evaluate Chasham’s usability and validity. The preliminary results of this study are promising and indicate a positive impact on student learning experiences. This research represents a significant step towards filling the void in comprehensive digital learning tools for visually impaired individuals, particularly in the context of education. Integrating the Braille system into a digital learning environment for the Urdu language is a novel and thoughtful approach. It leverages technology to overcome traditional barriers and enhance the learning experience.}, archive = {J_ACCESS}, author = {Farzana Jabeen and Mehvish Rashid}, doi = {10.1109/ACCESS.2025.3598856}, journal = {IEEE Access}, pages = {145243-145255}, shortjournal = {IEEE Access}, title = {Empowering individuals with visual impairment: A digital braille solution for learning the urdu language}, volume = {13}, year = {2025}, }
• (2025). Enhancing task-incremental learning via a prompt-based hybrid convolutional neural networks (CNNs)-vision transformer (ViT) framework. ACCESS, 13, 145223-145242. (www)

  @article{ , abstract = {Artificial neural network (ANN) models are widely used in various fields such as image classification, multi-object detection, intent prediction, military applications, and natural language processing. However, artificial intelligence (AI) models for continual learning (CL) are not yet mature, and “catastrophic forgetting (CF)” is still a typical problem. The study of biological neural networks (BNNs) and ANN models still needs further exploration. Therefore, this paper mainly explores the pre- and postsynaptic structures, the synaptic cleft, the early and late stages of long-term potentiation, and the effects of neurotransmitters on synaptic excitation and inhibition. We emphasize the necessity of integrating biological neural systems and ANN models in learning and memory. Based on the “Prompt Pool”, this paper designs a hybrid neural network (HNN) architecture that integrates convolutional neural networks (CNNs), vision transformers (ViT), prompt pools, and adapters to alleviate the “CF” problem in task incremental learning (TIL). Compared with the existing ViT and prompt pool architecture, this method shows higher performance in final task training and also shows certain advantages in the persistence of TIL. In the future, based on the principles of biological neuroscience, we will further apply the HNN model to image classification and multi-object detection tasks in autonomous driving. By gaining a deeper understanding of the BNN mechanisms, we will develop efficient HNN models that can adapt to dynamic environments and provide new solutions for CL.}, archive = {J_ACCESS}, author = {Zuomin Yang and Anis Salwa Mohd Khairuddin and Joon Huang Chuah and Wei Ru Wong and Xin Xu and Hafiz Muhammad Fahad Noman and Qiyuan Qin}, doi = {10.1109/ACCESS.2025.3597020}, journal = {IEEE Access}, pages = {145223-145242}, shortjournal = {IEEE Access}, title = {Enhancing task-incremental learning via a prompt-based hybrid convolutional neural networks (CNNs)-vision transformer (ViT) framework}, volume = {13}, year = {2025}, }
• (2025). Data-driven neural differential equation model and stochastic dynamics for glioma prediction. ACCESS, 13, 145211-145222. (www)

  @article{ , abstract = {Low-grade gliomas are infiltrative, incurable primary brain tumors that usually grow slowly and cause death. This study presents a unique low-grade glioma mathematical model and predicts the parameters of the model through real data using deep learning. We combine the advantages of mathematical models with deep learning features to provide results with precise solutions and high-performance prediction. The global stability of treatment success and failure equilibrium is effectively analysed using the Lyapunov method. Next, we estimate the parameters involved in the mathematical model by fitting them into a set of clinical data and employing a neural ordinary differential equations algorithm. Compared to baseline mechanistic tumor models, our approach reduces prediction root mean squared error to 3.37, demonstrating improved data alignment and forecasting accuracy. We also investigate the impact of stochastic perturbation in the model and the effect of time delay parameter on the rate of drug concentration. As an alternative to conducting clinical trials on patients, practitioners can make more informed decisions about patient treatment by studying the numerous models mentioned above that are appropriate for the patient’s condition.}, archive = {J_ACCESS}, author = {Mohammed Salman and Sanjay Kumar Mohanty}, doi = {10.1109/ACCESS.2025.3598932}, journal = {IEEE Access}, pages = {145211-145222}, shortjournal = {IEEE Access}, title = {Data-driven neural differential equation model and stochastic dynamics for glioma prediction}, volume = {13}, year = {2025}, }
• (2025). Design of an integrated space-based information support digital infrastructure. ACCESS, 13, 145200-145210. (www)

  @article{ , abstract = {To address systemic challenges including decentralized construction, functional redundancy, and network heterogeneity in space-based information systems in the New Space Age, For the first time, this study formulates a unified, modular, and service-oriented digital infrastructure specifically tailored for integrated space-based information support. The architecture integrates four core components: 1) A localized hardware infrastructure ensures autonomous and controllable space information infrastructure; 2) A multisource data governance infrastructure establishes unified data resource pools; 3) An integrated space-terrestrial network protocol system constructs elastic transmission frameworks; 4) A microservice-oriented infrastructure achieves cross-domain functional integration of communication, navigation, and remote sensing (CNR) systems. The proposed architecture innovatively combines localized autonomous hardware, multisource data governance, an elastic space-terrestrial network protocol system, and cross-domain microservices for Communication, Navigation, and Remote Sensing (CNR) integration—moving beyond separate subsystem approaches seen in previous studies. Practical applications demonstrate its capability to enable flexible networking and synergistic operations of CNR systems, providing scalable technological foundations for constructing smart Earth, advancing space economy, and fostering aerospace scientific innovation. The research establishes a “horizontal convergence and vertical integration” architectural paradigm for space digital infrastructure, offering novel solutions for enhancing space-based intelligence support system effectiveness.}, archive = {J_ACCESS}, author = {Pengbo Li and Gang Wan and Zhongmin Pei}, doi = {10.1109/ACCESS.2025.3598613}, journal = {IEEE Access}, pages = {145200-145210}, shortjournal = {IEEE Access}, title = {Design of an integrated space-based information support digital infrastructure}, volume = {13}, year = {2025}, }
• (2025). Minimal and simplified analysis of hierarchical density-based spatial clustering. ACCESS, 13, 145191-145199. (www)

  @article{ , abstract = {Hierarchical density based spatial clustering is a state-of-the-art clustering algorithm that is widely used by the research community for the analysis of spatial data. This popularity is in part due to its accessibility in well-known open-source libraries, which allow researchers to easily install and use the algorithm for their use cases. Although easy to use, the underlying algorithmic steps are quite complex and difficult to understand, which can lead to potential misuse or misinterpretation. Therefore, we aim to provide a step-by-step tutorial of the algorithm’s underlying computations using a simple and minimal two-dimensional example. Specifically, we guide the reader through the example by illustrating the steps of density estimation, minimum spanning tree computation, hierarchy construction, and extraction of flat clustering results. In addition, we provide notes on recent studies for further exploration by readers interested in a deeper analysis of each step of the algorithm. We believe that this tutorial provides the reader with a better understanding of the algorithm, helping them grasp both the strengths and limitations of the algorithm through a hands-on approach that can easily be reproduced with just pen and paper.}, archive = {J_ACCESS}, author = {Kayumov Abduaziz and Shukurillo Makhammadjonov and Ji Sun Shin}, doi = {10.1109/ACCESS.2025.3596634}, journal = {IEEE Access}, pages = {145191-145199}, shortjournal = {IEEE Access}, title = {Minimal and simplified analysis of hierarchical density-based spatial clustering}, volume = {13}, year = {2025}, }
• (2025). Online trajectory regeneration for multirotors via a proportional-derivative physics-informed neural network. ACCESS, 13, 145168-145190. (www)

  @article{ , abstract = {This paper presents a novel framework based on a Proportional-Derivative Physics-Informed Neural Network (PD-PINN) for real-time trajectory regeneration. Unlike conventional methods that rely on static, precomputed paths, the proposed approach dynamically adapts the reference trajectory using real-time sensor feedback and the system’s physical model. The PD-PINN incorporates proportional-derivative error terms into a physics-informed learning process, where the loss function explicitly embeds the system’s governing equations into the backpropagation algorithm. This integration enables physically consistent, control-aware weight updates that preserve dynamic feasibility. The framework is evaluated on a multirotor UAV model under realistic simulation conditions, including sensor noise, wind disturbances, and parametric uncertainty. Results show that PD-PINN reduces the total three-dimensional root mean square error from 0.7178 meters to 0.1352 meters, an 81.2% improvement over classical methods. The normalized control effort also decreases from 82,600 to 78,200, representing a 5.3% reduction. These findings indicate that the proposed method holds strong potential for online operation in autonomous flight scenarios characterized by uncertainty and dynamic environmental changes. The results confirm that PD-PINN enhances trajectory guidance accuracy, robustness, and computational efficiency in multirotor UAVs, making it well-suited for practical applications such as aerial mapping, environmental monitoring, and package delivery in complex and evolving conditions.}, archive = {J_ACCESS}, author = {Mana Ghanifar and Amir Ali Nikkhah and Milad Kamzan and Mohammad Teshnehlab and Morteza Tayefi}, doi = {10.1109/ACCESS.2025.3598862}, journal = {IEEE Access}, pages = {145168-145190}, shortjournal = {IEEE Access}, title = {Online trajectory regeneration for multirotors via a proportional-derivative physics-informed neural network}, volume = {13}, year = {2025}, }
• (2025). CBMAP: Clustering-based manifold approximation and projection for dimensionality reduction. ACCESS, 13, 145158-145167. (www)

  @article{ , abstract = {Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional spaces. Among these, popular nonlinear methods such as t-SNE, UMAP, TriMap, and PaCMAP excel in capturing local relationships and nonlinear structures. However, they often distort the global arrangement of clusters, rely heavily on hyperparameter tuning, and exhibit sensitivity to initialization. Moreover, most of these methods cannot project unseen test samples, limiting their applicability in real-world scenarios. To address these challenges, this study introduces a novel approach, CBMAP (Clustering-Based Manifold Approximation and Projection), which explicitly incorporates clustering in the high-dimensional space to guide the embedding. CBMAP computes membership values based on cluster centers in the original space and preserves these memberships during the projection process. This design enables CBMAP to better retain the global layout of the data while maintaining meaningful local relationships. CBMAP demonstrates low sensitivity to initialization strategies, minimal dependence on hyperparameters, and supports projection of unseen test samples. Experimental evaluations on both toy and real-world benchmark datasets show that CBMAP consistently preserves global structures and inter-cluster distances more effectively than state-of-the-art methods, while delivering competitive results in local structure preservation. The method is freely available at https://github.com/doganlab/cbmap and can be installed via the Python Package Index with the command pip install cbmap.}, archive = {J_ACCESS}, author = {Berat Doğan}, doi = {10.1109/ACCESS.2025.3599722}, journal = {IEEE Access}, pages = {145158-145167}, shortjournal = {IEEE Access}, title = {CBMAP: Clustering-based manifold approximation and projection for dimensionality reduction}, volume = {13}, year = {2025}, }
• (2025). Expanding multilingual co-speech interaction: The impact of enhanced gesture units in text-to-gesture synthesis for digital humans. ACCESS, 13, 145144-145157. (www)

  @article{ , abstract = {In this study, we explore the effects of co-speech gesture generation on user experience in 3D digital human interaction by testing two key hypotheses. The first hypothesis posits that increasing the number of gestures enhances the user experience across criteria such as naturalness, human-likeness, temporal consistency, semantic consistency, and social presence. The second hypothesis suggests that language translation does not degrade the user experience across these criteria. To explore these hypotheses, we investigated three conditions using a digital human: voice only with no gestures, limited(56 gestures) co-speech gestures, and full system functionality with over 2000 unique gestures. For the second hypothesis, we used language translation to provide multilingual support, retrieving gestures from an English rule base. We obtained text and pose from English videos and matched the pose with gesture units derived from Korean speakers’ motion-capture sequences, enhancing a comprehensive rule base that we used for gesture retrieval for given text input. We used translation of non-English input language to English for text matching. Our novel method utilizes an improved pipeline to extract text, 2D pose data, and 3D gesture units. Incorporating a cutting-edge gesture-pose matching model with deep contrastive learning, we retrieved gestures from a comprehensive rule base containing 210,000 rules. This approach optimizes alignment and generates realistic, semantically consistent co-speech gestures adaptable to various languages. A comprehensive user study evaluated our hypotheses. The results underscored the positive impact of diverse gestures, supporting the first hypothesis. Additionally, multilingual capabilities did not degrade the user experience, confirming the second hypothesis. Highlighting the scalability and flexibility of our method, this study provides valuable insights into cross-lingual data and expert systems for gesture generation, contributing significantly to more engaging and immersive digital human interactions and the broader field of human-computer interaction.}, archive = {J_ACCESS}, author = {Ghazanfar Ali and Woojoo Kim and Muhammad Shahid Anwar and Jae-In Hwang and Ahyoung Choi}, doi = {10.1109/ACCESS.2025.3596328}, journal = {IEEE Access}, pages = {145144-145157}, shortjournal = {IEEE Access}, title = {Expanding multilingual co-speech interaction: The impact of enhanced gesture units in text-to-gesture synthesis for digital humans}, volume = {13}, year = {2025}, }
• (2025). Fairness and explanations in entity resolution: An overview. ACCESS, 13, 145127-145143. (www)

  @article{ , abstract = {Entity Resolution (ER) is a fundamental task in data integration, enabling the identification of records that refer to the same real-world entity across diverse and often heterogeneous data sources. Recent advances in Artificial Intelligence (AI) have significantly improved ER performance, particularly with deep learning and pre-trained embeddings. However, these AI-driven solutions introduce new challenges related to fairness and explainability. Fairness-aware ER seeks to mitigate bias that may arise from algorithmic decision-making or imbalanced training data, while eXplainable Entity Resolution (XER) aims to enhance transparency and trust in ER. In this work, we provide a comprehensive overview of fairness and explainability in ER, systematically analyzing existing techniques across the ER pipeline. We discuss challenges in ensuring unbiased and interpretable ER outcomes, with a special focus on streaming environments, where real-time decision-making intensifies the complexity of these concerns. Furthermore, we outline research opportunities and examine the trade-offs between schema-aware and schema-agnostic methods, as well as rule-based and machine learning-based comparison techniques, in ensuring fair and transparent ER. Our study highlights open research challenges and potential future directions, encouraging novel explainable AI methodologies and fairness-aware ER solutions that enhance the reliability, accountability, and societal impact of AI-driven ER systems.}, archive = {J_ACCESS}, author = {Tiago Brasileiro Araújo and Vasilis Efthymiou and Kostas Stefanidis}, doi = {10.1109/ACCESS.2025.3599990}, journal = {IEEE Access}, pages = {145127-145143}, shortjournal = {IEEE Access}, title = {Fairness and explanations in entity resolution: An overview}, volume = {13}, year = {2025}, }
• (2025). Are requirements really all you need? using LLMs to generate configuration code: A case study in automotive simulations. ACCESS, 13, 145115-145126. (www)

  @article{ , abstract = {Large Language Models (LLMs) are taking many industries by storm. They possess impressive reasoning abilities and are capable of handling complex problems, as shown by their steadily improving scores on coding and mathematical benchmarks. However, questions remain about their ability to tackle domain-specific, real-world challenges, especially in highly technical fields like the automotive industry. How well can these models understand high-level, abstract instructions commonly found in automotive standards and documentation? Can they translate such specifications directly into functional code, or do they still require human guidance and post-processing? In this work, we investigate the practical capabilities of a state-of-the-art LLM in the context of autonomous driving functionalities. Specifically, we assess the model’s ability to interpret abstract textual requirements extracted from real automotive regulations and transform them into executable configuration code for CARLA, a widely used autonomous driving simulation environment. Our evaluation focuses on the accuracy, completeness, and reliability of the generated code, as well as the model’s ability to reason about domain-specific constraints. The results offer insight into both the potential and current limitations of the models in supporting LLM-based automotive development workflows.}, archive = {J_ACCESS}, author = {Krzysztof Lebioda and Nenad Petrovic and Fengjunjie Pan and Vahid Zolfaghari and André Schamschurko and Alois Knoll}, doi = {10.1109/ACCESS.2025.3597748}, journal = {IEEE Access}, pages = {145115-145126}, shortjournal = {IEEE Access}, title = {Are requirements really all you need? using LLMs to generate configuration code: A case study in automotive simulations}, volume = {13}, year = {2025}, }
• (2025). Direct pixel-level tightly coupled navigation design with dual-pattern LED markers for spacecraft proximity operation. ACCESS, 13, 145105-145114. (www)

  @article{ , abstract = {This study proposes an advanced integrated navigation algorithm based on a monocular camera for cooperative proximity missions of spacecraft in a low Earth orbit (LEO) environment. The proposed framework involves a camera mounted on the chaser spacecraft and LED markers attached to the target spacecraft, where pixel coordinates extracted from camera images are directly utilized as measurements in a tightly coupled extended Kalman filter. Unlike conventional loosely coupled methods based on Perspective-n-Point (PnP) pose estimation, the proposed approach integrates pixel-level feature measurements directly into the filter, reducing computational cost and enabling robust state estimation with a significantly smaller number of valid feature points. To further enhance navigation accuracy, an extended observation matrix incorporating the relative geometric configuration of the LED markers is introduced. Additionally, a dual-pattern LED marker configuration is employed to overcome the limited field of view of the camera. The performance of the proposed algorithm is validated through both Gazebo simulations and lab-scale experiments, demonstrating that the extended observation model significantly contributes to reducing navigation errors.}, archive = {J_ACCESS}, author = {Jiyeon Lee and Sangkyung Sung}, doi = {10.1109/ACCESS.2025.3598708}, journal = {IEEE Access}, pages = {145105-145114}, shortjournal = {IEEE Access}, title = {Direct pixel-level tightly coupled navigation design with dual-pattern LED markers for spacecraft proximity operation}, volume = {13}, year = {2025}, }
• (2025). Hyperbolic lattices derived from denser tessellations. ACCESS, 13, 145088-145104. (www)

  @article{ , abstract = {The paper’s aims are threefold. First, to identify the Fuchsian groups associated with the $\{4\lambda, 4\}$ and $\{6\lambda, 3\}$ hyperbolic tessellations in quaternion orders, where $\lambda = 3^{n}$ . Second, to provide the bases of the maximal quaternion orders associated with such dense tessellation families. Third, to provide the algebraic structure for constructing hyperbolic lattices or geometrically uniform signal constellations leading to a complete algebraic labeling. These tessellations are relevant for being denser and having inherent mathematical properties, making them a favorite in several classical and quantum applications. However, to the best of our knowledge, a systematic approach and results are needed to put the applications of such relevant mathematical structures in perspective. Hence, the contributions of this paper are related to the presentation of the corresponding Fuchsian groups $\Gamma _{4\lambda }$ and $\Gamma _{6\lambda }$ derived from quaternion algebras $\mathcal {A}$ over a number field $\mathbb {K}$ whose primitive elements are established in Theorems 10 and 20, respectively. Consequently, Theorems 13 and 14, and Theorems 23 and 24 establish the bases of the maximal orders related to the $\{4\lambda, 4\}$ and $\{6\lambda,3\}$ tessellations, respectively, leading to the algebraic labeling to be complete. Therefore, these results provide the algebraic structures for constructing the corresponding hyperbolic lattices.}, archive = {J_ACCESS}, author = {Cátia Regina de Oliveira Quilles Queiroz and Vandenberg Lopes Vieira and Giuliano Gadioli la Guardia and Clarice Dias de Albuquerque and Anderson José de Oliveira and Leandro Bezerra de Lima and Reginaldo Palazzo}, doi = {10.1109/ACCESS.2025.3599469}, journal = {IEEE Access}, pages = {145088-145104}, shortjournal = {IEEE Access}, title = {Hyperbolic lattices derived from denser tessellations}, volume = {13}, year = {2025}, }
• (2025). Toward intelligent caching in DSR protocols: Route compression, hierarchical structures, and link-aware optimization in MANETs. ACCESS, 13, 145071-145087. (www)

  @article{ , abstract = {Mobile Ad Hoc Networks (MANETs) are decentralized, infrastructure-less wireless systems that rely heavily on efficient routing protocols due to their dynamic and resource-constrained nature. Dynamic Source Routing (DSR) is widely adopted for such environments; however, it suffers from notable limitations, including cache saturation, memory inefficiencies, and lack of link quality awareness. These weaknesses reduce performance in high-mobility or large-scale scenarios and limit the reliability of DSR in real-world applications like military or disaster recovery networks. Motivated by these challenges, this paper proposes a comprehensive enhancement to DSR through intelligent caching strategies to improve scalability, energy efficiency, and route reliability. The proposed model introduces a dynamic route compression technique that reduces route size by eliminating redundant intermediate nodes. It also incorporates a hierarchical caching scheme where clusters managed by energy-aware Cluster Heads (CHs) distribute cache responsibilities, thereby improving scalability and reducing overhead. In addition, link quality metrics such as residual energy and link duration are integrated into route selection, enabling more stable and energy-efficient communication paths. Simulations using NS-2 across varying network sizes (50, 75, 100 nodes) revealed that the enhanced DSR significantly improves key performance metrics such as Packet Delivery Ratio (PDR), routing overhead, cache hit rate, and energy consumption. Route compression alone yielded a 14.3 PDR improvement and 21% reduction in overhead, while the hierarchical approach further optimized network performance. These results confirm the feasibility and effectiveness of the proposed enhancements, addressing longstanding gaps in traditional DSR implementations and laying a foundation for scalable and intelligent MANET routing protocols.}, archive = {J_ACCESS}, author = {Ammar Kareem Obaid and Mohd Ezanee Bin Rusli and Salman Yussof}, doi = {10.1109/ACCESS.2025.3599779}, journal = {IEEE Access}, pages = {145071-145087}, shortjournal = {IEEE Access}, title = {Toward intelligent caching in DSR protocols: Route compression, hierarchical structures, and link-aware optimization in MANETs}, volume = {13}, year = {2025}, }
• (2025). Osteoarthritis classification using hybrid quantum convolutional neural network. ACCESS, 13, 145060-145070. (www)

  @article{ , abstract = {This work explores the integration of quantum computing within a classical Convolutional Neural Network (CNN) framework, leveraging a VGG16 model enhanced with a quantum Convolutional Neural Network (QCNN) for the classification of knee osteoarthritis (OA) severity. A custom quantum layer was incorporated to boost the model’s feature extraction capabilities, enabling effective differentiation between healthy and OA-affected knees in medical imaging data. The model utilises the Swish activation function, which combines smoothness and nonlinearity to enhance learning efficiency and gradient flow, which are critical in complex diagnostic tasks. Using a QCNN, this model harnesses the ability of quantum computing to represent high-dimensional data transformations, a novel approach that complements classical CNN layers by exploring patterns that are not captured in traditional networks. The initial results showed a high classification accuracy of 97.26%, suggesting that quantum-enhanced layers can significantly bolster feature extraction and classification in medical diagnostics. Furthermore, multiple variations of pre-trained models with Quantum Convolutional Layers, such as XceptionNet, ResNet, and InceptionNet, were developed and compared in terms of the evaluation metrics that achieved 87.84,89.70, and 86.64, respectively. This hybrid VGG16-QCNN model showed the highest accuracy, demonstrating the potential of QCNNs in advancing the precision and scalability of AI-driven healthcare solutions.}, archive = {J_ACCESS}, author = {Devansh Tikariha and Abdul Moomin and D. Jeyamani and P. Rukmani}, doi = {10.1109/ACCESS.2025.3599679}, journal = {IEEE Access}, pages = {145060-145070}, shortjournal = {IEEE Access}, title = {Osteoarthritis classification using hybrid quantum convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Additively manufactured RF electronics with structurally integrated physically unclonable functions for wireless system security. ACCESS, 13, 145042-145059. (www)

  @article{ , abstract = {Physically unclonable functions (PUFs) are alternatives to secret keys stored in non-volatile memory. PUFs derive secret keys on demand based on readings invoked from the complexity of their structures or their physical properties. Conventional PUFs rely on the variations and tolerances involved in their manufacturing processes. On the other hand, traditional manufacturing processes aim to achieve cost-effective device replication by minimizing tolerances often through the use of masks for patterning which in turn limits PUF security by reducing their response diversity across multiple devices. Additionally, the fact that manufacturing tolerances are openly known leads to higher quality adversarial attacks towards these conventional PUFs. In contrast to traditional volume manufacturing, emerging additive manufacturing (AM) techniques are mainly mask-free and therefore cost-effective for creating unique randomizations within devices to act as PUFs. With AM, randomizations can be deliberately introduced by the designers in a way to enhance security and/or complicate the PUF operation mechanisms. This manuscript, for the first time, utilizes laser-enhanced direct print additive manufacturing (LE-DPAM) technology to demonstrate that AM based novel PUF security primitives can be structurally integrated with RF electronics such as antennas. Specifically, we investigate the use of compact I/O expander IC packages and demonstrate a PUF that utilizes 4 such IC packages structurally integrated directly under the ground plane of a 5.8 GHz ISM band patch antenna. Each IC package exhibits 48 input/output (I/O) pins leading to a 192-bit PUF security primitive where the I/O pins are randomly connected to high or low digital states. The NIST statistical test results confirm that the generated PUF outputs exhibit a high degree of statistical randomness, as demonstrated with high confidence p-values across all NIST tests. Furthermore, we evaluate the security of the proposed PUF against widely used machine learning (ML) modeling attacks and demonstrate that the PUF is robust with none of the attacks achieving prediction success that exceeds random guessing.}, archive = {J_ACCESS}, author = {Aaron Pendino and Nghia Nguyen and Saif E. Nouma and Jing Wang and Attila A. Yavuz and Yasin Yilmaz and Gökhan Mumcu}, doi = {10.1109/ACCESS.2025.3600010}, journal = {IEEE Access}, pages = {145042-145059}, shortjournal = {IEEE Access}, title = {Additively manufactured RF electronics with structurally integrated physically unclonable functions for wireless system security}, volume = {13}, year = {2025}, }
• (2025). A scalable framework for deploying AI-powered wildlife monitoring in resource-limited field environments. ACCESS, 13, 145023-145041. (www)

  @article{ , abstract = {Wildlife monitoring in remote field environments presents significant challenges owing to energy constraints, limited network connectivity, and the need for real-time processing. This study proposes a scalable framework that integrates artificial intelligence (AI) and edge computing for autonomous wildlife detection, class-based object counting in frames, distance measurement, and geolocation estimation of key European wildlife species. The system leverages deep learning-based object detection models, specifically YOLOv8m and YOLOv10m trained by the European Animal Detection Dataset, deployed on power-efficient embedded platforms such as the NVIDIA Jetson Orin Nano and Raspberry Pi 5. The experimental evaluation assesses detection accuracy, computational efficiency, and energy consumption across different hardware configurations, ensuring an optimal trade-off between performance and resource utilization. Our findings demonstrate that the proposed approach enables real-time inference while maintaining high mean average precision (mAP) and minimizing power consumption, ensuring feasibility in resource-limited field conditions. A comprehensive cost-performance analysis highlights the Jetson Orin Nano as the optimal choice for balancing accuracy and efficiency, achieving 40.1 Frame Per Second (FPS) at just 8W in 25W power mode using a TensorRT-optimized YOLO model. Meanwhile, the Raspberry Pi 5 serves as a cost-effective alternative for lightweight models. Beyond technical performance, this work underscores the transformative role of edge AI in supporting ecologists and agronomists with actionable insights for biodiversity conservation, human wildlife conflict mitigation, and sustainable ecosystem management.}, archive = {J_ACCESS}, author = {Md. Auhidur Rahman and Tesfalem Mehari Berhe and Luca Borgianni and Md Sabbir Ahmed and Cristian Bua and Davide Adami and Stefano Giordano}, doi = {10.1109/ACCESS.2025.3598927}, journal = {IEEE Access}, pages = {145023-145041}, shortjournal = {IEEE Access}, title = {A scalable framework for deploying AI-powered wildlife monitoring in resource-limited field environments}, volume = {13}, year = {2025}, }
• (2025). Theory inspires, but examples engage: A mixed-methods analysis of worked examples from CoderBot in programming education. ACCESS, 13, 145007-145022. (www)

  @article{ , abstract = {Programming has become increasingly important in our society. However, the learning process presents significant challenges, particularly for novice students of introductory courses. From the students’ perspective, programming concepts are often perceived as complex and challenging to understand. Chatbots have emerged as promising and effective pedagogical agents, offering continuous support and personalized feedback throughout the programming learning process. In this paper, we present CoderBot, a pedagogical agent grounded in Example-Based Learning designed to assist novice students in comprehending programming concepts using correct and erroneous practical examples. To evaluate the self-efficacy and acceptance of CoderBot in the classroom, we conducted an exploratory study involving 103 undergraduate students from several regions of our country, all of whom were enrolled in introductory programming courses. The quantitative findings highlight the ease of use associated with CoderBot, along with noticeable improvements in students’ understanding of programming concepts and increased levels of motivation and self-confidence. Moreover, the qualitative results indicate that CoderBot holds the potential to be an effective pedagogical agent for supporting programming instruction, particularly in terms of clarity, accessibility, and ongoing assistance. However, the findings also suggest the need for further expansion of the available examples and improvements in the clarity of responses to realize the tool’s educational potential fully. These results offer valuable insights into integrating chatbots within academic environments, underscoring the role such tools can play in enhancing the learning experience for programming students.}, archive = {J_ACCESS}, author = {Renato De Souza Garcia and João Emilio Antonio Villa and Andre Luiz Mendes Miranda and Gilleanes Thorwald Araujo Guedes and Ana Carolina Oran and Paulo Silas Severo De Souza and Ricardo Ferreira Vilela and Pedro Henrique Dias Valle and Williamson Silva}, doi = {10.1109/ACCESS.2025.3599357}, journal = {IEEE Access}, pages = {145007-145022}, shortjournal = {IEEE Access}, title = {Theory inspires, but examples engage: A mixed-methods analysis of worked examples from CoderBot in programming education}, volume = {13}, year = {2025}, }
• (2025). Secure beamforming design for multi-color visible light communication systems. ACCESS, 13, 144995-145006. (www)

  @article{ , abstract = {Visible light communication (VLC), offering high bandwidth and resisting electromagnetic interference, is a promising optical wireless communication technology for next-generation wireless communication systems (NG-WCS). On the other hand, multi-color VLC (MC-VLC), enabling the use of wavelength-division multiplexing by exploiting multiple wavelengths within the visible spectrum, offers significant potential for high-speed data transmission, which plays a critical role in addressing the ultra-low latency demands in NG-WCS. Data security is crucial in NG-WCS due to the rapid expansion of Internet of Things (IoT) devices and the rise of ultra-low-latency and mission-critical applications. The literature on physical layer security (PLS), which plays a pivotal role in enabling data security, is limited for MC-VLC systems, and existing studies do not consider the impact of inter-color interference (ICI). Considering ICI, this paper focuses on solving the secrecy rate maximization problem in MC-VLC systems that use beamforming PLS techniques. The optimization problems are formulated to find optimal beamformers maximizing the secrecy rates in MC-VLC systems for the cases of no-ICI and ICI, resulting in non-convex and NP-hard problems. This paper proposes ZF beamforming designs that facilitate implementations into practical MC-VLC systems by converting non-convex optimization problems into easily solvable linear programming problems. Numerical results show that the proposed ZF beamformer achieves the optimal solution in the no-ICI case and remains near-optimal in the ICI case when optical filters with appropriate passband boundaries, effectively attenuating ICI, are used. Additionally, the proposed designs provide higher secrecy rates than traditional ZF beamformers.}, archive = {J_ACCESS}, author = {Cenk Albayrak}, doi = {10.1109/ACCESS.2025.3599792}, journal = {IEEE Access}, pages = {144995-145006}, shortjournal = {IEEE Access}, title = {Secure beamforming design for multi-color visible light communication systems}, volume = {13}, year = {2025}, }
• (2025). Macro-level energy demand model for cellular telecommunication networks. ACCESS, 13, 144982-144994. (www)

  @article{ , abstract = {The surge in digital services and video streaming has considerably increased wireless broadband traffic, prompting telecom operators to rapidly expand their network infrastructure. The infrastructure of base transceiver stations (BTSs) must be upgraded to meet the growing customer demand while ensuring efficient energy use. To date, no macro-level framework has been established for calculating the energy demand in cellular networks using BTS site representations and stock composition variations as essential tools for monitoring energy management. This study proposes a framework for modeling the energy demand for cellular networks with such analytical capability. It includes two key modeling elements: 1) BTS site stock, including the composition of site types at the municipal level, differentiated by socioeconomic status and 2) energy demand at the BTS site level, estimated using archetypes that represent physical and technical characteristics. The framework was validated through a case study of the Indonesian cellular network using data from >8,500 BTS site samples across 20 municipalities. Results revealed the spatiotemporal characteristics of energy demand, with a total electricity demand of 5.37 TWh/year or 613.5 MW on average, equivalent to ~2% of the total Indonesian electricity demand in 2022. The proposed framework can be extended to other regions with limited data availability using the developed BTS site archetypes and statistical models of BTS site stock. Using the characteristic representation of BTS sites, the archetype-based approach can be used for quantifying the potential contribution of hybrid renewable energy systems, thereby accelerating the telecommunication industry’s clean energy transition and fostering implementation sustainability.}, archive = {J_ACCESS}, author = {Mochamad Mardi Marta Dinata and Yohei Yamaguchi and Hideaki Uchida and Yoshiyuki Shimoda and Agus Subekti}, doi = {10.1109/ACCESS.2025.3599587}, journal = {IEEE Access}, pages = {144982-144994}, shortjournal = {IEEE Access}, title = {Macro-level energy demand model for cellular telecommunication networks}, volume = {13}, year = {2025}, }
• (2025). Computationally efficient artifact suppression for optical coherence tomography angiography. ACCESS, 13, 144975-144981. (www)

  @article{ , abstract = {In optical coherence tomography angiography (OCTA), vessels exhibit tail artifacts beneath them in B-scan images. These superficial vessel tails cast shadows on deeper vascular layers, significantly degrading image quality. Current artifact suppression methods often require complex algorithms and extensive computation, limiting their practicality. To address this, we propose a computationally efficient image processing technique that simultaneously reduces artifacts to advance clinical OCTA applications. Our method integrates OCT structural images and OCTA images, and selectively enhances the blood-vessel wall interface, using vessel wall signals as markers for vascular positioning, achieving shorter tail artifacts with minimal processing overhead. Experiment results demonstrate that our approach requires only one-tenth of the computation time of traditional cmOCTA techniques, showing more significant speed improvements compared to other artifact suppression methods. Moreover, our method achieves effective artifact suppression comparable to conventional artifact removal methods at this high speed. Experimental validation using palm vasculature demonstrates the potential of our approach for real-time imaging and suggests that it may support clinical scenarios requiring timely feedback.}, archive = {J_ACCESS}, author = {Wenxin Zhang and Jun Pan and Shiqi Tang and Ning Liu}, doi = {10.1109/ACCESS.2025.3599180}, journal = {IEEE Access}, pages = {144975-144981}, shortjournal = {IEEE Access}, title = {Computationally efficient artifact suppression for optical coherence tomography angiography}, volume = {13}, year = {2025}, }
• (2025). ArtStroke-GAN: A unified framework for interpreting watercolor aesthetics via personalized stroke-based graphic language. ACCESS, 13, 144954-144974. (www)

  @article{ , abstract = {Painting is an expressive art form traditionally associated with considerable technical skill and prolonged training, making it largely inaccessible to the general public. Computational stroke-based rendering (SBR) approaches have emerged to simulate human-like artistic processes digitally; however, current methods encounter notable limitations, such as spatial inconsistencies, stylistic discontinuities, and insufficient control over fine-grained stroke details. To address these challenges, this paper introduces ArtStroke-GAN, a novel generative adversarial network framework designed to synthesize realistic and personalized watercolor paintings. The proposed framework employs a modular three-part GAN architecture consisting of a stroke-generation network, a dedicated color-enhancement module, and an adversarial discriminator. Adaptive spline-based stroke modeling and iterative, semantic-aware stroke optimization strategies are integrated, enabling progressive refinement from coarse to detailed artistic representations. Experimental evaluations conducted on diverse image datasets (CelebA and ImageNet) demonstrate that ArtStroke-GAN achieves superior performance compared to state-of-the-art methods, including Stroke-GAN, Paint Transformer, and Neural-Paint, across quantitative metrics such as LPIPS, FID, style loss, and content fidelity. Qualitative assessments further confirm its ability to produce aesthetically coherent, structurally consistent, and stylistically diverse watercolor renderings, significantly advancing the capability of computational art generation frameworks.}, archive = {J_ACCESS}, author = {Laixi Zheng}, doi = {10.1109/ACCESS.2025.3598183}, journal = {IEEE Access}, pages = {144954-144974}, shortjournal = {IEEE Access}, title = {ArtStroke-GAN: A unified framework for interpreting watercolor aesthetics via personalized stroke-based graphic language}, volume = {13}, year = {2025}, }
• (2025). Estimating attenuation coefficients in wooded environments with ANN predictions. ACCESS, 13, 144941-144953. (www)

  @article{ , abstract = {The Amazon region wide forest extension and its limited infrastructure make it difficult to access essential services. In this context, efficient and reliable communication could reduce distances and facilitate access to these services. Considering infrastructural limitations, Low-Power Wide Area Networks (LPWAN), such as Long-Range (LoRa) networks, show promise for implementation in the region. However, it is important to understand how trees affect the propagation of electromagnetic waves to properly plan and install telecommunication services. Thus, this article aims to determine the attenuation coefficient for wooded environments in an Amazon region through a neural model. For this purpose, data were collected in a grove located at the Federal University of Pará (UFPA). The data were analyzed, and the impact of spreading factor and antenna polarizations on LoRa signal propagation was verified, with Vertical-Vertical (VV) polarization being the least affected by attenuation on average. Finally, the proposed neural model was compared with an adjusted version of the modified exponential decay model and with traditional models such as Weissberger, COST-235, ITU-R, FITU-R, ITU-R P.833-10, and Chen & Kuo. The neural model demonstrated its effectiveness, outperforming the others in all scenarios with an RMSE below 6.5 dB. This model offers a promising tool for designing and planning more efficient communication networks in Amazonian regions, thereby improving local communities access to essential services such as healthcare, education, and e-commerce.}, archive = {J_ACCESS}, author = {Caio Mateus Machado Cardoso and Brenda Silvana de Souza Barbosa and Alex Sanches Macedo and Hugo Alexandre Oliveira da Cruz and Filipe Cavalcanti Fernandes and Fabricio J. B. Barros and Jasmine Priscyla Leite e Araujo}, doi = {10.1109/ACCESS.2025.3599745}, journal = {IEEE Access}, pages = {144941-144953}, shortjournal = {IEEE Access}, title = {Estimating attenuation coefficients in wooded environments with ANN predictions}, volume = {13}, year = {2025}, }
• (2025). SB-tree: A b+-tree for in-memory time series databases with segmented block. ACCESS, 13, 144927-144940. (www)

  @article{ , abstract = {Various devices such as smart cars, healthcare systems, and IoT platforms generate a massive amount of time series data. This type of data exhibits unique characteristics that pose new challenges for in-memory indexes, including heavy insert rates, monotonically increasing keys, and workloads dominated by range queries. In this paper, we propose a new in-memory index, Segmented Block-based Tree (SB-Tree), designed specifically for time series workloads. To support high insert throughput, SB-Tree decouples the index into a search layer and a data layer, and updates the search layer asynchronously to reduce write amplification. It introduces a shortcut mechanism to reduce tree traversal overhead and uses segmented blocks with per-thread data blocks to minimize contention during inserts. In addition, SB-Tree employs a lightweight block allocator to reduce system call overhead during memory management. We evaluate SB-Tree using synthetic time series workloads with varying levels of delayed data, inserting up to 1 billion keys across 80 threads. Our results show that SB-Tree outperforms state-of-the-art in-memory indexes, achieving up to $7\times $ higher throughput on insert-only workloads and $2.4\times $ lower 99.99th percentile tail latency on scan workloads compared to state-of-the-art.}, archive = {J_ACCESS}, author = {Christine Euna Jung and Jaesang Hwang and Yedam Na and Haena Lee and Wook-Hee Kim}, doi = {10.1109/ACCESS.2025.3598736}, journal = {IEEE Access}, pages = {144927-144940}, shortjournal = {IEEE Access}, title = {SB-tree: A b+-tree for in-memory time series databases with segmented block}, volume = {13}, year = {2025}, }
• (2025). A machine learning approach to explore and predict student motivation types. ACCESS, 13, 144908-144926. (www)

  @article{ , abstract = {Motivation plays a significant role in shaping students’ educational outcomes. Understanding the factors that influence student motivation is crucial for enhancing academic performance and designing effective learning environments. This study utilizes Self-Determination Theory to examine various types of motivation, aiming to develop an integrated framework for analyzing and predicting student motivation. The proposed framework employs multi-source data and evaluates three machine learning techniques: Multinomial Regression, Support Vector Machine, and XGBoost. These models are applied to data collected from a UK-based institution, specifically from the Computer Science department. The findings highlight the superior performance of the XGBoost model in identifying learning analytics characteristics that influence each motivation type, achieving precision ranging from 95% to 100%. Additionally, this study explores the underlying philosophy of using LMS data and its features to classify student motivation, supporting the effectiveness of XGBoost in this context. While the results are promising within the context of a single-institution Computer Science setting, further studies are needed to validate the applicability of this methodology across broader educational frameworks.}, archive = {J_ACCESS}, author = {Hafsa Al Ansari and Rasha Shakir Abdulwahhab Al Jassim and Rupert Ward}, doi = {10.1109/ACCESS.2025.3599166}, journal = {IEEE Access}, pages = {144908-144926}, shortjournal = {IEEE Access}, title = {A machine learning approach to explore and predict student motivation types}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of blockchain-smart contracts-ERP integration strategies for supply network (SN) collaboration. ACCESS, 13, 144887-144907. (www)

  @article{ , abstract = {Potential benefits of utilizing blockchain-smart contracts technologies are relatively known for improving SN visibility and traceability. However, utilization of blockchain and smart contracts in integration with Enterprise Resources Planning (ERP) infrastructures of supply networks (SN) partners for advanced business intelligence (BI) and inter-company collaboration is an immature research field. This study investigates how blockchain and smart contracts can complement existing ERP foundations of SN partners for advanced BI and collaborative reporting based on a conceptual comparative study. After identifying main configuration dimensions of privacy setting (public/private/consortium), smart contract utilization for business logic implementation (full/none) and degree of transactional data decentralization (full/only critical data/none), 18 deployment scenarios are developed for advanced SN collaboration. Scenarios are then analyzed, mapped with SN collaboration maturity stages, and assessed in terms of various critical SN capabilities such as visibility, traceability, flexibility and scalability. Study is unique in providing a comprehensive treatment of the topic and mappings offered from supply network management (SNM) perspective. Findings reveal that use of blockchain technologies and smart contracts enhances critical SN capabilities and supports higher levels of collaboration maturity as compared to the baseline ERP-centric integration scenario. Some deployment scenarios support collaborative settings in make-to-order production environments, whereas others embrace relatively larger networks not as collaborative, targeting repetitive production settings.}, archive = {J_ACCESS}, author = {Goknur Arzu Akyuz and Omer Ileri}, doi = {10.1109/ACCESS.2025.3598783}, journal = {IEEE Access}, pages = {144887-144907}, shortjournal = {IEEE Access}, title = {A comparative analysis of blockchain-smart contracts-ERP integration strategies for supply network (SN) collaboration}, volume = {13}, year = {2025}, }
• (2025). A generation algorithm for “Text to image” based on multi-channel attention. ACCESS, 13, 144878-144886. (www)

  @article{ , abstract = {Research on text-to-image has gained significant attention. However, existing methods primarily rely on upsampling convolution operations for feature extraction during the initial image generation stage. This approach has inherent limitations, often leading to the loss of global information and the inability to capture long-range semantic dependencies. To address these issues, this study proposes a generation algorithm for “text to image” based on multi-channel attention (TTI-MCA). The method integrates a self-supervised module into the initial image generation phase, leveraging attention mechanisms to enable autonomous mapping learning between image features. This facilitates a deep integration of contextual understanding and self-attention learning. Additionally, a feature fusion enhancement module is introduced, which combines low-resolution features from the previous stage with high-resolution features from the current stage. This allows the generation network to fully utilize the rich semantic information of low-level features and the high-resolution details of high-level features, ultimately producing high-quality, realistic images. Experimental results show that TTI-MCA outperforms the baseline algorithm in both Inception Score (IS) and Fréchet Inception Distance (FID), achieving superior performance on the CUB and COCO datasets. This research provides a novel approach to generating high-quality images from text.}, archive = {J_ACCESS}, author = {Yang Yang and Ainuddin Wahid Bin Abdul Wahab and Norisma Binti Idris and Dingguo Yu and Chang Liu}, doi = {10.1109/ACCESS.2025.3596894}, journal = {IEEE Access}, pages = {144878-144886}, shortjournal = {IEEE Access}, title = {A generation algorithm for “Text to image” based on multi-channel attention}, volume = {13}, year = {2025}, }
• (2025). ACM-YOLOv10: Research on classroom learning behavior recognition algorithm based on improved YOLOv10. ACCESS, 13, 144863-144877. (www)

  @article{ , abstract = {To effectively integrate the research on learning engagement with teaching practices and accurately assess and analyze students’ learning behavior participation in the classroom to improve teaching quality, this paper proposes an improved YOLOv10 algorithm model, ACM-YOLOv10, targeting the issues of insufficient detection precision, missed detection, false detection, and slow speed of traditional recognition algorithms in classroom behavior detection under multi-scale scenarios and occluded targets. The model is designed with an Asymmetric Depthwise Separable Convolution (ADSConv) module to replace the traditional convolutional layers. This module, with its lightweight design, optimizes convolution operations to reduce computational complexity and parameter quantity, thereby accelerating the model’s inference speed. Additionally, the Spatial and Channel Reconstruction Convolution (C2f_SCConv) module is embedded in the backbone and neck networks, combining the characteristics of SCConv to reduce redundant features of CNN through spatial and channel reconstruction units, which effectively enhances the model’s ability to learn local features, particularly suitable for fine-grained feature extraction in complex backgrounds. Finally, the redesigned spatial Pyramid Pool Fast Learning Separable Kernel Attention (SPPF_LSKA) module is introduced into the backbone network, improving the multi-scale feature fusion method to more effectively handle features of different scales and enhancing the model’s sensitivity and detection capability for minority class behaviors. Experimental results demonstrate that the improved ACM-YOLOv10 model achieves mAP of 82.4% for six behavior categories on the SCB-Dataset3, which is a 5.3% improvement compared to the baseline model, outperforming other mainstream detection models and meeting the practical requirements for Student Behaviors (SB) detection. Additionally, generalization experiments conducted on another SB dataset confirm that the improved algorithm model possesses good generalization performance.}, archive = {J_ACCESS}, author = {Beichen Qin and Haoyan Hu and Shaowen Du}, doi = {10.1109/ACCESS.2025.3599686}, journal = {IEEE Access}, pages = {144863-144877}, shortjournal = {IEEE Access}, title = {ACM-YOLOv10: Research on classroom learning behavior recognition algorithm based on improved YOLOv10}, volume = {13}, year = {2025}, }
• (2025). Observer-based nonlinear control scheme to reduce oscillations and zero crossing in skid-steer vehicles. ACCESS, 13, 144851-144862. (www)

  @article{ , abstract = {Motion sickness is a common condition experienced by drivers of skid-steer vehicles, primarily caused by zero crossing and oscillations in undamped systems. This study proposes an observer-based nonlinear control scheme to reduce transient oscillations and zero-crossing phenomena in skid-steer vehicles, thereby potentially alleviating motion sickness. Reducing transient oscillations and zero crossing in the transient response may alleviate motion sickness. A nonlinear damping controller is designed to improve transient response by reducing oscillations and zero-crossing. To design the controller, a reduced-order kinematic model based on coordinate transformation is developed. This transformation not only converts the system modeling into a controllable form but also enhances control performance. Modeling error is addressed by considering the distance between the center of the vehicle and the sensor location. Despite these improvements, model uncertainties and external disturbances remain, which may degrade control performance. To ensure robustness and estimate such disturbances, a high-order sliding mode observer (HOSMO) is incorporated. The effectiveness of the proposed method is validated through MATLAB/Simulink and TruckMaker simulations. From the simulation results, it was shown that the proposed method reduced the mean squared error of the tracking error to within 10% compared to the state feedback controller with the HOSMO.}, archive = {J_ACCESS}, author = {Jiwon Seo and Kwankyun Byeon and Hojin Lee and Yunhyoung Hwang and Yonghao Gui and Wonhee Kim}, doi = {10.1109/ACCESS.2025.3599353}, journal = {IEEE Access}, pages = {144851-144862}, shortjournal = {IEEE Access}, title = {Observer-based nonlinear control scheme to reduce oscillations and zero crossing in skid-steer vehicles}, volume = {13}, year = {2025}, }
• (2025). Intersecting realms: Examining the convergence of vision and AI in extended reality graphics. ACCESS, 13, 144828-144850. (www)

  @article{ , abstract = {Exploring the integration of Artificial Intelligence (AI) with Extended Reality (XR), this research addresses four critical questions: (R.Q.1) How can AI enhance the realism of virtual environments through dynamic texture and material generation? (R.Q.2) What methods can be developed for AI-driven adaptive learning systems that adjust environment difficulty and content based on user performance? (R.Q.3) What are the best practices for training AI to achieve real-time object recognition, spatial mapping, and scene understanding in diverse environments? And (R.Q.4) How have the themes and focal areas within XR research evolved over time, and what are the predominant topics and trends as evidenced by bibliographic references and citations in scholarly reviews? A thorough literature review and systematic analysis confirm that AI significantly advances the fidelity and interactivity of XR environments. This research not only highlights how AI enriches user experience and procedural functioning in XR but also delves into the evolutionary trajectories in XR research, identifying major thematic trends and shifts over time. These findings underscore the complex interplay between technological advancements and thematic trends within the academic discourse on XR, advocating for consistent innovation and ethical considerations in the utilization of AI across various domains, including education, healthcare, and entertainment.}, archive = {J_ACCESS}, author = {A'Aeshah Alhakamy}, doi = {10.1109/ACCESS.2025.3599337}, journal = {IEEE Access}, pages = {144828-144850}, shortjournal = {IEEE Access}, title = {Intersecting realms: Examining the convergence of vision and AI in extended reality graphics}, volume = {13}, year = {2025}, }
• (2025). Evaluation of dynamic hydrophobicity behavior of silicone rubber insulators filled with 2D nanosheets using a direct current dynamic drop test. ACCESS, 13, 144814-144827. (www)

  @article{ , abstract = {The growing use of composite polymer insulators in high-voltage direct current (HVDC) transmission systems requires a thorough understanding of their dynamic hydrophobicity characteristics to ensure long-term reliability. This study investigates the retention, recovery, and transfer of hydrophobicity in silicone rubber (SiR) composites filled with two-dimensional (2D) nanosheets, specifically molybdenum disulfide (MoS2) and tungsten disulfide (WS2), under DC Dynamic Drop Test (DDT) conditions. The efficacy of these fillers is evaluated against traditional alumina trihydrate (ATH) and fumed silica (SiO2) fillers. The Weibull probability analysis of the hydrophobicity retention test indicates that MoS2-filled composites achieved the highest hydrophobicity retention time (773.4 min), representing a 324.3% increase compared to the unfilled sample (185.4 min), while WS2 composites showed a 223.1% increase (609.7 min). In terms of hydrophobic recovery after 24 hours, MoS2 composites demonstrated a 282.6% improvement (125.6 min), followed by WS2 with a 160.4% improvement ( $\eta =87.1$ min) relative to the unfilled reference (33.9 min). The hydrophobicity transfer test showed that fumed silica promoted faster low molecular weight (LMW) migration, while MoS2 and WS2 slowed LMW diffusion due to stronger filler-matrix interactions. Experimental improvements-such as optimized sample preparation, electrolyte control, and voltage ramping-significantly reduced test variability, improving DDT reproducibility. The findings confirm that MoS2 and WS2 nanosheets substantially enhance the long-term hydrophobic stability of SiR composites under HVDC stress, highlighting their suitability for outdoor insulation applications.}, archive = {J_ACCESS}, author = {Mahmoud A. Ali and Xingliang Jiang and Ran Li and Ahmed S. Menesy and Waleed M. Hamanah and Muhammad Khalid}, doi = {10.1109/ACCESS.2025.3599474}, journal = {IEEE Access}, pages = {144814-144827}, shortjournal = {IEEE Access}, title = {Evaluation of dynamic hydrophobicity behavior of silicone rubber insulators filled with 2D nanosheets using a direct current dynamic drop test}, volume = {13}, year = {2025}, }
• (2025). Path-aware knowledge injection for fine-grained emotion recognition in mental health counseling. ACCESS, 13, 144798-144813. (www)

  @article{ , abstract = {Fine-grained, multi-label emotion recognition in textual dialogues, particularly in contexts like mental health counseling, presents significant challenges due to the complexity and concurrency of human emotions. Standard pre-trained language models, while powerful, often struggle to capture the underlying reasoning and emotional interdependencies that are not explicitly stated in the text. To address this, we propose PK-GAT, a novel framework for Path-aware Knowledge-injected Graph Attention Networks. Our approach moves beyond traditional knowledge fusion by actively reasoning over a commonsense knowledge graph (i..e, SenticNet). It identifies, encodes, and ranks multi-hop semantic paths that connect textual concepts to emotion labels, thereby injecting rich, inferential logic into the model. This path-aware mechanism is integrated with a Graph Attention Network (GAT) that dynamically models the relationships between emotion labels, allowing them to mutually refine one another. We conduct extensive experiments on the GoeMotions and SemEval-2018 datasets. Our results demonstrate that PK-GAT achieves new state-of-the-art performance, attaining a Macro-F1 score of 58.72% and a Micro-F1 of 69.85% on GoeMotions, outperforming strong baselines and recent models. Additionally, through detailed ablation and case studies, we validate the significant contributions of both the path-based reasoning and the dynamic label graph modeling, offering a robust and effective solution for nuanced emotion recognition.}, archive = {J_ACCESS}, author = {Haili Zhang and Dun Niu}, doi = {10.1109/ACCESS.2025.3599547}, journal = {IEEE Access}, pages = {144798-144813}, shortjournal = {IEEE Access}, title = {Path-aware knowledge injection for fine-grained emotion recognition in mental health counseling}, volume = {13}, year = {2025}, }
• (2025). An enhanced time-series-based solar power forecast for microgrid system. ACCESS, 13, 144785-144797. (www)

  @article{ , abstract = {The world is moving towards net-zero emissions. Forecasting solar power with precision is a key enabler for the efficient integration of renewable energy into the grid for smarter grid management, and hence optimizing the balance between supply and demand. This paper develops and rigorously evaluates five forecasting models, namely standard LSTM, attention-enhanced custom LSTM, ARIMA, rolling window ARIMA, and Prophet. All models are implemented on the same test bed using SARAH satellite data for training and ERA5 reanalysis data for validation, ensuring a controlled and reproducible benchmark. Although rolling window ARIMA is a known strategy for capturing evolving time series patterns, its application to cross-domain solar forecasting and its direct comparison with modern deep learning models under unified conditions remain limited. The analysis in this paper indicates that among all the evaluated models, the ARIMA(1,1,1) rolling window, dynamically retrained using a 30-day sliding window, demonstrates the most consistent and superior performance, surpassing both conventional statistical models and deep learning approaches. It achieves the lowest RMSE ( $11.19~\pm ~1.34$ ), MAE ( $8.92~\pm ~1.01$ ), and MAPE (7.2% $\pm ~0.8$ %), along with the highest R2 value ( $0.93~\pm ~0.02$ ), indicating strong generalization with minimal computational complexity. In contrast to static ARIMA, diagnostic tests validate that the residuals of the rolling model are not correlated (Ljung-Box p =0.53), nearly normal (Jarque-Bera p = 0.96; kurtosis =3.89) and symmetric (skewness $= -0.01$ ). A comparative evaluation of rolling window ARIMA and attention-based LSTM models demonstrates forecast accuracy improvements of 71.9% and 50.9%, respectively, with results reported under 95% confidence intervals. These results support the conclusion that the rolling window ARIMA framework offers a statistically reliable and operationally efficient approach, making it an apt model for enhancing time series-based solar power forecasting in microgrid systems.}, archive = {J_ACCESS}, author = {Tejaswini Thota and Aleena Bosco Kurumthottam and Suvidha Rupesh Kumar and Jyotismita Mishra}, doi = {10.1109/ACCESS.2025.3588666}, journal = {IEEE Access}, pages = {144785-144797}, shortjournal = {IEEE Access}, title = {An enhanced time-series-based solar power forecast for microgrid system}, volume = {13}, year = {2025}, }
• (2025). Model-based control implementation of quadruped robots with MuJoCo. ACCESS, 13, 144770-144784. (www)

  @article{ , abstract = {Control algorithms rely on robot simulation platforms to evaluate the dynamics of the controlled robot. For legged robot simulators, the ability to handle contact and friction is fundamental. Many robot simulation platforms are designed for virtual prototyping, replicating the kinematics, dynamics, and real-world interactions of robotic systems. Among them, MuJoCo stands out. Despite MuJoCo’s widespread use in the literature, detailed documentation on the settings required for implementing model-based control techniques is lacking. We present the implementation of inverse dynamics impedance control within a locomotion control framework based on compliant foot trajectory tracking. We provide the theoretical foundation for defining the system model and control scheme, and explain how to extract the corresponding terms from MuJoCo. The settings for contact parameters and the computation of contact forces are detailed, with each linked to the corresponding theoretical background. To illustrate MuJoCo’s practicality, we present an application featuring a quadruped robot modeled on the MIT Mini Cheetah. We analyze and simulate how contact parameters affect the dynamics of the controlled robot. Finally, videos showcase simulations of the controlled quadruped robot, highlighting the effects of varying contact parameter values on contact dynamics.}, archive = {J_ACCESS}, author = {Mauricio Becerra-Vargas and Eduardo Paciência Godoy}, doi = {10.1109/ACCESS.2025.3598608}, journal = {IEEE Access}, pages = {144770-144784}, shortjournal = {IEEE Access}, title = {Model-based control implementation of quadruped robots with MuJoCo}, volume = {13}, year = {2025}, }
• (2025). A review: 3D sketching technology and applications in augmented reality. ACCESS, 13, 144745-144769. (www)

  @article{ , abstract = {AR (Augmented Reality) sketching technology has driven innovation in content creation and design, and the popularity of mobile devices has increased the usability of 3D (three-dimensional) sketching, but many challenges still exist, which limit the full potential of AR sketching. Among them, interaction and technical limitations, such as recognition accuracy, the diversity of interaction manipulations, and steep learning curves and other interaction issues, continuously affect user experience and creative efficiency. These aspects have not yet been comprehensively reviewed, leaving a crucial gap in the literature. This paper fills this gap by conducting an in-depth review of the interactive technologies, interactive manipulations, hardware devices, and basic software tools used in various AR sketching studies classified by interactive devices. By systematically analyzing these elements, this review clearly understands how current AR sketching methods promote or limit user interaction and creative expression, and identifies existing challenges in terms of operational limitations, technical limitations, user experience and other aspects. Furthermore, this review explores emerging trends including more natural user interaction, more comprehensive sketching manipulations, and enhanced real-time collaboration, and summarizes a conceptual framework for AR sketching research and future research directions. By filling these knowledge gaps, this review not only highlights the current limitations, but also provides guidance for future research, offers valuable insights, and promotes the development of more complex and humanized AR 3D sketching technologies.}, archive = {J_ACCESS}, author = {Jin Bai and Mohd Shahrizal Sunar and Norhaida Mohd Suaib}, doi = {10.1109/ACCESS.2025.3598869}, journal = {IEEE Access}, pages = {144745-144769}, shortjournal = {IEEE Access}, title = {A review: 3D sketching technology and applications in augmented reality}, volume = {13}, year = {2025}, }
• (2025). A novel stator tooth shape of axial flux permanent magnet motors based on soft magnetic composite for torque density enhancement. ACCESS, 13, 144733-144744. (www)

  @article{ , abstract = {This paper proposes a pyriform stator tooth body shape of axial flux permanent magnet motors (AFPMMs) based on soft magnetic composite (SMC) to enhance torque density. For the AFPMM with the conventional trapezoidal and proposed pyriform tooth body designs, the difference only lies in tooth body shape and slot area. Under the same tooth cross-sectional area and other motor geometric parameters, the proposed pyriform tooth body shape minimizes the coil length of the concentrated winding compared to the traditional tooth body shape. The electromagnetic performance of the 10-poles and 12-slots AFPMM with the conventional and proposed tooth body shapes is compared under the same copper loss by using 3-D finite element analysis (FEA). Compared to the traditional design, the investigated AFPMM with the proposed pyriform tooth body shape has a 10.02% lower phase resistance and, thus, a 5.54% higher torque density. Besides, the influence of geometric ratios and pole-slot combinations on the electromagnetic performance of the AFPMM with the proposed pyriform tooth body is investigated. It shows that the AFPMM has a significant increase in torque density by replacing the conventional design with the proposed pyriform stator tooth body. Adopting the proposed pyriform tooth body shape is a simple and viable method for higher torque density of the AFPMM under certain constraints. Finally, a prototype with traditional trapezoidal teeth was manufactured using the wire-cutting process to verify the effectiveness of FEA. The impact of wire-cutting processing on the motor performance can be equivalent to incorporating an eddy current layer into the finite element model.}, archive = {J_ACCESS}, author = {Lijian Wu and Xiaofeng Ren and Fangwei Zhao and Zhenyang Zhang and Shuai Ge and Yao Li}, doi = {10.1109/ACCESS.2025.3596952}, journal = {IEEE Access}, pages = {144733-144744}, shortjournal = {IEEE Access}, title = {A novel stator tooth shape of axial flux permanent magnet motors based on soft magnetic composite for torque density enhancement}, volume = {13}, year = {2025}, }
• (2025). A systematic literature mapping of machine learning for handover optimization in cellular networks. ACCESS, 13, 144699-144732. (www)

  @article{ , abstract = {Handover (HO) is a fundamental process in cellular networks (CNs) that ensures continuous connectivity as users move across different coverage areas. With the evolution of mobile communication technologies, the application of Machine Learning (ML) to optimize HO has emerged as a promising research avenue. However, the current literature is fragmented, spanning various cellular standards, ML approaches, datasets, and evaluation practices. This paper presents a comprehensive Systematic Literature Mapping (SLM) to consolidate and structure existing research on ML-based HO optimization. A total of 2,772 articles were identified through a carefully validated search strategy, from which 86 primary studies were selected based on strict inclusion and exclusion criteria. The analysis reveals a predominant focus on LTE (53.8%) and 5G (44.1%), a high reliance on simulated datasets (82.6%), and the frequent use of reinforcement learning (46.5%) for tasks such as handover prediction (45%), ping-pong reduction (22%), and base station selection (10%). Despite these advancements, critical gaps persist, including under-use of real-world data, limited exploration of legacy networks such as 3G, and insufficient methodological standardization. This study contributes a structured synthesis of the field, highlights research trends and deficiencies, and offers actionable insights to guide future work in ML-driven mobility management for evolving CNs.}, archive = {J_ACCESS}, author = {Viviana Parraga-Villamar and Danny S. Guamán and Felipe Grijalva and Pablo Lupera-Morillo}, doi = {10.1109/ACCESS.2025.3599475}, journal = {IEEE Access}, pages = {144699-144732}, shortjournal = {IEEE Access}, title = {A systematic literature mapping of machine learning for handover optimization in cellular networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing vision transformers for facial expression recognition. ACCESS, 13, 144689-144698. (www)

  @article{ , abstract = {Facial expression recognition (FER) remains a challenging problem due to subtle intra-class variations, strong inter-class similarities, and external factors including pose variations, illumination changes, and occlusions. While vision transformers (ViTs) have demonstrated success in general image classification, their ability to model both global semantic context and localized discriminative facial features remains suboptimal for FER. The enhanced vision transformer architecture for facial expression recognition in this study introduces two important innovations. We first create a two-stream representation that simultaneously employs the class token and global average pooling (GAP) to guarantee a more thorough representation that preserves sensitive spatial details while capturing the overall facial context. Second, we present enhanced conditional positional encoding (ECPE), a sophisticated positional encoding technique that captures both local and global spatial dependencies to improve feature learning, and weighted global average pooling (WGAP), a spatially aware pooling mechanism that dynamically emphasizes discriminative facial regions. We evaluate our framework using three prominent FER benchmarks: FER2013, RAF-DB, and FERPlus, exhibiting consistent performance enhancements over conventional transformer baselines and exceeding many state-of-the-art approaches. Ablation studies demonstrate that each element—two-stream representation, WGAP, and ECPE—independently enhances feature learning and classification accuracy. Our approach improves the capacity of vision transformers to recognize subtle facial expressions by integrating global and local feature learning. The approach we present, although specifically tailored for FER, can also be adapted for other fine-grained classification problems necessitating precise discrimination.}, archive = {J_ACCESS}, author = {Ali Mohammad Pazandeh and Emad Fatemizadeh}, doi = {10.1109/ACCESS.2025.3598917}, journal = {IEEE Access}, pages = {144689-144698}, shortjournal = {IEEE Access}, title = {Enhancing vision transformers for facial expression recognition}, volume = {13}, year = {2025}, }
• (2025). Minimum EVCS aggregation requirements for reliable customer baseline load in electricity markets. ACCESS, 13, 144678-144688. (www)

  @article{ , abstract = {The electrification of the transportation sector, driven by decarbonization policies, can significantly impact power demand patterns. Strategically integrated electric vehicles (EVs) can serve as flexible grid resources by enabling load shifting, voltage regulation, and frequency stabilization. In particular, to evaluate their potential participation in the wholesale electricity market, it is essential to assess whether aggregated electric vehicle charging stations (EVCSs) meet the eligibility and reliability criteria required for existing demand response (DR) programs. This study analyzes the reliability of aggregated EVCSs based on their aggregation level using an average-based Customer Baseline Load (CBL) method, performance metrics such as RRMSE and RMAE, and a two-stage random sampling approach. The analysis utilizes load data from 873 EVCSs located in Jeju, Korea, as of 2023. The results show that as the level of aggregation increases, both RRMSE and RMAE decrease, significantly reducing CBL estimation errors. Furthermore, it was confirmed that aggregating EVCSs beyond 10 MW of capacity or 500kWh of average hourly load achieves reliability levels comparable to those of traditional DR resources. These findings provide a practical foundation for the integration of EV-based loads into wholesale market participation and grid control strategies, while contributing to the development of CBL methods and evaluation metrics for small-scale resources.}, archive = {J_ACCESS}, author = {Yongwoo Jee and Jinho Kim}, doi = {10.1109/ACCESS.2025.3596545}, journal = {IEEE Access}, pages = {144678-144688}, shortjournal = {IEEE Access}, title = {Minimum EVCS aggregation requirements for reliable customer baseline load in electricity markets}, volume = {13}, year = {2025}, }
• (2025). Optimal design and control of high power density bi-directional CLLC converter with integrated transformer for wide-range EV battery charging. ACCESS, 13, 144662-144677. (www)

  @article{ , abstract = {This paper presents effective and practical control and design methodologies for electric vehicle (EV) on-board chargers (OBCs) utilizing bidirectional power factor correction (PFC) and CLLC resonant converters. The proposed system enables high-efficiency, high-power-density battery charging over a wide output voltage range from 320 V to 820 V. This is achieved by combining pulse frequency modulation (PFM) of the CLLC converter with dynamic DC-link voltage regulation through the PFC stage, ensuring optimal performance across varying operating conditions. A robust soft-start strategy is introduced that employs secondary-side precharging of the CLLC stage and gradual activation of phase-shift modulation (PSM). This approach effectively limits inrush current, minimizes switching stress, and reduces electromagnetic interference (EMI) during startup, thereby enhancing overall system reliability. To improve power density and thermal performance, a compact integrated transformer design is proposed. The structure leverages inherent leakage inductance to eliminate the need for discrete resonant inductors and incorporates a center multi-gap and center-hole configuration to facilitate efficient heat dissipation. A systematic transformer design methodology is also presented to realize the desired gain characteristics while meeting thermal and dimensional constraints. The effectiveness of the proposed control and design techniques is validated through an 11 kW hardware prototype, achieving a peak efficiency of 96.7% and a power density of 1.5 kW/L. These results demonstrate the proposed approach’s viability for wide-range, high-power battery charging applications in next-generation EV platforms.}, archive = {J_ACCESS}, author = {Joon H. Jeon and Seon H. Jeon and Eun S. Lee}, doi = {10.1109/ACCESS.2025.3597634}, journal = {IEEE Access}, pages = {144662-144677}, shortjournal = {IEEE Access}, title = {Optimal design and control of high power density bi-directional CLLC converter with integrated transformer for wide-range EV battery charging}, volume = {13}, year = {2025}, }
• (2025). VIE-net: Regressive U-net for vegetation index estimation. ACCESS, 13, 144650-144661. (www)

  @article{ , abstract = {Vegetation indexes (VIs) are important indicators in agriculture, revealing valuable information about the vegetative status of crops through nondestructive evaluation methods. Among these indexes, the Normalized Difference Vegetation Index (NDVI) is a key metric used for assessing plant cover and health by combining Near-Infrared (NIR) and Red reflectance. NDVI calculation is based on multispectral cameras equipped with NIR sensors. However, the presence of this sensor is what makes the device costly and therefore impractical for small-scale farms. To address this limitation, recent works have explored the use of artificial intelligence to build AI-powered RGB cameras as a more affordable alternative for NDVI estimation. This has been done by means of generative artificial intelligence (often prone to hallucinations) or via shallow neural networks (pixel-wise regression) with the drawback of a high computational cost. Here, we introduce an end-to-end non-generative approach for NDVI estimation from calibrated RGB images. The proposed model, called VIE-Net, is a convolutional neural network based on a regressive version of the U-Net architecture. The model is tested on two datasets with images captured at 25 m above ground level (remote sensing) and 1 meter from the subject (proximal sensing), achieving correlation performance up to $r^{2} = 0.98$ when non-vegetative background is removed. A lightweight version of the model was also tested, achieving $r^{2} = 0.84$ . This approach not only provides a cost-effective solution for NDVI estimation but also improves the reliability of vegetation health assessment using standard RGB images.}, archive = {J_ACCESS}, author = {Valerio Capparella and Eugenio Nerio Nemmi and Simona Violino and Corrado Costa and Simone Figorilli and Lavinia Moscovini and Federico Pallottino and Catello Pane and Alessandro Mei and Luciano Ortenzi}, doi = {10.1109/ACCESS.2025.3598124}, journal = {IEEE Access}, pages = {144650-144661}, shortjournal = {IEEE Access}, title = {VIE-net: Regressive U-net for vegetation index estimation}, volume = {13}, year = {2025}, }
• (2025). A character-image-modality and multi-label auxiliary model for chinese sentiment analysis. ACCESS, 13, 144632-144649. (www)

  @article{ , abstract = {In multimodal applications, the corresponding image data for a piece of text is not always directly available. However, for the Chinese language, since Chinese characters originate from pictorial forms, character images are closely linked to textual information. To extract useful information from different modalities, this paper proposes a model based on character-image modality and multi-label auxiliary information to improve the accuracy of Chinese sentiment analysis. First, oracle bone script characters are treated as an image modality. A multi-input, multi-output network architecture is then designed to handle both text sentiment analysis and speech recognition tasks. The overall text sentiment analysis task is divided into three unimodal and one multimodal sentiment analysis sub-tasks. These tasks utilize textual features, image features, text-image features, and speech features. Finally, a multi-task joint training framework guided by multi-labels is constructed, including a parameter-sharing optimization mechanism. The speech recognition labels and text sentiment analysis labels guide the model’s attention to these features. Comparison and ablation experiments demonstrate competitive performance, indicating that even without a traditional image modality dataset, the proposed model effectively advances Chinese sentiment analysis research and enhances the generalization capability of multimodal models across different datasets.}, archive = {J_ACCESS}, author = {Liya Wang and Zhe Chen and Ming Zhang and Vasile Palade}, doi = {10.1109/ACCESS.2025.3598452}, journal = {IEEE Access}, pages = {144632-144649}, shortjournal = {IEEE Access}, title = {A character-image-modality and multi-label auxiliary model for chinese sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). A review on quantum machine learning in applied systems and engineering. ACCESS, 13, 144607-144631. (www)

  @article{ , abstract = {Quantum Machine Learning (QML) has emerged as a promising frontier within artificial intelligence, offering enhanced data-driven modeling through quantum-augmented representation, optimization, and learning capabilities, particularly in applied systems and engineering. This review comprehensively analyzes QML applications across various fields, including healthcare, communications, energy, space technology, transportation, and finance. The studies are categorized by learning modalities, QML architectures, and domain-specific use cases, emphasizing hybrid quantum-classical models deployed on current quantum hardware and simulators. Key insights underscore QML’s strengths in handling high-dimensional, low-sample, and time-sensitive tasks. The review addresses critical infrastructural, operational, and ethical considerations for real-world implementation. The goal is to inform system engineers, researchers, and practitioners about the evolving capabilities and challenges of applying QML in practical domains.}, archive = {J_ACCESS}, author = {Ying-Yi Hong and Dylan Josh Domingo Lopez}, doi = {10.1109/ACCESS.2025.3599147}, journal = {IEEE Access}, pages = {144607-144631}, shortjournal = {IEEE Access}, title = {A review on quantum machine learning in applied systems and engineering}, volume = {13}, year = {2025}, }
• (2025). Mobility-aware resource allocation in D2D communications using genetic algorithms. ACCESS, 13, 144591-144606. (www)

  @article{ , abstract = {Smart resource allocation techniques are needed to meet the increasing need for low-latency and high-throughput communication in 5G and beyond networks. Direct user-to-user communication provided by Device-to-Device (D2D) communication increases spectrum efficiency but also makes power control, interference management, and mobility adaptation more difficult. Under dynamic user mobility, static allocation strategies frequently fail, resulting in poor performance and unequal resource distribution. This paper offers a machine learning (ML) based resource allocation framework guaranteeing fairness and minimizing interference that uses mobility prediction and a multi-objective Non-Dominated Sorting Genetic Algorithm 2 (NSGA-II) to optimize power control, channel assignment, and spectrum reuse in D2D-enabled cellular networks. The suggested method demonstrates through simulation a 25% improvement in throughput, a reduction in interference, and significant enhancement in Jain’s fairness index compared to baseline approaches. Higher Quality of Service (QoS) in dynamic settings depends on this performance improvement. The framework also offers insightful analysis of how mobility affects Signal to Interference plus Noise Ratio (SINR), power efficiency, and QoS, stressing the need for dynamic adaptability in real-time systems. While multiple works on resource optimization already exist, including those using deep learning (DL) and federated learning (FL) for mobility-aware ML optimization, our contribution lies in integrating zone-based mobility prediction with multi-objective optimization. Our approach provides a practical basis for future research, including deep reinforcement learning, federated learning, and large-scale real-world deployments.}, archive = {J_ACCESS}, author = {Qusay Alghazali and Husam Al-Amaireh and Tibor Cinkler}, doi = {10.1109/ACCESS.2025.3599051}, journal = {IEEE Access}, pages = {144591-144606}, shortjournal = {IEEE Access}, title = {Mobility-aware resource allocation in D2D communications using genetic algorithms}, volume = {13}, year = {2025}, }
• (2025). Utilizing advanced transformers for summarized review-aware recommender system. ACCESS, 13, 144578-144590. (www)

  @article{ , abstract = {With the rapid growth of e-commerce and increase in the volume of online user reviews, personalized recommender systems have become essential for helping users efficiently identify items of interest. Although user review sets offer rich information for capturing user preferences and item characteristics, full-length reviews sometimes introduce noise, redundancy, and high computational costs. These issues degrade the recommendation performance. To address these challenges, this study proposes a summarized review-aware recommender system (SRARS), which leverages abstractive summarization to extract essential information from review texts. Specifically, we employed the BART, bidirectional and autoregressive transformer model to generate concise and coherent summaries of user and item reviews, thus improving the representation of preferences and attributes. To further enhance recommendation accuracy, we modeled user–item interactions through an outer product operation that captures complex multidimensional relationships and refined these interactions using an attention mechanism to emphasize informative patterns. Experimental evaluations conducted on real-world review data from Amazon.com demonstrated that SRARS significantly outperformed the baseline models in terms of recommendation accuracy and efficiency. This study contributes to the development of review-aware recommender systems by integrating text summarization and interaction modeling to improve the performance under sparse data conditions.}, archive = {J_ACCESS}, author = {Suji Kim and Li Jin and Qinglong Li and Xinzhe Li and Chulkyoon Yoon and Jaekyeong Kim}, doi = {10.1109/ACCESS.2025.3598937}, journal = {IEEE Access}, pages = {144578-144590}, shortjournal = {IEEE Access}, title = {Utilizing advanced transformers for summarized review-aware recommender system}, volume = {13}, year = {2025}, }
• (2025). Secure knuckle print authentication: Template protection and attack analysis. ACCESS, 13, 144560-144577. (www)

  @article{ , abstract = {Biometric authentication using finger knuckle patterns offers a secure and forgery-resistant alternative for identity verification. Unlike fingerprints or facial recognition, knuckle prints are rarely exposed in daily activities, reducing the risk of unauthorized acquisition and spoofing. This study presents a privacy-preserving knuckle print authentication system using a fine-tuned DenseNet-121 model for feature extraction and homomorphic encryption for secure template matching. The IIT Delhi Finger Knuckle Database (version 1.0), consisting of 790 images from 158 individuals, is used. Data augmentation techniques, including rotation, shifting, zooming, shear transformations, and flipping, expanded the training dataset from 632 to 3,160 images, improving generalization. Feature extraction is performed using DenseNet-121, followed by homomorphic encryption of feature templates. Matching is conducted using Euclidean distance on encrypted vectors, ensuring privacy without compromising accuracy. At the optimal authentication threshold of 47, the system achieves 93.67% accuracy, with a False Acceptance Rate (FAR) of 1.099%, False Rejection Rate (FRR) of 6.329%, True Acceptance Rate (TAR) of 93.67%, and True Rejection Rate (TRR) of 98.901%. Euclidean distance outperforms cosine similarity in balancing accuracy and computational efficiency. A security analysis confirms resilience against replay attacks, feature spoofing, and template inversion through nonce-based authentication, homomorphic encryption, and secure key management. The system adheres to ISO/IEC 24745:2022 biometric security standards, ensuring irreversibility, unlinkability, and renewability. The proposed system is well-suited for high-security applications such as banking, healthcare, access control and forensic identification.}, archive = {J_ACCESS}, author = {U. Sumalatha and K. Krishna Prakasha and Srikanth Prabhu and Vinod C. Nayak}, doi = {10.1109/ACCESS.2025.3598926}, journal = {IEEE Access}, pages = {144560-144577}, shortjournal = {IEEE Access}, title = {Secure knuckle print authentication: Template protection and attack analysis}, volume = {13}, year = {2025}, }
• (2025). A robust and efficient 13-level common-ground SCMLI with sextuple voltage gain and fault-tolerant design for sustainable power applications. ACCESS, 13, 144539-144559. (www)

  @article{ , abstract = {This research paper introduces a new 13-level switched capacitor multilevel inverter (SCMLI) design that connects to a common ground, making it suitable for solar PV applications by reducing leakage current. This inverter design achieves balanced capacitor voltages and a six-times voltage boost using only 1 DC power source, 15 switches, 1 diode, and 4 capacitors. A level shifted modulation technique improves the output quality, reduces harmonic distortion, and simplifies control. Simulations in MATLAB/Simulink test the inverter’s steady and changing performance, while PLECS software evaluates energy efficiency through power loss analysis across different conditions. Experiments show that the inverter consistently produces a stable output, with balanced capacitor voltages, even without extra balancing circuits. A comparison with other 13-level inverters highlights this design’s benefits in terms of fewer components, common ground feature, higher voltage gain, and lower cost. Additionally, a reliability analysis confirms that this design is robust and ideal for renewable energy systems. Overall, this inverter offers a compact, efficient, and reliable solution for sustainable power conversion in modern energy systems.}, archive = {J_ACCESS}, author = {Md Ashraf Ali Khan and Yusra Wahab and Chun-Hsin Chang and Adil Sarwar and Mohammad Zaid and Hwa-Dong Liu and Mohd Tariq and Shafiq Ahmad}, doi = {10.1109/ACCESS.2025.3598460}, journal = {IEEE Access}, pages = {144539-144559}, shortjournal = {IEEE Access}, title = {A robust and efficient 13-level common-ground SCMLI with sextuple voltage gain and fault-tolerant design for sustainable power applications}, volume = {13}, year = {2025}, }
• (2025). RSSI guided localization using genetic neural fusion in multi-UAV network. ACCESS, 13, 144529-144538. (www)

  @article{ , abstract = {In disaster response scenarios, the formation of multi-Uncrewed Aerial Vehicles (UAVs) networks is a critical task. The dynamic nature of UAVs makes it difficult to employ traditional approaches. This research study presents an improved task allocation mechanism using UAV localization in disaster conditions. Due to the random movement of UAVs localization is quite a complex problem. Therefore, a novel approach is introduced which is based on received signal strength indicator controlled genetic neural fusion. Genetic neural fusion is the combination of a genetic algorithm and an artificial neural network that adjusts weights to improve localization. During simulation 3D environment is properly built for UAVs. The proposed algorithm GenNF is compared with other techniques like CNN-RNN, CNN-LSTM, CNN-GRU, ANN-LSTM, Particle Filter, and Kalman Filter. GenNF has shown better performance regarding the mean square error, root mean square error, and huber loss. RSSI enabled GenNF to accurately calculate signal strength between UAVs and base station. Therefore, if the signal strength is strong then task will be accordingly allocated to UAVs.}, archive = {J_ACCESS}, author = {Abdur Rehman Khan and Inam Ullah Khan and Hameedur Rahman and Muhammad Mansoor Alam and Mazliham Mohd Su’ud}, doi = {10.1109/ACCESS.2025.3580475}, journal = {IEEE Access}, pages = {144529-144538}, shortjournal = {IEEE Access}, title = {RSSI guided localization using genetic neural fusion in multi-UAV network}, volume = {13}, year = {2025}, }
• (2025). Improving microbiome-based disease prediction with SuperTML and data augmentation. ACCESS, 13, 144505-144515. (www)

  @article{ , abstract = {The efficacy of feedforward neural networks in analyzing high-dimensional tabular data is limited, especially in the presence of data scarcity, a substantial challenge in microbiome research. Limited data availability can lead to overfitting and poor predictive performance, reducing the applicability of classical neural network models. To address these challenges, this study evaluates SuperTML, a novel deep learning framework designed for small tabular datasets, in the context of microbiome-disease prediction, an unexplored domain for this approach. We compare SuperTML’s performance with classical neural networks and the state-of-the-art DeepMicro framework. Our results show that SuperTML, especially when combined with data augmentation techniques, achieves competitive performance, outperforming classical approaches in five out of six datasets. This study also highlights the importance of data augmentation in boosting model performance and suggests SuperTML as a valid alternative for microbiome-based disease prediction. Finally, we discuss the framework’s limitations, particularly those related to data dimensionality, computational requirements and interpretability, and provide insights into possible solutions for future applications.}, archive = {J_ACCESS}, author = {Gabriele Tazza and Dario Ruggeri and László Vidács}, doi = {10.1109/ACCESS.2025.3598868}, journal = {IEEE Access}, pages = {144505-144515}, shortjournal = {IEEE Access}, title = {Improving microbiome-based disease prediction with SuperTML and data augmentation}, volume = {13}, year = {2025}, }
• (2025). NeGLU: Negation-aware sparse retrieval with negative weights for product search. ACCESS, 13, 144492-144504. (www)

  @article{ , abstract = {Recent advances in deep learning-based retrieval have established models such as SPLADE—a prominent variant of Learned Sparse Retrieval (LSR)—as state-of-the-art approaches, particularly in benchmark tasks like TREC Product Search. However, conventional LSR models (e.g., SPLADE) often struggle to effectively capture the semantics of negative Query terms and their lexical variations, leading to misinterpretation of user intent. This limitation leads to retrieval results that do not accurately reflect user intent, especially in queries containing negations like “without”. To overcome this challenge, we propose a novel activation function, Negative Gaussian Linear Unit (NeGLU), designed to assign negative weights that better represent the semantics of negated terms. By integrating NeGLU into an LSR framework, our method improves the model’s ability to identify and suppress negatively influential terms, thereby enhancing ranking performance. Extensive experiments on the TREC2023 product search test collection demonstrate the effectiveness of our approach, achieving an NDCG@100 score of 0.745, significantly outperforming existing methods. To further validate its robustness, we evaluated NeGLU on 186 test queries, including 54 involving negation, where it showed consistent gains—with an average improvement rate of 26.95% overall and 35.}, archive = {J_ACCESS}, author = {Gi-Taek An and Jung-Min Park and Kyung-Soon Lee}, doi = {10.1109/ACCESS.2025.3596549}, journal = {IEEE Access}, pages = {144492-144504}, shortjournal = {IEEE Access}, title = {NeGLU: Negation-aware sparse retrieval with negative weights for product search}, volume = {13}, year = {2025}, }
• (2025). Secure and efficient logistic regression with secret-sharing MPC and differential privacy. ACCESS, 13, 144479-144491. (www)

  @article{ , abstract = {Machine learning models are being increasingly deployed in sensitive applications where data privacy and model security are of paramount importance. This paper introduces a novel privacy-preserving approach for logistic regression that integrates secret sharing-based multi-party computation (MPC) with differential privacy (DP). Our approach ensures input data confidentiality during training via a three-party MPC protocol that supports mini-batch gradient descent, reducing computational and communication overhead compared to prior MPC methods. To protect the model against membership inference and inversion attacks, we implement DP through direct gradient perturbation that scales efficiently to high-dimensional data while preserving strong $\epsilon $ -differential privacy guarantees. Experimental results demonstrate that our approach achieves state-of-the-art efficiency without compromising privacy or model accuracy. By addressing both collaborative training privacy and model-level vulnerabilities, our work enables the secure adoption of logistic regression in scenarios requiring strict data confidentiality and robust post-deployment privacy.}, archive = {J_ACCESS}, author = {Seyma Selcan Magara and Ipek Motorcu and Emin Sahin Mektepli and Cem Ata Baykara and Ali Burak Ünal and Mete Akgün}, doi = {10.1109/ACCESS.2025.3599456}, journal = {IEEE Access}, pages = {144479-144491}, shortjournal = {IEEE Access}, title = {Secure and efficient logistic regression with secret-sharing MPC and differential privacy}, volume = {13}, year = {2025}, }
• (2025). WeakSegNet: Combining unsupervised, few-shot, and weakly supervised methods for the semantic segmentation of low-magnification effusion cytology images. ACCESS, 13, 144467-144478. (www)

  @article{ , abstract = {Effusion cytology analysis can be time consuming for cytopathologists, but the burden can be reduced through automatic malignancy detection. The main challenge in the automation process is pixel-wise labeling. We proposed WeakSegNet, a new model that addresses the challenge of semantic segmentation in low-magnification images by utilizing only four images with pixel-wise labels. WeakSegNet combines unsupervised, few-shot, and weakly supervised learning methods. In the first stage, an unsupervised model, DeepClusterSeg, learns the homogeneous structures from different images. The few-shot method uses only four images with pixel-wise labels to map homogeneous structures to the required classes. The final stage utilized image-level labels to predict precise classes using weakly supervised learning. We conducted our experiments using a dataset from KMC Hospital, MAHE, which consisted of 345 images. We performed 5-fold cross-validation to evaluate the results. Our proposed model achieved promising results, with an F-score of 0.85 and an IoU of 0.81 for the malignant class, surpassing the performance of the standard k-means algorithm with weakly supervised learning (F-scores of 0.65 and an IoU of 0.61). The semantic segmentation of low-magnification images using our approach eliminated 47% of the sub-regions that need to be scanned at high magnification. This innovative approach reduces the workload of cytopathologists and maintains a high accuracy in effusion cytology malignancy detection.}, archive = {J_ACCESS}, author = {Shajahan Aboobacker and Deepu Vijayasenan and Sumam David S. and Pooja K. Suresh and Saraswathy Sreeram}, doi = {10.1109/ACCESS.2025.3598953}, journal = {IEEE Access}, pages = {144467-144478}, shortjournal = {IEEE Access}, title = {WeakSegNet: Combining unsupervised, few-shot, and weakly supervised methods for the semantic segmentation of low-magnification effusion cytology images}, volume = {13}, year = {2025}, }
• (2025). Flight maneuver evaluation method based on similarity metric via triplet network. ACCESS, 13, 144456-144466. (www)

  @article{ , abstract = {Accurately evaluating the quality of flight training and providing practical feedback are essential for enhancing the skills of flight students. Flight maneuver evaluation using flight data offers a promising solution to this challenge. However, traditional evaluation methods have consistently faced significant challenges in terms of generalizability, primarily due to the scarcity of labeled flight data. We propose a novel flight maneuver evaluation framework based on a triplet network and utilizing contrastive learning. This framework employs a specially designed Flight Data Feature Extraction Network (FDFEN) and a customized triplet loss function to project flight maneuver data into embedding vectors. Through training, the network learns to place embedding vectors of maneuvers with the same score close together while pushing apart those with different scores. The score of a flight maneuver is then determined through nearest-neighbor analysis between its embedding vector and those of other maneuvers. In our experiments, accuracy (ACC) and mean absolute error (MAE) are used to assess model performance. Additionally, an adjusted AIC, incorporating penalty terms for neural network complexity, is employed to holistically evaluate the quality of deep learning models. Experimental results demonstrate that our method achieves an accuracy (ACC) at least 5.03% higher and reduces the mean absolute error (MAE) by 0.064 compared to baseline methods. Furthermore, it exhibits enhanced robustness across various types of flight maneuvers.}, archive = {J_ACCESS}, author = {Qingchao Wang and Chengliang Liu and Shoushuo Liu and Yingyue Zhang and Jinze Yang}, doi = {10.1109/ACCESS.2025.3598248}, journal = {IEEE Access}, pages = {144456-144466}, shortjournal = {IEEE Access}, title = {Flight maneuver evaluation method based on similarity metric via triplet network}, volume = {13}, year = {2025}, }
• (2025). Dynamic radar cross section analysis: Effects of pulsed signals and target motion on cylindrical objects. ACCESS, 13, 144445-144455. (www)

  @article{ , abstract = {This study examines the Radar Cross Section (RCS) of a cylindrical target, with a focus on the effects of pulsed signal transmission and target motion. Traditional RCS analyses often rely on continuous wave (CW) excitation and assume stationary targets, which may not accurately capture the dynamics encountered in practical radar scenarios. The RCS of the cylindrical target is computed under pulsed signal excitation and compared with the results obtained under CW conditions. To assess the influence of geometry, the model is extended to a disk-shaped target by reducing the height of the cylinder. The impact of target motion is incorporated using Doppler-based modeling, revealing how the interaction between signal characteristics and target dynamics affects the RCS. These results provide valuable insights into improving radar detection accuracy and refining RCS reduction techniques. Key strategies such as scattering control, absorption, and the use of radar-absorbing materials (RAMs) and metasurfaces must account for these dynamic conditions to achieve optimal performance. Although this investigation focuses on cylindrical structures, the methodology presented can be applied to other simple geometries, enhancing its relevance to practical RCS management. Future extensions may include modeling targets with more complex structures and incorporating advanced source modeling techniques, such as different types of pulsed and modulated signals, to further approximate real-world radar environments.}, archive = {J_ACCESS}, author = {Kamran Salehian and Ayaz Ghorbani and Abdolali Abdipour and Raed A. Abd-Alhameed}, doi = {10.1109/ACCESS.2025.3599092}, journal = {IEEE Access}, pages = {144445-144455}, shortjournal = {IEEE Access}, title = {Dynamic radar cross section analysis: Effects of pulsed signals and target motion on cylindrical objects}, volume = {13}, year = {2025}, }
• (2025). Global evidence on the impact of ICT on innovation among knowledge workers in the context of digital transformation. ACCESS, 13, 144434-144444. (www)

  @article{ , abstract = {Under the background of digital transformation, the relationship between knowledge workers and innovation has been widely discussed, but few studies have discussed the relationship between knowledge workers and different types of innovation activities at the macro level. There is also a lack of empirical evidence on the role of ICT. This paper provides an empirical analysis of the relationship between knowledge workers and different national innovation outputs based on the panel data of 122 countries from 2015-2022 and highlights Information and Communication Technology (ICT) as the moderating variable. The results show that first, knowledge workers are significantly positively correlated with total national innovation output, knowledge and technology output, and creative output. Compared with knowledge and technology output, knowledge workers play a stronger role in promoting national creative output. In addition, the utilization rate of ICT, the penetration rate of ICT and government network service all have positive moderating effects on the relationship between knowledge workers and national innovation output. This study is helpful to deepen the understanding of the relationship between innovation individuals and different types of innovation activities, and also validate the importance of ICT for knowledge workers in the era of big data and the Internet of Things.}, archive = {J_ACCESS}, author = {Xue Wang and Mengya Zhang and Maping Zhang and Zongjun Wang}, doi = {10.1109/ACCESS.2025.3597603}, journal = {IEEE Access}, pages = {144434-144444}, shortjournal = {IEEE Access}, title = {Global evidence on the impact of ICT on innovation among knowledge workers in the context of digital transformation}, volume = {13}, year = {2025}, }
• (2025). A comparative study of deep learning semantic segmentation models for kidney segmentation in ultrasound images using the open kidney ultrasound dataset. ACCESS, 13, 144417-144433. (www)

  @article{ , abstract = {Accurate segmentation of kidney ultrasound (KUS) images is essential for various diagnostic procedures. However, challenges such as variability in image quality and the need for real-time processing remain significant obstacles. This study presents a comparative evaluation of eleven well-established deep learning (DL) semantic segmentation architectures, including SegFormer, U-Net, HED-UNet, DSEU-Net, UNet++, Multi-scale Attention Net (MA-Net), BiSeNet V2, Pyramid Attention Network (PAN), LinkNet, SegNet, and DeepLabV3+. Different backbones are examined for three architectures (SegFormer, SegNet, and DeepLabV3+), resulting in a total of twenty distinct DL models. The models are trained and evaluated using the publicly available Open Kidney Ultrasound dataset. Additionally, the models are assessed based on three main indicators: kidney segmentation performance measured by six metrics, inference time per KUS image, and statistical differences quantified using the F1-score and intersection over union metrics. To the best of our knowledge, the SegFormer, HED-UNet, MA-Net, BiSeNet V2, and PAN models have not been previously applied to kidney segmentation in KUS images. The results indicate that the SegFormer models with the B5 and B4 MiT backbones have achieved the highest segmentation performance while maintaining interactive processing speeds. By using a publicly available dataset, this study supports reproducibility and standardized benchmarking, helping to overcome challenges associated with the use of private KUS image datasets in earlier studies. The findings highlight the strengths and limitations of each model, providing insights to guide future research and improve clinical applicability of DL-based kidney segmentation in KUS imaging.}, archive = {J_ACCESS}, author = {Mohammad I. Daoud and Faisal Abunameh and Khaled Shweikeh and Seif K. Alzamer and Mostafa Z. Ali and Rami Alazrai}, doi = {10.1109/ACCESS.2025.3596051}, journal = {IEEE Access}, pages = {144417-144433}, shortjournal = {IEEE Access}, title = {A comparative study of deep learning semantic segmentation models for kidney segmentation in ultrasound images using the open kidney ultrasound dataset}, volume = {13}, year = {2025}, }
• (2025). Toward sustainable rural electrification: Optimization of hybrid renewable energy systems in mediterranean climates. ACCESS, 13, 144406-144416. (www)

  @article{ , abstract = {Renewable energy systems have become increasingly vital in addressing the needs of rural communities, particularly in regions affected by socio-economic instability and displaced populations. This study proposes a comprehensive framework for the optimal design and evaluation of a hybrid renewable energy system tailored to a typical remote village with a decentralized utility grid in Jordan. The methodology relies on detailed site-specific surveys and datasets, which were thoroughly verified prior to system modeling. Multiple operational scenarios were examined, with a focus on optimizing economic, environmental, and operational (EEO) performance. A comparative cost analysis of the different system configurations is integrated, alongside a sensitivity analysis over key parameters such as load growth. Results demonstrate that the proposed optimal design consistently satisfies the village’s annual load demand while achieving the lowest cost of energy (COE) and net present cost (NPC). Moreover, increasing photovoltaic (PV) penetration levels significantly reduced harmful emissions, reinforcing the potential of renewable energy to replace conventional sources in rural applications. The findings underscore the viability and sustainability of hybrid renewable energy systems for electrifying rural communities, offering a replicable model for similar regions seeking resilient and low-cost energy solutions.}, archive = {J_ACCESS}, author = {Laith M. Halabi and Samer Saleh Hakami and Mahmoud Kassas}, doi = {10.1109/ACCESS.2025.3597085}, journal = {IEEE Access}, pages = {144406-144416}, shortjournal = {IEEE Access}, title = {Toward sustainable rural electrification: Optimization of hybrid renewable energy systems in mediterranean climates}, volume = {13}, year = {2025}, }
• (2025). Legal tensions and institutional innovations: The role of local self-government in international economic agreements and dispute resolution. ACCESS, 13, 144397-144405. (www)

  @article{ , abstract = {This study explores the evolving role of local self-governments in China in shaping international economic law through a detailed comparative analysis of Shanghai, Shenzhen, and Hangzhou. By examining key dimensions of institutional autonomy, governance innovation, legal tensions, and dispute resolution mechanisms, the paper investigates how these cities strategically navigate their growing influence in global economic governance. Findings reveal that Shanghai leads in global integration and dispute resolution efficiency, while Shenzhen excels in legal experimentation but faces higher tensions. Hangzhou, on the other hand, focuses on digital governance and procedural innovation. Through a combination of legal-textual coding, policy analysis, and advanced quantitative metrics, the paper demonstrates that local governments are not merely passive implementers of national law but rather active participants in multi-level governance and paradiplomacy. The research contributes to the understanding of how local actors shape global legal norms and how subnational innovation can influence international economic policy and dispute resolution.}, archive = {J_ACCESS}, author = {Yuyuan Yang}, doi = {10.1109/ACCESS.2025.3599200}, journal = {IEEE Access}, pages = {144397-144405}, shortjournal = {IEEE Access}, title = {Legal tensions and institutional innovations: The role of local self-government in international economic agreements and dispute resolution}, volume = {13}, year = {2025}, }
• (2025). Modified hybrid synchronization control for GFM inverters in output power tracking, oscillation suppression, and transient synchronization stability improvement. ACCESS, 13, 144385-144396. (www)

  @article{ , abstract = {Conventional hybrid synchronization control (HSC) schemes integrating power-based synchronization (PBS) and voltage-based synchronization (VBS) techniques suffer from inaccurate power reference tracking and susceptibility to low-frequency oscillation. To address these limitations, this paper proposes a modified HSC method featuring two improvements: a refined PBS loop with accurate power reference tracking ca pability and a modified VBS loop incorporating an oscillation damping mechanism. The proposed approach significantly enhances transient synchronization stability. Systematic stability verification is conducted through both small-signal stability analysis and transient stability assessment. Comprehensive simulation and Hardware-in-the-loop test results validate the effectiveness of the proposed control method.}, archive = {J_ACCESS}, author = {Wenhang Diao}, doi = {10.1109/ACCESS.2025.3598741}, journal = {IEEE Access}, pages = {144385-144396}, shortjournal = {IEEE Access}, title = {Modified hybrid synchronization control for GFM inverters in output power tracking, oscillation suppression, and transient synchronization stability improvement}, volume = {13}, year = {2025}, }
• (2025). A wireless sensor network LCS-AQMS: Design, instrumentation, calibration modelling, and data analysis. ACCESS, 13, 144362-144384. (www)

  @article{ , abstract = {The utilization of low-cost sensors (LCS) of air quality monitoring systems (AQMS) such as MQ7, GP2Y-Sharp, ZE15-Winsen, and ZH03 in air quality monitoring systems has seen a marked increase. This can be attributed to the cost advantages and ease of integration these sensors offer. However, the performance of these sensors is significantly impacted by environmental factors, including temperature and humidity, which result in substantial deviations from the reference values. The objective of this study is to develop a reliable AQMS system that applies a machine learning-based environmental compensation calibration model to improve the accuracy of CO and PM sensor readings, particularly in the campus environment of the Bandung Institute of Technology. A combination of CO and PM sensors is used to collect data, which is then temporally synchronized with data from a gravimetric reference sensor. This model was developed using a supervised learning approach with three main algorithms: linear regression, random forest, and gradient boosting. The input features encompassed temperature, humidity, the day of the week, and the raw PM sensor readings. The evaluation process employed hold-out validation to ensure the consistency of the model. The results of the study show that, despite its simplicity, linear regression provides the most stable performance, as indicated by the highest ${R}^{2}$ value and lowest error when used with limited data. This model is considered the most viable option for direct implementation on edge devices, due to its high computational efficiency and minimal resource requirements. Meanwhile, random forest or gradient boosting models are expected to yield better results when supplemented with training data such as wind direction, wind speed, and rainfall. In parallel, this study also developed a trustworthy wireless sensor network architecture that incorporates a Markov Chain-based reliability model to mitigate the risk of data transmission failure. This probabilistic approach enhances the robustness and continuity of environmental data collection in real-time deployments. The results of this study indicate that, with optimal model design parameters, LCS sensors can be reliably used for field measurements, thereby increasing the acceptability and credibility of the data. These results support the development of adaptive and energy-efficient air quality monitoring systems.}, archive = {J_ACCESS}, author = {Galang Adira Prayoga and Emir Husni and Reza Darmakusuma}, doi = {10.1109/ACCESS.2025.3598855}, journal = {IEEE Access}, pages = {144362-144384}, shortjournal = {IEEE Access}, title = {A wireless sensor network LCS-AQMS: Design, instrumentation, calibration modelling, and data analysis}, volume = {13}, year = {2025}, }
• (2025). Adaptive modulation for non-orthogonal multiple access (NOMA) with imperfect SIC. ACCESS, 13, 144344-144361. (www)

  @article{ , abstract = {The increasing demand for high-capacity wireless communications has always been a challenge for researchers. Non-Orthogonal Multiple Access (NOMA) is one of the emerging schemes that can improve the capacity of wireless networks. In the power-domain NOMA, users share the same frequency and time but with different power levels. In this paper, we propose a novel adaptive modulation scheme for a NOMA-based wireless network to achieve better throughput efficiency and improve the system spectral efficiency by considering imperfect Successive Interference Cancellation (SIC). This scheme allocates the appropriate modulation orders by solving an optimization problem constrained by a maximum Bit Error Rate (BER). We investigate the BER and throughput calculations to assess the efficacy of the proposed scheme. The results show that our adaptive scheme greatly improves the spectral efficiency compared to the traditional fixed modulation NOMA systems, indicating its potential to satisfy the demanding performance standards of future wireless networks.}, archive = {J_ACCESS}, author = {Milad Heshmati and Ali H. Bastami and Lokman Sboui and Zbigniew Dziong}, doi = {10.1109/ACCESS.2025.3597579}, journal = {IEEE Access}, pages = {144344-144361}, shortjournal = {IEEE Access}, title = {Adaptive modulation for non-orthogonal multiple access (NOMA) with imperfect SIC}, volume = {13}, year = {2025}, }
• (2025). Enhancing fisheye object detection using frequency-domain attention and dual aggregation transformer. ACCESS, 13, 144325-144343. (www)

  @article{ , abstract = {This paper presents a comprehensive evaluation of multiple YOLO (You Only Look Once) model variants for object detection in fisheye lens images, specifically utilizing the FishEye8K dataset. We propose a novel framework that combines frequency-domain preprocessing using FFTformer and post-processing with Dual Aggregation Transformer (DAT) to enhance detection accuracy. The study benchmarks ten variants across YOLOv5, YOLOv8, and YOLOv9, evaluating their performance on five object classes: Pedestrian, Bike, Car, Truck, and Bus. Our preprocessing strategy employs FFTformer to improve image clarity and reduce noise, while the post-processing approach utilizes DAT for super-resolution enhancement. Results demonstrate significant improvements in precision, recall, mAP50, and mAP metrics across all object categories after implementing the proposed preprocessing and post-processing techniques. Notably, YOLOv5l6 exhibited superior performance in detecting smaller objects, while YOLOv8x excelled at larger object detection. The integration of FFTformer and DAT led to substantial improvements in detection accuracy, particularly for challenging scenarios involving distorted or low-quality fisheye images.}, archive = {J_ACCESS}, author = {Oreoluwa Alade and Noreen Fayyaz Khan and Armstrong Aboah}, doi = {10.1109/ACCESS.2025.3596026}, journal = {IEEE Access}, pages = {144325-144343}, shortjournal = {IEEE Access}, title = {Enhancing fisheye object detection using frequency-domain attention and dual aggregation transformer}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection in photovoltaic systems using improved independent component analysis. ACCESS, 13, 144307-144324. (www)

  @article{ , abstract = {Reliable operation of photovoltaic (PV) systems requires effective monitoring strategies to detect sensor faults that may degrade performance or compromise safety. This paper proposes an enhanced data-driven fault detection framework that combines Improved Independent Component Analysis (ICA) with the Kantorovich Distance (KD) and Kernel Density Estimation (KDE) for robust anomaly detection. ICA is employed to model multivariate dependencies among PV sensor measurements, such as irradiance, temperature, and power output, enabling the extraction of residuals that isolate abnormal variations. The KD metric is then used to quantify distributional shifts in the residuals between healthy and testing conditions, capturing both mean and covariance changes. To enable adaptive and non-parametric thresholding, KDE is applied to the KD values computed under normal conditions, facilitating statistically grounded fault detection. The proposed ICA–KD–KDE framework is validated on PV datasets with injected sensor faults, including bias, drift, and intermittent errors. Experimental results demonstrate superior sensitivity and low false alarm rates compared to conventional ICA-based methods, particularly in detecting subtle anomalies in environmental sensors. This approach provides a flexible and interpretable monitoring solution for real-time PV system diagnostics.}, archive = {J_ACCESS}, author = {Fouzi Harrou and K. Ramakrishna Kini and Muddu Madakyaru and Ying Sun}, doi = {10.1109/ACCESS.2025.3599314}, journal = {IEEE Access}, pages = {144307-144324}, shortjournal = {IEEE Access}, title = {Anomaly detection in photovoltaic systems using improved independent component analysis}, volume = {13}, year = {2025}, }
• (2025). Investigation of deep learning techniques used in medicinal plants identification and classification. ACCESS, 13, 144287-144306. (www)

  @article{ , abstract = {There is a global dependence on medicinal plants for the treatment of diverse health conditions, particularly in developing countries, where government medical facilities are inadequate. Conventional machine learning techniques and traditional methodologies are inadequate for rapid, accurate, and reliable identification and classification of medicinal plants. This has paved the way for deep learning(DL) models to identify and classify medicinal plants. This review focuses on the following questions: 1) Which deep learning models were used for medicinal plant identification and classification and how did they perform? 2) Did hybrid models perform better than ensemble models? 3) What common datasets are used for deep learning-based medicinal plant identification and classification studies? 4) Which data augmentation techniques were prevalent in medicinal plant identification and classification studies? 5) Which performance metrics are frequently used to evaluate medicinal plant identification models? 6) What data splitting ratios are commonly used? 7) Was transfer learning prevalent in medicinal plant identification and classification studies? This review complements the limited literature on medicinal plant identification and classification using deep learning. This study analyzes gaps in the existing literature and provides recommendations for future research. This will enhance the development of robust deep learning models to identify and classify medicinal plants for indigenous knowledge preservation, biodiversity monitoring, and their proper use in pharmaceutical processes.}, archive = {J_ACCESS}, author = {Thon Malek Garang Ok and Malusi Sibiya and Zenghui Wang and Ernest Mnkandla}, doi = {10.1109/ACCESS.2025.3598636}, journal = {IEEE Access}, pages = {144287-144306}, shortjournal = {IEEE Access}, title = {Investigation of deep learning techniques used in medicinal plants identification and classification}, volume = {13}, year = {2025}, }
• (2025). FeSATLock: An energy efficient and SAT attack resilient logic locking design with FeFET LUT architecture for enhanced hardware security. ACCESS, 13, 144271-144286. (www)

  @article{ , abstract = {Boolean satisfiability (SAT) attacks have been proven to be highly effective against logic locking techniques that secure intellectual property (IP). Prior research has improved the output corruptibility and SAT attack resiliency of logic locking, but often results in large overheads, higher design effort, increased delay and area/energy consumption. This work presents FeSATLock- a novel ferroelectric FET (FeFET) lookup table (LUT) based energy efficient and secure logic locking technique exploring the FeFET tunable device characteristics leading to both steep-slope characteristics for energy efficient circuit design and hysteresis behavior for non-volatile (NV) memory design. A FeFET LUT based key gate architecture has been proposed for key management and obfuscating the original circuit. A complete logic locking framework is demonstrated utilizing the proposed FeFET LUT based key gates, and performance has been benchmarked with baseline 40nm Complementary Metal Oxide Semiconductor Static Random Access Memory (CMOS SRAM) LUT based design at ${\mathrm{V}}_{\mathbf {DD}} =0.5$ V. Due to the steep slope characteristics, at an optimal ferroelectric layer thickness ( ${\mathrm{t}}_{\mathbf {fe}}$ ), FeFET LUT key gate design achieves $\sim 2.68\times $ lower energy consumption, and $\sim 6.01\times $ higher speed with ~23% reduction in transistor count, compared to the baseline CMOS SRAM based key gate designs. The proposed FeFET LUT based locked circuit with key gates demonstrate $\sim 2.21\times $ reduction in energy consumption and ~4.75x improvement in circuit speed in comparison to baseline CMOS SRAM based locked designs. The proposed logic locking design methodology is further evaluated against SAT attack, robustness is compared with the existing XOR, MUX based techniques and demonstrate higher SAT attack resiliency.}, archive = {J_ACCESS}, author = {Tirumala Rao Kadiyam and Venu Birudu and Aditya Japa and Fadi N. Sibai and Venkateswarlu Gonuguntla and Ramesh Vaddi}, doi = {10.1109/ACCESS.2025.3598581}, journal = {IEEE Access}, pages = {144271-144286}, shortjournal = {IEEE Access}, title = {FeSATLock: An energy efficient and SAT attack resilient logic locking design with FeFET LUT architecture for enhanced hardware security}, volume = {13}, year = {2025}, }
• (2025). Deep learning-centric task offloading in IoT-fog-cloud continuum: A state-of-the-art review, open research issues, and future directions. ACCESS, 13, 144241-144270. (www)

  @article{ , abstract = {The rapid growth of IoT and real-time applications has led to a surge in data generation, traditionally processed in cloud-centric architectures. However, this paradigm introduces high latency, bandwidth bottlenecks, and privacy concerns. Fog computing, supported by edge devices, addresses these challenges by enabling computation closer to data sources. This survey presents a comprehensive review of recent studies on task offloading and resource allocation in fog computing, with a focus on Machine Learning (ML) and Deep Learning (DL)-based techniques. We analyze strategies across the fog–cloud continuum, considering factors such as latency, energy consumption, network utilization, and cost. The review also highlights simulation tools, architectural models, and placement policies. Unresolved challenges and interdependencies among research issues are discussed, and future directions are outlined with actionable evaluation metrics. This article serves as a valuable reference for researchers and practitioners aiming to optimize intelligent resource management in fog-enabled IoT environments.}, archive = {J_ACCESS}, author = {Gurpreet Singh Chhabra and Goluguri N. V. Rajareddy and Abhijeet Mahapatra and S. Sudheer Mangalampalli and Kshira Sagar Sahoo and Deepak Sethi and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3599190}, journal = {IEEE Access}, pages = {144241-144270}, shortjournal = {IEEE Access}, title = {Deep learning-centric task offloading in IoT-fog-cloud continuum: A state-of-the-art review, open research issues, and future directions}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning control of direct-driven wave energy converter incorporating constrained field weakening optimization. ACCESS, 13, 144226-144240. (www)

  @article{ , abstract = {Wave energy converters have recently attracted significant attention in control methodology. The complex and variable marine environment makes modeling difficult, thus requiring model-free control methods. To maximize power capture while satisfying physical constraints, this study proposes a model-free control method based on Deep Q-Network (DQN), incorporating field weakening optimization (FWO) mechanism. The DQN-based control primarily optimizes the damping and stiffness coefficients, then the field weakening optimization determines the optimal output under linear/nonlinear constraints. Experimental results show that the proposed FWO enhances power capture by 37%-65% under various sea conditions while extending the operational speed range by 10%. Moreover, compared with resistive control and MPC, the DQN-FWO method improves power capture by 330% and 70% respectively, demonstrating validated effectiveness for maximal power extraction.}, archive = {J_ACCESS}, author = {Chuan Qin and Ying Tang and Wei Li and Anni Jiang and Feng Wu}, doi = {10.1109/ACCESS.2025.3599215}, journal = {IEEE Access}, pages = {144226-144240}, shortjournal = {IEEE Access}, title = {Reinforcement learning control of direct-driven wave energy converter incorporating constrained field weakening optimization}, volume = {13}, year = {2025}, }
• (2025). Enhanced formation control for heterogeneous multi-agent systems with nonlinear dynamics and time-varying delays under switching networks. ACCESS, 13, 144215-144225. (www)

  @article{ , abstract = {In this investigation, we tackle the complexities associated with the formation control (FC) for heterogeneous multi-agent systems (HMASs) that exhibit nonlinear dynamics and are subject to time delays and dynamic networks. Considering the dynamic topology structure, a distributed FC protocol is designed. Furthermore, the stability of the heterogeneous multi-agent system (HMAS) is analyzed using Lyapunov theory and matrix theory, and the sufficient conditions for achieving FC for the HMAS with time delays are provided. Finally, numerical simulations demonstrate that the system achieves formation convergence within $t\approx 24s$ under time-varying delays up to $\delta =3$ , and maintains robustness even with non-identical delays. Physical validations in Gazebo confirm the protocol’s applicability in real-world multi-robot systems.}, archive = {J_ACCESS}, author = {Runxi Cao and Yunxi Zhang and Jia Liu and Jiayi Du}, doi = {10.1109/ACCESS.2025.3598840}, journal = {IEEE Access}, pages = {144215-144225}, shortjournal = {IEEE Access}, title = {Enhanced formation control for heterogeneous multi-agent systems with nonlinear dynamics and time-varying delays under switching networks}, volume = {13}, year = {2025}, }
• (2025). Blockchain and internet of things (BIoT) in smart Government—Impact analysis. ACCESS, 13, 144195-144214. (www)

  @article{ , abstract = {Smart information and communications technology and its extensive use usher significant changes in all spheres of society. As a result, there emerge new business models, new organisational and management forms, as well as new demands, both in the private and the public sectors. As far as the developments in the administrative paradigm are concerned, increasingly more governments are making efforts to move from e-government to smart government. The Internet of Things (IoT), blockchain, artificial intelligence are the technologies which enable smart government and make it possible to develop and provide smart services to citizens, organisations and public agencies in a variety of fields. Taking into account the current incipient stage of blockchain and IoT (BIoT) adoption and of the transition to smart government, our study focuses on the identification of the main characteristics of smart government, as well as on the main benefits and challenges related to BIoT in e-government. Moreover, in order to capture the impact of BIoT on e-government, the study proposes a conceptual model in which we define and describe a set of hypotheses on how the BIoT adoption in e-government will impact: 1) the government provision of public services to the citizens (G2C), 2) to the business environment (G2B) and 3) the intergovernmental processes and relations (G2G). The proposed model relies on the current stage of research and is validated through the data collected from a Romanian survey. The study uses the Partial Least Squares Structural Equation Modelling (PLS-SEM model) in order to test and analyse the impact of the identified variables on the use the BIoT based public services. The results indicate that the use of BIoT may contribute to delivering higher quality public services. Furthermore, our paper also lists the main recommendations that may step up the transition to smart government.}, archive = {J_ACCESS}, author = {Marian Stoica and Marinela Mircea}, doi = {10.1109/ACCESS.2025.3599108}, journal = {IEEE Access}, pages = {144195-144214}, shortjournal = {IEEE Access}, title = {Blockchain and internet of things (BIoT) in smart Government—Impact analysis}, volume = {13}, year = {2025}, }
• (2025). Dynamic state estimation of power systems using a modified unscented kalman filter and wavelet transform considering missing and noisy PMU data. ACCESS, 13, 144183-144194. (www)

  @article{ , abstract = {The power system is a quasi-static system which experiences change in its state continuously. The advent of Phasor Measurement Units (PMUs) has brought another dimension to wide area monitoring, protection and control (WAMPAC) through the process of dynamic state estimation. However, the existence of noise or missing PMU data due to failure of the measuring unit or communication channel can result in the mal-operation of the control system and also result in erroneous state estimation. This paper proposes a simplified yet robust solution for estimating the dynamic state of the power system under missing PMU data or noisy conditions. In order to obtain PMU data which reflects the real time scenario of the power system, the PMU is modelled in MATLAB Simulink in accordance with IEEE C37.118 standard. The proposed algorithm is based on de-noising technique using the Wavelet Transform (WT) and dynamic state estimation (DSE) technique based on a Modified Unscented Kalman Filter (Modified UKF). This approach improves the robustness of the classical UKF based DSE algorithm, in the absence of PMU data without introducing extra computational complexity. The proposed method, when combined with wavelet-based de-noising, demonstrated superior performance by significantly reducing the estimation errors for both angular speed ( $\omega $ ) rotor angle ( $\delta $ ), thereby clearly outperforming the conventional UKF method. Other simulation results on the standard IEEE 14-bus test system also validate the superiority of the proposed algorithm under various missing and noisy PMU conditions.}, archive = {J_ACCESS}, author = {Subin Koshy and V. Sreehari Venugopal and R. Sunitha and Elizabeth P. Cheriyan and K. Ananthakrishnan}, doi = {10.1109/ACCESS.2025.3598922}, journal = {IEEE Access}, pages = {144183-144194}, shortjournal = {IEEE Access}, title = {Dynamic state estimation of power systems using a modified unscented kalman filter and wavelet transform considering missing and noisy PMU data}, volume = {13}, year = {2025}, }
• (2025). Investigating the impact of p-bit non-idealities on solving complex optimization problems. ACCESS, 13, 144173-144182. (www)

  @article{ , abstract = {We perform a simulation study to effectively utilize SiOx-based threshold switching (TS) devices as probabilistic bits (p-Bits) for solving complex optimization problems (COPs), providing design guidelines for p-Bit development. Thin SiOx films, incorporating Ti scavenging layers to induce oxygen vacancies as intrinsic entropy sources, exhibit TS behavior with high variability. This variability gives rise to stochastic output voltage ( ${\mathrm{V}}_{\mathrm {out}}$ ) spike generation in response to input voltage ( ${\mathrm{V}}_{\mathrm {in}}$ ) pulses. Notably, the number of Vout spikes is inversely correlated with the Vin amplitude, resulting in a sigmoidal probability distribution. However, in practical scenarios, deviations from ideal probability distributions are frequently observed due to device variability or failure. Thus, we categorize the experimentally observed p-Bit behaviors into several notable types: 1) ideal sigmoidal response, 2) linear response, 3) Vout oscillations with deterministic outcomes (100% or 0% spike probability), and 4) transitions from sigmoid or linear behavior to random output. We then investigate how these non-ideal, device-level p-Bit characteristics affect system-level performance in two representative COP applications. First, by implementing simulated annealing algorithms in MATLAB to solve the vehicle routing problem, we demonstrate that p-Bits with sigmoidal probability responses can accelerate the discovery of optimal routing paths. Second, we show that the tunable stochastic behavior of p-Bits facilitates inverted logic gate operations, where outputs are fixed and inputs are inferred probabilistically. This probabilistic logic approach enables robust clause evaluation and effective resolution of the maximum satisfiability problem, even under device-level uncertainty. Furthermore, the demonstrated logic gate operations using stochastic p-Bits enable secure and parallelizable encryption and decryption schemes, offering a reconfigurable, memory-less alternative to conventional hardware-based cryptographic systems. The ability to construct reliable logic gates from stochastic p-Bits represents a foundational advancement toward implementing hardware-level probabilistic computing systems.}, archive = {J_ACCESS}, author = {Jihyun Kim and Hyeonsik Choi and Jiyong Woo}, doi = {10.1109/ACCESS.2025.3598102}, journal = {IEEE Access}, pages = {144173-144182}, shortjournal = {IEEE Access}, title = {Investigating the impact of p-bit non-idealities on solving complex optimization problems}, volume = {13}, year = {2025}, }
• (2025). Modeling of negative feedback refined via LLM in recommender systems. ACCESS, 13, 144160-144172. (www)

  @article{ , abstract = {Most recommender systems have been proposed by utilizing the user feedback data, where implicit feedback is often used. However, in this setting, the dispreference signals in the original data get simply removed and treated the same as the unrated items. To address this limitation, recent studies have been proposed to utilize the negative feedback, and have shown how the recommender systems can be improved. One major limitation in these approaches is that the negative feedback data is generated through some fixed threshold. A recent work presented a new perspective by leveraging a Large Language Model (LLM) to flexibly distinguish positive and negative signals based on individual user intent. In this paper, we review this latest work, and extend it in three directions. First, we address the structural limitations in modeling negative feedback in the previous work. Second, we replace the existing loss function with a more robust approach, and show its effectiveness. Third, we propose a method for generating unified embeddings that integrate both positive and negative embeddings, thereby capturing user preferences and dispreferences in a holistic manner. Experimental results on five real-world datasets demonstrate that our model achieves state-of-the-art performance. The code is available at https://github.com/Chanwoo-Jeong-2000/ReFINe_plus}, archive = {J_ACCESS}, author = {Chanwoo Jeong and Yoon-Sik Cho}, doi = {10.1109/ACCESS.2025.3599176}, journal = {IEEE Access}, pages = {144160-144172}, shortjournal = {IEEE Access}, title = {Modeling of negative feedback refined via LLM in recommender systems}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Copy-move forgery detection technique using graph convolutional networks feature extraction”. ACCESS, 13, 144159. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction”).}, archive = {J_ACCESS}, author = {Varun Shinde and Vineet Dhanawat and Ahmad Almogren and Anjanava Biswas and Muhammad Bilal and Rizwan Ali Naqvi and Ateeq Ur Rehman}, doi = {10.1109/ACCESS.2025.3596281}, journal = {IEEE Access}, pages = {144159}, shortjournal = {IEEE Access}, title = {Corrections to “Copy-move forgery detection technique using graph convolutional networks feature extraction”}, volume = {13}, year = {2025}, }
• (2025). A GAT-transformer-based framework for lane-change conflict prediction in highway merging and diverging zones. ACCESS, 13, 144143-144158. (www)

  @article{ , abstract = {Frequent lane-changing maneuvers significantly increase the risk of traffic accidents on highways and constitute a critical factor affecting road safety. This study investigates the complex dynamic interactions that occur during vehicle lane changes in highway merging and diverging zones. Based on vehicle trajectory data captured from an autonomous aerial vehicle (AAV) perspective, a conflict prediction modeling approach is proposed that incorporates both trajectory features and spatiotemporal conflict characteristics. The method employs the attention mechanism of a graph attention network (GAT) to dynamically aggregate the spatial features of neighboring vehicles and integrates the self-attention mechanism of a Transformer to capture the temporal evolution of trajectories within a defined time window, thereby uncovering latent patterns and key distinguishing features of lane-changing conflicts in merging and diverging areas. Experimental results indicate that vehicles in merging zones exhibit higher lane-changing frequencies and greater lateral accelerations. Compared with baseline methods, the proposed GAT-Transformer-based conflict prediction model achieves superior performance, reaching a maximum prediction accuracy of 95.49% and an F1 score of 97.64%. These results underscore the model’s effectiveness in balancing predictive precision with comprehensive detection of true conflicts. Furthermore, the model consistently maintains prediction accuracies above 90% under varying penetration rates and demonstrates strong robustness and reliability across different levels of data availability.}, archive = {J_ACCESS}, author = {Xiaojia Liu and Jun Yang and Dexin Yu and Bowei Liu and Yu Yang}, doi = {10.1109/ACCESS.2025.3599104}, journal = {IEEE Access}, pages = {144143-144158}, shortjournal = {IEEE Access}, title = {A GAT-transformer-based framework for lane-change conflict prediction in highway merging and diverging zones}, volume = {13}, year = {2025}, }
• (2025). AI governance in the context of the EU AI act. ACCESS, 13, 144126-144142. (www)

  @article{ , abstract = {The rapid advancement of artificial intelligence (AI) has brought about significant societal changes, necessitating robust AI governance frameworks. This study analyzed the research trends in AI governance within the framework of the European Union Artificial Intelligence Act (EU AI Act). This study conducted a bibliometric analysis to examine the publications indexed in the Web of Science database. Our findings reveal that research on AI governance, particularly concerning AI systems regulated by the EU AI Act, remains relatively limited compared to the broader AI research landscape. Nonetheless, a growing interdisciplinary interest in AI governance is evident, with notable contributions from multi-disciplinary journals and open-access publications. Analysis of publications per country revealed that while the United States and China dominate AI governance research, European countries, along with the United Kingdom, also contribute significantly with a focus on specific systems restricted by the Act. Dominant research themes include ethical considerations, privacy concerns, and the growing impact of generative AI, such as ChatGPT. Notably, education, healthcare, and worker management are prominent application domains. Keyword network analysis highlights education, ethics, and ChatGPT as central keywords, underscoring the importance of these areas in current AI governance research. Subsequently, a comprehensive literature review was undertaken based on the bibliometric analysis findings to identify research trends, challenges, and insights within the categories of the EU AI Act. This review revealed critical gaps in research concerning regulated AI systems, highlighting the need for more focused research aligned with the Act’s regulatory framework. The findings provide valuable insights for researchers and policymakers, informing future research directions and contributing to developing comprehensive AI governance frameworks beyond the EU AI Act. Crucially, the study identifies a significant lag between AI technological advancement and the development of policy and regulation, especially concerning specific AI systems categorized as high-risk by the EU AI Act.}, archive = {J_ACCESS}, author = {Byeong-Je Kim and Seunghoo Jeong and Bong-Kyung Cho and Ji-Bum Chung}, doi = {10.1109/ACCESS.2025.3598023}, journal = {IEEE Access}, pages = {144126-144142}, shortjournal = {IEEE Access}, title = {AI governance in the context of the EU AI act}, volume = {13}, year = {2025}, }
• (2025). FedXHDP: A federated XGBoost framework with hierarchical differential privacy for horizontally partitioned data. ACCESS, 13, 144104-144125. (www)

  @article{ , abstract = {Federated learning facilitates collaborative model training across decentralized data sources while safeguarding user privacy, making it particularly suitable for sensitive biological applications. However, achieving robust privacy guarantees in decision tree-based models remains a challenge. This paper presents FedXHDP, a federated decision tree-learning framework that combines the predictive capabilities of XGBoost with Hierarchical Differential Privacy (HDP). Our approach utilizes a layered privacy architecture at local, node, and global levels, efficiently safeguarding against data reconstruction, membership inference, and model inversion attacks. Compared to traditional methods that rely on cryptographic techniques, FedXHDP ensures robust privacy with reduced computational demands. To improve generality and mitigate overfitting, we incorporate DART, a dropout-based regularization method, into the boosting process. Experimental evaluations of the datasets indicate that FedXHDP attains a superior privacy-utility trade-off relative to current methodologies, particularly in horizontal partitioning. While our model emphasizes binary classification and multi-class classification in our work, subsequent research will investigate advanced optimization methods.}, archive = {J_ACCESS}, author = {B. Sasirekha and C. Gunavathi}, doi = {10.1109/ACCESS.2025.3596974}, journal = {IEEE Access}, pages = {144104-144125}, shortjournal = {IEEE Access}, title = {FedXHDP: A federated XGBoost framework with hierarchical differential privacy for horizontally partitioned data}, volume = {13}, year = {2025}, }
• (2025). Toward reliable fire detection in indoor CCTV footage: Reducing false alarms from benign flames using 3D attention CNNs. ACCESS, 13, 144092-144103. (www)

  @article{ , abstract = {Reliable early fire detection is essential for protecting life and property. While deep learning–based fire detection systems have demonstrated potential, many remain untested under real-world conditions, particularly in indoor environments where false alarms are prevalent. Existing studies primarily address limited false-positive cases—such as bright illumination or red-colored objects—while neglecting realistic, benign flame sources including vented fireplaces, gas fire pits, candles, and LED flame simulators. To address this gap, we introduce FalseNet, the first curated video dataset encompassing a diverse set of non-hazardous indoor flame scenarios alongside genuine fire incidents. We also propose a 3D attention-based convolutional neural network that integrates ResNet with (2+1)D convolutions—where a 3D convolution is factorized into sequential 2D spatial and 1D temporal operations—and Convolutional Block Attention Modules (CBAM) to enhance spatiotemporal feature extraction. The proposed model achieves an accuracy of 90.44%, precision of 90.46%, and recall of 90.44%, demonstrating improved robustness in reducing false-positive rates. Compared to a baseline model without CBAM, the results confirm a notable performance gain, validating the role of attention mechanisms in enhancing model generalization. Grad-CAM visualizations illustrate the model’s ability to localize spatial features associated with benign flames, highlighting its capacity to differentiate benign fire instances from actual fire events. These findings support the viability of the proposed approach for intelligent, CCTV-based fire detection with enhanced reliability in complex indoor environments.}, archive = {J_ACCESS}, author = {Mostafa M. E. H. Ali and Maryam Ghodrat}, doi = {10.1109/ACCESS.2025.3598919}, journal = {IEEE Access}, pages = {144092-144103}, shortjournal = {IEEE Access}, title = {Toward reliable fire detection in indoor CCTV footage: Reducing false alarms from benign flames using 3D attention CNNs}, volume = {13}, year = {2025}, }
• (2025). Image coding with data-driven fast transforms based on approximate givens factorizations. ACCESS, 13, 144079-144091. (www)

  @article{ , abstract = {In image compression, adaptive transform coding with optimal linear transforms computed from data being coded has been shown to outperform the widely used two-dimensional discrete cosine transform (2D-DCT). However, unlike the 2D-DCT for which fast computation algorithms exist, data-driven transforms are random matrices with no particular structure that can be exploited for fast computations. We present an approach to low-complexity data-driven image coding using structured orthonormal transform matrices constructed from approximate Givens factorizations optimized for transform coding. These Givens factorization-based fast transforms (GFFTs) are optimized by a tree-search algorithm on the orthonormal matrix manifold to minimize the mean square error of high-rate transform coding. Experimental results obtained with an adaptation of the baseline JPEG algorithm are presented which show that, for many images, the GFFTs outperform the 2D-DCT at comparable or lower computational complexity, with peak signal-to-noise ratio (PSNR) improvements as high as 6 dB in some cases.}, archive = {J_ACCESS}, author = {Dilshan Morawaliyadda and Pradeepa Yahampath}, doi = {10.1109/ACCESS.2025.3597217}, journal = {IEEE Access}, pages = {144079-144091}, shortjournal = {IEEE Access}, title = {Image coding with data-driven fast transforms based on approximate givens factorizations}, volume = {13}, year = {2025}, }
• (2025). KinTransformNet: A transformer-assisted approach with hough-HoG features for kinship verification and classification. ACCESS, 13, 144062-144078. (www)

  @article{ , abstract = {Kinship verification has been actively researched over the last decade for blood relation verification between two or more people using facial features. Kinship verification and classification domains have faced major challenges in accurately identifying familial relationships from ambiguous and intricate datasets. This research proposes “KinTransformNet,” a novel hybrid framework for kinship verification by integrating feature extraction techniques using the Histogram of Oriented Gradients based on Hough Transform (Hough-HoG) with a pre-trained Bidirectional Encoder Image Transformer (BEIT) model. Here, a novel Hough-HoG algorithm is used to encode spatial relationships of image gradient directions relative to a reference point to specifically construct the unique R-table for the edge pixel orientations with the most drastic changes. Image properties have been captured using this R-Table by calculating and normalizing HoG, magnitudes. Concurrently, high-level semantic features from image patches have been extracted using the pre-trained BEIT model. A set of features is generated using this model by first dividing the input image into patches, then linearly embedding them with positional encodings, and finally passing them through a stack of Transformer encoder layers with Multi-Head Self-Attention, Layer Normalization, and MLP blocks. A feature vector is extracted by pooling these embeddings. An absolute difference between the Hough-HoG feature and the BEIT feature vector is computed. Deep-learning-based semantic features were strengthened by Hough-HoG features; this feature vector is fed into a classifier to predict kinship relations between image pairs. The proposed model used the Family in the Wild (FIW), KinFaceW-I, II, and Cornell KinFace datasets and achieved an impressive accuracy of 95.25%, 93.79%, 94.57% and 91.62% respectively, which demonstrated its effectiveness in handling the complexities in kinship verification.}, archive = {J_ACCESS}, author = {Asifa Bibi and Syed Muhammad Adnan}, doi = {10.1109/ACCESS.2025.3599229}, journal = {IEEE Access}, pages = {144062-144078}, shortjournal = {IEEE Access}, title = {KinTransformNet: A transformer-assisted approach with hough-HoG features for kinship verification and classification}, volume = {13}, year = {2025}, }
• (2025). Investigation on intermittent XLPE-SiR insulation interface breakdown for medium-voltage cable joints. ACCESS, 13, 144051-144061. (www)

  @article{ , abstract = {Intermittent XLPE-SiR insulation interface breakdown for medium-voltage cable joints is investigated in this paper. Firstly, hypotheses on the intermittent interface breakdown mechanism of cable joints are proposed based on the equivalent interface breakdown circuit model. Secondly, an arc-thermal ablation model is constructed to validate the correlations among the states of the XLPE-SiR insulation interface and the semi-conductive layer, the arc energy, and the range of thermal ablation. Thirdly, Marx’s circuit experiment is conducted to validate the model and hypotheses. The model and experimental results show that the extra heat absorption caused by the insulation ablation and the current limitation of the semi-conductive layer are the two major factors, accelerating the extinction of arcs at the XLPE-SiR insulation interface of cable joints. This negative feedback mechanism will be weakened as the insulation and semi-conductive layer ablate due to long-term discharges. Ultimately, the formation of uncontrollable arcs may trigger serious cable joint faults.}, archive = {J_ACCESS}, author = {Yanqi Zeng and Gang Liu and Yifeng Zhao and Ran Hu}, doi = {10.1109/ACCESS.2025.3597734}, journal = {IEEE Access}, pages = {144051-144061}, shortjournal = {IEEE Access}, title = {Investigation on intermittent XLPE-SiR insulation interface breakdown for medium-voltage cable joints}, volume = {13}, year = {2025}, }
• (2025). Target detection and positioning of AGV based on YOLO-F network and monocular ranging. ACCESS, 13, 144039-144050. (www)

  @article{ , abstract = {High-precision and low-latency target detection and positioning are critical for autonomous Automated Guided Vehicle (AGV) operations in complex environments. This paper proposes a vision-based target detection and positioning method for detecting and estimating the position and attitude of artificial underwater targets. First, an improved lightweight target detection algorithm YOLO-F is designed, and the structure of the YOLOv8 algorithm is modified by introducing the FEM module, the FFM module, and the SCAM module. At the same time, the number of model parameters is optimized to improve the detection speed and accuracy. Secondly, the artificial targets are colabeled by design, and the position and attitude of the targets are calculated based on the geometric information of the designed markers. In addition, for the problem of monocular ranging error, a visual ranging compensation model based on Gaussian process regression is proposed to optimize the distance estimation by quantifying the nonlinear error confidence interval to improve the positioning accuracy effectively. Experiments verify the method’s effectiveness and robustness, providing a vision solution for AGV autonomous operation.}, archive = {J_ACCESS}, author = {Tingting Yang and Qiyao Guo and Pengfei Zhang}, doi = {10.1109/ACCESS.2025.3598852}, journal = {IEEE Access}, pages = {144039-144050}, shortjournal = {IEEE Access}, title = {Target detection and positioning of AGV based on YOLO-F network and monocular ranging}, volume = {13}, year = {2025}, }
• (2025). Planar microwave sensors: State of the art and applications. ACCESS, 13, 143985-144038. (www)

  @article{ , abstract = {This review paper focuses on the latest advances and applications of planar microwave sensors contributed by the most prominent researchers in the field. The paper presents the different working principles, design approaches, fabrication technologies, materials, and applications of a wide variety of planar sensors operating at microwave and millimeter-wave frequencies, including contact and contactless sensors, wired and wireless sensors, microfluidic sensors, “green” sensors, wearables, biosensors, physical sensors, chemical sensors, and more. Advanced techniques for sensor performance optimization (e.g., sensitivity, resolution, selectivity, etc.), based on artificial intelligence, active feedback loops, microwave spectroscopy, losses engineering, etc., will also be discussed in the paper.}, archive = {J_ACCESS}, author = {Carlos G. Juan and Katia Grenier and Mohammad H. Zarifi and Amir Ebrahimi and Ferran Martin}, doi = {10.1109/ACCESS.2025.3599112}, journal = {IEEE Access}, pages = {143985-144038}, shortjournal = {IEEE Access}, title = {Planar microwave sensors: State of the art and applications}, volume = {13}, year = {2025}, }
• (2025). Face swap detection: A systematic literature review. ACCESS, 13, 143957-143984. (www)

  @article{ , abstract = {Face swap technology, often associated with deepfakes, has rapidly advanced in recent years, raising serious concerns around misinformation, digital impersonation, and privacy. As a result, the development of reliable face swap detection methods has become a critical area of research. This survey provides a comprehensive review of existing approaches to face swap detection, addressing key research questions such as commonly used datasets, evaluation metrics, comparative model performance, and persistent challenges in the field. It includes a detailed taxonomy of detection methods, categorizing approaches into spatial, temporal, and spatiotemporal techniques. The survey further examines cross-dataset generalization performance to assess how well models adapt to domain shifts between training and testing data. Recent innovative directions are explored, covering adversarial defense strategies, explainability techniques, lightweight models for edge deployment, and privacy-preserving training. Additionally, best practices for building and releasing face swap detection tools are discussed to promote ethical, robust, and practical implementations. Finally, the paper outlines future research directions aimed at advancing model robustness, generalization, and compliance with legal and ethical standards. The discussions provide valuable insights that help researchers and practitioners gain a clear understanding of the face swap detection field, supporting and guiding their future research efforts.}, archive = {J_ACCESS}, author = {Safaa Hriez}, doi = {10.1109/ACCESS.2025.3598980}, journal = {IEEE Access}, pages = {143957-143984}, shortjournal = {IEEE Access}, title = {Face swap detection: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Hardware platforms enabling edge AI for space applications: A critical review. ACCESS, 13, 143939-143956. (www)

  @article{ , abstract = {Artificial Intelligence is an emerging technology in the space industry as it enables real-time data processing and autonomous navigation. We aim to facilitate mission development by providing an extensive state-of-the-art review. We analyze recent advancements in hardware architectures for space on-the-edge applications. We explore the unique challenges of space environments, including radiation hardening, flexibility, and real-time processing needs. We identify the possible hardware accelerator architectures and assess their applicability on-board spacecraft. We examine key developments in hardware accelerators, general-purpose processors, and neuromorphic chips, with an emphasis on adaptability for space applications. We compare the available chips in terms of performance capabilities, power consumption, technology node and radiation hardening. We showcase future directions, offering a comprehensive overview of cutting-edge solutions for AI-driven space missions.}, archive = {J_ACCESS}, author = {Gabriela Mystkowska and Matteo Monopoli and Pietro Nannipieri and Luca Zulberti and David Merodio Codinachs and Luca Fanucci}, doi = {10.1109/ACCESS.2025.3596326}, journal = {IEEE Access}, pages = {143939-143956}, shortjournal = {IEEE Access}, title = {Hardware platforms enabling edge AI for space applications: A critical review}, volume = {13}, year = {2025}, }
• (2025). Collaborative mobile manipulators for distributed task execution in smart city applications. ACCESS, 13, 143925-143938. (www)

  @article{ , abstract = {Robotic technologies offer feasible solutions to address rising costs and labour shortages by automating physically demanding tasks. This paper explores the integration of collaborative dual-arm car-like mobile manipulators equipped with obstacle detection sensors as a multi-agent system to enhance efficiency in smart city applications such as material handling and load transportation. While multi-robot coordination has been investigated extensively in the literature, a critical research gap remains in achieving seamless collaboration and formation control among mobile manipulators for distributed task execution. This study addresses this gap by presenting a set of continuous acceleration-based controllers using the Lyapunov-based Control Scheme (LbCS) to enable collision-free navigation of the mobile manipulators operating as a coordinated team of leader-carrier pairs. Computer simulations verify the effectiveness of the proposed method and ensure smooth navigation in obstacle-ridden environments. The findings demonstrate the potential of multi-agent robotic systems in addressing labour shortages and enhancing smart city operations, particularly in vulnerable Pacific Island nations like Fiji, where skilled workforce migration has led to significant labour shortages.}, archive = {J_ACCESS}, author = {Ravinesh Chand and Bibhya Sharma and Sandeep Ameet Kumar}, doi = {10.1109/ACCESS.2025.3599125}, journal = {IEEE Access}, pages = {143925-143938}, shortjournal = {IEEE Access}, title = {Collaborative mobile manipulators for distributed task execution in smart city applications}, volume = {13}, year = {2025}, }
• (2025). Optimum distance for in-flight UAV-to-UAV wireless charging. ACCESS, 13, 143914-143924. (www)

  @article{ , abstract = {Wireless charging is a promising technology for communications using battery-powered unmanned aerial vehicles (UAVs). In this paper, the optimal distance for UAV-to-UAV in-flight wireless charging and communications is studied. Considering the practical applications, two schemes are proposed. In the first scheme, the discharging UAV (D-UAV) and the charged UAV (C-UAV) are aligned during charging, which requires the D-UAV and the C-UAV to remain relatively stationary. In the second scheme, the D-UAV and the C-UAV move during charging. For both schemes, we aim to maximize the received energy at the C-UAV under the condition that the minimum achievable rate for communications is met. Numerical results show that the optimal distance exists in the Fresnel zone. They also show that the optimal distance increases with the charging frequency. This work provides useful guidance for UAV in-flight wireless charging and communications system designs.}, archive = {J_ACCESS}, author = {Hua Yan and Yunfei Chen}, doi = {10.1109/ACCESS.2025.3598733}, journal = {IEEE Access}, pages = {143914-143924}, shortjournal = {IEEE Access}, title = {Optimum distance for in-flight UAV-to-UAV wireless charging}, volume = {13}, year = {2025}, }
• (2025). Simultaneous overfill and foveated rendering in VR streaming: Metric modeling and joint optimization using deep reinforcement learning. ACCESS, 13, 143896-143913. (www)

  @article{ , abstract = {Offloading heavy VR rendering to edge/cloud computation entities and streaming rendered VR images to user headsets over networks is a promising solution for achieving widespread adoption of VR services. However, this solution faces the challenge of significant latency. To address black borders caused by such latency in a computationally efficient manner, VR streaming requires both overfill rendering (OR) and foveated rendering (FR), significantly influence the trade-off between visual quality and computational load based on their configuration parameters. In this paper, we develop a visual quality metric for VR images where OR and FR are simultaneously applied under latency, which models the impact of the overfill factor and foveal layers on user-perceived visual quality. Then, we design a deep reinforcement learning (DRL)-based solution to maximize a unified objective that combines visual quality and computational overhead by jointly optimizing the overfill factor and the sizes of the foveal layers. Experimental results obtained using a real-world wireless VR streaming testbed for an interactive VR game demonstrate the effectiveness of the proposed solution. Compared to benchmarks, our solution adapts OR and FR operations to varying environmental conditions, achieving superior performance in terms of overall reward.}, archive = {J_ACCESS}, author = {Yakub Fahim Luckyarno and Tae-Wook Kim and Dukyoung Jung and Jin Sam Kwak and Ji-Hoon Yun}, doi = {10.1109/ACCESS.2025.3598809}, journal = {IEEE Access}, pages = {143896-143913}, shortjournal = {IEEE Access}, title = {Simultaneous overfill and foveated rendering in VR streaming: Metric modeling and joint optimization using deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Experimental validation of crosstalk reduction for wideband MIMO transceiver systems. ACCESS, 13, 143883-143895. (www)

  @article{ , abstract = {A method for reducing crosstalk interference (or coupling) between multichannel radio frequency (RF) components in a wideband multiple input multiple output (MIMO) RF transceiver was developed. We devised feasible crosstalk parameter estimation, compensation, and calibration methods for joint transmitter (TX) and receiver (RX) coupling. Training signals are used to estimate the crosstalk parameters through the least-squares method. Crosstalk calibration, involving crosstalk estimation and compensation, is then performed sequentially for the RX followed by the TX. A crosstalk circuit board produced by the Industrial Technology Research Institute (ITRI) of Taiwan was used as a testbed for compensation for crosstalk interference for both single-carrier and multicarrier orthogonal frequency division multiplexing (OFDM) test signals. The method was tested on a system comprising a commercial AD9371 wideband RF module and the ITRI RF crosstalk circuit board. Our proposed techniques suppressed crosstalk by approximately 30 dB for single-carrier signals and reduced the error vector magnitude by approximately 20 dB for OFDM signals, effectively proving that this is a practical and effective method for improving wideband MIMO communications.}, archive = {J_ACCESS}, author = {Juinn-Horng Deng and Keng-Hwa Liu and Wei-Cheng Huang and Pin-Nian Chen and Meng-Lin Ku and Soo Yong Lim}, doi = {10.1109/ACCESS.2025.3599165}, journal = {IEEE Access}, pages = {143883-143895}, shortjournal = {IEEE Access}, title = {Experimental validation of crosstalk reduction for wideband MIMO transceiver systems}, volume = {13}, year = {2025}, }
• (2025). Electro-hydraulic brake system of uncrewed aerial vehicle based on high-speed on-off valve group. ACCESS, 13, 143875-143882. (www)

  @article{ , abstract = {To solve the problems of heavy volume, low integration and slow response of the existing industrial uncrewed aerial vehicle (UAV) braking system, an aircraft electro-hydrostatic braking system with integrated function and structure, high integration and adaptability to extreme environment was designed and developed based on the high-speed switch valve group. The system uses a single hydraulic pump source and four high-speed on-off valves to control the braking pressure, and a pair of high-speed on-off valves control the braking pressure of all the way, and an overflow valve is added to the circuit to realize the safety redundancy of the hydraulic circuit. The high-speed on-off valve gives the system high precision and high response. Under the condition of target pressure of 6 MPa, the pressure error can be controlled within 0.1 MPa and the response time is less than 0.25s. The differential anti-skid control of two braking pressures can be realized by four switch valve groups, and the steering movement and parking behavior of the aircraft can be completed.}, archive = {J_ACCESS}, author = {Yanjun Jiang and Renming Li and Yanfeng Jiang and Yuan Qiu and Benqin Jing and Xiaofeng Tang and Zheng Liu}, doi = {10.1109/ACCESS.2025.3595441}, journal = {IEEE Access}, pages = {143875-143882}, shortjournal = {IEEE Access}, title = {Electro-hydraulic brake system of uncrewed aerial vehicle based on high-speed on-off valve group}, volume = {13}, year = {2025}, }
• (2025). Toward AI in 6G: Concepts, techniques, and standards. ACCESS, 13, 143843-143874. (www)

  @article{ , abstract = {The rapid evolution of cellular networks, driven by the proliferation of mobile devices and the exponential growth of the Internet of Things (IoT), has significantly advanced wireless communication technologies. Fifth generation of wireless communications technology (5G) enhanced data rates, latency, and network capacity, resulting in the emergence of new applications. However, the sixth generation (6G) is foreseen to support a new set of use cases with diverse requirements. This paper explores the critical role of artificial intelligence (AI) in shaping the trajectory from 5G to 6G. We discuss AI applications in 5G for network planning, resource allocation, traffic management, and security, as well as propose infrastructure upgrades, like edge servers and enhanced network topologies, to support AI in 6G. Additionally, we outline a visionary perspective on AI’s potential contributions to 6G, highlighting its role in enabling innovative services and applications. By providing this forward-looking perspective, this paper aims to stimulate discussion and guide the development of intelligent and autonomous 6G networks.}, archive = {J_ACCESS}, author = {Sina Shafaei and Alex Palaios and Zied Ennaceur and Jiajing Zhang and Vedhas Pandit and Pramesh Gautam and Afsaneh Gharouni and Borislava Gajic and Bitan Banerjee and Sachinkumar B. Mallikarjun and Mohammad Asif Habibi and Phuong Bich Duong and Simone Mangiante and Muhammad Qurratulain Khan and Mohammad Parvini and Philipp Schulz and Faizan Rauf and Norman Franchi and Renato L. G. Cavalcante and Jochen Fink and Philipp Reitz and Maximilian Lübke and Torsten ReißLand and Ali El Husseini and Reem Karaki and Zahra Seifaei and Marco Danger and Carsten Bockelmann and Arne Bröring and Guenter Klas and Slawomir Stanczak and Hans D. Schotten and Gerhard P. Fettweis and Admela Jukan and Giang T. Nguyen and Rafael F. Schaefer and Ralf Irmer and Christian Wietfeld and Armin Dekorsy}, doi = {10.1109/ACCESS.2025.3595752}, journal = {IEEE Access}, pages = {143843-143874}, shortjournal = {IEEE Access}, title = {Toward AI in 6G: Concepts, techniques, and standards}, volume = {13}, year = {2025}, }
• (2025). SMILE-VLM: Self-supervised multi-view representation learning using vision-language model for 3D/4D facial expression recognition. ACCESS, 13, 143831-143842. (www)

  @article{ , abstract = {Facial expression recognition (FER) is a fundamental task in affective computing with applications in human-computer interaction, mental health analysis, and behavioral understanding. In this paper, we propose SMILE-VLM, a self-supervised vision-language model for 3D/4D FER that unifies multiview visual representation learning with natural language supervision. SMILE-VLM learns robust, semantically aligned, and view-invariant embeddings by proposing three core components: multiview decorrelation via a Barlow Twins-style loss, vision-language contrastive alignment, and cross-modal redundancy minimization. Our framework achieves the state-of-the-art performance on multiple benchmarks. We further extend SMILE-VLM to the task of 4D micro-expression recognition (MER) to recognize the subtle affective cues. The extensive results demonstrate that SMILE-VLM not only surpasses existing unsupervised methods but also matches or exceeds supervised baselines, offering a scalable and annotation-efficient solution for expressive facial behavior understanding.}, archive = {J_ACCESS}, author = {Muzammil Behzad}, doi = {10.1109/ACCESS.2025.3598019}, journal = {IEEE Access}, pages = {143831-143842}, shortjournal = {IEEE Access}, title = {SMILE-VLM: Self-supervised multi-view representation learning using vision-language model for 3D/4D facial expression recognition}, volume = {13}, year = {2025}, }
• (2025). A review of wearables-based activities of daily living recognition with LLMs: Overview, progress, and trends. ACCESS, 13, 143813-143830. (www)

  @article{ , abstract = {Over the past few years, driven by advancements in wearable devices and intelligent algorithms, numerous works have emerged in the field of wearables-based activities of daily living recognition. However, most existing methods still face challenges in addressing activity recognition in complex scenarios and generalizing to different devices. Many recent methods use Large Language Models to address these challenges and have made significant progress. LLMs enhance complex scenario perception by capturing rich contextual knowledge and improve generalization across tasks, enabling better adaptability to diverse conditions. In this paper, we present a comprehensive review of wearables-based ADL recognition with LLMs, covering 204 related works. We firstly introduce the overall pipeline of adapting pretrained LLMs to the field of wearables-based ADL recognition. Then the progress on data processing, LLMs finetuning and inference, and ADL applications enabled by LLMs are reported systematically. In the end, we summarize current research trends and discuss the future directions towards effective and generalized activity recognition with different wearable devices.}, archive = {J_ACCESS}, author = {Wenqi Zheng and Yutaka Arakawa}, doi = {10.1109/ACCESS.2025.3598088}, journal = {IEEE Access}, pages = {143813-143830}, shortjournal = {IEEE Access}, title = {A review of wearables-based activities of daily living recognition with LLMs: Overview, progress, and trends}, volume = {13}, year = {2025}, }
• (2025). Exploring the moderating effect of price on the relationship between intention and use of voice assistants. ACCESS, 13, 143800-143812. (www)

  @article{ , abstract = {This study aimed to identify the moderating effect of price on the relationship between usage intention and the actual use of voice assistants. A quantitative approach was adopted, with a correlational scope and non-experimental, cross-sectional design. The sample consisted of 329 participants from Generations X, Y, and Z residing in various cities across Ecuador, selected through non-probability convenience sampling. Data were collected using a self-administered questionnaire comprising 26 items, measured on a five-point Likert scale. The questions were adapted from previously validated studies in the field of technology acceptance. Reliability tests, confirmatory factor analysis, and structural equation modeling were conducted using SmartPLS 4 software. The results showed that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influenced usage intention, which in turn had a direct impact on the actual use of voice assistants. Moreover, price positively moderates this relationship. Together, these findings offer an expanded theoretical model that addresses the previous gaps in the literature, providing a more comprehensive view of consumer behavior toward AI-based technologies.}, archive = {J_ACCESS}, author = {Gabriel Usiña-Báscones and Nelson Carrión-Bósquez and Iván Veas-González and Andrés García-Umaña and Luis Ulloa-Meneses and Wilson Guerra-Regalado and Andrea Velasco-Donoso and Mayra Samaniego-Arias}, doi = {10.1109/ACCESS.2025.3598009}, journal = {IEEE Access}, pages = {143800-143812}, shortjournal = {IEEE Access}, title = {Exploring the moderating effect of price on the relationship between intention and use of voice assistants}, volume = {13}, year = {2025}, }
• (2025). Pattern recognition in intra-breath oscillometry measurements. ACCESS, 13, 143781-143799. (www)

  @article{ , abstract = {Intra-breath oscillometry (IBOsc) offers a high-resolution, non-invasive assessment of respiratory mechanics by tracking within-breath variations in respiratory impedance. Unlike traditional oscillometric approaches, which provide averaged impedance values over the entire respiratory cycle, IBOsc captures dynamic, nonlinear changes by analyzing single-frequency excitation signals. This study introduces a novel machine learning-based framework for automated pattern recognition in IBOsc data. The proposed pipeline incorporates artifact-tolerant preprocessing, impedance loop generation, feature engineering, and classification. Using carefully curated datasets from healthy individuals and patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and obesity hypoventilation syndrome (OHS), the proposed framework successfully identifies clinically relevant patterns such as tidal expiratory flow limitation (tEFL). In the binary classification task distinguishing tEFL in healthy versus COPD patients, the best-performing model achieved an F1-score of 0.98 and an overall accuracy of 98.5% on a held-out test set of 204 samples. In the more complex three-class scenario involving healthy, COPD, and ILD patients, the model sustained strong performance, reaching a macro-averaged accuracy of 86.3% across 156 test samples, with a class-wise accuracy of 96.2% for tEFL detection. Beyond binary classification, the method proved effective in identifying both the presence and the resolution of tEFL patterns, which is a key clinical indicator for tracking therapeutic outcomes. The methodology demonstrated robustness across varying conditions and measurement setups, highlighting the potential of automated IBOsc analysis for enhancing clinical diagnostics and phenotyping of respiratory diseases.}, archive = {J_ACCESS}, author = {Á. Sára Németh and Gergely Makan and Szabolcs Baglyas and András Lorx and Chung-Wai Chow and Joyce K. Y. Wu and Ronald J. Dandurand and Zoltán Hantos and György Kalmár}, doi = {10.1109/ACCESS.2025.3598615}, journal = {IEEE Access}, pages = {143781-143799}, shortjournal = {IEEE Access}, title = {Pattern recognition in intra-breath oscillometry measurements}, volume = {13}, year = {2025}, }
• (2025). Entity recognition for power equipment data based on optional word vectors and feature fusion. ACCESS, 13, 143767-143780. (www)

  @article{ , abstract = {Power equipment is an essential part of the power system. Regular inspection and maintenance of power equipment are of great significance. However, the existing entity recognition methods for power equipment data have certain limitations. Therefore, this paper introduces Continuous Bag of Words to optimize Convolutional Neural Network and Long Short-Term Memory network. Meanwhile, Variational Autoencoder is introduced to improve Conditional Generative Adversarial Network. These two methods are combined to form an algorithm that integrates textual data and image information features. On this basis, conditional random field is used for secondary optimization, and an intelligent recognition model is constructed. Its superiority and feasibility are verified by comparison with three traditional models. The results show that the proposed model achieves an accuracy of 96.8%, a recall of 97.6%, an average error of 0.11%, and a loss rate of 0.18%. In practical applications, the model achieves a recall of 97.6% and an Area Under the Curve value of 0.831. When facing different datasets, its average sensitivity reaches 97.6%. Comparative experiments also demonstrate its robustness and generalization ability. All experimental data and results outperform the three comparative models. In conclusion, the proposed model ensures the quality of power equipment data entity recognition and effectively improves recognition efficiency, making an important contribution to the healthy development of the power system in China.}, archive = {J_ACCESS}, author = {Min Chen and Feixiang Liu and Donggui Liang and Shuying Zhong and Yunting Li}, doi = {10.1109/ACCESS.2025.3598316}, journal = {IEEE Access}, pages = {143767-143780}, shortjournal = {IEEE Access}, title = {Entity recognition for power equipment data based on optional word vectors and feature fusion}, volume = {13}, year = {2025}, }
• (2025). Enhancing zero-shot word sense disambiguation via clustered sense vectors. ACCESS, 13, 143751-143766. (www)

  @article{ , abstract = {Recent approaches to Word Sense Disambiguation (WSD) often rely on the use of either clustered sense labels or sense vectors. The former reduces the output label space and helps handle untrained senses while maintaining fine-grained sense distinctions. The latter leverages latent representations to capture contextual information. In this study, we propose Clustered Sense Vectors, a method that integrates both strategies to enhance fine-grained WSD performance. In order to create clustered sense vectors, we convert clustered sense labels into clustered sense vectors. For this, we propose Sense-aware Clustering (SaC), a method for clustering sense labels more effectively. Then, for a WSD system, we present CluBE, a bi-encoder model that utilizes these clustered sense vectors by converting clustered sense labels into representative vectors through cluster vector normalization. The experimental results show that SaC improves clustering quality, and that CluBE not only achieves 1.6%p higher F1 scores but also reduces memory usage by 82% compared to a baseline bi-encoder model, while significantly enhancing zero-shot learning capability by effectively handling untrained word senses through clustered representations.}, archive = {J_ACCESS}, author = {Jeongyeon Park and Hyeong Jin Shin and Donghyeok Lee and Jae Sung Lee}, doi = {10.1109/ACCESS.2025.3599179}, journal = {IEEE Access}, pages = {143751-143766}, shortjournal = {IEEE Access}, title = {Enhancing zero-shot word sense disambiguation via clustered sense vectors}, volume = {13}, year = {2025}, }
• (2025). Domain-specific multi-document political news summarization using BART and ACT-GAN. ACCESS, 13, 143737-143750. (www)

  @article{ , abstract = {The exponential growth of digital content has made it increasingly difficult for users to understand the information, particularly in domains like political news. The study provides a framework for domain-specific multi-document political news summarization by using the transformer-based models that are fine-tuned on the synthetic dataset. The use of a generative Artificial Intelligence (AI) model, specifically the Bidirectional and Autoregressive Transformer (BART) model, for summarizing multi-document political news was explored in this paper. To improve the summarization process, real-world data was augmented, and a synthetic dataset was generated using the Adversarial Contextualized Text - Generative Adversarial Network (ACT-GAN) model to create a diverse and domain-specific training dataset. All four models, including BART, Pegasus, Text-To-Text Transfer Transformer (T5), and Seq2Seq, were fine-tuned and evaluated using a synthetic dataset generated via ACT-GAN and benchmarked using ROUGE metrics. BART performs the best in accuracy, recall, and F1-score, showing it can create clear and logical summaries while keeping the important context and structure intact. The results show that the transformer with the synthetic data helps to improve the quality of the political data summarization. In addition to addressing the challenges of extensive information, this framework also employs generative AI models in text summarization domains.}, archive = {J_ACCESS}, author = {Zekai Nie and Masooma Arshad and Syed Khaldoon Khurshid and Abqa Javed and Talha Waheed and Qianqian Zhang and Muhammad Farrukh Shahzad}, doi = {10.1109/ACCESS.2025.3598058}, journal = {IEEE Access}, pages = {143737-143750}, shortjournal = {IEEE Access}, title = {Domain-specific multi-document political news summarization using BART and ACT-GAN}, volume = {13}, year = {2025}, }
• (2025). Harvesting insights on AI-driven technologies in modern agriculture: A systematic literature review. ACCESS, 13, 143713-143736. (www)

  @article{ , abstract = {This review addresses critical gaps in the understanding of AI integration across modern agricultural systems through a systematic analysis of hybrid architectures, adaptation strategies, learning approaches, and knowledge integration methods. Following the PRISMA guidelines, we reviewed 200 papers published over the period 2019-2024, identifying key trends in AI implementation, including the dominance of hybrid approaches and evolution towards data-efficient methodologies. Our meta-analytical synthesis of 51 high-quality studies revealed that hybrid adaptation approaches achieve superior performance (91-94% accuracy) in disease detection and crop monitoring tasks, whereas environmental adaptation strategies offer an optimal balance between performance and resource requirements. Data-efficient learning approaches have shown significant growth, with transfer learning adoption increasing from 30% to 45% of studies, addressing the critical challenge of limited training data in agricultural contexts. We examined implementation challenges, including economic feasibility, ethical considerations, system robustness, and adoption barriers for smallholder farmers. This review provides a comprehensive framework for understanding the current AI applications in agriculture and establishes priorities for sustainable and inclusive agricultural technology development.}, archive = {J_ACCESS}, author = {Marieme Seif-Ennasr and Mustapha Naimi and Mohamed Chikhaoui and Mostafa Bellafkih and Khalid Benabbes and Zainab Azough and Zhor Abail}, doi = {10.1109/ACCESS.2025.3597844}, journal = {IEEE Access}, pages = {143713-143736}, shortjournal = {IEEE Access}, title = {Harvesting insights on AI-driven technologies in modern agriculture: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of recent advances in multimodal multimedia indexing and retrieval. ACCESS, 13, 143688-143712. (www)

  @article{ , abstract = {With the exponential growth of digital data across several domains, multimedia retrieval has emerged as a very critical research area. While much development has been witnessed in feature extraction, indexing methods, and deep learning-based retrieval models, many challenges persistently hamper the creation of efficient and scalable multimedia retrieval systems. This review systematically analyzes recent research in multimedia retrieval, highlighting key methodologies, findings, and limitations across various studies. The major gaps identified include the semantic disparity between low-level features and high-level semantics, as well as challenges of large datasets, privacy and security concerns, and the explainability of deep learning models. Besides, challenges on noisy and imbalanced data, multimodal data sources, and a lack of standardized benchmarking frameworks further limit the performance of existing systems. This paper comprehensively presents these gaps and proposes future research directions to bridge them, thereby directing the development of more robust, scalable, and user-centered multimedia retrieval systems. To overcome persistent challenges in versatility, semantic alignment, and trustworthiness, we introduce a unified framework that seamlessly integrates multimodal semantic fusion, explainable AI, privacy-preserving learning, and continual adaptation for robust and future-ready multimedia retrieval.}, archive = {J_ACCESS}, author = {Chethan Sharma and G. Poornalatha and K. B. Ajitha Shenoy}, doi = {10.1109/ACCESS.2025.3597396}, journal = {IEEE Access}, pages = {143688-143712}, shortjournal = {IEEE Access}, title = {A comprehensive review of recent advances in multimodal multimedia indexing and retrieval}, volume = {13}, year = {2025}, }
• (2025). Image enhancement method for detection in logistics loading and unloading robots. ACCESS, 13, 143678-143687. (www)

  @article{ , abstract = {This paper presents a novel method in the field of robotic vision, specifically designed to operate effectively under various lighting conditions. We propose an Image Enhancement Method for parcel loading status recognition, which addresses the challenge of insufficient lighting inside freight trucks without requiring lighting adjustments. Our approach integrates GAN and YOLO v5 into an end-to-end structure, where the detection network’s outputs influence the training of the generative network. Unlike traditional methods that focus solely on enhancing low-light image visibility, our method is optimized to maximize detection accuracy. To demonstrate the superiority of our approach, we constructed a setup resembling a freight truck, collected a dataset under varying lighting conditions, and conducted extensive experiments. The latter sections of the paper review relevant studies on vision data control in computer vision, provide a detailed explanation of our method, describe the experimental setup and database acquisition process, and discuss the results and analyses. The conclusion is presented in Section VI.}, archive = {J_ACCESS}, author = {Jeon-Seong Kang and Jin Kyu Kang and Soeun Son and Sung Ho Park and Eui-Jung Jung and Gun Rae Cho}, doi = {10.1109/ACCESS.2024.3434599}, journal = {IEEE Access}, pages = {143678-143687}, shortjournal = {IEEE Access}, title = {Image enhancement method for detection in logistics loading and unloading robots}, volume = {13}, year = {2025}, }
• (2025). Enhancing noise reduction in quadrature amplitude modulation receivers through advanced circuit design. ACCESS, 13, 143668-143677. (www)

  @article{ , abstract = {Wireless communication is experiencing exponential growth, driven by the increasing adoption of mobile devices and the Internet of Things (IoT). One of the biggest challenges in achieving reliable, high-speed data transmission is the effect of noise on transmitted signals. This research paper introduces a novel circuit designed to mitigate noise, positioned between the front-end and demodulation stages of a Quadrature Amplitude Modulation (QAM) receiver. The circuit’s effectiveness has been validated through rigorous MATLAB Simulink simulations across various signal-to-noise ratio (SNR) values and operating under an Additive White Gaussian Noise (AWGN) channel. The results demonstrate a significant enhancement in signal integrity, with noise reduction reaching up to 100 dB, which leads to increased SNR, improved reliability, extended coverage area, and enhanced power efficiency. Furthermore, since this method relies solely on hardware, it requires no computing power, resulting in minimal latency and complexity, which are critical factors for many wireless applications. These advantages make this approach highly effective for optimizing wireless communication performance, including 6G and beyond.}, archive = {J_ACCESS}, author = {Niyaz Othman Ali and Jalal Jamal Hamad Ameen}, doi = {10.1109/ACCESS.2025.3599026}, journal = {IEEE Access}, pages = {143668-143677}, shortjournal = {IEEE Access}, title = {Enhancing noise reduction in quadrature amplitude modulation receivers through advanced circuit design}, volume = {13}, year = {2025}, }
• (2025). Survey of attacks and defenses on consensus algorithms for secure data replication in distributed systems. ACCESS, 13, 143631-143667. (www)

  @article{ , abstract = {Distributed systems are fundamental to modern computing infrastructures, enabling scalability, fault tolerance, and efficient resource sharing. To maintain a consistent view of resources and support data replication across network nodes, systems such as distributed databases, cloud infrastructures, and decentralized storage rely on consensus algorithms. However, these algorithms are increasingly targeted by sophisticated attacks, threatening not only system integrity but also network-wide consistency. In this survey, we 1) systematically classify consensus algorithms employed for data replication in distributed systems, covering their operation, strengths, limitations, and deployment contexts, and 2) analyze attacks that compromise their integrity, categorizing them according to strategy and targeted vulnerabilities. We review mitigation mechanisms and evaluate algorithmic resilience, drawing from both peer-reviewed and grey literature. Beyond literature aggregation, we identify cross-cutting weaknesses, such as overreliance on benign fault models, and highlight the lack of robust defenses against strategic adversaries. Our analysis reveals that many systems continue to treat targeted attacks as transient faults, underestimating their impact on consistency guarantees. By exposing these gaps and synthesizing known defenses, our study provides a foundation for future research aimed at securing consensus protocols in distributed data replication settings.}, archive = {J_ACCESS}, author = {Luigi Mastromauro and Dhiego Souto Andrade and Muslum Ozgur Ozmen and Michel A. Kinsy}, doi = {10.1109/ACCESS.2025.3597260}, journal = {IEEE Access}, pages = {143631-143667}, shortjournal = {IEEE Access}, title = {Survey of attacks and defenses on consensus algorithms for secure data replication in distributed systems}, volume = {13}, year = {2025}, }
• (2025). ShellBox: Adversarially enhanced LLM-interactive honeypot framework. ACCESS, 13, 143618-143630. (www)

  @article{ , abstract = {Honeypot technology is an active defence strategy designed to mitigate the asymmetry inherent in network attacks and defence dynamics. In recent years, honeypot systems powered by large language models (LLMs) have become a focal point of research owing to their ability to simulate complex network environments and generate highly deceptive virtual assets. However, response inconsistency in multi-turn dialogues and prompt injection vulnerabilities inherent to LLMs significantly reduce the deceptive capability of honeypots. This study first defines the threat model, and then introduces a relevance-based interaction history pruning algorithm and dynamic error simulation strategy to mitigate these challenges. Considering practical issues such as response latency and network instability, our experiments were conducted using multiple locally deployed open-source LLMs. The experimental results demonstrated that the proposed dynamic error simulation mechanism achieved a maximum accuracy of 81.63% for the DeepSeek-R1 model. Furthermore, applying the interaction history pruning algorithm improved the turn-level coherence score (TCS) by 34.5% compared with the baseline. Finally, this paper outlines potential future research directions for LLM-based honeypot technologies in active multi-turn mechanisms.}, archive = {J_ACCESS}, author = {Guan Yang and Zhengzheng Sun and Yu Wang}, doi = {10.1109/ACCESS.2025.3598779}, journal = {IEEE Access}, pages = {143618-143630}, shortjournal = {IEEE Access}, title = {ShellBox: Adversarially enhanced LLM-interactive honeypot framework}, volume = {13}, year = {2025}, }
• (2025). Automatic curriculum design for zero-shot human-AI coordination. ACCESS, 13, 143606-143617. (www)

  @article{ , abstract = {Zero-shot human-AI coordination is the training of an ego-agent to coordinate with humans without human data. Most studies on zero-shot human-AI coordination have focused on enhancing the ego-agent’s coordination ability in a given environment without considering the issue of generalization to unseen environments. Real-world applications of zero-shot human-AI coordination should consider unpredictable environmental changes and the varying coordination ability of co-players depending on the environment. Previously, the multi-agent UED (Unsupervised Environment Design) approach has investigated these challenges by jointly considering environmental changes and co-player policy in competitive two-player AI-AI scenarios. In this paper, our study extends a multi-agent UED approach to zero-shot human-AI coordination. We propose a utility function and co-player sampling for a zero-shot human-AI coordination setting that helps train the ego-agent to coordinate with humans more effectively than a previous multi-agent UED approach. The zero-shot human-AI coordination performance was evaluated in the Overcooked-AI environment, using human proxy agents and real humans. Our method outperforms other baseline models and achieves high performance in human-AI coordination tasks in unseen environments. The source code is available at https://github.com/Uwonsang/ACD_Human-AI}, archive = {J_ACCESS}, author = {Won-Sang You and Tae-Gwan Ha and Seo-Young Lee and Kyung-Joong Kim}, doi = {10.1109/ACCESS.2025.3596640}, journal = {IEEE Access}, pages = {143606-143617}, shortjournal = {IEEE Access}, title = {Automatic curriculum design for zero-shot human-AI coordination}, volume = {13}, year = {2025}, }
• (2025). Discrete-time inverted decoupling internal model control of multivariable systems based on improved fractional-order butterworth filter. ACCESS, 13, 143591-143605. (www)

  @article{ , abstract = {A novel discrete-time inverted decoupling internal model control (IMC) strategy is proposed for discrete-time multivariable systems with time-delays and RHP zeros. The strategy integrates an improved fractional-order Butterworth filter (FOBF) into the control framework. Key contributions include the design of a high-order improved FOBF by cascading stable fractional-order and integer-order filters, guaranteeing stability through W-plane mapping analysis, and developing an efficient direct discretization method based on frequency response matching. Simulation examples demonstrate the controller’s effectiveness in enhancing tracking performance and robustness against disturbances and model uncertainties.}, archive = {J_ACCESS}, author = {Kaiyue Liu and Shuke Lyu and Rui Wang and Chenkang Gao and Xiangyu Yang and Yongtao Liu}, doi = {10.1109/ACCESS.2025.3598740}, journal = {IEEE Access}, pages = {143591-143605}, shortjournal = {IEEE Access}, title = {Discrete-time inverted decoupling internal model control of multivariable systems based on improved fractional-order butterworth filter}, volume = {13}, year = {2025}, }
• (2025). Multi-eavesdropper detection through PHY-aware cell-free AP selection. ACCESS, 13, 143576-143590. (www)

  @article{ , abstract = {The ability to provide reliable data rates across several coverage areas establishes massive multiple-input multiple-output (m-MIMO) cell-free (CF) systems as a pivotal technology for future sixth-generation (6G) systems. CF networks do, however, introduce additional security and network integrity vulnerabilities. For that, to complement the traditional cryptographic algorithms, physical layer security (PLS) can be an effective strategy for acquiring essential wireless network information in order to develop authentication methods against impersonation attacks. To prevent these spoofing attacks, we propose leveraging the wireless channel and the access point selection (APS) allocation schemes as authentication mechanisms. Our approach begins with a threshold-based analysis of spectral efficiency (SE) losses across different APS schemes. We then propose an algorithm capable of estimating the number of eavesdroppers executing active attacks while identifying the targeted user equipment (UE). Finally, we test the robustness of our detection scheme by examining how SE loss and achievable secrecy SE change when a single attacker is positioned at various locations relative to the targeted UE. Results demonstrate that monitoring these parameters provides critical insights into network performance and the impact of active eavesdropping. These findings highlight the potential of integrating PLS with upper-layer authentication protocols to significantly enhance wireless network security.}, archive = {J_ACCESS}, author = {João Martins and Filipe Conceição and Marco Gomes and Vitor Silva and Rui Dinis}, doi = {10.1109/ACCESS.2025.3598502}, journal = {IEEE Access}, pages = {143576-143590}, shortjournal = {IEEE Access}, title = {Multi-eavesdropper detection through PHY-aware cell-free AP selection}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization in a boost converter for photovoltaic systems based on environmental data. ACCESS, 13, 143550-143575. (www)

  @article{ , abstract = {Boost converters are widely used in photovoltaic systems for maximum power point tracking (MPPT). Their performance, however, depends significantly on the environmental conditions in which the dc-dc converter operate. This study aims to design a robust boost converter through a multi-objective optimization approach using the NSGA-II metaheuristic, seeking a single solution that minimizes sensitivity to climatic variations while maintaining satisfactory performance across different regions. To achieve this, a detailed model of both the photovoltaic panel and the boost converter—including parasitic elements—will be developed. Energy losses will be evaluated, and key parameters related to size, weight, and efficiency will be optimized under realistic climatic conditions. The methodology involves simulating the converter in closed loop alongside the photovoltaic panel using state-space modeling, with the goal of accurately determining the duty cycle at which the converter operates. This includes accounting for parasitic resistances and verifying operation at the maximum power point. Finally, the performance of one of the optimal converters will be evaluated using climate data from other cities. The results presented in this work are based on detailed simulations under varying climatic conditions. Experimental validation is planned for future studies to confirm the findings and assess practical implementation aspects.}, archive = {J_ACCESS}, author = {André Luiz Silva Crivellari and André Salume Lima Ferreira Leão and Bartolomeu F. Dos Santos Junior and Roberto Francisco Coelho and Lenon Schmitz and Denizar Cruz Martins and Walbermark M. Dos Santos}, doi = {10.1109/ACCESS.2025.3593619}, journal = {IEEE Access}, pages = {143550-143575}, shortjournal = {IEEE Access}, title = {Multi-objective optimization in a boost converter for photovoltaic systems based on environmental data}, volume = {13}, year = {2025}, }
• (2025). Research on heating and cooling characteristics of high-power permanent magnet traction motor under non-powered return transportation conditions. ACCESS, 13, 143537-143549. (www)

  @article{ , abstract = {This study innovatively integrates electromagnetic field finite element analysis, the Bertotti iron loss model, fluid dynamics modeling, and SKF bearing friction calculation models to establish, for the first time, a thermal network model for permanent magnet traction motors in unpowered transportation scenarios. This model enables precise analysis of the complex heating patterns within the motor and their heat transfer mechanisms. Through in-depth examination of this model, key factors influencing the motor’s temperature distribution are revealed. Based on these findings, during the design phase, methods such as optimizing the water-cooling structure and adding heat sinks to the end caps are proposed to effectively reduce the motor’s temperature in unpowered transportation scenarios, thereby ensuring safe transportation of the motor. Furthermore, for motors already installed on vehicles, this model can be utilized for precise analytical calculations to predict critical parameters such as safe towing speeds or continuous towing durations for safe operation. Therefore, this study provides practically guidance-oriented solutions for the optimal design and safe operation of permanent magnet traction motors, demonstrating its practical application value.}, archive = {J_ACCESS}, author = {Weiye Li and Gangyan Li and Kui Li and Jun Peng and Kaicheng Xu and Jun Xu and Ao Hu and Faquan Yin and Yinglu Luo}, doi = {10.1109/ACCESS.2025.3531972}, journal = {IEEE Access}, pages = {143537-143549}, shortjournal = {IEEE Access}, title = {Research on heating and cooling characteristics of high-power permanent magnet traction motor under non-powered return transportation conditions}, volume = {13}, year = {2025}, }
• (2025). Optimization of age of information in adaptive FD/HD cooperative SWIPT NOMA/OMA system. ACCESS, 13, 143522-143536. (www)

  @article{ , abstract = {The Age of Information (AoI) is a key metric in monitoring and control applications of Internet of Things (IoT) networks, where real-time decision-making relies on the freshness of the information. This paper presents an innovative approach to optimizing the AoI in downlink NOMA/OMA systems. We propose an AoI minimization-oriented adaptive framework that selects the optimal transmission mode from four operational strategies: full-duplex (FD) cooperative simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA), half-duplex (HD) cooperative SWIPT NOMA, regular NOMA, and orthogonal multiple access (OMA). Since the FD cooperative SWIPT NOMA requires the near user to be equipped with two antennas to enable FD operation, we propose enhancing the HD, OMA, and regular NOMA modes of operation by applying beamforming diversity. The proposed work analytically evaluates the system error performance by deriving closed-form expressions of the average block error rate (BLER) for the four operating modes and validates the results through Monte Carlo simulations. Based on the average BLER, the AoI for each operational mode is analysed, illustrating the necessity of the adaptive system. Furthermore, we utilize the finite state Markov decision process to devise an optimal adaptive policy that selects the best transmission strategy based on the AoI in the transmission time slot. Finally, we present a suboptimal policy using the drift-plus-penalty algorithm to reduce system complexity while maintaining near-optimal performance. The results demonstrate that the proposed approach minimizes the AoI, providing valuable insights into system design.}, archive = {J_ACCESS}, author = {Simon Kaboyo and Mohammed Abo-Zahhad and Osamu Muta and Ahmed H. Abd El-Malek and Maha M. Elsabrouty}, doi = {10.1109/ACCESS.2025.3598362}, journal = {IEEE Access}, pages = {143522-143536}, shortjournal = {IEEE Access}, title = {Optimization of age of information in adaptive FD/HD cooperative SWIPT NOMA/OMA system}, volume = {13}, year = {2025}, }
• (2025). Vision transformer applied for time series based prediction of sea surface temperature. ACCESS, 13, 143509-143521. (www)

  @article{ , abstract = {Sea surface temperature (SST) is a critical variable for ocean monitoring and ecosystem protection, particularly in the context of climate change. Although LSTM-based models have been widely adopted for SST prediction, they often struggle to capture localized spatial patterns effectively. To address this, we propose TS-ViT-SST, a Token Sparsification-based Vision Transformer that selectively attends to informative regions within SST data, thereby enhancing the modeling of fine-grained spatial variations. Furthermore, we introduce a physics-inspired temperature difference loss to suppress unrealistic fluctuations and improve prediction accuracy. Model performance is evaluated primarily using RMSE, with additional comparisons of parameter counts, computation times, AIC, BIC, and other relevant metrics across different models. Experiments on the SST-RSS dataset demonstrate that TS-ViT-SST surpasses state-of-the-art (SOTA) methods, achieving a 0.11 reduction in RMSE while requiring fewer computational resources. Additional validation on the ICAR-ENSO dataset for El Niño—Southern Oscillation (ENSO) prediction confirms the robustness and generalization capability of our approach. By integrating sparsified attention mechanisms with physical constraints, TS-ViT-SST establishes a new benchmark for SST forecasting. The code is available at: https://github.com/zhzhao2020/TS-ViT-SST.}, archive = {J_ACCESS}, author = {Qi Liang and Zhou Pan and Xinlong Zhang and Ziyun Ye and Zihao Zhao and Shang Guo}, doi = {10.1109/ACCESS.2025.3595579}, journal = {IEEE Access}, pages = {143509-143521}, shortjournal = {IEEE Access}, title = {Vision transformer applied for time series based prediction of sea surface temperature}, volume = {13}, year = {2025}, }
• (2025). A hybrid PLO-transformer-CEEMDAN model for natural gas price forecasting. ACCESS, 13, 143489-143508. (www)

  @article{ , abstract = {To address the nonlinear and non-stationary characteristics of natural gas prices, this study proposes a hybrid PLO-Transformer-CEEMDAN forecasting framework. The model integrates weight optimization, multi-scale trend extraction, and residual correction, effectively utilizing 27 heterogeneous features—including Google Trends indicators—to improve predictive accuracy. Empirical evaluations on Henry Hub monthly data (2004–2024) show that the model achieves exceptional forecasting performance (RMSE: 0.0257, R2: 0.9998), representing a 92.87% RMSE reduction compared to the baseline Transformer. Notably, incorporating Google Trends alone leads to a 30.91% improvement in accuracy, underscoring its value in capturing market sentiment prior to price changes. Overall, the proposed method significantly outperforms conventional benchmarks in both predictive precision and generalization ability, offering a robust tool for energy price forecasting and market risk assessment.}, archive = {J_ACCESS}, author = {Xiang Wang and Hao Wang and Zirong Wang and Di Wu and Yuan Xue and Lingxiao Ye and Yucong Zhang}, doi = {10.1109/ACCESS.2025.3597657}, journal = {IEEE Access}, pages = {143489-143508}, shortjournal = {IEEE Access}, title = {A hybrid PLO-transformer-CEEMDAN model for natural gas price forecasting}, volume = {13}, year = {2025}, }
• (2025). Research on a two-stage multi-objective optimization design method for permanent magnet synchronous motors utilizing partial correlation coefficients for parameter screening. ACCESS, 13, 143479-143488. (www)

  @article{ , abstract = {Permanent Magnet Synchronous Motors have become widely adopted in new energy vehicles, industrial drives, and various other applications, thanks to their remarkable advantages such as high efficiency, high power density, and a broad speed regulation range. However, in the realm of multi-objective optimization design for PMSMs, traditional approaches often utilize the Pearson correlation coefficient for parameter screening, aiming to enhance computational efficiency. Unfortunately, the Pearson correlation coefficient does not adequately account for the interactions among optimization parameters, which can result in deviations in the optimization results. To address this concern, this study presents a multi-objective optimization design method utilizing partial correlation coefficients for parameter screening. By quantifying and comparing these coefficients, we can effectively screen key design parameters. Following this, an optimization design model is developed using a back propagation neural network, with a genetic algorithm employed for global optimization. Simulation results demonstrate that the proposed method exhibits significant advantages in optimization effectiveness compared to traditional methods, with a 78.5% reduction in torque ripple, 27.3% improvement in average torque, and 1% improvement in efficiency, providing more precise and efficient theoretical support for PMSM design.}, archive = {J_ACCESS}, author = {Bin Zhang and Jinghong Zhao and Yihui Xia and Xuedong Zhu}, doi = {10.1109/ACCESS.2025.3598076}, journal = {IEEE Access}, pages = {143479-143488}, shortjournal = {IEEE Access}, title = {Research on a two-stage multi-objective optimization design method for permanent magnet synchronous motors utilizing partial correlation coefficients for parameter screening}, volume = {13}, year = {2025}, }
• (2025). Joint trajectory and power optimization for UAV-SAR based ISAC system. ACCESS, 13, 143465-143478. (www)

  @article{ , abstract = {Integrated Sensing and Communication (ISAC) for unmanned aerial vehicles (UAVs) has emerged as a promising paradigm for next-generation wireless systems. In this paper, we address the joint problem of resource allocation and trajectory planning for a UAV platform that integrates synthetic aperture radar (SAR) imaging and communication capabilities. The UAV operates at a fixed altitude, performing high-resolution SAR imaging of designated target areas in spotlight mode while simultaneously transmitting the collected data to a ground data center in real time. Given that the total energy consumption significantly impacts the overall performance of the UAV-SAR based ISAC system, we reformulate the trajectory planning task as a non-convex optimization problem focused on selecting optimal entry points into predefined sensing areas, with the objective of minimizing the UAV’s total energy expenditure. To solve this challenging problem, we propose an efficient optimization algorithm that achieves a high-quality suboptimal solution. Extensive simulations validate the effectiveness of the proposed hybrid algorithm, which incorporates Genetic Algorithm (GA) for speed planning. It achieves faster convergence than the conventional GA-ACO method and outperforms Particle Swarm Optimization (PSO) with 3.74% reduction in total system energy consumption.}, archive = {J_ACCESS}, author = {Jiayi Zhou and Xiangyin Zhang and Kaiyu Qin and Feng Yang and Libo Wang}, doi = {10.1109/ACCESS.2025.3598359}, journal = {IEEE Access}, pages = {143465-143478}, shortjournal = {IEEE Access}, title = {Joint trajectory and power optimization for UAV-SAR based ISAC system}, volume = {13}, year = {2025}, }
• (2025). Smishing detection from a messaging platform view. ACCESS, 13, 143449-143464. (www)

  @article{ , abstract = {Application-to-person messages for mobile marketing democratized the way enterprises interact with their clients. Messaging services are booming across all sectors allowing companies to solve real business problems and make services more efficient. Unfortunately, the ease and affordability of this service has led to a surge in fraudulent activities in recent years. Our work proposes a proactive and multivariate end-to-end method for determining whether a short text message belongs to a legitimate or malicious industry. The main goal is to empower messaging platforms to prevent and block SMS phishing attacks by combining multiple features from the same message/situation into a single model. To achieve this, we adopted and customized an unmasked large-language model and expressed its input into individual segments separated with a particular token. For phishing detection, we leverage the URL, the text message, the message intent, and the URL page’s title. The best strategy of the proposed detection method achieves an F1-score of 96.1% and an AUC of 97.1%, outperforming the state-of-the-art approaches. We also analyze the model’s gradients and explore an explainable mechanism for understanding the model’s decision, highlighting the most critical pieces related to a decision. We perform an ablation study that underlines the importance of each input segment for the final classification decision, dissecting how all input components correlate with the result.}, archive = {J_ACCESS}, author = {Stephane F. Schwarz and Paulo Fonseca and Anderson Rocha}, doi = {10.1109/ACCESS.2025.3597903}, journal = {IEEE Access}, pages = {143449-143464}, shortjournal = {IEEE Access}, title = {Smishing detection from a messaging platform view}, volume = {13}, year = {2025}, }
• (2025). SiC MOSFET based 10 kW interleaved boost converter interface for regenerative power electronic system. ACCESS, 13, 143440-143448. (www)

  @article{ , abstract = {DC-dc converters are used widely in applications such as industrial, renewable energy, and electric vehicles, etc. In the present scenario, there is a need for high-power and compact dc-dc converters, which can be achieved by interleaving multiple power stages of a particular topology. In industries, a step-up dc-dc converter interface is required to test and validate various industrial power supplies with different voltage ratings. The step-up dc-dc converter will serve as a universal step-up interface between an industrial power supply, which is referred to as Device Under Test (DUT), and a bidirectional power supply to recirculate the power drawn to the grid, saving a great amount of power. In this work, a high-power, three-phase/stage step-up interleaved dc-dc converter of 10 kW is developed and experimentally validated for input voltages ranging between 120 V and 200 V for the fixed output voltage of 450 V. The maximum power that can be obtained is 32 kW. The setup is tested for 10 kW power. The converter is developed using Silicon Carbide MOSFET devices. SiC devices help in energy conservation. A double pulse test is performed to compare switching losses between a second-generation Silicon Carbide (SiC) MOSFET and a fifth-generation Silicon IGBT. The performance of the SiC MOSFET is found to be better. Therefore, SiC MOSFETs are used. The advantage of inherent phase current balancing is achieved by using Peak Current Mode Control (PCMC), and the proposed control strategy can be applied for any number of phases. Three-phase boost converters have been chosen because it is practically feasible to achieve higher power density compared to a single-stage boost converter. Thermal imaging of the converter is obtained, and the measured temperature rise in the MOSFETs was found to be 51.40°C, which is less than the maximum allowed temperature of 100°C. The results are compared with the existing topologies. In the literature, the topologies are developed for low power ratings, and the component count is also higher. In this work, an efficient, high-power, interleaved dc-dc converter is developed with a less number of components.}, archive = {J_ACCESS}, author = {R. G. Arrshith and M. R. Rashmi and Vigna K. Ramachandaramurthy and Hoong Pin Lee and Umashankar Subramaniam}, doi = {10.1109/ACCESS.2025.3596903}, journal = {IEEE Access}, pages = {143440-143448}, shortjournal = {IEEE Access}, title = {SiC MOSFET based 10 kW interleaved boost converter interface for regenerative power electronic system}, volume = {13}, year = {2025}, }
• (2025). Leveraging mobile processors for ISAR image generation and classification in radar platforms. ACCESS, 13, 143429-143439. (www)

  @article{ , abstract = {With the growing threats posed by small airborne platforms such as drones in modern warfare, there is an increasing demand for small target identification technology. Portable radar systems offer an effective solution for detecting small targets with unpredictable movement patterns. However, unlike large-scale radar platforms with fixed positions, portable radar systems must be lightweight and energy-efficient while maintaining high performance to ensure real-time operation. This paper presents a case study on automating and accelerating radar-based small target identification using a low-power mobile application processor (AP). We utilize a mobile AP to preprocess raw data received from an Inverse Synthetic Aperture Radar (ISAR), generate ISAR images, and classify them using a convolutional neural network (CNN). The computing system is integrated with a radar system to implement an automated small target detection platform. We develop the ISAR image generation algorithm as a native library optimized for a mobile AP and design a CNN model to classify ISAR images. Experimental results show that the ISAR-based object detection platform running on a mobile AP takes approximately 890 ms to generate an ISAR image and 3 ms to classify it using the CNN model.}, archive = {J_ACCESS}, author = {Seongwook Kim and Sukhyun Han and Woojin Cho and Youngjae Choi and Seungeui Lee and Youngseok Bae and Seokin Hong}, doi = {10.1109/ACCESS.2025.3596559}, journal = {IEEE Access}, pages = {143429-143439}, shortjournal = {IEEE Access}, title = {Leveraging mobile processors for ISAR image generation and classification in radar platforms}, volume = {13}, year = {2025}, }
• (2025). DiDbKDT: A cross-SNR modulation recognition scheme with dual-input and dual-branch based on knowledge distillation for IQ-AP data. ACCESS, 13, 143414-143428. (www)

  @article{ , abstract = {Modulation recognition accuracy is crucial in communication reconnaissance. In this study, we propose DiDbKDT, a dual-input dual-branch network architecture for cross-SNR modulation recognition. The model leverages knowledge distillation and integrates IQ-AP data as inputs. Considering the differences in data features under different SNR scenarios and combining the respective features of IQ and AP data, we divide the SNR interval into two segments and use the IQ and AP data as the inputs of the low-SNR and high-SNR networks respectively. To balance the computational complexity and recognition rate, in the DiDbKDT network model, we utilize the knowledge distillation technique. We deploy the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) in the low-SNR and high-SNR scenarios respectively as teacher models to quickly learn the internal modulation features of IQ and AP data. In the student model, we adopt the Transformer to further mine the global features of the data and the correlation relationships between different features, thus achieving more accurate modulation recognition and classification. We also introduce a data augmentation technique based on the channelization scenario to further improve the robustness of the model. Experimental results on six types of datasets, namely RML2016.10a/b/c, RML2018.01a, HisarMod, and MiGouMod, demonstrate that the proposed DiDbKDT outperforms the existing Automatic Modulation Recognition (AMR) methods and has higher parameter efficiency.}, archive = {J_ACCESS}, author = {Guan Wang and Xiaoyang Hao and Chenchen Tian}, doi = {10.1109/ACCESS.2025.3598978}, journal = {IEEE Access}, pages = {143414-143428}, shortjournal = {IEEE Access}, title = {DiDbKDT: A cross-SNR modulation recognition scheme with dual-input and dual-branch based on knowledge distillation for IQ-AP data}, volume = {13}, year = {2025}, }
• (2025). Integrated passenger flow analysis and street-level monitoring for public transport management using deep learning and IoT. ACCESS, 13, 143401-143413. (www)

  @article{ , abstract = {This paper proposes an integrated method for real-time passenger counting and street-level monitoring in public transportation vehicles. Unlike previous flow-based approaches, the proposed system leverages pre-trained deep convolutional neural networks, specifically the YOLO model, to analyze video streams captured both inside and outside the vehicle. The approach is implemented on GPU-enabled PCs installed onboard and connected to the cloud for transmitting data on vehicle occupancy and street-level observations. Internal video streams are processed to perform accurate passenger counting after doors are closed and flow analysis during stops, improving precision over flow-only methods. For external monitoring, additional cameras capture the environment around the vehicle to detect key objects such as potholes, garbage, and bicycles. These detections are geo-referenced using GPS data to generate informative maps highlighting urban street features. Furthermore, the passenger flow data is shown to support public transport network analysis, enabling insights into route usage and bottlenecks. The solution was validated in a large-scale deployment on 50 buses operating across 30 routes in the public transportation network of Bari, Italy, demonstrating high accuracy and practical applicability for urban mobility management.}, archive = {J_ACCESS}, author = {Mariano G. Paganelli and Marco Gallo and Paolo R. Massenio and David Naso}, doi = {10.1109/ACCESS.2025.3597327}, journal = {IEEE Access}, pages = {143401-143413}, shortjournal = {IEEE Access}, title = {Integrated passenger flow analysis and street-level monitoring for public transport management using deep learning and IoT}, volume = {13}, year = {2025}, }
• (2025). SDR-based all-optical MIMO wireless hybrid IoT sensor networks. ACCESS, 13, 143392-143400. (www)

  @article{ , abstract = {In this paper, we experimentally demonstrate and analyze the performance of a software-defined radio (SDR)-based all-optical multiple-input multiple-output (MIMO) wireless hybrid communication link for indoor Internet of Things (IoT) sensor networks. We propose an all-optical hybrid scheme using two light-emitting diodes (LEDs) as transmitters (Tx) and photodiode (PD)- and image sensor (IS)-based receivers for simultaneous high- and low-speed data transmission capabilities using visible light communication (VLC) and optical camera communication (OCC) links, respectively. We present a testbed using an SDR platform to transmit and receive high- and low-speed signals and a media converter to introduce them into an optical link. We evaluate the performance of the proposed SDR-based all-optical MIMO wireless hybrid scheme: 1) a single-input multiple-output (SIMO) link employing a single-chip LED for reference, and 2) a MIMO link employing two-chip LEDs modulated with a quadrature phase shift keying signal, in terms of the bit error rate and reception success rate of high-speed VLC and low-speed OCC links, respectively. We show that the proposed scheme can provide independent link performance, irrespective of the significant differences between the operating $R_{b}$ of VLC and OCC. By means of the proposed testbed and measurements, we provide a versatile solution to implement all-optical hybrid links for low-rate IoT environments.}, archive = {J_ACCESS}, author = {Shivani Rajendra Teli and Carlos Guerra-Yanez and Zabih Ghassemlooy and Stanislav Zvanovec}, doi = {10.1109/ACCESS.2025.3598994}, journal = {IEEE Access}, pages = {143392-143400}, shortjournal = {IEEE Access}, title = {SDR-based all-optical MIMO wireless hybrid IoT sensor networks}, volume = {13}, year = {2025}, }
• (2025). BreNet: Attention-enhanced multi-scale CNN framework for breast cancer classification in histopathological images. ACCESS, 13, 143377-143391. (www)

  @article{ , abstract = {Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the critical importance of early and accurate detection in improving patient outcomes and treatment plans. Histopathological analysis of breast tissue samples is the gold standard for breast cancer diagnosis, yet it is often labor-intensive, subjective, and prone to inter-observer variability. Recent advancements in deep learning have shown significant promise in enhancing the accuracy and efficiency of breast cancer classification. In this study, we introduce BreNet, a robust and empirically validated multi-scale CNN ensemble tailored for breast cancer classification in histopathological images. By combining three pre-trained CNN backbones with complementary channel and spatial attention mechanisms, BreNet refines feature representations across multiple scales. The model achieves strong and consistent performance on both the BreakHis and IDC datasets, reaching classification accuracies of 99.96% and 88.26% respectively, while maintaining low false-positive and false-negative rates. Through thorough evaluations including patient-level cross-validation, stain normalization, and magnification-specific analysis, BreNet demonstrates reliable generalization in clinically realistic settings. These results underscore its potential as a practical decision-support tool to assist pathologists in routine diagnostic workflows.}, archive = {J_ACCESS}, author = {Helala M. Alshehri}, doi = {10.1109/ACCESS.2025.3598450}, journal = {IEEE Access}, pages = {143377-143391}, shortjournal = {IEEE Access}, title = {BreNet: Attention-enhanced multi-scale CNN framework for breast cancer classification in histopathological images}, volume = {13}, year = {2025}, }
• (2025). Neuro-symbolic AI for advanced signal and image processing: A review of recent trends and future directions. ACCESS, 13, 143360-143376. (www)

  @article{ , abstract = {Neuro-Symbolic Artificial Intelligence (NeSyAI) is a paradigm that combines neural networks with symbolic reasoning, building upon foundations laid in the 1990s and early 2000s, yet gaining increased attention through practical applications. This paper provides a comprehensive review of NeSyAI techniques and their applications in advanced signal and image processing. The paper begins by introducing the fundamentals of NeSyAI and a taxonometry analysis, highlighting how it bridges the gap between data-driven learning and knowledge-based reasoning. It then surveys several key application domains, such as biomedical, autonomous robotics, and IoT, in order to understand the benefits and challenges of that integration. For each domain, how NeSyAI methods improve upon traditional purely neural or purely symbolic approaches is illustrated. A comparative analysis with conventional AI techniques is presented, underscoring the advantages of NeSyAI in terms of interpretability, generalization, and flexibility. The challenges that arise in developing NeSyAI systems, such as computational complexity, integration of heterogeneous models, and ethical considerations, are also discussed. Finally, future research trends in NeSyAI for signal and image processing, as well as the path toward more explainable and generalizable AI, are synthesized.}, archive = {J_ACCESS}, author = {Ricardo Fitas}, doi = {10.1109/ACCESS.2025.3598909}, journal = {IEEE Access}, pages = {143360-143376}, shortjournal = {IEEE Access}, title = {Neuro-symbolic AI for advanced signal and image processing: A review of recent trends and future directions}, volume = {13}, year = {2025}, }
• (2025). An image semantic representation method based on cross-modal adaptive multi-layer perceptron. ACCESS, 13, 143351-143359. (www)

  @article{ , abstract = {With the development of Multimodal information research, the latest image captioning aims to generate variable-length sentences following cross-modal signals (e.g., visual and text). While Transformer-based methods have shown impressive progress for image captioning, they are complex and computationally expensive. Recent work has shown Multi-layer Perceptron’s (MLP) potential for lower computational cost. However, fixed-parameter weights of MLP make it difficult for models to adapt to producing variable-length sentences in cross-modal scenarios. To address these challenges, we propose the novel Cross-Modal Adaptive Network (CMANet) to improve MLP by dynamic parameter weight settings and bidirectional semantic alignment of cross-modal features, while enhancing computational efficiency. Specifically, a network architecture consisting of two independent fixed-weight two-layer MLP modules and a dynamic-weight bidirectional linear layer module was designed. Visual and textual features are first processed by two independent two-layer MLP modules to extract their intrinsic properties, thereby improving the training and computational efficiency of the model through a simple MLP structure. Subsequently, the processed visual and textual features are used as dynamic weight matrices for each other. Through the dynamic-weight bidirectional linear layer, CMANet can adapt to the requirement of generating variable-length sentences in cross-modal scenarios, addressing the issue of traditional MLPs being unable to handle variable text lengths in cross-modal feature relationships. Results on the MS-COCO dataset show that CMANet outperforms Transformer-based methods by 2.7% across the CIDEr score, while reducing the number of parameters and GFLOPs by 42.9% and 69.9%, respectively. In comparison to large-scale pre-trained models such as LEMON, CMANet achieves a performance improvement of 1.3%, while the number of parameters is only 3% of the LEMON.}, archive = {J_ACCESS}, author = {Yang Liu and Xiulei Liu and Chengli Peng}, doi = {10.1109/ACCESS.2025.3598598}, journal = {IEEE Access}, pages = {143351-143359}, shortjournal = {IEEE Access}, title = {An image semantic representation method based on cross-modal adaptive multi-layer perceptron}, volume = {13}, year = {2025}, }
• (2025). Self-attentive quantile GAN with causally grounded machine learning for vocal biomarker discovery in parkinson’s disease detection. ACCESS, 13, 143332-143350. (www)

  @article{ , abstract = {Parkinson’s disease (PD) detection through voice analysis faces two fundamental challenges: traditional machine learning methods lack clinical interpretability due to their correlational nature, while existing causal inference approaches struggle with the high-dimensional, complex relationships inherent in vocal biomarkers. We address these limitations by developing a causal inference framework that establishes interpretable relationships between vocal features and PD diagnosis. Our approach centers on estimating the Average Treatment Effect (ATE) of vocal frequency spread using directed acyclic graphs (DAGs) and backdoor adjustment, implemented through CatBoost for robust nonlinear modeling. To overcome data scarcity constraints, we introduce a Self-Attentive Quantile GAN (SAQ-GAN) that preserves empirical feature distributions through quantile-based transformation and self-attention mechanisms. The framework includes systematic preprocessing for causal validity and comprehensive refutation tests to validate causal assumptions. Experimental evaluation on the Oxford Parkinson’s dataset demonstrates that our method achieves 95.03% precision and 97.84% AUC-ROC while providing clinically interpretable causal estimates (ATE = 0.1844, 95% CI: [0.0743, 0.2945]). Comparative analysis with state-of-the-art causal methods and extensive ablation studies confirm the effectiveness of combining synthetic data augmentation with principled causal inference for medical diagnosis.}, archive = {J_ACCESS}, author = {Meghana Sunil and V. Shravya and S. Nachiyappan}, doi = {10.1109/ACCESS.2025.3596114}, journal = {IEEE Access}, pages = {143332-143350}, shortjournal = {IEEE Access}, title = {Self-attentive quantile GAN with causally grounded machine learning for vocal biomarker discovery in parkinson’s disease detection}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal meta-graph learning for recommendation on heterogeneous graphs. ACCESS, 13, 143317-143331. (www)

  @article{ , abstract = {Spatio-temporal information plays a crucial role in recommendation systems, and effectively capturing the spatiotemporal characteristics of user-item interactions is essential to enhance the performance of such systems. Conventional recommendation systems primarily focus on capturing interaction features within the data, frequently neglecting the significant influence of this information. To address this issue, this paper proposes a framework that combines graph ODE and meta-graph (MG) search. The graph ODE effectively captures spatiotemporal features across different time steps, resulting in node features that are enriched with semantic information. In addition, the MG search diverges from the conventional method of predefining MGs using a neural architecture search technique to derive MGs directly from the network. It also employs an attention mechanism to recommend the most suitable MGs once they have been generated. The proposed framework effectively integrates the spatiotemporal information of the nodes into the features, thereby enhancing understanding of the users’ needs and improving recommendation accuracy. The proposed STMGL framework was compared against the FMRec, HeteRec, SemRec, and GraphRec models on the MovieLens, Amazon, Douban, and Yelp datasets. The experimental results demonstrate a significant improvement the proposed STMGL framework’s performance compared with these existing recommendation techniques.}, archive = {J_ACCESS}, author = {Xiaofei Yang and Wenjing Yang and Xiaoshuang Wang and Mei Gao and Lu Zhang}, doi = {10.1109/ACCESS.2025.3597596}, journal = {IEEE Access}, pages = {143317-143331}, shortjournal = {IEEE Access}, title = {Spatio-temporal meta-graph learning for recommendation on heterogeneous graphs}, volume = {13}, year = {2025}, }
• (2025). Implementation of a voltage-dependent transmission line model with corona effect consideration. ACCESS, 13, 143302-143316. (www)

  @article{ , abstract = {This paper presents two models for overhead transmission lines that incorporate the voltage-dependent corona effect directly into the line representation, offering a more accurate representation of the line. The first model is an enhanced version of the Voltage-Dependent Line Model (VDLM), which calculates the shunt conductance and capacitance based on the average voltage across each line section. The second model, referred to as the Alternative Voltage-Dependent Line Model (AVDLM), determines these parameters using the voltages at the ends of the line sections. Both models demonstrate comparable accuracy, high efficiency, and compatibility with EMT-type programs. The models employ a discretization approach, utilizing a cascade connection of interfaces derived from the Bergeron model. Current sources and resistive elements are determined from preceding nodal voltages, effectively capturing the voltage dependence of the shunt capacitance and conductance of the line. The models have been implemented in MATLAB and validated against field measurements, demonstrating high accuracy and stability in predicting overvoltage propagation, as well as flexibility in tuning parameters related to the corona effect through the use of a genetic algorithm.}, archive = {J_ACCESS}, author = {Luis Carlos Timaná and Jaimis S. L. Colqui and Andrés Rodríguez and José Pissolato Filho}, doi = {10.1109/ACCESS.2025.3598646}, journal = {IEEE Access}, pages = {143302-143316}, shortjournal = {IEEE Access}, title = {Implementation of a voltage-dependent transmission line model with corona effect consideration}, volume = {13}, year = {2025}, }
• (2025). Enhancing motor imagery EEG classification in Brain–Computer interfaces via TransformerNet. ACCESS, 13, 143291-143301. (www)

  @article{ , abstract = {Brain-computer interfaces (BCIs) offer promising solutions for assisting individuals with disabilities, supporting neurorehabilitation, and enhancing human capabilities. However, the limited decoding accuracy of EEG-based motor imagery (MI) signals poses a major challenge for the practical deployment of BCI systems. A common approach involves using signals from opposite hemispheres to boost classification accuracy. Yet, to control devices such as robotic hands or prosthetics in a human-like manner, it is essential to accurately classify hand opening and closing tasks using EEG signals from the same motor cortex region. This study introduces TransformerNet, a novel deep learning architecture specifically designed to classify hand open-close MI tasks from the same brain region. The task is particularly challenging due to the high similarity and overlapping nature of the EEG signals. TransformerNet combines a convolutional module, inspired by EEGNet, to extract local spatial features, with a Transformer encoder that captures long-range temporal dependencies. Furthermore, a channel attention mechanism enhances the model’s ability to focus on the most informative features. In experimental evaluations, TransformerNet achieved an average classification accuracy of 85.97%, outperforming traditional deep learning methods. The model effectively captures high-level temporal-spectral patterns and uncovers hidden dependencies within the EEG signals. These results demonstrate the potential of integrating attention mechanisms with Transformer-based architectures to improve MI-based BCI performance. This advancement holds promise for real-world applications such as brain-controlled prosthetics, assistive devices, and human-computer interaction, moving BCI technologies closer to practical and reliable implementation.}, archive = {J_ACCESS}, author = {Ulvi Baspinar and Yahya Taştan}, doi = {10.1109/ACCESS.2025.3594083}, journal = {IEEE Access}, pages = {143291-143301}, shortjournal = {IEEE Access}, title = {Enhancing motor imagery EEG classification in Brain–Computer interfaces via TransformerNet}, volume = {13}, year = {2025}, }
• (2025). Patient-specific digital twins for personalized healthcare: A hybrid AI and simulation-based framework. ACCESS, 13, 143277-143290. (www)

  @article{ , abstract = {Digital twins (DTs) represent a transformative paradigm in personalized medicine, enabling real-time, patient-specific simulations that support precision diagnosis, continuous monitoring, and adaptive treatment planning. This paper presents a novel hybrid framework that integrates mechanistic physiological modeling with deep learning to construct patient-specific digital twins. The proposed architecture couples a dynamic simulation engine—capable of modeling organ-system interactions—with neural encoder-decoder networks that extract latent representations from heterogeneous clinical data sources, including biosignals (e.g., ECG), medical imaging, and wearable sensor streams. A probabilistic learning module further enables predictive adaptation under uncertainty, leveraging Bayesian inference and reinforcement learning. The framework supports continuous synchronization between a patient’s real-world physiological state and its digital counterpart, enabling individualized risk assessment, disease trajectory forecasting, and therapy simulation. Extensive experiments on large-scale benchmark datasets (MIMIC-IV and PhysioNet) demonstrate that our hybrid approach significantly outperforms conventional machine learning and standalone AI models across multiple clinical prediction tasks. By fusing physiological realism with data-driven intelligence, the proposed digital twin framework offers a scalable, interpretable, and clinically actionable foundation for next-generation precision healthcare systems.}, archive = {J_ACCESS}, author = {Harshit Sharma and Simran Kaur}, doi = {10.1109/ACCESS.2025.3598130}, journal = {IEEE Access}, pages = {143277-143290}, shortjournal = {IEEE Access}, title = {Patient-specific digital twins for personalized healthcare: A hybrid AI and simulation-based framework}, volume = {13}, year = {2025}, }
• (2025). DHPE-DETR: A dynamic hybrid position encoding-based multi-scale traffic sign detection algorithm. ACCESS, 13, 143261-143276. (www)

  @article{ , abstract = {As a core component of autonomous driving technology, traffic sign detection is directly linked to driver safety. However, traffic signs often exhibit multi-scale characteristics in real-world road scenarios, posing challenges for existing detection algorithms to meet accuracy requirements. To address this issue, this paper proposes a multi-scale enhanced traffic sign detection algorithm (DHPE-DETR) based on RT-DETR-r18. First, a Dynamic Hybrid Position Encoding (DHPE) method with scale-adaptive weight adjustment is introduced. This method enhances high-frequency detail representation for small targets and low-frequency position encoding for large targets, thereby improving the algorithm’s multi-scale perception capabilities. Second, a P2 detection head is incorporated to extract and detect shallow features, alleviating the loss of small object details and promoting multi-scale feature fusion. Finally, a novel loss function NWD-Focaler-MPDIoU is proposed based on Gaussian distribution-based probabilistic modeling of bounding boxes, further enhancing overall model accuracy. Experimental results demonstrate that the DHPE-DETR algorithm achieves 85.1% mAP@0.5 and 65.0% mAP@0.5:0.95 on the TT100K dataset, outperforming the baseline model by 3.1% and 2.8%, respectively.}, archive = {J_ACCESS}, author = {Yingxin Ren and Juwei Zhang and Bo Liu and Bingyi Ren}, doi = {10.1109/ACCESS.2025.3598815}, journal = {IEEE Access}, pages = {143261-143276}, shortjournal = {IEEE Access}, title = {DHPE-DETR: A dynamic hybrid position encoding-based multi-scale traffic sign detection algorithm}, volume = {13}, year = {2025}, }
• (2025). CAXF-LCCDE: An enhanced feature extraction and ensemble learning model for XSS detection. ACCESS, 13, 143250-143260. (www)

  @article{ , abstract = {Threats to businesses, organizations, and individuals have become more complicated and varied due to the constant evolution of cyberattack strategies brought about by the quick development of Internet technologies. Cross-Site Scripting (XSS) remains one of the most pervasive and dangerous threats among the many that target online application security. This emphasizes the importance of strong detection mechanisms to protect user information and preserve system integrity. Combining machine learning with intrusion detection systems successfully addresses this problem and enhances XSS detection capabilities. This work provides a new feature extraction technique designed specifically for XSS, called Comprehensive Analysis of XSS Features (CAXF). We combine it with Leader Class and Confidence Decision Ensemble (LCCDE), an ensemble machine learning approach. This combination efficiently identifies various XSS attack types, such as reflected, stored, and DOM-based variations. Extensive trials were carried out on well-annotated datasets with typical samples of these XSS categories. The findings demonstrate that the suggested CAXF-LCCDE framework significantly outperforms conventional resilience and detection accuracy models. Furthermore, the results highlight how crucial tailored feature engineering and algorithm selection are for cybersecurity applications. This work provides important information for creating stronger and more dependable XSS attack defenses.}, archive = {J_ACCESS}, author = {Man Hua and Zhiyu Luan and Yanling Li and Qijian Liu}, doi = {10.1109/ACCESS.2025.3598858}, journal = {IEEE Access}, pages = {143250-143260}, shortjournal = {IEEE Access}, title = {CAXF-LCCDE: An enhanced feature extraction and ensemble learning model for XSS detection}, volume = {13}, year = {2025}, }
• (2025). MADN: Multi-attention with diffusion network for scene text image super-resolution. ACCESS, 13, 143233-143249. (www)

  @article{ , abstract = {Scene Text Image Super-Resolution (STISR) enhances the recognition accuracy of degraded textual imagery that requires fine-grained character reconstruction. Despite incorporating text-specific prior knowledge, existing methods suffer from suboptimal feature discrimination and inadequate sequential dependency modeling, resulting in information dilution and spatial discontinuity that compromise textual legibility. In this paper, we propose a Multi-Attention with Diffusion Network (MADN), a hierarchical attention framework that addresses these limitations through dual-domain feature refinement. Our Attention-Guided Feature Extraction (AGFE) module implements Channel Attention (CA) for adaptive feature recalibration and Spatial Attention (SA) for region-specific localization, enabling selective amplification of text-critical features while suppressing background interference. We further introduce Multi-Attention Residual Blocks (MARB), which integrate Bidirectional Long Short-Term Memory (BLSTM) networks for modeling sequential dependencies and refining multi-dimensional features. This multi-attention-based model acquires inter-character contextual relations while preserving the fine-grained textual structure. MADN adopts a hybrid dual-branch architecture, comprising a Super-Resolution (SR) branch equipped with AGFE and cascaded MARB modules to process low-resolution inputs and a Guidance Branch, along with a Text Prior Enhancement Module (TPEM) for forward diffusion and text feature generation. The Feature Fusion Module (FFM) fuses multi-scale features of two branches, enabling information exchange across the branches and refinement of features through diffusion mechanisms. Extensive experiments on the TextZoom dataset demonstrate state-of-the-art performance with significant improvements in Recognition Accuracy, PSNR, and SSIM. The code for MADN is available at: https://github.com/cuee-mdap/madn-net}, archive = {J_ACCESS}, author = {Amir Hajian and Tithnorakneath Em and Watchara Ruangsang and Supavadee Aramvith}, doi = {10.1109/ACCESS.2025.3597298}, journal = {IEEE Access}, pages = {143233-143249}, shortjournal = {IEEE Access}, title = {MADN: Multi-attention with diffusion network for scene text image super-resolution}, volume = {13}, year = {2025}, }
• (2025). An enhanced geometric featured extraction method for real-time oscillation detection in power system. ACCESS, 13, 143223-143232. (www)

  @article{ , abstract = {This paper proposes a real-time oscillation monitoring framework based on an enhanced Geometric Feature Extraction (GFE) algorithm. The method utilizes high-resolution synchrophasor measurements from Phasor Measurement Units (PMUs) and employs a sliding window approach to identify oscillatory behavior in power systems. To improve detection robustness, the algorithm integrates signal-to-noise ratio (SNR) filtering and adaptive estimation of the Radius of Trajectory (RoT), which characterizes geometric patterns in reconstructed phase-space trajectories. A real-time alarming index is computed for each sample, and an oscillation alarm is triggered when the proportion of oscillatory samples within a window exceeds a predefined threshold. The proposed method was validated using actual PMU data recorded during a forced oscillation event caused by a Thyristor-Controlled Series Capacitor (TCSC) in the KEPCO power system. Experimental results demonstrate that the algorithm accurately captures the initiation, persistence, and attenuation of oscillatory behavior, offering both high detection sensitivity and low computational overhead suitable for real-time deployment.}, archive = {J_ACCESS}, author = {Beomju Kim and Jeonghoo Park and Hwanhee Cho and Suchul Nam and Byongjun Lee}, doi = {10.1109/ACCESS.2025.3597375}, journal = {IEEE Access}, pages = {143223-143232}, shortjournal = {IEEE Access}, title = {An enhanced geometric featured extraction method for real-time oscillation detection in power system}, volume = {13}, year = {2025}, }
• (2025). Enhancing glass segmentation accuracy via boundary-context guided physics-aware modeling. ACCESS, 13, 143210-143222. (www)

  @article{ , abstract = {Transparent objects such as glass pose significant challenges and potential safety risks to vision systems due to their transparency and complex light interactions, which often lead to blurred boundaries and contextual ambiguity. Existing methods typically suffer from weak edge responses and insufficient cross-scale context integration. To address these issues, we propose the Boundary-Context Guided Glass Detection Network (BCGNet), a novel encoder-decoder architecture that combines global context modeling with boundary-aware refinement for accurate glass segmentation. Specifically, we design a dual-path Boundary Feature Refinement (BFR) module, where the spatial branch captures position-sensitive boundary features, and the channel branch, inspired by physical light propagation, leverages optical flow-guided feature warping to model geometry-aware deformations along transparent edges, thereby mitigating semantic confusion. Additionally, we develop a Multi-scale Context Aggregator that integrates a Contextual Cross-Attention Module (CCAM) and a Depthwise Separable Feed-Forward Network (D-FFN) through hierarchical feature interaction. CCAM captures long-range dependencies via dual-stream attention, while D-FFN reconstructs boundary-sensitive local representations using depthwise separable convolutions. Extensive experiments on four public datasets demonstrate that BCGNet consistently outperforms existing methods, validating its robustness and effectiveness in complex real-world scenarios.}, archive = {J_ACCESS}, author = {Guojun Chen and Jianqiang Yuan and Haozhen Chen and Huihui Li and Jiale Chen}, doi = {10.1109/ACCESS.2025.3598465}, journal = {IEEE Access}, pages = {143210-143222}, shortjournal = {IEEE Access}, title = {Enhancing glass segmentation accuracy via boundary-context guided physics-aware modeling}, volume = {13}, year = {2025}, }
• (2025). Finite time sliding mode control for chattering reduction in unmanned aerial vehicles with dynamic payloads. ACCESS, 13, 143196-143209. (www)

  @article{ , abstract = {This study proposes a finite-time tracking control system for an unmanned aerial vehicle (UAV) in the presence of dynamic payloads and external disturbances. Changes in payload result in time-varying parametric uncertainties, making traditional control strategies ineffective. A sliding mode control (SMC) scheme is designed to handle uncertainties in both translational and rotational dynamics. A finite-time SMC (FTSMC) approach is developed to handle unknown time-varying mass and external disturbances in the translational dynamics. Furthermore, differences in the payload influence the inertia characteristics of the UAV, introducing further uncertainties in the feedback control design. A finite-time sliding mode controller is also implemented for rotational dynamics to guarantee the attitude stabilization under unknown inertia parameter variations. The proposed method has the objective to attenuate the chattering effect associated with traditional SMC within a finite time frame. Theoretical stability analysis with rigorous mathematical proofs is presented to confirm the effectiveness of the control scheme. Numerical simulations are provided to validate its performance under dynamic operating conditions.}, archive = {J_ACCESS}, author = {Imil Hamda Imran and Dilek Funda Kurtulus and Taiba Kouser and Azhar M. Memon and Luai Muhammad Alhems and Srikanth Goli}, doi = {10.1109/ACCESS.2025.3598131}, journal = {IEEE Access}, pages = {143196-143209}, shortjournal = {IEEE Access}, title = {Finite time sliding mode control for chattering reduction in unmanned aerial vehicles with dynamic payloads}, volume = {13}, year = {2025}, }
• (2025). Robust visual servoing and CNN-based thermal imaging for sugarcane row following with a skid-steering mobile robot. ACCESS, 13, 143166-143195. (www)

  @article{ , abstract = {The escalating demand for accuracy and efficiency in agriculture—driven by population growth and climate change—has increased the use of mobile robots in precision agriculture. Sugarcane production is particularly significant, providing both sugar yield and renewable energy sources such as bioplastic and biofuel from cane bagasse. Like other grass-type crops (e.g., wheat, corn, rice, barley, and bamboo), sugarcane fields pose unique challenges due to vast plantation areas and dense vegetation forming hard-to-access crop row tunnels. These conditions hinder both manual labor and autonomous robot operation, requiring higher investments in robot swarms with robust navigation systems to handle noisy imaging and low Global Navigation Satellite System coverage. This paper introduces a visual navigation system for TIBA, a cost-effective, compact, rugged skid-steering mobile robot equipped with customized sensors and designed for swarm-based operations in sugarcane fields. We propose a combination of infrared (thermal) imaging with robust control to address the locomotion challenge in sugarcane fields. Thermal images reveal temperature differences between ground and cane stalks, enabling clearer visualization of the row corridor despite occluding leaves. Consequently, computationally efficient algorithms, such as a simple convolutional neural network, can generate reliable trajectories for the robot to follow along the crop row, avoiding lateral collisions with the side plants. A sliding mode control approach ensures robust tracking under uncertainties and disturbances in both robot and camera models. We also introduce IRNavSim, a Matlab-based simulation environment tailored for crop row following tasks using thermal imaging, which helps us to evaluate the system’s performance and behavior.}, archive = {J_ACCESS}, author = {Marco F. S. Xaud and Pål J. From and Antonio C. Leite}, doi = {10.1109/ACCESS.2025.3598792}, journal = {IEEE Access}, pages = {143166-143195}, shortjournal = {IEEE Access}, title = {Robust visual servoing and CNN-based thermal imaging for sugarcane row following with a skid-steering mobile robot}, volume = {13}, year = {2025}, }
• (2025). A ground current suppression method for single-phase photovoltaic (PV) inverters. ACCESS, 13, 143155-143165. (www)

  @article{ , abstract = {For single-phase PV inverter systems, when relays connected to the grid live line are switched on and the DC/AC has not yet entered Pulse Width Modulation (PWM) mode, the ground current is distorted. Because the ground current is not sinusoidal, high-frequency harmonics can easily trip the ground current protection breaker, causing the system to be disconnected from the grid. The cause of ground current distortion is investigated in this paper, and solutions to suppress ground current distortion are proposed. First, the topology and control logic for the single-phase PV inverter are explained, followed by illustration of ground current distortion. The analysis for the operation during the ground current distortion is then presented, which includes four stages. Furthermore, the voltage of the DC-bus to ground and the ground current are expressed mathematically. Based on the analysis, solutions to suppress ground current distortion are proposed, as well as the detailed concept and implementation. The ground current suppression concept is to regulate the DC-bus voltage, and four detailed implementations are presented. Finally, an experimental platform is built, and the experimental results are presented to validate the proposed method.}, archive = {J_ACCESS}, author = {Wenping Zhang and Yiming Wang and Po Xu and Baosong Liu and Ke Ma}, doi = {10.1109/ACCESS.2025.3598291}, journal = {IEEE Access}, pages = {143155-143165}, shortjournal = {IEEE Access}, title = {A ground current suppression method for single-phase photovoltaic (PV) inverters}, volume = {13}, year = {2025}, }
• (2025). Analysis of control characteristics for circular tracking movements with rapidly changing target speeds in 3D VR environment. ACCESS, 13, 143138-143154. (www)

  @article{ , abstract = {Visual information-based movements are crucial in everyday life, and movements involving rapid speed changes (e.g., acceleration and deceleration) are essential for analyzing actual human motor control characteristics. This study is relevant because it was conducted in 3D VR, which reflects similar movements as real movements, unlike motor control research conducted in 2D. In addition, the human motor control characteristics were quantitatively analyzed by extracting accurate positional data. This study selected three parameters ( $\Delta $ D, $\Delta \boldsymbol {\theta }$ , and $\Delta \boldsymbol {\omega }$ ) to analyze the motor control characteristics resulting from rapid speed changes. We performed circular tracking movements with a target exhibiting speed changes in the frontal and sagittal planes. Unlike for uniform and decelerated motion, the largest errors in all the parameters for accelerated motion were observed owing to the influence of feedforward control, with the most impact occurring when the speed increased between 0.375 Hz and 0.438 Hz. During decelerated motion, two parameters ( $\Delta $ D, $\Delta \boldsymbol {\theta }$ ) were used to observe the effects of brain time delay (sensorimotor delay, somatosensory delay) and trajectory learning models, with the most significant influence occurring as the speed decreased between 0.188 Hz and 0.156 Hz. By comparing the frontal and sagittal planes for each speed change, we confirmed that the accuracy of the visual information in the planes significantly influenced motor control more than the speed changes. Furthermore, by analyzing the quadrants in the sagittal plane, we found that the control strategies for adapting to speed changes were more impactful than the accuracy of the visual information based on the target position.}, archive = {J_ACCESS}, author = {Kwangyong Han and Woong Choi and Jaehyo Kim}, doi = {10.1109/ACCESS.2025.3592702}, journal = {IEEE Access}, pages = {143138-143154}, shortjournal = {IEEE Access}, title = {Analysis of control characteristics for circular tracking movements with rapidly changing target speeds in 3D VR environment}, volume = {13}, year = {2025}, }
• (2025). On-chip flow cytometry utilizing the di-electrophoresis and ring oscillator-based capacitive sensor. ACCESS, 13, 143127-143137. (www)

  @article{ , abstract = {Flow Cytometry (FC) is a powerful technique used to detect and measure various cellular characteristics, including size, count, morphology, and DNA content. It plays a vital role in clinical diagnostics and biomedical research, with widespread use in hospitals and medical institutions worldwide. In this work, an on-chip, capacitance-based flow cytometer is developed to manipulate, characterize, detect, and quantify microbeads based on their electrical properties, specifically, capacitance. The proposed biochip consists of two primary subsystems: a manipulation system for directing particles and a detection system for characterizing them. The manipulation system integrates a microfluidic flow control design with dielectrophoresis (DEP) microelectrodes. DEP enables label-free particle manipulation in non-uniform electric fields by inducing polarization, allowing for precise control of particle trajectories with minimal sample preparation. The detection system employs a capacitive sensor array coupled with a ring oscillator-based readout circuit for real-time electrical sensing. Capacitance-based biosensors offer rapid, accurate, and efficient detection without the need for bulky optical equipment or chemical labeling. Leveraging printed circuit board (PCB) technology, the DEP electrodes and capacitive sensors are seamlessly integrated into a compact, low-cost, and scalable platform. The inclusion of a gold surface further enhances electrical conductivity and biocompatibility, making the system suitable for biological applications. Experimental and simulation results confirm that the integrated manipulation and sensing components effectively characterize and quantify carboxylate polystyrene microbeads. The sensor exhibits high sensitivity, achieving 184 fF per 1% w/v concentration change, and demonstrates excellent linearity with a coefficient of determination (R2) of 0.9755, indicating a reliable and consistent response. The use of a 7-stage Miller ring oscillator enables precise capacitance-to-frequency conversion with minimal error, achieving a root mean square error (RMSE) of 0.57 across a capacitance range of 1–100 pF. This allows for reliable detection of concentrations as low as 0.15625% w/v.}, archive = {J_ACCESS}, author = {Reda Abdelbaset and Yehia El-Sehrawy and Hanan Shaat and Sherif M. Shawky and Mohammed A. A. Abdullah and Mohamed A. K. Hussein and Marina Nabil and Rania Siam and Yehea Ismail}, doi = {10.1109/ACCESS.2025.3598510}, journal = {IEEE Access}, pages = {143127-143137}, shortjournal = {IEEE Access}, title = {On-chip flow cytometry utilizing the di-electrophoresis and ring oscillator-based capacitive sensor}, volume = {13}, year = {2025}, }
• (2025). Hybrid resilient framework for self-driving car. ACCESS, 13, 143110-143126. (www)

  @article{ , abstract = {Self-driving cars are expected to become a convenient source of transportation in near future. However, achieving a self-driving car at Level 5 still poses challenges. The challenges could be attributed to maintaining desirable functionality under uncertainty while accomplishing a task. This paper proposes a hybrid N-redundancy-based resilient framework for the perception module that integrates abstract sensor fusion and a probabilistic deep learning model to ensure resilient performance. Experimental results using the CARLA simulator demonstrate that our model consistently outperforms existing approaches in runtime efficiency while maintaining functional integrity under attack. Time-based performance evaluations reveal that our hybrid model achieves faster and more reliable outputs, validating its effectiveness in resilient autonomous perception.}, archive = {J_ACCESS}, author = {Junaid M. Qurashi and Kamal Jambi and Fathy E. Eassa and Maher Khemakhem and Fawaz Alsolami and Reem Abdulaziz Alnanih}, doi = {10.1109/ACCESS.2025.3597990}, journal = {IEEE Access}, pages = {143110-143126}, shortjournal = {IEEE Access}, title = {Hybrid resilient framework for self-driving car}, volume = {13}, year = {2025}, }
• (2025). An improved SiC MOSFET with integrated schottky contact super barrier rectifier. ACCESS, 13, 143102-143109. (www)

  @article{ , abstract = {This study focuses on the SiC MOSFET with integrated Schottky contact super barrier rectifier (SiC SSBR-MOSFET) through the application of TCAD simulation methodologies. During its operation as a freewheeling diode, the parasitic bipolar body diode’s activity is effectively suppressed as the SSBR has a lower turn-on voltage. Thus, the problem of bipolar degradation is entirely mitigated. Furthermore, the SiC SSBR-MOSFET demonstrates outstanding dynamic performance. In contrast to the conventional SiC MOSFET (SiC C-MOSFET), it exhibits decrease in input capacitance ( $C_{\mathrm {ISS}}$ ), reverse transfer capacitance ( $C_{\mathrm {RSS}}$ ), gate charge ( $Q_{\mathrm {G}}$ ) and gate-to-drain charge ( $Q_{\mathrm {GD}}$ ) by approximately 2, 6, 2 and 12 times, respectively. Despite a slight increase in specific on-resistance ( $R_{\mathrm {ON\cdot SP}}$ ), the SiC SSBR-MOSFET’s figures of merit, including $R_{\mathrm {ON\cdot SP}}\times Q_{\mathrm {G}}$ and $R_{\mathrm {ON\cdot SP}}\times Q_{\mathrm {GD}}$ , are significantly enhanced, reducing by 1.8 and 11 times, respectively. The SiC SSBR-MOSFET’s exceptional performance and straightforward implementation render it highly appealing for advanced power electronics applications.}, archive = {J_ACCESS}, author = {Xintian Zhou and Jinglong Yan and Yun Tang and Dongqing Hu and Yu Wu and Yunpeng Jia and Bodian Li and Yuanfu Zhao}, doi = {10.1109/ACCESS.2025.3598275}, journal = {IEEE Access}, pages = {143102-143109}, shortjournal = {IEEE Access}, title = {An improved SiC MOSFET with integrated schottky contact super barrier rectifier}, volume = {13}, year = {2025}, }
• (2025). YOLO-DefXpert: An advanced defect detection on PCB surfaces using improved YOLOv11 algorithm. ACCESS, 13, 143085-143101. (www)

  @article{ , abstract = {Surface defects in Printed Circuit Boards (PCBs), which arise during manufacturing, significantly impact product quality and directly influence equipment performance, stability and reliability. Accurately identifying small defects on PCB surfaces remains a considerable challenge, particularly under complex background conditions, due to the intricate and compact layout of the boards. This study introduces an improved PCB defect detection model, YOLO-DefXpert, using the YOLOv11 algorithm to address the low accuracy and efficiency challenges in detecting tiny-sized defects on PCBs. First, the standard backbone network of the YOLOv11 algorithm is replaced with the Swin Transformer to extract more robust features of defects, and the Convolutional Block Attention Module (CBAM) is added in the Patch Merging modules to alleviate feature leakage during the downsampling operation. Second, the standard convolutional operations are replaced with Deformable Convolutional Networksv2 (DCNv2) in the neck section to improve robustness in identifying multi-scale defects. Finally, an Additional Feature Fusion Layer (AFFL) is introduced in the neck to enhance the performance of the small defect identification. The effectiveness of the proposed YOLO-DefXpert is validated through experimental results obtained from publicly available PCB datasets. The proposed model achieves a mAP50 of 99.0% and a mAP95 of 60.6% in the HRIPCB benchmark dataset and 99.3% in mAP50 and 63.4% in mAP95 in the PCB dataset. Compared to the standard YOLOv11 model, the proposed YOLO-DefXpert attained an improvement of 9.3% and 13.2% in mAP50 and mAP95, an 11.25% increase in frames per second, and a 69.85MB decrease in model size. These findings highlight a notable enhancement in both accuracy and model efficiency in detecting tiny defects in the PCB board.}, archive = {J_ACCESS}, author = {Prabu Selvam and R. Rajasekar and C. Gunasundari and S. Janu Priya and M. Murugappan and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3595048}, journal = {IEEE Access}, pages = {143085-143101}, shortjournal = {IEEE Access}, title = {YOLO-DefXpert: An advanced defect detection on PCB surfaces using improved YOLOv11 algorithm}, volume = {13}, year = {2025}, }
• (2025). Toward varying bipedal walking gaits using rolling ball-tray dynamics at the walker’s core. ACCESS, 13, 143070-143084. (www)

  @article{ , abstract = {Actuated compass-gait walking machines have the potential to navigate areas with gentle slopes using very little energy, but a technological solution for applying a torque that will drive its natural gait is still proving elusive. In this paper, we investigate a mechatronic solution that could provide a driving force to a compass-gait walker: a heavy ball rolling in a V-shaped tray that is composed of circular arcs and straight segments. By tilting the tray, the ball can be made to roll back and forth. The equations of motion derived for this system are numerically simulated and compare favorably with the ball’s response in experiments. We then interpret the ball’s motion as a center-of-mass shift at the hips of a simulated walker to investigate what effects a ball-tray system would have on the compass-gait. When the ball’s motion under suitable tray geometry and tilt profile was applied as a mass shift at the hip to a compass-gait walking simulation, several feasible gait solutions were found. This suggests that the tilting ball-tray actuation method may be a suitable drive strategy for operating physical bipeds. In the long term, these results may be used to develop a system that can excite the walking dynamics of the biped from the core to create variable styles of walking gaits as described in Laban/Bartenieff Movement Analysis (L/BMA).}, archive = {J_ACCESS}, author = {Caleb Fuller and Umer Huzaifa and Joshua Schultz}, doi = {10.1109/ACCESS.2025.3598692}, journal = {IEEE Access}, pages = {143070-143084}, shortjournal = {IEEE Access}, title = {Toward varying bipedal walking gaits using rolling ball-tray dynamics at the walker’s core}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning agents with collective situational awareness for beyond visual range air combat. ACCESS, 13, 143052-143069. (www)

  @article{ , abstract = {This work explores Beyond Visual Range (BVR) air combat simulations, focusing on two-versus-two scenarios involving autonomous agents. The engagement phase in BVR combat presents complex and unpredictable situations, as it is difficult to anticipate the behavior of opposing aircraft and the outcomes of tactical decisions, especially in multi-agent settings. A promising approach is the use of Deep Reinforcement Learning (DRL), which enables agents to learn from dynamic environments. According to fighter pilots, collective situational awareness, defined as understanding the spatial distribution and orientation of allies and opponents, is essential for executing coordinated tactical maneuvers. The main contribution of this work is AsaGym, a library for developing and training DRL-based fighter agents in BVR scenarios. A case study demonstrates its use, applying a reward function that promotes coordination based on collective situational awareness, and compares different DRL algorithms to assess their ability to foster cooperative behavior. The results highlight DRL’s potential to address the complexities of modern air combat and support the development of more adaptive and effective tactics in multi-agent BVR scenarios.}, archive = {J_ACCESS}, author = {Joao P. A. Dantas and Felipe L. L. Medeiros and Adrisson R. Samersla and Pedro L. R. Botelho and Vitor C. F. Gomes and Samara R. Silva and Yuri D. Ferreira and Alessandro O. Arantes and Marcia R. C. Aquino and Marcos R. O. A. Maximo}, doi = {10.1109/ACCESS.2025.3597199}, journal = {IEEE Access}, pages = {143052-143069}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning agents with collective situational awareness for beyond visual range air combat}, volume = {13}, year = {2025}, }
• (2025). Rehabilitation device for low-speed gait therapy using a passive stephenson mechanism on a commercial treadmill. ACCESS, 13, 143042-143051. (www)

  @article{ , abstract = {This paper introduces a linkage-based rehabilitation device designed for low-speed gait therapy for individuals with neurological gait deficits. Unlike existing devices that operate above 0.5m/s, our system enables training at speeds between 0.1–0.4m/s, aligning with optimal rehabilitation thresholds. The device features a passive single DOF, six-link Stephenson-III mechanism mounted on a commercial treadmill. The system is designed to accurately follow an ankle-to-hip meta-trajectory, which has been extracted from 400 gait cycles of healthy individuals and adjusted for a leg length of 800mm. To ensure adjustability to users with varying physiologies, the design allows for scaling and parameter tuning based on individual anthropometrics. The final optimized mechanism was synthesized using the shadow robot algorithm, exploring 5000 initial configurations. The resulting design supports different gait trajectories through profile blending, enabling motion customization across a wide range of rehabilitation stages. A unique driving accessory transmits treadmill motion directly to the crank links, eliminating the need for separate actuators. A bodyweight support system reduces up to 80% of the patient’s weight and assists in balance control. The setup process has been streamlined for ease of use by inexperienced users by reducing the number of interaction points to two. This minimal constraint allows relative freedom for the remaining joints of the leg, enabling a more natural and patient-friendly walking experience. The device’s performance was validated using OpenSim simulations: hip and knee angle trajectories remained within ±1 standard deviation of experimental data, and root-mean-square errors were below 5 degrees throughout the gait cycle. These results demonstrate the device’s potential to provide safe, accurate, and adjustable gait rehabilitation for individuals with severe mobility impairments.}, archive = {J_ACCESS}, author = {Ashkan Vali and Reyhaneh Chegini and Mehdi Bakhtiari and Mohammad Reza Haghjoo and Borhan Beigzadeh}, doi = {10.1109/ACCESS.2025.3597996}, journal = {IEEE Access}, pages = {143042-143051}, shortjournal = {IEEE Access}, title = {Rehabilitation device for low-speed gait therapy using a passive stephenson mechanism on a commercial treadmill}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral methane plume segmentation through foundation computer vision models. ACCESS, 13, 143031-143041. (www)

  @article{ , abstract = {Efficient large-scale monitoring of methane emissions is crucial for sustainable environmental management, necessitating advanced remote sensing techniques that process hyperspectral imagery to provide detailed insights into the Earth’s surface and atmosphere. This technology is vital for identifying methane plumes from point sources within the fossil fuel industry, essential for mitigating climate change and minimizing environmental degradation. Despite advancements, current remote sensing methods often generate significant false alarms that require manual verification by experts, limiting scalability and practicality. To overcome these challenges, our study introduces a novel hybrid model combining a matched filter with the Segment Anything Model (SAM), a foundation model renowned for its zero-shot generalization capabilities. This model efficiently segments methane plumes from hyperspectral images without additional training on customized datasets, streamlining the detection process and enhancing both efficiency and scalability. Additionally, we have developed an iterative scheme to refine methane signatures and improve anomaly detection accuracy. This approach addresses the challenge of acquiring data for cold-start segmentation and reduces the computational burden of training on specialized datasets. The combined capabilities of our methodology not only make it cost-effective but also robust for sensor-agnostic implementation. By leveraging remote sensing and cutting-edge machine learning, our method improves the precision and reliability of environmental monitoring, crucial for advancing global environmental management and sustainability efforts.}, archive = {J_ACCESS}, author = {Yuan Zi and Chenyu Zhou and Jiefu Chen and Zhu Han and Yueqin Huang and Xuqing Wu}, doi = {10.1109/ACCESS.2025.3597991}, journal = {IEEE Access}, pages = {143031-143041}, shortjournal = {IEEE Access}, title = {Hyperspectral methane plume segmentation through foundation computer vision models}, volume = {13}, year = {2025}, }
• (2025). ARI-ORB: Adaptive rotation-invariant ORB for enhanced feature matching in dental X-ray imaging. ACCESS, 13, 143020-143030. (www)

  @article{ , abstract = {Feature matching occupies a significant place in computer vision and some algorithms for speed and accuracy with an equal ratio includes ORB, SIFT, and AKAZE. In this paper, we propose a new modified ORB feature matching approach named ARI-ORB, incorporating the Rotation- Invariant capability for generating a more adaptive. Recent developments have pointed out a need for permutation invariant deep learning models that can be applied on Dental Panoramic X-Ray images. Thus, ARI-ORB coping with problems studied from noisy and rotationally varied datasets employs the three sub-modules: descriptor aggregation, rotation invariance, and adaptive key-point selection. Experimental analysis of the proposed ARI-ORB approach on separate dental X-ray datasets yields 82.55% precision, 83.13% accuracy, 82.55% recall, 82.55% F1 score, and a PDR of 41.28%. Compared to baseline algorithms, ARI-ORB significantly outperforms ORB (F1: 58.40, PDR: 29.20), SIFT with (F1 value: 46.71, PDR: 23.35), AKAZE with (F1 value of: 53.74 PDR: 26.87), BRISK with (F1 value: 36.03, PDR: 18.01), FAST (F1: 17.42 PDR: 8.71). Moreover, the average execution time was 0.1548 seconds per image, indicating good computational performance. Described visualizations of feature matches and match distance distributions show that the method is viable, especially in adverse imaging conditions.}, archive = {J_ACCESS}, author = {Sarab Mohammed Taher and Chen Soong der}, doi = {10.1109/ACCESS.2025.3590077}, journal = {IEEE Access}, pages = {143020-143030}, shortjournal = {IEEE Access}, title = {ARI-ORB: Adaptive rotation-invariant ORB for enhanced feature matching in dental X-ray imaging}, volume = {13}, year = {2025}, }
• (2025). QoE-aware computational resource allocation for connected vehicles in smart urban environments. ACCESS, 13, 142998-143019. (www)

  @article{ , abstract = {Smart urban environments aim to enhance living standards by delivering effective and responsive services to residents. The growing number of connected objects, however, places increasing demands on computational efficiency and service provisioning. Leveraging the advancements in Information and Communications Technology (ICT), Connected and Autonomous Vehicles (CAVs) can serve as valuable computational resources to support service delivery. These vehicles can play the role of Vehicles as Computational Resources (VaCRs) by sharing their computational resources within smart cities. However, ensuring Quality of Experience (QoE) for service requesters, based on their diverse preferences, poses significant challenges in selecting and allocating resources. This paper presents a QoE-aware computational resource allocation system for Connected Vehicles (CVs), aimed at enhancing service delivery and computational efficiency in dynamic urban settings. The system models user requests based on key QoE factors and employs Performance Evaluation (PE) and QoE models developed using Multi-Criteria Decision-Making (MCDM) and machine learning techniques. A hierarchical multiagent architecture supports system deployment and coordination, while a QoE-aware game-theoretic model guides fair and efficient resource allocation. Compared to prior work, the proposed system demonstrates significantly improved performance in simulations, achieving higher classification accuracy (up to 96.5%) and lower average costs for service delivery. These results confirm the system’s effectiveness in harnessing vehicular computational resources and optimizing QoE in smart city environments.}, archive = {J_ACCESS}, author = {Abdallah H. Salem and Issam W. Damaj and Jibran K. Yousafzai and Hussein T. Mouftah}, doi = {10.1109/ACCESS.2025.3598725}, journal = {IEEE Access}, pages = {142998-143019}, shortjournal = {IEEE Access}, title = {QoE-aware computational resource allocation for connected vehicles in smart urban environments}, volume = {13}, year = {2025}, }
• (2025). On the effectiveness of automatic code generation for synthetic dataset creation. ACCESS, 13, 142990-142997. (www)

  @article{ , abstract = {This paper compares synthetic and real-world code datasets for machine learning applications in cybersecurity by examining the relationships between machine code and Low-Level Virtual Machine Intermediate Representation (LLVM IR). This study analyzes 1000 randomly generated programs from a compiler fuzzer against 1000 randomly selected samples from AnghaBench to evaluate suitability for security analysis tasks. Statistical analysis revealed that the code generated with fuzzers consistently produces more complex instruction patterns and achieves broader coverage of the available instruction sets, when compared to real-world samples, with statistically significant differences across all measured categories ( $p \lt 0.001$ ). The research examines instruction distributions, coverage metrics, program complexity, and statistical properties to characterize synthetic and real-world code differences. Our findings have important implications for vulnerability detection and malware analysis systems, and the research shows that synthetic data generation can effectively complement or potentially surpass real-world samples. These insights help security researchers and practitioners select training datasets for machine learning applications in cybersecurity.}, archive = {J_ACCESS}, author = {Josh Mitchell and Varghese Mathew Vaidyan and Yong Wang}, doi = {10.1109/ACCESS.2025.3587104}, journal = {IEEE Access}, pages = {142990-142997}, shortjournal = {IEEE Access}, title = {On the effectiveness of automatic code generation for synthetic dataset creation}, volume = {13}, year = {2025}, }
• (2025). Beyond relevance: Enhancing serendipity in content-based recommendations with knowledge graphs. ACCESS, 13, 142980-142989. (www)

  @article{ , abstract = {Content-based recommender systems suffer from overspecialization, creating filter bubbles that limit user exploration. Serendipitous recommendations that are unexpected and novel yet relevant offer a solution to this problem. Thus, this paper proposes a novel approach to enhance both precision and serendipity in content-based recommender systems using Knowledge Graph (KG) representations by enhancing the distance-based recommendation combined with LDA topic enrichment strategy. Our method combines the MovieLens SAC dataset (which includes details like actors, directors, and genres) with the Movie Plot Synopses with Tags (MPST) dataset to enrich item details. Using Latent Dirichlet Allocation (LDA), we generate topics from plot synopsis and integrate them into the KG alongside other features. User profiles are developed from movies rated $\ge 3.5$ and strengthened using INTERESTED_IN relationships for features with occurrence counts $\ge 2$ in the KG. We implement a two-hop distance approach on the KG to identify items that are semantically related yet sufficiently distant from user preferences. The semantic richness of KGs enables the exploration of hidden relationships between items and user preferences overlooked by conventional systems. Experiments demonstrate that our method significantly outperforms five baseline models (TF-IDF, LDA, rTF-IDF, rLDA, and dTF-IDF) across precision and serendipity measures. Results show our approach successfully discovers novel and unexpected items without sacrificing relevance, addressing the overspecialization problem in traditional content-based recommender systems. Future work will focus on enhancing the graph by integrating it with more contextual data, providing recommendation explanations, and evaluating through real user interactions.}, archive = {J_ACCESS}, author = {Nur Izyan Yasmin Saat and Masnizah Mohd and Shahrul Azman Mohd Noah and Sumaia Mohammed Al-Ghuribi}, doi = {10.1109/ACCESS.2025.3598342}, journal = {IEEE Access}, pages = {142980-142989}, shortjournal = {IEEE Access}, title = {Beyond relevance: Enhancing serendipity in content-based recommendations with knowledge graphs}, volume = {13}, year = {2025}, }
• (2025). Fabrication and characterization of physical vapour deposition derived amorphous Co₁₇Fe₅₄Ni₂₉ thin film for magnetic sensing applications. ACCESS, 13, 142961-142979. (www)

  @article{ , abstract = {The tri-metallic amorphous alloy Co17Fe54Ni29 is highly desirable for electronics and magnetic sensing applications, owing to its unique properties that stem from disordered atomic configurations. Due to the material’s intrinsic complexity, achieving uniform thin films and conducting comprehensive analyses has been challenging. The objective of this study is to gain a comprehensive understanding of Co17Fe54Ni29 by optimizing PVD deposition parameters and characterizing thin films sputtered onto glass substrates. Characterization techniques include FTIR and UV–Vis spectroscopy (for chemical and optical analysis), profilometry (to assess surface features), XRD (to confirm the amorphous structure), XPS (to determine elemental composition and bonding states), and SEM (to analyse surface morphology and particle size). By varying RF power from 100W to 150W, enhanced chemical interaction between metal species and oxygen was observed, accompanied by an increase in particle size, film thickness, and surface roughness. Surface wettability also changed, with the film transitioning from hydrophilic to hydrophobic behavior as contact angles exceeded 90°. The films exhibited reduced absorbance and improved optical transmittance, indicating potential for optoelectronic applications. This study provides important insights into how sputtering parameters influence film structure and properties. VSM analysis revealed that the film deposited at 130 W exhibited the most favorable soft magnetic properties (M ${}_{\mathbf {s}} = 7.02$ emu/g, H ${}_{\mathbf {c}} = 9.75$ Oe), making it the optimal choice for magnetic sensor applications.}, archive = {J_ACCESS}, author = {S. Vimala Gayathri and D. Subbulekshmi}, doi = {10.1109/ACCESS.2025.3597353}, journal = {IEEE Access}, pages = {142961-142979}, shortjournal = {IEEE Access}, title = {Fabrication and characterization of physical vapour deposition derived amorphous Co₁₇Fe₅₄Ni₂₉ thin film for magnetic sensing applications}, volume = {13}, year = {2025}, }
• (2025). Lightweight DWT steganography with ECC-ChaCha20 for secure medical IoT systems. ACCESS, 13, 142948-142960. (www)

  @article{ , abstract = {The rapid integration of the Internet of Things (IoT) into healthcare systems has raised concerns about the confidentiality and integrity of patient data transmitted over these networks. Although traditional cryptographic solutions, such as Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), are secure, they often introduce significant computational and energy overheads that are unsuitable for constrained Internet of Things (IoT) devices. This paper proposes a lightweight stego-crypto framework that combines ChaCha20 encryption and Elliptic Curve Cryptography (ECC) for efficient key management with a 2D Discrete Wavelet Transform (DWT)-based steganographic technique. The proposed model embeds encrypted diagnostic text within medical images, ensuring secure and imperceptible transmission of sensitive information. Extensive evaluations on grayscale medical images demonstrate superior performance across various metrics, including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bit Error Rate (BER), Correlation, and system-level metrics such as CPU, memory, and power usage. Robustness against common image perturbations such as Gaussian noise, salt-and-pepper noise, JPEG compression, blurring, rotation, and translation is assessed using Bit Error Rate (BER) and payload correlation. Compared to RSA-AES and other conventional stego-crypto systems, our approach achieves a better trade-off between security, imperceptibility, and computational efficiency, making it highly suitable for real-time healthcare applications over IoT networks.}, archive = {J_ACCESS}, author = {Kamalakanta Sethi and Bishwajeet Sahoo and Shrishti and B. N. Pavan Kumar and Kasturi Dhal and Woong Cho and Gyanendra Prasad Joshi}, doi = {10.1109/ACCESS.2025.3598099}, journal = {IEEE Access}, pages = {142948-142960}, shortjournal = {IEEE Access}, title = {Lightweight DWT steganography with ECC-ChaCha20 for secure medical IoT systems}, volume = {13}, year = {2025}, }
• (2025). An eight-port frequency reconfigurable MIMO antenna using liquid dielectrics. ACCESS, 13, 142938-142947. (www)

  @article{ , abstract = {In this paper, an 8-port compact fluidic-based frequency reconfigurable MIMO antenna is designed and developed. The antenna employs eight modified U-shaped slots arranged in a $2\times 4$ array on the top surface of a substrate integrated waveguide (SIW) cavity. Each slot is excited through a dedicated coaxial feedline. To achieve frequency reconfigurability, four fluidic microwells are integrated at the corners of each U-shaped slot. By filling the microwells with different liquid dielectrics (acetone, ethyl acetate, or distilled water) or air, the antenna’s resonant frequencies can be tuned. Additional reconfigurability is realized by selectively filling microwells with air and distilled water in various configurations. The proposed antenna demonstrates frequency agility from 1.29 GHz to 2.51 GHz. The measured results of the fabricated prototype show good agreement with simulated data. Furthermore, the antenna exhibits high inter-port isolation (>28 dB) in all configurations within a compact footprint of $0.114~\boldsymbol {\lambda }_{\mathbf {g}}^{\mathbf {2}}$ . Peak gains of 4.75 dBi and 3.62 dBi are achieved when all microwells are filled with air and distilled water, at their respective resonant frequencies of 2.51 GHz and 1.29 GHz. The MIMO performance, evaluated in terms of diversity gain (DG) and envelope correlation coefficient (ECC), remains within acceptable limits, validating the effectiveness of the proposed design.}, archive = {J_ACCESS}, author = {Gunjan Srivastava and Amit Kumar and Akhilesh Mohan and Binod Kumar Kanaujia and Ladislau Matekovits and Ildiko Peter}, doi = {10.1109/ACCESS.2025.3594828}, journal = {IEEE Access}, pages = {142938-142947}, shortjournal = {IEEE Access}, title = {An eight-port frequency reconfigurable MIMO antenna using liquid dielectrics}, volume = {13}, year = {2025}, }
• (2025). Digital information systems in hospitality: A comparative study of user feedback and rating mechanisms across booking websites. ACCESS, 13, 142922-142937. (www)

  @article{ , abstract = {In today’s digital landscape, the effectiveness of online booking platforms significantly impacts user experience and decision-making processes. This study investigates the role of digital information systems in enhancing user interactions with star ratings and customer reviews on various booking websites. Using a mixed-methods approach, we analyze user behavior patterns through quantitative data analysis and qualitative insights from user feedback. Rather than developing algorithmic models, our contribution lies in offering a comparative, interface-level evaluation of how sorting defaults, sentiment polarity, and booking types interact in real-world settings. We examine how different presentation formats of star ratings and customer reviews influence user perceptions and choices. Our research identifies critical factors that affect user trust and satisfaction, including the clarity of information, accessibility of reviews, and the overall design of the digital interface. Through the application of data analytics techniques, we uncover trends that highlight the importance of integrating advanced digital information systems to optimize user engagement and operational efficiency. Unlike prior studies that focus on reviewing content in isolation, this research introduces a comparative, interface-oriented approach by analyzing how sorting mechanisms and booking model types affect sentiment, ratings, and user trust across multiple platforms. The findings reveal that effective digital information systems not only improve user satisfaction but also enhance the decision-making process by providing relevant, timely, and trustworthy information. This research contributes to the ongoing discourse on digital transformation in the hospitality industry by offering practical implications for the design and implementation of user-centric information systems. By leveraging insights from this study, stakeholders can better understand the dynamics of user experience and the transformative potential of digital technologies in fostering customer loyalty and satisfaction.}, archive = {J_ACCESS}, author = {Abdulmohsen Saud Albesher and Fatimah Alhelal and Abdul-Rahim Ahmad}, doi = {10.1109/ACCESS.2025.3598242}, journal = {IEEE Access}, pages = {142922-142937}, shortjournal = {IEEE Access}, title = {Digital information systems in hospitality: A comparative study of user feedback and rating mechanisms across booking websites}, volume = {13}, year = {2025}, }
• (2025). Tagging of multimedia contents on the web 3.0 using semantic artificial intelligence: A systematic literature review. ACCESS, 13, 142900-142921. (www)

  @article{ , abstract = {With the World Wide Web transitioning from Web 2.0 to the more intelligent and reliable Web 3.0, the importance of effective tagging methods for multimedia data has become increasingly important. In contrast to text documents, where keyword extraction is quite straightforward, tagging multimedia objects such as images, audio, video, and infographics is computationally expensive and highly demanding. This paper thoroughly defines the common multimedia data tagging methods and contrasts their effectiveness in semantic intelligence systems as well as usage in general and domain-specific scenarios. Systematic searching, inclusion, and exclusion were used to choose 90 peer-reviewed articles from an initial pool of 136 papers. The paper then contrasts the performance of current multimedia tagging technologies based on 11 benchmark datasets. This review paper concentrates specifically on semantic-focused artificial intelligence (AI) and factual knowledge as major enablers towards enhancing the scalability and accuracy of multimedia tagging in the highly coherent Web 3.0 environment. The survey points out the implications of knowledge graphs (KG), ontologies, and semantic reasoning (SR) in boosting semantic comprehension, as well as outlines how these technologies tackle issues such as serendipity, overspecialization, and the cold start problem. Through the synthesis of insights from current work, this paper points out gaps in prevailing strategies and outlines paths towards the construction of computationally feasible and trustworthy semantic models for multimedia tagging in the future. The survey seeks to offer a general perspective on the state-of-the-art in multimedia tagging and contribute to the development of the Semantic Web as a web of interconnected and contextually informative data.}, archive = {J_ACCESS}, author = {M. Hemashree and Shreya Banerjee and R. Rashmi}, doi = {10.1109/ACCESS.2025.3597801}, journal = {IEEE Access}, pages = {142900-142921}, shortjournal = {IEEE Access}, title = {Tagging of multimedia contents on the web 3.0 using semantic artificial intelligence: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Intelligent human gait phase classification using machine learning models. ACCESS, 13, 142879-142899. (www)

  @article{ , abstract = {Advancements in machine learning (ML) have facilitated the prediction of key aspects of human locomotion, particularly in identifying subject gait trajectories essential for recognizing neurological and pathological gait patterns. In this study, data from 40 healthy people were used to predict gait phases using lower limb joint angles as the inputs. The gait cycle’s main and sub-phases were predicted using Artificial Neural Networks (ANN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Ensemble and Phase-aware Attention-based Convolutional and Pooling Deep Neural Network (pACP-DNN) learning techniques. A comprehensive evaluation was performed using 5-fold and 10-fold cross-validation to verify model robustness. The proposed approach achieved an accuracy of 0.99 for main phase prediction and 0.94 for sub-phases, exceeding previous research and confirming its efficacy in gait phase categorization. These well-performing models hold significant promise for contributing to the development of assistive equipment for individuals with physical disabilities.}, archive = {J_ACCESS}, author = {Sithara Mary Sunny and Arun P. Parameswaran}, doi = {10.1109/ACCESS.2025.3598038}, journal = {IEEE Access}, pages = {142879-142899}, shortjournal = {IEEE Access}, title = {Intelligent human gait phase classification using machine learning models}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Chronic diseases prediction using machine learning with data preprocessing handling: A critical review”. ACCESS, 13, 142878. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review”).}, archive = {J_ACCESS}, author = {Nur Ghaniaviyanto Ramadhan and Adiwijaya and Warih Maharani and Alfian Akbar Gozali}, doi = {10.1109/ACCESS.2025.3595638}, journal = {IEEE Access}, pages = {142878}, shortjournal = {IEEE Access}, title = {Corrections to “Chronic diseases prediction using machine learning with data preprocessing handling: A critical review”}, volume = {13}, year = {2025}, }
• (2025). Balancing performance and Cost—FPGA-based CNN accelerators for edge computing: Status quo, key challenges, and prospective innovations. ACCESS, 13, 142852-142877. (www)

  @article{ , abstract = {The development of AI accelerators for real-time edge computing applications is a robust and ever-growing field that offers countless improvements upon classical CPU, GPU, or ASIC-based designs. In 2023, the edge AI accelerator market was valued at nearly 6 billion with a compound annual growth rate (CAGR) of 30.4% over the next ${\$}6$ years. However, there are currently many limitations present in the designs that have been proposed. This paper presents a comprehensive overview of FPGA-based CNN accelerators, their architectural innovations, real-world deployments, and optimization techniques. These accelerators are highly applicable for countless edge computing applications including smart surveillance systems, autonomous vehicles, traffic monitoring systems, and drone applications. Through comparative analysis, we extract key trade-offs between power consumption, throughput, hardware resource utilization, and system cost. This work not only covers architectural design strategies but also introduces a scoring framework for low-cost designs. Based on how each low-cost system performs in a specific performance field, a final score is determined. These scores allow us to identify key performance trends amongst low-cost designs, proposing solutions for the future development of such systems. Overall, our findings show that dynamically reconfigurable FPGA architectures offer appealing adaptability and performance for edge computing, at the expense of increased complexity and power consumption. Alternatively, static accelerators exhibit great potential when optimized for power and memory efficiency, at the cost of throughput. Key design considerations and future directions, addressing the prominent tradeoff between performance, efficiency, and scalability in edge AI accelerator development are discussed in the paper. We conclude that no current design fully satisfies every edge computing constraint, highlighting the need for hybrid solutions that balance reconfigurability, power, and performance at a reduced cost.}, archive = {J_ACCESS}, author = {John S. Fata and Wafa M. Elmannai and Khaled Elleithy}, doi = {10.1109/ACCESS.2025.3594348}, journal = {IEEE Access}, pages = {142852-142877}, shortjournal = {IEEE Access}, title = {Balancing performance and Cost—FPGA-based CNN accelerators for edge computing: Status quo, key challenges, and prospective innovations}, volume = {13}, year = {2025}, }
• (2025). Hierarchical reinforcement learning for navigation among movable and immovable obstacles. ACCESS, 13, 142844-142851. (www)

  @article{ , abstract = {Path planning is a key technique for vehicle navigation, and significant research has focused on how to manage obstacles. Previous methods have addressed either removing movable obstacles or avoiding immovable ones. However, real-world environments contain both movable and immovable obstacles. To address this, we propose a path planning system for Navigation Among Movable and IMmovable Obstacles (NAMIMO) based on hierarchical reinforcement learning. In our system, lower-level agents generate paths for the vehicle to either avoid or remove obstacles, while the higher-level agent dynamically selects which lower-level agent to utilize based on the movability of the obstacle, incorporating visual and linguistic knowledge.}, archive = {J_ACCESS}, author = {Han Jun Bae and Juyoun Park}, doi = {10.1109/ACCESS.2025.3598230}, journal = {IEEE Access}, pages = {142844-142851}, shortjournal = {IEEE Access}, title = {Hierarchical reinforcement learning for navigation among movable and immovable obstacles}, volume = {13}, year = {2025}, }
• (2025). NAS-DG: Ultra-fast NAS via uncertainty modeling for domain generalization. ACCESS, 13, 142829-142843. (www)

  @article{ , abstract = {Designing neural network architectures manually is time-consuming and labor-intensive when applying deep learning to various tasks and datasets. Neural architecture search (NAS) has been proposed to automate this process, but existing methods mainly aim at improving generalization on in-distribution data. NAS methods for out-of-distribution data have been developed to address performance degradation on unseen domains, but they require training large super-networks during the search, which results in long search times and high computational costs. There is a need for an efficient NAS method that can reduce search time while maintaining high performance. In this work, we propose a training-free NAS method for domain generalization. Our method extends zero-cost proxy-based NAS by incorporating uncertainty modeling. We compute architecture scores using autoencoders with uncertainty-based data augmentation and apply feature augmentation during training. In addition, we provide a theoretical connection between our method and domain generalization error bounds. Experiments on standard benchmarks, including PACS, Office-Home, and NICO, show that our method achieves comparable or superior accuracy to existing domain generalization methods while reducing search time from over one hour to under ten minutes and using fewer parameters.}, archive = {J_ACCESS}, author = {Keigo Wakayama and Takafumi Kanamori}, doi = {10.1109/ACCESS.2025.3597980}, journal = {IEEE Access}, pages = {142829-142843}, shortjournal = {IEEE Access}, title = {NAS-DG: Ultra-fast NAS via uncertainty modeling for domain generalization}, volume = {13}, year = {2025}, }
• (2025). DatApollo: Orchestration of serverless functions for scalable data mining. ACCESS, 13, 142813-142828. (www)

  @article{ , abstract = {With the exponential growth of data generated from enterprise systems, social networks, and the Internet of Things, traditional data mining techniques face major challenges in terms of scalability and efficiency. As a foundational unsupervised learning method for detecting patterns in transactional datasets, Association Rule Mining (ARM) is commonly encountered in distributed environments with performance bottlenecks due to excessive memory consumption, static resource provisioning, and costly data shuffle. The present paper presents DatApollo, a novel serverless orchestration framework that enables the execution of distributed ARM workflows in a scalable and efficient manner. DatApollo, based on the Apollo orchestration engine, offers stateless cloud functions, dynamic scheduling, intermediate state persistence, and fault-tolerant coordination in order to address the limitations of both traditional cluster-based architectures and existing Function-as-a-Service models. By decomposing ARM pipelines into orchestrated microfunctions, the framework enables elastic, cloud-native execution with minimal idle time. Using real-world healthcare and meteorological datasets, we describe the architectural design, algorithmic components, and computational complexity of DatApollo and perform a comprehensive experimental evaluation. DatApollo provides up to five times faster execution time compared to Apache Spark and lowers infrastructure costs by utilizing elastic scaling and event-driven function invocations. The results demonstrate that DatApollo is a robust, cost-effective and high-performance alternative to ARM in dynamic, large-scale data environments.}, archive = {J_ACCESS}, author = {Mahtab Shahin and Markus Bertl and Nasim Janatian and Juan Aznar-Poveda and Syed Attique Shah and Thomas Fahringer and Sijo Arakkal Peious and Dirk Draheim}, doi = {10.1109/ACCESS.2025.3591712}, journal = {IEEE Access}, pages = {142813-142828}, shortjournal = {IEEE Access}, title = {DatApollo: Orchestration of serverless functions for scalable data mining}, volume = {13}, year = {2025}, }
• (2025). Abnormal evolution pattern recognition of spatial communities based on random walk with restart. ACCESS, 13, 142798-142812. (www)

  @article{ , abstract = {Identifying abnormal evolution patterns of spatial communities using vehicle trajectories provides valuable insights into urban traffic irregularities. Existing methods have primarily detected anomalies by tracking changes in the size of spatial communities. However, their effectiveness is limited by the inherent instability of spatial community detection methods. Additionally, these methods often fail to detect abnormal evolution patterns related to variations in internal connectivity and interaction intensity within communities. To address these limitations, we developed an Abnormal Evolution Pattern recognition method based on the Random Walk with Restart mechanism, referred to as AEP-RWR. First, a consensus clustering strategy was employed to derive stable community structures for each temporal snapshot, thereby mitigating the inherent instability in community detection. Then, the random walk with restart mechanism was used to calculate vertex intimacy matrices for matched communities across adjacent temporal snapshots, effectively capturing variations in internal connectivity and interaction intensity. Finally, the Matusita distance between the vertex intimacy matrices was calculated, and the evolutionary patterns exceeding a given threshold were identified as anomalies. Experimental results on simulated data and a real-world case study in Beijing demonstrate that AEP-RWR outperforms existing methods in detecting stable communities and accurately capturing microstructure perception. The AEP-RWR method can effectively detect abnormal evolution patterns affected by factors such as road conditions, weather, festivals, and social events, offering valuable insights for urban traffic optimization, safety monitoring, and policy development.}, archive = {J_ACCESS}, author = {Ke Tao and Zexin Lu and Zhaoyi Hou and Fei Luo and Meihua Chen and Min Deng}, doi = {10.1109/ACCESS.2025.3597935}, journal = {IEEE Access}, pages = {142798-142812}, shortjournal = {IEEE Access}, title = {Abnormal evolution pattern recognition of spatial communities based on random walk with restart}, volume = {13}, year = {2025}, }
• (2025). Theoretical and experimental investigation of the influence of rotor claw pole on electromagnetic vibration noise of alternators. ACCESS, 13, 142788-142797. (www)

  @article{ , abstract = {To investigate the electromagnetic vibration and noise of vehicle-mounted alternators during low-speed operation, an analytical model for the electromagnetic force was established. Furthermore, the harmonic characteristics of the electromagnetic force were thoroughly examined. Based on the developed analytical model, a method is proposed to calculate the relationship between surface intercept line of the claw pole and the harmonic components of the electromagnetic force, the influence of the former on the latter is analyzed. An optimization scheme for the rotor claw pole aimed at reducing the electromagnetic force is proposed. The impacts of the rotor claw pole, both prior to and following optimization, on electromagnetic vibration and noise are modeled and analyzed. The results show that, with the rotor claw pole having undergone optimization, the 36th-order acceleration of the electromagnetic vibration has been reduced by 53%, while the electromagnetic noise also decreases significantly. Tests on the developed prototype show consistency with the calculated results. This work can provide a reference for controlling the electromagnetic vibration noise of alternators.}, archive = {J_ACCESS}, author = {Zhibo Li and Xiping He and Shihua Zhao}, doi = {10.1109/ACCESS.2025.3595505}, journal = {IEEE Access}, pages = {142788-142797}, shortjournal = {IEEE Access}, title = {Theoretical and experimental investigation of the influence of rotor claw pole on electromagnetic vibration noise of alternators}, volume = {13}, year = {2025}, }
• (2025). Lyapunov-based finite control set applied for current control of grid-connected single-phase inverter under distorted voltage conditions. ACCESS, 13, 142776-142787. (www)

  @article{ , abstract = {The growing use of renewable energy sources, primarily driven by climate change, necessitates synchronization with the electricity grid for the effective and reliable operation of single-phase inverters connected to the grid. However, when the system is subject to distortions and variations in the grid voltage, inverter control can be compromised. To mitigate these problems, this article presents a predictive controller based on the Finite Control Set Model Predictive Control, which utilizes a Lyapunov stability criterion in discrete time. The proposed controller operates on a single-phase inverter connected to the grid, aiming to enhance total harmonic distortion performance. The control system, which uses a phase-locked loop for synchronization, incorporates Lyapunov stability concepts to ensure that the controlled variables remain close to their reference values, even in the face of grid disturbances. Experimental tests show that the proposal is viable in practice, keeping total harmonic distortion below 5%, within the limits set by power quality standards. The controller’s performance was evaluated under ideal and distorted grid conditions, and compared with a repetitive controller and a sliding mode controller combined with PI. Additionally, an analysis of parametric variations in the system was conducted. The results obtained show that the proposed strategy has superior performance in terms of harmonic suppression, validating its effectiveness and practical applicability.}, archive = {J_ACCESS}, author = {Lucas Demetrius Augusto and Yuri de Oliveira Cota and José Alberto Torrico Altuna and Alfeu Joãozinho Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3598607}, journal = {IEEE Access}, pages = {142776-142787}, shortjournal = {IEEE Access}, title = {Lyapunov-based finite control set applied for current control of grid-connected single-phase inverter under distorted voltage conditions}, volume = {13}, year = {2025}, }
• (2025). Masked and noise-masked multimodal brain tumor image segmentation using SegFormer and shared encoder framework. ACCESS, 13, 142759-142775. (www)

  @article{ , abstract = {Medical image segmentation is a critical task in clinical diagnosis and treatment, particularly for brain tumor analysis using imaging modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. However, segmentation is often hindered by noise introduced through scanner artifacts, motion blur, and inconsistent acquisition conditions. To address this limitation and the challenges of missing and noisy pixels in an MRI image, this work introduces the Masking and Noise-masking Multimodal SegFormer (MNMS) - a transformer-based framework designed for brain tumor segmentation, which collectively combines complementary information from multiple MRI modalities while integrating dual masking strategies, one to handle incomplete data and another to overcome noise artifacts. Being a multimodal framework, MNMS can effectively work with and provide valuable segmentation results for any single modality which it has been trained for, ensuring robustness in real-world clinical scenarios where multimodal data may not always be available. MNMS employs masking to overcome computational complexity while maintaining local and global features essential for accurate medical image segmentation. Additionally, noise-masking introduces a controlled Gaussian noise into the MRI images creating random variations in the pixel intensities and thereby encouraging the model to learn the invariant and essential patterns in MRI images. Evaluation on Brain Tumor Segmentation (BraTS2020) challenge dataset demonstrates that MNMS outperforms conventional convolutional neural network (CNN) based methods, achieving superior accuracy and Dice Similarity Coefficient (DSC) scores. Specifically, the proposed MNMS model achieved a DSC score of 0.9341, outperforming UNEt TRansformers (UNETR), which has a DSC of 0.8208 and 3D U-Net (0.8179). These results highlight its effectiveness in multimodal brain tumor segmentation, ultimately contributing to improved diagnostic accuracy and patient care.}, archive = {J_ACCESS}, author = {K. Hemalatha and P. R. Vishnu Vardhan and Alfred Dharmaraj Aravindraj and S. Hari Hara Sudhan}, doi = {10.1109/ACCESS.2025.3596643}, journal = {IEEE Access}, pages = {142759-142775}, shortjournal = {IEEE Access}, title = {Masked and noise-masked multimodal brain tumor image segmentation using SegFormer and shared encoder framework}, volume = {13}, year = {2025}, }
• (2025). Exploring motion information in homography calculation for football matches with moving cameras. ACCESS, 13, 142742-142758. (www)

  @article{ , abstract = {In football, where minor differences can significantly affect outcomes and performance, automatic video analysis has become a critical tool for analyzing and optimizing team strategies. However, many existing solutions require expensive and complex hardware comprising multiple cameras, sensors, or GPS devices, limiting accessibility for many clubs, particularly those with limited resources. Using images and video from a moving camera can help a wider audience benefit from video analysis, but it introduces new challenges related to motion. To address this, we explore an alternative homography estimation in moving camera scenarios. Homography plays a crucial role in video analysis, but presents challenges when keypoints are sparse, especially in dynamic environments. Existing techniques predominantly rely on visible keypoints and apply homography transformations on a frame-by-frame basis, often lacking temporal consistency and facing challenges in areas with sparse keypoints. This paper explores the use of estimated motion information for homography computation. Our experimental results reveal that integrating motion data directly into homography estimations leads to reduced errors in keypoint-sparse frames, surpassing state-of-the-art methods, filling a current gap in moving camera scenarios.}, archive = {J_ACCESS}, author = {Carlos Gomes and Christina Mastralexi and Pedro Carvalho}, doi = {10.1109/ACCESS.2025.3597803}, journal = {IEEE Access}, pages = {142742-142758}, shortjournal = {IEEE Access}, title = {Exploring motion information in homography calculation for football matches with moving cameras}, volume = {13}, year = {2025}, }
• (2025). Automated cricket analytics for player classification and commentary generation. ACCESS, 13, 142728-142741. (www)

  @article{ , abstract = {Cricket is one of the most popular sports around the world, and there is an increasing demand for intelligent commentary generation and real-time analytics. In this paper, we propose a system called Automated Cricket Analytics for Player Classification and Commentary Generation that uses computer vision and deep learning to analyze cricket match footage using a pre-trained VGG16 model to generate visual features from extracted frames of video input, which are then processed by a bidirectional LSTM-based neural network to generate descriptive commentary. In addition, a custom object detection model is deployed via the Roboflow API that detects which player is doing what (i.e. whether they are a bowler or a batsman) in each frame. The generated textual commentary is further refined using transformer-based NLP pipelines for summarization and grammatical correction prior to text-to-speech synthesis (gTTS). The experimental results show that the model can correctly recognize the role of each player and provide a suitable commentary with high BLEU scores (BLEU-1: 0.877; BLEU-2: 0.818; BLEU-3: 0.798; BLEU-4: 0.712). The average confidence score for the player classification and detection module was over 90%, showing that this system can recognize players correctly in all match frames. This system could be used to create highlights, automate sports broadcasting, or even allow fans to interact with a game.}, archive = {J_ACCESS}, author = {Rithvik Pabbati and Bijjula Sai Srujan Reddy and K. Karthik}, doi = {10.1109/ACCESS.2025.3598037}, journal = {IEEE Access}, pages = {142728-142741}, shortjournal = {IEEE Access}, title = {Automated cricket analytics for player classification and commentary generation}, volume = {13}, year = {2025}, }
• (2025). Classifying workers for mitigating adversarial attacks in crowdsourcing. ACCESS, 13, 142713-142727. (www)

  @article{ , abstract = {Crowdsourcing is adopted as a fast and cost-effective system for human computation and acquiring data for training models in machine learning. Although crowdsourcing has broad applicability, it still has the following challenges. Normally, workers have access to crowdsourcing platforms with simple authentication mechanisms. As a result, malicious workers may get into the system and submit unreliable answers rendering the platform fraudulent. Moreover, when workers perform tasks, their lack of expertise and the difficulty of tasks affects the accuracy. Truth inference benefits the data quality and worker reliability. A truth inference algorithm estimates workers’ trustworthiness and correctness of the answers from their responses. Besides, it helps in filtering out low-quality answers. However, due to adversarial attacks by malicious workers, ground truth inference algorithms perform poorly. This research considers how to defend against adversarial attacks on crowdsourcing platforms and improve the truth inference process. The proposed method estimates the trust and reliability scores of the workers and classifies them as normal and malicious workers. Based on this classification, tasks are assigned to the workers. Moreover, these predicted scores are used for inferring the correct answers, thereby improving the ground truth inference. Experiments consistently show that the proposed truth inference method is tolerant to adversarial attacks with competent accuracy.}, archive = {J_ACCESS}, author = {Ayswarya R. Kurup and G. P. Sajeev}, doi = {10.1109/ACCESS.2025.3598463}, journal = {IEEE Access}, pages = {142713-142727}, shortjournal = {IEEE Access}, title = {Classifying workers for mitigating adversarial attacks in crowdsourcing}, volume = {13}, year = {2025}, }
• (2025). A dynamic branch automatic modulation recognition method for heterogeneous data-driven. ACCESS, 13, 142703-142712. (www)

  @article{ , abstract = {To address insufficient feature complementarity mining and limited recognition accuracy in end-to-end deep learning models under complex channel environments, this paper proposes a dynamic branch automatic modulation recognition method driven by heterogeneous data. A multi-modal parallel feature extraction architecture is designed to learn time-frequency synergistic features, enhancing discriminative representation of modulation characteristics. Specifically, data preprocessing intensifies phase information in original I/Q data, while a sparse multi-scale convolutional module strengthens spatial feature extraction. The LSTM network has been developed to capture the time-dependent interactions of the three channels I/Q, I, and Q. A residual-based recursive encoder maps hierarchical features of Amplitude-Phase (AP) data to jointly extract spatiotemporal characteristics. Modulation classification is achieved through a fully connected layer. Experiments on the RadioML2016.10A dataset demonstrate superior performance: the proposed model achieves 1%–13.5% higher average accuracy than mainstream models at 0–18dB SNR, with peak accuracy reaching 94.68% at 14dB. This method improves robustness in complex channels through heterogeneous data-driven multi-modal fusion, offering new insights for intelligent communication signal demodulation.}, archive = {J_ACCESS}, author = {Yecai Guo and Mengjie Wang and Meiyu Liang}, doi = {10.1109/ACCESS.2025.3596617}, journal = {IEEE Access}, pages = {142703-142712}, shortjournal = {IEEE Access}, title = {A dynamic branch automatic modulation recognition method for heterogeneous data-driven}, volume = {13}, year = {2025}, }
• (2025). Feasibility study of marine CSEM in monitoring CO2 deposits. ACCESS, 13, 142695-142702. (www)

  @article{ , abstract = {The global search for renewable energy sources is intensifying, intending to replace fossil fuels and thereby reduce the emission of carbon dioxide gas (CO2). A further method of reducing atmospheric gas levels involves capturing and trapping such gases in subsurface reservoirs. It is becoming increasingly evident that this second strategy is gaining momentum, and geophysics plays a pivotal role in the success of this advance. After the capture and storage of CO2, monitoring its behavior in the subsurface is imperative to facilitate the control of these underground reservoirs. In this context, searching for geophysical methodologies optimally suited to this monitoring process is paramount. The present study investigates the application of the marine controlled source electromagnetic method (MCSEM) to the monitoring of carbon dioxide (CO2). This method has been extensively used within the oil and gas industry for detecting hydrocarbons. Given the physical property of CO2 (i.e., resistivity) that is analogous to those of hydrocarbons, identifying suitable matches for adequate gas monitoring is a conceivable proposition. The model selected for the assessment of the method’s viability is based on the Sleipner field situated in the northern part of the North Sea Basin, where gas injection has been ongoing since 1996. This field has achieved notable success in injecting substantial quantities of CO2 from the Utsira formation. It is evident that, from this model, a realistic approximation of all the horizons of this plume was created. This approximation was derived from seismic and well data obtained from the site. In the context of direct modeling of the MCSEM, the implementation of the vector finite element technique, utilizing tetrahedral elements, proved to be a successful approach. The efficacy of the method was evaluated concerning both frequency and the growth of the CO2 plume. It was determined that the method possesses the capacity to facilitate the requisite monitoring. However, it is imperative to possess prior knowledge of the injection site to select the most optimal survey arrangement and determine the optimal investigation frequencies.}, archive = {J_ACCESS}, author = {Jéssica L. S. Pinheiro Itó and Marcos W. C. Silva}, doi = {10.1109/ACCESS.2025.3598701}, journal = {IEEE Access}, pages = {142695-142702}, shortjournal = {IEEE Access}, title = {Feasibility study of marine CSEM in monitoring CO2 deposits}, volume = {13}, year = {2025}, }
• (2025). Advancing AIoMT-enabled healthcare system-of-systems using multi-agent reinforcement learning. ACCESS, 13, 142674-142694. (www)

  @article{ , abstract = {This paper presents a Multi-Agent Reinforcement Learning (MARL) framework designed to optimize coordination within a System-of-Systems (SoS) composed of six heterogeneous healthcare entities: hospitals, clinics, telemedicine platforms, wearable monitoring devices, rural health centers, and virtual triage hubs. These entities are organized into four distinct constituent systems—Directed, Acknowledged, Collaborative, and Virtual—each reflecting a unique coordination model that governs control, autonomy, and communication. Within each constituent system, agents independently learn optimal policies using Q-learning to enhance resource allocation, inter-agent cooperation, and service efficiency. We describe simulation experiments that span 1000 episodes, with evaluation metrics including cumulative rewards, resource utilization, and system-level efficiency. The results show that Acknowledged and Collaborative coordination models achieve faster policy convergence and superior operational outcomes, while the Virtual model provides flexibility with reduced coordination overhead. Our framework demonstrates the potential of MARL to enable adaptive, decentralized decision-making in Artificial Intelligence of Medical Things (AIoMT)-enabled healthcare environments, particularly under dynamic and resource-constrained conditions. Importantly, this approach can help improve care access in remote and underserved regions and enable more dynamic, responsive triage and resource allocation in real-world healthcare delivery.}, archive = {J_ACCESS}, author = {Arifuzzaman Sheikh and Edwin K. P. Chong}, doi = {10.1109/ACCESS.2025.3596921}, journal = {IEEE Access}, pages = {142674-142694}, shortjournal = {IEEE Access}, title = {Advancing AIoMT-enabled healthcare system-of-systems using multi-agent reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Optimizing internet-of-things energy management: Integrating theory of inventive problem solving with transfer learning and advanced optimization algorithms. ACCESS, 13, 142651-142673. (www)

  @article{ , abstract = {This article introduces the Energy-Efficient Theory of Inventive Problem Solving (EETRIZ) approach, designed to reduce electrical energy waste resulting from human mistakes in IoT-enabled environments. EETRIZ utilizes a novel integration of transfer learning and an activity-dependent environmental management algorithm to adjust settings dynamically according to real-time occupancy and activity data, hence improving energy efficiency. This system efficiently utilizes the advantages of the artificial intelligence-based adaptive gradient algorithm (AdaGrad) and root mean squared propagation (RMSProp) optimization methods to improve prediction accuracy through enhanced weight determination. EETRIZ is developed in the C programming language and is underpinned by comprehensive platforms and libraries, such as MPLAB, Nuvoton 8051 Series microcontroller unit (MCU) programming, GNU’s Not Unix multi-precision library (GMPLibrary)-GMP-5.1.1, and Miracle Library. Thorough hardware testing verifies that EETRIZ surpasses current solutions in energy efficiency, cost-effectiveness, accuracy, and user-friendliness. The system’s capacity to simultaneously control numerous IoT devices enhances its utility in various environments, including residences, workplaces, and educational facilities, providing a scalable solution to mitigate excessive energy consumption resulting from human mistakes.}, archive = {J_ACCESS}, author = {Abdul Razaque and Meer Jaro Khan and Dina S. M. Hassan and Aizhan Kassymova and Syed Rizvi and Arslan Ali and Vasily Valerievich Serbin}, doi = {10.1109/ACCESS.2025.3590050}, journal = {IEEE Access}, pages = {142651-142673}, shortjournal = {IEEE Access}, title = {Optimizing internet-of-things energy management: Integrating theory of inventive problem solving with transfer learning and advanced optimization algorithms}, volume = {13}, year = {2025}, }
• (2025). Electromagnetic characterization of concrete by jonscher universal dielectric model: Numerical validation of different nonlinear optimization algorithms. ACCESS, 13, 142635-142650. (www)

  @article{ , abstract = {The dielectric permittivity model of concrete plays a key role in characterizing its interaction with electromagnetic (EM) waves but also in determining its mechanical properties. Among various EM models, the Jonscher universal dielectric response has been favored by scientists and practitioners in the field for the above purposes. Despite its widespread use, to the best of our knowledge, no study has focused its attention on the problem of choosing the most appropriate numerical method and the related starting point solution to solve the nonlinear least squares problem for fitting the Jonscher model to the experimental data. In this work, considering their broad adoption within software packages such as R, Python, or MATLAB, the performances of the Nelder-Mead, the Broyden-Fletcher-Goldfarb-Shanno, and the Levenberg-Marquardt nonlinear optimization algorithms have been compared among them to obtain practical guidance on which nonlinear optimization procedure to choose and how to generate a valid initial guess solution to achieve optimal fitting with the experimental data.}, archive = {J_ACCESS}, author = {Giovanni Angiulli and Mario Versaci}, doi = {10.1109/ACCESS.2025.3598642}, journal = {IEEE Access}, pages = {142635-142650}, shortjournal = {IEEE Access}, title = {Electromagnetic characterization of concrete by jonscher universal dielectric model: Numerical validation of different nonlinear optimization algorithms}, volume = {13}, year = {2025}, }
• (2025). DOC* algorithm: Docking orientation constrained path planning for robust autonomous docking. ACCESS, 13, 142623-142634. (www)

  @article{ , abstract = {Autonomous docking is a crucial technology for unmanned surface vehicles (USVs). Due to the inherent challenges of lateral control in ships, it is essential to account for their kinematic constraints when generating safe docking paths without relying on auxiliary systems such as lateral thrusters or external assistance from other vessels. In particular, rapid orientation changes near docking facilities can lead to collisions. Therefore, minimizing the difference between the ship’s current orientation and the docking orientation prior to approach is critical for safety. To address these challenges, we propose a novel cost function integrated into a 3D A* path planning algorithm. The algorithm operates in two stages: 1) graph generation, which constrains the orientation transitions based on the ship’s maneuverability; and 2) node search, which employs a cost function that jointly minimizes lateral distance and orientation deviation. The effectiveness of the proposed method is demonstrated through case studies and simulations under various docking scenarios, and its feasibility is validated through real-world experiments conducted with an actual ship, where the results are compared against other path planning algorithms.}, archive = {J_ACCESS}, author = {Yeongha Shin and Hanguen Kim}, doi = {10.1109/ACCESS.2025.3594400}, journal = {IEEE Access}, pages = {142623-142634}, shortjournal = {IEEE Access}, title = {DOC* algorithm: Docking orientation constrained path planning for robust autonomous docking}, volume = {13}, year = {2025}, }
• (2025). Integrating machine learning and observational causal inference for enhanced spectral and energy efficiency in wireless networks. ACCESS, 13, 142603-142622. (www)

  @article{ , abstract = {Ensuring transparency and explainability in Artificial Intelligence (AI)/Machine Learning (ML) models is crucial for their effective deployment in wireless networks. This paper addresses the challenge of enhancing the interpretability and trustworthiness of ML-based decisions in wireless networks by integrating root cause analysis with observational causal inference. It proposes a causal framework aimed at optimising the energy sustainability class of Base Stations (BSs) - a composite metric that classifies each BS into four possible classes (A, B, C or D) based on its spectral and energy efficiency. This causal framework goes beyond traditional root cause analysis by establishing causal relationships among performance and energy consumption indicators, and other network topology data, quantifying their impacts. The proposed framework combines domain expertise and ML to construct causal graphs of the relevant indicators, enabling quantifiable causal effects between the energy sustainability class of BSs and a chosen treatment variable to support targeted optimisation. The framework was tested with live data from a fourth generation (4G)/fifth generation (5G) network, demonstrating a causal link between the average Channel Quality Index (CQI) and the energy sustainability class of BSs. The causal analysis revealed an Average Treatment Effect (ATE) ranging from 12% to 14%, indicating that improving average CQI has a positive impact on the likelihood of a BS being in class “A”. A continuous marginal structural model further showed that incremental improvements in average CQI raise the class “A” probability by approximately 11%.}, archive = {J_ACCESS}, author = {Luís Mata and Marco Sousa and Pedro Vieira and Maria Paula Queluz and António Rodrigues}, doi = {10.1109/ACCESS.2025.3598434}, journal = {IEEE Access}, pages = {142603-142622}, shortjournal = {IEEE Access}, title = {Integrating machine learning and observational causal inference for enhanced spectral and energy efficiency in wireless networks}, volume = {13}, year = {2025}, }
• (2025). The decision-making mechanisms of the livestreaming E-commerce supply chain based on a three-party evolutionary game. ACCESS, 13, 142591-142602. (www)

  @article{ , abstract = {The diversified development of media forms has led to the creation of livestreaming e-commerce as a sales model. Unlike the traditional sales model, this paper constructs a three-party game model composed of manufacturers, social media influencers, and consumers on the basis of livestreaming model. It explores the equilibrium strategy selections of each participant in the supply chain system. In analyzing the evolutionary game stability of each participant, this paper obtains the equilibrium strategy and stability factors by analysis of the income and expenditure matrix. It uses the Jacobian matrix and the simulation model to analyze the reasons behind the strategy selections of different game participants in the livestreaming e-commerce model. The results find the optimal decision of each participant in the livestreaming model and show that the strategies of manufacturers and social media influencers and the selections of consumers have different impacts on their decision-making mechanisms. The results also reveal that manufacturers are more sensitive to consumers’ active participation in livestreaming e-commerce.}, archive = {J_ACCESS}, author = {Hao Song and Taoxing Zhu and Jixiao Wu}, doi = {10.1109/ACCESS.2025.3596847}, journal = {IEEE Access}, pages = {142591-142602}, shortjournal = {IEEE Access}, title = {The decision-making mechanisms of the livestreaming E-commerce supply chain based on a three-party evolutionary game}, volume = {13}, year = {2025}, }
• (2025). Design of esophageal nursing needs identification model for inpatients based on attention mechanism. ACCESS, 13, 142578-142590. (www)

  @article{ , abstract = {In alignment with the scope in Computer Science, which emphasizes intelligent systems and human-centric computing, this study addresses the critical gap in clinically interpretable, physiologically grounded modeling for patient-specific decision support. Conventional computational approaches to esophageal care often rely on static classifiers or linear regressions that lack adaptability, multimodal integration, and physiological interpretability, limiting their deployment in real-time clinical workflows. Moreover, they typically disregard structural constraints intrinsic to the esophagus, resulting in clinically implausible outputs and reduced generalizability across diverse patient populations. An integrated approach combining the Structured Esophageal Dynamics Network (SED-Net) with a Domain-guided Esophageal Adaptation Strategy (DEAS) is developed to tackle prevailing limitations in modeling esophageal dynamics. SED-Net is a modular neural architecture designed to capture the topological and temporal intricacies of esophageal dynamics using graph-based spatial encoding, constrained temporal recurrence, and structure-aware decoding. Complementarily, DEAS injects domain knowledge through physiological constraint regularization, topological flow preservation, hierarchical attention calibration, and patient-specific adaptive feedback. This hybrid system aligns with key of the journal such as biomedical informatics, adaptive systems, and machine learning interpretability. Experimental evaluations demonstrate significant improvements in predictive accuracy, anatomical coherence, and cross-patient generalization, positioning the proposed framework as a promising advancement in computational health informatics for dynamic esophageal assessment and intervention.}, archive = {J_ACCESS}, author = {Huarong Mao}, doi = {10.1109/ACCESS.2025.3596787}, journal = {IEEE Access}, pages = {142578-142590}, shortjournal = {IEEE Access}, title = {Design of esophageal nursing needs identification model for inpatients based on attention mechanism}, volume = {13}, year = {2025}, }
• (2025). A timed-permission access control profile within MARTE. ACCESS, 13, 142561-142577. (www)

  @article{ , abstract = {Android applications often interact through inter-component communication (ICC), which can be exploited for privilege escalation attacks. Traditional permission-based access control (PBAC) mechanisms lack the ability to express temporal constraints, making them insufficient for preventing time-sensitive threats. This paper introduces a novel methodology based on the MARTE-TPAM profile, which extends PBAC with explicit temporal semantics and conforms to ISO/IEC 29146:2016 access management standards. We define a structured TPAM domain model, develop a UML-based profile for semi-formal modeling, and propose a Z-based formal verification workflow. Through detailed modeling and formal analysis of an Android news recommendation scenario, we demonstrate that our method enables rigorous detection of unauthorized privilege flows that violate both permission and timing policies. The proposed approach supports early-stage access policy validation and provides a mathematically grounded foundation for Android security assurance. Compared to existing permission-based models, our approach offers enhanced expressiveness and early-stage validation capability by explicitly capturing temporal constraints in both graphical and formal specifications.}, archive = {J_ACCESS}, author = {Xinwen Hu}, doi = {10.1109/ACCESS.2025.3596928}, journal = {IEEE Access}, pages = {142561-142577}, shortjournal = {IEEE Access}, title = {A timed-permission access control profile within MARTE}, volume = {13}, year = {2025}, }
• (2025). RECTV: Enhancing agile decision-making in startups through value-driven requirements prioritization. ACCESS, 13, 142546-142560. (www)

  @article{ , abstract = {Effective requirements prioritization is a success factor for software development. Models of prioritization that are traditionally used like MoSCoW, RICE, AHP and the Kano Model are deficient in responding to what is essential for startups in terms of speed and delivery of value, efficiency in cost, and mitigation of risk. In this paper, we introduce RECTV, a new startup requirements prioritization framework. Essential decision making components including Cost, Risk, Technical Feasibility, Confidence Level (C), and Time to Value (TtV) are integrated as key pieces of the RECTV tool to help startups prioritize based on informed, data based decision making. In contrast to traditional models, RECTV validates the outcomes of the features while delivering business value in a way where startups can adapt to market feedback and uncertainty rapidly. This work positions RECTV as a practical alternative to traditional models, offering startups a structured and responsive approach to prioritize features under real-world constraints. By conducting a comparative analysis with other models, the framework is evaluated and proved to be superior at tackling the startup glitches such as low funds, unspecified features, and the necessity of fast market adjustment. For startups, RECTV helps them to make better use of resources and develop features that avoid unnecessary risks.}, archive = {J_ACCESS}, author = {Frédéric Pattyn and Rafaqat Kazmi and Peter Goetz and Afnan Aldhahri and Aqsa}, doi = {10.1109/ACCESS.2025.3589942}, journal = {IEEE Access}, pages = {142546-142560}, shortjournal = {IEEE Access}, title = {RECTV: Enhancing agile decision-making in startups through value-driven requirements prioritization}, volume = {13}, year = {2025}, }
• (2025). AI-based prediction of warpage in organic substrates. ACCESS, 13, 142535-142545. (www)

  @article{ , abstract = {In substrate fabrication, thermal mismatch between materials and structural configurations can readily induce warpage deformation, which severely impacts subsequent chip mounting and solder joint alignment processes, ultimately compromising the reliability of packaging integration. Therefore, investigating the effects of material types and structural layouts on warpage is essential for design optimization. However, traditional experimental approaches incur high costs, while case-by-case finite element method (FEM) modeling presents computational bottlenecks. This study proposes an artificial intelligence (AI)-based method to predict the warpage behavior of organic substrates with diverse material and structural configurations. Through material preparation, substrate manufacturing, and comprehensive characterization testing, process-matched material parameters and warpage data were obtained. Combined with thermomechanical simulation, a high-precision dataset comprising 972 cross-case studies was constructed, achieving a prediction error of less than 10%. Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. The results indicate that MLP demonstrates rapid loss decay, a training time of 135 seconds, a fitting slope of 0.98, minimal dependency on dataset size, and outperforms the other algorithms. This approach reduces warpage evaluation from traditional day scale to AI-driven second scale, facilitating data-driven rapid design iterations for multi-material variable-structure substrates.}, archive = {J_ACCESS}, author = {Jingyi Zhao and Meiying Su and Rui Ma}, doi = {10.1109/ACCESS.2025.3595163}, journal = {IEEE Access}, pages = {142535-142545}, shortjournal = {IEEE Access}, title = {AI-based prediction of warpage in organic substrates}, volume = {13}, year = {2025}, }
• (2025). Optimizing ResNet-50 for multiclass classification: A multi-stage learning approach. ACCESS, 13, 142517-142534. (www)

  @article{ , abstract = {In this study, we present a multistage learning pipeline that utilizes the ResNet-50 architecture as a static feature extractor for multiclass image classification problems. This methodology integrates transfer learning, data augmentation, and adaptive learning techniques to enhance generalization across unbalanced and diverse datasets. We evaluated our approach using HAM10000, CIFAR-10, and CIFAR-100 to indicate its impact in both the medical and natural image domains. In contrast to providing a new network architecture, our contribution highlights a realistic and reproducible training schedule that compares effectively with present soft models such as EfficientNet-V2 and MobileViT-v2. Experimental results validate that our pipeline provides strong classification performance with minimal CPU resources, underscoring its applicability to practical image classification applications.}, archive = {J_ACCESS}, author = {Mustafa Kemal Ambar and Hüseyin Öztoprak and Kamil Yurtkan}, doi = {10.1109/ACCESS.2025.3597227}, journal = {IEEE Access}, pages = {142517-142534}, shortjournal = {IEEE Access}, title = {Optimizing ResNet-50 for multiclass classification: A multi-stage learning approach}, volume = {13}, year = {2025}, }
• (2025). Hierarchical reinforcement learning for energy-efficient API traffic optimization in large-scale advertising systems. ACCESS, 13, 142493-142516. (www)

  @article{ , abstract = {Digital advertising infrastructure represents a substantial component of global computing resources, with significant environmental impact due to its massive energy consumption and carbon footprint. This work addresses the sustainability challenges posed by inefficient API traffic management in large-scale advertising systems, where conventional static approaches lead to resource overprovisioning and energy waste. We propose AdaptiveGate, a sustainability-oriented hierarchical reinforcement learning framework that dynamically optimizes API traffic flows to enhance resource efficiency and reduce environmental impact. The proposed methodology employs a constrained Markov Decision Process formulation with a multi-objective reward function explicitly designed to balance system performance with resource efficiency. Our framework implements a two-tier architecture of twin delayed Deep Deterministic Policy Gradient agents: global agents minimize cross-datacenter energy expenditure through intelligent traffic routing, while local agents maximize resource utilization through service-specific load balancing. Empirical evaluation on production advertising systems processing over 2.5 million requests per second reveals significant sustainability improvements: 42.3% reduction in tail latency, 35.7% increase in throughput, and 18% decrease in overall energy consumption compared to state-of-the-art methods. The system demonstrates exceptional adaptability across diverse traffic conditions and operational scales, providing compelling evidence that AI-driven methods can substantially improve digital infrastructure sustainability. This work contributes to sustainable computing by establishing a framework that optimizes computational resource allocation, minimizes energy waste, and advances environmentally responsible high-performance computing systems, aligning with multiple Sustainable Development Goals including responsible consumption and affordable clean energy.}, archive = {J_ACCESS}, author = {Enkai Ji and Yihan Wang and Suchuan Xing and Jianian Jin}, doi = {10.1109/ACCESS.2025.3598712}, journal = {IEEE Access}, pages = {142493-142516}, shortjournal = {IEEE Access}, title = {Hierarchical reinforcement learning for energy-efficient API traffic optimization in large-scale advertising systems}, volume = {13}, year = {2025}, }
• (2025). Dielectric nanofluids for transformers: A review on stability, dielectric, thermophysical, and chemical properties. ACCESS, 13, 142467-142492. (www)

  @article{ , abstract = {Dielectric nanofluids are an advanced form of transformer liquid insulators with better performance as compared to traditional dielectric fluids. This review systematically examines the existing literature on stability of dielectric nanofluids, including influencing factors, stabilization methods, and long-term stability, as well as nanoparticle-enhanced insulating fluids in terms of dielectric, thermophysical, and chemical properties. This review shows that dielectric nanofluids have multifaceted advantages if size, agglomeration, and zeta potential can be controlled for improved stability, through surface functionalization, ultrasonication, and surfactants. However, aging effects necessitate periodic stability checks for industrial applications. Moreover, nanofluids offer improved dielectric properties (higher AC/Lightning impulse (LI) -breakdown voltage, higher partial discharge inception voltage (PDIV), streamers propagation inhibited, lower dissipation factor (tan $\delta $ )); enhanced thermal performance (higher thermal conductivity and heat dissipation); favourable physicochemical properties (moderate viscosity rise controllable through surfactants/nanoparticle reduced loading and higher flash/fire points than base fluids, the low pour points maintained, and chemical benefits (lower acidity, slower degradation of cellulose and higher flash points). While mineral oil based nanofluids exhibit the best performance. A standardized nanoparticle dispersion protocol and validation of stability over time in realistic operational scenarios is paramount for industrial uptake. As shown through experimental evidence, nanofluids have been shown to outperform the ordinary insulating oils in many parameters, with an average enhancement of approximately 21% in AC breakdown voltage. The review also provides an overview of challenges and directions for future research. Such improvements make nanofluids a candidate for the next generation of reliable, high-efficiency transformers.}, archive = {J_ACCESS}, author = {Rizwan A. Farade and Noor Izzri Abdul Wahab and Diaa-Eldin A. Mansour and Muhammad Rafiq and T. M. Yunus Khan and Abdul Saddique Shaik and Maciej Zdanowski and Sharanabasava V. Ganachari and Reji Kumar Rajamony and Ramjan Ahamad Khatik and Abdul Nazeer}, doi = {10.1109/ACCESS.2025.3597740}, journal = {IEEE Access}, pages = {142467-142492}, shortjournal = {IEEE Access}, title = {Dielectric nanofluids for transformers: A review on stability, dielectric, thermophysical, and chemical properties}, volume = {13}, year = {2025}, }
• (2025). Single-channel sEMG hand gesture classification using an artificial neural network implemented on an ESP32 microcontroller. ACCESS, 13, 142457-142466. (www)

  @article{ , abstract = {The implementation of embedded systems for myoelectric signal classification is key to the development of prosthetic and other real-time assistive technologies. This work presents a hand gesture classification system based on an artificial neural network (ANN) deployed on an ESP32 microcontroller. In contrast to approaches that require multiple acquisition channels or complex configurations, the proposed system uses a single surface electromyography (sEMG) channel, reducing cost and complexity. Data were collected from eight participants performing eight hand and wrist gestures. The system achieved classification accuracies of up to 93.05% for six gestures, including rest. Furthermore, the ESP32 implementation demonstrated inference times compatible with real-time operation, achieving an average inference time of 0.17 ms. When using a majority voting strategy, classification accuracy increased to 99%, confirming the feasibility of implementing efficient and accurate gesture recognition on low-cost embedded hardware. These results support its integration into portable and autonomous systems for prosthetic control and other interactive applications.}, archive = {J_ACCESS}, author = {José Antonio Marbán Salgado and Jorge Arturo Sandoval Espino and Roberto Alan Beltrán Vargas and Jacob Licea-Rodríguez and J. Jesús Escobedo-Alatorre and Héctor Miguel Buenabad Arias and Eliel Rodríguez Salgado}, doi = {10.1109/ACCESS.2025.3598649}, journal = {IEEE Access}, pages = {142457-142466}, shortjournal = {IEEE Access}, title = {Single-channel sEMG hand gesture classification using an artificial neural network implemented on an ESP32 microcontroller}, volume = {13}, year = {2025}, }
• (2025). A contrast-source inversion-assisted attention-unet for microwave imaging. ACCESS, 13, 142443-142456. (www)

  @article{ , abstract = {This paper introduces a physics-based intermediate estimate as input to the Attention-Unet (ATTN-Unet) architecture for solving the electromagnetic inverse scattering problem in microwave imaging. This input is calculated from the tenth iteration results of the conventional contrast source inversion (CSI) algorithm, which is referred to as ITER10. This input incorporates more physical domain knowledge than the widely used backpropagation (BP) estimate, which corresponds to the zeroth iteration of the CSI. While the non-iterative BP estimate is popular due to its simplicity, this work demonstrates that using ITER10 enhances reconstruction accuracy without significantly increasing computational cost. For comparison and the validation of choosing ITER10, the performance of the ATTN-Unet is evaluated using estimates from other intermediate CSI iterations, namely ITER5, 20, 30, and 40. Further, the network outputs are the reconstructed relative complex permittivity values (real and imaginary) of an imaged object. The networks are tested using synthetic and experimental datasets. The results show that the ITER10-ATTN-Unet significantly enhances reconstruction accuracy, outperforming both the BP-ATTN-Unet and the conventional CSI method. Furthermore, the results demonstrate that the ITER10-ATTN-Unet achieves a better balance between accuracy and computational cost compared to the other ITER-based models. These findings highlight the effectiveness of ITER10 as a strong alternative to BP in improving neural network reconstructions in microwave imaging.}, archive = {J_ACCESS}, author = {Mohammed Farook Maricar and Amer Zakaria and Nasser Qaddoumi}, doi = {10.1109/ACCESS.2025.3598475}, journal = {IEEE Access}, pages = {142443-142456}, shortjournal = {IEEE Access}, title = {A contrast-source inversion-assisted attention-unet for microwave imaging}, volume = {13}, year = {2025}, }
• (2025). Cyber threats in the pharmaceutical industry: A deep dive into recent attacks and future implications. ACCESS, 13, 142429-142442. (www)

  @article{ , abstract = {The pharmaceutical industry faces significant cybersecurity challenges, with increasing threats to sensitive data, intellectual property, and patient information. Traditional cybersecurity models, including signature-based and threshold-based intrusion detection systems (IDS), often fail to address the dynamic and evolving nature of cyber threats. This paper introduces a novel Artificial Intelligence (AI-driven) cryptographic system designed to enhance cybersecurity in the pharmaceutical sector, integrating advanced AI techniques such as reinforcement learning, game theory, and C*-algebra to optimize resource allocation, improve threat detection, and enhance cryptographic protocols. Through comprehensive evaluation, the AI model demonstrates superior performance over traditional methods, achieving a detection rate of 97.4%, significantly reducing false positive rates (2.6%) and response time (50ms), and attaining an F1-score of 0.95. Specifically, in the CICIDS 2017 dataset, the AI model reached a detection rate of 97.4%, compared to 85.2% for traditional IDS, while MITRE ATT&CK saw a detection rate of 91.5% and a false positive rate of 12.0%. The MIMIC-III dataset achieved 93.0% detection with a false positive rate of 6.5%, and in the UWF-ZeekData22 dataset, the AI model recorded a detection rate of 96.0% and a false positive rate of 3.5%. The AI model’s ability to continuously adapt to emerging cyber threats makes it a future-proof solution for the pharmaceutical industry, with scalability ensuring applicability across various organizational sizes. Future research directions include integrating blockchain, cloud computing, and post-quantum cryptography to further strengthen system resilience. This paper presents a scalable, adaptive, and efficient solution to address growing cybersecurity challenges in the pharmaceutical industry, ensuring critical data protection in an increasingly complex threat landscape.}, archive = {J_ACCESS}, author = {Krishna Chaitanya Chaganti and Subba Reddy Inta and Sri Lekha Bandla and Rajesh Chilukuri and Pavan Paidy and Subash Muthuveeran and Naresh Dulam}, doi = {10.1109/ACCESS.2025.3583667}, journal = {IEEE Access}, pages = {142429-142442}, shortjournal = {IEEE Access}, title = {Cyber threats in the pharmaceutical industry: A deep dive into recent attacks and future implications}, volume = {13}, year = {2025}, }
• (2025). Design of multi-source heterogeneous data fusion algorithm for distribution networks based on improved kalman filter. ACCESS, 13, 142416-142428. (www)

  @article{ , abstract = {The complexity of multi-source heterogeneous data in distribution network is high and Significant deviations in multi-source heterogeneous data increase the complexity of fusion multi-source heterogeneous data in distribution network. Therefore, a multi-source heterogeneous data fusion algorithm based on Improved Kalman filter is designed. The correction process adjusts deviations in the initial multi-source heterogeneous data. Subsequently, the least squares method aligns the time series, while the Lagrange interpolation fills missing values. The distribution map method and Kalman filter algorithm are fused to improve the Kalman filter algorithm. The processed multi-source heterogeneous data are input into the improved algorithm. After removing the disturbed data from the distribution map, the data fusion is completed by Kalman filter. The experimental results show that the algorithm can achieve accurate missing data filling processing, and the average error between the current data, voltage data and actual data is 0.0082 and 0.0149 respectively, which are within the ideal error range. For all kinds of distribution network data with disturbance data, it can achieve high-quality data fusion processing, and the covariance difference of the fusion result is close to the demand covariance difference of 0.06, which meets the actual application requirements.}, archive = {J_ACCESS}, author = {Min Li and Jing Tan and Juncheng Zhang and Xiaohong Tan and Tianlu Luo}, doi = {10.1109/ACCESS.2025.3590400}, journal = {IEEE Access}, pages = {142416-142428}, shortjournal = {IEEE Access}, title = {Design of multi-source heterogeneous data fusion algorithm for distribution networks based on improved kalman filter}, volume = {13}, year = {2025}, }
• (2025). A survey on modeling of blockchain oriented software systems and smart contracts. ACCESS, 13, 142397-142415. (www)

  @article{ , abstract = {Modeling is the process of developing models representing software systems from different perspectives using a modeling language that includes graphical notation. This process assists software developers in understanding the system functionality, evaluating design proposals, and documenting the software to be implemented. Blockchain is a popular, decentralized, efficient, and secure technology that enables transparent and tamper-resistant transactions across distributed networks. However, blockchain systems lack a dedicated modeling language to represent Blockchain-Based Applications (BBAs) and related Smart Contracts (SCs). Recent research has introduced adaptations of well-known graphical notations to meet the unique modeling needs of blockchain systems. This paper proposes a structured review of graphical models, techniques, and languages to enhance Blockchain-Oriented Software Engineering (BOSE) modeling. A detailed analysis of 36 studies published between 2018 and 2023 highlights the trends and developments in blockchain modeling, and a classification of modified modeling techniques and languages for BBAs and SCs is presented. A modeling framework that guides blockchain developers in describing, designing, and documenting BBAs and related SCs is developed, and an example is provided to demonstrate how this framework is used. These insights can help software engineers select suitable modeling strategies to improve the reliability and quality of BBAs.}, archive = {J_ACCESS}, author = {Ibtisam El Gaddafi and Magdi Zakaria Rashad and Amal Abou Eleneen}, doi = {10.1109/ACCESS.2025.3598627}, journal = {IEEE Access}, pages = {142397-142415}, shortjournal = {IEEE Access}, title = {A survey on modeling of blockchain oriented software systems and smart contracts}, volume = {13}, year = {2025}, }
• (2025). Enhanced multi-class driver behavior detection in IoMT environments using hybrid LSTM-GRU model. ACCESS, 13, 142379-142396. (www)

  @article{ , abstract = {The global rise in traffic accidents is significantly attributed to driver distractions —as a primary safety motivator—that can lead to collisions, injuries, fatalities, and property damage. Distractions such as using cell phones, eating, drinking, and interacting with in-vehicle systems are critical factors contributing to unsafe driving conditions. Addressing this issue necessitates advanced, real-time monitoring systems that can accurately detect and analyze driver behaviors. In this paper, we introduce an enhanced framework for real-time driver behavior detection within the Internet of Medical Things (IoMT) environment— i.e., a wearable-sensor edge platform compliant with IoMT architectures—that utilizes bio-signal data from wearable sensors. Our proposed system integrates Shimmer3 EMG sensors and a Raspberry Pi device to collect and process data, employing a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for classification. This framework includes three key modules: the data acquisition module, the hybrid LSTM-GRU training module, and the real-time analysis and feedback module. Experimental evaluations conducted on two real-life public datasets demonstrate that our hybrid model achieves a classification accuracy of 83.40% and 77.90% for the first and second datasets, significantly improving the detection of non-aggressive and aggressive driving behaviors. These results underscore the effectiveness of combining LSTM and GRU layers in capturing complex temporal dependencies, offering a scalable and reliable solution for enhancing road safety through improved driver behavior detection.}, archive = {J_ACCESS}, author = {Hussain AlSalman}, doi = {10.1109/ACCESS.2025.3598005}, journal = {IEEE Access}, pages = {142379-142396}, shortjournal = {IEEE Access}, title = {Enhanced multi-class driver behavior detection in IoMT environments using hybrid LSTM-GRU model}, volume = {13}, year = {2025}, }
• (2025). The ultimate evaluation of current consumption and QoS metrics in 5G mobile networks over IP multimedia subsystem (IMS). ACCESS, 13, 142358-142378. (www)

  @article{ , abstract = {With the widespread deployment of 5G, understanding its energy impact is crucial. This work evaluates the energy efficiency and Quality of Service (QoS) performance of 5G networks, comparing Non-Standalone (NSA) and Standalone (SA) architectures. We investigated the current consumption and QoS within 5G mobile networks, leveraging the IP Multimedia Subsystem (IMS) for Voice over New Radio (VoNR) and 5G Voice over Long-Term-Evolution using Dual-Connectivity feature (5G VoLTE). The analysis considers key QoS metrics: Mean Opinion Score (MOS), delay, and jitter, across all the communication paths from User Equipment (UE) to the 5G network and IMS core. Our results demonstrate that 5G NSA networks exhibit higher energy consumption compared to 5G SA deployments. Furthermore, we observed a correlation between energy behavior and QoS performance. Specifically, higher energy consumption in NSA networks and 5G VoLTE was associated with measurable impacts on MOS, delay, and jitter. These findings highlight the potential for SA deployments and VoNR to offer improved QoS and greater energy efficiency. Therefore, this study provides valuable insights for mobile network operators seeking to optimize resource allocation and reduce operational costs while maintaining a high-quality user experience in 5G networks.}, archive = {J_ACCESS}, author = {Jussif Junior Abularach Arnez and Walmir Acioli E. Silva and Wederson Medeiros Silva and Ana Flavia de Oliveira Ribeiro and Janislley Oliveira de Sousa}, doi = {10.1109/ACCESS.2025.3594242}, journal = {IEEE Access}, pages = {142358-142378}, shortjournal = {IEEE Access}, title = {The ultimate evaluation of current consumption and QoS metrics in 5G mobile networks over IP multimedia subsystem (IMS)}, volume = {13}, year = {2025}, }
• (2025). Machine learning in biomedical informatics: Optimizing resource allocation and energy efficiency in public hospitals. ACCESS, 13, 142331-142357. (www)

  @article{ , abstract = {This paper presents a computational framework that combines supervised machine learning and multi-objective optimization to support data-driven decision-making for resource allocation in public healthcare systems. The study uses real-world data from 51 public hospitals across Southern and Northern Italy, ensuring a representative sample of diverse healthcare structures. It analyzes the interplay between hospital admission volumes, adherence to quality standards, operational efficiency, and energy costs. The framework integrates several predictive models—including Random Forest, Support Vector Machines, and Logistic Regression—developed in Python using the scikit-learn library. Model performance was optimized using calibration techniques, while interpretability was enhanced through SHAP (SHapley Additive exPlanations), which identified the most influential clinical areas affecting both efficiency and energy expenditure. Feature importance results were used in a dual-objective linear optimization function to determine the optimal distribution of hospital admissions across nosological areas, balancing scale efficiency and energy cost reduction. Additionally, a multi-objective genetic algorithm (NSGA-II) was applied to optimize quality standard adherence levels, generating trade-off solutions along a Pareto frontier. The proposed approach offers a transparent, explainable, and scalable tool for healthcare administrators, providing actionable insights for strategic resource allocation. By integrating interpretable machine learning with computational optimization, the model contributes to building sustainable and high-performing hospital systems aligned with both operational and environmental objectives.}, archive = {J_ACCESS}, author = {Agostino Marengo and Vito Santamato and Massimo Iacoviello}, doi = {10.1109/ACCESS.2025.3597373}, journal = {IEEE Access}, pages = {142331-142357}, shortjournal = {IEEE Access}, title = {Machine learning in biomedical informatics: Optimizing resource allocation and energy efficiency in public hospitals}, volume = {13}, year = {2025}, }
• (2025). A novel seal packaging structure applied in sub miniature push-on connector based on Ni47Ti44Nb9 shape memory alloy seal ring. ACCESS, 13, 142318-142330. (www)

  @article{ , abstract = {At present, the tin-lead vacuum soldering process is commonly used to complete the seal packaging of Sub miniature push-on (SMP) connectors. However, when the number of SMP connectors exceeds thousands, it is difficult to guarantee the consistency of soldering, which greatly restricts the large-scale application of SMP connectors. Fortunately, Shape Memory Alloy (SMA) materials have the good low-temperature phase change characteristics and could produce a big fasten force, thus can be used for seal packaging of SMP connectors. Therefore, this paper proposes a novel seal packaging structure for SMP connectors based on the Ni47Ti44Nb9 SMA seal ring to solve the problem of seal packaging of SMP connectors. In view of the SMA’s good performance, three seal packaging structure forms for SMP connectors based on the SMA seal ring are proposed, which are named as structure A, structure B and structure C, respectively. The characteristics of the seal packaging structures of each form is briefly described. The comparative study is carried out on the three structure forms with the seal packaging leakage rate as the target. The test results demonstrate that structure A can provide the best sealing performance while taking up the least space, which meets the requirement of the normal application of electronic communications equipment. Then, the further research on structure A is conducted, a prediction model of seal packaging leakage rate is established with the response surface method (RSM), by which the relationship between the structural parameters including the wall thickness of seal ring, the roughness, the thin wall thickness of cavity and the seal packaging leakage rate is investigated. Finally, to reduce the seal packaging leakage rate, the structural parameters of the seal ring are further optimized with an improved Adaptive Genetic Algorithm (IAGA), and the efficiency of the IAGA is verified through comparison with the conventional Adaptive Genetic Algorithm (AGA).}, archive = {J_ACCESS}, author = {Yang Hai and Cong Lu}, doi = {10.1109/ACCESS.2025.3597999}, journal = {IEEE Access}, pages = {142318-142330}, shortjournal = {IEEE Access}, title = {A novel seal packaging structure applied in sub miniature push-on connector based on Ni47Ti44Nb9 shape memory alloy seal ring}, volume = {13}, year = {2025}, }
• (2025). A soft error self-resilience radiation-hardened 14T SRAM for aerospace applications. ACCESS, 13, 142304-142317. (www)

  @article{ , abstract = {Various charged particles in space threaten memory circuit integrity and dependability, including photons, alpha particles, and high-energy ions outside the Low Earth Orbit region. These particles particularly affect conventional 6T SRAM by disrupting stored bits, leading researchers to explore radiation-hardened SRAM chips and the addition of extra nodes to memory cells to recover lost data. A novel self resilience radiation-hardened 14T (SRRH-14T) SRAM cell with redundant nodes is presented in this work to solve the soft error problem. The suggested SRRH-14T memory performance compared to well-known radiation-hardened cells, such as 6T-SRAM, Quatro-10T, SEA-14T, RH-14T, QCCS-12T, and RRS-14T. The proposed SRRH-14T memory cell applies to a minimal sensitive node layout area separation to protect against multiple node interruptions. Additionally, the proposed SRRH-14T demonstrates performance enhancements of 1.22x, 1.03x, 1.09x, 1.06x, and 1.02x relative to 6T-SRAM, Quatro-10T, SEA-14T, RH-14T, and RRS-14T, respectively.}, archive = {J_ACCESS}, author = {Guguloth Anjaneyulu and Asisa Kumar Panigrahy and Mukku Pavan Kumar and Shams Ul Haq and Abdolreza Darabi and Erfan Abbasian and Priyanka Sharma and M. Durga Prakash}, doi = {10.1109/ACCESS.2025.3598000}, journal = {IEEE Access}, pages = {142304-142317}, shortjournal = {IEEE Access}, title = {A soft error self-resilience radiation-hardened 14T SRAM for aerospace applications}, volume = {13}, year = {2025}, }
• (2025). Implementation of the TCG DICE specification into the keystone TEE framework. ACCESS, 13, 142284-142303. (www)

  @article{ , abstract = {The rapid adoption of IoT devices introduced significant security challenges because they are often resource-constrained and operate in untrusted environments. Their adoption into critical scenarios makes it paramount they remain trustworthy. A possible solution is the Trusted Execution Environment (TEE), which isolates and protects sensitive code and data in use. Many TEE implementations exist (e.g., ARM TrustZone), yet most are closed-source, with specific hardware requirements. To overcome these issues, open-source solutions like Keystone have been proposed. Keystone is a framework for building customizable TEEs targeting RISC-V devices, based on the Physical Memory Protection security extension. Enforcing local protection must be coupled with the ability to verify the device is behaving as intended. This can be achieved with attestation techniques, but for the highest security level, some additional components are required. While Keystone defines basic requirements for attestation, it does not support architectures based on standard specifications. The Trusted Computing Group developed the Device Identifier Composition Engine (DICE) specifications to establish strong identity and integrity for IoT devices. In this paper, we propose the DICE integration in Keystone, to support secure boot and attestation. We detail the design, implementation, and evaluation of this solution, demonstrating its effectiveness in both security and performance.}, archive = {J_ACCESS}, author = {Enrico Bravi and Silvia Sisinni and Lorenzo Ferro and Valerio Donnini and Antonio Lioy}, doi = {10.1109/ACCESS.2025.3596829}, journal = {IEEE Access}, pages = {142284-142303}, shortjournal = {IEEE Access}, title = {Implementation of the TCG DICE specification into the keystone TEE framework}, volume = {13}, year = {2025}, }
• (2025). A resource-efficient edwards25519 point multiplication technique for resource-constrained devices. ACCESS, 13, 142274-142283. (www)

  @article{ , abstract = {Implementing elliptic curve point multiplication (ECPM), the core operation in elliptic curve cryptography (ECC), on microcontrollers is challenging due to limited processing power and memory, restricting its application in environments like embedded systems and autonomous vehicles, where security and performance are essential. This study presents an optimized ECPM approach for the Arduino Atmega 2560, leveraging low-level C and assembly programming for enhanced hardware resource control. By incorporating the cyclic group property of elliptic curve points, the additive inverse property in group theory, and the elimination of a curve-shaping parameter (specifically the parameter a, which influences point arithmetic in twisted Edwards curves) during doubling operations in the Extended Twisted Edwards Coordinates, along with the windowed method on Edwards25519, the approach achieves 14% to 71% reductions in execution time and cycle count, a 15% decrease in program storage space (flash memory) usage, and 77% to 80% lower dynamic memory (SRAM) consumption compared to previous studies. These results highlight the method’s efficiency, providing an optimal balance of speed, memory utilization, and security for constrained microcontroller applications.}, archive = {J_ACCESS}, author = {Nawras H. Sabbry and Alla Levina}, doi = {10.1109/ACCESS.2025.3596685}, journal = {IEEE Access}, pages = {142274-142283}, shortjournal = {IEEE Access}, title = {A resource-efficient edwards25519 point multiplication technique for resource-constrained devices}, volume = {13}, year = {2025}, }
• (2025). Early detection of voltage instability: A transparent, rule-based method for cyber-resilient grids. ACCESS, 13, 142262-142273. (www)

  @article{ , abstract = {This paper proposes a rule-based algorithm for fast prediction of long-term voltage stability status immediately after a disturbance, eliminating the need for post-disturbance measurements. Unlike traditional Phasor Measurement Unit (PMU) dependent methods vulnerable to cyber threats like data spoofing, the proposed approach uses PMU data only for initial system updates. It first validates measurements against the last trusted system state using consistency checks, corrects any discrepancies, and then bases stability decisions on inherent characteristics—power flow convergence and generator reactive power margins—ensuring cyber-resilient operation. The method’s rule-based logic ensures intrinsic immunity to cybersecurity risks while maintaining compatibility with both large and small disturbances triggering long-term voltage instability. Optimized for real-time operation, the simulation-based algorithm meets stringent computational speed requirements for online stability assessment. Comprehensive simulations on IEEE test systems under N-1/N-2 contingencies and load disturbances demonstrate three key advantages: 1) Early and accurate voltage stability prediction (96.8% detection accuracy), 2) Reliable identification of critical generators/loads contributing to instability, and 3) Cyber-resilient operation verified through adversarial test cases. The results establish the method as a transparent, infrastructure-independent solution for grid stability monitoring, particularly valuable for cybersecurity-sensitive environments.}, archive = {J_ACCESS}, author = {Mahtab Khalilifar and Seyed Mohammad Shahrtash}, doi = {10.1109/ACCESS.2025.3597555}, journal = {IEEE Access}, pages = {142262-142273}, shortjournal = {IEEE Access}, title = {Early detection of voltage instability: A transparent, rule-based method for cyber-resilient grids}, volume = {13}, year = {2025}, }
• (2025). Enhancing global and local context modeling in time series through multi-step transformer-diffusion interaction. ACCESS, 13, 142251-142261. (www)

  @article{ , abstract = {Multivariate time-series forecasting (MTSF) has become crucial across various domains, with transformer-based architectures emerging as the primary choice because of their excellent ability to capture long-term dependencies. However, these models often fail to represent fine-grained local patterns, which are critical for accurate forecasting. To address this limitation, we propose a novel MTSF method based on the diffusion process, utilizing the transformer as prior knowledge. By conditioning the denoising process on global embeddings derived from the transformer, our approach effectively captures both global and local patterns. However, when transformers are used as conditional priors in the diffusion process, errors in their predictions can propagate, adversely affecting overall performance. Thus, we also introduce a mechanism that mitigates the impact of such errors by enabling effective interactions between transformers and diffusion models. Furthermore, the proposed multi-step denoising process progressively refines temporal patterns, preserving both global structures and local variations to enhance robustness and generalization. Experimental results demonstrate that the proposed approach overcomes the limitations of existing models, consistently outperforming previous state-of-the-art models (TMDM), achieving up to 97.9% lower mean squared error (MSE) on Weather and 96.3% on ETTm2.}, archive = {J_ACCESS}, author = {Dagyeong Na and Jinho Kang and Byoungwoo Kang and Junseok Kwon}, doi = {10.1109/ACCESS.2025.3598141}, journal = {IEEE Access}, pages = {142251-142261}, shortjournal = {IEEE Access}, title = {Enhancing global and local context modeling in time series through multi-step transformer-diffusion interaction}, volume = {13}, year = {2025}, }
• (2025). Federated deep learning for scalable and explainable load forecasting in privacy-conscious smart cities. ACCESS, 13, 142237-142250. (www)

  @article{ , abstract = {As smart cities evolve, energy infrastructures are becoming more decentralized and dynamic due to the increased integration of renewables, electric vehicles, and consumer-driven usage behaviors. These shifts introduce significant complexity into electricity load forecasting, challenging conventional centralized AI models. Such models often face limitations in scalability, generalization across heterogeneous environments, and preserving user data privacy. Furthermore, their lack of interpretability impedes transparent decision-making, an essential requirement in critical energy management and policy planning contexts. To address these issues, this study proposes HHCTE-FL, a Hierarchical Hybrid Convolutional Transformer Extractor embedded within a federated learning framework. The architecture supports multi-horizon electricity load forecasting while ensuring model transparency through Layer-wise Interpretative Attention Maps (LIAM) and robustness via personalized federated optimization. Functional modules such as Dynamic Temporal Refinement (DTR) and Multi-Stage Adaptive Feature Selection (MSAFS) enable adaptive temporal modeling and feature prioritization across non-IID client data. Evaluation on real-world smart grid datasets demonstrates that HHCTE-FL consistently surpasses 12 benchmark methods, attaining 98.7% accuracy, with the lowest forecast errors (MAE: 0.081, RMSE: 0.114). The model achieves convergence within just 28 communication rounds and operates with low overhead, while attaining a Federated Stability Index (FSI) of 0.96, indicating high training consistency. Statistical significance analysis further affirms its superiority in performance and reliability. By integrating explainable deep learning with privacy-preserving federated learning, HHCTE-FL establishes a scalable and trustworthy paradigm for intelligent electricity load forecasting—aligned with the demands of modern urban energy systems and smart city sustainability goals.}, archive = {J_ACCESS}, author = {Ibrahim Alzamil}, doi = {10.1109/ACCESS.2025.3587058}, journal = {IEEE Access}, pages = {142237-142250}, shortjournal = {IEEE Access}, title = {Federated deep learning for scalable and explainable load forecasting in privacy-conscious smart cities}, volume = {13}, year = {2025}, }
• (2025). Genetic algorithms for a scheduling problem of parcel delivery and pickup by drones. ACCESS, 13, 142221-142236. (www)

  @article{ , abstract = {Recent studies have been conducted actively on scheduling in a parcel delivery system using a truck and drones to deliver parcels more effectively. In logistics, it is necessary to provide not only delivery service but also pickup service, and drones can be used for the pickup service. This paper extends our previous delivery system to a new one handling both delivery and pickup and then formulates a scheduling problem for the new system. A challenge in this problem is to consider how to appropriately select one of three options: whether a drone only delivers a parcel, only picks up, or both delivers and picks up. This paper proposes two methods based on a genetic algorithm (GA) introducing procedures to select an appropriate option for solving the problem. The first method uses a maximum weight matching procedure before executing a GA, and the procedure is applied to a bipartite graph of customers whose parcels are delivered and customers whose parcels are picked up. The second uses a greedy pairing procedure to reduce drone flight time in evaluating each individual of a GA. Experimental results show that the two proposed methods can successfully find good feasible solutions. The second method can often find better solutions but consumes longer computation time than the first.}, archive = {J_ACCESS}, author = {Yohei Hazama and Hitoshi Iima and Yoshiyuki Karuno}, doi = {10.1109/ACCESS.2025.3597781}, journal = {IEEE Access}, pages = {142221-142236}, shortjournal = {IEEE Access}, title = {Genetic algorithms for a scheduling problem of parcel delivery and pickup by drones}, volume = {13}, year = {2025}, }
• (2025). Joint resource allocation and power control in rate-splitting multiple access-based integrated terrestrial and non-terrestrial networks with HAP assistance. ACCESS, 13, 142203-142220. (www)

  @article{ , abstract = {This paper proposes a novel network architecture integrating a non-terrestrial network (NTN) with low-earth orbit (LEO) satellites and high-altitude platforms (HAPs) and terrestrial network (TN) with ground base stations under rate-splitting multiple access (RSMA) to enhance spectral efficiency (SE). A new resource allocation strategy is developed for efficient resource utilization across all layers. A joint resource block and power control framework (JRPS) is formulated to maximize sum-rate under quality-of-service (QoS) constraints. The non-convex JRPS problem is reformulated as a linear program and solved using an iterative successive convex approximation algorithm that ensures local optimality. Additionally, a heuristic resource allocation and power control method is also proposed to provide an effective initialization for JRPS and to serve as a performance baseline. Simulations confirm the superior performance of the proposed JRPS framework over existing benchmarks. Specifically, the proposed scheme achieves an average sum-rate improvement of up to 80% compared to conventional baselines without RSMA and up to 60% compared to RSMA combined with other allocation methods. Moreover, it ensures that approximately 90% of ground stations achieve an SE above 0.5 bps/Hz, significantly outperforming alternative strategies. Notably, integrating both LEO satellites and HAPs yields better performance than using either one alone.}, archive = {J_ACCESS}, author = {Thong-Nhat Tran and Young Jeon and Heejung Yu and Taejoon Kim}, doi = {10.1109/ACCESS.2025.3597313}, journal = {IEEE Access}, pages = {142203-142220}, shortjournal = {IEEE Access}, title = {Joint resource allocation and power control in rate-splitting multiple access-based integrated terrestrial and non-terrestrial networks with HAP assistance}, volume = {13}, year = {2025}, }
• (2025). Secondary hybrid decomposition strategy for wind power prediction using long short-term memory with crisscross optimization. ACCESS, 13, 142186-142202. (www)

  @article{ , abstract = {This paper presents a hybrid forecasting model for a wind power named Secondary Hybrid Decomposition (SHD)-Long Short-Term Memory (LSTM) with Crisscross Optimization (CSO).The model integrates three key techniques to improve prediction accuracy. Firstly, the SHD mitigates the limitations of single-stage decomposition by employing a two-step Empirical Mode Decomposition (EMD) process. This improves the predictability of wind power series by decomposing it into its Intrinsic Mode Functions (IMFs) and residual components. Secondly, LSTM networks are utilized to forecast each subseries and residue generated by SHD, capturing temporal dependencies within wind power data effectively. Finally, the CSO is used to optimize the hyperparameter of individual LSTM networks, enhancing global search performance. Two sets of wind farm data are used to validate the accuracy of the model. A comparative study shows that the proposed SHD-LSTM-CSO model performs better than other hybrid models such as SHD-LSTM, EMD-LSTM, LSTM based on variable mode decomposition (VMD) (VMD-LSTM), Gray Worm Optimization-based backpropagation neural network (GWO-BPANN) and EMD-based Artificial Neural Network (EMD-ANN) methods.}, archive = {J_ACCESS}, author = {Yoseph Mekonnen Abebe and Habtamu Kassa Bayu and Tekalign Tesfaye Mengistu and Abera Tullu and Sunghun Jung}, doi = {10.1109/ACCESS.2025.3597646}, journal = {IEEE Access}, pages = {142186-142202}, shortjournal = {IEEE Access}, title = {Secondary hybrid decomposition strategy for wind power prediction using long short-term memory with crisscross optimization}, volume = {13}, year = {2025}, }
• (2025). A review of data-driven models for electromagnetic devices design and analysis. ACCESS, 13, 142161-142185. (www)

  @article{ , abstract = {In recent years, the design and optimization of electromagnetic devices have grown increasingly complex, driven by the demand for higher efficiency, greater power density, and cost-effectiveness. Traditional approaches such as finite element analysis (FEA) offer precise simulations but can be time-consuming and computationally intensive. To address these challenges, data-driven methods have gained traction as efficient alternatives. This review, focusing on recent deep learning advances, presents a comprehensive review on the application of data-driven models in the design and optimization of electromagnetic devices, summarizing the statistical models such as Response Surface Methodology (RSM) and recent popular machine learning (ML) methods in handling multiple variables, as well as the deep learning (DL) models, in predicting various electromagnetic device parameters and optimizing electromagnetic models. This paper highlights the latest advances in DL models for electromagnetic device applications, including motors, transformers, and electrical wires. It discusses their potential to assist FEA to accelerate design and optimization. Future key directions are proposed to improve efficiency and expand the versatility of data-driven models.}, archive = {J_ACCESS}, author = {Zihan Li and Mengyu Cheng and Andy Tyrrell and Xing Zhao}, doi = {10.1109/ACCESS.2025.3591965}, journal = {IEEE Access}, pages = {142161-142185}, shortjournal = {IEEE Access}, title = {A review of data-driven models for electromagnetic devices design and analysis}, volume = {13}, year = {2025}, }
• (2025). Design of an optimized feature driven severity stage classifier for parkinson’s disease prediction using deep learning. ACCESS, 13, 142140-142160. (www)

  @article{ , abstract = {Parkinson’s Disease (PD) is a degenerative neurological condition that seriously affects both motor and non-motor abilities. Among many biomarkers, speech abnormalities have become a potential sign of PD as the disease affects the motor control of the vocal point. Therefore, speech data is a convenient, non-invasive, and valuable diagnostic tool. This work presents a novel methodology for classifying the severity stage of parkinson’s disease using speech data, addressing a notable deficiency in existing research works that have focused on binary classification. Stage classification distinguishes PD’s early, mid, and late phases, offering essential insights for stage-specific therapy strategies. We proposed a Parkinson’s Speech Stage-Net model (PSS-Net) for the prediction of the severity of PD that includes Isolation Forest-based outlier detection and removal, KMeansSMOTE data balancing, and Optimized Dimensionality Reduction techniques. The essential speech features were found using a tree-based feature selection method. Selected features are fed into the Optimized and Fused Neural Network model. The proposed model effectively classified the stages of PD with an accuracy of 89%. Moreover, this research study stresses openness using SHAP (SHapley Additive exPlanations) to understand feature contributions, thereby improving the clinical relevance and credibility of the model.}, archive = {J_ACCESS}, author = {K. Shyamala and T. M. Navamani}, doi = {10.1109/ACCESS.2025.3597851}, journal = {IEEE Access}, pages = {142140-142160}, shortjournal = {IEEE Access}, title = {Design of an optimized feature driven severity stage classifier for parkinson’s disease prediction using deep learning}, volume = {13}, year = {2025}, }
• (2025). Hybrid model for 6G network traffic prediction and wireless resource optimization. ACCESS, 13, 142129-142139. (www)

  @article{ , abstract = {The fast change from 5G to 6G networks calls for extremely accurate network traffic prediction and effective resource allocation to meet rising data volumes and ultra-low latency requirements. To deal with the complicated time and space based aspects of 6G network traffic, an AI based hybrid model is developed that combines random forest (RF), gated recurrent units (GRU), and a mechanism for paying attention is proposed. Large-scale 6G traffic data with varied channel conditions and user scenarios was used to validate the model. An algorithm is presented to describe the training process of the proposed hybrid model. The results of the proposed hybrid model are presented and compared with baseline methods, including LSTM, GRU, random forest, and XGBoost. Our model obtains a Root Mean Squared Error (RMSE) of 0.0049, an Mean Absolute Error (MAE) of 0.0034, a mean absolute percentage error (MAPE) of 0.46%, and a coefficient of determination $R^{2}$ of 0.9970 according to experimental findings on a whole dataset. The suggested technique lowers the RMSE by over 69% and increases $R^{2}$ by up to 2.88% compared to baseline GRU and LSTM respectively. These results highlight how well combining deep sequence modelling with ensemble learning works. In next-generation wireless systems, the framework opens the path for proactive resource allocation, strong security, and real-time optimisation outside of improving forecast accuracy. Moreover, this paper provides a critical review of open research directions including the scalability of hybrid AI models, edge intelligence integration, and the evolution of standardised protocols for safe and smooth AI deployment in 6G networks.}, archive = {J_ACCESS}, author = {Mohammed Anis Oukebdane and A. F. M. Shahen Shah and Md Baharul Islam and John Ekoru and Milka Madahana}, doi = {10.1109/ACCESS.2025.3597877}, journal = {IEEE Access}, pages = {142129-142139}, shortjournal = {IEEE Access}, title = {Hybrid model for 6G network traffic prediction and wireless resource optimization}, volume = {13}, year = {2025}, }
• (2025). Data-driven multiple ARIMA through neural fusion for enhanced wind power prediction with missing data. ACCESS, 13, 142117-142128. (www)

  @article{ , abstract = {Robust wind power forecasting in the presence of missing data remains a critical challenge for efficient grid management and energy trading. This paper proposes a novel methodology that integrates a data-driven approach for creating multiple Autoregressive Integrated Moving Average (ARIMA) models with with neural network-based fusion strategy. We first employ statistical properties to identify distinct operational regimes within the wind power data, training specialized ARIMA models. Then, we introduce neural networks for the robust fusion of these segmented ARIMA forecasts. These networks are engineered to: 1) learn and exploit intricate relationships between the predictions originating from different operational regimes, and 2) dynamically adapt its fusion strategy based on the availability of current data and the predicted reliability of each ARIMA model, effectively compensating for missing data points. Extensive experiments conducted on real-world wind power datasets characterized by varying missing data patterns demonstrate the superior accuracy and robustness of our proposed approach compared to conventional forecasting methods.}, archive = {J_ACCESS}, author = {Xiaoou Li and Wen Yu}, doi = {10.1109/ACCESS.2025.3596855}, journal = {IEEE Access}, pages = {142117-142128}, shortjournal = {IEEE Access}, title = {Data-driven multiple ARIMA through neural fusion for enhanced wind power prediction with missing data}, volume = {13}, year = {2025}, }
• (2025). Optimization of adaptive I2H ∞ control method based on multiple input sensors. ACCESS, 13, 142103-142116. (www)

  @article{ , abstract = {With the growing demand for sustainable urban transportation, electric bicycles (E-bikes) have become an important solution due to their environmental friendliness and convenience. However, traditional control methods (such as PID and fuzzy logic) have limitations in dynamically adapting to rider behavior, ecological changes, and mechanical uncertainties, and it is difficult to balance accuracy, robustness, and rider comfort. To this end, this paper proposes a human-centric adaptive control framework that combines multi-sensor fusion with advanced control theory to achieve efficient coordination between rider intention and motor assistance. The core contributions of this study include: 1) Designing a multi-sensor current reference estimator to dynamically generate the optimal electromagnetic torque through state variables such as wheel speed, acceleration, slope, and human factor database (heart rate, subjective score, fatigue index) to achieve real-time prediction of rider demand; 2) Proposing an adaptive current reference value estimation algorithm that integrates feedforward compensation and error feedback to ensure smooth switching of assistance modes and suppress sensor noise; 3) Developing an intention-induced H $\infty $ robust current tracking controller that significantly enhances the system’s robustness to parameter fluctuations and external disturbances by optimizing the H $\infty $ norm of the closed-loop transfer function, while supporting personalized riding assistance.}, archive = {J_ACCESS}, author = {Yu Gu and Hanyang Li and Zeting Mei and Hao Wen and Yuanxiong Jin and Wenxuan Dong}, doi = {10.1109/ACCESS.2025.3593069}, journal = {IEEE Access}, pages = {142103-142116}, shortjournal = {IEEE Access}, title = {Optimization of adaptive I2H ∞ control method based on multiple input sensors}, volume = {13}, year = {2025}, }
• (2025). Enhanced SWIPT with cooperative relaying for energy efficient and reliable NOMA-based underwater acoustic sensor networks. ACCESS, 13, 142089-142102. (www)

  @article{ , abstract = {Non-orthogonal multiple access (NOMA) based underwater acoustic sensor networks (UASNs) face several critical challenges, including limited bandwidth, energy constraints, and unreliable communication links. To address these issues, we propose an enhanced simultaneous wireless information and power transfer (ESWIPT) scheme with cooperative relaying (CR). Unlike terrestrial communication channels, underwater channels have distance-dependent bandwidth characteristics, where near users experience higher bandwidth than far users. Based on this, we propose enhanced power splitting (PS) and time switching (TS) SWIPT techniques combined with CR for NOMA-based UASNs. In the proposed scheme, the additional bandwidth available at the near user, by virtue of the lesser distance from the source, is utilized to harvest energy, and also the near user acts as a relay to forward messages to the far user. By combining enhanced SWIPT with CR, the proposed scheme achieves higher data rates, better energy efficiency, and lower outage probability. We derive closed-form expressions for the ergodic rate, energy efficiency, and outage probability for both enhanced PS-SWIPT (EPS-SWIPT) and enhanced TS-SWIPT (ETS-SWIPT) with CR. Analytical and simulation results show that the proposed scheme outperforms conventional CR and SWIPT methods in UASNs, making it a potential scheme for next-generation underwater sensor networks.}, archive = {J_ACCESS}, author = {R. Deepa and V. P. Harigovindan and Veerapu Goutham and Shaeen Kalathil}, doi = {10.1109/ACCESS.2025.3588063}, journal = {IEEE Access}, pages = {142089-142102}, shortjournal = {IEEE Access}, title = {Enhanced SWIPT with cooperative relaying for energy efficient and reliable NOMA-based underwater acoustic sensor networks}, volume = {13}, year = {2025}, }
• (2025). Design and evaluation of AgileBPM SDLC–An agile SDLC for business process management systems. ACCESS, 13, 142058-142088. (www)

  @article{ , abstract = {Business Process Management Systems (BPMS) are critical for enabling organizations to automate, monitor, and optimize their core processes. While agile software development approaches have transformed traditional systems engineering, their systematic application to BPMS development remains limited and underexplored. Existing agile SDLCs for BPMS are often incomplete, lack standardization, and provide insufficient documentation, hindering their adoption in both academic and professional contexts. This study addresses this gap by designing and validating AgileBPM SDLC, a novel, fine-grained Agile Software Development Life Cycle tailored specifically for BPMS. Grounded in Design Science Research Methodology (DSRM), the proposed model integrates best practices from the Scrum-XP framework and insights from four existing agile BPMS methodologies. The resulting AgileBPM SDLC includes three agile roles, four phases, fifteen activities, and twelve structured artifacts, all comprehensively documented through an open-access Electronic Process Guide (EPG). Validation involved expert review by senior evaluators and comparative assessment using a Rigor-Agility Framework. Results demonstrate the SDLC’s theoretical soundness, alignment with agile principles, and practical applicability. By providing a detailed, reusable framework for agile BPMS development, this study makes a significant contribution to researchers, developers, and organizations seeking faster, more flexible, and well-documented approaches to process-aware information systems.}, archive = {J_ACCESS}, author = {Roberto Davila-Campos and Manuel Mora and Paola Yuritzy Reyes Delgado and Sergio Galván-Cruz and Gabriela Citlalli López Torres}, doi = {10.1109/ACCESS.2025.3594684}, journal = {IEEE Access}, pages = {142058-142088}, shortjournal = {IEEE Access}, title = {Design and evaluation of AgileBPM SDLC–An agile SDLC for business process management systems}, volume = {13}, year = {2025}, }
• (2025). Research on cooperative search and dynamic target encirclement of unmanned surface vehicle swarms based on improved pheromone algorithm. ACCESS, 13, 142045-142057. (www)

  @article{ , abstract = {Concentrating on the practical needs of effective collaborative search and encirclement techniques for unmanned surface vehicles (USVs). This study proposes a collaborative search technique, focusing on enhancing the efficiency, preventing early pheromone accumulation, and achieving local optimality, in response to challenges in the regional coverage search of USVs in unfamiliar settings. The system creates a technique for revising hazardous grids and identifying adjacent locations. Global guidance is provided by the primary grid, while the sub-grid serves the purpose of local optimization. The efficiency of searching sub-grids is enhanced by dynamically modifying their search density via the mechanism of pheromone decay or enhancement. A method of dynamically distributed encirclement is suggested to address the challenge of dynamically allocating encirclement roles and preserving the stability of formation during the encirclement of dynamic targets. Encirclement occurs through the creation of encirclement contracts and the creation of potential points for encirclement, while the potential points for encirclement in dynamic targets are judiciously distributed, facilitating rapid formation of an encirclement by several USVs. Following this, simulations are conducted to test the aforementioned algorithms, leading to the optimization of their control parameters. A total of six USVs were employed to conduct a search for coverage with in a 400 m $\times 400$ m zone. The outcomes of the simulations revealed that a single USV located the target in 53.5 seconds, while the rest ceased their search swiftly, created an encirclement at 85.3 seconds, attained the threshold in 101.3 seconds, autonomously avoid obstacles, and finished the encirclement task. Ultimately, the lake study was confirmed using four USVs, which located the target upon achieving 64% area coverage, smoothly transitioned from search to encirclement mode, and ultimately accomplished the encirclement task. The effectiveness and rationality of this method were successfully confirmed through simulations and experiments.}, archive = {J_ACCESS}, author = {Antong Zhang and Yunfan Yang and Zhuo Chen and Tao Bao and Yu Guo and Xu Liu and Wenhui Yin}, doi = {10.1109/ACCESS.2025.3596844}, journal = {IEEE Access}, pages = {142045-142057}, shortjournal = {IEEE Access}, title = {Research on cooperative search and dynamic target encirclement of unmanned surface vehicle swarms based on improved pheromone algorithm}, volume = {13}, year = {2025}, }
• (2025). Mapping the carrier phase of a propagating wave by wireless two-way interferometry scanned by a mobile robot. ACCESS, 13, 142030-142044. (www)

  @article{ , abstract = {This study proposes a wireless high-precision carrier phase mapping system that removes the need for wired synchronization, which has been a significant limitation of conventional methods. The system achieves a root mean square error (RMSE) of $\leq 0.2\,\mathrm {rad}$ across five trials. It employs wireless two-way interferometry (Wi-Wi) with 920 MHz modules, a mobile robot, and a high-precision positioning device to enable real-time and wide-area spatial sensing. To validate its effectiveness, mapping experiments were conducted over a $3\,\mathrm {m} \times 2\,\mathrm {m}$ area in an anechoic chamber. The system is particularly suitable for challenging environments such as large indoor facilities or elevated outdoor locations. This technology contributes to applications including wireless communication optimization and environmental sensing, especially in visualizing multipath, diffraction, and interference effects.}, archive = {J_ACCESS}, author = {Takaaki Nara and Yoshito Okada and Shotaro Kojima and Ranulfo Bezerra and Kazunori Ohno and Suguru Kameda and Nobuyasu Shiga and Satoshi Yasuda and Kenichi Takizawa and Satoshi Tadokoro}, doi = {10.1109/ACCESS.2025.3597419}, journal = {IEEE Access}, pages = {142030-142044}, shortjournal = {IEEE Access}, title = {Mapping the carrier phase of a propagating wave by wireless two-way interferometry scanned by a mobile robot}, volume = {13}, year = {2025}, }
• (2025). Vision-based fall risk assessment through attention augmented neural encoding and data augmentation. ACCESS, 13, 142015-142029. (www)

  @article{ , abstract = {Falls among elderly individuals and other at-risk populations represent a significant public health concern, underscoring the need for proactive and accessible fall risk assessment methods. Although prior systems using wearable sensors or depth cameras have demonstrated potential, their widespread adoption is often constrained by user discomfort, setup complexity, and the high cost of specialized equipment such as Kinect. To address these limitations, we propose GTAE-FRA, a novel vision-based deep learning framework for fall risk assessment that leverages standard, widely available RGB cameras to record Five times Sit-To-Stand (FSTS) test videos. GTAE-FRA begins with a robust video processing pipeline that includes 3D pose estimation, noise filtering, and Dynamic Time Warping (DTW)-based matching to extract reliable Body Skeleton Key Point (BSKP) sequences. These sequences are then fed into GTrans, an attention-augmented encoder combining message passing aggregation mechanism with Transformer-based spatial-temporal modeling for nuanced feature extraction. To overcome limited data availability, six skeleton-oriented data augmentation strategies are applied, which significantly enhance the diversity and robustness of training samples. Experimental results on a dataset of 450 FSTS videos demonstrate the effectiveness of GTAE-FRA, achieving impressive performance with detection accuracy, weighted F1 score, macro F1 score, and AUC of 87.00%, 87.01%, 88.92%, and 97.96%, respectively. These results represent average improvements of 2.49%, 2.66%, 2.24% and 1.78% over baseline methods across the respective metrics.}, archive = {J_ACCESS}, author = {Chunhua Pan and Rui Miao and Qing Zhang and Boting Qu and Xin Wang}, doi = {10.1109/ACCESS.2025.3596942}, journal = {IEEE Access}, pages = {142015-142029}, shortjournal = {IEEE Access}, title = {Vision-based fall risk assessment through attention augmented neural encoding and data augmentation}, volume = {13}, year = {2025}, }
• (2025). Machine learning-enabled fast prediction of GGNMOS performance and inverse design for electrostatic discharge applications. ACCESS, 13, 142007-142014. (www)

  @article{ , abstract = {Electrostatic discharge (ESD) protection is generally required in integrated circuit (IC) chips. The grounded-gate n-channel metal-oxide-semiconductor (GGNMOS) is a popular device for ESD protection in circuit design. However, the design optimization of GGNMOS for every circuit is carried out using the trail-and-error method and requiring iterative device simulations. The device simulation is usually performed using technology computer-aided design (TCAD) software and is time-consuming. To address this issue, we developed machine learning models for fast prediction of GGNMOS performance and inverse design of its structure according to performance metrics. Here, we generated data for machine learning using Sentaurus TCAD. We applied AutoML and weight-sharing deep neural networks to predict current-voltage (I-V) characteristics of GGNMOS and extract performance metrics: triggering point ( $I_{t1}, V_{t1}$ ), holding point ( $I_{h}, V_{h}$ ), thermal breakdown point ( $I_{t2}, V_{t2}$ ), and discharge resistance $R_{on}$ . Additionally, Bayesian optimization was employed for inverse design, allowing rapid identification of optimal structural parameters for desired performance metrics. This approach significantly accelerates the ESD design process, minimizing dependency on costly and time-consuming TCAD simulations. Our work represents an advancement in design of electronic devices and circuits using machine learning.}, archive = {J_ACCESS}, author = {Zihan Wang and Ruichen Chen and Shengyao Lu and Ian Then and Di Niu and Xihua Wang}, doi = {10.1109/ACCESS.2025.3596439}, journal = {IEEE Access}, pages = {142007-142014}, shortjournal = {IEEE Access}, title = {Machine learning-enabled fast prediction of GGNMOS performance and inverse design for electrostatic discharge applications}, volume = {13}, year = {2025}, }
• (2025). LYRICEL: Knowledge graphs combined with large language models and machine learning for cross-cultural analysis of Lyrics—The case of greek songs. ACCESS, 13, 141985-142006. (www)

  @article{ , abstract = {This paper presents LYRICEL, a framework integrating Knowledge Graph (KG) representation learning, Large Language Models (LLMs), and machine learning for reliable, explainable, and validatable cross-cultural lyric analysis. The core component, Sequential Language Model Integration (SLMI), enhances the interpretability and reliability of transformer-based LLMs by addressing explainability and validation challenges through Retrieval-Augmented Generation (RAG), hybrid search, and rule-based evaluation. An important feature of LYRICEL is its use of KG visualizations, which serve as dynamic links to improve interpretability and validatability by structuring data relationships and sources. These visualizations are central to advancements in four areas: KG representation learning, knowledge acquisition, temporal KGs, and knowledge-aware applications. Tested on Greek folk music with models like GPT-4o and BERT, LYRICEL’s trustworthiness is assessed using the VIRTSI model, which quantifies cognitive trust in human-computer interactions. The framework shows strong potential for cross-cultural applications, particularly in languages such as Modern Greek which encompasses a rich cultural heritage spanning centuries of history and traditions resulting in a complex study. The outcomes of GPT-enabled LYRICEL are compared to ChatGPT alone and show a significant improvement in the reliability and efficiency of interactions that can reach a global audience, enhancing the accessibility and understanding of diverse cultural heritages.}, archive = {J_ACCESS}, author = {Dimitrios P. Panagoulias and Evangelia-Aikaterini Tsichrintzi and Dionisios N. Sotiropoulos and Konstantina Chrysafiadi and Evangelos Sakkopoulos and George A. Tsihrintzis and Maria Virvou}, doi = {10.1109/ACCESS.2025.3597213}, journal = {IEEE Access}, pages = {141985-142006}, shortjournal = {IEEE Access}, title = {LYRICEL: Knowledge graphs combined with large language models and machine learning for cross-cultural analysis of Lyrics—The case of greek songs}, volume = {13}, year = {2025}, }
• (2025). Research on the real-time scoring of chest ring target based on transfer learning and improved lightweight neural network. ACCESS, 13, 141975-141984. (www)

  @article{ , abstract = {To achieve real-time scoring of chest ring targets during outdoor shooting training while addressing the issues of limited memory and low performance in portable devices, this paper proposes a real-time scoring method for chest ring targets based on an improved lightweight neural network architecture. To enable efficient and high-precision real-time bullet hole detection, the paper lightweight processes the YOLOv10n end-to-end regression framework. First, the Repvit-MobileNet block (re-parameterized depth wise separable mobile network module) is introduced into its backbone network to reduce model size and computational complexity while improving detection speed. Then, grouped shuffle convolution (GSConv) is used to reconstruct the standard down sampling convolutions in the PANFPN structure, promoting feature information extraction and fusion at bullet hole edges, reducing model computational complexity, and maintaining detection accuracy. Additionally, to enhance the model’s ability to extract local feature information, the efficient channel attention (ECA) mechanism is integrated to replace the PSA attention mechanism in the backbone network, strengthening the edge features of bullet holes on chest ring targets and improving the recall rate for small target detection. Finally, transfer learning is incorporated to accelerate model convergence. The lightweight neural network model is deployed on portable mobile devices, and a real-time scoring system for chest ring targets is developed using MFC and C++. Experiments show that this method exhibits good adaptability and robustness in different environments. Compared with the original YOLOv10n model, the improved model achieves a 12.1% increase in mAP50, a 10% increase in mAP50-95, an inference speed of 25.43 FPS, and a model weight of 5.149 MB, meeting the real-time scoring requirements for chest ring targets in outdoor shooting training.}, archive = {J_ACCESS}, author = {Minghui Meng and Chuande Zhou}, doi = {10.1109/ACCESS.2025.3594477}, journal = {IEEE Access}, pages = {141975-141984}, shortjournal = {IEEE Access}, title = {Research on the real-time scoring of chest ring target based on transfer learning and improved lightweight neural network}, volume = {13}, year = {2025}, }
• (2025). A review of explainable AI for android malware detection and analysis. ACCESS, 13, 141958-141974. (www)

  @article{ , abstract = {Recent advances in complex machine learning models have significantly enhanced Android malware detection and analysis. However, these models often operate as closed boxes, making it difficult to understand which aspects of the input data influence their decisions. Such interpretability is essential for building trust and improving model robustness and performance. This paper reviews and analyzes recent research on explainable artificial intelligence (XAI) techniques applied to Android malware detection. We identify key objectives for integrating explainability, examine current XAI techniques used for explaining the results of Android malware detectors, and their limitations. We also examine the metrics used to evaluate explanation quality. Furthermore, we introduce a system that utilizes the MITRE ATT&CK framework to enhance and structure feature-based explanations. Lastly, we highlight current challenges and suggest directions for future research in this emerging field.}, archive = {J_ACCESS}, author = {Maryam Tanha and Somayeh Kafaie}, doi = {10.1109/ACCESS.2025.3597575}, journal = {IEEE Access}, pages = {141958-141974}, shortjournal = {IEEE Access}, title = {A review of explainable AI for android malware detection and analysis}, volume = {13}, year = {2025}, }
• (2025). How predictable is electric vehicle adoption? exploring the broader role of renewables in transportation using a data-driven approach. ACCESS, 13, 141937-141957. (www)

  @article{ , abstract = {This study harnesses data from a questionnaire carried out in May 2022 from the Pew Research Center to explore energy sources, climate change issues and factors influencing the adoption of Electric Vehicles (EVs). It was administered to a sample of 10,282 U.S. adults from a total of 11,674, achieving an 88% response rate. The survey was conducted to gauge public opinion on topics related to climate change. Cross-tabulation between the six questions related to EVs and four questions on demographics is performed. The interpretation of the results is essential to support EV adoption. Equally important is the capacity to predict the likelihood of EV adoption. A classification model is proposed, embedding several cutting-edge classifiers. As the neutral segment of population is significant (over 22%), the 1st mapping included this segment associated to the “Likely” (to adopt) class, whereas the 2nd mapping included this segment associated to the “Unlikely” class. Several classifiers are tested as baseline (Logistic Regression) or as cutting-edge algorithms (Random Forest-RF, eXtreme Gradient Boost-XGB, Light Gradient Boosting Machine-LGBM, Support Vector Classifier). For the 1st mapping, the RF model shows the best performance AUC=0.83. For the 2nd mapping, RF again performs well with the highest accuracy and AUC (0.86). XGB and LGBM have higher precision, but significantly lower recall compared to RF, which reduces their F1 scores. The most influential features for EV adoption are identified with feature importance: 1) Favor or oppose phasing out new gasoline cars and trucks by 2035; 2) Favor or oppose providing incentives to increase use of hybrid and EV; 3) More important priority for addressing America’s energy supply; (4) Family income.}, archive = {J_ACCESS}, author = {Simona-Vasilica Oprea and Adela Bâra}, doi = {10.1109/ACCESS.2025.3597101}, journal = {IEEE Access}, pages = {141937-141957}, shortjournal = {IEEE Access}, title = {How predictable is electric vehicle adoption? exploring the broader role of renewables in transportation using a data-driven approach}, volume = {13}, year = {2025}, }
• (2025). Study on vibration characteristics of three-dimensional coil core transformer under interturn short circuit fault. ACCESS, 13, 141920-141936. (www)

  @article{ , abstract = {In this study, the vibration characteristics of the core and winding of an SG-RL-50 / 0.38 / 0.22 open three-dimensional wound core transformer were studied. Firstly, the magnetostrictive characteristics and stress-strain characteristics of oriented electrical steel and copper wire are measured, and the B- $\lambda $ curve and stress-strain $\sigma $ - $\varepsilon $ curve are given. Secondly, the theory and calculation method of the short-circuit current, leakage magnetic field and axial spring mass model of the transformer were analyzed. On this basis, electromagnetic-vibration research of a three-dimensional wound core transformer under different interturn short circuit was carried out. The magnetic induction intensity, vibration acceleration and vibration displacement characteristic curves of normal operation conditions, short-circuit 1 turn, 3 turns, 9 turns, and 19 turns are given, and their amplitude-frequency characteristics are analyzed. Finally, a prototype of three-dimensional wound core transformer was manufactured. An experimental platform was built to measure the vibration of the core and winding under an interturn short circuit fault, and the results were compared with the simulation results. The results show that the interturn short-circuit fault significantly change the vibration characteristics of the transformer, and the characteristic frequency and amplitude of the winding vibration are much higher than those of the core. This finding provides a key basis for monitoring transformer interturn short-circuit faults.}, archive = {J_ACCESS}, author = {Xianghui Chang and Guoxin Zhao and Dezhi Chen and Ziyuan Xin and Zhonghuan Su}, doi = {10.1109/ACCESS.2025.3591629}, journal = {IEEE Access}, pages = {141920-141936}, shortjournal = {IEEE Access}, title = {Study on vibration characteristics of three-dimensional coil core transformer under interturn short circuit fault}, volume = {13}, year = {2025}, }
• (2025). Asymmetric network based on CNN and attention mechanisms for thyroid nodule segmentation. ACCESS, 13, 141908-141919. (www)

  @article{ , abstract = {Thyroid nodules are detected in up to 65% of the general population, and their early identification is clinically significant for formulating personalized treatment plans. Precise segmentation of nodules in ultrasound images is essential for assessing their morphological and pathological characteristics. In current clinical practice, the annotation of nodule regions primarily relies on manual operations performed by physicians. This process is not only time-consuming but also labor-intensive. Furthermore, due to variations in physicians’ professional experience, the annotation results are difficult to ensure consistency and reliability. This study proposes an asymmetric encoder-decoder segmentation architecture based on Convolutional Neural Networks (CNN) and attention mechanisms for automatically and precisely segmenting thyroid nodules in ultrasound images. The framework introduces an Efficient Convolutional Block (ECB) to extract high-level semantic features, constructs a Convolutional Modulation Module (CMM) to enhance feature representation, and incorporates a Spatial Semantic Enhancement Module (SSEM) to optimize detail reconstruction. Experimental results on a public thyroid nodule ultrasound dataset demonstrate that the proposed method significantly outperforms existing models’ overall segmentation performance, achieving a Dice Similarity Coefficient (DSC) of 0.8332 and an Intersection over Union (IOU) of 0.7473 on the test set. Ablation studies and visual analysis further confirm the effectiveness and interpretability of each module.}, archive = {J_ACCESS}, author = {Zhiheng Zhang and Lin Li and Cheng Zhao and Peng Ren and Ran Zhang}, doi = {10.1109/ACCESS.2025.3597460}, journal = {IEEE Access}, pages = {141908-141919}, shortjournal = {IEEE Access}, title = {Asymmetric network based on CNN and attention mechanisms for thyroid nodule segmentation}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable CPW band-stop filter based on loaded T-shaped defected ground structure. ACCESS, 13, 141897-141907. (www)

  @article{ , abstract = {This paper presents the design and implementation of a reconfigurable band-stop filter based on a coplanar waveguide (CPW) transmission line incorporating defected ground structures (DGS). The proposed filter consists of two pairs of T-shaped resonators loaded with varactor diodes and embedded in the ground plane of the CPW. The biasing configuration in the pair of resonators, either symmetric or asymmetric, enables dynamic tuning of the center stopband frequency and its bandwidth. The filter also features switchable functionality, when the resonators are deactivated via forward-biased diodes, the structure behaves as an all-pass filter, exhibiting insertion loss below 1 dB over the 525 MHz to 3.255 GHz range. A parametric study investigates the influence of the spacing between resonators on the filter’s response to account for the mode splitting effect. Full-wave simulations and experimental results demonstrate tunable stopbands with isolation levels better than 20 dB from 1.40 to 1.65 GHz, with bandwidths ranging from 50 to 175 MHz. The proposed filter exhibits sharp roll-off characteristics, effective tuning, and multifunctional behavior with large isolation in the stopband and low insertion loss in the passband, making it a strong candidate for integration in adaptive RF front-ends, cognitive radio systems, and frequency-agile microwave circuits.}, archive = {J_ACCESS}, author = {Claudia E. Perez-Martinez and Lourdes Martinez-Lopez and Rosalba Martinez-Lopez and Jose I. Martinez-Lopez}, doi = {10.1109/ACCESS.2025.3594721}, journal = {IEEE Access}, pages = {141897-141907}, shortjournal = {IEEE Access}, title = {Reconfigurable CPW band-stop filter based on loaded T-shaped defected ground structure}, volume = {13}, year = {2025}, }
• (2025). FMCNN: Raw-data type identification using feature matrix and CNN. ACCESS, 13, 141886-141896. (www)

  @article{ , abstract = {This paper introduces FMCNN, a novel classification method that combines a two-dimensional feature matrix capturing raw data characteristics with a CNN-based classifier. Raw data are the most fundamental form of digital information, and accurate identification of data type and bit lengths per sample is essential for meaningful use and automated processing in the absence of metadata. Existing file type identification techniques remain inadequate for effectively handling such data. Before feeding raw data into the CNN model, FMCNN extracts feature matrices from the raw data to enable classification regardless of content or format. The method was evaluated using a dataset of twenty-one data types and comparisons with several state-of-the-art classification models. It achieved an average accuracy of 89.65 percent and a weighted average $F1$ score of 0.8938, outperforming all comparison models despite using between five and two hundred times fewer parameters. These results demonstrate the effectiveness of the proposed method in achieving simplicity and high classification accuracy.}, archive = {J_ACCESS}, author = {Eunsu Lee and Hoon Yoo}, doi = {10.1109/ACCESS.2025.3597684}, journal = {IEEE Access}, pages = {141886-141896}, shortjournal = {IEEE Access}, title = {FMCNN: Raw-data type identification using feature matrix and CNN}, volume = {13}, year = {2025}, }
• (2025). Key-audit chain (KAC): A resilient and reliable key management framework for DID-based decentralized environments. ACCESS, 13, 141871-141885. (www)

  @article{ , abstract = {This paper proposes an integrated authentication framework to enhance trust, key security, and auditability in Self-Sovereign Identity (SSI) environments. Existing SSI systems face structural limitations: users must manage private keys directly, and there is a lack of mechanisms to verify the state of Verifiable Credentials (VCs) and trace the submission history of Verifiable Presentations (VPs). These gaps lead to difficulties in detecting forgery and ensuring authentication integrity. To address these challenges, this study introduces a FROST (Flexible Round-Optimized Schnorr Threshold Signature)-based threshold signature scheme combined with a Trusted Third Party (TTP) that performs key status verification and metadata auditing. The user wallet automatically queries the status of each selected VC via the issuer’s registry and excludes any revoked or expired credentials from VP generation. When a proof signature is created, related metadata such as signature hashes and device cluster IDs are submitted to the TTP for integrity checks and real-time anomaly detection. The proposed framework is implemented atop the WACI (Wallet and Credential Interaction) protocol and supports interoperability across decentralized systems. Experiments evaluated end-to-end processing time, audit logging performance, and key recovery efficiency. As a result, the system maintained high levels of security and trust while achieving an average response time of less than one second, demonstrating comparable or improved performance compared to related studies on recent SSI-based frameworks such as FutureDID, Kim et al.’s work, and Bisht et al’s work proving its suitability for practical deployment.}, archive = {J_ACCESS}, author = {Jihwan Kim and Younho Lee and Daeseon Choi}, doi = {10.1109/ACCESS.2025.3597593}, journal = {IEEE Access}, pages = {141871-141885}, shortjournal = {IEEE Access}, title = {Key-audit chain (KAC): A resilient and reliable key management framework for DID-based decentralized environments}, volume = {13}, year = {2025}, }
• (2025). Predicting adolescent concern over unhealthy food ads using explainable AI. ACCESS, 13, 141858-141870. (www)

  @article{ , abstract = {Machine learning and explainable AI techniques are powerful mechanisms to analyze the influence of unhealthy food advertisements on health awareness and concern levels. This research aims to predict levels of concern about unhealthy food advertisements and identify significant demographic and behavioral variables that modify the need for these concerns. A survey was conducted, and responses from 1030 adolescents were gathered on their knowledge about food advertisements and what affects their perceptions of health. Stacking Ensemble, Random Forest, and XGBoost with hyperparameter optimization and synthetic oversampling techniques were applied to predict three categories of concern levels in unhealthy food advertisements. Stacking Ensemble, utilizing Random Forest, K-Nearest Neighbors, and Support Vector Machines as base learners and Logistic Regression as the meta-learner, yielded the highest prediction accuracy of 98.54% and an F1 score of 0.99. The most relevant features for predicting the food advertisement concern levels were age, parental education and advertisement types, such as free toys and celebrity endorsement. LIME and SHAP-integrated interpretability methods have been utilized for a proper understanding of how advertisement strategies influence adolescent health awareness. The developments resulted from growing research efforts in which predictive analytics is connected to explainability in understanding and mitigating the adverse influence of unhealthy food advertisements. The results bear implications for public health policies, advertising regulations, and educational campaigns moving against improved adolescent dietary behaviors and awareness. The private dataset and implementation codes are at the following link: https://github.com/MdFahimShahoriar/adolescent-food-ad-concern-prediction}, archive = {J_ACCESS}, author = {Md. Fahim Shahoriar Titu and Md Rohanur Rahman and Afifa Zain Apurba and Mohammad Abdul Qayum and Riasat Khan}, doi = {10.1109/ACCESS.2025.3588190}, journal = {IEEE Access}, pages = {141858-141870}, shortjournal = {IEEE Access}, title = {Predicting adolescent concern over unhealthy food ads using explainable AI}, volume = {13}, year = {2025}, }
• (2025). Applying causal machine learning to spatiotemporal data analysis: An investigation of opportunities and challenges. ACCESS, 13, 141832-141857. (www)

  @article{ , abstract = {Traditional spatiotemporal data analysis often relies on predictive models that overlook causal relationships, making it difficult to identify true drivers and formulate effective interventions. To bridge this gap, we review causal machine learning (CML) techniques for spatiotemporal data, aiming to provide robust insights into their unique advantages. Our literature review reveals that fewer than 1% of studies in major databases explicitly integrate CML with spatiotemporal analysis. After rigorous screening, we analyze 51 relevant papers, categorizing their contributions into four key areas (totaling 62 methodological approaches due to multi-category papers): 1) causal effect discovery and estimation (32 approaches), 2) prediction accuracy enhancement (19), 3) pattern recognition limitations (10), and 4) interpretability (1). This distribution highlights a critical research gap, particularly in interpretability and comprehensive frameworks. We further examine unique challenges in spatiotemporal data, such as spatial autocorrelation and temporal dependencies, that complicate causal inference but also present opportunities for innovation. Promising approaches include the synergy of spatiotemporal Granger causality and structural equation modeling with spatial lags, which capture complex interdependencies while preserving interpretability. Future directions include developing interpretable causal models, advancing real-time causal inference in dynamic environments, and addressing computational challenges (scalability, efficiency, and complexity-interpretability trade-offs). We also discuss ethical considerations, such as bias mitigation in causal discovery and societal implications of spatiotemporal causal inference. By synthesizing challenges and opportunities, this work advances the application of CML in spatiotemporal analysis, with implications for climate science, economics, epidemiology, and urban planning.}, archive = {J_ACCESS}, author = {Christian M. Mulomba and Vogel M. Kiketa and David M. Kutangila and Pescie H. K. Mampuya and Junior N. Mukenze and Landry M. Kasunzi and Kyandoghere Kyamakya and Tasho Tashev and Selain K. Kasereka}, doi = {10.1109/ACCESS.2025.3596680}, journal = {IEEE Access}, pages = {141832-141857}, shortjournal = {IEEE Access}, title = {Applying causal machine learning to spatiotemporal data analysis: An investigation of opportunities and challenges}, volume = {13}, year = {2025}, }
• (2025). Toward specialized wireless networks using an ML-driven radio interface. ACCESS, 13, 141814-141831. (www)

  @article{ , abstract = {Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Therefore, meeting the stringent requirements of next-generation applications incorporating technology advances and operating in novel scenarios will necessitate wireless specialized networks which we refer to as SpecNets. These networks, equipped with cognitive capabilities, dynamically adapt to the unique demands of each application, e.g., by automatically selecting and configuring network mechanisms. An enabler of SpecNets are the recent advances in artificial intelligence and machine learning (AI/ML), which allow to continuously learn and react to changing requirements and scenarios. By integrating AI/ML functionalities, SpecNets will fully leverage the concept of AI/ML-defined radios (MLDRs) that are able to autonomously establish their own communication protocols by acquiring contextual information and dynamically adapting to it. In this paper, we introduce SpecNets and explain how MLDR interfaces enable this concept. We present three illustrative use cases for wireless local area networks (WLANs): bespoke industrial networks, traffic-aware robust THz links, and coexisting networks. Finally, we showcase SpecNets’ benefits in the industrial use case by introducing a lightweight, fast-converging ML agent based on multi-armed bandits (MABs). This agent dynamically optimizes channel access to meet varying performance needs: high throughput, low delay, or fair access. Results demonstrate significant gains over IEEE 802.11, highlighting the system’s autonomous adaptability across diverse scenarios.}, archive = {J_ACCESS}, author = {Kamil Szczech and Maksymilian Wojnar and Katarzyna Kosek-Szott and Krzysztof Rusek and Szymon Szott and Dileepa Marasinghe and Nandana Rajatheva and Richard Combes and Francesc Wilhelmi and Anders Jonsson and Boris Bellalta}, doi = {10.1109/ACCESS.2025.3597400}, journal = {IEEE Access}, pages = {141814-141831}, shortjournal = {IEEE Access}, title = {Toward specialized wireless networks using an ML-driven radio interface}, volume = {13}, year = {2025}, }
• (2025). Formal verification for preventing misconfigured access policies in kubernetes clusters. ACCESS, 13, 141798-141813. (www)

  @article{ , abstract = {Kubernetes clusters now underpin the bulk of modern production workloads, recent 2024 Cloud Native Computing Foundation surveys report >96% enterprise adoption, stretching from 5G edge nodes and AI/ML pipelines to heavily-regulated fintech and healthcare back-ends. Every action in those environments funnels through the API server, so a single access-control slip can jeopardise an entire fleet. Yet most deployments still rely on a patchwork of Role-Based Access Control (RBAC) rules and policy-as-code admission controllers such as OPA Gatekeeper or Kyverno. In practice these controls are brittle: minor syntactic oversights, wildcard privileges, or conflicting rules can silently create privilege-escalation paths that elude linters and manual review. This paper presents a framework that models both RBAC and admission policies as first-order logic and uses an SMT solver to exhaustively search for counter-examples to stated security invariants before policies reach the cluster. The approach detects policy conflicts, unreachable denies, and unintended permissions. Three real-world case studies are presented to illustrate how the framework reveals latent misconfigurations and validates the soundness of the corrected rules. These case studies include a supply-chain image bypass, an RBAC “shadow-admi” escalation, and a multi-tenant namespace breach. To aid replication and further study, we release a fully scripted GitHub testbed: a Minikube cluster, AuthzForce PDP, admission-webhook adapter, and Z3-backed CLI that recreates each scenario and verifies policies end-to-end. While the framework does not address runtime threats, it closes a critical verification gap and substantially raises the bar for attackers targeting the most widely deployed orchestration platform.}, archive = {J_ACCESS}, author = {Aditya Sissodiya and Eric Chiquito and Ulf Bodin and Johan Kristiansson}, doi = {10.1109/ACCESS.2025.3597504}, journal = {IEEE Access}, pages = {141798-141813}, shortjournal = {IEEE Access}, title = {Formal verification for preventing misconfigured access policies in kubernetes clusters}, volume = {13}, year = {2025}, }
• (2025). Efficient wheat disease identification using hybrid swin-SHARP vision model. ACCESS, 13, 141781-141797. (www)

  @article{ , abstract = {Accurate identification of wheat diseases is an essential component for increasing crop yields and guaranteeing global food security. However, subjective opinions, errors, and laborious procedures frequently limit traditional approaches, which are based on expert knowledge. To address these challenges, we propose a Swin-Streamlined High Accuracy and Reduced Parameters (Swin-SHARP) Transformer for classifying wheat diseases. The feature extraction capability of the Swin-SHARP model is enhanced by optimizing hierarchical representations and eliminating the requirement for intricate layers and attention heads. The proposed model reduces the parameters by 82.5%, from 48.9 to 8.5 million, without compromising accuracy. A Swin-SHARP Vision Hybrid (SVH) model integrates two powerful transformer architectures, the Swin-SHARP transformer that uses shifted windows, and the Vision Transformer (ViT) for wheat disease classification. The model features a customized convolutional auto-encoder decoder that inputs 3-dimensional features. The dual convolutional layer in the encoder with a stride of 2 and kernel size 3 is activated by ReLU, that maps a 3-channel input to 16 and then 32 feature maps. This architecture reduces dimensionality by 33% while retaining crucial information for precise disease detection. Our proposed model achieved an accuracy of 98.2% on the Wheat Diseases dataset, which is also cross-validated using the YELLOW-RUST-19 benchmark by achieving 97.2% accuracy. The proposed model outperformed state-of-the-art existing models, including CNN-CGLCM-HSV-SVM, GhostNet V2, ResNet 50, MobileNet V2–VGG-16, CNN-CGL CM-HSV-SVM, and Swin Transformer by 4.8%, 1.76%, 1.2%, 13.6%, 4.8%, and 1.4% respectively, on the YELLOW-RUST-19 dataset. These findings concluded that the SVH model is a strong approach to accurately detect yellow rust disease and can be used in real time, particularly in resource-constrained agricultural settings.}, archive = {J_ACCESS}, author = {Waqar Khalid and Yazeed Alkharijah and Syed Muhammad Usman and Shehzad Khalid}, doi = {10.1109/ACCESS.2025.3597539}, journal = {IEEE Access}, pages = {141781-141797}, shortjournal = {IEEE Access}, title = {Efficient wheat disease identification using hybrid swin-SHARP vision model}, volume = {13}, year = {2025}, }
• (2025). GCML: Geometric correlation encoding network with multi-scale local feature extraction for accurate point cloud registration. ACCESS, 13, 141765-141780. (www)

  @article{ , abstract = {Point cloud registration, a critical component of various applications, aims to establish correspondences between two point clouds. While detector-free methods exhibit outstanding accuracy, they only encode simple geometric features of point clouds while failing to comprehensively model rich geometric correlations between points. Moreover, they treat point clouds as sequential data and only employ the transformer to perform global message propagation during feature enhancement. This approach inevitably neglects the extraction of multi-scale local features, thereby compromising the descriptive capability of point cloud features. This study introduces GCML, a novel detector-free approach that tackles these issues. For the first problem, GCML develops a geometric correlation encoding module (GCEM) that draws inspiration from the Denavit-Hartenberg (DH) modeling method in robotics to effectively encode the geometric correlations between each pair of points within point clouds. This method substantially captures rich geometric structure information inherent in point clouds, thereby improving registration accuracy. For the second problem, GCML proposes a multi-scale local feature perception module (MLFP) that integrates multi-scale local features of point clouds during the feature enhancement stage. Subsequently, GCML employs the transformer architecture to facilitate global message propagation. This design effectively captures both local and global features of point clouds, hence enhancing the descriptive capability of point cloud features. Extensive experiments demonstrate that GCML achieves superior performance on all ModelNet40, DModelNet40, and KITTI datasets.}, archive = {J_ACCESS}, author = {Jinlei Zhuang and Ziteng Wang and Weiqiang Ma}, doi = {10.1109/ACCESS.2025.3597310}, journal = {IEEE Access}, pages = {141765-141780}, shortjournal = {IEEE Access}, title = {GCML: Geometric correlation encoding network with multi-scale local feature extraction for accurate point cloud registration}, volume = {13}, year = {2025}, }
• (2025). Dynamically tunable multidimensional feature focusing and diffusion networks for water surface debris detection. ACCESS, 13, 141748-141764. (www)

  @article{ , abstract = {With the increasing level of industrialization, water surface debris detection has emerged as a significant research topic. However, challenges such as complex backgrounds, overlapping targets, and reflections on water surfaces often hinder effective waste detection, resulting in low model accuracy and unreliable recognition. To address these challenges, the Water Surface Debris DEtection TRansformer (WSD-DETR) was proposed. First, a Self-moving Point Convolutional Gating Network (SPCG-Net) was designed, which integrated an adaptive point-moving mechanism with a convolutional gating linear unit to enhance the flexibility and accuracy of feature extraction. Second, an Attention-based Re-parameterization of Intra-scale Feature Interactions (ARIFI) module was constructed to process high-level features extracted from the backbone. This module employed a single-scale transformer encoder with Re-parameterized Batch Normalization (RepBN) to improve focus on small and medium-sized targets in water surface waste detection, thereby capturing relationships between semantic concepts and conceptual entities. Furthermore, a Focal-Diffuse Feature Pyramid Network (FD-FPN) was introduced to accurately capture and integrate key feature information through focused feature fusion techniques while utilizing cross-scale diffusion analysis to efficiently transfer and enhance feature information across different scales. This approach significantly improved feature expression capability and overall model performance. Experimental results indicated that WSD-DETR achieved a precision of 88.1%, and reduced model parameters by 12.6% compared to the Real-Time DEtection TRansformer (RT-DETR), and increased mAP@50 and mAP@50:90 values by 4.4% and 5.5%, respectively. These outcomes demonstrated substantial potential for water surface debris detection.}, archive = {J_ACCESS}, author = {Chong Zhang and Jie Yue and Jianglong Fu}, doi = {10.1109/ACCESS.2025.3597727}, journal = {IEEE Access}, pages = {141748-141764}, shortjournal = {IEEE Access}, title = {Dynamically tunable multidimensional feature focusing and diffusion networks for water surface debris detection}, volume = {13}, year = {2025}, }
• (2025). Adaptive flight control of fixed-wing UAVs for enhanced optical signal line-of-sight maintenance. ACCESS, 13, 141732-141747. (www)

  @article{ , abstract = {Uncrewed aerial vehicles (UAVs) based wireless free space optics communication (FSOC) is one of the strong candidate 6G technologies for 3rd Generation Partnership Project (3GPP) standardization. For reliable FSOC, maintenance of the optical signal’s line of sight (LoS) between UAVs and ground terminals (GTs) is important as it has a significant impact on the link quality and throughput. Previous studies have considered various factors that affect LoS in FSOC, including atmospheric attenuation, turbulence, and pointing error loss. However, practical aspects of the UAV physics and flight characteristics have not been properly considered. Among UAV types, fixed-wing UAVs have many advantages, which make them a strong candidate for FSOC. Low altitudes up to 1 km provide the best channel characteristics as UAVs fly closest to the GTs and are the easiest to deploy and operate, which is why fixed-wing UAVs that serve as low altitude platforms (LAPs) are the focus in this paper. The details of fixed-wing UAVs, including flight characteristics as well as physical limitations, are considered in the proposed LoS maintenance control scheme, which supports the FSOC link between the UAV and GT. The objective of the proposed scheme is to formulate an optimal control-based flight trajectory that minimizes the energy consumption while enhancing the communication throughput. This optimal control is designed based on the UAV’s three-dimensional (3D) position, attitude (i.e., roll, pitch, yaw), as well as its kinematic and dynamic physics. The UAV’s flight trajectory needs to consider these practical physical constraints to maintain LoS between the UAV and GT FSOC transceivers. The simulation experiment results show that the energy consumption remains similar, while the proposed scheme eliminates throughput degradation caused by LoS loss. This demonstrates that the proposed FSOC LoS maintenance (FLM) scheme can provide an improved performance compared to the convex area energy-efficient trajectory optimization (CA2ET) control scheme.}, archive = {J_ACCESS}, author = {Dae-Kwang Kim and Minsu Choi and Minseung Park and Jong-Moon Chung}, doi = {10.1109/ACCESS.2025.3597351}, journal = {IEEE Access}, pages = {141732-141747}, shortjournal = {IEEE Access}, title = {Adaptive flight control of fixed-wing UAVs for enhanced optical signal line-of-sight maintenance}, volume = {13}, year = {2025}, }
• (2025). Optimization-based path planning with artificial potential function. ACCESS, 13, 141717-141731. (www)

  @article{ , abstract = {Path planning remains a critical challenge in autonomous ground vehicle (AGV) systems, particularly in dynamic environments where real-time adaptation is essential. Unlike existing methods that often struggle with computational efficiency or environmental adaptability, this study introduces a novel optimization-based approach that seamlessly integrates artificial potential functions with the Levenberg-Marquardt optimization method. The proposed algorithm addresses the key limitations of traditional path planning by simultaneously handling static obstacles and dynamic environmental changes through a unified cost function framework. The innovation lies in formulating the dynamic environment using traversable and non-traversable regions defined by artificial potential functions a computationally efficient approach for real-time obstacle representation. The Levenberg-Marquardt method is specifically chosen for its superior convergence properties and robustness in nonlinear optimization problems compared to gradient-based alternatives. The optimization problem incorporates vehicle dynamics, control constraints, and environmental variations into a single framework. An integral controller ensures a precise path following the generated optimal trajectory. Experimental validation in both simulation and real-world scenarios demonstrates the algorithm’s effectiveness and practical applicability. A validation video is available at https://youtu.be/XVLes855hSw}, archive = {J_ACCESS}, author = {Seongyeon Kim and Kiyun Gil and Jongho Shin}, doi = {10.1109/ACCESS.2025.3597311}, journal = {IEEE Access}, pages = {141717-141731}, shortjournal = {IEEE Access}, title = {Optimization-based path planning with artificial potential function}, volume = {13}, year = {2025}, }
• (2025). TSB-forecast: A short-term load forecasting model in smart cities for integrating time series embeddings and large language models. ACCESS, 13, 141694-141716. (www)

  @article{ , abstract = {In smart cities, energy management systems are essential for efficient resource utilization, enhanced operational efficiency, and sustainability promotion. This work presents a novel load forecasting model, TSB-Forecast (Time Series BERT), a hybrid machine learning model aiming to improve short-term electrical demand forecasting by integrating structured and unstructured data. The model uses Sentence Bidirectional Encoder Representations from Transformers (SBERT) to extract semantic characteristics from textual news and Time to Vector (Time2Vec) to capture temporal patterns, acquiring cyclical behavior and context-sensitive impacts. TSB-Forecast shows significant improvements reducing MAE by 38.7%, RMSE by 18.2%, and SMAPE by 50.6% compared to several baselines, including the official ENTSO-E forecast, Extra Trees Regressor (ETR), a hybrid model with basic text processing (M6), and a deep learning model combining GRU and CNN (CNN-GRU). TSB-Forecast demonstrates a stronger ability to combine time-related and contextual information, which helps reduce errors. Our contributions in this paper are fourfold and can be summarized as follows: The model initially integrates multi-source inputs, encompassing load demand, BBC News, meteorological data, and holiday information. Secondly, it utilizes Time2Vec and SBERT for improved temporal and semantic feature encoding. Subsequently, Extreme Gradient Boosting (XGBoost) and Extra Trees Regressor (ETR) collectively enhance the robustness of a stacked ensemble. Finally, a thorough empirical analysis showing consistent outperformance of the proposed model over all the forecasting measures against baseline models.}, archive = {J_ACCESS}, author = {Mohamed Mahmoud Hasan and Neamat El-Tazi and Ramadan Moawad and Amany H. B. Eissa}, doi = {10.1109/ACCESS.2025.3597421}, journal = {IEEE Access}, pages = {141694-141716}, shortjournal = {IEEE Access}, title = {TSB-forecast: A short-term load forecasting model in smart cities for integrating time series embeddings and large language models}, volume = {13}, year = {2025}, }
• (2025). Smart fault detection, classification, and localization in distribution networks: AI-driven approaches and emerging technologies. ACCESS, 13, 141664-141693. (www)

  @article{ , abstract = {Distribution networks play a vital role in bridging transmission systems and end users, offering enhanced flexibility, decentralization, and the capacity to integrate distributed generation. However, with nations worldwide actively pursuing carbon neutrality and emission peak goals, sustainable energy sources such as solar and wind are increasingly penetrating distribution networks, posing significant challenges to conventional fault detection, classification, and localization techniques due to bidirectional power flows, dynamic fault currents, and rising network complexity. These challenges manifest as reduced sensitivity of protection systems in distribution networks, increased difficulty in identifying high impedance faults, and frequent misclassification or mislocation of faults under dynamic network conditions. To address these limitations, this paper presents a comprehensive review of artificial intelligence-driven approaches and emerging technologies that are specifically tailored for fault analysis in distribution networks, to enhance diagnostic accuracy, adaptability, and real-time decision-making efficiency. Following the chronological development of artificial intelligence, the review systematically investigates smart fault detection methods applied to fault scenarios in distribution networks, with a particular emphasis on presenting fault type classification and fault localization separately to facilitate a logically structured understanding. In addition, common types of distribution network faults are examined, and the impact of distributed generation on fault behavior, electrical characteristics, and protection coordination is critically assessed. The review further distinguishes between artificial intelligence-based smart approaches that directly process raw distribution networks signal data and those that rely on advanced feature extraction techniques to enhance functional performance. This review also explores the emerging potential of large language models to enhance the explainability of diagnostics, support multi-agent coordination, and enable natural language-based fault reasoning. The insights offered herein are expected to provide practical guidance for engineers and researchers for selecting and deploying intelligent fault diagnosis strategies in future distribution networks with high distributed generation penetration.}, archive = {J_ACCESS}, author = {Jianxian Wang and Hazlie Mokhlis and Nurulafiqah Nadzirah Mansor and Hazlee Azil Illias and Agileswari K. Ramasamy and Xingyu Wu and Siqi Wang}, doi = {10.1109/ACCESS.2025.3596791}, journal = {IEEE Access}, pages = {141664-141693}, shortjournal = {IEEE Access}, title = {Smart fault detection, classification, and localization in distribution networks: AI-driven approaches and emerging technologies}, volume = {13}, year = {2025}, }
• (2025). Detection and tracking of MAVs using a rosette scanning pattern LiDAR. ACCESS, 13, 141651-141663. (www)

  @article{ , abstract = {The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A pan-tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The system’s tracking capabilities (both in coverage and distance), as well as its accuracy are validated and compared to State Of The Art (SOTA) models, demonstrating improved performance, particularly in terms of coverage and maximum tracking distance. Our approach achieved accuracy on par with the SOTA indoor method while increasing the maximum detection range by approximately $85\;\%$ beyond the SOTA outdoor method to $130\;m$ . Additionally, our method yields at least a twofold increase in track coverage and returned point counts.}, archive = {J_ACCESS}, author = {Sándor Gazdag and Tom Möller and Anita Keszler and András L. Majdik}, doi = {10.1109/ACCESS.2025.3596857}, journal = {IEEE Access}, pages = {141651-141663}, shortjournal = {IEEE Access}, title = {Detection and tracking of MAVs using a rosette scanning pattern LiDAR}, volume = {13}, year = {2025}, }
• (2025). Advancing NR-V2X: Evaluating mode 2 performance through analytical modeling and simulations. ACCESS, 13, 141636-141650. (www)

  @article{ , abstract = {The third-generation partnership project (3GPP) has introduced a new radio (NR) for vehicle-to-everything (V2X). It assigns Mode 2 to allow user equipment (UEs) to reserve and select radio resources autonomously. This paper comprehensively analyzes the NR-V2X Mode 2 performance, focusing on the packet reception ratio (PRR). PRR is a critical metric in evaluating the efficacy of vehicular networks. We propose an analytical model that correlates PRR with the distance between the transmitter and receiver. This model helps us understand NR-V2X Mode 2 communication in autonomous operation. We also conduct simulations to validate our model. These simulations reflect various vehicular traffic scenarios. Our findings reveal strategies to optimize resource allocation in NR-V2X Mode 2 networks. Specifically, altering specific parameters can notably enhance network performance. This research is valuable for both academic and industry circles. It provides insights into improving communications in autonomous vehicles. Moreover, it offers practical guidance for industry experts aiming to advance V2X communication in the era of autonomous driving.}, archive = {J_ACCESS}, author = {Mahmoud Elsharief and Saifur Rahman Sabuj and Nathan Jeong and Han-Shin Jo}, doi = {10.1109/ACCESS.2025.3596946}, journal = {IEEE Access}, pages = {141636-141650}, shortjournal = {IEEE Access}, title = {Advancing NR-V2X: Evaluating mode 2 performance through analytical modeling and simulations}, volume = {13}, year = {2025}, }
• (2025). Multimodality-based situational knowledge for obstacle detection and alert generation to enhance the navigation assistive systems. ACCESS, 13, 141619-141635. (www)

  @article{ , abstract = {Recently, Deep Learning (DL) architectures have increasingly contributed to technological advancements, particularly for people with disabilities, by improving assistive systems. By adopting advanced DL models, intelligent assistive systems have recently become powerful with object detection, image captioning, and speech generation. The navigation system requires a concise and comprehensive alert generation that depends on an efficient caption generation model to describe the obstacles in the surroundings. This work introduces a Multimodality-based Situational Knowledge-aware (MuSK) system designed for obstacle alert generation in outdoor sidewalk environments by integrating visual and non-visual cues through contextual multimodal processing. To enhance obstacle detection, a layered attention mechanism fuses multi-sensory data with the visual feature maps extracted from the multimodal images, enabling obstacle-specific knowledge learning from diverse modalities. A critical innovation of this work is the personalized attention score, computed by a custom logic gate-based approach that dynamically adjusts obstacle weights based on situational knowledge based on multi-sensory data. Consequently, the logic gate mechanism refines attention scores by filtering and prioritizing relevant obstacle cues, improving obstacle detection accuracy. Furthermore, the cross-attention modeling of multimodal cues generates real-time, context-aware captions of detected obstacles, enabling precise situational awareness and safer navigation for the visually impaired. Thus, the experimental outcome reveals that the proposed MuSK system proves an accurate obstacle alert generation while tested on benchmark datasets, emphasizing the importance of personalized attention mechanisms and multi-sensory integration in improving obstacle detection in dynamic outdoor environments.}, archive = {J_ACCESS}, author = {Raniyah Wazirali and Eman Alkhamash}, doi = {10.1109/ACCESS.2025.3596720}, journal = {IEEE Access}, pages = {141619-141635}, shortjournal = {IEEE Access}, title = {Multimodality-based situational knowledge for obstacle detection and alert generation to enhance the navigation assistive systems}, volume = {13}, year = {2025}, }
• (2025). DropKAN: Dropout Kolmogorov–Arnold networks. ACCESS, 13, 141609-141618. (www)

  @article{ , abstract = {We propose DropKAN (Dropout Kolmogorov—Arnold Networks), a regularization method that introduces dropout masks at the edge level within Kolmogorov—Arnold Networks (KANs) layers, randomly masking a subset of activation outputs in the computation graph. Forward pass analysis reveals that DropKAN, when combined with scaling, accurately preserves the expected output signal magnitude in line with theoretical expectations. In contrast, conventional neuron-level Dropout—with its scaling—distorts signal propagation due to the nonlinear nature of KANs activations. Through extensive experiments on several classical benchmark datasets, DropKAN consistently achieves superior test accuracy compared to both unregularized KANs and KANs employing standard Dropout. Sensitivity analysis across dropout rates reveals DropKAN’s robustness, effectively mitigating underfitting at high dropout levels by preserving partial neuron activity via edge-level masking. Additionally, DropKAN demonstrates enhanced sample efficiency under limited training data conditions, outperforming baselines on multiple large-scale datasets. In computer vision benchmarks (MNIST, Fashion MNIST, EMNIST, CIFAR-10), DropKAN further validates its regularization efficacy by consistently improving generalization over standard KAN and Dropout configurations. These results establish DropKAN as a principled and practical regularization technique for KANs architectures. Our implementation of DropKAN is available at (https://github.com/ghaith81/dropkan).}, archive = {J_ACCESS}, author = {Mohammed Ghaith Altarabichi}, doi = {10.1109/ACCESS.2025.3597554}, journal = {IEEE Access}, pages = {141609-141618}, shortjournal = {IEEE Access}, title = {DropKAN: Dropout Kolmogorov–Arnold networks}, volume = {13}, year = {2025}, }
• (2025). Safeguarding the integrity of online social networks (OSN): Leveraging the efficacy of conv-LSTM-based siamese network to predict hate speech in low resource hindi-english code-mixed text. ACCESS, 13, 141598-141608. (www)

  @article{ , abstract = {In the era of free speech and rapid internet expansion, curbing the dissemination of offensive content on social media has become a pressing concern for linguists and regulatory bodies. Hate speech not only targets individuals or groups but also impacts their mental well-being, often leading to feelings of anxiety, isolation, and helplessness. Therefore, hate speech detection should be viewed not just as a linguistic task but also as a public health imperative. In multilingual and culturally diverse countries like India, the challenge is heightened by the presence of code-mixed language. While most existing studies focus on monolingual data, our work addresses hate speech detection in Hindi-English code-mixed text. We propose a Convolution-LSTM network that incorporates spatial and temporal features. Furthermore, the model’s performance is enhanced by constructing an ensemble network using an early-fusion-based Siamese architecture. Experimental results demonstrate that our approach outperforms existing baselines in identifying hate speech in low-resource, code-mixed scenarios.}, archive = {J_ACCESS}, author = {Shankar Biradar and Sunil Saumya and Sanjana Kavatagi}, doi = {10.1109/ACCESS.2025.3597144}, journal = {IEEE Access}, pages = {141598-141608}, shortjournal = {IEEE Access}, title = {Safeguarding the integrity of online social networks (OSN): Leveraging the efficacy of conv-LSTM-based siamese network to predict hate speech in low resource hindi-english code-mixed text}, volume = {13}, year = {2025}, }
• (2025). CKACL: A collaborative knowledge-aware contrast learning framework for addressing sparsity and cold-start problems in recommendation system. ACCESS, 13, 141585-141597. (www)

  @article{ , abstract = {Knowledge graphs have emerged as a powerful tool for enhancing recommendation systems by encoding rich side information to model user preferences with improved accuracy. Despite their potential, most knowledge graph-based recommendation systems still face two persistent challenges: data sparsity and cold-start problems. To address these challenges, we propose a novel framework called Collaborative Knowledge-Aware recommendation system with Contrast Learning (CKACL), which effectively integrates collaborative information and knowledge-aware contrastive learning. Our approach derives user and item collaborative graphs, enriching the representation of the user preference with both direct interactions and global collaborative patterns. In addition, CKACL introduces an innovative structural consistency-guided contrastive learning mechanism, where perturbed knowledge graph views with high-order semantics are generated via consistency scores. Comprehensive experiments on three real-world datasets demonstrate CKACL’s superiority in addressing cold-start and data sparsity problems, outperforming state-of-the-art baselines with an average improvement of 20% in recall@20.}, archive = {J_ACCESS}, author = {Xintao Ma and Shuai Wu and Hao Zhang}, doi = {10.1109/ACCESS.2025.3596091}, journal = {IEEE Access}, pages = {141585-141597}, shortjournal = {IEEE Access}, title = {CKACL: A collaborative knowledge-aware contrast learning framework for addressing sparsity and cold-start problems in recommendation system}, volume = {13}, year = {2025}, }
• (2025). Impact of safety signage placement on evacuation behavior in virtual fire scenarios based on EDA data. ACCESS, 13, 141572-141584. (www)

  @article{ , abstract = {Evacuation efficiency during fire emergencies directly influences casualty rates. Traditional fire drills suffer from limited scenario realism, elevated safety risks, and single-modal data acquisition, making it difficult to objectively quantify participants’ physiological and behavioral responses under authentic fire conditions. To address these limitations, this paper employs a high-immersion, controllable virtual reality (VR) hotel environment to simulate fire evacuation while concurrently recording electrodermal activity (EDA). Five features are extracted from the EDA signal: PhasicData, PhasicDriver, Skin Conductance (SC), TonicData, and TonicDriver. Three variables are evaluated, signage height (1m, 0.5m, and 0m), spacing (5m and 10m), and presence of active fire, using a hybrid classification model that integrates an im-proved convolutional neural network (CNN), a Transformer-based sequence encoder, and a multi-layer spiking neural network. The CNN autonomously captures multi-scale local features to heighten sensitivity to stress-related signal changes, the Trans-former encoder models long-range temporal dependencies, and the spiking neural network simulates neural dynamics to accurately reconstruct transient stress spikes. Experimental results yield average classification accuracies of 93.36%, 97.71%, and 95.99% across the three scenarios, demonstrating the neuro-engineering value of combining VR-based simulations with EDA monitoring to optimize safety-sign placement.}, archive = {J_ACCESS}, author = {Wenqi Song and Xu Feng and Yu Song}, doi = {10.1109/ACCESS.2025.3597305}, journal = {IEEE Access}, pages = {141572-141584}, shortjournal = {IEEE Access}, title = {Impact of safety signage placement on evacuation behavior in virtual fire scenarios based on EDA data}, volume = {13}, year = {2025}, }
• (2025). Finite-time and fixed-time synchronization of coupled chaotic HR neurons with chemical and electrical autapses: Sensitivity analysis and control design. ACCESS, 13, 141560-141571. (www)

  @article{ , abstract = {Synchronization of biological neurons plays a significant role in the processing and transmission of biological information, especially for therapeutic purposes. Therefore, designing accurate synchronization control methods is of great importance. On the other hand, the behavior of neurons is strongly influenced by the autapse terms, which are usually not considered in the design of synchronization control. In this paper, using sensitivity analysis, we demonstrate the important role of autapse effects on the behaviors of an isolated Hindmarsh-Rose (HR) neuron. Also, the effects of the chemical and electrical autapses on the behavioral properties of the neuron, such as spike frequency, bursting phenomena, etc., are investigated in several simulations. Furthermore, using a Lyapunov-like method, an asymptotic synchronization controller is designed for coupled HR neurons that are affected by the autapses. Using numerical simulations, the average norm of the synchronization error in 100 seconds without considering the autapse term is 2.1, and with considering its effect, it is 0.02. To ensure synchronization in finite time as well as convergence independent of initial conditions, a finite-time controller and a fixed-time controller are also designed, respectively. Simulations show faster convergence of the finite-time and fixed-time controls, 1.8 seconds and 1 second, respectively, compared to the asymptotic control scheme. Also, the average norm of the synchronization error using these methods is about 0.001 at 100 seconds, while it is 0.02 by using the asymptotic control method. Practically, these findings could directly assist therapists in applying appropriate control signals to achieve desired responses from neuronal networks for therapeutic purposes.}, archive = {J_ACCESS}, author = {Mashkour Mansouri and Fatemeh Jahangiri and Ali Akbar Afzalian}, doi = {10.1109/ACCESS.2025.3596612}, journal = {IEEE Access}, pages = {141560-141571}, shortjournal = {IEEE Access}, title = {Finite-time and fixed-time synchronization of coupled chaotic HR neurons with chemical and electrical autapses: Sensitivity analysis and control design}, volume = {13}, year = {2025}, }
• (2025). Autonomous robotic system for snow removal in large-scale photovoltaic arrays. ACCESS, 13, 141547-141559. (www)

  @article{ , abstract = {Snow accumulation on photovoltaic (PV) panels drastically reduces energy output and can induce uneven mechanical loads that damage the panels. We present a novel autonomous robotic system for efficient PV panel snow removal, featuring two core engineering innovations: (1) an adaptive dual-track suspension locomotion system that maintains traction on slippery, inclined panel surfaces and traverses wide gaps between panels, and (2) a hybrid brush–auger–impeller snow-clearing mechanism that mechanically sweeps snow off the panel and then forcefully ejects it away. These are integrated with a distributed control architecture and a multi-sensor navigation module for fully autonomous operation. In this study, a prototype was developed and tested. The experimental results demonstrated stable motion on 30° sloped panels and effective removal of approximately 95% of snow in a single pass. The robot clears up to 135 m2/h of fresh light snow and maintains a clearing efficiency of over 85% even on refrozen icy layers. This translates to a reduction in manual labor and preserves over 90% of the PV array’s energy output compared with the no-nothing scenario. These results validate the robot design and are in close agreement with theoretical predictions of its stability and performance.}, archive = {J_ACCESS}, author = {Jijiang Wu and Liqun Liu and Qingfeng Wu and Junqiang He and Wanjun Li}, doi = {10.1109/ACCESS.2025.3597534}, journal = {IEEE Access}, pages = {141547-141559}, shortjournal = {IEEE Access}, title = {Autonomous robotic system for snow removal in large-scale photovoltaic arrays}, volume = {13}, year = {2025}, }
• (2025). Comprehensive electromechanical analysis of high-temperature low-sag (HTLS) conductors. ACCESS, 13, 141536-141546. (www)

  @article{ , abstract = {The rising energy consumption in transmission networks has markedly increased the burden on transmission lines, necessitating an expansion in their capacity to accommodate this surge. Consequently, high-temperature low-sag (HTLS) conductors are emerging as substitutes for conventional conductors in recent network upgrades due to their superior power transmission capabilities, especially under elevated temperatures. This paper critically analyzes the feasibility of adopting HTLS conductors over conventional counterparts for future single-circuit overhead transmission lines. Specifically, we evaluate leading HTLS conductors, such as ACCC and ZTACIR, against the conventional AAAC conductor. Following a two-stage methodology, this study presents a comprehensive electromechanical assessment of overhead transmission line. First, ampacity calculations were performed to determine the required conductor cross-sections. Subsequently, mechanical performance was evaluated to quantify structural loads and their impact on tower design. Among the HTLS conductors analyzed, the ACCC exhibited the lowest mechanical loads, indicating a better performance in terms of structural efficiency. ACCC and ZTACIR conductors significantly reduced the weight imposed on transmission towers compared to conventional AAAC, primarily due to their lower tensile forces. Furthermore, our results demonstrate that HTLS conductors present nearly identical electrical parameters across a broad frequency spectrum, conversely to the more variable behavior of conventional alternatives. Based on these results, ACCC conductors are strongly recommended for future projects, pending detailed economic analysis.}, archive = {J_ACCESS}, author = {Jesus E. Guevara Asorza and Jaimis S. L. Colqui and José Pissolato Filho}, doi = {10.1109/ACCESS.2025.3597535}, journal = {IEEE Access}, pages = {141536-141546}, shortjournal = {IEEE Access}, title = {Comprehensive electromechanical analysis of high-temperature low-sag (HTLS) conductors}, volume = {13}, year = {2025}, }
• (2025). Adsorption mechanism and electronic behavior of pd- and pt-modified SnS₂ in detection and adsorption of characteristic gases for oil. ACCESS, 13, 141525-141535. (www)

  @article{ , abstract = {This study utilizes DFT to explore how doping SnS2-based gas sensors with Pd and Pt atoms influences their sensing performance. A comprehensive analysis was conducted on the adsorption properties and gas detection capabilities of these modified sensors toward key dissolved gases commonly found in insulating oil, specifically H2 and C2H2. The findings demonstrate that SnS2 sensors doped with Pd and Pt possess outstanding gas-sensing capabilities. Specifically, the Pd-SnS2 sensor demonstrates a high sensing response of 342.57 to H2, enabling rapid detection of H2 concentration in insulating oil. For C2H2, the recovery time of Pd-SnS2 is only $1.5\times 10 ^{-2}$ s at 498 K, showcasing remarkable fast response capabilities. The adsorption performance follows the order: Pd-SnS2/ $\text{H}_{2} \gt $ Pt-SnS2/ $\text{H}_{2} \gt $ Pt-SnS2/C2 $\text{H}_{2} \gt $ Pd-SnS2/C2H2, with all adsorption processes characterized as chemical adsorption. This study proposes a highly efficient gas-sensing material capable of accurately detecting dissolved gases generated by arc discharge in oil-immersed transformers. The findings provide technical support for early fault warning in transformers and lay a foundation for the safe operation of power equipment and the prevention of explosion accidents.}, archive = {J_ACCESS}, author = {Yulin Yan and Tianmei Sun and Shaopeng Xu and Fan Yu}, doi = {10.1109/ACCESS.2025.3597454}, journal = {IEEE Access}, pages = {141525-141535}, shortjournal = {IEEE Access}, title = {Adsorption mechanism and electronic behavior of pd- and pt-modified SnS₂ in detection and adsorption of characteristic gases for oil}, volume = {13}, year = {2025}, }
• (2025). ThresholdFP: Enhanced durability in browser fingerprinting. ACCESS, 13, 141509-141524. (www)

  @article{ , abstract = {Browser fingerprinting is a powerful tool for user identification in financial and other security-critical applications that require strong authentication. However, due to the instability of browser attributes, fingerprints often change rapidly, reducing their lifespan and negatively impacting user convenience. We propose, a novel linking algorithm designed to extend the durability of browser fingerprints without compromising precision. Instead of replacing the fingerprint after even a small change, computes a difference score between fingerprints and tolerates variations as long as they stay within a predefined threshold. To ensure realistic performance evaluation, we collected and utilized two real-world datasets. Comparative results show that the fingerprints generated by are significantly more persistent than those produced by existing methods, while maintaining near-perfect precision. Notably, our approach achieves an improvement in average tracking duration ranging from 24.33% to 106.30% compared to rival schemes in the literature.}, archive = {J_ACCESS}, author = {Elif Ecem Şamlioğlu and Sinan Emre Taşçi and Muhammed Fatih Gülşen and Albert Levi}, doi = {10.1109/ACCESS.2025.3597821}, journal = {IEEE Access}, pages = {141509-141524}, shortjournal = {IEEE Access}, title = {ThresholdFP: Enhanced durability in browser fingerprinting}, volume = {13}, year = {2025}, }
• (2025). A deterministic time-dependent resilience-based measure for disaster recovery in business continuity applications. ACCESS, 13, 141496-141508. (www)

  @article{ , abstract = {Resilience is generally defined as the ability of a system or entity to recover from a disruptive event. From an engineering perspective, the resilience of a system needs to be quantified to measure and assess its recoverability. Current resilience-based metrics focus on complex engineering and networked systems. However, businesses are adapting the concept of resilience, translated into the known term business continuity. Yet, the common metrics for business continuity and disaster recovery are static in nature and mathematically expressed as single-point values, which can be insufficient for comprehensive decision-making. This article introduces the Business Continuity Recovery Measure (BCRM), a time-dependent resilience metric designed to quantify disaster recovery for business continuity applications. By developing a service-centric measure, businesses can quantify their continuity plans with a suitable measure. The proposed metric takes into account business dynamics, such as allowing organizations to assign weights of importance to their services, accounting for interdependencies across services and business units, and allowing partial operation of services. As illustrated in the article, the proposed BCRM facilitates the comparison of disaster recovery plans for a business by evaluating alternative recovery strategies and focuses not only on restoration time but also progress over time. Utilizing the BCRM, the provided illustrative example showed that a heuristic-based approach can improve the recovery trajectory of weighted operational services by over 100% at specific time intervals compared to a random approach.}, archive = {J_ACCESS}, author = {Basem A. Alkhaleel}, doi = {10.1109/ACCESS.2025.3597335}, journal = {IEEE Access}, pages = {141496-141508}, shortjournal = {IEEE Access}, title = {A deterministic time-dependent resilience-based measure for disaster recovery in business continuity applications}, volume = {13}, year = {2025}, }
• (2025). Deepfakes in visual art: Differentiating AI-generated art from human art using convolutional neural networks (CNN). ACCESS, 13, 141484-141495. (www)

  @article{ , abstract = {As AI technology evolves, seeing is not believing. The boundary between human and machine creativity is increasingly blurred, presenting challenges for the art industry. This is more pronounced nowadays as advancements in AI technology make it increasingly easy to create highly realistic synthetic art. This study explores the use of Convolutional Neural Networks (CNNs) to differentiate AI-generated art from human-created art. By employing Error Level Analysis (ELA), an image forensic technique for detecting fake and real images, this study develops a robust CNN classifier. Using the AI-ArtBench dataset, the optimal model achieves a 99% classification accuracy, even when tested on art from a different generative model. While AI-image detection remains a “cat and mouse” pursuit due to advancements in generative AI, the findings of this study highlight that there are clear, discriminable differences between AI-generated and human-created art. The implications of this research extend beyond academic inquiry. They offer support for artists, collectors, curators, and policymakers as they navigate the complexities of AI’s expanding availability and address the evolving role of AI in the art world. It sets the groundwork for application within fields faced with the same or similar challenges.}, archive = {J_ACCESS}, author = {Ngonidzashe Tinago and Silas Formunyuy Verkijika and Kelibone Eva Mamabolo}, doi = {10.1109/ACCESS.2025.3596882}, journal = {IEEE Access}, pages = {141484-141495}, shortjournal = {IEEE Access}, title = {Deepfakes in visual art: Differentiating AI-generated art from human art using convolutional neural networks (CNN)}, volume = {13}, year = {2025}, }
• (2025). NAIA: A multi-technology virtual assistant for boosting academic Environments—A case study. ACCESS, 13, 141461-141483. (www)

  @article{ , abstract = {Virtual assistants have become essential tools for improving productivity and efficiency in various domains. This paper presents NAIA (Nimble Artificial Intelligence Assistant), an advanced multi-role and multi-task virtual assistant enhanced with artificial intelligence, designed to serve a university community case study. The system integrates AI technologies including Large Language Models (LLM), Computer Vision, and voice processing to create an immersive and efficient interaction through animated digital avatars. NAIA features five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide, each equipped with specific capabilities to support different aspects of academic life. The system’s Computer Vision capabilities enable it to comment on users’ physical appearance and environment, enriching the interaction. Through natural language processing and voice interaction, NAIA aims to improve productivity and efficiency within the university environment while providing personalized assistance through a ubiquitous platform accessible across multiple devices. NAIA is evaluated through a user experience survey involving 30 participants with different demographic characteristics, this is the most accepted way by the community to evaluate this type of solution. Participants give their feedback after using one role of NAIA after using it for 30 minutes. The experiment showed that 90% of the participants considered NAIA-assisted tasks of higher quality and, on average, NAIA has a score of 4.27 out of 5 on user satisfaction. Participants particularly appreciated the assistant’s visual recognition, natural conversation flow, and user interaction capabilities. Results demonstrate NAIA’s capabilities and effectiveness across the five roles.}, archive = {J_ACCESS}, author = {Adrián Pabón Mendoza and Kenneth J. Barrios Quiroga and Samuel D. Solano Celis and Christian M. Quintero}, doi = {10.1109/ACCESS.2025.3597565}, journal = {IEEE Access}, pages = {141461-141483}, shortjournal = {IEEE Access}, title = {NAIA: A multi-technology virtual assistant for boosting academic Environments—A case study}, volume = {13}, year = {2025}, }
• (2025). Benchmarking sentence encoders in associating indicators with sustainable development goals and targets. ACCESS, 13, 141434-141460. (www)

  @article{ , abstract = {The United Nations’ 2030 Agenda for Sustainable Development balances the economic, environmental, and social dimension of sustainable development in 17 Sustainable Development Goals (SDGs), monitored through a well-defined set of targets and global indicators. Although essential for humanity’s future well-being, this monitoring is still challenging due to the variable quality of the statistical data of global indicators compiled at the national level and the diversity of indicators used to monitor sustainable development at the subnational level. Associating indicators other than the global ones with the SDGs/targets may help not only to expand the statistical data, but to better align the efforts toward sustainable development taken at (sub)national level. This article presents a model-agnostic framework for associating such indicators with the SDGs and targets by comparing their textual descriptions in a common representation space. While removing the dependence on the quantity and quality of the statistical data of the indicators, it provides human experts with data-driven suggestions on the complex and not always obvious associations between the indicators and the SDGs/targets. A comprehensive domain-specific benchmarking of a diverse sentence encoder portfolio was performed first, followed by fine-tuning of the best ones on a newly created dataset. Five sets of indicators used at the (sub)national level of governance (around 800 indicators in total) were used for the evaluation. Finally, the influence of 40 factors on the results was analyzed using explainable artificial intelligence (xAI) methods. The results show that 1) certain sentence encoders are better suited to solving the task than others (potentially due to their diverse pre-training datasets), 2) the fine-tuning not only improves the predictive performance over the baselines but also reduces the sensitivity to changes in indicator description length (performance drops even by up to 17% for baseline models as length increases, but remains comparable for fine-tuned models), and 3) better selected training instances have the potential to improve the performance even further (taking into account the limited fine-tuning dataset currently used and the insights from the xAI analysis). Most importantly, this article contributes to filling the existing gap in comprehensive benchmarking of AI models in solving the problem.}, archive = {J_ACCESS}, author = {Ana Gjorgjevikj and Kostadin Mishev and Dimitar Trajanov and Ljupco Kocarev}, doi = {10.1109/ACCESS.2025.3595894}, journal = {IEEE Access}, pages = {141434-141460}, shortjournal = {IEEE Access}, title = {Benchmarking sentence encoders in associating indicators with sustainable development goals and targets}, volume = {13}, year = {2025}, }
• (2025). Automated code comments generation using large language models: Empirical evaluation of t5 and BART. ACCESS, 13, 141420-141433. (www)

  @article{ , abstract = {Source code documentation plays a significant role in the software development lifecycle, substantially improving the comprehensibility and maintainability of software projects. Despite its importance, documentation is usually dismissed or fails to satisfy the envisaged standards. Recently, Large Language Models (LLMs) have demonstrated the ability to address these challenges by automating code comments generation. Nevertheless, the empirical evaluation of these models is essential to assess their capabilities to produce accurate, contextually relevant and coherent documentation. In this paper, we have conducted an empirical study to investigate the capabilities of two prominent open-source LLMs, such as T5 and BART, developed by Google AI and Facebook AI, respectively, in automating the code comments generation for Python and Java code snippets. We have rigorously evaluated the performance of these models against four key metrics, such as BLEU, ROUGE, METEOR, and Smoothed BLEU. The comprehensive analysis of the evaluation results clearly highlights BART as the superior model over T5 for single-intent code comment generation.}, archive = {J_ACCESS}, author = {Dhan Prasad Ghale and Mohammad Dabbagh}, doi = {10.1109/ACCESS.2025.3597601}, journal = {IEEE Access}, pages = {141420-141433}, shortjournal = {IEEE Access}, title = {Automated code comments generation using large language models: Empirical evaluation of t5 and BART}, volume = {13}, year = {2025}, }
• (2025). A novel energy adaptive neural network and deep Q-learning network for improved energy efficiency in dynamic underwater IoT environment. ACCESS, 13, 141403-141419. (www)

  @article{ , abstract = {Optimizing energy efficiency and communication reliability is essential for an underwater Internet of Things (IoT) network that utilizes hybrid optical-acoustic communication system. The proposed research work has an objective to reduce the energy consumption in a dynamic underwater IoT network by adjusting the transmission power and frequency selection using a novel Energy Adaptive Neural Network (EANN). The communication environment is continuously monitored by the proposed EANN to adjust the transmission power and frequency to adapt the dynamic changes in the network. The next objective of the research work is to enhance the reliability and robustness of the communication model by incorporating a Deep Q-Learning network for adaptive error correction and modulation (DQL-AECM). The reinforcement learning model optimizes the error correction and modulation schemes to ensure that the systems effectively adapt the variations while maintain high communication reliability in an underwater IoT network. The multiple deep learning models in the proposed work provides a complete solution to reduce the energy consumption and ensure reliable communication which is essential for an underwater IoT network. The experimental analysis of proposed model is considering the traditional LEACH, PEGASIS, TEEN and MTP models to comparatively evaluate the performances. The proposed model exhibits better performance with energy consumption of 22 Joules and an energy per bit of $2.0\times 10^{-6}$ Joules/bit, SNR of 21dB, low BER of $10^{-8}$ , throughput of 6.2 Mbps and latency of 35ms which is superior than the existing LEACH, PEGASIS, TEEN and MTP models.}, archive = {J_ACCESS}, author = {Judy Simon and Nellore Kapileswar and Anoop Mohanakumar}, doi = {10.1109/ACCESS.2025.3597025}, journal = {IEEE Access}, pages = {141403-141419}, shortjournal = {IEEE Access}, title = {A novel energy adaptive neural network and deep Q-learning network for improved energy efficiency in dynamic underwater IoT environment}, volume = {13}, year = {2025}, }
• (2025). A precise and efficient frequency estimation method for single-phase grid with DC offset. ACCESS, 13, 141395-141402. (www)

  @article{ , abstract = {The frequency component of the grid voltage plays a crucial role in ensuring the stable operation of power electronic grid-connected systems. When the grid voltage contains a DC offset disturbance, the parameter estimation methods in the literature suffer from estimation errors, and a filter is always required to counteract its impact. In this paper, a nonlinear algebraic transformation method is proposed to eliminate the influence of the DC offset completely. By applying the nonlinear algebraic transformation to four consecutive sampling points, a first-order linear regression model of voltage is constructed for a single-phase grid affected by DC offset. This model inherently eliminates the offset’s impact on the model parameters. A parameter estimator is then developed for the linear regression model, and the frequency can be directly derived from the parameters of this model. Rigorous theoretical analyses confirm the stability and convergence of the proposed parameter estimator. Meanwhile, the results demonstrate that the proposed parameter estimator can globally track the frequency variations in single-phase grids with DC offset. Experimental results further confirm the efficacy of the proposed nonlinear algebraic transformation method.}, archive = {J_ACCESS}, author = {Ji Xiao and Jiaming Zhang and Shishun Tan and Youfeng Zhou}, doi = {10.1109/ACCESS.2025.3597600}, journal = {IEEE Access}, pages = {141395-141402}, shortjournal = {IEEE Access}, title = {A precise and efficient frequency estimation method for single-phase grid with DC offset}, volume = {13}, year = {2025}, }
• (2025). A STacked adaptive residual PINN (STAR-PINN) approach to 2D time-domain magnetic diffusion in nonlinear materials. ACCESS, 13, 141380-141394. (www)

  @article{ , abstract = {This work explores the use of Physics-Informed Neural Networks (PINNs) and a newly proposed approach, called the STacked Adaptive Residual PINN (STAR-PINN), to solve magnetic diffusion problems in the magneto quasi static regime. The study covers both one- and two-dimensional domains. The key advantage of this new architecture is the ability to refine predictions through multiple lightweight PINN blocks to achieve accurate results with lower computational cost and less architectural complexity than more advanced neural networks like Recurrent Neural Networks or Convolutional Neural Networks. The simplicity and efficiency of STAR-PINN make it a promising solution for tackling large-scale and nonlinear challenges in computational electromagnetics.}, archive = {J_ACCESS}, author = {Shayan Dodge and Sami Barmada and Alessandro Formisano}, doi = {10.1109/ACCESS.2025.3597869}, journal = {IEEE Access}, pages = {141380-141394}, shortjournal = {IEEE Access}, title = {A STacked adaptive residual PINN (STAR-PINN) approach to 2D time-domain magnetic diffusion in nonlinear materials}, volume = {13}, year = {2025}, }
• (2025). A low-discrepancy and latency-aware, scenario-sensitive resource allocation approach for cloud systems using latin square-based improvised genetic optimization (LSBGO). ACCESS, 13, 141344-141379. (www)

  @article{ , abstract = {Efficient resource allocation in cloud networks is a complex challenge, demanding adaptive strategies to sustain processing millions of tasks whose CPU, memory, and priority profiles changes rapidly. Static heuristics, such as Round Robin and Weighted Least Connection, react quickly but neglect the heterogeneity of resources, thereby creating hotspots and long-tail delays. Meta-heuristics, although excellent, tend to converge slowly and are thus not well-suited for bursty real-time traffic. Hence, the core problem is how to place tasks while ensuring that no virtual machine begins its execution overloaded with work, and how to adjust this placement on the fly as demand, failures, and scale changes occur. This paper presents Hybrid Latin Square-Based Genetic Optimization (LSBGO), a novel framework that integrates deterministic Latin square-based task placement with scenario-sensitive genetic optimization to enable robust, low-discrepancy scheduling across scalable cloud environments. The deterministic Latin-square initializer generates a mathematically uniform random initial distribution over the spatial and temporal task grid. An adaptive genetic phase then evolves the mapping based on live feedback about load and priority, supporting quick rebalance, elastic growth, and failure recovery with minimum tuning of parameters. The framework is extensively evaluated using both steady-state scenarios driven by Google Cluster-Usage traces and stress tests using real-world PlanetLab-style CPU bursts replayed in CloudSim. LSBGO keeps SLA violations below 4 %, maintains a mean time to recovery of 6.9 s, and sustains more than 90 % CPU utilization while holding latency up to 45 ms. The results confirm that LSBGO delivers scalable, low-complexity scheduling that harmonises utilisation, fairness, and responsiveness, making it well-suited for next-generation cloud resource management.}, archive = {J_ACCESS}, author = {Aman Kumar Routh and Prabhat Ranjan and Asisa Kumar Panigrahy}, doi = {10.1109/ACCESS.2025.3597627}, journal = {IEEE Access}, pages = {141344-141379}, shortjournal = {IEEE Access}, title = {A low-discrepancy and latency-aware, scenario-sensitive resource allocation approach for cloud systems using latin square-based improvised genetic optimization (LSBGO)}, volume = {13}, year = {2025}, }
• (2025). D-band channel modeling in data centers and industrial environments. ACCESS, 13, 141328-141343. (www)

  @article{ , abstract = {This paper presents D-band channel modeling campaigns for short-range line-of-sight (LOS) communications in representative data center and industrial environments. An accurate multipath estimation framework, exploiting measured antenna radiation patterns, is utilized to extract multipath components (MPCs) with 0.1° angular resolution for precise large- and small-scale channel characterization. In the data center, directional inter-rack communication links exhibit a high path loss exponent (PLE) of 2.39 and a low mean delay spread of 1.32 ns, with cable obstructions increasing the PLE to 3.24 but further decreasing the mean delay spread to 0.59 ns. The omnidirectional measurement in the data center shows significant clustering effects in spatial-temporal domain. In contrast, industrial machine-to-machine (M2M) communication links, characterized through omnidirectional measurements, exhibit a lower PLE of 1.67. Mean values for delay spread, angular spread, and K-factor in industrial environments are moderate, which are 4.35 ns, 16.46°, and 8.11 dB, respectively, with weak clustering effects. The measurement results are compared and discussed with the literature across different environments and frequency bands for an overall understanding of unique propagation characteristics in these two environments. The quantitative measurement results contribute to D-band channel standardization.}, archive = {J_ACCESS}, author = {Yunqi Feng and Mamoun Guenach and Nazar Muhammad Idrees and Claude Desset and David Plets and Wout Joseph and Emmeric Tanghe}, doi = {10.1109/ACCESS.2025.3597478}, journal = {IEEE Access}, pages = {141328-141343}, shortjournal = {IEEE Access}, title = {D-band channel modeling in data centers and industrial environments}, volume = {13}, year = {2025}, }
• (2025). Faster Image2Video generation: A closer look at CLIP image embedding’s impact on spatio-temporal cross-attentions. ACCESS, 13, 141313-141327. (www)

  @article{ , abstract = {This paper explores the effectiveness—specifically in improving video consistency—and the computational burden of Contrastive Language-Image Pre-Training (CLIP) embeddings in video generation. The investigation is conducted using the Stable Video Diffusion (SVD) framework, a state-of-the-art method for generating high-quality videos from image inputs. The diffusion process in SVD generates videos by iteratively denoising noisy inputs over multiple steps. Our analysis reveals that employing CLIP in the cross-attention mechanism at every step of this denoising process has limited impact on maintaining subject and background consistency while imposing a significant computational burden on the video generation network. To address this, we propose Video Computation Cut (VCUT), a novel, training-free optimization method that significantly reduces computational demands without compromising output quality. VCUT replaces the computationally intensive temporal cross-attention with a one-time computed linear layer, cached and reused across inference steps. This innovation reduces up to 322T MACs per 25-frame video, decreases model parameters by 50M, and cuts latency by 20% compared to baseline methods. By streamlining the SVD architecture, our approach makes high-quality video generation more accessible, cost-effective, and eco-friendly, paving the way for real-time applications in telemedicine, remote learning, and automated content creation.}, archive = {J_ACCESS}, author = {Ashkan Taghipour and Morteza Ghahremani and Mohammed Bennamoun and Aref Miri Rekavandi and Zinuo Li and Hamid Laga and Farid Boussaid}, doi = {10.1109/ACCESS.2025.3595822}, journal = {IEEE Access}, pages = {141313-141327}, shortjournal = {IEEE Access}, title = {Faster Image2Video generation: A closer look at CLIP image embedding’s impact on spatio-temporal cross-attentions}, volume = {13}, year = {2025}, }
• (2025). CoPaD-mark: A coded parallelizable deep learning-based scheme for robust image watermarking. ACCESS, 13, 141295-141312. (www)

  @article{ , abstract = {In this work, we propose a coded, parallelizable, and deep learning-based image watermarking scheme (CoPaD-Mark). The embedding and extraction layers both undergo iterative training to achieve robust image watermarking. The embedding layer employs a parallel structure with convolutional neural networks inspired by the Inception Net, while the extraction layer uses deformable convolutions. These convolutions enable dynamic feature extraction by adjusting convolutional filters based on image content. The information bits embedded in the image are derived from the parity bits of an error-correcting code. This code’s message is constructed by combining a chaotic binary sequence with the original watermark bits. The inclusion of chaotic sequences adds randomness and complexity to the encoding process, enhancing robustness against attempts to alter the watermark. The proposed method produces watermarked images with peak signal-to-noise ratios (PSNR) above 36 dB. CoPaD-Mark is evaluated under several common attacks, including noise addition, filtering, cropping, compression, and resizing, and demonstrates robust performance compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Andy M. Ramos and Cecilio Pimentel and Daniel P. B. Chaves}, doi = {10.1109/ACCESS.2025.3597853}, journal = {IEEE Access}, pages = {141295-141312}, shortjournal = {IEEE Access}, title = {CoPaD-mark: A coded parallelizable deep learning-based scheme for robust image watermarking}, volume = {13}, year = {2025}, }
• (2025). User intent-based music generation model combining actor-critic approach with MusicVAE. ACCESS, 13, 141281-141294. (www)

  @article{ , abstract = {This research proposes a way for music generation models to more clearly reflect user intent. Previous studies have attempted to generate personalized music through various inputs such as text and images, but these expressions have ambiguous meanings and are difficult to reflect directly in music. To solve this problem, this study proposes a method to define the user’s emotional state based on color and use it as a condition for music generation. The colors were classified into saturation, luminance, and hue, and the pitch and density information corresponding to each emotional condition was extracted through a Mood Classifier trained on them. This information was incorporated into a loss function that redefined the MusicVAE’s latent vector, which was then subjected to Actor-Critic-based condition injection. The model performance was evaluated using Spotify Energy-Valence analysis, PCA-based latent space visualization, and listening tests (200 subjects), and found to be superior to existing models in both conditioned performance and musical naturalness.}, archive = {J_ACCESS}, author = {Soyoung Jang and Jaeho Lee}, doi = {10.1109/ACCESS.2025.3597741}, journal = {IEEE Access}, pages = {141281-141294}, shortjournal = {IEEE Access}, title = {User intent-based music generation model combining actor-critic approach with MusicVAE}, volume = {13}, year = {2025}, }
• (2025). Modeling and control of a ballbot: A systematic approach. ACCESS, 13, 141263-141280. (www)

  @article{ , abstract = {The ballbot is a type of mobile robot that achieves dynamic stability by maintaining balance on a spherical ball. Its control poses significant challenges due to its nonlinear and unstable nature, as well as its five degrees of freedom and underactuated system dynamics with dynamic constraints. To the authors’ knowledge, there is a lack of a systematic approach in the literature that addresses these considerations, particularly when it comes to the parameter identification problem. This paper aims to provide a complete and straightforward approach for developing a model-based control of the ballbot, including hardware design and fabrication, detailed system modeling, advanced parameter identification, and control algorithm design. A planar dynamic model is adopted in this work to reduce system complexity and enable real-time control feasibility, serving as a tractable foundation for future extension to a fully coupled 3D dynamic model. The proposed approach is validated through several real-time experiments, demonstrating its robustness and validity. For instance, the identified model achieved a root mean square error (RMSE) of 0.0091 for the yz-plane and 0.0495 for the xy-plane, demonstrating accurate predictive performance, and the controller successfully maintained a tilt angle within ±1° under disturbances. The approach demonstrates a modular, low-complexity framework suitable for both research and educational platforms. Future work may explore extending this approach to more complex dynamic environments, advanced hardware, and nonlinear intelligent control strategies such as fuzzy logic and model predictive control.}, archive = {J_ACCESS}, author = {Mahmoud Abdelrahim and Mahmoud A. Thabet and Hossam S. Abbas and Mohamed M. M. Hassan and Mohamed H. Amin and Abdelrahman Morsi}, doi = {10.1109/ACCESS.2025.3597797}, journal = {IEEE Access}, pages = {141263-141280}, shortjournal = {IEEE Access}, title = {Modeling and control of a ballbot: A systematic approach}, volume = {13}, year = {2025}, }
• (2025). Traffic animation of pedestrian and vehicle interactions in non-signalized mid-block regions. ACCESS, 13, 141245-141262. (www)

  @article{ , abstract = {Traffic animation enables the visualization of potential conflicts among road users, supporting the development of safety measures and serving applications in education and entertainment. This paper presents a factor-based approach to generate animations of pedestrians interacting with vehicles in mid-block crossing scenarios. A mid-block refers to a road segment between intersections without traffic signals. In these settings, vehicles and pedestrians should coordinate dynamically to avoid accidents as there is a lack of designated crossing control. Furthermore, this dynamic environment provides an opportunity to produce traffic animation that depicts the diverse and intricate interactions between vehicles and pedestrians. We propose a dynamic graph that tracks leader-follower relationships among vehicles, as well as the evolving gap regions between them. In addition, pedestrians evaluate these gap regions using five intuitive factors, which are derived from gap characteristics. These factors are adjusted region distance, degree of passage, region growth rate, region flow ratio, and vacancy ratio. By tuning the influences of these factors, animators can simulate a wide range of pedestrians behaviors, from cautious waiting to assertive movement. To evaluate the performance of the proposed approach, we conducted experiments assessing effects of these factors on route decisions and pedestrian interaction styles. Additionally, we analyzed the effects of varying pedestrian and vehicle volumes on average travel time and distance. The results demonstrated the system’s ability to produce plausible animations of pedestrian-vehicle interactions. Our system serves as a customizable tool for animators to generate diverse pedestrian crossing behaviors adapted to varying vehicle gap scenarios. Our approach could be applied to various domains, such as education, training, and immersive entertainment. In these areas, tailored pedestrian crossing behaviors enhance the richness and engagement of traffic scenarios.}, archive = {J_ACCESS}, author = {Sai-Keung Wong and Pin-Chieh Huang and Chun-Han Shih and Guan-Ting Lin and Wen-Chieh Lin}, doi = {10.1109/ACCESS.2025.3597330}, journal = {IEEE Access}, pages = {141245-141262}, shortjournal = {IEEE Access}, title = {Traffic animation of pedestrian and vehicle interactions in non-signalized mid-block regions}, volume = {13}, year = {2025}, }
• (2025). Computational issues in nonlinear dynamical systems: Creating bifurcation diagrams of lyapunov exponents using a novel adaptive approach. ACCESS, 13, 141234-141244. (www)

  @article{ , abstract = {Nonlinear dynamical systems with oscillatory (periodic, quasi-periodic and chaotic) responses are analyzed in this paper through the method of Lyapunov exponents. The main goal is to present 1D (one varying parameter) and 2D (two varying parameters) bifurcation diagrams of Lyapunov exponents created via a new adaptive approach, for selected two systems, describing an electric arc system and a model of calcium oscillatory phenomenon. First, the well-known Lyapunov exponents approach is discussed, and the orthogonalization procedure of the Parker-Chua method is modified to deal with a possibility of existence of co-linear vectors of Jacobian matrix (singularity of the matrix) in the orthogonalization algorithm. The co-linearity of vectors may lead to an extremely high matrix conditional coefficient, and, as a consequence, a failure of the procedure of orthogonalization in the Lyapunov exponents computation. Thus, a new adaptive approach is proposed to avoid the co-linearity issue. Four corrections of the parameters involved in the algorithm are proposed in the new adaptive approach. The advantage of using the newly design adaptive method to improve the procedure of the Lyapunov exponents computation is illustrated through a series of numerical examples. Visual results in the forms of 1D and 2D bifurcation diagrams are presented for an electric arc system and calcium oscillatory model.}, archive = {J_ACCESS}, author = {Maciej Walczak and Wieslaw Marszalek}, doi = {10.1109/ACCESS.2025.3587410}, journal = {IEEE Access}, pages = {141234-141244}, shortjournal = {IEEE Access}, title = {Computational issues in nonlinear dynamical systems: Creating bifurcation diagrams of lyapunov exponents using a novel adaptive approach}, volume = {13}, year = {2025}, }
• (2025). AI driven fraud detection models in financial networks: A comprehensive systematic review. ACCESS, 13, 141204-141233. (www)

  @article{ , abstract = {Rapid advancements in digital innovation and globalization has significantly increased the complexity of financial networks, making them more vulnerable to fraud. Traditional fraud detection methods struggle to keep pace with evolving fraudulent strategies, contributing to an estimated global financial loss of ${\$}~5$ trillion. In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. By analyzing vast datasets, AI can uncover hidden fraud patterns and dynamically adapt to emerging threats. Techniques such as supervised and unsupervised learning, along with advanced approaches like Graph Neural Networks (GNNs), have proven particularly effective in detecting various types of financial fraud, including payment fraud, identity theft, and money laundering. This paper presents a comprehensive taxonomy of AI-driven fraud detection methodologies, synthesizing insights from a substantial number of research papers. It systematically categorizes fraud detection techniques based on their application in different types of fraud, providing a structured framework to understand their effectiveness. In addition, it examines the role of cloud computing, edge AI, and distributed systems in enabling real-time transaction monitoring and fraud detection. Although AI significantly improves detection accuracy, reduces operational costs, and strengthens regulatory compliance, challenges such as model explainability, data privacy concerns, algorithmic bias, and the dynamic nature of fraud remain critical barriers to widespread adoption. Our review highlights the need for collaborative efforts among financial institutions, regulators, and technology providers to address these challenges. Future research should focus on improving the transparency of the AI model, integrating AI with blockchain for secure data sharing, and leveraging federated learning to enhance fraud detection capabilities. By addressing these challenges, AI can play a pivotal role in securing financial systems, minimizing fraud risks, and fostering cross-industry collaboration for more resilient fraud detection frameworks.}, archive = {J_ACCESS}, author = {Nusrat Jahan Sarna and Farzana Ahmed Rithen and Umme Salma Jui and Sayma Belal and Al Amin and Tasnim Kabir Oishee and A. K. M. Muzahidul Islam}, doi = {10.1109/ACCESS.2025.3596060}, journal = {IEEE Access}, pages = {141204-141233}, shortjournal = {IEEE Access}, title = {AI driven fraud detection models in financial networks: A comprehensive systematic review}, volume = {13}, year = {2025}, }
• (2025). Enhanced teleoperation of robotic arms through master-slave configuration with multithreading. ACCESS, 13, 141187-141203. (www)

  @article{ , abstract = {Teleoperation systems are crucial in various industrial applications, including medical surgeries and the operation of remotely controlled robots. However, latency issues significantly impact precision and responsiveness, presenting a critical problem that requires extensive attention. Moreover, the dissimilarities in design, functionality, and control mechanisms between the master device and the slave robot arm introduce additional layers of complexity, making the operation and synchronization of the system more challenging. This paper aims to address these gaps by pursuing two primary objectives: to design and fabricate a structurally similar master device that replicates the joint configuration and range of motion of the Universal Robot’s UR10e robot arm, and to implement control strategies that minimize latency and improve stability. The master device was implemented using a modified Hiwonder Synchronization Controller configured to replicate the UR10e’s kinematic structure. It was integrated with six connected potentiometers in series and evaluated along with the UR10e robotic arm in a lab-scale setup, with all components connected via ROS Noetic. The system was tested in point-to-point motion tasks and a newly added obstacle-constrained task to assess latency, stability, and robustness to sensor noise. Low-pass filtering techniques were applied to suppress signal noise and improve trajectory smoothness, while multithreading methods were used to separate sensor polling and data transmission processes. Experimental results showed that latency was reduced by 91%, from an average of 119.0 ms to 10.2 ms in all tasks. The total system latency decreased by over 85%, from 228.0 ms to as low as 32.0 ms. For stability, the peak overshoot on the Y-axis was reduced by 98% from 104.0°to 2.3°, and the steady-state error on all axes was minimized, with overall improvements exceeding 95%. A preliminary user study showed that task completion times significantly improved after training, demonstrating the system’s intuitive design and low learning curve. These outcomes were visualized using error bar latency plots and histogram latency distribution, confirming improved system responsiveness, reduced jitter, and the elimination of catastrophic delays. This work presents a low-cost, scalable solution for improving the fidelity and responsiveness of master-slave teleoperation, with potential applications in industrial automation, agriculture, and remote manipulation in hazardous environments. Future work will focus on wireless communication, haptic feedback integration, and expanded validation under real-world agricultural conditions.}, archive = {J_ACCESS}, author = {Ahmad Faiz Fawzan Ahmad Zainudin and Mohd Hairi Mohd Zaman and Mohd Faisal Ibrahim and Mohd Asyraf Zulkifley and M. Marzuki Mustafa}, doi = {10.1109/ACCESS.2025.3596637}, journal = {IEEE Access}, pages = {141187-141203}, shortjournal = {IEEE Access}, title = {Enhanced teleoperation of robotic arms through master-slave configuration with multithreading}, volume = {13}, year = {2025}, }
• (2025). Advanced anomaly detection in smart grids using graph convolutional networks with integrated node and line sensor data. ACCESS, 13, 141170-141186. (www)

  @article{ , abstract = {Identifying the location of faults, anomalies, and failures is a long-standing but critical challenge in power system networks. The implementation of smart meters and advanced sensor measurement technologies in recent years has allowed power systems operators to more accurately identify fault locations, quickly resolve problems, and improve the overall reliability of power networks. However, the presence of complex topology changes, penetration of renewable energy resources, stochastic propagation of anomalies in the network, and missing data require the use of new approaches that are sensitive to these issues. This article introduces an innovative method that uses a graph convolutional network (GCN) combined with a modified probability propagation matrix and dual graphs to identify and locate node/line anomalies using network sensors installed on both nodes and lines. Also, an optimization model was developed to find the most likely sources of anomalies across all nodes and edges of the network. The proposed method, which was evaluated on the IEEE 118-bus system and a set of simulated data, demonstrated outstanding performance in handling complex topologies and missing data. Although the proposed model is designed for power networks, its flexible characteristics make it applicable to many sensor-intensive networks or graph structures (e.g., transportation and social networks) where anomaly detection at nodes and/or edges is critical.}, archive = {J_ACCESS}, author = {Mahdi Zarif and Ramin Moghaddass}, doi = {10.1109/ACCESS.2025.3595431}, journal = {IEEE Access}, pages = {141170-141186}, shortjournal = {IEEE Access}, title = {Advanced anomaly detection in smart grids using graph convolutional networks with integrated node and line sensor data}, volume = {13}, year = {2025}, }
• (2025). Advanced feature analysis of eddy current testing signals for rail surface defect characterization. ACCESS, 13, 141156-141169. (www)

  @article{ , abstract = {The maintenance of the railways is of paramount importance for safe and reliable transport. Eddy Current Testing (ECT) provides high-resolution time-series signals that capture subtle anomalies on the rail surface. This paper expands on previous analyses by combining classical time-frequency methods (short-time Fourier transform and continuous wavelet transform) and estimation of fractal dimensions with advanced feature extraction approaches, including wavelet sub-band decomposition, Hilbert–Huang transform, peak analysis and entropy metrics. Subsequently, a Random Forest classifier is applied to each set of characteristics, and we report comparative accuracy results on a dataset comprising rail segments with joints, welds, or squats. Experimental findings reveal that the Hilbert–Huang transform features yield the highest accuracy (93.28%), while simpler features, such as peak counts, are less discriminative (46.93%). These results underscore the effectiveness of using multiple signal-decomposition strategies and advanced analytics to robustly detect and categorize surface defects for better rail-maintenance decisions.}, archive = {J_ACCESS}, author = {Michele Quercio and Luca Santoro and Raffaella Sesana and Francesco Riganti Fulginei}, doi = {10.1109/ACCESS.2025.3597079}, journal = {IEEE Access}, pages = {141156-141169}, shortjournal = {IEEE Access}, title = {Advanced feature analysis of eddy current testing signals for rail surface defect characterization}, volume = {13}, year = {2025}, }
• (2025). BiLSTM-based parallel CNN models with attention and ensemble mechanism for twitter sentiment analysis. ACCESS, 13, 141139-141155. (www)

  @article{ , abstract = {Due to the increased use of social networks, word-of-mouth analysis has become an effective tool in market research since firms can determine the users’ attitudes toward their brands. However, the current approaches to analyzing word-of-mouth on social media face significant challenges like lack of feature integration, low sentiment analysis efficiency, low topic identification efficiency, issues with data scarcity, and the high costs of annotations. These problems limit the ability to offer detailed and comprehensive analyses of user sentiment. Sentiment analysis has been found to have positive results when using deep learning. When used together, models like the Convolutional Neural Networks (CNN) and LSTM networks have significant high-performance results for text feature extraction and semantic relationship of the word. This article proposes a new model based on attention mechanisms and ensemble learning. Our model uses Google’s pre-trained Word2Vec embeddings to represent text as dense vectors. Three parallel CNN layers extract complex low-level features from embeddings after the embedding layer. Batch normalization and dropout are implemented to prevent overfitting. A bidirectional LSTM is incorporated on the CNN layers to capture semantic relationships between words. An attention mechanism is implemented to prioritize words that convey substantial sentiment information. Our methodology incorporates four classifiers to produce text class predictions. Among them, five algorithms are selected for evaluation: Ridge Classifier (RC), Linear Discriminant Analysis (LDA), Extra Trees (ET), and Light Gradient Boosting Machine (LightGBM). In addition, we set up the BiLSTM-based CNN model as our basic model and added it to this ensemble to enhance the model’s performance. To evaluate the validity of the developed model, experiments were performed on two sentiment-labeled datasets commonly used, and we compared it to some of the most efficient baseline models in the field of sentiment analysis and achieved better results.}, archive = {J_ACCESS}, author = {Anas W. Abulfaraj}, doi = {10.1109/ACCESS.2025.3597413}, journal = {IEEE Access}, pages = {141139-141155}, shortjournal = {IEEE Access}, title = {BiLSTM-based parallel CNN models with attention and ensemble mechanism for twitter sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). AI-driven smart agriculture: An integrated approach for soil analysis, irrigation, and crop-fertilizer recommendations. ACCESS, 13, 141124-141138. (www)

  @article{ , abstract = {Modern agriculture faces critical challenges, including climate change induced water shortage, inefficient resource utilization, and reducing soil health which reduce sustainability and reduce crop yields. Traditional farming methods, dependent on manual observation and generalized practices, often lack the precision needed to address these growing issues. To meet these demands, this study presents an artificial intelligence (AI) powered smart agriculture system that integrates deep learning (DL), Internet of Things (IoT), and explainable artificial intelligence (XAI) to enable intelligent, data-driven farming. This study addresses key challenges such as ineffective soil management, poor irrigation practices, and the absence of personalized crop and fertilizer recommendations. IoT-based sensors provide real-time soil analysis, measuring critical parameters such as nutrient levels, moisture, and temperature. Advanced DL models were employed for various tasks in the AI-Driven Smart Agriculture System. The Transformer-based Tabular Learning (TTL) model for irrigation advice gives an accuracy of 99.13%. For soil analysis, the TabNet regressor achieved a root mean square error (RMSE) of 1.51 and an R2 score of 98.7%, while XAI techniques were used to explain and interpret its predictions. The Sparse Weighted Fusion Transformer (SwiFT) model for crop recommendation gives an accuracy of 98.75%. For fertilizer recommendation, the TabNet classifier achieved an accuracy of 99.3%, with XAI techniques applied to explain the reasoning behind its predictions. These results demonstrate that the system delivers precise and actionable insights. A weather Application Programming Interface (API) is integrated to support timely irrigation decisions through accurate forecasting. A chatbot interface is also added which enhances system usability, accessibility and user-friendliness for farmers. The proposed model improves crop productivity, optimizes resource use, and supports adaptation to environmental changes. By leveraging AI and IoT technologies, the proposed model gives a solution which helps in sustainable agriculture, environmental resilience, and global food security, thus benefiting stakeholders ranging from individual farmers to large-scale agricultural business. Future work will explore real-time adaptive control mechanisms and expand model generalization across diverse geographical region.}, archive = {J_ACCESS}, author = {Ayesha Khaliq and Atif Khan and Salman Jan and Muhammad Umair and Asad Gulshair and Ahmad Ali and Usman Ali Shah}, doi = {10.1109/ACCESS.2025.3594162}, journal = {IEEE Access}, pages = {141124-141138}, shortjournal = {IEEE Access}, title = {AI-driven smart agriculture: An integrated approach for soil analysis, irrigation, and crop-fertilizer recommendations}, volume = {13}, year = {2025}, }
• (2025). Mechanical design of a switched reluctance motor with small airgap length. ACCESS, 13, 141108-141123. (www)

  @article{ , abstract = {This paper presents the mechanical design of a 70 kW Switched Reluctance Motor prototype with a nominal airgap length of 0.4 mm with a relatively large stator outer diameter of 280 mm. It presents the mechanical design considerations and manufacturing tolerances that are vital for an electric motor to maintain component integrity and meet performance requirements. Multi-stage design of the mechanical components and fittings for the complete mechanical system are presented and justified with finite element analysis results. The radial and axial stack-up analysis are presented which account for the dimensional variations during the manufacturing of motor components. The assembly of the motor prototype is described, which demonstrates that the axial and radial alignment of the motor components are achieved while maintaining the small airgap length required. The motor assembly is then verified with static end-of-line tests, such as winding insulation and housing leakage tests. Additionally, experimental modal analysis of the housing assembly is performed to examine the influence of the winding, housing, and potting on its modal frequencies and damping characteristics.}, archive = {J_ACCESS}, author = {Alexander Forsyth and Sanjeev Ravichandran and Thisuri H. Indiketiya and Ashish Kumar Sahu and Sudesh V. Pathirannahalage and Batuhan S. Yilmaz and Brock Howey and Nir Vaks and Mohammad E. Abdollahi and Berker Bilgin}, doi = {10.1109/ACCESS.2025.3597296}, journal = {IEEE Access}, pages = {141108-141123}, shortjournal = {IEEE Access}, title = {Mechanical design of a switched reluctance motor with small airgap length}, volume = {13}, year = {2025}, }
• (2025). SCUP-HPC: System for constructing and utilizing provenance on high-performance computing systems. ACCESS, 13, 141090-141107. (www)

  @article{ , abstract = {Provenance, which documents how data are produced and processed, plays an important role in improving research reproducibility. In computing systems, provenance typically describes a suite of data relationships between simulation and analysis results, programs that generate results, and the input data fed to the programs. Recent research has introduced methods for automatically collecting such data relationships by embedding plugins in the operating system to trace file access and process creation. However, these methods fail to fully describe data processing from the perspective of research reproducibility, particularly in high-performance computing (HPC) systems. To ensure the reproducibility on an HPC system, the provenance must describe the following relationships: 1) executed programs and the corresponding job definition submitted to a workload manager, 2) the program and its source code, and 3) the program and its input/output files. We propose SCUP-HPC, a system for constructing and utilizing data provenance that targets modern HPC systems to support the reproducibility of research activities. We developed a suite of techniques to capture data from the operating system and the workload manager. In addition, we developed a suite of techniques to transform the captured data into provenance by extracting the relationships among programs, files, jobs, and source code from the user’s perspective. We evaluated SCUP-HPC with well-known HPC applications, assessing both the functionality of the constructed provenance and the performance impacts on the applications. The results demonstrate that SCUP-HPC can provide a provenance that supports reproducibility with less than 3% performance overhead.}, archive = {J_ACCESS}, author = {Yuta Namiki and Takeo Hosomi and Hideyuki Tanushi and Akihiro Yamashita and Susumu Date}, doi = {10.1109/ACCESS.2025.3597361}, journal = {IEEE Access}, pages = {141090-141107}, shortjournal = {IEEE Access}, title = {SCUP-HPC: System for constructing and utilizing provenance on high-performance computing systems}, volume = {13}, year = {2025}, }
• (2025). Small target detection for power line components in complex UAV imagery. ACCESS, 13, 141079-141089. (www)

  @article{ , abstract = {Transmission lines play a critical role in the stable operation of the power grid, and the proper functioning of their components is equally essential. However, these lines are often situated in harsh environments and continuously exposed to external challenges such as lightning strikes, heavy rain, and environmental contamination. Accurate detection of component damage is crucial to prevent potential safety hazards and equipment failures caused by transmission line faults. To address the limitations of existing algorithms—particularly their low efficiency in detecting small objects and poor accuracy in complex backgrounds—we propose a defect detection model for transmission line components based on YOLOv5. First, to enhance the model’s ability to detect small and low-resolution targets, we incorporate SPDConv into the backbone of the feature extraction network, significantly improving the extraction of small object features. In the Neck, we replace the original C3 module with the lightweight C3-Faster module to strike a balance between accuracy and model efficiency, thereby reducing latency without sacrificing performance. Moreover, the original YOLOv5 detection head integrates classification and localization, which helps reduce parameter count but introduces mutual interference between the two tasks, potentially compromising accuracy. To mitigate this issue, we redesign the decoupled detection head to decouple classification from localization. As a result, our improved model achieves a 3.88% increase in mAP, a 4.85% boost in precision, and a 3.96% improvement in recall compared to the baseline YOLOv5. Under identical conditions, our algorithm outperforms existing methods in terms of accuracy and exhibits superior small-target detection capability in complex backgrounds.}, archive = {J_ACCESS}, author = {Zhiyuan Sun and Xu Wang}, doi = {10.1109/ACCESS.2025.3594903}, journal = {IEEE Access}, pages = {141079-141089}, shortjournal = {IEEE Access}, title = {Small target detection for power line components in complex UAV imagery}, volume = {13}, year = {2025}, }
• (2025). Improved measurement method for locating winding faults in on-board transformers in high-speed trains. ACCESS, 13, 141064-141078. (www)

  @article{ , abstract = {Frequency response analysis (FRA) is a widely used fault diagnosis approach for transformer windings. However, its application to on-board transformers in high-speed trains faces challenges because the four groups of high-voltage (HV) windings are often connected in parallel, making it impossible to measure them separately outside the oil tank. Additionally, locating winding faults becomes more difficult because the condition of the windings changes over time, and prior knowledge about the winding status is not always readily available. This paper proposes an improved measurement method for locating winding faults without reference data. The method establishes multiple signal transmission paths between the HV windings and traction windings through a superimposed series–parallel connection to the winding terminals. The responses of the traction windings are measured simultaneously when excitation is imposed on the HV windings, and the faulty windings are detected based on the symmetry of the responses between the terminals. The correlation coefficient and the sum of resonant peak amplitudes are proposed to evaluate the windin status. First, a finite element method model and an experimental transformer are established to evaluate the efficacy of the proposed method. Experimental studies are then conducted. Test results show that the shift direction, correlation coefficient, and the sum of resonant peak amplitudes are effective in identifying faulty windings. For the FRA signatures of the faulty winding, the correlation coefficients remain around 5 in the low-frequency band, while they all drop below 1 in the mid-to-high frequency bands. The shift direction of the FRA signatures are closely correlated with the type of winding fault. Regarding resonant peak amplitudes, their sum remains stable in the low-frequency band. However, it increases in the medium-frequency band before decreasing in the high-frequency band. This finding confirms that the proposed measurement method can successfully capture fault diagnostic information and locate the faults without the need for reference data. The accuracy of the fault location of the on-board transformer is significantly improved.}, archive = {J_ACCESS}, author = {Zhenyu Wu and Tao Wang and Juan Yan and Kang Li and Hui Wang and Cungang Hu}, doi = {10.1109/ACCESS.2025.3597011}, journal = {IEEE Access}, pages = {141064-141078}, shortjournal = {IEEE Access}, title = {Improved measurement method for locating winding faults in on-board transformers in high-speed trains}, volume = {13}, year = {2025}, }
• (2025). StyleGraph: A heterogeneous graph neural framework for stylistic and semantic rumor detection on social media. ACCESS, 13, 141055-141063. (www)

  @article{ , abstract = {Identifying and controlling misinformation circulated on social media is known as rumor detection. The growing prevalence of deceptive content presents a significant challenge that necessitates advanced detection techniques, beyond classical machine learning. This study introduced StyleGraph, a novel graph-based framework that enhances rumor detection by jointly leveraging semantic and stylistic information. Unlike previous methods that focused solely on textual content or propagation structures, Style-Graph constructs a heterogeneous graph that models interactions among authors, phrases, and comments. We integrate stylistic features derived from author-writing patterns with semantic representations to enrich node embeddings. StyleGraph was evaluated using Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Cluster GCNs on the PHEME dataset under both 5-event and 9-event settings. The Cluster GCN model achieved the highest accuracy of 62.7% and an F1-score of 55.5% on the 9-event dataset, while the GAT model achieved 77.7% accuracy on the 5-event dataset. Further improvements were observed with the use of data augmentation and sentence-BERT (SBERT) embeddings, with GAT achieving an F1-score of 75.3%. The proposed framework demonstrates scalability and effectiveness in real-time rumor detection scenarios.}, archive = {J_ACCESS}, author = {Haider Jaffar and Ali Mohades and Mohammad Ebrahim Shiri}, doi = {10.1109/ACCESS.2025.3595498}, journal = {IEEE Access}, pages = {141055-141063}, shortjournal = {IEEE Access}, title = {StyleGraph: A heterogeneous graph neural framework for stylistic and semantic rumor detection on social media}, volume = {13}, year = {2025}, }
• (2025). Teaching software engineering: A continuously evolving perspective. ACCESS, 13, 141038-141054. (www)

  @article{ , abstract = {Software engineering is in a continuous process of change and evolution led by the dynamicity of our world, and hence, by the aim to adapt to and meet the current and future society’s needs. From the educational point of view, one of the main challenges consists in what is meaningful and useful to teach in a software engineering course to enable students to have a solid background and to address and lead current and future software development. In this paper, we outline the importance of continuously evolving education (e.g., course material, and teaching methods) to keep up with the changes in software engineering. This enables students to learn the actual and relevant topics, skills, and technologies in a continuously evolving field where new methodologies, technologies, and best practices are constantly being added. We consider this a valuable development in software engineering education, as it allows for an active and relevant learning experience for students. The novelty of our paper consists of 1) a continuous evolution perspective on software engineering education, 2) exploiting various types of inputs driving evolution, and 3) a set of concrete examples and guidelines that can be generalized and applied to similar courses or in other fields.}, archive = {J_ACCESS}, author = {Claudia Raibulet and Francesca Arcelli Fontana}, doi = {10.1109/ACCESS.2025.3596845}, journal = {IEEE Access}, pages = {141038-141054}, shortjournal = {IEEE Access}, title = {Teaching software engineering: A continuously evolving perspective}, volume = {13}, year = {2025}, }
• (2025). Toward fairer and more accurate real-time pedestrian attribute recognition for enhanced women’s safety: A domain-adversarial multi-head model with agent-based reporting. ACCESS, 13, 141018-141037. (www)

  @article{ , abstract = {Effective surveillance systems play a vital role in improving public security, most important among which are applications related to women’s security, where the capacity to correctly carry out Pedestrian Attribute Recognition (PAR) is of utmost importance. Current PAR models are susceptible to being thrown off by dataset bias, mainly gender bias due to training set imbalance, resulting in suboptimal generalization and incorrect prediction across groups. This paper solves the challenges above by creating a Domain-Adversarial Training for Multi-Head Pedestrian Attribute Recognition (DAMH-PAR) model. DAMH-PAR uses a domain-adversarial training method combined with a multi-head structure to provide specialized training to various sets of attributes. The model learns invariant domain features upon training independent “expert” heads per dataset, which are chosen during inference to produce detailed pedestrian descriptions. The usefulness of the model is established by its capacity to predict pedestrian attributes accurately from security feeds even under heavy lighting. Such details, combined with violence detection and proximity modules, forms critical contextual information for automated safety systems. Testing DAMH-PAR model on PETA and PA-100K datasets reveals significant performance improvement compared to existing top-performing benchmarks. The DAMH-PAR model achieves 90.50% Mean Accuracy on PETA and 94.31% accuracy on PA-100K, which is more than the previously established standards. The performance highlights the potential of the suggested methodology to develop more effective and unbiased PAR models for meaningful development in security and safety surveillance tasks.}, archive = {J_ACCESS}, author = {M. Balaji and G. Anitha}, doi = {10.1109/ACCESS.2025.3592975}, journal = {IEEE Access}, pages = {141018-141037}, shortjournal = {IEEE Access}, title = {Toward fairer and more accurate real-time pedestrian attribute recognition for enhanced women’s safety: A domain-adversarial multi-head model with agent-based reporting}, volume = {13}, year = {2025}, }
• (2025). Information extraction from blood test PDFs: Systematic review and case study. ACCESS, 13, 140984-141017. (www)

  @article{ , abstract = {Automated extraction of information from files is increasingly essential due to the growing volume of data. However, extracting information from blood test results remains a relatively unexplored area. Therefore, the objective of this paper is to present a system that is capable of automatically extracting blood test variables. To this end, this study proposes a comprehensive review of the methodologies and tools employed in extracting information from blood test PDFs based on a systematic review and a case study that presents the developed system in detail in all its stages, in order to validate the proposed system. The proposed methodology is validated based on the use of precision, recall and F1 Score evaluation metrics. To the best of our knowledge, there is no existing literature that conducts a study analogous to the one presented and validated in this paper through the provided case study.}, archive = {J_ACCESS}, author = {Alice T. G. Pintanel and Graçaliz P. Dimuro and Eduardo N. Borges and Mateus O. Jung and Pedro G. Machado and Eric L. Corrêa and Beatriz S. De M. Bernardo}, doi = {10.1109/ACCESS.2025.3593427}, journal = {IEEE Access}, pages = {140984-141017}, shortjournal = {IEEE Access}, title = {Information extraction from blood test PDFs: Systematic review and case study}, volume = {13}, year = {2025}, }
• (2025). Power transmission corridors wildfire detection for multi-scale fusion and adaptive texture learning based on transformers. ACCESS, 13, 140973-140983. (www)

  @article{ , abstract = {Ensuring the operational safety of power transmission corridors is critical for maintaining a stable electricity supply. However, their geospatial complexity makes them increasingly vulnerable to wildfires. Wildfires vary dramatically in scale—from pixel-level ignition to kilometer-wide spread—and often occur in visually cluttered environments. These conditions challenge existing Transformer- and YOLO-based detectors, which struggle with multi-scale feature conflicts and fine-grained inter-class distinctions. To address this, we propose a unified wildfire detection framework incorporating two key modules: the Multi-scale Adaptive Texture Encoding (MATE) module and the Dynamic Feature Pyramid Integration (DFPI) module. MATE leverages heterogeneous receptive fields to capture wildfire patterns across spatial scales, while DFPI enhances feature discrimination via scale-aware attention and adaptive resolution fusion. Additionally, we construct a large-scale wildfire dataset with 5,000 annotated images covering diverse terrains, weather, lighting, and fire stages. Extensive experiments demonstrate that our method achieves superior performance, with an mAP50 of 72.9% and an mAP50-95 of 39.3%, significantly outperforming state-of-the-art baselines in detecting wildfires under real-world, cluttered conditions.}, archive = {J_ACCESS}, author = {Yicen Liu and Xi Liu and Zhikun Zheng and Shimin Luo and Yongjun Xiao and Shuxian Wang and Xiaojiang Liu and Zhenhong Sun}, doi = {10.1109/ACCESS.2025.3588517}, journal = {IEEE Access}, pages = {140973-140983}, shortjournal = {IEEE Access}, title = {Power transmission corridors wildfire detection for multi-scale fusion and adaptive texture learning based on transformers}, volume = {13}, year = {2025}, }
• (2025). Focal mechanism and moment tensor determination for tsunami early warning systems: A literature study (2018–2024). ACCESS, 13, 140933-140972. (www)

  @article{ , abstract = {Focal mechanism and moment tensor are critical parameters for seismology required to be calculated promptly after the location and magnitude of earthquake have been determined. The calculations are needed to identify fault geometry, interpret forces acting on the fault, and evaluate potential damage in affected areas. These processes are complex and time-consuming, requiring significant effort to produce accurate results. Therefore, this review aims to present a systematic overview of recent studies and trends related to both concepts. The process was achieved by collecting quality studies from academic databases and digital libraries focusing on 2018 to 2024, using keywords such as earthquake, focal mechanism, inversion, polarity, moment tensor, and tsunami. A total of 113 relevant studies were selected and the results showed attention was mostly on wide usage of conventional classical methods. However, the methods require more calculation time, are subjective, have limitations in analyzing complex data, and challenging to apply in emergency situations demanding rapid decision-making due to high operator control. Several studies proposed improving objectivity and accuracy through artificial intelligence (AI), including machine learning and deep learning. AI methods could offer novel opportunities in seismology considering the potential to enhance analysis efficiency and accuracy of focal mechanism and moment tensor, providing additional insights into earthquake characteristics quickly, accurately, and automatically. This review aimed to guide the selection of appropriate methods for determining focal mechanism and moment tensor in Tsunami Early Warning Systems (TEWS) while identifying challenges and potential future study directions.}, archive = {J_ACCESS}, author = {Titi Handayani and Wijayanto and Adhi Harmoko Saputro and Mohd Asyraf Zulkifley and Dede Djuhana}, doi = {10.1109/ACCESS.2025.3594776}, journal = {IEEE Access}, pages = {140933-140972}, shortjournal = {IEEE Access}, title = {Focal mechanism and moment tensor determination for tsunami early warning systems: A literature study (2018–2024)}, volume = {13}, year = {2025}, }
• (2025). DA+BMAC: Distance-aware bidirectional medium access control for mesh wireless network-on-chip. ACCESS, 13, 140914-140932. (www)

  @article{ , abstract = {Medium access control (MAC) protocol and routing strategy significantly influence Wireless Network-on-Chip (WiNoC) performance and energy characteristics. Conventional WiNoC MAC typically utilizes a daisy-chained ring topology, which limits wireless channel performance due to maximum waiting times when a radio hub misses the token before packet arrival. Additionally, due to limited wireless channel bandwidth compared to aggregate wired interconnect capacity, radio hubs become susceptible to congestion when multiple processing cores simultaneously request wireless access. The lack of intelligent traffic control leads to indiscriminate wireless transmission even when efficient wired paths are available. This paper proposes DA+BMAC, a Distance-Aware (DA) Bidirectional Medium Access Control (BMAC) scheme for mesh WiNoC architecture. BMAC employs bidirectional links allowing token reversal when it overshoots an idle radio hub, reducing worst-case token wait time. DA+BMAC implements distance-aware routing to reserve single-hop wireless transmission exclusively for source-destination pairs beyond a specific distance threshold, while dedicating wired interconnects with higher aggregate bandwidth to short-range communication. Comprehensive validation using a cycle-accurate Noxim simulator on an $8\times 8$ mesh with 16 radio hubs demonstrates up to $11.49\times $ throughput improvement and 15% energy savings compared to a baseline token-ring WiNoC. Scalability analysis confirms performance benefits extend to larger networks (256 and 1024 cores), making DA+BMAC suitable for future many-core systems. The proposed approach has been validated using both synthetic traffic distributions (random, shuffle, transpose, and hotspot) and real-application traces (Barnes and Fluidanimate) from PARSEC and SPLASH-2 benchmark suites, confirming its effectiveness across diverse workloads.}, archive = {J_ACCESS}, author = {Mohd Shahrizal Rusli and Asrani Lit and Muhammad Nadzir Marsono and Ab Al-Hadi Ab Rahman and Shahidatul Sadiah and Michael Loong Peng Tan and Suhaila Isaak and Norlina Paraman}, doi = {10.1109/ACCESS.2025.3594190}, journal = {IEEE Access}, pages = {140914-140932}, shortjournal = {IEEE Access}, title = {DA+BMAC: Distance-aware bidirectional medium access control for mesh wireless network-on-chip}, volume = {13}, year = {2025}, }
• (2025). Impact of normalization techniques on synthetic load profile generation using deep generative models. ACCESS, 13, 140900-140913. (www)

  @article{ , abstract = {Synthetic load profiles are increasingly employed in power system studies as a cost-effective and privacy-preserving alternative to extensive smart meter deployments, with deep generative models (DGMs) showing promising results in capturing complex demand patterns. However, the impact of data normalization on their performance remains insufficiently explored. Using datasets from a university smart grid and industrial consumers in Germany, this work systematically evaluates five normalizations techniques-Min-Max, Standard, Robust, Max-Abs, and Quantile—on four representative DGMs: Wasserstein GAN with gradient penalty (WGAN-GP), variational autoencoder (VAE), nonlinear independent component estimation (NICE), and denoising diffusion implicit models (DDIM). Additionally, this study introduces DDIM for synthetic load profile generation, providing a deterministic and faster sampling approach compared to traditional probabilistic denoising models. Results based on statistical and temporal metrics indicate that Max-Abs normalization consistently yields more accurate and stable synthetic profiles across all models and datasets, while Robust and Quantile methods often degrade essential distributional features. These findings highlight the critical role of normalization in developing effective synthetic data generation pipelines for power system applications.}, archive = {J_ACCESS}, author = {Luís H. T. Bandória and Walquiria N. Silva and Madson C. De Almeida}, doi = {10.1109/ACCESS.2025.3597160}, journal = {IEEE Access}, pages = {140900-140913}, shortjournal = {IEEE Access}, title = {Impact of normalization techniques on synthetic load profile generation using deep generative models}, volume = {13}, year = {2025}, }
• (2025). Human-aware robot collaborative task planning using artificial potential field and DQN reinforcement learning. ACCESS, 13, 140889-140899. (www)

  @article{ , abstract = {This paper presents a novel way for Robot-Robot-Human interaction in a shared workspace for collaborative tasks and uses a multi-modal means of communication that includes hand gestures, voice commands, end-effector gestures, and marker tracking. The system consists of a human operator working along with a Task robot (UR5) and a helper robot (OpenManipulatorX) to perform assembly and disassembly tasks. A Deep Q Network (DQN) reinforcement learning model is used to train the robot to perform the goal reaching task while avoiding obstacles to ensure safety. The DQN algorithm makes use of the end-effector position and the relative positions with the goal and obstacles to train a policy that guides the robot arm safely. Then 4 different training models are created and their ability to avoid obstacles and reach the goal are compared along with the point-to-point Bezier interpolation path planning method in different scenarios such as varying height, size, and number of obstacles. The proposed system has been simulated and then experimentally validated. Experimental results show that DQN trained model performed better than Bezier interpolation in reaching the final goal position with an accuracy of 74mm while avoiding obstacles at the same time in a shared environment. It is also observed that of the different trained models, the model with a larger action space and reduced observation space gave better results compared to others in terms of accuracy and goal completion rate. Also, from experimental data its observed that Improved Artificial Potential Field (IAPF) only took 4.7s as the median time to reach the goal whereas Goal Directed Approach (GDA) took 7.62s and Rapidly Exploring Random Tree Star (RRT*) took 6.22s in different scenarios.}, archive = {J_ACCESS}, author = {Jayesh Prakash and Sam Altnji and Karthick Thiyagarajan and Jogesh S. Nanda and Abhijit Biswas and Abhra Roy Chowdhury}, doi = {10.1109/ACCESS.2025.3595995}, journal = {IEEE Access}, pages = {140889-140899}, shortjournal = {IEEE Access}, title = {Human-aware robot collaborative task planning using artificial potential field and DQN reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). GResilience: Decision-making policies for trading off greenness and resilience in online collaborative AI systems. ACCESS, 13, 140867-140888. (www)

  @article{ , abstract = {Problem: An Online Collaborative AI System (OL-CAIS) learns online from human collaboration to achieve a common goal. It may be subjected to environmental events that disrupt the system’s decision-making policies, resulting in performance degradation. Decision-makers need to develop appropriate policies to restore performance from degradation (i.e., ensure resilience) while managing the energy’s adverse effects that may result from these policies (i.e., ensure greenness). These policies should aim to select actions within OL-CAISs that lead to green recovery. Methodology: In this paper, we introduce the GResilience framework, which automatically recommends OL-CAIS actions to balance greenness and resilience. It quantifies the actions’ contribution to the two system’s qualities and then solves optimization and game theory to propose one-agent and two-agent policies, respectively. The internal policies and the GResilience ones are implemented and evaluated in three lab experiments on a real-world OL-CAIS experiencing performance degradation. In each experiment, we model system resilience and use the models with metrics to evaluate the overall effectiveness of the policies for greenness and resilience. Results: For resilience, two-agent policies achieved faster recovery and steadier performance than internal policies. Although one-agent improved performance steadiness, the system did not recover. As for greenness, both GResilience policies halved the dependency on human interactions. However, this improvement comes with a marginal increase in CO2 emissions. Conclusion: Our results demonstrate the effectiveness of the GResilience framework, emphasizing that two-agent policies provide a more balanced trade-off between greenness and resilience compared to the other policies.}, archive = {J_ACCESS}, author = {Diaeddin Rimawi and Antonio Liotta and Marco Todescato and Barbara Russo}, doi = {10.1109/ACCESS.2025.3596836}, journal = {IEEE Access}, pages = {140867-140888}, shortjournal = {IEEE Access}, title = {GResilience: Decision-making policies for trading off greenness and resilience in online collaborative AI systems}, volume = {13}, year = {2025}, }
• (2025). EvalRound+ bootstrapping and its rigorous analysis for CKKS scheme. ACCESS, 13, 140847-140866. (www)

  @article{ , abstract = {Bootstrapping stands as a fundamental component of fully homomorphic encryption schemes. This work is aimed to significantly reduce the consumption of modulus in bootstrapping and increase the reservoir of modulus in general purpose, thereby enhancing the efficiency of fully homomorphic encryption performance, specifically for Cheon-Kim-Kim-Song scheme [Asiacrypt’17]. Kim et al. [Asiacrypt’22] suggested EvalRound bootstrapping which achieved remarkable reduction of modulus in CoeffToSlot, but there was the price to pay for the benefit. The consumption of modulus in SlotToCoeff was rather increased, and the increase in some cases amounts to invalidate the reduction. Our proposal, referred to as $\textrm {EvalRound}^{+}$ , inherits the advantage of EvalRound bootstrapping in CoeffToSlot and resolved its disadvantage in SlotToCoeff. Through a set of rigorous and comprehensive analyses including causality tracing, momentum analysis and sparsity pattern, $\text {EvalRound}^{+}$ bootstrapping achieved about 36% of modulus reductions in CoeffToSlot and SlotToCoeff compared to state-of-art bootstrapping suggested by Bossuat et al. [Eurocrypt’21] and about 35% compared to EvalRound bootstrapping in tests with commonly used parameter sets.}, archive = {J_ACCESS}, author = {Hyewon Sung and Sieun Seo and Taekyung Kim and Chohong Min}, doi = {10.1109/ACCESS.2025.3595177}, journal = {IEEE Access}, pages = {140847-140866}, shortjournal = {IEEE Access}, title = {EvalRound+ bootstrapping and its rigorous analysis for CKKS scheme}, volume = {13}, year = {2025}, }
• (2025). An efficient topology construction scheme designed for graph neural networks in hyperspectral image classification. ACCESS, 13, 140830-140846. (www)

  @article{ , abstract = {Superpixel-based Graph Neural Networks (GNNs) have achieved remarkable success in hyperspectral image (HSI) classification tasks, primarily due to their ability to capture the implicit topological structure in the data while maintaining low computational complexity by propagating information between spatially adjacent superpixels. However, the assumption of treating pixel features within superpixels as identical representations may limit the model’s expressive power. This is because land cover regions in HSI are often irregular, which leads to a difficult-to-resolve contradiction between the superpixel segmentation scale and the homogeneity of pixel labels within superpixels. To fundamentally address this issue, we reinterpreted the implicit topological structure between pixels on the basis of their spectral feature similarity and spatial position dependencies. Considering computational bottlenecks, we proposed a new subgraph partitioning method and sorting selection technique to choose important relationships from the graph, thereby constructing an a priori topology conducive to downstream tasks on HSI of any scale. Based on this a priori topology, we designed a GCN model for learning pixel feature representations and integrated it into a unified framework. We conducted comprehensive experiments on three widely used benchmark datasets. The results show that, compared to the mainstream superpixel-level GCN models, the proposed method improved the overall accuracy (OA), the average accuracy (AA), and the Kappa coefficient (Kappa) by an average of 2.0%, 5.4%, and 2.3%, respectively, across the three datasets. Moreover, on some datasets, our method outperformed several recent multi-scale feature fusion models. We also observed that different models exhibit different performances when dealing with land cover areas with different characteristics. In particular, by combining these models with our method, the classification performance was significantly improved. Our code is open-source on the public platform GitHub: https://github.com/LittleBlackBearLiXin/An-Efficient-Topology-Construction-Scheme-Designed-for-Graph-Neural-Networks}, archive = {J_ACCESS}, author = {Yu Zhang and Xin Li and Yaoqun Xu}, doi = {10.1109/ACCESS.2025.3595997}, journal = {IEEE Access}, pages = {140830-140846}, shortjournal = {IEEE Access}, title = {An efficient topology construction scheme designed for graph neural networks in hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). Spatiotemporal flood hazard classification in bangkok using graph convolutional network and temporal fusion transformer. ACCESS, 13, 140816-140829. (www)

  @article{ , abstract = {Urban flooding is a significant and increasing phenomenon in Bangkok that threatens human life. High population density, low elevation, and seasonal monsoons contribute to increased vulnerability to flooding. Traditional flood prediction models often fail to capture spatial correlations across districts and the temporal patterns within different types of features. To address this problem, this study proposes a hybrid deep learning framework combining Graph Convolution Network (GCN) and the Temporal Fusion Transformer (TFT) for predicting flood hazard levels in 50 Bangkok districts. The GCN component learns spatial dependencies from a graph constructed based on district relations, while the TFT learns temporal sequences based on spatiotemporal inputs. The model is trained on a historical 22-year dataset incorporating meteorological, hydrological, and socioeconomic variables. Experimental evaluation demonstrates that the proposed GCN-TFT model achieves superior classification performance compared to the existing models. Interpretability is achieved using attention-based variable importance and regime shift detection. The predicted flood hazard levels are mapped into a high-resolution map produced through QGIS that indicates a strong correlation with flood-prone areas. This study offers a robust, interpretable, and spatially consistent framework for urban flood hazard assessment with practical applications in disaster mitigation and urban resilience planning.}, archive = {J_ACCESS}, author = {Pakpoom Chaimook and Nirattaya Khamsemanan and Cholwich Nattee and Alice Sharp}, doi = {10.1109/ACCESS.2025.3597328}, journal = {IEEE Access}, pages = {140816-140829}, shortjournal = {IEEE Access}, title = {Spatiotemporal flood hazard classification in bangkok using graph convolutional network and temporal fusion transformer}, volume = {13}, year = {2025}, }
• (2025). A survey on integration of empathy cues into empathetic text generation. ACCESS, 13, 140791-140815. (www)

  @article{ , abstract = {In recent years, affective computing has seen a surge in empathetic response generation algorithms. These incorporate different empathy-related concepts to increase their perceived behavioral empathy. This survey aims to define the predominant research directions and offer structure to the field through the lens of empathy cues. In this definition, we include the concepts, factors, and components of interpersonal communication that relate to the expression of empathy. We describe the four general methods taken for extracting empathy cues from multiple sources. Subsequently, we explore three major strategies used to incorporate these cues into the text generation pipeline. Then, we present the methods used by the most prominent algorithms for evaluating their results and present a novel hierarchical view of performance across the field. Finally, we present areas of opportunity for novel approaches.}, archive = {J_ACCESS}, author = {Edwin C. Montiel-Vázquez and Christian Arzate Cruz and Jorge Adolfo Ramírez Uresti and Miguel González Mendoza and Randy Gomez}, doi = {10.1109/ACCESS.2025.3597304}, journal = {IEEE Access}, pages = {140791-140815}, shortjournal = {IEEE Access}, title = {A survey on integration of empathy cues into empathetic text generation}, volume = {13}, year = {2025}, }
• (2025). 3D convolutional neural networks for brain tumor analysis in multimodal MRI: A systematic review. ACCESS, 13, 140771-140790. (www)

  @article{ , abstract = {Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. The dimensionality of images is a principal factor in CNN models, as they are designed to interpret 2D or 3D image data. This study provides a comprehensive assessment of 3D CNN-based models for brain tumor segmentation and classification of magnetic resonance imaging (MRI) scans, focusing on their performance in terms of accuracy and the Dice Similarity Coefficient (DSC), along with an analysis of self-reported limitations. The integration and processing of diverse data from different MRI sequences, such as resolution, contrast, and noise, pose challenges in the construction of robust models. Consequently, the review focused on multimodal MRIs to provide a comprehensive overview of peer-reviewed literature on 3D CNN models. Therefore, the study performed a systematic literature review (SLR) of articles published between 2019 and 2024 in PubMed, Scopus, and IEEE Xplore databases. The SLR initially identified 554 potentially eligible studies screened for relevance and quality, resulting in the inclusion of 32 studies. Based on these studies, the article conducted systematic and quantitative analysis from technical and task perspectives. The findings indicate that the technical aspects of 3D CNN-based models for brain tumor segmentation and classification can be further improved. The study also discussed the limitations of implementing 3D models for brain tumor analysis. Furthermore, it explores the challenges of translating deep learning (DL) techniques into clinical settings and offers insights into future research trends and advancements.}, archive = {J_ACCESS}, author = {Hamza S. Alsmadi and Hazlina Hamdan and Norwati Mustapha and Noridayu Manshor}, doi = {10.1109/ACCESS.2025.3597130}, journal = {IEEE Access}, pages = {140771-140790}, shortjournal = {IEEE Access}, title = {3D convolutional neural networks for brain tumor analysis in multimodal MRI: A systematic review}, volume = {13}, year = {2025}, }
• (2025). User-perceived quality and functional suitability of representations of DEMO’s process and fact models: Conclusions from a family of three studies. ACCESS, 13, 140742-140770. (www)

  @article{ , abstract = {Clear and cognitively compelling diagrams representing organizational reality are crucial for structured communication, proper information systems implementation, and ultimately, organizational success. Design and Engineering Methodology for Organizations (DEMO), a standard from the field of Enterprise Engineering, provides representations for Process and Fact Models. However, the complexity of its notations can impede stakeholder understanding. New representations for these models have been proposed, but lacked formal evaluation across diverse user groups. This paper addresses this gap by assessing user perception of both standard and newly proposed DEMO representations. We conducted a family of three evaluation studies using mixed research methods across distinct user groups ( ${N} =32$ ). Results show a unanimous preference for the new representations in terms of perceived quality and functional suitability, supporting most of our hypotheses. Our study does not support the claim that familiarity with the modeling language improves perception of empirical quality. This research expands the knowledge base on the underexplored area of user experience within DEMO models and representations.}, archive = {J_ACCESS}, author = {Dulce Pacheco and David Aveiro and Duarte Pinto and Vítor Freitas}, doi = {10.1109/ACCESS.2025.3597072}, journal = {IEEE Access}, pages = {140742-140770}, shortjournal = {IEEE Access}, title = {User-perceived quality and functional suitability of representations of DEMO’s process and fact models: Conclusions from a family of three studies}, volume = {13}, year = {2025}, }
• (2025). Cross-view correspondence modeling for joint representation learning between egocentric and exocentric videos. ACCESS, 13, 140733-140741. (www)

  @article{ , abstract = {Joint analysis of human action videos from egocentric and exocentric views enables a more comprehensive understanding of human behavior. While previous works leverage paired videos to align clip-level features across views, they often ignore the complex spatial and temporal misalignments inherent in such data. In this work, we propose a Cross-View Transformer that explicitly models fine-grained spatiotemporal correspondence between egocentric and exocentric videos. Our model incorporates self-attention to enhance intra-view context and cross-view attention to align features across space and time. To train the model, we introduce a hybrid loss function combining a triplet loss and a domain classification loss, further reinforced by a sample screening mechanism that emphasizes informative training pairs. We evaluate our method on multiple egocentric action recognition benchmarks, including Charades-Ego and EPIC-Kitchens. Experimental results demonstrate that our method consistently outperforms existing approaches, achieving state-of-the-art performance on several egocentric video understanding tasks.}, archive = {J_ACCESS}, author = {Zhehao Zhu and Yoichi Sato}, doi = {10.1109/ACCESS.2025.3593474}, journal = {IEEE Access}, pages = {140733-140741}, shortjournal = {IEEE Access}, title = {Cross-view correspondence modeling for joint representation learning between egocentric and exocentric videos}, volume = {13}, year = {2025}, }
• (2025). Design challenges for an autonomous unmanned aerial vehicle with hybrid lift and propulsion. ACCESS, 13, 140719-140732. (www)

  @article{ , abstract = {Minimization of energy consumption with the longest flight time and distance is one of the main challenges in the development of unmanned aerial vehicles (UAV). Vertical take-off and landing (VTOL) is another feature that makes handling such platforms easier. Considering these requirements, we propose the architectural design of a double-hybrid UAV, conceptualized and built as a flying wing with tractor configuration with a combustion engine propeller and an electric multirotor with 3 propeller sets. We performed several tests to validate the proposed UAV model, including take-off, horizontal flight, transition tests, and also tests on autonomy (flight time and distance). As a verification of the tool usefulness, the architectural sketch of our proposal has been granted a patent from the Intellectual Property Institute (Brazil) under the number BR-10-2015-032525-8. The main contribution is the architectural design and conceptualization of the double-hybrid UAV, which consists of easy-to-follow procedural steps that can be reproduced in a simple fashion anywhere.}, archive = {J_ACCESS}, author = {Alysson Nascimento de Lucena and Neusa Maria Franco de Oliveira and Bruno Marques Ferreira da Silva and Raimundo Carlos Silverio Freire Júnior and Luiz Marcos Garcia Gonçalves}, doi = {10.1109/ACCESS.2025.3596604}, journal = {IEEE Access}, pages = {140719-140732}, shortjournal = {IEEE Access}, title = {Design challenges for an autonomous unmanned aerial vehicle with hybrid lift and propulsion}, volume = {13}, year = {2025}, }
• (2025). Research on highway lane change control strategy considering driving styles. ACCESS, 13, 140709-140718. (www)

  @article{ , abstract = {To address the insufficient adaptability of existing automatic lane-change control technologies to driving styles, a highway lane-change control strategy with driving styles is proposed. Based on the highD natural driving dataset, 19 characteristic parameters are extracted, including lateral speed, acceleration, and headway distance, etc. using the principal component analysis (PCA). Driving styles are classified into three styles: cautious, normal, and aggressive, using the K-means clustering algorithm. A lane change trajectory planning model, based on a fifth-degree polynomial, is developed to balance safety and efficiency by dynamically adjusting the weight coefficients of lane change time and acceleration. Subsequently, LTV MPC tracking controller is designed which adapts to the dynamic needs of different driving styles by adjusting cost function weights and input constraints. Simulation results from CarSim and MATLAB/Simulink show that the cautious controller exhibits the smallest lateral tracking error and optimal yaw rate fluctuation range under various speed conditions. The aggressive controller has the fastest response speed, and the moderate controller provides the best overall performance. Compared to the traditional fixed parameter models, the proposed method significantly enhances lane change safety, efficiency, and personalized adaptation by the parameterized modeling of the driving style.}, archive = {J_ACCESS}, author = {Yi Ren and Lei Zhang and Xiyao Liu}, doi = {10.1109/ACCESS.2025.3596337}, journal = {IEEE Access}, pages = {140709-140718}, shortjournal = {IEEE Access}, title = {Research on highway lane change control strategy considering driving styles}, volume = {13}, year = {2025}, }
• (2025). CAFiKS: Communication-aware federated IDS with knowledge sharing for secure IoT connectivity. ACCESS, 13, 140694-140708. (www)

  @article{ , abstract = {Federated learning has recently gained significant attention as a method for building machine learning models due to its ability to preserve data privacy while delivering improved performance. In many cases, deep neural networks (DNNs) serve as the backbone algorithm in federated processes. However, their computational demands make them impractical for deployment in Internet of Things (IoT) environments, which are characterised by resource-constrained devices; hence, the need for lightweight and adaptable solutions. Currently, the most widely used aggregation method only assumes that data is independently and identically distributed (IID), which is impractical for real-world applications. Additionally, to cater for robust security measures to protect the vast number of devices and data in IoT networks, federated learning-based methods are increasingly being employed to develop intrusion detection systems (IDS) that monitor IoT traffic for malicious activities. In this paper, we introduce CAFiKS (Communication-Aware Federated Intrusion Detection System with Knowledge Sharing), a novel FL method designed to address the challenges of data heterogeneity, model complexity, and convergence rates. CAFiKS integrates knowledge sharing and parameter compression techniques to achieve a lightweight model that is both resource-efficient and adaptable to heterogeneous IoT settings. Specifically, CAFiKS leverages knowledge distillation (KD), where a high-capacity teacher model trains a smaller, more efficient student model by transferring essential knowledge. This approach significantly reduces model size while preserving high detection accuracy. We evaluated CAFiKS on several novel datasets under both data and system heterogeneity scenarios to ensure scalability. The experimental results demonstrate that the CAFiKS achieves high detection accuracy (up to 99%) on the selected datasets in identifying network traffic anomalies while using a compact network architecture for predictions. By incorporating knowledge distillation, FL clients effectively reduce communication bottlenecks and mitigate communication threats.}, archive = {J_ACCESS}, author = {Ogobuchi Daniel Okey and Demóstenes Zegarra Rodríguez and Frederico Gadelha Guimarães and João Henrique Kleinschmidt}, doi = {10.1109/ACCESS.2025.3596374}, journal = {IEEE Access}, pages = {140694-140708}, shortjournal = {IEEE Access}, title = {CAFiKS: Communication-aware federated IDS with knowledge sharing for secure IoT connectivity}, volume = {13}, year = {2025}, }
• (2025). A multi-hop semantic communication system enhanced by semantic importance. ACCESS, 13, 140685-140693. (www)

  @article{ , abstract = {Semantic communication is envisioned as a promising solution for tackling the data transmission challenges in future wireless communication systems. In this paper, we propose a novel semantic importance-enhanced multi-hop semantic communication (SIMSC) framework that integrates semantic communication with a map-and-forward (MF) relaying scheme. To enhance transmission reliability, the framework employs a semantic importance (SI) calculation module to aid in training joint source-channel coding (JSCC) with relays, thereby better protecting the delivery of crucial semantic information over wireless channels with long-distance signal propagation. Different from conventional amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes, the proposed MF scheme leverages a deep neural network (DNN) to adaptively transform the received signal, optimizing it for multi-hop transmission. This enhances robustness and flexibility in varying relay conditions. Numerical results demonstrate that the proposed SIMSC framework performs notably better than conventional solutions that do not utilize semantic coding or relays. Moreover, the MF relaying scheme outperforms the AF and DF schemes, especially when relay distances vary across hops.}, archive = {J_ACCESS}, author = {Xin Huang and Chao Wang and Xuming Tian and Zuxing Li and Cong Zhao and Ming Xiao}, doi = {10.1109/ACCESS.2025.3593341}, journal = {IEEE Access}, pages = {140685-140693}, shortjournal = {IEEE Access}, title = {A multi-hop semantic communication system enhanced by semantic importance}, volume = {13}, year = {2025}, }
• (2025). Proposal of a methodology for mechatronic design from ideation to embodiment design: Application in a masonry robot case study design. ACCESS, 13, 140667-140684. (www)

  @article{ , abstract = {The growing complexity of industrial automation requires advanced design methodologies that integrate mechanical, electronic, and software systems into cohesive mechatronic products. This paper introduces an innovative engineering design methodology focused on the development of mechatronic products, covering from ideation to embodiment design. For its validation, the methodology was applied in a case study involving the design of a construction robot that automates key tasks such as block transportation, mortar application, and block placement. The robot was designed using a modular approach, allowing for scalability and adaptability to various tasks within the construction environment. To evaluate the methodology’s effectiveness, a comparative assessment was conducted using five key criteria: Iterative Process, Multidisciplinary Work, Design Complexity, Usability, and Adaptability. The proposed methodology scored 3, 3, 3, 2, and 2 respectively, on a 0-3 scale, demonstrating strong performance in early-stage iteration, cross-disciplinary integration, and handling of complex designs, with moderate usability and adaptability. These results position the methodology among the most balanced approaches, bridging both classical and modern design methods, such as those proposed by Ulrich or the V-model. Initial validation of this case study, through simulations and conceptual design, highlights the robot’s potential to improve efficiency and reduce labor costs. Although the case study targets construction, the methodology is adaptable to various industrial contexts, particularly manufacturing processes requiring automation, modularity, and flexibility. Future work includes the development of a physical prototype and further validation, with the potential to expand the methodology to broader manufacturing environments, fostering human-robot collaboration and adaptive production systems.}, archive = {J_ACCESS}, author = {Luis A. Salazar-Calderón and Javier Izquierdo-Reyes and Javier A. de la Tejera}, doi = {10.1109/ACCESS.2025.3595509}, journal = {IEEE Access}, pages = {140667-140684}, shortjournal = {IEEE Access}, title = {Proposal of a methodology for mechatronic design from ideation to embodiment design: Application in a masonry robot case study design}, volume = {13}, year = {2025}, }
• (2025). MLDSS: Customer-centric retail recommendation via multi-layered decision support system. ACCESS, 13, 140644-140666. (www)

  @article{ , abstract = {In the ever-evolving landscape of retail, the need for an advanced recommendation system has become crucial to enhance customer experience and drive sales. This research introduces a novel multilayered recommendation system designed to provide personalized product recommendations by leveraging a combination of machine learning techniques and association rule mining (ARM). To design this system, we made the following contributions: Our first contribution is to generate association rules from the transaction dataset. The second contribution is to extract features from the association rules to have a better input to the proposed model. The third contribution is to design a multilayered recommendation system where, at each layer, we have applied and synchronized different ML techniques to generate customer-centric recommendations. The performance of the proposed system achieves a comparative accuracy of 99.09%, F1 score of 0.95, precision of 0.94, recall of 0.925, and AUC value of 0.987, demonstrating the ability to provide accurate, personalized, and dynamic recommendations. Furthermore, the system is rigorously validated under noise, drift, seasonality, and anomaly simulations, confirming its robustness and fault tolerance for real-world retail deployment. This approach improves the accuracy of recommendations and ensures adaptability to changing customer preferences and market trends, offering a robust solution for modern retail environments.}, archive = {J_ACCESS}, author = {Santilata Champati and Bijayini Moahanty and Swadhin Kumar Barisal}, doi = {10.1109/ACCESS.2025.3596795}, journal = {IEEE Access}, pages = {140644-140666}, shortjournal = {IEEE Access}, title = {MLDSS: Customer-centric retail recommendation via multi-layered decision support system}, volume = {13}, year = {2025}, }
• (2025). ‘Over time everyone’s gonna be open to it’: User attitudes toward security and privacy in continuous authentication for smart homes. ACCESS, 13, 140629-140643. (www)

  @article{ , abstract = {Continuous authentication (CA), a user authentication approach that continuously verifies a person’s identity without requiring explicit input, is increasingly being deployed in smart homes to maintain security posture throughout user sessions. However, prior research has overlooked user attitudes toward the increased data collection and surveillance associated with CA in smart homes. To bridge this gap, we conducted a focus group study with 33 participants, using a design probe video to simulate various CA implementation scenarios in smart homes. We explored participants’ current authentication methods (e.g., passwords and physiological biometrics) and examined their perceptions of CA. Through affinity diagramming, we found that participants perceive smart-home CA as presenting privacy and security challenges yet possessing great potential for enhanced usability. Participants also envisioned CA systems that offer more granular permission controls over personal data. Our findings indicate the contextual dependencies in balancing usability with privacy and security concerns. Our contributions include a comprehensive empirical dataset featuring the design probe video, participant transcripts, and a conceptual model of users’ nuanced understanding. We provide design recommendations for smart home CA systems, emphasizing transparency as a bridging factor that connects user perceptions with design features related to technology, implementation flexibility, and user interface.}, archive = {J_ACCESS}, author = {Heting Wang and Yu-Peng Chen and Dinank Bista and Rodrigo Calvo and Niriksha Regmi and Xuanpu Zhang and Tempestt Neal and Jaime Ruiz and Lisa Anthony}, doi = {10.1109/ACCESS.2025.3596512}, journal = {IEEE Access}, pages = {140629-140643}, shortjournal = {IEEE Access}, title = {‘Over time everyone’s gonna be open to it’: User attitudes toward security and privacy in continuous authentication for smart homes}, volume = {13}, year = {2025}, }
• (2025). The impact of AI, XR, and combined AI-XR on student satisfaction: A moderated mediation analysis of engagement and learner characteristics. ACCESS, 13, 140614-140628. (www)

  @article{ , abstract = {Emerging educational technologies such as Artificial Intelligence (AI) and Extended Reality (XR) promise to transform traditional classrooms into dynamic, personalized, and immersive learning environments. Yet, the potential of combining these innovative technologies and understanding their nuanced impacts on learners remains underexplored. This study addresses this gap by examining how AI, XR, and a combined AI-XR approach influence student satisfaction, mediated by student engagement and moderated by individual learner characteristics. Utilizing a cluster-randomized experimental design with 888 high school students, the research compares traditional teaching methods against AI-enhanced, XR-enhanced, and combined AI-XR instructional settings. Grounded in Kolb’s Experiential Learning Theory and Self-Determination Theory, findings reveal that student engagement significantly mediates the relationship between advanced learning environments and student satisfaction, with the combined AI-XR condition demonstrating the most potent effects. Further analysis highlights the moderating role of learner characteristics: students with high technological proficiency and male students show heightened engagement and satisfaction in immersive XR-based contexts, whereas AI-based personalized support notably benefits learners with lower initial motivation. Self-efficacy showed no significant moderating effect. The study underscores engagement as a critical mechanism for achieving positive outcomes through innovative educational technology and emphasizes the importance of designing instructional experiences that account for diverse learner profiles. The research offers valuable in-sights for educators and instructional designers seeking to harness AI and XR technologies effectively to enhance learner experiences in secondary education.}, archive = {J_ACCESS}, author = {Mohammad Hmoud and Wajeeh Daher and Abedalkarim Ayyoub}, doi = {10.1109/ACCESS.2025.3597239}, journal = {IEEE Access}, pages = {140614-140628}, shortjournal = {IEEE Access}, title = {The impact of AI, XR, and combined AI-XR on student satisfaction: A moderated mediation analysis of engagement and learner characteristics}, volume = {13}, year = {2025}, }
• (2025). Enhancing cloud job failure prediction with a novel multilayer voting-based framework. ACCESS, 13, 140600-140613. (www)

  @article{ , abstract = {In modern cloud data centers, accurately predicting job failures before they occur is essential for ensuring system reliability, availability, and efficiency. To address this challenge, researchers have progressively developed machine learning and deep learning techniques that examine cloud logs to identify patterns linked to such failures. To surpass the prediction accuracy of earlier models, this paper presents the Multilayer Multi-Prediction Framework (MMPF), a novel approach specifically designed to enhance failure prediction accuracy. The framework is divided into two distinct layers. The first layer aims to detect job failures using a fine-tuned voting mechanism, while the second layer identifies the specific failure type. In the first layer, several classifiers are integrated to build a robust predictive model, which outperforms conventional methods that rely on a single classifier. By aggregating the predictions from multiple base classifiers and making the final decision based on a weighted average of the predicted probabilities, this approach delivers significantly higher accuracy in failure prediction. Decision Trees, K-Nearest Neighbors, Extreme Gradient Boosting, Adaptive Boosting, and Artificial Neural Networks are the classifiers implemented in this layer. Once the failed jobs are detected, the second layer takes over to determine the failure type employing the Random Forest algorithm. Based on experiments conducted with the Google Cluster 2019 trace dataset, the framework achieved impressive accuracy: 99.83% in detecting failed jobs and 99.97% in identifying the nature of the failure.}, archive = {J_ACCESS}, author = {Ahmed Elkaradawy and Ayman Elshenawy and Hany Harb}, doi = {10.1109/ACCESS.2025.3593808}, journal = {IEEE Access}, pages = {140600-140613}, shortjournal = {IEEE Access}, title = {Enhancing cloud job failure prediction with a novel multilayer voting-based framework}, volume = {13}, year = {2025}, }
• (2025). Integrating IT and OT for cybersecurity: A stochastic optimization approach via attack graphs. ACCESS, 13, 140578-140599. (www)

  @article{ , abstract = {This paper proposes an attack graph-based optimization model to enable cybersecure digital manufacturing. Cybersecurity has become imperative as manufacturing systems continue to increase connectivity through Industrial Internet of Things (IIoT) devices. However, modeling cyber threats in manufacturing environments remains underexplored. This work addresses this gap by presenting an approach to represent a manufacturing IT and OT network as an attack graph that captures vulnerabilities in components, such as the motion control system, spindle, tool changer, sensors, network interfaces, and connectivity through potential vectors. A two-stage stochastic programming model is formulated based on the attack graph to optimize the allocation of countermeasures under budget constraints considering distinct defense strategies to minimize expected cyber risk. A hybrid solution approach that integrates the Sample Average Approximation (SAA) and Benders Decomposition (BD) algorithms is used to efficiently solve the resulting large-scale problem. We demonstrate the efficacy of this proposed approach in cybersecure digital manufacturing via a real-life Computer Numerical Control (CNC) machining process. The computational results demonstrate that the proposed solution approach can solve the problem for a large-scale complex network, compromising more than 1000 connections between components, within 13 minutes. The defense strategies identified by our approach demonstrate that robust security protection can be achieved with optimal resource allocation, providing robust protection while minimizing implementation costs across the most critical vulnerabilities in the manufacturing network.}, archive = {J_ACCESS}, author = {Gonzalo Martinez Medina and Krystel K. Castillo-Villar and Tanveer Hossain Bhuiyan}, doi = {10.1109/ACCESS.2025.3596837}, journal = {IEEE Access}, pages = {140578-140599}, shortjournal = {IEEE Access}, title = {Integrating IT and OT for cybersecurity: A stochastic optimization approach via attack graphs}, volume = {13}, year = {2025}, }
• (2025). Adaptive window multi-feature fusion point cloud semantic segmentation network. ACCESS, 13, 140559-140577. (www)

  @article{ , abstract = {With the widespread application of 3D point cloud data, point cloud semantic segmentation technology has shown tremendous potential in fields such as autonomous driving, robot navigation, and urban modeling. However, the high dimensionality, sparsity, and complex local structures of 3D point cloud data make it challenging for traditional point cloud processing methods to effectively capture fine-grained features. This challenge is particularly evident when dealing with point clouds that have different scales, densities, and structural characteristics. To address these issues, this research proposes Adaptive Window Multi-Feature Fusion Point Cloud Semantic Segmentation (AWFusionNet), aimed at simultaneously considering both global and local features of point clouds, optimizing their representation capability and segmentation accuracy. The method combines dynamic and fixed window feature extraction mechanisms, using dynamic windows to model global features and fixed windows to enhance local features, effectively improving segmentation accuracy and robustness. Specifically, the dynamic window utilizes the farthest point sampling (FPS) algorithm for division and performs global information fusion through inter-window relative attention and global cross-attention. The fixed window employs a local relative attention feature expansion module to extract fine-grained local features. Additionally, the method improves edge feature recognition during the upsampling stage through an inter-layer edge enhancement and suppression module. Experimental results demonstrate that AWFusionNet achieves high accuracy and better robustness when processing point cloud data in complex scenarios.}, archive = {J_ACCESS}, author = {Lie Zhu}, doi = {10.1109/ACCESS.2025.3596511}, journal = {IEEE Access}, pages = {140559-140577}, shortjournal = {IEEE Access}, title = {Adaptive window multi-feature fusion point cloud semantic segmentation network}, volume = {13}, year = {2025}, }
• (2025). Development of plucked piezoelectric energy harvesting from human’s upper limb motion. ACCESS, 13, 140546-140558. (www)

  @article{ , abstract = {This paper presents the design, modeling, and implementation of a plucked piezoelectric energy harvester (PEH) optimized for human elbow motion. The proposed system leverages the advantages of upper-limb biomechanics, particularly elbow flexion during walking, to generate electrical energy suitable for powering wearable electronics. A compact bimorph PZT-based PEH was selected and integrated with a mechanical plucking mechanism to enable frequency up-conversion from low-frequency body motion to high-frequency piezoelectric vibration. An electromechanical model predicting the electrical output and energy harvesting performance of a plucked PEH system was developed using Hamilton’s Principle and validated through experiments, showing strong agreement in voltage response and energy output. Implemented in MATLAB/Simulink, the model also served as a design tool for parametric optimization, allowing efficient tuning of system parameters prior to prototyping. Furthermore, parameter optimization through experimental tests identified a plucking distance of 0.16 mm and a load resistance of $800\Omega $ as optimal for the decided bimorph PZT in energy harvesting. The developed prototype successfully demonstrated repeatable energy generation ranging from 22– $33~\mu $ J per elbow movement cycle under simulated motion. This work offers a validated framework for future development of energy-autonomous wearable devices that are lightweight, compact, and ergonomically compatible.}, archive = {J_ACCESS}, author = {Kaweepak Satjasai and Matniwit Chariyasethapong and Khem Submee and Pheerawit Inpra and Parineak Romtrairat and Thitima Jintanawan and Gridsada Phanomchoeng}, doi = {10.1109/ACCESS.2025.3597015}, journal = {IEEE Access}, pages = {140546-140558}, shortjournal = {IEEE Access}, title = {Development of plucked piezoelectric energy harvesting from human’s upper limb motion}, volume = {13}, year = {2025}, }
• (2025). Student’s engagement detection based on computer vision: A systematic literature review. ACCESS, 13, 140519-140545. (www)

  @article{ , abstract = {Student engagement detection is receiving increasing attention due to its crucial role in influencing academic performance and learning outcomes. As learning environments increasingly shift toward online and hybrid modes, understanding and monitoring student engagement through automated means has become an essential focus in educational technology. Computer vision techniques offer promising capabilities for detecting and interpreting student behaviors indicative of engagement. This paper presents a systematic literature review (SLR) on engagement detection in learning environments using computer vision methods. The objective of this study is to examine and categorize the types of student engagement detected, the learning contexts in which detection occurs, and the methods employed for such detection. Specifically, we analyze the types of data sources, datasets, and features used, as well as the preprocessing and feature engineering techniques applied to enhance model accuracy. Furthermore, we investigate the types of machine learning/Deep learning models adopted and how their performance is evaluated. Based on the findings from selected studies, this review aims to identify key contributions, existing challenges, and potential directions for future research in the domain of automated student engagement detection.}, archive = {J_ACCESS}, author = {Ikram Qarbal and Nawal Sael and Sara Ouahabi}, doi = {10.1109/ACCESS.2025.3596885}, journal = {IEEE Access}, pages = {140519-140545}, shortjournal = {IEEE Access}, title = {Student’s engagement detection based on computer vision: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Extremist ideology classification in kazakh: A multi-class approach using machine learning and psycholinguistic analysis. ACCESS, 13, 140500-140518. (www)

  @article{ , abstract = {This paper presents a new approach for analyzing extremist content in the Kazakh language on social media using advanced machine learning and natural language processing techniques. With the rapid growth of online data, especially on social networks, there is an urgent need for tools that can identify and classify extremist ideologies. Our study focuses on four primary categories: propaganda, recruitment, radicalization, and neutral content. We employ a hybrid methodology that combines traditional text vectorization techniques, machine learning algorithms, and a psycholinguistic analysis module (PLAM) specifically adapted for the Kazakh language. To improve the accuracy of classification and capture subtle emotional signals, we integrate psycholinguistic features extracted through PLAM. The experimental results demonstrate the effectiveness of our hybrid approach. The combination of CountVectorizer + Logistic Regression + PLAM achieved the highest performance among traditional models (F1-score: 0.9305, Accuracy: 0.9308, ROC AUC: 0.9892). Among deep learning models, the BERT + LSTM model yielded the best results (F1-score: 0.9481, Accuracy: 0.9485, ROC AUC: 0.9918), followed by the standalone BERT model (F1-score: 0.9412, Accuracy: 0.9414, ROC AUC: 0.9901). These findings confirm that combining contextual embeddings with sequential modeling improves classification performance, particularly for ideologically complex categories. This research provides an effective framework for multilingual text analysis. It also contributes to improved monitoring and prevention of extremist content in underrepresented languages, such as Kazakh. Future work will focus on refining these methods and exploring their application in other domains for robust content moderation and security in the digital space.}, archive = {J_ACCESS}, author = {Shynar Mussiraliyeva and Milana Bolatbek and Kymbat Baisylbayeva}, doi = {10.1109/ACCESS.2025.3596601}, journal = {IEEE Access}, pages = {140500-140518}, shortjournal = {IEEE Access}, title = {Extremist ideology classification in kazakh: A multi-class approach using machine learning and psycholinguistic analysis}, volume = {13}, year = {2025}, }
• (2025). A review of engineering techniques for CMOS on-chip inductor design and quality factor enhancement from MHz-to-GHz frequency domains. ACCESS, 13, 140473-140499. (www)

  @article{ , abstract = {This paper presents a comprehensive review of engineering techniques employed to enhance the quality factor (Q-factor) of silicon-based on-chip inductors across MHz-to-GHz frequency domains. The performance of CMOS on-chip inductors is often bottlenecked by the substrate losses, parasitic effects and resistive losses, which degrade the Q-factor. Numerous engineering techniques including substrate engineering, ground plane engineering, layout engineering and material engineering are analyzed and discussed. Substrate engineering such as substrate isolation and micromachining, ground plane engineering such as patterned ground shield and defected ground shield, layout engineering such as multilayer stacking and geometry modulation, material engineering such as magnetic core integration and conductor lamination are all investigated. A comparative evaluation of these techniques highlights the optimum approaches for attaining high-Q inductors in silicon process, mainly for CMOS compatibility.}, archive = {J_ACCESS}, author = {Selvakumar Mariappan and Jagadheswaran Rajendran and Norhamizah Idros and Asrulnizam Abd Manaf and Narendra Kumar and Abdullah Alghaihab and Arokia Nathan and Binboga Siddik Yarman}, doi = {10.1109/ACCESS.2025.3596323}, journal = {IEEE Access}, pages = {140473-140499}, shortjournal = {IEEE Access}, title = {A review of engineering techniques for CMOS on-chip inductor design and quality factor enhancement from MHz-to-GHz frequency domains}, volume = {13}, year = {2025}, }
• (2025). Overhead transmission line modeling strategies for EMT-based traveling-wave analysis and fault location. ACCESS, 13, 140462-140472. (www)

  @article{ , abstract = {This paper presents a systematic evaluation of overhead transmission line (OHTL) modeling approaches for traveling-wave (TW)–based fault analysis in power systems. The study examines five critical modeling aspects: soil resistivity representation, shield wire inclusion and Kron reduction, modal decomposition techniques, fault location accuracy, and tower grounding representation. Comprehensive electromagnetic transient (EMT) simulations of a 220 kV, 330 km OHTL were performed using the Carson and Pettersson formulations to calculate electrical parameters, both with and without considering the frequency dependence of ground parameters. The results show that: 1) uniform soil resistivity assumptions introduce negligible errors in TW arrival times despite minor amplitude variations; 2) shield wires significantly affect modal structure, compromising the effectiveness of Clarke transformation for ground quasi-mode decoupling while preserving aerial quasi-mode reliability; 3) exact eigenvector-based decomposition improves ground mode identification but remains impractical for field applications; 4) the classical two-terminal fault location method maintains high accuracy across all modeling configurations; and 5) simplified OHTL modeling uniformly distributed sections at both terminals achieves an optimal balance between accuracy and computational efficiency for simulating TWs. These findings provide practical guidelines for EMT simulations and TW-based fault location schemes, particularly regarding appropriate model simplification.}, archive = {J_ACCESS}, author = {Adrian Wilmer Diaz Sarmiento and Jaimis Sajid Leon Colqui and José Pissolato Filho}, doi = {10.1109/ACCESS.2025.3597218}, journal = {IEEE Access}, pages = {140462-140472}, shortjournal = {IEEE Access}, title = {Overhead transmission line modeling strategies for EMT-based traveling-wave analysis and fault location}, volume = {13}, year = {2025}, }
• (2025). Quality of experience under huge load for WebRTC applications: A case study of three media servers. ACCESS, 13, 140440-140461. (www)

  @article{ , abstract = {Videoconference applications are becoming increasingly popular, and the demand for these applications is growing. The availability of a standard for building videoconference application on the web, the W3C WebRTC standard, boosted the development of such applications. With so many alternatives available, an impact on quality due to an overload of such applications might cause users to leave and choose another service instead. This makes stress testing mandatory in order to understand the limits of these videoconference solutions and how these limits impact the quality of experience (QoE) of the users. However, most testing tools are not designed to calculate QoE, which is essential for real-time videoconference applications, because QoE calculation is complex and a computationally intensive process. This paper focuses on how load impacts QoE for WebRTC applications and presents OpenVidu QoE and Load Testing Tool (OQLT), a load and stress testing tool for WebRTC applications which measures the QoE of users in videoconference applications. In this work, we make use of this tool to help researchers and practitioners understand the impact of server load on the QoE of users in WebRTC applications, by analyzing three different communication systems: Kurento, Mediasoup, and Pion. We study which quality of service (QoS) metrics can be used to prevent an impact on the QoE of users in these servers. We also analyze different session sizes and topologies to understand the impact of server load on the QoE of users under different circumstances. Our findings show that in two of the three media servers (Kurento and Pion), CPU alone is a good indicator of QoE degradation, whereas for Mediasoup, additional WebRTC metrics are needed, because under high CPU usage Mediasoup can still provide a good QoE to its users. We also found that the behavior of the three media servers under load with respect to the QoE perceived by users is different, which might be important for practitioners, and that not all users are impacted equally by an overload on the server, and how users are impacted under such a load depends as well on the media server. From our extensive analysis of the data collected, we provide detailed implications for practitioners when using WebRTC applications.}, archive = {J_ACCESS}, author = {Iván Chicano-Capelo and Francisco Gortázar and Micael Gallego}, doi = {10.1109/ACCESS.2025.3589785}, journal = {IEEE Access}, pages = {140440-140461}, shortjournal = {IEEE Access}, title = {Quality of experience under huge load for WebRTC applications: A case study of three media servers}, volume = {13}, year = {2025}, }
• (2025). An efficient model for real-time traffic density analysis and management using visual graph networks. ACCESS, 13, 140413-140439. (www)

  @article{ , abstract = {Real-time traffic management systems are needed to manage urbanization’s impact on traffic conditions. Traffic dynamics in cities are complex, and traditional signal timing methods and simple vehicle detection cannot handle them. The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. This study aims to develop a robust, dynamic, and accurate approach to traffic density analysis, vehicle classification, and dynamic signal control in order to achieve high accuracy in traffic flow analysis. The proposed RDAMVGN framework incorporates both a LACF-YOLO detection model and a Faster Region-Based Convolutional Neural Network (Faster RCNN) detection model for high-speed and high-accuracy vehicle identification. This framework applies transfer learning to adapt pre-trained features to new traffic environments. This enhances vehicle classification in complex urban scenes and improves the model’s ability to distinguish vehicles from non-vehicle objects. Traffic flow optimization is achieved by using Mask RCNN and LSTM. The comparative analysis includes Fine-Tuned YOLOv8 with Fine-Tuned Faster RCNN, Fine-Tuned YOLOv3 with Fine-Tuned Faster RCNN, YOLOv5 with Faster RCNN, YOLOv3 with Faster RCNN, standalone Faster RCNN, YOLOv5, YOLOv3, Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) models, evaluate across precision, recall, accuracy, under pre-emption and non-pre-emption scenarios. The RDAMVGN-based detection component exhibits demonstrably superior performance across all evaluation metrics in real-time traffic management systems. It achieves a high precision of 97.75 %, indicating accurate vehicle identification with minimal false positives. Its recall rate of 96.17 % reflects strong detection capability, minimizing missed vehicles. The overall accuracy stands at 98.48 %, indicating robust classification and localization. In pre-emption scenario, the model maintains its lead with pre-emption precision of 94.79 %, pre-emption recall of 98.66 %, and pre-emption accuracy of 97.43 %, showcasing its reliability in real-time traffic prioritization scenarios.}, archive = {J_ACCESS}, author = {Nikhil Nigam and Dhirendra Pratap Singh and Jaytrilok Choudhary and Surendra Solanki}, doi = {10.1109/ACCESS.2025.3593136}, journal = {IEEE Access}, pages = {140413-140439}, shortjournal = {IEEE Access}, title = {An efficient model for real-time traffic density analysis and management using visual graph networks}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Converging technologies for health prediction and intrusion detection in internet of healthcare things with matrix-valued neural coordinated federated intelligence”. ACCESS, 13, 140412. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix-Valued Neural Coordinated Federated Intelligence”).}, archive = {J_ACCESS}, author = {Sarah A. Alzakari and Arindam Sarkar and Mohammad Zubair Khan and Amel Ali Alhussan}, doi = {10.1109/ACCESS.2025.3596365}, journal = {IEEE Access}, pages = {140412}, shortjournal = {IEEE Access}, title = {Corrections to “Converging technologies for health prediction and intrusion detection in internet of healthcare things with matrix-valued neural coordinated federated intelligence”}, volume = {13}, year = {2025}, }
• (2025). Low weight and inertia self-balancing testbed for a 3U CubeSat attitude control system. ACCESS, 13, 140392-140411. (www)

  @article{ , abstract = {The design, implementation, and testing of an Attitude Control System (ACS) for nanosatellites require a simulated environment that replicates the conditions of Low Earth Orbit. Key factors such as the absence of friction and gravitational effects must be achieved in an attitude control testbed. Additionally, a platform with low weight and inertia ensures that the small actuators of a CubeSat can rotate the system without requiring excessive effort. The testbed developed at Universidad Católica San Pablo was built using lightweight materials, including 3D-printed PLA parts, carbon fiber tubes, and acrylic boards. Unlike other self-balancing testbeds, this work introduces a shifting-mass approach, where a DC motor serves as both actuator and shifting-mass, significantly reducing weight. The position of the shifting masses along their axis is controlled by a Linear Quadratic Regulator (LQR). The platform’s attitude estimation is performed using an Extended Kalman Filter (EKF), which combines data from low-cost accelerometer, gyroscope, and magnetometer sensors. A nonlinear control system is implemented for the automatic balancing procedure, mitigating gravitational disturbance torque by using the estimated gravity vector to drive the shifting masses and minimize platform tilt. Finally, a Least Squares Method (LSM) compensates for residual vertical misalignment after the balancing procedure, enabling precise center of mass (CM) control in all axes. Obtained results demonstrate the DC motor shifting-mass approach reduces total platform weight by 72% compared to other works while achieving precise balancing with less than $210~\mu $ m residual misalignment between the center of mass (CM) and center of rotation (CR) and 92% reduction in kinetic energy variance during operation.}, archive = {J_ACCESS}, author = {Josue Pareja Contreras and Nilton Anchayhua Arestegui and Eber Huanca Cayo and Erick Mayta Pumacota}, doi = {10.1109/ACCESS.2025.3597232}, journal = {IEEE Access}, pages = {140392-140411}, shortjournal = {IEEE Access}, title = {Low weight and inertia self-balancing testbed for a 3U CubeSat attitude control system}, volume = {13}, year = {2025}, }
• (2025). Artificial blur effect for optical see-through near-eye displays. ACCESS, 13, 140382-140391. (www)

  @article{ , abstract = {Saliency modulation has significant potential for various applications. In our pursuit of implementing saliency modulation for optical see-through near-eye displays, we introduce a blur effect to reduce the sharpness of specific regions while preserving the sharpness of others. In this study, we use a digital micromirror device (DMD) to separate incoming light from a scene into sharp and blurred regions. To achieve this, we integrate an electrically tunable lens (ETL) that operates in its zero optical power mode when the reflected light from the DMD represents the sharp region (i.e., masking the blur region). Conversely, when the reflected light indicates the blur region, the ETL adjusts to non-zero optical powers. Importantly, these modulations occur at a speed that surpasses the critical flicker frequency threshold of the human eye. Furthermore, we propose an algorithm to mitigate artifacts around the border region between the sharp and blur regions that appear due to the magnification of the ETL. We have also developed a prototype system to demonstrate the feasibility of our method.}, archive = {J_ACCESS}, author = {Shiva Sinaei and Daisuke Iwai and Kosuke Sato}, doi = {10.1109/ACCESS.2025.3595907}, journal = {IEEE Access}, pages = {140382-140391}, shortjournal = {IEEE Access}, title = {Artificial blur effect for optical see-through near-eye displays}, volume = {13}, year = {2025}, }
• (2025). The current landscape of scalable dynamic graph processing. ACCESS, 13, 140364-140381. (www)

  @article{ , abstract = {With the rapid growth in data volume, workloads from various domains have undergone drastic changes in recent years. Today, streaming workloads are commonplace. This generates the need for systems and algorithms that can receive and process streams of data with high throughput. Various graph applications are shifting away from the static graph model and incorporating a dynamic model, where updates to the graph can be received. In a dynamic setting, solutions to algorithms need to be updated alongside the graph. However, re-processing the whole graph every time can be infeasible given the size of current graphs. This raises a series of questions regarding how to process dynamic graph algorithms in a reasonable amount of time. In this paper, we explore the existing methods in the literature used to achieve scalable dynamic graph processing. We define different aspects and abstractions used for dynamic graph processing and categorize all approaches on the basis of their scalability.}, archive = {J_ACCESS}, author = {Gabriel G. Dos Santos and César A. F. de Rose and Kartik Lakhotia}, doi = {10.1109/ACCESS.2025.3596871}, journal = {IEEE Access}, pages = {140364-140381}, shortjournal = {IEEE Access}, title = {The current landscape of scalable dynamic graph processing}, volume = {13}, year = {2025}, }
• (2025). Strong directional source effect and coping strategy in passive data detection. ACCESS, 13, 140350-140363. (www)

  @article{ , abstract = {In passive source exploration, seismic records are typically assumed to satisfy the equipartition condition. However, in urban environments, buildings and factories act as strong, directionally biased seismic sources, causing recorded signals to deviate from equipartition. To investigate this effect, we numerically simulate seismic records received by orthogonal arrays under varying source distributions. The influence of the wavefield propagation direction is analyzed by MUSIC (multiple signal classification), energy-phase velocity curves, and extracted dispersion. Our results demonstrate that strong directional sources introduce significant discrepancies in energy-phase velocity spectra across different array orientations. However, optimizing the observation system can mitigate this effect. When the illumination angle between two receivers sufficiently covers the dominant propagation directions, the extracted dispersion aligns more closely with theoretical predictions. We carry out the field test in Qingdao with the L-shaped array. Because of the influence of the village and factories, the recorded data also show the same phenomena. Dispersion obtained by linear arrays in different directions are not accurate. The observation system should be optimized and the illumination of the estimated two receivers’ path should cover the propagation directions. Therefore, it is necessary to design new observation system and calculate the illumination of the two receivers’ path to obtain accurate dispersion data in passive source exploration.}, archive = {J_ACCESS}, author = {Yuefeng Yan and Shaodong Jing and Jiao Wang and Xuedong Ma and Xiaozhong Liu and Xu Zhang and Liu Liu and Wei Fang}, doi = {10.1109/ACCESS.2025.3594285}, journal = {IEEE Access}, pages = {140350-140363}, shortjournal = {IEEE Access}, title = {Strong directional source effect and coping strategy in passive data detection}, volume = {13}, year = {2025}, }
• (2025). Evaluating the consistency between numerical calculation and acoustic measurement of head-related transfer functions. ACCESS, 13, 140337-140349. (www)

  @article{ , abstract = {In the validation of numerically calculated Head-Related Transfer Functions (HRTFs), acoustic measurements are often considered the ground truth. This study indicates that discrepancies between numerical and measured HRTFs may result from various factors and should not be directly attributed to inaccuracies in the numerical calculations themselves. Factors contributing to these discrepancies were systematically examined using KEMAR by calculating HRTFs with two mainstream numerical methods: Mesh2HRTF and the Finite Difference Time Domain (FDTD) approaches. For the FDTD method, the optimal virtual microphone placement was found to be within 3 mm of the ear canal mesh entrance. In numerical calculations, the spatial correlation of spectra computed with shoulder-inclusive meshes and full-torso meshes exceeded 0.9 in the low-frequency (¡4 kHz) region of the median plane. This suggests that the shoulders within the torso are the primary contributors to elevation-direction HRTF variations. Furthermore, consistencies among different numerical methods, measurement datasets, and simulated versus measured HRTFs were evaluated. The results show that, for the dummy head, the similarity between the numerical and measured HRTFs was no less than the similarity observed among separate measurement datasets. Numerical calculations demonstrated greater stability than acoustic measurements, particularly in human-related scenarios where maintaining measurement consistency is challenging. These findings suggest that accurate head modeling combined with numerical simulation offers a promising approach for obtaining high-accuracy individualized HRTFs.}, archive = {J_ACCESS}, author = {Chang Sun and Kan Okubo}, doi = {10.1109/ACCESS.2025.3596332}, journal = {IEEE Access}, pages = {140337-140349}, shortjournal = {IEEE Access}, title = {Evaluating the consistency between numerical calculation and acoustic measurement of head-related transfer functions}, volume = {13}, year = {2025}, }
• (2025). Deployable deep learning for cross-domain plant leaf disease detection via ensemble learning, knowledge distillation, and quantization. ACCESS, 13, 140313-140336. (www)

  @article{ , abstract = {Accurate leaf disease detection via smartphone-based deep learning holds immense potential for mitigating global crop losses. However, significant deployment challenges persist when transitioning from controlled laboratory environments to real-world agricultural conditions. Despite recent advances, three fundamental barriers remain: cross-domain generalization, severe class imbalance, and computational limitations for edge deployment. This study introduces the first open cross-domain benchmark for tomato leaf disease detection, unifying PlantVillage and TomatoVillage datasets into 15 harmonized disease classes to enable reproducible evaluation across domains. We propose a unified optimization approach integrating ensemble learning, knowledge distillation, and quantization across 24 deep learning architectures for edge-compatible disease detection. Strategic data augmentation and ADASYN-based balancing mitigate the severe 75:1 class imbalance, while systematic hyperparameter tuning optimizes model configurations. Our four-model ensemble (DenseNet-121, ResNet-101, DenseNet-201, EfficientNet-B4) achieves 99.15% accuracy via soft-voting. Knowledge distillation transfers ensemble capabilities to compact ShuffleNetV2, maintaining 98.53% accuracy with $163 \times $ parameter reduction and $43.6 \times $ speedup. INT8 quantization provides $671 \times $ compression (1.46 MB) while sustaining 97.46% accuracy, enabling 0.29ms inference. Cross-dataset validation demonstrates robust generalization with only 3.45% performance degradation. Grad-CAM++ and LIME-based explainability confirm biologically grounded attention patterns aligned with plant pathology principles. Finally, field deployment via a multilingual and multi-platform Flutter application validates real-world feasibility, establishing the first scalable framework bridging research and practical agricultural deployment. This work sets a standardized benchmark and extensible methodology for future multi-dataset precision agriculture research. Codes and implementations are publicly available at: https://github.com/junayed-hasan/tomato-leaf-ai}, archive = {J_ACCESS}, author = {Mohammad Junayed Hasan and Suvodeep Mazumdar and Sifat Momen}, doi = {10.1109/ACCESS.2025.3595390}, journal = {IEEE Access}, pages = {140313-140336}, shortjournal = {IEEE Access}, title = {Deployable deep learning for cross-domain plant leaf disease detection via ensemble learning, knowledge distillation, and quantization}, volume = {13}, year = {2025}, }
• (2025). Partial-nodes-based finite-time fault estimator design for nonlinear time-varying complex networks utilizing binary encoding schemes. ACCESS, 13, 140297-140312. (www)

  @article{ , abstract = {This paper is mainly concerned with the finite-time partial-nodes-based fault estimation issue for time-varying complex networks with binary encoding schemes subject to multiplicative noises. Binary encoding schemes are employed concerning the communication from the sensor to the remote estimator. Random bit error is taken into account as a result of channel noise, whose influence is represented via an equivalent stochastic noise. In view of some unavailable measurements, only measurements from partial nodes are adopted to carry on the estimation work, and the model of time-varying fault and state estimators is constructed. By virtue of performance analysis of stochastic finite-time boundedness for the estimation error dynamics, sufficient condition is developed by which the existence of desired estimators is guaranteed and such time-varying estimators are designed. Via solving a sequence of linear matrix inequalities, gains of the finite-time estimators are obtained straightforward. The correctness of the designed finite-time time-varying fault estimators is testified using two illustrative simulation examples.}, archive = {J_ACCESS}, author = {Nan Hou and Dandi Yang and Xianye Bu and Zhongrui Hu and Dianjie Sui}, doi = {10.1109/ACCESS.2025.3596099}, journal = {IEEE Access}, pages = {140297-140312}, shortjournal = {IEEE Access}, title = {Partial-nodes-based finite-time fault estimator design for nonlinear time-varying complex networks utilizing binary encoding schemes}, volume = {13}, year = {2025}, }
• (2025). Tensor-based channel estimation considering beam squint effects in LEO satellite communications. ACCESS, 13, 140282-140296. (www)

  @article{ , abstract = {In this paper, we propose a tensor-based channel estimation framework for beam squint-aware low Earth orbit (LEO) downlink communication at a ground station. We first demonstrate that the downlink signals received at the ground station from multiple LEO satellites can be represented as a structured tensor using mode-n products. Based on this tensor representation, we formulate the channel estimation problem as a tensor decomposition problem to mitigate beam squint effects and inter-satellite interference in downlink communication. To solve this problem, we propose constrained versions of CANDECOMP/PARAFAC (CP) and Tucker decompositions. Specifically, our modified approach incorporates additional constraints, such as a power constraint, which are not considered in the standard CP and Tucker decompositions. Finally, we validate the proposed methods through numerical simulations. Results show that the Tucker-based method achieves lower computational error than the CP-based method, albeit with higher computational cost. Furthermore, the proposed methods incorporating power constraints demonstrate comparable convergence performance to conventional approaches while requiring fewer iterations.}, archive = {J_ACCESS}, author = {Kyungrak Son and Pansoo Kim and Jung Hoon Lee}, doi = {10.1109/ACCESS.2025.3596393}, journal = {IEEE Access}, pages = {140282-140296}, shortjournal = {IEEE Access}, title = {Tensor-based channel estimation considering beam squint effects in LEO satellite communications}, volume = {13}, year = {2025}, }
• (2025). A gradient-based optimization algorithm for optimal control problems with general conformable fractional derivatives. ACCESS, 13, 140270-140281. (www)

  @article{ , abstract = {This paper presents an extended gradient-based optimization framework for optimal control problems governed by general conformable fractional derivatives (GCFDs), which unify various fractional operators and provide greater modeling flexibility than classical conformable derivatives. The proposed study derives necessary optimality conditions for GCFD systems by reformulating the Hamiltonian and adjoint equations to accommodate the general operator. A novel discretization scheme with adaptive collocation points is introduced to efficiently handle kernel-dependent dynamics. Numerical experiments demonstrate the method’s effectiveness, showing up to 20.2% improvement in terminal constraint satisfaction and 11.8% reduction in total cost compared to conformable-only approaches, with similar computational times. Comprehensive comparisons including Caputo derivatives demonstrate 17.8% cost reduction and 40.1% lower terminal state error. Parameter sensitivity analyses further validate kernel selection strategies. Overall, the proposed framework broadens the applicability of gradient-based optimization to a wider class of fractional-order systems, enhancing both performance and flexibility, with future work aimed at convergence analysis and real-world implementation.}, archive = {J_ACCESS}, author = {Essia Ben Alaia and Slim Dhahri and Omar Naifar}, doi = {10.1109/ACCESS.2025.3595958}, journal = {IEEE Access}, pages = {140270-140281}, shortjournal = {IEEE Access}, title = {A gradient-based optimization algorithm for optimal control problems with general conformable fractional derivatives}, volume = {13}, year = {2025}, }
• (2025). Optimized partial band preamble and timing synchronization algorithm for orthogonal time frequency space system. ACCESS, 13, 140263-140269. (www)

  @article{ , abstract = {A novel optimized partial band preamble is designed and the corresponding filtering processing of received signal and the timing synchronization algorithm are also proposed for orthogonal time frequency space (OTFS) systems in high mobility scenarios. To optimize timing synchronization performance, the occupied bandwidth of the preamble is formulated as an optimization problem and solved via the offline exhaustive search. Simulation results show that, compared with the available best performance benchmark algorithm, the proposed method achieves a 2dB signal-to-noise ratio gain under the same capture probability, and its root mean square error of timing offset estimation is only 1/3 that of the available benchmark algorithm.}, archive = {J_ACCESS}, author = {Wenchao Zhang and Xiaochen Lu and Guangliang Ren and Lirui Shen}, doi = {10.1109/ACCESS.2025.3597086}, journal = {IEEE Access}, pages = {140263-140269}, shortjournal = {IEEE Access}, title = {Optimized partial band preamble and timing synchronization algorithm for orthogonal time frequency space system}, volume = {13}, year = {2025}, }
• (2025). Optimizing rural connectivity through hybrid wireless-wireline and satellite network integration. ACCESS, 13, 140251-140262. (www)

  @article{ , abstract = {Connectivity remains a significant challenge in rural regions, where existing network infrastructures often fail to meet the growing need for connectivity. To address these limitations, hybrid networking solutions—such as hybrid wireless-wireline access networks (HWWANs) and hybrid satellite-3GPP networks (HSNs)—have emerged as promising alternatives, garnering considerable research attention. Building on these advancements, this paper introduces a novel hybrid wireless-wireline and satellite access network (HWWSAN) framework, which integrates both technologies to enhance network performance, service reliability, and user experience. The proposed HWWSAN framework strategically assigns latency-critical users and hybrid customer premises equipment (HCPE) to terrestrial base stations, while allowing latency-tolerant users to access both the base station and the satellite network. This dual-access approach not only improves system robustness but also ensures efficient resource utilization. To further optimize network performance, we introduce two schemes: a user association scheme and a resource allocation scheme. In the user association scheme, the user association problem is formulated as a combinatorial optimization problem and solved using a greedy algorithm with a fairness-throughput trade-off that efficiently allocates resources based on network conditions and service priorities. The resource allocation scheme is designed based on modeling and solving a quality of service (QoS)-constrained optimization problem that maximizes the weighted system throughput using convex optimization techniques. The proposed methodologies incorporate base station and satellite resource availability, HCPE throughput gap (TG), and diverse user QoS requirements. Extensive simulations demonstrate that the HWWSAN framework reduces call-blocking probability by up to 50%, improves user satisfaction rates beyond 90%, and nearly doubles aggregate network throughput, validating its effectiveness for enhanced rural connectivity.}, archive = {J_ACCESS}, author = {Fareha Nizam and Teong Chee Chuah and Ying Loong Lee}, doi = {10.1109/ACCESS.2025.3595938}, journal = {IEEE Access}, pages = {140251-140262}, shortjournal = {IEEE Access}, title = {Optimizing rural connectivity through hybrid wireless-wireline and satellite network integration}, volume = {13}, year = {2025}, }
• (2025). EEG-SKDNet: A self-knowledge distillation model with scaled weights for emotion recognition from EEG signals. ACCESS, 13, 140239-140250. (www)

  @article{ , abstract = {Electroencephalogram-based emotion recognition has garnered increasing attention due to its potential in human–computer interaction and affective computing. While recent deep learning methods have achieved remarkable performance in this task, most approaches emphasize accuracy at the expense of computational efficiency, making them impractical for real-time applications or deployment on resource-constrained devices. This paper addresses the critical challenge of achieving high-performance electroencephalography-based emotion recognition with low computational cost by introducing a lightweight yet robust learning strategy. In this paper, we propose a novel self-knowledge distillation framework that requires no teacher model. Unlike conventional knowledge distillation approaches that rely on large pre-trained teacher networks, our method leverages two different augmented views of the electroencephalography input, which are passed through a single student model to generate diverse predictions. These predictions are then used to transfer knowledge internally within the model. To enhance this self-distillation process, we introduce a scaled-weights mechanism that dynamically adjusts the contribution of each soft label based on the original input, allowing the model to focus on electroencephalography segments with more informative or high-intensity signal regions. The experiment results have shown that our proposed framework consistently outperforms the baseline and even state-of-the-art deep models, achieving a superior trade-off among performance, model size, computation cost and inference time. This makes our proposed framework highly suitable for deployment in real-time and edge-based emotion recognition applications.}, archive = {J_ACCESS}, author = {Thuong Duong Thi Mai and Duc-Quang Vu and Huy Nguyen Phuong and Trung-Nghia Phung}, doi = {10.1109/ACCESS.2025.3594671}, journal = {IEEE Access}, pages = {140239-140250}, shortjournal = {IEEE Access}, title = {EEG-SKDNet: A self-knowledge distillation model with scaled weights for emotion recognition from EEG signals}, volume = {13}, year = {2025}, }
• (2025). QFCOI-enhancing objective quality assessment for compressed omnidirectional images with fusion of measures. ACCESS, 13, 140223-140238. (www)

  @article{ , abstract = {This paper introduces Quality Fusion of Compressed Omnidirectional Images (QFCOI), an enhanced objective quality assessment method for compressed 360° images. QFCOI integrates linear fusion of feature metrics. Established conventional state-of-the-art measures were analyzed to select specific ones for effective fusion and to mitigate eventual overfitting. The feature metrics selection was based on statistical performance and ability to capture key aspects of image quality, including structural preservation, visual information fidelity, and artifact sensitivity. To optimize predictive performance of QFCOI, a genetic algorithm was utilized to determine optimal weight coefficients, maximizing the monotonic correlation with subjective quality scores. The QFCOI performance was validated through correlation coefficients, statistical significance testing, and Receiver Operating Characteristic (ROC) analyses. Results confirmed that QFCOI outperformed other conventional metrics, achieving the highest performance on the OMNIQAD dataset across multiple emerging compression algorithms, including High Efficiency Image File Format (HEIC), Joint Photographic Experts Group XL (JPEG XL), and AV1 Image File Format (AVIF). Further, validation on the relevant public CVIQ dataset for HEIC-compressed images confirmed the robustness of QFCOI, and the method modification on only 25% of the data, achieved the best performance on the CVIQ dataset. These results highlight the generalization properties and versatility of QFCOI. In contrast to learning-based models, the proposed method offers a transparent and interpretable alternative while achieving high accuracy and statistical reliability in objective quality assessment.}, archive = {J_ACCESS}, author = {Marek Simka and Ladislav Polak and Adam Zizien and Karel Fliegel}, doi = {10.1109/ACCESS.2025.3597214}, journal = {IEEE Access}, pages = {140223-140238}, shortjournal = {IEEE Access}, title = {QFCOI-enhancing objective quality assessment for compressed omnidirectional images with fusion of measures}, volume = {13}, year = {2025}, }
• (2025). A linguistic features-based approach for the functional analysis of disinformation in spanish. ACCESS, 13, 140205-140222. (www)

  @article{ , abstract = {Information disorder has significant negative impacts on contemporary societies. This study presents a hybrid methodology that combines machine learning and natural language processing to analyze corpora of disinformation texts in Spanish. The approach not only adapts linguistic features originally developed for English to another major but less researched language, but also incorporates 251 features organized into six categories, surpassing previous methods in both the number and organization of features. Applied to the CLNews dataset of Spanish rumors, the analysis identified 17 features with statistically significant differences between false and real rumors. Linguistic analysis reveals that false rumors are characterized by more emotional language, greater sentence fragmentation, frequent use of auxiliary verbs, and lower information density, which creates an appearance of detail. Additionally, using BERT, a large language model (LLM), five topics were identified among false rumors, each exhibiting different strategies in terms of fragmentation, grammatical complexity, and information density. Given the above, linguistic features were employed to develop machine learning classifiers, with a linear SVM achieving 86% accuracy. This methodology offers a replicable framework for future research on disinformation and text analysis in Spanish, enhancing the interpretability of results. The methodology shows that classical machine learning models trained on carefully chosen linguistic features can deliver competitive results, surpassing BETO (57%) and RoBERTa-BNE (64%) in accuracy on the CLNews dataset. Moreover, these models demonstrate strong performance when the same features are applied to a different dataset and continue to perform well when the feature selection is adjusted to fit the new context.}, archive = {J_ACCESS}, author = {Eduardo Puraivan and Fabián Riquelme and René Venegas}, doi = {10.1109/ACCESS.2025.3595750}, journal = {IEEE Access}, pages = {140205-140222}, shortjournal = {IEEE Access}, title = {A linguistic features-based approach for the functional analysis of disinformation in spanish}, volume = {13}, year = {2025}, }
• (2025). A review of motors with reconfigurable windings for automotive traction drives. ACCESS, 13, 140187-140204. (www)

  @article{ , abstract = {This paper provides an overview of the application of reconfigurable windings in traction motors and their prospective benefits for electric vehicles. Methods of implementation discussed are series–parallel, star-delta, and tapped windings with series-parallel configurations having the greatest potential for system performance improvements. The review provides valuable insights into the impact on e-drive system mass and volume, showing significant improvements to torque and power densities of approximately 40-80% can be achieved. Other investigations targeting motor efficiency show total motor loss reductions of approximately 30-60% are also possible. The published literature on the topic tends to be case-specific, indicating that the generalisation of the technology applicability is lacking, with a particular need for design tools and methodologies early in the propulsion system design stage, to explore how the technology could be best applied. This is also highlighted by the system level impacts shown in a case study on the Nissan Leaf drive, with series-parallel reconfigurable windings resulting in a 41.67% inverter current reduction while maintaining baseline performance characteristics. Other key challenges to the technology uptake include the cost and complexity of the reconfiguration device, however If these can be overcome, and the design advantages better understood, traction drives with reconfigurable windings show potential in reducing the size, weight, and cost of the electric machines, while improving their efficiency and power density.}, archive = {J_ACCESS}, author = {Joshua Best and Pedram Asef and Christopher Vagg}, doi = {10.1109/ACCESS.2025.3591184}, journal = {IEEE Access}, pages = {140187-140204}, shortjournal = {IEEE Access}, title = {A review of motors with reconfigurable windings for automotive traction drives}, volume = {13}, year = {2025}, }
• (2025). XAI for point cloud data using perturbations based on meaningful segmentation. ACCESS, 13, 140175-140186. (www)

  @article{ , abstract = {In this work, we propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we also propose a novel point-shifting mechanism to introduce perturbations in point cloud data. In the last decade, Artificial intelligence (AI) has seen an exponential growth. However, due to the “black-box” nature of many of these AI algorithms, it is important to understand their decision-making process when it comes to their application in critical areas. Our work focuses on explaining AI algorithms that classify point cloud data. An important aspect of the methods used for explaining AI algorithms is their ability to produce explanations that are easy for humans to understand. This allows the users to analyze the performance of AI algorithms better and make appropriate decisions based on that analysis. Therefore, in this work, we intend to generate meaningful explanations that can be easily interpreted by humans. The point cloud data considered in this work represents 3D objects such as cars, guitars, and laptops. We make use of point cloud segmentation models to generate explanations for the working of classification models. The segments are used to introduce perturbations into the input point cloud data and generate saliency maps. The perturbations are introduced using the novel point-shifting mechanism proposed in this work which ensures that the shifted points no longer influence the output of the classification algorithm. In contrast to any previous methods, the segments used by our method are meaningful, i.e. humans can easily interpret the meaning of these segments. Thus, the benefit of our method over other methods is its ability to produce more meaningful saliency maps. We compare our method with the use of classical clustering algorithms to generate explanations. We also analyze the saliency maps generated for some example inputs using our method to demonstrate the usefulness of our proposed method in generating meaningful explanations.}, archive = {J_ACCESS}, author = {Raju Ningappa Mulawade and Christoph Garth and Alexander Wiebel}, doi = {10.1109/ACCESS.2025.3597094}, journal = {IEEE Access}, pages = {140175-140186}, shortjournal = {IEEE Access}, title = {XAI for point cloud data using perturbations based on meaningful segmentation}, volume = {13}, year = {2025}, }
• (2025). Three years of VoiceMOS challenges: Lessons learned by the UWB-NTIS-TTS team. ACCESS, 13, 140152-140174. (www)

  @article{ , abstract = {Automatic prediction of mean-opinion scores (MOS) promises a faster, cheaper alternative to listening tests, yet robust generalization across speakers, languages, and domains remains a significant challenge. This article presents our system designs and experimental results from three years of participation in the VoiceMOS Challenges (2022–2024), covering MOS prediction for synthesized or voice-converted speech and singing voice, including out-of-domain and cross-language conditions. We evaluate six neural architectures – wav2vec 2.0, QuartzNet, CNN-RNN, LDNet, RawNet3, and HiFi-GAN – and their ensembles. Across all tasks, we find that 1) self-supervised acoustic encoders are the most consistently reliable foundation, 2) ensembling yields rapidly diminishing returns once complementary representations are covered, and 3) the diversity and balance of training data outweigh architectural complexity. Notably, the indiscriminate fusion strategy that performed well in 2022 degrades under the mismatched French TTS conditions of 2023, emphasizing the importance of out-of-domain validation. Further experiments show that carefully pruned ensembles can modestly outperform the best single model while remaining within real-time constraints. We conclude with several observations to guide the development of computationally efficient, domain-robust MOS prediction systems.}, archive = {J_ACCESS}, author = {Marie Kunešová and Jindřich Matoušek and Jan Lehečka and Jan Švec and Daniel Tihelka and Zdeněk Hanzlíček}, doi = {10.1109/ACCESS.2025.3596644}, journal = {IEEE Access}, pages = {140152-140174}, shortjournal = {IEEE Access}, title = {Three years of VoiceMOS challenges: Lessons learned by the UWB-NTIS-TTS team}, volume = {13}, year = {2025}, }
• (2025). SAGCN: Self-attention graph convolutional network for human pose embedding. ACCESS, 13, 140139-140151. (www)

  @article{ , abstract = {Accurate human pose embedding is crucial for action recognition. While traditional convolutional neural networks (CNNs) have advanced pose feature extraction, they struggle to model structural relationships and long-range dependencies between keypoints, and are less robust to occlusions. To address these limitations, we present SAGCN, a novel model integrating graph convolutional network (GCN) with self-attention. SAGCN leverages GCN to preserve keypoint structure and self-attention to capture long-range dependencies. We further introduce probabilistic pose embeddings to represent inherent multi-view pose uncertainty. Evaluated on Human 3.6M and MPI-INF-3DHP for cross-view retrieval, and on PenAction for sequence alignment, SAGCN outperforms existing methods in retrieval and achieves competitive alignment results compared to specialized approaches.}, archive = {J_ACCESS}, author = {Zhongxiong Xu and Jiajun Hong and Yicong Yu and Chengzhu Lin and Linfei Yu and Meixian Xu}, doi = {10.1109/ACCESS.2025.3596651}, journal = {IEEE Access}, pages = {140139-140151}, shortjournal = {IEEE Access}, title = {SAGCN: Self-attention graph convolutional network for human pose embedding}, volume = {13}, year = {2025}, }
• (2025). Mental workload detection and assessment through statistical features extraction and optimization using GEL-RF method for EEG signals using N-back dataset. ACCESS, 13, 140118-140138. (www)

  @article{ , abstract = {Mental workload (MW) assessment measures the cognitive effort required to perform tasks and is crucial in fields such as aviation, clinical medicine, and healthcare. This study addresses the critical need to optimize cognitive load and resource allocation by evaluating MW through EEG-based analysis. Recently, MW assessment using EEG signals has gained importance, as they show a high correlation with specific cognitive processes and MW. Although previous studies have employed various techniques for MW assessment using EEG, detailed statistical feature selection remains underexplored. This work analyzes the N-back dataset, a standard benchmark for cognitive load, using four combinations of binary and multiclass classification to assess three MW levels. We propose a novel model, which integrates genetic, evolutionary and linear (GEL) feature selection techniques with a Random Forest (RF) classifier as GEL-RF model. Experimental results demonstrate that the GEL-RF model achieves classification accuracies of 97% for binary tasks and 96.3% for multiclass tasks MW assessment, outperforming existing methods. These results shows that our proposed model can improve MW assessment accuracy, helping to increase safety and efficiency in demanding mental tasks.}, archive = {J_ACCESS}, author = {Waleed Manzoor and Noman Naseer and Imran Fareed Nizami and Syed Hammad Nazeer and Husam A. Neamah}, doi = {10.1109/ACCESS.2025.3596517}, journal = {IEEE Access}, pages = {140118-140138}, shortjournal = {IEEE Access}, title = {Mental workload detection and assessment through statistical features extraction and optimization using GEL-RF method for EEG signals using N-back dataset}, volume = {13}, year = {2025}, }
• (2025). Roadmap on optics and photonics for security and encryption. ACCESS, 13, 140087-140117. (www)

  @article{ , abstract = {In 1994, Javidi and Horner published a paper in Optical Engineering that highlighted the ability of free space optical systems to manipulate sensitive data for authentication purposes. The underlying idea was effective yet surprisingly simple: an optical nonlinear joint transform using a random phase mask in both the input and the reference could produce a correlation peak to indicate whether the input object is authentic or not. This seminal paper fueled the development of this new discipline. After three decades, optical encryption and security have matured into a field that plays a central role in the development of photonics techniques. While the pioneering work was mainly focused on the field of optical information processing, nowadays, a broad spectrum of disciplines are contributing to developing security solutions, including nanotechnology, materials science, quantum information, and deep learning, just to cite a few. The present roadmap paper gathers 28 leading authors in the field from 21 academic institutions across nine different countries. It is organized into 17 sections which discuss the present and future challenges, state-of-the-art technology, and real-world solutions to address the security challenges facing our society.}, archive = {J_ACCESS}, author = {Bahram Javidi and Artur Carnicer and Kavan Ahmadi and Yasuhiro Awatsuji and Wen Chen and Thierry Fournel and Patrice Genevet and Jingying Guo and Wenqi He and Mathieu Hébert and Aloke Jana and Edmund Y. Lam and Gui-Lu Long and Osamu Matoba and Zhaoke Mi and Inkyu Moon and Naveen K. Nishchal and Dong Pan and Xiang Peng and Pepijn W. H. Pinkse and Yishi Shi and Guohai Situ and Adrian Stern and Xiaogang Wang and Tian Xia and Yin Xiao and Xie Zhenwei and Shuo Zhu}, doi = {10.1109/ACCESS.2025.3597226}, journal = {IEEE Access}, pages = {140087-140117}, shortjournal = {IEEE Access}, title = {Roadmap on optics and photonics for security and encryption}, volume = {13}, year = {2025}, }
• (2025). Analysis of magnetic barkhausen noise to reveal domain wall dynamics in Amorphous/Nanocrystalline ribbons. ACCESS, 13, 140079-140086. (www)

  @article{ , abstract = {Understanding and minimizing iron loss in soft magnetic materials is essential for the development of high-efficiency next-generation power electronics systems. Excess eddy current loss, which arises from localized eddy currents induced by moving domain walls (DWs), constitutes a major fraction of the total iron loss at high frequencies. However, the detailed physical mechanisms underlying excess eddy current loss remain unclear, primarily due to the lack of suitable experimental and analytical techniques. In this study, a wide-band and high-sensitivity magnetic Barkhausen noise (MBN) measurement system was developed to investigate DW dynamics in $25~\mu $ m-thick amorphous/nanocrystalline Fe–Si–B–P–Cu (NANOMET)® ribbons. This system enabled high-fidelity and single-shot capture of individual MBN pulses, which provided direct experimental evidence of DW relaxation in metallic ribbons. In the non-annealed amorphous state, isolated MBN pulses with steep leading edges and exponentially decaying trailing edges were clearly observed. Statistical analysis of these events yielded a mean relaxation time of $3.8~\mu $ s with a standard deviation of $1.8~\mu $ s. Analytical modelling suggests that eddy current damping exceeds intrinsic DW damping in the relaxation process. These findings advance the understanding of excess eddy current loss mechanism and thereby contribute to the microstructural design of low-loss soft magnetic materials.}, archive = {J_ACCESS}, author = {Takahiro Yamazaki and Shingo Tamaru and Masato Kotsugi}, doi = {10.1109/ACCESS.2025.3596507}, journal = {IEEE Access}, pages = {140079-140086}, shortjournal = {IEEE Access}, title = {Analysis of magnetic barkhausen noise to reveal domain wall dynamics in Amorphous/Nanocrystalline ribbons}, volume = {13}, year = {2025}, }
• (2025). Multi-band microwave rectifiers for RF energy harvesting: A comprehensive review of design techniques and performance analysis. ACCESS, 13, 140061-140078. (www)

  @article{ , abstract = {RF energy harvesting (RF-EH) technology has emerged as a key research direction driven by the increasing demand for sustainable power solutions in applications such as low-power wireless sensors, radio frequency identification (RFID) tags, IoT, and biotelemetry. The need to eliminate frequent battery replacements in large-scale sensor deployments has made RF-EH a promising alternative. At the core of these systems is the rectenna, a key component responsible for capturing and converting ambient RF energy into usable DC power. Research efforts have primarily focused on enhancing power conversion efficiency, ensuring compatibility with various sensors, and maximizing harvested power. Multi-band rectennas have gained significant attention for their ability to capture RF energy across multiple frequency bands, enabling higher overall power generation compared to narrowband counterparts. Consequently, multi-band rectifier circuits play a critical role in RF-EH systems. This paper presents a comprehensive review and performance analysis of multi-band rectifier designs, encompassing over 110 research studies published in the last decade. To enable objective comparison, we propose a novel power/frequency-weighted efficiency (PFE) metric, which accounts for operating frequency, input power, and the number of bands. This metric provides valuable insights into the efficiency of various rectifier architectures and serves as a benchmark for future advancements in RF energy harvesting.}, archive = {J_ACCESS}, author = {Md Ahsan Halimi and Ibraheem Al-Naib and Hussein Attia}, doi = {10.1109/ACCESS.2025.3597133}, journal = {IEEE Access}, pages = {140061-140078}, shortjournal = {IEEE Access}, title = {Multi-band microwave rectifiers for RF energy harvesting: A comprehensive review of design techniques and performance analysis}, volume = {13}, year = {2025}, }
• (2025). A multi-criteria perceptual visual complexity metrics-based optimization approach to user interface design layouts. ACCESS, 13, 140046-140060. (www)

  @article{ , abstract = {Generating and evaluating alternative UI design layouts with minimum perceptual visual complexity is highly desirable but is a complex and time-consuming process. Various constraints affecting visual complexity such as screen size, number and size of components and semantic relations of components to each other are to be considered. To this aim, we developed a method that relies on perceptual visual complexity metrics specifically, alignment, balance, density and an introduced measure called distance to focal area. Differential evolution algorithm is adapted to the problem to generate a set of pareto optimal alternative layout designs. A series of experiments to evaluate the performance and the validity of our approach showed that, pareto optimum UI design layout alternatives could be produced in a reasonable time where better visual complexity values could be achieved for the set of UI components in layouts.}, archive = {J_ACCESS}, author = {Eren Akça and Ömer Özgür Tanrıöver and Asım Egemen Yılmaz}, doi = {10.1109/ACCESS.2025.3596286}, journal = {IEEE Access}, pages = {140046-140060}, shortjournal = {IEEE Access}, title = {A multi-criteria perceptual visual complexity metrics-based optimization approach to user interface design layouts}, volume = {13}, year = {2025}, }
• (2025). Scalable mobility proactive-reactive load balancing (PRLB) algorithm for high-density environments of future wireless networks. ACCESS, 13, 140028-140045. (www)

  @article{ , abstract = {Mobile networks are Heterogeneous networks (HetNets) comprised of different cell types, including small cells that can suffer from imbalanced load distribution caused by unexpected surges in user density and user mobility across small cells. This can lead to network performance degradation due to several factors, the most important of which are decreased handover (HO) success rates and increased HO-related issues. To mitigate performance fluctuations, especially in ultra-dense user environments like Karbala City during significant events, this research paper proposes a novel Proactive-Reactive Load Balancing (PRLB) algorithm. The PRLB algorithm considers dynamic adjustments of HO controllable (HOC) parameters of the overloaded cell and its adjacent cells. It activates when any cell’s load, measured based on the Physical Resource Block (PRB) utilization ratio, reaches the overload (OL) state representing the proactive mechanism. At the same time, the reactive mechanism reviews and analyzes the load records of the network cells during specific time intervals for potential offloading. Dynamic thresholds, determined based on overall network load conditions, are utilized to identify loaded cells and determine the offloading ratio. The PRLB algorithm through extensive simulations, demonstrates a significant reduction in the OL scenarios duration, achieves effective load distribution across HetNets, and improves overall network performance by reducing attach failures, HO failures (HOF), radio-link failures (RLF), and HO ping-pong (HOPP) events.}, archive = {J_ACCESS}, author = {Mustafa Mohammed Hasan Alkalsh and Adrian Kliks}, doi = {10.1109/ACCESS.2025.3596682}, journal = {IEEE Access}, pages = {140028-140045}, shortjournal = {IEEE Access}, title = {Scalable mobility proactive-reactive load balancing (PRLB) algorithm for high-density environments of future wireless networks}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of power management system in multi-string solar-interfaced DC microgrid with energy storage system. ACCESS, 13, 140012-140027. (www)

  @article{ , abstract = {The DC microgrid (DCMG) offers a promising approach to efficiently distribute and manage renewable energy sources (RES), enhancing energy resilience and reducing reliance on traditional fossil fuel-based power generation. The intermittent nature of RES in DCMG can cause voltage and power fluctuations, potentially impacting the microgrid’s reliability, power quality, and stability. To bridge the gap between intermittent RES power generation and consistent load demand, energy storage devices (ESD) are necessary. Excessive charging and discharging cycles can degrade the performance of ESD. Hence, in this article, a condition-based power management algorithm (PMA) is proposed for a multi-string solar-interfaced DCMG with electric vehicle (EV) batteries and the grid for powering residential loads. The primary aim of the PMA is to maintain a stable DC bus voltage irrespective of variations in irradiance and load, to promote effective power sharing between the available resources, and to mitigate the risks of excessive charging and discharging of ESD. The performance and efficacy of the PMA are validated in a hardware setup using a low-cost FPGA-based controller. Different modes of operation are considered in this study to analyze the performance of the PMA, and their experimental results are presented.}, archive = {J_ACCESS}, author = {V. Srimathi and D. Vijayakumar}, doi = {10.1109/ACCESS.2025.3595594}, journal = {IEEE Access}, pages = {140012-140027}, shortjournal = {IEEE Access}, title = {Design and implementation of power management system in multi-string solar-interfaced DC microgrid with energy storage system}, volume = {13}, year = {2025}, }
• (2025). Network-based fractional-order control algorithms for vehicle platooning. ACCESS, 13, 139992-140011. (www)

  @article{ , abstract = {Fractional-order controllers have been shown to be an effective solution for improving the tracking performance of closed-loop control systems in various engineering applications. However, the use of fractional-order solutions has only been marginally investigated for controlling platoons of vehicles. Hence, this paper proposes three novel distributed fractional-order controllers, in which the vehicle platooning control problem of a set of homogeneous followers, characterised by either second- or third-order systems, is reformulated as a consensus control problem. The resulting closed-loop systems are analysed using the root locus approach to determine the region of control gains that ensures asymptotic closed-loop stability. Furthermore, the residual spacing errors to constant leader accelerations and disturbances are computed by analysing the error dynamics in the Laplace domain. The genetic algorithm is then employed for parameter optimisation within the stable region for different scenarios, and numerical analysis supports the theoretical findings and shows reduced tracking error when the fractional-order solutions replace their integer-order counterparts.}, archive = {J_ACCESS}, author = {Omar Hanif and Patrick Gruber and Aldo Sorniotti and Umberto Montanaro}, doi = {10.1109/ACCESS.2025.3595116}, journal = {IEEE Access}, pages = {139992-140011}, shortjournal = {IEEE Access}, title = {Network-based fractional-order control algorithms for vehicle platooning}, volume = {13}, year = {2025}, }
• (2025). Toward enhancing LightWeight GAN for text-guided generation of animated character faces. ACCESS, 13, 139979-139991. (www)

  @article{ , abstract = {Traditional text-guided image generation methods primarily focus on modifying existing images or altering specific elements, which limits their applicability. This paper introduces a significant enhancement to the LightWeight-GAN model, originally designed for image generation from random noise, by transforming it into a text-guided generative system. The proposed approach enables the generation of high-quality animated character faces directly from textual descriptions. To achieve this, we incorporate a mapping network that refines textual inputs before feeding them into the generator, ensuring more precise feature representation. Additionally, we integrate contrastive language-image pretraining (CLIP) to verify the generated images and enforce stronger alignment between textual prompts and visual outputs. The model is trained on a specialized facial dataset, demonstrating its ability to generate semantically accurate and visually compelling character faces. Extensive experiments using the CartoonSet dataset validate the effectiveness of our approach, achieving an FID score of 29.8 across 10 000 generated images. These improvements significantly outperform existing text-to-image generation models, making our system a promising tool for applications in game development, animation, and virtual reality.}, archive = {J_ACCESS}, author = {Sameh Zarif and Abdalfatah Najjar and Khalid Mohamed Amin and Abdullah Alharbi and Wail S. Elkilani and Mahmoud A. Shawky and Marian Wagdy}, doi = {10.1109/ACCESS.2025.3595928}, journal = {IEEE Access}, pages = {139979-139991}, shortjournal = {IEEE Access}, title = {Toward enhancing LightWeight GAN for text-guided generation of animated character faces}, volume = {13}, year = {2025}, }
• (2025). Modularized learning for hate speech detection in korean: Integrating emotions and multi-faceted attributes. ACCESS, 13, 139968-139978. (www)

  @article{ , abstract = {Detecting hate speech is critical both online and offline, particularly on social media, in personal reviews, and during interpersonal conversations. In this study, we present a modularized learning algorithm for hate speech detection in Korean that integrates diverse features, including emotional indicators and attributes capturing expressions of hatred. To investigate the influence of complex human emotions on hate speech, we leverage multiple datasets for both emotion classification and hate speech detection. Our approach, which integrates these varied datasets, enhances performance and examines the impact of multi-label annotation on detection accuracy. Built on pre-trained Korean language models, our framework extracts features related to emotion, multiple attributes, and similarity, and then employs a classifier for final prediction. Experimental results demonstrate that our proposed method outperforms existing algorithms by achieving a higher f1-score in hate speech recognition.}, archive = {J_ACCESS}, author = {Hyeun Jeong Min}, doi = {10.1109/ACCESS.2025.3596551}, journal = {IEEE Access}, pages = {139968-139978}, shortjournal = {IEEE Access}, title = {Modularized learning for hate speech detection in korean: Integrating emotions and multi-faceted attributes}, volume = {13}, year = {2025}, }
• (2025). A novel approach for classification and detection of apple leaf disease using enhanced RBVT-net with transfer learning and YoloV7. ACCESS, 13, 139953-139967. (www)

  @article{ , abstract = {Apples are a popular fruit worldwide, valued for their rich nutritional content and associated health benefits, such as reducing the risks for cancer, diabetes, and heart disease. However, apple production faces significant challenges from diseases and pests, which can lead to substantial losses for farmers. Early and accurate detection of apple diseases is crucial for effective disease management and improved yield, however the identification process is complicated by the similarity of symptoms across different diseases. Variations in symptom expression, such as leaf discoloration and spot patterns, further complicate disease diagnosis, making it challenging to distinguish between pathogens. This study utilizes a manually collected and expertly validated dataset of 8,000 apple leaf images from orchards in Himachal Pradesh and Uttarakhand, India, encompassing three common diseases: Marssonina leaf blotch, Alternaria leaf spot, and powdery mildew. A novel approach is proposed that utilizes a Transfer Learning (TL) Enhanced Residual BottleNeck Vision Transformer (RBVT-Net) model for the classification of Apple Leaf Disease (ALD) and disease detection, leveraging YOLOv7 for high-precision identification. Extensive experiments demonstrated the effectiveness of the proposed model, achieving a classification accuracy of 98.58% and a mean Average Precision (mAP) of 0.599 for disease detection using YOLOv7. These results underscore the potential of TL-enhanced models in aiding apple disease management, offering a promising solution to support farmers in early disease identification and better crop quality.}, archive = {J_ACCESS}, author = {Satish Kumar and Rakesh Kumar and Meenu Gupta and Korhan Cengiz and Nikola Ivković}, doi = {10.1109/ACCESS.2025.3596451}, journal = {IEEE Access}, pages = {139953-139967}, shortjournal = {IEEE Access}, title = {A novel approach for classification and detection of apple leaf disease using enhanced RBVT-net with transfer learning and YoloV7}, volume = {13}, year = {2025}, }
• (2025). OmniOcc: Cylindrical voxel-based semantic occupancy prediction for omnidirectional vision systems. ACCESS, 13, 139944-139952. (www)

  @article{ , abstract = {Accurate 3D perception is essential for autonomous driving. Traditional methods often struggle with geometric ambiguity due to a lack of geometric prior. To address these challenges, we use omnidirectional depth estimation to introduce geometric prior. Based on the depth information, we propose a Sketch-Coloring framework OmniOcc. Additionally, our approach introduces a cylindrical voxel representation based on polar coordinate to better align with the radial nature of panoramic camera views. To address the lack of fisheye camera dataset in autonomous driving tasks, we also build a virtual scene dataset with six fisheye cameras. Experimental results demonstrate that our Sketch-Coloring network significantly enhances 3D perception performance.}, archive = {J_ACCESS}, author = {Chaofan Wu and Jiaheng Li and Jinghao Cao and Ming Li and Sidan Du and Yang Li}, doi = {10.1109/ACCESS.2025.3595898}, journal = {IEEE Access}, pages = {139944-139952}, shortjournal = {IEEE Access}, title = {OmniOcc: Cylindrical voxel-based semantic occupancy prediction for omnidirectional vision systems}, volume = {13}, year = {2025}, }
• (2025). Robust and privacy-preserving federated learning against malicious clients: A bulyan-based adaptive differential privacy framework. ACCESS, 13, 139931-139943. (www)

  @article{ , abstract = {Federated learning (FL) enables collaborative model training across decentralized clients without sharing raw data. However, malicious or byzantine clients may compromise global model integrity through adversarial updates, while the addition of differential privacy (DP) noise can significantly reduce accuracy if not carefully managed. To address these dual challenges, we propose a robust and privacy-preserving FL framework featuring three core innovations: (i) a Bulyan-based aggregator that discards outlier gradients to neutralize adversarial behaviors, (ii) a GroupNorm-based convolutional neural network (CNN) design for DP compatibility, and (iii) an adaptive noise-scheduling mechanism that gradually reduces noise variance across training rounds. Specifically, we configure the Bulyan aggregator with $k=7$ and retain $K=5$ gradients per round, using a trim ratio of $\gamma =0.2$ . The DP noise follows an exponential decay schedule $\sigma _{r} = 0.30\exp (-0.01\,r)$ . Experimental results on CIFAR-10 under a 10% malicious-client scenario demonstrate that our method consistently mitigates poisoned updates and achieves a final global accuracy of 23.58% while maintaining a rigorous privacy budget of $\varepsilon =1.5$ , $\delta =10^{-5}$ . Despite an unavoidable performance gap relative to non-private or non-adversarial baselines, legitimate clients reach local accuracies near 80%. This outcome underscores the interplay between robust aggregation and flexible DP tuning to preserve model integrity and privacy. Our framework thus paves the way for future research on advanced aggregator heuristics, per-layer noise calibration, and refined defenses against sophisticated adversaries in large-scale FL.}, archive = {J_ACCESS}, author = {Stuti Pandey and Onkar Singh and Ashish Pandey and Chandrasen Pandey}, doi = {10.1109/ACCESS.2025.3596627}, journal = {IEEE Access}, pages = {139931-139943}, shortjournal = {IEEE Access}, title = {Robust and privacy-preserving federated learning against malicious clients: A bulyan-based adaptive differential privacy framework}, volume = {13}, year = {2025}, }
• (2025). A dual-broadband dual-polarized patch antenna array antenna-in-package (AiP) module on asymmetric substrate stack-up. ACCESS, 13, 139920-139930. (www)

  @article{ , abstract = {This study presents a broadband, dual-polarized stacked patch antenna array module developed for mobile Antenna-in-Package (AiP) applications utilizing an asymmetric substrate stack-up. A key design challenge in 5G mmWave AiP systems is achieving wideband operation, dual-polarization, and compact form factor simultaneously-particularly under the spatial constraints of mobile devices. To address this, the proposed antenna incorporates an L-probe feeding, which significantly enhances impedance bandwidth through capacitive coupling and broadband matching. Built on a 14-layer asymmetric substrate with a total thickness of 1, $205 \; \mu $ m, the antenna achieves dual-band impedance bandwidths of $24.2\sim 30.9$ GHz and $35.8\sim 41.9$ GHz, effectively covering all 5G mmWave bands from n258 to n261. The antenna demonstrates low envelope correlation and high polarization isolation, essential for massive MIMO implementations. Integrated with RFICs, the AiP module achieves measured gains of 10.2 dBi and 11.1 dBi at 28 GHz and 39 GHz, respectively, within a compact form factor of $5.9 \times 23 \times 2.1$ mm ${}^{\mathbf {3}}$ ( $0.48 \; \lambda _{\mathbf {0}} \times 1.86 \; \lambda _{\mathbf {0}} \times 0.17 \; \lambda _{\mathbf {0}}$ ). The active module supports beam-steering up to ±45° and achieves EIRP values of 29.5 dBm at both frequency bands. It is also compatible with high-order modulation schemes (up to 256-QAM), maintaining an EVM below 4.5%. This work provides a practical and scalable AiP solution, addressing the key design constraints of next-generation mmWave mobile devices.}, archive = {J_ACCESS}, author = {Dohyun Kim and Gyoungdeuk Kim and Kyungho Lim and Chanwoo Yang and Chisang You and Sangkil Kim}, doi = {10.1109/ACCESS.2025.3596653}, journal = {IEEE Access}, pages = {139920-139930}, shortjournal = {IEEE Access}, title = {A dual-broadband dual-polarized patch antenna array antenna-in-package (AiP) module on asymmetric substrate stack-up}, volume = {13}, year = {2025}, }
• (2025). Demonstration platform to emulate cognitive GEO-LEO dual satellite system. ACCESS, 13, 139904-139919. (www)

  @article{ , abstract = {Cognitive radio technology represents a transformative advancement in modern telecommunications, enabling real-time adaptation and optimisation of communication systems through sensing, analysis, and decision-making. In the context of satellite communications, cognitive satellite networks leverage these capabilities to enable dynamic spectrum access, dynamic resource management, and adaptive network optimisation. These enhancements improve spectral efficiency, maximise bandwidth utilisation, and enhance overall network performance, leading to more scalable, resilient, and cost-effective satellite operations. In this study, we develop a laboratory-scale demonstration platform to emulate a cognitive GEO-LEO dual satellite system using software-defined radios, which operate under controlled conditions without incorporating orbital dynamics such as Doppler effects or variable angular velocities. The geostationary satellite is designated as the primary user (PU), while the LEO satellite operates as the secondary user (SU). A preamble detection-based sensing algorithm is developed to identify PU transmissions, ensuring efficient spectrum utilisation. Additionally, a radio environment map (REM) server is implemented to manage control-plane data required for network decision-making. A dynamic spectrum access algorithm, integrated into the REM server, utilises spectrum sensing reports to enable cognitive network behaviour. We evaluate the performance of the PU and SU in terms of spectral occupancy and throughput. Furthermore, the performance of the proposed sensing algorithm is assessed based on missed detection rates. The results demonstrate that the cognitive system’s dynamic spectrum access capabilities significantly enhance spectral occupancy and system throughput, validating its effectiveness in optimising satellite communication networks.}, archive = {J_ACCESS}, author = {Kithmini Weththasinghe and Ying He and Quynh Tu Ngo and Beeshanga Jayawickrama and Eryk Dutkiewicz}, doi = {10.1109/ACCESS.2025.3595193}, journal = {IEEE Access}, pages = {139904-139919}, shortjournal = {IEEE Access}, title = {Demonstration platform to emulate cognitive GEO-LEO dual satellite system}, volume = {13}, year = {2025}, }
• (2025). Real-time defect detection for fast-moving fabrics on circular knitting machine under various illumination conditions. ACCESS, 13, 139890-139903. (www)

  @article{ , abstract = {In industrial production, automated inspection methods for circular knitting machines often encounter several challenges. First, the rapid movement of fabrics on these machines makes it difficult for existing fabric defect detection methods to effectively capture and process the motion. Next, due to practical constraints aimed at maintaining high yield rates, collecting sufficient abnormal fabric samples for model training is costly and limited. Furthermore, circular knitting machines typically operate under varying illumination conditions, further complicating the task of accurate fabric defect detection. Additionally, these methods usually fail to identify the cutline patterns that are integral to the design of the fabric and mistake cutlines for v-line defects. Therefore, existing fabric defect detection methods often struggle to balance real-time processing, few-shot learning, and high accuracy under various illumination conditions To address the aforementioned challenges, we adopt a few-shot learning approach and propose a novel real-time fabric defect detection method for circular knitting machines, aiming to achieve high accuracy even under varying illumination conditions. The proposed mechanism consists of two components, the LBUnet and the false alarm filter for cutlines. First, to tackle the challenges of real-time detection, limited training data, and varying illumination conditions, we develop a lightweight semantic segmentation model, LBUnet, which leverages local binary (LB) convolution to effectively handle variable lighting conditions. Next, to address the specific challenge of detecting V-line defects, we propose a false-alarm filtering method that ensures accurate defect identification by utilizing time-series data composed of consecutive segmentation maps generated by LBUnet. Extensive experiments demonstrate that the proposed method delivers both high defect detection accuracy and real-time processing performance for fast-moving fabrics on circular knitting machines under diverse lighting conditions. Specifically, using only LBUnet, our approach achieved an average Mean Intersection over Union (mIoU) of 86.24% with an average processing time of just 4 milliseconds per image. When the false-alarm filtering component was incorporated, the system achieved 100% accuracy in detecting cutlines.}, archive = {J_ACCESS}, author = {Yan-Qin Ni and Pei-Kai Huang and Ching-Han Yang and Chin-Chun Chang and Wei-Jen Wang and Deron Liang}, doi = {10.1109/ACCESS.2025.3593335}, journal = {IEEE Access}, pages = {139890-139903}, shortjournal = {IEEE Access}, title = {Real-time defect detection for fast-moving fabrics on circular knitting machine under various illumination conditions}, volume = {13}, year = {2025}, }
• (2025). Multi-dimensional arms for combinatorial multi-armed bandit. ACCESS, 13, 139883-139889. (www)

  @article{ , abstract = {The multi-armed bandit (MAB) problem with concave rewards has indeed become a significant area of research in recent years. In MAB problem, maximizing total rewards within a fixed number of pulls is a common objective. Previous works have indeed made substantial advancements in designing efficient online selection algorithms. However, the limitation of these works is that they fail to achieve a sublinear regret bound with multi-dimensional arms. In this paper, we study a combinatorial MAB problem with concave rewards and multi-dimensional arms for online advertising delivery. At first, we treat a multi-dimensional arm as a vector and adapt the choice of arm based on contextual information. Moreover, we utilize the confidence ellipsoid to construct optimistic estimate. Based on optimistic estimate, we combine online convex optimization with bandit methods to design selection algorithm. Our algorithm’s can achieve a sublinear regret bound of $\mathcal {O}(\sqrt {T})$ with probability guarantees in T selection rounds. At last, we evaluate the performance of our algorithm through extensive simulations and demonstrate that it is better than the baseline algorithms.}, archive = {J_ACCESS}, author = {Qi Li and Lijun Cai}, doi = {10.1109/ACCESS.2025.3590438}, journal = {IEEE Access}, pages = {139883-139889}, shortjournal = {IEEE Access}, title = {Multi-dimensional arms for combinatorial multi-armed bandit}, volume = {13}, year = {2025}, }
• (2025). Enhanced hybrid deep learning model with improved self-attention mechanism for legal judgment prediction. ACCESS, 13, 139868-139882. (www)

  @article{ , abstract = {The dawn of Natural Language Processing in the legal research field has achieved great heights. Legal Judgment Prediction (LJP) is one of the important tasks in legal intelligence that can assist attorneys and litigators in predicting judgments. Although current research techniques work effectively, they still have many drawbacks. Firstly, encoding lengthy case facts into vectors without losing information is one of the challenging tasks in LJP. Choosing an encoder that captures the syntax, semantics, and contexts of words can be extremely important for all downstream natural language tasks. Secondly, the features on which the deep learning model is trained also play an important role in testing the real-time cases. This research focuses on Indian cases that follow the common law, unlike civil law. Most of the existing LJP considers only civil law and does not emphasize the extraction of textual features. In this research a novel LJP approach has been suggested to overcome the issues raised above by improving the encoding part using the hybrid embedding method ELMo (Embeddings from Language Model) with Improved Principal Component Analysis (IPCA). Training a crucial set of features is done with a hybrid model, with a combination of Bi-GRU along with a modified attention mechanism and a deep-max-out network. The proposed Hybrid Deep Learning Model with Score Level Fusion (HDLMSF) is experimented with real-time Madras High Court criminal cases and compared with baseline classifier models. The results show that the proposed HDLMSF model has better prediction accuracy, 94.16% than other baseline classifiers.}, archive = {J_ACCESS}, author = {G. Sukanya and J. Priyadarshini}, doi = {10.1109/ACCESS.2025.3596180}, journal = {IEEE Access}, pages = {139868-139882}, shortjournal = {IEEE Access}, title = {Enhanced hybrid deep learning model with improved self-attention mechanism for legal judgment prediction}, volume = {13}, year = {2025}, }
• (2025). Electrical fault diagnosis method for tunneling equipment based on fault simulation and optimized particle swarm algorithm. ACCESS, 13, 139853-139867. (www)

  @article{ , abstract = {To address the challenges of scarce training data and poor adaptability to dynamic working conditions in electrical fault diagnosis for tunneling equipment, this study proposes an intelligent diagnosis model based on fault simulation and Improved Particle Swarm Optimization (IPSO). By constructing multi-physics coupling dynamic equations to accurately simulate fault effects and designing an IPSO algorithm integrating Lévy flight random walk with dynamic neighborhood topology optimization, the model significantly outperforms benchmarks in dynamic load tracking error (0.13%) and vibration suppression (28.7 dB), achieving 34.7% higher parameter identification accuracy (MSE = 0.19). With 34.5% reduction in hardware resource consumption (0.9 W power, 57 MB memory), 1% false alarm/missed detection rate, and 27.9 ms response latency, the solution enables practical edge deployment. The physics-data fusion framework ensures robust generalization under load mutations and high-frequency interference, realizing high-precision low-latency diagnosis with minimal resource requirements for tunnel engineering equipment.}, archive = {J_ACCESS}, author = {Huan Zou and Xueping Zhang and Xin Wang and Yu Li}, doi = {10.1109/ACCESS.2025.3594564}, journal = {IEEE Access}, pages = {139853-139867}, shortjournal = {IEEE Access}, title = {Electrical fault diagnosis method for tunneling equipment based on fault simulation and optimized particle swarm algorithm}, volume = {13}, year = {2025}, }
• (2025). A frequency-aware transformer for multiscale fault diagnosis in electrical machines. ACCESS, 13, 139831-139852. (www)

  @article{ , abstract = {Motor fault diagnosis is a critical technology for ensuring the reliability of industrial equipment and enabling predictive maintenance. However, conventional fault diagnosis methods struggle to effectively capture complex time-frequency patterns, limiting their ability to perform early fault detection and accurate classification. To address these challenges, this study proposes the Frequency-Aware Motor Fault Transformer (FAMFT), which integrates a self-attention mechanism with multi-scale feature analysis to comprehensively analyze the time-frequency characteristics of multidimensional power quality data, including voltage, current, and harmonics. FAMFT overcomes the limitations of conventional CNN- and RNN-based models through three key innovations: 1) Multi-scale feature extraction via parallel analysis of fine-, intermediate-, and long-term temporal scales, 2) Selective feature enhancement through a frequency gating mechanism, and 3) An interpretable fault diagnosis framework based on SHAP (SHapley Additive Explanations). Experimental results demonstrate that the proposed model achieves 99.9% diagnostic accuracy by maintaining an exceptionally low false alarm rate and missed detection rate, thereby ensuring high reliability. Notably, FAMFT exhibits consistent performance across various load conditions, demonstrating a level of robustness suitable for direct implementation of real-time predictive maintenance systems in industrial environments. This study introduces a novel transformer-based fault diagnosis paradigm, contributing to the stable operation of power systems and improving maintenance efficiency.}, archive = {J_ACCESS}, author = {Yurim Choi and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3596859}, journal = {IEEE Access}, pages = {139831-139852}, shortjournal = {IEEE Access}, title = {A frequency-aware transformer for multiscale fault diagnosis in electrical machines}, volume = {13}, year = {2025}, }
• (2025). TRIDENT: Text-free data augmentation using image embedding decomposition for domain generalization. ACCESS, 13, 139816-139830. (www)

  @article{ , abstract = {Deep learning has advanced vision tasks such as classification, segmentation, and detection. However, in real-world scenarios, models often encounter domains that differ from the ones seen during training, which can lead to substantial performance degradation. To mitigate the effects of distribution shifts, domain generalization (DG) aims to enable models to generalize effectively to unseen target domains. Recent DG approaches use generative models like diffusion models to augment data with text prompts. However, these methods rely on domain-specific textual inputs and require costly fine-tuning, which limits their scalability. We propose TRIDENT, a framework that overcomes these limitations by eliminating the need for text prompts and leveraging the linear structure of CLIP embeddings. TRIDENT decomposes image embeddings into three components—domain, class, and attribute—enabling precise control over semantic content. By reassembling each embedding component, we generate semantically valid and structurally coherent synthetic samples across domains. This allows efficient and diverse data synthesis without retraining diffusion models. TRIDENT operates through lightweight embedding-space manipulation, significantly reducing computational overhead. Extensive experiments on standard DG benchmarks (e.g., PACS, VLCS, and OfficeHome) demonstrate that TRIDENT achieves competitive or superior performance compared with existing approaches. Furthermore, qualitative evaluations and comprehensive analyses confirm that TRIDENT not only enables efficient and diverse data synthesis but also demonstrates the effectiveness of the proposed decomposition strategy.}, archive = {J_ACCESS}, author = {Yoonyoung Choi and Geunhyeok Yu and Hyoseok Hwang}, doi = {10.1109/ACCESS.2025.3596371}, journal = {IEEE Access}, pages = {139816-139830}, shortjournal = {IEEE Access}, title = {TRIDENT: Text-free data augmentation using image embedding decomposition for domain generalization}, volume = {13}, year = {2025}, }
• (2025). GA4RF: An effective fall detection system through optimizing random forest hyperparameters using genetic algorithm with mobile sensor data. ACCESS, 13, 139802-139815. (www)

  @article{ , abstract = {Research and development of highly accurate falling detection systems (FDSs) for individuals with medical conditions or the elderly are crucial for mitigating the risks associated with falls. These systems are of great significance for real-world applications in healthcare and elderly care. However, improving the accuracy of the FDS is a significant challenge, particularly due to the difficulty in collecting comprehensive and accurate fall data which causes imbalance in datasets. In this paper, we introduce a Genetic Algorithm (GA) based Random Forest (RF) method, named GA4RF, to enhance the accuracy of fall detection models by optimizing their hyperparameters. Specifically, we propose a fitness function based on the Matthews Correlation Coefficient (MCC) to enable the GA to identify the optimal hyperparameter set for the RF classifier. This targeted optimization ensures that the algorithm prioritizes balanced accuracy across both fall and non-fall events, mitigating the bias inherent in imbalanced datasets. Additionally, we select a chromosome structure focusing on three hyperparameters to effectively narrow the search space, reducing computational complexity while maintaining high detection performance. Experimental evaluations on the MobiAct v2.0 and UP-Fall detection datasets demonstrate that GA4RF achieves high human activity recognition accuracy, especially a significant improvement of fall detection performance. Compared to the conventional RF method, the F1-score increases from 95.23% to 96.91% on the MobiAct v2.0 dataset and from 96.16% to 97.67% on the UP-Fall dataset. These results indicate that GA4RF is a promising approach for improving FDS, especially when dealing with highly imbalanced data and meeting performance requirements in real-world applications.}, archive = {J_ACCESS}, author = {Ha-Nam Nguyen and Hong-Lam Le and Ngo-Thi-Thu-Trang and Duc-Nhan Nguyen}, doi = {10.1109/ACCESS.2025.3596520}, journal = {IEEE Access}, pages = {139802-139815}, shortjournal = {IEEE Access}, title = {GA4RF: An effective fall detection system through optimizing random forest hyperparameters using genetic algorithm with mobile sensor data}, volume = {13}, year = {2025}, }
• (2025). A quantum differential attack on ChaCha and related resources estimation. ACCESS, 13, 139791-139801. (www)

  @article{ , abstract = {Existing classical cryptanalytic techniques against ChaCha primarily rely on differential cryptanalysis, leveraging probabilistic neutral bits (PNBs) introduced by Aumasson et al. Separately, Bathe et al. proposed a quantum brute force search attack using Grover’s algorithm with complexity $2^{128}$ . However, since the setups for brute force attacks and differential attacks with probabilistic neutral bits differ significantly, the feasibility of applying Grover’s algorithm to the existing differential attacks on ChaCha remained unclear. In this paper, we demonstrate for the first time that a quantum setup for a PNB-based differential attack is indeed possible on ChaCha, where we can utilize the power of Grover’s algorithm to improve further. By combining Grover’s algorithm with PNB-based differential attack, we propose a quantum algorithm for PNB based differential attack and present a quantum attack on 6-round ChaCha that provides more than a quadratic speedup. Specifically, our attack achieves a complexity of $2^{70}$ , surpassing the quadratic speedup of the brute-force attack applying Grover’s algorithm, proposed by Bathe et al. We achieve a circuit depth of $2^{83}$ with our algorithm, marking a substantial advancement in efficiency.}, archive = {J_ACCESS}, author = {Mintu Mandal and Hirendra Kumar Garai and Sabyasachi Dey}, doi = {10.1109/ACCESS.2025.3593658}, journal = {IEEE Access}, pages = {139791-139801}, shortjournal = {IEEE Access}, title = {A quantum differential attack on ChaCha and related resources estimation}, volume = {13}, year = {2025}, }
• (2025). Advanced technologies for smart fertilizer management in agriculture: A review. ACCESS, 13, 139766-139790. (www)

  @article{ , abstract = {Precision fertilization has emerged as a critical component of modern smart agriculture, providing a transformative approach to optimizing nutrient application, enhancing crop productivity, and minimizing environmental harm while improving resource use efficiency. However, key challenges hinder its widespread adoption, including regional disparities in data quality, scalability constraints in low-resource settings, regulatory inconsistencies, and the high costs of technology adoption for smallholder farmers. This article reviews current challenges and examines proposed solutions, with a focus on precision fertilization enabled by advanced technologies such as geographic information systems, global positioning systems, the internet of things, artificial intelligence and machine learning algorithms, and unmanned aerial and ground vehicles for fertilizer management. These innovations have facilitated the development of site-specific nutrient management systems, real-time soil and crop monitoring, and automated variable-rate application strategies, all of which demonstrate superior efficiency compared to conventional blanket fertilization methods. Additionally, the study explores key applications of precision fertilization, including data-driven fertilizer recommendations, aerial crop monitoring, real-time nutrient deficiency prediction, automated and optimized fertilization systems, and organic and eco-friendly nutrient management practices. Future research directions highlight the need for integrating multi-sensor feedback loops, expanding open-access agronomic datasets, and advancing eco-friendly, AI-driven, and fully automated fertilization solutions. In conclusion, modern precision fertilization technologies hold significant potential to address existing challenges, offering a pathway toward resilient and sustainable agricultural systems that align with global food security and environmental conservation objectives.}, archive = {J_ACCESS}, author = {Jian-Jun Liu and Hao Wu and Imran Riaz}, doi = {10.1109/ACCESS.2025.3594361}, journal = {IEEE Access}, pages = {139766-139790}, shortjournal = {IEEE Access}, title = {Advanced technologies for smart fertilizer management in agriculture: A review}, volume = {13}, year = {2025}, }
• (2025). Spectrum sensing using fast slepian transform for cognitive radio networks. ACCESS, 13, 139758-139765. (www)

  @article{ , abstract = {Efficient and accurate spectrum sensing is vital for cognitive radio networks, especially in environments with low Signal-to-Noise Ratio (SNR). Traditional methods like energy detection often suffer from high false alarm rates and poor detection performance under noise. This paper presents a spectrum sensing approach based on the Fast Slepian Transform (FST), which leverages the time-frequency concentration properties of Discrete Prolate Spheroidal Sequences (DPSS) to project received signals into a sparse spectral domain. This enables robust detection of occupied subbands. We evaluated fixed and adaptive thresholding strategies, with the adaptive threshold optimized using the F1 score to balance detection accuracy and false alarms. Additionally, we introduce a computational efficiency metric based on floating-point operations (FLOPs) to evaluate the energy cost of detection. The FST method achieves reliable sensing with reduced complexity, making it well-suited for real-time applications. Simulation results across various SNR conditions demonstrate that the proposed FST-based method outperforms traditional techniques like Welch’s method, achieving higher detection probability, lower false alarm rates, and improved energy efficiency. These results confirm the method’s effectiveness for reliable and low-power spectrum sensing in cognitive radio networks.}, archive = {J_ACCESS}, author = {Aman Kumar and Indrasen Singh and Dilip Kumar Choudhary}, doi = {10.1109/ACCESS.2025.3596336}, journal = {IEEE Access}, pages = {139758-139765}, shortjournal = {IEEE Access}, title = {Spectrum sensing using fast slepian transform for cognitive radio networks}, volume = {13}, year = {2025}, }
• (2025). SUPERMAGOv2: Protein function prediction via transformer embeddings and bitscore-weighted features. ACCESS, 13, 139743-139757. (www)

  @article{ , abstract = {Sequencing technologies have advanced considerably in recent years, leading to the sequencing of a vast number of proteins through laboratory methods. However, the functional annotation of these proteins has not kept pace with sequencing efforts, creating a significant gap between sequenced proteins and those with known functions. To address this challenge, computational approaches based solely on amino acid sequence features have been developed to improve functional predictions. In this study, we introduce two novel approaches, one based on machine learning and another using an ensemble of machine learning with local alignment. Our machine learning-based model (SUPERMAGOv2) utilizes transformer-based backbones to extract features from multiple layers, which are then processed by six multilayer perceptrons that incorporate a novel bitscore-weighted input derived from DIAMOND alignments, and by an image classification model that converts the extracted feature vectors into images. Furthermore, we present SUPERMAGOv2+, an ensemble model that combines SUPERMAGOv2 with enhanced DIAMOND-based predictions. In addition, we introduce SUPERMAGOv2+Web, a lightweight web server version of SUPERMAGOv2+. Both proposed methods consistently outperform state-of-the-art approaches across various analyses, establishing themselves as leading methodologies for protein function classification based on amino acid sequences.}, archive = {J_ACCESS}, author = {Gabriel Bianchin de Oliveira and Helio Pedrini and Zanoni Dias}, doi = {10.1109/ACCESS.2025.3596851}, journal = {IEEE Access}, pages = {139743-139757}, shortjournal = {IEEE Access}, title = {SUPERMAGOv2: Protein function prediction via transformer embeddings and bitscore-weighted features}, volume = {13}, year = {2025}, }
• (2025). S₂Head: Small-scale human head detection algorithm by improved YOLOv8n architecture. ACCESS, 13, 139732-139742. (www)

  @article{ , abstract = {Analyzing crowd density and distribution through human head detection in surveillance footage is crucial for intelligent public safety management. Existing head detection algorithms face challenges in accurately detecting small and densely packed head targets, resulting in lower detection accuracy in complex scenarios. To address this, we propose a novel lightweight head detection algorithm named S2Head, based on an improved YOLOv8n framework. Specifically, we incorporate RepBlock into the backbone for effective multi-scale feature extraction and utilize structural reparameterization techniques to significantly reduce model complexity during inference. Additionally, a small object detection branch and a reparameterizable BiFPN (Rep-BiFPN) structure are incorporated into the neck to improve the model’s sensitivity to small-scale features. Finally, a lightweight MSBlock is also integrated into the head to reduce computational overhead and parameter count without sacrificing detection accuracy. Extensive experiments and ablation studies conducted on the SCUT-HEAD dataset, a benchmark for small-scale head detection, demonstrate that S2Head achieves a 3% improvement in mean average precision ( $\text {mAP}_{0.5}$ ) compared to YOLOv8n while reducing model parameters by approximately 46%. These results highlight S2Head’s effectiveness in balancing detection accuracy and computational efficiency, particularly for small-scale head detection tasks.}, archive = {J_ACCESS}, author = {Yuteng Sui and Xinghua Shan and Linlin Dai and Hui Jing and Bo Li and Jianjun Ma}, doi = {10.1109/ACCESS.2025.3596785}, journal = {IEEE Access}, pages = {139732-139742}, shortjournal = {IEEE Access}, title = {S₂Head: Small-scale human head detection algorithm by improved YOLOv8n architecture}, volume = {13}, year = {2025}, }
• (2025). Novel evaluation techniques for outlier detection methods: A case study with RCOD. ACCESS, 13, 139719-139731. (www)

  @article{ , abstract = {Outlier detection remains a key challenge in data analysis, with applications spanning cybersecurity, finance, medicine, and more. This paper introduces a comprehensive evaluation framework for comparing outlier detection methods, using the Random Clustering-based Outlier Detector (RCOD) as a case study. RCOD groups data points around randomly selected cluster centers and identifies outliers based on distance-based criteria and statistical thresholds. To enable more reliable assessment, two novel evaluation strategies are proposed: one based on deviations from the best-performing method per dataset, and another based on rank-based comparison. Experiments conducted on 30 benchmark datasets and 13 detection methods demonstrate RCOD’s superior performance and stability across accuracy, precision, and F1-score metrics. The proposed evaluation techniques provide a deeper insight into the effectiveness of outlier detectors than traditional performance metrics alone. Statistical validation confirms the significance of RCOD’s advantage, highlighting its robustness and applicability to diverse data environments.}, archive = {J_ACCESS}, author = {Adam Kiersztyn and Krystyna Kiersztyn and Michał Horodelski and Dorota Pylak}, doi = {10.1109/ACCESS.2025.3594986}, journal = {IEEE Access}, pages = {139719-139731}, shortjournal = {IEEE Access}, title = {Novel evaluation techniques for outlier detection methods: A case study with RCOD}, volume = {13}, year = {2025}, }
• (2025). A DNA-level convolutional neural network based on strand displacement reaction for image recognition. ACCESS, 13, 139704-139718. (www)

  @article{ , abstract = {Over the past few decades, convolutional neural network (CNN) has found broad applications in image recognition. Nevertheless, the operational environment of CNN is facing significant challenges, including poor parallelism and high energy consumption due to the explosive growth in data scale and the rise of multi-modal data. DNA-based molecular computing systems, which are known for their high parallelism and low energy consumption, have the potential to provide an ideal operating platform for CNN. Here, we employed strand displacement reaction (SDR) to construct a DNA-level CNN. Firstly, we integrated DNA-based weighted-sum module, subtraction activation module, and reporter module using SDR to implement CNN, and designed the weighted shared boxes to perform the function of convolutional kernel. The feasibility and parallelism of the proposed DNA-level CNN were verified by simultaneous recognition of three different categories of images, including handwritten numbers, letters and Chinese characters. We further applied the DNA-level CNN to parallel recognition of multi-class medical images. Visual DSD software was employed to perform all simulation experiments. The efficient parallel computing characteristics of DNA-level CNN provides a new approach for image recognition, and show great potential in multi-class medical image recognition.}, archive = {J_ACCESS}, author = {Shiyin Li and Zhixiang Yin and Chunlin Chen and Jing Yang and Zhen Tang}, doi = {10.1109/ACCESS.2025.3596786}, journal = {IEEE Access}, pages = {139704-139718}, shortjournal = {IEEE Access}, title = {A DNA-level convolutional neural network based on strand displacement reaction for image recognition}, volume = {13}, year = {2025}, }
• (2025). Fuzzy logic and deep learning for brand attention: A consumer-centric approach to product packaging design. ACCESS, 13, 139696-139703. (www)

  @article{ , abstract = {In the competitive world of today, brands cannot find an effective way of designing a package that attracts attention without considering the jungle that consumer preferences are. The problem with existing techniques is that they are incapable of adequately assessing subjective responses, and much to the dismay of designers, they are incapable of adjudicating between competing priorities, e.g. eye candy versus environmental friendliness. The three smart technologies compiled in our solution are Dempster-Shafer Fuzzy Sets to address the answers given, encouraging including the type “I kinda like it”, AI vision analysis to immediately rate the designs, and MACROS ranking to come up with the most having the ideal middle-ground when features are conflicting. A test with 2,100 actual shoppers showed game-changing results: designs in our system got a 32 percent higher score in recall tests, elicited 25 percent more smiles and hit the sweet spot between what looks good (70 percent) and what is green (30 percent). Better still, with our tool, brands would be able to sanction final designs 4 times faster. It is not mere theory and we have developed the art of packaging into a science which puts an upper hand to the companies once on the venture in the crowded aisles of stores.}, archive = {J_ACCESS}, author = {Heng Wu}, doi = {10.1109/ACCESS.2025.3596858}, journal = {IEEE Access}, pages = {139696-139703}, shortjournal = {IEEE Access}, title = {Fuzzy logic and deep learning for brand attention: A consumer-centric approach to product packaging design}, volume = {13}, year = {2025}, }
• (2025). Optimizing UAV location for deployment in cell-free massive MIMO networks using a soft actor-critic reinforcement learning. ACCESS, 13, 139680-139695. (www)

  @article{ , abstract = {The uncrewed aerial vehicles (UAVs) communication network has recently garnered significant attention and motivation in academic and industrial research due to its flexible mobility concept in providing dynamic coverage in hotspot or congested areas with frequent user demand. While UAVs offer reliable connectivity in such challenging scenarios, integrating them with Cell-Free Massive MIMO (CF-mMIMO) networks presents severe issues in optimal deployment and coverage efficiency. In addressing these issues, we propose a deployment technique using Soft Actor-Critic (SAC), a deep reinforcement learning method, to autonomously determine the optimal placement of the UAV within a given pre-defined coverage area, thereby maximizing channel capacity and throughput. UAVs are tactically deployed to offload traffic from overloaded locations and provide robust communication links with CF-mMIMO access points. We employ a hybrid user association mechanism that combines gradient descent and an enhanced K-means algorithm for efficient user-to-UAV cluster assignment. Simulation results indicate that the SAC-based deployment considerably outperforms traditional static K-means gradient descent-based deployments, achieving a throughput gain of 152.93 Mbps and overall channel capacity gain of 15.709 Gbps. These findings verify the ability of the SAC algorithm to dynamically adjust UAV positions for enhanced performance and smooth connectivity within CF-mMIMO networks, particularly in urban and high-density scenarios.}, archive = {J_ACCESS}, author = {Okello Kenneth and Elijah Mwangi and Dominic Bernard Onyango Konditi}, doi = {10.1109/ACCESS.2025.3592733}, journal = {IEEE Access}, pages = {139680-139695}, shortjournal = {IEEE Access}, title = {Optimizing UAV location for deployment in cell-free massive MIMO networks using a soft actor-critic reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). BCSM-YOLO: An improved product package recognition algorithm for automated retail stores based on YOLOv11. ACCESS, 13, 139665-139679. (www)

  @article{ , abstract = {With the rapid growth of automated retail and smart supermarkets, commodity package recognition faces challenges like complex backgrounds, multi-scale targets, and dense occlusion. To address YOLOv11’s limitations in supermarket scenarios, such as missed small targets and low positioning accuracy, this paper proposes BCSM-YOLO, an improved algorithm based on YOLOv11. Firstly, introducing the Space-to-Depth Convolution (SPD-Conv) can maximize the preservation of detailed information such as commodity texture and shape in the downsampling stage, which provides a rich information base for the subsequent feature extraction. Then, the Convolutional Block Attention Module (CBAM) screens the processed data, adaptively focuses on the key regions, and suppresses the background interference. Subsequently, the Bidirectional Feature Pyramid Network (BiFPN) performs cross-layer bi-directional fusion of the multi-scale features processed by the CBAM to ensure that the feature information at different scales fully interacts and effectively solves the multi-scale target recognition challenges. Finally, based on the Minimum Point Distance-IoU (MPD-IoU) loss function, the alignment relationship between the bounding box output from BiFPN is precisely optimized to improve bounding box regression accuracy, and the key innovation of BCSM-YOLO is its unique combination of these advanced technologies that work together to address the specific challenges of the supermarket environment. According to the experimental results, BCSM-YOLO improved precision, recall, mAP@0.5, and mAP@0.5-0.95 by 5.5%, 6.2%, 6%, and 5.8%, respectively, under identical hyperparameter and training dataset (Ultralytics SKU-110K). Robustness tests confirm its strong anti-interference ability, demonstrating its effectiveness for detection and identification of supermarket goods.}, archive = {J_ACCESS}, author = {Pingqing Hou and Shaoze Huang}, doi = {10.1109/ACCESS.2025.3595175}, journal = {IEEE Access}, pages = {139665-139679}, shortjournal = {IEEE Access}, title = {BCSM-YOLO: An improved product package recognition algorithm for automated retail stores based on YOLOv11}, volume = {13}, year = {2025}, }
• (2025). A latency-aware and resource-efficient content caching scheme for content-centric networks. ACCESS, 13, 139650-139664. (www)

  @article{ , abstract = {The proliferation of smart devices and bandwidth-intensive applications challenges traditional IP networks, driving interest in Content-Centric Networking (CCN). CCN decouples content from location using persistent names and leverages in-network caching to improve delivery efficiency. However, existing caching strategies often optimize isolated metrics like hit ratio, leading to suboptimal performance in latency or resource usage, employ inefficient eviction policies like Least Recently Used (LRU)/Least Frequently Used (LFU) unsuitable for dynamic content, or create excessive redundancy. They struggle to balance conflicting factors such as content popularity, freshness, latency, diversity, and resource constraints. To address this, we propose Latency-Aware and Resource-efficient Content-caching (LARC), a multi-objective heuristic approach that effectively improves latency performance while balancing resource constraints. LARC utilizes a lightweight, self-tuning heuristic featuring a Content Access Score (CAS) that dynamically integrates request frequency, content freshness (via adaptive decay), and hop distance. This score guides both an adaptive cache placement strategy, sensitive to real-time network conditions, and a novel freshness-aware eviction mechanism. By enhancing latency performance while considering resource efficiency, LARC implicitly enhances hit ratio, content freshness, and diversity. Extensive simulations on large-scale topologies showed that LARC achieves up to 9% improvement in hit ratio, up to 3% reduction in average latency, up to 3% enhancement in path-stretch, and up to 4% improvement in link-load compared to the state-of-the-art caching schemes.}, archive = {J_ACCESS}, author = {Yasar Khan and Rao Naveed Bin Rais and Osman Khalid and Imran Ali Khan}, doi = {10.1109/ACCESS.2025.3596376}, journal = {IEEE Access}, pages = {139650-139664}, shortjournal = {IEEE Access}, title = {A latency-aware and resource-efficient content caching scheme for content-centric networks}, volume = {13}, year = {2025}, }
• (2025). An affordable AI-driven and 3D-printed personalized myoelectric prosthesis: Design, development, and assessment. ACCESS, 13, 139631-139649. (www)

  @article{ , abstract = {Upper-limb amputations significantly affect independence and quality of life, particularly in low-income regions where advanced prosthetic technology is costly and lacks adequate personalization. Conventional myoelectric prostheses, while offering functional restoration, have limited adaptability and high cost. This study presents a personalized transradial myoelectric prosthesis that combines additive manufacturing and Artificial Intelligence (AI) control, offering an accessible and high-performance solution. The prosthesis design utilizes additive manufacturing (3D printing) for anatomical personalization via 3D scanning and parametric modeling. An AI-driven control system utilizes machine learning to classify electromyography (EMG) signals in real-time, specifically detecting the user’s intention to perform flexion or extension movements, and tailoring responses to individual users. Evaluation employed the “Brief Activity Measure for Upper Limb Amputees (BAM-ULA)” protocol with nine participants with transradial amputations. Trials with the nine participants yielded an average BAM-ULA score of 7.4 out of 10 (Standard Deviation (SD) 0.7). This demonstrated robust functional performance, comparable to high-end commercial devices in initial tests. Gross motor tasks saw 100% success rates; fine motor tasks, 22.2%. Integrating AI and additive manufacturing resulted in an affordable, high-performance, personalized prosthesis. This work highlights how localized digital manufacturing enables accessible customization for users in low-resource settings. The main novelty is this validated integration of personalized additive manufacturing and adaptive AI control in an affordable transradial prosthesis addressing the needs of developing countries.}, archive = {J_ACCESS}, author = {Enzo Romero and Jose G. Garcia and Magno Parra and Sebastian Caballa and Alejandro M. Saldarriaga and Edson F. Luque and Dante J. Rodriguez and Victoria E. Abarca and Dante A. Elias}, doi = {10.1109/ACCESS.2025.3596475}, journal = {IEEE Access}, pages = {139631-139649}, shortjournal = {IEEE Access}, title = {An affordable AI-driven and 3D-printed personalized myoelectric prosthesis: Design, development, and assessment}, volume = {13}, year = {2025}, }
• (2025). Dynamic energy sparse self-attention based on informer for remaining useful life of rolling bearings. ACCESS, 13, 139616-139630. (www)

  @article{ , abstract = {Predicting the remaining useful life of rolling bearings presents significant challenges due to their nonstationary degradation patterns. Conventional methods struggle with long sequences and are strongly dependent on manual feature extraction, which can lead to inefficiencies and biased predictions. To address these issues, this paper proposes the dynamic energy sparse self-attention model based on Informer. The proposed model uses the auto-reassignment transform to extract more accurate features from the raw vibration signals, and the dynamic energy sparse self-attention mechanism adapts the attention weights based on the energy distribution. In addition, the Monte Carlo dropout method is employed to quantify uncertainty and enhance model robustness. Experimental results obtained on two benchmark datasets demonstrate that the proposed model outperforms existing advanced methods, achieving a reduction in mean absolute error and root mean square error, demonstrating its superior ability to handle complex bearing degradation prediction tasks.}, archive = {J_ACCESS}, author = {Cheng Shi and Qifei Li and Hui Chen and Yuanwei Song and Qianqi Le}, doi = {10.1109/ACCESS.2025.3594077}, journal = {IEEE Access}, pages = {139616-139630}, shortjournal = {IEEE Access}, title = {Dynamic energy sparse self-attention based on informer for remaining useful life of rolling bearings}, volume = {13}, year = {2025}, }
• (2025). MolAttnNet: A predictive model for organic drug solubility based on graph convolutional networks and transformer-attention. ACCESS, 13, 139605-139615. (www)

  @article{ , abstract = {The solubility of a drug in water is a crucial indicator of its therapeutic efficacy and safety. However, traditional prediction methods often overlook the fine local structural dependencies within molecules, limiting the accuracy of solubility predictions. To address this issue, we propose MolAttnNet, an innovative deep learning framework that combines Graph Neural Networks, Transformer encoders, and Long Short-Term Memory networks to enhance solubility prediction accuracy. The framework comprises three specialized modules: a Graph Convolutional Network for extracting local molecular structural features, a multi-granularity attention mechanism for capturing both local and global molecular dependencies, and an adaptive LSTM with chemically-informed forget gates for selective feature retention and noise attenuation. On an anticancer compound test set, MolAttnNet achieved an R2 of 0.5921, an RMSE of 0.5681, and an MSE of 0.3227, representing an 11.15% reduction in MSE compared to the SolPredictor baseline model. Ablation studies systematically validate the contribution of each module, demonstrating that their synergistic integration is essential for optimal performance. Furthermore, interpretability analysis confirms the model’s reliability in feature extraction and decision-making processes. The proposed framework provides a practical approach for drug solubility prediction and contributes to computational drug discovery methodologies.}, archive = {J_ACCESS}, author = {Chenxu Wang and Yijun Feng and Zhejie Xu and Xiaohui Xu and Bangguo Peng}, doi = {10.1109/ACCESS.2025.3596145}, journal = {IEEE Access}, pages = {139605-139615}, shortjournal = {IEEE Access}, title = {MolAttnNet: A predictive model for organic drug solubility based on graph convolutional networks and transformer-attention}, volume = {13}, year = {2025}, }
• (2025). A near-real-time model for predicting electricity disruptions in texas during winter storms. ACCESS, 13, 139583-139603. (www)

  @article{ , abstract = {There has been an increase in extreme weather events, posing a threat to power grid systems, potentially influenced by factors such as population growth, changes in ecosystems, land cover, and land use in the service area, as well as the growth of certain vegetation types. This research seeks to develop a predictive model to mitigate potential damages caused by future winter storms. This research utilizes the Light Gradient Boosting Machine (LightGBM), incorporating the number of power outages experienced at the county level, geographic details, weather information, and lagged outage and lagged weather data. The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). However, in terms of Mean Directional Accuracy (MDA), while all results in Group 1 were below the baseline accuracy of 0.33, all results in Group 2 exceeded 0.33, with some cases showing an increase of more than three times the baseline. The results indicated that, in the optimized model group, Population and Pressure were the most influential factors when using current weather data and geographical information. When using lagged data, lagged recorded outages and lagged Pressure emerged as the most significant factors. Among the 12 developed models, the L-1-2-O model showed the lowest RMSE and MAE, as well as the highest accuracy, with values of 390.62 households and 168.13 households, respectively. To normalize the RMSE and MAE values, each metric was divided by the average number of households among the counties in Texas. For the L-1-2-O model, the scaled RMSE was 0.88% and the scaled MAE was 0.38%. In terms of MDA, which indicates the accuracy of the prediction direction, the L-1-O model achieved the highest score of 0.41. Although this study focused on Texas, which suffered the greatest impact from the winter storms in 2021, with additional validation, the methodology used in this research could be applied to other regions.}, archive = {J_ACCESS}, author = {Jangjae Lee and Sangkeun Lee and Supriya Chinthavali and Stephanie Paal}, doi = {10.1109/ACCESS.2025.3596531}, journal = {IEEE Access}, pages = {139583-139603}, shortjournal = {IEEE Access}, title = {A near-real-time model for predicting electricity disruptions in texas during winter storms}, volume = {13}, year = {2025}, }
• (2025). A systematic review and evaluation of sustainable AI algorithms and techniques in healthcare. ACCESS, 13, 139547-139582. (www)

  @article{ , abstract = {Concerns regarding energy use, environmental effects, and long-term sustainability have been highlighted in recent years by the expanding application of Artificial Intelligence (AI) in healthcare. This systematic review paper categorizes and classifies AI algorithms and tools in the healthcare sector to support more sustainable practices, focusing on reducing energy use while maintaining high standards in diagnostic accuracy and patient outcomes. AI algorithms and tools are categorized into three groups: explicit AI algorithms for sustainability for energy efficiency (e.g., Federated Learning, Hybrid Quantum-Classical Optimization, Modified Lempel-Ziv-Welch (mLZW)), traditional AI algorithms for sustainable healthcare (e.g., Bidirectional Long Short-Term Memory (Bi-LSTM), Backpropagation Neural Networks (BPNNs), Convolutional Neural Networks (CNNs)), and sustainable AI techniques (e.g., Adaptive Sampling, AutoML for Model Compression (AMC)) that support low-power computing (e.g., edge computing, neuromorphic hardware, adaptive sampling). A comprehensive performance analysis is presented across five dimensions: energy consumption, latency, accuracy, complexity, and cost. The review highlights mLZW as promising for energy efficiency, complexity, and cost, OFA for low-latency deployment, and Hybrid Quantum Classical Optimization for diagnostic accuracy. We propose an integration framework for deploying these methods in resource-constrained healthcare environments, identifying open research challenges and future directions. This work provides a foundational guide for researchers and sector practitioners to build energy-aware, high-performance AI systems in healthcare.}, archive = {J_ACCESS}, author = {Yehia Ibrahim Alzoubi and Ahmet E. Topcu and Ersin Elbasi}, doi = {10.1109/ACCESS.2025.3596189}, journal = {IEEE Access}, pages = {139547-139582}, shortjournal = {IEEE Access}, title = {A systematic review and evaluation of sustainable AI algorithms and techniques in healthcare}, volume = {13}, year = {2025}, }
• (2025). On evaluation of data stream clustering algorithms: A survey. ACCESS, 13, 139524-139546. (www)

  @article{ , abstract = {Data stream mining is a research area that has grown enormously in recent years. The main challenge is extracting knowledge in real-time from a possibly unbounded data stream. Clustering, a process in which groupings within the data are identified, is a valuable technique to extract and identify underlying structures of the data. An open question in stream clustering is how to evaluate the proposed algorithms. In this survey, we review the literature in the domain to identify common methodologies, datasets, and evaluation measures, used to evaluate the algorithms. We provide a short summary of the stream clustering algorithms in the literature, but our primary focus lies in the survey of cluster validation relevant to the evaluation of data stream clustering algorithms. We begin our literature review with the inception of clustering incrementally, namely with the introduction of the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. We identify that the evaluation methodologies primarily focus on performance, and that aspects such as cluster quality are rarely considered. Performance has been the focal point of all evaluations, both in terms of computational performance and accuracy, since the inception of clustering data streams. We also identify that issues in the conventional clustering domain are present in the data stream clustering. However, minor additions to the evaluation methods can improve both the applicability and usefulness of the algorithms.}, archive = {J_ACCESS}, author = {Christian Nordahl and Veselka Boeva and Håkan Grahn and Marie Persson-Netz}, doi = {10.1109/ACCESS.2025.3596435}, journal = {IEEE Access}, pages = {139524-139546}, shortjournal = {IEEE Access}, title = {On evaluation of data stream clustering algorithms: A survey}, volume = {13}, year = {2025}, }
• (2025). Optimization of relay node deployment in wireless sensor communication networks based on IGA-RNDP algorithm. ACCESS, 13, 139506-139523. (www)

  @article{ , abstract = {Reasonably deploying wireless sensor communication network relay nodes can not only expand network coverage and ensure that signals can span longer distances, but also enhance signal strength and reduce loss and errors during data transmission. To achieve reasonable deployment of relay nodes, a relay node deployment algorithm on the basis of improved genetic algorithm is proposed. By adjusting the operation method of traditional genetic algorithms and introducing relevant algorithm optimization mechanisms, the research method can effectively handle the deployment of relay nodes. This algorithm was compared with four other algorithms in the deployment of relay nodes in real traffic datasets to prove the effectiveness. The method had a maximum fitness value of 0.98 in the testing set and required 48 iterations to achieve convergence. When the communication radius of the sensor node was 20m, the coverage rate for different numbers of sensors was 100%. The average number of deployed nodes was 154.38, which was better than other methods. When the communication radius was 40m, the coverage redundancy always remained at a low level. The solution time for the problem model was within 2 seconds. In large-scale network environments, the research method deployed the fewest number of relay nodes and had a concentrated distribution, with statistically significant differences compared to other algorithms. The research method could achieve 100% coverage and low coverage redundancy with fewer relay nodes in a shorter period of time. The statistically significant differences existed between the research method and other algorithms.}, archive = {J_ACCESS}, author = {Shubo Xu and Cheng Zhong}, doi = {10.1109/ACCESS.2025.3596296}, journal = {IEEE Access}, pages = {139506-139523}, shortjournal = {IEEE Access}, title = {Optimization of relay node deployment in wireless sensor communication networks based on IGA-RNDP algorithm}, volume = {13}, year = {2025}, }
• (2025). An improved low-bit-rate image compression framework based on semantic-aware model and neighborhood attention. ACCESS, 13, 139490-139505. (www)

  @article{ , abstract = {In recent years, image compression techniques based on deep neural networks have achieved significant advancements, outperforming traditional methods in delivering higher compression efficiency at lower bit rates. However, existing approaches often result in degraded image quality at low bit rates, leading to distortions in critical regions such as faces and text during decoding. To address these limitations, we propose a robust image compression framework based on a semantic-aware model and a hyper-prior encoder with neighborhood attention. First, we utilize the encoder of the semantic-aware model to transform the input image into a latent space Z. To further improve the information representation within the latent space Z, we design a hyper-prior encoder, which leverages neighborhood attention to perform feature enhancement and transformation. This process can minimize quantization errors and facilitate efficient vector quantization. The refined latent space is then quantized using a residual vector quantization technique to ensure efficient and compact representation. Finally, entropy coding is applied to the quantized latent space, enabling high compression efficiency. Experimental results on public benchmark datasets show that our proposed framework achieves comparable results to current mainstream image compression methods. In addition, our proposed algorithm effectively mitigates distortions in facial and textual regions while preserving the structural integrity and visual fidelity. In future work, we plan to improve the model’s robustness under low-light conditions and enhance compression efficiency through lightweight optimization techniques, enabling broader real-world deployment.}, archive = {J_ACCESS}, author = {Chengbin Zeng and Liang Zhang}, doi = {10.1109/ACCESS.2025.3596711}, journal = {IEEE Access}, pages = {139490-139505}, shortjournal = {IEEE Access}, title = {An improved low-bit-rate image compression framework based on semantic-aware model and neighborhood attention}, volume = {13}, year = {2025}, }
• (2025). Minimum link flow problem and its solution with sparse modeling. ACCESS, 13, 139479-139489. (www)

  @article{ , abstract = {Recently, a statistical modeling approach called sparse modeling has attracted much attention, especially in the fields of signal and image processing. Sparse modeling is applicable to solve problems in a variety of fields, and a few applications have begun to be explored in the field of information networking. Sparse modeling is expected to be applicable to many information networking problems, but it has not yet been well investigated. In this study, we examine how sparse modeling can be applied to solve a new network flow problem called minimum link flow problem. The minimum link flow problem, which includes problems such as the minimum cost flow problem and the minimum Steiner tree problem under certain conditions, is a combinatorial optimization problem, and to the best of our knowledge, no effective solution has been proposed for the minimum link flow problem. In this study, we formulate the minimum link flow problem using sparse modeling and propose a greedy algorithm called Constrained Orthogonal Matching Pursuit (COMP), which imposes both upper and lower bounds on the solution. Through various experiments, we investigate how effectively the COMP can solve the minimum link flow problem. Our findings include that COMP can solve the minimum link flow problem with multiple sources and sinks 1.1–1.3 times more effectively than benchmark conventional methods.}, archive = {J_ACCESS}, author = {Ryotaro Matsuo and Ryo Nakamura and Hiroyuki Ohsaki}, doi = {10.1109/ACCESS.2025.3596267}, journal = {IEEE Access}, pages = {139479-139489}, shortjournal = {IEEE Access}, title = {Minimum link flow problem and its solution with sparse modeling}, volume = {13}, year = {2025}, }
• (2025). Proactive cyber resilience: A unified assessment methodology for incident forecasting with cyber threat intelligence integration. ACCESS, 13, 139460-139478. (www)

  @article{ , abstract = {Traditional cybersecurity assessments rely on the qualitative aspects of framework implementation, limiting the ability to quantify an organization’s cybersecurity posture and prioritize measures for incident prevention. In the first phase of our research, we designed a Center for Internet Security (CIS) Controls v8.0 Ranking-Weight scoring system to enhance vulnerability assessment and management in real organizations. In this study, we go a step further in adopting a proactive defense approach by incorporating the forecast of MITRE ATT&CK techniques frequently used to breach security controls that lack implementation. The second phase of our research integrates Cyber Threat Intelligence (CTI) gathered from industry data into the CIS Controls v8.1 quantitative assessment, through automatic mapping and risk categorization. We evaluated our methodology in 13 real case studies from U.S. organizations across different sectors after signing data usage approval agreements. The results show that these organizations have variable cybersecurity posture scores, ranging between 2.92/100 and 62.25/100. The forecast number of exploitable techniques was also variable and dependent on the risk categories. To address some of the limitations introduced by our real-world dataset’s scarcity and unbalanced characteristics, we expanded the number of observations 15 times by generating and balancing over 200 synthetic assessment results using Random-OverSampling and SMOTE. Experimental results show that synthetic data contribute positively to identifying risk categories based on cybersecurity posture scores below 45/100 and above 61/100, predicting additional attack techniques under variable implementation conditions, such as T1027, and confirming a multifold reduction in assessment time from months to days.}, archive = {J_ACCESS}, author = {Hayat Abdulla Asad Cue and Thirimachos Bourlai and Mark Lupo}, doi = {10.1109/ACCESS.2025.3596252}, journal = {IEEE Access}, pages = {139460-139478}, shortjournal = {IEEE Access}, title = {Proactive cyber resilience: A unified assessment methodology for incident forecasting with cyber threat intelligence integration}, volume = {13}, year = {2025}, }
• (2025). Noisier2Inverse: Self-supervised learning for image reconstruction with correlated noise. ACCESS, 13, 139445-139459. (www)

  @article{ , abstract = {We propose Noisier2Inverse, a correction-free, self-supervised deep learning method for general inverse problems. Our approach learns a reconstruction function without requiring ground truth data and is applicable in settings where measurement noise is statistically correlated. This includes applications such as computed tomography, where detector imperfections and photon scattering induce structured noise, as well as microscopy and seismic imaging, where physical interactions during acquisition lead to noise dependencies. Like Noisier2Noise, our method relies on generating noisier inputs to guide training via a network operating in image space. However, in contrast to Noisier2Noise, the loss function in Noisier2Inverse is defined in the measurement space and targets the recovery of an extrapolated image rather than the original noisy one. This design avoids the need for an explicit extrapolation step during inference, which would otherwise be affected by the ill-posedness of the inverse problem. We provide numerical evidence that Noisier2Inverse outperforms prior self-supervised methods designed to handle correlated noise. This indicates that Noisier2Inverse fills a gap in current self-supervised image reconstruction methods for correlated noise.}, archive = {J_ACCESS}, author = {Nadja Gruber and Johannes Schwab and Markus Haltmeier and Ander Biguri and Clemens Dlaska and Gyeongha Hwang}, doi = {10.1109/ACCESS.2025.3596557}, journal = {IEEE Access}, pages = {139445-139459}, shortjournal = {IEEE Access}, title = {Noisier2Inverse: Self-supervised learning for image reconstruction with correlated noise}, volume = {13}, year = {2025}, }
• (2025). Sparse adaptive optimization based on low rank decomposition for image defect detection. ACCESS, 13, 139433-139444. (www)

  @article{ , abstract = {Low-rank optimization plays a pivotal role in image processing due to its inherent ability to capture low-dimensional structures and promote sparsity. Traditional low-rank decomposition methods aim to recover low-rank components and isolate sparse elements, but the structural integrity of the sparse components is often compromised. Moreover, the interplay between low-rank and sparse terms can lead to conflicting effects in practical scenarios. To address these limitations, this paper proposes a novel low-rank decomposition model that introduces a new form of regularization, effectively balancing low-rank representation and structured sparsity. The proposed model enhances the structural expressiveness of the sparse component while preserving the global low-rank structure. To solve the resulting optimization problem efficiently, we develop a tailored algorithm based on operator splitting techniques, significantly improving computational efficiency without sacrificing accuracy. Extensive experiments on tasks such as defect detection and background reconstruction demonstrate that the proposed method outperforms existing approaches, achieving superior accuracy and robustness across various real-world scenarios.}, archive = {J_ACCESS}, author = {Daihong Jiang and Zhixiang Chen and Sanyou Zhang and Yunfei Li and Lu Zhao}, doi = {10.1109/ACCESS.2025.3596642}, journal = {IEEE Access}, pages = {139433-139444}, shortjournal = {IEEE Access}, title = {Sparse adaptive optimization based on low rank decomposition for image defect detection}, volume = {13}, year = {2025}, }
• (2025). Smart elderly healthcare services in industry 5.0: A survey of key enabling technologies and future trends. ACCESS, 13, 139419-139432. (www)

  @article{ , abstract = {This survey presents a comprehensive analysis of the transformative role of Industry 5.0 technologies in advancing smart elderly healthcare services, focusing on China’s evolving digital landscape. By examining the opportunities and challenges unique to China’s aging population in the context of rapid technological integration, the study outlines a system architecture that underpins next-generation elderly healthcare ecosystems. Central to this framework are five pivotal enabling technologies: the Internet of Things (IoT), Edge Computing, Service Robots, Big Data Analytics, and Digital Twin, each systematically explored to highlight their contributions to personalized, proactive, and decentralized elderly healthcare. The survey identifies critical application challenges, including interoperability gaps, ethical concerns in human-robot interaction, and data heterogeneity, while forecasting future trends poised to accelerate the adoption of Industry 5.0 principles. Emerging directions such as context-aware robotic collaboration, federated learning for privacy-preserving analytics, and metaverse-integrated digital twins are underscored as catalysts for sustainable, human-centric solutions. By bridging technological innovation with socio-ethical considerations, this work not only maps current advancements but also advocates for a paradigm shift toward empathetic, adaptive, and equitable elderly healthcare systems. The findings aim to inspire interdisciplinary collaboration among researchers, policymakers, and healthcare providers, fostering a transition to intelligent, dignity-preserving care models that align with the human-centered ethos of Industry 5.0.}, archive = {J_ACCESS}, author = {Xiaoling Wang and Li Huang and Dongni Wang and Linyu Liu and Peng Guo}, doi = {10.1109/ACCESS.2025.3596694}, journal = {IEEE Access}, pages = {139419-139432}, shortjournal = {IEEE Access}, title = {Smart elderly healthcare services in industry 5.0: A survey of key enabling technologies and future trends}, volume = {13}, year = {2025}, }
• (2025). Trusted blockchain-based clinical decision and medication management system for movement disorders. ACCESS, 13, 139404-139418. (www)

  @article{ , abstract = {Centralized healthcare information systems face significant challenges related to security, interoperability, and real-time monitoring, particularly in the management of movement disorders. This paper proposes a novel blockchain-based framework that integrates wearable inertial measurement units (IMUs), AI-driven analytics, and the Hyperledger Fabric blockchain to enable secure, decentralized decision-making in the management of movement disorders. The system employs a voting-based consensus mechanism, where medication dosage recommendations proposed by an AI model are validated by clinicians, ensuring transparency and accountability in treatment decisions. Experimental evaluations demonstrate the framework’s scalability, achieving a throughput of 185 transactions per second (TPS) and reducing latency to 0.285 seconds under a 300 TPS load, using an 8-core CPU configuration. The proposed architecture guarantees 100% auditability and tamper-evident records, significantly improving the security and efficiency of clinical workflows. Notably, the system achieves a 96.3% reduction in latency and shows promising performance in both medication safety and treatment response times. The integration of blockchain and AI-based dosage recommendations offers a scalable and low-latency solution for remote patient monitoring and personalized healthcare management in patients with movement disorders. This work provides a practical solution to the challenges of decentralized decision-making and real-time data integration, offering a pathway to more resilient and secure healthcare systems.}, archive = {J_ACCESS}, author = {Behnaz Behara and Mehdi Delrobaei and Nima Afraz}, doi = {10.1109/ACCESS.2025.3596693}, journal = {IEEE Access}, pages = {139404-139418}, shortjournal = {IEEE Access}, title = {Trusted blockchain-based clinical decision and medication management system for movement disorders}, volume = {13}, year = {2025}, }
• (2025). Aggregated distribution grid flexibilities in the overlaying grid operational management. ACCESS, 13, 139388-139403. (www)

  @article{ , abstract = {Distribution grid aggregated flexibilities or PQ-capabilities (active and reactive power capabilities) are termed in literature as Feasible Operating Regions (FORs). The FORs from underlying active distribution grids can effectively contribute to the operational management at the HV grid level. The HV buses are allocated aggregated FORs from the underlying MV grids, which are inherently nonlinear and non-convex. Therefore, two approaches are proposed in the paper to apply the FOR constraints in the HV grid operational management. First, a mixed integer linear programming (MILP) based optimization approach for alleviating the HV grid constraint violations is proposed, which addresses the non-convexity of the FOR using piecewise segmentation. Furthermore, the MILP method is enhanced to consider the influence of the HV bus voltage on the underlying MV grid flexibilities resulting in a 3D PQ(V)-FOR. Second, a convexification approach is proposed, which uses a convex approximation of the non-convex 3D PQ(V)-FOR for implementation in a linear optimization method. Results reveal a robust utilization of the distribution flexibilities to maintain grid security and reliability at the HV grid level. Comparisons present significantly reduced computation times for the convexification method.}, archive = {J_ACCESS}, author = {Neelotpal Majumdar and Lutz Hofmann}, doi = {10.1109/ACCESS.2025.3596884}, journal = {IEEE Access}, pages = {139388-139403}, shortjournal = {IEEE Access}, title = {Aggregated distribution grid flexibilities in the overlaying grid operational management}, volume = {13}, year = {2025}, }
• (2025). Delta-adjust: Minimum distance interpolation. ACCESS, 13, 139377-139387. (www)

  @article{ , abstract = {We present Delta-Adjust, a novel interpolation method that extends local neighborhood interpolation techniques by introducing per-feature neighbor selection and feature-vector difference bias adjustment. Unlike conventional prediction models, Delta-Adjust operates without explicit training phases. It leverages the structure of information entangled within the dataset to interpolate across a domain using small, local adjustments. This mechanism enables Delta-Adjust to infer a valid global representation of data only from local relationships. This formulation applies indifferently to both regression and classification problems, and potentially to broader predictive tasks. Though not a machine learning model in the conventional sense, Delta-Adjust can be used for predictive modeling due to its structure-preserving nature. Our experiments demonstrate strong performance across standard benchmarks (Iris: F1=0.94; Breast Cancer: F1=0.86; California Housing: MSE=0.29), while offering competitive interpolation accuracy across synthetic surfaces with discontinuities, curvature, and high-frequency patterns (e.g. Circular Ridges). As a general-purpose interpolation technique intended for ML applications, Delta-Adjust holds potential as a computational primitive for AI pipelines. Its framework, based on local propagation of deltas, naturally accommodates a wide range of predictive tasks and may support future applications in geospatial modeling, computer graphics, sensor networks, and representation learning. This work proposes a new direction for information-aware computation in data-driven systems—relying on local interpolations that cooperatively induce a global model of feature–output relationships. Delta-Adjust achieves this independently of the underlying data distribution, without any training steps, and without requiring explicit assumptions about global model structure.}, archive = {J_ACCESS}, author = {Ziad F. Doughan and Sari S. Itani}, doi = {10.1109/ACCESS.2025.3596384}, journal = {IEEE Access}, pages = {139377-139387}, shortjournal = {IEEE Access}, title = {Delta-adjust: Minimum distance interpolation}, volume = {13}, year = {2025}, }
• (2025). Design and deployment of swarm engineering systems: Insights, challenges, and innovations. ACCESS, 13, 139345-139376. (www)

  @article{ , abstract = {In this study, we explore the fundamentals of design and deployment of Multi-Agent Swarm Engineering Systems (MASESs), a comprehensive framework aimed at improving decision-making and operational efficiency in industrial environments. Swarm engineering systems draw inspiration from collective behaviors observed in natural swarms (such as ants or bees), offering a decentralized approach to problem-solving in complex systems. We provide a detailed theoretical foundation for MASES, focusing on key elements such as spatial control formation, adaptive coordination, decision-making dynamics, and critical factors such as scalability, adaptability, and robustness in practical applications. The analysis is enriched with insightful tables that highlight current innovations and challenges, underscoring the importance of balancing decentralization with scalability. In transitioning to practical applications, we discuss design methodologies and performance evaluation strategies, emphasizing the transformative potential of swarm engineering systems. Crucially, we highlight how artificial intelligence (AI) and machine learning (ML) are being integrated into MASES to enhance decision-making capabilities, adaptability, and scalability. Advanced frameworks and simulation environments have been shown to play a crucial role in ensuring that MASES operates effectively, fostering intelligence and adaptability to meet complex industrial demands. By showcasing various applications, we highlight the potential of MASES to enhance operational landscapes across industries and identify pathways for future research to refine and perfect this integration.}, archive = {J_ACCESS}, author = {Raza Muhammad Kazim and Guoxin Wang and Zhenjun Ming and Janet K. Allen and Farrokh Mistree}, doi = {10.1109/ACCESS.2025.3592950}, journal = {IEEE Access}, pages = {139345-139376}, shortjournal = {IEEE Access}, title = {Design and deployment of swarm engineering systems: Insights, challenges, and innovations}, volume = {13}, year = {2025}, }
• (2025). QUIRCY: QUantum integrated ResilienCY for power systems. ACCESS, 13, 139330-139344. (www)

  @article{ , abstract = {The increasing frequency and severity of disruptive weather events, such as windstorms and other high-impact low-probability (HILP) occurrences, emphasize the critical necessity for enhanced preparedness and resilience strategies in power systems. Operators face significant challenges in optimally deploying mobile generating resources to areas experiencing outages of crucial infrastructure components, including generators, transmission lines, and transformers. These disruptions often compromise the reliability and stability of power delivery, necessitating temporary load shedding to ensure grid stability until full restoration is achieved. This paper introduces quantum integrated resiliency for power systems (QUIRCY), a new resilience approach aimed at optimizing resource allocation in power systems. The proposed framework integrates a two-level quantum algorithm that combines the Harrow-Hassidim-Lloyd (HHL) algorithm for highly efficient quantum power flow analysis with quadratic unconstrained binary optimization (QUBO) to strategically allocate distributed energy resources (DERs). The HHL algorithm significantly outperforms classical fast decoupled load flow (FDLF) methods by requiring only $\log _{2} N$ qubits to effectively represent an N-dimensional system. Simulation case studies on IEEE test systems have demonstrated the effectiveness and efficiency of the proposed quantum approach, indicating consistent convergence to optimal solutions under various severe system constraints. These results highlight the significant promise of quantum computing approaches for improving power system resilience, particularly as advancements in noisy intermediate-scale quantum (NISQ) technology continue to evolve.}, archive = {J_ACCESS}, author = {Umar T. Salman and Zongjie Wang and Timothy M. Hansen}, doi = {10.1109/ACCESS.2025.3592830}, journal = {IEEE Access}, pages = {139330-139344}, shortjournal = {IEEE Access}, title = {QUIRCY: QUantum integrated ResilienCY for power systems}, volume = {13}, year = {2025}, }
• (2025). LARNet-SAP-YOLOv11: A joint model for image restoration and corrosion defect detection of transmission line fittings under multiple adverse weather conditions. ACCESS, 13, 139310-139329. (www)

  @article{ , abstract = {Adverse weather conditions such as haze, rain, and snow degrade images captured by uncrewed aerial vehicles (UAVs) during transmission line inspections and severely affect the detection of corrosion defects in transmission line fittings. To address this challenge, we propose LARNet-SAP-YOLOv11, a unified end-to-end model that integrates lightweight image restoration and defect detection. The proposed model comprises LARNet, a lightweight all-in-one image restoration network, and SAP-YOLOv11, an enhanced object detector based on YOLOv11. LARNet is built upon the DehazeFormer architecture and introduces a Triplet Attention Block (TAB) to improve adaptability to various weather degradations. SAP-YOLOv11 enhances the baseline YOLOv11n by incorporating a Shallow Robust Feature Downsampling (SRFD) module, an Adaptive Fine-Grained Channel Attention (AFGCAttention) mechanism, and a Pixel-level Cross-Attention Feature Fusion (PCAFFusion) module, significantly improving corrosion area perception. Experimental results show that LARNet achieves an average PSNR of 30.43 dB and SSIM of 0.951 across different conditions. For defect detection, SAP-YOLOv11 improves the mAP@50 by 2.1% compared to the original YOLOv11n. When jointly applied, LARNet-SAP-YOLOv11 achieves an mAP@50 of 88.6%, outperforming the baseline YOLOv11n by 12.1% in challenging weather conditions. This unified model offers an efficient and reliable solution for UAV-based intelligent inspection of transmission lines under diverse environmental conditions.}, archive = {J_ACCESS}, author = {Yuxiang Gong and Lin Gao and Hao Zhang and Huaguo Liu and Yongdan Zhu and Yu Yang}, doi = {10.1109/ACCESS.2025.3596300}, journal = {IEEE Access}, pages = {139310-139329}, shortjournal = {IEEE Access}, title = {LARNet-SAP-YOLOv11: A joint model for image restoration and corrosion defect detection of transmission line fittings under multiple adverse weather conditions}, volume = {13}, year = {2025}, }
• (2025). Calculation method and influencing factors of the maximum breaking current of SF6 circuit breaker. ACCESS, 13, 139300-139309. (www)

  @article{ , abstract = {At present, there is a lack of research on the evaluation of the maximum breaking current of circuit breakers in arc simulation of SF6 circuit breakers. Therefore, a coupling simulation model of electromagnetic, thermal, fluid and radiation physical fields is established for the breaking process of circuit breakers, and a breaking criterion based on temperature distribution is proposed to study the changing law of the maximum breaking current of circuit breakers. It is found that the ablation angle of the contact decreases the maximum breaking current ability of the circuit breaker rapidly. For the 126 kV circuit breaker, the maximum contact ablation angle to achieve the rated breaking condition is $12\sim 13^{\circ }$ of ablation from the intact contact angle. When the opening speed decreases, the maximum breaking current decreases as a whole, and the breaking capacity decreases the most when the contact is intact, but there is still a large margin from the rated current value. The maximum breaking current decreases with the increase of the breaking phase angle. When the breaking phase angle is [ $0.8\pi $ , $0.9\pi $ ], the circuit breaker cannot be broken in rated working condition. The research results will provide a theoretical basis for SF6 circuit breaker engineering to break short circuit current.}, archive = {J_ACCESS}, author = {Li Duanjiao and Wang Wei and Congwei Yao and Liu Miao and Peng Jiajun and Xiaofeng Pang and Wenxing Sun and Jianjun Li}, doi = {10.1109/ACCESS.2025.3526147}, journal = {IEEE Access}, pages = {139300-139309}, shortjournal = {IEEE Access}, title = {Calculation method and influencing factors of the maximum breaking current of SF6 circuit breaker}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based indirect tip force sensing and estimation for robotic uterine manipulation system. ACCESS, 13, 139284-139299. (www)

  @article{ , abstract = {Robotic-assisted Minimally Invasive Surgery (RMIS) has advanced laparoscopic gynecological procedures by improving precision and reducing invasiveness. However, the lack of direct force sensing during uterine manipulation remains a challenge, potentially increasing the risk of tissue damage and compromising surgical accuracy. This study presents an indirect force estimation method for robotic uterine manipulators using machine learning techniques to predict tip forces from handle force measurements. A dataset was developed by systematically varying uterine weight and ligament resistance, simulated through elastic bands of different colors and lengths. Three regression models—Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Support Vector Regression (SVR)—were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination $(\text {R}^{2})$ . All models demonstrated reliable performance across diverse testing conditions, with XGBoost and SVR achieving a balance of predictive accuracy and computational efficiency, while LSTM effectively leveraged temporal dependencies. These findings underscore the viability of machine learning-based indirect force sensing as a practical alternative in scenarios where direct measurement is infeasible, paving the way for improved haptic feedback and surgical safety.}, archive = {J_ACCESS}, author = {Songphon Namkhun and Apiwat Boonkong and Piroon Kaewfoongrungsi and Kovit Khampitak and Daranee Hormdee}, doi = {10.1109/ACCESS.2025.3596104}, journal = {IEEE Access}, pages = {139284-139299}, shortjournal = {IEEE Access}, title = {Machine learning-based indirect tip force sensing and estimation for robotic uterine manipulation system}, volume = {13}, year = {2025}, }
• (2025). Softness perception interfered by friction in gliding virtual surfaces. ACCESS, 13, 139275-139283. (www)

  @article{ , abstract = {Recent studies have shown that surface friction affects softness perception when objects are slid over using fingers. This study examines whether this phenomenon extends to interactions with virtual objects using a stylus-based haptic interface, which independently controls friction and object stiffness. In Experiment 1, we investigated how kinematic friction coefficients influence perceived stiffness. Results showed that surfaces with minimal friction (coefficient =0) were perceived as stiffer than those with higher friction ( $\geq 0.2$ ). For instance, a surface with a stiffness of 500 N/m at minimal friction was perceived as equivalent to 581.7 N/m at a friction coefficient of 0.4. However, the perceived stiffness was not different under frictional conditions, with coefficients ranging from 0.2 to 0.6. Experiment 2 evaluated participants’ ability to discriminate stiffness levels (400 N/m vs. 500 N/m and 500 N/m vs. 600 N/m) under three friction conditions (0, 0.25, and 0.5). Discrimination accuracy declined with increasing friction. These findings provide valuable insights for designing haptic interfaces and virtual reality applications, enabling developers to optimize user experiences by accounting for the interplay between friction and perceived softness.}, archive = {J_ACCESS}, author = {Hongbo Wang and Shogo Okamoto}, doi = {10.1109/ACCESS.2025.3594240}, journal = {IEEE Access}, pages = {139275-139283}, shortjournal = {IEEE Access}, title = {Softness perception interfered by friction in gliding virtual surfaces}, volume = {13}, year = {2025}, }
• (2025). AG-Net3D: Embedding attention gate into U-net for 3D seismic data fault detection. ACCESS, 13, 139267-139274. (www)

  @article{ , abstract = {Automatic fault detection of seismic data plays a remarkable role in ensuring the efficiency even effectiveness of oil and gas field exploration. Deep learning methods such as U-Net are becoming competitive alternatives for traditional ones such as coherency and curvature. However, conventional U-Net may cause the loss of underlying features of faults, resulting in poor performance of complex field data in terms of details. Besides, the open data set we obtained is insufficient to train an excellent deep model. In this paper, we propose a new approach called AG-Net3D. First, AG-Net3D incorporates Attention Gate into the decoders of basic U-Net structure, allowing the network to focus more attention on the region where faults are located, effectively improving the accuracy of fault detection. Second, for the high imbalance between fault and non-fault in the image, we propose an improved BCE loss function to regulate the weights of the fault and non-fault images and increase reasonable attention to fault. Moreover, to increase the volume and diversity of training data, we synthesize images based on the given ones by changing the dip angle, amplitude, etc. Experiments are undertaken on synthetic and field data in comparison with several popular methods. Results show that our model has stronger noise resistance, higher generalization ability, higher accuracy and better fault continuity.}, archive = {J_ACCESS}, author = {Mingchun Chen and Hui Cao and Luofei Jia}, doi = {10.1109/ACCESS.2025.3596339}, journal = {IEEE Access}, pages = {139267-139274}, shortjournal = {IEEE Access}, title = {AG-Net3D: Embedding attention gate into U-net for 3D seismic data fault detection}, volume = {13}, year = {2025}, }
• (2025). Adaptive control strategy for power flow controller with disturbance mitigation using discrete wavelet transform in renewable energy source integrated railway systems. ACCESS, 13, 139256-139266. (www)

  @article{ , abstract = {The growing direct integration of renewable energy sources (RES) into railway power networks has increased their vulnerability to power quality (PQ) disturbances and dynamic variations. To address these challenges, a novel control strategy for the railway power flow controller (RPFC) to increase its capacity for compensation and dynamic performance is presented. An adaptive discrete wavelet transform-based control scheme, incorporated into a twofold instantaneous reactive power method (ADW-TIRP) is proposed to enhance RPFC performance under fluctuating operating conditions. The proposed adaptive method is designed to operate effectively with various traction substation transformer configurations, including the main ones V/V and Scott transformers. The key advantages of the ADW-TIRP approach lie in its superior suppression of a wide spectrum of harmonic components, its robust dynamic response against time-varying train loads, and its enhanced robustness against disturbances caused by the integration of RESs into the railway network. The suggested control strategy’s efficacy is demonstrated through MATLAB/SIMPOWER simulations for different scenarios, validating its capability to improve PQ and system stability under different disturbances situations. Simulation results show that the proposed ADW-TIRP method consistently maintains low THD, significantly reduces negative-sequence currents, and outperforms conventional methods such as TLPF and VFR-based strategies in transient immunity, harmonic suppression, and current balancing, confirming its suitability for modern, large-scale electrified railway systems.}, archive = {J_ACCESS}, author = {Hamed Jafari Kaleybar and Morris Brenna and Mariacristina Roscia}, doi = {10.1109/ACCESS.2025.3594572}, journal = {IEEE Access}, pages = {139256-139266}, shortjournal = {IEEE Access}, title = {Adaptive control strategy for power flow controller with disturbance mitigation using discrete wavelet transform in renewable energy source integrated railway systems}, volume = {13}, year = {2025}, }
• (2025). Collision constraints and diffeomorphisms: Dealing with physics in deformable image registration. ACCESS, 13, 139236-139255. (www)

  @article{ , abstract = {Discontinuous displacements pose a substantial challenge to image registration algorithms, which generally assume smooth deformations. Two prominent cases occur in 4DCT lung scans. The first instance exists at the lung-thorax-boundary, where due to high elasticities and expansion properties, lungs exhibit higher displacements than the surrounding thoracic cavity, leading to sliding motions along their interface. Discontinuous motion is facilitated through a piecewise registration framework, in which inter-domain boundary interactions are constrained via collision detection, and intra-domain deformation smoothness is governed by the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. The second case with discontinuous displacements is found between lung lobes along the separating lung fissures. A fissure reconstruction algorithm for sparse segmentation is presented, improving geometric lung representation. Utilizing a refined Free-Form Deformation model for lobe-on-lobe registration that permits lobar sliding and prevents self-intersection, this approach examines discontinuities around the pulmonary fissures. Both cases are evaluated on synthetic and medical data, with boundary inconsistency results and average registration metrics on the DIRLAB dataset comparable to other approaches addressing sliding motion.}, archive = {J_ACCESS}, author = {Thomas Alscher and Jens Petersen and François Lauze and Kenny Erleben and Sune Darkner}, doi = {10.1109/ACCESS.2025.3588760}, journal = {IEEE Access}, pages = {139236-139255}, shortjournal = {IEEE Access}, title = {Collision constraints and diffeomorphisms: Dealing with physics in deformable image registration}, volume = {13}, year = {2025}, }
• (2025). Algebraic expressions for complex deep learning systems modeling and analysis. ACCESS, 13, 139225-139235. (www)

  @article{ , abstract = {Many modern Deep Learning (DL) systems have achieved impressive state-of-the-art results by combining individual sub-systems, including foundation models, to form increasingly more complex DL systems. However, this growing complexity also introduces new challenges in effectively modeling and analyzing these systems. In this paper, we introduce a novel approach to represent complex DL systems using algebraic expressions, by decomposing them into sets of sub-systems arranged in parallel and serial configurations, allowing for abstract and efficient representations. We further show how these algebraic expressions can be readily used to compute specific performance metrics of DL systems by formulating specific operations on our algebraic expressions. Specifically, we present the operations for computing system inference time and accuracy. Finally, we present two practical real case studies addressing multi-class visual classification of retail products, demonstrating the effectiveness and advantages of using our methodology in modeling and analyzing these systems.}, archive = {J_ACCESS}, author = {Rhandall Valdez-Yepez and Bryan V. Piguave and Jennifer C. Pita and Andres G. Abad}, doi = {10.1109/ACCESS.2025.3595486}, journal = {IEEE Access}, pages = {139225-139235}, shortjournal = {IEEE Access}, title = {Algebraic expressions for complex deep learning systems modeling and analysis}, volume = {13}, year = {2025}, }
• (2025). DeSFAM: An adaptive eBPF and AI-driven framework for securing cloud containers in real time. ACCESS, 13, 139203-139224. (www)

  @article{ , abstract = {Containerized applications offer lightweight and scalable deployment but remain exposed to security risks due to a shared kernel. We present DeSFAM (Dynamic eBPF-driven Syscall Filtering and Anomaly Mitigation), a real-time security framework that enforces least-privilege syscall usage and detects behavioral anomalies. DeSFAM integrates: 1) hybrid syscall profiling through static analysis and dynamic eBPF tracing; 2) SyscallAD (System call Anomaly Detection), a low-latency anomaly detector combining Variational Autoencoder (VAE) and Isolation Forest (iForest); 3) contextual risk scoring based on MITRE ATT&CK mappings and CVE correlations; and 4) adaptive syscall enforcement using eBPF maps and LSM hooks. Evaluations using the DongTing dataset and real-world CVE attack scenarios show DeSFAM achieves 94% precision, 90% recall, sub-millisecond enforcement latency, and less than 1% performance overhead. DeSFAM effectively blocks privilege escalation, container escape attempts, and syscall injection attacks in modern container environments.}, archive = {J_ACCESS}, author = {Sehar Zehra and Hassan Jamil Syed and Fahad Samad and Ummay Faseeha}, doi = {10.1109/ACCESS.2025.3592192}, journal = {IEEE Access}, pages = {139203-139224}, shortjournal = {IEEE Access}, title = {DeSFAM: An adaptive eBPF and AI-driven framework for securing cloud containers in real time}, volume = {13}, year = {2025}, }
• (2025). Estimation and assessment of partial shading patterns in large PV farms using ANN algorithm. ACCESS, 13, 139189-139202. (www)

  @article{ , abstract = {The use of technology to optimize electricity from photovoltaic (PV) farms is growing. Partial shading often occurs in PV farms owing to cloud movement or dust on the PV modules. Therefore, estimating shading patterns is essential as preliminary information for subsequent power optimization. This paper proposed an artificial neural network (ANN) to estimate the pattern of partial shading conditions. Simulation using a 160kW peak PV farm consisting of 600 modules in a 20 series - 30 parallel configuration is conducted in this research. The proposed technique measures each string’s current and the substring’s voltages. The Matrix of the current and voltages represents the shaded area of the PV farm and is used as a dataset. When partial shading (PS) occurs on the PV farm, the ANN model receives input data from current and voltage. Then, the trained ANN processes the input to estimate shading patterns. Assessment of the estimation results was carried out to determine the shaded PV module ratio and the shading strength. Comparative analysis explains that the proposed method outperforms other techniques such as Multiple-output Support Vector Regression (M-SVR), ANN with Levenberg–Marquardt (LM) training, and conventional backpropagation networks. In a typical shading condition, the best estimation depicts the accuracy of the shaded PV module ratio as 97% and shading strength as 98%. While the low accuracy results in the shaded PV module ratio of 94% and shading strength of 96%. Thus, the proposed technique can satisfactorily estimate the pattern caused by clouds in large-scale PV farms.}, archive = {J_ACCESS}, author = {Efendi S Wirateruna and Mochamad Ashari and Dedet Candra Riawan}, doi = {10.1109/ACCESS.2025.3596268}, journal = {IEEE Access}, pages = {139189-139202}, shortjournal = {IEEE Access}, title = {Estimation and assessment of partial shading patterns in large PV farms using ANN algorithm}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Task scheduling in cloud Environment—Techniques, applications, and tools: A systematic literature review”. ACCESS, 13, 139188. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Task Scheduling in Cloud Environment—Techniques, Applications, and Tools: A Systematic Literature Review”).}, archive = {J_ACCESS}, author = {Olanrewaju L. Abraham and Md Asri Bin Ngadi and Johan Bin Mohamad Sharif and Mohd Kufaisal Mohd Sidik}, doi = {10.1109/ACCESS.2025.3596406}, journal = {IEEE Access}, pages = {139188}, shortjournal = {IEEE Access}, title = {Corrections to “Task scheduling in cloud Environment—Techniques, applications, and tools: A systematic literature review”}, volume = {13}, year = {2025}, }
• (2025). The effective evaluation of emotions in the visual emotion images using convolutional neural networks. ACCESS, 13, 139174-139187. (www)

  @article{ , abstract = {This paper develops a model for recognizing emotions in visual images. The integration of contrastive-center loss optimization is proposed in this paper. This effectively improves the recognition of emotions when training a convolutional neural network against the baseline. The proposed contrastive-center loss function optimizes deep neural networks by enhancing feature discriminability. This loss function includes two key components: intra-class compactness and inter-class separability. We have suggested controlling the impact of the inter-class separability on the loss function. Moreover, we suggest combining cross-entropy and contrastive-center loss to calculate the total loss. In addition, we have proposed to apply the dimensionality reduction (visualization) for interactive evaluation of how the objects in the test set are arranged and how this arrangement, as well as the classification as a whole, can be improved by choosing the best combination of the strength of contrastive-center loss impact on the total loss. The efficiency of the developed model improvements is examined on three datasets: WEBEmo, FI-8, and EmoSet-118K. Our research allows us to improve the performance of visual emotion classification: for the WEBEmo dataset by 1.6%, the FI-8 dataset by 2.2%, and for the EmoSet-118K dataset by 2.52% higher accuracies than the baseline case.}, archive = {J_ACCESS}, author = {Modestas Motiejauskas and Gintautas Dzemyda}, doi = {10.1109/ACCESS.2025.3596484}, journal = {IEEE Access}, pages = {139174-139187}, shortjournal = {IEEE Access}, title = {The effective evaluation of emotions in the visual emotion images using convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Analysis of the mental workload associated with the use of virtual reality technology as support in the higher educational model”. ACCESS, 13, 139173. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Analysis of the Mental Workload Associated With the Use of Virtual Reality Technology as Support in the Higher Educational Model”).}, archive = {J_ACCESS}, author = {Santiago Criollo-C and Jose Cerezo and Andrea Guerrero-Arias and Agariadne Dwinggo Samala and Soha Rawas and Sergio Luján-Mora}, doi = {10.1109/ACCESS.2025.3596405}, journal = {IEEE Access}, pages = {139173}, shortjournal = {IEEE Access}, title = {Corrections to “Analysis of the mental workload associated with the use of virtual reality technology as support in the higher educational model”}, volume = {13}, year = {2025}, }
• (2025). Distributed adaptive coding optimization for IoT using fulcrum code and model-agnostic meta-learning (MAML) in ultra-low latency environments. ACCESS, 13, 139155-139172. (www)

  @article{ , abstract = {The exponential growth of the Internet of Things (IoT) has amplified the demand for energy-conscious, low-latency, and efficient data transmission techniques. This study proposes an adaptive distributed coding scheme combining Fulcrum Code and Model-Agnostic Meta-Learning (MAML) to optimize IoT communications in ultralow-latency environments. Fulcrum Code improves packet resilience by adjusting redundancy to channel conditions, while MAML optimizes coding parameters in real-time to improve transmission efficiency. FEC and HARQ further balance error correction and retransmission overhead. Simulations show significant reductions in transmission time and energy consumption, particularly in high-packet-loss scenarios. This approach ensures robust performance in resource-constrained IoT devices, preserving battery life while supporting critical applications such as smart cities, Industry 4.0, and the tactile Internet. By optimizing trade-offs between resilience and efficiency, this scheme advances scalable and energy-efficient communication for next-generation IoT systems.}, archive = {J_ACCESS}, author = {Yair Rivera Julio and Ángel Pinto and Rodrigo García and Jose Aguilar and Nelson A. Pérez-García}, doi = {10.1109/ACCESS.2025.3569165}, journal = {IEEE Access}, pages = {139155-139172}, shortjournal = {IEEE Access}, title = {Distributed adaptive coding optimization for IoT using fulcrum code and model-agnostic meta-learning (MAML) in ultra-low latency environments}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning meets custom autoencoder classifier: A semi-supervised approach for encrypted traffic anomaly detection. ACCESS, 13, 139141-139154. (www)

  @article{ , abstract = {The widespread adoption of encryption in computer networks has made detecting malicious traffic, especially at network perimeters, increasingly challenging. As packet contents are concealed, traditional monitoring techniques such as Deep Packet Inspection (DPI) become ineffective. Consequently, researchers have started employing data-driven methods based on Machine and Deep Learning (ML & DL) to identify malicious behavior even from encrypted traffic, typically within Anomaly-based Network Intrusion Detection Systems (A-NIDS). Existing approaches rely heavily on supervised learning, which requires large volumes of labeled benign and malicious traffic. Generating these labels is time-consuming, error-prone, and often requires expert knowledge. In this paper, we propose a semi-supervised learning framework that leverages Self-Supervised Learning (SSL) to learn discriminative representations from unlabeled network traffic. We design a novel pretext task that predicts important masked features, enabling the model to capture meaningful structure in the data. The learned representations are fine-tuned with minimal labeled data using a Custom-Autoencoder (Custom-AE) classifier. Experimental results show that the representation learned from our proposed pretext task outperforms the best competing method in terms of accuracy by 3.41% on UNSW-NB15 (NB15) and 1.53% on CSE-CIC-IDS2018 (CSE18) when evaluated using linear probing. When fine-tuned with the Custom-AE on only 100 benign and 10 malicious samples, it achieves 83.51% (NB15) and 87.43% (CSE18) accuracy, representing gains of 4.55% and 5.08% over the initial features, respectively. This demonstrates stronger suitability for label-scarce real-world scenarios compared to existing approaches.}, archive = {J_ACCESS}, author = {A. Ramzi Bahlali and Abdelmalik Bachir and Abdeldjalil Labed}, doi = {10.1109/ACCESS.2025.3596179}, journal = {IEEE Access}, pages = {139141-139154}, shortjournal = {IEEE Access}, title = {Self-supervised learning meets custom autoencoder classifier: A semi-supervised approach for encrypted traffic anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Optimized AlexNet pruning for edge-based medical diagnostics. ACCESS, 13, 139129-139140. (www)

  @article{ , abstract = {Medical diagnostics demand rapid and accurate disease detection to ensure timely treatment, directly impacting human lives. Deep neural networks (DNNs) have shown unparalleled success in medical applications, surpassing traditional methods. However, their computational and memory requirements often exceed the capabilities of resource-constrained edge devices, such as mobile and hand held platforms, limiting their deployment in real-world scenarios. To address this, model compression techniques have emerged as a key solution. This paper focuses on pruning AlexNet to significantly reduce its size. The reduced network effectively processes the PAD-UFES-20 dataset, distinguishing melanoma, a serious type of skin cancer, from benign skin lesions, while maintaining high classification accuracy. A meticulous layer-by-layer investigation evaluates magnitude-based pruning at varying ratios, analyzing its effects on accuracy and resource efficiency. The results reveal a clear difference between fully connected (FC) and convolutional layers: pruning FC layers substantially reduces memory consumption, while pruning convolutional layers significantly boosts inference speed. Careful analysis resulted in reaching 91% pruning, which resulted in accuracy dropping from 99.16% to 97.48%, with big reduction in memory usage. This balance demonstrates the feasibility of deploying pruned DNNs for real-time, edge-based medical diagnostics without compromising diagnostic precision. Furthermore, the proposed pruning approach was also applied to the full PAD-UFES-20 dataset across all classes, yielding similar results to the two-class (melanoma vs. benign) case. This work provides a robust framework for optimizing DNNs for resource-constrained environments, offering valuable insights for designing efficient, practical models in medical applications.}, archive = {J_ACCESS}, author = {Yasser A. Amer and Hassan I. Saleh and Omar A. Nasr}, doi = {10.1109/ACCESS.2025.3593453}, journal = {IEEE Access}, pages = {139129-139140}, shortjournal = {IEEE Access}, title = {Optimized AlexNet pruning for edge-based medical diagnostics}, volume = {13}, year = {2025}, }
• (2025). A robust adaptive signal decomposition method for enhanced mode extraction in financial time series. ACCESS, 13, 139110-139128. (www)

  @article{ , abstract = {Accurately forecasting energy commodity prices and financial time series has long been a challenge for policymakers and energy market participants. Various existing decomposition techniques were introduced in this scenario, like EMD, EEMD, and CEEMDAN but these techniques in the majority of the cases perform poorly in cases when a time series data exhibits irregular fluctuations and has a trend. This study introduces a novel decomposition, the Robust Adaptive Decomposition (RAD) technique, for time-series data forecasting. As its name implies robust means not duly affected by the presence of outliers, here Adaptive means having the ability to change to suit changing conditions and in signal processing decomposition is the process of dividing a signal into its subcomponents. So, this novel technique can decompose complex time-series signals like stock prices or Brent Oil prices. The RAD technique consists of several steps (i) choosing appropriate weights for each value of the dataset (ii) finding the weighted mean of adjacent points (iii) connecting these weighted points with cubic spline (iv) performing sifting as given in methodology (v) stop sifting when stopping criteria reach. The performance of RAD-ARIMA and RAD-LSTM was compared with six hybrid models such as EMD-ARIMA, EMD-LSTM, EEMD-ARIMA, EEMD-LSTM, CEEMDAN-ARIMA, CEEMDAN-LSTM, this novel technique outperformed the other six hybrid models for the simulated scenarios and a real-world data set of daily Brent Spot prices. Also, the RAD technique copes with the problem of model fitting and the labor involved with the classical decomposition techniques.}, archive = {J_ACCESS}, author = {Laiba Sultan Dar and Muhammad Aamir and Muhammad Hamraz and Nosheen Faiz and Walid Emam and Yusra Tashkandy}, doi = {10.1109/ACCESS.2025.3596524}, journal = {IEEE Access}, pages = {139110-139128}, shortjournal = {IEEE Access}, title = {A robust adaptive signal decomposition method for enhanced mode extraction in financial time series}, volume = {13}, year = {2025}, }
• (2025). Uncovering the uncertainties and variability of travel time and fuel consumption using floating car data: A case study in wuhan. ACCESS, 13, 139098-139109. (www)

  @article{ , abstract = {Understanding the probabilistic behavior of both travel time and fuel consumption is critical for improving urban traffic efficiency and developing reliable urban transportation systems. While previous studies have extensively examined travel time distributions, the joint distributional patterns of fuel consumption remain underexplored, limiting the effectiveness of green routing strategies. A novel data-driven framework, integrating map-matching, second-by-second trajectory interpolation, and microscopic fuel consumption estimation (CMEM), is developed for jointly analyzing the distributions and spatiotemporal variability characteristics of link-level travel time and fuel consumption using floating car data (FCD) in Wuhan, China. The results reveal that hourly travel time follows a Lognormal distribution for over 80% of road links. In comparison, fuel consumption is best represented by a Gamma distribution in more than 30% of links. Peak-hour analyses (08:00 and 18:00) indicate substantial increases in both the mean and 90th-percentile values of the two metrics, reflecting heightened uncertainty during congestion. Coefficients of variation are consistently higher on weekdays than on weekends, emphasizing increased variability in urban mobility. Hot spot analysis further shows that areas with dense traffic signals and commercial activities tend to form clusters of high travel time and fuel consumption. These findings highlight the necessity of incorporating reliability and energy efficiency considerations into urban traffic management and eco-routing navigation systems.}, archive = {J_ACCESS}, author = {Wenxin Teng and Fei Liu and Jianbing Yang and Chaoyang Shi and Yunfei Zhang and Fuqiang Wang}, doi = {10.1109/ACCESS.2025.3596034}, journal = {IEEE Access}, pages = {139098-139109}, shortjournal = {IEEE Access}, title = {Uncovering the uncertainties and variability of travel time and fuel consumption using floating car data: A case study in wuhan}, volume = {13}, year = {2025}, }
• (2025). Application of metalines to a low-profile antenna array for CP multibeam generation in the elevation plane. ACCESS, 13, 139090-139097. (www)

  @article{ , abstract = {A novel application of metalines to a low-profile antenna array is presented, enabling circularly polarized (CP) multiple beams within the elevation plane. The realization is based on five key specifications at the design frequency of $f_{\mathrm {D}} =3$ GHz: a low-profile antenna structure of less than 0.05 wavelengths, beam directions at elevation angles of approximately $\theta = - 20^{\circ }$ , 0°, +20°, and +40°, a gain of at least 7 dBic, an axial ratio not exceeding 3 dB in the beam direction, and a reflection coefficient scattering parameter below −10 dB. The realization process consists of three main steps. First, dispersion diagrams are created for four types of CP cells (U1, U2, U3, and U4) in compliance with the specified low-profile structure and beam directions at $f_{\mathrm {D}}$ . Second, four metalines M1, M2, M3, and M4 are individually constructed using U1, U2, U3, and U4, respectively, and their radiation characteristics are analyzed to determine the number of cells that meet the specified gain characteristics. Third, the individually constructed metalines are integrated into an antenna array, and simulations are conducted to optimize the space between neighboring metalines, considering the specified axial ratio. Note that the simulated performance, which meets all specifications, is validated through measurements of a fabricated metaline antenna array.}, archive = {J_ACCESS}, author = {Hisamatsu Nakano and Tomoki Abe}, doi = {10.1109/ACCESS.2025.3595218}, journal = {IEEE Access}, pages = {139090-139097}, shortjournal = {IEEE Access}, title = {Application of metalines to a low-profile antenna array for CP multibeam generation in the elevation plane}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Hybrid approach of cotton disease detection for enhanced crop health and yield”. ACCESS, 13, 139089. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield”).}, archive = {J_ACCESS}, author = {Rahul Kumar and Ashok Kumar and Karamjit Bhatia and Kottakkaran Sooppy Nisar and Siddharth Singh Chouhan and Priti Maratha and Anoop Kumar Tiwari}, doi = {10.1109/ACCESS.2025.3596404}, journal = {IEEE Access}, pages = {139089}, shortjournal = {IEEE Access}, title = {Corrections to “Hybrid approach of cotton disease detection for enhanced crop health and yield”}, volume = {13}, year = {2025}, }
• (2025). The polarization and impedance controlled car: An interactive tool for teaching electromagnetic principles. ACCESS, 13, 139080-139088. (www)

  @article{ , abstract = {The Polarization and Impedance Controlled Car (PICC) presents an innovative approach for demonstrating and teaching key principles of electromagnetic field theory, specifically polarization and impedance. Designed as an interactive learning tool, the PICC utilizes a radio-controlled car, NanoVNA, Raspberry Pi, and handheld antennas to visualize real-time effects of polarization and impedance. The system incorporates two orthogonal linearly polarized patch antennas on a shared ground plane connected to a NanoVNA. A strip grid is utilized to affect the reflection and transmission properties of the two antennas, which in turn enables controlled car movements. This paper details the design, measurements, and simulations involved in creating the PICC and introduces an open-source Python library for NanoVNA data streaming. The PICC illustrates a practical application of electromagnetic principles potentially providing a more engaging learning experience for undergraduate students.}, archive = {J_ACCESS}, author = {Teo Bergkvist and Otto Edgren and Oscar Gren and Måns Jacobsson and Christian Nelson and Johan Lundgren}, doi = {10.1109/ACCESS.2025.3595273}, journal = {IEEE Access}, pages = {139080-139088}, shortjournal = {IEEE Access}, title = {The polarization and impedance controlled car: An interactive tool for teaching electromagnetic principles}, volume = {13}, year = {2025}, }
• (2025). A family of almost MDS symbol-pair codes of length 8p. ACCESS, 13, 139071-139079. (www)

  @article{ , abstract = {In high-density data storage systems, symbol-pair codes are commonly used to prevent symbol-pair errors. Designing maximum distance separable (MDS) codes is crucial in symbol-pair coding theory because MDS symbol-pair codes are the best at meeting the Singleton bound, which is a measure of their effectiveness. However, obtaining MDS codes is challenging due to the difficulty of meeting the strict requirements of the Singleton bound. As an alternative, almost maximum distance separable (AMDS) symbol-pair codes are explored since they are not as perfect as MDS codes according to the Singleton bound. In this paper, we introduce a new family of AMDS symbol-pair cyclic codes of length $8p$ for every prime number $p \equiv 1 \mod 8$ . This results in a useful collection of AMDS symbol-pair cyclic codes applicable directly in various real-world scenarios.}, archive = {J_ACCESS}, author = {Hai Q. Dinh and Bac T. Nguyen and Hiep L. Thi and Woraphon Yamaka}, doi = {10.1109/ACCESS.2025.3590462}, journal = {IEEE Access}, pages = {139071-139079}, shortjournal = {IEEE Access}, title = {A family of almost MDS symbol-pair codes of length 8p}, volume = {13}, year = {2025}, }
• (2025). Application of uncertain rényi entropy as a metric for risk in portfolio optimization. ACCESS, 13, 139062-139070. (www)

  @article{ , abstract = {This research introduces a novel multi-objective framework for portfolio optimization that integrates uncertainty theory with Rényi entropy as a risk metric. Unlike traditional models that operate within a probability space and rely on historical data— a challenge for public investors— our approach leverages an uncertain space. Our primary contribution is the development of the Uncertain Rényi Entropy-Mean Portfolio Optimization (REMPO) model. This framework simultaneously maximizes uncertain returns and minimizes risk, which is quantified by Rényi entropy. By using Rényi entropy, a generalized form of other entropy measures with a flexible parameter “v”, our model more effectively captures the tail behavior of return distributions, a limitation of conventional metrics like variance. Three meta-heuristic optimization techniques are employed to tackle this uncertain Rényi entropy-mean portfolio optimization (REMPO) challenge. In conclusion, a comparative analysis with the Markowitz Model and another model utilizing Shannon entropy reveals that the portfolio derived from the suggested model consistently outperforms in terms of returns while exhibiting reduced risk across all specified parameters.}, archive = {J_ACCESS}, author = {Mahsa Mahmoodvand Gharahshiran and Gholamhossein Yari and Mohammad Hassan Behzadi}, doi = {10.1109/ACCESS.2025.3595489}, journal = {IEEE Access}, pages = {139062-139070}, shortjournal = {IEEE Access}, title = {Application of uncertain rényi entropy as a metric for risk in portfolio optimization}, volume = {13}, year = {2025}, }
• (2025). The application of digital twin technology in the evaluation of environmental design solutions. ACCESS, 13, 139053-139061. (www)

  @article{ , abstract = {As urbanization accelerates, the need for effective environmental design grows. This paper proposes the Digital Twin-Based Environmental Multi-Objective Optimization System (DTEMOS) to address challenges in environmental evaluation and optimization. By integrating multi-source heterogeneous data using tensor decomposition and deep neural networks, DTEMOS enables comprehensive environmental assessment. An adaptive evaluation framework based on meta-learning and reinforcement learning is introduced, alongside a value-aligned reward mechanism. A dynamic weight adjustment system enhances optimization flexibility, while human-in-the-loop feedback incorporates expert judgment. The proposed system provides a robust and automated approach to optimizing environmental design.}, archive = {J_ACCESS}, author = {Haoxuan Feng and Rongbing Mu and Yue Cheng}, doi = {10.1109/ACCESS.2025.3594374}, journal = {IEEE Access}, pages = {139053-139061}, shortjournal = {IEEE Access}, title = {The application of digital twin technology in the evaluation of environmental design solutions}, volume = {13}, year = {2025}, }
• (2025). Integrated reliability assessment and optimal reconfiguration of islanded microgrids under load growth using fick’s law optimization algorithm and long short-term memory technique. ACCESS, 13, 139033-139052. (www)

  @article{ , abstract = {This paper proposes an integrated framework for reliability assessment, load growth evaluation, and optimal reconfiguration of islanded microgrids under increasing electricity demand. The studied microgrid consists of solar photovoltaic (PV) panels, wind turbines, and diesel generators, operating independently from the main grid. First, as load demand grows over time, the microgrid must be reassessed to ensure that reliability metrics such as Loss of Load Probability (LOLP), Energy Not Supplied (ENS), and Loss of Power Supply Probability (LPSP) remain within acceptable limits. Second, a maximum allowable load growth is determined under current generation capacity constraints. Third, a long-term load forecasting model based on the Long Short-Term Memory (LSTM) technique is developed to predict future demand accurately. Fourth, if reliability thresholds are violated, the microgrid is reconfigured by optimally expanding generation capacity, focusing on diesel generators and battery storage units. To address the complex, mixed-variable nature of the reconfiguration problem, the Fick’s Law Optimization (FLO) algorithm is proposed. The FLO algorithm is compared with other metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) algorithm, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Artificial Bee Colony (ABC) algorithm, Grey Wolf Optimizer (GWO) algorithm, Equilibrium Optimizer (EO) algorithm, Runge-Kutta Optimizer (RUN) algorithm, and Honey Badger Algorithms (HBA) demonstrating superior performance in convergence speed, exploration–exploitation balance, and global optimality. Numerical results validate the proposed approach, confirming its effectiveness in ensuring reliable and cost-efficient microgrid operation under load growth scenarios.}, archive = {J_ACCESS}, author = {Hieu M. Pham and Duy C. Huynh and Loc D. Ho and Matthew W. Dunnigan and Corina Barbalata}, doi = {10.1109/ACCESS.2025.3596185}, journal = {IEEE Access}, pages = {139033-139052}, shortjournal = {IEEE Access}, title = {Integrated reliability assessment and optimal reconfiguration of islanded microgrids under load growth using fick’s law optimization algorithm and long short-term memory technique}, volume = {13}, year = {2025}, }
• (2025). Analysis of multivariate indoor building data: A comparative study of time-series clustering methods. ACCESS, 13, 139018-139032. (www)

  @article{ , abstract = {Clustering multivariate time-series data is crucial for uncovering complex temporal patterns in dynamic environments, such as building indoor conditions and behavior where variables like temperature, humidity, and CO2 concentration evolve simultaneously. This study conducts a comparison of the performance of six clustering techniques: KMeans with Euclidean and Dynamic Time-Warping (DTW) distances, HDBSCAN, KShapes, Self-Organizing Maps (SOM), and Bi-Clustering combined with different normalization methods, applied to real-world indoor sensor datasets. Results show that applying a first-order derivative transformation notably improved clustering quality across all methods by highlighting temporal dynamics. Bi-Clustering and KMeans (Euclidean) consistently outperformed others, achieving higher Silhouette scores, Calinski-Harabasz indices, and lower Davies-Bouldin scores. These findings underscore the necessity of multivariate time-series clustering in capturing latent dependencies across variables and time-relevant data that are often missed in univariate analyses. Moreover, incorporating derivative-based preprocessing enhances the ability of clustering algorithms to distinguish subtle temporal dynamics, making this approach highly relevant for applications in smart building monitoring and environmental control.}, archive = {J_ACCESS}, author = {Quoc-Dung Ngo and Ly-Huynh Phan}, doi = {10.1109/ACCESS.2025.3596297}, journal = {IEEE Access}, pages = {139018-139032}, shortjournal = {IEEE Access}, title = {Analysis of multivariate indoor building data: A comparative study of time-series clustering methods}, volume = {13}, year = {2025}, }
• (2025). Challenges related to approximating the energy consumption of a website. ACCESS, 13, 139001-139017. (www)

  @article{ , abstract = {Based on a rough estimate, the ICT industry consumes 7 to 10% of the world’s energy, and around 70% of this is related to usage while the rest can be considered embodied due to manufacturing, logistics, and related activities. Simultaneously, the web has become an important channel for consuming ICT services as it is the largest available application platform globally. Especially energy consumption of mobile web applications has been studied in detail, but there is a clear research gap for web applications because suitable measurement tooling has not been available earlier. The purpose of this article is to review the current state of the art and understand how to approximate the energy consumption of web applications effectively by measuring an existing website that has been implemented with two different web frameworks—Qwik and Next.js. Our main findings indicate that although services that approximate energy consumption of web applications exist, they tend to overestimate consumption when compared to our measurements and they may even show contradictory results between different web frameworks. Further, we found that Firefox Profiler can be used to measure energy consumption with high precision. We also found that Website Carbon service fails to acknowledge techniques, such as lazy rendering, and there were open questions related to hosting detection (green or not) while the service was not transparent in calculating the results—not disclosing intermediary results or exposing the scope of the calculation. Our key recommendation is to use CPU-based measurement methods in estimating web energy consumption.}, archive = {J_ACCESS}, author = {Janne Kalliola and Juho Vepsäläinen}, doi = {10.1109/ACCESS.2025.3596459}, journal = {IEEE Access}, pages = {139001-139017}, shortjournal = {IEEE Access}, title = {Challenges related to approximating the energy consumption of a website}, volume = {13}, year = {2025}, }
• (2025). Least to most range (LMR): A novel consistent weighting multicriteria method applied to green supplier selection. ACCESS, 13, 138986-139000. (www)

  @article{ , abstract = {Decision-making is a complex task for people, professionally or personally, since the decision has several positive and negative consequences and cannot always be undone. Multicriteria decision-making models (MCDM) aim to reduce the complexity of decision-making by using mathematical and algorithmic procedures to guide the process. Given the various issues and complexities that current methods for weighting criteria present; this study aims to develop a new subjective multicriteria method for criteria weighting applied in Green Supplier Selection (GSS). To solve this GSS problem, the Least to Most Range (LMR) method was developed and applied. The LMR is characterized by presenting a new logic for weighting criteria using two distinct and complementary scales. This method has a series of advantages over some well-established methods (e.g. AHP, BWM) in the literature. To illustrate these advantages, a numerical application was developed in GSS containing a literature review to survey relevant criteria for the problem. The LMR has proven robust compared to other methods because it can treat many criteria simultaneously without presenting inconsistency, satisfactorily dealing with some of the problems most commonly found in weighting methods in the literature.}, archive = {J_ACCESS}, author = {Vinicius Moretti and Everton L. Melo and João Carlos Colmenero}, doi = {10.1109/ACCESS.2025.3596799}, journal = {IEEE Access}, pages = {138986-139000}, shortjournal = {IEEE Access}, title = {Least to most range (LMR): A novel consistent weighting multicriteria method applied to green supplier selection}, volume = {13}, year = {2025}, }
• (2025). AMNED: An efficient framework for spiking neuron coding in AirComp federated learning. ACCESS, 13, 138970-138985. (www)

  @article{ , abstract = {This paper advances ACFL technologies by proposing the Adaptive Memristor Neuron Encoding-Decoding (AMNED) framework for AirComp Federated Learning (ACFL), enabling efficient, privacy-preserving model aggregation optimized for resource-constrained wireless environments. In advancing future ACFL technologies, Over-the-Air Computation (AirComp) has emerged as a groundbreaking innovation. AirComp Federated Learning (ACFL) integrates AirComp with federated learning, transforming distributed machine learning by enhancing data privacy and leveraging network device computation. The ACFL employs a novel aggregation technique leveraging the superposition property of wireless signals, enabling simultaneous model updates from multiple devices. This reduces communication overhead and alleviates computational burdens at central aggregation points. Despite these advantages, challenges such as device location variability, channel quality instability, and limited computational and energy resources persist. Motivated by these challenges, we introduce the AMNED framework to address these issues. By integrating spiking neural networks with memristive techniques, the proposed framework significantly improves signal reconstruction accuracy and energy utilization. This paper proposes the AMNED framework for ACFL to support efficient and secure model aggregation in ACFL networks. The framework combines spiking neural networks with memristor-based techniques to improve signal reconstruction and reduce energy use. Experiments in an ACFL with SNRs and interference from 10 devices show that AMNED achieves 79.3% communication efficiency, compared to 73.2% for the Stepwise Forward algorithm. It reaches an MMSE of 0.3044 and converges 7% faster in a toy FL task using synthetic gradients. A dynamic threshold adjustment improves efficiency by 2% at 10 dB SNR, and a hybrid temporal-rate encoding method lowers MMSE by 5% under high interference, tested on MNIST data. AMNED accurately reconstructs signals from spike sequences, enabling stable model updates. Its low energy use makes it suitable for edge devices and large-scale federated learning. Additionally, our proposed framework accurately reconstructs signals from spike sequences, ensuring precise model updates. Consequently, its energy efficiency makes it ideal for energy-constrained devices and scalable federated learning.}, archive = {J_ACCESS}, author = {Juncheng Ji and Chan-Tong Lam and Ke Wang and Benjamin K. Ng}, doi = {10.1109/ACCESS.2025.3595270}, journal = {IEEE Access}, pages = {138970-138985}, shortjournal = {IEEE Access}, title = {AMNED: An efficient framework for spiking neuron coding in AirComp federated learning}, volume = {13}, year = {2025}, }
• (2025). Tabular data augmentation using artificial intelligence: A systematic review and taxonomic framework. ACCESS, 13, 138950-138969. (www)

  @article{ , abstract = {Context: Tabular data predominate in machine learning applications; however, data scarcity, class imbalance, and privacy-related constraints often impair the model performance. Therefore, AI-centric data-synthesis techniques have been adopted to mitigate these challenges. Objective: To systematically map the state of the art in AI-driven tabular-data augmentation, identifying trends, methodological gaps, and best practices. Method: The review followed Kitchenham’s guidelines and covered the ACM Digital Library, Compendex, IEEE Xplore, ScienceDirect, and Scopus for the period 2020–2024. After deduplication and application of the inclusion and exclusion criteria, 55 primary studies were selected and analyzed with respect to the eight research questions. Results: Of the 55 studies, 210 quantitative results were extracted: 70.95% employed utility metrics, and only 7.14% assessed privacy. To organize this heterogeneous landscape, we propose a taxonomic framework: solution type $\times $ evaluation $\times $ metric, highlighting gaps and redundancies. Conclusions: Although research has progressed from conventional synthesizers to hybrid and novel architectures, metric standardization and systematic privacy assessments remain limited. Future work should address these gaps and apply fidelity and privacy metrics to underrepresented tasks such as regression problems and ultra-rare-class datasets.}, archive = {J_ACCESS}, author = {Mauro Henrique Lima de Boni and Iwens Gervasio Sene Junior and Ronaldo Martins da Costa}, doi = {10.1109/ACCESS.2025.3593449}, journal = {IEEE Access}, pages = {138950-138969}, shortjournal = {IEEE Access}, title = {Tabular data augmentation using artificial intelligence: A systematic review and taxonomic framework}, volume = {13}, year = {2025}, }
• (2025). SAFH-net: A hybrid network with shuffle attention and adaptive feature fusion for enhanced retinal vessel segmentation. ACCESS, 13, 138932-138949. (www)

  @article{ , abstract = {Segmenting retinal blood vessels is critical for the early detection of retinal abnormalities. While significant progress has been achieved in vessel segmentation through deep learning techniques, existing methodologies still struggle with the effectiveness of extracting and integrating local-global features. To overcome these challenges, this paper introduces SAFH-Net, a hybrid end-to-end network architecture that synergistically integrates Swin Transformer and CNN with innovative shuffle attention mechanisms and adaptive feature fusion. Specifically, a parallel encoder architecture employs a Convolutional (Conv) block with a residual structure for local feature extraction alongside a hierarchical Swin Transformer with Shifted-Window Multi-head Self-Attention (SW-MSA) for global context modeling, thereby achieving comprehensive feature capture with minimal additional parameter overhead. Then, an improved Spatial Attention Feature Fusion (SAFF) module is used to enable pixel-level adaptive weighting for optimal local-global feature integration. Additionally, cross-channel and cross-spatial shuffle operations enhance the interaction between local details and global information efficiency while suppressing redundant information. We achieved accuracies of 97.28%, 97.55%, and 97.60% on the DRIVE, STARE, and CHASE_DB1 datasets, respectively. A series of experimental results demonstrates that the proposed model significantly outperforms other advanced methods in segmentation performance.}, archive = {J_ACCESS}, author = {Yang Zhou Ling Ou and Joon Huang Chuah and Hua Nong Ting and Shier Nee Saw and Jun Zhao}, doi = {10.1109/ACCESS.2025.3596113}, journal = {IEEE Access}, pages = {138932-138949}, shortjournal = {IEEE Access}, title = {SAFH-net: A hybrid network with shuffle attention and adaptive feature fusion for enhanced retinal vessel segmentation}, volume = {13}, year = {2025}, }
• (2025). Dual-wideband MIMO antenna using characteristic mode theory for mobile terminal applications. ACCESS, 13, 138914-138931. (www)

  @article{ , abstract = {This paper presents a minimum-profile dual-wideband MIMO antenna. Modal analysis using the Theory of Characteristic Modes (TCM) was applied to improve the design through a series of minor modifications to the terminal chassis. A hybrid mode-tracking algorithm was applied to benefit all possible radiating modes in both frequency bands. The phase of the normalized envelope correlation coefficient (NECC) was studied to make the two ports excite the same modes while maintaining minimum coupling and correlation. As a proof of the concept, a dual-wideband (0.8-1GHz, 2.4-3GHz), dual-port MIMO antenna was designed and fabricated on a 150 mm $\times 76$ mm chassis. A simple matching network was designed to match the two ports over the entire frequency range. The impedance matching was less than −6 dB, the isolation between the two adjacent ports was over 12.8 dB, the correlation coefficient between the two adjacent ports $\rho _{S}$ was less than 0.07, the correlation coefficient between the radiation patterns resulting from each port $\rho _{3D}$ was less than 0.185, the average total efficiency was 84%, the peak realized gain was 4.38 dB, and the diversity performance was good, over the entire frequency range. Moreover, two reconfigurable resonant antennas were added to the two short edges of the chassis. By controlling the variable capacitor of a varactor diode and the state of a PIN diode, each resonant antenna can scan the entire upper or lower frequency bands.}, archive = {J_ACCESS}, author = {Ali Mohammad and Ali M. Hassan}, doi = {10.1109/ACCESS.2025.3596502}, journal = {IEEE Access}, pages = {138914-138931}, shortjournal = {IEEE Access}, title = {Dual-wideband MIMO antenna using characteristic mode theory for mobile terminal applications}, volume = {13}, year = {2025}, }
• (2025). Quantitative analysis of operator influence on IEC 61000-4-2 indirect ESD testing and recommendations for enhanced reproducibility. ACCESS, 13, 138900-138913. (www)

  @article{ , abstract = {The reproducibility of system-level electrostatic discharge (ESD) testing per IEC 61000-4-2 remains challenged by waveform variability, notably in the second peak region, partly due to human operator influence. Although recent Standard revisions introduce parameters like ${\mathrm {I}}_{\mathbf {P2}}$ , operator effects during indirect discharges are still unaddressed. This study examines human presence impacts on ESD waveforms during indirect discharges to horizontal and vertical coupling planes, using a SpaceWire link as the device under test. Injected and shield currents were measured under various operator conditions, alongside tests in operator-free setups with different grounding cable arrangements and protective bodywear. Results confirm significant operator-induced waveform alterations. While operator-free setups reduce variability, grounding cable layout remains crucial. Standardized, operator-free configurations with defined grounding are recommended. Where manual operation is unavoidable, ESD gloves and shoes partially improve test consistency.}, archive = {J_ACCESS}, author = {Panagiotis K. Papastamatis and Eleni P. Nicolopoulou and Christos A. Christodoulou and Ioannis F. Gonos}, doi = {10.1109/ACCESS.2025.3596287}, journal = {IEEE Access}, pages = {138900-138913}, shortjournal = {IEEE Access}, title = {Quantitative analysis of operator influence on IEC 61000-4-2 indirect ESD testing and recommendations for enhanced reproducibility}, volume = {13}, year = {2025}, }
• (2025). OcularAge: A comparative study of iris and periocular images for pediatric age estimation. ACCESS, 13, 138883-138899. (www)

  @article{ , abstract = {Estimating a child’s age from ocular biometric images is challenging due to subtle physiological changes and the limited availability of longitudinal datasets. Although most biometric age estimation studies have focused on facial features and adult subjects, pediatric-specific analysis, particularly of the iris and periocular regions, remains relatively unexplored. This study presents a comparative evaluation of iris and periocular images for estimating the ages of children aged between 4 and 16 years. We utilized a longitudinal dataset comprising more than 21,000 near-infrared (NIR) images, collected from 288 pediatric subjects over eight years using two different imaging sensors. A multi-task deep learning framework was employed to jointly perform age prediction and age-group classification, enabling a systematic exploration of how different convolutional neural network (CNN) architectures, particularly those adapted for non-square ocular inputs, capture the complex variability inherent in pediatric eye images. The results show that periocular models consistently outperform iris-based models, achieving a mean absolute error (MAE) of 1.33 years and an age-group classification accuracy of 83.82%. These results mark the first demonstration that reliable age estimation is feasible from children’s ocular images, enabling privacy-preserving age checks in child-centric applications. This first longitudinal benchmark for pediatric ocular age estimation provides a foundation for designing robust, child-focused biometric systems. The developed models proved resilient across different imaging sensors, confirming their potential for real-world deployment. They also achieved inference speeds of less than 10 milliseconds per image on resource-constrained VR headsets, demonstrating their suitability for real-time applications.}, archive = {J_ACCESS}, author = {Naveenkumar G. Venkataswamy and Poorna Ravi and Stephanie Schuckers and Masudul H. Imtiaz}, doi = {10.1109/ACCESS.2025.3592879}, journal = {IEEE Access}, pages = {138883-138899}, shortjournal = {IEEE Access}, title = {OcularAge: A comparative study of iris and periocular images for pediatric age estimation}, volume = {13}, year = {2025}, }
• (2025). Non-negative matrix factorization and latent semantic analysis for hybrid feature selection: A proposed machine learning system for the detection of malicious executable files. ACCESS, 13, 138867-138882. (www)

  @article{ , abstract = {During a typical cyber-attack lifecycle, several key phases are involved, including footprinting and reconnaissance, scanning, exploitation, and covering tracks. The successful delivery of a payload lies at the heart of ensuring the effectiveness of cyberattacks, which is typically executed following the exploitation of vulnerabilities. This allows adversaries to gain backdoor access to their target and accomplish their objectives. With the increasing use of generative Artificial Intelligence (AI), adversaries are leveraging AI to enhance their attack strategies, ranging from creating more credible phishing attacks and social engineering tactics to developing advanced viruses that are delivered through various means such as phishing attacks. Efforts to devise AI techniques for the detection of malicious executable files have garnered significant attention in the research community. While numerous Machine Learning (ML) techniques have been proposed for this purpose, a significant challenge arises due to the memory requirements for storing the extracted features. These features, resembling unstructured vocabulary features in natural language processing, need to be converted into a rectangular matrix for input into a classification model during training. The resulting matrix is sparse and its size depends on the unique features extracted, leading to a substantial increase in memory requirements, posing a significant challenge. This research proposes a novel ML-based intrusion detection system designed for the detection of malicious executable files. The proposed system utilises each of Non-Negative Matrix Factorization (NMF) and Latent Semantic Analysis (LSA) as an individual technique for feature selection. In addition to these two individual techniques, this system introduces a hybrid feature selection approach that combines both NMF and LSA. The proposed system was assessed using a dataset containing benign and malicious executable files, yielding a performance accuracy of over 96% and False Positive Rate (FPR) score of less than 2.2% across several ML models.}, archive = {J_ACCESS}, author = {Moemedi Lefoane and Ibrahim Ghafir and Sohag Kabir and Irfan-Ullah Awan and Khalil El Hindi and Anand Mahendran}, doi = {10.1109/ACCESS.2025.3596483}, journal = {IEEE Access}, pages = {138867-138882}, shortjournal = {IEEE Access}, title = {Non-negative matrix factorization and latent semantic analysis for hybrid feature selection: A proposed machine learning system for the detection of malicious executable files}, volume = {13}, year = {2025}, }
• (2025). An integrated approach to stock selection and ranking: Combining shannon entropy technique, DEA, and inverse DEA. ACCESS, 13, 138851-138866. (www)

  @article{ , abstract = {This study proposes a novel, integrated framework that combines Shannon Entropy Technique (SET), Data Envelopment Analysis (DEA), and Inverse DEA (IDEA) for the objective evaluation and ranking of equities, with a case application to the tourism sector of the Taiwan Stock Exchange (TWSE). SET is employed for data-driven variable selection to reduce dimensionality across five financial perspectives, enhancing the discriminatory power of DEA. DEA identifies efficient stocks, while IDEA ranks them based on adaptability to output perturbations, providing a dynamic and interpretable view of efficiency. Empirical analysis reveals that only five out of sixteen stocks achieved full efficiency. The IDEA-based ranking uncovered distinct adaptability profiles, with some firms requiring minimal input changes to accommodate output growth, indicating operational flexibility. Sensitivity analysis and model comparisons with super-efficiency and cross-efficiency DEA confirm the robustness and consistency of the proposed method, even under adverse scenarios. This work advances financial decision-making by offering a reproducible, bias-minimized, and scenario-sensitive ranking model. Its transparent structure makes it especially valuable for investors, policymakers, and portfolio managers navigating volatile or crisis-prone markets.}, archive = {J_ACCESS}, author = {Temitope Olubanjo Kehinde and Sai-Ho Chung and Oludolapo A. Olanrewaju}, doi = {10.1109/ACCESS.2025.3586417}, journal = {IEEE Access}, pages = {138851-138866}, shortjournal = {IEEE Access}, title = {An integrated approach to stock selection and ranking: Combining shannon entropy technique, DEA, and inverse DEA}, volume = {13}, year = {2025}, }
• (2025). A data-driven approach for predicting remaining useful life of semiconductor devices based on machine learning and synthetic data generation: A review and case study on SiC MOSFETs. ACCESS, 13, 138834-138850. (www)

  @article{ , abstract = {Predicting the remaining useful life of electronic components is a crucial aspect for predictive maintenance and system reliability across multiple fields and applications. Data-driven approaches, particularly those methods based on machine learning, are currently being used due to their ability to model complex degradation patterns without the need for explicit physical modeling. However, several challenges remain, including the availability and quality of data, as well as the uncertainty quantification of the results. To tackle these obstacles, this work explores the use of synthetic data for augmenting datasets, as well as feature selection and the assessment of different neural network architectures, including models with recurrent layers and probabilistic output. The proposed approach was evaluated on a silicon carbide metal-oxide-semiconductor field-effect transistor dataset. The best results for modeling the remaining useful life of these devices were obtained with a model trained on an augmented dataset that included synthetic data. This model’s probabilistic output allows building a confidence interval for the predictions, which is helpful to identify outliers. This model outperformed other state-of-the-art algorithms using only 4 out of 22 features, demonstrating the effectiveness of the feature selection procedure, the data augmentation method, and the neural network architecture for this case study.}, archive = {J_ACCESS}, author = {Yarens J. Cruz and Fernando Castano and Alberto Villalonga and Madhav Mishra and Rodolfo E. Haber}, doi = {10.1109/ACCESS.2025.3596444}, journal = {IEEE Access}, pages = {138834-138850}, shortjournal = {IEEE Access}, title = {A data-driven approach for predicting remaining useful life of semiconductor devices based on machine learning and synthetic data generation: A review and case study on SiC MOSFETs}, volume = {13}, year = {2025}, }
• (2025). Grid integration of PV systems with advanced control and machine learning strategies. ACCESS, 13, 138820-138833. (www)

  @article{ , abstract = {In the pursuit of sustainable and efficient energy solutions, Photovoltaic (PV) systems have emerged as a prominent player in the domain of renewable energy generation. Particularly, grid-tied PV systems have gained substantial attention due to their potential to contribute to stability and reliability of existing power grid infrastructure. Accordingly, an innovative approach to enhance grid supply using PV systems with Machine Learning Strategy is proposed in this research. The primary objective is to optimize voltage output from PV system while concurrently maximizing power using a novel Modified Zeta-Cuk converter, coupled with Hybrid Maximum Power Point Tracking (MPPT) algorithm combining Incremental Conductance and Bat Optimization Algorithm (InC-BOA). The stabilized DC link resulting from this process is directed to a 3-phase Voltage Source Inverter (VSI) to facilitate conversion of DC supply to AC. To further improve the efficiency and accuracy of system, current produced by inverter is subjected to Discrete Wavelet Transform (DWT) analysis followed by Principal Component Analysis (PCA) for feature extraction. The final step involves implementation of Recurrent Neural Network (RNN) controller, enabling the generation of a refined reference current. The generated reference current is then compared with actual current using Hysteresis Current Controller (HCC). This comparison yields an output which is subsequently employed to Pulse Width Modulation (PWM) generator facilitating the achievement of effective grid synchronization, enhancing overall performance and stability of the system. The validation is performed using MATLAB Simulink software and the outcomes reveals the dominance of proposed work.}, archive = {J_ACCESS}, author = {Venkata Reddy Kota and Bapayya Naidu Kommula and Asif Afzal and Mohammad Asif and Liew Tze Hui}, doi = {10.1109/ACCESS.2025.3593044}, journal = {IEEE Access}, pages = {138820-138833}, shortjournal = {IEEE Access}, title = {Grid integration of PV systems with advanced control and machine learning strategies}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv12 through sliced contrastive supervision and full scene fine-tuning. ACCESS, 13, 138813-138819. (www)

  @article{ , abstract = {Real-time detection of objects in drone-based imagery has proven to be a challenging task for even the most state-of-the-art deep learning models. Due to computational limitations, images are often scaled down during training, reducing the feature space and leading to decreased overall accuracy during validation and inference. This work proposes a two-stage training strategy and several key improvements to one of the most recent You Only Look Once (YOLO) models, YOLOv12. An extra P2 branch and its corresponding scale were added to the head of the network to improve detection of small-scale objects. An additional CIoU-like penalty term was combined with the standard CIoU-based loss used in YOLO to improve detection accuracy. Finally, a contrastive loss function and an associated embedding branch were introduced to help discriminate between features in the embedding space, grouping instances of the same classes closer together and instances of different objects further apart. The first stage of training leverages these improvements on a sliced, full-resolution version of the VisDrone2019-DET dataset, which maintains 15% overlap between slices, and the second stage continues to leverage these improvements for fine-tuning using the full images in a scaled down configuration to provide full scene context. Results demonstrate a 35.5% Mean Average Precision ( $mAP_{50:95}$ ) and a 56.6% mAP50 on the validation split. On the test split, results demonstrate a 36.2% $mAP_{50:95}$ and a 57.7% mAP50.}, archive = {J_ACCESS}, author = {Javier E. Garza and Muhammad F. Islam}, doi = {10.1109/ACCESS.2025.3596039}, journal = {IEEE Access}, pages = {138813-138819}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv12 through sliced contrastive supervision and full scene fine-tuning}, volume = {13}, year = {2025}, }
• (2025). DAWN: Designing distributed agents in a worldwide network. ACCESS, 13, 138795-138812. (www)

  @article{ , abstract = {The rapid evolution of Large Language Models (LLMs) has transformed them from basic conversational tools into sophisticated entities capable of complex reasoning and decision-making. These advancements have led to the development of specialized LLM-based agents designed for diverse tasks such as coding and web browsing. As these agents become more capable, the need for a robust framework that facilitates global agentic communication and collaboration for building sophisticated software solutions has become increasingly important. Distributed Agents in a Worldwide Network (DAWN) addresses this need by providing an architectural framework that allows globally distributed agents of any provenance to be registered, discovered, and organized for building AI-based applications and solutions. In DAWN, a Principal Agent Service composes and oversees the execution of agentic applications. It delegates tasks to one or more Gateway Agent Services that provide for the discovery, registration, and connection of the most suitable agents to fit each application’s needs. DAWN offers three operational modes: No-LLM mode for deterministic and classical software development, Copilot for decision-making augmented using AI, and LLM Agent for autonomous operations. Last but not least, DAWN ensures the safety and security of agent collaborations globally through a dedicated safety, security, and compliance layer, protecting the network against attackers and adhering to stringent security and compliance standards. These features make DAWN a robust framework for designing, developing, and deploying agent-based applications across business and consumer applications.}, archive = {J_ACCESS}, author = {Zahra Aminiranjbar and Jianan Tang and Qiudan Wang and Shubha Pant and Mahesh Viswanathan}, doi = {10.1109/ACCESS.2025.3588425}, journal = {IEEE Access}, pages = {138795-138812}, shortjournal = {IEEE Access}, title = {DAWN: Designing distributed agents in a worldwide network}, volume = {13}, year = {2025}, }
• (2025). High-voltage TX/RX switches for efficient sensor interfaces across various technologies in ultrasound imaging and beyond: A review. ACCESS, 13, 138776-138794. (www)

  @article{ , abstract = {High-voltage (HV) transmit/receive (TX/RX) switches are pivotal components in systems requiring efficient control of signal flow between high-power transmitters and sensitive receivers, particularly in ultrasound imaging. These switches must meet stringent performance criteria, including high voltage-handling capability, low insertion loss, high isolation, and minimal distortion, all while adhering to compact and low-power design constraints. This review paper provides a comprehensive exploration of the design challenges, emerging technologies, and diverse applications of high-voltage TX/RX switches, with a particular focus on their critical role in medical ultrasound imaging systems. Key design considerations, such as the optimization of on-state resistance, off-state capacitance, and voltage stress management, are discussed in detail. Recent advancements in semiconductor technologies, including the adoption of wide-bandgap materials like silicon carbide (SiC) and gallium nitride (GaN), are highlighted as transformative innovations that enable superior performance and integration. Furthermore, the paper examines the practical implications of these technologies in industrial and biomedical contexts, emphasizing their potential to enhance image quality, reduce power consumption, and enable miniaturization in portable ultrasound devices. By bridging the gap between academic research and industrial practice, this review aims to provide valuable insights for circuit designers, researchers, and industry professionals working on next-generation high-voltage TX/RX switches. It also identifies future trends and opportunities for innovation, setting the stage for advancements in this critical field.}, archive = {J_ACCESS}, author = {Abdollah Amini and Giulio Ricotti and Piero Malcovati and Edoardo Bonizzoni}, doi = {10.1109/ACCESS.2025.3592814}, journal = {IEEE Access}, pages = {138776-138794}, shortjournal = {IEEE Access}, title = {High-voltage TX/RX switches for efficient sensor interfaces across various technologies in ultrasound imaging and beyond: A review}, volume = {13}, year = {2025}, }
• (2025). Opportunities and challenges of foundation models in industrial manufacturing. ACCESS, 13, 138745-138775. (www)

  @article{ , abstract = {The field of artificial intelligence (AI) is undergoing a transformative shift, propelled by the emergence of Foundation Models such as BERT, DALL-E, and GPT. These models, trained on large and diverse datasets using self-supervision techniques at unprecedented scales, demonstrate remarkable adaptability across numerous downstream tasks. Their versatility underscores their pivotal role in the evolving AI landscape. In this paper, we explore the complexities of Foundation Models, delving into their broad capabilities that span from natural language processing to advanced robotic manipulation. We also examine their potential industrial applications, recognizing the profound impact they could exert across sectors. However, integrating these models into industrial settings presents significant challenges. While rooted in traditional deep learning and transfer learning principles, the scale and intricacy of Foundation Models introduce new behaviors and characteristics that demand careful consideration. Industries often gravitate toward standardized AI application methods to streamline processes, yet this approach risks embedding inherent flaws or biases within the adapted models. Addressing these challenges requires a nuanced understanding that extends beyond technical proficiency. As industries move toward more widespread adoption of Foundation Models, the intersection of technological, ethical, and domain-specific considerations becomes increasingly important. Navigating this complexity calls for collaborative efforts across disciplines, ensuring that these powerful tools are deployed effectively, responsibly, and with a holistic awareness of their broader implications.}, archive = {J_ACCESS}, author = {Mohammed Ayyat and Mohamed Osman and Tamer Nadeem}, doi = {10.1109/ACCESS.2025.3589777}, journal = {IEEE Access}, pages = {138745-138775}, shortjournal = {IEEE Access}, title = {Opportunities and challenges of foundation models in industrial manufacturing}, volume = {13}, year = {2025}, }
• (2025). An improved quadratic boost converter with lower input current ripple and reduced power components for fuel cell systems. ACCESS, 13, 138733-138744. (www)

  @article{ , abstract = {This paper presents an improved quadratic boost converter designed to achieve lower input current ripple and reduce the number of power components, specifically for fuel cell systems. The proposed converter is derived from a cascaded circuit that combines a conventional boost converter with a quasi Z-source boost converter. This configuration allows for higher voltage gain, a reduced number of power components, and the elimination of current spikes. In the proposed design, the rear-end boost inductor is connected to the input side of the front-end circuit. Additionally, two switches with the same duty cycle, but a phase difference of 180 degrees, are used. This interleaved structure significantly reduces input current ripple and doubles the equivalent switching frequency, thereby reducing the required input capacitance. Compared to a conventional quadratic boost converter, the proposed design decreases the voltage stress on power components and increases the voltage gain by a factor of ( $4D$ - $D^{2}$ -1) through the introduction of a quasi-Z-source network. The working principle, steady-state characteristics, small signal model, and parameter designs of the proposed converter are discussed in detail. The theoretical analysis of the proposed converter is validated through comprehensive experimentation with a 300 W/50 kHz prototype. The maximum and minimum measured efficiencies are approximately 95.9% and 95.4%, respectively, at an output voltage of 400 V under full load conditions, with input voltages ranging from 40 V to 80 V.}, archive = {J_ACCESS}, author = {Yuan Zhu and Kazuhiro Ohyama}, doi = {10.1109/ACCESS.2025.3596095}, journal = {IEEE Access}, pages = {138733-138744}, shortjournal = {IEEE Access}, title = {An improved quadratic boost converter with lower input current ripple and reduced power components for fuel cell systems}, volume = {13}, year = {2025}, }
• (2025). Domain adaptation across geographic regions through region-specific feature learning and distribution matching. ACCESS, 13, 138718-138732. (www)

  @article{ , abstract = {Deep neural networks achieve high accuracy in image recognition. However, they struggle with domain shifts, particularly from geographic variations. Although conventional domain adaptation methods address generalization across domains, such as real-to-clipart transfer, they do not address geographic variations for two key reasons: first, they assume uniform domain shifts across all classes, whereas geographic variations affect object categories non-uniformly, and second, they align global feature distributions without accounting for the selective influence of region-specific characteristics on different semantic categories. This challenge is compounded by the fact that most real-world datasets comprise images captured primarily in Western regions, which complicates generalization to Asian or other underrepresented regions. This study addresses geographic domain shift by incorporating both design and context shifts through a two-part approach: 1) a mechanism for learning region-specific features across object classes and 2)a distribution-matching technique that aligns features across regions while preserving class discrimination. In contrast to existing methods that eliminate all domain differences, our approach effectively distinguishes between universal object features and region-specific variations. We also introduce a novel feature alignment strategy at the superclass level that leverages hierarchical relationships between classes, enabling more effective knowledge transfer across regions. Experiments on the GeoNet benchmark show that the proposed framework outperforms existing methods, with improvements of up to 8.84 percentage points in top-1 accuracy and 17.26 percentage points in top-5 accuracy in cross-region tasks. Visualizations further confirm that the model attends to relevant image regions while incorporating appropriate contextual information for accurate classification when necessary. The reported implementation is available at https://github.com/meruemon/GDA}, archive = {J_ACCESS}, author = {Takashi Horihata and Soh Yoshida and Mitsuji Muneyasu}, doi = {10.1109/ACCESS.2025.3596221}, journal = {IEEE Access}, pages = {138718-138732}, shortjournal = {IEEE Access}, title = {Domain adaptation across geographic regions through region-specific feature learning and distribution matching}, volume = {13}, year = {2025}, }
• (2025). Predicting freshman recruitment rates: A comparative analysis of metropolitan and non-metropolitan universities in south korea. ACCESS, 13, 138699-138717. (www)

  @article{ , abstract = {South Korea’s declining school-age population has intensified competition among universities, particularly in freshman recruitment, with non-metropolitan institutions facing disproportionate challenges. This study investigates regional disparities in recruitment rates by applying a range of statistical and deep learning models—including Generative Adversarial Networks (GAN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, and Transformer architectures—to predict freshman enrollment outcomes. These models integrate conventional institutional indicators such as scholarship amounts, graduate employment rates, and internationalization metrics, alongside newly emphasized variables like professors’ research performance. Findings demonstrate that including faculty research data significantly enhances model accuracy and predictive power, with Transformer-based models consistently outperforming others. The results underscore the methodological advantage of AI-driven modeling in educational analytics and offer strategic insights for policymakers: strengthening academic research capacity, especially in non-metropolitan universities, may serve as a vital policy lever for improving regional recruitment balance and sustaining higher education competitiveness.}, archive = {J_ACCESS}, author = {Hyung Jong Na and Ha-Young Shin and Yongsun Cho}, doi = {10.1109/ACCESS.2025.3574140}, journal = {IEEE Access}, pages = {138699-138717}, shortjournal = {IEEE Access}, title = {Predicting freshman recruitment rates: A comparative analysis of metropolitan and non-metropolitan universities in south korea}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal data augmentation method for network traffic prediction. ACCESS, 13, 138686-138698. (www)

  @article{ , abstract = {Enhancing the generalization performance of CNN-based deep learning models for network traffic prediction requires a substantial amount of data. However, collecting large-scale network traffic data is costly, making data augmentation a practical alternative for improving model performance without additional data acquisition costs. Network traffic data inherently possesses both spatial and temporal characteristics, necessitating data augmentation methods that effectively incorporate both aspects. Despite this need, existing studies have largely overlooked data augmentation techniques that simultaneously address spatial and temporal features. Moreover, network traffic data often exhibits localized and granular patterns, meaning that augmented data with significant spatial deviations from the original distribution can undermine structural consistency, leading to severe performance degradation in prediction models. To address these challenges, we propose a novel spatio-temporal data augmentation framework based on a UNet architecture. The proposed method treats UNet as an inpainting model to generate augmented data by masking and reconstructing partial regions of input samples. A spatial attention module is embedded within the UNet structure to better capture localized features, while the temporal aspect is modeled by providing a sequence of n consecutive time steps as conditional input. This enables the model to generate spatially and temporally coherent augmented samples. By leveraging this inpainting-based approach, we are able to generate subtle augmentations that maintain structural consistency and temporal context, ultimately leading to improved prediction performance. All experiments were conducted on the Telecom Italia Milano dataset, a widely used real-world network traffic dataset. Experimental results demonstrate that the proposed method consistently outperforms baseline models and conventional augmentation techniques, achieving performance improvements in MSE of 15.07% in SMS, 32.7% in CALL, and 10.15% in INTERNET traffic prediction.}, archive = {J_ACCESS}, author = {Sung Oh and Myeong-Jun Oh and Jong-Kyung Im and Ji-Yeon Park and Joung-Sik Kim and Na-Rae Yi and Myung-Ho Kim and Sung-Ho Bae}, doi = {10.1109/ACCESS.2025.3595120}, journal = {IEEE Access}, pages = {138686-138698}, shortjournal = {IEEE Access}, title = {Spatio-temporal data augmentation method for network traffic prediction}, volume = {13}, year = {2025}, }
• (2025). Enhancing long-duration multi-person tracking in hospitality settings through random-skip sub-track correction. ACCESS, 13, 138677-138685. (www)

  @article{ , abstract = {Long-duration customer monitoring with overhead cameras is essential for service analytics in restaurants and cafés, yet state-of-the-art multi-object trackers still suffer from track fragmentation whenever individuals are briefly missed or occluded during multi-hour recordings. We introduce Random-Skip Sub-Track Correction (RSSC) – a tracker-agnostic, post-hoc module that retrospectively joins fragmented tracklets without altering the real-time pipeline. RSSC generates R auxiliary frame streams by randomly skipping up to S consecutive frames, re-runs the baseline tracker on each, and validates candidate linkages wherever any sub-track bridges a temporal gap in the original trajectories. Because it operates solely on sparse frame indices and trajectory meta-data, the extra cost is negligible on low-power edge devices. Field experiments on five one-hour videos captured in a live doughnut shop show that RSSC reduces mean-absolute error in stay-time estimation by up to 23.5% and root-mean-square error by up to 18.2% across four representative trackers (SORT, OC-SORT, DeepSORT, Norfair) while adding virtually no runtime overhead. Although systematic detector failures at extreme viewing angles remain challenging, the results confirm that RSSC is an effective, lightweight remedy for sporadic fragmentation and a practical step toward robust, long-term identity maintenance in resource-constrained hospitality environments.}, archive = {J_ACCESS}, author = {Yu Nakayama and Masayuki Mikuriya and Fumitoshi Ogino}, doi = {10.1109/ACCESS.2025.3596264}, journal = {IEEE Access}, pages = {138677-138685}, shortjournal = {IEEE Access}, title = {Enhancing long-duration multi-person tracking in hospitality settings through random-skip sub-track correction}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Impact of artificial intelligence in nursing for geriatric clinical care for chronic diseases: A systematic literature review”. ACCESS, 13, 138676. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review”).}, archive = {J_ACCESS}, author = {Mahdieh Poodineh Moghadam and Zabih Allah Moghadam and Mohammad Reza Chalak Qazani and Paweł Pławiak and Roohallah Alizadehsani}, doi = {10.1109/ACCESS.2025.3596408}, journal = {IEEE Access}, pages = {138676}, shortjournal = {IEEE Access}, title = {Corrections to “Impact of artificial intelligence in nursing for geriatric clinical care for chronic diseases: A systematic literature review”}, volume = {13}, year = {2025}, }
• (2025). Enhancing microgrid protection through adaptive decentralized relay coordination: A solution to blinding and sympathetic tripping. ACCESS, 13, 138660-138675. (www)

  @article{ , abstract = {Integrating Distributed Energy Resources into microgrids modifies the network’s system parameters and causes it to exhibit behaviour, particularly during fault conditions. This is due to the greater variations in current direction and magnitude. When a microgrid runs in autonomous mode, Distributed Generators result in a reduced short circuit current compared to a system working in grid-connected mode. Microgrid protection issues, namely, blinding of protection, sympathetic tripping, and bidirectional power flow, occur in the power network under such circumstances. The impact of microgrid protection issues and changes in fault current leads to variations of overcurrent relay settings, their operating time and relay pair coordination. This paper presents an adaptive decentralized protection technique for ensuring the coordination of overcurrent relays in a microgrid network, even under situations of uncertainty. This method involves estimating the relay settings in the microgrid without considering DG and establishing relay coordination. The fault currents of the system, including DG, are calculated using the Thevenin equivalent circuit. These currents are derived from the perspective of the relay by analyzing the current and voltage measured at relay position before system fault. The proposed adaptive decentralized protection is developed and implemented in an IEEE-modified 9-bus and 14-bus system. The proposed method of coordination is compared against the conventional relay coordination method. The simulation studies are carried out in the MATLAB simulation and Digsilent Powerfactory 2023 version. The simulation results demonstrate effectiveness of proposed decentralized adaptive protection strategy in addressing all protection issues in the DG-integrated microgrid network.}, archive = {J_ACCESS}, author = {Sujatha Balasubramanian and J. Preetha Roselyn and Prabha Sundaravadivel}, doi = {10.1109/ACCESS.2025.3594099}, journal = {IEEE Access}, pages = {138660-138675}, shortjournal = {IEEE Access}, title = {Enhancing microgrid protection through adaptive decentralized relay coordination: A solution to blinding and sympathetic tripping}, volume = {13}, year = {2025}, }
• (2025). RTF-SAW-YOLOv11: A bolt defect detection model for power transmission lines under low-light conditions. ACCESS, 13, 138640-138659. (www)

  @article{ , abstract = {To address the performance degradation of transmission line bolt defect detection under low-light conditions, we propose a unified model named RTF-SAW-YOLOv11, which integrates image enhancement and object detection into a single end-to-end framework. The model incorporates the Retinexformer (RTF) module for illumination compensation, noise suppression, and texture enhancement, and employs an improved YOLOv11-based SAW-YOLOv11 detector with Shallow Robust Feature Downsampling (SRFD) and Deep Robust Feature Downsampling (DRFD) to strengthen small-object feature extraction. An auxiliary detection branch (Aux Head) is introduced to enhance training stability, and the Wise IoU (WIoU) loss is applied to improve localization for irregular bolt targets. Experimental results on the low-light BoltData dataset show that RTF-SAW-YOLOv11 achieves 85.5% precision, 83.2% recall, and 88.0% mAP@0.5, outperforming the base RTF-YOLOv11 by 4.8%, 1.5%, and 2.7%, respectively. Compared with SAW-YOLOv11 alone, it achieves improvements of 11.1% in precision, 14.1% in recall, and 13.6% in mAP@0.5. RTF-SAW-YOLOv11 maintains real-time inference at 18.6 ms, with a total parameter size of 4.16 M, ensuring deployment feasibility. Generalization tests on the ExDark dataset confirm SAW-YOLOv11’s robustness. RTF-SAW-YOLOv11 offers an effective and lightweight solution for accurate bolt defect detection in real-world power transmission scenarios.}, archive = {J_ACCESS}, author = {Hao Zhang and Lin Gao and Yuxiang Gong and Huaguo Liu and Yongdan Zhu and Yu Yang}, doi = {10.1109/ACCESS.2025.3596308}, journal = {IEEE Access}, pages = {138640-138659}, shortjournal = {IEEE Access}, title = {RTF-SAW-YOLOv11: A bolt defect detection model for power transmission lines under low-light conditions}, volume = {13}, year = {2025}, }
• (2025). Improving token-based object detection with video. ACCESS, 13, 138622-138639. (www)

  @article{ , abstract = {This paper improves upon the Pix2Seq object detector by extending it for videos. In the process, it introduces a new way to perform end-to-end video object detection that improves upon existing video detectors in two key ways. First, by representing objects as variable-length sequences of discrete tokens, we can succinctly represent widely varying numbers of video objects, with diverse shapes and locations, without having to inject any localization cues in the training process. This eliminates the need to sample the space of all possible boxes that constrains conventional detectors and thus solves the dual problems of loss sparsity during training and heuristics-based postprocessing during inference. Second, it conceptualizes and outputs the video objects as fully integrated and indivisible 3D boxes or tracklets instead of generating image-specific 2D boxes and linking these boxes together to construct the video object, as done in most conventional detectors. This allows it to scale effortlessly with available computational resources by simply increasing the length of the video subsequence that the network takes as input, even generalizing to multi-object tracking if the subsequence can span the entire video. We compare our video detector with the baseline Pix2Seq static detector on several datasets and demonstrate consistent improvement, although with strong signs of being bottlenecked by our limited computational resources. We also compare it with several video detectors on UA-DETRAC to show that it is competitive with the current state of the art even with the computational bottleneck. We make our code and models publicly available.}, archive = {J_ACCESS}, author = {Abhineet Singh and Nilanjan Ray}, doi = {10.1109/ACCESS.2025.3596215}, journal = {IEEE Access}, pages = {138622-138639}, shortjournal = {IEEE Access}, title = {Improving token-based object detection with video}, volume = {13}, year = {2025}, }
• (2025). GHMSA-net: Gated hierarchical multi-scale self-attention for perceptually-guided AV1 post-processing. ACCESS, 13, 138601-138621. (www)

  @article{ , abstract = {The AOMedia Video 1 (AV1) codec achieves excellent compression efficiency but often introduces visually distracting artifacts at high quantization parameters (QPs), impairing perceptual quality. We propose Gated Hierarchical Multi-Scale Attention Network (GHMSA-Net), a post-processing model that leverages multi-scale self-attention and dynamic gating to adaptively suppress compression artifacts across varying quantization levels while preserving structural fidelity. The network architecture captures both fine-grained details and global context through a hierarchical attention design, enabling robust restoration under diverse compression strengths. We also explore an efficient training scheme that combines unified pretraining on a representative QP with lightweight QP-specific fine-tuning, offering a favorable trade-off between performance and training cost. Results show that, relative to the AV1 anchor, GHMSA-Net achieves BD-rate savings of 11.79% (Y), 21.24% (Cb), and 20.11% (Cr) for BD-PSNR; 10.55% (Y), 22.49% (Cb), and 21.44% (Cr) for BD-MS-SSIM; and 15.44% for BD-VMAF across QPs 20, 32, 43, 55, and 63. Visual assessments validate the model’s effectiveness in artifact removal and perceptual quality enhancement.}, archive = {J_ACCESS}, author = {Bopu Zhao and Woowoen Gwun and Kiho Choi}, doi = {10.1109/ACCESS.2025.3596303}, journal = {IEEE Access}, pages = {138601-138621}, shortjournal = {IEEE Access}, title = {GHMSA-net: Gated hierarchical multi-scale self-attention for perceptually-guided AV1 post-processing}, volume = {13}, year = {2025}, }
• (2025). INS-based aeromagnetic compensation: Sliding window optimization for enhanced accuracy. ACCESS, 13, 138589-138600. (www)

  @article{ , abstract = {Currently, aeromagnetic compensation research primarily relies on fluxgate magnetometers. However, with the rapid advancement of unmanned aerial vehicle (UAV) technology, foldable aeromagnetic survey UAVs emerging as a research hotspot. Unlike traditional method, small UAVs cannot use long booms to extend magnetometers away from the airframe, posing significant design and operational challenges. Moreover, complex electromagnetic environments introduce unpredictable magnetic interference, severely affecting the accuracy of magnetic compensation techniques. To address these challenges, this paper proposes a novel magnetic compensation method based solely on an inertial navigation system (INS). INS that must be equipped on the aircraft is used to obtain aircraft attitude information, and a geomagnetic model is introduced to complete the function of the fluxgate in aeromagnetic detection. Experimental results demonstrate that the proposed method achieves compensation performance comparable to that of conventional methods. Additionally, by using sliding window technique, this method mitigates the “jumping” issue of the Euler angle approach near “gimbal lock”, the compensation performance is improved by 27.6%. The findings indicate that this method not only optimizes the magnetic compensation capability of small UAVs but also provides a new technical pathway for aeromagnetic measurement.}, archive = {J_ACCESS}, author = {Xiaodong Liu and Jiarui Liu and Junqian Zhang and Wanhua Zhu and Qimao Zhang and Guangyou Fang}, doi = {10.1109/ACCESS.2025.3590411}, journal = {IEEE Access}, pages = {138589-138600}, shortjournal = {IEEE Access}, title = {INS-based aeromagnetic compensation: Sliding window optimization for enhanced accuracy}, volume = {13}, year = {2025}, }
• (2025). A fault diagnosis method for dry vacuum pump bearing based on finite element simulation with deep transfer learning. ACCESS, 13, 138568-138588. (www)

  @article{ , abstract = {Dry vacuum pumps play a vital role in chip manufacturing, and failure of their core components, bearings, can lead to severe downtime and economic losses. Traditional fault diagnosis methods face challenges due to difficult data collection and inconsistent working conditions. In this paper, a dry vacuum pump bearing fault diagnosis method based on finite element simulation and deep transfer learning is proposed. First, finite element simulation is used to model the bearing dynamics and create a fault dataset to address data collection challenges and verify the feasibility of the method. Secondly, an attention mechanism is introduced in the feature extraction layer, combined with a deep transfer learning framework based on domain adversarial networks, to optimize feature extraction and align source and target domain features, thereby improving the accuracy of fault diagnosis and model generalization ability. Experimental results show that the proposed method performs well on multiple datasets, especially when there is a large speed difference between domains, as shown in the PU dataset, and the accuracy is significantly improved compared with the deep transfer learning methods DANN and DDTLN. The t-SNE visualization technique is used to further confirm the advantages of the method in cross-conditional fault diagnosis.}, archive = {J_ACCESS}, author = {Yanfeng Wei and Haibin Liu and Min Wei and Mingfei Li}, doi = {10.1109/ACCESS.2025.3584099}, journal = {IEEE Access}, pages = {138568-138588}, shortjournal = {IEEE Access}, title = {A fault diagnosis method for dry vacuum pump bearing based on finite element simulation with deep transfer learning}, volume = {13}, year = {2025}, }
• (2025). Echoes from the void: Detecting DNS tunneling with blackhole features in encrypted scenarios with high accuracy. ACCESS, 13, 138551-138567. (www)

  @article{ , abstract = {Domain name system (DNS) tunneling is a covert technique for data exfiltration and command-and-control communication, often bypassing traditional security mechanisms. It exploits the DNS, making it difficult to detect, especially when it uses encryption protocols such as DNS-over-HTTPS (DoH). This paper describes a novel detection framework analyzing client behavior towards simulated blackhole events that occur when a DNS query is dropped deliberately. The new approach introduces six new behavioral features concerning retry and domain-switching behavior in combination with eleven traditional DNS metrics. Experiments were conducted using the GraphTunnel data set (2,975,353 records) and the CIC-Bell-DNS-EXF-2021 (1,019,318 records). A five-fold cross-validation with a random forest classifier achieved 99.9% classification accuracy, a 99.9% F1 score, and 100% precision and recall. Feature importance analysis indicates that blackhole-related features contribute to the detection query-to-blackhole ratios (Gini importance of 0.35) and inter-blackhole intervals (0.20), which help focus on stealthy tunneling behavior. The system does not inspect the payload, so it works with a lightweight mechanism even under encrypted DNS traffic, enabling its real-time deployment. Apart from the computational capability, such a practical field deployment in commercial environments presents integration challenges. Integration within existing security information and event management (SIEM) systems or DNS infrastructures implies API compatibility, scaling for heterogeneous networks, and minimizing the disruption of established workflow. Enterprises also face limitations concerning regulatory compliance, data privacy, and operational costs in integrating new detection mechanisms. In order to address these issues, our system is designed modularly and protocol-independently so that it can be easily integrated with mainstream SIEM tools (i.e., Splunk, QRadar) and enterprise DNS resolvers using standard interfaces. Future end uses include validating the approach in live environments as a fast response to future evolutive threats such as wildcard DNS abuse and tunneling over amplified queries.}, archive = {J_ACCESS}, author = {Wafa S. Alorainy}, doi = {10.1109/ACCESS.2025.3595455}, journal = {IEEE Access}, pages = {138551-138567}, shortjournal = {IEEE Access}, title = {Echoes from the void: Detecting DNS tunneling with blackhole features in encrypted scenarios with high accuracy}, volume = {13}, year = {2025}, }
• (2025). Developing a multi-layer ontology construction framework for arabic language processing: Focus on figurative language potential. ACCESS, 13, 138535-138550. (www)

  @article{ , abstract = {Figurative language, encompassing metaphor, hyperbole, and metonymy, is deeply embedded in Arabic discourse and presents considerable challenges related to Arabic’s rich morphology, dialectal diversity, and complex syntax. In response to these challenges, this study introduces a novel multi-layered ontology construction framework aimed at systematically capturing and formalizing linguistic features essential for the annotation of figurative language in Arabic texts. The proposed framework comprises four interrelated layers–Lexical, Grammatical, Inflectional, and Categorical–and integrates external lexical resources, such as Wiktionary, dependency parsing, and morphological analysis to model syntactic, morphological, and semantic relationships. The ontology supports key NLP applications, including word sense disambiguation, semantic search, and text summarization. Experimental evaluation demonstrates the scalability and effectiveness of the framework, resulting in the structured representation of over 41,000 Arabic lexical entries and approximately 830,000 morphological inflections. Theoretically, this work advances the formal modeling of figurative language in morphologically rich languages, while practically, it enables the development of more linguistically grounded and semantically aware Arabic NLP systems.}, archive = {J_ACCESS}, author = {Zouheir Banou and Sanaa El Filali and El Habib Benlahmar and Laila Eljiani and Fatima-Zahra Alaoui}, doi = {10.1109/ACCESS.2025.3596130}, journal = {IEEE Access}, pages = {138535-138550}, shortjournal = {IEEE Access}, title = {Developing a multi-layer ontology construction framework for arabic language processing: Focus on figurative language potential}, volume = {13}, year = {2025}, }
• (2025). Hybrid energy storage systems, converter topologies, energy management systems, and future prospects of green marine technology: A comprehensive review. ACCESS, 13, 138509-138534. (www)

  @article{ , abstract = {Over the decades, significant research on renewable energy systems (RESs) and their diverse applications has been conducted. Various RESs, including fuel cells (FCs), solar, wind, biomass, and hydroenergy, have emerged as sustainable substitutes for fossil fuels. Unlike fossil fuels, these renewable energy sources are environmentally friendly, applicable to rural areas, user-friendly, and cost-effective. Various sectors have adopted these energy sources based on their specific requirements and applications. Recently, FCs, particularly proton exchange membrane FCs (PEMFCs), have been employed in various industrial sectors, notably in hybrid electric vehicles. Additionally, PEMFCs have significantly transformed the marine industry. To enhance performance, energy storage system (ESS) components, such as batteries and supercapacitors, are often combined with PEMFCs to create hybrid energy storage systems (HESSs). HESSs can mitigate sudden fluctuations in load demand, especially when FCs alone are insufficient to meet power requirements. Various modeling techniques have been proposed to effectively configure HESSs. However, a critical challenge is to ensure efficient power sharing among the different ESSs. For this purpose, numerous research articles on optimizing the performance of HESS control strategies have been published. These strategies have been categorized into groups based on their specific applications. This article provides a comprehensive review by summarizing, elucidating, and consolidating the characteristics, limitations, future directions, and real-time applications of various HESS converter topologies and energy management systems by analyzing current and previous studies.}, archive = {J_ACCESS}, author = {Md. Rubel Sarkar and Yunho Park and Haneul Park and Junghyun Woo and Hyunwoo Son}, doi = {10.1109/ACCESS.2025.3595762}, journal = {IEEE Access}, pages = {138509-138534}, shortjournal = {IEEE Access}, title = {Hybrid energy storage systems, converter topologies, energy management systems, and future prospects of green marine technology: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Multi-technology integration in hybrid V2X networks: A survey, challenges, and open issues. ACCESS, 13, 138475-138508. (www)

  @article{ , abstract = {The rapid advancement of autonomous and connected vehicles has driven the development of sophisticated vehicular communication networks that integrate several communication technologies, such as 802.11p, 4G/LTE, 5G, and soon 6G technologies. These hybrid V2X networks represent a key element for the future of smart mobility and connected cities, promising more improved safety, efficiency and comfort. However, due to their heterogeneous nature, integrating these different technologies together also poses several significant challenges and issues that require advanced solutions to take full advantage of them. This survey provides an in-depth review of latest research on solutions addressing various challenges in hybrid V2X communication systems. Firstly, we provide an overview of V2X communication and examine the different standards and emerging technologies that structure and enable these networks. Next, we discuss the top issues facing the practical implementation of hybrid V2X systems, as well as the latest proposed solutions that have been developed to counter these challenges. We offer in addition a qualitative evaluation of these identified approaches to highlight current research limitations. Finally, we highlight key open research issues, which require innovative approaches to fill the gaps in current approaches and ensure reliable communication for future applications of V2X technology.}, archive = {J_ACCESS}, author = {Sihem Baccari and Mohamed Hadded and Haifa Touati and Hakim Ghazzai and Anis Laouiti and Gianluca Setti}, doi = {10.1109/ACCESS.2025.3595461}, journal = {IEEE Access}, pages = {138475-138508}, shortjournal = {IEEE Access}, title = {Multi-technology integration in hybrid V2X networks: A survey, challenges, and open issues}, volume = {13}, year = {2025}, }
• (2025). A cyber secure and scalable blockchain-based framework for monitoring and controlling distributed energy resources. ACCESS, 13, 138460-138474. (www)

  @article{ , abstract = {The ever-growing share of behind-the-meter distributed energy resources (DERs) is radically reshaping conventional distribution systems by incorporating advanced control and monitoring schemes. However, the efficiency and reliability of these mechanisms profoundly depend on the integrity of the data (e.g., measurements, control commands, etc.) exchanged among this ecosystem’s stakeholders (i.e., prosumers, aggregators and grid operators). Besides, traditional power systems depend solely on trusted aggregators for the collection of these DERs’ data, which might jeopardise the security of the whole system if these aggregators are compromised. Thus, in this paper, we address these concerns by proposing a hierarchical blockchain-based framework that incorporates a distributed integrity auditing scheme for DERs’ measurements. Specifically, by leveraging hash functions and Merkle trees, a secure and lightweight blockchain-based hash aggregation protocol is designed to ensure the authenticity of behind-the-meter DERs’ measurements. Further, an automated distributed sanity-check of DERs’ set-points (i.e., control commands) is also proposed to mitigate the risk of coordinated cyber-attacks on large shares of DERs. The proposed framework is implemented and evaluated under various scenarios to assess its effectiveness and security. The obtained results indicate the scalability of the framework as it is capable of achieving a reduction of roughly 47% and 44% in terms of its runtime and storage overheads.}, archive = {J_ACCESS}, author = {Raifa Akkaoui and Peter Palensky and Dick H. J. Epema and Alexandru Ştefanov}, doi = {10.1109/ACCESS.2025.3595944}, journal = {IEEE Access}, pages = {138460-138474}, shortjournal = {IEEE Access}, title = {A cyber secure and scalable blockchain-based framework for monitoring and controlling distributed energy resources}, volume = {13}, year = {2025}, }
• (2025). Design and theoretical analysis of polarization manipulation metasurface based on generalized fresnel’s law. ACCESS, 13, 138449-138459. (www)

  @article{ , abstract = {The electromagnetic (EM) metasurface, or dielectric interfaces with conductive surface structures, can adjust the parameters including amplitude, phase, and wavefront of the incident EM waves by reflection or transmission, and have been widely used in antennas, absorbers, polarization converters and many other applications. However, the theoretical model of the EM metasurface is still mostly based on the generalized Snell’s law, which only studies the phase abrupt discontinuity on the metasurface and lacks theoretical guidance for the changes of reflection and transmission amplitudes. In this paper, “equivalent surface conductivity $\sigma $ ” is defined to describe the effect of artificially designed conductive structures at the dielectric interface, and analytical formulas for reflection and transmission coefficients are established based on the Maxwell’s equations. The formulas are natural expansion of the traditional Fresnel’s law, so named as the generalized Fresnel’s law, with both co-polarization and cross-polarization components included. In this paper, the practicality of the Generalized Fresnel’s law in the case of normal incident was discussed. It can also be applied to more general situations such as anisotropic dielectric interfaces. Simulation and experiment results confirm that the proposed theoretical model can be used to analyze and design various EM metasurfaces, and can be further used to analyze EM devices with the sandwich structures. The fabricated reflective polarization converter achieved over 80% PCR in (14.86-21.09) GHz.}, archive = {J_ACCESS}, author = {Yanghui Wu and Fanghui Sang and Enling Dai and Wenhua Gu}, doi = {10.1109/ACCESS.2025.3593246}, journal = {IEEE Access}, pages = {138449-138459}, shortjournal = {IEEE Access}, title = {Design and theoretical analysis of polarization manipulation metasurface based on generalized fresnel’s law}, volume = {13}, year = {2025}, }
• (2025). Optimizing accounting and auditing principles: A fuzzy MCDM approach to ranking key influencing factors. ACCESS, 13, 138429-138448. (www)

  @article{ , abstract = {Accounting and auditing principles ensure financial transparency, regulatory compliance, and efficient decision-making in organizations. These principles face constant impact from such uncertainties as changing financial regulations, subjective expert decisions, and complicated financial information. This research creates a multi-criteria decision-making (MCDM) approach linking step-wise weight assessment ratio analysis (SWARA) with combined compromise solution (CoCoSo) through complex spherical fuzzy sets (CSFS) to handle existing uncertainties. Through this approach, decision-makers can make more precise choices because it successfully measures expert evaluation ambiguity and imprecision. Three decision-makers prioritize accounting and auditing alternatives by considering key evaluation criteria, including international financial reporting standards (IFRS) compliance, accuracy, cost efficiency implementation, ease of risk mitigation, data security, and adaptability. Internal control mechanisms ( $A_{10}$ ) maintained the most preferred position in the results because they demonstrate fundamental importance for financial governance. The sensitivity analysis of the ranking system shows that changes in parameter values do not affect the stability of the proposed method. The proposed method outperforms other MCDM techniques regarding calculation speed and improved decision quality. The SWARA-CoCoSo approach is an optimal auditing solution in the CSFS, as shown by the research findings guiding policymakers, financial institutions, and regulators to implement the most resilient audit strategies.}, archive = {J_ACCESS}, author = {Weiwen Wang and Jiajia Pan and Jinjin Pan and Qiang Guo and Desong Kong and Zhuo Sun}, doi = {10.1109/ACCESS.2025.3594621}, journal = {IEEE Access}, pages = {138429-138448}, shortjournal = {IEEE Access}, title = {Optimizing accounting and auditing principles: A fuzzy MCDM approach to ranking key influencing factors}, volume = {13}, year = {2025}, }
• (2025). Defining preventable boundaries in automated driving systems: A driver behavior model for scenario-based assessments. ACCESS, 13, 138415-138428. (www)

  @article{ , abstract = {This study presents a quantitative framework for defining preventable boundaries in scenario-based assessments of Automated Driving Systems (ADS) grounded in a novel driver behavior model. This approach integrates data from real traffic and two driving simulation studies focusing on cut-in maneuvers on highways. In the first experiment, the evasive responses and decision times of drivers were statistically modeled based on real traffic data to characterize the role of cut-in responders, highlighting the behavioral differences between experienced and inexperienced drivers. Building on the first experiment findings, the second experiment analyzed the behavior of the maneuvering initiator in scenarios in which an ADS performed a cut-in maneuver, capturing reaction times, braking strategies, and risk perception. Statistical comparisons and regression analyses were used to derive preventability criteria, revealing that human drivers prioritize steering over braking in imminent-risk situations, and that experience level significantly influences response timing and choice. By combining empirical data, systematic reviews, and scenario-specific statistical modeling, this study quantifies the variability in human responses, challenging the sufficiency of the fixed-threshold safety benchmarks currently used in ADS validation. Additionally, cross-validation with real-world lane-change datasets demonstrated discrepancies with the prevailing safety models, suggesting overconservatism in the current assumptions. The results provide methodological insights for refining ADS testing standards, and contribute to data-driven regulatory guidance grounded in human performance analytics.}, archive = {J_ACCESS}, author = {Sou Kitajima and Husam Muslim and Ryosuke Katoh and Joohyeong Lee and Hideaki Satoh and Koichiro Ozawa and Eiichi Kitahara and Hiroki Nakamura}, doi = {10.1109/ACCESS.2025.3594900}, journal = {IEEE Access}, pages = {138415-138428}, shortjournal = {IEEE Access}, title = {Defining preventable boundaries in automated driving systems: A driver behavior model for scenario-based assessments}, volume = {13}, year = {2025}, }
• (2025). Toward sustainable agriculture: DPA-UNet for semantic segmentation of landscapes using remote sensing imagery. ACCESS, 13, 138400-138414. (www)

  @article{ , abstract = {Particularly in agricultural contexts where exact delineation of land cover types is vital for resource management and planning, semantic segmentation is a key technique for high-resolution image interpretation. Although U-Net-based designs have shown significant success in Land Use and Land Cover (LULC) identification, they sometimes struggle when segmenting classes with comparable spectral, textural, and intensity traits. Moreover, generating large-scale ground truth across wide agricultural areas is still time consuming and labor intensive, which greatly limits the creation of scalable segmentation solutions. This work presents a new architecture called Deep Pro Agri-UNet (DPA-UNet) meant specifically for agricultural field segmentation with high-resolution images sourced from Google Earth Pro in order to solve these problems. DPA-UNet improves the model’s ability to extract discriminative features and properly separate spectrally similar land classes by means of a multi-branch spatial and channel attention mechanism at the model’s bottleneck layer. A Dice loss function is included into the training goal to offset the consequences of class imbalance common in agricultural datasets. Attention gates are also included into the decoder path to selectively hone feature maps from the encoder to emphasize spatially relevant areas during the upsampling process. Experimental results show that DPA-UNet significantly outperforms traditional U-Net models, with an 81.8% total accuracy and an 82.8% Intersection over Union (IoU). While keeping a lower computational load, the proposed model efficiently lowers segmentation mistakes in heterogeneous agricultural areas. The findings confirm that DPA-UNet provides a scalable, precise, and computationally efficient solution for large scale agricultural monitoring, therefore supporting applications in sustainable land management, informed policy decision making, and precision farming.}, archive = {J_ACCESS}, author = {J. Kavipriya and G. Vadivu}, doi = {10.1109/ACCESS.2025.3595836}, journal = {IEEE Access}, pages = {138400-138414}, shortjournal = {IEEE Access}, title = {Toward sustainable agriculture: DPA-UNet for semantic segmentation of landscapes using remote sensing imagery}, volume = {13}, year = {2025}, }
• (2025). GenCoder++: A protocol-aware and adversarially robust adaptive intrusion detection framework for hybrid CAN-ethernet vehicular networks. ACCESS, 13, 138381-138399. (www)

  @article{ , abstract = {This paper presents GenCoder++, a robust and adaptive Intrusion Detection System (IDS) framework designed for hybrid vehicular networks that incorporates the Controller Area Network (CAN) and Ethernet protocols. Unlike traditional static IDS approaches, GenCoder++ integrates a compression-aware deep neural classifier, generative variational autoencoder (VAE), and Adversarial Machine Learning (AML)-Shield defense module to proactively handle evolving adversarial threats and concept drift. The proposed system dynamically retrains itself using realistic synthetic samples, generated by an entropy-regularized VAE, enhancing resilience against unseen attack patterns. A novel communication layer coordinates the decision logic across modules and triggers retraining based on entropy and anomaly density metrics. Experimental evaluations on a hybrid CAN-Ethernet dataset revealed that GenCoder++ outperformed state-of-the-art models in terms of accuracy, generalization, and adversarial robustness, achieving 92.5% accuracy and recovering up to 18.8% F1-score under Projected Gradient Descent (PGD) attacks. Furthermore, the compressed GenCoder-Light variant demonstrates real-time efficiency with $4.2 \times $ faster inference and 50% less memory usage, making it suitable for deployment in embedded Electronic Control Units (ECU). Comprehensive ablation and energy profiling analyses confirmed the modular contribution and practical viability of GenCoder++ for the next-generation intelligent automotive systems.}, archive = {J_ACCESS}, author = {Mikhail Smolin}, doi = {10.1109/ACCESS.2025.3595879}, journal = {IEEE Access}, pages = {138381-138399}, shortjournal = {IEEE Access}, title = {GenCoder++: A protocol-aware and adversarially robust adaptive intrusion detection framework for hybrid CAN-ethernet vehicular networks}, volume = {13}, year = {2025}, }
• (2025). Zero-knowledge proof in 5G and beyond technologies: State of the arts, practical aspects, applications, security issues, open challenges, and future trends. ACCESS, 13, 138352-138380. (www)

  @article{ , abstract = {This study explored the diverse applications of zero-knowledge proofs (ZKPs) in next-generation network technologies, particularly in fifth-generation (5G) and emerging sixth-generation (6G) systems. ZKPs are cryptographic methods that enable one party to prove the validity of a statement without revealing the statement itself, thereby offering significant advantages in privacy-preserving authentication and authorization. Given these properties, ZKPs have garnered increasing research attention in contexts such as the Internet of Things (IoT), vehicular communications, and telecommunication protocols. To the best of our knowledge, this is the first study to provide a comprehensive, taxonomy-driven analysis of ZKP applications specifically designed for 5G and beyond. We categorize existing solutions according to the type of application, the underlying cryptographic technology, maturity level, and relevance to 6G. Furthermore, this paper examines how ZKPs can help mitigate various cybersecurity threats, such as distributed denial-of-service (DDoS) attacks, man-in-the-middle attacks, and location tracking. We also assess recent advancements in ZKP acceleration techniques and highlight the key implementation challenges. Finally, this study outlines promising directions for future research in this rapidly evolving field.}, archive = {J_ACCESS}, author = {Aleksandra Szczegielniak-Rekiel and Krzysztof Kanciak and Jan M. Kelner}, doi = {10.1109/ACCESS.2025.3596122}, journal = {IEEE Access}, pages = {138352-138380}, shortjournal = {IEEE Access}, title = {Zero-knowledge proof in 5G and beyond technologies: State of the arts, practical aspects, applications, security issues, open challenges, and future trends}, volume = {13}, year = {2025}, }
• (2025). An integrated decision-making framework for enhancing blockchain scalability and efficiency: Layer-2, off-chain storage, and smart contracts. ACCESS, 13, 138328-138351. (www)

  @article{ , abstract = {Blockchain technology has advanced trust and transparency across various sectors, yet persistent limitations remain in scalability, data storage, and smart contract efficiency. Existing research addresses these issues individually, via Layer 2 (L2) scaling, off-chain storage, or contract-level optimizations, but lacks an integrated methodology to guide system-level design. This paper proposes a unified decision-making framework for systematically evaluating and balancing trade-offs among performance, cost, and security across blockchain layers. Drawing on a comprehensive review of foundational and recent literature, the framework introduces quantifiable metrics, including the Scalability Necessity Factor (ScaNF), the Cost Efficiency Factor (CEF), and the Security Necessity Factor (SecNF), to support architecture-level decisions. Through formalized evaluation criteria, it enables developers to identify when and how to adopt L2 rollups, off-chain storage models, and smart contract optimizations. The framework is positioned as a generalizable tool for designing efficient, scalable, and secure blockchain applications in practice.}, archive = {J_ACCESS}, author = {Khoa Tan Vo and Hong-Tri Nguyen and Tu-Anh Nguyen-Hoang}, doi = {10.1109/ACCESS.2025.3596021}, journal = {IEEE Access}, pages = {138328-138351}, shortjournal = {IEEE Access}, title = {An integrated decision-making framework for enhancing blockchain scalability and efficiency: Layer-2, off-chain storage, and smart contracts}, volume = {13}, year = {2025}, }
• (2025). Automated cardiac disease prediction using composite GAN and DeepLab model. ACCESS, 13, 138313-138327. (www)

  @article{ , abstract = {Cardiovascular diseases remain the leading global cause of mortality, resulting in over 17 million deaths annually. Manual cardiac image interpretation is often subjective and varies significantly among clinicians. However, constraints like limited annotation and model generalization persist. We introduce GenDeep, a novel framework integrating an unsupervised Generative Adversarial Network (GAN) and DeepLab model for robust cardiac pathology classification from cine-MRI scans. The GAN component performs data augmentation to synthesize realistic pathological imagery, overcoming dataset constraints. Meanwhile, the DeepLab segmentation network exploits inter-slice spatial contexts for precise anatomical quantification. GenDeep is trained on over 4000 expert-annotated scans from the ACDC dataset, leveraging Apache Spark and Hadoop for efficient parallel data loading and preprocessing. The Generator maps noise vectors to synthetic MRIs while the Discriminator predicts disease labels and classifies images as real/fake. Weights are updated through backpropagation to refine image realism and classification accuracy. Once trained, the Generator produces additional pathological data to boost model generalization. The Discriminator then serves as the diagnostic classifier based on ventricular morphology from DeepLab segmentation. Extensive comparative testing on a held-out test set achieves 97% accuracy and 93% F1 Score, significantly exceeding benchmarks. Smooth convergence is verified with a low 2.21 MSE. These results highlight the effective integration of generative learning and segmentation for automated and reliable cardiac diagnosis.}, archive = {J_ACCESS}, author = {Sohail Jabbar and Umar Raza and Muhammad Asif Habib and Muhammad Farhan and Saqib Saeed}, doi = {10.1109/ACCESS.2025.3589529}, journal = {IEEE Access}, pages = {138313-138327}, shortjournal = {IEEE Access}, title = {Automated cardiac disease prediction using composite GAN and DeepLab model}, volume = {13}, year = {2025}, }
• (2025). Strengths and knowledge gaps identified from cybersecurity education and training programs, and the implications for operational technology personnel in critical infrastructures: A scoping review. ACCESS, 13, 138265-138312. (www)

  @article{ , abstract = {Critical infrastructures, such as energy or water management, provide services that are essential to society’s function. With the advent of digitalization and Industry 4.0, these infrastructures have transitioned from offline, self-contained systems to digitally interconnected networks. While these advancements have streamlined operations, they have also left critical infrastructures vulnerable to cyber threats. Training all users, including non-technical personnel, in cybersecurity is crucial to safeguard these systems. However, current trainings primarily focus on information technology personnel rather than addressing the needs of non-information technology personnel such as operational technology. This scoping review aimed to address this gap by systematically mapping the available academic literature on cybersecurity education and training programs, particularly focusing on operational technology. The objectives were to identify what different training tools and approaches existed, what knowledge gaps from published literature remained, and what were the potential facilitators and barriers with developing cybersecurity trainings tailored for operational technology environments. Following the JBI methodology, 837 articles were retrieved from 8 databases, with 132 articles included in the final analysis. 39 studies specifically targeted operational technology. The results showed several studies utilized active learning approaches including testbeds, simulations, and gamification. There was, however, a lack of consistent application of performance measurements, with 9 papers specifying self-efficacy as a performance measure. From mapping facilitators, the review presents that, through a co-creative process, tailored cybersecurity education and training programs can be developed to address the needs of operational technology, in turn contributing to the overall cyber-resilience of organizations through increased self-efficacy of personnel.}, archive = {J_ACCESS}, author = {Leanne Noelle Strom Torgersen and Elizabeth Rebecca Noble and Torvald Fossåen Ask and Benjamin J. Knox}, doi = {10.1109/ACCESS.2025.3594045}, journal = {IEEE Access}, pages = {138265-138312}, shortjournal = {IEEE Access}, title = {Strengths and knowledge gaps identified from cybersecurity education and training programs, and the implications for operational technology personnel in critical infrastructures: A scoping review}, volume = {13}, year = {2025}, }
• (2025). A fixed-time convergence control of roll-to-roll systems with a fault-tolerant mechanism. ACCESS, 13, 138247-138264. (www)

  @article{ , abstract = {The operation of Roll-to-roll systems in complex manufacturing industries continuously confronts challenges primarily due to external disturbances, actuator failures, and model uncertainties. These factors directly deteriorate the system’s productivity and quality, resulting in low-quality products and potentially unstable operation. In order to overcome the aforementioned disadvantage, this paper aims to construct a fixed-time control strategy to enhance the system’s tracking performance of material surface tension and transport speed. First, a fixed-time sliding mode observer is developed to estimate disturbances and actuator faults. Subsequently, using the disturbance estimation from the observer, the fixed-time sliding mode control is designed to generate the required control moments for this studied system. Moreover, the control framework is mathematically proven to be fixed-time stable through the Lyapunov criteria, with both the tracking and the observation errors converging to zero over a fixed period. Finally, extensive comparative simulations with other nonlinear controllers are conducted and discussed to demonstrate the validity and feasibility of the proposed method in terms of tracking accuracy and fast settling time with the existence of disturbances, uncertainties, and actuator faults.}, archive = {J_ACCESS}, author = {Hien Thi Nguyen and Van Trong Dang and Mai Thi Hoang and Duc Thinh Le and Dzung Manh do and Minh Nhat Vu and Tung Lam Nguyen}, doi = {10.1109/ACCESS.2025.3593826}, journal = {IEEE Access}, pages = {138247-138264}, shortjournal = {IEEE Access}, title = {A fixed-time convergence control of roll-to-roll systems with a fault-tolerant mechanism}, volume = {13}, year = {2025}, }
• (2025). An instantaneous minimum input energy control strategy for isolation systems with co-varying stiffness and damping. ACCESS, 13, 138235-138246. (www)

  @article{ , abstract = {The seismic isolation stiffness and damping of magnetorheological elastomer (MRE) isolation bearings can adapt instantly to seismic load variations which can provide more intelligent seismic protection for engineering structures. The stiffness and damping change simultaneously following a defined functional relationship. For such systems, an instantaneous minimum input energy (IMIE) control strategy is proposed. This algorithm explicitly establishes the functional relationship between damping and stiffness, converting the optimization problem into a single-variable search for optimal stiffness while automatically determining the corresponding optimal damping. The IMIE strategy operates through total minimization of seismic-induced kinetic energy transmission to the structure and isolation system’s actuation energy consumption during each discrete time interval. A bridge isolation system with MRE isolation bearings is controlled by proposed IMIE algorithm. The control effects under different seismic excitation are compared with passive control, Linear Quadratic Regulation (LQR) control and fuzzy control method. The results demonstrate that the IMIE algorithm effectively mitigates structural responses and exhibits distinct advantages in reducing input energy and MRE bearing displacements. It provides a new control thought and method for the engineering application of hybrid isolation systems.}, archive = {J_ACCESS}, author = {Leilei Xia and Dehong Wu and Lu Chen and Jingjing Li}, doi = {10.1109/ACCESS.2025.3592026}, journal = {IEEE Access}, pages = {138235-138246}, shortjournal = {IEEE Access}, title = {An instantaneous minimum input energy control strategy for isolation systems with co-varying stiffness and damping}, volume = {13}, year = {2025}, }
• (2025). Enriched HARQ feedback for link adaptation in 6G: Optimizing uplink overhead for enhanced downlink spectral efficiency. ACCESS, 13, 138215-138234. (www)

  @article{ , abstract = {Conventional hybrid automatic repeat request (HARQ) feedback uses a single-bit acknowledgment, ensuring reliability but lacking the detail required for effective link adaptation (LA) during retransmissions. To address this, we introduce an enriched HARQ feedback (EHF) framework that goes beyond reliability gains to enhance spectral efficiency by reporting delta adjustments in modulation and coding scheme (MCS) levels from user equipment (UE) to the gNodeB (gNB). This framework features two innovations. First, our learning-driven adaptive quantization (LAQ) employs a-priori statistics to refine delta MCS quantization within fixed-size UE feedback. By tailoring quantization levels to channel-condition–based clusters, LAQ enhances downlink (DL) performance over existing techniques and provides strong out-of-distribution generalization. Second, we extend the EHF framework with a flexible variant where feedback bit size is optimized using a novel LA metric to boost DL performance and minimize uplink (UL) overhead, overcoming the rigidity of fixed-size feedback. Simulation results in a dense urban-macro deployment with file transfer protocol model 3 traffic show gains in DL indicators, including average physical resource block utilization (APU). EHF with LAQ improves DL APU by about 22% over conventional HARQ with Chase combining (CC) at 1-bit resolution. The flexible variant reduces average UL overhead by 1 bit in clusterless feedback for comparable performance and favors LAQ by 23% in end-user experienced DL throughput over CC. These findings underscore the effectiveness of LAQ and our optimized feedback metric in improving retransmission efficiency and feedback in HARQ systems. Complexity analysis supports the feasibility of both gNB and UE-side operations for deployment.}, archive = {J_ACCESS}, author = {Aritra Mazumdar and Abolfazl Amiri and Stefano Paris and Klaus I. Pedersen and Ramoni Adeogun}, doi = {10.1109/ACCESS.2025.3595675}, journal = {IEEE Access}, pages = {138215-138234}, shortjournal = {IEEE Access}, title = {Enriched HARQ feedback for link adaptation in 6G: Optimizing uplink overhead for enhanced downlink spectral efficiency}, volume = {13}, year = {2025}, }
• (2025). FL-QNNs: Memory efficient and privacy preserving framework for peripheral blood cell classification. ACCESS, 13, 138197-138214. (www)

  @article{ , abstract = {Human blood predominantly consists of plasma, erythrocytes, leukocytes, and thrombocytes. It is crucial for the transportation of oxygen and nutrients and for storing the health information of the human body. The body uses blood cells to fight against diseases and infections. Consequently, blood analysis enables clinicians to evaluate a person’s physiological state. These blood cells are categorized into eight classes such as neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes, erythroblasts, and platelets based on nuclear characteristics, morphology, and cytoplasmic composition. Historically, pathologists and hematologists have discovered and analyzed specimens using microscopy before physical categorization, a method that is time consuming and susceptible to human error. Consequently, automating this procedure is imperative. This study proposes a resource efficient, privacy preserving, optimized memory framework by incorporating two approaches: Federated learning and quantized neural network (FL-QNNs) for peripheral blood cell (PBC) image classification. The data set include the total 17,092 images of eight classes. The study began by training the model with seven Deep learning Convolutional neural network (DL-CNN) model such as LeNet, SimpleCNN, MiniAlexNet, SmallVGG, TinyResNet, MiniMobileNet, and Multiple layer perceptron (MLP) and consider them as a baseline models achieving a 98-99% accuracy. Then implement the federated learning (FL) framework with IID (Independent and identically distributed) and Non-IID datasets maintain similar results by preserving data. Further FL-QNNs approach is used with these baseline models and achieved 98-99% accuracy and while optimizing memory, preserving data and resources. This makes the technique exceptionally appropriate for implementation in real-world, privacy-sensitive, and resource-limited healthcare settings. FL-QNN is recognized as an effective approach for minimizing substantial computational resources and memory associated with several Deep Learning (DL) methods.}, archive = {J_ACCESS}, author = {Meenakshi Aggarwal and Vikas Khullar and Nitin Goyal and Bhavani Sankar Panda and Hardik Doshi and Nafeesh Ahmad and Vivek Bhardwaj and Gaurav Sharma}, doi = {10.1109/ACCESS.2025.3595861}, journal = {IEEE Access}, pages = {138197-138214}, shortjournal = {IEEE Access}, title = {FL-QNNs: Memory efficient and privacy preserving framework for peripheral blood cell classification}, volume = {13}, year = {2025}, }
• (2025). Model-based posture estimation with structured light system. ACCESS, 13, 138185-138196. (www)

  @article{ , abstract = {Mechanical inaccuracies and assembly tolerances are inevitable in a wide range of engineered systems, often leading to errors in relative position and orientation, referred to as “posture.” These posture errors can reduce the control precision and overall system performance. To mitigate this problem, a model-based method was developed for estimating the six-degree-of-freedom posture of a structured light module (SLM) that integrates projection and sensing within a compact structure. In this approach, three laser devices emit rays, and the resulting points are captured by an imaging device. The spatial configuration of these points in a global coordinate system is utilized to determine the posture of the SLM. In the first stage, a Jacobian-based iterative estimation algorithm updates the posture by minimizing discrepancies between the laser points predicted from the initial estimate and those obtained through measurements. In the second stage, a misalignment parameter characterizing the geometric deviations among the laser axes is introduced and refined using nonlinear optimization. This two-stage process makes robust convergence possible and sustains estimation accuracy, even when laser axis misalignments are present. The method was validated through simulations and physical experiments on a custom-designed testbed under nominal and perturbed conditions. Simulation results demonstrated stable estimation performance despite laser axis deviations and testbed imperfections. In contrast, physical experiments showed increased estimation errors, primarily owing to subpixel inaccuracies in laser point localization by the imaging system. The method can be applied to manipulators, industrial automation systems, and other engineered platforms that support posture estimation in dynamic or sensor-constrained environments.}, archive = {J_ACCESS}, author = {Jeongyeop Lee and Bumjoo Lee}, doi = {10.1109/ACCESS.2025.3593270}, journal = {IEEE Access}, pages = {138185-138196}, shortjournal = {IEEE Access}, title = {Model-based posture estimation with structured light system}, volume = {13}, year = {2025}, }
• (2025). VRU safety at road intersections: An ontology-driven digital twin. ACCESS, 13, 138166-138184. (www)

  @article{ , abstract = {Although the role of optimization in transportation systems is widely recognized as key to future sustainable development, alarming statistics related to accidents and deaths continue to be overlooked. Among the various components of road networks, road intersections pose the greatest safety challenges for smart and human cities. Digital twins (DT) have emerged as a promising alternative to assist intelligent transportation system (ITS) decision-makers in recent years. However, a critical component is still lacking in developing DTs for ITS: a robust information modeling framework that effectively integrates the physical world with cyberspace in these intricate and dynamic environments. Furthermore, the current emphasis on the efficiency of ITS solutions often overshadows pressing safety concerns for vulnerable road users (VRUs). This paper tackles the challenge of modeling transportation scenarios to enhance VRU safety, focusing on a crucial element: a road intersection, in particular, the semantic issues of such elements when addressing VRU safety. For the first time, we propose an ontology-based information modeling method for road intersections using recent advancements from market-driven, regulatory, and standardization bodies. It specifies the information classification, content to be modeled, and modeling methodology. Nevertheless, inexpensive, swift, and friendly prototyping remains a bottleneck for innovation in this area. Practitioners currently require more means and methods to realize ontology-based DTs for ITS. Another contribution is to provide information model instantiation, visualization, and simulation, as well as fully based open-source tools, as a case study.}, archive = {J_ACCESS}, author = {Victor M. G. Martinez and Salim S. Mussi and Moises R. N. Ribeiro and Divanilson R. Campelo and Tereza C. Carvalho}, doi = {10.1109/ACCESS.2025.3595998}, journal = {IEEE Access}, pages = {138166-138184}, shortjournal = {IEEE Access}, title = {VRU safety at road intersections: An ontology-driven digital twin}, volume = {13}, year = {2025}, }
• (2025). Global trends and empirical metrics in the evaluation of code smells and technical debt: A bibliometric study. ACCESS, 13, 138143-138165. (www)

  @article{ , abstract = {Software quality and long-term maintainability represent fundamental challenges in modern software engineering. Among the critical factors that affect these attributes are code smells, indicators of structural deficiencies in the source code, which, although they do not directly affect functionality, significantly increase technical debt and maintenance costs. This study presents a comprehensive bibliometric analysis of the scientific literature published between 2020 and 2024, with the objective of identifying the main trends, authors, sources, metrics and mitigation techniques associated with code smells and their relationship with technical debt. Seventy-eight articles extracted from the Scopus and Web of Science databases were analyzed using Bibliometrix (R), Biblioshiny and VOSviewer tools, applying productivity indicators, co-authorship networks, term co-occurrence analysis and thematic evolution. The results reveal a growing annual scientific production $(+35.79\%)$ , a concentration of publications in Q1 journals such as Journal of Systems and Software and IEEE Transactions on Software Engineering, and a strong presence of key authors such as Fabio Palomba and Tomas Cerny. The most prominent topics include the application of artificial intelligence techniques for automated detection of code smells, the use of empirical metrics such as cyclomatic complexity and data dependencies, and the implementation of strategies such as automated refactoring and peer review for mitigation. In addition, research gaps were identified in the evaluation of emerging code smells in modern architectures such as microservices or scientific software. This study not only consolidates existing knowledge, but also proposes new lines of research aimed at improving software sustainability through the use of predictive models and hybrid techniques. The bibliometric evidence obtained provides a solid framework for researchers and practitioners interested in optimizing code quality and proactively managing technical debt.}, archive = {J_ACCESS}, author = {Ronald Henry Díaz Arrieta and Byron Leoncio Díaz Monroy and Luis Javier Castillo Heredia and Alexandra Gabriela Valenzuela Cobos}, doi = {10.1109/ACCESS.2025.3594657}, journal = {IEEE Access}, pages = {138143-138165}, shortjournal = {IEEE Access}, title = {Global trends and empirical metrics in the evaluation of code smells and technical debt: A bibliometric study}, volume = {13}, year = {2025}, }
• (2025). Leveraging the power of zero-shot learning for malware detection using application programming interface call sequences. ACCESS, 13, 138125-138142. (www)

  @article{ , abstract = {Currently one of the biggest threats to computer security is malware, which is software designed specifically to harm or infiltrate computers without the owner’s consent. Malware developers often utilize Application Programming Interface (API) calls to carry out these attacks. Recently, the use of Deep Learning (DL) has emerged as a possible solution for recognizing malware from these API calls. Supervised deep learning methods have proven their usefulness in recognizing exploitative code patterns in known big data sets by crafting super complicated systems. These approaches are ineffective against concealed or zero-day malware variants as they depend on vast amounts of datasets that are updated frequently. To solve these problems, this paper employs zero-shot learning to analyze the model’s ability to recognize unseen malware samples without any labeling. The proposed DL system can use zero-shot learning to recognize new evolution of malware before they come into existence, thus decreasing the reliance on up-to-date threat databases. The proposed method achieved accuracies of 0.98 on the Kaggle Malware Detection dataset, 0.98 on the API Call Sequences dataset, and 1.0 on the UCI Malware Detection dataset. The research results show that deep learning is effective for malware detection and exemplify how online emerging threats can be countered with zero-shot learning.}, archive = {J_ACCESS}, author = {P. Meena and K. P. Rama Prabha}, doi = {10.1109/ACCESS.2025.3594087}, journal = {IEEE Access}, pages = {138125-138142}, shortjournal = {IEEE Access}, title = {Leveraging the power of zero-shot learning for malware detection using application programming interface call sequences}, volume = {13}, year = {2025}, }
• (2025). Current-voltage modeling of transistors based on two-dimensional molybdenum disulfide. ACCESS, 13, 138112-138124. (www)

  @article{ , abstract = {This paper presents a compact model for the current-voltage (I–V) characteristics of field-effect transistors (FETs) based on two-dimensional molybdenum disulfide (MoS2) channels. The proposed model is fully analytical, explicit, and physics-based, ensuring compatibility with circuit simulators while providing a comprehensive description of the device behavior. A key starting point is the derivation of a closed-form expression for the electrostatic potential, achieved through a novel solution of the Poisson equation, by means of a Taylor series expansion method using a sliding expansion point. Our modelling approach avoids iterative procedures and special functions commonly used in the literature and allows a more detailed and physically grounded analysis of device operation into a unified analytical framework, including effects often overlooked or fragmented in previous works, as the model incorporates critical non-idealities, such as short-channel effects, interface traps, carrier mobility degradation, and velocity saturation. The results are validated against experimental and simulation data from the literature, demonstrating excellent agreement. This work offers a robust and accessible modeling approach for 2D-FETs, enabling the design of high-performance integrated circuits and favoring the practical implementation of two-dimensional materials in nanoelectronics.}, archive = {J_ACCESS}, author = {Adelcio M. de Souza and Daniel R. Celino and Regiane Ragi and Murilo A. Romero}, doi = {10.1109/ACCESS.2025.3595055}, journal = {IEEE Access}, pages = {138112-138124}, shortjournal = {IEEE Access}, title = {Current-voltage modeling of transistors based on two-dimensional molybdenum disulfide}, volume = {13}, year = {2025}, }
• (2025). Equilibrium strategy and application of pareto cooperative game with linear quadratic positive system. ACCESS, 13, 138095-138111. (www)

  @article{ , abstract = {In this study, we study the equilibrium strategy of a Pareto cooperative differential game with a continuous-time linear positive system in the infinite time domain and a quadratic performance index. In view of the deterministic linear feedback strategy of the gamer strategy and the situation of only two players and multiple players, the equilibrium problem of the Pareto cooperative game in the corresponding situation is studied. The main results are as follows: First, the stability conditions of the corresponding deterministic feedback system under the deterministic feedback strategy of two players and multiple players are studied, and the existence conditions of the equilibrium deterministic closed-loop feedback strategy of Pareto cooperative differential game and the optimal performance index values of the corresponding deterministic feedback control system are obtained, respectively. Second, the conditions for the existence of non-negative solutions of the Pareto equilibrium solutions of positive systems are updated. In addition, a numerical simulation of the deterministic feedback strategy is carried out using a four-dimensional numerical example, which shows the effectiveness of the conclusions. Finally, the application of the Pareto equilibrium strategy in water pollution control cases is confirmed by studying the application of Pareto equilibrium strategy with certainty feedback.}, archive = {J_ACCESS}, author = {Wenxin Yao and Chengke Zhang}, doi = {10.1109/ACCESS.2025.3595538}, journal = {IEEE Access}, pages = {138095-138111}, shortjournal = {IEEE Access}, title = {Equilibrium strategy and application of pareto cooperative game with linear quadratic positive system}, volume = {13}, year = {2025}, }
• (2025). Robust seizure prediction model using EEG signal for temporal lobe epilepsy leveraging deep learning and continual learning. ACCESS, 13, 138083-138094. (www)

  @article{ , abstract = {Epilepsy is characterized by abnormal electrical activity in the brain that affects millions worldwide and poses significant challenges to patients and healthcare providers. Seizure prediction has emerged as a pivotal area of research in epilepsy management aiming to mitigate the unpredictability of seizures and enhance patient quality of life. This research focuses on leveraging memory-based learning approaches including transfer learning and recurrent neural networks (RNNs) for seizure prediction. The dataset comprises time-domain signals embedded with noise necessitating preprocessing techniques such as filtering for optimal utilization. By employing deep learning methodologies, the work seeks to classify seizure in temporal lobe epilepsy from the EEG signals to improve seizure prediction accuracy. With deep learning ability to capture temporal dependencies in sequential data, they offer promising avenues for modeling the temporal dynamics of epileptic seizures. Among the performed architecture and their merits when compared to other, “Googlenet” proved to be the best architecture for training the model by achieving the accuracy of 97.5%. Through continual learning strategies, the model’s training process adapts dynamically enabling it to incorporate new information while retaining knowledge from previous tasks. We used “Elastic Weight Consolidation (EWC)” as the continual learning technique which enforces adaptability and robustness of the trained model. With a custom learning rate scheduler that adjust the learning rate during the training based on the current epoch in the EWC technique we enhanced the accuracy to 98.7% which is pretty much promising result. The model improved by 1.2% over the baseline GoogLeNet (97.5%) to reach an accuracy of 98.7% by including Elastic Weight Consolidation (EWC) for continuous learning. When paired with a customized learning rate scheduler, EWC accelerates convergence and improves model adaptability by eliminating catastrophic forgetting.}, archive = {J_ACCESS}, author = {Bikash Chandra Sahoo and Shruti Mishra and Sandeep Kumar Satapathy and Sachin Kumar}, doi = {10.1109/ACCESS.2025.3595578}, journal = {IEEE Access}, pages = {138083-138094}, shortjournal = {IEEE Access}, title = {Robust seizure prediction model using EEG signal for temporal lobe epilepsy leveraging deep learning and continual learning}, volume = {13}, year = {2025}, }
• (2025). A review of resilience enhancement techniques for cyber-physical systems. ACCESS, 13, 138061-138082. (www)

  @article{ , abstract = {Cyber-physical systems (CPS) integrate computational and physical components, enabling interaction between the digital and physical worlds. As CPS become increasingly prevalent across various domains, ensuring their resilience against disturbances and anomalies, stemming from both cyberattacks and system faults, has become a critical concern. These systems are exposed to risks that can jeopardize safety, reliability, and operational continuity. This study presents a comprehensive and structured review of state-of-the-art resilience enhancement techniques in CPS, with a focus on key aspects that define the concept of resilience adopted in this work: anomaly detection, attack mitigation, fault recovery, and system reconfiguration. We analyze and classify recent approaches such as robust control, fault-tolerant architectures, and secure state estimation, illustrating how these methods address threats like cyber-physical attacks and hardware failures. Our main contribution lies in identifying current gaps within existing resilience frameworks, particularly the need for integrated and adaptive solutions. By synthesizing insights across disciplines, this review serves as a foundational reference for researchers and practitioners aiming to design robust and secure CPS for adversarial environments.}, archive = {J_ACCESS}, author = {Gomez-Cabrera Alain and Ponciano Jorge Escamilla-Ambrosio and Mohammad Abdullah Al Faruque}, doi = {10.1109/ACCESS.2025.3593257}, journal = {IEEE Access}, pages = {138061-138082}, shortjournal = {IEEE Access}, title = {A review of resilience enhancement techniques for cyber-physical systems}, volume = {13}, year = {2025}, }
• (2025). Detection of inverted channels using sentinel-1 data and sentinel-2 data on google earth engine: A comparative analysis. ACCESS, 13, 138052-138060. (www)

  @article{ , abstract = {Inverted fluvial channels are significant geomorphological features, particularly in arid regions, offering insights into past hydrological regimes and paleo-climatic conditions. Accurate mapping of these features is crucial for understanding landscape evolution in arid regions. However, it is challenging to detect these inverted channels over large areas based on traditional field methods. This study develops and evaluates a workflow for inverted channel detection by comparing the performance of a Sentinel-1 Synthetic Aperture Radar (SAR) only approach with a synergistic data fusion approach. The first method relies on thresholding the backscatter intensity of Sentinel-1 imagery. The second method is to fuse Sentinel-1 SAR data and Sentinel-2 multispectral data for classification using Random Forest (RF) classifier to generate classification probability maps, which are then used for channel detection. The methodologies are tested and validated across four distinct study sites. The results demonstrate the superiority of the data fusion approach. Accuracy assessments show that the integration of Sentinel-2 data improve the classification performance, with Overall Accuracy (OA) increasing from a range of 0.82-0.86 to 0.84-0.90 and Kappa coefficients rising from 0.66-0.77 to 0.69-0.79. Receiver operating characteristic (ROC) analysis further confirms this enhancement, with an increase in the area under the curve (AUC). Visual comparison also shows that the fusion approach produces a more coherent and complete channel network, overcoming the inherent problems of the SAR-only approach. We conclude that the synergy between Sentinel-1’s sensitivity to surface roughness and Sentinel-2’s spectral information provides a more comprehensive characterization of the landscape, leading to a highly accurate and robust delineation of inverted channels. The proposed workflow provides an efficient method for detecting inverted channel, offering a valuable tool for geomorphological research, landscape evolution studies, and hydrological exploration in arid environments.}, archive = {J_ACCESS}, author = {Xuhua Weng}, doi = {10.1109/ACCESS.2025.3591178}, journal = {IEEE Access}, pages = {138052-138060}, shortjournal = {IEEE Access}, title = {Detection of inverted channels using sentinel-1 data and sentinel-2 data on google earth engine: A comparative analysis}, volume = {13}, year = {2025}, }
• (2025). Guided texture transfer network for mid-infrared satellite image super resolution. ACCESS, 13, 138037-138051. (www)

  @article{ , abstract = {Mid-infrared (MIR) satellite imagery captures thermal information and supports a wide range of remote sensing applications. However, its inherently low spatial resolution limits its utility for detailed spatial analysis. In this work, we formulate MIR super-resolution as a guided super-resolution task using geometrically aligned high-resolution RGB images. We present a Guided Texture transFormer (GTFormer) that transfers fine textures from RGB to MIR while preserving thermal semantics. Also, we propose a two-stage learning strategy to prevent the model from simply copying guidance values. We evaluate the proposed method on real satellite data from KOMPSAT-3A and KOMPSAT-2. Extensive experiments demonstrate that our method outperforms state-of-the-art techniques in both visual quality and thermal information preservation.}, archive = {J_ACCESS}, author = {Yeji Jeon and Youkyung Han and Kwang-Jae Lee and Yeseul Kim and Hanul Kim}, doi = {10.1109/ACCESS.2025.3595829}, journal = {IEEE Access}, pages = {138037-138051}, shortjournal = {IEEE Access}, title = {Guided texture transfer network for mid-infrared satellite image super resolution}, volume = {13}, year = {2025}, }
• (2025). IMed-CNN: Ensemble learning approach with systematic model dropout for enhanced medical image classification using image channels and pixel intervals. ACCESS, 13, 138020-138036. (www)

  @article{ , abstract = {To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image classification tasks. Despite numerous deep learning (DL) models were developed to solve the generalization problem in DL through ensemble learning, most of the models lacks the evaluation of the effects of each ensemble member for the final ensemble model. Additionally, existing ensemble models tend to include huge number of parameters and use images only in RGB channels or greyscale forms missing the crucial representations of the dataset to reach robust classification outcomes, particularly in medical applications. In this study, novel ensemble model IMed-CNN proposed solution to above-mentioned gaps in the field by introducing the data in ten various forms to the model and applying systematic model dropout (SMDE) with unique true prediction (UTP) analysis which insures to choose only useful ensemble members. To verify the performance of the IMed-CNN, extensive experiments was designed for testing. Results illustrate that IMed-CNN outperformed baseline models.}, archive = {J_ACCESS}, author = {Javokhir Musaev and Abdulaziz Anorboev and Yeong-Seok Seo and Odil Fayzullaev and Akobir Musaev and Ngoc Thanh Nguyen and Dosam Hwang}, doi = {10.1109/ACCESS.2025.3593046}, journal = {IEEE Access}, pages = {138020-138036}, shortjournal = {IEEE Access}, title = {IMed-CNN: Ensemble learning approach with systematic model dropout for enhanced medical image classification using image channels and pixel intervals}, volume = {13}, year = {2025}, }
• (2025). Octascope: A lightweight pre-trained model for optical coherence tomography. ACCESS, 13, 138005-138019. (www)

  @article{ , abstract = {Optical coherence tomography (OCT) imaging enables high resolution visualization of sub-surface tissue microstructures. However, OCT image analysis using deep learning is hampered by limited diverse training data to meet performance requirements and high inference latency for real-time applications. To address these challenges, we developed Octascope, a lightweight domain-specific convolutional neural network (CNN) - based model designed for OCT image analysis. Octascope was pre-trained using a curriculum learning approach, which involves sequential training, first on natural images (ImageNet), then on OCT images from retinal, abdominal, and renal tissues, to progressively acquire transferable knowledge. This multi-domain pre-training enables Octascope to generalize across varied tissue types. In two downstream tasks, Octascope demonstrated notable improvements in predictive accuracy compared to alternative approaches. In the epidural tissue detection task, our method surpassed single-task learning with fine-tuning by 9.13% and OCT-specific transfer learning by 5.95% in accuracy. Octascope outperformed VGG16 and ResNet50 by 5.36% and 6.66% in a retinal diagnosis task, respectively. In comparison to a Transformer-based OCT foundation model - RETFound, Octascope delivered 2 to 4.4 times faster inference speed with slightly better predictive accuracies in both downstream tasks. Octascope represented a significant advancement for OCT image analysis by providing an effective balance between computational efficiency and diagnostic accuracy for real-time clinical applications.}, archive = {J_ACCESS}, author = {Haoyang Cui and Chen Wang and Paul Calle and Yunlong Liu and Qinghao Zhang and Sinaro Ly and Justin Reynolds and Feng Yan and Ke Zhang and Ronghao Liu and Junyuan Liu and Kar-Ming Fung and Zhongxin Yu and Ajay Jain and Qinggong Tang and Chongle Pan}, doi = {10.1109/ACCESS.2025.3595838}, journal = {IEEE Access}, pages = {138005-138019}, shortjournal = {IEEE Access}, title = {Octascope: A lightweight pre-trained model for optical coherence tomography}, volume = {13}, year = {2025}, }
• (2025). A novel UX-based approach for ontology evaluation: Applying tree testing to the agricultural product types ontology. ACCESS, 13, 137986-138004. (www)

  @article{ , abstract = {This paper introduces a novel approach to evaluating ontologies by integrating user experience (UX) metrics, specifically the Tree Testing protocol, into the validation process. Traditional ontology validation often focuses on verifying competency questions via SPARQL queries, overlooking the critical role of domain specialists in assessing usability and conceptual alignment. To address this gap, we applied Tree Testing to the Agricultural Product Types Ontology (APTO), tracking specialists’ navigation paths as they completed 11 domain-specific tasks. This method provided actionable insights into APTO’s usability, revealing structural weaknesses and areas needing refinement. The study involved experts in ontological modeling within the agricultural domain, ensuring feedback was relevant and domain-specific. The findings underscore the value of UX metrics, such as Tree Testing, in identifying user navigation patterns and conceptual misunderstandings. This research illustrates the importance of integrating UX-driven methodologies into ontology design to foster more user-centered and practical knowledge representations for real-world applications. By bridging the gap between technical validation and user-centered evaluation, this work contributes to developing ontologies that are not only technically sound but also intuitive and aligned with the needs of domain experts.}, archive = {J_ACCESS}, author = {Filipi Miranda Soares and Antonio Mauro Saraiva and Luís Ferreira Pires and Debora Pignatari Drucker and Kelly Rosa Braghetto and Luiz Olavo Bonino da Silva Santos and Dilvan de Abreu Moreira and Fernando Elias Corrêa and Alexandre Cláudio Botazzo Delbem}, doi = {10.1109/ACCESS.2025.3595447}, journal = {IEEE Access}, pages = {137986-138004}, shortjournal = {IEEE Access}, title = {A novel UX-based approach for ontology evaluation: Applying tree testing to the agricultural product types ontology}, volume = {13}, year = {2025}, }
• (2025). Decentralized digital product passport building blocks for enhancing supply chain sovereignty and circular economy practices. ACCESS, 13, 137973-137985. (www)

  @article{ , abstract = {This research article explores the decentralization of Digital Product Passport (DPP) building blocks for Asset Administration Shells (AAS), aiming to enhance the management and traceability of digital assets. The DPP system is conceptualized to incorporate three critical elements: identity management, traceability, and access permissions. We present a comprehensive architecture that integrates these elements, ensuring a resilient supply chain. Furthermore, we delve into the custodianship of the DPP data, examining the roles and responsibilities of various stakeholders in maintaining and securing the system. Finally, we experiment with existing technologies to conceptualize a decentralized DPP system for AASs, showcasing its practical application and effectiveness in real-world scenarios. This study sheds some light into the potential of decentralized systems in digital product management, highlighting the benefits and challenges associated with their deployment.}, archive = {J_ACCESS}, author = {Santiago de Diego and Iván Gutiérrez-Aguero}, doi = {10.1109/ACCESS.2025.3594826}, journal = {IEEE Access}, pages = {137973-137985}, shortjournal = {IEEE Access}, title = {Decentralized digital product passport building blocks for enhancing supply chain sovereignty and circular economy practices}, volume = {13}, year = {2025}, }
• (2025). Federated learning-based trust evaluation with fuzzy logic for privacy and robustness in fog computing. ACCESS, 13, 137952-137972. (www)

  @article{ , abstract = {Trust evaluation significantly influences security and reliability in fog computing-based Wi-Fi mesh networks. This paper introduces a Federated Learning Trust Model (FLTM) to assess trustworthiness across 2000 resources while preserving data privacy. FLTM incorporates six critical metrics: Availability, Reliability, Data Integrity, Identity, Computational Capability, and Throughput. To address challenges related to unlabeled datasets, fuzzy logic techniques are employed to generate initial trust labels for local neural network training at fog nodes. The key contributions include a privacy-preserving federated learning framework specifically tailored for robust trust evaluation in hierarchical fog computing architectures, robust fuzzy logic-based labeling enhancing interpretability and reliability in the trust assessment process, extensive empirical validations demonstrating superior prediction accuracy, computational efficiency, enhanced privacy preservation, and scalability compared to traditional centralized approaches. Additionally, comprehensive comparative analyses against existing federated learning approaches validated on different datasets, and against non-federated methods using identical datasets, confirm FLTM’s practical advantages. Experimental results validate the robustness, scalability, and suitability of FLTM for real-time trust assessments in distributed fog computing-enabled Wi-Fi mesh networks.}, archive = {J_ACCESS}, author = {Thinh Le Vinh and Huan Thien Tran and Huyen Trang Phan and Samia Bouzefrane}, doi = {10.1109/ACCESS.2025.3596093}, journal = {IEEE Access}, pages = {137952-137972}, shortjournal = {IEEE Access}, title = {Federated learning-based trust evaluation with fuzzy logic for privacy and robustness in fog computing}, volume = {13}, year = {2025}, }
• (2025). DroneRiver: An aerial semantic segmentation dataset for analysis of river leakage. ACCESS, 13, 137942-137951. (www)

  @article{ , abstract = {Currently, existing drone datasets mainly focus on collecting data in urban environments. However, there are numerous residential and farming areas located alongside the river in rural regions. If there is heavy rain and flooding, the river is likely to overflow, posing a threat to human lives and property. In this paper, we collect a novel drone-captured dataset, called DroneRiver, tailored for water management of pre-disaster scenarios and analysis of preliminary river leakage in rural areas. DroneRiver provides annotations for the objects on both sides of the river, such as people, vehicles, levee roads, farms, vegetation, etc. Additionally, to overcome the difficulty of collecting real-world river leakage scenes without environmental harm, based on the recent generative model, preliminary river leakage images can be generated in our datasets for pre-disaster prevention and alerts. Finally, our dataset annotates the original and generated aerial at the pixel level for semantic segmentation. We also conduct extensive experiments using state-of-the-art segmentation models and evaluate the quality of the generated aerial images. The results demonstrate the significance of the DroneRiver dataset for river leakage analysis.}, archive = {J_ACCESS}, author = {Yi Tang and Pengfei Lyu and Hidekazu Shirai and Yasuharu Watanabe and Hiroshi Masui}, doi = {10.1109/ACCESS.2025.3595602}, journal = {IEEE Access}, pages = {137942-137951}, shortjournal = {IEEE Access}, title = {DroneRiver: An aerial semantic segmentation dataset for analysis of river leakage}, volume = {13}, year = {2025}, }
• (2025). FOPID-controlled self-propelled pipeline inspection robot for internal mapping of small-diameter pipelines. ACCESS, 13, 137931-137941. (www)

  @article{ , abstract = {Conventional pipeline route mapping systems frequently depend on external sensing technology, like cameras and laser scanners. Although useful in certain situations, these devices are often costly, cumbersome, and significantly vulnerable to environmental factors, such as inadequate lighting and reflecting internal surfaces—particularly in constricted pipelines. These constraints render them inappropriate for small-scale industrial pipes, generally ranging from 3 to 4 inches in diameter. This paper presents a compact and cost-effective Pipeline Route Mapping System (PRMS) that solely uses internal sensing techniques to tackle these difficulties. The design features a self-propelled Pipeline Inspection Robot (PIR) that autonomously traverses restricted pipeline geometries. The robot’s movement is meticulously controlled by a Fractional Order Proportional Integral Derivative (FOPID) controller, chosen for its superior adaptability and resilience in nonlinear and limited settings. The suggested method facilitates the extraction of essential pipeline characteristics, encompassing the identification of transition sites between linear and curved segments, precise alignment of linear sections in accordance with curvature, and interpolation of bends and directional changes. The system has been validated by extensive MATLAB/Simulink simulations and empirical testing, demonstrating its capacity to accurately reconstruct pipeline pathways with minimal sensor hardware. This research facilitates the development of a novel category of economical, compact, and intelligent PIRs that can enhance sophisticated mapping algorithms. Its applicability encompasses several industrial inspection and maintenance contexts, enhancing the safety and efficiency of pipeline infrastructure management.}, archive = {J_ACCESS}, author = {Rizwan Aziz Mangi and Ghulam E. Mustafa Abro and Sufyan Ali Memon and Aneela Pathan and Khalil Muhammad Zuhaib and Maran Marimuthu}, doi = {10.1109/ACCESS.2025.3595951}, journal = {IEEE Access}, pages = {137931-137941}, shortjournal = {IEEE Access}, title = {FOPID-controlled self-propelled pipeline inspection robot for internal mapping of small-diameter pipelines}, volume = {13}, year = {2025}, }
• (2025). Time-domain versus frequency-embedded EEG sequences for sensorimotor BCI using 1D-CNN. ACCESS, 13, 137919-137930. (www)

  @article{ , abstract = {This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. The objective was to classify right hand (RH) versus right foot (RF) MI tasks in both intra- and inter-subject (pairwise and pooled) BCI settings using a 1D-CNN architecture trained on time-domain bandpass filtered electroencephalography (EEG) signals, frequency-embedded power spectral density (PSD) and cross-power spectral density (CPSD) sequences. The EEG signals were bandpass filtered with 4 Hz and 32 Hz cut-off frequencies, and PSD/CPSD sequences were estimated in the same frequency range. Thus, the number of input channels for 1D-CNN was N, N or $N\times N$ for EEG signals, PSD or CPSD sequences. We used dataset IVa from BCI Competition III in 5−fold cross-validation settings to evaluate intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCI classification accuracies. We compared the performance of the proposed methods with classification algorithms featuring common spatial patterns (CSP) for benchmarking. The best overall classification accuracies (%) for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs were $86.57\pm 11.69$ , $70.80\pm 9.21$ and $76.61\pm 12.37$ using 1D−CNN with time-domain EEG signals. The average classification accuracies using 1D-CNN with frequency-embedded PSD sequences were $82.57\pm 10.20$ , $69.32\pm 7.46$ and $71.32\pm 7.96$ for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs. The proposed time/frequency feature representation techniques with 1D-CNN outperformed CSP-based algorithms (p-value <0.05). The comparative results suggest the utility of the proposed methods for MI classification, especially for a fully zero-training inter-subject BCI.}, archive = {J_ACCESS}, author = {Simanto Saha and Mathias Baumert and Alistair Mcewan}, doi = {10.1109/ACCESS.2025.3595953}, journal = {IEEE Access}, pages = {137919-137930}, shortjournal = {IEEE Access}, title = {Time-domain versus frequency-embedded EEG sequences for sensorimotor BCI using 1D-CNN}, volume = {13}, year = {2025}, }
• (2025). A review of urban building extraction from synthetic aperture radar imagery based on deep learning. ACCESS, 13, 137896-137918. (www)

  @article{ , abstract = {Urban building extraction from Synthetic Aperture Radar (SAR) imagery is a hot topic and remains challenging for wide applications. Recently, deep learning approaches, which have achieved remarkable success in computer vision, are increasingly becoming a major trend in SAR image urban building extraction. However, there remains a lack of systematic reviews on urban building extraction from SAR imagery based on deep learning. This article aims to present a comprehensive review of methodologies in this field since 2015. It includes detailed analyses and discussions of approaches, datasets, and evaluation metrics. The fundamental imaging principles of urban buildings in SAR imagery are first explored to clarify existing challenges. Then, the review is conducted from two aspects: urban building extraction and building height estimation. The methods are further classified as only-SAR image-based methods and SAR-optical data-based methods, depending on the data sources. Additionally, given the distinct feature representations across SAR resolutions, investigations are differentiated between building-level analysis for high-resolution images and built-up area level analysis for medium-resolution data. Benchmark datasets are summarized, and evaluation metrics are reviewed. Finally, the article conducts discussions, proposes potential solutions, and outlines future development directions.}, archive = {J_ACCESS}, author = {Fan Wu and Lixia Gong and Bo Zhang and Chao Wang and Hong Zhang and Yue Wang}, doi = {10.1109/ACCESS.2025.3593955}, journal = {IEEE Access}, pages = {137896-137918}, shortjournal = {IEEE Access}, title = {A review of urban building extraction from synthetic aperture radar imagery based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Service-level objective-aware load-adaptive timeout: Balancing failure rate and latency in microservices communication. ACCESS, 13, 137881-137895. (www)

  @article{ , abstract = {Microservices architectures enable scalable and modular application design but introduce reliability challenges due to their distributed nature. Timeout configurations are critical for maintaining system reliability, as they directly impact latency and failure rate Service-Level Objective (SLO) compliance. However, current timeout settings are often based on best practices rather than systematic optimization, as determining the optimal timeout is challenging. The difficulty arises from the need to balance SLO constraints while adapting to dynamically changing load and system capacity, making static configurations inherently suboptimal. To address this, this study proposes a load-adaptive timeout mechanism that dynamically adjusts timeout values to optimize reliability across different load conditions. Under normal load, the method minimizes latency while maintaining failure rate SLO compliance. Under overload, where meeting both objectives becomes infeasible, it prioritizes failure rate reduction while ensuring latency SLO compliance. By allocating the initial portion of the timeout duration to transmission delay during downstream overload and failure, the method naturally exhibits load shedding and circuit-breaking behavior, preventing the bottleneck service from being overwhelmed. The proposed method was implemented as an open-source Go library and evaluated using the Online Boutique benchmark under various load conditions. Results show that it reduces average and tail latencies by 40% and 55%, respectively, under normal load and short-lived overload. Under prolonged overload, it minimizes failure rates, reducing deviations from the failure rate SLO by 18%. These findings demonstrate the effectiveness of adaptive timeout control in maintaining microservices reliability while dynamically responding to changing system conditions.}, archive = {J_ACCESS}, author = {Hiroki Hanada and Keisuke Ishibashi}, doi = {10.1109/ACCESS.2025.3596118}, journal = {IEEE Access}, pages = {137881-137895}, shortjournal = {IEEE Access}, title = {Service-level objective-aware load-adaptive timeout: Balancing failure rate and latency in microservices communication}, volume = {13}, year = {2025}, }
• (2025). A method to simulate the heave-pitch coupled motion of spar platforms. ACCESS, 13, 137869-137880. (www)

  @article{ , abstract = {The purpose of this work is to explore the internal resonance exhibited by the heave-pitch coupled motion of a classic Spar platform via an equivalent analog circuit method. First, the pitch motion equation is established on the basis of the motion characteristics of the Spar platform, and a damped Mathieu equation is obtained via normalization. Then, the equivalent analog circuit model of the motion equation is obtained by using operational amplifiers and multipliers as circuit components. The circuit equation is found to be consistent with the motion equation. Finally, the multiparameter equivalent analog circuit model is used to study the coupled motion of a Spar platform. The stable range of the pitch motion of the Spar platform is determined according to threshold conditions. The feasibility of the proposed method is then verified. The results show that the equivalent analog circuit method is effective for obtaining the time history of the pitch motion and predicting the parametric resonance of the Spar platform.}, archive = {J_ACCESS}, author = {Zhenhua Zhang and Chuyi Wang and Min Li}, doi = {10.1109/ACCESS.2025.3595536}, journal = {IEEE Access}, pages = {137869-137880}, shortjournal = {IEEE Access}, title = {A method to simulate the heave-pitch coupled motion of spar platforms}, volume = {13}, year = {2025}, }
• (2025). Enhanced DL-based breast cancer diagnosis and classification using modified DenseNet-121, DenseNet-201, and MobileNetV2: Optimized architectures and refined activation functions. ACCESS, 13, 137851-137868. (www)

  @article{ , abstract = {Deep learning has revolutionized medical image analysis, particularly in the domain of breast cancer detection. Despite notable progress, further optimization of neural network architectures and activation functions remains critical for enhancing classification accuracy and model generalization. This study explores the effect of architectural refinements and activation function choices on the performance of three widely adopted convolutional neural networks: DenseNet-121, DenseNet-201, and MobileNetV2. Specifically, we introduce a modified framework that incorporates selective freezing of early convolutional layers and replaces the conventional ReLU activation function with the smoother GELU activation in the fully connected layers, aiming to improve feature representation and classification robustness. The proposed enhancements are rigorously evaluated across three balanced and widely used benchmark mammography datasets: MIAS, INbreast, and DDSM, using standard performance metrics including Precision, Recall, F1-score, and Accuracy. Experimental findings show that the modified models consistently surpass their original counterparts. Notably, the enhanced DenseNet-201 achieves the highest accuracy of 99.6% on the DDSM dataset. Similarly, the modified DenseNet-121 attains 97% accuracy on MIAS and 98% on INbreast, while the improved DenseNet-201 reaches 98% on both MIAS and INbreast. Furthermore, the lightweight yet optimized MobileNetV2 achieves impressive results, with 99.4% accuracy on DDSM and 99% on INbreast. To validate the statistical significance of these performance gains, paired t-tests were conducted, confirming that the improvements are not only consistent but also statistically meaningful. Our results emphasize the effectiveness of architectural tuning and activation function refinement in advancing deep learning-based breast cancer classification.}, archive = {J_ACCESS}, author = {Khaddouj Taifi and Yassine Sabbar and Hanin Ardah and Abdel-Haleem Abdel-Aty}, doi = {10.1109/ACCESS.2025.3594827}, journal = {IEEE Access}, pages = {137851-137868}, shortjournal = {IEEE Access}, title = {Enhanced DL-based breast cancer diagnosis and classification using modified DenseNet-121, DenseNet-201, and MobileNetV2: Optimized architectures and refined activation functions}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Advanced intrusion detection in MANETs: A survey of machine learning and optimization techniques for mitigating Black/Gray hole attacks”. ACCESS, 13, 137850. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Advanced Intrusion Detection in MANETs: A Survey of Machine Learning and Optimization Techniques for Mitigating Black/Gray Hole Attacks”).}, archive = {J_ACCESS}, author = {Saad M. Hassan and Mohd Murtadha Mohamad and Farkhana Binti Muchtar}, doi = {10.1109/ACCESS.2025.3595635}, journal = {IEEE Access}, pages = {137850}, shortjournal = {IEEE Access}, title = {Corrections to “Advanced intrusion detection in MANETs: A survey of machine learning and optimization techniques for mitigating Black/Gray hole attacks”}, volume = {13}, year = {2025}, }
• (2025). Supporting the evolution of event-based choreographies of BPMN fragments in microservices environments. ACCESS, 13, 137827-137849. (www)

  @article{ , abstract = {Business Processes (BPs) describe how organizations should perform to achieve their goals. Many times, these BPs are defined and executed in a decentralized way, which forces organizations to find mechanisms to achieve coordination in their processes. Within this context, microservice-based architectures are offered as a solution to achieve this coordination. To maintain a lower coupling between different microservices supporting these distributed BPs, this type of architecture proposes using event-based choreographies. Nevertheless, this distribution makes it hard to analyse the business requirements of the composition since the flow is split among different microservices. Our prior work addresses this by using BPMN diagrams for microservices composition, executing it through an event-based BPMN fragment choreography. In this work, we aim to support the evolution of a BP choreography when changes occur from the local perspective of a microservice by automatically selecting adaptation actions to maintain the functional integrity of the composition. To achieve this, we have formally defined our microservices approach and a catalogue of adaptation rules. These rules have been automated by integrating a new microservice implementing an MAPE-K control loop into the application. This extended architecture was successfully evaluated in a user experiment.}, archive = {J_ACCESS}, author = {Jesús Ortiz and Victoria Torres and Pedro Valderas}, doi = {10.1109/ACCESS.2025.3595387}, journal = {IEEE Access}, pages = {137827-137849}, shortjournal = {IEEE Access}, title = {Supporting the evolution of event-based choreographies of BPMN fragments in microservices environments}, volume = {13}, year = {2025}, }
• (2025). Diffusion model-based virtual 3D city augmentation for EM-wave propagation analysis. ACCESS, 13, 137816-137826. (www)

  @article{ , abstract = {We propose a method to generate a 3D model of virtual cities for electromagnetic-wave propagation data generation by ray tracing. In constructing propagation estimation models using machine learning, insufficient or biased training data may worsen the estimation performance. However, in cases where simulation-generated data are used, this effect could be suppressed if we prepare a variety of 3D models and generate propagation data. Therefore, the objective of this study is to generate a map of naturally occurring building layouts using an image generation model, and to create a 3D model on the basis of this map to ensure a larger number of practical propagation environment models.}, archive = {J_ACCESS}, author = {Rento Hagiwara and Kazuki Yuasa and Koichi Ichige and Tatsuya Nagao and Takahiro Hayashi}, doi = {10.1109/ACCESS.2025.3595524}, journal = {IEEE Access}, pages = {137816-137826}, shortjournal = {IEEE Access}, title = {Diffusion model-based virtual 3D city augmentation for EM-wave propagation analysis}, volume = {13}, year = {2025}, }
• (2025). Challenges and opportunities in business process elicitation. ACCESS, 13, 137793-137815. (www)

  @article{ , abstract = {Context: Eliciting business processes is critical for understanding and optimizing organizational workflows. This study examines methodologies, tools, and techniques that aim to enhance the accuracy and reliability of process modeling while addressing challenges such as inconsistent data and the lack of standards for unstructured processes. Objectives: The primary goal is to identify and classify approaches and techniques used in process elicitation, categorizing them into key areas and assessing their impact on continuous improvement in organizations. Methodology: A systematic mapping was conducted to review existing literature, classifying selected studies into methodologies, modeling tools, and automation frameworks. Current trends and gaps in the field were also analyzed. Results: The study highlights the growing adoption of tools such as BPMN, collaborative approaches, and advanced technologies to address data inconsistencies. The findings emphasize the importance of integrating interdisciplinary approaches and adaptive tools to align with dynamic business environments. This work lays the groundwork for further research to enhance organizational process elicitation practices. Future work: Future research should explore the implementation of emerging technologies, such as artificial intelligence and big data analytics, to further automate process elicitation. Additionally, evaluating the effectiveness of collaborative approaches across diverse industries and analyzing the impact of tool customization in specific contexts is recommended to ensure scalability and efficiency. Ensure that your abstract reads well and is grammatically correct.}, archive = {J_ACCESS}, author = {Jorge Hochstetter-Diez and Marlene Negrier-Seguel and Claudio Navarro-Cruces and Mauricio Diéguez-Rebolledo and Marco Cáceres-Senn and Yolanda Valdés-Rodríguez and Jeferson Arango-López}, doi = {10.1109/ACCESS.2025.3590295}, journal = {IEEE Access}, pages = {137793-137815}, shortjournal = {IEEE Access}, title = {Challenges and opportunities in business process elicitation}, volume = {13}, year = {2025}, }
• (2025). Flow matching posterior estimation for simulation-based atmospheric retrieval of exoplanets. ACCESS, 13, 137773-137792. (www)

  @article{ , abstract = {The characterization of exoplanetary atmospheres allows a deeper understanding of planetary formation, evolution, and habitability through atmospheric retrieval, which consists in inferring various properties of exoplanetary atmospheres given their spectroscopic observations. Traditional atmospheric retrieval methods based on Bayesian inference, such as Nested Sampling, require significant computational resources to compute the full posterior distribution of atmospheric parameters, limiting their scalability for future large-scale surveys and high-resolution characterizations. Additionally, the rise of modern density estimation techniques poses a fundamental need for comprehensive evaluation frameworks to objectively compare the posterior distributions of heterogeneous probabilistic estimators. Within the scope of the 2023 edition of the Ariel Data Challenge, this work proposes a novel, scalable atmospheric retrieval framework based on Flow Matching Posterior Estimation (FMPE) and Continuous Normalizing Flows (CNFs), leveraging transmission spectra, instrumental uncertainties across wavelength channels, and auxiliary information about planetary systems, to retrieve the posterior distribution of atmospheric parameters in a significantly reduced computational time compared to conventional techniques. Through the fair definition of an extensive posterior evaluation framework, our approach demonstrates superior performance in target prediction accuracy, uncertainty quantification, calibration, and posterior coverage—consistently outperforming existing neural- and sampling-based retrieval methods. In addition, complementary ablation studies emphasize the value of incorporating auxiliary planetary system data, enhancing the reliability, explainability, and interpretability of atmospheric inferences. Together, these contributions establish the proposed approach as a robust, scalable, and adaptable framework for the analysis of exoplanetary atmospheres.}, archive = {J_ACCESS}, author = {Massimiliano Giordano Orsini and Alessio Ferone and Laura Inno and Angelo Casolaro and Antonio Maratea}, doi = {10.1109/ACCESS.2025.3594751}, journal = {IEEE Access}, pages = {137773-137792}, shortjournal = {IEEE Access}, title = {Flow matching posterior estimation for simulation-based atmospheric retrieval of exoplanets}, volume = {13}, year = {2025}, }
• (2025). Machine learning for non-destructive prediction of sunflower leaf area. ACCESS, 13, 137761-137772. (www)

  @article{ , abstract = {Leaf area is an important parameter in plant growth, physiology, and productive potential studies. However, its measurement using traditional methods can be limited. The search for non-destructive approaches based on leaf dimensions is essential, and machine learning offers promising alternatives for accurate estimation. Therefore, the present study aimed to develop and compare linear regression models and machine learning algorithms for the non-destructive prediction of leaf area in four sunflower cultivars (BRS 323, Altis 99, Sany 66, and BRS 442). The best-performing model was also made available through an interactive and freely accessible web interface. Models based on artificial neural networks (ANN), random forest, and linear regression were developed using leaf length and width as input variables, and leaf area as the output. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Willmott’s agreement index (d). The ANN model with a 2-4-1 architecture showed the highest accuracy ( $\text{R}^{2} =0.9937$ ; RMSE =1.1060; MAE =0.7851; MAPE =4.7916; $d =0.9984$ ), outperforming the other models. This ANN model was subsequently implemented in the web interface, offering an efficient, non-destructive tool for estimating sunflower leaf area based on linear dimensions.}, archive = {J_ACCESS}, author = {João Everthon da Silva Ribeiro and Antonio Gideilson Correia da Silva and Pablo Henrique de Almeida Oliveira and Josiana Micarla da Silva Oliveira and Alessandra Nunes da Silva and John Victor Lucas Lima and Ivan Euzébio da Silva and Ester Dos Santos Coêlho and Isaque de Oliveira Leite and Elania Freire da Silva and Toshik Iarley da Silva and Lindomar Maria da Silveira and Aurélio Paes Barros Júnior}, doi = {10.1109/ACCESS.2025.3595153}, journal = {IEEE Access}, pages = {137761-137772}, shortjournal = {IEEE Access}, title = {Machine learning for non-destructive prediction of sunflower leaf area}, volume = {13}, year = {2025}, }
• (2025). Community clustering and recommendation toward elderly health records: Probabilistic representation learning and large graph search. ACCESS, 13, 137742-137760. (www)

  @article{ , abstract = {We develop a probabilistic representation learning method to automatically divide a large-scale elderly population into distinct health management communities by leveraging shared health conditions, treatment preferences, and behavioral tendencies. This approach enables more precise, efficient, and personalized healthcare interventions, wherein the key steps include: 1) Geometry-based feature selection from high-dimensional health data-This effectively reduces dimensionality while preserving discriminative health patterns. 2) Probabilistic multi-topic representations of individual health profiles-These latent space embeddings capture complex health-need interactions. 3) Graph-based community detection via shared clinical/behavioral traits-The algorithm identifies natural clusters with medical relevance. 4) Personalized intervention ranking by community relevance-This ensures tailored care plan recommendations for each subgroup. Validated on >1M health records, the method achieves a 0.5049 Balanced Error Rate, outperforming six baselines across 42 health categories. Ablation studies show $2.8-9.3\%$ accuracy drops when removing key components, with graph mining and ranking mechanisms being most impactful (91.4% baseline precision).}, archive = {J_ACCESS}, author = {Huijiong Ding}, doi = {10.1109/ACCESS.2025.3594764}, journal = {IEEE Access}, pages = {137742-137760}, shortjournal = {IEEE Access}, title = {Community clustering and recommendation toward elderly health records: Probabilistic representation learning and large graph search}, volume = {13}, year = {2025}, }
• (2025). GU-net3+: A global-local feature fusion algorithm for building extraction in remote sensing images. ACCESS, 13, 137724-137741. (www)

  @article{ , abstract = {In remote sensing image building extraction, image regions with similar textures or colors often cause false positives and false negatives in building-detection. Global features can help the model better recognize the overall structure of large buildings and provide contextual background information when segmenting small buildings to avoid mis-segmentation. Local features focus on extracting details and edge information, enabling a precise description of the target’s local shape and texture details. In this study, we propose a building detection method that integrates global and local features. First, frequency domain transformation and a Convolutional Block Attention Module (CBAM) were applied to preprocess the remote sensing images, enhancing building details while suppressing irrelevant noise interference. Next, an auxiliary encoder was designed to dynamically adjust the orientation and scale of the receptive field, improving the precise localization of building edges and contours. Finally, a Global Cross-Attention module is developed to aggregate global features from different spatial locations, which are then fused with local features from skip connections as input to the decoder, enhancing information exchange across multi-scale and hierarchical features to improve building extraction accuracy. The experimental results demonstrate that the proposed algorithm achieves mIoU values of 93.8% and 81.05%, and F1-scores of 96.93% and 89.10% on the WHU and Massachusetts datasets, respectively, effectively addressing the false detection issues caused by similar textures or colors in the building extraction process.}, archive = {J_ACCESS}, author = {Yali Liu and Cui Ni and Peng Wang and Dongqing Yang and Hexin Yuan and Chao Ma}, doi = {10.1109/ACCESS.2025.3588206}, journal = {IEEE Access}, pages = {137724-137741}, shortjournal = {IEEE Access}, title = {GU-net3+: A global-local feature fusion algorithm for building extraction in remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Intelligent virtual assistant for calculating technology readiness levels using large language models (LLM). ACCESS, 13, 137709-137723. (www)

  @article{ , abstract = {The Technology Readiness Level (TRL) is a metric developed by NASA in the 1970s to assess the maturity of a technology and guide decision-making regarding its development. Although it is standardized, in practice, evaluations are often manual and subjective, leading to inconsistencies. In this study, an intelligent virtual assistant was developed based on a large language model (LLM), using GPT-3.5-Turbo as the foundation, selected over LlaMa-2 after rigorous performance testing. The model was trained using optimization techniques on a custom dataset built from reliable sources such as NASA, government entities, and universities, enabling a more accurate evaluation of the TRL.}, archive = {J_ACCESS}, author = {Juan Betancourt and Andrés Coral and Anabel Fraga and Cristhian Figueroa and Gustavo Ramirez-Gonzalez}, doi = {10.1109/ACCESS.2025.3595699}, journal = {IEEE Access}, pages = {137709-137723}, shortjournal = {IEEE Access}, title = {Intelligent virtual assistant for calculating technology readiness levels using large language models (LLM)}, volume = {13}, year = {2025}, }
• (2025). Self attention GAN and SWIN transformer-based pothole detection with trust region-based LSM and hough line transform for 2D to 3D conversion. ACCESS, 13, 137695-137708. (www)

  @article{ , abstract = {Vehicles and roads play a crucial role in connecting peoples across the world for their every day needs, activities and life style. This has enormously increased the need for maintaining the road surface with better quality and maintenance to prevent accidents and vehicles damages due to road hazards such as cracks and potholes. In this paper, we have addressed the aforementioned problem with deep learning based General Adversarial Networks (GAN) along with Data Augmentation considering both road damaged and road damage free images from the datasets. Additionally, we have also employed SWIN transformer with Self-Attention Mechanism where 2D images are transformed into 3D images for better detection of potholes with improved accuracy using Trust Region Reflective Least Square Fitting Method and Hough line Transform along with Inverse Perspective Mapping is employed for dimensionality conversion of pothole dimensions. The proposed approach has considered modified YOLOv81 and the results were compared with basic YOLOv81 with various performance metrics. Our proposed approach has achieved 93.54% accuracy, minimum accuracy loss of 6.46% and highest F1-score of 76.42% while compared with ESRGAN-YOLOv5, ESRGAN-YOLOv7 and Potholes state of art approaches.}, archive = {J_ACCESS}, author = {R. Kothai and N. Prabakaran}, doi = {10.1109/ACCESS.2025.3595190}, journal = {IEEE Access}, pages = {137695-137708}, shortjournal = {IEEE Access}, title = {Self attention GAN and SWIN transformer-based pothole detection with trust region-based LSM and hough line transform for 2D to 3D conversion}, volume = {13}, year = {2025}, }
• (2025). Vigilance state classification of comatose patients based on multifractal analysis of EEG signals. ACCESS, 13, 137684-137694. (www)

  @article{ , abstract = {Various information can be inferred from electrical signals generated by the brain using the electroencephalogram (EEG). This information can be used for the detection or classification of several diseases using many signal processing methods. Multifractal analysis has been widely used in the last two decades to examine various electrical signals generated by the human body. In this study, we analyze the multifractal characteristics of a data set comprising 27 EEG signals where the objective is to demonstrate the efficacy of this method in differentiating the states of vigilance in patients. These signals are obtained from comatose patients in the intensive care unit of the National Institute of Neurology of Tunis, each of whom had a documented Glasgow coma scale score. The purpose of analyzing these data is to identify the connection between the coma scales and the signals generated by some regions of the cerebral cortex in the brain and how this could lead to determining the state of the coma of these patients based on their electrical brain activities, especially due to the number of limitations of the behavioral and physiological coma scales currently used. Because EEG signals are multifractal, we applied the wavelet leader method to extract features from the singularity spectra of the recorded signals with a known Glasgow coma scale and classify them. We found that the singularity spectrum curves of patients in deep, mild, and light coma are distant from each other. Furthermore, we found that the spectra of patients in a light coma are shifted to the right compared to those of patients in a deep coma. On the other hand, curves calculated from the sedated patients are on the left of the curves of those in deep coma and are shrinking in shape compared to non-sedated patients. The lighter the coma, the more distinct the curves, especially the frontal channel curves. We also calculated the h values at the peak of the singularity spectrum curves of three different channels from the three other areas of the brain for the three groups. We found that the results from the back areas of the brain are aligned with those from the frontal lobe area. The h value averages of sedated patients, deep coma patients, and light coma patients are 0.116, 0.196, and 0.317, respectively. These results provided strong evidence that the multifractal analysis of EEG signals can be utilized to classify the vigilance states of comatose patients. However, although this analysis overcomes the limited physiological scales currently used, it still needs more investigation with a large sample of comatose patients.}, archive = {J_ACCESS}, author = {Bechir Hbibi and Lamine Mili and Kamel Baccar and Abdelkader Mami}, doi = {10.1109/ACCESS.2025.3592518}, journal = {IEEE Access}, pages = {137684-137694}, shortjournal = {IEEE Access}, title = {Vigilance state classification of comatose patients based on multifractal analysis of EEG signals}, volume = {13}, year = {2025}, }
• (2025). A zero-sequence current-based cluster energy balancing control in single-star multilevel converter with zigzag transformer. ACCESS, 13, 137665-137683. (www)

  @article{ , abstract = {In a power system, when a fault occurs, a Static Synchronous Compensator (STATCOM) is often required to inject negative-sequence current to compensate for unbalanced conditions. In high-voltage applications, single-star multilevel converters are commonly used in STATCOM systems to efficiently interface with high-voltage levels. However, during unbalanced condition compensation, these circuits experience cluster energy imbalance, which must be addressed through the balancing control. The conventional cluster energy balancing method utilizes zero-sequence voltage to redistribute active power for system balance. Under severe unbalance, a large negative-sequence current must be injected to compensate grid or load asymmetry. In conventional single-star multilevel converters, the zero-sequence component of the cluster output voltage is then actively regulated to eliminate the resulting cluster-energy imbalance; however, the required increase in this component can drive the converter into over-modulation. This limitation reduces the effectiveness of negative-sequence current injection, making it difficult to effectively compensate for unbalanced loads and grid conditions. To overcome this limitation, this paper proposes a modified power converter structure that retains the single-star multilevel converter topology while incorporating a zigzag transformer, along with a new cluster energy balancing control method. The proposed approach significantly enhances the range and capability of negative-sequence current compensation. The effectiveness of the proposed control strategy and converter design is validated through mathematical analysis, simulations, and experimental results.}, archive = {J_ACCESS}, author = {Yeongung Kim and Shenghui Cui and Jae-Jung Jung}, doi = {10.1109/ACCESS.2025.3595810}, journal = {IEEE Access}, pages = {137665-137683}, shortjournal = {IEEE Access}, title = {A zero-sequence current-based cluster energy balancing control in single-star multilevel converter with zigzag transformer}, volume = {13}, year = {2025}, }
• (2025). Neural networks for solving PDEs on edge computing platforms: A comprehensive analysis. ACCESS, 13, 137652-137664. (www)

  @article{ , abstract = {Solving elliptic partial differential equations (PDEs) with traditional methods is challenging on edge devices due to their reliance on fine meshes. This study evaluates neural networks as an efficient alternative, comparing 60 architectures and five optimizers on three single-board computers. Our methodology uniquely tested spatial interpolation by training on 900 points and verifying on 6,400, demonstrating robustness to mesh density. Deeper architectures (4–5 layers, 30–50 neurons) achieved errors of $10^{-4}$ , with Adamax consistently proving the best-performing optimizer. Hardware benchmarks revealed critical trade-offs. The NVIDIA Jetson Nano offered optimal overall performance (8–12 ms inference, $\approx 1W$ power consumption, temperatures $\lt 32^{o}$ C), while the TPU-accelerated ASUS Tinker Edge T provided superior linear scalability, essential for varying problem sizes. However, this finding highlights that specialized hardware requires software co-optimization, as unoptimized TPU inference was unexpectedly slow. The Raspberry Pi required active cooling due to significant overheating (up to $\lt 80^{o}$ C). Across all platforms, neural networks maintained high solution accuracy (<0.05% error deviation), overcoming the limitations of traditional mesh refinement. We conclude that neural networks are a compelling alternative for solving PDEs on edge devices.}, archive = {J_ACCESS}, author = {Segundo Francisco Segura Altamirano and Diana M. Castro Cárdenas}, doi = {10.1109/ACCESS.2025.3590766}, journal = {IEEE Access}, pages = {137652-137664}, shortjournal = {IEEE Access}, title = {Neural networks for solving PDEs on edge computing platforms: A comprehensive analysis}, volume = {13}, year = {2025}, }
• (2025). A novel variable-order threshold serial belief propagation-based decoding algorithm for LT codes. ACCESS, 13, 137642-137651. (www)

  @article{ , abstract = {The belief propagation (BP) algorithm is a widely adopted iterative decoding method for Luby transform (LT) codes. However, the conventional log-likelihood ratio BP (LLR-BP) decoder exhibits inherent limitations in additive white Gaussian noise (AWGN) channels. These limitations include high computational complexity, slow convergence speed, and limited decoding efficiency. To address these challenges, we propose a variable-order threshold serial belief propagation (VOT-SBP) algorithm. The proposed algorithm integrates the serial belief propagation (SBP) method with a reliability metric for check nodes, enabling accurate determination of the sequence for message updates. In addition, by introducing an adaptive threshold for correcting check node information, the algorithm effectively eliminates redundant update actions throughout the decoding process. Simulation results demonstrate a 63.6% reduction in iteration count compared to BP for long-length codes at medium to high signal-to-noise ratio (SNR) and a 17.4% decrease in computational overhead. This approach enhances both decoding performance and convergence efficiency.}, archive = {J_ACCESS}, author = {Weibai Sun and Shuyan Ni and Tuofeng Lei and Rui Yang}, doi = {10.1109/ACCESS.2025.3592429}, journal = {IEEE Access}, pages = {137642-137651}, shortjournal = {IEEE Access}, title = {A novel variable-order threshold serial belief propagation-based decoding algorithm for LT codes}, volume = {13}, year = {2025}, }
• (2025). Improved design of finite-set sliding mode predictive control for AC microgrids with multiple distributed generators. ACCESS, 13, 137623-137641. (www)

  @article{ , abstract = {Microgrids offer a reliable way to integrate renewable energy sources into the main power grid. Connecting distributed generators (DGs) to the grid requires robust control strategies to manage power flow without disruption. This study presents a novel finite-set sliding mode predictive control (FS-SMPC) framework that supports microgrid operation in both grid-connected and islanded modes. Each DG operates with its own FS-SMPC and inverse droop controller, enabling decentralized control. The control goals change based on the current mode. In grid-connected operation, each DG regulates its power output, while voltage and frequency are maintained by the main grid. In islanded mode, the FS-SMPC takes over to stabilize voltage and frequency within the microgrid. The proposed FS-SMPC design is validated using MATLAB/Simulink® simulations. Its performance is further confirmed through a real-time Hardware-in-the-Loop (HiL) test setup, using the OPAL-RT OP5600 digital simulator. Results show effective power tracking and dynamic load management.}, archive = {J_ACCESS}, author = {Houssam Eddine Mansouri and Billel Talbi and Idris Messaoudene and Moussa Abderrahim Mehiris and Abdelbasset Krama and Abdeslem Sahli}, doi = {10.1109/ACCESS.2025.3595467}, journal = {IEEE Access}, pages = {137623-137641}, shortjournal = {IEEE Access}, title = {Improved design of finite-set sliding mode predictive control for AC microgrids with multiple distributed generators}, volume = {13}, year = {2025}, }
• (2025). Visual saliency design for AR-HUD navigation in extreme weather: Reducing inattentional blindness. ACCESS, 13, 137613-137622. (www)

  @article{ , abstract = {Background: Current research on AR-HUD visual icons focuses primarily on interface, color, and motion trajectory design. However, when displayed in complex environments, AR-HUD might cause inattentional blindness, particularly during extreme weather conditions where occurrence rates increase significantly. Methods: This study employed a two-factor within-subjects design to examine how dynamic saliency and contour saliency in AR-HUD navigation graphics affect inattentional blindness and user experience. Eye-tracking metrics with total fixation time and first fixation time, driver reaction time, and user experience scores were analyzed. Results: Objective measures revealed significant interaction effects between saliency types, though user experience feedback showed no significance. Both contour and dynamic saliency effectively reduced inattentional blindness and improved information-seeking efficiency. Conclusions: The combination of dynamic and contour saliency produced the lowest inattentional blindness rates, fastest reaction times, and highest user satisfaction. We recommend: 1) adding distinct contours to prevent environmental visual fusion, 2) implementing temporary icon enlargement during critical moments to enhance attentional capture. These findings provide actionable design principles for improving AR-HUD safety in adverse weather conditions.}, archive = {J_ACCESS}, author = {Qi Zhu and Jiale Li and Yixiang Liu}, doi = {10.1109/ACCESS.2025.3588576}, journal = {IEEE Access}, pages = {137613-137622}, shortjournal = {IEEE Access}, title = {Visual saliency design for AR-HUD navigation in extreme weather: Reducing inattentional blindness}, volume = {13}, year = {2025}, }
• (2025). Dynamic group key exchange protocol with lattice trapdoor and re-encryption mechanisms. ACCESS, 13, 137603-137612. (www)

  @article{ , abstract = {Post-quantum group key exchange protocols have become an increasingly critical area of research due to the potential threat quantum computers pose to classical cryptographic schemes. Traditional group key exchange protocols, often based on Diffie-Hellman and other number-theoretic assumptions, are vulnerable to quantum attacks. To address this challenge, we propose a novel group key exchange protocol that leverages lattice-based cryptography. Specifically, our approach utilizes lattice trapdoors and a re-encryption mechanism based on the Ring Learning With Errors (RLWE) problem. This design not only achieves post-quantum security but also enhances efficiency and flexibility in dynamic group settings. A common challenge in most protocols is the computational cost of deriving new keys as group members join and leave. Our approach effectively reduces this cost, making key updates more efficient and practical. Our protocol is proven secure under standard lattice assumptions, providing a promising solution for quantum-resistant group key exchange.}, archive = {J_ACCESS}, author = {Narn-Yih Lee and Yuan-Ting Tsai}, doi = {10.1109/ACCESS.2025.3595584}, journal = {IEEE Access}, pages = {137603-137612}, shortjournal = {IEEE Access}, title = {Dynamic group key exchange protocol with lattice trapdoor and re-encryption mechanisms}, volume = {13}, year = {2025}, }
• (2025). PRIVOT: Privacy-resilient intelligent DAG blockchain architecture for IoT. ACCESS, 13, 137592-137602. (www)

  @article{ , abstract = {The rapid growth of the Internet of Things (IoT) demands solutions that can secure massive streams of sensitive data without sacrificing performance. Traditional blockchains struggle in IoT environments, facing significant challenges with transaction speed, scalability, and privacy. This paper introduces PRIVOT, a novel blockchain architecture that integrates a Directed Acyclic Graph (DAG) for high-throughput consensus with lightweight zero-knowledge proofs (ZKPs) for confidential transactions, rateless coded computation for private analytics, and an AI-driven manager that dynamically balances security and efficiency. Our simulations show that PRIVOT significantly outperforms traditional blockchain approaches, achieving high transaction throughput (up to 480 TPS on a 500-device network) with confirmation latencies under 2.1 seconds, even under heavy load. The framework provides robust privacy, limiting data leakage to less than 0.1% against significant node collusion, while keeping computational overhead low enough for resource-constrained IoT devices. By unifying these techniques, PRIVOT offers a scalable and resilient solution ideal for large-scale IoT deployments where both high performance and strong privacy are paramount.}, archive = {J_ACCESS}, author = {Faisal Alanazi and Mahdi Zareei and Alberto Rodríguez Arreola}, doi = {10.1109/ACCESS.2025.3593365}, journal = {IEEE Access}, pages = {137592-137602}, shortjournal = {IEEE Access}, title = {PRIVOT: Privacy-resilient intelligent DAG blockchain architecture for IoT}, volume = {13}, year = {2025}, }
• (2025). Generating parathyroid reports using YOLO-based large language models. ACCESS, 13, 137579-137591. (www)

  @article{ , abstract = {Different tissues and structures in ultrasound produce varying sound wave reflections, resulting in black-and-white grayscale images. Radiologists review thousands of medical images daily and write detailed reports. In parathyroid ultrasound imaging, their reports must go beyond routine observations, providing a comprehensive analysis highlighting the lesion’s type, size, and exact location. This critical information forms the foundation for accurate diagnoses and personalized treatment plans, underscoring the vital role of radiologists in modern medical care. This study presents a novel system for generating automated medical reports from parathyroid ultrasound images focusing on critical diagnostic parameters such as size, type, structure, location, homogeneity, and echogenicity through effective lesion recognition and localization. The proposed system, medical report generation for parathyroid ultrasound (MrGPU-GPT), integrates advanced image processing and natural language generation to streamline clinical workflows and improve diagnostic accuracy. The MrGPU-GPT employs a modified YOLOv7-based architecture, YOLO-BiFPN, to detect and localize lesions within the ultrasound images. Subsequently, key lesion characteristics, including homogeneity and echogenicity, are extracted and utilized for prompt engineering. These prompts are then fed into a fine-tuned Vicuna language model, which generates detailed and precise medical reports. The system provides annotated ultrasound images with bounding boxes and offers comprehensive-textual descriptions in the form of medical reports, significantly reducing the reporting burden on radiologists. Combining image-based diagnostic insights with advanced natural language processing minimizes errors caused by human factors such as inexperience or fatigue, offering robust support for radiologists. The resulting system improves the efficiency and reliability of clinical workflows, delivering accurate diagnostic information while improving patient care outcomes. The experimental results show that, in classifying the lesion, detecting objects, and recognizing position markers, the system achieved a mAP of 93.32%, 91.04%, and 99.65% in the validation of the K-Fold. In medical report generation, compared to MiniGPT, our method, using cosine similarity, ROUGE, and BLEU metrics, along with physician feedback, can generate medical reports with higher similarity scores. (MrGPU-GPT) was deployed on a local computer, developing a medical report in about 3-5 seconds to identify parathyroid images and a medical report.}, archive = {J_ACCESS}, author = {Chuan-Yu Chang and Abida Khanum and Chiao-Yin Sun and Ying-Ting Chen and Yu-Chen Tsai and Chih-Chin Hsu}, doi = {10.1109/ACCESS.2025.3595504}, journal = {IEEE Access}, pages = {137579-137591}, shortjournal = {IEEE Access}, title = {Generating parathyroid reports using YOLO-based large language models}, volume = {13}, year = {2025}, }
• (2025). Optimizing mRNA vaccine degradation prediction via penalized dropout approaches. ACCESS, 13, 137561-137578. (www)

  @article{ , abstract = {Predicting mRNA vaccine degradation rates with precision is essential for ensuring stability, efficacy, and optimal deployment strategies, particularly given the unique challenges posed by their rapid degradation. This study introduces a comprehensive approach that integrates bioinformatic insights with advanced computational methodologies to address these challenges. A novel tetramer-label encoding approach (4-mer-lbA) was proposed, integrating biological relevance with data-driven analysis to enhance predictive accuracy. To further optimize model performance, two advanced hyperparameter optimization (HPO) techniques—Dropout-Enhanced Technique (DEet) and Hyperparameter Optimization Algorithm Penalizer (HOPeR)—are proposed to mitigate overfitting, address inefficiencies in conventional HPO algorithms (HPOAs), and accelerate model convergence. The methodologies were validated on mRNA degradation datasets using deep neural network (DNN) architectures, with particular attention to the comparative performance of sequential and wrapped architectural designs. Results demonstrate that sequential architectures outperform wrapped models by reducing overfitting and computational demands. The integration of DEet and HOPeR further optimized hyperparameter exploration, with DEet enhancing model robustness through dropout regularization and HOPeR introducing adaptive penalties to systematically eliminate suboptimal configurations. The experimental outcomes highlight significant advancements in convergence rates and error reduction, particularly in complex models like 3-layer-wrapped Bidirectional Long Short-Term Memory (3wBiLSTM). By the 100th epoch, training and validation losses reached 0.0023 and 0.0029, respectively, indicating a substantial improvement over baseline models. These methodologies extend beyond mRNA vaccine research, demonstrating versatility across diverse machine learning domains. By addressing critical challenges in HPO and predictive modeling, the study offers scalable and robust solutions for advancing biotechnology and interdisciplinary research.}, archive = {J_ACCESS}, author = {Hwai Ing Soon and Azian Azamimi Abdullah and Hiromitsu Nishizaki and Latifah Munirah Kamarudin}, doi = {10.1109/ACCESS.2025.3595155}, journal = {IEEE Access}, pages = {137561-137578}, shortjournal = {IEEE Access}, title = {Optimizing mRNA vaccine degradation prediction via penalized dropout approaches}, volume = {13}, year = {2025}, }
• (2025). Rectification behavior evaluation of si cone diodes in dependence of contact metallization and printable nanoparticle thin film modification. ACCESS, 13, 137552-137560. (www)

  @article{ , abstract = {Recently, we were able to demonstrate Si cone Schottky diodes processed from printable nanoparticle dispersion which can operate at switching speeds well beyond 4 GHz. The challenge of these devices, however, is a relatively low rectification ratio in the order of 10 to 100. Therefore, in this contribution the rectification behavior of such diodes is evaluated in terms of effective Schottky barrier height (SBH), ideality factor and rectification ratio for different contact metals as well as Si cone height distributions. We find that the effective SBH is only weakly dependent on the metal work function of thermally evaporated metals due to strong Fermi-level pinning. In consequence, this parameter has limited influence on the rectification behavior of this diode type. However, in previous theoretical publications, we demonstrated that the non-ideal rectification behavior of cone diodes can be explained by Gaussian distributed SBHs as the result of the variation in Si cone heights. In accord with this model, we investigate different cone height distributions generated from varying nanoparticle deposition profiles, which are formed by spin coating and by controlling the coffee ring-effect in inkjet printed droplets. With this variation in cone height distribution, we observe a linear relation between SBH and ideality factor, which indicates a Gaussian SBH distribution. Therefore, to improve the rectification ratio, the standard deviation of the cone height distribution needs to be reduced, which leads to a lower ideality factor and a greater effective SBH. This is facilitated by using small-area cone diode devices.}, archive = {J_ACCESS}, author = {Fabian Langer and Niels Benson}, doi = {10.1109/ACCESS.2025.3588347}, journal = {IEEE Access}, pages = {137552-137560}, shortjournal = {IEEE Access}, title = {Rectification behavior evaluation of si cone diodes in dependence of contact metallization and printable nanoparticle thin film modification}, volume = {13}, year = {2025}, }
• (2025). XLSTMKT: XLSTM for knowledge tracing. ACCESS, 13, 137540-137551. (www)

  @article{ , abstract = {Knowledge Tracing focuses on modeling a student’s evolving knowledge state to accurately predict their responses to future questions across a set of knowledge concepts. In recent years, KT models based on Long Short-Term Memory (LSTM) and Transformer architectures have achieved significant performance improvements, surpassing traditional Bayesian approaches. However, existing methods fuse question-specific and knowledge concepts-specific information at an early stage, limiting the model’s ability to fully capture the fine-grained details of student learning dynamics. To address these limitations, we propose a novel framework called xLSTM Knowledge Tracing (xLSTMKT), that explicitly separates student interactions into question-level and skill-level streams, generating dynamic embeddings for each at every time step. These embeddings are then jointly utilized to enhance next-response prediction. Extensive experiments conducted on several datasets demonstrate that our model consistently outperforms state-of-the-art KT methods, achieving at least a 1.42% improvement in AUC. Furthermore, we introduce a new problem formulation for the KT task, which yields additional performance gains, notably improving AUC scores on the ALGEBRA2005 dataset.}, archive = {J_ACCESS}, author = {Yasser Aderghal and Hakim Hafidi and Abdellatif El Ghazi}, doi = {10.1109/ACCESS.2025.3595176}, journal = {IEEE Access}, pages = {137540-137551}, shortjournal = {IEEE Access}, title = {XLSTMKT: XLSTM for knowledge tracing}, volume = {13}, year = {2025}, }
• (2025). Prediction of pilot performance during startle events based on neuropsychophysiological features of stress resilience and cognitive task scores. ACCESS, 13, 137525-137539. (www)

  @article{ , abstract = {The disruptive impact of the startle effect on pilots can lead to potentially fatal outcomes, highlighting the importance of identifying novel predictors of pilot performance during unexpected and unpredictable startle events. Accordingly, this study investigates the potential of optimally selected stress resilience features and cognitive task scores to predict pilot performance during startle events. Seventeen military pilot cadets participated in a two-phase experiment conducted over two weeks. In Phase 1, context-free stimulation paradigms including neuropsychophysiological measurements were administered. In Phase 2, specific flight simulator sessions with startle events were undertaken. Optimal neuropsychophysiological stress resilience features and cognitive task scores from Phase 1 were selected as predictors of pilot simulator performance in Phase 2 using multiple regression analysis (MRA) within a leave-one-out cross-validation (LOOCV) procedure. The obtained MRA models demonstrated significant predictive validity across left-out participants, as indicated by a Pearson correlation coefficient of 0.7039 (p =0.0034) between the actual and predicted simulator performance, with a mean absolute error (MAE) of 2.5457° versus a dummy median regressor MAE of 3.4881°. The optimally predictive features/scores from Phase 1 were related to startle reflex habituation, multitasking ability, heart rate recovery after threat, and resting respiratory sinus arrhythmia. These findings may improve the accuracy of predicting pilot performance during startle events, with implications for pilot selection and training.}, archive = {J_ACCESS}, author = {Mate Gambiraža and Siniša Popović and Matej Kurtak and Tanja Jovanovic and Seth Norrholm and Krešimir Ćosić}, doi = {10.1109/ACCESS.2025.3594493}, journal = {IEEE Access}, pages = {137525-137539}, shortjournal = {IEEE Access}, title = {Prediction of pilot performance during startle events based on neuropsychophysiological features of stress resilience and cognitive task scores}, volume = {13}, year = {2025}, }
• (2025). Noise-based frequency spectrum correction method using interior point approach. ACCESS, 13, 137514-137524. (www)

  @article{ , abstract = {The spectral correction of noisy real sinusoidal signals is of critical importance; however, the presence of spectral leakage and noise introduces significant challenges. This paper presents a novel approach based on the interior point (IP) method to address key limitations of conventional techniques, including high sensitivity to noise, low computational efficiency, and parameter decoupling. The correction problem is reformulated as an unconstrained optimization task. Following Welch spectral estimation, peak detection is performed using an adaptive threshold to identify the spectral peaks adjacent to the true peak, which serve as boundary constraints for the IP solution. This enables accurate estimation of the amplitude, frequency, and phase of the signal. This study innovatively formulates the correction process as a bounded optimization problem and incorporates an L1-norm sparse penalty term into the interior point (IP) framework to enhance robustness against noise. Additionally, an efficient adaptive solver is developed to improve convergence speed. Simulation results demonstrate that the proposed method offers significant advantages, particularly in terms of computational efficiency. Notably, for a signal containing 2048 samples, the IP method achieves a 4.2-fold reduction in execution time compared to the FFT+FT refinement approach, while maintaining comparable accuracy. More importantly, the method exhibits superior estimation accuracy. Under simulated conditions, the root mean square errors of amplitude, frequency, and phase estimation are reduced by over 20% relative to the ratio correction and energy centroid methods. In challenging scenarios such as short signals and non-integer period sampling, the frequency estimation error is decreased by approximately 10–20% compared to spectral refinement techniques, and the phase estimation error is reduced by 20–28% relative to the energy centroid method. These outcomes validate the method’s enhanced accuracy and robustness. The performance improvements primarily result from joint parameter optimization and adaptive boundary enforcement, enabling strong effectiveness under low signal-to-noise ratios, non-ideal sampling conditions, and short signal lengths.}, archive = {J_ACCESS}, author = {Guichun Yang and Shengyou Zhou and Han Chen and Huiyue Yang and Weixing Hua and Zhaojie Wu and Yan Chen}, doi = {10.1109/ACCESS.2025.3589500}, journal = {IEEE Access}, pages = {137514-137524}, shortjournal = {IEEE Access}, title = {Noise-based frequency spectrum correction method using interior point approach}, volume = {13}, year = {2025}, }
• (2025). From prompts to motors: Man-in-the-middle attacks on LLM-enabled vacuum robots. ACCESS, 13, 137505-137513. (www)

  @article{ , abstract = {The integration of large language models (LLMs) into robotic platforms is transforming human–robot interaction by enabling more natural communication and adaptive task execution. However, this advancement also introduces new security vulnerabilities, particularly in networked environments. In this study, we present a systematic analysis of man-in-the-middle (MITM) attacks targeting an LLM-enabled vacuum robot. Our research follows a three-phase development process: 1) command-line simulation of LLM–robot interactions, 2) tabletop setup, and 3) implementation of a physical robot using a commercial vacuum platform enhanced with a Raspberry Pi–hosted ChatGPT application programming interface (API) and you only look once (YOLO, v8) object detection. We define a gray-box threat model in which an attacker can intercept, inject, and manipulate JavaScript object notation (JSON)-formatted messages exchanged between the robot and the LLM. We evaluate four attack scenarios, two based on prompt injection and two on output manipulation, across three LLM configurations (ChatGPT-4, ChatGPT-4o mini, and ChatGPT-3.5 Turbo). While prior work on LLM security assumes secure communication channels and overlooks network-level threats, our experimental results demonstrate that a remote attacker can bypass safety protocols, override motor commands, and deliver deceptive feedback to users, ultimately leading to unsafe robot behavior. These findings reveal a critical and underexplored attack surface in LLM-integrated robotic systems and highlight the urgent need for secure-by-design communication architectures.}, archive = {J_ACCESS}, author = {Asif Shaikh and Aygün Varol and Johanna Virkki}, doi = {10.1109/ACCESS.2025.3595424}, journal = {IEEE Access}, pages = {137505-137513}, shortjournal = {IEEE Access}, title = {From prompts to motors: Man-in-the-middle attacks on LLM-enabled vacuum robots}, volume = {13}, year = {2025}, }
• (2025). Routine Testing–Oriented method for evaluating inductive current transformers accuracy under harmonic distortion, based on maximum current error and phase displacement. ACCESS, 13, 137491-137504. (www)

  @article{ , abstract = {In the case of the modern inductive current transformers the most important factor that have influence on their wideband accuracy is the self-distortion of the secondary current caused by the nonlinearity of the magnetization characteristic of the magnetic core. In response to the industry requirement to simplify the measuring procedure in this paper the idea is proposed to evaluate their transformation accuracy from tests made for increased frequency sinusoidal current with consideration of the impact of self-generated low-order higher harmonics. Moreover, this proposed method ensures simple and fast determination of the maximum values of current error and phase displacement for transformation of distorted current harmonics by inductive current transformers essential for accuracy class assessment. This is important due to the fact, that both the phase angles and the RMS values of higher harmonics depend on the source of disturbances in the power network and have significant influence on their transformation accuracy. Therefore, different results for different current distortion is possible and these multiple conditions should be considered for their reliable test of transformation accuracy for distorted currents. However, for accuracy class evaluation they are correctly represented by distorted waveform containing main component and single higher harmonics the maximum values of current error and phase displacement are determined. The presented analyses aim to justify the application of this simplified method for testing the transformation accuracy of inductive current transformers for harmonics of distorted currents in routine test in contrast to the full procedure required to be used in type test.}, archive = {J_ACCESS}, author = {Michal Kaczmarek}, doi = {10.1109/ACCESS.2025.3593690}, journal = {IEEE Access}, pages = {137491-137504}, shortjournal = {IEEE Access}, title = {Routine Testing–Oriented method for evaluating inductive current transformers accuracy under harmonic distortion, based on maximum current error and phase displacement}, volume = {13}, year = {2025}, }
• (2025). Deep learning and edge computing in agriculture: A comprehensive review of recent trends and innovations. ACCESS, 13, 137464-137490. (www)

  @article{ , abstract = {Rice is a vital staple for over half of the global population, yet its production is significantly threatened by leaf diseases caused by fungal and bacterial pathogens. Early and accurate detection of such diseases is critical to minimizing crop loss, particularly under conditions of labor shortages and climate variability. Traditional inspection methods are labor-intensive and error-prone, highlighting the need for automated, intelligent solutions. This systematic literature review explores recent advancements in deep learning and edge computing for rice leaf disease detection, focusing on challenges such as small and overlapping lesion detection and real-time model deployment. These lesions are difficult to detect due to their limited size, irregular shapes, and visual similarity to healthy areas, especially in field conditions. A comprehensive search across five databases (2020–2024) using the keywords “rice leaf disease detection,” “object detection,” and “edge computing” identified 2,072 articles. After applying inclusion criteria, 76 peer-reviewed journal articles were selected. Among the models reviewed, You Only Look Once version 7 (YOLOv7) achieved the highest accuracy (99% mAP), outperforming Faster Region-Based Convolutional Neural Network (Faster R-CNN) and YOLOv8. Edge devices like NVIDIA Jetson Nano demonstrated real-time capabilities, achieving 0.0549 seconds latency per image using YOLOv7-tiny at $640\times 640$ resolution. Vision Transformers (ViTs) also performed well (up to 98% mAP) but faced challenges related to computational and data efficiency. Future directions include leveraging lightweight models, solar-powered edge devices, and hybrid ViT–CNN architectures. This review underscores the transformative role of AI technologies in precision agriculture, offering scalable tools to improve crop monitoring and global food security.}, archive = {J_ACCESS}, author = {Apri Junaidi and Siti Zaiton Mohd Hashim and Mohd Shahizan Bin Othman and Mohd Murtadha Bin Mohamad and Hitham Alhussian and Said Jadid Abdulkadir and Maged Nasser and Yunusa Adamu Bena}, doi = {10.1109/ACCESS.2025.3595621}, journal = {IEEE Access}, pages = {137464-137490}, shortjournal = {IEEE Access}, title = {Deep learning and edge computing in agriculture: A comprehensive review of recent trends and innovations}, volume = {13}, year = {2025}, }
• (2025). Security design for data distribution service based on secure onboard communication. ACCESS, 13, 137446-137463. (www)

  @article{ , abstract = {Data Distribution Service (DDS) middleware plays a critical role in intelligent connected vehicle (ICV) communication; however, its existing security mechanisms often compromise real-time performance. This study proposes a hierarchical dynamic security protection architecture that achieves deep integration of Secure Onboard Communication(SecOC) and DDS security through protocol extension, policy mapping, and resource degradation. The core innovations are as follows: 1) Dynamic Security Policy Adaptation across Protocol Layers: By extending the Real-Time Publish-Subscribe protocol of DDS, SecOC security fields (Freshness Value + Message Authentication Code) are embedded into the message structure, and a dynamic mapping mechanism is established between the topic-level Quality of Service policies and the Protocol Data Unit-level security parameters is established. Security levels are defined based on Extensible Markup Language configuration files to achieve automated policy loading from application layer encryption to transport layer authentication, thereby solving the problem of consistency in security policies between heterogeneous protocol stacks. 2) Lightweight Security Grading Method: A security grading method: A security grading framework was constructed based on the sensitivity of DDS topics. This method balances security strength and computational overhead through differentiated security processing while considering the ISO 26262 functional safety standard. Compared with the original security plugin, experimental verification in typical communication scenarios demonstrates its superiority in jitter reduction by 35%, latency reduction by 50%, and throughput improvement of approximately 12%, thereby effectively fulfilling the dual requirements of ICV for robust security and high real-time performance.}, archive = {J_ACCESS}, author = {Fengjun Zhou and Gang Wang and Qun Wang and Yuxing Wang and Jun Wang}, doi = {10.1109/ACCESS.2025.3595598}, journal = {IEEE Access}, pages = {137446-137463}, shortjournal = {IEEE Access}, title = {Security design for data distribution service based on secure onboard communication}, volume = {13}, year = {2025}, }
• (2025). Quality evaluation of culture-tourism integration based on a 3E multi-dimensional Subjective–Objective hybrid weighting method. ACCESS, 13, 137436-137445. (www)

  @article{ , abstract = {The development of culture–tourism is a complex process that requires a comprehensive and balanced assessment of economic, social, cultural, and environmental factors. While hybrid models that combine subjective and objective weighting exist in other domains, their application to the culture–tourism sector remains limited. This study addresses this gap by introducing a domain-specific Multi-Dimensional Hybrid Weighting Model that integrates expert-based judgments with data-driven evaluations, tailored specifically to the contextual challenges of culture–tourism integration. The proposed model combines Analytic Hierarchy Process (AHP) for subjective weighting and entropy-based methods for objective weighting, synthesizing them through a hybrid weighting function. Applied to a case study in Florence, Italy, the model demonstrates enhanced robustness, interpretability, and fairness—defined here as balanced consideration of diverse stakeholder perspectives and equitable weighting of evaluation criteria—compared to traditional approaches. It captures regional disparities and provides actionable insights for more equitable and sustainable tourism policy-making. The results highlight how multi-dimensional assessments can support responsive planning and effective decision-making in complex cultural environments. The framework is highly adaptable and offers significant value for urban planning, cultural resource management, and strategic tourism development across diverse contexts.}, archive = {J_ACCESS}, author = {Lingyun Huang and Zhanqi Li}, doi = {10.1109/ACCESS.2025.3595448}, journal = {IEEE Access}, pages = {137436-137445}, shortjournal = {IEEE Access}, title = {Quality evaluation of culture-tourism integration based on a 3E multi-dimensional Subjective–Objective hybrid weighting method}, volume = {13}, year = {2025}, }
• (2025). Optimized and routed wiring harness based on zonal clustering concept using AI in the automotive industry. ACCESS, 13, 137417-137435. (www)

  @article{ , abstract = {This paper presents an AI-driven approach for multi-zonal clustering and harness routing optimization in automotive electrical/electronic (E/E) systems. A methodology integrating K-means clustering with dynamic grid-based routing algorithms (A* and Bresenham’s line algorithm) is applied to optimize the wiring harness layout across the vehicle’s zones, covering six key domains (comfort, chassis, drive assist, infotainment, system, and powertrain). The proposed method accounts for realistic vehicle constraints, including restricted zones and diverse wire types, which are often overlooked in prior work. A 12-Zone architecture under a high application-load scenario is examined, demonstrating that this configuration achieves an optimal trade-off between harness length, complexity, and cost efficiency. Compared to manual design approaches and existing zonal architectures in the literature, the AI-based method reduced design time, produced a leaner harness layout, and achieved measurable material and cost savings. Specifically, the 12-Zone cluster arrangement achieved a total wiring distance of 241.056 m and a total wire mass of 22.92 kg for 417 ECUs, outperforming both lower and higher zone counts in overall efficiency. Scalability tests from 1 to 14 zones (under Low, Mid, and High load variants) confirmed the robustness of the approach and identified the 12-Zone configuration as the best-balanced solution. These findings highlight the potential of AI-optimized zonal E/E architectures to significantly reduce wiring weight, complexity, and design effort while maintaining system performance and cost competitiveness, providing valuable insights for next-generation automotive E/E design.}, archive = {J_ACCESS}, author = {Md Sanowar Hossain and Hafiz Abdul Quddus and Ziya Cevahir and Alexander Jesser}, doi = {10.1109/ACCESS.2025.3594845}, journal = {IEEE Access}, pages = {137417-137435}, shortjournal = {IEEE Access}, title = {Optimized and routed wiring harness based on zonal clustering concept using AI in the automotive industry}, volume = {13}, year = {2025}, }
• (2025). Theory of broadband transmission of frequency modulated signals through high-Q resonators with continuous time-varying matching networks. ACCESS, 13, 137396-137416. (www)

  @article{ , abstract = {Impedance match to a high-Q load with a passive network is fundamentally narrow band as its linear, time-invariant (LTI) property is subject to the Bode-Fano limit. However, such a limit may be partially lifted for frequency modulated signals when a time-varying matching network is used whose reactance varies continuously following the instantaneous frequency of the signal. The mismatch, however, may increase for high-speed modulations as the result of transient changes of energy balance among the different reactive components. As the information bandwidth of a modulated signal is primarily determined by the modulation speed, a general theory quantifying the modulation speed limit for such dynamic impedance matching is derived based on circuit laws. The analyses are applied to parallel LC resonators with one type of time-varying reactance, two types of time-varying reactance, and gyrator coupled time-varying reactance. The analyses are validated with nonlinear circuit simulations. It is concluded that while a single type of time-varying reactance can help to broaden the modulation rates of impedance matching, two paired types of time-varying reactance (inductance and capacitance) can lift the modulation speed limit completely. Furthermore, when a pair of single-type time-varying reactance coupled with a gyrator is used, the modulation speed limit can be similarly removed. In general, the gyrator adds another dimension in dynamic impedance matching in either lowering the resonant frequency or suppressing the mismatch. The proposed theory can be applied to the dynamic matching of electrically small antennas, which may help to achieve a radiation efficiency bandwidth product that goes beyond the limit imposed by its radiation quality factor, whose lower bound is defined by the famous Harrington-Chu’s limit.}, archive = {J_ACCESS}, author = {Yuanxun Ethan Wang}, doi = {10.1109/ACCESS.2025.3595056}, journal = {IEEE Access}, pages = {137396-137416}, shortjournal = {IEEE Access}, title = {Theory of broadband transmission of frequency modulated signals through high-Q resonators with continuous time-varying matching networks}, volume = {13}, year = {2025}, }
• (2025). CAFNet: Cross-modal adaptive fusion network with attention and gated weighting for RGB-T semantic segmentation. ACCESS, 13, 137384-137395. (www)

  @article{ , abstract = {Advanced applications such as autonomous vehicles and intelligent surveillance systems require strong environmental awareness abilities, and RGB-T semantic segmentation technology which fuses visible details and thermal imaging contour features can effectively improve perception capabilities. However, current methods typically use basic fusion strategies like channel concatenation or weighted summation, neglecting feature distribution differences from distinct imaging mechanisms. We propose a cross-modal adaptive fusion network (CAFNet) for multimodal feature extraction and fusion that uses the ConvNeXt V2 architecture for multistage feature learning and integrates a channel-spatial attention module (CSAM) and an adaptive weighted fusion module (AWFM) in a dual-stream encoder. The CSAM enhances thermal radiation contour recognition under low-light conditions through cross-modal spatial enhancement and channel selection. The AWFM adaptively balances the weights of the RGB detail and thermal contour through dynamic gating, enabling the complementary fusion of contextual features. Finally, the decoder combines multistage features to predict semantics. The experimental results show that CAFNet achieves a 60.1% mIoU on the MFNet dataset, which is 1.2% higher than that of EAEFNet (58.9% mIoU), and the computational cost (110.61G FLOPs) and parameter count (68.13 M) are also reduced by 25% and 66.1%, respectively. The proposed method achieves exceptional results on both daytime and nighttime images, with mIoU scores of 51.3% and 60.5%, respectively.}, archive = {J_ACCESS}, author = {Meili Fu and Huanliang Sun and Zhihan Chen and Lulin Wei}, doi = {10.1109/ACCESS.2025.3595811}, journal = {IEEE Access}, pages = {137384-137395}, shortjournal = {IEEE Access}, title = {CAFNet: Cross-modal adaptive fusion network with attention and gated weighting for RGB-T semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Evaluation of LSTM, GRU, and ANFIS models for ankle angle and ankle moment prediction using biomechanical data. ACCESS, 13, 137364-137383. (www)

  @article{ , abstract = {Accurate prediction of joint angles and moments is crucial for understanding human gait and developing assistive technologies, such as exoskeletons and prosthetics. This study compares the performance of Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for predicting ankle angle and ankle moment at walking speeds of 1 km/h, 2 km/h, and 3 km/h. A multimodal sensor fusion approach was employed, incorporating electromyography (EMG), Ground Reaction Forces (GRF), and knee angle data as model inputs to enhance predictive accuracy. The performance of the models were assessed using Root Mean Square Error (RMSE) and Coefficient of Determination (R2) metrics, under both subject-specific and Leave-One-Subject-Out (LOSO) cross-subject validation frameworks. The results demonstrate that, under subject-specific evaluation, ANFIS achieved superior accuracy for ankle angle prediction at higher walking speeds, outperforming LSTM and GRU by up to 10.79% and 7.08% in RMSE at 3km/h, respectively. Conversely, GRU provided better performance for ankle moment prediction under subject-specific evaluation, achieving up to 29.04% and 18.27% lower RMSE than ANFIS and LSTM at 1km/h, respectively, highlighting its robustness under higher gait variability. Under the LOSO design, GRU consistently demonstrated superior generalization capability across subjects and walking speeds, reducing RMSE by 13.5% over ANFIS at 1km/h and 2.84% at 3km/h for ankle angle prediction, and achieving up to 17.5% and 4.5% lower RMSE compared to ANFIS and LSTM for ankle moment prediction. The comparative results were further validated using paired t-tests on RMSE values, confirming statistically significant performance differences between models. These findings highlight the importance of selecting appropriate modeling architectures for biomechanical prediction tasks and identify GRU as a promising candidate for robust gait estimation applicable to assistive and rehabilitative technologies.}, archive = {J_ACCESS}, author = {T. S. Sangeetha and S. N. Muralikrishna and Advay Mandar Naik and Arun P. Parameswaran}, doi = {10.1109/ACCESS.2025.3591227}, journal = {IEEE Access}, pages = {137364-137383}, shortjournal = {IEEE Access}, title = {Evaluation of LSTM, GRU, and ANFIS models for ankle angle and ankle moment prediction using biomechanical data}, volume = {13}, year = {2025}, }
• (2025). Investigating the relationship between text vectorization cosine similarity and classification performance. ACCESS, 13, 137348-137363. (www)

  @article{ , abstract = {In recent years, optimizing classification pipelines has become increasingly critical due to the growing volume of textual data and the computational challenges associated with exhaustive hyperparameter tuning. This paper proposes a similarity-based approach for selecting the most promising vectorization configurations–specifically, Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec–by analyzing the average cosine similarity of the generated vectors; by preselecting configurations that yield more diverse textual representations, our method relies on the hypothesis that increased diversity in text representations enhances the discriminative capacity of classification models. Experimental evaluations conducted on five different datasets demonstrate that the similarity-based approach achieves accuracy and F1-Score results very close to those obtained via exhaustive search, with notable reductions in processing time; furthermore, correlation analyses reveal a strong inverse relationship between vector similarity and model performance for BoW and TF-IDF, and a moderate relationship for Word2Vec. These findings validate the efficacy of the proposed method as a practical alternative to hyperparameter selection in vectorization pipelines, offering significant benefits for applications where exhaustive exploration is unfeasible.}, archive = {J_ACCESS}, author = {Fernando Rezende Zagatti and Gilson Yuuji Shimizu and Daniel Lucrédio and Helena de Medeiros Caseli}, doi = {10.1109/ACCESS.2025.3595423}, journal = {IEEE Access}, pages = {137348-137363}, shortjournal = {IEEE Access}, title = {Investigating the relationship between text vectorization cosine similarity and classification performance}, volume = {13}, year = {2025}, }
• (2025). SynAdult: Multimodal synthetic adult dataset generation via diffusion models and neuromorphic event simulation for critical biometric applications. ACCESS, 13, 137327-137347. (www)

  @article{ , abstract = {We propose SynAdult, a multimodal synthetic data generation framework designed to address the scarcity of diverse and privacy-compliant senior adult face datasets for biometric applications and facial analysis. Our pipeline begins with the rendering of high-fidelity 2D adult facial images using parameter-efficient LoRA-based tuning of the state-of-the-art hyperrealism Stable Diffusion XL (SDXL) model, producing photorealistic outputs across diverse ethnicities and age-specific features. Next, we integrate a video retargeting pipeline to synthesize temporally consistent head pose and facial expression sequences, ensuring naturalistic dynamics crucial for downstream video-based facial analysis. In the third stage, we generate neuromorphic event data to introduce a privacy-preserving modality aligned with real-world edge deployment scenarios, such as ambient monitoring and in-vehicle sensing, where high temporal resolution and minimal identity leakage are beneficial. Finally, we reconstruct detailed 3D facial meshes from single 2D frames using 2D-to-3D morphing techniques to capture fine-grained structural details. This modality enhances geometric understanding and supports applications in AR/VR and affective computing. To validate the robustness and utility of the generated dataset, we perform a comprehensive evaluation using Kernel Inception Distance (KID), BRISQUE, CLIP score, and identity similarity metrics. We further assess downstream applicability employing the state-of-the-art facial expression classification networks and event facial landmarks tests for downstream machine learning tasks. As a key contribution, we open-source a large-scale, multimodality, multi-race adult dataset, enabling future research in secure and ethically grounded synthetic data for facial biometrics and facial analysis applications. The project website, along with the complete adult multimodality dataset and the fine-tuned model, is available at https://mali-farooq.github.io/SynAdult/}, archive = {J_ACCESS}, author = {Muhammad Ali Farooq and Paul Kielty and Wang Yao and Peter Corcoran}, doi = {10.1109/ACCESS.2025.3594875}, journal = {IEEE Access}, pages = {137327-137347}, shortjournal = {IEEE Access}, title = {SynAdult: Multimodal synthetic adult dataset generation via diffusion models and neuromorphic event simulation for critical biometric applications}, volume = {13}, year = {2025}, }
• (2025). Practical DU-CU placement in an enhanced mobile BroadBand request during 5G traffic. ACCESS, 13, 137317-137326. (www)

  @article{ , abstract = {5G technology offers numerous advantages, including high data rates, enhanced connectivity, low latency, and high reliability. These benefits have driven the evolution of network architecture, particularly the disaggregation of radio access networks (RAN) into three distinct functional components: radio units (RU), distributed units (DU), and centralized units (CU). This modular segmentation directly impacts deployment costs and energy consumption. In this study, we propose a Mixed-Integer Linear Programming (MILP) model to minimize the deployment costs of DU and CU functions while considering network performance metrics and resource constraints in the context of enhanced Mobile Broadband (eMBB) services. The model is evaluated on a network topology in which radio units (RUs), candidate sites (CSs), and data centers (DCs) are distributed across a territory. The objective is to identify the optimal candidate sites for deploying the DU and CU functions to ensure a continuous service path from the RU to the DC. To ensure the scalability of the solution for large-scale networks and reduce computation time, we also developed a heuristic algorithm capable of providing solutions close to those obtained by the MILP solver. The simulation results indicate that the operator-defined latency constraints and the functional split type significantly influence deployment costs.}, archive = {J_ACCESS}, author = {Ange Joel Nounga Njanda and Joao Filipe Muchanga and Hojjat Baghban and Karcius Day R. Assis}, doi = {10.1109/ACCESS.2025.3594555}, journal = {IEEE Access}, pages = {137317-137326}, shortjournal = {IEEE Access}, title = {Practical DU-CU placement in an enhanced mobile BroadBand request during 5G traffic}, volume = {13}, year = {2025}, }
• (2025). Innovative charging solution using a three-winding transformer at the battery swapping station. ACCESS, 13, 137303-137316. (www)

  @article{ , abstract = {Electric Vehicles (EVs) are gaining global popularity as a sustainable solution to rising energy costs and environmental concerns. However, EV integration introduces power quality issues into the grid, coupled with the growing demand for accessible charging infrastructure. This work addresses these challenges by proposing a novel charging system for Battery Swapping Stations (BSS) that optimizes power utilization and improves grid stability. The proposed system utilizes a star-star-delta transformer to recover harmonic power from the tertiary winding, which is typically wasted as heat, to supplement fundamental power during battery charging. An interleaved bidirectional multiport converter ensures efficient power management, enabling optimized battery charging and discharging based on electrical parameters. Reactive power compensation via a Shunt Active Filter during peak hours further enhances voltage stability, power factor, and overall system efficiency. The system is modelled in MATLAB Simulink, with performance analyzed under peak and off-peak load conditions. A 3 kVA transformer-based hardware prototype is constructed, incorporating a Push-Pull converter for harmonic power recovery and battery charging. Experimental results, including waveforms, power analyzer data, and insights into converter design, validate the system’s functionality. Key metrics such as Total Harmonic Distortion (THD), power factor, drainage power recovery, and efficiency are also examined. This work demonstrates a cost-effective and technically viable solution for improving power quality and optimizing EV charging at BSS.}, archive = {J_ACCESS}, author = {M. V. Athul and C. Umayal}, doi = {10.1109/ACCESS.2025.3595164}, journal = {IEEE Access}, pages = {137303-137316}, shortjournal = {IEEE Access}, title = {Innovative charging solution using a three-winding transformer at the battery swapping station}, volume = {13}, year = {2025}, }
• (2025). Calibration of electron microscopes through deep learning and bayesian optimization. ACCESS, 13, 137291-137302. (www)

  @article{ , abstract = {Electron microscopes can create images at atomic resolution, enabling key developments across many scientific fields. To obtain high-resolution images, the inherent imperfections in the electromagnetic lenses have to be overcome by frequent and extensive calibration. Currently, this calibration is typically performed or monitored by experts, making it expensive and time-consuming. To resolve this, this paper proposes a data-driven solution for automated, image-based calibration of a Scanning Transmission Electron Microscope (STEM). The method uses deep learning to interpret the high-dimensional image, also called the Ronchigram, produced by the STEM, providing a quality index that quantifies the distance of the current image from its calibrated state. Specifically, we perform transfer learning on an adapted ResNet18 architecture using a large data set of simulated Ronchigrams. Afterward, Gaussian process regression models how calibration settings affect this quality index from limited samples. Subsequently, candidate calibration settings are selected online using Bayesian optimization with a novel acquisition function tailored to this problem. When applied to the STEM, the proposed method consistently calibrates defocus and two-fold astigmatism in just a few steps. Compared to existing automated calibration methods, the proposed method can consistently achieve similar performance in less time. For this reason, our approach potentially reduces expert involvement and improves the overall throughput of the STEM. Moreover, the data-driven nature of the proposed method suggests it could be adapted to calibrate additional types of imperfections, positioning it as a flexible tool for enhancing STEM performance, provided that representative data can be generated from the simulator.}, archive = {J_ACCESS}, author = {Jilles S. van Hulst and Roy A. C. van Zuijlen and Narges Javaheri and Maurits Diephuis and Duarte J. Antunes and W. P. M. H. Heemels}, doi = {10.1109/ACCESS.2025.3595430}, journal = {IEEE Access}, pages = {137291-137302}, shortjournal = {IEEE Access}, title = {Calibration of electron microscopes through deep learning and bayesian optimization}, volume = {13}, year = {2025}, }
• (2025). Meta-ensemble learning for heart disease prediction: A stacking-based approach with explainable AI. ACCESS, 13, 137271-137290. (www)

  @article{ , abstract = {Heart disease remains a leading cause of mortality globally, necessitating the development of accurate, interpretable, and reliable predictive models to support early diagnosis and enhance patient care. The work demonstrates a robust machine learning framework for classifying heart disease on three widely used benchmark datasets for heart disease such as Heart_2020_Cleaned, Heart Statlog Cleveland Hungary, and Cardio Train. A comparative analysis was conducted across these datasets to evaluate model performance under varying data characteristics. Similarly, to ensure optimal model performance and generalizability, k-fold cross-validation was employed for hyperparameter tuning, enabling systematic evaluation and fine-tuning of model parameters while reducing the risk of overfitting. The proposed model attained an accuracy of 97.72% with a F1-Score of 97.89% on the Heart_2020_Cleaned dataset comprising 319,795 samples, 95.50% accuracy with 95.24% F1-Score on the Cardio Train dataset containing 70,000 samples, and 98.90% accuracy with 98.86% F1-Score on the Heart Statlog Cleveland Hungary dataset, which includes 1,190 instances. Among the three datasets, the Heart Statlog Cleveland Hungary dataset exhibited the highest performance matrices, indicating the model’s enhanced ability to accurately identify positive cases within this dataset. To mitigate class imbalance, the SMOTEENN resampling technique was applied, which improved the model’s generalization performance across both majority and minority classes. Additionally, the integration of SHAP-based Explainable AI (XAI) techniques enhanced the interpretability of the model by offering clear and meaningful explanations of how individual features influenced the predictions. These insights contribute to greater transparency and trust in the model’s decision-making process. Overall, the results demonstrate the robustness and practical applicability of the proposed framework for early detection and clinical management of heart disease.}, archive = {J_ACCESS}, author = {Mehwish Naz and Aqsa Khalid and Abdul Hameed and Rabia Taj and Waleed Mumtaz and Faiz Abdullah Alotaibi and Mrim M. Alnfiai}, doi = {10.1109/ACCESS.2025.3588683}, journal = {IEEE Access}, pages = {137271-137290}, shortjournal = {IEEE Access}, title = {Meta-ensemble learning for heart disease prediction: A stacking-based approach with explainable AI}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of on-chip reconfigurable SCVR with wide output voltage range for SoCs. ACCESS, 13, 137255-137270. (www)

  @article{ , abstract = {A comprehensive framework for the analysis and design of reconfigurable two-phase switched-capacitor (SC) dc-dc converters is introduced. Initially, a generalized approach leveraging graph theory and network analysis is presented, enabling the enumeration of all possible ideal conversion ratios for a given SC converter topology. This methodology extends the performance boundaries of two-phase SC converters, leading to the proposal of a reconfigurable, two-phase SC dc-dc converter. The proposed converter incorporates a programmable, optimized switching configuration that enhances the number of achievable ideal conversion ratios for a fixed set of capacitors and switching states. This flexibility enables the converter to operate with high efficiency across a broad spectrum of operating conditions while achieving significantly high step-down conversion ratios. This work presents a reconfigurable, fully integrated, two-phase interleaved switched-capacitor voltage regulator (SCVR) with an on-die MIM capacitor intended for low-power system-on-chips (SoCs). High power density and efficiency are made possible in on-chip switched-capacitor (SC) converter implementations using MIM capacitors with a low bottom-plate parasitic. The charge-multiplier model for reconfigurable SCVR is investigated and verified through modeling and simulation. With varying power supplies of 1.24 V, 1.15 V, and 1.03 V, a reconfigurable SCVR having four voltage conversion ratios (VCRs) has been designed to provide dynamic voltage scaling (DVS) of many-core microprocessors on a per-core basis. Further, the converter’s parasitic and charge-sharing losses are efficiently reduced by utilizing switch and frequency scaling techniques. The proposed SCVR is reconfigured into four gain topologies having VCRs 5:4, 4:3, 3:2, and 1:1 respectively. The nominal output voltage of the converter ranges from 0.6 V to 1 V, from three different power supplies. The proposed converter has a maximum load current of 625 mA and a peak efficiency of 81.09%. Further, it delivers load current ranging from 200 mA to 625 mA with an efficiency greater than 70%. To validate the design, it is developed and simulated using a 22 nm CMOS process. MATLAB and PSpice simulation tools are used to verify the results.}, archive = {J_ACCESS}, author = {Sunita Saini and Davinder Singh Saini and Vipin Balyan}, doi = {10.1109/ACCESS.2025.3593997}, journal = {IEEE Access}, pages = {137255-137270}, shortjournal = {IEEE Access}, title = {Design and analysis of on-chip reconfigurable SCVR with wide output voltage range for SoCs}, volume = {13}, year = {2025}, }
• (2025). Advanced signal processing algorithm-based state estimation and denoising of ECG signals for biomedical applications. ACCESS, 13, 137240-137254. (www)

  @article{ , abstract = {State estimation of electrocardiography (ECG) signals plays a crucial role in effective cardiac monitoring systems and assists in the early detection of abnormalities. However, ECG signals are often contaminated by interfering noise such as baseline wander, power-line interference, and motion artifacts, which significantly hinder precise state estimation. To address these challenges, this paper proposes a particle filter (PF)-based method for ECG signal estimation, which effectively preserves clinically relevant waveform characteristics under nonlinear and non-stationary noise conditions. The technique is implemented in MATLAB using a synthetic ECG signal and is evaluated using fast Fourier transform, spectrogram analysis, power spectral density, autocorrelation, and wavelet transform. A comparative analysis reveals that the proposed PF-based method significantly outperforms baseline denoising techniques. Quantitatively, the process achieves a mean square error (MSE) of 0.001382, representing a 48.5% improvement over the variant without the attention mechanism (MSE =0.002682). Additionally, the signal-to-noise ratio is enhanced by 6.3 dB, reaching 15.87 dB, the highest among all tested methods. The percentage root mean square difference is also reduced to 4.62%, indicating low signal distortion. Furthermore, the accuracy of R-peak identification reaches 98.5%, ensuring a better extraction of cardiac features. The proposed method demonstrates a runtime of 0.75 s for a 10-s ECG segment, confirming its suitability for real-time biomedical applications. The simulation results validate that the PF-based state estimation framework is not only more precise and robust to noise but also computationally efficient, making it ideal for ECG signal processing in wearable health monitoring and real-time cardiac diagnostics.}, archive = {J_ACCESS}, author = {Saad Arif and Mohammad Maroof Siddiqui and Nayef Alqahtani and Kamran Shah and Muhammad Attique Khan and Naoufel Kraiem}, doi = {10.1109/ACCESS.2025.3595453}, journal = {IEEE Access}, pages = {137240-137254}, shortjournal = {IEEE Access}, title = {Advanced signal processing algorithm-based state estimation and denoising of ECG signals for biomedical applications}, volume = {13}, year = {2025}, }
• (2025). Advanced computer vision methods for avian risk assessment using multispectral drones. ACCESS, 13, 137219-137239. (www)

  @article{ , abstract = {The growing threat of the Avian Influenza virus has raised the alarm for conservationists, ecologists and researchers to develop innovative solutions to monitor wildlife birds using drones and early diagnosing through advanced image processing methods. In recent years, outbreaks of the Avian Influenza virus have devastated avian populations worldwide, leading to significant declines in bird colonies, particularly in vulnerable species such as the Dalmatian Pelicans (Pelecanus crispus). These significant changes to the environment may contribute to a Cascade effect impacting the food chain of both predators and preys. This study’s goal is to monitor wildlife birds in the Gulf of Amvrakikos and build an intelligent system that utilizes images derived from drones equipped with thermal imagery. This system leverages state-of-the-art computer vision techniques with spatial analysis characteristics to analyze wildlife bird behavior through optical cameras and extract features for each bird. Coupled with data derived from thermal imagery as a crucial factor, it can perform a comprehensive risk assessment by correlating thermal patterns with behavioral insights, utilizing the Analytic Hierarchy Process (AHP) method. This study pioneers a novel methodology for avian health assessment using drones and identifies abnormal patterns to detect the presence of avian influenza.}, archive = {J_ACCESS}, author = {Dimitrios Mpouziotas and Petros Karvelis and Ioulia Kapsali and Chrysostomos Stylios and Vasilios Tsiouris}, doi = {10.1109/ACCESS.2025.3595389}, journal = {IEEE Access}, pages = {137219-137239}, shortjournal = {IEEE Access}, title = {Advanced computer vision methods for avian risk assessment using multispectral drones}, volume = {13}, year = {2025}, }
• (2025). MFSE-TransUNet: A thyroid nodule ultrasound image segmentation network integrated with dynamic feature calibration and edge enhancement. ACCESS, 13, 137209-137218. (www)

  @article{ , abstract = {Ultrasound imaging is a commonly used auxiliary diagnostic method for detecting thyroid nodules. However, its low resolution, high noise interference, numerous artifacts, and blurred boundaries make manual annotation time-consuming and highly subjective. Thus, accurate pixel-level segmentation is of great value for quantifying nodule morphology, tracking lesion progression, and planning surgery. Although the existing TransUNet balances local and global features through a hybrid CNN-Transformer architecture, it still faces the following challenges in thyroid nodule segmentation: firstly, the fixed convolutional kernels struggle to adapt to nodule morphological diversity; Secondly, existing multi-scale feature fusion methods fail to consider hierarchical contribution differences; Furthermore, significant edge information is easily lost during upsampling. Accordingly, this study proposes the MFSE-TransUNet model to address these issues. Experimental results on the TUD, TN3K, and DDTI datasets demonstrate the model’s effectiveness, achieving MIoU improvements of 10.89%, 6.02%, and 7.83%, and Dice coefficient increases of 7.79%, 3.79%, and 5.21% compared to the TransUNet baseline. All metrics show consistent improvements, with cross-dataset Dice fluctuations of less than 3%, strongly demonstrating the model’s generalization capability.}, archive = {J_ACCESS}, author = {Ye Lu and Jiaojiao Jing and Wenbo Zhang and Yali Kong}, doi = {10.1109/ACCESS.2025.3595458}, journal = {IEEE Access}, pages = {137209-137218}, shortjournal = {IEEE Access}, title = {MFSE-TransUNet: A thyroid nodule ultrasound image segmentation network integrated with dynamic feature calibration and edge enhancement}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based classification of suspension droplet-solid wall impacts for control of droplet fragmentation. ACCESS, 13, 137179-137208. (www)

  @article{ , abstract = {This study explores the collision of suspension droplets against solid dry surfaces (substrates). It applies and compares multiple machine learning (ML) models for the classification of impact outcomes: splashing, breaking up, and rebound. A total of 372 experiments were performed with varied particle properties, liquid parameters, substrate properties, droplet velocity, and substrate inclination. The suggested markup indicators form the basis for computer vision processing of drop-wall impact videos. From 12 dimensionless features, seven are selected to train ML classifiers. We tested ten models, including Logistic Regression, KNN, SVM, ensemble methods (CatBoost, XGBoost, LightGBM, Random Forest, AdaBoost), and neural networks (MLP, TabNet). The gradient boosting model CatBoost showed the best results: accuracy and macro-averaged F1-score improved by more than 15% over the baseline, ROC-AUC exceeded 96%. Logistic Regression demonstrated robust generalization capabilities. The key physical finding is that greater sedimentation Stokes number, particle size, and inclination increase the likelihood of splashing and fragmentation. The models allow prediction of no-fragmentation outcomes, important for droplet-based 3D-printing and coating. To the best of our knowledge, this is the first study to use ML for the classification of suspension drop-wall impacts, including data from experiments with both heavier and lighter-than-liquid particles and inclined surfaces. The results obtained assist in controlling suspension droplet impact to avoid fragmentation.}, archive = {J_ACCESS}, author = {Mikhail Vulf and Dmitry Zharikov and Dmitry Kolomenskiy and Dmitry Eskin and Pavel Osinenko}, doi = {10.1109/ACCESS.2025.3594877}, journal = {IEEE Access}, pages = {137179-137208}, shortjournal = {IEEE Access}, title = {Machine learning-based classification of suspension droplet-solid wall impacts for control of droplet fragmentation}, volume = {13}, year = {2025}, }
• (2025). Multi-agent reinforcement learning with cross-layered adaptive wireless video streaming for road traffic monitoring. ACCESS, 13, 137155-137178. (www)

  @article{ , abstract = {This paper presents the design and implementation of a multi-agent reinforcement learning framework for adaptive wireless image sequence streaming in road traffic monitoring systems. This work extends previous research that utilizes Apache Kafka for real-time wireless image transmission. To promote cooperation and fairness among agents, a multi-agent architecture with independent learners employing a social welfare function as a joint reward is implemented. The learning agents are trained and evaluated under various scenarios, and their performance is compared to a baseline without learning agents. Experimental results show that, after sufficient training, the proposed approach outperforms the baseline by 3.98% to 31.55% in joint reward. An emulated software-defined wireless mesh network is built with Mininet-WiFi to test the scalability and convergence time. This study highlights the potential of multi-agent reinforcement learning for improving adaptive wireless image streaming in road traffic monitoring, with significant implications for future research and real-world applications.}, archive = {J_ACCESS}, author = {Aung Myo Htut and Hideya Ochiai and Chaodit Aswakul}, doi = {10.1109/ACCESS.2025.3593616}, journal = {IEEE Access}, pages = {137155-137178}, shortjournal = {IEEE Access}, title = {Multi-agent reinforcement learning with cross-layered adaptive wireless video streaming for road traffic monitoring}, volume = {13}, year = {2025}, }
• (2025). A transformerless bidirectional active switched inductor-based SEPIC high-gain DC–DC converter with Buck–Boost capability. ACCESS, 13, 137139-137154. (www)

  @article{ , abstract = {The growing demand for efficient and compact power conversion systems in electric vehicles (EVs), renewable energy systems, DC microgrids, and both portable and stationary medical equipment has intensified research into non-isolated high-gain bidirectional DC-DC converters. Existing solutions often employ transformer-based topologies or coupled inductors, which introduce increased cost, size, and control complexity. This paper presents a novel transformerless bidirectional high-gain DC-DC converter based on a modified Single-Ended Primary Inductor Converter (SEPIC) architecture. The proposed topology incorporates an Active Switched Inductor (ASL) at the input stage to achieve a wide voltage conversion ratio while ensuring reduced voltage stress on the maximum power switches. A key feature of the converter is its ability to provide bidirectional buck–boost operation in both power flow directions, while maintaining a reduced component count and improved efficiency through synchronous rectification. The converter’s performance is thoroughly analyzed under both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). Furthermore, detailed small-signal modeling and closed-loop controller design are developed for both voltage-mode and current-mode control. A 200 W experimental prototype employing SiC MOSFETs is implemented to validate the theoretical analysis. Experimental results confirm the high efficiency, robust dynamic response, and practical feasibility of the proposed converter for next-generation power conversion applications.}, archive = {J_ACCESS}, author = {Sunil Mandal and Prajof Prabhakaran and D. Arun Dominic and Arun P. Parameswaran}, doi = {10.1109/ACCESS.2025.3595442}, journal = {IEEE Access}, pages = {137139-137154}, shortjournal = {IEEE Access}, title = {A transformerless bidirectional active switched inductor-based SEPIC high-gain DC–DC converter with Buck–Boost capability}, volume = {13}, year = {2025}, }
• (2025). Research on high-value patent prediction method based on technology component mapping network. ACCESS, 13, 137129-137138. (www)

  @article{ , abstract = {This research aims to develop a sophisticated method for precisely predicting high-value patents by integrating a technology component network with patent metric features. This integration is based on the potential semantic correlations between the technical components embedded in patents, thereby providing robust technical support for optimizing technology innovation management. The proposed method initially extracts Subject-Action-Object (SAO) triplets and constructs a technology component network by leveraging the semantics of the Action component. This process maps the relationships between technology components to the relationships among patent documents. Subsequently, patent document metric features are utilized as node attributes, while the mapped patent document relationships serve as edges to form a patent semantic association network. Ultimately, a graph convolutional neural network (GCN) model is employed to predict high-value patents. The experimental results demonstrate that the method based on the patent technology component mapping network (CMN) generally outperforms the prediction method relying on the patent citation network (PCN) and the patent semantic similarity network (PSSN). Specifically, the GraphSAGE-based CMN prediction method achieves the best performance, with an F1 score of 0.81, which represents a notable improvement over the PCN-based method and the PSSN-based method. Additionally, case study analysis further substantiates the effectiveness and superiority of the proposed high-value patent prediction method.}, archive = {J_ACCESS}, author = {Yuepeng Li}, doi = {10.1109/ACCESS.2025.3595596}, journal = {IEEE Access}, pages = {137129-137138}, shortjournal = {IEEE Access}, title = {Research on high-value patent prediction method based on technology component mapping network}, volume = {13}, year = {2025}, }
• (2025). Integrated communication, sensing, computing, and control for industrial power protection systems: Analysis and performance optimization. ACCESS, 13, 137117-137128. (www)

  @article{ , abstract = {With the continuous advancement of industrial automation technology, industrial power security systems are playing an increasingly important role in ensuring production stability and improving efficiency. This paper studies an industrial power security system that integrates sensing, communication, computation, and control (ISCC), focusing on the performance optimization issues related to latency and reliability. Addressing the trade-offs between latency and reliability across the four components of sensing, communication, computation, and control, and assuming no errors in sensing, closed-form expressions for the maximum latency of the communication-sensing-computation-control system and the maximum packet loss probability of communication are derived. By optimizing the optimal control law and the system’s uplink and downlink bandwidth under constraints of communication rate, maximum available bandwidth, and power, the control cost of the system is minimized. To further enhance system performance, this paper proposes a joint optimization method aimed at simultaneously optimizing the control law and uplink-downlink bandwidth allocation to minimize the system’s control cost.}, archive = {J_ACCESS}, author = {Tongshan Liu and Jiangtao Zhang and You Liu and Chunyou Zhuang and Hao Li and Yuanyuan Chen}, doi = {10.1109/ACCESS.2025.3595277}, journal = {IEEE Access}, pages = {137117-137128}, shortjournal = {IEEE Access}, title = {Integrated communication, sensing, computing, and control for industrial power protection systems: Analysis and performance optimization}, volume = {13}, year = {2025}, }
• (2025). An adaptive content caching model to improve the NDN-based IoT network performance. ACCESS, 13, 137102-137116. (www)

  @article{ , abstract = {The current Internet of Things (IoT) networks experience considerable challenges due to the limitation of device capacities, energy constraints, and data accessibility problems. The existing IP-based Internet architecture is inadequate to handle the huge volume of interconnected devices and data because of restricted address capacity and ineffective data retrieval processes. To address these challenges, Named Data Networking (NDN) appears as a promising solution, offering content-centric approaches and proficient in-network caching. This study presents a novel caching technique, named the Efficient Pre-Caching Technique (EPCT), which depends on two mechanisms, such as content selection using similarity functions and content placement. The proposed technique aims to improve cache hit ratios, latency, and hop count ratios. A full performance test in a simulated environment shows that EPCT is better than other NDN-based IoT caching techniques by highlighting its ability to make NDN-based IoT networks more efficient overall. Our simulation results represent that EPCT considerably improves the performance through key metrics. In particular, it increases the cache hit ratio up to 38%, reduce the average hop count by 26%, and decreases the content retrieval latency around 31% when it is compared with existing caching techniques such as CEE, MPC, and CFPC. These considerable improvements highlight the EPCT potential to increase data availability and ease the network traffic in dynamic IoT settings.}, archive = {J_ACCESS}, author = {Qaizar Javed and Arfan Jaffar and Muhammad Ali Naeem and Izhar Ahmed Khan and Mohammed Alsuhaibani}, doi = {10.1109/ACCESS.2025.3591612}, journal = {IEEE Access}, pages = {137102-137116}, shortjournal = {IEEE Access}, title = {An adaptive content caching model to improve the NDN-based IoT network performance}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid analysis method of LLC resonant converter for high accuracy and low computational burden. ACCESS, 13, 137086-137101. (www)

  @article{ , abstract = {Recently, as the demand for LLC resonant converters has increased in various power electronics industry sectors, research on accurate analysis methods necessary for effectively designing converters has been conducted. This paper proposes an analysis model that can estimate the efficiency of the converter and the temperature of component parts with high accuracy while requiring a relatively low computational burden when analyzing LLC resonant converters. The proposed analysis model performs circuit and thermal analysis using advanced mathematical modeling methods based on time analysis, and uses a hybrid structure analysis model that analyzes magnetic components through an AI-based analysis model. Time analysis-based analysis is a method that accurately estimates the magnitude of voltage and current of resonant converters by equivalent modeling of circuit configurations for each operating state during a switching cycle using nonlinear circuit equations. The AI-based analysis model is a method that generates the relationship between design input variables and output variables of magnetic components through AI algorithms and uses this as an analysis model. To verify the effectiveness of the proposed method, a 1kW LLC resonant converter prototype is used, and the efficiency and temperature estimation accuracy of the proposed method are measured under various capacity conditions, input and output voltage conditions, resonant network conditions, and load conditions. The proposed method estimates efficiency with an error within 2% and estimates temperature with an error within 10% under various verification conditions. In terms of computation time, it takes about 7000[s] for 100 analyses, which shows relatively low computational burden compared to existing methods. Through this, the proposed method can effectively analyze LLC resonant converters with high accuracy and relatively low computational burden.}, archive = {J_ACCESS}, author = {Su-Seong Park and Rae-Young Kim}, doi = {10.1109/ACCESS.2025.3593996}, journal = {IEEE Access}, pages = {137086-137101}, shortjournal = {IEEE Access}, title = {A novel hybrid analysis method of LLC resonant converter for high accuracy and low computational burden}, volume = {13}, year = {2025}, }
• (2025). Indicator engineering on a cloud-edge GIS platform assisting post-seismic reconstruction and rehabilitation. ACCESS, 13, 137067-137085. (www)

  @article{ , abstract = {In every sphere, for policy-makers, geography increasingly proves to be a key discipline for understanding and knowing the territory, making informed decisions, and taking consequent actions for the welfare of citizens. Spatial planning for post-seismic reconstruction emphasizes safety and memories, and is closely linked to recovery plans. This paper focuses on the case of 56 small Italian municipalities that were severely damaged by a devastating earthquake in 2009. An innovative ICT platform for services on spatial databases has been designed and developed to support the activities of a special Public Administration established ad hoc to guide the post-seismic reconstruction process in Italy, for which monitoring the status of works is a milestone, as well as having a clear and official informative tool. With concurrent and collaborative access to georeferenced data, this is a hybrid cloud/edge/on-premise architecture that leverages the capabilities of GIS, new disruptive technologies, indicator engineering, and database performance optimization. Its implementation and application are presented so that it can also be used in other (similar) contexts, with any adaptations. In addition, the proposed solution can be useful for both short-term and long-term developments, such as analysis of (cyber-)security scenarios, integration with 5G networks, and provision of public smart utilities.}, archive = {J_ACCESS}, author = {Fabio Franchi and Francesco Zullo and Alessandra Galassi}, doi = {10.1109/ACCESS.2025.3595708}, journal = {IEEE Access}, pages = {137067-137085}, shortjournal = {IEEE Access}, title = {Indicator engineering on a cloud-edge GIS platform assisting post-seismic reconstruction and rehabilitation}, volume = {13}, year = {2025}, }
• (2025). Enhancing web development education with game-based and gamification learning: A study of engagement, motivation, and performance. ACCESS, 13, 137048-137066. (www)

  @article{ , abstract = {Web development is a dynamic and complex subject in the computer science field that can be challenging to learn among higher education students. We investigated the impact of game-based learning and gamification on students’ engagement, motivation, and performance in a web development course in this study. We tested 60 university students, who were expected to learn from a novice or beginner level. The experimental group used a gamified system, while the control group used a non-gamified system. All game elements in this study were grounded in self-determination theory (SDT), which suggests that intrinsic motivation increases when learners feel autonomous, competent, and connected, and flow theory, which emphasizes that when challenge and skill are balanced, learners are more likely to experience a state of flow. Quantitative and qualitative data were both collected and analyzed. Results showed that the gamified system positively impacted students’ performance, compared to the non-gamified system. Interestingly, there was no significant difference in the engagement and motivation of the students. The findings of this study suggest that while the game elements addressed students’ psychological needs, gamified learning outcomes depend not only on the inclusion of these game elements but also on individual learner differences such as prior game experience, learners’ expectations of the gamified system and how well the designed system sustains engagement over time to overcome the novelty effect. In conclusion, these findings have significant implications for web development education and suggest that game-based learning and gamification are promising approaches for web development education.}, archive = {J_ACCESS}, author = {Suzan Ejura Ojonuba and Güzin Türkmen and Sacip Toker}, doi = {10.1109/ACCESS.2025.3595510}, journal = {IEEE Access}, pages = {137048-137066}, shortjournal = {IEEE Access}, title = {Enhancing web development education with game-based and gamification learning: A study of engagement, motivation, and performance}, volume = {13}, year = {2025}, }
• (2025). Dynamic orbital resource management (DORM) for 6G networks: Enhancing cybersecurity in non-terrestrial systems. ACCESS, 13, 137033-137047. (www)

  @article{ , abstract = {The integration of Non-Terrestrial Networks (NTNs) into 6G systems promises seamless global connectivity but also introduces unprecedented cybersecurity challenges. Due to their wide coverage area, satellite mobility, and open communication channels, NTNs are uniquely vulnerable to jamming, spoofing, and eavesdropping attacks. Their limited physical security further exposes them to cyber-physical threats, including advanced persistent threats (APTs) targeting satellite control systems and denial-of-service (DoS) attacks that can degrade services critical to autonomous vehicles and positioning systems. To address these issues, this paper introduces the Dynamic Orbital Resource Management (DORM) framework—a machine learning-driven system for cybersecurity threat detection and resource optimization in 6G NTNs. DORM leverages predictive modeling and real-time monitoring to detect anomalies such as jamming, spoofing, and DoS attacks while simultaneously optimizing network performance. It processes over 10,000 network events across various orbital configurations to learn threat patterns and resource usage behaviors. Simulation results show that DORM achieves 96.8% overall accuracy in detecting threats, with specific detection rates of 96.2% for jamming, 94.8% for spoofing, and 93.9% for DoS attacks. Even under high-intensity attack scenarios, the framework maintains 91.4% detection accuracy and 78.7% system utility. Compared to existing methods, DORM reduces latency by 35.7% (from 85.6 ms to 30.2 ms) and interference by 28.9% (from 8.1 dB to 2.5 dB), while preserving 92.1% overall system utility in standard conditions. These results demonstrate DORM’s potential to deliver both security and performance in dynamic and adversarial NTN environments. The proposed framework lays the groundwork for building resilient, adaptive, and secure 6G NTNs, offering a significant leap forward in protecting global communication infrastructures against emerging threats.}, archive = {J_ACCESS}, author = {Fatemah Alharbi and Abeer Alhuzali and Easa Alalwany and Abdullah Alfahaid}, doi = {10.1109/ACCESS.2025.3590218}, journal = {IEEE Access}, pages = {137033-137047}, shortjournal = {IEEE Access}, title = {Dynamic orbital resource management (DORM) for 6G networks: Enhancing cybersecurity in non-terrestrial systems}, volume = {13}, year = {2025}, }
• (2025). People and management debt in ML-integrated software projects: Structuring industry insights. ACCESS, 13, 137012-137032. (www)

  @article{ , abstract = {The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). Traditional TD research focuses primarily on technical issues, but in ML systems, people and management factors, referred to as nontechnical debt (NTD), play a critical role in TD accumulation and persistence. In this study, we investigate the underexplored dimension of NTD in ML-integrated software systems, focusing on people- and management-related factors. Using Design Science Research (DSR) methodology, we developed an artifact in an iterative incremental manner that categorizes NTD issues in ML systems. As part of this process, we conducted semi-structured interviews with 18 professionals from 15 companies, examining 22 ML projects. Through thematic analysis, we identified 15 NTD categories, 10 of which relate to people debt, and the remaining 5 to management debt. Each category is associated with underlying causes, short-term fixes, and potential solutions. Our findings show that NTD in ML projects frequently arise from inadequate decision-making practices, particularly those related to technology adoption, knowledge management, and human resource planning. Additional sources of NTD include challenges in team dynamics, such as insufficient collaboration, poor skill integration, and ineffective team structuring, as well as communication barriers rooted in organizational culture and team interactions. These factors collectively and substantially impact project outcomes. While band-aid solutions may provide short-term relief, they frequently contribute to accumulation over time. To support practitioners and researchers, our study complements the proposed artifact with actionable recommendations informed by expert perspectives and literature.}, archive = {J_ACCESS}, author = {Pelin Dayan-Akman and Özden Özcan-Top and Tugba Taskaya Temizel}, doi = {10.1109/ACCESS.2025.3595609}, journal = {IEEE Access}, pages = {137012-137032}, shortjournal = {IEEE Access}, title = {People and management debt in ML-integrated software projects: Structuring industry insights}, volume = {13}, year = {2025}, }
• (2025). Toward a holistic framework for robotic assessment: A survey on performance, software, and environmental adaptability. ACCESS, 13, 136996-137011. (www)

  @article{ , abstract = {The evaluation of robotic systems has traditionally centered on task performance metrics such as accuracy, efficiency, and execution time. However, as robotics continues to expand across diverse domains, a broader and more structured assessment framework is necessary. This survey examines metrics from 113 research papers published between 2020 and 2024 to identify trends in robotic evaluation. The findings reveal a wide variety of metrics applied based on different research objectives, making standardization difficult. Despite this diversity, these metrics can generally be classified into task performance and non-task performance categories. Many studies prioritize task performance, often placing less emphasis on software contributions, which are frequently underrepresented in evaluations despite their integral role in control and decision-making. Additionally, robotic adaptability in harsh environments is often inconsistently assessed, limiting comparability across different studies. To address these challenges, this paper advocates for clearer frameworks that systematically integrate both hardware and software contributions. Furthermore, a standardized approach to evaluating robotic adaptability across various environmental conditions is recommended. Establishing structured methodologies will enhance comparability and lead to more precise assessments of robotic performance.}, archive = {J_ACCESS}, author = {Daniel M. Muepu and Yutaka Watanobe and Keitaro Naruse}, doi = {10.1109/ACCESS.2025.3595386}, journal = {IEEE Access}, pages = {136996-137011}, shortjournal = {IEEE Access}, title = {Toward a holistic framework for robotic assessment: A survey on performance, software, and environmental adaptability}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A comprehensive review of unimodal and multimodal fingerprint biometric authentication systems: Fusion, attacks, and template protection”. ACCESS, 13, 136995. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection”).}, archive = {J_ACCESS}, author = {U. Sumalatha and K. Krishna Prakasha and Srikanth Prabhu and Vinod C. Nayak}, doi = {10.1109/ACCESS.2025.3595616}, journal = {IEEE Access}, pages = {136995}, shortjournal = {IEEE Access}, title = {Corrections to “A comprehensive review of unimodal and multimodal fingerprint biometric authentication systems: Fusion, attacks, and template protection”}, volume = {13}, year = {2025}, }
• (2025). Machine learning-enhanced model-based optical proximity correction framework with convolutional neural network-based variable threshold method near the diffraction limit. ACCESS, 13, 136985-136994. (www)

  @article{ , abstract = {As semiconductor manufacturing approaches the diffraction limit of lithography, traditional model-based optical proximity correction (MBOPC) techniques face accuracy bottlenecks due to physical modeling errors. This study proposes a Machine Learning (ML)-enhanced MBOPC framework that employs a convolutional neural network (CNN) to predict mask edge imaging thresholds, thereby mitigating modeling deviations caused by complex lithographic conditions in the 28 nm technology node under immersion lithography. Compared to the conventional polynomial-based variable threshold method, the CNN-based variable threshold strategy significantly improves threshold prediction accuracy and critical dimension (CD) simulation consistency by integrating geometric features of local patterns and slice’s optical intensity distribution parameters. Specifically, the CNN module reduces the root mean square error (RMSE) by approximately 78% on training datasets and 69% on testing datasets versus the constant threshold strategy. In CD simulations for typical patterns, the hybrid model reduces error medians and confines the statistical upper and lower limits of the distribution ranges to ±5 nm. The simulation results demonstrate tighter error distributions across typical 1D and 2D patterns in the metal layer of 28 nm node, highlighting the method’s robustness. This work presents a combined approach that integrates physical models with data-driven methods for the OPC model, providing a new paradigm for next-generation MBOPC technologies that balance physical interpretability and high precision in advanced nodes.}, archive = {J_ACCESS}, author = {Jinhao Zhu and Liwan Yue and Ying Li and Xianhe Liu and Qiang Wu and Qi Wang and Yanli Li}, doi = {10.1109/ACCESS.2025.3592971}, journal = {IEEE Access}, pages = {136985-136994}, shortjournal = {IEEE Access}, title = {Machine learning-enhanced model-based optical proximity correction framework with convolutional neural network-based variable threshold method near the diffraction limit}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of trajectory and handover strategy for cellular-enabled UAV. ACCESS, 13, 136970-136984. (www)

  @article{ , abstract = {We consider a scenario where a UAV travels from an initial point to a final point while maintaining connectivity with the cellular network. For this scenario, the problem of jointly optimizing UAV trajectory and handover strategy to travel between the two points swiftly while avoiding overly frequent handovers is formulated. For this optimization problem, we propose an algorithm that involves converting the original problem into the problem of finding the minimum weight path over a weighted finite graph which can be solved in polynomial time. Our proposed algorithm is compared with some existing algorithms in terms of performance and time complexity, and it is shown that our algorithm is the only one that outputs an optimal solution in polynomial time. Furthermore, various numerical results are presented to illustrate optimal UAV trajectories and handover strategies according to environments.}, archive = {J_ACCESS}, author = {Hyeon-Seong Im and Kyu-Yeong Kim and Jaehoon Chung and Si-Hyeon Lee}, doi = {10.1109/ACCESS.2025.3595184}, journal = {IEEE Access}, pages = {136970-136984}, shortjournal = {IEEE Access}, title = {Joint optimization of trajectory and handover strategy for cellular-enabled UAV}, volume = {13}, year = {2025}, }
• (2025). FedSensor: Federated learning framework for secure sensor-based IoT at the extreme edge. ACCESS, 13, 136945-136969. (www)

  @article{ , abstract = {Ultra-low-power IoT devices at the extreme edge generate valuable data for machine learning applications, however, these devices face stringent limitations in processing power, memory, and energy capacity. The current paper introduces FedSensor, a federated learning framework specifically designed to securely deliver global model updates and enable inference on highly constrained devices, without requiring firmware alterations. The architecture safeguards both data and metadata through end-to-end encryption and enforces edge-based device management to enhance system resilience. FedSensor attains energy efficiency by delegating training tasks to edge servers while maintaining localized inference at the device level. Experimental results demonstrate that battery longevity is preserved, with energy consumption increasing by less than 8% for inference intervals of 10 seconds, and remaining below 5% for longer intervals. These results substantiate the viability of deploying secure, autonomous, and energy-efficient federated learning on ultra-low-power IoT devices.}, archive = {J_ACCESS}, author = {Norisvaldo Ferraz Junior and Anderson Aparecido Alves da Silva and Adilson Eduardo Guelfi and Eduardo Takeo Ueda and Sergio Takeo Kofuji}, doi = {10.1109/ACCESS.2025.3595490}, journal = {IEEE Access}, pages = {136945-136969}, shortjournal = {IEEE Access}, title = {FedSensor: Federated learning framework for secure sensor-based IoT at the extreme edge}, volume = {13}, year = {2025}, }
• (2025). Enhancing emergency response in road accidents: A severity prediction framework using RF-RFE and deep learning model. ACCESS, 13, 136927-136944. (www)

  @article{ , abstract = {Road accidents, particularly in urban areas, pose significant challenges due to their complexity and severe consequences. Rapid emergency response is crucial to mitigating their impact, especially for cases requiring urgent medical intervention. This study aims to enhance emergency response strategies by developing a model that prioritizes severe accidents, ensuring faster ambulance dispatch and timely medical assistance. We utilize multi-source accident data from France, incorporating factors related to road conditions, user demographics, vehicle characteristics, and environmental conditions. The dataset is refined using Random Forest Recursive Feature Elimination (RF-RFE) to select key features influencing accident severity. To address class imbalance, we apply the Synthetic Minority Over-sampling Technique with Tomek Links (SMOTE-Tomek) to oversample minority cases and reduce noise. Based on this preprocessed data, we propose a hybrid Convolutional Neural Network with Bidirectional Long Short-Term Memory and Attention Mechanism (CNN-BiLSTM-Attention) model. The convolutional layers capture spatial patterns, while BiLSTM layers learn sequential dependencies. The attention mechanism further refines predictions by emphasizing critical features. This deep learning model significantly outperforms traditional machine learning methods, achieving accuracy score of 94.99%. Finally, a Shapley Additive Explanations (SHAP) analysis ensures interpretability by identifying key factors influencing accident severity. These insights provide actionable guidance for optimizing emergency response strategies and improving road safety measures.}, archive = {J_ACCESS}, author = {Chaimaa Chaoura and Hajar Lazar and Zahi Jarir}, doi = {10.1109/ACCESS.2025.3592609}, journal = {IEEE Access}, pages = {136927-136944}, shortjournal = {IEEE Access}, title = {Enhancing emergency response in road accidents: A severity prediction framework using RF-RFE and deep learning model}, volume = {13}, year = {2025}, }
• (2025). A stacking ensemble framework leveraging synthetic data for accurate and stable crop yield forecasting. ACCESS, 13, 136909-136926. (www)

  @article{ , abstract = {With the rapid increase in world’s population and changing climate patterns, accurate crop yield forecasting is essential to ensure food security and sustainable agriculture. This study presents a yield prediction framework consisting of Stacking Ensemble Model (SEM) and its Optimized variant (OSEM), which integrates real-world agricultural data with synthetic data generated using the Prophet time-series model. The ensemble comprises Random Forest, XGBoost, Decision Tree, and K-Nearest Neighbors as base learners, with an Extra Trees Regressor as the meta-learner. OSEM incorporates hyperparameter tuning and dimensionality reduction using Singular Value Decomposition to enhance predictive performance and reduce feature noise. Extensive experiments on globally distributed and region-specific crop datasets demonstrate that OSEM consistently outperforms both SEM and state-of-the-art baselines, achieving $R^{2} = 0.996$ and MAE = 0.185 t/ha. Stability and convergence analysis, supported by Wilcoxon and Friedman tests, further confirm the robustness and reliability of the proposed model for scalable crop yield forecasting.}, archive = {J_ACCESS}, author = {Muhammad Waqar and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3591802}, journal = {IEEE Access}, pages = {136909-136926}, shortjournal = {IEEE Access}, title = {A stacking ensemble framework leveraging synthetic data for accurate and stable crop yield forecasting}, volume = {13}, year = {2025}, }
• (2025). Spatio SLAM: An integrated strategy for robot autonomy in variable indoor luminance. ACCESS, 13, 136894-136908. (www)

  @article{ , abstract = {Perceiving and understanding dynamic scenes by a robot vision system requires crucial spatio-temporal knowledge and geometry to enable long-term autonomy. Existing Dynamic SLAM (Simultaneous Localization And Mapping) addresses definite portions of robot pose estimation to make it accurate. On the contrary scene understanding and rational integration with position is hard to find. In this context, a comprehensive realisation of a variable scene shared with other dynamic agents is still a challenge to execute within a shifting drift. This challenge is even increased when the environment is with variable luminance. To address these challenges, we propose a spatial-geometric SLAM (Spatio SLAM) solution that unifies the exiting connotations of long and short-term dynamics that constructs a real-time dense spatial-geometric map. The geometric representation of the scene gives the autonomous agent a prior map to factorize the navigation framework. In this work an end-to-end DA-SSOD (Domain Adaptive Single Stage Object Detection) deep network is introduced into the object detection module. This DA-SSOD introduction experimentally confirms the accurate prediction of obstacles in complex indoor surroundings with variable luminance. Geometric maps built by Spatio SLAM (as the real-time reconstruction of a 3D scene over an instance), results in 86.6%precision in static and 87.4%precision in varying environment (with dynamic obstacles). SOTA (State Of The Art) analysis presents the superiority of Spatio SLAM on other SLAM techniques implemented across multiple indoor structures.}, archive = {J_ACCESS}, author = {Rapti Chaudhuri and Suman Deb and Abhijit Das}, doi = {10.1109/ACCESS.2025.3595572}, journal = {IEEE Access}, pages = {136894-136908}, shortjournal = {IEEE Access}, title = {Spatio SLAM: An integrated strategy for robot autonomy in variable indoor luminance}, volume = {13}, year = {2025}, }
• (2025). Detecting falls and slips of wheelchair users using low-resolution thermal image analysis. ACCESS, 13, 136881-136893. (www)

  @article{ , abstract = {As the aging population grows, there is increasing demand for privacy-preserving monitoring of accidents in private rooms, for which thermal cameras are well suited. This paper focuses specifically on wheelchair users who are more prone to falls. Falls can be classified into high-risk falls that cause injury and low-risk slips where users gradually slide out of the wheelchair. Treating both equally risks generating excessive warnings and missing true emergencies. In this study, we used low-resolution thermal images to detect and distinguish high-risk falls and low-risk slips, thereby reducing caregiver burden and improving safety. However, video analysis requires clear images of the limbs and other details of the body, but thermal cameras cannot consistently capture such detailed features. To overcome this, we proposed a video analysis scheme using torso features and developed a system called “Fall Detection using CNN and Torso Features (FDCTF).” Additionally, to enhance the generalizability of the system to various environmental conditions, we incorporated multi-task learning and a two-layer LSTM network, extending FDCTF. As a result, in a single-environment evaluation, our system achieved an F1 score of 1.0 for fall detection. Furthermore, in a multi-environment evaluation, the F1 score reached 0.82, demonstrating the model’s adaptability to various settings. Moreover, the system significantly reduced caregiver burden compared to conventional methods, with reductions of 0.19 in false alarms, and 0.18 in missed serious falls.}, archive = {J_ACCESS}, author = {Shisei Nakamura and Masaaki Yamauchi and Miwa Sugita and Yoshihiro Aso and Yuichi Ohsita and Hideyuki Shimonishi}, doi = {10.1109/ACCESS.2025.3591606}, journal = {IEEE Access}, pages = {136881-136893}, shortjournal = {IEEE Access}, title = {Detecting falls and slips of wheelchair users using low-resolution thermal image analysis}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection system framework for SDN-based IoT networks using deep learning approaches with XAI-based feature selection techniques and domain-constrained features. ACCESS, 13, 136864-136880. (www)

  @article{ , abstract = {The proliferation of Internet of Things (IoT) applications impact many aspects of life, including smart homes, smart offices, and smart cities, among others. However, it poses significant cybersecurity threats. Intrusion detection systems (IDSs) utilize artificial intelligence, especially deep learning, to mitigate these threats. The design of deep learning models and the quality of datasets are two key factors in creating effective IDSs. Randomly selecting hyperparameters and using datasets with irrelevant features can negatively affect model performance and computational complexity. This study proposes an IDS framework to detect various cyberattacks in SDN-based IoT networks utilizing three deep learning algorithms that incorporate hyperparameter tuning and the feature selection process based on explainable artificial intelligence (XAI), which uses domain-constrained features to improve performance and reduce computational complexity. Two recent flow-based datasets were used to train and assess models to validate the proposed framework. We conducted an extensive set of experiments using the subsets of features derived by XAI-based feature selection techniques, and compared their performance against each other, the baseline, and state-of-the-art models. The experimental results reveal that Shapley Additive Explanations and Random Forest feature importance are the reliable feature selection techniques, as they yield consistent results across all deep learning models and different feature subsets. Furthermore, the convolutional neural network model produced a top performance with an accuracy of 99.9% in the InSDN and 98% in the X-IIoTID datasets for multi-class classification. Our study provides guidelines for selecting suitable XAI-based feature selection techniques that incorporate domain-constrained features in the development of IDSs for SDN-based IoT networks.}, archive = {J_ACCESS}, author = {Manlaibaatar Tserenkhuu and Md Delwar Hossain and Yuzo Taenaka and Youki Kadobayashi}, doi = {10.1109/ACCESS.2025.3595595}, journal = {IEEE Access}, pages = {136864-136880}, shortjournal = {IEEE Access}, title = {Intrusion detection system framework for SDN-based IoT networks using deep learning approaches with XAI-based feature selection techniques and domain-constrained features}, volume = {13}, year = {2025}, }
• (2025). Improved RTAB-map algorithm based on visible light positioning. ACCESS, 13, 136854-136863. (www)

  @article{ , abstract = {Visible Light Positioning (VLP) is an emerging technology that enables highly accurate indoor localization, especially in areas where GPS is not effective, such as inside buildings. VLP makes use of existing lighting systems but typically requires a high density of Light Emitting Diodes (LEDs) and a clear line-of-sight (LOS) between the light sources and the receivers. It is also crucial to recognize that sensors can have limitations, and their readings may not always be precise. This research presents an improved RTAB-Map algorithm based on visible light positioning. By using a custom modulator, we send signals to the LEDs to create a striped pattern. A rolling shutter camera captures these LED signals and uses template matching techniques to accurately locate the region of interest (ROI). Within this ROI, we identify the LED contours and extract the pixels from the central row into a one-dimensional array for decoding. The decoded information is then compared to a pre-existing database. Finally, we apply the solvePnP algorithm to determine the three-dimensional position and orientation, which is integrated with Kalman Filters to combine the data into the RTAB-Map algorithm for mapping purposes. This multi-sensor localization system shows strong and accurate capabilities for robot localization and navigation, even in scenarios with limited LEDs or power outages. Experimental results reveal that the proposed method achieves an average accuracy of 2.5 cm and an average positioning delay of about 5.3 microseconds.}, archive = {J_ACCESS}, author = {Po-Hao Hunag and Si-Chuan Liu and Cheng-Hsiung Yang}, doi = {10.1109/ACCESS.2025.3590961}, journal = {IEEE Access}, pages = {136854-136863}, shortjournal = {IEEE Access}, title = {Improved RTAB-map algorithm based on visible light positioning}, volume = {13}, year = {2025}, }
• (2025). Enabling secure and scalable GDPR-compliant blockchain-based e-KYC with efficient redaction. ACCESS, 13, 136834-136853. (www)

  @article{ , abstract = {As electronic Know Your Customer (e-KYC) systems become integral to modern financial services, ensuring compliance with stringent privacy regulations—particularly the General Data Protection Regulation (GDPR)—has become paramount. However, most existing blockchain-based e-KYC solutions are limited by two critical shortcomings. First, they lack native support for data redaction, conflicting with GDPR’s “right to be forgotten,” since data once recorded on-chain becomes immutable and globally persistent. Second, they fail to scale under high-volume cross-institutional transactions, where centralized authentication models and monolithic blockchain structures lead to computational bottlenecks, increased latency, and communication overhead across financial institutions FIs. To address these gaps, we propose a novel blockchain-based e-KYC framework that is GDPR-compliant, privacy-preserving, and highly scalable. Our solution introduces three key innovations: 1) ZK-Rollup-based authentication integrated with sharded blockchain architecture, enabling off-chain proof batching and distributed verification across shards for high-throughput, low-latency validation; 2) adaptive proxy re-encryption to support secure and dynamic customer data portability between host and requesting FIs; and 3) an optimized redactable blockchain protocol using off-chain Chameleon Hash collision generation and threshold cryptography-based key rotation to enable efficient, secure, and verifiable data updates in compliance with GDPR. Our experimental results show substantial performance gains in authentication verification, redaction throughput and cross-FI transaction efficiency, validating the practical applicability of our scheme in real-world, regulation-sensitive e-KYC deployments.}, archive = {J_ACCESS}, author = {Somchart Fugkeaw and Surabordin Sungchai and Supawit Nakprame and Piphattra Sreekongpan}, doi = {10.1109/ACCESS.2025.3594656}, journal = {IEEE Access}, pages = {136834-136853}, shortjournal = {IEEE Access}, title = {Enabling secure and scalable GDPR-compliant blockchain-based e-KYC with efficient redaction}, volume = {13}, year = {2025}, }
• (2025). Transcending fixed infrastructure: Leveraging mobile RSUs for dynamic collective perception services in VANETs. ACCESS, 13, 136816-136833. (www)

  @article{ , abstract = {Road safety has become a major concern in recent decades due to the growing number of vehicles, which is expected to double in the next decade. This increase is likely to result in more accidents and greater financial costs due to traffic congestion. Vehicular Ad Hoc Networks (VANETs) offer a promising solution to address these issues by facilitating information exchange, thereby enhancing situational awareness. One of the key VANET safety applications, Collective Perception (CP) services, aims to improve awareness of both V2X and non-V2X objects during the transition period before full V2X vehicle market penetration. Traditionally, Collective Perception Messages (CPMs) are generated by V2X vehicles or specialized Road Side Units (RSUs) equipped with sensors and are shared with other V2X vehicles within a single-hop communication range. This work proposes using mobile RSUs to disseminate RSU-assisted CPMs by aggregating received CPM objects, rather than relying on conventional fixed RSUs. The mobility of RSUs can increase the chances of distributing CPMs to a larger number of vehicles, thereby enhancing overall situational awareness. Additionally, we introduce a novel dynamic CPM generation mechanism for RSU-assisted CPMs, which adjusts the CPM dissemination rate based on the current environment, reducing channel load compared to the traditional periodic CPM generation approach. We begin by conducting simulations in a city scenario to evaluate the performance of fixed and mobile RSU-assisted dissemination methods. We explore the critical question of whether mobile RSUs are more effective for CPM dissemination than fixed RSUs. Our results show that the proposed mobile RSU-assisted CPM dissemination mechanism distributes on average 3.7 times more CPM objects to V2X vehicles than fixed RSUs. Moreover, the proposed dynamic CPM generation rate control mechanism maintains overall latency below 100 ms while adapting the CPM generation interval according to the current environment. After confirming the feasibility of using mobile RSUs for CPM dissemination with simulations, we model the proposed system for an n-lane road segment scenario, validating its performance by comparing it with simulation results. Finally, we perform a detailed parameter analysis on the model to evaluate the benefits of the proposed CPM dissemination mechanism across various scenarios.}, archive = {J_ACCESS}, author = {Thenuka Karunathilake and Anna Förster}, doi = {10.1109/ACCESS.2025.3595416}, journal = {IEEE Access}, pages = {136816-136833}, shortjournal = {IEEE Access}, title = {Transcending fixed infrastructure: Leveraging mobile RSUs for dynamic collective perception services in VANETs}, volume = {13}, year = {2025}, }
• (2025). TriageHD: A hyper-dimensional learning-to-rank framework for dynamic micro-segmentation in zero-trust network security. ACCESS, 13, 136806-136815. (www)

  @article{ , abstract = {Network security faces major challenges from sophisticated cyber attacks that exploit lateral movement and evade traditional network intrusion detection mechanisms. To address these challenges, micro-segmentation has proven to be an effective defense strategy for isolating network components and limiting breach propagation. This paper presents TriageHD, a novel framework that integrates graph-based Hyper-Dimensional Computing (HDC) with a learning-to-rank algorithm to strengthen zero-trust network security. TriageHD constructs dynamic scene graphs from time-based network flow data, integrating feature representations extracted via a self-attention-based payload encoder. It employs a learning-to-rank algorithm with an approximated nDCG loss function, incorporating time-aware relevance and graph-aware HDC to prioritize nodes for segregation, thereby mitigating attack propagation. Experiments on the CIC-IDS-2017 dataset demonstrate that TriageHD outperforms state-of-the-art graph neural networks, including graph convolutional networks, graph attention networks, and graph transformer models, in threat prioritization accuracy. By providing a dynamic micro-segmentation approach, TriageHD significantly enhances automated threat detection and response. This work bridges traditional network security measures with zero-trust paradigms, laying the groundwork for future advancements in dynamic micro-segmentation.}, archive = {J_ACCESS}, author = {Ryozo Masukawa and Sanggeon Yun and Sungheon Jeong and Nathaniel D. Bastian and Mohsen Imani}, doi = {10.1109/ACCESS.2025.3592877}, journal = {IEEE Access}, pages = {136806-136815}, shortjournal = {IEEE Access}, title = {TriageHD: A hyper-dimensional learning-to-rank framework for dynamic micro-segmentation in zero-trust network security}, volume = {13}, year = {2025}, }
• (2025). Modeling point source potentials in horizontally layered soils. ACCESS, 13, 136793-136805. (www)

  @article{ , abstract = {This paper presents a novel model for electric current dissipation in horizontally stratified multilayer soils, applicable to arbitrary point source locations and evaluated at any spatial coordinate. The soil characteristic functions, which govern the boundary conditions across layer interfaces and the solution domain, are obtained by solving a linear system. Once these functions are established, the electric potential is computed using a Bessel-type integral, efficiently evaluated via the Ogata-Sugihara quadrature method, which has demonstrated high accuracy for this class of problems. Several case studies are conducted, and the results are validated through comparison with simulations from existing literature using Finite Element Analysis.}, archive = {J_ACCESS}, author = {Rooney R. A. Coelho and Gabriel de Castro Biage and José Roberto Cardoso}, doi = {10.1109/ACCESS.2025.3577069}, journal = {IEEE Access}, pages = {136793-136805}, shortjournal = {IEEE Access}, title = {Modeling point source potentials in horizontally layered soils}, volume = {13}, year = {2025}, }
• (2025). Multistage training of fuzzy cognitive maps to predict preeclampsia and fetal growth restriction. ACCESS, 13, 136779-136792. (www)

  @article{ , abstract = {Preeclampsia (PE) and fetal growth restriction (FGR) are pregancy complications related to placental dysfunction that pose significant challenges in terms of morbidity and mortality worldwide. Addressing these challenges involves early identification of the disease, which could reduce both the burden on healthcare systems and associated morbidity rates. In this study, we propose an innovative strategy using multistage training of fuzzy cognitive maps (FCM) to predict specific pregnancy disorders such as PE and FGR. The objective was to develop a predictive approach as a result of multistage training to simulate disease progression in a human individual. The models were rigorously evaluated for their predictive ability using datasets containing characteristics related to the mother, fetus, signs, symptoms, Doppler studies, and laboratory tests. The results conclusively reveal that multistage training better uncovers patterns in the data, leading to significantly improved predictive performance for these disorders. Convergence analysis demonstrated the stability of the FCM generated during the training stages. Also, the comparison with other machine learning models demonstrates that our approach is competitive to predict PE and FGR. The application of these models in healthcare settings holds promise as a valuable tool for the early detection of PE and FGR, contributing to the reduction of morbidity and mortality rates.}, archive = {J_ACCESS}, author = {William Hoyos and Rodrigo García and Jose Aguilar}, doi = {10.1109/ACCESS.2025.3595758}, journal = {IEEE Access}, pages = {136779-136792}, shortjournal = {IEEE Access}, title = {Multistage training of fuzzy cognitive maps to predict preeclampsia and fetal growth restriction}, volume = {13}, year = {2025}, }
• (2025). Exploring the use of virtual reality and AI to create immersive, interactive music experiences for performance, education, and therapy. ACCESS, 13, 136768-136778. (www)

  @article{ , abstract = {This study investigates the synchronization of real-time music generation with visual elements in Virtual Reality (VR) environments, leveraging Artificial Intelligence (AI) to create immersive, interactive music experiences for performance, education, and therapy. By leveraging deep learning techniques, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, the study investigates real-time music generation and synchronization with VR environments. The optimization of music-visual alignment was achieved through Genetic Algorithms (GAs), enhancing the overall coherence and responsiveness of the system. Key findings include significant improvements in user engagement, learning outcomes, and audience satisfaction in educational and performance contexts. The system achieved a high degree of musical coherence, with sequence prediction accuracy of 92.3% and sub-50ms latency, providing a seamless VR music experience. Case studies focused on interactive music education, immersive performance, and personalized music therapy demonstrated the system’s potential in diverse settings, with improvements in user retention, stress reduction, and overall satisfaction. This study highlights the transformative potential of combining VR and AI in music, paving the way for innovative applications in music education, performance, and therapy. The findings offer a foundation for future research and development in immersive music technologies.}, archive = {J_ACCESS}, author = {Jing Zhao and Kun Cheng}, doi = {10.1109/ACCESS.2025.3595591}, journal = {IEEE Access}, pages = {136768-136778}, shortjournal = {IEEE Access}, title = {Exploring the use of virtual reality and AI to create immersive, interactive music experiences for performance, education, and therapy}, volume = {13}, year = {2025}, }
• (2025). Collision risk perception models using physiological and eye-tracking signals. ACCESS, 13, 136745-136767. (www)

  @article{ , abstract = {Accurate risk perception is essential for safe driving, particularly in dynamic and high-risk traffic environments. This study develops machine learning (ML)-based user risk perception models using physiological recording systems to assess driving risks across various scenarios. We evaluate model performance at different granularity levels, ranging from binary classification (e.g., risk versus no-risk), to detailed classifications, resulting in more classes (e.g., incorporating collision subject types, pedestrian types, numbers, vehicle speeds and deceleration rates). Results demonstrate that despite increased model complexity, the proposed risk perception models consistently achieve high performance, with accuracy exceeding 0.92 in most cases except for high-granularity models in vehicle collision scenarios. Models trained for pedestrian-related risks outperformed those for vehicle-related risks, indicating a stronger physiological response to pedestrian hazards. Feature importance analysis reveals that electroencephalogram (EEG) signals (midline channels from frontal to parietal lobes: Cz, Fz and Pz) play a dominant role, with pupil center shift degree (PCD) and pupil center shift magnitude (PCM) as secondary key contributors. In contrast, features derived from skin temperature and electrodermal activity (EDA) exhibit limited importance due to slower response times. These findings suggest that EEG and pupil features are optimal for real-time risk perception models, with heart activity features serving as complementary factors in enhancing model accuracy and reliability. We also discuss practical applications of these models in driver-vehicle interaction and intelligent transportation systems. By integrating physiological data with environmental perception sensors, these models offer a promising approach to enhancing safety in semi-autonomous driving systems.}, archive = {J_ACCESS}, author = {Hyowon Lee and Ocktaeck Lim and Amandeep Singh and Siby Samuel}, doi = {10.1109/ACCESS.2025.3594806}, journal = {IEEE Access}, pages = {136745-136767}, shortjournal = {IEEE Access}, title = {Collision risk perception models using physiological and eye-tracking signals}, volume = {13}, year = {2025}, }
• (2025). Comprehensive evaluation of techniques for intelligent chatter detection in micro-milling processes. ACCESS, 13, 136731-136744. (www)

  @article{ , abstract = {Frictional and regenerative chatter are the most relevant unstable dynamic conditions in micro-milling. Both phenomena promote chatter, affecting the surface finishing and reducing the tool life. Therefore, in-process chatter detection strategies are of utmost importance to avoid adverse effects on productivity. These strategies can use machine learning (ML) and deep learning (DL) classifiers, but they should be able to lead with tight computational requirements. A smaller yet relevant set of features could aid these classifiers in coping with these requirements. This work proposed using feature selection to evaluate the impact of several statistical features on the performance of ML classifiers for chatter detection during micro-milling operations, compare them to the performance of the Convolutional Neural Network algorithm, and discuss the employability of the techniques on the STM32F446RE microcontroller. This study exploits experimental data from chatter and chatter-free cuts during machining operations with commercial COSAR-60 low-carbon steel under two different grain sizes (as received and ultra-fined). Random Forest, Pearson’s Correlation, and collinearity analysis are the strategies used to identify the most relevant features in these operations. The performance of several ML classifiers is compared in each feature reduction stage with the Deep Learning algorithm. The results discuss the need for applying complex algorithms since the accuracy and F1-score indices presented similar values regardless of the number of features. Moreover, reducing the dataset dimension significantly decreased training/testing time, and mainly in execution latency (FLOPs). This reduction is essential for implementing strategies for in-process chatter detection.}, archive = {J_ACCESS}, author = {Guilherme Serpa Sestito and Wesley Angelino De Souza and Alessandro Roger Rodrigues and Maíra Martins Da Silva}, doi = {10.1109/ACCESS.2025.3595058}, journal = {IEEE Access}, pages = {136731-136744}, shortjournal = {IEEE Access}, title = {Comprehensive evaluation of techniques for intelligent chatter detection in micro-milling processes}, volume = {13}, year = {2025}, }
• (2025). Integration of photovoltaic systems with hydrogen production: A review of current technologies and future perspective. ACCESS, 13, 136716-136730. (www)

  @article{ , abstract = {The integration of photovoltaic (PV) systems with hydrogen production offers a sustainable method to utilize solar energy for the manufacturing of clean fuel. This paper examines recent breakthroughs in the integration of photovoltaic technology with water electrolysis, highlighting the technical feasibility and economic viability of these systems. Principal hydrogen production technologies, such as alkaline, proton exchange membrane (PEM), and solid oxide electrolyzers, are assessed regarding their compatibility with photovoltaic power outputs. Direct coupling solutions, including photovoltaic-electrochemical (PV-EC) systems, are emphasized for their capacity to optimize energy conversion processes. Notwithstanding the potential, obstacles such as the variability of solar irradiation, the responsive behavior of electrolyzers to changing power inputs, and the necessity for effective energy management systems remain. Recent research has illustrated kilowatt-scale solar hydrogen generation systems, with demonstrated production rates of up to 0.5–1.2 Nm3/h under peak irradiance, and overall solar-to-hydrogen efficiencies reaching $10^{}18\%$ . Levelized cost of hydrogen (LCOH) values from PV-powered systems currently range from 4 to 8 USD/kg H2, depending on the technology and location. Advancements in power electronics, particularly maximum power point tracking (MPPT) techniques, are essential for enhancing the efficiency of photovoltaic-powered electrolysis. The novelty of this study lies in its comprehensive and current synthesis of PV-electrolysis integration techniques, with a specific emphasis on direct coupling configurations, system scalability, and the enhancement of energy management through advanced MPPT techniques. It is essential to tackle these issues with creative system designs and control methodologies to facilitate the wider deployment of PV-hydrogen systems, thereby considerably aiding global decarbonization efforts.}, archive = {J_ACCESS}, author = {Mourad Yessef and Youness Hakam and Mohamed Tabaa and Shaik Mohammad Irshad and Z. M. S. El-Barbary}, doi = {10.1109/ACCESS.2025.3595057}, journal = {IEEE Access}, pages = {136716-136730}, shortjournal = {IEEE Access}, title = {Integration of photovoltaic systems with hydrogen production: A review of current technologies and future perspective}, volume = {13}, year = {2025}, }
• (2025). H∞ sliding mode control: A recent review of applications and design methods. ACCESS, 13, 136687-136715. (www)

  @article{ , abstract = {The $H_{\infty }$ Sliding Mode Control ( $H_{\infty }$ SMC) approach was introduced at the onset of this millennium as a proficient strategy integrating sliding mode and $H_{\infty }$ control principles to leverage their robust attributes while avoiding inherent limitations, thereby facilitating the control of complex perturbed nonlinear systems. This review study explores noteworthy advances in the academic community of $H_{\infty }$ SMC from January 2019 to September 2024, revealing notable progress with cutting-edge achievements in design methods and applications. First, the importance of $H_{\infty }$ SMC is explained with its primary perspectives. The motivation and contribution of this paper are then discussed in light of the lack of review works on the topic of interest. After that, the problem systems controlled using $H_{\infty }$ SMC and the challenges and perturbations included in each previous study are presented. In addition, the $H_{\infty }$ SMC design methods are qualitatively and quantitatively analyzed, with extensive critique reported for each. Figures and tables are provided for overall review. Finally, remaining issues and new study directions are provided to help scholars improve and grow the research subject. The observations of this review show that more contributions are needed to compensate different applications using further novel design methods.}, archive = {J_ACCESS}, author = {Ali H. Mhmood and Muhammad Nasiruddin Mahyuddin}, doi = {10.1109/ACCESS.2025.3594707}, journal = {IEEE Access}, pages = {136687-136715}, shortjournal = {IEEE Access}, title = {H∞ sliding mode control: A recent review of applications and design methods}, volume = {13}, year = {2025}, }
• (2025). Channel access probability in unslotted IEEE 802.15.4 csma under correlated clear channel assessments. ACCESS, 13, 136671-136686. (www)

  @article{ , abstract = {Correlation between successive clear channel assessments in unslotted carrier sense multiple access with collision avoidance (CSMA/CA) is commonly assumed in the literature to be negligible for modeling the medium access control algorithm. However, in previous research, the authors found this assumption to be questionable under certain realistic conditions. In this paper, the correlation between successive clear channel assessments is studied and its effect on the probability and delay of accessing the medium by a node following the IEEE 802.15.4 Standard is modeled for the unslotted CSMA/CA mode. The results obtained are used for evaluating the delay probability distributions of the three outcome events of the CSMA/CA algorithm: frame transfer success, channel access failure and frame transfer failure. For scenarios in which the duration of contentions is similar to, or shorter than, the duration of the frames propagating in the air, the correlation effect is strong, making classical models to yield results that deviate from a realistic scenario. The theoretical results are validated by simulations. It is shown that neglecting correlation between successive clear channel assessments leads to overestimating the expected delay of frame transfer success and channel access failure by up to 15%. The results also reveal that the probability of frame transfer success can be overestimated by up to 30%, the probability of frame transfer failure by up to 80% and the probability of channel access failure is underestimated by up to a factor 6 when clear channel assessment correlation is ignored.}, archive = {J_ACCESS}, author = {Miguel Espinoza and Christian Oberli and Miguel Gutiérrez Gaitán and Alejandro Maass}, doi = {10.1109/ACCESS.2025.3593810}, journal = {IEEE Access}, pages = {136671-136686}, shortjournal = {IEEE Access}, title = {Channel access probability in unslotted IEEE 802.15.4 csma under correlated clear channel assessments}, volume = {13}, year = {2025}, }
• (2025). Short and medium-term power load anomaly detection method based on convolutional neural network and EL-DCC. ACCESS, 13, 136656-136670. (www)

  @article{ , abstract = {Power load anomaly detection is critical to grid stability and security. With the increasing complexity of load patterns, it is difficult for traditional methods to meet the requirements of accurate detection. Therefore, this study proposes a short and medium-term power load anomaly detection method that combines convolutional neural networks and ensemble learning with deep convolutional classifiers to improve detection accuracy and robustness. The model first extracts local features using convolutional neural network, then captures the time series dependencies by bi-directional long short-term memory, and finally the classification judgment is made by random forest. The experimental results indicated that on the Global Energy Forecasting Competition 2012 Dataset, the convergence speed of the power load anomaly detection model proposed by the research was faster, and the loss value was stabilized at about 0.2 after about 40 iterations. Moreover, the detection accuracy gradually increased and stabilized with the increase of the quantity of samples, and the maximum value was about 0.98. The accuracy of the power load forecasting detection model proposed by the study was maximum about 0.97 when the quantity was 1000. Its accuracy gradually increased with the increase of the iterations to reach the maximum value of about 0.95. The study shows that the proposed model can effectively detect and predict power load anomalies with good precision and accuracy. The proposed method provides an efficient anomaly detection and forecasting scheme for the power system and improves the intelligent management level of the power grid.}, archive = {J_ACCESS}, author = {Zhipeng Li and Shaobo Liu and Yang Zhang and Zhaowei Wang and Lu Wang and Lu Huang}, doi = {10.1109/ACCESS.2025.3594024}, journal = {IEEE Access}, pages = {136656-136670}, shortjournal = {IEEE Access}, title = {Short and medium-term power load anomaly detection method based on convolutional neural network and EL-DCC}, volume = {13}, year = {2025}, }
• (2025). Ultralight situation-aware hypervisor with adaptive starvation-free feedback control for embedded system. ACCESS, 13, 136643-136655. (www)

  @article{ , abstract = {In recent years, microcontrollers are increasingly using hypervisors to achieve efficient and adaptable real-time performance in resource-constrained environments, enabling better resource management and system isolation. However, traditional hypervisor scheduling approaches often rely on static scheduling or require all scheduling decisions to be determined at compile time, limiting their ability to adapt to dynamic workload changes and real-time demands. To address these issues, this study introduces a scheduling technique that combines round-robin and priority scheduling. Rather than simply merging the two methods—which would also combine their disadvantages—the proposed approach resolves the fixed time-quantum issue of round-robin by dynamically adjusting the time-quantum based on priority for each round. In addition, to address the starvation problem in priority scheduling, a feedback control system is designed to maintain the average waiting time at an acceptable level. To mitigate the temporal overhead of determining time quanta, the design incorporates memory and functional isolation, significantly reducing context switching overhead. Our experimental results show a 54% reduction in context switching overhead compared to FreeRTOS, requiring only 44us for switching operations. In dynamic priority scenarios using computationally intensive dummy tasks to simulate real workloads, the system reduced the execution time by up to 38% for high priority conditions while appropriately deferring lower priority tasks. By improving resource utilization and responsiveness to external environmental changes, this approach enables robust and adaptive software execution, providing an effective solution for real-time embedded systems.}, archive = {J_ACCESS}, author = {Minjung Kim and Daejin Park}, doi = {10.1109/ACCESS.2025.3594619}, journal = {IEEE Access}, pages = {136643-136655}, shortjournal = {IEEE Access}, title = {Ultralight situation-aware hypervisor with adaptive starvation-free feedback control for embedded system}, volume = {13}, year = {2025}, }
• (2025). Applying the FAIR principles to open educational resources: A semantic similarity approach to improve resource discovery. ACCESS, 13, 136626-136642. (www)

  @article{ , abstract = {Open educational resources (OER) are teaching, learning, or research resources freely available for use and reuse. Despite their potential, OER uptake in existing education systems remains low, primarily due to challenges in locating suitable resources. This study addresses this challenge by proposing and implementing a workflow applying the FAIR (Findable, Accessible, Interoperable, and Reusable) principles to OER. We demonstrated this framework within the Earth System Sciences as an application domain. We constructed a knowledge graph of approximately 500 FAIR OER, each annotated with structured metadata using the Schema.org vocabulary and made accessible through a SPARQL endpoint. To bridge the gap between making resources queryable and enabling their practical reuse, we employed a transformer-based language model (Sentence-BERT). The model was fine-tuned using few-shot learning on a domain-specific dataset of course-description pairs. This specialized model was then used to map the OER collection against over 200 university courses across five academic programs at a German university, based on semantic similarity between OER descriptions and university course descriptions. Expert evaluation of the model’s recommendations demonstrated 74% accuracy in identifying reusable OER for university courses. Notably, even with limited training data, fine-tuning the Sentence-BERT model significantly improved performance, resulting in a 16% reduction in mean squared error compared to the base model. This study provides both a generalizable methodology and a practical demonstration of how FAIR principles can streamline OER discovery, potentially accelerating OER uptake in higher education.}, archive = {J_ACCESS}, author = {Farzaneh Sadeghi and Auriol Degbelo and Carsten Kessler and Reza Zolnouri}, doi = {10.1109/ACCESS.2025.3594963}, journal = {IEEE Access}, pages = {136626-136642}, shortjournal = {IEEE Access}, title = {Applying the FAIR principles to open educational resources: A semantic similarity approach to improve resource discovery}, volume = {13}, year = {2025}, }
• (2025). Corrections to “St-hot: A prospect of price equilibrium in a multi-player game for electric vehicle charging application”. ACCESS, 13, 136625. (www)

  @article{ , abstract = {Due to some formatting error on page 3 of Gupta and Bhatnagar (2024), the variables on the left-hand side of (1) and (2) are incorrectly prefixed with “rCl.” Specifically, (1) currently reads $rCl\mathcal {P}_{c_{n}}$ but should simply be $\mathcal {P}_{c_{n}}$ . Likewise, in (2), it should be ${\mathcal {U}}_{n}$ instead of $rCl {\mathcal {U}}_{n}$ .}, archive = {J_ACCESS}, author = {Ajay Kumar Gupta and Manav R. Bhatnagar}, doi = {10.1109/ACCESS.2025.3593713}, journal = {IEEE Access}, pages = {136625}, shortjournal = {IEEE Access}, title = {Corrections to “St-hot: A prospect of price equilibrium in a multi-player game for electric vehicle charging application”}, volume = {13}, year = {2025}, }
• (2025). YOLOv5-MDS: Target detection model for PCB defect inspection based on YOLOv5 integrated with mamba architecture. ACCESS, 13, 136612-136624. (www)

  @article{ , abstract = {The Printed Circuit Board (PCB), which serves as the foundational component of numerous electronic devices, exhibits a complex relationship between its quality and the lifespan and performance of those products. However, annual losses are significantly contributed to by PCB defects arising from various production factors. PCB defects present significant challenges in detection, due to their small size and irregular distribution. Existing methods struggle to effectively detect these minute and scattered defects, especially in the context of industrial production, where high accuracy and speed are paramount. To address this issue, YOLOv5-MDS has been proposed, a convolutional neural network (CNN) specifically designed for image segmentation and classification. This model is derived from and enhanced on the YOLOv5 model. Our primary objective was to refine the neck section of the YOLOv5 model by integrating the Mamba architecture within it. Additionally, we optimized the intersection over union (IoU) calculation method and the upsampling module. This strategic integration aims to overcome the inherent limitation of convolutional operations’ local receptive fields, thereby enabling the model to capture features more effectively. Our proposed improvements enhance detection accuracy while maintaining computational efficiency. Experimental results demonstrate that our model achieves significant improvements in accuracy for PCB defect detection tasks compared to the baseline model. Compared to the baseline model, our proposed model achieves a 34.3% performance improvement. When benchmarked on edge devices, YOLOv5-MDS demonstrates superior detection capabilities with mAP95 values exceeding YOLOv8n by 11.0% and YOLOv11n by 0.9%, while simultaneously showing significant efficiency advantages - operating 29% faster than YOLOv8n and 57% faster than YOLOv11n in inference speed. These comparative results comprehensively validate the practical applicability of YOLOv5-MDS in industrial deployment scenarios.}, archive = {J_ACCESS}, author = {Deming Guo and Qi Xi and Long Zhang and Jing Wang and Yilong Ye and Ziyi Zeng}, doi = {10.1109/ACCESS.2025.3591987}, journal = {IEEE Access}, pages = {136612-136624}, shortjournal = {IEEE Access}, title = {YOLOv5-MDS: Target detection model for PCB defect inspection based on YOLOv5 integrated with mamba architecture}, volume = {13}, year = {2025}, }
• (2025). Assessing head injury of civilian UAV safety systems: A test apparatus and spherical shell example. ACCESS, 13, 136598-136611. (www)

  @article{ , abstract = {In recent years, flying vehicles have become an innovative way to transfer parcels efficiently over a short distance. Existing airspace regulations are slow to adapt to the anticipated rise in aircraft traffic resulting from this development. This is because the increase in unmanned aerial vehicles (UAVs) comes with an increased risk of personal injuries from faulty drones carrying a payload. Many national regulatory agencies, such as the FAA, rely on the Abbreviated Injury Scale (AIS) to determine the weight threshold of a UAV by evaluating the impact energy and correlating it to the severity of a potential injury. This research paper presents a test apparatus to investigate the effect of different safety systems on AIS.As an example, a passive safety system in the form of a geodesic shell is used to investigate the change in impact acceleration and therefore, the head injury sustained. The spherical shell is designed as a geodesic sphere. Its struts are carbon fiber reinforced polymer (CFRP) rods and connectors made from a flexible filament, TPU-95A. The apparatus enables droppings of the geodesic shell from a height of 4.48 meters,varying the payload weight in two weight classes between 2.5 lb to 5 lb attached to the drone. The impact acceleration is recorded, and the impact velocity is calculated using slow-motion video. The Head Injury Criterion (HIC) scale and the Abbreviated Injury Scale (AIS) are used to measure the predicted head injury level. The test apparatus designed establishes a standard of testing for other safety systems, in addition to geodesic shells like airbags, to be tested for their efficacy in reducing the head injury level sustained.}, archive = {J_ACCESS}, author = {Eric J. Hettel and Hossein Eslamiat and Mohammed Aleemuddin and Mohsen Farajijalal}, doi = {10.1109/ACCESS.2025.3593937}, journal = {IEEE Access}, pages = {136598-136611}, shortjournal = {IEEE Access}, title = {Assessing head injury of civilian UAV safety systems: A test apparatus and spherical shell example}, volume = {13}, year = {2025}, }
• (2025). Data-driven vehicle dynamics: Lever-aging SINDy for optimization-based vehicular motion planning. ACCESS, 13, 136584-136597. (www)

  @article{ , abstract = {Motion planning remains a crucial challenge for the widespread adoption of autonomous vehicles. This paper presents a novel approach that integrates an empirical plant model within an optimization-based motion planning architecture. The model prioritizes performance and efficiency while maintaining interpretability. We introduce a methodology that utilizes a data-driven approach to derive an interpretable description of the evolution of vehicle states over time using sparse regression. This method allows effective learning from limited datasets, eliminating the need for extensive and expensive data collection. Our approach addresses the trade-off between performance and accuracy, enabling adaptation to diverse driving scenarios. We affirm the efficacy of our methodology via an extensive analysis, evaluating the independent prediction performance across diverse metrics. Additionally, we examine the overall tracking performance when incorporated into an optimization-based framework. Finally, we present a comparative analysis and discuss the subsequent impact on overall motion planning and decision-making in relation to a state-of-the-art single-track model.}, archive = {J_ACCESS}, author = {Daniel Weihmayr and Christian Birkner and Hormoz Marzbani and Reza Nakhaie Jazar}, doi = {10.1109/ACCESS.2025.3594892}, journal = {IEEE Access}, pages = {136584-136597}, shortjournal = {IEEE Access}, title = {Data-driven vehicle dynamics: Lever-aging SINDy for optimization-based vehicular motion planning}, volume = {13}, year = {2025}, }
• (2025). Robust semantic segmentation of wafer transmission electron microscopy image using multi-task learning with edge detection. ACCESS, 13, 136568-136583. (www)

  @article{ , abstract = {Semantic segmentation for wafer transmission electron microscopy (TEM) images plays a crucial role in the automated measurement of semiconductors. However, the automated measurement of wafer TEM images presents three main challenges: difficulty in image acquisition, significant noise, and ambiguous object boundaries. While existing methods for automated measurement have used semantic segmentation algorithms, they often lead to inaccurate object boundary detection, resulting in over- or under-estimation. In this study, we propose a multi-task pre-training with masked autoencoder and virtual edge detection (MTP-MAVED) to improve object recognition and boundary detection in wafer TEM images with consideration of three challenges. Our approach includes three primary components: a pre-training phase using self-supervised representation learning to extract meaningful representations from unlabeled wafer TEM images, a multi-task learning-based fine-tuning phase incorporating both semantic segmentation and edge detection tasks, and a boundary-aware loss function to enhance boundary recognition accuracy. We demonstrate that MTP-MAVED outperforms existing methods in both object recognition and boundary detection, even with limited labeled data. This framework offers a more robust solution for addressing the complexities of wafer TEM image analysis and advances the field of automated semiconductor analysis.}, archive = {J_ACCESS}, author = {Yongwon Jo and Jinsoo Bae and Hansam Cho and Sungsu Kim and Heejoong Roh and Kyunghye Kim and Munki Jo and Munuk Kim and Jaeung Tae and Seoung Bum Kim}, doi = {10.1109/ACCESS.2025.3594581}, journal = {IEEE Access}, pages = {136568-136583}, shortjournal = {IEEE Access}, title = {Robust semantic segmentation of wafer transmission electron microscopy image using multi-task learning with edge detection}, volume = {13}, year = {2025}, }
• (2025). Calibrating f1 scores for fair performance comparison of binary classification models with application to student dropout prediction. ACCESS, 13, 136554-136567. (www)

  @article{ , abstract = {The F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, $\pi $ . Depending on how large $\pi $ is, it can be underestimated or overestimated, making it difficult to fairly compare the performance of models. In this study, we discuss how to calibrate the F1 score for fair performance comparison of binary classification models trained on data with different positive class ratios. We initially demonstrated that the F1 score is inverse proportional to accuracy according to the change in $\pi $ . From the relationship, the calibrated F1 score was defined as an arithmetic mean of the two measures, which we named the F1* score. Since many prior studies only presented the F1 score or accuracy for model performance, but not both, we provided additional equations to estimate the expected F1 score or accuracy when one of the two measures is available. The accuracy of the presented equations was examined through experiments with a real dataset aimed at student dropout prediction, and the results showed that the mean absolute difference between the derived and actual values was less than 0.01, inferring that the proposed F1* score can calibrate a given F1 score with a high level of accuracy. We also conducted an example analysis comparing the performance of existing models using the F1* score to highlight its efficacy.}, archive = {J_ACCESS}, author = {Hyeon Gyu Kim and Yoohyun Park}, doi = {10.1109/ACCESS.2025.3594735}, journal = {IEEE Access}, pages = {136554-136567}, shortjournal = {IEEE Access}, title = {Calibrating f1 scores for fair performance comparison of binary classification models with application to student dropout prediction}, volume = {13}, year = {2025}, }
• (2025). YOLOv9-LSBN: An improved YOLOv9 model for cotton pest and disease. ACCESS, 13, 136536-136553. (www)

  @article{ , abstract = {To achieve accurate identification of cotton aphids and diseases in natural complex environments, an enhanced YOLOv9 model named YOLOv9-LSBN (Large Selective Kernel Network with Bidirectional Feature Pyramid) is proposed. The RepLanLsk module replaces RepNCSPELAN4 in YOLOv9, dynamically adjusting receptive fields through reparameterizable convolutions and spatial attention, enhancing multi-scale feature diversity while maintaining computational efficiency via structural reparameterization during inference. A simplified weighted bidirectional feature pyramid network is integrated to strengthen bidirectional feature fusion and suppress background interference. Experiments on cotton aphids, Verticillium wilt, brown spot, and healthy leaves show that YOLOv9-LSBN achieves 93.1% precision, 92.5% recall, and 96.3% mAP@0.5, outperforming the original YOLOv9 by 1.8%, 0.3%, and 1.0%, respectively. Compared to YOLOv7, YOLOv8x, and lightweight models (e.g., YOLOv12), YOLOv9-LSBN demonstrates superior accuracy in complex backgrounds (96.3% vs. 94.6% mAP@0.5) with lower misjudgment rates, balancing real-time detection speed (28.3 ms/frame) and precision. Statistical significance (p <0.01) is validated through five randomized training trials, ensuring the robustness of improvements. This work provides a robust reference for crop pest detection in natural environments.}, archive = {J_ACCESS}, author = {Ruohong He and Fengkui Zhang and Jikui Zhu and Yulong Wang and Daorina Yang and Ting Zhang and Ping Li}, doi = {10.1109/ACCESS.2025.3578967}, journal = {IEEE Access}, pages = {136536-136553}, shortjournal = {IEEE Access}, title = {YOLOv9-LSBN: An improved YOLOv9 model for cotton pest and disease}, volume = {13}, year = {2025}, }
• (2025). Quantization-based jailbreaking vulnerability analysis: A study on performance and safety of the llama3-8B-instruct model. ACCESS, 13, 136524-136535. (www)

  @article{ , abstract = {This study systematically investigates how quantization, a key technique for the efficient deployment of large language models (LLMs), affects model safety. We specifically focus on jailbreaking vulnerabilities that emerge when models are subjected to quantization, particularly in multilingual and tense-shifted scenarios. Using Llama3-8B-Instruct as a representative model, we evaluate 23 quantization levels across two languages and three tenses. Our experimental results reveal a critical trade-off: lower-bit quantization degrades the model’s core reasoning abilities, which directly correlates with a higher Attack Success Rate (ASR). Within this context, for the model tested, 4-bit quantization appears as a practical “sweet spot,” maintaining near-baseline performance while significantly reducing computational costs. However, even at this level, substantial vulnerabilities persist—Korean prompts exhibit attack success rates 25.5 percentage points higher than English, and past-tense transformations increase vulnerability by 39.3 percentage points. These findings highlight that safety mechanisms are often compromised by quantization-induced performance degradation and are biased toward English, present-tense prompts. Although this study has clear limitations, it provides the first quantitative analysis of these combined vulnerabilities, underscoring the need for more comprehensive safety evaluations in quantized LLM deployment.}, archive = {J_ACCESS}, author = {Jaesik Lee}, doi = {10.1109/ACCESS.2025.3594287}, journal = {IEEE Access}, pages = {136524-136535}, shortjournal = {IEEE Access}, title = {Quantization-based jailbreaking vulnerability analysis: A study on performance and safety of the llama3-8B-instruct model}, volume = {13}, year = {2025}, }
• (2025). LookupForensics: A large-scale multi-task dataset for multi-phase image-based fact verification. ACCESS, 13, 136505-136523. (www)

  @article{ , abstract = {Amid the proliferation of forged images, notably the tsunami of deepfake content, extensive research has been conducted on using artificial intelligence (AI) to identify forged content in the face of continuing advancements in counterfeiting technologies. We have investigated the use of AI to provide the original authentic image after deepfake detection, which we believe is a reliable and persuasive solution. We call this “image-based automated fact verification,” a name that originated from a text-based fact-checking system used by journalists. We have developed a two-phase open framework that integrates detection and retrieval components. Additionally, inspired by a dataset proposed by Meta Fundamental AI Research, we further constructed a large-scale dataset that is specifically designed for this task. This dataset simulates real-world conditions and includes both content-preserving and content-aware manipulations that present a range of difficulty levels and have potential for ongoing research. This multi-task dataset is fully annotated, enabling it to be utilized for sub-tasks within the forgery identification and fact retrieval domains. This paper makes two main contributions: 1) We introduce a new task, ‘‘image-based automated fact verification,” and present a novel two-phase open framework combining “forgery identification” and “fact retrieval.” 2) We present a large-scale dataset tailored for this new task that features various hand-crafted image edits and machine learning-driven manipulations, with extensive annotations suitable for various sub-tasks. Extensive experimental results validate its practicality for fact verification research and clarify its difficulty levels for various sub-tasks.}, archive = {J_ACCESS}, author = {Shuhan Cui and Huy H. Nguyen and Trung-Nghia Le and Chun-Shien Lu and Isao Echizen}, doi = {10.1109/ACCESS.2025.3584395}, journal = {IEEE Access}, pages = {136505-136523}, shortjournal = {IEEE Access}, title = {LookupForensics: A large-scale multi-task dataset for multi-phase image-based fact verification}, volume = {13}, year = {2025}, }
• (2025). Optimized machine learning-augmented hybrid empirical models for AlGaN/GaN HEMTs: A comprehensive analysis. ACCESS, 13, 136483-136504. (www)

  @article{ , abstract = {Precise modeling of gallium nitride (GaN) high-electron mobility transistors (HEMTs) is vital for ensuring reliable and scalable RF circuit design, and efficient characterization of the device behavior. This article presents robust hybrid equivalent circuit (EC)–machine learning (ML) frameworks for significantly streamlining the extraction of small-signal model parameters of AlGaN/GaN HEMTs. The extrinsic and intrinsic parameters of the devices are initially extracted using physics-relevant empirical models in Keysight’s advanced design system. Thereafter, six extensively optimized ML regression models, namely decision tree (DT), ensemble learning (EL), support vector regression (SVR), kernel approximation regression (KAR), Gaussian process regression (GPR), and neural networks (NN) are employed to simulate the intrinsic behavior of GaN HEMTs. The models are trained on GaN HEMTs of geometries $4\times 100~\mu $ m, $10\times 220~\mu $ m, and $10\times 250~\mu $ m, while tested on GaN HEMTs of geometries $2\times 200~\mu $ m and $10\times 200~\mu $ m across diverse biasing and frequency conditions. The input features to models include gate–source voltage (VGS), drain–source voltage (VDS), frequency, number of fingers (NF), unity gate width (Wg), and effective gate width (Weff). Finally, a thorough quantitative assessment and detailed comparisons are performed in terms of standard regression tests, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, training and prediction speed, reliability of model parameters, and simulation agreement with the measured S-parameters. The results demonstrate that among the tested ML models, EL exhibited the lowest mean relative S-parameter errors (2.78–3.75 %), followed by NNs (2.05–6.98 %), DT (2.29–7.35 %), GPR (2.86-8.91 %) SVR (7.83–9.88 %), and KAR (8.26–10.54 %) across diverse GaN HEMTs geometries. This hybrid modeling strategy provides a practical alternative to conventional parameter extraction, offering speed, accuracy, and broader applicability.}, archive = {J_ACCESS}, author = {Ahmad Khusro and Saddam Husain and Mohammad Hashmi}, doi = {10.1109/ACCESS.2025.3594339}, journal = {IEEE Access}, pages = {136483-136504}, shortjournal = {IEEE Access}, title = {Optimized machine learning-augmented hybrid empirical models for AlGaN/GaN HEMTs: A comprehensive analysis}, volume = {13}, year = {2025}, }
• (2025). Adaptive sampled value differential protection with high penetration of inverter-based resource based on error function fitting. ACCESS, 13, 136473-136482. (www)

  @article{ , abstract = {As power systems continue to evolve and modernize, the integration level of renewable energy sources in distribution grids has been steadily increasing. Traditional protection schemes in distribution grids, such as directional current protection, are influenced by the fault characteristics of inverters, posing challenges such as fault current limiting and frequency deviation. Moreover, the conventional phasor-based protection principles struggle to accurately characterize non-fundamental frequency fault currents. While Sampled Value Differential Protection (SVD) overcomes these limitations, it remains vulnerable to measurement errors under low-current conditions, compounded by the absence of industry standards for error handling. This issue leads to a lack of foundation for setting of SVD and hinders quantitative operation, maintenance, and testing. To address these gaps, this paper introduced an adaptive sampled value differential protection (ASVD) scheme. The proposed method utilized pre-fault steady-state current for time-domain error estimation, establishing a floating threshold to dynamically adjust protection characteristics. The effectiveness and reliability of this scheme were successfully verified through simulations. Compared to conventional differential protection methods, the ASVD scheme demonstrates adaptability, sensitivity and reliability to different operating conditions of high penetration inverter-based resource (HPIB). This promising approach provides a new solution to address the practical engineering challenges posed by the high integration of inverter-based resource into distribution grids.}, archive = {J_ACCESS}, author = {Wenyue Zhou and Zhaowei Zhou and Xiaopeng Li and Wei Chen and Hao Wang and Bing Luo}, doi = {10.1109/ACCESS.2025.3592952}, journal = {IEEE Access}, pages = {136473-136482}, shortjournal = {IEEE Access}, title = {Adaptive sampled value differential protection with high penetration of inverter-based resource based on error function fitting}, volume = {13}, year = {2025}, }
• (2025). Automated design method based on boosting algorithms for improving the radiation performance of microstrip antenna arrays. ACCESS, 13, 136458-136472. (www)

  @article{ , abstract = {This paper presents an automated design methodology to improve the radiation performance of microstrip antenna arrays using boosting-based machine learning (ML) algorithms in the X-band frequency range. The proposed approach replaces computationally expensive full-wave simulations with an ML-driven framework trained on a large dataset of wide-angle impedance matching (WAIM) and microstrip antenna structures. To address various design requirements, two types of microstrip antennas are incorporated into the framework. The behavior of both antenna configurations is predicted with only one network achieved by adding preprocessing and postprocessing modules to the method. This reduces the number of trained networks while maintaining the prediction accuracy. Training networks for WAIM and antennas involve four different boosting algorithms: AdaBoost, Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Light Gradient Boosting (LightGB). Among the evaluated boosting algorithms, LightGB achieved the highest prediction accuracy for both WAIM and antenna models. Two design examples are investigated to demonstrate the framework’s capability in extending the microstrip array scanning range. The results confirm no grating lobes and improved gain at extreme scanning angles across the frequency range. Compared to traditional full-wave solvers, the ML-based method significantly reduces the order of computation time from several hours to seconds while minimizing hardware resource requirements. This automated method offers an efficient framework for designing wide-angle microstrip arrays and expanding their applications without requiring designer expertise.}, archive = {J_ACCESS}, author = {Sina Hasibi Taheri and Ali Lalbakhsh and Amirhassan Zareanborji and Slawomir Koziel}, doi = {10.1109/ACCESS.2025.3593900}, journal = {IEEE Access}, pages = {136458-136472}, shortjournal = {IEEE Access}, title = {Automated design method based on boosting algorithms for improving the radiation performance of microstrip antenna arrays}, volume = {13}, year = {2025}, }
• (2025). IoTShield: Defending IoT systems against prevalent attacks using programmable networks. ACCESS, 13, 136446-136457. (www)

  @article{ , abstract = {The growing proliferation of Internet of Things (IoT) devices in smart homes, smart agriculture, and smart energy grids has greatly improved their functionality, efficiency, and responsiveness — but it has also widened the attack surface of these networks. The inherent security vulnerabilities of IoT devices, have rendered them susceptible to a variety of flow-based attacks such as Distributed Denial of Service (DDoS), scanning, spoofing, data exfiltration and web-based attacks, thereby diminishing their potential benefits. This paper presents IoTShield, a Software Defined Network (SDN) based dual-stage defensive framework, designed to mitigate different flow-based attacks targeting IoT systems. Leveraging recent advancements in programmable networks, our defensive framework enables each programmable switch within the connectivity layer of the network to be responsible of identifying a single attack category among prevalent attacks. Furthermore, to effectively mitigate the spread of these attacks, detected attacks are classified at the network controller, facilitating timely updates to the data plane defensive rules. As a proof of concept, using CICIoT2023 dataset, we first illustrate that deploying separate detectors for DDoS and Web-based attack categories on programmable data planes reduces false alarms by 58% and 97%, respectively. Furthermore, a single DDoS attacks detector based on lightweight Decision Tree (DT) model in the data plane, achieves 80-99% of accuracy in detecting different types of attack flows, with fine-grained classification offloaded to the control plane where a Convolutional Neural Network (CNN) classifier achieves 99% accuracy. Besides, IoTShield significantly reduces the latency and load on controller to perform the attack detection; with only 0.14 milliseconds of additional median queuing delay.}, archive = {J_ACCESS}, author = {Mah-Rukh Fida and Azza H. Ahmed and Ameer Shakayb Arsalaan}, doi = {10.1109/ACCESS.2025.3594580}, journal = {IEEE Access}, pages = {136446-136457}, shortjournal = {IEEE Access}, title = {IoTShield: Defending IoT systems against prevalent attacks using programmable networks}, volume = {13}, year = {2025}, }
• (2025). Dual transformers with latent amplification for multivariate time series anomaly detection. ACCESS, 13, 136433-136445. (www)

  @article{ , abstract = {Anomaly detection in multivariate time series is crucial for applications such as industrial monitoring, cybersecurity, and healthcare. Transformer-based reconstruction methods have recently shown strong performance but often suffer from overgeneralization, where anomalies are reconstructed too accurately, thereby reducing the separability between normal and abnormal patterns. Prior works have attempted to mitigate this by incorporating two-stage frameworks or external memory modules to explicitly store normal patterns and amplify deviations from abnormal patterns. However, such approaches increase model complexity and incur additional computational overhead. In this paper, we propose Dual Transformers with Latent Amplification (DT-LA), a novel framework designed to mitigate overgeneralization within a unified architecture. The core idea of DT-LA is to enhance anomaly separability by jointly leveraging both input and latent space reconstructions, rather than merely improving reconstruction fidelity. In particular, we propose the Modified Reverse Huber (MRH) loss that amplifies meaningful deviations in the latent space by applying inverse scaling. It allows the model to retain informative discrepancies that would otherwise be suppressed, thereby improving its ability to detect subtle anomalies. Second, we incorporate sparse self-attention with entropy-based regularization to capture essential inter-sensor relationships and suppress redundancy. Third, we refine the anomaly scoring process using a scaled-softmax function, which balances relative and absolute deviations to reduce softmax-induced bias. Extensive experiments on four benchmark datasets (SMAP, MSL, PSM, and SMD) demonstrate that DT-LA achieves state-of-the-art performance, with F1-scores of 97.02% on SMAP and 98.42% on PSM, highlighting its robustness and practical competitiveness as a single-stage framework.}, archive = {J_ACCESS}, author = {Yeji Choi and Kwanghoon Sohn and Ig-Jae Kim}, doi = {10.1109/ACCESS.2025.3594473}, journal = {IEEE Access}, pages = {136433-136445}, shortjournal = {IEEE Access}, title = {Dual transformers with latent amplification for multivariate time series anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Toward inclusive healthcare: An LLM-based multimodal chatbot for preliminary diagnosis. ACCESS, 13, 136420-136432. (www)

  @article{ , abstract = {This paper presents the design and development of a multimodal medical chatbot that leverages Gemini-2.0-Flash Model alongside a novel Retrieval-Augmented Generation (RAG) architecture to support preliminary medical diagnosis and recommendations. The system integrates textual prompt analysis and medical image interpretation, aiming to improve healthcare accessibility, particularly for underserved populations. Focused on data-rich medical conditions, the chatbot generates reliable diagnostic insights based on natural language inputs and/or medical images, requiring minimal user expertise. The proposed RAG-based architecture incorporates a curated medical knowledge base and structured retrieval mechanisms, significantly reducing hallucinations and enhancing response credibility compared to direct Large Language Model (LLM) querying. By demonstrating the efficacy of multimodal reasoning in conjunction with structured retrieval, this work paves the way for more accessible, accurate, and scalable AI-driven health support systems.}, archive = {J_ACCESS}, author = {Ishita Agarwal and V. Sakthivel and P. Prakash}, doi = {10.1109/ACCESS.2025.3594218}, journal = {IEEE Access}, pages = {136420-136432}, shortjournal = {IEEE Access}, title = {Toward inclusive healthcare: An LLM-based multimodal chatbot for preliminary diagnosis}, volume = {13}, year = {2025}, }
• (2025). Character-level adversarial samples generation method based on dual-layer text watermark. ACCESS, 13, 136409-136419. (www)

  @article{ , abstract = {Textual adversarial samples are widely used to assess the robustness and security of language models. Most existing methods generate these samples by substitution or deletion. However, such approaches are often easy to detect and show limited attack effectiveness. To address these issues, we propose a character-level adversarial attack framework based on dual-layer watermarking. The method embeds explicit and implicit watermarks in the original text to interfere with downstream models. First, we apply character-level gradient optimization to perturb explicit statistical features. Then, we use reinforcement learning to fine-tune semantic and encoding characteristics, guided by feedback from the target model. This approach ensures that the generated samples significantly reduce the performance of target models while preserving semantic meaning. Experiments on four benchmark datasets show that our method outperforms state-of-the-art baselines. It reduces average word distance (WD) by 12.5%, improves BERTScore by 0.9%, lowers the attack success rate (TMASR) by 11.0%, and increases the concealment rate (Hide) by 5.6%.}, archive = {J_ACCESS}, author = {Houyue Wu and Xianwei Li and Shunxiang Zhang and Honghao Zhu}, doi = {10.1109/ACCESS.2025.3594727}, journal = {IEEE Access}, pages = {136409-136419}, shortjournal = {IEEE Access}, title = {Character-level adversarial samples generation method based on dual-layer text watermark}, volume = {13}, year = {2025}, }
• (2025). Low complexity concurrent multi-band modeling and digital predistortion for harmonically driven wireless amplifiers. ACCESS, 13, 136395-136408. (www)

  @article{ , abstract = {With the cutting-edge 5G/6G signals and corresponding demands of the multi-band and ultra-wideband transmitters, numerous digital predistortion (DPD) models have been proposed for the removal of Power Amplifier’s (PA) distortions in concurrent multi-band and ultra-wideband communication. These demands result in challenges such as 5G/6G communication when frequency bands are harmonic or near-harmonic related. This paper proposes modeling approach and novel DPD model for removing PA’s nonlinearity, intermodulation distortions (IMDs), cross-modulation distortions (CMDs), and harmonic distortions for carrier aggregated harmonic related frequencies. This model is a neural network (NN) based and its input consists of basis corresponding to harmonic distortions, CMDs, and IMDs. The proposed NN DPD model is shallow, robust, and scalable to the growth in the dimension of multi-band transmission at harmonic frequencies, it delivers better removal of distortions with reduced complexity than the state-of-the-art DPDs. Furthermore, the proposed model also compensates for I/Q imbalance in the transmitting path. To test the robustness of the proposed shallow NN DPD, the performance is measured in adjacent channel power ratio (ACPR), normalized mean square error (NMSE) and error vector magnitude (EVM) for different 5G New Radio (NR) relevant signals scenarios.}, archive = {J_ACCESS}, author = {Praveen Jaraut and Mohamed Helaoui and Meenakshi Rawat and Wenhua Chen and Fadhel M. Ghannouchi}, doi = {10.1109/ACCESS.2025.3594685}, journal = {IEEE Access}, pages = {136395-136408}, shortjournal = {IEEE Access}, title = {Low complexity concurrent multi-band modeling and digital predistortion for harmonically driven wireless amplifiers}, volume = {13}, year = {2025}, }
• (2025). Linking independent 5G bubbles using tactical IP Radios—Part i: Laboratory deployment and testing. ACCESS, 13, 136383-136394. (www)

  @article{ , abstract = {In the realm of military operations, this research paper investigates the complex implications surrounding the integration of fifth-generation (5G) wireless technology, aiming to serve the enhanced and diverse military user requirements for established Quality of Service (QoS) provisioning in modern-era military environments. This work presents a testing campaign conducted within a controlled laboratory environment, aimed at facilitating the interconnection of two independent 5G networks (5G Bubbles), through the utilization of a conventional military Internet Protocol (IP) radio. The testing campaign was executed as part of the 5G COMPAD Project, co-funded by the European Union through the European Defence Fund (EDF). The core objective of the campaign was to demonstrate that text, voice and video services can be efficiently provisioned and sustained between 5G User Equipments (UEs) situated within these independent 5G Bubbles, interconnected solely via a standard military IP radio. Various performance metrics were defined, measured and evaluated during the testing campaign, while the results indicate that legacy military radios and 5G systems can be successfully integrated, thereby offering robust support for the diverse communication needs of tactical units, despite the performance constraints set by military links. The main contribution of this study is the practical integration of commercial 5G components with existing military communication assets, marking the first European experimental campaign in this domain. This effort provides critical insights into the feasibility and performance of linking independent 5G Bubbles over legacy military infrastructure in realistic defense-oriented scenarios.}, archive = {J_ACCESS}, author = {Georgios Vardoulias and Ioannis A. Bartsiokas and Nikolaos Nikopoulos and Georgios Karachalios and Georgios Gardikis and Nick Alampasis and Ioannis Giannoulakisgeorgios Karachalios and Emmanouil Kafetzakis}, doi = {10.1109/ACCESS.2025.3594800}, journal = {IEEE Access}, pages = {136383-136394}, shortjournal = {IEEE Access}, title = {Linking independent 5G bubbles using tactical IP Radios—Part i: Laboratory deployment and testing}, volume = {13}, year = {2025}, }
• (2025). Hyperparameter optimization for problem-based custom CNN architectures using a smart grid search method. ACCESS, 13, 136374-136382. (www)

  @article{ , abstract = {Recent advancements in deep learning architectures have made it possible to solve a wide range of classification problems present in industrial applications. However, many CNN architectures are complex, with large model sizes and high inference times, making them less suitable for deployment in resource-constrained environments. This study proposes an improved grid search based hyperparameter optimization method to design lightweight CNN architectures for task-specific classification problems. In order to test the performance of the proposed algorithm, a dataset of ripe and unripe pistachios was used. To classify the ripe and unripe pistachios with a small-sized and high test accuracy model, a two-layer CNN architecture’s hyperparameters were optimized with the proposed algorithm. The implementation of the proposed algorithm took 14 hours and 17 minutes, and the algorithm calculated 173 different CNN architectures with different parameters. Among these 173 CNN architectures, the model with higher test accuracy than the literature and the smallest size was determined to have a size of 4.74 MB and achieve 98.70% test accuracy. When compared to the literature, this model shows 0.26% improvement in test accuracy and 36.6 times smaller than the AlexNet model. The proposed method was also compared with Bayesian optimization using a similar search space. While Bayesian optimization achieved the same highest test accuracy (99.22%), it produced a model that was 3.7 times larger. Moreover, when small-sized CNN architectures with identical accuracies (98.19%) were compared, the proposed method produced a model that was 2.5 times smaller than the Bayesian result in terms of model size. These results demonstrate the success of the method in efficiently discovering lightweight, high-accuracy CNN architectures.}, archive = {J_ACCESS}, author = {H. Aktas}, doi = {10.1109/ACCESS.2025.3594212}, journal = {IEEE Access}, pages = {136374-136382}, shortjournal = {IEEE Access}, title = {Hyperparameter optimization for problem-based custom CNN architectures using a smart grid search method}, volume = {13}, year = {2025}, }
• (2025). Optimization of convolutional neural networks with multi-objective function metaheuristics for melanoma detection. ACCESS, 13, 136352-136373. (www)

  @article{ , abstract = {Early and accurate detection of melanoma remains a critical challenge in medical imaging. Convolutional Neural Networks (CNNs) have demonstrated superior classification performance, often surpassing dermatologists in diagnostic accuracy. However, optimizing CNN architectures for clinical applications requires careful consideration of performance metrics that reflect both overall accuracy and Recall (sensitivity) to malignant cases. This article explores 45 recent research contributions addressing melanoma classification, offering a critical overview of current trends and techniques. Based on these findings, we propose a novel weighted multi-objective function (MOF) that balances key performance metrics according to clinical needs, with particular emphasis on Accuracy for overall correctness, Recall for reducing false negatives, and AUC for enhancing class discrimination in melanoma detection, thus ensuring both general reliability and high sensitivity in detecting melanoma. The Grey Wolf Optimizer (GWO), a population-based metaheuristic known for its effective exploration of high-dimensional search spaces, is employed for CNN hyperparameter tuning. Experimental results demonstrate that the proposed approach enhances model performance across key clinical metrics, obtaining a MOF score of 0.9032, with Accuracy, Recall, and AUC values of 0.9025, 0.9101 and 0.8925, respectively. This confirms the effectiveness of the strategy in achieving both high diagnostic accuracy and sensitivity, offering a robust and scalable strategy for medical image classification in computer-aided diagnosis systems.}, archive = {J_ACCESS}, author = {Pamela Hermosilla and Ricardo Soto and Eric Monfroy and Emanuel Vega and Lucas Erazo and Valentina Guzmán}, doi = {10.1109/ACCESS.2025.3594706}, journal = {IEEE Access}, pages = {136352-136373}, shortjournal = {IEEE Access}, title = {Optimization of convolutional neural networks with multi-objective function metaheuristics for melanoma detection}, volume = {13}, year = {2025}, }
• (2025). Accurate and efficient fluid flow regime classification using localized texture descriptors and machine learning. ACCESS, 13, 136336-136351. (www)

  @article{ , abstract = {This paper presents an image-based framework for classifying fluid flow regimes into low and high-speed states by utilizing spatially localized texture features combined with machine learning techniques. Traditional approaches, such as Computational Fluid Dynamics (CFD) and Direct Numerical Simulations (DNS), often require extensive post-processing to extract fluid flow properties. This makes them time-consuming and less practical for real-time applications. To address this, the proposed method leverages the Local Binary Pattern (LBP) feature extraction technique. LBP effectively captures local neighborhood patterns and converts complex flow behaviors into quantifiable texture features from images of CFD. These features are then classified using various machine learning models, namely Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). The LBP-based approach demonstrates excellent performance, with the k-NN classifier achieving a maximum accuracy of 0.9879 in the case of flow past an elliptical cylinder. Similarly, the SVM classifier attains up to 0.9540 accuracy for the flow past an airfoil. Evaluations cover a range of Reynolds numbers from 200 to 5000 and turbulence intensities of 5% and 20%, confirming the robustness and effectiveness of the method. A comparative analysis with other texture-based techniques, namely Local Ternary Pattern (LTP) and Gray Level Co-occurrence Matrix (GLCM), further highlights the advantages of the proposed method. The LBP approach outperforms LTP and GLCM by 14.5% and 2.4%, respectively, in terms of prediction accuracy. This demonstrates the superior capability of LBP in flow regime classification. The dataset used in this study is publicly available at: https://www.kaggle.com/datasets/palanithanarajk/fluid-flow-images}, archive = {J_ACCESS}, author = {Manimaran Renganathan and Palani Thanaraj Krishnan and C. Christopher Columbus and T. Sunil Kumar}, doi = {10.1109/ACCESS.2025.3594850}, journal = {IEEE Access}, pages = {136336-136351}, shortjournal = {IEEE Access}, title = {Accurate and efficient fluid flow regime classification using localized texture descriptors and machine learning}, volume = {13}, year = {2025}, }
• (2025). Automated skin cancer report generation via a knowledge-distilled vision-language model. ACCESS, 13, 136320-136335. (www)

  @article{ , abstract = {Artificial Intelligence (AI)’s capacity to analyze dermoscopic images promises a groundbreaking leap in skin cancer diagnostics, offering exceptional accuracy and an effortlessly non-invasive image acquisition process. However, this immense potential, which has ignited widespread research enthusiasm, is critically undermined due to the lack of transparency and interpretability. The automated generation of articulate and comprehensive diagnostic reports will bridge this critical gap by not only illuminate the AI’s diagnostic rational but also substantially reduce the demanding workload of the medical professionals. This study presents a multimodal vision-language model (VLM) trained using a two-stage knowledge distillation (KD) framework to generate structured medical reports from dermoscopic images, with descriptive features based on the 7-point melanoma checklist. The reports are organized into clinically relevant sections—Findings, Impression, and Differential Diagnosis—aligned with dermatological standards. Experimental evaluation demonstrates the system’s ability to produce accurate and interpretable reports. Human feedback from a medical professional, assessing clinical relevance, completeness, and interpretability, supports the utility of the generated reports, while computational metrics validate their accuracy and alignment with reference pseudo-reports, achieving a SacreBLEU score of 55.59, a ROUGE-1 score of 0.5438, a ROUGE-L score of 0.3828, and a BERTScore F1 of 0.9025. These findings underscore the model’s ability to generalize effectively to unseen data, enabled by its multimodal design, clinical alignment, and explainability.}, archive = {J_ACCESS}, author = {Lawhori Chakrabarti and Boyu Zhang and Hengyi Tian and Aleksandar Vakanski and Min Xian}, doi = {10.1109/ACCESS.2025.3591750}, journal = {IEEE Access}, pages = {136320-136335}, shortjournal = {IEEE Access}, title = {Automated skin cancer report generation via a knowledge-distilled vision-language model}, volume = {13}, year = {2025}, }
• (2025). VIOLET: Vectorized invariance optimization for language embeddings using twins. ACCESS, 13, 136312-136319. (www)

  @article{ , abstract = {We present VIOLET, a novel positive pair-based information maximisation strategy for fine-tuning BERT to generate robust, invariant, and semantically meaningful sentence embeddings. VIOLET extends the Barlow Twins framework by addressing both redundancy reduction and invariance preservation within the embedding space. This is achieved through a combination of text-specific augmentations tailored for the nuances of natural language and a mixup-based regularisation mechanism that promotes smoother representation learning. Unlike conventional contrastive learning methods that rely on large batch sizes and hard negative mining to achieve performance, VIOLET operates exclusively on positive pairs. This eliminates the need for complex sampling strategies and significantly reduces training overhead. A key strength of VIOLET is its ability to perform consistently and robustly even with smaller batch sizes, making it an appealing choice for training on limited computational resources. Empirical results on the Semantic Textual Similarity Benchmark (STS-B) demonstrate that VIOLET achieves correlation scores on par with or exceeding several state-of-the-art sentence embedding models. These findings underscore the method’s effectiveness, scalability, and practical utility in a wide range of downstream natural language understanding tasks, particularly in settings where efficiency and stability are critical. Our implementation is provided at (https://github.com/mikhail-ram/VIOLET)}, archive = {J_ACCESS}, author = {Mikhail E. Ram and G. Manju}, doi = {10.1109/ACCESS.2025.3590971}, journal = {IEEE Access}, pages = {136312-136319}, shortjournal = {IEEE Access}, title = {VIOLET: Vectorized invariance optimization for language embeddings using twins}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection algorithms for real-time log data analysis at scale. ACCESS, 13, 136288-136311. (www)

  @article{ , abstract = {In recent years, Artificial Intelligence for IT Operations (AIOps) has gained popularity as a solution to various challenges in IT operations, particularly in anomaly detection. Although numerous studies have focused on anomaly detection, they often overlook cloud-based systems and the vast amount of data they generate. Moreover, the practical application of these results and managing multiple IT systems within diverse computing environments present notable challenges. In this paper, we explore anomaly detection in real-world environments, including legacy systems and cloud platforms, by evaluating existing methods and explicitly introducing four novel anomaly detection algorithms: Llama Model-based Log Anomaly Detection (LMLAD), Log Clustering with Cosine Similarity for Log Anomaly Detection (LCCLAD), Convolutional Auto-Encoder based Log Anomaly Detection (CAELAD), and Statistical Clustering for Log Anomaly Detection (SCLAD). These algorithms vary in complexity and accuracy, and offer tunable performance based on the specific requirements of the environment. In experiments on four log datasets (two private and two public), our best models improved F1-scores by 6–10% over state-of-the-art baselines, demonstrating the efficacy of our approach. Through empirical analysis, we demonstrate that while advanced techniques such as CAELAD often deliver high accuracy, simpler methods such as SCLAD can be equally effective in practical settings, depending on the complexity of the problem. Our results underscore the importance of selecting the right balance between sophistication and simplicity, challenging the assumption that the most sophisticated methods are necessary for effective anomaly detection in real-world log data.}, archive = {J_ACCESS}, author = {András Horváth and András Oláh and Attila Pintér and Bálint Siklósi and Gergely Lukács and István Z. Reguly and Kálmán Tornai and Tamás Zsedrovits and Zoltán Máthé}, doi = {10.1109/ACCESS.2025.3594469}, journal = {IEEE Access}, pages = {136288-136311}, shortjournal = {IEEE Access}, title = {Anomaly detection algorithms for real-time log data analysis at scale}, volume = {13}, year = {2025}, }
• (2025). Impossible 4-tuple attack: A new mixture-based cryptanalysis applied to reduced-round AES-128. ACCESS, 13, 136274-136287. (www)

  @article{ , abstract = {Mixture differential cryptanalysis is a powerful technique for evaluating the robustness of Substitution-Permutation Network (SPN)-structured block ciphers, including the Advanced Encryption Standard (AES). In this work, we propose a novel mixture-based cryptanalysis and apply it successfully to reduced-round AES-128 to recover the master key. Accordingly, we initially define several 4-tuple categories. Based on these definitions, we construct a new distinguisher, referred to as the “impossible 4-tuple property.” Exploiting this property enables us to recover the master key using a new technique called “impossible 4-tuple cryptanalysis.” To evaluate the effectiveness of the proposed method, we apply impossible 4-tuple cryptanalysis to the 5-round AES-128. We first introduce the proposed impossible 4-tuple property, which covers four rounds of AES. Subsequently, impossible 4-tuple cryptanalysis is applied to recover the 5-round AES-128 master key using $2^{34}$ chosen plaintexts, $2^{33.2}$ encryptions, and $2^{36}$ bytes of memory. Furthermore, we propose a specific version of the method, “fast impossible 4-tuple cryptanalysis,” which utilizes a time-memory trade-off. This version can recover the master key using $2^{36}$ chosen plaintexts, $2^{30.2}$ encryptions, and $2^{38}$ bytes of memory, making it the fastest attack among differential-based and mixture-based cryptanalysis methods applied to 5-round AES. Finally, to demonstrate the validity of the proposed impossible 4-tuple cryptanalysis methods, a simplified version of the methods was applied to the miniAES algorithm. Accordingly, two corresponding open-source tools, “ $\mathsf {miniAES\_Typical\_Imp\_4tuple.py}$ ” and “ $\mathsf {miniAES\_Fast\_Imp\_4tuple.py}$ ,” were developed to recover the master key of the miniAES block cipher.}, archive = {J_ACCESS}, author = {Masroor Hajari and Mahmoud Salmasizadeh and Javad Mohajeri and Siavash Ahmadi and Atiyeh Mirzaie}, doi = {10.1109/ACCESS.2025.3591639}, journal = {IEEE Access}, pages = {136274-136287}, shortjournal = {IEEE Access}, title = {Impossible 4-tuple attack: A new mixture-based cryptanalysis applied to reduced-round AES-128}, volume = {13}, year = {2025}, }
• (2025). Improving rotational stability of radial permanent magnetic bearings for compact rotors via axial offset and optimal sizing. ACCESS, 13, 136255-136273. (www)

  @article{ , abstract = {Permanent magnetic bearings (PMBs) provide a passive, contactless alternative to mechanical bearings, yet their inherent instability complicates their adoption in small-scale rotating machinery. While axial instability is well-known for radially stable PMBs, rotational instability can arise when the bearing radius exceeds the lever arm between the bearing center and the rotor’s center of rotation. This study addresses this limitation by introducing an axial offset between magnets to improve rotational stability, enabling the integration of large-diameter PMBs in flat-profile rotors and enhancing overall system compactness. A semi-analytical model for calculating bearing stiffness is employed to investigate how bearing offset and other geometric parameters can be used to tailor stiffness characteristics. Parametric analysis reveals that a positive axial offset effectively increases rotational stability at the expense of reduced radial stiffness. The low computational cost of the model enables further optimization of the bearing dimensions. An optimization strategy is proposed to size single-layer radial PMBs for compact rotors, tuning both radial and rotational stiffness while minimizing volume. The design approach is extendable to multi-layer topologies. Additionally, a practical expression is derived to estimate the theoretical upper bound of radial stiffness based on bearing outer radius and clearance.}, archive = {J_ACCESS}, author = {Mante Baekeland and Konstantinos Gryllias and Dirk Vandepitte}, doi = {10.1109/ACCESS.2025.3592602}, journal = {IEEE Access}, pages = {136255-136273}, shortjournal = {IEEE Access}, title = {Improving rotational stability of radial permanent magnetic bearings for compact rotors via axial offset and optimal sizing}, volume = {13}, year = {2025}, }
• (2025). Leveraging machine learning for enhanced bug triaging in open-source software projects. ACCESS, 13, 136237-136254. (www)

  @article{ , abstract = {Bug triaging–the process of classifying and assigning software issues to appropriate developers–is a critical yet challenging task in large-scale software development. Manual triaging is time-consuming, inconsistent, and prone to human bias, which often delays issue resolution and misallocates developer resources. This study explores the application of machine learning to automate and improve bug triaging efficiency and accuracy. Using a dataset of over 122,000 issues from the microsoft/vscode GitHub repository, we evaluate several machine learning models including Bidirectional LSTM, CNN-LSTM, Random Forest, and Multinomial Naive Bayes. Our primary contribution is the development of an Augmented Bidirectional LSTM model that integrates enriched textual features and contextual metadata. This model, optimized using Optuna, outperforms traditional baselines, achieving a Micro F1-score of 0.6469 and Hamming Loss of 0.0133 for label prediction, and a Micro F1-score of 0.5974 with Hamming Loss of 0.0062 for assignee recommendation. In addition to demonstrating strong predictive performance, we present a robust end-to-end pipeline for data preprocessing, augmentation, model training, and evaluation using multi-label classification techniques. The study highlights how deep learning architectures, in combination with feature engineering and hyperparameter tuning, can provide scalable and generalizable components to support the automation of bug triaging. These findings contribute to the growing field of intelligent software maintenance by offering data-driven approaches that can support developer workflows and improve issue management efficiency in open-source environments.}, archive = {J_ACCESS}, author = {Nitanta Adhikari and Rabindra Bista and João Carlos Ferreira}, doi = {10.1109/ACCESS.2025.3595011}, journal = {IEEE Access}, pages = {136237-136254}, shortjournal = {IEEE Access}, title = {Leveraging machine learning for enhanced bug triaging in open-source software projects}, volume = {13}, year = {2025}, }
• (2025). OCR-augmented GPT for accurate text extraction in industrial environments. ACCESS, 13, 136226-136236. (www)

  @article{ , abstract = {Optical character recognition (OCR) in industrial environments often struggles with degraded text, such as handwriting or text obscured by complex backgrounds. Traditional methods address these challenges by re-identifying handwritten text or modifying backgrounds, but these approaches are costly and time-consuming. To overcome these limitations, we propose an OCR-augmented GPT framework, where GPT infers text from images with support from an initial OCR-based coarse estimation. The OCR layer first generates preliminary text outputs by processing multiple image sequences of the same text, capturing variations in lighting, noise, and distortion. These outputs, which may contain errors or inconsistencies, are then fed to a GPT layer along with the original images. By extracting information from both degraded images and preliminary OCR results, the framework produces accurate text by leveraging contextual understanding. In experimental evaluations, including real-world industrial environments, the proposed framework achieved more accurate OCR results than standalone OCR or GPT, demonstrating its scalability and cost-effectiveness for digital documentation across various environments.}, archive = {J_ACCESS}, author = {Jonghyeok Park and Jaehyung Cho and Soohee Han and Kyungjun Kim}, doi = {10.1109/ACCESS.2025.3594682}, journal = {IEEE Access}, pages = {136226-136236}, shortjournal = {IEEE Access}, title = {OCR-augmented GPT for accurate text extraction in industrial environments}, volume = {13}, year = {2025}, }
• (2025). Bridging the sim-to-real gap in motion planning for autonomous electric vehicles using autoware: A comparative study of simulation and real-world deployment. ACCESS, 13, 136210-136225. (www)

  @article{ , abstract = {Road traffic accidents result in over 1.35 million fatalities annually, highlighting the urgent need for safer transportation systems. Autonomous Electric Vehicles (AEVs), powered by advancements in Intelligent Transportation Systems (ITS), offer a promising solution by enhancing road safety and efficiency. This study focuses on trajectory planning for AEVs, a critical component for safe navigation in dynamic environments. Using Autoware.ai, an open-source modular platform, a novel interface integrating custom point cloud (PCD) and vector maps for precise localization and navigation was developed. The proposed method was tested in both simulation and real-world scenarios on the SRM IST campus, addressing challenges such as the “sim-to-real gap” caused by discrepancies between simulated and real-world conditions. Quantitative analysis of planned, simulated, and measured trajectories revealed significant improvements in localization accuracy and trajectory adherence, with Root Mean Square Error (RMSE) values for longitudinal and lateral positions reduced to 0.839 m and 0.1044 m, respectively. Velocity and steering angle tracking demonstrated minor deviations due to actuator constraints and road conditions. This research provides valuable insights into bridging the sim-to-real gap in AEV trajectory planning, paving the way for safer and more reliable autonomous driving systems.}, archive = {J_ACCESS}, author = {Manikandan Ganesan and Bharatiraja Chokkalingam and Sivanathan Kandhasamy and Rajesh Verma and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3587991}, journal = {IEEE Access}, pages = {136210-136225}, shortjournal = {IEEE Access}, title = {Bridging the sim-to-real gap in motion planning for autonomous electric vehicles using autoware: A comparative study of simulation and real-world deployment}, volume = {13}, year = {2025}, }
• (2025). Integrating SAR-ADC in GaN-based totem pole PFC: A step toward smart power conversion. ACCESS, 13, 136191-136209. (www)

  @article{ , abstract = {This research investigates whether Successive Approximation Register Analog-to-Digital Converters (SAR-ADCs) might be included in (PFC) converters constructed to improve control precision and power conversion efficiency. The suggested approach enhances real-time voltage and current measurement using the high-speed capabilities of SAR-ADCs. This lets the converter’s operating parameters alter dynamically. Using a Proportional-Integral (PI) approach, a novel control algorithm has been created to optimize the duty cycle and switching frequency consistent with the instantaneous power needs. MATLAB/Simulink simulations have shown significant increases in efficiency and power factor under a variety of load scenarios. The real data supporting the simulation findings is proof of the practical usage of SAR-ADC in improving the performance of GaN-based PFC systems. The findings of the research suggest that power electronics might be opened by adding current ADC technology into more smart and more efficient energy solutions. This integration not only solves the issues now being faced in power factor correction but also prepares the system to manage future developments in smart power control.}, archive = {J_ACCESS}, author = {Xu Cheng}, doi = {10.1109/ACCESS.2025.3594333}, journal = {IEEE Access}, pages = {136191-136209}, shortjournal = {IEEE Access}, title = {Integrating SAR-ADC in GaN-based totem pole PFC: A step toward smart power conversion}, volume = {13}, year = {2025}, }
• (2025). Transfer learning for photovoltaic power forecasting across regions using large-scale datasets. ACCESS, 13, 136175-136190. (www)

  @article{ , abstract = {Accurate photovoltaic (PV) power forecasting enables stable grid operation; however, acquiring sufficient and diverse data remains challenging. Although numerous deep learning models have been employed, most rely on proprietary datasets or data spanning only a few years, limiting the generalizability of forecasting applications. This study proposes a cross-continental transfer learning framework for PV power forecasting using a large-scale dataset with 4.5 million data points, including a source domain dataset with over 3.5 million data points. The prediction model, which is a transformer-based approach, compares the results of zero-shot learning, linear probing, fine-tuning, and training on the target dataset alone. The target datasets are located on different continents with different meteorological characteristics. Results demonstrate that the proposed transfer learning approach significantly improves prediction accuracy. It reduces the mean absolute percentage error by up to 38.8% compared with models trained solely on the target data. Further analysis reveals that freezing a few transformer blocks is beneficial when the target dataset is sufficiently large, whereas freezing most layers is effective for smaller datasets. This study analyzes the impact of source and target datasets on prediction performance, demonstrating that larger datasets in both domains enhance forecasting accuracy. These findings offer a robust approach for cross-continental knowledge transfer in renewable energy forecasting, particularly benefiting regions with limited historical data or newly installed systems. The source code is available on https://github.com/gist-ailab/transfer-learning-PV-forecasting}, archive = {J_ACCESS}, author = {Seongho Bak and Sowon Choi and Donguk Yang and Doyoon Kim and Heeseon Rho and Kyoobin Lee}, doi = {10.1109/ACCESS.2025.3591040}, journal = {IEEE Access}, pages = {136175-136190}, shortjournal = {IEEE Access}, title = {Transfer learning for photovoltaic power forecasting across regions using large-scale datasets}, volume = {13}, year = {2025}, }
• (2025). Utilization of ensemble techniques in machine learning to predict the porosity and hardness of plasma-sprayed ceramic coating. ACCESS, 13, 136160-136174. (www)

  @article{ , abstract = {Ceramic coatings play a vital role in protecting steel components by significantly enhancing their corrosion and wear resistance, thereby extending service life. The performance of these coatings critically depends on surface characteristics, such as porosity and hardness, which are traditionally assessed through time-consuming and labour-intensive experimental methods. To address this challenge, the present study employs advanced machine learning ensemble techniques, including bagging, boosting, stacking, weighted averaging, voting, and hybrid methods, to accurately predict the porosity and hardness of plasma-sprayed ceramic coatings based on key process parameters. Feature engineering identifies input power and spray distance as the most influential factors affecting coating properties. Hyperparameter optimization is performed using the random search technique and is compared with the conventional grid search method. The hybrid ensemble technique demonstrates exceptional predictive performance, achieving a coefficient of determination (R2) of 0.92 with a Root Mean Square Error (RMSE) of 2.01 % and a Mean Absolute Error (MAE) of 1.62 % for porosity prediction, and R2 of 0.94 with the RMSE of 82.03 HV and MAE of 66.42 HV for hardness prediction. Experimental validation confirms the model’s reliability, through minimal error deviation between predicted and actual values for porosity and hardness. This ML approach provides a robust framework for optimizing coating processes and ensuring superior steel protection through data-driven decision-making.}, archive = {J_ACCESS}, author = {N. Radhika and M. Sabarinathan and S. Sivaraman}, doi = {10.1109/ACCESS.2025.3594679}, journal = {IEEE Access}, pages = {136160-136174}, shortjournal = {IEEE Access}, title = {Utilization of ensemble techniques in machine learning to predict the porosity and hardness of plasma-sprayed ceramic coating}, volume = {13}, year = {2025}, }
• (2025). Burned area mapping using SAR and multispectral data integration via generative adversarial networks. ACCESS, 13, 136144-136159. (www)

  @article{ , abstract = {Multispectral data have been successfully exploited in the literature for burned area detection. However, the occurrence of suitable weather and illumination conditions limits their availability, especially at high latitudes and tropical environments. The usage of synthetic aperture radar (SAR) data, due to their all-weather and all-time characteristics, can potentially be a breakthrough, but it is still underexploited due to the ambiguity of the radar reflectivity of burned vegetation. The recent availability of generative networks is providing a new tool for combining such complementary information. This paper introduces a new framework for burned area mapping based on image domain translation. The main novelties against the related literature are the i) exploitation of SAR phase information as an input for the translation and the pointing to multispectral indices as a translation target. In particular, a bi-temporal coherent change detection-oriented SAR product is used as input for generative networks targeting the post-event normalized burned ratio index. Then, generated data are combined with native optical pre-event ones to produce the difference image typically used for thresholding segmentation. Experimental results obtained using a dataset extracted from the Copernicus Emergency Management Service database show that the proposed methodology outperforms the literature reaching a burned area mapping accuracy of above 90% with negligible false alarm, thus introducing a new robust solution for the integration of SAR and multispectral data.}, archive = {J_ACCESS}, author = {Donato Amitrano}, doi = {10.1109/ACCESS.2025.3593878}, journal = {IEEE Access}, pages = {136144-136159}, shortjournal = {IEEE Access}, title = {Burned area mapping using SAR and multispectral data integration via generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). Future of breast cancer diagnosis: A review of DL and ML applications and emerging trends for multimodal data. ACCESS, 13, 136101-136143. (www)

  @article{ , abstract = {Breast cancer diagnosis has greatly benefited from the use of deep learning (DL) and machine learning (ML) methods on multimodal data, including mammography, magnetic resonance imaging (MRI), ultrasound, histopathology, genomic data, and clinical reports. Every modality presents its challenges and opportunities, demanding a thorough understanding of used methods for accurate and early diagnosis. Our review provides an exhaustive survey of the latest advances in DL and ML applications to detect, classify, and segment breast cancer in these modalities. Our work also addresses basic issues of data limitations, model generalizability, and interpretability. To overcome these issues we explore the state-of-the-art Vision-Language Models (VLMs) as an emerging approach for multimodal learning. Through incorporation of the current advances and identification of areas of future improvement, this review is a precious source of information to researchers and practitioners. The findings highlight the importance of interdisciplinary collaboration, application of standardized data sets, and continuous improvement of the methodology to facilitate clinical uptake of AI-based breast cancer diagnostic systems.}, archive = {J_ACCESS}, author = {Beibit Abdikenov and Tomiris Zhaksylyk and Olzhas Shortanbaiuly and Yerzhan Orazayev and Nursultan Makhanov and Temirlan Karibekov and Victor Suvorov and Aruzhan Imasheva and Kurmash Zhumagozhayev and Aigerim Seitova}, doi = {10.1109/ACCESS.2025.3585377}, journal = {IEEE Access}, pages = {136101-136143}, shortjournal = {IEEE Access}, title = {Future of breast cancer diagnosis: A review of DL and ML applications and emerging trends for multimodal data}, volume = {13}, year = {2025}, }
• (2025). Recursive class connectivity classification (R3C) applied to binary image segmentation for improved infant fingerprint enhancement. ACCESS, 13, 136087-136100. (www)

  @article{ , abstract = {Image enhancement plays a crucial role in infant fingerprint matching, as child-specific characteristics such as smaller finger dimensions and thinner ridge structures often degrade image quality during acquisition. To address these limitations, enrollment typically depends on specialized high-resolution scanners, which most existing enhancement methods are not designed to support. Consequently, identification rates for children remain significantly lower than those achieved with adult fingerprints. This study introduces Recursive Class Connectivity Classification (R3C), a novel framework that iteratively refines binary segmentation outputs from existing enhancement methods by extending ridge structures. R3C does not require modifications to the underlying classifier and operates without training data, which is not currently available for infant fingerprints. Instead, the method improves segmentation by repeatedly feeding the classified image back into the classification process, while combining each intermediate segmentation with the original input image. Experiments conducted on three fingerprint datasets using four different enhancement classifiers show that R3C can increase the True Acceptance Rate (TAR) by up to 4% for children and over 40% for newborns, compared to using the enhancement methods alone. A qualitative analysis further demonstrates that R3C reconnects fragmented ridge patterns, improving the visual quality of segmentation. Because it functions independently of the enhancement method used, R3C provides a flexible and broadly applicable solution for improving binary segmentation.}, archive = {J_ACCESS}, author = {João Leonardo Harres Dall Agnol and Luiz Fernando Puttow Southier and Jefferson Tales Oliva and Marcelo Teixeira and Rodrigo Minetto and Marcelo Filipak and Dalcimar Casanova and Érick Oliveira Rodrigues}, doi = {10.1109/ACCESS.2025.3594912}, journal = {IEEE Access}, pages = {136087-136100}, shortjournal = {IEEE Access}, title = {Recursive class connectivity classification (R3C) applied to binary image segmentation for improved infant fingerprint enhancement}, volume = {13}, year = {2025}, }
• (2025). A temporal attention-based SARIMA-BiLSTM residual learning model tuned by grey wolf optimizer for parallel urban traffic forecasting. ACCESS, 13, 136073-136086. (www)

  @article{ , abstract = {Accurate and timely traffic forecasting is essential for ensuring the efficiency, reliability, and safety of modern transportation networks. However, urban traffic exhibits high levels of temporal variability, spatial complexity, and nonlinear dependencies, posing challenges to traditional time series and machine learning models. In this study, a novel hybrid residual learning framework is proposed that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a temporal attention mechanism. The SARIMA component captures underlying seasonal and linear patterns in traffic flow, while the residuals—representing unmodeled nonlinear dynamics—are learned by the BiLSTM module. To improve the model’s generalization and predictive accuracy, a Grey Wolf Optimizer (GWO) is employed for automated hyperparameter tuning of both SARIMA and BiLSTM components. Furthermore, the entire pipeline is adapted for parallel execution to improve scalability and reduce computational latency, enabling faster processing of large-scale traffic data. Experimental evaluations on real-world traffic datasets demonstrate the effectiveness of the proposed model, achieving a processing speedup of $10.11\times $ and a forecasting accuracy of 99.31%, outperforming conventional single-threaded and baseline hybrid models. The results indicate that the proposed attention-enhanced SARIMA-BiLSTM model, optimized via GWO and accelerated through parallel processing, provides a robust and scalable solution for real-time urban traffic forecasting applications.}, archive = {J_ACCESS}, author = {Manas Kamal Das and Christopher Columbus Chinnappan and E. Elakiya}, doi = {10.1109/ACCESS.2025.3590104}, journal = {IEEE Access}, pages = {136073-136086}, shortjournal = {IEEE Access}, title = {A temporal attention-based SARIMA-BiLSTM residual learning model tuned by grey wolf optimizer for parallel urban traffic forecasting}, volume = {13}, year = {2025}, }
• (2025). Self-assembling families of LC ladder circuits with frequency-controlled growth. ACCESS, 13, 136062-136072. (www)

  @article{ , abstract = {A family of LC ladder circuits is analyzed with an abstract model for growth in a diverse set of systems, with possible applications to biological organisms, self-assembly of nanostructures, models of topological insulators, and classical simulation of quantum circuits. In the LC circuit tile assembly model (lc-CTAM), tiles of inductors and capacitors attach to a growing assembly if the magnitude of the electric node potential at the tip is greater than a threshold. The frequency response, including poles and zeros, and time and space behavior of the node potentials are characterized as a function of length and the circuit parameters. When a resistance is present, growth is always bounded. As the resistance goes to zero, growth is bounded for low frequencies. At higher frequencies, growth can be both bounded or unbounded, depending on frequency and circuit parameters. In some instances, the distribution of node potentials as a function of length exhibits complex, aperiodic behavior. Both the lc-CTAM model and the methods used to analyze it, including Chebyshev polynomials, are a novel application of lumped circuit analysis. Through exact mathematical characterization, the lc-CTAM demonstrates how complex behavior can arise from simple systems, has the potential to inform investigations of biological growth, and may assist in the design of new materials and computational paradigms.}, archive = {J_ACCESS}, author = {Russell Deaton and Max Garzon and Rojoba Yasmin and Andrew Garth}, doi = {10.1109/ACCESS.2025.3594624}, journal = {IEEE Access}, pages = {136062-136072}, shortjournal = {IEEE Access}, title = {Self-assembling families of LC ladder circuits with frequency-controlled growth}, volume = {13}, year = {2025}, }
• (2025). CPO-VMD combined with multiscale permutation entropy for noise reduction in GNSS vertical time series in mining areas. ACCESS, 13, 136050-136061. (www)

  @article{ , abstract = {The vertical time series of deformation monitoring data are often interfered with by multiple noises, which makes it difficult to extract useful information. In order to effectively filter out the noise components in the deformation monitoring data, this paper proposes a time series noise reduction method (CPO-VMD-MPE) that integrates the crested porcupine optimizer (CPO), the variational modal decomposition (VMD), and the multiscale permutation entropy (MPE). The method uses the CPO algorithm to optimize the key parameters of the VMD, determines the high-frequency components with MPE values higher than a set threshold as noise components and removes them, and then reconstructs the remaining components in order to obtain the noise-reduced time series. The results of simulation and example analysis show that compared with wavelet denoising (WD), empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), this method shows significant advantages in the evaluation indexes of noise reduction effect—Pearson’s correlation coefficient (R), the signal-to-noise ratio (SNR), and the root-mean-square error (RMSE)—and all the indexes are better than the comparative methods. Taken together, the CPO-VMD-MPE method proposed in this paper significantly reduces the noise in the time series and provides a better theoretical and methodological reference for deformation analysis and prediction.}, archive = {J_ACCESS}, author = {Xu Yang and Xinxin Yao and Xinjian Fang and Xuexiang Yu and Yi Wu and Shicheng Xie}, doi = {10.1109/ACCESS.2025.3594011}, journal = {IEEE Access}, pages = {136050-136061}, shortjournal = {IEEE Access}, title = {CPO-VMD combined with multiscale permutation entropy for noise reduction in GNSS vertical time series in mining areas}, volume = {13}, year = {2025}, }
• (2025). Communication-balancing threshold for event-triggered federated learning. ACCESS, 13, 136038-136049. (www)

  @article{ , abstract = {Federated Learning (FL) enables training models across distributed devices while preserving data privacy by avoiding raw data sharing. However, it suffers from significant communication overhead. Event-Triggered FL (ETFL) addresses this issue by allowing devices to transmit updates only when substantial changes occur in the model. Nevertheless, this approach may result in imbalanced communication, where some devices communicate more frequently than others, leading to uneven model performance and slower overall convergence. To address this, we propose a new threshold-based method that dynamically adjusts each device’s communication frequency. Our method ensures balanced communication across devices and reduces the time required for each training iteration, ultimately accelerating convergence time. Furthermore, we analyze how a device’s communication affects the difference between its local model and the global model. Through extensive experiments, we demonstrate that the proposed method significantly reduces communication imbalance and achieves faster convergence compared to existing approaches. This result highlights the importance of balancing communication in federated learning to improve overall performance and ensure fairness across devices.}, archive = {J_ACCESS}, author = {Juhyeong Yoon and Jun-Pyo Hong and Jaeyoung Song}, doi = {10.1109/ACCESS.2025.3592860}, journal = {IEEE Access}, pages = {136038-136049}, shortjournal = {IEEE Access}, title = {Communication-balancing threshold for event-triggered federated learning}, volume = {13}, year = {2025}, }
• (2025). AI-driven background segmentation for high-throughput 3D plant scans. ACCESS, 13, 136027-136037. (www)

  @article{ , abstract = {Accurate background segmentation in 3D plant phenotyping is crucial for reliable trait assessment but remains challenging. Current methods are either excessively complex, developed for a different domain, or lead to data loss (coordinate-based). This paper addresses these issues by introducing an AI-driven approach using a Multi-Layer Perceptron (MLP) model, leveraging RGB, spatial (XYZ), and near-infrared (NIR) data to enhance precision. The method was evaluated on high-throughput phenotyping data, achieving a classification accuracy of 0.9993, significantly reducing false positives and false negatives compared to coordinate-based segmentation. The proposed approach improved segmentation, particularly in early growth stages and for prostrate species, where traditional methods often fail. The model’s impact on leaf area estimation was validated against destructive measurements, demonstrating substantial accuracy improvements, especially for species with small and prostrate canopies. Additionally, the model exhibited strong generalization capabilities when applied to an external 3D dataset, confirming its reusability beyond plant phenotyping tasks. Integrating this simple method into phenotyping pipelines will enhance efficiency and accuracy in high-throughput trait estimation, supporting advancements in plant science and precision agriculture.}, archive = {J_ACCESS}, author = {Serkan Kartal and Jan Masner and Jana Kholová and Alexander Galba and Tharanya Murugesan and Rekha Baddam and Vojtěch Mikeš and Eva Kánská}, doi = {10.1109/ACCESS.2025.3594406}, journal = {IEEE Access}, pages = {136027-136037}, shortjournal = {IEEE Access}, title = {AI-driven background segmentation for high-throughput 3D plant scans}, volume = {13}, year = {2025}, }
• (2025). NMPC-based performance evaluation of active balancing networks of li-ion batteries for overnight EV charging. ACCESS, 13, 136016-136026. (www)

  @article{ , abstract = {A battery pack plays a vital role in an Electric Vehicle (EV), for it stores the energy and releases it to provide a reliable energy output. However, because of variations and imbalance in individual cell characteristics, the cells connected in a pack do not contain the same State of Charge (SoC) level—leading to poor EV performance and battery health deterioration. Therefore, in this work, active cell balancing is performed during overnight charging to ensure the same charge level among the cells by the end of the charging cycle. For this purpose, a high-fidelity mean current model is presented for a switched capacitor-(SC) based architecture comprising N cells for a generalized battery pack. Furthermore, this model is incorporated into a feedback control system utilizing Non-linear Model Predictive Control (NMPC). Since balancing losses are a primary factor during overnight charging, the SC-based NMPC is also compared with a buck-boost (BB) converter architecture under the same conditions. For each framework, extensive simulations are conducted by varying the weights of different terms in the cost function of the NMPC problem. The simulation results demonstrate that both SC- and BB-based architectures have been able to achieve balancing, wherein the average balancing time of SC is $186~\%$ more than that of BB. However, in terms of average power losses and energy dissipation, SC incurred $288~\%$ and $31~\%$ fewer losses than BB, respectively.}, archive = {J_ACCESS}, author = {Muhammad Azmat Ullah and Afaq Ahmed and Ali Arshad Uppal and Qadeer Ahmed}, doi = {10.1109/ACCESS.2025.3594129}, journal = {IEEE Access}, pages = {136016-136026}, shortjournal = {IEEE Access}, title = {NMPC-based performance evaluation of active balancing networks of li-ion batteries for overnight EV charging}, volume = {13}, year = {2025}, }
• (2025). Decentralized detection and mitigation of false data injection attacks in DC microgrids using artificial neural network. ACCESS, 13, 136002-136015. (www)

  @article{ , abstract = {Cooperative and distributed control strategies for direct current (DC) microgrids have made significant advancements in recent years. However, integrating a cyber layer to enhance resilience, scalability, and reliability also exposes the system to potential cyberattacks. This paper leverages data-driven methods to propose a novel approach for detecting cyberattacks in the secondary control layer. The proposed decentralized framework employs multilayer perceptron (MLP) neural networks to detect false data injection attacks (FDIA) on DC bus voltage while simultaneously mitigate their adverse effects on system operation. In this framework, the MLP neural networks are trained offline using local data under various conditions and are subsequently deployed online within the distributed generator units for fault detection and mitigation. Resilience is achieved through a pinning-node consensus-based secondary control strategy, which enables attack detection via MLP on other units regardless of whether all units are pinned. To demonstrate the effectiveness of the proposed method, simulation studies are conducted in MATLAB/Simulink on a DC microgrid across eight scenarios. The results and comparisons demonstrate that the proposed approach achieves a high detection accuracy of 99.96% under ideal conditions and 99.4% under sensor noise, enhancing security while reducing communication overhead.}, archive = {J_ACCESS}, author = {Omid Danaei Koik and Shahram Karimi and Khaled M. Almustafa and Juliano Katrib}, doi = {10.1109/ACCESS.2025.3594245}, journal = {IEEE Access}, pages = {136002-136015}, shortjournal = {IEEE Access}, title = {Decentralized detection and mitigation of false data injection attacks in DC microgrids using artificial neural network}, volume = {13}, year = {2025}, }
• (2025). Analysis-runtime co-design for EDF-VD-based mixed-criticality scheduling: Improving completion rates of low-criticality jobs. ACCESS, 13, 135990-136001. (www)

  @article{ , abstract = {In this paper, we explore mixed criticality systems, where traditional approaches often adopt a pessimistic stance in managing low-criticality tasks, focusing primarily on ensuring the timely completion of high-criticality tasks. We optimized the “Analysis-Runtime Co-design” for the preemptive single-processor EDF-VD algorithm, and introduces the EDF-VD-DL protocol. The main adjustment refines the conditions for transitioning between high and low criticality levels. When a task shows high-criticality behavior, dynamic relaxation is used to delay the switch to high-criticality mode, reducing the frequency of such transitions. Additionally, the transition from high-criticality mode to low-criticality mode is expedited, reducing the duration spent in high-criticality mode. This effectively minimizes the risk of missing deadlines or leaving low-criticality jobs incomplete, thereby improving the completion rate of low-criticality jobs. Finally, we validated the schedulability of the new protocol through testing and experimental evaluation.}, archive = {J_ACCESS}, author = {Zhenkai Hao}, doi = {10.1109/ACCESS.2025.3594017}, journal = {IEEE Access}, pages = {135990-136001}, shortjournal = {IEEE Access}, title = {Analysis-runtime co-design for EDF-VD-based mixed-criticality scheduling: Improving completion rates of low-criticality jobs}, volume = {13}, year = {2025}, }
• (2025). State-of-the-art on IoV-based deep learning framework for enhanced driving behavior recognition: Recent progress, technology updates, challenges, and future direction. ACCESS, 13, 135969-135989. (www)

  @article{ , abstract = {In recent years, the application of deep learning (DL) models to identify dangerous driving behaviors has emerged as a novel approach to enhance road safety and detect high-risk driving behaviors. However, these advanced algorithms still face several challenges. We analyze relevant literature from 2015 to 2024, to uncover key trends and gaps in the development of applications for identifying dangerous driving behaviors, with a primary focus on optimizing integration to improve the accuracy and real-time capability of driving behavior prediction. The analysis reveals that data quality, computational demands, and privacy issues are currently hindering the full realization of this technology. The Internet of Vehicles (IoV) collects data based on real driving environments, offering unique advantages in terms of data timeliness, sufficiency, and diversity. Therefore, this paper proposes an active IoV based DL framework, emphasizing the feasibility of enhancing data processing techniques and algorithmic improvements to boost model accuracy and generalization ability, and highlighting the potential of integrating edge and cloud computing to support real-time data analysis and decision-making. Future research should concentrate on multimodal data fusion and federated learning to further enhance recognition performance and ensure broader applicability of deep learning in the internet of vehicles environments.}, archive = {J_ACCESS}, author = {Hongguang Li and Shafrida Sahrani and Mahidur R. Sarker and Yinglin Xiao}, doi = {10.1109/ACCESS.2025.3594091}, journal = {IEEE Access}, pages = {135969-135989}, shortjournal = {IEEE Access}, title = {State-of-the-art on IoV-based deep learning framework for enhanced driving behavior recognition: Recent progress, technology updates, challenges, and future direction}, volume = {13}, year = {2025}, }
• (2025). Enhancing fake review detection using linguistic exaggeration, BERT embeddings, and fuzzy logic. ACCESS, 13, 135957-135968. (www)

  @article{ , abstract = {The rapid proliferation of online reviews has made them a crucial factor in consumer decision-making. However, the presence of fake reviews threatens the credibility of review platforms, which requires advanced detection mechanisms. The core objective of this work is to develop a hybrid model that combines interpretable handcrafted linguistic cues with deep semantic features for more accurate, robust, and accurate fake review detection. In this study, we propose a novel hybrid approach to fake review detection that integrates fuzzy logic, BERT embeddings, attention-based feature fusion, and an enhanced multicentroid K-means classifier. Our method first preprocesses reviews and extracts key linguistic features based on extravagant words using a fuzzy logic-based membership function that highlights extreme linguistic patterns. Simultaneously, BERT embeddings capture the deep semantic meaning of the text. These features are then combined using a softmax-based attention mechanism to optimize the contribution of each component. To classify the reviews, we introduce a personalized multicentroid K-means algorithm, which improves traditional K-means by allowing a more precise clustering of false and genuine reviews. We evaluated our approach against traditional classifiers such as Support Vector Machines (SVM), Logistic Regression. Our final model demonstrated statistically significant improvements across key metrics, supported by cross-validation and hypothesis testing (p ¡ 0.01). The confidence intervals were calculated to assess the reliability of the observed performance gains. To validate the effectiveness of our proposed method, we conducted extensive evaluations on two real-world datasets. The results confirm that our approach effectively balances semantic understanding, linguistic heuristics, and clustering efficiency, leading to enhanced detection performance. This research provides a scalable and interpretable solution to the problem of detecting fake reviews, with potential applications in e-commerce platforms, online marketplaces, and digital content verification systems.}, archive = {J_ACCESS}, author = {Mohammed Ennaouri and Ahmed Zellou}, doi = {10.1109/ACCESS.2025.3594629}, journal = {IEEE Access}, pages = {135957-135968}, shortjournal = {IEEE Access}, title = {Enhancing fake review detection using linguistic exaggeration, BERT embeddings, and fuzzy logic}, volume = {13}, year = {2025}, }
• (2025). Where does mBERT understand code-mixing? layer-dependent performance on semantic tasks. ACCESS, 13, 135950-135956. (www)

  @article{ , abstract = {Semantic tasks like lexical relation prediction and word analogy are crucial for deep language understanding, yet pose significant challenges when applied to code-mixed text, where multiple languages are interwoven. This study investigates the ability of multilingual BERT (mBERT) to capture semantic nuances in code-mixed scenarios (English-Spanish and English-Telugu) through layer-wise probing across four distinct tasks: lexical relation prediction, word analogy prediction, sentiment analysis, and intent classification. We employ probing classifiers trained on representations extracted from each of mBERT’s 12 layers to assess semantic knowledge capture throughout the model. Our comprehensive experiments reveal that mid-level layers (approximately 4-7) generally exhibit the strongest performance for tasks like lexical relation and intent classification in code-mixed settings. However, performance varies significantly by task, with sentiment analysis proving particularly challenging (achieving near chance-level accuracy), highlighting mBERT’s limitations in capturing certain code-mixed semantic subtleties. This work contributes a multi-task layer-wise analysis of mBERT’s semantic capabilities on code-mixed data, providing insights into optimal layer usage and pinpointing areas requiring further research for robust multilingual, code-mixed natural language understanding.}, archive = {J_ACCESS}, author = {Aditya Somani and S. R. Mithun Kumar and Aruna Malapati}, doi = {10.1109/ACCESS.2025.3594135}, journal = {IEEE Access}, pages = {135950-135956}, shortjournal = {IEEE Access}, title = {Where does mBERT understand code-mixing? layer-dependent performance on semantic tasks}, volume = {13}, year = {2025}, }
• (2025). A method for automatically locating defects in CCTV inspection data of sewer pipes. ACCESS, 13, 135931-135949. (www)

  @article{ , abstract = {Identifying and locating sewer defects is crucial for minimizing the risk of sewer-related accidents. Currently, spatial localization of sewer defects in closed circuit television (CCTV) inspection data is primarily performed through manual visual inspections by professional technicians, which is time-consuming and costly. Consequently, there is an urgent need to develop automated solutions. This study proposes an innovative method for automatic defect location, which involves defect tracking based on rules, calculating the distance of the defect relative to the pipe endpoint, and converting this distance into physical world coordinates. Following experimental validation, the multiple object tracking accuracy (MOTA) metric of defect tracking within the proposed method ranged from -1.46 to 0.89 across ten test videos. The proposed method can be easily integrated into practical engineering applications, thereby alleviating the workload of professional technicians in obtaining the geographic locations of sewer defects in CCTV inspection data and reducing both time and labor costs associated with this process.}, archive = {J_ACCESS}, author = {Jun Tang and Jisheng Xia and Zhiqiang Xie and Zhaoyong Li and Yuting Zhang}, doi = {10.1109/ACCESS.2025.3591031}, journal = {IEEE Access}, pages = {135931-135949}, shortjournal = {IEEE Access}, title = {A method for automatically locating defects in CCTV inspection data of sewer pipes}, volume = {13}, year = {2025}, }
• (2025). An energy-aware, self-adaptive, battery-free smart wristband for long-term health monitoring. ACCESS, 13, 135915-135930. (www)

  @article{ , abstract = {Continuous monitoring of physiological parameters, including heart rate and oxygen saturation, is important in healthcare applications. Modern wearables are expected to support wireless connectivity, be small and lightweight, and offer long-term use. These features introduce challenges in power consumption and management. As a result, battery-powered wearables often face trade-offs between size, energy storage, performance, and operating time. This research presents a self-sustainable, battery-free smart wristband for continuous heart rate monitoring. The wristband extends operational life by integrating solar energy harvesting with supercapacitor technology in an environmentally sustainable design. Power consumption is minimized through onboard processing, which reduces inefficient data transmission, and adaptive sensor sampling, which dynamically allocates energy based on heart rate, supercapacitor voltage, and harvesting conditions. Benefiting from hardware and software co-design, the proposed wearable device achieves self-sustainability at a sensor sampling rate of 50 Hz with only 1.45 hours of indoor light exposure (1000 lux) per day and at 200 Hz with 4.74 hours. The in-field experiments show that onboard processing reduces Bluetooth Low Energy (BLE) data transmission to essential information, saving about 2 J of energy. The system shortens supercapacitor charging time by 17 minutes per hour through its ability to automatically reduce the sampling frequency from 200 Hz to 50 Hz when harvesting energy is scarce. The proposed system can even operate effectively in around 200 lux of light, equivalent to a dimly lit living room. These results underscore the system’s ability to dynamically balance power-performance trade-offs, offering a promising long-term solution for continuous health monitoring in various environments.}, archive = {J_ACCESS}, author = {Shihong Chen and Esther Rodriguez-Villegas}, doi = {10.1109/ACCESS.2025.3593936}, journal = {IEEE Access}, pages = {135915-135930}, shortjournal = {IEEE Access}, title = {An energy-aware, self-adaptive, battery-free smart wristband for long-term health monitoring}, volume = {13}, year = {2025}, }
• (2025). PercepWeldNet: A novel perception-guided deep model for welding defect detection. ACCESS, 13, 135899-135914. (www)

  @article{ , abstract = {In this study, we introduce PercepWeldNet, a deep architecture designed for visual perception in welding defect detection, addressing the challenge of identifying semantically significant features within complex welding scenes. Our approach integrates multi-channel perceptual visual features to effectively represent welding images with intricate spatial configurations. The core contribution is a multi-layer hierarchical model, LPL, which simulates human gaze behavior by utilizing the BING objectness metric. This enables efficient identification of semantically and visually important patches across various scales and contexts. Through locality-preserved learning (LPL), our model generates gaze shift paths (GSP) that preserve local coherence and focus on regions that align with human visual attention. These GSP-based deep features are incorporated into a Gaussian mixture model for welding defect classification. Extensive empirical validation on our welding defect detection dataset and several generic datasets demonstrates the effectiveness of our approach, with significant advantages in defect categorization accuracy. The proposed method achieves superior results in welding defect detection, as well as in scenery retargeting tasks.}, archive = {J_ACCESS}, author = {Xingxing Zhang}, doi = {10.1109/ACCESS.2025.3594359}, journal = {IEEE Access}, pages = {135899-135914}, shortjournal = {IEEE Access}, title = {PercepWeldNet: A novel perception-guided deep model for welding defect detection}, volume = {13}, year = {2025}, }
• (2025). Dynamic cluster selection DAG scheduling for clustered many-core processor considering inter-cluster communication. ACCESS, 13, 135876-135898. (www)

  @article{ , abstract = {The advancement of embedded systems, such as autonomous driving systems, has led to a need for platforms that can cope with severe real-time constraints. In this context, research on real-time scheduling of Directed Acyclic Graph (DAG) using clustered many-core processors has been actively conducted. Clustered many-core processors are suitable for systems with severe real-time constraints due to their structure with low memory contention. However, improper selection of clusters can result in a significant volume of inter-cluster communication, which introduces considerable overhead. Despite its importance, reducing inter-cluster communication has not been extensively studied, and when studied, the focus has often been limited to specific cluster architectures. Furthermore, in DAG scheduling, inter-cluster communication occurs when a single node uses multiple clusters and when different clusters are used between nodes. For example, in embedded systems such as autonomous driving systems, where a single node may be processed by multiple cores and modeled in a DAG, the reduction of inter-cluster communication both within a node and between nodes should be aimed at. To address these issues, this paper proposes the dynamic cluster selection DAG scheduling method considering the reduction of inter-cluster communication. The proposed method dynamically selects the clusters to be used based on the number of cores remaining in each cluster and information on the clusters used by dependent jobs. The proposed method is independent of cluster architectures used and can be used on various clustered many-core processors. Through experiments using two task sets and 12 cluster architectures with DAG modeling autonomous driving systems, we analyzed inter-cluster communication occurrences and response times. When compared to three baseline cluster selection methods, which are the index-based approach, the best-fit strategy for the number of cores within a cluster, and the worst-fit strategy for the number of cores within a cluster, our proposed method achieves significant reductions. Specifically, it reduces the average number of inter-cluster communication occurrences within a node by up to 96%, 65%, and 59%, respectively. Similarly, the average number of inter-cluster communication occurrences between nodes is reduced by up to 53%, 61%, and 59%, respectively. Furthermore, the average maximum response time is reduced by up to 31%, 32%, and 23%, respectively. The results show that for all cluster architectures, the proposed method is able to suppress the occurrence of inter-cluster communication well both within a node and between nodes of the DAG and suppress response times.}, archive = {J_ACCESS}, author = {Yutaro Nozaki and Fuma Omori and Takuya Azumi}, doi = {10.1109/ACCESS.2025.3593662}, journal = {IEEE Access}, pages = {135876-135898}, shortjournal = {IEEE Access}, title = {Dynamic cluster selection DAG scheduling for clustered many-core processor considering inter-cluster communication}, volume = {13}, year = {2025}, }
• (2025). Federated learning for distributed IoT security: A privacy-preserving approach to intrusion detection. ACCESS, 13, 135863-135875. (www)

  @article{ , abstract = {Billions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. At its core is Hybrid Adaptive-Weight Aggregation (HADA), a new server-side rule that weights client updates by their SHAP-based feature stability and individual differential-privacy (DP) budgets. HADA down-weights noisy or low-privacy updates, speeding convergence and hardening the global model against poisoning. Evaluated on the CIC-BCCC-NRC TabularIoTAttack-2024 and Edge-IIoTset benchmarks with 100 simulated ARM-class devices, the proposed FL-IDS achieves 85–89 % detection accuracy—on par with centralized training—while reducing per-round uplink traffic by an order of magnitude. Under strong adversarial perturbations (FGSM, PGD-10, label-flip) it retains 66–73 % accuracy, outperforming vanilla FedAvg by up to 22 percentage points. A formal convergence theorem is proved for non-IID, DP-noisy updates, and the privacy–utility curve shows $\lt \!1.5$ pp accuracy loss at $\varepsilon = 1.0$ . These results demonstrate that HADA-FL delivers a scalable, privacy-preserving, and attack-resilient intrusion-detection system suitable for real-world heterogeneous IoT deployments.}, archive = {J_ACCESS}, author = {Chandu Gutti and Karthik Thumula and Parag Balbudhe}, doi = {10.1109/ACCESS.2025.3592481}, journal = {IEEE Access}, pages = {135863-135875}, shortjournal = {IEEE Access}, title = {Federated learning for distributed IoT security: A privacy-preserving approach to intrusion detection}, volume = {13}, year = {2025}, }
• (2025). One-step graph fusion fuzzy clustering network. ACCESS, 13, 135848-135862. (www)

  @article{ , abstract = {Graph clustering plays a crucial role in uncovering implicit information within data which can be used to rationally classify potential data samples in unsupervised scenarios. In recent years, investigators have essentially focused on deep graph clustering, where deep learning techniques are effectively utilized to transform nodes in the graphs into representative vectors for subsequent clustering. Nevertheless, the existence of two main constraints within current methodologies warrants their resolutions: 1) the majority of graph clustering methodologies are bifurcated into two-step, potentially culminating in clustering outcomes that deviate unpredictably from the actual truths; 2) the prevailing graph clustering techniques chiefly employ hard clustering paradigms, that exhibit a lack of interpretability concerning data points situated in boundary regions that may pertain to multiple clusters. To address these challenges, we herein introduce a one-step graph fusion fuzzy clustering network (GFFCN) that incorporates fuzzy clustering into the graph structure by modulating its fuzziness coefficient to reveal the degree of membership of each data point to various clusters. Additionally, an expanded mutual information regularization component is incorporated to enhance its equilibrium and precision, and to introduce additional clustering layers that fortify the overarching network. Collectively, the GFFCN adeptly surmounts the constraints of the conventional two-step clustering methodology, facilitating the entire clustering framework to yield outcomes in a seamless end-to-end fashion, which in turn diminishes the discrepancies associated with the bifurcated clustering process. The extensive experiments conducted demonstrate that the proposed approach achieves state-of-the-art results in six data sets and effectively enhances the determinism and balance of clustering outcomes.}, archive = {J_ACCESS}, author = {Bin Tang and Zhongyang Zhou and Xuewen Liu and Feiyu Chen}, doi = {10.1109/ACCESS.2025.3593844}, journal = {IEEE Access}, pages = {135848-135862}, shortjournal = {IEEE Access}, title = {One-step graph fusion fuzzy clustering network}, volume = {13}, year = {2025}, }
• (2025). Asymmetric space vector modulation with positive and negative DC-bus clamping: A strategy for enhanced THD and efficiency. ACCESS, 13, 135834-135847. (www)

  @article{ , abstract = {This work proposes a two-stage modified T-type multilevel boost inverter and an asymmetric space vector modulation (ASVM) technique based on DC-bus clamping. In the conventional two-stage T-type multilevel boost inverters, the mid-point of the DC-link capacitors is taken as the neutral point (NP), whereas in the modified converter, the positive DC source terminal is taken as the NP. The modified structure helps in mitigating the NP voltage unbalancing and has the feature of reduced power processing. The pole voltages of the proposed converter include the input DC source and the boost converter’s output voltage with respect to the ground. Here, based on the duty cycle, the inverter pole voltage levels can be either symmetric or asymmetric; therefore, to utilize the asymmetric voltage levels, an ASVM technique is proposed, which is based on the positive and negative DC-bus clamping of each phase. This proposed ASVM technique also helps in minimizing the current THD and the switching loss of the converter. Further, the dependency of the output voltage on the DC-DC converter’s duty and inverter’s modulation index gives a feature to choose the converter’s operational point for minimum current THD and higher efficiency. A 1kW-rated prototype of the proposed converter is made and tested using the ASVM technique under different operating points. Further, a topological comparison with state-of-the-art multilevel inverters (MLIs) is provided across the key performance metrics of the proposed converter.}, archive = {J_ACCESS}, author = {Balram Kumar and Sankar Peddapati and Praveen Kumar Bonthagorla}, doi = {10.1109/ACCESS.2025.3593812}, journal = {IEEE Access}, pages = {135834-135847}, shortjournal = {IEEE Access}, title = {Asymmetric space vector modulation with positive and negative DC-bus clamping: A strategy for enhanced THD and efficiency}, volume = {13}, year = {2025}, }
• (2025). Torque reduction of 1-DOF transformable wheel with passive springs. ACCESS, 13, 135822-135833. (www)

  @article{ , abstract = {Transformable wheels provide an effective solution for mobile robots to overcome structured obstacles by altering their shape. However, they often encounter a peak torque during the critical climbing phase, particularly at contact-point transitions. This significantly increases actuator requirements and overall system costs (e.g., motor size and price), ultimately limiting the practicality of transformable wheels in mobile service robots. To address this issue, we integrated a passive spring into the previously developed one-degree-of-freedom (1-DOF) RPRP transformable wheel (STEP II), aiming to reduce peak torque and redistribute internal forces during obstacle climbing. A quasi-static model was first developed to analyze torque behavior and simulate a 6 kg wheel climbing a 100 mm step. Simulations identified an optimal spring constant of k =0.9 N/mm, achieving approximately 12% peak torque reduction compared to the no-spring baseline. Additional simulations indicated that this approach remains effective for various obstacle dimensions. An experimental prototype validated this result and further evaluated five spring configurations (k =0 to 3.0 N/mm) across four obstacle heights (50–140 mm). The results confirmed that moderate stiffness values (k =1.8–2.4 N/mm) yield the greatest benefit, with peak torque reductions of up to 35% depending on the obstacle height. These results demonstrate that passive spring integration effectively mitigates the high-torque issue inherent in transformable wheels, paving the way for enhanced energy efficiency and cost-effectiveness, and potentially enabling smaller actuators and more practical service robot deployments.}, archive = {J_ACCESS}, author = {Jaebaek Lee and Jaeseong Park and Youngsoo Kim}, doi = {10.1109/ACCESS.2025.3594567}, journal = {IEEE Access}, pages = {135822-135833}, shortjournal = {IEEE Access}, title = {Torque reduction of 1-DOF transformable wheel with passive springs}, volume = {13}, year = {2025}, }
• (2025). An XGBoost-SHAP model for energy demand prediction with Boruta–Lasso feature selection. ACCESS, 13, 135806-135821. (www)

  @article{ , abstract = {Energy demand prediction is essential in ensuring national energy security, promoting high-quality economic development, advancing sustainable development, optimizing the energy structure, and achieving dual carbon goals. In recent years, machine learning (ML) algorithms have been extensively used in energy demand prediction but with poor interpretability. This study proposes an interpretable ML framework for energy demand prediction based on the Boruta-Lasso two-stage feature selection model, extreme gradient boosting (XGBoost) regression model, grid search optimization algorithm, and Shapley additive explanations (SHAP) algorithm. Taking China as an example, the results show that the XGBoost-based model achieves R2, MAE, and RMSE of 0.9975, 0.0108, and 0.0157, respectively, with higher prediction accuracy compared to other models. In addition, the analysis based on the SHAP algorithm shows that gross national income (GNI), gross domestic product (GDP), total population at the year-end, per capita consumption expenditure, and the proportion of added value of the secondary industry are the main drivers of energy demand. The framework established in this study helps to screen influencing factors, predict energy demand, and simultaneously explain the relationship between these factors and energy demand.}, archive = {J_ACCESS}, author = {Yiwen Wang and Weibin Cheng and Yuting Jin and Jifei Li and Yantian Yang and Shaobing Hu}, doi = {10.1109/ACCESS.2025.3593558}, journal = {IEEE Access}, pages = {135806-135821}, shortjournal = {IEEE Access}, title = {An XGBoost-SHAP model for energy demand prediction with Boruta–Lasso feature selection}, volume = {13}, year = {2025}, }
• (2025). Leveraging spatial and temporal data to predict heavy freight vehicle traffic flow on rural road network. ACCESS, 13, 135791-135805. (www)

  @article{ , abstract = {Accurately predicting heavy freight vehicle (HFV) traffic flow is essential for optimizing service levels and improving road safety management. This study compares the performance of three machine learning approaches—classical learning, reinforcement learning, and artificial neural networks—in predicting the traffic flow of trucks and tractor-trailers. The dataset consists of hourly traffic records spanning three years and seven months on rural road network segments in southern Iran. The modeling framework integrates temporal and spatial variables along with lagged traffic volumes from upstream counters. Model performance is assessed through an expanding window cross-validation approach, which defines separate training, validation, and testing datasets. The results indicate that while the seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model performs well on the training data, it exhibits limitations in predicting test data. The extreme gradient boosting (XGBoost) model surpasses the time-series model in predictive accuracy, yielding average R-squared values of 84.7% and 85.8% on the test data for trucks and tractor-trailers, respectively. In comparison, the multi-layer perceptron (MLP) model achieves average R-squared values of 86.8% for trucks and 82.9% for tractor-trailers. In addition to R-squared, model performance has been further evaluated using RMSE and MAE metrics, offering a more nuanced and reliable understanding of prediction accuracy across different error dimensions. Analysis based on SHAP values and sensitivity assessment demonstrates that particular times of day and traffic counter classifications exert the greatest influence on HFV traffic flow predictions. Overall, the MLP model demonstrates superior predictive performance.}, archive = {J_ACCESS}, author = {Alireza Gholami and Seyedehsan Seyedabrishami}, doi = {10.1109/ACCESS.2025.3593202}, journal = {IEEE Access}, pages = {135791-135805}, shortjournal = {IEEE Access}, title = {Leveraging spatial and temporal data to predict heavy freight vehicle traffic flow on rural road network}, volume = {13}, year = {2025}, }
• (2025). CRGAN: A context-aware clothing design and recommendation system for young sri lankan females using generative adversarial networks. ACCESS, 13, 135776-135790. (www)

  @article{ , abstract = {The fashion industry is constantly undergoing notable transformations fueled by advancements in technology, particularly AI and ML. This shift is driven by the desire for personalized clothing recommendations, especially among young females, who seek tailored suggestions based on their preferences, climate, and style. While traditional recommendation systems rely on existing databases to suggest predefined outfit options, this research takes a novel approach by integrating generative modeling techniques to create unique outfit designs based on contextual factors. The proposed system leverages a Conditional Generative Adversarial Network (GAN) model, trained on three predefined parameters–attire type, temperature conditions, and the user’s skin tone. This innovative integration of context-aware features marks a significant advancement over prior research, which has largely neglected these combined parameters, particularly in the context of cultural demographics. Targeting young Sri Lankan females, this study fills a critical gap in generative clothing systems by addressing the unique cultural and environmental needs of this population. Experiment results on a labeled dataset demonstrate the model’s ability to generate accurate and personalized outputs based on up to three input parameters. Results are verified through quantitative model evaluations and user studies, underscoring the system’s potential to redefine personalized fashion recommendations.}, archive = {J_ACCESS}, author = {Nethmi Pathirana and Azma Imtiaz and Shakir Saheel and Kasun Karunanayaka and Adrian David Cheok}, doi = {10.1109/ACCESS.2025.3593498}, journal = {IEEE Access}, pages = {135776-135790}, shortjournal = {IEEE Access}, title = {CRGAN: A context-aware clothing design and recommendation system for young sri lankan females using generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). #GAMEADDICTED: A machine learning framework for digital game addiction detection and early intervention. ACCESS, 13, 135757-135775. (www)

  @article{ , abstract = {This study addresses the growing concern of digital game addiction (DGA) by developing an information system for detection and early intervention, to predict an individual’s propensity toward DGA. The study employed machine learning models to process unstructured data collected from online game platforms between May 1, 2023, and June 1, 2023. A total of 660 comments were automatically extracted. Following an expert review process, 245 ambiguous or inconsistent entries were removed, resulting in a final dataset of 415 textual comments for model training. The comments were manually annotated into two categories: addiction-related (1) and non-addiction-related (0) to ensure data consistency and reliability. Data augmentation techniques, such as BERT-based augmentation, deep translation, and synonym replacement, were applied to increase dataset diversity and enhance the model’s generalization capability. After extensive preprocessing, we trained and evaluated six machine learning models: XGBoost, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. XGBoost consistently outperformed all other models, achieving the highest accuracy of 93.06% on the binary-labeled dataset and 92.49% on the expanded dataset. Random Forests and Logistic Regression also exhibited strong classification performance, making them viable alternatives depending on the requirements. In contrast, SVM and Decision Trees showed lower predictive reliability, indicating potential challenges in classifying text-based addiction indicators effectively. As a practical outcome, we developed an interface using the best-performing model, capable of performing preliminary diagnoses based on user-provided sentences. This innovative approach introduces a new dimension in addiction studies by presenting a practical tool for early detection and intervention in DGA.}, archive = {J_ACCESS}, author = {Esra Kahya Özyírmídokuz and Bekír Asim Çelík and Eduard Alexandru Stoica}, doi = {10.1109/ACCESS.2025.3587622}, journal = {IEEE Access}, pages = {135757-135775}, shortjournal = {IEEE Access}, title = {#GAMEADDICTED: A machine learning framework for digital game addiction detection and early intervention}, volume = {13}, year = {2025}, }
• (2025). Leveraging automated market maker for decentralized NFT valuation and inflation management in P2E web3 applications. ACCESS, 13, 135741-135756. (www)

  @article{ , abstract = {Non-fungible tokens (NFTs) are increasingly popular in Play-to-Earn (P2E) Web3 applications as a means of incentivizing user engagement. However, the lack of a standardized valuation mechanism has led to tokenomic pressures, where ‘tokenomics’ refers to the monetary policies of coins or tokens, contributing to inflationary trends. We propose a method to mitigate these inflationary tendencies associated with NFTs in P2E applications, leveraging a contribution-reward model coupled with an Automated Market Maker (AMM) mechanism. Our method suggests that burning NFTs, which permanently removes them from circulation, contributes to managing tokenomics inflation. As a reward for this contribution, our method mints a compensation (CP) token as an ERC-20 token. To further enhance the CP token’s value, we propose creating liquidity pools with governance tokens within the AMM, where the CP token’s value is derived from the market-driven swap rate between it and the governance token. To illustrate our approach, we introduce a robust model integrating our valuation method for upgradeable NFTs, highlighting their potential and utility. Additionally, by comparing models with and without our method, we demonstrate the benefits of our approach in game-theoretic mechanism design. This approach incentivizes participants to align their strategies with governance goals.}, archive = {J_ACCESS}, author = {Hyoungsung Kim and Yong-Suk Park and Hyun-Sik Kim}, doi = {10.1109/ACCESS.2025.3591800}, journal = {IEEE Access}, pages = {135741-135756}, shortjournal = {IEEE Access}, title = {Leveraging automated market maker for decentralized NFT valuation and inflation management in P2E web3 applications}, volume = {13}, year = {2025}, }
• (2025). An efficient quantum blockchain framework with edge computing for privacy-preserving 6G networks. ACCESS, 13, 135722-135740. (www)

  @article{ , abstract = {The advent of 6G networks places very high demands on ultra-low latency, high throughput, and quantum-secure communication to power Industry 5.0 use cases. Traditional blockchain architectures, given their decentralized and secure nature, often fall short in meeting the performance and security requirements of such an ecosystem. In this paper, we present a post-quantum blockchain architecture that employs CRYSTALS-Dilithium and SPHINCS+ for digital signatures and block and transaction verification, respectively, along with zk-STARKs to facilitate scalable zero-knowledge proof-based privacy, and a DPoS+VDFs consensus protocol to satisfy fairness and efficiency. We prototyped and evaluated the proposed framework with a benchmarking setup composed of Python, PQClean, liboqs, and Google Benchmark tools. Experimental results demonstrate that the system achieves a 40% reduction in latency, a 35% increase in transaction throughput, and a 25% reduction in computational overhead due to the integration of zk-STARK. Furthermore, finality time for consensus was reduced by 30% by using the hybrid DPoS-VDF consensus approach. Comparative studies with various lattice-, hash-, and code-based quantum cryptographic primitives have shown that CRYSTALS-Dilithium and SPHINCS+ outperform others in key generation, signing, and verification performance indicators, and thus qualify as optimal solutions for edge-centric 6G infrastructures. Conversely, zk-Starks showed near-optimal timeliness and verification effectiveness among the several examined zero-knowledge proof schemes. These findings validate the proposed framework as an efficient, scalable, and performance-enhanced blockchain solution for securing industrial ecosystems with latency sensitivity in a 6G-enabled environment.}, archive = {J_ACCESS}, author = {Kranthi Kumar Singamaneni and Budati Anil Kumar and Raenu A. L. Kolandaisamy and Vijaya Saradhi Dommeti and Swetha Katragadda}, doi = {10.1109/ACCESS.2025.3592793}, journal = {IEEE Access}, pages = {135722-135740}, shortjournal = {IEEE Access}, title = {An efficient quantum blockchain framework with edge computing for privacy-preserving 6G networks}, volume = {13}, year = {2025}, }
• (2025). LSV-MAE: A masked-autoencoder pre-training approach for large-scale 3D point cloud data. ACCESS, 13, 135708-135721. (www)

  @article{ , abstract = {Masked language modeling (MLM) and masked image modeling (MIM) pretraining paradigms have achieved remarkable success in both natural language processing (NLP) and computer vision (CV). However, extending MIM to large-scale outdoor point cloud data presents significant challenges due to the inherent sparsity and wide spatial coverage of such data. To address this issue, we develop a masked autoencoding pre-training model, LSV-MAE, making it possible to train detection models on large-volume unlabeled point cloud data. Our approach pre-trains the backbone to reconstruct masked voxel features extracted by PointNN. To enhance the feature extraction capability of the encoder, the point cloud is voxelized with different voxel sizes at different pre-training stages. Meanwhile, to avoid the effect of masking key points, the masked voxel features are re-integrated into the decoder during pretraining. To verify the proposed approach, experiments are conducted on well-known datasets, showing that our method not only can avoid tedious labeling work but improve the detection accuracy by up to 18%, compared with that using the model without pre-training, across datasets of different sizes.}, archive = {J_ACCESS}, author = {Nuo Cheng and Chuanyu Luo and Han Li and Sikun Ma and Shengguang Lei and Pu Li}, doi = {10.1109/ACCESS.2025.3594614}, journal = {IEEE Access}, pages = {135708-135721}, shortjournal = {IEEE Access}, title = {LSV-MAE: A masked-autoencoder pre-training approach for large-scale 3D point cloud data}, volume = {13}, year = {2025}, }
• (2025). 3DB-ROC data structure organization for three-dimensional building models. ACCESS, 13, 135689-135707. (www)

  @article{ , abstract = {With the rapid development of technologies such as smart city, digital twin, and metaverse, the data volume of three-dimensional building models is experiencing explosive growth. To address the issue of efficient organization and management, existing approaches primarily rely on single-structure spatial indices (e.g., R-tree, Octree) or hybrid indices. However, single-structure methods often suffer from limitations like node overlap (R-tree) or unbalanced structures (Octree) with uneven data distribution. While hybrid indices are effective for point clouds, their application to 3D building models remains limited, and existing implementations may introduce inefficiencies during construction or for fine-grained data organization within complex buildings. Hereby, this paper proposes a 3DB-ROC data structure organization method for three-dimensional building models. This method combines the advantages of R-tree and octree. Firstly, using a single building as the basic unit, the R-tree organizes the 3D building model data in a coarse-grained manner. Then, in a fine-grained manner, the octree is employed to organize the individual building model data within a single building. This hierarchical strategy minimizes tree depth, balances structure, and boosts data handling and query performance. Experimental evaluation on metaverse platform data demonstrates that: Firstly, 3DB-ROC outperforms R-tree, octree, KD-tree, BVH, and 3DOR in construction speed and insertion efficiency; Secondly, it achieves faster region retrieval than octree, particularly for small-area queries; Moreover, the method maintains balanced spatial utilization and acceptable global retrieval latency. These results confirm that 3DB-ROC effectively addresses the requirements of efficient organization and management of 3D building models, especially in applications requiring frequent data construction and insertion.}, archive = {J_ACCESS}, author = {Jiangtao Lei and Bin Fan and Qing Liu and Shuhong Mei and Yongsheng Huang and Xiaoli Yang}, doi = {10.1109/ACCESS.2025.3594352}, journal = {IEEE Access}, pages = {135689-135707}, shortjournal = {IEEE Access}, title = {3DB-ROC data structure organization for three-dimensional building models}, volume = {13}, year = {2025}, }
• (2025). Optimization of multi-energy grid integration and energy storage in low-carbon power systems based on the TCM-MBZOA algorithm: A case study of yunnan province. ACCESS, 13, 135669-135688. (www)

  @article{ , abstract = {With the rapid transition toward green and low-carbon energy systems, efficient scheduling of power systems is crucial for improving energy utilization and reducing carbon emissions. However, existing converter stations still rely heavily on single-source energy models, which limit the integration of renewables and lead to high carbon footprints. To address this limitation, this paper proposes a multi-source coordinated optimization strategy based on a bi-level programming model and an improved tent chaotic mapping-memory backtracking zebra optimization algorithm (TCM-MBZOA). The outer model maximizes the annual revenue of third-party energy storage operators, while the inner model minimizes operational costs, carbon emissions, and renewable energy curtailment within multiple virtual power plants. The TCM-MBZOA enhances the algorithm’s performance through Tent Chaotic Mapping for diverse initialization, a Memory-Backtracking Strategy for adaptive exploration, and an Adaptive T-Distribution for improved convergence. Applied to a real converter station in Yunnan Province, the proposed method achieves a 10.51% reduction in total energy consumption and significantly improves photovoltaic utilization and energy storage efficiency, outperforming benchmark algorithms. The results demonstrate the practical effectiveness and engineering feasibility of the proposed strategy in enabling low-carbon, high-efficiency operation of modern power systems.}, archive = {J_ACCESS}, author = {Yang Li and Guoen Zhou and Jiaqi Xue and Junwei Yang and Shi Yin}, doi = {10.1109/ACCESS.2025.3592970}, journal = {IEEE Access}, pages = {135669-135688}, shortjournal = {IEEE Access}, title = {Optimization of multi-energy grid integration and energy storage in low-carbon power systems based on the TCM-MBZOA algorithm: A case study of yunnan province}, volume = {13}, year = {2025}, }
• (2025). Sustainable development of rural E-commerce: Exploring the cognitive themes and emotional expressions of consumers when purchasing agricultural and sideline foodstuffs online. ACCESS, 13, 135652-135668. (www)

  @article{ , abstract = {Maintaining stable sales of agricultural and sideline foodstuffs is vital for the sustainable development of rural e-commerce. Exploring consumers’ thematic cognition and emotional expressions can help better understand their consumption behavior, thereby boosting the sales of agricultural and sideline foodstuffs. This paper applies the customer-value hierarchy model (C-VHM) as the theoretical research framework. It employs Mini Tag Cloud, a text analysis software, to analyze 8,227 consumer reviews on agricultural and sideline foodstuffs purchases from a well-known e-commerce platform in China. The research reveals that when purchasing agricultural and sideline foodstuffs online, customers primarily focus on four themes: product taste, product logistics, seller service, and cooking methods and target consumers. Among these, product taste and cooking methods and target consumers are the themes most likely to trigger consumer dissatisfaction. Furthermore, our semantic network analysis, theme classification, and sentiment analysis support Propositions of the C-VHM, particularly at the attribute and consequence levels. However, no factors related to social value goals were identified in the review texts. Instead, we observed personal goals such as care for family members, especially children and the elderly. Our study enriches the literature on consumer behavior related to rural e-commerce and the purchase of agricultural and sideline foodstuffs. It is expected to provide practical insights for stakeholders such as rural e-commerce platforms and logistics service providers, promoting the sustainable development of rural e-commerce.}, archive = {J_ACCESS}, author = {Guojie Xie and Haiquan Chen and Wenjuan Wu and Jebbouri Abdelhamid and Weiwei Cai and Yu Tian}, doi = {10.1109/ACCESS.2025.3589869}, journal = {IEEE Access}, pages = {135652-135668}, shortjournal = {IEEE Access}, title = {Sustainable development of rural E-commerce: Exploring the cognitive themes and emotional expressions of consumers when purchasing agricultural and sideline foodstuffs online}, volume = {13}, year = {2025}, }
• (2025). An improved rule-based energy management system: Effective solution to achieve collaborative control of batteries in multi-prosumers community microgrid. ACCESS, 13, 135634-135651. (www)

  @article{ , abstract = {The integration of renewable energy sources into community microgrids is increasingly critical for reducing reliance on utility grids and promoting local energy consumption. In this paper, an improved rule-based energy management system (EMS) for the community microgrid is proposed, which optimizes energy scheduling to enable the system to achieve real-time supply-demand balance in both the grid-connected mode and the off-grid mode. To realize the proposed EMS function, an improved peer-to-peer fast settlement transaction mode is proposed, which is suitable for both the grid-connected mode and the off-grid mode, and fuzzy control is employed to manage complex real-time control problems. Additionally, a non-communication internal price transmission mechanism is proposed to ensure the EMS operates effectively even under harsh communication conditions. Simulation results show that the proposed EMS can rapidly respond to supply-demand variations, maintaining near self-sufficiency in both the grid-connected and the off-grid mode. Specifically, the EMS achieves at least a 55% reduction in the net exchange power deviation from zero and the supply-demand ratio (SDR) deviation from the target level of 1 compared to the rule-based EMS used for comparison. Overall, the proposed EMS demonstrates excellent real-time performance, robustness, and immunity to communication issues.}, archive = {J_ACCESS}, author = {Jiawei Huang and Chengxiong Mao and Bin Liu and Dan Wang and Rui Gao and Bo Gu}, doi = {10.1109/ACCESS.2025.3588035}, journal = {IEEE Access}, pages = {135634-135651}, shortjournal = {IEEE Access}, title = {An improved rule-based energy management system: Effective solution to achieve collaborative control of batteries in multi-prosumers community microgrid}, volume = {13}, year = {2025}, }
• (2025). Next generation human action recognition: A comprehensive review of state-of-the-art signal processing techniques. ACCESS, 13, 135609-135633. (www)

  @article{ , abstract = {Human Action Recognition is a rapidly evolving field at the intersection of computational intelligence, signal processing, and machine learning, driving innovations in healthcare, fitness, surveillance, and smart environments. This review bridges the gap between classical techniques, such as Fourier Transform (FT), Wavelet Transform (WT), and Principal Component Analysis (PCA), and advanced Deep Learning (DL) approaches, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and emerging Transformer-based architectures. A key focus is the integration of multimodal sensing modalities, including accelerometers, gyroscopes, Electromyography (EMG), and electroencephalography (EEG), in conjunction with noncontact technologies such as thermal and infrared imaging, enhancing HAR adaptability in diverse environments. Unlike prior surveys, this work presents a comprehensive synthesis of HAR methodologies, systematically analyzing 217 studies to quantify trends, assess challenges such as noise resilience, real-time constraints, and scalability, and highlight innovative solutions, including advanced multimodal fusion frameworks and energy-efficient architectures. Furthermore, a research roadmap is proposed to advance scalable, real-time HAR systems with deeper multimodal data integration and lightweight computational models, paving the way for robust and adaptable solutions in real-world applications.}, archive = {J_ACCESS}, author = {Misha Karim and Shah Khalid and Sungyoung Lee and Sulaiman Almutairi and Abdallah Namoun and Mohammed Abohashrh}, doi = {10.1109/ACCESS.2025.3590073}, journal = {IEEE Access}, pages = {135609-135633}, shortjournal = {IEEE Access}, title = {Next generation human action recognition: A comprehensive review of state-of-the-art signal processing techniques}, volume = {13}, year = {2025}, }
• (2025). Diagnostic analysis of crosswalk safety hazards in pedestrian environments: A SHAP-enhanced machine learning approach with street-view imagery. ACCESS, 13, 135589-135608. (www)

  @article{ , abstract = {This study investigated the influence of built environment features on crosswalk safety in dense urban settings, with a focus on visual streetscape characteristics extracted from street-view imagery using both semantic and instance segmentation. It used data from 36,750 crosswalks in Seoul, South Korea, to rigorously evaluate multiple machine learning algorithms for predicting pedestrian crash risk. Among the models assessed, the Random forest (RF) demonstrated the highest precision, aligning with the objective of enhancing pedestrian safety through accurate risk identification. The RF model enhanced by SHapley Additive exPlanations (SHAP) achieved strong predictive performance (precision: 0.91), and SHAP analysis identified visual features, particularly sky openness ratio, building coverage, and sidewalk ratio, as influential factors affecting crash risk. A lower sky openness ratio combined with a higher building ratio was associated with increased crash likelihood, whereas greater sidewalk coverage and the presence of traffic control measures, including traffic lights and crosswalk time indicators, mitigated risk. Interaction effects further highlighted the complexity of urban safety, showing that combinations of streetscape and infrastructural elements can amplify or reduce hazards. These results highlight the importance of combining visual and structural data for thorough risk assessment and further the use of interpretable machine learning in urban safety research. The findings imply that to address particular combinations of built environment elements that increase the risk of crosswalk crashes, policy and planning initiatives should concentrate on context-sensitive interventions, particularly by placing bus stops strategically, maintaining tree canopies for visibility, clearing visual clutter, and improving pedestrian infrastructure.}, archive = {J_ACCESS}, author = {Caryl Anne M. Barquilla and Jeongwoo Lee}, doi = {10.1109/ACCESS.2025.3592445}, journal = {IEEE Access}, pages = {135589-135608}, shortjournal = {IEEE Access}, title = {Diagnostic analysis of crosswalk safety hazards in pedestrian environments: A SHAP-enhanced machine learning approach with street-view imagery}, volume = {13}, year = {2025}, }
• (2025). Theoretical and numerical analyses of a quasi-isotropic antenna with almost everywhere isotropy. ACCESS, 13, 135579-135588. (www)

  @article{ , abstract = {A fundamental radiation limit for quasi-isotropic antennas is rigorously presented utilizing vector spherical modes within the framework of a mode-matching technique. The theoretical derivation is substantiated through diverse numerical analyses demonstrating that quasi-isotropic radiation employing an omnidirectional antenna is realizable with unity gain across nearly all angles and a minimizable antenna gain of 1.1929 (0.7660 dBi) at specific finite angles attributable to the Gibbs phenomenon. Ideal implementation necessitates the introduction of a linear array comprising traditional element antennas such as Hertzian dipoles, half-wave dipole antennas, and microstrip patch antennas. The validation of our array configuration is conducted using the NEC-2 software package, and the resulting quasi-isotropic radiation patterns demonstrate a high degree of agreement. The ratio between the numbers of element antennas and effective Legendre modes governs a relative convergence that influences radiation characteristics, thereby indicating the existence of an optimal ratio for achieving quasi-isotropic radiation while minimizing the mean absolute error (MAE).}, archive = {J_ACCESS}, author = {Yong Heui Cho and Jae-Yong Kwon}, doi = {10.1109/ACCESS.2025.3594282}, journal = {IEEE Access}, pages = {135579-135588}, shortjournal = {IEEE Access}, title = {Theoretical and numerical analyses of a quasi-isotropic antenna with almost everywhere isotropy}, volume = {13}, year = {2025}, }
• (2025). Heuristic binary search for modulated predictive control. ACCESS, 13, 135567-135578. (www)

  @article{ , abstract = {Predictive Control optimization often relies on exhaustive enumeration of voltage vectors within the finite control set. Using Virtual Voltage Vector strategies tend to increase the number of predictions to enhance dynamic and steady state performance, leading to a higher computational burden. Modulated Model Predictive Control offers a solution by incorporating a modulation stage, but duty-cycle-based implementations can restrict the inclusion of constraints and nonlinearities in the cost function. This paper introduces the Heuristic Binary Search, an intelligent search algorithm that efficiently explores the solution space, accommodating arbitrary cost function constraints similar to Finite Control Set, while maintaining a fixed switching frequency. Applicable to various machine and inverter topologies, the proposed method is experimentally validated on a two-level voltage source inverter driving an induction motor using Predictive Torque Control and Deadbeat Robust Predictive Control. Experimental results comparing three variants of the Predictive Torque Control (one vector, three vector and modulated) show improvements in torque and flux ripple and improvements of current THD up to 30% over classic Modulated Predictive Torque Control implementation with reduced or similar computational cost. Results compared with the Finite Control Set Deadbeat Robust Predictive Control shows an improvement in THD of 40% with negligible impact in computational time.}, archive = {J_ACCESS}, author = {Rafael FIgueiredo and Igor Oliani and Angelo Lunardi and Alfeu J. Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3591064}, journal = {IEEE Access}, pages = {135567-135578}, shortjournal = {IEEE Access}, title = {Heuristic binary search for modulated predictive control}, volume = {13}, year = {2025}, }
• (2025). The design method of enhanced unscented kalman filter considering UT transform truncation error. ACCESS, 13, 135558-135566. (www)

  @article{ , abstract = {Although the Unscented Kalman Filter (UKF) generally outperforms the Extended Kalman Filter (EKF) due to its superior approximation of nonlinear state transition functions, the core Unscented Transform (UT) still cannot precisely capture complex nonlinear dynamics. This inherent limitation introduces non negligible truncation errors into state estimation, leading to significantly degraded accuracy in practical applications such as high-maneuvering target tracking, robotic localization in complex environments, and high-precision inertial navigation. Crucially, UKF performance deteriorates further as system non-linearity intensifies, exemplified by scenarios like aggressive UAV maneuvers in strong winds or rapid spacecraft attitude adjustments. To address this challenge, this paper proposes a novel estimation algorithm that explicitly accounts for the UT truncation error. We developed an improved UKF method that uses this error estimate. Rigorous performance analysis and numerical simulations closely replicating real-world scenarios demonstrate the proposed method’s significant effectiveness in boosting estimation accuracy for highly complex nonlinear systems.}, archive = {J_ACCESS}, author = {Ziran Luo and Chenglin Wen}, doi = {10.1109/ACCESS.2025.3592813}, journal = {IEEE Access}, pages = {135558-135566}, shortjournal = {IEEE Access}, title = {The design method of enhanced unscented kalman filter considering UT transform truncation error}, volume = {13}, year = {2025}, }
• (2025). Acceleration of urdu optical character recognition on zynq UltraScale+ MPSoC using deep convolutional neural network. ACCESS, 13, 135538-135557. (www)

  @article{ , abstract = {Deploying deep learning–based optical character recognition (OCR) systems for low-resource, complex-script languages like Urdu remains a major challenge due to high computational costs, lack of annotated datasets, and limited hardware support for real-time applications. Existing FPGA-based OCR implementations have primarily focused on simplified datasets such as MNIST digits, limiting their generalizability to scripts like Urdu that exhibit extensive intra-class variability, contextual shaping, and diacritics. This study presents a hardware-accelerated Urdu OCR framework using a custom-designed Convolutional Neural Network (CNN) optimized for deployment on the Xilinx Zynq UltraScale+ MPSoC (ZCU104). The proposed CNN is trained on a novel large-scale dataset of 336,000 labeled images spanning 48 Urdu characters across 230 font styles. Compared to MNIST-based FPGA implementations, our approach addresses significantly higher script complexity while achieving a classification accuracy of 96.73% (FP32) and 94.06% (INT8). Hardware-aware quantization and deployment using the Vitis AI toolchain enabled 75% model compression with minimal accuracy loss, achieving real-time inference of 0.189 ms per character and 4,886.95 FPS, while consuming only 1.32 W. Benchmarking against CPU and GPU platforms confirmed substantial improvements in speed and energy efficiency. This work establishes a high-performance, scalable, and energy-efficient FPGA-based OCR framework for Urdu and sets the foundation for extending such solutions to other cursive, low-resource languages like Arabic, Pashto, and Persian.}, archive = {J_ACCESS}, author = {Fauzia Yasir and Majida Kazmi}, doi = {10.1109/ACCESS.2025.3593294}, journal = {IEEE Access}, pages = {135538-135557}, shortjournal = {IEEE Access}, title = {Acceleration of urdu optical character recognition on zynq UltraScale+ MPSoC using deep convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Skew cyclic codes over ℤ4 + uℤ4 with derivation based on a new gray map and automorphism. ACCESS, 13, 135527-135537. (www)

  @article{ , abstract = {This paper investigates the algebraic structure and properties of skew cyclic codes over the finite chain ring $R = \mathbb {Z}_{4} + u\mathbb {Z}_{4}$ , where $u^{2} = 0$ . A central contribution of this work is the introduction and application of a novel Gray map, establishing a distance-preserving link between codes over R and linear codes over $\mathbb {Z}_{4}$ . We employ a specific, compatible pair consisting of a ring automorphism $\theta $ and a $\theta $ -derivation $\eta $ to define the appropriate skew polynomial ring structure $R[x; \theta , \eta]$ . Within this algebraic framework, we provide a comprehensive analysis of the fundamental structure of free $(\theta , \eta)$ -cyclic codes, detailing their generator polynomial structure and establishing their precise relationship with classical cyclic or quasi-cyclic codes. Furthermore, the structure of Euclidean dual codes for these free codes is examined for even lengths, and a construction for double $(\theta , \eta)$ -cyclic codes from free constituent codes is also presented.}, archive = {J_ACCESS}, author = {Eda Tekin}, doi = {10.1109/ACCESS.2025.3594631}, journal = {IEEE Access}, pages = {135527-135537}, shortjournal = {IEEE Access}, title = {Skew cyclic codes over ℤ4 + uℤ4 with derivation based on a new gray map and automorphism}, volume = {13}, year = {2025}, }
• (2025). A ka-band modular dual switchable circularly polarized phased array transmitter for LEO satellite communications. ACCESS, 13, 135515-135526. (www)

  @article{ , abstract = {This article presents a scalable Ka-band phased array transmitter designed for the upload channel of terrestrial mobile transceivers in satellite communications (SATCOM, 27.5–30.0 GHz). The transmitter consists of a 16-channel active phased array system with digitally switchable circular polarization, enabling seamless alternation between right-hand (RHCP) and left-hand circular polarization (LHCP). A linearly polarized patch antenna is utilized, achieving circular polarization through the sequential rotation technique. By integrating the radiating element within a modular multilayer printed circuit board (PCB) and employing a superheterodyne architecture, the system supports full digital beamforming in a compact, scalable design. The array achieves a scan range of ±50° with a scan loss of approximately 2 dB in both principal planes ( $\varphi = 0^{\circ }$ and $\varphi = 90^{\circ }$ ) and maintains a roll-off factor ${cos}^{1.03}(\theta)$ . The operational bandwidth of 15.4% (26.99 - 31.49 GHz) is accompanied by an axial ratio with great polarization purity across the scanning range. These features, combined with a thermal management strategy, highlight the system’s innovation as a high-performance, adaptable solution for next-generation SATCOM applications.}, archive = {J_ACCESS}, author = {Amélia Ramos and Raul Arruela and Bernardo Lopes and Tiago Varum and Ricardo Correia and João N. Matos}, doi = {10.1109/ACCESS.2025.3593954}, journal = {IEEE Access}, pages = {135515-135526}, shortjournal = {IEEE Access}, title = {A ka-band modular dual switchable circularly polarized phased array transmitter for LEO satellite communications}, volume = {13}, year = {2025}, }
• (2025). Improving BI-RADS mammographic classification with self-supervised vision transformers and cascade learning. ACCESS, 13, 135500-135514. (www)

  @article{ , abstract = {Accurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that rely heavily on annotated datasets, PatchCascade-ViT leverages Self Patch-level Supervision (SPS) to learn meaningful mammographic representations from unlabeled data, significantly enhancing classification performance. Our framework operates through a two-stage cascade classification process. In the first stage, the model differentiates non-cancerous from potentially cancerous mammograms using SelfPatch, an innovative self-supervised learning task that enhances patch-level feature learning by enforcing consistency among spatially correlated patches. The second stage refines the classification by distinguishing Scattered Fibroglandular from Heterogeneously and Extremely Dense breast tissue categories, enabling more precise breast cancer risk assessment. To validate the effectiveness of PatchCascade-ViT, we conducted extensive evaluations on a dataset of 4,368 mammograms across three BI-RADS classes. Our method achieved a system sensitivity of 85.01% and an F1-score of 84.90%, outperforming existing deep learning-based approaches. By integrating self-supervised learning with a cascade vision transformer architecture, PatchCascade-ViT reduces reliance on annotated datasets while maintaining high classification accuracy. These findings demonstrate its potential for enhancing breast cancer screening, aiding radiologists in early detection, and improving clinical decision-making.}, archive = {J_ACCESS}, author = {Abdelrahman Abdallah and Mahmoud Salaheldin Kasem and Ibrahim Abdelhalim and Norah Saleh Alghamdi and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3581582}, journal = {IEEE Access}, pages = {135500-135514}, shortjournal = {IEEE Access}, title = {Improving BI-RADS mammographic classification with self-supervised vision transformers and cascade learning}, volume = {13}, year = {2025}, }
• (2025). Multimodal data mining trends, achievements, and challenges: Systematic literature review. ACCESS, 13, 135489-135499. (www)

  @article{ , abstract = {This paper aims to analyze state-of-the-art in multimodal data mining research through a systematic literature review. It provides a documented answer on the research evolution of multimodal data mining, the methods used for multimodal data modeling, their frequency of use, advantages, and limitations. This analysis allows us to identify the challenges and opportunities associated with this emerging research area, which in turn leads to the discovery of insights that support decisions aimed at sustainably addressing these challenges while also identifying new areas of potential applications.}, archive = {J_ACCESS}, author = {Bogdănel P. Constantin Gradinaru and Mirela Danubianu and Adina Luminiţa Bărîlă}, doi = {10.1109/ACCESS.2025.3590921}, journal = {IEEE Access}, pages = {135489-135499}, shortjournal = {IEEE Access}, title = {Multimodal data mining trends, achievements, and challenges: Systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Recurrence-based techniques for data-driven fault diagnosis and monitoring in neutral-point-clamped inverters. ACCESS, 13, 135475-135488. (www)

  @article{ , abstract = {The high cost and operational complexity of inverter technology make system reliability a key concern, which can be enhanced through advanced monitoring techniques. This paper introduces a novel data-driven condition monitoring (CM) approach for Neutral-Point-Clamped (NPC) inverters based on Recurrence Plot Analysis (RPA) and Recurrence Quantification Analysis (RQA). Unlike conventional deep learning-based approaches, the proposed method requires minimal data, offers high interpretability, and enhances the feature extraction process by capturing nonlinear dynamics and hidden patterns in the time-series data, which are often not accessible through traditional statistical or spectral techniques. The study investigates both normal operation with varying load conditions and open-circuit faults in power switches. A detailed theoretical background on RPA is provided, emphasizing the impact of parameter selection on performance. RQA metrics are extracted from PWM voltage and current signals and employed as diagnostic features to identify operating conditions and fault states. The proposed approach is experimentally validated using a laboratory prototype, confirming its effectiveness in fault detection and classification. Three operating conditions were evaluated: an RL load, an induction motor, and an open-circuit fault scenario. The experimental results demonstrate that the proposed method achieves high diagnostic accuracy (up to 100%), exhibits strong robustness to noise, and maintains consistent classification performance across varying load conditions. By employing a decision tree classifier applied to RQA metrics, the approach effectively distinguishes between healthy and faulty states, providing an interpretable solution for real-time fault diagnosis. These innovations position the proposed technique as accurate, and hardware-friendly alternative to complex neural-network-based solutions. These findings highlight the method’s potential for integration into practical data-driven fault diagnosis frameworks for power electronic converters.}, archive = {J_ACCESS}, author = {Lincoln Moura De Oliveira and Menaouar Berrehil El Kattel and Esio Eloi Dos Santos Filho and Juan C. Vasquez and Josep M. Guerrero and Fernando Luiz Marcelo Antunes}, doi = {10.1109/ACCESS.2025.3593168}, journal = {IEEE Access}, pages = {135475-135488}, shortjournal = {IEEE Access}, title = {Recurrence-based techniques for data-driven fault diagnosis and monitoring in neutral-point-clamped inverters}, volume = {13}, year = {2025}, }
• (2025). Design of a microstrip line fed SIW ring antenna with sixth-order circular polarized conical pattern. ACCESS, 13, 135466-135474. (www)

  @article{ , abstract = {This paper presents the design of a microstrip line-fed substrate-integrated waveguide (SIW) ring antenna designed to excite a sixth-order circularly polarized conical radiation pattern. Characteristic mode theory indicates that antennas can theoretically support an infinite number of Orbital Angular Momentum (OAM) modes; however, practically exciting higher-order OAM beams introduces significant challenges. This work addresses the difficulties associated with generating higher-order OAM waves using a single patch antenna by proposing a straightforward design featuring a single-ring radiator with vias positioned on its inner side. A comparative analysis highlights the advantages of the SIW configuration in effectively realizing the left hand circularly polarized (LHCP) OAM beam with sixth mode order from the proposed antenna. The dual feed single port mechanism successfully radiates the desired radiation pattern at 6.55 GHz, making the design suitable for satellite communication applications. The validation of the theoretical analysis is demonstrated through the prototype, which shows good agreement between simulation and measured performance parameters. The simplicity of the antenna’s configuration, combined with its enhanced radiation performance, distinguishes it from existing designs and offers a promising solution for practical implementation in OAM-based communication systems.}, archive = {J_ACCESS}, author = {Umar Fayyaz and Shahab Ahmad Niazi and Khaled Al-Jaloud and Waqar Ahmad Malik and Abdul Aziz and Rifaqat Hussain and Ali H. Alqahtani and Akram Alomainy}, doi = {10.1109/ACCESS.2025.3593651}, journal = {IEEE Access}, pages = {135466-135474}, shortjournal = {IEEE Access}, title = {Design of a microstrip line fed SIW ring antenna with sixth-order circular polarized conical pattern}, volume = {13}, year = {2025}, }
• (2025). Adaptive load regulation technique of distribution transformer based on power electronic switch. ACCESS, 13, 135455-135465. (www)

  @article{ , abstract = {To cope with the loss and voltage issues caused by the load long-term change and short-term fluctuation in the power system, an adaptive load regulation technique for the distribution transformer is proposed in this paper. Since the operation of the existing transformer capacity regulation methods is complex, the multi-winding parallel-wound (MWPW) structure is proposed, which can flexibly change the transformer capacity mode. Meanwhile, conventional on-load tap changer (OLTC) employs the mechanical switch, which suffers from electrical contact wear, mechanical component degradation, and operating speed limitation. Power electronic OLTC (PE-OLTC) has been proposed to improve performances during the voltage regulation. Consequently, an effective switch control strategy is required. Therefore, a dual zero-crossing detection (DZCD) control strategy for PE-OLTC is proposed. The proposed strategy mitigates the stress on IGBT during the voltage regulation process and overcomes the drawbacks of mechanical OLTC. The simulation model is built to validate the effectiveness of the proposed adaptive load regulation technique.}, archive = {J_ACCESS}, author = {Jinwei Lin and Tao Ji and Yuhong Wu and Xianglong He and Peilin Han and Wei Wang}, doi = {10.1109/ACCESS.2025.3594084}, journal = {IEEE Access}, pages = {135455-135465}, shortjournal = {IEEE Access}, title = {Adaptive load regulation technique of distribution transformer based on power electronic switch}, volume = {13}, year = {2025}, }
• (2025). Improved air mesh refinement for accurate strand-solid and self-collision handling. ACCESS, 13, 135440-135454. (www)

  @article{ , abstract = {This paper presents a novel method for efficiently handling strand-solid and self-collisions using air meshes. Traditional collision handling at the primitive level requires extensive computations, such as time-stepping and solving cubic equations, to ensure simulation stability. Moreover, depending on scene complexity, both discrete collision detection (DCD) and continuous collision detection (CCD) often need to be considered. In this study, we propose an improved approach to collision handling based on the previously introduced air mesh technique. The original air mesh approach does not rely on the simulation mesh itself but instead meshes the surrounding air, approximating its deformation as volumetric changes to detect and predict collisions. To ensure numerical convergence during air mesh refinement, a constraint was imposed to maintain equilateral triangles. However, this method often produced noisy results depending on the scene, and boundary issues became more pronounced in line-based simulations such as hair or fur simulation. To address these issues, we introduce a new constraint during the air mesh refinement process, leading to a more stable and noise-reduced collision handling approach. Our method demonstrates stable results not only for hair simulations but also across various scene types.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3594080}, journal = {IEEE Access}, pages = {135440-135454}, shortjournal = {IEEE Access}, title = {Improved air mesh refinement for accurate strand-solid and self-collision handling}, volume = {13}, year = {2025}, }
• (2025). Enhancing spectrum license valuation with probabilistic net present value analysis. ACCESS, 13, 135429-135439. (www)

  @article{ , abstract = {This paper proposes a probabilistic methodology for valuing spectrum licenses based on Net Present Value (NPV). Unlike traditional approaches that rely on deterministic forecasts, the proposed method treats the NPV as a random variable, allowing the derivation of a probability density function (PDF) using forecasting models, sensitivity analysis, and Monte Carlo simulations. This probabilistic framework accounts for uncertainties in key variables such as revenue, user growth, and network costs, and provides a more comprehensive and realistic assessment of investment viability. A structured methodology is introduced, involving network modeling, variable selection via Sobol analysis, time-series forecasting, and probabilistic simulation of future cash flows. The resulting PDF for NPV supports more informed decision-making for both regulators and operators by quantifying investment risks and enabling confidence-based pricing strategies. A simulation illustrates the application of the methodology and demonstrates how regulators can define license pricing criteria based on probabilistic thresholds. The results highlight the advantages of probabilistic over scenario-based NPV analysis, offering greater flexibility and insight into risk.}, archive = {J_ACCESS}, author = {Gabriel Zauza Braun and Xisto Lucas Travassos and Richard Demo Souza}, doi = {10.1109/ACCESS.2025.3594161}, journal = {IEEE Access}, pages = {135429-135439}, shortjournal = {IEEE Access}, title = {Enhancing spectrum license valuation with probabilistic net present value analysis}, volume = {13}, year = {2025}, }
• (2025). A deep reinforcement learning framework for adaptive resiliency enhancement in smart power grids. ACCESS, 13, 135420-135428. (www)

  @article{ , abstract = {This paper addresses the critical challenge of enhancing resiliency in smart power grids against cyber-physical disturbances, including cyber-attacks, system faults, and dynamic load variations. Traditional control methods often lack adaptability and robustness required to maintain stable grid operation under such evolving threats. To overcome these limitations, we propose a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm, optimized using the Root Mean Square Propagation (RMSprop) method for stable and efficient training. The proposed framework features a two-layered control architecture that models grid states, actions, and rewards to enable continuous, real-time adaptive control of distributed energy resources, microgrids, and control centers. Key contributions include the integration of DDPG with RMSprop optimizer tailored for power system stability, a comprehensive reward design reflecting resiliency metrics, and a resiliency adaptation layer for rapid response to disturbances and cyber-attacks. Simulation results conducted on a realistic smart grid testbed demonstrate superior performance in system stability, control accuracy, convergence speed, and resiliency score compared to classical Proportional-Integral-Derivative (PID) controllers and alternative reinforcement learning approaches such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The findings highlight the framework’s potential for practical deployment, offering a scalable and intelligent solution to enhance smart grid resiliency in the face of increasing cyber-physical challenges.}, archive = {J_ACCESS}, author = {Boyu Wang and Aliasghar Baziar and Mohammad Reza Askari}, doi = {10.1109/ACCESS.2025.3593903}, journal = {IEEE Access}, pages = {135420-135428}, shortjournal = {IEEE Access}, title = {A deep reinforcement learning framework for adaptive resiliency enhancement in smart power grids}, volume = {13}, year = {2025}, }
• (2025). Wavelet-enhanced desnowing: A novel single image restoration approach for traffic surveillance under adverse weather conditions. ACCESS, 13, 135407-135419. (www)

  @article{ , abstract = {Image restoration under adverse weather conditions refers to the process of removing degradation caused by weather particles while improving visual quality. Most existing deweathering methods rely on increasing network scale and data volume to achieve better performance, which requires more expensive computing power. Also, many methods lack generalization for specific applications. In the traffic surveillance screener, the main challenges are snow removal and veil effect elimination. In this paper, we propose a wavelet-enhanced snow removal method that uses a Dual-Tree Complex Wavelet Transform feature enhancement module and a dynamic convolution acceleration module to address snow degradation in surveillance images. We also use a residual learning restoration module to remove veil effects caused by rain, snow, and fog. The proposed architecture extracts and analyzes information from snow-covered regions, significantly improving snow removal performance. And the residual learning restoration module removes veiling effects in images, enhancing clarity and detail. Experiments show that it performs better than some popular desnowing methods in terms of PSNR and SSIM on some public datasets. Our approach also demonstrates effectiveness and accuracy when applied to real traffic surveillance images.}, archive = {J_ACCESS}, author = {Zihan Shen and Yu Xuan and Qingyu Yang}, doi = {10.1109/ACCESS.2025.3594065}, journal = {IEEE Access}, pages = {135407-135419}, shortjournal = {IEEE Access}, title = {Wavelet-enhanced desnowing: A novel single image restoration approach for traffic surveillance under adverse weather conditions}, volume = {13}, year = {2025}, }
• (2025). Survey on hand gesture recognition from visual input. ACCESS, 13, 135373-135406. (www)

  @article{ , abstract = {Hand gesture recognition has become an important research area, driven by the growing demand for human-computer interaction in fields such as sign language recognition, virtual and augmented reality, and robotics. Despite the rapid growth of the field, there are few surveys that comprehensively cover recent research developments, available solutions, and benchmark datasets. This survey addresses this gap by examining the latest advancements in hand gesture and 3D hand pose recognition from various types of camera input data including RGB images, depth images, and videos from monocular or multiview cameras, examining the differing methodological requirements of each approach. Furthermore, an overview of widely used datasets is provided, detailing their main characteristics and application domains. Finally, open challenges such as achieving robust recognition in real-world environments, handling occlusions, ensuring generalization across diverse users, and addressing computational efficiency for real-time applications are highlighted to guide future research directions. By synthesizing the objectives, methodologies, and applications of recent studies, this survey offers valuable insights into current trends, challenges, and opportunities for future research in human hand gesture recognition.}, archive = {J_ACCESS}, author = {Manousos Linardakis and Iraklis Varlamis and Georgios Th. Papadopoulos}, doi = {10.1109/ACCESS.2025.3593428}, journal = {IEEE Access}, pages = {135373-135406}, shortjournal = {IEEE Access}, title = {Survey on hand gesture recognition from visual input}, volume = {13}, year = {2025}, }
• (2025). Neural machine translation in electrical engineering with cross-layer information fusion and multiple positional mapping. ACCESS, 13, 135359-135372. (www)

  @article{ , abstract = {This study focuses on the English-Chinese neural machine translation task of professional texts in the field of electrical engineering. Such texts are usually dense in terms, complex in sentence structure, deeply nested in structure, and have low resources, which poses significant challenges to existing neural machine translation models. Aiming at the problem of attenuation of inter-layer semantic information and position information in text encoding of Transformer model in the field of electrical engineering, an optimization method based on cross-layer information fusion and multiple position mapping is proposed. This method integrates the Long Short-Term Memory (LSTM) and the attention mechanism to construct a cross-layer information compensation architecture consisting of a cross-layer feature propagation module and a feature fusion module to compensate for the attenuation of cross-layer semantic information and position information. In addition, in view of the fact that Transformer position information is easily distorted, this study also designed a multiple position mapping module based on dynamic linear transformation, which enhances the robustness of position representation through a multiple superposition mechanism and improves the position sensitivity of the model during deep calculations. Experimental results on translation tasks in the electrical engineering field show that the improved model outperforms the baseline and other comparison models in terms of BLEU, METEOR and TER scores, verifying the effectiveness and superiority of this method in semantic modeling and position information retention in professional field translation tasks.}, archive = {J_ACCESS}, author = {Zhenyu Zhang and Yuan Chen and Juwei Zhang}, doi = {10.1109/ACCESS.2025.3594132}, journal = {IEEE Access}, pages = {135359-135372}, shortjournal = {IEEE Access}, title = {Neural machine translation in electrical engineering with cross-layer information fusion and multiple positional mapping}, volume = {13}, year = {2025}, }
• (2025). YOLOv8-eRFD-AP: A novel domain generalization model for UAV-based insulator inspection under adverse weather conditions. ACCESS, 13, 135336-135358. (www)

  @article{ , abstract = {The deployment of artificial intelligence (AI)-powered uncrewed aerial vehicles (UAVs) for high-voltage power line inspection has become a crucial advancement in ensuring the stability and reliability of electrical transmission networks. Among various deep learning architectures, You Only Look Once (YOLO)-based models are widely used due to their real-time object detection efficiency. However, these models frequently struggle to generalize when exposed to diverse environmental conditions. To address this, we present YOLOv8-eRFD-AP, an enhanced YOLOv8 variant designed to improve generalization ability across varied weather scenarios for insulator anomaly detection. The model is trained under clear-weather conditions and evaluated on the IDID_Weather dataset, which includes rain, fog, and snow, to assess its performance under real-world challenges. Our model introduces a novel version of Robust Feature Downsampling (RFD) method, enhanced with Convolutional Block Attention Modules (CBAM) and Normalization Perturbation (NP) to preserve key spatial information and improve feature discrimination across different weather conditions. Experimental results confirm that YOLOv8-eRFD-AP outperforms state-of-the-art models, including YOLOv11, YOLOv12, and Real-Time Detection Transformer (RT-DETR), in both clear and adverse weather settings. The model achieves a mean average precision at 0.5 intersection over union (mAP@0.5) of 92.0%, exceeding the second-best model by 2.7% under clear weather. Under rain and snow, it attains 85.2% and 73.1%, reflecting improvements of 10.3% and 13.3%, respectively, over prior best-performing models. Despite the improved generalization ability, the proposed model remains real-time capable, achieving an inference speed of 12.5 ms per image (~80 frames per second, FPS) on a Tesla T4 graphics processing unit (GPU). This work demonstrates the potential of YOLOv8-eRFD-AP to enhance UAV-based power line inspections, particularly by improving generalization to previously unseen scenarios. Future research will focus on refining NP strategies for more efficient handling of both uniform and non-uniform distributions, as well as developing a lightweight version optimized for low-power edge computing environments.}, archive = {J_ACCESS}, author = {Badr-Eddine Benelmostafa and Rita Aitelhaj and Hicham Medromi}, doi = {10.1109/ACCESS.2025.3593201}, journal = {IEEE Access}, pages = {135336-135358}, shortjournal = {IEEE Access}, title = {YOLOv8-eRFD-AP: A novel domain generalization model for UAV-based insulator inspection under adverse weather conditions}, volume = {13}, year = {2025}, }
• (2025). IoT in smart urban planning: A comprehensive review of applications, developments, and engineering perspectives. ACCESS, 13, 135316-135335. (www)

  @article{ , abstract = {The rapid evolution of urbanization necessitates the deployment of smart technologies to create sustainable, efficient, and resilient cities. This paper presents a comprehensive review of the role of Internet of Things (IoT) technologies in enabling smart urban environments, with a focus on practical applications, investment trends, and electronics engineering contributions. The review covers real-world IoT use cases across key urban domains mobility, energy, environment, infrastructure, and governance supported by insights into recent global developments (2022-2024). It also analyzes smart city investment patterns, highlighting the growth of IoT-driven infrastructure and service models. From a technical perspective, the paper examines common urban IoT sensors (e.g., air quality, water, noise, and motion), their performance metrics, power profiles ( $\mu $ W to mW), and communication technologies (LoRaWAN, BLE, Wi-Fi, Zigbee). Comparative evaluations emphasize trade-offs between accuracy, energy efficiency, and range. Challenges related to power management, network scalability, cybersecurity, and sustainability are discussed, along with future research directions including tinyML, 6G networks, and biodegradable sensor platforms. The findings reinforce that electronics engineers play a pivotal role in designing and securing next-generation smart city systems by integrating innovation with ecological responsibility.}, archive = {J_ACCESS}, author = {Ahmed A. Zakaria and Tasneem Amr and Amany A. Ragheb}, doi = {10.1109/ACCESS.2025.3594019}, journal = {IEEE Access}, pages = {135316-135335}, shortjournal = {IEEE Access}, title = {IoT in smart urban planning: A comprehensive review of applications, developments, and engineering perspectives}, volume = {13}, year = {2025}, }
• (2025). Data-centric fine-tuning of small language models for automatic extraction of technical requirements. ACCESS, 13, 135301-135315. (www)

  @article{ , abstract = {Training small language models for specific tasks often encounters a significant challenge: the limited availability of high-quality labeled data, which can restrict model performance. This constraint is especially critical in organizations handling sensitive data, where large language models cannot be readily deployed due to privacy concerns, high costs, and dependency on external providers. While existing research demonstrates the effectiveness of large language models in automating text-based tasks, there is a lack of methods tailored to fine-tuning small language models for secure, domain-specific applications with minimal labeled data. To address this gap, we introduce a data-centric fine-tuning method that systematically enhances training data through prompt engineering, making small language model fine-tuning feasible and effective in data-constrained environments. This study evaluates the proposed method on a real-world use case with an industry partner, focusing on the automatic extraction of technical requirements from full-text documents and using both quantitative metrics and qualitative expert assessments. Our findings reveal that the fine-tuned small language model achieves accuracy and consistency comparable to human service providers, while outperforming baseline models, including GPT-4-turbo, on key evaluation metrics. These results underscore the potential of data-centric fine-tuning for adapting small language models to high-stakes, privacy-sensitive tasks, offering a scalable alternative to LLMs enabling their applications to real-world use cases.}, archive = {J_ACCESS}, author = {Leopold Müller and Nina Schwarz and Lars Böcking and Andreas Bereczuk and Hanno Stagge and Wolfgang Kratsch and Niklas Kühl}, doi = {10.1109/ACCESS.2025.3591739}, journal = {IEEE Access}, pages = {135301-135315}, shortjournal = {IEEE Access}, title = {Data-centric fine-tuning of small language models for automatic extraction of technical requirements}, volume = {13}, year = {2025}, }
• (2025). Analysis of power generation in electric scooters with photovoltaic panel integration: A monte carlo simulation approach. ACCESS, 13, 135290-135300. (www)

  @article{ , abstract = {This paper analyzes power generation and corresponding additional driving range of various photovoltaic array configurations mounted on an electric scooter. Shared electric scooters have been widely used for short-distance travel. However, they are often unavailable due to insufficient charging after use. To address this issue, photovoltaic panels can be installed on the electric scooter to enable self-charging capabilities. The power generation of photovoltaic panels is affected by continuously varying factors. These include irradiance and shading conditions. These conditions make it difficult to accurately estimate the power generation from a photovoltaic array. This study statistically analyzes the power generation of each photovoltaic array configuration using the Monte Carlo method. The power at the global maximum power point is obtained under random irradiance and shading conditions. Based on these results, the annual power generation and the corresponding additional driving range are calculated.}, archive = {J_ACCESS}, author = {Jae-Hwan Ko and Sung-Min Park}, doi = {10.1109/ACCESS.2025.3592259}, journal = {IEEE Access}, pages = {135290-135300}, shortjournal = {IEEE Access}, title = {Analysis of power generation in electric scooters with photovoltaic panel integration: A monte carlo simulation approach}, volume = {13}, year = {2025}, }
• (2025). Seamless integration of grid-forming and grid-following capabilities in a single VSI within a unified hybrid control framework under islanded conditions. ACCESS, 13, 135273-135289. (www)

  @article{ , abstract = {To achieve seamless integration of grid-forming (GFM) and grid-following (GFL) capabilities in a single voltage-source inverter (VSI) under islanded conditions, this study introduces a unified hybrid control (UHC) scheme with minimal control parameters and measurement sensors. The UHC scheme enables a single voltage-controlled VSI to emulate the behavior of two virtual parallel VSIs, each operating independently as a GFM and GFL inverter, thereby ensuring voltage and frequency stability, as well as reliable power sharing. The proposed UHC approach is designed specifically for islanded microgrids, providing functionalities for voltage-controlled inverters to perform multiple operational tasks simultaneously. These include voltage and frequency regulation, provision of local grid support services, and tracking of reference powers using output current control. The implementation utilizes a dual-loop parallel control strategy based on a proportional-resonant (PR) controller in the $\alpha \beta $ reference frame, which operates without requiring a phase-locked loop (PLL) in the islanded operational context. The control structure requires tuning of only three control parameters (two PR controller parameters and one active damping term), while the other parameters represent physical system values. Under the tested islanded operating conditions, VSIs equipped with the UHC scheme demonstrate the ability to mimic virtual inertia, contributing to voltage and frequency stability during sudden load changes, reference power variations, line outages, and short-circuit fault scenarios with appropriate dynamic and transient responses, addressing the specific challenges of islanded microgrid stability and control. Comprehensive simulation studies on single- and multi-inverter systems operating in the islanded mode validate the effectiveness and feasibility of the UHC scheme for practical implementation in power systems.}, archive = {J_ACCESS}, author = {Iman Lorzadeh and Omid Lorzadeh and Dimitar V. Bozalakov and Luc Dupré and Lieven Vandevelde}, doi = {10.1109/ACCESS.2024.3450611}, journal = {IEEE Access}, pages = {135273-135289}, shortjournal = {IEEE Access}, title = {Seamless integration of grid-forming and grid-following capabilities in a single VSI within a unified hybrid control framework under islanded conditions}, volume = {13}, year = {2025}, }
• (2025). Exploring diffusion models for oral health applications: A conceptual review. ACCESS, 13, 135254-135272. (www)

  @article{ , abstract = {Recent advances in generative modeling have revolutionized data synthesis, simplifying the creation of realistic data and addressing challenges related to data scarcity and intensive data collection. This review provides a comprehensive overview of the use of diffusion models in oral health, highlighting their effectiveness in dental therapy and care, especially given the complexities of data collection and alignment in this field. We explore the potential of diffusion models for robust learning with noisy and misaligned data, such as panoramic X-rays, PET and CT scans, and Photoacoustic data. We also discuss how diffusion models go beyond data generation to support multiple tasks, a critical requirement for practical machine-learning applications in the medical domain. This includes their integration with other machine learning techniques, such as object detection and representation learning, which necessitates significant structural innovations in diffusion models, a topic we explore in depth in this review. Lastly, we identify current challenges and propose future research directions for these applications, highlighting novel areas for further study. This review aims to support the advancement of diffusion modeling in dental data science and facilitate its practical application to real-world challenges in the field.}, archive = {J_ACCESS}, author = {Mohsen Tabejamaat and Amira Soliman}, doi = {10.1109/ACCESS.2025.3593933}, journal = {IEEE Access}, pages = {135254-135272}, shortjournal = {IEEE Access}, title = {Exploring diffusion models for oral health applications: A conceptual review}, volume = {13}, year = {2025}, }
• (2025). Operational performance assessment of PV-powered street lighting: A comparative study of different machine learning prediction models. ACCESS, 13, 135232-135253. (www)

  @article{ , abstract = {The widespread adoption of photovoltaic (PV) energy contributes to carbon emissions reduction, cost minimization, and climate change mitigation, particularly in applications such as street lighting. However, efficiency is negatively impacted by variations in weather and temperature. Despite the growing interest in PV-powered street lighting, a significant research gap remains in predicting the operational performance of such systems, as research on PV performance prediction is constrained by the scarcity of accurate data on energy consumption values corresponding to varying lighting intensities and the impact of environmental factors. In this paper, a comparative analysis of five machine learning algorithms—Linear Regression (LR), Expectation Maximization (EM), Random Forest (RF), Deep Belief Networks (DBNs), and Deep Neural Networks (DNNs)—is conducted to predict the operational conditions of PV-powered streetlights based on luminance levels and energy consumption. A comprehensive database is created and used to train the proposed algorithms. The results indicate that DNNs and DBNs algorithms achieve the lowest error rate (2.5%) and highest accuracy (97%) with high-quality data. LR, meanwhile, is distinguished by its rapid response time, enabling predictions with minimal training on large datasets. The computational expense differs among algorithms; DNNs and DBNs require significant resources and have lengthy training periods, whereas RF and LR are faster and better suited for real-time applications. DNNs demonstrate superior performance in recall, F1 scores, and ROC-AUC, while LR shows the lowest performance.}, archive = {J_ACCESS}, author = {Safwan Nadweh and Nabil Mohammed and Charalambos Konstantinou and Shehab Ahmed}, doi = {10.1109/ACCESS.2025.3594171}, journal = {IEEE Access}, pages = {135232-135253}, shortjournal = {IEEE Access}, title = {Operational performance assessment of PV-powered street lighting: A comparative study of different machine learning prediction models}, volume = {13}, year = {2025}, }
• (2025). An efficient radio map construction method using region-aware adaptive ensemble regression for indoor localization. ACCESS, 13, 135220-135231. (www)

  @article{ , abstract = {The increasing adoption of indoor localization has increased demand for location-based services (LBS) to enhance daily efficiency. Fingerprinting is a widely used method due to its high accuracy and resilience to multipath fading, but it requires dense sampling and extensive data collection, making large-scale site surveys labor-intensive and often impractical. To alleviate these challenges, interpolation and regression-based methods have been explored for their scalability and reduced manual effort. However, these approaches often struggle to model the complex, multimodal nature of real-world Received Signal Strength Indicator (RSSI) distributions. To address this limitation, we propose a novel Region-Aware Adaptive Ensemble Regressor (RAER) for generating synthetic RSSI values. RAER adapts to the statistical characteristics of the training data to efficiently produce high-quality virtual RSSI maps with low computational cost. The model integrates two components: 1) a clustering algorithm that partitions the radio map based on fingerprint signal similarity, and 2) an adaptive ensemble regression framework that combines multiple base regressors using weighted averaging, where weights are inversely proportional to each regressor’s mean absolute error (MAE). By prioritizing regressors with lower localization error, RAER improves the realism of synthetic RSSI data and enhances radio map accuracy. Experimental evaluations conducted in a multi-story university building demonstrate that RAER can reconstruct radio maps, which reduces the need for site surveys by up to 25%. Furthermore, it improves localization accuracy by 5.06%, outperforming existing methods and offering a scalable and practical solution for fingerprint-based indoor positioning systems.}, archive = {J_ACCESS}, author = {Farid Yuli Martin Adiyatma and Panarat Cherntanomwong}, doi = {10.1109/ACCESS.2025.3592753}, journal = {IEEE Access}, pages = {135220-135231}, shortjournal = {IEEE Access}, title = {An efficient radio map construction method using region-aware adaptive ensemble regression for indoor localization}, volume = {13}, year = {2025}, }
• (2025). Prediction of remaining useful life of operating mechanism driven by deep feature in classification model. ACCESS, 13, 135208-135219. (www)

  @article{ , abstract = {Accurate evaluation of the remaining service life of the operating mechanism plays an important role in ensuring the reliable operation of power transmission and distribution network. To address the drawbacks of high computational complexity and slow convergence speed in existing data-driven regression models, in this research, based on deep feature linear fitting of classification models, a remaining useful life prediction method for operating mechanism is proposed. With the sampled acoustic signals of operating mechanisms, a residual neural network is employed to continuously evaluate the remaining service life grade. For a given sample, with the principal component analysis method, the extracted deep features from the trained network can be further optimized. Finally, with the assistance of linear fitting of a few sample data, the prediction of the remaining service life of the operating mechanism can be achieved. The full life cycle experiment of the operating mechanism verifies the effectiveness of the proposed hybrid model.}, archive = {J_ACCESS}, author = {Xilei Dong and Fuping Zhang and Yunyang Ye and Huan Zhang}, doi = {10.1109/ACCESS.2025.3594021}, journal = {IEEE Access}, pages = {135208-135219}, shortjournal = {IEEE Access}, title = {Prediction of remaining useful life of operating mechanism driven by deep feature in classification model}, volume = {13}, year = {2025}, }
• (2025). Stackelberg game based on trajectory prediction for lane change in mixed traffic. ACCESS, 13, 135196-135207. (www)

  @article{ , abstract = {To address lane-changing conflicts between intelligent and human-driven vehicles in mixed traffic environments, this study proposes a Stackelberg game-based decision-making method for autonomous vehicles. A Stackelberg game framework is established between autonomous vehicles (leaders) and target-lane human-driven vehicles (followers) in three typical scenarios. The method develops a utility function for human-driven vehicles incorporating driving styles and safety-comfort-efficiency factors, with a corresponding cost function for autonomous vehicles. An improved Stackelberg game model integrates trajectory prediction of human-driven vehicles, while a bi-level optimization algorithm combining model predictive control and genetic algorithms jointly optimizes acceleration sequences and lane-change timing. Simulations demonstrate 60% reduction in heading angle variation and 67.59% decrease in yaw rate compared to non-game strategies, confirming enhanced safety, comfort, and efficiency of the proposed method.}, archive = {J_ACCESS}, author = {Baichuan Shi and Li Zhai and Chang Liu}, doi = {10.1109/ACCESS.2025.3594031}, journal = {IEEE Access}, pages = {135196-135207}, shortjournal = {IEEE Access}, title = {Stackelberg game based on trajectory prediction for lane change in mixed traffic}, volume = {13}, year = {2025}, }
• (2025). SH-YOLO: Enhanced real-time detection of laparoscopic surgical instruments in computer-aided surgery based on star operation and hybrid attention mechanisms. ACCESS, 13, 135179-135195. (www)

  @article{ , abstract = {Real-time surgical instrument detection is essential in computer-aided surgery systems for procedure identification, quality maintenance, and operation evaluation. To meet the real-time detection requirements of laparoscopic surgical instruments, a dataset for laparoscopic surgery is established, and an enhanced YOLOv5 algorithm named SH-YOLO is proposed. SH-YOLO incorporates star operation, denoted as element-wise multiplication, for efficient feature extraction and a hybrid attention mechanism (HAM) for adaptive feature fusion, facilitating real-time detection of laparoscopic surgical instruments. Specifically designed for computer-assisted surgical environments, SH-YOLO addresses critical challenges including non-central object localization, subtle feature discrimination, and motion blur resilience. Experimental results show that, compared to YOLOv5, SH-YOLO significantly enhances detection speed and accuracy. Specifically, SH-YOLO achieves a mean Average Precision (mAP) of 91.10%, an F1 Score of 0.941, and a detection speed of 97.4 FPS, outperforming YOLOv5 by 8.77%, 0.03, and 35.1 FPS, respectively. These results validate SH-YOLO’s capability to meet the high accuracy and real-time requirements of instrument detection in computer-assisted laparoscopic procedures, which provides reliable technical support for intelligent surgical navigation and robotic control systems.}, archive = {J_ACCESS}, author = {Yiping Shao and Zhilong Xu and Qicong Zhu}, doi = {10.1109/ACCESS.2025.3593635}, journal = {IEEE Access}, pages = {135179-135195}, shortjournal = {IEEE Access}, title = {SH-YOLO: Enhanced real-time detection of laparoscopic surgical instruments in computer-aided surgery based on star operation and hybrid attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Research on path planning of manipulator based on the improved RRT-connect algorithm. ACCESS, 13, 135165-135178. (www)

  @article{ , abstract = {With the development of intelligent manufacturing, manipulators are being increasingly used in the tobacco industry. Path planning of manipulators is a key technology for achieving high-efficiency production. The traditional RRT-Connect algorithm has several problems, such as low efficiency, high randomness, long search time, excessive number of iterations and nodes, and long path planning. An improved RRT-Connect path-planning algorithm was proposed. First, two strategies, namely constraining the sampling area and target-oriented growth, are proposed to reduce the number of sampling times and randomness of the algorithm and shorten the planning time. Second, key points are obtained through path pruning to shorten the path length. Finally, a spline interpolation method is used to smooth the path and improve the operational stability of the manipulator. Through MATLAB simulation verification, the improved RRT-Connect algorithm can significantly reduce the number of sampling nodes, shorten the planning time, reduce randomness, and generate a continuous and smooth trajectory during path planning.}, archive = {J_ACCESS}, author = {Han Shaolong and Liu Wenqi and Liu Yuanshang and Zhang Jianlin and Liu Kaihui and Zheng Xiaoyun and Li Haolun and Wang Jianchao and Wang Xiang}, doi = {10.1109/ACCESS.2025.3586358}, journal = {IEEE Access}, pages = {135165-135178}, shortjournal = {IEEE Access}, title = {Research on path planning of manipulator based on the improved RRT-connect algorithm}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of cryptographic techniques in federated learning for secure data sharing and applications. ACCESS, 13, 135138-135164. (www)

  @article{ , abstract = {The demand for secure data sharing is growing fast in sensitive domains like healthcare, finance, and IoT. Federated Learning (FL) introduces a decentralised machine learning paradigm whereby models can be trained over distributed nodes without sharing data. Despite its promise, FL faces significant security challenges, such as gradient inversion, model poisoning, and privacy leakage, which involve strong cryptographic techniques. Some cryptographic techniques have been proposed to address potential security concerns in the FL environment. This study provides an overview of the major techniques that include Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), Differential Privacy (DP), blockchain integration, and emerging hybrid approaches, and their applications from different perspectives. The techniques, along with their respective strengths and weaknesses, are systematically compared to ensure the identification of appropriate application domains. This review addresses hybrid approaches that combine multiple techniques to achieve an optimal trade-off between privacy, computational efficiency, and scalability. Key challenges such as computational overhead, scalability limitations, and the privacy-utility trade-off are identified, along with notable research gaps in the field. Future directions emphasise on the development of optimised hybrid techniques and strategies to alleviate computational and communication overheads in resource-constrained environments. This study therefore reviews those aspects that might provide useful insights to researchers and practitioners in the development of secure, scalable, and computationally efficient FL systems, and hence facilitate their practical implementation in privacy-sensitive domains.}, archive = {J_ACCESS}, author = {Anik Sen and Swee-Huay Heng and Shing-Chiang Tan}, doi = {10.1109/ACCESS.2025.3593953}, journal = {IEEE Access}, pages = {135138-135164}, shortjournal = {IEEE Access}, title = {A comprehensive review of cryptographic techniques in federated learning for secure data sharing and applications}, volume = {13}, year = {2025}, }
• (2025). Intelligent interior design systems: Optimizing layouts and aesthetics using AI and user data. ACCESS, 13, 135127-135137. (www)

  @article{ , abstract = {This paper introduces an advanced intelligent interior design system that synergizes deep learning, mathematical optimization, and user data integration to automate and personalize layout generation. Interior design traditionally involves significant manual effort, subjective aesthetic evaluation, and inefficient space utilization. To address these challenges, we mathematically formulated the layout generation problem as a constrained multi-objective optimization task, balancing aesthetics, functionality, and user preferences. Our computational framework leverages convolutional neural networks (CNNs) for layout parsing, graph neural networks (GNNs) for modeling spatial relationships, and Transformer-based architectures for context-aware reasoning. Optimization algorithms, including hybrid evolutionary strategies, are guided by learned aesthetic and functionality scores to efficiently explore the solution space. User data is integrated through latent embeddings and continual learning mechanisms, enabling dynamic personalization and feedback adaptation. Experimental evaluations on both synthetic and real-world datasets demonstrate superior performance compared to rule-based, pure ML, and pure optimization approaches, with notable improvements in layout efficiency, aesthetic quality, and user satisfaction. The proposed system presents significant implications for AI-driven architecture, offering a scalable and adaptive solution for intelligent space design. Future work includes real-time interaction through AR/VR platforms and broader generalization to diverse architectural styles.}, archive = {J_ACCESS}, author = {Zhe Ji and Yan Yu}, doi = {10.1109/ACCESS.2025.3591135}, journal = {IEEE Access}, pages = {135127-135137}, shortjournal = {IEEE Access}, title = {Intelligent interior design systems: Optimizing layouts and aesthetics using AI and user data}, volume = {13}, year = {2025}, }
• (2025). Validity and reliability of a mobile device application for assessing motor performance in the 30-second sit-to-stand test. ACCESS, 13, 135115-135126. (www)

  @article{ , abstract = {This study aimed to test the validity and reliability of a mobile device application for capturing center of mass (CoM) acceleration signals in analyzing and describing events during the 30-second chair stand test. A sample of 25 healthy young adults was evaluated using a smartphone with an Android-based application positioned at the fifth lumbar vertebra level (L5). Performance data from the mobile device were compared to those captured by a three-camera motion capture system, which served as the gold-standard reference. Signal synchronization between the smartphone and the kinematic system was achieved using an initial vertical jump, during which the peak in vertical acceleration from both systems was used as a temporal alignment marker. The motor behavior of the CoM was subsequently observed during task execution. Data were processed offline using routines developed for MatLab software, focusing on the longitudinal axis and describing the event cycles. A total of 21 variables were extracted for analysis. Each movement cycle was divided into three phases: impulse (DP1), sit-to-stand (DP2), and stand-to-sit (DP3). This approach allowed for the observation of cycle duration, the number of cycles performed, execution frequency, positive and negative acceleration areas (AUC+ and AUC-) for each phase, maximum and minimum acceleration peaks (Max Acc and Min Acc) per phase, and the amplitude between these peaks. Mean values for each variable were tested for sample normality, followed by criterion validation and reliability analyses. The hypothesis was relatively validated against the gold standard for 18 out of 21 variables in the smartphone app. Only the for P2 duration and accelerations in AUC+P1 and AUC+P2 showed correlation values <0.70. Reliability was confirmed for 16 out of 21 variables; however, for DP1, DP2, and DP3 duration, as well as accelerations for Max Acc P2 and Min Acc P1, exhibited reliability values <0.70. Despite some limitations, the mobile application demonstrated partial effectiveness in observing CoM motor behavior during the proposed functional test.}, archive = {J_ACCESS}, author = {Eduardo Candido Veloso Ferreira and Amélia Pasqual Marques and Szymon Kulis and Jan Gajewski and Anderson Antunes da Costa Moraes and Manuela Brito Duarte and Gizele Cristina da Silva Almeida and Enzo Gabriel da Rocha Santos and Gustavo Henrique Lima Pinto and André Dos Santos Cabral and Givago da Silva Souza and Anselmo De Athayde Costa E Silva and Bianca Callegari}, doi = {10.1109/ACCESS.2025.3593215}, journal = {IEEE Access}, pages = {135115-135126}, shortjournal = {IEEE Access}, title = {Validity and reliability of a mobile device application for assessing motor performance in the 30-second sit-to-stand test}, volume = {13}, year = {2025}, }
• (2025). The prediction of cerebral palsy in infants using pose estimation techniques within a hybrid deep learning framework. ACCESS, 13, 135098-135114. (www)

  @article{ , abstract = {Cerebral palsy (CP) is a neurological disorder that affects movement and posture, and early diagnosis is crucial for timely interventions to improve long-term outcomes. Despite advancements in diagnostic techniques, accurate early detection remains challenging. This study aimed to investigate the effectiveness of using pose estimation techniques and hybrid deep learning (DL) approaches for predicting CP in infants. Three hybrid DL models were explored: a convolutional neural network (CNN) combined with long short-term memory (LSTM) networks (CNN-LSTM), a CNN with Gated Recurrent Units (CNN-GRU), and a Graph Convolutional Network (GCN) with GRU (GCN-GRU). To further enhance prediction accuracy, an ensemble method using majority voting was employed to aggregate the predictions from these three models to maximize their strengths while minimize individual weaknesses. Performance evaluation shows that the CNN-LSTM model achieves the highest accuracy, with 100% on training and 90% on testing datasets. The CNN-GRU model achieved a training accuracy of 96% and testing 90%, and the GCN-GRU model achieved a training accuracy of 90% and testing accuracy of 90%. These findings highlight the potential of integrating pose estimation with DL techniques to enhance early CP diagnosis, offering a promising tool for clinicians to improve predictive accuracy and enable earlier, more targeted interventions.}, archive = {J_ACCESS}, author = {Lama A. Alghamdi and Maryam A. Aldahri and Saja A. Alzahrani and Rowla A. Alaofi and Rawaf F. Nafea and Noura M. Alotaibi}, doi = {10.1109/ACCESS.2025.3592798}, journal = {IEEE Access}, pages = {135098-135114}, shortjournal = {IEEE Access}, title = {The prediction of cerebral palsy in infants using pose estimation techniques within a hybrid deep learning framework}, volume = {13}, year = {2025}, }
• (2025). Graph-based user-centric recommender system using neo4j, cypher, and jaccard similarity in the field of e-commerce. ACCESS, 13, 135089-135097. (www)

  @article{ , abstract = {This paper presents a scalable and interpretable recommender system architecture that uses a property graph model implemented in Neo4j to generate personalized product recommendations. By representing customers and products as nodes and purchases as edges, and leveraging Jaccard similarity over shared purchases, the proposed system identifies nearest neighbors and recommends items accordingly. The use of graph queries allows for explainability, flexibility and improved performance compared to traditional collaborative filtering methods. Visualization with networkx further enhances transparency. Our research contributes a novel integration of graph-based storage, similarity computation and visual analytics in the domain of recommender systems. We further propose an algorithm for building a graph-based recommendation system for e-commerce to offer personalized suggestions. It is implemented by batching relationships and using the UNWIND clause in Cypher to minimize memory and network overhead. Experimental results transaction data show that the graph-based method delivers interactive performance and high-quality recommendations, which are validated by comparing Jaccard similarity scores with results from the Surprise recommendation library.}, archive = {J_ACCESS}, author = {Simona-Vasilica Oprea and Adela Bâra}, doi = {10.1109/ACCESS.2025.3591705}, journal = {IEEE Access}, pages = {135089-135097}, shortjournal = {IEEE Access}, title = {Graph-based user-centric recommender system using neo4j, cypher, and jaccard similarity in the field of e-commerce}, volume = {13}, year = {2025}, }
• (2025). A novel single-source 13-level switched- capacitor inverter with triple voltage gain. ACCESS, 13, 135074-135088. (www)

  @article{ , abstract = {In recent years, the growing demand for efficient voltage boosting solutions has been driven by advancements in renewable energy systems, electric vehicles (EVs), and photovoltaic (PV) arrays. However, conventional magnetic-based inverters remain bulky and inefficient for compact, high-performance applications, limiting their use in emerging technologies. To address this, the objective of this study is to develop a compact, single-source switched-capacitor multilevel inverter (SC-MLI) topology that achieves high voltage gain with minimal component count. The proposed 13-level SC-MLI employs a novel switched-capacitor structure and is evaluated under Natural Level Control (NLC) and Sinusoidal PWM (SPWM) schemes. Theoretical analysis, MATLAB/Simulink simulations, and experimental validation on a 100–1000 W prototype are carried out, along with thermal modeling in PLECS. The results show that the topology achieves a voltage gain of 3, maintains capacitor self-balancing without auxiliary circuits, and reaches a peak efficiency of 97.2% (simulation) and 95.3% (experiment). Moreover, it meets harmonic standards, reduces total harmonic distortion (THD), and outperforms recent single-source designs in terms of accuracy, cost, and control simplicity. This makes the proposed topology highly suitable for grid-connected PV systems, electric vehicle chargers, and compact renewable energy interfaces, with theoretical scalability toward medium- and high-power applications.}, archive = {J_ACCESS}, author = {Kyrmyzy Taissariyeva and Murat Karakiliç and Kuanysh Mussilimov and Hasan Hataş}, doi = {10.1109/ACCESS.2025.3594159}, journal = {IEEE Access}, pages = {135074-135088}, shortjournal = {IEEE Access}, title = {A novel single-source 13-level switched- capacitor inverter with triple voltage gain}, volume = {13}, year = {2025}, }
• (2025). Enhanced lung disease classification using CALMNet: A hybrid CNN-LSTM-TimeDistributed model for respiratory sound analysis. ACCESS, 13, 135053-135073. (www)

  @article{ , abstract = {Lung diseases, such as asthma, pneumonia, and chronic obstructive pulmonary disease (COPD), pose considerable global health issues, making early diagnosis essential for effective treatment. Traditional diagnostic techniques for these ailments, especially auscultation using a stethoscope, are subjective and susceptible to inaccuracies. This study introduces CALMNet, a deep learning model that uses Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a TimeDistributed layer to accurately analyze lung sounds by considering both their spatial and time-related features. The model is trained and assessed utilizing the ICBHI dataset, which encompasses a varied collection of lung sounds from patients with various respiratory ailments. Preprocessing techniques, including double denoising (FFT and high-pass filtering), were utilized to enhance the audio data quality, while feature extraction methods such as Mel Spectrograms, Mel-Frequency Cepstral Coefficients (MFCCs), and Chroma Short-Time Fourier Transform (ChromaSTFT) were employed to capture the fundamental characteristics of lung sounds. The results show that CALMNet achieves an impressive accuracy of 97.65%, with an F1-score of 0.909, precision of 0.911, and recall of 0.90, outperforming other models like CNNs and LSTMs. CALMNet exhibits enhanced efficacy in differentiating various lung illnesses, as evidenced by the confusion matrix and ROC curve analysis. The model’s exceptional accuracy and strong performance indicate its promise as an effective instrument for the automated classification of lung illnesses, providing a dependable, objective, and scalable option for clinical use. Future endeavors will concentrate on augmenting the dataset, enhancing model interpretability, and executing real-time diagnostic applications to further elevate its applicability in healthcare environments.}, archive = {J_ACCESS}, author = {Reshma Sreejith and R.Kanesaraj Ramasamy and Wan-Noorshahida Mohd-Isa and Junaidi Abdullah}, doi = {10.1109/ACCESS.2025.3591061}, journal = {IEEE Access}, pages = {135053-135073}, shortjournal = {IEEE Access}, title = {Enhanced lung disease classification using CALMNet: A hybrid CNN-LSTM-TimeDistributed model for respiratory sound analysis}, volume = {13}, year = {2025}, }
• (2025). Graph neural network classification in EEG-based biometric identification: Evaluation of functional connectivity methods using time-frequency metric. ACCESS, 13, 135043-135052. (www)

  @article{ , abstract = {The growing demand for secure identification has spurred interest in EEG-based biometric systems, which leverage unique brain activity patterns. Traditional univariate feature extraction methods, analyzing EEG channels independently, suffer from sensitivity to signal fluctuations. In contrast, bivariate features based on Functional Connectivity (FC) capture statistical interactions between brain regions, offering superior discriminability. However, conventional FC metrics like Correlation (COR) and Phase Locking Value (PLV) assume signal stationarity, failing to model EEG’s dynamic and directional nature. To address this, we introduce two novel time-frequency FC metrics: 1) Multi-band Coherency, which integrates features across delta, theta, alpha, and beta bands to enhance robustness and discriminability; and 2) wavelet coherency, addressing the non-stationarity of EEG signals, models phase lead-lag interactions and temporal variations. Integrated with Graph Convolutional Neural Networks (GCNNs), our approach leverages graph-structured FC data for superior classification. We propose a realistic experimental framework, recording training and testing signals in separate sessions under varying states, using only 19 EEG channels and single-trial signals. Despite reduced setup complexity, our GCNN achieves over 98% identification accuracy, comparable to CNN-based studies using 64 channels, with significantly lower computational cost and trainable variables reduced to less than 0.25 of those in a Convolutional Neural Network (CNN). In this study, self-recorded Auditory Evoked Potentials (AEPs), alongside public PhysioNet database, were used for their rapid acquisition, controllability, and ability to isolate individuals from external auditory stimuli. Statistical tests and connectivity visualizations validate our methods’ reliability, demonstrating advancements in EEG-based biometrics for secure and practical applications.}, archive = {J_ACCESS}, author = {Roghaieh Ashenaei and Ali Asghar Beheshti Shirazi}, doi = {10.1109/ACCESS.2025.3590304}, journal = {IEEE Access}, pages = {135043-135052}, shortjournal = {IEEE Access}, title = {Graph neural network classification in EEG-based biometric identification: Evaluation of functional connectivity methods using time-frequency metric}, volume = {13}, year = {2025}, }
• (2025). Early career developers’ perceptions of code understandability: A study of complexity metrics. ACCESS, 13, 135027-135042. (www)

  @article{ , abstract = {Context: Code understandability is crucial for developers, who continually modify the code to achieve changing business needs over time. Low understandability is associated with a significant increase in coding efforts and defect-proneness. To help assess code understandability, developers often use complexity metrics. Objectives: Our work investigates the predictive power of code understandability of two metrics: The first is the well-known cyclomatic complexity metric. In contrast, the second one is the recently introduced cognitive complexity metric by SonarQube. Moreover, we also distinguish the predictions of understandability in general and the severity of issues in particular. Methods: We designed and conducted an empirical study with 216 early career developers with professional experience ranging from one to four years. We asked them to manually inspect and rate the understandability of 12 Java classes that exhibit different levels of Cyclomatic and Cognitive Complexity. Results: Our findings show that the two complexity metrics are modest predictors of code understandability. Particularly, when controlling the sizes of classes, the predictive power of McCabe complexity drops substantially, while the same is noticeable for cognitive complexity. Furthermore, the two metrics have no predictive power for the severity of issues. We also discuss particular cases and provide qualitative insights into these observations. Conclusions: We concluded that low complexity values indicate good understandability, but high values do not necessarily show low understandability. We found no evidence that the measures can predict severity at all. Furthermore, we provided explanations of understandability and seriousness in light of both developers’ comments and the metric values.}, archive = {J_ACCESS}, author = {Matteo Esposito and Andrea Janes and Terhi Kilamo and Valentina Lenarduzzi}, doi = {10.1109/ACCESS.2025.3585777}, journal = {IEEE Access}, pages = {135027-135042}, shortjournal = {IEEE Access}, title = {Early career developers’ perceptions of code understandability: A study of complexity metrics}, volume = {13}, year = {2025}, }
• (2025). Improving electrical distance accuracy for cable fault location based on the prony method. ACCESS, 13, 135019-135026. (www)

  @article{ , abstract = {In recent years, the cable fault detection method centered on the traveling wave method has been widely used. But the method has the problem of large deviation in the electrical distance accuracy of cable fault location due to the aging of cable segments. Therefore, an improving electrical distance accuracy for cable fault location based on the Prony method is proposed, obtaining the wave velocity of different sections of the cable to improve the cable fault electrical distance positioning accuracy. By establishing a frequency-variable coupling model containing the transfer function of the cable with double joint structure, studying the mapping relationship between the cable attenuation constant and the electromagnetic wave velocity, and adopting the Prony estimation method combined with the singular value decomposition noise reduction algorithm to achieve the extraction of the attenuation constant and the decoupling of the segmented wave velocity, thus improving the electrical accuracy of fault location. Finally, the feasibility and effectiveness of the method are verified by simulation and field 680 m/10 kV power cable. Experimental results demonstrate that the method efficiently addresses the issue of significant deviations in electrical distance accuracy for cable fault location, which arise from uneven wave velocity distribution resulting from cable aging.}, archive = {J_ACCESS}, author = {Xiang Ren and Zhiqiang Feng and Kai Zhou and Xueming Zhou and Jianjun Yuan and Yefei Xu and Li Zhang}, doi = {10.1109/ACCESS.2025.3593833}, journal = {IEEE Access}, pages = {135019-135026}, shortjournal = {IEEE Access}, title = {Improving electrical distance accuracy for cable fault location based on the prony method}, volume = {13}, year = {2025}, }
• (2025). Adaptive optimization and planning design method for DC distribution grid system based on optical storage direct and flexible constructed cluster. ACCESS, 13, 135007-135018. (www)

  @article{ , abstract = {The topology design of DC distribution networks has a natural advantage in accommodating distributed DC power sources such as photovoltaic (PV) and energy storage systems, which is one of the effective ways to achieve carbon peaking and carbon neutrality goals. However, random power fluctuations of distributed PV can cause serious problems in DC distribution networks, such as bi-directional power flow variations, frequent node voltage violations and increased network losses. To address these challenges in the design of a DC distribution network integrating distributed PV and energy storage, this paper proposes a coordinated optimal design approach that simultaneously considers voltage regulation and network loss optimization. An adaptive coordinated optimization design scheme featuring adaptive switching between voltage correction and network loss optimization modes is proposed. The design scheme achieves coordinated control of the voltage source converter (VSC), DC/DC converter and controllable energy storage resources, balancing the requirements of system loss reduction and voltage safety control. It also provides guidance for the planning and design of future PV-storage DC distribution network systems based on flexible DC technology.}, archive = {J_ACCESS}, author = {Chenghui Lin and Zhanpeng Xu and Qihui Feng and Wuliang Chen and Jianyang Zhu}, doi = {10.1109/ACCESS.2025.3593822}, journal = {IEEE Access}, pages = {135007-135018}, shortjournal = {IEEE Access}, title = {Adaptive optimization and planning design method for DC distribution grid system based on optical storage direct and flexible constructed cluster}, volume = {13}, year = {2025}, }
• (2025). A novel skeletonization algorithm for topologically complex structures: Comparative analysis and application to renal arterial trees. ACCESS, 13, 134989-135006. (www)

  @article{ , abstract = {Accurate skeletonization of complex vascular structures is essential for clinical applications and anatomical research. This paper presents a domain-adapted skeletonization algorithm specifically developed for the intrarenal arterial tree, with a focus on high-resolution $\mu $ -CT scans of human renal corrosion cast specimens. These datasets pose challenges due to the complexity of the vascular tree, making it difficult to convert raw $\mu $ -CT data into meaningful representations. Without proper reconstruction, skeletonization, and graph representation, raw $\mu $ -CT data remains an unstructured point cloud, unsuitable for quantitative analysis. State-of-the-art methods, including voxel coding, geometric flow, gradient vector flow, and learning-based approaches, fail to meet key performance indicators (KPIs), such as one-voxel-wide centerline extraction, topological preservation, suppression of false bifurcations, and robustness to noise. To address these limitations, the proposed method integrates vascular-specific termination criteria, branch-preserving pruning, and voxel-based interpolation to ensure anatomically accurate, topologically consistent skeletons suitable for graph-based vascular modeling. The method was quantitatively evaluated on artificial renal tree models using metrics such as branch count per hierarchical level and bifurcation angle fidelity. Qualitative assessments were performed on standardized 3D benchmark models and $\mu $ -CT scans of human renal arterial corrosion cast specimens. Results demonstrate superior performance compared to state-of-the-art techniques, particularly in preserving connectivity, minimizing spurious branches, and achieving consistent one-voxel skeleton width all while maintaining a favorable balance between accuracy and computational efficiency. The algorithm enables detailed morphological and topological analysis of renal vascular trees, supporting the identification of intra- and inter-tree structural dependencies. These capabilities contribute to improved anatomical insight and have potential applications in surgical planning, where accurate vascular mapping enhances arterial localization and patient safety. Beyond renal vasculature, the method’s adaptability makes it applicable to other tree-like anatomical systems, supporting 3D centerline extraction and structural analysis in broader clinical and research contexts. Future work will focus on integrating a priori vascular pattern knowledge into the segmentation framework to enhance performance on patient-specific $\mu $ -CT datasets, where resolution is often constrained by radiation dose limitations.}, archive = {J_ACCESS}, author = {Katarzyna Heryan and Ştefan-Daniel Caliman}, doi = {10.1109/ACCESS.2025.3594114}, journal = {IEEE Access}, pages = {134989-135006}, shortjournal = {IEEE Access}, title = {A novel skeletonization algorithm for topologically complex structures: Comparative analysis and application to renal arterial trees}, volume = {13}, year = {2025}, }
• (2025). Improving LLM outputs against jailbreak attacks with expert model integration. ACCESS, 13, 134976-134988. (www)

  @article{ , abstract = {Using LLMs in a production environment presents security challenges that include vulnerabilities to jailbreaks and prompt injections, which can result in harmful outputs for humans or the enterprise. The challenge is amplified when working within a specific domain, as topics generally accepted for LLMs to address, may be irrelevant to that field. These problems can be mitigated for example, by fine-tuning large language models with domain-specific and security-focused data. However, these alone are insufficient, as jailbreak techniques evolve. Additionally, API-accessed models do not offer the flexibility needed to tailor behavior to industry-specific objectives, and in-context learning is not always sufficient and reliable. In response to these challenges, we introduce Archias, an expert model, adept at distinguishing between in-domain and out-of-domain communications. Archias classifies user inquiries into several categories: in-domain (specifically for the automotive industry), malicious questions, price injections, prompt injections, and out-of-domain examples. Our methodology integrates outputs from the expert model (Archias) into prompts, which are then processed by the LLM to generate responses. This method increases the model’s ability to understand the user’s intention and give appropriate answers. Archias can be adjusted, fine-tuned, and used for many different purposes due to its small size. Therefore, it can be simply customized to the needs of any industry. To validate our approach, we created a benchmark dataset for the automotive industry. Furthermore, in the interest of advancing research and development, we release our benchmark dataset to the community.}, archive = {J_ACCESS}, author = {Tatia Tsmindashvili and Ana Kolkhidashvili and Dachi Kurtskhalia and Nino Maghlakelidze and Elene Mekvabishvili and Guram Dentoshvili and Orkhan Shamilov and Zaal Gachechiladze and Steven Saporta and David Dachi Choladze}, doi = {10.1109/ACCESS.2025.3592458}, journal = {IEEE Access}, pages = {134976-134988}, shortjournal = {IEEE Access}, title = {Improving LLM outputs against jailbreak attacks with expert model integration}, volume = {13}, year = {2025}, }
• (2025). Dual branch graph representation learning-based approach for next point-of-interest recommendation. ACCESS, 13, 134966-134975. (www)

  @article{ , abstract = {Next Point-of-Interest (POI) recommendation, a sub-task of POI recommendation, focuses on predicting the next POI a user will visit, relying on the user’s sequential check-in history. In this paper, we observe that existing methods for this task have a fundamental limitation: they find it difficult to comprehensively model the associations between POIs from both explicit and implicit perspectives. To bridge this gap, we present a new method DBGR. Specifically, DBGR first introduces a language model to extract semantic representations from the text-based features of POIs, such as category information. Subsequently, it constructs a semantic association graph which preserves the semantic relations between POIs and are further fed into a graph neural network-based backbone to learn the representations of POIs in the semantic feature space. Complementarily, based the global and local check-in patterns, the Transformer model is deployed to extract the representations of POIs and users from historical check-in records. Extensive results on real-world datasets have showcased the effectiveness of DBGR for next POI recommendation, compared to mainstream approaches.}, archive = {J_ACCESS}, author = {Guoning Lv and Min Gao}, doi = {10.1109/ACCESS.2025.3593978}, journal = {IEEE Access}, pages = {134966-134975}, shortjournal = {IEEE Access}, title = {Dual branch graph representation learning-based approach for next point-of-interest recommendation}, volume = {13}, year = {2025}, }
• (2025). Enhanced photodetector field of view for IoT-driven VLC systems using fluorescent optical antennas. ACCESS, 13, 134955-134965. (www)

  @article{ , abstract = {The rapid advancements in 5G and IoT have significantly increased global mobile network traffic. Consequently, there is growing interest in alternative communication technologies such as Visible Light Communication (VLC). This technology presents several advantages over conventional Radio Frequency (RF) communications, including lower latency, reduced implementation costs, and immunity to electromagnetic interference. However, VLC faces challenges like limited photodetector Field of View (FOV) and line-of-sight requirements. To address these challenges, this paper explores the utilization of a short segment of Fluorescent Plastic Optical Fiber (FPOF) as an optical antenna designed to extend the photodetector’s FOV in VLC systems. The effectiveness of the proposed method is evaluated through experimental measurements and simulation analyses, comparing its performance against conventional VLC configurations. Experimental results showed that a 6 cm red FPOF antenna increased the FOV by over 10° at the mark of 3dB, achieving up to 25° at misaligned angles (above 70°), with around 10 dB gain at orthogonal conditions (90°), underscoring the potential of fluorescent optical antennas in overcoming traditional limitations of VLC systems, allowing new designs for detectors models, thereby enhancing their applicability for IoT-driven communication networks.}, archive = {J_ACCESS}, author = {Antonio A. C. Batista and Tiago D. Amorim and Ricardo M. Ribeiro and Andrés P. L. Barbero and Roberto B. Di Renna and Fernando C. Peixoto and Vinicius N. H. Silva}, doi = {10.1109/ACCESS.2025.3593261}, journal = {IEEE Access}, pages = {134955-134965}, shortjournal = {IEEE Access}, title = {Enhanced photodetector field of view for IoT-driven VLC systems using fluorescent optical antennas}, volume = {13}, year = {2025}, }
• (2025). Fake news detection with credibility scoring on turkish twitter data. ACCESS, 13, 134943-134954. (www)

  @article{ , abstract = {The widespread use of social media tools today has led to a rapid increase in newsworthy data on the internet. Some of the news data may contain inaccurate content, and the repeated sharing of these by users can lead to significant problems. Preventing these problems before they happen is very important today. Automatically detecting data referred to as fake news can help prevent such situations. In this study, fake news detection was performed using deep learning methods on tweets and their credibility scores from a Turkish Twitter news dataset. As a deep learning model, a fine-tuned version of the large language model llama-2-7b, called “mohammedbriman/llama-2-7b-chat-turkish-instructions,” was used for Turkish instructions. Four models were fine-tuned for four different scenarios, and results were obtained. Additionally, to achieve higher performance, considering the resource needs and response times of large language models, training was also conducted with the smaller language model Phi-3 (Phi-3-mini-4k-instruct) in four scenarios, and the results were analyzed. In the developed models, F1 scores ranged from 0.74 to 0.99. In another study that also used deep learning methods for fake news detection in Turkish, the models achieved F1 scores between 0.828 and 0.947. When comparing the performance of the models we designed with this study, it was concluded that our models performed better. Furthermore, these results show that the developed models are effective in detecting fake news on social media platforms and represent an important step in protecting users from misleading content.}, archive = {J_ACCESS}, author = {Özlem Aydin Fidan and Emir Öztürk and Hüsein Kantarci}, doi = {10.1109/ACCESS.2025.3594010}, journal = {IEEE Access}, pages = {134943-134954}, shortjournal = {IEEE Access}, title = {Fake news detection with credibility scoring on turkish twitter data}, volume = {13}, year = {2025}, }
• (2025). Indoor human motion recognition based on FMCW radar and threshold comparison algorithm. ACCESS, 13, 134930-134942. (www)

  @article{ , abstract = {Radar technology, particularly frequency-modulated continuous wave (FMCW) radar, has garnered attention in the field of smart home monitoring owing to its high sensitivity, long-range surveillance capabilities, and privacy-preserving characteristics. This study proposed a human motion state recognition system to recognize motion states based on the spectra from radar signals in a home environment. The velocity of a target is assessed by comparing a micro-Doppler signature against a velocity threshold, and its height signature discerns between high and low postures against a height threshold. The horizontal position of the target is determined using a range–angle map. The proposed threshold algorithm defines the motion state using the velocity, height postures and indoor position. Subsequently, the performance of the system is evaluated by conducting experiments on 10 subjects. The results demonstrate the efficacy of the proposed method, achieving an accuracy of approximately 85%. In continuous-action experiments involving sequences of movements, the system achieved recognition accuracy exceeding 90%.}, archive = {J_ACCESS}, author = {Ruoyu He and Masaki Kurosawa and Guanghao Sun}, doi = {10.1109/ACCESS.2025.3593957}, journal = {IEEE Access}, pages = {134930-134942}, shortjournal = {IEEE Access}, title = {Indoor human motion recognition based on FMCW radar and threshold comparison algorithm}, volume = {13}, year = {2025}, }
• (2025). A new hybrid wavelet transform-deep learning for smart resilient inverters in microgrids against cyberattacks. ACCESS, 13, 134917-134929. (www)

  @article{ , abstract = {Smart inverters are vital to the functioning and stability of contemporary microgrids (MG). Nevertheless, their growing dependence on digital communication and control exposes them to cyberattacks, which can disrupt grid stability and lead to equipment damage or outages. False Data Injection Attacks (FDIA), a severe cyberattack that intentionally falsifies sensor data, are particularly dangerous as they can mislead control systems. This paper proposes a new hybrid deep learning framework that integrates Long Short-Term Memory (LSTM) networks with the Gaussian Continuous Wavelet Transform (GCWT), a signal processing technique excelling at time-frequency feature extraction from power signals to detect severe FDIA in smart inverters, which are designed to significantly alter operational data and destabilize the grid. Comparative experiments with other wavelet transforms and deep learning architectures, such as Convolutional Neural Networks (CNN), Morlet continuous wavelet transform (MCWT), and Haar discrete wavelet transform (HDWT)-based models, validate the better performance of the proposed LSTM-GCWT model. It achieved 99.9% accuracy, surpassing all the baseline and hybrid methods. Moreover, a prediction-level comparison illustrates the strength of the model with 99% classification accuracy in identifying specific FDIA scenarios. The findings confirm the effectiveness of the LSTM-GCWT model in enhancing the security and reliability of smart inverters against FDIAs in MGs. While the model shows high performance, further research is needed to validate its generalizability across different inverter hardware and against novel, zero-day attack variants.}, archive = {J_ACCESS}, author = {Chou-Mo Yang and Pei-Min Huang and Chun-Lien Su and Mahmoud Elsisi}, doi = {10.1109/ACCESS.2025.3594216}, journal = {IEEE Access}, pages = {134917-134929}, shortjournal = {IEEE Access}, title = {A new hybrid wavelet transform-deep learning for smart resilient inverters in microgrids against cyberattacks}, volume = {13}, year = {2025}, }
• (2025). ABI-net: Attention-based inception U-net for brain tumor segmentation from multimodal MRI images. ACCESS, 13, 134898-134916. (www)

  @article{ , abstract = {Magnetic Resonance Imaging (MRI) is widely used for glioma evaluation, but manual segmentation is impractical due to the large data volume. Automated techniques are necessary for precise clinical measurements. U-Net has shown promise in volumetric segmentation, but brain tumor segmentation remains challenging due to tumor diversity in type, location, and structure. This study introduces ABI-Net, an advanced U-Net variant integrating Attention-based Inception blocks for improved segmentation of brain tumor sub-regions in 3D multimodal MRI images. ABI-Net leverages the Inception module for spatial feature extraction and an attention mechanism to enhance cancerous region detection. Trained on the BraTS 2020 dataset, ABI-Net achieved dice scores of 0.8354, 0.8505, and 0.8782 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively, outperforming state-of-the-art models. On the validation dataset (125 patients without segmentation masks), ABI-Net obtained average dice scores of 0.8189, 0.8401, and 0.8673 for ET, TC, and WT. ABI-Net provides an accurate, efficient solution for automated brain tumor segmentation, with significant potential for clinical applications, including diagnosis, treatment planning, and patient monitoring.}, archive = {J_ACCESS}, author = {Evans Kipkoech Rutoh and Qin ZhiGuang and Joyce C. Bore-Norton and Noor Bahadar}, doi = {10.1109/ACCESS.2025.3585926}, journal = {IEEE Access}, pages = {134898-134916}, shortjournal = {IEEE Access}, title = {ABI-net: Attention-based inception U-net for brain tumor segmentation from multimodal MRI images}, volume = {13}, year = {2025}, }
• (2025). SPPMFN: Efficient multimodal financial time-series prediction network with self-supervised learning. ACCESS, 13, 134883-134897. (www)

  @article{ , abstract = {Financial time series exhibit high volatility and non-linearity, making analysis particularly challenging. Traditional statistical methods like ARIMA and GARCH struggle with non-linear data. At the same time, despite capturing complex price dynamics, machine learning and deep learning approaches often face the risk of overfitting. Additionally, the limited parameters of one-dimensional financial time series signals constrain feature representation. To tackle these challenges, we propose an efficient multimodal financial time series prediction network utilizing self-supervised learning based on the custom-designed SPPMFN network for stock trend forecasting. Our approach introduces a novel signal transformation strategy to extract richer multi-scale feature representations from financial time series signals. Specifically, we convert one-dimensional stock price time series into two-dimensional image sequences across varying time intervals using Gramian Angular Fields. These transformed data modalities are then simultaneously processed by the SPPMFN, enabling the extraction of features from multiple dimensions. Furthermore, we introduce a self-supervised learning framework that enhances the model’s ability to capture intrinsic relationships within the data, allowing it to detect underlying patterns while effectively mitigating overfitting. Experimental results on the CSI300E, CSI100E, and S&P500 datasets demonstrate the effectiveness of our method, showing superior performance in accurately predicting high-yield stocks and significantly outperforming industry benchmarks. Specifically, our model improves accuracy by over 3% and achieves a cumulative return of 21.62% on the CSI300E dataset. On the S&P500 dataset, accuracy increased by over 1.4% and cumulative return reached 18.66%. These findings highlight the robustness and practical applicability of our method across diverse financial markets.}, archive = {J_ACCESS}, author = {Ningxin Li and Gang Chao and Jianke Zou and Gaozhe Jiang}, doi = {10.1109/ACCESS.2025.3593851}, journal = {IEEE Access}, pages = {134883-134897}, shortjournal = {IEEE Access}, title = {SPPMFN: Efficient multimodal financial time-series prediction network with self-supervised learning}, volume = {13}, year = {2025}, }
• (2025). Visual similarity assessment for product aesthetic properties using single reference training. ACCESS, 13, 134864-134882. (www)

  @article{ , abstract = {Traditional product aesthetic evaluation requires extensive preference labeling for each design, limiting scalability and increasing costs. This study presents a novel single reference training approach that enables aesthetic preference assessment through visual similarity to a single highly preferred product. Using 30 home audio speakers, we collected aesthetic preference ratings from 44 participants on a 7-point Likert scale. Four deep learning approaches—Pre-trained CNN, Auto-encoder, Siamese Network, and Triplet Network—measured visual similarity between the most preferred reference products and remaining samples. Results demonstrated significant correlations between visual similarity and aesthetic preference: the Triplet Network achieved Pearson correlation coefficient $r = 0.448$ ( $p = 0.013$ ), while Pre-trained CNN approach yielded $r = 0.478$ ( $p = 0.008$ ). After filtering high-variance products, correlations substantially improved to $r = 0.738$ ( $p \lt 0.001$ ). Principal component analysis of embedding vectors revealed interpretable aesthetic dimensions, with specific components significantly correlating with novelty ( $r = 0.51-0.61$ ), harmony ( $r = 0.36-0.37$ ), dynamics ( $r = 0.55-0.65$ ), and complexity ( $r = 0.42-0.56$ ). Interaction models of multiple principal components increased explanatory power ( $R^{2} = 0.4065$ ), demonstrating that aesthetic preferences emerge from complex relationships among visual features. The findings prove that single reference training effectively extracts interpretable aesthetic properties from embedding spaces without explicit labeling. Our results suggest that highly preferred products contain multiple aesthetic preference-related properties, and deep learning models can successfully extract features corresponding to these properties. This approach provides an alternative aesthetic assessment from labor-intensive individual evaluation to efficient similarity-based inference, offering a methodology with reduced data requirements for product design evaluation and enabling evaluation of new designs.}, archive = {J_ACCESS}, author = {Seok Young Hwang and Juseong Kim and Kicheol Pak}, doi = {10.1109/ACCESS.2025.3594058}, journal = {IEEE Access}, pages = {134864-134882}, shortjournal = {IEEE Access}, title = {Visual similarity assessment for product aesthetic properties using single reference training}, volume = {13}, year = {2025}, }
• (2025). Enhanced spatial clustering for crime analysis: Novel advances in ward-like and SKATER algorithms for brazilian public security. ACCESS, 13, 134846-134863. (www)

  @article{ , abstract = {This study investigates how spatially constrained clustering can reveal actionable crime patterns to support public security planning. Using neighbourhood–level crime records for Recife, Brazil (2007–2015), we present new versions of two established methods —Ward–like hierarchical clustering and the graph-based spatial kluster analysis by tree edge removal (SKATER) algorithm– designed to: (i) enforce geographical contiguity and (ii) handle mixed (qualitative and quantitative) variable types through the Gower dissimilarity. Validation with the Calinski–Harabasz, Dunn, and Davies–Bouldin indices shows that the new Ward–like method provides the strongest compactness and inter–cluster separation, whereas the Gower–based SKATER method achieves the highest variance-based discrimination. The resulting clusters partly, but not fully, coincide with Recife’s official Integrated Security Areas (Áreas Integradas de Segurança in Portuguese), suggesting that a data-driven redrawing of boundaries could improve police resource allocation and the monitoring of local safety policies. All computations were performed in the R software and both the code and methods are readily transferable to other cities facing similar crime-mapping challenges.}, archive = {J_ACCESS}, author = {Raydonal Ospina and Alexsandra Gomes De Lima and Cristiano Ferraz and Cecilia Castro and Carlos Martin-Barreiro and Víctor Leiva}, doi = {10.1109/ACCESS.2025.3586941}, journal = {IEEE Access}, pages = {134846-134863}, shortjournal = {IEEE Access}, title = {Enhanced spatial clustering for crime analysis: Novel advances in ward-like and SKATER algorithms for brazilian public security}, volume = {13}, year = {2025}, }
• (2025). Fault-tolerant control of steer-by-wire system based on sliding mode observer with neural network. ACCESS, 13, 134831-134845. (www)

  @article{ , abstract = {This paper proposes a novel automatic disturbance rejection fault-tolerant control method based on a sliding mode observer with neural network for steer-by-wire (SbW) systems subjected to internal uncertainty, external disturbance, and steering motor fault. Firstly, to mitigate the adverse effects of non-smooth desired steering angle signals on the transient performance of the control system, a third-order tracking-differentiator is employed to smooth the desired signal. Secondly, a sliding mode observer with neural network is designed, which utilizes an adaptive radial basis function (RBF) neural network to reconstruct the internal and external uncertainties, as well as the fault-induced uncertainty, and employs the terminal sliding mode algorithm to ensure fast convergence of observation errors. The asymptotic stability of the observer is demonstrated by Lyapunov stability theory. Subsequently, a finite-time feedback control law is developed. To compensate for these uncertainties in the closed-loop system, the reconstruction results from the observer are incorporated into the control channel. Based on this framework, stability analysis shows that the tracking errors can reach a time-varying residual set within finite time and subsequently converge asymptotically to the origin as this residual set contracts to zero. Finally, the effectiveness and superiority of the proposed control method are verified through simulations conducted using Matlab/Simulink and CarSim.}, archive = {J_ACCESS}, author = {Rongji Yang and Daozheng Liao}, doi = {10.1109/ACCESS.2025.3593492}, journal = {IEEE Access}, pages = {134831-134845}, shortjournal = {IEEE Access}, title = {Fault-tolerant control of steer-by-wire system based on sliding mode observer with neural network}, volume = {13}, year = {2025}, }
• (2025). A software synchronization method for high-frequency circulating current in multiple inverter parallel systems. ACCESS, 13, 134822-134830. (www)

  @article{ , abstract = {To increase system power, multiple inverters are connected in parallel. However, if multiple inverters are connected in parallel but without carrier synchronization, it is possible to generate both high-frequency circulating current at the switching frequency and excessive ground current. To avoid high-frequency circulating current problems caused by unsynchronized carriers, the traditional solution is to synchronize them. Currently, the majority of carrier synchronization techniques are hardware based. This paper proposes a novel software synchronization method for multi-inverter parallel systems, eliminating the need for additional hardware. First, a parallel inverter system is modelled, and high-frequency circulating current analysis is performed. The high-frequency circulating current in the presence of an unsynchronized carrier is computed. The circulating current under unsynchronized carriers is quantitatively calculated. Next, a software synchronization mechanism for carriers in multi-inverter parallel systems is presented. Two different approaches are proposed: alignment at the zero crossing point and alignment at the peak point. The concept and implementation of these two options are thoroughly explained. Finally, the experiment is implemented and results show that the proposed method can achieve full carrier synchronization and significantly reduces the circulating current.}, archive = {J_ACCESS}, author = {Wenping Zhang and Yiming Wang and Po Xu and Yiming Wang and Ke Ma and Yongheng Yang}, doi = {10.1109/ACCESS.2025.3591914}, journal = {IEEE Access}, pages = {134822-134830}, shortjournal = {IEEE Access}, title = {A software synchronization method for high-frequency circulating current in multiple inverter parallel systems}, volume = {13}, year = {2025}, }
• (2025). A network traffic anomaly classification model based on self-attention mechanism and convolutional gated recurrent unit. ACCESS, 13, 134804-134821. (www)

  @article{ , abstract = {In the context of accelerated digital transformation, cybersecurity has become a global strategic issue. With the rapid growth of network traffic and the evolving complexity of attack patterns, the stability of information systems and data security face significant challenges. The detection of minority-class traffic (anomalous traffic) is crucial for network security. However, traditional methods struggle to effectively identify minority-class traffic under conditions of data imbalance, which affects detection accuracy. To address this issue, this paper proposes a Custom Synthetic Minority Over-Sampling Technique (Custom SMOTE) algorithm, which precisely handles minority-class samples while significantly reducing memory consumption. Additionally, this paper introduces a Self-Attention based Convolutional Gated Recurrent Unit (SA_CGRU) model. By combining self-attention mechanisms, Convolutional Neural Networks (CNN), and Gated Recurrent Units (GRU), the model captures key features and models temporal dependencies. Experimental results on the CIC-IDS 2017, UNSW-NB15, and CIC-IDS 2018 datasets demonstrate that Custom SMOTE reduces memory usage by an average of 50% and improves overall detection accuracy by 5.35% compared to traditional SMOTE. The SA_CGRU model further enhances minority-class recognition and boosts overall classification accuracy, demonstrating the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Yulian Li and Yang Su}, doi = {10.1109/ACCESS.2025.3593613}, journal = {IEEE Access}, pages = {134804-134821}, shortjournal = {IEEE Access}, title = {A network traffic anomaly classification model based on self-attention mechanism and convolutional gated recurrent unit}, volume = {13}, year = {2025}, }
• (2025). Integrating OPAL into CAVISE for connected vehicle simulation with 3D signal propagation. ACCESS, 13, 134793-134803. (www)

  @article{ , abstract = {Population growth has led to an increased demand for transportation. This increase has subsequently resulted in problems such as road accidents, greenhouse gas emissions, and others. Automated and connected vehicles could be a solution to these problems. However, before they can be released on public roads, they must go through extensive testing, including computer simulation. State-of-the-art open-source connected and automated vehicle simulation environments do not provide signal propagation modeling in 3D environments with elevation differences for vehicular communications. While open-source 3D signal propagation models are available. To avoid redeveloping complex existing technology, it is desirable to integrate such models into state-of-the-art simulation environments. This paper proposes integrating a 3D signal propagation model OPAL into the CAVISE simulation environment as an alternative to the available GEMV2 2D signal propagation model. A comparison of GEMV2 and OPAL models is provided, and an extended CAVISE architecture that can interchange between the two models is synthesized and described. Approaches to further integrate OPAL into the architecture by porting it to Unreal Engine are explored. Various scenarios of information exchange between vehicles using the GEMV2 and OPAL models are simulated. The results are compared, and the models are validated against each other. Current limitations of OPAL capabilities regarding 3D scenario generation are discussed, and possible approaches to improvement are suggested. The findings will be used to develop signal propagation simulation in scenarios with elevation differences and 3D structures in CAVISE.}, archive = {J_ACCESS}, author = {Vitaly G. Stepanyants and Garik A. Mkrtchyan and Valeria E. Rakhimullina and Aleksandr Y. Romanov}, doi = {10.1109/ACCESS.2025.3593477}, journal = {IEEE Access}, pages = {134793-134803}, shortjournal = {IEEE Access}, title = {Integrating OPAL into CAVISE for connected vehicle simulation with 3D signal propagation}, volume = {13}, year = {2025}, }
• (2025). Severity classification of freezing of gait using machine-learning algorithms: A hidden state model approach. ACCESS, 13, 134781-134792. (www)

  @article{ , abstract = {E-health sensors and wearables play an important role in the detection and classification of many chronic diseases. A chronic disease requires active monitoring and its severity increases over time. Parkinson’s disease is one such chronic disease resulting in motor as well as non-motor impairments. Freezing of gait (FOG) is one of the motor symptoms that could affect the quality of life of patients. Diagnosing the presence and severity of Parkinson’s disease and its symptoms can be done subjectively and objectively. However, subjective analysis might suffer from biases and variability as it involves human emotions and need objective method or measurable data to make decisions. So, in this regard, we conducted a feasibility study by adopting an objective approach for labeling the FOG severity levels and then classifying them using the data from various sensors. We use the Hidden Markov Model (HMM) for labeling the severity levels. We also trained an ensemble algorithm to model the prediction of severity levels marked by HMM model. We could obtain severity level classification accuracy between $87-88\%$ using the XGBoost algorithm. Moreover, we also used model explainability approach to explain the predictions on severity levels using only objective methods, in this case sensor data. We found that features from EEG show a positive correlation with severe FOG events and gyroscope sensors show a negative correlation for predicting severe FOG events.}, archive = {J_ACCESS}, author = {Aditi Site and Elena Simona Lohan and Jari Nurmi}, doi = {10.1109/ACCESS.2025.3591270}, journal = {IEEE Access}, pages = {134781-134792}, shortjournal = {IEEE Access}, title = {Severity classification of freezing of gait using machine-learning algorithms: A hidden state model approach}, volume = {13}, year = {2025}, }
• (2025). Leader propagation characteristics under positive lightning impulse voltages. ACCESS, 13, 134772-134780. (www)

  @article{ , abstract = {Lightning strikes on transmission lines often generate overvoltage, which is the primary reason for insulator string flashover. This overvoltage, with a rapid rise time of just a few microseconds, causes leader propagation characteristics to differ significantly from those under an impulse with a slower rise time. However, most existing studies focus on leader behavior under slow-rise-time impulses, like switching impulse voltage, while research on fast-rise-time impulses remains limited. To address this gap, this study conducted a discharge experiment using a 5 m air gap under lightning impulse voltage in a lab setting. Voltage, current, and high-speed images were recorded simultaneously. The results revealed how leader velocity and charge per unit length relate to the leader’s position and voltage amplitude. When compared to switching impulse voltage, these characteristics were found to be ten times greater under lightning impulse voltage. The study also examined the leader’s branching behavior, including the number of branches and the spatial distribution of bifurcation points. It explored how voltage amplitude affects these traits, showing that higher voltage increases the likelihood of branching, with three or four branches becoming more common. Additionally, the research analyzed the leader’s attachment points and, for the first time, observed two attachment points occurring simultaneously. These findings enhance our understanding of how leaders behave dynamically under fast-rise-time impulse voltage.}, archive = {J_ACCESS}, author = {Lei Jia and Song Zhang and Lu Qu and Yongxiang Cai and Huaifei Chen and Xiaobing Xiao and Ruihan Qi and Minchuan Liao and Jian Hu}, doi = {10.1109/ACCESS.2025.3592978}, journal = {IEEE Access}, pages = {134772-134780}, shortjournal = {IEEE Access}, title = {Leader propagation characteristics under positive lightning impulse voltages}, volume = {13}, year = {2025}, }
• (2025). Color-selective channel modeling for visible light communication (VLC). ACCESS, 13, 134758-134771. (www)

  @article{ , abstract = {Color-selective attenuation, a specific case of wavelength- or frequency-selective attenuation, is an under-investigated but significant factor affecting the performance of color-modulated systems, particularly in Non-Line-of-Sight (NLOS) links. Therefore, it must be mitigated to provide reliable links for the Visible Light Communications (VLC) technology. In this sense, this article proposes a novel color-selective channel model, covering multiple time and space (in)variability scenarios for Color-Shift Keying (CSK) modulation. This paper also derives the ergodic capacity of color-selective channels for the high Signal-to-Noise Ratio (SNR) regime. Numerical analysis evaluates the effects of color-selective attenuation on received symbol color, signal space mapping, and channel condition degradation. Additionally, this article evaluates the impact of particular cases of color-selective attenuation – monochromatic, bichromatic, and trichromatic – on 4-CSK systems, comparing them to non-selective attenuation. Simulations also compare the performance of the Color-Hopping Space-Time (CHST) scheme to conventional CSK systems for color-selective channels. Results show that color-selective attenuation causes significant color imbalance and severe performance loss compared to non-selective attenuation, limiting achievable diversity gains and reducing channel capacity. However, the CHST coding scheme mitigates these effects, providing diversity, coding, and channel capacity gains compared to conventional 4-CSK systems.}, archive = {J_ACCESS}, author = {Igor S. C. Rodrigues and Leandro R. Ximenes and Rangel Arthur and Rafael Perez-Jimenez}, doi = {10.1109/ACCESS.2025.3590208}, journal = {IEEE Access}, pages = {134758-134771}, shortjournal = {IEEE Access}, title = {Color-selective channel modeling for visible light communication (VLC)}, volume = {13}, year = {2025}, }
• (2025). Reference model-based event-triggered impulsive control for fixed-time consensus of nonlinear multi-agent system. ACCESS, 13, 134748-134757. (www)

  @article{ , abstract = {This paper is concerned with the fixed-time event-triggered impulsive consensus problem of nonlinear multi-agent systems (MASs), where the leader-following and leaderless consensus problems are considered simultaneously. Two new reference model-based event-triggered impulsive controllers with trigger functions are proposed to make all agents achieve consensus within a fixed time, which can substantially increase the convergence speed and reduce energy consumption. Then, some sufficient conditions are presented to guarantee that nonlinear MASs can reach fixed-time consensus in the case of agents’ dynamics being high-order. Moreover, it is proved that Zeno behavior can be avoided with the proposed control protocols. Finally, two numerical simulations are provided to validate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Yaping Xia and Haodong Li}, doi = {10.1109/ACCESS.2025.3593656}, journal = {IEEE Access}, pages = {134748-134757}, shortjournal = {IEEE Access}, title = {Reference model-based event-triggered impulsive control for fixed-time consensus of nonlinear multi-agent system}, volume = {13}, year = {2025}, }
• (2025). Accurate modulation voltage tracking with reduced switching frequency for modular multilevel converters in offshore wind power systems. ACCESS, 13, 134735-134747. (www)

  @article{ , abstract = {In recent years, modular multilevel converter (MMC) technology has become a cornerstone of offshore wind power development. To optimize MMC system performance, this paper proposes an enhanced control scheme, where the number of inserted submodules (SMs) per phase is dynamically adjusted according to real-time capacitor voltage fluctuations to enable precise tracking of the modulation voltage reference. Circulating currents are intrinsically suppressed, reducing arm inductance by 95.6%. In addition, the proposed scheme optimizes SM sequencing by leveraging historical SM switching states, achieving 42.6% switching frequency reduction. Furthermore, an arm energy balancing control is implemented to ensure precise energy equilibrium across the MMC arms. The effectiveness of the proposed strategy is validated through PSCAD/EMTDC simulations of an offshore wind farm integrated via a point-to-point high voltage DC (HVDC) link.}, archive = {J_ACCESS}, author = {Rui Li and Zhaogang Li and Ran Guo and Zhiwen Suo and Yi Liang}, doi = {10.1109/ACCESS.2025.3591958}, journal = {IEEE Access}, pages = {134735-134747}, shortjournal = {IEEE Access}, title = {Accurate modulation voltage tracking with reduced switching frequency for modular multilevel converters in offshore wind power systems}, volume = {13}, year = {2025}, }
• (2025). Knowledge graph representation of felder-silverman learning style model for computing education. ACCESS, 13, 134721-134734. (www)

  @article{ , abstract = {This research focuses on adapting the Felder-Silverman Learning Style Model (FSLSM) questionnaire according to a specific computing course. In this approach, we transform FSLSM questions based on the course’s algorithmic structure. Using these transformed questions, we create Learning Style Knowledge Graphs (LSKGs), where each learner has their own LSKG based on their learning style. Once these LSKGs are generated, we use them to form clusters of learners who share similar features. These clustered learners can enhance their learning experience through their shared learning profiles, as they exhibit similar characteristics. To increase learners’ engagement with the questionnaire, we have made the questions interactive and aligned with the algorithmic course. Our analysis indicates that around 26 clusters are formed, which, according to Silhouette and Calinski-Harabasz scores, provide the best clustering results. We observed that when the threshold is set above 0.5, approximately 76% of learners have matching profiles with each other. This highlights the need to consider individual learning styles when designing educational strategies. The LSKG approach can be used to pinpoint students’ strengths and weaknesses, providing a foundation for AI-based adaptive learning systems that tailor educational content to the needs of each student. Moreover, LSKGs enable effective resource allocation by aligning teaching methods with learning styles.}, archive = {J_ACCESS}, author = {Mubasher Yousuf and Syed Imran Jami and Shaukat Wasi and Muhammad Shoaib Siddiqui}, doi = {10.1109/ACCESS.2025.3593429}, journal = {IEEE Access}, pages = {134721-134734}, shortjournal = {IEEE Access}, title = {Knowledge graph representation of felder-silverman learning style model for computing education}, volume = {13}, year = {2025}, }
• (2025). Performance boundaries and related improvement method of sub-oscillation- suppression capabilities in continuously adjustable phase-shifting transformer. ACCESS, 13, 134683-134698. (www)

  @article{ , abstract = {The increasing penetration of power electronic devices has elevated the possibility of sub-synchronous oscillation (SSO) in the power grid. At the same time, the continuously adjustable phase-shifting transformer (CAPST) exhibits a certain capability for SSO suppression while regulating power flow. To clarify the SSO suppression mechanism and enhance its suppression capability, this study first analyzes the oscillation suppression mechanism of the CAPST, quantifies the boundaries of its oscillation suppression performance, and reveals inherent limitations of CAPST, including inadequate SSO suppression capability and the inability to simultaneously achieve effective power flow regulation and SSO suppression. To further extend the SSO suppression performance boundary, a capacitive series-compensated phase-shifting transformer (CSCPST) is introduced, accompanied by a design method for the series compensation capacitor. Additionally, to address the associated issue of power-flow reversal, a hybrid open-closed-loop power flow regulation strategy is proposed. Finally, simulation studies are conducted on both the CAPST and the CSCPST. The results verified the limitations of CAPST in SSO suppression capability and confirmed the superior performance of the CSCPST in enhancing SSO suppression and simultaneously achieving effective power flow regulation and SSO suppression.}, archive = {J_ACCESS}, author = {Jinjiao Lin and Jingyan Du and Shi Chen and Xinying Wang and Haodong Long and Chuyang Wang}, doi = {10.1109/ACCESS.2025.3582834}, journal = {IEEE Access}, pages = {134683-134698}, shortjournal = {IEEE Access}, title = {Performance boundaries and related improvement method of sub-oscillation- suppression capabilities in continuously adjustable phase-shifting transformer}, volume = {13}, year = {2025}, }
• (2025). A distributed coalition selection of distribution centers for delivery stations. ACCESS, 13, 134669-134682. (www)

  @article{ , abstract = {In modern logistics networks, an essential challenge is determining how delivery stations autonomously select the appropriate distribution center for dispatching their shipments. Traditional optimization approaches often rely on centralized decision-making and global computations, leading to high computational costs and limited adaptability in dynamic environments. To address these limitations, this paper proposes a coalition game-based distributed decision-making algorithm, allowing delivery stations to independently form coalitions and optimize their dispatch strategies. Unlike centralized optimization methods, the proposed approach enables real-time decision-making with reduced computational complexity while maintaining both system-wide efficiency and individual delivery station satisfaction. Extensive Monte Carlo simulations demonstrate that our algorithm achieves 8% higher average delivery station utility than Genetic Algorithms and 11% higher than Particle Swarm Optimization when the scale of the delivery stations is large, whereas maintaining comparable distribution center utility. Additionally, the proposed method reduces computational time by over 50% compared to baseline, making it a scalable and practical solution for real-time logistics scheduling. These results confirm that the coalition game-based framework effectively balances individual autonomy and global efficiency, offering a promising alternative for optimizing decentralized logistics dispatch decisions.}, archive = {J_ACCESS}, author = {Fan Wu and Ming Yu and Jingya Zhang and Yicheng Fan and Ming Li}, doi = {10.1109/ACCESS.2025.3592667}, journal = {IEEE Access}, pages = {134669-134682}, shortjournal = {IEEE Access}, title = {A distributed coalition selection of distribution centers for delivery stations}, volume = {13}, year = {2025}, }
• (2025). Toward an onboard anomaly detection and identification method for satellites. ACCESS, 13, 134655-134668. (www)

  @article{ , abstract = {Unexpected anomalies can occur when satellites are exposed to harsh environments. In addition, operators cannot directly repair or inspect satellites, making stable operations reliant on the early detection and identification of anomalies. However, conventional data-driven satellite monitoring methods often lack explainability and feasibility for onboard implementation. This study proposes a novel onboard satellite monitoring method that detects anomalies, identifies their types, and locates specific abnormal channels within housekeeping data. The proposed method employs the Mahalanobis distance for anomaly detection owing to its low computational cost, making it suitable for onboard applications. Feature contribution analysis using orthogonal arrays and signal-to-noise ratios was used to enhance explainability, attempting to identify the type and location of detected anomalies. Through experiments using benchmark datasets, we demonstrated that the proposed method achieves detection performance comparable to that of conventional machine learning-based methods expected to be implemented at ground stations, and can identify anomaly locations and anomaly types to a limited extent. In addition, execution speed tests conducted on a Raspberry Pi Zero confirmed its feasibility for onboard implementation. These results suggest that the proposed method contributes to the realization of a self-diagnostic capability for satellites, thereby enhancing their reliability and operational stability.}, archive = {J_ACCESS}, author = {Shun Katsube and Hironori Sahara}, doi = {10.1109/ACCESS.2025.3593489}, journal = {IEEE Access}, pages = {134655-134668}, shortjournal = {IEEE Access}, title = {Toward an onboard anomaly detection and identification method for satellites}, volume = {13}, year = {2025}, }
• (2025). Standards: Exposure limits for brief high intensity pulses of radiofrequency energy between 6 and 300 GHz. ACCESS, 13, 134648-134654. (www)

  @article{ , abstract = {This Standards paper considers limits of the International Commission on Nonionizing Radiation (ICNIRP) and Institute of Electrical and Electronics Engineers (IEEE C95.1-2019) on “brief exposures” to radiofrequency energy (RF-EMF), that vary significantly within the 6 minute averaging time specified in the limits. This situation arises in two important contexts: exposures from cellular base stations employing Multiple Input Multiple Output (MIMO) antennas with beamforming that sweep beams around the service area, and exposure to pulsed RF-EMF with high peak power but low duty cycle. The ICNIRP and IEEE limits on brief exposures between 6 and 300 GHz are assessed using numerical and analytical solutions to a one-dimensional thermal model of tissue. Analysis is done both for rectangular pulses of RF-EMF (i.e., the step response to a suddenly imposed RF-EMF field), and to impulses of RF-EMF energy. The analysis shows that both sets of guidelines are uneven in the level of protection they offer against excessive transient heating of skin, with the IEEE limits being far more conservative than the ICNIRP limits. The thermal model suggests a simple modification to the pulse fluence limits that provides a more consistent level of protection against excessive temperature increases for pulses of varying duration and frequency. The Standards Paper does not consider possible nonthermal hazards or possible thermoacoustic effects produced by brief but very high amplitude RF-EMF pulses that produce high rates of change of tissue temperature but small temperature increases. The conclusion points to the need for further research and development to establish and refine exposure limits for pulsed RF-EMF.}, archive = {J_ACCESS}, author = {Kenneth R. Foster and Ilkka Laakso and Quirino Balzano}, doi = {10.1109/ACCESS.2025.3592532}, journal = {IEEE Access}, pages = {134648-134654}, shortjournal = {IEEE Access}, title = {Standards: Exposure limits for brief high intensity pulses of radiofrequency energy between 6 and 300 GHz}, volume = {13}, year = {2025}, }
• (2025). A biomechanical analysis of weighted lifting with an active knee exoskeleton. ACCESS, 13, 134634-134647. (www)

  @article{ , abstract = {Manual material handlers experience some of the highest injury rates among professions monitored by the Bureau of Labor Statistics. Most of these injuries are musculoskeletal strains, sprains, and tears linked to repeated lifting and lowering of heavy objects. Powered exoskeletons may help to lessen the strain on the musculoskeletal system during lifting. Previous studies have indicated that powered knee exoskeletons can reduce muscle activation and metabolic cost while lifting. However, no prior studies have conducted comprehensive biomechanical analyses using motion capture during exoskeleton-assisted lifting, which is crucial for examining joint-level effects of the assistance. In this study, we employed a bilateral powered knee exoskeleton to assist eight healthy subjects as they repeatedly lifted and lowered a 9 kg box. We utilized electromyography sensors to record quadriceps muscle activity and full-body motion capture to evaluate angles, torques, and positions of the lower limb joints. The exoskeleton decreased the peak electromyography activations of four quadriceps muscles — the right and left vastus medialis and vastus lateralis – with three reductions being statistically significant (29.2%, 31.2%, and 30.9% reduction compared to lifting without the exoskeleton, p <0.05). Additionally, we observed significant decreases in biological knee torque with the exoskeleton (53.4% and 53.58% reduction compared to lifting without the exoskeleton, p <0.05). These findings suggest that powered knee exoskeletons could serve as an effective tool for reducing effort during occupational lifting, potentially lowering the risk of injury among manual material handlers by encouraging a safer, knee-centric lifting form.}, archive = {J_ACCESS}, author = {Margaret A. Meagher and Grace R. Hunt and Brendon Ortolano and Rosemarie Murray and Lukas Gabert and Kenneth Bo Foreman and Tommaso Lenzi}, doi = {10.1109/ACCESS.2025.3593446}, journal = {IEEE Access}, pages = {134634-134647}, shortjournal = {IEEE Access}, title = {A biomechanical analysis of weighted lifting with an active knee exoskeleton}, volume = {13}, year = {2025}, }
• (2025). Synthetic data generation using large language models: Advances in text and code. ACCESS, 13, 134615-134633. (www)

  @article{ , abstract = {This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment or even substitute for real-world datasets, particularly in scenarios where labeled data is scarce, expensive, or sensitive. This paper surveys recent advances in leveraging LLMs to create synthetic text and code, highlighting key techniques such as prompt-based generation, retrieval-augmented pipelines, and iterative self-refinement. We examine how these methods can enrich low-resource tasks (e.g. classification, question answering) and facilitate code-centric applications (e.g. instruction tuning, code translation, bug repair) through automated verification of functional correctness. Alongside potential benefits—cost-effectiveness, broad coverage, and controllable diversity—we discuss the accompanying challenges, including factual inaccuracies in generated text, insufficient stylistic or distributional realism, and risks of bias amplification. Proposed mitigation strategies range from filtering and weighting synthetic outputs to reinforcement learning with execution feedback in code domains. We conclude by outlining open research directions, such as automated prompt engineering, cross-modal data synthesis, and robust evaluation frameworks, underscoring the growing importance of LLM-generated synthetic data in accelerating AI development while emphasizing ethical and quality safeguards.}, archive = {J_ACCESS}, author = {Mihai Nadǎş and Laura Dioşan and Andreea Tomescu}, doi = {10.1109/ACCESS.2025.3589503}, journal = {IEEE Access}, pages = {134615-134633}, shortjournal = {IEEE Access}, title = {Synthetic data generation using large language models: Advances in text and code}, volume = {13}, year = {2025}, }
• (2025). MRFB-net: A novel attention pooling network with modified receptive field block for uterine fibroid segmentation. ACCESS, 13, 134601-134614. (www)

  @article{ , abstract = {The segmentation of uterine fibroid in ultrasound image is very important for the implementation of ultrasound-guided high-intensity focused ultrasound therapy. However, due to the inherent limitations of ultrasound imaging, such as poor contrast, speckled noise, and variability of fibroid appearance, reliable and effective segmentation algorithms still face great challenges. To overcome these limitations, we introduce a deep learning-based architecture that leverages attention pooling decoder module to enhance the segmentation of uterine fibroids in preoperative ultrasound images, named MRFB-Net. In our framework, we utilize an encoder-decoder structure inspired by U-Net to capture image features. Then, we design an attention pooling module (APM) into the decoder to effectively alleviate the loss of spatial details caused by multiple pooling operations. Subsequently, we integrate modified receptive field block (MRFB) into the skip connection to learn more robust feature representation. Furthermore, we introduce a multilayer feature fusion module (MFFM) to harness complementary information from different layers. Finally, we conducted a series of comparative experiments using two in-house uterine fibroid clinical datasets and one publicly available brain tumor dataset. The evaluation results demonstrate the accuracy and reliability of the proposed MRFB-Net, as well as its adaptability to different types of medical images. In addition, ablation experiments also verified the effectiveness of the attention pooling module, modified receptive field block and multilayer feature fusion module.}, archive = {J_ACCESS}, author = {Yun Jiang and Xiaokang Ding and Hongmei Zhou}, doi = {10.1109/ACCESS.2025.3593364}, journal = {IEEE Access}, pages = {134601-134614}, shortjournal = {IEEE Access}, title = {MRFB-net: A novel attention pooling network with modified receptive field block for uterine fibroid segmentation}, volume = {13}, year = {2025}, }
• (2025). RIM-net: A real-imaginary-magnitude network for NLOS/LOS identification in UWB indoor positioning systems. ACCESS, 13, 134590-134600. (www)

  @article{ , abstract = {In UWB-based indoor positioning research, NLOS signals severely degrade localization accuracy. To address the NLOS/LOS discrimination challenge, this paper proposes the RIM-Net model, which enhances CIR signal feature learning by integrating residual blocks and LSTM architectures. Unlike conventional methods that process only the magnitude of CIR signals, RIM-Net explicitly models both real and imaginary components of CIR signals to improve classification accuracy. Experimental results demonstrate that RIM-Net achieves an average accuracy of 92.36% on benchmark datasets while maintaining generalization capabilities above 85% in unseen environments. Furthermore, the optimization of the TOA algorithm validated the effectiveness of RIM-Net in localization, achieving an average performance improvement of 9.66%. This approach provides an effective solution for NLOS/LOS identification in complex channel conditions within UWB positioning systems.}, archive = {J_ACCESS}, author = {Jiacheng Ni and Fang Li and Shuai Cao and Linsong Li}, doi = {10.1109/ACCESS.2025.3593578}, journal = {IEEE Access}, pages = {134590-134600}, shortjournal = {IEEE Access}, title = {RIM-net: A real-imaginary-magnitude network for NLOS/LOS identification in UWB indoor positioning systems}, volume = {13}, year = {2025}, }
• (2025). Energy efficiency in network slicing: Survey and taxonomy. ACCESS, 13, 134570-134589. (www)

  @article{ , abstract = {Network Slicing (NS) is a fundamental feature of 5G, 6G, and future mobile networks, enabling logically isolated virtual networks over shared infrastructure. As data demand increases and services diversify, ensuring Energy Efficiency (EE) in NS is vital (not only for operational cost savings but also to reduce the Information and Communication Technology (ICT) sector’s environmental footprint). This survey addresses the need for a comprehensive and holistic perspective on energy-efficient NS by reviewing and classifying recent strategies across the NS life cycle. Our contributions are threefold: (i) a thorough review of state-of-the-art techniques aimed at reducing energy consumption in NS; (ii) a novel taxonomy that organizes strategies into infrastructure, path/route, and slice operation levels; and (iii) the identification of open challenges and research directions, with a focus on systemic, cross-layer, and AI-driven approaches. By consolidating insights from recent developments, our work bridges existing gaps in the literature, offering a structured foundation for researchers and practitioners to design, evaluate, and improve energy-efficient network slicing systems.}, archive = {J_ACCESS}, author = {Adnei Willian Donatti and Marcia Cristina Machado and Marvin Alexander Lopez Martinez and Sabino S. Rogério Antunes and Eli Carlos Figueiredo Souza and Sand L. Corrêa and Tiago C. Ferreto and José Augusto Suruagy and Joberto S. B. Martins and Tereza Cristina Carvalho}, doi = {10.1109/ACCESS.2025.3590365}, journal = {IEEE Access}, pages = {134570-134589}, shortjournal = {IEEE Access}, title = {Energy efficiency in network slicing: Survey and taxonomy}, volume = {13}, year = {2025}, }
• (2025). CoDAC: Autonomous obstacle avoidance optimization for unmanned surface vehicle clusters via multi-modal dynamic perception and collaborative community detection. ACCESS, 13, 134552-134569. (www)

  @article{ , abstract = {Autonomous obstacle avoidance for unmanned surface vehicle (USV) clusters in dynamic marine environments faces challenges including heterogeneous sensor fusion, multi-objective optimization conflicts, and scalable swarm coordination. This paper proposes CoDAC, an autonomous obstacle avoidance optimization method that synergizes multimodal dynamic perception and incremental community detection. The method establishes an integrated “perception-planning-collaboration” framework. First, cross-modal feature fusion and spatio-temporal alignment techniques significantly enhance environmental perception accuracy and robustness, effectively addressing heterogeneous sensor data fusion challenges. Second, an incremental dynamic community detection mechanism achieves adaptive task group partitioning for clusters, substantially reducing communication loads and computational complexity while ensuring high-efficiency collaboration at scale. Meanwhile, the Improved Velocity Obstacle Model (IVO-DWA) integrates Triangle Obstacle Zone (TOZ) prediction with multi-objective optimization, enabling real-time trade-offs among path length, smoothness, and compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). Simulation experiments demonstrate that CoDAC exhibits superior real-time performance and stability in complex scenarios, strictly adheres to maritime rules, and improves the emergency obstacle avoidance success rate by 26.7% (to 96.7%). The proposed method provides a highly reliable and scalable solution for the collaboration of unmanned systems in complex and dynamic marine environments.}, archive = {J_ACCESS}, author = {Youhong Li and Jiawei Ye and Le Gao and Min Cai}, doi = {10.1109/ACCESS.2025.3593236}, journal = {IEEE Access}, pages = {134552-134569}, shortjournal = {IEEE Access}, title = {CoDAC: Autonomous obstacle avoidance optimization for unmanned surface vehicle clusters via multi-modal dynamic perception and collaborative community detection}, volume = {13}, year = {2025}, }
• (2025). Improving railway track detection with a mixed-modality deep learning approach. ACCESS, 13, 134541-134551. (www)

  @article{ , abstract = {Artificial intelligence (AI) integration has become crucial in augmenting safety systems at train stations and along railway lines. It plays a crucial role in identifying and monitoring individuals, detecting hazardous items, classifying unusual movements, and recognizing obstructions on railway lines. Artificial intelligence’s capacity to swiftly and precisely analyze data enables systems to react to possible dangers promptly. The present study introduces a sophisticated railway track identification system that utilizes semantic segmentation methods and deep learning models to effectively distinguish between items and persons in close proximity to railway zones. This work combines ResNet-50 with five segmentation models: SegNet, U-Net, FCN-8, FCN-16, and FCN-32. Refinements to the model’s underlying architecture are intended to enhance its performance, while a dataset of 5,000 images from the internet is utilized for testing. This paper compares traditional and improved model architectures, including precise learning rate adjustment to maximize accuracy. Empirical findings demonstrate that incorporating ResNet-50 substantially enhances accuracy and precision. The FCN-16+ ResNet-50 model had a mean accuracy coefficient of 98.72%. Despite the increased computational parameters, the improved models paradoxically enable quicker processing across all models.}, archive = {J_ACCESS}, author = {Wichian Ooppakaew and Jakkrit Onshaunjit and Saifun Khrueakhrai and Jakkree Srinonchat}, doi = {10.1109/ACCESS.2025.3593629}, journal = {IEEE Access}, pages = {134541-134551}, shortjournal = {IEEE Access}, title = {Improving railway track detection with a mixed-modality deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Edge computing application-specific integrated circuit with two serial communication protocols and a backpropagation neural network. ACCESS, 13, 134529-134540. (www)

  @article{ , abstract = {This study developed an edge computing application-specific integrated circuit (ASIC) with two serial communication protocols, adjustable weights, and a backpropagation neural network (BPNN) for edge computing. The serial peripheral interface and I2C serial communication protocols are used in the developed ASIC to ensure its communication reliability. Moreover, adjustable weights are employed to achieve high edge computing performance and minimize the computational load. An Arduino development board captures sensing data and sends them to the designed ASIC through the I2C communication protocol. A 25LC640 EEPROM memory microchip is mounted externally on the ASIC and stores the BPNN model, training data, and adjustable weights. The BPNN model is constructed using the inverse transfer algorithm, and this model’s training process is optimized by employing the stochastic gradient descent method. This approach not only ensures a high learning rate but also leads to relatively few iterations. Once the training procedure is complete, the obtained weights are stored in the EEPROM. Moreover, the BPNN model can be trained on a computer or other external device and then loaded into the plug-in EEPROM directly. This design not only enhances the tunability of the model but also facilitates migration and application of the learning results among devices. The designed ASIC was fabricated through the 90-nm cell-based complementary metal–oxide–semiconductor process of Taiwan Semiconductor Manufacturing Company. According to the simulation results, the proposed ASIC has a gate count of 85526, a chip area of $0.896\times 0.896$ mm2, and power consumption of 0.7459 mW at a voltage of 1.0 V and frequency of 20 MHz.}, archive = {J_ACCESS}, author = {Guo-Ming Sung and Sachin D. Kohale and Chao-Yu Chen and Ying-Tzu Lai}, doi = {10.1109/ACCESS.2025.3592315}, journal = {IEEE Access}, pages = {134529-134540}, shortjournal = {IEEE Access}, title = {Edge computing application-specific integrated circuit with two serial communication protocols and a backpropagation neural network}, volume = {13}, year = {2025}, }
• (2025). Linking electrocardiogram and echocardiogram: Comparing classical machine learning and deep learning neural networks for the detection of regional wall motion abnormalities. ACCESS, 13, 134519-134528. (www)

  @article{ , abstract = {Historically, electrocardiogram (ECG) datasets have been created based on physicians’ interpretation of the ECG, which may introduce human biases and errors. Nightingale Open Science provides an open-source ECG dataset linking to an imaging marker, regional wall motion abnormality (RWMA), that is primarily associated with myocardial ischemia or infarction. RWMA refers to an area of the heart muscle that does not contract properly and can be identified on an imaging modality such as an echocardiogram or cardiac magnetic resonance imaging (MRI). Our study aimed to predict RWMA using both classical machine learning (ML) methods and a one-dimensional convolutional neural network (1D CNN) model on the 12-lead ECG data from 3,750 unique patients provided by the Nightingale Open Science platform. Of the 3,750 patients, 341 (9.1%) were diagnosed with positive RWMA on their echocardiogram within a year of the ECG. After filtering out patients with missing data, we identified 313 patients with positive RWMA label and randomly undersampled 313 from the remaining patients to keep the data balanced. We repeated the experiments with five random selections of the 313 patients without RWMA to ensure the robustness of the models. Our results indicate that the optimized 1D CNN model achieved an area under the receiver operating characteristic curve (AUROC) of 73.40% (1D CNN) versus 63.58% (Random Forest), an area under the precision-recall curve (AUPRC) of 72.45% (1D CNN) versus 72.64% (Random Forest), and accuracy of 71.15% (1D CNN) versus 63.58% (Random Forest). The Random Forest model revealed that QT duration and maximal amplitude differential of ST elevation or depression as the key features associated with RWMA. This study demonstrated that CNN models and classical ML methods offer complementary advantages. Additionally, we compared the performance, training time and model complexity of 1D CNN model with the latest foundation model, HuBERT-ECG. This is a preliminary step for further elucidation of the relationship between ECG and echocardiogram. Future studies are needed to determine the optimal CNN architecture that balances the target classifier performance and computational efficiency for ease of deployment with larger datasets.}, archive = {J_ACCESS}, author = {Shantanu M. Joshi and Hana R. Shaik and Shivam Rai Sharma and Philip Strong and Uma Srivatsa and Imo Ebong and Hyoyoung Jeong and Chen-Nee Chuah and Lihong Mo}, doi = {10.1109/ACCESS.2025.3592693}, journal = {IEEE Access}, pages = {134519-134528}, shortjournal = {IEEE Access}, title = {Linking electrocardiogram and echocardiogram: Comparing classical machine learning and deep learning neural networks for the detection of regional wall motion abnormalities}, volume = {13}, year = {2025}, }
• (2025). Fine tuning hyperparameters of deep learning models using metaheuristic accelerated particle swarm optimization algorithm. ACCESS, 13, 134506-134518. (www)

  @article{ , abstract = {In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing. The success of CNNs can be attributed to their ability to learn hierarchical representations from data. However, achieving optimal performance with CNNs often necessitates fine-tuning a myriad of hyperparameters, such as learning rates, batch sizes, and network architectures. This tuning process typically relies on expert judgment and can be time-consuming and resource-intensive. To address this challenge, a novel metaheuristic-based optimization approach called Accelerated Particle Swarm Optimization for CNNs (APSO-CNN) is proposed. APSO-CNN uses the global search capabilities of Particle Swarm Optimization (PSO) to automatically optimize hyperparameter configurations for architecture-determined Convolutional Neural Networks (CNNs). The main contribution of this work is the development of the APSO-CNN framework, which introduces an improved PSO variant specifically tailored for Convolutional Neural Networks (CNNs) hyperparameter tuning, thereby reducing manual intervention and significantly enhancing model performance. The proposed APSO-CNN is evaluated across various Convolutional Neural Networks (CNNs) architectures and the Faces94 dataset, representing a diverse range of image processing tasks. Experiments demonstrate that the proposed automated hyperparameter fine-tuning approach consistently yields significant improvements in performance metrics, including Accuracy, Precision, Recall, and F1 Score. The APSO-CNN framework was experimentally validated on the Faces94 dataset comprising 3,060 facial images. These enhancements underscore the effectiveness of APSO-CNN in optimizing Convolutional Neural Networks (CNNs) for image processing applications. Accelerated Particle Swarm Optimization for CNNs (APSO-CNN) presents a promising solution to the challenge of hyperparameter optimization in Convolutional Neural Networks (CNNs). By automating this critical aspect of model development, Accelerated Particle Swarm Optimization for CNNs (APSO-CNN), streamlines the process, making it more efficient and accessible to researchers and practitioners. This contribution has the potential to accelerate advancements in image processing and related fields by enabling the rapid development of high-performance Convolutional Neural Networks (CNNs) models.}, archive = {J_ACCESS}, author = {Abdel-Hamid M. Emara and Ghada Atteia and Jawad Hasan Alkhateeb}, doi = {10.1109/ACCESS.2025.3591403}, journal = {IEEE Access}, pages = {134506-134518}, shortjournal = {IEEE Access}, title = {Fine tuning hyperparameters of deep learning models using metaheuristic accelerated particle swarm optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Learning with partial-label and unlabeled data: Contrastive with negative example separation. ACCESS, 13, 134497-134505. (www)

  @article{ , abstract = {Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. In SSPLL, the existence of unlabeled examples presents a great challenge to train a model with good generalization ability. To tackle this challenge, in this work we propose a new method termed Learning with Partial-Label and Unlabeled Data: Contrastive with Negative Example Separation (LPU-CNES), which leverages the powerfulness of Contrastive Learning in extracting high-level semantic representations for weakly supervised and unsupervised scenarios. Specifically, LPU-CNES first transforms unlabeled examples into partial label ones by assigning pseudo candidate label sets to them, and then introduces negative example separation to construct the contrastive loss, and finally trains the model by minimizing the sum of contrastive loss, regularization loss and other classic PLL classification losses. We demonstrate the effectiveness of our method in terms of classification accuracy across multiple benchmarks.}, archive = {J_ACCESS}, author = {Bangfa Jiang and Chengkun Liu and Jing Chai}, doi = {10.1109/ACCESS.2025.3593642}, journal = {IEEE Access}, pages = {134497-134505}, shortjournal = {IEEE Access}, title = {Learning with partial-label and unlabeled data: Contrastive with negative example separation}, volume = {13}, year = {2025}, }
• (2025). MRTMD: A multi-resolution dataset for evaluating object detection in traffic monitoring systems. ACCESS, 13, 134460-134483. (www)

  @article{ , abstract = {Traffic monitoring reduces congestion, improves safety, and supports environmental sustainability. Real-time flow tracking, anomaly detection, and efficient management are key. Convolutional Neural Networks (CNNs) have become integral due to their compact size and easy deployment. However, their effectiveness depends heavily on the quality of the input data, especially image resolution. With high-resolution cameras, especially 4K, balancing image quality, detection accuracy, and system efficiency is critical. We propose the Multi-Resolution Traffic Monitoring Dataset (MRTMD), which captures transport scenes at resolutions ranging from 2160p to 360p. This dataset serves as a benchmark for standard object detection models, enabling the development of more efficient and cost-effective traffic monitoring solutions. MRTMD will be freely available on GitHub, offering a valuable resource for researchers and practitioners. We evaluate leading object detection models—YOLOv9, YOLOv8, YOLOv7, Faster R-CNN, FCOS, SSD, and RT-DETR—across varied resolutions. Our analysis focuses on mean Average Precision (mAP), recall, and processing time. We also assess the accuracy of Number Plate Recognition (NPR) for tasks that require fine-grained detail extraction. Our findings show that detection performance typically varies within ±0.01 to ±0.03 in mAP and recall across resolutions, suggesting higher resolutions are not always advantageous. However, they remain crucial for tasks like NPR. The multi-resolution dataset enables a comprehensive evaluation of the trade-off between image quality and task performance. Ultimately, our analysis highlights the importance of resolution selection in large-scale deployments, informing system designers and policymakers. This dataset is a vital tool for balancing performance, cost, and practical constraints in real-world traffic monitoring.}, archive = {J_ACCESS}, author = {Mark Bugeja and Matthias Bartolo and Matthew Montebello and Dylan Seychell}, doi = {10.1109/ACCESS.2025.3585986}, journal = {IEEE Access}, pages = {134460-134483}, shortjournal = {IEEE Access}, title = {MRTMD: A multi-resolution dataset for evaluating object detection in traffic monitoring systems}, volume = {13}, year = {2025}, }
• (2025). Voltage-based load recognition and its integration into an application use case. ACCESS, 13, 134423-134440. (www)

  @article{ , abstract = {The expansion of renewable energies, the growing number of consumers, e.g., in e-mobility, and their connection to low voltage grids pose new challenges for grid operation, particularly regarding voltage control and energy losses. In this context, comprehensive knowledge about the surrounding grid facilitates the intelligent control of photovoltaic (PV)-battery systems, creating a need to observe grid participants. In that regard, a previously developed load recognition method is successfully applied in a use case to recognize two electric vehicles and a heat pump in a simulative grid environment, yielding an accuracy of around 96%. Considering to couple that method with a subsequent control algorithm, an approach is developed to estimate a virtual voltage signal, which represents the grid voltage without the impact of control actions. It is validated that this enables the effective application of load recognition in control conditions. Finally, it is demonstrated how to integrate the load recognition method into a control setup using the virtual voltage concept. The corresponding control algorithm manages the power flow between a PV-battery system and the grid in conjunction with a static Q(U) algorithm. It leverages the information about active loads with the objectives of maintaining a stable and balanced grid voltage and optimizing energy consumption in the example use case. Thereby, the voltage deviation is reduced by around 26% and the grid unbalance by approximately 38%, compared to only using the Q(U) algorithm without a battery. It can be concluded that the load recognition method can successfully gather information from the surroundings of a grid node even in control applications.}, archive = {J_ACCESS}, author = {Henning Schlachter and Stefan GeißEndörfer and Holger Behrends and Karsten Von Maydell and Carsten Agert}, doi = {10.1109/ACCESS.2025.3592476}, journal = {IEEE Access}, pages = {134423-134440}, shortjournal = {IEEE Access}, title = {Voltage-based load recognition and its integration into an application use case}, volume = {13}, year = {2025}, }
• (2025). System-level analysis of LED retrofit solutions for t8 fluorescent tubes: Harmonic emissions, photometric impacts, and compliance considerations. ACCESS, 13, 134403-134422. (www)

  @article{ , abstract = {This study investigates the harmonic emissions, photometric performance, and techno-economic implications of retrofitting T8 fluorescent tubes with LED alternatives under various ballast configurations. Experimental results reveal that while legacy fluorescent installations—whether magnetic or electronic—generally comply with EN IEC 61000-3-2 harmonic limits, most retrofit LED tube scenarios do not. The lack or ineffectiveness of power factor correction circuits in low-power LED tubes is identified as a key contributor to non-compliance. Moreover, the presence of magnetic ballasts introduces unpredictable effects: in some cases acting as harmonic filters, but in others exacerbating distortion, particularly when interacting with internal LED drivers. Photometric assessments further highlight inconsistent luminous output and flicker performance, especially when magnetic ballasts remain in place. Lighting simulations demonstrate that although ballast-based retrofits may yield tolerable illuminance levels, they consistently exceed ASHRAE 90.1 power density limits and may lead to overillumination when luminaires are retrofitted one by one without considering the photometric quantities, which are unknown when LED tubes are placed in an existing luminaire. Techno-economic analysis confirms that full luminaire replacement with appropriately selected LED systems—combined with design optimization—achieves higher energy efficiency, shorter return on investment, and reliable compliance. Furthermore, the study includes an assessment of colorimetric characteristics and metrics relevant to human-centric lighting, such as the melanopic daylight efficacy ratio (MDER) and equivalent melanopic daylight illuminance, revealing significant variations between fluorescent and LED retrofit solutions. These findings emphasize the importance of evaluating not only energy and photometric performance but also biological lighting impacts when considering retrofit strategies. The findings emphasize that lighting retrofitting should not be treated as a simple lamp replacement but as a system-level intervention. Properly engineered LED replacements provide a more consistent, efficient, and sustainable solution, supporting the objectives of long-term reliability, regulatory alignment, and Sustainable Development Goals 3, 7, 9, 11, and 12.}, archive = {J_ACCESS}, author = {Themistoklis L. Athanasiadis and Eleni P. Nicolopoulou and Evangelos-Nikolaos D. Madias and Aikaterini D. Polykrati and Christos A. Christodoulou and Lambros T. Doulos and Ioannis F. Gonos}, doi = {10.1109/ACCESS.2025.3593623}, journal = {IEEE Access}, pages = {134403-134422}, shortjournal = {IEEE Access}, title = {System-level analysis of LED retrofit solutions for t8 fluorescent tubes: Harmonic emissions, photometric impacts, and compliance considerations}, volume = {13}, year = {2025}, }
• (2025). GLOPS: A hybrid approach for enhanced scheduling in cloud computing environments via machine learning-based process prediction. ACCESS, 13, 134385-134402. (www)

  @article{ , abstract = {Cloud computing enables on-demand access to scalable computing resources, fostering agility and cost-effectiveness. However, effective resource allocation strategies are crucial to ensuring optimal utilization and meet dynamic workload demands. Traditional heuristic approaches often lead to suboptimal decisions, performance degradation, and resource wastage in dynamic cloud environments due to multifarious shortcomings. Furthermore, the need for a robust process prediction method is crucial, as doing so would make it possible to schedule said processes before arrival, in turn increasing performance metrics. In order to mitigate these challenges, this research proposes a comprehensive strategy that incorporates machine learning based process prediction and metaheuristic optimization techniques in the process scheduling. The research includes a process prediction pipeline, employing Linear Regression and Long Short Term Memory. However, early predictions would seem futile without efficient scheduling, which is why the article further proposes Global and Local Optimized Preemptive Scheduling (GLOPS), an enhanced metaheuristic scheduling algorithm employing Particle Swarm Optimization and Simulated Annealing. The performance of the proposed work has been evaluated on relevant data, and the results demonstrate how well the method performs, considerably enhancing system performance above conventional heuristics in terms of assessment criteria. The proposed approach positions the method as a competent solution in the field of process prediction and efficient scheduling, all the while outperforming the existing approaches by managing to reduce overall turn around time by up to 48.79%.}, archive = {J_ACCESS}, author = {Syed Abu Ammar Muhammad Zarif and Layla Kader Suma and Md. Sadat Rahman and Md. Motaharul Islam and Phuoc Hung Pham and Phong Dung Thi Nguyen and Nguyen Thanh Binh}, doi = {10.1109/ACCESS.2025.3592211}, journal = {IEEE Access}, pages = {134385-134402}, shortjournal = {IEEE Access}, title = {GLOPS: A hybrid approach for enhanced scheduling in cloud computing environments via machine learning-based process prediction}, volume = {13}, year = {2025}, }
• (2025). A self-balancing robot with redundant actuators for obstacle avoidance. ACCESS, 13, 134376-134384. (www)

  @article{ , abstract = {Planar robots, such as self-balancing two-wheeled systems, are designed to operate in two-dimensional space and maintain stability while in motion. While these systems can navigate simple terrains, they struggle with complex obstacles requiring traversal from above or below. This paper presents a novel self-balancing robot capable of autonomous morphological reconfiguration between two-wheeled and four-wheeled configurations, enabling it to overcome such challenges. To ensure robust stability throughout these transitions, an adaptive PID control strategy is implemented, facilitating real-time gain adjustment based on the robot’s dynamic physical state. Experimental results demonstrate that the adaptive controller maintains balance during both transformation phases, in contrast, a fixed-gain PID controller fails to maintain balance during these phases. The system illustrates promising capabilities for mobile robotics applications such as search and rescue or navigation in unstructured environments, while demonstrating a practical method for analytically adjusting PID gains in response to measured tracking error under changing dynamic conditions.}, archive = {J_ACCESS}, author = {Ariel Hess and Oded Medina}, doi = {10.1109/ACCESS.2025.3590812}, journal = {IEEE Access}, pages = {134376-134384}, shortjournal = {IEEE Access}, title = {A self-balancing robot with redundant actuators for obstacle avoidance}, volume = {13}, year = {2025}, }
• (2025). Social compliance with NPIs, mobility patterns, and reproduction number: Lessons from COVID-19 in europe. ACCESS, 13, 134357-134375. (www)

  @article{ , abstract = {Non-pharmaceutical interventions (NPIs), such as lockdowns, travel restrictions, and social distancing mandates, play a critical role in controlling the spread of infectious diseases by shaping human mobility patterns. Using COVID-19 as a case study, this research investigates the relationships between NPIs, mobility, and the effective reproduction number (Rt) across 13 European countries. We employ XGBoost regression models to estimate missing mobility data from NPIs and missing Rt values from mobility, achieving high accuracy. Additionally, using clustering techniques, we uncover national distinctions in social compliance. Northern European countries demonstrate higher adherence to NPIs than Southern Europe, which exhibits more variability in response to restrictions. These differences highlight the influence of cultural and social norms on public health outcomes. In general, our analysis reveals a strong correlation between NPIs and mobility reductions, highlighting the immediate impact of restrictions on population movement. However, the relationship between mobility and Rt is weaker and more nuanced, reflecting the time delays involved, as changes in mobility take time to influence transmission rates. These results underscore the interdependence of restrictions, mobility, and disease spread while demonstrating the potential for data-driven approaches to guide policy decisions. Our approach offers valuable insights for optimizing public health strategies and tailoring interventions to diverse cultural contexts during future health crises.}, archive = {J_ACCESS}, author = {Daniele Baccega and Jose Aguilar and Carlos Baquero and Antonio Fernández Anta and Juan Marcos Ramirez}, doi = {10.1109/ACCESS.2025.3586508}, journal = {IEEE Access}, pages = {134357-134375}, shortjournal = {IEEE Access}, title = {Social compliance with NPIs, mobility patterns, and reproduction number: Lessons from COVID-19 in europe}, volume = {13}, year = {2025}, }
• (2025). Artificial general intelligence: Advancements, challenges, and future directions in AGI research. ACCESS, 13, 134325-134356. (www)

  @article{ , abstract = {Artificial General Intelligence (AGI) is a transformative shift in artificial intelligence that aims to match human-like reasoning, flexibility, and self-learning. Unlike Narrow AI, which is capable of performing only a limited set of tasks, AGI aspires to handle any intellectual task by exhibiting human-like learning, reasoning, and behavior. This enables AGI to offer extraordinary potential in various domains such as healthcare, education, transportation, and more. This paper provides a comprehensive review of the fundamental concepts, applications, and challenges associated with AGI’s development. This systematic review of the literature explored AGI’s transformative potential, from personalized healthcare and adaptive learning systems to advanced autonomous vehicles and predictive analytics. Although, AGI has great potential, multiple challenges, such as ethical issues, data privacy, and other technical challenges, must be addressed before its launch in the real world.}, archive = {J_ACCESS}, author = {Gokul Yenduri and Ramalingam Murugan and Praveen Kumar Reddy Maddikunta and Sweta Bhattacharya and Devulapalli Sudheer and Bharath Bhushan Savarala}, doi = {10.1109/ACCESS.2025.3592708}, journal = {IEEE Access}, pages = {134325-134356}, shortjournal = {IEEE Access}, title = {Artificial general intelligence: Advancements, challenges, and future directions in AGI research}, volume = {13}, year = {2025}, }
• (2025). Research on surface defects classification for PET preform by fusing multi-scale features. ACCESS, 13, 134312-134324. (www)

  @article{ , abstract = {Product quality control is an important component of PET preform production process. In order to improve the accuracy and reliability of PET preform detection, reduce the cost of product quality defects, and improve the overall level of product quality, this paper proposes a surface defect detection method for PET preform based on multi-scale feature fusion. Multi-scale features fusion combines features from different scales to produce more accurate and robust feature representations, which improve the accuracy, stability and adaptability of PET preform detection model. This feature processing model constructs a three-branch network, generating feature maps of different sizes. Then, the low-level feature, the middle-level feature and the high-level feature are fused to obtain a PET preform feature map based on multi-scale feature fusion by using a maximum value fusion strategy. The original image and feature map of the PET preform are respectively input into the deep convolutional neural network for feature extraction and fusion. And finally the fusion features are input into the ECOC-SVM to achieve surface defect classification of the PET preform. The machine vision platform is designed for collecting and detecting PET preform data. By importing the model designed in this paper, surface defect recognition and defect category judgment of PET preform can be achieved. Through experimental data analysis, the higher testing detection accuracy of this method is 98.8%, with an average testing accuracy of 97.1%.}, archive = {J_ACCESS}, author = {Chunmei Duan and Taochuan Zhang and Lei Han and Huilin Tan}, doi = {10.1109/ACCESS.2025.3589559}, journal = {IEEE Access}, pages = {134312-134324}, shortjournal = {IEEE Access}, title = {Research on surface defects classification for PET preform by fusing multi-scale features}, volume = {13}, year = {2025}, }
• (2025). A machine learning-based framework for measuring attack surfaces of IoT systems. ACCESS, 13, 134297-134311. (www)

  @article{ , abstract = {With the increasing demand for utilizing IoT devices in many cases, in recent years, a great risk raises as many of those devices will be vulnerable to many types of cyberattacks. Moreover, the latest increase in intelligent cyberattacks along with the complexity increase in IoT architectures place a greater risk that many of those cyberattacks will not be discovered until very late stages. However, continuous evolution in Artificial Intelligence (AI) methods, such as machine learning models, can provide an intelligent and effective approach to accommodate and mitigate such risks in the early stages. If such models are used to analyze previous cyberattacks on IoT architectures to predict their behaviors on different layers of the IoT architecture, then this will definitely help in reducing impact of such cyberattacks. This presents the main objective of this work, in which risks associated with IoT cyberattacks are mitigated by measuring the attack surface size of an IoT architecture from the perspective of the information flow exposed by these surfaces. The paper proposes a novel approach that shows how to map certain cyberattacks to specific layers of the IoT architecture. Based on that mapping, several security metrics are defined to measure that architecture’s attack surface size. This paper also shows an experimental study of how the framework proposed here can be applied to a three-layer IoT architecture using a publicly available dataset. Several machine learning models have been used to show how to evaluate and validate the mapping process between different layers and various cyberattacks. Once higher performance results are obtained of the defined mapping, the security metrics proposed here are applied to measure the attack surface size of that particular architecture.}, archive = {J_ACCESS}, author = {Bandar M. Alshammari}, doi = {10.1109/ACCESS.2025.3593516}, journal = {IEEE Access}, pages = {134297-134311}, shortjournal = {IEEE Access}, title = {A machine learning-based framework for measuring attack surfaces of IoT systems}, volume = {13}, year = {2025}, }
• (2025). Convolutional versus large language models for software log classification in edge-deployable cellular network testing. ACCESS, 13, 134283-134296. (www)

  @article{ , abstract = {Software logs generated by sophisticated network emulators in the telecommunications industry, such as VIAVI TM500, are extremely complex, often comprising tens of thousands of text lines with minimal resemblance to natural language. Only specialised expert engineers can decipher such logs and troubleshoot defects in test runs. While AI offers a promising solution for automating defect triage, potentially leading to massive revenue savings for companies, state-of-the-art large language models (LLMs) suffer from significant drawbacks in this specialised domain. These include a constrained context window, limited applicability to text beyond natural language, and high inference costs. To address these limitations, we propose a compact convolutional neural network (CNN) architecture that offers a context window spanning up to 200,000 characters and achieves over 96% accuracy (F $1\gt 0.9$ ) in classifying multifaceted software logs into various layers in the telecommunications protocol stack. Specifically, the proposed model is capable of identifying defects in test runs and triaging them to the relevant department, formerly a manual engineering process that required expert knowledge. We evaluate several LLMs; LLaMA2-7B, Mixtral_ $8\times 7$ B, Flan-T5, BERT and BigBird, and experimentally demonstrate their shortcomings in our specialized application. Despite being lightweight, our CNN achieves strong performance compared to LLM-based approaches in telecommunications log classification while minimizing the cost of production. Our defect triaging AI model is deployable on edge devices without dedicated hardware and is applicable across software logs in various industries.}, archive = {J_ACCESS}, author = {Achintha Ihalage and Sayed Taheri and Faris Muhammad and Hamed Al-Raweshidy}, doi = {10.1109/ACCESS.2025.3587029}, journal = {IEEE Access}, pages = {134283-134296}, shortjournal = {IEEE Access}, title = {Convolutional versus large language models for software log classification in edge-deployable cellular network testing}, volume = {13}, year = {2025}, }
• (2025). A black box approach to EV energy consumption: Integrating key parameters for drive range optimization. ACCESS, 13, 134269-134282. (www)

  @article{ , abstract = {This paper aims to develop a black box model to improve the accuracy of drive range prediction in electric vehicles. Traditional models are typically based on limited parameters, often overlooking critical factors significantly impacting electric vehicles’ (EVs’) performance and energy consumption. The paper aims to address the gaps in current electric vehicle energy usage models by developing a more comprehensive EV energy consumption model, known as the Black Box Model. The paper incorporates and integrates twenty-three diverse parameters into a single mathematical framework, including conventional factors and auxiliary variables, to positively influence drive range and enhance overall EV performance. The study classifies EVs into eight distinct energy consumption groups, identifying key parameters that lead to sub-optimal drive range predictions. These parameters are systematically incorporated into the model to improve the precision of energy consumption monitoring and drive range estimation. An EV model is created in AVL eSuite and, through simulation, as it plays a critical role in artificial intelligence (AI) workflows by providing a controlled environment, the model was validated using real-world data from Nissan Leaf SV Plus. The model’s results positively demonstrate the role of multi-parameter integration in energy consumption analysis and lay the groundwork for future advancements in optimizing EV drive range, contributing to the broader goal of sustainable and efficient electric mobility.}, archive = {J_ACCESS}, author = {Muhammad Arsalan Jalees Abro and Abdul Sattar Larik and Ali Alqazzaz and Irfan Ahmed Halepoto and Mana Saleh Al Reshan and Asadullah Shaikh and Samar M. Alqhtani and Jotee Kumari Lohana}, doi = {10.1109/ACCESS.2025.3591371}, journal = {IEEE Access}, pages = {134269-134282}, shortjournal = {IEEE Access}, title = {A black box approach to EV energy consumption: Integrating key parameters for drive range optimization}, volume = {13}, year = {2025}, }
• (2025). New counterexamples to sequential composition adaptive insecurity. ACCESS, 13, 134261-134268. (www)

  @article{ , abstract = {At CRYPTO 2005, Pietrzak demonstrated an intriguing result: the sequential composition of two non-adaptively secure pseudorandom functions does not necessarily imply adaptive security, assuming the decisional Diffie-Hellman (DDH) assumption holds. To support this claim, they constructed a cleverly designed counterexample consisting of two functions, $\textsf {F}$ and $\textsf {G}$ . While both $\textsf {F}$ and $\textsf {G}$ are non-adaptively secure, their sequential composition $\textsf {G} \circ \textsf {F}$ fails to achieve adaptive security. Such a counterexample is highly non-trivial, requiring sophisticated designs and a deep understanding of the distinctions between non-adaptive and adaptive security. In this work, we show that such well-crafted counterexample is not unique by presenting two novel counterexamples derived from entirely different intuitions. These new constructions imply that there may be potential systematic approaches to designing such counterexamples, providing new insights into the relationship between non-adaptive and adaptive security.}, archive = {J_ACCESS}, author = {Liqing Yu}, doi = {10.1109/ACCESS.2025.3592932}, journal = {IEEE Access}, pages = {134261-134268}, shortjournal = {IEEE Access}, title = {New counterexamples to sequential composition adaptive insecurity}, volume = {13}, year = {2025}, }
• (2025). Hierarchical RUL prediction for turbofan engines based on health stage classification and change point-guided data augmentation. ACCESS, 13, 134241-134260. (www)

  @article{ , abstract = {In prognostics, it is essential to accurately predict the Remaining Useful Life (RUL) of complex systems like turbofan engines. Most studies adopt data-driven methods and rely on piecewise linear (PwL) labeling, where a fixed initial RUL is assigned to all engines. To better reflect real-world degradation, this assumption is refined using change point detection to assign engine-specific initial RUL values, offering a more realistic problem formulation. In line with PwL-based labeling approaches, where mechanical systems shift from a normal to a degradation phase at a specific change point, distinct RUL prediction models are developed for each health stage. A classification model is proposed using a novel training methodology to identify the current health stage of an engine. This method segments the dataset into pre- and post-change intervals and applies data augmentation both to simulate the truncated nature of the test data—where engines are monitored up to unknown failure points—and to construct a balanced training set that fairly represents both healthy and degraded conditions, thereby improving the robustness and generalization of the classification model. Two deep learning models are used for RUL prediction in engines: an LSTM model for degraded engines, which captures long-term dependencies in deterioration data, and a feature-level fusion model for healthy engines with sparse sensor data, which enhances prediction accuracy by integrating CNN and LSTM, addressing unique problems in different engine health stages. Model interpretability is enhanced through post-hoc SHAP analysis, revealing stage-specific sensor importance and explaining predictions across different health stages.}, archive = {J_ACCESS}, author = {Kiymet Ensarioglu}, doi = {10.1109/ACCESS.2025.3593206}, journal = {IEEE Access}, pages = {134241-134260}, shortjournal = {IEEE Access}, title = {Hierarchical RUL prediction for turbofan engines based on health stage classification and change point-guided data augmentation}, volume = {13}, year = {2025}, }
• (2025). A PLS-SEM for ERP adoption readiness in government organizations: A TOEPP conceptual model. ACCESS, 13, 134225-134240. (www)

  @article{ , abstract = {Enterprise Resource Planning (ERP) is an integrated system widely used by large private companies. Its adoption in the public sector has enormous potential to improve operational efficiency and effectiveness. However, government organizations need to first assess their level of readiness to ensure the success of ERP implementation. Despite its importance, research on ERP readiness is still limited, especially in developing countries. This study develops and empirically tests an ERP adoption readiness model for government organizations in Indonesia using the Partial Least Squares-Structural Equation Modeling (PLS-SEM) approach. To fill the gap in the literature, this study proposes the Technology, Organization, Environment, People, and Process (TOEPP) framework, which is an extension of the classic TOE model by adding the dimensions of People and Process. Based on data from 268 local government employees, the findings show that the five TOEPP constructs have a significant effect on ERP readiness. The theoretical contribution of this study lies in strengthening the TOEPP framework in the context of public sector digital transformation. Practically, this model serves as an evaluative guide for policymakers and IT managers in designing effective ERP adoption strategies in bureaucratic environments. This study emphasizes the importance of a holistic approach to assessing digital transformation readiness in the public sector. The results provide important insights to strengthen ERP initiatives, improve organizational readiness, and increase the chances of successful implementation. In addition, this study fills the research gap related to ERP pre-implementation preparation and offers a comprehensive, evidence-based framework for government agencies.}, archive = {J_ACCESS}, author = {R. Wahjoe Witjaksono and Tri Kuntoro Priyambodo and Mardhani Riasetiawan}, doi = {10.1109/ACCESS.2025.3592650}, journal = {IEEE Access}, pages = {134225-134240}, shortjournal = {IEEE Access}, title = {A PLS-SEM for ERP adoption readiness in government organizations: A TOEPP conceptual model}, volume = {13}, year = {2025}, }
• (2025). Adaptive coordinated protection for DC microgrids with lunar surface power applications. ACCESS, 13, 134212-134224. (www)

  @article{ , abstract = {The future of deep space exploration is becoming increasingly dependent on a reliable and resilient electric power system (EPS) to support several subsystems including life support, electric propulsion, autonomous rovers, and more. The planned growth of NASA’s Artemis base camp will present new challenges in the operation and control of the expanding EPS. In particular, improved fault detection and isolation methods are needed to protect the microgrid from faults as it undergoes expansion and modifications given the limitations and constraints of a space-based power system. This paper proposes a pragmatic technique for achieving coordinated zonal protection against direct current (DC) line-to-ground faults with autonomous adaptive tuning for topological and operational changes to the EPS. Here a dynamic method is developed to tune circuit breaker settings based on the given system topology, generation capacity, and expected load. The proposed adaptive coordinated protection approach is demonstrated using real-time simulation and hardware-in-the-loop (HIL) experiments to evaluate its effectiveness in achieving zonal fault isolation.}, archive = {J_ACCESS}, author = {Marc A. Carbone and Matthew Muscatello and Xavier Collazo Fernandez and Brent Gardner and Jeffrey Csank}, doi = {10.1109/ACCESS.2025.3593439}, journal = {IEEE Access}, pages = {134212-134224}, shortjournal = {IEEE Access}, title = {Adaptive coordinated protection for DC microgrids with lunar surface power applications}, volume = {13}, year = {2025}, }
• (2025). Emulator forensics investigation model (EFIM). ACCESS, 13, 134189-134211. (www)

  @article{ , abstract = {Emulator forensics has become critical in combating cybercrime, providing deep insight into digital artifacts generated within virtual environments. This study introduces the Emulator Forensics Investigation Model (EFIM), a nine-phase, platform-agnostic forensic framework designed to guide systematic investigations across Android emulator platforms. To implement this model, we developed the Emulator Forensics Tool (EFT), a lightweight, modular tool that supports the detection and analysis of widely used emulators, including Bluestacks, NoxPlayer, and Waydroid. EFT achieved a $48\times $ speed improvement over commercial tools on Windows and a $7\times $ gain on Unix systems, while also demonstrating lower RAM usage (80 MB vs. 1811 MB) and higher disk I/O throughput. EFIM formalizes its process via Set Theory and Finite State Machines (FSMs), ensuring auditability and reproducibility. EFT supports the binary-level extraction of encrypted BLOB artifacts (e.g., Gmail, Telegram), enabling deferred cryptographic analysis. The model is resilient to anti-forensic techniques such as timestomping and emulator fingerprinting and is validated through controlled experiments with synthetic datasets and statistical analysis. EFIM’s modular design enables future support for iOS simulators and game emulators. All non-sensitive EFT components are open-sourced to promote transparency. This research significantly enhances the forensic community’s capabilities in analyzing evasive emulator environments and contributes to legally reliable, scalable investigation practices.}, archive = {J_ACCESS}, author = {Sedat Şen and Harun Artuner}, doi = {10.1109/ACCESS.2025.3585096}, journal = {IEEE Access}, pages = {134189-134211}, shortjournal = {IEEE Access}, title = {Emulator forensics investigation model (EFIM)}, volume = {13}, year = {2025}, }
• (2025). System-wide analysis of electric power system for a reference short-range electrified aircraft. ACCESS, 13, 134172-134188. (www)

  @article{ , abstract = {This study investigates the electrification of a reference short-range passenger aircraft, focusing on optimizing the Electric Power System (EPS). An iterative optimization method is employed to identify the optimal specifications for individual EPS components, considering gravimetric and volumetric power densities and efficiency as single objectives. The study introduces a structured, decoupled optimization framework that isolates the impact of each design variable on weight, volume, and efficiency, providing clearer insights into system-level trade-offs and offering a novel perspective for EPS design. By incorporating component characteristics into the design process, optimal configurations are achieved. Results indicate that certain motor designs enhance EPS performance by reducing weight, increasing efficiency, or minimizing volume, depending on the optimization objective. Similarly, during the optimization process and selection of power electronics configurations, certain topologies demonstrated superior efficiency, while others excelled in different performance criteria. In transmission line selection, superconducting cables are more efficient than conventional cables in minimizing electrical losses at lower voltages, despite being bulkier. Conversely, conventional cables are more advantageous at higher voltage levels due to their lighter weight. Moreover, modular and scalable circuit breakers are recommended for their adaptability across varying voltage levels. The optimization results are further supported by sensitivity analyses that reveal the influence of voltage level, shaft power, and cable technology on overall system metrics. Additionally, feasibility assessments benchmarked against a conventional ATR-72 propulsion system highlight the current limitations of battery integration and the challenging energy density requirements for full electrification.}, archive = {J_ACCESS}, author = {Ahmad Naderi and Robert Keilmann and Arsham Asgari and Abbas Mehraban and Regine Mallwitz and Markus Henke and Michael Terörde}, doi = {10.1109/ACCESS.2025.3592792}, journal = {IEEE Access}, pages = {134172-134188}, shortjournal = {IEEE Access}, title = {System-wide analysis of electric power system for a reference short-range electrified aircraft}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning-machine learning stacking model for yemeni arabic dialect sentiment analysis. ACCESS, 13, 134160-134171. (www)

  @article{ , abstract = {With the rise of online communities, Yemeni Arabic has gained increasing exposure to written social media content. Nevertheless, sentiment analysis studies have largely centered on Modern Standard Arabic (MSA) and other regional varieties (e.g., Egyptian, Levantine, Gulf), leaving the Yemeni dialect understudied, with insufficient specialized resources. This study bridges this gap by introducing the largest sentiment-annotated corpus for the Yemeni Arabic dialect, consisting of 45,862 manually labeled Facebook comments collected from the Facebook pages of the main telecommunications companies in Yemen (Yemen Telecom, Yemen Mobile, YOU, and Sabafon). These comments comprise user feedback on public service matters related to these companies. Multiple reviewers annotated the data to ensure reliability, with disagreements resolved through discussion. Moreover, we constructed a novel Yemeni dialect sentiment lexicon, classifying words/phrases according to polarity. We systematically evaluate sentiment analysis approaches, including lexicon-based, machine learning, deep learning, and hybrid, in terms of user sentiment regarding Yemeni telecom services. This study implemented different advanced deep-learning models in our dataset to demonstrate superior performance compared to other methods. The experimental results indicate a hybrid approach that combines advanced deep learning models with a stacked approach, leveraging deep networks for hierarchical feature extraction and RF/LinearSVC as meta-learners, achieving the highest accuracies of 94.71% and 94.28%, respectively. This study provides a preliminary reference for sentiment analysis in low-resource Arabic dialects such as Yemeni. It highlights the effectiveness of stacked feature engineering and meta-learning in dialectal NLP tasks.}, archive = {J_ACCESS}, author = {Alaa Abdulkareem Hameed Brihi and Mossa Ghurab}, doi = {10.1109/ACCESS.2025.3593204}, journal = {IEEE Access}, pages = {134160-134171}, shortjournal = {IEEE Access}, title = {A hybrid deep learning-machine learning stacking model for yemeni arabic dialect sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Efficient image super-resolution using dynamic quality control with recursive model structures. ACCESS, 13, 134143-134159. (www)

  @article{ , abstract = {Nowadays, as the demand for accurate object detection (OD) applications is increasing, several attempts have been made to introduce convolutional neural network (CNN)-based super-resolution (SR) into these applications to further improve their target accuracy. OD systems require real-time processing because they are widely used in latency-critical applications such as autonomous driving, augmented reality, and surveillance cameras. However, due to their high computational and memory requirements, the introduction of SR in OD systems often makes real-time processing difficult. To reduce computation, one possible solution is to make the SR network size smaller, but this may not be the best solution because it lowers the quality of the reconstructed image. In addition, performing SR on the entire frame is inefficient because there are unnecessary background elements other than the objects that need to be detected within a single frame. Performing SR on specific regions of interest (ROIs) instead can improve the overall efficiency. Therefore, we propose an efficient dynamic quality control SR (DQC-SR) system that can dynamically adjust the inference rate according to the number of ROIs by introducing recursive and early-exit architectures into the baseline SR network. The DQC-SR network (DQC-SRNet) adopts a recursive architecture in which layers share identical parameters and an early-exit architecture that adjusts the network’s depth through bypassing paths. The recursive architecture can reduce memory consumption, while the early-exit architecture can adapt computation dynamically. With these two architectures, the DQC-SR system enables real-time SR inference on edge devices. For evaluation, we conducted example scenarios based on license plate recognition (LPR) situations and demonstrated the effectiveness of the DQC-SR system.}, archive = {J_ACCESS}, author = {Inho Lee and Jaemin Park and Seunghwan Lee and Tae Hyun Kim and Jiwon Seo and Hunjun Lee and Yongjun Park}, doi = {10.1109/ACCESS.2025.3582604}, journal = {IEEE Access}, pages = {134143-134159}, shortjournal = {IEEE Access}, title = {Efficient image super-resolution using dynamic quality control with recursive model structures}, volume = {13}, year = {2025}, }
• (2025). Research on multi-objective coordinated planning of distribution networks based on improved generative adversarial networks. ACCESS, 13, 134129-134142. (www)

  @article{ , abstract = {This paper addresses the significant challenges that the uncertainty of renewable energy (RE) outputs, such as wind and solar power, bring to distribution network planning and operation by proposing a multi-objective bi-level distribution network planning model based on an improved generative adversarial network and carbon footprint analysis. Firstly, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to simulate numerous wind and solar output scenarios, which are then reduced using the K-medoids clustering algorithm. Secondly, carbon footprint coefficients for each generation unit are determined through a life cycle assessment method. Next, a bi-level distribution network planning model considering carbon footprints is established: the upper level minimizes the annual comprehensive cost by optimizing the planning schemes of distributed generation (DG), energy storage systems (ESS), and capacitor banks (CB); the lower level minimizes operating costs, voltage deviations, and carbon emissions by formulating operation strategies under typical scenarios, considering on-load tap changers (OLTC), controllable loads, capacitor banks, energy storage, and distributed generation. Then, the upper and lower levels of the model are coupled and unified into a single-level model. The normalized normal constraint (NNC) method is used to solve the single-level multi-objective model. Finally, simulation analyses are conducted on the IEEE 33-node distribution system to verify the model’s effectiveness.}, archive = {J_ACCESS}, author = {Li Zhu}, doi = {10.1109/ACCESS.2025.3592675}, journal = {IEEE Access}, pages = {134129-134142}, shortjournal = {IEEE Access}, title = {Research on multi-objective coordinated planning of distribution networks based on improved generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). A fluid mixing benchmark for anomaly detection in CPS with real and simulated data. ACCESS, 13, 134113-134128. (www)

  @article{ , abstract = {Ensuring the safety and reliability of Cyber-Physical Systems requires effective anomaly detection. However, research in this field is often limited by the lack of publicly available benchmark datasets that accurately capture real-world system behavior and provide sufficient documentation. This paper addresses this gap by defining requirements for anomaly detection datasets, evaluating existing benchmarks, and introducing a novel dataset collected from a real-world fluid mixing system augmented with a simulation model. The dataset captures diverse operational states, fault scenarios, and non-linear system dynamics, while the simulation model enables further system understanding. To demonstrate its utility, we apply six state-of-the-art unsupervised anomaly detection models on the dataset. The results confirm the dataset’s suitability for anomaly detection research. To promote reproducibility, we publicly provide the software framework for model implementation, along with the datasets, simulation model, and evaluation results, accompanied by comprehensive documentation.}, archive = {J_ACCESS}, author = {Malte Ramonat and Bernd Zimmering and Silke Merkelbach and Felix Gehlhoff and Oliver Niggemann and Alexander Fay}, doi = {10.1109/ACCESS.2025.3592815}, journal = {IEEE Access}, pages = {134113-134128}, shortjournal = {IEEE Access}, title = {A fluid mixing benchmark for anomaly detection in CPS with real and simulated data}, volume = {13}, year = {2025}, }
• (2025). LIoTning: A peer-supervised payment channel approach for secure micropayments in IoT ecosystem. ACCESS, 13, 134080-134112. (www)

  @article{ , abstract = {The rapid proliferation of the Internet of Things has heightened the demand for efficient, low-cost micropayments to support seamless service provisioning—a requirement fundamentally at odds with the inherent scalability issues of blockchain technology. Digital assets like Bitcoin, despite offering a decentralized and cryptographically secure foundation for the machine-to-machine (M2M) economy, struggle to accommodate high-frequency transactions. While payment channel networks (PCNs) propose an innovative off-chain solution to bypass blockchain bottlenecks, they remain impractical for resource-constrained IoT devices, where stringent storage, memory, and processing restrictions make PCN architectures infeasible. Previous studies have attempted to integrate off-chain payments with IoT devices while addressing threats in this domain—particularly broadcasting old states. However, their reliance on third-party entity/entities, software modifications, trusted hardware, or overhead-intensive protocols leaves critical gaps unresolved. To bridge this gap, we propose LIoTning, a peer-supervised payment channel protocol with $N+2$ participants operating under the honest majority assumption: two devices (sender and receiver) and N peers, including resource-efficient IoT devices and blockchain nodes—that collaboratively track and maintain full-state commitment transactions. To minimize storage and communication overhead, each full-state commitment embeds intermediate transactions. We formally prove the security of the LIoTning protocol in the Universal Composability (UC) framework, analyze its resilience against broadcasting revoked states, collusion, and ransom attacks, and implement the proposed model to evaluate its performance under different scenarios. Our evaluation results show that the model enables fast payments over low-bandwidth wireless networks. It demonstrates that, with practical parameters, the proposed model can achieve a throughput of at least 200 transactions per second with a network bandwidth of 300 kBps, which is fairly within the capabilities of IoT devices.}, archive = {J_ACCESS}, author = {Erfan Bahrami and Amir M. Aghapour and Morteza Amini}, doi = {10.1109/ACCESS.2025.3592816}, journal = {IEEE Access}, pages = {134080-134112}, shortjournal = {IEEE Access}, title = {LIoTning: A peer-supervised payment channel approach for secure micropayments in IoT ecosystem}, volume = {13}, year = {2025}, }
• (2025). AI-driven detection of alkali-silica reaction in concrete structures using feature-enhanced deep learning models. ACCESS, 13, 134070-134079. (www)

  @article{ , abstract = {Concrete’s affordability, adaptability, and resilience make it a cornerstone of construction, yet its vulnerability to degradation, particularly Alkali-Silica Reaction (ASR), poses significant challenges. ASR induces cracking and structural instability, necessitating efficient detection methods to mitigate its impacts. This study explores the use of Artificial Intelligence (AI) techniques for ASR crack identification, employing image enhancement and advanced models such as ResNet-18, InceptionV3, and AlexNet. A dataset of ASR-affected images was developed and augmented through feature enhancement processes, improving crack visibility and classification accuracy. Among the tested models, InceptionV3 demonstrated superior performance with high accuracy and robustness. The findings reveal that AI-based approaches, combined with image enhancement, effectively identify ASR cracks without requiring structural surface treatment. This research offers a scalable, automated solution to ASR detection, advancing structural health monitoring technologies and contributing to the preservation of critical infrastructure.}, archive = {J_ACCESS}, author = {Yujie Wu and Mengze Wu and Tianyi Cui and Jiani Lin and Qingke Liao and Jinqiu Shu}, doi = {10.1109/ACCESS.2025.3591865}, journal = {IEEE Access}, pages = {134070-134079}, shortjournal = {IEEE Access}, title = {AI-driven detection of alkali-silica reaction in concrete structures using feature-enhanced deep learning models}, volume = {13}, year = {2025}, }
• (2025). Q-fractional hesitant fuzzy sets and their correlation coefficients: Multi-criteria decision making technique for selection of agricultural land to cultivate apples crops. ACCESS, 13, 134057-134069. (www)

  @article{ , abstract = {The q-Fractional Fuzzy Sets (q-FrFSs) offers information in Membership Grade (MG) and Non-membership Grade (NMG) of an object; however, both grades have the hesitancy factor because complex information usually does not give single MG and single NMG. Therefore, in this study we initiate the concept of q-Fractional Hesitant Fuzzy Sets (q-FrHFSs) and its basic properties. In q-FrHFSs not only hesitancy factor is taken into account but it also consider all possible values of uncertainties in $[{0,1}]\times [{0,1}]$ . Thus Correlation Coefficients (CCs) on q-FrHFSs are necessary to cope uncertain information with hesitancy, MGs and NMGs. In this study we introduce two types of CCs namely CCs on q-FrHFSs and weighted CCs on q-FrHFSs. We investigate underlying properties of these CCs and give a MCDM method on q-FrHFSs environment. We consider an application of our method to agricultural land selection across a set of cities for cultivation of apples crop. At the end, we compare our method of q-FrHFSs to some existing frameworks.}, archive = {J_ACCESS}, author = {Nadeem Ajaib and Khizar Hayat and Dragan Pamucar and Kamran Kausar}, doi = {10.1109/ACCESS.2025.3582884}, journal = {IEEE Access}, pages = {134057-134069}, shortjournal = {IEEE Access}, title = {Q-fractional hesitant fuzzy sets and their correlation coefficients: Multi-criteria decision making technique for selection of agricultural land to cultivate apples crops}, volume = {13}, year = {2025}, }
• (2025). FPGA SoC implementation of adaptive deep neural network-based multimodal edge intelligence for internet of medical things. ACCESS, 13, 134041-134056. (www)

  @article{ , abstract = {In emergency healthcare services, accurate and timely decision-making is critical for the patient’s life and death. The emergence of edge intelligence enables these service goals achievable for Internet of Medical Things (IoMT) compared with cloud-centric approaches. To assist medical personnel in intensive care units (ICU), we present the design of a network edge gateway that performs resource-efficient, real-time data analytics. We develop a cloud-configurable deep neural network (DNN) intellectual property (IP) core with an adaptable hardware architecture that executes four different types of analysis on an edge gateway. Our developed IP core adaptively switches from one architecture to another only in one clock cycle, based on the type of input features. The proposed IP core analyzes raw multimodal signals such as ECG, PPG, accelerometer, and other to discover anomalies in critically ill patients and their surroundings. We have validated the robustness of our developed model by comparing it with benchmark machine learning models and their previous implementations. The results show that our adaptive DNN model has obtained a software accuracy of 99.2% for ECG, 91.4% for PPG, 95% for activity classification, and 98.7% for smoke detection with a five-fold cross-validation strategy. Three versions of adaptive DNN IP cores (8-bit, 16-bit, 24-bit) are implemented on SoC/FPGA and compared together to study the effect of bit precision on accuracy, resource utilization, and power consumption. The developed adaptive DNN IP cores with 16-bits require 680 nanoseconds with a power consumption of 309 milliwatts for a single inference with a speed of 1.47 mega samples per second. Our analysis shows that the decentralization of intelligence in the IP core reduces data size from 96.25% to 98.75%. This flexible IP core has achieved significant power and resource utilization performance compared to independent implementation without compromising latency and throughput.}, archive = {J_ACCESS}, author = {Nikhil B. Gaikwad and Smith K. Khare and Dinesh Mendhe and Hasan Mir and Sokol Kosta and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3592729}, journal = {IEEE Access}, pages = {134041-134056}, shortjournal = {IEEE Access}, title = {FPGA SoC implementation of adaptive deep neural network-based multimodal edge intelligence for internet of medical things}, volume = {13}, year = {2025}, }
• (2025). A deep learning method for pneumoconiosis staging on chest X-ray under label noise. ACCESS, 13, 134031-134040. (www)

  @article{ , abstract = {The ambiguous properties of small opacities in pneumoconiosis chest radiographs can cause diagnostic drift, which in turn leads to the presence of noisy labels in the datasets collected from hospitals that can negatively impact the generalization of deep learning models. To tackle this issue, we propose COFINE, a novel coarse-to-fine noise-tolerant deep learning method for the staging of pneumoconiosis chest radiographs, which comprises two procedures: coarse screening and fine learning. In the coarse screening procedure, the proposed sample selection strategy divides the pneumoconiosis dataset into ‘confident’ and ‘less-confident’ subsets based on the logical relationship between the prediction correctness and confidence of multiple expert networks. During the fine learning procedure, we apply two different strategies to fit the sample features in the above two subsets. For the samples in ‘confident’ subset, we specifically design a novel soft label relaxation learning strategy (SLRL) to mine the explicit features and overcome the overfitting problem caused by traditional one-hot labels. To parse the implicit features within the less-confident samples, the augmentation-based self-supervised learning method is employed. On the pneumoconiosis dataset containing 1,372 chest X-rays collected from West China Fourth Hospital, the proposed method achieves a sensitivity of 82.4% in identifying ‘stage-1’ cases, which is higher than that of other algorithms (70.6%), and is especially beneficial for clinical radiologists in pneumoconiosis staging.}, archive = {J_ACCESS}, author = {Wenjian Sun and Dongsheng Wu and Jiang Shen and Yang Luo and Hao Wang and Li Min and Chunbo Luo}, doi = {10.1109/ACCESS.2025.3590783}, journal = {IEEE Access}, pages = {134031-134040}, shortjournal = {IEEE Access}, title = {A deep learning method for pneumoconiosis staging on chest X-ray under label noise}, volume = {13}, year = {2025}, }
• (2025). Emphasizing electro-thermal dynamics for design, modeling, and control of microgrids. ACCESS, 13, 134014-134030. (www)

  @article{ , abstract = {This article studies the design, modeling, and control of microgrid systems with the inclusion of internal electro-thermal dynamics. Microgrids play a vital role in integrating renewable energy and enabling distributed energy systems. However, their complexity arising from diverse and dynamic components necessitates advanced control strategies. While existing works often utilize model-based controllers, the focus is primarily on electrical dynamics, with limited attention to the thermal behavior of components. The intricate interplay between electrical and thermal power terms heavily impacts component behavior, exemplified by the dependency of photovoltaic (PV) module electricity production on temperature. This article addresses the limited studies on electro-thermal microgrid dynamics through three contributions. First, a candidate microgrid design is developed to utilize electro-thermal knowledge, incorporating active cooling for PVs. Second, a graph-based modeling methodology is expanded to represent microgrid component- and system-level dynamics. Third, a hierarchical control framework is developed to define controllers for microgrids using the graphical model. Controllers produced from the framework enable management of electro-thermal behavior while adhering to battery charge limits. Case studies utilizing realistic environmental data are explored to evaluate the performance of the proposed system. Results indicate design and model-based control that integrate electro-thermal dynamics provide improvements to energy generation and performance even under nonideal conditions.}, archive = {J_ACCESS}, author = {Asmaou S. Ouedraogo and Donald J. Docimo}, doi = {10.1109/ACCESS.2025.3593387}, journal = {IEEE Access}, pages = {134014-134030}, shortjournal = {IEEE Access}, title = {Emphasizing electro-thermal dynamics for design, modeling, and control of microgrids}, volume = {13}, year = {2025}, }
• (2025). Strategic human resource development for emerging technologies: A scoping review in ICT, robotics, data science, and CPS. ACCESS, 13, 133994-134013. (www)

  @article{ , abstract = {The rapid advancement of ICT, robotics, data science, and cyber-physical systems (CPS) has intensified the need for agile, future-ready human resource development (HRD) strategies. This scoping review explores current HRD practices, challenges, and opportunities across these technological domains. Guided by the PRISMA-ScR framework, 791 records were initially retrieved from four databases, with 43 peer-reviewed publications ultimately included following a rigorous screening process. Thematic analysis revealed a strong emphasis on digital and AI literacy, interdisciplinary training, and the adoption of immersive technologies such as virtual reality and AI-driven competency tracking systems. Persistent gaps, including misalignment between academic training and industry needs, a lack of scalable HRD models, and underdeveloped policy frameworks. Statistical comparisons revealed no significant differences across fields, indicating the cross-cutting relevance of the identified strategies. The findings underscore the need for structured, empirically grounded, and industry-integrated HRD approaches to prepare the workforce for the demands of the Fourth Industrial Revolution.}, archive = {J_ACCESS}, author = {Leonard Chukwualuka Nnadi and Dake Ding and Ebubechukwu Delight Godwin-Ojukwu and Yutaka Watanobe and Keitaro Naruse}, doi = {10.1109/ACCESS.2025.3592301}, journal = {IEEE Access}, pages = {133994-134013}, shortjournal = {IEEE Access}, title = {Strategic human resource development for emerging technologies: A scoping review in ICT, robotics, data science, and CPS}, volume = {13}, year = {2025}, }
• (2025). A custom reinforcement learning environment for hybrid renewable energy systems: Design and implementation. ACCESS, 13, 133984-133993. (www)

  @article{ , abstract = {We present HybridEnergyEnv, an open-source, Gym-style simulation environment designed for reinforcement learning (RL) research in hybrid renewable energy systems (HRES) combining wind, solar, and battery storage. The environment incorporates realistic component models, including intermittent renewable generation profiles, a synthetic electricity price signal inversely correlated with renewable availability, and a detailed Battery Energy Storage System (BESS) model accounting for state-of-charge (SoC) dynamics, self-discharge, efficiency losses, thermal derating, and rainflow-based capacity degradation. To validate the framework, we evaluate three dispatch strategies implemented with algorithms available in the Stable-Baselines3 (SB3) library: Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), and Double Deep Q-Network (DDQN). Results show that DRL-based policies increase operational revenue by up to 10.05% and reduce curtailment by up to 84.60% compared to the no-storage baseline. Additionally, DDQN achieves the longest episode durations and highest rewards during training, indicating greater stability under strict curtailment constraints. We describe the environment architecture, component models, and API, demonstrating the potential of HybridEnergyEnv as a high-fidelity, extensible platform for the development of intelligent, degradation-aware dispatch strategies in modern power systems.}, archive = {J_ACCESS}, author = {Dalton F. Guedes Filho and Marcelo A. Moret and Erick G. Sperandio Nascimento}, doi = {10.1109/ACCESS.2025.3593064}, journal = {IEEE Access}, pages = {133984-133993}, shortjournal = {IEEE Access}, title = {A custom reinforcement learning environment for hybrid renewable energy systems: Design and implementation}, volume = {13}, year = {2025}, }
• (2025). Unsupervised anomaly detection of forceps force by localizing the region of interest. ACCESS, 13, 133971-133983. (www)

  @article{ , abstract = {The lack of haptic feedback in robotic surgical systems can lead to unintended tissue damage as a result of excessive mechanical force. To address this issue, many studies in vision-based force sensing have focused on supervised learning approaches, which require labeled force data for training. However, obtaining force labels in real surgical scenarios remains challenging. In this study, we propose an alternative approach by formulating the force feedback problem as an image-based anomaly detection task. By leveraging unsupervised learning, we overcome the limitations posed by the scarcity of labeled abnormal force data and present a novel abnormal force anomaly detection method. Our method employs a bidirectional generative adversarial network (BiGAN) and integrates a region-of-interest (ROI) guided module to enhance attention on the contact area between surgical instruments and tissue. The model is trained to distinguish between normal and abnormal force samples based on deformation features in an organ. Extensive experiments on an ex vivo porcine spleen manipulation dataset demonstrate the efficacy of the proposed ROI-guided BiGAN in accurately detecting abnormal forces. The ROI-guided BiGAN exhibits an area under the curve of 0.7963 and a recall rate of 0.9174 when applied to the test data. The proposed framework is expected to be useful as a practical tool for abnormal forceps force detection and to serve as a useful alarm in robot-assisted surgery.}, archive = {J_ACCESS}, author = {Wenhui Zhuang and Kimihiko Masui and Naoto Kume and Megumi Nakao}, doi = {10.1109/ACCESS.2025.3592960}, journal = {IEEE Access}, pages = {133971-133983}, shortjournal = {IEEE Access}, title = {Unsupervised anomaly detection of forceps force by localizing the region of interest}, volume = {13}, year = {2025}, }
• (2025). Investigation of VLF electromagnetic undersea positioning system using receiving antenna array in a simulation. ACCESS, 13, 133958-133970. (www)

  @article{ , abstract = {In this paper, we propose a 3D undersea positioning system using VLF electromagnetic waves as a system to support divers’ activities in seawater and show results of a basic study for the proposed system in electromagnetic field simulations. Assuming that a transmitting antenna is attached to a diver, the antenna’s position in seawater is estimated using the Received Signal Strength (RSS [dB]) of multiple receiving antennas placed near the sea surface. To assist in the installation and retrieval of the proposed system, we propose a receiving antenna array that integrates twelve dipole antennas into a single receiver. Furthermore, we also propose a position estimation algorithm optimized for the proposed receiver. In the proposed algorithm, Support Vector Regression (SVR) was utilized to estimate the distance between the transmitting antenna and the receiver from the RSS. We confirmed that position estimation using the receiver was possible within a circle with an approximately 12 m radius centered on the receiver, demonstrating the viability of our proposed system for implementation.}, archive = {J_ACCESS}, author = {Shinnosuke Sakaya and Masaharu Takahashi}, doi = {10.1109/ACCESS.2025.3593248}, journal = {IEEE Access}, pages = {133958-133970}, shortjournal = {IEEE Access}, title = {Investigation of VLF electromagnetic undersea positioning system using receiving antenna array in a simulation}, volume = {13}, year = {2025}, }
• (2025). An AI-based decision support framework for clinical screening of iron deficiency anemia and thalassemia. ACCESS, 13, 133937-133957. (www)

  @article{ , abstract = {Iron deficiency anemia (IDA) and thalassemia (THL) are common hematological disorders that necessitate efficient and accurate screening for early diagnosis. Traditional blood smear analysis is labor-intensive, prone to subjectivity, and lacks reproducibility, highlighting the urgent need for AI-driven methods to improve diagnostic sensitivity and specificity. This study proposes a novel hybrid AI framework for patient-level classification, integrating soft voting with optimized probability-based thresholds. The model was trained and validated using a real-world dataset from Hatyai Hospital, Thailand, and evaluated at both the patch and patient levels. The proposed approach achieved 96% accuracy on the test set. Specifically, it yielded precision-recall values of 1.00 and 0.83 for IDA, and 0.95 and 1.00 for THL, respectively. At the patient level, sensitivity reached 1.00 for THL and 0.83 for IDA. Bayesian probability updates further confirmed prediction reliability, yielding post-test probabilities exceeding 99.99% for IDA and 80% for THL. The model explained 62.84% of the variance in patient classifications, demonstrating strong discriminatory power. Model interpretability, assessed using SHAP and Grad-CAM, highlighted key red blood cell morphological features. The proposed framework thus serves as a cost-effective screening tool. Limitations include the use of a single-center dataset and the need for adaptive threshold optimization. Future work will focus on multi-center validation and real-world clinical integration. This study thereby establishes a structured baseline for AI-assisted hematology screening, fostering early detection and improved clinical decision-making in resource-limited settings.}, archive = {J_ACCESS}, author = {Kasikrit Damkliang and Thakerng Wongsirichot and Komsai Suwanno and Chutima Cheranakhorn and Sorawat Sangkeaw and Satanun Sottisataworn}, doi = {10.1109/ACCESS.2025.3592652}, journal = {IEEE Access}, pages = {133937-133957}, shortjournal = {IEEE Access}, title = {An AI-based decision support framework for clinical screening of iron deficiency anemia and thalassemia}, volume = {13}, year = {2025}, }
• (2025). Dual-mode 2T1C DRAM process-in-memory architecture for boolean and MAC operations. ACCESS, 13, 133925-133936. (www)

  @article{ , abstract = {With the increasing demand for intelligent memory, the conventional memory system is more and more equipped with computational logic to support simple arithmetic and Boolean operations required by many real-world applications. Such a trend, called ‘Process-In-Memory’ architecture, tries to utilize most of the memory candidates ranging from the conventional charge-based memory such as SRAM, DRAM, and Flash to the emerging memory devices such as RRAM, PRAM and MRAM. From the application perspective, many researchers are putting efforts to develop efficient memory peripherals with CMOS circuits to support one of two operations: 1) Boolean function, and 2) simple arithmetic operations such as multiplication and accumulation (addition), which is typically referred to as MAC operation. However, there are not many previous works that support both operations in a single system. In this article, we propose a 2T1C DRAM-based PIM architecture that supports dual-mode operation with minimal additional hardware. The proposed architecture has been fabricated using a commercial 65nm CMOS technology and successfully proves that the target operations are performed with a reasonably good accuracy.}, archive = {J_ACCESS}, author = {Yerim An and Honggu Kim and Dongjun Son and Hakrae Yu and Yong Shim}, doi = {10.1109/ACCESS.2025.3592352}, journal = {IEEE Access}, pages = {133925-133936}, shortjournal = {IEEE Access}, title = {Dual-mode 2T1C DRAM process-in-memory architecture for boolean and MAC operations}, volume = {13}, year = {2025}, }
• (2025). Reacting like humans: Incorporating intrinsic human behaviors into NAO through sound-based reactions to fearful and shocking events for enhanced sociability. ACCESS, 13, 133909-133924. (www)

  @article{ , abstract = {As robots increasingly enter human-centered environments, the ability to exhibit natural, emotionally resonant behaviors becomes crucial for effective interaction. In this work, we present a multi-modal system designed to enable the NAO robot to sense its environment, react naturally to sudden loud sounds with human-like fear responses, and locate the source of the sound. The system comprises a motion generator, a sound classifier, and an object detector. For motion generation, we developed a deep network-based model to synthesize diverse movements. Sound detection leveraged a transfer learning model utilizing spectrograms of sound signals, while object detection employed a YOLO model trained specifically for this task. These individual components were integrated into a comprehensive “fear” module, which was implemented on the NAO robot. The module’s performance was evaluated through practical testing, during which both experts and non-experts in robotics completed a questionnaire on the robot’s performance. Results demonstrated that the proposed system effectively convinced participants that the NAO robot could perceive and respond to its environment with human-like reasoning and behavior. Additionally, findings revealed that non-experts held higher expectations regarding the performance of social robots. Given our promising results, this preliminary exploratory research provides a fresh perspective on social robotics and could be a starting point for modeling immediate, emotion-driven human behaviors in robots.}, archive = {J_ACCESS}, author = {Ali Ghadami and Mohammadreza Taghimohammadi and Mohammad Mohammadzadeh and Mohammad Hosseinipour and Alireza Taheri}, doi = {10.1109/ACCESS.2025.3592922}, journal = {IEEE Access}, pages = {133909-133924}, shortjournal = {IEEE Access}, title = {Reacting like humans: Incorporating intrinsic human behaviors into NAO through sound-based reactions to fearful and shocking events for enhanced sociability}, volume = {13}, year = {2025}, }
• (2025). Parameter estimation for three-phase induction motor based on performance curves and differential evolution. ACCESS, 13, 133891-133908. (www)

  @article{ , abstract = {The estimation of electrical parameters in three-phase induction motors is necessary for tasks such as performance analysis, control design, and predictive maintenance. Nonetheless, manufacturers typically provide limited electrical data, and existing methods often require invasive testing or additional sensors, increasing system complexity and cost. Existing approaches predominantly focus on online estimation, which is based on continuous data acquisition and may be impractical in industrial environments with limited hardware resources. Moreover, many offline techniques still require experimental measurements or specific instrumentation, limiting their applicability. This paper introduces a novel and easy-to-implement offline parameter estimation method that eliminates the need for additional instrumentation by leveraging manufacturer performance curves and differential evolution optimization algorithm. Four equivalent circuit models are analyzed to evaluate the impact of core loss resistance and supplementary losses on estimation accuracy. The methodology is experimentally validated on a 1 HP induction motor under varying voltage, frequency, and load conditions, with estimated parameters compared with experimental measurements and IEEE Std 112-2017 test results. Furthermore, the study includes a comparative analysis that includes seven evolutionary algorithms, focusing on solution distribution, computational complexity, and convergence characteristics. The results demonstrate that the proposed method provides accurate parameter estimation while reducing computational effort, establishing it as a low-cost and practical alternative for industrial applications requiring reliable motor modeling and control optimization.}, archive = {J_ACCESS}, author = {Marcelo F. Castoldi and Gustavo Henrique Bazan and Wesley A. Souza and Clayton L. Graciola and Rafael M. Silva and Alessandro Goedtel}, doi = {10.1109/ACCESS.2025.3592886}, journal = {IEEE Access}, pages = {133891-133908}, shortjournal = {IEEE Access}, title = {Parameter estimation for three-phase induction motor based on performance curves and differential evolution}, volume = {13}, year = {2025}, }
• (2025). Enhancing text classification through quantum transfer learning: A hybrid quantum-classical approach with complex kernel self-attention networks. ACCESS, 13, 133882-133890. (www)

  @article{ , abstract = {Quantum computing, with its unique principles such as superposition and entanglement, promises to transform various domains, including artificial intelligence. This study investigates the fusion of quantum computing with contemporary AI technologies through quantum transfer learning. We introduce the Complex Quantum Kernel Self-Attention Network (CQKSAN), a novel model that leverages quantum circuits and self-attention mechanisms to boost text classification performance. By applying quantum transfer learning, CQKSAN harnesses the strengths of pre-trained classical models and enhances them with quantum capabilities. Experimental results on multiple binary classification datasets demonstrate that CQKSAN achieves a more efficient learning process. Specifically, it improves training accuracy and validation accuracy by up to 24.31% and 28.35%, respectively, and increases the Matthews Correlation Coefficient by 113.10%. Moreover, CQKSAN consistently outperforms the BERT baseline in both training and validation accuracy across various datasets, showcasing its strong learning capability and generalization performance in handling tasks with diverse linguistic structures and complex semantics. Our findings underscore the potential of hybrid quantum-classical approaches, while also identifying the current limitations and paving the way for future research in fully quantum-based learning systems.}, archive = {J_ACCESS}, author = {Xiaoxiao Chen and Xiaoping Lou}, doi = {10.1109/ACCESS.2025.3593052}, journal = {IEEE Access}, pages = {133882-133890}, shortjournal = {IEEE Access}, title = {Enhancing text classification through quantum transfer learning: A hybrid quantum-classical approach with complex kernel self-attention networks}, volume = {13}, year = {2025}, }
• (2025). Visualizing UNet decisions: An explainable AI perspective for brain MRI segmentation. ACCESS, 13, 133869-133881. (www)

  @article{ , abstract = {In recent years, medical image analysis, particularly neuroimaging, has experienced remarkable advancements, with Magnetic Resonance Imaging (MRI) greatly helping in diagnosing complex neurological disorders, including brain tumors. However, accurately segmenting brain tumors from MRI scans remains a significant challenge, necessitating sophisticated computational techniques. This article presents research findings from brain MRI segmentation utilizing the UNet architecture and enhancing model interpretability using explainable AI methods to harness UNet’s effectiveness in semantic segmentation tasks. We study intricacies of UNet’s adaptation to brain MRI segmentation, the dataset employed, and the methodology for model development, training, and validation. In addition to discussing segmentation outcomes, we incorporate several explainable AI (XAI) techniques like Grad-CAM, Saliency Maps, Vanilla Gradient and Layer-wise Relevance Propagation (LRP) to generate necessary visualizations showing the internal workings of the opaque system nature of UNet. A comprehensive analysis of the results highlights the clinical implications of these findings, addressing the relative utility of different XAI methods in visualizing UNet’s outputs using metrics like fidelity, unambiguity and stability. The Vanilla Grad method stands out with its high unambiguity and consistent fidelity scores in complex scenarios. We also find that while LRP also offers high stability, the combination of high fidelity and clarity from the Vanilla Grad model makes it the preferred method for enhancing the interpretability of AI systems in brain tumor segmentation. Overall, this research work represents a significant advancement in leveraging the trustworthiness of UNet in accurate and efficient brain tumor segmentation via XAI methods, ultimately aiming to support clinicians in diagnosis and treatment planning while fostering a deeper understanding of the model’s decision-making processes.}, archive = {J_ACCESS}, author = {D. Jeya Mala and Mainak Chattopadhyay and Parthiba Mukhopadhyay and Roopak Sinha}, doi = {10.1109/ACCESS.2025.3592239}, journal = {IEEE Access}, pages = {133869-133881}, shortjournal = {IEEE Access}, title = {Visualizing UNet decisions: An explainable AI perspective for brain MRI segmentation}, volume = {13}, year = {2025}, }
• (2025). Addressing urban food insecurity through data-driven and community-centric smart city frameworks. ACCESS, 13, 133847-133868. (www)

  @article{ , abstract = {In the evolving landscape of smart city development, addressing food insecurity remains a critical challenge. This paper presents an innovative interdisciplinary approach that combines sensing technologies, data analytics, and community engagement to deliver holistic solutions to food access disparities. Through a comprehensive analysis, we first identify key factors that contribute to food insecurity, and then present a system-theoretic model to address challenges in the fundamental components of the food system: production, processing, distribution, and consumption. We also share insights from a virtual workshop with community advocates and organizations, highlighting promising practices in data collection, experiences during the COVID-19 pandemic, and the ongoing need for equity in food access efforts. Finally, we present a case study from the Greater Richmond Region, Virginia that further exemplifies the potential of geospatial data and predictive analytics. This work enables the identification of food deserts at a granular scale and provides estimates of household food service demand. By integrating data on grocery store locations, urban road networks, demographic indicators, and public transit accessibility, we apply predictive modeling techniques that can be adapted to other urban areas. Our findings demonstrate the power of interdisciplinary research combined with community-centered strategies to drive sustainable and equitable improvements in food access within smart city frameworks. This study bridges technological innovation—including predictive analytics, geospatial modeling, and system-theoretic tools—with lived community insights to identify food system gaps and support data-informed, locally relevant interventions.}, archive = {J_ACCESS}, author = {Nasibeh Zohrabi and John C. Jones and Brittany Keegan and Sarin Adhikari and Brian C. Verrelli and Sherif Abdelwahed}, doi = {10.1109/ACCESS.2025.3589963}, journal = {IEEE Access}, pages = {133847-133868}, shortjournal = {IEEE Access}, title = {Addressing urban food insecurity through data-driven and community-centric smart city frameworks}, volume = {13}, year = {2025}, }
• (2025). An industrial add-on for precision die-centering in catheter extrusion using visual servoing. ACCESS, 13, 133837-133846. (www)

  @article{ , abstract = {This paper presents a real-time visual servoing system for automating die-centering in catheter extrusion processes. By leveraging state-of-the-art image processing algorithms, the system dynamically adjusts the positions of the extrusion head and tip in real time, ensuring precise alignment and significantly enhancing product consistency and quality. The proposed algorithm, a cornerstone of intelligent manufacturing, has been rigorously tested under diverse extrusion head types and operational conditions, demonstrating robust performance and precise detection of the die’s inner circle and the tip’s center position. In particular, the developed algorithm achieved a mean IoU (Intersection over Union) of 0.95, demonstrating exceptional robustness in inference performance even under dynamic conditions, such as the positional movements of the add-on system. The control signals generated by the algorithm were employed to align the tip and die, achieving precise operations at a scale of tens of micrometers, thereby validating the system’s high accuracy and reliability. Designed for seamless integration into existing manufacturing environments, the system requires minimal modifications, making it highly adaptable. By addressing long-standing challenges in die-centering operations, this intelligent manufacturing innovation not only increases production efficiency but also elevates product reliability.}, archive = {J_ACCESS}, author = {Han Chang Lee and Byeong Hak Kim and Jong Pil Yun and Gyu Man Kim and Woojin Kim and Hong-In Won}, doi = {10.1109/ACCESS.2025.3593277}, journal = {IEEE Access}, pages = {133837-133846}, shortjournal = {IEEE Access}, title = {An industrial add-on for precision die-centering in catheter extrusion using visual servoing}, volume = {13}, year = {2025}, }
• (2025). A unified approach to voice classification: Leveraging spectrograms, mel spectrograms, and statistical features. ACCESS, 13, 133827-133836. (www)

  @article{ , abstract = {This study presents a multi-input neural network architecture for voice classification that integrates two parallel convolutional neural networks (CNNs) for spectrogram and Mel spectrogram images, along with a fully connected dense network for six handpicked numerical statistical features from time domain signal. The outputs from these branches are flattened and merged, enabling the model to learn complementary patterns from both visual and numerical modalities. A new dataset, voice-18, was developed, consisting of one-second audio clips from 18 speakers across 18 classes. Extensive experiments evaluated the performance of individual and combined inputs. Results demonstrate that the multi-input model, particularly when using spectrograms, Mel spectrograms, and statistical features together, achieves the highest accuracy. The model best performed when all the three inputs were used together attained accuracies of $0.9849~\pm ~0.0093$ on voice-18, $0.8825~\pm ~0.0137$ on urban sound (US)8K, and $0.9220~\pm ~0.0276$ on environmental sound classification (ESC)-50. While models trained solely on less than three inputs underperformed. These findings confirm the effectiveness of the proposed multimodal architecture for accurate voice and sound classification across different datasets.}, archive = {J_ACCESS}, author = {Muhammad Talha and Huma Ghafoor and Seung Yeob Nam}, doi = {10.1109/ACCESS.2025.3593440}, journal = {IEEE Access}, pages = {133827-133836}, shortjournal = {IEEE Access}, title = {A unified approach to voice classification: Leveraging spectrograms, mel spectrograms, and statistical features}, volume = {13}, year = {2025}, }
• (2025). Lightweight elliptic curve cryptography accelerator over 25519 curves. ACCESS, 13, 133817-133826. (www)

  @article{ , abstract = {This paper presents a lightweight hardware accelerator optimized for elliptic curve cryptography (ECC), supporting three standardized curves over the prime field GF( $2^{255}-19$ ): Weierstrass, Montgomery, and Twisted Edwards. In implementations of these 25519 curves, the Montgomery ladder method with projective coordinates is widely adopted for efficient point multiplication (PntMlt); however, this work deliberately employs affine coordinates to reduce hardware complexity and area. Although several affine-coordinate optimization techniques were originally proposed for the NIST Curve P-256, they could not be directly applied to the targeted curves due to fundamental differences in the underlying finite field structure. To address this incompatibility, we reformulated the core mathematical expressions, enabling effective adaptation of the P-256-based methods to our context in a lightweight and efficient manner. The proposed architecture supports all three curves interchangeably through lightweight curve mapping, allowing integration into a wide range of ECC-based systems. Synthesized under a 180 nm application specific integrated circuit (ASIC) process technology, the design achieves full PntMlt computation within 1 ms at 200 MHz, consuming only 60 k gate equivalents.}, archive = {J_ACCESS}, author = {Piljoo Choi and Dong Kyue Kim}, doi = {10.1109/ACCESS.2025.3588734}, journal = {IEEE Access}, pages = {133817-133826}, shortjournal = {IEEE Access}, title = {Lightweight elliptic curve cryptography accelerator over 25519 curves}, volume = {13}, year = {2025}, }
• (2025). Process injection using return-oriented programming. ACCESS, 13, 133790-133816. (www)

  @article{ , abstract = {Return-oriented programming (ROP) is a code-reuse attack that uses borrowed chunks of executable code for arbitrary computation. On Windows, ROP is often used solely to bypass Data Execution Prevention, rather than realizing its full potential; indeed, the bulk of advanced, malicious functionality is typically invoked through shellcode. This paper demonstrates an approach to advanced process injection using only ROP that works without shellcode or higher-level code, providing significantly more advanced functionality than is typically achieved with ROP. We show how to generalize a complex exploit chain that invokes advanced malicious functionality through multiple function calls, each implemented solely with ROP. We generalize this approach by creating a library of nearly 150 parameter-loading patterns, making our ROP-only process-injection technique portable across dissimilar binaries. Previously, only a few APIs were documented with parameter-loading patterns, making advanced ROP techniques on Windows less straightforward. Experimental validation on several builds of modern Windows confirms our approach is reliable on modern Windows variants. This work advances the state of the art in offensive security and provides a foundation for future work in cyber defense and software exploitation.}, archive = {J_ACCESS}, author = {Bramwell Brizendine and Shiva Shashank Kusuma and Bhaskar P. Rimal}, doi = {10.1109/ACCESS.2025.3592128}, journal = {IEEE Access}, pages = {133790-133816}, shortjournal = {IEEE Access}, title = {Process injection using return-oriented programming}, volume = {13}, year = {2025}, }
• (2025). A practical algorithm for physical layer encryption under constant envelope constraint. ACCESS, 13, 133783-133789. (www)

  @article{ , abstract = {Physical Layer Encryption (PLE) techniques have gained increasing attention recently as they ensure security without requiring heavy coordination or time-consuming secret key establishment. In particular, masking encryption techniques rely on transmitting a masking signal along with the data signal to prevent eavesdroppers from deciphering the transmitted information by degrading its channel. These methods are especially practical, as they do not require any knowledge of the eavesdroppers channel state. Several mask designs have been proposed in the literature in recent years. However, these methods suffer from various limitations, including vulnerability to brute-force attacks, and a limited range of potential use cases. In this paper, we present a novel mask design algorithm that offers three key advantages compared to state-of-the-art methods: optimal Peak-to-Average Power Ratio (PAPR), applicability to both Gaussian and flat fading channels, and a high level of confidentiality. These advantages are validated through our simulations.}, archive = {J_ACCESS}, author = {Clément Leroy and Tarak Arbi and Oudomsack Pierre Pasquero and Benoit Geller}, doi = {10.1109/ACCESS.2025.3592479}, journal = {IEEE Access}, pages = {133783-133789}, shortjournal = {IEEE Access}, title = {A practical algorithm for physical layer encryption under constant envelope constraint}, volume = {13}, year = {2025}, }
• (2025). Multimodal rumor detection by online balance multimodal representation learning. ACCESS, 13, 133773-133782. (www)

  @article{ , abstract = {Multimodal approaches have been theoretically and empirically shown to outperform unimodal methods. Paradoxically, leading unimodal architectures sometimes surpass multimodal systems trained in a joint framework. Previous studies have shown that this counterintuitive outcome stems from the disparate convergence speeds and generalization capabilities inherent to different modalities. In joint training, this imbalance can lead to the over-representation of one modality while others remain underfit. To overcome this limitation in multimodal rumor detection, we employ a training strategy that dynamically scales the logits of each modality. By applying adaptive coefficients, our method normalizes the output magnitudes to better align with the target values, thereby ensuring a more balanced contribution from all modalities. Extensive evaluations on two real-world multimodal datasets confirm that our approach stabilizes the training process and yields an embedding space that effectively discriminates between rumors and verified information.}, archive = {J_ACCESS}, author = {Jianing Ren and Tingting Zhong}, doi = {10.1109/ACCESS.2025.3587007}, journal = {IEEE Access}, pages = {133773-133782}, shortjournal = {IEEE Access}, title = {Multimodal rumor detection by online balance multimodal representation learning}, volume = {13}, year = {2025}, }
• (2025). PEARL: An adaptive and explainable hardware trojan detection using open source and enterprise large language models. ACCESS, 13, 133755-133772. (www)

  @article{ , abstract = {The Integrated Circuit (IC) supply chain risk allows attackers to implant hardware Trojans (HT) in various stages of chip production. To counter this, different machine learning (ML) and deep learning (DL)-based methods have been developed to detect HTs. However, these methods require massive amounts of high-quality labeled data for effective training, extended training times for accurate HT detection, limited generalization to novel or unseen HTs, and insufficient capability to explain the detected HTs. Recent studies have started exploring the potential of Large language models (LLMs) for hardware security tasks. However, there are no current studies that explore, study, and compare the applicability of open-source vs. enterprise LLMs for efficient HT detection and explanation of the detected HT. To close the gap, we propose an innovative HT detection and explanation method by leveraging the knowledge of a pre-trained LLM, namely enterprise application programming interface (API)-enabled a Generative Pre-trained Transformer (GPT)-3.5 Turbo, Google Gemini 1.5 Pro and open-source LLMs: Meta AI Llama-3.1 and DeepSeek AI DeepSeek-V2 models, which have already been trained on massive and diverse datasets and is capable of providing the reasoning of the detected HT. Specifically, we apply In-Context Learning (ICL)-based mechanisms: zero-shot, one-shot, and few-shot learning strategies (e.g., register transfer level (RTL) files (Verilog) of the circuit) to adopt this model for HT detection and explanation tasks. We validate our proposed approach on diverse circuit design benchmarks from Trust-Hub and ISCAS (85 and 89). Our experimental results show that the proposed few-shot learning-based enterprise-API-enabled GPT-3.5 Turbo and open-source DeepSeek-V2 LLM models detect unknown HTs with an accuracy of 97% and 91% and drastically reduce the training time compared to state-of-the-art techniques. Furthermore, after detecting the HT, they provide human-centric reasoning/explanation, reinforcing transparency and trust in the IC supply chain through its understanding.}, archive = {J_ACCESS}, author = {Ripan Kumar Kundu and Khurram Khalil and Eric Garcia and Ethan Grassia and Prasad Calyam and Khaza Anuarul Hoque}, doi = {10.1109/ACCESS.2025.3592030}, journal = {IEEE Access}, pages = {133755-133772}, shortjournal = {IEEE Access}, title = {PEARL: An adaptive and explainable hardware trojan detection using open source and enterprise large language models}, volume = {13}, year = {2025}, }
• (2025). Social collective as a modular group modeling tool. ACCESS, 13, 133737-133754. (www)

  @article{ , abstract = {This paper presents a comprehensive description of the social collective, an approach to group modeling for computational collective intelligence purposes. The social collective is independent of the structure of connections between members of the group, therefore it may use any existing representation of a social graph as a basis. Its modular form describes the internal mechanisms of group members: what type of data or knowledge do they use, how is it modified based on internal factors and on communication, as well as how communication between members works. A measure of drift is described in several variants, to better represent the behavior of the social collective. The measure is derived from behavior of social groups and past multi-agent models. Simulation experiments are used to show the observable effects in terms of drift, and to compare to non-collective approaches to group modeling. Social collective is specialized for computational collective intelligence tasks, but it may be fine-tuned for classical tasks, e.g., knowledge diffusion.}, archive = {J_ACCESS}, author = {Marcin Maleszka}, doi = {10.1109/ACCESS.2025.3592944}, journal = {IEEE Access}, pages = {133737-133754}, shortjournal = {IEEE Access}, title = {Social collective as a modular group modeling tool}, volume = {13}, year = {2025}, }
• (2025). Enhanced vehicle tracking with discretization error control for OFDM-based radar system. ACCESS, 13, 133724-133736. (www)

  @article{ , abstract = {Orthogonal frequency division multiplexing (OFDM)-based radar systems are considered a key technology for enhancing spectral efficiency in 6G wireless networks by enabling simultaneous target detection and data transmission using shared frequency resources. However, the discrete sample acquisition process inherent in OFDM radar introduces quantization errors in the measured distance and relative velocity. The quantization errors pose significant challenges for vehicle tracking applications, as directly relying on discretized measurements can lead to considerable inaccuracies in position and velocity estimation. The impact of the discretization errors is especially pronounced in low-resolution scenarios, where coarse quantization bins severely limit measurement precision. In this paper, we propose a vehicle tracking algorithm designed to correct discretization errors arising during the radar sample acquisition process. The algorithm adaptively computes measurement correction factors by leveraging the statistical properties of quantization bins in the two-dimensional radar image profile. The proposed error correction technique is integrated into the measurement update step of the Kalman filter-based vehicle tracking framework, enhancing state estimation accuracy. Furthermore, we investigate how radar resolution constraints affect vehicle state estimation accuracy by analyzing variations in the derived measurement correction factors. Numerical results demonstrate that the estimation performance of the proposed algorithm outperforms that of existing algorithms that rely on uncorrected discrete measurement samples.}, archive = {J_ACCESS}, author = {Seong-Hwan Hyun and Jiho Song and Keunwoo Kim and Jong-Ho Lee and Seong-Cheol Kim}, doi = {10.1109/ACCESS.2025.3592947}, journal = {IEEE Access}, pages = {133724-133736}, shortjournal = {IEEE Access}, title = {Enhanced vehicle tracking with discretization error control for OFDM-based radar system}, volume = {13}, year = {2025}, }
• (2025). Time-optimal model predictive control for linear time-variant systems based on configuration-constrained backward reachability. ACCESS, 13, 133711-133723. (www)

  @article{ , abstract = {This paper presents a robust formulation of Time-Optimal Model Predictive Control for Linear Time-Variant systems that leverages backward reachability analysis for time-optimality while achieving constraint handling and disturbance rejection. The formulation minimizes the time required to reach a terminal set while maintaining recursive feasibility and robustness. Unlike most Time-Optimal Model Predictive Control approaches that depend on time-scaling or iterative horizon reduction, the proposed framework computes a backward reachable tube offline. Each reachable set contains the largest set of states that can reach the terminal set within a fixed number of steps. A controller that constrains the state to remain within the backward reachable tube achieves time-optimality while ensuring real-time applicability and feasibility under disturbances, also with a short prediction horizon. The central innovation lies in the use of configuration-constrained polytopes to construct backward reachable tubes offline with fixed complexity, which permits propagation over long time horizons without growing computational cost. Notably, our method introduces a containment check that dynamically identifies the set in the tube that contains the current system state and lies closer to the terminal set, thereby providing an updated time index for the controller. This effectively replaces the originally planned initial constraint with one that reflects the actual progress. As a result, the controller exploits favorable disturbance realizations, accelerates convergence to the terminal set, and significantly reduces conservatism without compromising robustness. The approach is validated in a vertical farming application, where the objective is to drive crop growth toward desired biomass and ripeness levels as quickly as possible.}, archive = {J_ACCESS}, author = {Michael Fink and Annalena Daniels and Dirk Wollherr and Marion Leibold}, doi = {10.1109/ACCESS.2025.3592981}, journal = {IEEE Access}, pages = {133711-133723}, shortjournal = {IEEE Access}, title = {Time-optimal model predictive control for linear time-variant systems based on configuration-constrained backward reachability}, volume = {13}, year = {2025}, }
• (2025). Embodied learning in virtual reality: Enhancing japanese psychomimetic word acquisition through emotional experiences. ACCESS, 13, 133694-133710. (www)

  @article{ , abstract = {Virtual reality (VR) offers the potential for embodied learning through multisensory engagement. However, empirical research on the direct impact of emotional experiences on learning outcomes remains limited. This study investigated the influence of direct emotional experiences in VR on learners’ acquisition of Japanese psychomimetic words, examining how different emotional styles, self-perception, and empathy affect learning outcomes. Based on embodied learning theory, this study explored how emotional experiences shape vocabulary acquisition. Thirty participants used a VR system designed to elicit emotions, such as joy, anger, and anxiety through scenes that reflected 13 psychomimetic words. The VR system provided immersive scenes that allowed learners to connect emotions with word meanings through emotional experience. This investigation assessed whether immersive VR enhanced the understanding of psychomimetic words and explored how self-perception and empathy influenced the relationships between emotional experiences, engagement, attention, and learning outcomes using eye-tracking and controller data. Self-perception and empathy enhance learning at different effectiveness levels. Self-perception significantly improved immediate and long-term retention, whereas empathy contributed to long-term retention. In self-perception, emotional experiences affect learning outcomes, with attention significantly influencing emotional experiences and learning outcomes. Engagement primarily affects emotional experiences. These results highlight the roles of emotional experiences in VR, suggesting their potential to enhance language education through tailored emotional engagement. The findings emphasize the importance of strategic practices and learner-centered designs for optimizing VR’s effectiveness in language learning.}, archive = {J_ACCESS}, author = {Tang Li and Xuewang Geng and Masanori Yamada}, doi = {10.1109/ACCESS.2025.3590748}, journal = {IEEE Access}, pages = {133694-133710}, shortjournal = {IEEE Access}, title = {Embodied learning in virtual reality: Enhancing japanese psychomimetic word acquisition through emotional experiences}, volume = {13}, year = {2025}, }
• (2025). Minimum mismatch modeling (3M) hyperparameter selection in autoregressive moving average (ARMA) modeling. ACCESS, 13, 133681-133693. (www)

  @article{ , abstract = {Hyperparameters of Autoregressive Moving Average (ARMA) modeling are the number of AR coefficients and the number of MA coefficients. The hyperparameter selection (HS) in ARMA modeling plays a critical role and can dominate the coefficient (parameter) estimation process. This work provides a novel method of HS estimation that works with the Conditional Least Square Estimator (CLSE), which is the most efficient ARMA parameter estimator. The proposed HS method focuses on a rational cost function in the form of mismatch modeling error. The error aims to capture the estimation difference between the true and unknown HS parameters and the competing hyperparameters. This error can be calculated using the available mean square error (MSE) in the parameter estimation step. The proposed method, denoted by the minimum mismatch modeling (3M) approach, has already shown superiority over other HS approaches in AR modeling. In AR modeling, the parameter estimator is based on the Yule-Walker method, which is a linear estimator, and the 3M calculation process using the available MSE has been provided for this modeling. However, in ARMA modeling the CLSE estimator is a nonlinear estimator, and one main challenge is to solve for calculation of the 3M using the MSE of CLSE. The method proposed here, denoted by 3M-CLSE, provides the steps to get to the desired 3M from the available CLSE MSE. It can be shown that the criteria of most used HS methods Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are special cases of 3M-CLSE for particular choices of confidence and validation probabilities. The simulation results confirm the superiority of 3M-CLSE over the existing HS approaches in terms of HS accuracy, as well as in terms of modeling MSE error.}, archive = {J_ACCESS}, author = {Soosan Beheshti and Vedant Bommanahally}, doi = {10.1109/ACCESS.2025.3592088}, journal = {IEEE Access}, pages = {133681-133693}, shortjournal = {IEEE Access}, title = {Minimum mismatch modeling (3M) hyperparameter selection in autoregressive moving average (ARMA) modeling}, volume = {13}, year = {2025}, }
• (2025). Background-masked lightweight approach for pear leaf disease recognition. ACCESS, 13, 133668-133680. (www)

  @article{ , abstract = {Deep learning for plant disease recognition faces challenges in real-world scenarios, especially on resource-constrained devices and with complex backgrounds. While deep neural networks excel with such images, lightweight approaches struggle due to the complexities in feature extraction, affecting classification accuracy. To overcome this, we introduce a novel pipeline that masks and removes complex backgrounds, enabling accurate disease recognition from affected leaves. This approach also significantly increases model accuracy and reduces training time compared to baseline models due to object-cropped input. Using various lightweight neural networks as backbones for the classification task, we validated the efficiency of our approach, which outperformed the baseline in terms of performance. Specifically, our proposed background masking model achieved an IoU of 93.93% for background masking. Furthermore, for disease recognition, our approach using the SqueezeNet-1.1 backbone demonstrated the highest accuracy of 90.84%, surpassing the baseline’s highest average accuracy of 89.22%. These results demonstrate the efficacy of our proposed method in enhancing disease recognition in agricultural imagery, thereby contributing to the advancement of deep learning techniques in this domain. Moreover, the comparison of training times demonstrates that our approach achieved a considerable reduction compared to the benchmark.}, archive = {J_ACCESS}, author = {Romiyal George and Selvarajah Thuseethan and Roshan G. Ragel and Sutharshan Rajasegarar and Mamoun Alazab and Hamish Campbell and John Yearwood}, doi = {10.1109/ACCESS.2025.3593413}, journal = {IEEE Access}, pages = {133668-133680}, shortjournal = {IEEE Access}, title = {Background-masked lightweight approach for pear leaf disease recognition}, volume = {13}, year = {2025}, }
• (2025). A linear programming approach for battery impairment analysis and optimization in decentralized power systems for agricultural operations with solar energy integration. ACCESS, 13, 133650-133667. (www)

  @article{ , abstract = {Energy system researchers are concentrating on reserve systems and reserve synthesis as these subjects show good potential for reducing the price of sustainable energy production. Especially, decentralized power systems have drawn much attention all around the globe as they enable rural areas to get cheap energy without having to spend a fortune enlarging current infrastructure. Such systems often include batteries as well as other backup mechanisms. A key factor affecting the cost-effectiveness of decentralized systems is the impairment of batteries caused by continuous filling and draining. We provide linear range models for the optimal management of distributed systems for agricultural and farming uses where steady energy is required for activities such as watering crops, maintaining greenhouse temperatures, and food reserve for later use. By means of defining battery impairment pricing and creating a method to directly insert battery impairment processes into optimization models, this work mostly adds Given the lack of data linking battery usage to impairment effects, we recommend sensitivity studies to explore the interaction between impairment costs and different operating patterns. We want to identify the best cost-performance profile for batteries that will enable farmers to make more financially viable use of distributed energy systems. This will let them operate more efficient, robust, and eco-friendly businesses.}, archive = {J_ACCESS}, author = {M. Saivineeth and Venkateswarlu. B}, doi = {10.1109/ACCESS.2025.3586439}, journal = {IEEE Access}, pages = {133650-133667}, shortjournal = {IEEE Access}, title = {A linear programming approach for battery impairment analysis and optimization in decentralized power systems for agricultural operations with solar energy integration}, volume = {13}, year = {2025}, }
• (2025). Neural network-based projective grid model for learning representation of surface and wave foams. ACCESS, 13, 133635-133649. (www)

  @article{ , abstract = {In this paper, we propose a projective grid model that enables learning representation of foam effects using artificial neural networks. In 3D fluid simulations, foam is one of the most representative secondary effects in water. Consequently, the processes of foam generation, advection, and dissolution are computationally expensive. However, no prior research has explored representing these processes using neural networks. This paper introduces a constitutive model for learning foam effects in projective space with neural networks. Learning 3D fluid simulation is a complex and multi-faceted challenge, but our approach simplifies the design of foam effects by leveraging a 2D projective space rather than a fully 3D space. The proposed projective grid model consists of the following components: 1) learning the conditions under which foam particles are generated in the projective shape, 2) distinguishing between surface foam and wave foam based on varying conditions instead of relying on a single foam texture, 3) learning the advection process of different foam types, and 4) learning the dissolution process through representation learning. As a result, our method enables efficient representation of 3D foam effects without the need for complex numerical calculations, demonstrating its effectiveness across various scenarios.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3593120}, journal = {IEEE Access}, pages = {133635-133649}, shortjournal = {IEEE Access}, title = {Neural network-based projective grid model for learning representation of surface and wave foams}, volume = {13}, year = {2025}, }
• (2025). Model identification, dynamic positioning, and thrust allocation system for the milliAmpere1 autonomous ferry prototype: Field trial results. ACCESS, 13, 133609-133634. (www)

  @article{ , abstract = {Autonomous electric ferries offer an eco-friendly solution for sustainable and intelligent urban mobility. Currently, these ferries are undergoing successful trial operations on short routes for passenger transportation in European and Scandinavian cities. This paper presents experimental results on model identification, thrust allocation (TA), and dynamic positioning (DP) system for the milliAmpere1 (mA1) autonomous electric ferry prototype. The mA1 is a small prototype ferry used as a test platform in several research projects at the Norwegian University of Science and Technology (NTNU). It recently went through a system upgrade and is now equipped with four symmetrically distributed azimuth thrusters. These new systems require a new mathematical model and a reliable DP and TA system to enable autonomous operation. The DP system is responsible for calculating the total force and moment required to keep position or move between waypoints. At the same time, the TA algorithm coordinates the thrusters to ensure that the resulting force they generate matches the request from the DP control algorithm. The DP is designed with a horizontal plane control law, using a feedforward plus feedback control. The TA is formulated as a quadratic programming problem, which includes cost functions for power consumption. A third-order reference guidance system is implemented to ensure smooth and continuous signals for the DP desired position, velocity, and acceleration. Additionally, system model identification was performed to determine the parameters of the actuator dynamics. Full-scale trials were conducted to evaluate the performance of the proposed system in a sheltered basin situated in Trondheim, Norway, under low-wind conditions. The field trial results indicate that the proposed approach successfully stabilized and maintained a desired trajectory.}, archive = {J_ACCESS}, author = {Miguel Hinostroza and Camilla Fruzzetti and Andreas Gudahl Tufte and Simon J. N. Lexau and Alexey Gusev and Egil Eide and Edmund F. Brekke and Anastasios M. Lekkas and Roger Skjetne and Michele Martelli and Morten Breivik}, doi = {10.1109/ACCESS.2025.3593251}, journal = {IEEE Access}, pages = {133609-133634}, shortjournal = {IEEE Access}, title = {Model identification, dynamic positioning, and thrust allocation system for the milliAmpere1 autonomous ferry prototype: Field trial results}, volume = {13}, year = {2025}, }
• (2025). Efficient text encoders for labor market analysis. ACCESS, 13, 133596-133608. (www)

  @article{ , abstract = {Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this paper, we propose ConTeXT-match, a novel contrastive learning approach with token-level attention that is well-suited for the extreme multi-label classification task of skill classification. ConTeXT-match significantly improves skill extraction efficiency and performance, achieving state-of-the-art results with a lightweight bi-encoder model. To support robust evaluation, we introduce Skill-XL a new benchmark with exhaustive, sentence-level skill annotations that explicitly address the redundancy in the large label space. Finally, we present JobBERT V2, an improved job title normalization model that leverages extracted skills to produce high-quality job title representations. Experiments demonstrate that our models are efficient, accurate, and scalable, making them ideal for large-scale, real-time labor market analysis.}, archive = {J_ACCESS}, author = {Jens-Joris Decorte and Jeroen van Hautte and Chris Develder and Thomas Demeester}, doi = {10.1109/ACCESS.2025.3589147}, journal = {IEEE Access}, pages = {133596-133608}, shortjournal = {IEEE Access}, title = {Efficient text encoders for labor market analysis}, volume = {13}, year = {2025}, }
• (2025). Islanding detection methods and challenges for distribution generation: A technological review. ACCESS, 13, 133568-133595. (www)

  @article{ , abstract = {Islanding detection poses a significant technical challenge for the reliable operation of grid-connected photovoltaic (PV) systems, particularly as the deployment of distributed generation (DG) increases across modern power networks. An undetected islanding event may jeopardize the safety of utility personnel, degrade power quality, cause voltage and frequency instability, and lead to malfunction or damage of sensitive electrical equipment. This paper presents a structured and comprehensive review of local islanding detection methods (IDMs), which are categorized into three principal classes: passive, active, and hybrid approaches, with the associated challenges. Each category is examined in terms of its core detection principle and is evaluated based on key performance indicators, including detection time, non-detection zone (NDZ), threshold dependency, false and nuisance tripping probability, implementation complexity, and impact on system power quality. By comparing IDM performance across diverse operating conditions, analyzing implementation trade-offs, and synthesizing recent advancements, this review highlights key features, limitations, operational challenges, and emerging trends that affect the reliability of islanding detection. It aims to provide useful insights to assist researchers in selecting appropriate methods and in designing effective IDMs. Special emphasis is placed on the influence of evolving grid dynamics on detection performance, encouraging the development of more accurate and system-specific detection strategies. A comparative evaluation supported by standardized criteria further facilitates the development of adaptive, robust, and standards-compliant IDM strategies that enhance overall grid stability, detection accuracy, and operational resilience.}, archive = {J_ACCESS}, author = {Farshad Aslani and Shuhui Li}, doi = {10.1109/ACCESS.2025.3591392}, journal = {IEEE Access}, pages = {133568-133595}, shortjournal = {IEEE Access}, title = {Islanding detection methods and challenges for distribution generation: A technological review}, volume = {13}, year = {2025}, }
• (2025). Coded caching: User to access points distribution in wireless local area networks. ACCESS, 13, 133557-133567. (www)

  @article{ , abstract = {Modern wireless networks face significant challenges in high-density deployments, where the existing approaches fail to fully exploit the cooperative potential of coded caching, intelligent user clustering, and graph-theoretic optimization. This paper proposes a novel coded caching framework that optimizes content delivery in a wireless local area network where users connect to cache-less access points. The system proposes a novel user-to-access-point assignment approach and leverages coded multicast opportunities across carefully paired users and access points to maximize the caching gain and improve load balancing. Our key contribution is the development of a clustering algorithm that efficiently distributes users across access points. This algorithm was inspired by the enhanced K++ means initialization technique that determines a centroid for each cluster, leveraging the benefits of coded caching to maximize caching gains. To validate its performance and complexity, we compare our clustering algorithm against a random User-to-AP assignment algorithm. In this heuristic baseline, users connect to access points randomly, ignoring load balance. Our simulations reveal the dual benefits of the clustering approach, highlighting enhanced caching performance through optimized content placement and improved network efficiency via balanced user distribution.}, archive = {J_ACCESS}, author = {Mirna Haidar and Yasser Fadlallah and Hadi Sawaya and Abed Ellatif Samhat}, doi = {10.1109/ACCESS.2025.3589318}, journal = {IEEE Access}, pages = {133557-133567}, shortjournal = {IEEE Access}, title = {Coded caching: User to access points distribution in wireless local area networks}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence-based transmission frequency prediction in cable systems using electromagnetic field data. ACCESS, 13, 133546-133556. (www)

  @article{ , abstract = {This study presents an AI-driven framework for predicting transmission frequency in cables using magnetic field data, offering a modern alternative to conventional analytical methods. A reference model based on transmission line theory analyzes twisted-pair cables via distributed parameters (R, L, C, G), yielding an initial frequency estimate of 750 MHz. Concurrently, an MLP neural network trained on HFSS-simulated magnetic field data predicts 745 MHz, deviating by 5 MHz. To enhance precision, a hybrid neural network—combining transformer, LSTM, and a physics-informed layer grounded in Maxwell’s equations—is proposed, reducing the error to 0.2 MHz (749.8 MHz prediction). The AI approach demonstrates superior computational efficiency while maintaining high accuracy. Key preprocessing steps include data normalization, noise reduction, and magnetic field vectorization. Findings validate AI’s potential to streamline frequency estimation in cable systems, bridging theoretical and data-driven paradigms. This work advances applications in electrical and telecommunications engineering, emphasizing scalable, intelligent solutions for electromagnetic analysis.}, archive = {J_ACCESS}, author = {Abbas Seif and Hamid Radmanesh and Amir Ahmarinejad and Ahmad Ali Ashrafian}, doi = {10.1109/ACCESS.2025.3589943}, journal = {IEEE Access}, pages = {133546-133556}, shortjournal = {IEEE Access}, title = {Artificial intelligence-based transmission frequency prediction in cable systems using electromagnetic field data}, volume = {13}, year = {2025}, }
• (2025). Changes in research trends and topics on carbon neutrality before and after the 2016 paris agreement. ACCESS, 13, 133525-133545. (www)

  @article{ , abstract = {Carbon neutrality has become a critical global objective that influences climate governance and shapes research trends, particularly in G20 nations. This study systematically analyzes how academic research on carbon neutrality has evolved in these countries by comparing the periods before and after the 2016 Paris Agreement. A quantitative bibliometric analysis, combined with latent Dirichlet allocation (LDA) topic modeling, was conducted on 2,376 peer-reviewed articles published between 2005 and 2023; these were retrieved from the Web of Science database. The findings indicate a significant increase in research volume and notable thematic shifts following the Paris Agreement, moving from technology-oriented topics towards comprehensive, policy-driven themes such as renewable energy integration, regulatory frameworks, economic incentives, and urban sustainability. Country-specific comparisons highlight various national emphases. China has intensified its research on industrial decarbonization and sustainable economic strategies. The United States has emphasized renewable energy technologies and economic viability. The European Union has focused on urban innovation, regulation, and green finance. Japan has prioritized catalytic processes and industrial efficiency. Finally, South Korea has integrated technological advancements with practical urban applications. These results underscore the need for tailored national policies that leverage economic strengths complemented by intensified international collaboration, including technology transfer and green finance.}, archive = {J_ACCESS}, author = {Insung Hwang and Woojun Jung and Keuntae Cho}, doi = {10.1109/ACCESS.2025.3583438}, journal = {IEEE Access}, pages = {133525-133545}, shortjournal = {IEEE Access}, title = {Changes in research trends and topics on carbon neutrality before and after the 2016 paris agreement}, volume = {13}, year = {2025}, }
• (2025). Stability improvement of renewable energy integrated power network through coordination of PSS, ISFCL, and STATCOM. ACCESS, 13, 133510-133524. (www)

  @article{ , abstract = {Wind energy is a promising solution to meet growing global energy demands; however, its intermittent nature and low system inertia introduce significant challenges to power system stability. This paper addresses these issues by applying a coordinated deployment of a Power System Stabilizer (PSS), an Inductive Superconducting Fault Current Limiter (ISFCL), and a Static Synchronous Compensator (STATCOM) to the IEEE 9-bus system. Type 1 and Type 3 wind farms are integrated into the network, and system performance is evaluated using Sum of Mean Deviations (SMD) and Critical Clearing Time (CCT). The dual-input PSS enhances damping of rotor oscillations, ISFCL limits peak fault currents by up to 37%, and STATCOM, optimized using the Hooke & Jeeves method, reduces voltage dip by 0.3 pu and strengthens Low Voltage Ride Through (LVRT) capability. The combined deployment of all three devices restores the stability indices to levels comparable to those in the base case without wind integration. Among the two wind farm types, the coordinated application proves more effective for Type 3 wind farms, which show superior damping, reduced fault current, and enhanced voltage recovery, making them more suitable for reliable grid integration. Simulations were performed in PSCAD software.}, archive = {J_ACCESS}, author = {Afrasiyab Khan and Babar Hussain and Addy Wahyudie and Ruhul Amin Khalil}, doi = {10.1109/ACCESS.2025.3592940}, journal = {IEEE Access}, pages = {133510-133524}, shortjournal = {IEEE Access}, title = {Stability improvement of renewable energy integrated power network through coordination of PSS, ISFCL, and STATCOM}, volume = {13}, year = {2025}, }
• (2025). Big data and I2X communication infrastructure for traffic optimization and accident prevention on automated roads. ACCESS, 13, 133497-133509. (www)

  @article{ , abstract = {This paper presents a scalable big data infrastructure designed to support traffic optimization and accident prevention in automated and connected road environments. The proposed system integrates real-time data acquisition from heterogeneous sources, including multichannel roadside camera gantries and IoT-enabled vehicle telemetry. The architecture is built upon technologies such as Apache Kafka, Apache Beam, and MongoDB, enabling high-throughput data ingestion, processing, and storage. To validate its performance, two experimental use-cases were developed: one for large-scale image ingestion and another for vehicle telemetry data streaming. The system successfully handled over 48 GB of image data and more than 3.4 million telemetry messages under real-time constraints. Results show that applying data compression techniques—such as resolution reduction and transmission throttling—reduced image upload durations by up to 77%, improving ingestion efficiency without compromising system robustness. These findings demonstrate the feasibility of deploying the proposed infrastructure as a foundational layer for future intelligent traffic management systems.}, archive = {J_ACCESS}, author = {Jorge García-González and Javier Fernández-Andrés and Nourdine Aliane and Javier Sánchez-Soriano}, doi = {10.1109/ACCESS.2025.3592310}, journal = {IEEE Access}, pages = {133497-133509}, shortjournal = {IEEE Access}, title = {Big data and I2X communication infrastructure for traffic optimization and accident prevention on automated roads}, volume = {13}, year = {2025}, }
• (2025). LSD-det: A lightweight detector for small ship targets in SAR images. ACCESS, 13, 133483-133496. (www)

  @article{ , abstract = {Synthetic Aperture Radar (SAR) plays a vital role in ship safety monitoring and marine environmental protection. However, ship targets in SAR images are often small, have blurred edges, and suffer from strong background interference, making it difficult for traditional detection algorithms to balance accuracy and real-time performance. To address these challenges, this paper proposes LSD-Det, a lightweight SAR ship detection model improved from YOLOv8n. First, the YOLOv8n architecture is streamlined to reduce redundancy, enhancing suitability for SAR detection. Then, a Grouped Split Enhanced Channel Attention (GSECA) module is introduced in the backbone, combining average and max pooling with channel shuffle to improve recognition of small targets and suppress background noise. Additionally, a Global-Enhanced Dilated Wavelet Transform (GEDWT) module is embedded in the neck’s C2f structure to enhance multi-scale feature representation with minimal computational overhead. Furthermore, the original CIoU loss is replaced with PIoUv2, which accelerates convergence and improves bounding box regression accuracy. Experiments conducted on two publicly available SAR ship detection datasets, SSDD and HRSID, demonstrate that LSD-Det significantly reduces computational cost while maintaining high detection performance. Specifically, parameters are reduced by 65.7%, GFLOPs by 20.7%, and inference speed is notably improved. LSD-Det also improves mAP on SSDD by 1.2% (mAP@0.5) and 2.2% (mAP@0.5:0.95), with corresponding gains of 0.8% and 1.3% on HRSID. The experimental outcomes demonstrate that the proposed approach maintains a desirable trade-off between precision and computational efficiency, making it suitable for real-time SAR ship detection under limited-resource conditions.}, archive = {J_ACCESS}, author = {Zhen Wang and Bin Qin and Shang Gao}, doi = {10.1109/ACCESS.2025.3593021}, journal = {IEEE Access}, pages = {133483-133496}, shortjournal = {IEEE Access}, title = {LSD-det: A lightweight detector for small ship targets in SAR images}, volume = {13}, year = {2025}, }
• (2025). Revealing depression through social media via adaptive gated cross-modal fusion augmented with insights from personality traits. ACCESS, 13, 133465-133482. (www)

  @article{ , abstract = {The escalating prevalence of mental health disorders, particularly depression, underscores the urgent need for early and accurate detection systems. The widespread use of social media has produced an extensive repository of user-generated content, offering critical insight into individuals’ emotional and psychological states. This development enables novel approaches for automated depression detection. However, existing multimodal depression detection approaches often adopt rigid fusion strategies and disregard individual differences in expressive behavior by adopting generalized, one-size-fits-all frameworks. To bridge this gap, we introduce DeXMAG, a novel personalized depression detection framework that integrates a Cross-Modal Attention mechanism with an Adaptive Gated Fusion strategy. DeXMAG effectively combines textual content, visual imagery, and user-level personality traits to capture the heterogeneous and individualized patterns of depressive expression across modalities. Leveraging domain-specific transformer-based language models and a convolutional neural networks-based model, we extract modality-specific features and fuse them via a novel adaptive gating mechanism. Unlike traditional fusion techniques, our model leverages modality-aware attention signals and gating coefficients to enable flexible and personalized integration of features. An extensive ablation study reveals that the most substantial performance gain occurs when DeXMAG is augmented with insights from Myers–Briggs Type Indicator (MBTI) personality traits in conjunction with textual and visual features. Comprehensive experiments on multimodal social media datasets demonstrate that DeXMAG consistently outperforms unimodal baselines and state-of-the-art approaches, achieving an accuracy of 95.20% and an F1-score of 95.94%. These results reinforce the effectiveness of adaptive gated fusion and personality-aware modeling in advancing robust and personalized depression detection.}, archive = {J_ACCESS}, author = {Gede Aditra Pradnyana and Wiwik Anggraeni and Eko Mulyanto Yuniarno and Mauridhi Hery Purnomo}, doi = {10.1109/ACCESS.2025.3593273}, journal = {IEEE Access}, pages = {133465-133482}, shortjournal = {IEEE Access}, title = {Revealing depression through social media via adaptive gated cross-modal fusion augmented with insights from personality traits}, volume = {13}, year = {2025}, }
• (2025). SLGMA-UNet: Comprehensive feature aggregation with context-sensitive attention for medical image segmentation. ACCESS, 13, 133457-133464. (www)

  @article{ , abstract = {Medical image segmentation is essential for clinical diagnosis and treatment planning. Existing segmentation methods encounter challenges such as managing size variations, interpreting contextual relationships, and integrating multi-source data. This paper introduces SLGMA-UNet, an enhanced architecture that incorporates a multi-level attention system, adaptive scale processing Efficient Multi-Scale Attention (EMA), and a combined focus mechanism Spatial & Local-Global Attention (SLGA). The EMA module integrates multi-scale information, avoids channel dimensionality reduction, facilitates cross-space learning, and effectively establishes dependencies. The SLGA module balances local and global feature processing through an adaptive fusion strategy. Experiments conducted on five medical datasets, including ISIC2017, ISIC2018, CVC-ClinicDB, Kvasir-SEG, and GLAS, demonstrate that SLGMA-UNet outperforms existing models, achieving accuracy improvements of 4.2-7.8% (mean Intersection over Union, mIoU) and 3.6-6.4% (Dice Similarity Coefficient, DSC), along with enhanced edge detection and reduced errors in complex cases. Ablation studies confirm the positive and synergistic effects of the additional components. SLGMA-UNet offers a more effective solution for accurate medical image segmentation and shows promise for practical clinical applications.}, archive = {J_ACCESS}, author = {Xinghuo Ye and Na Wang}, doi = {10.1109/ACCESS.2025.3584832}, journal = {IEEE Access}, pages = {133457-133464}, shortjournal = {IEEE Access}, title = {SLGMA-UNet: Comprehensive feature aggregation with context-sensitive attention for medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). TOP: A combined logical and physical obfuscation method for securing networks-on-chip against reverse engineering attacks. ACCESS, 13, 133438-133456. (www)

  @article{ , abstract = {The Network-on-Chip (NoC) plays an important role in high-speed and efficient communication in System-on-Chip architectures. However, the NoC topology can be extracted through reverse engineering attacks with the goal of making illegal copies or unauthorized access to interconnect structure and system parameters, including routing protocols and data traffic patterns, thereby posing a threat to the security of the entire system. Simultaneously, the demand for energy efficiency has resulted in the extensive use of power-gating techniques that selectively shut down specific components in the NoC when they are not used to reduce power consumption significantly. This paper introduces TOP, a scalable obfuscation method that can be integrated into power-gating controllers, which aims to fortify the NoC against reverse engineering attacks while providing near-zero additional latency during operation. TOP operates using security keys to assume control of network components and reconfigure its structure and routing paths, utilizing existing units designed for use by power-gating controllers. The mechanism incorporates two distinct obfuscation approaches for strong levels of protection. First, our centralized approach combines power-gating and structure obfuscation, ensuring efficiency and scalability to corrupt the function and traffic pattern of the network when an incorrect key is presented. Second, our distributed approach specifically addresses timing-based attacks by introducing timing violations and triggering malfunctions upon applying wrong key. The scalability, energy efficiency, and minimal latency overhead of TOP, as evidenced by the average power, latency, and PDP overhead results across seven PARSEC programs in five different security levels (1.7-4.3%, 6-14.8%, and 7.8-19.7%, respectively), make it a suitable solution for safety-critical, high-performance, and resource-limited designs.}, archive = {J_ACCESS}, author = {Mona Hashemi and Siamak Mohammadi and Trevor E. Carlson}, doi = {10.1109/ACCESS.2025.3590967}, journal = {IEEE Access}, pages = {133438-133456}, shortjournal = {IEEE Access}, title = {TOP: A combined logical and physical obfuscation method for securing networks-on-chip against reverse engineering attacks}, volume = {13}, year = {2025}, }
• (2025). A novel picture fuzzy similarity measure: Theory and practical applications. ACCESS, 13, 133426-133437. (www)

  @article{ , abstract = {Similarity metrics serve as valuable tools for evaluating the resemblance between two datasets. While intuitionistic fuzzy sets have been widely used, picture fuzzy sets present enhanced capabilities in capturing ambiguity and uncertainty within real-world applications. The role of similarity measures becomes pivotal when analyzing such sets, yet existing methodologies, despite extensive research, frequently generate illogical outcomes in various scenarios. Addressing this gap, our study introduces an advanced similarity measure tailored for picture fuzzy sets, demonstrating superior efficacy and reliability compared to conventional approaches. Through comprehensive benchmarking against established metrics, we validate its effectiveness in both classification tasks and diagnostic analyses. Furthermore, we propose a robust decision-making framework within the picture fuzzy domain, designed to surpass the limitations of the traditional TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, offering improved accuracy in complex decision environments.}, archive = {J_ACCESS}, author = {Abdul Haseeb Ganie and Yousef Al-Qudah and Zabidin Salleh and Ahmad Alhawarat}, doi = {10.1109/ACCESS.2025.3588325}, journal = {IEEE Access}, pages = {133426-133437}, shortjournal = {IEEE Access}, title = {A novel picture fuzzy similarity measure: Theory and practical applications}, volume = {13}, year = {2025}, }
• (2025). A novel SDR-based P-band PRN radar with real time range compression and coherent integration. ACCESS, 13, 133418-133425. (www)

  @article{ , abstract = {Active microwave remote sensing at P-band offers the potential to map vegetation biomass and subsurface soil moisture. This paper presents the development of a novel Software-Defined Radio (SDR)-based Pseudo-Random Noise (PRN) radar operating at P-band. Utilizing GNU Radio, it is the first one of its kind performing real-time range compression and coherent integration, achieving a signal-to-noise ratio (SNR) of up to 64.4 dB. Operating at a low transmit power of 10 dBm, experimental results validate the system’s performance, demonstrating its potential for remote sensing applications.}, archive = {J_ACCESS}, author = {Edwar Edwar and Yasir M. O. Abbas and Shah Zahid Khan and Abdul-Halim Jallad and Adriano Camps}, doi = {10.1109/ACCESS.2025.3590285}, journal = {IEEE Access}, pages = {133418-133425}, shortjournal = {IEEE Access}, title = {A novel SDR-based P-band PRN radar with real time range compression and coherent integration}, volume = {13}, year = {2025}, }
• (2025). Handwriting difficulties: A review of recent advances in the identification and intervention. ACCESS, 13, 133397-133417. (www)

  @article{ , abstract = {Handwriting difficulties can significantly affect education and daily functioning. Despite recent advances, challenges in standardization, dataset diversity, and model explainability still limit cross-study comparability and real-world applicability. This narrative review synthesizes recent work on identifying and addressing handwriting difficulties, covering both traditional and technology-based approaches. Rather than proposing a new method or computational framework, it highlights trends, challenges, and opportunities in current research. We systematically reviewed 107 primary studies published between 2019 and 2024, sourced from IEEE Xplore and SpringerLink. Key techniques examined include the use of spatiotemporal signals, image data, feature-extraction methods, and algorithmic classification. We also explored key experimental design elements and assistive interventions aimed at improving handwriting performance. Our review underscores advances in handwriting analysis—particularly the role of technology in assessing and improving handwriting legibility and fluency—and shows how these methods address limitations of conventional assessments. The findings demonstrate the potential of technology-based approaches for assessing and mitigating handwriting difficulties, while revealing ongoing challenges such as limited dataset diversity, the need to integrate cognitive and motor aspects, and inadequate model transparency. Future work should focus on standardizing methods, increasing dataset representation, and developing interpretable, multimodal solutions.}, archive = {J_ACCESS}, author = {Suatmi Murnani and Kazuharu Hashitsume and Tomohiko Igasaki}, doi = {10.1109/ACCESS.2025.3593240}, journal = {IEEE Access}, pages = {133397-133417}, shortjournal = {IEEE Access}, title = {Handwriting difficulties: A review of recent advances in the identification and intervention}, volume = {13}, year = {2025}, }
• (2025). Sentiment analysis algorithm based on deep transfer learning for multi-source data fusion. ACCESS, 13, 133381-133396. (www)

  @article{ , abstract = {To tackle the challenge of data diversity in sentiment analysis and improve the accuracy and generalization ability of sentiment analysis, this study first cleans, denoises, and standardizes multi-source data, and then proposes an improved sentiment analysis framework based on deep transfer learning technology. The swarm intelligence optimization algorithm is introduced to optimize the deep transfer learning architecture. The experiment outcomes indicate that the artificial bee colony-improved particle swarm optimization algorithm designed in the study has the minimum convergence value under single-peak and multi-peak test functions, with a standard deviation of less than 0.25 and a success rate of 100% in optimization. The solution set obtained by the algorithm performs well in terms of hyper-volume and inverse generation distance, and is closest to the true Pareto frontier. The diversity and convergence of the algorithm’s solutions outperform other algorithms, ensuring the optimization requirements of the sentiment analysis architecture. The deep transfer learning sentiment analysis model designed based on this achieves the best classification accuracy, with a minimum mean absolute error of 0.128, and good performance in accuracy recall and receiver operation characteristics. The recognition of emotional diversity has high accuracy, with an accuracy of 0.928 for 5 categories. The cross entropy loss and Matthews correlation coefficient confirm the reliability of this method for sentiment orientation recognition in multi-source data. The research explores the use of deep transfer learning in multi-source data fusion sentiment analysis, enriches and improves sentiment analysis techniques, and promotes innovation and development of sentiment analysis technology.}, archive = {J_ACCESS}, author = {Long Qin}, doi = {10.1109/ACCESS.2025.3593022}, journal = {IEEE Access}, pages = {133381-133396}, shortjournal = {IEEE Access}, title = {Sentiment analysis algorithm based on deep transfer learning for multi-source data fusion}, volume = {13}, year = {2025}, }
• (2025). Flexible liquid-cooled electromagnetic gripper for grasping heavy objects. ACCESS, 13, 133370-133380. (www)

  @article{ , abstract = {This paper presents a novel electromagnetic gripper designed for grasping heavy objects (up to 20 kg) with self-adaptability grasping. The gripper features Liquid-Cooled Electromagnetic Actuators with Dual-Pole Actuation (LEA-DAs) serving as fingertips and a Wire Length Retention Mechanism (WLRM). Both poles are used simultaneously to provide a high payload and grip stability. The LEA-DAs are cooled by a closed-loop oil system, which allows the gripper to maintain high power output while ensuring safe operating temperatures for an electromagnetic drive. A method has been adopted to enable the grasping of objects of various sizes and shapes, where the object is enclosed and grasped using a wire. The wire’s length is fine-tuned to match the object’s shape and size through a Rear Pole Actuation Mechanism (RPAM), while a WLRM keeps the wire in its fully extended state, enabling it to conform to the shape of the object and facilitating its release. The RPAM enhances grip stability across diverse geometries. Testing with objects of various cross-sections, including circular, hexagonal, and rectangular shapes, demonstrates that the combination of high grasping force and an adaptable grip structure allows the gripper to grasp heavy objects effectively. This study presents an analysis of the mechanisms to achieve the desired results. The gripper is studied experimentally through grasping tests.}, archive = {J_ACCESS}, author = {Mauricio Rodriguez Calvo and Toshihiro Nishimura and Tetsuyou Watanabe}, doi = {10.1109/ACCESS.2025.3593041}, journal = {IEEE Access}, pages = {133370-133380}, shortjournal = {IEEE Access}, title = {Flexible liquid-cooled electromagnetic gripper for grasping heavy objects}, volume = {13}, year = {2025}, }
• (2025). Using variational autoencoders for out of distribution detection in histological multiple instance learning. ACCESS, 13, 133351-133369. (www)

  @article{ , abstract = {In the context of histological image classification, Multiple Instance Learning (MIL) methods only require labels at Whole Slide Image (WSI) level, effectively reducing the annotation bottleneck. However, for their deployment in real scenarios, they must be able to detect the presence of previously unseen tissues or artifacts, the so-called Out-of-Distribution (OOD) samples. This would allow Computer Assisted Diagnosis systems to flag samples for additional quality or content control. In this work, we propose an OOD-aware probabilistic deep MIL model that combines the latent representation from a variational autoencoder and an attention mechanism. At test time, the latent representations of the instances are used in the classification and OOD detection tasks. We also propose a deterministic version of the model that uses the reconstruction error as OOD score. Panda (prostate tissue) and Camelyon16 (lymph node tissue) are used as train/test in-distribution datasets, obtaining bag classification results competitive with current state-of-the-art models. OOD detection is evaluated performing two experiments for each in-distribution dataset. For Panda, Camelyon16 and ARTIF (prostate tissue contaminated with artifacts) are used as OOD datasets, obtaining 100% AUC in both cases. For Camelyon16, Panda and BCELL (lymph node tissue diagnosed with diffuse large B-cell lymphoma) are used as OOD datasets, obtaining AUCs of 100% and 97%, respectively. Experimental validation demonstrates the models’ strong classification performance and effective OOD slide detection, highlighting their clinical potential.}, archive = {J_ACCESS}, author = {Francisco Javier Sáez-Maldonado and Luz García and Lee A. D. Cooper and Jeffery A. Goldstein and Rafael Molina and Aggelos K. Katsaggelos}, doi = {10.1109/ACCESS.2025.3593420}, journal = {IEEE Access}, pages = {133351-133369}, shortjournal = {IEEE Access}, title = {Using variational autoencoders for out of distribution detection in histological multiple instance learning}, volume = {13}, year = {2025}, }
• (2025). MFENet: A multi-feature extraction network for enhanced emotion detection using EEG and STFT. ACCESS, 13, 133338-133350. (www)

  @article{ , abstract = {Developing computationally efficient models for EEG-based emotion recognition is essential for enabling scalable and responsive brain-computer interface (BCI) systems. However, the inherent nonstationarity of EEG signals and individual variability across subjects complicate the development of models that generalize reliably across diverse affective states. To address these challenges, this paper introduces MFENet, a lightweight convolutional neural network tailored for emotion recognition from electroencephalogram (EEG) data. The proposed approach leverages short-time Fourier transform (STFT) to convert one-dimensional EEG signals into two-dimensional spectrograms, thereby capturing rich time-frequency information while preserving signal structure. MFENet employs multi-feature extraction blocks and grouped convolutions to enhance representational diversity without incurring high computational costs. Comparative evaluation on the SEED and SEED-IV datasets demonstrates that MFENet achieves consistently superior performance over GoogLeNet, ResNet-101, and Inception-v3, with improvements of 11.7%, 16.3%, and 8.6% on the SEED dataset, and 4.9%, 2.5%, and 1.4% on the SEED-IV dataset, respectively. These results demonstrate that MFENet achieves an effective balance between classification performance and architectural simplicity, making it well-suited for deployment in real-time, emotion-aware BCI environments.}, archive = {J_ACCESS}, author = {N. Ramesh Babu and Viswanathan Vadivel}, doi = {10.1109/ACCESS.2025.3591202}, journal = {IEEE Access}, pages = {133338-133350}, shortjournal = {IEEE Access}, title = {MFENet: A multi-feature extraction network for enhanced emotion detection using EEG and STFT}, volume = {13}, year = {2025}, }
• (2025). Analysis on solar power plant placement and distribution grid issues utilizing monte carlo simulation and teaching learning-based optimization. ACCESS, 13, 133321-133337. (www)

  @article{ , abstract = {This study investigates the impact that uncertainties in photovoltaic (PV) performance under a variety of solar irradiance scenarios have on the expected power losses that occur when probabilistic distribution network restrictions are taken into consideration. Through the use of Teaching–Learning–Based Optimization (TLBO) and Monte Carlo Simulation (MCS) in conjunction with an emphasis on chance-constrained approaches, this study contributes to the enhancement of the body of literature for optimal PV installation in distribution networks. The outputs of the optimization are validated using MCS under a variety of uncertainty scenarios, and the variables of the distribution network are evaluated in terms of the probability of exceeding constraints. This is done in order to demonstrate that the suggested technique is effective. A comparison is made between the outcomes of TLBO and the implementation of Artificial Bee Colony (ABC) technique, which make use of probabilistic evaluation and modeling. It has been shown via simulation that the TLBO methodology is superior than the ABC method in terms of efficiently minimizing power losses.}, archive = {J_ACCESS}, author = {Ersan Omer Yuzer and Ibrahim Cagri Barutcu}, doi = {10.1109/ACCESS.2025.3593155}, journal = {IEEE Access}, pages = {133321-133337}, shortjournal = {IEEE Access}, title = {Analysis on solar power plant placement and distribution grid issues utilizing monte carlo simulation and teaching learning-based optimization}, volume = {13}, year = {2025}, }
• (2025). Robust time-of-arrival-based localization with unknown transmission time using convex relaxation and penalty term. ACCESS, 13, 133310-133320. (www)

  @article{ , abstract = {This paper considers the time-of-arrival (TOA) based source localization problems when the start transmission time is unknown for both cases of accurate and inaccurate sensor locations. For the case where the sensor locations are accurate, the weighted least squares (WLS) criterion is adopted, leading to a constrained weighted least squares (CWLS) problem. By using the convex relaxation technique, the CWLS problem is relaxed into a novel quasi-convex fractional programming (FP) problem, which is then transformed into an equivalent mixed semi-definite and second-order cone programming (SD/SOCP) problem. This SD/SOCP problem is further tightened by adding a series of second-order cone constraints and a penalty term. For the case where the sensor locations are inaccurate, Taylor series expansion is applied to formulate the new expression of the error term, resulting in the CWLS problem considering the sensor location errors. The solution of this problem can be approximately obtained through similar operations. The results from the simulations and experiments confirm the proposed method can achieve the Cramer-Rao lower bound (CRLB) accuracy and possess a good robustness against the selection of the penalty factor.}, archive = {J_ACCESS}, author = {Ruichao Zheng and Jinliang Dong and Zhao Han and Wen Xi}, doi = {10.1109/ACCESS.2025.3592926}, journal = {IEEE Access}, pages = {133310-133320}, shortjournal = {IEEE Access}, title = {Robust time-of-arrival-based localization with unknown transmission time using convex relaxation and penalty term}, volume = {13}, year = {2025}, }
• (2025). Metaheuristic-tuned droop control for PV-based DC microgrid optimization under dynamic load conditions. ACCESS, 13, 133293-133309. (www)

  @article{ , abstract = {Sustainable energy solutions from photovoltaic (PV)-based direct current (DC) microgrids (MG) encounter issues in maintaining voltage stability and power-sharing efficiency during dynamic demand fluctuation. System instability and poor performance are caused by traditional droop control methods, which rely on predetermined parameters and are unable to handle fluctuating loads. Due to dynamic load circumstances, DC microgrids powered by PV may experience voltage fluctuations, power imbalances, and significant power losses. Traditional droop control is unstable and inefficient because it cannot make real-time corrections. Microgrid stability requires constant droop optimization. Metaheuristic-Tuned Droop Control (MTDC) uses the Self-Adaptive Particle Swarm Optimization (SA-PSO) technique to calculate the dynamic droop coefficient. With an inertia weight adjustment method, SA-PSO improves convergence and flexibility. Optimization goals include minimizing power loss, enhancing power-sharing accuracy, and mitigating voltage fluctuations. This study simulates the proposed approach using MATLAB/Simulink under various stress conditions to verify its effectiveness. Unlike traditional droop control, MTDC minimizes voltage fluctuation, improves power-sharing accuracy, and lowers power losses. Metaheuristic optimization improves PV-based DC microgrid control. Systems become more stable, respond faster, and withstand disturbances. According to this research, adaptive droop control can enhance the efficiency and reliability of microgrids powered by renewable energy.}, archive = {J_ACCESS}, author = {G. R. Prudhvi Kumar and Sattianadan Dasarathan}, doi = {10.1109/ACCESS.2025.3592751}, journal = {IEEE Access}, pages = {133293-133309}, shortjournal = {IEEE Access}, title = {Metaheuristic-tuned droop control for PV-based DC microgrid optimization under dynamic load conditions}, volume = {13}, year = {2025}, }
• (2025). Joint P- and S-wave VSP traveltime tomography via well log-guided physics-informed neural networks. ACCESS, 13, 133280-133292. (www)

  @article{ , abstract = {Traveltime tomography is widely used in seismology to construct accurate long-wavelengthsubsurface velocity models and to investigate the Earth’s internal structure and dynamic processes. The joint inversion of vertical seismic profile (VSP) P- and S-wave velocities provides opportunities, but alsopresents challengesdue to the limitations of conventional numerical solvers, which are generally constrained to first-order accuracy and perform poorly on irregular computational grids. To overcome these limitations, physics-informed neural networks (PINNs) have been introduced, enabling the PINN-based tomography framework (PINNtomo) to utilize the representational strength of neural networks to solve theeikonal equation, reconstruct velocity models in a data-consistent and physically constrained method, and address these challenges more effectively. We propose a physics-informed neural network framework guided by well log information for the joint P-wave and S-wave VSP traveltime tomography (W-PINNPStomo). Specifically, W-PINNPStomo performs joint multi-parameter inversion of P-wave and S-wave velocities, with model updates constrained by P- and S-wave log data serving as softconstraints. The W-PINNPStomo framework incorporates two neural networks: a travel time network dedicated to predicting P- and S-wave traveltimes, and a velocity network tasked with estimating the corresponding wave velocities. By minimizing the misfit between initial P- and S-wave traveltimes derived from the eikonal equation and those predicted by the model, the associated velocity fields can be iteratively updated. Since the original PINNtomo loss function relies on multiplicative-factor eikonal equations that depend on background traveltimes based on uniform velocities at the source location, we introduce a source-independent multiplicative decomposition of the eikonal equation to eliminate this velocity dependence. Numerical experiments conducted on the Marmousi, Fault and Salt velocity models under VSP acquisition geometries confirm the effectiveness of the proposed improvements. By incorporating well log information as softconstraints, the W-PINNPStomo framework achieves faster convergence and improved multi-parameter inversion accuracy compared to PINNPStomo.}, archive = {J_ACCESS}, author = {Tengyu Wang and Dingding Deng and Duoming Zheng and Zhen Zhang and Wenjun Luo and Yang Liu}, doi = {10.1109/ACCESS.2025.3591876}, journal = {IEEE Access}, pages = {133280-133292}, shortjournal = {IEEE Access}, title = {Joint P- and S-wave VSP traveltime tomography via well log-guided physics-informed neural networks}, volume = {13}, year = {2025}, }
• (2025). GWSC-SegMamba: Gate wavelet spatial convolution enhanced state space model for multi-temporal agricultural land segmentation. ACCESS, 13, 133246-133279. (www)

  @article{ , abstract = {The study of multi-temporal satellite data for agricultural land segmentation faces significant computational challenges when processing extended temporal sequences, particularly due to CNNs’ limited receptive fields and Transformers’ quadratic complexity, since convolutional neural networks are constrained by local receptive fields, whereas Transformers experience quadratic complexity in their self-attention mechanisms. These constraints particularly impede the precise identification of spectrally analogous crop varieties and minority classes within agricultural landscapes. We present GWSC-SegMamba, an innovative hybrid architecture that integrates State Space Models (SSMs) with Gate Wavelet Convolution (GWC) and Gate Spatial Convolution (GSC) components to tackle multi-temporal agricultural segmentation issues. The GWC component conducts multi-resolution analysis with discrete wavelet transforms to address spatial resolution constraints for medium-resolution satellite data, whereas the GSC component identifies spatial correlations essential for delineating crop boundaries. Our thorough assessment of three benchmark datasets (Munich, Lombardia, and PASTIS) reveals substantial performance enhancements: a 7.65% increase in mIoU relative to the conventional SegMamba, a 4.43% improvement over the Swin-UNETR baseline, and an impressive 53.13% augmentation in IoU for difficult minority classes, including winter triticale. The design processes temporal sequences with linear computational cost, incurring about 8-10% additional computational overhead relative to the basic SegMamba and demonstrating enhanced scalability properties compared to transformer-based approaches. These findings provide enhanced crop monitoring systems vital for precision agriculture, especially in differentiating spectrally analogous crop varieties needed for yield estimation and sustainable land management decisions.}, archive = {J_ACCESS}, author = {Yohanes Fridolin Hestrio and Aprinaldi Jasa Mantau and Wisnu Jatmiko}, doi = {10.1109/ACCESS.2025.3593389}, journal = {IEEE Access}, pages = {133246-133279}, shortjournal = {IEEE Access}, title = {GWSC-SegMamba: Gate wavelet spatial convolution enhanced state space model for multi-temporal agricultural land segmentation}, volume = {13}, year = {2025}, }
• (2025). Node-based steady-state and dynamic gas grid calculation method with electrical analogies. ACCESS, 13, 133218-133245. (www)

  @article{ , abstract = {For joint intelligent and safe operation of electric power and gas grids, network condition estimates and the knowledge of propagating effects are important. For considering these two systems on a common level, this investigation proposes electric equivalents for gas grid components and defines them in the context of the “Extended Node Method”. Hence, it enhances this efficient node-based method, originally for power grids, to also be applied to gas networks. For this, the isothermal Euler equations are interpreted within the electric context. While the steady-state electric equivalent circuit diagram of a gas pipeline mainly consists of an ohmic resistance, the transient flow characteristics also show inductive and capacitive behavior. It is derived, how the network components are classified in terms of their terminal behavior and how this affects the characterization of network nodes and the structure of the equation system. An iterative algorithm for steady gas flow and a straightforward calculation process for unsteady flow are developed based only on node equations. Study cases for sample gas networks of different pressure levels are investigated with varying spatial resolutions of pipeline segments. The steady-state results show very high agreement compared to the established tools Simulink/Simscape and pandapipes and the procedure is computationally efficient. From a number of four segments, the results hardly vary at all. Small deviations are observed for dynamic considerations. These depend on the spatial resolution and are attributed to the electromagnetic effects of the electrical analogies. The computing time for the models increases with higher spatial resolution.}, archive = {J_ACCESS}, author = {Daniela Vorwerk and Detlef Schulz}, doi = {10.1109/ACCESS.2025.3592754}, journal = {IEEE Access}, pages = {133218-133245}, shortjournal = {IEEE Access}, title = {Node-based steady-state and dynamic gas grid calculation method with electrical analogies}, volume = {13}, year = {2025}, }
• (2025). FedDAR: Federated learning with data-quantity aware regularization for heterogeneous distributed data. ACCESS, 13, 133208-133217. (www)

  @article{ , abstract = {Federated learning (FL) has emerged as a promising approach for collaboratively training global models and classifiers without sharing private data. However, existing studies primarily focus on distinct methodologies for typical and personalized FL (tFL and pFL), representing a challenge in exploring cross-applicable training methods. Moreover, previous approaches often rely on data and feature augmentation branches, overlooking data-quantity considerations, leading to suboptimal performance and inefficient communication costs, particularly in multi-class classification tasks. To address these challenges, we propose a novel add-on regularization technique for existing FL methods, named Data-quantity Aware Regularization (FedDAR), seamlessly integrating with existing tFL and pFL frameworks. This network-agnostic methodology reformulates the local training procedure by incorporating two crucial components: 1) enriched-feature augmentation, where features of the local model are coordinated with pre-initialized features to ensure unbiased-representations with efficient global communication rounds for unbalanced data distribution, and 2) data-quantity aware branch, which associates with local data size to improve the optimization of the local model using both supervised and self-supervised labels. We demonstrate significant performance improvements in tFL and pFL, achieving state-of-the-art results across MNIST, F-MNIST, CIFAR-10/100, and Tiny-ImageNet benchmarks.}, archive = {J_ACCESS}, author = {Youngjun Kwak and Minyoung Jung}, doi = {10.1109/ACCESS.2025.3591839}, journal = {IEEE Access}, pages = {133208-133217}, shortjournal = {IEEE Access}, title = {FedDAR: Federated learning with data-quantity aware regularization for heterogeneous distributed data}, volume = {13}, year = {2025}, }
• (2025). ECGNet: High-precision ECG classification using deep learning and advanced activation functions. ACCESS, 13, 133191-133207. (www)

  @article{ , abstract = {The electrocardiogram (ECG) is an important tool in diagnosing heart diseases. In this study, we introduce ECGNet a customized deep learning model that utilizes advanced activation functions and modified classifiers to enhance ECG classification. ECGNet’s performance is compared with three benchmark deep learning models. Image transformation and optimization are performed using the LabelIMG tool. ECGNet unifies an embedding layer for feature extraction, followed by two dense layers for disease detection. The proposed AELU (Advanced Exponential Linear Unit) activation, with a learning rate of 0.001, addresses the vanishing gradient issues in ReLU and Sigmoid function, allowing the absolute values of the small negative gradient to be preserved, thereby introducing higher nonlinearity in the output and higher model flexibility. Additionally, the Modified SparseMax classifier is employed to enhance the classification by mapping logits to sparse probability distributions. The ECGNet model achieves 99% accuracy, outperforming the existing methods. These matrices reflected their ability to efficiently classify the five different classes. The proposed model also compared with most trending pretrained models such as LSTM, GRU and TabNet to validate the effectiveness.}, archive = {J_ACCESS}, author = {Muhammad Abubakar and Ahsan Raza and Arfan Ali Nagra and Anas Bilal and Ahmad Alshammari and Monir Abdullah and Sahraoui Dhelim}, doi = {10.1109/ACCESS.2025.3592240}, journal = {IEEE Access}, pages = {133191-133207}, shortjournal = {IEEE Access}, title = {ECGNet: High-precision ECG classification using deep learning and advanced activation functions}, volume = {13}, year = {2025}, }
• (2025). Effect of noises and GSM codings on pre-trained speaker embedding models in forensic voice comparison. ACCESS, 13, 133172-133190. (www)

  @article{ , abstract = {Deep learning has witnessed extensive application within speaker verification (SV) in recent years, particularly in feature extraction or embedding features. This study delves into examining the impact of noise and Global System for Mobile Communications (GSM) coding, with a particular emphasis on metrics relevant to forensic voice comparison, and examining how enrollment methods (averaging embeddings) can mitigate these adverse effects. It further evaluates the influence of practical variables such as speech sample duration and enrollment on verification reliability. It provides critical recommendations for forensic practice, especially when involving GSM coding and different types of noises. In this study, the assessment was based on the ForVoice 120+ Hungarian speech dataset. Samples are compared pairwise with and without enrollment for suspect speakers (known) because, often, forensic voice comparisons don’t have multiple samples of offenders. Speaker embedding features are extracted using four pre-trained techniques: X-vector, Emphasized channel attention, propagation and aggregation in time delay neural network (ECAPA-TDNN), large-scale self-supervised (Wavlm) and framework for self-supervised learning (Wav2vec) pre-trained. The evaluation of speaker verification performance employed the likelihood-ratio framework, utilizing measures such as log-likelihood ratio cost ( ${C} _{\text {llr}}$ ) and equal error rate (EER). Results show that environmental noise and GSM coding significantly degrade speaker verification performance. The best-performing model, ECAPA-TDNN, achieved a minimum EER of 1% and a ${C} _{\text {llr}}$ of 0.048 in clean conditions with speaker enrollment. However, under degraded conditions such as white noise at 10 dB SNR and half-rate GSM coding, performance dropped notably, with EER increasing to 6.4% and ${C} _{\text {llr}}$ rising to 0.241. Despite this, speaker enrollment mitigated some performance loss. These findings highlight the need for forensic practitioners to account for the effects of noise and coding when conducting speaker verification.}, archive = {J_ACCESS}, author = {Mohammed Hamzah Alsalihi and Dávid Sztahó}, doi = {10.1109/ACCESS.2025.3593024}, journal = {IEEE Access}, pages = {133172-133190}, shortjournal = {IEEE Access}, title = {Effect of noises and GSM codings on pre-trained speaker embedding models in forensic voice comparison}, volume = {13}, year = {2025}, }
• (2025). Leveraging LLMs for COVID-19 fake news generation and detection: A comparative analysis on twitter data. ACCESS, 13, 133160-133171. (www)

  @article{ , abstract = {The rapid spread of rumors on social media, especially during crises like the COVID-19 pandemic, highlights an urgent need for advanced tools to detect fake news. Large Language Models (LLMs), with their vast knowledge and emergent abilities, show great promise in tackling this challenge. This study investigates five state-of-the-art LLMs — DeepSeek, GPT-3.5, GPT-4, Gemini, and Claude— in creating and detecting fake news. Especially, we consider not only the performance but also the similarity of the models. Regarding tweet generation, we identify interesting patterns about similarity among the outputs of the models. Specifically, the outputs of DeepSeek are most similar to those of GPT models, while Gemini’s outputs are more distinct from the others. Regarding detection, no model demonstrates strong performance, even on its own generated dataset. Still, the decisions of DeepSeek are most similar to those of GPT-3.5, while the decisions of Gemini are the least similar to those of the other models. This study provides valuable insights into the dual role of LLMs as both detectors and potential sources of rumors, contributing to the development of more robust and reliable fake news detection systems.}, archive = {J_ACCESS}, author = {Hong N. Dao and Yasuhiro Hashimoto and Incheon Paik and Truong Cong Thang}, doi = {10.1109/ACCESS.2025.3592943}, journal = {IEEE Access}, pages = {133160-133171}, shortjournal = {IEEE Access}, title = {Leveraging LLMs for COVID-19 fake news generation and detection: A comparative analysis on twitter data}, volume = {13}, year = {2025}, }
• (2025). Automated overtake assist system using YOLOv4 and mask R-CNN for enhanced autonomous driving. ACCESS, 13, 133145-133159. (www)

  @article{ , abstract = {The advent of computer vision technology has significantly advanced the development of automated driving systems, enabling more sophisticated and safer driving aids. This paper presents an Automated Overtake Assist (AOA) system that leverages state-of-the-art computer vision algorithms—YOLOv4 and Mask R-CNN—for lane and vehicle detection. Additionally, an ultrasonic sensor is employed to accurately measure the distance to the vehicle directly in front. The system is implemented on a Raspberry Pi microcontroller, integrated with a high-definition webcam to capture real-time visual data, facilitating intelligent decision-making and enhancing the safety and efficiency of overtaking maneuvers in Autonomous vehicles(AVs). The error rate of the system is notably smoother than typical human error, ranging between approximately 0.1% and 0.25%. Training yielded a total loss of 0.5702 (0.6646), with loss components including: lossclassifier at 0.0922 (0.1262), ${\mathrm{loss}}_{\mathrm {box\_reg}}$ at 0.1815 (0.2140), lossmask at 0.1725 (0.1695), lossobjectness at 0.0209 (0.0226), and ${\mathrm{loss}}_{\mathrm {rpn\_box\_reg}}$ at 0.0748 (0.1323). Consequently, the system achieves an accuracy of approximately 98%. Our results demonstrate the AOA system’s capability to perform accurate lane detection, vehicle recognition, and distance measurement, ensuring a seamless and safe overtaking process in response to the driver’s intent.}, archive = {J_ACCESS}, author = {Navya Thirumaleshwar Hegde and Kapileswar Rana and S. S. Barath and Florence Nishmitha and Vishnu G. Nair}, doi = {10.1109/ACCESS.2025.3593023}, journal = {IEEE Access}, pages = {133145-133159}, shortjournal = {IEEE Access}, title = {Automated overtake assist system using YOLOv4 and mask R-CNN for enhanced autonomous driving}, volume = {13}, year = {2025}, }
• (2025). REST network: An ensemble deep learning approach for EV charging load forecasting in artificial port supply chains. ACCESS, 13, 133128-133144. (www)

  @article{ , abstract = {Electric vehicles (EVs) are increasingly used in logistics and port operations, making charging requirements prediction difficult. Energy supply management, infrastructural readiness, and daily operations depend on these estimates. Traditional forecasting algorithms can fail to address logistics’ complicated, variable charge patterns and uneven data. The ResNet-SENet-Transformer Ensemble (REST) Network, a novel forecasting model for EV charging demand prediction in artificial port supply chains, addresses these issues. Our model improves feature selection and corrects data imbalances using Texas data from January 2020 to June 2024 and sophisticated approaches including PSS and DSRS. REST outperforms standard methods with a MAPE of 6.5%, an RMSE of 9.8 kW, and strong results across several custom metrics, including Energy Efficiency Error (3.2%), Load Variability Deviation (4.1%), Cost of Prediction Error (12.50), and Time-Aware Accuracy (92.3%). These findings demonstrate that REST may improve port EV fleet energy management and operational planning. This study prepares next-generation EV forecasting systems that are more flexible, dependable, and sustainable logistics-focused by reducing energy waste and enhancing responsiveness.}, archive = {J_ACCESS}, author = {Elham Abdullah Alghamdi and Monagi Hassan Alkinani and Abdulwahab Ali Almazroi and Mohammed Alqarni and Eman Ali Aldhahri and Nasir Ayub}, doi = {10.1109/ACCESS.2025.3589292}, journal = {IEEE Access}, pages = {133128-133144}, shortjournal = {IEEE Access}, title = {REST network: An ensemble deep learning approach for EV charging load forecasting in artificial port supply chains}, volume = {13}, year = {2025}, }
• (2025). CIA-UNet: An attention-enhanced multi-scale U-net for single tree crown segmentation. ACCESS, 13, 133113-133127. (www)

  @article{ , abstract = {Accurate segmentation of single tree crowns enables precise measurement of tree height, DBH, and stock volume, facilitating effective assessment of forest growth and productivity. However, single tree crown segmentation currently faces several challenges, including insufficient segmentation accuracy, mis-segmentation and crown adhesion. To address these issues, this study conducts experiments using UAV data and proposes a model named CIA-UNet. In this model, the multiscale feature extraction capability of the InceptionV2 module is leveraged to compensate for U-Net’s limitation in capturing different scales of tree crowns and fine details using fixed convolutional kernels. The Convolutional Block Attention Module (CBAM) is employed to increase the weight of key features, and mitigate false segmentation caused by feature confusion. Additionally, the Attention Gate module is utilized to improve the model’s sensitivity to foreground pixels, adjust the weighting of skip connection features and prevent segmentation errors due to semantic mismatches. The experimental results show that CIA-UNet achieves outstanding performance on TREE512 dataset, with overall accuracy (OA), mean precision (mPrecision), mean recall (mRecall) and mean intersection over union (mIoU) reaching 90.79%, 88.08%, 89.41% and 79.9% respectively. Compared with U-Net, these metrics have improved by 1.07%, 1.63%, 1.5%, and 2.41% respectively. Furthermore, CIA-UNet performs better than DeepLabV3+, PSPNet and FCN in accuracy evaluation indicators and visual segmentation results. The results demonstrate the efficacy of CIA-UNet model in enhancing the accuracy of tree crown segmentation and minimizing mis-segmentation, and provide experience for the study of single tree crown segmentation in the Millennium Forest of Xiongan New Area.}, archive = {J_ACCESS}, author = {Jiapeng Duan and Chuanzhao Tian and Wansi Liu and Lixiang Cao and Teng Feng and Xiaomin Tian}, doi = {10.1109/ACCESS.2025.3591400}, journal = {IEEE Access}, pages = {133113-133127}, shortjournal = {IEEE Access}, title = {CIA-UNet: An attention-enhanced multi-scale U-net for single tree crown segmentation}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence (AI) in neurofeedback therapy using electroencephalography (EEG), heart rate variability (HRV), and galvanic skin response (GSR): A review. ACCESS, 13, 133078-133112. (www)

  @article{ , abstract = {Attention problems, such as attention deficit hyperactivity disorder (ADHD), have a significant impact on quality of life and academic achievement. Using Electroencephalogram (EEG), Heart Rate Variability (HRV), and Galvanic Skin Response (GSR), this literature review analyzes the use of Artificial Intelligence (AI) in Neurofeedback Therapy (NFT). The proposed framework, detailed and illustrated in AI-powered NFT applications, emphasizes AI’s pivotal role in preprocessing data, feature extraction, and AI model analysis, enabling dynamic adjustment of therapy protocols based on real-time physiological responses and facilitating remote therapy via Cloud AI. Although artificial intelligence has begun to be integrated into NFTs, its use is still in its early stages and presents a few challenges. The lack of representative datasets for training AI models, the complexities of processing biometric data (EEG, HRV, GSR), and the high infrastructure costs required for implementation are the key barriers to widespread adoption. Additionally, the general adoption of this technology is restricted by concerns regarding patient security and data privacy. The paper emphasizes the role of artificial intelligence in preprocessing data, feature extraction, and AI model analysis by integrating current research findings. The authors propose a novel approach to enhance the efficacy of neurofeedback therapy by integrating artificial intelligence with EEG, HRV, and GSR data, even though most of the content is derived from previous research. The researchers achieved substantial results by employing CNN, SVM, and LSTM to identify EEG, HRV, and GSR signals. However, challenges such as overfitting, data noise, and ethical concerns persist. The objective of this review is to offer a fair evaluation of the current state of artificial intelligence in NFT, identify gaps in the literature, and propose future research directions. These directions include the development of robust AI algorithms, the integration of NFT with emerging technologies such as virtual reality, ethical and societal implications, and AI-powered NFT applications. Additionally, this review proposes a framework for the integration of AI into NFT. AI-powered NFT has the potential to revolutionize the treatment of attention disorders and enhance the outcomes for those who are affected by surmounting these obstacles.}, archive = {J_ACCESS}, author = {Teddy Marcus Zakaria and Armein Zainal R. Langi and Muhamad Sophian Nazaruddin and Isa Anshori}, doi = {10.1109/ACCESS.2025.3582805}, journal = {IEEE Access}, pages = {133078-133112}, shortjournal = {IEEE Access}, title = {Artificial intelligence (AI) in neurofeedback therapy using electroencephalography (EEG), heart rate variability (HRV), and galvanic skin response (GSR): A review}, volume = {13}, year = {2025}, }
• (2025). Comparing machine learning-based crime hotspots versus police districts: What’s the best approach for crime forecasting?. ACCESS, 13, 133053-133077. (www)

  @article{ , abstract = {Criminal activity poses a significant challenge in urban environments, impacting public safety, economic stability, and overall quality of life. As a result, the efficient allocation of public security resources based on spatio-temporal crime prediction models has become a critical concern for urban management. To this purpose, many crime forecasting approaches first split city territories into partitions based on crime rates and trends, with each partition reflecting criminal dynamics of its specific area. Then, crime forecasting models are extracted for each area, to monitor and predict how crime rates evolve over time within each partition. However, traditional spatial partitioning approaches, which divide cities into predefined police districts based on geographic and operational considerations, often fail to account for variations in crime patterns. In contrast, machine learning-based approaches could dynamically adapt to areas with differing crime frequencies and densities, making them particularly effective in cities characterized by diverse population distributions and crime activity levels. This study examines the impact of various partitioning techniques on crime forecasting performance, comparing the traditional static division of the city into police districts with machine learning approaches, specifically density clustering algorithms, for detecting crime hotspots. The experimental evaluation, conducted on two real-world case studies, i.e. Chicago and Los Angeles crime data, demonstrates the effectiveness of density-based clustering in identifying multi-density crime hotspots. Compared to traditional police district partitioning, these data-driven methods offer significant advantages in improving crime forecasting accuracy across urban environments.}, archive = {J_ACCESS}, author = {Eugenio Cesario and Paolo Lindia and Andrea Vinci}, doi = {10.1109/ACCESS.2025.3592668}, journal = {IEEE Access}, pages = {133053-133077}, shortjournal = {IEEE Access}, title = {Comparing machine learning-based crime hotspots versus police districts: What’s the best approach for crime forecasting?}, volume = {13}, year = {2025}, }
• (2025). Design and evaluation of ADANet: A high-fidelity motion acquisition framework for assistive gesture-based interfaces. ACCESS, 13, 133035-133052. (www)

  @article{ , abstract = {Accurate and low-latency gesture recognition is critical for real-time assistive technologies that enable individuals with motor impairments to interact more intuitively with their environment. However, current systems often suffer from poor signal fidelity, limited adaptability, and high computational overhead. To address these limitations, this article presents ADANet — Advanced Disability Assistive Neural Network-driven framework for accelerometer-based gesture recognition, tailored for wearable assistive applications. The proposed system combines a high-fidelity ADXL335 triaxial accelerometer with an ESP32 microcontroller, forming a lightweight and cost-efficient motion acquisition pipeline. A structured preprocessing architecture is developed, incorporating zero-lag Butterworth filtering, entropy-based temporal smoothing, and computation of 33 handcrafted statistical features, including axis-specific jerk, signal magnitude area (SMA), and range-normalized entropy. ADANet’s compact yet expressive neural architecture is optimized through comprehensive ablation studies to ensure strong generalization with minimal latency. Our model was trained on data from 21 healthy participants(12 male & 9 female) performing eight functional gestures demonstrate a test accuracy of 84.47%, F1-score of 0.84, and AUC $\geq 0.96$ , outperforming traditional classifiers such as XGBoost, SVM, and KNN across multiple evaluation metrics. This work validates a robust end-to-end instrumentation-to-inference framework and establishes ADANet as a practical solution for embedded gesture recognition in assistive devices, mostly for disabilities, where signal integrity, adaptability, and inference efficiency are jointly critical.}, archive = {J_ACCESS}, author = {Md Ettashamul Haque and Atique Tajwar and Akm Azad and Salem A. Alyami and Md Mehedi Hasan}, doi = {10.1109/ACCESS.2025.3592079}, journal = {IEEE Access}, pages = {133035-133052}, shortjournal = {IEEE Access}, title = {Design and evaluation of ADANet: A high-fidelity motion acquisition framework for assistive gesture-based interfaces}, volume = {13}, year = {2025}, }
• (2025). Accelerated ElasticTrainer with elastic layer selection. ACCESS, 13, 133025-133034. (www)

  @article{ , abstract = {On-device training consumes a lot of training time due to the limited computing resources of edge devices. ElasticTrainer reduces training time by selecting important tensors from the model and then training them. However, selection at the tensor level leads to reduced arithmetic intensity, failing to fully utilize GPU resources. In this paper, we propose a layer-level selection method considering arithmetic intensity to further reduce training time. Compared to the existing tensor selection method, ElasticTrainer, our method reduces training time by up to 25% with less than 0.1% accuracy loss.}, archive = {J_ACCESS}, author = {Sangho Ha and Hyungshin Kim}, doi = {10.1109/ACCESS.2025.3591772}, journal = {IEEE Access}, pages = {133025-133034}, shortjournal = {IEEE Access}, title = {Accelerated ElasticTrainer with elastic layer selection}, volume = {13}, year = {2025}, }
• (2025). A review of vehicle dynamics and control approaches for mitigating motion sickness in autonomous vehicles. ACCESS, 13, 132990-133024. (www)

  @article{ , abstract = {This study highlights the challenge of motion sickness (MS) in autonomous vehicles (AVs), providing a comprehensive review of assessing, predicting, and preventing this issue with a special focus on vehicle dynamics and control-based approaches. Unlike previous studies, this review bridges the gap between MS prediction models and vehicle dynamics-based mitigation strategies by presenting an integrated perspective. Effective mitigation requires accurate and reliable prediction. In this context, motion-based prediction approaches, recognised for their practicality, cost-effectiveness, and promising results, are examined in detail with particular focus on ISO-based methods and sensory conflict theory-based models. The importance of identifying MS triggers and validating these models experimentally is also emphasised, alongside recent trends in customised approaches addressing individual variability in MS susceptibility. The study then investigates mitigation strategies centred on vehicle dynamics and control systems, due to their potential for directly controlling motion triggers, calling for tailored and integrated approaches. Furthermore, the critical role of trajectory planning and tracking algorithms in mitigating MS is reviewed, emphasising their potential through optimal control and the incorporation of MS metrics into cost functions. Additionally, integrating trajectory planning with active chassis systems is identified as a promising direction for reducing MS. The study concludes by underscoring the importance of optimised, personalised, integrated and connected vehicle dynamics and control-based methods to effectively mitigate MS in AVs. Finally, a future horizons approach, supported by a vision roadmap, is introduced as a means to address current challenges, define research directions, and ultimately advance the adoption of AVs with minimum MS.}, archive = {J_ACCESS}, author = {Ilhan Yunus and Georgios Papaioannou and Jenny Jerrelind and Lars Drugge}, doi = {10.1109/ACCESS.2025.3592407}, journal = {IEEE Access}, pages = {132990-133024}, shortjournal = {IEEE Access}, title = {A review of vehicle dynamics and control approaches for mitigating motion sickness in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Medical report generation with knowledge distillation and multi-stage hierarchical attention in vision transformer encoder and GPT-2 decoder. ACCESS, 13, 132973-132989. (www)

  @article{ , abstract = {Automated medical report generation is a challenging task that involves synthesizing diagnostic findings and clinical observations from medical images. In this study, we propose a novel framework that integrates knowledge distillation and multi-stage hierarchical attention mechanisms to enhance the generation of comprehensive and accurate medical reports. Our approach leverages knowledge distillation with Vision Transformer (ViT) as the image encoder to capture complex visual features, the model benefits from knowledge distillation, transferring knowledge from an ensemble of Convolutional Neural Networks (CNNs) – including VGG16, InceptionV3, and DenseNet121 – to the ViT, ensuring rich and diverse feature extraction. The GPT-2 used as decoder for generating coherent and contextually relevant narratives. The multi-stage hierarchical attention mechanism further refines this process by progressively focusing on key image regions and aligning them with the generated textual content. On the MIMIC-CXR dataset, our model achieved a BLEU score of 0.127 with precision 0.8832 for the abnormalities, demonstrating notable improvements over previous methods. Further analysis reveals that our approach enhances the generation of detailed and accurate medical reports, as validated by both quantitative metrics and qualitative assessments, reinforcing its effectiveness in capturing critical clinical information.}, archive = {J_ACCESS}, author = {Hilya Tsaniya and Chastine Fatichah and Nanik Suciati and Takashi Obi and Joong-Sun Lee}, doi = {10.1109/ACCESS.2025.3588344}, journal = {IEEE Access}, pages = {132973-132989}, shortjournal = {IEEE Access}, title = {Medical report generation with knowledge distillation and multi-stage hierarchical attention in vision transformer encoder and GPT-2 decoder}, volume = {13}, year = {2025}, }
• (2025). High-efficiency wireless power transfer for electric vehicles: A coil geometry and PSO-based optimization framework. ACCESS, 13, 132952-132972. (www)

  @article{ , abstract = {This paper investigates the optimization of wireless power transfer (WPT) systems for electric vehicle (EV) charging applications using a combination of analytical modeling, numerical simulation, and particle swarm optimization (PSO). A comprehensive mathematical model is developed for the electromagnetic (EM) coil, incorporating key geometrical parameters and accounting the presence of a ferrite core. This model is integrated with a PSO algorithm to maximize the power transfer efficiency (PTE) by identifying the optimal coil geometry. The optimized parameters are subsequentially applied to a double-sided inductor-capacitor-capacitor (LCC) compensation WPT system. Additionally, a sensitivity analysis is conducted to evaluate the influence of various design parameters on system performance and to identify critical parameters for robust operation. The proposed approach successfully captures the complex relationships among coil geometry, ferrite material characteristics, and system operating conditions, leading to high-efficiency outcomes. Optimization results reveal AC-side efficiencies up to 99.5% for identical coils at a 100 mm air gap, with performance exceeding 98% at 150 mm and 200 mm. For non-identical coils, efficiencies range from 97.1% to 98.45% across the same air gap range. To validate the proposed design, a full-system simulation model is developed, incorporating inverter and rectifier losses, while operating at a frequency of 85 kHz with a coil air gap of 150 mm. Under perfect alignment ( ${X}=0$ mm, ${Y}=0$ mm), the system achieves a peak DC-side efficiency of 96.9% with a coupling coefficient of 0.43. Under 150 mm lateral misalignment (corresponding to a coupling coefficient of 0.3), the system maintains a high DC-side efficiency of 95.47%.}, archive = {J_ACCESS}, author = {Tarek S. Hassan and Mohamed M. Zakaria Moustafa and Nabil H. Abbasy}, doi = {10.1109/ACCESS.2025.3592070}, journal = {IEEE Access}, pages = {132952-132972}, shortjournal = {IEEE Access}, title = {High-efficiency wireless power transfer for electric vehicles: A coil geometry and PSO-based optimization framework}, volume = {13}, year = {2025}, }
• (2025). Analyzing the integration of future skills in top universities for engineering education in the age of industry 4.0. ACCESS, 13, 132942-132951. (www)

  @article{ , abstract = {This study investigates the integration of future skills in engineering education at leading global universities, within the framework of Industry 4.0. We systematically analyzed academic publications from the top 10 universities listed in the QS World University Rankings since 2020, aiming to assess the extent of integration, key skill dimensions emphasized, and patterns of institutional collaboration. The results reveal significant trends in embedding future-oriented competencies into curricula, particularly through interdisciplinary and international initiatives. Our findings underscore the pivotal role of global collaboration in enhancing educational frameworks and equipping graduates with the skills demanded by the evolving industrial landscape. The study advocates for adaptable, dynamic educational models to align with rapid technological advancement and shifting industry needs. Future research should address the scalability of these integration efforts and their long-term impact on student performance and university-industry collaboration.}, archive = {J_ACCESS}, author = {Jose Daniel Azofeifa and Valentina Rueda-Castro and Patricia Caratozzolo}, doi = {10.1109/ACCESS.2025.3590290}, journal = {IEEE Access}, pages = {132942-132951}, shortjournal = {IEEE Access}, title = {Analyzing the integration of future skills in top universities for engineering education in the age of industry 4.0}, volume = {13}, year = {2025}, }
• (2025). Resource integrity vulnerabilities and protections in RPG games: A case study of elancia. ACCESS, 13, 132926-132941. (www)

  @article{ , abstract = {The rapid advancement of network and internet technologies has profoundly impacted the online gaming industry, particularly in the domain of Massively Multiplayer Online Role-Playing Games (MMORPGs). These games, which allow players to interact in a shared virtual world, have witnessed a surge in popularity. However, the rise of cheating and malicious activities facilitated by rogue users and cheat developers poses a significant threat to the gaming industry and the experiences of fair-playing users. In this study, we analyzed the client resource files of the MMORPG Elancia, developed by Nexon, to understand their structure. Our findings revealed that critical game resource files, including graphics, music, and character settings, could be modified by unauthorized users, with the altered content taking effect during gameplay. To address this vulnerability, we proposed both client-side and server-side integrity verification methods for client resource files. On the client side, we suggested verifying the integrity of resource files by comparing their Merkle Root with the Merkle Root stored on the server. On the server side, we proposed a cloud gaming environment where resource files are not stored on the client; instead, the client sends input to the server, which renders the results and transmits them back to the client. Additionally, the server verifies client inputs and events, storing the results for further validation. To validate the proposed methods, we customized Minecraft’s Forge mod to evaluate the performance of our approaches. The results underscore the critical role of client-side file integrity in maintaining game security and highlight the potential impact of robust protection measures on safeguarding the gaming ecosystem.}, archive = {J_ACCESS}, author = {Pyo Gil Hong and Sung Won Lee and Dohyun Kim}, doi = {10.1109/ACCESS.2025.3592760}, journal = {IEEE Access}, pages = {132926-132941}, shortjournal = {IEEE Access}, title = {Resource integrity vulnerabilities and protections in RPG games: A case study of elancia}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of spectrum and power control in 6G multi-UAV networks: Managing data redundancy. ACCESS, 13, 132913-132925. (www)

  @article{ , abstract = {This paper investigates the joint spectrum and energy efficiency in multimanned aerial vehicle (Multi-UAV) communication network in environments beyond the fifth generation and sixth generation (B5G / 6G), with a focus on the challenges posed by redundant data. We propose a linear optimization model for bandwidth allocation that accounts for both correlated and uncorrelated data scenarios. Additionally, we enhance energy efficiency through the Augmented Lagrangian method for power control optimization. Our approach achieves superior joint spectrum and energy efficiency, with spectrum efficiency reaching 0.75 bits/Hz, outperforming other methods by effectively exploiting data correlation. Furthermore, the proposed approach demonstrates a significant improvement in energy efficiency under varying levels of source correlation, achieving up to 7 Mb/J as the correlation variance increases. These results highlight the crucial role of data correlation in optimizing joint spectrum and energy efficiency, making the proposed approach highly effective for future 6G multi-UAV networks in smart city environments, with an emphasis on reliability.}, archive = {J_ACCESS}, author = {Mohammad Ahmed Alnakhli}, doi = {10.1109/ACCESS.2025.3587960}, journal = {IEEE Access}, pages = {132913-132925}, shortjournal = {IEEE Access}, title = {Joint optimization of spectrum and power control in 6G multi-UAV networks: Managing data redundancy}, volume = {13}, year = {2025}, }
• (2025). Transfer kernel extreme learning machine based on bidirectional cross domain approximation. ACCESS, 13, 132899-132912. (www)

  @article{ , abstract = {Kernel extreme learning machine (KELM) not only inherits the high learning efficiency of the traditional extreme learning machine (ELM), but also mitigates the instability caused by random initialization of input layer parameters, thus establishing its success as a machine learning model. However, KELM will be degraded when there is an insufficient amount of labeled training data available. In this study, we propose a novel approach called transfer kernel extreme learning machine based on bidirectional cross domain approximation (TKELM-BCDA) to address this issue within the context of unsupervised transfer learning (UTL). Firstly, TKELM-BCDA decomposes cross domain mean approximation (CDMA) into two single directional cross domain mean approximations (SDCDMA), aiming to rectify the distribution discrepancy between source and target domains in UTL. Subsequently, these SDCDMAs are integrated with KELM to construct two transfer KELMs respectively. Furthermore, we introduce two output weight approximation terms into these transfer KELMs - referred to as source transfer KELM and target transfer KELM - in order to enhance knowledge transfer across domains. Finally, by combining the predictions from both transfer KELMs, our model significantly boosts its robustness. To validate the effectiveness of our proposed method, we conducted classification experiments on four publicly available datasets. An analysis of the experimental results reveals that TKELM-BCDA achieved an average classification accuracy improvement of 14.14% compared to the KELM baseline method. These comparative results clearly demonstrate that our proposed algorithm offers satisfactory classification accuracy and strong robustness.}, archive = {J_ACCESS}, author = {Yuanxiao Zeng and Huimin Li and Yanbing Song}, doi = {10.1109/ACCESS.2025.3591599}, journal = {IEEE Access}, pages = {132899-132912}, shortjournal = {IEEE Access}, title = {Transfer kernel extreme learning machine based on bidirectional cross domain approximation}, volume = {13}, year = {2025}, }
• (2025). PassAI: An explainable machine learning framework for predicting soccer pass outcomes using multimodal match data. ACCESS, 13, 132884-132898. (www)

  @article{ , abstract = {As sports data, increasingly shaped by recent advances in in-game data acquisition technologies, become more complex and high-dimensional, analyzing such multimodal datasets presents challenges in both predictive performance and interpretability. Therefore, in this study, we introduce PassAI, a novel machine learning framework for classifying soccer passes success or failure using spatiotemporal tracking images and player-specific statistical profiles. PassAI addresses these challenges with an original dual-stream architecture that processes image-based and tabular features separately and then combines them for final prediction. Additionally, a two-stage explanation module was implemented to provide interpretable insights from both inter- and intra-modality perspectives. This module allows end users (e.g., coaches and analysts) understand whether pass outcomes depend more on contextual dynamics or individual player traits. In experiments involving 6,349 passes from professional Japanese soccer league games, PassAI outperformed state-of-the-art models—including convolutional neural network-based and graph neural network-based approaches, as well as existing pass prediction models—by more than 5% in accuracy. It also provided interpretable feedback through visual saliency maps and feature sensitivity analysis. Furthermore, RemOve And Retrain was also performed to verify the faithfulness of the generated explanation, and the visual saliency maps were highly faithful. Although there remains room for improvement from a practical standpoint, the results of this study indicate the potential of explainable multimodal artificial intelligence systems in real-world, high-stakes decision-making environments such as professional sports.}, archive = {J_ACCESS}, author = {Ryota Takamido and Jun Ota and Hiroki Nakamoto}, doi = {10.1109/ACCESS.2025.3589903}, journal = {IEEE Access}, pages = {132884-132898}, shortjournal = {IEEE Access}, title = {PassAI: An explainable machine learning framework for predicting soccer pass outcomes using multimodal match data}, volume = {13}, year = {2025}, }
• (2025). Heterogeneous AI music generation technology integrating fine-grained control. ACCESS, 13, 132870-132883. (www)

  @article{ , abstract = {As artificial intelligence algorithms continue to advance, researchers have increasingly harnessed their capabilities to generate music that resonates with human emotions, offering a novel means of alleviating the escalating pressures of contemporary life. To tackle the persistent issue of low accuracy in current emotion recognition and music generation systems, an innovative approach was proposed that fused a graph convolutional neural network with a channel attention mechanism for emotion recognition. This integrated model was subsequently paired with a Transformer architecture, creating a sophisticated framework capable of fine-grained control and heterogeneous music generation. In comparing the performance of the emotion recognition model against other leading models, the results underscored its exceptional accuracy across five distinct electroencephalogram signal bands: 97.3%, 95.8%, 96.9%, 98.4%, and 97.6%, respectively. Crucially, all these accuracy metrics exceeded the 95% benchmark, clearly demonstrating superiority over the comparative models. Additionally, a rigorous performance assessment was conducted to evaluate the music generation model’s capabilities against alternative approaches. The findings revealed that the suggested model achieved an average mean square error of 0.27 and an average root mean square error of 0.24. These error rates were notably lower than those of the competing models, highlighting the enhanced precision and fidelity of the music generated. Together, these results validated the effectiveness of both the emotion recognition and music generation models developed in this research. This research not only propelled the existing frontiers of emotion detection and musical composition forward, but also laid a robust theoretical framework to facilitate subsequent investigations into the emerging field of emotion-aware music generation.}, archive = {J_ACCESS}, author = {Hongtao Wang and Li Gong}, doi = {10.1109/ACCESS.2025.3592699}, journal = {IEEE Access}, pages = {132870-132883}, shortjournal = {IEEE Access}, title = {Heterogeneous AI music generation technology integrating fine-grained control}, volume = {13}, year = {2025}, }
• (2025). Novel passivity analysis for wind turbine systems via decentralized event-triggered approach. ACCESS, 13, 132849-132869. (www)

  @article{ , abstract = {This paper focuses on an innovative passivity analysis of wind turbine systems employing a decentralized event-triggered control approach. To establish new stability conditions, a novel Lyapunov-Krasovskii functional (LKF) is introduced, incorporating information from both the upper and lower bounds of the interval time-varying delay. We provide a new approach to identify the time instants of transmission from the sensors to the controller using just locally available information in event-triggered communication schemes. By using the relaxed integral inequality and the delay decomposition methodology, novel delay-dependent criteria guarantee that the considered system is passive. The passification problem is then handled by designing a decentralized event-triggered controller based on the acquired passivity conditions. The established stability conditions are expressed as linear matrix inequalities (LMIs), which may be numerically verified using the MATLAB LMI toolkit. Finally, a numerical example clearly demonstrates the superiority of our theoretical results using the simulation model.}, archive = {J_ACCESS}, author = {R. Vadivel and T. K. Santhosh and R. Suresh and Nallappan Gunasekaran and Mohammed H. Alsharif and Mun-Kyeom Kim}, doi = {10.1109/ACCESS.2025.3591485}, journal = {IEEE Access}, pages = {132849-132869}, shortjournal = {IEEE Access}, title = {Novel passivity analysis for wind turbine systems via decentralized event-triggered approach}, volume = {13}, year = {2025}, }
• (2025). VPN traffic analysis: A survey on detection and application identification. ACCESS, 13, 132830-132848. (www)

  @article{ , abstract = {Network traffic analysis is fundamental for cybersecurity, network management, and policy enforcement. The widespread adoption of encryption, particularly through Virtual Private Networks (VPNs), presents a significant challenge by obscuring traditional visibility methods. While VPNs enhance user privacy and security, they also create blind spots for network operators, potentially concealing malicious activities or hindering performance management. Analyzing the characteristics of traffic flowing through encrypted VPN tunnels, without decryption, has become a critical yet difficult task. This survey provides a comprehensive review of the state-of-the-art in VPN traffic analysis research published over the past decade (2016-2025). We specifically focus on three key tasks: detecting the presence of VPN traffic, identifying the specific VPN protocol or service used, and classifying the application traffic encapsulated within VPN tunnels. Based on a systematic review of the literature, we provide an in-depth analysis of the features, methodologies (including traditional and learning-based approaches), and datasets employed in recent studies. We synthesize reported performance results, analyze trends in feature and methodology evolution, and highlight the prevalent use and limitations of benchmark datasets. The survey identifies key technical challenges, discusses the implications of VPN traffic analysis for network security and Quality of Service (QoS), and proposes promising future research directions. This work serves as a vital resource for researchers and practitioners navigating the complexities of analyzing encrypted VPN traffic in modern networks.}, archive = {J_ACCESS}, author = {Yasameen Sajid Razooqi and Adrian Pekar}, doi = {10.1109/ACCESS.2025.3592152}, journal = {IEEE Access}, pages = {132830-132848}, shortjournal = {IEEE Access}, title = {VPN traffic analysis: A survey on detection and application identification}, volume = {13}, year = {2025}, }
• (2025). Bandwidth extension of resistive-feedback CMOS inverter amplifier using T-coil peaking. ACCESS, 13, 132822-132829. (www)

  @article{ , abstract = {This paper presents a broadband CMOS three-stage resistive-feedback inverter (RFI) amplifier for high-speed applications. A novel T-coil-based peaking technique is proposed, combining over-shunt and gate peaking to extend the bandwidth with reduced inductance. Theoretical analysis derives design equations and shows that the proposed structure achieves a bandwidth extension ratio of 2.3, matching conventional gate peaking performance with only one-third the inductance, thereby enabling chip area reduction. Fabricated in a 28 nm fully depleted silicon-on-insulator CMOS process, the amplifier occupies an active area of only 0.0094 mm2. Small-signal measurements show a gain of 16.2 dB, a 3-dB bandwidth of 31.5 GHz (gain-bandwidth product (GBW) =203.38 GHz), and low power consumption of 5.6 mW. The measured eye diagram confirms reliable performance, with an output amplitude of 333.4 mVpp, signal-to-noise ratio of 8.9, rise time of 15.0 ps, and fall time of 15.3 ps, meeting the 28 Gbps non-return-to-zero signaling requirements. Compared to previous broadband CMOS amplifiers, the proposed T-coil peaking RFI amplifier demonstrates superior GBW performance in terms of active area and power consumption.}, archive = {J_ACCESS}, author = {Minkyung Choi and Hyungeun Kim and Jaehyun Park and Jongshin Shin and Jinho Jeong}, doi = {10.1109/ACCESS.2025.3592770}, journal = {IEEE Access}, pages = {132822-132829}, shortjournal = {IEEE Access}, title = {Bandwidth extension of resistive-feedback CMOS inverter amplifier using T-coil peaking}, volume = {13}, year = {2025}, }
• (2025). Non-invasive glucose monitoring using optical sensors and machine learning: A predictive model for nutritional and health assessment. ACCESS, 13, 132804-132821. (www)

  @article{ , abstract = {Glucose monitoring plays a vital role in maintaining metabolic and nutritional balance. However, invasive methods, while accurate, are often painful and impractical for routine use. This study presents a non-invasive glucose monitoring framework that integrates a high-intensity Superbright optical sensor with an IoT-enabled data acquisition system using ESP32 microcontrollers and Raspberry Pi. The IoT-based architecture enables seamless integration with cloud computing platforms, allowing remote access and scalability for large-scale population-level screening and monitoring. The system captures glucose-related optical signals, which are analyzed using various machine learning algorithms, including a novel Convolutional Neural Network–Attention Hybrid Model (CNN-AHM). CNN-AHM combines spatial feature extraction with attention-based prioritization of relevant signal patterns, enhancing both accuracy and interpretability. Among all models tested, CNN-AHM demonstrated the best standalone performance (RMSE =14.06 mg/dL), representing a 13.18% improvement over its baseline configuration. When combined with complementary strength of the second-best performing Random Forest Regressor (RF-R), the system achieved a substantially improved RMSE of 7.62 mg/dL and an R2 of 0.83. This work underscores the importance of integrating sensor technology, deep learning, and explainable modeling to advance non-invasive glucose monitoring solutions. Future directions include extending the architecture with explainable AI using SHAP (SHapley Additive Explanations) to provide feature-level interpretability, and exploring real-world deployment scenarios.}, archive = {J_ACCESS}, author = {Heru Agus Santoso and Nur Setiawati Dewi and Susilo and Arga Dwi Pambudi and Hanif Pandu Suhito and Iman Dehzangi}, doi = {10.1109/ACCESS.2025.3591505}, journal = {IEEE Access}, pages = {132804-132821}, shortjournal = {IEEE Access}, title = {Non-invasive glucose monitoring using optical sensors and machine learning: A predictive model for nutritional and health assessment}, volume = {13}, year = {2025}, }
• (2025). Optimal autonomous control for distribution transformer area with high photovoltaic penetration based on CSBO-LSTM. ACCESS, 13, 132793-132803. (www)

  @article{ , abstract = {With the large-scale deployment of distributed photovoltaic (PV) and energy storage system (ESS) in distribution network transformer area, problems such as low management efficiency and difficult collaborative regulation have become increasingly prominent. To enhance the autonomous control capability of transformer area with high PV penetration, this paper proposes a “data-model” dual-driven collaborative optimization model that integrates the Circulatory System Based Optimization (CSBO) algorithm and Long Short-Term Memory (LSTM), constructing an architecture of “offline optimization-knowledge migration-online inference”. The model takes maximizing PV power consumption as the core objective, while considering voltage stability regulation and minimum network loss, to construct a multi-objective optimization function. Firstly, the improved CSBO is used to solve the multi-objective optimization problem, accurately formulate the PV reactive power regulation and ESS charging/discharging strategies, and generate the optimal operation dataset of the transformer area through multi-scenario simulation. Furthermore, leveraging the advantage of LSTM in processing time-series data, a real-time response model is constructed through deep training to achieve rapid perception of grid status and dynamic control decisions. Experimental results show that the model maintains the voltage of the transformer area near 1.00 p.u., while significantly reducing network losses. The organic combination of CSBO and LSTM effectively improves data completeness construction, model complexity control, and real-time decision response, providing new ideas and implementation solutions for the autonomous control of transformer areas with high PV penetration.}, archive = {J_ACCESS}, author = {Yukai Wei and Chun He and Zhuo Chen and Yinyuan Guo and Zongyuan Li}, doi = {10.1109/ACCESS.2025.3592674}, journal = {IEEE Access}, pages = {132793-132803}, shortjournal = {IEEE Access}, title = {Optimal autonomous control for distribution transformer area with high photovoltaic penetration based on CSBO-LSTM}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization of machine learning-based nonlinear equalizers for digital coherent optical interconnects. ACCESS, 13, 132782-132792. (www)

  @article{ , abstract = {The growing demand for high-capacity optical networks has driven the advancement of digital coherent optical communication systems, which rely on sophisticated signal processing to mitigate transmission impairments, including nonlinear fiber distortions. Traditional nonlinear compensation techniques, such as the Inverse Volterra Series Transfer Function (IVSTF) and Digital Back Propagation (DBP), are computationally expensive and require oversampling. Machine learning-based equalizers offer reduced complexity and improved adaptability. However, for dispersion-uncompensated systems with a high bandwidth-reach product, the computational complexity increases, becoming a significant concern. This work presents a multi-objective optimization approach for nonlinear equalization, balancing performance and computational cost. A sequentialized multilayer perceptron (MLP)-based nonlinear equalizer is applied to a 16-QAM digital coherent optical system over a 150 km uncompensated link at 112 Gbps. Various hyperparameter configurations, tap and neuron counts, are evaluated to identify optimal trade-offs. A Pareto front analysis quantifies the complexity-performance trade-off, providing insights into selecting an optimal equalizer configuration based on system constraints.}, archive = {J_ACCESS}, author = {Lucas C. Dantas and Hildo Guillardi Júnior and Plinio Dester and Rafael A. Penchel and José Augusto de Oliveira and Marcelo L. F. Abbade and Leandra I. Abreu and Jinlong Wei and Ivan Aldaya}, doi = {10.1109/ACCESS.2025.3588443}, journal = {IEEE Access}, pages = {132782-132792}, shortjournal = {IEEE Access}, title = {Multi-objective optimization of machine learning-based nonlinear equalizers for digital coherent optical interconnects}, volume = {13}, year = {2025}, }
• (2025). Intelligent layer sharing (ILASH): A predictive neural architecture search framework for multi-task applications. ACCESS, 13, 132767-132781. (www)

  @article{ , abstract = {Artificial intelligence (AI) is widely used in various fields including healthcare, autonomous vehicles, robotics, traffic monitoring, and agriculture. Many modern AI applications in these fields are multi-tasking in nature (i.e. perform multiple analyses on the same data) and are deployed on resource-constrained edge devices, requiring the AI models to be efficient across different metrics such as power, frame rate, and size. For these specific use-cases, we propose a new paradigm of intelligent neural network architecture search framework (ILASH) that leverages a layer sharing concept for minimizing power utilization, increasing frame rate, and reducing model size. ILASH utilizes a data-driven intelligent approach to make the search efficient in terms of energy, time, and frames per second (FPS). We perform extensive evaluations of the proposed layer shared architecture paradigm and the ILASH framework using three open-source datasets (UTKFace, MTFL, and CelebA). We compare ILASH with two different neural architecture search libraries that support multi-task applications (LibMTL and AutoKeras). We also evaluate ILASH against two standard neural architecture search frameworks (DARTS and ENAS). ILASH was able to surpass state-of-the-art performance across most comparison metrics (e.g. task accuracy, search/inference energy, and fps).}, archive = {J_ACCESS}, author = {Md Hafizur Rahman and Zafaryab Haider and Md Mashfiq Rizvee and Sumaiya Shomaji and Prabuddha Chakraborty}, doi = {10.1109/ACCESS.2025.3592039}, journal = {IEEE Access}, pages = {132767-132781}, shortjournal = {IEEE Access}, title = {Intelligent layer sharing (ILASH): A predictive neural architecture search framework for multi-task applications}, volume = {13}, year = {2025}, }
• (2025). Link prediction in social networks using the HTOA. ACCESS, 13, 132750-132766. (www)

  @article{ , abstract = {Social networks are complex and dynamic data structures whose analysis holds significant importance. Among various tasks, link prediction stands out as a fundamental aspect of social network analysis, focusing on identifying potential future relationships between nodes. Despite considerable advancements in this field, existing methods often face challenges related to computational efficiency and prediction accuracy. This study introduces a novel approach to link prediction in social networks by employing the Heat Transfer Optimization Algorithm (HTOA) for topological feature selection. HTOA, inspired by the physical phenomenon of heat transfer, identifies an optimal subset of topological features to feed XGBoost machine learning model. The primary advantage of the proposed method lies in its ability to substantially reduce feature dimensionality without compromising prediction accuracy. Experiments conducted on two real-world datasets, Facebook and CollegeMsg, demonstrate that the proposed method, can reduce the number of utilized features by up to 75% while maintaining a prediction accuracy (AUC) of 0.99. Furthermore, this dimensionality reduction results in a significant improvement in prediction time (up to 73.8%) compared to traditional methods. Comparisons with other optimization techniques, such as the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), reveal that the proposed method outperforms them in selecting key features and achieving faster convergence. These characteristics make the proposed method a suitable choice for real-world applications, including recommender systems, large-scale social network analysis, and interaction pattern detection. Additionally, the analysis of selected features provides valuable insights into the underlying mechanisms of link formation in social networks.}, archive = {J_ACCESS}, author = {Foad Asef and Vahid Majidnezhad and Mohammad-Reza Feizi-Derakhshi}, doi = {10.1109/ACCESS.2025.3586898}, journal = {IEEE Access}, pages = {132750-132766}, shortjournal = {IEEE Access}, title = {Link prediction in social networks using the HTOA}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection in IoT networks: A deep learning approach using autoencoders. ACCESS, 13, 132739-132749. (www)

  @article{ , abstract = {The current fast proliferation of the Internet of Things (IoT) networks has made anomaly detection and security more difficult. Traditional methods are not able to detect hostile activities because they cannot understand dynamic, complex, and high-dimensional data. In this paper, a deep learning framework-based autoencoder is designed to efficiently identify the anomalies primarily focusing on attack types in IoT environments. This model identifies the deviations through reconstruction error metrics, dimensionality reduction and studies normal behavior. The study experiments and observes how the performance of machine learning algorithms varies according to the way of distribution of the datasets, and it was found that the Random forest and the Decision tree are efficient in most of the distributions. The analysis of various loss function types also revealed that, although the Arcface and AM Softmax loss functions are noticeably more accurate and efficient, the All Combined Loss Function does remarkably well in clustering. Moreover, while keeping low false positive rates, the proposed design exhibits sensitivity against significant attack types. Considering its efficiency, this framework can be used for real-time applications in a range of IoT scenarios. Furthermore, we compared the proposed model with that of Isolation Forest, and show that, the proposed model outperforms it significantly.}, archive = {J_ACCESS}, author = {R. Tejaswini and P. Abinaya and S. S. Anuprabha and Saranya Karattupalayam Chidambaram and Sudhanshu Arya and Yogesh Kumar Choukiker and Abhijit Bhowmick}, doi = {10.1109/ACCESS.2025.3587727}, journal = {IEEE Access}, pages = {132739-132749}, shortjournal = {IEEE Access}, title = {Anomaly detection in IoT networks: A deep learning approach using autoencoders}, volume = {13}, year = {2025}, }
• (2025). Federated learning for privacy-preserving employee performance analytics. ACCESS, 13, 132724-132738. (www)

  @article{ , abstract = {With the increasing sensitivity surrounding employee performance data, there is a pressing need for predictive systems that preserve privacy while delivering actionable insights to organizations. This paper introduces HFAN-Priv, a hierarchical federated attention network designed to predict employee resignation risk and evaluate performance trends without sharing raw data across organizations. The framework integrates feature-level and instance-level attention to model complex workforce patterns, applies differential privacy through gradient masking to ensure compliance with data protection regulations, and enhances interpretability using local SHAP and LIME explanations. Experiments conducted on a real-world employee productivity dataset show that HFAN-Priv achieves near-perfect predictive accuracy, robust cross-client generalization, and transparent decision-making, all while maintaining strong privacy guarantees. The proposed approach presents a scalable, ethical, and effective solution for HR analytics in decentralized environments.}, archive = {J_ACCESS}, author = {Jay Barach}, doi = {10.1109/ACCESS.2025.3591360}, journal = {IEEE Access}, pages = {132724-132738}, shortjournal = {IEEE Access}, title = {Federated learning for privacy-preserving employee performance analytics}, volume = {13}, year = {2025}, }
• (2025). Robust prediction of wildfire spread in australia. ACCESS, 13, 132703-132723. (www)

  @article{ , abstract = {Wildfires can have devastating effects on urban infrastructure and natural ecosystems, making wildfire management an important, but yet complex and difficult task. The systematic collection of data, increased computing power, and the development of physical models made it possible to get an understanding of the dynamics of wildfire spread. As exact computational simulations of wildfires are not feasible yet, several subtasks such as the estimation of the spread rate were analyzed with various methods in the literature. In this paper, different types of predictive models are evaluated to forecast the spread of wildfires on a daily and weekly basis in a comparative study. These models are tested on real-world data of wildfires from the seven Australian regions New South Wales, Northern Territory, Queensland, South Australia, Tasmania, Victoria, and Western Australia from 2005 to 2020, including weather, vegetation, and land cover class data, in a univariate and multivariate setting. Furthermore, relevant features are identified and discussed which can have an important influence on wildfire spread. We find that robust models, which are less sensitive to outliers, capture the dynamics of wildfire spread most accurately.}, archive = {J_ACCESS}, author = {Michael Palk and Katharina Knappmann and Stefan Voss and Raka Jovanovic}, doi = {10.1109/ACCESS.2025.3592124}, journal = {IEEE Access}, pages = {132703-132723}, shortjournal = {IEEE Access}, title = {Robust prediction of wildfire spread in australia}, volume = {13}, year = {2025}, }
• (2025). Complex short circuit ratio to evaluate grid robustness for inverter integration. ACCESS, 13, 132693-132702. (www)

  @article{ , abstract = {So far, the short circuit ratio (SCR) has been widely used to assess grid robustness at inverter points of interconnection. However, its mathematical and geometric foundations have not been fully established, and its applicability across various X/R ratios and power factors remains an open question. This paper provides a geometric interpretation of the SCR based on the two-dimensional direct-quadrature axis framework from the perspective of voltage stability. The analysis incorporates the grid’s equivalent impedance, specifically accounting for the X/R ratio, as well as the inverter’s power factor and the reactive power contribution of shunt filter capacitors. The proposed method is validated through multiple case studies conducted under diverse conditions using an electromagnetic transient (EMT) model derived from a real power system. Simulations are performed with the Power System Computer Aided Design/Electromagnetic Transient including DC (PSCAD/EMTDCTM) platform.}, archive = {J_ACCESS}, author = {Soo Hyoung Lee and Donghee Choi and Seung-Mook Baek}, doi = {10.1109/ACCESS.2025.3589357}, journal = {IEEE Access}, pages = {132693-132702}, shortjournal = {IEEE Access}, title = {Complex short circuit ratio to evaluate grid robustness for inverter integration}, volume = {13}, year = {2025}, }
• (2025). Encoding holographic data into synthetic video streams for enhanced microplastic detection. ACCESS, 13, 132680-132692. (www)

  @article{ , abstract = {This study presents a novel deep learning pipeline for the detection and classification of microplastics using digital holography, with a focus on synthetic microfibers released during laundry. We introduce HMPD 2.0, an enhanced version of the Holography MicroPlastic Dataset featuring 24 amplitude and phase channels per sample, obtained through varying spatial filtering, numerical aberration correction, and propagation. To reduce input dimensionality, we propose a pseudo-RGB compression technique that groups grayscale channels into synthetic frames, which are then interpreted as a video sequence. This allows the use of transformer-based video architectures, particularly TimeSformer, for spatiotemporal modeling. Experimental results demonstrate that TimeSformer achieves a classification accuracy of up to 97.91%, with compressed 8-frame inputs maintaining high performance while significantly reducing inference time (from 42 ms to 16 ms per sample). These findings validate the effectiveness and efficiency of our approach, which supports real-time deployment on edge devices.}, archive = {J_ACCESS}, author = {Paolo Russo and Fabiana Di Ciaccio and Pasquale Santaniello and Teresa Cacace and Pierluigi Carcagnì and Marco del Coco and Melania Paturzo}, doi = {10.1109/ACCESS.2025.3592323}, journal = {IEEE Access}, pages = {132680-132692}, shortjournal = {IEEE Access}, title = {Encoding holographic data into synthetic video streams for enhanced microplastic detection}, volume = {13}, year = {2025}, }
• (2025). Verifying the safety of formal specification using fault tree analysis and animation. ACCESS, 13, 132670-132679. (www)

  @article{ , abstract = {Formal specifications can effectively guide the implementation of intended functionalities and improve the quality of software during its creation phase. However, a remaining challenge is how to verify the system’s formal specification to ensure its safety. In this paper, we describe a systematic approach that integrates fault tree techniques and animation technique to address this issue. Our method offers new ways to: 1) define the safety of processes, 2) establish a comprehensive set of rules that can effectively construct fault tree structures and reasonably integrate animation technique into this framework, and 3) propose a new algorithm for generating animation data for numeric type variables. We discuss these contributions and present a case study to demonstrate how our method can be applied in practice. We also report an experiment on applying our method to an automatic overtaking system to validate this technique.}, archive = {J_ACCESS}, author = {Wen Jiang and Shaoying Liu}, doi = {10.1109/ACCESS.2025.3592224}, journal = {IEEE Access}, pages = {132670-132679}, shortjournal = {IEEE Access}, title = {Verifying the safety of formal specification using fault tree analysis and animation}, volume = {13}, year = {2025}, }
• (2025). On the design of junction termination for 4H-SiC high-voltage devices. ACCESS, 13, 132659-132669. (www)

  @article{ , abstract = {Junction termination design has become a crucial process in ultrahigh-voltage 4H-SiC device design since it enhances the reliability and ensures that the device can reach the designed breakdown voltage. In this work, we review the blocking performances, fabrication considerations and area efficiencies of several typical termination structures widely used for ultrahigh-voltage 4H-SiC devices, and aim to optimize the termination design of next generation devices, focusing on improved termination efficiency, simultaneous design of breakdown voltage and surface field without introducing extra fabrication complexity and costs. The relationship between area efficiency, surface electric field and breakdown voltage is first described, indicating that improving the uniformity of electric field at the SiC/oxide interface is essential to improve the area efficiency. A buried termination structure, where implanted zones are buried under a thin field buffer layer is proposed to obtain a nearly rectangular field distribution at the SiC/oxide interface. The termination pattern is then directly scaled without any iterative design process to optimize the termination area, and the simulation results show that the field distribution can be mostly preserved. Optimization and limitations that are related to fabrication and design considerations are also addressed in the end.}, archive = {J_ACCESS}, author = {Zimo Yuan and Anders Hallén and Mietek Bakowski}, doi = {10.1109/ACCESS.2025.3592759}, journal = {IEEE Access}, pages = {132659-132669}, shortjournal = {IEEE Access}, title = {On the design of junction termination for 4H-SiC high-voltage devices}, volume = {13}, year = {2025}, }
• (2025). Pigment-aware: Understanding skin tone variations in digital image analysis. ACCESS, 13, 132649-132658. (www)

  @article{ , abstract = {Optical sensors, such as the RGB cameras embedded in smartphones, often fail to accurately capture the full spectrum of skin tones. As a result, individuals with darker skin may experience reduced performance in machine vision-based security systems. Insufficient attention to human diversity, including variations in skin tone, can contribute to biased training data and, subsequently, to disparities in AI systems, especially in biometric recognition. This paper highlights the need for more consistent and objective approaches to assessing skin tone, which are often treated subjectively or applied inconsistently. We address this issue by analyzing RGB finger photo data using colorimetric techniques to support the development of more inclusive machine vision systems.}, archive = {J_ACCESS}, author = {Emanuela Marasco and Luismar B.Cruz Junior}, doi = {10.1109/ACCESS.2025.3592845}, journal = {IEEE Access}, pages = {132649-132658}, shortjournal = {IEEE Access}, title = {Pigment-aware: Understanding skin tone variations in digital image analysis}, volume = {13}, year = {2025}, }
• (2025). Patterns of digital transformation: Stories from afghanistan’s public administration. ACCESS, 13, 132624-132648. (www)

  @article{ , abstract = {In its digital transformation, Afghanistan faces numerous challenges. This paper brings twenty eight patterns of digital transformation related to people and to systems and services that address these challenges. The patterns are organized into a pattern language with connections that indicate typical directions in which these patterns are applied, forming meaningful pattern sequences. We discovered the patterns by observing the actual efforts of digital transformation of public administration in Afghanistan, which we present in the paper as stories: stories about the challenges of digital transformation and patterns that help address these challenges. At the time of our observations, some patterns were already present, which is evident in the stories of digital transformation in Afghanistan we observed in reality and which we included in the paper, but they were not recognized as such and not referred to by the names we set for them in this paper. We assessed the relevance of the patterns we identified in a broader context via a survey we conducted with the participants actively engaged in or influenced by the implementation of digital initiatives in Afghanistan. With the pattern language presented in this paper, practitioners can learn from proven solutions and consolidate their vocabulary so they can build their own solutions in a more synergistic way.}, archive = {J_ACCESS}, author = {Mohammad Yusuf Momand and Valentino Vranić}, doi = {10.1109/ACCESS.2025.3591745}, journal = {IEEE Access}, pages = {132624-132648}, shortjournal = {IEEE Access}, title = {Patterns of digital transformation: Stories from afghanistan’s public administration}, volume = {13}, year = {2025}, }
• (2025). Robust monocular visual-inertial odometry for agricultural vehicles based on IMU-augmented 3D feature point correction. ACCESS, 13, 132611-132623. (www)

  @article{ , abstract = {This paper presents a new monocular visual-inertial odometry (VIO) system designed to achieve precise and robust localization for autonomous vehicles in challenging agricultural environments, where unstable and low-texture features often degrade performance. Unlike existing VIO methods, which are primarily optimized for urban environments and structured settings, the proposed approach specifically addresses the unique challenges of agricultural landscapes, such as illumination, vegetation occlusions, and irregular terrain. To enhance robustness and precision, the system integrates an error state Kalman filter (ESKF)-based attitude estimation, improving IMU preintegration accuracy even in visually degraded conditions. Additionally, a map point correction technique is introduced, leveraging IMU motion data to eliminate distortions in 3D map points and mitigate the effects of low-texture environments. By tightly coupling these advancements, the proposed system achieves robustness and accuracy in pose estimation. The effectiveness of the method is validated through real-time experiments across various scenarios, with the proposed system achieving approximately an 80% reduction in translation error and about a 60% reduction in rotation error compared to state-of-the-art methods. These results demonstrate its superiority over existing VIO algorithms in terms of localization precision and stability.}, archive = {J_ACCESS}, author = {Quoc Duy Tran and Quoc Hung Hoang and Gon-Woo Kim}, doi = {10.1109/ACCESS.2025.3582252}, journal = {IEEE Access}, pages = {132611-132623}, shortjournal = {IEEE Access}, title = {Robust monocular visual-inertial odometry for agricultural vehicles based on IMU-augmented 3D feature point correction}, volume = {13}, year = {2025}, }
• (2025). Vibration analysis and optimization of iron-core reactors based on fe-based soft magnetic composite materials. ACCESS, 13, 132599-132610. (www)

  @article{ , abstract = {To effectively reduce the vibration of iron-core reactors, a vibration optimization method considering the magnetostrictive properties of Fe-based soft magnetic composite materials is proposed. First, an improved magnetostrictive model incorporating stress effects is established based on the classical Jiles–Atherton (J-A) model and the quadratic domain rotation theory. The characteristic parameters of the improved model are identified using the particle swarm optimization–simulated annealing (PSO-SA) algorithm, with the identified root mean square error not exceeding 3.5, verifying the model’s accuracy. Then, an electromagnetic-structural coupled simulation model of the iron-core reactor is developed to calculate the magnetic field and vibration distribution. Based on multiphysics simulation and Latin hypercube sampling, combined with sensitivity analysis techniques, the influence of each parameter on vibration is identified, and optimization objectives and variables are hierarchically classified. Finally, response surface methodology (RSM) and Kriging methods are employed for the parameter optimization design of the reactor, yielding the optimal structural parameters under different optimization strategies. The results show that, compared to the initial parameters, the maximum vibration displacement of the iron core is reduced by 13.93% and 24.64% using the RSM and Kriging methods, respectively. Additionally, both core loss and conductor consumption are significantly reduced. Therefore, under the premise of meeting performance requirements, the Kriging optimization method can significantly reduce the vibration displacement of the iron-core reactor, providing valuable guidance for its vibration reduction and optimization.}, archive = {J_ACCESS}, author = {Yangyang Ma and Wenle Song and Jie Gao and Yang Liu and Yilei Shang and Weimei Zhao and Fuyao Yang}, doi = {10.1109/ACCESS.2025.3592728}, journal = {IEEE Access}, pages = {132599-132610}, shortjournal = {IEEE Access}, title = {Vibration analysis and optimization of iron-core reactors based on fe-based soft magnetic composite materials}, volume = {13}, year = {2025}, }
• (2025). Large language models in transportation: A comprehensive bibliometric analysis of emerging trends, challenges, and future research. ACCESS, 13, 132547-132598. (www)

  @article{ , abstract = {This paper presents a comprehensive review and bibliometric analysis of Large Language Models (LLMs) in transportation, exploring emerging trends, challenges and future research. Understanding their evolution and impact in transportation research is essential. The study used Scopus as the primary data source, applying Bibliometrix, VOSviewer, and Python for performance analysis and science mapping. This study analyzes 161 peer-reviewed articles and reveals a 25.74% annual growth in scholarly output. IEEE Transactions on Intelligent Transportation Systems and IEEE Transactions on Intelligent Vehicles emerge as the most influential journals by publication volume and impact on LLM research. The findings highlight global disparities in research contributions, with China and the United States dominating by publication volume, followed by Germany and Canada, while developing regions exhibit lower scientific productivity. In addition, the study provides qualitative insights by reviewing recent LLM applications in transportation, examining their key contributions, methodological approaches, inherent limitations, and domain-specific challenges. Key research themes focus on autonomous mobility, traffic optimization, and sustainable transportation networks. Despite significant progress, several challenges remain, including decision-making uncertainties, computational scalability, and high energy consumption. Overcoming these challenges requires greater transparency through causal learning, enhanced reasoning via hybrid AI models, and inclusive frameworks that address algorithmic bias and ensure equitable adoption.}, archive = {J_ACCESS}, author = {Mahbub Hassan and Md. Emtiaz Kabir and Muzammil Jusoh and Hong Ki An and Michael Negnevitsky and Chengjiang Li}, doi = {10.1109/ACCESS.2025.3589319}, journal = {IEEE Access}, pages = {132547-132598}, shortjournal = {IEEE Access}, title = {Large language models in transportation: A comprehensive bibliometric analysis of emerging trends, challenges, and future research}, volume = {13}, year = {2025}, }
• (2025). GoalNet: Goal areas oriented pedestrian trajectory prediction. ACCESS, 13, 132537-132546. (www)

  @article{ , abstract = {Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian’s intentions and decision-making, which is a multi-modal problem. Relying solely on historical coordinates pedestrian data represents the most straightforward method for pedestrian trajectory prediction. Nevertheless, the accuracy achieved by this method is limited, primarily because it fails to account for the crucial scene paths impacting the pedestrian. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the “goals” of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian’s trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.}, archive = {J_ACCESS}, author = {Amar Fadillah and Ching-Lin Lee and Zhi-Xuan Wang and Kuan-Ting Lai}, doi = {10.1109/ACCESS.2025.3588812}, journal = {IEEE Access}, pages = {132537-132546}, shortjournal = {IEEE Access}, title = {GoalNet: Goal areas oriented pedestrian trajectory prediction}, volume = {13}, year = {2025}, }
• (2025). Quality-aware PPG-based blood pressure classification for energy-efficient trustworthy BP monitoring devices with reduced false alarms. ACCESS, 13, 132514-132536. (www)

  @article{ , abstract = {Early detection of hypertension (HT) is crucial for significantly reducing the risk of serious health complications, which demands continuous or long-term blood pressure (BP) monitoring to ensure the timely management of hypertension. This paper proposes a new quality-aware photoplethysmogram (PPG) feature-based hypertension recognition framework to reduce false alarms and overall energy consumption by considering the affordable resource-constrained continuous BP monitoring devices. The proposed framework includes a high-pass filter (HPF), PPG signal quality assessment (PPG-SQA), PPG waveform feature extraction (FE), and BP classification (hypertension and non-hypertension (NHT)). In this paper, we present four SQA methods and nine machine learning (ML) based BP classification models, including logistic regression, decision tree, random forest, multilayer perceptron, k-nearest neighbours, XGBoost, AdaBoost, Bagged Tree, and one-dimensional convolutional neural network (1D-CNN). Four SQA methods are based on the average magnitude difference function (AMDF/(SQA-M1)) features and the AMDF features with the total number of zero-crossings present in raw/original (SQA-M2), derivative (SQA-M3), and smoothed derivative (SQA-M4) PPG segment features. On three standard databases, results showed that the SQA-M4 method outperforms the other three SQA methods with an overall false alarm reduction rate (FARR) of 50.8% in the case of motion artifact (MA) corrupted PPG signals and FARR of 91.0% in the case of additive white Gaussian noise (AWGN)-corrupted PPG signals. The quality-aware PPG feature extraction is presented to extract twelve features, which are combined with nine ML classifiers to explore the best model with higher recognition accuracy. Among the nine BP-classify models, the random forest (RF)-based BP model achieved the highest accuracy of 92.1% with a specificity of 93.8% for the BP model with SQA approach as compared to the BP model without SQA approach. On the implementation of the best model using the Raspberry Pi (R-Pi) platform, the quality-aware BP (QA-BP) model had FARR ranging from 32.4% to 62.2% for the NHT events and FARR ranging from 30.8% to 99.7% for the HT events in the case of random noise corrupted PPG signals, and the QA-BP model can reduce false alarms from 11.9% to 52.5% for NHT events and from 7.1% to 55.5% for HT events in the case of motion corrupted PPG signals. For the case of processing a 60s noisy PPG signal, the QA-BP method had an energy saving of 41.3% with display mode and an energy saving of 95.3% with the data transfer mode as compared to the BP classification method without the SQA approach. The proposed QA-BP method had energy saving of 8.1% - 85.6% for the 10% - 90% noisy portions in 24-hour recordings and has great potential in the development of affordable, energy-efficient cuffless BP monitoring devices with reduced false alarms.}, archive = {J_ACCESS}, author = {Yalagala Sivanjaneyulu and M. Sabarimalai Manikandan and Srinivas Boppu and Linga Reddy Cenkeramaddi}, doi = {10.1109/ACCESS.2025.3591977}, journal = {IEEE Access}, pages = {132514-132536}, shortjournal = {IEEE Access}, title = {Quality-aware PPG-based blood pressure classification for energy-efficient trustworthy BP monitoring devices with reduced false alarms}, volume = {13}, year = {2025}, }
• (2025). New energy and CCUS thermal power synergistic peaking cost model and apportionment optimization strategy. ACCESS, 13, 132501-132513. (www)

  @article{ , abstract = {Driven by the global carbon neutral strategy, the large-scale application of Carbon Capture, Utilization and Storage (CCUS) technology has significantly weakened the peaking margin of thermal power units, and the traditional peaking cost allocation model fails to take into account the inequitable distribution of costs faced by the carbon capture power plants participating in peaking. This study proposes a peaking cost quantification method based on multidimensional characterization. In terms of temporal characteristics, the scenario substitution method is used to quantify the participation of different peaking entities in peaking; in terms of spatial characteristics, the initiative constraints of unit peaking are taken into account to ensure that each peaking entity actively participates in peaking and profits from it. Considering the impact of carbon capture power plants’ participation in peak shifting on carbon emission reduction and carbon cost, we constructed a dynamic peak shifting model for multiple subjects. We innovatively construct a two-layer architecture of “spatio-temporal two-dimensional quantization and dynamic game sharing”, and adopt the improved kernel method to establish a coalition reorganization trigger mechanism to solve the unfair sharing of peaking costs when carbon capture power plants participate in peaking. Taking the improved IEEE34 node as an example, the results show that the established cost calculation model can accurately calculate the peak shaving cost of each unit in different scenarios. The total cost of thermal power peak shaving using CCUS is 6.5% lower than that of conventional thermal power units, which significantly verifies the effectiveness of CCUS technology in reducing the economic burden of peak shaving. When the carbon price is 150-250 yuan/ton, the carbon emission is reduced by 40%-50%, which highlights the dual advantages of the model in deep emission reduction and carbon price co-optimization, and the cost increase is $\le 5$ %, which proves the feasibility of achieving large-scale carbon emission reduction under the premise of strictly controlling the economic cost. On the basis of completing energy conservation and emission reduction, the peak regulation task is completed at the minimum cost, and the unity of economy and environmental protection is achieved. Compared with the Shapley value method, the correlation coefficient of the contribution of the kernel method is increased to 0.91, which indicates that the accuracy of the proposed method for the evaluation of multi-agent contribution is significantly improved, and the fairness of the peak-shifting cost-sharing model of the kernel method is significantly improved, which provides a reliable solution for solving the problem of cost allocation imbalance between traditional power supply and CCUS units.}, archive = {J_ACCESS}, author = {Yihui Song and Gang Wang and Hanlin Li and Yunpeng Sun and Hongyang Jin and Tianyang Su and Xueqing Wu and Qiang Zhang}, doi = {10.1109/ACCESS.2025.3585989}, journal = {IEEE Access}, pages = {132501-132513}, shortjournal = {IEEE Access}, title = {New energy and CCUS thermal power synergistic peaking cost model and apportionment optimization strategy}, volume = {13}, year = {2025}, }
• (2025). Exploring knowledge sharing strategies in competing supply chains: A behavioral economics approach to incentivized knowledge dissemination. ACCESS, 13, 132475-132500. (www)

  @article{ , abstract = {This study examines knowledge-sharing strategies within two competing supply chains, each consisting of one supplier and one manufacturer. Manufacturers possess proprietary information on product innovations and face strategic choices regarding knowledge dissemination: sharing horizontally with competitors, vertically with suppliers, or both. Using a behavioral economics framework, a Bertrand competition-based model is developed to evaluate three approaches: complete knowledge sharing, partial (incomplete) knowledge sharing, and non-knowledge sharing. The model incorporates payment incentives for purchasers, challenging conventional norms, and reveals critical insights. Non-knowledge sharing policies primarily benefit manufacturers, often to the detriment of other supply chain members. In contrast, partial knowledge sharing results in more balanced outcomes, fostering mutual benefits across the chain. Furthermore, the findings demonstrate that incentivizing knowledge dissemination can significantly enhance overall supply chain profitability. By rewarding purchasers for sharing knowledge, this strategy increases the financial gains for both purchasers and producers, making it a more advantageous policy compared to incomplete or non-knowledge sharing. This research addresses notable gaps in the literature by advancing beyond the binary focus of previous studies on either complete or non-sharing. Instead, it evaluates alternative approaches, such as incentivized and partial sharing mechanisms. A subscription-based model is proposed, allowing purchasers to access comprehensive knowledge in exchange for payment. This innovative approach broadens the scope of knowledge-sharing strategies and underscores the importance of incentivized dissemination as a means to optimize supply chain performance and foster collaborative profitability.}, archive = {J_ACCESS}, author = {Mostafa Jafari and Shayan Naghdi Khanachah}, doi = {10.1109/ACCESS.2025.3589327}, journal = {IEEE Access}, pages = {132475-132500}, shortjournal = {IEEE Access}, title = {Exploring knowledge sharing strategies in competing supply chains: A behavioral economics approach to incentivized knowledge dissemination}, volume = {13}, year = {2025}, }
• (2025). MagicCraft: Natural language-driven generation of dynamic and interactive 3D objects for commercial metaverse platforms. ACCESS, 13, 132459-132474. (www)

  @article{ , abstract = {Metaverse platforms are rapidly evolving to provide immersive spaces for user interaction and content creation. However, the generation of dynamic and interactive 3D objects remains challenging due to the need for advanced 3D modeling and programming skills. To address this challenge, we present MagicCraft, a system that generates functional 3D objects from natural language prompts for metaverse platforms. MagicCraft uses generative AI models to manage the entire content creation pipeline: converting user text descriptions into images, transforming images into 3D models, predicting object behavior, and assigning necessary attributes and scripts. It also provides an interactive interface for users to refine generated objects by adjusting features such as orientation, scale, seating positions, and grip points. Implemented on Cluster, a commercial metaverse platform, MagicCraft was evaluated by 7 expert CG designers and 51 general users. Results show that MagicCraft significantly reduces the time and skill required to create 3D objects. Users with no prior experience in 3D modeling or programming successfully created complex, interactive objects and deployed them in the metaverse. Expert feedback highlighted the system’s potential to improve content creation workflows and support rapid prototyping. By integrating AI-generated content into metaverse platforms, MagicCraft makes 3D content creation more accessible.}, archive = {J_ACCESS}, author = {Ryutaro Kurai and Takefumi Hiraki and Yuichi Hiroi and Yutaro Hirao and Monica Perusquía-Hernández and Hideaki Uchiyama and Kiyoshi Kiyokawa}, doi = {10.1109/ACCESS.2025.3587232}, journal = {IEEE Access}, pages = {132459-132474}, shortjournal = {IEEE Access}, title = {MagicCraft: Natural language-driven generation of dynamic and interactive 3D objects for commercial metaverse platforms}, volume = {13}, year = {2025}, }
• (2025). Cooperative graph-based predictive collision avoidance (CGPCA): A decentralized framework for safe drone traffic management. ACCESS, 13, 132450-132458. (www)

  @article{ , abstract = {Cooperative Graph-based Predictive Collision Avoidance (CGPCA) is a novel, decentralized approach designed to manage and control drone traffic while preventing collisions with both other drones and surrounding obstacles. It combines real-time communication, Graph Neural Networks (GNNs), and Decentralized Model Predictive Control (DMPC) to achieve this goal. Each drone continuously broadcasts its state—such as position, velocity, and intended path—to nearby units, dynamically constructing a “traffic graph” that includes both drones and detected obstacles. Unlike traditional methods that rely on fixed geometric rules or purely reactive algorithms, CGPCA leverages predictive learning. A GNN, trained on a wide range of simulated traffic scenarios, processes the traffic graph to forecast near-future positions and assign collision risk scores. These predictions then feed into a DMPC module onboard each drone, which optimizes safe, efficient trajectories in real time to mitigate potential conflicts. By integrating these advanced technologies, CGPCA provides a scalable and robust solution that adapts dynamically to rapid changes in drone behavior and environmental conditions, paving the way for safer and more efficient U-space operations.}, archive = {J_ACCESS}, author = {Fariborz Rasoulie}, doi = {10.1109/ACCESS.2025.3592446}, journal = {IEEE Access}, pages = {132450-132458}, shortjournal = {IEEE Access}, title = {Cooperative graph-based predictive collision avoidance (CGPCA): A decentralized framework for safe drone traffic management}, volume = {13}, year = {2025}, }
• (2025). Modeling spectral LED degradation using an unsupervised machine learning approach. ACCESS, 13, 132440-132449. (www)

  @article{ , abstract = {The modeling of spectral characteristics of light-emitting diodes (LED) has been addressed in various studies. We extend the current state of knowledge by modeling the spectral characteristics of commercially available high-power LEDs, exhibiting a temperature-dependent degradation, by using a different modeling strategy. To this end, the state of the art approach of an additive superposition of probability density functions (PDF) is compared with an unsupervised machine learning approach called non-negative matrix factorization (NMF). The stress test data used in our modeling routine was collected for a period of 6000 hours at four different case temperatures between 55 C and 120 C. The results of the accelerated stress tests indicate a temperature-activated aging process, which can be described using the Arrhenius equation. By combining the Arrhenius equation with the modeling parameters, the spectral characteristics can be modeled for 6000 hours of stress at four different stress test temperatures. The introduced spectral modeling approach using non-negative matrix factorization achieves CIE 1976 UCS chromaticity differences primarily smaller than $\Delta u'v'\le 0.001$ and proves to be superior to superimposed probability density functions in terms of colorimetric reconstruction accuracy, modeling complexity and robustness against spectral outliers.}, archive = {J_ACCESS}, author = {Alexander Herzog and Benoit Hamon and Paul Myland and Peter Foerster and Simon Benkner and Babak Zandi and Victor Guerra and Sebastian Schoeps and Willem D. van Driel and Tran Quoc Khanh}, doi = {10.1109/ACCESS.2025.3592806}, journal = {IEEE Access}, pages = {132440-132449}, shortjournal = {IEEE Access}, title = {Modeling spectral LED degradation using an unsupervised machine learning approach}, volume = {13}, year = {2025}, }
• (2025). A tubular linear-rotary generator with dual stators for direct drive wind-wave complementary energy generation platform. ACCESS, 13, 132426-132439. (www)

  @article{ , abstract = {So as to the efficient and low-cost utilization of offshore renewable energy, a novel direct-drive wind-wave complementary energy generation (D-D WWCEG) platform based on a tubular permanent magnet (PM) linear-rotary generator (TPMLRG) with dual stator is proposed in this study. According to the demand of the load and the energy storage, the D-D WWCEG system has stable output power, few non-operating hours and good anti-disturbance performance under different sea conditions by energy storage. Firstly, combine the design steps of traditional motors, the design process of TPMLRG is given, and the key parameters of linear power generation module (LPGM) and rotating power generation module (RPGM) are optimized by combining the finite element analysis (FEA) and multi-objective optimization method. The magnet field, thermal distribution and the magnetic properties of the PMs are analyzed by multiple coupled models. Secondly, the direct circuit - direct circuit converter (DC/DC) circuit for energy storage is designed and tested, the results show that under a wide fluctuation input voltage, a constant charging current of 0.2A is obtained for the lithium battery. Finally, based on the optimized parameters of TPMLRG, the energy conversion efficiencies of LPGM and RPGM are respectively obtained as 61.0% and 49.8%.}, archive = {J_ACCESS}, author = {Jing Zhang and Minshuo Chen and Yulei Liu}, doi = {10.1109/ACCESS.2025.3592709}, journal = {IEEE Access}, pages = {132426-132439}, shortjournal = {IEEE Access}, title = {A tubular linear-rotary generator with dual stators for direct drive wind-wave complementary energy generation platform}, volume = {13}, year = {2025}, }
• (2025). Research on natural language misleading content detection method based on attention mechanism. ACCESS, 13, 132410-132425. (www)

  @article{ , abstract = {The rapid evolution of digital communication and the corresponding surge in deceptive or misleading content have underscored the critical need for reliable and domain-adaptive detection technologies. Within the scope of the Frontiers in Computer Science, which emphasizes intelligent information systems, trustworthy AI, and content safety, this study introduces a robust and generalizable method for detecting misleading content across diverse linguistic and contextual domains. Traditional approaches, typically relying on static feature extraction or simple classifier pipelines, often fail under domain shifts and lack the semantic flexibility required for accurate real-world deployment. These models struggle particularly with ambiguous expressions, weak supervision, and multi-domain variability, resulting in reduced generalization and inconsistent performance. To address these challenges, we propose a novel dual-component system consisting of the Content-Driven Encoder (CoDE) and the Domain-Informed Detection Adapter (DiDA). CoDE employs a dynamic attention mechanism with hierarchical semantic encoding and domain-aware feature modulation, allowing adaptive representation learning across unimodal and multimodal inputs. Complementing this, DiDA injects domain priors via contrastive learning and retrieval, enhancing inference robustness even under severe domain shift. This modular architecture not only supports weakly supervised training but also integrates with large-scale content management pipelines. Comprehensive experiments across heterogeneous datasets–including social media, academic texts, and multimedia transcripts–demonstrate significant improvements over state-of-the-art baselines, particularly in transfer settings. This research contributes to the special issue’s objectives by promoting semantically grounded, scalable, and explainable AI systems that enhance digital content safety and integrity.}, archive = {J_ACCESS}, author = {Boning Liu}, doi = {10.1109/ACCESS.2025.3591455}, journal = {IEEE Access}, pages = {132410-132425}, shortjournal = {IEEE Access}, title = {Research on natural language misleading content detection method based on attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Data augmentation for improving convergence speed in federated sequential recommendation system. ACCESS, 13, 132398-132409. (www)

  @article{ , abstract = {A federated sequential recommendation system enables personalized temporal recommendations while safeguarding user privacy. However, the statistical heterogeneity of independent user records often necessitates extensive communication to achieve high-performing models. To address this challenge, prior research in other domains has employed data augmentation techniques to mitigate heterogeneity by generating synthetic datasets. Despite their potential, data augmentations have not been systematically explored in the context of federated recommendation systems. We aim to systematically evaluate six data augmentation methods and their effectiveness in mitigating statistical heterogeneity for efficient federated sequential recommendation. Our findings indicate that augmentation techniques introducing variation in sequence lengths can enhance convergence speed and improve the generalizability of federated models, while reducing communication overhead. To our knowledge, this is the first study to systematically evaluate data augmentation in federated recommendation systems.}, archive = {J_ACCESS}, author = {Donghoon Lee and Hyunsouk Cho}, doi = {10.1109/ACCESS.2025.3590762}, journal = {IEEE Access}, pages = {132398-132409}, shortjournal = {IEEE Access}, title = {Data augmentation for improving convergence speed in federated sequential recommendation system}, volume = {13}, year = {2025}, }
• (2025). Frequency stability evaluation based on conservative piecewise-linearized frequency nadir model in south korea. ACCESS, 13, 132385-132397. (www)

  @article{ , abstract = {Synchronous generators (SGs) provide the inertia and reserve power required to ensure frequency stability in the power system, while renewable resources do not contribute effectively. Maintaining frequency stability is challenging because of the increase in renewable resources and corresponding decline in SGs in response to climate change. Therefore, developing grid operation and planning strategies based on frequency stability evaluation has become increasingly important. In South Korea, frequency stability is evaluated based on frequency nadir during disturbances such as generator failures. This paper proposes a conservative piecewise-linearized frequency nadir (C-PLFN) model based on linearized relationships between the frequency nadir and system inertia, frequency regulating reserve space, governor-free reserve (GFR), and load size. The main features of the proposed methodology include linear regression on the frequency nadir against datasets of system inertia and GFR by load sizes, linear regression on the coefficients in the equation obtained by load sizes, and formalizing the C-PLFN model. This method was validated using the 2024 power system data for South Korea and the transmission security management data saved as real-time steady-state data for power system simulation for engineering simulations in South Korea.}, archive = {J_ACCESS}, author = {Yongbeom Son and Minhyeok Chang and Chiwon Seo and Sungwoo Kang and Yonggu Ha and Jongan Kim and Myunghwan Choi and Minhan Yoon and Gilsoo Jang}, doi = {10.1109/ACCESS.2025.3592248}, journal = {IEEE Access}, pages = {132385-132397}, shortjournal = {IEEE Access}, title = {Frequency stability evaluation based on conservative piecewise-linearized frequency nadir model in south korea}, volume = {13}, year = {2025}, }
• (2025). Learnable diffusion distances for link prediction. ACCESS, 13, 132366-132384. (www)

  @article{ , abstract = {In this paper, we address the problem of link prediction (LP) in Graph Neural Networks (GNNs) by learning approximated diffusion distances. Since diffusion distances have a spectral interpretation, we learn the smallest eigenvectors of the normalized Laplacian using the training edges. The resulting “empirical eigenfunctions” are reactive both to the Dirichlet loss that finds the eigenvectors and to the prediction loss. This allows us to chase the spectrum of the network in addition to provide competitive results in many LP benchmarks without precomputing subgraphs or subgraphs sketches (e.g., SEAL, WalkPooling, and ELPH/BUDDY). In addition, we provide a scalable approach for large graphs, where we do not rely on the matrix of diffusion distances.}, archive = {J_ACCESS}, author = {Ahmed Begga and Miguel Ángel Lozano and Francisco Escolano}, doi = {10.1109/ACCESS.2025.3590610}, journal = {IEEE Access}, pages = {132366-132384}, shortjournal = {IEEE Access}, title = {Learnable diffusion distances for link prediction}, volume = {13}, year = {2025}, }
• (2025). Generative models, attention mechanisms, and adaptive methods for robot navigation in complex Environments—A survey. ACCESS, 13, 132332-132365. (www)

  @article{ , abstract = {Autonomous mobile robots, equipped with multiple sensors, have been traditionally used to perform search, rescue and other tasks. A number of new scenarios for application of mobile robots have emerged in the last decade. These include automated logistics handling in warehouse-like environments (in the context of e-commerce) and robot-assisted personal care. In these scenarios, there is a need for highly accurate object recognition and semantic knowledge. Also, enhanced safety requirements have come up as robots attempt to interact more with humans and make efforts to recognize their gestures and movements. Mobile robots also increasingly operate in malls and other zones which are pedestrian-rich. Thus, a need has arisen for a relook at navigation strategies for mobile robots. Classical approaches are typically inadequate in these new settings. This survey is aimed at studying the role of contemporary approaches in artificial intelligence in enabling successful robotic navigation in a variety of complex environments. In particular, we discuss how generative models, attention mechanisms and adaptive methods have helped mobile robots navigate in cluttered, uneven and even unknown indoor and outdoor environments. We also point to several interesting possibilities in the future.}, archive = {J_ACCESS}, author = {Harinath Sridharan and Nambala Ramsai and Bhaskar Vundurthy}, doi = {10.1109/ACCESS.2025.3590587}, journal = {IEEE Access}, pages = {132332-132365}, shortjournal = {IEEE Access}, title = {Generative models, attention mechanisms, and adaptive methods for robot navigation in complex Environments—A survey}, volume = {13}, year = {2025}, }
• (2025). A multi-branch-based capsule network with structural reparameterization. ACCESS, 13, 132318-132331. (www)

  @article{ , abstract = {The capsule network (CapsNet) is an advanced network model. However, the performance of CapsNet on complex datasets (such as CIFAR10) is limited. Improving the network architecture serves as one of the crucial approaches to enhancing the performance of capsule networks. Among these improvements, adopting a multi-branch structure is an effective way to achieve this goal. Unlike directly modifying the architecture, we introduce multi-branch into the routing process and propose multi-branch routing. The capsule network constructed using this routing mechanism is referred to as ReCapsNet. In an effort to mitigate the parameter expansion caused by the multi-branch structure, structural reparameterization is introduced into ReCapsNet. This approach aims to curtail the parameters needed for inference and simplify the architecture. In addition, we utilize the pooling operation based on the attention mechanism to suppress non-important capsules and improve model performance. Experiments on four datasets (CIFAR10, CIFAR100, SVHN, and FMNIST) demonstrate that ReCapsNet has excellent classification performance. Moreover, the reparameterized ReCapsNet model can retain performance comparable to that of the original model, while significantly reducing parameters. Finally, the affine robustness of ReCapsNet is verified on the MNIST dataset and the affNIST dataset.}, archive = {J_ACCESS}, author = {Kun Sun and Yuqi Bai and Huishi Yin}, doi = {10.1109/ACCESS.2025.3592328}, journal = {IEEE Access}, pages = {132318-132331}, shortjournal = {IEEE Access}, title = {A multi-branch-based capsule network with structural reparameterization}, volume = {13}, year = {2025}, }
• (2025). Disturbance observer-based optimal active suspension control for vehicle-trailer systems. ACCESS, 13, 132303-132317. (www)

  @article{ , abstract = {This paper presents an active suspension controller for a vehicle-trailer system aimed at improving ride comfort by accounting for the trailer’s effect. Previous studies have primarily focused on controller design for vehicles with active suspension systems alone. However, with the increasing number of drivers towing trailers, there is a growing need for an active suspension controller that considers disturbances caused by the trailer—without requiring additional sensors. In this work, trailer-induced disturbances are estimated using a disturbance observer based on a half-car model. The control input is determined through a combination of feedback control, using a linear quadratic regulator and feedforward control. The proposed algorithm was evaluated through simulation studies involving four consecutive bump scenarios. Simulation results demonstrated that the proposed algorithm effectively reduced vertical acceleration caused by the trailer when compared to baseline algorithms.}, archive = {J_ACCESS}, author = {Yonghwan Jeong}, doi = {10.1109/ACCESS.2025.3592193}, journal = {IEEE Access}, pages = {132303-132317}, shortjournal = {IEEE Access}, title = {Disturbance observer-based optimal active suspension control for vehicle-trailer systems}, volume = {13}, year = {2025}, }
• (2025). Robust predictive current control using luenberger observer applied to an induction motor for agricultural electrical traction. ACCESS, 13, 132293-132302. (www)

  @article{ , abstract = {Induction motors are broadly used in various industrial applications due to their durability, simplicity, and cost-effectiveness. In agricultural electrical traction systems, they are crucial for providing reliable, efficient, and high-torque performance. Their ability to deliver consistent power at varying speeds and loads makes them ideal for such applications, where robust and low-maintenance solutions are essential for operational efficiency. However, parameter mismatches can compromise the performance of the motor control. Advanced control strategies, such as finite control set-model predictive control (FCS-MPC), have been developed to address these challenges. This paper introduces a novel robust predictive current control method for induction motors using an improved Luenberger observer to cope with parameter mismatches. By deriving a discrete-time voltage model from the motor’s dynamic model, a Luenberger observer with an inverse linear gain is integrated to predict future stator current and disturbance values, thereby improving the controller’s performance under parameter mismatches. Unlike traditional FCS-MPC approaches, this method evaluates the stator voltage in the cost function rather than using current, torque, or magnetic flux. Experimental evaluations with the induction motor under steady-state and dynamic conditions demonstrate the proposed method’s superior robustness compared to traditional predictive current control in electrical traction applications. The new method significantly reduces oscillations under substantial variations in inductance and resistance, confirming its effectiveness as a prospective solution.}, archive = {J_ACCESS}, author = {Gabriel Caramori and Igor Oliani and Angelo S. Lunardi and Alfeu J. Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3592018}, journal = {IEEE Access}, pages = {132293-132302}, shortjournal = {IEEE Access}, title = {Robust predictive current control using luenberger observer applied to an induction motor for agricultural electrical traction}, volume = {13}, year = {2025}, }
• (2025). How the risk of tin whiskers affects design decisions: Is the aerospace and defense approach to tin whiskers the right approach. ACCESS, 13, 132287-132292. (www)

  @article{ , abstract = {Most of the electronics industry, including piece part (electronic component) suppliers, have been involved with developing and implementing lead (Pb)-free risk reduction methods since the introduction and enforcement of RoHS in 2006. The most widely used standard to assess the risk of tin whiskers has been JESD201 “Environmental Acceptance Requirements for Tin Whisker Susceptibility of Tin and Tin Alloy Surface Finishes”. The JESD201 standard requires that aged samples be put through thermal cycling, humidity, high temperature, and ambient testing. Another companion standard, JESD22-A121 “Test Method for Measuring Whisker Growth on Tin and Tin Alloy Surface Finishes” can be used to assess the results of the JESD201 tests. If assemblies and piece parts meet the requirements of JESD201, it is generally expected that their use of Pb-free solders, finishes and plating should not result in tin whiskers that can cause failures. This approach is currently used in the commercial industry with products having lifetimes of up to 7 years, as well as in the automotive and gas industry with much longer expected lifetimes. This paper investigates the benefits of using this approach by industries, including the medical devices, aerospace and defense industries, that are still producing lead-based electronics.}, archive = {J_ACCESS}, author = {Michael Pecht and Michael Osterman and Edwin C. Tinsley and Chang Lu}, doi = {10.1109/ACCESS.2025.3587034}, journal = {IEEE Access}, pages = {132287-132292}, shortjournal = {IEEE Access}, title = {How the risk of tin whiskers affects design decisions: Is the aerospace and defense approach to tin whiskers the right approach}, volume = {13}, year = {2025}, }
• (2025). Leveraging feedback and causality-enriched multimodal context for predictive maintenance. ACCESS, 13, 132269-132286. (www)

  @article{ , abstract = {We propose an anomaly detector-agnostic framework to exploit heterogeneous and multidimensional streams in industrial predictive maintenance, with the main objective of detecting early data anomalies preceding asset failures. Our novelty lies in fusing multiple data streams, combining both discrete and continuous sources with potentially different sample rates, exploiting causality graphs, and leveraging (automated) feedback on past false positive alarms. This forms the contextual information, which, combined with feedback on produced alarms, aims to prune new false positive alarms of similar context before they are raised, in an inherently explainable manner. The framework is applied in two predictive maintenance case studies with different characteristics, and the results show that it can significantly enhance standalone anomaly detectors; e.g., we have observed decreases in false positive rates up to 7 times. Our implementation is provided as a python library enabling experiment repeatability and application to arbitrary other cases.}, archive = {J_ACCESS}, author = {Apostolos Giannoulidis and Anastasios Gounaris and Athanasios Naskos and Nikodimos Nikolaidis and Daniel Caljouw}, doi = {10.1109/ACCESS.2025.3592775}, journal = {IEEE Access}, pages = {132269-132286}, shortjournal = {IEEE Access}, title = {Leveraging feedback and causality-enriched multimodal context for predictive maintenance}, volume = {13}, year = {2025}, }
• (2025). A CNN-based framework for geometric alignment of historical and satellite imagery. ACCESS, 13, 132259-132268. (www)

  @article{ , abstract = {This paper addresses the difficulty of aligning historical aerial photographs with other archive images or modern satellite imagery, which is crucial for applications such as landscape comparison, shoreline erosion monitoring, and urban expansion evaluation. Traditional approaches, such as SIFT for key point identification and RANSAC for feature alignment, suffer with complex geometric transformations. To address these limitations, we present a Machine Learning (ML)-based technique that uses Convolutional Neural Networks (CNNs) for geometric matching. We use a custom-labeled dataset from Google Maps satellite imagery to build paired images with known homograph matrices using controlled transformations such as translations, warps, and rotations. A CNN is trained to predict the homographs between these pairings, allowing for accurate alignment. The suggested CNN-based approach achieved great accuracy for minor translations ( $\le 20$ pixels) with an MSE of 0.0002, but performance decreased as translation magnitude grew. Minor distortions were noticed for moderate translations (40 pixels) with an MSE of 0.0028, while large translations (60 pixels) resulted in severe mistakes (MSE: 0.0735).}, archive = {J_ACCESS}, author = {Manaswi Kulahara and Abdul Khader Jilani Saudagar and Sahil Tripathi and Md Azizul Hoque}, doi = {10.1109/ACCESS.2025.3589497}, journal = {IEEE Access}, pages = {132259-132268}, shortjournal = {IEEE Access}, title = {A CNN-based framework for geometric alignment of historical and satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Enhanced stability of cislunar-based small satellite constellations via preferential-EKF. ACCESS, 13, 132237-132258. (www)

  @article{ , abstract = {In the NewSpace era, space systems are rapidly advancing to meet increasing competition and the need for robust, value-added satellite operations in Cislunar space. Small satellites play a pivotal role in this evolution, supporting Earth-based applications while enabling deep space exploration. This paper tackles the critical challenge of maintaining stable relative motion within small satellite constellations operating in dynamic orbital environments. Precise relative orbital positioning is vital to ensure reliable data delivery with minimal latency. To address this, a novel Preferential-EKF technique is presented as an autonomous enhancement to the traditional Extended Kalman Filter (EKF). This approach enables self-organizing behavior when known or unknown in-orbit anomalies occur. The method accounts for key Cislunar dynamics, including J2 perturbations, atmospheric drag, solar radiation pressure, and interactions with debris and other satellites. Comprehensive orbital stability analyses, supported by detailed simulations and parametric studies, demonstrate the technique’s effectiveness. The results confirm Preferential-EKF’s ability to improve constellation coordination and resilience. This highlights the growing importance of autonomy for future Cislunar operations involving small satellite constellations.}, archive = {J_ACCESS}, author = {Mohammed Irfan Rashed and Hyochoong Bang}, doi = {10.1109/ACCESS.2025.3589481}, journal = {IEEE Access}, pages = {132237-132258}, shortjournal = {IEEE Access}, title = {Enhanced stability of cislunar-based small satellite constellations via preferential-EKF}, volume = {13}, year = {2025}, }
• (2025). A four-port MIMO antenna featuring multi-band functionality with improved isolation for terahertz systems. ACCESS, 13, 132223-132236. (www)

  @article{ , abstract = {The increasing saturation of microwave frequencies poses challenges for modern networks, making terahertz (THz) communication a promising solution for higher data rates, provided energy-efficient devices are developed. This study presents an innovative quad port multi-input multi-output (MIMO) antenna for sixth-generation (6G) terahertz wireless communications applications. The single-element radiator featuring a patch of modified elliptical quadrant shape in the presence of a C-shaped ground plane is conceptualized at an initial stage. Furthermore, two and four elements-based MIMO structures are constructed by placing the conceptualized single antenna elements. Additionally, the proposed configuration of the quad-element MIMO antenna comprises a plus-shaped isolation structure with a circular ring on a low-cost, transparent polyimide substrate. This innovative design enhances multi-resonance behavior, optimizes impedance matching, and achieves miniaturization while maintaining high isolation. The antenna operates across five distinct frequency bands: 0.97–1.27, 3.37–3.9, 8.68–9.17, 11.02–11.79, and 13.36–13.80 THz, with fractional bandwidths (FBW) of 26.79%, 14.59%, 5.49%, 6.75%, and 3.24%, respectively. It achieves a peak gain of 8.09 dBi and a radiation efficiency exceeding 82%. The antenna demonstrates exceptionally low mutual coupling (<30 dB) between adjacent elements along with attractive MIMO metrics, offering a low ECC of 0.0001, DG close to 10, a TARC of less than -10 dB, and a CCL of 0.4 bps/Hz. These results confirm the antenna’s potential as an effective solution for THz MIMO systems, positioning it as a strong candidate for next-generation 6G wireless communications utilizing terahertz spectrum.}, archive = {J_ACCESS}, author = {Said Douhi and Salma Mazroui and Zahriladha Zakaria and Sudipta Das and Adil Eddiai}, doi = {10.1109/ACCESS.2025.3582542}, journal = {IEEE Access}, pages = {132223-132236}, shortjournal = {IEEE Access}, title = {A four-port MIMO antenna featuring multi-band functionality with improved isolation for terahertz systems}, volume = {13}, year = {2025}, }
• (2025). ETIA: Enhancing Text2Image surround view scene generation with semantic annotation via diffusion for autonomous driving. ACCESS, 13, 132209-132222. (www)

  @article{ , abstract = {Generating high-fidelity surround view images from text prompts is a complex task that requires balancing contextual coherence with computational efficiency. The proposed work introduces a novel methodology that combines recurrent attention-based encoder-decoder architectures with text-to-image diffusion models to produce coherent and continuous surround view images. The approach utilizes a custom text encoder to convert input text prompts into contextual embeddings, which are then processed by the proposed ViewNet Unet2d architecture within the decoder. This architecture employs dual cross-attention mechanisms: one aligns text embeddings with corresponding noise image latents, while the other integrates previously generated image latents to ensure continuity across the sequence. This method guarantees that each generated image adheres to its specific prompt, while maintaining coherence with preceding images. In addition, an annotation decoder was introduced that generates semantic segmentation maps, instance segmentation masks, and object detection annotations. The annotation decoder processes latent image maps using a shared feature extraction backbone and dedicated heads for each annotation task. Experimental results on the nuScenes validation set demonstrate the effectiveness of the proposed model in producing high-quality contextually aligned surround view images. The proposed model achieves an FVD of 99 and an FID of 12.6, outperforming existing methods such as Panacea+ and DriveDreamer-2. Furthermore, our approach improves segmentation and detection accuracy, achieving a PQ of 67.4, mIoU of 80.1, and mAP of 65.4, surpassing methods like OpenSeeD and D2Det. An ablation study highlights the contributions of key components in our architecture. Integrating positional encoding, self-attention, and concurrent attention significantly enhances generation quality, reducing FVD to 99 and FID to 12.6. Experimental results demonstrate the effectiveness of proposed work in producing high-quality, contextually aligned surround view images with comprehensive annotations, pushing the boundaries of text-to-image synthesis and scene understanding.}, archive = {J_ACCESS}, author = {Ramyashree and S. Raghavendra and S. K. Abhilash and Venu Madhav Nookala and P. V. Arun Kumar and P. Malashree}, doi = {10.1109/ACCESS.2025.3591146}, journal = {IEEE Access}, pages = {132209-132222}, shortjournal = {IEEE Access}, title = {ETIA: Enhancing Text2Image surround view scene generation with semantic annotation via diffusion for autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Quantum vision theory in deep learning for object recognition. ACCESS, 13, 132194-132208. (www)

  @article{ , abstract = {We introduce a new perspective and a theory, called Quantum Vision (QV) theory in deep learning, for object recognition. The proposed theory is based on particle-wave duality of quantum physics. In quantum-scale, an object appears as a wave until it is observed, but after observation the object collapses into a solid object, called particle. In quantum world, every object has a ‘wave function’ that contains all the information about the object permitted by the uncertainty principle. Quantum-scale world looks different from our human-scale world. Attempts to relate the microscopic quantum world to our macroscopic world led to philosophical issues and questions. But what if the objects in human-scale world such as cats, dogs and bicycles have wave functions as well? And what will happen if we feed waves of objects to Deep Neural Networks (DNN) instead of collapsed still images of objects captured by cameras? This is the main contribution of our work. Inspired from quantum physics, we introduce a new perspective and theory, called Quantum Vision (QV) theory in deep learning that is a completely new perspective for object recognition. The proposed QV theory takes captured still images of objects, and converts them to information wave functions using a deep learning block that is called QV block. The proposed QV block is integrated into sequential CNNs, vision transformers and convolutional vision transformer to generate QV model variants for object classification. Extensive experiments are carried out on several datasets, and results demonstrate that QV model variants perform consistently better than standalone versions.}, archive = {J_ACCESS}, author = {Cem Direkoglu and Melike Sah}, doi = {10.1109/ACCESS.2025.3592037}, journal = {IEEE Access}, pages = {132194-132208}, shortjournal = {IEEE Access}, title = {Quantum vision theory in deep learning for object recognition}, volume = {13}, year = {2025}, }
• (2025). Safe switching model-free value iteration for general nonlinear systems. ACCESS, 13, 132170-132193. (www)

  @article{ , abstract = {This paper presents a solution to the well-known challenge of ensuring safety guarantees during the evaluation of controllers tuned using Value Iteration (VI) techniques on real systems. We propose an approach called Safe Switching Model-Free Value Iteration (SSMFVI), which guarantees both stability and safety of a system in closed loop with a controller optimized with Value Iteration in a model-free manner. A state dependent switching rule is designed to alternate between the VI tuned controller and an initial known stabilizing admissible controller. Using the initial controller Q-function and the one continuously actualized during learning, the stability of the switching mechanism in closed loop with the system is derived using the Multiple Lyapunov Functions (MLF) framework. To guarantee safety during runtime operation, the systems maximum one-step transition is estimated. Then, the switching control signal is designed to select the MFVI controller only in the region of the state space both covered by the collected transitions during the exploration phase and with the distance to the unsafe set greater than the computed maximum one-step transition. This subset of the state space is determined using single-class Support Vector Machine (SVM) classification. The method includes mechanisms for early instability detection and chattering reduction near switching surfaces. The validation is conducted on a linear first order system for visualization of the results and on a real Electric Braking System circuit system, demonstrating the effectiveness of the proposed control method.}, archive = {J_ACCESS}, author = {Timotei Lala}, doi = {10.1109/ACCESS.2025.3567496}, journal = {IEEE Access}, pages = {132170-132193}, shortjournal = {IEEE Access}, title = {Safe switching model-free value iteration for general nonlinear systems}, volume = {13}, year = {2025}, }
• (2025). Two-stage robust optimization considering the uncertainty of sources and loads in virtual power plants. ACCESS, 13, 132155-132169. (www)

  @article{ , abstract = {Virtual power plants (VPPs) aggregate controllable distributed power sources, distributed new energy sources, distributed energy storage devices, loads, and other resources. As the proportion of new energy in VPPs continues to rise, the impacts of the uncertainty of internal new energy output and load have become increasingly significant, and the demand for flexible resources has also become more urgent. To address the challenges of system operation optimization and resource allocation caused by the integration of a large amount of renewable energy on the supply side of the energy system into VPPs and the flexible and changeable load on the demand side, this paper proposes a distributed robust optimization method that simultaneously considers the uncertainties of both the source and the load. First, regarding the uncertainties of wind power, photovoltaic output, and load, Monte Carlo scenario sampling and scenario reduction methods are employed to depict the typical scenarios of uncertainty, thereby determining the output interval ranges of wind power, photovoltaic, and load. On this basis, considering the uncertainties of renewable energy and load in multiple VPPs simultaneously, a distributed two-stage robust optimization model based on the min-max-min structure is established to obtain the optimal dispatching scheme for VPP operating costs under the most extreme scenarios. The model takes into account system power balance and output power constraints, demand response load constraints, and the interaction constraints of multiple VPPs. The uncertainty in VPPs is handled through an adjustable uncertainty control factor. Subsequently, the Benders decomposition algorithm is used to split the model into a master problem and a subproblem. Combining the duality theory, KKT conditions, and the Big-M method, the inner layer max-min optimization problem is transformed into a mixed integer linear programming problem. Finally, numerical simulations verify the correctness and effectiveness of the proposed model, as well as the positive role of this model in the economic and low-cost operation of VPPs when considering capacity configuration.}, archive = {J_ACCESS}, author = {Shixun Qi and Duo Li and Liyong Hou and Haili Wang and Li Xin and Yaojie Jin and Yuanji Cai and Mingfei Ban}, doi = {10.1109/ACCESS.2025.3591459}, journal = {IEEE Access}, pages = {132155-132169}, shortjournal = {IEEE Access}, title = {Two-stage robust optimization considering the uncertainty of sources and loads in virtual power plants}, volume = {13}, year = {2025}, }
• (2025). A dual-linearly-polarized center-fed quadrifilar helix antenna loaded with reverse helices. ACCESS, 13, 132146-132154. (www)

  @article{ , abstract = {This paper proposes a dual-linearly-polarized (DLP) center-fed quadrifilar helix antenna (CF-QHA) loaded with reverse helices. It features a small transverse area, hollow-core structure, enhanced bandwidth, high cross-polarization discrimination (XPD), and high gain. The proposed antenna comprises two pairs of diagonal helical arms along ±45° directions. By changing the feeding point to the center of each helical arm, the center feed mechanism is introduced first to improve the impedance matching, compared with the previous end-fed DLP-QHA. Loading reverse helices with the CF-QHA in parallel further enhances the XPD performance. In addition, each pair of diagonal helical arms employs a differential feed to ensure LP radiations and high isolation. For demonstration, a prototype is fabricated and tested. According to the measured results, the 10-dB return loss bandwidths of the proposed ±45° DLP CF-QHA respectively range over $2.13-2.68$ GHz (22.87%) and $2.1-2.69$ GHz (24.63%), where XPDs higher than 21 dB and isolation better than 24 dB are achieved. The DLP CF-QHA is expected to provide the industry with more design options in base station applications.}, archive = {J_ACCESS}, author = {Xunting Yang and Yifeng Xu and Yuhang Dou and Miao Zhang}, doi = {10.1109/ACCESS.2025.3592776}, journal = {IEEE Access}, pages = {132146-132154}, shortjournal = {IEEE Access}, title = {A dual-linearly-polarized center-fed quadrifilar helix antenna loaded with reverse helices}, volume = {13}, year = {2025}, }
• (2025). Refining software clustering: The impact of code co-changes on architectural reconstruction. ACCESS, 13, 132126-132145. (www)

  @article{ , abstract = {Version control systems are essential for tracking and managing changes in software code. They also provide information about the relationships between software entities: when multiple entities (e.g., classes or interfaces) are frequently modified together, it may indicate an underlying connection between them. These code co-changes can be analyzed and filtered to uncover logical dependencies, which complement structural or lexical dependencies. While structural dependencies are extracted using code analysis techniques, logical dependencies can be extracted solely from version control system logs. This makes their extraction independent of the programming language. Our work investigates filtering techniques to eliminate co-change situations that are not meaningful as logical dependencies. The effectiveness of these filtering techniques is investigated by using the obtained logical dependencies in architectural reconstruction, a reverse engineering activity aimed at recovering a system’s modular structure. This paper explores the use of logical dependencies as input for architectural reconstruction. The main goal is to investigate whether logical dependencies can complement or even fully replace structural dependencies in this process. We use clustering based on software dependencies to group related entities into modules. We conduct experiments on four open-source Java projects using three clustering algorithms (Louvain, Leiden, and DBSCAN) and two evaluation metrics (Modularization Quality and MoJoFM). We consider three different dependency configurations: 1) only structural dependencies, 2) only logical dependencies, and 3) their combination. Our goal is to assess which configuration performs best and to examine the advantages and limitations of using logical dependencies.}, archive = {J_ACCESS}, author = {Adelina Diana Stana and Ioana Şora}, doi = {10.1109/ACCESS.2025.3592777}, journal = {IEEE Access}, pages = {132126-132145}, shortjournal = {IEEE Access}, title = {Refining software clustering: The impact of code co-changes on architectural reconstruction}, volume = {13}, year = {2025}, }
• (2025). Breaking and healing: GAN-based adversarial attacks and post-adversarial recovery for 5G IDSs. ACCESS, 13, 132109-132125. (www)

  @article{ , abstract = {Generative adversarial networks (GANs) have advanced rapidly in data augmentation and generation, and researchers have been exploring their applications in other areas, including adversarial attack generation. GANs have significantly improved the field of adversarial attacks, especially against intrusion detection systems (IDSs). These highly sophisticated GAN-based attacks pose a significant threat to IDS security. Addressing the challenges posed by GAN-based adversarial attacks is crucial for assessing their impact and developing robust defense mechanisms. In this paper, we propose new adversarial attack generation techniques derived from advanced GAN architectures. The effectiveness of these attacks, which are based on sophisticated types of GANs, is evaluated against three multi-class IDSs designed for 5G networks. Comprehensive experiments are conducted to evaluate the effectiveness of each GAN in exploiting IDS vulnerabilities and examine the transferability of adversarial attacks across different IDSs, considering the quality of the adversarial samples generated. Exploring these sophisticated GANs for adversarial attack generation enables us to develop a new post-adversarial recovery mechanism based on reconstructing the adversarial samples. Moreover, to thoroughly assess the capabilities of the proposed techniques, new evaluation metrics are introduced to facilitate a comprehensive analysis of the system’s vulnerabilities. Our results show that the proposed GAN-based adversarial attacks can significantly impact IDSs by achieving a high attack success rate (ASR) and drastically reducing accuracy, recall, precision, and F1 score. However, the proposed post-adversarial recovery process effectively restores the IDSs’ performance while significantly reducing the ASR.}, archive = {J_ACCESS}, author = {Yasmeen Alslman and Mouhammd Alkasassbeh and Mohammad J. Abdel-Rahman}, doi = {10.1109/ACCESS.2025.3587605}, journal = {IEEE Access}, pages = {132109-132125}, shortjournal = {IEEE Access}, title = {Breaking and healing: GAN-based adversarial attacks and post-adversarial recovery for 5G IDSs}, volume = {13}, year = {2025}, }
• (2025). Metering automation system 3.0 base version based on machine learning. ACCESS, 13, 132097-132108. (www)

  @article{ , abstract = {The accurate identification of equipment base versions in Metering Automation System 3.0 (MAS 3.0) is critical for ensuring interoperability and maintenance efficiency in modern smart grids. However, traditional machine learning methods and standalone deep learning architectures struggle to balance spatiotemporal feature extraction, computational efficiency, and deployment constraints for high-frequency multivariate metering data. This study proposes a hybrid DSCNN-CBAM-BiLSTM framework that synergistically integrates depthwise separable convolutions, dual attention mechanisms, and bidirectional temporal modeling to address these challenges. The depthwise separable convolutional neural network (DSCNN) minimizes parameter overhead while capturing spatial correlations across distributed grid nodes, followed by convolutional block attention modules (CBAM) that dynamically recalibrate channel and spatial features to amplify discriminative patterns. Bidirectional LSTM (BiLSTM) layers then model long-range temporal dependencies in both forward and backward directions, enabling robust contextual analysis of energy consumption sequences. Validated on 14 TB of operational data from China Southern Power Grid, the framework achieves 96.7% classification accuracy with an inference latency of 8.9 ms—outperforming CNNs (89.2%), Transformers (90.5%), and GRUs (92.1%) while reducing GPU memory usage by 35.7–72.7%. Edge deployment tests on NVIDIA Jetson AGX Xavier demonstrate real-time compatibility with IEC 61850-7-420 protocols, maintaining <15 ms latency at 200-node resolution. These advancements establish a highly effective and resource-efficient framework. For resource-efficient, edge-deployable analytics in smart grid infrastructure, effectively bridging the gap between high-accuracy version identification and industrial computational constraints.}, archive = {J_ACCESS}, author = {Sheng Li and Leping Zhang and Hang Dai and Lukun Zeng and Yuan Ai and Shuang Qi and Yuanzhai Cui}, doi = {10.1109/ACCESS.2025.3592384}, journal = {IEEE Access}, pages = {132097-132108}, shortjournal = {IEEE Access}, title = {Metering automation system 3.0 base version based on machine learning}, volume = {13}, year = {2025}, }
• (2025). MT-EfficientNetV2: A multi-temporal scale fusion EEG emotion recognition method based on recurrence plots. ACCESS, 13, 132079-132096. (www)

  @article{ , abstract = {Emotion recognition based on electroencephalography (EEG) signals has garnered significant research attention in recent years due to its potential applications in affective computing and brain-computer interfaces. Despite the proposal of various deep learning-based methods for extracting emotional features from EEG signals, most existing models struggle to effectively capture both long-term and short-term dependencies within the signals, failing to fully integrate features across different temporal scales. To address these challenges, we propose a deep learning model that combines multi-temporal-scale fusion, termed MT-EfficientNetV2. This model segments one-dimensional EEG signals using combinations of varying window sizes and fixed step lengths. The Recursive Plot (RP) algorithm is then employed to transform these segments into RGB images that intuitively represent the dynamic characteristics of the signals, facilitating the capture of complex emotional features. Additionally, a three-branch input feature fusion module has been designed to effectively integrate features across different scales within the same temporal domain. The model architecture incorporates DEconv and the SimAM attention mechanism with EfficientNetV2. This integration enhances the global fusion and expression of multi-scale features while strengthening the extraction of key emotional features at the local level, thereby suppressing redundant information. Experiments conducted on the public datasets SEED and SEED-IV yielded accuracies of 98.67% and 96.89%, respectively, surpassing current mainstream methods and validating the feasibility and effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Zihan Zhang and Zhiyong Zhou and Jun Wang and Hao Hu and Jing Zhao}, doi = {10.1109/ACCESS.2025.3592336}, journal = {IEEE Access}, pages = {132079-132096}, shortjournal = {IEEE Access}, title = {MT-EfficientNetV2: A multi-temporal scale fusion EEG emotion recognition method based on recurrence plots}, volume = {13}, year = {2025}, }
• (2025). An optical and mm-wave converged, dual-band, multi-beam rotman lens antenna array system enabling simplified designs of B5G/mmW base stations for ultra dense wireless networks. ACCESS, 13, 132067-132078. (www)

  @article{ , abstract = {With the exponential demand for additional capacity, ultra-dense wireless networks (UDNs) have become an interesting area of study. One of the major challenges in UDN deployment is the complexity and cost of the base stations, which affect scalability. This study proposes an integrated system combining optical and millimeter-wave (mm-Wave/mmW) technologies to address these challenges by centralizing processing tasks and simplifying the base station. The need for bulky, power-intensive components at each base station is significantly reduced, by transmitting signals over fiber from the central location to the base stations. This architecture opens the door to centralized artificial intelligence-based control of the base station. A dual-band Rotman lens antenna array is integrated into the system to provide flexible, passive beamforming capabilities, supporting multiple frequencies and multiple beams in a compact form, further reducing the overall number of devices required at the base station. The multi-layer Rotman lens antenna achieves a total -3-dB realized gain coverage of ±42° at both 28 GHz and 39 GHz. The maximum realized gain is 12.6 dBi and 12.9 dBi, at 28 GHz and 39 GHz respectively. To demonstrate the capabilities of the proposed optical and mm-Wave converged Rotman-lens enabled simplified base station architectures, a proof-of-concept experiment is performed integrating optical multi-carrier generation, optical modulation, fiber transmission, optical-to-electrical conversion and transmission through the presented dual-band, Rotman lens antenna array. The results demonstrate BER below the hard-decision FEC threshold, EVM meeting IEEE standard requirements, and open eye diagrams, confirming acceptable performance of the proposed architecture for simplified UDN base stations.}, archive = {J_ACCESS}, author = {Lauryn P. Smith and Charles A. Lynch and C. Alex Kaylor and L. Alberto Campos and Lin Cheng and Stephen E. Ralph and Manos M. Tentzeris}, doi = {10.1109/ACCESS.2025.3592213}, journal = {IEEE Access}, pages = {132067-132078}, shortjournal = {IEEE Access}, title = {An optical and mm-wave converged, dual-band, multi-beam rotman lens antenna array system enabling simplified designs of B5G/mmW base stations for ultra dense wireless networks}, volume = {13}, year = {2025}, }
• (2025). Implementation of an AI english-speaking interactive training system using multi-model neural networks. ACCESS, 13, 132052-132066. (www)

  @article{ , abstract = {Many people can read and listen to English well but may need help to speak it well. This paper aims to implement an AI English-speaking interactive training (AIESIT) system based on AI for special-purpose English-speaking training research, enabling students to express and communicate in professional English naturally. We will provide new tools for solving the software design in English-speaking training. The proposed AIESIT system integrates generative AI, speech recognition, and body recognition. The AIESIT system uses generative AI to generate an AI agent with mouth shapes that match the English speech, which enhances the user’s feeling of being in the real world. The speech recognition system recognizes the voice content of the user’s response for passing the evaluation. Since the AIESIT system does not have a natural person online, users can speak English boldly to improve their oral skills. During the learning process, OpenPoseNet is used to recognize whether the user has poor posture or leaves the seat during the learning process. Eye CNN is used to recognize whether the user falls asleep during the learning process and as a reference for continuing the lesson. Finally, the learning trajectory, including the recognition rate and response time, is output to score the performance of the English dialogues. The results of multiple user tests have shown that increasing the number of practice sessions can improve the English speaking, encouraging users to keep practicing, and this system helps improve their English speaking.}, archive = {J_ACCESS}, author = {Ching-Ta Lu and Yen-Yu Lu and Yi-Ru Lu and Ying-Chen Pan and Yu-Chun Liu}, doi = {10.1109/ACCESS.2025.3592632}, journal = {IEEE Access}, pages = {132052-132066}, shortjournal = {IEEE Access}, title = {Implementation of an AI english-speaking interactive training system using multi-model neural networks}, volume = {13}, year = {2025}, }
• (2025). Source-agnostic single-ended protection and fault location for double-circuit lines connected to power electronics-based sources. ACCESS, 13, 132038-132051. (www)

  @article{ , abstract = {Double-circuit transmission line installations are rising due to their enhanced reliability, power transfer capability and operational flexibility, particularly in grids with significant share of power electronics-based sources. Reliable protection of these lines ensures the isolation of faulty sections, while precise fault location enables maintenance teams to quickly address the cause, thereby facilitating faster restoration and avoiding unnecessary curtailment of the clean power. Existing single-ended protection and fault location methods have limitations when applied to such double-circuit lines connected to power-electronics based renewable energy sources, mainly due to the source-dependent, variable, and controlled nature of their transient response. In this paper, limitations of existing methods when applied to such systems are demonstrated with several illustrative cases, and a reliable single-ended protection and an accurate fault location method are proposed. Protection requires the faulted line to be identified; this is achieved based on the polarity of the angle of an operating quantity evaluated at two extreme boundaries of the zone of protection. The operating quantity is defined as the apparent power flowing into the fault resistance path expressed as the function of fault location on the line of interest. Further, based on the same principle, accurate fault location is also identified without any additional measurement or complex calculations. Source-agnostic performance is accomplished through observability of the remote end using locally measured healthy line current. The proposed method is verified for multiple system configurations with power electronics-based resources including system with 100% such sources. The method is validated using experimental and field data and it is found to be reliable. Performance comparison with traditional distance, and existing single-ended protection and fault location methods for lines connected with power electronics-based resources are also conducted and improved performance is demonstrated.}, archive = {J_ACCESS}, author = {Neethu George and O. D. Naidu and Ashok Kumar Pradhan}, doi = {10.1109/ACCESS.2025.3590962}, journal = {IEEE Access}, pages = {132038-132051}, shortjournal = {IEEE Access}, title = {Source-agnostic single-ended protection and fault location for double-circuit lines connected to power electronics-based sources}, volume = {13}, year = {2025}, }
• (2025). Designing a four-group permutational hybrid feature selection scheme for transient stability prediction based on multivariate trajectory data. ACCESS, 13, 132022-132037. (www)

  @article{ , abstract = {Collecting the optimal transient features (OTFs) from multivariate transient time series data is crucial for ensuring precise and prompt transient analysis (TA) in power systems. Realizing the OTFs requires designing a comprehensive hybrid feature selection scheme (FSS). Hence, this work offers a four-group permutational hybrid FSS called FGPHFSS to select OTFs from transient trajectory data. The FGPHFSS includes four hybrid FSS (4HFSS) groups decorated by filter-wrapper mechanisms. The two 4HFSS groups comprise a relevance-based filter and another two 4HFSS groups derived from a conditional relevance-based filter. The 4HFSS groups includes incremental wrapper methods namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr), which are fed by the support vector machine (SVM) and twin SVM (TWSVM) classifiers. The nonlinear space of transient data causes the SVM and TWSVM to be equipped with elastic and non-elastic kernels. Generally, we have $\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Relevance}}$ , $\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF}/\text {POL}}^{\text {RBF/DTW}} }\limits ^{\text {Relevance}}$ , $\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSS}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. Relevance}}$ , and $\mathop {{}_{\text {TWSVM}}^{\text {SVM}} \textrm {IWSSr}_{\text {RBF/POL}}^{\text {RBF/DTW}}}\limits ^{\text {Cond. relevance}}$ , which are permuted in a 24-way manner. After selecting OTFs, the OTFs’ performance in transient stability prediction (TSP) is evaluated by a cross-validation technique. The results show that FGPHFSS has a prediction accuracy of 99 % and a processing time of 101.819 milliseconds for TSP.}, archive = {J_ACCESS}, author = {Seyed Alireza Bashiri Mosavi and Omid Khalaf Beigi}, doi = {10.1109/ACCESS.2025.3592712}, journal = {IEEE Access}, pages = {132022-132037}, shortjournal = {IEEE Access}, title = {Designing a four-group permutational hybrid feature selection scheme for transient stability prediction based on multivariate trajectory data}, volume = {13}, year = {2025}, }
• (2025). ProBoost: Reducing uncertainty using a boosting method for probabilistic models. ACCESS, 13, 132006-132021. (www)

  @article{ , abstract = {Uncertainty analysis of classification or regression models is a key feature of probabilistic approaches to supervised learning, allowing the assessment of how trustworthy predictions are. Just as boosting algorithms aim at obtaining accurate ensembles of simple classifiers, using a process guided by the accuracy of each of these classifiers, the method proposed in this paper builds an ensemble guided by the uncertainty of each of its individual models. The proposed method, named ProBoost (probabilistic boosting), uses the epistemic uncertainty of each training sample to determine those about which each model is most uncertain; the importance of these samples is then increased for the next learner, producing a sequence that progressively focuses on samples found to have the highest uncertainty. In the end, the learned models are combined into an ensemble. Thus, the approach goes beyond standard boosting methods, which usually focus on deterministic error correction, by quantifying predictive uncertainty to guide sequential training through dataset manipulation. Three methods are proposed to update the importance of the samples according to the uncertainty estimates at each stage: undersampling, oversampling, and weighting. Furthermore, two approaches are studied regarding the final ensemble combination. The learners herein considered are standard convolutional neural networks, and the probabilistic models underlying the uncertainty estimation use either variational inference or Monte Carlo dropout. The experimental evaluation carried out on MNIST and CIFAR 10 benchmark datasets shows that ProBoost yields significant performance improvement, compared to not using ProBoost, and outperforms a wider single model with a similar number of parameters.}, archive = {J_ACCESS}, author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Fernando Morgado-Dias and Antonio G. Ravelo-García and Mário A. T. Figueiredo}, doi = {10.1109/ACCESS.2025.3592797}, journal = {IEEE Access}, pages = {132006-132021}, shortjournal = {IEEE Access}, title = {ProBoost: Reducing uncertainty using a boosting method for probabilistic models}, volume = {13}, year = {2025}, }
• (2025). CATCH-FORM-ACTer: Compliance-aware tactile control and hybrid deformation regulation-based action transformer for viscoelastic object manipulation. ACCESS, 13, 131998-132005. (www)

  @article{ , abstract = {Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and ±5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus $10\%-20\%$ higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.}, archive = {J_ACCESS}, author = {Haobo Kang and Hongjun Ma and Weichang Li}, doi = {10.1109/ACCESS.2025.3591499}, journal = {IEEE Access}, pages = {131998-132005}, shortjournal = {IEEE Access}, title = {CATCH-FORM-ACTer: Compliance-aware tactile control and hybrid deformation regulation-based action transformer for viscoelastic object manipulation}, volume = {13}, year = {2025}, }
• (2025). Compression-aware hybrid framework for deep fake detection in low-quality video. ACCESS, 13, 131980-131997. (www)

  @article{ , abstract = {Deep fakes pose a growing threat to digital media integrity by generating highly realistic fake videos that are difficult to detect, especially under the high compression levels commonly used on social media platforms. These compression artifacts often degrade the performance of deep fake detectors, making reliable detection even more challenging. In this paper, we propose a handcrafted deep fake detection framework that integrates wavelet transforms and Conv3D-based spatiotemporal descriptors for feature extraction, followed by a lightweight ResNet-inspired classifier. Unlike end-to-end deep neural networks, our method emphasizes interpretability and computational efficiency, while maintaining high detection accuracy under diverse real-world conditions. We evaluated four configurations based on input modality and attention mechanisms: RGB with attention, RGB without attention, grayscale with attention, and grayscale without attention. Experiments were conducted on the FaceForensics++ dataset (C23 and C40 compression levels) and Celeb-DF v2 (C0 and C40), across intra- and inter-compression settings, as well as cross-dataset scenarios. Results show that RGB inputs without attention achieve the highest accuracy on FaceForensics++, while grayscale inputs without attention perform best in cross-dataset evaluations on Celeb-DF v2, attaining strong AUC scores. Despite its handcrafted nature, our approach matches or surpasses the existing state-of-the-art (SOTA) methods. Grad-CAM visualizations further reveal both strengths and failures (e.g., occlusion and misalignment), offering valuable insights for refinement. These findings underscore the potential of our framework for efficient and effective deep fake detection in low-resource and real-time environments.}, archive = {J_ACCESS}, author = {Lagsoun Abdel Motalib and Oujaoura Mustapha and Hedabou Mustapha}, doi = {10.1109/ACCESS.2025.3592358}, journal = {IEEE Access}, pages = {131980-131997}, shortjournal = {IEEE Access}, title = {Compression-aware hybrid framework for deep fake detection in low-quality video}, volume = {13}, year = {2025}, }
• (2025). PHILDER: Lightweight framework for intelligent phishing detection on resource-limited devices. ACCESS, 13, 131967-131979. (www)

  @article{ , abstract = {The increasing sophistication of phishing attacks represents a significant challenge to cybersecurity, particularly in the context of fraudulent emails crafted to deceive users and steal confidential information. This paper proposes a deep learning-based approach for the automatic detection of phishing in emails, the PHILDER (PHishing Intelligent Lightweight DEtection on Resource-limited devices) model. The study employs a dataset composed of real emails extracted from PhishTank and the SpamAssassin Public Corpus, spanning both legitimate and fraudulent samples. To address the natural imbalance between spam and phishing emails, two strategies were applied: undersampling, which reduces the number of legitimate emails to balance the classes, and oversampling using SMOTE (Synthetic Minority Over-sampling Technique) to generate new synthetic samples of the minority class. Five models (ALBERT, DistilBERT, MobileBERT, MiniLM, and TinyBERT) were trained: one using the original dataset, one with undersampling, and a third with oversampling. The experimental results show that the oversampling approach had to be discarded due to computational cost issues, the tendency to overfit, and inferior performance compared to the other two approaches. The main contributions of this paper include the application of computational efficiency metrics for model evaluation, which not only consider traditional metrics but also enable assessment in real-world applications under hardware computational constraints, and the exploration of different data composition strategies for training. The use of PHILDER (TinyBERT combined with data balancing techniques) demonstrates a promising approach for the automatic detection of phishing in emails, contributing to strengthening digital security against social engineering attacks.}, archive = {J_ACCESS}, author = {Guilherme Dantas Bispo and César Augusto Borges de Andrade and Gabriela Mayumi Saiki and Raquel Valadares Borges and André Luiz Marques Serrano and Geraldo Pereira Rocha Filho and Vinícius Pereira Gonçalves}, doi = {10.1109/ACCESS.2025.3592541}, journal = {IEEE Access}, pages = {131967-131979}, shortjournal = {IEEE Access}, title = {PHILDER: Lightweight framework for intelligent phishing detection on resource-limited devices}, volume = {13}, year = {2025}, }
• (2025). Adaptive passivity-based control for DC motor speed regulation in DC-DC converter-fed systems. ACCESS, 13, 131957-131966. (www)

  @article{ , abstract = {This paper presents a unified adaptive passivity-based control strategy using incremental modeling to regulate the angular speed of a DC series motor driven by DC-DC converters operating in both buck and boost configurations. The proposed approach leverages an incremental port-Hamiltonian framework to design control laws that ensure the global asymptotic stability of the closed-loop system. To address the challenge posed by unknown load torques, a nonlinear disturbance observer is incorporated, allowing for the real-time estimation required for an accurate computation of equilibrium points and reference tracking. These theoretical developments are validated through experimental implementation and compared against an inverse optimal control (IOC) strategy. The results show that the proposed IDA-PBC significantly outperforms the IOC in terms of transient response, tracking accuracy, and disturbance rejection. In the buck configuration, the IDA-PBC reduces the rise time by up to 50.24% and completely eliminates the overshoot. Similarly, in the boost configuration, the rise time is improved by 20.63%, with enhanced stability and lower phase lag under sinusoidal tracking. These findings confirm the robustness and effectiveness of the proposed control strategy for real-time applications in electromechanical systems.}, archive = {J_ACCESS}, author = {Diego Montoya Acevedo and Ignacio Parraguez-Garrido and Walter Gil-González and Oscar Danilo Montoya and Catalina González-Castaño}, doi = {10.1109/ACCESS.2025.3592594}, journal = {IEEE Access}, pages = {131957-131966}, shortjournal = {IEEE Access}, title = {Adaptive passivity-based control for DC motor speed regulation in DC-DC converter-fed systems}, volume = {13}, year = {2025}, }
• (2025). Gradient flow decoding. ACCESS, 13, 131937-131956. (www)

  @article{ , abstract = {This paper presents the Gradient Flow (GF) decoding for LDPC codes. GF decoding, a continuous-time methodology based on gradient flow, employs a potential energy function associated with bipolar codewords of LDPC codes. The decoding process of the GF decoding is concisely defined by an ordinary differential equation and thus it is well suited to an analog circuit implementation. We experimentally demonstrate that the decoding performance of the GF decoding for AWGN channels is comparable to that of the multi-bit mode gradient descent bit flipping algorithm. We further introduce the negative log-likelihood function of the channel for generalizing the GF decoding. The proposed method is shown to be tensor-computable, which means that the gradient of the objective function can be evaluated with the combination of basic tensor computations. This characteristic is well-suited to emerging AI accelerators, potentially applicable in wireless signal processing. The paper assesses the decoding performance of the generalized GF decoding in LDPC-coded MIMO channels. For LDPC-coded MIMO channels, our method achieves approximately 1.6 dB performance gain over MMSE + BP. Furthermore, an exploration of score-based channel learning for capturing statistical properties is also provided.}, archive = {J_ACCESS}, author = {Tadashi Wadayama and Lantian Wei}, doi = {10.1109/ACCESS.2025.3592296}, journal = {IEEE Access}, pages = {131937-131956}, shortjournal = {IEEE Access}, title = {Gradient flow decoding}, volume = {13}, year = {2025}, }
• (2025). Subliminal channels in CRYSTALS-kyber key-encapsulation mechanism and their use in quantum-resistant TLS protocols. ACCESS, 13, 131922-131936. (www)

  @article{ , abstract = {Cryptographic protocols can be used to covertly exchange information without arousing suspicion. The covert channels created in this way are called subliminal channels. In this work, three different subliminal channels using CRYSTALS-Kyber are discovered. Kyber is employed in the Module-Lattice-based Key-Encapsulation Mechanism (ML-KEM) standard published in FIPS 203. In the found subliminal channels, the covert message is embedded in the random data needed by the encapsulation or the key generation algorithms. Two settings are differentiated depending on if the covert receiver is an overt entity executing the key exchange protocol or a man-in-the-middle. An important feature achieved by the proposed subliminal channels is plausible deniability. Covert entities can convince a jury that they did not use a subverted version of the original Kyber algorithms by showing the random data used in the protocol, the values exchanged, and the outputs of the algorithms. The proposed subliminal channels can be used in quantum-resistant proposals of TLS (Transport Layer Security). Concretely, this work explores the use of the proposed subliminal channels in PQTLS and KEMTLS. Also, some countermeasures are proposed in the paper. Experimental results show that the overhead in execution times is not significant and that from 2 to 34 bytes of covered information can be transmitted per TLS handshake.}, archive = {J_ACCESS}, author = {Roberto Román and Rosario Arjona and Iluminada Baturone}, doi = {10.1109/ACCESS.2025.3590863}, journal = {IEEE Access}, pages = {131922-131936}, shortjournal = {IEEE Access}, title = {Subliminal channels in CRYSTALS-kyber key-encapsulation mechanism and their use in quantum-resistant TLS protocols}, volume = {13}, year = {2025}, }
• (2025). FADQN: A heuristic reinforcement learning mechanism for UAV path planning in unknown environment. ACCESS, 13, 131909-131921. (www)

  @article{ , abstract = {Path planning remains a focal point in Unmanned Aerial Vehicle (UAV) research, with autonomous path planning in unknown environments emerging as a particularly active area. Deep Reinforcement Learning (DRL) has become a standard approach for addressing path planning challenges in such environments. However, traditional DRL algorithms often suffer from slow convergence. To address this issue, this paper introduces an enhanced deep reinforcement learning algorithm, termed Firefly Algorithm-enhanced Deep Q-Network (FADQN). In FADQN, the action output from the Firefly Algorithm is integrated into the Deep Q-Network to guide early action selection, thereby accelerating convergence. To balance exploration and exploitation, an adaptive $\varepsilon $ -greedy strategy is proposed. Furthermore, a prioritized experience replay mechanism is employed to increase the likelihood of the agent learning from critical experiences. Simulation experiments demonstrate that FADQN achieves shorter paths (reducing the path length by 7.32% in complex environments), fewer turns, faster and more stable convergence, and higher success rates across various environments compared to traditional DQN. These results validate the effectiveness of FADQN in solving UAV path planning problems.}, archive = {J_ACCESS}, author = {Wei Sun and Jing Huan and Qi Wang and Hualong Yu and Shang Gao}, doi = {10.1109/ACCESS.2025.3591457}, journal = {IEEE Access}, pages = {131909-131921}, shortjournal = {IEEE Access}, title = {FADQN: A heuristic reinforcement learning mechanism for UAV path planning in unknown environment}, volume = {13}, year = {2025}, }
• (2025). A systematic open source modeling method for energy and power systems: Solar PV application. ACCESS, 13, 131869-131908. (www)

  @article{ , abstract = {Open source modeling has emerged as a transformative paradigm in energy and power systems studies by enabling transparent, reproducible and collaborative development of models and tools. However, limited access to high-resolution, localized data reduces model accuracy, especially in regions with infrastructural constraints and complex weather conditions. This paper introduces a novel, systematic Open Source Modeling Method for Energy and Power Systems (OSM-MEPS). Derived and synthesized from foundational steps outlined in modeling literature, OSM-MEPS is grounded in first-principles thinking, the scientific method and the engineering design process frameworks, ensuring technical rigor, empirical validation and adaptability. To demonstrate effectiveness, OSM-MEPS guided the modeling and simulation of a solar photovoltaic (PV) system using high-resolution weather and irradiance data for the year 2024 from Solcast-DNV. At the Serres-C site in Greece, OSM-MEPS model results achieved a strong fit with real-world solar PV energy data (MAE: 469.57 kWh, RMSE: 584.62 kWh, $R^{2}$ : 0.887), outperforming PVLIB (MAE: 1,400.39 kWh, RMSE: 1,526.11 kWh, $R^{2}$ : 0.232). Seasonal testing showed similar results: in May 2024, the OSM-MEPS model achieved $R^{2}$ : 0.917 vs. PVLIB’s −0.039; in December, it maintained $R^{2}$ : 0.873 vs. PVLIB’s 0.638. Geographic validation demonstrated generalizability. In Pretoria, the OSM-MEPS model outperformed PVLIB ( $R^{2}$ : 0.443 vs. −0.239); in Adelaide, it maintained accuracy ( $R^{2}$ : 0.866) while PVLIB failed ( $R^{2}$ : −0.959). In Kisumu, both models had low correlation despite matching MAE (0.88 kWh), due to data gaps and limited shading metadata. A rooftop energy feasibility study was carried out in Njagu-Mhasibu-Ruiru Estate in Kenya, based on a solar PV installed capacity of 8.75 kW. The OSM-MEPS model predicted an annual yield of 14,329 kWh/year, compared to PVLIB’s estimate of 10,048 kWh/year and the GSA reference of 12,784 kWh/year. Overall, the results confirm that the OSM-MEPS method and its resulting model are robust and adaptable tools for high-resolution solar PV energy modeling across diverse geographic regions and weather conditions.}, archive = {J_ACCESS}, author = {Peter Munyao Mutuku and Alan L. Leigh Jarvis and Andrew G. Swanson and Mohamed F. Khan}, doi = {10.1109/ACCESS.2025.3592577}, journal = {IEEE Access}, pages = {131869-131908}, shortjournal = {IEEE Access}, title = {A systematic open source modeling method for energy and power systems: Solar PV application}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of dual-input frequency-periodic load modulated power amplifier at 1–5.7-GHz bandwidth with co-designed biasing network. ACCESS, 13, 131856-131868. (www)

  @article{ , abstract = {This study proposes a novel dual input power amplifier (PA) with a frequency-periodic load modulated output matching network supported by a broadband biasing network. The output matching network consists of two transmission lines which enable the dual-input PA to operate in Doherty or outphasing modes depending on the frequency. The proposed broadband biasing network simply consists of a short stub circuit that cooperates with the output matching network. The biasing network not only provides a bias to PA, but also improves the bandwidth in the back-off operation over the broadband characteristic of 145%. The two independent input signals are utilized to optimize the operation of Doherty and outphasing mode. The dual-input PA is implemented using a $0.15~\mu $ m GaN HEMT process. The fabricated PA shows a saturated output power of 35.2 dBm to 38.1 dBm with a power added efficiency (PAE) of 36.6% to 62% for the broadband 1 GHz to 5.7 GHz. The fabricated PA demonstrated an averaged output power of 27.7 dBm to 31.8 dBm, a PAE of 35% to 56.2% and an adjacent channel power Ratio (ACPR) of -41 to -55 dBc.}, archive = {J_ACCESS}, author = {Takuma Torii and Yuji Komatsuzaki and Shintaro Shinjo and Ryo Ishikawa}, doi = {10.1109/ACCESS.2025.3592238}, journal = {IEEE Access}, pages = {131856-131868}, shortjournal = {IEEE Access}, title = {Performance enhancement of dual-input frequency-periodic load modulated power amplifier at 1–5.7-GHz bandwidth with co-designed biasing network}, volume = {13}, year = {2025}, }
• (2025). Automated class imbalance learning via few-shot multi-objective bayesian optimization with deep kernel gaussian processes. ACCESS, 13, 131839-131855. (www)

  @article{ , abstract = {Automated Class Imbalance Learning (AutoCIL) is an emerging paradigm that leverages Combined Algorithm Selection and Hyperparameter Optimization (CASH) to automate the configuration of resampling strategies and classifiers for imbalanced classification tasks. Existing AutoCIL methods focus solely on single-objective optimization. However, real-world applications often involve multiple, conflicting objectives—such as predictive performance and computational cost—that must be jointly optimized. Ignoring such trade-offs limits the adaptability and practicality of current methods. In this work, we propose a novel approach called AutoCIL-FMOBO (AutoCIL via Few-shot Multi-Objective Bayesian Optimization). Specifically, we design meta-learned deep kernel Gaussian process surrogates trained on a meta-dataset constructed from pre-evaluated results obtained by running configurations in the search space on class-imbalanced datasets. Then, these surrogate models with prior optimization knowledge are combined with the Expected Hypervolume Improvement (EHVI) acquisition function in a Bayesian optimization framework to efficiently discover Pareto-optimal configurations for the target task, which enables AutoCIL-FMOBO to jointly optimize key components, such as resampling methods, classifiers, and their hyperparameters, under a multi-objective setting. Experimental results on 15 real-world class-imbalanced datasets demonstrate that our approach outperforms baselines in both effectiveness and sample efficiency, while maintaining generalization across tasks and achieving competitive performance under a multi-objective setting.}, archive = {J_ACCESS}, author = {Zhaoyang Wang and Shuo Wang and Damien Ernst and Chenguang Xiao}, doi = {10.1109/ACCESS.2025.3591034}, journal = {IEEE Access}, pages = {131839-131855}, shortjournal = {IEEE Access}, title = {Automated class imbalance learning via few-shot multi-objective bayesian optimization with deep kernel gaussian processes}, volume = {13}, year = {2025}, }
• (2025). Hypergraph neural sheaf diffusion: A symmetric simplicial set framework for higher-order learning. ACCESS, 13, 131823-131838. (www)

  @article{ , abstract = {The absence of intrinsic adjacency relations and orientation systems in hypergraphs creates fundamental challenges for constructing sheaf Laplacians of arbitrary degrees. We resolve these limitations through symmetric simplicial sets derived directly from hypergraphs, called symmetric simplicial lifting, which encode all possible oriented subrelations within each hyperedge as ordered tuples. This construction canonically defines adjacency via facet maps while inherently preserving hyperedge provenance. We establish that the normalized degree zero sheaf Laplacian on our symmetric simplicial lifting reduces exactly to the traditional graph normalized sheaf Laplacian when restricted to graphs, validating its mathematical consistency with prior graph-based sheaf theory. Furthermore, the induced structure preserves all structural information from the original hypergraph, ensuring that every multi-way relational detail is faithfully retained. Leveraging this framework, we introduce Hypergraph Neural Sheaf Diffusion (HNSD), the first principled extension of neural sheaf diffusion to hypergraphs. HNSD operates via normalized degree zero sheaf Laplacian over symmetric simplicial lifting, resolving orientation ambiguity and adjacency sparsity inherent to hypergraph learning. Experimental evaluations demonstrate HNSD’s competitive performance across established benchmarks.}, archive = {J_ACCESS}, author = {Seongjin Choi and Gahee Kim and Yong-Geun Oh}, doi = {10.1109/ACCESS.2025.3592104}, journal = {IEEE Access}, pages = {131823-131838}, shortjournal = {IEEE Access}, title = {Hypergraph neural sheaf diffusion: A symmetric simplicial set framework for higher-order learning}, volume = {13}, year = {2025}, }
• (2025). Life pattern-based human attribute estimation using only GPS data. ACCESS, 13, 131803-131822. (www)

  @article{ , abstract = {Analysis of large-scale GPS data enables the summarization and structuring of human mobility characteristics, providing insights into residents’ behavior patterns. However, existing methods analyze sequences of individual-derived stay labels, such as home or workplace, which do not accurately reflect actual area functions and can fail to distinguish individuals based on differences in workplaces. This limitation arises because annotating stay locations and purposes in GPS data is challenging due to the complexity of urban structures and constraints on data quality. In this study, we propose ArLPH, a framework that takes only GPS data as input, models mobility from the perspective of “when and what types of places he/she visited,” and enables the interpretation of life patterns. ArLPH consists of two modules: “Area Modeling,” which represents urban areas based on usage patterns, and “Human Modeling,” which represents individuals based on behavior patterns. By the processing flow of area modeling to human modeling, it becomes possible to analyze human mobility while considering area characteristics. Through an evaluation of a large-scale, real-world GPS dataset collected from tens of thousands of smartphone users, we demonstrate that ArLPH enables the characterization of individuals based on their life patterns and estimates attributes such as occupation (e.g., office workers or homemakers) and lifestyle (e.g., those who work frequently on weekends).}, archive = {J_ACCESS}, author = {Kazuyuki Shoji and Haru Terashima and Naoki Tamura and Shin Katayama and Kenta Urano and Takuro Yonezawa and Nobuo Kawaguchi}, doi = {10.1109/ACCESS.2025.3591811}, journal = {IEEE Access}, pages = {131803-131822}, shortjournal = {IEEE Access}, title = {Life pattern-based human attribute estimation using only GPS data}, volume = {13}, year = {2025}, }
• (2025). Adaptive spelling in immersive reality: The impact of gamified VR and LLMs on young learners’ english color word acquisition. ACCESS, 13, 131789-131802. (www)

  @article{ , abstract = {Spelling is a crucial language skill, yet traditional instruction often relies on rote memorization rather than meaningful learning. Despite various approaches to spelling instruction, the potential of virtual reality (VR) along with the integration of gamification and LLMs, remains underexplored. This study explores a VR-based, gamified approach using LLM-driven adaptive learning to improve spelling acquisition of English color words among young learners. The study employed a quasi-experimental, pre-test-post-test design with a control group. The participants were 50 male students aged 10, divided into an experimental group (N=25) that used the LLM-enhanced VR game and a control group (N=25) that received traditional instruction. The VR intervention consisted of a three-stage game built on the whole-word approach, featuring gamified elements and adaptive feedback from an LLM. Data were collected via spelling tests (pre-test, immediate post-test, and delayed post-test), user experience surveys, and semi-structured interviews. Results showed that the VR-based approach, significantly improved spelling performance and engagement. Specifically, the experimental group demonstrated substantially higher scores in both immediate vocabulary uptake and long-term retention after one week compared to the control group. Furthermore, qualitative and survey data indicated the VR experience was perceived as significantly more interesting, effective, and motivating. These findings highlight the potential of immersive, gamified learning environments to enhance spelling education, offering an effective alternative to conventional methods.}, archive = {J_ACCESS}, author = {Jalal Safari Bazargani and Abolghasem Sadeghi-Niaraki and Xinyu Shi and Soo-Mi Choi}, doi = {10.1109/ACCESS.2025.3591114}, journal = {IEEE Access}, pages = {131789-131802}, shortjournal = {IEEE Access}, title = {Adaptive spelling in immersive reality: The impact of gamified VR and LLMs on young learners’ english color word acquisition}, volume = {13}, year = {2025}, }
• (2025). Estimation of performance bounds for computational models of visual saliency in immersive videos. ACCESS, 13, 131772-131788. (www)

  @article{ , abstract = {The omnidirectional (360°) video format favors user interaction within an immersive virtual environment, in a manner that is closer to real-world situations. Several studies have been proposing computational models to predict salient regions for this type of media. Currently, strategies inspired by methods originally applied to 2D videos have been adapted to assess the performance of attention models for omnidirectional videos, ignoring the fact that immersive media provides the observer with a different experience when compared with conventional media. Furthermore, the lack of consolidated proposals for performance limits for 360° videos represents a relevant gap in the literature. This paper evaluates the applicability of 2D approaches (Mean Eye Position and Human Infinite), introduces Equator Bias as a performance bound, and also proposes Saliency Sum as an approach for estimating upper limits in visual attention models for immersive media. For validation purposes, we compare these bounds with outputs from three attention models (DAVE, Static CP360 and Spherical U-Net) for six metrics (AuC-Judd, AuC-Borji, NSS, $D_{\text {KL}}$ , CC and SIM). The experiments were conducted on a subset of 6 videos from the PAVS10K dataset, which includes eye-tracking data from approximately 20 observers. In our experiments, the proposed Saliency Sum scored best on three metrics and Human Infinite achieved the best scores in two metrics and was never ranked as the worst. In contrast, Mean Eye Position showed the lowest performance, obtaining the worst score in five metrics and not ranking as the best in any. The Equator Bias showed worse score in one metric and scores close to the worst scores in three metrics. These results suggest that these approaches may serve as upper and lower performance bounds for evaluating saliency models in immersive videos.}, archive = {J_ACCESS}, author = {Aline F. G. De Sousa and Clebson Ismael S. Silva and Aldebaro Klautau and Ronaldo F. Zampolo}, doi = {10.1109/ACCESS.2025.3592569}, journal = {IEEE Access}, pages = {131772-131788}, shortjournal = {IEEE Access}, title = {Estimation of performance bounds for computational models of visual saliency in immersive videos}, volume = {13}, year = {2025}, }
• (2025). WISP: Workframe for interferogram signal phase-unwrapping. ACCESS, 13, 131757-131771. (www)

  @article{ , abstract = {This paper proposes an iterative framework WISP (Workframe for Interferogram Signal Phase-unwrapping for phase reconstruction from interferograms of complex phase objects. The framework processes each interferogram through the sequential stages: dark fringe tracing, isophase distribution, local gradient direction estimation, anisotropic (local direction dependent) diffusion smoothing, phase unwrapping, and convergence testing. Iterations continue until the difference between the reconstructed and experimental phase distributions reaches an asymptotic minimum. A key contribution is the proposed loss function for isophase fitting, which directly optimizes curve quality and enhances reconstruction accuracy. Experimental results confirm the algorithm’s highest precision. A study of the framework’s resistance to noise was conducted, showing high stability even in the case of noise with an amplitude half the amplitude of the image brightness. Comparative analysis against established baselines reveals that the WISP consistently outperforms alternative approaches in accurately unwrapping the phase, particularly under high noise conditions. Evaluated using RMSD metrics, WISP achieves the lowest reconstruction errors, reducing them by 39.7% compared to the next best method (Deep Convolutional Neural Network), highlighting its superior robustness and accuracy.}, archive = {J_ACCESS}, author = {Timofey F. Khirianov and Aleksandra I. Khirianova and Egor V. Parkevich and Ilya Makarov}, doi = {10.1109/ACCESS.2025.3591959}, journal = {IEEE Access}, pages = {131757-131771}, shortjournal = {IEEE Access}, title = {WISP: Workframe for interferogram signal phase-unwrapping}, volume = {13}, year = {2025}, }
• (2025). Secret key generation driven by attention-based convolutional autoencoder and quantile quantization for IoT security in 5G and beyond. ACCESS, 13, 131744-131756. (www)

  @article{ , abstract = {Physical-layer secret key generation (PSKG) has emerged as a promising technique for enhancing wireless security in Internet of Things (IoT) networks by exploiting the reciprocity of uplink and downlink channels. However, in time-division duplex (TDD) systems, hardware impairments and channel noise disrupt channel reciprocity, degrading key generation performance. To overcome these challenges, this paper introduces a deep learning–enhanced PSKG framework that effectively mitigates channel discrepancies and improves key generation reliability under imperfect channel state information (CSI). Specifically, a two-dimensional convolutional neural network–based autoencoder (2D CNN–AE) with a spatial self-attention (SSA) mechanism is developed to efficiently extract and learn channel reciprocity features in time-division duplex (TDD)-based fifth-generation (5G) networks. Additionally, a quantile-based quantization scheme is proposed to enhance key randomness and entropy, thereby strengthening security and resilience against potential threats. To facilitate comprehensive performance evaluation, a wiretap channel dataset is generated in accordance with 5G standards, encompassing diverse propagation conditions and including both legitimate users and an eavesdropper. Extensive simulation results demonstrate that the proposed 2D CNN–AE–SSA-based PSKG framework significantly reduces the key disagreement ratio (KDR), enhances key randomness, and maintains low computational complexity. These findings establish the proposed method as a robust and practical solution for securing wireless communications in resource-constrained IoT environments.}, archive = {J_ACCESS}, author = {Anas Alashqar and Ehsan Olyaei Torshizi and Raed Mesleh and Werner Henkel}, doi = {10.1109/ACCESS.2025.3590926}, journal = {IEEE Access}, pages = {131744-131756}, shortjournal = {IEEE Access}, title = {Secret key generation driven by attention-based convolutional autoencoder and quantile quantization for IoT security in 5G and beyond}, volume = {13}, year = {2025}, }
• (2025). A comprehensive approach to residual value analysis in the luxury automotive market. ACCESS, 13, 131733-131743. (www)

  @article{ , abstract = {Global automotive markets have introduced new complexities, from the surge in powertrain diversity to evolving consumer purchasing habits. In the luxury car segment, residual value (RV), the car’s actual value at the end of ownership, is particularly significant. A high RV translates into lower overall ownership costs, as the car retains more of its value over time, which can boost demand as well as leasing margin. For this reason, the analysis of RV offers key insights for strategic decision-making. The present study leverages a large-scale global dataset spanning a 10-year period, capturing both internal vehicle features and three available external market conditions (CPI, unemployment rate, and 10-year bond yield). Our approach employs machine learning techniques, particularly CatBoost, achieving a mean absolute percentage error of around 5%, deemed highly acceptable within the industry. Moreover, a novel method to enhance the reliability and interpretability of RV estimations is proposed by quantifying depreciation thresholds and mitigating distortions related to sample composition via a “Standard Vehicle” concept. The approach has been validated by Ferrari S.p.A., the provider of the data, serving as a robust tool for automotive industry stakeholders.}, archive = {J_ACCESS}, author = {A. Ghibellini and A. Scioletti and M. Coletto and L. Bononi and M. Gabbrielli}, doi = {10.1109/ACCESS.2025.3591765}, journal = {IEEE Access}, pages = {131733-131743}, shortjournal = {IEEE Access}, title = {A comprehensive approach to residual value analysis in the luxury automotive market}, volume = {13}, year = {2025}, }
• (2025). Scalable nonparametric supervised learning for streaming and massive data: Applications in healthcare monitoring and credit risk. ACCESS, 13, 131716-131732. (www)

  @article{ , abstract = {This paper introduces novel nonparametric supervised learning techniques for classifying massive datasets, addressing key limitations of existing methods in Big and Streaming Data framework. We propose an offline kernel-based classifier enhanced by Batch Principal Component Analysis (PCA) for dimensionality reduction to mitigate the “curse of dimensionality”. Additionally, an online classifier is developed for streaming data, combining online PCA with a kernel-based recursive classifier using a stochastic approximation algorithm. Application to fetal well-being monitoring demonstrates that the online classifier achieves a competitive median misclassification rate (11.92%), comparable to the offline classifier (11.54%) and Random Forest (11.31%), while requiring only 1/15th of the offline classifier’s computation time. Receiver Operating Characteristic (ROC) analysis shows superior Area Under the Curve (AUC) for the offline classifier but at a significant computational cost. A second study on larger database of credit scoring confirms these findings, showing that the online classifier achieves an F1-score of 96.40% and an accuracy of 93.08%, closely matching the performance of neural networks (96.46%, 93.22%) and boosting (96.51%, 93.31%). Notably, the online classifier accomplishes this with a CPU time of only 0.87 seconds per classification - over 600 times faster than neural networks - demonstrating its effectiveness for high-frequency, real-time financial decision-making.}, archive = {J_ACCESS}, author = {Mohamed Chaouch and Omama M. Al-Hamed}, doi = {10.1109/ACCESS.2025.3591883}, journal = {IEEE Access}, pages = {131716-131732}, shortjournal = {IEEE Access}, title = {Scalable nonparametric supervised learning for streaming and massive data: Applications in healthcare monitoring and credit risk}, volume = {13}, year = {2025}, }
• (2025). Model-driven architecture for accelerating the development of tourism applications. ACCESS, 13, 131696-131715. (www)

  @article{ , abstract = {The tourism sector has witnessed a significant shift towards digital transformation, with mobile and web applications becoming essential tools for enhancing tourist experiences. Despite their potential, the development of these applications is often hindered by challenges such as platform heterogeneity, rapid adaptability, and integration complexity. This paper proposes a Model-Driven Architecture (MDA) approach to address these challenges and accelerate the development of tourism applications. The approach introduces the use of the 6As framework as a domain model, alongside systematic model transformations, to streamline the development process. A detailed case study demonstrates the transformation of a class diagram into a functional backend, showcasing the advantages of automation, scalability, and maintainability. Validation results reveal significant improvements in development time compared to traditional manual methods. This contribution highlights the potential of MDA to simplify the development of complex systems while ensuring quality and adaptability. Future work aims to enhance the methodology by incorporating additional architectural patterns, expanding support for diverse platforms, and integrating advanced validation techniques.}, archive = {J_ACCESS}, author = {Lahbib Naimi and Lamya Benaddi and Charaf Ouaddi and Adnane Souha and Hamza Abdelmalek and Abdeslam Jakimi}, doi = {10.1109/ACCESS.2025.3592552}, journal = {IEEE Access}, pages = {131696-131715}, shortjournal = {IEEE Access}, title = {Model-driven architecture for accelerating the development of tourism applications}, volume = {13}, year = {2025}, }
• (2025). Adaptive federated learning with reinforcement learning-based client selection for heterogeneous environments. ACCESS, 13, 131671-131695. (www)

  @article{ , abstract = {This study introduces an Adaptive Federated Learning (AFL) framework designed to address the challenges of data heterogeneity, resource imbalance, and communication constraints in decentralized learning environments. The framework integrates reinforcement learning (RL) based client selection using both Tabular Q-Learning and Deep Q-Network (DQN) strategies to dynamically identify clients that most positively impact global model performance. A multi-objective reward function, combining model accuracy and execution time, guides the RL agent toward performance- and efficiency-aware client selection. For local model training, Random Forest (RF) classifiers are employed to ensure robustness to noise, class imbalance, and limited computational resources, particularly in privacy-sensitive healthcare settings. The AFL framework is evaluated on two real-world healthcare datasets BRFSS2015 and Diabetes Prediction, and extended to benchmark FL datasets (CIFAR-10 and FEMNIST) to assess scalability and generalization. Experimental results demonstrate that the DQN-based AFL achieves superior global accuracy (up to 91.3%) compared to Tabular Q-Learning and baseline methods such as FedAvg, while also reducing execution time by up to 15%. Client-level accuracy remains stable across rounds, with reward progression confirming effective RL policy convergence. These findings underscore the AFL framework’s capability to adaptively balance performance and efficiency, offering a practical and scalable solution for federated learning in heterogeneous, resource-constrained environments.}, archive = {J_ACCESS}, author = {Shamim Ahmed and M. Shamim Kaiser and Sudipto Chaki and A. B. M. Shawkat Ali}, doi = {10.1109/ACCESS.2025.3591699}, journal = {IEEE Access}, pages = {131671-131695}, shortjournal = {IEEE Access}, title = {Adaptive federated learning with reinforcement learning-based client selection for heterogeneous environments}, volume = {13}, year = {2025}, }
• (2025). An integrated onboard EV charger with wide voltage range compatibility. ACCESS, 13, 131654-131670. (www)

  @article{ , abstract = {Electric vehicles (EVs) typically employ separate onboard chargers and traction inverters for battery charging and motor operation, respectively. This paper proposes a bidirectional integrated onboard charger (BIOC) that utilizes a single power converter to perform both functions, thereby reducing cost, improving system compactness, and supporting fast charging. The proposed BIOC architecture is implemented using a totem-pole power factor correction (PFC) stage combined with an additional coupling unit. This configuration supports a wide range of battery voltages and is compatible with both AC and DC grid inputs. The BIOC incorporates active power decoupling (APD) to minimize voltage ripple at the battery terminals during high-voltage (HV) battery charging. Leakage current analysis of the totem-pole PFC stage confirms compliance with safety standards, and a detailed switch loss calculation procedure is presented. A comprehensive controller design is developed using State-Space Averaging (SSA) and Small Signal Modelling (SSM), followed by a K-factor method to ensure optimal performance across various operating modes. The proposed system is validated through simulations on a 2 kW Simulink model and experimental testing on a 400 W scaled-down prototype, demonstrating effective charging of an EV battery across a wide voltage range from both AC and DC grids. This work offers a unified, efficient, and cost-effective solution for integrated onboard EV charging.}, archive = {J_ACCESS}, author = {Vivek Kumar and Prajof Prabhakaran and B. Dastagiri Reddy and J. L. Febin Daya}, doi = {10.1109/ACCESS.2025.3590958}, journal = {IEEE Access}, pages = {131654-131670}, shortjournal = {IEEE Access}, title = {An integrated onboard EV charger with wide voltage range compatibility}, volume = {13}, year = {2025}, }
• (2025). An accurate and reliable behavioral modeling technique for fully printed vanadium dioxide RF switches using model ensembling approach. ACCESS, 13, 131638-131653. (www)

  @article{ , abstract = {This paper develops and showcases a model ensembling-based accurate, reliable and computer-aided design integrable behavioral modeling technique for emerging fully printed Vanadium dioxide (VO2) based Radio Frequency (RF) switches. Initially, separate and independent models are trained using Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) gradient boosting frameworks. The hyperparameters of the standalone XGBoost, CatBoost, and LightGBM based models are optimized using random search optimization coupled with a cross-validation scheme. Subsequently, weighted ensemble models are constructed by leveraging the optimally trained XGBoost, CatBoost, and LightGBM based models. It is vital to carefully calibrate the ensembling weights; therefore, an optimization algorithm, namely Tuna Swarm Optimization (TSO), is employed. Finally, all the developed models are tried and validated on standard regression tests, including mean relative error across all operating temperature conditions. The proposed weighted ensemble models have achieved remarkable accuracy and efficiency in simulating the behavior of VO2 RF switches.}, archive = {J_ACCESS}, author = {Saddam Husain and Bagylan Kadirbay and Mohammad Vaseem and Atif Shamim and Mohammad Hashmi}, doi = {10.1109/ACCESS.2025.3591890}, journal = {IEEE Access}, pages = {131638-131653}, shortjournal = {IEEE Access}, title = {An accurate and reliable behavioral modeling technique for fully printed vanadium dioxide RF switches using model ensembling approach}, volume = {13}, year = {2025}, }
• (2025). Edge-LLM inference with cost-aware layer allocation and adaptive scheduling. ACCESS, 13, 131614-131637. (www)

  @article{ , abstract = {This paper addresses two key challenges in distributed Large Language Model (LLM) inference at the edge: 1) cost-efficient and fair task allocation, and 2) dynamic scheduling under deadline constraints. We propose two mechanisms: the Fair Cost-Efficient Incentive Mechanism (FCIM) for task and layer assignment, and the Adaptive Dynamic Scheduling Algorithm (ADSA) for execution scheduling on individual devices. FCIM is an auction-based mechanism that selects cost-effective, memory-feasible devices while minimizing task latency, reward cost, and device usage. Its adaptive reward design ensures positive utility and fairness, even under shifting system priorities. ADSA enables preemption-aware, deadline-driven scheduling by dynamically reordering tasks based on arrival time and workload characteristics. Simulations demonstrate that FCIM reduces communication overhead by 54.7% and task completion time by 36.9% compared to static and performance-driven baselines, while ADSA reduces queueing delay by 39% under strict deadline constraints.}, archive = {J_ACCESS}, author = {Sama Habibi and Ozgur Ercetin}, doi = {10.1109/ACCESS.2025.3592308}, journal = {IEEE Access}, pages = {131614-131637}, shortjournal = {IEEE Access}, title = {Edge-LLM inference with cost-aware layer allocation and adaptive scheduling}, volume = {13}, year = {2025}, }
• (2025). Research on control strategy of 3-DOF electro-hydraulic servo manipulator based on active disturbance rejection controller. ACCESS, 13, 131600-131613. (www)

  @article{ , abstract = {To address the high-precision control requirements of a three-degree-of-freedom (3-DOF) electro-hydraulic servo robotic arm, this paper innovatively integrates the funnel control (FC) concept into the compensation structure of an active disturbance rejection controller (ADRC), designing a triple-parallel decoupled active disturbance rejection controller (TPDADRC). By establishing a precise global dynamic model of the electro-hydraulic servo system and the 3-DOF robotic arm, complex factors such as unmodeled dynamics, external disturbances, and nonlinear characteristics are elegantly unified and represented as a total disturbance term. An extended state observer (ESO) is employed for real-time precise estimation. The TPDADRC constrains the transient behavior of the error through performance funnel constraints, not only enhancing the estimation and compensation capabilities for the total disturbance but also significantly improving control performance. Based on Lyapunov stability theory and linear matrix inequalities (LMI), the Lurie system is constructed to rigorously prove the stability of TPDADRC. Comparison of simulation results shows that compared to sliding mode funnel control (SMC-FC), TPDADRC significantly improves trajectory and velocity tracking accuracy through a synergistic mechanism of dynamic compensation and decoupling, while achieving fast response and demonstrating robust robustness under disturbance conditions. This method provides an innovative new approach to addressing the multi-degree-of-freedom coupled control problem in hydraulic robotic arms. The integration of FC and ADRC effectively optimizes error dynamics, improves disturbance estimation accuracy, and enhances interference resistance, thereby advancing the development of robotic arm control technology.}, archive = {J_ACCESS}, author = {Chungeng Sun and Zhenlong Deng and Zhijie Duan}, doi = {10.1109/ACCESS.2025.3591982}, journal = {IEEE Access}, pages = {131600-131613}, shortjournal = {IEEE Access}, title = {Research on control strategy of 3-DOF electro-hydraulic servo manipulator based on active disturbance rejection controller}, volume = {13}, year = {2025}, }
• (2025). Development of adaptive drone swarm networks. ACCESS, 13, 131582-131599. (www)

  @article{ , abstract = {Drone swarms demonstrate significant advantages in executing complex missions. However, existing systems are constrained by preprogrammed algorithms and rigid communication frameworks, making them less adaptable to dynamic environments. To address this issue, this study proposes “AeroSyn”, a drone swarm network architecture designed to operate efficiently across various communication conditions, ensuring stable and effective communication under different infrastructure constraints. The AeroSyn integrates MQLink and UAVConnector to accommodate different network requirements. MQLink utilizes an event-driven publish-subscribe mechanism, making it suitable for cellular networks by enabling dynamic data exchange and decentralized role adaptation, which is particularly beneficial for high-mobility drone formations. UAVConnector is designed for infrastructure-less environments, ensuring minimal data transmission while allowing UAV swarms to maintain autonomous coordination through a Leader-Follower approach, even when disconnected from ground control stations. By incorporating a dual-mode communication strategy, AeroSyn seamlessly adapts to high-mobility cellular networks and mission-oriented operations in infrastructure-free environments, providing a robust and scalable communication solution. Systematic testing has validated AeroSyn’s adaptability and feasibility, positioning it as a flexible and globally applicable framework for future drone swarm deployments.}, archive = {J_ACCESS}, author = {Yang-Yi Chen and Wen-Chun Huang and Chun-Liang Lin and Shih-Hao Chen and Chian-Yu Lu}, doi = {10.1109/ACCESS.2025.3591763}, journal = {IEEE Access}, pages = {131582-131599}, shortjournal = {IEEE Access}, title = {Development of adaptive drone swarm networks}, volume = {13}, year = {2025}, }
• (2025). Advances in liver tumour diagnosis and treatment: Etiology, classification, and the emerging role of machine learning. ACCESS, 13, 131549-131581. (www)

  @article{ , abstract = {Liver Tumours represent a significant global health challenge due to their diverse etiology, complex classification, and high mortality rates. This literature offers a comprehensive review of advancements in the diagnosis and treatment of liver tumours, encompassing their causes, classifications, symptoms, staging, and current treatment modalities. It begins with an exploration of the etiology and risk factors contributing to liver tumours, including genetic, environmental, and lifestyle influences, and a detailed classification into benign and malignant tumours. Staging systems, particularly the Barcelona Clinic Liver Cancer system, are discussed to elucidate diagnostic pathways and therapeutic implications. The paper delves into various treatment options, ranging from surgical approaches and locoregional therapies to systemic treatments such as targeted therapies and immunotherapies. Emerging treatment strategies, including the combination of Stereotactic Body Radiotherapy and immunotherapy, are highlighted for their potential to enhance survival outcomes. Additionally, significant attention is given to the transformative role of Artificial Intelligence in liver tumour diagnosis and treatment. This includes Artificial Intelligence applications in imaging modalities like Computed Tomography and Magnetic Resonance Imaging for segmentation, histopathological analysis, and predictive modeling for tumour growth and survival. The review also explores architectures such as Convolutional Neural Networks, U-Net and its variants, and Transformer-based models, which have been pivotal in segmentation, classification and prognosis. Furthermore, it discusses annotation tools that streamline dataset preparation, ensuring accurate and efficient training of Artificial Intelligence models. By providing a comprehensive analysis of existing literature and advancements, this review aims to support researchers and practitioners in advancing the field of liver tumour diagnosis and treatment.}, archive = {J_ACCESS}, author = {Yana Gaur and Abhay Chaudhary and Ajith Abraham}, doi = {10.1109/ACCESS.2025.3591622}, journal = {IEEE Access}, pages = {131549-131581}, shortjournal = {IEEE Access}, title = {Advances in liver tumour diagnosis and treatment: Etiology, classification, and the emerging role of machine learning}, volume = {13}, year = {2025}, }
• (2025). Advances in virtual power plant operations: A review of optimization models. ACCESS, 13, 131525-131548. (www)

  @article{ , abstract = {In recent decades, the global shift toward sustainable energy solutions has accelerated, prompting nations to integrate renewable energy sources (RES) into their electricity grids and adhere to international environmental protocols. This transition has catalyzed the evolution from traditional power systems to smart grids, which form the backbone of contemporary energy management systems. However, smart grids often show limited adaptability to the large-scale integration of RES, underscoring a critical need for more dynamic solutions. Against this backdrop, Virtual Power Plants (VPPs) have emerged as a pivotal innovation, enhancing grid management through their robust handling of RES fluctuations and sophisticated distributed control systems. VPPs not only increase the flexibility, reliability, scalability, and performance of power systems but also improve asset utilization, bolster system resilience, and foster more effective market interactions. This surge in VPP deployment has sparked extensive research focused on optimizing VPP operations and their engagement with electricity markets. Given the rapid advancements in VPP technology and market integration, this review is critical for consolidating existing knowledge, guiding effective implementation strategies, and identifying emerging trends and challenges within the field. This review presents a comprehensive overview of prior research on VPP optimization, delineating the key methodologies and outcomes that have shaped current practices. Specifically, this paper discusses the fundamental concepts of VPPs, provides an overview of their integration into electricity markets, and examines the various optimization formulations and methodologies that have been proposed in the literature for enhancing VPP operational efficiency.}, archive = {J_ACCESS}, author = {Fatemeh Marzbani and Ahmed H. Osman and Mohamed S. Hassan}, doi = {10.1109/ACCESS.2025.3591697}, journal = {IEEE Access}, pages = {131525-131548}, shortjournal = {IEEE Access}, title = {Advances in virtual power plant operations: A review of optimization models}, volume = {13}, year = {2025}, }
• (2025). Invisible manipulation: Deep reinforcement learning-enhanced stealthy attacks on battery energy management systems. ACCESS, 13, 131509-131524. (www)

  @article{ , abstract = {This paper introduces an innovative cyber-attack scheme, “invisible manipulation,” utilizing timed-stealthy false data injection attacks (Timed-SFDIAs). By subtly altering critical measurements ahead of a target period, the attacker covertly steers system operations toward a specific failure state, evading detection while enabling repeated attacks over time. Using Battery Energy Management System (BEMS) as a case study, we demonstrate the scheme’s effectiveness in manipulating Battery Energy Storage Systems (BESS), critical for grids with high renewable penetration. Our method employs deep reinforcement learning (DRL) to generate synthetic measurements (e.g., battery voltage, current) that mimic real data, bypassing residual-based bad data detection (BDD) and misleading Extended Kalman-filter (EKF) based State-of-Charge (SoC) estimations. This allows the BEMS to operate the BESS per the attacker’s objectives. To minimize real-time computational demands, we transform this online optimization problem into an offline DRL training problem, utilizing high-fidelity simulation data from a digital twin-based microgrid testbed. The testbed incorporates real load and solar generation profiles with BESS models in the electromagnetic transient (EMT) domain at a 100- $\mu $ s resolution, capturing rapid system dynamics and ensuring robust performance in real-time scenarios. Testing on the same testbed allows real-time evaluation of microgrid responses, where the BEMS, EKF-based SoC estimation algorithms interact dynamically with the injected false measurements. This unique DRL training and testing setup not only showcases the effectiveness of the Timed-SFDIA algorithm in evading detection and achieving diverse attack objectives but also underscores the critical role of high-fidelity, digital-twin based real-time simulation testbeds. Such testbeds are invaluable for training and validating data-driven machine learning algorithms, especially when field tests and real-world validation are challenging to conduct, as they ensure robustness and adaptability under realistic operational conditions.}, archive = {J_ACCESS}, author = {Qi Xiao and Lidong Song and Jong Ha Woo and Rongxing Hu and Bei Xu and Kai Ye and Ning Lu}, doi = {10.1109/ACCESS.2025.3590437}, journal = {IEEE Access}, pages = {131509-131524}, shortjournal = {IEEE Access}, title = {Invisible manipulation: Deep reinforcement learning-enhanced stealthy attacks on battery energy management systems}, volume = {13}, year = {2025}, }
• (2025). Millimeter wave dual polarized huygens lens of 0.14 λ₀Thickness. ACCESS, 13, 131498-131508. (www)

  @article{ , abstract = {A new dual polarized Huygens unit cell of $0.14~\lambda _{0}$ thickness with two metal layers at W-band frequency range is designed, simulated and experimentally tested. The proposed unit cell has an electric dipole and a magnetic dipole for both horizontal and vertical polarizations to generate the Huygens resonance. The electric dipole has a metal strip with slot on the top and a strip on the bottom. The magnetic dipole has metal strips on the top and bottom surfaces. The electric surface admittance and magnetic surface impedance confirms the Huygens resonance at 78.49 GHz. A planar Huygens lens is designed by arranging twenty-one different unit cells whose parameters are varied to realize ${360}^{^{\circ }}$ transmission phase range. The Huygens lens increased the simulated realized gain of the horn antenna to 23.3 dBi in vertical polarization and to 20.6 dBi in horizontal polarization at 78.5 GHz when simulated with the horn antenna of gain 13.8 dBi. The simulated 3 dB beam widths are reduced to ${3.48}^{^{\circ }}$ for vertical polarization and ${4.36}^{^{\circ }}$ for horizontal polarization at 78.5 GHz. The Huygens lens is tested experimentally, and the measured realized gain of the horn antenna is increased to 24.42 dBi for vertical polarizations and 18. 74 dBi for horizontal polarization at 78.7 GHz when the horn antenna gain is 20.2 dBi at 78. 7 GHz. The measured beam widths are reduced to ${2.66}^{^{\circ }}$ for the vertical polarization and ${2.7}^{^{\circ }}$ for the horizontal polarization from ${13.06}^{^{\circ }}$ of the feed horn antenna.}, archive = {J_ACCESS}, author = {Manoj Prabhakar Mohan and Juan Miguel Jimeno and Sun Mei and Arokiaswami Alphones and Mohammed Yakoob Siyal and Muhammad Faeyz Karim}, doi = {10.1109/ACCESS.2025.3591249}, journal = {IEEE Access}, pages = {131498-131508}, shortjournal = {IEEE Access}, title = {Millimeter wave dual polarized huygens lens of 0.14 λ₀Thickness}, volume = {13}, year = {2025}, }
• (2025). A dual-branch deep learning framework combining xception and ResNet for accurate lung and colon cancer detection. ACCESS, 13, 131483-131497. (www)

  @article{ , abstract = {Lung and colon cancers are among the leading causes of cancer-related deaths worldwide. Early detection significantly enhances survival rates, but traditional diagnostic methods, which rely on manual analysis of histopathological images, are labor-intensive, error-prone, and inconsistent. While deep learning has shown promise in automating medical image analysis, existing models often struggle with issues like overfitting, poor generalization, and class imbalance, which limit their clinical effectiveness. This research proposes a hybrid deep learning model combining the strengths of Xception and ResNet architectures. The Xception model excels at feature extraction, while ResNet’s residual connections improve training stability and address issues like gradient vanishing. The model is trained using a large dataset of lung and colon cancer images, with a dynamic learning rate and backpropagation via Stochastic Gradient Descent (SGD) momentum, optimizing performance. Advanced data augmentation techniques, such as rotations and flips, further enhance model generalization. The proposed hybrid model achieves an impressive accuracy of 98.96%, demonstrating high effectiveness in distinguishing between different cancer classes. The model outperforms traditional diagnostic methods, offering a robust and reliable tool for automated cancer detection with significant clinical potential.}, archive = {J_ACCESS}, author = {Chandrasekar Venkatachalam and Priyanka Shah}, doi = {10.1109/ACCESS.2025.3589390}, journal = {IEEE Access}, pages = {131483-131497}, shortjournal = {IEEE Access}, title = {A dual-branch deep learning framework combining xception and ResNet for accurate lung and colon cancer detection}, volume = {13}, year = {2025}, }
• (2025). Spatiotemporal wind energy forecasting: A comprehensive survey and a deep equilibrium-based case study with StemGNN. ACCESS, 13, 131461-131482. (www)

  @article{ , abstract = {Accurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehensive survey of existing spatiotemporal forecasting methods and introducing an innovative deep learning approach. The proposed model integrates a Graph Neural Network (GNN) to represent wind turbines as nodes within a graph, capturing spatial relationships, while a Deep Equilibrium Model (DEQ) enables equilibrium-based inference to handle highly nonlinear wind patterns. A Sequence-to-Sequence (Seq2Seq) architecture further manages temporal dependencies. The method was validated using a real-world dataset of wind power generation, outperforming baseline models across multiple forecast horizons and maintaining stable accuracy across short- and mid-term predictions. Results demonstrate that the proposed GNN with DEQ effectively models both spatial and temporal dynamics for Seq2Seq data, improving prediction accuracy while maintaining computational efficiency. This study highlights the potential of equilibrium-based spatiotemporal graph models for wind energy forecasting and provides a robust tool for better integration of wind power into modern power grids.}, archive = {J_ACCESS}, author = {Luiza Scapinello Aquino and Laio Oriel Seman and Viviana Cocco Mariani and Leandro Dos Santos Coelho and Stefano Frizzo Stefenon and Gabriel Villarrubia González}, doi = {10.1109/ACCESS.2025.3586997}, journal = {IEEE Access}, pages = {131461-131482}, shortjournal = {IEEE Access}, title = {Spatiotemporal wind energy forecasting: A comprehensive survey and a deep equilibrium-based case study with StemGNN}, volume = {13}, year = {2025}, }
• (2025). Cooperative control of hydrogen electrolyzers and HVDC links for frequency regulation sharing in asynchronous power system. ACCESS, 13, 131446-131460. (www)

  @article{ , abstract = {Frequency stability in low-inertia power systems has become a significant concern due to the growing integration of inverter-based distributed renewable generation. High-voltage direct current (HVDC) systems offer an effective solution for enhancing frequency stability in asynchronous multi-area power grids. Additionally, hydrogen electrolyzers, such as proton exchange membrane electrolyzers (PEMELs), can support frequency regulation. This paper proposes a cooperative control strategy involving HVDC systems and PEMEL controllers to share frequency regulation responsibilities in asynchronously interconnected power systems. The benefits of utilizing PEMELs for frequency regulation are demonstrated, enabling the coordinated distribution of frequency control between two interconnected power areas via HVDC links. Distributed model predictive controls (DMPCs) are employed to synchronize the operation of the HVDC systems and PEMEL controllers. The effectiveness of the DMPC-based coordinated control of PEMEL and HVDC systems is thoroughly investigated and analyzed. Simulation results confirm that the proposed cooperative control strategy significantly improves the frequency stability of the studied system, even under disturbance conditions.}, archive = {J_ACCESS}, author = {Jonglak Pahasa and Nattachote Rugthaicharoencheep and Boonruang Marungsri and Chawasak Rakpenthai}, doi = {10.1109/ACCESS.2025.3592091}, journal = {IEEE Access}, pages = {131446-131460}, shortjournal = {IEEE Access}, title = {Cooperative control of hydrogen electrolyzers and HVDC links for frequency regulation sharing in asynchronous power system}, volume = {13}, year = {2025}, }
• (2025). SMILE: A small multimodal dataset capturing roadside behavior in indian driving conditions. ACCESS, 13, 131432-131445. (www)

  @article{ , abstract = {The advancement of autonomous systems, including self-driving and robotics depends on diverse, high-quality datasets. While existing datasets often focus on standard driving scenarios, they frequently lack challenging edge cases, particularly those involving Vulnerable Road Users (VRUs) in complex and dynamic roadside environments. To address this gap, we introduce a novel Small Multimodal Indian Dataset for Learning and Exploration (SMILE) captured in the unique Indian context, showcasing a level of traffic complexity and diversity underrepresented in current benchmarks. We incorporate synchronized data from LiDAR, a stereo camera, and a monocular camera. This resource aims to facilitate the development of more robust autonomous systems. Additionally, we provide a baseline for depth estimation and set a benchmark for future research.}, archive = {J_ACCESS}, author = {Mayur Anand Pandya and Aaryan Takayuki Panigrahi and Subham Patra and Asmit Paul and Sucharitha Shetty}, doi = {10.1109/ACCESS.2025.3589781}, journal = {IEEE Access}, pages = {131432-131445}, shortjournal = {IEEE Access}, title = {SMILE: A small multimodal dataset capturing roadside behavior in indian driving conditions}, volume = {13}, year = {2025}, }
• (2025). Hybrid feature and optimized deep learning model fusion for detecting hateful arabic content. ACCESS, 13, 131411-131431. (www)

  @article{ , abstract = {Detecting hate speech in Arabic social media content is critical for ensuring safe, inclusive, and respectful online communication. However, this task remains challenging due to Arabic’s morphological richness, dialectal variations such as Levantine, and the scarcity of high-quality annotated data. This study proposes a comprehensive and language-aware approach to Arabic hate speech detection that integrates advanced preprocessing, targeted data augmentation, hybrid feature extraction, and deep ensemble learning. Our experiments are conducted on a Levantine Arabic tweet dataset labeled hateful or non-hateful. To address lexical variability and noise common in user-generated content, we apply a dedicated preprocessing pipeline that includes normalization, diacritic removal, and emoji filtering. To further enhance generalization and mitigate data imbalance, we employ two augmentation strategies: synonym replacement using a curated Arabic lexicon and semantic-preserving back-translation through English. We investigate lexical and contextual approaches for feature extraction, including TF-IDF vectors, contextualized AraBERT embeddings, and a hybrid combination of both. These features are input into multiple deep learning classifiers, including CNN-BiGRU, BiLSTM, and DNN architectures. To maximize predictive performance, we develop an ensemble framework that integrates these models. The final prediction is obtained through a weighted fusion of individual model outputs, where the optimal weights are selected using the Grey Wolf Optimizer (GWO), aiming to maximize classification accuracy. Experimental results demonstrate that our proposed hybrid and ensemble-based architecture achieves superior performance, with an accuracy of 83.33% and a ROC-AUC score of 89.5%, outperforming individual models and conventional baselines. These findings highlight the effectiveness of hybrid feature representations and nature-inspired optimization in enhancing Arabic hate speech detection. Our approach offers a scalable, linguistically informed solution for robust content moderation in Arabic digital spaces.}, archive = {J_ACCESS}, author = {Karim Gasmi and Ibtihel Ben Ltaifa and Alameen Eltoum Abdalrahman and Omer Hamid and Mohamed Othman Altaieb and Shahzad Ali and Lassaad Ben Ammar and Manel Mrabet}, doi = {10.1109/ACCESS.2025.3591673}, journal = {IEEE Access}, pages = {131411-131431}, shortjournal = {IEEE Access}, title = {Hybrid feature and optimized deep learning model fusion for detecting hateful arabic content}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of 5G new radio V2X sidelink for coexisting traffic. ACCESS, 13, 131400-131410. (www)

  @article{ , abstract = {In the last releases, 5G New Radio V2X enabled the Sidelink vehicle-to-vehicle communication to accommodate low-latency and highly reliable vehicular applications. To support the direct vehicle-to-vehicle communication, without the need to rely on the cellular network, dynamic scheduling and random resource selection channel access techniques must be adopted. In this paper, we assess the performance of the 5G NR V2X Sidelink technology, in Mode 2, for the transmission of different traffic types: only broadcast, only unicast, and when both traffic types coexist. We deeply investigate the impact of several PHY layer parameters of the resource allocation procedure, for safety-related broadcast messages, with different message transmission rates and vehicle density. Next, we expand the analysis to include unicast communications. Finally, we assess the impact of this vehicular communication technology when both traffic applications coexist, comparing the results for two scheduling access schemes, in static and mobile setups. The outcomes of the experiments emphasize the importance of optimal resource utilization, achieved through MCS and numerology, for example, when employing dynamic scheduling. For unicast communications, we evaluate the influence of the packet sizes for different application rates, which proves to be minor as the application rates get substantial. The scheduling scheme selection presented clear behavior across the different applications: the dynamic scheme outperformed the semi-persistent scheme for lower densities of unicast vehicles, and vice versa for higher densities. As for the ITS traffic, and considering a coexistence traffic scenario, better results were achieved with the semi-persistent approach.}, archive = {J_ACCESS}, author = {João Guerra and Miguel Luís and Pedro Rito}, doi = {10.1109/ACCESS.2025.3590868}, journal = {IEEE Access}, pages = {131400-131410}, shortjournal = {IEEE Access}, title = {Performance evaluation of 5G new radio V2X sidelink for coexisting traffic}, volume = {13}, year = {2025}, }
• (2025). PhishingGNN: Phishing email detection using graph attention networks and transformer-based feature extraction. ACCESS, 13, 131390-131399. (www)

  @article{ , abstract = {Phishing emails remain a critical cybersecurity challenge, demanding detection frameworks that capture both textual semantics and structural relationships in email data. This study introduces PhishingGNN, a hybrid model that integrates DistilBERT for context-aware text analysis with Graph Attention Networks (GAT) to model email metadata and content as graph structures, detecting subtle phishing patterns overlooked by traditional methods. By transforming email bodies into relational graphs, PhishingGNN leverages Graph Neural Networks (GNNs) to analyze textual interactions while retaining computational efficiency. Evaluated on an expanded CEAS_08 dataset (39,154 samples: 17,312 non-phishing and 21,842 phishing emails), PhishingGNN achieves state-of-the-art performance: 0.9939 accuracy, balanced precision, recall, and F1-scores of 0.99, and an AUC of 1.00. Cross-dataset validation on the Nazario Corpus confirms robustness (0.9910 accuracy), outperforming contemporary few-shot learning approaches. PhishingGNN’s key innovations include a transformer-GNN architecture unifying semantic and structural reasoning, a novel graph-based email representation methodology, and comprehensive validation confirming real-world scalability. PhishingGNN advances graph-based deep learning in cybersecurity, offering a modular benchmark solution with demonstrated cross-dataset efficacy.}, archive = {J_ACCESS}, author = {Mejdl Safran and Abdulbaset Musleh}, doi = {10.1109/ACCESS.2025.3592135}, journal = {IEEE Access}, pages = {131390-131399}, shortjournal = {IEEE Access}, title = {PhishingGNN: Phishing email detection using graph attention networks and transformer-based feature extraction}, volume = {13}, year = {2025}, }
• (2025). Forecasting transmission line loss using a cluster-based refinement framework and scheduled outage data. ACCESS, 13, 131378-131389. (www)

  @article{ , abstract = {Transmission line loss forecasting is an important power system forecasting task, yet, existing methods often overlook qualitative operational data such as scheduled outages, which directly impact network topology and losses. This paper introduces a new framework that integrates scheduled outage reports with a two-stage cluster-based refinement solution to enhance forecasting accuracy. First, we process and utilize outage data in a way that preserves its temporal and contextual relevance, using Natural Language Processing (NLP) technique. This addresses a key gap in prior works that relies solely on quantitative inputs. Next, the proposed framework employs an initial baseline model in the first stage, followed by cluster-refinement using submodels trained on grouped data patterns. The proposed framework is applied to 24 hour ahead forecasts of transmission losses on an IEEE-118 bus test system, and in Alberta, Canada. We compare the results to benchmark methods from existing state-of-the-art transmission loss forecasting models. Our findings indicate that the proposed framework offers an accurate forecasting solution, outperforming the benchmark techniques. Moreover, these results highlight the value of integrating qualitative information into forecasting models for more accurate and reliable predictions.}, archive = {J_ACCESS}, author = {Gideon Egharevba and Arne Dankers and Hamidreza Zareipour}, doi = {10.1109/ACCESS.2025.3591059}, journal = {IEEE Access}, pages = {131378-131389}, shortjournal = {IEEE Access}, title = {Forecasting transmission line loss using a cluster-based refinement framework and scheduled outage data}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of risk assessment frameworks in blockchain applications: Research gaps and key lessons. ACCESS, 13, 131347-131377. (www)

  @article{ , abstract = {Blockchain is a decentralised and distributed digital ledger technology that is used to record and store data securely and transparently and has attracted the attention of both academia and industry. It is imperative to ensure the security of this technology and to understand the potential associated risks. However, in the recent past, blockchain-based applications have suffered from series of security breaches ranging from fraud to smart contract vulnerabilities, wallet theft, private key compromise, and non-regulatory compliance. One of the best ways to identify security threats and the associated risks in blockchain applications is to undertake a proper risk assessment. This paper presents a systematic review of the risk assessment frameworks in blockchain-based applications. We first review the general overview of blockchain technology and examine the up-to-date concepts of blockchain risk management, focusing on risk assessment. Although many scholars have worked on blockchain applications and have published surveys on challenges and opportunities, some on security threats, vulnerabilities, and attacks of blockchain technology, very few have researched blockchain risk management and assessment, and none has proposed a framework for assessing risk in blockchain-based applications. Following our proposed survey methodology, the key contributions of this literature review include a comprehensive assessment of various blockchain applications and their adoption challenges, risks in centralised versus decentralised systems, risk assessment and management in blockchain technologies, and three case studies on the risks of blockchain applications and their real-world implications. Applications discussed include blockchain in healthcare, supply chain management, government, and the Internet of Things (IoT). This review serves as a benchmark for researchers to propose an appropriate risk assessment framework for blockchain applications and to gain a deeper understanding of the associated risks.}, archive = {J_ACCESS}, author = {Rapheal Sunday Amadi and A. S. M. Kayes and Eric Pardede and Mohammad Jabed Morshed Chowdhury and Khandakar Ahmed}, doi = {10.1109/ACCESS.2025.3590963}, journal = {IEEE Access}, pages = {131347-131377}, shortjournal = {IEEE Access}, title = {A comprehensive review of risk assessment frameworks in blockchain applications: Research gaps and key lessons}, volume = {13}, year = {2025}, }
• (2025). Evolved motor design process to reduce the design expense of axial flux permanent magnet motor. ACCESS, 13, 131334-131346. (www)

  @article{ , abstract = {This paper proposes a design process that combines an initial electromagnetic design using analytical techniques with an optimal design based on a multi-objective optimization algorithm, aiming to reduce the design cost of an axial flux permanent magnet motor for urban air mobility traction. In the initial design step, key parameters are determined using an analytical method, followed by a detailed design that accounts for magnetic saturation. Design variables with significant impact on performance are selected, and an optimal design is conducted using a multi-objective optimization algorithm. The analytical method employs the space harmonic method to calculate the motor’s magnetic field distribution using Fourier series, while the conformal mapping method considers the slot effect by incorporating it into the air gap flux density. The Quasi-3D method approximates 3D analysis with multiple 2D analyses, enabling rapid evaluation of various models. The optimization algorithm reduces computational time by constructing a meta-model using a kriging function, which enables direct addition of solutions within the objective function area, thereby avoiding repeated evaluations of the actual objective function. Finally, a prototype motor was fabricated and tested to validate the proposed process. The experimental results showed strong agreement with the simulation outcomes, confirming the accuracy and applicability of the proposed design approach.}, archive = {J_ACCESS}, author = {Jong-Min Ahn and Dong-Kuk Lim and Min-Mo Koo}, doi = {10.1109/ACCESS.2025.3592274}, journal = {IEEE Access}, pages = {131334-131346}, shortjournal = {IEEE Access}, title = {Evolved motor design process to reduce the design expense of axial flux permanent magnet motor}, volume = {13}, year = {2025}, }
• (2025). Hyperledger fabric graph isomorphism network for conflict transactions detection in multi-version concurrency control. ACCESS, 13, 131314-131333. (www)

  @article{ , abstract = {Multi-Version Concurrency Control (MVCC) is a critical mechanism in blockchain systems such as Hyperledger Fabric. MVCC is crucial for managing concurrent transactions ensuring integrity through versioning and timestamp techniques. Enhancing MVCC is essential due to the significant overhead involved in maintaining multiple versions of data and resolving conflict transactions, particularly in enterprise blockchain applications where data integrity and system reliability are of utmost importance. Conflict transactions are the predominant cause of transaction failures in Hyperledger Fabric where multiple transactions attempt to update data items based on outdated data versions. This study introduces the Hyperledger Fabric-based Modified Graph Isomorphism Network (HFGIN). Employing advanced graph neural networks, HFGIN utilizes node and edge data representations within a graph-based framework, which significantly increases the efficiency of detecting MVCC conflicts. The proposed model aims to detect conflict transactions at an earlier stage in Hyperledger Fabric blockchain systems. Evaluation results indicate that HFGIN substantially outperforms baseline models such as the Graph Convolutional Network (GCN) and Graph Isomorphism Network (GIN). HFGIN achieves a test accuracy of 82.20%, demonstrating a 14.67% improvement over GCN and a 4.12% improvement over GIN. Moreover, HFGIN records a precision of 86.06%, marking an 8.97% enhancement over GCN and 9.46% over GIN. It also shows a recall of 79.56%, which is 17.03% higher than GCN and 1.39% greater than GIN, besides an F1 score improvement of 23.37% over GCN and 5.27% over GIN. HFGIN also demonstrates computational efficiency with low inference latency and minimal resource usage and maintains scalability when tested under larger transaction loads. The model achieves these improvements while maintaining computational efficiency and system scalability by applying inference selectively and enabling parallel execution of non-conflicting transactions. The enhancements contribute to detection rate increase of conflict transactions over GCN and GIN in an earlier stage, demonstrating the model’s potential to transform MVCC conflict management in high-demand blockchain environments such as banking operations.}, archive = {J_ACCESS}, author = {Fawaz Alzahrani and Mohd Yazid Idris and Mohd Fo’Ad Rohani and Rahmat Budiarto}, doi = {10.1109/ACCESS.2025.3589165}, journal = {IEEE Access}, pages = {131314-131333}, shortjournal = {IEEE Access}, title = {Hyperledger fabric graph isomorphism network for conflict transactions detection in multi-version concurrency control}, volume = {13}, year = {2025}, }
• (2025). Robust optimal control for a microbial batch culture processes: Incorporating free terminal time and sensitivity analysis. ACCESS, 13, 131303-131313. (www)

  @article{ , abstract = {This paper focuses on the robust optimal control problem of an enzymatic-catalytic dynamic system in batch culture with uncertain parameters. To enhance the productivity of 1,3-propanediol (1,3-PD) in batch culture, we define the initial concentrations of biomass and glycerol as well as the terminal time of the fermentation process as control variables. By considering the nonlinear characteristics of the enzymatic-catalytic system as a primary constraint, we formulate a robust optimal control model that simultaneously evaluates the production efficiency of 1,3-PD and its sensitivity to uncertain parameters. The main objective of this model is to identify an optimal control strategy that not only strengthens the robustness of the system but also maximizes the yield of 1,3-PD. Given that the terminal time of this robust optimal control problem is unfixed and non-standard terms exist in the optimal control formulation, we adopt the time-scale transformation method and introduce an auxiliary dynamic system for calculating system sensitivity. Subsequently, we develop an improved particle swarm optimization algorithm to solve this equivalent problem. Finally, the trade-off between production efficiency and process robustness is systematically assessed through numerical simulations. The results demonstrate that sacrificing a marginal 3.9% of 1,3-PD production efficiency can lead to a 25.3% enhancement in system robustness. The primary contribution of this study lies in proposing a novel control strategy that effectively addresses the uncertainty of system parameters, while enhancing the production efficiency of 1,3-PD and the stability of the production process. The innovation of this control strategy resides in its comprehensive consideration of the impacts of multiple variables on system robustness, providing valuable theoretical support for practical applications.}, archive = {J_ACCESS}, author = {Yang Qi and Zheng Shijin and Xie Suzhen and Feng Enmin}, doi = {10.1109/ACCESS.2025.3592428}, journal = {IEEE Access}, pages = {131303-131313}, shortjournal = {IEEE Access}, title = {Robust optimal control for a microbial batch culture processes: Incorporating free terminal time and sensitivity analysis}, volume = {13}, year = {2025}, }
• (2025). Entity pair relation classification based on contrastive learning and biaffine model. ACCESS, 13, 131289-131302. (www)

  @article{ , abstract = {In natural language processing, the biaffine model can effectively captures sentence structure and word relationships for tasks like text classification and relation extraction. However, it struggles with entity pair relation classification, particularly in overlapping or complex scenarios. To address this, this paper proposes BERT-CL-Biaffine, an improved relation classification model integrating bidirectional entity contrastive learning and a global pointer network. The model enhances the biaffine architecture by training it to identify entity boundaries and leveraging contrastive learning to strengthen semantic associations between overlapping entity pairs. Experiments on the NYT and WebNLG datasets demonstrate that BERT-CL-Biaffine outperforms baseline models, achieving F1 score improvements of 1% and 1.2%, respectively. The model excels in classifying overlapping entity pairs and handles challenges like imbalanced relation types and ambiguous entity features, particularly in complex scenarios. The results validate that bidirectional entity contrastive learning and global pointer networks significantly enhance the biaffine model’s feature representation and classification performance. This approach offers a robust solution for relation extraction in intricate textual contexts.}, archive = {J_ACCESS}, author = {Songhua Hu and Ziming Zhang and Hengxin Wang and Lihui Jiang}, doi = {10.1109/ACCESS.2025.3592203}, journal = {IEEE Access}, pages = {131289-131302}, shortjournal = {IEEE Access}, title = {Entity pair relation classification based on contrastive learning and biaffine model}, volume = {13}, year = {2025}, }
• (2025). Optimizing the learnable RoPE theta parameter in transformers. ACCESS, 13, 131271-131288. (www)

  @article{ , abstract = {Rotary Position Embedding (RoPE) enhances Transformer models by encoding relative positions through a frequency parameter $\theta $ , but conventional implementations fix $\theta $ , constraining adaptability. We conduct the first systematic study of learnable RoPE $\theta $ , introducing four optimization strategies—separate learning rates, layer-wise initialization, cosine annealing scheduling, and sigmoid-based constraints—to stabilize and refine positional learning. Our approach demonstrates modest but consistent benefits across multiple datasets including Tiny Shakespeare, WikiText-103, and IWSLT’14, achieving measurable gains in validation loss, perplexity, and BLEU scores relative to a fixed- $\theta $ baseline while maintaining high inference throughput and requiring minimal architectural modifications. Ablation experiments quantify each strategy’s contribution and offer practical integration guidelines. This adaptive position encoding framework provides a foundation for large-scale pretraining and diverse sequence modeling applications.}, archive = {J_ACCESS}, author = {Zhigao Huang and Musheng Chen}, doi = {10.1109/ACCESS.2025.3590604}, journal = {IEEE Access}, pages = {131271-131288}, shortjournal = {IEEE Access}, title = {Optimizing the learnable RoPE theta parameter in transformers}, volume = {13}, year = {2025}, }
• (2025). YOLO-WTB: Improved YOLOv12n model for detecting small damage of wind turbine blades from aerial imagery. ACCESS, 13, 131257-131270. (www)

  @article{ , abstract = {Wind energy has been extensively studied worldwide to advance technology, reduce operating costs, and improve performance. A key challenge in this field is ensuring the optimal performance of wind turbines through proactive and effective maintenance strategies. In particular, wind turbine blade inspection and fault detection play an important role in minimizing the risk of unexpected failures, downtime, and operational disruptions. Although predictive maintenance methods based on machine learning, deep learning, and traditional visual inspection have been widely studied, detecting small faults from aerial images remains a major challenge. The main obstacles include data shortages, high computational complexity, limited labelled datasets, and difficulty in accurately identifying faults under real-world conditions. Notably, one of the most pressing problems that modern deep learning models face today is the detection of small-sized objects in images. To address these challenges, we propose an improved model based on the You Only Look Once version 12n model, which enhances the accuracy of wind turbine blade surface damage detection while maintaining real-time processing capability. The improvements are made by adding a very small target Head and removing the two Heads for medium and large targets. In addition, in the backbone part, we also propose to remove a Convolution module and an Area Attention Concatenate-Convolution-Fusion module and add an improved SoftPool Feature Spatial Pyramid Pooling - Fast module to increase the feature extraction ability while maintaining the complexity of the model. The proposed model not only optimizes wind turbine maintenance efficiency but also contributes to advancements in the field of computer vision.}, archive = {J_ACCESS}, author = {Phat T. Nguyen and Duy C. Huynh and Loc D. Ho and Matthew W. Dunnigan}, doi = {10.1109/ACCESS.2025.3589225}, journal = {IEEE Access}, pages = {131257-131270}, shortjournal = {IEEE Access}, title = {YOLO-WTB: Improved YOLOv12n model for detecting small damage of wind turbine blades from aerial imagery}, volume = {13}, year = {2025}, }
• (2025). SWIPT-enabled relaying networks for next-generation wireless systems: A review of achievable rates and future challenges. ACCESS, 13, 131234-131256. (www)

  @article{ , abstract = {Efficiently powering the billions of mobile devices currently in use is a growing challenge, as most of these devices are battery-operated and require substantial energy for reliable signal transmission. To address this, harvesting energy from radio frequency (RF) signals has emerged as a promising solution. This approach, known as Simultaneous Wireless Information and Power Transfer (SWIPT), has garnered increasing attention from both researchers and industry due to its potential to extend battery life. Among its various applications, relaying networks represent a key area in which SWIPT can make a significant impact. These networks offer improved coverage, higher data rates, lower latency, and better energy and spectral efficiency, which are critical attributes for meeting the demands of next-generation (5G/6G) ultra-dense wireless networks. In this paper, we provide a comprehensive review of SWIPT in relaying networks, exploring its principles, structures, protocols, and technical aspects. We also examine the achievable rates in SWIPT-enabled relaying networks and discuss how machine learning could enhance these systems, presenting it as a promising direction for future research. Finally, we highlight several key challenges that remain to be addressed, emphasizing the need for continued exploration in the rapidly evolving landscape of wireless technology.}, archive = {J_ACCESS}, author = {Rami Zaino and Teong Chee Chuah and Mohamed S. Hassan}, doi = {10.1109/ACCESS.2025.3591764}, journal = {IEEE Access}, pages = {131234-131256}, shortjournal = {IEEE Access}, title = {SWIPT-enabled relaying networks for next-generation wireless systems: A review of achievable rates and future challenges}, volume = {13}, year = {2025}, }
• (2025). Multi-view attention network with iterative feature refinement and boundary awareness for endoscopic image segmentation. ACCESS, 13, 131217-131233. (www)

  @article{ , abstract = {Endoscopic image segmentation plays a critical role in diagnosing early gastrointestinal tumors, which is essential for preventing colorectal and gastric cancers. However, achieving accurate segmentation is challenging due to issues such as boundary blurring, low contrast, and small lesion sizes. This paper proposes a novel Multi-view Attention Network (MVANet) to address these challenges by incorporating several specialized blocks. The first, a Composite Block (CMB) enhances feature representations across multiple dimensions, improving segmentation accuracy. To bridge the semantic gap between different feature layers, we propose an Attention-based Cross-layer Feature Fusion (ACFF) block, which incorporates a Triplet Efficient Transformer Attention (TETA) mechanism to capture long-range dependencies across multiple views. Additionally, to enhance the model’s boundary-awareness, the paper presents a Prior Knowledge-guided Boundary Awareness (PKBA) block, which aids in capturing irregular boundaries, and a Boundary-driven Scale Awareness and Semantic Complementarity (BSASC) block, designed to improve boundary localization during the decoder stage. Moreover, a Multi-scale Feature Integration (MFI) block is proposed to integrate multi-scale features during the decoder stage to capture potentially useful features. An iterative feature refinement module, composed of four Image-Guided Feature Refinement (IFR) blocks, is used to further refine local information. Extensive experiments conducted on six endoscopic image datasets demonstrate that MVANet achieves state-of-the-art performance, with mDice values of 93.1% and 94.9%, and mIoU values of 88.4% and 90.7% on the Kvasir-SEG and CVC-ClinicDB datasets, respectively. MVANet demonstrates strong potential for enhancing segmentation performance in early tumor detection.}, archive = {J_ACCESS}, author = {Dongzhi He and Rui Zhang and Yu Liang and Jiaping Chen and Yunqi Li}, doi = {10.1109/ACCESS.2025.3592229}, journal = {IEEE Access}, pages = {131217-131233}, shortjournal = {IEEE Access}, title = {Multi-view attention network with iterative feature refinement and boundary awareness for endoscopic image segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhancing generalization in phishing URL detection via a fine-tuned BERT-based multimodal approach. ACCESS, 13, 131197-131216. (www)

  @article{ , abstract = {Phishing has emerged as a prevalent cyber threat aimed at stealing confidential information through diverse delivery media such as emails, instant messages, and social media posts, often resulting in severe identity theft and financial loss. Traditional approaches typically rely on machine learning models combined with handcrafted, specialized feature engineering. More recent efforts have shifted toward fully automated, featureless strategies using deep learning in an end-to-end manner. However, many existing studies are highly data-dependent and lack practical validation across diverse scenarios, thus leaving their real-world applicability uncertain. In this study, we propose a multimodal approach that enhances the generalization capability of phishing URL detection systems by combining a fine-tuned BERT model for URL processing with auxiliary external features derived from public Internet resources. In addition, we share a publicly available dataset named PhishMail, which contains 8,937 phishing samples collected over an extended period from daily incoming malicious emails. This dataset serves as a valuable testing resource for simulating zero-day detection scenarios. The proposed framework is evaluated through a series of extensive experiments and is thoroughly validated, with results across multiple cross-dataset evaluations demonstrating significant improvements in detection effectiveness and generalization capability.}, archive = {J_ACCESS}, author = {Yi Wei and Masaya Nakayama and Yuji Sekiya}, doi = {10.1109/ACCESS.2025.3591843}, journal = {IEEE Access}, pages = {131197-131216}, shortjournal = {IEEE Access}, title = {Enhancing generalization in phishing URL detection via a fine-tuned BERT-based multimodal approach}, volume = {13}, year = {2025}, }
• (2025). DC-link capacitors online condition monitoring using repetitive RLS in hybrid-electric aircraft. ACCESS, 13, 131166-131176. (www)

  @article{ , abstract = {In the future hybrid-electric aircraft, power distribution systems will play curial role as these would not only provide energy for in-cabin systems but propulsion as well. In the turboelectric architecture of these airplanes ac/dc/ac converters are present. DC-link capacitors, that are part of the voltage converters, have high percentage of faults occurring among power electronics. As reliability is important in the aviation industry, condition monitoring of DC-link is required. Different types of capacitors may be used in the future hybrid-electric aircraft power converters. DC-link voltage circuit capacitors are key elements of power electronic systems and are subject to aging and damage processes. It is therefore important to conduct permanent diagnostic and quickly detect any anomaly in the DC circuit. Among the most popular signals in diagnostic process, capacitance drop is a common degradation indicator. In order to estimate the DC-link capacitance, repetitive recursive least squares (RRLS) method was developed. The proposed capacitor health monitoring algorithm has low complexity and operates on data that has size constraints. Such solutions are extremely important in aviation systems, where there are large limitations in the amount of data transmitted by the control bus. The proposed capacitor capacitance estimation algorithm was verified in a simulation environment and on a test bench, where it is possible to simulate capacitor degradation processes. Strict aviation requirements were applied regarding the amount of data and the speed of the algorithm. Range of different capacitance values and datasets were taken into consideration. Results obtained provide information on what type of DC-link capacitors can be monitored using RRLS.}, archive = {J_ACCESS}, author = {Stanisław Oliszewski and Marcin Pawlak and Mateusz Dybkowski}, doi = {10.1109/ACCESS.2025.3591882}, journal = {IEEE Access}, pages = {131166-131176}, shortjournal = {IEEE Access}, title = {DC-link capacitors online condition monitoring using repetitive RLS in hybrid-electric aircraft}, volume = {13}, year = {2025}, }
• (2025). Multi-stage image aesthetic assessment via chain-of-thought reasoning. ACCESS, 13, 131159-131165. (www)

  @article{ , abstract = {Image Aesthetic Assessment (IAA) is an crucial task in computer vision, aiming to quantify the aesthetic quality of images. Existing methods face two main challenges: neglecting the sequential modeling of human visual perception, and the fact that multi-attribute annotation is extremely time-consuming and labor-intensive. This letter proposes a novel Multi-stage IAA framework, leveraging Chain-of-Thought (CoT) reasoning and Multimodal Large Language Models (MLLMs). The framework designs a system that mimics the chain-like progression of human cognitive processing, with three dedicated modules: Low-level Stimulus Assessment (e.g., color harmony), Holistic Organizing Assessment (e.g., scene semantics), and High-level Perceiving Assessment (e.g., emotional resonance). These modules progressively analyze images, from basic visual features to high-level emotional understanding, by employing a step-by-step reasoning process characteristic of CoT. Furthermore, we explore an MLLM-oriented data transformation paradigm to convert multi-source IAA datasets into structured, Chain-of-Thought-compatible data, facilitating convenient attribute annotation. This enables the MLLMs to effectively learn and apply the CoT reasoning for aesthetic evaluation. Experiment on the PARA dataset achieves state-of-the-art results. Moreover, the framework exhibits generalization capabilities on the unseen FLICKR-AES dataset.}, archive = {J_ACCESS}, author = {Zhenglang Jiang and Jianhao Liu and Hongming Li and Yue Liu and Yu Song}, doi = {10.1109/ACCESS.2025.3590925}, journal = {IEEE Access}, pages = {131159-131165}, shortjournal = {IEEE Access}, title = {Multi-stage image aesthetic assessment via chain-of-thought reasoning}, volume = {13}, year = {2025}, }
• (2025). Interdisciplinary methodology for resource allocation problems using artificial neural networks and software robots. ACCESS, 13, 131141-131158. (www)

  @article{ , abstract = {This study presents an interdisciplinary methodology for the Resource Allocation Problem (RAP) in a textile manufacturing environment, aligned with the principles of Industry 5.0, which emphasizes the convergence of advanced technologies and the human factor. The core of this research lies in the integration of an Artificial Neural Network (ANN) to predict worker performance, coupled with process automation, to optimize the allocation of workers to machines. A key ethical consideration addressed by this methodology is the focus on psychosocial factors and workplace well-being, recognizing their contribution to sustainability and organizational success. The benefits of this interdisciplinary approach to RAP are manifold. By considering psychosocial and productivity factors in the optimization model, the study moves beyond traditional methods that often prioritize efficiency at the expense of human considerations. This human-centered approach leads to a more sustainable and socially responsible production environment. The methodology contributes to the development of new tools for managing production in ways that are not only efficient but also significantly aligned with worker well-being. By embedding human-centered constraints directly into the optimization process, this study offers a pathway to mitigate potential issues such as worker overload and improve overall worker satisfaction. Ultimately, this research underscores the value of interdisciplinarity in operational research and production management by demonstrating a cohesive methodology that balances efficiency with ethical considerations for the workforce.}, archive = {J_ACCESS}, author = {Marta Lilia Eraña-Díaz and Marco Antonio Cruz-Chávez and Mario Acosta-Flores and Juana Enríquez-Urbano and Nadia Lara Ruiz and Jorge Pablo Oseguera Gamba}, doi = {10.1109/ACCESS.2025.3591710}, journal = {IEEE Access}, pages = {131141-131158}, shortjournal = {IEEE Access}, title = {Interdisciplinary methodology for resource allocation problems using artificial neural networks and software robots}, volume = {13}, year = {2025}, }
• (2025). Optimizing EV charging in real-time with a distributed game-theoretic framework. ACCESS, 13, 131128-131140. (www)

  @article{ , abstract = {In light of the difficulties posed by optimizing and scheduling the integration of large-scale electric vehicles (EVs) into the power grid, this study introduces a real-time optimization approach rooted in dynamic non-cooperative game theory. An equivalent model of the EV cluster is constructed, and the unique Nash equilibrium of the game model is proven using complete potential game theory. A distributed real-time optimization approach is then implemented through the alternating direction method of multipliers (ADMM). Simulations are conducted across three scenarios: disordered charging, orderly charging, and orderly charging with energy storage. The results indicate that disordered charging increases the grid load peak and exacerbates the peak-valley difference, while orderly charging reduces the peak-valley difference by 15.35%. When energy storage is optimally configured, the peak-valley difference is reduced by up to 20.65%. Additionally, in the orderly charging scenario, the average electricity purchasing cost for EVs drops by 8.72%, and the peak cost decreases by 10.4%. The system demonstrates high computational efficiency with response times in the millisecond range for different EVA (Electric Vehicle Aggregator) charging strategies. The probabilistic model used for predicting EV charging loads achieves an average error rate of less than 5%, ensuring accurate and stable scheduling. These findings show that the proposed method not only reduces grid load fluctuations and achieves peak shaving and valley filling but also lowers charging aggregators’ (EVA) purchasing costs, offering stability in optimization results and excellent performance in computation time and user privacy protection.}, archive = {J_ACCESS}, author = {Aifang Yan and Xiaopeng Chen}, doi = {10.1109/ACCESS.2025.3589297}, journal = {IEEE Access}, pages = {131128-131140}, shortjournal = {IEEE Access}, title = {Optimizing EV charging in real-time with a distributed game-theoretic framework}, volume = {13}, year = {2025}, }
• (2025). Hybrid bonding with polymeric interlayer dielectric layers patterned by nanoimprint lithography. ACCESS, 13, 131120-131127. (www)

  @article{ , abstract = {Recent advancements in semiconductor technology have shifted the focus of innovation toward advanced packaging technologies featuring heterogeneous integration. Among these, hybrid bonding has garnered significant attention due to its potential for achieving higher integration density and reduced interconnect lengths. To alleviate thermal stress during high-temperature processes, polymeric interlayer dielectric (ILD) offers a promising solution owing to their compliant mechanical properties and strong bonding strength. In this work, we propose a simplified patterning method for polymeric dielectric layers based on thermal nanoimprint lithography (NIL). NIL enables the patterning of ILD layers using conventional polymers such as epoxy, as it does not require photoactive materials typically used in photolithography. In this study, a thermosetting epoxy resin based on diglycidyl ether of bisphenol A (DGEBA) was employed as the dielectric material. Using NIL followed by thermo-compression bonding, we achieved hybrid bonding with a $3\mu $ m linewidth. The bonding strength of the Cu/epoxy interfaces was measured to lie between that of conventional Cu–Cu and epoxy–epoxy bonding, reflecting the dual contributions of metal diffusion and polymer crosslinking. These results demonstrate the feasibility of nanoimprint-based dielectric patterning for fine-pitch hybrid bonding and highlight its potential for high-density packaging and 3D integration, offering a viable alternative to traditional Through-Silicon Via (TSV)-based approaches.}, archive = {J_ACCESS}, author = {Sungwoo Jeon and Sohwi Lee and Hyunsik Yoon}, doi = {10.1109/ACCESS.2025.3591107}, journal = {IEEE Access}, pages = {131120-131127}, shortjournal = {IEEE Access}, title = {Hybrid bonding with polymeric interlayer dielectric layers patterned by nanoimprint lithography}, volume = {13}, year = {2025}, }
• (2025). From age of information to age of digital twin: A review on synchronization metrics for IoT networks. ACCESS, 13, 131102-131119. (www)

  @article{ , abstract = {Recently, the advent of digital twins (DTs) has sparked significant interest in both business and academics offering new perspectives into our increasingly digitalized society. The aim of DT is to create a digital representation of a physical entity, be it a process, an object, or a system, by using the entity’s characteristics and behavior. This enables real-time prediction, optimization, monitoring, control, and better decision-making. The adoption of digital twins is growing in various industry verticals due to the rapid proliferation of the Internet of Things (IoT), computing power, artificial intelligence, and big data. Furthermore, the incorporation of metaverse and augmented reality technologies with digital twins bridges the gap between the physical and virtual worlds in manufacturing by providing intuitive ways to visualize and interact with DT data. Identifying the main performance metrics of a DT is crucial to develop a high-fidelity replica of the physical entity to ensure better informed decisions. Research and industry have focused on accuracy and synchronization as key performance metrics with varying definitions. To this end, the aim of this study is to survey the synchronization metrics used in the existing related literature and to establish a more representative synchronization measure for digital twins. Among all metrics, the freshness of the information delivered to the DT, coined as Age of Information (AoI), remains the most adopted measure. In this paper, we review the evaluation of AoI in different network scenarios and introduce Age of Digital Twin (AoDT) to reliably measure the synchronization gap between digital and physical twins. We proceed to formulating AoDT expressions in common network scenarios to be used for planning and developing high-fidelity digital twins.}, archive = {J_ACCESS}, author = {Ali Loubany and May Itani and Sanaa Sharafeddine}, doi = {10.1109/ACCESS.2025.3591589}, journal = {IEEE Access}, pages = {131102-131119}, shortjournal = {IEEE Access}, title = {From age of information to age of digital twin: A review on synchronization metrics for IoT networks}, volume = {13}, year = {2025}, }
• (2025). Machine learning approach to aerodynamic analysis of NACA0005 airfoil: ANN and CFD integration. ACCESS, 13, 131088-131101. (www)

  @article{ , abstract = {This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. Data generated by computational fluid dynamics (CFD) is used to train the model for Reynolds numbers $Re \in [{1000-5000}]$ and angles of attack ranging from 9° to 11°. A robust Scaled Conjugate Gradient (SCG) algorithm is employed for efficient training of data. The ANN has a two-layer architecture, with 9 fixed neurons in the first hidden layer and a varying number of neurons in the second layer to achieve optimal performance. The model yielded coefficients of determination ( $R^{2}$ ) of 0.994 (Coefficient of lift ( $C_{l}$ )) and 0.9615 (Coefficient of drag ( $C_{d}$ )) for training, and 0.9563 ( $C_{l}$ ) and 0.9085 ( $C_{d}$ ) for testing. Overall mean errors are found to be less than 1%. It offers a powerful surrogate modeling approach for aerodynamic studies at ultra-low Reynolds numbers. Moreover, it provides rapid and reliable alternatives to traditional CFD simulations in aerodynamic analysis for unseen cases.}, archive = {J_ACCESS}, author = {Taiba Kouser and Dilek Funda Kurtulus and Srikanth Goli and Abdulrahman Aliyu and Imil Hamda Imran and Luai M. Alhems and Azhar M. Memon}, doi = {10.1109/ACCESS.2025.3592338}, journal = {IEEE Access}, pages = {131088-131101}, shortjournal = {IEEE Access}, title = {Machine learning approach to aerodynamic analysis of NACA0005 airfoil: ANN and CFD integration}, volume = {13}, year = {2025}, }
• (2025). AAV parameters estimation based on improved time-frequency ridge extraction and hough transform. ACCESS, 13, 131074-131087. (www)

  @article{ , abstract = {The micro-Doppler effect caused by the rotation of autonomous aerial vehicle (AAV) rotors plays a crucial role in AAV detection and identification, as it can reflect the micro-movement characteristics of the target, enabling the estimation of the blade length and rotation speed. However, existing methods are prone to noise interference and exhibit poor performance in extracting multi-rotor and multi-component signals. In this paper, we first construct a AAV rotor echo model for frequency-modulated radar systems and derive the mapping relationship between rotor parameters and micro-Doppler characteristic components. First-Order short-time Fourier transform synchrosqueezed transform (FSST) is proposed for extracting micro-Doppler features. Specifically, a novel AAV parameter estimation method is investigated, which is based on an improved time-frequency ridge extraction and Hough transform, following a detailed analysis of the micro-Doppler time-frequency spectrum. Finally, the effectiveness of the method is validated through experimental data. Compared to traditional methods, this approach improves the accuracy of multi-rotor, multi micro-Doppler signal parameter estimation.}, archive = {J_ACCESS}, author = {Yongji Yu and Yonghong Ruan and Junjie Zhong}, doi = {10.1109/ACCESS.2025.3591787}, journal = {IEEE Access}, pages = {131074-131087}, shortjournal = {IEEE Access}, title = {AAV parameters estimation based on improved time-frequency ridge extraction and hough transform}, volume = {13}, year = {2025}, }
• (2025). A simple extension of bayer demosaicing for lightweight non-bayer demosaicing. ACCESS, 13, 131061-131073. (www)

  @article{ , abstract = {Recent advancements in image sensor technology have introduced non-Bayer color filter arrays (CFAs), such as Quad ( $2\times 2$ ), Nona ( $3\times 3$ ), and Q $\times $ Q ( $4\times 4$ ) patterns, which improve performance under low-light conditions compared to the traditional Bayer CFA. Although numerous deep learning models exist for Bayer demosaicing, directly applying them to non-Bayer CFAs often yields suboptimal results, despite identical input-output dimensions and differences only in pixel-level color arrangements. In this work, we identify pixel discontinuity, a structural mismatch introduced by modern non-Bayer CFAs, as the key factor behind this performance gap. Then, based on our investigation, we propose a simple yet effective solution to mitigate it: inserting a pixel unshuffle layer at the input of existing Bayer demosaicing networks. This key operation spatially restructures the input, enabling seamless adaptation to non-Bayer patterns without altering the original network architecture. Our method significantly improves demosaicing performance with almost no increase in both network size and computational cost. Moreover, it generalizes across various CFA patterns, including Quad, Nona, and Q $\times $ Q, facilitating the reuse of Bayer-trained models for a wide range of sensor designs. Experimental results on synthetic and real non-Bayer data demonstrate the practical effectiveness of our approach, removing the need for custom model design for other non-Bayer CFA types.}, archive = {J_ACCESS}, author = {Ki-Wook Hong and Minhyeok Jo and Haechang Lee and Se Young Chun and Albert No}, doi = {10.1109/ACCESS.2025.3589295}, journal = {IEEE Access}, pages = {131061-131073}, shortjournal = {IEEE Access}, title = {A simple extension of bayer demosaicing for lightweight non-bayer demosaicing}, volume = {13}, year = {2025}, }
• (2025). Millimeter-wave 2D retrodirective beamformer based on waveguide rotman lens. ACCESS, 13, 131053-131060. (www)

  @article{ , abstract = {Millimeter-wave (mmWave) technologies offer high directivity and narrow beamwidth with compact size, making them suitable for wireless power transfer (WPT). To support efficient beam steering, retrodirective operation is advantageous. While prior Rotman lens-based retrodirective systems have focused on one-dimensional (1D) beamforming, their applicability is limited. To enable more adaptive and efficient beamforming, two-dimensional (2D) beamforming is essential which can be achieved with Rotman lens as it features fully passive, true-time delay operation. Therefore, this paper proposes a waveguide-based Rotman lens beamformer capable of two-dimensional (2D) retrodirective beamforming implemented using a two-stage stacked lens structure that independently handles azimuth and elevation planes. Operating at 33 GHz in the Ka-band, the beamformer enables beam steering at 10° intervals within ±20° (azimuth) and ±10° (elevation) using 15 input and 16 output ports. Simulation and measurement validate the feasibility of the proposed design for high-performance mmWaves WPT applications.}, archive = {J_ACCESS}, author = {Sooyoung Oh and Yeonsoo Kim and Kyungbin Cho and Hojin Lee and Juntaek Oh and Sun K. Hong}, doi = {10.1109/ACCESS.2025.3591778}, journal = {IEEE Access}, pages = {131053-131060}, shortjournal = {IEEE Access}, title = {Millimeter-wave 2D retrodirective beamformer based on waveguide rotman lens}, volume = {13}, year = {2025}, }
• (2025). Federated mental wellbeing assessment using smartphone sensors under unreliable participation. ACCESS, 13, 131042-131052. (www)

  @article{ , abstract = {Today’s smartphones are equipped with sensors that can track and collect data about users’ everyday activities, which can then be transformed into behavioural indicators of users’ health and wellbeing. Prior studies were focused on centralised machine learning techniques, which transfers all the data to a central server. With modern smartphones being powerful enough to process data locally on a user’s device, federated learning (FL) has emerged as a promising alternative that addresses privacy concerns inherent in centralised setups. This study explores the feasibility of FL models to predict mental wellbeing in a decentralised setting. We also closely evaluate how FL can be applied in such applications in the wild, i.e., where user participation may be inconsistent due to device limitations or privacy concerns inherent in mental health monitoring. To further alleviate demands on edge clients, we incorporate federated continual learning, allowing for adaptive, timely model updates that enhance robustness in real-world mental health applications. In our experiments, we trained tree-based, fully-connected and recurrent neural networks, comparing each time with the centralised approach and random baselines. We also assess the model’s ability to generalise across different users and adapt to temporal changes, ensuring reliability across diverse real-world contexts. The findings suggested that given the widespread use of such devices, FL holds great potential in mood and depression detection while protecting data privacy. Our continual FL achieves similar performance to standard FL, but with added benefit of faster model updates.}, archive = {J_ACCESS}, author = {Gavryel Martis and Ryan McConville}, doi = {10.1109/ACCESS.2025.3591310}, journal = {IEEE Access}, pages = {131042-131052}, shortjournal = {IEEE Access}, title = {Federated mental wellbeing assessment using smartphone sensors under unreliable participation}, volume = {13}, year = {2025}, }
• (2025). Flexible PCB windings size optimization for winding AC resistance minimization under the sinusoidal voltage regime. ACCESS, 13, 131030-131041. (www)

  @article{ , abstract = {In this paper, optimization of the winding AC resistance of the flexible printed circuit board (FPCB) wire windings is performed. A one-dimensional model of the FPCB winding is introduced and an equation for the FPCB winding AC resistance and its low and medium frequency approximation are derived. The approximate FPCB winding AC resistance equation is used to derive optimum thickness of the FPCB winding conductor thickness at which minimum of winding AC resistance (global optimum) is achieved. The Finite Element Method analysis and experimental verification of derived equations (winding resistance, impedance, power loss, and temperature measurements) is performed in order validate derived model and winding resistance equation.}, archive = {J_ACCESS}, author = {Rafal P. Wojda}, doi = {10.1109/ACCESS.2025.3589166}, journal = {IEEE Access}, pages = {131030-131041}, shortjournal = {IEEE Access}, title = {Flexible PCB windings size optimization for winding AC resistance minimization under the sinusoidal voltage regime}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of the FCI-DAB as a single-stage and storage capacitorless PFC wireless charger. ACCESS, 13, 131015-131029. (www)

  @article{ , abstract = {This paper compares two single-stage (1-S) topologies for Electric Vehicle (EV) wireless battery chargers. The Single Stage Resonant-Dual Active Bridge (SSR-DAB) topology is a very compact topology due to the lack of input and output interfaces, reducing considerably the passive components of the circuit. However, its main disadvantage compared to two-stage (2-S) solutions is the fact that it transmits the low frequency fluctuation to the output, which is solved with the recently presented Floating Capacitor Integrated-Dual Active Bridge (FCI-DAB) topology, thanks to the active filter connected in series on the primary-side of the converter. The topologies are compared in terms of controllability, bifurcation, grid current quality and system power losses, taking into account two different control strategies: duty-cycle control on the primary-side and duty-cycle control on the secondary-side of the circuit. The study concludes that the FCI-DAB works better dealing with bifurcation and grid current distortion, while the SSR-DAB converter is not able to guarantee full performance with a single control strategy. The study is validated by experimental results obtained from a scaled down GaN-based Inductive Power Transfer (IPT) 1.5 kW battery charger, achieving 93 % of efficiency and PF=0.99.}, archive = {J_ACCESS}, author = {Itziar Alzuguren and Asier Garcia-Bediaga and Ander Avila and Alejandro Rujas and Miroslav Vasić}, doi = {10.1109/ACCESS.2025.3591847}, journal = {IEEE Access}, pages = {131015-131029}, shortjournal = {IEEE Access}, title = {Performance evaluation of the FCI-DAB as a single-stage and storage capacitorless PFC wireless charger}, volume = {13}, year = {2025}, }
• (2025). Fast field strength prediction using modern machine learning in european cities from few RF-EMF measurements: A neural tangent kernel perspective. ACCESS, 13, 131003-131014. (www)

  @article{ , abstract = {The rapid growth of 5G and 6G networks, with their dense deployments, millimeter-wave communications, and dynamic beamforming, necessitates scalable simulation tools for performance evaluation, network design, and electromagnetic field (EMF) exposure assessment. EMF simulations model exposure across frequency, space, and time, typically based on base stations, user devices, and deterministic and empirical models. These simulations are essential for network operators and researchers for network planning, assessing human exposure to RF-EMF, and ensuring compliance with safety regulations considering factors like frequency, power levels, antenna configurations, and the environment, but they often require extensive computational resources and large simulation time. To address this, we propose an infinitely wide convolutional neural network approach for fast and accurate EMF exposure estimation. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. We compute a convolutional neural tangent kernel from the infinite-width network to perform matrix imputation for exposure estimation. Proposed method estimates exposure fields using less than 7% of simulation points and outperforms other machine learning models, predicting exposure levels which fall below the safety limit set by ICNIRP in under ${3,66 \times 10^{-3}}$ seconds.}, archive = {J_ACCESS}, author = {Mohammed Mallik and Jorn Schampheleer and Laurent Clavier and Margot Deruyck}, doi = {10.1109/ACCESS.2025.3589492}, journal = {IEEE Access}, pages = {131003-131014}, shortjournal = {IEEE Access}, title = {Fast field strength prediction using modern machine learning in european cities from few RF-EMF measurements: A neural tangent kernel perspective}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning-based adaptive nulling in phased array under dynamic environments. ACCESS, 13, 130988-131002. (www)

  @article{ , abstract = {A phased array consists of multiple antenna elements that can control the direction of the radiated signal by adjusting each antenna element’s phase and amplitude, which are encapsulated in the phased array weights. To obtain better communication quality, nulling, which can weaken the interference signal, is helpful by adjusting the phased array weights. In dynamic environments, rapid changes in the directions of both interference and desired signals demand equally rapid, continual updates of phased-array weights. Traditional heuristic optimizers—such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA)—struggle to keep up because their iterative searches depend on pre-computed configurations that are unrealistic to obtain on the fly. To date, no heuristic, supervised-learning, or reinforcement-learning method simultaneously achieves all three requirements: fast adaptation, dataset-free operation, and continuous complex-weight control under highly dynamic environments. In this paper, an innovative deep reinforcement learning-based adaptive nulling, called DRLNuller, is proposed. DRLnuller adopts the Proximal Policy Optimization (PPO) algorithm, a typical reinforcement learning algorithm, to dynamically optimize phased array weights through continuous interaction with the environment without relying on pre-computed or labeled data. In experiments, DRLNuller after the training process outperforms PSO and GA in computation speed by $2.83\times 10^{5}$ times faster and maintains effective communication quality, an average Signal-to-Interference Ratio (SIR) of 25.06 dB, under different conditions.}, archive = {J_ACCESS}, author = {Ying-Dar Lin and Jen-Hao Chang and Yuan-Cheng Lai}, doi = {10.1109/ACCESS.2025.3591643}, journal = {IEEE Access}, pages = {130988-131002}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning-based adaptive nulling in phased array under dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Assessment of time-varying waveform distortion measured in a railway traction converter station. ACCESS, 13, 130978-130987. (www)

  @article{ , abstract = {The waveform distortion interactions and time-varying behavior in electric railway power systems (ERPS) are explored in this paper by investigating measurements from a traction converter station. The paper proposes a methodology aimed at contributing to three aspects: screening detailed measurements with high resolution in time and frequency, comprehensively assessing waveform distortion parameters of the system, and developing a time-varying framework to assess waveform distortion interactions and sub-1-min variations in the railway grid. Specific focus is given to identifying disturbance sources and patterns during operation, including limitations of traditional methodologies based on aggregated values and addressing the challenges posed by the differences between the two electromagnetic environments (railway grid and public grid). The results propose new indices ( $z\text {-}WDSV{,z\text {-}WDSV}_{w}$ ) for evaluating waveform distortion in ERPS, highlighting the suitability of the methodology for assessing time-varying interactions and probabilistic aspects. The proposed framework can determine and quantify the frequency-domain components impacted by the operation of vehicles in the traction sections. Additionally, the limitation of longer measurement intervals is confirmed by the relationship between the aggregated value and variance due to the rapid variation caused by rolling stock operations. Other aspects of mechanisms involving waveform distortion on both sides of the traction converter station are discussed.}, archive = {J_ACCESS}, author = {Rafael S. Salles and Tommy Hjertberg and Mats Wahlberg and Sarah K. Rönnberg}, doi = {10.1109/ACCESS.2025.3591979}, journal = {IEEE Access}, pages = {130978-130987}, shortjournal = {IEEE Access}, title = {Assessment of time-varying waveform distortion measured in a railway traction converter station}, volume = {13}, year = {2025}, }
• (2025). A multilayer residual dendritic neural model for predicting stroke prognosis. ACCESS, 13, 130963-130977. (www)

  @article{ , abstract = {Stroke, caused by occlusion or rupture of cerebral blood vessels, is a leading cause of disability and death globally. Accurate stroke prognosis can enhance clinical decisions and rehabilitation strategies. The dendritic neural model (DNM), inspired by biological neurons, shows strong predictive capability, but struggles with real-world small-scale tabular stroke data. Therefore, an improved residual dendritic neural model (RDNM) is proposed. It contains a series of stacked synaptic and dendritic layers to enhance the power. Residual connections are added between layers to address the vanishing gradient problem. Evaluations using one public and two private stroke prognosis datasets demonstrate that RDNM significantly outperforms original DNM and state-of-the-art deep-learning methods, highlighting its potential for clinical applications. Source code is available at https://github.com/jhc050998/RDNM.}, archive = {J_ACCESS}, author = {Yuxi Wang and Haochang Jin and Maocheng Cao and Xiong Xiao and Li Wang}, doi = {10.1109/ACCESS.2025.3591799}, journal = {IEEE Access}, pages = {130963-130977}, shortjournal = {IEEE Access}, title = {A multilayer residual dendritic neural model for predicting stroke prognosis}, volume = {13}, year = {2025}, }
• (2025). Fine-tuned YOLO model for monitoring children across medical scenes based on a large-scale real-world dataset for children detection. ACCESS, 13, 130953-130962. (www)

  @article{ , abstract = {Accurate detection and monitoring of children in medical settings using computer vision systems present unique challenges due to anatomical differences, environmental complexity, and stringent privacy constraints. This paper introduces YOLOCDD, a fine-tuned YOLOv11-based model optimized for child detection in medical scenes, supported by the Child Detection Dataset (CDD)—a large-scale, real-world dataset comprising 1,928 annotated images of children across diverse age groups and interaction scenarios. Unlike existing datasets that rely heavily on synthetic data or controlled environments, CDD captures realistic medical and everyday settings, including occlusions, multi-child interactions, and dynamic lighting conditions. Our model achieves a mean average precision (mAP@50) of 0.953 in medical environments, significantly outperforming general-purpose detectors like YOLOv11x (mAP@50: 0.606).This work bridges critical gaps in pediatric medical AI by providing a scalable, privacy-compliant dataset, delivering a high-precision detection model, and showcasing clinical applicability in neurological diagnostics. The dataset is publicly available to foster further research in child-centric computer vision.}, archive = {J_ACCESS}, author = {Samuel Diop and François Jouen and Jean Bergounioux and Imen Trabelsi}, doi = {10.1109/ACCESS.2025.3588316}, journal = {IEEE Access}, pages = {130953-130962}, shortjournal = {IEEE Access}, title = {Fine-tuned YOLO model for monitoring children across medical scenes based on a large-scale real-world dataset for children detection}, volume = {13}, year = {2025}, }
• (2025). Managing timing uncertainties in worst-case design of machine learning applications. ACCESS, 13, 130941-130952. (www)

  @article{ , abstract = {Achieving reliable worst-case timing poses a challenge for modern, high-performance, commercial off-the-shelf hardware platforms deployed for industrial applications. Particularly for safety-critical industrial systems, e.g., robot-human collaboration using convolutional neural networks, timing must be considered to operate safely. Although state-of-the-art real-time operating systems and isolation techniques provide predictable timing, they restrict design decisions as many modern hardware platforms are not supported, introducing serious performance penalties. Besides traditional timing considerations, such as the number of cache misses, process variations due to chip manufacturing become more prominent, causing chips from the same model series to exhibit different timing behavior. This circumstance complicates achieving reliable timing on a system level even further. In this work, we present examples of physical variations using most recent hardware platforms, including 12th-generation Intel-based embedded hardware and GPU-based platforms using an Nvidia Jetson AGX Xavier. We elaborate on a potential solution from the avionics domain, called Timing Diversity, which allows for masking unexpected occurrences of worst-case timing behavior by exploiting modular redundancy inherent to safety-critical systems. A key result of our work is that Timing Diversity enables the safe usage of high-performance platforms such as the Nvidia Jetson AGX Xavier, consequently yielding a significant performance boost of nearly 6x.}, archive = {J_ACCESS}, author = {Robin Hapka and Rolf Ernst}, doi = {10.1109/ACCESS.2025.3591986}, journal = {IEEE Access}, pages = {130941-130952}, shortjournal = {IEEE Access}, title = {Managing timing uncertainties in worst-case design of machine learning applications}, volume = {13}, year = {2025}, }
• (2025). Taxonomy of existing sustainable smart city IoT projects. ACCESS, 13, 130909-130940. (www)

  @article{ , abstract = {There has been a significant increase in the inclusion of the Internet of Things (IoT) into systems that affect everyone daily. The lessons learned from past experiences (i.e., case studies) can give a clearer understanding of the various options useful for potential improvements. Moreover, given the diversity of projects across privately funded versus government-funded, we took an independent, objective and scientific approach to understanding the differences comparatively. Starting with the funding sources and feature rationale, a clearer picture has emerged. Consequently, the composite picture enables a different, perhaps more valuable, baseline from which to develop future smart city strategies. Moreover, there can be many positives and negatives to the way these projects were implemented and carried out (i.e., commercial and political stakes). This study was limited from the post-implementation perspective, which emphasizes success stories, an inherent bias, as well as a disparity in level-of-funding available for each of the projects. Nevertheless, we have provided an original and consolidated perspective of the project outcomes and results. This represents a useful opportunity to explore an array of Smart City implementations that depend on evolving IoT technology solutions. Unfortunately, not all solutions being employed today are interoperable and/or extensible in an open systems sense. Modification, extensions, and support toward addressing emerging needs and achieving better more sustainable community mobility therefore may be disadvantaged and/or impeded; Thus, to better manage the expectations of Smart City planners and developers, this article has assessed and organized the outcomes of the eight projects posthumously and comparatively.}, archive = {J_ACCESS}, author = {Frederick T. Sheldon and Youssef Saleh and Matthew Cox and Ananth A. Jillepalli and Bhaskar P. Rimal and Ahmed Abdel-Rahim}, doi = {10.1109/ACCESS.2025.3591230}, journal = {IEEE Access}, pages = {130909-130940}, shortjournal = {IEEE Access}, title = {Taxonomy of existing sustainable smart city IoT projects}, volume = {13}, year = {2025}, }
• (2025). Adaptive virtual inertia and voltage estimation: Enabled model predictive control for improved performance in islanded DC microgrids. ACCESS, 13, 130896-130908. (www)

  @article{ , abstract = {The low inertia of voltage estimation degrades system performance in islanded DC microgrids (DC MGs). To mitigate this issue, we propose an Adaptive Virtual Inertia and Voltage Estimation with Model Predictive Control (AVIE-MPC) approach, which enhances DC MGs performance while ensuring input-to-state stability (ISS). First, the voltage-source converter generates a stochastic state-space model of the DC MG. The virtual DC grid voltage is estimated using covariance adaptation in a standard Kalman filter algorithm. State estimation via feedback control stabilizes the voltage. The feedback gain is derived from the dynamic algebraic Riccati equation (DARE). Integral action eliminates estimation errors through DARE-based operations. Second, references to the expected cost function, which plays a crucial role in MPC performance, are determined by virtual DC grid voltage estimation and the integral of the virtual voltage estimation error. During each sample period, measurements of the virtual DC grid voltage and the current at each power converter output are fed into the expected cost function. The cost function ensures equal current sharing among converters. The DC MG state estimation is compared with the switching control input, and the optimal control signals are iteratively sent to converters. Finally, the proposed AVIE-MPC approach is validated through co-simulation in Simulink and real-time testing on an Opal-RT platform. T he ISS property bounds the DC grid voltage estimation error via Lyapunov stability analysis.}, archive = {J_ACCESS}, author = {Salisu Abdullahi and Khaled Eltag and Lei Weining and Chen Xiaohu}, doi = {10.1109/ACCESS.2025.3591518}, journal = {IEEE Access}, pages = {130896-130908}, shortjournal = {IEEE Access}, title = {Adaptive virtual inertia and voltage estimation: Enabled model predictive control for improved performance in islanded DC microgrids}, volume = {13}, year = {2025}, }
• (2025). Facial beauty prediction combining dual-branch feature fusion with a stacked broad learning system. ACCESS, 13, 130883-130895. (www)

  @article{ , abstract = {Facial beauty prediction (FBP) is a key computer vision task that uses algorithms to assess facial attractiveness. Current models rely on single feature extraction, such as using a single convolutional neural network to extract local feature, failing to capture other potentially more important information contained within facial data and limiting feature diversity. Dual-branch feature fusion complements key features from multiple models, reducing reliance on single features. A stacked broad learning system (BLS) increases network depth with residual connections, addressing the feature insensitivity of traditional BLS. Therefore, this paper combines dual-branch feature fusion with a stacked BLS for FBP. First, facial features are extracted by the ConvNeXtBase and MobileNetV3Large models through transfer learning. Second, a squeeze-and-excitation (SE) network attention-based connection layer dynamically adjusts feature weights, emphasizing important features and reducing irrelevant ones. Then, facial features are input into the BLS network, and the outputs are horizontally concatenated to form fused features. Finally, the fused features are reinput into the BLS to obtain the final FBP result. This model is referred to as DB-BLS (dual branch broad learning system). Building upon DB-BLS, this paper introduces a stacked BLS module to replace the BLS module. Stacked BLS enhancing the network’s ability to perceive features at different levels. This model is named SDB-BLS (stacked dual branch broad learning system). Experiments show that DB-BLS and SDB-BLS outperform traditional methods, respectively achieving accuracies of 76.86% and 78.78% on the SCUT-FBP5500 dataset, with potential applications in image classification and object detection.}, archive = {J_ACCESS}, author = {Junying Gan and Hantian Chen and Wenchao Xu and Huicong Li and Zhenxin Zhuang and Zhen Chen}, doi = {10.1109/ACCESS.2025.3588197}, journal = {IEEE Access}, pages = {130883-130895}, shortjournal = {IEEE Access}, title = {Facial beauty prediction combining dual-branch feature fusion with a stacked broad learning system}, volume = {13}, year = {2025}, }
• (2025). Perfect gaussian integer sequences with two cycles. ACCESS, 13, 130872-130882. (www)

  @article{ , abstract = {The complex sequences including Gaussian integers have received considerable attention in the past due to their wide applications in communications and cryptosystems. This paper proposes three new base sequences along with six known ones to construct two novel classes of perfect Gaussian integer sequences (PGISs). The first is two-cycle PGIS, where each Gaussian integer has an absolute value such that these values in a $2p$ -periodic sequence form two cycles of period p. Compared to the conventional PGISs, the computational complexity of determining a two-cycle PGIS is reduced by approximately one-half. The second is zero-deletion PGIS (ZDPGIS) when the resulting shorter PGIS is obtained from a long even-length PGIS by deleting zero elements. Experimental results show that the shorter PGISs have flexible lengths including odd and even, most of which are either optimal or almost optimal two-cycle. Such ZDPGISs outperform conventional ones in reducing the computational complexity up to 62.5%.}, archive = {J_ACCESS}, author = {Kun-Lin Lee and Chong-Dao Lee and Yan-Haw Chen}, doi = {10.1109/ACCESS.2025.3591976}, journal = {IEEE Access}, pages = {130872-130882}, shortjournal = {IEEE Access}, title = {Perfect gaussian integer sequences with two cycles}, volume = {13}, year = {2025}, }
• (2025). Memory-efficient annotation techniques for autonomous drone navigation. ACCESS, 13, 130846-130871. (www)

  @article{ , abstract = {The rapid adoption of autonomous drones in mission-critical applications such as infrastructure inspection and emergency response demands highly accurate real-time (RT) environmental perception, yet faces fundamental bottlenecks in its perception system due to memory-intensive data representations. We present a novel end-to-end framework that synergistically unifies: 1) dynamic scene complexity-aware perception-mode selection, 2) ASPP-optimized sparse mask encoding achieving >90% memory reduction, 3) seamless Datumaro integration for version-controlled, AES-256 encryption data management with just 9.1ms overhead. Our hybrid bounding-box/semantic segmentation (BB/SS) switching mechanism dynamically adapts annotation granularity based on RT scene complexity, achieving notable memory-accuracy trade-off for edge-deployment.The holistic system demonstrates considerable performance through large-scale benchmarking across seven standardized data formats (JSON/CSV/Parquet/HDF5/YAML/Avro/SQLite3) and 90+ reference methods: With an energy efficiency of 10.4 FPS/W on NVIDIA hardware, achieving 92.5% mIoU (outperforming comparable lightweight methods by 23.6%) at a 1.1GB memory footprint - a $2.3\times $ throughput gain over fragmented approaches enabled by TensorRT-optimized execution (56% latency reduction). These advances optimize three competing factors: RT performance (sub-5.22 ms storage latency), memory efficiency (1.1GB footprint), and perception accuracy (92.5% mIoU), demonstrating significant improvements for UAV perception pipelines as quantified in our systematic comparison against 24 reference systems. Future research will explore explainable AI (XAI), adaptive annotation, and hybrid storage to extend this integrated framework’s capabilities for next-gen aerial mobility applications.}, archive = {J_ACCESS}, author = {Pratibha Vittal Hegde and Mohammed Riyaz Ahmed and Abdul Haq Nalband}, doi = {10.1109/ACCESS.2025.3590402}, journal = {IEEE Access}, pages = {130846-130871}, shortjournal = {IEEE Access}, title = {Memory-efficient annotation techniques for autonomous drone navigation}, volume = {13}, year = {2025}, }
• (2025). SSAT: Sensor-satellite auto-correlation transformer for enhanced aerosol optical depth prediction. ACCESS, 13, 130832-130845. (www)

  @article{ , abstract = {Satellite-derived aerosol optical depth (AOD) observations are highly valuable to describe the horizontal distribution of aerosols, but are hampered by spatial data gaps and limited temporal coverage (typically once a day). The synergism of these measurements with chemistry-transport models (CTM) may be used to overcome these limitations. Although physically constrained methods (e.g. data assimilation) are a common practice for addressing this synergism, these methods may be difficult to implement and are highly computationally demanding. On the other hand, statistical or learning-based techniques can still offer flexible and effective solutions. In this work, we present SSAT, a transformer-based approach that fuses satellite and model outputs to refine AOD predictions without modifying the underlying CTM itself. Our design leverages the auto-correlation mechanism from Autoformer and introduces GridMSE, a specialized loss function aimed at improving spatial coherence and handling imbalanced data. Extensive experiments show that SSAT has higher accuracy and better visual correspondence compared to existing tree-based and deep learning baselines, particularly in underrepresented aerosol load regimes. While it does not replace physically rigorous data assimilation, SSAT can serve as a complementary tool for quickly refining AOD maps, ultimately offering a more complete basis for monitoring aerosol distributions and informing environmental analyses.}, archive = {J_ACCESS}, author = {Maoquan Zhang and Bisser Raytchev and Juan Cuesta and Farouk Lemmouchi and Maithili Karle and Daniel Andrade}, doi = {10.1109/ACCESS.2025.3591479}, journal = {IEEE Access}, pages = {130832-130845}, shortjournal = {IEEE Access}, title = {SSAT: Sensor-satellite auto-correlation transformer for enhanced aerosol optical depth prediction}, volume = {13}, year = {2025}, }
• (2025). Optimizing hyperparameters in meta-learning for enhanced image classification. ACCESS, 13, 130816-130831. (www)

  @article{ , abstract = {This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification applications often suffer from data inadequacy. Few-shot learning addresses this challenge by enabling learning from limited samples. Meta-learning, a prominent tool for few-shot learning, learns across multiple classification tasks. We explore different types of meta-learners, with a particular focus on metric-based models. We analyze the potential of hyperparameter optimization techniques, specifically Bayesian optimization and its variants, to enhance the performance of these models. Experimental results on the Omniglot and ImageNet datasets demonstrate that incorporating Bayesian optimization, particularly its evolutionary strategy variant, into meta-learning frameworks leads to improved accuracy compared to settings without hyperparameter optimization. Here, we show that by optimizing hyperparameters for individual tasks rather than using a uniform setting, we achieve notable gains in model performance, underscoring the importance of tailored hyperparameter configurations in meta-learning.}, archive = {J_ACCESS}, author = {Amala Mary Vincent and P. Jidesh and A. A. Bini}, doi = {10.1109/ACCESS.2025.3591142}, journal = {IEEE Access}, pages = {130816-130831}, shortjournal = {IEEE Access}, title = {Optimizing hyperparameters in meta-learning for enhanced image classification}, volume = {13}, year = {2025}, }
• (2025). Exploring new paradigms in time series prediction by integrating computer simulations and machine learning. ACCESS, 13, 130796-130815. (www)

  @article{ , abstract = {Time series prediction is a challenging task that requires modeling complex temporal dependencies and structural priors. In this work, we propose a novel framework that fundamentally advances existing Transformer-based methods through the introduction of the Dynamic Temporal Attention Network (DTAN) and the Hierarchical Temporal Optimization Strategy (HTOS), which jointly address long-term dependency capture, interpretability, and domain-specific adaptation in a unified architecture. To address existing limitations, we propose a novel paradigm that integrates machine learning with computer simulations by embedding simulation-derived constraints and structural priors directly into the model training process. These simulations serve not merely as synthetic data generators but as sources of domain-specific knowledge, including physics-informed constraints and governing equations, which are used to guide attention mechanisms and regularization strategies within our architecture. Our framework introduces DTAN, which combines hierarchical temporal encoding with adaptive attention mechanisms to dynamically capture both short- and long-term dependencies. Additionally, we incorporate HTOS, which integrates auxiliary tasks such as trend decomposition and seasonality estimation while enforcing structured learning through hierarchical regularization. By merging data-driven learning with simulation-informed constraints, our approach enhances both predictive accuracy and interpretability. Empirical evaluations across diverse benchmark datasets validate the effectiveness of our framework, demonstrating its robustness, scalability, and ability to uncover meaningful temporal patterns in complex time series data.}, archive = {J_ACCESS}, author = {Pengcheng Wu and Yan Shi and Pengfei Zhao and Zhengzhao Gu}, doi = {10.1109/ACCESS.2025.3589239}, journal = {IEEE Access}, pages = {130796-130815}, shortjournal = {IEEE Access}, title = {Exploring new paradigms in time series prediction by integrating computer simulations and machine learning}, volume = {13}, year = {2025}, }
• (2025). Serious games in entrepreneurship classroom: Pedagogical implications and outcomes. ACCESS, 13, 130782-130795. (www)

  @article{ , abstract = {Serious games are increasingly recognized as an effective pedagogical tool in entrepreneurship education, providing experiential learning opportunities to develop entrepreneurial mindsets and skills. This study examines the pedagogical implications of integrating the serious online game Paradox of Theron (POT) into a large entrepreneurship classroom at a Malaysian university. A total of 332 undergraduate students from diverse disciplines, supported by six tutors, engaged in the game for over 21 days. At the end of the semester, data was collected from 256 students and five tutors through closed- and open-ended surveys to evaluate the game’s impact on students’ entrepreneurial mindset, skill development, and overall learning experience. Thematic analysis revealed that POT positively impacts students’ entrepreneurial mindset and skill acquisition. These findings highlight the importance of pairing serious games with structured pedagogical support to foster students’ development of entrepreneurial mindset and skills. The study also noted that some students’ learning through POT was affected by unclear guidelines, insufficient support resources, and limited opportunities for reflection and feedback. Recommendations include the need for a realistic student-tutor ratio, bite-size video tutorials on how to play POT, and the integration of debriefing, and guided reflection to help students make meaningful connections between gameplay and entrepreneurial concepts. Limitations related to context, sampling, and reliance on self-reported data suggest the need for future research across diverse educational settings using mixed-methods or longitudinal approaches. Such studies could further examine how pedagogical design influences learning outcomes and guide more effective implementation of serious games in entrepreneurship education.}, archive = {J_ACCESS}, author = {Hui Yan Yeong and Ann Rosnida Md Deni}, doi = {10.1109/ACCESS.2025.3591486}, journal = {IEEE Access}, pages = {130782-130795}, shortjournal = {IEEE Access}, title = {Serious games in entrepreneurship classroom: Pedagogical implications and outcomes}, volume = {13}, year = {2025}, }
• (2025). Flight test results of an adaptive RF front-end for multi-band SDR in avionics. ACCESS, 13, 130770-130781. (www)

  @article{ , abstract = {This paper presents an in-depth evaluation of the reconfigurable and agile RF front-end (RFFE) architecture, previously demonstrated in laboratory settings through real-world flight tests. The study aims to validate the practical performance, reliability, and robustness of the RFFE architecture, developed in conjunction with software-defined radios (SDRs) by the LASSENA lab, in dynamic aviation environments. By transitioning from controlled lab conditions to actual flight scenarios, we assess the ability of the architecture to adapt to varying signal requirements, frequencies, and protocols. Preparation for flight situations includes temperature resilience, vibration tolerance, and mobility tests. An initial airport ground test will be conducted to ensure the system’s readiness before actual flight deployment. Key performance metrics such as spectrum utilization, signal integrity, receiver performance, and transmitter linearity are examined to ensure compliance with stringent aviation safety and performance standards. The results provide critical insights into the operational benefits and potential enhancements of the RFFE architecture, supporting its adoption in aviation and potentially other fields requiring high communication and system efficiency levels.}, archive = {J_ACCESS}, author = {Behnam Shakibafar and Abdessamad Amrhar and Jean-Marc Gagné and Rene Jr Landry and Frederic Nabki}, doi = {10.1109/ACCESS.2025.3591591}, journal = {IEEE Access}, pages = {130770-130781}, shortjournal = {IEEE Access}, title = {Flight test results of an adaptive RF front-end for multi-band SDR in avionics}, volume = {13}, year = {2025}, }
• (2025). An autonomous drone for avalanche search and rescue: Integrating ArduPilot and tracking-beacon detection. ACCESS, 13, 130758-130769. (www)

  @article{ , abstract = {Avalanches claim numerous lives annually, underscoring the critical need for rapid and reliable rescue solutions. This paper presents an innovative autonomous drone system explicitly designed to enhance avalanche search and rescue (SAR) operations in challenging mountainous terrains. Utilising a hexacopter equipped with the CubePilot flight controller and integrated with a Raspberry Pi onboard computer, our system uniquely combines ArduPilot’s precise navigation capabilities and GNU Radio’s sophisticated signal processing techniques to detect avalanche transceiver signals swiftly and accurately. Rigorous testing through simulation and extensive field trials conducted in real alpine conditions demonstrate the drone’s robust performance. In our field trials at a controlled avalanche debris site, the drone detected a buried standard avalanche transceiver and autonomously home in its location in only several minutes. This is a noteworthy improvement compared to conventional search times – manual rescuer sweeps can take 20–30 minutes or more to locate a victim, even when using transceivers. Furthermore, the cost-effective and open-source nature of our drone solution promotes wide accessibility and adaptability for various SAR scenarios. By bridging advanced aerial robotics with practical rescue operations, this study delivers a vital technological advancement, significantly boosting the effectiveness and safety of mountain rescue missions. Code is available on GitLab: https://gitlab.com/albatros-uav/albatros}, archive = {J_ACCESS}, author = {Jedrzej Stasik and Tomasz Szandala}, doi = {10.1109/ACCESS.2025.3585245}, journal = {IEEE Access}, pages = {130758-130769}, shortjournal = {IEEE Access}, title = {An autonomous drone for avalanche search and rescue: Integrating ArduPilot and tracking-beacon detection}, volume = {13}, year = {2025}, }
• (2025). Long-term, multivariate time series generation with the capture of intervariate correlations and variatewise characteristics. ACCESS, 13, 130747-130757. (www)

  @article{ , abstract = {This paper proposes a novel Time Series Generation (TSG) model, the Attended Variate-Conditioned GAN (AVC-GAN), for generating multivariate long-term time series data. Recently, generative approaches to TSG have been explored for applications such as privacy protection, anomaly detection, and time series classification/forecasting. Since existing TSG methods, particularly Generative Adversarial Networks (GANs)-based methods, focus on modeling long-term time series and capturing intervariate correlations, they often struggle to adequately capture the characteristics of each variate, such as specific patterns of waveforms. To address these issues with TSG simultaneously, we propose a novel generative model, AVC-GAN. The key components of AVC-GAN are as follows: 1) a nonautoregressive architecture for modeling long-term time series, 2) variate conditioning for capturing variatewise characteristics, and 3) a multihead attention mechanism for capturing intervariate correlations. Our experiments on multiple benchmark datasets for Long-Term Time Series Forecasting (LTSF) demonstrate that AVC-GAN outperforms state-of-the-art GAN-based generative models across a range of evaluation metrics. In terms of distance-based metrics, calculated as the MSE between real and generated data, our method achieves improvements of 29.8% and 33.7% over state-of-the-art methods in assessing overall intervariate correlations and variatewise characteristics, respectively. Visualization also confirms the superior quality of the generated time series.}, archive = {J_ACCESS}, author = {Kasumi Ohno and Kohei Makino and Makoto Miwa and Yutaka Sasaki}, doi = {10.1109/ACCESS.2025.3590728}, journal = {IEEE Access}, pages = {130747-130757}, shortjournal = {IEEE Access}, title = {Long-term, multivariate time series generation with the capture of intervariate correlations and variatewise characteristics}, volume = {13}, year = {2025}, }
• (2025). Credit scoring prediction using deep learning models in the financial sector. ACCESS, 13, 130731-130746. (www)

  @article{ , abstract = {The increasing complexity and volume of financial and behavioral data in modern credit scoring and risk assessment present significant challenges to traditional modeling methods. Existing approaches often struggle with integrating structured numerical records and unstructured user behavior signals, limiting their ability to capture meaningful temporal and non-linear patterns. In the swiftly transforming domain of computational science, the incorporation of sophisticated machine learning algorithms has emerged as a critical driver in addressing these challenges. Although traditional statistical approaches provide foundational value, they frequently fall short when faced with the task of capturing the nuanced, non-linear associations embedded within extensive datasets, thereby limiting predictive precision. To address these shortcomings, we propose an innovative deep learning paradigm that effectively integrates structured financial information with unstructured behavioral data, thereby bolstering the reliability and accuracy of predictive analytics. Our approach features a thorough feature engineering pipeline that includes statistical indicators, temporal trends, and aggregated financial attributes—aimed at constructing a comprehensive representation of the data environment. At the core of our framework is a hybrid neural network architecture, which leverages Long Short-Term Memory (LSTM) units to handle sequential dependencies alongside dense layers that model complex interactions among features. This configuration enables the simultaneous learning of both temporal dynamics and high-level abstractions. To prevent overfitting and to promote better generalization, we incorporate adaptive regularization strategies that adjust penalization levels in response to validation metrics. We confront the issue of class imbalance by applying dynamic re-sampling and weight adjustment techniques, ensuring balanced model performance across varied data segments. Extensive evaluations on standard datasets validate the effectiveness of our proposed model, revealing enhanced prediction accuracy and model interpretability when compared to more traditional techniques. These results highlight the promise of advanced deep learning integration in computational science applications, offering a pathway toward more insightful and dependable predictive solutions.}, archive = {J_ACCESS}, author = {Xi Shi and Dingfen Tang and Yike Yu}, doi = {10.1109/ACCESS.2025.3591005}, journal = {IEEE Access}, pages = {130731-130746}, shortjournal = {IEEE Access}, title = {Credit scoring prediction using deep learning models in the financial sector}, volume = {13}, year = {2025}, }
• (2025). State of charge estimation in li-ion batteries using a parallel LSTM-based approach: The impact of modeling based on operating states. ACCESS, 13, 130719-130730. (www)

  @article{ , abstract = {In Li-ion battery applications, effective energy management relies heavily on accurate knowledge of the state of charge (SOC). As SOC cannot be directly measured, it must be estimated using several methods. Deep learning has emerged as one of the most widely used approaches in machine learning. However, in cases where the input data exhibit limited variation over time and consist of low-dimensional features, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) may tend toward overfitting. To address this, deep learning algorithms such as long short-term memory (LSTM) have been focused on for SOC prediction. Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. This variability negatively impacts the performance of conventional LSTM models. To overcome this limitation, this study proposes a parallel LSTM architecture composed of three distinct models, each tailored to a specific battery operating condition. Both the proposed and conventional models were evaluated using various standardized driving cycles. Mean absolute error, mean squared error, and boxplot analysis were employed for performance comparison. Across all metrics, the proposed method consistently outperformed the standard model. The best mean absolute error result was achieved with the proposed method, at 0.75% under the LA92 driving cycle. These results demonstrate the effectiveness of the proposed approach in accurately and reliably estimating SOC in dynamic battery applications.}, archive = {J_ACCESS}, author = {Osman Ozer and Hayri Arabaci}, doi = {10.1109/ACCESS.2025.3591970}, journal = {IEEE Access}, pages = {130719-130730}, shortjournal = {IEEE Access}, title = {State of charge estimation in li-ion batteries using a parallel LSTM-based approach: The impact of modeling based on operating states}, volume = {13}, year = {2025}, }
• (2025). A comparative study for localization of forgery regions in images. ACCESS, 13, 130701-130718. (www)

  @article{ , abstract = {As computer technologies and image processing software have advanced, it has become progressively easier to produce simple fake or forged images by altering digital images without leaving any discernible trace. There is a significant need to detect manipulated regions in images in crucial fields such as politics, law, and forensic medicine. In this study, we propose a method that combines the traditional techniques, such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), with the advantages of deep learning methods to detect manipulated regions in forged images. The proposed method involves designing an architecture where DWT and DCT are used in parallel with DenseNet based Convolutional Neural Network (CNN). To evaluate the effectiveness of this method, we implemented three alternative approaches: one that uses only DCT and CNN, another that uses only DWT and CNN, and a third that employs only CNN without either transformation. In total, four different methods were tested on eight datasets, and their performance was compared using metrics such as accuracy, precision, recall, dice similarity coefficient, and F1 score. The results from these comparisons clearly indicate the effectiveness and high classification accuracy of the proposed method. By leveraging the combined strengths of traditional image processing techniques and advanced deep learning algorithms, the proposed method demonstrates superior capability in detecting manipulated regions in forged images, thus offering a robust solution for applications in forensic field.}, archive = {J_ACCESS}, author = {Mustafa Özden and Canberk Şahin}, doi = {10.1109/ACCESS.2025.3591571}, journal = {IEEE Access}, pages = {130701-130718}, shortjournal = {IEEE Access}, title = {A comparative study for localization of forgery regions in images}, volume = {13}, year = {2025}, }
• (2025). Health monitoring of carbon fiber reinforced building materials based on phase unwrapping algorithm. ACCESS, 13, 130684-130700. (www)

  @article{ , abstract = {During the construction process, building structures are affected by environmental factors such as earthquakes. Engineers cannot monitor real-time data related to the status of building materials, such as whether the structure is complete, whether nodes are effective, and specific displacement situations. Therefore, a hybrid model to monitor carbon fiber reinforced building materials based on the least squares phase unwrapping and deep convolutional neural networks is developed to provide timely warning of damage factors. When constructing the model, deep convolutional neural network is used to effectively extract the features of carbon fiber reinforced building materials, and improve the analysis ability of the building materials monitoring model. The experimental results showed that the hybrid algorithm had higher computational accuracy and smaller errors compared with the discrete cosine transform algorithm, fast Fourier transform algorithm, and quality map guided method. When the signal-to-noise ratio of these four algorithms was 6%, the average errors were 0.3147, 0.8462, 0.7836, and 0.8147, respectively. When conducting denoising tests on noisy images, the hybrid algorithm showed good denoising results, with clear images. In addition, empirical analysis was conducted on the constructed hybrid model for health status monitoring. When the iteration was 120, the loss curve gradually smoothed and the loss rate remained between 0.2 and 0.3. When testing the fracture of a single suspension rod, the difference in cable force detected was 115kN, with a variation amplitude of 12.15%. When the mixed model was tested on the training set and the test set, the accuracy rates of building material monitoring were 98.2% and 95.5% respectively, which were significantly higher than those of the comparison models. Meanwhile, after introducing the multi-feature fusion strategy, the gap between the training set and the test set was significantly narrowed, significantly improving the generalization ability of the model. The above results indicate that the carbon fiber reinforced building material health monitoring hybrid model can timely detect the damage status of building materials and transmit and process data. This study will better monitor the status in future building structures and provide support for emergency response decisions in structures.}, archive = {J_ACCESS}, author = {Min Yan and Jianjun Zhou and Shuai Guo and Yanlong Li}, doi = {10.1109/ACCESS.2025.3587689}, journal = {IEEE Access}, pages = {130684-130700}, shortjournal = {IEEE Access}, title = {Health monitoring of carbon fiber reinforced building materials based on phase unwrapping algorithm}, volume = {13}, year = {2025}, }
• (2025). An RIS-assisted integrated deep learning framework for MIMO-OFDM-IM. ACCESS, 13, 130672-130683. (www)

  @article{ , abstract = {Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM) offers a flexible trade-off between error performance and spectral efficiency. Meanwhile, reconfigurable intelligent surface (RIS)-assisted communication has emerged as a promising technology for optimizing wireless propagation environments, significantly enhancing signal quality and system performance. Integrating RIS into MIMO-OFDM-IM systems can improve communication reliability. However, efficient signal detection remains challenging due to severe interchannel interference and the correlation among subcarrier symbols within each subblock. To address this challenge, we propose InDeep, a robust deep learning (DL)-based detection framework designed for RIS-assisted MIMO-OFDM-IM systems. InDeep leverages one-dimensional convolutional neural network (1D-CNN) layers for feature extraction, followed by bidirectional gated recurrent unit (Bi-GRU) networks to capture temporal dependencies in the received signal. Additionally, we incorporate domain knowledge into the preprocessing of the channel matrix and received signal to enhance detection accuracy. Simulation results demonstrate that the proposed RIS-assisted MIMO-OFDM-IM system with InDeep detection significantly improves communication reliability, making it a compelling solution for next-generation wireless networks.}, archive = {J_ACCESS}, author = {Md Abdul Aziz and Md Habibur Rahman and Mohammad Abrar Shakil Sejan and Rana Tabassum and Hyoung-Kyu Song}, doi = {10.1109/ACCESS.2025.3587847}, journal = {IEEE Access}, pages = {130672-130683}, shortjournal = {IEEE Access}, title = {An RIS-assisted integrated deep learning framework for MIMO-OFDM-IM}, volume = {13}, year = {2025}, }
• (2025). Improved distributed backdoor attacks in federated learning by density-adaptive data poisoning and projection-based gradient updating. ACCESS, 13, 130651-130671. (www)

  @article{ , abstract = {While federated learning enables collaborative model training with preserved data locality, it remains vulnerable to evolving backdoor attacks that exploit its distributed architecture. Compared with centralized backdoor attacks, a distributed backdoor attack (DBA) poses a greater threat to FL system due to its spatially distributed trigger mode. Existing DBA methods are typically based on a uniform trigger design and update, which compromises the stealthiness and reduces the attack effect. This paper proposes a density-adaptive data poisoning method for backdoor attacks, which effectively evades data purification defenses by decomposing a global trigger into localized sub-triggers that adapt to the data distribution of malicious clients to maintain the attack effect without compromising attack stealthiness. To further improve attack stealthiness, we propose a constrained gradient projection method that dynamically limits the boundaries of malicious parameter updates to ensure their consistency with normal update patterns. This dual-layer approach, spanning both poison-release triggering and training parameter update control, significantly enhances the stealthiness of the attack while maintaining the attack effect. Experimental results on three benchmark datasets demonstrate superior performance. On COCO dataset, our method achieves 92.44% main task accuracy with 91.68% attack success rate, outperforming DBA by +8.04% MTA and +10.29% ASR. In addition, we also propose targeted defense strategies based on the mechanism of attack mode, which promotes the research of attack and defense methods in FL security.}, archive = {J_ACCESS}, author = {Jian Wang and Hong Shen and Wei Ke and Xue Hua Liu}, doi = {10.1109/ACCESS.2025.3586416}, journal = {IEEE Access}, pages = {130651-130671}, shortjournal = {IEEE Access}, title = {Improved distributed backdoor attacks in federated learning by density-adaptive data poisoning and projection-based gradient updating}, volume = {13}, year = {2025}, }
• (2025). Pharmacophore-aware dual-view learning with bidirectional cross-attention for drug-drug interaction prediction. ACCESS, 13, 130638-130650. (www)

  @article{ , abstract = {Accurate drug-drug interaction (DDI) prediction is critical for ensuring patient safety and guiding clinical decision-making. Existing methods often rely on single-view molecular representations, limiting their ability to capture the complex structural and spatial properties of drugs. In this study, we propose a novel pharmacophore-aware dual-view learning framework (PharmaDual) that integrates both 2D and 3D representations of pharmacophores for enhanced DDI prediction. Specifically, we first extract pharmacophore fragments as key substructures, and independently encode their 2D and 3D spatial information using specialized graph-and geometry-based encoders. To effectively combine the complementary views, we introduce a bidirectional cross-attention fusion module that dynamically aligns and integrates 2D and 3D pharmacophore representations. Extensive experiments on benchmark DDI datasets demonstrate that our method consistently outperforms existing approaches, highlighting the benefit of dual-view modeling and cross-attentive fusion in capturing nuanced pharmacophore-level interactions. The code is available at https://github.com/ZWX1289/PharmaDual}, archive = {J_ACCESS}, author = {Wenxiao Zhang and Seong Yoon Shin and Hailiang Tang}, doi = {10.1109/ACCESS.2025.3591823}, journal = {IEEE Access}, pages = {130638-130650}, shortjournal = {IEEE Access}, title = {Pharmacophore-aware dual-view learning with bidirectional cross-attention for drug-drug interaction prediction}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of deep learning-based feature extractors for change detection in automotive radar maps. ACCESS, 13, 130629-130637. (www)

  @article{ , abstract = {The Siamese network architecture has been applied by deep learning practitioners to find similarities between images. In the domain of autonomous driving, this network configuration has recently gained attention for solving the change detection task, which involves identifying changes in a previously known map of a vehicle’s environment. This is vital, as such deviations may compromise the accuracy and reliability of the map, which is essential for the vehicle’s ability to localize itself and navigate effectively. In this paper, we present a set of experiments involving state-of-the-art deep learning architectures based on both convolution (CNN) and attention mechanisms such as AlexNet, GoogLeNet, VGG, ResNet, Vision Transformer, and Shifted Windows Transformer as possible candidates for the feature extractor backbone module in the Siamese architecture to detect changes caused by the disappearance and appearance of construction zones. Also, we evaluate the performance of these architectures using fine-tuning, i.e., initializing the convolutional layers with pre-trained weights. In our experimentation, the best results were obtained using VGG16 (CNN), especially when it was initialized using pre-trained weights from the ImageNet-1K dataset. In particular, VGG16 with an average F1 score of 92% on highway datasets outperformed the baseline residual network composed of ResNet18 convolutions by about 13.5%.}, archive = {J_ACCESS}, author = {Harihara Bharathy Swaminathan and Aron Sommer and Uri Iurgel and Andreas Becker and Martin Atzmueller}, doi = {10.1109/ACCESS.2025.3591272}, journal = {IEEE Access}, pages = {130629-130637}, shortjournal = {IEEE Access}, title = {Comparative analysis of deep learning-based feature extractors for change detection in automotive radar maps}, volume = {13}, year = {2025}, }
• (2025). Ethical foundations of AI-driven avatars in the metaverse for innovation and user privacy. ACCESS, 13, 130610-130628. (www)

  @article{ , abstract = {The rapid proliferation of AI-powered avatars in the Metaverse is fundamentally transforming user interactions through unprecedented levels of realism and personalization. However, these advances also introduce critical ethical and legal dilemmas surrounding privacy, identity theft, algorithmic bias, and data governance. This study employs a comprehensive multimethodological approach combining systematic literature review, regulatory gap analysis, and ethical framework synthesis. Our methodology integrates qualitative analysis of primary sources, comparative assessment of international legal frameworks (GDPR, CCPA, EU,AI Act), and development of a five pillar ethical framework validated through real-world case studies across educational, commercial, and healthcare Metaverse applications. Building on these insights, we propose an ethical framework emphasizing transparency, fairness, accountability, privacy by design, and user autonomy. We then examine practical solutions such as blockchain-based identity management, privacy tools, regulatory sandboxes, and global standards harmonization. Through computational simulation of representative Metaverse environments, we explore how stakeholders can apply these guidelines to balance innovation with user protection. Finally, we explore future directions, including integrating brain computer interfaces (BCIs), emotion detection, quantum-resistant cryptography, and international governance models. Validation through case study analysis demonstrates significant improvements in privacy protection metrics, substantial reduction in identity theft vulnerabilities, and marked enhancement in user autonomy controls compared to existing frameworks. Our proposed blockchain-based identity management shows effective prevention of deepfake avatar creation while maintaining high user experience satisfactio. By proactively embracing ethical and legal safeguards, we envision a Metaverse where AI-driven avatars deliver transformative experiences while upholding individual rights and societal well-being.}, archive = {J_ACCESS}, author = {Ammar Almomani and Ahmad Al-Qerem and Mohammad Alauthman and Amjad Aldweesh and Samer Aoudi and Said A. Salloum}, doi = {10.1109/ACCESS.2025.3589714}, journal = {IEEE Access}, pages = {130610-130628}, shortjournal = {IEEE Access}, title = {Ethical foundations of AI-driven avatars in the metaverse for innovation and user privacy}, volume = {13}, year = {2025}, }
• (2025). Soft error in saddle fin-based DRAM at cryogenic temperature. ACCESS, 13, 130603-130609. (www)

  @article{ , abstract = {This study examines the impact of soft error by heavy ions on saddle fin-based dynamic random access memory (DRAM). The investigation is conducted using technology computer-aided design (TCAD) simulation at different temperatures ranging from 77 to 300 K. At 300 K, charge sharing is greater compared to 77 K due to the increased prominence of the bipolar amplification effect. The decrease in storage node potential ( $V_{SN}$ ) caused by charge sharing varies by up to 1.31% between 77 and 300 K. Nevertheless, if the linear energy transfer (LET) of the ion is below 1 MeV $\cdot $ cm2/mg, temperature increase does not result in enhanced charge sharing. This is because there is an insufficient generation of electron-hole pairs (EHPs) to trigger a bipolar amplification effect. On the other hand, the amount collected charge is greater at 77 compared to 300 K because the mobility of the carriers increased as the temperature decreased. The variation in $V_{SN}$ due to the collected charge is as high as 13.19% between 77 and 300 K. When comparing the reduction in $V_{SN}$ caused by collected charge and charge sharing, it is seen that the influence of collected charge is more pronounced at 77 and 300 K. TCAD simulations are used to investigate strategies for mitigating the heavy ion effect. Enhancing the bit-line junction can reduce the impact of heavy ions on the saddle fin-based DRAM. As a result, several EHPs generated by heavy ions can be moved towards the junction of the bit-line.}, archive = {J_ACCESS}, author = {Minsang Ryu and Minki Suh and Jonghyeon Ha and Minji Bang and Dabok Lee and Hojoon Lee and Hyunchul Sagong and Dong-Seok Kim and Jungsik Kim}, doi = {10.1109/ACCESS.2025.3591488}, journal = {IEEE Access}, pages = {130603-130609}, shortjournal = {IEEE Access}, title = {Soft error in saddle fin-based DRAM at cryogenic temperature}, volume = {13}, year = {2025}, }
• (2025). Physics-based AI-driven surrogate modeling for structural displacement prediction in mechanical systems with limited sensor data. ACCESS, 13, 130585-130602. (www)

  @article{ , abstract = {This study introduces a machine learning (ML)-based surrogate model for finite element analysis, designed to predict structural strain distributions using a minimal number of strategically placed virtual sensors. The proposed approach eliminates the dependency on external force measurements, leveraging local strain measurements to infer global strain responses across a two-dimensional truss structure. Various regression algorithms, including decision trees and deep neural networks (DNNs), were evaluated, with DNNs achieving superior accuracy ( $\text {R}^{2}$ > 0.996, MAE < 4%, RMSE < 5.5%). The methodology significantly reduces sensor requirements and computational overhead, offering a practical, scalable solution for structural health monitoring (SHM) in complex mechanical systems. The results underscore the potential of ML-based surrogate models to enhance the efficiency and accuracy of continuous monitoring and dynamic analysis in large-scale infrastructure applications, setting the stage for future advancements in sensor optimization and three-dimensional system extensions.}, archive = {J_ACCESS}, author = {Ali Hashemi and Javad Beheshti and Mahdieh Mohammadi}, doi = {10.1109/ACCESS.2025.3590664}, journal = {IEEE Access}, pages = {130585-130602}, shortjournal = {IEEE Access}, title = {Physics-based AI-driven surrogate modeling for structural displacement prediction in mechanical systems with limited sensor data}, volume = {13}, year = {2025}, }
• (2025). Exploring virtual reality-induced anxiety in iatrophobia: A pilot study for future exposure therapy. ACCESS, 13, 130570-130584. (www)

  @article{ , abstract = {This pilot study explores the feasibility of using Virtual Reality (VR) to simulate medically related environments that elicit anxiety responses in individuals with iatrophobia—a specific phobia characterized by an intense fear of medical professionals and procedures. Rather than providing a therapeutic intervention, the aim is to validate the capacity of VR-based scenarios to induce physiological and emotional reactions associated with medical anxiety, thereby laying the groundwork for future therapeutic applications. Ten participants—five with self-reported iatrophobia and five without—were exposed to a series of VR scenarios simulating clinical settings, including a waiting room, a medical consultation, and a diagnostic procedure. The study was conducted at the Ecuadorian Institute of Social Security (IESS), Ambato Hospital, Ecuador. Physiological responses, including heart rate and electrodermal activity, were measured and analyzed using statistical methods. The results demonstrate significant differences between the experimental and control groups, with individuals with Iatrophobia exhibiting heightened anxiety responses in VR environments. Furthermore, correlation analysis reveals a strong positive association between heart rate and electrodermal response, indicating the reliability of these physiological indicators in assessing anxiety levels. The study also discusses the implications of these findings for phobia treatment and highlights future research directions, including the integration of advanced VR technologies and exploring VR’s applicability in treating other specific phobias and anxiety disorders. These findings support the use of VR for eliciting controlled emotional responses in medical contexts, which may inform the design of future exposure-based interventions for iatrophobia and other medical-related phobias.}, archive = {J_ACCESS}, author = {Revelo-Aguilar Fabian and Miró-Amarante Lourdes and Gómez-Rodríguez Francisco}, doi = {10.1109/ACCESS.2025.3591396}, journal = {IEEE Access}, pages = {130570-130584}, shortjournal = {IEEE Access}, title = {Exploring virtual reality-induced anxiety in iatrophobia: A pilot study for future exposure therapy}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient multi-hop routing protocol for tag-to-tag communication in passive RFID networks using reinforcement learning. ACCESS, 13, 130549-130569. (www)

  @article{ , abstract = {With recent advancements in Radio Frequency Identification (RFID) technology, tag-to-tag communication among passive RFID tags using backscattering has emerged as a breakthrough development. Unlike traditional RFID systems where tags only communicate with a reader, this innovation enables tags to interact directly when RF signals are present in the environment. However, due to the absence of internal power sources, the communication range of these tags is significantly limited, necessitating multi-hop communication to connect distant tags. In this paper, we propose a novel multi-hop routing protocol tailored for passive RFID tag-to-tag networks utilizing a reinforcement learning method. Our design features a circular sector-based area with a central controller that acts as the RF signal source. The area is divided into sectors based on the communication range of both the controller and the tags. To evaluate the performance of our protocol, we developed a simulation environment where passive tags are uniformly distributed within the circular area. Through multi-hop communication facilitated by the controller, energy-efficient tag-to-tag communication is achieved. The results demonstrate that our proposed protocol effectively minimizes energy consumption while ensuring reliable multi-hop communication among passive tags.}, archive = {J_ACCESS}, author = {Guowei Guo and Zeli Xi and Zhixin Lu and Ximei Zhan and Xinsen Yang and Peisong Li}, doi = {10.1109/ACCESS.2025.3591375}, journal = {IEEE Access}, pages = {130549-130569}, shortjournal = {IEEE Access}, title = {Energy-efficient multi-hop routing protocol for tag-to-tag communication in passive RFID networks using reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Design of a drivable area segmentation network using a field programmable gate array based on light detection and ranging. ACCESS, 13, 130535-130548. (www)

  @article{ , abstract = {With the continuing development of autonomous driving systems, the drivable area segmentation problem has become an indispensable part of self-driving cars. The drivable area segmentation technology introduces many features to self-driving car technology, such as providing information about the surrounding environment, assisting decision-making mechanisms, selecting appropriate driving paths, and avoiding obstacles. In addition, accurate segmentation of drivable areas is crucial for improving self-driving navigation and avoiding obstacles. To enable effective identification of drivable areas on the basis of environmental information, this study designed a drivable area segmentation network named DASNet. The proposed DASNet utilizes depthwise separable convolution as a basis/platform for feature extraction to enable features to be efficiently extracted to reduce both the computational load and required network parameters. Additionally, the proposed DASNet enhances the inference speed of the network while maintaining high accuracy. In order to reduce point cloud density without compromising essential information, we perform sampling and fusion on the point clouds in both Cartesian and spherical coordinate spaces during data preprocessing. The fused point cloud serves as the input to DASNet, while the output is the drivable area map. Finally, the proposed DASNet is ported to a field programmable gate array to achieve real-time drivable area detection. This study employs the publicly available KITTI dataset and proposed wooded environment dataset for experimental evaluation. By evaluating the model on two distinct datasets, we aim to demonstrate the capabilities across three types of urban road scenes and more densely wooded environments. The experimental results indicate that DASNet achieved an F1-score of 0.9449 and an inference speed of 9.32 ms on the KITTI dataset. Furthermore, the proposed DASNet was applied to autonomous vehicles operating in a wooded environment, achieving an F1-score of 98.64%.}, archive = {J_ACCESS}, author = {Xue-Qian Lin and Jyun-Yu Jhang and Cheng-Jian Lin and Sheng-Fu Liang}, doi = {10.1109/ACCESS.2025.3591974}, journal = {IEEE Access}, pages = {130535-130548}, shortjournal = {IEEE Access}, title = {Design of a drivable area segmentation network using a field programmable gate array based on light detection and ranging}, volume = {13}, year = {2025}, }
• (2025). Cheby-KANs: Advanced Kolmogorov–Arnold networks for applying geometric deep learning in quantum chemistry applications. ACCESS, 13, 130525-130534. (www)

  @article{ , abstract = {In this work, we present an enhanced version of the Kolmogorov–Arnold network (KAN) algorithm, called Cheby-KAN, that offers a more efficient, more reliable, and faster alternative to conventional KANs. We integrated our algorithm with a geometric deep learning model (SchNet) to assess the effect of integrating KANs representation on geometric deep learning by combining domain knowledge of quantum chemistry and our new representation. We initially tested our model on benchmark datasets, then we integrated Cheby-KAN with SchNet to obtain an enhanced model, called Cheby-KAN-SchNet. We used Cheby-KAN-SchNet to predict six quantum properties of molecules, and compared our results against the original SchNet results and another model integrating SchNet with the original KANs using basis splines to give a fair and objective comparison of our model. We expected to obtain the benefits of the powerful KANs representation and to reduce the high time and computational costs of KANs. We applied the algorithm to quantum chemistry because quantum chemistry requires extremely high precision modeling of the system where error rates increase exponentially. Our algorithm outperformed the original KANs with B-splines in terms of both speed and accuracy, and outperformed Schnet in terms of accuracy and consistency. These results demonstrate the potential of Cheby-KAN in addressing the approximation of multi-variate and complex functions under high levels of uncertainty, while offering enhanced interpretability compared to traditional neural network models or KANs with B-splines.}, archive = {J_ACCESS}, author = {Amgad Abdallah Mahmoud and Andreas Pester and Maha M. Muttardi and Frederic Andres and Shihori Tanabe and Nicolas Greneche and Hesham H. Ali}, doi = {10.1109/ACCESS.2025.3566551}, journal = {IEEE Access}, pages = {130525-130534}, shortjournal = {IEEE Access}, title = {Cheby-KANs: Advanced Kolmogorov–Arnold networks for applying geometric deep learning in quantum chemistry applications}, volume = {13}, year = {2025}, }
• (2025). A programmable active recharge circuit for SPAD in 110-nm BSI CMOS. ACCESS, 13, 130517-130524. (www)

  @article{ , abstract = {This paper presents a programmable active recharge (AR) circuit optimized for a single-photon avalanche diode (SPAD) fabricated in a 110-nm BSI CMOS process. The SPAD employs a p-well and deep n-well junction. A retrograde p-substrate guard-ring, which is located at the junction edges, suppresses the premature breakdown of the SPAD. An NMOS recharge transistor is connected to the SPAD anode and is driven by an active recharge signal ( $\Phi _{\mathrm {AR}}$ ), which is generated by the AR circuit. The AR circuit consists of an NMOS recharge transistor ( ${\mathrm{M}}_{\mathrm {AR}}$ ), a current-starved inverter chain, and an AND gate, which detects the SPAD pulse and recharges the SPAD for the consecutive photon detection. By controlling the bias of the inverter chain, the delay and pulse width of $\Phi _{\mathrm {AR}}$ (DEL and WID) can be externally controlled. To optimize the timing of the AR circuit, an afterpulsing probability (APP) is characterized using inter-avalanche histogramming technique. The APP is measured at 0.1% with an interval range of $20~\mu $ s. When adjusting the delay of $\Phi _{\mathrm {AR}}$ , the number of irregular peaks within the first 2 ns time interval are increased by a factor of 3.2. In contrast, varying the pulse width of $\Phi _{\mathrm {AR}}$ shows no significant correlation with the number of irregular peaks. These results demonstrate that the delay of $\Phi _{\mathrm {AR}}$ should be controlled precisely for minimizing APP and improving the SPAD performance in high-speed photon counting applications.}, archive = {J_ACCESS}, author = {Hyunho Moon and Byungchoul Park and Hyeon-June Kim}, doi = {10.1109/ACCESS.2025.3591115}, journal = {IEEE Access}, pages = {130517-130524}, shortjournal = {IEEE Access}, title = {A programmable active recharge circuit for SPAD in 110-nm BSI CMOS}, volume = {13}, year = {2025}, }
• (2025). Decoupled latent diffusion model for enhancing image generation. ACCESS, 13, 130505-130516. (www)

  @article{ , abstract = {Latent Diffusion Models have emerged as an efficient alternative to conventional diffusion approaches by compressing high-dimensional images into a lower-dimensional latent space using a Variational Autoencoder (VAE) and performing diffusion in that space. In standard Latent Diffusion Model (LDM), the latent code is formed by sampling from a Gaussian distribution (i.e., combining both the mean and the standard deviation), which helps regularize the latent space but appears to contribute little beyond the deterministic component. Motivated by recent empirical observations that the decoder relies primarily on the latent mean, our work reexamines this paradigm and proposes a decoupled latent diffusion model that focuses on a simplified latent representation. Specifically, we compare three configurations: (i) the standard latent code, (ii) a concatenated representation that explicitly preserves both mean and variance, and (iii) a deterministic mean-only representation. Our extensive experiments on multiple benchmark datasets demonstrate that, when compared to the standard approach, the mean-only configuration not only maintains but in many cases improves synthesis quality by producing sharper and more coherent images while reducing unnecessary noise. These findings suggest that a simplified, deterministic latent representation can yield more stable and efficient generative models, challenging the conventional reliance on latent sampling in diffusion-based image synthesis.}, archive = {J_ACCESS}, author = {Hyun-Tae Choi and Kensuke Nakamura and Byung-Woo Hong}, doi = {10.1109/ACCESS.2025.3592163}, journal = {IEEE Access}, pages = {130505-130516}, shortjournal = {IEEE Access}, title = {Decoupled latent diffusion model for enhancing image generation}, volume = {13}, year = {2025}, }
• (2025). Graphormer boosted: Molecular property prediction with enhanced graph spatial and edge encodings. ACCESS, 13, 130492-130504. (www)

  @article{ , abstract = {Transformer-based architectures have gained popularity across various domains, including graph representation learning. However, selecting an optimal transformer configuration remains challenging, as attention-based models are highly sensitive to parameter choices and input value ranges. Even state-of-the-art architectures can underperform without proper tuning, while simple yet thoughtful modifications can unlock significant performance gains by better leveraging graph structures. In this work, we propose an enhancement to the Graphormer architecture that refines the attention mechanism by unifying spatial encodings, edge encodings, and similarity matrices. Specifically, we introduce nonlinear transformations, such as softmax, sigmoid, or tanh, to normalize these encodings within the attention calculation. This ensures balanced contributions from all terms, mitigating drawbacks caused by disparate value ranges. We explore two main approaches: 1) element-wise application of nonlinearities to bound spatial and edge encodings and 2) row-wise softmax normalization to emphasize their relative importance within the graph structure. The latter preserves relational information, enhancing the expressiveness of the model and improving the prioritization of node and edge. Experiments on molecular datasets demonstrate consistent performance improvements over the baseline Graphormer, highlighting the effectiveness of our approach. Additionally, our model outperforms traditional graph learning models. Our findings suggest that carefully designed nonlinear transformations over structural encodings significantly boost transformer-based graph models, offering a simple yet powerful strategy for improved graph representation learning.}, archive = {J_ACCESS}, author = {Sarah A. Fadlallah and Francesc Serratosa and Carme Julià}, doi = {10.1109/ACCESS.2025.3591966}, journal = {IEEE Access}, pages = {130492-130504}, shortjournal = {IEEE Access}, title = {Graphormer boosted: Molecular property prediction with enhanced graph spatial and edge encodings}, volume = {13}, year = {2025}, }
• (2025). Group-specific SVM with bilevel programming methods for parameter optimization and explainable AI in urban quality of life prediction. ACCESS, 13, 130475-130491. (www)

  @article{ , abstract = {The assessment of urban quality of life (UQoL) is a multidimensional challenge involving socioeconomic, environmental, and infrastructure-related variables that, due to changes in the behavior and structures of social issues, requires robust and effective analytical techniques. This study introduces a novel machine learning-based approach incorporating bilevel programming methods to optimize predictive models for UQoL. A key contribution is the development of a Support Vector Machine (SVM) model for group-specific regression, enhanced by a bilevel programming reformulation that optimizes the hyperparameters of the model over groups of data. The bilevel models consider linear and quadratic loss functions. The proposed approach is benchmarked against Linear Regression, Regression Tree, Random Forest, and Gradient Boosting models. The evaluation is conducted using a cross-validation procedure computing the Mean Absolute Error, the Mean Squared Error, and the Mean Absolute Percentage Error as performance metrics. The results indicate that the bilevel quadratic loss SVM model outperforms other models, achieving the lowest prediction error. Furthermore, explainable AI techniques are applied with the usage of Shapley values to analyze feature importance, revealing that own incomes, teacher-to-population ratios, and birth rates are key predictors of UQoL. These findings provide valuable insights for policymakers, supporting data-driven decision-making processes for urban development and resource allocation. This study highlights the potential of AI-driven methodologies in advancing UQoL assessments and urban planning strategies as support systems.}, archive = {J_ACCESS}, author = {Gonzalo Ríos-Vásquez and Hanns De La Fuente-Mella and José Ceroni-Díaz}, doi = {10.1109/ACCESS.2025.3592156}, journal = {IEEE Access}, pages = {130475-130491}, shortjournal = {IEEE Access}, title = {Group-specific SVM with bilevel programming methods for parameter optimization and explainable AI in urban quality of life prediction}, volume = {13}, year = {2025}, }
• (2025). AI-based forecasting in renewable-rich microgrids: Challenges and comparative insights. ACCESS, 13, 130446-130474. (www)

  @article{ , abstract = {Amid the accelerating global transition to renewable energy, accurate forecasting has become the cornerstone for unlocking the full potential of solar and wind power in modern power grids, especially in regions with high resource variability. This study begins with a review of forecasting challenges in microgrids located in developing areas where issues related to data sparsity, model limitations, environmental variability, and operational limitations are prevalent. To address some of these challenges, we benchmarked twelve machine learning (ML) and deep learning (DL) models using high-resolution hourly data from South Africa’s national grid data, covering Energy Demand, Photovoltaic (PV), and Wind generation over five years. Our results show that Bidirectional-Long Short-term Memory (Bi-LSTM) achieved the best demand and PV forecasting performance with a Root Mean Square Error (RMSE) / R2 of 303.95, Megawatt MW/ 0.9877 and 48.04 MW / 0.996, while XGBoost delivered a competitive accuracy for PV (RMSE: 49.74 MW, R2: 0.996) and Wind (RMSE: 119.77 MW, R2: 0.964). Classical ML models outperformed most DL architectures, including Transformer and Convolutional Neural Network (CNN)-LSTM, which underperformed despite their complexity. Statistical testing via Friedman ( $\chi ^{2} = 10$ ,858.39, p <0.00001) and Nemenyi post-hoc tests confirmed significant differences in model performance. The study demonstrates that, with effective feature engineering, classical ML models can rival deep learning counterparts in forecasting accuracy. This finding provides actionable insights for utilities and policymakers by highlighting robust, low-latency models suitable for practical deployment in operational power systems.}, archive = {J_ACCESS}, author = {Martins Osifeko and Josiah Lange Munda}, doi = {10.1109/ACCESS.2025.3591091}, journal = {IEEE Access}, pages = {130446-130474}, shortjournal = {IEEE Access}, title = {AI-based forecasting in renewable-rich microgrids: Challenges and comparative insights}, volume = {13}, year = {2025}, }
• (2025). Power equipment image recognition method based on feature extraction and deep learning. ACCESS, 13, 130430-130445. (www)

  @article{ , abstract = {Traditional image recognition methods for power equipment face challenges such as difficulty in distinguishing target features from background features and insufficient feature extraction capabilities. This paper proposes an improved attention mechanism-based network for image detection and recognition of power equipment. The proposed method introduces a target feature prediction strategy tailored to power equipment: it incorporates a learning mechanism for depth variation to extract deep semantic information from images; enhances the global structure learning network module by stacking convolutional kernels and removing pooling layers in the front-end network, thereby acquiring prior information rich in detailed and correlated image features of power equipment. Furthermore, a long short-term memory (LSTM) gate mechanism is employed to predict power equipment target features at different levels of image feature information, constructing an attention mechanism network based on the LSTM gating mechanism. Additionally, the method introduces a deep-shallow feature interaction strategy: it integrates shallow and deep feature information through matrix outer product operations, enabling the model to fully learn multi-level features of power equipment. Compared with traditional power equipment image recognition methods, the proposed approach enhances the recognition and extraction of detailed target features, accurately distinguishes blurred boundaries between background and targets, and improves the interaction between deep and shallow features, effectively increasing recognition accuracy in complex background environments. Experimental results show that, on image datasets of five types of power equipment—insulators, transformers, circuit breakers, transmission poles, and transmission towers—the proposed model achieves a recognition accuracy of 92%, which is 1.6% higher than that of the CvT model. Future research will focus on further enhancing the model’s robustness and generalization ability in complex scenarios. We plan to introduce a lightweight convolutional structure combined with a graph neural network mechanism to strengthen global context modeling and device structural awareness. This will enable efficient and interpretable identification and localization of power equipment in application scenarios such as automated substation inspections and real-time monitoring with drones.}, archive = {J_ACCESS}, author = {Shuang Lin}, doi = {10.1109/ACCESS.2025.3592017}, journal = {IEEE Access}, pages = {130430-130445}, shortjournal = {IEEE Access}, title = {Power equipment image recognition method based on feature extraction and deep learning}, volume = {13}, year = {2025}, }
• (2025). MEIL-NeRF: Memory-efficient incremental learning of neural radiance fields. ACCESS, 13, 130420-130429. (www)

  @article{ , abstract = {Hinged on the representation power of neural networks, neural radiance fields (NeRF) have recently emerged as one of the promising and widely applicable methods for 3D object and scene representation. However, NeRF faces challenges in practical applications, such as large-scale scenes and edge devices with a limited amount of memory, where data needs to be processed sequentially. Under such incremental learning scenarios, neural networks are known to suffer catastrophic forgetting: easily forgetting previously seen data after training with new data. We observe that previous incremental learning algorithms are limited by either low performance or memory scalability issues. As such, we develop a Memory-Efficient Incremental Learning algorithm for NeRF (MEIL-NeRF). MEIL-NeRF takes inspiration from NeRF itself in that a neural network can serve as a memory that returns pixel RGB values, given rays as queries. Upon the motivation, our framework learns which rays to query NeRF to extract previous pixel values. The extracted pixel values are then used to train NeRF in a self-distillation manner to prevent catastrophic forgetting. As a result, MEIL-NeRF demonstrates constant memory consumption and competitive performance.}, archive = {J_ACCESS}, author = {Jaeyoung Chung and Kanggeon Lee and Sungyong Baik and Kyoung Mu Lee}, doi = {10.1109/ACCESS.2025.3578393}, journal = {IEEE Access}, pages = {130420-130429}, shortjournal = {IEEE Access}, title = {MEIL-NeRF: Memory-efficient incremental learning of neural radiance fields}, volume = {13}, year = {2025}, }
• (2025). Joint channel allocation and power control for mission-critical applications in sustainable underwater acoustic networks. ACCESS, 13, 130406-130419. (www)

  @article{ , abstract = {Underwater Acoustic Networks (UANs) are gaining popularity for underwater communication due to their long-range capabilities. However, they experience significant challenges, including limited bandwidth and spectrum overlap with marine mammal vocalizations, which raises concerns about the sustainable and ethical use of underwater acoustic resources. This study proposes an eco-friendly approach that integrates joint channel allocation and power control to minimize harmful interference with marine life while ensuring reliable communication for mission-critical applications, such as diver safety. To balance marine protection and communication reliability, this work introduces a strategy for intelligent spectrum management, where nodes first sense the acoustic spectrum to detect the presence of marine mammals, followed by channel gain calculations. Based on these insights, a spectrum decision mechanism allocates appropriate channels, and the power control mechanism determines optimal transmission power levels that minimize interference with marine mammals. The model also guarantees the minimum required transmitter power for critical applications, thereby avoiding network disconnections and ensuring the required Quality of Service (QoS) rate. Results show that the proposed solution achieves a practical trade-off by improving total network capacity and reducing end-to-end delay, while minimizing environmental impact and lowering computation complexity compared to related studies. This work promotes the environmental sustainability of underwater acoustic networks, aligning with the UN Sustainable Development Goals.}, archive = {J_ACCESS}, author = {Sadaf Vahabli and Iftekhar Ahmad and Quoc Viet Phung and Daryoush Habibi}, doi = {10.1109/ACCESS.2025.3590778}, journal = {IEEE Access}, pages = {130406-130419}, shortjournal = {IEEE Access}, title = {Joint channel allocation and power control for mission-critical applications in sustainable underwater acoustic networks}, volume = {13}, year = {2025}, }
• (2025). Swin transformer with late-fusion feature aggregation for multi-modal vehicle reidentification. ACCESS, 13, 130393-130405. (www)

  @article{ , abstract = {Vehicle reidentification is a problem that coexisted with the advent of CCTV technology for road monitoring. Vehicle image data from low-light environments is very challenging for reidentification tasks, and multi-modal data (visible, near-infrared, and thermal) is often used to improve model performance. In this paper, we proposed a Swin Transformer classifier with late-fusion feature aggregation networks called SAFA (Self-Attention Feature Aggregation) for multi-modal vehicle reidentification problems. The proposed SAFA classifier is constructed by attaching multi-head networks with CBAM (Convolutional Block Attention Module) and MWN (Modality Weighted Network) to the three parallel shared Swin Transformer architecture for each modality input (visual, near-infrared, and thermal). Three multi-modal vehicle reidentification datasets were used to evaluate our proposed classifier, including RGBN300, RGBNT100, and WMVeID863 datasets. Experiments on the RGBN300, RGBNT100, and WMVeID863 datasets show that our proposed classifier can achieve good performance, with an mAP of 85.1% on the RGBN300 dataset, an mAP of 88.4% on the RGBNT100 dataset, and an mAP of 70.5% on the WMVeID863 dataset. Further analysis using t-SNE and GradCAM visualization shows that our proposed classifier can effectively distinguish different vehicle IDs by extracting strong features, with the headlight and backlight of the vehicle being the main regions extracted in the SAFA network.}, archive = {J_ACCESS}, author = {Reza Fuad Rachmadi and Supeno Mardi Susiki Nugroho and I. Ketut Eddy Purnama}, doi = {10.1109/ACCESS.2025.3591251}, journal = {IEEE Access}, pages = {130393-130405}, shortjournal = {IEEE Access}, title = {Swin transformer with late-fusion feature aggregation for multi-modal vehicle reidentification}, volume = {13}, year = {2025}, }
• (2025). High-performance modeling and optimization of wireless networks using bottleneck structures. ACCESS, 13, 130363-130392. (www)

  @article{ , abstract = {Bottleneck structures have been recently introduced as an efficient mathematical framework for modeling communication systems. Leveraging fast computational graph algorithms from the field of automatic differentiation such as backpropagation, these structures can solve certain wired network problems two to three orders of magnitude faster than traditional network simulators, without losing significant precision. In this paper, we extend the theory of bottleneck structures to incorporate channel interference, enabling their application to wireless network modeling. This generalization leads to the development of a novel mathematical simulator for wireless networks. By introducing a new class of water-filling algorithms that exploit the structure of the computational graph, we demonstrate that bottleneck structures can simulate wireless networks with thousands of user equipments (UEs) in just a few seconds—achieving speedups of two to four orders of magnitude compared to state-of-the-art linear and non-linear programming solvers. For example, in a network with approximately 3000 UEs and 30 base stations, our approach reduces computation time from 145 to 1.08 seconds when assuming generalized processor sharing (GPS) schedulers, and from 2470 to 0.04 seconds when assuming proportional fair (PF) schedulers. To showcase the practical utility of this framework in network optimization, we also integrate bottleneck structures into a mixed-integer linear programming solver and apply it to the antenna placement problem, demonstrating scalability to networks with thousands of UEs.}, archive = {J_ACCESS}, author = {Aleix Torres-Camps and Alex Batlle Casellas and Yuyang Wang and Mauro Filomeno Rivero and Naga Bhushan and Jordi Ros-Giralt}, doi = {10.1109/ACCESS.2025.3586752}, journal = {IEEE Access}, pages = {130363-130392}, shortjournal = {IEEE Access}, title = {High-performance modeling and optimization of wireless networks using bottleneck structures}, volume = {13}, year = {2025}, }
• (2025). ML-augmented optimization of LoRa antennas for drone telemetry. ACCESS, 13, 130353-130362. (www)

  @article{ , abstract = {A compact printed monopole antenna for drone telemetry communication, operating at 433 MHz, with a gain of 2.2 dBi, is designed. The antenna is fabricated on an FR-4 substrate with dimensions of $0.116~\lambda _{0} \times 0.073~\lambda _{0}$ and is optimized for long-range communication. A Machine Learning-augmented Optimization (MLaO) method is proposed to reduce the antenna design time compared to traditional electromagnetic simulation techniques. Typically, antenna design involves complex computer simulations like CST and computationally intensive parameter sweeps. However, in this work a surrogate Artificial Neural Network (ANN) model trained on 1080 antenna designs replaces the heavy CST simulations. This ANN model is then coupled with a Simulated Annealing (SA) optimizer to generate antennas with the desired characteristics, reducing the total design time to 57% compared to traditional techniques. Three antenna designs were simulated using MLaO for different long-range (LoRa) frequency bands, with Ata1 (433 MHz) achieved a return loss (S11) of −23.3 dB, Ata2 (865 MHz) had an S11 of −30.6 dB, and Ata3 (dual band at 433 MHz and 865 MHz) with S11 of −15.6 dB and −35.4 dB respectively. The fabricated antenna (433 MHz) was mounted on a drone and tested with a 3DR-433 telemetry transceiver, recording an average Received Signal Strength Indicator (RSSI) of −57.8 dBm up to 470 m. These results demonstrate the proposed antenna’s efficiency, compactness, and the effectiveness of the MLaO approach for fast and accurate antenna design.}, archive = {J_ACCESS}, author = {Pothala Chaya Devi and Ramarakula Madhu}, doi = {10.1109/ACCESS.2025.3592096}, journal = {IEEE Access}, pages = {130353-130362}, shortjournal = {IEEE Access}, title = {ML-augmented optimization of LoRa antennas for drone telemetry}, volume = {13}, year = {2025}, }
• (2025). EvSat3D: Satellite pose estimation and 3D reconstruction with event camera. ACCESS, 13, 130340-130352. (www)

  @article{ , abstract = {Accurate object pose estimation is vital in robotics and satellite operations, especially during rendezvous missions involving proximity interactions with orbiting satellites, non-cooperative objects, or space debris. Traditional frame-based cameras struggle in the space environment due to limitations such as motion blur, low dynamic range, and low frame rates—conditions exacerbated by rapid relative motion, abrupt lighting changes, and high contrast variations in orbit. Event cameras offer a compelling alternative with their unique advantages: ultra-high temporal resolution (>10,000 fps), wide dynamic range (up to 140 dB), and low power consumption (1mW), making them especially suited for the extreme visual conditions in space. This paper introduces EvSat3D, a comprehensive and robust pipeline for event-based satellite pose estimation and 3D reconstruction. The pipeline leverages event-to-frame (E2F) conversion algorithms to generate frames from asynchronous event data and applies Structure-from-Motion (SfM) techniques for accurate camera pose recovery and 3D scene reconstruction. We validate EvSat3D using the only publicly available event-based satellite dataset and demonstrate its effectiveness under challenging conditions. Our results highlight the feasibility and performance benefits of using event cameras for satellite navigation and pose estimation tasks in space.}, archive = {J_ACCESS}, author = {Shahid Shabeer Malik and Maryam Moshrefizadeh and Omar Tahri and Xiaoli Bai and Erik Blasch and Vasit Sagan and Hadi AliAkbarpour}, doi = {10.1109/ACCESS.2025.3589506}, journal = {IEEE Access}, pages = {130340-130352}, shortjournal = {IEEE Access}, title = {EvSat3D: Satellite pose estimation and 3D reconstruction with event camera}, volume = {13}, year = {2025}, }
• (2025). A comprehensive analysis of bandwidth and size for four-types of series-combined transformer-based doherty output networks: Theory and examples. ACCESS, 13, 130323-130339. (www)

  @article{ , abstract = {This paper presents theory-driven design guidelines for four types of series-combined transformer (SCT)-based Doherty output matching networks to maximize bandwidth (BW). Novel and intuitive methods were proposed for analyzing the series Doherty combiners. Compared with prior analyses relying solely on ideal or insufficient models, this study systematically derives closed-form design equations based on non-ideal transformer-based practical circuit models. Doherty load modulation was clearly demonstrated for all four types using two combinations of SCT models and four capacitor configurations. Furthermore, the design parameters were simplified, and the equations were validated for both symmetrical and asymmetrical SCTs. To estimate the maximum achievable BW and identify the key design factors, four design examples of each type were compared using circuit simulations and numerical analysis. A clear trend observed across all four types is that a higher magnetic coupling coefficient results in a larger fractional BW. It is also demonstrated that the distinct “sweet spots” for the four types, when maximizing BW, depend mainly on only 2 or 3 variables, including turn ratios and/or self-inductances. SCT sizes were compared based on the sum of the two transformer dimensions. To validate the circuit model, a design example was built through a detailed procedure as the best practice, and a layout-based full-EM structure achieved close-to-theoretical BW. For further verification, four SCT-based Doherty power amplifiers were simulated and compared.}, archive = {J_ACCESS}, author = {Seokchul Lee and John D. Cressler}, doi = {10.1109/ACCESS.2025.3589508}, journal = {IEEE Access}, pages = {130323-130339}, shortjournal = {IEEE Access}, title = {A comprehensive analysis of bandwidth and size for four-types of series-combined transformer-based doherty output networks: Theory and examples}, volume = {13}, year = {2025}, }
• (2025). Enhancing sentiment-driven recommender systems with LLM-based feature engineering: A case study in drug review analysis. ACCESS, 13, 130304-130322. (www)

  @article{ , abstract = {Sentiment analysis is vital for evaluating user feedback in drug reviews because understanding patient experiences leads to more personalized treatment recommendations by providing insights into the real-world effectiveness and tolerability of medications, which are often overlooked in clinical trials. This study evaluates the effectiveness of word-level and sentence-level embeddings for feature extraction in sentiment analysis. These embeddings are used in sequential models (Bi-LSTM, CNN) and non-sequential models (Random Forest, DNN, ExtraTreesClassifier). The Random Forest model with LLM2Vec achieves the best performance, with 0.93 accuracy, F1-scores of 0.95 (positive) and 0.88 (negative), and precision scores of 0.93 (positive) and 0.94 (negative). This approach detects subtle negative feedback often missed by standard models. To capture social consensus in patient feedback, we introduce Adaptive Confidence-Weighted Scoring. This method leverages social validation as an implicit confidence signal, enabling sentiment scores to reflect both individual experiences and community agreement. It enhances trust and interpretability while standardizing sentiment polarity on a $[-5, +5]$ scale. Clinically validated drug-related information, scraped from trusted sources, is encoded using the Llama-3.2-3B-Instruct model to extract context-aware representations. Features derived from this external medical knowledge improve semantic understanding, ensure grounding and safety, and enhance robustness even when reviews are sparse or noisy. The final vectors are employed within a cosine similarity framework to recommend relevant drugs, aligning recommendations with user satisfaction. This work demonstrates how LLM-based feature engineering can advance clinically valid, patient-aligned healthcare recommender systems informed by real-world feedback.}, archive = {J_ACCESS}, author = {Samuel Matia Kangoni and Obed Tshimanga Tshipata and Pierre Sedi Nzakuna and Vincenzo Paciello and Jean-Gilbert Mbula Mboma and Jean-Robert Makulo and Kyandoghere Kyamakya}, doi = {10.1109/ACCESS.2025.3590326}, journal = {IEEE Access}, pages = {130304-130322}, shortjournal = {IEEE Access}, title = {Enhancing sentiment-driven recommender systems with LLM-based feature engineering: A case study in drug review analysis}, volume = {13}, year = {2025}, }
• (2025). Cutting-edge EMS technologies for EVs and HEVs: Recent developments and future directions. ACCESS, 13, 130256-130303. (www)

  @article{ , abstract = {Energy Management Strategies (EMS) are critical for optimizing energy utilization, improving performance, and reducing environmental impact in Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs). This study aims to address the growing complexities in EMS development by introducing a novel classification framework that organizes existing methodologies into rule-based, optimization-based, and intelligent transportation system (ITS)-based approaches. The research systematically explores the influence of advanced powertrain architectures and charging technologies on EMS objectives. Recent advancements, such as the integration of machine learning, predictive controls, and multi-objective optimization, are analyzed to highlight their contributions to addressing challenges in modern electrified transportation systems. Key findings demonstrate the importance of emerging trends like vehicle-to-everything (V2X) communication and renewable energy integration in enhancing EMS capabilities. The study concludes by emphasizing the necessity of adaptive and innovative solutions to meet the increasing demands of energy-efficient and sustainable transportation. This work provides a comprehensive and objective representation of current advancements, offering insights to guide future research and practical applications in the field.}, archive = {J_ACCESS}, author = {Yashar Farajpour and Armin Lotfy and Hicham Chaoui and Sousso Kelouwani}, doi = {10.1109/ACCESS.2025.3582527}, journal = {IEEE Access}, pages = {130256-130303}, shortjournal = {IEEE Access}, title = {Cutting-edge EMS technologies for EVs and HEVs: Recent developments and future directions}, volume = {13}, year = {2025}, }
• (2025). AEQUAM: Accelerating quantum algorithm validation through FPGA-based emulation. ACCESS, 13, 130232-130255. (www)

  @article{ , abstract = {This work presents AEQUAM (Area Efficient QUAntum eMulation), a toolchain that enables faster and more accessible quantum circuit verification. It consists of a compiler that translates OpenQASM 2.0 into RISC-like instructions, Cython software models for selecting number representations and simulating circuits, and a VHDL generator that produces RTL descriptions for FPGA-based hardware emulators. The architecture leverages a SIMD approach to parallelize computation and reduces complexity by exploiting the sparsity of quantum gate matrices. The VHDL generator allows customization of the number of emulated qubits and parallelization levels to meet user requirements. Synthesized on an Altera Cyclone 10LP FPGA with a 20-bit fixed-point representation and nearest-type approximation, the architecture demonstrates better scalability than other state-of-the-art emulators. Specifically, the emulator has been validated by exploiting the well consolidated benchmark of mqt bench framework.}, archive = {J_ACCESS}, author = {Lorenzo Lagostina and Deborah Volpe and Maurizio Zamboni and Giovanna Turvani}, doi = {10.1109/ACCESS.2025.3589746}, journal = {IEEE Access}, pages = {130232-130255}, shortjournal = {IEEE Access}, title = {AEQUAM: Accelerating quantum algorithm validation through FPGA-based emulation}, volume = {13}, year = {2025}, }
• (2025). Resource allocation in user-centric cell-free massive MIMO URLLC systems with network slicing. ACCESS, 13, 130215-130231. (www)

  @article{ , abstract = {With the development of sixth-generation mobile communication technology (6G), ultra-reliable and low latency communication (URLLC) is vital for latency-sensitive applications. User-centric cell-free massive MIMO (CF-mMIMO) architectures meet these demands by removing cell boundaries and ensuring fair service for edge users. However, resource allocation in user-centric CF-mMIMO networks with URLLC constraints presents significant challenges. In this paper, we present a novel resource allocation scheme for network slicing based user-centric CF-mMIMO networks to maximize the achievable sum data rate while maintaining the quality of URLLC services. To solve the inherently non-convex optimization problem, the original problem is decomposed into two subproblems: the user-slice association problem, which optimizes the user-slice mapping variables, and the resource allocation problem, which optimizes the bandwidth allocation and power control coefficients. The user-slice association problem is addressed using the simulated annealing algorithm. For the resource allocation problem, a successive rate lower bound maximization algorithm is employed to obtain the convex lower bound of the data rate under short packet transmission, and the successive convex approximation (SCA) method is used to determine the optimal bandwidth and frequency allocation. Simulation results show that the proposed scheme improves the sum data rate by at least 10.24% and reduces the average transmission delay by at least 33.40% compared to the existing algorithms.}, archive = {J_ACCESS}, author = {Han Lu and Weiwei Xia and Weiwei Miao and Mingxuan Zhang and Feng Yan and Lianfeng Shen}, doi = {10.1109/ACCESS.2025.3591008}, journal = {IEEE Access}, pages = {130215-130231}, shortjournal = {IEEE Access}, title = {Resource allocation in user-centric cell-free massive MIMO URLLC systems with network slicing}, volume = {13}, year = {2025}, }
• (2025). Tracing data origins in smart cities: An IoT perspective. ACCESS, 13, 130201-130214. (www)

  @article{ , abstract = {The rapid growth in the quantity of interconnected Internet of Things devices causes the continuous production of complex and diverse heterogeneous data sets, which encounter significant challenges in big data processing and security. This study highlights the critical function that data provenance serves within smart cities and IoT frameworks. An important task solved by data provenance is tracking the entire lifecycle of the data: their creation, transmission, storage, and processing. This paper emphasizes the critical role of data quality in the reliability of IoT applications. This review presents a comprehensive analysis of current IoT provenance methods and technologies, and we propose a taxonomy that classifies the key components of IoT data provenance systems in smart cities. The findings aim to assist researchers and practitioners with the design and application of effective data provenance approaches suited to smart city systems. Additionally, a case study on secure air quality monitoring demonstrates how provenance mechanisms can be implemented in a real-world IoT network.}, archive = {J_ACCESS}, author = {Binara Imankulova and Wael M.S. Yafooz and Kaznah Alshammari and Basemah Alshemali and Abdulsamad E. Yahya and Ali Abd Almisreb}, doi = {10.1109/ACCESS.2025.3591277}, journal = {IEEE Access}, pages = {130201-130214}, shortjournal = {IEEE Access}, title = {Tracing data origins in smart cities: An IoT perspective}, volume = {13}, year = {2025}, }
• (2025). Exploring tensor-based optimization for missing EEG signal recovery: A comparative study of optimization methods across different tensor decomposition frameworks. ACCESS, 13, 130185-130200. (www)

  @article{ , abstract = {Electroencephalography (EEG) signals are frequently compromised by missing data due to electrode contact issues or subject movement. Tensor decomposition has emerged as a powerful technique for analyzing multidimensional EEG data. This study evaluates various tensor-based methods for reconstructing structured and unstructured missing EEG signals, including recovery methods based on canonical polyadic (CP) decomposition, tensor singular value decomposition (t-SVD), and tucker decomposition. Notably, this research represents the first application of t-SVD-based recovery to missing EEG data. To rigorously assess the efficacy of our proposed method, we implement a dual evaluation framework encompassing: a) signal recovery indices and b) classification performance metrics. In the most challenging structured missing data scenario (missing segments $N=80$ , duration $d=1.8s$ ), the t-SVD-based method demonstrate superior performance based on signal recovery indices, followed by the tucker-based method, while CP-based approaches exhibited inferior results. We further evaluate the impact of signal recovery on classification accuracy by comparing performance on complete, missing, and recovered data using six classifiers and five metrics. In the most severe structured-missing case, employing a Linear Discriminant Analysis classifier, the t-SVD method enhance accuracy from 46.36% (missing data) to 56.50%, surpassing the tucker-based method (54.29%) and both CP-based methods (48.21% and 49.07%), approaching the accuracy achieved with the original data (58.57%). These results demonstrate the superior capability of t-SVD-based recovery method in preserving discriminative features within EEG signals. This research offers promising solutions for enhancing EEG signals quality and subsequent analysis in challenging real-world scenarios.}, archive = {J_ACCESS}, author = {Yue Zhang and Huanmin Ge and Chencheng Huang and Xinhua Su}, doi = {10.1109/ACCESS.2025.3591278}, journal = {IEEE Access}, pages = {130185-130200}, shortjournal = {IEEE Access}, title = {Exploring tensor-based optimization for missing EEG signal recovery: A comparative study of optimization methods across different tensor decomposition frameworks}, volume = {13}, year = {2025}, }
• (2025). Hybrid learning framework for explainable cardiovascular disease detection. ACCESS, 13, 130168-130184. (www)

  @article{ , abstract = {Cardiovascular disease (CVD) is a leading cause of global mortality, necessitating predictive models that are both accurate and interpretable. This study introduces the Hybrid Polynomial Ensemble Model (HPEM), a novel ensemble learning framework that combines Random Forest (RF), Gradient Boosting (GB), and Support Vector Machines (SVM), with Extreme Gradient Boosting (XGBoost) as a meta-learner. The model integrates advanced feature engineering, including second-degree polynomial expansion and mutual information-based selection, to enhance learning from heterogeneous patient data. HPEM was evaluated on three publicly available datasets, achieving an accuracy of 91%, 88%, and 89% respectively. It also attained F1-scores of 0.91, 0.88, and 0.89, and Area Under the Curve (AUC) values up to 0.96, outperforming baseline models across all metrics. To ensure transparency, explainability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were applied, identifying key clinical predictors including cholesterol levels, exercise-induced angina, and age. The model also achieved Cohen’s Kappa scores above 0.79 and Matthews Correlation Coefficient (MCC) values above 0.80, demonstrating reliability and robustness. These results underscore the potential of HPEM as a clinically relevant tool for early detection and risk stratification of cardiovascular disease.}, archive = {J_ACCESS}, author = {Saeed Al Gharib and Jinan Charafeddine and Fadi Dornaika and Samir Haddad}, doi = {10.1109/ACCESS.2025.3591241}, journal = {IEEE Access}, pages = {130168-130184}, shortjournal = {IEEE Access}, title = {Hybrid learning framework for explainable cardiovascular disease detection}, volume = {13}, year = {2025}, }
• (2025). Enhanced spectral efficiency in RIS-assisted MIMO systems through joint precoding and RIS configuration. ACCESS, 13, 130159-130167. (www)

  @article{ , abstract = {Reconfigurable Intelligent Surfaces (RIS) offer a promising solution to enhance wireless communication performance, particularly for 6G networks. This paper proposes a unified joint optimization framework to enhance spectral efficiency in reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems. Unlike conventional approaches that optimize RIS phase shifts and transmitter precoding separately, our method jointly optimizes both using an iterative strategy. Water-filling is used for power allocation across channel eigenmodes, while manifold optimization ensures efficient phase shift updates under unit-modulus constraints. Extensive simulations under diverse channel conditions reveal a consistent spectral efficiency improvement of up to 39.38%, outperforming across RIS sizes and transmit powers. These results highlight the contribution of combining RIS configuration and transmitter precoding into a coordinated optimization loop, guided by channel eigenmode alignment and practical implementation constraints. The enhanced performance stems from the algorithm’s ability to dynamically coordinate power allocation with channel eigenmode alignment, making it a viable solution for next-generation wireless systems requiring high spectral efficiency.}, archive = {J_ACCESS}, author = {Mohammed Adil Abbas and Aqiel N. Almamori and Ahmed Jumaa Lafta and Asaad H. Sahar}, doi = {10.1109/ACCESS.2025.3590964}, journal = {IEEE Access}, pages = {130159-130167}, shortjournal = {IEEE Access}, title = {Enhanced spectral efficiency in RIS-assisted MIMO systems through joint precoding and RIS configuration}, volume = {13}, year = {2025}, }
• (2025). Star-YOLO: A lightweight real-time wheat grain detection model for embedded deployment. ACCESS, 13, 130147-130158. (www)

  @article{ , abstract = {With the rapid advancement of precision agriculture, traditional object detection algorithms struggle with limited efficiency and accuracy in wheat grain detection and counting, while the need for real-time deployment of deep learning models on embedded devices becomes increasingly critical. To this end, this paper introduces Star-YOLO, a lightweight wheat grain detection model built upon YOLOv11n. The model employs StarNet to refine the C3k2 structure, reducing computational complexity without compromising detection accuracy, and integrates the MBConv module into the detection head to boost feature extraction while further minimizing computational load. A Shape-NWD loss function is designed, incorporating shape and scale information of target bounding boxes to refine regression, tackling the challenge of distinguishing overlapping wheat grains. Experimental results indicate that Star-YOLO attains a mean Average Precision (mAP) of 97.3% in wheat grain detection, outperforming models like YOLOv5n and RT-DETR in both accuracy and efficiency, with a reduced parameter count. Tests on embedded devices reveal a 36.8% performance enhancement over the baseline model, fulfilling the demands of real-time detection. The Star-YOLO model markedly enhances both the accuracy and efficiency of wheat grain detection while underscoring the potential of deep learning in agricultural image analysis and smart wheat industry advancements.}, archive = {J_ACCESS}, author = {Zhihang Qu and Xiao Liang and Sicheng Liang and Xiumei Guo}, doi = {10.1109/ACCESS.2025.3591374}, journal = {IEEE Access}, pages = {130147-130158}, shortjournal = {IEEE Access}, title = {Star-YOLO: A lightweight real-time wheat grain detection model for embedded deployment}, volume = {13}, year = {2025}, }
• (2025). Broadband dual-sense circular-polarization reconfigurable metasurface antenna for wearable applications. ACCESS, 13, 130138-130146. (www)

  @article{ , abstract = {This manuscript introduces a broadband dual-sense circular-polarization (CP) reconfigurable coplanar waveguide (CPW)-fed metasurface (MS) antenna for wearable applications. The proposed antenna employs an integration of two vertical stubs, protruding from the ground plane of a CPW-fed monopole antenna, through two p-i-n (PIN) diodes. The proposed antenna has several unique features, including low specific absorption rate (SAR), wide impedance bandwidth (IBW) and axial ratio bandwidth (ARBW), and a simple reconfigurability using only two PIN diodes. The polarization reconfiguration is achieved using a biasing circuit that selectively switches one diode to the ON state while keeping the other in the OFF state. This configuration produces perturbations in the current distribution along the horizontal and vertical axes, exciting CP radiation. By alternating the state of the PIN diodes, the direction of circular polarization is changed. To enhance the ARBW, radiation directivity, and SAR, an MS structure is placed under the fundamental radiator. Two square ring resonators are also added to further improve the ARBW. The proposed antenna is fabricated and verified by measurement in free space, human body, and fresh pork environments, demonstrating good agreement between simulation and measurement results. The antenna is also evaluated on a human tissue model, confirming that its SAR complies with the US and EU safety standards.}, archive = {J_ACCESS}, author = {Tu Tuan Le and Yong-Deok Kim and Tae-Yeoul Yun}, doi = {10.1109/ACCESS.2025.3590767}, journal = {IEEE Access}, pages = {130138-130146}, shortjournal = {IEEE Access}, title = {Broadband dual-sense circular-polarization reconfigurable metasurface antenna for wearable applications}, volume = {13}, year = {2025}, }
• (2025). Optimal charging algorithm based on receding horizon framework for electric two-wheeler charging stations. ACCESS, 13, 130125-130137. (www)

  @article{ , abstract = {In Vietnam urban traffic, electric two-wheelers have emerged as a potential alternative to gasoline-powered motorcycles. Thus, charging infrastructure for electric two-wheelers is being in the front line of research and development. Moreover, high rooftop power potential and the encouragement of self-generation-self-consumption propel the need for photovoltaic-integrated charging solutions which in turn might pose technical challenges relating to optimal charging operation. In this work, an optimal real-time scheduling algorithm for photovoltaic-integrated electric two-wheeler charging stations is proposed. The algorithm aims at improving the total load profile as well as load leveling, peak shaving and valley filling. By leveraging receding horizon framework combined with grouping approach, the proposed algorithm can effectively perform optimal scheduling while covering the uncertainties of arrival/departure and energy level variation of vehicles. The effectiveness of the algorithm is verified and analyzed through various case studies and operation scenarios.}, archive = {J_ACCESS}, author = {Van Nguyen Ngoc and Duc Nguyen Huu}, doi = {10.1109/ACCESS.2025.3591193}, journal = {IEEE Access}, pages = {130125-130137}, shortjournal = {IEEE Access}, title = {Optimal charging algorithm based on receding horizon framework for electric two-wheeler charging stations}, volume = {13}, year = {2025}, }
• (2025). KAS: A kernel alignment-based sampling probability approach for medical data valuation. ACCESS, 13, 130110-130124. (www)

  @article{ , abstract = {Data valuation, critical for quantifying individual data points’ contributions to model performance, faces challenges in noisy, high-dimensional medical datasets due to inherent feature correlations and measurement uncertainties. Existing Shapley value-based methods struggle with an accuracy-efficiency tradeoff and fail to address medical data characteristics like noise and missing values. This paper introduces Kernel-Aligned Sampling (KAS), a dual-mechanism framework that innovates through: 1) A kernel-aligned sampling probability strategy that aligns valuation distributions with feature-space similarity to stabilize Shapley approximations and resolve the speed-accuracy dilemma; 2) A prior-guided data refinement mechanism that leverages domain knowledge to implicitly decompose data into signal and noise components, mitigating misattribution of noisy or incomplete data as low-value; 3) Experiments on 10 datasets and 4 baselines demonstrate that KAS outperforms baseline methods by 5.7%-8.8% in high-value data ablation tests and achieves an average improvement of 4.3% in noisy data detection accuracy compared to the second-best method.}, archive = {J_ACCESS}, author = {Mingming Jiang and Qinghe Liu and Hongli Xu and Wenting Liao and Rilige Wu and Longji Li and Yawei Zhao and Kunlun He}, doi = {10.1109/ACCESS.2025.3587136}, journal = {IEEE Access}, pages = {130110-130124}, shortjournal = {IEEE Access}, title = {KAS: A kernel alignment-based sampling probability approach for medical data valuation}, volume = {13}, year = {2025}, }
• (2025). SHAP-based feature selection for enhanced unsupervised labeling. ACCESS, 13, 130098-130109. (www)

  @article{ , abstract = {Manual dataset labeling is expensive, time-consuming, and susceptible to noise and inaccuracies, often necessitating significant financial investments with risks of inconsistencies from human annotations. These challenges are further extended in domains such as fraud detection because of privacy concerns due to manual annotations and severe class imbalance, which negatively impact machine learning models. Our unsupervised approach integrates SHapley Additive exPlanations (SHAP) for feature selection with our novel unsupervised labeling method which uses an ensemble unsupervised method in conjunction with a percentile-based threshold technique on the widely used Kaggle Credit Card Fraud Detection dataset. We create subsets with three and five features using unsupervised SHAP-based feature selection to determine the most impactful features, as well as use the full-featured dataset. To evaluate, we compare the newly generated binary class labels to the actual labels, which were only used for evaluation, and calculate Matthews Correlation Coefficient (MCC), Jaccard Index (JI), and Precision. Furthermore, we compare our method to an unsupervised baseline and show significant improvements. Our empirical results demonstrate that unsupervised SHAP-based feature selection consistently improves the quality of our labels, when compared to the baseline unsupervised method. Lastly, unsupervised SHAP-based feature selection improves label quality when comparing feature subsets to the full-feature dataset while reducing computational complexity. Our work provides an unsupervised framework capable of addressing the challenges of labeling highly imbalanced and unlabeled data while preserving data privacy concerns given the unsupervised nature of our methodology and application of unsupervised SHAP-based feature selection.}, archive = {J_ACCESS}, author = {Mary Anne Walauskis and Taghi M. Khoshgoftaar}, doi = {10.1109/ACCESS.2025.3591554}, journal = {IEEE Access}, pages = {130098-130109}, shortjournal = {IEEE Access}, title = {SHAP-based feature selection for enhanced unsupervised labeling}, volume = {13}, year = {2025}, }
• (2025). MITSGRN: A novel computational framework for reconstructing sleep rhythm gene regulatory networks based on mutual information and time-series big data. ACCESS, 13, 130088-130097. (www)

  @article{ , abstract = {Disruptions in sleep rhythms have emerged as a global health concern, posing serious risks to the physical and mental well-being of modern populations. Elucidating the molecular regulatory mechanisms underlying the periodic nature of sleep rhythms remains a critical scientific challenge. In this study, we propose an innovative computational framework for gene regulatory network (GRN) reconstruction based on mutual information and large-scale time-series data. The proposed framework leverages the temporal characteristics of gene expression profiles associated with sleep rhythms, and integrates k-means clustering, mutual information, and Pearson lag correlation analysis in a synergistic manner to support GRN reconstruction. We systematically evaluate the performance of our method using BEELINE open-source datasets of varying scales, with precision, recall, and cross-validation accuracy as evaluation metrics. Experimental results demonstrate that our approach significantly outperforms existing methods such as dynGENIE3 and transfer entropy in terms of both accuracy and generalization capability. Furthermore, we successfully applied the proposed framework to reconstruct the GRN governing sleep rhythms in rats. The resulting network exhibits topological features and identifies key regulatory components that are highly consistent with previously published findings. Our results highlight the advantages of mutual information-based GRN reconstruction in deciphering complex biological rhythm regulatory systems. This method not only provides a novel perspective for investigating the gene regulatory mechanisms underlying sleep rhythms, but also establishes a solid methodological foundation for exploring the pathogenesis of sleep-related disorders and advancing the development of targeted therapies.}, archive = {J_ACCESS}, author = {Zhenyu Liu and Jiangqian Zuo and Qian Cao and Zheng Lu and Tao Li}, doi = {10.1109/ACCESS.2025.3591304}, journal = {IEEE Access}, pages = {130088-130097}, shortjournal = {IEEE Access}, title = {MITSGRN: A novel computational framework for reconstructing sleep rhythm gene regulatory networks based on mutual information and time-series big data}, volume = {13}, year = {2025}, }
• (2025). Iterative configuration of T-timed colored petri nets by a MILP model for rail freight volume and rolling stock planning. ACCESS, 13, 130066-130087. (www)

  @article{ , abstract = {This study presents a hybrid simulation-optimization framework for the strategic long-term planning of railway operations. The proposed model aims to optimize railway transport planning by ensuring efficient asset allocation, maximizing financial returns, and minimizing asset utilization. It integrates constraints related to operational capacity, such as track and terminal limitations, and market demand, while also assessing the risks of executing planned volumes amid operational uncertainties. By utilizing Mixed Integer Linear Programming (MILP) and T-Timed Colored Petri Net simulations, the model effectively handles the complexities of various cargo types, including grains, fuels, steel products, batch operations, return loads, and mixed trains. Developed in MATLAB, the T-Timed Colored Petri Nets-based simulator enhances the model’s capability to analyze stochastic behaviors and complex correlations. A probabilistic risk assessment (P80) was used to verify the feasibility of the planned volumes. This study provides a detailed guide for implementing this planning tool, demonstrating its adaptability to other planning levels, and suggests potential areas for future enhancements and adaptations.}, archive = {J_ACCESS}, author = {Grazielle F. da Silva and Lucas S. M. Guedes and Rodney R. Saldanha and Carlos Andrey Maia and Tomas B. F. Tamantini}, doi = {10.1109/ACCESS.2025.3581175}, journal = {IEEE Access}, pages = {130066-130087}, shortjournal = {IEEE Access}, title = {Iterative configuration of T-timed colored petri nets by a MILP model for rail freight volume and rolling stock planning}, volume = {13}, year = {2025}, }
• (2025). CAN-GraphiT: A graph-based IDS for CAN networks using transformer. ACCESS, 13, 130015-130028. (www)

  @article{ , abstract = {As the integration of electronic control units in vehicles continues to advance, the inherent security limitations of the Controller Area Network (CAN) protocol cause it to be vulnerable to cyberattacks. The advancement of intrusion detection systems (IDS) seeks to shield vehicles from malicious threats. While Recurrent Neural Networks (RNNs) have been integral in these efforts, their sequential processing method introduces certain constraints. These limitations arise from their feature extraction process, which solely depends on the hidden state of previously observed data, potentially leading to the omission of critical context features. In response to these challenges, we propose CAN-GraphiT, a graph-based intrusion detection solution that combines graph and temporal features with a transformer-based attention network (TAN) for in-vehicle CAN networks. Eliminating the need for RNNs, our approach harnesses the self-attention mechanism, which enables effective attack detection in CAN data. Experimental results indicate that the CAN-GraphiT model is a powerful and robust approach for bolstering CAN security. It outperforms some state-of-the-art models, achieving 98.45% accuracy.}, archive = {J_ACCESS}, author = {Md Mahbub Hasan and Sanchita Ghose and Krishna Chandra Roy}, doi = {10.1109/ACCESS.2025.3590241}, journal = {IEEE Access}, pages = {130015-130028}, shortjournal = {IEEE Access}, title = {CAN-GraphiT: A graph-based IDS for CAN networks using transformer}, volume = {13}, year = {2025}, }
• (2025). From code to concept: A semantic approach to AI innovation discovery in open source software repositories. ACCESS, 13, 129995-130014. (www)

  @article{ , abstract = {Artificial Intelligence (AI) is a transformative force driving innovation, yet tracking AI-related advancements remains challenging due to the rapid pace of development and unstructured data from platforms like GitHub. This paper proposes an AI-driven approach to innovation detection, leveraging GitHub as a data source to systematically identify and link AI projects to organizations. Key contributions include a domain-specific taxonomy comprising 7,490 AI topics, a modular pipeline for semantic annotation and entity linking, and a trend detection framework based on Singular Spectrum Analysis (SSA). A knowledge graph is constructed to represent relationships among AI topics, projects, and companies, thereby enabling structured innovation tracking. The approach addresses challenges such as data sparsity and noise, demonstrating strengths in semantic annotation and topic categorization. Results highlight the potential for accurately detecting AI innovations and linking them to organizational entities, offering valuable insights for researchers, companies, and policymakers. This work contributes a scalable, automated approach for AI innovation tracking, with future directions focusing on refining entity linking and expanding the knowledge graph to capture emerging trends.}, archive = {J_ACCESS}, author = {Inna Novalija and Dumitru Roman and Federico Belotti and Vladimir Alexiev and Luis Rei and Roberto Avogadro and Babak Khalilvandian and Boyan Bechev and Catalina Alexandra Chinie and Iulia Ciurea and Janez Brank and Cosmin Udroiu and Ahmet Soylu and Matteo Palmonari}, doi = {10.1109/ACCESS.2025.3590135}, journal = {IEEE Access}, pages = {129995-130014}, shortjournal = {IEEE Access}, title = {From code to concept: A semantic approach to AI innovation discovery in open source software repositories}, volume = {13}, year = {2025}, }
• (2025). A hybrid multi-criteria decision-making and multi-objective framework for optimal sizing of PV-powered EV charging station with battery storage. ACCESS, 13, 129977-129994. (www)

  @article{ , abstract = {The fast-paced expansion of Electric Vehicles (EVs) necessitates the establishment of efficient, sustainable, and resilient infrastructure for charging. The design of EV charging stations (EVCS) should incorporate renewable energy sources to reduce grid stress and adopt a multifaceted approach that considers economic, social, reliability, and environmental parameters. However, existing optimization studies primarily focus on economic and reliability criteria and often tend to neglect social and environmental considerations, leading to suboptimal and inequitable solutions. This study proposes a hybrid optimization and decision-making framework to determine the optimal configuration of a PV-powered EVCS having battery backup simultaneously considering all 4 parameters- economic, social, reliability, and environmental factors through a two-layer approach. A Multi-Objective Particle Swarm Optimization (MOPSO) approach is employed to identify optimal configurations in the first layer, which are then evaluated using a hybrid TOPSIS-AHP-based Multi-Criteria Decision-Making (MCDM) method to select the most balanced solution in the second layer. The proposed framework was implemented using MATLAB, which enabled efficient development and testing of the algorithm. Three case studies were conducted to evaluate the influence of Analytic Hierarchy Process (AHP) derived weights on the system design obtained using the proposed framework. In Case Study 1, where greater emphasis was placed on economic performance, the resulting configuration featured high PV capacity, low battery storage, and minimal grid dependence ensuring cost-effectiveness but introducing potential intermittency risks. Case Study 2, which prioritized reliability, yielded a design with both high PV and battery capacities and reduced grid reliance, thereby enhancing system resilience at the expense of higher capital investment. Case Study 3 adopted a balanced weighting approach, resulting in a configuration that moderately satisfied all performance criteria. These findings underscore the pivotal role of weight selection in multi-criteria optimization and emphasize the need for a balanced strategy that considers economic, environmental, reliability, and social parameters, thereby enabling EV charging station operators to tailor system design to their specific operational priorities.}, archive = {J_ACCESS}, author = {Soumya Sathyan and V. Ravikumar Pandi and Preetha Sreekumar and Nishant Thakkar and Surender Reddy Salkuti}, doi = {10.1109/ACCESS.2025.3590521}, journal = {IEEE Access}, pages = {129977-129994}, shortjournal = {IEEE Access}, title = {A hybrid multi-criteria decision-making and multi-objective framework for optimal sizing of PV-powered EV charging station with battery storage}, volume = {13}, year = {2025}, }
• (2025). Hybrid tree-based machine learning models for state-of-charge and core temperature estimation in EV batteries. ACCESS, 13, 129962-129976. (www)

  @article{ , abstract = {Accurate estimation of State-of-Charge (SoC) and core temperature is fundamental to optimizing the performance, safety, and longevity of Lithium-Ion Batteries (LiBs), particularly in Electric Vehicles (EVs). Traditional estimation methods fail to account for the complex, non-linear interactions between thermal and electrical dynamics and the challenges posed by data uncertainty. This paper introduces a comprehensive framework to estimate core temperature and SoC, considering diverse charging levels and uncertainties. For the data generation phase, first, features are extracted from a control-oriented electro-thermal coupling model, offering a computationally efficient alternative to resource-intensive experiments and avoiding a lack of data. Then, a correlation analysis between the ambient temperature and each feature (e.g., internal resistances, thermal capacity, and time) is performed, with linear regression applied to generate features showing strong linear relationships, and a Gaussian Multivariate Copula model is used to generate features with weak or non-linear dependencies. For the estimation phase, hybrid tree-based models were employed due to their robustness in handling complex and noisy datasets, and computational efficiency while integrating the complementary strengths of individual models. Among the combinations tested, the Extra Trees Regressor-Random Forest (ETR-RF) model delivered the highest estimation accuracy, while the Decision Tree-LightGBM (DT-LGBM) model exhibited the fastest training time. The ETR-RF model consistently reduced estimation errors, achieving RMSE values of 0.047°C and 1.25°C for core temperature and 0.5% and 0.56% for SoC estimation across white Gaussian noise levels, with standard deviations of 0.02 and 0.2, respectively. In contrast, the DT-LGBM model prioritized computational efficiency, requiring 1 second (average training time) for SoC estimation and 0.66 seconds for core temperature estimation, performed on a system equipped with an Intel Core i7-7500U CPU (2.70GHz base, 2.90GHz boost).}, archive = {J_ACCESS}, author = {Aya Haraz and Khalid Abualsaud and Ahmed M. Massoud}, doi = {10.1109/ACCESS.2025.3591057}, journal = {IEEE Access}, pages = {129962-129976}, shortjournal = {IEEE Access}, title = {Hybrid tree-based machine learning models for state-of-charge and core temperature estimation in EV batteries}, volume = {13}, year = {2025}, }
• (2025). Bridging the Question–Answer gap in retrieval-augmented generation: Hypothetical prompt embeddings. ACCESS, 13, 129952-129961. (www)

  @article{ , abstract = {Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of responses. However, bridging the style gap between user queries and relevant information in document text remains a persistent challenge in retrieval-augmented systems, often addressed by runtime solutions (e.g., Hypothetical Document Embeddings (HyDE)) that attempt to improve alignment but introduce extra computational overhead at query time. To address these challenges, we propose Hypothetical Prompt Embeddings (HyPE), a framework that shifts the generation of hypothetical content from query time to the indexing phase. By precomputing multiple hypothetical prompts for each data chunk and embedding the chunk in place of the prompt, HyPE transforms retrieval into a question-question matching task, bypassing the need for runtime synthetic answer generation. This approach does not introduce latency but also strengthens the alignment between queries and relevant context. Our experimental results on six common datasets show that HyPE can improve retrieval context precision by up to 42 percentage points and claim recall by up to 45 percentage points, compared to standard approaches, while remaining compatible with re-ranking, multi-vector retrieval, query decomposition, and other RAG advancements.}, archive = {J_ACCESS}, author = {Domen Vake and Jernej Vičič and Aleksandar Tošić}, doi = {10.1109/ACCESS.2025.3589499}, journal = {IEEE Access}, pages = {129952-129961}, shortjournal = {IEEE Access}, title = {Bridging the Question–Answer gap in retrieval-augmented generation: Hypothetical prompt embeddings}, volume = {13}, year = {2025}, }
• (2025). Advancing agricultural machinery maintenance: Deep learning-enabled motor fault diagnosis. ACCESS, 13, 129933-129951. (www)

  @article{ , abstract = {Condition monitoring and fault diagnosis of the agricultural machinery are critical for ensuring the safety and stability of agricultural production processes. Timely detection of machinery failures, particularly in motor-driven systems, is essential to prevent unexpected shutdowns, maintain operational continuity, and avoid economic losses. Recent advancements in artificial intelligence (AI), particularly deep learning (DL)-based techniques, have demonstrated significant potential in machinery condition monitoring and fault diagnosis. These technologies offer reliable and efficient solutions for predictive maintenance in agricultural machinery, enabling proactive intervention in the early stages of catastrophic failures. This article reviews the application of DL-based technologies in the motor fault detection of agricultural machinery, highlighting their effectiveness in identifying early-stage anomalies and classifying fault types. The integration of DL-based fault detection systems into agricultural machinery not only enhances diagnostic accuracy but also reduces maintenance costs and downtime. However, challenges remain in implementing these systems, including data acquisition limitations, computational resource requirements, and algorithm adaptability to diverse operational conditions. This article further discusses future research directions, such as optimizing DL models for real-time processing, improving robustness under varying agricultural conditions, and developing user-friendly interfaces for farmers and technicians. By addressing these challenges, AI-driven fault detection systems can significantly contribute to the reliability and sustainability of agricultural production.}, archive = {J_ACCESS}, author = {Xusong Bai and Qian Chen and Xiangjin Song and Weihang Hong}, doi = {10.1109/ACCESS.2025.3591279}, journal = {IEEE Access}, pages = {129933-129951}, shortjournal = {IEEE Access}, title = {Advancing agricultural machinery maintenance: Deep learning-enabled motor fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Hybrid transformer-KAN within federated learning framework: A novel machine learning approach for improved short-term weather forecasting. ACCESS, 13, 129920-129932. (www)

  @article{ , abstract = {Among the various methods used for time series prediction tasks in weather forecasting, Transformer can effectively extract global information and has achieved tremendous success. However, Transformer is challenged in forecasting series with large lookback windows due to performance degradation and computation explosion. At the same time, a new architecture of Kolmogorov–Arnold Network (KAN) has been proposed, which dramatically improves the fitting ability in constructing neural networks widely used in machine learning techniques. In order to take both advantages of Transformer and KAN, in this paper a novel method of Transformer-KAN is proposed. To be specific, the Transformer is used to extract global features and pass them to KAN, and the latter is responsible for learning global features and optimizing them with high precision. With a deep integration of Transformer and KAN, the proposed Transformer-KAN exhibits powerful feature extraction capability and fitting ability. Furthermore, federated learning is applied to the proposed method to exploit the spatio-temporal dependencies while protecting data privacy. As a result, a novel framework of Federated-Transformer-KAN is proposed to collaboratively forecast weather variables in a distributed manner, without sharing data that is collected by the intelligent sensors equipped at each weather station locally. Compared with the benchmark methods, extensive experimental results shown that better prediction accuracy and generalization are achieved by the proposed Federated-Transformer-KAN. For instance, in the task of predicting the next 1 ambient temperature at station 68328, the mean squared error of Transformer and KAN individually is reduced by 33.9% and 40.9%, respectively.}, archive = {J_ACCESS}, author = {Shuai Jin and Qiang Li and Sanqiu Liu and Olebogeng Kevin Joel and Xiaohu Ge}, doi = {10.1109/ACCESS.2025.3591119}, journal = {IEEE Access}, pages = {129920-129932}, shortjournal = {IEEE Access}, title = {Hybrid transformer-KAN within federated learning framework: A novel machine learning approach for improved short-term weather forecasting}, volume = {13}, year = {2025}, }
• (2025). Identifying G-protein coupled receptors using mixed-feature extraction methods and machine learning methods. ACCESS, 13, 129911-129919. (www)

  @article{ , abstract = {G-protein-coupled receptors (GPCRs) are important protein molecules in the field of cell signaling and are widely found in various organisms. GPCRs play an important role in a variety of physiological processes and are important drug targets for a variety of diseases. Accurate prediction of GPCRs using machine learning is useful for drug design in a variety of related diseases. In this paper, we propose a method for identifying GPCRs based on mixed-feature vectors. We combine three individual features, such as 400D, N-gram and Parallel correlation pseudo amino acid composition (PC-PseAAC), using mixed-feature representation methods, which are evaluated by Random Forest, Naïve Bayes, and J48 for classification purposes. To measure the performance of this classifier, ten-fold cross-validation is used. Two dimensionality reduction methods—the max-relevance-max-distance (MRMD) and t-Distributed Stochastic Neighbor Embedding (t-SNE)—are applied to reduce the feature dimension. The 400D and PC-PseAAC feature extraction methods are combined, the random forest is used as the classifier, and the area under the curve (AUC) is up to 0.9413. Therefore, among these methods, the new feature vector obtained by combining the two features shows the best performance, and the mixed feature is better than the single feature.}, archive = {J_ACCESS}, author = {Chunyan Ao and Lin Gao and Liang Yu}, doi = {10.1109/ACCESS.2020.2983105}, journal = {IEEE Access}, pages = {129911-129919}, shortjournal = {IEEE Access}, title = {Identifying G-protein coupled receptors using mixed-feature extraction methods and machine learning methods}, volume = {13}, year = {2025}, }
• (2025). Assessing the value of heterogeneous elasticities for incentive-based residential demand response. ACCESS, 13, 129900-129910. (www)

  @article{ , abstract = {Incentive-based demand response (IBDR) programs play a crucial role in enhancing grid stability and reducing peak loads in modern power systems. However, existing IBDR programs often rely on aggregate demand models, overlooking the impact of heterogeneous consumer behaviors and appliance-specific demand elasticities. This study assesses the value of incorporating heterogeneous elasticity values in IBDR programs by developing three optimization models with increasing levels of granularity: 1) an aggregate elasticity model, 2) an appliance-specific elasticity model, and 3) a customer and appliance-specific elasticity model. Furthermore, this study incorporates transmission line losses into the models, providing a realistic assessment of distribution system efficiency. Comparative analysis using realistic residential electricity consumption data reveals that integrating appliance-specific elasticity significantly improves economic efficiency, while adding customer-specific granularity yields marginal additional benefits. Comparative analysis using realistic residential electricity consumption data reveals that integrating appliance-specific elasticity improves economic efficiency by 6.29%, while customer-specific granularity yields only a marginal additional benefit of 0.92%. These findings offer valuable insights for load-serving entities (LSEs) and policymakers in designing more efficient IBDR programs.}, archive = {J_ACCESS}, author = {Bahareh Kargar and Elson Cibaku and Sangwoo Park}, doi = {10.1109/ACCESS.2025.3591723}, journal = {IEEE Access}, pages = {129900-129910}, shortjournal = {IEEE Access}, title = {Assessing the value of heterogeneous elasticities for incentive-based residential demand response}, volume = {13}, year = {2025}, }
• (2025). Solving the dynamic stability challenge of automatic gain-controlled phase-locked loops: A robust alternative via enhanced SRF-PLL design and implementation. ACCESS, 13, 129883-129899. (www)

  @article{ , abstract = {This study presents a robust alternative to conventional phase-locked loop (PLL) architectures through the design and real-time implementation of an enhanced Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) for single-phase inverter systems. Targeting the inherent dynamic stability limitations of Automatic Gain-Controlled PLLs (AGC-PLLs), the proposed SRF-PLL demonstrates superior performance in transient response and tracking accuracy. Comparative evaluations under diverse dynamic operating conditions—including phase angle jumps, voltage sags/swells, frequency steps, noise, and harmonic distortion—highlight its resilience and adaptability. Experimental results reveal a substantial improvement in dynamic response, achieving 2.5 to 6 times faster settling times depending on the operating condition. Moreover, peak-to-peak oscillations were significantly suppressed, with deviations reduced from 2° to nearly 0° under a 5 Hz frequency step scenario, even in the presence of $3^{\text {rd}}$ order harmonic injection. For a 40° phase angle step, the proposed system achieves a fivefold improvement in attenuation time (0.03 s vs. 0.11 s) compared to the AGC-PLL. Under voltage sag conditions with noise and $3^{\text {rd}}$ order harmonic injection, the enhanced SRF-PLL reduces the attenuation time from 0.2 s to 0.08 s. During voltage swell scenarios, it further improves the response time from 0.025 s to 0.01 s. These findings confirm the practical benefits and efficiency of the enhanced SRF-PLL for applications requiring precise and reliable phase tracking and synchronization.}, archive = {J_ACCESS}, author = {Cağfer Yanarateş and Aytaç Altan}, doi = {10.1109/ACCESS.2025.3591724}, journal = {IEEE Access}, pages = {129883-129899}, shortjournal = {IEEE Access}, title = {Solving the dynamic stability challenge of automatic gain-controlled phase-locked loops: A robust alternative via enhanced SRF-PLL design and implementation}, volume = {13}, year = {2025}, }
• (2025). Scattering parameter characterization of a novel four-port fixture using time domain gating and mason’s gain formula. ACCESS, 13, 129870-129882. (www)

  @article{ , abstract = {Modern electronic devices comprise a complex structure of interconnected components, and signals passing through various fixtures are prone to attenuation and distortion across various frequencies. This paper presents a novel four-port fixture characterization scheme that converts the S-parameters of a 2x-Thru structure into time-domain reflectometry data. This data is then separated into the two sides of the fixture via time-domain gating. After the remaining S-parameters are calculated using Mason’s gain formula, fixture effects are removed through a de-embedding process involving matrix operations to yield the S-parameters of the device under test. The efficacy of the proposed scheme is demonstrated through comparison with the industry-standard automatic fixture removal (AFR) algorithm.}, archive = {J_ACCESS}, author = {Jim-Wei Wu and Bo-Jun Chen and Jia-Cheng Li}, doi = {10.1109/ACCESS.2025.3590875}, journal = {IEEE Access}, pages = {129870-129882}, shortjournal = {IEEE Access}, title = {Scattering parameter characterization of a novel four-port fixture using time domain gating and mason’s gain formula}, volume = {13}, year = {2025}, }
• (2025). ASIC design for real-time CAN-bus intrusion detection and prevention system using random forest. ACCESS, 13, 129856-129869. (www)

  @article{ , abstract = {A controller area network (CAN) is the most widely used protocol to communicate between electronic control units (ECUs) inside a vehicle. However, it lacks basic security functions and a structure vulnerable to external attacks. Therefore, it is important to develop an intrusion detection system (IDS) that can effectively identify malicious and legitimate CAN messages, as well as an intrusion prevention system (IPS) that can block such malicious messages in real time. In this study, a random-forest-based intrusion detection system (RF-IDS) was proposed and integrated with a CAN controller to design a system that can perform real-time detection and attack blocking. Existing IDS studies aim for high detection performance. However, the real-time blocking function is limited or the possibility of hardware implementation is yet to be sufficiently verified. To address this issue, we designed an IDS that can immediately determine if an attack is present when receiving a CAN frame and blocking the attack node. Fast and efficient detection is possible complex matrix operations because the random forest model performs detection based on comparison operations. This confirmed that the corresponding structure achieved an average detection time of $1.86~\mu $ s and detection accuracy of more than 99%, allowing real-time intrusion blocking. Finally, hardware implementation was achieved by completing the design of the ASIC chip through a multiproject wafer process.}, archive = {J_ACCESS}, author = {Junseok Lee and Sangmin Park and Sua Shin and Hyungchul Im and Joosock Lee and Seongsoo Lee}, doi = {10.1109/ACCESS.2025.3585956}, journal = {IEEE Access}, pages = {129856-129869}, shortjournal = {IEEE Access}, title = {ASIC design for real-time CAN-bus intrusion detection and prevention system using random forest}, volume = {13}, year = {2025}, }
• (2025). Optimal coil design and irDA-based communication method for auto-guided vehicles with thermally stable and high-efficiency operation. ACCESS, 13, 129846-129855. (www)

  @article{ , abstract = {In this paper, an optimal coil design methodology for inductive power transfer (IPT) systems is proposed, providing a systematic and practical approach for coil designers working with voltage-source-based series-series topologies. Unlike conventional designs, the proposed methodology explicitly considers frequency splitting phenomena, ensuring both thermally stable and high-efficiency operation while simultaneously identifying the optimal frequency range for effective load power regulation. Using finite element method (FEM) analysis and circuit simulations, the transmitter (Tx) and receiver (Rx) coils are designed to achieve stable power delivery across varying conditions. Even in the presence of frequency splitting, the proposed IPT system ensures robust power transfer through frequency modulation control. Furthermore, an infrared (irDA)-based communication protocol is implemented to maintain precise load power delivery, such as constant current-constant voltage (CCCV) battery charging, even under coil misalignment. A 2.2 kW IPT prototype for automated guided vehicle (AGV) wireless charging was fabricated and experimentally validated. The prototype achieved a maximum coil efficiency of 94.5% at a 35 mm coil distance and maintained coil temperatures below 60°C during a 2-hour AGV operation cycle, confirming the thermal stability and practical viability of the proposed design approach.}, archive = {J_ACCESS}, author = {Kang M. Seo and Eun S. Lee}, doi = {10.1109/ACCESS.2025.3591615}, journal = {IEEE Access}, pages = {129846-129855}, shortjournal = {IEEE Access}, title = {Optimal coil design and irDA-based communication method for auto-guided vehicles with thermally stable and high-efficiency operation}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Performance analysis and model improvements for electromagnetic transient state estimators”. ACCESS, 13, 129845. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Performance Analysis and Model Improvements for Electromagnetic Transient State Estimators”).}, archive = {J_ACCESS}, author = {Humberto Cunha De Oliveira and Vinicius Henrique Faria Brito and José Carlos De Oliveira and Isaque Nogueira Gondim}, doi = {10.1109/ACCESS.2025.3590130}, journal = {IEEE Access}, pages = {129845}, shortjournal = {IEEE Access}, title = {Corrections to “Performance analysis and model improvements for electromagnetic transient state estimators”}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of the proportional distribution method and the random forest algorithm for predicting pedestrian traffic accident risk. ACCESS, 13, 129828-129844. (www)

  @article{ , abstract = {The risk of pedestrian-involved traffic accidents represents a significant challenge to road safety and necessitates objective methods for analyzing the contributing factors. This study presents a comparative analysis of two methodologies for predicting the risk of pedestrian traffic accidents: a methodology based on proportional risk distribution and the Random Forest algorithm. The analysis utilizes data derived from real court cases, where linguistic variables defined as risk factors are categorized and quantified based on expert evaluations. The results demonstrate that both approaches are applicable for risk assessment, with Random Forest exhibiting higher accuracy and robustness in handling complex and heterogeneous data. Correlation analysis confirms a statistically significant linear relationship between the outputs of the two methods, supporting their validity. Graphical representations derived from the results offer a visual interpretation of risk severity and facilitate comparison between the two approaches. The proposed method is intended for road safety experts, engineers, analysts, and institutions in the field of transportation safety. Its primary aim is to provide an objective and quantitative tool for evaluating the risk factors contributing to pedestrian-related incidents. The method supports informed decision-making regarding preventive measures and awareness campaigns targeting both drivers and pedestrians.}, archive = {J_ACCESS}, author = {Hristo V. Uzunov and Plamen G. Matzinski and Vasil H. Uzunov and Silvia V. Dechkova}, doi = {10.1109/ACCESS.2025.3591297}, journal = {IEEE Access}, pages = {129828-129844}, shortjournal = {IEEE Access}, title = {Comparative analysis of the proportional distribution method and the random forest algorithm for predicting pedestrian traffic accident risk}, volume = {13}, year = {2025}, }
• (2025). Research on adaptive planning of three-dimensional trajectory for uncrewed aerial vehicle inspection based on nonlinear weibull algorithm. ACCESS, 13, 129814-129827. (www)

  @article{ , abstract = {Traditional methods for solving the path planning problem of inspection UAVs in complex environments often suffer from issues such as convergence to locally optimal paths and poor global search accuracy. To address these shortcomings, this study proposes a Non-linear Weibull Flight Reptile Search Algorithm (NWFRSA) for adaptive path planning of inspection UAVs. By introducing an improved Iterative Chaotic Map with Infinite Collapses (ICMIC) initialization strategy, the diversity and quality of initial path solutions are enhanced. An S-shaped characteristic function-based nonlinear evolution factor is employed to balance global exploration and local exploitation of the optimal path, while a Weibull flight operator mutation strategy is designed to enable the algorithm to escape from locally optimal paths, enrich the search space, and improve convergence accuracy. Meanwhile, this study constructs a path planning model for inspection UAVs and designs a weighted objective function considering total path length, flight altitude, flight turning angle, and threat model, thereby transforming path planning in three-dimensional space into a constrained multi-objective optimization problem. The results show that under flight environments with varying complexity of obstacle and threat region distribution, compared with the Particle Swarm Optimization algorithm (PSO), Butterfly Optimization Algorithm (BOA), and Reptile Search Algorithm (RSA), NWFRSA can effectively reduce the path cost (by 4.53%–34.47%), contributing to improved.}, archive = {J_ACCESS}, author = {Zhuang Liu and Ning Yang and Jiaxing Fu and Huanqing Cai and Xuebei Wei}, doi = {10.1109/ACCESS.2025.3590477}, journal = {IEEE Access}, pages = {129814-129827}, shortjournal = {IEEE Access}, title = {Research on adaptive planning of three-dimensional trajectory for uncrewed aerial vehicle inspection based on nonlinear weibull algorithm}, volume = {13}, year = {2025}, }
• (2025). Fault-tolerant control of a quadrotor unmanned aerial vehicle with single rotor failure in dense obstacle environments. ACCESS, 13, 129803-129813. (www)

  @article{ , abstract = {Due to the motor degradation from long-term flight and the underactuated dynamics of quadrotor unmanned aerial vehicles (UAV), rotor failures are more likely to occur, posing significant challenges to stable flight. To address the impact of a single rotor completely failure, a quaternion-based decoupled degraded fault-tolerant control strategy is proposed to ensure accurate trajectory tracking performance of the quadrotor UAV. When a single rotor completely failure, the attitude control is intentionally degraded to retain altitude and attitude regulation capabilities in complex environments. To enhance robustness, a sliding mode observer (SMO) is designed to estimate and compensate for external unknown wind disturbances. Furthermore, to enable obstacle avoidance in three-dimensional environments, a geometric position controller integrated with the artificial potential field (APF) method is developed. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. Numerical simulation results demonstrate that the proposed control strategy provides strong robustness and high-precision pose tracking, even under complete single-rotor failure scenarios in complex environments. These findings suggest that the control framework is suitable for reliable autonomous flight missions under actuator efficiency degradation.}, archive = {J_ACCESS}, author = {Xun Gu and Pengbo Wang and Tao Lu}, doi = {10.1109/ACCESS.2025.3588128}, journal = {IEEE Access}, pages = {129803-129813}, shortjournal = {IEEE Access}, title = {Fault-tolerant control of a quadrotor unmanned aerial vehicle with single rotor failure in dense obstacle environments}, volume = {13}, year = {2025}, }
• (2025). Cobots designed for strength, not stiffness. ACCESS, 13, 129787-129802. (www)

  @article{ , abstract = {Conventional industrial robots are designed to be as stiff as possible to ensure high positioning accuracy. The stiffness of a structure is, however, strongly related to its mass, leading to heavy structures. This paper aims to quantify the potential gain of reducing and eliminating the stiffness constraints, which is of lesser importance for collaborative robots, by investigating the effect of applying different optimization objectives. The resulting optimized designs are quantitatively compared using a set of performance measures and evaluated against the traditional stiffness-designed approach. It was concluded that significant improvements can be made, e.g. the robot’s mass can be reduced up to 74% compared to traditionally stiff-designed robots. The dependency of the payload as well as the structural/actuator mass distribution on the optimized results is investigated and proved to have a significant influence on the potential improvements when allowing reduced structural rigidity. The relationship between structural mass and actuator mass is investigated and compared to commercially available cobots.}, archive = {J_ACCESS}, author = {Thierry Hubert and Amin Khorasani and Muhammad Usman and Hafsa Nouhi and Raphaël Furnémont and Bram Vanderborght and Greet Van De Perre and Tom Verstraten}, doi = {10.1109/ACCESS.2025.3591262}, journal = {IEEE Access}, pages = {129787-129802}, shortjournal = {IEEE Access}, title = {Cobots designed for strength, not stiffness}, volume = {13}, year = {2025}, }
• (2025). Quadratic boost DC–DC converters capable of being extended to improved topologies. ACCESS, 13, 129760-129786. (www)

  @article{ , abstract = {Luo converters represent the most extensive family of non-isolated DC-DC converters, yet achieving high voltage gain in these topologies often compromises simplicity. This study introduces a novel family of quadratic boost converters that address this challenge by combining six key features: 1) continuous input current, 2) reduced component count, 3) optimized voltage/current stress on semiconductors, 4) higher voltage gain than conventional designs, 5) high efficiency across a wide duty-cycle range, and 6) topological extensibility comparable to Luo converters. The proposed topologies are analyzed in both ideal and non-ideal operating modes, with derived expressions for voltage gain and efficiency sensitivity. Design requirements are detailed for continuous conduction mode (CCM), complemented by small-signal analysis and compensator design for the primary family members. Experimental results validate the theoretical models, demonstrating the converters’ performance and robustness. Additionally, voltage gain tests for extended topologies confirm their adherence to theoretical predictions. The proposed family offers a streamlined, high-performance alternative to existing Luo converters, with potential applications in high-gain, non-isolated power conversion systems.}, archive = {J_ACCESS}, author = {Mohammadfazel Dehghan and Hossein Gholizadeh and Reza Sharifi Shahrivar and Saeed Amini and Hani Vahedi}, doi = {10.1109/ACCESS.2025.3589641}, journal = {IEEE Access}, pages = {129760-129786}, shortjournal = {IEEE Access}, title = {Quadratic boost DC–DC converters capable of being extended to improved topologies}, volume = {13}, year = {2025}, }
• (2025). Voltage control via tap-changing transformer using the holomorphic embedding load flow method based on total multiplication of polynomials. ACCESS, 13, 129741-129759. (www)

  @article{ , abstract = {This paper presents an innovative strategy for voltage control in electric power systems by integrating tap-changing transformers with the Holomorphic Embedding Load Flow Method based on Total Multiplication of Polynomials (HELM/TMP). Using an alternating adjustment scheme, the proposed approach takes advantage of the superior numerical robustness and computational efficiency of the HELM/TMP over conventional power flow methods. The method is validated through extensive simulations performed on IEEE 14, 30, 57, and 118-bus test systems, including load variations and wind power. Representative wind generation scenarios produced by applying the modified Iterative Self-Organizing Data Analysis Technique Algorithm are used, considering annual historical generation data. The results show that the proposed technique maintains the voltage within the specified limits with an accuracy comparable to Newton-Raphson-based approaches while achieving an approximately 90% reduction in computation time. These case studies confirm the robustness, scalability, and real-time applicability of the proposed HELM/TMP-based voltage control. The main contribution lies in the use of HELM/TMP, which significantly accelerates the voltage control solution process, thus validating the applicability of the framework for real-time scenarios. This is particularly relevant for complex power systems, including those with critical loads, intermittent generators, and demanding operating conditions.}, archive = {J_ACCESS}, author = {Glaucus R. S. Lima and Edimar J. Oliveira and Leonardo W. Oliveira}, doi = {10.1109/ACCESS.2025.3591261}, journal = {IEEE Access}, pages = {129741-129759}, shortjournal = {IEEE Access}, title = {Voltage control via tap-changing transformer using the holomorphic embedding load flow method based on total multiplication of polynomials}, volume = {13}, year = {2025}, }
• (2025). Autoencoder-augmented graph neural networks for accurate and scalable structure recognition in Analog/Mixed-signal schematics. ACCESS, 13, 129721-129740. (www)

  @article{ , abstract = {The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data scarcity and confidentiality constraints limit model training. In this work, a novel framework has been proposed that combines the generative augmentation capabilities of convolutional Autoencoders with the structural analysis power of Graph Convolutional Networks (GCNs). Realistic schematic variants have been synthesized from limited proprietary data to enhance model generalization, while the GCN has been used to capture topological features critical to substructure recognition. The method has been validated on a curated AMS dataset, where it surpassed a GCN-only baseline by reducing reconstruction error and achieving a balanced classification accuracy of 96.7%, thereby exceeding the long-standing 95% accuracy threshold. Inference latency was measured at 5–10ms per schematic on standard GPU hardware, confirming its applicability to interactive industrial Electronic Design Automation (EDA) workflows. These results highlight the potential of the Autoencoder–GCN pipeline as a scalable and reliable solution for AMS structure recognition under real-world constraints.}, archive = {J_ACCESS}, author = {Mohamed Salem and Witesyavwirwa Vianney Kambale and Ali Deeb and Sergii Tkachov and Anjeza Karaj and Joachim Pichler and Manuel Ludwig Lexer and Kyandoghere Kyamakya}, doi = {10.1109/ACCESS.2025.3591720}, journal = {IEEE Access}, pages = {129721-129740}, shortjournal = {IEEE Access}, title = {Autoencoder-augmented graph neural networks for accurate and scalable structure recognition in Analog/Mixed-signal schematics}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection for GOOSE spoofing attacks in digital substations using deep learning models: A DNN and LSTM approach. ACCESS, 13, 129709-129720. (www)

  @article{ , abstract = {Digital substations have significantly increased the accuracy of power grid operations thus advancing high efficiency and reliability in grid operations. While this advancement has brought new cybersecurity challenges, in particular, for security of communication protocols such as Generic Object-Oriented Substation Event (GOOSE) messages are subject to spoofing attacks. These attacks can disrupt substation operations such that incorrect control actions are taken by operators, equipment gets damaged and large scale blackouts can occur. One of the contributions of this research is the proposal of a novel approach of detecting GOOSE spoofing attacks in digital substations using deep learning models, such as Deep Neural Networks (DNN) and Long Short Term Memory (LSTM) networks. To achieve this, the proposed models are trained on a multi-modal dataset consisting of GOOSE message attributes and physical electrical system measurements, and achieve the ability to accurately detect anomalies. Furthermore, we present an autoencoder based anomaly detection system in order to augment the deep learning models in discovering the subtle changes in system behavior. Extensive experiments show the effectiveness of the models in obtaining high detection accuracy on both training and test datasets. This result demonstrates that the integrated deep learning technique, capable of handling dynamic attack patterns, real time data, provides a robust and scalable cyber security solution to digital substations. The proposed methodology is contributing significantly to the field of anomaly detection in critical infrastructure, and acts as foundations for future research on real time threat mitigation strategies.}, archive = {J_ACCESS}, author = {Trinal Fernando and Gowri Ramachandran and Mahinda Vilathgamuwa and Dhammika Jayalath}, doi = {10.1109/ACCESS.2025.3591695}, journal = {IEEE Access}, pages = {129709-129720}, shortjournal = {IEEE Access}, title = {Anomaly detection for GOOSE spoofing attacks in digital substations using deep learning models: A DNN and LSTM approach}, volume = {13}, year = {2025}, }
• (2025). Learning face pareidolia via global feature transfer. ACCESS, 13, 129702-129708. (www)

  @article{ , abstract = {Convolutional Neural Networks learn different details of features across various layers, progressively extracting features from low-level aspects such as edges to high-level semantic concepts. In this work, we investigate which feature representations are more effective for pareidolic face detection. We demonstrate that high-level human face representations optimally enhance both pareidolic face classification and localization. In contrast, animal face transfers fail to provide significant improvements over a no-transfer model, as species-specific features mismatch the abstract patterns of pareidolia. Class activation maps of human face feature-transferred models and the last-layer transferred animal feature model reveal that the models focus on precise locations of face-like patterns, whereas the no-transfer model fails to do so. Our results yield several findings. First, effective transfer requires alignment between source and target feature abstractions. Second, human face topology provides ideal priors for face pareidolia. These findings offer valuable insights for challenging visual recognition tasks using advanced cross-domain feature transfer methodologies.}, archive = {J_ACCESS}, author = {Usfita Kiftiyani and Seungkyu Lee}, doi = {10.1109/ACCESS.2025.3585523}, journal = {IEEE Access}, pages = {129702-129708}, shortjournal = {IEEE Access}, title = {Learning face pareidolia via global feature transfer}, volume = {13}, year = {2025}, }
• (2025). Current signature-based bearing fault severity classification using a robust multilevel cascaded framework. ACCESS, 13, 129678-129701. (www)

  @article{ , abstract = {Bearing faults are a critical concern in electrical machines, particularly permanent magnet synchronous motors (PMSMs), commonly used in electric vehicles. Early and accurate classification of bearing fault severity is essential for predictive maintenance, as it enhances cost-effectiveness, ensures safety, and extends product life. Although vibration-based monitoring offers rich diagnostic information, it remains costly and requires excess modifications. In contrast, current-based non-invasive techniques offer advantages in cost and integration but face challenges with accuracy due to operational complexities. This study presents six distinct artificial neural networks (ANNs)-based cascaded classification schemes for bearing fault severity classification. Discrete wavelet transform (DWT) with Symlet (Sym) is used for multi-resolution feature extraction in currents, combined with motor speed data to generate multi-channel features. These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. One approach employs a multi-input ANN for level I, integrated with an ANN-based level II for refined severity classification. This two-level cascaded approach achieves an accuracy over 99% on the Paderborn University dataset in various operational scenarios. The model is trained and analyzed using MATLAB. The proposed cascaded algorithms outperform single-stage models, and enhanced signal preprocessing improves accuracy and noise resilience. Additionally, the proposed risk-based performance indicator offers insights into maintenance strategies, while an optimum algorithm selection framework identifies an algorithm by considering a trade-off between computational complexity and accuracy.}, archive = {J_ACCESS}, author = {Korawege N. C. Jayasena and Battur Batkhishig and Babak Nahid-Mobarakeh and Ali Emadi}, doi = {10.1109/ACCESS.2025.3589750}, journal = {IEEE Access}, pages = {129678-129701}, shortjournal = {IEEE Access}, title = {Current signature-based bearing fault severity classification using a robust multilevel cascaded framework}, volume = {13}, year = {2025}, }
• (2025). Economic analysis of critical distance based on frequency in low frequency AC for offshore wind farms. ACCESS, 13, 129659-129677. (www)

  @article{ , abstract = {This paper presents a frequency-dependent cost analysis model for optimizing the economic performance of Low-Frequency AC (LFAC) transmission systems for offshore wind integration. Unlike previous studies that assumed fixed frequencies (e.g., 16.7 Hz or 20 Hz), the proposed model evaluates how variations in frequency affect the cost of key components, including transformers, offshore platforms, and submarine cables. By modeling the cost of the total system as a function of frequency, transmission capacity, and distance, the system’s optimal operating frequency is identified under various conditions. In addition, LFAC, High-Voltage AC (HVAC), and High-Voltage DC (HVDC) systems are compared based on the concept of economic critical distance, which defines the boundary between the most economically favorable transmission technologies depending on distance and capacity conditions. The results show that HVAC is the most cost-effective solution for short transmission distances, LFAC is optimal for medium-range transmission, and HVDC is favorable for long-distance applications. Notably, LFAC outperforms HVDC when cable multiplication is avoided, confirming its competitiveness in midrange scenarios. These findings demonstrate that considering frequency as a design parameter significantly improves the cost efficiency of LFAC systems and should be reflected in the early-stage planning of offshore transmission system design.}, archive = {J_ACCESS}, author = {Byeonghyeon An and Jeongsik Oh and Taesik Park}, doi = {10.1109/ACCESS.2025.3589579}, journal = {IEEE Access}, pages = {129659-129677}, shortjournal = {IEEE Access}, title = {Economic analysis of critical distance based on frequency in low frequency AC for offshore wind farms}, volume = {13}, year = {2025}, }
• (2025). Dual-stream contrastive learning for medical visual representations using synthetic images generated by latent diffusion model. ACCESS, 13, 129648-129658. (www)

  @article{ , abstract = {Deep learning-based medical image processing methods can enhance diagnostic accuracy while significantly accelerating clinical decision workflows. However, in order to learn better visual representations, such approaches usually need substantial amount of expert-annotated data, which are highly costly. To address this issue, we propose a novel approach called Dual-Stream Contrastive Learning with Cross-Scale Token Projection (DCL-CsTP), which aims to enhance visual representations and transferable initializations. Specifically, a latent diffusion model (LDM) is leveraged to generate high-quality synthetic medical images in order to expand the dataset. Then we utilize the proposed dual-stream architecture that consists of a global semantic relations stream and a local detail relations stream to learn discriminative medical image representations from the dataset. Furthermore, a cross-scale token projection is designed to enable the model to capture various scales of focus in medical images. Comprehensive experiments are performed on two downstream tasks: medical image classification and segmentation. For multi-classification of pneumonia, our DCL-CsTP method achieves 95.90% accuracy. For lesions segmentation, our DCL-CsTP method attains 89.73% dice coefficient on the International Skin Imaging Collaboration 2018 (ISIC 2018) dataset and 82.50% dice coefficient on the Kvasir-SEG dataset. The performance superiority of the model pre-trained by DCL-CsTP is conclusively demonstrated through the above experiments on various dataset, which shows that DCL-CsTP can enhance diagnostic precision and alleviate radiologists’ image screening burdens.}, archive = {J_ACCESS}, author = {Weitao Ye and Longfu Zhang and Xiaoben Jiang and Dawei Yang and Yu Zhu}, doi = {10.1109/ACCESS.2025.3591544}, journal = {IEEE Access}, pages = {129648-129658}, shortjournal = {IEEE Access}, title = {Dual-stream contrastive learning for medical visual representations using synthetic images generated by latent diffusion model}, volume = {13}, year = {2025}, }
• (2025). Research on polyphonic music generation algorithm based on GPT large model. ACCESS, 13, 129638-129647. (www)

  @article{ , abstract = {Along with the rapid technological progress in the field of artificial intelligence, music generation algorithms based on large-scale pre-trained models have increasingly become the focus of academic attention. Existing polyphonic music generation techniques have limitations in terms of melodic complexity and harmonic diversity. To address the challenges of structural accuracy and long-range dependency modelling in polyphonic music generation, this paper proposes a targeted fine-tuning algorithm based on GPT macromodels, which improves the generation quality by incorporating domain-specific mechanisms. On the basis of GPT, the method embeds directional cross-track attention to enhance the modelling of vocal interactions, designs dynamic interval weight mask constraints and acoustic compliance, and introduces beat-phase embedding to enhance the temporal structure perception, forming a “GPT + domain-enhanced” generation framework. Experimental results show that the method demonstrates significant advantages in objective evaluation dimensions, such as note accuracy and harmonic consistency. The proposed method opens up an innovative path for polyphonic music composition and demonstrates the great potential of this technology in practical scenarios.}, archive = {J_ACCESS}, author = {Lin Zhu and Wenjuan Zhang}, doi = {10.1109/ACCESS.2025.3588847}, journal = {IEEE Access}, pages = {129638-129647}, shortjournal = {IEEE Access}, title = {Research on polyphonic music generation algorithm based on GPT large model}, volume = {13}, year = {2025}, }
• (2025). YOLOv9-based human face detection and counting under human-animal faces, complex imaging environments, and image qualities. ACCESS, 13, 129600-129637. (www)

  @article{ , abstract = {Automatic human face detection and counting can play a vital role in the recognition and tracking of infant and adult faces in both outdoor and indoor human surveillance applications and facial-vital sign measurement. Despite the advancements in deep learning networks, accurate and reliable detection is still a challenging task in the presence of different kinds of objects, animal faces, and image characteristics. In this paper, we study the effectiveness of the YOLOv9-based face detection and counting method under skin color variations, different face sizes and number of faces, mixture of human and animal faces, image properties (brightness, contrast, illumination, glare), image qualities (image blurring, low-light facial images, lens flared images, grainy images taken at night, different types of noises), and pareidolia effects. The proposed method is trained and validated using the Wider Face database. In addition, we created image databases with different kinds of image qualities and image characteristics with the above-mentioned challenges. The YOLOv9-based face detection model achieves a precision of 86%, a recall of 62.8%, and a mean average precision of 70.8% at an inference time of 15.2 ms on the Wider Face database. Evaluation results demonstrate that the YOLOv9-based face counting outperforms most of the state-of-the-art face detection and people counting methods with a mean absolute error (MAE) of 3.36 and root mean square error (RMSE) of 22.38. The model was also deployed on the Raspberry Pi edge computing platform to study the real-time performance. The YOLOv9-based method achieves an MAE of 0.53-5.76 on the untrained infant database with Gaussian, salt and pepper, and speckle noises and an MAE of 0.43-2.87 on faces inside vehicles. The study further highlights the effectiveness of the YOLOv9 model in achieving promising face detection and counting results under a range of illumination and skin color variations. Evaluation results on a wide variety of animal faces and pareidolia-induced faces demonstrate more false positives due to the lack of contextual intelligence in the generation of deep-face models. Further, results show that the deep-face model detects artificial faces (statues, art, paintings, posters) if the model is deployed in uncontrolled face-based application environments. The performance of the model is degraded under different kinds of noise and blurred images. The results of this study highlight that performance can be improved by using noise-specific filtering techniques with optimal filtering parameters, but this requires the automatic identification of noise types and their corresponding parameters.}, archive = {J_ACCESS}, author = {Sivaranjini Perikamana Narayanan and M. Sabarimalai Manikandan and Linga Reddy Cenkeramaddi}, doi = {10.1109/ACCESS.2025.3591247}, journal = {IEEE Access}, pages = {129600-129637}, shortjournal = {IEEE Access}, title = {YOLOv9-based human face detection and counting under human-animal faces, complex imaging environments, and image qualities}, volume = {13}, year = {2025}, }
• (2025). Scalable FPGA implementation of a reliability-based direct turbo decoder for short block codes. ACCESS, 13, 129588-129599. (www)

  @article{ , abstract = {Turbo codes are widely used in satellite and mobile communication systems for their strong error-correcting performance. However, conventional iterative decoders such as Log-MAP suffer from high complexity and exhibit error floors in short block-length regimes. To address this, a Reliability-Based Compact Direct Decoder (RCODD) algorithm has been proposed as a non-iterative alternative that leverages encoder state information and demodulator reliability metrics for efficient decoding. This paper presents the first hardware implementation of the RCODD algorithm on an FPGA, featuring a serial architecture optimized for minimal memory usage and logic complexity. Synthesized on a Zynq-7010 FPGA, the design achieves a BER of $0.7\times 10^{-4}$ at 2 dB SNR for a 16,000-bit stream while consuming only 49% of available BRAM and 56% of LUTs. A 4-way parallel version of the architecture is also proposed and simulated, demonstrating a $3.8\times $ reduction in latency while preserving decoding performance. Additionally, a fixed-point quantization study confirms that a 16-bit representation of reliability values introduces negligible error, offering further scope for area and power optimization. Comparative analysis with existing turbo decoder designs shows that the proposed RCODD-based architecture is uniquely positioned to serve low-power, high-reliability applications such as satellite telecomm and systems, while remaining scalable to higher-throughput needs.}, archive = {J_ACCESS}, author = {Senthil Murugan and Ramesh Bhakthavatchalu and Karthi Balasubramanian and B. Yamuna and Deepak Mishra and Sree Ranjani Rajendran}, doi = {10.1109/ACCESS.2025.3589156}, journal = {IEEE Access}, pages = {129588-129599}, shortjournal = {IEEE Access}, title = {Scalable FPGA implementation of a reliability-based direct turbo decoder for short block codes}, volume = {13}, year = {2025}, }
• (2025). Enhancing traffic flow in heterogeneous freeways: Integration of multivariable extremum seeking and filtered feedback linearization control. ACCESS, 13, 129573-129587. (www)

  @article{ , abstract = {This paper presents a novel control approach for managing traffic flow on heterogeneous freeways, including both human-driven vehicles and autonomous vehicles classes. We introduce an enhanced version of the multi-class METANET model that incorporates state-dependent parameters, enabling a more accurate representation of the macroscopic dynamics of traffic systems. Building on this improved traffic model, we propose a two-level hierarchical control framework to manage traffic flow and reduce congestion effectively. At the upper level, a distributed multi-variable extremum seeking (DMV-ES) control approach determines the optimal densities for different class of vehicles within controlled traffic cells to maximize overall traffic flow while minimizing flow variations between adjacent cells. These densities are then provided as reference inputs to the lower-level controller, where a distributed filtered feedback linearization (D-FFL) control is employed to determine the desired speed commands for the vehicles. The key advantage of the proposed controller is that it requires only limited knowledge of the system, making it well-suited for the uncertain and nonlinear nature of heterogeneous traffic systems. The effectiveness of the proposed scheme is validated through numerical simulations and VISSIM-generated traffic scenarios, demonstrating significant improvements in congestion management and enhanced traffic flow compared to conventional methods.}, archive = {J_ACCESS}, author = {Pouria Karimi Shahri and Arash Rahmanidehkordi and Azad Ghaffari and Amir H. Ghasemi}, doi = {10.1109/ACCESS.2025.3585431}, journal = {IEEE Access}, pages = {129573-129587}, shortjournal = {IEEE Access}, title = {Enhancing traffic flow in heterogeneous freeways: Integration of multivariable extremum seeking and filtered feedback linearization control}, volume = {13}, year = {2025}, }
• (2025). Unit-centric regularization for efficient deep neural networks. ACCESS, 13, 129551-129572. (www)

  @article{ , abstract = {Deep neural networks excel by learning hierarchical representations, often requiring architectural enhancements like increased width, normalization layers, or skip connections, each adding complexity and computational cost. This paper proposes Jumpstart, a novel regularization technique that enables the use of simpler architectures by promoting efficient utilization of both network units and data points. The method penalizes units that become inactive (dead) or operate strictly in the linear regime, as well as data points whose activations within a layer are uniformly zero or strictly positive. This strategy enables the training of plain ReLU networks without relying on overparameterization, specialized initialization, normalization layers, or architectural modifications like skip connections. As a result, it promotes more efficient use of units and data, maintaining performance while avoiding waste of computational resources during both training and inference. On the ImageNet benchmark, it matches the top-1 accuracy of a standard ResNet50 with Batch Normalization and skip connections. On UCI tabular datasets, it consistently outperforms batch normalization and often surpasses residual connections. The method is evaluated using four global metrics: Dead Units, Linear Units, Trainability, and Convergence. Jumpstart significantly reduces the presence of inactive and linear units (0.07 and 0.12, respectively), outperforming most baselines and achieves superior trainability (1.0) and convergence (-0.03). These results demonstrate that simpler, regularized networks can maintain competitive accuracy while significantly lowering architectural complexity and computational burden. Jumpstart offers a sustainable and effective alternative to conventional deep learning design strategies, facilitating efficient training without compromising performance.}, archive = {J_ACCESS}, author = {Carles R. Riera Molina and Eloi Puertas and Oriol Pujol}, doi = {10.1109/ACCESS.2025.3591313}, journal = {IEEE Access}, pages = {129551-129572}, shortjournal = {IEEE Access}, title = {Unit-centric regularization for efficient deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Joint modulation identification and IQ impairment handling in two-path relayed multiple users OFDMA over unknown channels. ACCESS, 13, 129539-129550. (www)

  @article{ , abstract = {The design of effective modulation identification (MI) schemes in orthogonal frequency division multiple access (OFDMA) transmissions poses a considerable challenge for autonomous radio systems, owing to system complexity and dynamic channel conditions. While prior research has addressed this problem, it has predominantly considered non-cooperative single-user scenarios and overlooked practical transmission constraints. In this study, we develop an advanced maximum-likelihood (ML) method to perform MI in the presence of in-phase and quadrature-phase (IQ) impairments, which are commonly introduced by fabrication flaws in transceiver components. The focus is placed on a multiple users OFDMA uplink configuration that employs amplify-and-forward relays within a two-path consecutive relayed (TCR) system. To better capture realistic circumstances, we represent IQ impairments as impacting each node individually, mirroring variances seen in practical installations. We further assume that the channel parameters across all links are unknown and must be implicitly handled during processing. The proposed solution is reinforced by utilizing the data detection mechanism included in TCR systems. Theoretical analysis confirms that the cumulative IQ distortions from each node can be effectively embedded within the link parameters, leading to the formulation of aggregated link parameters (ALPs). The conceptual analysis indicates that the direct ML solution for combining MI with ALPs is computationally prohibitive for practical implementation. To address this challenge, we adopt a lightweight estimation scheme derived from the space-alternating generalized expectation-maximization (SAGE) strategy. The proposed scheme includes a feedback-driven cycle that enables dynamic interaction among the MI, ALPS estimator, and data discovery components to optimize their collective efficacy. Simulation results substantiate the effectiveness of the proposed approach, indicating its performance exceeds that of existing methods.}, archive = {J_ACCESS}, author = {Hala Mostafa and Mohamed Marey and Khaled Mohamad Almustafa and Juliano Katrib}, doi = {10.1109/ACCESS.2025.3589780}, journal = {IEEE Access}, pages = {129539-129550}, shortjournal = {IEEE Access}, title = {Joint modulation identification and IQ impairment handling in two-path relayed multiple users OFDMA over unknown channels}, volume = {13}, year = {2025}, }
• (2025). Compensation method, diakoptics, and MATE. ACCESS, 13, 129534-129538. (www)

  @article{ , abstract = {This paper demonstrates how the Compensation method relates to the bordered-block- diagonal solution of power grids for the computation of power system transients. It is shown that it also relates to diakoptics and englobes other algorithms, such as the Multi Area Thevenin Equivalent (MATE) method. Implementation issues and potential inefficiencies are also discussed. The aim is to create a reference for avoiding confusion in the literature, for eliminating misconceptions and for supporting future research.}, archive = {J_ACCESS}, author = {Jean Mahseredjian and Boris Bruned and Anas Abusalah}, doi = {10.1109/ACCESS.2025.3589280}, journal = {IEEE Access}, pages = {129534-129538}, shortjournal = {IEEE Access}, title = {Compensation method, diakoptics, and MATE}, volume = {13}, year = {2025}, }
• (2025). Explainable graph neural networks for power grid fault detection. ACCESS, 13, 129520-129533. (www)

  @article{ , abstract = {This paper proposes the application of explanation methods to enhance the interpretability of graph neural network (GNN) models in fault location for power grids. GNN models have exhibited remarkable precision in utilizing phasor data from various locations around the grid and integrating the system’s topology, an advantage rarely harnessed by alternative machine learning techniques. This capability makes GNNs highly effective in identifying fault occurrences in power grids. Despite their greater performance, these models can encounter criticism for their “black box” nature, which conceals the reasoning behind their predictions. Lack of transparency significantly hinders power utility operations, as interpretability is crucial to building trust, accountability, and actionable insights. This research presents a comprehensive framework that systematically evaluates state-of-the-art explanation strategies, representing the first use of such a framework for Graph Neural Network models for defect location detection. By assessing the strengths and weaknesses of different explanatory methods, it identifies and recommends the most effective strategies for clarifying the decision-making processes of GNN models. These recommendations aim to improve the transparency of fault predictions, allowing utility providers to better understand and trust the models’ output. The proposed framework not only enhances the practical usability of GNN-based systems but also contributes to advancing their adoption in critical power grid applications.}, archive = {J_ACCESS}, author = {Richard Bosso and Corey Chang and Mahdi Zarif and Yufei Tang}, doi = {10.1109/ACCESS.2025.3591604}, journal = {IEEE Access}, pages = {129520-129533}, shortjournal = {IEEE Access}, title = {Explainable graph neural networks for power grid fault detection}, volume = {13}, year = {2025}, }
• (2025). Lightweight dual-backbone detection transformer for infrared insulator detection. ACCESS, 13, 129504-129519. (www)

  @article{ , abstract = {To address the challenges of high computational complexity and complex background interference in infrared insulator detection systems, especially on resource-constrained devices, this study proposes a lightweight dual-backbone detection transformer (LWDB-DETR). To extract target features from complex infrared backgrounds more accurately while reducing computational cost, we propose a multi-scale attention network (MSANet) as the adaptive backbone, which employs a multi-branch parallel structure and dynamically adjusts feature weights through a multi-scale gated attention mechanism. Additionally, we propose a contextual and geometric pyramid network (CGPN) to handle multi-scale feature fusion more effectively, which integrates context-aware modules with dynamic interpolation and implements geometric calibration through rectangular calibration modules. This enhances the fusion of low and high-resolution features while optimizing feature representation in infrared images with complex backgrounds. Furthermore, the lightweight context gate block (CGBlock) is introduced into the model, improving the model’s spatial awareness and detection accuracy by integrating gated convolution mechanisms and adopting adaptive feature channel weighting strategies. To validate our approach, an extensive infrared insulator dataset encompassing 3,790 images under various environmental conditions is constructed. Experimental results indicate that compared to RT-DETR-R18, the LWDB-DETR model reduces parameters by 53.2% and GFLOPs by 70.7% on the infrared insulator dataset. Moreover, mAP50 reaches 97.2%. On the CPLID dataset, it retains 94.3% mAP50 while maintaining computational efficiency. Deployment on the NVIDIA Jetson Xavier NX platform achieves 32.5 FPS, highlighting its effectiveness for real-time infrared insulator detection in power system monitoring applications.}, archive = {J_ACCESS}, author = {Boyang Zhang and Yanpeng Zhang and Liming Sun}, doi = {10.1109/ACCESS.2025.3591296}, journal = {IEEE Access}, pages = {129504-129519}, shortjournal = {IEEE Access}, title = {Lightweight dual-backbone detection transformer for infrared insulator detection}, volume = {13}, year = {2025}, }
• (2025). Simulation of E-mode β -Ga2O3 interlayer-based vertical trench power MOSFET with fast-switching performance at high temperatures. ACCESS, 13, 129493-129503. (www)

  @article{ , abstract = {In this paper, an interlayer-based vertical trench power $\beta $ -Ga2O3 MOSFET with nitrogen-doped current blocking layers (CBLs) is designed and simulated in Sentaurus TCAD. The proposed MOSFET achieves Enhancement-mode (E-mode) operation and better performance than the reported E-mode $\beta $ -Ga2O3 transistors with CBLs. The DC characteristics present a higher on/off current ratio of $10^{8}$ than the reported $\beta $ -Ga2O3 U-shaped MOSFETs, and a higher on-current density of 94 A/cm2, a lower specific on-resistance of 26.5 m $\Omega \cdot $ cm2, and a larger breakdown voltage of 483 V than the reported $\beta $ -Ga2O3 current aperture vertical MOSFETs. The thermal characteristic study shows that the proposed device can work up to $500~^{\circ }$ C in E-mode operation with an on-current density of 35 A/cm2 and on-resistance of 104 m $\Omega $ .cm2. The dynamic characteristics are explored, and excellent switching performance with a turn-on time of 29 ns and a turn-off time of 48 ns at room temperature is achieved. At $500~^{\circ }$ C, the turn-on time slightly increases to 38 ns due to the reduced electron mobility, and the turn-off time is still in the order of 40 ns because of the less discharge time of the lower on-state gate charge. The proposed MOSFET is examined in a boost converter application, and an efficiency of 89% and a voltage conversion ratio of 1.42 for a duty cycle of 40% are obtained. Our work shows the proposed $\beta $ -Ga2O3 MOSFET has great potential as a high-speed switch in power electronics applications.}, archive = {J_ACCESS}, author = {Xiaoqing Chen and Feng Li and Herbert Hess}, doi = {10.1109/ACCESS.2025.3589533}, journal = {IEEE Access}, pages = {129493-129503}, shortjournal = {IEEE Access}, title = {Simulation of E-mode β -Ga2O3 interlayer-based vertical trench power MOSFET with fast-switching performance at high temperatures}, volume = {13}, year = {2025}, }
• (2025). Hierarchical structure estimation of financial networks and sentiment analysis of financial news headlines. ACCESS, 13, 129476-129492. (www)

  @article{ , abstract = {In financial markets, there have been several crises, and it is often suggested that early signs of these crises can be observed in market structures before they fully emerge. In this study, we apply the one-layer Hierarchical Stochastic Block Model (HSBM) to detect anomalies by analyzing market changes and the underlying spatial structure, providing crucial insights to support investors’ investment decisions. Moreover, understanding and explaining these market shifts is essential, as it enables investors to respond effectively to fluctuations and proactively prepare for similar major changes in the future. To deepen this analysis, we extended to two-layer HSBM, treating the blocks in the lower layer as nodes in the upper layer, using a two-layer approach for cause analysis. Additionally, the sentiment score of financial news headlines is widely regarded as a strong indicator of market conditions. During periods of market prosperity, sentiment scores tend to be more positive, while in downturns, they become more negative. In this study, we analyzed the structural changes in the S&P 100 indices and compared them with the sentiment scores of financial news headlines from the same period. Our findings reveal a correlation between sentiment scores and market structure changes under normal market conditions. However, during the pandemic, discrepancies between the two emerged, suggesting that crisis periods may disrupt this relationship. These insights provide a new perspective for understanding financial market dynamics.}, archive = {J_ACCESS}, author = {Naijia Liu and Kaira Sekiguchi and Yoshiyuki Nakata and Toshiaki Sugie and Takaaki Yoshino and Yukio Ohsawa}, doi = {10.1109/ACCESS.2025.3588385}, journal = {IEEE Access}, pages = {129476-129492}, shortjournal = {IEEE Access}, title = {Hierarchical structure estimation of financial networks and sentiment analysis of financial news headlines}, volume = {13}, year = {2025}, }
• (2025). Cross-filter structured pruning for efficient sparse CNN acceleration. ACCESS, 13, 129461-129475. (www)

  @article{ , abstract = {Convolutional Neural Networks (CNNs) are widely used in vision tasks for resource-constrained environments due to their computational efficiency and strong generalization. However, the dominance of $1 \times 1$ convolutions in modern CNN architectures introduces challenges for sparsity-aware hardware accelerators, particularly in processing element (PE) load balancing, which limits speedup in sparse inference. To address this issue, this paper proposes a cross-filter structured pruning method, enforcing a uniform sparsity pattern across multiple filters to ensure balanced workload distribution among PEs. This approach is further extended to k $\times $ k convolutions by decomposing them into $1 \times 1$ filters, improving applicability across various CNN layers. This paper also proposes an intra-kernel parallelism technique which significantly reduces the size of PE’s local buffers, a critical bottleneck in sparse CNN accelerators. Experimental results show that the proposed approach maintains accuracy comparable to globally unstructured pruning while significantly enhancing inference speed. FPGA implementation and cycle-accurate simulations confirm improvements in processing speed, energy efficiency, and hardware utilization, making this method well-suited for edge and mobile AI applications. Specifically, the proposed architecture achieves $1.14\times $ to $1.6\times $ speedup over Sparten for various CNN models and delivers $7.6\times $ and $1.9\times $ higher energy efficiency compared to Sparten and StarSPA, respectively. In terms of area efficiency, synthesis results show a $1.73\times $ – $10.95\times $ reduction in required hardware primitives compared to Sparten and StarSPA.}, archive = {J_ACCESS}, author = {Ngoc-Son Pham and Sangwon Shin and Lei Xu and Weidong Shi and Taeweon Suh}, doi = {10.1109/ACCESS.2025.3587027}, journal = {IEEE Access}, pages = {129461-129475}, shortjournal = {IEEE Access}, title = {Cross-filter structured pruning for efficient sparse CNN acceleration}, volume = {13}, year = {2025}, }
• (2025). Aspect-oriented approach of non-functional requirements for embedded system-based IoT applications. ACCESS, 13, 129451-129460. (www)

  @article{ , abstract = {Designing high-performance embedded devices is usually time consuming and error-prone due to conflicting non-functional requirements (NFRs) like performance, timeliness, and sustainability. This paper proposes an Aspect-Oriented Programming (AOP) approach using AspectJ to streamline IoT middleware design by modularizing NFRs and communicating domain knowledge to optimizers and mapping tools. Leveraging AspectJ, our method enhances modularity by separating crosscutting concerns—such as scheduling, resource allocation, and synchronization—from core functionality. Our novel contribution lies in refactoring crosscutting concerns— task scheduling, resource management, and task synchronization —into reusable aspects, achieving a Cohesion Index (CoI) increase from 0.19 (Java) to 0.69 (AspectJ), a 263% improvement, while reducing code tangling. By leveraging context primitives and behavior functions aligned with standards like OSEK OS, we address IoT challenges such as real-time criticality and resource scarcity Validated across a real-time audio switch and simulated FreeRTOS environments, our method outperforms traditional Java designs, cutting memory usage by 20% (24.25% to 20.01%) and CPU utilization by 9% (44% to 40%), with a 50% runtime reduction in critical tasks (e.g., $92.03\mu $ s to $46.19\mu $ s). Unlike prior AOP efforts focused on desktop systems, this approach targets embedded IoT, offering a scalable, standards-integrated solution. A survey of real-time embedded system development highlights the superiority of Aspect-Oriented Software Development (ASOD) in enhancing schedulers, resource allocation agents, and other synchronization mechanisms with minimal overhead, setting a foundation for broader IoT adoption.}, archive = {J_ACCESS}, author = {Anirban Bhar and Sabnam Sengupta and Sankhayan Choudhury}, doi = {10.1109/ACCESS.2025.3585417}, journal = {IEEE Access}, pages = {129451-129460}, shortjournal = {IEEE Access}, title = {Aspect-oriented approach of non-functional requirements for embedded system-based IoT applications}, volume = {13}, year = {2025}, }
• (2025). Enhancing LoRa-based outdoor localization accuracy using machine learning. ACCESS, 13, 129432-129450. (www)

  @article{ , abstract = {The Internet of Things is gaining significant relevance, driving increasing interest in location-based services using wireless signals, particularly Low Power Wide Area Network (LPWAN) technology. LoRa (Long Range), together with LoRaWAN, is a prominent LPWAN standard that provides long-range connectivity and low energy consumption, making it viable for IoT-based positioning systems in smart cities. For localization systems leveraging LoRa signals, Machine Learning (ML) approaches are being increasingly explored, as ML-based solutions offer a powerful way to enhance the accuracy of positioning. In this study, we propose various ML approaches for LoRa-based positioning in outdoor environments. We evaluate six different ML models: k-NN, CNN, SVR, ANN, XG-Boost, and LightGBM-using an open-source urban LoRaWAN dataset. We further propose a Hybrid Model that combines convolutional feature extraction with gradient-boosted regression. This architecture integrates the strengths of Deep Learning and tree-based models, aiming to capture both temporal signal patterns and structured input correlations for improved localization accuracy. The models are trained offline and tested for performance in terms of localization accuracy, mean square error, and computational efficiency. Additionally, we investigate the impact of different Feature Vector (FV) subsets on localization performance by analyzing the significance of LoRaWAN signal attributes. Our results highlight the effectiveness of ML models in enhancing localization accuracy for LoRa-based outdoor positioning systems, demonstrating performance improvements ranging from 10% to 73% compared to previous ML studies in outdoor localization.}, archive = {J_ACCESS}, author = {Nur Keleşoğlu and Marzena Halama and Anna Strzoda}, doi = {10.1109/ACCESS.2025.3589032}, journal = {IEEE Access}, pages = {129432-129450}, shortjournal = {IEEE Access}, title = {Enhancing LoRa-based outdoor localization accuracy using machine learning}, volume = {13}, year = {2025}, }
• (2025). A big data framework for scalable and cross-dataset capable machine learning in network intrusion detection systems. ACCESS, 13, 129419-129431. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDS) are widely used to secure modern networks, but deploying accurate and scalable Machine Learning (ML)-based detection in high-speed environments remains challenging. Traditional approaches often fail to generalize across different network environments, leading to significant performance degradation in cross-dataset evaluations. Additionally, ensuring near real-time inference while ingesting large volumes of network events requires efficient processing pipelines. In this work, we propose a distributed ensemble-based NIDS designed to improve both accuracy and scalability in large-scale network environments. Our approach leverages a Big Data framework to decouple event ingestion from inference, ensuring high-speed processing without sacrificing detection performance. We implement our system using Apache Spark and Apache Kafka, enabling real-time event ingestion, efficient model inference, and periodic model updates through distributed storage. The ensemble classification scheme enhances generalization capabilities by combining multiple classifiers, reducing accuracy loss in cross-dataset scenarios. Experimental evaluations conducted on three benchmark datasets—UNSW-NB15, CS-CIC-IDS, and BoT-IoT—demonstrate that our proposed approach consistently outperforms traditional techniques. Our model achieves an F-Measure improvement of up to 0.46 in cross-dataset evaluations, addressing the generalization limitations of individual classifiers. Additionally, it achieves near real-time inference throughput comparable to traditional classifiers, processing up to 1.07M events per second with three workers, while our distributed training pipeline scales efficiently, reducing model training time by up to 62% in the same setup.}, archive = {J_ACCESS}, author = {Vinicius M. de Oliveira and Henrique M. de Oliveira and Gabriel M. Santos and Jhonatan Geremias and Eduardo K. Viegas}, doi = {10.1109/ACCESS.2025.3589872}, journal = {IEEE Access}, pages = {129419-129431}, shortjournal = {IEEE Access}, title = {A big data framework for scalable and cross-dataset capable machine learning in network intrusion detection systems}, volume = {13}, year = {2025}, }
• (2025). A unified framework for fault and performance prediction using spatio-temporal geometric features based on STSFE. ACCESS, 13, 129400-129418. (www)

  @article{ , abstract = {This paper proposes a unified deep-learning framework for fault and performance prediction in communication equipment by utilizing spatiotemporal geometric features. The core methodology, Spatio-Temporal Slope Feature Extraction (STSFE), transforms irregular time-series data into slope-, area-, and volume-based representations, capturing both temporal dynamics and spatial correlations. We develop three distinct yet structurally aligned prediction models: 1) passive MUX fault classification, 2) SFP port-level fault detection, and 3) regression-based forecasting of Rx signal degradation. All models employ a multi-branch neural architecture that integrates MLP, CNN, and attention mechanisms, along with customized loss functions designed to enhance sensitivity to tail-zone deviations. To evaluate the generalization capability of the proposed framework, we conduct a comparative analysis using the NASA Battery dataset, which is reshaped via STSFE to emulate industrial signal characteristics. Experimental results demonstrate that our models outperform existing approaches in terms of classification accuracy, mean absolute error, and tail prediction performance. This research provides a flexible and robust methodology for predictive maintenance across diverse time-series domains in industrial communication networks.}, archive = {J_ACCESS}, author = {Dong-Hyun Kang and A-Youn Yang and Jong-Min Lee and Jong-Gu Lee}, doi = {10.1109/ACCESS.2025.3590393}, journal = {IEEE Access}, pages = {129400-129418}, shortjournal = {IEEE Access}, title = {A unified framework for fault and performance prediction using spatio-temporal geometric features based on STSFE}, volume = {13}, year = {2025}, }
• (2025). Team recruitment of collaborative crowdsensing: A graph-based approach. ACCESS, 13, 129385-129399. (www)

  @article{ , abstract = {Collaborative crowdsensing is a teamwork approach that leverages the collective intelligence of participants. Unlike traditional crowdsensing user recruitment, collaborative crowdsensing not only considers users’ intrinsic abilities (sensing ability, reliability) but also places a greater emphasis on their extrinsic abilities(collaborative abilities). Initially, we calculated user’s collaborative ability based on an undirected graph, which is modeled by user’s interaction relationships and collaborative characteristics. Besides, a directed graph representing evaluation relationships is constructed through user ratings, and the PageRank algorithm is utilized to assess a user’s reliability. Taking into account users’ intrinsic abilities, extrinsic capabilities and cost quotations, we propose a team recruitment mechanism. which transforms the recruitment problem into a maximum spanning tree problem in graph theory. Furthermore, an enhanced Prim algorithm is utilized to select efficient users. To address the issue of recruiting new users lacking prior knowledge, an improved UCB algorithm is introduced on top of the proposed team recruitment mechanism. Additionally, to prevent unfair payments resulting from selfish users misreporting costs, the team recruitment process is modeled as a reverse auction, and a reward payment method based on Myerson theory is designed. This method ensures fair compensation based on a user’s utility contribution to the team. Finally, experimental comparisons on real datasets validate the effectiveness of the algorithms.}, archive = {J_ACCESS}, author = {Hui Liu and Xiaowan Li and Chuang Zhang and Xiaodong Chen and Weipeng Tai}, doi = {10.1109/ACCESS.2024.3406519}, journal = {IEEE Access}, pages = {129385-129399}, shortjournal = {IEEE Access}, title = {Team recruitment of collaborative crowdsensing: A graph-based approach}, volume = {13}, year = {2025}, }
• (2025). Dynamic adaptive parametric social network analysis using reinforcement learning: A case study in topic-aware influence maximization. ACCESS, 13, 129372-129384. (www)

  @article{ , abstract = {Current network analysis algorithms often rely on search methods or centrality measures but face challenges such as 1) The solution space is large, resulting in high computational complexity. 2) Algorithms may be instance-dependent, relying considerably on network structure and characteristics, which may result in varying performance across different networks. 3) Most existing centrality measures are inherently static, which fail to capture the dynamic nature inherent in network analysis problems. To address these issues, this paper introduces a dynamic adaptive parametric (DAP) approach using reinforcement learning. As a case study, the method has been applied to the topic-aware influence maximization (TIM) problem, where the objective is to identify k influential nodes that maximize influence spread under a given topic vector and diffusion model. The paper introduces two dynamic centrality measures that capture the evolving importance of nodes during topic propagation in the network. To avoid instance-dependence, an adaptive reinforcement learning technique is used to adjust the significance of each measure based on the current network structure, tailoring solutions to the specific network. The parametric approach further reduces the search space by transforming TIM into a parametric optimization task, where the goal is to determine the optimal importance of each centrality measure. The proposed algorithm is evaluated on both real-world and synthetic networks. Experimental results show that the method outperforms conventional centrality-based greedy algorithms and other existing approaches in terms of solution quality, running time, and scalability. Also, as a part of our research, we propose a topic-aware benchmark dataset by augmenting the Deezer music-based social network with labeled nodes and edges, providing a valuable resource for evaluating future research.}, archive = {J_ACCESS}, author = {Mohammad Hossein Ahmadikia and Mehdy Roayaei}, doi = {10.1109/ACCESS.2025.3590835}, journal = {IEEE Access}, pages = {129372-129384}, shortjournal = {IEEE Access}, title = {Dynamic adaptive parametric social network analysis using reinforcement learning: A case study in topic-aware influence maximization}, volume = {13}, year = {2025}, }
• (2025). Event-triggered inverse control for parametric feedback systems with asymmetric dead-zone input. ACCESS, 13, 129359-129371. (www)

  @article{ , abstract = {In this paper, an event triggered control scheme is proposed for strict feedback nonlinear systems with dead-zone input, and external disturbance. In the threshold setting, inevitable measurement interference is considered and an unknown non-static threshold is constructed. Then, an error representation between the event triggered control signal and the conventional control signal is established at the output end of the dead-zone actuator. Of course, the unknown threshold disturbance and smooth inverse of dead-zone make it more difficulty. The Zone behavior analysis is established to address the existence of this unknown threshold disturbance. Compared to the linear approximation of dead-zone, the application of the smooth inverse makes this result effective for both symmetric and asymmetric dead-zone input.}, archive = {J_ACCESS}, author = {Jiamin Cui and Dong Guo and Gang Chen and Jianping Cai and Jianning Li}, doi = {10.1109/ACCESS.2025.3590439}, journal = {IEEE Access}, pages = {129359-129371}, shortjournal = {IEEE Access}, title = {Event-triggered inverse control for parametric feedback systems with asymmetric dead-zone input}, volume = {13}, year = {2025}, }
• (2025). Enhancing adaptive spectrum access: An intelligent reflecting surface assisted CRN for future wireless communication. ACCESS, 13, 129348-129358. (www)

  @article{ , abstract = {Effective spectrum management remains a critical challenge in modern wireless communication due to the growing demand for high-speed and reliable connectivity. Cognitive radio (CR) has emerged as a transformative solution, enabling more efficient spectrum utilization through adaptive spectrum access mode (ASAM). In ASAM, the secondary transmitter (ST) selects either underlay or overlay transmission based on the sensing methodology. In the underlay mode, ST operates alongside the primary transmitter (PT) at low power levels to prevent interference with the primary receiver (PR), ensuring that transmission remains within the interference threshold. However, the dynamic and unpredictable nature of wireless environment makes it challenging to maintain transmit power within this limit. To address this, we propose an intelligent reflecting surface (IRS)-assisted enhanced ASAM (EASAM) CR network (CRN). Additionally, we optimize the IRS phase shifts and ST’s transmit power using the Grey Wolf Optimization (GWO) algorithm. This approach dynamically adjusts the ST’s transmit power within interference constraints while fine-tuning IRS phase shifts to enhance spectral efficiency. Extensive simulations demonstrate that the proposed EASAM with optimized IRS significantly outperforms the EASAM without IRS, achieving nearly a four times higher throughput. Furthermore, IRS optimization using the GWO yields a 7.1% increase in throughput compared to other meta-heuristic-based IRS optimization methods, and a 47.9% improvement over the unoptimized IRS. These findings highlight the potential of integrating GWO-based IRS approach in CRN to enhance performance of adaptive spectrum access.}, archive = {J_ACCESS}, author = {Vishwas Srivastava and Binod Prasad}, doi = {10.1109/ACCESS.2025.3590978}, journal = {IEEE Access}, pages = {129348-129358}, shortjournal = {IEEE Access}, title = {Enhancing adaptive spectrum access: An intelligent reflecting surface assisted CRN for future wireless communication}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of alternative configurations of battery electric mining haul trucks. ACCESS, 13, 129331-129347. (www)

  @article{ , abstract = {Mining is at the heart of the global decarbonisation push, providing the minerals and metals needed for renewable and sustainable development. Minerals and metals are the foundation of almost all industries, so these goals will remain unattainable without the mining and metals industry’s own decarbonisation journey. Efforts to develop solutions for decarbonising mining operations support the perspective that electrification is a key solution for transitioning the mining fleet and equipment away from fossil fuels. This paper describes three battery electric mining truck configurations: battery-only truck (BOT), battery trolley with stationary charging (BT-S), and battery trolley with dynamic charging (BT-D). For better battery-electric mining truck deployment, this study discusses 1) battery-electric mining truck powertrains based on current industry technologies and readiness; 2) the reasonable battery size design of various applications; 3) performance of various truck models, and; 4) the influence of battery size and truck performance on operations due to dynamic mining haulage routes in surface mines. The study establishes a 3D mining discrete event simulation (DES) scenario and initial and final haulage routes are selected according to conventional mine design and planning. The simulation results show BOT needs the largest battery size while BT-D needs the smallest due to trolley power and dynamic charging. In scenarios of equal cycle times, the BT-S configuration typically demonstrates a lower duty cycle relative to the BOT while BT-D achieves a duty cycle of 100% within a single work shift. Assuming no trolley power limitations, the BT-D configuration demonstrates the most favourable energy efficiency and productivity. Further study and analysis are ongoing in terms of fleet implications for energy management systems. These research findings will help decision makers design effective solutions for mining electrification and decarbonisation.}, archive = {J_ACCESS}, author = {Haiming Bao and Peter Knights and Mehmet Kizil and Micah Nehring}, doi = {10.1109/ACCESS.2025.3588871}, journal = {IEEE Access}, pages = {129331-129347}, shortjournal = {IEEE Access}, title = {Performance evaluation of alternative configurations of battery electric mining haul trucks}, volume = {13}, year = {2025}, }
• (2025). Behavioral modeling and predistortion of dual-band power amplifiers. ACCESS, 13, 129305-129330. (www)

  @article{ , abstract = {Dual-band power amplifiers (PAs) are essential components in modern wireless communication systems. Compared to single band PAs, dual-band PAs enable efficient utilization of spectrum resources with lower capital and operational expenses. In modern applications, inherent nonlinearities in PAs are unavoidable and can significantly degrade signal quality causing significant spectrum regrowth. These nonlinearities, already problematic in the case of single band systems, become considerably worse in dual-band systems with the generation of a plethora of intermodulation and cross-modulation products. These are even more challenging since the dominant distortions products can vary depending on the carrier aggregation scenario and, most importantly, the spacing between the two bands. Digital predistortion (DPD) techniques are mature for single band PAs; however, there are still numerous challenges for their adoption in dual-band and multi-band systems. This paper provides a comprehensive review of dual-band digital predistorters, including polynomial based, hybrid, and neural networks based predistorters. The different models are presented using consistent formulations to better highlight their commonalities and differences. Finally, a discussion of the presented models is included along with some insight about potential areas of improvement. This review aims to provide a valuable resource for researchers and engineers working on the design and optimization of dual-band communication systems.}, archive = {J_ACCESS}, author = {Majid Ahmed and Ahmad Dalbah and Oualid Hammi and Wenhua Chen and Fadhel M. Ghannouchi}, doi = {10.1109/ACCESS.2025.3590752}, journal = {IEEE Access}, pages = {129305-129330}, shortjournal = {IEEE Access}, title = {Behavioral modeling and predistortion of dual-band power amplifiers}, volume = {13}, year = {2025}, }
• (2025). A 7-bit vector-sum phase shifter for LEO satellite communication in 90-nm CMOS process. ACCESS, 13, 129296-129304. (www)

  @article{ , abstract = {This paper presents a 7-bit vector-sum phase shifter for low-earth-orbit satellite communication. The phase shifter consists of a differential-type quadrature all-pass filter (QAF) and an analog differential vector adder, where a matching network is inserted between the QAF and vector adder to minimize insertion loss further. Theoretical analyses are performed to account for the loading effect on the QAF while providing design methodology to minimize phase and amplitude errors. Two 8-bit current digital-to-analog converters (IDACs) are used to supply the bias current and determine the weighting of the I/Q signals for the vector adder, enabling full-range phase shifting with steps smaller than 3°. Fabricated in the TSMC 90-nm CMOS process, the digital phase shifter occupies a chip area of 0.63 mm ${}^{\mathbf {2}}$ . Over the frequency band of 27 GHz to 31 GHz, the phase shifter achieves an average gain between 0.13 dB to 1.36 dB, an RMS gain error between 0.68 dB to 0.9 dB, and a high phase shift accuracy with an RMS phase error between 0.18° to 0.21°. Compared with other vector-sum phase shifters, this work achieves the highest figure-of-merit while consuming the lowest power of 10 mW.}, archive = {J_ACCESS}, author = {Hao-Jen Tu and Hsiao-Chin Chen and Pin-Hsuan Chen and Po-Yen Huang}, doi = {10.1109/ACCESS.2025.3591240}, journal = {IEEE Access}, pages = {129296-129304}, shortjournal = {IEEE Access}, title = {A 7-bit vector-sum phase shifter for LEO satellite communication in 90-nm CMOS process}, volume = {13}, year = {2025}, }
• (2025). Pre-processing-based walsh code with switched system in secure image steganography enhancement. ACCESS, 13, 129284-129295. (www)

  @article{ , abstract = {This paper proposes a pre-processing encryption then hiding scheme that enhances image security in steganography. The method combines a Walsh code with switched linear system encryption, followed by Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD) for secure image hiding. The Walsh code is considered as the switching signal of the switched linear system, which offers benefits in image encryption due to its characteristics of left invertibility and flatness. Additionally, with the aid of the Walsh code generator, the key matrix will be formed to enhance the image encryption security. To hide the encrypted secret image in another image, the DWT-SVD method will be applied to obtain the frequency-domain representation, which is then transformed into the singular values of the secret image. Consequently, the matrix manipulations between SVD matrices of both the encrypted secret image and cover image complete the embedding process of steganography. The contribution of this scheme lies in its simplicity of hardware use while achieving high security. Finally, the simulations are shown to demonstrate the effectiveness of this scheme. The metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Bit Correction Ratio (BCR), and Structural Similarity Index Measurement (SSIM) are examined to evaluate the security level of the proposed scheme.}, archive = {J_ACCESS}, author = {Uyen L. P. Nguyen and Long Tonthat and Vu Dai Tran and Lap Luat Nguyen}, doi = {10.1109/ACCESS.2025.3589630}, journal = {IEEE Access}, pages = {129284-129295}, shortjournal = {IEEE Access}, title = {Pre-processing-based walsh code with switched system in secure image steganography enhancement}, volume = {13}, year = {2025}, }
• (2025). Cellular network densification: A system-level analysis with IAB, NCR, and RIS. ACCESS, 13, 129269-129283. (www)

  @article{ , abstract = {As the number of user equipments increases in 5G and beyond, it is desirable to densify the cellular network with auxiliary nodes assisting the base stations. Examples of these nodes include IAB nodes, NCR and RIS. In this context, this work presents a system level overview of these three nodes. Moreover, this work evaluates, through simulations, the impact of network planning aiming at enhancing the performance of a network used to cover an outdoor sport event. We show that, in the considered scenario, in general, IAB nodes provide an improved signal-to-interference-plus-noise ratio and throughput compared to NCR and RIS. However, there are situations where NCR outperforms IAB due to higher level of interference caused by the latter. Finally, we show that the deployment of these nodes in UAV also achieves performance gains due to their aerial mobility. However, UAV constraints related to aerial deployment may prevent these nodes from reaching results as good as those achieved by their stationary deployment.}, archive = {J_ACCESS}, author = {Gabriel C. M. Da Silva and Victor F. Monteiro and Diego A. Sousa and Darlan C. Moreira and Tarcisio F. Maciel and Fco. Rafael M. Lima and Behrooz Makki}, doi = {10.1109/ACCESS.2025.3587493}, journal = {IEEE Access}, pages = {129269-129283}, shortjournal = {IEEE Access}, title = {Cellular network densification: A system-level analysis with IAB, NCR, and RIS}, volume = {13}, year = {2025}, }
• (2025). DP-FWCA: A prompt-enhanced model for named entity recognition in educational domains. ACCESS, 13, 129250-129268. (www)

  @article{ , abstract = {The digital transformation in education has spurred a rapid expansion of academic literature, pedagogical resources, and institutional knowledge, intensifying the demand for efficient automated information extraction. However, the inherent complexity and contextual variability of educational texts, compounded by a limited supply of domain-specific annotated data, impose formidable challenges on conventional NER methods. To address these challenges, we propose the Domain-adaptive Prompt Feature-Weighted CNN-Attention-CRF (DP-FWCA), a novel framework specifically designed for educational NER. Our approach integrates domain-adaptive prompting to direct attention toward critical educational semantics, BERT-based contextual embeddings for robust representation learning, multi-scale convolutional neural networks (CNNs) with gated feature fusion to capture fine-grained local features, bidirectional LSTM networks augmented with self-attention to model long-range dependencies, and conditional random fields (CRFs) for structured sequence labeling. Evaluations on the EduNER and MSRA datasets reveal that the DP-FWCA model achieves F1-scores of 87.72% and 95.12%. These results underscore the promise of our integrated approach in overcoming the intrinsic challenges of educational texts, thereby advancing automated knowledge extraction and supporting the development of more intelligent educational systems.}, archive = {J_ACCESS}, author = {Zhenkai Qin and Dongze Wu and Jiajing He and Jingming Xie and Aimin Wei}, doi = {10.1109/ACCESS.2025.3590851}, journal = {IEEE Access}, pages = {129250-129268}, shortjournal = {IEEE Access}, title = {DP-FWCA: A prompt-enhanced model for named entity recognition in educational domains}, volume = {13}, year = {2025}, }
• (2025). Bio-inspired fine-tuning for selective transfer learning in image classification. ACCESS, 13, 129234-129249. (www)

  @article{ , abstract = {Deep learning has significantly advanced image analysis across diverse domains but often depends on large, annotated datasets for success. Transfer learning addresses this challenge by utilizing pre-trained models to tackle new tasks with limited labeled data. However, discrepancies between source and target domains can hinder effective transfer learning. We introduce BioTune, a novel adaptive fine-tuning technique utilizing evolutionary optimization. BioTune enhances transfer learning by optimally choosing which layers to freeze and adjusting learning rates for unfrozen layers. Through extensive evaluation on nine image classification datasets, spanning natural and specialized domains such as medical imaging, BioTune demonstrates superior accuracy and efficiency over state-of-the-art fine-tuning methods, including AutoRGN and LoRA, highlighting its adaptability to various data characteristics and distribution changes. Additionally, BioTune consistently achieves top performance across four different CNN architectures, underscoring its flexibility. Ablation studies provide valuable insights into the impact of BioTune’s key components on overall performance.}, archive = {J_ACCESS}, author = {Ana Davila and Jacinto Colan and Yasuhisa Hasegawa}, doi = {10.1109/ACCESS.2025.3587524}, journal = {IEEE Access}, pages = {129234-129249}, shortjournal = {IEEE Access}, title = {Bio-inspired fine-tuning for selective transfer learning in image classification}, volume = {13}, year = {2025}, }
• (2025). A framework for qualitative analysis of switching techniques of inductive pulse power supply. ACCESS, 13, 129213-129233. (www)

  @article{ , abstract = {The inductive pulse power supply (IPPS) features energy densities about an order of magnitude greater than those of a capacitive power supply, making it attractive for researchers. The literature documents various IPPS topological circuits and their derivative circuits. The literature has presented and examined several reviews on IPPS topologies; however, most of them concentrate on various Meatgrinder and XRAM-based topologies, while others lack detail and comprehensiveness. It is important for the design of the IPPS to carefully look into how to choose the primary switch, switching circuits, and topological circuits based on the pulse transformer, Meatgrinder, and XRAM. This research examines the functionality, results, and constraints of the pulse transformer, Meatgrinder, and XRAM-based IPPS circuit topologies using different switching techniques, such as stretch Meatgrinder, ICCOS, SECT, CPFU, ACC, BCSC, and TWPT. The performance of the IPPS circuits is comprehensively analyzed by examining voltage stress on the primary switch, current multiplication, and the repetition rate. Additionally, a new framework has been proposed to assist researchers in identifying switching approaches, selecting the primary switch, and determining the topology for a specific application. The purpose of this work is to serve as a valuable resource for academics and professionals interested in conducting more studies on the topic’s practical implications for IPPS.}, archive = {J_ACCESS}, author = {Ayaz Ahmed and Zulkurnain Abdul-Malek and Hasmat Malik and Majed A. Alotaibi}, doi = {10.1109/ACCESS.2025.3574691}, journal = {IEEE Access}, pages = {129213-129233}, shortjournal = {IEEE Access}, title = {A framework for qualitative analysis of switching techniques of inductive pulse power supply}, volume = {13}, year = {2025}, }
• (2025). Latent space classification for cardiovascular disease detection: A deep convolutional autoencoder-based approach for telemedicine applications. ACCESS, 13, 129195-129212. (www)

  @article{ , abstract = {Cardiovascular disease is a leading global cause of mortality, often due to abnormal heart function. Early detection and timely treatment are essential to prevent fatalities. Electrocardiograms (ECGs) are critical non-invasive tools for diagnosing such conditions. With the rise of telemedicine, remote monitoring of cardiovascular patients has become more accessible. However, telemedicine sensor’s limited bandwidth and battery life demand efficient data transmission. This study introduces a Latent Space Classification System (LSCS) that compresses ECG signals into lower dimensions while preserving diagnostic accuracy, enhancing performance, and energy efficiency in telemedicine. The study analyses energy consumption through FLOPs, inference time, and transmission size to overcome sensor limitations across various feature extraction techniques. The proposed LSCS encompasses a deep convolutional autoencoder trained on the MIT-BIH arrhythmia database that compresses ECG signals. The compressed features are classified using seven ML models: K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Histogram Gradient Boosting Trees (HGBT), and three Support Vector Machine variants (SVML, SVMP, SVMR). LSCS-KNN, LSCS-XGBoost, and LSCS-HGBT achieved top performance, with over 98.8% accuracy, 98.7% precision, and 98.7% F1 scores. These results confirm LSCS’s ability to classify cardiovascular diseases accurately from compressed ECG data. Additionally, deep learning-based ECG sensors significantly reduced inference time and energy use (0.000107 J) compared to traditional methods. LSCS offers an efficient ECG transmission approach for telemedicine, effectively addressing bandwidth and energy limitations in remote cardiovascular monitoring.}, archive = {J_ACCESS}, author = {G Antoni Gracy and Sheena Christabel Pravin}, doi = {10.1109/ACCESS.2025.3579999}, journal = {IEEE Access}, pages = {129195-129212}, shortjournal = {IEEE Access}, title = {Latent space classification for cardiovascular disease detection: A deep convolutional autoencoder-based approach for telemedicine applications}, volume = {13}, year = {2025}, }
• (2025). Perceptual carlini-wagner attack: A robust and imperceptible adversarial attack using LPIPS. ACCESS, 13, 129185-129194. (www)

  @article{ , abstract = {Adversarial attacks on deep neural networks (DNNs) present significant challenges by exploiting model vulnerabilities using perturbations that are often imperceptible to human observers. Traditional approaches typically constrain perturbations using p-norms, which do not effectively capture human perceptual similarity. In this work, we propose the Perceptual Carlini-Wagner (PCW) attack, which integrates the Learned Perceptual Image Patch Similarity (LPIPS) metric into the adversarial optimization process. By replacing p-norm constraints with LPIPS, PCW generates adversarial examples that are both highly effective at inducing misclassification and visually indistinguishable from the original images. We evaluate PCW on the CIFAR-10, CIFAR-100, and ImageNet datasets. On ImageNet, adversarial examples crafted using PCW achieve an LPIPS distance of only 0.0002 from clean images, in contrast to 0.3 LPIPS for those produced by the CW and PGD attacks. In terms of robustness, PCW shows superior performance under common image processing defenses such as JPEG compression and bit-depth reduction, outperforming CW and SSAH and rivaling PGD. Additionally, we test PCW against adversarially trained models from RobustBench and find that it maintains high attack success rates, significantly outperforming CW and PGD in this more challenging setting. Finally, we assess the transferability of PCW across model architectures. While LPIPS contributes to perceptual alignment, it does not significantly improve transferability, with results comparable to those of the original CW attack.}, archive = {J_ACCESS}, author = {Liming Fan and Anis Salwa Mohd Khairuddin and Haichuan Liu and Khairunnisa Binti Hasikin}, doi = {10.1109/ACCESS.2025.3588113}, journal = {IEEE Access}, pages = {129185-129194}, shortjournal = {IEEE Access}, title = {Perceptual carlini-wagner attack: A robust and imperceptible adversarial attack using LPIPS}, volume = {13}, year = {2025}, }
• (2025). Approach to represent run-of-river hydropower plants in the short-term hydrothermal system operation planning. ACCESS, 13, 129172-129184. (www)

  @article{ , abstract = {The operation planning of hydrothermal systems is characterized by high complexity due to the inherent temporal and spatial coupling of the problem. To address this complexity, the process is traditionally structured into sequential stages, each focusing on specific aspects according to its time discretization and study horizon. In the context of the Brazilian power system, these stages are referred to as long-, mid-, and short-term planning, with interdependencies formalized through a cost-to-go function. Reservoir storage is one of the main state variables in this function, linking different temporal stages. In long- and mid-term planning, hydropower plants with small reservoirs — typically designed for daily or weekly regulation — are usually modeled as run-of-river units and not considered as state variables, as they do not significantly contribute to interstage coupling. However, in short-term planning, such plants must be explicitly modeled with their corresponding reservoirs to properly account for their ability to respond to load fluctuations. This discrepancy can create challenges when coupling mid- and short-term models, as the cost-to-go function lacks forward-looking information on small reservoirs not represented in the mid-term model. This paper proposes an approach for representing small reservoirs in short-term hydrothermal operation planning. The objective is to capture short-term storage variations while imposing a condition that the final storage level matches the initial level — a necessary criterion for the plant to be treated as run-of-river in the mid-term model. The proposed approach is applied, as a case study, to the Brazilian power system.}, archive = {J_ACCESS}, author = {Tiago Norbiato Dos Santos and André Luís Marques Marcato and Amanda Pávila Silva}, doi = {10.1109/ACCESS.2025.3589178}, journal = {IEEE Access}, pages = {129172-129184}, shortjournal = {IEEE Access}, title = {Approach to represent run-of-river hydropower plants in the short-term hydrothermal system operation planning}, volume = {13}, year = {2025}, }
• (2025). SVHAE: Spectral variability-aware hybrid autoencoder for hyperspectral underwater target detection. ACCESS, 13, 129161-129171. (www)

  @article{ , abstract = {Hyperspectral imaging (HSI) has evolved as an important tool for many applications, including remote sensing, crime investigation, target detection, disease diagnosis, and anomaly detection. Among these, hyperspectral underwater target detection (HUTD) presents unique challenges due to spectral distortions caused by water absorption and scattering. Conventional methods often struggle with spectral variability, complicating detection accuracy. This paper introduces spectral variability-aware hybrid autoencoder (SVHAE) for HUTD, a novel autoencoder-based unmixing network incorporating parallel linear and nonlinear decoders to improve underwater target detection. Our method effectively reduces the effect of spectral distortions and addresses variability using a combined loss function integrating Kullback-Leibler divergence, mean squared error, and spectral angle distance. Experimental validation on real-world and simulated datasets demonstrates that our proposed SVHAE outperformed state-of-the-art methods by achieving superior AUC values. These advancements contribute to the progressing field of HUTD, making the way for robust solutions in marine exploration and detecting targets under the water.}, archive = {J_ACCESS}, author = {Suresh Aala and Sravan Kumar Sikhakolli and Sunil Chinnadurai and Anuj Deshpande and Karthikeyan Elumalai and Md. Abdul Latif Sarker and Hala Mostafa}, doi = {10.1109/ACCESS.2025.3587438}, journal = {IEEE Access}, pages = {129161-129171}, shortjournal = {IEEE Access}, title = {SVHAE: Spectral variability-aware hybrid autoencoder for hyperspectral underwater target detection}, volume = {13}, year = {2025}, }
• (2025). A conceptual model for project observatories. ACCESS, 13, 129143-129160. (www)

  @article{ , abstract = {The use of observatories in various fields of knowledge has added a relevant characteristic to them, typological diversity. This has created the need to develop models that assist in the conceptualization and design of these observatories, considering the specificities of their application contexts. Thus, the main objective of this work is to propose a conceptual model for project observatories, considering aspects related to their structures, processes, and involved actors. To achieve the proposed objective, this research was conducted in two phases: 1) conception and 2) evolution and evaluation. The conception phase, which gave rise to the first version of the model, included an ad hoc literature review and a systematic mapping of the literature on observatories, as well as the development of ten pilot projects on project observatories. During the second phase, the model was evaluated and evolved into three additional versions through three focus groups, a survey, and a multiple case study. The final version of the model, named Model for Project Observatories (MPO), is organized from a set of 61 concepts structured into three hierarchical levels (general, intermediate, and specific concepts).}, archive = {J_ACCESS}, author = {Ivaldir H. de Farias Junior and Jeferson Kenedy Morais Vieira and Hermano Perrelli de Moura and Lucas Tito Sampaio}, doi = {10.1109/ACCESS.2025.3589743}, journal = {IEEE Access}, pages = {129143-129160}, shortjournal = {IEEE Access}, title = {A conceptual model for project observatories}, volume = {13}, year = {2025}, }
• (2025). Numerical and experimental analysis of photovoltaic-integrated energy storage system for electric vehicle fast charging. ACCESS, 13, 129127-129142. (www)

  @article{ , abstract = {Electric vehicles (EVs) have emerged as a pivotal technology for environmental protection, driving the development of battery energy storage systems (BESS) for sustainable charging solutions in smart cities. This paper investigates the implementation of BESS in smart cities to facilitate the charging of EVs, with the aim of improving air quality and promoting sustainable practices. The proposed model involves the selection of seven distinct EV models, each with varying battery capacities and ranges. The model has been developed further by the addition of an annual travel scenario, in which EVs are assumed to be used for daily commuting activities, such as work, school and travel based on family usage profiles. Charging EVs is facilitated by DC fast-charging units, with the charging stations themselves being equipped with BESS capable of charging up to 70 EVs on a daily basis. Proposed BESS is connected to a grid that utilizes electricity generated by a solar power plant (SPP). The SPP has been experimentally designed to generate sufficient electricity to power 10 charging points, with a capacity of up to 700 EVs per day. Thus, a single SPP installation is capable of supplying the entire network of 10 fast-charging stations deployed across the city. Furthermore, a detailed assessment of different state of charge analysis (95–70%) shows a trade-off between battery life and flexibility. The system is economically viable (8.06 years payback) and enables up to 1,518 tons/year CO2 reduction.}, archive = {J_ACCESS}, author = {Furkan Dincer and Emre Ozer}, doi = {10.1109/ACCESS.2025.3590468}, journal = {IEEE Access}, pages = {129127-129142}, shortjournal = {IEEE Access}, title = {Numerical and experimental analysis of photovoltaic-integrated energy storage system for electric vehicle fast charging}, volume = {13}, year = {2025}, }
• (2025). Large scale asset detection within railway scene point cloud data from mobile laser scanning. ACCESS, 13, 129114-129126. (www)

  @article{ , abstract = {To reduce greenhouse gas emissions from the transport sector, shifting to rail transport is crucial. This transition will increase the demand on existing rail infrastructure, necessitating large-scale monitoring to maintain its resilience. Point cloud data are an ideal candidate for this purpose, as they provide immediate, precise 3D geometric information independent of illumination conditions. This study investigates two object detection models, the PointPillar and the CenterPoint model, to automatically create a digital representation of the rail environment. Using a custom open dataset, these two models are evaluated to detect masts, tension rods, signals, and relay cabinets. A mean Average Precision (mAP@0.5) of 70.6% is achieved. A unique contribution of this study is an in-depth analysis of the locational error in terms of the x and y components of the detected positions. This analysis reveals that location accuracy is not yet sufficient for engineering applications. The analysis indicates that the largest contribution to this error originates from the random error. Additionally, this study demonstrates that transfer learning effectively reduces the labeling burden. For instance, when using 25% of the training data, the average Precision (AP) for the tension rod class improves from 9.5% without transfer learning to 70.8% with transfer learning.}, archive = {J_ACCESS}, author = {Bram Ton and Rick Akster}, doi = {10.1109/ACCESS.2025.3590779}, journal = {IEEE Access}, pages = {129114-129126}, shortjournal = {IEEE Access}, title = {Large scale asset detection within railway scene point cloud data from mobile laser scanning}, volume = {13}, year = {2025}, }
• (2025). Turning energy-aware route planning with an extended TSP and double-layer state graph: Experimental evaluation with a crawler mobile robot. ACCESS, 13, 129104-129113. (www)

  @article{ , abstract = {For mobile robots with multiple destinations that must be visited only once, the Traveling Salesman Problem (TSP) provides an optimal route. The optimality may not be achieved for crawler robots, which are often used in the forestry industry and have a higher energy consumption during turning. This is because, in the standard TSP, travel points are represented as nodes of a graph, and travel costs are represented by edges, which do not account for the cost of turning. Our method solves this problem by a double-layer state graph to express the change in the orientation by choosing a combination of two points associated with each travel point. Since the standard TSP algorithm cannot be applied to this graph, we use the Generalized Traveling Salesman Problem (GTSP) algorithm, an extension of the TSP. To confirm the effectiveness of our method, we evaluated the planned routes through experiments conducted with a prototype crawler robot. The routes that minimized energy consumption showed an average 17 % reduction in energy consumption, while the routes that minimized travel time achieved an average 5 % reduction in travel time compared to the standard TSP algorithm.}, archive = {J_ACCESS}, author = {Namihei Adachi and Hisashi Date}, doi = {10.1109/ACCESS.2025.3586876}, journal = {IEEE Access}, pages = {129104-129113}, shortjournal = {IEEE Access}, title = {Turning energy-aware route planning with an extended TSP and double-layer state graph: Experimental evaluation with a crawler mobile robot}, volume = {13}, year = {2025}, }
• (2025). Configuration design and experiment research of MRI-compatible cable-driven puncture surgical robot. ACCESS, 13, 129087-129103. (www)

  @article{ , abstract = {Magnetic resonance imaging (MRI)-guided robot for puncture surgery has developed rapidly in recent years. However, the robot’s design is challenged by the limited workspace and strong magnetic field inside the MRI scanner. For prostate puncture surgery, this paper proposes an MRI-compatible cable-driven robot for puncture surgery. The wrench feasible workspace of the robot under three different cable layout structures is analyzed, and the optimal configuration is selected to design the robot. The cable layout size of the robot is reduced to a circle with a diameter of 150 mm, which can meet the workspace requirements inside the scanner. Different puncture trajectories are planned in the simulation, which proves the feasibility of the cable-driven puncture surgical robot. In the real-time experiment, the average response time of the robot control system is 0.024 s. In the translational motion experiment, the cable length error is ±0.28mm, and in the process of repeated insertion, the cable length error is ±0.5mm. The average position error of the puncture needle in the process of puncture at different angles is 0.31 mm, and the average position error when subjected to external shocks is 0.32 mm. The robot proposed in this study has potential application value in special environments. It proposes a new solution for the design of puncture surgical robots used in strong magnetic fields.}, archive = {J_ACCESS}, author = {Duanjiao Li and Wenxing Sun and Junwen Yao and Yilong Chen and Yuhui Chen and Yanxing Zhang and Yupeng Zou}, doi = {10.1109/ACCESS.2025.3530982}, journal = {IEEE Access}, pages = {129087-129103}, shortjournal = {IEEE Access}, title = {Configuration design and experiment research of MRI-compatible cable-driven puncture surgical robot}, volume = {13}, year = {2025}, }
• (2025). A novel GRU-augmented time-frequency estimator for IGBT remaining useful life prediction. ACCESS, 13, 129074-129086. (www)

  @article{ , abstract = {Aging-induced failure of Insulated Gate Bipolar Transistors (IGBTs) significantly restricts the reliability of power electronic systems. Accurate and efficient prediction of IGBT Remaining Useful Life (RUL) is critical for proactive risk mitigation and ensuring system stability. Despite numerous existing aging models and data-driven methodologies, maintaining prediction robustness and accuracy under diverse and complex operational scenarios remains challenging. To overcome these limitations, we introduce a novel GRU-Augmented Time-Frequency Estimator (GATE) tailored for IGBT lifetime prediction. GATE utilizes an autoregressive time-series prediction framework trained via the Teacher Forcing strategy to recursively decode the electrical parameters indicative of the IGBT’s aging state from rich time-frequency features. Experimental validations are performed using the square-wave power cycling dataset from the NASA Prognostics Data Repository. The results demonstrate that GATE significantly enhances prediction accuracy, reducing Mean Squared Error (MSE) to 0.0026 and Mean Absolute Error (MAE) to 0.045, representing improvements of 38.1% and 19.6%, respectively, compared to the leading baseline method. Moreover, recursive forecasting experiments show that GATE precisely predicts the remaining power cycles until the aging threshold (defined as a 15% increase in Vce(on)) at various aging stages (10–60%). Ablation analyses further underline the critical contribution of the frequency-domain component. Collectively, these findings underscore GATE’s capability to reliably decode IGBT RUL directly from historical operational data, bypassing intricate electrical or mechanical modeling, thereby offering a practically deployable and broadly generalizable solution for lifetime management in power electronic devices.}, archive = {J_ACCESS}, author = {Yuankuang Li and Baohua Tan and Yingjie Zhang and Shuo Wang and Bolin Lian and Yuan Li and Zongming Tan}, doi = {10.1109/ACCESS.2025.3590001}, journal = {IEEE Access}, pages = {129074-129086}, shortjournal = {IEEE Access}, title = {A novel GRU-augmented time-frequency estimator for IGBT remaining useful life prediction}, volume = {13}, year = {2025}, }
• (2025). An ontology-enabled approach for the detection of software design smells. ACCESS, 13, 129056-129073. (www)

  @article{ , abstract = {Software evolution and bad smells have been widely studied over past two decades, occurring at architecture, design, implementation, and requirement levels. Design smells, which result from poor software design or violations of best programming practices, often seep into the implementation stage, causing hindrances for writing cleaner and maintainable code. Detecting design smells at design stage significantly reduces their proliferation to implementation stage. This paper introduces an innovative Ontology based Detection of Design Smell (ODDS) approach for detecting software design smells at the design level. We analyzed 4,820 classes and 27,563 methods across various Java projects, proposing new design-level metrics and axioms tailored for identifying 10 design smells. Our approach also identified that some design smells serve as precursors to the occurrence of other design smells. Unlike existing approaches that rely on source code metrics for detecting architectural and design smells, our method emphasizes using metrics, corresponding to the specific level of granularity—architectural, design, and code. Our methodology facilitates research community by early detection during the design phase, ensures design consistency by supporting automated reasoning, and reducing propagation of design smells in to implementation phase. This highlights the importance of clear and understandable designs to facilitate refactoring and minimize design smells. The efficacy of our approach is validated through its application to open-source software projects, giving precision and recall of 0.92, 0.91 and demonstrating significant potential in enhancing the detection and management of design smells.}, archive = {J_ACCESS}, author = {Sabiyyah Sabir and Ghulam Rasool and Tariq Umer and Naoufel Kraiem and Yunyoung Nam}, doi = {10.1109/ACCESS.2025.3590267}, journal = {IEEE Access}, pages = {129056-129073}, shortjournal = {IEEE Access}, title = {An ontology-enabled approach for the detection of software design smells}, volume = {13}, year = {2025}, }
• (2025). Energy management system comparison among fuzzy strategies. ACCESS, 13, 129040-129055. (www)

  @article{ , abstract = {This study evaluates and compares energy management systems (EMS) for hybrid vehicles using fuzzy logic strategies to optimize energy distribution between fuel cells (FC) and batteries. Key components, including the Proton Exchange Membrane Fuel Cell (PEMFC) and the Permanent Magnet Synchronous Motor (PMSM), were modeled and validated using MATLAB-Simulink, LabVIEW, and Hardware-in-the-Loop (HIL) testing. The proposed EMS achieves the highest efficiency, up to a fuel cell average power output of 8.5 kW, an efficiency of 74.35%, and a state of charge (SoC) improvement of 34.25%. Other strategies, including those of (Esoufi et al., 2020, Hemi et al., 2014, and Zhao et al. 2021), exhibited average power outputs ranging from 7.28 to 7.79 kW and SoC improvements of 22.96% to 28.35%. These results emphasize the advantages of the proposed strategy fuzzy logic-based EMS in balancing energy demands, reducing battery stress, and extending battery life under urban driving conditions. The findings highlight fuzzy logic as a superior approach to traditional methods for managing hybrid vehicle powertrains, particularly in scenarios with frequent acceleration and deceleration.}, archive = {J_ACCESS}, author = {Julio C. Peña-Aguirre and Francisco J. Pérez-Pinal and Yosafat J. Samano-Flores and Alejandro I. Barranco-Gutierrez and Gilberto Gonzalez Gomez and Tomas Serrano-Ramirez}, doi = {10.1109/ACCESS.2025.3586754}, journal = {IEEE Access}, pages = {129040-129055}, shortjournal = {IEEE Access}, title = {Energy management system comparison among fuzzy strategies}, volume = {13}, year = {2025}, }
• (2025). Priority-aware multi-agent deep reinforcement learning for resource scheduling in C-V2X mode 4 communication. ACCESS, 13, 129024-129039. (www)

  @article{ , abstract = {The need for vehicular networks with exceptional levels of reliability and negligible delay in communication, especially with the ongoing 5G and the upcoming generation of 6G systems, has given rise to Cellular-Vehicle-to-Anything C-V2X systems. This paper proposes a novel framework of Priority-Aware Multi-Agent Deep Reinforcement Learning (PA-MADRL) to address with issues such as high interference, dynamic allocation of critical signals, resource contention in density traffic, dynamic fading, and safety-critical message ratio. This approach is aimed at the development of an effective and adaptive resource allocation mechanism that will improve the likelihood of transmission of safety critical signals and improve overall network performance in metropolitan areas. PA-MADRL allows autonomous vehicles to efficiently allocate resources despite varied congestion along with interference conditions by combining centralized training for global optimization and decentralized execution for scalable decision-making. PA-MADRL is derived from the concept of optimal global allocation of resources in central training and intelligent instantaneous distribution in decentralized setting. Several tests carried out at different levels of interference and traffic densities demonstrate that PA-MADRL improves throughput by 35%, lowers latency by 40% and increases Packet Delivery Ratio (PDR) by nearly 50%, especially when the traffic density increases. These results show that PA-MADRL has the capability to adaptively work in dense vehicular networks with low communication overhead. Ultimately, the framework enhances safety in autonomous transportation by facilitating the timely delivery of critical messages, thus contributing to sustainable and intelligent transportation systems as part of next-generation smart cities.}, archive = {J_ACCESS}, author = {Ahmed Thair Shakir and Barbara M. Masini and Nemer Radhwan Khudhair and Rosdiadee Nordin and Angela Amphawan}, doi = {10.1109/ACCESS.2025.3586804}, journal = {IEEE Access}, pages = {129024-129039}, shortjournal = {IEEE Access}, title = {Priority-aware multi-agent deep reinforcement learning for resource scheduling in C-V2X mode 4 communication}, volume = {13}, year = {2025}, }
• (2025). Efficient resource allocation for blockchain-enabled mobile edge computing: A joint optimization approach. ACCESS, 13, 129011-129023. (www)

  @article{ , abstract = {This paper addresses the critical challenge of optimizing resource allocation for task offloading in blockchain-enabled mobile edge computing (MEC), aiming to minimize the total energy consumption within wireless networks equipped with edge servers (ESs). We propose a novel joint resource allocation framework that integrates task offloading and blockchain processes within MEC, formulating it as a mixed-integer nonlinear programming (MINLP) problem. The solution assigns mobile terminals (MTs) to ESs and optimally allocates computational resources at ESs for both task computation and block generation. To manage the complexity of this optimization, we employ a two-stage dual decomposition approach. Initially, the problem is separated into subproblems for MEC and blockchain functionalities. These subproblems are further decomposed across ESs and MTs, enabling us to derive analytical solutions for optimal computational frequency allocation for both task offloading and blockchain operations. Leveraging these insights, we develop two low-complexity algorithms, which utilizes a greedy assignment strategy for MTs to ESs, and optimally allocates computational frequencies within the MEC and blockchain components. Performance evaluation results demonstrate the effectiveness of these algorithms, achieving significant reductions in total energy consumption while maximizing the efficiency of communication and computational resources. The approach also contributes to reducing network outage probability. This work presents a promising framework for developing resource-efficient blockchain-enabled MEC systems, positioning it as a scalable solution for future wireless networks.}, archive = {J_ACCESS}, author = {Moein Valitabar and Mohammad Fathi and Keivan Navaie}, doi = {10.1109/ACCESS.2025.3590431}, journal = {IEEE Access}, pages = {129011-129023}, shortjournal = {IEEE Access}, title = {Efficient resource allocation for blockchain-enabled mobile edge computing: A joint optimization approach}, volume = {13}, year = {2025}, }
• (2025). An augmented reality-based pre-surgical navigation system for ventriculoperitoneal shunt placement: Development and phantom skull assessment with unity-vuforia. ACCESS, 13, 128996-129010. (www)

  @article{ , abstract = {Ventriculoperitoneal (VP) shunt placement is a vital technique for managing patients with hydrocephalus. However, the procedure encounters challenges due to the insufficient accuracy of anatomical landmarks and the high costs associated with advanced surgical navigation systems. To address these limitations, this study aims to assist in the pre-surgical planning phase of VP shunt placements using 3D-printed skulls. We developed the AR system using Unity and Vuforia to help surgeons visualise the trajectory of a catheter from an entry point on the skull to an intercept point in a ventricle. Experiments were conducted on one hundred commonly used freehand entry points across ten 3D-printed skulls to evaluate the accuracy of the intercept points generated by the AR system. The results indicate that the ventricle intercept points obtained through the system are on average 6 mm away from the ground truth, which falls within an acceptable range. This pilot study demonstrates the potential to enhance pre-surgical planning, particularly in resource-limited settings.}, archive = {J_ACCESS}, author = {Tandin Dorji and Vich Yindeedej and Pakinee Aimmanee}, doi = {10.1109/ACCESS.2025.3588098}, journal = {IEEE Access}, pages = {128996-129010}, shortjournal = {IEEE Access}, title = {An augmented reality-based pre-surgical navigation system for ventriculoperitoneal shunt placement: Development and phantom skull assessment with unity-vuforia}, volume = {13}, year = {2025}, }
• (2025). Optimizing machine learning-based ovarian cancer prediction through normalization strategies. ACCESS, 13, 128974-128995. (www)

  @article{ , abstract = {Ovarian cancer is one of the most challenging cancers to detect early, often leading to poor survival rates. This study explores supervised and unsupervised machine learning and deep learning approaches to improve predictive performance using clinical and biomarker-based data which was scaled through two popular techniques: Min-Max scaling and Z-Score normalization. The research begins by carefully preprocessing the dataset including feature selection to ensure high-quality inputs. Various baseline and ensemble classifiers, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Logistic Regression (LR), are tested, for better model efficiency on both datasets. To further boost performance, ensemble methods like Stacking, Bagging, and Gradient Boosting, are incorporated. Additionally, unsupervised models like K-Means and DBSCAN clustering are implemented to study further subgroups of the Ovarian Cancer dataset optimizing results. The effects of different feature selection techniques and the impact of standardization versus normalization are compared on both datasets. The Min-Max normalization technique outperformed Z-Score and it is observed that, the Stacking classifier achieved the highest accuracy of 100%, followed by SVM, Logistic Regression, and Bagging, each recording an accuracy of 97%. Further, DBSCAN, a clustering technique outperformed K-Means with a Silhouette Score of 0.7245 and it is observed that clustering performed well with Min-Max when compared with Z-Score normalization technique. The findings highlight that a well-optimized combination of feature selection, ensemble learning, and clustering significantly enhances ovarian cancer prediction, providing a valuable foundation for early diagnosis and clinical decision support.}, archive = {J_ACCESS}, author = {Roopashri Shetty and Siddhant Gupta and Vansh Mediratta and Shwetha Rai and M. Geetha}, doi = {10.1109/ACCESS.2025.3590871}, journal = {IEEE Access}, pages = {128974-128995}, shortjournal = {IEEE Access}, title = {Optimizing machine learning-based ovarian cancer prediction through normalization strategies}, volume = {13}, year = {2025}, }
• (2025). Integration of MATH41 and generative AI in pre-service mathematics teacher education: An empirical study on lesson design competency. ACCESS, 13, 128959-128973. (www)

  @article{ , abstract = {Generative artificial intelligence (AI) tools are rapidly transforming mathematics education by enabling automated problem generation, dynamic visualizations, and adaptive learning experiences. This study presents an empirical study on incorporating MATH41 into a 15-week pre-service teacher preparation course for mathematics majors. Twenty-four participants learned to create parameterized math problems, generate vector graphics. Following guided training, each participant designed and micro-taught a 20-minute lesson incorporating MATH41-generated resources. Reflection journals and final lesson plans were analyzed thematically, while 20 participants completed a self-assessment survey on lesson design competency. Results reveal that the automated problem generation capabilities motivated pre-service teachers to explore a broader range of instructional strategies, including personalized tasks and diverse problem variants. Reflection data indicate that while integrating AI tools can significantly boost confidence and creativity in lesson planning, careful pedagogical alignment remains essential to avoid superficial learning. Participants underscored the importance of maintaining teacher oversight—especially when adapting AI-generated problems for particular learner needs. Additionally, their post-course self-assessments showed high confidence in digital tool integration, yet they acknowledged that anticipating student misconceptions requires further field experience. Overall, this study contributes to understanding how teacher education programs can enhance lesson design competencies via structured AI tool integration. It also highlights the critical role of reflective practice in ensuring that automated content creation fosters deeper instructional effectiveness rather than uncritical AI dependency.}, archive = {J_ACCESS}, author = {Sejun Oh}, doi = {10.1109/ACCESS.2025.3586593}, journal = {IEEE Access}, pages = {128959-128973}, shortjournal = {IEEE Access}, title = {Integration of MATH41 and generative AI in pre-service mathematics teacher education: An empirical study on lesson design competency}, volume = {13}, year = {2025}, }
• (2025). Smart grid intrusion detection for IEC 60870-5-104 with feature optimization, privacy protection, and honeypot-firewall integration. ACCESS, 13, 128938-128958. (www)

  @article{ , abstract = {The increase in Supervisory Control and Data Acquisition (SCADA) solutions in industrial systems has made these systems more vulnerable to cyberattacks due to insecure communication protocols. This paper proposes a privacy-aware intrusion detection framework that reduces these risks. The proposed framework implements, through machine learning, anomaly detection methods, classification techniques and feature selection methodologies, integrating these approaches to optimize the identification of potential cyber threats, improve model accuracy, and enhance computational efficiency in SCADA systems. Anomaly detection is strengthened through Isolation Forests, time-series analysis, and Fourier-based burst detection while dynamically adapting to concept drift adaptation with Kullback-Leibler divergence. It also utilizes important feature optimization methods, implemented in multiple ways, such as SHAP, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA). Furthermore, the proposed framework includes a federated learning-based scheme that utilizes differential privacy and homomorphic encryption to ensure the privacy and integrity of the data to enhance model interpretability and efficiency with feature ranking to provide insights into attack patterns and anomaly characteristics. Defences against adversarial attacks use FGSM-based training, feature smoothing, and ensemble-based defences, to reduce susceptibility to evasion tactics. Honeypots and automated IP firewalls enhance attack profiling and proactive mitigation of incidents. The experimental results demonstrate the framework’s operational capability and effectiveness for SCADA network security, achieving 99.29% accuracy, 94.8% recall and 4.1% false positive rates.}, archive = {J_ACCESS}, author = {Pedamallu Sai Mrudula and Rayappa David Amar Raj and Archana Pallakonda and Yanamala Rama Muni Reddy and K. Krishna Prakasha and V. Anandkumar}, doi = {10.1109/ACCESS.2025.3590151}, journal = {IEEE Access}, pages = {128938-128958}, shortjournal = {IEEE Access}, title = {Smart grid intrusion detection for IEC 60870-5-104 with feature optimization, privacy protection, and honeypot-firewall integration}, volume = {13}, year = {2025}, }
• (2025). Integrated additive manufacturing trajectory control and repair management for spiral bevel gear using fractional-order PID and adaptive fuzzy neural inference system. ACCESS, 13, 128920-128937. (www)

  @article{ , abstract = {Design optimization of mechanical system components, such as spiral bevel gears, is important due to their complex geometry and design constraints. However, the complex geometry of these components introduces great challenges in manufacturing and maintenance. As inspired by the need to improve gear maintenance practices through advanced technologies, the integration of additive manufacturing (AM) is an opportunity to revolutionize gear repair processes, which potentially reduces downtime and extends gear life cycles. The research objectives concentrate on constructing an adaptive control algorithm for gear repair using metal powder bed fusion technology and assessing these algorithms against state-of-the-art methods. The repair phase provided an approach with a control system combining fractional-order PID (FOPID) controllers with adaptive neuro–fuzzy inference systems (ANFIS) to manage the complex and nonlinear process of gear repair using AM. By integrating these techniques in ANFIS, neural networks can adapt to varying conditions and fuzzy logic can efficiently handle imprecise data and mimic human decisions. This integration produces a robust and adaptive system that can handle the uncertainties inherent in gear inspection and repair. The FOPID controllers outperform traditional PID controllers in terms of rise time, settling time, and steady-state error. The ANFIS model achieves good overall tracking of amplitude variations and accurate classification of repair methods. The adaptive control system utilizing FOPID controllers with ANFIS demonstrates superior performance in handling the complex repair process. The framework established for gear repair offers a blueprint for future advancements in predictive maintenance and automated repair systems. Its potential applications extend beyond gear systems to other essential mechanical components.}, archive = {J_ACCESS}, author = {Hiyam Adil Habeeb and Abdul Hadi Azman and Mohd Rizal Alkahari and Dzuraidah Abd Wahab and Nurhasyimah Abd Aziz}, doi = {10.1109/ACCESS.2025.3590636}, journal = {IEEE Access}, pages = {128920-128937}, shortjournal = {IEEE Access}, title = {Integrated additive manufacturing trajectory control and repair management for spiral bevel gear using fractional-order PID and adaptive fuzzy neural inference system}, volume = {13}, year = {2025}, }
• (2025). From convolution to attention: Transformer-based modeling for multi-day wildfire spread forecasting. ACCESS, 13, 128895-128919. (www)

  @article{ , abstract = {Wildfire prediction and management is a critical component of environmental risk assessment, especially in the face of increasing climate volatility. Recent advances in remote sensing and artificial intelligence have enabled more precise forecasting of wildfire spread dynamics. However, existing deep learning models often struggle to capture complex spatiotemporal interactions and translate predictions into actionable decisions. This paper addresses this gap by integrating transformer-based prediction with a BDI (Belief–Desire–Intention) reasoning framework for real-time wildfire response. The study leverages four open-access datasets: WFED, FIRMS, Sentinel Hub, and a custom ERA5 + MODIS + SRTM fusion, encompassing fire masks, vegetation indices, meteorological parameters, and topographic features. The proposed methodology constructs spatiotemporal tensors with learnable positional encodings, processed through a multi-head attention transformer to predict burnt area masks and spread direction vectors. These predictions inform a role-specific BDI reasoning layer to generate adaptive action plans. The novelty lies in combining attention-driven deep learning with symbolic goal-based decision modelling in an operational wildfire management context. We evaluated the model performance using F1-score, IoU, MAE, directional accuracy, and AIC-based feature importance. Results show the proposed Transformer + BDI system significantly outperforms prior CNN-based approaches, achieving an F1 score of 0.75 and reducing prediction MAE to 7.4 km2 across datasets, while offering interpretable and actionable wildfire response insights.}, archive = {J_ACCESS}, author = {Parul Dubey and Nitin Rakesh and Pushkar Dubey and Pitshou Ntambu Bokoro}, doi = {10.1109/ACCESS.2025.3586922}, journal = {IEEE Access}, pages = {128895-128919}, shortjournal = {IEEE Access}, title = {From convolution to attention: Transformer-based modeling for multi-day wildfire spread forecasting}, volume = {13}, year = {2025}, }
• (2025). Using EEG signals and AI to predict neurodegenerative diseases. ACCESS, 13, 128879-128894. (www)

  @article{ , abstract = {Diseases like Alzheimer’s and Parkinson’s pose significant challenges because of their complex and progressive characteristics. Addressing these conditions requires advanced diagnostic methods that support value-based care. This study proposes NeuroPredictNet, a deep learning-based predictive framework designed for early and accurate disease identification. NeuroPredictNet integrates multimodal data—EEG, neuroimaging, genetic, and clinical features—through an attention-based fusion mechanism that dynamically weights modality contributions. We introduce the Adaptive Knowledge Integration Strategy (AKIS) to enhance model robustness by addressing modality-specific noise, data imbalance, and temporal consistency. Experimental evaluations on four benchmark datasets demonstrate that our method achieves superior prediction accuracy and interpretability compared to state-of-the-art approaches. These results underscore the framework’s clinical potential in supporting personalized, value-based neurological care.}, archive = {J_ACCESS}, author = {Juan Li and Dongyuan Zhang and Wei Lin and Wei Liu}, doi = {10.1109/ACCESS.2025.3586363}, journal = {IEEE Access}, pages = {128879-128894}, shortjournal = {IEEE Access}, title = {Using EEG signals and AI to predict neurodegenerative diseases}, volume = {13}, year = {2025}, }
• (2025). Multi-modal comparative analysis on audio dub detection using artificial intelligence. ACCESS, 13, 128856-128878. (www)

  @article{ , abstract = {Audio dubbing, or inducing fake audio clips to a genuine video or audio file, has shown growing adverse effects in the speech domain sectors, causing concerns to individuals, organizations, and society as a whole. With the advent and rapid popularity of the artificial intelligence (AI) sector, inducing these fake clips is being made possible using generative AI that enables voice cloning and real-time voice conversion from one individual to another. Misuse of this technology would lead to a breach in one’s privacy and misinterpretation, thus requiring an urgent need to address the issue of real-time detection of AI-generated audio dubs and fake audio identification and prevention. This research aims at discovering dubs in audio files using machine learning and deep learning techniques in various modes of execution, such as offline or static, batch, and online or streaming means. The audio files are segmented using libROSA, the Python package for analysis of music and audio files. The river framework works in an online mode, where the processing is done faster using dictionaries with less storage space consumption, leading to a better mode of execution for critical applications in real time. Incremental processing yields improved results in real-time audio applications by enabling low-latency responses and adaptive system behavior. The proposed hybrid deep neural network (hyDNN) gave an accuracy of 99.19% on custom data in incremental mode.}, archive = {J_ACCESS}, author = {Divya Jennifer Dsouza and Anisha P. Rodrigues and Roshan Fernandes}, doi = {10.1109/ACCESS.2025.3591306}, journal = {IEEE Access}, pages = {128856-128878}, shortjournal = {IEEE Access}, title = {Multi-modal comparative analysis on audio dub detection using artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). IoT-enhanced smart parking management with IncepDenseMobileNet for improved classification. ACCESS, 13, 128838-128855. (www)

  @article{ , abstract = {With the fast progression of urbanization, efficient parking management is crucial for high-traffic areas like Los Angeles International Airport (LAX). This research introduces an AI-driven approach to improve smart parking using deep learning and IIoT-based monitoring for real-time analysis. The data from January 2021 to June 2024 includes parking occupancy, vehicle classification, payment behaviors, and violations. Advanced preprocessing techniques, such as Temporal Variability Adjustment and Harmonic Noise Compensation, enhanced data quality, while Proportional Adaptive Balancing and Augmentation (PABA) addressed class imbalance. The Hybrid Adaptive Feature Selector (HAFS) enhanced critical attributes via statistical and genetic diversity techniques. The IncepDenseMobileNet (IDMN) model integrates Inception, DenseNet, and MobileNet architectures to proficiently capture complex patterns via multi-scale feature extraction and efficient depthwise separable convolutions. Simulations show that IDMNachieved remarkable results, including 98.6% accuracy, 0.95 precision, and 0.96 recall. The model demonstrated a log loss of 0.18 and outstanding performance on new metrics, including Weighted Error Impact Score (WEIS), Dynamic Class Stability Index (DCSI), and Harmonized Classification Risk (HCR), signifying its effectiveness. The examination of the confusion matrix indicated a high predictive accuracy, with AUC values over 97% for both “Occupancy Status” and “Vehicle Type.” Statistical testing validated the robustness of the technique, while sensitivity analysis identified optimal hyperparameter combinations. The findings indicate significant improvement in parking management via accurate prediction of occupancy trends, identification of peak demand, and detection of payment and violation issues. This paper presents an innovative framework for improving parking operations, dynamic pricing, and resource allocation in urban environments.}, archive = {J_ACCESS}, author = {Xiaoxia Zheng and Wenxi Feng and Ning Wang and Huhemandula}, doi = {10.1109/ACCESS.2025.3589468}, journal = {IEEE Access}, pages = {128838-128855}, shortjournal = {IEEE Access}, title = {IoT-enhanced smart parking management with IncepDenseMobileNet for improved classification}, volume = {13}, year = {2025}, }
• (2025). Efficient satellite image retrieval using shearlet transforms and spiking neurons-based liquid autoencoder model. ACCESS, 13, 128817-128837. (www)

  @article{ , abstract = {This paper introduces an innovative content-based image retrieval system for precise and effective retrieval of satellite images. The system integrates liquid autoencoders with shearlet transforms to derive robust feature representations from satellite images. Empirical results demonstrate that the proposed system achieves a high retrieval accuracy of more than 95% on different datasets and is comparable with several state-of-the-art approaches. The efficiency of the system is further indicated by its fast inference time and moderate memory needs, making it suitable for large image databases. This study contributes to the literature on satellite image analysis by introducing a valid tool for several applications, including environmental monitoring and land-use mapping. The new method contributes value to the existing literature in Content-based image retrieval by showing the suitability of liquid autoencoders for satellite image retrieval.}, archive = {J_ACCESS}, author = {Sai Bhargav Kasetty and K. Rajakumar}, doi = {10.1109/ACCESS.2025.3586866}, journal = {IEEE Access}, pages = {128817-128837}, shortjournal = {IEEE Access}, title = {Efficient satellite image retrieval using shearlet transforms and spiking neurons-based liquid autoencoder model}, volume = {13}, year = {2025}, }
• (2025). Temporal and modality awareness-based lightweight residual network with attention mechanism for human activity recognition using a lower-limb exoskeleton robot. ACCESS, 13, 128802-128816. (www)

  @article{ , abstract = {Although many human activity recognition (HAR) models have achieved high accuracy, their computational complexity often limits deployment in systems with constrained hardware resources, such as wearable lower-limb exoskeletons. In addition, existing models frequently overlook the complementary nature of multimodal sensor signals, focusing primarily on temporal dynamics while underutilizing modality-specific information. To address these issues, this study proposes a lightweight residual network for recognizing diverse locomotion activities across varying terrains using multimodal sensing data. The model adopts an asymmetric convolutional architecture composed of depthwise and pointwise layers to efficiently capture temporal and modality-specific features while significantly reducing the number of trainable parameters. A channel-attention block is further integrated to emphasize salient fused features. Evaluations on the Walking Assist Wearable Robot Motion dataset, which contains kinematic and postural signals from 500 adults using a lower-limb exoskeleton, demonstrated that the proposed model achieves an accuracy of 98.23% and a macro F1 score of 98.21%, with only 48,037 parameters. This outperforms four hybrid deep learning baselines while reducing the parameter count by 5.5–12.9 times. To assess generalizability, additional experiments were conducted on four benchmark datasets—UCI-HAR, HAPT, PAMAP2, and WISDM—under varying batch size conditions. The proposed model consistently achieved competitive or superior macro F1 scores of $0.9627~\pm ~0.0046$ , $0.776~\pm ~0.0138$ , $0.8937~\pm ~0.0121$ , and $0.9736~\pm ~0.0025$ , respectively, confirming its robustness and adaptability across diverse real-world HAR scenarios.}, archive = {J_ACCESS}, author = {Chang-Sik Son and Won-Seok Kang}, doi = {10.1109/ACCESS.2025.3590407}, journal = {IEEE Access}, pages = {128802-128816}, shortjournal = {IEEE Access}, title = {Temporal and modality awareness-based lightweight residual network with attention mechanism for human activity recognition using a lower-limb exoskeleton robot}, volume = {13}, year = {2025}, }
• (2025). H-ConvLSTM to estimate reference evapotranspiration from air temperature and relative humidity. ACCESS, 13, 128789-128801. (www)

  @article{ , abstract = {The estimation of evapotranspiration reference is significant importance in the agricultural sector, as it allows for the precise determination of irrigation water distribution times. A few deep learning architecture models are employed by researchers in the estimation of evapotranspiration reference (ETo), including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). These three algorithms have been extensively evaluated and validated due to their ability to predict and estimate ETo data using temperature (T), relative humidity (RH), and solar radiation (Rs) as variables. This paper presents a comparison of the results of the three algorithms using only two variables, namely temperature and relative humidity, without the inclusion of solar radiation variables. This paper presents a novel approach to compare the ConvLSTM algorithm with a newly developed algorithm, called Hybrid Convolutional Neural Network Long Short-Term Memory (H-ConvLSTM). Furthermore, this study employs a minimal set of variables, comprising temperature and relative humidity. This study aims to facilitate the computational process on devices with limited processing capabilities. The results of the comparison demonstrate that the determinant coefficient values for RNN, LSTM, GRU, and ConvLSTM are 0.8354 (84%), 0.8506 (85%), 0.8496 (85%), and 0.8536 (85%), respectively. In contrast, the new architecture of the proposed algorithm exhibits a value of 0.8942 (89%), which is superior to that of other algorithm architectures. The objective of this paper is to serve as a reference for determining the reference value of evapotranspiration with limited device computation. Additionally, this paper presents the results of measuring the error comparison of each algorithm utilized.}, archive = {J_ACCESS}, author = {Abdul Haris and M. Marimin and Sri Wahjuni and Budi Indra Setiawan}, doi = {10.1109/ACCESS.2025.3585771}, journal = {IEEE Access}, pages = {128789-128801}, shortjournal = {IEEE Access}, title = {H-ConvLSTM to estimate reference evapotranspiration from air temperature and relative humidity}, volume = {13}, year = {2025}, }
• (2025). A lightweight two-step detection method for real-time small UAV detection. ACCESS, 13, 128771-128788. (www)

  @article{ , abstract = {Uncrewed aerial vehicles (UAVs) have been widely adopted across various domains; however, their potential for malicious use cases, including espionage, illicit trafficking, and unauthorized surveillance, has escalated security threats. This growing risk underscores the urgent need for precise and timely detection systems that can ensure robust countermeasures against UAV-related threats. In response to this demand, various studies have explored deep learning-based UAV detection approaches that can attain increased detection accuracy and efficiency. However, several major challenges remain in this domain, including the difficulty of detecting small UAVs and the increased computational cost associated with achieving performance improvements for deep learning models. To address the frequent misclassification of small UAVs in one-stage object detectors, this study proposes a two-step detection method, as illustrated in the graphical abstract, which summarizes the proposed approach. First, YOLO object detection model identifies candidate small UAVs. Then, a Vision Transformer classifier refines these results by filtering out false positives. This integrated approach significantly improves detection accuracy for small UAVs that are easily confused with other flying objects. Furthermore, we propose a compression strategy that integrates pruning, weight initialization, and knowledge distillation (KD) to reduce the computational costs of real-time applications. Experimental results demonstrate that the proposed two-step method outperforms a diverse set of state-of-the-art object detection and classification models. Specifically, in experiments where multiple types of aerial vehicles are present, the proposed method achieves a 5.6% improvement in mean average precision (mAP) compared to conventional object detection models. Additionally, our compression approach preserves critical UAV features by selectively removing low-importance parameters, significantly reducing the degree of redundancy while minimizing the induced detection performance loss.}, archive = {J_ACCESS}, author = {Sungkyu Jung and Jaeyeon Jang and Chang Ouk Kim}, doi = {10.1109/ACCESS.2025.3590473}, journal = {IEEE Access}, pages = {128771-128788}, shortjournal = {IEEE Access}, title = {A lightweight two-step detection method for real-time small UAV detection}, volume = {13}, year = {2025}, }
• (2025). Machine learning in education: Innovations, impacts, and ethical considerations. ACCESS, 13, 128741-128770. (www)

  @article{ , abstract = {Machine Learning (ML) has recently emerged as a powerful tool with significant potential to revolutionize education, bringing about fundamental changes in pedagogy and research. Its applications span various domains within academia, including administration, instructional method enhancement, and grade prediction. This study illustrates how ML can enhance the effectiveness of research, instruction, and study strategies by adapting to student needs and leveraging new communication tools within virtual learning environments. The literature review included various research articles sourced IEEE Xplore, Scopus, Web of Science, PubMed, Google Scholar, and ScienceDirect. The inclusion criteria encompassed studies that explicitly defined Artificial Intelligence (AI) within the medical education sector and were published in English with peer review. This study delves into the potential applications of ML and its associated benefits, which can aid researchers in implementing AI-based educational systems.}, archive = {J_ACCESS}, author = {Ersin Elbasi and Muhammad Nadeem and Yehia Ibrahim Alzoubi and Ahmet E. Topcu and Greeshma Varghese}, doi = {10.1109/ACCESS.2025.3590134}, journal = {IEEE Access}, pages = {128741-128770}, shortjournal = {IEEE Access}, title = {Machine learning in education: Innovations, impacts, and ethical considerations}, volume = {13}, year = {2025}, }
• (2025). Few-shot named entity recognition based on the collaborative graph attention network. ACCESS, 13, 128729-128740. (www)

  @article{ , abstract = {Few-shot Named Entity Recognition (NER) aims to extract entity information from limited annotated samples, addressing the scarcity of data in specialized domains. However, existing few-shot NER methods relying on data augmentation struggle to adequately augment semantic features, limiting their learning and representation capabilities. To overcome this, we introduce a novel few-shot NER encoder based on a Collaborative Graph Attention network (ColGAT). This encoder utilizes a collaborative graph-based data augmentation mechanism to thoroughly extract latent semantic features of entities within sentences, enabling precise entity recognition. Furthermore, to facilitate information interaction between support and query sets, we develop an entity classifier with Match Processing (MP), where adaptive weights allow the support set to flexibly adapt to different query instances, enhancing entity classification performance. Our model achieved an average F1 result of 65.87% across six datasets, surpassing the second-ranked model by 2.19% and achieving state-of-the-art performance, demonstrating significant improvements over previous methods.}, archive = {J_ACCESS}, author = {Haoran Niu and Zhaoman Zhong}, doi = {10.1109/ACCESS.2024.3520971}, journal = {IEEE Access}, pages = {128729-128740}, shortjournal = {IEEE Access}, title = {Few-shot named entity recognition based on the collaborative graph attention network}, volume = {13}, year = {2025}, }
• (2025). A single-ended protection scheme for flexible HVdc transmission lines based on the fréchet distance of the rate of change of fault voltage traveling waves. ACCESS, 13, 128716-128728. (www)

  @article{ , abstract = {Flexible DC transmission technology is a technical solution for the large-scale integration of renewable energy. To ensure the stable operation of transmission systems, the rapid and selective protection of transmission lines is crucial. However, the existing traveling wave (TW) protection methods have several main problems, such as insufficient tolerance to fault resistance at the remote ends. To address this problem, a protection scheme based on the overall curve characteristics of TWs is proposed. Specifically, a mathematical analysis is conducted on the initial line-mode TW curves, focusing on the differences between the rate-of-change characteristics exhibited by the initial TW under internal and external faults. On the basis of the analyzed mathematical expression, the reference curves of the rate-of-change of traveling waves (ROCOTW) under internal and external faults are subsequently established. The Fréchet distance (FD) is used to quantify the similarity between the reference and the measured curves of the ROCOTW. If the FD between the measured curve and the internal fault reference curve is less than the FD between the measured curve and the external fault reference curve, the simulated fault is determined to be an internal fault. Otherwise, it is determined to be an external fault. Finally, the results show that the proposed scheme can tolerate a fault resistance of $\,600{\Omega }$ and 35 dB of noise under different grid topologies and a current-limiting reactor (CLR) of 70 mH. This scheme can quickly and accurately distinguish between internal and external faults.}, archive = {J_ACCESS}, author = {Hui Qin and Faxi Peng and Zhen Pan and Yilin Zhong and Tao Liu and Runbin Cao}, doi = {10.1109/ACCESS.2025.3590150}, journal = {IEEE Access}, pages = {128716-128728}, shortjournal = {IEEE Access}, title = {A single-ended protection scheme for flexible HVdc transmission lines based on the fréchet distance of the rate of change of fault voltage traveling waves}, volume = {13}, year = {2025}, }
• (2025). A simple and effective KAN-based architecture for accurate battery RUL prediction. ACCESS, 13, 128700-128715. (www)

  @article{ , abstract = {Accurately estimating a lithium-ion battery’s Remaining Useful Life (RUL) is crucial for ensuring the safety and reliability of battery management systems. However, the performance of emerging architectures, such as Kolmogorov-Arnold Networks (KANs), is often hindered by the significant noise and complex temporal dynamics present in real-world battery data. To overcome this, we propose a novel Knowledge Distillation-Based Denoising and Channel-Independent Kolmogorov-Arnold Networks (DCI-KANs) architecture, specifically designed to enhance robustness and accuracy for multivariate time series RUL prediction. Our approach integrates a VMD-based denoising mechanism, compressed into a more compact KAN model via knowledge distillation, to mitigate noise efficiently. It also incorporates a channel-independent KANs structure with a regularized weighted loss function to handle variable-specific degradation patterns. Experimental results on two public battery datasets show that DCI-KANs substantially outperform existing state-of-the-art methods in RUL prediction accuracy, achieving a 43 % and 22 % reduction in Absolute Relative Error (ARE) on the NASA and CALCE datasets, respectively. This work presents a simple yet effective framework that makes KANs a practical, robust, and computationally efficient solution for the challenging task of real-world battery RUL prediction.}, archive = {J_ACCESS}, author = {Guangzai Ye and Li Feng and Jianlan Guo and Yuqiang Chen and Shufei Li}, doi = {10.1109/ACCESS.2025.3590480}, journal = {IEEE Access}, pages = {128700-128715}, shortjournal = {IEEE Access}, title = {A simple and effective KAN-based architecture for accurate battery RUL prediction}, volume = {13}, year = {2025}, }
• (2025). Analytical modeling of forced-air cooling heatsink by considering air temperature rise in channel and coupling of multiple heat sources. ACCESS, 13, 128685-128699. (www)

  @article{ , abstract = {With the rapid development of renewable energy, data centers, and new energy vehicles, the power density of power electronic converters is continuously increasing. Higher power density requires more advanced thermal dissipation system designs. Consequently, designing an efficient cooling system is crucial for improving the power density of converters. Due to its simplicity and efficiency, the rectangular extruded-fin air cooling heat sink is the most common cooling system design for converters. With increasing power density, multiple power devices are often arranged along the airflow path in forced-air cooling systems. However, existing analytical thermal models for forced-air cooling systems typically focus only on the system’s maximum or average temperature, neglecting the temperature prediction of each individual device. In response, this paper proposes a novel distributed thermal resistance network model for rectangular extruded-fin air cooling heat sink. The model accounts for air temperature rise along the flow path and includes thermal coupling between heat sources, enabling accurate temperature estimation of individual devices. Furthermore, the proposed model is primarily based on analytical formulations, which ensures fast computation and makes it well-suited for rapid thermal evaluation and efficient optimization of cooling system designs. The accuracy of this thermal resistance network is verified through computational fluid dynamics (CFD) simulations and experiments, with an average prediction error of less than 6%, maximum error less than 14%. These results demonstrate that the model can accurately predicted temperature of multiple devices and assist in efficient thermal system design.}, archive = {J_ACCESS}, author = {Jianxiong Yu and Kaiyang Bu and Ye Tian and Bowen Liu and Yongjun Zheng and Bin Guo and Chushan Li}, doi = {10.1109/ACCESS.2025.3589206}, journal = {IEEE Access}, pages = {128685-128699}, shortjournal = {IEEE Access}, title = {Analytical modeling of forced-air cooling heatsink by considering air temperature rise in channel and coupling of multiple heat sources}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient coverage path planning for a reconfigurable robot. ACCESS, 13, 128673-128684. (www)

  @article{ , abstract = {Reconfigurable robots have been introduced for many application domains, including area coverage. Energy-efficient complete coverage planning is foremost expected in robots used in coverage applications. This paper proposes a novel energy-efficient coverage path planner for a reconfigurable robot. The proposed method decides the reconfigurations to reduce energy usage while ascertaining the complete coverage, yielding an optimum coverage plan. A Modified Genetic Algorithm (MGA) with a unique chromosome representation is proposed for handling the reconfiguration and coverage order. A cell decomposition and clustering are proposed to divide the map into smaller cells and later merge them to form bigger cellular regions. The A* path planner, modified with new parameters to facilitate search and consider reconfiguration requirements, is proposed for inter-cluster movements. The proposed method has been compared with the state-of-the-art coverage methods of reconfigurable and fixed-shape robots through experiments. Based on the experimental results, the proposed method effectively reduces the total energy consumption of a reconfigurable robot while ensuring complete coverage in a given environment. With this approach, nearly 100% coverage was achieved, whereas existing methods failed to provide complete coverage in some cases. Path costs were reduced by 47% and 24% compared to existing approaches for reconfigurable and fixed robots, respectively, in cases where complete coverage was achievable.}, archive = {J_ACCESS}, author = {W. K. R. Sachinthana and I. D. Wijegunawardana and S. M. Bhagya P. Samarakoon and M. A. Viraj J. Muthugala and Mohan Rajesh Elara}, doi = {10.1109/ACCESS.2025.3590638}, journal = {IEEE Access}, pages = {128673-128684}, shortjournal = {IEEE Access}, title = {Energy-efficient coverage path planning for a reconfigurable robot}, volume = {13}, year = {2025}, }
• (2025). A comprehensive deep learning system with MGRF modeling for predicting breast cancer response to neoadjuvant chemotherapy. ACCESS, 13, 128654-128672. (www)

  @article{ , abstract = {Accurate prediction of breast cancer (BC) response to neoadjuvant chemotherapy (NAC) is critical for tailoring treatment strategies and improving patient outcomes. This study introduces a novel deep learning-based framework that integrates multi-parametric magnetic resonance imaging (MRI) (i.e., T1, T2, STIR, and DWI), along with clinical and molecular subtype markers, to classify tumor response into pathological complete response (pCR), partial response (PR), and stable disease (SD). First, tumor regions are delineated across MRI modalities and then modeled using a translation-invariant Markov-Gibbs random field (MGRF) with analytical parameter estimation to capture modality-specific spatial appearance patterns correlated with NAC response. Subsequently, diffusion-weighted MRI is processed to generate apparent diffusion coefficient (ADC) maps, offering quantitative assessment of intratumoral water diffusion and cellularity. Afterward, an adaptive rescaling module (ARM) is proposed to adjust spatial resolution and project volumetric inputs into 2D, enabling compatibility with pretrained convolutional networks. Finally, a customized SEResNet architecture, augmented with Squeeze-and-Excitation (SE) blocks, is introduced to extract modality-specific features which are then fused with clinical and molecular subtypes descriptors for final classification. Evaluated on a cohort of 109 BC patients using leave-one-subject-out (LOSO) cross-validation method, the system achieved an accuracy of 96.33%, a precision of 96.51%, a recall of 96.33%, an F1-score of 96.23%, and a Cohen’s kappa of 94.08%, outperforming its individual components, various pretrained deep learning models, and a state-of-the-art method. These results underscore the value of integrating the appearance model, functional (i.e., ADC) model, adaptive rescaling module, SE blocks, and clinical and molecular subtype markers for the precise prediction of NAC outcomes.}, archive = {J_ACCESS}, author = {Ahmed Sharafeldeen and Fatma Taher and Norah Saleh Alghamdi and Eman Alnaghy and Reham Alghandour and Khadiga M. Ali and Sameh Shamaa and Abdelrahman Gamal and Mohammed Ghazal and Sohail Contractor and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3590649}, journal = {IEEE Access}, pages = {128654-128672}, shortjournal = {IEEE Access}, title = {A comprehensive deep learning system with MGRF modeling for predicting breast cancer response to neoadjuvant chemotherapy}, volume = {13}, year = {2025}, }
• (2025). BrainAR: Automated brain tumor diagnosis with deep learning and 3D augmented reality visualization. ACCESS, 13, 128639-128653. (www)

  @article{ , abstract = {Augmented Reality (AR) technology offers promising applications in healthcare by enabling interactive 3D visualization of anatomical structures. However, current AR implementations often lack patient-specific detail, limiting their effectiveness in clinical settings. In this paper, we present BrainAR, an innovative mobile AR-based application designed for the automatic segmentation, 3D visualization, localization, and interaction with brain tumors using multiparametric 3D Magnetic Resonance Imaging (MRI) data. Our method leverages a 3D Residual U-Net, trained on the BraTS2021 dataset, achieving a mean Dice score of 0.886 for accurate tumor segmentation. The segmentation outputs are integrated into a real-time 3D engine to enable precise and dynamic visualization of brain tumors. Key contributions of our work include: 1) a server-side deployment of the segmentation model for online, patient-specific inference; 2) seamless AR integration enabling interactive exploration through hand gestures and voice commands; and 3) a mobile-based platform aimed at enhancing accessibility and usability in clinical environments. The proposed solution facilitates early detection and diagnosis by providing clinicians with an intuitive, immersive, and patient-specific tool for enhanced medical imaging interaction.}, archive = {J_ACCESS}, author = {Meriem Khedir and Kahina Amara and Nassima Dif and Oussama Kerdjidj and Shadi Atalla and Naeem Ramzan}, doi = {10.1109/ACCESS.2025.3590291}, journal = {IEEE Access}, pages = {128639-128653}, shortjournal = {IEEE Access}, title = {BrainAR: Automated brain tumor diagnosis with deep learning and 3D augmented reality visualization}, volume = {13}, year = {2025}, }
• (2025). Computer vision-based detection and classification of road obstacles: Systematic literature review. ACCESS, 13, 128603-128638. (www)

  @article{ , abstract = {Road obstacles are a major contributor to traffic accidents, making their accurate detection and classification vital for road safety and infrastructure maintenance. This paper presents a systematic literature review (SLR) of 95 peer-reviewed studies published between 2015 and 2024, specifically focusing on computer vision-based approaches for detecting and classifying road anomalies, including potholes, cracks, manhole covers, and speed bumps. Potholes were the most frequently studied obstacle (56 studies), followed by cracks (19), while manholes and speed bumps were the least represented (6 each). Detection tasks dominated (44.29%), with classification (30%) and combined approaches (25.75%) also explored. Deep learning techniques, particularly YOLO variants and CNNs, were the most used (87.14%), achieving state-of-the-art performance—e.g., YOLOv8 attained 100% accuracy and 99.50% mAP on benchmark datasets. Public datasets were used in 36.59% of cases, and 91.55% of studies relied on image-based inputs. Preprocessing techniques, such as data augmentation (30.86%) and annotation (24.69%), were prevalent. Although this review strictly focused on computer vision techniques, it identifies future research directions such as multi-sensor fusion, machine learning forecasting, crowdsourced data integration, and smart city infrastructure alignment. These broader approaches, though beyond this review’s scope, are critical for developing more comprehensive, real-time, and context-aware road obstacle detection systems.}, archive = {J_ACCESS}, author = {Hamza Assemlali and Soukaina Bouhsissin and Nawal Sael}, doi = {10.1109/ACCESS.2025.3588668}, journal = {IEEE Access}, pages = {128603-128638}, shortjournal = {IEEE Access}, title = {Computer vision-based detection and classification of road obstacles: Systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Improving data entry quality in enterprise applications with NLP methods: A model proposal based on BERT and deep learning. ACCESS, 13, 128592-128602. (www)

  @article{ , abstract = {In digital transformation, which is one of the most important keywords of our time, the completeness and accuracy of the data that users enter into applications directly affects the quality of the process, the accuracy of decision-making systems, and the speed at which data turns into information. Incorrect or incomplete data causes many problems such as prolonged approval processes, decreased trust in data, and negative impact on analysis capabilities. In this study, a data validation system was developed to improve the accuracy of risk management data collected from an ERP application and to minimize data entry errors. In order to prevent users from incorrectly entering or confusing important data such as Potential Risk, Internal Control, Control and Impact of the Risk during data entry, it is aimed to ensure accurate data entry by using NLP methods. Within the scope of the study, training was conducted on historical data and errors in user data entry were detected with various classification methods. Different methods such as BERT, RoBERTa, GPT-2, TFIDF+SVM, Word2Vec+SVM, Embedding GRU and Embedding LSTM were used to prevent these errors. The results show that the BERT model achieves the highest success rate with 94% accuracy. The strong language modelling capabilities of BERT gave it a significant advantage over other methods in detecting errors in data input.}, archive = {J_ACCESS}, author = {H. Canli}, doi = {10.1109/ACCESS.2025.3590983}, journal = {IEEE Access}, pages = {128592-128602}, shortjournal = {IEEE Access}, title = {Improving data entry quality in enterprise applications with NLP methods: A model proposal based on BERT and deep learning}, volume = {13}, year = {2025}, }
• (2025). Mixed Time/Event-triggered model predictive tracking control for networked mobile robots. ACCESS, 13, 128582-128591. (www)

  @article{ , abstract = {Focusing on the tracking control challenges in networked mobile robot systems, this research formulates a mixed time/event-triggered model predictive control (MPC) method. The method integrates time-triggered and event-triggered mechanisms, where the time-triggered module improves the performance of the MPC, and the event-triggered module reduces the resource consumption without sacrificing the control performance. The MPC algorithm is developed based on the auxiliary optimization problem (OP), which is constrained by linear matrix inequalities. The feasibility of the auxiliary OP is evaluated, and the mean square stability of the closed-loop system is investigated. The research results are expected to save network resources while ensuring the performance of networked mobile robots and provide strong support for practical applications of networked mobile robots.}, archive = {J_ACCESS}, author = {Huixin Liu and Yonghua Lai and Hongsong Lian and Guobin Wang and Dongsheng Zheng}, doi = {10.1109/ACCESS.2025.3589791}, journal = {IEEE Access}, pages = {128582-128591}, shortjournal = {IEEE Access}, title = {Mixed Time/Event-triggered model predictive tracking control for networked mobile robots}, volume = {13}, year = {2025}, }
• (2025). Optimal cogeneration scheduling: A comparison of genetic and POMDP-based deep reinforcement learning approaches. ACCESS, 13, 128562-128581. (www)

  @article{ , abstract = {Large processing facilities require multiple types of energy, such as electrical and thermal (hot water or steam). Cogeneration, or Combined Heat and Power (CHP), can provide significant economic and energy savings. However, scheduling its operation in real-time is challenging. This work compares deep reinforcement learning (DRL) and genetic algorithm (GA) approaches to control a real CHP in a processing facility. Traditionally, the CHP economic dispatch problem is modelled as a Markov Decision Process (MDP) with the assumption of complete observability. Due to the uncertainty of future electric and thermal demands, this assumption is unrealistic in real-world scenarios. Thus, this work proposes using a partially observable MDP (POMDP) for hourly CHP dispatch scheduling to address this partial observability. The selected DRL algorithms are Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Soft Actor-Critic (SAC), along with six GA variants. Performance was evaluated using multiple economic metrics, including Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), an environmental analysis, and a sensitivity analysis under variable electric pricing. This work shows that POMDP effectively models the hourly dispatch scheduling problem of CHPs. The insights gained from this analysis offer multiple potential avenues for future research, including the development more advanced DRL algorithms for CHP economic dispatch and the evaluation of their resilience when inaccurate measurements and anomalous conditions occur.}, archive = {J_ACCESS}, author = {Giorgia Ghione and Vincenzo Randazzo and Eros Pasero and Marco Badami}, doi = {10.1109/ACCESS.2025.3590255}, journal = {IEEE Access}, pages = {128562-128581}, shortjournal = {IEEE Access}, title = {Optimal cogeneration scheduling: A comparison of genetic and POMDP-based deep reinforcement learning approaches}, volume = {13}, year = {2025}, }
• (2025). Advanced modulation techniques and topological innovations in high-frequency link inverters: A comprehensive review. ACCESS, 13, 128541-128561. (www)

  @article{ , abstract = {High-Frequency Link inverters (HFLIs) have attracted significant research attention owing to their compact design, high power density, and high efficiency. HFLI systems achieve power conversion by integrating a High-Frequency Transformer (HFT) between a full-bridge (FB) inverter and cycloconverter. The coupling of HFT between the primary and secondary sides of the converter poses challenges for devising appropriate modulation strategies. The voltage stress on the secondary-side cycloconverter switches arises from the abrupt changes in the output filter inductor current. Another cause of voltage stress is the resonance between the switch output capacitance and the HFT leakage inductance. Given these challenges, examining and investigating modulation strategies in depth is essential. Existing review studies have primarily concentrated on topological configurations and their operational principles, with little attention paid to the modulation strategies. This review examines the topological designs of single-phase HFLI, followed by an in-depth analysis of the modulation strategies, operational principles, and switching signal generation logic diagrams. The limitations of each modulation strategy and their solutions are presented. The primary side modulation (PSM) and secondary side modulation (SSM) strategies are comprehensively reviewed for the operation of the HFLI. A comparative analysis of existing HFLIs in terms of switching frequency, soft-switching capability, modulation strategies, power rating, and efficiency is discussed. Recommendations on various modulation strategies are also provided. This review study is valuable for academic and industry-oriented researchers investigating HFLI.}, archive = {J_ACCESS}, author = {Asif Ali and Jianhui Su and Abdelrahman Farghly and Zeeshan Anjum Memon and Pervez Hameed Shaikh}, doi = {10.1109/ACCESS.2025.3535776}, journal = {IEEE Access}, pages = {128541-128561}, shortjournal = {IEEE Access}, title = {Advanced modulation techniques and topological innovations in high-frequency link inverters: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Cohesion metrics for microservices. ACCESS, 13, 128532-128540. (www)

  @article{ , abstract = {Software development requires new technologies, such as service-oriented architectures, that resolve quality problems such as maintenance and costs, which promote improvements in software development. Recently, software architectures have evolved from monolithic architectures to service-oriented architectures, and microservice architectures. In this process, the mechanisms used to evaluate the quality of architectures require changes to be applied to microservices. Cohesion is a quality attribute worthy of consideration. This work describes three metrics for microservices evaluation: Nop to determine the number of used operations, Nap to calculate the number of used endpoints, and the average of the sum of Nap and Nop is used to calculate Cohesion. The metrics are the result of the analysis and tests of microservices from several microservices projects. The cohesion thresholds range from 1 to 0, where one represents a fully cohesive microservice and 0 indicates that the microservice lacks cohesion. The metrics were tested in 41 microservices contained in 9 open-access repositories such as GitHub, Amazon, and Netflix. Results show that the proposed metrics provide cohesion values indicating whether the microservices code require restructuring due to unused operations and endpoints. 12 microservices had a Cohesion value of 1. In the remaining microservices, no pattern of behavior was found. It was observed that programming languages did not influence cohesion results; this is due to poor design or changes in the architecture of the finished and operational system. The metrics should be used as a complement to those already proposed in other works that address other objectives and scopes.}, archive = {J_ACCESS}, author = {C. Rojas-Pérez Juan and G. Fragoso-Díaz Olivia and A. Sánchez-González Guadalupe}, doi = {10.1109/ACCESS.2025.3589789}, journal = {IEEE Access}, pages = {128532-128540}, shortjournal = {IEEE Access}, title = {Cohesion metrics for microservices}, volume = {13}, year = {2025}, }
• (2025). Thermal analysis of outer rotor BLDC motor for drone considering airflow ventilation cooling. ACCESS, 13, 128518-128531. (www)

  @article{ , abstract = {This paper presents a refined thermal analysis of a semi-closed outer rotor BLDC motor designed for drone applications, with a focus on ventilation cooling effects induced by motor-generated airflow. To reduce weight, small drone motors are typically designed without dedicated cooling systems, relying instead on airflow generated during flight. Considering these operating conditions, thermal analysis that accurately reflects the airflow characteristics around the motor is essential in the motor design process. Existing thermal analysis methods each have limitations. Computational Fluid Dynamics (CFD) can analyze flow and heat transfer phenomena with high resolution but involves high computational cost, making it impractical for iterative design processes. Conversely, Lumped-Parameter Thermal Network (LPTN) models offer fast computation but require pre-defined convective heat transfer coefficients and have difficulty capturing complex flow conditions. In this study, a hybrid thermal analysis approach combining CFD and LPTN methods is proposed. First, motor losses under various load conditions are calculated using 2D and 3D electromagnetic Finite Element Analysis (FEA), incorporating the effect of axial leakage flux. Next, CFD simulations are performed on a coaxial rotor drone system to evaluate airflow characteristics near the motor. Based on the CFD results, convective heat transfer coefficients are determined and applied to the LPTN model, achieving both high accuracy and computational efficiency in thermal analysis. The proposed method was validated through prototyping and propeller load tests simulating various operating conditions. The comparison between the measured and analyzed winding temperatures showed good agreement, confirming the accuracy and reliability of the proposed approach. Notably, the results confirmed that most of the airflow entering the motor is generated by the motor’s own rotation rather than by the propeller, for both single-rotor and coaxial-rotor configurations. This CFD–LPTN-based thermal analysis framework provides a practical and validated method to evaluate the thermal performance of air-cooled drone motors under various operating conditions.}, archive = {J_ACCESS}, author = {Jaebeom Kang and Jiheon Lee and Hyeong-Jin Kim and Thanh-Tuan Nguyen and Jiyoung Lee and Yi-Hwa Song and Sung-Joo Yoo}, doi = {10.1109/ACCESS.2025.3586240}, journal = {IEEE Access}, pages = {128518-128531}, shortjournal = {IEEE Access}, title = {Thermal analysis of outer rotor BLDC motor for drone considering airflow ventilation cooling}, volume = {13}, year = {2025}, }
• (2025). Extending Artery/OMNeT++ for evaluating V2X sensor fusion algorithms with infrastructure support in cooperative advanced driver assistance systems. ACCESS, 13, 128492-128517. (www)

  @article{ , abstract = {Artery is a well-known V2X (Vehicle-to-Everything) simulation framework for evaluating vehicular communication systems. It is built on top of the OMNeT++ simulation environment. Although the framework is considered one of the best V2X simulators, serious deficiencies related to the error model in the representation of positioning were identified, prohibiting modeling of various advanced sensor fusion solutions and related ADAS technologies that rely on Cooperative Awareness Messages (CAMs), Collective Perception Messages (CPMs), and Vulnerable Road User Awareness Messages (VAMs). The main contribution lies in error models that were introduced to the framework to incorporate realistic measurement uncertainties, enhancing the fidelity of V2X message simulations and enabling a more accurate representation of real-world GPS and sensor errors. This paper extends Artery by addressing these challenges through improved modeling of position error, implementing geodetic-to-Cartesian coordinate transformations, correcting inconsistent scaling methods, and improper validation mechanisms leading to positional mismatches and unreliable object perception within the simulations. The study introduces robust geodetic transformations ensuring precise coordinate alignment between ego vehicles, roadside units (RSUs), and detected objects. A key enhancement includes extending RSU capabilities within Artery, enabling RSUs to actively generate and transmit CPM messages based on their own sensor data rather than solely receiving vehicle-originated messages. This allows RSUs to detect non-V2X-equipped vehicles and vulnerable road users (VRUs), allowing the RSU to share the data via V2X, thereby improving situational awareness and cooperative perception. Our framework treats RSUs as active CPM generators to model infrastructure-supported collective perception, seamlessly integrates configurable error models for the kinematic state vector of V2X messages as well as for perceived objects, and exposes a realistic and appropriate approach for V2X-based object-level sensor fusion algorithms. The paper presents a comprehensive evaluation that includes reproducible scenarios, visualization tools, and metrics for perception accuracy and scalability under varying vehicle and RSU densities to enable systematic benchmarking of collective perception for cooperative ADAS applications. The validation is conducted through diverse urban and industrial scenario simulations. The results show that the refined coordinate handling significantly improves the model of object positioning and the reliability of the synthetic message generation, while the applied error models implement highly realistic behavior in a configurable manner.}, archive = {J_ACCESS}, author = {Hamdan Hejazi and András Wippelhauser and László Bokor}, doi = {10.1109/ACCESS.2025.3591028}, journal = {IEEE Access}, pages = {128492-128517}, shortjournal = {IEEE Access}, title = {Extending Artery/OMNeT++ for evaluating V2X sensor fusion algorithms with infrastructure support in cooperative advanced driver assistance systems}, volume = {13}, year = {2025}, }
• (2025). A novel digital twin-based framework and methodology for enhancing the contribution of loads to frequency control. ACCESS, 13, 128475-128491. (www)

  @article{ , abstract = {The integration of renewable energy sources into power grids is crucial for reducing carbon emissions, yet it introduces challenges related to frequency stability due to reduced inertia and renewable intermittency. Although many solutions have been explored, the role of loads in frequency control remains underdeveloped. This work presents a novel Digital Twin framework for frequency control, incorporating six load frequency control schemes implemented on a thermal load. Additionally, it proposes a methodology for constructing frequency-control-oriented Digital Twins of power systems. The framework uses real-time measurements to estimate grid parameters—via the swing equation—and continuously tune control gains for enhanced performance. Complementarily, an online inertia estimation technique is integrated to enable fully adaptive strategies, further improving frequency control. The proposed approach is validated through computational and Hardware-in-the-Loop experimentation, showing robust performance under real-world conditions such as measurement noise and delays. The results indicate that integrating Digital Twins with load-based frequency control significantly enhances the power system’s resilience, offering a promising direction for future improvements in grid stability.}, archive = {J_ACCESS}, author = {Jesús Araúz and Antoine Labonne and Yvon Besanger and Fréderic Wurtz and Simon Waczowicz and Veit Hagenmeyer}, doi = {10.1109/ACCESS.2025.3590215}, journal = {IEEE Access}, pages = {128475-128491}, shortjournal = {IEEE Access}, title = {A novel digital twin-based framework and methodology for enhancing the contribution of loads to frequency control}, volume = {13}, year = {2025}, }
• (2025). Development of a compact optical tracking system with motorized variable zoom and rotation for tracking range adjustment in image-guided surgery. ACCESS, 13, 128465-128474. (www)

  @article{ , abstract = {Interest in remote robot-assisted image-guided surgery is increasing to improve medical accessibility in areas where physical visits by medical staff are challenging. However, existing optical tracking systems (OTS) cannot accommodate intraoperative changes in tracking range because they use fixed magnification lenses and mounts. We develop a new OTS that includes a motorized variable zoom lens system and a two-degree of freedom (DOF) rotation module, along with control algorithms to adjust the tracking range for targets in remote image-guided surgery. The variable zoom lens system automatically switches among three preoperatively calibrated zoom lenses using a lead-screw-based linear guide and a photo interrupter sensor. Based on a remote center of motion (RCM) mechanism, a rotation module can adjust the OTS’s orientation while keeping its position fixed. In addition, we implemented a reinforcement learning-based control algorithm to ensure that the target always remains within the OTS’s tracking range. The variable zoom lens system showed high iterative precision at the micrometer level, and the positional tracking error of target was less than 0.1 mm. The rotation module achieved a resolution of 0.1° in the yaw and pitch axes. We trained the control algorithm in a virtual simulator environment and deployed it in the actual system to keep the rigid body within the tracking range. The proposed system can adapt to intraoperative changes in tracking range. This will contribute to increased utilization of OTS in various surgical fields and enhance the safety of surgeries.}, archive = {J_ACCESS}, author = {Deokgi Jeung and Daekeun Ji and Hyunseok Choi and Joonho Seo and Seongbo Shim}, doi = {10.1109/ACCESS.2025.3588572}, journal = {IEEE Access}, pages = {128465-128474}, shortjournal = {IEEE Access}, title = {Development of a compact optical tracking system with motorized variable zoom and rotation for tracking range adjustment in image-guided surgery}, volume = {13}, year = {2025}, }
• (2025). An automated method inspired by taxonomic classification for distinguishing chilean pelagic fish species. ACCESS, 13, 128447-128464. (www)

  @article{ , abstract = {In this study, we designed an automated classification method, inspired by human taxonomic principles, to distinguish visually similar species of pelagic fish in images through the integration of morphological feature analysis with a hierarchical classification technique. By adapting the Keypoint R-CNN model for automated extraction of morphological characteristics, we accurately classified images of anchovies, mackerel, jack mackerel, and sardines, outperforming the results of the direct use of deep learning-based computer vision algorithms. Our method includes taxonomic analysis, exploiting geometric characteristics such as distances and angles between key body parts, segmenting patterned areas, and extracting texture features. Furthermore, we developed hierarchical classification models that employ a dichotomous key based on these key morphological traits to assess specific fish features such as size, shape, mouth orientation, and color patterns, simulating taxonomic classification. We achieved macro-precisions of up to 1.00 for small fish species and 0.98 for larger species, highlighting the pivotal role of keypoint detection combined with hierarchical classification in addressing challenging taxonomic tasks in marine organisms, and providing a scalable and adaptable solution for further applications.}, archive = {J_ACCESS}, author = {Vincenzo Caro Fuentes and Danny Luarte and Ariel Torres and Jorge E. Pezoa and Sebastián E. Godoy and Sergio N. Torres and Mauricio A. Urbina}, doi = {10.1109/ACCESS.2025.3590378}, journal = {IEEE Access}, pages = {128447-128464}, shortjournal = {IEEE Access}, title = {An automated method inspired by taxonomic classification for distinguishing chilean pelagic fish species}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal travel speed estimation in mixed traffic conditions: A probe vehicle-based approach with autonomous vehicle sensor integration. ACCESS, 13, 128432-128446. (www)

  @article{ , abstract = {The increasing adoption of autonomous vehicles (AVs) presents new opportunities for traffic monitoring and spatiotemporal travel speed estimation. Traditional traffic detection methods, relying on fixed-point sensors, face inherent limitations, including high installation costs, limited spatial coverage, and accuracy degradation in congested conditions. To address these issues, this study introduces a novel approach utilizing AVs as mobile detectors to estimate travel speeds in mixed traffic conditions. The proposed methodology examines the impact of varying sampling rates and spatiotemporal intervals on estimation accuracy, using probe vehicle sensor data. Three distinct scenarios are evaluated, each incorporating progressively advanced sensor configurations, ranging from GPS-only data to forward- and rear-facing sensor integration. The results indicate that estimation accuracy significantly improves with higher sampling rates, with a minimum threshold of 30% required for reliable performance. Additionally, larger spatial intervals enhance estimation stability by mitigating fluctuations in speed data. The findings demonstrate that AV-based mobile detection systems provide a scalable and cost-effective alternative to traditional fixed detection methods. This research contributes to the evolving field of intelligent transportation systems by offering insights into optimizing data collection strategies for real-time traffic management. Future research directions include integrating vehicle-to-everything (V2X) communication and machine learning models to further refine estimation accuracy and predictive capabilities in dynamic urban mobility environments.}, archive = {J_ACCESS}, author = {Hyungjoo Kim and Seongeun Na and Jongho Kim and Sangsoo Lee and Jiho Yeo}, doi = {10.1109/ACCESS.2025.3587137}, journal = {IEEE Access}, pages = {128432-128446}, shortjournal = {IEEE Access}, title = {Spatio-temporal travel speed estimation in mixed traffic conditions: A probe vehicle-based approach with autonomous vehicle sensor integration}, volume = {13}, year = {2025}, }
• (2025). Cancelable face biometrics with soft-biometric privacy enhancement. ACCESS, 13, 128420-128431. (www)

  @article{ , abstract = {The storage of biometric data has raised significant privacy concerns, necessitating robust measures for secure storage. While traditional Privacy-Enhancing Technologies (PETs), like Cancelable Biometric (CB) schemes, excel at creating protected templates that fulfill criteria such as irreversibility and unlinkability, they often fail to preserve the privacy of soft-biometric information. To address this issue, we propose a hybrid technology that combines PETs, leveraging their different properties to comprehensively address multiple privacy requirements and enhance overall protection for biometric templates. In our approach, we integrate Multi Incremental Variable Elimination (Multi-IVE), a recent technology designed to remove soft-biometric information from biometric templates, with conventional CB schemes. We apply our hybrid technology to facial templates and assess the properties of the resulting protected templates. In the event of stolen secrets, the combination of Multi-IVE with CB schemes helps decrease the accuracy of estimating soft-biometric attributes without affecting recognition performance, compared to CB schemes alone.}, archive = {J_ACCESS}, author = {Pietro Melzi and Hatef Otroshi Shahreza and Christian Rathgeb and Ruben Tolosana and Ruben Vera-Rodriguez and Julian Fierrez and Sébastien Marcel and Christoph Busch}, doi = {10.1109/ACCESS.2025.3590989}, journal = {IEEE Access}, pages = {128420-128431}, shortjournal = {IEEE Access}, title = {Cancelable face biometrics with soft-biometric privacy enhancement}, volume = {13}, year = {2025}, }
• (2025). Human behavior analysis: A comprehensive survey on techniques, applications, challenges, and future directions. ACCESS, 13, 128379-128419. (www)

  @article{ , abstract = {Human Behavior Analysis (HBA) has emerged as a critical interdisciplinary field, combining psychology, sociology, artificial intelligence, and data science to model, understand, and predict human behavior across diverse domains. This paper provides a comprehensive survey, addressing gaps in existing literature by exploring applications, techniques, challenges, and future directions. We begin by defining HBA, tracing its historical roots, and outlining core concepts such as behavioral patterns, cognitive processes, and emotional states. The survey then explores traditional and modern techniques, from manual observation to AI-driven methods such as deep learning, natural language processing, and computer vision. A key contribution is our extensive coverage of HBA applications in healthcare, marketing, education, workplace productivity, activity recognition, and criminal justice. For each domain, we provide detailed examples of how HBA enhances outcomes and decision-making. The survey also delves into data sources and methodologies used in HBA, such as sensor data, social media data, physiological signals, and multimodal analysis. We discuss major challenges such as data privacy, generalization, real-time processing, and scalability. Finally, we highlight emerging trends and future directions, including edge computing, Large Language Models, privacy-preserving techniques, and cross-disciplinary approaches. By offering a holistic review, this survey aims to guide future research and innovation in the evolving field of HBA.}, archive = {J_ACCESS}, author = {Siham Essahraui and Ismail Lamaakal and Yassine Maleh and Khalid El Makkaoui and Mouncef Filali Bouami and Ibrahim Ouahbi and Ahmed A. Abd El-Latif and May Almousa and Joel J. P. C. Rodrigues}, doi = {10.1109/ACCESS.2025.3589938}, journal = {IEEE Access}, pages = {128379-128419}, shortjournal = {IEEE Access}, title = {Human behavior analysis: A comprehensive survey on techniques, applications, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on urdu hate speech detection: Methods, evaluation, and challenges. ACCESS, 13, 128360-128378. (www)

  @article{ , abstract = {Hate speeches has risen significantly because of ever expanding online communities, easy accessibility of social media platforms, anonymity of the users, rapid spread of information, and increased visibility of hate related incidents. Detection of such hate speech, especially in low-resource language i.e. Urdu, on social media has become active research area for safe, and inclusive online environment. This paper introduces a new taxonomy, and presents a survey of Urdu hate speech detection techniques. It also explores future research directions, and highlights the main challenges which persist in this field such as limited data availability, code-switching, and requirements for adaptability to contextual perspectives. The taxonomy, and the survey form the basis for continued advancement in Urdu hate speech detection that yields improved insights regarding Urdu hate speech patterns, and contributes to better detection system’s development. It serves a comprehensive review of Urdu hate speech detection methods available in the research literature, and compares those methods at an abstract level, discusses their limitations, and provides future research directions for new researches in this domain.}, archive = {J_ACCESS}, author = {Ijaz Hussain and Umar Farooq and Ammara Nawaz Cheema and Ibrahim M. Almanjahie}, doi = {10.1109/ACCESS.2025.3591143}, journal = {IEEE Access}, pages = {128360-128378}, shortjournal = {IEEE Access}, title = {A comprehensive survey on urdu hate speech detection: Methods, evaluation, and challenges}, volume = {13}, year = {2025}, }
• (2025). An inverse modeling multi-objective optimization technique based on incremental learning and fuzzy clustering. ACCESS, 13, 128337-128359. (www)

  @article{ , abstract = {The use of inverse modeling-based crossover operators in multi-objective evolutionary algorithms (MOEAs) has recently received much attention. Sampling in the objective space is advantageous over sampling in the decision space as it allows selecting promising areas worthy to explore. This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. Several inverse SVR models are constructed and the samples in the objective space are partitioned among them based on fuzzy clustering instead of hard clustering to enrich the training process. Extensive simulations on various benchmark problems show that the proposed algorithm drastically reduces the number of function evaluations required to reach an optimal solution compared to existing methods. The algorithm is also tested on the pathfinding problem, the community detection problem, the sparse portfolio problem, and other real-world problems, all of which confirmed the scalability and superiority of the proposed algorithm.}, archive = {J_ACCESS}, author = {Gadallah Mohamed Abd Elaziz and Yasmine Abouelseoud and Sara H. Kamel}, doi = {10.1109/ACCESS.2025.3590300}, journal = {IEEE Access}, pages = {128337-128359}, shortjournal = {IEEE Access}, title = {An inverse modeling multi-objective optimization technique based on incremental learning and fuzzy clustering}, volume = {13}, year = {2025}, }
• (2025). Internet exchange points IXPs: The backbone of efficient 5G data routing. ACCESS, 13, 128325-128336. (www)

  @article{ , abstract = {The accelerated deployment of 5G networks is bringing about a new age of high-speed connectivity, low-latency services, and large-scale device interconnectivity. The Internet Exchange Point (IXP) is at the forefront of this transformation and is essential to facilitate smooth data exchange among network operators, content providers, and end-users. IXPs are vital in lowering latency, optimizing bandwidth utilization, and enabling best possible data routing, all of which are essential to address the performance requirements of the 5G technology. Current IXP architectures, however, must be modified to support the explosive growth in data traffic, complex routing demands, and ultra-low latency needs of 5G applications such as autonomous vehicles, IoT, and virtual reality. This article proposes a next-generation IXP architecture optimized for 5G data routing. The architecture dramatically improves scalability, performance, security, and cost-efficiency by bringing together Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing to enable dynamic and localized traffic management. In addition, sophisticated security mechanisms, such as DDoS mitigation and Zero Trust models, are embedded in this architecture. The main findings are that the performance is improved and latency decreased with direct peering and optimized resource allocation. Despite implementation challenges, such as significant upfront investment and integration with existing systems, the architecture offers a future-proof and scalable solution. With Internet Exchange Points (IXPs) becoming ever more critical to the 5G ecosystem, their modernization is key to unlocking the future of global connectivity.}, archive = {J_ACCESS}, author = {Osman Diriye Hussein and Adam Muhudin}, doi = {10.1109/ACCESS.2025.3589653}, journal = {IEEE Access}, pages = {128325-128336}, shortjournal = {IEEE Access}, title = {Internet exchange points IXPs: The backbone of efficient 5G data routing}, volume = {13}, year = {2025}, }
• (2025). Temporal coupling of brain signals and fine motor output using affordable EEG. ACCESS, 13, 128314-128324. (www)

  @article{ , abstract = {This study introduces a novel publicly accessible EEG dataset, acquired through an evoked motor execution paradigm, with a primary objective to examine the relationship between frequency activity of the sensorimotor cortex and index finger movements. A primary and distinctive contribution of this work is our precise temporal integration methodology that synchronizes EEG signals, task-related visual and auditory cues, and exact muscle activation events (finger trigger presses) through the Lab Streaming Layer protocol. This multimodal approach enhances dataset quality by enabling millisecond-precise alignment between stimulus presentation, cortical activation patterns, and resultant motor behavior—a critical feature lacking in most publicly available motor-related EEG datasets. A group consisting of 26 participants completed a total of 127 experimental sessions, during which EEG signals were simultaneously recorded along with corresponding trigger presses. Further underscoring the dataset’s broad accessibility, all recordings were performed using an affordable, widely available EEG headset device. We demonstrate the dataset’s practical applicability for EEG-based motor decoding by developing and evaluating deep learning classification models, highlighting dataset suitability despite using an affordable EEG recording headset. A real-time demonstration validated our approach in a virtual environment, where the highest-performing model successfully translated EEG signals into predicted finger movements. Our dataset, the detailed experimental methods for synchronized multimodal recording, as well as the codebase for data preprocessing and deep-learning model training, are all publicly available and open-source. The dataset is publicly available online. Codebase is published at github.com/nemesgyadam/NeuroJoystickVault}, archive = {J_ACCESS}, author = {Ádám Gyula Nemes and Ádám Gyulai and Ádám Szarvas and Ernő Németh and Kristóf Tajti and Viktor Tóth and György Eigner}, doi = {10.1109/ACCESS.2025.3587262}, journal = {IEEE Access}, pages = {128314-128324}, shortjournal = {IEEE Access}, title = {Temporal coupling of brain signals and fine motor output using affordable EEG}, volume = {13}, year = {2025}, }
• (2025). Research on change information retrieval method for energy infrastructure based on optical remote sensing images. ACCESS, 13, 128297-128313. (www)

  @article{ , abstract = {With the rapid advancement of satellite remote sensing technology, high-resolution optical remote sensing images have become increasingly crucial for monitoring energy infrastructure. Traditional change detection methods identify land-use and land cover variations over time, while image retrieval extracts pertinent information from large datasets. By integrating both approaches, change information retrieval allows for identifying images with features similar to those in a query time-series from vast archives of temporal data. Research in this direction remains relatively limited at present. To address this, we propose a novel framework that integrates change detection with image retrieval, focusing specifically on the change information in bi-temporal remote sensing images. Our model, SCanNet-CDH (Semantic Change Network and Convolutional Deep Hashing), integrates convolutional neural networks with Transformer architecture, utilizing multi-scale features from ResNet-34 and enhancing them with the CSWin Transformer for improved global feature modeling. A deep hashing technique is applied to the extracted features, optimizing retrieval accuracy via contrastive loss. To address the lack of publicly available biphasic datasets in the energy infrastructure domain, we introduce EICIRD (Energy Infrastructure Change Information Retrieval Dataset), featuring multi-class variation annotations for model training and evaluation. Experimental results demonstrate that the proposed model outperforms existing methods in terms of mean Average Precision, showcasing superior performance, practical applicability, and its potential to advance remote sensing image analysis and energy infrastructure monitoring.}, archive = {J_ACCESS}, author = {Zhibao Wang and Ying Yuan and Man Zhao and Dan Zhou and Lu Bai and Jinhua Tao and Anna Jurek-Loughrey}, doi = {10.1109/ACCESS.2025.3590987}, journal = {IEEE Access}, pages = {128297-128313}, shortjournal = {IEEE Access}, title = {Research on change information retrieval method for energy infrastructure based on optical remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Clients purchasing tendency community classification in E-commerce scenario: Multi-feature search for densely distributed clients in huge network. ACCESS, 13, 128280-128296. (www)

  @article{ , abstract = {We propose a scalable, multi-feature graph-based mining framework. It segments large populations of online users—such as those on digital commerce or social networking platforms—into well-defined communities. These communities reflect shared purchasing tendencies. The term “huge network search” in the title refers to conducting dense subgraph search on our constructed huge network. This network models the relations of massive-scale clients. Our approach captures and encodes user preferences (e.g., favored dress colors or styles). It extracts a broad range of behavioral features, including those from weakly supervised learning. A geometric feature selection process refines these attributes, ensuring that only the most informative factors are retained. This provides a precise depiction of each client’s buying behavior. User preferences are modeled as probabilistic distributions across multiple latent topics. This allows us to project complex purchasing patterns into a structured, hidden feature space. From this space, we construct a similarity-driven huge network. This network maps the relational landscape of users with comparable tastes. A dedicated community search algorithm uncovers densely interconnected subgroups. These subgroups are termed purchasing tendency communities. They exhibit consistent preference profiles, such as a collective inclination toward bright, saturated tones versus subtle, muted color palettes. To further enhance the personalization experience, the framework incorporates a tailored recommendation engine. This engine ranks and suggests products—such as clothing items or color themes—based on each userąŕs affiliation with learned communities. This ensures that suggestions are contextually appropriate and finely attuned to the individual’s behavioral signature. The method was validated on a real-world dataset of over one million users. It demonstrates the ability to reliably uncover distinct consumer communities and drive large-scale, individualized recommendation services. The results affirm the commercial and experiential value of the system. This positions it as a powerful tool for user-centric product personalization in dynamic online environments.}, archive = {J_ACCESS}, author = {Mingyuan Li and Chun-Ming Yang and Yi-Wei Kao}, doi = {10.1109/ACCESS.2025.3590976}, journal = {IEEE Access}, pages = {128280-128296}, shortjournal = {IEEE Access}, title = {Clients purchasing tendency community classification in E-commerce scenario: Multi-feature search for densely distributed clients in huge network}, volume = {13}, year = {2025}, }
• (2025). Investigation of the vibration effects on an outer rotor brushless direct current motor. ACCESS, 13, 128262-128279. (www)

  @article{ , abstract = {Light electric vehicles used as scooters, electric bikes, tricycles and mini cars are mostly preferred today, especially in urban transportation. Outer rotor Brushless Direct Current Motor (BLDCM) is frequently used in the powertrain of light electric vehicles in terms of efficiency and reliable or appreciable torque per both developed power and weight densities. Nevertheless, the in-wheel BLDCM is sensitive to torque ripple and electromagnetic vibrations due to the imbalances of d-q-axis inductance. Vibration in in-wheel electric motors is one of the disruptive effects that affects both driving comfort and operating performance of the motor. This paper presents the experimental and simulation results of vibration effects on outer rotor BLDCM using ANSYS Workbench. The design model is obtained with the results of the electromagnetic analysis of the outer rotor BLDCM with 3kW power and 1000rpm speed, and random vibration and directional velocity analyzes were carried out in accordance with real operating conditions. The random and harmonic analysis of vibrations revealed that they create different vibration effects on the stator and rotor. Simulation results show that high vibrations occur in the stator and low vibrations occur in the rotor at low frequencies. The measurement results on the simulated design prototype are consistent with the simulation results and show that the vibration effect decreases with the increase in frequency.}, archive = {J_ACCESS}, author = {Ali Sinan Cabuk}, doi = {10.1109/ACCESS.2025.3590163}, journal = {IEEE Access}, pages = {128262-128279}, shortjournal = {IEEE Access}, title = {Investigation of the vibration effects on an outer rotor brushless direct current motor}, volume = {13}, year = {2025}, }
• (2025). FPGA-accelerated sparse subset segmentation using ADMM for high-resolution imagery. ACCESS, 13, 128249-128261. (www)

  @article{ , abstract = {Applications in computer vision and image analysis, including object recognition and diagnostic imaging, are reliant on a fundamental competency in image segmentation. However, high-computation methods are occasionally exceeded by the energy economy and processing speed of typical CPU-based systems. To surpass these limitations, a hardware-accelerated picture segmentation method is introduced, leveraging the Alternating Direction Method of Multipliers (ADMM) technology, FPGA parallel processing, and sparse subset selection. ADMM algorithms are designed in high-level synthesis (HLS) C code for deployment on Xilinx Zynq UltraScale+ MPSoC. This approach simplifies hardware integration and maintains accuracy while reducing latency and improving energy efficiency. Significant energy savings and decreased execution times are indicated by experimental results, with FPGA achieving segmentation in 9 ms as opposed to 13 ms on a CPU, thereby proving the tremendous computational efficiency of FPGA-based solutions. These results demonstrate how hardware acceleration can enable scalable real-time applications in limited resources by overcoming computational constraints.}, archive = {J_ACCESS}, author = {Rupali Karthikeyan and Deep Amit Lodaya and Rama Muni Reddy Yanamala and Rayappa David Amar Raj and K. Krishna Prakasha and T. Subeesh and V. Anandkumar and Archana Pallakonda}, doi = {10.1109/ACCESS.2025.3590167}, journal = {IEEE Access}, pages = {128249-128261}, shortjournal = {IEEE Access}, title = {FPGA-accelerated sparse subset segmentation using ADMM for high-resolution imagery}, volume = {13}, year = {2025}, }
• (2025). Early warning of nerve agent release in large indoor environments based on encoder-decoder coupling physics-informed neural network. ACCESS, 13, 128231-128248. (www)

  @article{ , abstract = {Accurately perceiving spatial distribution patterns, detecting dynamic evolution characteristics, and issuing early warnings set higher standards for indoor safety and protection efforts. Computational fluid dynamics (CFD) methods provide accurate predictions but struggle with real-time performance, while neural networks offer rapid predictions, though their performance declines with high-dimensional fluid flow. Nerve agents were used in a subway attack in Japan. For counter-terrorism surveillance purposes, the real-time and accurate detection of nerve agents is critical for providing early warnings to the public and coordinating subsequent rescue operations. Therefore, in this work, a new model called Encoder-Decoder Coupling Physics-Informed Neural Network is proposed, which learns from concentration data generated by experimentally validated CFD simulations and the spatio-temporal information to solve high-dimensional partial differential equations and provide predictions of nerve agents distribution that more closely align with objective physical laws. Extensive experiments are conducted, and the results indicate that our model attains the highest performance among all the algorithms proposed in this paper. The difference between the actual and predicted results is small, and the scatter points are distributed around the fitted curves. In addition, it can make predictions with millisecond-level response time to achieve real-time monitoring. We propose a data-physics dual-driven surrogate model for real-time monitoring and early warning of the distribution of nerve agents in indoor environments.}, archive = {J_ACCESS}, author = {Shuobei Sun and Yang Peng and An Wang and Yiwen Xie and Yang Hu and Zhongyu Hou}, doi = {10.1109/ACCESS.2025.3587245}, journal = {IEEE Access}, pages = {128231-128248}, shortjournal = {IEEE Access}, title = {Early warning of nerve agent release in large indoor environments based on encoder-decoder coupling physics-informed neural network}, volume = {13}, year = {2025}, }
• (2025). Reliability and economic analysis of a modular converter with three vienna converters connected in parallel. ACCESS, 13, 128216-128230. (www)

  @article{ , abstract = {This paper analyzes the reliability of a 90kW modular converter with three Vienna converters in parallel modules. We designed a Markov model for partial failures and analyzed reliability for a configuration where each module has 30 kW without redundancy and a configuration where each module has 45 kW and 90 kW with redundancy. In the case of 45kW and 90kW modules with redundancy effects, if a failure occurs in any of the three parallel modules, the module that can operate normally will share the power of the failed module. Therefore, the current stress applied to the components of the Vienna converter increases, and the junction temperature of the switching devices increases, which raises the failure rate of the Vienna converter. This paper improves the accuracy of reliability analysis by reflecting the increase in failure rate according to the operating temperature of the components when the Vienna converter operates under maximum load conditions. Simulations are performed for various partial failure conditions, and Markov models and cost models are designed to analyze the correlation between reliability and price according to the redundancy effect. The redundancy effect due to module margin increases the mean time to failure (MTTF) by up to 4.6 times. However, it should be noted that when each Vienna converter module is designed as 90kW, the cost increases by USD 253 compared to when it is intended as 30kW.}, archive = {J_ACCESS}, author = {Jae-Seong Jo and Sang-Hyeok Lee and Guangxu Zhou and Feel-Soon Kang}, doi = {10.1109/ACCESS.2025.3590352}, journal = {IEEE Access}, pages = {128216-128230}, shortjournal = {IEEE Access}, title = {Reliability and economic analysis of a modular converter with three vienna converters connected in parallel}, volume = {13}, year = {2025}, }
• (2025). LCAT: A lightweight color-aware transformer with hierarchical attention for leaf disease classification in precision agriculture. ACCESS, 13, 128202-128215. (www)

  @article{ , abstract = {Leaf disease classification plays a critical role in ensuring healthy crop production and preventing agricultural losses. This research proposes a novel deep learning-based method for classifying leaf diseases using a dataset of 21,733 images across six distinct disease categories. We introduce the Lightweight Color-Aware Transformer (LCAT), a model designed to achieve high accuracy and computational efficiency for practical deployment, particularly in environments with limited hardware resources. LCAT uses a special attention system that combines color-focused attention with attention to different sizes of distortions, helping the model to accurately identify disease-related changes that usually appear as slight color changes and small deformations on leaves. A key contribution of LCAT lies in its use of smaller patch sizes to extract features in high-resolution regions while maintaining shallow depth, which significantly reduces model complexity. By focusing attention on a small, highly relevant region of leaf images, LCAT maintains strong classification performance without the computational overhead typically encountered in deeper transformer architectures. The experimental results show that LCAT outperforms standard Vision Transformer (ViT) models with a similar number of parameters. In particular, LCAT achieves a mean average precision (mAP) of 0.81 and a classification accuracy of 0.75, compared to ViT’s mAP of 0.75 and an accuracy of 0.68, while using significantly fewer floating-point operations (FLOPs), at 3.33G vs. 17.58G. This work highlights the potential of transformer-based models in plant pathology and provides a lightweight, high-performance alternative suitable for real-time field or mobile applications. Future work includes extending LCAT to support more crop species and plant diseases and integrating it into comprehensive decision support systems for precision agriculture.}, archive = {J_ACCESS}, author = {Parkpoom Chaisiriprasert and Khachonkit Chuiad}, doi = {10.1109/ACCESS.2025.3590764}, journal = {IEEE Access}, pages = {128202-128215}, shortjournal = {IEEE Access}, title = {LCAT: A lightweight color-aware transformer with hierarchical attention for leaf disease classification in precision agriculture}, volume = {13}, year = {2025}, }
• (2025). Deep learning for sector-specific labor market forecasting: Integrating job postings and macroeconomic indicators. ACCESS, 13, 128184-128201. (www)

  @article{ , abstract = {This paper presents a sector-specific employment forecasting framework that integrates deep learning with heterogeneous labor market data, including job postings and macroeconomic indicators. Traditional statistical methods often fail to capture the non-linear, high-dimensional dynamics of modern labor markets, especially under volatile economic conditions. To address this, we propose the Stochastic Adaptive Labor Integration Network (SALIN) and the Hierarchical Opportunity-Aware Mechanism (HOAM). SALIN models the labor market as a stochastic bipartite graph enhanced with latent behavioral embeddings and temporal learning components, enabling fine-grained simulation of adaptive interactions between job seekers and firms. HOAM supports multi-level policy interventions through fairness-aware optimization and dynamic opportunity reallocation. Together, these modules facilitate short-to-medium term forecasting at both national and regional levels, offering decision support for policymakers, economic analysts, and labor market platforms. Empirical evaluations across diverse datasets demonstrate improved accuracy and robustness in forecasting employment trends, particularly in sector-sensitive and disruption-prone contexts. Empirical results show that our framework reduces MAE by up to 11.2% and MAPE by up to 10.6% compared to state-of-the-art baselines such as FEDformer, across diverse real-world labor datasets.}, archive = {J_ACCESS}, author = {Haojun Ding}, doi = {10.1109/ACCESS.2025.3590788}, journal = {IEEE Access}, pages = {128184-128201}, shortjournal = {IEEE Access}, title = {Deep learning for sector-specific labor market forecasting: Integrating job postings and macroeconomic indicators}, volume = {13}, year = {2025}, }
• (2025). AI-driven optimization of multilayer thin-film structures for advanced optical applications. ACCESS, 13, 128173-128183. (www)

  @article{ , abstract = {Designing multilayer thin-film structures with tailored optical responses is a complex and computationally intensive task, often requiring repeated simulations and limited physical intuition about the role of each layer. In this study, we propose a data-driven framework that leverages a fully connected neural network (FCNN) as a surrogate model to accelerate and interpret the design of a high-reflectivity multilayer structure composed of alternating Ta2O5 and MgF2 layers. The FCNN model is trained to predict reflectance spectra with high accuracy over a wide design space defined by the thicknesses of individual layers. Beyond fast prediction, we analyze the physical influence of each design parameter using Principal Component Analysis (PCA), Global Sensitivity Analysis, and SHapley Additive exPlanations (SHAP). These techniques provide deep insights into layer significance, symmetry, and redundancy in the design, shedding light on the complex relationships between structure and spectral response. Finally, we integrate the surrogate model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization algorithm to maximize both the transmission peak and its full width at half maximum (FWHM) for a desired wavelength band. We then elect the best solution using a Multi-Criteria Decision-Making (MCDM) approach. This work demonstrates the synergy between AI and photonic design, enabling efficient optimization and facilitating physical interpretability. The framework provides a robust pathway for the intelligent design of optical filters and mirrors, with potential applications across telecommunications, sensing, and laser systems.}, archive = {J_ACCESS}, author = {Emad Alkhazraji and Sani Mukhtar and Haitham Khaled and Jaime Viegas}, doi = {10.1109/ACCESS.2025.3591001}, journal = {IEEE Access}, pages = {128173-128183}, shortjournal = {IEEE Access}, title = {AI-driven optimization of multilayer thin-film structures for advanced optical applications}, volume = {13}, year = {2025}, }
• (2025). A deep learning approach to goal-based portfolio optimization in non-stationary environments. ACCESS, 13, 128158-128172. (www)

  @article{ , abstract = {Goal-based portfolio optimization is a portfolio design technique that tailors investment strategies to an investor’s specific financial objective. Traditional approaches to this paradigm often assume that market dynamics are stationary, meaning that factors such as mean returns, volatility, and asset correlations remain constant over time. In reality, market dynamics are often non-stationary, posing challenges to traditional methods. In this paper, we present a deep reinforcement learning framework that adapts to evolving market conditions, enabling more robust investment strategies. Additionally, we leverage deep probabilistic regression techniques for data generation and market state estimation, ensuring the model accurately reflects changing financial environments. The presented approach offers a flexible solution for goal-based investing in dynamic markets and outperforms benchmarks on historical multi-asset data.}, archive = {J_ACCESS}, author = {Tessa Bauman and Lovre Mrčela and Sven Goluža and Zvonko Kostanjčar}, doi = {10.1109/ACCESS.2025.3588247}, journal = {IEEE Access}, pages = {128158-128172}, shortjournal = {IEEE Access}, title = {A deep learning approach to goal-based portfolio optimization in non-stationary environments}, volume = {13}, year = {2025}, }
• (2025). Automatic leather defect detection and classification using single-channel and multi-channel neural networks. ACCESS, 13, 128139-128157. (www)

  @article{ , abstract = {Timely and accurate defect detection is essential in the leather industry, as the quality of raw leather directly impacts both the usability and value of finished products. This paper provides a systematic overview of state-of-the-art solutions and proposes a novel approach for automated detection of leather surface defects using deep neural networks based on the Inception-V3 architecture. Five defect categories are introduced, focusing on their impact on leather quality. In addition, two deep neural network architectures were analyzed and implemented for defect detection and classification: a single-channel model and a multi-channel model with arbitration. The evaluation was carried out using a combination of a custom-developed dataset and publicly available datasets, assessed with standard performance metrics. Moreover, an image annotation tool was developed to facilitate precise defect labeling and the creation of variable-size datasets. Both models demonstrated promising results on the custom dataset, achieving accuracy rates exceeding 93%. The suggested methodology enhances the research domain of leather inspection automation by creating an openly accessible image dataset, performing a comparative analysis of detection models and creating software tools for data preparation. These contributions lay the foundation for further research in leather defect detection and potential industrial implementation.}, archive = {J_ACCESS}, author = {Majda Curtic-Hodzic and Aldina Ajkunic and Emir Sokic and Almir Salihbegovic and Lejla Arapovic and Nedim Osmic and Samim Konjicija}, doi = {10.1109/ACCESS.2025.3590789}, journal = {IEEE Access}, pages = {128139-128157}, shortjournal = {IEEE Access}, title = {Automatic leather defect detection and classification using single-channel and multi-channel neural networks}, volume = {13}, year = {2025}, }
• (2025). Comparison of multi-criteria decision analysis methods under comprehensive sensitivity analysis. ACCESS, 13, 128122-128138. (www)

  @article{ , abstract = {In many contemporary decision-making environments, practitioners must navigate complex problems characterized by multiple, conflicting criteria. Multi-Criteria Decision Analysis (MCDA) methods have become essential tools for structuring such evaluations, enabling more transparent and data-driven recommendations. However, their practical usefulness depends on both their theoretical foundations and their ability to perform reliably under uncertain or changing input data. Despite their widespread integration into Decision Support Systems (DSS), there remains a lack of systematic investigation into how robust different MCDA methods are when exposed to variability in the decision data. This study addresses this gap by investigating the stability and sensitivity of four selected used MCDA methods, Root Assessment Method (RAM), Complex Proportional Assessment (COPRAS), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). These methods were chosen based on their diverse computational assumptions and approaches to preference calculation, providing a representative basis for comparing stability across different methodological principles. The analysis employs the Comprehensive Sensitivity Analysis Method (COMSAM), a systematic framework that introduces simultaneous perturbations into the decision matrix to simulate real-world uncertainty. Through both simulation experiments and a practical facility location problem, the study reveals significant differences in method stability. RAM demonstrated the highest robustness, maintaining consistent preference rankings, while TOPSIS was most sensitive to matrix fluctuations. The findings highlight the critical role of method selection in DSS design, particularly in dynamic environments. The study contributes a structured comparative framework and empirical benchmarks that support the development of reliable MCDA-based decision tools under conditions of uncertainty.}, archive = {J_ACCESS}, author = {Jakub Wiȩckowski and Marcin Hernes and Wojciech Sałabun}, doi = {10.1109/ACCESS.2025.3588166}, journal = {IEEE Access}, pages = {128122-128138}, shortjournal = {IEEE Access}, title = {Comparison of multi-criteria decision analysis methods under comprehensive sensitivity analysis}, volume = {13}, year = {2025}, }
• (2025). From vision to voice: A multi-modal assistive framework for the physically impaired. ACCESS, 13, 128106-128121. (www)

  @article{ , abstract = {Providing people with visual and physical limitations their ability to access textual content continues to be a difficult challenge. A desktop-assisted system with automated computer processing enables the conversion of text found in images into audible speech. The application uses Python to develop its interface with Tkinter libraries and implements Tesseract OCR for optical character recognition that receives images through real-time capture enabled by OpenCV. Through the googletrans library, the system enables multilingual operations (more than 100 languages) for text processing and translation across all languages accessible via Google Translate. The system converts extracted or translated text into speech output using Google Text-to-Speech (gTTS) that plays back audio through system default media players as. mp3 files. Users experience intuitive interaction with the interface because it features hover effect characteristics, accessible control elements, and language selection through a dropdown menu. Through an expandable structural design, the system delivers multilingual text-to-speech capabilities, which prove useful in assistive technology applications for accessibility needs.}, archive = {J_ACCESS}, author = {Suhas Bhat and Prajwal Bhat and Sucheta V. Kolekar}, doi = {10.1109/ACCESS.2025.3590237}, journal = {IEEE Access}, pages = {128106-128121}, shortjournal = {IEEE Access}, title = {From vision to voice: A multi-modal assistive framework for the physically impaired}, volume = {13}, year = {2025}, }
• (2025). Resilient multimedia embedding in DICOM images: A robustness evaluation under multiple attacks. ACCESS, 13, 128086-128105. (www)

  @article{ , abstract = {Digital watermarking in medical imaging serves as a critical technique for ensuring data integrity, authentication, and copyright protection. This study explores an innovative approach to embed diverse file types, including text, PDF, video, and audio, into DICOM images using the Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) techniques. The primary objective is to evaluate the performance of these embedding techniques in terms of insertion time, extraction time, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Mean Squared Error (MSE). To assess the robustness of the embedded data, a series of attacks are applied to the DICOM images, including zero attack, salt and pepper attack, Gaussian attack, and speckle noise attack. The impact of these attacks on the integrity and retrieval of the embedded files is meticulously analyzed. The experimental analysis reveals that under Speckle Noise Attack (SNA), the character extraction accuracy remains relatively high across all file types, highlighting its minimal impact compared to other attacks. The results demonstrate the resilience of the embedded data against various forms of attacks, providing insights into the effectiveness and security of the proposed method. Speckle Noise Attack (SNA), LSB achieves higher accuracy in text files (85.76%) and PDF files (82–85%) compared to DCT, while DCT performs better in audio files with 86–86.5% accuracy. Overall, LSB is more effective for text and PDF watermarking, whereas DCT is more robust for audio files. The proposed framework uniquely enables the embedding of diverse multimedia content into DICOM images, addressing a critical gap in existing medical watermarking studies that focus mainly on text or visual data. This multi-format capability supports hospital systems in securing comprehensive patient records such as diagnostic notes, doctor-patient consultations, and procedural videos. Furthermore, the method demonstrates strong resilience against various attacks ensuring robustness and integrity validated by superior PSNR, SSIM, and MSE metrics. This comprehensive analysis not only validates the robustness of the embedding techniques but also lays the groundwork for future research in secure medical image processing.}, archive = {J_ACCESS}, author = {Ramyashree and P. S. Venugopala and S. Raghavendra and Vijay S. Kubihal and B. Ashwini}, doi = {10.1109/ACCESS.2025.3590013}, journal = {IEEE Access}, pages = {128086-128105}, shortjournal = {IEEE Access}, title = {Resilient multimedia embedding in DICOM images: A robustness evaluation under multiple attacks}, volume = {13}, year = {2025}, }
• (2025). Simple ultrasound imaging system based on dense and sparse selection and activation of array transducer elements. ACCESS, 13, 128071-128085. (www)

  @article{ , abstract = {Ultrasound imaging is typically performed using an array transducer system. To obtain a direct 3D image instead of stacking 2D cross-sectional images, a 2D array is required, which requires a large amount of electronic circuitry. Therefore, simple single transducer systems are being investigated. However, these single-transducer systems require multiple echo receptions by rotating or translating the transducer to achieve sufficient image quality, making them difficult to apply to specific imaging targets. In this study, we propose a method that selectively connects multiple elements instead of using all elements of the array to receive independently, aiming to reduce the size of the receiving electronic circuitry. By selecting and connecting elements in dense and sparse patterns, we have developed a method that considers both resolution and signal-to-noise ratio. Through phantom experiments and human in vivo imaging experiments, it has been demonstrated that the proposed method achieves image quality equivalent to that of conventional array transducers.}, archive = {J_ACCESS}, author = {Mohammad Syaryadhi and Eiko Nakazawa and Norio Tagawa}, doi = {10.1109/ACCESS.2025.3590062}, journal = {IEEE Access}, pages = {128071-128085}, shortjournal = {IEEE Access}, title = {Simple ultrasound imaging system based on dense and sparse selection and activation of array transducer elements}, volume = {13}, year = {2025}, }
• (2025). Transport-of-intensity equation for real-time tomographic experiments: Projection or slices?. ACCESS, 13, 128060-128070. (www)

  @article{ , abstract = {In this manuscript, we present a computational imaging discussion on the classical Paganin approach for recovering the phase of X-ray tomographic measurements using the Transport-of-Intensity Equation (TIE) with a single-material parameter. In practical experiments, this parameter often carries significant uncertainty, leading tomographic users to iteratively test various values until a reconstruction provides useful information. From a computational perspective, this trial-and-error process affects user experience, as it is both time-consuming and resource-intensive. We compare the classical formulation of the problem in the projection domain (also referred to as the frame domain) with an alternative filtering strategy in the sinogram domain (also referred to as the slice domain). Additionally, we propose bounds to quantify the differences between these approaches. Processing directly in the slice domain is significantly faster, as the phase retrieval is integrated with the reconstruction process, effectively combining two steps into one. This integration enhances the user experience in tomographic beamline settings.}, archive = {J_ACCESS}, author = {Eduardo X. Miqueles and Paola C. Ferraz and Goran Lovric and David M. Paganin}, doi = {10.1109/ACCESS.2025.3585781}, journal = {IEEE Access}, pages = {128060-128070}, shortjournal = {IEEE Access}, title = {Transport-of-intensity equation for real-time tomographic experiments: Projection or slices?}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven secrecy energy efficiency maximization in RIS-enabled communication systems. ACCESS, 13, 128048-128059. (www)

  @article{ , abstract = {Reconfigurable intelligent surfaces (RISs) are becoming an innovative technology for sixth-generation (6G) wireless networks, providing improved coverage and spectral efficiency. Nonetheless, incorporating RIS into 6G systems brings forth considerable security challenges, especially given the potential for multiple eavesdroppers. This study explores the use of physical layer security (PLS) to facilitate secure communications in RIS-assisted networks, tackling the important issues of safeguarding sensitive information while also addressing limited energy resources. This study examines the optimization of RIS passive beamforming, active beamforming, and power budgets with the aim of enhancing secrecy energy efficiency (SEE). Given the intricate nature of this problem, we utilize artificial intelligence, particularly deep reinforcement learning. By treating the problem as a Markov decision process (MDP), we make it easier to make decisions in real-time by creating specific states and actions, along with a reward system designed to balance privacy and energy efficiency. We introduce an innovative framework that utilizes the deep deterministic policy gradient (DDPG) algorithm to address the challenges of the MDP. Our extensive simulations show that our method works much better than current techniques using deterministic policy gradients, like the proximal policy optimization (PPO) framework, in improving SEE. Our results demonstrate the ability of DDPG to improve secure and energy-saving communications with RIS, offering a robust and flexible solution for the changing and tough conditions expected in future 6G networks.}, archive = {J_ACCESS}, author = {Akram Y. Sarhan and Osamah A. Abdullah and Hayder Al-Hraishawi and Faisal S. Alsubaei}, doi = {10.1109/ACCESS.2025.3591009}, journal = {IEEE Access}, pages = {128048-128059}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven secrecy energy efficiency maximization in RIS-enabled communication systems}, volume = {13}, year = {2025}, }
• (2025). SAGNet: Small-target attention guided network for urban remote sensing image real-time segmentation. ACCESS, 13, 128034-128047. (www)

  @article{ , abstract = {High-precision real-time segmentation of urban remote sensing (URS) images is crucial for emergency response such as urban flood warning and fire spread monitoring. Existing image segmentation methods based on feature fusion and attention mechanisms effectively enhance the features of large targets in URS images, but introduce noise, which leads to blurring of small target features in the feature space. In this paper, we propose a small-target attention-guided real-time segmentation network (SAGNet) for URS images to optimize spatial features and highlight small targets. Specifically, a feature-guided enhancement (FGE) model is designed by two nonlinear learning operators to learn more discriminative features and filter background noise. In addition, a scale-aware weighted (SAW) loss function is designed to adaptively adjust the loss of various scale objects by dynamic weighting to highlight the features of small scale objects. After quantization and compression of SAGNet, the network parameters was reduced to 11.8 M. Experimental results on three URS datasets show that our network can effectively improve the overall performance compared to the state-of-the-art methods by 6.40% mIoU. And the model segmentation speed reaches 64.3 FPS when processing $512\times 512$ images on a laptop device (NVIDIA GTX Geforce 2080 Ti GPU), which meets the demand of processing images or videos in real-time on mobile devices.}, archive = {J_ACCESS}, author = {Shasha Ren and Xiaodong Zhang}, doi = {10.1109/ACCESS.2025.3584952}, journal = {IEEE Access}, pages = {128034-128047}, shortjournal = {IEEE Access}, title = {SAGNet: Small-target attention guided network for urban remote sensing image real-time segmentation}, volume = {13}, year = {2025}, }
• (2025). Advancements in AI-powered electric vehicle routing: Multi-constraint optimization and infrastructure integration approaches for evolving EVs—A survey. ACCESS, 13, 127941-128033. (www)

  @article{ , abstract = {Electric vehicles (EVs) have become essential tools in the worldwide shift to sustainable transportation, driven by advances in battery technology, charging infrastructure, and routing algorithms. This paper presents a comprehensive investigation of the emerging EV ecosystem through three key domains: electric automobiles, routing algorithms, and charging infrastructure. The study uses a dual methodology combining automated data collection with manual filtering to ensure research quality. The investigation spans EV evolution from the 1820s to 2024+, tracking battery technology advancement from early Lead-acid systems (20-30km range) to future Solid-State batteries (800-1000km range), alongside the development of charging infrastructure from basic Level 1 systems to advanced wireless and vehicle-to-grid technologies. The research examines how routing algorithms have evolved from traditional approaches like Dijkstra’s and Bellman-Ford to advanced AI-driven methods. A key focus is multi-constraint optimization that simultaneously manages State of Charge, waiting times, traffic conditions, energy consumption, and charging needs. The study evaluates modern AI techniques including Graph Neural Networks, Transformers, Multi-Agent Reinforcement Learning, Deep Reinforcement Learning, and Neuro-Fuzzy systems, demonstrating their superior performance over conventional methods. Major EV challenges such as range anxiety, charging delays, poor station distribution, and traffic unpredictability are addressed through intelligent solutions. These include reinforcement learning for adaptive routing, predictive analytics for demand forecasting, and smart charging systems for grid optimization. Experimental validation shows significant improvements in energy efficiency, route optimization, and system adaptability. Future directions include Vehicle-to-Everything communication, multi-agent coordination, next-generation batteries, and renewable energy integration. This study establishes a multi-constraint optimization framework with hybrid AI integration, providing a foundation for scalable and intelligent electric vehicle ecosystems.}, archive = {J_ACCESS}, author = {P. Anandha Prakash and R. Radha}, doi = {10.1109/ACCESS.2025.3589363}, journal = {IEEE Access}, pages = {127941-128033}, shortjournal = {IEEE Access}, title = {Advancements in AI-powered electric vehicle routing: Multi-constraint optimization and infrastructure integration approaches for evolving EVs—A survey}, volume = {13}, year = {2025}, }
• (2025). Head and hands tunneling pipeline for enhancing sign language recognition. ACCESS, 13, 127926-127940. (www)

  @article{ , abstract = {Sign Language Recognition (SLR) presents a significant challenge as a fine-grained, scene- and subject-invariant video classification task, primarily relying on hand gestures and facial expressions to convey meaning. Vision foundation models, such as Vision Transformers (ViTs), trained on general human action recognition datasets, often struggle to capture the nuanced features of signs. We highlight two main challenges: 1) the loss of critical spatial features in the head and hand regions due to video downscaling during preprocessing, and 2) the lack of sufficient domain-specific knowledge of sign gestures in ViTs. To tackle these, we propose a pipeline comprising our Head & Hands Tunneling (H&HT) preprocessor and a domain-specifically pre-trained 32-frame ViT classifier. The H&HT preprocessor, incorporating the MediaPipe pose predictor, maximizes the preservation of critical spatial details from the signer’s head and hands in raw sign language videos. When the ViT model is pre-trained on a domain-specific, large-scale SLR dataset, the two parts complement each other. As a result, the 32-frame H&HT pipeline achieves a Top-1 accuracy of 62.82% on the WLASL2000 benchmark, surpassing the performance of the 32-frame models and ranking second among the 64-frame models. We also provide benchmarking results on the ASL-Citizen dataset and two revised versions of the WLASL2000 dataset. All weights and codes are available in this link.}, archive = {J_ACCESS}, author = {Ganzorig Batnasan and Munkh-Erdene Otgonbold and Qurban Ali Memon and Timothy K. Shih and Munkhjargal Gochoo}, doi = {10.1109/ACCESS.2025.3591123}, journal = {IEEE Access}, pages = {127926-127940}, shortjournal = {IEEE Access}, title = {Head and hands tunneling pipeline for enhancing sign language recognition}, volume = {13}, year = {2025}, }
• (2025). Classification of apolipoprotein e ϵ4 allele carriers in cognitively healthy older adults and alzheimer’s disease patients using event-related potentials and event-related spectral perturbation. ACCESS, 13, 127911-127925. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is the most common neurodegenerative disease in older adults. With increasing life expectancy, the incidence of this disease is expected to increase over time. Individuals who are carriers of the $\varepsilon 4$ allele of Apolipoprotein E (APOE $\varepsilon 4$ ) have a greater chance of developing AD. Electroencephalography (EEG) is a relatively inexpensive and non-invasive method that can help diagnose neurodegenerative diseases. Event-Related Potentials (ERP) analysis is an EEG tool widely used in the literature to help identify AD. In this study, the Event-Related Spectral Perturbation (ERSP) tool was added, a novel application for identifying carriers of the APOE $\varepsilon 4$ allele. We subdivided the individuals into four groups: control group of APOE $\varepsilon 4$ carriers (CT-C), control group of non-APOE $\varepsilon 4$ carriers (CT-N), AD carrying APOE $\varepsilon 4$ (AD-C), and AD without APOE $\varepsilon 4$ (AD-N). The extraction of ERP and ERSP features from EEG can be used as classifier features using Machine Learning (ML) techniques. The EEGLAB tool was used for the data preparation and feature extraction pipeline to search for statistically significant differences between the groups. Python programming language was also used with the aid of the scikit-learn library to perform feature selection and classification. Among the classifiers we used, we identified greater accuracy and technical advantage in the use of Support Vector Machine (SVM). We also found that ERSP was more effective than ERP in terms of the ML accuracy. Interestingly, this study also observed statistically significant differences in the pre-stimulus interval (−500 to 0 ms) for ERSP, and not only in the post-stimulus interval that is generally used for cognitive studies. This study proposed a machine learning classification method to identify APOE $\varepsilon 4$ allele carriers in older adults with AD and cognitively healthy older adults. Using ERP alone, the performance was poor, but with the use of ERSP, the performance was much better, and with the fusion of ERP and ERSP, the accuracy values were even higher. Besides the classification accuracy, we added the F1 score as a performance metric.INDEX TERMS Alzheimer’s disease, Apolipoprotein E, event-related potentials, event-related spectral perturbation, machine learning, feature selection.}, archive = {J_ACCESS}, author = {Kauê O. Frassão and Sandro M. S. Filho and Renata Valle Pedroso and Carla M. Crispim Nascimento and Henrique Pott and Marcia Regina Cominetti and Francisco J. Fraga}, doi = {10.1109/ACCESS.2025.3590656}, journal = {IEEE Access}, pages = {127911-127925}, shortjournal = {IEEE Access}, title = {Classification of apolipoprotein e ϵ4 allele carriers in cognitively healthy older adults and alzheimer’s disease patients using event-related potentials and event-related spectral perturbation}, volume = {13}, year = {2025}, }
• (2025). Iteration overlap and dual-sided LSOVA for efficient parallel turbo decoding architectures. ACCESS, 13, 127902-127910. (www)

  @article{ , abstract = {An efficient turbo decoder architecture is proposed by jointly exploring advancements in code design, decoding algorithms, and hardware implementation to achieve high-throughput and low-latency performance. At the algorithmic level, we introduce Dual-Sided Local-SOVA (DS-LSOVA), an enhanced variant of Local-SOVA that significantly lowers the complexity of extrinsic information computation while preserving decoding accuracy. To further improve implementation efficiency, iteration overlap interleavers are developed specifically for fully pipelined, iteration-unrolled UXMAP decoders, enabling full iteration overlap. This design reduces decoding latency and boosts hardware area efficiency by up to 22%. Importantly, these gains are achieved without sacrificing error-correcting performance: DS-LSOVA matches the Frame Error Rate (FER) of conventional max-Log-MAP decoding, while the proposed interleavers deliver FER results comparable to the Quadratic Permutation Polynomial (QPP) interleavers used in LTE.}, archive = {J_ACCESS}, author = {Stefan Weithoffer and Charbel Abdel Nour}, doi = {10.1109/ACCESS.2025.3590986}, journal = {IEEE Access}, pages = {127902-127910}, shortjournal = {IEEE Access}, title = {Iteration overlap and dual-sided LSOVA for efficient parallel turbo decoding architectures}, volume = {13}, year = {2025}, }
• (2025). Flexible job shop scheduling based on energy consumption of method research. ACCESS, 13, 127885-127901. (www)

  @article{ , abstract = {In current manufacturing processes, the importance of sustainable development concepts in helping enterprises reduce costs and improve efficiency is increasing. The workshop scheduling problem considering energy consumption has become a meaningful research direction. This paper focuses on the solution methods for the flexible job-shop scheduling problem (FJSP) considering energy consumption. Deep reinforcement learning, with its self-learning and adaptive characteristics, has become an important means for solving scheduling problems. By establishing a multi-objective optimization model aimed at minimizing the maximum completion time and energy consumption, this paper solves the flexible job-shop scheduling problem considering energy consumption (GFJSP) based on an improved deep reinforcement learning algorithm, D3QN. This paper proposes an improved MD3QN algorithm by introducing a dual experience pool, a noisy neural network, and a reward mechanism combining single-step and multi-step rewards on the basis of the D3QN algorithm, thereby further improving the algorithm’s convergence, generalization ability, and solution stability. The effectiveness of the algorithm improvement is verified through a test dataset expanded based on standard instances, and the practical application effect is validated through a case study using the production data of hub bearings from a bearing company. The improved effectiveness of the MD3QN algorithm is thus demonstrated.}, archive = {J_ACCESS}, author = {Yajie Li and Longlong Li and Xiaoying Yang and Bingfeng Zhao}, doi = {10.1109/ACCESS.2025.3589064}, journal = {IEEE Access}, pages = {127885-127901}, shortjournal = {IEEE Access}, title = {Flexible job shop scheduling based on energy consumption of method research}, volume = {13}, year = {2025}, }
• (2025). Relay-assisted wireless sensor networks using KNN-DBSCAN static clustering. ACCESS, 13, 127868-127884. (www)

  @article{ , abstract = {Wireless sensor networks (WSNs) play a vital role in modern applications such as healthcare, smart cities, and environmental monitoring. However, their potential is often limited by energy constraints, which reduce the lifetime of the network and the data collection capabilities. To address this challenge, we propose KDL, a novel hybrid clustering algorithm that combines K-Nearest Neighbor (KNN) and Density-Based Spatial Clustering (DBSCAN) to optimize energy efficiency in WSNs. KDL first uses KNN to analyze internode distances and determine optimal clustering parameters, which guide DBSCAN in forming robust clusters. After clustering, KNN reassigns noise points to appropriate clusters, improving coverage. Next, an energy-aware cluster head (CH) selection mechanism, inspired by LEACH but enhanced with node energy levels and cluster centroid distances, ensures balanced energy consumption. In addition, a relay-assisted communication strategy optimizes data transmission by strategically placing relay nodes between the CH and the base station. Through extensive simulations, KDL shows significant performance improvements over existing approaches, substantially enhancing network lifetime while maintaining energy efficiency. These advancements position KDL as a promising solution for real-world energy-efficient WSN deployments.}, archive = {J_ACCESS}, author = {Thierry Taning Longla and Tansal Gucluoglu and Tamer Dag}, doi = {10.1109/ACCESS.2025.3590790}, journal = {IEEE Access}, pages = {127868-127884}, shortjournal = {IEEE Access}, title = {Relay-assisted wireless sensor networks using KNN-DBSCAN static clustering}, volume = {13}, year = {2025}, }
• (2025). T3SSLNet: Triple-method self-supervised learning for enhanced brain tumor classification in MRI. ACCESS, 13, 127852-127867. (www)

  @article{ , abstract = {Classification of brain tumors from MRI images is crucial for early diagnosis and effective treatment planning. However, there are still obstacles to overcome, including low image quality, sparsely labeled data, and variability in tumor characteristics. In this study, we explored the use of self-supervised learning techniques to improve the classification performance for brain tumors. Specifically, we tested three SSL approaches SimCLR, MoCo, and BYOL, with ResNet-50 as the backbone architecture on a newly constructed dataset created by combining five public datasets. We further extended our work by integrating EfficientNet to evaluate its computational efficiency, demonstrating its feasibility for low-processor systems. We introduce T3SSLNet, a novel framework consisting of four key components: the imaging spectrum enhancement block for data augmentation, the Frozen Feature Extractor block for hierarchical feature extraction, Neural Representation Projection Learning block for contrastive-positive pair learning, and Unfrozen Classification block for tumor classification. Our experimental results paired with ResNet-50 indicate that, without fine-tuning, MoCo achieved the highest accuracy at 95.76%, followed by SimCLR at 92.25% and BYOL at 81.80%. Following fine-tuning, BYOL showed a significant improvement, reaching 96.42%, while MoCo and SimCLR reached 96.87% and 97.02%, respectively.}, archive = {J_ACCESS}, author = {Md. Nasif Safwan and Souhardo Rahman and Mahamodul Hasan Mahadi and Md Iftekharul Mobin and Taharat Muhammad Jabir and Zeyar Aung and M. F. Mridha}, doi = {10.1109/ACCESS.2025.3589619}, journal = {IEEE Access}, pages = {127852-127867}, shortjournal = {IEEE Access}, title = {T3SSLNet: Triple-method self-supervised learning for enhanced brain tumor classification in MRI}, volume = {13}, year = {2025}, }
• (2025). Feature-driven EnsembleX: An advanced ensemble framework for enhanced MRI abdomen image classification using feature refinement and boosting techniques. ACCESS, 13, 127837-127851. (www)

  @article{ , abstract = {The classification of MRI abdomen images, particularly for liver conditions, remains a complex task due to the intricate anatomy and variability of abdominal structures and the subtle imaging features that distinguish specific diagnoses. In this study, we introduce a new framework that utilizes MRI T1 water-only images generated using the Deep-Dixon technique. This technique processes Dual-sequence T1 MRI scans, comprising in-phase and out-phase images, to enhance tissue differentiation. These MRI T1 water-only images are then used for classifying liver tissue to distinguish between healthy and unhealthy liver classes. The framework begins with the extraction of detailed features, including texture, edge information, and intensity patterns from the MRI T1 water-only images. To refine the extracted features, Principal Component Analysis (PCA) was used to reduce dimensionality, followed by Recursive Feature Elimination (RFE) to select the most relevant attributes for classification. The classification is powered by an ensemble learning approach, incorporating boosting to improve weak learners and enhance model accuracy iteratively. Additionally, bagging is applied to train multiple models on bootstrapped subsets of the data, aggregating predictions to reduce variance and improve robustness. The model’s effectiveness was assessed using statistically validated performance metrics across 5-fold cross-validation, achieving an accuracy of 98.84% $\pm ~0.12$ %, a precision of 99.03% $\pm ~0.10$ %, a recall of 99.51% $\pm ~0.10$ %, specificity of 80.00% $\pm ~0.18$ %, an F1-score of 99.27% $\pm ~0.10$ %, and a ROC-AUC score of 89.76% $\pm ~0.32$ %, demonstrating its effectiveness in accurately classifying liver tissue into healthy and unhealthy categories based on MRI T1 water-only images. The evaluation was conducted on the publicly available CHAOS dataset, which demonstrates improved classification accuracy and reliability over existing state-of-the-art models, also reproducibility and validation through the use of publicly accessible data. This approach offers a promising tool for medical practitioners, facilitating precise diagnostic assessments and informed treatment planning, especially in resource-limited settings where radiological expertise may be scarce.}, archive = {J_ACCESS}, author = {Snehal V. Laddha and Rohini S. Ochawar}, doi = {10.1109/ACCESS.2025.3584785}, journal = {IEEE Access}, pages = {127837-127851}, shortjournal = {IEEE Access}, title = {Feature-driven EnsembleX: An advanced ensemble framework for enhanced MRI abdomen image classification using feature refinement and boosting techniques}, volume = {13}, year = {2025}, }
• (2025). ORB-SLAM2 system enhanced with SiaT-hough module on mars digital twin platform: For rock counting and 3D reconstruction in martian environments. ACCESS, 13, 127824-127836. (www)

  @article{ , abstract = {This paper proposes a digital twin platform for Mars environment based on NVIDIA Isaac Sim to create a unique random environment with multi-scale fine-grained terrain features through programmed terrain generation and asset management. The SiaT-Hough module was added to the ORB-SLAM2 system for Mars rock segmentation and 3D modeling, as well as rock counting. We propose a lightweight Siamese Transformer (SiaT) network combined with Hough transform for small sample semantic segmentation tasks, especially for rock counting in Mars exploration scenarios. Experimental results demonstrate that the SiaT-Hough model achieves an average segmentation accuracy of 87.86% and a processing speed of 57.34 FPS on the Mars simulation dataset, striking an optimal balance between accuracy and real-time performance. In addition, we also carried out 3D reconstruction positioning accuracy evaluation and point cloud quality evaluation, and the positioning absolute track error on the two public data sets was less than 0.1m, 0.0639m and 0.0106m, respectively. The Pearson linear correlation coefficient of mean point cloud mass in Mars rendering dataset reaches 0.852, and the mean relative error of rock count is 8.63%, which proves the feasibility of the improved ORB-SLAM2 system for rock count in Mars environment.}, archive = {J_ACCESS}, author = {Wei Liu and Gang Wan and Jia Liu and Dianwei Cong}, doi = {10.1109/ACCESS.2025.3585918}, journal = {IEEE Access}, pages = {127824-127836}, shortjournal = {IEEE Access}, title = {ORB-SLAM2 system enhanced with SiaT-hough module on mars digital twin platform: For rock counting and 3D reconstruction in martian environments}, volume = {13}, year = {2025}, }
• (2025). Improvements to the cavity simulator for european spallation source. ACCESS, 13, 127815-127823. (www)

  @article{ , abstract = {The Cavity Simulator for the European Spallation Source (ESS) is a specialized device created to evaluate the Low-Level Radio Frequency control systems utilized in the medium- and high-beta sections of the ESS’ linear accelerator (linac). This equipment replicates the behavior of a single RF station, including a high-power RF amplifier and a superconducting cavity with piezo tuners. This is the world’s first device capable of providing such complex functionalities and simulation scope. The device was used to install LLRF control systems in dedicated sections of the ESS’ linac. The Cavity Simulator is continuously being developed to extend its scope and functionality. This paper presents some recent improvements implemented in this device. These include slow resonance frequency drift simulation, beam current, amplifier nonlinearity, data acquisition system, and synchronization. In addition, the device hardware and operating principles are discussed, along with the measurement results that demonstrate the proper operation of the designed system.}, archive = {J_ACCESS}, author = {Maciej Grzegrzółka and Krzysztof Czuba}, doi = {10.1109/ACCESS.2025.3590288}, journal = {IEEE Access}, pages = {127815-127823}, shortjournal = {IEEE Access}, title = {Improvements to the cavity simulator for european spallation source}, volume = {13}, year = {2025}, }
• (2025). End-to-end multi-speaker FastSpeech2 with hierarchical decoder. ACCESS, 13, 127805-127814. (www)

  @article{ , abstract = {Multi-speaker text-to-speech (TTS) systems play a crucial role in different applications, such as personalized voice assistants, audiobooks, and multilingual speech synthesis. These systems aim to generate high-quality, natural-sounding speech while preserving the distinct characteristics of different speakers. In this paper, we strive to enhance the naturalness and speaker similarity of the FastSpeech2 model in multi-speaker text-to-speech synthesis across closed and open-set speaker scenarios while preserving its high inference speed and lightweight architecture. Specifically, we introduce a hierarchical decoder structure and a speaker similarity loss function to enhance speaker fidelity in synthesized speech. Additionally, we investigate various methods for integrating speaker embeddings within the model and propose an end-to-end training strategy to mitigate error propagation, an inherent limitation of cascaded models. Experimental results demonstrate that our modified FastSpeech2 model significantly outperforms the baseline in closed and open-set scenarios. The proposed model achieves an absolute improvement of 0.89 in Mean Opinion Score (MOS) and 0.44 in Speaker Similarity MOS (SMOS) while maintaining the high inference speed of FastSpeech2.}, archive = {J_ACCESS}, author = {Majid Adibian and Hossein Zeinali}, doi = {10.1109/ACCESS.2025.3589120}, journal = {IEEE Access}, pages = {127805-127814}, shortjournal = {IEEE Access}, title = {End-to-end multi-speaker FastSpeech2 with hierarchical decoder}, volume = {13}, year = {2025}, }
• (2025). Thermally switchable auxetic cell structures for adaptive morphing surfaces: Design, simulation, and demonstrator validation. ACCESS, 13, 127788-127804. (www)

  @article{ , abstract = {This paper introduces a morphing surface concept based on thermally switchable auxetic unit cells for adaptive aerodynamic applications. The structure combines a flexible membrane made of thermoplastic polyurethane (TPU) with a Polyethylene Terephthalate Glycol-modified (PETG)-based auxetic lattice, embedded with resistive heating wires using the Wire Encapsulating Additive Manufacturing (WEAM) process. Upon heating, the stiffness of the auxetic cells is significantly reduced, enabling pneumatic deformation. Once cooled, the structure retains its shape due to the thermomechanical properties of the materials. Finite element simulations and mechanical testing confirm that geometric parameters, particularly arm width, strongly influence stiffness. Switching factors of up to 32 were achieved. A demonstrator featuring 60 active cells validated the concept, demonstrating reversible shape changes and programmable deformation without the need for mechanical actuators. The system offers a scalable and energy-efficient solution for morphing surfaces in automotive, aerospace, and soft robotics applications.}, archive = {J_ACCESS}, author = {Fabian Ziervogel and Lukas Boxberger and Nataliia Gromberg and Linda Weisheit and Dilip Chalissery and Welf-Guntram Drossel}, doi = {10.1109/ACCESS.2025.3590629}, journal = {IEEE Access}, pages = {127788-127804}, shortjournal = {IEEE Access}, title = {Thermally switchable auxetic cell structures for adaptive morphing surfaces: Design, simulation, and demonstrator validation}, volume = {13}, year = {2025}, }
• (2025). A survey on survivable safety-critical systems. ACCESS, 13, 127770-127787. (www)

  @article{ , abstract = {Safety-critical systems (SCS) are essential in domains such as automotive, railway, e-health, nuclear power, and air transportation, where failure can result in catastrophic consequences. Due to their crucial functions, these systems are frequent targets of various attacks, including Denial of Service (DoS) attacks, the execution of improper commands, code injection, and data manipulation, which can compromise system safety and put lives and critical infrastructure at risk. While numerous studies and surveys have investigated the security of SCS, they focus predominantly on recognition mechanisms and often neglect a broader and equally critical concept: survivability. This pioneering survey presents a comprehensive and systematic literature review on the survivability of SCS. It delves into the resilience of these systems in the face of adverse events, examining how they maintain essential services under attack. Our first contribution is the introduction of a novel taxonomy that comprehensively classifies existing survivability approaches based on application fields, types of attacks, survivability properties, and evaluation methodologies. This taxonomy serves as a foundational framework for researchers and practitioners, facilitating the systematic analysis and design of survivable SCS. Our second contribution is the proposition of the survivability by design paradigm, which advocates for the proactive incorporation of survivability considerations into the early stages of SCS development. This paradigm aims to support the holistic integration of preventive, reactive, and tolerant security mechanisms throughout the entire SCS engineering life-cycle, particularly in the context of next-generation networks.}, archive = {J_ACCESS}, author = {Helber Silva and Marco Vieira and Augusto Neto}, doi = {10.1109/ACCESS.2025.3590244}, journal = {IEEE Access}, pages = {127770-127787}, shortjournal = {IEEE Access}, title = {A survey on survivable safety-critical systems}, volume = {13}, year = {2025}, }
• (2025). Thermal analysis of system in package considering boundary conditions for long-term reliability studies. ACCESS, 13, 127758-127769. (www)

  @article{ , abstract = {This paper proposes an integrated Foster-based thermal network for a System in Package (SiP) that models thermal interaction between package layers to predict the transient temperature of junctions and interlayer compression in SiP. The critical factors contributing to impedance mismatch are detailed to ensure modeling accuracy. Important factors include the boundary conditions and the interface layer of the thermal material. With the help of the Finite Element Method (FEM) and data curve fitting, thermal parameters are derived and expressed as a function of boundary conditions. The proposed modeling method is demonstrated with 3D heterogeneous System in Package models implemented in Simulink for long-term temperature predictions. Predicted junction and interlayer temperatures show good accuracy, confirmed by reported results obtained by Finite Element Analysis (FEA). The importance of considering the boundary conditions and the materials used in the various interfaces is shown through simulation results. Neglecting one of these key factors, the predicted temperature differs by as much as 14.5 °C in the reported results. The proposed thermal network is consistent with FEA, while computing much faster and producing results that differ by no more than 0.5 °C, unlike previously reported models.}, archive = {J_ACCESS}, author = {Djallel Eddine Touati and Aziz Oukaira and Ahmad Hassan and Mohamed Ali and Yvon Savaria and Ahmed Lakhssassi}, doi = {10.1109/ACCESS.2024.3396373}, journal = {IEEE Access}, pages = {127758-127769}, shortjournal = {IEEE Access}, title = {Thermal analysis of system in package considering boundary conditions for long-term reliability studies}, volume = {13}, year = {2025}, }
• (2025). Research on the construction and application of earthquake emergency information knowledge graph based on large language models. ACCESS, 13, 127742-127757. (www)

  @article{ , abstract = {To address the challenges of semantic parsing of multi-source heterogeneous information and the delayed emergency response decisions caused by insufficient relational reasoning capabilities in earthquake emergency management, this study proposes a domain knowledge extraction method for earthquakes based on a large language model combined with a three-level prompt engineering system (TPES-LLM) of “instruction fine-tuning - demand awareness - case matching. ”The method deploys a local large language model using LangChain +QWEN2.5-7B, integrates earthquake domain knowledge through LoRa fine-tuning based on earthquake experts’ classifications and industry standards, and injects seismic knowledge into the model. The multi-head attention mechanism weights are optimized based on the co-occurrence frequency of historical earthquake entities, and demand-aware knowledge identifies key textual features that significantly impact knowledge extraction. Training is performed on 36 known earthquake disaster events to learn the association patterns of entities, relationships, and events hidden within the earthquake case data for case matching. This method significantly enhances the accuracy of entity recognition and the efficiency of relation extraction for complex disaster-related texts. Additionally, a bidirectional graph attention network (Bi-GAT) is designed to enable bidirectional propagation and dynamic aggregation of node features. The path confidence constraint algorithm (PCCA) is used to achieve deep semantic associations of earthquake disaster elements. Based on the Neo4j graph database, an earthquake emergency knowledge graph is constructed. Experimental results from real earthquake events such as the 2022 Luding 6.8-magnitude earthquake, the 2024 Jishishan 6.2-magnitude earthquake, and the 2025 Dingri 6.8-magnitude earthquake show that the accuracy of intelligent Q&A retrieval for the earthquake emergency knowledge graph reaches 89.62%, 87.28%, and 90.23%, respectively. The earthquake emergency knowledge graph based on large language models constructed in this study provides intelligent decision support for earthquake emergencies, with significant application value.}, archive = {J_ACCESS}, author = {Wentao Zhou and Meng Huang and Shuai Liu and Qiao You and Fanxin Meng}, doi = {10.1109/ACCESS.2025.3586370}, journal = {IEEE Access}, pages = {127742-127757}, shortjournal = {IEEE Access}, title = {Research on the construction and application of earthquake emergency information knowledge graph based on large language models}, volume = {13}, year = {2025}, }
• (2025). Ten years of asset administration shell: Developments, research opportunities, and adoption challenges. ACCESS, 13, 127721-127741. (www)

  @article{ , abstract = {Over the past decade, the Asset Administration Shell (AAS) has emerged as a cornerstone of digital transformation in Industry 4.0 (I4.0), providing a standardized approach to managing digital representations of industrial assets. With 2025 marking approximately ten years since its introduction, this article aims to provide a comprehensive analysis and discussion of AAS development over the past decade, potential research opportunities, and the challenges associated with its adoption. To this end, the study combines a literature survey with an examination of specifications from key organizations, such as the Plattform Industrie 4.0 and the Industrial Digital Twin Association (IDTA), which play a central role in the AAS standardization and development. A key insight from this survey is that AAS is progressing toward becoming a game-changer in realizing I4.0. Unlike a decade ago, AAS has now reached a level of maturity that enables its increasing adoption, supported by specifications and standards, dedicated development platforms for its implementation, and several examples in the literature showing a wide range of applications. Additionally, research opportunities for AAS align with emerging industrial trends and contribute to addressing them. However, several challenges must still be addressed to facilitate the widespread adoption of the AAS.}, archive = {J_ACCESS}, author = {Lucas Sakurada and Fernando de la Prieta and Paulo Leitao}, doi = {10.1109/ACCESS.2025.3586716}, journal = {IEEE Access}, pages = {127721-127741}, shortjournal = {IEEE Access}, title = {Ten years of asset administration shell: Developments, research opportunities, and adoption challenges}, volume = {13}, year = {2025}, }
• (2025). Noise-robust local ternary pattern center for noisy texture classification. ACCESS, 13, 127690-127720. (www)

  @article{ , abstract = {Local Binary Pattern (LBP) has been widely used for texture analysis in machine vision and image processing. However, most of these descriptors provide a high number of features that are sensitive to noise and rotation. In this paper, a version of LTP is proposed that is named Local Ternary Pattern Center (LTPC). This descriptor provides noise-robust and low feature numbers with a better classification rate than LTP and advanced LBP versions for noisy textures. The proposed method has three steps. First a criterion is proposed that estimates the quality of the texture. According to the estimated quality of the texture, an average kernel is applied to the noisy texture. The more level of noise the more number of applying average kernel must be convolved to each texture. Convolution is a time-consuming operation, so here, a fast average method is proposed that can do this operation around 2 to 3 times faster than the traditional convolution method. In the second step LTPC is used to extract the features of filtered textures. The third innovation of this paper is related to the mapping of the features. Most of the advanced LBP versions used the riu2 mapping method to decrease the number of features and make them rotation-invariant. In this research, a novel mapping method is proposed. It has all advantages of the riu2 and it provides features that they are more robust to noise than riu2. The implementations on different datasets such as Outex suits (TC10, TC12(t), TC12(h), TC13), CUReT, UIUC, USPTex, Virus, RSMAS, KTHTIPS2, UMD and ORL indicate that the proposed method can provide very high discriminative and noise-robust features. The higher the level of noise the better classification accuracy can be obtained by the proposed method compared to some state-of-the-art LBP versions. Furthermore the classification rate of proposed method is better than some deep neural network results. Whereas, in term of speed the local descriptors provide very shorter operation time than most of the deep learning methods.}, archive = {J_ACCESS}, author = {Farhan A. Alenizi and Mokhtar Mohammadi and Mohammad Hossein Shakoor}, doi = {10.1109/ACCESS.2025.3585596}, journal = {IEEE Access}, pages = {127690-127720}, shortjournal = {IEEE Access}, title = {Noise-robust local ternary pattern center for noisy texture classification}, volume = {13}, year = {2025}, }
• (2025). Stochastic portfolio planning for virtual power plants using stratified sampling. ACCESS, 13, 127674-127689. (www)

  @article{ , abstract = {The rise of Virtual Power Plants represents a major shift in the energy sector, promoting decentralized and sustainable power systems. Virtual Power Plants integrate distributed energy resources such as energy storage systems, distributed generators, photovoltaic systems, and wind turbines. This integration improves system efficiency by enabling unified operational schedules, reducing the need to manage each resource separately. Allocating these resources efficiently is critical due to the growing use of renewable energy and the changing nature of electricity markets. To address challenges caused by variable renewable energy generation and market price fluctuations, this study proposes a scenario modeling method using Kernel Density Estimation combined with Stratified Sampling. Forecast errors are further modeled using a copula-based approach that captures spatial and temporal correlations. A two-stage mixed-integer programming method is employed to optimize resource allocation within Virtual Power Plants, aiming to increase revenue and reduce imbalance penalties. Numerical results show that the proposed method improves resource allocation under uncertainty, increases revenue from market prices and renewable energy credits, and lowers imbalance penalties while meeting operational constraints. Specifically, total revenue increased by 1.6%, with a 2.6% improvement in market price revenue and a 5.6% reduction in imbalance penalties.}, archive = {J_ACCESS}, author = {Dosung Kim and Sung-Kwan Joo}, doi = {10.1109/ACCESS.2025.3588003}, journal = {IEEE Access}, pages = {127674-127689}, shortjournal = {IEEE Access}, title = {Stochastic portfolio planning for virtual power plants using stratified sampling}, volume = {13}, year = {2025}, }
• (2025). Reducing delivery time in E-commerce orders with advanced ant colony hybridization by GRU algorithm. ACCESS, 13, 127650-127673. (www)

  @article{ , abstract = {The efficiency of the supply chain is highly corelated with the acceleration of preparing orders from the warehouse to meet customer requests on time. A unified framework optimising the movement of item pickers throughout the warehouse aisles and shelves via reducing their motion route length during orders collected. The framework consists of three stages. The preamble stage aims to sort the items according to XYZ/ABC pattern “assignment policy”. The 2nd stage classifies the customers’ orders according to route similarity via hybridising the (Extreme Gradient Boosting) XGboost and (gate recurrent unit) GRU algorithms that generate all possible orders’ lists/route in 1.2586 sec with RMSE 0.01395 and called “initial layout structure”, which is filtered in the 3rd stage via creating their shortest route via hybridising (Ant Colony Optimisation) ACO and GRU algorithms with innovative tuning parameters to give workers to pick up more items in minimum time to accelerate preparing orders “priority pace motion”. The 3rd creates the minimum route via two steps, the 1st the original route sketched in Map(A), which is tracked until out of the determined zone of orders picked, then updates the remaining route via a nine-step approach hybridised to get the shortest route served more customers as sketched in Map ( $B_{i}$ ) to accelerate the total order collection in the 2nd step. The proposed model called “Acceleration Customer Orders’ Preparation” (ACOP). The challenge lies in assigning a new order to Map (A) lists and creating Map ( $B_{i}$ ). The paper presents innovative tuning parameters for ACO that help in accelerating orders preparation by 152.49%/worker/day. According to OEE, the proposed strategy is approximately 88% effective. The ACO_GRU superior to Regular Ant Colony (RgACO), Elitist Ant Colony (EACO), Ranked Ant Colony (RnACO), Best-Worst Ant Colony (BWACO), Min-Max Ant Colony (MMACO), Mat-ACO, A ${}^{\ast }$ algorithm, Dijkstra’s algorithm, and MRL-SA with 19%, 16%, 9%, 12%, 6%, 6%, 5%, 5%, 3%, respectively. Choosing a suitable hybridization model according to order contents increased the customer serviced mainly by 52.5% daily.}, archive = {J_ACCESS}, author = {Ahmed M. Abed and Ali Alarjani and Tamer S. Gaafar}, doi = {10.1109/ACCESS.2025.3582800}, journal = {IEEE Access}, pages = {127650-127673}, shortjournal = {IEEE Access}, title = {Reducing delivery time in E-commerce orders with advanced ant colony hybridization by GRU algorithm}, volume = {13}, year = {2025}, }
• (2025). RIS-assisted NOMA systems: Performance analysis under HWI and channel estimation error. ACCESS, 13, 127637-127649. (www)

  @article{ , abstract = {This paper inspects a reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA) wireless network where users convey to the base station (BS) through the support of RIS. The study investigates the impact of channel estimation error (CEE) and hardware impairment (HWI) at BS and end users in this RIS-enabled NOMA scheme. Additionally, phase errors in the RIS and the influence of imperfect successive interference cancellation (SIC) on NOMA users are incorporated into the analysis. Exact closed-form analytical expressions for the outage probability (OP) and ergodic rate (ER) of the NOMA users are derived to evaluate system performance. Furthermore, the asymptotic OP was also examined. The theoretical findings are validated through simulations, which demonstrate that 1) HWI and CEE significantly affect the OP of the NOMA near user (NU) more than far user (FU); 2) CEE impacts the OP of FU more than HWI; 2) the effect of phase noise at the RIS on OP is substantially less significant than that of transceiver HWI and imperfect SIC; 4) the number of RIS reflecting elements plays a crucial role in enhancing the system performance of the RIS-assisted NOMA system; 5) varying power allocation coefficient between users can improve user fairness.}, archive = {J_ACCESS}, author = {Merve Uçar-Gül and Mustafa Namdar and Ozgur Ergul}, doi = {10.1109/ACCESS.2025.3590655}, journal = {IEEE Access}, pages = {127637-127649}, shortjournal = {IEEE Access}, title = {RIS-assisted NOMA systems: Performance analysis under HWI and channel estimation error}, volume = {13}, year = {2025}, }
• (2025). Predicting object communication errors in constructor development. ACCESS, 13, 127626-127636. (www)

  @article{ , abstract = {An important challenge in dynamic software development is to predict object formation run-time object communication errors in complex environments involving multiple and multi-level object inheritance. This paper proposes a technique for doing so. The technique is intended to stop or fix software bugs, particularly in situations where it is believed that object communications would persist across different settings. The research addresses a critical gap in existing methodologies by integrating static and dynamic object-oriented metrics, providing a holistic approach to defect prediction. Additionally, the paper presents a software testing defect prediction model that categorizes problematic classes according to inheritance defects found in a particular class. Earlier researchers studied various methods for predicting and mitigating software defects in object-oriented programming. We propose a defect prediction model that categorizes problematic classes based on inheritance defects to overcome the gaps in existing methodologies by introducing them. We evaluated this object communication error prediction using a set of 150 common errors drawn primarily from real-world open-source repositories and enriched with synthesized cases reflecting rare but critical inheritance-related bugs, ensuring comprehensive and realistic error representation. The methodology employs classification techniques, including K-Nearest Neighbors (KNN) for k-fold cross-validation, Random Forest, Decision Trees, and Support Vector Machines (SVM), alongside object-oriented metrics such as inheritance, cohesion, and coupling. Key performance metrics precision (78%), F1 score (76.4%), recall (74.9%), and ROC AUC (89%), demonstrate the model’s superiority over prior approaches. These results underscore the practical applicability of the model in improving defect detection accuracy and reducing software failures.}, archive = {J_ACCESS}, author = {Abdul Majid Soomro and Awad Bin Naeem and Susama Bagchi and Babul Salam KSM Kader Ibrahim and Sanjoy Kumar Debnath}, doi = {10.1109/ACCESS.2025.3590239}, journal = {IEEE Access}, pages = {127626-127636}, shortjournal = {IEEE Access}, title = {Predicting object communication errors in constructor development}, volume = {13}, year = {2025}, }
• (2025). Comparison of linear and nonlinear methods for decoding selective attention to speech from ear-EEG recordings. ACCESS, 13, 127614-127625. (www)

  @article{ , abstract = {Many people with hearing loss struggle to comprehend speech in crowded auditory scenes, even when they are using hearing aids. However, the focus of a listener’s selective attention to speech can be decoded from their electroencephalography (EEG) recordings, raising the prospect of smart EEG-steered hearing aids which restore speech comprehension in adverse acoustic environments. Here, we assess the feasibility of using a novel, ultra-wearable, ear-EEG device to classify the selective attention of normal-hearing listeners who participated in a two-talker competing-speakers experiment. State-of-the-art auditory attention decoding algorithms are compared, including stimulus-reconstruction algorithms based on linear regression as well as non-linear deep neural networks, and canonical correlation analysis (CCA). Meaningful markers of selective auditory attention could be extracted from the ear-EEG signals of all participants, even when those markers are derived from relatively short EEG segments of just 5 s in duration. Algorithms which relate the EEG signals to the rising edges of the speech temporal envelope are more successful than those which make use of the temporal envelope itself. The CCA algorithm achieves the highest mean attention decoding accuracy, although differences between the performances of the three algorithms are both small and not statistically significant when EEG segments of short durations are employed. In summary, our ultra-wearable ear-EEG device offers promising prospects for wearable auditory monitoring.}, archive = {J_ACCESS}, author = {Mike D. Thornton and Danilo P. Mandic and Tobias Reichenbach}, doi = {10.1109/ACCESS.2025.3590490}, journal = {IEEE Access}, pages = {127614-127625}, shortjournal = {IEEE Access}, title = {Comparison of linear and nonlinear methods for decoding selective attention to speech from ear-EEG recordings}, volume = {13}, year = {2025}, }
• (2025). SN360: Semantic and surface normal cascaded multi-task 360 monocular depth estimation. ACCESS, 13, 127599-127613. (www)

  @article{ , abstract = {Omnidirectional images carry comprehensive scene representation and are widely useful for applications like AR/VR, robotics, and autonomous driving that require holistic scene understanding. Depth estimation, being the core component of scene understanding, has been widely researched, achieving significant improvement for perspective inputs. However, for 360 input, the methods still produce low-quality, globally inconsistent depths, indicating poor generalization ability due to challenges such as inherent spherical distortion and relatively few training data. Recent state-of-the-art (SOTA) methods utilize multiple projected distortionless tangent patches to mitigate spherical distortion, but they lose the learning of holistic contextual information, leading to global discrepancies and merging artifacts in the final merged-back 360 depths. In this paper, we propose to mitigate the existing global inconsistency and merging artifact issue via a new initial depth estimation network that directly takes a panorama image to learn holistic features with enhanced global awareness via latent attention. We further present a novel semantic and surface normal cascaded multi-task model agnostic framework that mitigates the negative transfer effect observed in current multi-task 360-depth approaches, to produce fine-grained, structure-detailed depths. Specifically, our approach utilizes an initial depth estimation to simulate RGBD input to enhance the performance of semantic segmentation and surface normal estimation, which is, in turn, leveraged to explicitly guide the final depth prediction. Our approach shows significant improvement in Abs Rel by 19.62%, 21.45%, and zero-shot depths by 22.8% using real-world Stanford2D3D and Matterport2D3D benchmark datasets, respectively, over the SOTA while producing structurally detailed globally consistent high-quality depths.}, archive = {J_ACCESS}, author = {Payal Mohadikar and Ye Duan}, doi = {10.1109/ACCESS.2025.3590646}, journal = {IEEE Access}, pages = {127599-127613}, shortjournal = {IEEE Access}, title = {SN360: Semantic and surface normal cascaded multi-task 360 monocular depth estimation}, volume = {13}, year = {2025}, }
• (2025). Analysis, design, and hardware implementation of phase shifted isolated DC–DC converter for low voltage electric vehicle battery charging applications. ACCESS, 13, 127580-127598. (www)

  @article{ , abstract = {Electric vehicles are gaining importance due to their clean and environmentally friendly nature, contributing to the reduction of pollution and global warming caused by the fossil fuels. With ongoing improvements and decreasing prices, renewable energy technology is expected to significantly impact global energy systems.This article proposes an isolated DC-DC converter that combines the H-bridge converter and the IPOP (Input Parallel Output Parallel) bridgeless luo converter. The bridgeless luo converter on the secondary side stabilizes current and voltage reversals at the high-frequency transformer terminals. The H-bridge converter gate pulses are phase-shifted to ensure exact control of the output. This work discusses the different operating modes of the proposed isolated DC-DC converter, along with the associated dynamic equations. The proposed converter is simulated, with waveforms presenting the voltages and currents through various components provided. A hardware prototype is implemented with a power output of 100 W to assess the efficacy of the proposed converter in charging a lithium-ion battery at a voltage of $48V$ . The losses in different components of the converter are analyzed. This prototype employs silicon carbide (SiC) MOSFETs and diodes to facilitate high-frequency switching operations. This approach makes the components lighter and smaller. The proposed converter is a novel isolated DC-DC converter that is characterized by its low output ripples and lower losses, and thus it can be suitable for low-voltage electric vehicle battery charging.}, archive = {J_ACCESS}, author = {Pulipaka Srikanth and G. Arunkumar}, doi = {10.1109/ACCESS.2025.3590029}, journal = {IEEE Access}, pages = {127580-127598}, shortjournal = {IEEE Access}, title = {Analysis, design, and hardware implementation of phase shifted isolated DC–DC converter for low voltage electric vehicle battery charging applications}, volume = {13}, year = {2025}, }
• (2025). Study of optimal base station deployment for UAM operations in an urban environment based on a genetic algorithm. ACCESS, 13, 127570-127579. (www)

  @article{ , abstract = {In this paper, we propose a method for determining the optimal base station deployment to establish a stable communication environment for urban air mobility (UAM) operation in urban areas. To realistically model the UAM operating environment, we utilize DEM files that include data on terrain and buildings. Furthermore, the radiation patterns of the BS’s antenna are generated according to the 3GPP-standard, and the receiver antenna patterns are obtained through analysis of the UAM’s mounted antenna. The proposed method uses binary chromosomes to determine the locations of BSs and the orientations of the antennas. A genetic algorithm is then used to determine the optimal deployment of the base stations. When applying the proposed method to optimize BS deployment in the UAM corridor (area of approximately 5.4 km ${}^{\mathbf {2}}$ ), the optimization process resulted in the deployment of two base stations at 3.5 GHz, achieving an average received power of −61.36 dBm across the corridor. At 5.4 GHz, three base stations are deployed, with an average received power of −63.74 dBm. To validate the reliability of the simulation, measurements are conducted in a real urban environment, comparing the results from measurement, theory, and simulation. The received power values obtained from measurement, theory, and simulation are −110.21 dBm, −110.82 dBm, and −110.38 dBm, respectively. These results demonstrate that the proposed BS deployment method can be used to establish a stable communication environment in a real UAM corridor.}, archive = {J_ACCESS}, author = {Minsang Yoon and Jiseok Park and Bosung Park and Taekyeong Jin and Hosung Choo}, doi = {10.1109/ACCESS.2025.3590069}, journal = {IEEE Access}, pages = {127570-127579}, shortjournal = {IEEE Access}, title = {Study of optimal base station deployment for UAM operations in an urban environment based on a genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Knowledge improved hybrid DNN–KAN framework for intrusion detection in wireless sensor networks. ACCESS, 13, 127558-127569. (www)

  @article{ , abstract = {Wireless Sensor Networks (WSNs) are increasingly vulnerable to sophisticated cyber threats, necessitating advanced intrusion detection systems (IDS) that balance high accuracy with interpretability. This paper presents a Knowledge-Improved Hybrid Deep Neural Network-Kolmogorov Arnold Network (DNN-KAN) Framework for intrusion detection in WSNs, integrating data-driven learning with domain-specific knowledge to enhance detection performance. The proposed framework preprocesses and merges multiple datasets (WSN, NSL-KDD, and CICIDS2017), extracts features using Principal Component Analysis (PCA), and constructs a knowledge graph to embed expert-defined rules via Graph Convolutional Networks (GCNs). The hybrid architecture employs a DNN for hierarchical feature extraction and replaces the fully connected layer with a KAN layer, leveraging the Kolmogorov-Arnold Theorem to decompose complex relationships into interpretable univariate functions. Evaluated on key metrics, the model achieves 99.87% accuracy, 99.85% precision, and a 0.9985 ROC-AUC score, significantly outperforming traditional DNNs, standalone KANs, and state-of-the-art models like CNN-LSTM and Transformer-based IDS. By incorporating domain knowledge into the loss function, the framework reduces false positives and improves generalization, even with limited training data. This work demonstrates that combining knowledge-driven constraints with hybrid neural architectures can yield highly accurate, interpretable, and robust intrusion detection for WSN security.}, archive = {J_ACCESS}, author = {M. Sriraghavendra and Muna Elsadig and Ines Hilali Jaghdam and S. Abdel-Khalek and B. Galeebathullah and Salem Alkhalaf}, doi = {10.1109/ACCESS.2025.3586783}, journal = {IEEE Access}, pages = {127558-127569}, shortjournal = {IEEE Access}, title = {Knowledge improved hybrid DNN–KAN framework for intrusion detection in wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Differential injection locked frequency divider with dual darlington pairs for wideband ÷4 frequency division in CMOS. ACCESS, 13, 127545-127557. (www)

  @article{ , abstract = {This paper presents a 4.08 mW injection-locked frequency divider (ILFD) achieving division-by-4 with a 21.3% locking range (16.4-20.3 GHz) in 65 nm CMOS. The design employs dual CMOS Darlington pairs in a fully differential injection topology, simultaneously enhancing trans-conductance, switching speed, and power efficiency. Key innovations include a high-transit frequency ( $f_{T}$ ) topology for robust high-frequency division and optimized harmonic rejection. Measured results show consistent 12 dB phase noise reduction ( $20\cdot \mathbf {log_{10}(4)}$ ) across offsets up to 1 MHz, with harmonic rejection of 23.4 dB (3rd) and 49.3 dB (2nd) at 16.4 GHz. Competitive figures of merit of 0.95 GHz/mW and 19.4 GHz2/mW demonstrate state-of-the-art efficiency.}, archive = {J_ACCESS}, author = {Akram Muhamad Rafli and Muhammad Fakhri Mauludin and Sanghun Lee and Kunhee Cho and Jusung Kim}, doi = {10.1109/ACCESS.2025.3590205}, journal = {IEEE Access}, pages = {127545-127557}, shortjournal = {IEEE Access}, title = {Differential injection locked frequency divider with dual darlington pairs for wideband ÷4 frequency division in CMOS}, volume = {13}, year = {2025}, }
• (2025). A pre-Selection–Enhanced arbiter PUF for strengthening PUF-based authentication. ACCESS, 13, 127526-127544. (www)

  @article{ , abstract = {Physical Unclonable Functions (PUFs) have emerged as a compelling solution for hardware-based authentication, yet arbiter PUFs often require additional mechanisms to address reliability challenges and vulnerabilities to attacks. In this paper, we propose a Pre-Selection-based Enhanced Arbiter PUF that filters noise-induced bit flips through repeated sampling without imposing substantial hardware overhead. We evaluate our design via a two-step methodology: an initial screening on a subset of boards, followed by a comprehensive assessment on 56 Field Programmable Gate Arrays (FPGA) boards—yielding 336 million challenge-response pairs (CRPs). Our findings indicate that the Pre-Selection process notably boosts reliability, with median values rising from about 72.63% up to 89.60% under higher sampling conditions. Even with a broader spread in uniqueness (majorly near the ideal value), most devices remain within a practically acceptable range for authentication. Crucially, False Acceptance Rate (FAR) and False Rejection Rate (FRR) stay below or near 2.5% in most configurations, underscoring the approach’s real-world viability. Additionally, bit aliasing reaches near-excellent levels, whereas Bit Error Rate (BER) remains below 0.2 or 0.3 for most boards. Entropy estimation consistently exceeds 95%, verifying that Pre-Selection retains the global randomness essential for security. A modeling attack test using Artificial Neural Network (ANN) model further yielded near-random prediction accuracy (ranging from 50% - 56%), confirming that enhanced reliability does not compromise unpredictability. These results suggest that a lightweight, modular post-processing scheme can balance reliability, distinctiveness, and randomness in arbiter PUFs, offering a scalable pathway to secure authentication in resource-constrained devices.}, archive = {J_ACCESS}, author = {Rizka Reza Pahlevi and Hirokazu Hasegawa and Yukiko Yamaguchi and Hajime Shimada}, doi = {10.1109/ACCESS.2025.3590476}, journal = {IEEE Access}, pages = {127526-127544}, shortjournal = {IEEE Access}, title = {A pre-Selection–Enhanced arbiter PUF for strengthening PUF-based authentication}, volume = {13}, year = {2025}, }
• (2025). Reducing false alarm in target sidelobes using time-varying trajectories in range-doppler diagram with conditional 3D-DCNN. ACCESS, 13, 127518-127525. (www)

  @article{ , abstract = {The increasing use of drones in civil and defense applications poses threats to critical infrastructure, with low-radar cross section (RCS) targets detection in cluttered environments challenging conventional radar methods like constant false alarm rate (CFAR), which often result in high false alarm rates (FAs). This paper proposes a conditional-three dimensional-deep convolutional neural networks (C-3D-DCNN) that reduces FAs by utilizing the time-varying target trajectories observed over consecutive frames in the range-Doppler diagram with velocity information as a conditional input. By adjusting a Gaussian filter in accordance with the target’s velocity in the suggested C-3D-DCNN structure, the algorithm distinguishes real targets from clutter, noise, and target sidelobes that appear along the Doppler axis. The proposed method was validated using frequency-modulated continuous wave radar to detect low-RCS targets and demonstrated a reduction in FAs in clutter environments. Verification with 200 simulation datasets showed a 99.8 % reduction in FAs excluding the sidelobe region along the range axis compared to CFAR and a 95.22 % reduction compared to 3D-DCNN, while all FAs in the Doppler sidelobe region were eliminated (100 % reduction). We also verified the effectiveness of this method in experiments with real drones by removing 100 % of the FA in both the excluding sidelobe in range axis and Doppler sidelobe regions when tested with two drones. This approach simplifies previous two-step processes into a one-step solution, offering a more efficient framework for drone surveillance systems.}, archive = {J_ACCESS}, author = {Jiyeon Choi and Daegun Oh and Youngwook Kim}, doi = {10.1109/ACCESS.2025.3588460}, journal = {IEEE Access}, pages = {127518-127525}, shortjournal = {IEEE Access}, title = {Reducing false alarm in target sidelobes using time-varying trajectories in range-doppler diagram with conditional 3D-DCNN}, volume = {13}, year = {2025}, }
• (2025). Individual differences in subjective image quality assessment: A bayesian mixed-effects modeling approach. ACCESS, 13, 127504-127517. (www)

  @article{ , abstract = {Traditional approaches to subjective image quality assessment often rely on Mean Opinion Scores (MOS), which overlook individual differences among observers. This study addresses this limitation by introducing a novel experimental paradigm and Bayesian mixed-effects modeling framework, specifically Cumulative Link Mixed Models (CLMMs), to quantify these differences. A balanced dataset of 256 high-resolution images across four content categories, each with three color-based distortions, was rated by 16 observers. Our analysis demonstrates that models accounting for individual-level preferences outperform group-level models, offering more accurate predictions of subjective image quality ratings. The best-performing model accounted for both content and distortion preferences, highlighting the critical role of observer variability in image quality assessment. A follow-up experiment conducted one week later revealed trait-like stability in observers’ preferences for content and distortion types, with medium-to-large correlations in ratings (r =0.43 to 0.97). These results indicate that individual differences are enduring traits rather than transient states. This study highlights the importance of considering human variability in subjective assessments, with significant implications for image quality methodologies and content delivery strategies. By embracing individual differences as valuable information, we can improve user experience, develop personalized content delivery systems, optimize compression techniques, and create more accurate predictive models for image quality. This work lays the foundation for a more individualized approach to image quality assessment, with applications in digital media, telecommunications, and human-computer interaction.}, archive = {J_ACCESS}, author = {Simon Hviid Del Pin and Seyed Ali Amirshahi}, doi = {10.1109/ACCESS.2025.3579016}, journal = {IEEE Access}, pages = {127504-127517}, shortjournal = {IEEE Access}, title = {Individual differences in subjective image quality assessment: A bayesian mixed-effects modeling approach}, volume = {13}, year = {2025}, }
• (2025). Influence maximization in social networks using improved genetic algorithm. ACCESS, 13, 127490-127503. (www)

  @article{ , abstract = {Influence maximization is one of the important problems in network science, data mining, and social media analysis. It focuses on identifying the most influential individuals (or nodes) in a social network to maximize the spread of information, ideas, or behaviors. Most existing studies have used centrality measures such as degree, closeness, or PageRank to identify the most influential individuals, but they have not obtained good results. To overcome this issue and improve the results, in this paper, an improved genetic algorithm is proposed to solve the influence maximization problem in social networks, and it is called IGAIM. In different operators of the IGAIM, a new criterion called selection score is employed to rank the nodes in terms of influence extent. In addition, a new local search method is presented that uses the selection score to search in the neighborhood of a given solution. By utilizing the proposed local search, the search space is efficiently explored, leading to an increase in the convergence speed of the algorithm. Experimental results on four real-world benchmark networks reveal that IGAIM considerably outperforms the compared methods in terms of influence spread and computation time. On average, IGAIM achieves a 65.89% improvement in terms of influence spread over greedy algorithms, a 37.59% improvement over metaheuristic algorithms, and a 10.94% improvement compared to the top three performing algorithms. In terms of computation time, IGAIM is significantly faster, outperforming greedy algorithms by 99.56%, metaheuristic algorithms by 34.82%, and top three performing algorithms by 14.70%.}, archive = {J_ACCESS}, author = {Ali Chodari Khosroshahi and Saeid Taghavi Afshord and Bagher Zarei and Bahman Arasteh}, doi = {10.1109/ACCESS.2025.3589189}, journal = {IEEE Access}, pages = {127490-127503}, shortjournal = {IEEE Access}, title = {Influence maximization in social networks using improved genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). A meta-framework for virtual entities within digital twins: Better understanding for stakeholders and technologies. ACCESS, 13, 127476-127489. (www)

  @article{ , abstract = {Digital Twins (DT) represent a transformative technology that bridges the physical and digital worlds. The rapid advancement of DT, providing a virtual representation of a physical asset called Virtual Entity (VE), has led to the development of various DT solutions. However, research indicates a lack of a unified understanding of the VE’s role among stakeholders, creating challenges when comparing existing DT frameworks, technologies, and components. This paper introduces a meta-framework encompassing three main concepts: First, it defines three specific Perspectives to cover all aspects of the VE. Second, it introduces three Levels of Abstraction to identify underlying features and models of existing technologies, as well as to integrate stakeholder views represented through cognitive mental models. Third, through the concept of Technological Interlocking, the interconnected nature of technologies at the implementation stage is recognized and enables the alignment between mental models and current technologies and frameworks. As a tool, the meta-framework serves three main functions: First, it provides insights and knowledge about VEs within existing frameworks. Second, it allows for their comparison. Third, it promotes the reuse of existing components as a groundwork for DT development. By applying the meta-framework to three commonly used DT frameworks, we can extract their single features and models and place them within the context of the VE. This analysis reveals that certain VE representations lack behavioral models, a key concept of DTs. Furthermore, based on an industry-specific use-case, mapping the mental models of specific stakeholders to existing technologies lays the groundwork for new DT solutions. Overall, the presented meta-framework promotes a unified understanding of VEs that is independent of technology and stakeholder specifics.}, archive = {J_ACCESS}, author = {Christoph Klaassen and Gernot Steindl and Lukas Kasper and René Hofmann}, doi = {10.1109/ACCESS.2025.3589787}, journal = {IEEE Access}, pages = {127476-127489}, shortjournal = {IEEE Access}, title = {A meta-framework for virtual entities within digital twins: Better understanding for stakeholders and technologies}, volume = {13}, year = {2025}, }
• (2025). Circularly polarized cross-dipole array antenna for GNSS and L-band satellite applications. ACCESS, 13, 127462-127475. (www)

  @article{ , abstract = {This study introduces a dual-sense circularly polarized (CP) crossed-dipole array antenna designed for Global Navigation Satellite System (GNSS) frequency bands. The antenna employs an optimized double-branch line coupler (ODBLC) and a single pair of dual-band linearly polarized dipole antennas (LPDAs) to achieve impedance bandwidths of 44.83% (1092.1–1723.3 MHz) for right-hand circular polarization (RHCP) and 46.30% (1070.2–1715.1 MHz) for left-hand circular polarization (LHCP), with axial ratio bandwidths of 47.69% and 44.78%, respectively. With peak gains of 7.08–7.10 dBic and a compact form factor of $160\times 160\times 44.6$ mm3, the simplified feed architecture enhances manufacturability and reduces fabrication costs. The dual-sense circular polarization mitigates multipath interference in urban, forested, and maritime environments, facilitating applications in precision navigation, autonomous vehicle localization, geodetic surveying, and meteorological data acquisition.}, archive = {J_ACCESS}, author = {Aliakbar Dastranj and Hamed Shirzad}, doi = {10.1109/ACCESS.2025.3589526}, journal = {IEEE Access}, pages = {127462-127475}, shortjournal = {IEEE Access}, title = {Circularly polarized cross-dipole array antenna for GNSS and L-band satellite applications}, volume = {13}, year = {2025}, }
• (2025). Multi-level voltage high fault-tolerance modular SRM converter with simultaneous charge and discharge capability for HEV. ACCESS, 13, 127447-127461. (www)

  @article{ , abstract = {The air pollution problem has drawn the attention of researchers to hybrid electric vehi- cles (HEV). Among electric motors, the switched reluctance motor (SRM) is an appropriate option because of its low cost, reliability, non-existing magnet and winding on the rotor, and etc. In the SRM drive for HEV application, researchers are interested in subjects, such as fault-tolerance, modular structure, battery life, low cost and low size drive, high performance, etc. Therefore, this paper proposes a modular structure-based switched reluctance motor drive for HEV application. The proposed topology can provide multi-level voltage, fault tolerance and flexible charging and discharging modes through its modular structure. The specific structure of the topology reduces the diodes of the power path and the gate drive supply, which reduces losses, cost and size. The simultaneous charging and discharging can established using the switches of the modules and the topology relays which increase electrical capability, thus, it can discharge two packs while two other packs are charged simultaneously. The front-end converter can be connected to any SRM drive, which is beneficial for mass production. The battery packs can be charged in the states of running, idling and resting at home, which increases the applicability of the drive. The experimental results are established on the prototype DSP F28355 and SRM 8/6. The simulation and experimental results confirmed the effectiveness of the proposed topology for the SRM drive.}, archive = {J_ACCESS}, author = {Hamid Malekpour and Amir Rashidi and Sayed Morteza Saghaian Nejad and Dong-Hee Lee}, doi = {10.1109/ACCESS.2025.3587508}, journal = {IEEE Access}, pages = {127447-127461}, shortjournal = {IEEE Access}, title = {Multi-level voltage high fault-tolerance modular SRM converter with simultaneous charge and discharge capability for HEV}, volume = {13}, year = {2025}, }
• (2025). Partial discharge (PD) signal recognition in fiber-optic acoustic emission using a lightweight attention mechanism (ECA). ACCESS, 13, 127436-127446. (www)

  @article{ , abstract = {Partial discharge (PD) is a primary cause of insulation degradation in electrical equipment. Consequently, the investigation of PD signals is of paramount importance for enhancing the safe operation of power devices. This paper delves into the principles of four typical PD signals and designs experiments to simulate four common PD mechanisms: Along Surface, Igniter, Suspension and Tip. Utilizing the latest fiber-optic acoustic emission system, we constructed an experimental platform for PD signal acquisition to capture acoustic emission signals under various voltage gradients. These signals are then denoised using a wavelet threshold algorithm. Subsequently, the denoised acoustic emission signals are segmented into training set samples. Based on the ResNet model, we incorporate an ELU activation function to enhance the model and introduce both CA and ECA attention mechanisms. Through extensive comparative experiments, we select the most effective model. The experimental results demonstrate that the ELU activation function significantly improves the model’s recognition performance. Additionally, the ECA attention module, added after stage13, exhibits excellent recognition capabilities for the four typical PD signals, achieving an accuracy rate of 98%.}, archive = {J_ACCESS}, author = {Yang Feng and Dong Yuhan and Jiang Peng and Liu Guolen}, doi = {10.1109/ACCESS.2025.3582870}, journal = {IEEE Access}, pages = {127436-127446}, shortjournal = {IEEE Access}, title = {Partial discharge (PD) signal recognition in fiber-optic acoustic emission using a lightweight attention mechanism (ECA)}, volume = {13}, year = {2025}, }
• (2025). A robust fractional-order nonsingular terminal sliding mode control with deep learning-based lie derivative estimation for maximum power point tracking in wind turbine. ACCESS, 13, 127423-127435. (www)

  @article{ , abstract = {This paper presents a Robust Fractional-Order Sliding Mode Control (FOSMC) with Nonsingular Integral Terminal Dynamics, integrated with Densely Connected Convolutional Networks (DenseNet) for Lie Derivatives Estimation, to achieve Maximum Power Point Tracking (MPPT) in Wind Energy Conversion Systems (WECS) based on Permanent Magnet Synchronous Generators (PMSG). The proposed method effectively addresses the challenges of nonlinear dynamics, uncertain wind conditions, and chattering effects, which are common in traditional control approaches. The core innovation lies in fractional-order sliding mode control, which enhances convergence speed and robustness while ensuring finite-time stability. Unlike classical Sliding Mode Control (SMC), the proposed Nonsingular Terminal Sliding Mode (NTSM) formulation eliminates singularities and improves tracking accuracy. Additionally, to overcome inaccuracies in numerical differentiation, a Densely Connected Convolutional Neural Network (DenseNet) is employed to estimate higher-order Lie derivatives, providing real-time system state approximation and improving control precision. A rigorous Lyapunov stability theorem guarantees the finite-time convergence of the considered system. Extensive MATLAB/Simulink simulations validate the effectiveness of the proposed control law by comparing it with the existing classical controllers. The results demonstrate superior MPPT efficiency, faster transient response, reduced chattering, and enhanced robustness under varying wind conditions.}, archive = {J_ACCESS}, author = {Ahmed S. Alsafran and Safeer Ullah and Ameen Ullah and Ghulam Hafeez and Muhammad Zeeshan Babar and Baheej Alghamdi and Abdullah A. Algethami}, doi = {10.1109/ACCESS.2025.3586279}, journal = {IEEE Access}, pages = {127423-127435}, shortjournal = {IEEE Access}, title = {A robust fractional-order nonsingular terminal sliding mode control with deep learning-based lie derivative estimation for maximum power point tracking in wind turbine}, volume = {13}, year = {2025}, }
• (2025). Next-generation ultrasound propagation in suspended core optical fibers. ACCESS, 13, 127411-127422. (www)

  @article{ , abstract = {We demonstrate the temporal propagation of high-frequency ultrasonic pulses inside of a suspended core fiber (SCF) for the first time. An SCF composed of a $5~\mu $ m core suspended by nano bridges and four air holes is analytically and numerically investigated using the 2D and 3D finite element method. We reveal the propagation of ultrasonic modes in the SCF and describe the variation of their properties with increasing frequency from 1 to 100 MHz. Ultrasonic Gaussian-modulated pulses traveling with distinct frequencies (25 and 72 MHz) and bandwidths (56 and 10 MHz) are evaluated along the SCF length and time. The induced strain, pressure, and sensing responsivity are reviewed and discussed. Analytical and numerical results show highly confined effective single-mode propagation in the SCF core, amplifying strains along the fiber (up to 18 times) and pressures at the fiber output (up to 3 times). Compared to previous studies, the demonstrated SCF provides the widest 86 MHz high-responsivity bandwidth (with a -218 dB average and -75 dB maximum responsivity at 93 MHz). These achievements are promising for developing smaller, faster, and more efficient acousto-optic modulators for fiber lasers, enhancing simultaneous light-sound guidance and spatial resolution of fiber-based optoacoustic imaging sensors and neurostimulators in biomedicine.}, archive = {J_ACCESS}, author = {Ricardo E. da Silva and David J. Webb}, doi = {10.1109/ACCESS.2025.3589995}, journal = {IEEE Access}, pages = {127411-127422}, shortjournal = {IEEE Access}, title = {Next-generation ultrasound propagation in suspended core optical fibers}, volume = {13}, year = {2025}, }
• (2025). Circular intuitionistic fuzzy dombi aggregation operators and their application to marine fuel selection. ACCESS, 13, 127391-127410. (www)

  @article{ , abstract = {Higher-order fuzzy sets have emerged as effective tools for decision-making under uncertainty. Among these, the circular intuitionistic fuzzy set (CIFS) has recently been introduced to better represent uncertain information. In a CIFS, each element is modelled as a circular area defined by a radius, with membership and non-membership degrees located at its center. Aggregation operators play a crucial role in synthesizing multiple inputs into representative ones. Among the well-known operators, Dombi operators, comprising the Dombi t-norm and t-conorm, offer significant flexibility due to their adjustable parameters. However, existing CIFS-based aggregation operators are typically defined for $r \in [{0,1}]$ , which only partially covers the intuitionistic fuzzy interpretation triangle. This study extends Dombi aggregation operators to the full CIFS domain, $r \in [0,\sqrt {2}]$ and examines their mathematical properties in accordance with fundamental algebraic laws. Specifically, we introduce the CIF Dombi Weighted Averaging, CIF Dombi Ordered Weighted Averaging, CIF Dombi Weighted Geometric, and CIF Dombi Ordered Weighted Geometric operators. In addition, existing score functions for ranking CIFS values do not adequately account for the influence of the radius, which should prioritize smaller r values due to their lower uncertainty. Therefore, we propose an alternative score function to improve the consistency and accuracy of CIFS value ranking. Based on these developments, a multi-criteria decision-making (MCDM) model is constructed within the CIFS environment. The model is applied to a real-world problem of marine fuel selection, demonstrating its practicality and robustness. Comparative and sensitivity analyses further validate the model’s performance. The findings highlight the effectiveness of the proposed operators in enhancing decision-making under uncertainty, offering valuable insights for policymakers and stakeholders in the marine fuel sector.}, archive = {J_ACCESS}, author = {Nuraini Rahim and Binyamin Yusoff and Lazim Abdullah and Dian Pratama}, doi = {10.1109/ACCESS.2025.3590550}, journal = {IEEE Access}, pages = {127391-127410}, shortjournal = {IEEE Access}, title = {Circular intuitionistic fuzzy dombi aggregation operators and their application to marine fuel selection}, volume = {13}, year = {2025}, }
• (2025). Bridging explainability and security: An XAI-enhanced hybrid deep learning framework for IoT device identification and attack detection. ACCESS, 13, 127368-127390. (www)

  @article{ , abstract = {The rapid expansion of the Internet of Things (IoT) has made accurate and interpretable device identification essential for maintaining network security, managing traffic, and enforcing security policies. However, traditional identification methods, often reliant on handcrafted features or shallow models, struggle to adapt to the highly dynamic and heterogeneous nature of modern IoT environments. They also offer limited transparency, making it difficult for analysts to trust or understand their decisions. To address these challenges, we propose a hybrid machine learning framework that combines deep feature extraction using Convolutional Neural Networks (CNNs) with the robust classification capabilities of XGBoost. This design eliminates the need for manual feature engineering and enhances detection accuracy by learning directly from raw packet-level network data. To ensure interpretability, which is critical in security-sensitive applications, we integrate Explainable AI (XAI) techniques using SHAP values. These explanations help identify the key features that influence each classification decision, increasing trust, and supporting informed actions. We evaluated the proposed system using the CIC IoT2024-DIAD dataset, which includes traffic data from seven device classes and 25 network features. The model achieved strong performance across multiple metrics: 99.92% accuracy, 99.97% F1-Score, 99.62% precision, 99.13% recall, and 99.27% specificity. Regularization and dropout layers were used to mitigate overfitting and ensure generalizability. Unlike existing solutions, our framework balances high accuracy with real-time interpretability, making it scalable, lightweight, and suitable for deployment in resource-constrained IoT settings.}, archive = {J_ACCESS}, author = {Prabhav Jain and Anshika Rathour and Aashima Sharma and Gurpal Singh Chhabra}, doi = {10.1109/ACCESS.2025.3590159}, journal = {IEEE Access}, pages = {127368-127390}, shortjournal = {IEEE Access}, title = {Bridging explainability and security: An XAI-enhanced hybrid deep learning framework for IoT device identification and attack detection}, volume = {13}, year = {2025}, }
• (2025). GeoAT: Geometry-aware attention feature matching network. ACCESS, 13, 127351-127367. (www)

  @article{ , abstract = {Attention mechanisms have achieved remarkable success in the field of computer vision. However, in the context of image matching with large deformations, attention often suffers from divergence issues. To address this, we propose a geometry-aware attention-based matching method called GeoAT. This method leverages low-resolution image features to obtain global geometric constraint information between images and uses an affine transformation matrix to guide the subsequent attention computation on high-resolution features, achieving efficient and accurate matching in a coarse-to-fine manner. One of the key innovations of GeoAT is leveraging the affine transformation matrix derived from the coarse matching stage to guide the cross-attention process in the intermediate matching stage. This enables the neural network to focus primarily on regions with potential correspondences while minimizing attention to irrelevant areas, thereby improving the precision and efficiency of the correspondence identification process. GeoAT employs a flexible window-based attention mechanism, integrating self-attention and cross-attention structures, while fusing multi-scale features and positional information. It demonstrates excellent performance in scenarios with large angle variations and achieves highly accurate matching results even under high matching thresholds. We evaluated GeoAT on two well-known datasets, HPatches and MegaDepth, where it outperformed the current state-of-the-art algorithm, LoFTR, with improvements of 3-5% across various metrics. In the future, GeoAT will focus on further optimizing computational efficiency and enhancing robustness.}, archive = {J_ACCESS}, author = {Yan Li and Yingdan Wu and Yang Ming and Yong Zhang and Zhesheng Cheng}, doi = {10.1109/ACCESS.2025.3590252}, journal = {IEEE Access}, pages = {127351-127367}, shortjournal = {IEEE Access}, title = {GeoAT: Geometry-aware attention feature matching network}, volume = {13}, year = {2025}, }
• (2025). Smart agile prioritization and clustering: An AI-driven approach for requirements prioritization. ACCESS, 13, 127335-127350. (www)

  @article{ , abstract = {In Agile software development, requirements prioritization plays a crucial role in ensuring that critical functionalities are delivered efficiently. Traditional prioritization methods often suffer from scalability limitations, lack of automation, and difficulty in handling dependencies. This paper proposes Smart Agile Prioritization and Clustering (SAPC), an AI-driven approach that enhances requirements prioritization by leveraging Natural Language Processing (NLP), BERT embeddings, graph-based dependency modeling, and optimization techniques. The proposed model extracts and processes textual requirements, constructs a dependency graph to model interrelations, and applies PageRank to compute requirement importance. Feature fusion and dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP) facilitate clustering, while Particle Swarm Optimization (PSO) determines the optimal number of clusters for efficient backlog prioritization. The effectiveness of SAPC is evaluated using functional requirements extracted from Software Requirement Specifications (SRS), product backlogs, and customer requests, along with a benchmark dataset for validation. Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. The results highlight the potential of AI-based methods in automating and optimizing backlog management within Agile methodologies, offering a scalable and data-driven approach to enhanced requirements prioritization.}, archive = {J_ACCESS}, author = {Aya M. Radwan and Manal A. Abdel-Fattah and Wael Mohamed}, doi = {10.1109/ACCESS.2025.3589959}, journal = {IEEE Access}, pages = {127335-127350}, shortjournal = {IEEE Access}, title = {Smart agile prioritization and clustering: An AI-driven approach for requirements prioritization}, volume = {13}, year = {2025}, }
• (2025). Semantic parsing for aspect-based sentiment analysis. ACCESS, 13, 127322-127334. (www)

  @article{ , abstract = {Aspect-Based Sentiment Analysis (ABSA) faces significant challenges in accurately identifying sentiment polarity for specific aspects within complex sentences, particularly when dealing with implicit sentiments, nested structures, and nuanced semantic relationships. This study introduces the Semantic Parsing Tree (SPT), a novel framework designed to enhance ABSA by addressing these limitations. By integrating advanced attention mechanisms, our approach overcomes the limitations of traditional dependency trees, which often fail to capture the complex semantic relationships crucial for accurate sentiment prediction, particularly in intricate sentence constructs such as nested clauses or implicit sentiments. Converting syntactic trees into SPTs enables our model to preserve and analyze key semantic roles and relationships, facilitating precise sentiment analysis at the aspect level. The integration of SPT with an advanced graph-based attention mechanism, augmented by relational heads, enhances the deep encoding of semantic nuances, significantly improving sentiment analysis accuracy. Comprehensive evaluations across benchmark datasets, including SemEval 2014, Restaurant, and Twitter, indicate that this approach outperforms conventional models in both accuracy and adaptability.}, archive = {J_ACCESS}, author = {Muhammad Aqeel and Francesco Setti}, doi = {10.1109/ACCESS.2025.3588301}, journal = {IEEE Access}, pages = {127322-127334}, shortjournal = {IEEE Access}, title = {Semantic parsing for aspect-based sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). LUCID: A framework for reducing false positives and inconsistencies among container scanning tools. ACCESS, 13, 127307-127321. (www)

  @article{ , abstract = {Containerization has emerged as a revolutionary technology in the software development and deployment industry. Containers offer a portable and lightweight solution that allows for packaging applications and their dependencies systematically and efficiently. In addition, containers offer faster deployment and near-native performance with isolation and security drawbacks compared to Virtual Machines. To address the security issues, scanning tools that scan containers for preexisting vulnerabilities have been developed, but they suffer from false positives. Moreover, using different scanning tools to scan the same container provides different results, which leads to inconsistencies and confusion. Limited work has been done to address these issues. This paper provides a fully functional and extensible framework named LUCID that can reduce false positives and inconsistencies provided by multiple scanning tools. We use a database-centric approach and perform query-based analysis, to pinpoint the causes for inconsistencies. Our results show that our framework can reduce inconsistencies by 70%. The framework has been tested on both Intel64/AMD64 and ARM architecture. We also create a Dynamic Classification component that can successfully classify and predict the different severity levels with an accuracy of 84%. We believe this paper will raise awareness regarding security in container technologies and enable container scanning companies to improve their tool to provide better and more consistent results.}, archive = {J_ACCESS}, author = {Md Sadun Haq and Ali Şaman Tosun and Turgay Korkmaz}, doi = {10.1109/ACCESS.2025.3587626}, journal = {IEEE Access}, pages = {127307-127321}, shortjournal = {IEEE Access}, title = {LUCID: A framework for reducing false positives and inconsistencies among container scanning tools}, volume = {13}, year = {2025}, }
• (2025). Optimizing XGBoost hyperparameters for credit scoring classification using weighted cognitive avoidance particle swarm. ACCESS, 13, 127292-127306. (www)

  @article{ , abstract = {Decision trees in machine learning achieved satisfactory performance in classification. Decision trees offer the advantage of handling high-dimensional and complexly correlated data through feature combination and selection. Extreme Gradient Boosting (XGBoost) overcomes the issue of overfitting in decision trees by integrating multiple tree models. Weighted cognitive avoidance particle swarm optimization for the XGBoost (WCAPSO-XGB) model has been proposed for credit score classification. The structure of the XGBoost (XGB) model is determined by the hyperparameters, which must be initialized prior to model evaluation. The optimal hyperparameter values for the XGBoost model can vary significantly depending on the specific problem at hand. Manually setting these hyperparameters can be a time-consuming process. Therefore, to automate this process, weighted cognitive avoidance particle swarm optimization (WCAPSO) is employed for hyperparameter optimization. The novelty of the proposed work includes a modified version of the particle swarm optimizer (PSO), which is used to address the issue of getting trapped into a local optimal solution. The proposed WCAPSO restricts the movement of particles toward local optima and enhances exploration of the search space for better solutions. Here, the hyperparameters of XGBoost are represented as particles in WCAPSO, which defines the state and its movement in a continuous search space. The proposed WCAPSO-XGB model tunes the hyperparameters of XGBoost and classifies the credit scoring, and the experimental results are compared with various classifier such as Random Forest (RF), K-neighbors (KNN), Gaussian Naive Bayes (NB), AdaBoost, Gradient Boosting, Logistic Regression (LR), Neural Network (NN), Decision Tree (DT) and Linear Discriminant Analysis (LDA), and hyperparameter optimization methods, such as Grid Search (GS), Random Search (RS), Bays Optimization, Optuna Optimization, Hybrid Snake Optimizer Algorithm (HSOA), Exploratory Cuckoo Search, island Cuckoo Search (iCSPM and iCSPM2), and Improved SSA (ISSA) with HDPM, on four different datasets with a varying number of instances from small to large. The experimental results and analysis show that the proposed model secures better results in accuracy of 76.75%, 82.43%, 88.98%, and 88.74%; F1-score of 66.52%, 89.44%, 93.53%, and 90.64%; precision of 78.79%, 84.31%, 88.46%, and 90.41%; and recall of 64.07%, 95.24%, 99.21%, and 90.87% in the UCI German, UCI Taiwan dataset, P2P US lending club, and Credit scoring Kaggle dataset, respectively. Further, the overall confidence interval of the proposed model ranges from 82% to 83% in accuracy, 73.1% to 74.2% in F1-score, and 71% to 72% in AUC, with a confidence of 95% measured over 20 evaluations on each dataset.}, archive = {J_ACCESS}, author = {Atul Vikas Lakra and Sudarson Jena and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3585980}, journal = {IEEE Access}, pages = {127292-127306}, shortjournal = {IEEE Access}, title = {Optimizing XGBoost hyperparameters for credit scoring classification using weighted cognitive avoidance particle swarm}, volume = {13}, year = {2025}, }
• (2025). A comprehensive compact model for multilevel switching in TaOx-based memristive 1T-1R cells. ACCESS, 13, 127280-127291. (www)

  @article{ , abstract = {Filamentary switching memristive devices based on the valence change mechanism (VCM) are promising for non-volatile memory applications due to their ability to store multiple resistance states within a single device. To facilitate the integration into circuits, this study presents an advanced compact model designed for multilevel switching in the VCM devices serially connected with transistors in a 1T-1R configuration. This model is an extension of the existing JART (Jülich Aachen Resistive Switching Tools) VCM v1b model, by incorporating state-dependent effective thermal resistance ( $R_{\mathrm {th,eff}}$ ) based on an electro-thermal continuum model. This enables precise modeling of multilevel behavior and includes the variability in switching cycles to reflect experimental conditions. The validation with TaOx-based VCM devices co-integrated with 180 nm n-MOS transistors demonstrates the model’s accuracy, achieving consistent multilevel programming across 7-states and capturing cycle-to-cycle variability effectively. This model offers a robust tool for designing reliable, high-density multilevel ReRAM memory system.}, archive = {J_ACCESS}, author = {Seokki Son and Ankit Bende and Daniel Schön and Rana Walied Ahmad and Dennis Nielinger and Vikas Rana and Stephan Menzel}, doi = {10.1109/ACCESS.2025.3590140}, journal = {IEEE Access}, pages = {127280-127291}, shortjournal = {IEEE Access}, title = {A comprehensive compact model for multilevel switching in TaOx-based memristive 1T-1R cells}, volume = {13}, year = {2025}, }
• (2025). Cognitive agent-based modeling to optimize complex project management under uncertainty. ACCESS, 13, 127263-127279. (www)

  @article{ , abstract = {This paper presents an agent-based simulation model with cognitive elements, developed to explore strategies for managing complex projects under uncertainty. The model was implemented using NetLogo, which provided a matrix-structured environment where agents—identified as assigners and workers—interact based on factors such as expertise level, intensity of collaboration, frequency of task rotation, and workload balance. The system was simulated over 100 time steps, allowing the identification of behavioral patterns related to organizational learning and performance under varied experimental conditions. Methodologically, the model integrates mechanisms for distributing tasks, rules for agent assignment, adjustable collaboration ranges, and decision-making based on utility functions. Cognitive aspects are addressed through decentralized decision processes and adaptive behaviors that evolve during the simulation. A total of eight configurations were analyzed, varying turnover rates, collaboration distances, and the initial competencies of the agents to assess how these variables influence the accumulation of experience and project effectiveness. The results indicate that when agents build up experience over time, project outcomes improve, following a growth curve with diminishing marginal returns. Conversely, high turnover interferes with knowledge retention and weakens learning processes. Additionally, too much collaboration can overwhelm highly connected agents, thereby decreasing the system’s overall efficiency. Statistical validation was carried out through ANOVA and Spearman’s correlation tests, confirming the consistency and relevance of the observed effects. Overall, the study underscores the importance of organizational continuity and the thoughtful design of collaboration parameters to achieve efficient performance in complex project settings. The proposed model serves as a useful framework for examining how task distribution, talent retention, and interaction patterns can be optimized, and it opens the door to future developments involving more advanced cognitive modeling techniques.}, archive = {J_ACCESS}, author = {Liliana M. Pantoja-Rojas and Marco A. Alzate-Monroy and Vãctor H. Medina-Garcãa and Luis G. Moreno-Sandoval}, doi = {10.1109/ACCESS.2025.3588816}, journal = {IEEE Access}, pages = {127263-127279}, shortjournal = {IEEE Access}, title = {Cognitive agent-based modeling to optimize complex project management under uncertainty}, volume = {13}, year = {2025}, }
• (2025). An adaptive fusion path tracking strategy for autonomous vehicles based on improved ACO algorithm. ACCESS, 13, 127252-127262. (www)

  @article{ , abstract = {Path tracking system is a key component in autonomous vehicles research. It is a challenge for a single controller to achieve accurate tracking in complex scenarios with dynamic curvatures and errors. Although methods based on dynamic models and optimization theory can improve tracking performance, most autonomous systems lack high-fidelity models and the complexity of optimization processes lead to increase computational burden. Hence, the aim of this study is to develop an accurate tracking control strategy for autonomous vehicles in complex scenarios by proposing an adaptive fusion control tracking scheme based on an intelligent optimization algorithm. Firstly, this scheme is built with a PP algorithm with forward-looking distance and a PID model for direct error feedback as the base controller. Secondly, the PID and PP algorithms are integrated through the ant colony optimization (ACO) algorithm, with adaptive fusion to adjust the weights and reduce tracking errors quickly and effectively. Finally, an improved ACO (IMACO) algorithm is designed by establishing the natural logarithm function to address the blind search problem in the ACO algorithm. Experimental results showed that the IMACO-based fusion controller achieves significant enhancements in path-tracking accuracy, with root mean square error (RMSE) reductions of 68.3%, 74.5%, 35.8%, and 21.8% when compared to PP controller, PID controller, original ACO-based fusion controller, and the Cuckoo-based fusion controller, respectively. This study of path tracking strategy for autonomous vehicles can be devoted to providing a new perspective of the controllers’ development to improve computational efficiency and its application in complex traffic scenarios.}, archive = {J_ACCESS}, author = {Jihan Zhang and Yuan Wang and Jinyan Hu and Hongwu You}, doi = {10.1109/ACCESS.2025.3590633}, journal = {IEEE Access}, pages = {127252-127262}, shortjournal = {IEEE Access}, title = {An adaptive fusion path tracking strategy for autonomous vehicles based on improved ACO algorithm}, volume = {13}, year = {2025}, }
• (2025). Leveraging synthetic data to develop a machine learning model for voiding flow rate prediction from audio signals. ACCESS, 13, 127240-127251. (www)

  @article{ , abstract = {Sound-based uroflowmetry (SU) is a non-invasive technique emerging as an alternative to traditional uroflowmetry (UF) to calculate the voiding flow rate based on the sound generated by the urine impacting the water in a toilet, enabling remote monitoring and reducing the patient burden and clinical costs. This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. The models were trained with a dataset that contains sounds from synthetic void events generated with a high precision peristaltic pump and a traditional toilet. Sound was simultaneously recorded with three devices: Ultramic384k, Mi A1 smartphone and Oppo Smartwatch. To extract the audio features, our analysis showed that segmenting the audio signals into 1000 ms segments with frequencies up to 16 kHz provided the best results. Results show that random forest achieved the best performance in both regression and classification tasks, with a mean absolute error (MAE) of 0.9, 0.7 and 0.9 ml/s and quadratic weighted kappa (QWK) of 0.99, 1.0 and 1.0 for the three devices. To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. The results reported an MAE below 2.5 ml/s and a QWK above 0.86 for regression and classification tasks, respectively.}, archive = {J_ACCESS}, author = {Marcos Lazaro Alvarez and Alfonso Bahillo and Laura Arjona and Diogo Marcelo Nogueira and Elsa Ferreira Gomes and Alípio M. Jorge}, doi = {10.1109/ACCESS.2025.3590626}, journal = {IEEE Access}, pages = {127240-127251}, shortjournal = {IEEE Access}, title = {Leveraging synthetic data to develop a machine learning model for voiding flow rate prediction from audio signals}, volume = {13}, year = {2025}, }
• (2025). A multi-modal approach using a hybrid vision transformer and temporal fusion transformer model for stock price movement classification. ACCESS, 13, 127221-127239. (www)

  @article{ , abstract = {Stock market price movement primarily focuses on accurately classifying buy and sell signals, which enables traders to maximize profits with well-timed market entry and exit trading positions. This study presents and implements a multi-modal deep learning approach to classifying stock price movement. Our approach adequately captures potential price reversals or continuations by utilizing two modalities (candlestick chart patterns and historical price data). Specifically, the proposed framework converts the historical data into candlestick charts of $256\times 256$ -pixel images where both modalities are effectively integrated and processed. A key innovation employed is the application of the histogram of oriented gradients (HOG) to extract relevant descriptors, including the candlestick colour, body-to-wick proportions, and wick size. Concurrently, the vision transformer (ViT) model is used to process the images using an embedded projection and multi-head self-attention to extract salient spatial features into a non-overlapping patch of $16\times 16$ pixels, which are treated as input tokens for the model. After which, the temporal fusion transformer (TFT) model processes the historical features, candlestick chart features, and the extracted HOG features via a decision-level (late feature fusion) strategy that concatenates these inputs to predict short-term price movements over different horizons (1 day, 3 days, 7 days, and 10 days ahead). We systematically evaluate the model performance using a time series cross-validation split to demonstrate the proposed model’s efficacy and generalization across eight indices (BSE, IXIC, N225, NIFTY-50, NSE-30, NYSE, S&P 500, and SSE). The results demonstrate the superior performance of our multi-modal approach, achieving average accuracy, precision, recall, and matthew correlation coefficient (MCC) of 96.17%, 96.24%, 96.15%, and 0.9367, respectively across all evaluated indices. Furthermore, using a real-time trading simulation, the study assesses the practical implications of different window sizes (5, 10, and 15 days). A paired t-test is also conducted to validate the proposed model against benchmarks statistically. The analysis provides valuable insights into how short and long-term traders can effectively maximize the proposed model, highlighting its adaptability for real-world applications.}, archive = {J_ACCESS}, author = {Ibanga Kpereobong Friday and Sarada Prasanna Pati and Debahuti Mishra}, doi = {10.1109/ACCESS.2025.3589063}, journal = {IEEE Access}, pages = {127221-127239}, shortjournal = {IEEE Access}, title = {A multi-modal approach using a hybrid vision transformer and temporal fusion transformer model for stock price movement classification}, volume = {13}, year = {2025}, }
• (2025). A robust and efficient machine learning framework for enhancing early detection of android malware. ACCESS, 13, 127183-127220. (www)

  @article{ , abstract = {The advancement of information technology has introduced new challenges in cybersecurity, especially related to the Android platform which is the main target of malicious software (malware) attacks. The National Cyber and Crypto Agency (BSSN) of Indonesia reported millions of incidents involving Android Package Kit (.apk) files related to electronic wedding invitations. This study aims to develop a robust and efficient static analysis-based machine learning framework for early detection of Android malware. Six machine learning algorithms Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes, AdaBoost, and Gradient Boosting are used to identify malicious behavior in APK files. The dataset used consists of 2,084 Android applications, including 1,314 malware samples and 770 benign applications, obtained through a reverse engineering process. Data pre-processing, feature extraction, and training using supervised learning are carried out to optimize detection accuracy. The experimental results show that the Random Forest algorithm achieves the best performance with 97% accuracy and 95% precision, surpassing the performance of other algorithms.}, archive = {J_ACCESS}, author = {Fandi Kurniawan and Deris Stiawan and Darius Antoni and Mohd Yazid Idris and Rahmat Budiarto}, doi = {10.1109/ACCESS.2025.3589656}, journal = {IEEE Access}, pages = {127183-127220}, shortjournal = {IEEE Access}, title = {A robust and efficient machine learning framework for enhancing early detection of android malware}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of GPT-4 and LLaMA 3.2 integration with speech processing models for enhancing Human–Robot interaction and motion control in real-world applications. ACCESS, 13, 127170-127182. (www)

  @article{ , abstract = {Human-Robot Interaction (HRI) in robots finds wide applications today such as personal assistant robots, autonomous vehicles, healthcare support robots, industrial robots and many more, which receive interpreted commands to perform functions ranging from home automation to real-time assembly line operations. This paper provides a comparative study of different Natural Language Processing (NLP) models that are created by combining advanced Large Language Models (LLMs) with speech processing technologies to create a more intuitive, adaptable, and accurate system for robots. By assessing performance metrics including accuracy, response time and robustness, this paper identifies the best generalized model for the real-world application in robotics. In fact, this framework enables natural language understanding and speech generation to be combined effectively, which can help robots respond quickly to spoken requests, even in dynamic environments.}, archive = {J_ACCESS}, author = {Sheeba Uruj and Riddhi Goswami and Sujala D. Shetty and Kalaichelvi Venkatesan and Karthikeyan Ramanujam}, doi = {10.1109/ACCESS.2025.3590592}, journal = {IEEE Access}, pages = {127170-127182}, shortjournal = {IEEE Access}, title = {Comparative analysis of GPT-4 and LLaMA 3.2 integration with speech processing models for enhancing Human–Robot interaction and motion control in real-world applications}, volume = {13}, year = {2025}, }
• (2025). A TD3-based reinforcement learning algorithm with curriculum learning for adaptive yaw control in all-wheel-drive electric vehicles. ACCESS, 13, 127150-127169. (www)

  @article{ , abstract = {A novel artificial intelligence-based approach for the direct yaw control (DYC) of an all-wheel drive (AWD) electric vehicle (EV) is proposed in this paper. To improve adaptability and ability to handle nonlinearities via continuous learning, the proposed algorithm is built upon a twin delayed deep deterministic policy gradient (TD3) reinforcement learning (RL) algorithm for the optimal torque distribution across four wheels of the vehicle. The proposed model-free torque vectoring algorithm performs based on the interaction of an agent with an environment to learn the optimal policy in a reward-driven manner and obtain the ability to dynamically adapt to varying conditions, such as different roads and vehicle speeds. Unlike conventional control methods that rely on precise system modeling and may struggle to adapt under varying conditions, no model of the vehicle is required in the proposed method. This work proposes a model-free RL-based controller with curriculum learning to train the strategy, where the model learns simpler tasks first, progressively increasing difficulty to enhance stability and convergence. A detailed reward function and well-structured actor-critic networks are devised, and the proposed algorithm is compared with a conventional model-based linear quadratic regulator (LQR) approach. A nonlinear model with 7 degrees of freedom is used to model the dynamic behavior of the vehicle in MATLAB/Simulink, and the results are further verified through the implementation of IPG CarMaker under realistic driving scenarios. The performance of the proposed algorithm is studied across different maneuvers, demonstrating reduced yaw rate error and sideslip angle, resulting in enhanced dynamic stability.}, archive = {J_ACCESS}, author = {Reza Jafari and Pouria Sarhadi and Amin Paykani and Shady S. Refaat and Pedram Asef}, doi = {10.1109/ACCESS.2025.3587938}, journal = {IEEE Access}, pages = {127150-127169}, shortjournal = {IEEE Access}, title = {A TD3-based reinforcement learning algorithm with curriculum learning for adaptive yaw control in all-wheel-drive electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Temperature-dependent optical absorption study of exciton binding energy in organolead halide perovskites. ACCESS, 13, 127142-127149. (www)

  @article{ , abstract = {Organolead halide perovskites have recently garnered significant attention for their potential in solar cell applications, as well as their promise for light-emitting and lasing devices. In this study, we investigate the excitonic and electronic properties of mixed halide organolead perovskite CH3NH3PbCl ${}_{3\text {-}\text {x}}$ Ix layers through temperature-dependent optical absorption spectroscopy. By analyzing spectral features across the 77–300 K range, we determined the exciton binding energy ( $E_{b} \approx ~14$ meV) and the bandgap energy. The experimentally derived $E_{b}$ , obtained from absorption-edge shifts, is nearly half the theoretical value calculated using the Wannier–Mott model with a high-frequency dielectric constant ( $\varepsilon _{\infty } =6.55$ ). The $E_{b}$ values close to the thermal energy at room temperature, confirmed by both the experimental and theoretical approaches, indicate predominantly non-excitonic behavior, with approximately 95% of excitons dissociating into free carriers, which is essential for high photovoltaic efficiency. A discontinuity in the bandgap (from 1.57 eV to 1.72 eV) near 150–160 K reflects the impact of the tetragonal-to-orthorhombic phase transition on the material’s optoelectronic properties. While a more accurate treatment of dielectric screening, for example through in situ studies or by considering effective dielectric constants ( $\varepsilon _{eff} \approx ~10$ ), is needed, our results quantitatively confirm the weakly bound excitonic character of mixed-halide perovskites under operational conditions. These findings provide useful insights for their optimized design in solar energy applications.}, archive = {J_ACCESS}, author = {Gagik Ayvazyan and Harutyun Dashtoyan and Surik Khudaverdyan and Lenrik Matevosyan and Ashok Vaseashta}, doi = {10.1109/ACCESS.2025.3589255}, journal = {IEEE Access}, pages = {127142-127149}, shortjournal = {IEEE Access}, title = {Temperature-dependent optical absorption study of exciton binding energy in organolead halide perovskites}, volume = {13}, year = {2025}, }
• (2025). Load observing system based on wave reflection for soft-switching control of MHz-frequency resonant inverter against load fluctuations. ACCESS, 13, 127126-127141. (www)

  @article{ , abstract = {Applying MHz-frequency inverters to radio frequency (RF) power sources for plasma applications has been challenging because capacitive loads caused by load fluctuations cause destructive hard-switching. This paper proposes a MHz-frequency load impedance estimation method for soft-switching control. The proposed system estimates the load impedance by utilizing a directional coupler that is typically built into an RF power source to observe traveling and reflected waves. Therefore, unlike typical low-frequency power electronics systems, the proposed system does not require output current sensors which are not suitable for MHz-frequencies. In the experiment, the proposed method was tested using the auxiliary switch-inductor circuit of a 3.75MHz gallium nitride (GaN) full-bridge inverter as the control target. Capacitive load detection by the proposed method was experimentally verified, and soft-switching control was realized under capacitive load. The transient response of the control under a fluctuating load that changes from inductive to capacitive was also measured. The inverter transitioned to soft-switching within 2.31 $\mu $ s after a load change, and hard-switching took only 8 cycles.}, archive = {J_ACCESS}, author = {Aoi Oyane and Kazuhiro Umetani and Eiji Hiraki}, doi = {10.1109/ACCESS.2025.3587595}, journal = {IEEE Access}, pages = {127126-127141}, shortjournal = {IEEE Access}, title = {Load observing system based on wave reflection for soft-switching control of MHz-frequency resonant inverter against load fluctuations}, volume = {13}, year = {2025}, }
• (2025). A pipelined architecture for interatomic interactions computation considering atomic distribution. ACCESS, 13, 127113-127125. (www)

  @article{ , abstract = {Domain-specific computing architectures significantly enhance the scale and performance of scientific simulations. In molecular dynamics, optimization of interatomic interaction computations is crucial for simulation accuracy. However, current methods aimed at optimizing accuracy rely on predefined interaction pipelines whose static computational configurations often lead to compromised resource efficiency. To address the difficulty mentioned above in calculating interatomic forces, we propose a Distribution-Aware Pair-Interaction Calculation (DAPIC) architecture that dynamically senses atomic distribution and allocates computational resources adaptively. This DAPIC architecture assigns computational weights to critical pair-interactions, enhancing the accuracy of essential distance calculations. DAPIC integrates Taylor series-based interpolation with a dynamically reloadable lookup table, emphasizing high-impact interaction regions. The experimental results demonstrate that comparable computational accuracy is achieved with significantly reduced resource demands, alleviating performance bottlenecks in interatomic force calculations. Specifically, the DAPIC-based design reduced the cumulative interatomic force error per atom by 70.8% and mitigated the atomic position and velocity drift errors by 86.2% and 85.6%, respectively, compared with single-precision GROMACS simulations. In long-term energy conservation tests, the total energy fluctuations closely align with those observed in double-precision GROMACS, underscoring the effectiveness of the architecture in preserving simulation fidelity.}, archive = {J_ACCESS}, author = {Chengyang Han and Jifeng Luo and Yan Pei and Qianjian Xing and Feng Yu}, doi = {10.1109/ACCESS.2025.3587457}, journal = {IEEE Access}, pages = {127113-127125}, shortjournal = {IEEE Access}, title = {A pipelined architecture for interatomic interactions computation considering atomic distribution}, volume = {13}, year = {2025}, }
• (2025). Transformer and large language models for automatic multiple-choice question generation: A systematic literature review. ACCESS, 13, 127100-127112. (www)

  @article{ , abstract = {Developing multiple-choice questions manually requires a lot of time and effort. Automatic multiple-choice question generation is one of the solutions to alleviate the problem. The research in automatic multiple-choice question generation has been growing with the recent use of Transformer and Large-Language Models. However, existing literature reviews have not thoroughly covered the recent advances in methods and evaluation conducted on the multiple-choice question generation domain. This research conducted a systematic literature review on multiple-choice question generation using Transformer and Large Language Models. This research aims to discover recent methods and evaluation strategies that have been used in the domain. We obtained 28 primary studies. We presented a taxonomy covering strategy of using the Transformer and Large Language Models for multiple-choice question generation, including fine-tuning and prompt engineering with zero-shot, few-shot, chain-of-thought, and retrieval augmented generation. Primary studies used either or both automatic and manual evaluation for the generated questions from Transformer and LLM. We found that studies are still primarily in English, with few studies utilizing learning components such as learning objective, limited use of chain-of-thought, retrieval augmented generation, and open problem in automatic evaluation.}, archive = {J_ACCESS}, author = {Halim Wildan Awalurahman and Rizal Fathoni Aji and Indra Budi}, doi = {10.1109/ACCESS.2025.3590423}, journal = {IEEE Access}, pages = {127100-127112}, shortjournal = {IEEE Access}, title = {Transformer and large language models for automatic multiple-choice question generation: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A multi-criteria comparison of large language model powered assistants in pre-research studies for the academia. ACCESS, 13, 127086-127099. (www)

  @article{ , abstract = {Large Language Models (LLMs), including Generative Pre-trained Transformers (GPT), a specific type of Large Language Model Powered Assistants (LLM-PA), have emerged as powerful tools in academic research and education. They offer capabilities ranging from language understanding to content generation, and serve as the foundation for LLM-PA-powered assistants, such as ChatGPT, DeepSeek, and Gemini, which facilitate interactive learning, research support, and intelligent tutoring. This study aims to guide researchers in choosing and ranking various LLM-PA alternatives in their preliminary research for academic studies. However, selecting the appropriate alternatives requires considering a large number of distinct criteria. Therefore, we conducted a multi-criteria comparison of different LLM-PAs employed in academic research. These assistants are evaluated based on criteria including performance metrics, user experience, ethical issues, and technical constraints. Examining the strengths and limitations of each tool across these dimensions, it is aimed to provide insights into their performance and suitability for academic applications. Throughout the solution procedure, we first define the criteria and sub-criteria affecting the preferences and sort them by the G1 method. Subsequently, we evaluate nine commonly used LLM-PA using the Simple Additive Weighting Method. According to the results, Gemini 2.0, Claude 3.7 Sonnet and ChatGPT-4o are the most preferred tools.}, archive = {J_ACCESS}, author = {Murat Akin and Gül Didem Batur Sir and Ayyüce Aydemir Karadağ and Hakan Çerçioğlu}, doi = {10.1109/ACCESS.2025.3586502}, journal = {IEEE Access}, pages = {127086-127099}, shortjournal = {IEEE Access}, title = {A multi-criteria comparison of large language model powered assistants in pre-research studies for the academia}, volume = {13}, year = {2025}, }
• (2025). SEMG-based control of prosthesis hand using LSTM classification and SMC. ACCESS, 13, 127068-127085. (www)

  @article{ , abstract = {In this paper, an approach for controlling an prosthesis hand using surface Electromyography (sEMG) signal, artificial neural network (ANN) and sliding mode control (SMC) is presented. An artificial hand mechanism also known as a prosthetic hand or bionic hand, is device designed to replace the function of a missing or non-functioning hand. surface Electromyography (sEMG) were used to detect hand muscle activities which were interpreted into six specific hand movements (gestures). By sensing the activities of two muscle groups of the user’s arm, the fingers in the robotic hand were controlled to follow specific trajectories. The classification of the sEMG signals into one of the six gestures was done using Long Short-Term Memory (LSTM) neural network that was trained from a dataset collected from five people. To improve the performance of the LSTM during classification, feature extraction operation of the sEMG signal was performed during training and classification. This increased the accuracy of the neural network from 52.3%, using raw data, to 99.7%, using extracted features. Once the required hand gesture was identified from the sEMG signal, interpretation of the gesture into individual fingers joint angles was done using cubic polynomial path-planning algorithm. These joint angle trajectories were used to command the robotic hand using Sliding Mode Control (SMC) controller. The use of SMC instead of other existing controllers such as PIDs also improved parameter uncertainty handling capabilities of the robotic had control system by a significant amount.}, archive = {J_ACCESS}, author = {Amanual Tesfaye Takele and Dereje Shiferaw Negash}, doi = {10.1109/ACCESS.2025.3589294}, journal = {IEEE Access}, pages = {127068-127085}, shortjournal = {IEEE Access}, title = {SEMG-based control of prosthesis hand using LSTM classification and SMC}, volume = {13}, year = {2025}, }
• (2025). A novel measurement error identification framework for multi-branch CT system. ACCESS, 13, 127054-127067. (www)

  @article{ , abstract = {The current transformer (CT) is recognized as a critical power conversion and metering device in substations, with its operational stability being considered crucial for grid reliability. A measurement error identification framework for error threshold-exceeding in multi-branch CT systems is proposed in the study. First, the secondary-side currents of each phase in multi-branch CTs are decomposed using robust empirical mode decomposition (REMD), with residuals being extracted to characterize current trend variations. The current residuals are then mapped into principal component space and residual space through kernel principal component analysis (KPCA), where the squared prediction error (SPE) in residual space is calculated as the error characteristic.The total SPE is generated as the summation of phase SPEs through accumulation of all branch CTs. During normal multi-branch CT operation, the total SPE is employed to establish error monitoring thresholds via adaptive kernel density estimation (AKDE) at 99% confidence level. When real-time total SPE surpasses the predetermined threshold, threshold-exceeding CT measurement errors are detected within the multi-channel CT system. Subsequently, the contribution rate of each branch CT’s relative threshold exceedance is calculated. The CT phase demonstrating the highest contribution rate is identified as the tolerance threshold-violating component. The proposed threshold-exceeding identification methodology has been validated through multiple experimental cases at the China Electric Power Research Institute. In all test scenarios, threshold-exceeding conditions were accurately detected within multi-branch CT systems, with both the non-compliant CTs and their specific phases being precisely identified.}, archive = {J_ACCESS}, author = {Bolun Du and Yinglong Diao and Huan Wang and Xiujuan Zeng and Tong Liu and Yiyi Peng}, doi = {10.1109/ACCESS.2025.3590166}, journal = {IEEE Access}, pages = {127054-127067}, shortjournal = {IEEE Access}, title = {A novel measurement error identification framework for multi-branch CT system}, volume = {13}, year = {2025}, }
• (2025). Multiscale convolutional fusion network for efficient monaural speech separation. ACCESS, 13, 127045-127053. (www)

  @article{ , abstract = {Speech separation is crucial for robust speech processing in real-world acoustic environments. To enable deployment in resource-limited scenarios, a speech separation system must balance separation performance, computational efficiency, and inference speed. In time-domain approaches, recurrent convolutional neural network (RCNN)-based models employ multiscale convolutional architectures to model the long sequences of encoded mixture signals. Compared to dual-path models, RCNN-based models offer higher computational efficiency but often struggle to achieve competitive performance. To address this limitation, we propose a multiscale convolutional fusion network (MSCF-Net), an efficient RCNN-based architecture designed to enhance the performance of lightweight speech separation models. The MSCF-Net follows the encoder-mask estimation-decoder pipeline, where the mask estimation process consists of parameter-shared multiscale convolutional fusion (MSCF) modules. MSCF first employs dynamic convolution-based downsampling to enhance the multiscale feature representation. Adaptive gating and multiscale convolutions are then utilized to capture complex acoustic patterns. Finally, cross-scale modulation upsampling efficiently reconstructs the acoustic features. Experiments on three datasets demonstrate that our method achieves state-of-the-art performance among lightweight speech separation models, with low computational complexity and fast inference. Specifically, MSCF-Net achieves 19.1 dB SI-SNRi on the WSJ0-2Mix test set with only 3.4 M parameters and 6.67 G/s multiply-accumulate operations (MACs) per second, and achieves 6 times real-time inference speed on the test CPU.}, archive = {J_ACCESS}, author = {Rui Yang and Shanliang Pan}, doi = {10.1109/ACCESS.2025.3587085}, journal = {IEEE Access}, pages = {127045-127053}, shortjournal = {IEEE Access}, title = {Multiscale convolutional fusion network for efficient monaural speech separation}, volume = {13}, year = {2025}, }
• (2025). VBerlinV2N: Recreating a cellular network measurement campaign with simulations. ACCESS, 13, 127023-127044. (www)

  @article{ , abstract = {System-level simulations play a crucial role in evaluating the behaviors of cellular networks, yet most studies rely on synthetic simulation scenarios rather than reproducing realistic network deployments. This paper presents vBerlinV2N, a system-level simulation model based on the network simulator 3 (ns-3) that replicates the BerlinV2N measurement campaign conducted in a real-live Long Term Evolution (LTE) network. We describe the methodology for setting up and calibrating the simulation to match real-world observations, including network topology, radio conditions, and user mobility. We discuss the challenges of accurately recreating real deployments in simulations by identifying missing information in the public domain and limitations of ns-3. Furthermore, we generate a simulation dataset with vBerlinV2N that closely resembles the BerlinV2N measurement dataset. A statistical evaluation of the measured and simulated datasets validates that the simulated vBerlinV2N dataset captures the essential statistical patterns of the measured BerlinV2N dataset. We analyze the distributions as well as the temporal and spatial variations of the received signal quality metrics of the radio environments and compare the end-to-end throughput, delay, and handovers.}, archive = {J_ACCESS}, author = {Christian L. Vielhaus and Mauri Seidel and Vincent Latzko and Alexander Groß and Peter Sossalla and Martin Reisslein and Frank H. P. Fitzek}, doi = {10.1109/ACCESS.2025.3588416}, journal = {IEEE Access}, pages = {127023-127044}, shortjournal = {IEEE Access}, title = {VBerlinV2N: Recreating a cellular network measurement campaign with simulations}, volume = {13}, year = {2025}, }
• (2025). Intelligent counterforce allocation method using multi-agent reinforcement learning for ground operations. ACCESS, 13, 127009-127022. (www)

  @article{ , abstract = {Modern military operations require commanders to make complex tactical decisions involving the effective allocation of multiple friendly forces to counter enemy threats while managing diverse streams of battlefield information. This study proposes the Intelligent Counterforce Allocation for Ground Operations (ICAGO) method, which leverages multi-agent reinforcement learning (MARL) to support command-level decision-making in ground warfare. Designed for brigade-level and higher force optimization, ICAGO enables effective counterforce deployment in defensive, offensive, and simultaneous operations. The method models key tactical actions, such as maneuver force allocation by avenue of approach and artillery targeting, as MARL agent actions, with doctrinal tactical factors embedded into the reward function. The MARL agent is implemented using the Soft Actor-Critic algorithm and scaled through a Graph Attention Network to manage over 40 heterogeneous agents (infantry, tanks, and artillery). Compared to rule-based approaches, ICAGO demonstrates lower performance variance and significantly improves doctrinal metrics, including up to a 30% increase in win rate and enhanced protection of critical friendly zones, even under uncertainty and diverse operational scenarios. These results highlight the potential of MARL to enhance intelligent, large-scale force allocation under constrained human resources.}, archive = {J_ACCESS}, author = {Kiwoong Park and Sangheun Shim}, doi = {10.1109/ACCESS.2025.3590019}, journal = {IEEE Access}, pages = {127009-127022}, shortjournal = {IEEE Access}, title = {Intelligent counterforce allocation method using multi-agent reinforcement learning for ground operations}, volume = {13}, year = {2025}, }
• (2025). A 3-port network-based capacitance measurement method for asymmetric half-bridge power modules. ACCESS, 13, 127000-127008. (www)

  @article{ , abstract = {This paper proposes a method to measure the capacitance of an asymmetric half-bridge power module using a single 3-port S-parameter measurement. The capacitance of a power module originates from power semiconductors including the package and affects switching timing and loop impedance changes during operation, making it essential to determine these values before design. Conventionally, capacitance between terminals is measured using the principle of LCR meters, which extract capacitance by measuring the ratio of voltage to current. However, asymmetric power modules, such as those designed for synchronous buck converters with asymmetric high-side and low-side semiconductors, require a new approach. Asymmetric half-bridge modules with new packaging structures have six different capacitances, which complicates measurement. Therefore, this study proposes a method to measure the capacitance of asymmetric power modules using a single measurement based on a 3-port network theory. This approach simplifies the process by leveraging a commonly used network analyzer, achieving capacitance extraction with a single measurement. The proposed method was compared with vendor-provided capacitance values, showing a maximum difference of approximately 10.9%.}, archive = {J_ACCESS}, author = {Jaewon Rhee and Seongho Woo and Yujun Shin and Jiseong Kim and Seungyoung Ahn}, doi = {10.1109/ACCESS.2025.3588841}, journal = {IEEE Access}, pages = {127000-127008}, shortjournal = {IEEE Access}, title = {A 3-port network-based capacitance measurement method for asymmetric half-bridge power modules}, volume = {13}, year = {2025}, }
• (2025). LegalMind: Agentic AI-driven process optimization and cost reduction in legal services using DeepSeek. ACCESS, 13, 126981-126999. (www)

  @article{ , abstract = {The legal industry struggles with inefficiencies, high costs, and manual-intensive workflows. Traditional AI lacks adaptability in optimizing legal operations. To address this, we propose LegalMind, an agentic AI-driven framework leveraging DeepSeek R1 for intelligent legal process automation and cost reduction. LegalMind integrates a structured legal dataset and fine-tunes DeepSeek R1 to enhance decision-making and workflow efficiency. Experimental results show a 42.6% cost reduction and a 60.8% improvement in document processing speed over baseline AI models. Scalability tests confirm the system’s ability to handle 100,000 queries efficiently. Real-world case studies validate LegalMind’s effectiveness in law firms, corporate legal departments, and government agencies, demonstrating significant reductions in case preparation time and operational costs. These findings highlight the transformative potential of agentic AI in legal automation, optimizing workflows and improving decision support.}, archive = {J_ACCESS}, author = {Nidadavolu Venkat Durga Sai Siva Vara Prasad Raju and Nuruzzaman Faruqui and Nikhil Patel and Olivia-Roxana Alecsoiu and Priyabrata Thatoi and Salem A. Alyami and AKM Azad}, doi = {10.1109/ACCESS.2025.3586781}, journal = {IEEE Access}, pages = {126981-126999}, shortjournal = {IEEE Access}, title = {LegalMind: Agentic AI-driven process optimization and cost reduction in legal services using DeepSeek}, volume = {13}, year = {2025}, }
• (2025). Empirical evaluation on GPU, overclocking, and LoRA for deep learning on embedded systems. ACCESS, 13, 126953-126980. (www)

  @article{ , abstract = {The use and optimization of deep learning models for embedded systems and mobile devices present real-world challenges, such as limited computational resources, memory size, inference latency, and high-power consumption requirements. This study evaluated 14 models from nine popular deep learning architectures using three hardware platforms (Jetson Nano B01, Jetson AGX Xavier, and GTX 1650 laptop). The trade-offs between reducing training time, image throughput, accuracy loss, temperature increase, and power consumption were analyzed using two strategies: 1) overclocking and 2) low-rank adaptation (LoRA). The results in the overclocking mode show a reduction in the training time from 3.7% (GoogLeNet) to 38.9% (EfficientNet-B0), and the temperatures have an average increase of 0.5-21.9°C when considering the CPU, GPU, and all devices. Our accuracy results for all devices were superior to those of the PyTorch baseline on ImageNet and aligned with the performance of each device. Regarding LoRA, we observed an average reduction in training time of 70% in Jetson Nano, but an average accuracy loss ranging from 21% to 40% in all models. What distinguishes our study is its multi-platform, multi-strategy evaluation, which combines hardware tuning with software training strategies. The findings of this study will contribute to other researchers in discerning and choosing algorithms to be used in the deployment and retraining of deep learning models on embedded and mobile platforms that have not yet been well explored in the literature.}, archive = {J_ACCESS}, author = {Evandro Raphaloski and Mariana Caravanti De Souza and Edson Takashi Matsubara}, doi = {10.1109/ACCESS.2025.3590316}, journal = {IEEE Access}, pages = {126953-126980}, shortjournal = {IEEE Access}, title = {Empirical evaluation on GPU, overclocking, and LoRA for deep learning on embedded systems}, volume = {13}, year = {2025}, }
• (2025). Optimizing heart disease prediction: A comparative analysis of tree-based ensembles with feature expansion and selection. ACCESS, 13, 126935-126952. (www)

  @article{ , abstract = {Cardiovascular disease (CVD) is the leading cause of death worldwide, emphasizing the importance of accurate early detection. This study examines the efficacy of tree-based ensemble machine learning models that have been improved using Feature Expansion and Selection (FES-EM). We considered the Mendeley CVD dataset of 1,000 patient records for this study, where new features were generated with feature generation operators, and the most significant ones were then retained by applying four feature selection techniques. Three hyperparameter tuning strategies were used to train and tune seven tree-based ensemble models to identify the best-performing approach. The results identified Decision Tree-Based Recursive Feature Elimination (DTRFECV) with AdaBoost optimized for grid search as the most effective model, achieving 98.20% sensitivity, 97.98% F1 score, and 97.75% accuracy. Validation on both primary and secondary datasets demonstrated that models incorporating FES outperformed those without FES. These findings underscore the novelty of a multi-stage machine learning pipeline that integrates clinically driven feature expansion with systematic optimization to build a robust and generalizable framework for early and accurate CVD diagnosis.}, archive = {J_ACCESS}, author = {K. Aswini and Kriti Arya}, doi = {10.1109/ACCESS.2025.3590492}, journal = {IEEE Access}, pages = {126935-126952}, shortjournal = {IEEE Access}, title = {Optimizing heart disease prediction: A comparative analysis of tree-based ensembles with feature expansion and selection}, volume = {13}, year = {2025}, }
• (2025). Pwnobd: Offensive cybersecurity toolkit for vulnerability analysis and penetration testing of OBD-II devices. ACCESS, 13, 126925-126934. (www)

  @article{ , abstract = {The research field of vehicle cybersecurity has experienced a significant growth in interest due to the attack surface that the information systems comprising a vehicle provides and the ever-expanding body of regulations that provide special focus on cybersecurity on vehicular systems. Of particular interest is the attack surface exposed by OBD dongles, wireless devices that connect to the vehicle’s diagnostic port, whose access to the vehicle’s CAN buses could potentially be exploited by adversaries. However, acquiring a vehicle for use in the security assessment of these devices may not be possible for the researcher. In this article, we propose a software tool, pwnobd, that assists in developing proof-of-concept attacks seeking to take advantage of the found vulnerabilities, alongside an architecture for a research and demonstration platform that provides a testbed for vulnerability analysis and penetration testing for attacks towards these devices. A small battery of tests is then performed on several diagnostic devices using this platform, along with a focused study on one such device, proving the potential benefit of such platform for security researchers.}, archive = {J_ACCESS}, author = {Roberto Gesteira-Miñarro and Ignacio Gutiérrez and Rafael Palacios and Gregorio López}, doi = {10.1109/ACCESS.2025.3589867}, journal = {IEEE Access}, pages = {126925-126934}, shortjournal = {IEEE Access}, title = {Pwnobd: Offensive cybersecurity toolkit for vulnerability analysis and penetration testing of OBD-II devices}, volume = {13}, year = {2025}, }
• (2025). LossFIQA: A shortcut solution to image quality assessment using loss for faces and beyond. ACCESS, 13, 126915-126924. (www)

  @article{ , abstract = {We introduce a novel approach to model-based quality assessment of input images. Our approach is very simple, and we demonstrate experimentally that it is not limited to a single domain (typically face recognition in the literature). Our approach generates per-sample quality pseudo-labels directly from the objective function used during the training of the target model. We evaluate the proposed method on eight large and respected datasets (from the face recognition on LFW, CALFW, CPLFW, XQLFW, CFP-FP, AgeDB, IJB-C, and retinopathy detection domain on EyePACS dataset) and using multiple state-of-the-art models (AdaFace, MagFace, ArcFace, ElasticFace, and CuricularFace). Compared to state-of-the-art methods for face quality assessment that are considerably more complex, our solution yields competitive results while being much simpler and not limited to one application.}, archive = {J_ACCESS}, author = {Marek Vaško and Adam Herout}, doi = {10.1109/ACCESS.2025.3589778}, journal = {IEEE Access}, pages = {126915-126924}, shortjournal = {IEEE Access}, title = {LossFIQA: A shortcut solution to image quality assessment using loss for faces and beyond}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv7-tiny for small-scale fire detection via multi-scale channel spatial attention and dynamic upsampling. ACCESS, 13, 126901-126914. (www)

  @article{ , abstract = {Fire detection remains a challenging task, particularly in distinguishing small smoke and flames from complex backgrounds. This study proposes an enhanced YOLOv7-tiny model tailored for small-scale fire detection. The model incorporates a Multi-Scale Channel Spatial Attention (CAS-ELAN) module, which combines Convolutional Additive Self-attention (CAS) and Efficient Layer Aggregation Networks (ELAN) to extract discriminative fire features and suppress irrelevant background information. To improve detection performance for small targets, we introduce the DySample module, a dynamic upsampling strategy that enhances feature map resolution. Furthermore, the Focaler-IoU loss function is employed to focus the model on hard samples, enhancing the accuracy of small fire detection. Extensive experiments on a self-collected dataset demonstrate that our method achieves a performance improvement of 3.49% in mAP@0.5 and 6.62% in APs over the baseline YOLOv7-tiny model. These results highlight the potential of our model as a competitive tool for detecting fires, especially at small scales. The source code and datasets are publicly available to facilitate reproducibility and further research in this domain. Code and datasets are available at https://github.com/exoplantlalala/DCF-YOLO}, archive = {J_ACCESS}, author = {Shihao Wu and Yi Xia}, doi = {10.1109/ACCESS.2025.3590036}, journal = {IEEE Access}, pages = {126901-126914}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv7-tiny for small-scale fire detection via multi-scale channel spatial attention and dynamic upsampling}, volume = {13}, year = {2025}, }
• (2025). LitePipeNet: Research on a lightweight and efficient segmentation model for UAV pipeline inspection in mining areas. ACCESS, 13, 126886-126900. (www)

  @article{ , abstract = {In response to the challenges of UAV-based pipeline inspection in mining areas—including difficulties in small object detection, limited adaptability to complex environments, and constraints on efficient edge deployment—this paper proposes LitePipeNet, a lightweight segmentation model tailored for edge computing. To support model development, we construct the MinePipe-UAV dataset, containing 7,960 high-resolution images with typical inspection targets such as pipelines, corrosion, and leakage. LitePipeNet integrates Multi-Path Weight Convolution (MPWConv) for enhanced perception, a Lightweight Bidirectional Feature Pyramid Network (LBiFPN) for efficient feature fusion, and a Multi-Level Decoupled Segmentation Head (MLHead) to optimize segmentation. This architecture significantly improves small-object recognition and environmental adaptability. Experiments show LitePipeNet achieves a Mask mAP50 of 89.74% on MinePipe-UAV, outperforming mainstream models (YOLOv8n, DeepLabV3+) by 3–6% in segmentation accuracy. With only 2.9M parameters and 10.5 GFLOPs, it reaches real-time inference at 28 FPS on the RKNN 3588 platform, making it well-suited for industrial edge deployment. Extensive comparisons and ablation studies confirm its robustness and generalization. The main contributions are: 1) construction of the MinePipe-UAV dataset for UAV-based pipeline inspection; 2) design of the compact, high-performance LitePipeNet model; 3) systematic validation of our approach’s effectiveness and scalability in industrial visual edge applications.}, archive = {J_ACCESS}, author = {Huang Yong and Xiao Shengwang and Xia Xing and Huang Qiu}, doi = {10.1109/ACCESS.2025.3587196}, journal = {IEEE Access}, pages = {126886-126900}, shortjournal = {IEEE Access}, title = {LitePipeNet: Research on a lightweight and efficient segmentation model for UAV pipeline inspection in mining areas}, volume = {13}, year = {2025}, }
• (2025). Drone detection using YOLOv5 with GhostNet backbone and adaptive fourier neural operator (AFNO 2D). ACCESS, 13, 126874-126885. (www)

  @article{ , abstract = {The growing presence of autonomous aerial vehicles (AAVs) in civilian, commercial, and military sectors has raised urgent and widespread security concerns. Real-time and reliable AAV detection remains a formidable challenge owing to the small size, rapid movement, and visual similarity of AAVs to birds and other airborne objects in dynamic environments. This study proposes a lightweight yet high-accuracy drone detection framework based on the YOLOv5 object detection model, enhanced with a GhostNet backbone and Adaptive Fourier Neural Operator (AFNO 2D). The proposed architecture integrates Ghost Bottleneck modules, Spatial Pyramid Pooling Fast (SPPF), and frequency-domain learning via AFNO 2D to significantly improve global feature extraction while maintaining a compact model footprint. Additionally, Depthwise Convolution (DWConv) and C3 Ghost modules further optimize multi-scale feature fusion, ensuring robust and precise detection performance across varying altitudes, lighting conditions, and complex backgrounds. Compared with existing lightweight variants of You Only Look Once (YOLO), the proposed model demonstrates superior accuracy, achieving mAP@50:80.4% and mAP@50:95.5% while utilizing fewer parameters (2.6M) and offering faster inference across two distinct datasets. These characteristics make the proposed framework promising for real-time aerial surveillance and anti-drone defense applications on resource-constrained embedded platforms.}, archive = {J_ACCESS}, author = {K. Kavin Akash and Uma Kuppusamy}, doi = {10.1109/ACCESS.2025.3589102}, journal = {IEEE Access}, pages = {126874-126885}, shortjournal = {IEEE Access}, title = {Drone detection using YOLOv5 with GhostNet backbone and adaptive fourier neural operator (AFNO 2D)}, volume = {13}, year = {2025}, }
• (2025). Exergaming versus on-court tennis: Comparing enjoyment, exertion, and energy expenditure in training. ACCESS, 13, 126862-126873. (www)

  @article{ , abstract = {Exergaming provides a convenient way for individuals to engage in physical activity. However, studies on the comparison between exergaming with traditional sports in terms of enjoyment, rate of perceived exertion (RPE), training intensity, and calories burned were limited. This study recruited 57 participants to determine the differences and relationships between these variables in tennis sport. Participants played three matches each of exergaming tennis (EBTT group) and on-court tennis (OCTT group) over six days. The average heart rate, RPE, enjoyment, and calories burned were recorded for each match and comparisons were done between the groups. Overall, results suggested that OCTT group’s RPE (median=6, IQR=1) was significantly higher (p<0.01) compared to the EBTT group (median=5, IQR=2) by 1.2 times. In addition, the calories burned in OCTT group (mean=515.52 kCal ±134.10), and training intensity (mean=62.06% ±4.59) were also significantly higher (p<0.01) when compared to EBTT group’s calories burned (mean=421.09 kCal ±140.17), and training intensity (mean=57.47 % $\pm ~5.47$ ) by 1.22 times, and 1.08 times respectively. However, EBTT group’s enjoyment level (mean $= 113.89~\pm 4.58$ ) was significantly higher (p<0.01) than OCTT group (mean $= 107.35~\pm 4.62$ ) by 1.06 times. The enjoyment level was also found to be negatively correlated with RPE (rs=- 0.37), training intensity (r=- 0.02), and calories burned (r=-0.16). In conclusion, although EBTT group’s RPE, calories burned, and training intensity were lower compared to OCTT, the enjoyment level was higher. This could encourage longer exercise hours, thus improving overall physical wellness.}, archive = {J_ACCESS}, author = {Jia Sheng Ngo and See Ziau Hoe and Maziah Mat Rosly}, doi = {10.1109/ACCESS.2025.3588890}, journal = {IEEE Access}, pages = {126862-126873}, shortjournal = {IEEE Access}, title = {Exergaming versus on-court tennis: Comparing enjoyment, exertion, and energy expenditure in training}, volume = {13}, year = {2025}, }
• (2025). SDN-based VANET routing: A comprehensive survey on architectures, protocols, analysis, and future challenges. ACCESS, 13, 126801-126861. (www)

  @article{ , abstract = {As the automotive and telecommunication industries advance, more vehicles are becoming connected, leading to the realization of intelligent transportation systems (ITS). Vehicular ad-hoc network (VANET) supports various ITS services, including safety, convenience, and infotainment services for drivers and passengers. Generally, such services are realized through data sharing among vehicles and nearby infrastructures or vehicles over multi-hop data routing mechanisms. Vehicular data routing faces many challenges caused by vehicle dynamicity, intermittent connectivity, and diverse application requirements. Consequently, the software-defined networking (SDN) paradigm offers unique features such as programmability and flexibility to enhance vehicular network performance and management and meet the quality of services (QoS) requirements of various VANET services. Recently, VANET routing protocols have been improved using multilevel knowledge and an up-to-date global view of traffic conditions offered by SDN technology. The primary objective of this study is to furnish comprehensive information regarding the current SDN-based VANET routing protocols, encompassing intricate details of their underlying mechanisms, forwarding algorithms, and architectural considerations. Each protocol will be thoroughly examined individually, elucidating its strengths, weaknesses, and proposed enhancements. Also, the software-defined vehicular network (SDVN) architectures are presented according to their operation modes and controlling degree. Then, the potential of SDN-based VANET is explored from the aspect of routing and the design requirements of routing protocols in SDVNs. SDVN routing algorithms are uniquely classified according to various criteria. In addition, a complete comparative analysis will be achieved to analyze the protocols regarding performance, optimization, and simulation results. Finally, the challenges and upcoming research directions for developing such protocols are widely stated here. By presenting such insights, this paper provides a comprehensive overview and inspires researchers to enhance existing protocols and explore novel solutions, thereby paving the way for innovation in this field.}, archive = {J_ACCESS}, author = {Nehad Hameed Hussein and Johnny Siaw Paw Koh and Chong Tak Yaw and Sieh Kiong Tiong and Foo Benedict and Talal Yusaf and Kumaran Kadirgama and Tan Chung Hong}, doi = {10.1109/ACCESS.2024.3355313}, journal = {IEEE Access}, pages = {126801-126861}, shortjournal = {IEEE Access}, title = {SDN-based VANET routing: A comprehensive survey on architectures, protocols, analysis, and future challenges}, volume = {13}, year = {2025}, }
• (2025). Fault detection technique for active phased array antenna using current sensing. ACCESS, 13, 126789-126800. (www)

  @article{ , abstract = {This article presents the design and implementation of a built-in fault detection technique for an active phased array antenna. The proposed method employs a current monitoring circuit integrated into the power protection board of the transmit-receive unit (TRU). This circuit consists of a current sense amplifier and an operational amplifier, which detect the drain current of the high-power amplifier within the transmit-receive module (TRM) and compare it against a predefined detection threshold. To facilitate channel control and status reporting of individual TRMs, a built-in test function was implemented in the field-programmable gate array within both the antenna controller and the TRU control board. The test logic monitors the pulse output of the current monitoring circuit and requires four pulse transmissions to confirm a fault. The effectiveness of the proposed technique was evaluated through an initial test using a TRU with an intentionally damaged TRM. Further validation was conducted at both the antenna and radar system levels. The results demonstrated that the antenna can accurately detect and isolate failed TRMs, thereby confirming the reliability of the proposed fault detection method.}, archive = {J_ACCESS}, author = {Hongwoo Park and Shin Young Cho and Won Jin Lee and Seunghee Seo and Hongjoon Kim}, doi = {10.1109/ACCESS.2025.3589236}, journal = {IEEE Access}, pages = {126789-126800}, shortjournal = {IEEE Access}, title = {Fault detection technique for active phased array antenna using current sensing}, volume = {13}, year = {2025}, }
• (2025). Process for simultaneous removal of s8, DBDS, and PCBs from mineral insulating oils. ACCESS, 13, 126780-126788. (www)

  @article{ , abstract = {The presence of corrosive sulfur and polychlorinated biphenyls (PCBs) in mineral insulating oils poses significant challenge to the reliability of power transformers and presents serious environmental risks. Elemental sulfur (S8) and dibenzyl disulfide (DBDS) are well-known for causing the formation of metal sulfides on transformer components, leading to equipment failures, while PCBs present in insulating oils are persistent environmental pollutants with long-term health hazards. Current insulating oil purification techniques, including adsorbents, metal passivators and reagents, exhibit limited effectiveness, particularly if elemental sulfur is present in oil. This study introduces a novel reagent-based desulfurization process which uses copper deposited on iron particles dispersed in polyethylene glycol, for the complete removal of S8 from mineral insulating oil. The integration of S8 removal process with the KOH/PEG complex enables the simultaneous removal of S8, DBDS, and PCBs, with no residual metal particles detected in the oil following the treatment. Laboratory-scale experiments have shown the high efficiency of the developed process resulting in the improved physical-chemical and electrical properties of the oil, which was further verified on pilot scale unit and finally on the industrial scale process unit.}, archive = {J_ACCESS}, author = {Dejan M. Kolarski and Jelena M. Lukić and Jelena U. Janković and Sandra B. Glišić and Aleksandar M. Orlović}, doi = {10.1109/ACCESS.2025.3589171}, journal = {IEEE Access}, pages = {126780-126788}, shortjournal = {IEEE Access}, title = {Process for simultaneous removal of s8, DBDS, and PCBs from mineral insulating oils}, volume = {13}, year = {2025}, }
• (2025). Road safety risk assessment approach for freight vehicles using warning data. ACCESS, 13, 126769-126779. (www)

  @article{ , abstract = {Road transport is a vital pillar of the national economy. However, freight vehicles face significant safety challenges during operation due to characteristics such as heavy-load transportation, long-distance travel, and complex cargo types. Traditional accident data analysis methods rely on historical accident statistics, which suffer from strong lagging effects and difficulties in achieving proactive risk identification. To address this, this paper proposes a real-time early-warning-data-based approach for road risk assessment. First, the global Moran’s I index is employed to analyze the spatial clustering characteristics of warning points. Second, an evaluation system is constructed, with the relative occurrence rates of improper driving behavior and abnormal vehicle status warnings as core indicators. The entropy weight method is applied to determine the weights of each indicator, enabling the quantitative calculation of road segment risk values. Finally, cluster analysis is used to determine optimal risk classification thresholds. The method is validated using warning data and historical accident records from 47 road segments across three major roads in Nanning. The results demonstrate that spatial clusters of warning points strongly correlate with high-accident-frequency road segments. The risk classification thresholds exhibit excellent discriminative performance, with boundary values of 0.038 (94.44% accuracy) between low- and medium-risk segments and 0.075 (96.00% accuracy) between medium- and high-risk segments. Significant differences in accident occurrence rates are observed across risk levels: high-risk segments average 7.73 accidents, far exceeding medium-risk (3.53) and low-risk segments (1.35). This study confirms the efficacy of early-warning data in risk assessment, providing transportation authorities with a data-driven risk management tool. The proposed method offers a novel approach for proactive safety control in freight vehicle transportation.}, archive = {J_ACCESS}, author = {Cheng Yang and Xiaoling Zhai and Xiaoqin Zhou and Tao Wang and Shiyi Chen and Xiyuan Zhang}, doi = {10.1109/ACCESS.2025.3588502}, journal = {IEEE Access}, pages = {126769-126779}, shortjournal = {IEEE Access}, title = {Road safety risk assessment approach for freight vehicles using warning data}, volume = {13}, year = {2025}, }
• (2025). Evaluating the impact of feature engineering in phishing URL detection: A comparative study of URL, HTML, and derived features. ACCESS, 13, 126756-126768. (www)

  @article{ , abstract = {Phishing attacks have evolved into sophisticated threats, making effective cybersecurity detection strategies essential. While many studies focus on either URL or HTML features, limited work has explored the comparative impact of engineered feature sets across different machine learning models. This study aims to bridge that empirical gap by evaluating the effectiveness of URL-based, HTML-based, and derived features, individually and in combination, on phishing URL detection. The proposed approach utilizes the PhishOFE dataset of 101,063 phishing and legitimate URLs. Features are organized into four sets: 1) URL only, 2) HTML only, 3) URL + HTML, and 4) URL + HTML + derived features. Ten machine learning models are employed, including Random Forest, k-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, and advanced ensemble methods such as LightGBM, XGBoost, and CatBoost. Performance is assessed using accuracy, precision, recall, and F1-score, while permutation importance is used to evaluate feature significance. Experimental results demonstrate that ensemble models outperform traditional classifiers, with CatBoost achieving the highest accuracy of 99.45% using the complete feature set. Moreover, URL features like URLLength and NoOfSubDomain consistently rank high in importance, while derived features such as SuspiciousCharRatio and URLComplexityScore notably enhance detection performance in specific models.}, archive = {J_ACCESS}, author = {Yanche Ari Kustiawan and Khairil Imran Ghauth}, doi = {10.1109/ACCESS.2025.3579223}, journal = {IEEE Access}, pages = {126756-126768}, shortjournal = {IEEE Access}, title = {Evaluating the impact of feature engineering in phishing URL detection: A comparative study of URL, HTML, and derived features}, volume = {13}, year = {2025}, }
• (2025). Employee turnover prediction model based on feature selection and imbalanced data handling. ACCESS, 13, 126736-126755. (www)

  @article{ , abstract = {Employee turnover prediction is a critical task for organizations aiming to retain talent and reduce costs. In this study, we utilized the Kaggle HR dataset to predict employee turnover by employing a comprehensive machine learning pipeline. The dataset underwent rigorous preprocessing and exploratory data analysis (EDA) to identify key patterns and relationships. Feature selection was performed using correlation matrix analysis, Chi-Square tests, and Recursive Feature Elimination (RFE) to identify the most relevant features. To address the class imbalance issue, we applied both Synthetic Minority Over-sampling Technique (SMOTE) and Generative Adversarial Network (GAN-based) oversampling techniques. Eight models—Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), and a hybrid model (RF+DNN) —were evaluated under different scenarios, including before and after feature selection and imbalance treatment. Model performance was assessed using recall, F1-score, Cohen’s Kappa, ROC-AUC, PR-AUC, and confusion matrix. To evaluate potential overfitting, we compared performance across training and validation sets. Model generalization was further validated using cross-validation techniques. Results showed that the RF model, trained on GAN-balanced data and using RFE-selected only five features, achieved the best performance with an accuracy of 0.98, precision of 0.98, recall of 0.92, F1-score of 0.95, and Cohen’s Kappa of 0.94. XGBoost model also showed outstanding performance, with evaluation metrics closely matching those of RF. To validate the robustness and generalizability of our approach, the same methodology was applied to an external dataset—the IBM HR dataset. The application of RFE and GAN-based oversampling significantly improved minority class detection in this independent dataset. Notably, XGBoost and the hybrid RF+DNN model performed best, further demonstrating the effectiveness and generalization of the proposed framework across different datasets.}, archive = {J_ACCESS}, author = {Yuan Fang and Zhongqiu Zhang}, doi = {10.1109/ACCESS.2025.3589491}, journal = {IEEE Access}, pages = {126736-126755}, shortjournal = {IEEE Access}, title = {Employee turnover prediction model based on feature selection and imbalanced data handling}, volume = {13}, year = {2025}, }
• (2025). Scalable and fast electrical modeling method for bonding-wire memory packages. ACCESS, 13, 126718-126735. (www)

  @article{ , abstract = {With the increasing demand for high-speed and cost-effective memory solutions, bonding wire (BW)-based memory packages are adopted in various applications. Designing BW based memory packages requires numerous changes in design parameters of multiple components. However, using traditional 3D electromagnetic (EM) simulations to accommodate these changes results in long computation times and high design cost. To address this limitation, a method is needed that allows rapid evaluation of the entire memory package’s performance, regardless of design parameter changes. Thus, we propose a scalable and fast modeling method for the full memory package. The proposed method provides modeling methods for each package component, including BW, pad, transmission line (TL), via, and ball. The BW, via, and ball modeling rely solely on simple formulas, enabling rapid computation. Similarly, pad and TL modeling leverages 2D EM simulations with low computational overhead and employs an S-parameter extension method to achieve fast modeling even in multi-conductor scenarios. To accelerate the modeling process, matrix reduction and parallel processing are utilized. Finally, the in-house code connects the component models, creating the full memory package. The proposed method’s consistency, scalability, and fast computation times are validated through comparisons with 3D EM simulations. Validation is performed in both the frequency domain, covering a wide bandwidth up to 40 GHz, and the time domain. The results demonstrate that the proposed modeling technique is scalable, accurate, and reduces computation time by 99% compared to 3D EM simulations while maintaining consistency.}, archive = {J_ACCESS}, author = {Dongryul Park and Seonghi Lee and Seunghun Ryu and Hyunwoo Kim and Duksoo Kim and Hyunsik Kim and Jongwook Kim and Seungyoung Ahn}, doi = {10.1109/ACCESS.2025.3585957}, journal = {IEEE Access}, pages = {126718-126735}, shortjournal = {IEEE Access}, title = {Scalable and fast electrical modeling method for bonding-wire memory packages}, volume = {13}, year = {2025}, }
• (2025). Resource management for URLLC–eMBB coexistence in sub-band full-duplex systems. ACCESS, 13, 126700-126717. (www)

  @article{ , abstract = {With the growing adoption of uplink (UL)-intensive services such as video conferencing, high-definition (HD) streaming, and real-time gaming, the demand for UL resources has increased significantly. Traditional time division duplexing (TDD) networks often struggle to meet these dynamic traffic demands, necessitating innovative solutions such as sub-band full-duplex (SBFD) to enhance resource utilization and network efficiency. To enable scalable and deployment-aware SBFD integration, we consider a hybrid deployment of TDD and SBFD BSs, taking into account practical constraints such as hardware cost and backward compatibility. This study proposes a resource management strategy for multicell networks that facilitates the coexistence of SBFD and TDD to effectively support ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services for both UL and downlink (DL) communication. The proposed coexistence-based approach significantly reduces delay and increases the maximum number of supportable user equipments (UEs) compared to traditional TDD-only methods. Specifically, we propose a random access-based scheme to facilitate UL URLLC UEs during the flexible SBFD slots. The proposed scheme allows short packets to opportunistically access SBFD UL subbands during flexible scheduling intervals, effectively addressing the stringent latency requirements of high-frequency URLLC transmissions. By dynamically prioritizing URLLC traffic while optimizing resources for eMBB, our strategy provides a balanced and efficient solution for managing diverse traffic demands in dynamic environments.}, archive = {J_ACCESS}, author = {Yukai Wang and Bowen Fu and Najam Us Saqib and Gyeong-June Hahm and Kyung-Yul Cheon and Hyenyeon Kwon and Seungkeun Park and Sang-Woon Jeon}, doi = {10.1109/ACCESS.2025.3588994}, journal = {IEEE Access}, pages = {126700-126717}, shortjournal = {IEEE Access}, title = {Resource management for URLLC–eMBB coexistence in sub-band full-duplex systems}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based sign language recognition using efficient multi-feature attention mechanism. ACCESS, 13, 126684-126699. (www)

  @article{ , abstract = {Sign language is a communication system used by Deaf and hard of hearing people and serves as a bridge between Deaf and hearing communities. Since sign language uses numerous visuomotor elements that include both visual perception (hand shapes, facial expressions) and physical movements (hand and arm movements), it represents a multimodal input source for Sign Language Recognition (SLR) systems. In this study, a novel deep learning-based architecture using EfficientNet and multi-feature attention mechanism is proposed to accurately recognize SL signs. Initially, general visual features are acquired through the EfficientNet model, leveraging the transfer learning paradigm. Subsequently, dataset-specific contextual features are extracted utilizing distinct network types; spatial dependencies are modeled via Convolutional Neural Networks (CNNs), whereas temporal dynamics are learned through Recurrent Neural Networks (RNNs). These features are adaptively weighted using an attention mechanism and focus on the most critical information for the classification task. This approach ensures that the most information-rich and useful components of both methods are emphasized, leading to a significant increase in final performance. Utilizing RGB video images, the proposed model, on the BosphorusSign22k General dataset comprising Turkish Sign Language (TSL) signs, achieved accuracies of 99.01% and 96.84% for sign classes of 50 and 174, respectively. Furthermore, the generalization ability of the model was demonstrated by its high accuracy of 99.84% in the Argentinian Sign Language dataset (LSA64) and 98.41% in the Indian Sign Language dataset (INCLUDE50). Experimental results indicated that the proposed model architecture has a competitive performance compared to existing SLR models reviewed in the literature.}, archive = {J_ACCESS}, author = {Esma Yenisari and Sirma Yavuz}, doi = {10.1109/ACCESS.2025.3586096}, journal = {IEEE Access}, pages = {126684-126699}, shortjournal = {IEEE Access}, title = {Deep learning-based sign language recognition using efficient multi-feature attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Techno-economic viability of fuel cell emulator toward sustainable energy systems: A survey. ACCESS, 13, 126649-126683. (www)

  @article{ , abstract = {This paper reviews the enhancement and implementation of a Fuel Cell (FC) emulator design to mimic the performance characteristics of a real FC. Unlike conventional methods, which require expensive and complex FC systems, the emulators are worthwhile solutions for steering cost-effective and harmless development. The system has fascinated the attention of worldwide producers and research groups in the field of grid, microgrid and electric vehicle applications. Emulators have made it possible to develop and optimise the system in both steady-state and transient behaviour, as well as power electronics and control, without the cost or harm of a physical FC system, making it a valuable device for researchers and engineers in the design and optimisation of the FC technologies. These systems enable the expansion and optimisation of FC-based power electronics, control strategies, and hybrid configurations without relying on or risking damage to actual FC stacks. Despite their critical role in accelerating research, comprehensive studies on FC emulators remain limited. This review provides an in-depth analysis of the working principles of fuel cells, emphasising the importance of FC stacks and the configuration of multi-stack fuel cells (MSFC). It explores fuel cell (FC) emulator evolution, categorizing them into pseudo, mathematical modelling, and electronic types, ensuring reliable performance comparable to real FC systems and assessing accuracy through the margin of error. The paper discusses control strategies, designing fuel cell emulators, and implementation techniques, addressing FC degradation issues and covering fault detection while suggesting solutions. It highlights current challenges in the reliability, durability, and scalability of fuel cells, supported by statistical analysis and techno-economic evaluations that demonstrate the benefits of emulator-based approaches. A case study illustrates hydrogen fuel cells’ role in sustainable development goals (SDGs), with a review of government policies contextualizing the push for clean energy technologies. This review provides researchers and engineers with essential tools to create advanced FC emulators, fostering innovation and overcoming barriers to FC adoption.}, archive = {J_ACCESS}, author = {M. S. Priya and S. Saravanan and K. R. M. Vijaya Chandrakala and Umashankar Subramaniam and Sivakumar Selvam}, doi = {10.1109/ACCESS.2025.3588696}, journal = {IEEE Access}, pages = {126649-126683}, shortjournal = {IEEE Access}, title = {Techno-economic viability of fuel cell emulator toward sustainable energy systems: A survey}, volume = {13}, year = {2025}, }
• (2025). Adding data quality to federated learning performance improvement. ACCESS, 13, 126623-126648. (www)

  @article{ , abstract = {Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (AI) training models while preserving data privacy. However, selecting high-quality data for training remains a critical challenge in FL environments with non-independent and identically distributed (non-iid) data. Poor-quality data introduces errors, delays convergence, and increases computational costs. This study develops a data quality analysis algorithm for both FL and centralized environments to address these challenges. The proposed algorithm reduces computational costs, eliminates unnecessary data processing, and accelerates the convergence of AI models. The experiments utilized the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets, and performance evaluation was based on main literature metrics, including accuracy, recall, F1 score, and precision. Results show a maximum observed execution time reduction of up to 56.49%, with an accuracy loss of approximately 0.50%.}, archive = {J_ACCESS}, author = {Ernesto Gurgel Valente Neto and Solon Alves Peixoto and Valderi Reis Quietinho Leithardt and Juan Francisco de Paz Santana and Julio C. S. Dos Anjos}, doi = {10.1109/ACCESS.2025.3578301}, journal = {IEEE Access}, pages = {126623-126648}, shortjournal = {IEEE Access}, title = {Adding data quality to federated learning performance improvement}, volume = {13}, year = {2025}, }
• (2025). MLCRP: ML-based GPU cache performance modeling featured with reuse profiles. ACCESS, 13, 126610-126622. (www)

  @article{ , abstract = {Accurate cache performance prediction is critical for designing efficient memory hierarchies in high-performance computing systems. While cyclic simulators provide high accuracy, they require significant computational cost and time, making them inefficient for large-scale design space exploration. Analytical models are faster but lack accuracy in complex cache scenarios. This paper proposes MLCRP, a machine learning-based GPU cache performance prediction framework that utilizes the reuse profile (RP) as a key feature. RP captures memory access locality through a histogram of reuse distances. MLCRP consists of three main stages: data preparation, training, and inference. In the data preparation stage, synthetic RP-based traces are generated from parameterized distributions to simulate diverse and non-stationary memory patterns. In the training stage, a regression-based ML model is trained to capture the relationship between RP features, cache configurations, and performance metrics such as miss rate and miss status holding register (MSHR) merge rate. Finally, we propose a method to extract RP features from real GPU application traces, enabling the trained model to predict cache performance. Experimental results demonstrate that MLCRP significantly improves prediction accuracy compared to existing analytical models, maintaining the mean absolute error (MAE) within 5%. Furthermore, it successfully reduces simulation time by an average of four orders of magnitude compared to cycle-accurate simulators. Combining the strengths of analytic speed and simulation accuracy, MLCRP offers a scalable and generalizable solution for GPU cache modeling.}, archive = {J_ACCESS}, author = {Minjung Cho and Eui-Young Chung}, doi = {10.1109/ACCESS.2025.3589802}, journal = {IEEE Access}, pages = {126610-126622}, shortjournal = {IEEE Access}, title = {MLCRP: ML-based GPU cache performance modeling featured with reuse profiles}, volume = {13}, year = {2025}, }
• (2025). Why asset administration shells: A survey on uses and challenges. ACCESS, 13, 126582-126609. (www)

  @article{ , abstract = {In the cadre of the Industry 4.0 initiative, and its proposed RAMI 4.0 reference architecture, the Asset Administration Shell (AAS) is gaining traction in the industrial sector, ushering production practices to new precedents and enabling the incorporation of the latest digital technologies. However, the task of instigating the new era of industry is not without difficulties. The current work performs a systematic literature review of AAS by thoroughly reviewing 86 recently published journal articles. The aim is to put in scope current use cases of the AAS, categorise them and, thus, paint a comprehensive picture of AAS usage, by synthesizing all recent journal literature. The second part of the work focuses on highlighting current challenges that appear at the intersection of AAS with necessities of the industrial sector, as well as with the AAS’s integration with novel concepts and technologies.}, archive = {J_ACCESS}, author = {Angelos Alexopoulos and Georgios Kalogeras and Konstantinos Koutras and Athanasios Kalogeras}, doi = {10.1109/ACCESS.2025.3589931}, journal = {IEEE Access}, pages = {126582-126609}, shortjournal = {IEEE Access}, title = {Why asset administration shells: A survey on uses and challenges}, volume = {13}, year = {2025}, }
• (2025). A novel design of non-uniform limited scan array based on tri-polarized metamaterial element. ACCESS, 13, 126574-126581. (www)

  @article{ , abstract = {Limited scan arrays have attracted a lot of attentions in recent years, due to its many merits, such as fewer array elements, lower weight and easy heat dissipation of transmit and receive(T/R) channels. In the conventional literature, the subarray is usually chosen as the radiation element, however the subarray need an additional passive feeding network, which will cause an additional insertion loss and cost. Also, as far as we know, the grating lobes caused by large array element spacing haven’t been perfectly eliminated in the published literature. In this paper, we firstly design a tri-polarized metamaterial element with the “flat-top” pattern, and then design a non-uniform linear limited scan array with large spacing based on the proposed metamaterial element. Measurement results demonstrate that the metamaterial element exhibits nearly identical voltage standing wave ratio and radiation patterns under excitation for all three polarizations, indicating promising application prospects in polarization-diversity radar. Furthermore, simulation results indicate that this element also exhibits a certain level of grating lobe suppression effect in large-element-spacing, non-uniform linear arrays within the limited-scan domain.}, archive = {J_ACCESS}, author = {Jianqing Sun and Ke Zhang and Jianyu Ye and Xiaoliang Wu and Guang Hua}, doi = {10.1109/ACCESS.2025.3587747}, journal = {IEEE Access}, pages = {126574-126581}, shortjournal = {IEEE Access}, title = {A novel design of non-uniform limited scan array based on tri-polarized metamaterial element}, volume = {13}, year = {2025}, }
• (2025). GO-GASA: Grid optimization genetic a* algorithm for mobile robots path planning utilizing grid optimization. ACCESS, 13, 126557-126573. (www)

  @article{ , abstract = {Path planning, as a central challenge in mobile robotics, directly impacts the operational efficiency and safety of robotic systems. To enhance navigation performance in complex environments, this paper proposes a two-stage optimization framework: First, a Grid-Optimized Genetic A ${}^{\ast }$ Algorithm (GO-GASA) is developed by integrating the global search capability of genetic algorithms with an enhanced A ${}^{\ast }$ algorithm for optimizing the generation of initial populations. This integration not only improves the initialization performance of genetic algorithms but also enhances their overall optimization capacity. Second, a Grid-Optimized Redundant Path Removal Algorithm (GO-RPRA) is introduced to perform secondary optimization on the paths generated by GO-GASA, further improving path smoothness and efficiency. Simulation results demonstrate that in initialization experiments, the individual diversity of GO-GASA is 7.8 times higher than that of traditional genetic algorithms (GA). Under identical testing conditions, compared to six other popular metaheuristic algorithms, GO-GASA reduces the number of turns by 53.85%, the max turning angle by 50%, and the total distance by 25.24%. Overall, these improvements significantly enhance the quality of path planning and strengthen the capability of mobile robots to handle complex tasks.}, archive = {J_ACCESS}, author = {Lishu Qin and Yu Gao and Ye Zheng}, doi = {10.1109/ACCESS.2025.3587069}, journal = {IEEE Access}, pages = {126557-126573}, shortjournal = {IEEE Access}, title = {GO-GASA: Grid optimization genetic a* algorithm for mobile robots path planning utilizing grid optimization}, volume = {13}, year = {2025}, }
• (2025). An efficient and cost-effective vehicle detection and tracking system for collision avoidance in foggy weather. ACCESS, 13, 126525-126556. (www)

  @article{ , abstract = {An Efficient and Cost-Effective Vehicle Detection and Tracking (ECE-VDT) system is proposed and is essential for real-time collision avoidance and driver assistance in foggy weather conditions. The proposed ECE-VDT system encompasses an optimized SimYOLO-V5s algorithm and its five variants for vehicle detection, while the optimized deep-SORT algorithm is proposed for vehicle tracking. Several design changes are proposed to enhance the SimYOLO-V5s variants, aiming to optimize the baseline YOLO-V5s algorithm. The backbone network of the baseline YOLO-V5s algorithm is optimized by integrating the SimSPPF module, a variant of the standard SPPF module. Furthermore, diverse distance metrics and loss functions, such as CIOU, DIOU, EIOU, GIOU, and SIOU, are considered to enhance the baseline YOLO-V5s bounding box localization accuracy. These design changes significantly improved the baseline YOLO-V5s vehicle detection performance. The optimized deep-SORT algorithm is proposed by utilizing the SiLU activation function in the CNN network of the baseline deep-SORT algorithm, compared to the ReLU activation function. The self-annotated and customized state-of-the-art foggy weather vehicle detection datasets, Vehicle Detection in Adverse Weather Nature (DAWN), Foggy Driving (FD), and Foggy Cityscapes (FC), are considered for evaluating the vehicle detection performance of baseline and SimYOLO-V5s variants in adverse weather conditions. Extensive and rigorous experiments on cloud and local GPU-enabled systems show that the optimized SimYOLO-V5s variants, more specifically SimYOLO-V5s_GIOU outperformed in mAP50 multi-class vehicle detection by $1.6~\%$ on the DAWN dataset, $15.2~\%$ on the FD dataset, and $3.4~\%$ on the FC dataset compared to baseline YOLO-V5s. The optimized SimYOLO-V5s variants also outperformed state-of-the-art algorithms in accuracy and speed. The optimized deep-SORT outperforms pre-processing, inference, NMS post-processing, and update time, achieving higher FPS on foggy and the BDD100K dataset video sequences compared to the baseline deep-SORT and strong-SORT vehicle tracking algorithms. The proposed ECE-VDT system is an efficient and cost-effective solution to assist human-centric drivers of low-end vehicles and autonomous vehicles to avoid road collisions in foggy weather.}, archive = {J_ACCESS}, author = {Naeem Raza and Muhammad Asif Habib and H. M. Sajid Imran and Abdul Qayum and Sajida Perveen and Sohail Jabbar and Shahzad Sarwar and Mudassar Ahmad}, doi = {10.1109/ACCESS.2025.3588376}, journal = {IEEE Access}, pages = {126525-126556}, shortjournal = {IEEE Access}, title = {An efficient and cost-effective vehicle detection and tracking system for collision avoidance in foggy weather}, volume = {13}, year = {2025}, }
• (2025). A high-efficiency silicon-carbide bidirectional industrial switched reluctance motor drive. ACCESS, 13, 126513-126524. (www)

  @article{ , abstract = {Switched reluctance motors (SRMs), known for their simple construction, high efficiency, and robustness, are increasingly being adopted across various applications. In an industrial setting, these motors offer a promising alternative to traditional induction motors (IMs), particularly due to their lower sensitivity to temperature and ability to perform reliably in harsh environments. While commercially available variable frequency drives (VFDs) are readily accessible for IMs, the same is not true for SRMs, limiting their broader adoption in the industry. This paper presents the design and implementation of a high-efficiency, integrated, bidirectional drive for an SRM for industrial applications. An analysis of the current SRM market, along with a review of related research, has been conducted. Based on this analysis, existing gaps in SRM drive solutions have been identified, and the design requirements for the proposed drive have been defined. Subsequently, a 10 kW silicon-carbide (SiC)-based converter topology is designed, developed, and experimentally validated. The proposed converter achieves a peak efficiency of 97.03% at the power factor correction (PFC) stage and 98.90% at the motor drive stage under nominal power conditions.}, archive = {J_ACCESS}, author = {Francisco Juarez-Leon and Berker Bilgin}, doi = {10.1109/ACCESS.2025.3590302}, journal = {IEEE Access}, pages = {126513-126524}, shortjournal = {IEEE Access}, title = {A high-efficiency silicon-carbide bidirectional industrial switched reluctance motor drive}, volume = {13}, year = {2025}, }
• (2025). Enhancing energy efficiency in IoT-WSNs through optimized PSO cluster head selection. ACCESS, 13, 126496-126512. (www)

  @article{ , abstract = {In IoT-based wireless sensor networks (WSNs), clustering is one of the most effective energy-saving approaches for optimizing the life cycle of networks. The primary challenge in the multi-hop technique arises when cluster heads (CHs) located near the base station quickly deplete their energy owing to the high volume of inter-cluster relay traffic. Therefore, a clustering protocol must be both fault-tolerant and energy-efficient. This paper proposes a particle swarm optimization (PSO) based clustering algorithm that incorporates a double-exponential adaptive inertia weight update to balance global exploration and local exploitation dynamically. This adaptive mechanism mitigates premature convergence, improves the accuracy of CH selection, and enhances overall energy efficiency in the network. By classifying sensor nodes into cluster members, CHs, and free nodes, this intelligent swarm-based algorithm is used to maximize CH selection in IoT-WSNs. The residual energy of the nodes is utilized to determine the optimal number of clusters during cluster formation dynamically. Subsequently, the algorithm allocates nodes into clusters of uniform size, ensuring that high-energy nodes are efficiently assigned as CHs based on their location data. The proposed algorithm is evaluated across various network topologies and compared with four benchmark WSN routing algorithms: LEACH, LEACH-FL, LEACH-FC, and KM-PSO. The results indicate that the proposed algorithm outperforms LEACH by approximately 28%, LEACH-FL and LEACH-FC by around 20%, and KM-PSO by nearly 10% in terms of node survivability and network longevity. Besides that, the proposed algorithm enhances energy efficiency in IoT-based WSNs by selecting cluster heads with high neighborhood density and strategically using intermediate nodes, thereby reducing communication distances, balancing transmission load, and extending network lifetime. These findings highlight the effectiveness of the proposed algorithm in enhancing energy efficiency and prolonging the operational lifetime of IoT-enabled WSNs.}, archive = {J_ACCESS}, author = {Muhammad Haris and Haewoon Nam}, doi = {10.1109/ACCESS.2025.3583922}, journal = {IEEE Access}, pages = {126496-126512}, shortjournal = {IEEE Access}, title = {Enhancing energy efficiency in IoT-WSNs through optimized PSO cluster head selection}, volume = {13}, year = {2025}, }
• (2025). TREET: TRansfer entropy estimation via transformers. ACCESS, 13, 126477-126495. (www)

  @article{ , abstract = {Transfer entropy (TE) is an information theoretic measure that reveals the directional flow of information between processes, providing valuable insights for a wide range of real-world applications. This work proposes Transfer Entropy Estimation via Transformers (TREET), a novel attention-based approach for estimating TE for stationary processes. The proposed approach employs Donsker-Varadhan representation to TE and leverages the attention mechanism for the task of neural estimation. We propose a detailed theoretical and empirical study of the TREET, comparing it to existing methods on a dedicated estimation benchmark. To increase its applicability, we design an estimated TE optimization scheme that is motivated by the functional representation lemma, and use it to estimate the capacity of communication channels with memory, which is a canonical optimization problem in information theory. We further demonstrate how an optimized TREET can be used to estimate underlying densities, providing experimental results. Finally, we apply TREET to feature analysis of patients with Apnea, demonstrating its applicability to real-world physiological data. Our work, applied with state-of-the-art deep learning methods, opens a new door for communication problems which are yet to be solved.}, archive = {J_ACCESS}, author = {Omer Luxembourg and Dor Tsur and Haim Permuter}, doi = {10.1109/ACCESS.2025.3588009}, journal = {IEEE Access}, pages = {126477-126495}, shortjournal = {IEEE Access}, title = {TREET: TRansfer entropy estimation via transformers}, volume = {13}, year = {2025}, }
• (2025). Incorporation of piezoelectric materials in military uniforms. ACCESS, 13, 126465-126476. (www)

  @article{ , abstract = {This research deals with one of the possible ways of energy harvesting in military missions, using piezoelectricity as a fundamental basis for generating this energy. It harnesses the energy of the soldier when moving, especially on the battlefield or during long missions where a soldier needs external sources of energy to charge the batteries of their electronic devices. Since piezoelectric materials generate electrical potential at their terminals when mechanically stressed, this property makes them useful and favourable for realising this project. These materials can be either piezoceramic or piezopolymer and are available in various forms in the market. Therefore, to enhance the system’s efficiency, an electronic circuit for energy harvesting and self-powering is implemented to harvest the maximum energy generated by the piezoelectrics. Several circuits are studied and the best one is implemented in a Printed Circuit Board (PCB) prototype. Currents between 40 and $80~\mu $ A and voltages between 3.5 and 10.1 V are obtained before the voltage regulator. The maximum recorded power was approximately $812~\mu $ W, higher than the power reported in several recent articles. However, between 100 and $200~\mu $ W are obtained after the voltage regulation to 4.2 V. At the end of a day, the storage is expected to be around a unit of mAh, which could be enough to increase the autonomy of various devices used on missions, such as radios, GPS and torches, and even the possibility of activating emergency systems such as very lights or audible warning systems.}, archive = {J_ACCESS}, author = {Rodrigues de Fátima Jacinto and Ricardo A. Marques Lameirinhas and Catarina P. Correia V. Bernardo and João Paulo N. Torres and P. Mendonça Dos Santos and Marcelino Santos and António Baptista}, doi = {10.1109/ACCESS.2025.3588258}, journal = {IEEE Access}, pages = {126465-126476}, shortjournal = {IEEE Access}, title = {Incorporation of piezoelectric materials in military uniforms}, volume = {13}, year = {2025}, }
• (2025). Investigating the transferability of TOG adversarial attacks in YOLO models in the maritime domain. ACCESS, 13, 126444-126464. (www)

  @article{ , abstract = {In recent years, CNN-based object detectors have been widely adopted in autonomous systems. Although their capabilities are employed across various industries, these detectors are inherently susceptible to adversarial attacks. Despite extensive studies on their effects on image classification, adversarial attacks remain largely unexplored in object detection. In particular, we note the reduced number of studies employing benchmarks for these types of attacks. Object detectors can be easily deceived by adding carefully devised perturbations to their inputs, rendering them unreliable. This study investigates the transferability of one such adversarial attack type, the Targeted Objectness Gradient (TOG), on different variations of the YOLO architecture to formally assess its vulnerability under different scenarios in the maritime domain. To investigate the significance of TOG adversarial attacks across variations of YOLO architectures and combinations of maritime datasets (all publicly available), we conducted a statistical analysis of black-box and white-box attacks. Our research questions were formulated to address a range of concerns that encompass various complexities to be considered in the detection of maritime objects. Our presented results underline the transferable nature of TOG adversarial attacks and the compelling need to benchmark such attacks in the maritime object detection domain.}, archive = {J_ACCESS}, author = {Phornphawit Manasut and Md Saleh Ibtasham and Zeynep Yaradanakul and Sepinoud Azimi and Sébastien Lafond and Bogdan Iancu}, doi = {10.1109/ACCESS.2025.3589512}, journal = {IEEE Access}, pages = {126444-126464}, shortjournal = {IEEE Access}, title = {Investigating the transferability of TOG adversarial attacks in YOLO models in the maritime domain}, volume = {13}, year = {2025}, }
• (2025). An iterative power gradient searching algorithm toward the slot and power joint scheduling problem in MANET broadcast. ACCESS, 13, 126432-126443. (www)

  @article{ , abstract = {Broadcast is a fundamental network service in mobile ad hoc networks (MANETs). Despite a plethora of research on ad hoc networks’ broadcast, there has been minimal research on slot and power joint scheduling. In this study, we focused our investigation on slot and power joint broadcast scheduling to maximize the throughput under a dynamic topology with a minimum delay and energy. Due to the difficulty of multi-target optimization in the dynamic topology, we decoupled the problem into two subproblems iterating with each other: minimum-delay slot scheduling and maximum-throughput minimum-energy power gradient searching, both of which process the primary topology considering the mobility of the nodes, thus forming the algorithm Iterative Power Gradient Searching. In the power gradient searching, we exploited the Monte Carlo Tree Search by utilizing the hierarchical nature of multihop transmission to reduce complexity. Through theoretical proof and extensive simulations, we demonstrated that Iterative Power Gradient Searching achieved a maximum throughput in the dynamic topology with a minimum delay and energy approximately.}, archive = {J_ACCESS}, author = {Xu Zhao and Zuyao Ni and Hanyu Tian}, doi = {10.1109/ACCESS.2024.3397792}, journal = {IEEE Access}, pages = {126432-126443}, shortjournal = {IEEE Access}, title = {An iterative power gradient searching algorithm toward the slot and power joint scheduling problem in MANET broadcast}, volume = {13}, year = {2025}, }
• (2025). The dangerous effects of a frustratingly easy LLMs jailbreak attack. ACCESS, 13, 126418-126431. (www)

  @article{ , abstract = {Large Language Models (LLMs) are employed in various applications, including direct end-user interactions. Ideally, they should consistently generate both factually accurate and non-offensive responses, and they are specifically trained and safeguarded to meet these standards. However, this paper demonstrates that simple, manual, and generalizable jailbreaking attacks, such as reasoning backward, can effectively bypass the safeguards implemented in LLMs, potentially leading to harmful consequences. These include the dissemination of misinformation, the amplification of harmful recommendations, and toxic comments. Furthermore, these attacks have been found to reveal latent biases within LLMs, raising concerns about their ethical and societal implications. In particular, the vulnerabilities exposed by such attacks appear to be generalizable across different LLMs and languages. This paper also assesses the effectiveness of a straightforward architectural framework to mitigate the impact of jailbreak attacks on end users.}, archive = {J_ACCESS}, author = {Marco Bombieri and Simone Paolo Ponzetto and Marco Rospocher}, doi = {10.1109/ACCESS.2025.3589112}, journal = {IEEE Access}, pages = {126418-126431}, shortjournal = {IEEE Access}, title = {The dangerous effects of a frustratingly easy LLMs jailbreak attack}, volume = {13}, year = {2025}, }
• (2025). The ITTT—Intelligent touch, talk, teach: Smart sculpture for gamifying heritage. ACCESS, 13, 126406-126417. (www)

  @article{ , abstract = {Over the centuries, mankind has created and utilized many values. It is the task of subsequent generations to preserve and protect these values and pass them on to future generations. These values vary in size, from very small to gigantic. The question naturally arises as to how this cultural heritage can not only be protected but also made accessible to a wider audience. In this study, new technologies, especially from the field of information technology, are used to enable a playful approach to cultural heritage. Technologies such as 3D printing, 3D scanning are used to print a three-dimensional sculpture which, with gamification approaches, becomes an intelligent tool. With the help of this intelligent sculpture, which enables an interaction through touch and voice, access to the history and background of the artefact is made possible in a playful way. The interaction with the sculpture is presented to different user groups and experiences and expectations are analyzed. Although this work is mainly focused on the gamification of cultural heritage, the approach can be extended and applied to many different areas of learning and teaching.}, archive = {J_ACCESS}, author = {Samsun Mustafa Başarici and Ahmet Çağdaş Seçkin and Célio Gonçalo Marques and Tatiana Barradas}, doi = {10.1109/ACCESS.2025.3589666}, journal = {IEEE Access}, pages = {126406-126417}, shortjournal = {IEEE Access}, title = {The ITTT—Intelligent touch, talk, teach: Smart sculpture for gamifying heritage}, volume = {13}, year = {2025}, }
• (2025). Tracking E-learning for arabic dyslexia through published papers: A systematic review. ACCESS, 13, 126390-126405. (www)

  @article{ , abstract = {E-learning provides asynchronous learning to anyone regardless of time and location. It can be useful for individuals with dyslexia. Dyslexia is a reading difficulty that affects individuals’ lives. It can have a wide range of symptoms that vary depending on language. Since Arabic is a widely used language, it was targeted in this review. This paper aims to highlight existing research on e-learning for individuals with Arabic dyslexia in order to understand the current state of research, understand the benefits of existing e-learning systems, discover the limitations of this research and point to future research directions. Therefore, we conducted a systematic literature review (SLR) by comprehensively searching six scientific literature databases, selecting studies through inclusion and exclusion criteria, assessing the quality of the selected studies and analysing and synthesising these studies to draw conclusions. The main findings are there is a dearth of e-learning research targeting individuals with Arabic dyslexia, recent research has focused on training individuals with Arabic dyslexia on already-taught topics, primarily in the domain of reading skills, and there were widespread weaknesses with experimental samples, evaluation methods, and the analysis and interpretation of results. Therefore, this research contributes to directing the research proposals by considering these findings. Also, a conceptual model is proposed to facilitate the design of inclusive e-learning systems for Arabic dyslexia to balance learner requirements with adaptive design and accessible experiences.}, archive = {J_ACCESS}, author = {Al-Dawsari M. Hadeel and Weam G. Alghabban}, doi = {10.1109/ACCESS.2025.3590287}, journal = {IEEE Access}, pages = {126390-126405}, shortjournal = {IEEE Access}, title = {Tracking E-learning for arabic dyslexia through published papers: A systematic review}, volume = {13}, year = {2025}, }
• (2025). True-time-delay unit cell for ultra-wideband single- and wideband dual-polarized large reflectarrays. ACCESS, 13, 126376-126389. (www)

  @article{ , abstract = {This paper presents the design and experimental validation of novel ultra-wideband true-time-delay (TTD) reflectarray unit cells for large-scale antenna applications. Two configurations are proposed: a single-polarized unit cell and a dual-polarized unit cell, both utilizing aperture-coupled stacked microstrip circular patches. The single-polarized unit cell employs a parasitic patch, an hourglass-shaped aperture, and a fork-shaped delay line, achieving a linear phase range exceeding 1040° at 13.1 GHz with a phase error below 25° and a linear phase bandwidth of 53% (9.6–16.6 GHz). A reflectarray prototype demonstrates a 3-dB gain bandwidth of 44.4% (10.5–16.5 GHz), with cross-polarization below -30 dB, sidelobe levels better than −13.5 dB, and aperture efficiency up to 55%. The dual-polarized unit cell incorporates perpendicular bowtie-shaped apertures with fork-shaped feeds, enabling independent phase control for both polarizations with a phase range over 1140° at 12.6 GHz, a phase error below 40°, and a linear phase bandwidth of 32% (10.7–14.5 GHz). A dual-polarized reflectarray achieves a 3-dB gain bandwidth of 36.1%, maximum gains of 32 dBi and 31.75 dBi for orthogonal polarizations, cross-polarization below −26 dB, sidelobe levels below −13 dB, and aperture efficiency up to 50%. Both designs exhibit low loss, stable beam direction, and high scalability, significantly advancing wideband reflectarray performance for satellite and communication systems.}, archive = {J_ACCESS}, author = {Reza Shamsaee Malfajani and Karim Glatre and Jean-Jacques Laurin}, doi = {10.1109/ACCESS.2025.3590261}, journal = {IEEE Access}, pages = {126376-126389}, shortjournal = {IEEE Access}, title = {True-time-delay unit cell for ultra-wideband single- and wideband dual-polarized large reflectarrays}, volume = {13}, year = {2025}, }
• (2025). Characterization of wi-fi access point energy consumption and the impact of IEEE 802.11 standard evolutions. ACCESS, 13, 126355-126375. (www)

  @article{ , abstract = {Wi-Fi networks have become increasingly prevalent, contributing notably to global energy consumption—particularly through access points (APs). This study offers a rigorous analysis of the primary energy consumption characteristics of modern enterprise-grade APs, focusing on advancements in the IEEE 802.11 standard from Wi-Fi 4 (802.11n) to Wi-Fi 7 (802.11be). Addressing existing gaps in the literature, we first provide practical guidance for network engineers on how parameters such as transmit power, communication bandwidth, and standard evolution affect AP energy usage. Second, we deliver insights for AP manufacturers—particularly those developing future Wi-Fi generations—by identifying key design considerations to enhance energy efficiency. In this regard, our findings reveal that newer Wi-Fi standards do not inherently result in proportionally higher power consumption. Instead, energy usage is more significantly influenced by factors such as multiple input multiple output (MIMO) configurations, the number of spatial streams, supported frequency bands, bandwidth capabilities, and the number of active clients or SSIDs. However, if we consider that the evolution of the IEEE 802.11 standard has consistently introduced new features at both the physical and upper layers—such as increased spatial streams through larger antenna arrays in MIMO configurations—then it becomes evident that standard evolution does contribute to a rising energy cost in APs. As for AP components like the power amplifier, we observe that while higher transmit power increases consumption under heavy traffic, simply lowering it does not necessarily improve efficiency. In some cases, reduced transmit power degrades the modulation and coding scheme (MCS), potentially leading to greater energy expenditure. Bandwidth utilization also plays a critical role: contrary to common assumptions, increasing bandwidth does not always equate to higher energy usage. Rather, dynamic bandwidth management—responsive to traffic conditions—can contribute meaningfully to improved energy efficiency. These findings emphasize the importance of designing Wi-Fi APs that balance energy efficiency with high performance, aligning with the growing demand for sustainable wireless infrastructure. Furthermore, this study proposes a unified and comprehensive model for AP energy consumption that incorporates both hardware and software contributions, along with key operational parameters such as transmission states, MCS level, channel bandwidth, and traffic load. Unlike traditional static or linear models that overlook dynamic behavior and upper-layer processing costs, our model better captures the complexity of real-world AP usage.}, archive = {J_ACCESS}, author = {Hamidou Dembélé}, doi = {10.1109/ACCESS.2025.3590176}, journal = {IEEE Access}, pages = {126355-126375}, shortjournal = {IEEE Access}, title = {Characterization of wi-fi access point energy consumption and the impact of IEEE 802.11 standard evolutions}, volume = {13}, year = {2025}, }
• (2025). An intelligent audio encryption and compression algorithm inspired by the encoding of various biological sequences. ACCESS, 13, 126334-126354. (www)

  @article{ , abstract = {During the last decade, audio streams became an essential and fast means of communication through personal and business applications including social media and telehealth applications. Thus, various research efforts tried to develop robust and secure audio encryption algorithms that keep audio communications secure to the highest extent. Biological sequences retain huge amount of information which present new horizon over legacy encryption algorithms in terms of encoding capacity. This article introduces an intelligent audio encryption and compression framework, namely Audio-to-Peptide (A2P), that mimics the successive generation of biological sequences to successively encrypt and compress sequences of frames in raw WAV audio files. The parameters of the basic encryption key include some general information of the audio file in addition to some technical information that is based on the frequency of the audio to be encrypted. Hence, the proposed framework uses an Artificial Neural Network (ANN) model that was trained to accurately determine these technical parameters of the basic encryption key without any user involvement. The experimental results showed that the proposed algorithm is robust and secure against known security attacks.}, archive = {J_ACCESS}, author = {Mohammad Nassef}, doi = {10.1109/ACCESS.2025.3588764}, journal = {IEEE Access}, pages = {126334-126354}, shortjournal = {IEEE Access}, title = {An intelligent audio encryption and compression algorithm inspired by the encoding of various biological sequences}, volume = {13}, year = {2025}, }
• (2025). DB-net: A dual-branch hybrid network for stroke lesion segmentation on non-contrast CT images. ACCESS, 13, 126319-126333. (www)

  @article{ , abstract = {Lesion segmentation in acute ischemic stroke (AIS) is critical for accurate diagnosis, with non-contrast CT (NCCT) serving as the primary imaging modality. However, the nature of NCCT often results in low contrast and blurred lesion boundaries, creating significant challenges for accurate segmentation. These limitations can lead to omissions, misdiagnoses, or inaccurate segmentations, directly impacting clinical assessment and timely intervention. To address these challenges, this study proposes a two-branch hybrid network combining a convolutional neural network (CNN) with a Transformer framework. The proposed architecture features the aforementioned dual-branch encoder, as well as a spatial channel difference learning module and parallel attention module. The CNN branch of the encoder performs local feature extraction whereas the Transformer branch captures global dependencies, overcoming limitations of CNN in modeling long-range relationships. The spatial channel difference learning module with multiscale parallel subnetworks enhances feature interaction between branches. Additionally, the parallel attention module in the decoder improves the detection of low-contrast lesion boundaries. Experiments on publicly available AIS datasets demonstrate the effectiveness of the proposed method, achieving a Dice score of 59.11%, with improvements of 4.2% in Dice, 10.6% in Precision, and 5.0% in Jaccard over the best-performing state-of-the-art method. The proposed approach overcomes segmentation challenges in noisy NCCT images and enhances automated methods for AIS diagnosis.}, archive = {J_ACCESS}, author = {Xiao Jia and He Dong and Jiashu Xu and Yanhong Zhang and Yihua Lan}, doi = {10.1109/ACCESS.2025.3586745}, journal = {IEEE Access}, pages = {126319-126333}, shortjournal = {IEEE Access}, title = {DB-net: A dual-branch hybrid network for stroke lesion segmentation on non-contrast CT images}, volume = {13}, year = {2025}, }
• (2025). Enhanced transformer network with high-dimensional attention mechanism for diabetic retinopathy classification. ACCESS, 13, 126307-126318. (www)

  @article{ , abstract = {Diabetic Retinopathy (DR) is a severe condition that affects diabetic patients, potentially leading to irreversible vision loss if not addressed in its early stage. DR is classified into two types: Non-Proliferative DR (NPDR), the initial stage, and Proliferative DR (PDR), the advanced stage. The progression of DR includes mild NPDR, moderate NPDR, severe NPDR, and PDR. Various deep learning-based detection and classification algorithms have been developed to identify and categorize the disease. Prominent among them are Convolutional Neural Networks (CNN), Recurrent Neural Networks, Generative Adversarial Networks, and the Vision Transformer (ViT). This study aims at developing an efficient model for precise DR classification. This work proposes an enhanced transformer network, termed Vision Transformer with High Dimensional Attention (HDA-ViT), which incorporates high-dimensional spatial and channel attention ahead of patch embedding in ViT. The rationale behind incorporating this dual attention mechanism is to make the ViT focus on the most relevant portion of images. In addition, the classification head present in the ViT block incorporates sequential dropout layer in addition to the standard linear dropout layer. For experimentation, APTOS 2019 fundus image dataset containing 3662 images falling under 5 classes is used. Experimental results show that the proposed HDA-ViT network results in a classification accuracy of 99.32%, and outperforms the state-of-the-art techniques for DR classification.}, archive = {J_ACCESS}, author = {M. Rizvana and Sathiya Narayanan}, doi = {10.1109/ACCESS.2025.3589449}, journal = {IEEE Access}, pages = {126307-126318}, shortjournal = {IEEE Access}, title = {Enhanced transformer network with high-dimensional attention mechanism for diabetic retinopathy classification}, volume = {13}, year = {2025}, }
• (2025). 3D-printed lightweight metamaterial absorber using rectangular perforations. ACCESS, 13, 126299-126306. (www)

  @article{ , abstract = {Metamaterial absorbers (MMAs) are designed to achieve near-total absorption of electromagnetic radiation. In radio-frequency fields, MMAs exhibit absorption behavior that stems from RF resonance. Thus, MMAs are useful in fields such as stealth coatings. However, the use of MMA unit cells for stealth applications is limited due to their heavy weight. In this paper, we demonstrate that a weight reduction of up to 61.31% and a 66.22% decrease in PLA volume can be achieved in a Jerusalem Cross MMA unit cell, with minimal impact on key absorptive properties such as resonant frequency and absorptivity. This was accomplished by selectively removing material from the corners and near-edge regions of the dielectric layer within the unit cell. The simulation results were verified with an array consisting of $12\times 12$ MMA unit cells, where resonant behavior was observed at 10.33 GHz with an absorptivity of 99.84%. Polylactic acid was used as the dielectric material for 3D printing, and the conductive surfaces were fabricated using silver paste. The results of this study demonstrate the feasibility of lightweight MMA designs for real-life applications.}, archive = {J_ACCESS}, author = {Junhyuk Ahn and Heeju Jwa and Minjun Kim and Yongwoo Cho and Kyounghwan Kim and Prabhakar Jepiti and Sungjoon Lim}, doi = {10.1109/ACCESS.2025.3589588}, journal = {IEEE Access}, pages = {126299-126306}, shortjournal = {IEEE Access}, title = {3D-printed lightweight metamaterial absorber using rectangular perforations}, volume = {13}, year = {2025}, }
• (2025). Continuous mixture of uniforms framework for modeling residual lifetime distribution. ACCESS, 13, 126279-126298. (www)

  @article{ , abstract = {In this paper, a novel mathematical framework based on Continuous Mixture of Uniforms (CMU) for modeling the Residual Lifetime physical phenomenon is proposed. Through an alternative and intuitively comprehensible mathematical analysis, both the mixing distribution and the Residual Lifetime Theorem as a function of the Lifetime distribution are derived, providing a novel perspective compared to existing literature. The applicability of the derived mathematical expressions is demonstrated by obtaining the probability distribution function and statistical properties of the residual lifetime when the lifetime follows various commonly used distributions, including the negative exponential, log-normal, gamma, n-th order hyper-exponential, Pareto, and Weibull distributions. Furthermore, we present new mathematical expressions for generating random numbers with the Residual Lifetime distribution when the Lifetime follows either a log-normal or Weibull distribution. The proposed CMU framework is also employed to establish explicit relationships between distribution and moments of the Residual Lifetime and of the Lifetime. By using the conditional moments of the uniform random variables that constitute the CMU, functional relationships between both raw and standardized moments of residual lifetime and standardized moments of the lifetime are derived. Finally, numerical results graphically illustrate the statistical relationships between both the distribution and moments of the lifetime, mixing distribution, and residual lifetime, providing valuable insights into their interdependencies.}, archive = {J_ACCESS}, author = {Felipe A. Cruz-Pérez and Sandra Lirio Castellanos-López and Genaro Hernández-Valdez and Mario Eduardo Rivero-Ángeles}, doi = {10.1109/ACCESS.2025.3589531}, journal = {IEEE Access}, pages = {126279-126298}, shortjournal = {IEEE Access}, title = {Continuous mixture of uniforms framework for modeling residual lifetime distribution}, volume = {13}, year = {2025}, }
• (2025). Distinctive contribution of sound spectral features in enhancing vibration-based multi-component fault classification under non-stationary speed conditions. ACCESS, 13, 126261-126278. (www)

  @article{ , abstract = {Rotational machines under variable speed conditions often confront challenges to diagnose various component faults as an existing problem with less exploration of sound signals in this subject area. This premise motivates the paper to contribute the prodigious sound signal features to the vibration-based multi-component fault recognition at speeds ranging from 200 to 2200 rpm. With this novelty, as an added innovation, how each signal-based metric is influencing the classification of different faults in each component is examined. To begin with, speed synchronizing instantaneous frequency (IF) with two signal envelopes of vibration signals are individually fed to the machine learning (ML) classifier, such as Decision Tree (DT), Support Vector Machine-Radial Basis Function (SVM-RBF), and Artificial Neural Network (ANN), to verify the model performance. It is realized that the shaft is highly misclassified by fusing these vibration signal features. To enhance the ML effects and also to reduce the shaft’s misclassification, sound signal features such as Mel Frequency Cepstral Coefficients (MFCCs), spectral centroid, and zero-crossing rate are incorporated to evidence its significance. Besides this feature fusion, the comparison of vibration and sound data properties for bearing, gear, and shaft proves the effectiveness of sound-based parameters towards shaft and gear classification but less impact on bearings. Further reassessment of sound spectral attributes in differentiating gear faults clarifies its utility through SHAP, cross-domain adaptation, covariance, and correlation analysis. This imparts an interpretation of integrating vibration- and sound-based features, which attains 83.84% classification accuracy for ANN with a better F1 score than vibration-based features. Thus, distinctively integrating the sound spectral attributes to the vibration signal features under varying speeds demonstrates an improvement in multi-component fault prediction.}, archive = {J_ACCESS}, author = {S. Sowmya and M. Saimurugan and Naveen Venkatesh}, doi = {10.1109/ACCESS.2025.3587652}, journal = {IEEE Access}, pages = {126261-126278}, shortjournal = {IEEE Access}, title = {Distinctive contribution of sound spectral features in enhancing vibration-based multi-component fault classification under non-stationary speed conditions}, volume = {13}, year = {2025}, }
• (2025). Digital presentation and interactive learning for intangible cultural heritage preservation using artificial intelligence. ACCESS, 13, 126245-126260. (www)

  @article{ , abstract = {The preservation of intangible cultural heritage (ICH) faces significant and multifaceted challenges due to its ephemeral nature, reliance on oral traditions, and contextual embeddedness within lived cultural experiences. Traditional preservation approaches—such as textual documentation, static archiving, and audiovisual recordings—often fall short in capturing the dynamic, embodied, and performative characteristics that define ICH practices. To overcome these limitations, we propose an innovative computational framework that integrates advanced neural representations with structured symbolic logic and contextual grounding mechanisms. We introduce a novel neural-symbolic architecture capable of modeling the fluid, multimodal, and socially constructed nature of intangible cultural knowledge. Our approach includes a culturally informed reasoning strategy that enables the system to align observed cultural signals with both canonical forms and evolving variants within a specific tradition. This is further enhanced by a self-supervised semiotic alignment module, which dynamically adapts through iterative engagement with context-specific cues and emergent performative deviations. By leveraging cutting-edge artificial intelligence, our framework enables the digital preservation, interactive representation, and inclusive transmission of ICH, ensuring its resilience, relevance, and accessibility across generations and communities in a rapidly evolving global landscape.}, archive = {J_ACCESS}, author = {Liuxun Zhang and Zhouluo Wang and Rulan Yang and Qiang Yi}, doi = {10.1109/ACCESS.2025.3588520}, journal = {IEEE Access}, pages = {126245-126260}, shortjournal = {IEEE Access}, title = {Digital presentation and interactive learning for intangible cultural heritage preservation using artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Transport capacity maximization in rail mass transit stations under uncertainty. ACCESS, 13, 126227-126244. (www)

  @article{ , abstract = {Urban railways play a critical role in promoting sustainable transportation by providing high-capacity, efficient, and low-emission alternatives to road-based travel. As cities continue to grow, the demand for mass transit rail transport is steadily increasing, posing significant challenges to existing infrastructure, particularly at terminal stations, which often become bottlenecks due to their complex operations. Addressing these capacity limitations is essential not only for improving the quality of service but also for supporting the broader goals of urban sustainability. This paper introduces a two-level optimization model designed to maximize the practical transport capacity of mass transit railway stations. At the upper level, a genetic algorithm determines the optimal sequence of train services and platform assignments. These solutions are then evaluated by a linear programming model at the lower level to estimate the resulting transport capacity. For a more realistic assessment of practical capacity, fuzzy modelling is employed to manage uncertainties in train delays and operational variations. The model incorporates critical operational constraints of urban railways, including minimum turn-around times, track occupancy, and constant headways between train services of the same type. A detailed simulation model is used to evaluate route intervals based on train dynamics and signalling systems. Simulation results based on real-world data demonstrate the model’s effectiveness in maximizing capacity under uncertain conditions and different operational schemes, contributing to more efficient and sustainable mass transit railway systems. This research highlights the importance of optimizing rail transport capacity as a key component of sustainable urban development.}, archive = {J_ACCESS}, author = {Adrián Fernández-Rodríguez and María Domínguez and Asunción P. Cucala and Antonio Fernández-Cardador}, doi = {10.1109/ACCESS.2025.3589009}, journal = {IEEE Access}, pages = {126227-126244}, shortjournal = {IEEE Access}, title = {Transport capacity maximization in rail mass transit stations under uncertainty}, volume = {13}, year = {2025}, }
• (2025). Rethinking vehicle architecture through softwarization and servitization. ACCESS, 13, 126213-126226. (www)

  @article{ , abstract = {The automotive industry is undergoing a foundational transformation driven by software-defined vehicles (SDVs), where software not only orchestrates vehicle functionality but also redefines the value proposition through servitization, making services a key differentiator rather than the physical product. This paper explores the industry’s shift from a hardware-centric paradigm to one defined by software and presents a comprehensive review of SDVs, structured around a systematic analysis of scientific publications and patents. It lays out the evolution from a distributed to a centralized compute architecture and discusses the promises of SDVs and their challenges. In particular, the paper highlights critical obstacles including fragmented certification frameworks, lack of standardized toolchains, limited support for continuous validation, and rising complexity in software maintenance and repair. Additional concerns include vulnerability management, long-term software support, supply chain decentralization, regulatory compliance, and the absence of open datasets, simulators, and benchmarking tools. The software-centric nature of SDVs also imposes new demands on hardware scalability and future-proofing. Finally, we highlight future directions and research opportunities such as ecosystem collaboration, open innovation, open and standardized datasets, open education simulators and testbeds, quantifiable Quality of Experience (QoX), cognitive data sampling, and contextual observability. This work offers a structured and forward-looking perspective on how SDVs are fundamentally reshaping automotive technology and business models toward intelligent, service-centric mobility ecosystems. To fully realize this potential, several technical, operational, regulatory, and organizational challenges must be addressed across the SDV lifecycle.}, archive = {J_ACCESS}, author = {Alaa Khamis and Partha Goswami}, doi = {10.1109/ACCESS.2025.3588432}, journal = {IEEE Access}, pages = {126213-126226}, shortjournal = {IEEE Access}, title = {Rethinking vehicle architecture through softwarization and servitization}, volume = {13}, year = {2025}, }
• (2025). Localized sequential component selection and matching scheme based on control protection collaboration. ACCESS, 13, 126202-126212. (www)

  @article{ , abstract = {To solve the problem of failure of photovoltaic phase selection element in grid-connected systems, this paper proposes a phase selection capability recovery scheme for phase selection element based on control and protection coordination. The key condition for restoring the phase-selective capability is to utilize the inverter power supply to adjust the positive and negative-sequence current distribution factors. Through theoretical analysis, load and energy storage are utilized to adjust the above two values to the same, which in turn achieves the correct action of relay protection on the PV storage side. The simulation results based on the PSCAD/EMTDC model show that the control strategy proposed in this paper can effectively restore the phase selection ability of the phase selection element under various complex fault conditions, and has high sensitivity.}, archive = {J_ACCESS}, author = {Di Cheng and Qunshan Li and Yang Wang and Yanbo Huang and Zheng Yang and Tao Kang and Fanrong Wei and Xiangning Lin}, doi = {10.1109/ACCESS.2024.3396188}, journal = {IEEE Access}, pages = {126202-126212}, shortjournal = {IEEE Access}, title = {Localized sequential component selection and matching scheme based on control protection collaboration}, volume = {13}, year = {2025}, }
• (2025). The determinants of two-wheeled vehicle riders’ riding behavior and their consequences: A literature review, conceptual E-TPB model, and research agenda. ACCESS, 13, 126183-126201. (www)

  @article{ , abstract = {Human error, particularly risky riding behaviour, dominates the causes of 30% of the global fatalities involving two-wheeled vehicles such as bicycles, motorcycles, e-bikes and e-scooters. Previous review studies failed to develop an extension of the theory of Planned behaviour to investigate two-wheeler riders’ risk-taking behaviour on the road and overlook the environment, such road conditions, and vehicle-relevant factors that lead to risk behaviours. The current studies focus on cyclists, motorcyclists and e-bike and e-scooter riders to explore the underlying mechanisms of riding behaviours. This review used riding behaviour keywords to filter existing studies in the Web of Science to provide better insights into the complex dynamics of two-wheeled vehicle riders’ behaviours. Riding simulator, observation, naturalistic riding, interview, and questionnaire methods can be recombined in the study on two-wheeler riders’ riding behaviour. Moral norm is a significant predictor of intention. Younger and lower-income two-wheeler riders were more inclined to engage in risky riding behaviours, while marital status, experience, and education factors have varying influences under cultural content differences. This paper develops additional factors such as environment, vehicle usage, and vehicle control beliefs from selected papers’ findings, and incorporates other factors and these novel variables into the TPB. The research agenda is demonstrated to conduct the conceptual E-TPB model. The environment-vehicle-human E-TPB model highlights the psychological and non-psychological factors influencing rider behaviours. This paper bridges the gaps between under-explored factors for data collection and data processing methods. This study provides a unique perspective for improving road safety and a theoretical contribution.}, archive = {J_ACCESS}, author = {Haibing Yang and Muhamad Nazri Borhan and Ahmad Nazrul Hakimi Ibrahim and Fazilatulaili Ali}, doi = {10.1109/ACCESS.2025.3589125}, journal = {IEEE Access}, pages = {126183-126201}, shortjournal = {IEEE Access}, title = {The determinants of two-wheeled vehicle riders’ riding behavior and their consequences: A literature review, conceptual E-TPB model, and research agenda}, volume = {13}, year = {2025}, }
• (2025). Reflections of Fitness—Investigating user acceptance of smart fitness mirrors based on UTAUT2. ACCESS, 13, 126167-126182. (www)

  @article{ , abstract = {Driven by the growing demand for home-based exercise alternatives and further fuelled by the COVID-19 pandemic, smart fitness mirrors have rapidly emerged as a prominent solution in the fitness industry. While their technological and experiential features have been extensively explored, research on user acceptance—particularly the role of perceived interactivity—remains limited. This study extends the UTAUT2 model by integrating the construct of perceived interactivity and employs a mixed-methods approach combining PLS-SEM and fsQCA, based on 621 valid survey responses. The results reveal that habit, social influence, performance expectancy, and perceived interactivity are key determinants of behavioral intention, while effort expectancy, hedonic motivation, price value, and facilitating conditions were not significant among digitally fluent, higher-income users. Perceived interactivity not only directly influences behavioral intention but also significantly enhances performance expectancy and effort expectancy. Furthermore, fsQCA analysis identifies multiple causal configurations, confirming the central role of perceived interactivity in driving continued use. This study contributes to theory by refining the UTAUT2 model with an experiential dimension and demonstrates practical implications for designing interactive fitness technologies that foster user engagement, routine formation, and social reinforcement.}, archive = {J_ACCESS}, author = {Yejing Zhang and Shu Yang and Wei Wei}, doi = {10.1109/ACCESS.2025.3589248}, journal = {IEEE Access}, pages = {126167-126182}, shortjournal = {IEEE Access}, title = {Reflections of Fitness—Investigating user acceptance of smart fitness mirrors based on UTAUT2}, volume = {13}, year = {2025}, }
• (2025). Gradient-based optimization of all-pass filter networks for non-invasive electroanatomical mapping. ACCESS, 13, 126147-126166. (www)

  @article{ , abstract = {Existing approaches to non-invasive electroanatomical mapping face a fundamental challenge: accurately representing the continuous propagation velocities crucial for cardiac arrhythmia localization. Current methods either sacrifice precision by using discrete delays or require computationally intensive biophysical models that limit clinical applicability. We investigated interconnected all-pass filter networks as a novel middle ground that enables continuous, differentiable representation of cardiac propagation velocities while maintaining computational tractability. Through systematic analysis of these networks’ fundamental properties and scaling behavior, we demonstrate successful gradient-based optimization of propagation velocities in networks of up to 50 filters and 2D arrangements of $5\times 5$ voxels using magnetocardiographic measurements, while identifying critical scaling challenges in more complex geometries. Our experiments establish that reliable convergence requires at least 32-48 magnetic sensors operating below a noise threshold of approximately $\mathrm {7~pT/\sqrt {Hz}}$ . Runtime analysis shows linear computational scaling with system size, with GPU implementations achieving up to $\mathrm {50 \times }$ acceleration over CPU versions, processing a realistic cardiac model with about 30000 voxels in under 5 s per epoch. These findings establish the theoretical feasibility of all-pass filter networks for cardiac propagation velocity modeling while identifying practical implementation requirements for clinical applications. This approach could reduce patient risks by eliminating invasive catheterization procedures and enable longitudinal studies and research applications not feasible with current invasive methods.}, archive = {J_ACCESS}, author = {Erik Engelhardt and Johannes Hoffmann and Moritz Boueke and Lukas Elsner and Marius Leye and Norbert Frey and Gerhard Schmidt}, doi = {10.1109/ACCESS.2025.3588293}, journal = {IEEE Access}, pages = {126147-126166}, shortjournal = {IEEE Access}, title = {Gradient-based optimization of all-pass filter networks for non-invasive electroanatomical mapping}, volume = {13}, year = {2025}, }
• (2025). Demographic insights into autonomous taxi adoption: A multi-group analysis extending UTAUT2 with trust, perceived risk, and personal innovativeness. ACCESS, 13, 126130-126146. (www)

  @article{ , abstract = {The adoption of autonomous taxis (ATs) is expected to revolutionize urban mobility, yet user skepticism and demographic disparities present significant barriers to widespread acceptance. To address crucial gaps in AT adoption research, this study explores direct determinants of AT adoption using an enhanced UTAUT2, the moderating role of personal innovativeness, and demographic variations in these relationships. Based on a survey of 417 respondents in Wuhan, China, relationships between the proposed theoretical constructs and AT adoption intention are examined using structural equation modeling (SEM), complemented by multi-group analysis (MGA) to investigate demographic variations. The findings indicate that performance expectancy, effort expectancy, facilitating conditions, perceived cost, trust, and perceived risk significantly influence AT adoption, while social influence, hedonic motivation, and personal innovativeness do not. Trust plays a particularly strong role among experienced users, while perceived risk remains a universal concern. MGA reveals that lower-income and lower-education groups are more sensitive to effort expectancy, whereas higher-income users are primarily influenced by performance expectancy. Personal innovativeness moderates the effects of performance expectancy and trust, with stronger impacts observed in higher-income groups. This research advances the autonomous transportation technology acceptance theory in the AT domain and provides actionable insights for policymakers, technology developers, and transportation planners to promote AT adoption through targeted strategies.}, archive = {J_ACCESS}, author = {Chuan Zheng and Danling Sun and Munirah Khamarudin and Azanin Ahmad and Han Wei and Jie Xu}, doi = {10.1109/ACCESS.2025.3588588}, journal = {IEEE Access}, pages = {126130-126146}, shortjournal = {IEEE Access}, title = {Demographic insights into autonomous taxi adoption: A multi-group analysis extending UTAUT2 with trust, perceived risk, and personal innovativeness}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection model in network security situational awareness based on machine learning: Limitation, techniques, and future trends. ACCESS, 13, 126084-126129. (www)

  @article{ , abstract = {This study focuses on the anomaly detection problem in Network Security Situational Awareness (NSSA). We systematically review traditional approaches and recent advancements based on Machine Learning (ML) and Deep Learning (DL), and assess the application status of various anomaly detection models within the context of NSSA. Building upon this comprehensive review, we identify and analyze the primary challenges currently faced by the field, including the technical complexity of multimodal ML data fusion, insufficient real-time adaptive detection capabilities, limited model interpretability, and bottlenecks in privacy protection and security collaboration. To address these issues, we propose an integrated conceptual model, AFEMAD, which incorporates multimodal ML data fusion, online adaptive detection, enhanced interpretability, Federated Learning (FL), and collaborative privacy-preserving mechanisms, thereby providing an innovative technical framework for anomaly detection in NSSA. Finally, this study deepens the understanding of the anomaly detection problem in NSSA and provides both theoretical and practical guidance for the development of efficient and scalable anomaly detection systems.}, archive = {J_ACCESS}, author = {Li Zhen and Nazhatul Hafizah Kamarudin and Ven Jyn Kok and Faizan Qamar}, doi = {10.1109/ACCESS.2025.3589620}, journal = {IEEE Access}, pages = {126084-126129}, shortjournal = {IEEE Access}, title = {Anomaly detection model in network security situational awareness based on machine learning: Limitation, techniques, and future trends}, volume = {13}, year = {2025}, }
• (2025). The bregman modified second APG method for DC optimization problems. ACCESS, 13, 126070-126083. (www)

  @article{ , abstract = {The Difference of Convex (DC) optimization problem holds significant importance in both optimization theory and practical applications. In recent years, numerous algorithms have been developed to solve DC problems, demonstrating strong performance across various applications. However, most of these methods typically require the L-smoothness property of the objective function; without this condition, the optimization process may exhibit slow convergence or even fail to converge. To address this limitation, researchers often employ Bregman distance as an alternative to Euclidean distance in existing DC algorithms. While this substitution relaxes the requirements on DC functions, it simultaneously introduces greater complexity in theoretical analysis. In this article, we present the Bregman modified second Accelerated Proximal Gradient method (BAPGs), which incorporates Bregman distance into the modified second APG framework (APGs) introduced in Lin and Liu (2019). We establish fundamental decreasing and convergence properties of the sequence generated by BAPGs under standard assumptions. Moreover, with the additional Kurdyka-Łojasiewicz property, we provide a rigorous proof of global convergence and derive explicit convergence rate estimates. Our numerical experiments on two practical problems demonstrate the theoretical validity of BAPGs while showcasing its performance compared to: 1) the original APGs method, 2) existing Bregman-type algorithms, and 3) classical DC approaches. The results confirm that BAPGs effectively balances computational efficiency with relaxed smoothness requirements.}, archive = {J_ACCESS}, author = {Lumiao Wang and Ziye Liu and Chunguang Liu}, doi = {10.1109/ACCESS.2025.3589260}, journal = {IEEE Access}, pages = {126070-126083}, shortjournal = {IEEE Access}, title = {The bregman modified second APG method for DC optimization problems}, volume = {13}, year = {2025}, }
• (2025). Agentic AI systems: Architecture and evaluation using a frictionless parking scenario. ACCESS, 13, 126052-126069. (www)

  @article{ , abstract = {An AI agent is a goal-oriented autonomous computational entity that connects reasoning and action using large language models (LLMs) or Large Reasoning Models (LRMs), memory systems, and external tools to achieve contextually intelligent outcomes. An agentic AI system comprises and coordinates multiple specialized AI agents to achieve complex goals with minimal or no human supervision. In this paper, agentic AI systems are elucidated through a frictionless-parking scenario that illustrates core components of the system, namely the design of individual AI agents, their interaction mechanisms (handoff and cueing), and potential cooperation patterns (augmentative, integrative, and debative). This scenario provides an experimental test-bed for demonstrating how agentic AI can deliver context-aware, personalized services. A six-factor factorial experiment evaluates performance of the implemented AI agents across five user profiles, four GPT backbones, three entropy levels, three verbosity settings, three query complexities, and three prompt specificity levels. To guarantee that each agent’s recommendation is both plausible and constraint-compliant, the system combines guardrails, which reject or rewrite answers that violate user requirements, with Chain-of-Thought prompting that exposes intermediate reasoning steps for internal self-checks. Key metrics (agent’s response time or latency and lexical consistency) show that a lightweight gpt-4o-mini backbone and concise verbosity minimize latency, while medium prompt specificity and moderate query complexity optimize consistency. Decoding entropy influences stylistic diversity without significant latency costs but reduces consistency at high settings. User intent, particularly for creative or ambiguous profiles, drives variability. A SHAP analysis ranks model size, verbosity, and prompt specificity as top performance drivers.}, archive = {J_ACCESS}, author = {Alaa Khamis}, doi = {10.1109/ACCESS.2025.3590264}, journal = {IEEE Access}, pages = {126052-126069}, shortjournal = {IEEE Access}, title = {Agentic AI systems: Architecture and evaluation using a frictionless parking scenario}, volume = {13}, year = {2025}, }
• (2025). Nighttime vehicle detection algorithm based on improved YOLOv7. ACCESS, 13, 126043-126051. (www)

  @article{ , abstract = {Aiming at the problems of low visibility, fuzzy target features and high leakage rate of small targets in nighttime vehicle detection, this paper proposes a nighttime vehicle detection algorithm E-YOLOv7 based on the improved YOLOv7. Firstly, a hybrid feature enhancement module (SFE) is designed to enhance the expression of key features through the grouped-channel attention mechanism and feature reorganization to alleviate the loss of semantic information caused by fuzzy features of nighttime vehicles. Second, a lightweight efficient feature fusion network (GS-EFF) is constructed to optimize multi-scale feature alignment by combining the jump connection and GSConv modules to reduce the information loss in the feature fusion process and lower the number of parameters. The Soft-EloU-NMS post-processing algorithm is further proposed to effectively reduce the leakage detection rate of dense small targets by fusing the multi-dimensional evaluation of overlap, center distance and aspect ratio. Experiments on the BDD100K nighttime vehicle dataset show that E-YOLOv7 achieves an AP value of 94.9%, which is a 6.6% improvement over the original YOLOv7, with a significant increase in recall. The model also demonstrates strong real-time performance. Ablation experiments verify the synergistic optimization effect and efficiency of each module. Furthermore, a comparison with other state-of-the-art algorithms like SSD and DETR confirms the superiority of our approach. Visualization results validate the robustness of the algorithm in long-distance, multi-target, and complex lighting scenarios. This study provides a high-precision real-time detection solution for nighttime environment sensing for autonomous driving.}, archive = {J_ACCESS}, author = {Fan Zhang}, doi = {10.1109/ACCESS.2025.3587717}, journal = {IEEE Access}, pages = {126043-126051}, shortjournal = {IEEE Access}, title = {Nighttime vehicle detection algorithm based on improved YOLOv7}, volume = {13}, year = {2025}, }
• (2025). Application of artificial intelligence for selected photovoltaic faults detection based on electroluminescence images. ACCESS, 13, 126030-126042. (www)

  @article{ , abstract = {Photovoltaics (PV), like other renewable sources, is characterized by a steadily growing public interest. However, the efficient generation of green energy from photovoltaic panels requires overcoming a number of barriers, including structural and technological barriers to achieve ever greater efficiency in the conversion of solar energy into electrical current. One of the known reasons leading to efficiency limitations in crystalline silicon photovoltaic cells is the different types of defects and damage, the causes of which can appear already in the production stage of the PV cells themselves. This article describes a brief overview of selected artificial intelligence-based solutions and their application to detect and identify defects in photovoltaic cells and panels using electroluminescence (EL) imaging, and is divided into two main subsections – the first is the comparison of selected solutions of using the AI in imaging the PV cells and panels using EL imaging and infrared (IR) imaging, as the most common ways to determine the presence of inhomogeneities or faults. The second part consists of the exemplary engineering application of the AI algorithms – the binary classification and multi-criteria analysis of the defects’ appearance on the photovoltaic cell EL images. The best results in case of binary classification was the use of Logistic Regression classifier with AUC of 0.9569 with the use of ResNet50 feature extraction. In the case of multicriteria analysis the best performance was achieved for the Faster R-CNN ResNet50 detector with mAP@0.5 of 0.985. The performed analysis shows the usefulness of AI in the field of solar cell diagnostics.}, archive = {J_ACCESS}, author = {Laura Jasińska and Maciej Zagórski and Jarosław Domaradzki and Maurycy Maziuk and Paweł Chodasewicz}, doi = {10.1109/ACCESS.2025.3589674}, journal = {IEEE Access}, pages = {126030-126042}, shortjournal = {IEEE Access}, title = {Application of artificial intelligence for selected photovoltaic faults detection based on electroluminescence images}, volume = {13}, year = {2025}, }
• (2025). SE-COLLAB: Achieving fine-grained and efficiently verifiable searchable encryption with boolean multi-keyword search for collaborative IIoT data sharing. ACCESS, 13, 126012-126029. (www)

  @article{ , abstract = {In Industrial Internet of Things (IIoT) environments, the secure and efficient retrieval of sensitive data from outsourced storage remains a critical challenge, especially under collaborative and multi-authority settings. Existing searchable encryption (SE) techniques either suffer from limited access control flexibility or impose high computation and verification costs, making them unsuitable for large-scale, real-time IIoT applications. To address these challenges, we propose a novel hybrid searchable encryption framework that combines Attribute-Based Searchable Encryption (ABSE) for fine-grained access control for efficient keyword indexing and Boolean query processing. Our system supports multi-authority users by allowing each Data User (DU) to receive attribute-based keys from different trusted domains, enabling decentralized search authorization. A Proxy Re-Encryption (PRE) mechanism further ensures secure and privacy-preserving query delegation across organizations. To optimize search performance, we design a Bitwise Posting List (BPL) structure for scalable multi-keyword Boolean search. Moreover, we introduce a tamper-resistant search result validation mechanism based on Merkle Tree aggregation and Zero-Knowledge Proofs (ZKP), enabling users to verify the integrity and completeness of retrieved results with minimal overhead. Our revocation mechanism leverages smart contracts and Bloom filters to minimize ciphertext re-encryption while ensuring that revoked users cannot perform valid searches. Comprehensive evaluations demonstrate that the proposed scheme outperforms existing approaches in terms of access control granularity, search efficiency, and verifiable result assurance in multi-entity IIoT data-sharing environments.}, archive = {J_ACCESS}, author = {Somchart Fugkeaw and Jirakit Deevijit}, doi = {10.1109/ACCESS.2025.3590026}, journal = {IEEE Access}, pages = {126012-126029}, shortjournal = {IEEE Access}, title = {SE-COLLAB: Achieving fine-grained and efficiently verifiable searchable encryption with boolean multi-keyword search for collaborative IIoT data sharing}, volume = {13}, year = {2025}, }
• (2025). Active multifunctional frequency selective surfaces: An insight into design, behavior analysis, and applications. ACCESS, 13, 125993-126011. (www)

  @article{ , abstract = {This investigation provides a critical review of the design methodologies for multifunctional active frequency selective surfaces (MAFSSs) and reconfigurable frequency selective surfaces (RFSSs), collectively referred to as active multifunctional frequency selective surfaces (AMFSSs). The advanced AMFSS structures plays a crucial role in electromagnetic shielding, wireless communication systems, and stealth technology. The review systematically examines the fundamental performance parameters of AMFSS. These parameters include absorption, transmission, reflection, fractional bandwidth (FBW), insertion loss, absorptivity, polarization stability, and angular stability. The design techniques explored, include the integration of active elements (PIN and varactor diodes). Additionally, the incorporation of lumped components (resistors, inductors, and capacitors), the implementation of multilayer architectures, and the utilization of distributed elements (slots and stubs). These techniques facilitate dynamic switching between operational states, enhancing the adaptability and overall performance of AMFSS. Furthermore, a detailed comparative analysis of AMFSS performance metrics—encompassing absorption, transmission, and reflection bandwidths, along with their respective frequency ranges, FBW, polarization characteristics, and angular stability—is presented, offering valuable insights into their practical applications and future advancements.}, archive = {J_ACCESS}, author = {Harienthiran Maheswaran and Gulam Nabi Alsath Mohammed and Geethanjali Govindarajan and Malathi Kanagasabai and Kirubaveni Savarimuthu and Sandeep Kumar Palaniswamy and Sachin Kumar and Bhawna Goyal and Mousa I. Hussein}, doi = {10.1109/ACCESS.2025.3586046}, journal = {IEEE Access}, pages = {125993-126011}, shortjournal = {IEEE Access}, title = {Active multifunctional frequency selective surfaces: An insight into design, behavior analysis, and applications}, volume = {13}, year = {2025}, }
• (2025). Dynamic efficiency analysis of airborne cross-eye jamming operations. ACCESS, 13, 125982-125992. (www)

  @article{ , abstract = {Cross-eye jamming represents an effective angle deception technique against monopulse seekers, capable of electromagnetic jamming against precision-guided weapons deployed on airborne platforms. While existing research primarily concentrates on minimizing the amplitude and phase tolerance of cross-eye jamming to enhance jammer performance, it often overlooks in-depth analysis of its practical combat application, leading to fragmented and nonspecific evaluations of its combat effectiveness. This study decouples the amplitude and phase error composition characteristics of an airborne retrodirective cross-eye jamming prototype by conducting a thorough analysis of its system architecture and jamming signal generation principles. Additionally, it introduces a vector guidance algorithm incorporating physical constraints to model and assess the combat effectiveness of cross-eye jamming in dynamic operational scenarios. To validate the scientific robustness of the research findings, a comprehensive verification framework is established, integrating numerical simulations and air interface experiments. Through the systematic combination of diverse experimental configurations, this research summarizes and proposes a methodology for analyzing the hardware specifications and combat effectiveness baseline of cross-eye jamming systems.}, archive = {J_ACCESS}, author = {Sun Jiandong and Chen Jiabei and Gong Songtao and Xu Zhiwei and You Shixun}, doi = {10.1109/ACCESS.2025.3589882}, journal = {IEEE Access}, pages = {125982-125992}, shortjournal = {IEEE Access}, title = {Dynamic efficiency analysis of airborne cross-eye jamming operations}, volume = {13}, year = {2025}, }
• (2025). Sheath voltage limiter defects detection method based on harmonic phasor differences of sheath current for cross-bonded HV cable. ACCESS, 13, 125970-125981. (www)

  @article{ , abstract = {The sheath voltage limiter (SVL) defect is a typical defect in HV cable cross-bonding systems that threatens cable system safety. Traditional surge arrester detection and time-domain sheath current analysis methods fail to accurately identify SVL defects due to environmental complexities and parameter variations. This paper proposes a defect diagnosis method based on harmonic phasor difference of sheath currents. By establishing a frequency-domain equivalent model of cross-bonded HV cables combined with SVL nonlinear resistance characteristics, harmonic currents under defect conditions are treated as harmonic sources injected into the sheath loop. Transfer functions analyze the impacts of SVL defects on sheath currents, revealing that harmonic content during SVL defects is significantly higher than under normal conditions and other defect types. Through simulation analysis using 110kV cable as the case study, diagnostic criteria based on harmonic content of sheath current phasor differences were established: When SVL defects occur in sheath loop 1, the 3rd, 5th, 7th, and 9th harmonic content variation ranges of the head-to-tail sheath current phasor difference in this loop are 1.88%~21.31%, 0.79%~8.98%, 0.17%~1.94%, and 0.11%~1.10% respectively; When SVL defects occur in sheath loop 2, the 3rd, 5th, 7th, and 9th harmonic content variation ranges of the head-to-tail sheath current phasor difference in this loop are 1.30%~23.71%, 0.55%~9.99%, 0.12%~2.16%, and 0.07%~1.22% respectively; When SVL defects occur in sheath loop 3, the 3rd, 5th, 7th, and 9th harmonic content variation ranges of the head-to-tail sheath current phasor difference in this loop are 1.53%~16.15%, 0.65%~6.81%, 0.14%~1.47%, and 0.08%~0.83% respectively. The harmonic content of phasor differences is significantly affected by ground impedance and phase currents, while the influence of phase voltage is insignificant. Compared with existing methods, this approach takes into account the effects of complex environmental factors and effectively resolves the issue of traditional diagnostic methods being unable to distinguish between SVL defects and other types of grounding defects, thereby improving the accuracy of SVL defect diagnosis.}, archive = {J_ACCESS}, author = {Hao Hu and Jing Tu and Yongheng Ai and Zhanran Xia and Ruoxin Song and Bin Yang and Xinyun Gao and Zhi Li and Fuyuan Liu and Hang Wang}, doi = {10.1109/ACCESS.2025.3587895}, journal = {IEEE Access}, pages = {125970-125981}, shortjournal = {IEEE Access}, title = {Sheath voltage limiter defects detection method based on harmonic phasor differences of sheath current for cross-bonded HV cable}, volume = {13}, year = {2025}, }
• (2025). The rise of artificial intelligence phobia! unveiling news-driven spread of AI fear sentiment using ML, NLP, and LLMs. ACCESS, 13, 125944-125969. (www)

  @article{ , abstract = {Contemporary public discourse surrounding artificial intelligence (AI) often displays disproportionate fear and confusion relative to AI’s actual potential. This study examines how the use of alarmist and fear-inducing language by news media contributes to negative public perceptions of AI. Nearly 70,000 AI-related news headlines were analyzed using natural language processing (NLP), machine learning (ML), and large language models (LLMs) to identify dominant themes and sentiment patterns. The theoretical framework draws on existing literature that posits the power of fear-inducing headlines to influence public perception and behavior, even when such headlines represent a relatively small proportion of total coverage. This research applies topic modeling and fear sentiment classification using BERT, LLaMA, and Mistral, alongside supervised ML techniques. The findings show a persistent presence of emotionally negative and fear-laden language in AI news coverage. This portrayal of AI as dangerous to humans or as an existential threat profoundly shapes public perception, fueling AI phobia that leads to behavioral resistance toward AI, which is ultimately detrimental to the science of AI. Furthermore, this can have an adverse impact on AI policies and regulations, leading to a stunted growth environment for AI. The study concludes with implications and recommendations to counter fear-driven narratives and suggests ways to improve public understanding of AI through responsible news media coverage, broad AI education, democratization of AI resources, and the drawing of clear distinctions between AI as a science versus commercial AI applications, to promote enhanced fact-based mass engagement with AI while preserving human dignity and agency.}, archive = {J_ACCESS}, author = {Jim Samuel and Tanya Khanna and Julia Esguerra and Srinivasaraghavan Sundar and Alexander Pelaez and Soumitra S. Bhuyan}, doi = {10.1109/ACCESS.2025.3588179}, journal = {IEEE Access}, pages = {125944-125969}, shortjournal = {IEEE Access}, title = {The rise of artificial intelligence phobia! unveiling news-driven spread of AI fear sentiment using ML, NLP, and LLMs}, volume = {13}, year = {2025}, }
• (2025). System-level assessment of shaped elevation beam patterns for hybrid beamforming in mm-wave 5G networks with spatially heterogeneous traffic. ACCESS, 13, 125933-125943. (www)

  @article{ , abstract = {The throughput performance of intelligently shaped and fixed analog elevation beam patterns in millimeter-wave (mm-wave) base stations with hybrid beamforming (HBF) is assessed for the first time. Distinct spatially heterogeneous user distributions (i.e., uniform, near-site, cell-edge, and weighted uniform and near-site) and propagation environments (i.e., line-of-sight (LoS) with multipath and non-line-of-sight (NLoS) with multipath) are considered. The cosecant-squared and flat-top shaped beam patterns are compared to the benchmark pencil beam pattern with a straightforward electrical downtilt. The LoS simulation results show that in case of unknown weight of user distribution scenarios, the cosecant-squared pattern is the most robust, with a gain of up to 16% in the average system throughput and up to 34% in the 90th percentile user throughout compared to the benchmark. If the near-site case has a greater probability of occurrence than the uniform user distribution (e.g., due to daily events and festivals), the flat-top pattern becomes preferable. In the NLoS scenario, the considered HBF architectures with elevation beam pattern shaping do not bring any performance disadvantages compared to the benchmark HBF.}, archive = {J_ACCESS}, author = {Wenxu Chen and Yanki Aslan and Remco Litjens and Alexander Yarovoy}, doi = {10.1109/ACCESS.2025.3588913}, journal = {IEEE Access}, pages = {125933-125943}, shortjournal = {IEEE Access}, title = {System-level assessment of shaped elevation beam patterns for hybrid beamforming in mm-wave 5G networks with spatially heterogeneous traffic}, volume = {13}, year = {2025}, }
• (2025). Impact of input sequence types on healthcare intrusion prediction models. ACCESS, 13, 125897-125932. (www)

  @article{ , abstract = {Prediction models are vital for sensing zero-day and even n-day cyberattacks, particularly in healthcare infrastructure. Most existing research focuses on developing classifiers also known as IDS to enhance detection and accuracy. However, predictive intrusion models for healthcare remain underexplored, with limited studies investigating the comparative performance of univariate and multivariate inputs against single-step and multi-step outputs in time series models. This study aims to address these gaps by evaluating the accuracy and error performance of selected predictive models across various input and output configurations. The methodology involves transforming input data sequences into univariate $1^{\ast } $ n and multivariate m ${}^{\ast }$ n formats, establishing single-step and multi-step splitting functions, and evaluating these configurations using the benchmark CIRA-CIC-DoHBrw-2020 dataset. Algorithms including Bidirectional LSTM, Stacked LSTM, Vanilla LSTM, Transformer Encoder-Decoder, Vector Output LSTM (GRU core), and CNN were applied, with results visualized to assess performance. The findings reveal that the Multivariate LSTM model, when trained on a sequence of multivariate inputs, demonstrates superior predictive performance, achieving low MAE error rates of 0.4% for single-step predictions and 0.1% for multi-step predictions. Additionally, GRU and Transformer models exhibit heightened sensitivity to specific input sequence configurations. In conclusion, our study demonstrates that Transformer Encoder-Decoder based prediction models exhibit exceptional prediction performance. This effectiveness is attributed to their ability to capture contextual and critical information from input sequences. These findings provide valuable insights for designing advanced intrusion prediction models, paving the way for improved prediction capabilities in future systems.}, archive = {J_ACCESS}, author = {Mohammad Hafiz Mohd Yusof and Mohammed Balfaqih and Md Munir Hayet Khan and Akram A. Almohammedi and Zain Balfagih}, doi = {10.1109/ACCESS.2025.3584741}, journal = {IEEE Access}, pages = {125897-125932}, shortjournal = {IEEE Access}, title = {Impact of input sequence types on healthcare intrusion prediction models}, volume = {13}, year = {2025}, }
• (2025). Speaker-independent phoneme-based automatic quranic speech recognition using deep learning. ACCESS, 13, 125881-125896. (www)

  @article{ , abstract = {An automatic speech recognition system is important to help Muslims recite the Holy Quran accurately. Most existing research ignores a wide range of potential users (reciters) in their systems by focusing on professional adult male reciters due to the abundance of this group’s recordings and the lack of annotated data for other groups. This work bridges this gap by developing a speaker-independent system that recognizes Quranic recitations of different genders, ages, accents, and Tajweed levels. Our recognizer is designed on the phoneme level to offer Tajweed detection. Using a private dataset, rich of non-transcribed recitations, we propose training the DeepSpeech model with Transfer Learning and semi-supervised learning techniques. The performance of our model is evaluated using several proposed language models and evaluation metrics, including Word Error Rate (WER) and Phoneme Error Rate (PER). The goal is to show how our model would perform in regard to diverse reciter groups. Starting with a typical test set of unseen professional adult male recitations, the WER/PER of our model are 3.11% and 6.18%, respectively. More interestingly, our model achieves a WER of 25.39% and 17.93% when tested on recitations of non-professional (normal) females and children, respectively. The results are very promising and ensure the ability of our model to recognize recitations of various groups of normal reciters. Moreover, the latter results were done on the public “in-the-wild” Tarteel dataset, hoping this will be useful for comparison with future research and building more practical recitation teaching applications. In fact, a major limitation of existing systems (including ours) is the ability to handle diverse in-the-wild scenarios, such as when the reciter is reciting the verses in a very high tempo (common for those trying to memorize the Quran.}, archive = {J_ACCESS}, author = {Samah Al-Zaro and Mahmoud Al-Ayyoub and Osama Al-Khaleel}, doi = {10.1109/ACCESS.2025.3589252}, journal = {IEEE Access}, pages = {125881-125896}, shortjournal = {IEEE Access}, title = {Speaker-independent phoneme-based automatic quranic speech recognition using deep learning}, volume = {13}, year = {2025}, }
• (2025). Improved RT-DETR framework for railway obstacle detection. ACCESS, 13, 125869-125880. (www)

  @article{ , abstract = {Obstacle intrusion detection in railway systems is a critical technology for ensuring the operational safety of trains. However, existing algorithms face challenges related to insufficient multiscale object detection, high model redundancy, and poor real-time performance. Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. Additionally, a Deformable Agent Attention (DAA) mechanism is designed to optimize attention weights through sparse queries, effectively improving detection accuracy for small targets and enhancing inference speed in complex scenarios. Experimental results demonstrate that the improved model achieves 87.9% mean average precision (mAP) on a railway dataset, with a detection speed of 90 frames per second (FPS). The proposed model achieves a +1.7% mAP improvement and 13.9% faster inference speed compared to RT-DETR, while simultaneously reducing model parameters by 24.6%. As a result, the proposed model is highly effective for multiple obstacle intrusion detection in complex real-world scenarios.}, archive = {J_ACCESS}, author = {Peng Li and Yanhui Peng and Su-Mei Wang and Cheng Zhong}, doi = {10.1109/ACCESS.2025.3589159}, journal = {IEEE Access}, pages = {125869-125880}, shortjournal = {IEEE Access}, title = {Improved RT-DETR framework for railway obstacle detection}, volume = {13}, year = {2025}, }
• (2025). HeRD: Modeling heterogeneous degradations for federated super-resolution in satellite imagery. ACCESS, 13, 125857-125868. (www)

  @article{ , abstract = {Federated learning (FL) offers a privacy-preserving solution for single-image super-resolution (SR) on sensitive satellite imagery, but its performance is often hindered by simplistic data models. Existing methods that rely on simple bicubic downsampling fail to capture the complex, client-specific degradations found in real-world satellite data, creating a significant domain gap. To address this, we propose a novel strategy, Heterogeneous Realistic Degradation (HeRD), which models data heterogeneity by generating realistic low-resolution images based on the unique, device-locked characteristics of different satellites. Unlike conventional approaches, HeRD systematically applies diverse, anisotropic degradations to enable fine-grained control over non-Independent and Identically Distributed (non-IID) conditions. Our extensive evaluations demonstrate the robustness of FL when trained with HeRD. The proposed federated pipeline outperforms traditional bicubic-based setups by over 0.5 dB in PSNR. Notably, even in highly heterogeneous environments, our approach achieves performance within just 0.2–0.4 dB of a fully centralized training model. These findings confirm that HeRD provides a viable, high-performance, and privacy-preserving alternative for super-resolving distributed satellite imagery where data sovereignty and disparate hardware characteristics are paramount.1}, archive = {J_ACCESS}, author = {Bostan Khan and Seyedhamidreza Mousavi and Masoud Daneshtalab}, doi = {10.1109/ACCESS.2025.3590171}, journal = {IEEE Access}, pages = {125857-125868}, shortjournal = {IEEE Access}, title = {HeRD: Modeling heterogeneous degradations for federated super-resolution in satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Adaptive robust low-beam feature point detection algorithm based on convex hull. ACCESS, 13, 125841-125856. (www)

  @article{ , abstract = {The existing algorithms for detecting low-beam feature points are neither universally applicable nor effective. Additionally, such algorithms typically involve numerous parameters, making them difficult to tune and lacking in robustness. In response to these challenges, this paper proposes a novel algorithm for low-beam feature point detection that incorporates convex hull detection capabilities, enabling the automatic localization of the detection area. This algorithm achieves parameter adaptation through the analysis of the inherent characteristics of low-beam light distributions. It is designed to be applicable to the detection of feature points in “two-segment,” “three-segment,” and “five-segment” low-beam light patterns. Furthermore, two application modes have been developed: one for elbow point detection and another for the simultaneous detection of multiple feature points. The experimental results demonstrate that the proposed algorithm can accurately detect and differentiate all types of low-beam elbow points and other feature points and is applicable to both right-hand and left-hand traffic low-beam light patterns. It offers advantages such as high detection accuracy, ease of debugging, strong versatility, real-time performance, and high robustness, making it well suited for applications in automotive lamp optical inspection and alignment adjustment.}, archive = {J_ACCESS}, author = {Qiqi Shen and Jie Wu}, doi = {10.1109/ACCESS.2025.3589593}, journal = {IEEE Access}, pages = {125841-125856}, shortjournal = {IEEE Access}, title = {Adaptive robust low-beam feature point detection algorithm based on convex hull}, volume = {13}, year = {2025}, }
• (2025). ARIA: Additive ReRAM-based integrity and aging monitoring for ICs. ACCESS, 13, 125830-125840. (www)

  @article{ , abstract = {This paper reports an approach for monitoring aging and integrity of CMOS circuits through additively manufactured Resistive Random-Access Memory (ReRAM) based test structures. MgO-based ReRAM devices demonstrated excellent temperature sensing and aging modalities with simultaneous storage of sensed temperature and age as a change in the resistive state. The Process Voltage Temperature (PVT) characteristics, aging, and temperature sensitivity of MgO-ReRAM devices were experimentally studied and modeled to capture resistance distributions and temperature-based modalities. This in-memory sensing feature of ReRAM was integrated with specially designed read circuitry using 180 nm CMOS technology, to produce a measurable change in spiking-frequency over the lifetime of the ReRAM under normal aging conditions with the underlying CMOS circuits. Large feature sizes were used so these circuits can be fabricated in-house in trusted foundry. Temporal changes in temperature of underlying CMOS circuit could be captured by instantaneous change in resistive state of ReRAM with local temperature fluctuations which translated to a change in read circuit output. The characteristics of this circuit is studied in detail using simulations. Due to additive integration of ReRAM and associated circuitry, this approach for aging and integrity monitoring (AIM) ensures large spatial and accurate temporal monitoring of underlying CMOS die with minimal loss of the functional chip area for these added security features. The passive, in-memory sensing, and non-volatile nature of ReRAM also ensures low-power consumption in these circuits. The devices resistance states and material composition are specific to every device preventing reverse engineering and tampering of the devices, thus making it an attractive approach for adding customized security and trust features in advanced CMOS nodes-based circuits.}, archive = {J_ACCESS}, author = {Thomas Schultz and Rashmi Jha and Brian Dupaix and Matt Casto}, doi = {10.1109/ACCESS.2019.2951661}, journal = {IEEE Access}, pages = {125830-125840}, shortjournal = {IEEE Access}, title = {ARIA: Additive ReRAM-based integrity and aging monitoring for ICs}, volume = {13}, year = {2025}, }
• (2025). GS-GVINS: A tightly-integrated GNSS-visual-inertial navigation system augmented by 3D gaussian splatting. ACCESS, 13, 125817-125829. (www)

  @article{ , abstract = {Accurate navigation is critical for autonomous vehicles in today’s diverse traffic environments. Integrating Global Satellite Navigation System (GNSS), Inertial Navigation System (INS), and camera has demonstrated significant robustness and high accuracy for navigation in complex environments. However, most integrated systems rely on feature-tracking based visual odometry, which suffers from the problem of feature sparsity, high dynamics, significant illumination changes, etc. Recently, the emergence of 3D Gaussian Splatting (3DGS) has drawn significant attention in the area of 3D map reconstruction and visual SLAM. While extensive research has explored 3DGS for indoor trajectory tracking using visual sensor alone or in combination with Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU), its integration with GNSS for large-scale outdoor navigation remains underexplored. To address these concerns, we propose GS-GVINS: a tightly-integrated GNSS-Visual-Inertial Navigation System augmented by 3DGS. This system leverages 3D Gaussian as a continuous differentiable scene representation in large-scale outdoor environments, enhancing navigation performance through the constructed 3D Gaussian map. Notably, GS-GVINS is the first GNSS-Visual-Inertial navigation application that directly utilizes the analytical jacobians of SE3 camera pose with respect to 3D Gaussians. To maintain the quality of 3DGS rendering in extreme dynamic states, we introduce a motion-aware 3D Gaussian pruning mechanism, updating the map based on relative pose translation and the accumulated opacity along the camera ray. For validation, we test our system under different driving environments: open-sky, suburban, and urban. Both self-collected and public datasets are used for evaluation. The results demonstrate the effectiveness of GS-GVINS in enhancing navigation accuracy across diverse driving environments.}, archive = {J_ACCESS}, author = {Zelin Zhou and Shichuang Nie and Saurav Uprety and Hongzhou Yang}, doi = {10.1109/ACCESS.2025.3589161}, journal = {IEEE Access}, pages = {125817-125829}, shortjournal = {IEEE Access}, title = {GS-GVINS: A tightly-integrated GNSS-visual-inertial navigation system augmented by 3D gaussian splatting}, volume = {13}, year = {2025}, }
• (2025). Joint extraction of hazard source knowledge in integrated utility corridor based on knowledge graph. ACCESS, 13, 125805-125816. (www)

  @article{ , abstract = {The current knowledge graph-based joint extraction method for hazard source identification in integrated utility tunnels has been applied to emergency response management, assisting personnel in making rapid and accurate decisions. The core steps in building a knowledge graph involve entity recognition and relationship extraction. Joint extraction models overcome the limitations of pipeline extraction methods by enhancing the interaction between entities and relationships. However, existing joint models tend to overlook the semantic information between words and have room for improvement in the accuracy of extracting overlapping entities and relationships. This paper proposes an end-to-end, relation-specific attention network model, RAJE (Joint extraction model based on relational attention mechanism), to address semantic information and entity-relationship overlap issues. The RAJE model integrates active learning, data augmentation, BERT, a relation-specific attention network, and a sequence labeling approach to simultaneously extract all entities and relationships, demonstrating the model’s effectiveness in resolving nested entity and relationship problems. First, an active learning-based sampling method selects more valuable training data, and data augmentation is performed on the labeled dataset. Then, an end-to-end joint extraction method is used to obtain triplets. The joint extraction includes using the BERT model to obtain sentence representations, employing a relation-specific attention network to capture relation-based sentence features, followed by sequence labeling to extract entity pairs. Finally, the proposed model is evaluated using both a collected integrated utility tunnel fault engineering dataset and a public dataset.}, archive = {J_ACCESS}, author = {Shanshan Wan and Houchen Lv and Yuhan Zhu and Yiran Zhao}, doi = {10.1109/ACCESS.2025.3589760}, journal = {IEEE Access}, pages = {125805-125816}, shortjournal = {IEEE Access}, title = {Joint extraction of hazard source knowledge in integrated utility corridor based on knowledge graph}, volume = {13}, year = {2025}, }
• (2025). Critical issues of alignment procedures for a novel single-stage current source inverter with discharge path. ACCESS, 13, 125796-125804. (www)

  @article{ , abstract = {The intrinsic boost capability of the current source inverter (CSI) increase the power density of the machine and this is of interest in aerospace and electric vehicles (EVs) applications. With the classical power converter, the machine can work only in a limited range above a base speed. In this scenario the alignment of the rotor is forbidden due to the uncontrolled increase of the DC current. A recently proposed single-stage CSI with discharge path allows to achieve the whole speed range extending the operating region of the converter down to zero speed. In this way it is possible to align the rotor during the power-up phase in a controlled way avoiding unwanted behaviors of the DC current. Unlike voltage source inverter (VSI) architectures that have a constant DC voltage, with single-stage CSI the DC current depends on the working condition and is not constant. For this reason, it is not possible to use the common alignment on the a-axis of the stationary reference frame because the active vectors exhibit different mean values due to the variations of the DC current. An alignment procedure for electric drives that employ an incremental encoder for position sensing is designed for this power converter, using a rotor position alignment on one of the active vectors with a predefined startup procedure without the use of any signal injection. Simulations and experimental results show the effectiveness of the proposed solution.}, archive = {J_ACCESS}, author = {Dario Benatti and Giovanni Migliazza and Emilio Carfagna and Fabio Immovilli and Emilio Lorenzani}, doi = {10.1109/ACCESS.2025.3588694}, journal = {IEEE Access}, pages = {125796-125804}, shortjournal = {IEEE Access}, title = {Critical issues of alignment procedures for a novel single-stage current source inverter with discharge path}, volume = {13}, year = {2025}, }
• (2025). Modeling 5G-NR user blocking probability in conditional handover-enabled heterogeneous networks. ACCESS, 13, 125782-125795. (www)

  @article{ , abstract = {Conditional Handover (CHO), introduced in the 16th edition of the 3GPP standard, serves as an alternative to Baseline Handover (BHO). CHO aims to mitigate radio link failures (RLFs) during handover preparation by incorporating a dedicated base station (BS) reservation phase. While BS reservation effectively reduces RLF rates, it concurrently occupies valuable radio resources, potentially leading to user blockages in congested scenarios. In this research, we develop a mathematical model to quantify user blocking probability by employing Markov models and connecting this probability to handover management parameters using stochastic geometry to characterize user mobility and BS coverage. This formulation establishes a probabilistic link between handover management parameters and user blocking occurrences. It further reveals that optimization solely focused on minimizing user blocking can paradoxically increase RLF rates, highlighting the need to consider trade-offs among performance metrics. To demonstrate the practical applicability of the proposed model, we present a case study involving a multi-objective optimization problem solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This example highlights how the model can be used to balance key performance trade-offs, including user blocking probability, RLF probability, latency, and network data rate. The presented model can be utilized independently or integrated with a variety of optimization techniques according to the needs of service providers. These configurations may include the distribution intensity and biasing factor of BSs at each tier during the network establishment phase, as well as the optimal handover initiation parameters during network operation. Operators can use the model to guide network planning and tuning for improved stability, seamless connectivity, and enhanced user experience.}, archive = {J_ACCESS}, author = {Amiraslan Haghrah and Javad Musevi Niya and Jafar Pourrostam}, doi = {10.1109/ACCESS.2025.3583791}, journal = {IEEE Access}, pages = {125782-125795}, shortjournal = {IEEE Access}, title = {Modeling 5G-NR user blocking probability in conditional handover-enabled heterogeneous networks}, volume = {13}, year = {2025}, }
• (2025). Optimum medium PRF selection using reinforcement learning. ACCESS, 13, 125772-125781. (www)

  @article{ , abstract = {Determining the best medium pulse repetition frequency (PRF) set for radar has long been a problem. Because it cannot be represented in an analytical form, it is not possible to solve it using conventional optimization methods. Although heuristic methods can provide a good PRF set, determining the best PRF set remains an open question. In this study, we propose a new method to find the best PRF set that can provide the smallest blind region in the range-Doppler domain. We applied a reinforcement learning algorithm to determine the PRF set. By applying the Deep Q-network (DQN) algorithm with hybrid rewards, we can find the PRF sets that yield the minimum blind region (maximum visible region). The proposed method can find the global optimum PRF sets with a high probability even if it begins from initial values close to the local optima.}, archive = {J_ACCESS}, author = {Hyunhak Kim and Yeo-Sun Yoon and Jinho An}, doi = {10.1109/ACCESS.2025.3588896}, journal = {IEEE Access}, pages = {125772-125781}, shortjournal = {IEEE Access}, title = {Optimum medium PRF selection using reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). TEMPO: Timestep explanations for modeling preferences in online preference-based RL. ACCESS, 13, 125759-125771. (www)

  @article{ , abstract = {Human-in-the-loop reinforcement learning enables non-expert users to train agents through interactive feedback. While preference-based methods (PbRL) have gained popularity by allowing humans to express preferences between trajectory pairs, they are limited by their narrow feedback interface - humans can only indicate which trajectory they prefer but not why. This work introduces a more expressive preference-based learning framework that allows humans to provide step-level explanations alongside their preferences, indicating which specific timesteps influenced their decisions. Our key technical innovation is a training approach that incorporates these explanations using a novel loss function combining reward learning, saliency-based explanations, and structural regularization. We evaluate our method across multiple continuous control environments using both synthetic oracles and human trainers. Results show that our approach accelerates learning compared to standard preference-based methods while maintaining robustness across different types of human feedback patterns. The improved learning speed and more natural interaction suggest that this is a promising direction for making human-in-the-loop RL more practical.}, archive = {J_ACCESS}, author = {Jakob Karlaus and Friedhelm Schwenker}, doi = {10.1109/ACCESS.2025.3588936}, journal = {IEEE Access}, pages = {125759-125771}, shortjournal = {IEEE Access}, title = {TEMPO: Timestep explanations for modeling preferences in online preference-based RL}, volume = {13}, year = {2025}, }
• (2025). Wide load range, high current accuracy low drop-out regulator with load tracking compensation and dual soft-start for wireless charging system. ACCESS, 13, 125746-125758. (www)

  @article{ , abstract = {This study introduces a High Voltage Low Dropout (LDO) regulator featuring a dual soft-start mechanism and load tracking compensation technique for Wireless Power Transfer (WPT) systems. The LDO design efficiently handles a broad input voltage spectrum from 5.15V to 20V and maintains stable performance under fluctuating load conditions ranging from 1mA to 1A. A notable advancement in this design is the dual soft-start mechanism, which enhances startup stability by separately regulating the pass transistor gate and the error amplifier input. Additionally, the load tracking compensation technique optimizes phase margin across diverse load currents, enhancing transient response and output stability. The regulator incorporates a current-sensing circuit for instantaneous overcurrent protection, accommodating load currents between 100mA and 1A. An integrated charge pump expands the error amplifier’s operational range, allowing high-voltage compatibility while running on a 5V supply. Utilizing a BCD process, the proposed circuit delivers high power efficiency and robust performance within a compact layout measuring 0.8814 mm2. These design features contribute to enhanced reliability and efficiency in WPT systems.}, archive = {J_ACCESS}, author = {Jae Bin Kim and Ji Hoon Song and Sang Sun Yoo and Seok Kee Kim and Yeon Jae Jung and Young Gun Pu and Kang Yoon Lee}, doi = {10.1109/ACCESS.2025.3585921}, journal = {IEEE Access}, pages = {125746-125758}, shortjournal = {IEEE Access}, title = {Wide load range, high current accuracy low drop-out regulator with load tracking compensation and dual soft-start for wireless charging system}, volume = {13}, year = {2025}, }
• (2025). SPATL-XLC: An explainability-driven framework for efficient and robust federated learning under non-IID data. ACCESS, 13, 125732-125745. (www)

  @article{ , abstract = {Federated learning (FL) enables multiple devices to collectively train a machine learning model without sharing private data. However, when data across devices differ significantly (non-IID),training becomes less accurate and difficult to understand in terms of how the model makes decisions (explainability). To address this problem, we propose two methods: SPATL-XL and SPATL-XLC. SPATL-XL applies SHAP, a model explainability technique, to identify and remove less important model parameters before sending updates to the server. SPATL-XLC builds on this by clustering similar clients based on their similarity patterns and sending the aggregated cluster updates, thereby improving learning stability and transparency. Experiments on public datasets (CIFAR-10 and Fashion MNIST) show that SPATL-XLC improves explanation fidelity from 30% to 89% and reduces the training time by 13% while maintaining practical accuracy. Although the communication cost slightly increases, the method delivers a clearer and more stable explanation with a reduced per-round bandwidth, making it well-suited for real-world FL applications in which explainability and efficiency are critical.}, archive = {J_ACCESS}, author = {Samuel Hailemariam Seifu and Beakal Gizachew Assefa}, doi = {10.1109/ACCESS.2025.3589535}, journal = {IEEE Access}, pages = {125732-125745}, shortjournal = {IEEE Access}, title = {SPATL-XLC: An explainability-driven framework for efficient and robust federated learning under non-IID data}, volume = {13}, year = {2025}, }
• (2025). Low-complexity bayesian-GLRT-based target detection for MIMO radar in gaussian clutter. ACCESS, 13, 125723-125731. (www)

  @article{ , abstract = {This paper proposes an unstructured adaptive moving target detection approach for a multiple-input multiple-output (MIMO) radar to obtain reduced computational complexity. Moreover, the MIMO radar operates in a Gaussian clutter with an unknown but stochastic covariance matrix. At the design stage, the Bayesian unstructured generalized likelihood ratio test (UGLRT), namely the BUGLRT detector, which inherits from the UGLRT approach, has been derived. The BUGLRT approach has transformed 2D searching of structured generalized likelihood ratio test (SGLRT) based on the Bayesian framework, namely BSGLRT, detector into a 1D searching procedure that drastically reduces the computational complexity. The asymptotic probability of false alarms and computation complexity have also been analyzed. Both theoretical analysis and numerical simulation results show that BSGLRT and BUGLRT detectors outperform their non-Bayesian counterparts, especially for a small number of snapshots. Moreover, the detection performance of BUGLRT is better than that of BSGLRT, but the performance gap decreases as the number of snapshots increases. In addition, the detectors based on the Bayesian framework are sensitive to the existing mismatches of parameters for inverse complex Wishart distribution. Compared with BSGLRT, the BUGLRT can obtain a noticeable complexity reduction along with a slightly improved detection performance.}, archive = {J_ACCESS}, author = {Yingtai Li and Lisheng Yang and Haowei Wu}, doi = {10.1109/ACCESS.2024.3369061}, journal = {IEEE Access}, pages = {125723-125731}, shortjournal = {IEEE Access}, title = {Low-complexity bayesian-GLRT-based target detection for MIMO radar in gaussian clutter}, volume = {13}, year = {2025}, }
• (2025). Generation and balancing capacity in future electric power systems-scenario analysis using bayesian networks. ACCESS, 13, 125705-125722. (www)

  @article{ , abstract = {This paper examines the evolution of the Finnish electric energy system up to 2035, focusing on the likelihood of different development paths. The primary contribution of this paper is the development of an extensive Bayesian Network, designed to model and analyze the evolution of power generation capacity mix, assess the likelihood of different grid management scenarios, and understand the causal relationships underlying these scenarios. A target optimization was carried out using the constructed Bayesian Network to explore possibilities to minimize grid management complexity. The results of the optimization reveal that the authorities and stakeholders should prioritize increasing demand response, gas power, and battery storage capacities. These mature technologies are well-suited to guarantee energy adequacy during peak consumption periods, which in Finland typically occur during consecutive cold, dark and windless winter weeks. Although this study focuses on the evolution of the Finnish power grid, the constructed Bayesian Network approach is broadly applicable and can be utilized to explore causal relationships in other countries by employing the designed questionnaire and engaging a panel of experts specific to the country’s energy infrastructure.}, archive = {J_ACCESS}, author = {Seppo Borenius and Pekka Kekolahti and Petri Mähönen and Matti Lehtonen}, doi = {10.1109/ACCESS.2025.3589799}, journal = {IEEE Access}, pages = {125705-125722}, shortjournal = {IEEE Access}, title = {Generation and balancing capacity in future electric power systems-scenario analysis using bayesian networks}, volume = {13}, year = {2025}, }
• (2025). Optimal scheduling of electrolytic hydrogen-based steelmaking facility participating in capacity auction market. ACCESS, 13, 125689-125704. (www)

  @article{ , abstract = {The adoption of electrolytic hydrogen-based steelmaking technology has recently garnered increasing attention as a promising solution for reducing carbon emissions within the framework of industrial decarbonization. Nonetheless, the high costs associated with hydrogen production via electrolysis stand as a key challenge to the widespread deployment of this technology, necessitating strategies to improve its economic feasibility. To that end, this paper introduces an optimal scheduling model for a hydrogen-based steelmaking facility equipped with an on-site electrolysis-based hydrogen production and storage system. The model mainly aims to: 1) exploit lower electricity prices to minimize the overall operating costs of the entire system, 2) contribute to the capacity auction market as a demand response resource for the joint benefit of the steel sector and electricity grid operator, and 3) ensure the safe operation of the hydrogen production system via keeping the hydrogen content in oxygen within safety permissible limits. As such, the economic feasibility of the hydrogen-based steelmaking technology would be further improved due to energy cost savings and extra financial settlements from capacity auction participation. The efficacy of the proposed model is validated using numerical case studies, demonstrating its potential to support the transition to low-carbon steel production.}, archive = {J_ACCESS}, author = {Heba Nabil Khalil and Hany Essa Zidan Farag and Amir Asif}, doi = {10.1109/ACCESS.2025.3588822}, journal = {IEEE Access}, pages = {125689-125704}, shortjournal = {IEEE Access}, title = {Optimal scheduling of electrolytic hydrogen-based steelmaking facility participating in capacity auction market}, volume = {13}, year = {2025}, }
• (2025). DCW-YOLO: Road object detection algorithms for autonomous driving. ACCESS, 13, 125676-125688. (www)

  @article{ , abstract = {Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. First, a dynamic head framework is used to unify the object detection head and the attention mechanism, and the attention mechanism is used for scale-awareness, spatial-awareness, and task-awareness, respectively, which significantly improves the representation capability of the object detection head without increasing the computational overhead. Second, the Coordinate Attention mechanism is embedded in the SPPF layer, which embeds the target’s location information into the channel attention to offer more precise localization for the model, suppress irrelevant aspects, and enable greater integration of local and global characteristics. Finally, the deleterious gradients generated by low-quality examples are reduced using the Wise-IoU v3 bounding box loss function in conjunction with a dynamic non-monotonic focusing mechanism utilizing an anchor box gradient gain assignment strategy. On the challenging public dataset KITTI, the accuracy is improved by 2.1% compared to the benchmark algorithm. In addition, the excellent performance on CCTSDB2021 and VOC highlights the generalization performance of the improved model.}, archive = {J_ACCESS}, author = {Hongge Ren and Fangke Jing and Song Li}, doi = {10.1109/ACCESS.2024.3364681}, journal = {IEEE Access}, pages = {125676-125688}, shortjournal = {IEEE Access}, title = {DCW-YOLO: Road object detection algorithms for autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Design of a high-specific-power traction motor: Innovations and strategies for superior performance. ACCESS, 13, 125659-125675. (www)

  @article{ , abstract = {In electric vehicle (EV) traction, energy conversion by electric motors must be material and energy efficient to reduce environmental burden caused by the manufacture and operation of the systems and to help in efficient transition towards net zero future. The scarcity of key materials like rare earths and to some extent copper must be addressed in the design. Novel traction motors need a different approach compared to traditional industrial motor designs. Here, we focus on innovative design and optimization strategies to enhance material and energy efficiency and meet the escalating demand for sustainable transportation solutions. We are developing a prototype motor that achieves a continuous specific power of 7 kW/kg, significantly exceeding current automotive standards. This will be achieved by elevated operational speed of the traction motor, integrating advanced materials, and innovative cooling techniques such as direct liquid cooling (DLC) using hollow hairpin conductors, and insulating them with polyether-ether-ketone (PEEK) extrusion and expandable mainwall insulation material. The approach reduces reliance on rare earth permanent magnet (PM) materials by 60% compared to existing motors, aligning with global sustainability objectives. Through simulation, modeling, and practical case studies, the research demonstrates the feasibility of these innovations in real-world applications, highlighting potential advancements in EV propulsion systems. The article not only represents the insights into the latest developments in the design of electric motors for EV but also delineates the journey towards the creation of such a motor with considering accompanying electromagnetic and mechanical challenges.}, archive = {J_ACCESS}, author = {Juha J. Pyrhönen and Ilya Petrov and Daniil Zadorozhniuk and Lasse Laurila and Miika Parviainen and Giota Goswami and Charles Nutakor and Jussi Sopanen and Iikka Martikainen and Dieter Zeppei and Jonas Bickel and Jenni Pippuri-Mäkeläinen and Janne Keränen and Kalle Kinnunen and Juho Montonen}, doi = {10.1109/ACCESS.2025.3589939}, journal = {IEEE Access}, pages = {125659-125675}, shortjournal = {IEEE Access}, title = {Design of a high-specific-power traction motor: Innovations and strategies for superior performance}, volume = {13}, year = {2025}, }
• (2025). Accurate time-to-target forecasting for autonomous mobile robots. ACCESS, 13, 125645-125658. (www)

  @article{ , abstract = {The use of Autonomous Mobile Robots in various industries is on the rise. Accurate estimation of payload delivery times poses a significant challenge for efficient planning across different sectors. This paper addresses this challenge by evaluating the effectiveness of four time forecasting methods: Linear Regression, Random Forest Regression, Transformer and Bidirectional Long Short-Term Memory (BiLSTM) networks. These models are compared to a previous approach that uses Long Short-Term Memory neural networks. Among the evaluated models, BiLSTM and Transformers were the only methods able to outperform the Long Short-Term Memory implementation, achieving a Mean Average Error of 1.26 seconds and 1.27 seconds, respectively. In particular, the newly proposed BiLSTM model could train twice as fast as the Long Short-Term Memory network, despite the increased complexity generated by the addition of the backward layer. To our knowledge, this is the first time BiLSTM and Transformers are evaluated with the aim to identify the best approach for forecasting the time left to reach a destination in a dynamic industrial environment, where the presence of numerous potential obstacles adds to the complexity of the task.}, archive = {J_ACCESS}, author = {Stefan-Alexandru Precup and Arpad Gellert and Alexandru Matei and Bogdan-Constantin Pirvu and Constantin-Bala Zamfirescu}, doi = {10.1109/ACCESS.2025.3588300}, journal = {IEEE Access}, pages = {125645-125658}, shortjournal = {IEEE Access}, title = {Accurate time-to-target forecasting for autonomous mobile robots}, volume = {13}, year = {2025}, }
• (2025). Image generation and lesion segmentation of brain tumors and stroke based on GAN and 3D ResU-net. ACCESS, 13, 125629-125644. (www)

  @article{ , abstract = {In recent years, with the development of medical image analysis, the image processing of brain tumor and stroke has been deeply studied. However, when processing clinical medical imaging data with different characteristics or information collected from different sensors or modalities, that is, multi-modal imaging data, the segmentation accuracy is low. Therefore, the research is based on generative adversarial networks and three-dimensional residual U-shaped networks to study brain tumor and stroke image generation and lesion segmentation. Experimental results showed the three models performed best in various conversions. For example, in T1 $\to $ Flair conversion, the generative multi-modal image analysis model based on perceptual loop consistency had an average peak signal-to-noise ratio of $23.951~\pm ~2.735$ , an average structural similarity of $0.873~\pm ~0.046$ , and an average root mean square error of $16.998~\pm ~6.184$ . All three models significantly raised the segmentation effectiveness of lesions, such as the combination of dual-scale perceptual loop generation adversarial network and three-dimensional residual U-shaped network for generative multi-modal image generation and lesion segmentation algorithm. Using three real input modalities, its HD index value of 75.082 and precision index value of 0.696 were better than the HD index value of 84.776 and precision index value of 0.686. In addition, the study also conducted ablation experiments on a generative multi-modal image analysis model based on dual-scale perceptual loop consistency, indicating that the cavity residual module is hoped to have a good influence on lesion segmentation. Overall, the algorithm model proposed in the study has high effectiveness in the generation and segmentation of brain tumors and stroke images, and is of great significance for the development of medicine.}, archive = {J_ACCESS}, author = {Mingkang Sun and Xiang Li and Weiye Sun}, doi = {10.1109/ACCESS.2024.3383880}, journal = {IEEE Access}, pages = {125629-125644}, shortjournal = {IEEE Access}, title = {Image generation and lesion segmentation of brain tumors and stroke based on GAN and 3D ResU-net}, volume = {13}, year = {2025}, }
• (2025). A 1.2-v, 106.4-dB DR discrete-time sigma-delta modulator with feedback adaptive cascaded integrator for sensor applications. ACCESS, 13, 125618-125628. (www)

  @article{ , abstract = {This innovative research work introduces a novel second-order discrete-time Sigma-Delta ( $\Sigma \Delta $ ) Analog-to Digital Converter (ADC) design for high performance signal conditioning sensors. A dual-phase noise rejection adaptive frequency gain (AFG) amplifier is designed with an output-stage CMFB circuit to ensure improved stability and performance. These circuits minimize flicker noise, refine noise shaping, and ensure precision, linearity, and stability. They enhance signal integrity, eliminate the need for high-gain amplifiers, and provide optimal loop gain, enabling an efficient modulator design. A highly efficient switched-capacitor feedback DAC enhances the loop filter’s input path. Switched-Capacitor feedback amplifier enables frequency-dependent gain control, signal integration, and differentiation, while miller compensation enhances stability and bandwidth. CMFB circuit is applied at the output of the AFG amplifier to enhance the power supply rejection ratio (PSRR). Low power dynamic latch comparator is used for high-speed, low offset, efficient noise performance and effective clock synchronization. A second-order discrete time modulator model was successfully fabricated using 0.13- $\mu $ m CMOS technology. The structure exhibits an exceptional dynamic range (106.4dB) and 17.38-bit ENOB across 1.95 KHz bandwidth at 2 MHz sampling. The circuit consumes only 1.44 mW using 1.2 V supply. It occupies a minimal chip area of $0.14~\text {mm}^{2}$ .}, archive = {J_ACCESS}, author = {Ghayur Hussain and Nguyen van Quang and Muhammad Basim and Chang Hyeon Kim and Nabeel Ahmad and Sung June Byun and Kang-Yoon Lee}, doi = {10.1109/ACCESS.2025.3589250}, journal = {IEEE Access}, pages = {125618-125628}, shortjournal = {IEEE Access}, title = {A 1.2-v, 106.4-dB DR discrete-time sigma-delta modulator with feedback adaptive cascaded integrator for sensor applications}, volume = {13}, year = {2025}, }
• (2025). A flexible and configurable system to author name disambiguation. ACCESS, 13, 125606-125617. (www)

  @article{ , abstract = {Author Name Disambiguation (AND) is critical in maintaining the integrity of bibliographic databases, especially under data sparsity and large-scale ambiguity. This paper introduces a configurable and scalable AND system that combines transformer-based embeddings (MiniLM), Graph Convolutional Networks (GCN), and hierarchical clustering. The framework enables fine-grained parameterization of GCN depth, training epochs, and embedding models to adapt to datasets with varying structural and semantic complexity. Extensive evaluations on three benchmark datasets, including AMiner-12, DBLP, and LAGOS-AND, demonstrate consistent improvements over state-of-the-art baselines. On DBLP, our system achieves a pF1 of 0.878 and a K-Metric of 0.976, outperforming prior work by over 15% and 20%, respectively. On AMiner-12, despite sparse metadata, the method attains a 13.8% gain in average cluster purity and 10.1% in K-Metric. On the large-scale LAGOS-AND dataset, the system reaches a B-cubed F1-score of 0.908, surpassing the best-reported baseline by more than 9%. These results validate the system’s ability to integrate semantic and relational signals for robust and accurate AND across diverse contexts.}, archive = {J_ACCESS}, author = {Natan de Souza Rodrigues and Célia Ghedini Ralha}, doi = {10.1109/ACCESS.2025.3589957}, journal = {IEEE Access}, pages = {125606-125617}, shortjournal = {IEEE Access}, title = {A flexible and configurable system to author name disambiguation}, volume = {13}, year = {2025}, }
• (2025). Effectiveness evaluation of penetration-detection scheme for surgical drill using thrust force and positional information. ACCESS, 13, 125594-125605. (www)

  @article{ , abstract = {The risk of spinal cord injury when cutting into the spine is a particular concern in orthopedic surgery. Surgeons must rely entirely on their haptic senses to determine bone penetration. Therefore, surgeons incur heavy burdens. Some studies have introduced penetration-detection systems using torque information. However, these method requires obtaining the characteristics of the bone before the actual cutting. Our previous study introduced an orthopedic haptic drill equipped with a penetration-detection system. This drill detects penetration by monitoring changes in the force of a linear motor. In this study, the advantage of using force over torque for penetration detection was verified through theoretical modeling and experiments. The theoretical model indicates that the thrust force reacts more sensitively than the torque at penetration. The detection methods using the force-based and torque-based methods were compared through experiments. The experimental results verified that the force-based detection method has advantages in terms of detection accuracy and detection time. The results demonstrated the efficacy of the proposed method, highlighting its potential utility in improving the accuracy and safety of orthopedic procedures.}, archive = {J_ACCESS}, author = {Shunya Takano and Tomoyuki Shimono and Takuya Matsunaga and Mitsuru Yagi and Kouhei Ohnishi and Masaya Nakamura and Yuichiro Mima and Kento Yamanouchi and Go Ikeda}, doi = {10.1109/ACCESS.2025.3587283}, journal = {IEEE Access}, pages = {125594-125605}, shortjournal = {IEEE Access}, title = {Effectiveness evaluation of penetration-detection scheme for surgical drill using thrust force and positional information}, volume = {13}, year = {2025}, }
• (2025). Development of a torque sensor for robot actuators with overload protection. ACCESS, 13, 125587-125593. (www)

  @article{ , abstract = {This study proposes the development of a one-axis torque sensor that can be integrated into robotic joint actuators, featuring an innovative structural design with enhanced overload protection. The developed torque sensor employs a strain gauge-based measurement principle and adopts a binocular structure to induce stress concentration, thereby improving signal sensitivity. In particular, a dedicated stopper mechanism is incorporated into the design to limit deformation and prevent irreversible damage under excessive torque loads. This mechanism plays a critical role in preventing plastic deformation, thereby ensuring long-term structural stability and extending the operational lifespan of the sensor. Finite Element Analysis (FEA) was conducted to simulate the deformation and stress distribution of the torque sensor, leading to the optimization of key design parameters. Based on the simulation results, a physical prototype was fabricated, and experimental validation was performed to evaluate the sensor’s performance. The experimental results showed strong agreement with the FEA outcomes, confirming the validity of the proposed design and demonstrating the effectiveness of the overload protection feature implemented in the sensor. Furthermore, the sensor maintains a compact form factor compatible with CPDS-type high-reduction actuators, while offering enhanced reliability and robustness against unexpected mechanical stress. The findings of this study contribute to the development of high-performance torque sensors suitable for applications in robotics, industrial automation, and precision control systems, and highlight the feasibility of sensor designs with built-in mechanical safety features.}, archive = {J_ACCESS}, author = {Kwon-Hui Lee and Bumjoo Lee}, doi = {10.1109/ACCESS.2025.3588846}, journal = {IEEE Access}, pages = {125587-125593}, shortjournal = {IEEE Access}, title = {Development of a torque sensor for robot actuators with overload protection}, volume = {13}, year = {2025}, }
• (2025). PROPER: Personality recognition based on public speaking using electroencephalography recordings. ACCESS, 13, 125570-125586. (www)

  @article{ , abstract = {Personality trait recognition is an important psychological paradigm to understand the differences in people’s behavior. This paper presents a new dataset, which we dubbed as PROPER (Personality Recognition based On Public Speaking using Electroencephalography Recordings) that connects the personality traits of an individual with public speaking activity via electroencephalography (EEG) signals. EEG data of 40 healthy individuals is recorded before, during, and after public speaking activity using Muse headband. A score from the Big Five Personality Trait questionnaire is used to label the participant’s EEG data. A statistical analysis of EEG signals for each personality trait during different phases of the experiment is performed. The personality recognition process involves data acquisition, pre-processing, feature extraction and selection, and classification. Five feature groups are extracted from the frequency bands of EEG data of each channel. Feature selection is applied to the extracted features via the wrapper method. Support vector machine, the Naive Bayes, and multilayer perceptron (MLP) are used to classify the personality traits. An average F1-score of 0.95 for extroversion, 0.94 for openness to experience, 0.90 for conscientiousness, 0.84 for neuroticism, and 0.85 for agreeableness is achieved using the MLP classifier using pre-stimulus, during activity, and post-stimulus EEG data respectively.}, archive = {J_ACCESS}, author = {Muhammad Majid and Amna Rauf Butt and Imran Fareed Nizami and Aamir Arsalan and Jihyoung Ryu}, doi = {10.1109/ACCESS.2024.3395434}, journal = {IEEE Access}, pages = {125570-125586}, shortjournal = {IEEE Access}, title = {PROPER: Personality recognition based on public speaking using electroencephalography recordings}, volume = {13}, year = {2025}, }
• (2025). Scale is not all you need: Revisiting the biomimetic roots of deep learning to overcome fundamental limitations. ACCESS, 13, 125537-125569. (www)

  @article{ , abstract = {Deep Learning has historically drawn direct inspiration from nature, with Artificial Neural Networks initially grounded in neurobiological principles. However, the field has increasingly diverged from its biomimetic origins in favor of scaling-driven approaches. While this shift has delivered remarkable technical progress, it introduced fundamental limitations relative to natural intelligence. This review distills a vast body of literature into a uniquely comprehensive synthesis of these limitations, challenging the prevailing belief that scaling current methods is sufficient for achieving human-level intelligence. Drawing on recent advances in neuroscience and deep learning, we then connect each of the limitations with promising neuro-inspired solutions. Specifically, we explore the emerging topics of Liquid Neural Networks, recurrent vision architectures, and Predictive Coding as representative examples of such solutions. A number of interesting insights emerge from the literature covered throughout this synthesis, including but not limited to the common roots of model fragility and poor generalization, relationships between model nonlinearity and adversarial robustness, parallels between Predictive Coding and Joint-Embedding Predictive Architectures (JEPA), and how backpropagation constrains more radical neuro-inspired innovations. By drawing novel connections across the covered limitations and solutions, this review bridges previously disconnected lines of research and illuminates promising next steps for neuro-inspired AI, explicitly contextualizing each direction in terms of the limitations it aims to overcome. More broadly, we hope this work offers a coherent roadmap for pushing the frontiers of artificial intelligence by reverse engineering natural intelligence.}, archive = {J_ACCESS}, author = {Malek Wahidi and Anthony Rizk and Rodrigue Imad}, doi = {10.1109/ACCESS.2025.3589514}, journal = {IEEE Access}, pages = {125537-125569}, shortjournal = {IEEE Access}, title = {Scale is not all you need: Revisiting the biomimetic roots of deep learning to overcome fundamental limitations}, volume = {13}, year = {2025}, }
• (2025). Multi-scale venation pattern analysis for medicinal plant species recognition. ACCESS, 13, 125526-125536. (www)

  @article{ , abstract = {This research addresses the challenge of medicinal plant species recognition based on leaf images by focusing on venation patterns as discriminative features. Venation patterns—defined by the hierarchical arrangement of veins within a leaf—carry significant taxonomic information that is often overlooked by conventional plant classification approaches. We propose a novel, venation-aware methodology that combines specialized image preprocessing techniques with both transfer learning and custom-designed deep learning architectures. Our method extracts and analyzes venation patterns at multiple spatial scales, capturing both global and fine-grained structural details to improve classification performance. To validate the effectiveness of our approach, we developed and evaluated three distinct model architectures: 1) a modified ResNet-50 model utilizing transfer learning with an adapted input pipeline for venation-aware channels; 2) a custom-built convolutional neural network, VenationNet, explicitly designed for multi-scale venation analysis; and 3) a Dual-Stream CNN architecture that processes leaf texture and venation maps independently before merging via attention-based fusion. Preprocessing involves contrast enhancement, Frangi filtering for venation extraction, and edge detection to create a three-channel input comprising RGB, venation, and edge maps. Experimental evaluation using the Indian Medicinal Plants Dataset demonstrates that our venation-centric strategy significantly outperforms traditional CNN-based approaches, achieving higher accuracy, precision, recall, and F1-scores across diverse plant categories. This research contributes a practical and scalable solution for reliable medicinal plant identification, which is crucial for pharmacological research, biodiversity monitoring, and traditional medicine practices. Moreover, our approach is well-suited for deployment in real-time mobile and edge computing environments.}, archive = {J_ACCESS}, author = {Arnav Sanjay Karnik and Nikhil Nair and Yashas Sagili and P. B. Shanthi}, doi = {10.1109/ACCESS.2025.3589278}, journal = {IEEE Access}, pages = {125526-125536}, shortjournal = {IEEE Access}, title = {Multi-scale venation pattern analysis for medicinal plant species recognition}, volume = {13}, year = {2025}, }
• (2025). Defect detection and correction in OpenMP: A static analysis and machine learning-based solution. ACCESS, 13, 125499-125525. (www)

  @article{ , abstract = {Concurrency defects such as race conditions, deadlocks, and improper synchronization remain a critical challenge in developing reliable OpenMP-based parallel applications. Traditional static analysis tools often focus only on defect detection, offering limited or no automated correction capabilities. This paper presents a novel static analysis tool designed to detect and automatically correct concurrency-related defects in OpenMP programs. The tool performs lexical and syntactic analysis to extract OpenMP constructs, verify directive usage, and identify incorrect synchronization patterns. A rule-based correction engine is employed to repair detected defects through minimally invasive code transformations, such as inserting critical sections, correcting directive placement, and adjusting data-sharing clauses. To enhance predictive accuracy, the tool incorporates machine learning classifiers—Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Linear Support Vector Machine (LSVM)—trained on various feature combinations, including Abstract Features (AF), Halstead Features (HF), and Semantic Features (SF). Evaluation results show that NB and LSVM achieved up to 99% accuracy with simple feature sets, while DT and RF exhibited lower performance across all combinations. The tool was validated on a curated dataset of annotated OpenMP programs, achieving a 99.15% correction rate with minimal execution overhead. These results confirm the effectiveness and practicality of the proposed solution in improving the correctness and maintainability of OpenMP applications. This work bridges the gap between static defect detection and automated correction, contributing a scalable and intelligent approach to reliable parallel software development.}, archive = {J_ACCESS}, author = {Norah A. Al-Johany and Fathy E. Eassa and Sanaa A. Sharaf and Eynas H. Balkhair and Sara M. Assiri}, doi = {10.1109/ACCESS.2025.3589175}, journal = {IEEE Access}, pages = {125499-125525}, shortjournal = {IEEE Access}, title = {Defect detection and correction in OpenMP: A static analysis and machine learning-based solution}, volume = {13}, year = {2025}, }
• (2025). URDS: A dual-branch ViTs and CNNs framework for unpaired raindrop and rain streak removal. ACCESS, 13, 125485-125498. (www)

  @article{ , abstract = {Rain streaks and raindrops severely degrade image quality and adversely affect vision-based systems in outdoor environments. Removing such rain artifacts is critical for reliable visual recognition. Although supervised deraining methods achieve strong performance, unpaired data remains a major challenge due to the absence of pixel-level supervision. In this study, we propose a novel dual-branch GAN-based framework for unpaired rain detection and removal. For rain detection, we introduce a rain perception block that integrates channel sparse transformer and spatial gated convolution, enhancing feature extraction across channel and spatial dimensions and to generate precise rain masks. For rain removal, we design a rain-attentive block that leverages the detected rain mask as guidance, enabling high-performance deraining in an unsupervised manner. Extensive experiments on benchmark datasets demonstrate that our method achieves state-of-the-art performance in unsupervised rain removal. Specifically, it outperforms existing unsupervised approaches on the RainDS dataset. On Rain200L, Rain200H, and RainDrop, it achieves PSNR/SSIM scores of 34.78 dB/0.9637, 24.85 dB/0.8017, and 29.00 dB/0.9095, respectively, with average improvements of 1.50 dB in PSNR and 0.03 in SSIM. Comprehensive ablation studies further validate the critical contributions of both the Rain Perception Block (RPB) and Rain Attentive Block (RAB) modules to the overall system performance.}, archive = {J_ACCESS}, author = {Nianyun Liu and Kebin Sha and Junwei Yan and Xinxi Xie}, doi = {10.1109/ACCESS.2025.3588508}, journal = {IEEE Access}, pages = {125485-125498}, shortjournal = {IEEE Access}, title = {URDS: A dual-branch ViTs and CNNs framework for unpaired raindrop and rain streak removal}, volume = {13}, year = {2025}, }
• (2025). Computing lightning-induced voltages on overhead distribution lines using the RBF-FDTD approach. ACCESS, 13, 125473-125484. (www)

  @article{ , abstract = {This paper presents an advanced application of the Radial Basis Function-Finite-Difference Time-Domain (RBF-FDTD) method, as introduced in Vu et al. (2023), to solve the field-to-line coupling equations for simulating lightning-induced voltages on overhead distribution lines. Simulation results for a typical 1-km, 110-kV line obtained using both the RBF-FDTD and conventional FDTD methods are compared against benchmark solutions from the Lightning-Induced Overvoltage (LIOV) software to validate accuracy and determine the optimal shape parameters for the RBFs. The RBF-FDTD method exhibits high accuracy and reduced numerical dispersion, particularly when the shape parameters are optimally selected. Furthermore, the effects of critical physical factors—including lightning electromagnetic field models, imperfect ground conductivity, and corona discharge dynamics—on the induced voltages are analyzed through error evaluation. The results confirm the proposed method’s capability for reliable and efficient simulation of lightning transients, providing valuable insights for the design and protection of power distribution systems against fast transient overvoltages.}, archive = {J_ACCESS}, author = {Duc-Quang Vu and Nhat-Nam Nguyen and Tinh-Minh T. Le and Phan-Tu Vu}, doi = {10.1109/ACCESS.2025.3588253}, journal = {IEEE Access}, pages = {125473-125484}, shortjournal = {IEEE Access}, title = {Computing lightning-induced voltages on overhead distribution lines using the RBF-FDTD approach}, volume = {13}, year = {2025}, }
• (2025). Strategic human capital management in engineering workplaces: A fuzzy logic-based decision framework. ACCESS, 13, 125458-125472. (www)

  @article{ , abstract = {Strategic human capital (HC) management is a key driver of organizational success, particularly in highly technical fields such as engineering. This study presents an innovative approach to human resource (HR) decision-making by applying fuzzy logic to evaluate and prioritize HC opportunities. Through a structured survey of 70 experts from industry and academia, the most critical HC opportunities were identified and analyzed based on their impact on project objectives and the likelihood of organizational adoption. Using a Mamdani fuzzy inference system (FIS) developed in MATLAB, this research provides a data-driven framework for optimizing HR strategies. The results highlight that factors such as a “favorable work environment” and “effective internal communication” are essential for project success, as they directly influence talent retention, productivity, and organizational alignment. Given that project performance is a cornerstone of organizational competitiveness, the strategic management of HC emerges as vital for ensuring both project excellence and sustained business success. This study provides HR professionals with a systematic, scalable model that strengthens strategic decision-making, optimizes workforce management, and promotes evidence-based leadership in specialized industries. This study underlines that robust strategic HC management is not only essential for high-impact project outcomes, but also for achieving sustained organizational success.}, archive = {J_ACCESS}, author = {Belen Maria Moreno-Cabezali}, doi = {10.1109/ACCESS.2025.3589207}, journal = {IEEE Access}, pages = {125458-125472}, shortjournal = {IEEE Access}, title = {Strategic human capital management in engineering workplaces: A fuzzy logic-based decision framework}, volume = {13}, year = {2025}, }
• (2025). Self-attention-based text encoder for enhancing DMGAN text-to-image generation. ACCESS, 13, 125442-125457. (www)

  @article{ , abstract = {Generating images that align with textual input using text-to-image (TTI) generation models is a challenging task. Generative adversarial network (GAN) based TTI models can produce realistic and semantically consistent images. A bidirectional long-short-term memory (LSTM) network is commonly employed with the text encoder of GAN-based TTI models to extract text features. However, the text encoder faces the risk of information loss with longer input text or keyword removal due to the inherent sequential nature of LSTM. The popular text attention mechanism that captures relevant textual information is an option to tackle this challenge. In this paper, we propose a text encoding approach with a text self-attention mechanism to produce a superior TTI output quality. For this purpose, we modified and trained the dynamic memory GAN (DMGAN) TTI model. In our experiments, we trained and tested the TTI model on the CUB and MS-COCO datasets. Our results show that our modified DMGAN TTI model generates realistic images and outperforms the base TTI model. We analyzed the TTI models qualitatively and quantitatively in terms of FID, IS, R-precision, and CLIP score values.}, archive = {J_ACCESS}, author = {Remya Gopalakrishnan and Sambangi Naveen and Shaeen Kalathil and P. V. Sudeep}, doi = {10.1109/ACCESS.2025.3588762}, journal = {IEEE Access}, pages = {125442-125457}, shortjournal = {IEEE Access}, title = {Self-attention-based text encoder for enhancing DMGAN text-to-image generation}, volume = {13}, year = {2025}, }
• (2025). KeyMPs: One-shot vision-language guided motion generation by sequencing DMPs for occlusion-rich tasks. ACCESS, 13, 125420-125441. (www)

  @article{ , abstract = {Dynamic Movement Primitives (DMPs) provide a flexible framework wherein smooth robotic motions are encoded into modular parameters. However, they face challenges in integrating multimodal inputs commonly used in robotics like vision and language into their framework. To fully maximize DMPs’ potential, enabling them to handle multimodal inputs is essential. In addition, we also aim to extend DMPs’ capability to handle object-focused tasks requiring one-shot complex motion generation, as observation occlusion could easily happen mid-execution in such tasks (e.g., knife occlusion in cake icing, hand occlusion in dough kneading, etc.). A promising approach is to leverage Vision-Language Models (VLMs), which process multimodal data and can grasp high-level concepts. However, they typically lack enough knowledge and capabilities to directly infer low-level motion details and instead only serve as a bridge between high-level instructions and low-level control. To address this limitation, we propose Keyword Labeled Primitive Selection and Keypoint Pairs Generation Guided Movement Primitives (KeyMPs), a framework that combines VLMs with sequencing of DMPs. KeyMPs use VLMs’ high-level reasoning capability to select a reference primitive through keyword labeled primitive selection and VLMs’ spatial awareness to generate spatial scaling parameters used for sequencing DMPs by generalizing the overall motion through keypoint pairs generation, which together enable one-shot vision-language guided motion generation that aligns with the intent expressed in the multimodal input. We validate our approach through experiments on two occlusion-rich tasks: object cutting, conducted in both simulated and real-world environments, and cake icing, performed in simulation. These evaluations demonstrate superior performance over other DMP-based methods that integrate VLM support.}, archive = {J_ACCESS}, author = {Edgar Anarossi and Yuhwan Kwon and Hirotaka Tahara and Shohei Tanaka and Keisuke Shirai and Masashi Hamaya and Cristian C. Beltran-Hernandez and Atsushi Hashimoto and Takamitsu Matsubara}, doi = {10.1109/ACCESS.2025.3588975}, journal = {IEEE Access}, pages = {125420-125441}, shortjournal = {IEEE Access}, title = {KeyMPs: One-shot vision-language guided motion generation by sequencing DMPs for occlusion-rich tasks}, volume = {13}, year = {2025}, }
• (2025). Efficient IDS for IoT networks using host-based data aggregation and multi-entropy analysis. ACCESS, 13, 125406-125419. (www)

  @article{ , abstract = {IoT devices have limited computational resources, posing challenges to implementing adequate security measures. As a result, numerous attacks targeting vulnerabilities in IoT devices have been observed. Against this backdrop, research on Intrusion Detection Systems (IDSs) leveraging machine learning in IoT environments has been actively conducted. However, packet-based and flow-based IDSs proposed in existing studies are vulnerable to attacks such as DoS and DDoS, which involve numerous packet or flow combination patterns. These methods also face challenges related to computational resource burdens caused by the increased volume of input data. This study proposes a lightweight IDS with the host-based approach, representing communication behaviors with multiple entropies. The host-based approach aggregates features from different communications sent by the same host, enabling a reduction in input data. Additionally, the method captures host-level communication behaviors by leveraging multiple entropies, focusing on characteristic patterns of IoT devices, such as periodic communication with specific servers during normal operation. This enables the reduction of computational resources during detection processing while maintaining detection accuracy, even when using fewer features and lightweight machine learning algorithms. The evaluation results demonstrate that the proposed method achieves a maximum reduction of 99.7% (2916 milliseconds) in processing time and 86.4% (633 MiB) in memory usage while maintaining an intrusion detection accuracy of 99.97%, proving its feasibility in constrained environments comparable to IoT gateways.}, archive = {J_ACCESS}, author = {Yusei Katsura and Arata Endo and Ismail Arai and Kazutoshi Fujikawa}, doi = {10.1109/ACCESS.2025.3589057}, journal = {IEEE Access}, pages = {125406-125419}, shortjournal = {IEEE Access}, title = {Efficient IDS for IoT networks using host-based data aggregation and multi-entropy analysis}, volume = {13}, year = {2025}, }
• (2025). Optimal configuration of ESS in distribution network considering generation of PV-load time series scenarios. ACCESS, 13, 125397-125405. (www)

  @article{ , abstract = {With the accelerating application demand for new energy-based power systems, a energy storage system (ESS) optimization configuration method is proposed to fully consider the load demand and the uncertainty of photovoltaic (PV) output and improve the adaptability of the ESS configuration scale. Firstly, the Gaussian kernel function of the kernel method is used to map the temporal variation characteristics of the PV output scenario to establish a comprehensive evaluation index scenario for the uncertainty and temporal correlation of PV-Load. The iterative self-organizing data analysis algorithm (ISODATA) is used to optimize clustering of the comprehensive evaluation index scenario to generate PV-Load typical scenarios. A distribution network ESS configuration model is constructed with the objectives of minimizing voltage fluctuation indicator, line loss rate, and minimizing ESS investment cost. To solve the high-dimensional multi-objective functions in the model, an non-dominated sorting genetic algorithm-III (NSGA-III) was employed, and the optimal solution is selected by using the entropy weight method (EWM). Finally, the proposed method and model are analyzed and verified by case simulation, and the results show that the proposed model and method can comprehensively consider the actual operating characteristics of PV and load, and can effectively formulate the ESS configuration scale and operation strategy in the distribution network.}, archive = {J_ACCESS}, author = {Xiaolong Xiao and Mingming Shi and Fan Wu and Yukai Wei and Bo Zhao and Fang Zhang}, doi = {10.1109/ACCESS.2024.3392390}, journal = {IEEE Access}, pages = {125397-125405}, shortjournal = {IEEE Access}, title = {Optimal configuration of ESS in distribution network considering generation of PV-load time series scenarios}, volume = {13}, year = {2025}, }
• (2025). Robust adaptive multiple-input multiple-output radar array beamforming using advanced generalized sidelobe canceller against random scenario mismatches. ACCESS, 13, 125383-125396. (www)

  @article{ , abstract = {Nowadays, one of the core issues regarding multiple-input multiple-out (MIMO) radar is its robustness against scenario mismatches owing to its sensitivity to array scenario mismatch. This paper presents a method to enhance the performance of an advanced generalized sidelobe canceller based MIMO radar beamformer recently presented in the literature in dealing with scenario mismatches. We formulate an optimization problem of maximizing the output power gain of the beamformer for effectively finding the optimal estimate of the desired signal’s direction angle. An efficient iterative algorithm is proposed based on a well-known early late gate synchronization to solve the optimization problem without requiring complicated algorithmic complexity. The proposed method is not only efficient but also effective to cure the performance deterioration under random multiple scenario mismatches. The convergence and computation complexity regarding the proposed method are analyzed. Simulation results are provided for confirmation and making comparisons with the existing robust methods.}, archive = {J_ACCESS}, author = {Cheng-Jie Wang and Ju-Hong Lee}, doi = {10.1109/ACCESS.2025.3589417}, journal = {IEEE Access}, pages = {125383-125396}, shortjournal = {IEEE Access}, title = {Robust adaptive multiple-input multiple-output radar array beamforming using advanced generalized sidelobe canceller against random scenario mismatches}, volume = {13}, year = {2025}, }
• (2025). A pad-focused PCB routing algorithm using polygon-based dynamic partitioning. ACCESS, 13, 125374-125382. (www)

  @article{ , abstract = {Routing plays a pivotal role in the design of printed circuit boards (PCBs). Existing automated routers typically tackle the routing problem by dividing it into two separate phases: escape routing and area routing. However, this approach often leads to suboptimal solutions or even the absence of solutions when transitioning from escape routing to area routing. In this paper, we propose a novel pad-focused, net-by-net, two-stage PCB routing approach comprising of a Monte Carlo tree search (MCTS)-based global routing stage, followed by an A*-based detailed routing stage. To bridge the gap between the global and detailed routing stages, we introduce a polygon-based dynamic routable region partitioning mechanism, ensuring that a detailed routing solution exists when a global routing solution is present. Experimental results demonstrate that our approach outperforms state-of-the-art routers in terms of the success rate and total wirelength on the test set.}, archive = {J_ACCESS}, author = {Youbiao He and Hebi Li and Ge Luo and Forrest Sheng Bao}, doi = {10.1109/ACCESS.2024.3386921}, journal = {IEEE Access}, pages = {125374-125382}, shortjournal = {IEEE Access}, title = {A pad-focused PCB routing algorithm using polygon-based dynamic partitioning}, volume = {13}, year = {2025}, }
• (2025). Graph-based radiomics feature extraction from 2D retina images. ACCESS, 13, 125359-125373. (www)

  @article{ , abstract = {Medical image analysis offers valuable visual support for clinical decision-making, yet the incorporation of quantitative data is essential for deeper diagnostic insight. The radiomics approach addresses this need by combining quantitative image analysis with Machine Learning (ML) techniques, further enhancing Explainable Artificial Intelligence (XAI) for clinical applications. While working with two-dimensional (2D) images derived from volumetric data offers computational advantages, accurately estimating structural properties within these images remains challenging. Within the radiomics framework, this study introduces a methodology to distinguish bifurcations from other structural variations in 2D local fragments of retinal vasculature. Using a publicly available dataset of 29 retinal images, we extracted 1003 feature fragments for experiments. The regions of interest (ROIs) are identified using morphological image processing techniques. Specifically, candidate points are detected by applying structuring elements (SEs) to the skeletonized and binarized vasculature. From each candidate point, a local fragment of $35\times 35$ pixels is extracted and used as input to the classification model. A Convolutional Neural Network (CNN) model, tailored for small image datasets and binary classification tasks is created. The trained model achieved an accuracy of 94.95% in correctly identifying bifurcation points. Based on predicted bifurcation points and blood vessel segments, we use the Graph-Based Radiomics Feature Extraction Algorithm (Graph-BRFExtract) to extract the adjacency matrix. This matrix serves as mathematical representation of the retinal vascular network, constituting a novel form of graph-based radiomic features.}, archive = {J_ACCESS}, author = {Ofélio Jorreia and Nuno Gonçalves and Rui Cortesão}, doi = {10.1109/ACCESS.2025.3588817}, journal = {IEEE Access}, pages = {125359-125373}, shortjournal = {IEEE Access}, title = {Graph-based radiomics feature extraction from 2D retina images}, volume = {13}, year = {2025}, }
• (2025). Broadband low-cost microstrip arrays for water-level radar with frequency-stable beams and side-sector sidelobe suppression. ACCESS, 13, 125348-125358. (www)

  @article{ , abstract = {Water-level radar systems often rely on horn or lens antennas to achieve narrow-beam coverage and high gain, but scaling these designs for bistatic setups significantly increases size and installation complexity. This paper addresses these challenges by introducing a single-layer microstrip patch array, fabricated on a standard printed circuit board (PCB) substrate and fed by only uniform excitations, for bistatic water-level radar. The key novelty lies in systematically comparing two widely used feed architectures—series-fed and planar-fed—while demonstrating that the planar-fed structure not only avoids the beam steering observed in series-fed arrays but also achieves side-sector sidelobe suppression without complex amplitude tapers. A $16\times 16$ planar-fed prototype demonstrates wide impedance and 3-dB gain bandwidths, near-constant beamwidth across its operational range, and suppressed side-sector sidelobes, marking a clear improvement over earlier microstrip arrays that rely on amplitude tapers or show frequency-dependent beam steering. These results confirm that uniform excitation is sufficient for maintaining broadside radiation and controlling sidelobes, offering a compact, broadband, and low-cost alternative to large horn assemblies. This work thus provides an effective solution that bridges theoretical array concepts and real-world radar deployment, meeting the stringent needs of water-level monitoring.}, archive = {J_ACCESS}, author = {Kuang-Hsuan Huang and Yen-Sheng Chen}, doi = {10.1109/ACCESS.2025.3589884}, journal = {IEEE Access}, pages = {125348-125358}, shortjournal = {IEEE Access}, title = {Broadband low-cost microstrip arrays for water-level radar with frequency-stable beams and side-sector sidelobe suppression}, volume = {13}, year = {2025}, }
• (2025). A joint knowledge extraction model for tobacco pest and disease prevention based on BERT+BA+CASREL. ACCESS, 13, 125332-125347. (www)

  @article{ , abstract = {To address the issues of long-text overlapping triplets and data imbalance in text knowledge extraction for tobacco pest and disease prevention, this study first constructed a corpus of tobacco pest and disease prevention texts based on published books and relevant websites, and established a dataset. Subsequently, a joint knowledge extraction model based on BERT+BA+CASREL was proposed, and its precision was validated. In this model, BERT leverages the Transformer architecture to generate deep contextual semantic representations, effectively capturing complex semantic associations between words. The BA module consists of BiLSTM and Self-Attention, where BiLSTM performs bidirectional sequence modeling on the features extracted by BERT, enhancing the understanding of contextual and long-distance dependencies; Self-Attention assigns different weights to each input, strengthening the ability to capture long-term dependencies. The CASREL module, as the core component of the framework, employs a cascade binary tagging approach to achieve joint extraction of entities and relations, effectively improving the precision of complex relation recognition and end-to-end optimization. Additionally, the GHM loss function is adopted to replace the traditional cross-entropy loss function, alleviating the data imbalance issue. Experimental results demonstrate that the proposed model achieves a precision of 93.32%, a recall of 92.51%, and an F1-score of 92.91%, validating its effectiveness in addressing long-text overlapping triplet extraction and other related issues. The proposed model not only enhances the precision of text knowledge extraction in the field of tobacco pest and disease prevention but also provides new perspectives and methodologies for related research areas.}, archive = {J_ACCESS}, author = {Kehan Liu and Feng Zhang and Qiulan Wu and Xiang Sun and Huarui Wu and Ziruo Sun}, doi = {10.1109/ACCESS.2025.3586989}, journal = {IEEE Access}, pages = {125332-125347}, shortjournal = {IEEE Access}, title = {A joint knowledge extraction model for tobacco pest and disease prevention based on BERT+BA+CASREL}, volume = {13}, year = {2025}, }
• (2025). A dual-strategy framework for cyber threat detection in imbalanced, high-dimensional data across heterogeneous networks. ACCESS, 13, 125313-125331. (www)

  @article{ , abstract = {As cyber threats grow in complexity, ensuring robust network security has become increasingly critical. Intrusion Detection Systems (IDS) serve as a key defense mechanism, detecting potential threats and unauthorized activities that may evade traditional firewalls. Intrusion Detection Systems (IDS) face significant challenges in detecting cyber threats due to highly imbalanced datasets, high-dimensional feature spaces, and computational inefficiencies. Existing methods often struggle to maintain detection accuracy for minority attack classes while dealing with redundant and irrelevant features, leading to degraded model performance. This study addresses these gaps by introducing a novel IDS framework that optimizes data balancing, feature selection, and classification. First, the Variance Split Adaptive Sampling KD-SMOTE (VAST-KD-SMOTE) technique addresses data imbalance by strategically under-sampling majority class instances using a variance-based KD-Tree. This preserves meaningful data patterns while reducing computational costs. Minority class instances are simultaneously oversampled using SMOTE, where diversity is enhanced by selecting k-nearest neighbors from KD-Tree leaf nodes. Sampled data quality is validated using Jensen-Shannon Divergence, Silhouette Score, and Davies-Bouldin Index to ensure realistic synthetic sample generation. Second, the Cauchy-Gaussian Genetic-Arithmetic Optimizer (CG-GAO) addresses the challenge of high-dimensional data by combining a genetic algorithm (GA) and an arithmetic optimization algorithm (AOA), enhancing exploration and preventing premature convergence. The proposed IDS employs classifiers such as Decision Tree, Random Forest, Cat Boost, Ada Boost, XG Boost, and Bagging Classifier to improve detection performance. Experiments are conducted on the CICIDS2017, IoTID20, and ToN-IoT datasets. The proposed IDS is evaluated using metrics that specifically evaluate anomaly detection capabilities, such as Accuracy, Precision, DR (Detection Rate), Specificity, MR (Miss Rate), FAR (False Alarm Rate), F1-Score, Cohen’s Kappa, MCC, ROC-AUC. The proposed IDS outperforms conventional AOA and GA, achieving 99.72%,99.29%, and 99.97% accuracy with the Bagging classifier. Feature selection and data balancing technique improves Detection Rate and reduce complexity by a 37% reduction in computational overhead, making it a breakthrough in IDS for imbalanced, high-dimensional cybersecurity data.}, archive = {J_ACCESS}, author = {T. Saranya and S. Indra Priyadharshini}, doi = {10.1109/ACCESS.2025.3582788}, journal = {IEEE Access}, pages = {125313-125331}, shortjournal = {IEEE Access}, title = {A dual-strategy framework for cyber threat detection in imbalanced, high-dimensional data across heterogeneous networks}, volume = {13}, year = {2025}, }
• (2025). Low-power portable system for power grid foreign object detection based on the lightweight model of improved YOLOv7. ACCESS, 13, 125301-125312. (www)

  @article{ , abstract = {Foreign objects caught and entangled on power grid transmission lines, power towers and other equipment can pose potential threats to the power system. The detection algorithm for foreign objects in the power grid cannot achieve the optimal balance in terms of accuracy and efficiency. The increasing demand for embedded devices in practical applications has led to the development of efficient, lightweight networks as a trend. In order to meet the actual power grid foreign object detection needs, this paper proposes a low-power portable power grid detection system based on lightweight improved YOLOv7, which is designed to be deployed on low-power portable embedded devices while ensuring a balance between detection efficiency and accuracy. Through the parallel module of segmentation and detection, the model completes instance segmentation while detecting, reducing the missed detection of object bounding boxes and increasing the attention to small objects. In addition, lightweight improvements are made to each branch of the network to ensure detection efficiency while significantly reducing the amount of calculation. Furthermore, a professional power grid foreign object detection data set is proposed, and the proposed model is experimentally verified on the dataset. The experimental results demonstrate that the model proposed has higher detection performance in foreign object detection in power grids. Finally, the model is deployed on the Jetson Nano embedded device and showed superior adaptability and inference speed, proving the practicability of the entire system for foreign object detection in the power grid.}, archive = {J_ACCESS}, author = {Yonghuan He and Rong Wu and Chao Dang}, doi = {10.1109/ACCESS.2024.3352277}, journal = {IEEE Access}, pages = {125301-125312}, shortjournal = {IEEE Access}, title = {Low-power portable system for power grid foreign object detection based on the lightweight model of improved YOLOv7}, volume = {13}, year = {2025}, }
• (2025). X-band waveguide mechanical damper enabled by 3D printed flexible corrugations and dielectric loaded chokes. ACCESS, 13, 125293-125300. (www)

  @article{ , abstract = {In modern Radio Frequency (RF) systems, long waveguide chains are routed as transmission lines to connect the RF source to the antenna. These systems are subjected to mechanical shock and vibration, along with undesired thermal expansion during transportation or operation. Since waveguides are rigid mechanical pipes, they may face catastrophic failure due to mechanical stresses. Thus, this paper investigates a novel waveguide-based damper in the X-band with 93.33% transmission efficiency from 9.5 GHz to 11.5 GHz, which is enabled by 3D printing of flexible dielectrics to act as a mechanical isolator. The 3D-printed flexible dielectric used is Thermoplastic Polyurethane (TPU), which allows for flexible corrugations to be designed between two waveguide interfaces that are separated by some distance. To reduce the RF leakage due to the gap between the waveguide flanges, dielectric-loaded RF chokes are introduced. The dielectric-loaded RF chokes also provide mechanical functionality of the waveguide mechanical damper by implementing corrugations as part of the choke design, allowing for shocks and acceleration to 20G. This allows the two gap waveguides to support the transient mechanical displacements of the waveguide flange during transportation, acting as a mechanical damper between two waveguide subassemblies.}, archive = {J_ACCESS}, author = {Jie Xiang Chia and King Ho Holden Li}, doi = {10.1109/ACCESS.2025.3588380}, journal = {IEEE Access}, pages = {125293-125300}, shortjournal = {IEEE Access}, title = {X-band waveguide mechanical damper enabled by 3D printed flexible corrugations and dielectric loaded chokes}, volume = {13}, year = {2025}, }
• (2025). A wireless-powered endovascular stent system for neuromodulation: A simulation-based feasibility study. ACCESS, 13, 125287-125292. (www)

  @article{ , abstract = {Neurological disorders present a growing global health challenge, contributing to an increasing disease burden. While invasive treatments, such as deep brain stimulation, offer therapeutic benefits, they require surgical implantation, posing risks such as infection, bleeding, and the need for ongoing maintenance. To overcome these limitations, this study proposes a novel wirelessly powered endovascular stent system designed to enable continuous electrical stimulation. By leveraging wireless power transmission technology, the stent receives energy from external electromagnetic sources, eliminating the need for implanted batteries. Simulation experiments revealed that both external and stent coils play a crucial role in shaping the electric field distribution within the head and influencing the energy transfer efficiency. Our proposed interventional system demonstrated the feasibility of wireless power transmission via intermediate fields, enabling induced electrical stimulation. These findings provide a foundation for further optimization and development of efficient and interventional neuromodulation therapies.}, archive = {J_ACCESS}, author = {Yuru Tian and Jianwei Pan and Minmin Wang}, doi = {10.1109/ACCESS.2025.3586782}, journal = {IEEE Access}, pages = {125287-125292}, shortjournal = {IEEE Access}, title = {A wireless-powered endovascular stent system for neuromodulation: A simulation-based feasibility study}, volume = {13}, year = {2025}, }
• (2025). Task-ready PanNuke and NuCLS datasets: Reorganization, synthetic data generation, and experimental evaluation. ACCESS, 13, 125275-125286. (www)

  @article{ , abstract = {Automating nuclei analysis in histopathology is essential for enhancing disease diagnosis; however, training reliable models necessitates well-structured datasets. This paper addresses the gap in standardized data preparation workflows for two critical histopathology datasets: PanNuke and NuCLS. First, we organize histopathology images and masks into training-validation splits, extract subsets specific to cell types, and generate multi-scale patches to enable robust model training across various resolutions using the PanNuke dataset. Second, we curate task-specific subsets for object detection and semantic segmentation, ensuring consistency across splits while addressing annotation inconsistencies with the NuCLS dataset. Third, we conduct experiments on two reorganized datasets, including cell-type-specific binary classification, multi-task evaluation, and extension to synthetic datasets. Our workflows address common histopathology data challenges, including fragmented annotations, class imbalance, and mismatched metadata. The processed datasets are shared in standardized formats, allowing researchers to train models directly for critical tasks such as detecting cancerous nuclei or segmenting inflammatory cells in histopathology images.}, archive = {J_ACCESS}, author = {Sai Chandana Koganti and Siri Yellu and Jihoon Yun and Sanghoon Lee}, doi = {10.1109/ACCESS.2025.3589477}, journal = {IEEE Access}, pages = {125275-125286}, shortjournal = {IEEE Access}, title = {Task-ready PanNuke and NuCLS datasets: Reorganization, synthetic data generation, and experimental evaluation}, volume = {13}, year = {2025}, }
• (2025). PARS: A position-based attention for rumor detection using feedback from source news. ACCESS, 13, 125260-125274. (www)

  @article{ , abstract = {Rumor detection focuses on verifying unconfirmed information. Using user feedback within a timeframe enhances the quality of detection. Given the rapid spread and societal impact of rumors, both accurate validation and early detection are critical. Despite numerous studies in this field, designing effective feature representations still remains a central challenge, especially for models aiming to capture the dynamics of rumor propagation. This paper introduces PARS (Position-based Attention for Rumor detection using feedback from Source news), a novel method that treats a rumor and its associated feedback as a unified unit. Each post is considered like a word, and the sequence of posts forms a sentence that conveys the collective meaning. This sentence-level-like structure captures the semantic and structural aspects of rumor spread. PARS incorporates a position-aware multi-head attention layer, which attends to posts based on their roles in the propagation tree. A post reordering mechanism is applied to reflect the actual dissemination pattern, ensuring that position-sensitive attention captures the most relevant signals. Experimental results show that PARS achieves superior performance compared to existing methods, particularly in terms of accuracy, F1-score, and early detection, demonstrating its effectiveness in real-world rumor classification scenarios.}, archive = {J_ACCESS}, author = {Hanieh G. Asadi and Ali Hamzeh and Niloofar Mozafari}, doi = {10.1109/ACCESS.2025.3583544}, journal = {IEEE Access}, pages = {125260-125274}, shortjournal = {IEEE Access}, title = {PARS: A position-based attention for rumor detection using feedback from source news}, volume = {13}, year = {2025}, }
• (2025). Are we there yet? a study of decentralized identity applications. ACCESS, 13, 125232-125259. (www)

  @article{ , abstract = {The development of Decentralized Identities (DI) and Self-Sovereign Identities (SSI) aim to address the critical limitations of centralized identity management by enhancing user data control. This has been accompanied by numerous contributions to the development of principles, standards, and systems. While prior surveys have focused primarily on academic literature, they often neglect industry-led efforts and gray literature. Furthermore, no existing survey has thoroughly analyzed real-world deployments to understand the barriers to the adoption of DI and SSI systems. This paper addresses the gap by providing an overview of both academic and gray literature, examining industry-led and governmental initiatives, and presenting a comprehensive landscape of DI and SSI systems. Additionally, the survey identifies the practical challenges and limitations that slow down the transition from centralized to decentralized identity management systems. By shifting the focus from purely technological constraints to real-world deployment challenges, this paper identifies the underlying reasons preventing the adoption of decentralized identities despite their evident benefits to the data owner.}, archive = {J_ACCESS}, author = {Daria Schumm and Katharina O. E. Müller and Burkhard Stiller}, doi = {10.1109/ACCESS.2025.3588170}, journal = {IEEE Access}, pages = {125232-125259}, shortjournal = {IEEE Access}, title = {Are we there yet? a study of decentralized identity applications}, volume = {13}, year = {2025}, }
• (2025). Fairness-oriented charging station location optimization driven by deep reinforcement learning. ACCESS, 13, 125217-125231. (www)

  @article{ , abstract = {Against the backdrop of growing global attention to sustainable development and environmental protection, electric vehicles (EVs) have become an important choice for green transportation due to their zero-emission advantage. In recent years, the number of EVs in Beijing has rapidly increased, leading to an urgent demand for charging infrastructure. However, the existing layout of charging stations is imbalanced, with an over-concentration of stations in central urban areas and insufficient coverage in peripheral regions, hindering the further promotion of electric vehicles. Existing studies mostly rely on static data and traditional optimization methods, which are unable to adapt to the dynamic changes in the city, necessitating innovative strategies to improve both fairness and efficiency in location planning. This study aims to optimize the layout of charging stations in Beijing, balancing overall coverage with regional fairness to support the green transportation system. First, multi-source data including POI, population, GDP, and traffic networks are integrated, and GIS technology is used to analyze spatial distribution characteristics. Geographic detectors are then used to quantify the weight of influencing factors. Next, a fairness-oriented maximum coverage model is constructed, and Deep Reinforcement Learning (DRL) is employed to solve the problem, evaluating its advantages over traditional methods. The results show that the charging station layout in Beijing exhibits a “dense center, sparse periphery” pattern, with schools, hospitals, and other public facilities being the main driving factors. The DRL approach outperforms traditional methods in terms of coverage rate, fairness, and optimization efficiency, and can balance benefits and fairness by adjusting the parameter $\lambda $ This study provides theoretical and practical guidance for the planning of EV charging infrastructure in Beijing.}, archive = {J_ACCESS}, author = {Siyue Yuan and Jiaying Fu and Xiaoyin Ma}, doi = {10.1109/ACCESS.2025.3588880}, journal = {IEEE Access}, pages = {125217-125231}, shortjournal = {IEEE Access}, title = {Fairness-oriented charging station location optimization driven by deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Style-VT: Style conditioned chord generation by variational transformer with chord substitution. ACCESS, 13, 125207-125216. (www)

  @article{ , abstract = {Recent advancements in music modeling have shown the potential of Transformer in tasks such as chord progression generation. Despite these successes, controlling chord progressions to reflect genre-specific characteristics remains underexplored. In this work, we present Style-VT, a style-conditioned chord generation model that combines the Transformer architecture with a variational autoencoder. Our model leverages genre embeddings to generate chord progressions that align with the stylistic norms of specific genres. We also introduce a newly defined chord substitution into the objective function, allowing for greater flexibility in generated chord progressions. By extensive experiments, we demonstrate that Style-VT generates a more flexible chord progressions and achieves higher harmonic similarities with human-composed chords than the existing methods.}, archive = {J_ACCESS}, author = {Kyowon Song and Dongyoung Seo and Junsu Na and Heecheol Yang}, doi = {10.1109/ACCESS.2025.3589525}, journal = {IEEE Access}, pages = {125207-125216}, shortjournal = {IEEE Access}, title = {Style-VT: Style conditioned chord generation by variational transformer with chord substitution}, volume = {13}, year = {2025}, }
• (2025). Decentralized and dynamic zero-touch provisioning of leaf-spine EVPN data centers. ACCESS, 13, 125190-125206. (www)

  @article{ , abstract = {Data centers are becoming increasingly large and complex due to the exponential growth of data and the rising demand for cloud services. To manage this complexity, modern data center networks employ leaf-spine topologies and EVPN overlays, enabling efficient scaling and supporting multi-tenancy. These networks often include thousands of servers and network devices, making configuration and management highly challenging. This paper proposes a decentralized and dynamic Zero-Touch Provisioning (ZTP) solution for leaf-spine EVPN data centers. Our approach employs autonomous agents on each data center router to provision both the underlay and the overlay infrastructure without manual intervention, handling both initial deployments and disruptive events (e.g., router or link failures). The underlay is assumed to use a link-state routing protocol, and we introduce a novel algorithm that infers the role of each router (e.g., leaf or spine) from the network topology provided by the protocol, a feature central to our approach. We experimentally validated our solution using SR Linux, a network operating system for data centers that supports non-native agent integration. Our results demonstrate that the proposed ZTP solution can fully provision leaf-spine data center networks automatically while achieving very fast convergence times.}, archive = {J_ACCESS}, author = {Martim Tavares and Rui Valadas and Tiago Amado and Marlon Paz and Sérgio Santos}, doi = {10.1109/ACCESS.2025.3589509}, journal = {IEEE Access}, pages = {125190-125206}, shortjournal = {IEEE Access}, title = {Decentralized and dynamic zero-touch provisioning of leaf-spine EVPN data centers}, volume = {13}, year = {2025}, }
• (2025). Integrated thermal management methodology based on proximal policy optimization with memory function for battery electric vehicles. ACCESS, 13, 125180-125189. (www)

  @article{ , abstract = {Thermal management strategy (TMS) plays a pivotal role in ensuring operational safety, energy efficiency in battery electric vehicles (BEVs) by maintaining critical components within optimal thermal operating ranges. This study proposes the integrated TMS for BEVs by integrating proximal policy optimization (PPO) with a gated recurrent unit (GRU)-based memory mechanism. By leveraging GRU-enhanced PPO (HPPO), proposed TMS captures long-term temporal dependencies in thermal dynamics, enabling adaptive coordination of cooling components while maintaining battery, motor, and cabin temperatures within optimal ranges. Simulation results demonstrate that the HPPO-based TMS reduces energy consumption by 25.76% compared to conventional rule-based TMS, achieving a balance between thermal stability and energy efficiency. Further analysis reveals a significant improvement in root mean square error of controlled components’ temperature. Moreover, in the three cases where the sensitivity of the proposed TMS is verified, the proposed TMS achieved energy improvement of 23.61%, 25.19% and 21.54% compared to traditional rule-based TMS, respectively. These advancements highlight the potential of memory-augmented reinforcement learning for multi-objective thermal optimization in next-generation BEVs, offering a scalable solution for extreme climate adaptability.}, archive = {J_ACCESS}, author = {Guanghui Cao and Yongqiang Jia and Jinrui Gao}, doi = {10.1109/ACCESS.2025.3589659}, journal = {IEEE Access}, pages = {125180-125189}, shortjournal = {IEEE Access}, title = {Integrated thermal management methodology based on proximal policy optimization with memory function for battery electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Ensemble methods for peristaltic pump accuracy enhancement. ACCESS, 13, 125157-125179. (www)

  @article{ , abstract = {This study investigates how ensemble learning techniques can be employed for enhancing peristaltic pump accuracy in pharmaceutical manufacturing, and demonstrates significant accuracy improvements through the novel E-AR implementation, with gains of up to 53.93% at 0.3 ml volume compared to 47% achievable with single models. To establish the foundation for ensemble methods evaluation, we first conduct a comprehensive validation of traditional Adaptive Dosing Control System (ADCS) across an extended volume range (0.1-2.0 ml), demonstrating base performance improvements. In this investigation, we develop a novel offline performance indicator enabling rapid assessment of compensation strategies without extensive physical testing, showing strong correlation with actual measurements. These premises enable a thorough investigation of various ensemble configurations, revealing volume-dependent performance patterns where different models excel under specific conditions, suggesting that practical applications may benefit from volume-specific model selection. The comparison with a very accurate reference mechanical pump, demonstrates that our ADCS solutions achieve comparable or superior performance across most volumes while maintaining the cost-effectiveness. Statistical validation via a multi-dimensional framework confirms the significance of these improvements through multiple complementary tests: paired t-tests showing significant mean differences with p $\leq 0.001$ , Mann-Whitney U tests confirming distributional shifts, Levene tests demonstrating variance modifications with statistics up to 801.65, and mixed linear model analysis with F-statistics ranging from 0.004 to 1497.75 confirming global effects.}, archive = {J_ACCESS}, author = {Davide Privitera and Alessandro Mecocci and Sandro Bartolini}, doi = {10.1109/ACCESS.2025.3589947}, journal = {IEEE Access}, pages = {125157-125179}, shortjournal = {IEEE Access}, title = {Ensemble methods for peristaltic pump accuracy enhancement}, volume = {13}, year = {2025}, }
• (2025). Microservices-driven automation in full-stack development: Bridging efficiency and innovation with FSMicroGenerator. ACCESS, 13, 125131-125156. (www)

  @article{ , abstract = {The development of modern web applications presents major challenges due to increasingly complex architectures, scalability requirements, and the need to reduce time to market. These challenges are exacerbated by the shortage of talent, making the implementation of modern architectures more difficult for many companies. In response, this article presents FSMicroGenerator, an innovative code generation tool based on a low-code approach, which exploits UML class diagrams and templates to generate multilingual, full-stack web applications based on a microservices architecture. FSMicroGenerator automates the entire process of creating IT solutions, from development to deployment, reducing technical barriers and technical debt, and enabling teams to focus on business innovation while ensuring compliance with industry standards and best practices. By facilitating the adoption of DevSecOps practices, it improves collaboration between the development and operations teams, ensuring that security is integrated at every stage of the development process. This enables continuous management of the application lifecycle, faster and more reliable releases, and proactive identification and mitigation of security vulnerabilities. FSMicroGenerator also stands out for its ability to create a catalog of ready-to-use functional blocks, enabling their reuse and integration into other solutions. In addition, it offers a secure ecosystem, protecting companies’ assets against the risks of data exposure and leakage thanks to a robust architecture and integrated security mechanisms. As part of our open innovation approach, we plan to make FSMicroGenerator open source, to broaden its adoption and enable the community to contribute to its continuous improvement.}, archive = {J_ACCESS}, author = {Samira Khalfaoui and Hafida Khalfaoui and Abdellah Azmani}, doi = {10.1109/ACCESS.2025.3589285}, journal = {IEEE Access}, pages = {125131-125156}, shortjournal = {IEEE Access}, title = {Microservices-driven automation in full-stack development: Bridging efficiency and innovation with FSMicroGenerator}, volume = {13}, year = {2025}, }
• (2025). Embedding-enhanced graph attention networks for imbalanced industrial fault diagnosis. ACCESS, 13, 125119-125130. (www)

  @article{ , abstract = {Intricate nonlinear interactions among measurements and imbalanced distribution of fault samples pose considerable challenges for accurate fault diagnosis in modern industrial processes. To address these challenges, this study proposes an embedding-enhanced graph attention network to learn efficient representations, which captures the delicate interactions among sensors with learnable node embeddings, facilitating accurate fault diagnosis. First, sensor interactions are transformed into graphs using K-nearest neighbors using the similarity of nodes, with node features and edge weights learned through embedding and attention mechanisms, respectively. Subsequently, a dynamic attention mechanism, incorporating both node embedding and measurements, is adopted to update sample representations for fault diagnosis. During training, the focal loss, instead of cross-entropy loss, is adopted to emphasize hard classes and address category imbalance. Finally, the proposed model is tested on two real-world industrial process datasets, and the results demonstrate the efficiency and superiority of the proposed model compared to existing state-of-the-art models.}, archive = {J_ACCESS}, author = {Chi Zhang and Yuliang Li and Lifeng Fan and Lixiang Shen and Ting Qin}, doi = {10.1109/ACCESS.2024.3392422}, journal = {IEEE Access}, pages = {125119-125130}, shortjournal = {IEEE Access}, title = {Embedding-enhanced graph attention networks for imbalanced industrial fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Scalable cold-start optimization in serverless computing: Leveraging function fusion with PanOpticon simulator. ACCESS, 13, 125101-125118. (www)

  @article{ , abstract = {Serverless computing has transformed cloud computing with its inherent scalability, pay-as-you-go pricing model, and low-latency execution. However, cold-start delays arising from the on-demand initialization of execution environments such as containers or virtual machines remain a persistent bottleneck, particularly for latency-sensitive applications. This paper addresses this challenge by proposing a novel function fusion approach to minimize cold-start latency in serverless environments. The proposed strategy leverages the PanOpticon simulator, a comprehensive platform for deploying and evaluating Function-as-a-Service (FaaS) applications. The proposed approach reduces redundant initialization overhead and improves resource utilization by dynamically merging functions within a workflow. Unlike conventional frameworks, the proposed method integrates function fusion directly into the simulation and evaluation pipeline, enabling more scalable and performance-aware optimizations. Extensive simulation-based evaluations demonstrate that the fusion strategy significantly reduces cold-start delays while enhancing system throughput and responsiveness across diverse workloads. The results confirm that function fusion not only minimizes latency but also maximizes efficiency, making it a robust solution for real-time, scalable serverless applications. This work contributes a practical framework for cold-start mitigation and lays the groundwork for future research in optimizing FaaS-based cloud architectures.}, archive = {J_ACCESS}, author = {Ranjan Kumar Behera and Anisha Kumari and Sung-Bae Cho}, doi = {10.1109/ACCESS.2025.3589393}, journal = {IEEE Access}, pages = {125101-125118}, shortjournal = {IEEE Access}, title = {Scalable cold-start optimization in serverless computing: Leveraging function fusion with PanOpticon simulator}, volume = {13}, year = {2025}, }
• (2025). Dual-branch neural network for bridging semantic gap in harmful meme detection. ACCESS, 13, 125090-125100. (www)

  @article{ , abstract = {Memes, cultural entities that spread predominantly through social media, have recently seen an influx of harmful content used for trolling and bullying. Detecting such harmful memes presents a significant challenge due to the semantic disparity between their textual and visual components. To address this issue, we propose a Dual-Branch Neural Network (DBNN) for bridging the semantic gap in harmful meme detection. The proposed DBNN framework utilizes global visual and textual embeddings to learn discriminative joint representations. By capturing the context between the textual and visual modalities, DBNN bridges the semantic gap, enabling robust detection of harmful content. We evaluate the performance of the proposed network on the Harm-C and Harm-P benchmark datasets, employing Accuracy and Macro-Averaged Mean Absolute Error (MMAE) as evaluation metrics. Our experiments demonstrate that DBNN significantly outperforms state-of-the-art approaches, achieving higher accuracy and robustness in detecting harmful memes. This work highlights the potential of DBNN in effectively identifying harmful memes, contributing to a safer online environment.}, archive = {J_ACCESS}, author = {Muhammad Shoib Amin and Summaira Jabeen and Ahmad A. Alzahrani and Abdolraheem Khader and Ali Ahmed and Sadaf Hussain}, doi = {10.1109/ACCESS.2025.3586505}, journal = {IEEE Access}, pages = {125090-125100}, shortjournal = {IEEE Access}, title = {Dual-branch neural network for bridging semantic gap in harmful meme detection}, volume = {13}, year = {2025}, }
• (2025). Refining ℌ∞ controller performance with extremum seeking control for improved disturbance attenuation. ACCESS, 13, 125080-125089. (www)

  @article{ , abstract = {Robust disturbance attenuation is critical in automotive, active suspension systems to ensure vehicle stability and passenger comfort. Traditional ${\mathcal {H}}_{\infty } $ control methods effectively minimize disturbances but often result in high-order controllers, which are challenging to implement practically due to complexity and reduced robustness to unmodeled dynamics. Although fixed-order structured controllers mitigate these implementation difficulties, they inherently suffer from a performance gap compared to their full-order counterparts. This paper presents a novel hybrid tuning strategy that combines structured ${\mathcal {H}}_{\infty } $ control with Extremum Seeking Control (ESC) to optimize disturbance attenuation in a quarter-car active suspension system, specifically targeting the ${\mathcal {H}}_{\infty } $ norm within a critical frequency band. Initially, a structured sub-optimal ${\mathcal {H}}_{\infty } $ controller is designed using conventional robust control methodologies. Subsequently, ESC is uniquely employed to iteratively fine-tune controller parameters, exploiting known disturbance characteristics without requiring explicit system modeling. The proposed ESC-based tuning significantly enhances the sub-optimal controller’s performance, narrowing the gap between structured and full-order optimal controllers. Stability of the closed-loop system is maintained throughout the tuning process by employing small perturbations, slow adaptation rates, and continuous evaluation of the ${\mathcal {H}}_{\infty } $ norm. Simulation results clearly demonstrate the effectiveness of the proposed method, highlighting its potential to improve practical disturbance attenuation and overall performance.}, archive = {J_ACCESS}, author = {Bilal Erol}, doi = {10.1109/ACCESS.2025.3586136}, journal = {IEEE Access}, pages = {125080-125089}, shortjournal = {IEEE Access}, title = {Refining ℌ∞ controller performance with extremum seeking control for improved disturbance attenuation}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in data science: Evaluating forecasting models for solar energy in the amazon basin. ACCESS, 13, 125066-125079. (www)

  @article{ , abstract = {Forecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazon rainforest. This study introduces a novel approach using ML and DL methods—integrated with Universal Kriging and Holt-Winters (time series) models — to forecast solar irradiance (kWh/m2) in cities across the state of Amazonas. The analysis is grounded in the Data Science cycle, with input data sourced from both ground stations and satellite products. Forecasting performance was evaluated for short-term horizons (one to three days ahead) across three representative cities. The hybrid SARIMAX-CNN-LSTM, SARIMAX-CNN-Transformer, and SARIMAX-TCN models achieved MAPE values ranging from 18.1% to 26.6% for the different forecast horizons and cities. These results are consistent with existing literature and reinforce the suitability of advanced ML/DL approaches for solar energy forecasting in highly variable and challenging environments such as the Amazon Basin.}, archive = {J_ACCESS}, author = {André Luis Ferreira Marques and Ricardo Sbragio and Pedro Luiz Pizzigatti Corrêa and Marcelo Ramos Martins}, doi = {10.1109/ACCESS.2025.3589275}, journal = {IEEE Access}, pages = {125066-125079}, shortjournal = {IEEE Access}, title = {Artificial intelligence in data science: Evaluating forecasting models for solar energy in the amazon basin}, volume = {13}, year = {2025}, }
• (2025). A modulation classification algorithm based on feature-embedding graph convolutional network. ACCESS, 13, 125057-125065. (www)

  @article{ , abstract = {Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data. Considering the signals as graph nodes and the similarity of each signal as an edge, the GCN propagates node information to similar nodes along the edges. GCN extracts more features and achieves better classification results, particularly for characterless examples. In this paper, we propose a modulation classification algorithm based on a feature-embedding GCN (FE-GCN). It comprises three parts: feature-embedding network (FEN), similarity adjacent matrix calculation network (SAMCN), and graph convolutional classification network (GCCN). The FEN embeds the signal data into a one-dimensional feature vector. The SAMCN calculates the similarity of all signal feature vectors to a matrix using a single convolutional neural network (CNN). The GCCN is used to extract the final features and classify the signals in a graph. Simulation results on the public dataset RML2016.10A show that the FE-GCN performs effectively and outperforms a series of advanced deep-learning methods.}, archive = {J_ACCESS}, author = {Huali Zhu and Hua Xu and Yunhao Shi and Yue Zhang and Lei Jiang}, doi = {10.1109/ACCESS.2024.3385663}, journal = {IEEE Access}, pages = {125057-125065}, shortjournal = {IEEE Access}, title = {A modulation classification algorithm based on feature-embedding graph convolutional network}, volume = {13}, year = {2025}, }
• (2025). Smart power systems transformation: Advanced fault detection with edge computing and signal processing in LV networks with EV charging stations. ACCESS, 13, 125029-125056. (www)

  @article{ , abstract = {This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. At the core of the approach is an innovative time-frequency analysis method that enhances fault discrimination, even in noisy environments. By strategically positioning smart meters and Electric Vehicle (EV) charging stations, the framework improves fault detection efficiency and overall system reliability. The Adaptive SBCT index dynamically fine-tunes fault identification, ensuring a more responsive power grid. Additionally, Kernel Principal Component Analysis (KPCA) streamlines data processing without compromising critical information, enhancing real-time performance. Extensive simulations and case studies validate the framework’s effectiveness across diverse low-voltage networks, demonstrating its potential to minimize power outages, reduce maintenance costs, and strengthen grid reliability. Future directions include large-scale real-world deployment and integration with renewable energy sources to further enhance system sustainability and scalability.}, archive = {J_ACCESS}, author = {Partha Chakraborty and Pampa Sinha and Subhra Debdas and Kaushik Paul and Chidurala Saiprakash and Vasupalli Manoj and Taha Selim Ustun and Ahmet Onen}, doi = {10.1109/ACCESS.2025.3586062}, journal = {IEEE Access}, pages = {125029-125056}, shortjournal = {IEEE Access}, title = {Smart power systems transformation: Advanced fault detection with edge computing and signal processing in LV networks with EV charging stations}, volume = {13}, year = {2025}, }
• (2025). Metal-stamped and IR-UWB-optimized air-filled cavity-backed antenna array for cost-effective single-anchor 3D localization. ACCESS, 13, 125016-125028. (www)

  @article{ , abstract = {A cost-effective, yet high-performance, time-domain-optimized air-filled cavity-backed metal-stamped slot antenna is presented for next-generation multi-channel, multi-antenna IR-UWB applications. Equipping a quarter-mode metal-stamped cavity with a novel feeding mechanism yields a compact 0.43 $\lambda _{{0}}$ 0.43 $\lambda _{{0}}$ antenna that is compatible with standard low-cost manufacturing processes and that has a fractional bandwidth of 34.4%, covering UWB channels 5, 6, 8 and 9. The antenna is optimized in the frequency and time domains for IR-UWB applications. It is robust against variations in material properties and manufacturing inaccuracies in the metal stamping process. Furthermore, it retains its performance when exploited in a three-element V-shaped antenna array for single-anchor distance and 2D angle-of-arrival estimation. Measurements demonstrate excellent estimation performance in both free-space conditions and challenging real-life deployment scenarios. Specifically, 97% of all distance estimation errors are below 2cm, with a mean absolute error (MAE) of 7.1 mm. In terms of angle estimation, 91% of all errors are below $10^{\scriptscriptstyle \circ }$ , with an MAE of $5.0^{\scriptscriptstyle \circ }$ . These results highlight the antenna’s excellent IR-UWB and isolation performance, enabling single-anchor 3D localization for Industry 4.0 and Healthcare 4.0 applications.}, archive = {J_ACCESS}, author = {Gert-Jan Gordebeke and Sam Lemey and Hendrik Rogier}, doi = {10.1109/ACCESS.2025.3589930}, journal = {IEEE Access}, pages = {125016-125028}, shortjournal = {IEEE Access}, title = {Metal-stamped and IR-UWB-optimized air-filled cavity-backed antenna array for cost-effective single-anchor 3D localization}, volume = {13}, year = {2025}, }
• (2025). AlignFusionNet: Efficient cross-modal alignment and fusion for 3D semantic occupancy prediction. ACCESS, 13, 125003-125015. (www)

  @article{ , abstract = {The environmental perception system is a critical component of autonomous vehicles, and multimodal perception systems significantly enhance perception capabilities by integrating camera and LiDAR data. This paper proposes a novel framework, AlignFusionNet. It effectively combines image and point cloud data to construct an occupancy network, thereby improving target detection and representation. The framework introduces two innovative modules: a point-level data alignment module based on geometric transformations and an enhanced fusion module utilizing cross-attention mechanisms. These modules achieve precise point-level alignment and seamless feature fusion between point clouds and RGB images. Experiments on the nuScenes-Occupancy dataset demonstrate that the proposed AlignFusionNet outperforms baseline methods, achieving a significant 15.9% improvement in mIoU and a 4% increase in IoU. Compared to the previous state-of-the-art method, OccGen, mIoU is improved by 5.9%. Further qualitative visualization analysis shows that the proposed method achieves higher representation accuracy for small objects.}, archive = {J_ACCESS}, author = {Ziyi Xu and Legan Qi and Hongzhou Du and Jiaqi Yang and Zhenglin Chen}, doi = {10.1109/ACCESS.2025.3589858}, journal = {IEEE Access}, pages = {125003-125015}, shortjournal = {IEEE Access}, title = {AlignFusionNet: Efficient cross-modal alignment and fusion for 3D semantic occupancy prediction}, volume = {13}, year = {2025}, }
• (2025). Few-shot SAR target recognition via causal inference and deep metric learning. ACCESS, 13, 124988-125002. (www)

  @article{ , abstract = {Deep learning, with large-scale annotated datasets, has demonstrated remarkable success in synthetic aperture radar automatic target recognition (SAR-ATR). However, the collecting of SAR images is expensive and complex, and manually labeling them requires expert knowledge. To overcome these limitations, we propose a few-shot learning model capable of accurate recognition of novel targets with minimal training samples. Our model innovatively integrates causal inference with mutual centralized learning (MCL) to address few-shot SAR-ATR tasks. First, we establish a causal inference framework to identify and model the dependencies among target characteristics, imaging conditions, and category labels. This framework incorporates a novel causal intervention method based on multi-scale random convolution to eliminate spurious correlations caused by imaging variations, thereby enhancing feature stability. Second, we introduce an advanced MCL module to effectively evaluate feature similarity in few-shot settings. MCL breaks through the unidirectional matching paradigm adopted by conventional metric learning. Through its bidirectional feature interactions and dense feature accessibility mechanisms, MCL achieves more robust feature discrimination in few-shot learning tasks. Comprehensive experiments demonstrate that our model outperforms existing advanced few-shot SAR-ATR methods, achieving superior recognition accuracy while maintaining robustness in data-scarce scenarios.}, archive = {J_ACCESS}, author = {Ke Wang and Yuqian Mao and Qi Qiao}, doi = {10.1109/ACCESS.2025.3589192}, journal = {IEEE Access}, pages = {124988-125002}, shortjournal = {IEEE Access}, title = {Few-shot SAR target recognition via causal inference and deep metric learning}, volume = {13}, year = {2025}, }
• (2025). Nonlinear regressive maximum correntropy extended kalman filter with student’s t-kernel for GPS navigation. ACCESS, 13, 124979-124987. (www)

  @article{ , abstract = {State estimation is a critical task across many disciplines, with the extended Kalman filter (EKF) being a widely adopted solution. However, the EKF is built on the mean square error criterion, which limits it to capturing only second-order noise statistics, making it vulnerable to significant performance drops in the presence of outliers. In practical engineering scenarios, noise often deviates from a Gaussian distribution, and outliers naturally occur, further complicating the estimation process. To overcome these challenges, this study proposes a novel filtering method, the Student’s t kernel-based nonlinear regressive maximum correntropy extended Kalman filter, which is robust to non-Gaussian noise and outliers. Additionally, the algorithm’s convergence is examined due to its reliance on a fixed-point iteration approach to derive the optimal state estimate. The robustness for the proposed method has been verified in various noise scenario and dynamic conditions. Performance evaluations through various error technique demonstrate the effectiveness of the proposed method in improving GPS positioning accuracy, highlighting its broader applicability in real-world systems such as GPS navigation and radar-based measurements.}, archive = {J_ACCESS}, author = {Amita Biswal and Dah-Jing Jwo}, doi = {10.1109/ACCESS.2025.3587939}, journal = {IEEE Access}, pages = {124979-124987}, shortjournal = {IEEE Access}, title = {Nonlinear regressive maximum correntropy extended kalman filter with student’s t-kernel for GPS navigation}, volume = {13}, year = {2025}, }
• (2025). Simplified carrier-based SVDPWM methods using reduced common mode voltage vector redundancy for improving output current ripple in four-level NPC inverter. ACCESS, 13, 124962-124978. (www)

  @article{ , abstract = {In this article, new fast and Three-level reduced common-mode voltage (RCMV) space vector Discontinuous pulse width modulation (SVDPWM) strategies are proposed to achieve high performance in four-level neutral point clamped (4L-NPC) inverters. Redundant reduced common-mode voltage (RCMV) vectors in the space vector diagram (SVD) of the Four-level inverter are utilized to design three discontinuous pulse width modulation (DPWM) control strategies with Three-level CMV (3L-CMV) characteristic, SVDPWM0, SVDPWM1 and HSVDPWM. The last and best one is deduced based on harmonic flux and harmonic distortion factor (HDF) analysis, to improve the output current ripple. For analysis and easy implementation, the SVPWM of the four-level inverter will be solved in the two-level SVD. A simple carrier-based PWM implementation of the RCMV SVDPWM methods will be developed to reduce the computational burden. The proposed 3L-CMV methods significantly reduce harmonic distortion compared to both Two-level CMV (2L-CMV) and Four-level CMV (4L-CMV) SVPWM methods. They exhibit a reduction in peak-to-peak CMV from approximately 40% to 60% and a lower CMV magnitude in the sampling frequency component compared to the 4L-CMV methods. As a result, the proposed system exhibits a considerable reduction in RMS leakage current. Additionally, they can achieve a reduction in switching loss up to 40% in specific conditions, offering improved efficiency compared to continuous PWM schemes.}, archive = {J_ACCESS}, author = {Le Nam Pham and Quoc Dung Phan and Nho-van Nguyen}, doi = {10.1109/ACCESS.2025.3589572}, journal = {IEEE Access}, pages = {124962-124978}, shortjournal = {IEEE Access}, title = {Simplified carrier-based SVDPWM methods using reduced common mode voltage vector redundancy for improving output current ripple in four-level NPC inverter}, volume = {13}, year = {2025}, }
• (2025). Numerical study on human brain cortical electrostimulation assessment during uniform magnetic field exposure at intermediate frequencies. ACCESS, 13, 124955-124961. (www)

  @article{ , abstract = {Permissible limits have been established by international guidelines and standards for human protection against electromagnetic field exposure to prevent adverse health effects stemming from electrostimulation in the most sensitive body part. That is the peripheral nervous system (PNS) in the intermediate frequency range (300 Hz to 100 kHz) and the central nervous system (CNS) at lower frequencies. However, there is a need to reevaluate protection limits against CNS electrostimulation in the intermediate frequency range, considering the importance of brain tissues during electromagnetic head exposure. This study aims to derive the level of CNS cortical stimulation to evaluate compliance with existing protection limits. To achieve this, a numerical multi-scale model was used to evaluate neuron stimulation thresholds by integrating individual neurons into realistic anatomical head models. Five different excitable membrane models within the motor cortex were examined across three human head models, providing the most comprehensive and extensive evaluation to date. The results show that current protection limits are confirmed as conservative, with non-compliance observed in only 0.02% and 2.4% of axons under clamped and sealed boundary conditions, respectively. The study also highlights significant intersubject variability (up to a 600% mean threshold) and clarifies the influence of neural excitation models on permissible level assessments. In conclusion, current electric field limits are conservative for CNS electrostimulation in the intermediate frequency range, but the margin of safety decreases at higher frequencies, warranting further evaluation. The study’s findings and methodology contribute to the rationale and provide valuable insights for re-evaluating electromagnetic safety exposure guidelines.}, archive = {J_ACCESS}, author = {Jose Gomez-Tames and Thomas Tarnaud and Wout Joseph and Emmeric Tanghe}, doi = {10.1109/ACCESS.2025.3586112}, journal = {IEEE Access}, pages = {124955-124961}, shortjournal = {IEEE Access}, title = {Numerical study on human brain cortical electrostimulation assessment during uniform magnetic field exposure at intermediate frequencies}, volume = {13}, year = {2025}, }
• (2025). Public investments and forecasting in renewable energy technologies: A comparative analysis using ARIMA and GARCH models. ACCESS, 13, 124941-124954. (www)

  @article{ , abstract = {This paper analyzes public investments in relation to the promotion of renewable energy technologies (RET) within the solar, wind and hydropower, bioenergy industries. Based on historical information, the ARIMA (Auto Regressive Integrated Moving Average) model is used to predict future investment trends, and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model analyzes the volatility of these investments. Both the models utilized univariate time series data to observe the temporal trends behavior purely based on past investment values without any external variables. Furthermore, a correlation matrix is prepared to determine the degree of interaction between investments in different RETs. The result indicates that public investment is important in supporting RET markets and thus promoting a more sustainable energy transition. Thus, the results of the study presented in the paper are quite encouraging and valuable in terms of policy perspective and future investments in the field of renewable energy generation.}, archive = {J_ACCESS}, author = {Bhanu Sharma and Chandra Prakash and Bijesh Dhyani and Chirag Joshi}, doi = {10.1109/ACCESS.2025.3588615}, journal = {IEEE Access}, pages = {124941-124954}, shortjournal = {IEEE Access}, title = {Public investments and forecasting in renewable energy technologies: A comparative analysis using ARIMA and GARCH models}, volume = {13}, year = {2025}, }
• (2025). Preset-trajectory-based adaptive neural autopliot control for uncertain surface vehicles via dynamic event-triggered mechanism. ACCESS, 13, 124932-124940. (www)

  @article{ , abstract = {This paper investigates the tracking control problem of autopliot for maritime autonomous surface ships (MASSs) in the presence of the uncertain dynamics and external disturbances, and proposes a dynamic event triggered adaptive mechanism based on a preset-trajectory function. In this paper, to handle the problem of lumped uncertainties, neural netwok and adaptive control technique are employed. A preset-trajectory function is designed, which can guarantee the autopliot error arrive the pre-set accuracy. A dynamic event triggering protocol is established, to reduce the acting frequency of actuators, and decrease the mechanical wear of the MASS actuators. Finally, a new dynamic event-triggered control mechanism is proposed, integrating adaptive neural control and a preset-trajectory function. Based on Lyapunov control theory, all signals in the autopliot control system are proven to be bounded. The numerical simulation results sufficiently demonstrate the effectiveness of the control strategy.}, archive = {J_ACCESS}, author = {Yongyi Lin and Zonglian Guo}, doi = {10.1109/ACCESS.2025.3588820}, journal = {IEEE Access}, pages = {124932-124940}, shortjournal = {IEEE Access}, title = {Preset-trajectory-based adaptive neural autopliot control for uncertain surface vehicles via dynamic event-triggered mechanism}, volume = {13}, year = {2025}, }
• (2025). Smart data and decision support system for local optimization of electric charging stations. ACCESS, 13, 124910-124931. (www)

  @article{ , abstract = {This paper introduces an innovative Decision Support System (DSS) for the local optimization of Electric Vehicles (EV) charging station placement, integrating open data with advanced operational research. In contrast to existing works that typically isolate economic, geographic, or routing factors, our approach synergistically incorporates demographic statistics (ISTAT), geographic data (OpenStreetMap), and commuting patterns to optimize infrastructure deployment. The system is implemented through a Web Application that enables real-time geospatial simulation and cost analysis. Key contributions include a hybrid mathematical model combining Integer Linear Programming with algorithmic strategies—Greedy heuristics, Simple Plant Location Problem (SPLP), and Analytic Hierarchy Process (AHP). This multi-method approach allows users to explore and compare diverse planning scenarios based on multiple optimization criteria. By enabling both global and local optima evaluation, the DSS advances the state of the art in EV infrastructure planning, offering scalable, user-centered, and open-data-driven tools for sustainable urban mobility.}, archive = {J_ACCESS}, author = {Mauro Mazzei and Cristian Michelotti}, doi = {10.1109/ACCESS.2025.3586099}, journal = {IEEE Access}, pages = {124910-124931}, shortjournal = {IEEE Access}, title = {Smart data and decision support system for local optimization of electric charging stations}, volume = {13}, year = {2025}, }
• (2025). Optimized placement of distributed fiber optic sensors for accurate strain monitoring of buried pipelines in landslide-prone areas. ACCESS, 13, 124899-124909. (www)

  @article{ , abstract = {Buried pipelines are vulnerable to damage from geohazards such as landslides, making accurate strain monitoring essential for early hazard detection and integrity management. While conventional strain monitoring tools face limitations in long-distance applications, distributed fibre optic sensing (DFOS) offers continuous strain measurement with high spatial resolution along extended pipeline networks. This study proposes an optimised DFOS placement strategy for early-stage strain detection induced by lateral soil movement. A novel laboratory-scale sandbox model was developed to simulate soil–pipeline interaction, with fibre optic cables installed at varying positions relative to the pipe. Complementary finite element analysis using ABAQUS was conducted to replicate and validate the physical test conditions. Results indicate that placing DFOS cables at a distance of 1.5D to 2D from the pipe (where D is the pipe diameter) provides optimal strain detection. Experimental and numerical results showed strong agreement, with an average strain deviation of less than 11%. The proposed placement approach enhances DFOS performance for buried pipeline monitoring and offers a practical, scalable solution for early-warning applications in geohazard-prone environments.}, archive = {J_ACCESS}, author = {Alarifi Hamzh and Hisham Mohamad and Phromphat Thansirichaisree}, doi = {10.1109/ACCESS.2025.3588181}, journal = {IEEE Access}, pages = {124899-124909}, shortjournal = {IEEE Access}, title = {Optimized placement of distributed fiber optic sensors for accurate strain monitoring of buried pipelines in landslide-prone areas}, volume = {13}, year = {2025}, }
• (2025). Intention recognition of AAV swarm based on GAT-EPool-BiGRU model. ACCESS, 13, 124882-124898. (www)

  @article{ , abstract = {Against the backdrop of rapid advancements in drone intelligence within military applications, the real-time and accurate identification of enemy drone swarm operational intent has become crucial for battlefield decision-making. Addressing the limitations of existing methods—such as the low feature transfer efficiency of stacked autoencoders (SAE) and the tendency of panoramic convolutional long short-term memory networks (PC-LSTM) to lose tactical details—this paper proposes a novel deep learning model called GAT-EPool-BiGRU, which integrates Graph Attention Networks (GAT), Edge Pooling (EPool), and Bidirectional Gated Recurrent Units (BiGRU). The experiments were conducted using 13 features, including altitude, velocity, and acceleration, covering 7 types of tactical intent such as attack, feint, and reconnaissance. With recognition accuracy as the primary evaluation metric, the results demonstrate that the proposed model achieves an intent recognition accuracy of 95.5%, outperforming mainstream models—5.4% higher than Transformer and 8% higher than GCN-LSTM. This performance improvement stems from GAT’s dynamic graph construction capability, EPool’s key feature retention mechanism, and BiGRU’s bidirectional temporal processing, collectively enhancing both robustness and accuracy in intent recognition. Additionally, the study enhances decision-making credibility through interpretability analyses, including attention heatmaps and SHAP values, which reveal critical features and their correlations with tactical intent. This approach not only improves model transparency but also provides actionable insights for real-world military applications.}, archive = {J_ACCESS}, author = {Jiajun Yuan and Xiang Jia and Yu An and Liang Geng and Lei Shu}, doi = {10.1109/ACCESS.2025.3588557}, journal = {IEEE Access}, pages = {124882-124898}, shortjournal = {IEEE Access}, title = {Intention recognition of AAV swarm based on GAT-EPool-BiGRU model}, volume = {13}, year = {2025}, }
• (2025). A transition-based neural framework for chinese nested entity and relation recognition. ACCESS, 13, 124874-124881. (www)

  @article{ , abstract = {Entity and relation extraction for Chinese texts are typical performed in a pipelined fashion in the sense that by first segmenting sequence into words then recognizing entities and relations subsequently. However, this process often leads to the problem of error propagation and prevents cross-task information integration. To address this issue, we propose a novel transition-based model that performs nested entity recognition and relation extraction jointly without the need of word segmentation beforehand, which is achieved by leveraging the recent advance in using a lattice structure for Chinese sentence encodings. On standard ACE benchmarks, our model gives the best results in the literature. Further analyses show the effectiveness of the proposed architecture in capturing structural outputs.}, archive = {J_ACCESS}, author = {Junchi Zhang and Mengchi Liu}, doi = {10.1109/ACCESS.2020.3006350}, journal = {IEEE Access}, pages = {124874-124881}, shortjournal = {IEEE Access}, title = {A transition-based neural framework for chinese nested entity and relation recognition}, volume = {13}, year = {2025}, }
• (2025). Continual learning with neuromorphic computing: Foundations, methods, and emerging applications. ACCESS, 13, 124824-124873. (www)

  @article{ , abstract = {The challenging deployment of compute- and memory-intensive methods from Deep Neural Network (DNN)-based Continual Learning (CL), underscores the critical need for a paradigm shift towards more efficient approaches. Neuromorphic Continual Learning (NCL) appears as an emerging solution, by leveraging the principles of Spiking Neural Networks (SNNs) and their inherent advantages (e.g., sparse spike-driven operations and bio-plausible learning rules) for improving energy efficiency and performance, thereby enabling efficient CL algorithms (e.g., unsupervised learning approach) executed in dynamically-changed environments with resource-constrained computing systems. Though in its early stages, NCL is already a major research field with an increasing interest in novel SNN-based techniques for different CL methods (e.g., regularization-, replay-, and architecture-based). Motivated by the need for a holistic study of NCL, in this survey, we first provide a detailed background on CL, encompassing the desiderata, settings, metrics, scenario taxonomy, Online Continual Learning (OCL) paradigm, recent DNN-based methods proposed in the literature to address catastrophic forgetting (CF). Then, we analyze these methods based on their achieved CL desiderata, computational and memory costs, as well as network complexity, hence emphasizing the need for energy-efficient CL. After introducing the CL background and the energy efficiency challenges, we provide an extensive background of low-power neuromorphic computing systems including encoding techniques, neuronal dynamics, network architectures, learning rules, neuromorphic hardware processors, software and hardware frameworks, neuromorphic datasets, benchmarks, and evaluation metrics. Then, this survey comprehensively reviews and analyzes state-of-the-art works in the NCL field. The key ideas, implementation frameworks, and performance assessments (including CL, OCL, neuromorphic hardware compatibility aspects) are provided. This survey also covers several hybrid approaches that combine supervised and unsupervised learning paradigms and categorizes them into three main classes. It also covers optimization techniques including SNN operations reduction, weight quantization, and knowledge distillation. Then, this survey covers the progress of real-world NCL applications categorized into adaptive robots and autonomous vehicles with a wide range of use-cases i.e., object recognition, robotic arm control, cars and road lane detection, Simultaneous Localization and Mapping (SLAM), people detection and robotic navigation and provides their specific case-studies with empirical results. Finally, this paper provides a future perspective on the open research challenges for NCL, since the purpose of this study is to be useful for the wider neuromorphic AI research community and to inspire future research in bio-plausible OCL.INDEX TERMS Continual learning (CL), neuromorphic computing, spiking neural networks (SNNs), neuromorphic continual learning (NCL), event-based processing, energy efficiency, online continual learning (OCL), catastrophic forgetting (CF), deep neural networks (DNNs), artificial intelligence (AI), embedded AI systems.}, archive = {J_ACCESS}, author = {Mishal Fatima Minhas and Rachmad Vidya Wicaksana Putra and Falah Awwad and Osman Hasan and Muhammad Shafique}, doi = {10.1109/ACCESS.2025.3588665}, journal = {IEEE Access}, pages = {124824-124873}, shortjournal = {IEEE Access}, title = {Continual learning with neuromorphic computing: Foundations, methods, and emerging applications}, volume = {13}, year = {2025}, }
• (2025). An optimized hybrid framework based on long-short term memory neural networks and fourier SynchroSqueezed transform for photovoltaic power forecasting. ACCESS, 13, 124807-124823. (www)

  @article{ , abstract = {Accurate prediction of photovoltaic power is crucial to optimize its integration into the power grid. However, this task is highly complex due to the inherently stochastic nature of photovoltaic power generation. To address this challenge, this paper proposes a novel hybrid framework that combines a Long Short-Term Memory network with the Fourier SynchroSqueezed Transform, and Bayesian Optimization. The model integrates the Fourier SynchroSqueezed Transform with the Long Short-Term Memory network to enhance the identification of very short-term patterns in photovoltaic power production. Additionally, Bayesian Optimization is used to determine the most relevant hyperparameters of the Long Short-Term Memory network. The model was evaluated using real-world data from the Desert Knowledge Australian Solar Centre project, considering different data segmentation sizes, ranging from 15 days to four months, and input types, including univariate and multivariate data, with prediction horizons ranging from five minutes to three days. Significant improvements in prediction accuracy were observed. For example, the Root Mean Squared Error for the 15-day data segmentation decreased by 19.48% when using multivariate inputs and by 29.59% for univariate inputs. For a four-month data segmentation, improvements reached 40.12% and 64.47%, respectively. Furthermore, the model demonstrated robust performance across various prediction horizons. For the 15-day segmentation, the Root Mean Squared Error was approximately 0.707 kW with an average power of 8.540 kW, while for the four-month segmentation, the error ranged around 0.467 kW with an average power of 4.733 kW. These results demonstrate the effectiveness and consistency of the proposed model in enhancing photovoltaic power forecasts across different time scales and data segmentations. The demonstrated ability to provide consistent and accurate predictions across multiple time horizons and data segmentations represents a significant advancement toward seamless integration of solar energy into the electricity grid.}, archive = {J_ACCESS}, author = {Samer Rajah and Francisco J. Muñoz and Alejandro Rodríguez}, doi = {10.1109/ACCESS.2025.3589131}, journal = {IEEE Access}, pages = {124807-124823}, shortjournal = {IEEE Access}, title = {An optimized hybrid framework based on long-short term memory neural networks and fourier SynchroSqueezed transform for photovoltaic power forecasting}, volume = {13}, year = {2025}, }
• (2025). Your cursor reveals: On analyzing workers’ browsing behavior and annotation quality in crowdsourcing tasks. ACCESS, 13, 124792-124806. (www)

  @article{ , abstract = {In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions. Both aspects of user attention can be modeled using different types of browsing behavior features but most previous research mostly focuses on the former. This work therefore proposes to model semantic user attention by capturing the worker’s understanding of task content using task-semantics specific behavior features. We develop a web-based annotation interface for gathering user behavior data when workers perform a knowledge path retrieval task. With the collected data, we train several prediction models using behavior features corresponding to different aspects of user attention and conduct experiments on a set of annotation tasks performed by 51 Amazon Mechanical Turk workers. We show that the prediction model using both general and semantic user attention features can achieve the best performance of nearly 75% accuracy.}, archive = {J_ACCESS}, author = {Pei-Chi Lo and Ee-Peng Lim}, doi = {10.1109/ACCESS.2022.3212080}, journal = {IEEE Access}, pages = {124792-124806}, shortjournal = {IEEE Access}, title = {Your cursor reveals: On analyzing workers’ browsing behavior and annotation quality in crowdsourcing tasks}, volume = {13}, year = {2025}, }
• (2025). Transfer learning-based super-resolution for high-precision medical imaging. ACCESS, 13, 124776-124791. (www)

  @article{ , abstract = {High-resolution medical images are critical for preserving intricate anatomical details essential for accurate diagnosis, effective surgical planning, and creating precise digital twins. However, acquiring such images often requires expensive equipment, specialized personnel, considerable time, and significant financial investment. This research addresses these challenges by proposing an AI-driven specialized super-resolution (SR) framework tailored for medical imaging. Our approach leverages transfer learning by fine-tuning high-performing general-domain SR models (BSRGAN, DPSR, HAT, RealESRGAN, and SwinIR) using approximately 190,000 images from KISTI’s Digital Korean dataset. On average, the fine-tuned SR models exhibited a 3.28% improvement in PSNR and a 0.6% increase in SSIM compared to their zero-shot counterparts, underscoring the effectiveness of transfer learning in enhancing both image quality and structural fidelity for medical applications. Notably, the fine-tuned HAT model achieved the highest performance, recording a Peak Signal-to-Noise Ratio (PSNR) of 39.95 and a Structural Similarity Index Map (SSIM) of 0.9461. The DPSR model also demonstrated robust detail preservation, with a PSNR of 39.60 and an SSIM of 0.9421. Qualitative evaluations by anatomy and medical imaging specialists confirmed the enhanced quality of the high-resolution outputs. These advancements highlight the transformative potential of specialized SR models to overcome the limitations of general-domain approaches in medical imaging. By laying the groundwork for developing high-resolution medical datasets, the proposed framework supports more precise diagnostics and advances the creation of accurate 3D human models and digital twins. Overall, this work underscores the vital role of AI-driven innovation in shaping the future of medical imaging and precision healthcare.}, archive = {J_ACCESS}, author = {Jae Yong Lee and Muhammad Ishfaq Hussain and Kyong-Ha Lee and Hyoung Seop Shim and Seung-Ho Han and Donghun Yang}, doi = {10.1109/ACCESS.2025.3587263}, journal = {IEEE Access}, pages = {124776-124791}, shortjournal = {IEEE Access}, title = {Transfer learning-based super-resolution for high-precision medical imaging}, volume = {13}, year = {2025}, }
• (2025). Incentive schemes for centralized material planning and allocation with asymmetric information in construction supply chain. ACCESS, 13, 124763-124775. (www)

  @article{ , abstract = {In construction projects, a centralized management mode can effectively address the material management issues. Specifically, the Material Management Department (MMD) is responsible for material purchase, storage, and allocation based on the demand information reported by contractors. However, material demand are best observed by the contractors. They may manipulate the material takeoff (i.e. misrepresent the material demand) to increase the expected material allocation and avoid the project delay. Motivated by this situation, this paper focuses on the incentives to elicit truthful reporting from contractors. Cases of having prior information and no prior information about the material takeoff, as well as priority allocation and proportional allocation policies are considered. Under the case of having prior information, an incentive scheme is designed based on the Groves payment rule, while under the case of no prior information, two incentive schemes are designed based on the proper scoring rules. The comparison and numerical analysis show that the total three incentive schemes can induce the MMD to choose a system-optimal inventory plan, as well as satisfy the participation constraints of contractors. Besides, the results also demonstrate that prior information is only the prerequisite for implementing the Groves incentive scheme and cannot improve the performance of the whole system in other aspects.}, archive = {J_ACCESS}, author = {Hui Lu and Hongwei Wang and Chao Qi and Yong Xie and Dian Liu}, doi = {10.1109/ACCESS.2020.3009151}, journal = {IEEE Access}, pages = {124763-124775}, shortjournal = {IEEE Access}, title = {Incentive schemes for centralized material planning and allocation with asymmetric information in construction supply chain}, volume = {13}, year = {2025}, }
• (2025). OFDM-based carrier phase localization with integer ambiguity resolution. ACCESS, 13, 124752-124762. (www)

  @article{ , abstract = {This study introduces a two-stage Time Difference of Arrival (TDoA) localization method using OFDM signals with carrier-phase measurements. Unlike conventional methods, our approach uniquely resolves integer ambiguities in carrier-phase signals by combining the coarse estimates derived from multiple long-wavelength subcarrier phase differences within a single OFDM symbol. Subsequently, fine TDoA measurements from high-frequency carrier-phase signals utilize these coarse references for precise positioning. We present a maximum-likelihood estimator (MLE), analyze its performance using the Fisher information matrix and Cramér-Rao Lower bound (CRLB), and validate our method through simulations and field tests, demonstrating sub-meter accuracy and significant robustness improvements over existing techniques.}, archive = {J_ACCESS}, author = {Jai Hyung Cho and Jie Hyun Lee}, doi = {10.1109/ACCESS.2025.3585495}, journal = {IEEE Access}, pages = {124752-124762}, shortjournal = {IEEE Access}, title = {OFDM-based carrier phase localization with integer ambiguity resolution}, volume = {13}, year = {2025}, }
• (2025). Advancing industry 5.0: Collaborative welding robotics with precision, safety, and flexibility. ACCESS, 13, 124734-124751. (www)

  @article{ , abstract = {This study presents a digital twin-based seam correction framework for collaborative robotic laser welding. The framework addresses key manufacturing challenges, including dimensional inconsistencies, motion inversion, and lightweight part deformation, which significantly impact welding quality. Digital twin technology enables comprehensive analysis and supports real-time deviation monitoring and compensation. Experimental results demonstrate a reduction in maximum seam deviation from 2.9 mm to <0.2 mm, achieving an average deviation <0.1 mm. The system ensures precise laser beam positioning, leading to waterproof welding. Bézier curves are used in the seam correction, providing a flexible and scalable approach. This research contributes to improving welding accuracy and adaptability in flexible production environments while minimizing resource consumption and material waste.}, archive = {J_ACCESS}, author = {Kantawatchr Chaiprabha and Gridsada Phanomchoeng and Parames Chutima and Ninshu Ma and Kaamutsup Sungkaksem and Setthibhak Suthithanakom and Ratchatin Chancharoen}, doi = {10.1109/ACCESS.2025.3588515}, journal = {IEEE Access}, pages = {124734-124751}, shortjournal = {IEEE Access}, title = {Advancing industry 5.0: Collaborative welding robotics with precision, safety, and flexibility}, volume = {13}, year = {2025}, }
• (2025). A data-driven approach to aircraft engine MRO using enhanced ANNs based on FMECA. ACCESS, 13, 124710-124733. (www)

  @article{ , abstract = {Aircraft engine MRO is essential for safe, reliable, and cost-effective aviation operations. Traditional maintenance methods, such as scheduled and condition-based maintenance, often result in excessive downtime, higher costs, and inefficient resource use. AI-driven predictive maintenance, combined with Reliability Engineering, enhances efficiency but typically lacks integration with systematic reliability assessment frameworks, limiting its ability to prioritize critical failures. This study introduces a hybrid predictive maintenance framework integrating artificial neural networks (ANN) with failure modes, effects, and criticality analysis (FMECA). Historical engine sensor data (temperature, pressure, vibration, and oil analysis) trains an ANN that predicts failure probabilities, repair durations, and costs. FMECA, utilizing the Risk Priority Number (RPN), ranks failures by severity, ensuring that the most critical issues are addressed first Weibull distribution analysis models component reliability, confirming wear-out failure modes, and supporting scheduled predictive maintenance. Validation with real aircraft engine data demonstrates the effectiveness of the ANN-FMECA model, achieving 94.3% accuracy in failure prediction and surpassing conventional methods. Maintenance prioritization efficiency improves by 15.7%, reducing maintenance costs by 35.3% and unplanned outages by 40.5%. This enhances fleet availability, improves flight safety, and reduces environmental impact.}, archive = {J_ACCESS}, author = {Idriss Dagal and Bilal Erol and Wulfran Fendzi Mbasso and Ambe Harrison and Alpaslan Demirci and Umit Cali}, doi = {10.1109/ACCESS.2025.3587090}, journal = {IEEE Access}, pages = {124710-124733}, shortjournal = {IEEE Access}, title = {A data-driven approach to aircraft engine MRO using enhanced ANNs based on FMECA}, volume = {13}, year = {2025}, }
• (2025). Innovative sandia frequency shift islanding detection for microgrids using interval type-II fuzzy logic and VP-PSO optimization. ACCESS, 13, 124697-124709. (www)

  @article{ , abstract = {This paper presents an innovative islanding detection method (IDM) that combines interval type-2 fuzzy logic with the Sandia frequency shift (SFS) technique for enhanced microgrid protection. The study examines a hybrid microgrid incorporating photovoltaic and wind farm distributed generation sources, explicitly accounting for uncertainties in solar irradiation, wind speed, and load demand through probabilistic modeling. Using probability density functions (PDFs), multiple uncertainty scenarios were generated, with representative scenarios selected via Monte Carlo simulation (MCS) for IDM development. The proposed approach further integrates a variable-parameter PSO (VP-PSO) algorithm to optimize both fuzzy membership functions and controller parameters. Comprehensive simulations demonstrate 100% detection accuracy for both PV and wind farm islanding scenarios, significantly outperforming conventional methods: standard SFS (84-86%), type-1 fuzzy SFS (89-91%), and neural network approaches (94-95%). This consistent 12-16% performance improvement establishes the method’s superior reliability for practical microgrid applications.}, archive = {J_ACCESS}, author = {Mehrdad Ahmadi Kamarposhti and Hassan Shokouhandeh and Yeonwoo Lee and Sun-Kyoung Kang and Ilhami Colak and El Manaa Barhoumi and Kei Eguchi}, doi = {10.1109/ACCESS.2025.3585786}, journal = {IEEE Access}, pages = {124697-124709}, shortjournal = {IEEE Access}, title = {Innovative sandia frequency shift islanding detection for microgrids using interval type-II fuzzy logic and VP-PSO optimization}, volume = {13}, year = {2025}, }
• (2025). AMC with a BP-ANN scheme for 5G enhanced mobile broadband. ACCESS, 13, 124689-124696. (www)

  @article{ , abstract = {In 5G enhanced mobile broadband (eMBB), applications that require high spectral efficiency and transmission speeds employ adaptive modulation and coding (AMC) technology. Such technology enables various levels of modulation and coding. AMC technologies use channel state information to select the optimal modulation or coding scheme as well as transmission parameters to improve the transmission speed, transmission quality, and spectrum efficiency of links. Because of equipment limitations at the receiving end, channel estimation algorithms cannot be used to acquire ideal solutions, and thus estimation errors are inevitable. Consequently, parameter information returned from the receiving end may be inaccurate and the radio link is affected by interference, which may impede the reliability of data transmission and reduce spectrum efficiency. This study proposed an AMC with a backpropagation artificial neural network (BP-ANN) scheme (AMC-BP-ANN). This study addressed problems regarding reduced efficiency in conventional AMC technology caused by channel, interference and noise estimation errors. In particular, this study used channel estimation, interference and noise estimation errors as features for machine learning, which allowed eMBB systems to accurately estimate the signal-to-interference plus noise ration and ensure that the estimated value approximated the value was determined through ideal channel estimation. The simulation results indicated that the AMC-BP-ANN scheme could achieve ideal performance and a superior bit error rate compared with the AMC technologies using lookup table and genetic algorithm, and furthermore, a lower bit error rate could improve the transmission reliability and spectral efficiency in 5G eMBB.}, archive = {J_ACCESS}, author = {Li-Sheng Chen and Wei-Ho Chung and Ing-Yi Chen and Sy-Yen Kuo}, doi = {10.1109/ACCESS.2020.3024726}, journal = {IEEE Access}, pages = {124689-124696}, shortjournal = {IEEE Access}, title = {AMC with a BP-ANN scheme for 5G enhanced mobile broadband}, volume = {13}, year = {2025}, }
• (2025). Context-aware gene embedding pipeline (CGEP): An accessible, regulatory-region-inclusive embedding framework for interpretable disease-agnostic prediction. ACCESS, 13, 124665-124688. (www)

  @article{ , abstract = {In this study, we propose a novel Gene Embedding Pipeline (CGEP) designed to address critical limitations in genomic deep learning by integrating both functional and regulatory genomic contexts. Unlike conventional approaches, our framework prioritizes disease-associated genes identified through contribution analysis (validated via a breast cancer case study) while remaining extensible to all pathologies with patient-derived FASTA sequences. The pipeline uniquely processes each target gene alongside its upstream/downstream regulatory regions (±2.5 kbp), capturing promoter/enhancer dynamics critical for disease mechanisms. To enable flexible downstream analysis, CGEP generates four distinct embeddings per gene, which may be utilized independently via task-specific models or fused for enhanced predictive power. By leveraging publicly available reference genomes (GRCh37/GRCh38) as healthy baselines, our method minimizes data procurement barriers and supports decentralized training paradigms that align with institutional data governance requirements. Implementation relies on lightweight feed-forward architectures, ensuring computational accessibility without sacrificing performance. Benchmarking against state-of-the-art models demonstrates competitive accuracy as good as (99.0%), F1-score (0.99), and AUC-ROC (0.99), with superior GC (Generalization Capacity) and reduced OL (Overfitting Likelihood). To foster reproducibility, we provide open-source access to the entire pipeline, including modular scripts for data curation, embedding generation, and model training. This work bridges computational innovation with clinical pragmatism, enabling scalable and interpretable genomic analysis for precision medicine.}, archive = {J_ACCESS}, author = {Twaha Ahmed Minai and Zubair Ahmed Shaikh and Asim Imdad Wagan and M. Kamran Azim and Syed Muhammad Muaz and Muhammad Shoaib Siddiqui}, doi = {10.1109/ACCESS.2025.3582511}, journal = {IEEE Access}, pages = {124665-124688}, shortjournal = {IEEE Access}, title = {Context-aware gene embedding pipeline (CGEP): An accessible, regulatory-region-inclusive embedding framework for interpretable disease-agnostic prediction}, volume = {13}, year = {2025}, }
• (2025). PatchTSFL: Patch fourier enhanced linear for long-term time-series forecasting. ACCESS, 13, 124651-124664. (www)

  @article{ , abstract = {Long-term time series forecasting presents a critical challenge across numerous application domains. Recently, various transformer-based models have been employed for this task; however, these methods face two key challenges: difficulty in retaining local series information and failure to fully capture the overall trend of time series. To address these limitations, we propose a novel model called Patch Time Series Fourier-former Linear (PatchTSFL), which incorporates three innovative features: 1) A patching operation that splits long-term series into multiple patches, using the number of patches as the input length of the encoder, which preserves local sequence information while reducing model complexity; 2) A Fourier-enhanced block that replaces the traditional transformer’s multi-attention mechanism, capturing important information by converting time domain data into frequency domain mapping, further reducing computational complexity; 3) A Mixture Of Experts Decomposition block (MOEDecomp) that decomposes the series, enabling comprehensive capture of the overall time series trend. We conducted extensive experiments on nine widely-used long-term time series datasets, comparing PatchTSFL with state-of-the-art transformer-based models. Results demonstrate that PatchTSFL significantly improves forecasting accuracy (31.9% reduction in MSE and 19.0% reduction in MAE on average) while maintaining the lowest model complexity and runtime (4.3 times faster than FEDformer). These findings establish PatchTSFL as an effective and efficient solution for long-term time series prediction. The source code is available at: https://github.com/WESTBROOK-0/PatchTSFL.}, archive = {J_ACCESS}, author = {Ling Li and Xianyun Wen and Weibang Li and Chengjie Li and Chengfang Zhang}, doi = {10.1109/ACCESS.2025.3588672}, journal = {IEEE Access}, pages = {124651-124664}, shortjournal = {IEEE Access}, title = {PatchTSFL: Patch fourier enhanced linear for long-term time-series forecasting}, volume = {13}, year = {2025}, }
• (2025). Benchmarking the mapping of address and port with encapsulation border relay routers. ACCESS, 13, 124628-124650. (www)

  @article{ , abstract = {Mapping of Address and Port with Encapsulation (MAP-E) is considered one of the most important IPv6 transition technologies, providing a high level of scalability thanks to its utterly stateless behavior in the operator network. The primary device executing this technology is the Border Relay (BR), which represents the pivot of its scalability. Thus, benchmarking the performance and scalability of this device using a solution based on existing accredited standards is crucial for service providers before its deployment in their core networks. The Benchmarking Working Group (BMWG) of the Internet Engineering Task Force (IETF) has published a comprehensive guide for such a task in its Request for Comments (RFC) 8219. The research work presented in this paper involves conducting comprehensive benchmarking for the MAP-E BR implementation of the FD.io Vector Packet Processing (VPP), a well-known high-performance open-source networking software, and analyzing its performance and scalability, using two different test systems. To accomplish the research, a MAP-E encapsulation capability has been integrated into “Maptperf,” an RFC 8219 compliant research Tester developed originally to benchmark the Mapping of Address and Port using Translation (MAP-T) BR. The results of benchmarking the MAP-E BR of VPP demonstrated the functionality and efficiency of the Tester encapsulation plugin as well as the high scalability of the tested implementation, even when serving an increasing number of Customer Edge (CE) devices or deploying a varying number of worker threads in the test. Moreover, they showed that the tested implementation achieved high performance levels in throughput, Frame Loss Rate (FLR), latency, and Packet Delay Variation (PDV) tests, despite utilizing a low number of worker threads.}, archive = {J_ACCESS}, author = {Ahmed Al-Hamadani and Gábor Lencse}, doi = {10.1109/ACCESS.2025.3587302}, journal = {IEEE Access}, pages = {124628-124650}, shortjournal = {IEEE Access}, title = {Benchmarking the mapping of address and port with encapsulation border relay routers}, volume = {13}, year = {2025}, }
• (2025). A novel printed monopole antenna with a tapered-line resonator loading. ACCESS, 13, 124622-124627. (www)

  @article{ , abstract = {In this paper, a novel compact printed monopole antenna is presented. The antenna consists of a planar monopole antenna and a tapered-line resonator with inner coupled section, which works as a near-field resonant parasitic (NFRP) element. The resonant frequency can be decreased dramatically by the strong coupling between the resonator and the monopole antenna. Thus, the miniaturization of the antenna is realized. The simulated results show that the resonant frequency decreases from 2.98 GHz to 2.4 GHz and the electric length of the antenna is reduced from $0.32~\lambda $ to $0.25~\lambda $ by loading a tapered-line resonator as NFRP element. After theoretical analysis is presented, sample antenna is manufactured and measured to verify the predictive performance of the proposed antenna. The overall size of the antenna is only $44.6\times 31.8\times 0.5$ mm3, and the operation frequency of the antenna ranges from 2.59 GHz to 2.85 GHz (9.5%) based on $\vert $ S $11\vert \le -10$ dB, the gain is about 1.98dBi, which are in well agreement with the simulated results.}, archive = {J_ACCESS}, author = {Dong Chen and Chen Zhang and Honglin Zhang and Chunlan Zhao}, doi = {10.1109/ACCESS.2021.3057591}, journal = {IEEE Access}, pages = {124622-124627}, shortjournal = {IEEE Access}, title = {A novel printed monopole antenna with a tapered-line resonator loading}, volume = {13}, year = {2025}, }
• (2025). Coalition game-based reinforcement learning in underwater acoustic networks. ACCESS, 13, 124602-124621. (www)

  @article{ , abstract = {Underwater Acoustic (UWA) communication plays a pivotal role in maritime operations, yet it faces persistent challenges such as limited bandwidth, high interference, and dynamic environmental conditions. The purpose of this work is to address the limitations of existing resource allocation strategies that rely on static optimization or centralized control, which lack scalability and adaptability in large, dynamic UWA networks. Hence, we propose a novel and scalable framework, herein referred to as Cooperative Coalition Optimization based on Reinforcement Learning for Underwater network (U-CoCRL) that integrates coalition game theory with Q-learning (QL) to optimize resource allocation in UWA networks. The proposed approach dynamically forms coalitions based on real-time conditions through K-means clustering, ensures fair payoff distribution via the Shapley value and continuously adapts to changing environmental conditions and interference levels via Reinforcement learning (RL). Simulation results demonstrate that the U-CoCRL method outperforms baseline algorithms across all interference levels. It achieves up to 18.1% higher global utility and 33.9% better performance indices under high interference, 13.6% and 33.9% improvements under medium interference, and 6.1% and 27.7% gains under low interference, respectively. Additionally, tests with up to 20 users validate the framework’s scalability and robustness under increased network size. These results highlight the potential of combining cooperative game theory with QL and demonstrate the robustness of the method in optimizing resource allocation and fairness in dynamic underwater communication environments.}, archive = {J_ACCESS}, author = {Khouloud Gharsalli and Sameh Najeh and Thierry Le Pors and Leila Najjar and Pierre-Jean Bouvet and Hichem Besbes}, doi = {10.1109/ACCESS.2025.3582918}, journal = {IEEE Access}, pages = {124602-124621}, shortjournal = {IEEE Access}, title = {Coalition game-based reinforcement learning in underwater acoustic networks}, volume = {13}, year = {2025}, }
• (2025). Dynamic adaptation in an intelligent multi-tutoring system: A multi-agent approach. ACCESS, 13, 124587-124601. (www)

  @article{ , abstract = {Online learning provides new educational opportunities but continues to face high dropout rates compared to traditional classroom settings. To address this issue, we propose IMTS (Intelligent Multi-Tutoring System), an intelligent tutoring system that integrates both multi-tutoring and multidisciplinary capabilities. IMTS synergistically integrates automated tutoring (intelligent agents) and human tutoring (teachers and peers) to deliver personalized support to learners. The proposed architecture is based on a multi-agent system (MAS) that dynamically adapts pedagogical interventions according to the learners’ profiles and needs. By analysing learning progress in real time, the system continuously adjusts its recommendations and guides the student toward the most appropriate support (educational resources, peer interaction, or teacher intervention). IMTS was developed using the JADE platform and integrated into a customized Learning Management System (LMS). An experiment conducted with students demonstrated notably improved academic performance (e.g., a 20% relative score gain compared to control), increased engagement, and significantly reduced dropout rates (e.g., halved). These findings suggest that IMTS represents a significant advancement in online education by enabling more interactive, responsive, and personalised tutoring.}, archive = {J_ACCESS}, author = {Rida Zakaria and Boukachour Hadhoum and Ennaji Mourad and Machkour Mustapha}, doi = {10.1109/ACCESS.2025.3576651}, journal = {IEEE Access}, pages = {124587-124601}, shortjournal = {IEEE Access}, title = {Dynamic adaptation in an intelligent multi-tutoring system: A multi-agent approach}, volume = {13}, year = {2025}, }
• (2025). SiamMCE: An efficient siamese network for real-time object tracking with dual-correlation strategy and dynamic template updating. ACCESS, 13, 124569-124586. (www)

  @article{ , abstract = {Real-time object tracking in dynamic environments poses significant challenges in balancing computational efficiency with robust performance under complex scenarios such as occlusion and illumination changes. This paper presents SiamMCE (Siamese MobileNet-CE) tracker, an optimized Siamese network variant that integrates three key innovations to address these challenges. First, we design a lightweight backbone network MobileNet-CE which is based on MobileNetV3-Small through strategic integration of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention Module (ECAM), reducing parameters by 18% while enhancing feature discriminability. Second, we propose a Dual-Correlation Strategy combining Pixel-wise Correlation (PWC) and Channel-wise Correlation (CWC) operations to improve localization precision through complementary spatial-channel feature fusion. Third, a Dynamic Template Adaptation mechanism leverages response map analysis via UpdateNet to enable online template refinement, mitigating drift accumulation during long-term tracking. Extensive experiments on benchmarks (OTB-2015, VOT2018, UAV123) demonstrate that SiamMCE achieves robust performance across mainstream tracking tasks, balancing competitive accuracy with real-time operation on embedded platforms. This capability enables new applications in dynamic environments, such as UAV-based detection and mobile surveillance, where sustained reliability is critical.}, archive = {J_ACCESS}, author = {Xin Wang and Dejiang Wang and Mingchao Sun and Hang Ren and Yulian Zhang and Songwei Han and Ligang Liu}, doi = {10.1109/ACCESS.2025.3588568}, journal = {IEEE Access}, pages = {124569-124586}, shortjournal = {IEEE Access}, title = {SiamMCE: An efficient siamese network for real-time object tracking with dual-correlation strategy and dynamic template updating}, volume = {13}, year = {2025}, }
• (2025). A safety detection method for electric bike riding incorporating license plate region recognition. ACCESS, 13, 124556-124568. (www)

  @article{ , abstract = {The widespread use of electric bikes has made rider safety a crucial issue in intelligent transportation research. To address the limitations of existing methods in terms of accuracy and robustness in electric bike license plate recognition, helmet detection, and rider detection tasks, this paper proposes a novel electric bike safety detection method, F-YOLOv8, which integrates license plate region recognition. Based on the YOLOv8 architecture, this method incorporates a feature pyramid network (FPN) to optimize multi-scale object detection capabilities, while introducing an attention mechanism to enhance the extraction of key features. The study constructs a high-quality dataset covering various riding scenarios and violations, including image data collected in Guangzhou and Foshan, Guangdong Province, annotated using LabelImg.The label types include Ebike (rider), plate (license plate), gzplace (license plate region), helmet (wearing a helmet), nohelmet (not wearing a helmet), and manned (carrying a passenger). Experimental results demonstrate significant improvements across all metrics: the precision on Ebike reached 96.1%, on Plate reached 92.9%, on Helmet reached 84.6%, and on Manned tasks reached 67.9%. This study provides an efficient and reliable detection method for enhancing electric bike riding safety management, laying a technical foundation for the development of intelligent transportation systems.}, archive = {J_ACCESS}, author = {Shaohui Zhong and Xiaofei Liu}, doi = {10.1109/ACCESS.2025.3588506}, journal = {IEEE Access}, pages = {124556-124568}, shortjournal = {IEEE Access}, title = {A safety detection method for electric bike riding incorporating license plate region recognition}, volume = {13}, year = {2025}, }
• (2025). Integrated OVMD-BiGRU-SMAC framework for forecasting construction accidents in the kingdom of saudi arabia. ACCESS, 13, 124543-124555. (www)

  @article{ , abstract = {Construction Accidents (CA) remain a major concern for occupational safety, especially within high-risk environments such as those found across the expanding construction sector in the Kingdom of Saudi Arabia (KSA). This research introduces an innovative prediction framework that combines signal decomposition with advanced deep learning methods to estimate CA trends. Initially, the framework applies Optimized Variational Mode Decomposition (OVMD) to break down historical CA time series into distinct temporal components known as Intrinsic Mode Functions (IMFs). These IMFs are individually forecasted using Bidirectional Gated Recurrent Unit (BiGRU) models, which are capable of learning sequential patterns in both temporal directions. To enhance the predictive accuracy, the hyperparameters of each BiGRU model are optimized using the Sequential Model-based Algorithm Configuration (SMAC) technique. The proposed framework is trained on monthly CA data in the KSA from June 2010 to March 2023. Among the tested configurations, the proposed OVMD–BiGRU–SMAC model produced the most reliable and better results and achieves RMSE value of 17.26, MAE of 14.02, and R2 of 0.874. In comparison, the OVMD–TCN–SMAC model showed the weakest performance, with an RMSE of 23.93, MAE of 19.11, and R2 of 0.742. These results demonstrate the effectiveness of combining signal decomposition with deep learning techniques in order to caputer the irregular and nonstationary patterns of CA data and provide more reliable forecasts to support safety management and proactive planning efforts.}, archive = {J_ACCESS}, author = {Badr T. Alsulami and Afaq Khattak}, doi = {10.1109/ACCESS.2025.3589024}, journal = {IEEE Access}, pages = {124543-124555}, shortjournal = {IEEE Access}, title = {Integrated OVMD-BiGRU-SMAC framework for forecasting construction accidents in the kingdom of saudi arabia}, volume = {13}, year = {2025}, }
• (2025). Automated business decision-making using generative AI in online A/B testing: Comparative analysis with human decision-making. ACCESS, 13, 124530-124542. (www)

  @article{ , abstract = {Online A/B testing is widely used as an experimental methodology for product improvement and business optimization. However, interpreting experimental results often involves subjective judgment and biases from experiment designers, which can undermine the reliability and reproducibility of test outcomes. In particular, experiment designers frequently exhibit inconsistent decision-making when dealing with neutral results—cases where neither statistically significant positive nor negative effects are observed. This study aims to explore the feasibility of automating A/B test decision-making using Generative AI and empirically analyze how well AI decisions align with those of experiment designers and experts. Utilizing 1,407 experimental cases from 48 companies on the Hackle online experimentation platform, the study compares decision-making outcomes between experiment designers and Generative AI, analyzing agreement rates and identifying patterns across companies. Statistical analyses, including chi-square tests and inter-rater agreement evaluation, were employed to assess differences and reliability. The findings indicate meaningful discrepancies between AI and experiment designers but demonstrate that AI decisions closely align with expert judgments. These results suggest that Generative AI can serve as a complementary tool to enhance the consistency and reliability of A/B test result interpretation.}, archive = {J_ACCESS}, author = {Changhak Sunwoo and Hyunjin Kwon and Jong Min Kim and Ho-Hyun Lim and Yongwoo Kim and Dongwook Hwang and Jingoo Kim}, doi = {10.1109/ACCESS.2025.3588480}, journal = {IEEE Access}, pages = {124530-124542}, shortjournal = {IEEE Access}, title = {Automated business decision-making using generative AI in online A/B testing: Comparative analysis with human decision-making}, volume = {13}, year = {2025}, }
• (2025). Robust target detection and estimation for airborne STAP radar with arbitrary array errors and target uncertainty. ACCESS, 13, 124520-124529. (www)

  @article{ , abstract = {In the presence of arbitrary array errors and angle mismatch, performance on target detection and angle estimation will be degraded due to steering vector mismatch. Thus, a robust target detection and estimation algorithm for airborne STAP radar is developed. First, utilizing the spatial-temporal coupling property of the ground clutter, array steering vectors are well estimated by fine Doppler localization of the mainlobe clutter. Then, the robust subspace detector spanned by these estimated array steering vectors is developed, which can improve the detection performance for the targets not located in the look direction. Finally, target angle estimation using subspace coefficients, which implements the ML estimator in a reduced-dimensional version, is presented to reduce the computational complexity of the ML estimator. Numerical examples are given to demonstrate the effectiveness of the presented algorithm.}, archive = {J_ACCESS}, author = {Jianxin Wu and Yang Zhao and Changxian Li and Peng Shen}, doi = {10.1109/ACCESS.2020.2989341}, journal = {IEEE Access}, pages = {124520-124529}, shortjournal = {IEEE Access}, title = {Robust target detection and estimation for airborne STAP radar with arbitrary array errors and target uncertainty}, volume = {13}, year = {2025}, }
• (2025). Configuration synthesis of a spherical coaxial-actuated parallel mechanism for in-situ machining robots. ACCESS, 13, 124504-124519. (www)

  @article{ , abstract = {Large-scale curved structural components often exhibit irregular and continuously varying geometries, requiring precise machining with high smoothness and microstructural integrity. In-situ machining robots offer a flexible and efficient solution, with coaxial-driven mechanisms standing out for their self-centering motion and structural simplicity, effectively reducing the coupling issues common in hybrid systems. This study investigates the topological synthesis of such mechanisms using Finite Instantaneous Screw (FIS) theory. The motion characteristics of the end-effector are first described via finite screws, then embedded into standardized limb structures to construct viable topologies. By applying the properties of the screw triple product, equivalent rotational and translational motions are derived, leading to extended and variant limb configurations. The feasibility is verified through FIS-based configuration synthesis. Three fully symmetrical coaxial-driven parallel mechanisms are designed to meet assembly and application requirements. A systematic topological synthesis method is proposed, providing new models that lay a theoretical foundation for future development of in-situ machining systems.}, archive = {J_ACCESS}, author = {Yang Qi and Yuhuan Wu and Wenrui Yu}, doi = {10.1109/ACCESS.2025.3589124}, journal = {IEEE Access}, pages = {124504-124519}, shortjournal = {IEEE Access}, title = {Configuration synthesis of a spherical coaxial-actuated parallel mechanism for in-situ machining robots}, volume = {13}, year = {2025}, }
• (2025). YOLOv11-ND: A method for identifying traffic targets in nighttime urban environments. ACCESS, 13, 124483-124493. (www)

  @article{ , abstract = {Traffic target recognition is a crucial technology that has drawn a lot of interest due to the quick development of unmanned and assisted driving systems. However, the precision and performance of target recognition for the more complicated nighttime environment are lower, and the majority of the present research on traffic target recognition concentrates on the daytime. By using nighttime traffic targets as the research object, this paper suggests YOLOv11-ND, an enhanced target recognition method, to address the aforementioned issues. First, based on WTConv, the WTC3k2 module is intended to take the position of C3k2 in the backbone part. This reduces the amount of model parameters without sacrificing precision. Then, to boost the fusion ability of multi-scale features, the HS-FPN network structure is adopted in the neck section. This improves the detection performance. Lastly, the model is optimized using the Focaler-GIoU loss function to further enhance the detection performance. In comparison to the baseline model YOLOv11, the enhanced model YOLOv11-ND improved the P, R, mAP50, and mAP50-95 measures by 3.1%, 3.4%, 4%, and 3.6%, respectively, according to experimental validation using the FLIR dataset.The method can successfully increase the precision of traffic target detection in urban settings at night, according to the testing results.}, archive = {J_ACCESS}, author = {Danyang Zhu and Hao Zhou and Yunlong Gao and Yongjuan Wang}, doi = {10.1109/ACCESS.2025.3589010}, journal = {IEEE Access}, pages = {124483-124493}, shortjournal = {IEEE Access}, title = {YOLOv11-ND: A method for identifying traffic targets in nighttime urban environments}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of active control method for minimizing circulating current in MMC-VSC system. ACCESS, 13, 124471-124482. (www)

  @article{ , abstract = {Modular Multilevel Converters (MMCs) have emerged as a key technology for large-scale renewable energy integration due to their scalability, fault tolerance, and superior output quality. However, internal circulating currents remain a major barrier to efficiency and long-term reliability. It causes power losses, increased thermal stress, and excessive capacitor voltage fluctuations. Existing passive and active methods often lack clear design guidelines or fail to achieve robust suppression under varying operating conditions. This paper introduces a comprehensive hybrid strategy that addresses these gaps through two key innovations. Firstly, an analytical expression for arm inductor sizing is derived using instantaneous power theory and the harmonic addition theorem. It offers an explicit passive design method rather than relying on heuristic selection. This analytical formulation ensures optimal passive suppression within practical inductor size constraints. Then an advanced active suppression scheme is developed. Unlike conventional approaches, the circulating current is regulated using a vector control strategy formulated in the dq reference frame. It enables precise control of the dominant second-order harmonic. The PI controller is tuned through a direct pole placement method. A high-pass filter is integrated upstream of the controller to eliminate the DC offset. The simulation studies demonstrates that the proposed methods outperforms traditional direct modulation by significantly reducing circulating current amplitude, lowering power losses and improving thermal performance. The results confirm that the passive and active control framework delivers a robust, scalable, and practically implementable solution for next-generation MMC-based renewable energy systems.}, archive = {J_ACCESS}, author = {A. Aslam and M. Raza}, doi = {10.1109/ACCESS.2025.3588713}, journal = {IEEE Access}, pages = {124471-124482}, shortjournal = {IEEE Access}, title = {Design and implementation of active control method for minimizing circulating current in MMC-VSC system}, volume = {13}, year = {2025}, }
• (2025). Multi-harmonic currents control strategy for five-phase permanent magnet machine with non-sinusoidal back-EMF. ACCESS, 13, 124463-124470. (www)

  @article{ , abstract = {This paper describes an optimal torque per peak current control method for a five-phase permanent magnet (PM) machine considering both $3^{\mathrm {rd}}$ and $5^{\mathrm {th}}$ harmonic currents. These optimal ratios to the fundamental component are analytically derived to maximize the output torque. It is found that except for the $3^{\mathrm {rd}}$ harmonic current contributing to the output torque, the $5^{\mathrm {th}}$ harmonic current can also produce the additional positive torque. However, the $5^{\mathrm {th}}$ harmonic is zero sequence component for the five-phase machines, which does not exist in the phase windings. Hence, the neutral point is required to connect the middle point of the DC link capacitors for constructing flowing path. The conventional vector space decomposition (VSD) control is extended to zero sequence sub-plane, which can quantitatively control $5^{\mathrm {th}}$ harmonic current. For a prototype five-phase PM machine, the average torque can be increased by 21.4% with $3^{\mathrm {rd}}$ harmonic current injection. Meanwhile, 10.7% additional positive torque is achieved together with $3^{\mathrm {rd}}$ and $5^{\mathrm {th}}$ harmonic injection. The torque ripple remains similar to that without harmonics injection. Finally, the experiments are given to demonstrate the theoretical analysis.}, archive = {J_ACCESS}, author = {J. Y. Zhang and S. J. He and K. Wang}, doi = {10.1109/ACCESS.2020.2989323}, journal = {IEEE Access}, pages = {124463-124470}, shortjournal = {IEEE Access}, title = {Multi-harmonic currents control strategy for five-phase permanent magnet machine with non-sinusoidal back-EMF}, volume = {13}, year = {2025}, }
• (2025). Finding misstatement accounts in financial statements through ontology reasoning. ACCESS, 13, 124449-124462. (www)

  @article{ , abstract = {Finding misstatement accounts in financial statements, is a key problem of fraud detection. Potential applications include external audit, internal controls, investment decision and securities market regulation. However, most existing intelligent methods just detect financial statements fraud at the company level, while little research can detect financial statements fraud at the account level. For this, to achieve intelligent fraud detection at the accounts level, an ontology-based fraud detection framework was proposed. To be specific, the proposed framework mainly combines the articulation between different accounts and periods, and 30 financial indicators (ratios) as the knowledge basis of ontology. Notably, with OWL (Ontology Web Language), SWRL (Semantic Web Rule Language) and Protégé ontology editor, the case study not only completed the fraud detection in a fast and timely manner, but also provided logical explanation and risk warning at the accounts level. This fully shows the great advantages and applicability of the proposed framework in the detection of misstatements accounts. Moreover, the proposed framework is of great significance for timely detection, prevention and response of financial statements fraud. More importantly, the proposed framework opens-up a new direction of using ontology reasoning techniques to find misstatement accounts in financial statements, which provides an interpretable and fine-grained way for fraud detection.}, archive = {J_ACCESS}, author = {Liming Chen and Baoxin Xiu and Zhaoyun Ding}, doi = {10.1109/ACCESS.2020.3014620}, journal = {IEEE Access}, pages = {124449-124462}, shortjournal = {IEEE Access}, title = {Finding misstatement accounts in financial statements through ontology reasoning}, volume = {13}, year = {2025}, }
• (2025). A model-based tightly coupled architecture for low-cost unmanned aerial vehicles for real-time applications. ACCESS, 13, 124429-124448. (www)

  @article{ , abstract = {This paper investigates the navigation performance of a vehicle dynamic model-based (VDM-based) tightly coupled architecture for a fixed-wing Unmanned Aerial Vehicle (UAV) during a global navigation satellite system (GNSS) outage for real-time applications. Unlike an Inertial Navigation System (INS) which uses inertial sensor measurements to propagate the navigation solution, the VDM uses control inputs from either the autopilot system or direct pilot commands to propagate the navigation states. The proposed architecture is tested using both raw GNSS observables (Pseudorange and Doppler frequency) and Micro-Electro-Mechanical Systems-grade (MEMS) Inertial Measurement Unit IMU) measurements fused using an extended Kalman filter (EKF) to aid the navigation solution. Other than the navigation states, the state vector also includes IMU errors, wind velocity, VDM parameters, and receiver clock bias and drift. Simulation results revealed significant performance improvements with a decreasing number of satellites in view during 140 seconds of a GNSS outage. With two satellites visible during the GNSS outage, the position error improved by one order of magnitude as opposed to a tightly coupled INS/GNSS scheme. Real flight tests on a small fixed-wing UAV show the benefits of the approach with position error being an order of magnitude better as opposed to a tightly coupled INS/GNSS scheme with two satellites in view during 100 seconds of a GNSS outage.}, archive = {J_ACCESS}, author = {Hery A. Mwenegoha and Terry Moore and James Pinchin and Mark Jabbal}, doi = {10.1109/ACCESS.2020.3038530}, journal = {IEEE Access}, pages = {124429-124448}, shortjournal = {IEEE Access}, title = {A model-based tightly coupled architecture for low-cost unmanned aerial vehicles for real-time applications}, volume = {13}, year = {2025}, }
• (2025). Modeling purposes of public transportation trips for human need-responsive urban mobility efficiency. ACCESS, 13, 124413-124428. (www)

  @article{ , abstract = {Public transportation (PT) systems are the artery systems for urban residents to access resources essential for fulfilling their daily needs. Enhancing the operational efficiency of PT systems is thus of critical importance in urban mobility improvement and sustainable city development. However, current PT system operations do not account for the impact of operation decisions on the satisfaction of diverse needs of riders, irresponsive to the fundamental human needs driving mobility behaviors of PT riders. To address this limitation, it is imperative to model and infer the purposes of PT trips to understand the types of human needs that these trips aim to satisfy. As such, this paper presents a Bayesian probabilistic method for station-level, hourly PT trip purpose modeling and inference. The proposed method integrates 1) the gravity model for modeling spatial trip purpose distributions and 2) a temporal variation model for modeling temporal trip purpose distributions to enable station-level, hourly PT trip purpose modeling and inference. The method was validated by comparing the modeled PT trip purpose distributions to those obtained from established mobility surveys. The validation results showed a strong alignment of the two types of distributions, with a Kullback-Leibler divergence score of 0.061 and a Jensen-Shannon divergence score of 0.014. Building upon the validation, this paper further implemented and demonstrated the proposed method in modeling and analyzing shifts in PT trip purposes during the COVID-19 pandemic in New York City. Temporal-spatial analysis revealed distinct patterns in the trip purpose shifts across time and space.}, archive = {J_ACCESS}, author = {Lan Zhang and Kaijian Liu}, doi = {10.1109/ACCESS.2025.3587994}, journal = {IEEE Access}, pages = {124413-124428}, shortjournal = {IEEE Access}, title = {Modeling purposes of public transportation trips for human need-responsive urban mobility efficiency}, volume = {13}, year = {2025}, }
• (2025). Research on resource reservation strategy for edge federation. ACCESS, 13, 124402-124412. (www)

  @article{ , abstract = {With the increasing number of users and the continuous expansion of task scales, the resource constraints faced by edge computing are becoming increasingly pronounced. To address these challenges, edge computing has gradually evolved into an edge federation computing model. This model enhances overall resource utilization through multi-node resource sharing within the federation, effectively alleviating the issue of insufficient resources at individual edge nodes. This paper first analyzes the necessity of implementing a resource reservation strategy within the edge federation. It proposes a load prediction-based resource reservation strategy and optimizes this strategy according to the load levels present in the edge federation. Subsequently, we introduce a multi-agent deep deterministic policy gradient (RRP-MADDPG) approach based on a multi-agent deep reinforcement learning algorithm aimed at reducing average task delay. Simulation results demonstrate that both proposed resource reservation strategies can significantly reduce average task delays. Furthermore, the RRP-MADDPG strategy exhibits excellent convergence performance and outperforms both the load prediction-based reservation strategy and other similar deep reinforcement learning algorithms.}, archive = {J_ACCESS}, author = {Hengzhou Ye and Huangran Li and Jiaming Li and Qiu Lu and Gong Chen}, doi = {10.1109/ACCESS.2025.3588224}, journal = {IEEE Access}, pages = {124402-124412}, shortjournal = {IEEE Access}, title = {Research on resource reservation strategy for edge federation}, volume = {13}, year = {2025}, }
• (2025). A lightweight caching decision strategy based on node edge-degree for information centric networking. ACCESS, 13, 124389-124401. (www)

  @article{ , abstract = {In-networkcaching as a core function in ICN can greatly improve the content distribution efficiency and reduce redundant network traffic. It is a challenge to design caching strategies in ICN to improve the performance of in-network caching. Traditional lightweight strategies are inefficient due to problems such as edge caching pollution and slow diffusion speed. Current caching strategy with popularity prediction does not fully consider the content capacity of ICN network. They usually have huge computation overhead and cannot meet line-speed requirement. This paper focuses on the research of lightweight on-path caching. Our caching strategy is proposed according to the edge-degree of routers along the forwarding path. This strategy can quickly form a hierarchical content placement with little overhead. A large number of experiments are conducted to examine the performance of our strategy in different topologies and workloads. The simulation result reveals that our caching strategy can reduce the average download latency by 10.2% and increase the cache hit ratio by 9.6% at most compared with other lightweight strategies.}, archive = {J_ACCESS}, author = {Qifeng Yang and Haojiang Deng and Lingfang Wang}, doi = {10.1109/ACCESS.2020.2993853}, journal = {IEEE Access}, pages = {124389-124401}, shortjournal = {IEEE Access}, title = {A lightweight caching decision strategy based on node edge-degree for information centric networking}, volume = {13}, year = {2025}, }
• (2025). An overview study of deep learning in geophysics: Cross-cutting research to advance geoscience. ACCESS, 13, 124364-124388. (www)

  @article{ , abstract = {In recent years, Artificial Intelligence technology (AI) has driven rapid advances across various sciences. As a new data-driven technology, Deep Learning (DL) is widely utilized for data processing and adaptive tasks in multiple fields due to its high automation, accuracy, and scalability. DL has garnered widespread attention and developed rapidly in geophysics. DL provides a new power for geophysical exploration and is becoming an essential tool for geophysical data processing, modeling, and analysis. With the proposal and effective application of various new deep learning-based technologies and methods for geophysical data processing, the efficiency and accuracy of geophysical exploration have been significantly improved. This advancement is accelerating the rapid development of geophysics toward intelligent interpretation. This paper reviews the latest research and application status of DL in geophysics, including seismic exploration, electrical prospecting, earthquake science, remote sensing, and other fields. Through systematic analysis of recent literature, it summarizes mainstream technical approaches of DL for addressing diverse geophysical challenges, and the limitations of these technologies in specific application scenarios are discussed. In addition, this paper analyses and prospects for research trends of DL in geophysics. This paper serves as a relevant reference for hobbyists and researchers to understand the latest advances, unresolved issues, and future trends in related fields.}, archive = {J_ACCESS}, author = {Zhao Wenxue and Dai Shikun and Tian Hongjun and Zhu Dexiang and Zhang Ying and Jiang Fan}, doi = {10.1109/ACCESS.2025.3586693}, journal = {IEEE Access}, pages = {124364-124388}, shortjournal = {IEEE Access}, title = {An overview study of deep learning in geophysics: Cross-cutting research to advance geoscience}, volume = {13}, year = {2025}, }
• (2025). A novel permanent magnet assisted brushless wound rotor synchronous machine with improved torque characteristics. ACCESS, 13, 124351-124363. (www)

  @article{ , abstract = {In this manuscript, a novel permanent magnet assisted brushless wound rotor synchronous machine (PMa-BLWRSM) has been presented. Contrary to the existing configurations requiring two inverters for BLWRSMs, the proposed brushless topology requires a single 3-phase inverter. Two dominant components of magneto motive force (MMF) are available in the stator MMF (sMMF) of the suggested PMa-BLWRSM: subharmonic component (SHC) and fundamental component (FC). The SHC of sMMF is utilized to obtain the brushless working of the proposed machine. The rotor design includes two windings: 1) harmonic winding (HW), and 2) field winding (FW). A rotating bridge rectifier is embedded between these two windings. The SHC of the sMMF produces an AC voltage in the HW of the rotor. The produced voltage in HW is given to the rotating rectifier, which converts it into DC. This DC is given to the FW of the rotor. A 2-dimensional finite element analysis is performed using JMAG designer to verify the operating principle of the suggested 8-pole and 12-slot PMa-BLWRSM. In comparison to existing BLWRSM, the proposed machine provides high starting and average torques with less input stator winding current and low torque ripples. The proposed machine can be used for applications requiring high starting torque.}, archive = {J_ACCESS}, author = {Muhammad Ahsan Ul Haq and Haroon Farooq and Rehan Liaqat and Zafar A. Khan and Abdullah Altamimi}, doi = {10.1109/ACCESS.2025.3588513}, journal = {IEEE Access}, pages = {124351-124363}, shortjournal = {IEEE Access}, title = {A novel permanent magnet assisted brushless wound rotor synchronous machine with improved torque characteristics}, volume = {13}, year = {2025}, }
• (2025). Trends and future directions of metaverse in education: A bibliometric analysis. ACCESS, 13, 124333-124350. (www)

  @article{ , abstract = {The objective of this study is to analyze metaverse topics and education through bibliometric methods to identify research trends, leading researchers, institutions, and prevalent themes from the past decade (2010–2020) and developing themes for the next five years (2021–2024). This study followed the PRISMA framework to extract data from the Scopus and Web of Science (WoS) databases, resulting in 475 publications. Biblioshiny was used for descriptive bibliometric analysis and thematic mapping, whereas VOS Viewer co-occurrence and trending analysis were used to identify clusters and trending topics in this domain. Thematic mapping and cluster analysis revealed that Virtual Reality (VR) and Augmented Reality (AR) technologies are dominant forces shaping the future evolution of education in a metaverse context. The co-occurrence analysis revealed that future studies could focus on customized education, dynamic teaching methods, and a sustainability-oriented educational framework. This study suggests the potential of an interactive, dynamic, and scalable learning environment using metaverse learning. Policymakers, educators, and developers should establish collaborations, enhance infrastructure, and remove barriers to realize this potential. By combining the results from both the Scopus and WoS databases, this study offers a thorough literature overview of the applications of the metaverse in learning and development while proposing future research areas for academics to address existing gaps and explore the potential of metaverse technology in this field.}, archive = {J_ACCESS}, author = {Samsudeen Sabraz Nawaz and Mohamed Ismail Mohamed Riyath and Roshan Nayak and Mohamed Najim Mohamed Mujithaba and Bader Alhafi Alotaibi and Mohamed B. Fathima Sanjeetha and Ghadah Al Murshidi}, doi = {10.1109/ACCESS.2025.3588007}, journal = {IEEE Access}, pages = {124333-124350}, shortjournal = {IEEE Access}, title = {Trends and future directions of metaverse in education: A bibliometric analysis}, volume = {13}, year = {2025}, }
• (2025). Optimization of customer feedback summarization using large language models (LLM) and advanced retrieval-augmented generation. ACCESS, 13, 124319-124332. (www)

  @article{ , abstract = {Customer feedback, often shared through online reviews, plays a crucial role in shaping business strategies. However, the overwhelming volume of such reviews poses two major challenges: valuable insights often go unnoticed, and manual analysis introduces human bias. To address this, we propose a system that leverages large language models (LLMs) integrated with the LangChain framework to answer natural language queries over customer reviews. A synthetic dataset was created to resemble food delivery reviews typically seen on the Play Store and was stored in a vector database. On receiving a user query, relevant reviews are retrieved using advanced Retrieval-Augmented Generation (RAG) techniques, namely Hierarchical Chunk Retrieval and RAG Fusion that are further refined using the Declarative Self-improving Python (DSPy) framework to generate accurate, grounded responses. The system was evaluated using LLaMA-3-8B-InstructLite, GPT-3.5-Turbo, and Gemini-1.5-Pro, and compared against two non-LLM baselines: BM25 and a fine-tuned BERT model. Results show that our LLM-based pipeline outperforms by an average 15% in semantic and factual accuracy. Component-level analysis showed that enhanced retrieval strategies showed aggregate improvements of 4.9% in semantic relevance, 12.1% in lexical coverage, and 9.9% in factual consistency over traditional RAG. Further integration of DSPy led to an additional 10.8% boost in linguistic fluency and a 9.0% gain in factual alignment. Among the evaluated models, Gemini-1.5-Pro combined with RAG Fusion and DSPy produced the most fluent and factually accurate responses, demonstrating the effectiveness of combining hybrid retrieval with LLM-driven reasoning for query-based feedback response system.}, archive = {J_ACCESS}, author = {Buchepalli Praneeth and Mohana and Eshitha Chowdary Nattem and Kamala Jetti and B. K. Kavyashree and D. Rakshitha and P. Ramakanth Kumar and K. Sreelakshmi}, doi = {10.1109/ACCESS.2025.3588337}, journal = {IEEE Access}, pages = {124319-124332}, shortjournal = {IEEE Access}, title = {Optimization of customer feedback summarization using large language models (LLM) and advanced retrieval-augmented generation}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of non-pairing hierarchical identity-based encryption in active distribution system. ACCESS, 13, 124303-124318. (www)

  @article{ , abstract = {The evolution of traditional power grids into smart grids has enabled two-way Internet of Things (IoT)-based communication between utilities and devices or consumers, improving energy distribution and efficient service to the end-users. However, the deployment of IoT technology can expose cyber-attack vulnerabilities, which can lead to the tampering of critical electricity-based data in the distribution sector, where the consumers are directly involved in the energy sector through various initiatives of the smart grid, such as demand side management, demand response and Transactive Energy Management System (TEMS). As a result, this may lead to malicious decisions regarding market clearing prices in the TEMS. To address these vulnerabilities, this paper proposes a lightweight and secure cryptographic protocol that integrates non-pairing-based Hierarchical Identity-Based Encryption (HIBE) with Shamir’s Secret Sharing (SSS) to achieve mutual authentication and establish a secure session key without relying on third-party authorities. The session key enables the secure exchange of sensitive information between utilities and consumers. An analytical evaluation of the proposed protocol demonstrates that it achieves low computational and communication overhead. Furthermore, the protocol has been evaluated in an IoT-based smart grid environment using the NS3 simulator, with network performance metrics including Packet Delivery Ratio (PDR), Average End-to-End Delay, and network throughput. The results confirm that the proposed protocol outperforms existing protocols by achieving low communication overhead and maintaining a high PDR above 98% with less than 0.0165326 (sec) transmission delay across various participant scenarios.}, archive = {J_ACCESS}, author = {Naveen Tatipatri and S. L. Arun}, doi = {10.1109/ACCESS.2025.3588106}, journal = {IEEE Access}, pages = {124303-124318}, shortjournal = {IEEE Access}, title = {Performance analysis of non-pairing hierarchical identity-based encryption in active distribution system}, volume = {13}, year = {2025}, }
• (2025). Noise-suppressing newton algorithm for kinematic control of robots. ACCESS, 13, 124295-124302. (www)

  @article{ , abstract = {In this paper, armed with the integral control method, a new noise-suppressing Newton (NSN) algorithm is proposed for the redundancy resolution of redundant robot manipulators efficiently. For practical hardware implementation, the discrete-time noise-suppressing Newton (abbreviated as DTNSN) algorithm is discretized from the continues NSN algorithm. Specifically, the distinguishing feature of the proposed DTNSN algorithm is that it can rigorously converge with inherent tolerance to noises induced by communication jamming and computational systematical errors. In contrast, considerable traditional algorithms often dispose of noises with the high-degree filter from the viewpoint of signal processing, which requires a complex system structure and further results in a heavy computational burden. Note that theoretical analyses are provided to elaborate the convergent property of the DTNSN algorithm polluted with constant bias, time-dependent linear noises and bounded random noises. Besides, by the proposed DTNSN algorithm, the end effector of both serial and parallel redundant robot manipulators complete the allocated motion planning and are impervious to the noisy simulated environment.}, archive = {J_ACCESS}, author = {Xiuchun Xiao and Lin Wei and Dongyang Fu and Jingwen Yan and Huan Wang}, doi = {10.1109/ACCESS.2019.2937686}, journal = {IEEE Access}, pages = {124295-124302}, shortjournal = {IEEE Access}, title = {Noise-suppressing newton algorithm for kinematic control of robots}, volume = {13}, year = {2025}, }
• (2025). SCiMA: A systolic CiM-based accelerator with a new weight mapping for CNNs—A virtual framework approach. ACCESS, 13, 124278-124294. (www)

  @article{ , abstract = {Efficient execution of deep convolutional neural networks (DCNNs) on von Neumann platforms has significantly encountered the memory wall. Computation-in-Memory (CiM) aims to integrate computation and storage by performing analog dot product operations within memory cells. Despite advancements, CiM-based architectures still face bottlenecks in transferring input and partial result data to and from CiM units. To address this, a programmable CiM-based Systolic Accelerator (SCiMA) is proposed, featuring an array of Tiles with deep systolic dataflow within architectural levels, providing a scalable and efficient dataflow for DCNN inference. To reduce data movement more efficiently for convolutional layers, which are feature-dominant, proposed inter- and intra-crossbar weight mapping strategies aim to minimize Tile buffering requirements and enhance data reuse. For dense layers, which have the highest weight dominance, Sparse SCiMA leverages structured pruning to exploit sparsity and enhance area efficiency. Additionally, a virtual SystemC-Python framework is introduced to compile CNN applications onto SCiMA and to explore and evaluate architectural, power, and timing aspects using back-annotated circuit-level information. This framework leveraged matrix-based emulation of the analog computations to calculate the impact of non-idealities in results, thus enabling efficient emulation. Experimental results demonstrate that the proposed CiM-based systolic dataflow and weight mapping for convolutional layers improve computation cycles and data movement by 62% and 57%, respectively, compared to the conventional shared bus structure and weight mapping used in state-of-the-art architectures. Overall, SCiMA improves throughput and power efficiency by 38% and 42% on average, respectively, compared to other architectures. Sparse SCiMA reduces the area and power overhead by 43% and 40% respectively compared to SCiMA.}, archive = {J_ACCESS}, author = {Rezgar Sadeghi and Zainalabedin Navabi}, doi = {10.1109/ACCESS.2025.3585746}, journal = {IEEE Access}, pages = {124278-124294}, shortjournal = {IEEE Access}, title = {SCiMA: A systolic CiM-based accelerator with a new weight mapping for CNNs—A virtual framework approach}, volume = {13}, year = {2025}, }
• (2025). A neural operator unifying graph neural networks and point transformers. ACCESS, 13, 124264-124277. (www)

  @article{ , abstract = {Neural operators have emerged as a powerful tool for learning mappings between function spaces, particularly for solving partial differential equations (PDEs). This study introduces a novel framework that unifies Graph Neural Networks (GNNs) and Transformers, combining their complementary strengths to enhance the approximation of operators derived from solutions of PDEs. By integrating the feature extraction capabilities of GNNs with the attention mechanisms of Transformers, this approach efficiently captures information across multiple scales, from local patterns to global structures, thereby enhancing both the accuracy and efficiency of operator learning.The proposed model consistently outperforms existing baseline models in experiments across various datasets, reducing relative errors. Notably, the model demonstrates robustness in handling multi-input/output problems on irregular meshes, whereas other models struggle to converge. The results demonstrate the effectiveness and generalization capacity of the framework across different types and scales of PDEs. Overall, the integration of GNNs with attention mechanisms represents a significant advancement in operator learning for PDEs, offering a reliable and efficient solution for practical applications.}, archive = {J_ACCESS}, author = {Junseung Ryu and Namkyeong Cho and Hyung Ju Hwang}, doi = {10.1109/ACCESS.2025.3585920}, journal = {IEEE Access}, pages = {124264-124277}, shortjournal = {IEEE Access}, title = {A neural operator unifying graph neural networks and point transformers}, volume = {13}, year = {2025}, }
• (2025). Transformer-based vulnerability detection in IoT firmware binaries using opcode sequences. ACCESS, 13, 124250-124263. (www)

  @article{ , abstract = {Firmware security is critical for maintaining the integrity of embedded systems. However, detecting vulnerabilities in firmware binaries is a challenging task. This is due to the absence of source code, the inherent complexity of binary structures, the diversity of hardware architecture, and the difficulty of extracting deep contextual representations from binaries. In the proposed approach, the Decoding-enhanced BERT with Disentangled Attention (DeBERTa), a novel transformer-based model is used to detect vulnerabilities in firmware binaries. Initially, firmware binaries are disassembled to extract opcode sequences, which are then tokenized and encoded as inputs to the proposed DeBERTa model. The model processes instruction opcode sequences and generates meaningful embeddings, which are utilized for classification tasks. The classifiers used in the proposed approach are Random Forest, Multi-Layer Perceptron, and GAN-based classifier, which operate on the DeBERTa-generated embeddings. The proposed model learns deep contextual representations of firmware code, effectively capturing intricate syntactic and semantic relationships. The evaluation is conducted on IoT firmware binaries collected from real-world IoT projects, reflecting practical and diverse vulnerability scenarios. Experimental results demonstrate that the proposed DeBERTa-based model achieves 97% accuracy, 97% recall, and 94.6% F1-score, outperforming conventional embedding techniques. The experimental findings demonstrate that the opcode sequence feature effectively and reliably detects different types of vulnerable and benign IoT samples.}, archive = {J_ACCESS}, author = {M. Nandish and Jalesh Kumar and H. G. Mohan and M. V. Manoj Kumar}, doi = {10.1109/ACCESS.2025.3588950}, journal = {IEEE Access}, pages = {124250-124263}, shortjournal = {IEEE Access}, title = {Transformer-based vulnerability detection in IoT firmware binaries using opcode sequences}, volume = {13}, year = {2025}, }
• (2025). Carbon black-coated fabric blend as piezoresistive flexible pressure sensor for motion monitoring applications. ACCESS, 13, 124234-124249. (www)

  @article{ , abstract = {Flexible pressure sensors (FPSs) have garnered significant interest among researchers focusing on wearable electronics for motion recognition, health care, rehabilitation therapy, athletic performance monitoring, and more applications. Recently, fabrics known for their flexibility and comfort have become popular substrates for fabricating various flexible sensors. Of these flexible sensors, piezoresistive devices are favored for their simplicity and notable sensitivity to physical movements. This study presents a fabric composite created by coating a viscose staple fiber/linen blend with functionalized conductive carbon black (CB) using a simple drop-coating method and evaluates its pressure-sensing performance. The FPS with optimum CB content exhibited sensitivities of 41.1 kPa ${}^{\mathbf {-1}}$ and 1.4 kPa ${}^{\mathbf {-1}}$ in the pressure ranges of 0-1 kPa and 1-20 kPa, respectively. With a low detection limit of 10 Pa, a response time of 16 ms, a relaxation time of 63 ms, durability exceeding 5,400 cycles, and stable performance in various ambient conditions, the FPS has exhibited potential for applications in bending motion monitoring and information transmission.}, archive = {J_ACCESS}, author = {R. Srinivasan and A. Ravi Sankar}, doi = {10.1109/ACCESS.2025.3588328}, journal = {IEEE Access}, pages = {124234-124249}, shortjournal = {IEEE Access}, title = {Carbon black-coated fabric blend as piezoresistive flexible pressure sensor for motion monitoring applications}, volume = {13}, year = {2025}, }
• (2025). A backdoor approach with inverted labels using dirty label-flipping attacks. ACCESS, 13, 124225-124233. (www)

  @article{ , abstract = {Audio-based machine learning systems frequently use public or third-party data, which might be inaccurate. This exposes deep neural network (DNN) models trained on such data to potential data poisoning attacks. In this type of assault, attackers can train the DNN model using poisoned data, potentially degrading its performance. Another type of data poisoning attack that is extremely relevant to our investigation is label flipping, in which the attacker manipulates the labels for a subset of data. It has been demonstrated that these assaults may drastically reduce system performance, even for attackers with minimal abilities. In this study, we propose a backdoor attack named “DirtyFlipping”, which uses dirty label techniques, ‘label-on-label‘, to input triggers (clapping) in the selected data patterns associated with the target class, thereby enabling a stealthy backdoor.}, archive = {J_ACCESS}, author = {Orson Mengara}, doi = {10.1109/ACCESS.2024.3382839}, journal = {IEEE Access}, pages = {124225-124233}, shortjournal = {IEEE Access}, title = {A backdoor approach with inverted labels using dirty label-flipping attacks}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence algorithm for optimal time series data model. ACCESS, 13, 124211-124224. (www)

  @article{ , abstract = {In order to solve the limitation of a large number of literatures on the study of modeling, simulation and prediction of time series data, there is no model selection, and a certain model is directly used for analysis. For three types of artificial intelligence models often applied to time series analysis: hidden Markov Carrier model, artificial neural network model and autoregressive moving average model are used to study model selection based on simulation comparison method. The study of nonlinear integration methods, using intelligent system methods to learn the weighting mode, has made the model’s generalization ability and the degree is of fit to the sample data have been significantly improved. At the same time, numerical simulations are performed on various models, and the characteristics of the time series generated by various models are investigated. Based on the characteristics, the theory and algorithm of model selection are proposed. The model selection theory and algorithm in this paper is used for empirical analysis. For the artificial intelligence models commonly used in time series analysis such as autoregressive moving average model, artificial neural network model, hidden Markov model, etc., when selecting the research model, the method of simulation comparison can be used. The experimental results show that the time series data generated by various models have different mathematical and physical characteristics, which provide a basis for model selection. At the same time, the selection theory is practical. The model selected by the theory has a good fit and prediction effect. The generation of different models has different mathematical characteristics of time series data, which also provides a basis for selecting models.}, archive = {J_ACCESS}, author = {Kang Wang}, doi = {10.1109/ACCESS.2020.2981488}, journal = {IEEE Access}, pages = {124211-124224}, shortjournal = {IEEE Access}, title = {Artificial intelligence algorithm for optimal time series data model}, volume = {13}, year = {2025}, }
• (2025). Information as interpretation: Measuring learning behavior for knowledge insight. ACCESS, 13, 124197-124210. (www)

  @article{ , abstract = {Traditional Learning Analytics (LA) has been primarily focused on the behavioral aspects of learners’ learning due to its data-driven nature, often lacking analysis of the knowledge-related aspects of what learners have learned. To tackle this issue, the Information Theory on Learning Analytics and Knowledge (ITLAK) framework uses information theory to quantify the informational value of learners’ learning behavior regarding their interaction with knowledge. This paper shows the foundation of ITLAK and its case study, demonstrating its theoretical validity and practical usefulness. ITLAK was applied to the context of English Intensive Reading (IR) specialized in English grammar learning, and a field experiment was conducted with Japanese junior high school third-year students using an IR system. The results showed the possibility that the information content calculated by ITLAK is an indicator that can capture the behavioral and knowledge-related aspects of learning. In particular, it was suggested that the information content metric functions in immediate feedback and captures aspects of learning distinct from the number of contacts with knowledge. However, this case study is limited by the small sample size, reliance on subjective self-assessment, and short intervention period, so further large-scale and long-term studies with objective proficiency measures are needed to validate and generalize the findings. This finding can indicate that ITLAK provides a theoretical foundation for advancing Knowledge-Aware Learning Analytics (KALA) and opens new possibilities for LA. Future research will involve revalidating the findings with large-scale data and designing learning support models.}, archive = {J_ACCESS}, author = {Kensuke Takii and Changhao Liang and Hiroaki Ogata}, doi = {10.1109/ACCESS.2025.3583311}, journal = {IEEE Access}, pages = {124197-124210}, shortjournal = {IEEE Access}, title = {Information as interpretation: Measuring learning behavior for knowledge insight}, volume = {13}, year = {2025}, }
• (2025). Secure federated matrix factorization via device-to-device model shuffling. ACCESS, 13, 124180-124196. (www)

  @article{ , abstract = {Location-Based Recommendation Systems (LBRS) use device location data to suggest nearby hotels, restaurants, and points of interest. Since directly collecting location data from users can raise privacy concerns, there is growing interest in building recommendation systems based on Federated Learning (FL). Under FL, parameters of recommendation model learned on each user’s device are collected on a single server to build aggregated model. While FL does not raise privacy concerns about data collection since it does not collect user data directly, it may construct unfair models that repeatedly recommend specific locations. Although there are training methods to achieve fair recommendations that prevent such bias, they require more training epochs than usual. In FL, a malicious server can infer the original location data by continuously tracking a specific user’s parameter updates, and the inference accuracy increases proportionally with the number of training epochs. This means that achieving fair location recommendations in FL puts the original data at risk. In this paper, we design a novel parameter aggregation method to build fair and secure FL recommendation models. In the proposed aggregation method, users exchange parameters with each other before model aggregation to prevent malicious servers from inferring the original data. Even if a server (adversary) continuously tracks a specific user’s device, it cannot get parameters from the same user, thus preventing inference of the original location data. An experiment result demonstrated that the proposed method can reduce training time while maintaining the same accuracy as homomorphic encryption approach.}, archive = {J_ACCESS}, author = {Taisho Sasada and Md. Delwar Hossain and Yuzo Taenaka and Md. Mahbubur Rahman and Youki Kadobayashi}, doi = {10.1109/ACCESS.2025.3588497}, journal = {IEEE Access}, pages = {124180-124196}, shortjournal = {IEEE Access}, title = {Secure federated matrix factorization via device-to-device model shuffling}, volume = {13}, year = {2025}, }
• (2025). Identifying non-functional requirements from unconstrained documents using natural language processing and machine learning approaches. ACCESS, 13, 124159-124179. (www)

  @article{ , abstract = {Requirements engineering is the first phase in software development life cycle and it also plays one of the most important and critical roles. Requirement document mainly contains both functional requirements and non-functional requirements. Non-functional requirements are significant to describe the properties and constraints of the system. Early identification of Non-functional requirement has direct impact on the system architecture and initial design decision. Practically, non-functional requirements are extracted manually from the document. This makes it tedious, time-consuming task and prone to various errors. In this paper, we propose an automatic approach to identify and classify non-functional requirements using semantic and syntactic analysis with machine learning approaches from unconstrained documents. We used A dataset of public requirements documents (PURE) that consists of 79 unconstrained requirements documents in different forms. In our approach, features were extracted from the requirement sentences using four different natural language processing methods including statistical and state-of-the-art semantic analysis presented by Google word2vec and bidirectional encoder representations from transformers models. The adopted approach can efficiently classify non-functional requirements with an accuracy between 84% and 87% using statistical vectorization method and 88% to 92% using word embedding semantic methods. Furthermore, by fusing different models trained on different features, the accuracy improves by 2.4% compared with the best individual classifier.}, archive = {J_ACCESS}, author = {Qais A. Shreda and Abualsoud A. Hanani}, doi = {10.1109/ACCESS.2021.3052921}, journal = {IEEE Access}, pages = {124159-124179}, shortjournal = {IEEE Access}, title = {Identifying non-functional requirements from unconstrained documents using natural language processing and machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). Correction to “Distribution network topology identification considering nonsynchronous multi-prosumer data measurement”. ACCESS, 13, 124158. (www)

  @article{ , abstract = {Presents corrections to the paper, (Correction to “Distribution Network Topology Identification Considering Nonsynchronous Multi-Prosumer Data Measurement”).}, archive = {J_ACCESS}, author = {Lizong Zhang and Fengming Zhang and Xiaolei Li and Chunlei Wang and Taotao Chen and Qingqing Wang and Huilin Hu}, doi = {10.1109/ACCESS.2022.3190185}, journal = {IEEE Access}, pages = {124158}, shortjournal = {IEEE Access}, title = {Correction to “Distribution network topology identification considering nonsynchronous multi-prosumer data measurement”}, volume = {13}, year = {2025}, }
• (2025). P4Muse: Enabling modular p4 programming via compiler-managed code merging without syntax modifications. ACCESS, 13, 124138-124157. (www)

  @article{ , abstract = {Domain-specific programming languages such as P4 enable flexible and high-performance packet processing for programming network data planes. However, many P4 programs remain monolithic, limiting the development of modular and reusable protocols and libraries. Introducing modularity to P4 has proven challenging, as existing approaches—such as trans-compilers and virtualization—often sidestep direct integration with the P4 language and compiler, constraining backward compatibility and extensibility. This paper introduces P4Muse (P4 Modularity and Unification for Seamless Extensibility), an open-source P4C compiler extension that enhances the modularity of P4 without requiring new syntax or annotations. P4Muse is developed by integrating new compiler passes for automatic code merging, fostering modular design and reuse. We demonstrate its benefits through three classes of use cases that support P4 modularity, enabling code reusability, data plane pipeline composition, and vendor-customer compatibility. using the V1Model architecture and the BMv2 software switch. Our results show that P4Muse effectively supports modular P4 program development without altering existing P4 syntax, providing a robust solution that significantly improves code reusability, flexibility, and extensibility while maintaining backward compatibility.}, archive = {J_ACCESS}, author = {Mohsen Rahmati and François-Raymond Boyer and Bill Pontikakis and Jean Pierre David and Yvon Savaria}, doi = {10.1109/ACCESS.2025.3589353}, journal = {IEEE Access}, pages = {124138-124157}, shortjournal = {IEEE Access}, title = {P4Muse: Enabling modular p4 programming via compiler-managed code merging without syntax modifications}, volume = {13}, year = {2025}, }
• (2025). Simplifying masked image modeling with symmetric masking and contrastive learning. ACCESS, 13, 124129-124137. (www)

  @article{ , abstract = {Masked image modeling (MIM) has emerged as an effective self-supervised learning paradigm for pre-training Vision Transformers (ViTs) by reconstructing missing pixels from masked image regions. While prior approaches have demonstrated strong performance, they typically rely on random masking strategies and require extensive hyperparameter tuning—particularly in searching for optimal masking ratios—leading to increased training cost and reduced generalizability. In this work, we propose a simple yet powerful symmetric masking strategy that eliminates the need for such ratio exploration by maintaining a fixed 50% masking pattern. Based on this strategy, we introduce SymMIM, a novel MIM framework that integrates both reconstruction and contrastive learning objectives to jointly capture local and global representations. Despite its simplicity, SymMIM achieves state-of-the-art accuracy of 85.9% on ImageNet with ViT-Large and consistently outperforms prior methods across various downstream tasks, including image classification, semantic segmentation, object detection, and instance segmentation—all while requiring only a single-stage pre-training process.}, archive = {J_ACCESS}, author = {Khanh-Binh Nguyen and Chae Jung Park}, doi = {10.1109/ACCESS.2025.3589488}, journal = {IEEE Access}, pages = {124129-124137}, shortjournal = {IEEE Access}, title = {Simplifying masked image modeling with symmetric masking and contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Hierarchical cascade deep learning for EMG-based behavioral biometrics: Gesture and subject classification. ACCESS, 13, 124115-124128. (www)

  @article{ , abstract = {The integration of electromyography (EMG) signals into biometric recognition has garnered significant attention due to their potential for highly secure and reliable identification. Unlike vision-based methods like cameras, EMG is immune to lighting conditions, clothing, or occlusions. This study presents a hierarchical cascade deep learning framework aimed at simultaneously performing hand gesture recognition and subject-specific biometric classification. Utilizing the publicly available Gesture Recognition and Biometrics ElectroMyogram (GRABMyo) dataset, which encompasses diverse EMG recordings from 43 individuals performing 17 unique gestures, this study proposes a two-staged classification approach. The first stage concentrates on recognizing the hand gesture, succeeded by a gesture-specific model that subsequently categorizes the subject associated with the identified gesture. The experimental results demonstrate the effectiveness of the proposed model, which achieved an average accuracy of 71.62% across gesture and subject classification, representing an improvement of approximately 5% and 21% compared to conventional single-model and multi-task strategies evaluated in this study, highlighting this approach’s effectiveness in handling the variability of EMG signals across different gestures and subjects. The findings underscore the potential of the proposed methodology for enhancing EMG-based biometric recognition systems.}, archive = {J_ACCESS}, author = {Berke Cansiz and Hatice Vildan Dudukcu and Murat Taskiran and Nihan Kahraman}, doi = {10.1109/ACCESS.2025.3588791}, journal = {IEEE Access}, pages = {124115-124128}, shortjournal = {IEEE Access}, title = {Hierarchical cascade deep learning for EMG-based behavioral biometrics: Gesture and subject classification}, volume = {13}, year = {2025}, }
• (2025). A study of reduction of time-to-CED in GPS CNAV message structure with reduced CED. ACCESS, 13, 124107-124114. (www)

  @article{ , abstract = {Legacy GNSS requires several minutes to obtain accurate user position, velocity, and time information under cold start. However, with the advancement of navigation aiding systems and telecommunication systems, fast positioning is increasingly preferred by general users in many areas, even if the initial positioning accuracy is somewhat lower. From this perspective, several methods have been proposed for fast positioning and the Galileo system has adopted reduced clock and ephemeris data (CED) in the I/NAV navigation message. This approach shortens the time required to read navigation data, thereby reducing the Time-to-First-Fix (TTFF). In a similar manner, this paper proposes an improved GPS CNAV message structure that incorporates reduced CED. By allocating reduced CED to each time slot, the TTFF is significantly reduced compared to the original GPS CNAV. Along with this, a functional TTFF calculation method is presented, as a comparison to the conventional approach. This method enables easy performance evaluation of new GNSS/RNSS signals while meeting users’ requirements.}, archive = {J_ACCESS}, author = {Byunghyun Choi and Donguk Kim}, doi = {10.1109/ACCESS.2025.3588680}, journal = {IEEE Access}, pages = {124107-124114}, shortjournal = {IEEE Access}, title = {A study of reduction of time-to-CED in GPS CNAV message structure with reduced CED}, volume = {13}, year = {2025}, }
• (2025). Insect detection using mm-waves: Integrated communication and biodiversity sensing based on micro-doppler effects. ACCESS, 13, 124096-124106. (www)

  @article{ , abstract = {Wing flapping of insects can cause micro-Doppler patterns in the reflected signals. Here we study the feasibility of insect sensing from a joint waveform from a near-field continuous-wave micro-Doppler radar and a digitally modulated signal at mm-Wave frequencies. We use experiments to capture reflected signals from live insects, and introduce a semi-supervised algorithm that automatically detects signal portions exhibiting micro-Doppler effects of wing flapping. Performance evaluation using precision, recall, F1-score, and ROC-AUC demonstrates the robustness of the approach, while the extracted wing-beat frequencies from various species further validate its effectiveness. Due to the simplicity of this proposed method, the technique can be integrated into the mm-Wave telecommunication system to monitor flying insects to bring about a step-change in our understanding of the dynamics of insect communities and the critical roles they play in ecosystem functioning and biodiversity.}, archive = {J_ACCESS}, author = {Linta Antony and Nicola Marchetti and Ian Donohue and Adam Narbudowicz}, doi = {10.1109/ACCESS.2025.3588780}, journal = {IEEE Access}, pages = {124096-124106}, shortjournal = {IEEE Access}, title = {Insect detection using mm-waves: Integrated communication and biodiversity sensing based on micro-doppler effects}, volume = {13}, year = {2025}, }
• (2025). Multitask analysis method for tongue image based on edge computing. ACCESS, 13, 124086-124095. (www)

  @article{ , abstract = {In response to the application scenarios of modernized Traditional Chinese Medicine (TCM) diagnostic and treatment equipment moving towards the user end, an effort has been made to enhance the user-friendliness of TCM diagnostic and treatment devices. This involves introducing the concept of edge computing into the mobile tongue diagnosis instrument, shifting the tasks of tongue image acquisition and analysis to portable auxiliary diagnostic devices. To improve the efficiency of edge computing devices in handling tongue image analysis tasks, a multi-task network model based on a lightweight network backbone is proposed. The model utilizes the lightweight feature extraction backbone of MobileNet to provide feature encoding for both the semantic segmentation branch and the multi-label classification branch. The semantic segmentation branch adopts a skip-layer connection structure with multi-scale feature maps, and an attention mechanism is incorporated into the classification branch to fuse the feature maps from the segmentation branch. This achieves tongue segmentation and multi-label classification tasks in a computationally efficient environment. The model achieves a pixel accuracy of 85.3% in semantic segmentation and an accuracy of 95.6% in multi-label classification. The network’s forward propagation speed on edge computing platforms reaches 7 frames per second (FPS). The proposed lightweight network backbone multi-task network model ensures a significant improvement in processing efficiency while maintaining the accuracy of segmentation and classification tasks. Additionally, the model exhibits advantages in terms of quantity and scale, saving both storage and computational resources. It not only enhances the accuracy and efficiency of tongue image real-time analysis in edge computing scenarios but also reduces the processing time, providing excellent precision and inference speed.}, archive = {J_ACCESS}, author = {Tingting Song and Bin Liu and Fengen Yuan and Yunfeng Wang and Kang Yu and Hao Yang and Qiuyan Li}, doi = {10.1109/ACCESS.2024.3382303}, journal = {IEEE Access}, pages = {124086-124095}, shortjournal = {IEEE Access}, title = {Multitask analysis method for tongue image based on edge computing}, volume = {13}, year = {2025}, }
• (2025). Deep learning for time series prediction of strata pressure in coal mining. ACCESS, 13, 124068-124085. (www)

  @article{ , abstract = {Hydraulic support plays a vital role in maintaining the structural integrity and safety of underground coal mines. We analyze a six-month dataset (May 1-October 31) of strata pressure from ten hydraulic supports (No. 65-74) in a 5966m $\times 280$ m longwall face, preprocessed into one-minute intervals, to predict strata pressure in underground coal mines, which is critical for ensuring safety and structural integrity. Using Pearson Correlation Coefficient (PCC), Fourier Transform (FT), and change point detection, we uncover strong intra-support correlations (PCC > 0.9), non-periodic patterns, and frequent abrupt shifts (3-5 events/hour). For short-term (one-minute) prediction, we propose a novel CNN-DLinear hybrid model that integrates DLinear’s interpretable trend-residual decomposition, tailored to strata pressure dynamics, with CNN’s localized spike detection for abrupt geological events. For long-term (30-minute) forecasting, we employ a smoothing technique to mitigate abrupt fluctuations and a sliding window approach to capture evolving trends. Experimental results show that our CNN-DLinear model achieves superior performance compared to ARIMA, LSTM, and Transformer models, with average reductions of 67% in MAE, 71% in MAPE, and 62% in RMSE, and an average $R^{2}$ of 0.96 across ten supports. Our approach excels in capturing non-periodic, noisy strata pressure dynamics with lower computational complexity ( $O(L)$ vs. $O(L^{2})$ for Transformers), enabling real-time safety monitoring. This work addresses the urgent need for accurate, efficient strata pressure forecasting in dynamic underground environments, thereby advancing operational safety and decision-making in coal mining.}, archive = {J_ACCESS}, author = {Xinyu Gu and Khay See and Xiuze Zhou}, doi = {10.1109/ACCESS.2025.3589493}, journal = {IEEE Access}, pages = {124068-124085}, shortjournal = {IEEE Access}, title = {Deep learning for time series prediction of strata pressure in coal mining}, volume = {13}, year = {2025}, }
• (2025). SVPDSA: Selective view perception data synthesis with annotations using lightweight diffusion network. ACCESS, 13, 124051-124067. (www)

  @article{ , abstract = {The generation of high-quality annotated image datasets with low computational cost and automated labeling is essential for advancing computer vision systems. However, manual labeling of real images is often labor intensive and expensive. To overcome these challenges, proposed a model named SVPDSA, a generic dataset generation model that incorporates residual and attention block pruning, reduced sampling steps, and network quantization. The model comprises two main training phases: Refined LDM((Latent Diffusion Model) retraining and P-Decoder(Perception decoder) training. A compressed Unet-based diffusion model, pre-trained on the LAION-5B dataset, serves as the foundation for efficient text-to-image synthesis. The model is trained for approximately 50,000 iterations with a learning rate of 0.0001, ensuring lightweight yet effective generation. The proposed model efficiently generates diverse synthetic images with high-quality perception annotations. The proposed approach utilizes a lightweight trained diffusion model and extends text-guided image synthesis to perception data generation, ensuring the quality of the generated datasets while offering a flexible solution for label generation. A decoder module is introduced to expand latent code features and generate labeled annotations for tasks such as semantic segmentation, instance segmentation, and depth estimation. Training the decoder requires fewer than 100 manually labeled images, enabling the creation of an infinitely large annotated dataset. Evaluation on the Cityscapes dataset demonstrates that SVPDSA matches or surpasses existing methods like Mask2Former and DatasetDM in key object classes, including cars, buses, and bicycles. It achieves a mean IoU of 42.7 with ResNet-50 and 41.4 with Swin-B using only 9 real images and 38k synthetic samples, showcasing its efficiency in generating high-quality annotations with minimal real data. Deploying the proposed models on edge devices results in less than a 5-second inference time.This research contributes toward building resource-efficient data generation systems suitable for constrained training environments.}, archive = {J_ACCESS}, author = {S. Raghavendra and Vijayalakshmi and Vainidhi and S. K. Abhilash and Venu Madhav Nookala and P. V. Arun Kumar and Ramyashree}, doi = {10.1109/ACCESS.2025.3588542}, journal = {IEEE Access}, pages = {124051-124067}, shortjournal = {IEEE Access}, title = {SVPDSA: Selective view perception data synthesis with annotations using lightweight diffusion network}, volume = {13}, year = {2025}, }
• (2025). AI and digital twin federation-based flexible safety control for Human–Robot collaborative work cell. ACCESS, 13, 124037-124050. (www)

  @article{ , abstract = {This paper proposes a safety response technology that leverages multi-domain vision sensors to monitor the positions of workers and robots in real-time, enabling dynamic calculation of Flexible Protective Separation Distances (FPSD) for adaptive robot velocity control and safety stops. Compliant with ISO/TS 15066 technical specifications for speed and separation monitoring, the method integrates AI-driven 3D pose estimation and Digital Twin visualization to enhance safety and productivity in human-robot collaborative (HRC) work cells. Experimental results demonstrate that FPSD significantly outperforms static Protective Separation Distance (PSD) methods, achieving collaboration times of 2.50 s (98.04%) and 5.40 s (99.08%) in gantry and collaborative robot experiments, respectively, compared to 0.25 s (9.80%) and 2.50 s (45.87%) for PSD method. The system reduces unnecessary robot pauses with average response times of 0.021 s for deceleration and 0.032 s for stops, ensuring robust collision prevention. Validated using multi-domain sensor data in dynamic HRC tasks, this approach confirms its potential for scalable deployment in smart manufacturing, offering a flexible and efficient safety framework.}, archive = {J_ACCESS}, author = {Ji Dong Choi and Seung-Hwan Choi and Min Young Kim and Inho Lee and Suwoong Lee and Byeong Hak Kim}, doi = {10.1109/ACCESS.2025.3586121}, journal = {IEEE Access}, pages = {124037-124050}, shortjournal = {IEEE Access}, title = {AI and digital twin federation-based flexible safety control for Human–Robot collaborative work cell}, volume = {13}, year = {2025}, }
• (2025). Deep neural framework with visual attention and global context for predicting image aesthetics. ACCESS, 13, 124025-124036. (www)

  @article{ , abstract = {Computational inference of aesthetics has recently become a hot topic due to its usefulness in widely applications such as evaluating image quality, retouching image and retrieving image. Owing to the subjectivity of this problem, there is no general framework to predict image aesthetics. In this paper, we propose a deep neural framework with visual attention module, self-generated global features and hybrid loss to address this problem. Specifically, the framework can be any state-of-the-art convolution classification network compatible with visual attention. Further, self-generated global feature compensates for the loss of global context information during training stage, and the hybrid loss guides the network to learn the similarity between the predicted aesthetic scores and the ground-truths through fusing soft-max-entropy and Earth Mover’s Distance(EMD). With the above-mentioned improvements, the proposed deep neural framework is capable of effectively predicting image aesthetics in an efficient way. In our experiments, we release a real-world aesthetic dataset that contains 1,800 2K photos labeled by several experienced photographers, and then provide a thorough ablation study of the design choices to better understand the superiority brought by each part of our framework, and design several comparisons with the state-of-the-art methods on a fraction of metrics. The experimental results on two datasets demonstrate that both accuracy and efficiency achieve favorably performance.}, archive = {J_ACCESS}, author = {Yifei Xu and Nuo Zhang and Pingping Wei and Genan Sang and Li Li and Feng Yuan}, doi = {10.1109/ACCESS.2020.3015060}, journal = {IEEE Access}, pages = {124025-124036}, shortjournal = {IEEE Access}, title = {Deep neural framework with visual attention and global context for predicting image aesthetics}, volume = {13}, year = {2025}, }
• (2025). The evolution of global and local scientist mobility network: Evidence from ORCID profiles. ACCESS, 13, 124010-124024. (www)

  @article{ , abstract = {The global and local mobility of scientists are two critical aspects influencing national innovation systems. However, existing research primarily analyzes the scientist mobility network at either a global or local scale, failing to capture the structure and dynamic of networks at the coupled global-local scale. To address this gap, we develop a conceptual model of the global and local scientist mobility network. Empirically, based on a dataset containing approximately two million profiles from ORCID, we construct a mobility network encompassing 206 countries and 16,049 universities. Using social network analysis methods and the core-periphery profile algorithm, we analyze the structural evolution of the network. Furthermore, we employ community detection algorithms and a random network null model to examine the driving role of the proximity in this evolution. The main findings are as follows: (1) Over time, the size of the scientist mobility network has expanded significantly, with increasingly small-world properties and network centralization, which are more evident at the global scale; (2) Geographical and institutional proximity play a crucial role in the evolution of the scientist mobility network, with geographical proximity primarily influencing local networks. The findings of this study provide more robust and generalizable empirical evidence for universities, local governments, and national policymakers to better understand the competitive dynamics of domestic and international talent, and also offer important implications for optimizing talent management policies.}, archive = {J_ACCESS}, author = {Ziyang Lin and Huiming Gu}, doi = {10.1109/ACCESS.2025.3588744}, journal = {IEEE Access}, pages = {124010-124024}, shortjournal = {IEEE Access}, title = {The evolution of global and local scientist mobility network: Evidence from ORCID profiles}, volume = {13}, year = {2025}, }
• (2025). Driving mechanisms of user engagement with AI-generated content on social media platforms: A multimethod analysis combining LDA and fsQCA. ACCESS, 13, 123994-124009. (www)

  @article{ , abstract = {With the rapid development of artificial intelligence (AI) technologies, AI-generated content (AIGC) on social media platforms has significantly increased. This study collected text data related to AIGC from mainstream social media platforms and employed the Latent Dirichlet Allocation (LDA) topic model to uncover the thematic characteristics of AIGC. The analysis was further integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT) to identify seven key conditional variables: the maturity of AIGC technology, users’ perception of the authenticity of AIGC, users’ perception of the usefulness of AIGC, users’ perception of the entertainment value of AIGC, the commercialization level of AIGC, the personalization level of AIGC recommendations on the platform, and the ecosystem management and interaction atmosphere of AIGC on the platform. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this study identified seven configurational paths that drive user engagement with AIGC on social media platforms, which were ultimately summarized into three core pathways: user perception—platform recommendation pathway, user perception—platform atmosphere pathway, and technology characteristics—user perception—platform recommendation—platform atmosphere pathway. The results indicate that users’ perceptions of the usefulness of AIGC are a key factor in driving user engagement with AIGC on social media platforms.}, archive = {J_ACCESS}, author = {Jiajun Hou and Hongju Lu and Baojun Wang}, doi = {10.1109/ACCESS.2025.3589286}, journal = {IEEE Access}, pages = {123994-124009}, shortjournal = {IEEE Access}, title = {Driving mechanisms of user engagement with AI-generated content on social media platforms: A multimethod analysis combining LDA and fsQCA}, volume = {13}, year = {2025}, }
• (2025). Enhancing FT-transformer with a matérn-driven kolmogorov-arnold feature tokenizer for tabular data-based in-bed posture classification. ACCESS, 13, 123979-123993. (www)

  @article{ , abstract = {In-bed posture classification plays a crucial role in health monitoring. In this paper, we explore in-bed posture classification using FT-Transformer, a model that employs 1D tabular inputs instead of the commonly used 2D pressure heatmaps. However, the Feature Tokenizer in FT-Transformer suffers from limited representational capacity—relying on simplistic numerical feature processing—and slow convergence due to learning separate embeddings for each feature, which increases training complexity and time. To address this, we propose a Matérn-driven Kolmogorov-Arnold Feature Tokenizer (MKAFT) that enhances the expressiveness of feature tokens in FT-Transformer, leading to faster training. This paper offers three major advancements: (1) Validation of Reduced Spatial Dependency – We demonstrate that in-bed posture classification does not heavily rely on the spatial information of 2D pressure heatmaps. By flattening the pressure data into 1D tabular inputs, we simplify the model structure while still achieving excellent classification performance; (2) A Faster KAN via Matérn kernel – by incorporating Matérn kernel into the Kolmogorov-Arnold Network (KAN), we accelerate both training and inference; and (3) Matérn-driven KAN for Optimizing the Feature Tokenizer in FT-Transformer – leveraging Matérn-driven KAN in the Feature Tokenizer stage of FT-Transformer enhances feature representation capacity, accelerating training with minimal impact on classification accuracy. Empirical results demonstrate that our method strikes a favorable balance between efficiency and performance.}, archive = {J_ACCESS}, author = {Bing Zhou and Weiwei Chen}, doi = {10.1109/ACCESS.2025.3586365}, journal = {IEEE Access}, pages = {123979-123993}, shortjournal = {IEEE Access}, title = {Enhancing FT-transformer with a matérn-driven kolmogorov-arnold feature tokenizer for tabular data-based in-bed posture classification}, volume = {13}, year = {2025}, }
• (2025). NeuroTutor: Neural decoding of student engagement during virtual and robotic tutoring. ACCESS, 13, 123967-123978. (www)

  @article{ , abstract = {The use of electroencephalography (EEG)-based analysis in educational research offers valuable insight into the dynamics of virtual and robot-based tutoring systems. This study aims to classify neural patterns associated with two distinct tutoring modalities (virtual tutor versus robot tutor) to understand how different educational interfaces affect brain activity and engagement biomarkers. This classification enables adaptive learning systems that can optimize educational environments based on real-time neural responses, potentially enhancing learning outcomes by matching tutoring modalities to individual neural preferences. We examine the performance of multiple EEG features such as power spectral density, Fast Fourier Transform (FFT) magnitude coefficients, amplitude and variance, coherence, Hjorth parameters, and wavelet transform coefficients in distinguishing between these two tutoring modalities. A transformer-based binary classifier was employed to evaluate the effectiveness of these characteristics in classifying EEG data collected during interactions with both types of tutors. The wavelet transform coefficients demonstrated the highest classification accuracy of 98.75%, precision of 99.17%, recall of 98.18%, F1 score of 98.61%, and Area Under the Curve (AUC) of 1.00, indicating exceptional performance in all metrics. This high accuracy represents a significant advancement in EEG-based educational classification systems. The Hjorth parameters (90.13% accuracy, 89.91% precision, 95.45% recall) and preprocessed EEG (91.73% accuracy, 91.57% precision, 90.91% recall) also showed strong performance. Simpler features, such as amplitude and variance, exhibited limited discriminatory power with only 72.67% accuracy. These findings underscore the importance of feature engineering and robust preprocessing in EEG-based educational studies and demonstrate the feasibility of creating personalized learning environments that can adapt to individual students’ neural preferences. Limitations such as a small sample size and topic-specific focus are noted, paving the way for future research to generalize findings and optimize methodologies.}, archive = {J_ACCESS}, author = {Shahd Elgammal and Shaymaa Alsereidi and Mahra Almansoori and Shamma Alblooshi and Abdelhadi Hireche and Abdelkader Nasreddine Belkacem}, doi = {10.1109/ACCESS.2025.3585826}, journal = {IEEE Access}, pages = {123967-123978}, shortjournal = {IEEE Access}, title = {NeuroTutor: Neural decoding of student engagement during virtual and robotic tutoring}, volume = {13}, year = {2025}, }
• (2025). Optimized trust prediction and multi-criteria framework for task and data collector selection using deep learning in ubiquitous environments. ACCESS, 13, 123942-123966. (www)

  @article{ , abstract = {In crowdsourcing and ubiquitous computing environments, efficient task assignment and data collector selection are vital for system performance and profitability. This paper presents a novel framework integrating deep learning models to predict trustworthiness for tasks and data collectors. Natural optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulating Annulling (SA), and Cuckoo Search are employed to fine-tune hyperparameters of the predictive models. Tasks and data collectors are classified as trusted or non-trusted, and fuzzy multi-criteria decision-making techniques—Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS)—are used to rank and select tasks and data collectors. Extensive experiments validate the proposed strategy’s efficacy, demonstrating superior performance in terms of total Profit, total Reward, trusted score, overall success probability, and total Selected data collectors compared to existing approaches.}, archive = {J_ACCESS}, author = {Ahmed A. A. Gad-Elrab}, doi = {10.1109/ACCESS.2025.3589065}, journal = {IEEE Access}, pages = {123942-123966}, shortjournal = {IEEE Access}, title = {Optimized trust prediction and multi-criteria framework for task and data collector selection using deep learning in ubiquitous environments}, volume = {13}, year = {2025}, }
• (2025). A new prediction method of wind power based on l2 norm cloud to erase the wind uncertainty. ACCESS, 13, 123931-123941. (www)

  @article{ , abstract = {Due to the large dispersion and uncertainty of wind power operation, it is very difficult to predict the wind power. Even though there are many prediction methods considering the uncertainty of wind power, there is still not a method to accurately fit the corresponding relationship between wind speed and wind power, and thus the wind power prediction is not accurate. According to this paper, the probability density curve of wind power under different wind speeds is firstly studied, and the combined cloud model is established with the peak value as the boundary to represent the uncertain corresponding relationship between wind speed and power. Secondly, in order to avoid the error increase of the combined cloud model caused by the sample peak disturbance, L2 norm theory is introduced to update the peak point to enhance the robustness of the model. Finally, The parameters of L2 norm cloud model are calculated based on Bayesian theory. The simulation results show that the combined cloud fitting method can obtain high fitting accuracy for the irregular single peak or multi peak wind speed power probability distribution with uncertainty.}, archive = {J_ACCESS}, author = {Fangyu Wang and Liping Zhu and Wenying Liu and Jie Qin}, doi = {10.1109/ACCESS.2020.2987938}, journal = {IEEE Access}, pages = {123931-123941}, shortjournal = {IEEE Access}, title = {A new prediction method of wind power based on l2 norm cloud to erase the wind uncertainty}, volume = {13}, year = {2025}, }
• (2025). Mobility-aware bivariate line-of-sight probability for air-to-ground communications using millimeter and terahertz waves. ACCESS, 13, 123913-123930. (www)

  @article{ , abstract = {This paper presents a mobility-aware bivariate line-of-sight (LoS) probability model for millimeter waves and terahertz air-to-ground communication. Current literature does not provide LoS models that consider using directional antennas on the unmanned aerial vehicle that acts as an air base station (airBS). Furthermore, in the context of having large stationary blockers, the impact of ground user terminals’ (gUTs) and airBSs’ mobility remains unexplored. To simulate the ray-tracing between gUTs and airBSs in both scenarios where the airBSs have omnidirectional or directional antennas, we develop a game engine-based synthetic urban layout generator. We analyze ray-tracing as a bivariate probability and formulated a unified joint probability function that could predict the LoS probability for omnidirectional and directional scenarios. The proposed model outperformed the existing models in the omnidirectional scenario. We also present a benchmark for the model’s performance in the directional scenario. Moreover, using ray-tracing simulation, we examine the influence of mobility on the service time, also known as the sojourn time, and developed two cubic polynomial models to forecast the service time for any urban layout that aligns with the ITU-R P.1410-5 build-up parameters.}, archive = {J_ACCESS}, author = {Basheer Ameen Raddwan and Ibrahim Ahmed Al-Baltah}, doi = {10.1109/ACCESS.2025.3582890}, journal = {IEEE Access}, pages = {123913-123930}, shortjournal = {IEEE Access}, title = {Mobility-aware bivariate line-of-sight probability for air-to-ground communications using millimeter and terahertz waves}, volume = {13}, year = {2025}, }
• (2025). Energy efficiency of substation buildings based on low power analysis technology. ACCESS, 13, 123896-123912. (www)

  @article{ , abstract = {To promote environmentally friendly and low-carbon growth of buildings, accelerate building energy efficiency renovation, and solve the substation energy consumption problem, the study proposes to analyse substation buildings with wireless sensor network low power technology. The study starts from three parts: building loss analysis, energy consumption simulation model building and single factor method design. It is also verified with the help of simulation software and orthogonal tests, taking into account the characteristics of energy consumption of substation buildings. The results show that the improved scheme proposed in this study is effective in analyzing the energy consumption of the equipment with a minimum success rate of more than 0.7. The average resource loss and the average delay are significantly better than the other protocols, and the maximum resource loss for the optimised substation construction is reduced by more than 15%. As the value of the roof heat transfer coefficient increases, it leads to a change in the unit air conditioning energy consumption of more than 9 kW.h/m2. the minimum annual cumulative cooling load value of 484.024 W.h/m2 is achieved when its value is 1.0. factors such as the building orientation, the indoor temperature setting, and the ratio of the window and wall area all have an effect on the building energy consumption. The research results can promote efficient energy-saving management of substation construction and sustainable development of power systems.}, archive = {J_ACCESS}, author = {Jingbo Song and Chen Chen and Liang Zhang and Han Yao and Kai Li and Jinfeng Zhang}, doi = {10.1109/ACCESS.2025.3587355}, journal = {IEEE Access}, pages = {123896-123912}, shortjournal = {IEEE Access}, title = {Energy efficiency of substation buildings based on low power analysis technology}, volume = {13}, year = {2025}, }
• (2025). EndoDSR: Automatic detection, segmentation, and restoration of artifacts in helicobacter pylori infection using endoscopic images. ACCESS, 13, 123881-123895. (www)

  @article{ , abstract = {Objective: To develop EndoDSR, an AI-based model for the automatic detection, segmentation, and restoration of artifacts in endoscopic images. This model aims to enhance the diagnostic accuracy of Helicobacter pylori (H. pylori), ultimately contributing to better clinical outcomes in the detection of gastric cancer. Methods and procedures: The proposed EndoDSR model makes use of the YOLOv8 architecture for artifact detection and segmentation, followed by an image restoration method, utilizing interpolation and extrapolation techniques to remove artifacts while retaining the endoscopic image features. The dataset comprises 170 endoscopic images, categorized as normal (85 images) and H. pylori positive (85 images) with classifications confirmed by biopsy reports. Results: The EndoDSR model for our endoscopic images is YOLOv8m, which has a precision of 0.92 and 0.93, a recall value of 0.89 and 0.87, a precision recall value of 0.772 and 0.749 for the boundary box and mask, respectively. These performance metrics indicate the capacity of the model to identify artifacts. Additionally, the mean Average Precision (mAP)@50 was 0.772 and the mean Average Precision (mAP)@50-95 was 0.7685, highlighting the robustness of YOLOv8m in endoscopic artifact detection. The restoration model depicted an average SSIM value of 0.879 and the Cov.PSNR is 0.078. Conclusion: The EndoDSR model tackles the important challenge in artifact removal and restoration of endoscopic images, providing a robust solution to enhance the diagnostic reliability of infection. The integration of artifact detection, segmentation, and restoration will help in real-time applications in clinical settings. Future work will focus on expanding the dataset, improving generalizability, and exploring advanced image restoration techniques to optimize gastroenterology diagnosis.}, archive = {J_ACCESS}, author = {Jovita Relasha Lewis and Sameena Pathan and Preetham Kumar and Cifha Crecil Dias}, doi = {10.1109/ACCESS.2025.3586736}, journal = {IEEE Access}, pages = {123881-123895}, shortjournal = {IEEE Access}, title = {EndoDSR: Automatic detection, segmentation, and restoration of artifacts in helicobacter pylori infection using endoscopic images}, volume = {13}, year = {2025}, }
• (2025). RAG-driven memory architectures in conversational LLMs—A literature review with insights into emerging agriculture data sharing. ACCESS, 13, 123855-123880. (www)

  @article{ , abstract = {Despite significant advances in natural language processing, conversational AI systems face persistent challenges in maintaining extensive and contextually coherent dialogues, particularly regarding long-term memory management. This literature review synthesizes current approaches to memory architectures in conversational AI, examining the transition from basic dialogue agents to more sophisticated, agentic frameworks. We analyze how vector databases and Retrieval-Augmented Generation (RAG) address fundamental challenges in storing and retrieving conversational context, maintaining system responsiveness, managing user-specific data ethically, and integrating domain-specific information. Through systematic review of papers, we identify critical limitations of vector embedding in capturing extended conversational context, particularly in agentic domains requiring semantic, episodic, procedural, and emotional memory. We evaluate how RAG frameworks can augment vector databases to handle memory-intensive tasks requiring real-time updates and domain-specific knowledge integration. Furthermore, we examine alternative architectures including knowledge graphs, finite state machines, and hybrid solutions, highlighting the data quality and ethical challenges that must be addressed for scalable, reliable AI memory management. Our analysis provides a structured framework for understanding memory evolution in conversational AI, identifies gaps in current RAG solutions, proposes hybrid memory designs, and outlines future research directions emphasizing cross-domain applications in agriculture.}, archive = {J_ACCESS}, author = {Nur Arifin Akbar and Rahool Dembani and Biagio Lenzitti and Domenico Tegolo}, doi = {10.1109/ACCESS.2025.3589241}, journal = {IEEE Access}, pages = {123855-123880}, shortjournal = {IEEE Access}, title = {RAG-driven memory architectures in conversational LLMs—A literature review with insights into emerging agriculture data sharing}, volume = {13}, year = {2025}, }
• (2025). C-coil: A high performance computing approach for magnetostatic circular coil calculations. ACCESS, 13, 123835-123854. (www)

  @article{ , abstract = {Accurate computation of magnetostatic coupling between non-coaxial circular coils remains prohibitively expensive when millions of configurations must be evaluated for design-space exploration. We propose a novel approach based on numerical methods to improve performance by 5 to 7 orders of magnitude while matching the accuracy of state-of-the-art semi-analytical methods. While other approaches strive to reduce the number of integration directions in the six-fold integral to 2 or 4, we propose a five-fold integral with simple-to-evaluate integrands. In place of the filament method, we employ the Gauss-Legendre quadrature due to its exponential convergence and find that numerical integration can be quicker than analytic integral evaluation. Furthermore, to tackle the complexity of allocating the computational resources to each of the five integration directions, we propose a heuristic that leads to 2 orders of magnitude lower computation time or 2 to 4 orders of magnitude higher accuracy. We also provide an implementation of our approach in C-Coil, an open-source C++ library with Python bindings that can also be used in MATLAB.}, archive = {J_ACCESS}, author = {Davor Dobrota and Lara Vrabac and Nikola Sočec and Filip Vučić and Dario Bojanjac}, doi = {10.1109/ACCESS.2025.3589331}, journal = {IEEE Access}, pages = {123835-123854}, shortjournal = {IEEE Access}, title = {C-coil: A high performance computing approach for magnetostatic circular coil calculations}, volume = {13}, year = {2025}, }
• (2025). Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage. ACCESS, 13, 123826-123834. (www)

  @article{ , abstract = {Application of local energy resources in the buildings encounters such systems with both uncertainties in the produced power and failures (events) of the resources. The energy planning excluding uncertainty and events may be economically efficient but it is not resilient and practical. This paper provides a platform for energy management in the building under a set of realistic failures and uncertainties. The energy resilience is improved and the energy cost is decreased at the same time while all events and uncertainties are modeled. A typical building integrated with solar-battery-grid is studied. The resilience index is to minimize the loss of demanded service following all possible events. Three objective functions are defined and compared including (i) resilience cost, (ii) energy cost, (ii) resilience and energy costs together. The stochastic mixed integer linear programming is implemented to minimize the objective functions. The results validate that the developed toolkit can cope with all events and uncertainties with minimum operating cost and maximum resilience. The optimal tradeoff between operating cost, resilience, and uncertainty is presented. The results demonstrate that the uncertainty and resilience increase the planning cost by 5.3% and 13.8%, respectively, and both of them together increase the cost by 19.5%.}, archive = {J_ACCESS}, author = {Hasan Mehrjerdi}, doi = {10.1109/ACCESS.2020.2981923}, journal = {IEEE Access}, pages = {123826-123834}, shortjournal = {IEEE Access}, title = {Resilience-uncertainty nexus in building energy management integrated with solar system and battery storage}, volume = {13}, year = {2025}, }
• (2025). TRIM: AI guided random number generation for resource-constrained IoT systems. ACCESS, 13, 123808-123825. (www)

  @article{ , abstract = {Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.}, archive = {J_ACCESS}, author = {Tasneem Suha and Md Hafizur Rahman and Ashley E. Rice and Colin Smith and Rima Asmar Awad and Prabuddha Chakraborty}, doi = {10.1109/ACCESS.2025.3586177}, journal = {IEEE Access}, pages = {123808-123825}, shortjournal = {IEEE Access}, title = {TRIM: AI guided random number generation for resource-constrained IoT systems}, volume = {13}, year = {2025}, }
• (2025). Proposal medical image protection system based on hybrid CNN-transformer model and chaotic maps. ACCESS, 13, 123793-123807. (www)

  @article{ , abstract = {Medical image encryption is a crucial area of research that focuses on protecting sensitive medical images from unauthorized access and ensuring patient confidentiality. Protecting medical images from data breaches and illegal usage is essential, given the growing use of digital imaging technology (such as magnetic resonance imaging, CT scans, X-rays, and ultrasound). In order to comply with laws such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), medical photographs must be protected since they include sensitive information about a patient’s health. This paper introduces a proposal to ensure that medical images are confidential during transmission or storage by encryption and intends to preserve their integrity for accurate diagnosis and treatment by hashing these images through constructing a feature map image. A hybrid model of CNN and transformer is suggested in this proposed system, which is used to extract a unique image’s hash of the original medical image, presenting a feature map of the image to guarantee the integrity. In the next step, the original and hashed images encrypted depending on the using of several chaotic maps, where the hash image is encrypted by making use of a logistic map, and the RGB channels of original image is encrypted separately by using a Henon map, an Arnold map, and the Lorenz system, and finally the complete encrypted image encrypts by a 9D chaotic map. This method greatly enhanced the security and complexity of the encryption process, making it very difficult for an attacker to access or recreate the original data. The combination of these chaotic maps guaranteed a higher level of secrecy and robustness in the encryption mechanism. Additionally, this approach effectively preserves time in diagnosing the medical image since the feature map is extracted on the client side. The experimental results show this approach’s ability to effectively maintain time in diagnosing the medical image since the feature map is extracted on the client side. This proposal successfully and safely protects medical images from various attacks on the client side, channels, and servers.}, archive = {J_ACCESS}, author = {Rasha S. Ali and Rajaa Kadhom Hassoun and Ayad Al-Adhami and Sanaa Ali Jabber and Soukaena H. Hashem}, doi = {10.1109/ACCESS.2025.3586562}, journal = {IEEE Access}, pages = {123793-123807}, shortjournal = {IEEE Access}, title = {Proposal medical image protection system based on hybrid CNN-transformer model and chaotic maps}, volume = {13}, year = {2025}, }
• (2025). Brain tumor identification and classification of MRI images using deep learning techniques. ACCESS, 13, 123783-123792. (www)

  @article{ , abstract = {The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the various anatomical structure of the human organ. Detection of human brain abnormal structures by basic imaging techniques is challenging. In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. The present work proposes the separation of the whole cerebral venous system into MRI imaging with the addition of a new, fully automatic algorithm based on structural, morphological, and relaxometry details. The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of hidden nodes. Such networks are used in various areas, including regression and classification. In brain MRI images, the probabilistic neural network classification system has been utilized for training and checking the accuracy of tumor detection in images. The numerical results show almost 98.51% accuracy in detecting abnormal and normal tissue from brain Magnetic Resonance images that demonstrate the efficiency of the system suggested.}, archive = {J_ACCESS}, author = {Zheshu Jia and Deyun Chen}, doi = {10.1109/ACCESS.2020.3016319}, journal = {IEEE Access}, pages = {123783-123792}, shortjournal = {IEEE Access}, title = {Brain tumor identification and classification of MRI images using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Pilot allocation in multi-cell MIMO systems based on missing data imputation. ACCESS, 13, 123764-123782. (www)

  @article{ , abstract = {We consider a pilot allocation algorithm to alleviate pilot contamination in a multi-cell massive MIMO network with missing channel information. We utilize a machine learning framework for the imputation of large-scale channel state information (CSI) features in neighboring cells. In the offline phase, a dataset with full information is created; the covariance CSI feature is estimated for each user at all base stations. The CSI features are exploited to create a multi-point channel chart (CC), where relative positions of UEs are preserved. We assume an incomplete information setup in the online phase, in which user channel covariance is known only at the serving BS. The CC locations in the online phase are predicted using a supervised machine learning framework. We consider a constrained weighted graph coloring approach to allocate the pilots. We make use of CC distances to construct similarity weights between users; nearby users have large similarities, whereas faraway users have small similarities. Numerical results show that the CC based approach outperforms the solution utilizing angle-of-arrival with full information, and nears the performance of the one based on covariance with full information. Furthermore, we consider a machine learning framework to predict the channel covariance matrices at other BSs. The performance of this scheme is slightly better than that of the CC based approach. However, its communication overhead and computational complexity is much larger, compared to the CC based scheme.}, archive = {J_ACCESS}, author = {Bushra Shaikh and Pere Garau Burguera and Hanan Al-Tous and Markku Juntti and Bilal Muhammad Khan and Olav Tirkkonen}, doi = {10.1109/ACCESS.2025.3586582}, journal = {IEEE Access}, pages = {123764-123782}, shortjournal = {IEEE Access}, title = {Pilot allocation in multi-cell MIMO systems based on missing data imputation}, volume = {13}, year = {2025}, }
• (2025). Generating pseudo-realistic infrared observations for observing system simulation experiments. ACCESS, 13, 123756-123763. (www)

  @article{ , abstract = {Observing System Simulation Experiments (OSSEs) are essential for evaluating and designing remote sensing systems using synthetic observations. A key aspect of OSSEs is tuning observation errors, typically by comparing data assimilation (DA) statistics from real and synthetic observations. However, during instrument design, only limited specifications, such as channels’ frequency and spectral response functions, are available. This paper presents a method for generating pseudo-realistic infrared observations for proposed instruments using high-resolution measurements from the Infrared Atmospheric Sounding Interferometer (IASI) as a reference. The approach involves converting IASI measurements into infrared spectra using the Fast Fourier Transform (FFT) and inverse FFT (iFFT), then deriving channel-specific radiances for new instruments by convolving sensor response functions. Validation using IASI and AIRS simulations shows a mean difference below 0.1 K and a standard deviation under 0.5 K when comparing Fourier-derived AIRS brightness temperatures with those from a radiative transfer model. The accuracy of these conversions is confirmed by reconstructing IASI brightness temperatures via FFT, yielding negligible differences. An example of how these pseudo-observations can be used in OSSE experiments is presented, and the results are discussed.}, archive = {J_ACCESS}, author = {Isaac Moradi and Will McCarty and Nikki Privé}, doi = {10.1109/ACCESS.2025.3588735}, journal = {IEEE Access}, pages = {123756-123763}, shortjournal = {IEEE Access}, title = {Generating pseudo-realistic infrared observations for observing system simulation experiments}, volume = {13}, year = {2025}, }
• (2025). Assessment of EMF exposure induced by wireless cellular phones in various usage scenarios in france. ACCESS, 13, 123743-123755. (www)

  @article{ , abstract = {With the ever-increasing demand for high-speed and low-latency wireless communications, new generation of base stations, i.e., the fifth generation (5G), has been deployed recently. However, this deployment has raised growing concerns regarding the potential public health implications associated with exposure to radio-frequency electromagnetic fields (RF-EMF). According to the type of sources, RF-EMF exposure can be categorized into uplink (device-to-base station) and downlink (base station-to-device) transmissions. In this paper, we aim to analyze uplink exposure induced by mobile devices operating on 5G and previous generations. A total of 380 measurement points are selected for evaluating three types of services, i.e., voice call, video call, file uploading, in the Greater Paris region. Data collected from those measurements cover technologies in 2G, 3G, 4G, and 5G, where 5G refers to non-standalone (NSA) in the current paper. We compare the average transmitted (TX) power, throughput, connection duration, and energy utilization efficiency for different services and frequency bands. To assess the impact of 5G, we used NEMO system from Keysight to conduct measurements with the 5G functionality enabled and disabled, respectively. The data analysis revealed that mobile phones emit higher power during data-intensive services. Additionally, mobile phones operating on 5G emit lower TX power than 4G. Furthermore, the UL exposure level also depends on the duration of emission. For a given file size to upload, the duration depends on the throughput. Consequently, we also investigated the emitted energy consumption per bit transmitted, referred to as radiated energy per bit transmitted (REBT). The comparison reveals that REBT in file uploading is 9.65 mJ/Mb for 4G and 5.1 mJ/Mb for 5G, indicating a significant decrease in radiated energy consumption with the adoption of 5G technology. This research enhances our understanding of uplink RF-EMF exposure associated with 5G and provides crucial insights for public health risk assessments.}, archive = {J_ACCESS}, author = {Jiang Liu and Yarui Zhang and Wassim Ben Chikha and Shanshan Wang and Theodoros Samaras and Ourouk Jawad and Lamine Ourak and Emmanuelle Conil and Joe Wiart}, doi = {10.1109/ACCESS.2024.3424305}, journal = {IEEE Access}, pages = {123743-123755}, shortjournal = {IEEE Access}, title = {Assessment of EMF exposure induced by wireless cellular phones in various usage scenarios in france}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on next-generation modeling and optimization for semiconductor devices. ACCESS, 13, 123724-123742. (www)

  @article{ , abstract = {The integration of physics-based modelling and artificial intelligence (AI) is transforming semiconductor device simulation, facilitating unparalleled precision, efficiency, and predictive power. Conventional semiconductor modelling is based on first-principles physics, including drift-diffusion equations, Boltzmann transport models, and quantum mechanical methods. Nonetheless, these methods frequently encounter computational constraints when tackling intricate nanoscale processes. Novel AI-driven approaches, including as deep learning, physics-informed neural networks (PINNs), and alternative modelling, provide innovative ways to address these difficulties. The article explores recent progress in the integration of AI with semiconductor device physics, highlighting hybrid methodologies that preserve physical interpretability while utilising data-driven insights. In response to these improvements, machine learning-assisted compact modelling (MLCM) has garnered considerable attention as an alternative to conventional white-box modelling. These opaque methodologies seek to deliver versatile modelling for intricate mathematical and physical events through the training of neural networks using empirical and simulated data. This facilitates the creation of a precise closed-form correlation between output attributes and input parameters associated with the fabrication process and device functionality. Primary applications encompass swift process optimisation, concise model formulation, and inverse design for advanced electronics. It addresses the benefits and constraints of AI-based modelling, emphasising prospective approaches for combining physics-driven and data-driven paradigms. This interdisciplinary synthesis aims to expedite semiconductor research and development, promoting more efficient and scalable device design methodologies.}, archive = {J_ACCESS}, author = {Pratikhya Raut and Deepak Kumar Panda and Amit Kumar Goyal}, doi = {10.1109/ACCESS.2025.3587721}, journal = {IEEE Access}, pages = {123724-123742}, shortjournal = {IEEE Access}, title = {A comprehensive review on next-generation modeling and optimization for semiconductor devices}, volume = {13}, year = {2025}, }
• (2025). JailbreakTracer: Explainable detection of jailbreaking prompts in LLMs using synthetic data generation. ACCESS, 13, 123708-123723. (www)

  @article{ , abstract = {The emergence of Large Language Models (LLMs) has revolutionized natural language processing (NLP), enabling remarkable advancements across various applications. However, these models remain susceptible to adversarial prompts, commonly referred to as jailbreaks, which exploit their vulnerabilities to bypass ethical and safety constraints. These prompts manipulate LLMs to produce harmful or forbidden outputs, posing serious ethical and security challenges. In this study, we propose JailbreakTracer, a novel framework leveraging synthetic data generation and Explainable AI (XAI) to detect and classify jailbreaking prompts. We first construct two comprehensive datasets: a Toxic Prompt Classification Dataset, combining real-world and synthetic jailbreak prompts, and a Forbidden Question Reasoning Dataset, categorizing forbidden queries into 13 distinct scenarios with clear reasoning labels. Synthetic toxic prompts are generated using a fine-tuned GPT model, achieving an attack success rate of 95.1%, effectively addressing the class imbalance. Using transformer-based architectures, we train classifiers that achieved 97.25% accuracy in detecting jailbreak prompts and 100% accuracy in categorizing forbidden questions. Our approach integrates XAI techniques, such as LIME, to ensure interpretability and transparency in the model’s predictions. Extensive evaluations demonstrate the efficacy of JailbreakTracer in detecting and reasoning about jailbreak prompts, providing a critical step toward enhancing the safety and accountability of LLMs. The dataset and code are available on GitHub: https://github.com/faiyazabdullah/JailbreakTracer}, archive = {J_ACCESS}, author = {Md. Faiyaz Abdullah Sayeedi and Maaz Bin Hossain and Md. Kamrul Hassan and Sabrina Afrin and Molla Md. Sabit Hossain and Md. Shohrab Hossain}, doi = {10.1109/ACCESS.2025.3579996}, journal = {IEEE Access}, pages = {123708-123723}, shortjournal = {IEEE Access}, title = {JailbreakTracer: Explainable detection of jailbreaking prompts in LLMs using synthetic data generation}, volume = {13}, year = {2025}, }
• (2025). Generalized PCA method and its application in uncertainty reasoning. ACCESS, 13, 123699-123707. (www)

  @article{ , abstract = {Principal Component Analysis (PCA) is an important mathematical dimension reduction method. In the process of uncertain reasoning, as the elements in the recognition framework increase, the evidence dimension increases exponentially, and the calculation amount also increases exponentially, which greatly affects the application of uncertainty theory in practical engineering. To solve this problem, the PCA algorithm is used to reduce the dimension of evidence in uncertain reasoning. However, in evidence theory, the focal elements in evidence are not completely independent, which is the essential difference between evidence theory and probability theory, and is also the advantage of evidence theory in dealing with uncertain data. Therefore, the PCA algorithm cannot be used to reduce the dimension of evidence directly. This paper proposes a generalized PCA method and gives strict mathematical proof. The traditional PCA algorithm is a special case of the generalized PCA algorithm proposed in this paper. Finally, the application of a generalized PCA algorithm in uncertainty reasoning is given, and the example results show that the generalized PCA proposed in this paper can greatly reduce the calculation amount and obtain good evidence combination effect.}, archive = {J_ACCESS}, author = {Bin Wu and Xiao Yi and Dong Ning Zhao}, doi = {10.1109/ACCESS.2019.2960261}, journal = {IEEE Access}, pages = {123699-123707}, shortjournal = {IEEE Access}, title = {Generalized PCA method and its application in uncertainty reasoning}, volume = {13}, year = {2025}, }
• (2025). Enhancing transient and steady-state performance in grid-connected VSCs: A comprehensive frequency region analysis of decoupling current units. ACCESS, 13, 123682-123698. (www)

  @article{ , abstract = {This paper introduces an innovative synthesis of inner loop control for grid-connected Voltage Source Converters (VSCs), comprising a Decoupling Current Unit (DCU) with a classic current tracking controller. The objective is to enhance both transient and steady-state performance by reducing the inherent coupling between the d- and q-axis current control loops. The proposed DCU is formulated through a generalized control design approach that can be configured according to the dynamics of practical filter types, including L, LC, and LCL structures. Decoupling effectiveness is assessed through a model-independent Relative Gain Array (RGA) analysis, while the robustness of the inner control loop under parameter uncertainties is evaluated to ensure reliable steady-state performance. Theoretical analysis confirms that the proposed DCU effectively suppresses inter-axis coupling, producing RGA profiles that closely align with ideal decoupling across different filter configurations. These findings are further validated through simulation studies under both linear and nonlinear load conditions. Compared to a classic non-decoupled control scheme, the proposed method achieves substantial improvements: approximately 55% reduction in overshoot, up to 70% suppression of cross-coupling effects following power step changes, more than 60% improvement in reactive power tracking accuracy.}, archive = {J_ACCESS}, author = {Mehdi Forouzanfar and Anna Pinnarelli and Seyyed Mohammad Sadegh Ghiasi and Daniele Menniti and Nicola Sorrentino and Giovanni Brusco and Pasquale Vizza and Hossein Safaiepour}, doi = {10.1109/ACCESS.2025.3588464}, journal = {IEEE Access}, pages = {123682-123698}, shortjournal = {IEEE Access}, title = {Enhancing transient and steady-state performance in grid-connected VSCs: A comprehensive frequency region analysis of decoupling current units}, volume = {13}, year = {2025}, }
• (2025). Lightweight and efficient protocol based on ECDH for securing smart grid communication infrastructure. ACCESS, 13, 123666-123681. (www)

  @article{ , abstract = {The smart electricity distribution network, known as Smart Grid (SG), represents an optimal and intelligent framework for electricity generation and distribution, facilitated through a bidirectional information and communication network. However, setting up such an expansive network presents considerable network security challenges. Consequently, numerous threats such as impersonation attacks, repeat attacks, and man-in-the-middle attacks can pose the network with substantial difficulties. To address security and privacy concerns in SG, researchers have proposed several security protocols intending to increase protection, but exchanging information in a public channel that does not have security conditions poses serious security risks. Despite numerous security protocols having been suggested to facilitate the authentication of network entities and the generation of session keys for ensuring secure communication, some of them have security flaws or high computational overhead that makes them unsuitable for resource-constrained components. This paper presents a lightweight protocol to address the security challenges in smart grid networks. The proposed scheme incorporates a multi-level authentication mechanism and the Elliptic Curve Diffie-Hellman (ECDH) technique to ensure the secure generation of session keys. The security of the proposed protocol has been formally verified using the AVISPA simulation tool and further validated through informal analysis against various attacks. Performance evaluation results demonstrate that the protocol is computationally efficient and well-suited to the resource-constrained environment of smart grid entities. Specifically, the session key generation process is more efficient compared to several existing approaches, thereby minimizing communication delays and enhancing the overall operational performance of the system.}, archive = {J_ACCESS}, author = {Ali Peivandizadeh and Rafe Alasem and Mostafa Farhadi Moghadam and Haitham Y. Adarbah and Behzad Molavi and Amirhossein Mohajerzadeh and Afzel Noore}, doi = {10.1109/ACCESS.2025.3585982}, journal = {IEEE Access}, pages = {123666-123681}, shortjournal = {IEEE Access}, title = {Lightweight and efficient protocol based on ECDH for securing smart grid communication infrastructure}, volume = {13}, year = {2025}, }
• (2025). 3D determination of message collection and delivery locations for UAV-enabled disaster recovery networks. ACCESS, 13, 123649-123665. (www)

  @article{ , abstract = {When a large-scale disaster happens, critical infrastructure is destroyed, and many people are displaced. Unmanned aerial vehicle (UAV)-enabled disaster recovery networks can be used to support people in disaster-hit areas. However, determining UAV routes is critical to communicate with refugees. In this work, we propose a method to determine UAV locations to collect and deliver messages for UAV-enabled disaster recovery networks. Our method involves two stages: the received signal strength sensing stage and the message collection and delivery stage. We theoretically analyzed the computational complexity in terms of the number of steps and consequently evaluated the performance of our method using experimental data obtained from rural, suburban, and urban areas.}, archive = {J_ACCESS}, author = {Azusa Danjo and Shinsuke Hara and Takahiro Matsuda and Fumie Ono and Ryu Miura}, doi = {10.1109/ACCESS.2021.3065698}, journal = {IEEE Access}, pages = {123649-123665}, shortjournal = {IEEE Access}, title = {3D determination of message collection and delivery locations for UAV-enabled disaster recovery networks}, volume = {13}, year = {2025}, }
• (2025). Multi-modal deep learning for lung cancer detection using attention-based inception-ResNet. ACCESS, 13, 123630-123648. (www)

  @article{ , abstract = {Lung cancer is one of the deadliest malignancies worldwide, demanding swift and accurate diagnosis for effective treatment. Traditional screening methods rely on manual interpretation of medical images. However, these methods are time-intensive and highly susceptible to human error. Deep learning (DL) has emerged as a powerful alternative to autonomously identify complex patterns within radiological and histopathological images. Nevertheless, existing DL models for lung cancer detection suffer from critical limitations, including reliance on single imaging modalities, insufficient datasets, suboptimal feature extraction techniques and constrained generalizability. Accordingly, we introduce a novel DL framework that blends diverse imaging modalities, including X-rays, computed tomography (CT) and histopathological images. The proposed framework employs Inception-ResNet module to extract multi-scale spatial features and refine deep feature representations through residual learning. This hybrid module combines the convolutional pathways of Inception architectures with the gradient optimization benefits of residual connections. Besides, the proposed architecture is embedded with sequential multi-scale convolutional fusion and efficient channel attention mechanisms to ameliorate feature diversity and optimize feature importance. These components aid the model to focus on highly discriminative regions within medical images. The proposed model attained an accuracy of 95.35%, 99.68%, 99.73% and 99.26% using X-ray, CT, histopathological and mega datasets, respectively. Comparative experiments unveiled that the proposed model outperformed conventional DL architectures in lung cancer detection. The proposed system, utilizing advanced attention mechanisms and multi-modal imaging capabilities, has the potential to revolutionize early lung cancer diagnosis and extend its impact to other critical diseases. This work represents a paradigm shift in medical image analysis through bridging the gap between DL and clinical applications. The proposed model is available at https://github.com/MohamedHosny90/Lung-Cancer-Detection.git}, archive = {J_ACCESS}, author = {Mohamed Hosny and Ibrahim A. Elgendy and Mousa Ahmad Albashrawi}, doi = {10.1109/ACCESS.2025.3588407}, journal = {IEEE Access}, pages = {123630-123648}, shortjournal = {IEEE Access}, title = {Multi-modal deep learning for lung cancer detection using attention-based inception-ResNet}, volume = {13}, year = {2025}, }
• (2025). Pixel-based change detection in moving-camera videos using twin convolutional features on a data-constrained scenario. ACCESS, 13, 123613-123629. (www)

  @article{ , abstract = {This work proposes the Pixel-Based Change Detection using a Moving Camera (PBCD-MC) algorithm for detecting anomalies in a cluttered industrial site. The surveillance of industrial environments may be challenging for an anomaly detection problem, as the frequent presence of pipes, cables, and heavy machinery in the scene can create occlusion, undesirable shadows, metallic reflections, etc. A moving camera, surveilling the environment based on reference videos, may also face parallax differences, tracking misalignments, and illumination changes throughout the day. To address these moving camera surveillance problems, the PBCD-MC method was developed based on a hybrid ensemble feature extractor, combining deep learning methods, responsible for generating high-level features, and tree-based algorithms, responsible for selecting and combining the deep features. In addition, in order to deal with the challenges of training with high-dimensional data, a complexity-reducing training strategy that eliminates the need for large training sets while maintaining a robust detection performance was proposed. The PBCD-MC is assessed using a newly developed pixel-level annotated dataset (VDAO-AS) and is shown to outperform state-of-the-art models, achieving overall $MCC=0.877$ and overall $DIS=0.092$ on a pixel-level detection, results that are significantly better than those of its competitors. Also, a series of ablation studies proves the importance of the adopted modules for the achieved performance. Software, datasets, results, and the pretrained weights of the proposed architecture are available at https://github.com/lgtavares/VDAO_Pixel.}, archive = {J_ACCESS}, author = {Luiz G. C. Tavares and Allan F. Da Silva and Rafael Padilla and Lucas A. Thomaz and Sergio L. Netto and Eduardo A. B. Da Silva}, doi = {10.1109/ACCESS.2025.3588450}, journal = {IEEE Access}, pages = {123613-123629}, shortjournal = {IEEE Access}, title = {Pixel-based change detection in moving-camera videos using twin convolutional features on a data-constrained scenario}, volume = {13}, year = {2025}, }
• (2025). Enhancing image quality by optimizing and fine-tuning multi-fidelity generative adversarial networks. ACCESS, 13, 123595-123612. (www)

  @article{ , abstract = {Achieving a balance between image quality and computational efficiency is one of the most challenging tasks in image enhancement. Conventional single-fidelity methods focus on structural integrity; yet, they are ineffective in improving perceptual quality, resulting in unrealistic images. The limitations identified in this paper are addressed by introducing a unique Multi-Fidelity Generative Adversarial Network (MF-GAN). The quality of the image is enhanced by the dynamic integration of low-fidelity and high-fidelity models using a composite loss function and dual-generator design. This method achieves high superior perceptual quality and structural integrity while preserving computational efficiency by gradually improving image details. The model was tested on standard natural image benchmarks, including Set5, Set14, and DIV2K, it achieved a Natural Image Quality Evaluator (NIQE) of 4.55, a Peak Signal-to-Noise Ratio (PSNR) of 30.5 dB, and a Structural Similarity Index (SSIM) of 0.92, outperforming existing methods in terms of fidelity and perceptual quality when compared to techniques like ESRGAN (Enhanced Super-Resolution GAN) and RDN (Residual Dense Network). Regarding to the computational efficiency, MF-GAN has 27.6M parameters which is competitive to other models. It achieves an inference time of 1.32 seconds with 203.7G FLOPs (Floating Point Operations). Although MF-GAN is a little faster than RDN, its computational cost is higher than models like ESRGAN and SRGAN (Super-resolution GAN). These results show that MF-GAN is a promising method for image enhancement, since it effectively balances computational efficiency and perceptual image quality.}, archive = {J_ACCESS}, author = {Noor Baha Aldin}, doi = {10.1109/ACCESS.2025.3588956}, journal = {IEEE Access}, pages = {123595-123612}, shortjournal = {IEEE Access}, title = {Enhancing image quality by optimizing and fine-tuning multi-fidelity generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). Experimental analysis on comprehensive combustion performances of vehicle-level thermal runaway. ACCESS, 13, 123586-123594. (www)

  @article{ , abstract = {As the growing population of electric vehicles, the increasing fire accidents have drawn much attention both for researcher and society. Unfortunately, the real-conditional thermal runaway is hard to recurrent according to diverse inducements. The comprehensive combustion behaviors for vehicle-level fire accident are still confusing and should be further investigated, and the results will provide guidance on further improvement of vehicle safety or further applicational situation. In this article, a vehicle-level thermal runaway experiment is carried out with a commercial SUV, and multi-information is collected to determine the combustion behavior and fire spreading path including temperature, voltage and gases. The artificial arson is selected as the inducement for simulating the real-conditional accident, and 4 periods on fire burning are divided according to combustion behaviors. An interval of 12 mins before the battery thermal runaway can be observed and provide probability for roadside assistance. Moreover, the safe distance of possible harmful hazards is less than 10 m, and should be warning when designing underground parking. Then, Limited voltage decrease of 10 mV is recognized as early-warning information before battery thermal runaway, and the large voltage jump or characteristic temperature help for diagnostic of thermal runaway. Finally, the established experimental workbench delivers the potential solution for vehicle-level thermal runaway analysis, and can be promoted to other experiments under various inducements. The article highlights the whole process of fire spreading of electric vehicle and help to improve the safety of vehicles according to the results.}, archive = {J_ACCESS}, author = {Haigang Cui and Xinan Zhou and Sida Zhou and Yu Lu and Desheng Zhang and Fei Chen and Xinhua Liu and Haiyang Zhang and Liang Zhang and Lingyun Xiao and Shichun Yang}, doi = {10.1109/ACCESS.2023.3242328}, journal = {IEEE Access}, pages = {123586-123594}, shortjournal = {IEEE Access}, title = {Experimental analysis on comprehensive combustion performances of vehicle-level thermal runaway}, volume = {13}, year = {2025}, }
• (2025). A review of knowledge distillation in object detection. ACCESS, 13, 123570-123585. (www)

  @article{ , abstract = {Target detection is a revolutionary advancement in computer vision that provides the ability to identify specific targets in images for a wide variety of applications, including but not limited to video surveillance, face recognition, and autonomous driving. Although target detection has been developed to a high level and can be deployed for applications in several fields, there are still some problems in practice, such as the two-phase detection algorithm has high detection accuracy but slow detection speed, while the one-phase detection algorithm is fast, but its accuracy is poor. We need to combine their respective advantages further for related algorithm research, and we need to reach a balance between detection speed and detection accuracy so that the algorithms can be deployed on edge devices with limited computational power. Knowledge Refinement, as one of the common means of model compression, can solve the above problems, and it reduces the deployment difficulty of the algorithms. In this paper, we summarize the use of knowledge compression on target detection. We conclude the methods mentioned in the paper from an objective and unbiased perspective and suggest possible improvement directions, and we provide an outlook on the future trend of combining distillation learning and target detection. This paper provides a clear overview of the field of target detection and provides an idea of future trends.}, archive = {J_ACCESS}, author = {Shengjie Cheng and Qiuxia Zhao and Xinyun Zhang and Nurbiya Yadikar and Kurban Ubul}, doi = {10.1109/ACCESS.2023.3288692}, journal = {IEEE Access}, pages = {123570-123585}, shortjournal = {IEEE Access}, title = {A review of knowledge distillation in object detection}, volume = {13}, year = {2025}, }
• (2025). Channel estimation and pilot design for uplink sparse code multiple access system based on complex-valued sparse autoencoder. ACCESS, 13, 123559-123569. (www)

  @article{ , abstract = {Channel estimation is one of the most important aspects of wireless communication. Especially, in sparse code multiple access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time–frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things (IoT) when time–frequency resources are restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in an SCMA system based on complex-valued sparse autoencoder which is effective to learn features of the wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In this paper, the encoder part is used to realize the pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of the linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates’ performance of the SCMA receiver in this paper is very close to that of the perfect channel state information (CSI).}, archive = {J_ACCESS}, author = {Quan Yuan and Dezhi Li and Zhenyong Wang and Chang Liu and Ci He}, doi = {10.1109/ACCESS.2019.2904990}, journal = {IEEE Access}, pages = {123559-123569}, shortjournal = {IEEE Access}, title = {Channel estimation and pilot design for uplink sparse code multiple access system based on complex-valued sparse autoencoder}, volume = {13}, year = {2025}, }
• (2025). Ensemble strategy with multi-step hard sample mining for improved UXO localisation and classification. ACCESS, 13, 123546-123558. (www)

  @article{ , abstract = {Ensembling of multiple models to achieve better overall performance than individual ones is an approach that has yielded promising results in recent years in multiple tasks. In this article, a novel strategy based on the iterative fine-tuning on hard-to-detect instances is presented. This is implemented specifically by oversampling instances from the training dataset resulted as false negative predictions of the previous models, to obtain expert models for different types and difficulties of the involved proprietary real-world UneXploded Ordnances (UXO) dataset. Along with the studying and integration of robust voting schemes for outputs distribution, this opens new opportunities for the localisation and classification of UXO. The purpose of this approach is to reduce false negative cases, which could lead to dangerous situations for Explosive Ordnance Disposal personnel as well as for the civilian population. Starting from the new small State-of-The-Art Real-Time Detection Transformer (RT-DETR) detection architecture for each expert model with 20 Milion parameters, the proposed ensemble strategy includes a methodology which concludes with improved results in reducing associated risks of misclassification, with an mAP of 52.4% and a Recall of 86.2% for the ensemble, surpassing the baseline model by 4.5% mAP, respectively by 3.9% Recall. Compared to a reference model with more parameters (76 million), the ensemble model shows a difference of 0.1% in mAP and 11.4% higher Recall than the reference, while having 21% fewer parameters. To enhance our overall work, the models designed to save human lives should be accessible and easy to implement in terms of computational costs which are also discussed in terms of frames per second and number of parameters, resulted from deploying the models and the ensembles in an Nvidia T4-based online environment.}, archive = {J_ACCESS}, author = {Marian Craioveanu and Grigore Stamatescu and Dan Popescu}, doi = {10.1109/ACCESS.2025.3588184}, journal = {IEEE Access}, pages = {123546-123558}, shortjournal = {IEEE Access}, title = {Ensemble strategy with multi-step hard sample mining for improved UXO localisation and classification}, volume = {13}, year = {2025}, }
• (2025). A benchmark review of YOLO algorithm developments for object detection. ACCESS, 13, 123515-123545. (www)

  @article{ , abstract = {You Only Look Once (YOLO) has established itself as a prominent object detection framework due to its excellent balance between speed and accuracy. This article provides a thorough review of the YOLO series, from YOLOv1 to YOLOv10, including YOLOX, emphasizing their architectural advancements, loss function improvements, and performance enhancements. We have benchmarked the officially released versions from YOLOv3 to YOLOv10 and YOLOX, using widely recognized datasets VOC07+12 and COCO2017, on diverse hardware platforms: NVIDIA GTX Titan X, RTX 3060, and Tesla V100. The benchmark provides significant insights, such as YOLOv9-E achieving the highest mean average precision (mAP) of 76.0% on VOC07+12 and also showing superior detection accuracy on COCO2017 with an mAP of 56.6% which is 1.2% higher than that of the latest YOLOv10-X. YOLOv9-E stands out for its superior detection accuracy making it more suitable for detection that needs high accuracy such as analysis of medical images, while some lightweight versions like YOLOv5-S, YOLOv7-S, YOLOv8-S, and YOLOv10-S offer the great balance of accuracy and speed, making them ideal for real-time applications. Among them, YOLOv7-S has the highest mAP value among these lightweight models. Inference benchmarks highlight lightweight YOLO models such as YOLOv10-S for their exceptional inference speed on all GPUs and results of training time also indicate YOLOv9-E would take the longest time to converge among all versions using both datasets. This study would provide researchers and developers with some strategies in choosing appropriate YOLO models based on accuracy, resource availability, and application-specific needs.}, archive = {J_ACCESS}, author = {Zhengmao Hua and Kaviya Aranganadin and Cheng-Cheng Yeh and Xinhe Hai and Chen-Yun Huang and Tsan-Chuen Leung and Hua-Yi Hsu and Yung-Chiang Lan and Ming-Chieh Lin}, doi = {10.1109/ACCESS.2025.3586673}, journal = {IEEE Access}, pages = {123515-123545}, shortjournal = {IEEE Access}, title = {A benchmark review of YOLO algorithm developments for object detection}, volume = {13}, year = {2025}, }
• (2025). A pareto front transformation model for multi- objective-based constrained optimization. ACCESS, 13, 123473-123486. (www)

  @article{ , abstract = {One of the most promising approaches of handling constrained optimization problems (COPs) is to adopt multi-objective methods, which can provide a trade-off between the objective and constraints. However, the multi-objective-based constraint-handling techniques take preference over infeasible solutions, some promising feasible solutions cannot survive during the course of search because they are dominated ones. Furthermore, some nondominated infeasible solutions with worse objective values should not be reserved in that they are far from the feasible optimal solution. To address these two problems, this paper proposes a Pareto front transformation model which transforms a part of potential feasible solutions into nondominated ones. Meanwhile, combined with the dynamic multi-objective technique, certain nondominated infeasible solutions with worse objective values will be rejected. In this way, the search can towards the global optimum from both the feasible and infeasible sides of the search space. The proposed Pareto front transformation model is integrated into a multi-objective-based constrained evolutionary algorithm (CEA). The new designed algorithm is named PT-MOEA, and it is compared with seven peer multi-objective-based CEAs and five state-of-the-art CEAs on solving IEEE CEC 2006 and IEEE CEC 2010 test suites, respectively. Experimental results demonstrate the competitiveness of the proposed method in comparison with its competitors for solving COPs.}, archive = {J_ACCESS}, author = {Sanyou Zeng and Rui Zhang and Ruwang Jiao and Qinghui Xu}, doi = {10.1109/ACCESS.2020.2976047}, journal = {IEEE Access}, pages = {123473-123486}, shortjournal = {IEEE Access}, title = {A pareto front transformation model for multi- objective-based constrained optimization}, volume = {13}, year = {2025}, }
• (2025). A reinforcement learning-based task mapping method to improve the reliability of clustered manycores. ACCESS, 13, 123460-123472. (www)

  @article{ , abstract = {The increasing scale of manycore systems poses significant challenges in managing reliability while meeting performance demands. Simultaneously, these systems become more susceptible to different aging mechanisms such as negative-bias temperature instability (NBTI), hot carrier injection (HCI), and thermal cycling (TC), as well as the electromigration (EM) phenomenon. In this paper, we propose a reinforcement learning (RL)-based task mapping method to improve the reliability of manycore systems considering the aforementioned aging mechanisms, which consists of three steps including bin packing, task-to-bin mapping, and task-to-core mapping. In the initial step, a density-based spatial application with noise (DBSCAN) clustering method is employed to compose some clusters (bins) based on the cores’ temperature. Then, the Q-learning algorithm is used for the two latter steps, to map the arrived task on a core such that the minimum thermal variation is occurred among all the bins. Compared to the state-of-the-art works, the proposed method is performed during runtime without requiring any parameter to be calculated offline. The effectiveness of the proposed technique is evaluated on 16, 32, and 64 cores systems using SPLASH2 and PARSEC benchmark suite applications. The results demonstrate up to 27% increase in the mean time to failure (MTTF) compared to the state-of-the-art task mapping techniques.}, archive = {J_ACCESS}, author = {Fatemeh Hossein-Khani and Omid Akbari}, doi = {10.1109/ACCESS.2025.3585768}, journal = {IEEE Access}, pages = {123460-123472}, shortjournal = {IEEE Access}, title = {A reinforcement learning-based task mapping method to improve the reliability of clustered manycores}, volume = {13}, year = {2025}, }
• (2025). Comparative study of disturbance observer-based control methods on an industrial gimbal system. ACCESS, 13, 123440-123459. (www)

  @article{ , abstract = {Disturbance-observer-based control (DOBC) methods are developed to approximate the closed-loop behavior of dynamic systems to the nominal system response determined during the design phase. In the literature, many different approaches have been derived for similar purposes. This paper presents a comprehensive experimental evaluation of notable DOBC structures, conducted in both a simulation environment with a comparative analysis and in real-time experiments. Unlike previous studies, which predominantly focus on theoretical analysis or simulation-based assessments, this study provides a systematic experimental comparison of DOBC methods implemented on a gimbal platform, offering insights into their practical performance. For this purpose, the mathematical representations of the DOBC methods have been rearranged in an application-oriented framework and employed for high-precision control of a gimbal platform. A dedicated simulation environment has been established to enable a proper comparison of the proposed DOBC methods in terms of applicability and performance evaluations, considering well-defined and measurable metrics within the experimental setup. The control system designs based on the methods evaluated in this paper were carried out in line with the original studies as originally suggested. The results of the investigation are interpreted using both time- and frequency-domain metrics, highlighting the practical implications of DOBC methods in real-world applications.}, archive = {J_ACCESS}, author = {Mücahid Rıdvan Kaplan and Burak Kürkçü and Mehmet Önder Efe and Zekı Yağiz Bayraktaroğlu}, doi = {10.1109/ACCESS.2025.3588595}, journal = {IEEE Access}, pages = {123440-123459}, shortjournal = {IEEE Access}, title = {Comparative study of disturbance observer-based control methods on an industrial gimbal system}, volume = {13}, year = {2025}, }
• (2025). A secure key-aggregate keyword retrieval scheme over encrypted data in cloud computing. ACCESS, 13, 123429-123439. (www)

  @article{ , abstract = {Key-aggregate keyword retrieval primitive enables a cloud server on behalf of delegated users to securely perform a keyword search over the data encrypted with various public keys. However, existing key-aggregate searchable encryption schemes are insecure against keyword guessing attacks, which result in the privacy leakage of keyword ciphertext and trapdoor. In this paper, we for the first time formulate a secure and efficient key-aggregate searchable encryption for cloud-assisted IoT applications, which not only enables a data owner to securely share the selected documents to multiple users, but also supports data users in delegating the capability of keyword search to a cloud server for searching the desired documents without leaking any privacy of keyword ciphertext and trapdoor. Then, the security of the proposed scheme is formally defined and proven to be secure against the indistinguishable selective-file chosen keyword attacks. The flexibility and practicability of the formulated scheme is also demonstrated by theoretical evaluations and experimental simulations.}, archive = {J_ACCESS}, author = {Chunwei Lou and Mingsheng Cao and Yaohua Luo and Hua Xu and Yuan Gao and Weiwei Wang and Dong Wang}, doi = {10.1109/ACCESS.2020.2980886}, journal = {IEEE Access}, pages = {123429-123439}, shortjournal = {IEEE Access}, title = {A secure key-aggregate keyword retrieval scheme over encrypted data in cloud computing}, volume = {13}, year = {2025}, }
• (2025). A novel framework for solar irradiance prediction integrating signal decomposition with hybrid time-series models. ACCESS, 13, 123414-123428. (www)

  @article{ , abstract = {This study aims to improve the accuracy of solar irradiance forecasting by integrating signal decomposition techniques with deep learning and statistical models. Three decomposition methods—EMD, EEMD, and CEEMDAN—were combined with models such as LSTM, Bi-LSTM, GRU, Transformer, and statistical models such as ARIMA, ARCH, and GARCH were also implemented. Hybrid model architecture was developed to process high and low frequency signals separately in this research, respectively. Among all configurations, the EEMD–Transformer–GRU model achieved the best performance, with a MAPE of 7.87%, reducing prediction error by approximately 57% compared to non-decomposed models. These findings demonstrate that the integration of signal decomposition and hybrid deep learning significantly improves forecasting accuracy and offers a promising approach for renewable energy applications.}, archive = {J_ACCESS}, author = {Keng-Hsi Lin and Po-Yen Hsu and Po-Han Chen and Mu-Yen Chen}, doi = {10.1109/ACCESS.2025.3580328}, journal = {IEEE Access}, pages = {123414-123428}, shortjournal = {IEEE Access}, title = {A novel framework for solar irradiance prediction integrating signal decomposition with hybrid time-series models}, volume = {13}, year = {2025}, }
• (2025). Observer-based distributed adaptive consensus tracking of nonlinear multi-agent systems on directed graphs. ACCESS, 13, 123403-123413. (www)

  @article{ , abstract = {This paper studies the leader-following consensus problem of nonlinear multi-agent systems (MAS) with one-sided Lipschitz conditions under the directed graph, and a novel observer-based distributed tracking protocol that only utilizes the relative output information of adjacent agents is proposed. To avoid the disadvantage of solving Laplace matrix eigenvalues, a fully distributed adaptive consensus scheme is constructed. The sufficient conditions for linear matrix inequalities (LMIs) for the consensus of MAS systems are derived using the Lyapunov function and the properties of one-sided Lipschitz nonlinear systems. Finally, the effectiveness of the proposed control method can be verified by two examples-based simulations with a leader and five followers.}, archive = {J_ACCESS}, author = {Bao Chen and Xuemei Qi and Chen Li and Xuelei Qi and Hongjun Ma}, doi = {10.1109/ACCESS.2022.3229775}, journal = {IEEE Access}, pages = {123403-123413}, shortjournal = {IEEE Access}, title = {Observer-based distributed adaptive consensus tracking of nonlinear multi-agent systems on directed graphs}, volume = {13}, year = {2025}, }
• (2025). ConstrainTree: Enhancing decision trees with semantic constraint validation. ACCESS, 13, 123379-123402. (www)

  @article{ , abstract = {A growing number of knowledge graphs are being released by diverse sources. The massive volume of linked data aims to provide entities with semantic context. The entities can be validated in relation to their context using the Shapes Constraint Language (SHACL). On the other hand, the increasing usage of Artificial Intelligence (AI) models in productive systems entails a tremendous deal of responsibility in a variety of domains. Predictive models such as linear, logistic regression, and tree-based models are still widely employed because they have a basic structure that allows for explanation. Explanation of models, however, requires determining if the model generated predictions based on human constraints or scientific facts. This work proposes using the semantic context of entities in knowledge graphs to assess prediction models against user-defined constraints, thus providing a theoretical framework for a model-agnostic validation engine based on SHACL. In a subsequent phase, the model validation results are summarized and model-coherently visualized in the case of a decision tree. The performance of the proposed framework is studied in an empirical evaluation suggesting its efficiency.}, archive = {J_ACCESS}, author = {Philipp D. Rohde and Maria-Esther Vidal}, doi = {10.1109/ACCESS.2025.3587477}, journal = {IEEE Access}, pages = {123379-123402}, shortjournal = {IEEE Access}, title = {ConstrainTree: Enhancing decision trees with semantic constraint validation}, volume = {13}, year = {2025}, }
• (2025). Occupancy-assisted surround-view images 3D object detection. ACCESS, 13, 123369-123378. (www)

  @article{ , abstract = {In the modern field of surround-view 3D detection, there has been a significant increase in interest. Existing methods are mainly focused on constructing dense Bird’s Eye View(BEV) features or utilizing sparse queries for detection. Additionally, there is a popular occupancy task aimed at predicting the semantic occupancy of 3D voxel space. In this paper, I innovatively improve the common 3D detection pipeline by leveraging the 3D positional and semantic information extracted from the semantic occupancy task. Specifically, I first generate a voxel feature and perform occupancy prediction. I use the results of occupancy prediction, which include the coordinates, categories, and features of the occupied voxels, to generate a series of occupied queries enriched with semantic and positional information. Then, I compress the voxel feature in height to obtain BEV features and use the coordinates of the occupied voxels to determine the occupied BEV queries, updating them through the aggregation of image features. Additionally, when performing temporal modeling, I only interact with the occupied BEV features. With the help of the occupancy task, I efficiently improve the existing 3D detection pipeline and achieve state-of-the-art results on the nuScenes test set, with an mAP of 61.4% and NDS of 52.6%, gaining 1.8% mAP and 1.5% NDS compared to MV2D. Code will be available.}, archive = {J_ACCESS}, author = {Jian Sun}, doi = {10.1109/ACCESS.2024.3428968}, journal = {IEEE Access}, pages = {123369-123378}, shortjournal = {IEEE Access}, title = {Occupancy-assisted surround-view images 3D object detection}, volume = {13}, year = {2025}, }
• (2025). Scrutinizing news media cooperation in facebook and twitter. ACCESS, 13, 123355-123368. (www)

  @article{ , abstract = {In recent times, news media avail oneself of online social media platforms for news promotion, sharing, and commentary to a large extent mainly in Twitter, Facebook, and Reddit. Therefore, in this paper, researchers have been used machine learning and text mining techniques to attain useful insights from the news media data in social media in-order to understand the factors for gaining large audience attention. Different to the previous studies, analyses of the news media in this paper are based on a set of new features: content features such as the originality of a news item, context features such as time and circadian patterns of a news media, and reader reactions. Our dataset includes 238 K tweets and 128 K Facebook posts of 48 most popular news media shared during May–June 2017. In this paper, we explored news producers, news consumers, inter news production patterns, inter news dissemination behaviors, sharing similar news items within Twitter and Facebook (cross-posts), and news readers reactions on news items. In addition, we investigated the best time period to receive the highest readers’ attention toward their news items as this information is useful for other news media to understand the best time duration to publish news items. Finally, we proposed a predictive model to increase news media popularity among readers and the results manifested that news media should disperse its own content and need to publish at first before other news media publish the same content in social media in order to be popular and attract the attention of readers.}, archive = {J_ACCESS}, author = {Praboda Rajapaksha and Reza Farahbakhsh and Noël Crespi}, doi = {10.1109/ACCESS.2019.2902491}, journal = {IEEE Access}, pages = {123355-123368}, shortjournal = {IEEE Access}, title = {Scrutinizing news media cooperation in facebook and twitter}, volume = {13}, year = {2025}, }
• (2025). An efficient uncertainty-driven learning for stochastic super-resolution. ACCESS, 13, 123344-123354. (www)

  @article{ , abstract = {Single image super-resolution (SISR) is inherently ill-posed because of information loss during degradation, where a single low-resolution (LR) image corresponds to multiple plausible high-resolution (HR) reconstructions. While recent stochastic SISR approaches address this by modeling conditional distributions, they commonly assume isotropic Gaussian priors, overlooking spatial variations in uncertainty across the image. In this paper, we propose a novel uncertainty-driven framework for stochastic SISR that incorporates anisotropic Gaussian priors modulated by estimated uncertainty maps. Our model explicitly learns the spatially varying aleatoric uncertainty from LR inputs and uses it to guide the sampling of latent noise, which is then injected into a decoder to reconstruct residuals over a deterministic SISR baseline. This approach not only captures the one-to-many nature of the SISR task but also preserves strong pixel-level fidelity by centering the generation around a pre-trained deterministic prediction. Extensive experiments show that our method achieves state-of-the-art performance in terms of both distortion and perceptual quality, especially outperforming the best generative-model-based methods by +0.44 in PSNR and -0.008 in LPIPS simultaneously with lower computational cost.}, archive = {J_ACCESS}, author = {Daeyoung Han and Seongmin Hwang and Hoyeon Ahn and Moongu Jeon}, doi = {10.1109/ACCESS.2025.3587789}, journal = {IEEE Access}, pages = {123344-123354}, shortjournal = {IEEE Access}, title = {An efficient uncertainty-driven learning for stochastic super-resolution}, volume = {13}, year = {2025}, }
• (2025). Identification of typical time-series house consumption profiles based on unsupervised learning techniques. ACCESS, 13, 123326-123343. (www)

  @article{ , abstract = {Energy consumption profiles are a critical component of energy management strategies. With the recent widespread adoption of smart meters, there is a pressing need to develop robust methodologies for obtaining, characterizing, and visualizing these profiles. This paper presents a comparative analysis of various clustering methods for deriving consumer electricity profiles, aiming to identify the most suitable techniques for big time-series data and to evaluate how the granularity of the data influences the final profiles. Four approaches were considered for obtaining house electricity consumption profiles: K-means, Principal Component Analysis (PCA) combined with K-means, Self-Organizing Maps (SOM), and SOM combined with K-means. Additionally, an iterative study with the scores silhouette coefficient and Davies-Bouldin index validated the subjective indications from the elbow method. The results demonstrate that hybrid approaches offer superior performance compared to conventional clustering models, highlighting the effectiveness of combining PCA and SOM in improving model generalization and supporting more targeted energy management strategies. SOM combined with K-means provides the best clustering quality across all tested scenarios, making it the preferred method when computational resources are not a limiting factor. On the other hand, PCA combined with K-means highlights a good balance between meaningful profiles and the capacity to handle large datasets effectively.}, archive = {J_ACCESS}, author = {Marcelo Forte and Cindy P. Guzman and Lucas Pereira and Hugo Morais}, doi = {10.1109/ACCESS.2025.3585204}, journal = {IEEE Access}, pages = {123326-123343}, shortjournal = {IEEE Access}, title = {Identification of typical time-series house consumption profiles based on unsupervised learning techniques}, volume = {13}, year = {2025}, }
• (2025). Retraction notice: Visistant: A conversational chatbot for natural language to visualizations with gemini large language models. ACCESS, 13, 123325. (www)

  @article{ , abstract = {Presents corrections to the paper, (Visistant: A Conversational Chatbot for Natural Language to Visualizations With Gemini Large Language Models).}, archive = {J_ACCESS}, author = {G. K. Santhosh Ram and V. Muthumanikandan}, doi = {10.1109/ACCESS.2025.3588243}, journal = {IEEE Access}, pages = {123325}, shortjournal = {IEEE Access}, title = {Retraction notice: Visistant: A conversational chatbot for natural language to visualizations with gemini large language models}, volume = {13}, year = {2025}, }
• (2025). A survey of automotive radar misalignment detection techniques. ACCESS, 13, 123314-123324. (www)

  @article{ , abstract = {The integration of reliable and accurate sensors is critical to the performance and safety of autonomous vehicles in the automotive industry. Sensors like LiDAR, cameras, and radar are essential to ensure proper vehicle functioning and perception capabilities. Failure or misalignment of any of these sensors degrades system performance and poses significant safety risks. In particular, radar is suitable for applications such as adaptive cruise control, collision avoidance, and lane change assistance systems and plays a crucial role in autonomous vehicle systems. Misalignments in radar, vertical or horizontal, can lead to severe performance degradation in these applications. This survey paper comprehensively analyzes radar misalignment issues, reviewing detection techniques that address various types of offset. Through a comparative evaluation of existing methods, this work aims to identify current gaps and provide valuable information for researchers to improve the reliability and accuracy of radar systems in autonomous vehicles.}, archive = {J_ACCESS}, author = {Danish Sharif and Sheriff Murtala and Gyu Sang Choi}, doi = {10.1109/ACCESS.2025.3584454}, journal = {IEEE Access}, pages = {123314-123324}, shortjournal = {IEEE Access}, title = {A survey of automotive radar misalignment detection techniques}, volume = {13}, year = {2025}, }
• (2025). FDAE: Lightweight privacy protection based on face detection and image encryption. ACCESS, 13, 123297-123313. (www)

  @article{ , abstract = {The increasing prevalence of facial privacy leakage in digital images has become a growing concern. To address this issue, we propose Face Detection and Encryption (FDAE), a lightweight privacy protection framework. FDAE integrates enhanced face detection with targeted encryption, offering an efficient alternative to full-image encryption. Specifically, we developed a YOLOv7-based face detection algorithm, further enhanced with Explicit Visual Center (EVC) and Spatial Context Pyramid (SCP) modules, to accurately localize facial regions. This approach achieves detection accuracies of 96.4% (Easy), 94.8% (Medium), and 90.3% (Hard) categories, representing improvements of 1.3%, 1.4%, and 4.8% over the baseline YOLOv7 model, respectively. Subsequently, a lightweight encryption method, which combines Logistic and PWLCM chaotic systems with DNA encoding, is applied to the detected facial regions. To further improve efficiency, we encrypt only the upper four bit-planes of the detected face regions after bit-plane decomposition. Experimental results demonstrate that, using a 44.42M-parameter lightweight model, the FDAE algorithm achieves significantly higher efficiency in facial region encryption compared to full-image encryption, with encryption and decryption speeds improved by 7.64 to 9 times. Moreover, the decrypted images exhibit negligible differences from the original ones, making FDAE well-suited for batch image processing tasks and practical privacy protection applications.}, archive = {J_ACCESS}, author = {Li You and Qianqian Liu and Shuai Li and Tianyu Yang}, doi = {10.1109/ACCESS.2025.3588006}, journal = {IEEE Access}, pages = {123297-123313}, shortjournal = {IEEE Access}, title = {FDAE: Lightweight privacy protection based on face detection and image encryption}, volume = {13}, year = {2025}, }
• (2025). Bandpass threshold models for activation and influence propagation in social networks. ACCESS, 13, 123282-123296. (www)

  @article{ , abstract = {We consider the models of activation/influence propagation in social networks based on the concept of “bandpass” thresholds: a node will “activate” if at least a certain minimum fraction of and no more than a certain maximum fraction of its neighbors are active. These interval-based activation processes are conceptually similar to a “bandpass filter” in signal processing, implying that activation occurs only for a signal in a certain range between a minimum and a maximum frequency values. The existence of such phenomena in social networks was originally proposed in the seminal work on threshold models by Granovetter (1978); however, these models have not yet been extensively studied from mathematical modeling and computational perspectives. In this study, we investigate two baseline versions of these models by conducting computational experiments on multiple publicly available social networks (with various sizes, edge densities, and clustering coefficients), as well as on very large (city-wide and country-wide) online social media networks constructed based on anonymized data collected from a well-known social media portal VK.com in December 2016. We also compare the results to the ones for classical linear threshold (“single-threshold”) models with the same lower threshold values. We observe that the processes of bandpass threshold activation/influence propagation can exhibit drastically different behaviors depending on initial choices of seed nodes, underlying network characteristics, and lower/upper threshold values.}, archive = {J_ACCESS}, author = {Alexander Semenov and Alexander Veremyev and Eduardo L. Pasiliao and Vladimir Boginski}, doi = {10.1109/ACCESS.2025.3587282}, journal = {IEEE Access}, pages = {123282-123296}, shortjournal = {IEEE Access}, title = {Bandpass threshold models for activation and influence propagation in social networks}, volume = {13}, year = {2025}, }
• (2025). Wildfire front monitoring with multiple UAVs using deep Q-learning. ACCESS, 13, 123269-123281. (www)

  @article{ , abstract = {Wildfires destroy thousands of hectares every summer all over the globe. To provide an effective response and to mitigate wildfires impact, firefighters require a real-time monitoring of the fire front. This article proposes a cooperative reinforcement learning (RL) framework that allows a team of autonomous unmanned aerial vehicles (UAVs) to learn how to monitor a fire front. In the literature, independent Q-learners were proposed to solve a wildfire monitoring task with two UAVs. Here we propose a framework that can be easily extended to a larger number of UAVs. Our framework builds on two methods: multiple single trained Q-learning agents (MSTA) and value decomposition networks (VDN). MSTA trains a single UAV controller, which is then “copied” to each of the UAVs in the team. In contrast, VDN trains agents to learn how to cooperate. We benchmarked in simulations our two considered methods – MSTA and VDN – against two state-of-the-art approaches: independent Q-learners and a joint Q-learner. Simulation results show that our considered methods outperform state-of-the-art approaches in a wildfire front monitoring task with up to 9 fixed-wing and multi-copter UAVs.}, archive = {J_ACCESS}, author = {Alberto Viseras and Michael Meissner and Juan Marchal}, doi = {10.1109/ACCESS.2021.3055651}, journal = {IEEE Access}, pages = {123269-123281}, shortjournal = {IEEE Access}, title = {Wildfire front monitoring with multiple UAVs using deep Q-learning}, volume = {13}, year = {2025}, }
• (2025). Compact liquid crystal polymer-based tri-band flexible antenna for WLAN/WiMAX/5G applications. ACCESS, 13, 123259-123268. (www)

  @article{ , abstract = {In this paper, a new compact coplanar waveguide (CPW)-fed liquid crystal polymer (LCP) based tri-band antenna is presented and fabricated for Wireless Local Area Network (WLAN), Worldwide Interoperability for Microwave Access (WiMAX) and 5th-Generation (5G) systems. The antenna combines two strips with the main radiation rectangular patch and coplanar waveguide ground. The proposed antenna is printed on a LCP substrate with a thickness of 0.1 mm and has a small overall dimensions of 20mm $\times 32$ mm $\times 0.1$ mm. To analyze the characteristics, the antenna is designed and fabricated, and then the performance of the antenna is measured and tested. The measurement results show that the proposed antenna has three operating bandwidths, including 2.38 – 2.79 GHz, 3.27 – 4.05 GHz, and 4.80 – 8.44 GHz. To test the flexibility of the antenna, the antenna is simulated and measured in bent configurations for radii of 10 mm and 50 mm. In addition, the antenna is attached to different parts of the human body to test the integration effect in wearable equipment. The experimental results show that the performance of the antenna remains reliable under bending conditions and that the specific absorption ratio (SAR) value meets the European Union (EU) standard. The proposed antenna shows reasonable gains and good radiation characteristics in the operating bands. The flexible, compact, simple design and multiband performance indicates that the antenna is suitable for integration in flexible electronic devices.}, archive = {J_ACCESS}, author = {Chengzhu Du and Xiaodi Li and Shunshi Zhong}, doi = {10.1109/ACCESS.2019.2941212}, journal = {IEEE Access}, pages = {123259-123268}, shortjournal = {IEEE Access}, title = {Compact liquid crystal polymer-based tri-band flexible antenna for WLAN/WiMAX/5G applications}, volume = {13}, year = {2025}, }
• (2025). A review of distributed ledger technologies for satellite operations. ACCESS, 13, 123230-123258. (www)

  @article{ , abstract = {As the density of objects in Low Earth Orbit continues to increase, greater levels of coordination among satellites are needed to ensure the continued operation and sustainability of the space environment. Existing centralised space governance frameworks suffer from delays, ambiguity, and vulnerability to manipulation, introducing further complexity and risk into satellite operations, and hindering the development of new space-based services. This review examines the feasibility of leveraging Distributed Ledger Technologies to facilitate secure, decentralised, and autonomous coordination among satellites, conceptualised as an extension of Internet of Things networks. By examining analogous applications in swarms of Uncrewed Aerial Systems, and the Internet of Things, key system requirements for decentralised satellite coordination are defined. It is found that traditional Blockchain systems using Proof of Work-based consensus mechanisms are impractical for use in space-based systems, whilst Directed Acyclic Graph architectures, Sharded Blockchain architectures, and non-deterministic Proof of Stake consensus mechanisms are the most suitable. Critical gaps preventing the implementation of Distributed Ledger Technologies in the space environment are identified. These include wider regulatory considerations, and technical obstacles relating to the storage of transactional data and reaching consensus in time-varying, asynchronous networks.}, archive = {J_ACCESS}, author = {Beth Probert and Ruaridh A. Clark and Erik Blasch and Malcolm Macdonald}, doi = {10.1109/ACCESS.2025.3588688}, journal = {IEEE Access}, pages = {123230-123258}, shortjournal = {IEEE Access}, title = {A review of distributed ledger technologies for satellite operations}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient computation offloading and resource allocation in SWIPT-based MEC networks. ACCESS, 13, 123218-123229. (www)

  @article{ , abstract = {Recent years, 5G networks have become an important role in accelerating the development of social intelligence. But it also increases energy consumption and data flow. In order to guarantee the experience of network users, an adaptive SWIPT-based MEC network is proposed. The network consists of multiple user equipment (UE) and multiple mobile edge computing (MEC) servers. The MEC server can make up for the shortcomings of the UE’s insufficient computing capability, and Simultaneous Wireless Information and Power Transfer (SWIPT) can send energy to the UE without pollution to make up for the shortcomings of limited battery energy. Added, we increase the utilization of sub-channels, improve the adaptability of the SWIPT-based MEC network to the environment, lengthen the battery life, and optimize the UE’s energy efficiency of the network. We also propose a three-part alternative optimization algorithm framework based on the categories of optimization variables. The first part combines the Alternating Direction Multiplier Method (ADMM) and Dinkelbach’s algorithm to optimize continuous variables. And for directly optimize the integer variables of the other two parts, without converting them to continuous variables. The second part adjusts the offloading decision by comparing the energy consumption of the two computation modes, and the third part proposes the integer Bat algorithm to assign sub-channel. The simulation results show that the energy efficiency of the binary offloading algorithm proposed in this article is 11% higher than the approximate algorithm based on the binary offloading KKT algorithm, and the relationship between various preset parameters and energy efficiency in the network is discussed.}, archive = {J_ACCESS}, author = {E. Xuefei and Zhonggui Ma and Kaihang Yu}, doi = {10.1109/ACCESS.2020.3047690}, journal = {IEEE Access}, pages = {123218-123229}, shortjournal = {IEEE Access}, title = {Energy-efficient computation offloading and resource allocation in SWIPT-based MEC networks}, volume = {13}, year = {2025}, }
• (2025). Robust multi-input multi-output analysis for crop row segmentation and furrow line detection in diverse agricultural fields. ACCESS, 13, 123199-123217. (www)

  @article{ , abstract = {Agricultural robots have transformed traditional crop cultivation and harvesting by addressing labor shortages and enhancing precision and quality through artificial intelligence. Precise detection of furrow centerlines is critical for the seamless navigation of these robots. Previous methods often focused on specific furrow types, leading to issues with generalization and adaptability. This paper introduces a comprehensive deep-learning model designed to detect furrow centerlines across diverse types of furrows, thereby improving accuracy and robustness. Our algorithm utilizes RGB and depth images, processed through a dual encoder that combines their features. These features are refined through a channel-limiting network and then enhanced by Deep Multi-scale Feature Fusion (DFF), which maintains feature correlations across different scales. Finally, two interlinked decoders re-utilize the multiscale features to compute segmentations and lines. Our model significantly outperforms state-of-the-art methods, achieving a minimal lateral distance deviation of just 7.8 pixels, well within an acceptable range for agricultural robotics and achieves a detection line ratio $(mLR)$ of 71.13%. Additionally, under a multi-task learning setup, our approach yields over a 10% improvement in mIOU for the furrow segmentation task. Our results demonstrate that the proposed model is robust and adaptable to various environments and conditions, ensuring reliable furrow navigation for agricultural robots.}, archive = {J_ACCESS}, author = {Muhammad Ibrahim Zain Ul Abideen and Dewa Made Sri Arsa and Talha Ilyas and Hyunggi Jo and Sang Cheol Kim and Hyongsuk Kim}, doi = {10.1109/ACCESS.2025.3584191}, journal = {IEEE Access}, pages = {123199-123217}, shortjournal = {IEEE Access}, title = {Robust multi-input multi-output analysis for crop row segmentation and furrow line detection in diverse agricultural fields}, volume = {13}, year = {2025}, }
• (2025). A novel discrete time series representation with de bruijn graphs for enhanced forecasting using TimesNet. ACCESS, 13, 123182-123198. (www)

  @article{ , abstract = {In this paper, we present a novel method for advancing time series forecasting by representing discretized time series data through de Bruijn Graphs (dBGs). This method harnesses the capability of dBGs to encapsulate and project future states from historical sequences, thus enhancing predictive analytics in time series. Our approach is multi-faceted, involving: 1) encoding time series data as a dBG; 2) the application of graph representation learning, specifically struct2vec, to distill salient features from dBG constructed from time series and 3) the seamless integration of these extracted features into the state of the art TimesNet model to bolster short-term forecasting accuracy. Empirical evaluations conducted on the M4 datasets illustrate that our approach not only maintains the intrinsic dynamics of the time series but also achieves notable improvements in forecasting performance across diverse datasets. All the code developed for this study can be found at: https://github.com/KurbanIntelligenceLab/dBGTime-Series-Library}, archive = {J_ACCESS}, author = {Mert Onur Cakiroglu and Hasan Kurban and Elham Buxton and Mehmet Dalkilic}, doi = {10.1109/ACCESS.2025.3588507}, journal = {IEEE Access}, pages = {123182-123198}, shortjournal = {IEEE Access}, title = {A novel discrete time series representation with de bruijn graphs for enhanced forecasting using TimesNet}, volume = {13}, year = {2025}, }
• (2025). Efficient data storage in DNA sequences aided by p-gram huffman coding. ACCESS, 13, 123169-123181. (www)

  @article{ , abstract = {Data storage and retrieval using DNA sequences have been extensively studied in computer and information sciences because of the increasing demand for archiving large amounts of data over long periods of time. This study introduces an efficient approach to DNA data encoding that takes advantage of p-gram Huffman coding, a highly effective technique for lossless data compression. The proposed method combines data compression and encoding, while inherently guaranteeing no-homopolymer and GC-content constraints, both of which are crucial for biologically synthesizing DNA, to ensure the longevity and durability of the generated sequences. This property is achieved using Huffman trees. The proposed method achieves a high efficiency of 2.72 bits per nucleotide when encoding text data. However, it can be extended to any type of data characterized by compressibility. In addition, we perform an error analysis of the method, considering substitution and deletion errors, which result in two more robust variants.}, archive = {J_ACCESS}, author = {Kun Tu and Dariusz Puchala}, doi = {10.1109/ACCESS.2025.3589079}, journal = {IEEE Access}, pages = {123169-123181}, shortjournal = {IEEE Access}, title = {Efficient data storage in DNA sequences aided by p-gram huffman coding}, volume = {13}, year = {2025}, }
• (2025). QoEMultiSDN: Management of multimedia services using MPTCP/SR in softwarized and virtualized networks. ACCESS, 13, 123151-123168. (www)

  @article{ , abstract = {5G networks are set to empower new user experiences for multimedia streaming services by offering high data rates and low latency. The rapid growth of multimedia services, such as video streaming services over future softwarized 5G networks, poses great challenges for both service providers and network operators regarding service provisioning with high Quality of Experience (QoE). Network evolution towards Software Defined Networking (SDN) and Network Function Virtualization (NFV) provides new opportunities to develop innovative ways to address the challenges. In this article, we present a novel QoE-aware management scheme based on SDN/NFV by utilizing Multi-Path-TCP/Segment-Routing (MPTCP/SR) techniques and exploring information from both the network and client. The new scheme, entitled QoEMultiSDN, provides an optimized end-to-end QoE for multimedia service delivery. QoEMultiSDN employs two novel strategies to achieve this: (a) QoE-based multisource routing and QoE optimization, and (b) multipath protection and dynamic link-failure-free mechanisms. We implemented the proposed scheme over SDN through Dynamic Adaptive Streaming over HTTP (DASH) experiments using Mininet, POX and OpenDaylight controllers, and compared its performance with MPTCP and regular TCP. Experimental results indicate that the QoEMultiSDN outperforms others in terms of system throughput, failure recovery time and the QoE of the end-user.}, archive = {J_ACCESS}, author = {Alcardo Alex Barakabitze and Is-Haka Mkwawa and Andrew Hines and Lingfen Sun and Emmanuel Ifeachor}, doi = {10.1109/ACCESS.2020.3039953}, journal = {IEEE Access}, pages = {123151-123168}, shortjournal = {IEEE Access}, title = {QoEMultiSDN: Management of multimedia services using MPTCP/SR in softwarized and virtualized networks}, volume = {13}, year = {2025}, }
• (2025). Operational readiness for large scale distributed applications: A comprehensive review. ACCESS, 13, 123136-123150. (www)

  @article{ , abstract = {The complexity, scale, and performance requirements of distributed applications continue to increase in the current era. This makes the launch of applications challenging, while keeping consistently high operational costs for enterprises. This necessitates a robust Operational Readiness (OR) strategy that will serve as guidance for enterprises to ensure successful application launches. This study provides a framework for achieving operational excellence, categorizing both frontend and backend systems. This paper also outlines how Artificial Intelligence (AI) has contributed to enhancing the Operational Readiness of highly scalable applications. This study provides actionable insights for software engineers and leaders to launch reliable, highly available, high-performance, and scalable applications in complex cloud environments. Furthermore, the study also addresses the requirements for security and privacy compliance that are crucial for maintaining customer trust, safeguarding sensitive data, and ensuring regulatory adherence. This paper’s recommendations aim to guide future research on improving operations of cloud applications, contributing to the advancement of operations and keeping the post-launch operations cost-effective.}, archive = {J_ACCESS}, author = {Apeksha Jain and Anujkumarsinh Donvir and Sai Sandeep Ogety and Srinivas Sandiri}, doi = {10.1109/ACCESS.2025.3586196}, journal = {IEEE Access}, pages = {123136-123150}, shortjournal = {IEEE Access}, title = {Operational readiness for large scale distributed applications: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Innamark: A whitespace replacement information-hiding method. ACCESS, 13, 123120-123135. (www)

  @article{ , abstract = {Large language models (LLMs) have gained significant popularity in recent years. Differentiating between a text written by a human and one generated by an LLM has become almost impossible. Information-hiding techniques such as digital watermarking or steganography can help by embedding information inside text in a form that is unlikely to be noticed. However, existing techniques, such as linguistic-based or format-based methods, change the semantics or cannot be applied to pure, unformatted text. In this paper, we introduce a novel method for information hiding called Innamark, which can conceal any byte-encoded sequence within a sufficiently long cover text. This method is implemented as a multi-platform library using the Kotlin programming language, which is accompanied by a command-line tool and a web interface. By substituting conventional whitespace characters with visually similar Unicode whitespace characters, our proposed scheme preserves the semantics of the cover text without changing the number of characters. Furthermore, we propose a specified structure for secret messages that enables configurable compression, encryption, hashing, and error correction. An experimental benchmark comparison on a dataset of 1 000 000 Wikipedia articles compares ten algorithms. The results demonstrate the robustness of our proposed Innamark method in various applications and the imperceptibility of its watermarks to humans. We discuss the limits to the embedding capacity and robustness of the algorithm and how these could be addressed in future work.}, archive = {J_ACCESS}, author = {Malte Hellmeier and Hendrik Norkowski and Ernst-Christoph Schrewe and Haydar Qarawlus and Falk Howar}, doi = {10.1109/ACCESS.2025.3583591}, journal = {IEEE Access}, pages = {123120-123135}, shortjournal = {IEEE Access}, title = {Innamark: A whitespace replacement information-hiding method}, volume = {13}, year = {2025}, }
• (2025). A zero-voltage-transition h5-type transformerless photovoltaic grid-connected inverter. ACCESS, 13, 123110-123119. (www)

  @article{ , abstract = {Efficiency and leakage current are two major issues for transformerless grid-connected inverters (TLI). Soft-switching technology can reduce switching losses, and is a good choice for high-frequency TLIs. Herein, a zero-voltage-transition H5 type (ZVT-H5) inverter with soft turn-on and turn-off transitions for high-frequency main switches is derived from basic resonant tanks in this paper. Meanwhile, the reverse recovery problem of freewheeling diodes is alleviated under the operation of the auxiliary resonant network. As a result, the conversion efficiency of ZVT-H5 can be significantly improved comparing with hard-switching H5 (HS-H5) inverter. Moreover, a diode clamping branch is employed to achieve constant common-mode characteristic for the ZVT-H5. The construction process of the proposed topology is described, and the operation principle of the auxiliary resonant network is analyzed. In addition, the resonant parameter design of ZVT-H5 and its circuit performance are discussed in detail. Finally, the experimental results of a 3-kW prototype at 100 kHz switching frequency are provided to verify the effectiveness of the ZVT-H5.}, archive = {J_ACCESS}, author = {Jicheng Fang and Mingming Shi and Huafeng Xiao and Ruibin Wang}, doi = {10.1109/ACCESS.2019.2946976}, journal = {IEEE Access}, pages = {123110-123119}, shortjournal = {IEEE Access}, title = {A zero-voltage-transition h5-type transformerless photovoltaic grid-connected inverter}, volume = {13}, year = {2025}, }
• (2025). Leveraging machine learning regression algorithms to predict mechanical properties of evaporitic rocks from their physical attributes. ACCESS, 13, 123097-123109. (www)

  @article{ , abstract = {Evaluating the geotechnical properties of evaporitic rocks is crucial for infrastructure stability; however, traditional methods are costly and labour-intensive. In this study, machine learning (ML) regression algorithms were applied to predict four key mechanical parameters, namely, uniaxial compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), and Schmidt hardness value (SHV), based on the physical attributes of evaporitic rocks. A comprehensive laboratory analysis of 149 block samples from Abu Dhabi was performed to measure their physical (density, porosity, unit weight, water content, specific gravity, and void ratio) and mechanical properties. Nine ML models were trained (80:20 data split) and validated using R-squared (R2), mean absolute error (MAE), and root-mean square error (RMSE). Nonlinear correlations demonstrated strong relationships between the mechanical properties and physical attributes, such as saturated density (s) and natural unit weight (n). After feature selection, random forest and XGBoost outperformed the other models with exceptional accuracy for UCS prediction (R ${}^{2} =0.95$ ) and robust performance for ITS (R ${}^{2} =0.84$ ) and SHV (R ${}^{2} =0.77$ ). This study advances previous research by simultaneously predicting multiple mechanical properties, offering an efficient alternative to expensive laboratory tests. The study findings highlight the potential of ML for improving geotechnical workflows by enabling rapid data-driven examinations of rock durability and stability.}, archive = {J_ACCESS}, author = {Ayham Zaitouny and Hasan Arman and Anusuya Krishnan and Alaa Ahmed and Ahmed Gad}, doi = {10.1109/ACCESS.2025.3588377}, journal = {IEEE Access}, pages = {123097-123109}, shortjournal = {IEEE Access}, title = {Leveraging machine learning regression algorithms to predict mechanical properties of evaporitic rocks from their physical attributes}, volume = {13}, year = {2025}, }
• (2025). DS-HPPO deep reinforcement learning for optimal DVFS control on an image signal processor. ACCESS, 13, 123083-123096. (www)

  @article{ , abstract = {In this paper, we introduce the optimal DVFS control, formulated as a sequential decision-making problem, which aims to maximize the overall energy efficiency of processing the specific image task on an ISP chip. We apply the Parameterized Action Markov Decision Process (PAMDP) to model the above optimization problem and solve the PAMDP with the proposed DS-HPPO deep reinforcement learning algorithm. We design and implement a verification system for the DS-HPPO-based optimal DVFS control policy by leveraging a self-designed ISP chip. Compared with the energy efficiency under the default operating condition of the ISP chip, the proposed DS-HPPO-based optimal DVFS control policy achieves a 33.988% improvement in energy efficiency. Furthermore, when accounting for the impact of manufacturing process variations on performance, power consumption, and chip area, the experimental result clearly demonstrates our work’s effectiveness and superiority over state-of-the-art works.}, archive = {J_ACCESS}, author = {Yang Du and Wenhong Li and Xiaoyang Zeng}, doi = {10.1109/ACCESS.2025.3577344}, journal = {IEEE Access}, pages = {123083-123096}, shortjournal = {IEEE Access}, title = {DS-HPPO deep reinforcement learning for optimal DVFS control on an image signal processor}, volume = {13}, year = {2025}, }
• (2025). Joint tomek links (JTL): An innovative approach to noise reduction for enhanced classification performance. ACCESS, 13, 123059-123082. (www)

  @article{ , abstract = {Noisy data is a prevalent issue in data mining, significantly impacting the performance of classification algorithms. Mathematical methods are crucial in tackling this obstacle, particularly in optimizing noise detection and data preprocessing. This study proposes a novel approach—Joint Tomek Links (JTL)— to identify and eliminate noisy instances by detecting pairs of nearest neighbors from different classes. It first finds the Tomek links and then refines a probabilistic method to determine which instance from a pair will be removed. In our approach, a random tree classifier serves as the base model. We conducted experiments on 40 benchmark datasets spanning various domains, achieving an average classification accuracy of 83.26% for JTL. The results demonstrate that the JTL attains an average improvement of 5.33% in accuracy compared to the original classification with a random tree. Furthermore, JTL surpasses existing techniques, delivering a noteworthy gain in accuracy by 12.30% on the same datasets. These findings underscore the effectiveness of JTL in enhancing data quality and boosting classification performance in data mining tasks.}, archive = {J_ACCESS}, author = {Goksu Tuysuzoglu and Yunus Dogan and Elife Ozturk Kiyak and Mustafa Ersahin and Bita Ghasemkhani and Kokten Ulas Birant and Derya Birant}, doi = {10.1109/ACCESS.2025.3580290}, journal = {IEEE Access}, pages = {123059-123082}, shortjournal = {IEEE Access}, title = {Joint tomek links (JTL): An innovative approach to noise reduction for enhanced classification performance}, volume = {13}, year = {2025}, }
• (2025). Cooperator and defector-based dynamic community detection in social networks. ACCESS, 13, 123041-123058. (www)

  @article{ , abstract = {Dynamic community detection in social networks requires advanced methods to capture the intricate patterns of user interactions. Traditional approaches often fail to account for the evolving nature of social relationships, leading to incomplete community representations. This work addresses this gap by distinguishing between two key interaction types: cooperators, who foster collaboration, and defectors, who disrupt collective action. This behavioral classification provides deeper insights into community formation and stability. By integrating interaction-based data, the model enhances the accuracy of dynamic community detection, revealing hidden patterns and relationships beyond structural analysis. The model was rigorously evaluated on two real-world datasets, demonstrating superior performance over state-of-the-art baseline methods across multiple metrics. Comparative analysis confirms its enhanced ability to detect meaningful community structures within dynamic networks. This interaction-focused framework offers significant improvements in understanding social dynamics, capturing evolving relationships, and accurately modeling human cooperation within social networks.}, archive = {J_ACCESS}, author = {Hui Jiang and Chenlin Zhao and Zhexi Guo and Tangyu Wang}, doi = {10.1109/ACCESS.2025.3581924}, journal = {IEEE Access}, pages = {123041-123058}, shortjournal = {IEEE Access}, title = {Cooperator and defector-based dynamic community detection in social networks}, volume = {13}, year = {2025}, }
• (2025). Dual-attention-based enhanced unified net for precise GTV segmentation of nasopharyngeal carcinoma in 3D MR images. ACCESS, 13, 123031-123040. (www)

  @article{ , abstract = {Accurate gross tumor volume (GTV) segmentation is essential for effective radiotherapy in nasopharyngeal carcinoma (NPC). However, challenges arise due to the nasopharyngeal region’s complex anatomy and the annotated data scarcity. Our study presents a dual-attention-based enhanced unified network (DAEU-Net) designed for precise NPC GTV segmentation utilizing 3D T1, T2, and T1C-weighted MR images. Our approach involves splitting large-scale MR data into multiple patches and then training every patch independently. This approach effectively captures localized and detailed information without downscaling the image resolution. The DAEU-Net integrates channel-attention and pixel-attention modules within the encoder section, eliminating background noise and reducing information loss by enhancing the network’s focus on detailed features. The decoder section incorporates bottleneck residual blocks to enhance the computing efficiency and robustness of the network. The proposed methodology surpasses the state-of-the-art models with a respective average symmetric surface distance (ASSD) of $0.920\pm 0.386$ mm, $0.987\pm 0.421$ mm, and $1.043\pm 0.457$ mm and a dice similarity coefficient (DSC) of 0.896, 0.871, and 0.851, respectively. Multi-viewed animated MR images in three orthogonal dimensions (axial, sagittal, and coronal) with predicted NPC tumors and real GTV masks were shown to assist in comprehending the tumor’s precise location. Our approach can significantly improves NPC tumor delineation, aids in automated tumor lesion segmentation, and reduces the annotation workload for oncologists.}, archive = {J_ACCESS}, author = {Hassan Ali Khan and Gong Xueqing and Muhammad Shoib Amin and Zeeshan Bin Siddique and Muhammad Ahtsam Naeem}, doi = {10.1109/ACCESS.2025.3580600}, journal = {IEEE Access}, pages = {123031-123040}, shortjournal = {IEEE Access}, title = {Dual-attention-based enhanced unified net for precise GTV segmentation of nasopharyngeal carcinoma in 3D MR images}, volume = {13}, year = {2025}, }
• (2025). A federated learning model for detecting cyberattacks in internet of medical things networks. ACCESS, 13, 123018-123030. (www)

  @article{ , abstract = {The Internet of Medical Things (IoMT) has significantly enhanced the healthcare sector by enabling advanced connectivity between smart medical devices, improving patient monitoring, and optimizing care quality. However, this increasing connectivity exposes these systems to cyberattacks that can compromise data confidentiality, disrupt device operations and endanger patient safety. Attacks on the IoMT, such as intrusions, ransomware, data tampering and denial-of-service, exploit vulnerabilities in medical devices, which often lack adequate security measures. Addressing these challenges requires robust attack detection solutions tailored to the constraints of IoMT environments. In this paper, we propose a novel approach based on Federated Learning (FL), enabling medical devices to collaboratively detect cyberattacks without directly sharing sensitive data. Unlike traditional FL systems that rely on computationally intensive neural networks, our approach leverages XGBoost—a lightweight yet powerful algorithm—to train detection models locally on devices. The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. We evaluate our approach using three IoMT datasets: CICIoMT2024, ECU-IoHT, and WUSTL-EHMS. Experimental results demonstrate that, in addition to minimizing the risk of medical data leakage, our method achieves superior detection accuracy compared to centralized approaches.}, archive = {J_ACCESS}, author = {Abdallah Ghourabi and Adel Alkhalil}, doi = {10.1109/ACCESS.2025.3588808}, journal = {IEEE Access}, pages = {123018-123030}, shortjournal = {IEEE Access}, title = {A federated learning model for detecting cyberattacks in internet of medical things networks}, volume = {13}, year = {2025}, }
• (2025). Integrating digital food nudges and recommender systems: Current status and future directions. ACCESS, 13, 123002-123017. (www)

  @article{ , abstract = {Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.}, archive = {J_ACCESS}, author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner}, doi = {10.1109/ACCESS.2025.3588663}, journal = {IEEE Access}, pages = {123002-123017}, shortjournal = {IEEE Access}, title = {Integrating digital food nudges and recommender systems: Current status and future directions}, volume = {13}, year = {2025}, }
• (2025). Lightweight pyramid cross-attention network for no-service rail surface defect detection. ACCESS, 13, 122989-123001. (www)

  @article{ , abstract = {Vision-based rail defect detection plays a crucial role in ensuring the safety and efficiency of railway transportation systems. However, many existing methods face challenges such as high parameters, complex computation, slow inspection speed, and low accuracy. To tackle these challenges, this paper introduces the Lightweight Pyramid Cross-Attention Network (LPCANet) for rail defect detection using RGB and depth images. Initially, a Lightweight Pyramid Module (LPM) is designed to extract multi-scale feature maps from depth images, while a backbone is used to capture pyramid feature details from RGB images. Subsequently, the cross-attention module (CAM) is applied across the two types of feature maps. Secondly, a structural feature extractor (SFE) is required to improve detection performance. Finally, a pixel shuffle operation is applied to restore the size of maps to align with the size of the ground truth labels (GT). Experimental results show that the LPCANet surpasses the performance of 18 state-of-the-art methods, with parameters of 9.90 M and a running speed of 162.60 frames per second (FPS).}, archive = {J_ACCESS}, author = {Sixu Guo and Jiyou Fei and Liying Wang and Hua Li and Xiaodong Liu}, doi = {10.1109/ACCESS.2025.3587348}, journal = {IEEE Access}, pages = {122989-123001}, shortjournal = {IEEE Access}, title = {Lightweight pyramid cross-attention network for no-service rail surface defect detection}, volume = {13}, year = {2025}, }
• (2025). Compact ku-band monopulse comparator using offset two-step rectangular-prism-based magic-T. ACCESS, 13, 122982-122988. (www)

  @article{ , abstract = {This paper presents a compact Ku-band monopulse comparator with enhanced inter-port balance and isolation. The key component, a magic-T, was designed using an offset two-step rectangular prism and an iris to improve impedance matching and isolation while maintaining ease of mechanical fabrication. The magic-T, fabricated as a two-split block, achieved a return loss >15.0 dB over 13.9–15.6 GHz, with insertion loss <0.3 dB, amplitude/phase imbalance <0.05 dB/0.5°, and isolation >45 dB. A monopulse comparator was developed using four magic-Ts, connected symmetrically to maintain balance among the output ports. To reduce reflections in the H-plane bends necessary for constructing the comparator, chamfered bends were employed. The monopulse comparator, fabricated as a six-split block, achieved a return loss >13.0 dB across 14.1–16.1 GHz at all ports, insertion loss from the input to each output port (one sum and two difference ports) was less than 0.5 dB. The amplitude and phase imbalances were less than 0.1 dB and 1.7°, respectively, with the isolation >50 dB. These results demonstrate superior performance compared to previously reported monopulse comparators.}, archive = {J_ACCESS}, author = {Jongmin Lee and Changil Choi and Sungmin Noh and Sangpil Lee and Jinho Jeong}, doi = {10.1109/ACCESS.2025.3586509}, journal = {IEEE Access}, pages = {122982-122988}, shortjournal = {IEEE Access}, title = {Compact ku-band monopulse comparator using offset two-step rectangular-prism-based magic-T}, volume = {13}, year = {2025}, }
• (2025). Dual-mode method for generating adversarial examples to attack deep neural networks. ACCESS, 13, 122972-122981. (www)

  @article{ , abstract = {Deep neural networks yield desirable performance in text, image, and speech classification. However, these networks are vulnerable to adversarial examples. An adversarial example is a sample generated by inserting a small amount of noise into an original sample (with minimal distortion) such that it is recognized incorrectly by the targeted model. A typical method of attack using adversarial examples must satisfy two conditions: the distortion of the original sample must be kept to a minimum and misrecognition must be induced in the targeted deep neural network. Therefore, considerable time and numerous iterations are required to generate an adversarial example because both the conditions must be satisfied during the generation process. However, there are cases in which it may be desirable to generate an adversarial example that acts quickly to induce misrecognition in the deep neural network without considering the amount of distortion applied to the original sample. In this paper, we propose a dual-mode method for creating adversarial examples that allows the user to prioritize the malfunctioning of deep neural networks according to the situation. The proposed method generates an adversarial example using one of two modes: mode 1, which takes the level of distortion into account, and mode 0, which does not consider distortion and can generate examples rapidly. To evaluate the method experimentally, MNIST and CIFAR10 were used as the datasets. The results show that the proposed method can generate a targeted or untargeted adversarial example for MNIST with 50% fewer iterations using mode 0 than using mode 1. For CIFAR10, the reduction in the number of iterations that can be achieved using mode 0 is 80% and 88% for targeted and untargeted adversarial examples, respectively, and the attack success rate is 100%.}, archive = {J_ACCESS}, author = {Hyun Kwon and Sunghwan Kim}, doi = {10.1109/ACCESS.2023.3245632}, journal = {IEEE Access}, pages = {122972-122981}, shortjournal = {IEEE Access}, title = {Dual-mode method for generating adversarial examples to attack deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Leveraging SPI-DMA for PROFINET isochronous IRT cyclic communication. ACCESS, 13, 122960-122971. (www)

  @article{ , abstract = {PROFINET IRT (Isochronous Real-Time) is a specialized communication protocol used in industrial automation to ensure high-speed, deterministic data transmission with minimal jitter. The ISO (Isochronous) mode within PROFINET IRT provides precise synchronization of data transmission cycles across communication components, ensuring optimized control and reliable timing, particularly in high-speed production environments and motion control applications. Typically, such systems integrate a PROFINET ASIC (Application-Specific Integrated Circuit), such as the ERTEC (Enhanced Real-Time Ethernet Controller), with a secondary MCU (Microcontroller Unit) to manage complex tasks while maintaining deterministic communication with minimal jitter, often within $1~\mu $ s. However, conventional communication interfaces like PCIe (Peripheral Component Interconnect Express) and XHIF (External Host Interface) introduce additional hardware and software complexity. This study presents a novel approach utilizing SPI (Serial Peripheral Interface) communication, enhanced with DMA (Direct Memory Access) hardware, for high-determinism applications within a PROFINET ISO IRT network. SPI, a synchronous communication protocol widely used in embedded systems, facilitates efficient data exchange between MCUs and peripheral devices. The proposed method demonstrates the performance of SPI-based communication between an ERTEC ASIC and a secondary MCU (STM32F401), applied in a real-world communication processor operating over PROFINET ISO IRT. This approach not only reduces hardware and software complexity but also enables the use of cost-effective MCUs as host controllers alongside PROFINET ASICs, thereby enhancing the scalability and affordability of industrial automation solutions.}, archive = {J_ACCESS}, author = {Oğuz Çiloğlu and İhsan Gökhun Önel and Ömer Korçak}, doi = {10.1109/ACCESS.2025.3585667}, journal = {IEEE Access}, pages = {122960-122971}, shortjournal = {IEEE Access}, title = {Leveraging SPI-DMA for PROFINET isochronous IRT cyclic communication}, volume = {13}, year = {2025}, }
• (2025). Technological evolvement in AAC modalities to foster communications of verbally challenged ASD children: A systematic review. ACCESS, 13, 122943-122959. (www)

  @article{ , abstract = {Augmentative and Alternative Communication (AAC) emerged as a combination of methods or strategies that constitute any device, such as Speech Generating Device (SGD), Program (mobile applications), Procedure (PECS, Picture Exchange Communication System), which enhances individual’s communication ability. Autism Spectrum Disorder (ASD) is a spectrum of comprehensive neurodevelopment disorder that leads to speech impairments, repetitive behavior, and social communication difficulties; therefore, it is imperative to underscore that at the core of all impediments are communication impairment. This article represents a systematic review of research initiatives that investigate multi-modal AAC strategies and functionality, features of mobile applications to reinforce communication and communal skills in verbally challenged ASD children because other researches are focused only on low or high-tech AAC or interventions to provide insights on ASD children respond to a particular approach. Following the PRISMA method, a total of 60 (January 2015 to October 2020) research articles were reviewed, indexed by Springer, Science Direct, Scopus, ACM, IEEE databases, and published in the AAC journal. The selected research articles are categorized into different themes where most of them focused on interactive mobile applications to improve emotional, social, learning, and overall communication skills in verbally challenged ASD children. This systematic review provides an outline of the paradigm shift in AAC modalities from PECS to Artificial Intelligence (AI), Machine Learning (ML), and Augmented Reality (AR) based applications. It opens up underline future opportunities to integrate intelligent analytics features in mobile applications to strengthen communication skills in verbally undermined ASD children.}, archive = {J_ACCESS}, author = {Walia Farzana and Farhana Sarker and Tom Chau and Khondaker A. Mamun}, doi = {10.1109/ACCESS.2021.3055195}, journal = {IEEE Access}, pages = {122943-122959}, shortjournal = {IEEE Access}, title = {Technological evolvement in AAC modalities to foster communications of verbally challenged ASD children: A systematic review}, volume = {13}, year = {2025}, }
• (2025). ADF-SL: An adaptive and fair scheme for smart learning task distribution. ACCESS, 13, 122928-122942. (www)

  @article{ , abstract = {Split Learning (SL) is an emerging decentralized paradigm that enables numerous participants, to train a deep neural network without disclosing sensitive information, such as patient data, in fields such as healthcare. In healthcare, SL enables distributed training across a variety of medical devices, hospitals, and organizations, improving model robustness while maintaining patient confidentiality. However, training models within SL is affected by data heterogeneity and sensitivity, and often requires more computational resources than an individual data provider can afford. This can result in significant model divergence and decreased performance due to differences in data distributions between various clients. To address this issue, we propose a framework that integrates fairness and adaptivity considerations, called ADF-SL. In particular, ADF-SL dynamically adjusts the total number of clients involved in model training and the number of iteration required to achieve convergence without compromising participant privacy. To evaluate performance, we compare the effectiveness of ADF-SL with that of the naive (Vanilla) SL approach, SplitFed and FairFed. Extensive experiments performed on time series electrocardiogram (ECG) databases (MITDB, SVDB, and INCARTDB) indicate that ADF-SL significantly outperforms the three existing algorithms that served as baselines. Compared to these baseline methods, ADF-SL accelerates model training on clients by up to 22.7%, 10.4%, and 5.8% compared to Vanilla SL, SplitFed, and FairFed, respectively, while maintaining model convergence and accuracy. Furthermore, the conducted ablation study has confirmed the importance of ADF-SL decay enrichment, which has outperformed non-decay ADF-SL for each used dataset by up to 15.8%, 43.9%, and 7.6%, respectively.}, archive = {J_ACCESS}, author = {Ahmed A. Al-Saedi and Veselka Boeva}, doi = {10.1109/ACCESS.2025.3586544}, journal = {IEEE Access}, pages = {122928-122942}, shortjournal = {IEEE Access}, title = {ADF-SL: An adaptive and fair scheme for smart learning task distribution}, volume = {13}, year = {2025}, }
• (2025). MulMatch-CL: A semi-supervised teacher-student framework for robust crop segmentation in UAV imagery. ACCESS, 13, 122914-122927. (www)

  @article{ , abstract = {Accurate crop segmentation using unmanned aerial vehicle (UAV) imagery is essential for efficient crop monitoring and management. While Transformer-based architectures have demonstrated exceptional performance in segmentation tasks, their application to UAV imagery remains challenging owing to limited labeled data and noisy annotations. To address these challenges, this study proposes MulMatch-CL, a novel teacher-student architecture that integrates the semi-supervised MulMatch framework with Confident Learning (CL). The proposed MulMatch component employs consistency regularization and multiple strong augmentation streams to effectively utilize unlabeled data and enhance model generalization. A teacher model is first trained on both labeled and unlabeled data to generate pixel-wise probability distributions. Confident Learning then identifies and filters noisy labels, refining the labeled dataset. The cleaned dataset, combined with the unlabeled data, is used to train a student model, resulting in a more robust segmentation performance. Experimental results on the Barley Remote Sensing Dataset show that MulMatch-CL achieves 78.42% mIoU, 89.10% pixel Acc, and 87.58% F1 score, outperforming supervised baselines, robust learning strategies, and semi-supervised methods. Ablation studies further confirm that both Confident Learning and MulMatch independently enhance performance, improving mIoU by 2.36% and 4.28% respectively, while their integration yields a 6.08% improvement over the baseline. These results demonstrate that MulMatch-CL provides a robust solution for applying Transformer models to UAV-based crop segmentation.}, archive = {J_ACCESS}, author = {Xiaoyu Xu and Dafang Zou and Jinding Zou and Shouhui Xia and Weiguo Sheng}, doi = {10.1109/ACCESS.2025.3586498}, journal = {IEEE Access}, pages = {122914-122927}, shortjournal = {IEEE Access}, title = {MulMatch-CL: A semi-supervised teacher-student framework for robust crop segmentation in UAV imagery}, volume = {13}, year = {2025}, }
• (2025). A user-friendly wearable telerehabilitation system based on neuromotor training for mild post-stroke patients: The DoMoMEA system. ACCESS, 13, 122900-122913. (www)

  @article{ , abstract = {Home-based telerehabilitation technologies can support the long-term rehabilitation needs of post-stroke patients. This work describes an Android-based wearable system for telerehabilitation (DoMoMEA), which engages such patients in a full-body customized neuromotor rehabilitation protocol. DoMoMEA features seven inertial units and five force sensors, which capture patient’s body posture, movements, and interactions with a non-immersive virtual reality environment, providing real time scores, motivational feedback, and online automatic corrections for performance errors. Clinically relevant parameters can be collected and sent to a remote server where they can be retrieved by a physiotherapist through a dedicated web application, according to a store-and-forward telemedicine approach. Clinicians can also personalize the training settings to ensure a high level of engagement and challenge for patients, based on their residual motor abilities, needs and progress. Preliminary tests performed on 11 healthy elderly volunteers revealed a high usability of the system ( $82~\pm ~6$ points in the System Usability Scale questionnaire). Furthermore, the system was presented to experienced physiatrists and physiotherapists, who provided their assessment by a semi-structured interview. The evidence underscored the usefulness of DoMoMEA not only at home, but also in daily clinical practice.}, archive = {J_ACCESS}, author = {A. Zedda and M. Caruso and S. Bertuletti and G. Baldazzi and G. Sedda and A. Spanu and D. Riboni and A. Pibiri and M. Monticone and A. Cereatti and D. Pani}, doi = {10.1109/ACCESS.2025.3587614}, journal = {IEEE Access}, pages = {122900-122913}, shortjournal = {IEEE Access}, title = {A user-friendly wearable telerehabilitation system based on neuromotor training for mild post-stroke patients: The DoMoMEA system}, volume = {13}, year = {2025}, }
• (2025). Graph neural network-based attribute auxiliary structured grouping for person re-identification. ACCESS, 13, 122888-122899. (www)

  @article{ , abstract = {Recently, person re-identification (re-ID) with weakly labeled or unlabeled data has drawn much attention in open-set and cross-domain re-ID systems especially for the attribute auxiliary weakly supervised person re-ID. Although state-of-the-art clustering-based methods have achieved good performance, the pseudo labels generated through clustering are often low-quality and noisy. To address this problem, we propose a graph neural network based Attribute Auxiliary structured Grouping ( $ \text {A}^{2}$ G) to improve the confidence of the pseudo labels. Different from the existing clustering-based approaches that only utilize the similarity in feature space, we learn the feature representation from the similarities in both attribute space and feature space by graph learning on the pedestrian attribute graph. Specifically, we first utilize the pair-wise attribute similarity to represent the relation between two pedestrians to construct a pedestrian attribute graph. Furthermore, we aggregate the features from their neighborhood on a pedestrian attribute graph by the graph neural network, which would make the attribute similar pairs closer and simultaneously take the dissimilar pairs further in the feature space. Finally, to avoid the over-confidence of the hard pseudo labels, we regularize the learning of the embedding model with the smoothed pseudo label (SPL) in the optimization of the feature embedding network. We conduct extensive experiments on several person re-ID datasets to validate the efficacy of our proposed method. The results demonstrate that our technique is effective and promising for person re-ID tasks.}, archive = {J_ACCESS}, author = {Geyu Tang and Xingyu Gao and Zhenyu Chen and Huicai Zhong}, doi = {10.1109/ACCESS.2021.3069915}, journal = {IEEE Access}, pages = {122888-122899}, shortjournal = {IEEE Access}, title = {Graph neural network-based attribute auxiliary structured grouping for person re-identification}, volume = {13}, year = {2025}, }
• (2025). Hybrid CNN and transformer-based sequential learning techniques for plant disease classification. ACCESS, 13, 122876-122887. (www)

  @article{ , abstract = {Plant diseases have important consequences for livelihoods and economies, both on local and global scales, whereby the spread of plant pathogens can lead to high levels of damage to agricultural productivity. In this regard, deep learning (DL) has evolved as a promising remedy. Nevertheless, the level of diversity among plant species still presents a constant challenge to effective plant disease classification. This research proposes a two-stream convolution operator that combines bidirectional long short-term memory (BiLSTM) for effective feature mapping and learning. Moreover, a transformer network (TransNet) is constructed based on sequential learning techniques (SLT) using long short-term memory (LSTM), bidirectional LSTM (BiLSTM), sequence-to-sequence, and gated recurrent unit models to highlight important plant disease features. The network is trained on the PlantVillage dataset, which includes 38 species of plant diseases. The experimental results indicate that the proposed model achieves scores of 97.88%, 97.93%, and 97.62% for Accuracy, Precision, and Recall, respectively, along with a low training loss of 0.0696 and a minimal training time of 83.17 minutes. The proposed approach represents a significant improvement over previous models and demonstrates the potential for further enhancing the efficiency and accuracy of plant disease classification, thereby contributing to the advancement of modern technology in the agricultural industry.}, archive = {J_ACCESS}, author = {Anuruk Prommakhot and Jakkrit Onshaunjit and Wichian Ooppakaew and Grianggai Samseemoung and Jakkree Srinonchat}, doi = {10.1109/ACCESS.2025.3586285}, journal = {IEEE Access}, pages = {122876-122887}, shortjournal = {IEEE Access}, title = {Hybrid CNN and transformer-based sequential learning techniques for plant disease classification}, volume = {13}, year = {2025}, }
• (2025). Modified model-free predictive control for reliable operation of multiple parallel grid-forming inverters. ACCESS, 13, 122862-122875. (www)

  @article{ , abstract = {The increasing integration of parallel grid-forming (GFM) inverters within islanded AC microgrids introduces significant challenges in maintaining robust voltage regulation and accurate power sharing. Central to controlling the output voltage is the need for a reference voltage, which can be derived using the virtual synchronous generator (VSG) concept. The VSG not only ensures active power sharing but furthermore provides inertia emulation, reducing the rate of change of frequency (ROCOF) in response to sudden load variations, thereby enhancing system stability and frequency support. To achieve precise voltage regulation, a model predictive control (MPC) strategy is commonly employed, particularly the finite control set-MPC (FCS-MPC). However, FCS-MPC relies on an accurate system model, which can limit performance under parameter mismatches or dynamic changes. To overcome this limitation, this paper proposes a modified model-free predictive control (MFPC) based on the dual second-order generalized integrator-quadrature signal generator (DSOGI-QSG) strategy. The MFPC eliminates the need for an explicit system model by using an ultra-local model (ULM) for prediction, with the unknown function in the ULM playing a critical role in the accuracy of output voltage prediction. Algebraic identification methods are typically used to estimate this unknown function. Nevertheless, as the sampling time increases, the estimation becomes increasingly distorted, leading to prediction inaccuracies. The DSOGI-QSG is introduced to extract a filtered version of the unknown function, improving the quality of the estimation and, consequently, the prediction accuracy. Simulation results using MATLAB Simulink demonstrate the effectiveness of the proposed method, showing enhanced robustness, improved dynamic response, and reduced harmonic distortion compared to conventional MFPC.}, archive = {J_ACCESS}, author = {Mohamed Islam Grairia and Riad Toufouti and Z. M. S. Elbarbary and Mamadou-Bailo Camara and Abderahmane Abid and Shaik Mohammad Irshad and Hafiz Ahmed}, doi = {10.1109/ACCESS.2025.3588042}, journal = {IEEE Access}, pages = {122862-122875}, shortjournal = {IEEE Access}, title = {Modified model-free predictive control for reliable operation of multiple parallel grid-forming inverters}, volume = {13}, year = {2025}, }
• (2025). 5G millimeter-wave fixed wireless access: Optimizing cost and coverage with antenna height variation using point-cloud path loss prediction. ACCESS, 13, 122852-122861. (www)

  @article{ , abstract = {The deployment of fifth generation (5G) mobile network millimeter-wave (mmWave) for cellular services faces challenges due to significant signal attenuation from blockages to the signal. 5G Fixed Wireless Access (FWA) has emerged as a promising mmWave application. The economical viability of high frequency FWA typically requires line-of-sight (LoS) connections. The amount of LoS FWA links can be increased by increasing the height of the antenna. In this paper, we introduce a novel approach for optimizing per-household cost effectiveness of a 5G FWA network. For the estimation of LoS connections, our approach relies on a highly accurate channel model derived from point-cloud data with terrain height information. Our empirical measurements confirm that the digital 3D point-cloud map model delivers significantly higher precision compared to other models. We examine how the heights of base transceiver station (BTS) towers and customer premise equipment (CPE) antennas impact the availability of FWA services in a real-world rural setting with 764 houses. For instance, with BTSs set at a height of 70 meters from the ground and CPEs height elevated up to 2 meters from the roof, we achieve a LoS coverage of 62.57%. By factoring in practical deployment costs for BTS and CPE antenna heights, we identify optimal heights that minimize the mmWave network investment per customer.}, archive = {J_ACCESS}, author = {Norshahida Saba and Jari Salo and Kalle Ruttik and Riku Jäntti}, doi = {10.1109/ACCESS.2025.3583995}, journal = {IEEE Access}, pages = {122852-122861}, shortjournal = {IEEE Access}, title = {5G millimeter-wave fixed wireless access: Optimizing cost and coverage with antenna height variation using point-cloud path loss prediction}, volume = {13}, year = {2025}, }
• (2025). Machine learning model for road anomaly detection using smartphone accelerometer data. ACCESS, 13, 122841-122851. (www)

  @article{ , abstract = {This paper presents a vibration-based machine learning approach for road surface monitoring using smartphone sensors. With Mexico’s road network experiencing significant deterioration and potholes ranking as citizens’ top concern, we propose a convolutional neural network (CNN) model that analyzes accelerometer and gyroscope data from Android smartphones to detect road anomalies. Our methodology includes a custom mobile application for data collection, feature extraction through moving average filtering, and a 2-CNN architecture for classification. Experimental results demonstrate 98% accuracy in distinguishing potholes from speed bumps when using six sensor features, compares favorably with previously reported vibration-based approaches. The system’s low-cost implementation and high accuracy indicate that it may be well suited for large-scale road condition monitoring using mobile crowd-sensing paradigms.}, archive = {J_ACCESS}, author = {Mahdi Zareei and Carlos Alonzo López Castañeda and Faisal Alanazi and Fausto Granda and Jesús Arturo Pérez-Díaz}, doi = {10.1109/ACCESS.2025.3586812}, journal = {IEEE Access}, pages = {122841-122851}, shortjournal = {IEEE Access}, title = {Machine learning model for road anomaly detection using smartphone accelerometer data}, volume = {13}, year = {2025}, }
• (2025). Synchronous recovery of waveforms from dynamic phasors models of LCC-HVDC links. ACCESS, 13, 122822-122840. (www)

  @article{ , abstract = {LCC-HVDC links are used worldwide for long-distance bulk power transmission in large scale power systems. In this technology, commutation failure phenomena may occur, where transient power interruptions of HVDC links may lead the system to electromechanical instability. For power system simulations with HVDC transmission, the dynamic phasor methodology has advantages for considering simultaneously the electromechanical and electromagnetic transients, in particular, the simulation of commutation failures and their impacts in transient stability. However, when using dynamic phasors, the full harmonic contents of the HVDC variables are missing, which may worsen the accuracy of commutation failure simulations. This paper proposes a methodology, named Synchronous Recovery of Waveforms from Dynamic Phasor Model (SRWDPM) for obtaining accurate waveforms of the HVDC variables from dynamic phasor models. The main benefits of SRWDPM are these accurate waveforms and their use for adequate commutation failure simulations using efficient fundamental frequency dynamic phasor models. These accurate waveforms were previously only available in EMT programs or in expensive dynamic phasor models of high-order harmonics. The methodology is based on numerical integration of a small subsystem of each HVDC converter using instantaneous values obtained from dynamic phasors as input variables. SRWDPM is solved synchronously with the dynamic phasor solution and the waveforms are comparable to those obtained in electromagnetic transient programs with discontinuities and without harmonic truncation. The proposed methodology is general, it can be applied to different dynamic phasor models for improving efficiency or accuracy. A benchmark system is used for illustrating the methodology application.}, archive = {J_ACCESS}, author = {Leonardo Pinto de Almeida and Sergio Gomes and Thiago J. Masseran A. Parreiras}, doi = {10.1109/ACCESS.2025.3586494}, journal = {IEEE Access}, pages = {122822-122840}, shortjournal = {IEEE Access}, title = {Synchronous recovery of waveforms from dynamic phasors models of LCC-HVDC links}, volume = {13}, year = {2025}, }
• (2025). Efficiently automating covering array generation: A model checking and metaheuristic algorithm approach. ACCESS, 13, 122804-122821. (www)

  @article{ , abstract = {The cost of software testing is a significant aspect of the software development life cycle, typically accounting for half of the total software production cost. Due to the large number of tests, it is impractical to perform a complete test of a system under test. Combinatorial testing is employed to identify errors that arise from the interaction among subsystems, with covering arrays being the most crucial type of combinatorial testing. Covering arrays consider parameter combinations using the t-way strategy. The goal is to reduce the number of test cases while ensuring adequate coverage. Fewer test cases result in greater efficiency, and less time spent generating the test suite leads to more effective testing. Various solutions have been proposed, with metaheuristic algorithms playing a vital role. Furthermore, solutions have been developed to automatically generate parameters and their corresponding values by utilizing a model of the system under test. In this paper, our objective is to employ model-based solutions for the automatic generation of a covering array. Specifically, we integrate the GROOVE model checker with a combination of Biogeography-Based Optimization (BBO) and Genetic Algorithm (GA) to enhance the efficiency and accuracy of the test suite generation. The GROOVE model checker extracts parameters and their values, which are then optimized by the BBO and GA algorithms to generate a minimal and effective test suite. The evaluation results show that our proposed solution outperforms existing algorithms, achieving higher efficiency and accuracy in test generation. This research highlights the novel integration of GROOVE with BBO and GA, contributing to the advancement of combinatorial testing techniques.}, archive = {J_ACCESS}, author = {Sajad Esfandyari and Davar Giveki and Amir Rastegarnia and Ali Farzamnia and Hua Zheng}, doi = {10.1109/ACCESS.2025.3587202}, journal = {IEEE Access}, pages = {122804-122821}, shortjournal = {IEEE Access}, title = {Efficiently automating covering array generation: A model checking and metaheuristic algorithm approach}, volume = {13}, year = {2025}, }
• (2025). Dental identification based on single tooth feature concatenation. ACCESS, 13, 122792-122803. (www)

  @article{ , abstract = {Forensic dentistry research indicates that teeth, as one of the hardest tissues in the human body, can be well-preserved even in disasters and crime scenes, providing a reliable means of human identification. Dental identification based on 3D tooth point clouds has made some progress recently. But the whole-dentition-based identification mode may fail in complex forensic application scenarios such as tooth loss, changes in arrangement or scattering. Therefore, we propose a dental identification deep neural network based on single tooth feature concatenation in this paper, offering more flexibility and accuracy as it can adapt to various tooth arrangements and different tooth numbers. To address the challenges of difficult samples with fewer teeth, a progressive random tooth dropout training strategy is devised to aid learning. For inadequate discrimination of individual tooth features, PointResNet is introduced as a feature extraction module. To handle significant differences in samples with varying tooth numbers and arrangements from the same individual, the additive angular margin loss is adopted. To verify the effectiveness of our method in various dental conditions, we apply random spatial transformations to the teeth of the same individual and simulate different cases of missing teeth on our self-constructed dataset. The results from comparative and ablation experiments confirmed the effectiveness of this approach.}, archive = {J_ACCESS}, author = {Li Yuan and Luoji Zhu and Kang-Hyun Jo and Yanfeng Li}, doi = {10.1109/ACCESS.2025.3585545}, journal = {IEEE Access}, pages = {122792-122803}, shortjournal = {IEEE Access}, title = {Dental identification based on single tooth feature concatenation}, volume = {13}, year = {2025}, }
• (2025). On the causal relationship between music virality and success. ACCESS, 13, 122782-122791. (www)

  @article{ , abstract = {Songs going viral is not a new phenomenon in the music industry. Still, this phenomenon has reached new heights with the popularization of the Web and social platforms, which allow songs to achieve worldwide hit status almost instantly. Although interconnected, musical virality and commercial success are distinct concepts, and platforms such as TikTok have had significant power in amplifying music virality and creating successful hits. In this work, we analyze the temporal connection between musical virality and success. Specifically, our goal is to investigate the causal relationship between such concepts. By using global chart data from streaming platforms, we model time series to represent songs’ viral and success evolution over time and then perform two distinct analyses over them. First, we use Granger Causality to assess whether musical virality can forecast success and vice versa. Then we address the causal discovery task to qualitatively uncover the underlying causal relationships between them. The results suggest there is potential for using music virality to forecast future success and vice versa, although this does not apply to all songs. Despite their symbiotic relationship influenced by social platforms, our findings reinforce the contrast between music virality and success as different facets of music popularity.}, archive = {J_ACCESS}, author = {Gabriel P. Oliveira and Ana Paula Couto Da Silva and Mirella M. Moro}, doi = {10.1109/ACCESS.2025.3589173}, journal = {IEEE Access}, pages = {122782-122791}, shortjournal = {IEEE Access}, title = {On the causal relationship between music virality and success}, volume = {13}, year = {2025}, }
• (2025). Research on the aided diagnosis method of diseases based on domain semantic knowledge bases. ACCESS, 13, 122769-122781. (www)

  @article{ , abstract = {The healthcare domain is a knowledge-intensive domain. The quality of clinical diagnosis relies mainly on the medical knowledge and experience held by doctors. However, the ability of a single doctor is very limited, so the quality of clinical diagnosis is currently not high. In this paper, an aided diagnosis method based on domain semantic knowledge bases is proposed. First, a domain semantic knowledge base is established by extracting and refining the knowledge of the medicine subject matter domain from the Freebase resource description framework dumps. Then, based on the semantic knowledge base, the algorithms for calculating the weights of the symptoms in the knowledge base, the relative weights of the diseases related to the input symptom set from a patient, and the related symptom set related to the input symptom set from the patient are proposed. Finally, the clinical medical record data of several common diseases are selected to make an evaluation on the proposed method. For each medical record, the symptom information is extracted from the chief complaint as the patient’s input symptom set. Based on the input symptom set, the method of this paper is used to obtain the list of related diseases and the ranking of disease relative weights. From the disease relevance rankings, the Top-1 (first diagnosis) and Top-3 (first three diagnoses) are compared with the doctor’s diagnoses in the medical records. Among them, the ovarian cyst has the highest Top-1 and Top-3 hit rates of 67.3% and 89.1%, respectively. Followed by acute upper respiratory tract infection, Top-1 and Top-3 hit rates are 56.6% and 85.2%, respectively. The average Top-1 and Top-3 hit rates are 47.9% and 79.7%, respectively. Compared with the relevant methods, the method of this paper is better. The evaluation results show that based on the domain semantic knowledge base and the aided diagnosis method of diseases constructed in this paper, it is possible to provide the aided diagnosis services of a large number of common diseases for general practitioners (especially inexperienced doctors) at the grass-roots level as well as self-diagnosis services of diseases for patients.}, archive = {J_ACCESS}, author = {Deyan Chen and Hong Zhao and Xia Zhang}, doi = {10.1109/ACCESS.2018.2875722}, journal = {IEEE Access}, pages = {122769-122781}, shortjournal = {IEEE Access}, title = {Research on the aided diagnosis method of diseases based on domain semantic knowledge bases}, volume = {13}, year = {2025}, }
• (2025). Hybrid DNN-HMM-based approach for telugu language speech recognition. ACCESS, 13, 122752-122768. (www)

  @article{ , abstract = {In this paper, we discuss our research work in Telugu Indian language speech data for building a large language vocabulary to build Telugu speech recognition system. We have collected speech research data around 2628 utterances in Telugu language among 14 Indian regional languages as part of Speech Quality Control Project under Bhashini-NLTM (National Language Translation Mission) in collaboration with IIT Madras. In this research work, we discuss our methodology for the collection of Telugu data and developing Telugu speech database. In our paper, we present the preliminary results that are obtained by building speech recognition using the acoustic models created on Telugu language database by implementing Kaldi toolkit for speech in building ASR in Telugu language. The proposed hybrid DNN-HMM acoustic model implemented to build Telugu ASR in Kaldi achieved a WER score of 0. 7% when the weight of the inverse_language_model is 17 in the test data set of the Telugu speech database. In the process of our work, some of the ASR models, methods and speech processing performance metrics that we used in ASR in order to maintain quality corpus are discussed.}, archive = {J_ACCESS}, author = {M. Rama Rajeswari and Suryakanth V. Gangashetty}, doi = {10.1109/ACCESS.2025.3588664}, journal = {IEEE Access}, pages = {122752-122768}, shortjournal = {IEEE Access}, title = {Hybrid DNN-HMM-based approach for telugu language speech recognition}, volume = {13}, year = {2025}, }
• (2025). 3D measurement of particle movement in a silo using magnetic positioning and inertial navigation technologies. ACCESS, 13, 122742-122751. (www)

  @article{ , abstract = {Magnetic positioning technology is a novel and direct method to measure the attitude and position information of discrete particles in dense granular flow. However, owing to the need to manually set the initial value and other boundary conditions in the process of algorithm solution, the deviation between the measured results and actual values cannot be ignored. To solve this problem, a hybrid optimization algorithm of particle swarm optimization algorithm and sequential quadratic programming algorithm (PSO-SQP) is proposed in this paper, which combines the advantages of PSO algorithm being insensitive to initial value with the advantages of SQP algorithm being fast and accurate to solve magnetic positioning parameters. The attitude calculated by magnetic positioning and calculated by inertial measurement are fused together through Kalman filter to accurately measure the attitude of discrete particles. The static experiments show that PSO-SQP can solve the position without setting the initial value. Meanwhile, the attitude accuracy is improved by data fusion. Finally, the method is applied to measure the motion of discrete particles in a 3D silo, and it is observed that the method can accurately obtain the information of the spatial position, translational motion, and rotational motion of the discrete particles in the silo. The experimental results reveal the laws of motion of granular flow in the silo, and provide data reference for improving the storage and transportation of particle materials in industrial production process.}, archive = {J_ACCESS}, author = {Hongyang Dai and Yiming Li and Shijie Wang and Ran Li and Hui Yang}, doi = {10.1109/ACCESS.2024.3351381}, journal = {IEEE Access}, pages = {122742-122751}, shortjournal = {IEEE Access}, title = {3D measurement of particle movement in a silo using magnetic positioning and inertial navigation technologies}, volume = {13}, year = {2025}, }
• (2025). ECG, respiration, fNIRS, and eye tracking for stress and mental workload monitoring in human-machine interaction. ACCESS, 13, 122726-122741. (www)

  @article{ , abstract = {Advanced human-machine interaction (AHMI) is a key concept in human factors and ergonomics (HFE), focusing on how individuals interact with systems to perform tasks efficiently. As AHMI becomes more integrated into fields such as Industry 4.0, aviation, automotive, and clinical applications, users face increasing complexity, leading to elevated mental workload (MWL) and stress. These factors can impair performance and cause accidents, emphasizing the need for AHMI systems capable of real-time monitoring of cognitive load and stress levels. This paper investigates the relationship between stress, MWL, and four physiological signals—electrocardiogram (ECG), respiration, functional near-infrared spectroscopy (fNIRS), and eye tracking—combined with a tailored Self-Assessment Questionnaire (SAQ), specifically designed for industrial applications. A study involving 20 participants was conducted using the Stroop, Visual, Auditory, and Dual N-Back tasks. During the study, 83 features were extracted from the physiological signals and linked to this four-level ratings SAQ of perceived stress and MWL. Statistical analysis using Kruskal-Wallis and Mann-Whitney tests assessed the ability of these features to differentiate stress and MWL levels. Over 50% of the features reliably distinguished between cognitive states, particularly in identifying relaxed versus altered conditions. Respiration, fNIRS, and eye movement signals provided higher granularity in differentiating multiple altered cognitive states, suggesting their potential for precise monitoring in AHMI systems. These findings underscore the value of physiological monitoring in AHMI systems, which can enhance user performance and safety by enabling adaptive interfaces tailored to real-time cognitive states, supporting future industrial applications.}, archive = {J_ACCESS}, author = {Gabriele Luzzani and Marco Pogliano and Irene Buraioli and Manuel Colavincenzo and Stefano Martorana and Giorgio Guglieri and Danilo Demarchi}, doi = {10.1109/ACCESS.2025.3588384}, journal = {IEEE Access}, pages = {122726-122741}, shortjournal = {IEEE Access}, title = {ECG, respiration, fNIRS, and eye tracking for stress and mental workload monitoring in human-machine interaction}, volume = {13}, year = {2025}, }
• (2025). Assessing industry 4.0 readiness and adoption barriers in textile sector: Strategic insights for sustainable industrial transformation. ACCESS, 13, 122698-122725. (www)

  @article{ , abstract = {The $4^{th}$ Industrial Revolution (Industry 4.0) signifies a revolutionary change in industrial production driven by digital technologies, and promises exceptional improvements in operational efficiency. Multiple studies have shown that the adoption of Industry 4.0 is still in its infancy in developing countries, owing to various barriers, which is a significant area of inquiry for researchers. Due to the newness of this concept, manufacturing industries must first assess their readiness for transitioning to smart manufacturing, guided by the Resource-Based View (RBV) theory, which emphasizes the strategic utilization of resources for successful transformation. In this context, this study has two objectives: first, to assess the current I4.0 readiness of Pakistan’s main manufacturing sector, the Textile Industry, and second, to present an overview of the barriers to adoption of I4.0. We employed the IMPULS readiness model developed by the German Mechanical Engineering Industry Association (VDMA) in collaboration with the Institute for Industrial Management (FIR) at RWTH Aachen University to assess current readiness. For the second objective, a systematic literature review approach was used to identify potential barriers. The identified barriers were later validated via surveys with Technical Directors, IT experts, and Industrial Automation Engineers from Pakistan’s textile sector. Data from the 106 textile units were collected online to assess the current I4.0 readiness. The results categorized firms into leaders, learners, and newcomers, detailing readiness scores across Industry 4.0. The findings, aligned with Sustainable Development Goals (SDGs) 8 (Decent Work and Economic Growth) and 9 (Industry, Innovation, and Infrastructure), guide stakeholders, including textile industry leaders, academic institutions, and policymakers, in prioritizing decisions and investments for advancing digital transformation for operational efficiency, competitive advantage, and sustainable industrial progress in the global markets.}, archive = {J_ACCESS}, author = {Arshad Mahmood and Muhammad Asif Habib and Mudassar Ahmad and Sajjad Ahmad Baig}, doi = {10.1109/ACCESS.2025.3586326}, journal = {IEEE Access}, pages = {122698-122725}, shortjournal = {IEEE Access}, title = {Assessing industry 4.0 readiness and adoption barriers in textile sector: Strategic insights for sustainable industrial transformation}, volume = {13}, year = {2025}, }
• (2025). Content-based brain magnetic resonance image retrieval and classification with the proposed deep learning and tissue-based system. ACCESS, 13, 122684-122697. (www)

  @article{ , abstract = {The exponential growth in the size of databases due to technological advancements has led to challenges in locating and accessing specific components of the data. While deep learning and other machine learning architectures have shown promise in retrieving data components, their efficacy is more pronounced when addressing disease cohorts. Contrarily, this effectiveness diminishes when accessing large datasets. This study focuses on the analysis of brain magnetic resonance imaging (MRI) images and, specifically, to differentiate between benign and malignant lesions associated with Alzheimer’s disease, multiple sclerosis (MS), and intracranial regions, all of which are medically significant with distinct treatment modalities. A hybrid model was first devised to facilitate image retrieval by employing a pre-trained EfficientNet-b0 and local binary pattern (LBP) for feature extraction. These extracted features were then amalgamated to encompass diverse aspects of each image. To improve model performance, redundant features were pruned using the minimum redundancy maximum relevance (mRMR) technique. As a result, the proposed model demonstrated efficacy in analyzing a diverse dataset encompassing three distinct diseases and eight unique classes. Notably, existing machine architectures already published in the literature have struggled to achieve comparable success rates in discerning such closely related yet distinct disease groups. Our study underscores the challenge posed by increasing class diversity on the performance of deep learning architectures and obtained an accuracy of 98.9% in classifying three diseases and eight unique classes. As a result, the same model was used as the base in both the classification and CBIR processes for MRI detection, yielding competitive results when compared with the literature and other models.}, archive = {J_ACCESS}, author = {Bedriye Dogan and Hursit Burak Mutlu and Muhammed Yildirim and Sercan Yalcin and Serpil Aslan and Niranjana Sampathila and Ozal Yildirim and Edward J. Ciaccio and Ru-San Tan and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3588211}, journal = {IEEE Access}, pages = {122684-122697}, shortjournal = {IEEE Access}, title = {Content-based brain magnetic resonance image retrieval and classification with the proposed deep learning and tissue-based system}, volume = {13}, year = {2025}, }
• (2025). MalFormer: A novel vision transformer model for robust malware analysis. ACCESS, 13, 122671-122683. (www)

  @article{ , abstract = {As traditional signature-based malware analysis struggles to detect malware variants, image-based malware analysis has been researched to overcome these limitations. Especially, Vision Transformer (ViT)-based malware analysis has achieved high performance by capturing global features of malware images. However, previous ViT-based methods struggle to effectively capture both local and global features of images and exhibit limited generalization performance. In this paper, we propose a novel malware analysis method that introduces MalFormer, which employs the Cross-Scale Embedding Layer, Long-Short Distance Attention, and Adaptive Token Masking. Also, we introduce Sharpness-Aware Minimization (SAM) to optimize the training process of MalFormer. The proposed method enhances sensitivity to key features while maintaining a well-balanced integration of local and global features. From the experimental results on various datasets, we show that the proposed method outperforms state-of-the-art ViT-based methods. We also show that the proposed method provides strong robustness against obfuscation techniques, achieving high performance on obfuscated samples.}, archive = {J_ACCESS}, author = {In-Woong Jeong and Han-Ju Lee and Gwang-Nam Kim and Seok-Hwan Choi}, doi = {10.1109/ACCESS.2025.3588232}, journal = {IEEE Access}, pages = {122671-122683}, shortjournal = {IEEE Access}, title = {MalFormer: A novel vision transformer model for robust malware analysis}, volume = {13}, year = {2025}, }
• (2025). Optimal trajectory optimization of 7R robot for space maintenance operation. ACCESS, 13, 122660-122670. (www)

  @article{ , abstract = {This paper studies the optimal trajectory optimization of a space robot for maintenance operations. A new trajectory planning algorithm is proposed to solve the problems of low maintenance efficiency and poor stationarity of space robots on-orbit. Firstly, considering the constraint conditions such as the speed, acceleration and secondary acceleration of each joint of the space robots, the cubic spline curve is used to plan the trajectory of the space robots in the joint space. Then, a comprehensive optimal planning model for time-jerk is established, and the optimal problem of space maintenance efficiency and stationarity is described as a nonlinear constrained optimization problem. Finally, a hybrid optimization algorithm is proposed to solve the trajectory optimization problem of space robots by combining genetic algorithm and particle swarm optimization method. The simulation results show that, compared with several classic optimization algorithms, the proposed hybrid optimization algorithm can obtain a better maintenance trajectory of the space robots under the constraints, which solves the low efficiency and poor stability of the space robots on-orbit maintenance problem.}, archive = {J_ACCESS}, author = {Qidong Li and Hehua Ju and Pengfei Xiao and Feifei Chen and Fei Lin}, doi = {10.1109/ACCESS.2020.3008754}, journal = {IEEE Access}, pages = {122660-122670}, shortjournal = {IEEE Access}, title = {Optimal trajectory optimization of 7R robot for space maintenance operation}, volume = {13}, year = {2025}, }
• (2025). Cultural bias in text-to-image models: A systematic review of bias identification, evaluation, and mitigation strategies. ACCESS, 13, 122636-122659. (www)

  @article{ , abstract = {Despite their continuous advancements, text-to-image (TTI) models often reflect and reinforce cultural biases, perpetuating stereotypes often inherent in their training data. This systematic review critically examines cultural bias in text-to-image (TTI) models, addressing gaps in existing research by analyzing its manifestations, evaluation methods, and mitigation strategies—both directly and through the lens of intersectionality with other bias dimensions. A comprehensive literature review was conducted across multiple major databases, following a rigorously structured search strategy, resulting in the selection of 58 studies spanning bias analysis, evaluation frameworks, and mitigation techniques. Thematic findings highlight that gender bias was the most extensively studied, appearing in 53 studies (91%), followed by racial/ethnic bias (42 studies) and other social biases (41 studies). Furthermore, the review explores how these biases intersect and compound in AI-generated imagery, shaping and reinforcing cultural bias. Our findings reveal the following key aspects: 1) the lack of standardization and scalability in bias evaluation, 2) the lack of a fully effective mitigation strategy, 3) contributed TTI benchmarks favoring Western-centric perspectives. We finally propose future directions to improve fairness and representation in TTI models.}, archive = {J_ACCESS}, author = {Wala Elsharif and Mahmood Alzubaidi and Marco Agus}, doi = {10.1109/ACCESS.2025.3585745}, journal = {IEEE Access}, pages = {122636-122659}, shortjournal = {IEEE Access}, title = {Cultural bias in text-to-image models: A systematic review of bias identification, evaluation, and mitigation strategies}, volume = {13}, year = {2025}, }
• (2025). Side-channel attack on ARADI in non-profiling scenarios. ACCESS, 13, 122628-122635. (www)

  @article{ , abstract = {ARADI is a low-latency block cipher introduced by the NSA (National Security Agency), designed with consideration for applications in memory encryption. In this study, we present the first results of side-channel attacks on ARADI, exposing its vulnerabilities to physical attacks in a non-profiling scenario. We propose a novel bitwise divide-and-conquer methodology tailored for ARADI, and extend the approach to recover the full 256-bit master key. Using power consumption traces from an ARM processor, our experimental results demonstrate the effectiveness of our attack method, achieving key recovery with only 2,000 traces. These findings highlight critical weaknesses in the physical security of ARADI and emphasize the need for robust countermeasures to address side-channel vulnerabilities without compromising performance. This research provides new insights into the practical security of ARADI, underscoring the importance of integrating physical attack resilience in cryptographic designs.}, archive = {J_ACCESS}, author = {Donggeun Kwon and Seokhie Hong}, doi = {10.1109/ACCESS.2025.3585559}, journal = {IEEE Access}, pages = {122628-122635}, shortjournal = {IEEE Access}, title = {Side-channel attack on ARADI in non-profiling scenarios}, volume = {13}, year = {2025}, }
• (2025). Remote irrigation control based on LoRa technology and fuzzy PID algorithm. ACCESS, 13, 122613-122627. (www)

  @article{ , abstract = {Due to the dual challenges of limited communication distance and insufficient adaptability to complex environments in remote irrigation control technology, this study combines Long Range Radio (LoRa) technology and Proportional-Integral-Derivative (PID) algorithm to construct a remote irrigation control system and improve the intelligence of irrigation modes. The perception layer architecture of the system and its terminal devices collect soil moisture and other parameters, which are then transmitted to the upper computer through the LoRa module. The fuzzy PID algorithm and its controller use soil moisture parameters to adjust irrigation parameters, enabling the LoRa network to effectively convey irrigation instructions and improve water resource utilization. After systematic simulation analysis and actual testing, when the communication distance was 700m, the packet loss rate of the collection node in complex terrain was 0.95%, and the packet loss rate of the receiving node was 1.06%. Compared with the packet loss rate in open terrain, the average value was increased by about 2.5%. In irrigation mode, the irrigation time of the orchard had an error of less than 0.7% compared to the system calculation, and the minimum irrigation time was 10.06 seconds. Therefore, the remote irrigation control system based on LoRa technology and fuzzy PID algorithm has stability and superiority, achieving high efficiency and precision applications, which is conducive to promoting the digitization and sustainable development of agricultural production activities.}, archive = {J_ACCESS}, author = {Yuliang Li}, doi = {10.1109/ACCESS.2025.3586708}, journal = {IEEE Access}, pages = {122613-122627}, shortjournal = {IEEE Access}, title = {Remote irrigation control based on LoRa technology and fuzzy PID algorithm}, volume = {13}, year = {2025}, }
• (2025). MSAN-net: An end-to-end multi-scale attention network for universal industrial defect detection. ACCESS, 13, 122603-122612. (www)

  @article{ , abstract = {With the rapid advancement of automation and intelligence in the electronics manufacturing industry, the throughput of a single production line was grown exponentially. Although high efficiency brought significant cost and time advantages, it also led to two major challenges: (1) extremely low tolerance for error—any slight defect might have caused the entire product to be scrapped; (2) increasingly diverse and more concealed types of defects—bubble defects, internal chip defects, printed circuit board (PCB) defects, and specific process defects were continuously emerged, posing significant challenges to the inspection process. Traditional manual visual inspection or single-task deep learning models were often struggled to balance detection efficiency and accuracy in complex industrial scenarios. To address the above challenges, a single-stage industrial defect detection model based on multi-dataset mixed training—MSAN-Net—was proposed in this paper. Representative datasets covering the typical scenarios mentioned above were collected and organized, and part of the data was re-annotated to ensure a high level of consistency with actual production environments. MSAN-Net was adopted an integrated architecture, deeply combining UnifiedViT, C2f modules, convolution operations, SPPF structure, and Bi-Level Routing Attention mechanism to achieve accurate identification of complex industrial defects. Extensive experiments (including comparisons with multiple methods, ablation studies, and external validation) showed that MSAN-Net was outperformed existing SOTA models in industrial defect detection tasks, significantly improving detection accuracy for multi-class defects in complex scenarios, reducing reliance on manual inspection, and effectively lowering scrap losses caused by defects, thus providing a reliable solution for intelligent quality inspection in the electronics manufacturing industry.}, archive = {J_ACCESS}, author = {Zelu Wang and Ming Luo and Xinghe Xie and Yue Sun and Xinyu Tian and Zhengxuan Chen and Junwei Xie and Qinquan Gao and Tong Tong and Yue Liu and Tao Tan}, doi = {10.1109/ACCESS.2025.3583589}, journal = {IEEE Access}, pages = {122603-122612}, shortjournal = {IEEE Access}, title = {MSAN-net: An end-to-end multi-scale attention network for universal industrial defect detection}, volume = {13}, year = {2025}, }
• (2025). Advancing early diagnosis: Predicting mild cognitive impairment progression in normal individuals using deep learning on MRI features. ACCESS, 13, 122591-122602. (www)

  @article{ , abstract = {One of the most challenging tasks for neurologists is the early diagnosis of Alzheimer’s disease (AD). Early and accurate diagnosis of the mild cognitive impairment (MCI) stage can enhance efforts to slow down the major consequences linked to this condition. Deep learning systems provide a promising performance in diagnosing the disease through neuroimaging analysis. This research aims to develop a deep learning-based system that efficiently identifies and analyzes the progression from Cognitively Normal (CN) to MCI, addressing the growing need for more accessible, accurate diagnostic tools. The proposed model comprises two distinct feature extraction paths to capture local and global image features. Each path includes advanced modules for feature refinement associated with the channel attention mechanism. The resultant output features are produced using a learned fusion technique from the two paths’ features and applied to the CN vs. MCI binary classifier. Furthermore, the proposed Suspected Subject Classifier (SSC) system applies various machine-learning methods to identify the suspected MCI subjects. The results showed a comparative performance for the binary diagnosis of CN individuals and those with MCI, achieving an accuracy of 91.6% and 88.4% for multi-class diagnoses, including the prediction of progression from normal to confirmed MCI. This study represents an exceptional stride toward predicting early MCI in normal individuals. By enhancing prediction efficiency for early disease progression in normal individuals, our method can potentially advance intervention strategies and improve patient care outcomes.}, archive = {J_ACCESS}, author = {Abdullah Baktash and Yashar Sarbaz and Saeed Meshgini and Reza Afrouzian}, doi = {10.1109/ACCESS.2025.3588801}, journal = {IEEE Access}, pages = {122591-122602}, shortjournal = {IEEE Access}, title = {Advancing early diagnosis: Predicting mild cognitive impairment progression in normal individuals using deep learning on MRI features}, volume = {13}, year = {2025}, }
• (2025). Refining the smart linkage across two fields: Bridging latent topics in artificial intelligence and social media marketing. ACCESS, 13, 122581-122590. (www)

  @article{ , abstract = {Apparently, distinct fields could possess shared knowledge, and leveraging such shared information could be aiming to implement solutions from one domain into another, enabling greater strategic importance for commercial enterprises. For example, employing solutions from “artificial intelligence” (AI) in designated “social media marketing” (SMM) programs could expedite cross-functional collaboration, potentially leading to a competitive advantage. This area of study is relatively underexplored. In this context, the present research introduces a resilient design model to uncover various linkage possibilities between AI and SMM from a commercial standpoint. Methodologically, the patent database related to AI and SMM is employed to generate latent topics independently, using the classic topic model, Latent Dirichlet allocation. Then, these topics, or implied concepts, are linked through bibliographic coupling. The study’s findings observed cross-concept networks; it reveals a range of concept pairs, and each pair has a unique value of coupling strength. For instance, representative keywords of the top five AI-SMM theme-pairs that show the highest coupling value and offer the maximum commonality of previous work(s) are: [accurate advertising; automation of customer response]; [automation of payment transaction; accuracy; and automation of network database, storage. This study contributes to the literature in three ways. First, each bibliographic coupling-based association shows a unique association of shared knowledge — an influence of common prior art(s). Second, use of citation-based linkage auto-updates to add sustainable features. Third, the patents database is the source of prior art to understand the current state of technology in a particular field. And its latent topics reflect the areas of focus within the broader field. In summary, while the present study maintains a specific focus, its overall analysis is adaptable to other fields, demonstrating its capacity to address intricate challenges in the introduction of automation in information sharing to reduce the knowledge gap. Such smart linkages between two fields may accelerate cross-field communication and foster intelligent cooperation for sustainable industry development by avoiding duplication of efforts.}, archive = {J_ACCESS}, author = {Noman Hanif and Omer Hanif and Salman Hanif and Liaqat Javeed and Muhammad Nauman Abbasi and Tayyaba Hanif and Sana Hanif}, doi = {10.1109/ACCESS.2023.3337427}, journal = {IEEE Access}, pages = {122581-122590}, shortjournal = {IEEE Access}, title = {Refining the smart linkage across two fields: Bridging latent topics in artificial intelligence and social media marketing}, volume = {13}, year = {2025}, }
• (2025). Continuous TOA measurement-based single-anchor indoor localization. ACCESS, 13, 122568-122580. (www)

  @article{ , abstract = {On the premise of guaranteeing high-quality measurement data, active wireless localization (A-WL) locates the target by at least three anchor nodes (ANs) with known coordinates, while passive wireless localization (P-WL) requires more ANs. In addition, P-WL requires a cumbersome training process to maintain the robustness in application scenarios as much as possible. However, the optimal deployment of ANs is not an easy task, and the application scenario will not remain unchanged. Therefore, a lightweight localization system that satisfies the localization accuracy requirements and can be quickly deployed should be one of the preferred solutions for WL. In this paper, we propose a single-anchor localization system based on continuous time-of-arrival (TOA) measurement. Specifically, we fix the AN on the blade with uniform rotation, construct the time-length distribution map by using the cyclically varying TOA caused by blade rotation, and intercept it according to the rotation period. We extract the change angle and distance information from the intercepted data in real time to achieve single-anchor localization. Meanwhile, we also construct a support vector machine classifier to identify the line-of-sight state of each interception period to assist the angle estimation in harsh environments and further improve the adaptability of the system. Experimental results show that the line-of-sight recognition accuracy of higher than 92% under continuous movement, and the average localization error of less than 0.6m. The average localization error is less than 0.3m in the mixed motion state.}, archive = {J_ACCESS}, author = {Ting Lei and Shengshan Pan and Kaiyu Wang and Yan Yu}, doi = {10.1109/ACCESS.2019.2941596}, journal = {IEEE Access}, pages = {122568-122580}, shortjournal = {IEEE Access}, title = {Continuous TOA measurement-based single-anchor indoor localization}, volume = {13}, year = {2025}, }
• (2025). Lightweight AI models in mineral processing: Classifying hydrocyclone underflow with ResNet-18 and MobileViT-v2. ACCESS, 13, 122556-122567. (www)

  @article{ , abstract = {This study investigates the use of lightweight deep learning models for classifying operational states of hydrocyclones in a copper mining process. A diverse data set was curated by capturing video footage of 10 hydrocyclones under varying operational conditions, including changes in lighting and underflow dynamics. The data were processed into 1217 images labeled “fanning” or “roping”. The data set was augmented to enhance model generalization, and two architectures, ResNet-18 and MobileViT-V2, were trained using a Stratified K-Fold Cross-Validation approach to ensure reliable performance evaluation. Both models were evaluated using precision, recall and F1 scores, with results averaged across all folds. The models were also tested on an independent dataset to simulate real-world classification tasks. ResNet-18 achieved an F1 score of 87.3%, while MobileViT-V2 achieved a higher F1 score of 94.5%. These results are consistent with those reported in the existing literature. Notably, the approach does not rely on ensembling methods or fixed camera positions, demonstrating that lightweight deep learning models can effectively enhance the operational control of hydrocyclone processes in mineral processing plants. This study underscores the potential of deep learning for operational monitoring in industrial settings and highlights the critical role of model fine-tuning for optimising performance in specific applications.}, archive = {J_ACCESS}, author = {Tomás Henrique Coelho E Silva and Ricardo Augusto Rabelo Oliveira}, doi = {10.1109/ACCESS.2025.3587227}, journal = {IEEE Access}, pages = {122556-122567}, shortjournal = {IEEE Access}, title = {Lightweight AI models in mineral processing: Classifying hydrocyclone underflow with ResNet-18 and MobileViT-v2}, volume = {13}, year = {2025}, }
• (2025). Reliability estimation for the gull alpha power pareto model under progressive type-I censoring. ACCESS, 13, 122540-122555. (www)

  @article{ , abstract = {A new family of Pareto distributions, known as the Gull Alpha Power Pareto (GAPP) model, has been derived and its statistical properties have been examined, followed by an application to life-testing, specifically under Type I progressive censoring schemes (PCS-TI). A comprehensive statistical analysis of the GAPP distribution was performed, including the estimation of its parameters and the reliability function. Estimation methods were investigated from an algorithmic perspective: for maximum likelihood estimation, the Newton-Raphson algorithm was adapted, while for Bayesian estimation, Markov Chain Monte Carlo methods were employed, utilizing the Metropolis-Hastings algorithm. Interval estimation was also addressed using two approaches: asymptotic confidence intervals based on MLE, and highest posterior density intervals in the Bayesian framework. A numerical study of the GAPP model under PCS-TI was performed using Monte Carlo simulations and real-world data related to the failure times of an airplane’s air conditioning system. The results provided valuable insights into the performance of the estimation methods and algorithms, along with comparisons across different censoring schemes. To determine the optimal PCS-TI scheme, the study further examined the sampling optimality criteria under various patterns.}, archive = {J_ACCESS}, author = {Mahmoud M. Abdelwahab and Ahmed R. El-Saeed and M. A. Abdelkawy and Mohamed Khalifa Ahmed Issa and Hanem Mohamed}, doi = {10.1109/ACCESS.2025.3588632}, journal = {IEEE Access}, pages = {122540-122555}, shortjournal = {IEEE Access}, title = {Reliability estimation for the gull alpha power pareto model under progressive type-I censoring}, volume = {13}, year = {2025}, }
• (2025). Use of asset administration shell for monitoring and control of industrial processes in legacy systems. ACCESS, 13, 122530-122539. (www)

  @article{ , abstract = {Industry 4.0 is revolutionizing industrial processes by promoting automation and digitalization across various sectors. However, it is some times necessary to replace legacy systems with those more suited to this new reality, which demands resources that, in the case of small and medium-sized enterprises, pose a challenge for this transition. This study investigates the application of the Asset Administration Shell (AAS) for monitoring and controlling industrial processes, using an Level, Flow, Pressure, and Temperature (NVPT) test bench as an example of a legacy system. The Reference Architectural Model for Industry 4.0 (RAMI 4.0) was adopted to structure the system architecture. Virtualization was carried out using three AASs: two passive ones, responsible for data acquisition, and one reactive one, in charge of temperature and level control. The AASs were modeled using the AASX Package Explorer and implemented in Python using the Opua asyncio library. Execution was performed on a Raspberry Pi to explore the benefits of edge computing. The solution enabled digitalization of the test bench, allowing remote monitoring and automated control of the system variables. The tests demonstrated the feasibility of the method, highlighting its applicability in integrating legacy systems into Industry 4.0, in an economical and efficient manner.}, archive = {J_ACCESS}, author = {Enrik Ramos Lima and Miguel Angel Orellana Postigo and João Victor Reis De Oliveira de Sicco and Raimundo Cláudio Souza Gomes and Fábio De Sousa Cardoso and Israel Gondres Torné}, doi = {10.1109/ACCESS.2025.3589172}, journal = {IEEE Access}, pages = {122530-122539}, shortjournal = {IEEE Access}, title = {Use of asset administration shell for monitoring and control of industrial processes in legacy systems}, volume = {13}, year = {2025}, }
• (2025). Attention-module-guided time-lapse leakage plume imaging driven by LeakInv-CUNet GPR inversion framework. ACCESS, 13, 122514-122529. (www)

  @article{ , abstract = {Accurate characterization of pipeline leakage by capturing time-lapse features and pathway orientations using ground penetrating radar (GPR) is crucial for optimizing the operational efficiency of water supply system and reducing water resource losses. However, achieving spatiotemporal leakage imaging remains challenging for current deterministic or probabilistic inversions due to the signal complexity, environmental interference, and computational burden. This paper develops LeakInv-CUNet, a novel attention-guided GPR inversion framework, to enable refined imaging of leakage plumes and their temporal-spatial evolution. To enhance network training, extensive GPR datasets are generated by augmenting simulated data and experimentally measured data, accounting for variations in injection orientation, plume dynamics, and subsurface media properties. By leveraging the dual advantages of the Convolutional Block Attention Module (CBAM) and U-Net architecture, the developed LeakInv-CUNet framework effectively extracts subtle leakage-induced response features, enabling refined imaging of leakage plumes and their orientations. Specifically, the training process utilizes the inversion result of GPR datasets corresponding to different leakage permittivity distributions, with feedbacks provided through functional mapping based on the Topp equation for water content distribution imaging. Special emphasis is placed on leakage features influenced by injection orientation and time-lapse characteristics. Simulations and on-site experiments demonstrate the framework’s superior noise robustness and practicality compared to conventional U-Net and Enc-Dec networks. With a mean absolute percentage error (MAPE) below 0.707%, structural similarity (SSIM) exceeding 0.924, and peak signal-to-noise ratio (PSNR) above 45.416, the attention-guided leakage imaging framework exhibits high efficiency and accuracy, showcasing its potential applications in early leakage warning and pipeline time-lapse monitoring.}, archive = {J_ACCESS}, author = {Honghua Wang and Shan Wang and Fei Zhou and Yi Lei and Bin Zhang}, doi = {10.1109/ACCESS.2025.3587902}, journal = {IEEE Access}, pages = {122514-122529}, shortjournal = {IEEE Access}, title = {Attention-module-guided time-lapse leakage plume imaging driven by LeakInv-CUNet GPR inversion framework}, volume = {13}, year = {2025}, }
• (2025). Anti-interference planning of flight path in terminal maneuvering area. ACCESS, 13, 122506-122513. (www)

  @article{ , abstract = {Determining the conflict-free aircraft path that includes uncertainty while efficiently using the available resource is critical for air transportation. However, many of these approaches assume perfect knowledge, and therefore, the interference immunity of these methods is questionable. In this paper, we propose an anti-interference technique that depends on the dynamic property of path planning. First, we propose a modeling approach to the problem of finish time for scheduled flights. Then, we discuss some cost criterions and demonstrate how they can be used to obtain optimal scheduling in a dynamic environment. The simulation results are shown to illustrate the model performance.}, archive = {J_ACCESS}, author = {Han Yun-Xiang and Zhang Jian-Wei and Zhu Jiang-Ping}, doi = {10.1109/ACCESS.2019.2914070}, journal = {IEEE Access}, pages = {122506-122513}, shortjournal = {IEEE Access}, title = {Anti-interference planning of flight path in terminal maneuvering area}, volume = {13}, year = {2025}, }
• (2025). A blockchain based data monitoring and sharing approach for smart grids. ACCESS, 13, 122496-122505. (www)

  @article{ , abstract = {With the development of science and technology, human beings cannot live without electricity. The introduction of smart grid systems brings new ideas to break the shackle of existing electricity systems. This paper proposes a mechanism with data monitoring and sharing capabilities based on the consortium blockchain, realizing comprehensive monitoring of smart devices, and promoting the effective sharing of electrical data in smart grids. When a smart device is out of order, the smart contract connected to it will be triggered, and the users can check the running status through the smart phone. This approach allows nodes in the consortium blockchain to request transactions, using the prepaid payment smart contract with time-lock script to protect the consumer right of request nodes. In addition, we use a $(t,n)$ -threshold secret sharing scheme to realize multiparty sharing of electrical data. Paillier encryption arithmetic is used to guarantee the confidentiality of messages in node transaction.}, archive = {J_ACCESS}, author = {Yanhan Yang and Mingzhe Liu and Qin Zhou and Helen Zhou and Ruili Wang}, doi = {10.1109/ACCESS.2019.2952687}, journal = {IEEE Access}, pages = {122496-122505}, shortjournal = {IEEE Access}, title = {A blockchain based data monitoring and sharing approach for smart grids}, volume = {13}, year = {2025}, }
• (2025). An enhanced precise carrier phase positioning method for vehicle navigation. ACCESS, 13, 122484-122495. (www)

  @article{ , abstract = {Precise carrier phase positioning is a widely used technique in vehicle navigation, localization, and mapping, which utilizes pseudorange and carrier phase measurements with the extended Kalman filter (EKF) to estimate position, velocity, and ambiguities. For highly accurate vehicle positioning requirements, propagation errors of measurements need to be considered, especially the ionospheric delay, which is the most significant error source. In EKF, a priori information of different error characteristics can be fully utilized as constraints to obtain more precise state estimation results, even when no corrections are available. This paper aims to improve the positioning accuracy for vehicle navigation and proposes a spherical harmonic function (SHF)-based undifferenced and uncombined (UDUC) carrier phase positioning method for vehicles to fit the ionospheric delays of multiple satellites in a single formula and reduce the marginal effect caused by polynomials. Better positioning results are expected compared to traditional UDUC and polynomial fitting approaches. The feasibility of the proposed method is tested on a kinematic dataset from a real-world car test. The experimental results show that the proposed method outperforms the traditional UDUC and polynomial fitting methods, respectively, with a root mean square error of $0.860m$ and a standard deviation of $0.579m$ when using broadcast ephemeris. In summary, the results demonstrate that the proposed method is highly beneficial for precise vehicle navigation.}, archive = {J_ACCESS}, author = {Qiangqiang Shuai and Qiyi Pang and Fangzhou Tang and Zun Niu and Bocheng Zhu}, doi = {10.1109/ACCESS.2023.3285818}, journal = {IEEE Access}, pages = {122484-122495}, shortjournal = {IEEE Access}, title = {An enhanced precise carrier phase positioning method for vehicle navigation}, volume = {13}, year = {2025}, }
• (2025). Optimized zero cross-correlation code for free space and underwater optical communication using a modular rotational matrix. ACCESS, 13, 122473-122483. (www)

  @article{ , abstract = {This paper introduces a novel code construction method called the modular rotation matrix zero cross-correlation code (MRM ZCCC), designed to enhance multi-user optical communication systems such as free space optics (FSO) and underwater wireless optical communication (UWOC). The proposed method generates zero cross-correlation codes through a modular and rotational strategy, ensuring scalability, minimal multi-user interference, and reduced computational complexity. Two variants—constant weight (CW) and variable weight (VW) MRM ZCCC—are developed to accommodate users with different data rate requirements. Simulation results demonstrate that the VW MRM-ZCCC reduces code length by up to 50% compared to Basic ZCCC and by 25% compared to Pascal ZCCC, thereby improving spectral efficiency. Moreover, under the most demanding underwater conditions, the VW MRM-ZCCC demonstrates up to a 60-dB enhancement in Log of BER compared to the Basic ZCCC. It also delivers an overall system performance boost of approximately 35–65% in both FSO and UWOC scenarios relative to conventional methods. In addition, its modular design supports up to 33% more simultaneous users within the same code length, making it a scalable, adaptable, and interference-tolerant solution for future optical wireless communication systems.}, archive = {J_ACCESS}, author = {Eddy Wijanto and Kevin Sutanto}, doi = {10.1109/ACCESS.2025.3587259}, journal = {IEEE Access}, pages = {122473-122483}, shortjournal = {IEEE Access}, title = {Optimized zero cross-correlation code for free space and underwater optical communication using a modular rotational matrix}, volume = {13}, year = {2025}, }
• (2025). Audio adversarial example detection using the audio style transfer learning method. ACCESS, 13, 122464-122472. (www)

  @article{ , abstract = {In this paper, we propose a method for defending against audio adversarial examples that operates by applying audio style transfer learning. The proposed method has the effect of maintaining the classification result produced by the target model and removing the adversarial noise by changing only the style while maintaining the content of the input audio sample. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method improved the target model’s accuracy on the adversarial examples from 2.1% to 79.2% while maintaining its accuracy on the original samples at 81.4%.}, archive = {J_ACCESS}, author = {Hyun Kwon and Kangjun Lee and Junyeol Ryu and Jun Lee}, doi = {10.1109/ACCESS.2022.3216075}, journal = {IEEE Access}, pages = {122464-122472}, shortjournal = {IEEE Access}, title = {Audio adversarial example detection using the audio style transfer learning method}, volume = {13}, year = {2025}, }
• (2025). Tencent text-video retrieval: Hierarchical cross-modal interactions with multi-level representations. ACCESS, 13, 122453-122463. (www)

  @article{ , abstract = {Text-Video Retrieval plays an important role in multi-modal understanding and has attracted increasing attention in recent years. Most existing methods focus on constructing contrastive pairs between whole videos and complete caption sentences, while overlooking fine-grained cross-modal relationships, e.g., clip-phrase or frame-word. In this paper, we propose a novel method, named Hierarchical Cross-Modal Interaction (HCMI), to explore multi-level cross-modal relationships among video-sentence, clip-phrase, and frame-word for text-video retrieval. Considering intrinsic semantic frame relations, HCMI performs self-attention to explore frame-level correlations and adaptively cluster correlated frames into clip-level and video-level representations. In this way, HCMI constructs multi-level video representations for frame-clip-video granularities to capture fine-grained video content, and multi-level text representations at word-phrase-sentence granularities for the text modality. With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i.e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities. Further boosted by adaptive label denoising and marginal sample enhancement, HCMI achieves new state-of-the-art results on various benchmarks, e.g., Rank@1 of 55.0%, 58.2%, 29.7%, 52.1%, and 57.3% on MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet, respectively.}, archive = {J_ACCESS}, author = {Jie Jiang and Shaobo Min and Weijie Kong and Hongfa Wang and Zhifeng Li and Wei Liu}, doi = {10.1109/ACCESS.2022.3227973}, journal = {IEEE Access}, pages = {122453-122463}, shortjournal = {IEEE Access}, title = {Tencent text-video retrieval: Hierarchical cross-modal interactions with multi-level representations}, volume = {13}, year = {2025}, }
• (2025). Design and control for piezoelectric energy harvester based on a heuristic optimization algorithms. ACCESS, 13, 122445-122452. (www)

  @article{ , abstract = {Energy harvesting represents a significant focus of attention for researchers to make optimal use of the energy available around in the environment, whether it is a natural phenomenon or resulting from human movement, equipment, and structures. This energy is available in several forms, such as solar, wind energy, temperature changes, and kinetic energy due to mechanical vibration. The framework of this paper includes the modeling of an intelligent controller for an efficient piezoelectric vibration energy harvester. In addition, it suggested two controllers to control the output voltage for this harvester. The Proportional-Integrated-Derivative (PID) control is applied in this paper as a main controller. The parameters of the second controller will be optimized by using various heuristic algorithms. (Ant Colony Optimization (ACO), Modified Camel Traveling Algorithm (MCTA), and Particle Swarm Optimization (PSO)). MATLAB/Simulink software will be used to build the model of the proposed circuit, where the model be verified under various testing conditions. The model output response is improved after adjusting the PID controller in line with optimization algorithms as compared with the conventional PID controller. The simulation findings indicate the capability of the proposed model of harvester with PSO-PID controller to give the best performance to get the desired output under different forces compared with the two other controllers (ACO-PID and MCTA-PID).}, archive = {J_ACCESS}, author = {Habeeb Jaber Nekad and Diyah Kammel Shary and Oday Alahmad}, doi = {10.1109/ACCESS.2025.3582521}, journal = {IEEE Access}, pages = {122445-122452}, shortjournal = {IEEE Access}, title = {Design and control for piezoelectric energy harvester based on a heuristic optimization algorithms}, volume = {13}, year = {2025}, }
• (2025). The looming privacy challenges posed by commercial satellite imaging: Remedies and research directions. ACCESS, 13, 122432-122444. (www)

  @article{ , abstract = {As commercial satellites proliferate and data-capture technologies become more diverse, accessible, and high-resolution, new privacy concerns emerge. Additionally, emerging trends such as direct-to-consumer image sales and AI-enhanced computer vision strongly impact the satellite imaging industry. While these technologies benefit applications such as natural disaster monitoring and precision agriculture, they also introduce novel privacy risks. This perspective paper provides several contributions: we start by analyzing privacy risks using the LINDDUN privacy framework, categorizing threats like linking, identification, and data disclosure. Moving on from these findings, we introduce and discuss a few mitigation strategies, including inpainting and access control. Finally, we highlight some research directions to address these emerging challenges. Overall, this work characterizes the major issues in the evolving privacy landscape of satellite imaging, provides actionable solutions to mitigate these privacy risks, and highlights future research directions, positioning itself as a reference for researchers, policymakers, and industry stakeholders.}, archive = {J_ACCESS}, author = {Nathanaël Denis and Roberto Di Pietro}, doi = {10.1109/ACCESS.2025.3588593}, journal = {IEEE Access}, pages = {122432-122444}, shortjournal = {IEEE Access}, title = {The looming privacy challenges posed by commercial satellite imaging: Remedies and research directions}, volume = {13}, year = {2025}, }
• (2025). A seamless switching strategy for hybrid AC/DC microgrids under varied control complexities. ACCESS, 13, 122420-122431. (www)

  @article{ , abstract = {In peer-to-peer controlled hybrid AC/DC microgrids, the grid-connected inverters switch between different control modes with the change of the operating conditions. However, the above-mentioned mode transitions trigger transient disturbances in voltage, frequency, and current, causing power mismatch, such that the smooth operation of the microgrid will be hampered. To solve the above-mentioned problems, a composite control strategy is proposed in this study following droop control and PQ control, with the aim of achieving seamless switching between islanded and grid-connected modes. The proposed strategy couples the current inner loops of the above control methods (droop control and PQ control) to develop a dual-mode combined controller operating based on current tracking. This configuration enables the reference current of the input current inner loop controller to track the pre-switching state while maintaining the correct initial value. Moreover, a compensatory pre-synchronization control strategy is developed to cope with variations in frequency, phase, and amplitude. The voltage offset is improved from 19% to synchronize with the grid, and the microgrid islanded/grid-connected mode is smoothly switched. The reference value of the outer loop is changed by PQ control, so that the exchange power of the grid side and the microgrid side is 0. Thus, seamless switching between grid-connected and islanded modes can be achieved. The effectiveness and feasibility of the control strategy proposed in this study are verified in the simulation system.}, archive = {J_ACCESS}, author = {Huayu Zhang and Shan He and Zhi Yuan and Zhijiang Cheng and Jing Cheng and Bing Hu and Lijun Xu and Xiaochao Fan}, doi = {10.1109/ACCESS.2023.3312509}, journal = {IEEE Access}, pages = {122420-122431}, shortjournal = {IEEE Access}, title = {A seamless switching strategy for hybrid AC/DC microgrids under varied control complexities}, volume = {13}, year = {2025}, }
• (2025). ViPeR: Vision-based surgical phase recognition. ACCESS, 13, 122408-122419. (www)

  @article{ , abstract = {Surgical phase recognition is a critical, yet challenging, problem in computer vision, with significant implications for automated surgical training, intraoperative assistance, and workflow optimization. However, the development of robust models is hindered by the scarcity of well-annotated medical datasets and the complexity of surgical workflows, which exhibit substantial inter- and intra-procedural variations. To address these challenges, we introduce UroSlice, a novel complex dataset that focuses on nephrectomy surgeries, capturing both radical and partial procedures performed using robotic-assisted techniques. In order to address the task of phase recognition in these videos, we propose a novel model named “ViPeR” (Vision-based Surgical Phase Recognition). Our model incorporates hierarchical dilated temporal convolution layers and inter-layer residual connections to capture temporal correlations at both fine and coarse granularities. Experimental evaluations validates that our approach achieves state-of-the-art performance of 91.7% on Cholec80 and 66.4% on UroSlice - a more challenging dataset due to its irregular phase durations, non-standardized phase order, and a smaller sample size. The code and dataset are publicly available at: https://github.com/soumyadeepchandra/ViPeR}, archive = {J_ACCESS}, author = {Soumyadeep Chandra and Sayeed Shafayet Chowdhury and Courtney Yong and Ginnie Jeng and Ashorne Mahenthiran and Kostantinos E. Morris and Harrison L. Love and Chandru P. Sundaram and Kaushik Roy}, doi = {10.1109/ACCESS.2025.3588428}, journal = {IEEE Access}, pages = {122408-122419}, shortjournal = {IEEE Access}, title = {ViPeR: Vision-based surgical phase recognition}, volume = {13}, year = {2025}, }
• (2025). An evaluation of LLMs and google translate for translation of selected indian languages via sentiment and semantic analyses. ACCESS, 13, 122386-122407. (www)

  @article{ , abstract = {Large Language models (LLMs) have been prominent for language translation, including low-resource languages. There has been limited study on the assessment of the quality of translations generated by LLMs, including Gemini, GPT, and Google Translate. This study addresses this limitation by using semantic and sentiment analysis of selected LLMs for Indian languages, including Sanskrit, Telugu and Hindi. We select prominent texts (Bhagavad Gita, Tamas and Maha Prasthanam) that have been well translated by experts and use LLMs to generate their translations into English, and provide a comparison with selected expert (human) translations. Our investigation revealed that while LLMs have made significant progress in translation accuracy, challenges remain in preserving sentiment and semantic integrity, especially in metaphorical and philosophical contexts for texts such as the Bhagavad Gita. The sentiment analysis revealed that GPT models are better at preserving the sentiment polarity for the given texts when compared to human (expert) translation. The results revealed that GPT models are generally better at maintaining the sentiment and semantics when compared to Google Translate. This study could help in the development of accurate and culturally sensitive translation systems for large language models.}, archive = {J_ACCESS}, author = {Rohitash Chandra and Aryan Chaudhari and Yeshwanth Rayavarapu}, doi = {10.1109/ACCESS.2025.3585629}, journal = {IEEE Access}, pages = {122386-122407}, shortjournal = {IEEE Access}, title = {An evaluation of LLMs and google translate for translation of selected indian languages via sentiment and semantic analyses}, volume = {13}, year = {2025}, }
• (2025). A direct gait kinematic model to estimate forward progression and spatial stepping features in cartesian space. ACCESS, 13, 122376-122385. (www)

  @article{ , abstract = {Robotic exoskeletons are usually controlled in joint-space, being spatio-temporal step features an uncontrolled consequence. However, clinical practice is focused on the cartesian movement of the foot rather than analytical hip/knee/ankle flexion/extension movements. In order to enable robotic exoskeletons to control the cartesian trajectory of the foot, a kinematic model to translate from joint-space to cartesian-space is needed. Besides, such a model requires to consider the forward progression of the direct (joints-to-foot) kinematic model to calculate the spatio-temporal stepping features. This paper presents a direct gait kinematic model including pelvis, hips, knees and ankles degrees of freedom based on Denavit-Hartenberg method and also proposes a method to iteratively compute the progression of the kinematic chain considering a straight walking over a flat floor. This model has been experimentally validated, comparing its results with the gait of 5 healthy subjects recorded by a Vicon Motion Capture System. Results show RMS errors lower than 1 cm and 5 cm in vertical and horizontal directions respectively.}, archive = {J_ACCESS}, author = {Julio S. Lora-Millan and Jaime Ramos-Rojas and Juan Carballeira and Juan A. Castano and Antonio J. del-Ama}, doi = {10.1109/ACCESS.2025.3587807}, journal = {IEEE Access}, pages = {122376-122385}, shortjournal = {IEEE Access}, title = {A direct gait kinematic model to estimate forward progression and spatial stepping features in cartesian space}, volume = {13}, year = {2025}, }
• (2025). Capability analysis of fault-tolerant control with differential braking for steer-by-wire failure. ACCESS, 13, 122354-122375. (www)

  @article{ , abstract = {This study proposes a fault-tolerant control strategy to solve the performance degradation problem due to kingpin offset, especially the negative kingpin offset condition, when steering torque failure occurs in a Steer-by-Wire (SbW) system. Previous studies overlooked kingpin offset, particularly negative offset, and lacked methods for practical control under such conditions. To this end, differential-braking experiments were conducted using an experimental vehicle with a negative kingpin offset to verify the vehicle model. Using the validated model, offline optimization was conducted to identify the control inputs that maximize yaw rate across varying offset and speed conditions, revealing limitations of the conventional approach. Based on this analysis, differential-braking distribution guidelines for each kingpin offset were derived, and a torque distribution controller that reflects kingpin offset was proposed by utilizing the steady-state yaw rate gain of the front and rear wheel differential braking. The proposed strategy was evaluated through open- and closed-loop tests in the full vehicle model and IPG CarMaker simulation environment. The evaluation results showed that the conventional strategy had problems such as performance degradation and unintended vehicle turning under some kingpin offset conditions; however, the proposed strategy showed improved and consistent turning performance within the tested kingpin offset range (–30 to +30 mm). The control error and control effort were reduced over the entire tested kingpin offset conditions compared with that of the conventional strategy.}, archive = {J_ACCESS}, author = {Yunchul Ha and Seunguk Jeon and Haekwon Park and Aldo Sorniotti and Seunghoon Woo}, doi = {10.1109/ACCESS.2025.3588473}, journal = {IEEE Access}, pages = {122354-122375}, shortjournal = {IEEE Access}, title = {Capability analysis of fault-tolerant control with differential braking for steer-by-wire failure}, volume = {13}, year = {2025}, }
• (2025). Elastic shifts: I/O sequence patterns of ransomware and detection evasion. ACCESS, 13, 122341-122353. (www)

  @article{ , abstract = {Cyber-criminals frequently use crypto-ransomware to gain financial benefit by encrypting victims’ valuable digital assets, such as photos and documents. The unique I/O behavior sequence patterns of such crypto-ransomware have been used as key features in ransomware detection. Prior behavior-profiling approaches built detection patterns from existing ransomware datasets using their own tools or third-party tools for behavior monitoring. In addition, these approaches applied simple rule-based matching. However, future ransomware may not consistently exhibit previous patterns, since its behavior can change significantly. Furthermore, the monitoring tools used in existing detection methods may not be sufficient to interpret the behavior of future ransomware. This study demonstrates that ransomware can effectively evade existing detection methods by changing its I/O behavior sequence patterns. We induce monitoring tools to misinterpret the semantics of ransomware I/O operations, which leads detection systems to construct incorrect behavioral patterns. Our findings expose weaknesses in current endpoint behavior-based ransomware detectors, including an antivirus program’s real-time detection, and underscore the need for methods that remain effective against previously unseen patterns.}, archive = {J_ACCESS}, author = {Il Hyeon Ju and Huy Kang Kim}, doi = {10.1109/ACCESS.2025.3587698}, journal = {IEEE Access}, pages = {122341-122353}, shortjournal = {IEEE Access}, title = {Elastic shifts: I/O sequence patterns of ransomware and detection evasion}, volume = {13}, year = {2025}, }
• (2025). Ensembles of gradient boosting recurrent neural network for time series data prediction. ACCESS, 13, 122331-122340. (www)

  @article{ , abstract = {Ensemble deep learning can combine strengths of neural network and ensemble learning, which gradually becomes a new emerging research direction. However, the existing methods either lack theoretical support or demand large integrated models. In this paper, Ensembles of Gradient Boosting Recurrent Neural Network (EGB-RNN) is proposed, which combines the gradient boosting ensemble framework with three types of recurrent neural network models, namely Minimal Gated Unit (MGU), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). RNN model is used as base learner to integrate an ensemble learner, through the way of gradient boosting. Meanwhile, for ensuring the ensemble model fit data better, Step Iteration Algorithm is designed to find an appropriate learning rate before models being integrated. The proposed method is tested on four time-series datasets. Experimental results demonstrate that with the number of integration increasing, the performance of three types of EGB-RNN models tend to converge and the best EGB-RNN model and the best degree of ensemble vary with data sets. It is also shown in statistical results that the designed EGB-RNN models perform better than six baselines.}, archive = {J_ACCESS}, author = {Shiqing Sang and Fangfang Qu and Pengcheng Nie}, doi = {10.1109/ACCESS.2021.3082519}, journal = {IEEE Access}, pages = {122331-122340}, shortjournal = {IEEE Access}, title = {Ensembles of gradient boosting recurrent neural network for time series data prediction}, volume = {13}, year = {2025}, }
• (2025). An enhanced multiple object tracking method with improved feature fusion and cross-camera trajectory matching for closed scenes. ACCESS, 13, 122315-122330. (www)

  @article{ , abstract = {Traditional MOT methods often struggle with issues such as occlusions, identity switches, and limited cross-camera trajectory matching accuracy. To overcome these challenges, this paper proposes a modified advanced multiple object tracking (MOT) method based on the CSTrack algorithm, named MACSTrack, which aims to address the competition between detection and re-identification (ReID) tasks in MOT and enhance its applicability to cross-camera scenarios. MACSTrack introduces a reciprocal inhibition network (REIN) to deeply mine and fuse the feature specificity and commonality between detection and ReID tasks, suppressing irrelevant features and enhancing task synergy. An improved scale-aware attention network (ISAAN) is employed to minimize information loss and feature distortion during feature fusion. Furthermore, a hierarchical association method based on ambiguous assignment modeling (HAAM) is proposed to improve the stability and robustness of object tracking in time series. To address the challenge of cross-camera trajectory matching in closed scenes, a two-stage strategy utilizing prior knowledge of the number of individuals is developed, which significantly improves matching accuracy. Experimental results on benchmark datasets demonstrate that MACSTrack outperforms the original CSTrack algorithm and achieves comparable performance to state-of-the-art MOT methods. Specifically, MACSTrack exhibits improvements in key metrics such as MOTA, IDF1, and a substantial reduction in identity switches. The proposed two-stage cross-camera trajectory matching strategy also achieves over 10% higher accuracy compared to conventional methods, highlighting the practicality and effectiveness of the approach.}, archive = {J_ACCESS}, author = {Fengjun Chen and Guangxun Zhu and Zhenyu Lv and Ke Wang and Ruiyan Xu and Jian Liu}, doi = {10.1109/ACCESS.2025.3586901}, journal = {IEEE Access}, pages = {122315-122330}, shortjournal = {IEEE Access}, title = {An enhanced multiple object tracking method with improved feature fusion and cross-camera trajectory matching for closed scenes}, volume = {13}, year = {2025}, }
• (2025). On usage of artificial intelligence for predicting neonatal diseases, conditions, and mortality: A bibliometric review. ACCESS, 13, 122294-122314. (www)

  @article{ , abstract = {Purpose: Care and attention during the neonatal period are crucial to preventing negative outcomes. The literature presents artificial intelligence models as promising tools to assist healthcare professionals in disease prediction and support clinical decision-making. Methods: This study conducts a bibliometric review of the use of artificial intelligence models in predicting neonatal diseases, conditions and mortality. The review analyzed publications from 2014 to 2024. A total of 629 studies were selected after applying selection criteria. Subsequently, analyses of collaboration networks, keyword co-occurrence, citations and cluster analysis were performed. Results: The results show that the United States, China and the United Kingdom lead scientific production and international collaborations. 12 neonatal diseases were identified, with emphasis on “retinopathy of prematurity”, “necrotizing enterocolitis” and “bronchopulmonary dysplasia”; 7 clinical conditions, including “prematurity”, “perinatal asphyxia” and “jaundice”; and 5 neonatal outcomes, mainly “sepsis”, “mortality” and “cerebral palsy.” Cluster analysis revealed that studies predominantly use clinical, laboratory, genetic and imaging data, with Logistic Regression, Random Forest and Convolutional. Conclusion: The study has growing interest in applying artificial intelligence to neonatal care. The models are increasingly used with clinical, laboratory, genetic and imaging data, enabling earlier and more accurate diagnoses. However, the study also underscores important ethical considerations, such as data quality, algorithmic transparency and equitable access to these technologies, particularly in underrepresented regions, with scientific production uneven and limited participation from low- and middle-income countries.}, archive = {J_ACCESS}, author = {Flávio Leandro de Morais and Raysa Carla Leal da Silva and Anna Beatriz Silva and Estefani Pontes Simão and Maria Eduarda Ferro de Mello and Stephany Paula da Silva Canejo and Katia Maria Mendes and Waldemar Brandão Neto and Jackson Raniel Florencio da Silva and Maicon Herverton Lino Ferreira da Silva Barros and Patricia Takako Endo}, doi = {10.1109/ACCESS.2025.3582503}, journal = {IEEE Access}, pages = {122294-122314}, shortjournal = {IEEE Access}, title = {On usage of artificial intelligence for predicting neonatal diseases, conditions, and mortality: A bibliometric review}, volume = {13}, year = {2025}, }
• (2025). Dual graph for traffic forecasting. ACCESS, 13, 122285-122293. (www)

  @article{ , abstract = {Traffic forecasting is the task of predicting future traffic based on historical traffic data. It is challenging due to the complex spatial-temporal correlation on road networks. Most existing research works use sequential Graph Neural Networks (GNN) to model traffic inference. However, they only focus on nodes (intersections) or edges (road segments) traffic forecasting alone. As a result, they could hardly provide a complete description of future traffic on road networks. Actually, nodes and edges traffic are interrelated. Both of them are important for traffic safety and efficiency, and neither one is negligible. In this paper, we exploit nodes and edges information together and make traffic forecasting on nodes and edges simultaneously. We propose a novel dual graph framework, called DualGraph, to model the propagation behavior of traffic on road networks. Inside our framework, we develop a DualMap block to simulate the recursive interactions between nodes and edges. The interaction process is realized by a message passing mechanism of nearby information flow. We employ the Simulation of Urban MObility (SUMO) software to generate real-world traffic data to illustrate the effectiveness of our method. We also empirically evaluate our model on public traffic datasets. The results show that even for node or edge traffic forecasting alone, our model still outperforms compared ones, especially for long term (one hour) prediction.}, archive = {J_ACCESS}, author = {Long Wei and Zhengxu Yu and Zhongming Jin and Liang Xie and Jianqiang Huang and Deng Cai and Xiaofei He and Xian-Sheng Hua}, doi = {10.1109/ACCESS.2019.2958380}, journal = {IEEE Access}, pages = {122285-122293}, shortjournal = {IEEE Access}, title = {Dual graph for traffic forecasting}, volume = {13}, year = {2025}, }
• (2025). Entropy field decomposition based outage detection for ultra-dense networks. ACCESS, 13, 122270-122284. (www)

  @article{ , abstract = {Ambitious quality of experience expectations from 5G mobile cellular networks have spurred the research towards ultra-dense heterogeneous networks (UDHNs). However, due to coverage limitations of millimeter wave cells and lack of coverage data in UDHNs, discovering coverage lapses in such 5G networks may become a major challenge. Recently, numerous studies have explored machine learning-based techniques to detect coverage holes and cell outages in legacy networks. Majority of these techniques are susceptible to noise in the coverage data and only characterize outages in the spatial domain. Thus, the temporal impact of an outage, i.e., the duration of its presence remains unidentified. In this paper, for the first time, we present an outage detection solution that characterizes outages in both space and time while also being robust to noise in the coverage data. We do so by employing entropy field decomposition (EFD) which is a combination of information field theory and entropy spectrum pathways theory. We demonstrate that compared to other techniques such as independent component analysis and k-means clustering, EFD returns accurate detection results for outage detection even in the presence of heavy shadowing in received signal strength data which makes it ideal for practical implementation in emerging mobile cellular networks.}, archive = {J_ACCESS}, author = {Ahmad Asghar and Hasan Farooq and Haneya Naeem Qureshi and Adnan Abu-Dayya and Ali Imran}, doi = {10.1109/ACCESS.2021.3056551}, journal = {IEEE Access}, pages = {122270-122284}, shortjournal = {IEEE Access}, title = {Entropy field decomposition based outage detection for ultra-dense networks}, volume = {13}, year = {2025}, }
• (2025). EEG functional connection analysis based on the weight distribution of convolutional neural network. ACCESS, 13, 122250-122269. (www)

  @article{ , abstract = {Functional connections are commonly used when exploring the human brain, especially in brain data analysis. However, most of the studies concentrate on traditional statistical analysis. In this paper, we innovatively combined the functional connection with the deep learning algorithms and analysed the matrices after the weight distribution of each layer of the convolutional neural network (CNN) to obtain the connections that play a vital role in the classification. The electroencephalogram (EEG) data used in this paper was acquired through a visual mismatch negativity (MMN) experiment. When dealing with this data, each electrode was regarded as a node in the network, and the phase lag index (PLI) was calculated to construct the functional connection matrices, which were used as inputs for the CNN classification and feature extraction. The matrices after the weight distribution were further analysed by means of graph theory. In this paper, the classification accuracy for deviation and standard stimuli are over 95%, and the theta band achieved the highest accuracy. Through the distributed matrices, we found that there are two regions that obtained larger weights from the convolutional layers, i.e., the temporal lobe and the occipital region. The occipital region is related to our visual experiment, and the temporal lobe region is connected with MMN mechanism. We also considered the strategy of the three-layer CNN according to weight distribution processing.}, archive = {J_ACCESS}, author = {Jinglong Wu and Peiwen Huang and Tiantian Liu and Go Ritsu and Duanduan Chen and Tianyi Yan}, doi = {10.1109/ACCESS.2020.2968717}, journal = {IEEE Access}, pages = {122250-122269}, shortjournal = {IEEE Access}, title = {EEG functional connection analysis based on the weight distribution of convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Optimal pricing and pre-sale policies for perishable product in an EOQ inventory model. ACCESS, 13, 122242-122249. (www)

  @article{ , abstract = {This paper considers an optimal pricing and pre-sale policy of an EOQ inventory model for perishable products, in which the demand of pre-sale period is dependent on price and pre-sale time, and the demand of spot-sale period is dependent on the price and stock level. Several theoretical results are derived to demonstrate the existence and uniqueness of the solution. For comparison, we also propose a benchmark inventory model in which all the products are to be sold in a spot-sale period, and the optimal policy of pricing and economic order quantity is provided. The numerical experiments are carried out and the sensitivity analysis of the parameters is presented to illustrate the theoretical results for the proposed inventory model, and the comparative analysis of the two models is provided to show if the pre-sale policy is better off the spot-sale policy under some conditions and vice versa.}, archive = {J_ACCESS}, author = {Lianxia Zhao and Li Zhang and Jianxin You and Chunyan Duan}, doi = {10.1109/ACCESS.2019.2916582}, journal = {IEEE Access}, pages = {122242-122249}, shortjournal = {IEEE Access}, title = {Optimal pricing and pre-sale policies for perishable product in an EOQ inventory model}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of full-duplex NOMA in RIS-assisted UAV networks through channel-aware transmission switching. ACCESS, 13, 122224-122241. (www)

  @article{ , abstract = {The integration of reconfigurable intelligent surfaces (RIS) and uncrewed aerial vehicles (UAVs) offers significant potential for enhancing spectral efficiency, connectivity, and coverage in future wireless networks. This paper examines a RIS-enabled, UAV-assisted non-orthogonal multiple access (NOMA) system, wherein the UAV operates in full-duplex mode to facilitate signal transmission. This work identifies the limitations of an existing joint transmission and selection combining (JT-SC) scheme, showing that simultaneous RIS and UAV transmissions can degrade system performance due to mutual interference and processing delays. However, the challenge of mitigating mutual interference and processing delays in simultaneous RIS and full-duplex UAV transmissions has not been adequately addressed in existing works, and a dynamic switching strategy between RIS and UAV based on channel conditions remains unexplored. To address these challenges, we propose a channel-aware hybrid switching (CA-HS) scheme that dynamically selects either RIS reflection or UAV transmission based on instantaneous channel conditions. Numerical and simulation results demonstrate that the proposed CA-HS scheme significantly improves both system throughput and reliability compared to existing baseline methods. Specifically, the achieved rate of the CA-HS scheme shows an improvement of approximately 8% and 20% compared to the UAV-only case and the JT-SC scheme, respectively, highlighting its effectiveness and practical relevance for next-generation wireless communication systems.}, archive = {J_ACCESS}, author = {Senthilkumar Dhanasekaran and Harini Grama Srinath}, doi = {10.1109/ACCESS.2025.3587252}, journal = {IEEE Access}, pages = {122224-122241}, shortjournal = {IEEE Access}, title = {Performance enhancement of full-duplex NOMA in RIS-assisted UAV networks through channel-aware transmission switching}, volume = {13}, year = {2025}, }
• (2025). DS-AdaptNet: An efficient retinal vessel segmentation framework with adaptive enhancement and depthwise separable convolutions. ACCESS, 13, 122207-122223. (www)

  @article{ , abstract = {Medical image segmentation plays a crucial role in diagnosis and treatment planning, yet faces persistent challenges including limited annotated data, boundary ambiguity, and high computational demands that hinder clinical deployment. This paper presents DS-AdaptNet, an efficient segmentation framework that addresses these challenges through two synergistic technical innovations. First, we introduce a Multi-Dimensional Morphological Perturbation Augmentation Technique (MD-PAT) that generates anatomically plausible variations through topologically-constrained deformation fields, significantly enhancing training data diversity while preserving critical structural properties. Second, we develop a Context-Aware Adaptive Threshold Optimization (CA-ATO) algorithm that dynamically determines optimal thresholds by integrating multi-scale contextual information and uncertainty estimates, substantially improving boundary delineation accuracy and fine structure preservation. These techniques are integrated with an Efficient Depthwise Convolutional Neural Network (ED-CNN) architecture that employs depth-separable convolutions, dramatically reducing computational complexity while maintaining high segmentation accuracy. Our comprehensive experiments on three benchmark retinal vessel segmentation datasets demonstrate that the proposed DS-AdaptNet achieves state-of-the-art performance while maintaining exceptional efficiency. Notably, our method attains a Dice coefficient of 0.8328 on DRIVE, 0.8110 on CHASE_DB1, and 0.8515 on STARE, consistently outperforming existing approaches. Most importantly, DS-AdaptNet achieves these results with only 1.57M parameters and 44.08 GFLOPs—a 94.9% reduction in parameters and 77.2% reduction in computational operations compared to standard U-Net. These efficiency gains enable real-time retinal vessel analysis on standard hardware without specialized acceleration, making DS-AdaptNet particularly suitable for resource-constrained clinical environments and telemedicine applications. The proposed framework establishes a foundation for developing practical computer-aided diagnostic systems that balance accuracy, efficiency, and clinical utility.}, archive = {J_ACCESS}, author = {Shuting Chen and Chengxi Hong and Hong Jia}, doi = {10.1109/ACCESS.2025.3585611}, journal = {IEEE Access}, pages = {122207-122223}, shortjournal = {IEEE Access}, title = {DS-AdaptNet: An efficient retinal vessel segmentation framework with adaptive enhancement and depthwise separable convolutions}, volume = {13}, year = {2025}, }
• (2025). Hybrid AI for predictive cyber risk assessment: Federated graph-transformer architecture with explainability. ACCESS, 13, 122187-122206. (www)

  @article{ , abstract = {The increasing complexity and dynamism of modern digital infrastructures have significantly elevated the difficulty of anticipating and mitigating cyber threats. Traditional risk assessment methods, often based on static rules and signature matching, fail to provide sufficient predictive capabilities in scenarios characterized by high-volume, heterogeneous data and evolving attack patterns. Furthermore, conventional machine learning models lack architectural flexibility and contextual awareness to detect stealthy or multi-stage attacks across distributed environments. This paper proposes a hybrid model for predictive cyber risk assessment that integrates Graph Neural Networks (GNNs) for relational pattern modeling, a Transformer-based language model (CyberBERT) for semantic representation of logs and traffic data, and a Federated Learning framework to preserve data privacy during training. The model was trained and evaluated using public datasets (CIC-IDS2017, UNSW-NB15, MITRE ATT&CK, TON_IoT) and synthetically generated traffic from honeypots and real anonymized networks. The system achieved an average F1-score of 0.947, significantly outperforming traditional classifiers such as SVM (0.836), Random Forest (0.873), and standard deep neural networks (0.901). Including adversarial training improved attack detection by 21.3 % under evasion scenarios. Furthermore, the model supports interpretability through SHAP and LIME, reaching an 83.5 % concordance in explanation consistency. The architecture demonstrates scalability and low-latency inference on edge and cloud platforms, making it suitable for real-time deployment in heterogeneous environments. These results highlight the model’s capability to deliver accurate, resilient, and privacy-preserving predictions of cyber risks across dynamic and distributed infrastructures.}, archive = {J_ACCESS}, author = {Jaime Govea and Rommel Gutierrez and William Villegas-Ch and Alexandra Maldonado Navarro}, doi = {10.1109/ACCESS.2025.3588076}, journal = {IEEE Access}, pages = {122187-122206}, shortjournal = {IEEE Access}, title = {Hybrid AI for predictive cyber risk assessment: Federated graph-transformer architecture with explainability}, volume = {13}, year = {2025}, }
• (2025). Hybrid physics-LSTM framework for wind power prediction and control in virtual microgrid simulations. ACCESS, 13, 122175-122186. (www)

  @article{ , abstract = {Three-dimensional physical systems play a pivotal role in the development of cyber-physical infrastructures, particularly in the implementation of digital twins that enable the evaluation of hypothetical and adverse scenarios through high-fidelity simulation. This work presents a real-time 3D monitoring and feedback system designed for a custom transverse-axis wind turbine, integrating physical modeling principles with simulation engines developed initially for game environments. This hybrid architecture facilitates the virtual prototyping, testing, and validation of wind energy systems under near-operational conditions. The proposed framework combines two key components: 1) a physics-based model grounded in the mechanical and electromagnetic dynamics of wind turbine operation, and 2) a data-driven architecture composed of multiple layers. The physical layer interfaces directly with the sensors and actuators of the turbine, ensuring real-time synchronization between the physical and virtual systems. Data acquisition and communication are managed through the MQTT protocol, enabling low-latency streaming and robust interoperability. A long short-term memory neural network is integrated into the architecture to enhance predictive capabilities and trained to forecast wind energy production. An intelligent battery management system subsequently utilizes the output of the model to optimize charging strategies.}, archive = {J_ACCESS}, author = {Sebastián López Flórez and Guillermo Hernández González and Javier Prieto and Fernando de la Prieta}, doi = {10.1109/ACCESS.2025.3586798}, journal = {IEEE Access}, pages = {122175-122186}, shortjournal = {IEEE Access}, title = {Hybrid physics-LSTM framework for wind power prediction and control in virtual microgrid simulations}, volume = {13}, year = {2025}, }
• (2025). Data-driven optimization of aspect ratio in permanent magnet machines using deep learning and SHAP analysis. ACCESS, 13, 122164-122174. (www)

  @article{ , abstract = {The aspect ratio, defined as the ratio of the outer diameter to the stack length, is a critical parameter in permanent magnet (PM) machine design, with a profound impact on motor performance. This study presents a novel framework integrating deep learning and Shapley additive explanations (SHAP) to analyze the influence of design variables on the optimal aspect ratio. To achieve this, extensive datasets are generated using a metaheuristic optimization algorithm, covering diverse scenarios and objectives to ensure robust generalization and accuracy. A deep learning model is then trained on these datasets to capture the complex, nonlinear relationships between design variables and the aspect ratio. To enhance the interpretability of the “opaque model”, SHAP is employed, providing a detailed attribution analysis of each design variable contribution to the aspect ratio. This dual approach successfully uncovers the complex relationships between the aspect ratio and design variables across diverse design scenarios, thereby enabling actionable guidelines for sizing the outer diameter and height of the motor in the early design phase. Furthermore, the proposed methodology offers a scalable framework for analyzing other key ratios in motor design, establishing itself as a foundational tool for future advancements in this field.}, archive = {J_ACCESS}, author = {Kyeong Jin Kim and Ji Hoon Park and Dong Hoo Min and Seun Guy Min}, doi = {10.1109/ACCESS.2025.3586216}, journal = {IEEE Access}, pages = {122164-122174}, shortjournal = {IEEE Access}, title = {Data-driven optimization of aspect ratio in permanent magnet machines using deep learning and SHAP analysis}, volume = {13}, year = {2025}, }
• (2025). Providing QoS and resiliency for video streaming over MPTCP. ACCESS, 13, 122151-122163. (www)

  @article{ , abstract = {The performance of Internet applications can be improved by using multipath transport protocols such as Multipath TCP (MPTCP), which leverages aggregated bandwidth from multiple paths for improved throughput. MPTCP can be utilized to apply Quality of Service (QoS) and address network resiliency in order to increase the Quality of Experience (QoE) of video streaming applications. This paper introduces a novel approach for providing service differentiation to video streaming clients across different classes, while ensuring robust communication to mitigate link failures. The proposed method aims to deliver seamless streaming sessions by enhancing resilience and maintaining QoS under challenging network conditions. We evaluate the proposed approach using Dynamic Adaptive Streaming over HTTP (DASH), a widely used video streaming application that delivers high-quality media content over the Internet. The aim is to evaluate the performance of the proposed approach by focusing on user groups belonging to different service classes, each with varying QoE requirements. Additionally, we assess the resiliency of the proposed algorithm in scenarios involving subflow failures within the MPTCP connection. The results demonstrate that the proposed approach outperforms alternative methods in maintaining network connectivity and sustaining a high level of QoE, even in the event of subflow disconnections.}, archive = {J_ACCESS}, author = {Shadi Bikas and Müge Sayit}, doi = {10.1109/ACCESS.2025.3586446}, journal = {IEEE Access}, pages = {122151-122163}, shortjournal = {IEEE Access}, title = {Providing QoS and resiliency for video streaming over MPTCP}, volume = {13}, year = {2025}, }
• (2025). Retraction notice research on GNSS-assisted low-cost INS combined navigation moving base initial alignment technique. ACCESS, 13, 122150. (www)

  @article{ , archive = {J_ACCESS}, author = {Mengbo Sun and Jian Huang and Pengcheng Hu and Xiaohui Song and Zhu Li}, doi = {10.1109/ACCESS.2025.3588308}, journal = {IEEE Access}, pages = {122150}, shortjournal = {IEEE Access}, title = {Retraction notice research on GNSS-assisted low-cost INS combined navigation moving base initial alignment technique}, volume = {13}, year = {2025}, }
• (2025). Efficient region-wise packing of stereoscopic ERP videos based on information loss minimization. ACCESS, 13, 122132-122149. (www)

  @article{ , abstract = {Utilizing frame-compatible (FC) formats for packing stereoscopic videos often comes with challenges, as they require higher transmission bandwidth and larger memory buffers on the decoder compared to single-view videos. When it comes to stereoscopic 360° videos, as the primary content consumed by virtual reality (VR) applications, these requirements become even more challenging since they ask for ultra-high-resolution formats with high frame rates (e.g., 6K, 8K, or 12K at 100 frames per second). To address these challenges, sub-sampled versions of the left and right views are usually used to form the spatial FC format, leading to a loss of visual quality. In this paper, we propose an efficient region-wise packing method for equirectangular projection (ERP) videos with minimum information loss by exploiting the uneven sampling characteristic of ERP. Moreover, we propose a content-adaptive (CA) packing method for ERP videos, where the sizes of partitions, each with a particular horizontal downsampling factor, are adaptively determined based on spatial complexity. We then utilize a low-complexity frequency-domain approach to estimate the optimal partition sizes of the CA packing. We use these proposed methods to determine the optimal packing of the stereoscopic ERP videos in the FC format. Experimental results, using the VVenC Versatile Video Coding (VVC) encoder, show that compared with the standard side-by-side (SbS) format, with uniform horizontal half-downsampling (UHHDS), the proposed CA packing method provides an average 13.84% and 12.02% Bjøntegaard-Delta bitrate (BD-BR) reduction for Random Access (RA) and Low Delay B (LDB) configurations, respectively, with an average encoding time comparable to SbS. In addition, when the performance is measured based on user attention probability, using the Laplacian Distribution model, the coding performance of our proposed packing methods outperforms the state-of-the-art packing method with significantly lower computational complexity.}, archive = {J_ACCESS}, author = {Hossein Pejman and Stéphane Coulombe and Carlos Vázquez and Ahmad Vakili}, doi = {10.1109/ACCESS.2025.3588095}, journal = {IEEE Access}, pages = {122132-122149}, shortjournal = {IEEE Access}, title = {Efficient region-wise packing of stereoscopic ERP videos based on information loss minimization}, volume = {13}, year = {2025}, }
• (2025). Integrated feature-temporal GAN for imbalanced transaction fraud detection. ACCESS, 13, 122116-122131. (www)

  @article{ , abstract = {Transaction fraud detection (TFD) poses a significant challenge due to the severe class imbalance, where fraudulent transactions, though rare, cause substantial financial losses. Existing methods often fail to adequately capture both the critical discriminative features of fraud and the temporal dependencies inherent in transaction sequences, leading to suboptimal detection performance. In this work, we introduce the Integrated Feature-Temporal-aware Generative Adversarial Network (IFT-GAN), a novel framework that simultaneously refines feature space and models temporal dynamics during the generation of synthetic samples. IFT-GAN introduces two key innovations: 1) A Feature-Aware Sample Selection (FASS) module that employs Fisher score-based feature ranking and weighted k-means clustering to create a balanced subset enriched with discriminative fraud signatures; and 2) A Temporal Dependency GAN (TD-GAN) architecture employing a novel Time-aware Gated Recurrent Unit (TaGRU) in both the generator and discriminator, explicitly modeling the temporal intervals between transactions through adaptive temporal gating. Distinct from existing methods, IFT-GAN establishes a unified framework where feature-space refinement and temporal sequence generation mutually reinforce each other. The FASS module effectively reduces the amplification of non-discriminative features, while the TD-GAN ensures temporal consistency through adversarial training. Extensive experiments on both real-world and publicly available datasets demonstrate that IFT-GAN outperforms its competitive peers across key detection metrics, indicating the effectiveness of IFT-GAN in addressing the class imbalance challenge while preserving the integrity of authentic fraud patterns.}, archive = {J_ACCESS}, author = {Yicen Zheng and Yu Xie and Jiamin Yao}, doi = {10.1109/ACCESS.2025.3587793}, journal = {IEEE Access}, pages = {122116-122131}, shortjournal = {IEEE Access}, title = {Integrated feature-temporal GAN for imbalanced transaction fraud detection}, volume = {13}, year = {2025}, }
• (2025). Experimental study and mechanism analysis of composite insulator chalking in extra-high voltage (EHV) transmission line. ACCESS, 13, 122107-122115. (www)

  @article{ , abstract = {The chalking of composite insulators is referred to a kind of abnormal aging of the silicone rubber sheds. It has serious adverse effects on the long-term operation of the insulators, but the mechanism behind its generation and development remains unclear. In order to deeply understand the phenomenon of composite insulator chalking, this paper took chalked composite insulators operating on extra-high voltage (EHV) AC 500 kV as samples for research. Appearance inspection, hydrophobicity test and tensile stress test were performed on the samples to determine the change of their surface properties after chalking. It is found that a layer of white powder was formed on the surface of the sheds, the hydrophobicity level of which come to a range from HC1 to HC4, and the tensile stress tolerance and mechanical strength of the silicone rubber decreased significantly. Further, XPS analysis, FTIR detection, and TG analysis were carried out to explore the microscopic process inside the material during its chalking. The results show that the oxidation reaction of Si-C bond transforms the composition of silicone rubber, bringing damage to its apparent performance; the generation of strong polar groups such as –C=O group is the main reason for the decrease in the flexibility of the silicone rubber structure and then the formation of a powdered layer; and above all, the chalking process is based on a series of chemical decomposition reactions, in which the decomposition of polydimethylsiloxane (PDMS) as silicone rubber matrix plays a predominant role, and the decomposition of aluminum hydroxide (ATH) serves as a secondary.}, archive = {J_ACCESS}, author = {Ming Lu and Miao Jiang and Chao Gao and Zehui Liu and Qiaoge Zou and Lee Li}, doi = {10.1109/ACCESS.2022.3140764}, journal = {IEEE Access}, pages = {122107-122115}, shortjournal = {IEEE Access}, title = {Experimental study and mechanism analysis of composite insulator chalking in extra-high voltage (EHV) transmission line}, volume = {13}, year = {2025}, }
• (2025). MVTC-sec: Lightweight timestamp correlation for securing RPL against DIO replay attacks. ACCESS, 13, 122088-122106. (www)

  @article{ , abstract = {The rapid expansion of the Internet of Things (IoT) has brought greater attention to the reliability and security of communication within Low-Power and Lossy Networks (LLNs) with constrained resources. Of all the protocols for such networks, the Routing Protocol for Low-Power and Lossy Networks (RPL) plays a central role in enabling effective routing in 6LoWPAN-based IoT systems. However, RPL does not possess any built-in security measures, making it vulnerable to a wide range of attacks, primarily DODAG Information Object (DIO) message-based attacks such as DIO suppression, neighbor, and copycat attacks. Such attacks destabilize the network topology, reduce the packet delivery ratio (PDR), and increase both latency and energy consumption. To address these issues, this paper proposes MVTC-Sec, a Mathematically Validated Timestamp Correlation method that detects replay-based DIO attacks by analyzing deviations from the expected Trickle algorithm timing. Passively observing DIO intervals, MVTC-Sec identifies attack nodes violating the exponential backoff behavior, with efficient and lightweight attack detection irrespective of cryptographic overhead. We evaluate MVTC-Sec using the Cooja simulator under both static and mobile RPL scenarios, with varying attacker behaviors and replay intervals. Results show that MVTC-Sec achieves a detection accuracy ranging from 90% to 99%, improves packet delivery ratio (PDR) to 0.50-0.96, and reduces end-to-end latency by up to 60%. The scheme proves to be of low overhead, requiring only (48.1 kB ROM, 6.3 KB RAM), making it suitable for resource-constrained devices. Compared to the existing solutions, MVTC-Sec offers higher detection accuracy, lower complexity, and improved adaptability, making it an efficient and scalable protection method for RPL-based IoT networks.}, archive = {J_ACCESS}, author = {Tahar Guerbouz and Akram Zine Eddine Boukhamla and Djalila Belkebir and Sahraoui Dhelim}, doi = {10.1109/ACCESS.2025.3587977}, journal = {IEEE Access}, pages = {122088-122106}, shortjournal = {IEEE Access}, title = {MVTC-sec: Lightweight timestamp correlation for securing RPL against DIO replay attacks}, volume = {13}, year = {2025}, }
• (2025). News image captioning via separate attention on entity categories. ACCESS, 13, 122071-122087. (www)

  @article{ , abstract = {News image captioning involves generating descriptive and informative captions for news images by utilizing news article context. This task aims to capture detailed information, including multiple types of named entities like person, organization, location, events etc. However, identifying named entities from an image is a challenging task; to address this, we propose a novel approach of categorizing the key named entities, into person entities and geoOrg entities, and providing distinct attention to these categories, which ensure the focused extraction of relevant information from the image. Despite this approach, a single news image falls short of providing comprehensive background details, leading to a lack of useful context. Nevertheless, it is possible that the missing context in one image is present in another image of the same article. To address this, we propose to incorporate the nearby image as an addon input, as structural proximity implies contextual relevance. This multimodal cue facilitates the accumulation of contextualized features that effectively capture contextually rich information from the article. Experimental results demonstrate the effectiveness of our proposed approach in the GoodNews, NYTimes800k and DM800K datasets for news image captioning, achieving an improvement of 0.4 BLEU-4 score over the state-of-the-art on the DM800K dataset.}, archive = {J_ACCESS}, author = {Sonali Ajankar and Tanima Dutta}, doi = {10.1109/ACCESS.2025.3582655}, journal = {IEEE Access}, pages = {122071-122087}, shortjournal = {IEEE Access}, title = {News image captioning via separate attention on entity categories}, volume = {13}, year = {2025}, }
• (2025). Introducing MLOps to facilitate the development of machine learning models in agronomy: A case study. ACCESS, 13, 122059-122070. (www)

  @article{ , abstract = {While machine learning (ML) and deep learning (DL) are increasingly being adopted in agronomy, the literature shows that the use of ML Operations (MLOps) frameworks remains scarce during the research stage. This study discusses the utility of MLOps in enhancing the reproducibility and transparency of ML projects even before deployment in production. Moreover, by showing the process of integrating these practices in a pre-existing codebase from an ML study in agronomy, this work showcases the transformative potential of MLOps and serves as a guideline for efficiently utilizing these frameworks. Among the many MLOps frameworks, ClearML was selected and integrated into all the project’s aspects, from data processing through hyper-parameter optimization to orchestrating the entire workflow. This integration not only streamlined dataset management, experiment tracking, and hyper-parameter optimization but also demonstrated the utility of a more structured approach to ML project design, leading to more informed design choices. Our findings underscore the potential of these practices to revolutionize the reproducibility, transparency, and design of ML projects in computational agronomy research.}, archive = {J_ACCESS}, author = {Dario Ruggeri and Gabriele Tazza and László Vidács}, doi = {10.1109/ACCESS.2025.3586691}, journal = {IEEE Access}, pages = {122059-122070}, shortjournal = {IEEE Access}, title = {Introducing MLOps to facilitate the development of machine learning models in agronomy: A case study}, volume = {13}, year = {2025}, }
• (2025). Reusability, reconfigurability and efficiency optimization of satellite network modeling and simulation. ACCESS, 13, 122035-122058. (www)

  @article{ , abstract = {Model-based system engineering (MBSE) with reusable mechanisms can serve as an effective way for complex system architecture design. Stakeholder needs should be satisfied while product and architecture design need to be consistent with user requirements in all stages during the whole product lifecycle. In this paper, satellite network as an example of complex system is modeled in a reusable, reconfigurable and efficient manner using the system modeling language (SysML) together with pattern viewpoints and simulation constructs. Based upon abstract syntax described using metamodels and a set of profiles, concept reusability is established for the specific domain. Additionally a reusable modeling framework is developed with tailored design patterns and multiple viewpoints. Analysis metamodel, profile and interface are further presented to preserve reusability during iterations among multiple optimization rounds. A novel satellite network simulation model is formulated and multi-objective optimization is solved by transformation under practical application scenarios. A set of metrics are designed to assess and validate the models. Results show that the proposed reusable model has viewpoint coverage of more than 80 percent compared to a half for the baseline OOSEM model. The proposed model thus covers the pattern viewpoints and ontologies in a wider and more frequent way and is more efficient. Design choices made based on the model can be incorporated into this mechanism which is extensible along the system lifespan.}, archive = {J_ACCESS}, author = {Su Gao and Yue Cao and Yinqiao Li and Yujun Chen and Song Jin and Shikun Xie and Jihong Liu}, doi = {10.1109/ACCESS.2023.3250426}, journal = {IEEE Access}, pages = {122035-122058}, shortjournal = {IEEE Access}, title = {Reusability, reconfigurability and efficiency optimization of satellite network modeling and simulation}, volume = {13}, year = {2025}, }
• (2025). Toward AI-enabled approach for urdu text recognition: A legacy for urdu image apprehension. ACCESS, 13, 122022-122034. (www)

  @article{ , abstract = {Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a large-scale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.}, archive = {J_ACCESS}, author = {Kamlesh Narwani and Hongzhi Lin and Sandeep Pirbhulal and Mir Hassan}, doi = {10.1109/ACCESS.2022.3203426}, journal = {IEEE Access}, pages = {122022-122034}, shortjournal = {IEEE Access}, title = {Toward AI-enabled approach for urdu text recognition: A legacy for urdu image apprehension}, volume = {13}, year = {2025}, }
• (2025). GCV-SLAM: Ground constrained visual SLAM through local ground planes. ACCESS, 13, 122010-122021. (www)

  @article{ , abstract = {The classical visual Simultaneous Localization and Mapping(SLAM) algorithms assume that the camera moves in a free 3D space, while it is not valid for ground vehicles whose poses are influenced by ground surfaces. In recent years, researchers have focused on improving pose estimation accuracy by assuming planar motion which is more suitable to indoor environments. In this paper, we propose a lightweight stereo-visual SLAM framework for ground vehicles in road environments that tightly integrates constraints imposed by ground surfaces. We assume that the ground vehicle exhibits planar motion locally, and extract the observed local ground plane in each keyframe. To avoid the over-parameterization problem in the graph-factor-based optimization process, the Closest Point(CP) representation is adopted to describe the local ground plane. The roll, pitch, and position information provided by the local ground plane can be utilized to constrain the pose of the ground vehicle. Moreover, local ground planes can also be regarded as geometric features, enabling the construction of coplanarity constraints between ground map points and local ground planes, as well as between local ground planes in different keyframes. The KITTI odometry dataset was selected to validate the performance of our system, and the results demonstrated that our system could improve the accuracy and efficiency of ground vehicle localization in road environments.}, archive = {J_ACCESS}, author = {Yu Fan and Peng Zhang and Zhi Wang and Chengbao Liu and Guang Zhang}, doi = {10.1109/ACCESS.2024.3351571}, journal = {IEEE Access}, pages = {122010-122021}, shortjournal = {IEEE Access}, title = {GCV-SLAM: Ground constrained visual SLAM through local ground planes}, volume = {13}, year = {2025}, }
• (2025). Analytic hierarchy process for an automated evaluation of learning difficulties of novice programmers. ACCESS, 13, 121995-122009. (www)

  @article{ , abstract = {Decision support systems represent a relevant tool for data-driven decision-making with limited human resources. It is critical to adopt enabling techniques that avoid automation distortion, that is, delegating the decision to the system without understanding the motivation. The utilization of state-of-the-art AI techniques for decision-making in specific application domains is considered a high-risk practice, such as in Education. In this context, we propose a general methodology that integrates a rule-based classification schema with the Analytic Hierarchy Process, and a practical application to identify and deal with students’ misconceptions through the automatic analysis of compiler logs. Novice programmers, beyond their problem-solving skills, often demonstrate difficulties in writing source code that is error-free. Behind the difficulties of mastering the syntax of a programming language and understanding the compiler messages, is often hidden a wrong comprehension of fundamental coding concepts. A typical decision problem is related to which exercise would allow the student to verify the learning outcome after some remedial activities. We applied the the proposed methodology to a real dataset of compiler logs. The dataset is composed of about two thousand programs developed by novice programmers, who addressed their assignment in five editions of a bachelor’s course in fundamental programming. It also includes compiling errors or warning messages introduced during the development cycle.}, archive = {J_ACCESS}, author = {Salvatore Venticinque and Alba Amato and Pietro Fusco and Francesco Palmieri}, doi = {10.1109/ACCESS.2025.3586198}, journal = {IEEE Access}, pages = {121995-122009}, shortjournal = {IEEE Access}, title = {Analytic hierarchy process for an automated evaluation of learning difficulties of novice programmers}, volume = {13}, year = {2025}, }
• (2025). Reliable event detection via multiple edge computing on streaming traffic social data. ACCESS, 13, 121981-121994. (www)

  @article{ , abstract = {Traffic event detection plays an essential role in flexible decision-making for sensor-cloud system(SCS). Since a social traffic event is often described by multiple social media texts, it is significant to perform traffic event detection on streaming social media texts. However, these social media texts with limited semantic features fail to describe a large amount of traffic event categories and a small number of samples per traffic event category, which are difficult to solve with conventional text classification methods and reduce the reliability in SCS. In this paper, we propose a reliable and streaming clustering algorithm for streaming traffic event detection via multiple edge computing. First, we combine traffic-related knowledge information to extract various types of elements from social media texts, and accordingly construct a traffic event-based heterogeneous information network (HIN) and proceed to calculate event similarity between social media texts through meta-path weights. Then, we utilize graph neural networks to perform semi-supervised learning on HIN to obtain the optimal meta-path weights. We also develop Binary Sample Graph Convolutional Neural Network (BS-GCN) and Binary Sample Graph Attention Network (BS-GAT) to improve the reliability of graph neural network models based on the characteristics of traffic event detection and design an incremental clustering algorithm based on event similarity to implement streaming social traffic event detection. We conduct experiments on social media text datasets describing various traffic events in cities such as Beijing and develop related social traffic event detection systems. The results indicate that our model can better implement streaming social traffic event detection, and is superior to most text classification methods.}, archive = {J_ACCESS}, author = {Yipeng Ji and Jingyi Wang and Yan Niu and Hongyuan Ma}, doi = {10.1109/ACCESS.2021.3060624}, journal = {IEEE Access}, pages = {121981-121994}, shortjournal = {IEEE Access}, title = {Reliable event detection via multiple edge computing on streaming traffic social data}, volume = {13}, year = {2025}, }
• (2025). Energy router for peer-to-peer trading under emergency with energy traceable functionality. ACCESS, 13, 121970-121980. (www)

  @article{ , abstract = {Multi-port bidirectional energy router is an important infrastructure for peer-to-peer (P2P) energy trading after power supply interruption. The state-of-the-art multi-port bidirectional energy router lacks the traceability function of an energy source. A triple active bridge (TAB) converter with partial power processing (PPP) progress is employed as multi-port bidirectional energy router. This proposed multi-port bidirectional energy router integrates the energy conversion and signal transmission in one converter, which supports peer-to-peer trading. In detail, the controlling approach of power transmission and the phase perturbation modulation (PPM) method of signal transmission, which realize the energy traceable functionality, are also illustrated in this paper. Finally, Simulation is performed and presented as proof of the feasibility of the designed method.}, archive = {J_ACCESS}, author = {Yu Nie and Yixuan Zhang and Yihua Hu and Mohammed Alkahtani and Mohammed Alquraish and Hui Xu}, doi = {10.1109/ACCESS.2023.3285198}, journal = {IEEE Access}, pages = {121970-121980}, shortjournal = {IEEE Access}, title = {Energy router for peer-to-peer trading under emergency with energy traceable functionality}, volume = {13}, year = {2025}, }
• (2025). A multi-modal attentive framework that can interpret text (MMAT). ACCESS, 13, 121955-121969. (www)

  @article{ , abstract = {Deep learning algorithms have demonstrated exceptional performance on various computer vision and natural language processing tasks. However, for machines to learn information signals, they must understand and have enough reasoning power to respond to general questions based on the linguistic features present in images. Questions such as “What temperature is my oven set to?” need the models to understand objects in the images visually and then spatially identify the text associated with them. The existing Visual Question Answering model fails to recognize linguistic features present in the images, which is crucial for assisting the visually impaired. This paper aims to deal with the task of a visual question answering system that can do reasoning with text, optical character recognition (OCR), and visual modalities. The proposed Visual Question Answering model focuses on the image’s most relevant part by using an attention mechanism and passing all the features to the fusion encoder after getting pairwise attention, where the model is inclined toward the OCR-Linguistic features. The proposed model uses the dynamic pointer network instead of classification for iterative answer prediction with a focal loss function to overcome the class imbalance problem. On the TextVQA dataset, the proposed model obtains an accuracy of 46.8% and an average of 55.21% on the STVQA dataset. The results indicate the effectiveness of the proposed approach and suggest a Multi-Modal Attentive Framework that can learn individual text, object, and OCR features and then predict answers based on the text in the image.}, archive = {J_ACCESS}, author = {Vijay Kumari and Sarthak Gupta and Yashvardhan Sharma and Lavika Goel}, doi = {10.1109/ACCESS.2025.3586900}, journal = {IEEE Access}, pages = {121955-121969}, shortjournal = {IEEE Access}, title = {A multi-modal attentive framework that can interpret text (MMAT)}, volume = {13}, year = {2025}, }
• (2025). YUNet_LLMClaimReport: An enhanced automobile insurance fraud detection and automated claim report generation using large language models. ACCESS, 13, 121935-121954. (www)

  @article{ , abstract = {The detection of fraud in automobile insurance and processing of automobile claims are still a challenge since manual inspections are carried out and automated systems are fragmented. The state-of-the-art approaches (convolutional neural networks, statistical models, etc.) tend to focus on separate tasks (damage detection, fraud classification, etc.), without being integrated with report generation on an end-to-end basis, and without making sufficient use of unstructured information (e.g., images, claim descriptions). In this research, YUNet_LLMClaimReport, a new framework is proposed that combines YOLOv11, U-Net, and a fine-tuned GPT-3.5-turbo large language model to automatically generate claim reports based on the detections and segmentation. The model is trained on a custom dataset of 4,000 vehicle images within the Egyptian insurance market (70:20:10 training, validation and test split), using data augmentation, dropout (0.3) and L2 regularisation (l =0.01) to ensure robustness. The evaluation measures are mean average precision (mAP50: 0.9424), intersection over union (IoU: 0.85), claim report accuracy (CRA: 0.92+/−0.008), BLEU (0.85+/−0.01), and ROUGE-L (0.82+/−0.01). The framework runs at 8.3 FPS on NVIDIA Jetson Nano and saves processing time by 60 percent. Weaknesses are single-run measures because of computational limitations and factual inconsistencies in reports (10%). The model can be generalized to the Kaggle Car Damage Detection dataset (mAP50: 0.90), providing a scalable solution to insurance processes. The code and sample data are open sourced.}, archive = {J_ACCESS}, author = {P. Anand Kumar and S. Sountharrajan}, doi = {10.1109/ACCESS.2025.3584927}, journal = {IEEE Access}, pages = {121935-121954}, shortjournal = {IEEE Access}, title = {YUNet_LLMClaimReport: An enhanced automobile insurance fraud detection and automated claim report generation using large language models}, volume = {13}, year = {2025}, }
• (2025). A new 5-D hyperchaotic system with a line equilibrium, its bifurcation analysis, circuit simulation, FPGA implementation, and data prediction using long-term-short memory. ACCESS, 13, 121918-121934. (www)

  @article{ , abstract = {This work proposes a new 5-D hyperchaotic system with a line of equilibrium points. The bifurcation properties of the new hyperchaotic system are analyzed using bifurcation diagrams and Lyapunov exponents spectra. Multistability and offset boosting properties of the new hyperchaotic system are also discussed. With the help of Multisim 14.0 circuit simulator, an electronic circuit design for the new 5-D hyperchaotic system with a line of equilibrium points has been constructed. Furthermore, an FPGA implementation of the proposed hyperchaotic system is performed by applying Forward Euler numerical methods using FPGA CYCLONE IV GX platform. Additionally, this study demonstrates the use of the Long-Short-Term Memory LSTM model to predict the future hyperchaotic behavior of the new 5-D hyperchaotic system. The Mean Square Error (MSE), Root Mean Square Error (RMSE), and the coefficient of determination ( $R^{2}$ ) metrics are used to evaluate the prediction performance of the proposed LSTM model. The small obtained values for MSE and RMSE respectively, along with $R^{2}$ scores near to 1, reveal the accuracy and reliability of the proposed prediction model.}, archive = {J_ACCESS}, author = {Talal Bonny and Sundarapandian Vaidyanathan and Wafaa Al Nassan and Aceng Sambas and Fareh Hannachi and Maher Alrahhal and Chittineni Aruna}, doi = {10.1109/ACCESS.2025.3587732}, journal = {IEEE Access}, pages = {121918-121934}, shortjournal = {IEEE Access}, title = {A new 5-D hyperchaotic system with a line equilibrium, its bifurcation analysis, circuit simulation, FPGA implementation, and data prediction using long-term-short memory}, volume = {13}, year = {2025}, }
• (2025). A systematic review of pretrained models in automated essay scoring. ACCESS, 13, 121902-121917. (www)

  @article{ , abstract = {Automated essay scoring (AES) uses artificial intelligence and machine learning methods to grade student essays and produce human-like scores. AES research has witnessed significant advancements over time by adopting diverse machine learning models. This evolution started with traditional techniques like regression and classification, and later advanced to deep learning models that leverage neural networks for enhancing scoring performance. This review focuses on the utilization of Transformer architectures, a sophisticated form of neural networks employing attention mechanisms, with an emphasis on pretrained models like BERT in AES research. The aim is to enhance the understanding of their applicability in advancing the AES research landscape. Additionally, this study selected and analyzed relevant papers from Scopus and Web of Science databases in the past five years, by adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. An intensive screening process was followed to shortlist studies within the defined scope, focusing on AES applications that employed pretrained models. From the comprehensive analysis of 354 studies, this review shortlisted 22 key papers and identified five distinct focus areas within the current AES research landscape: holistic scoring, trait scoring, cross-domain and generalization, model fairness, and robustness. The results highlight the potential of transformer-based pretrained models in improving AES systems’ accuracy. However, pretrained models face several limitations, such as high computational costs, long input text length, and explainability. Tackling these challenges and integrating emerging technologies, such as large language models (GPT4), is expected to foster the development of accurate, robust, and transparent AES systems.}, archive = {J_ACCESS}, author = {Ahmed M. Elmassry and Nazar Zaki and Negmeldin Alsheikh and Mohammed Mediani}, doi = {10.1109/ACCESS.2025.3584784}, journal = {IEEE Access}, pages = {121902-121917}, shortjournal = {IEEE Access}, title = {A systematic review of pretrained models in automated essay scoring}, volume = {13}, year = {2025}, }
• (2025). Privacy-aware detection for large language models using a hybrid BiLSTM-HMM approach. ACCESS, 13, 121880-121901. (www)

  @article{ , abstract = {Large Language Models (LLMs) have transformed natural language processing, enabling applications such as conversational agents and machine translation. However, their deployment introduces significant privacy concerns, including the memorization and unintended disclosure of sensitive data. Existing privacy-preserving techniques—such as Differential Privacy and federated learning—struggle to balance data protection, model utility, and computational efficiency. To address these limitations, we propose a lightweight privacy-disclosure detection system that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Hidden Markov Models (HMM) using a novel modeling pipeline. Our approach employs the Predefined and Sensitive Labeling (PSL) technique, a generative labeling approach that extracts meaningful patterns from data. These patterns are then used to train a BiLSTM model capable of proactively identifying sensitive information in real-time user interactions with LLMs. As BiLSTMs do not provide the probability of private data, we design a HMM that estimates the probability of occurrence for this private data. Utilizing the Forward algorithm, our system quantifies privacy risks, enabling users to revise inputs prior to submission and thereby enhancing data privacy. Trained on synthetic data using PSL technique, the model achieves approximately 99.94% precision, recall, and F1-score, and successfully detects previously unseen sensitive information in synthetic datasets with $\approx 99.98$ % accuracy across 55,000 sentences. Additionally, the generated model trained on patterns derived from synthetic data, achieved $\approx 99.99$ % accuracy when evaluated on a real-world dataset across varying sentence structures, demonstrating strong generalizability in detecting sensitive information regardless of the data source. Importantly, the model provides real-time predictions with an average execution time of 35.46 milliseconds, satisfying the speed requirements for practical deployment. It also trains 45.5 times faster than a state-of-the-art framework, offering high computational efficiency without compromising accuracy.}, archive = {J_ACCESS}, author = {Maryam Abbasalizadeh and Sashank Narain}, doi = {10.1109/ACCESS.2025.3587988}, journal = {IEEE Access}, pages = {121880-121901}, shortjournal = {IEEE Access}, title = {Privacy-aware detection for large language models using a hybrid BiLSTM-HMM approach}, volume = {13}, year = {2025}, }
• (2025). Investigation of water and mud effects on the propagation of real 5G signal by using the reverberation chamber. ACCESS, 13, 121868-121879. (www)

  @article{ , abstract = {We investigated the propagation condition in a particular environment, such as an area covered by water or mud. In particular, we analyzed the effect on signal propagation in a fifth-generation (5G) wireless communication system in that scenario. The experiments were carried out in a laboratory inside a reverberation chamber that emulates a complex propagation environment, equipped with a 5G base station connected to the TIM live network. We measured: 1) the permittivity of the medium under investigation; 2) the effects of the presence of such mixtures within the propagation environment to test the 5G system by checking the key performance indicators. The chosen situations emulate the radio channel propagation in a hostile scenario such as during a flood, to evaluate the performance of a 5G base station. For the characterization of the medium, we considered its physical properties such as the permittivity, conductivity, and reflection coefficients. Moreover, to assess radio performance of the 5G system, we report the following key performance indicators: RSRP, SINR, CQI, MCS and BLER. Experimental results show that water creates a greater multipath than mud, confirmed by direct measurements of permittivity and conductivity. Furthermore, experiments conducted in the laboratory reveal the same behavior of the propagation scenario in a realistic flood such as the lowering of about 3 dB in terms of RSRP between the normal situation and the flooded scenario.}, archive = {J_ACCESS}, author = {Luca Bastianelli and Riccardo Diamanti and Alfredo de Leo and Emanuel Colella and Michele Colombo and Valter Mariani Primiani and Franco Moglie and Davide Micheli}, doi = {10.1109/ACCESS.2025.3588297}, journal = {IEEE Access}, pages = {121868-121879}, shortjournal = {IEEE Access}, title = {Investigation of water and mud effects on the propagation of real 5G signal by using the reverberation chamber}, volume = {13}, year = {2025}, }
• (2025). Development of robust CNN architecture for grading and classification of renal cell carcinoma histology images. ACCESS, 13, 121849-121867. (www)

  @article{ , abstract = {Kidney cancer is a commonly diagnosed cancer disease in recent years, and Renal Cell Carcinoma (RCC) is the most common kidney cancer responsible for 80% to 85% of all renal tumors. The diagnosis of kidney cancer requires manual examination and analysis of histopathological images of the affected tissue. This process is time-consuming, prone to human error, and highly depends on the expertise of a pathologist. Early detection and grading of kidney cancer tissues enable doctors and practitioners to decide the further course of treatment. Therefore, quick and precise analysis of kidney cancer tissue images is extremely important for proper diagnosis. Recently, deep learning algorithms have proved to be very efficient and accurate in histopathology image analysis. In this paper, we propose a computationally efficient deep-learning architecture based on convolutional neural networks (CNNs) to automate the grading and classification task for kidney cancer tissue. The proposed Robust CNN (RoCNN) architecture is capable of learning features at varying convolutional filter sizes because of the inception modules employed in it. Squeeze and Extract (SE) blocks are used to remove unnecessary contributions from noisy channels and improve model accuracy. Concatenating samples from three different parts of architecture allows for the encompassing of varied features, further improving grading and classification accuracy. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on two well-known datasets, the KMC kidney dataset of five different grades and the TCGA dataset of four classes. Compared to the best-performing state-of-the-art model the accuracy of RoCNN shows a significant improvement of about 4.22% and 3.01% for both datasets respectively.}, archive = {J_ACCESS}, author = {Amit Kumar Chanchal and Shyam Lal and Shilpa Suresh}, doi = {10.1109/ACCESS.2025.3586935}, journal = {IEEE Access}, pages = {121849-121867}, shortjournal = {IEEE Access}, title = {Development of robust CNN architecture for grading and classification of renal cell carcinoma histology images}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence based high definition map generation from mobile mapping data. ACCESS, 13, 121838-121848. (www)

  @article{ , abstract = {The production of High-Definition (HD) maps has become an increasingly researched area in recent years, along with the rise of self-driving vehicles. One area is the automatic evaluation of road networks and their environment. This research presents a workflow for the evaluation of a point cloud dataset generated by a mobile mapping system. In the initial step of the procedure, a Shallow Neural Network (SNN) classification method is utilized to determine the segment of the point cloud corresponding to the path of the mapped area. Then, exploiting the GPS (Global Positioning System) time data of the points, the boundaries of the path are determined, which are used as a last step to carry out the OpenDRIVE structure. The presented workflow has been validated on real measurement data from the ZalaZONE Automotive Proving Ground. The results are convincing and justify the proposed methodology. The median cross-sectional deviation of the path achieved an accuracy of 5 cm, and the areas covered by the path showed a 97% similarity. The High Speed Handling course (one of the proving ground modules) has been used for the testing and validation of the semi-automatic HD map generation workflow. The generated HD maps and related models have been openly shared and also integrated into MATLAB software (since the 2026 release of MATLAB).}, archive = {J_ACCESS}, author = {Árpád József Somogyi and Dániel Baranyai and Mohammad Dowajy and Tamás Lovas and Zsolt Szalay and Tamás Tettamanti}, doi = {10.1109/ACCESS.2025.3587592}, journal = {IEEE Access}, pages = {121838-121848}, shortjournal = {IEEE Access}, title = {Artificial intelligence based high definition map generation from mobile mapping data}, volume = {13}, year = {2025}, }
• (2025). A novel smart molecular fuzzy decision support system for solid-state battery investments in grid-level renewable energy storage. ACCESS, 13, 121815-121837. (www)

  @article{ , abstract = {It is very necessary to determine the most critical indicators to improve the performance of solid-state battery investments. Although there are many factors affecting these investments in the literature, there is no consensus on the most important factors. Investors’ failure to focus on the most critical factors may cause businesses to fail to use their financial and human resources effectively. This situation significantly reduces the performance and efficiency of investments. To eliminate this deficiency, a priority analysis of performance indicators for solid-state battery investments is performed in this study. For this purpose, a new decision-making model is developed that includes expert weighting with the entropy game, evaluation balancing with the Q-learning algorithm, calculation of criterion weights with the least squares optimization (LSO) and alternative ranking with the molecular ranking (MORAN) method. Furthermore, molecular fuzzy sets are also integrated into the model to manage uncertainties. This study contributes to the literature by presenting an integrated multi-criteria decision-making model to define essential indicators to improve the performance of solid-state battery investments. The proposed model offers several advantages over existing methods. Unlike traditional multi-criteria decision-making methods such as AHP or MOORA, this study integrates entropy-based game theory, reinforcement learning, least squares optimization, and molecular fuzzy set theory to provide a more dynamic, precise, and uncertainty-aware evaluation framework tailored for solid-state battery investments. The proposed MORAN method introduces a novel molecular geometry-inspired ranking logic, which enables a more flexible modeling of performance indicators compared to linear or ratio-based methods. In addition, while methods such as AHP have been widely applied for battery technology selection, they often rely on fixed pairwise comparisons. They do not accommodate learning-based optimization or flexible uncertainty modeling. This proposed model integrates different techniques to address these gaps. The findings show that charging efficiency and thermal stability are the most important performance criteria. Financial incentives with technological advancements and direct funding of community-scale battery storage projects are the most critical alternative investment policies.}, archive = {J_ACCESS}, author = {Gang Kou and Hasan Dınçer and Serhat Yüksel and Edanur Ergün and Serkan Eti}, doi = {10.1109/ACCESS.2025.3588345}, journal = {IEEE Access}, pages = {121815-121837}, shortjournal = {IEEE Access}, title = {A novel smart molecular fuzzy decision support system for solid-state battery investments in grid-level renewable energy storage}, volume = {13}, year = {2025}, }
• (2025). Reliability and economic assessment of integrated distributed hybrid generation and battery storage for base transceiver stations in intermittent utility grids. ACCESS, 13, 121799-121814. (www)

  @article{ , abstract = {Reliable telecommunication tower operation is paramount for sustainable cities as it ensures uninterrupted communication, supports economic growth, facilitates smart city applications, and enables emergency response. This study evaluates the reliability and economic aspects of three hybrid system configurations aimed at providing an uninterrupted power supply to base transceiver stations (BTS) during power outages. A framework is developed to optimize power operation and assess the operational costs of these configurations. A case study is conducted to examine the effectiveness of the optimization framework. The study evaluates the system size and costs of solar PV, hydrogen fuel cell, and battery energy storage systems. The results demonstrate that system architecture combining a utility grid with battery energy storage and solar PV offers the most cost-effective option. The system architecture, incorporating a utility grid with battery energy storage and hydrogen fuel cells, provides the highest reliability. The daily operating cost of the solar PV-based architecture is 40.3% lower than that of the hydrogen fuel cell-based system and 35.8% lower than that of the utility grid and battery storage configuration. This study contributes to the integration of renewable power sources and optimization framework, enhancing energy supply and promoting society’s long-term well-being.}, archive = {J_ACCESS}, author = {Huzaifa Rauf and Hassan Abbas Khan and Muhammad Khalid and Naveed Arshad and Michael G. Pecht}, doi = {10.1109/ACCESS.2025.3588459}, journal = {IEEE Access}, pages = {121799-121814}, shortjournal = {IEEE Access}, title = {Reliability and economic assessment of integrated distributed hybrid generation and battery storage for base transceiver stations in intermittent utility grids}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey of explainable artificial intelligence techniques for malicious insider threat detection. ACCESS, 13, 121772-121798. (www)

  @article{ , abstract = {Malicious insider threats remain a persistent and formidable challenge for organizations, primarily due to their covert nature and the severe impact they can have on critical systems and sensitive data. Traditional detection mechanisms often struggle to uncover such threats, underscoring the need for more intelligent, interpretable, and trustworthy solutions. Although the research community has shown increasing interest in Insider Threat Detection (ITD), existing surveys rarely emphasize the integration of Explainable Artificial Intelligence (XAI) with machine learning (ML) and deep learning (DL) techniques. This oversight has limited progress in understanding how interpretability can improve both detection effectiveness and the trust of security analysts. To address this gap, this survey presents a comprehensive review of the application of XAI in ITD. It explores how ML and DL models, when combined with XAI techniques, can uncover anomalous behaviors and insider actions while enhancing the transparency of model decisions. Tools such as SHAP and LIME are examined for their role in revealing feature contributions and improving analyst insight. The paper also highlights critical data sources–ranging from behavioral logs and network activity to psychometric indicators–that support the development of interpretable detection models. We categorize existing literature based on XAI techniques, data modalities, and threat models, and propose a conceptual framework for aligning XAI methods with specific ITD challenges. Our findings reveal that while XAI enhances interpretability and trust in AI-driven threat detection, several challenges persist. These include class imbalance in datasets, integration of heterogeneous data streams, and the absence of standardized metrics for evaluating explainability in cybersecurity contexts. Finally, the survey identifies key directions for future research, including privacy-preserving AI, human-in-the-loop explainability, and the development of benchmarking frameworks tailored to ITD applications. By offering a structured and up-to-date overview of XAI-enhanced ITD approaches, this work supports the advancement of more transparent, accountable, and operationally effective insider threat detection systems.}, archive = {J_ACCESS}, author = {Khuloud Saeed Alketbi and Abid Mehmood}, doi = {10.1109/ACCESS.2025.3587114}, journal = {IEEE Access}, pages = {121772-121798}, shortjournal = {IEEE Access}, title = {A comprehensive survey of explainable artificial intelligence techniques for malicious insider threat detection}, volume = {13}, year = {2025}, }
• (2025). GMPP tracking of solar PV system using spotted hyena and quadratic approximation based hybrid algorithm under partially shaded conditions. ACCESS, 13, 121762-121771. (www)

  @article{ , abstract = {The demand for clean and renewable energy is increasing due to environmental concerns. Photovoltaic (PV) system is very popular from the renewable sources and its usage is increasing. The PV system power transfer efficiency depends on several factors such electrical characteristics of loads, solar irradiation, shading condition and panel temperature. The process of transferring maximum possible power to load from PV system by adjusting the system parameters is called maximum power point tracking (MPPT). Under the partially shaded conditions, multiple power peaks are created in the PV curves and makes the MPPT is a non-convex problem. It is a challenging problem to track the global maximum power point (GMPP) under partially shaded conditions. It is essential to recognize the GMPP and restart the string as early as possible to prevent the physical as well as economical damage. In this paper, a new hybrid algorithm for MPPT is proposed and called as QASHO, a combination of Spotted Hyena optimization (SHO) and Quadrature approximation (QA) technique. The QASHO is used to track the GMPP under different weather conditions such as partial shading. The simulation results are compared with Perturb and Observe (P&O) algorithm and Cuckoo Search optimization (CSO) algorithm and promised for better performance in tracking GMPP. The experimental results are provided to validate the proposed MPPT methodology.}, archive = {J_ACCESS}, author = {Parveen Kumar and Manish Kumar and Ajay Kumar Bansal}, doi = {10.1109/ACCESS.2023.3272554}, journal = {IEEE Access}, pages = {121762-121771}, shortjournal = {IEEE Access}, title = {GMPP tracking of solar PV system using spotted hyena and quadratic approximation based hybrid algorithm under partially shaded conditions}, volume = {13}, year = {2025}, }
• (2025). Counterfactual balancing feature alignment for few-shot cross-domain scene parsing. ACCESS, 13, 121748-121761. (www)

  @article{ , abstract = {Scene parsing becomes a key step to develop a visual autonomous driver. Real-world images are too expansive to annotate at scale, while few-shot cross-domain scene parsing (CSP) approaches only require a few labeled target images to train a model with source virtual data, thus, attracting more attention in the community. However, since the target training images are too few to support the cross-domain measures in statistics, it is inappropriate of resembling the spirit of conventional domain adaptation. In this paper, we reconsider this imbalance transfer learning demand as a covariate balancing issue regularly found in Rubin causal framework. We first consider the domain adaptation in pixels in the view of the average treatment effect (ATE), in which data are categorized into a treatment group or a control group in terms of the domain identity taken as the treatment. In this manner, the pair of domains could be perfectly aligned if the ATE converges to zero. It motivates Counterfactual Balance Feature Alignment (CBFA) to mitigate the cross-domain imbalance in the categories. CBFA revises existing adversarial adaptation techniques by modeling the propensity score for all pixels in their contexts, for the sake of predicting which groups they belong to. The propensity score for a pixel refers to its output of the domain discriminator and can be applied to balancing the adversarial adaptation objective. We evaluate our method on two suites of virtual-to-real scene parsing setups. Our method has obtained the new state of the art across 1-5 shot scenarios (in particular, 1-shot 56.79 in SYNTHIA-to-CITYSCAPES and 51.56 in GTA5-to-CITYSCAPES), demonstrating our motivation of building the connection between ATE and domain gap.}, archive = {J_ACCESS}, author = {Cody Mingzhan Sun and Ziliang Chen and Liang Lin}, doi = {10.1109/ACCESS.2023.3263561}, journal = {IEEE Access}, pages = {121748-121761}, shortjournal = {IEEE Access}, title = {Counterfactual balancing feature alignment for few-shot cross-domain scene parsing}, volume = {13}, year = {2025}, }
• (2025). Adapted volt-VAr extremum-seeking method for voltage regulation and loss minimization in low voltage distribution networks. ACCESS, 13, 121735-121747. (www)

  @article{ , abstract = {The high penetration of Distributed Generation (DG) in the low-voltage distribution grid has raised several concerns in recent years, mainly regarding overvoltage at the buses caused by the reversal of active power flow. In order to solve this problem, this work proposes an adaptation of the Volt-VAr Extremum-Seeking (Volt-Var-ES) method, which, in addition to promoting voltage regulation, also minimizes active losses in the network. This approach is framed within the concept of local Volt-VAr control since it requires only local measurements and does not depend on an accurate grid model. The IEEE European Low Voltage Test Feeder was used as the test network, with some adaptations to bring it closer to the Brazilian reality. The adapted method was applied in several allocation scenarios and at different levels of DG penetration, and its results were compared with those of the conventional Volt-VAr method, presented in IEEE 1547:2018. The simulations showed that the adapted Volt-VAr Extremum-Seeking method satisfactorily regulates the grid voltage and reduces active losses compared to the conventional Volt-VAr, generating financial savings for utilities and avoiding financial losses for photovoltaic (PV) system owners due to active power curtailment.}, archive = {J_ACCESS}, author = {J. H. S. Carvalho and W. M. Dos Santos and A. R. Almeida and B. F. S. Júnior}, doi = {10.1109/ACCESS.2025.3586872}, journal = {IEEE Access}, pages = {121735-121747}, shortjournal = {IEEE Access}, title = {Adapted volt-VAr extremum-seeking method for voltage regulation and loss minimization in low voltage distribution networks}, volume = {13}, year = {2025}, }
• (2025). Edge-fog computing-based blockchain for networked microgrid frequency support. ACCESS, 13, 121720-121734. (www)

  @article{ , abstract = {Microgrids have gained increasing adoption in recent years due to the growing demand for renewable energy integration. Frequency support has become a critical issue in networked microgrid operation, primarily due to the intermittent nature of renewable energy sources and the inherently low system inertia of microgrids. Sudden system events, such as faults that lead to generator disconnections, can result in significant frequency drops. This paper proposes a novel method for providing frequency support during generator disconnection events in networked microgrids. The proposed approach integrates an edge–fog hierarchical blockchain architecture with a Long Short-Term Memory Model-Free Predictive Controller (LSTM-MFPC). The parameters and hyperparameters of the LSTM-MFPC are optimized using the Bayesian Adaptive Direct Search (BADS) algorithm. The root mean square error (RMSE) of the current obtained using the traditional model predictive control (MPC) and the proposed LSTM-MFPC applied to the inverter are 0.1970 and 0.1432, respectively. These results were obtained through experiments conducted on MATLAB/Simulink-based power hardware-in-the-loop (PHIL) platforms. This demonstrates that the proposed method significantly improves the frequency response of networked microgrids following fault events and exhibits strong potential for real-world implementation.}, archive = {J_ACCESS}, author = {Ying-Yi Hong and Francisco I. Alano and Yih-der Lee and Chia-Yu Han}, doi = {10.1109/ACCESS.2025.3588109}, journal = {IEEE Access}, pages = {121720-121734}, shortjournal = {IEEE Access}, title = {Edge-fog computing-based blockchain for networked microgrid frequency support}, volume = {13}, year = {2025}, }
• (2025). Vision transformer for brain tumor detection using hyperspectral images with reduced spectral bands. ACCESS, 13, 121704-121719. (www)

  @article{ , abstract = {Brain tumors, particularly gliomas, pose a significant clinical challenge with rising incidence rates and high mortality. Artificial intelligence combined with Hyperspectral Imaging (HSI) offers promising tools to improve surgical precision and patients’ outcomes. HSI offers unique advantages, including non-invasiveness and detailed spectral data, for enhanced tumor tissue differentiation. This study tailored the Vision Transformer (ViT) with techniques from remote sensing to segment nineteen spectral images of low- and high-grade gliomas with limited spectral bands. The choice of the ViT was motivated by its attention mechanism, enabling fine-grained distinction of subtle details. Segmentation focused on four classes: healthy tissue, tumor tissue, blood vessels and dura mater. A careful hyperparameter optimization was performed, resulting in the selection of two models based on a defined quality index, which were evaluated using three experimental methodologies, achieving up to $98.24\pm 2.50$ % average Overall Accuracy (OACC) and $99.61\pm 0.66$ % average Area Under the Curve (AUC) in intra-patient classification. For inter-patient classification, the models achieved an average OACC up to $53.56\pm 24.91$ % and an average AUC score up to $79.27\pm 10.43$ %, highlighting areas of improvement. Comparable or improved performance was demonstrated versus other deep learning techniques applied to the same dataset, proving effectiveness with few spectral bands. Some results were lower than a similar application with more bands, but they also underscore the adaptability and potential of the ViT to handle challenging datasets. Insights from hyperparameter optimization shows the ViT’s promise as a robust tool for tumor identification, paving the way for integration into real-time clinical workflows and advancing precision medicine.}, archive = {J_ACCESS}, author = {Domenico Ragusa and Marco Gazzoni and Emanuele Torti and Elisa Marenzi and Francesco Leporati}, doi = {10.1109/ACCESS.2025.3588001}, journal = {IEEE Access}, pages = {121704-121719}, shortjournal = {IEEE Access}, title = {Vision transformer for brain tumor detection using hyperspectral images with reduced spectral bands}, volume = {13}, year = {2025}, }
• (2025). RFHS-RTDETR: Multi-domain collaborative network with hierarchical feature integration for UAV-based object detection. ACCESS, 13, 121686-121703. (www)

  @article{ , abstract = {To address UAV-based detection challenges including scale variation, high target density, and hardware limitations, we propose RFHS-RTDETR with four innovations: (1) RMConv, a reparameterized lightweight module for efficient multi-scale feature extraction; (2) FSConv fusing Scharr operator and Fourier transform to enhance edge preservation and robustness; (3) AH module combining HiLo attention for dense target recognition; (4) SOPS Feature Pyramid with hierarchical feature integration of P2 features and dynamic upsampling for small objects. On VisDrone2019, RFHS-RTDETR reduces FLOPs and parameters by 16.2% and 34.3% versus RT-DETR-R18, while improving precision by 2.2%, mAP50 by 2%, and FPS by 17.1%.These advancements demonstrate its practicality for resource-constrained aerial scenarios.}, archive = {J_ACCESS}, author = {Songtao Tang and Leiming Zhang and Xudong Liu and Rongfu Lv and Ruyi Qin}, doi = {10.1109/ACCESS.2025.3586649}, journal = {IEEE Access}, pages = {121686-121703}, shortjournal = {IEEE Access}, title = {RFHS-RTDETR: Multi-domain collaborative network with hierarchical feature integration for UAV-based object detection}, volume = {13}, year = {2025}, }
• (2025). MetaForecaster: A PSO-driven neural model for sustainable industrial air quality management. ACCESS, 13, 121670-121685. (www)

  @article{ , abstract = {Industrial carbon monoxide (CO) emissions significantly affect public health and environmental quality, necessitating advanced forecasting models for effective air quality management. Traditional neural network (NN)-based forecasting methods frequently exhibit limitations, including inadequate hyperparameter tuning and limited responsiveness to temporal variability in industrial emissions data. To address these challenges, this study proposes an optimized neural forecasting framework integrating Particle Swarm Optimization (PSO) with neural networks. The PSO algorithm strategically optimizes network weights and biases, utilizing the mean squared error (MSE) as the fitness metric to ensure prediction accuracy. The framework is validated using a segmented real-time dataset that distinguishes daytime and nighttime CO emissions, improving the adaptability and precision of the model. Comparative analyzes with established hybrid forecasting approaches, such as Genetic Algorithm-NN, Simulated Annealing-NN, and Differential Evolution-NN, demonstrate the superior performance of the proposed PSO-NN model, achieving notably low prediction errors (MSE: $1.1941 \times 10^{-7}$ ), MAPE: 0.0016 and a high coefficient of determination ( $R^{2}$ : 0.99999). Furthermore, Theil’s U statistic confirms the robustness and predictive reliability of the model. Consequently, the proposed PSO-NN framework emerges as an effective real-time decision support system, facilitating sustainable air quality governance and promoting environmentally responsible industrial production practices.}, archive = {J_ACCESS}, author = {Marzia Ahmed and Shahrin Islam and Mohd Herwan Sulaiman and Md Maruf Hassan and Touhid Bhuiyan}, doi = {10.1109/ACCESS.2025.3587716}, journal = {IEEE Access}, pages = {121670-121685}, shortjournal = {IEEE Access}, title = {MetaForecaster: A PSO-driven neural model for sustainable industrial air quality management}, volume = {13}, year = {2025}, }
• (2025). Marine object detection using LiDAR on an unmanned surface vehicle. ACCESS, 13, 121658-121669. (www)

  @article{ , abstract = {Marine object detection plays a crucial role in various applications such as collision avoidance and autonomous navigation in maritime environments. While most existing datasets focus on 2D object detection, this research introduces a novel 3D object detection approach that relies exclusively on LiDAR (Light Detection And Ranging) data, specifically tailored for small Unmanned Surface Vehicles (USVs), where energy efficiency and computational constraints are key challenges. This study contributes a new point cloud dataset collected from a 2-meter autonomous USV and augmented through a hardware-in-the-loop simulation environment. The PointPillars network, chosen for its efficiency in processing LiDAR data, was trained and evaluated in this maritime context. A comparative analysis was also conducted between the proposed LiDAR-only method and a multimodal (LiDAR-camera) approach. The core innovation of this work is a step for late fusion strategy, where object detection is performed independently across sensors before integration. This results in a significantly less resource-intensive solution compared to early fusion methods. Consequently, the LiDAR-only approach highly suitable for deployment on compact, low-power autonomous surface drones, marking a step forward in practical and scalable marine perception systems.}, archive = {J_ACCESS}, author = {Yvan Eustache and Cédric Seguin and Antoine Pecout and Alexandre Foucher and Johann Laurent and Dominique Heller}, doi = {10.1109/ACCESS.2025.3587315}, journal = {IEEE Access}, pages = {121658-121669}, shortjournal = {IEEE Access}, title = {Marine object detection using LiDAR on an unmanned surface vehicle}, volume = {13}, year = {2025}, }
• (2025). Compact 532 nm laser with 59 mJ pulse energy output. ACCESS, 13, 121652-121657. (www)

  @article{ , abstract = {We have developed an external frequency doubling Nd:YAG laser with high beam quality, side-pumped by a high-temperature semiconductor laser. The beam quality was optimized using an unstable resonator configuration combined with a variable reflectivity mirror (VRM). This setup, coupled with the high-temperature side-pumping of the Nd:YAG laser crystal and external frequency doubling, resulted in highly stable 532 nm laser output. At a pump current of 100 A and a repetition rate of 50 Hz, the laser produced single-pulse energy of over 50 mJ, with a pulse width of 10.5 ns, and a beam quality factor of $M^{2} \approx 5.5$ . The system demonstrated continuous operation for 300 seconds with output energy stability fluctuations of less than 5%. This work presents a compact, high pulse energy green laser, providing a technological reference for airborne high pulse energy laser systems.}, archive = {J_ACCESS}, author = {Ma Changzheng and Gu Zhenyu and Ning Tigang and Li Qingsong and Dai Shoujun}, doi = {10.1109/ACCESS.2025.3585974}, journal = {IEEE Access}, pages = {121652-121657}, shortjournal = {IEEE Access}, title = {Compact 532 nm laser with 59 mJ pulse energy output}, volume = {13}, year = {2025}, }
• (2025). Enhancing LLMs for sequential recommendation with reversed user history and user embeddings. ACCESS, 13, 121641-121651. (www)

  @article{ , abstract = {Inspired by the successful applications of Large Language Models (LLMs) in various fields, LLMs for sequential recommendation have also become an active research area. Recent studies have focused on leveraging the powerful capabilities of LLMs to enhance their alignment with sequential recommendation. While LLMs excel in sequence modeling and have inspired adaptations for sequential recommendation tasks, their potential to fully exploit sequential nature remains underexplored. In this paper, we propose two key strategies: Reversed User History Generation (RUHG) and Recency-based User Embedding. The first method, RUHG, forces LLM to generate the next item and then regenerate the user history in reverse order. Due to the autoregressive nature of LLM, this method allows for better understanding of the next item and user history. Our second method, Recency-based User Embedding, captures the dynamics of user preference by emphasizing recent interactions. This user embedding provides a global view of user history to LLM, rather than utilizing only individual items. Moreover, we leverage Curriculum Learning (CL) for effectively training, and provide insights into defining easy and hard tasks for LLMs within CL. Our methods effectively bridge the modality gap between LLMs and sequential recommendation while maximizing the capabilities of LLMs through our scheme. Extensive experiments demonstrate the effectiveness of proposed methods and show performance improvements across three benchmark datasets. Our code is available at https://github.com/Yeo-Jun-Choi/llmfor}, archive = {J_ACCESS}, author = {Yeo Jun Choi and Woo-Seong Yun and Yoon-Sik Cho}, doi = {10.1109/ACCESS.2025.3583094}, journal = {IEEE Access}, pages = {121641-121651}, shortjournal = {IEEE Access}, title = {Enhancing LLMs for sequential recommendation with reversed user history and user embeddings}, volume = {13}, year = {2025}, }
• (2025). An integrated neural network-based traffic congestion prediction for material handling systems of semiconductor manufacturing. ACCESS, 13, 121630-121640. (www)

  @article{ , abstract = {Rapid automated logistics within a factory are essential to maximize productivity. In semiconductor manufacturing, the most important logistics management is the efficient operation of overhead hoist transports (OHTs). To transfer wafers via OHTs without delays, it is necessary to predict short-term traffic congestion in the OHT railway accurately. However, the congestion prediction is a significant challenge due to the complexity of investigating all traffic conditions and dynamic traffic changes. Several studies have utilized machine learning approaches to address these concerns, but limitations arise in predicting the short-term congestion due to the performance bias stemming from large input features. Recurrent neural networks are effective in predicting traffic flow in transportation. However, they may not be suitable for OHT railway congestion prediction due to unpredictable loading/unloading events and varying traffic volumes. Therefore, this study proposes an integrated neural network-based method where multiple neural networks are trained considering current conditions of the railway network and expected changes in traffic conditions. To verify the effectiveness of the proposed method, a simulated dataset was used to reflecting real-world semiconductor fabrication. The experiment results demonstrate that the proposed method outperforms existing methods, including machine learning- and deep learning-based methods.}, archive = {J_ACCESS}, author = {Donghun Lee and Suhee Kim and Hoonseok Park and Haejoong Kim and Ri Choe and Younkook Kang and Jae-Yoon Jung and Kwanho Kim}, doi = {10.1109/ACCESS.2025.3585914}, journal = {IEEE Access}, pages = {121630-121640}, shortjournal = {IEEE Access}, title = {An integrated neural network-based traffic congestion prediction for material handling systems of semiconductor manufacturing}, volume = {13}, year = {2025}, }
• (2025). Optimized distributed control for power sharing and voltage-frequency regulation in islanded microgrids. ACCESS, 13, 121616-121629. (www)

  @article{ , abstract = {This study examines the interactions among multiple distributed generators in an islanded microgrid with predominantly resistive line impedances. The primary objective is to ensure stable frequency and voltage regulation under disturbances, such as load changes while achieving accurate power sharing. A multiagent distributed control system, based on a hierarchical control structure, is implemented to facilitate the seamless integration of energy resources into the microgrid. The proposed hierarchical control framework consists of Finite Control Set Model Predictive Control (FCS-MPC)-based zero-level control, inverse droop-based primary control, and a finite-time optimal distributed secondary controller (SC). The FCS-MPC ensures precise voltage and current regulation by generating optimal switching pulses for each distributed generator (DG), thereby improving power delivery accuracy. Inverse droop control effectively decouples active and reactive power in resistive-dominant lines, ensuring proper load sharing. The secondary controller restores voltage and frequency with finite-time convergence, enhancing overall system stability. A dedicated communication network enables real-time coordination among the DGs, ensuring efficient operation under varying grid conditions. The proposed distributed secondary control is assessed by incorporating FCS-MPC-based inner control. Controller hardware in the loop (C-HIL) experimental tests are conducted with three DGs having different line impedances and local loads. The results validate the controller’s performance in voltage and frequency restoration and power sharing.}, archive = {J_ACCESS}, author = {Neethu Sajeev and Stephen Arinze Obi and Jae-Jung Jung}, doi = {10.1109/ACCESS.2025.3586743}, journal = {IEEE Access}, pages = {121616-121629}, shortjournal = {IEEE Access}, title = {Optimized distributed control for power sharing and voltage-frequency regulation in islanded microgrids}, volume = {13}, year = {2025}, }
• (2025). RFHP-CD: A prompt-driven fine-tuning framework of remote sensing foundation model for building and cropland change detection. ACCESS, 13, 121601-121615. (www)

  @article{ , abstract = {With the widespread adoption of foundation models in computer vision, remote sensing foundation models have emerged as a key approach for advancing intelligent Earth observation. However, most existing foundation models are pre-trained on natural images, leading to semantic gaps and task mismatches when transferred to remote sensing downstream tasks—especially change detection, which requires fine-grained spatiotemporal modeling. To address these challenges, we propose Remote sensing Foundation models’ Hierarchical Prompt fine-tuning framework for Change Detection (RFHP-CD). Built upon the state-of-the-art remote sensing foundation model HyperSIGMA, RFHP-CD introduces a lightweight and efficient Tri-prompt module, which employs a tri-level prompt encoder and a hierarchical prompt interaction strategy. In addition, we design a Multi-Stage feature Difference modeling and Fusion (MSDF) mechanism to guide the model in deeply capturing bitemporal change information. Experiments conducted on three representative datasets—LEVIR-CD,WHU-CD (building/bare land change) and PX-CLCD (cropland change)—demonstrate that our method outperforms mainstream approaches across multiple evaluation metrics, achieving F1 scores of 91.15%, 91.39% and 73.88%, respectively. Ablation studies and t-SNE visualizations further verify the effectiveness of our modules in enhancing model discriminability and preserving feature separability. This work offers new insights and practical pathways for adapting remote sensing foundation models to downstream change detection tasks.}, archive = {J_ACCESS}, author = {Guofang Wang and Yi Ma and Fangrong Zhou and Yifan Wang and Yijun Yan and Hao Geng}, doi = {10.1109/ACCESS.2025.3587922}, journal = {IEEE Access}, pages = {121601-121615}, shortjournal = {IEEE Access}, title = {RFHP-CD: A prompt-driven fine-tuning framework of remote sensing foundation model for building and cropland change detection}, volume = {13}, year = {2025}, }
• (2025). Staff hiring and reassignment based on resource-constrained project scheduling. ACCESS, 13, 121586-121600. (www)

  @article{ , abstract = {The implementation of a set of projects within an organization can be scheduled through a project scheduling procedure, that is based on the organizational resources available, such as the number of teams and team members, and the tasks that each team must perform. Nevertheless, hiring new personnel (staff hiring) and rearranging the members among the existing teams (staff reassignment) can lead to better scheduling solutions. This can potentially have a large financial impact for the organization, as it will reduce the projects’ implementation cost and will bring products earlier to market. To this end, this work presents different approaches to solve the resource-constrained project scheduling problem considering the reassignment of existing personnel as well as the hiring of new personnel. This is achieved by modifying a previously presented integer linear program (ILP) formulation to solve the aforementioned problem and considering different objectives and scenarios for staff hiring and reassignment. From the results obtained, it is evident that by using the developed ILPs, an organization can schedule its projects in a more time- and cost-efficient manner through the newly proposed recruitment and staff reassignment processes, compared to the benchmark approach that considers only existing personnel and team compositions.}, archive = {J_ACCESS}, author = {Giannis Savva and Konstantinos Manousakis and Georgios Ellinas}, doi = {10.1109/ACCESS.2025.3588234}, journal = {IEEE Access}, pages = {121586-121600}, shortjournal = {IEEE Access}, title = {Staff hiring and reassignment based on resource-constrained project scheduling}, volume = {13}, year = {2025}, }
• (2025). A power quality enhanced push-pull converter-based two-stage onboard charger for electric vehicle applications. ACCESS, 13, 121556-121567. (www)

  @article{ , abstract = {This paper presents an efficient two-stage onboard charger (OBC) using AC-DC converters with improved power quality for electric vehicle (EV) applications. The first stage comprises a diode bridge rectifier (DBR) without an input AC filter and a front-end boost converter (FBC) as a power factor correction (PFC) circuit, stabilizing the output voltage for efficient power transfer. The boost inductor current control is facilitated using a phase-locked loop to achieve input current wave shaping. This FBC drives the second-stage back-end push-pull converter (BPPC) operating in buck mode to ensure the battery is charging in constant current (CC) control mode. The push-pull converter demands its inductor current to be continuous due to the CC mode of battery charging. A detailed analysis of the power converters is conducted through simulations performed using the MathWorks SIMULINK software. Furthermore, a scaled-down hardware prototype has been developed, utilizing a dSPACE 1202 controller, to evaluate the effectiveness of the charger for a 48 V, 100 Ah battery. The test results demonstrate satisfactory performance and compliance with the IEC 61000-3-2 standard. This design effectively maintains input AC power quality during battery charging, highlighting its potential for enhancing EV charging infrastructure.}, archive = {J_ACCESS}, author = {U. Suprabha Padiyar and R. Kalpana}, doi = {10.1109/ACCESS.2025.3585631}, journal = {IEEE Access}, pages = {121556-121567}, shortjournal = {IEEE Access}, title = {A power quality enhanced push-pull converter-based two-stage onboard charger for electric vehicle applications}, volume = {13}, year = {2025}, }
• (2025). Linearized MILP model with improved soft actor-critic algorithm for dynamic and efficient active distribution network planning. ACCESS, 13, 121544-121555. (www)

  @article{ , abstract = {With the rapid development of distributed generations (DGs), the operation scale of distributed networks is getting larger and larger, and the operation modes are becoming more diversified. The traditional AC-based nonlinear planning model has many shortcomings in computational analysis. To this end, an active distributed network planning model based on linear constraints is proposed. Firstly, the basic principle and analysis method of the distributed network model linearization are proposed from two aspects, namely, power flow linearization and distributed network connectivity linearization. Then, by analyzing the distributed network planning objective function and constraints, a mixed integer linear planning (MILP) model for distributed networks is proposed. And an improved Soft Actor-Critic (ISAC) algorithm is proposed to solve the model. Finally, the feasibility and efficiency of the proposed method are verified by comparing the proposed MILP model and the traditional nonlinear planning model for distributed networks through multi-scenario case analysis. The experimental results demonstrate that the proposed MILP model significantly improves computational efficiency while maintaining accuracy. Compared to the traditional AC-based nonlinear model, the MILP model achieves a maximum relative error of only 3.3%, with computation time reduced from over 460 seconds to less than 0.2 seconds. Furthermore, ISAC exhibits outstanding performance in dynamic scenarios, reducing total costs by 20.63% and 15.25% compared to Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG), respectively.}, archive = {J_ACCESS}, author = {Jinlin Liao and Guilian Wu and Jia Lin}, doi = {10.1109/ACCESS.2025.3581382}, journal = {IEEE Access}, pages = {121544-121555}, shortjournal = {IEEE Access}, title = {Linearized MILP model with improved soft actor-critic algorithm for dynamic and efficient active distribution network planning}, volume = {13}, year = {2025}, }
• (2025). Phase current equalization method of IPMSM-based on-board boost converter for electric vehicles. ACCESS, 13, 121533-121543. (www)

  @article{ , abstract = {As the distance traveled per charge increases, the adoption of 800V batteries is increasing. However, most electric vehicle (EV) charging stations are designed to charge 400V batteries. However, if the installed motor and inverter operate as a boost converter, the 800V battery can be charged using an existing 400V charger. As a result of this increased availability, on-board boost charging is becoming more common. When an internal permanent magnet synchronous machine (IPMSM) is used as the boost inductor, the shape of the three-phase current changes because the current ripple varies with the rotor angle due to the magnetic saliency of the rotor. In addition, if the averages of the phase currents are not equal, torque can be produced. In this study, the complete IPMSM inductance model is utilized to obtain an analytical representation of the current ripple as a function of the switching duty and rotor angle. Based on this, the maximum charging current is estimated and the maximum charging power is also derived as a function of the rotor angle. It also determines the optimal rotor angle that minimizes the current ripple. This study also addresses phase current equalization to avoid torque generation. To this end, a compensation method is proposed to obtain an accurate average current in the double-sampling method, which samples the top and valley. The derived formulas are verified via simulations and experiments.}, archive = {J_ACCESS}, author = {Yunkyung Hwang and Jaehong Kim and Minhyeok Lee and Juwon Choi and Kwanghee Nam}, doi = {10.1109/ACCESS.2023.3272382}, journal = {IEEE Access}, pages = {121533-121543}, shortjournal = {IEEE Access}, title = {Phase current equalization method of IPMSM-based on-board boost converter for electric vehicles}, volume = {13}, year = {2025}, }
• (2025). A clinically interpretable approach for early detection of autism using machine learning with explainable AI. ACCESS, 13, 121512-121532. (www)

  @article{ , abstract = {Autism Spectrum Disorder (ASD) is a genetic and neurological condition that leads to difficulties in communication and social interaction. The global concern associated with ASD diagnosis is increasing at a rapid rate due to its significant impact on quality of life. Early identification of ASD can significantly support timely intervention and treatment planning. While research in ASD diagnosis is evolving through the application of machine learning (ML) techniques, practical implementation in clinical settings has not progressed at the same pace. Although theoretical studies have demonstrated improved ML performance, they have not gained much interest among clinicians due to a lack of explainability. This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. The paper uses naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) as classifiers after careful investigation. Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. The RF model achieved up to 99% balanced accuracy on the adult dataset, with similarly strong performance on the children and adolescent datasets using five-fold cross-validation (CV). SHAP are also illustrated to improve model interpretability by highlighting the most influential features, thereby aiding physician understanding. The novelty of this work lies in the integration of explainable AI with robust preprocessing and age-specific modeling across multiple ASD datasets, addressing both diagnostic accuracy and clinical interpretability. It can be considered that the suggested method can efficiently diagnose ASD at a very early stage and enhance the understanding of ASD diagnosis clinically.}, archive = {J_ACCESS}, author = {Oishi Jyoti and Hafsa Binte Kibria and Zareen Tasnim Pear and Md Nahiduzzaman and Md. Faysal Ahamed and Khandaker Reajul Islam and Jaya Kumar and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3586314}, journal = {IEEE Access}, pages = {121512-121532}, shortjournal = {IEEE Access}, title = {A clinically interpretable approach for early detection of autism using machine learning with explainable AI}, volume = {13}, year = {2025}, }
• (2025). Blindfolded flight: A novel approach for secure drone flight. ACCESS, 13, 121497-121511. (www)

  @article{ , abstract = {This study proposes a positioning system that allows a drone to determine its location independently of external data sources. The system is one of the key factors in achieving blindfolded flight, which is the ability to follow a predetermined flight route using only the aircraft’s internal data, even in the presence of spoofing, interference, or unavailability of any positioning systems. The system remains operational even when the sensor signals are of low quality. The GPS spoofing detection method based on this system effectively manages slow drift attacks, and its stability is demonstrated by the small coefficients of variation (CVs) of detection delays. The prediction of the current position is based on deep learning rather than motion equations. Existing approaches to blindfolded flight are constrained by cost, flight time, or flight distance. The proposed system does not significantly suffer from these limitations. The flight time limitations of the latest studies are approximately 100 s. The system demonstrates no tendency for prediction error to increase over time during the 360-second flight, regardless of the presence of GPS spoofing attacks.}, archive = {J_ACCESS}, author = {Kazumasa Oida and Tomoya Korezawa and Taiki Yamada and Sojiro Eto}, doi = {10.1109/ACCESS.2025.3586933}, journal = {IEEE Access}, pages = {121497-121511}, shortjournal = {IEEE Access}, title = {Blindfolded flight: A novel approach for secure drone flight}, volume = {13}, year = {2025}, }
• (2025). Hybrid renewable energy systems in türkiye: A multi-scenario assessment of demand, metering, and carbon tax policies. ACCESS, 13, 121472-121496. (www)

  @article{ , abstract = {The accelerating demand for low-carbon energy solutions highlights the critical role of hybrid renewable energy systems (HRES) in achieving decarbonization, energy security, and economic resilience. This study offers a comprehensive techno-economic and environmental evaluation of HRES integrating photovoltaic, wind, and battery storage technologies across Türkiye’s diverse climatic regions and sectoral demand profiles (residential, commercial, industrial). Utilizing 48 scenario-based simulations via HOMER Pro, the analysis incorporates real-world policy instruments including carbon taxation, net metering (NM), and net billing (NB). A novel sensitivity analysis reveals that under NB, battery integration becomes economically viable at carbon prices above 40– $50~{\$}$ /tCO2, while NM lowers this threshold to around $20~{\$}$ /tCO2. Additionally, a $40~{\$}$ /tCO2 carbon tax increases the renewable energy fraction by 32% and reduces the levelized cost of energy (LCOE) by 11.75%. Commercial users in wind-rich regions benefit most under NM, achieving up to 22% lower net present cost (NPC). While NM maximizes renewable deployment and short-term returns, NB ensures greater economic discipline and supports battery investments under high tariff and carbon scenarios. Expanded performance indicators, including self-supply rate, self-consumption rate, and energy exchange rate, provide operational insights beyond conventional metrics. The findings offer region-specific and policy-aware recommendations, suggesting that hybrid models combining NM and NB, supported by moderate carbon pricing and targeted incentives, can optimize system performance while ensuring affordability and equity in Türkiye’s energy transition. This framework offers strategic guidance for regulators, investors, and planners in emerging economies.}, archive = {J_ACCESS}, author = {Musa Terkes and Kaan Mehmet Toprak and Alpaslan Demirci and Erdin Gökalp and Umit Cali}, doi = {10.1109/ACCESS.2025.3587879}, journal = {IEEE Access}, pages = {121472-121496}, shortjournal = {IEEE Access}, title = {Hybrid renewable energy systems in türkiye: A multi-scenario assessment of demand, metering, and carbon tax policies}, volume = {13}, year = {2025}, }
• (2025). Automated artery detection and stenosis classification in CTA using deep learning for peripheral arterial disease. ACCESS, 13, 121458-121471. (www)

  @article{ , abstract = {Peripheral Arterial Disease (PAD) is one of the most important cardiovascular diseases worldwide for several million humans, associated with substantial morbidity, mortality, and healthcare resource utilization. Computed Tomography Angiography (CTA) is a high-quality imaging modality in PAD diagnostics, which directly highlights the vasculature having high resolution. However, manual interpretation of large-scale CTA data is hindered by human errors and is time-consuming. In this study, we introduce an automated deep learning model for detecting the main arteries and stenosis severity along with occlusions inference in lower limb CTA scans. We use Faster R-CNN with a ResNet-101 backbone driven by a custom loss function to achieve good artery localization and reduce false positives. The evaluation of our model is done by experts on a 67,850 CTA slices dataset from eighty patients. Experts manually annotated a subset of 13,439 slices for use in training and evaluating the model. Post-processed with HU-based vessel analysis and intersection-over-union (IoU) filtering, stenosis classification was more refined. In this way, the proposed method resulted in an overall accuracy of 98.79%, with an AUC of 0.9534 for stenosis classification. Deep learning integration with automated stenosis quantification will thus present a tool for robust AI-driven diagnosis of PAD that will bring about a clinically viable solution towards enhancing diagnostic accuracy, reducing workload, and improving patient outcomes.}, archive = {J_ACCESS}, author = {Ali M. O. A. Anwer and Hacer Karacan and Muhammed Rabee and Levent Enver and Gonca Çabuk}, doi = {10.1109/ACCESS.2025.3588303}, journal = {IEEE Access}, pages = {121458-121471}, shortjournal = {IEEE Access}, title = {Automated artery detection and stenosis classification in CTA using deep learning for peripheral arterial disease}, volume = {13}, year = {2025}, }
• (2025). Ensuring semantic consistency in SysML v2 models through metamodel-driven validation. ACCESS, 13, 121444-121457. (www)

  @article{ , abstract = {Model-Based Systems Engineering (MBSE) relies on formal models for system lifecycle management, supporting model coherence and efficient reuse of components. Modelling languages, particularly SysML v2, provide a standardized framework for complex system modelling, overcoming key limitations of earlier versions. Unlike SysML v1, which was a UML profile and inherited UML’s complexities, SysML v2 is designed to better handle multi-disciplinary, large-scale, and emergent system behaviours. However, the validation of SysML v2 models is still an emerging area, and while some methods are beginning to surface, comprehensive and standardized validation approaches are not yet fully established. This may pose challenges to ensuring correctness and reliability in engineering workflows. This paper presents a systematic, metamodel-based method for validating SysML v2 models, utilising the SysML v2 metamodel as a formal specification. By defining validation rules derived from this metamodel, the method facilitates automated detection of structural and semantic inconsistencies. A practical case study validates the method on SysML v2 models of aerospace, automotive, and software development domains, demonstrating the systematic identification and resolution of errors. This research advances SysML v2 model validation, contributing to broader MBSE objectives by ensuring correct SysML v2 models for complex system development in multi-disciplinary environments.}, archive = {J_ACCESS}, author = {Eduardo Cibrián and Jose Olivert-Iserte and Carlos Díez-Fenoy and Roy Mendieta and Juan Llorens and Jose María Álvarez-Rodríguez}, doi = {10.1109/ACCESS.2025.3587786}, journal = {IEEE Access}, pages = {121444-121457}, shortjournal = {IEEE Access}, title = {Ensuring semantic consistency in SysML v2 models through metamodel-driven validation}, volume = {13}, year = {2025}, }
• (2025). Study of bursting oscillations in VSG system with external excitation. ACCESS, 13, 121432-121443. (www)

  @article{ , abstract = {When the virtual synchronous generator (VSG) is subjected to an external excitation and the frequency of the external excitation is much smaller than the intrinsic frequency of the VSG system, the VSG system will experience bursting oscillations, leading to system instability. In order to avoid the problem of VSG oscillating and then destabilizing under external excitation, this paper investigates the bursting oscillations and bifurcation mechanisms of VSG systems with external excitation. This demonstrates the VSG’s dynamic behavior and provides a theoretical basis for avoiding bursting oscillations, which is of great importance for the stable operation of the VSG system. Firstly, the mathematical model of VSG subjected to external excitation is developed by considering the external excitation as a slow-varying parameter. Secondly, the possible bifurcation of the system and the associated phenomenon of bursting oscillations are theoretically analyzed. Finally, the bursting oscillation process under different simulation conditions is revealed by nonlinear dynamics simulation. Hardware-in-the-loop experiment verifies the accuracy of the simulation results.}, archive = {J_ACCESS}, author = {Kuanmin Wei and Xiaobing Wang and Jingda Gu and Liuxue Li}, doi = {10.1109/ACCESS.2025.3588207}, journal = {IEEE Access}, pages = {121432-121443}, shortjournal = {IEEE Access}, title = {Study of bursting oscillations in VSG system with external excitation}, volume = {13}, year = {2025}, }
• (2025). Energy theft identification: A state estimation model for partial meter bypass. ACCESS, 13, 121412-121431. (www)

  @article{ , abstract = {Energy theft due to illegally bypassing the meter can happen in traditional and smart grids. It is still one of the most frequent types of non-technical losses (NTL) due to the simplicity of connecting the illegal load directly to the distribution cable. Fraudulent consumers tend to report partial loads to avoid suspicion. Connection of illicit loads consisting of one or more electrical appliances is addressed at length in this paper. A novel method is proposed to locate and estimate the amount of theft using a 2-step Kalman filtering process with bias filter for optimization. The schema facilitates estimation of shifting cable impedances under dynamic operating conditions, regardless of whether energy theft is present. On average, energy theft incidents could be identified up to 99% for thefts involving single appliance and 92% for thefts involving multiple appliances. The effectiveness also vary based on the number of premises with energy theft, the load pattern and the type of bias filter applied. Results affirmed that by incorporating an optimization stage with the inclusion of the bias filter eliminates poor estimation, yielding low mean absolute errors for apparent power and power factor of 0.3 volt-ampere and 0.02, respectively. The accuracy and precision of classifying theft-points are close to 100%. A schema is also presented to reduce false identification of honest consumer from 22.6% to 0.5%. In addition, the proposed framework demonstrates robustness against different cable lengths, changes in temperature, cable core material, cable core dimension and cable degradation.}, archive = {J_ACCESS}, author = {Hwee Ling Wong and Chia Kwang Tan and Wooi-Nee Tan and Ming-Tao Gan and Sook-Chin Yip and Ab Halim Abu Bakar}, doi = {10.1109/ACCESS.2025.3587208}, journal = {IEEE Access}, pages = {121412-121431}, shortjournal = {IEEE Access}, title = {Energy theft identification: A state estimation model for partial meter bypass}, volume = {13}, year = {2025}, }
• (2025). Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting. ACCESS, 13, 121399-121411. (www)

  @article{ , abstract = {River sediment plays a critical role in maintaining the health of river ecosystems, and accurately predicting suspended sediment load (SSL) is vital for effective river management. This study proposes a quantum bee colony algorithm based on Boltzmann’s selection and dimensional dynamic update strategy (BDQABC). The BDQABC is utilized to optimize artificial intelligence (AI) models, specifically BDQABC-SVR and BDQABC-ANN. Both models are applied to predict SSL at the Caguas Municipio station in Loíza River, Puerto Rico. To evaluate the predictive capability, the models are compared with quantum bee colony algorithm-optimized AI models (QABC-SVR and QABC-ANN), genetic algorithm-optimized AI models (GA-SVR and GA-ANN) and traditional AI models (SVR and ANN). The models using various combinations of lagged river flow and SSL values as inputs. The performance of the models is assessed using metrics such as root mean square error (RMSE), mean absolute error, nash-Sutcliffe efficiency, coefficient of determination, and graphical comparison methods. The results indicate that the BDQABC-SVR and BDQABC-ANN models exhibit stronger predictive capabilities for SSL than other models. Specifically, BDQABC-SVR improves the RMSE accuracy of the QABC-SVR model by 5.6% and 22.0% respectively, while BDQABC-ANN improves the RMSE accuracy of the QABC-ANN model by 13.3% and 9.7%, respectively for the two input scenarios.}, archive = {J_ACCESS}, author = {Peng Wei and Wang Yu}, doi = {10.1109/ACCESS.2023.3333051}, journal = {IEEE Access}, pages = {121399-121411}, shortjournal = {IEEE Access}, title = {Improved quantum artificial bee colony algorithm-optimized artificial intelligence models for suspended sediment load predicting}, volume = {13}, year = {2025}, }
• (2025). Correct/Incorrect analysis of responses when estimating the cognitive function of spontaneous language in the elderly. ACCESS, 13, 121378-121398. (www)

  @article{ , abstract = {In recent years, research utilizing conversational data from spontaneous speech to estimate cognitive functions in older adults has increased. While spontaneous speech from older adults has traditionally been directed toward physicians or licensed psychologists, conversations with AI agents are now also being considered. However, the differences between having a human or an AI agent as a conversational partner when communicating with older adults remain insufficiently examined. Furthermore, it is still unclear whether the accuracy of responses in conversations differs when the partner is a human versus an AI. This study investigates how cognitive function in older adults is influenced by responses to questions posed by either human or AI conversational partners. The participants were 14 older adults (3 males and 11 females) residing in Tokyo and utilizing a day service center. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants engaged in semi-structured everyday conversations with both human and AI conversational partners. This study analyzed the correctness of the responses to the questions. The results revealed that differences in conversational partners did not significantly impact the response content. Participants with lower cognitive function frequently exhibited “memory errors” or “lack of understanding” in their responses, while those with higher cognitive function displayed higher accuracy rates. Additionally, older participants demonstrated fewer instances of “socially acceptable responses” or “lack of understanding.” Furthermore, differences in question content had a substantial impact on the participants’ responses; for instance, questions related to ADL (activities of daily living) elicited more “socially acceptable responses,” whereas questions regarding orientation often resulted in “memory errors” or “lack of understanding.” These findings suggest that routine interaction with AI agents can potentially enable the early detection of cognitive decline in older adults, facilitating timely and appropriate interventions. This study aims to provide new insights into the effective utilization of AI agents in conversations with older adults, ultimately contributing to the enhancement of elderly welfare.}, archive = {J_ACCESS}, author = {Toshiharu Igarashi and Katsuya Iijima and Kunio Nitta and Yu Chen}, doi = {10.1109/ACCESS.2025.3587009}, journal = {IEEE Access}, pages = {121378-121398}, shortjournal = {IEEE Access}, title = {Correct/Incorrect analysis of responses when estimating the cognitive function of spontaneous language in the elderly}, volume = {13}, year = {2025}, }
• (2025). RecompGPT: Generative pre-trained transformers-assisted interactive human gaze pattern learning and distribution modeling for scene recomposition. ACCESS, 13, 121362-121377. (www)

  @article{ , abstract = {We introduce a cutting-edge, GPT-assisted approach to optimizing visual scene retargeting by learning human gaze behaviors. RecompGPT leverages the power of Generative GPT to model human gaze dynamics, providing an advanced mechanism for intelligent and interactive image reconstruction. The framework incorporates a hierarchical structure, utilizing the BING objectness metric to identify key visual elements in exhibits. Through the integration of multimodal data, RecompGPT employs a novel Locality-Preserved and Observer-Like Active Learning (LOAL) strategy to incrementally generate Gaze Shift Paths (GSPs). LOAL is an active-learning algorithm that selects multiple representative patches from each scene image. It simultaneously preserves the local distribution of image patches and chooses representative (or visually salient) regions in a way that mimics human gaze allocation. Then, we deploy GPT to learn the distribution of the initial human gaze fixation toward different sceneries. Afterward, these GSPs are refined via a multi-layer aggregation algorithm that encodes deep feature representations into a Gaussian Mixture Model (GMM) to model the distribution of human gaze patterns. The learned GMM guides the scene recomposition process by maximizing the posterior estimation. Empirical evaluations, including interactive user studies, demonstrate RecompGPT’s superiority over existing methods, achieving a 4.39% improvement in precision and reducing testing time by 50%. RecompGPT harmonizes cutting-edge algorithmic efficiency with human-centered aesthetics, pushing the boundaries of AI-driven scene analysis and visual recomposition. It enhances the interactivity and immersion of virtual reality, offering a next-generation approach to interactive scene recomposition that aligns with human gaze patterns and cognitive preferences.}, archive = {J_ACCESS}, author = {Wang Shang and Nassiriah Binti Shaari and Nur Sauri Bin Yahaya and Liu Hao}, doi = {10.1109/ACCESS.2025.3588221}, journal = {IEEE Access}, pages = {121362-121377}, shortjournal = {IEEE Access}, title = {RecompGPT: Generative pre-trained transformers-assisted interactive human gaze pattern learning and distribution modeling for scene recomposition}, volume = {13}, year = {2025}, }
• (2025). A lightweight tri-stream feature fusion network for speech emotion recognition. ACCESS, 13, 121351-121361. (www)

  @article{ , abstract = {Understanding and modeling emotions from speech is a fundamental challenge in speech processing and a key enabler of emotionally intelligent human-computer interaction. However, defining and extracting robust emotional features remains difficult due to the nuanced and context-dependent nature of human affect. Existing approaches, focusing on prosodic features or deep representations from pre-trained models, often struggle to capture the full spectrum of emotional cues present in real-world speech. To address these limitations, we introduce Tri-Stream, a novel speech emotion recognition (SER) framework that concurrently leverages spectrogram and waveform modalities. Tri-Stream integrates three complementary feature streams: spectral patterns extracted via a Swin Transformer, deep acoustic representations from HuBERT, and engineered prosodic features capturing rhythmic information. These streams are fused and processed by a GRU-based classifier for final emotion prediction. Extensive evaluations on four benchmark datasets (IEMOCAP, SAVEE, RAVDESS, EMO-DB) demonstrate that Tri-Stream consistently outperforms state-of-the-art baselines, achieving 79.86% unweighted accuracy on IEMOCAP and best performance on the remaining datasets, highlighting its effectiveness and robustness across diverse emotional speech corpora.}, archive = {J_ACCESS}, author = {Ronghe Cao and Yunxing Wang and Xiaolong Wu and Shuang Jin and Huiling Niu}, doi = {10.1109/ACCESS.2025.3587607}, journal = {IEEE Access}, pages = {121351-121361}, shortjournal = {IEEE Access}, title = {A lightweight tri-stream feature fusion network for speech emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Effectiveness of explainable artificial intelligence (XAI) techniques for improving human trust in machine learning models: A systematic literature review. ACCESS, 13, 121326-121350. (www)

  @article{ , abstract = {Most decision-making processes worldwide are increasingly relying on artificial intelligence (AI) algorithms to enhance human welfare. Explainable Artificial Intelligence (XAI) techniques are pivotal in addressing the bottlenecks of utilizing machine learning (ML) algorithms, aiming to enhance human trust by providing transparency and interpretability. This paper conducts a systematic literature review (SLR) to evaluate the effectiveness of various XAI techniques in improving human trust in ML models. Our methodology involves a comprehensive search and analysis of relevant literature from 2015 to 2024, using well-known databases and adhering to PRISMA guidelines. The results indicate that XAI techniques significantly enhance user confidence by making ML models more transparent and understandable, facilitating error identification, and promoting better decision-making. However, gaps remain, including the need for standardized evaluation metrics, more user-centric evaluations, and studies on the long-term impact of XAI on user trust. Future research should focus on these areas to further improve the applicability and effectiveness of XAI techniques in diverse domains.}, archive = {J_ACCESS}, author = {In-On Wiratsin and Chaiyong Ragkhitwetsagul}, doi = {10.1109/ACCESS.2025.3575022}, journal = {IEEE Access}, pages = {121326-121350}, shortjournal = {IEEE Access}, title = {Effectiveness of explainable artificial intelligence (XAI) techniques for improving human trust in machine learning models: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Optimizing access point placement in industrial IoT: A deep reinforcement learning approach with Q-learning verification and signal heatmap visualization. ACCESS, 13, 121300-121325. (www)

  @article{ , abstract = {Optimal access point (AP) placement inside an industrial layout is important to ensure excellent connectivity. However, wireless fidelity (Wi-Fi) AP placement is complicated because industries exhibit distinct radio propagation characteristics and channel models compared to typical indoor offices and hotspots. In this work, we employ deep reinforcement learning (DRL) and Q-learning to optimize AP placement, with circle packing as the baseline. The performance metrics used are average received signal strength (RSS), average interference, and signal coverage across the industrial floor. These performance metrics are used to tailor the reward function used in DRL and Q-learning. The results obtained are reasonable, with an increase in average RSS by 3.55% and a decrease in average interference by 5.73% in one instance, and a 4.59% increase in signal coverage in another instance, compared to the results obtained using circle packing. DRL performed the best, followed by Q-learning and circle packing. Q-learning, though, matched the performance of DRL when the number of APs was low. An exploration prioritized epsilon decay function provided better results than an exploitation prioritized epsilon decay function, while the latter took less time to complete. The optimized AP locations and the corresponding RSS are further visualized using Wi-Fi heat maps. The proposed technique is effective for both small and large scaled industrial areas. However, the learning process takes significantly longer to complete in larger industrial areas. The technique presented in this work can be used during the initial network setup or to improve an existing network.}, archive = {J_ACCESS}, author = {Manash Mahanta and Attaphongse Taparugssanagorn and Kenta Umebayashi}, doi = {10.1109/ACCESS.2025.3581267}, journal = {IEEE Access}, pages = {121300-121325}, shortjournal = {IEEE Access}, title = {Optimizing access point placement in industrial IoT: A deep reinforcement learning approach with Q-learning verification and signal heatmap visualization}, volume = {13}, year = {2025}, }
• (2025). Enhancing structured pathology report generation with foundation model and modular design. ACCESS, 13, 121290-121299. (www)

  @article{ , abstract = {Pathology report generation, which requires analyzing giga-pixel Whole Slide Images (WSIs), is a complex and labor-intensive task. Despite this, it is crucial for clinical decision-making. An AI-driven report generation system can assist in producing accurate and concise reports. To address these needs, we designed a two-stage framework consisting of image analysis and text generation. This framework improves diagnostic performance and optimizes it for real-world clinical application. In the first stage, using 752 high resolution WSIs from bladder tumor tissue samples collected at Korea University Medical Center, we leveraged foundation models to extract robust features. Then, we built a multi-label classification based on key diagnostic elements to achieve high performance with an Attention-based Multi-Instance Learning (MIL) model and a Transformer-based MIL with Knowledge Distillation. For the report generation stage, we designed a T5-based text-to-text model, simplifying input representations while integrating data augmentation to improve stability and generalization. The proposed model achieved a ROUGE score of 0.87, a BLEU-4 score of 0.94, a Jaccard score of 0.89 and a BioLLM score of 0.97. An additional evaluation conducted by the institution maintaining the exclusive K-MEDICON test datasets confirmed a consistent performance yielding an overall score of 0.88, calculated as a weighted sum of aforementioned metrics. Its consistency validated through a Mean Opinion Score (MOS) evaluation by pathologists. By systematically integrating foundation models and modular structure, the benchmark results demonstrate that the task can be effectively solved with limited data and computational resources, indicating promising potential for real-world clinical adaptation.}, archive = {J_ACCESS}, author = {Kyung A Kim and Sungman Hong and Sehwan Yoo and Yousun Kang and Hyo Sup Shim}, doi = {10.1109/ACCESS.2025.3588121}, journal = {IEEE Access}, pages = {121290-121299}, shortjournal = {IEEE Access}, title = {Enhancing structured pathology report generation with foundation model and modular design}, volume = {13}, year = {2025}, }
• (2025). Surface unequal charging effect of GEO satellite. ACCESS, 13, 121284-121289. (www)

  @article{ , abstract = {When a satellite is in orbit, the plasma environment interacts with the surface material, and the surface charge accumulation of different structures is inconsistent,resulting in an unequal charging effect. In serious cases, this leads to electrostatic discharge and affects the normal operation of the satellite. In this study, a three-dimensional model of a geosynchronous orbit (GEO) satellite is constructed, and the particle cloud grid algorithm (Particle-In-Cell,PIC) was used to simulate the charging process and the surface charge state of satellite in different lasma environment. The simulation results show that in the solar wind plasma environment, the charging balance potential of solar panels is low and the charging difference between panels is small, which does not affect the normal operation of the satellite. However, when the satellite is in an hot plasma environment, if some parts of the satellite suddenly encounter extremely strong interference, the charging balance of the satellite in the solar wind plasma will be destroyed, and there will be a large potential difference between the irradiated part and the adjacent structure, which can easily cause electrostatic discharge to harm the normal operation of the satellite.}, archive = {J_ACCESS}, author = {Jianhong Hao and Biao Ru and Jieqing Fan}, doi = {10.1109/ACCESS.2023.3235598}, journal = {IEEE Access}, pages = {121284-121289}, shortjournal = {IEEE Access}, title = {Surface unequal charging effect of GEO satellite}, volume = {13}, year = {2025}, }
• (2025). A review of acetone vapor sensors for non-invasive diabetes diagnosis and management: Technologies, challenges, and future directions. ACCESS, 13, 121261-121283. (www)

  @article{ , abstract = {DM is a chronic metabolic disorder characterized by insufficient insulin production or impaired use in the cells, leading to a high blood glucose level. This disease requires constant monitoring of blood glucose levels. Traditional blood glucose monitoring techniques are invasive and inconvenient, highlighting the need for non-invasive alternatives. Breath acetone, a byproduct of fat metabolism in diabetic patients, has been identified and used as a biomarker for diabetes as it is directly related to blood glucose levels. Its concentration is significantly higher in the breath of diabetic patients, making it an effective indicator of the disease’s progression. This review comprehensively analyzes various acetone sensor technologies, including electrochemical, capacitive, chemiresistive, mass spectrometry-based, piezoelectric, and optical sensors. This review also discusses the biological background of acetone as a diabetic biomarker, the mechanism of different sensor types, and their advantages and disadvantages. Further, it explores the future prospect of enhancing sensor performance through integrating artificial intelligence (AI), energy harvesting, and the Internet of Things (IoT). By addressing the gaps in the literature regarding the summary of acetone sensors, this review aims to guide future developments in non-invasive diabetic monitoring technologies.}, archive = {J_ACCESS}, author = {Joel Georgeous and Lutfi Albasha and Ghaleb A. Husseini}, doi = {10.1109/ACCESS.2025.3584863}, journal = {IEEE Access}, pages = {121261-121283}, shortjournal = {IEEE Access}, title = {A review of acetone vapor sensors for non-invasive diabetes diagnosis and management: Technologies, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Super-resolution reconstruction method of face image based on attention mechanism. ACCESS, 13, 121250-121260. (www)

  @article{ , abstract = {In recent years, convolutional neural network in Single image super-resolution field show good results. Deep networks can establish complex mapping between low-resolution and high-resolution images, making the reconstructed images quality a great progress over traditional methods. In order to be able to generate face images with rich texture details, the algorithm proposed in this paper captures implicit weight information in channel and space domains through dual attention modules, so as to allocate computing resources more effectively and speed up the network convergence. Fusion of global features through residual connections in this network not only focus on learning the high frequency information of images that has been lost, but also accelerate the network convergence through effective feature supervision. In order to alleviate the defects of MAE loss function, a special Huber loss function is introduced in the algorithm. The experimental results on benchmark show that the proposed algorithm has a significant improvement in image reconstruction accuracy compared with existed SISR methods.}, archive = {J_ACCESS}, author = {Chenglin Yu and Hailong Pei}, doi = {10.1109/ACCESS.2021.3070898}, journal = {IEEE Access}, pages = {121250-121260}, shortjournal = {IEEE Access}, title = {Super-resolution reconstruction method of face image based on attention mechanism}, volume = {13}, year = {2025}, }
• (2025). MixLVMM: A mixture of lightweight vision mamba model for enhancing skin lesion segmentation across high tone variability. ACCESS, 13, 121234-121249. (www)

  @article{ , abstract = {Accurate skin lesion segmentation remains a critical challenge in automated dermatological diagnosis due to heterogeneous lesion presentations, ambiguous boundaries, imaging artifacts, and significant variability in skin and lesion tones across diverse populations. Current segmentation methods inadequately address these multifaceted complexities, particularly failing to handle extreme tone variations that can lead to diagnostic bias. To address these limitations, we present the Mixture of Lightweight Vision Mamba Model (MixLVMM), a novel expert-based framework that enhances segmentation performance across high tone variability through specialized processing. Our approach employs a Siamese network with triplet loss as a gate mechanism to categorize lesions based on tonal characteristics, routing each image to specialized Vision Mamba Model (VMM) experts optimized for specific lesion categories. Each expert utilizes a U-shaped architecture incorporating Focused Vision Mamba blocks and Adaptive Salient Region Attention modules to capture lesion-specific features while maintaining computational efficiency. Comprehensive evaluation on ISIC and PH2 datasets demonstrates that MixLVMM achieves superior segmentation performance with an average Dice coefficient of 93%, surpassing state-of-the-art methods while maintaining efficiency with only 2.5M parameters. These results establish MixLVMM as a robust solution for addressing tone-related segmentation challenges in clinical dermatology, offering both high accuracy and practical deployment feasibility for real-world applications. Additional materials and code will be available at https://github.com/MOHAMEDLamine77/MixLVMM}, archive = {J_ACCESS}, author = {Mohamed Lamine Allaoui and Mohand Saïd Allili}, doi = {10.1109/ACCESS.2025.3588476}, journal = {IEEE Access}, pages = {121234-121249}, shortjournal = {IEEE Access}, title = {MixLVMM: A mixture of lightweight vision mamba model for enhancing skin lesion segmentation across high tone variability}, volume = {13}, year = {2025}, }
• (2025). Performance comparison of computationally efficient algorithms for drone localization on embedded systems. ACCESS, 13, 121217-121233. (www)

  @article{ , abstract = {Accuracy and computational complexity are the two most fundamental and critical factors for ensuring efficient performance of a real-time drone localization algorithm. This paper explores the time complexity of two popular state estimation algorithms: The Error State Kalman Filter (ESKF), and Particle Filter (PF). The capacity to estimate the state of dynamic systems in the presence of noise makes these algorithms popular in a wide range of applications, including robotics, navigation, and sensor fusion. The main objective of this study is to assess each algorithm’s computational performance, particularly with regard to its time complexity. The paper offers a thorough examination of the variables that affect these algorithms’ computational cost, such as their mathematical structure, the underlying systems’ characteristics, and the complexity of the required operations. Through this investigation, the study hopes to shed light on the trade-offs associated with choosing a suitable state estimation method in terms of computational efficiency. With the simulation results in this work, the computational time obtained with the proposed ESKF algorithm is found to be 0.21 seconds on average and for PF algorithm it is 0.54 milliseconds using the NVIDIA Jetson Xavier hardware platform. The PF’s faster runtime stems from its simplified motion and observation models, which maintain sufficient accuracy under test conditions. This concludes that the proposed PF strategy in this paper is more suitable for real-time drone localization on embedded systems.}, archive = {J_ACCESS}, author = {Muhammad Bilal Kadri}, doi = {10.1109/ACCESS.2025.3587882}, journal = {IEEE Access}, pages = {121217-121233}, shortjournal = {IEEE Access}, title = {Performance comparison of computationally efficient algorithms for drone localization on embedded systems}, volume = {13}, year = {2025}, }
• (2025). Investigation on the dynamic thermal behavior for high voltage cable with air gap considering the thermal expansion of insulation. ACCESS, 13, 121206-121216. (www)

  @article{ , abstract = {The air gap is reserved between the insulation and metal sheath within the cable to mitigate damage resulting from cable bending phenomena. Owing to the temperature sensitivity of insulation thermal expansion, the thermal parameter of air gap layer will undergo significant variations during the transient temperature rise of cable. However, the aforementioned phenomena have not been considered in the existing cable dynamic thermal evaluation methods. To address this issue, in this paper the thermo-force multi-field coupled simulation model of cable with air gap was established and the simulation analysis of dynamic thermal behavior inside the cable was achieved. The error analysis of existing IEC method was conducted in conjunction with the simulation results. Further, the impact of the radial dynamic thermal expansion of insulation on the thermal parameter of air gap layer was discussed. Then, the dynamic modification method of the thermal parameter of air gap layer was proposed and the optimized iterative algorithm for calculating cable transient temperature rise was demonstrated. The accuracy and superiority of the optimized algorithm were verified by the simulation method. The results show that the proposed methodology shows a satisfactory performance in the dynamic thermal evaluation for cable with air gap.}, archive = {J_ACCESS}, author = {Yanxiong Li and Xingjia Tang and Ziheng Gao and Jianrui Yang and Jiayu Luo and Daqi Zhao and Pengyu Wang and Gang Liu}, doi = {10.1109/ACCESS.2025.3587246}, journal = {IEEE Access}, pages = {121206-121216}, shortjournal = {IEEE Access}, title = {Investigation on the dynamic thermal behavior for high voltage cable with air gap considering the thermal expansion of insulation}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in quantum communications: A comprehensive survey. ACCESS, 13, 121174-121205. (www)

  @article{ , abstract = {The integration of Artificial Intelligence (AI) into the field of quantum communication has emerged in recent years as a promising avenue for enhancing secure data transmission, error correction, and scalability in quantum networks. This paper offers a comprehensive survey of AI applications in quantum communication, with a focus on machine learning (ML) models such as neural networks and reinforcement learning, which are adapted to manage complex quantum challenges. Despite the growing body of research, current studies often concentrate on specific AI techniques or quantum applications, lacking a unified framework that evaluates their practical effectiveness, scalability, and cost-efficiency. Addressing this gap, our survey systematically reviews and categorizes AI-driven methodologies across core aspects like system security, transmission speed, and reliability. Through a critical comparison and ranking of existing techniques, this study identifies best practices and key advancements while highlighting areas requiring further exploration. By providing an in-depth analysis of AI’s potential to transform quantum communication, this paper aims to serve as a foundational resource for researchers and practitioners seeking to develop resilient, adaptive, and economically viable quantum communication systems.}, archive = {J_ACCESS}, author = {Istiak Mahmud and Ahmed Abdelhadi}, doi = {10.1109/ACCESS.2025.3585799}, journal = {IEEE Access}, pages = {121174-121205}, shortjournal = {IEEE Access}, title = {Artificial intelligence in quantum communications: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Multi-task counterpropaganda semantic learning for detecting fake news. ACCESS, 13, 121158-121173. (www)

  @article{ , abstract = {With the surge in online information, unverified claims and fake news, especially in politics and economics, have become rampant. This rise makes distinguishing true from false news crucial, as fake news can manipulate public perception and cause socio-economic harm. Previous research indicates that human-written fake news often employs propaganda techniques to seem credible. However, the smaller volume of fake news data compared to real news leads to imbalances in training datasets. This imbalance can cause models to learn incorrect correlations instead of accurately identifying the semantic features of deceptive content. This paper introduces a counterpropaganda semantic learning framework for detecting fake news. Utilizing existing fake news datasets, we automatically generate propaganda-related summaries, reasons, and scores using a large language model. We then create counterpropaganda semantics by enhancing the potential propaganda elements in news content. The model learns the relationship between news and propaganda through a multi-task learning strategy. Experimental results demonstrate that this framework improves small language models’ accuracy and semantic understanding in detecting fake news while reducing the need for intermediary pre-training and manual review of generated training data. Our model, data, and code are open-source at https://github.com/Ryan-Hu-AI/MTCPSL}, archive = {J_ACCESS}, author = {Ming-Jhe Hu and Hung-Yu Kao}, doi = {10.1109/ACCESS.2025.3587145}, journal = {IEEE Access}, pages = {121158-121173}, shortjournal = {IEEE Access}, title = {Multi-task counterpropaganda semantic learning for detecting fake news}, volume = {13}, year = {2025}, }
• (2025). Deep residual network with integrated StarDist nuclei segmentation for papillary thyroid cancer identification: A pathologist-inspired approach. ACCESS, 13, 121139-121157. (www)

  @article{ , abstract = {Papillary Thyroid Carcinoma (PTC) is the most frequently occurring type of thyroid cancer. Diagnosing PTC using histopathological imaging as the gold standard in clinical practice remains challenging. The rise of Artificial Intelligence (AI) has accelerated the development of fast and accurate computer-aided pathology for PTC identification. Numerous studies have demonstrated that deep learning-based methods yield promising results; however, current approaches often overlook the nuclei, a key feature in PTC diagnosis. This study proposes a deep residual network with integrated StarDist nuclei segmentation to enhance the model’s ability to focus on critical cellular structures when making predictions. To assess its robustness, the model was trained on the Tharun & Thompson dataset and evaluated on three independent external datasets: an institutional hospital dataset, the Cancer Genome Atlas (TCGA) dataset, and the Nikiforov dataset. Our proposed method achieved 98.55% accuracy, 98.55% sensitivity, and 98.84% specificity in internal testing while also demonstrating an improved focus on nuclei-dense regions, aligning with the diagnostic approach used by pathologists. Our findings confirmed that incorporating nuclei segmentation significantly enhances classification performance and model interpretability. The proposed model enhances feature localization by integrating nucleus segmentation with Deep Residual Networks, yielding a practical and efficient solution for histopathological PTC analysis.}, archive = {J_ACCESS}, author = {Nabila Husna Shabrina and Dadang Gunawan and Mia Rizkinia and Agnes Stephanie Harahap and Mohammad Ikhsan and Rifai Chai and Maria Francisca Ham}, doi = {10.1109/ACCESS.2025.3584941}, journal = {IEEE Access}, pages = {121139-121157}, shortjournal = {IEEE Access}, title = {Deep residual network with integrated StarDist nuclei segmentation for papillary thyroid cancer identification: A pathologist-inspired approach}, volume = {13}, year = {2025}, }
• (2025). The effect of driver behavior on energy consumption using a microscopic traffic model. ACCESS, 13, 121118-121138. (www)

  @article{ , abstract = {Driver behavior significantly affects vehicle energy consumption and emissions which are critical global concerns. This paper proposes a microscopic traffic model to characterize energy consumption considering the physiological and psychological behavior of a driver. Physiological behavior reflects driver response to traffic conditions while psychological behavior includes driver sensitivity and awareness. Thus, the proposed model encompasses driver response, sensitivity, and awareness. Data was obtained using a sensor node installed at the roadside. Traffic was monitored for six days and the data was modeled using regression analysis. The models and vehicle fuel consumption rate are integrated into the proposed model. The Intelligent Driver (ID) model is based on a constant exponent so driver behavior does not vary with traffic conditions. Thus, fuel consumption is ignored. It is shown that the proposed model results in stable traffic, i.e. traffic returns to a steady state following a disturbance, whereas the ID model produces unstable traffic behavior. Further, it results in smaller fluctuations in speed, flow, and density. Thus, the proposed model can be used to lower fuel consumption and emissions, thereby reducing air pollution and contributing to climate change mitigation.}, archive = {J_ACCESS}, author = {Zawar Hussain Khan and Faryal Ali and Khurram Shehzad Khattak and Ahmed B. Altamimi and Mohammad Alsaffar and Wilayat Khan and Thomas Aaron Gulliver}, doi = {10.1109/ACCESS.2025.3587920}, journal = {IEEE Access}, pages = {121118-121138}, shortjournal = {IEEE Access}, title = {The effect of driver behavior on energy consumption using a microscopic traffic model}, volume = {13}, year = {2025}, }
• (2025). Integrated VLC/RF with adaptive power control for reliable cluster-based VANET communications. ACCESS, 13, 121099-121117. (www)

  @article{ , abstract = {In Vehicular Ad Hoc Networks (VANETs), ensuring reliable, energy-efficient communication under varying traffic densities remains a major challenge, particularly when relying completely on conventional Radio Frequency (RF) communication. This study presents an integrated communication framework that combines Visible Light Communication (VLC) with RF to improve vehicular connectivity. Motivated by the limitations of fixed-power techniques in dynamic environments, we introduce two power allocation algorithms based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN): one using Fixed Power Allocation (DFPA) and another employing Dynamic Power Allocation (DDPA). Both approaches are evaluated using realistic traffic simulations based on Simulation of Urban Mobility (SUMO) and OpenStreetMap (OSM) data, with a focus on energy efficiency and communication coverage. Simulation results show that DDPA outperforms DFPA in cases with cluster radii below 1159 m, reducing power consumption by up to 20% and serving 90% of vehicles. Conversely, DFPA proves more efficient at larger radii, saving up to 12% power. These results establish a scalable, integrated VLC/RF framework for next-generation VANETs, adaptable to diverse density and coverage requirements.}, archive = {J_ACCESS}, author = {Ahmed M. El-Eraki and Nancy A. Alshaer and Mohamed M. Fouad and Azhar A. Hamdi and Tawfik Ismail}, doi = {10.1109/ACCESS.2025.3586830}, journal = {IEEE Access}, pages = {121099-121117}, shortjournal = {IEEE Access}, title = {Integrated VLC/RF with adaptive power control for reliable cluster-based VANET communications}, volume = {13}, year = {2025}, }
• (2025). Enhancing weibo sentiment analysis with multi-modal learning: Integrating text and synthesized images with contrastive learning. ACCESS, 13, 121086-121098. (www)

  @article{ , abstract = {In this paper, we aim to improve sentiment analysis on weibo, a vital platform for sentiment and opinion expression. Current sentiment analysis methods struggle with the unique challenges of Weibo posts, which often contain informal language, sarcasm, and lack of context, making it difficult to capture complex emotions. Additionally, existing text-based models fail to account for visual cues and rely heavily on predefined emotion categories, limiting their ability to detect nuanced sentiments. To address these issues, we propose a novel multimodal approach that integrates both textual and visual elements, leveraging a pretrained text-to-image model to generate images from posts. Integrated with a self-supervised contrastive learning objective, this approach empowers our model to extract knowledge from unlabeled data and achieve enhanced comprehension of nuanced emotional cues. Our method enhances sentiment analysis on microblogs by introducing a framework that captures both text and synthesized visual cues, enabling deeper sentiment interpretation. By incorporating self-supervised learning, we significantly improve the detection of complex emotions, particularly those related to mental health, offering a more comprehensive solution for sentiment analysis on social platforms. The innovation of this method lies in the introduction of synthetic images and self-supervised contrastive learning into microblog sentiment analysis, and the accuracy and generalization ability of the model are significantly improved through multimodal feature fusion and contrastive learning.}, archive = {J_ACCESS}, author = {Chuyang Wang and Jessada Konpang and Adisorn Sirikham and Shasha Tian}, doi = {10.1109/ACCESS.2025.3579782}, journal = {IEEE Access}, pages = {121086-121098}, shortjournal = {IEEE Access}, title = {Enhancing weibo sentiment analysis with multi-modal learning: Integrating text and synthesized images with contrastive learning}, volume = {13}, year = {2025}, }
• (2025). SmartCamAirDetect: A contrastive approach for probabilistic ambient air pollution estimation with limited images for smart city development. ACCESS, 13, 121073-121085. (www)

  @article{ , abstract = {Effective air pollution monitoring is crucial for data-driven smart city initiatives. However, the sparse distribution of air quality monitoring stations (AQMSs) often results in localized measurements, which may not accurately represent broader air quality conditions. The cost of extensive AQMS expansion is prohibitive, whereas widely distributed stationary cameras offer a cost-effective alternative by providing high-density imagery for ambient air pollution estimation. Nonetheless, cameras distant from AQMSs or newly installed often lack sufficient labeled data, and conventional models trained on limited datasets tend to overfit, leading to substantial estimation errors. To address these challenges, we introduce SmartCamAirDetect, a novel AI-driven contrastive learning framework to enhance air quality estimation performance, tailored to stationary ground-level camera imagery under sparse labeling conditions. Unlike previous contrastive methods developed for satellite-based data, SmartCamAirDetect is specifically designed to handle the fixed scenery and high temporal variability unique to ground-level images. At the core of SmartCamAirDetect is ContraEst, a contrastive learning model that estimates pollution differentials between images and generates probabilistic pollution assessments by comparing a target image from a limited dataset to multiple images captured by other cameras with ample labels. To refine feature extraction, SmartCamAirDetect synthesizes scenery images to eliminate static background features, enabling the model to focus exclusively on pollution-related information such as sky appearance. SmartCamAirDetect is evaluated across various pollutants using multiple performance metrics. Compared to the baseline deterministic model, ContraEst reduces the mean absolute error by 11.61%, decreases the symmetric mean absolute percentage error by 14.20%, and improves the Pearson correlation coefficient by 50.97%. Incorporating synthetic scenery images further reduces the pinball loss and Winkler score for probabilistic estimations by 5.38% and 5.81%, respectively. These results underscore the efficacy and robustness of SmartCamAirDetect for image-based air pollution estimation, highlighting its potential for fine-grained, AI-driven air quality monitoring in smart cities. In future work, we aim to improve the interpretability and robustness of the scenery synthesis and removal module under dynamic scenes, enhance contrastive pair selection via optimal sampling strategies, incorporate additional probabilistic baselines to better contextualize uncertainty estimation performance, and strengthen generalizability across diverse urban environments.}, archive = {J_ACCESS}, author = {Yang Han and Victor O. K. Li and Jacqueline C. K. Lam and Shiguang Song and Tingyu Mo}, doi = {10.1109/ACCESS.2025.3583794}, journal = {IEEE Access}, pages = {121073-121085}, shortjournal = {IEEE Access}, title = {SmartCamAirDetect: A contrastive approach for probabilistic ambient air pollution estimation with limited images for smart city development}, volume = {13}, year = {2025}, }
• (2025). Data-driven robust tracking control for multi-player nonzero-sum games with constrained inputs. ACCESS, 13, 121061-121072. (www)

  @article{ , abstract = {In this paper, the robust tracking control for unknown multi-player nonlinear systems with constrained inputs and uncertainties is studied by data-driven reinforcement learning scheme. In this study, the dependence on system dynamics is relaxed through the use of system input and state data. First of all, an augmented multi-player uncertain system is built by integrating the multi-player uncertain nonlinear systems and the given reference signal system. Afterwards, a non-quadratic cost function including the bounded functions, system states and all players control inputs is designed to handle the input constraints and uncertainties, and the robust tracking control problem is therefore converted into a constrained optimal control problem of the nominal system corresponding to the augmented multi-player uncertain system. Further, we rigorously analyze the equivalence of this conversion under specific conditions. The solution of the constrained optimal control problem can be obtained by solving a coupled Hamilton-Jacobi (HJ) equations. Then, on the basis of a given policy iteration scheme, a data-driven reinforcement learning scheme utilizing actor-critic neural networks is designed to solve the coupled HJ equation under the restriction of unavailable system dynamics. The weights of the neural network are learned using the least squares method from the input and state data collected from the system. Finally, a two-player uncertain nonlinear system and an induction heater circuit system are simulated to support the theoretical analysis.}, archive = {J_ACCESS}, author = {Jingang Zhao and Jun Zhao and Yehan Chang and Guosheng Xu}, doi = {10.1109/ACCESS.2025.3587808}, journal = {IEEE Access}, pages = {121061-121072}, shortjournal = {IEEE Access}, title = {Data-driven robust tracking control for multi-player nonzero-sum games with constrained inputs}, volume = {13}, year = {2025}, }
• (2025). DialogueMLLM: Transforming multimodal emotion recognition in conversation through instruction-tuned MLLM. ACCESS, 13, 121048-121060. (www)

  @article{ , abstract = {Multimodal Emotion Recognition in Conversation (MERC) is an advanced research area that integrates cross-modal understanding and contextual reasoning through text-speech-visual fusion, with applications spanning diverse scenarios including student emotion monitoring in high school classroom interactions. Although existing research has made progress in multimodal alignment and dialogue relationship modeling through architectures such as graph neural networks and pre-trained language models, challenges persist in dataset overfitting and underexplored generative approaches. In this study, a generative MERC framework based on Multimodal Large Language Models (MLLMs) is proposed, employing Video-LLaMA, an open-source and advanced tri-modal foundation model, for end-to-end multimodal emotion reasoning. Carefully crafted structured prompts are used to align emotion semantics with dataset annotations, combined with Low-Rank Adaptation (LoRA) for parameter-efficient optimization. The method achieves a state-of-the-art weighted F1-score of 68.57% on the MELD benchmark. Further, exploratory experiments on dynamic modality combinations and fine-tuning strategies offer actionable insights for MLLM-based MERC research. This work not only advances emotion understanding in dialogues but also highlights MLLMs’ potential in complex multimodal reasoning tasks.}, archive = {J_ACCESS}, author = {Yuanyuan Sun and Ting Zhou}, doi = {10.1109/ACCESS.2025.3591447}, journal = {IEEE Access}, pages = {121048-121060}, shortjournal = {IEEE Access}, title = {DialogueMLLM: Transforming multimodal emotion recognition in conversation through instruction-tuned MLLM}, volume = {13}, year = {2025}, }
• (2025). Dynamic risk thresholds for SIEM alerting based on machine learning. ACCESS, 13, 121034-121047. (www)

  @article{ , abstract = {Almost every organization with an internet presence is nowadays exposed to increasing amounts of attempted cyber attacks year over year. Such an increase calls for a development of more effective ways of detecting such attempts at compromise. In the article, a theoretical concept of a Dynamic Risk-Based Alerting model for SIEM based on machine learning has been presented. An implementation of such a model in a production environment has also been showcased, with both qualitative and quantitative data indicators gathered from the environment. Conducting research on the effects of dynamic risk thresholds on incident detection quality, particularly regarding the count of false positives and the efficiency of threat detection, was a crucial part of this study and showed a 26% reduction in false positive/repeated alert volume. Based on the gathered data and survey responses, it can be concluded that the proposed framework has value and could be implemented as a novel alternative or supplementary method to typical, static risk-based alerting.}, archive = {J_ACCESS}, author = {Artur Kapera and Marcin Niemiec}, doi = {10.1109/ACCESS.2025.3588441}, journal = {IEEE Access}, pages = {121034-121047}, shortjournal = {IEEE Access}, title = {Dynamic risk thresholds for SIEM alerting based on machine learning}, volume = {13}, year = {2025}, }
• (2025). DMFFNet: Dual-mode multiscale feature fusion-based pedestrian detection method. ACCESS, 13, 121021-121033. (www)

  @article{ , abstract = {Most contemporary pedestrian detection algorithms are based on visible light image detection. However, in environments with dim light, small targets, and easily occluded and cluttered backgrounds, single-mode visible light images relying on color, texture, and other features cannot adequately represent the feature information of targets; as a result, a large number of targets are lost and the algorithm performance is not good. To address this problem, we propose a dual-modal multi-scale feature fusion network (DMFFNet). First, we use the MobileNet v3 backbone network to extract the features of dual-modal images as input for the multi-scale fusion attention (MFA) module, combining the idea of multi-scale feature fusion and attention mechanism. Second, we deeply fuse the multi-scale features output by the MFA with the double deep feature fusion (DDFF) module to enhance the semantic and geometric information of the target. Finally, we optimize the loss function to reflect the distance between the predicted box and the real box more realistically as well as to enhance the ability of the network toward predicting difficult samples. We performed multi-directional evaluations on the KAIST dual-light pedestrian dataset and the visible-thermal infrared pedestrian dataset (VTI) in our laboratory through comparative and ablation experiments. The overall MR−2 on the KAIST dual-light pedestrian dataset is 9.26%, and the MR−2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The overall MR−2 on the VIT dual-light pedestrian dataset is 9.26%, and the MR−2 in dim light, partial occlusion, and severe occlusion are 5.17%, 23.35%, and 47.31%, respectively. The results show that the algorithm performs well on pedestrian detection, especially in dim light and when the target was occluded.}, archive = {J_ACCESS}, author = {Ruizhe Hu and Ting Rui and Yan Ouyang and Jinkang Wang and Qunyan Jiang and Yinan Du}, doi = {10.1109/ACCESS.2022.3185986}, journal = {IEEE Access}, pages = {121021-121033}, shortjournal = {IEEE Access}, title = {DMFFNet: Dual-mode multiscale feature fusion-based pedestrian detection method}, volume = {13}, year = {2025}, }
• (2025). Self-organizing neighbor discovery and ranging for battery-powered ultra-wideband anchors. ACCESS, 13, 121003-121020. (www)

  @article{ , abstract = {Ultra-wideband (UWB) technology has emerged as a popular solution in support of autonomous navigation. Unfortunately, installing a localization system is still a significant challenge in many large-scale or secluded environments. Since most available solutions require anchors to be connected to the power grid, infrastructures turn up to be extremely expensive when not simply unfeasible. We address these issues with SONAR, an UWB localization system based on battery-powered anchors. SONAR supports multi-year deployments without wired backbones, covering a wide spectrum of scenarios: from planetary exploration, our motivating application, to more earthly uses in, e.g., agricultural fields, smart warehouses, or smart health. The proposed system can be easily tuned to satisfy different needs in terms of ranging rate and energy consumption. We evaluate SONAR in a multi-hop testbed, showing that multiple roaming users can quickly discover each other and the surrounding anchors, and self-organize in a contention-free ranging schedule.}, archive = {J_ACCESS}, author = {Davide Vecchia and Pablo Corbalán and Timofei Istomin and Gian Pietro Picco and Enrico Varriale}, doi = {10.1109/ACCESS.2025.3587489}, journal = {IEEE Access}, pages = {121003-121020}, shortjournal = {IEEE Access}, title = {Self-organizing neighbor discovery and ranging for battery-powered ultra-wideband anchors}, volume = {13}, year = {2025}, }
• (2025). Decom-UNet3+: A retinal vessel segmentation method optimized with decomposed convolutions. ACCESS, 13, 120993-121002. (www)

  @article{ , abstract = {The intricate and highly branched structure of retinal blood vessels, along with the fragility of fine vessels, makes segmentation a challenging task. To address this issue, we propose Decom-UNet3+, a model that optimizes the encoders by employing decomposed convolutions. Specifically, the encoders replace standard convolutional layers with asymmetric convolutions and depthwise separable convolutions, reducing the number of parameters while enhancing capability for feature extraction. Additionally, a spatial attention mechanism is integrated to improve focus on vessel regions and suppress background noise. The model is evaluated on high-resolution, expertly annotated datasets including CHASEDB1, DRIVE, STARE and HRF, achieving an average accuracy of 97.2% on CHASEDB1, 96.4% on DRIVE, 94.3% on STARE and 97.6% on HRF, outperforming the original UNet3+ model. The results demonstrates that Decom-UNet3+ effectively improves vascular segmentation performance with lower computational cost and parameter overhead, offering a more efficient and robust solution for automated retinal disease screening.}, archive = {J_ACCESS}, author = {Qun Li and Juntao Zhang and Licheng Hua and Songyin Fu and Chenjie Gu}, doi = {10.1109/ACCESS.2025.3588461}, journal = {IEEE Access}, pages = {120993-121002}, shortjournal = {IEEE Access}, title = {Decom-UNet3+: A retinal vessel segmentation method optimized with decomposed convolutions}, volume = {13}, year = {2025}, }
• (2025). Efficient multi-scale transformer with convolutional attention for high-definition image dehazing. ACCESS, 13, 120983-120992. (www)

  @article{ , abstract = {Image dehazing aims to recover clear images from hazy inputs, crucial for advanced computer vision tasks. While convolutional neural networks (CNNs) have traditionally been used, transformer-based methods have recently demonstrated superior performance in capturing global information. However, their computational complexity scales quadratically with image resolution, posing challenges for high-resolution tasks. To address these limitations, we propose Mulsormer, a novel transformer-based dehazing model tailored for high-definition images. Mulsormer incorporates Multi-Head Convolutional Attention (MHCA) and a Gated Feed-Forward Network (GFFN) to reduce computational complexity and control critical information flow, respectively. A multi-scale loss function combining perceptual and patch-based losses is utilized to improve detail restoration. Extensive experiments on the SOTS and RTTS datasets show that Mulsormer achieves state-of-the-art performance, with a PSNR of 37.49 dB and an SSIM of 0.993 on the SOTS Indoor dataset, significantly outperforming previous methods. Our approach demonstrates the potential of transformer-based models for efficient high-definition image dehazing. Additionally, our code is publicly available at https://github.com/Wang1666570/Efficient-Multi-Scale-Transformer}, archive = {J_ACCESS}, author = {Zhirui Wang}, doi = {10.1109/ACCESS.2025.3588390}, journal = {IEEE Access}, pages = {120983-120992}, shortjournal = {IEEE Access}, title = {Efficient multi-scale transformer with convolutional attention for high-definition image dehazing}, volume = {13}, year = {2025}, }
• (2025). Comparison of current ratio errors of support and toroidal type CTs in electric power systems: A case study. ACCESS, 13, 120973-120982. (www)

  @article{ , abstract = {A current transformer (CT) is a specialized device used to reduce high current values to safe and manageable levels in measurement and protection systems. Current ratio error (CRE) is an inevitable error in CTs due to factors such as magnetic saturation, magnetic core properties, primary and secondary winding errors, temperature effects and frequency changes, and manufacturing tolerances. Keeping this error close to zero is critical to the reliability of Electric Power System (EPS) because it directly affects the operation of protective relays and the accuracy of energy measurement systems. This study has performed a field-based quantitative comparison of absolute percentage current ratio error (AP-CRE) between S-CTs and T-CTs within ARAS’s (Erzurum/Turkey) operational electric power system (EPS). The AP-CRE measurement has been obtained from S-CTs and T-CTs in EPS for protection with accuracy class 5P. Experimental measurement tests on a total of 34 CTs, including 17 S-CT and 17 T-CT, actively used in EPS. AP-CREs have been obtained by means of the TURA-01 device. In addition, during each AP-CRE measurement, absolute phase displacement (A-PD) and excitation current values have been also measured in real time. The performance of S-CT and T-CT have been evaluated based on three main metrics: mean absolute error (MAE), root mean square error (RMSE) and standard deviation (STD) values of the AP-CRE values obtained from CTs. According to AP-CRE; T-CT: MAE=0.44, RMSE=0.47 and STD=0.15, while S-CT: MAE=0.55, RMSE=0.58 and STD=0.21. The AP-CRE performance proves that T-CTs are better than S-CTs in EPS, which increases reliability.}, archive = {J_ACCESS}, author = {Ishak Ozer and Ali Unluturk}, doi = {10.1109/ACCESS.2025.3587981}, journal = {IEEE Access}, pages = {120973-120982}, shortjournal = {IEEE Access}, title = {Comparison of current ratio errors of support and toroidal type CTs in electric power systems: A case study}, volume = {13}, year = {2025}, }
• (2025). MEC in 3D-networks: Supporting application controlled QoS. ACCESS, 13, 120960-120972. (www)

  @article{ , abstract = {Multi-Access Edge Computing (MEC) applied to three-dimensional (3D)-Networks comprising space, air, and ground layers, bear the potential to combine benefits of all these domains and globally provide low latency services. These networks leverage technologies such as non-terrestrial networks (NTN) and advanced computing frameworks to provide real-time services and extend connectivity to remote regions. Especially for MEC-App developers, knowledge of the underlying network topology and link characteristics is required to meet QoS targets, which has not been considered by current standardization. Based on the smart agriculture and video distribution use cases, we investigated on MEC in 3D-Networks, the architectural enablers, and necessary extension of current standards including mobility. For this we reviewed the current Application Programmable Interfaces (APIs) of the ETSI MEC standard and propose updates. Main update is the inclusion of topology information that can be used by MEC applications to achieve low latency and ultra-reliable services.}, archive = {J_ACCESS}, author = {Benjamin Barth and Shaden Baradie and Claus Keuker and Roman Zaitsev and Gayathri Guruvayoorappan and Tomaso De Cola}, doi = {10.1109/ACCESS.2025.3586811}, journal = {IEEE Access}, pages = {120960-120972}, shortjournal = {IEEE Access}, title = {MEC in 3D-networks: Supporting application controlled QoS}, volume = {13}, year = {2025}, }
• (2025). Predicting return-to-sport timeline: Classifying anterior cruciate ligament health levels post-reconstruction surgery using convolutional neural networks. ACCESS, 13, 120946-120959. (www)

  @article{ , abstract = {Sports-related anterior cruciate ligament (ACL) ruptures are linked to abnormal muscle activation patterns, extending treatment and delaying athletes’ return to sports (RTS) post-ACL reconstruction (ACLR). Given its prevalence among athletes, accurately estimating safe RTS time is crucial. This study introduces a deep convolutional neural network (DCNN) designed to classify ACL health levels in injured athletes, aiding in RTS estimation. Ten ACLR athletes and fourteen healthy controls participated. We defined three ACL health levels: healthy, six months post-ACLR, and nine months post-ACLR. Surface electromyography (sEMG) signals were recorded from five knee muscles during single-leg drop landing (SLDL) and single-leg forward hopping (SLFH) tasks. The time-frequency Pseudo-Wigner-Ville distribution (PWVD) calculated energy distribution patterns of non-stationary sEMG signals, generating heatmap images. The PWVD, preferred for its superior time-frequency localization over wavelet transform, processed these heatmaps, which were then analyzed by the custom-designed DCNN. The DCNN classifier achieved a 90% accuracy in categorizing ACL health levels. Additionally, it calculated the probability of data points belonging to specific classes, estimating safe RTS time for athletes. This study demonstrates the potential of DCNN classification using sEMG signals to estimate RTS time, providing valuable insights into ACLR recovery in athletes.}, archive = {J_ACCESS}, author = {Zeinab Jafari and Ali Sharifnezhad and Mohammad Razi and Mohammad Haghpanahi and Arash Maghsoudi}, doi = {10.1109/ACCESS.2025.3586408}, journal = {IEEE Access}, pages = {120946-120959}, shortjournal = {IEEE Access}, title = {Predicting return-to-sport timeline: Classifying anterior cruciate ligament health levels post-reconstruction surgery using convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). A deformation field-based approach for expression processing in head motion correction. ACCESS, 13, 120931-120945. (www)

  @article{ , abstract = {It is a routine procedure to use Iterative Closest Point (ICP) algorithm to correct motion in medical imaging of the head. However, the basic assumption of ICP is the rigid body motion, and it is not strictly valid when there is non-rigid motion (facial expression). In this study, we propose a novel method to reduce the adverse effects of facial expressions on head motion estimation. First, we design a network named DFG-Net to generate a deformation field on the RGB domain and compute a confidence map. We then extend the original ICP by embedding the confidence map representing each point’s degree of non-rigid motion. Compared to other techniques, our proposed method eliminates the negative effects of facial expression while making the most of pose information. The experiments demonstrate that our approach reduces the position error by 19.23% and orientation error by 36.37% compared to the original ICP algorithm. They also show that our method is robust to variations of expressions.}, archive = {J_ACCESS}, author = {Jiaqi Zhao and Jiaxu Zheng and Qun Chen}, doi = {10.1109/ACCESS.2023.3258999}, journal = {IEEE Access}, pages = {120931-120945}, shortjournal = {IEEE Access}, title = {A deformation field-based approach for expression processing in head motion correction}, volume = {13}, year = {2025}, }
• (2025). Cross-room CO2-based presence detection for occupancy profiling. ACCESS, 13, 120917-120930. (www)

  @article{ , abstract = {Control systems for building services, such as heating and cooling, often rely on fixed timing schemes. While such approaches are convenient, they make strong assumptions about room usage, often leading to inadequate comfort and energy efficiency. This study addresses this limitation by presenting two contributions aimed at automating the configuration of building control systems. The first contribution involves the development of a presence detection model based on CO2 data, which is easy to measure and non-privacy intrusive. Unlike existing literature, which typically focuses on single-room applications, this work introduces a dataset and machine learning methodology demonstrating the generalizability of a presence detection model across various real-world rooms, even among different building types. Sliding window normalization of the sensor data is the key to achieve this unsupervised cross-room adaptability. As second contribution, we propose an occupancy profiling technique that relies on the predicted presence information. This approach facilitates the automated configuration of building control systems by using historical presence probabilities to anticipate future occupancy. In contrast to fixed timing schemes, these occupancy profiles dynamically adapt over time, accommodating changes in occupant behavior. As such, this work improves the configuration of building control systems, leading to a more comfortable and energy-efficient environment.}, archive = {J_ACCESS}, author = {Jelle Vanhaeverbeke and Emiel Deprost and Steven Verstockt and Sofie van Hoecke}, doi = {10.1109/ACCESS.2025.3588031}, journal = {IEEE Access}, pages = {120917-120930}, shortjournal = {IEEE Access}, title = {Cross-room CO2-based presence detection for occupancy profiling}, volume = {13}, year = {2025}, }
• (2025). A novel long short-term memory-based approach for microgrid fault detection and classification using the wavelet scattering transform. ACCESS, 13, 120905-120916. (www)

  @article{ , abstract = {Protecting microgrids poses significant challenges owing to their diverse operational modes, multiple power infeed sources, variable fault current levels, heterogeneous control strategies, and the high penetration of intermittent renewable energy sources (RESs). These characteristics undermine the effectiveness of conventional protection schemes, particularly those based on overcurrent relays (OCRs) that are designed for systems with predictable and high fault currents. During islanded operation, a common mode in microgrids, fault currents are often reduced, making fault detection and isolation even more difficult. These limitations underscore the urgent need for intelligent, adaptive, and fast-responding fault detection and classification algorithms tailored specifically to the nature of microgrids. To address this gap, this study proposes a novel fault detection and classification approach that combines the Wavelet Scattering Transform (WST) for robust feature extraction with a Long Short-Term Memory (LSTM) network for accurate temporal pattern recognition and classification. Although WST has demonstrated remarkable performance in audio and image classification, its use in power system analysis remains largely unexplored. In this study, WST was used for the first time to process fault current signals in microgrids, resulting in significant feature matrices for training and testing. WST is especially excellent for capturing hidden properties in current waveforms, making it ideal for classification applications. The proposed methodology was validated using a Consortium for Electric Reliability Technology Solutions (CERTS) microgrid testbed. All simulations were performed in MATLAB, which served as the platform for modeling the CERTS microgrid, configuring the WST, and implementing the LSTM network. A comparative analysis with conventional techniques highlighted the superior classification accuracy and robustness of the proposed WST-LSTM framework.}, archive = {J_ACCESS}, author = {Naema M. Mansour and Abdelazeem A. Abdelsalam and Ibrahim A. Awaad}, doi = {10.1109/ACCESS.2025.3584355}, journal = {IEEE Access}, pages = {120905-120916}, shortjournal = {IEEE Access}, title = {A novel long short-term memory-based approach for microgrid fault detection and classification using the wavelet scattering transform}, volume = {13}, year = {2025}, }
• (2025). Correlation of scale factor non-linearity and vibration rectification error in vibrating beam accelerometers. ACCESS, 13, 120895-120904. (www)

  @article{ , abstract = {Vibration Rectification Error (VRE) is a critical parameter for high-performance accelerometers operating in dynamic environments. This paper presents a focused experimental study to rigorously investigate the relationship between VRE and Scale Factor Non-Linearity (SFNL) in vibrating beam MEMS accelerometers (VBAs). A robust experimental setup is developed to suppress external influences and measurement errors, ensuring that the observed VRE originates solely from intrinsic sensor behavior. By refining the measurement methodology and isolating SFNL contributions, this work establishes a clear and quantitative link between quadratic term of SFNL and VRE. After limiting the VRE contribution to the mechanical behavior of the MEMS, the understanding of amplification phenomenon allow explaining the bandwidth dependency. The results demonstrate that the dominant source of VRE is the 2nd-order term of SFNL, confirming the effectiveness of low-bias VBAs in mitigating 2nd-order SFNL, thereby reducing VRE. These findings define a robust framework for accurate VRE characterization and provide the foundation for advanced VRE reduction strategies.}, archive = {J_ACCESS}, author = {Theo Miani and Lokesh Gurung and Douglas Young and Madan Parajuli and Guillermo Sobreviela-Falces and Colin Baker and Ashwin A. Seshia}, doi = {10.1109/ACCESS.2025.3586063}, journal = {IEEE Access}, pages = {120895-120904}, shortjournal = {IEEE Access}, title = {Correlation of scale factor non-linearity and vibration rectification error in vibrating beam accelerometers}, volume = {13}, year = {2025}, }
• (2025). A CMOS fully integrated optical wireless power transfer SoC for biomedical IoT devices. ACCESS, 13, 120883-120894. (www)

  @article{ , abstract = {This paper reports on the design and experimental characterization of a fully integrated optical wireless power transfer for biomedical internet-of-things devices. The solution has been fabricated on-chip in TSMC $0.18~\mu $ m standard Si CMOS technology within an area of 0.466 mm2 integrating a $500\times 500~\mu $ m2 Si photodiode. If compared with the solutions using different methodologies, optical wireless systems allow for reaching the highest electromagnetic compatibility with the further advantage of transferring electrical power from long distances. The architecture employs inverter stages to maximize the value of the achievable output voltage starting from the input voltage and currents provided by the integrated photodiode. The electrical and optical characterizations of the fabricated device were performed under both continuous and pulsed operation modes. Depending on the impedance of the external load to be powered, output voltages up to 2.45 V have been obtained from an input voltage of 0.35 V with an energy conversion efficiency of 58 %. The comparison of the main features achieved with those of similar solutions reported in the Literature has been performed. By employing low input voltages, the proposed system requires a reduced Si area, works at a lower clock frequency, achieves the higher output voltages to supply external loads with satisfactory output currents and good energy conversion efficiencies.}, archive = {J_ACCESS}, author = {Guido Di Patrizio Stanchieri and Andrea de Marcellis and Elia Palange and Marco Faccio and Ulkuhan Guler}, doi = {10.1109/ACCESS.2025.3587474}, journal = {IEEE Access}, pages = {120883-120894}, shortjournal = {IEEE Access}, title = {A CMOS fully integrated optical wireless power transfer SoC for biomedical IoT devices}, volume = {13}, year = {2025}, }
• (2025). Analyzing gender-based satisfaction disparities in online learning: A quantitative survey in GCC nations. ACCESS, 13, 120869-120882. (www)

  @article{ , abstract = {Global educational organizations have embraced e-learning because of the COVID-19 pandemic. Although instructors and students have found difficulties with this move to online learning (OL), it has also encouraged creative teaching strategies. This study examines college students’ perspectives of OL, with a particular emphasis on gender inequalities in their experiences. Information from many college students in Gulf Cooperation Council (GCC) countries who underwent OL during the outbreak was obtained through a quantitative survey. Focusing on how these aspects differ between male and female students, the survey asked questions on satisfaction with OL and particular elements like course material delivery, interacting with educators, behavioral shifts, and obstacles with OL. A statistical comparison was conducted between the OL experiences of male and female students. The findings indicate that male students exhibit greater satisfaction than female learners. Students encountered difficulties with internet connectivity, online learning adaptation, concentration, and workload during OL. Comprehending such gender gaps in OL satisfaction among learners is essential for instructors and organizations aiming to enhance the efficacy of e-learning practices. This study corresponds with the United Nations Sustainable Development Goals (UN SDGs) about gender equality (SDG 5) and quality education (SDG 4), examining gender disparities in OL experiences among learners across the GCC countries. The findings illustrate the distinct requirements of each gender in OL, giving direction for more fair and successful educational initiatives in the GCC area, therefore advancing the worldwide objectives of equal opportunity as well as inclusive education.}, archive = {J_ACCESS}, author = {A. M. Mutawa}, doi = {10.1109/ACCESS.2025.3572351}, journal = {IEEE Access}, pages = {120869-120882}, shortjournal = {IEEE Access}, title = {Analyzing gender-based satisfaction disparities in online learning: A quantitative survey in GCC nations}, volume = {13}, year = {2025}, }
• (2025). 3D reconstruction with spherical cameras. ACCESS, 13, 120852-120868. (www)

  @article{ , abstract = {The goal of image-based 3D reconstruction is to establish a high-quality 3D expression from images. In order to achieve a high-resolution and real-time 3D model, inspired by the open source COLMAP, we propose a novel framework (3DMAP) to reconstruct 3D scenes based on spherical cameras. Unlike traditional methods which focus on building a 3D plane by Poisson distribution function, our method illustrates the key processes of 3D reconstruction: locating the camera based on global feature, estimating the scene’s relative depth from monocular panoramic images, and obtaining a high-quality 3D surface reconstruction. In the camera locating part, we use a global descriptor augmentation model to build a labeled panorama dataset GDAP, in which the images are captured by our designed spherical cameras; In the depth estimation part, we propose a new network UMDE that can estimate the depth of both indoor and outdoor scenes; Finally, in the 3D surface reconstruction section, we turn the reconstruction problem to a graph optimization problem, called GraphFit, in which, we optimize the point clouds with s-t graph and smoothing method successively. We conduct experiments on our own dataset to demonstrate the effectiveness of our proposed 3DMAP framework. Experimental results show that our 3DMAP has achieved good evaluation scores and visual effects.}, archive = {J_ACCESS}, author = {Yan Cui and Qingling Chang and Qiang Liu and Xin Yang and Yajiang Huang and Shichao Chen and Fei Ren and Didier Stricker}, doi = {10.1109/ACCESS.2021.3119367}, journal = {IEEE Access}, pages = {120852-120868}, shortjournal = {IEEE Access}, title = {3D reconstruction with spherical cameras}, volume = {13}, year = {2025}, }
• (2025). A survey of holistic approaches for distributed database systems: From conceptual model to deployment. ACCESS, 13, 120830-120851. (www)

  @article{ , abstract = {Database modeling defines the logical design, structure, and specifications for data storage and access within a DataBase Management System (DBMS), a core component of any Information System (IS). While conceptual database models (e.g., Entity-Relationship (ER) diagrams, Unified Modeling Language (UML)) facilitate the transition to physical models, they still fall short when it comes to supporting distributed and multi-layered architectures. Current approaches lack unified modeling abstractions, forcing manual reconciliation between high-level designs and their distributed implementations, leading to inefficiencies, deployment risks, and reliance on specialized teams. Existing tools (e.g., Kubernetes, Terraform) automate infrastructure but remain disconnected from Conceptual Models (CMs), requiring error-prone manual translation. This paper assesses the current state of the art in holistic approaches that aim to integrate modeling and deployment, identifies critical gaps in end-to-end automation, classifies the available deployment tools, and highlights advances that could enable building a distributed data management system based solely on its CM, potentially leading to greater agility and reduced operational complexity. Finally, the paper discusses open questions and research directions that indicate promising areas for future investigation.}, archive = {J_ACCESS}, author = {Gonçalo Carvalho and Jorge Bernardino and Bruno Cabral and Vasco Pereira}, doi = {10.1109/ACCESS.2025.3587670}, journal = {IEEE Access}, pages = {120830-120851}, shortjournal = {IEEE Access}, title = {A survey of holistic approaches for distributed database systems: From conceptual model to deployment}, volume = {13}, year = {2025}, }
• (2025). TEDformer: Temporal feature enhanced decomposed transformer for long-term series forecasting. ACCESS, 13, 120821-120829. (www)

  @article{ , abstract = {In recent years, Transformer-based models have achieved good results in the analysis and application of time series. In particular, the introduction of Autoformer has further improved the performance of the model in long-term sequence prediction. However, Transformer-based models, such as Autoformer, have not fully considered the local temporal features of the sequence, and have not addressed the impact of sequence anomalies on decomposition and the processing of trend terms. To address these issues, we combined the excellent performance of the time convolutional neural network (TCN) on time series data and the advantages of the STL inner-outer loop decomposition to design the TEDformer, a Transformer prediction model enhanced with global and local temporal features. The model decomposes the time series into trend and periodic terms using STL and extracts temporal features accordingly. We conducted experiments on six real-world datasets, and the results showed that our model improved by 10.8% on multivariate datasets and 15.7% on univariate datasets compared to state-of-the-art models.}, archive = {J_ACCESS}, author = {Jiayi Fan and Bingyao Wang and Dong Bian}, doi = {10.1109/ACCESS.2023.3287893}, journal = {IEEE Access}, pages = {120821-120829}, shortjournal = {IEEE Access}, title = {TEDformer: Temporal feature enhanced decomposed transformer for long-term series forecasting}, volume = {13}, year = {2025}, }
• (2025). Optimizing trade-offs in reliability, security, and freshness for integrated communication, sensing, and over-the-air computing. ACCESS, 13, 120807-120820. (www)

  @article{ , abstract = {This paper addresses the trade-offs and optimization problems among reliability, security, and information freshness in integrated communication, sensing, and over-the-air computation (AirComp) systems. Internet of things (IoT) applications with stringent latency requirements pose challenges to the reliability and information freshness of such integrated systems. Given the security risks and resource constraints, it is crucial to study the trade-offs among reliability, security, and information freshness. To tackle this issue, this paper proposes an integrated sensing, communication, and computation over-the-air (ISCCO) framework and presents effective system design and optimization strategies. Specifically, considering statistical CSI, the target detection and AirComp aggregation errors are derived, and the upper bound of the security information age violation probability is obtained using stochastic network calculus and moment generating function theory. This paper optimizes the AirComp transceiver design to balance reliability, security, and information freshness, subject to constraints on information leakage probability and resources. It proposes a multi-objective minimization problem that includes the security information age violation probability, AirComp error, and target detection error. To address the issues of slow convergence and local optima in traditional algorithms, a faster converging and lower complexity equilibrium stochastic continuous convex approximation (SSCA) algorithm is proposed. Simulation results validate that this algorithm exhibits better convergence and optimization performance compared to traditional convex optimization methods, approaching the ideal case of perfect CSI.}, archive = {J_ACCESS}, author = {Tianyu Ren and Miao Zhang and Fangyu Wang and Kaiyuan Zheng}, doi = {10.1109/ACCESS.2025.3588349}, journal = {IEEE Access}, pages = {120807-120820}, shortjournal = {IEEE Access}, title = {Optimizing trade-offs in reliability, security, and freshness for integrated communication, sensing, and over-the-air computing}, volume = {13}, year = {2025}, }
• (2025). A survey of post-hoc XAI methods from a visualization perspective: Challenges and opportunities. ACCESS, 13, 120785-120806. (www)

  @article{ , abstract = {XAI (eXplainable AI) has become a pivotal area of research with the advancement of deep learning (DL) technologies and applications. Post-hoc explanation methods interpret deep learning predictions by uncovering the significance of input features, while visualization tools can contribute to a deep understanding of AI model reasoning based on these methods. In this paper, we survey a broad spectrum of post-hoc explanation methods and the visual analytics work based on them. First, we categorize the computational methods into four main types: perturbation-based, gradient-based, decomposition-based, and concept-based. While the first three focus on attributing the model’s output to specific regions of the input image, concept-based methods provide global explanations by mapping human-understandable concepts to high-level features. Then, we examine the methodologies, features, strengths, and limitations of each approach. Moreover, we review existing visualization-focused work based on these computational methods. Finally, we discuss further research challenges and opportunities for XAI visualization with post-hoc explanation.}, archive = {J_ACCESS}, author = {Deepshikha Bhati and MD Amiruzzaman and Ye Zhao and Angela Guercio and Tram Le}, doi = {10.1109/ACCESS.2025.3581136}, journal = {IEEE Access}, pages = {120785-120806}, shortjournal = {IEEE Access}, title = {A survey of post-hoc XAI methods from a visualization perspective: Challenges and opportunities}, volume = {13}, year = {2025}, }
• (2025). Flexible edge-AI software execution architecture based on cloud-connected incremental learning. ACCESS, 13, 120772-120784. (www)

  @article{ , abstract = {Edge AI computing transcends the passive execution of predefined software by making inferences in response to inputs. However, AI inference at the edge is only matrix operations with statically loaded weights defined in on-chip code flash memory. The narrow weights configured in the offline learning stage do not produce accurate inferences for various input conditions. Therefore, AI-embedded edge systems have to be efficiently reconfigured in runtime to update the pre-defined weights and models to handle various scenarios. To address these issues this paper proposes flexible edge AI architecture with cloud-connected incremental learning. The proposed architecture updates weights and models through the cloud to adapt to external changes in edge AI in runtime. Also, offloading resource-intensive learning stages to the cloud with an event-driven approach reduces the load on edge. Cloud communication minimizes overhead by updating weights intermittently, based on accuracy under changing environmental conditions. The proposed system demonstrates an average accuracy improvement of over 10% in biased input scenarios, a 49% reduction in training time compared to the Jetson Nano board with learning capability, and a 70% reduction in communication volume compared to cloud code-streaming edge devices. This research focuses on integrating cloud and edge computing to overcome the limitations of embedded devices and develop flexible edge AI systems.}, archive = {J_ACCESS}, author = {Myeongjin Kang and Daejin Park}, doi = {10.1109/ACCESS.2025.3586940}, journal = {IEEE Access}, pages = {120772-120784}, shortjournal = {IEEE Access}, title = {Flexible edge-AI software execution architecture based on cloud-connected incremental learning}, volume = {13}, year = {2025}, }
• (2025). Linear potentiation in filamentary valence change mechanism with sub-100 ps pulses. ACCESS, 13, 120762-120771. (www)

  @article{ , abstract = {Redox-based resistive random access memory (ReRAM) has emerged as a promising technology for next-generation non-volatile memory and neuromorphic computing applications. Among the various ReRAM types, valence change mechanism (VCM) based devices have gained significant attention due to their fast switching, low energy consumption, good scalability, high endurance, and multilevel capability. However, for synaptic applications, gradual and linear conductance changes are required to mimic biological learning. Unfortunately, the typically abrupt SET switching in filamentary VCM makes this difficult, which is a key challenge for neuromorphic systems. In this study, we investigated the SET switching kinetics of $\mathrm {Pt/TaO_{x}/Ta/Pt} $ stack devices in response to repeated sub-100ps pulses, and observed a transition from abrupt to gradual, linear switching behavior. Our findings demonstrated that the pulse width and amplitude were crucial in facilitating this transition, with 50ps pulses enabling a linear conductance change ( $R^{2} = 99.5\,\%$ ) over 100 pulses in the linear region. Although variability remains a key challenge, the initial low conductive state (LCS) is expected to influence the switching dynamics, with previous reports suggesting that a low LCS typically leads to more abrupt switching. However, our findings reveal that the use of short pulses mitigates this effect, thereby allowing linear transitions even when starting from a low initial LCS.}, archive = {J_ACCESS}, author = {Faisal Munir and Jari Klinkmann and Pascal Stasner and Siyuan Jia and Rainer Waser and Stefan Wiefels}, doi = {10.1109/ACCESS.2025.3586720}, journal = {IEEE Access}, pages = {120762-120771}, shortjournal = {IEEE Access}, title = {Linear potentiation in filamentary valence change mechanism with sub-100 ps pulses}, volume = {13}, year = {2025}, }
• (2025). Comprehensive performance assessment of multi-neural ensemble model for mortality prediction in ICU. ACCESS, 13, 120750-120761. (www)

  @article{ , abstract = {The development of models to estimate the mortality rate of critically ill patients in the intensive care unit(ICU) is significantly enhanced by technologies based on artificial intelligence. But it can be quite difficult because most medical data are typically unbalanced and incomplete. This paper aims to address the missing data using advanced imputation algorithm like single center imputation from multiple chained equation (SICE) and synthetic minority oversampling technique with edited nearest neighbor (SMOTE-ENN) method is used to solve the imbalanced data problem. Wrapper-based genetic feature selection method is used for the feature selection. With a highly skewed dataset, the objective of this work is to propose an improved ensemble classifier that utilizes a combination of boosting and stacking ensemble strategies. Our work proposed a Stacking-based multi-layer perceptron ensemble learning method on bench-marking dataset namely 2020 WiDS (Women in Data Science) Challenge and got an accuracy of 93.67%. The results obtained using randomized five-fold cross-validation and hold-out techniques reveal that the performance of the proposed ensemble algorithm was better than that of all other predictors. In comparison to prior deep learning-driven mortality classification research, we designed a comprehensive structure that encompasses a novel feature pre-processing methods and stacking ensemble algorithm for classification.}, archive = {J_ACCESS}, author = {M. Fathima Begum and Subhashini Narayan}, doi = {10.1109/ACCESS.2023.3324459}, journal = {IEEE Access}, pages = {120750-120761}, shortjournal = {IEEE Access}, title = {Comprehensive performance assessment of multi-neural ensemble model for mortality prediction in ICU}, volume = {13}, year = {2025}, }
• (2025). Detection of sensitive malicious android functionalities using inter-component control-flow analysis. ACCESS, 13, 120732-120749. (www)

  @article{ , abstract = {Android Smartphone’s popularity among users and developers is due to its open architecture and third-party apps. The exponential growth of third-party developer apps needs a robust code audit before upload. The weak program analysis at app stores adversely affects security. Third-party developers can update the app to introduce malicious activities. Sensitive sensor hardware and software resources are exfiltrated for leaking sensitive user data like messages, contacts, audio, and images. Malicious hidden features undermine user permission, including short message service (SMS), audio and video recording, voice calls, and image capturing. This paper treats the covert activities as called malicious features. The paper proposes a robust inter-component communication (ICC) based detection approach to identify such hidden functionality exploited by adversaries. The proposed technique detects sensitive features exploited by persistent malware without user knowledge. Despite the asynchronous characteristics of the ICC Application Programming Interface (API), we develop a precise ICC-based component-interaction graph (CIG) from the app bytecode. Moreover, we enrich the CIG with data flow analysis to identify the component reachability utilizing malicious features from legitimate user interactions. The proposed static app analysis framework identifies the prominent malware which exploits sensitive device resources. We assessed 25K benign and 13.3K malicious apps from 211 malware families with a classification rate of 0.40 % false positive (FP) and 0.37 % false negative (FN), better than existing state-of-the-art. In addition, we detect hidden features in unseen apps that subvert traditional antimalware.}, archive = {J_ACCESS}, author = {Rati Bhan and Parvez Faruki and Rajendra Pamula}, doi = {10.1109/ACCESS.2023.3321383}, journal = {IEEE Access}, pages = {120732-120749}, shortjournal = {IEEE Access}, title = {Detection of sensitive malicious android functionalities using inter-component control-flow analysis}, volume = {13}, year = {2025}, }
• (2025). Switched-inductor extended-boost quasi-Z-source inverter. ACCESS, 13, 120717-120731. (www)

  @article{ , abstract = {This paper introduces two novel impedance source inverter topologies for switched-inductor extended-boost quasi-Z-source inverters: diode-assisted and capacitor-assisted switched-inductor extended-boost quasi-Z-source inverters. The proposed topologies exhibit a significant voltage boost capability while operating at a lower shoot-through duty ratio. These inverters can operate at higher modulation indices by employing a low shoot-through duty ratio, improving the output voltage performance and reducing the stress on inverter switches. A comprehensive analysis of the operation of inverters in both the shoot-through and non-shoot-through states is performed. Mathematical derivations are provided to determine the boost factor and design the impedance network parameters. The theoretical analysis is further validated through computer simulations using the PSCAD software and experimental results. A laboratory prototype is constructed to verify the simulation results and demonstrate the high-gain capabilities of the proposed inverters. A detailed analysis of the capacitor voltages, diode stress, and switch stress of the proposed inverters is performed. These parameters are compared with the existing inverter topologies documented in the literature. This comparison offers insights into the superior performance and advantages of the proposed diode-assisted and capacitor-assisted switched-inductor extended-boost quasi-Z-source inverter.}, archive = {J_ACCESS}, author = {Abhishek Paikray and Dezi Shrivastav and Sisir Kumar Nayak}, doi = {10.1109/ACCESS.2025.3569076}, journal = {IEEE Access}, pages = {120717-120731}, shortjournal = {IEEE Access}, title = {Switched-inductor extended-boost quasi-Z-source inverter}, volume = {13}, year = {2025}, }
• (2025). How to implemented knowledge management in supply chain management best-worst with D-number. ACCESS, 13, 120709-120716. (www)

  @article{ , abstract = {There is no doubt that in this hectic world, many companies seek to implement Knowledge Management (KM) as a part of their supply chain management (SCM). However, many factors affect them in many ways. As a result of this research, we will be able to find out which of these factors influenced the implementation of knowledge management in supply chain management in healthcare facilities. In order to identify the most prominent factors in this research, the Best-Worst Method (BWM) has been applied. Due to the fact that decision makers have to deal with a vague environment, DMs have to be integrated with proper methods in order to cope with this problem. The D-number is a kind of fuzzy number that is one of these methods, one of the types of fuzzy numbers. To find and prioritize the best criterion that will impact the implementation of Knowledge Management in Supply Chain Management with the Best-Worst Method, we conducted this research. This model of decision-making attempts to eliminate the pitfall of Analytical Hierarchy Process by using a Multi-Attribute Decision-Making strategy. Using multi-criteria decision-making in uncertain environments, this paper reviews the literature on knowledge management applied to Supply Chain Management (SCM). These methods have not been implemented in the healthcare industry in previous studies, both theoretically and methodologically. Based on the results of the study, it should be noted that, among the 12 criteria, two of them have been eliminated with the use of the Delphi method; culture capacity is the most important criterion and should be targeted by healthcare facilities companies. In order to implement KM in SCM to facilitate the healthcare system, these companies would need to focus on the culture capability factor in order to implement KM in SCM successfully.}, archive = {J_ACCESS}, author = {Javad Navaei and Soheila Sardar and Saber Saati}, doi = {10.1109/ACCESS.2023.3253785}, journal = {IEEE Access}, pages = {120709-120716}, shortjournal = {IEEE Access}, title = {How to implemented knowledge management in supply chain management best-worst with D-number}, volume = {13}, year = {2025}, }
• (2025). Dual attention-guided self-adaptive aware cascade network for multi-view stereo and stereo matching. ACCESS, 13, 120696-120708. (www)

  @article{ , abstract = {Extensive studies have been conducted on multi-view stereo and stereo matching for 3D reconstruction, whereas relatively few methods have been proposed for a large-scale environment. The difficulty of producing high-resolution depth/disparity maps is one of the main reasons. In this paper, we propose a dual attention-guided self-adaptive aware cascade network (DAscNet) that achieves state-of-the-art results for generating high-resolution depth/disparity maps of complex scenes by introducing a cascade inference strategy using a set of input views. A pyramid cost volume fusion and a self-adaptive cost volume cascade are built upon a dual attention-guided context multi-scale feature extraction encoding geometric, spatial and contextual information at gradually finer scales to achieve robust structural representation for predictions. The dual attention-guided context multi-scale feature extraction is made up of two distinct modules that are both based on the attention mechanism. In the pyramid cost volume fusion, an inter-cost attention aggregation module fuses multiple low-resolution dense cost volumes to achieve a robust structural representation for initial predictions. In the self-adaptive cost volume cascade, a changeable depth/disparity range estimation module is employed to alter the depth/disparity searching range interval of following stage based on the prediction information from the previous stage. This module can drive the network to gradually deal with complicated matching ambiguities and make better the accuracy of depth/disparity searching range interval prediction. Experiments on two publicly available datasets, the Tanks and Temples dataset and the DTU dataset, show that DAscNet outperforms prior work. The effectiveness of our proposed method is also supported by statistics on the accuracy, runtime, and GPU memory of other representative methods.}, archive = {J_ACCESS}, author = {Na Cheng and Hongye Xie and Zhixuan Sun and Xuanbing Zhu and Hongyu Wang}, doi = {10.1109/ACCESS.2023.3273903}, journal = {IEEE Access}, pages = {120696-120708}, shortjournal = {IEEE Access}, title = {Dual attention-guided self-adaptive aware cascade network for multi-view stereo and stereo matching}, volume = {13}, year = {2025}, }
• (2025). Fault diagnosis method of rolling bearing based on variational modal decomposition multisynchrosqueezing transform combined with long short-term memory networks. ACCESS, 13, 120684-120695. (www)

  @article{ , abstract = {Aiming at the characteristics of weak vibration signal, strong interference, unevenness and nonlinearity in rolling bearing faults, this paper proposes a bearing fault intelligent diagnosis method based on variational modal decomposition (VMD) and Multisynchrosqueezing transform (MSST). First, the original vibration signal of the bearing is decomposed into multiple intrinsic mode functions (IMF) through the optimal parameter VMD, and then an effective IMF is selected for signal reconstruction according to the kurtosis value and mutual information. Secondly, MSST is applied to the reconstructed signal to obtain a time-frequency (TF) images with high energy concentration, and then extract the time-frequency and time-amplitude signals of the vibration signal in the TF images according to the ridge detection algorithm. Finally, these time series are used as input, using Long short-term memory (LSTM) network is trained to complete the intelligent classification and diagnosis of bearing failure. This method is compared with other fault diagnosis methods through test data sets. The results show that the method proposed in this paper is superior to other methods in recognition accuracy and recognition type, can accurately identify and classify bearing faults, and has strong generalization. Ability to better meet the needs of actual engineering.}, archive = {J_ACCESS}, author = {Tao Liang and Qingzhao Lv and Jianxin Tan and Yanwei Jing and Hexu Sun}, doi = {10.1109/ACCESS.2022.3177650}, journal = {IEEE Access}, pages = {120684-120695}, shortjournal = {IEEE Access}, title = {Fault diagnosis method of rolling bearing based on variational modal decomposition multisynchrosqueezing transform combined with long short-term memory networks}, volume = {13}, year = {2025}, }
• (2025). Deep convolutional neural networks to automatically classify human heart diseases. ACCESS, 13, 120672-120683. (www)

  @article{ , abstract = {Automatic and reliable classification of human heart sounds is essential for self-monitoring heart conditions to improve life quality and public health. However, technology for self-monitoring of heart conditions is expensive, unavailable, and thus inaccessible to many people, especially in low-income regions. This study therefore presents a highly accurate automatic heart rate classification system using deep convolutional neural networks (DCNNs) conveniently implemented as a smartphone application that provide users with timely heart disease warnings. Two datasets were employed, and heart sound features were extracted using the Log-Mel spectrogram and the Mel-frequency cepstral coefficients (MFCCs). Five deep learning (DL) models were developed and combined through ensemble strategies that fused the predicted probabilities. When comparing the DL models using Log-Mel and the training technique two, the study found that Model 1c outperformed the others, attaining a 98.38% F1 score on the binary dataset. Meanwhile, Model 1b excelled with a 98.46% F1 score on the multi-class dataset. The improvements were statistically significant. The data augmentation method exhibited varying effects on model performance. Moreover, distinction in model predictions was observed when differentiating between normal and abnormal heart conditions using the Log-Mel feature and a Shapley Additive Explainability (SHAP) approach. The ensemble techniques were particularly successful, with the mean strategy in Ensemble C achieving a 98.62% F1 score for the binary dataset, surpassing existing methods, and reaching a 98.73% F1 score for the multi-class dataset.}, archive = {J_ACCESS}, author = {Yo-Ping Huang and Richard Mushi and Frode Eika Sandnes and Hsiao-Feng Hu and Jing-Huei Lee}, doi = {10.1109/ACCESS.2025.3586214}, journal = {IEEE Access}, pages = {120672-120683}, shortjournal = {IEEE Access}, title = {Deep convolutional neural networks to automatically classify human heart diseases}, volume = {13}, year = {2025}, }
• (2025). Energy management considering energy storage and demand response for smart energy hub in internet of things. ACCESS, 13, 120663-120671. (www)

  @article{ , abstract = {This paper proposes an energy optimization management method of interaction between the smart energy hub (SEH) and users in the energy system. Internet of Things (IoT) and energy storage are also considered in the system. First, the models of the SEH and users are constructed. Then the profit models under the influence of energy storage are discussed. Next the Stackelberg game model is applied in this paper, where the SEH acts as the leader and the users are the followers. A double-layer algorithm is proposed to solve the problem. Finally, a case is studied to verify the correctness of the method. The results show that the method can be used for effective energy management in integrated energy system and IoT plays an important role in information exchange.}, archive = {J_ACCESS}, author = {Jinyong Lei and Changcheng Zhou and Xiaolin Li and Andi Huang and Hao Bai and Zhiyong Yuan and Linjie Zhou}, doi = {10.1109/ACCESS.2020.2976781}, journal = {IEEE Access}, pages = {120663-120671}, shortjournal = {IEEE Access}, title = {Energy management considering energy storage and demand response for smart energy hub in internet of things}, volume = {13}, year = {2025}, }
• (2025). Probabilistic principal component analysis and channel attention for end-to-end image compression optimization. ACCESS, 13, 120648-120662. (www)

  @article{ , abstract = {In recent years, deep learning has shown significant progress for image compression compared to traditional image compression methods. Although conventional standard-based methods are still used, they are limited in handling repetitive patterns and complex calculations, which can lead to image reconstruction issues. In this study, we propose a novel learning-based image compression method that integrates both channel attention (CA) and probabilistic principal component analysis (PPCA) blocks as core components to enhance encoding efficiency. PPCA is used to focus on essential features and manage noise. Unlike traditional PCA, PPCA’s probabilistic approach better preserves meaningful data structure, enhancing compression and robustness. The CA mechanism in our model emphasizes significant image features by prioritizing dominant pixel values, allowing the compression process to retain essential details while minimizing less relevant information. Furthermore, a foveated image quality assessment metric is proposed, prioritizing visually significant regions to enhance the evaluation of dominant information guided by attention mechanisms and to assess the impact of the CA and PPCA blocks on image reconstruction. Experimental results demonstrate that the proposed method obtained significant coding efficiency across various metrics on the Kodak and Tecnick datasets compared with state-of-the-art methods.}, archive = {J_ACCESS}, author = {Andri Agustav Wirabudi and Sung-Chang Lim and Woong Lim and Jeongil Seo and Haechul Choi}, doi = {10.1109/ACCESS.2025.3586241}, journal = {IEEE Access}, pages = {120648-120662}, shortjournal = {IEEE Access}, title = {Probabilistic principal component analysis and channel attention for end-to-end image compression optimization}, volume = {13}, year = {2025}, }
• (2025). A novel solution technique for the expansion planning of modern distribution systems: A feasibility-driven approach. ACCESS, 13, 120632-120647. (www)

  @article{ , abstract = {This paper proposes a novel solution technique using a matheuristic approach to address the expansion planning of modern distribution systems. The planning problem can be accurately modeled as a non-convex mixed-integer non-linear programming (MINLP) model, but this formulation is extremely difficult to solve. In this context, traditional solution techniques often rely on convex relaxations or approximations. However, these approaches often result in infeasible solutions for the original non-convex MINLP model (especially in the context of modern distribution systems), and can also be computationally expensive for large-scale problems. The proposed technique overcomes these challenges with an iterative local search process that improves solution feasibility (regarding the original MINLP model) at each step until a high-quality local optimal solution is achieved. To do this, the problem is reformulated as a mixed-integer quadratic programming (MIQP) model that penalize the feasibility errors. The planning actions considered include the installation of distributed generation (DG) units, electrical energy storage (EES) systems, fixed and switchable capacitor banks (CBs) and line reconductoring. System variability in demand and energy resources is captured through representative days, preserving EES temporal transition. Furthermore, the model guarantees CO2 reduction in order to be on track to limit global warming. The effective performance of the proposed approach in terms of solution quality and computational time has been illustrated with two case studies based on a 33-node test system. Scalability is further evaluated using a real 135-node distribution system.}, archive = {J_ACCESS}, author = {Jonathan A. Marcelo and Gregorio Muñoz-Delgado and Javier Contreras and José R. Sanches Mantovani}, doi = {10.1109/ACCESS.2025.3586613}, journal = {IEEE Access}, pages = {120632-120647}, shortjournal = {IEEE Access}, title = {A novel solution technique for the expansion planning of modern distribution systems: A feasibility-driven approach}, volume = {13}, year = {2025}, }
• (2025). Toward 6G: Deep GRU and RNN empowered MVDR and LCMV adaptive beamformers for IRS-aided wireless environments. ACCESS, 13, 120619-120631. (www)

  @article{ , abstract = {6G systems require highly adaptive beamforming techniques to cope with rapid channel variations and strict latency constraints. The advent of Intelligent Reflecting Surfaces (IRS) has emerged as a transformative paradigm in the evolution of 6G wireless communication systems, enabling programmable radio environments through passive beam control. Driven by the potentials of IRS-assisted communication strategies, this work proposes a novel hybrid beamforming architecture for IRS-assisted Multiple-Input Multiple-Output (MIMO) systems. The architecture seamlessly integrates constrained optimization techniques—specifically Minimum Variance Distortionless Response (MVDR) and Linearly Constrained Minimum Variance (LCMV)—with advanced deep recurrent learning models, including Gated Recurrent Units (GRU) and Recurrent Neural Networks (RNN). The proposed approach enables precise signal alignment with improved spatial selectivity, effective interference suppression, and enhanced spectral efficiency. Monte Carlo simulations validate the efficient performance of the GRU-based MVDR beamforming scheme, achieving a spectral efficiency of ~5 bps/Hz observed at lower signal-to-noise ratios (SNRs), outperforming other hybrid approaches. Furthermore, Average Bit Error Probability (ABEP) analysis employing MVDR framework investigates the impact of transmit correlation and phase rotation in correlated Rayleigh fading scenarios. MVDR beamforming consistently boosts ABEP performance across all the transmit correlations by minimizing symbol misalignment during signal transmission. Additionally, the GRU-integrated MVDR approach achieves target BER levels at notably lower transmit power of ~8 dBm and enhancements of ~6 dBm under imperfect Channel State Information (CSI) scenarios in comparison with other schemes.}, archive = {J_ACCESS}, author = {D. L. Sharini and Ravilla Dilli and M. Kanthi and G. D. Goutham Simha}, doi = {10.1109/ACCESS.2025.3586727}, journal = {IEEE Access}, pages = {120619-120631}, shortjournal = {IEEE Access}, title = {Toward 6G: Deep GRU and RNN empowered MVDR and LCMV adaptive beamformers for IRS-aided wireless environments}, volume = {13}, year = {2025}, }
• (2025). A novel closed-loop deep learning-based smart infusion rate monitoring technique for safe intravenous medication administration. ACCESS, 13, 120603-120618. (www)

  @article{ , abstract = {Medication administration via an intravenous (IV) catheter is a widely used medical procedure; however, incidents related to IV administration have consistently been reported to regulatory agencies. To improve patient safety during these incidents, it is essential to enhance the monitoring of IV administration. This study proposes an artificial intelligence-based technique for real-time infusion rate (IR) monitoring that automates several processes: the initial setup for image monitoring, the monitoring of variations in in-bag liquid volume and infusion pump settings, the determination of the relevance between infusion status and in-bag liquid residue, and the alarm processes for early detection of IV administration-related emergencies, using deep learning models and mathematical estimations. The experimental results demonstrate that the average error rate for estimating in-bag liquid volume is less than 5.00%, the average mismatch between the bag-extracted IR and the pump-extracted IR is under 3.00%, the average error rate for the “time-to-bag-empty” alarm is below 6.00%, and the error rate for the “low in-bag liquid volume” alarm is under 10.00%. The accuracy of detecting abnormal IR settings of the infusion pump was 100%. Based on these results, we conclude that the proposed artificial intelligence-based smart IR status monitoring technique shows promise as a prototype for autonomous IV administration monitoring with minimal human intervention, serving as a foundational step toward clinically deployable solutions in future healthcare settings.}, archive = {J_ACCESS}, author = {Subrata Bhattacharjee and Gun Ho Kim and Hongje Lee and Kyoung Won Nam}, doi = {10.1109/ACCESS.2025.3587531}, journal = {IEEE Access}, pages = {120603-120618}, shortjournal = {IEEE Access}, title = {A novel closed-loop deep learning-based smart infusion rate monitoring technique for safe intravenous medication administration}, volume = {13}, year = {2025}, }
• (2025). AsymGroup: Asymmetric grouping and communication optimization for 2D tensor parallelism in LLM inference. ACCESS, 13, 120591-120602. (www)

  @article{ , abstract = {Recent advances in Large Language Models (LLMs), such as GPT and LLaMA, have demonstrated remarkable capabilities across a wide array of natural language processing tasks. Despite these successes, efficient inference at scale remains challenging, particularly in heterogeneous computing environments characterized by variations in GPU counts, and computational capacities across nodes. Conventional tensor parallelism approaches typically assume homogeneous hardware, resulting in significant performance degradation under asymmetric conditions. To address this challenge, we propose AsymGroup, which is an asymmetric 2D tensor parallelism framework that dynamically constructs groups of uneven sizes based on node- and GPU-level metrics. AsymGroup proportionally allocates both computational and communication workloads according to individual device capabilities. In addition, we introduce a formal communication cost model to accurately quantify bottlenecks and a group optimization algorithm to systematically determine efficient group configurations. Experimental evaluations demonstrate that AsymGroup reduces inference latency by up to 26.3% and communication overhead by up to 33.4% compared to state-of-the-art frameworks, while also maintaining competitive performance under symmetric conditions.}, archive = {J_ACCESS}, author = {Ki Tae Kim and Seok-Ju Im and Eui-Young Chung}, doi = {10.1109/ACCESS.2025.3587387}, journal = {IEEE Access}, pages = {120591-120602}, shortjournal = {IEEE Access}, title = {AsymGroup: Asymmetric grouping and communication optimization for 2D tensor parallelism in LLM inference}, volume = {13}, year = {2025}, }
• (2025). CNN assisted dual reservoir hybrid network for power consumption forecasting. ACCESS, 13, 120578-120590. (www)

  @article{ , abstract = {Developing a precise forecasting model for power usage is an essential requirement for modern power management systems. As a result, researchers are focusing their efforts on proposing effective and accurate energy forecasting techniques which are crucial for future energy management and planning in smart grids. The prevailing methods for energy forecasting rely on traditional methods which are challenged by the high level of nonlinearity among input data from smart sensors and output, requiring further improvements in terms of robustness, forecasting performance, and generalization ability. Therefore, in this study, a novel hybrid approach combining CNN and a dual reservoir is developed for future energy consumption prediction. Unlike existing methods, our model leverages CNN to extract spatial dependencies in historical data, which are then processed by a dual reservoir to capture complex temporal relationships. This unique combination enhances robustness, forecasting accuracy, and generalization ability. Additionally, Bayesian Optimization Methods (BOM) are employed to fine-tune hyperparameters, further improving performance. The proposed model is systematically developed through extensive experimentation with both standalone and hybrid models, demonstrating superior predictive capability compared to traditional approaches. The proposed model achieved RMSE values of 0.0245 and 0.0332 for hourly building data, and 0.0113 for regional data, which are significantly lower than the baseline methods, demonstrating its superior performance.}, archive = {J_ACCESS}, author = {Hammad I. Sherazi and Omar Alrumayh and Shabana Habib and Abdulrahman Alsafrani and Muhammad Islam and Hedi A. Guesmi}, doi = {10.1109/ACCESS.2025.3581194}, journal = {IEEE Access}, pages = {120578-120590}, shortjournal = {IEEE Access}, title = {CNN assisted dual reservoir hybrid network for power consumption forecasting}, volume = {13}, year = {2025}, }
• (2025). Recognition of handwritten characters in birch-bark manuscripts via object detection. ACCESS, 13, 120563-120577. (www)

  @article{ , abstract = {The automatic transcription of medieval birch-bark manuscripts remains a bottleneck in Slavic paleography and digital humanities. This study adapts the YOLO-based object detection framework for detection and recognition of handwritten characters in digital photographs of Novgorod birch-bark fragments. A synthetic dataset of over 2,500 glyphs was created by extracting and denoising character fragments with a convolutional autoencoder, then compositing them onto varied-resolution canvases with controlled distortions. This corpus was used to train and benchmark SSD, Faster R-CNN, and YOLOv5–v11 detectors for 50 epochs. The best-performing model, YOLOv10, reached an initial mAP of 0.85 on synthetic data prior to hyperparameter optimization (HPO) and was further optimized—via anchor reconfiguration and learning-rate scheduling—to achieve mean average precision (mAP) 0.89. On a hold-out set of 1,100 real manuscript images, it attained mAP =0.79, with median Levenshtein, Jaccard, and Cosine similarity scores of 0.81, 0.73, and 0.79, respectively. An error taxonomy identifies common failure modes and informs model refinements. These results support the scalable transcription of birch-bark texts and enable quantitative studies of medieval Novgorod’s language, scribal practices, and document production. Future work will integrate Slavic-specific language models and multimodal imaging data to improve character recognition accuracy.}, archive = {J_ACCESS}, author = {Ivan P. Malashin and Vadim S. Tynchenko and Andrei P. Gantimurov and Vladimir A. Nelyub and Aleksei S. Borodulin}, doi = {10.1109/ACCESS.2025.3586910}, journal = {IEEE Access}, pages = {120563-120577}, shortjournal = {IEEE Access}, title = {Recognition of handwritten characters in birch-bark manuscripts via object detection}, volume = {13}, year = {2025}, }
• (2025). Automatic multiclass classification of unlabeled ground-based sky images for minute-scale PV energy yield forecasting. ACCESS, 13, 120547-120562. (www)

  @article{ , abstract = {Ground-based Sky Images (GSIs) are widely recognized as one of the most promising inputs for intra-hour minute-scale photovoltaic (PV) generation forecasting. Given the ongoing challenges in capturing short-term solar power variability, GSI classification has emerged as a data-centric approach with the potential to create more informative inputs for PV generation forecasting models and to increase their robustness against imbalanced GSI datasets. However, preassigning ground-truth labels to GSIs is a very challenging task that limits the effectiveness and scalability of existing classification methods. This paper introduces a novel unsupervised learning method for multiclass classification of unlabeled GSIs. The proposed method combines enhanced handcrafted feature extraction with hybrid auto-encoders to create feature sets that fully represent GSIs and capture PV energy yield variations. GSI clusters are generated through a novel forecast-driven k-means clustering approach that integrates downstream evaluation with co-optimization. Classification performance is evaluated based on its impact on PV energy yield forecasts generated by a best-in-class minute-scale forecasting model. Experimental results from two case studies demonstrate the feasibility of automatic multiclass GSI classification that improves minute-scale PV energy yield forecasting without requiring preassigned ground-truth labels or computationally intensive methods.}, archive = {J_ACCESS}, author = {Markos A. Kousounadis-Knousen and Francky Catthoor and Apostolos Bakovasilis and Pavlos S. Georgilakis}, doi = {10.1109/ACCESS.2025.3587059}, journal = {IEEE Access}, pages = {120547-120562}, shortjournal = {IEEE Access}, title = {Automatic multiclass classification of unlabeled ground-based sky images for minute-scale PV energy yield forecasting}, volume = {13}, year = {2025}, }
• (2025). MSKFaceNet: A lightweight face recognition neural network for low-power devices. ACCESS, 13, 120533-120546. (www)

  @article{ , abstract = {In recent years, the rapid development of lightweight convolutional neural networks (CNNs) and lightweight vision transformers (ViTs) has led to significant progress in the field of mobile computing. However, deploying facial recognition models on low-power devices (with power consumption below 10 watts) remains challenging. To address this issue, we designed a lightweight facial recognition network specifically optimized for low-power devices–MSKFaceNet (Multi-Scale Kernels Face Network). First, we propose a novel lightweight convolutional neural network module called MSKFNet. MSKFNet adopts a bottleneck design and introduces variable kernel convolutions from VarKNet, combined with channel shuffle and structural re-parameterization techniques, establishing an efficient CNN module for embedded systems. Built upon the MSKFNet module, MSKFaceNet further integrates a lightweight SE module to enhance its feature representation capabilities. Finally, we designed a real-time facial recognition attendance system based on MSKFaceNet and developed a prototype device. Experimental results show that MSKFaceNet, with only 0.54M parameters and 0.25 GFLOPS, achieves a recognition accuracy of 99.39% on the LFW dataset while delivering an inference speed of 10.8ms on the Jetson Nano platform. The proposed attendance system effectively and accurately performs facial recognition and attendance recording, significantly improving the efficiency and fairness of attendance management.}, archive = {J_ACCESS}, author = {Peng Zhang and Qinghua Ma and Yi Li and Min Cui}, doi = {10.1109/ACCESS.2025.3584814}, journal = {IEEE Access}, pages = {120533-120546}, shortjournal = {IEEE Access}, title = {MSKFaceNet: A lightweight face recognition neural network for low-power devices}, volume = {13}, year = {2025}, }
• (2025). Estimation and reduction of CO₂ emissions from fossil fuel power plants in bangladesh. ACCESS, 13, 120517-120532. (www)

  @article{ , abstract = {Electricity generation in Bangladesh is predominantly from fossil fuel power plants. Bangladesh’s electricity generation from renewable sources is well below the global average, with less than 1% coming from hydropower and less than 1% from solar and wind. Estimating and reducing carbon emissions from gas, coal, and oil generation plants in Bangladesh is a burning issue. The carbon emission factor is used to compute the carbon dioxide emissions from fossil fuel plants based on their generation capacity, and fuel type. The Intergovernmental Panel on Climate Change (IPCC) framework is used to calculate the carbon emission factor of each fuel type. The paper presents Matpower Interior Point Solver (MIPS), and Dynamic Non-Linear Particle Swarm Optimization (DNPSO) algorithms to solve combined economic emission dispatch (CEED) to optimize generation dispatch and minimize emissions. A practical 14-bus test system in Southern Chattogram, consisting of 132 kV, 230 kV, 400 kV, and 765 kV lines, is constructed. The developed algorithms based on optimal power flow have applied on 14-bus test system and measured optimal dispatch and emissions of the generators and grid. The environmental emission factor is used to combine the economic and emission dispatch. The existing and future coal power plants at Matarbari will release from 1934 ton-CO2 to 4889 ton-CO2 in year 2025 to 2041 with current plant efficiency. The results indicate that enhancing plant efficiency to 47%, the carbon dioxide emissions from coal power plants may be decreased to 15%. Moreover, when CEED is implemented with MIPS and DNPSO, the daily carbon emissions from the 14-bus test system are reduced to 14%, while those from DNPSO decreased to 11% from their original levels. CEED prioritizes more cleaner generators in the system than higher emissions low-cost generators. Furthermore, transmission line losses and grid emissions are lower in economic-emission dispatch compared to economic dispatch.}, archive = {J_ACCESS}, author = {Deepak Kumar Chowdhury and Nur Mohammad and Tanjim Mahmud and Md. Khaliluzzaman and Karl Andersson and Mohammad Shahadat Hossain}, doi = {10.1109/ACCESS.2025.3586553}, journal = {IEEE Access}, pages = {120517-120532}, shortjournal = {IEEE Access}, title = {Estimation and reduction of CO₂ emissions from fossil fuel power plants in bangladesh}, volume = {13}, year = {2025}, }
• (2025). A coordinated operation control strategy of multiple phase shifting transformers based on the interaction sensitivity. ACCESS, 13, 120506-120516. (www)

  @article{ , abstract = {With the continuous expansion of power system scales and the increasing integration of renewable energies, the issues such as the line overload have become prominent, which result in the growing demand for power flow control in the power system. As a low-cost and effective power flow regulation device, Phase Shifting Transformers (PSTs) are gradually being deployed at a larger scale. However, the control characteristics and application scenarios of different types of PSTs are different. In the case of independent operation without considering multiple PSTs’ interactive effects, their control performances may be suboptimal or even counterproductive. In order to achieve coordinated optimization of multiple PSTs, this paper proposes a coordination control strategy based on the interaction sensitivity. By quantifying the interactions between PSTs, a multi-objective coordination control algorithm is designed to optimize the system’s power flow control performance, voltage stability, and loss rate. The simulation results based on the IEEE 57-bus system verify the effectiveness of the proposed strategy. Compared to independent control strategies, the coordination control strategy reduces the maximum line loading rate by 3.79%, the node voltage deviation index by 0.02%, and the system loss rate by 0.01%.}, archive = {J_ACCESS}, author = {Huarui Li and Ningyu Zhang and Yuqiao Jia and Xinyao Zhu}, doi = {10.1109/ACCESS.2025.3583692}, journal = {IEEE Access}, pages = {120506-120516}, shortjournal = {IEEE Access}, title = {A coordinated operation control strategy of multiple phase shifting transformers based on the interaction sensitivity}, volume = {13}, year = {2025}, }
• (2025). Assessing safety and sustainability of a monitoring system for nuclear waste management. ACCESS, 13, 120486-120505. (www)

  @article{ , abstract = {Nowadays, nuclear technologies are increasingly being integrated into industry, healthcare and manufacturing. As a side effect, waste materials are produced according to standard processes which are subject to international regulations. One of the most critical phases is the pre-disposal, due to the uncertainty related to the evolution of the materials and their potential impact on environmental protection. This paper introduces the architecture of a monitoring system able to accomplish safety goals and to guarantee energetic sustainability. The possibility of defining different system configurations (e. g., sensor scheduling policies, geometry of the sites, trustworthiness of the sensors) fosters a high adaptability to several monitoring scenarios, being characterised by different safety and sustainability levels. A methodology, integrating a model-based approach with data collection and processing, is proposed to quantitatively evaluate system configurations. This methodology is based on the definition of two metrics — one for safety and one for sustainability — and an assessment model. The model computes the metrics considering geometry of the place, scheduling and trustworthiness of monitoring sensors. This is a first step in the construction of a Decision Support System able to aid human operators in assessing system configurations and finding possible safety/sustainability trade-offs. A case study is used to show the feasibility of the approach: some configurations are evaluated on the real plant, placed at Řež in the Czech Republic, assessing them on the base of the defined metrics.}, archive = {J_ACCESS}, author = {Michele Di Giovanni and Laura Verde and Lelio Campanile and Mauro Romoli and Carlo Sabbarese and Stefano Marrone}, doi = {10.1109/ACCESS.2025.3586735}, journal = {IEEE Access}, pages = {120486-120505}, shortjournal = {IEEE Access}, title = {Assessing safety and sustainability of a monitoring system for nuclear waste management}, volume = {13}, year = {2025}, }
• (2025). A new approach to environmental economic dispatch using multiobjective differential evolution: A case study. ACCESS, 13, 120472-120485. (www)

  @article{ , abstract = {In electricity generation systems, problems related to economic and environmental dispatch represent potential for improvement in generating plants. The Environmental Economic Dispatch (EED) consists of minimizing the cost of generation and emission of pollutants such as CO2, SO2 and NOx. In addition to possible financial benefits, addressing economic and environmental aspects can also generate a business competitive advantage. The EED problem comprises a multi-objective optimization challenge in power plants and various approaches have been developed to formulate and solve this problem. Problems associated with EED generally adopt quadratic functions to directly relate cost and emissions to generation power. This paper presents a new modeling approach which consists of using a hyperbolic function to predict generation costs. This new approach was applied together with the Differential Evolution Algorithm in two case studies which comprise a real thermal power plant with ten generating units and an IEEE test system with thirteen generating units. The results obtained were compared with the application of the quadratic function, showing that the new approach successfully solves the multi-objective EED problem, providing better results.}, archive = {J_ACCESS}, author = {Carine Nogueira Santino and Jorge Laureano Moya Rodríguez and Cristiano Hora De Oliveira Fontes}, doi = {10.1109/ACCESS.2025.3585375}, journal = {IEEE Access}, pages = {120472-120485}, shortjournal = {IEEE Access}, title = {A new approach to environmental economic dispatch using multiobjective differential evolution: A case study}, volume = {13}, year = {2025}, }
• (2025). An overview on computer vision analysis in the airport applications. ACCESS, 13, 120446-120471. (www)

  @article{ , abstract = {This research serves a dual purpose: it aims to map the current landscape of knowledge in a field rich with technological challenges while simultaneously showcasing innovative techniques that demonstrate the transformative power of computer vision in airport applications. To achieve this, the study offers a thorough analysis of various object recognition and tracking methods, exploring their implications both in general contexts and specifically within airport environments. As airport operations become increasingly complex, the integration of advanced technologies such as computer vision and AI models is essential. This review examines a variety of object detection and tracking techniques, including deep learning models, traditional algorithms, and hybrid approaches, highlighting their effectiveness in addressing specific challenges to airport environments and detailing their performance under real-world conditions such as adverse weather, occlusion, low visibility, and data privacy concerns. A key insight is the superior capability of deep learning techniques, which, through multiscale feature extraction and region proposal strategies, provide enhanced detection accuracy and robustness in dynamic airport settings. In addition, emerging trends such as federated learning and hybrid sensor fusion are explored, emphasizing their potential to improve scalability and situational awareness by integrating data from various sources, including radar and optical sensors. The analysis also examines precise aircraft detection to optimize ground operations, real-time runway surveillance to ensure safe takeoffs and landings, and advanced passenger monitoring systems that improve security by identifying potential threats. These findings underscore the pivotal role of computer vision in improving operational efficiency and safety while laying the foundation for the future development of intelligent, integrated airport management systems.}, archive = {J_ACCESS}, author = {Amal Talbi and Mehrez Abdellaoui and Jordi Pons-Prats and Jovana Kuljanin}, doi = {10.1109/ACCESS.2025.3580299}, journal = {IEEE Access}, pages = {120446-120471}, shortjournal = {IEEE Access}, title = {An overview on computer vision analysis in the airport applications}, volume = {13}, year = {2025}, }
• (2025). An attention-enhanced 3D-CNN framework for spectrogram-based EEG analysis in epilepsy detection. ACCESS, 13, 120437-120445. (www)

  @article{ , abstract = {Epilepsy is a widespread neurological disorder affecting approximately 50 million individuals globally, with a disproportionately high burden in low- and middle-income countries. It is characterized by recurrent seizures caused by sudden and uncontrolled electrical discharges in brain cells, often leading to cognitive and motor impairments. Electroencephalography (EEG) remains the gold standard for epilepsy diagnosis, offering non-invasive monitoring of brain activity. However, the complexity and variability of epileptic patterns make traditional visual analysis subjective, time-consuming, and impractical for continuous monitoring. This underscores the need for automated deep learning solutions to improve diagnostic accuracy and operational efficiency. This study introduces a novel 3D Convolutional Neural Network with integrated Self-Attention (3D-ACNN), specifically designed for the volumetric analysis of multi-channel EEG data. The proposed algorithm preprocesses 21-channel EEG signals by segmenting them into 4-second Hann windows, applies the Short-Time Fourier Transform (STFT) to extract time-frequency features, and constructs stacked spectrograms that serve as three-dimensional inputs to the model. The proposed model (3D-ACNN), integrated with self-attention mechanisms, effectively captures spatiotemporal dependencies, enhancing seizure classification accuracy. The model achieved a classification accuracy of 99.89.}, archive = {J_ACCESS}, author = {Ziaullah Khan and Aakanksha Dayal and Hee-Cheol Kim}, doi = {10.1109/ACCESS.2025.3574646}, journal = {IEEE Access}, pages = {120437-120445}, shortjournal = {IEEE Access}, title = {An attention-enhanced 3D-CNN framework for spectrogram-based EEG analysis in epilepsy detection}, volume = {13}, year = {2025}, }
• (2025). FWFA: Fairness-weighted federated aggregation for privacy-aware decision intelligence. ACCESS, 13, 120417-120436. (www)

  @article{ , abstract = {Ensuring fairness in automated decision-making is a critical challenge, especially in organizational contexts like recruitment, performance evaluation, and promotion. As machine learning (ML) and artificial intelligence (AI) increasingly influence such decisions, promoting responsible AI that minimizes bias while preserving data privacy has become essential. However, existing fairness-aware models are often centralized or ill-equipped to handle non-IID data, limiting their real-world applicability. This study introduces a novel federated learning framework, Fairness-Weighted Federated Aggregation (FWFA), which integrates fairness-aware weighting into the model aggregation process. Each client’s contribution is scaled using a fairness score computed from key metrics Demographic Parity (DP), Statistical Parity Difference (SPD), and Disparate Impact Ratio (DIR). A synthetically generated dataset simulating diverse employee profiles across five professional domains was used to replicate real-world heterogeneity and imbalance. Across 20 communication rounds, FWFA achieved a DP of 0.91, an SPD of 0.06, and a DIR of 0.91, outperforming baseline methods WA+FL and SMOTE+FL, while maintaining an accuracy of 0.84. Additionally, a dynamic weighting mechanism was simulated by varying fairness thresholds to explore adaptive aggregation behavior, revealing a controllable trade-off between fairness and model performance. To further strengthen privacy guarantees, differential privacy was integrated into the FWFA framework, resulting in minimal performance degradation while retaining key fairness properties. These findings reinforce FWFA’s role as a robust, privacy-preserving solution for fair collaborative decision-making in federated environments, supporting the broader vision of ethical and trustworthy AI in real-world systems.}, archive = {J_ACCESS}, author = {Rahul Haripriya and Nilay Khare and Manish Pandey and Shrijal Patel and Jaytrilok Choudhary and Dhirendra Pratap Singh and Surendra Solanki and Duansh Sharma}, doi = {10.1109/ACCESS.2025.3587344}, journal = {IEEE Access}, pages = {120417-120436}, shortjournal = {IEEE Access}, title = {FWFA: Fairness-weighted federated aggregation for privacy-aware decision intelligence}, volume = {13}, year = {2025}, }
• (2025). MultiSenseNet: Multi-modal deep learning for machine failure risk prediction. ACCESS, 13, 120404-120416. (www)

  @article{ , abstract = {Predictive maintenance plays a key role in industrial manufacturing by identifying machine failure risks early. This prevents downtime and improves efficiency, contributing towards industry innovation. This paper presents a method for predicting equipment failures using multi-modal sensor data. Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. The model was tested on two real-world datasets: the Machine Failure Classification Dataset and the Machine Failure Prediction Dataset. MultiSenseNet outperformed existing benchmarks, achieving accuracy rates of 95.4% and 95.2%, respectively. It also excelled in precision, recall, F1-score, and AUC metrics compared to traditional machine learning models and recent deep learning architectures. The model performed consistently across different hyperparameter settings, reaching peak accuracy of 94.7% on the classification dataset and 95.6% on the prediction dataset after 40 training epochs. Training and validation curves showed effective convergence with minimal overfitting.}, archive = {J_ACCESS}, author = {Mostafijur Rahman and Md Sabbir Hossain and Uland Rozario and Satyabrata Roy and M. F. Mridha and Nilanjan Dey}, doi = {10.1109/ACCESS.2025.3586978}, journal = {IEEE Access}, pages = {120404-120416}, shortjournal = {IEEE Access}, title = {MultiSenseNet: Multi-modal deep learning for machine failure risk prediction}, volume = {13}, year = {2025}, }
• (2025). Design and development of a metamaterial enhanced microstrip antenna integrated with a monopulse comparator for tracking applications. ACCESS, 13, 120394-120403. (www)

  @article{ , abstract = {This paper presents the design and development of a metamaterial-enhanced pentagonal patch antenna integrated with a monopulse comparator operating at 2.4 GHz for S-band radar tracking and UAV applications. The metamaterial structure is specifically designed to reduce cross-polarization and enhance the gain of the conventional pentagonal patch antenna, thereby resulting in enhanced antenna performance. The primary contribution and novelty of this research work are the successful system-level integration of the metamaterial-based antennas with a monopulse comparator and the experimental validation of their tracking capabilities. The proposed system offers a cost-effective and affordable solution for S-band radar tracking applications. Each metamaterial enhanced antenna was individually characterized and validated prior to their final integration into the monopulse comparator. The measured gain of the monopulse tracking system is 19 dBi, and the integrated system achieves excellent sum and difference responses with a null depth of approximately −55 dB, indicating high tracking precision. Real-time tracking experiments were also conducted using transmitter antennas such as a log-periodic dipole array (LPDA) and a sleeve dipole, placed at a far-field distance from the integrated system in an indoor environment. The results demonstrate that, there is a strong agreement between simulated and measured responses and the designed system is capable of accurately tracking the target with reduced errors making it highly suitable for modern UAV and radar applications.}, archive = {J_ACCESS}, author = {V M Jayakrishnan and R. V. Sanjika Devi and Dhanesh G. Kurup}, doi = {10.1109/ACCESS.2025.3587084}, journal = {IEEE Access}, pages = {120394-120403}, shortjournal = {IEEE Access}, title = {Design and development of a metamaterial enhanced microstrip antenna integrated with a monopulse comparator for tracking applications}, volume = {13}, year = {2025}, }
• (2025). Experimental analysis of wi-fi 6, MulteFire, and 5G in real-time industrial connectivity. ACCESS, 13, 120378-120393. (www)

  @article{ , abstract = {Industry 4.0 is set to transform manufacturing through enhanced connectivity, where low-latency wireless infrastructure is essential for securely and efficiently linking devices and systems. While Wi-Fi has long served as a reliable solution in the unlicensed spectrum, unlicensed cellular technologies such as MulteFire and the forthcoming 5G NR-U are increasingly viewed as better suited for industrial environments, particularly for supporting large volumes of data with greater efficiency and security. This study experimentally evaluates the performance of 5G Non-Public Networks (NPN) as a proxy for 5G NR-U, benchmarking key metrics such as latency, scalability, and packet loss against MulteFire and Wi-Fi 6. Additionally, it investigates coexistence challenges by evaluating the interference impact between unlicensed networks operating in the 5 GHz band under both stationary and mobile conditions. The experiments present new scalability results for MulteFire and provide, for the first time, a detailed interference analysis comparing MulteFire and Wi-Fi 6, offering practical insights for optimizing industrial wireless deployments. These findings highlight the strengths and current limitations of unlicensed spectrum technologies, offer guidance for future research directions in Wi-Fi and cellular wireless systems design, and underscore the potential of 5G NR-U and needed research in Wi-Fi to drive the next generation of industrial connectivity in the context of Industry 4.0.}, archive = {J_ACCESS}, author = {Akif Kabaci and Sebastian Bro Damsgaard and Preben E. Mogensen}, doi = {10.1109/ACCESS.2025.3587452}, journal = {IEEE Access}, pages = {120378-120393}, shortjournal = {IEEE Access}, title = {Experimental analysis of wi-fi 6, MulteFire, and 5G in real-time industrial connectivity}, volume = {13}, year = {2025}, }
• (2025). Evaluating the application of a perturbation-based inertia estimation methodology with synchrophasor data of the brazilian interconnected power system. ACCESS, 13, 120369-120377. (www)

  @article{ , abstract = {This paper presents the application of a methodology for estimating the inertia of generator groups of the Brazilian Interconnected Power System (BIPS) using phasor measurement unit (PMU) data acquired at 230 kV transmission substations. The methodology has been applied using synchrophasor data collected during disturbances in a Brazilian subsystem with two hydroelectric power plants (HPPs), as well as two interconnections with the rest of the BIPS, composed of three 230 kV transmission lines and a back-to-back converter station. Simulated and real data have been analyzed to assess the limitations and to identify required improvements toward a practical application of the perturbation-based methodology in the estimation of the inertia of real systems. A by-product of the application of the methodology is the obtaining of a second-order dynamic equivalent, useful for model-based online applications.}, archive = {J_ACCESS}, author = {Juliana Luiza Pereira and Guido Rossetto Moraes and Raul Torres Bernardo and Daniel Dotta and Augusto Tietz and Antonio Felipe da Cunha De Aquino and Diego Issicaba}, doi = {10.1109/ACCESS.2025.3586839}, journal = {IEEE Access}, pages = {120369-120377}, shortjournal = {IEEE Access}, title = {Evaluating the application of a perturbation-based inertia estimation methodology with synchrophasor data of the brazilian interconnected power system}, volume = {13}, year = {2025}, }
• (2025). Beam-steerable Fabry–Pérot cavity antenna with reconfigurable liquid-based PRS. ACCESS, 13, 120360-120368. (www)

  @article{ , abstract = {The design of a beam steering antenna with a reconfigurable liquid-based metamaterial as a Partially Reflective Surface (PRS) in a Fabry–Pérot cavity (FPC) configuration is presented in this letter. The proposed system is based on a two-layer structure having a source antenna and metamaterial containing, 3D-printed Polylactic Acid (PLA) liquid channels, along with a $9\times 9$ square ring elements metamaterial. Beam steering is achieved by varying the reflection amplitude of the reconfigurable metamaterial through the injection of liquid. A prototype of the beam-steering system, operating at 8 GHz, has been fabricated and measured. It achieves beam steering in the range of ±35° for different configurations, with a realized gain of over 10 dBi in each configuration. There is good agreement between simulated and measured results, making the system a good candidate where beam steering with consistent gain is required at low cost and compact size.}, archive = {J_ACCESS}, author = {Syed Tamoor Shah and Muhammad Umar Khan and Rifaqat Hussain and James R. Kelly and Akram Alomainy and Khaled A. Aljaloud}, doi = {10.1109/ACCESS.2025.3586683}, journal = {IEEE Access}, pages = {120360-120368}, shortjournal = {IEEE Access}, title = {Beam-steerable Fabry–Pérot cavity antenna with reconfigurable liquid-based PRS}, volume = {13}, year = {2025}, }
• (2025). A hybrid large language model for context-aware document ranking in telecommunication data. ACCESS, 13, 120345-120359. (www)

  @article{ , abstract = {Large language models (LLMs) have drawn a lot of attention due to their exceptional comprehension and reasoning capabilities. The development of LLM methods are leading to countless prospects for the automation of numerous tasks in the telecommunication industry. Following pre-training and fine-tuning, LLMs are able to carry out a variety of downstream activities in response to human instructions. This paper presents hybrid document retrieval and ranking approach that integrates statistical, probabilistic, and neural network-based retrieval models to enhance information retrieval performance in telecommunication domain. Traditional methods such as Term Frequency–Inverse Document Frequency (TF-IDF), and Best Match 25 (BM25) provide effective lexical matching, while deep learning-based models like Sentence-BERT (SBERT), and Word to Vector (Word2Vec) improve semantic understanding by capturing contextual relationships between query and document representations. The proposed framework introduces a novel multi-stage ranking mechanism that strategically integrates term-frequency-based scoring with semantic similarity modelling using Sentence-BERT and Word2Vec. Unlike existing models, our method dynamically adjusts weights across lexical and semantic components based on query features, enabling real-time adaptation for telecom-specific QA tasks. Performance evaluation is conducted using BLEU Score, ROUGE metrics, Cosine Similarity, and Word2Vec Similarity, demonstrating that the hybrid model outperforms conventional retrieval baselines in both precision and recall-oriented tasks. The proposed model effectively aligns query intent with retrieved documents, increase in efficiency of domain-specific search. The future scope includes dynamic embedding techniques to handle domain adaptation and attention-based ranking optimizations for long-form information retrieval. This research enhances information retrieval by combining machine learning-based ranking with traditional methods, improving knowledge discovery and decision-making in telecommunications and technical document processing.}, archive = {J_ACCESS}, author = {Abhay Bindle and Preeti Singla and Sachin Sharma and Abdukodir Khakimov and Reem Ibrahim Alkanhel and Ammar Muthanna}, doi = {10.1109/ACCESS.2025.3585637}, journal = {IEEE Access}, pages = {120345-120359}, shortjournal = {IEEE Access}, title = {A hybrid large language model for context-aware document ranking in telecommunication data}, volume = {13}, year = {2025}, }
• (2025). A hybrid model for early melanoma detection: Integrating YOLOv9 and faster R-CNN for enhanced diagnostic accuracy. ACCESS, 13, 120327-120344. (www)

  @article{ , abstract = {Melanoma accounts for only 1% of skin cancer diagnoses yet causes the majority of skin cancer-related deaths due to its rapid progression and high metastatic potential. Early and accurate detection is crucial for improving patient outcomes; however, existing deep learning models often struggle to balance diagnostic precision with real-time efficiency. This study presents the Melano Hybrid Model, a novel architecture that integrates the rapid detection capabilities of YOLOv9 with the boundary localization accuracy of Faster R-CNN through an adaptive feature fusion mechanism. The model was rigorously evaluated on three benchmark datasets—ISIC 2019, HAM10000, and ISIC 2020—using 5-fold cross-validation. On ISIC 2020, the hybrid model achieved a 96.2% classification accuracy (95% CI: 95.8–96.6%) and a 95.1% F1-score (95% CI: 94.7–95.5%), significantly outperforming standalone models ( $p\lt 0.001$ ). The architecture delivers an average inference speed of 31.3 frames per second (FPS), surpassing clinical real-time thresholds. Additionally, computational profiling confirms its practical feasibility with 78.3 million parameters, 134.8 GFLOPs, and a 324 MB memory footprint. These results support the hybrid framework as a robust AI-assisted tool for real-world melanoma screening, offering an optimal trade-off between speed and diagnostic performance.}, archive = {J_ACCESS}, author = {Mohamed I. Marie and Mohamed S. Elredeny and Ahmad Essayed Yakoub}, doi = {10.1109/ACCESS.2025.3587625}, journal = {IEEE Access}, pages = {120327-120344}, shortjournal = {IEEE Access}, title = {A hybrid model for early melanoma detection: Integrating YOLOv9 and faster R-CNN for enhanced diagnostic accuracy}, volume = {13}, year = {2025}, }
• (2025). Isolated multi-port DC-DC converters for renewable energy sources: A review. ACCESS, 13, 120307-120326. (www)

  @article{ , abstract = {Multi-port DC-DC converters (MPCs) become increasingly popular in power conversion for renewable energy sources (RESs), battery storage systems (BSSs), and electric vehicles (EVs). MPCs can be categorized into isolated or non-isolated structures. Isolated MPCs (IMPCs) have the galvanic isolation, higher voltage gain, bidirectional capability, and are easier to operate under soft-switching. This paper comprehensively reviews IMPCs to assist in developing new converters. Based on isolation of all or some of the ports, IMPCs can be divided into two groups: partially isolated converters (PICs) and fully isolated converters (FICs). A comparison of their architectures and features, including the applications, component count and efficiency, is presented, along with the circuit selection of IMPCs for different applications. Future research directions in IMPCs are recommended in this paper.}, archive = {J_ACCESS}, author = {Alireza Asadi and Xiaodong Liang and Sobhan Sarani and Mohammad Saleh Karimzadeh}, doi = {10.1109/ACCESS.2025.3586151}, journal = {IEEE Access}, pages = {120307-120326}, shortjournal = {IEEE Access}, title = {Isolated multi-port DC-DC converters for renewable energy sources: A review}, volume = {13}, year = {2025}, }
• (2025). IARA: An underwater acoustic database. ACCESS, 13, 120293-120306. (www)

  @article{ , abstract = {Recent advancements in artificial intelligence have been driven by the availability of diverse and well-structured public databases; however public databases for passive sonar classification remain scarce. To address this gap, this paper presents a new public underwater acoustic labeled database collected during the Santos Basin Soundscape Monitoring Project carried out by PETROBRAS in compliance with conditions required by the federal environmental licensing process conducted by IBAMA. This database expands public resources, containing 129 h 34 min of audio across 1825 recordings (approximately 50 GB). In contrast, existing public databases include ShipsEar (3 h 9 min, 90 recordings) and DeepShip (47 h 4 min, 613 recordings). The dataset provides several hours of general-purpose recordings; however, in this work, only classification was explored, as this is a typical use of such databases in the literature. Baselines were established using Random Forest, MLP, and CNN classification models, with the MLP achieving the best performance: a balanced accuracy of $67.48~\pm ~1.24$ % and an F1-score of $66.72~\pm ~1.17$ %, evaluated using $5\times 2$ cross-validation. The primary contribution of this work is the introduction of the database and its associated baselines for underwater acoustic target recognition tasks, with the aim of providing a valuable resource for the research community. The study also emphasizes the importance of proper data selection, including the use of exclusion regions to enhance signal confidence. Generalization experiments highlight the critical role of dataset diversity, demonstrating that models often struggle to generalize to other datasets. These findings underscore the necessity of diverse, well-structured databases to improve model robustness across varied acoustics scenarios. The IARA an Acoustical Recordings Archive (IARA) is available at doi.org/10.5281/zenodo.15777429 and the code used in this paper is available at github.com/labsonar/IARA}, archive = {J_ACCESS}, author = {Fábio Oliveira Baptista Da Silva and Júlio De Castro Vargas Fernandes and William Soares Filho and João Baptista De Oliveira E Souza Filho and Ângela Spengler and Natanael Nunes De Moura}, doi = {10.1109/ACCESS.2025.3585467}, journal = {IEEE Access}, pages = {120293-120306}, shortjournal = {IEEE Access}, title = {IARA: An underwater acoustic database}, volume = {13}, year = {2025}, }
• (2025). Toward low-cost digital twins for urban transportation systems. ACCESS, 13, 120277-120292. (www)

  @article{ , abstract = {This study introduces an advanced Digital Twin (DT) framework for trams, transforming public transport maintenance through intelligent analytics and real-time data monitoring. Our approach includes developing the DT concept, identifying essential tram components for monitoring, such as the drive train, transmission, electric motor, inverter, suspension, and brakes, and determining the appropriate methods and metrics for their Predictive Maintenance (PM). Employing low-cost sensors coupled with advanced analytics, our system efficiently gathers accurate data and feeds it into PM models. A set of comprehensive rules is formulated to assess the tram’s maintenance status, enabling early detection of potential issues. Significantly, this system was successfully implemented on a real tram, contributing to the early detection and prevention of a major transmission failure. Beyond trams, our approach extends to assessing infrastructure like railway conditions and plays a significant role in enhancing passenger satisfaction. This application not only demonstrates the system’s practical efficacy but also underscores its role in enhancing the safety and reliability of urban tram services.}, archive = {J_ACCESS}, author = {Oskar Wysocki and Michał Kuziemski and André Freitas and Michał Wasilczuk and Jacek Czyżewicz}, doi = {10.1109/ACCESS.2025.3585362}, journal = {IEEE Access}, pages = {120277-120292}, shortjournal = {IEEE Access}, title = {Toward low-cost digital twins for urban transportation systems}, volume = {13}, year = {2025}, }
• (2025). An overview of integrating deep learning methods with close-range hyperspectral imaging for agriculture. ACCESS, 13, 120257-120276. (www)

  @article{ , abstract = {Hyperspectral imaging combines spectroscopy with imaging, thus capturing both spectral and spatial features. This makes it a useful technology in several application areas such as remote sensing and smart agriculture. Extracting spatial-spectral information of objects-of-interest from hyperspectral images requires sophisticated computational methods. The last decade saw the rapid advancement of deep learning methods due to their superior automatic feature extraction capability from images, and hence it is no surprise that these methods have been adapted and used for hyperspectral image analysis. Yet, while deep learning methods have achieved some success for hyperspectral remote sensing, it has been less explored in close range (or proximal) hyperspectral imaging, which is likely because at this range, it is more akin to spectroscopy with spatial information, rather than the case of remote sensing, which is more akin to imaging with higher spectral resolution. Close-range HSI allows for fine-scale analysis of plant health, nutrient levels, disease detection, and crop quality, which is very important in precision agriculture. In light of the new computational methods in deep learning, this review article provides an in-depth analysis and comparisons of such methods when applied to proximal hyperspectral imagery, with a particular emphasis on unsolved challenges (e.g., limited availability of annotated datasets, the need for robust models under real-world conditions, and the integration of spatial and spectral information) and potential future research directions for agricultural applications. The review emphasizes the importance of further explorations and has provided recommended directions for future research that could elevate close-range hyperspectral imaging technology from research to industry use for smart agriculture applications.}, archive = {J_ACCESS}, author = {Shah Faisal and Melanie Po-Leen Ooi and Ye Chow Kuang and Sanush K. Abeysekera and Dale Fletcher}, doi = {10.1109/ACCESS.2025.3587226}, journal = {IEEE Access}, pages = {120257-120276}, shortjournal = {IEEE Access}, title = {An overview of integrating deep learning methods with close-range hyperspectral imaging for agriculture}, volume = {13}, year = {2025}, }
• (2025). Low-profile dual-band and wideband optical mantle cloaks with electric and magnetic polarizabilities and high scattering efficiency. ACCESS, 13, 120244-120256. (www)

  @article{ , abstract = {A theoretical framework is developed to propose single- and dual-band mantle cloaks composed of low-profile single-layer electrically and magnetically polarizable (EMP) metasurfaces. The target is a dielectric cylinder with a circular cross-section. The metasurface immitances are modeled by Lorentzian functions to model the near-resonance behavior of realistic unit cells. For small targets, to suppress the dominant zeroth-order scattered harmonic, closed-form and semi-closed-form relations are derived for Lorentzian parameters to achieve dual-band and single-band operations, respectively. Compared to pure electric cloaks, the scattering efficiency (SE) for the single-band electric-magnetic cloak experiences a 12 dB improvement, and the cloaking bandwidth is nearly doubled at 350 THz. For larger targets with two significant scattered modes, a semi-analytical approach is developed. Accordingly, compared to a double-layer electric cloak of the same thickness, an improvement of about 7.5 dB in SE and a fivefold bandwidth enhancement at 500 THz are achieved. The proposed modal solution is verified by the method of moments (MoM). EMP metasurfaces mimic the behavior of dielectric metasurfaces, which are necessary for THz and optical bands due to their low loss.}, archive = {J_ACCESS}, author = {Keyhan Hosseini and Hemn Younesiraad and Alessio Monti and Alessandro Toscano and Filiberto Bilotti}, doi = {10.1109/ACCESS.2025.3586017}, journal = {IEEE Access}, pages = {120244-120256}, shortjournal = {IEEE Access}, title = {Low-profile dual-band and wideband optical mantle cloaks with electric and magnetic polarizabilities and high scattering efficiency}, volume = {13}, year = {2025}, }
• (2025). Privacy and security in the metaverse: Trends, challenges, and future directions. ACCESS, 13, 120209-120243. (www)

  @article{ , abstract = {The Metaverse represents a transformative virtual environment with vast potential for innovation, societal engagement, and economic growth. However, its rapid expansion also brings pressing concerns regarding privacy and security. Key challenges include data breaches, identity theft, unauthorized surveillance, malicious activities, the absence of robust and standardized security frameworks, and other emerging threats in Metaverse Security. This review presents a systematic literature review (SLR) to explore and analyze these challenges comprehensively. We propose a taxonomy that categorizes Metaverse privacy and security challenges, synthesizing insights from current literature. Furthermore, we compare existing research efforts, identifying trends, gaps, and limitations in addressing these issues. Based on our findings, we provide actionable solutions, such as developing privacy-preserving technologies, secure authentication mechanisms, and interoperable security standards. Finally, the review outlines future research directions to mitigate emerging risks and foster a secure and privacy-centric Metaverse ecosystem. By offering a structured analysis and clear guidance, this work aims to support researchers and practitioners in tackling privacy and security challenges effectively, contributing to the sustainable development of the Metaverse.}, archive = {J_ACCESS}, author = {Jagdeep Singh and Parminder Singh and Ranjodh Kaur and Avinash Kaur and Mustapha Hedabou}, doi = {10.1109/ACCESS.2025.3587015}, journal = {IEEE Access}, pages = {120209-120243}, shortjournal = {IEEE Access}, title = {Privacy and security in the metaverse: Trends, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Token mixing for breast cancer diagnosis: Pre-trained MLP-mixer models on mammograms. ACCESS, 13, 120190-120208. (www)

  @article{ , abstract = {Breast cancer remains a leading cause of mortality among women, necessitating accurate and computationally efficient diagnostic solutions. Deep learning, particularly convolutional neural networks (CNNs), has significantly advanced mammographic analysis by automating feature extraction and improving early detection. However, CNNs rely on localised feature extraction, limiting their ability to capture long-range dependencies essential for robust classification. This study introduces and evaluates the effectiveness of pre-trained MLP-Mixer models using transfer learning as an alternative to CNN-based approaches, utilising their token-mixing and channel-mixing mechanisms to integrate local and global spatial features in mammograms. Four MLP-Mixer variants (B/16, L/16, B/32, and L/32) were systematically assessed on three benchmark datasets: CBIS-DDSM, INbreast, and MIAS. The results demonstrate that MLP-Mixer models, particularly those with smaller patch sizes (L/16 and B/16), consistently achieve state-of-the-art accuracy and sensitivity, while also offering 30 – 50% faster inference times compared to leading CNNs such as ResNet and DenseNet. These models demonstrate strong generalisation across multiple benchmark datasets and strike an effective balance between diagnostic accuracy and computational efficiency, which are essential requirements for clinical deployment. Their performance underscores the importance of fine-grained feature extraction in mammographic analysis. Comparative results indicate that MLP-Mixer models offer a compelling alternative to conventional CNNs by efficiently capturing both local and global dependencies without the high computational demands of deep convolutional network architectures. These findings highlight the promise of token-based models for AI-assisted breast cancer diagnosis and suggest that MLP-Mixer architectures are well-suited for real-time medical imaging applications. By enabling direct global spatial interaction, reducing architectural complexity, and improving diagnostic precision across varied imaging conditions, MLP-Mixers offer a computationally efficient alternative to traditional CNNs without compromising accuracy.}, archive = {J_ACCESS}, author = {Hosameldin O. A. Ahmed and Asoke K. Nandi}, doi = {10.1109/ACCESS.2025.3586139}, journal = {IEEE Access}, pages = {120190-120208}, shortjournal = {IEEE Access}, title = {Token mixing for breast cancer diagnosis: Pre-trained MLP-mixer models on mammograms}, volume = {13}, year = {2025}, }
• (2025). One-to-many tendon driven system for robotic prosthetic hand. ACCESS, 13, 120181-120189. (www)

  @article{ , abstract = {In robotic prosthetic hand systems, with small sizes comparable to that of a human hand and consisting of numerous joints, increase in weight due to the use of numerous motors is a major drawback. In this study, a one-to-many system that can simultaneously and independently control the tension of multiple tendons with a single motor is proposed. The system comprises multiple clutch units, each containing a pulley that winds a tendon connected to a motor. Inspired by the power transmission function of planetary gears, a novel mechanism is proposed to control the power transmitted from the sun gear to the planetary gear by braking the rotation of the ring gear. The clutch unit implementing this mechanism can connect multiple clutch units on the same axis as that of the motor shaft, thus simplifying the conventional connection via gears and belts, resulting in a making the compact, lightweight, and highly scalable mechanism. The basic performance of the proposed mechanism is demonstrated through experiments in which multiple tendons are simultaneously driven under different target tensions. In addition, a tendon-driven robot that anatomically mimics a human index finger is driven, demonstrating the potential of the proposed mechanism for realizing a small robotic hand.}, archive = {J_ACCESS}, author = {Daigo Tokunaga and Satoshi Nishikawa and Kazuo Kiguchi}, doi = {10.1109/ACCESS.2025.3585143}, journal = {IEEE Access}, pages = {120181-120189}, shortjournal = {IEEE Access}, title = {One-to-many tendon driven system for robotic prosthetic hand}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of low pole variable flux reluctance machine. ACCESS, 13, 120169-120180. (www)

  @article{ , abstract = {This paper presents a comprehensive analytical model that reveals the detailed harmonic structure of flux linkage in the armature windings of a 6-slot/4-pole variable flux reluctance machine (VFRM). By integrating armature winding, field winding, and permeance functions, the model accurately characterizes the complex harmonic profile arising from the double-slot structure, which induces a modulation effect. In particular, even-order harmonics dominate the harmonic spectrum, contributing to a significant torque ripple. To address this issue, this paper proposes a novel dual-stator VFRM design, featuring reverse-direction armature windings for the front and rear stators, aimed at eliminating even-order harmonics. The proposed configuration successfully eliminates even-order harmonics, as confirmed through analytical modeling and experimental validation on a prototype machine. The results show a significant reduction in torque ripple. Furthermore, while traditional VFRM topologies with high pole combinations are typically limited by core losses at high speeds, the proposed design mitigates this constraint, providing enhanced performance across a wider range of operating conditions.}, archive = {J_ACCESS}, author = {Hilmi Gurleyen}, doi = {10.1109/ACCESS.2025.3587951}, journal = {IEEE Access}, pages = {120169-120180}, shortjournal = {IEEE Access}, title = {Design and analysis of low pole variable flux reluctance machine}, volume = {13}, year = {2025}, }
• (2025). Enhancing MANET security through long short-term memory-based trust prediction in location-aided routing protocols. ACCESS, 13, 120142-120168. (www)

  @article{ , abstract = {Mobile Ad-Hoc Networks (MANETs) face significant security threats due to their decentralized and dynamic topology, making them susceptible to malicious attacks. Traditional routing protocols struggle to maintain security while ensuring network performance. To address this, we propose LSTMT-LAR, an LSTM-based trust prediction mechanism integrated with Location-Aided Routing (LAR) to enhance MANET security. LSTMT-LAR utilizes a 13-feature behavioral model to assess node trustworthiness in real-time, enabling proactive detection of malicious nodes. The protocol was evaluated across four scenarios with varying node densities (30-100 nodes) and malicious node percentages (10-90%). Results show that LSTMT-LAR maintains a high Packet Delivery Ratio (PDR) of 0.786 even with 50% malicious nodes, outperforming conventional protocols. Furthermore, it optimizes energy consumption by reducing overhead while maintaining competitive end-to-end delays. These findings validate LSTMT-LAR’s effectiveness in securing MANET communications, demonstrating its potential for practical deployment.}, archive = {J_ACCESS}, author = {Saad Mohsen Hassan and Mohd Murtadha Mohamad and Farkhana Binti Muchtar}, doi = {10.1109/ACCESS.2025.3572619}, journal = {IEEE Access}, pages = {120142-120168}, shortjournal = {IEEE Access}, title = {Enhancing MANET security through long short-term memory-based trust prediction in location-aided routing protocols}, volume = {13}, year = {2025}, }
• (2025). Adaptive and asymmetrical blending for bi-directional inter prediction of various visual contents in video coding. ACCESS, 13, 120128-120141. (www)

  @article{ , abstract = {Versatile Video Coding (VVC) standard adopts the geometric partition mode (GPM) to enhance inter prediction by supporting non-rectangular partitions in a block. The GPM improves coding efficiency by effectively handling arbitrary partitions caused by the motion of irregularly shaped objects in a scene. However, the blending process of GPM prediction suffers from limitations, primarily because the equal treatment given to blending ranges for predictors, disregarding the unique visual characteristics of diverse video content, leads to suboptimal performance. This study presents a comprehensive approach using adaptive and asymmetrical blending for the GPM (AAGPM) to enhance the performance of the blending technique by introducing two novel blending methods: a difference-based adaptive blending method and an implicitly asymmetrical blending mechanism. The difference-based method enhances the prediction accuracy by adaptively assigning more relevant blending weights to the dominant predictors. In addition, the asymmetrical mechanism adaptively adjusts the blending ranges without additional signaling by analyzing the local and global characteristics based on the reconstructed signal and predictors. These methods enable adaptive and content-aware adjustment of blending matrices, improve edge quality by preserving intrinsic edges, and prevent over-smoothing in complex sequences across diverse video content, including camera-captured, computer-generated, and mixed content. The proposed algorithm is implemented using the VVC reference software, VTM-18.0, and enhanced compression model (ECM)-6.0, respectively. Experimental results demonstrate that the algorithm achieves a Bjøntegaard Delta bitrate (BD-rate) savings of 0.86% and 0.50% for low-delay B and random-access configurations, respectively, compared to VVC. It also shows improved compression performance compared to ECM.}, archive = {J_ACCESS}, author = {Minhun Lee and Donggyu Sim}, doi = {10.1109/ACCESS.2025.3587255}, journal = {IEEE Access}, pages = {120128-120141}, shortjournal = {IEEE Access}, title = {Adaptive and asymmetrical blending for bi-directional inter prediction of various visual contents in video coding}, volume = {13}, year = {2025}, }
• (2025). Emerging technologies and clinical practice: A systematic review of jugular venous assessment techniques. ACCESS, 13, 120102-120127. (www)

  @article{ , abstract = {Jugular venous (JV) assessment can be a fundamental component of cardiovascular examination, providing valuable insights into right-sided heart function and central venous pressure. Traditional methods are subjective and prone to inter-observer variability, affecting diagnostic accuracy. Technological advancements offer potential solutions by providing objective, consistent, and reproducible measurements. This review aims to present a comprehensive analysis of technology based JV assessment techniques reported in the literature from 2015 to 2024, focusing on their technical characteristics, diagnostic performance, clinical applications, and associated limitations. This represents the first systematic review across all technological platforms for JV assessment, providing evidence-based recommendations for method selection based on clinical context, accuracy requirements, and resource availability. A total of 44 studies were included, comprising a diverse range of JV assessment techniques. Ultrasound-based methods were the most extensively studied, demonstrating strong correlations with invasive measurements and high diagnostic accuracy confirming their suitability for acute care settings where high diagnostic accuracy is essential. Optical and camera-based methods, including photoplethysmographic imaging and infrared thermography, showed promise for non-contact and remote JV assessment, making them well-suited for applications in telemedicine and infection control. Wearable devices and sensors, such as plethysmography and gyroscope sensors, offer promising solutions for continuous and real-time monitoring in ambulatory and chronic disease management contexts. Other specialized modalities, such as MRI and near-infrared spectroscopy (NIRS), were primarily applied to specific populations, including pregnant women and dialysis patients, where conventional techniques may be limited. Despite these advancements, challenges such as small sample sizes, variability in methodologies, and technological limitations were identified. Recent advancements in JV assessment techniques offer enhancements in cardiovascular diagnostics and patient care. Future research should focus on standardizing measurement protocols, conducting larger multi-center studies to properly validate these technological approaches, and evaluating the integration of these innovative techniques into clinical practice to improve diagnostic precision and patient outcomes.}, archive = {J_ACCESS}, author = {Yichen Cao and Rawan S. Abdulsadig and Esther Rodriguez-Villegas}, doi = {10.1109/ACCESS.2025.3587885}, journal = {IEEE Access}, pages = {120102-120127}, shortjournal = {IEEE Access}, title = {Emerging technologies and clinical practice: A systematic review of jugular venous assessment techniques}, volume = {13}, year = {2025}, }
• (2025). Desired trajectory-based sliding mode control for multiple perishable inventory systems. ACCESS, 13, 120085-120101. (www)

  @article{ , abstract = {This paper tackles the problem of supply chain management for multiple warehouse perishable inventory system. The study takes into account a single product perishable inventory system combined of multiple smaller warehouses with one distribution warehouse as a supplier. Each of the endpoint warehouses realizes a predefined demand profile, resulting from its contractual obligations, plus answers to the random customers. As the demand is partially a priori known, their delivery schedule may be shaped in advance. Moreover, in each of the warehouses some product may perish during the delivery and storage process, which must be accounted for and appropriately compensated. The endpoint facilities are all supplied from one main warehouse, with multiple suppliers, characterized by different lead times. The main warehouse’s demand is a sum of the resupply orders of its dependent facilities. Therefore, the paper proposes a desired trajectory based sliding mode control strategy for all the warehouses in the system. The paper shows that application of a reference trajectory following control law ensures full demand satisfaction in all parts of the system while significantly simplifying the control design process. The features of the proposed control strategy are finally demonstrated with a simulation example.}, archive = {J_ACCESS}, author = {Tymoteusz Zwierzchowski and Katarzyna Adamiak}, doi = {10.1109/ACCESS.2025.3587812}, journal = {IEEE Access}, pages = {120085-120101}, shortjournal = {IEEE Access}, title = {Desired trajectory-based sliding mode control for multiple perishable inventory systems}, volume = {13}, year = {2025}, }
• (2025). Leveraging deep learning for fault detection and localization in distributed systems. ACCESS, 13, 120069-120084. (www)

  @article{ , abstract = {The dynamic and complex nature of distributed systems makes fault localization extremely difficult, frequently leading to extended outages and higher operating expenses. A deep learning-based fault localization framework that combines Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), LSTM+CNN, and Autoencoder+LSTM models is proposed in this study. These models undergo extensive preprocessing, including log parsing, feature extraction using TF-IDF and Word2Vec, and min-max normalisation, before being trained and assessed on five benchmark datasets: HDFS, OpenStack, Spark, Hadoop, and BGL. To ensure robustness, the methodology incorporates a 5-fold cross-validation strategy, model-specific architecture tuning, and 1-D sequence modelling. According to experimental results, CNN performs best overall on the HDFS dataset, with an Mean Squared Error (MSE) of 0.00002 and an Coefficient of Determination (R2 Score) Score of 0.996. CNN continuously beats other models in accuracy and performance across all datasets. The key contributions of this study are: 1) a thorough fault localization framework built with deep learning for distributed systems; 2) a comparison of five cutting-edge architectures on five real-world datasets; and 3) statistically validated performance benchmarks backed by Wilcoxon signed-rank tests and t-tests. These contributions provide useful information for implementing accurate and scalable fault localization in distributed computing environments found in the real world.}, archive = {J_ACCESS}, author = {Debolina Ghosh and Jay Prakash Singh}, doi = {10.1109/ACCESS.2025.3587529}, journal = {IEEE Access}, pages = {120069-120084}, shortjournal = {IEEE Access}, title = {Leveraging deep learning for fault detection and localization in distributed systems}, volume = {13}, year = {2025}, }
• (2025). Lightweight and high-payload robotic gripper using shape-memory-alloy actuator and self-locking mechanism. ACCESS, 13, 120056-120068. (www)

  @article{ , abstract = {This study proposes a novel lightweight robotic gripper with a high-payload capacity using shape-memory-alloy (SMA) actuators and a self-locking mechanism. The multi-joint fingers of the developed gripper are driven by flexible SMA actuators. This multi-link structure and the SMA actuator’s compliance property due to its flexibility provide self-adaptability, enabling the robotic gripper to grasp various shaped objects. To achieve the high-payload capacity while overcoming the limited actuation force inherent in SMA actuator systems, this study developed a new self-locking mechanism with a belt-shaped ratchet structure. The developed gripper is designed for vertical pick-and-place operations using an enveloping grasp strategy, where objects rest on the fingers secured by the self-locking mechanism. This self-locking mechanism also allows the developed gripper to hold objects without requiring a continuous power supply while transferring them. This integration of the SMA actuators and belt-shaped locking mechanism not only reduces power consumption but also simplifies the gripper’s structure for its lightweight design. These features–lightweight structure, high-payload capacity, self-adaptive grasping function, and low-power consumption–make the proposed gripper particularly suitable as an end-effector for drones. The paper presents a mechanical analysis of the proposed design, along with experimental validations. Payload tests were also conducted, demonstrating the proposed gripper achieved a payload capacity of 76.9 N (7.85 kg) while weighing only 265 g. Furthermore, grasping experiments with various objects confirmed the effectiveness and versatility of the gripper’s design.}, archive = {J_ACCESS}, author = {Toshihiro Nishimura and Keisuke Akasaka and Kosei Ueno and Yosuke Suzuki and Tokuo Tsuji and Tetsuyou Watanabe}, doi = {10.1109/ACCESS.2025.3587215}, journal = {IEEE Access}, pages = {120056-120068}, shortjournal = {IEEE Access}, title = {Lightweight and high-payload robotic gripper using shape-memory-alloy actuator and self-locking mechanism}, volume = {13}, year = {2025}, }
• (2025). Real-time vision-based wheel-rail interface monitoring for tramway running performance. ACCESS, 13, 120040-120055. (www)

  @article{ , abstract = {Accurate monitoring of the wheel-rail interface is essential for safe tram operation and optimal vehicle performance. Conventional measurement techniques, ranging from strain gauges to gap sensors and earlier vision-based systems, can involve substantial hardware complexity, sensitivity to environmental disturbances, and onerous maintenance. To address these challenges, we propose a scalable camera-vision monitoring system tailored to tramways. A single high-resolution camera, combined with rigorous calibration and two stabilization modules (Wheel Contour Stabilizer and Rail Edge Stabilizer), converts pixel coordinates into reliable distance measurements while effectively suppressing noise. Wheel-rail boundaries are detected in real time by a YOLOv8-segmentation model trained on a customized dataset, ensuring robust performance across diverse lighting and weather conditions. Field trials on straight and curved tracks at speeds from 10 to 50 km/h demonstrate submillimeter accuracy (mean error $\approx $ 0.46 mm) and consistent robustness. The proposed approach facilitates straightforward maintenance and enables early detection of the risk of derailment. In addition, thanks to its camera vision foundation, it can be easily extended to additional rail applications. This work establishes a foundation for future research and rapid technology transfer in intelligent rail transportation.}, archive = {J_ACCESS}, author = {Sangyup Lee and Jin Choi and Naekyung Kim and Janghun Hyeon}, doi = {10.1109/ACCESS.2025.3585740}, journal = {IEEE Access}, pages = {120040-120055}, shortjournal = {IEEE Access}, title = {Real-time vision-based wheel-rail interface monitoring for tramway running performance}, volume = {13}, year = {2025}, }
• (2025). Rectified tangent activation (RTA): A novel activation function for enhanced deep learning performance. ACCESS, 13, 120028-120039. (www)

  @article{ , abstract = {In deep learning, activation functions (AFs) influence a model’s performance, convergence rate, and generalization capability. Conventional activation functions such as ReLU, Swish, ELU, and Tanh have been widely utilized, each offering distinct advantages but also exhibiting intrinsic drawbacks. ReLU is computationally efficient but susceptible to the “dying ReLU” phenomenon, whereas Tanh has saturation problems in both its positive and negative ranges. This study presents the Rectified Tangent Activation (RTA) function, an innovative activation function developed to overcome these restrictions by integrating advantageous features of ReLU, Swish, ELU, and Tanh. We assess the efficacy of RTA by a comparison study with five prevalent activation functions: ELU, ReLU, Swish, and Tanh, utilizing four distinct datasets—CIFAR-10, CIFAR-100, Fashion MNIST, and Chest X-ray. The findings demonstrate that RTA regularly attains superior performance, ranking first on the CIFAR-100, Fashion MNIST, and Chest X-ray datasets, while achieving a robust second place on CIFAR-10, trailing only ELU. The versatility of RTA across diverse data sets, such as image classification and medical imaging, underscores its potential as a versatile AF for numerous deep-learning applications. Our findings indicate that RTA can alleviate problems such as gradient saturation and convergence delay while improving overall accuracy. Considering these encouraging outcomes, RTA offers a persuasive alternative for deep learning practitioners aiming for strong model performance with reduced computing demands.}, archive = {J_ACCESS}, author = {Gaurav Kumar Pandey and Sumit Srivastava}, doi = {10.1109/ACCESS.2025.3587602}, journal = {IEEE Access}, pages = {120028-120039}, shortjournal = {IEEE Access}, title = {Rectified tangent activation (RTA): A novel activation function for enhanced deep learning performance}, volume = {13}, year = {2025}, }
• (2025). System identification of vehicle’s lateral dynamics for open-source autonomous driving simulators. ACCESS, 13, 120018-120027. (www)

  @article{ , abstract = {Open-source autonomous driving simulators (ADSs) are widely used in the design of autonomous driving algorithms. An accurate estimation of the lateral dynamics of vehicles is essential for accurately controlling an autonomous vehicle, where vehicle parameters are required. However, these parameters are often not directly available in open-source simulation environments, making the estimation process more challenging. Therefore, robust system identification techniques become necessary. However, when estimating vehicle dynamics in an open-source autonomous driving simulator, it is often difficult to obtain a sufficient sampling time. In such cases, the impact of the numerical integration methods should be considered. In this study, we analyzed the impact of numerical integration methods on the system identification of a vehicle’s lateral dynamics within an open-source autonomous driving simulator. We considered the forward, backward, and trapezoidal rules, and our results clearly highlight their differences under various sampling times. In particular, while each method exhibited similar accuracy at a high sampling frequency of 50 Hz, the trapezoidal rule demonstrated the highest accuracy for a lower sampling frequency of 10 Hz, underscoring its robustness in scenarios with limited sensor update rates.}, archive = {J_ACCESS}, author = {Yong-Ha Lee and Young-Jin Roh and Dong-Hyuk Park and Jae-Un Lee and Jong-Hoon Won}, doi = {10.1109/ACCESS.2025.3585234}, journal = {IEEE Access}, pages = {120018-120027}, shortjournal = {IEEE Access}, title = {System identification of vehicle’s lateral dynamics for open-source autonomous driving simulators}, volume = {13}, year = {2025}, }
• (2025). IA-KNNR: A novel imbalance-aware approach for handling multi-label class imbalance problem. ACCESS, 13, 119999-120017. (www)

  @article{ , abstract = {Multi-label learning (MLL) is a supervised learning where the classifier needs to learn from the data where one instance can belong to more than one class (label). Due to its wide application, MLL has attracted great attention from academia and researchers. However, due to the complex nature of multi-label data (MLD), it suffers from the class imbalance problem, where data distribution is heterogeneous, and learning from that imbalanced data is challenging. To address this, we propose IA-KNNR, a novel imbalance-aware resampling framework that leverages distance-weighted K-nearest neighbors, label similarity, and a dynamic weighting strategy that accounts for the co-occurrence patterns between frequent and infrequent labels. In addition, DBSCAN-based prototype selection is used to select the most appropriate training data to reduce the complexity of the training time. Extensive experiments on ten multi-label benchmark datasets demonstrate that IA-KNNR performs significantly better than state-of-the-art approaches, up to 26.4%.}, archive = {J_ACCESS}, author = {Himanshu Suyal and Shiv Naresh Shivhare and Gulshan Shrivastava and Rohit Singh and Ansh Singhal}, doi = {10.1109/ACCESS.2025.3586146}, journal = {IEEE Access}, pages = {119999-120017}, shortjournal = {IEEE Access}, title = {IA-KNNR: A novel imbalance-aware approach for handling multi-label class imbalance problem}, volume = {13}, year = {2025}, }
• (2025). Cross-domain recommendation with personalized rating pattern compatibility as transfer rate between domains. ACCESS, 13, 119978-119998. (www)

  @article{ , abstract = {Cross-Domain Recommendation Systems (CDRs) address the data sparsity issue by transferring useful information from one domain to another. Despite advancements, these systems often face two main limitations. First, existing models transfer information without considering the magnitude of differences between domains, which can result in inaccurate information transfer when the characteristics of the domains diverge. Secondly, they prioritize domains based solely on user interaction counts, which biases the model toward high-frequency domains (e.g., Food) over low-frequency ones (e.g., Cars). To overcome these limitations, we propose a CDR that utilizes adaptive domain information transformation based on domain compatibility while prioritizing each domain’s information according to its relative domain density. Our proposed method compares the rating pattern of the target user across domains to calculate the compatibility level, which is used in the transfer function. This process prevents the leakage of irrelevant information by allowing the model to apply a lower transfer rate when the characteristics of the domains differ and a higher transfer rate when they are similar. To alleviate the bias caused by relying solely on the number of interactions, the proposed method prioritizes dense domains based on relative density. Specifically, relative density is computed by considering the ratio of the number of items in each domain to the number of interactions the target user has within that domain. In the experiments, the proposed method consistently outperformed all baselines across three benchmark datasets.}, archive = {J_ACCESS}, author = {Natthapol Maneechote and Saranya Maneeroj and Atsuhiro Takasu}, doi = {10.1109/ACCESS.2025.3586981}, journal = {IEEE Access}, pages = {119978-119998}, shortjournal = {IEEE Access}, title = {Cross-domain recommendation with personalized rating pattern compatibility as transfer rate between domains}, volume = {13}, year = {2025}, }
• (2025). Group-based ultralightweight authentication protocol for telecare medical information systems. ACCESS, 13, 119954-119977. (www)

  @article{ , abstract = {Telecare medical information systems (TMIS) integrate telecommunication and information technology with Internet of Things (IoT) devices, such as wearable sensors, to enable the remote delivery of healthcare services by seamlessly collecting and transmitting health data to the system. However, the integration of IoT in e-health raises significant security concerns, including increased accessibility of patients’ private information to adversaries, inadequate authentication mechanisms, and high server time complexity. Addressing these challenges, we propose a new group-based ultralightweight authentication protocol (GBULAP) for TMIS. The protocol introduces a novel indexing mechanism for the medical server, enabling it to directly identify the doctor’s ID and thereby reducing server time complexity. Furthermore, it ensures robust authentication among the doctor, server, and patient. We conduct a comprehensive security analysis of the proposed protocol using the real-or-random (ROR) model and BAN (Burrows-Abadi-Needham) logic. The results confirm the protocol’s resilience against common attacks targeting TMIS and IoT systems. Furthermore, formal security analysis of GBULAP, conducted using the widely adopted AVISPA (Automated Validation of Internet Security Protocols and Applications) framework, confirms the protocol’s robustness against replay and man-in-the-middle attacks. Additionally, performance analysis demonstrates that the protocol significantly reduces processing and communication costs.}, archive = {J_ACCESS}, author = {Divyanshu Gairola and Pramod Kumar Maurya and Ankush Chanda}, doi = {10.1109/ACCESS.2025.3586952}, journal = {IEEE Access}, pages = {119954-119977}, shortjournal = {IEEE Access}, title = {Group-based ultralightweight authentication protocol for telecare medical information systems}, volume = {13}, year = {2025}, }
• (2025). Impact of network complexity on the computational performance of ising machines. ACCESS, 13, 119946-119953. (www)

  @article{ , abstract = {Recent demonstrations of massively parallel probabilistic computing have shown a practical route to tackling computationally hard problems in various combinatorial optimizations, with impressive performance that can be further accelerated using emerging devices. A technique known as network sparsification has been adopted and adapted for hardware implementation, transforming dense networks into sparse ones by introducing additional nodes or p-bits. Here, we present the impact of network complexity on Boolean SATisfiability (SAT) and integer factorization problems using a more conventional compact network, as well as corresponding dense and sparse networks encoded with invertible logic. These networks are evaluated through both CPU-based simulations and prototyped hardware implementations on a Field-Programmable Gate Array (FPGA) with up to 1935 nodes. Among various network topologies, the success probability of finding optimal solutions using standard simulated annealing generally decreases with the addition of nodes, as expected, and several orders of magnitude more Markov Chain Monte Carlo (MCMC) samples are required to achieve comparable performance in larger networks. Graph sparsification provides a useful means of adjusting network complexity for practical and scalable applications with moderate computational overhead. However, its benefits should be weighed against those of optimized network connections, as the search space increases exponentially with the introduction of auxiliary nodes.}, archive = {J_ACCESS}, author = {Seokmin Hong}, doi = {10.1109/ACCESS.2025.3586179}, journal = {IEEE Access}, pages = {119946-119953}, shortjournal = {IEEE Access}, title = {Impact of network complexity on the computational performance of ising machines}, volume = {13}, year = {2025}, }
• (2025). Blockchain-enabled federated learning in healthcare: Survey and state-of-the-art. ACCESS, 13, 119922-119945. (www)

  @article{ , abstract = {Advances in Internet of Medical Things technology, information and communication technologies, and machine learning have initiated the shift in healthcare towards smart healthcare. Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. Federated learning solves such problems by distributing the learning process to several devices, but it also encounters problems like encouraging participants and model aggregation correctness. Combining blockchain and FL can solve such problems through a decentralized approach that provides greater security and privacy for intelligent healthcare. This survey provides a systematic review of blockchain-based federated learning (BCFL) systems in healthcare. Key design features of BCFLs are analyzed, such as consensus protocols, crypto protocols, storage topology, and integration processes relevant to healthcare use cases. Characteristics such as convergence delay, computation overhead, accuracy loss when privacy is an issue, and ledger scalability for different implementations are compared among common implementations. The works of recent FL-based healthcare frameworks have been discussed, along with determining the challenges and research directions for healthcare use cases.}, archive = {J_ACCESS}, author = {Nasim Nezhadsistani and Naghmeh S. Moayedian and Burkhard Stiller}, doi = {10.1109/ACCESS.2025.3587345}, journal = {IEEE Access}, pages = {119922-119945}, shortjournal = {IEEE Access}, title = {Blockchain-enabled federated learning in healthcare: Survey and state-of-the-art}, volume = {13}, year = {2025}, }
• (2025). Multi-unmanned aerial vehicles cooperative tactics: A survey. ACCESS, 13, 119897-119921. (www)

  @article{ , abstract = {With recent advances in unmanned aerial vehicles (UAVs), there is a growing need for cooperation among multiple vehicles. One approach to enable cooperation is through tactics. However, there is a lack of consensus on how these tactics should be defined or applied to cooperative UAV systems. Moreover, the existing literature does not clearly identify the main challenges inherent in cooperative tactics. We survey recent contributions across both military and civilian domains to consolidate how the term cooperative tactics is employed. We propose a framework that clarifies the core components of multi-UAV cooperation and identifies the main research blocks addressed in the existing work.}, archive = {J_ACCESS}, author = {Arthur Freitas and Camille Alain Rabbath and Craig Williams and Nicolas Lechevin and Sidney Givigi}, doi = {10.1109/ACCESS.2025.3583981}, journal = {IEEE Access}, pages = {119897-119921}, shortjournal = {IEEE Access}, title = {Multi-unmanned aerial vehicles cooperative tactics: A survey}, volume = {13}, year = {2025}, }
• (2025). Efficient power transfer to implanted antennas. ACCESS, 13, 119884-119896. (www)

  @article{ , abstract = {Wireless power transfer between an external electromagnetic source and implanted dipoles into the human body can be greatly assisted by employing a matching part atop of the skin. Two different approaches are proposed: one incorporating lossless layers that minimize the reflections and another dealing with active metasurfaces that boost the transmissivity. Obviously, the performance is higher in the latter scenario since gain media are involved; however, even with passive layers, significant enhancement in the coupling between the two radiators is achieved. These coupling scores exhibit substantial robustness under the condition of misaligned dipoles, a feature that renders them suitable to operate under real-world imperfections. The proposed concept has been experimentally demonstrated at bluetooth frequencies leading to strong enhancement of power transmission, even when the radiation patterns of the two antennas are heavily mismatched. Accordingly, the reported designs can be utilized in biomedical setups that call for fast and reliable wireless communication: from healthcare monitoring and biosensing to real-time drug delivery and bioimaging.}, archive = {J_ACCESS}, author = {Varvara Mouzi and Nektarios Moraitis and Constantine Kakoyiannis and Konstantina S. Nikita and Constantinos Valagiannopoulos}, doi = {10.1109/ACCESS.2025.3583230}, journal = {IEEE Access}, pages = {119884-119896}, shortjournal = {IEEE Access}, title = {Efficient power transfer to implanted antennas}, volume = {13}, year = {2025}, }
• (2025). Community aware source-based incentive strategy to mitigate node selfishness in social internet of things. ACCESS, 13, 119863-119883. (www)

  @article{ , abstract = {The Social Internet of Things (SIoT) enables autonomous nodes to form socially aware communities and collaborate based on trust and interaction history. However, ensuring fair communication and equitable resource allocation remains a challenge due to selfish or selectively cooperative node behavior. This work presents a socially adaptive framework that integrates Virtual Currency (VC), Reputation Scores (RS), and Social Preference (SP) within a community-based incentive model. Once communities are established, Community Heads (CHs) are elected to monitor node behavior, allocate VC based on RS, and enforce cooperation through localized control mechanisms. Nodes earn VC by participating in data relaying tasks. Conversely, nodes that avoid cooperation and fail to accumulate VC may become temporarily ineligible to initiate their own transmissions. In such cases, if a strong social bond exists, a destination node may assist the underfunded source by contributing to the route cost via an adjustment factor. This socially driven support mechanism enhances communication resilience under resource-limited conditions. The framework also supports a recovery mechanism that allows previously selfish nodes to rejoin the network by improving their behavior and regaining RS. Simulations conducted on a MATLAB-based SIoT testbed evaluate the framework against several recent and efficient cooperation models. Compared to the best-performing existing scheme, the proposed mechanism achieves notable improvements in packet delivery ratio (3.9% higher), energy efficiency (0.95% lower consumption), network throughput (11% higher), end-to-end delay (2% lower), and overhead control (9.4% lower). These results confirm that the proposed framework enhances communication efficiency, promotes sustainable cooperation, and mitigates fragmentation caused by non-cooperative behavior in SIoT environments.}, archive = {J_ACCESS}, author = {Saood Iqbal and Muhammad Muneer Umar and Noha Alnazzawi and Shafiullah Khan}, doi = {10.1109/ACCESS.2025.3585979}, journal = {IEEE Access}, pages = {119863-119883}, shortjournal = {IEEE Access}, title = {Community aware source-based incentive strategy to mitigate node selfishness in social internet of things}, volume = {13}, year = {2025}, }
• (2025). A blockchain-oriented task scheduling and allocation system for ROS enabled mobile robots. ACCESS, 13, 119842-119862. (www)

  @article{ , abstract = {In multi-mobile robot applications, the operational processes such as the management of robots, task assignment, monitoring of assigned tasks, communication and coordination between robots, and data storage are executed through a centralized server system. Therefore, most critical decisions are made on this centralized server rather than by the robots themselves. However, working in a centralized system has numerous disadvantages such as the obligation to maintain a server, routing all communication through a central unit, susceptibility to connectivity issues that render the system inoperable, and increased bandwidth requirements as the number of robots increases. Moreover, any communication issues between the server computers and any of the robots in centralized systems affect the entire system’s operation. To address these limitations, a blockchain-powered distributed communication system for inter-robot communication has been developed in this study. A task allocation application between robots has been implemented on this developed distributed communication system. In the application, Hyperledger Fabric (HLF) has been utilized as the blockchain platform due to its advantages. Each robot is a peer in the blockchain network in the proposed system. A cost function which is computed in all robots has been introduced to reduce the communication load in the blockchain network during task distribution and to enable optimal task allocation among robots. With the proposed system, robots compute and choose the most suitable tasks using the cost function, hence transactions on the blockchain network are kept at optimal level. After reaching consensus of peers on task allocations via HLF, task data are transmitted to robots by Robot Operating System (ROS) integration. With the proposed system, a dynamic and distributed architecture has been introduced and implemented where mobile robots can communicate with each other over a blockchain network without the need for a centralized server. In experimental studies conducted on real robots, the proposed system demonstrated optimal task allocation across multiple phases, effectively adapting to various task requirements in different scenarios. For instance, in one scenario, the system effectively allocated a total of 9 tasks, distributed across two phases: 3 tasks in the first phase and 6 tasks in the second phase. This study presents an innovative contribution to the literature on communication of robots and task allocation. Also, this study has a high potential to be adapted to industrial applications including robotic instruments.}, archive = {J_ACCESS}, author = {Mehmed Oğuz Şen and Fatıh Okumuş and Adnan Fatıh Kocamaz}, doi = {10.1109/ACCESS.2025.3585860}, journal = {IEEE Access}, pages = {119842-119862}, shortjournal = {IEEE Access}, title = {A blockchain-oriented task scheduling and allocation system for ROS enabled mobile robots}, volume = {13}, year = {2025}, }
• (2025). A layout-based method for analog fault injection in the context of functional safety. ACCESS, 13, 119831-119841. (www)

  @article{ , abstract = {Digital and analog circuits are fundamental components embedded across a wide range of applications, including but not limited to consumer electronics, automotive systems, aerospace technologies, medical instrumentation, and industrial automation. The critical role they play in ensuring the functionality and efficiency of modern technological systems introduces significant risks, as failures or defects can lead to catastrophic consequences. This work focuses on analog fault injection for automotive applications. ISO 26262 serves as the global standard for functional safety in automotive circuits, identifying fault injection as a fundamental approach to assess safety metrics and validate this category of circuits. Although fault injection is a well-known and widely adopted strategy in the digital domain, its usage is less widespread in analog circuits. The purpose of this work is to propose a layout-based method for analog circuits, where parasitic components in the post-layout extracted schematics are used as indicators to assign the likelihood of fault occurrence that may affect the functionality of circuits and impact the safety of the application. Moreover, the proposed approach leverages simulations to enable a quantitative assessment of the diagnostic coverage achieved by the implemented safety mechanisms. Although the results show a Diagnostic Coverage of 91.82%, which falls short of the assumed 99% by ISO 26262, the method provides valuable insights into identifying critical architecture or layout patterns that can be improved to achieve better safety metrics while maintaining electrical performance.}, archive = {J_ACCESS}, author = {Raffi Der Yeghiayan and Paolo Vilmercati and Giancarlo Storti Gajani and Gianluca D’Alesio}, doi = {10.1109/ACCESS.2025.3585636}, journal = {IEEE Access}, pages = {119831-119841}, shortjournal = {IEEE Access}, title = {A layout-based method for analog fault injection in the context of functional safety}, volume = {13}, year = {2025}, }
• (2025). Research on underwater target detection technology based on SMV-YOLOv11n. ACCESS, 13, 119820-119830. (www)

  @article{ , abstract = {High-precision underwater object detection is a crucial component for improving the efficiency of marine resource development. Traditional detection methods face challenges due to underwater environmental factors such as low brightness and high blurriness. To address these issues, this paper proposes an underwater object detection algorithm named SMV-YOLOv11. Firstly, the Swin Transformer is adopted to replace the backbone network, and a novel Multi-scale Spatial and Channel Attention module (MSCA) is designed and integrated into the backbone to enhance its feature extraction capability. Subsequently, the VoVGSCSP module is introduced to replace the C3K2 module, effectively leveraging contextual information to improve global detection performance. Furthermore, the WIoUv3 loss function replaces the CIoU loss function to enhance the model’s robustness against spatial transformations and scale variations. Comparative experiments and ablation studies conducted on publicly available datasets validate the superiority and generalization ability of the improved model. SMV-YOLOv11 achieves an 87.6 % mean Average Precision (mAP), representing a 1.4 % improvement over the original YOLOv11. It also demonstrates significant gains of 2.7 %, 2.8 %, and 1.9 % in Precision, Recall, and F1 Score, respectively. The experimental results demonstrate that SMV-YOLOv11 possesses significant advantages in underwater object detection, effectively mitigating interference from background noise, low-light conditions, and blur. This provides a new high-precision and high-speed solution for the field.}, archive = {J_ACCESS}, author = {Dongcheng Liao and Yijun Shen and Jinyu Ou and Yanlian Du}, doi = {10.1109/ACCESS.2025.3584313}, journal = {IEEE Access}, pages = {119820-119830}, shortjournal = {IEEE Access}, title = {Research on underwater target detection technology based on SMV-YOLOv11n}, volume = {13}, year = {2025}, }
• (2025). Early prediction of student suspension and dropout for counseling resource optimization using multilayer perceptron. ACCESS, 13, 119810-119819. (www)

  @article{ , abstract = {The sub-replacement fertility rate in Taiwan has caused many educational institutions to face significant challenges, making student suspension and dropout critical issues for sustainable operations. Addressing these challenges requires effective prediction tools to identify at-risk students and allow for timely intervention. This study applies a Multilayer Perceptron to predict student suspension and dropout, with the goal of assisting schools in better monitoring these situations and proactively deploying counseling resources. By enhancing the precision of resource allocation, schools can improve the effectiveness of their interventions. The dataset used in this study includes student records from Chaoyang University of Technology spanning five academic years (2017–2021). Data from the second semester of the 2021 academic year were designated for model testing, while data from the other nine semesters were used for training and validation. The proposed model achieved an accuracy of 81.70%, demonstrating its potential to provide valuable insights. Future efforts will focus on further optimizing the model and deploying it in real-world applications to serve as a critical tool for counseling resource allocation and institutional decision-making.}, archive = {J_ACCESS}, author = {Yu-Huei Cheng and Mu-Hsin Shih and Che-Nan Kuo}, doi = {10.1109/ACCESS.2025.3587484}, journal = {IEEE Access}, pages = {119810-119819}, shortjournal = {IEEE Access}, title = {Early prediction of student suspension and dropout for counseling resource optimization using multilayer perceptron}, volume = {13}, year = {2025}, }
• (2025). Design and development of a novel universal gripper having rigid mechanics with self-adaptable fingers for industrial applications. ACCESS, 13, 119789-119809. (www)

  @article{ , abstract = {In industrial assembly, intralogistics, and powertrain automation, it is essential to implement robust, safe, and efficient object-grasping solutions that are applicable to various scenarios. From this perspective, the design, kinematic analysis, and experimental evaluation of a novel three-finger universal gripper are presented in this paper. The aim of this research is to develop and validate a versatile grasping solution that combines the precision of rigid mechanics with the adaptability of soft robotics for handling diverse industrial components ranging from delicate items to complex automotive parts. The rigid mechanical structure of each finger integrates a Chebyshev-parallelogram linkage mechanism with thermoplastic polyurethane (TPU) finger for object interface. The gripper design addresses the challenge of handling diverse components through three key innovations integrated into a cohesive system: 1) a compound linkage mechanism that transforms the linear input at the Chebyshev linkage into angular motion driving the parallelogram linkage interfaced with the self-adaptable finger, maintaining near-perfect linear motion (±0.033 mm deviation) with a 6.06:1 mechanical advantage; 2) a modular design enabling rapid reconfiguration between parallel, equiangular, and envelope grasping configurations; and 3) independent actuation of each finger for asymmetric object handling. Moreover, the Fin Ray–inspired fingertip enables an overall lightweight gripper design with firm grasping capability, self-adaptivity, and finer object manipulation. Three soft finger designs were characterized through compression testing at multiple positions, revealing significant performance differences, with one finger demonstrating superior overall performance and selected for gripper integration. Tensile pull-out tests using the equiangular configuration demonstrated retention capabilities with peak forces of 45-77 N and sustained forces of 15-25 N, translating to practical load capacities of 1.5-2.5 kg. Furthermore, the gripper’s performance was validated through integration with a six degrees of freedom (6DOF) industrial robot, grasping objects ranging from delicate items like an orange to automotive components like a clutch plate. Objects weighing up to approximately 2.5 kg were grasped reliably, whereas objects exceeding 3 kg showed minor occasional slippage. The universal gripper weighs less than 3 kg and features integrated stepper motors with Modbus RTU control, thus offering a practical solution for industrial environments requiring versatile and adaptive handling.}, archive = {J_ACCESS}, author = {Muhammad Usman Khalid and Michal Jilich and Mattia Frascio and Benoît Lévesque and Alessandro Zanella and Matteo Zoppi}, doi = {10.1109/ACCESS.2025.3587328}, journal = {IEEE Access}, pages = {119789-119809}, shortjournal = {IEEE Access}, title = {Design and development of a novel universal gripper having rigid mechanics with self-adaptable fingers for industrial applications}, volume = {13}, year = {2025}, }
• (2025). Hardware-efficient neural network digital predistortion for terahertz power amplifiers using DeepShift and pruning. ACCESS, 13, 119772-119788. (www)

  @article{ , abstract = {With the increasing demand for high data rates in wireless communication systems, broadband signals with high peak-to-average power ratio (PAPR) are being utilized, and higher frequency bands such as terahertz waves are being adopted. These factors have led to significant nonlinear distortion in radio frequency (RF) power amplifiers (PAs). Particularly in high-frequency bands, PA nonlinearity becomes more pronounced, making it difficult to maintain signal quality. In this research, we propose a novel hardware-efficient neural network-based digital predistortion (DPD) technique that combines DeepShift and pruning. Our comprehensive evaluation using terahertz wave (256.4 GHz) PAs demonstrates that the proposed method, particularly when applying pruning before DeepShift (Strategy 1), maintains equivalent linearization performance with an EVM of 13.76% compared to 14.21% for conventional 32-bit fixed-point implementation, while reducing circuit area by 99.2% and energy consumption by 99.3% in the 45nm CMOS process. This remarkable hardware efficiency improvement without performance compromise is achieved through the synergistic combination of bit-shift operations replacing multiplications and strategic pruning of redundant connections. The proposed technique is particularly effective for PAs with complex nonlinearity in the terahertz band, providing an efficient DPD solution for high-performance linearization in resource-constrained environments.}, archive = {J_ACCESS}, author = {Taishi Watanabe and Takeo Ohseki and Issei Kanno}, doi = {10.1109/ACCESS.2025.3585066}, journal = {IEEE Access}, pages = {119772-119788}, shortjournal = {IEEE Access}, title = {Hardware-efficient neural network digital predistortion for terahertz power amplifiers using DeepShift and pruning}, volume = {13}, year = {2025}, }
• (2025). A study on an improved generative adversarial network model for super-resolution image reconstruction. ACCESS, 13, 119757-119771. (www)

  @article{ , abstract = {The SRGAN model based on Generative Adversarial Networks (GANs) has achieved breakthroughs in perceptual quality for image super-resolution reconstruction. However, existing models suffer from feature smoothing effects in the generator and unstable training of the discriminator. To address these limitations, we propose an improved SRGAN model (denoted as SRGAN-GBDB) through multi-strategy collaborative optimization to improve reconstruction quality. The specific improvements include: Firstly, in the generator, we remove batch normalization (BN) layers to alleviate feature degradation and embed the Convolutional Block Attention Module (CBAM) to enhance high-frequency feature modeling. Secondly, we implement spectral normalization for the discriminator and incorporate spectrally normalized residual blocks into the discriminator to strengthen its discriminative capability. Additionally, the joint perceptual-adversarial-content loss function of the SRGAN model balances reconstruction accuracy and visual quality. Experimental results show that the optimized model SRGAN-GBDB outperforms the traditional Bicubic interpolation method, SRGAN, and several other existing super-resolution algorithms on the Set5, Set14, BSD100, Urban100, and DIV2K datasets. It not only improves quantitative metrics such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) but also demonstrates superior visual quality through better detail recovery and fewer artifacts, validating the effectiveness and superiority of the optimization strategy. Future work will explore more efficient optimization methods and lightweight network architectures to facilitate the application of image super-resolution systems in mobile platforms and specialized fields like remote sensing and medical imaging.}, archive = {J_ACCESS}, author = {Ximin Qu and Cheng Peng}, doi = {10.1109/ACCESS.2025.3587201}, journal = {IEEE Access}, pages = {119757-119771}, shortjournal = {IEEE Access}, title = {A study on an improved generative adversarial network model for super-resolution image reconstruction}, volume = {13}, year = {2025}, }
• (2025). PRISE: A framework for AI product incubation from concept to implementation. ACCESS, 13, 119740-119756. (www)

  @article{ , abstract = {AI product incubation faces unique challenges such as difficulty in technical feasibility assessment, ambiguous value perception, and engineering transformation barriers. This research proposes the PRISE framework (Problem Definition, Rapid Validation, Impact Evaluation, Single-focus Development, End-to-End Productization), integrating design thinking, lean startup, and engineering practices with adaptive modifications for AI characteristics. Through analysis of seven cross-industry cases from Microsoft’s AI Vertical team, we validated the framework’s practicality. The research reveals five key patterns for successful AI product incubation: user-decision maker separation identification, lightweight validation priority, technology-value transformation mechanism, deep cultivation of vertical scenarios, and end-to-end delivery thinking. This framework fills the gap in AI product development methodology, providing systematic guidance and operational tools for practitioners while offering new perspectives for AI innovation management theory.}, archive = {J_ACCESS}, author = {Baoli Wang and Ye Li and Wen Lei}, doi = {10.1109/ACCESS.2025.3587943}, journal = {IEEE Access}, pages = {119740-119756}, shortjournal = {IEEE Access}, title = {PRISE: A framework for AI product incubation from concept to implementation}, volume = {13}, year = {2025}, }
• (2025). Quantifying haptic affection of car door through data-driven analysis of force profile. ACCESS, 13, 119726-119739. (www)

  @article{ , abstract = {Haptic affection plays a crucial role in user experience, particularly in the automotive industry where the tactile quality of components can influence customer satisfaction. This study aims to accurately predict the affective property of a car door by only watching the force or torque profile of it when opening. To this end, a deep learning model is designed to capture the underlying relationships between force profiles and user-defined adjective ratings, providing insights into the door-opening experience. The dataset employed in this research includes force profiles and user adjective ratings collected from six distinct car models, reflecting a diverse set of door-opening characteristics and tactile feedback. The model’s performance is assessed using Leave-One-Out Cross-Validation, a method that measures its generalization capability on unseen data. The results demonstrate that the proposed model achieves a high level of prediction accuracy, indicating its potential in various applications related to haptic affection and design optimization in the automotive industry.}, archive = {J_ACCESS}, author = {Mudassir Ibrahim Awan and Ahsan Raza and Waseem Hassan and Ki-Uk Kyung and Seokhee Jeon}, doi = {10.1109/ACCESS.2025.3585067}, journal = {IEEE Access}, pages = {119726-119739}, shortjournal = {IEEE Access}, title = {Quantifying haptic affection of car door through data-driven analysis of force profile}, volume = {13}, year = {2025}, }
• (2025). Simulation and analysis of distributed reaction systems. ACCESS, 13, 119709-119725. (www)

  @article{ , abstract = {Reaction systems (RSs) are a computational framework inspired by the interplay between biochemical interactions. Similarly to regulatory networks, reaction products can dynamically activate or inhibit other reactions. This provides a framework for discrete-time, interactive computation, where each state is determined by those reactions enabled in the immediately preceding state and by additional environmental interventions, if any. Since their introduction, RSs have been extended to study many aspects of complex systems: multi-agent collaborations, cause-effect relationships, model checking, and many others. The gap in the literature addressed in this paper is the lack of software tools for simulating and analysing distributed reaction systems (DRSs). We introduce a process algebraic approach to describing, simulating, and analysing DRSs. This allows designing complex models by re-using modular components in a well-structured and compositional way, and analysing them with the BioReSolve modelling software. We demonstrate our approach by experimenting with distributed Lotka-Volterra models, where multiple agents evolve according to their own periodic dynamics, but can also synchronise their cycles through diverse communication topologies.}, archive = {J_ACCESS}, author = {Linda Brodo and Roberto Bruni and Moreno Falaschi and Ion Petre}, doi = {10.1109/ACCESS.2025.3586078}, journal = {IEEE Access}, pages = {119709-119725}, shortjournal = {IEEE Access}, title = {Simulation and analysis of distributed reaction systems}, volume = {13}, year = {2025}, }
• (2025). Peak arm current reduction of modular multilevel converter based on particle swarm optimizer for circulating current injections. ACCESS, 13, 119682-119708. (www)

  @article{ , abstract = {Modular multilevel converters (MMCs) are widely used, and ongoing research aims to enhance and optimize their performance. One key improvement is reducing the peak arm current, which helps boost the current capability of the submodule switches and increase power handling capacity. One approach to achieve this target is to optimally shape the circulating current with a specified amount of AC components, which can be injected into the arm current. Circulating current arises from the mismatch between submodule capacitor voltages and the DC terminals. This paper uses a particle swarm optimisation (PSO) algorithm to determine the optimal values for the injected components. The magnitude and phase of the injected components are treated as variables in the optimisation process, with the goal of minimising the peak arm current as the cost function. The paper also investigates how these optimal injections affect peak-to-peak energy deviations and capacitor voltage ripples. Moreover, the response of the proposed method is investigated under multiple dynamic conditions, including a varying power factor, dynamically changing power references, and grid voltage sags, as the MMC interacts with the grid by exchanging real and reactive power. Results show that the proposed method can reduce the peak arm current by up to 27.42% for specific operating conditions. Additionally, the proposed approach performs better in reducing the peak arm current when the MMC supplies purely reactive current. In contrast, other methods may tend to be unproductive in these areas. An evaluation of losses, based on datasheet information of submodule switches, shows that the effect of the PSO-based optimal injections is marginal to the extent that no significant change is reported in converters’ efficiency compared to the case of zero injections.}, archive = {J_ACCESS}, author = {Riyadh Toman Thahab and Tahani Hamodi Al-Mhana}, doi = {10.1109/ACCESS.2025.3586725}, journal = {IEEE Access}, pages = {119682-119708}, shortjournal = {IEEE Access}, title = {Peak arm current reduction of modular multilevel converter based on particle swarm optimizer for circulating current injections}, volume = {13}, year = {2025}, }
• (2025). Measuring and improving the efficiency of python code generated by LLMs using CoT prompting and fine-tuning. ACCESS, 13, 119657-119681. (www)

  @article{ , abstract = {The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and facilitate the debugging of existing software systems. However, LLM-generated code often faces challenges due to inherent inefficiencies, including redundant logical structures, factually inconsistent content (hallucinations), and programming errors. To address this issue, our research rigorously evaluated the computational efficiency of Python code generated by three prominent LLMs: GPT-4o-Mini, GPT-3.5-Turbo, and GPT-4-Turbo. The evaluation metrics encompass execution time, memory utilization, and peak memory consumption, while maintaining the functional correctness of the generated code. Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. Furthermore, we investigated the impact of two distinct optimization strategies: Chain-of-Thought (CoT) prompting and model fine-tuning. Our findings reveal a significant enhancement in efficiency metrics for GPT-4o-Mini and GPT-3.5-Turbo when employing CoT prompting; however, this trend was not observed for GPT-4-Turbo. Based on its promising performance with CoT prompting, we selected the GPT-4o-Mini model for subsequent fine-tuning, aiming to further enhance both its computational efficiency and accuracy. However, contrary to our expectations, fine-tuning the GPT-4o-Mini model led to a discernible degradation in both its accuracy and computational efficiency. In conclusion, this study provides empirical evidence suggesting that the deployment of high-CPU machine configurations, in synergy with the utilization of the GPT-4o-Mini model and CoT prompting techniques, yields demonstrably more efficient and accurate LLM-generated Python code, particularly within computationally intensive application scenarios.}, archive = {J_ACCESS}, author = {Ramya Jonnala and Jeong Yang and Young Lee and Gongbo Liang and Zechun Cao}, doi = {10.1109/ACCESS.2025.3585742}, journal = {IEEE Access}, pages = {119657-119681}, shortjournal = {IEEE Access}, title = {Measuring and improving the efficiency of python code generated by LLMs using CoT prompting and fine-tuning}, volume = {13}, year = {2025}, }
• (2025). Performance analysis on massive random-access based on repetition and post-encoding processing. ACCESS, 13, 119646-119656. (www)

  @article{ , abstract = {The relentless advancement of wireless mobile communication technology, evolving from 1G to 5G and now venturing into 6G, has prompted an exploration of massive random-access (MRA) techniques to accommodate the anticipated massive communication demands. This paper delves into the domain of MRA technology, garnering increasing attention in 6G Internet of Things (IoT) networks. Based on extensive prior research on MRA, this paper analyzes the impact of repetitions and post-encoding processing on MRA performance. Two scenarios are considered: the ideal post-encoding processing scenario with the maximum processing gain, where each repetition is treated as independent; and the identical post-encoding processing scenario with the most basic processing gain, where all repetitions undergo the same processing steps. Through theoretical derivations, performance bounds for MRA with joint decoding across all actual IoT devices and all repetitions are established for both scenarios. The special scenario concerning performance bounds, where the repetition number is 1, aligns with existing results. Numerical evaluations have confirmed the accuracy of the theoretical derivations and further demonstrated that, as the number of repetitions increases, the performance disparity between the ideal post-encoding processing scenario and the identical post-encoding processing scenario widens. The performance bounds of other post-encoding processing schemes lie between the ideal and the identical post-encoding processing. These findings emphasize optimizing post-encoding processing schemes to bring actual performance closer to the ideal post-encoding processing scenario.}, archive = {J_ACCESS}, author = {Wei Bai and Shaoli Kang and Shaohui Sun and Shanzhi Chen}, doi = {10.1109/ACCESS.2025.3585185}, journal = {IEEE Access}, pages = {119646-119656}, shortjournal = {IEEE Access}, title = {Performance analysis on massive random-access based on repetition and post-encoding processing}, volume = {13}, year = {2025}, }
• (2025). TPVis: A temporal path visualization system for intuitive understanding of information diffusion inside temporal networks. ACCESS, 13, 119633-119645. (www)

  @article{ , abstract = {Analyses of temporal graphs provide valuable insights into temporal networks via two analytical approaches (temporal evolution and temporal information diffusion). The former explains how a network evolves over time; the latter shows how information diffuses over a temporal network. Various temporal graph visualization techniques could be applied to boost the comprehension of temporal evolution and make decisions more accurate. However, there are problems in the case of temporal information diffusion that worsen understanding: visual misalignment, visual disconnection, and time-constraint violation. In this paper, we propose a novel system that supports intuitive understanding of temporal information diffusion, TPVis. TPVis directly computes and visualizes temporal paths into a timeline-based layout, which is computationally challenging, while this approach is more scalable than existing approaches in terms of the number of required events and valid times. We present an efficient incremental algorithm to compute temporal paths and design the procedure for path-to-tree conversion to alleviate the problems above, which are shown in our case studies.}, archive = {J_ACCESS}, author = {Jincheol Oh and Haifa L. Gaza and Eunsol Gang and Jaewook Byun}, doi = {10.1109/ACCESS.2025.3586044}, journal = {IEEE Access}, pages = {119633-119645}, shortjournal = {IEEE Access}, title = {TPVis: A temporal path visualization system for intuitive understanding of information diffusion inside temporal networks}, volume = {13}, year = {2025}, }
• (2025). Risk indices assessment of different cyber-attack actions for an SDN-enabled IEC 61850 substation using markov decision process. ACCESS, 13, 119619-119632. (www)

  @article{ , abstract = {IEC61850-based digital substations are widely used for electricity network power flow to be controlled and directed safely and securely from generation to demand. As digital substations become software-intensive and digitally connected to the grid cyberspace, they are vulnerable to cyberattacks. Thus, Software-Defined Networking (SDN) technology can be utilized to enhance the security of digital substations. This paper aims to evaluate the security posture of SDN-enabled substations and to calculate the cyber risk indices of a substation under different attacks. The proposed approach uses 1) the Purdue Model to model attack scenarios using cyber-physical devices at different layers based on typical protection control device arrangement in a substation and 2) the Markov Decision Process (MDP) model to assess cybersecurity risk indices for the substation. The assessment evaluates all possible attack patterns by an attacker to determine the severity of each attack path within the substation. The paper further compares the cybersecurity risk indices of SDN-enabled substations with those of traditional switch-based substations. Results show that implementing SDN technology can significantly enhance substation security with improvements ranging from 6.9% to 100% and an average security enhancement of 68%.}, archive = {J_ACCESS}, author = {Zhihao Wu and Sugandh Seth and Haiyu Li and Zhirun Hu and James Yu}, doi = {10.1109/ACCESS.2025.3583120}, journal = {IEEE Access}, pages = {119619-119632}, shortjournal = {IEEE Access}, title = {Risk indices assessment of different cyber-attack actions for an SDN-enabled IEC 61850 substation using markov decision process}, volume = {13}, year = {2025}, }
• (2025). Conceptualizing clinicians’ trust in artificial intelligence as a function of their expertise, workload, patient outcome, diagnosis difficulty, and AI accuracy: A systems thinking approach. ACCESS, 13, 119601-119618. (www)

  @article{ , abstract = {This study applies a systems thinking approach to examine the complex dynamics influencing clinicians’ trust in artificial intelligence (AI). We propose a conceptual model that maps how trust in AI evolves in response to key factors such as workload, diagnostic difficulty, AI accuracy, patient outcomes, prior trust in AI, and user expertise. The interrelationships among these variables were synthesized from existing literature to explore hypothetical scenarios, rather than to predict specific outcomes. The data used in this study are assumed to be tabular in nature, with analytical challenges arising from inherent uncertainties affecting both humans and AI. Using simulation-based analysis, we observed that clinicians’ trust in AI decreases as diagnostic difficulty—both for the AI system and the human clinician—and clinical workload increase. Interestingly, trust remains relatively stable under rising workload when diagnostic tasks are simple but significantly declines when tasks are complex. Clinician expertise emerged as a critical moderating factor. Experienced clinicians tend to maintain higher trust levels in AI systems, even under challenging conditions, compared to their less experienced counterparts. This may be attributed to their enhanced ability to detect AI errors, allowing for more calibrated and resilient trust. These insights highlight the importance of designing AI systems and training interventions that support appropriate trust calibration, especially in high-pressure clinical environments. Our findings suggest that a one-size-fits-all approach to fostering trust in AI may be insufficient. Instead, targeted strategies that account for diagnostic complexity and clinician expertise are essential. This work contributes to the broader understanding of trust dynamics in clinical AI adoption and offers practical guidance for developers and healthcare organizations seeking to optimize human-AI collaboration.}, archive = {J_ACCESS}, author = {Hamid Shamszare and Zaira Chaudhry and Manijeh Berenji and Avishek Choudhury}, doi = {10.1109/ACCESS.2025.3586555}, journal = {IEEE Access}, pages = {119601-119618}, shortjournal = {IEEE Access}, title = {Conceptualizing clinicians’ trust in artificial intelligence as a function of their expertise, workload, patient outcome, diagnosis difficulty, and AI accuracy: A systems thinking approach}, volume = {13}, year = {2025}, }
• (2025). Short-term photovoltaic power combined prediction based on feature screening and weight optimization. ACCESS, 13, 119590-119600. (www)

  @article{ , abstract = {Aiming at the problem of low prediction accuracy caused by the intermittent and fluctuating characteristics of photovoltaic power, a short-term photovoltaic power combined prediction method based on feature screening and weight optimization is proposed. Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman’s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. Through the example verification, the MAPE values of sunny, cloudy and rainy weather are 1.80%, 5.00% and 21.80%, respectively. The prediction error is less than that of other comparison models, thus effectively solving the problem of low prediction accuracy of traditional models.}, archive = {J_ACCESS}, author = {Liqing Geng and Yadong Yang and Genghuang Yang and Yongfeng Zheng and Xiaocong Liu}, doi = {10.1109/ACCESS.2025.3584859}, journal = {IEEE Access}, pages = {119590-119600}, shortjournal = {IEEE Access}, title = {Short-term photovoltaic power combined prediction based on feature screening and weight optimization}, volume = {13}, year = {2025}, }
• (2025). Bridging language barriers in coding: An NLP-powered tool for programming education in regional languages. ACCESS, 13, 119577-119589. (www)

  @article{ , abstract = {In the digital age, coding has become a critical skill for improving career prospects and participating in the global economy. However, most educational tools primarily cater to English-speaking learners, limiting access for individuals in rural areas who are proficient only in regional languages, such as Kannada. This study presents an interactive web-based tool that leverages Natural Language Processing (NLP) to teach fundamental programming concepts in Kannada. The tool allows users to input algorithmic statements in Kannada, which are then translated into English and converted into Python code using a fine-tuned Salesforce CodeT5 model. The model, trained on a custom dataset, achieved strong performance with a BLEU score of 87.64 and an Exact Match score of 81.62%. The study also evaluated other models such as T5, PLBART, and GPT-Neo for the same task. Additionally, the platform offers tutorials, quizzes, and an embedded coding environment for learners to test the generated code. A user study involving Kannada-speaking students from diverse academic backgrounds was conducted to evaluate the usability and educational effectiveness of the tool. Results indicated that the majority of participants found the tool easy to use, the code generation accurate, and the overall experience beneficial in understanding programming logic. Notably, over 65% of students without prior programming experience expressed interest in pursuing programming careers after using the tool. By addressing language barriers, this research contributes to United Nations Sustainable Development Goal 4 (SDG 4) by fostering inclusive and equitable quality education, enabling learners to acquire programming skills in their native language.}, archive = {J_ACCESS}, author = {Goutami Sooda and Arya Hariharan and Ahana Patil and Arya Vinod and S. Chandana and L. B. Manvith and Nihar Mandahas and H. Siri and G. Shobha and N. Deepamala and Jyoti Shetty}, doi = {10.1109/ACCESS.2025.3587056}, journal = {IEEE Access}, pages = {119577-119589}, shortjournal = {IEEE Access}, title = {Bridging language barriers in coding: An NLP-powered tool for programming education in regional languages}, volume = {13}, year = {2025}, }
• (2025). Non-contact rapid neck socket depth and shoulder width measurement for pillow design. ACCESS, 13, 119563-119576. (www)

  @article{ , abstract = {Pillows have a significant impact on sleep quality and cervical spine health, and neck socket depth and shoulder width are the most critical parameters in their design and selection. Traditional measurement methods require subjects to maintain a standard posture for a long time and are done manually by the surveyor, which is time-consuming and prone to errors due to subjective factors. In this paper, we first propose an automated non-contact measurement pipeline based on the combination of deep learning and region of interest (ROI) geometric analysis, which can calculate the neck socket depth and shoulder width directly from a partially backside body point cloud wearing clothing. The core of the method is the proposed Internal Body ROI Recognition Network (IBROI-RNet), which realizes ROI label prediction and backside internal point cloud reconstruction through a two-branch cascade architecture. On this basis, the accurate computation of target dimensions is accomplished based on geometric analysis. Experimental results show that the method demonstrates excellent measurement accuracy and practicality while protecting user privacy, providing a feasible solution for the design and selection of personalized pillows. The code and model are available for research purposes at https://github.com/kumori97/neck-and-shoulder-measurement}, archive = {J_ACCESS}, author = {Xihang Li and Ming Li and Xianguo Cheng and Fang Chen}, doi = {10.1109/ACCESS.2025.3586930}, journal = {IEEE Access}, pages = {119563-119576}, shortjournal = {IEEE Access}, title = {Non-contact rapid neck socket depth and shoulder width measurement for pillow design}, volume = {13}, year = {2025}, }
• (2025). NFFNet: A deep learning framework for fault diagnosis using classifier-guided graph attention networks and blind deconvolution. ACCESS, 13, 119551-119562. (www)

  @article{ , abstract = {Ensuring the safety and reliability of train transmission systems is crucial for efficient rail transit operations. These systems are prone to faults that can lead to service disruptions or accidents, making effective fault diagnosis essential for operational safety. However, existing methods often fail to extract features effectively in noisy environments and neglect interactions between components, such as vibration propagation from motors to axle boxes. To address these limitations, this paper proposes NFFNet, a Graph Attention Network that incorporates component spatial relationships for fault diagnosis in train transmission systems. NFFNet employs a Blind Deconvolution (BD) technique combined with a Deep Residual Shrinkage Network (DRSN) for robust and adaptive feature extraction under noisy conditions. It further integrates spatial relationships within a graph attention network to model interactions between components, enhancing diagnostic accuracy. By recovering latent fault signals obscured by noise and dynamically modeling interactions among adjacent components, NFFNet achieves more reliable and precise fault diagnosis. Experimental results demonstrate that NFFNet significantly improves accuracy and robustness in high-noise environments compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Hongfei Yao and Qianzhe Xu and Qingtao Wu}, doi = {10.1109/ACCESS.2025.3584380}, journal = {IEEE Access}, pages = {119551-119562}, shortjournal = {IEEE Access}, title = {NFFNet: A deep learning framework for fault diagnosis using classifier-guided graph attention networks and blind deconvolution}, volume = {13}, year = {2025}, }
• (2025). Distributionally robust and learning-based vehicle routing for disaster blood logistics. ACCESS, 13, 119538-119550. (www)

  @article{ , abstract = {In humanitarian disasters, delivering blood and blood products rapidly and reliably is critical but logistically complex. This paper proposes a hybrid framework that integrates Distributionally Robust Optimization (DRO) for offline anticipative planning with Contextual Bandits for online adaptive vehicle dispatch. The system coordinates a heterogeneous fleet of ambulances, drones, and robots to navigate uncertain demand, infrastructure degradation, and time-sensitive deliveries. We formulate a two-level approach: a robust vehicle routing problem accounting for moment-based uncertainty in demand, and an online dispatch policy that learns context-aware allocation decisions in real time. Extensive simulations evaluate the proposed method against DRO-only, CB-only, and stochastic routing baselines. Results show that the hybrid approach outperforms all others in robustness, service rate, and delay, while maintaining reasonable computational efficiency.}, archive = {J_ACCESS}, author = {I. Hssini}, doi = {10.1109/ACCESS.2025.3584213}, journal = {IEEE Access}, pages = {119538-119550}, shortjournal = {IEEE Access}, title = {Distributionally robust and learning-based vehicle routing for disaster blood logistics}, volume = {13}, year = {2025}, }
• (2025). 3D reconstruction of the human colon from capsule endoscope video. ACCESS, 13, 119523-119537. (www)

  @article{ , abstract = {As the number of people affected by diseases in the gastrointestinal system is ever-increasing, a higher demand for preventive screening is inevitable. This will significantly increase the workload on gastroenterologists. To help reduce the workload, tools from computer vision may be helpful. In this paper, we investigate the possibility of constructing 3D models of whole sections of the human colon using image sequences from wireless capsule endoscope video, providing improved visualization as well as position localization of pathologies for gastroenterologists. As capsule endoscope images contain distortion and artifacts non-ideal for many 3D reconstruction algorithms, the problem is challenging. However, recent developments of virtual graphics-based models of the human gastrointestinal system, where distortion and artifacts can be enabled or disabled, make it possible to “dissect” the problem. The graphical model also provides a ground truth, enabling computation of geometric distortion introduced by the 3D reconstruction method. In this paper, most distortions and artifacts are first left out to determine if it is feasible to reconstruct whole sections of the human gastrointestinal system by existing methods. We demonstrate that 3D reconstruction is possible through simultaneous localization and mapping. Further, to reconstruct the gastrointestinal wall surface from resulting point clouds, which vary greatly in density, Poisson surface reconstruction is a good option. Noise and artifacts are then enabled to analyze their effect on the 3D reconstruction. The results are promising, encouraging further research and development on the topic.}, archive = {J_ACCESS}, author = {Pål Anders Floor and Ivar Farup and Marius Pedersen}, doi = {10.1109/ACCESS.2025.3587596}, journal = {IEEE Access}, pages = {119523-119537}, shortjournal = {IEEE Access}, title = {3D reconstruction of the human colon from capsule endoscope video}, volume = {13}, year = {2025}, }
• (2025). Piecewise dynamic time warping-based subsequence matching in data stream. ACCESS, 13, 119512-119522. (www)

  @article{ , abstract = {Dynamic Time Warping (DTW) is effective for subsequence matching but is limited by its high computational cost. To address this challenge, we propose a method that adaptively segments data streams and extracts Chebyshev features for piecewise approximation, enabling DTW computation in a low-dimensional space. This approach significantly reduces complexity while preserving accuracy. In addition, we introduce an efficient online matching algorithm that supports incremental DTW computation, eliminating redundant calculations on overlapping subsequences and greatly accelerating processing. Experimental results show that leveraging informative Chebyshev features allows our method to significantly outperform approximation-based DTW baselines and achieve accuracy comparable to the original DTW, with up to a three-order-of-magnitude speedup. The incremental algorithm further surpasses state-of-the-art baselines by up to two orders of magnitude in efficiency.}, archive = {J_ACCESS}, author = {Qinglin Cai and Leiying Chen and Jian Shao and Ling Chen}, doi = {10.1109/ACCESS.2025.3586847}, journal = {IEEE Access}, pages = {119512-119522}, shortjournal = {IEEE Access}, title = {Piecewise dynamic time warping-based subsequence matching in data stream}, volume = {13}, year = {2025}, }
• (2025). Exploring cross-substation transfer learning for improving cybersecurity in IEC 61850 substations. ACCESS, 13, 119500-119511. (www)

  @article{ , abstract = {The information security of IEC 61850-compliant substations is a growing concern for researchers and industry practitioners. IEC 62351, developed to address such concerns, recommends the use of intrusion detection systems (IDSs) as a defense, prompting extensive research on their development, particularly in data-driven approaches. Data-driven IDSs rely on high-quality and comprehensive training data. However, capturing complete datasets for each unique substation at scale is challenging due to the diverse and dynamic operating states between substations. Transfer learning (TL) has been shown to improve model performance in data-scarce environments; however, to the best of our knowledge, no prior work has formulated its use in the context of knowledge transfer between IEC 61850 substations. To address this gap, we propose cross-substation transfer learning (XSTL), a strategy that leverages knowledge transfer between substations that share the same protocol stack but differ in architecture. We demonstrate the value of XSTL using two publicly available datasets collected from substations with contrasting architectures, and show that XSTL can improve IDS performance compared to training IDSs in an isolated manner. Using data from a generic object-oriented substation event (GOOSE) flooding attack, we show that IDS performance is significantly improved in cross-domain tests (using data from two different substations) compared with baseline tests (using data from one substation), with statistical analyses confirming the significance of the improvement. These findings indicate that XSTL can reduce reliance on large datasets, thereby enabling more practical and scalable IDS development across substations where collecting diverse training data is challenging.}, archive = {J_ACCESS}, author = {Filip Natvig and Lars Nordström and Göran N. Ericsson}, doi = {10.1109/ACCESS.2025.3587923}, journal = {IEEE Access}, pages = {119500-119511}, shortjournal = {IEEE Access}, title = {Exploring cross-substation transfer learning for improving cybersecurity in IEC 61850 substations}, volume = {13}, year = {2025}, }
• (2025). A review of the current usage of AI/ML for radio access network (RAN). ACCESS, 13, 119457-119499. (www)

  @article{ , abstract = {Future cellular communication is envisioned to rely on intelligent networks enabled by Artificial Intelligence (AI)/Machine Learning (ML) methods. However, research on AI/ML-based networks is still in its infancy, and existing works do not address the whole process of integrating AI technologies into 5G and beyond networks. This systematic review assesses the current state of AI and ML applications for future wireless networks, focusing on four main topics: task offloading, resource allocation, spectrum management, and channel estimation. This work confirms that research in this area faces numerous challenges, including the lack of reliable datasets, limited information on tools and libraries, hardware impact, and simulation platforms. These factors make it difficult to compare and validate the performance of different AI/ML approaches, impairing their progress into standardized solutions. Additionally, the cost of deploying AI is often overlooked, undermining the credibility of the reported gains. Moreover, current surveys rarely analyze the types of datasets used, the runtime cost of models, or the simulation and ML tools adopted. These omissions hinder reproducibility and obscure the trade-offs between performance and implementation cost. The outcome of this review emphasizes the need for more efforts to address these challenges and advance the field of AI/ML-based network research.}, archive = {J_ACCESS}, author = {Alaa Alzailaa and Julio Camejo Corona and Rafael Teixeira and Hao Ran Chi and Mário Antunes and Ayman Radwan and Rui L. Aguiar}, doi = {10.1109/ACCESS.2025.3586800}, journal = {IEEE Access}, pages = {119457-119499}, shortjournal = {IEEE Access}, title = {A review of the current usage of AI/ML for radio access network (RAN)}, volume = {13}, year = {2025}, }
• (2025). Cache contention aware virtual machine placement and mitigation using adaptive ABC algorithm. ACCESS, 13, 119443-119456. (www)

  @article{ , abstract = {Cache contention in cloud computing can degrade system performance and lead to security vulnerabilities like side-channel attacks. Virtual Machine (VM) migration is a practical solution to mitigate cache contention by relocating affected VMs to optimal Physical Machines (PMs). The proposed Adaptive Artificial Bee Colony (AABC) algorithm for cache contention-aware VM migration improves upon the traditional Artificial Bee Colony (ABC) algorithm by introducing self-adaptive parameter tuning to enhance efficiency. Upon detecting a cache-based side-channel attack, the algorithm identifies the optimal PM for migration, ensuring minimal contention. The migration process is repeated iteratively until the cloud system achieves a contention-free state. The AABC algorithm evaluates metrics such as the total number of misses, computation time, and cache access, providing a comprehensive view of VM performance. By optimizing the migration process with a focus on these critical factors, it ensures minimal computational overhead and optimal memory performance. This innovative approach not only improves system performance by addressing cache contention but also enhances the security of cloud environments. It guarantees reliable VM allocation, providing a robust solution for secure and efficient cloud operations.}, archive = {J_ACCESS}, author = {Bharati Ainapure and Nishu Gupta and Mohammad Derawi}, doi = {10.1109/ACCESS.2025.3583067}, journal = {IEEE Access}, pages = {119443-119456}, shortjournal = {IEEE Access}, title = {Cache contention aware virtual machine placement and mitigation using adaptive ABC algorithm}, volume = {13}, year = {2025}, }
• (2025). Dynamic adjacency matrix learning and multi-order random walk aggregation for alzheimer’s disease diagnosis from resting-state fMRI. ACCESS, 13, 119427-119442. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder, and early diagnosis is critical for timely intervention and slowing down the course of the disease. Functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique, can be used to detect functional brain activity. However, most of the existing graph neural network methods based on static adjacency matrices are difficult to capture individual differences and remote brain region interactions, thus limiting their classification performance and interpretability. In this paper, we propose a Dynamic Multi-Scale Brain Network (DMSBN) model based on resting-state fMRI, which improves the discriminative ability and biological interpretability of the model through dynamic adjacency matrix learning and multistage stochastic wandering aggregation mechanism. The experimental results show that the classification accuracy of DMSBN on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database reaches 96.57%, which is significantly better than the existing baseline methods. In addition, the brain region analysis module identified high contributing brain regions associated with Alzheimer’s disease pathology, providing new insights for early intervention. This study validates the effectiveness of dynamic neighborhood learning and multi-scale feature fusion in brain network analysis, providing a powerful aid for early diagnosis of Alzheimer’s disease.}, archive = {J_ACCESS}, author = {Zhaoyang Guan and Jierui Li and Yuxin Liu and Xianrui Shan}, doi = {10.1109/ACCESS.2025.3587286}, journal = {IEEE Access}, pages = {119427-119442}, shortjournal = {IEEE Access}, title = {Dynamic adjacency matrix learning and multi-order random walk aggregation for alzheimer’s disease diagnosis from resting-state fMRI}, volume = {13}, year = {2025}, }
• (2025). Implementation of a multiple-transmitter RS-OFDM-based OCC system with advanced ByteTrack for mobility environments. ACCESS, 13, 119411-119426. (www)

  @article{ , abstract = {This paper addresses the challenge of achieving reliable, high-speed optical camera communication (OCC) in mobile environments with multiple LED transmitters. A novel RS-OFDM-based OCC system is proposed to improve decoding accuracy, tracking robustness, and throughput under motion and interference. This solution targets applications such as IoT, ITS, and e-health, where the RF spectrum is limited. While rolling-shutter OFDM (RS-OFDM) improves spectral efficiency, it still struggles in mobility scenarios with multiple transmitters. The proposed system incorporates a multiprocessing architecture, YOLOv11n, for real-time LED detection, an advanced ByteTrack with re-identification (ReID), and an extended Kalman filter (EKF) for reliable tracking. An adaptive region of interest (RoI) improves decoding efficiency, while the denoising autoencoder (DAE-RS) mitigates ambient noise. Parallel decoding enables simultaneous message extraction from multiple transmitters within a single frame. Message sequencing and buffer management ensure data integrity through track ID and sequence number alignment. Experiments show high tracking accuracy (MOTA: 0.9861, MOTP: 0.1075), zero ID switches, and effective noise reduction (RMSE: 65.2979). YOLOv11n provides fast inference (2.4 ms) and high detection accuracy (mAP@50: 0.989). The frame rate increases from 11.30 to 16.83 frames per second. The system maintains 5.03 kbps (single) and 4.16 kbps per LED (dual) at 10-4 BER over 4 meters.}, archive = {J_ACCESS}, author = {Herfandi Herfandi and Ones Sanjerico Sitanggang and Muhammad Rangga Aziz Nasution and Ida Bagus Krishna Yoga Utama and Md. Minhazur Rahman and Huy Nguyen and Su Mon Ko and Yeong Min Jang}, doi = {10.1109/ACCESS.2025.3587140}, journal = {IEEE Access}, pages = {119411-119426}, shortjournal = {IEEE Access}, title = {Implementation of a multiple-transmitter RS-OFDM-based OCC system with advanced ByteTrack for mobility environments}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of concept drift mitigation in time-series applications. ACCESS, 13, 119380-119410. (www)

  @article{ , abstract = {Machine Learning (ML) plays a key role in time-series applications because it analyzes observed data and predicts future values. The effectiveness of ML models in time-series forecasting is reduced by the occurrence of Concept Drift (CD). CD refers to continuous changes in the statistical properties of datasets. This affects the predictive performance of ML models. To address this issue, researchers have developed several CD detection and adaptation techniques. However, certain limitations exist, and most studies on CD detection and adaptation in time-series data have focused on classification learning, with minimal attention paid to regression learning. This study conducted a Systematic Literature Review (SLR) that classified, identified, and recommended an optimal method for the detection and adaptation of CD in regression and classification tasks involving time-series data. A systematic search was performed using the SCOPUS, ScienceDirect, IEEE Xplore, Web of Science, MDPI, and ACM databases. Based on the identified records, 60 studies published between 2013 and 2024 were thoroughly surveyed and evaluated using PRISMA guidelines. The findings show that Support Vector Machines (SVM) is the most effective learning algorithms for the detection and adaptation of CD in regression and classification tasks using time-series data. This is possible because of their high detection accuracy and effective memory. Moreover, this SLR presents a roadmap for detecting CDs using Artificial Intelligence (AI)-based learners, along with a comparative analysis of well-known baseline methods. Future work should focus on developing adaptive learning models capable of adapting to CD without explicit re-training, thereby, ensuring optimal performance.}, archive = {J_ACCESS}, author = {Mujaheed Abdullahi and Hitham Alhussian and Norshakirah Aziz and Said Jadid Abdulkadir and Yahia Baashar and Abdussalam Ahmed Alashhab and Afroza Afrin}, doi = {10.1109/ACCESS.2025.3587231}, journal = {IEEE Access}, pages = {119380-119410}, shortjournal = {IEEE Access}, title = {A systematic literature review of concept drift mitigation in time-series applications}, volume = {13}, year = {2025}, }
• (2025). Stakeholder perspectives on microgrid interoperability in energy access. ACCESS, 13, 119362-119379. (www)

  @article{ , abstract = {Interoperability is critical for the sustainability and scalability of decentralized energy systems. It facilitates self-coordination, component replacement, system expansion, upgrades, and integration with other systems, including the main grid. Despite its importance, achieving interoperability in the energy access sector remains a challenge, as stakeholders have diverse perspectives on approaches and strategies to achieve interoperability, creating further complexity. This study explores stakeholder perspectives on how microgrid interoperability can be achieved in the context of energy access, using Indonesia as a case study. To capture the diversity and subjectivity of expert opinions, the study employs the Q methodology, marking the first application of this method to the topic of interoperability. The study advances the theoretical understanding of interoperability in decentralized energy systems by revealing four distinct stakeholder perspectives, namely (1) Harmonization Promoters, (2) Industry Allies, (3) Tech Proponents, and (4) Public Advocates. Although there are some areas of consensus, stakeholders differ in their views on standardization, technical features, and stakeholder roles. These differences highlight the competing priorities that influence decision making on approaches to interoperability that balance technical, regulatory, and market considerations. This study provides practical insights for developing more inclusive and effective interoperability strategies, supported by empirical evidence derived from primary data gathered from relevant stakeholders.}, archive = {J_ACCESS}, author = {Amalia Suryani and Eko Agus Prasetio and Niek Moonen and Jelena Popovic}, doi = {10.1109/ACCESS.2025.3587347}, journal = {IEEE Access}, pages = {119362-119379}, shortjournal = {IEEE Access}, title = {Stakeholder perspectives on microgrid interoperability in energy access}, volume = {13}, year = {2025}, }
• (2025). An IoT-based hygiene monitoring system in the restroom. ACCESS, 13, 119348-119361. (www)

  @article{ , abstract = {Public restroom hygiene is a critical factor influencing public health and user comfort. Traditional maintenance approaches, which rely on manual labour and fixed cleaning schedules, are often inefficient and costly, requiring janitors regardless of actual conditions. This research presents an Internet of Things (IoT)-based restroom hygiene monitoring system designed to optimise cleaning by dispatching staff only when hygiene thresholds are exceeded, particularly during periods of elevated ammonia levels and poor Indoor Air Quality (IAQ). The system enables remote, continuous cleanliness assessment by monitoring ammonia levels and analyzing IAQ through humidity and gas resistance metrics. Alerts are triggered based on correlations among humidity, temperature, and occupancy data, enabling timely interventions. The system utilises the InfluxDB time-series database and the Message Queuing Telemetry Transport (MQTT) protocol over Wi-Fi to support real-time monitoring and long-term trend analysis, providing actionable insights for effective hygiene management. A five-month focused analysis within a two-year monitoring period (January 2023 to December 2024) revealed ammonia concentration spikes ranging from 0 ppm to $13 \times 10^{12}$ ppm. These peaks were linked to sewer issues and cleaning without detergent, while poor IAQ levels (up to $466 \times 10^{3}$ ppm) were associated with excessive use of detergent and air fresheners. Statistical analysis revealed a strong inverse correlation $(r = 0.91)$ between humidity and temperature regarding ammonia, as well as a moderate inverse correlation $(r = 0.45)$ between humidity and gas resistance. Despite 274 users recorded in a single day, effective ventilation was maintained at safe ammonia levels. These findings suggest that overuse of cleaning agents may worsen air quality. The study demonstrates the potential of IoT-enabled systems for responsive, data-driven, and cost-effective restroom hygiene management.}, archive = {J_ACCESS}, author = {F. I. Azman and N. L. Saleh and F. Hashim and A. Sali and A. M. Ali and A. S. M. Noor}, doi = {10.1109/ACCESS.2025.3586117}, journal = {IEEE Access}, pages = {119348-119361}, shortjournal = {IEEE Access}, title = {An IoT-based hygiene monitoring system in the restroom}, volume = {13}, year = {2025}, }
• (2025). Data driven inertia distribution index monitoring using dynamic mode decomposition. ACCESS, 13, 119335-119347. (www)

  @article{ , abstract = {The integration of inertia-less renewable energy sources reduces the overall system inertia and leads to an uneven inertia distribution, which can significantly impact system stability. Uneven inertia distribution amplifies localized oscillations relative to the center of inertia oscillation, making it necessary to assess inertia distribution accurately. This article proposes a novel approach for estimating the inertia distribution of a power system from bus frequency measurements using higher-order dynamic mode decomposition. The proposed methodology identifies major oscillatory modes in the system and quantifies their spatial distribution to provide insights into inertia distribution across the system. The effectiveness of the approach is tested and validated on two different test systems: the IEEE 39-bus system and the Northern Region Power Grid of India. Simulation results demonstrate that the proposed method effectively estimates the inertia distribution, providing valuable information for stability assessment and control in modern power grids.}, archive = {J_ACCESS}, author = {K. K Thoufeer and Manas Kumar Jena}, doi = {10.1109/ACCESS.2025.3587346}, journal = {IEEE Access}, pages = {119335-119347}, shortjournal = {IEEE Access}, title = {Data driven inertia distribution index monitoring using dynamic mode decomposition}, volume = {13}, year = {2025}, }
• (2025). Geometric deep learning for computer-aided design: A survey. ACCESS, 13, 119305-119334. (www)

  @article{ , abstract = {Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making better informed decisions, and create designs that are both innovative and practical. The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities among diverse CAD models, the proposition of alternative designs and enhancements, and even the generation of novel design alternatives. This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories, including similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds, and single/multi-view images. Additionally, it provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain. The final discussion delves into the challenges prevalent in this field, followed by potential future research directions in this rapidly evolving field.}, archive = {J_ACCESS}, author = {Negar Heidari and Alexandros Iosifidis}, doi = {10.1109/ACCESS.2025.3587121}, journal = {IEEE Access}, pages = {119305-119334}, shortjournal = {IEEE Access}, title = {Geometric deep learning for computer-aided design: A survey}, volume = {13}, year = {2025}, }
• (2025). AI veterinary assistance: Enhancing clinical decision-making in animal healthcare. ACCESS, 13, 119292-119304. (www)

  @article{ , abstract = {As the number of households raising companion animals increases, there is a corresponding increase in the demand for veterinary medical services. Furthermore, as companion animals age, the prevalence of chronic disease is increasing, leading companion animal owners to seek more help with long-term health management. However, the shortage of veterinarians and the growing complexity of the consultation process have led to a heavier workload for veterinarians, potentially hindering clinical decision-making efficiency. To address these challenges, we propose AI Veterinary Assistance (AVA), a framework leveraging a large language model. AVA automatically extracts symptoms from consultation records, predicts most probable disease using veterinarian certified disease-symptom database and recommend future consultation question. AVA achieved disease prediction accuracies of 91.4%, 93.4%, and 95.9% for Top-3, Top-5, and Top-10, respectively, along with a symptom extraction accuracy of 79.9% on a consultation records dataset constructed from a veterinarian-certified disease-symptom database. Furthermore, on a real-world dataset, AVA achieved disease prediction accuracies of 38.6%, 43.6%, and 51.5% for Top-3, Top-5, and Top-10, respectively. In both datasets, AVA outperformed baseline methods, demonstrating its effectiveness in supporting clinical decision-making. These results suggest that AVA can help reduce veterinarians’ workload while enhancing the efficiency and reliability of the consultation process.}, archive = {J_ACCESS}, author = {Youn-Gyu Jin and Guixin Wu and Ju-Won Seo and Seong-Jin Park and Sung-Ho Hur and Dinara Aliyeva and Jun-Hyung Park and Kang-Min Kim}, doi = {10.1109/ACCESS.2025.3587787}, journal = {IEEE Access}, pages = {119292-119304}, shortjournal = {IEEE Access}, title = {AI veterinary assistance: Enhancing clinical decision-making in animal healthcare}, volume = {13}, year = {2025}, }
• (2025). Quasi-3D calculation of stray losses in outer lamination stack of power transformer core. ACCESS, 13, 119279-119291. (www)

  @article{ , abstract = {This paper presents a quasi-3D method for calculating stray losses in the outer lamination stacks of a power transformer core. The method uses multiple 2D FEM models in several steps to capture the 3D nature of the problem. This paper builds on a previous study that presented a quasi-3D calculation of the magnetic field on the surface of the outer stack. Here, the focus is on the magnetic field and eddy currents within the stack. The magnetic field penetration is analyzed in two distinct regions. To analyze the first region, located at the stack edges where magnetic field penetration is significant, a magnetostatic 2D FEM model with non-linear and anisotropic material properties is used. This model incorporates the concept of equivalent permeability to capture the impact of eddy currents on the field distribution. The equivalent permeability is derived from a 1D analytical model of a metallic plate subjected to a time-varying magnetic field. For the second region, near the surface of the stack where eddy currents strongly affect the field, a basic 1D analytical model is used. This model describes the field behavior using an exponentially decaying function characterized by the skin depth parameter, and its linearization is based on the results of the previous model. The magnetic field distributions serve as inputs to two additional 2D FEM models used to calculate the distribution of eddy currents. By combining the results from these models, the total stray losses in the stack are obtained. The calculation results are then validated experimentally. The experimental setup consists of an analyzed lamination stack, excitation windings, a U-shaped core, and thermal insulation. Typically, magnetically induced losses are evaluated by comparing losses in a system with and without the analyzed metallic component. However, this conventional approach cannot isolate losses in a single metallic component when other metallic components are present. The presented experimental evaluation, based on temperature measurements, overcomes this limitation. Thermal insulation is used to minimize the losses needed to reach a target temperature range and to improve temperature distribution uniformity, facilitating more accurate average temperature measurements. In all test cases, the deviation between the calculated and experimentally evaluated losses remains within the expanded uncertainty of approximately 7%, corresponding to a 95% confidence level.}, archive = {J_ACCESS}, author = {Zvonimir Jurkovic and Bojan Trkulja and Karlo Petrovic and Mario Jurkovic}, doi = {10.1109/ACCESS.2025.3588066}, journal = {IEEE Access}, pages = {119279-119291}, shortjournal = {IEEE Access}, title = {Quasi-3D calculation of stray losses in outer lamination stack of power transformer core}, volume = {13}, year = {2025}, }
• (2025). Integrating immunotherapy with targeted therapy: Evaluating efficacy in NSCLC treatment through computational analysis of gene regulatory networks. ACCESS, 13, 119262-119278. (www)

  @article{ , abstract = {Non-Small Cell Lung Cancer (NSCLC) remains a leading cause of cancer-related deaths, often characterized by complex mutational landscapes and resistance to monotherapies. To address this, we developed a novel Boolean network (BN) model that integrates oncogenic and immunotherapy signaling pathways to evaluate the efficacy of FDA-approved drug combinations in NSCLC. This computational framework simulates the behavior of key cancer-related pathways under single and multiple mutation scenarios, offering a system-level understanding of drug response. Our model incorporates sixteen targeted inhibitors and simulates their effects on proliferation-promoting and apoptosis-regulating genes. Drug efficacy was quantitatively assessed using a normalized mean size difference (NMSD) metric. Unlike prior models that examine targeted therapy or immunotherapy in isolation, our integrated approach enables systematic evaluation of synergistic effects between these modalities. Key results show that the inclusion of immunotherapy—particularly PD-L1 inhibitors such as Durvalumab—significantly improves therapeutic outcomes, especially in networks with multiple co-occurring mutations. The most effective four-drug combination identified (Durvalumab + Lumakras + Ribociclib + Capivasertib) targets immune evasion, KRAS signaling, cell cycle regulation, and AKT activation, reducing tumor-promoting signals by 89.2% compared to the untreated state. This study provides a theoretical and mechanistic basis for combining immune checkpoint blockade with targeted therapies in NSCLC and demonstrates the utility of BN modeling in optimizing personalized, mutation-specific treatment strategies.}, archive = {J_ACCESS}, author = {Pranabesh Bhattacharjee and Aditya Lahiri and Tanmay Sindhwani and Norman Peter Reeves and Aniruddha Datta}, doi = {10.1109/ACCESS.2025.3586246}, journal = {IEEE Access}, pages = {119262-119278}, shortjournal = {IEEE Access}, title = {Integrating immunotherapy with targeted therapy: Evaluating efficacy in NSCLC treatment through computational analysis of gene regulatory networks}, volume = {13}, year = {2025}, }
• (2025). SGAM toolbox revisited: A standards-based domain-specific modeling language and toolset. ACCESS, 13, 119243-119261. (www)

  @article{ , abstract = {The SGAM Toolbox has established itself as a valuable modeling tool in the energy sector, particularly for interdisciplinary system-of-systems use cases. Built on a domain-specific modeling language that is anchored in European smart grid standardization, the toolbox has been adopted across academia and industry. Drawing on this extensive experience, we introduce the new and improved SGAM Toolbox. First, we review published applications of the tool; it has been widely used for high-level architecture modeling, often alongside other software in areas such as security, privacy, and e-mobility integration. Based on our findings, the key updates for the new toolbox include a strong formal foundation, a clear definition on how the tool should interface with requirements engineering, comprehensive semantics, and a viewpoint structure that segregates logical from technical aspects; these improvements enhance usability and real-world applicability. Second, the updated toolbox is presented in accordance with the TILO language-engineering stack; the specification includes the underlying ontology, a MOF-conformant metamodel, a UML-based implementation, and a modeling add-in for Enterprise Architect for advanced features. Third, we offer a practical demonstration of the toolbox, showing use case–driven architecture modeling. The SGAM Toolbox aims to strengthen its role as a platform for collaborating on system-of-systems use cases, bringing together the diverse stakeholders in smart grid projects.}, archive = {J_ACCESS}, author = {Dominik Vereno and Christian Neureiter and Simon Eschlberger and Mergim Millaku and René Kuchenbuch and Mathias Uslar}, doi = {10.1109/ACCESS.2025.3586722}, journal = {IEEE Access}, pages = {119243-119261}, shortjournal = {IEEE Access}, title = {SGAM toolbox revisited: A standards-based domain-specific modeling language and toolset}, volume = {13}, year = {2025}, }
• (2025). Channel sensing in molecular communications with multiple types of ligands and receptors. ACCESS, 13, 119235-119242. (www)

  @article{ , abstract = {In practical Molecular Communication (MC) environments, the similar binding characteristics among multiple ligands can lead to significant cross-talk, which can be reduced by the concurrent sensing of multiple ligands. This article derives the equilibrium probability of receptor states using the transfer rate matrix to establish the relationship between the number of bound receptors and multiple ligand concentrations. In cases where the number of receptor types exceeds, equals, or is less than that of ligand types, we derived the minimum variance of the concentration estimate by the Cramér-Rao Lower Bound and propose a maximum likelihood estimator for comparison. By analyzing the estimation performance based on normalized variance, we validate the efficacy and stability of our method.}, archive = {J_ACCESS}, author = {Haowei Chen and Xu Bao and Zhuo Sun and Xi Huang}, doi = {10.1109/ACCESS.2025.3586610}, journal = {IEEE Access}, pages = {119235-119242}, shortjournal = {IEEE Access}, title = {Channel sensing in molecular communications with multiple types of ligands and receptors}, volume = {13}, year = {2025}, }
• (2025). Estimating import lead times using business intelligence and machine learning within the CRISP-DM framework: A case study in oil and gas services industry. ACCESS, 13, 119215-119234. (www)

  @article{ , abstract = {This study explores the estimation of import lead times in the oil and gas services industry by combining Business Intelligence (BI) and Machine Learning (ML) within the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework. Addressing the challenge of limited data availability, we employ data synthesis techniques alongside machine learning algorithms to model and predict lead times accurately across diverse import scenarios. The methodology begins with a dual perspective on the problem, encompassing both business and technical requirements, followed by systematic data collection, cleansing, and preparation. BI tools facilitate the visualization of key insights, allowing for enhanced data comprehension and performance tracking. Machine learning models are then applied to predict lead times under varying import conditions, optimizing decision-making in terms of transport mode, entry port, and customs handling. The final solution is deployed through an interactive chatbot interface, streamlining access to lead time estimates for import-export specialists. This integrated approach enables proactive lead time management, cost optimization, and greater operational efficiency within import processes, providing a strategic advantage in the industry.}, archive = {J_ACCESS}, author = {Mohamed Annis Souames and Larbi Abderrahmane Mohammedi and Iskander Zouaghi and Angappa Gunasekaran and Samia Beldjoudi and Abderrazak Laghouag}, doi = {10.1109/ACCESS.2025.3584886}, journal = {IEEE Access}, pages = {119215-119234}, shortjournal = {IEEE Access}, title = {Estimating import lead times using business intelligence and machine learning within the CRISP-DM framework: A case study in oil and gas services industry}, volume = {13}, year = {2025}, }
• (2025). SMCLM: Semantically meaningful causal language modeling for autoregressive paraphrase generation. ACCESS, 13, 119197-119214. (www)

  @article{ , abstract = {This article introduces semantically meaningful causal language modeling (SMCLM), a self-supervised method of training autoregressive models to generate semantically equivalent text. Our approach involves using semantically meaningful text representation as an initial embedding in the autoregressive training and generation processes. The extensive empirical study demonstrates that the SMCLM approach makes autoregressive models capable of learning robust and high-quality paraphrase generation. The proposed method is competitive with the supervised method and achieves state-of-the-art results in unsupervised approaches. This article also presents a comprehensive set of automatic metrics that cover a wide range of autogenerated paraphrase evaluation aspects. Simultaneously, this article highlights the low reliability of the metrics that are widely used in paraphrase generation evaluation, including BLEU, ROUGE, and BERTScore.}, archive = {J_ACCESS}, author = {Michal Perełkiewicz and Sławomir Dadas and Rafał Poświata}, doi = {10.1109/ACCESS.2025.3585679}, journal = {IEEE Access}, pages = {119197-119214}, shortjournal = {IEEE Access}, title = {SMCLM: Semantically meaningful causal language modeling for autoregressive paraphrase generation}, volume = {13}, year = {2025}, }
• (2025). Overcoming data scarcity: Guiding citation function classification with prompt-based few-shot learning. ACCESS, 13, 119188-119196. (www)

  @article{ , abstract = {Citation function analysis is crucial for understanding how cited literature affects the narrative in scientific publications, as citations serve multiple purposes that need accurate distinction and classification. The field faces challenges due to insufficient labeled data and the complexity of defining and categorizing citation functions, requiring specialized knowledge and a deep understanding of scholarly literature. This limitation leads to the imprecise identification and classification of citation functions. To mitigate this challenge, we propose a meta-learning strategy that utilizes prompt learning with pre-trained language models, also known as prompt-based tuning, for the task of few-shot learning in citation function classification. Our findings demonstrate that prompt-based tuning with SciBERT surpasses state-of-the-art pre-trained language models with the conventional fine-tuning approach when labeled data is scarce. Furthermore, we present an analysis that sheds light on the impact of template selection on the prompt-based tuning methodology.}, archive = {J_ACCESS}, author = {Krittin Chatrinan and Thanapon Noraset and Suppawong Tuarob}, doi = {10.1109/ACCESS.2025.3586729}, journal = {IEEE Access}, pages = {119188-119196}, shortjournal = {IEEE Access}, title = {Overcoming data scarcity: Guiding citation function classification with prompt-based few-shot learning}, volume = {13}, year = {2025}, }
• (2025). A novel method for extending dynamic range in time-division-multiplexing linear sagnac systems based on time slot insertion. ACCESS, 13, 119177-119187. (www)

  @article{ , abstract = {The time-division multiplexed linear Sagnac sensing system significantly reduces the cost of interferometric fiber-optic sensor arrays and holds substantial potential for engineering applications. However, as the number of sensors in a time-division multiplexed array and the differences in the lengths of the channels increase, the dynamic range of the system decreases rapidly. This paper presents a method for shortening the pulse period to enhance the system’s dynamic range. It is mathematically proven that designing the pulse period based on the length differences of time-division multiplexed channels can prevent pulse overlap and further reduce the pulse period. The experimental results show that the system’s maximum detectable level increases by 10 dB, while the noise level decreases by 10 dB compared to the traditional method, resulting in a total dynamic range improvement of 20 dB. Moreover, this approach is not limited by the number of channels, which is significant for extending the dynamic range in various time-division multiplexing systems.}, archive = {J_ACCESS}, author = {Kun Jia and Yixiao Ma and Lai Zhang and Xin Lai and Qian Xiao and Bo Jia}, doi = {10.1109/ACCESS.2025.3586047}, journal = {IEEE Access}, pages = {119177-119187}, shortjournal = {IEEE Access}, title = {A novel method for extending dynamic range in time-division-multiplexing linear sagnac systems based on time slot insertion}, volume = {13}, year = {2025}, }
• (2025). Fractional order approach for conical active magnetic bearings under time delay and supply faults. ACCESS, 13, 119157-119176. (www)

  @article{ , abstract = {A conical magnetic bearing system (CAMBs) has emerged as a promising solution for various applications relying on magnetic force, thanks to its unique geometry, which reduces the need for multiple active magnets and reduces energy consumption for supporting maximum loads and lower copper losses. Due to the inherent nonlinearity and coupling characteristics of the 5-DOF CAMBs, a detailed mathematical model as well as the design of a highly accurate control scheme are essential, especially under the influence of external disturbances, unbalance disturbances, and supply faults. The proposed mathematical model integrates both the electrical and mechanical systems, making it subject to mismatched and matched disturbances. Furthermore, the system also accounts for issues such as supply faults, supply saturation, and time delays in both power supplies and sensors. To address these challenges, a cascade control structure for CAMBs is proposed, comprising an outer loop and an inner loop. The outer loop employs fractional order sliding mode control (FOSMC) to regulate the rotor’s displacement and rotational angle. Meanwhile, the inner loop utilizes a fractional-order PID (FOPID) controller to control coil currents, effectively mitigating the influence of eddy currents and the time delay issues associated with power supplies and sensors. Furthermore, disturbances are addressed through a hybrid extended state observer (ESO) structure, where a fast ESO is implemented in the inner loop, while a slow ESO is applied to the outer loop to observe mismatched and matched disturbances. Overall stability of the closed-loop system control is mathematically proven. Finally, the simulation evaluation using particle swarm optimization for CAMBs under disturbances, faults, and time delays demonstrates that the proposed control system achieves effectiveness, feasibility, and robustness.}, archive = {J_ACCESS}, author = {Duc Thinh Le and Danh Giang Nguyen and The Tai Ta and Tung Lam Nguyen and Minh Vu Nhat and Danh Huy Nguyen}, doi = {10.1109/ACCESS.2025.3580420}, journal = {IEEE Access}, pages = {119157-119176}, shortjournal = {IEEE Access}, title = {Fractional order approach for conical active magnetic bearings under time delay and supply faults}, volume = {13}, year = {2025}, }
• (2025). Positive emotion Recognition—A survey of computational models. ACCESS, 13, 119131-119156. (www)

  @article{ , abstract = {In this paper, we provide a systematic survey of works on positive emotion recognition and detection. We queried major research paper databases for 12 different emotional states: admiration, amusement, awe, compassion, contentment, elation, enthusiasm, excitement, gratitude, pride, relief, and sympathy. From an initial pool of more than 800 papers, we selected 81 that propose recognition models, and categorized them according to the modality and data type used for recognition. According to the results, the most frequently occurring labels are amusement and excitement, followed by compassion and contentment. In terms of data type, the highest number of solutions utilize various physiological signals, with visual data being the second most common. As the first survey to focus on a large number of distinct positive emotions, our paper contributes to Affective Computing and, more broadly, to Human-Computer Interaction by demonstrating that: 1) the topic of positive emotion recognition remains largely unexplored, despite numerous potential applications; 2) there are significant shortcomings in the research on positive emotion recognition, including the lack of relevant datasets, contextual information, and adequate data collection procedures. Finally, we provide suggestions and guidelines to support future research in this area.}, archive = {J_ACCESS}, author = {Irem Arici and Radoslaw Niewiadomski}, doi = {10.1109/ACCESS.2025.3585338}, journal = {IEEE Access}, pages = {119131-119156}, shortjournal = {IEEE Access}, title = {Positive emotion Recognition—A survey of computational models}, volume = {13}, year = {2025}, }
• (2025). A robust image encryption scheme based on quantum walks and dynamic DNA for secure cloud applications. ACCESS, 13, 119110-119130. (www)

  @article{ , abstract = {With the rapid development of cloud-based services, ensuring the confidentiality of image data in cloud environments has become increasingly critical. Furthermore, advances in quantum computing pose a significant threat to traditional cryptosystems, including those used in cloud systems, as they may become vulnerable to quantum-based attacks. This paper presents a robust image encryption approach based on integrating quantum walks, a 4-D hyperchaotic system, and dynamic DNA operations to ensure high security. To enhance sensitivity to the plain image, our encryption algorithm initializes the hyperchaotic system with key parameters, generating chaotic sequences that are used to substitute the plain image. It then extracts information from the initially substituted image to update the binary message that controls the operation of quantum walks while simultaneously updating the initial parameters of the hyperchaotic system. These updated parameters operate both the quantum walks and the hyperchaotic system. The probability distribution generated by the quantum walks is integrated with the chaotic sequences of the hyperchaotic system to permutate the DNA-encoded image sequence, perform dynamic DNA operations, and execute dynamic DNA decoding, thereby increasing complexity and enhancing security. Finally, after decoding, the pixel permutation and substitution processes are applied to produce the final cipher image. Experimental analysis demonstrates that the proposed approach achieves high encryption quality and robust resistance to cryptographic attacks, which makes it suitable for secure cloud-based services. The results show an average correlation coefficient of -0.00007, a Chi-square of 254.26013, NPCR of 99.61238%, UACI of 33.45688%, global entropy of 7.99954, local entropy of 7.90219, PSNR of 7.8537, key space of $10^{240}$ , and an encryption speed of 0.1426 megabits per second.}, archive = {J_ACCESS}, author = {Ahmed A. Abd El-Latif and May Almousa and Bassem Abd-El-Atty}, doi = {10.1109/ACCESS.2025.3582705}, journal = {IEEE Access}, pages = {119110-119130}, shortjournal = {IEEE Access}, title = {A robust image encryption scheme based on quantum walks and dynamic DNA for secure cloud applications}, volume = {13}, year = {2025}, }
• (2025). Cancelable multi-branch deep learning framework for privacy-preserving ECG biometric authentication. ACCESS, 13, 119090-119109. (www)

  @article{ , abstract = {Recently, biometric authentication systems have become essential tools for secure and reliable identity verification. Electrocardiogram (ECG) biometrics are particularly effective because of the unique electrical activity patterns of the heart. However, storing and processing raw ECG data raises privacy and security concerns, requiring advanced methods to safeguard user data. This paper presents a novel cancelable ECG biometric authentication framework that integrates a Cancelable Dynamic Biometric Transformation (CDBT) with a multi-branch deep learning architecture to enhance both privacy and performance. The proposed model captures varied ECG feature representations via parallel convolutional branches and transforms them into non-invertible templates using a fixed-key CDBT method. We evaluate the framework on two publicly available ECG datasets (PTB and ECG-ID) achieving high authentication performance with accuracies of 96.25% and 96.43%, respectively, and Equal Error Rates (EER) of 3.55% and 3.6%. The system meets critical privacy-preserving criteria, including irreversibility, unlinkability, diversity, and revocability. In addition, the model demonstrates robustness against spoofing and transformation-based attacks. These results confirm the practicality and scalability of our approach in secure, real-world biometric applications.}, archive = {J_ACCESS}, author = {Mohamed Hammad and Ali Abdullah S. AlQahtani and Mohammed ELAffendi and Abdelhamied A. Ateya and Nebojsa Bacanin}, doi = {10.1109/ACCESS.2025.3585673}, journal = {IEEE Access}, pages = {119090-119109}, shortjournal = {IEEE Access}, title = {Cancelable multi-branch deep learning framework for privacy-preserving ECG biometric authentication}, volume = {13}, year = {2025}, }
• (2025). Geometry meets attention: Interpretable transformers via SVD inspiration. ACCESS, 13, 119077-119089. (www)

  @article{ , abstract = {Self-attention is a cornerstone of modern deep learning, yet its dense dot-product formulation offers limited interpretability and lacks explicit structural constraints. We propose SVD-inspired Attention (SVDA), a novel self-attention mechanism that introduces normalized query/key projections and a learnable diagonal spectral modulation, drawing direct motivation from the structure of Singular Value Decomposition (SVD). This formulation separates directional alignment from spectral emphasis, offering a geometrically grounded and interpretable variant of attention. We formalize SVDA within a standard multi-head Transformer architecture and introduce a suite of structure-aware indicators—such as spectral entropy, effective rank, and selectivity—that quantify interpretability and sparsity in attention dynamics. Our analysis highlights SVDA’s capacity for structured, energy-aware attention without compromising architectural compatibility or expressiveness. This work provides a theoretical foundation and diagnostic framework for structured attention models aimed at interpretability, compression, and semantic transparency.}, archive = {J_ACCESS}, author = {Vasileios Arampatzakis and George Pavlidis and Nikolaos Mitianoudis and Nikos Papamarkos}, doi = {10.1109/ACCESS.2025.3586739}, journal = {IEEE Access}, pages = {119077-119089}, shortjournal = {IEEE Access}, title = {Geometry meets attention: Interpretable transformers via SVD inspiration}, volume = {13}, year = {2025}, }
• (2025). Application of simulated annealing algorithm in the construction of online examination system for tax law courses. ACCESS, 13, 119063-119076. (www)

  @article{ , abstract = {The increasing reliance on online examination systems in tax law education necessitates the development of intelligent frameworks that ensure fairness, syllabus coverage, and difficulty balance. Traditional exam construction methods, such as manual question selection and heuristic approaches, often result in inconsistencies, bias, and suboptimal difficulty distribution. This study proposes a simulated annealing (SA)-based optimization model for dynamically generating balanced online examinations in tax law courses. The SA algorithm efficiently selects exam questions while minimizing Exam Difficulty Variance (EDV), maximizing Syllabus Coverage Ratio (SCR), and reducing computational overhead. Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). Specifically, SA attained the lowest EDV (0.038), the highest SCR (94%), and the fastest execution time (ET) (1.5s), outperforming GA (5.2s) and ACO (3.8s). These findings highlight the effectiveness of SA in generating equitable and adaptive exams, providing a scalable and automated solution for tax law education. Future enhancements include integrating machine learning techniques to refine SA’s parameter tuning and further improving exam personalization.}, archive = {J_ACCESS}, author = {da Pan}, doi = {10.1109/ACCESS.2025.3584214}, journal = {IEEE Access}, pages = {119063-119076}, shortjournal = {IEEE Access}, title = {Application of simulated annealing algorithm in the construction of online examination system for tax law courses}, volume = {13}, year = {2025}, }
• (2025). Direct-driver wind turbine model: A bond graph approach. ACCESS, 13, 119050-119062. (www)

  @article{ , abstract = {This paper presents a comprehensive Bond Graph (BG) model for a Direct-Drive Wind Turbine (DDWT) that captures the energy interactions among aerodynamic, mechanical, and electromechanical subsystems. The model includes the blades, hub, bearings, shaft, and Permanent Magnet Synchronous Generator (PMSG), and is parameterized using real data from a 3 kW horizontal-axis turbine designed for residential applications. A key aspect of the proposed approach is the accurate representation of bearing dynamics within a modular, physically consistent, and multidomain framework. The modelling strategy seeks a balance between physical realism, coherent energy structuring, and computational feasibility. The aerodynamic response was characterized using a realistic blade profile, and the performance of the system was validated through a series of simulations under different operating conditions. The model was designed to support system-level analysis and serves as a foundation for future integration with control strategies, condition monitoring, and fault diagnosis schemes. By integrating components that are often treated in isolation, such as the rotor–bearing interaction, into a unified BG-based structure, this study advances the use of Bond Graphs in the modeling of wind energy conversion systems. The resulting model is reusable, scalable, and suitable for studies involving both transient and steady-state behaviors of small-scale wind turbines.}, archive = {J_ACCESS}, author = {América Torres-Tinoco and Roberto Tapia-Sánchez and Aurelio Medina-Ríos}, doi = {10.1109/ACCESS.2025.3587092}, journal = {IEEE Access}, pages = {119050-119062}, shortjournal = {IEEE Access}, title = {Direct-driver wind turbine model: A bond graph approach}, volume = {13}, year = {2025}, }
• (2025). An optimized FPGA-based FDIR system for sensor fault detection in satellite attitude estimation. ACCESS, 13, 119038-119049. (www)

  @article{ , abstract = {Sensor failure is a major issue in satellite attitude estimations, which widely employs the Inertial Measurement Unit (IMU). The failure of this sensor often causes severe accidents and may lead to mission failures. To solve this problem, a Fault Detection, Isolation, and Recovery (FDIR) was proposed, which integrates an adaptive unscented Kalman filter (AUKF), a radial basis function (RBF) neural network for fault detection, and a QUEST-based estimator. However, the FDIR algorithm is computation-intensive, taking up valuable hardware resources and slowing down processing speed, which presents a challenge for real-time attitude control systems. This paper addresses these challenges by optimizing FDIR hardware implementation through reshaping, parallelism, and pipelining to reduce computational load and latency while enhancing efficiency and processing speed. It also ensures high accuracy and resilience against sensor failures. The proposed design is divided into four stages. These stages are optimized through reshaping, parallelism, and pipeline processing on the FPGA. Compared to the GPU implementation, the FPGA-based FDIR implementation offers faster processing speed and reduces the power consumption while maintaining valid estimation under faulty conditions.}, archive = {J_ACCESS}, author = {Xianliang Chen and Zhicheng Xie and Jiashu Wu and Xiaofeng Wu}, doi = {10.1109/ACCESS.2025.3585415}, journal = {IEEE Access}, pages = {119038-119049}, shortjournal = {IEEE Access}, title = {An optimized FPGA-based FDIR system for sensor fault detection in satellite attitude estimation}, volume = {13}, year = {2025}, }
• (2025). A new minimum spanning tree-based analysis of EEG and MEG with application to classification of alzheimer’s disease. ACCESS, 13, 119023-119037. (www)

  @article{ , abstract = {Cognitive deficits in each brain are likely to be associated with alterations in brain connectivity. Given the vast number of connections, studying and identifying these changes is neither practical nor efficient. Therefore, we use a minimum spanning tree (MST) to address these challenges. We hypothesize that there is a global subgraph for each cognitive disease that consists of the most repeated connections for each group and because of the slow changes of the disease, we call this “disease-specific subgraph”. To validate this model, two datasets are used, a MEG dataset with 80 subjects with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and healthy control (HC), and an EEG dataset recorded from 88 subjects: 36 AD, 23 frontotemporal dementia (FTD), and 29 HC. The weighted phase lag index (wPLI), phase lag index (PLI), and phase locking value (PLV) were employed to assess the functional connectivity and brain graph construction. However, due to less sensitivity to volume conduction and noise, wPLI was utilized to construct the graph and MST matrices. The results and the location of hub nodes showed higher integrated graphs of AD, MCI and FTD which is related to poorer cognitive performance and compatible with physiological observations. Moreover, the characteristics of MST matrices of subjects were used as cognition features and showed high classification rates in comparison with state-of-the-art methods.}, archive = {J_ACCESS}, author = {Hadis Biglari and Reza Boostani and Mehran Yazdi and Saeid Sanei}, doi = {10.1109/ACCESS.2025.3585196}, journal = {IEEE Access}, pages = {119023-119037}, shortjournal = {IEEE Access}, title = {A new minimum spanning tree-based analysis of EEG and MEG with application to classification of alzheimer’s disease}, volume = {13}, year = {2025}, }
• (2025). Cross-domain fake review detection via orthogonal counterfactual representations. ACCESS, 13, 119004-119022. (www)

  @article{ , abstract = {The popularity of online review-based purchases has led many businesses to generate fake reviews. Manual detection of these fakes is a challenging task. Moreover, existing research focuses primarily on single domain. Further, labelled datasets are limited and restricted to a few domains. Hence, detecting fake reviews in less-explored domains is a significant challenge. Cross-domain fake review detection can be used to handle these challenges by using the knowledge from labeled domains to improve detection in less explored domains. For this, a novel framework, COAT, that trains on Counterfactuals generated by the Orthogonal Alignment of BER T-driven mask infillings for cross-domain fake review detection has been proposed in the paper. Working of the proposed COAT is divided in three phases: 1) two-step masking to remove source-specific cues, 2) counterfactual generation using BERT, followed by orthogonal alignment via Procrustes algorithm, and 3) training and prediction using a Bi-LSTM classifier in a zero-shot setting. Experiments are conducted on benchmark datasets of Online Products, Hotels, Yelp Restaurants, Doctors and Restaurant reviews, divided in two groups. Obtained results show that the proposed COAT framework exhibits an enhanced domain transfer with a significant improvement in average accuracy by 17.31% and 26.18%, as well as F1-score by 19.05% and 51.16% on the two groups, respectively, over its counterparts in consideration. This shows that COAT empowers businesses, online platforms, and consumers with more reliable insights and fosters informed decision-making and trust in e-commerce platforms.}, archive = {J_ACCESS}, author = {Richa Gupta and Vinita Jindal and Indu Kashyap}, doi = {10.1109/ACCESS.2025.3584731}, journal = {IEEE Access}, pages = {119004-119022}, shortjournal = {IEEE Access}, title = {Cross-domain fake review detection via orthogonal counterfactual representations}, volume = {13}, year = {2025}, }
• (2025). A review of robot fleet management. ACCESS, 13, 118975-119003. (www)

  @article{ , abstract = {This paper comprehensively reviews Robot Fleet Management (RFM), detailing current practices, challenges, and advancements in this critical domain. With the expansion of automation across industries such as manufacturing, logistics, and healthcare, effective coordination of robot fleets has become pivotal. This review focuses on key aspects such as task allocation, trajectory planning, and communication strategies, identifying prominent applications in industrial and delivery settings while exploring emerging sectors, such as marine operations and hospital environments. Challenges, such as dynamic interaction management and scalability, were examined, emphasizing the practical validation of algorithms and the integration of heterogeneous fleets. The literature was selected following the PRISMA methodology based on studies retrieved from Scopus, Web of Science, and the IEEE Xplore digital library. This study provides actionable insights into RFM methodologies and identifies research gaps, highlighting the need for innovative solutions for scalability, communication robustness, and real-world application testing. The findings contribute significantly to the advancement of autonomous systems and the optimization of logistical operations, offering a foundational resource for academics and practitioners driving innovation in multi-robot systems.}, archive = {J_ACCESS}, author = {Paulo H. C. Morais and Kelen C. T. Vivaldini and Edilson R. R. Kato and Roberto S. Inoue}, doi = {10.1109/ACCESS.2025.3586564}, journal = {IEEE Access}, pages = {118975-119003}, shortjournal = {IEEE Access}, title = {A review of robot fleet management}, volume = {13}, year = {2025}, }
• (2025). EHO-Q-LGAN: An EHO-based Q-learning GAN for the timely diagnosis of diabetic retinopathy. ACCESS, 13, 118959-118974. (www)

  @article{ , abstract = {Diabetic Retinopathy (DR) stands as a significant factor in the prevalence of blindness on a global scale, therefore, the timely identification and precise diagnosis of this condition are imperative for enabling proper management and care. Over the past few years, diverse methods have been employed for automated segmentation and classification of internal retinal components, but achieving high accuracy and efficiency remains a challenging task for clinical scenarios. This research introduces an innovative method for identifying and classifying internal retinal components that combine three advanced models: a dynamic segmentation model, an efficient Elephant Herding Optimization (EHO) Model, and an autoencoder model with Q-Learning Generative Adversarial Network (Q-LGAN) process. The dynamic segmentation model accurately identifies internal retinal components by incorporating advanced methods for image processing such as vessel enhancement, multi-scale analysis, and adaptive thresholding processes. The EHO Model extracts and selects multidimensional features from the segmented images, and the autoencoder model with Q-LGAN optimizes the classification and severity identification process. The proposed approach was evaluated on the EyePACS dataset and achieved efficient results in the context of accuracy, sensitivity, and specificity levels. Compared to existing methods, our approach demonstrated significant improvements in segmentation Accuracy and feature extraction efficiency, as well as improved classification and DR severity identification levels.}, archive = {J_ACCESS}, author = {Maneesha Vadduri and P. Kuppusamy}, doi = {10.1109/ACCESS.2025.3586702}, journal = {IEEE Access}, pages = {118959-118974}, shortjournal = {IEEE Access}, title = {EHO-Q-LGAN: An EHO-based Q-learning GAN for the timely diagnosis of diabetic retinopathy}, volume = {13}, year = {2025}, }
• (2025). Semi-supervised salient object detection for side-scan sonar images via entropy-based uncertainty and contrastive learning. ACCESS, 13, 118944-118958. (www)

  @article{ , abstract = {Recently, the field of salient object detection (SOD) in underwater sonar images has gradually gained attention. Benefiting from the advancements in deep learning technologies and insights from specific tasks, semi-supervised learning has demonstrated promising results for salient object detection in side-scan sonar (SSS) images. However, current methods often face the following challenges: 1) scarce data annotation; 2) complex and dynamically changing underwater environments; 3) difficulty in distinguishing similar target and background features; 4) unclear boundaries with high uncertainty. To address these challenges, we propose a semi-supervised SOD method for SSS image based on entropy uncertainty contrastive learning (EUCL). Our method aims to improve the quality of pseudo-labels and the overall performance of the model using a small amount of labeled data. Specifically, we design an uncertainty map evaluation module that effectively extracts global contextual information through an entropy-based partition loss and a contrastive learning mechanism, integrated with the uncertainty map to handle the complex underwater background. Moreover, we introduce an uncertainty weighting mechanism to enhance the detection performance of salient objects in different regions. Comparative experiments on the underwater scene SSS dataset against seven state-of-the-art semi-supervised methods demonstrate that our method achieves outstanding performance, with the mean absolute error (MAE) of 7.89% and an accuracy (Acc) of 94.69%. Implementation codes will be available on https://github.com/darkseid-arch/EUCL}, archive = {J_ACCESS}, author = {Shenao Yuan and Zhen Wang and Fu-Lin He and Shan-Wen Zhang}, doi = {10.1109/ACCESS.2025.3584647}, journal = {IEEE Access}, pages = {118944-118958}, shortjournal = {IEEE Access}, title = {Semi-supervised salient object detection for side-scan sonar images via entropy-based uncertainty and contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of resource allocation and task offloading strategies in multi-cell dynamic MEC systems using multi-agent DRL. ACCESS, 13, 118933-118943. (www)

  @article{ , abstract = {This paper focuses on minimizing the total energy consumption of a long-term delay-sensitive multi-cell mobile edge computing (MEC) system that serves continuously arriving mobile devices (MDs). The energy consumption minimization is achieved by jointly optimizing the task offloading proportions, transmit power allocations, and computational resource distributions while ensuring the overall deadline constraints and the minimum processing size requirements in each scheduling cycle. The optimization problem is then formulated as a multi-agent Markov decision process (MAMDP) to enable sequential optimization across multiple scheduling cycles. To efficiently solve the formulated problem, we develop a multi-agent deep reinforcement learning (MADRL) algorithm that integrates the actor-critic (AC) framework, the embedding techniques, and the centralized training and decentralized execution (CTDE) framework. Simulation results show that the proposed algorithm converges 14%-23% faster than benchmark methods and significantly outperforms benchmark methods in reducing the total energy consumption under specific constraints by up to 10%.}, archive = {J_ACCESS}, author = {Yuntao Hu and Ming Chen and Haowen Sun and Yinlu Wang and Yihan Cang}, doi = {10.1109/ACCESS.2025.3584471}, journal = {IEEE Access}, pages = {118933-118943}, shortjournal = {IEEE Access}, title = {Joint optimization of resource allocation and task offloading strategies in multi-cell dynamic MEC systems using multi-agent DRL}, volume = {13}, year = {2025}, }
• (2025). Reconstruction of the intrarenal vascular tree from μ-CT scans of corrosion cast specimens. ACCESS, 13, 118912-118932. (www)

  @article{ , abstract = {Reconstructing renal arterial trees from $\mu $ -CT scans of corrosion cast specimens is challenging due to imaging artifacts, noise, and inconsistent material properties that hinder automated analysis. This study aims to develop a robust methodology for transforming $\mu $ -CT data into semantically and topologically meaningful models, enabling accurate morphometric and topological analysis. We propose a three-stage algorithmic pipeline consisting of adaptive binarization tailored to each sample’s characteristics, artifact removal using region labeling and manual trimming, and multi-scale convolution with spherical kernels for smooth reconstruction. The methodology was validated using artificial renal models generated with FractaL-Tree software, 3D printed, $\mu $ -CT scanned, and processed using the proposed pipeline to replicate real-world conditions. Visual inspection confirmed that the algorithm effectively addressed discontinuities and preserved anatomical integrity. Numerical validation, based on ground-truth models, demonstrated high agreement in key geometric parameters such as branching angles and edge counts, with minor discrepancies limited to the smallest vessels. Of the 33 human intrarenal vascular trees, 30 were successfully reconstructed into clean, artifact-free, anatomically realistic models. These reconstructions support advanced structural analysis and have potential applications in anatomical research, disease modeling, and preoperative planning for minimally invasive kidney procedures.}, archive = {J_ACCESS}, author = {Katarzyna Heryan}, doi = {10.1109/ACCESS.2025.3583961}, journal = {IEEE Access}, pages = {118912-118932}, shortjournal = {IEEE Access}, title = {Reconstruction of the intrarenal vascular tree from μ-CT scans of corrosion cast specimens}, volume = {13}, year = {2025}, }
• (2025). Optimizing secure and efficient data aggregation in IoMT using NSGA-II. ACCESS, 13, 118890-118911. (www)

  @article{ , abstract = {The Internet of Medical Things (IoMT) enables real-time healthcare monitoring but faces challenges in energy efficiency, latency, security, and scalability. This paper presents a novel and comprehensive hierarchical data aggregation framework for the Internet of Medical Things (IoMT), optimized using a constrained multi-objective approach. Unlike existing methods that treat objectives in isolation, the model simultaneously minimizes energy consumption, latency, security risks, and authentication overhead under real-world constraints (500 mJ energy, 200 ms latency, 0.9 Sybil detection rate, 0.95 data integrity). Utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), a diverse Pareto front of solutions is generated, allowing adaptability to different healthcare scenarios. The framework is further validated through dual-environment simulations (MATLAB and NS-3) under realistic attack conditions (Sybil, spoofing, and DoS), demonstrating superior performance and robustness compared to centralized and fog-only baselines. For instance, the system achieves up to 43% energy savings and 84% latency reduction. These results illustrate the framework’s practical viability for scalable, secure, and efficient IoMT deployments.}, archive = {J_ACCESS}, author = {Maria Zuraiz and Muhammad Javed and Nasim Abbas and Waseem Abbass and Waqas Nawaz and Ashfaq Hussain Farooqi}, doi = {10.1109/ACCESS.2025.3587016}, journal = {IEEE Access}, pages = {118890-118911}, shortjournal = {IEEE Access}, title = {Optimizing secure and efficient data aggregation in IoMT using NSGA-II}, volume = {13}, year = {2025}, }
• (2025). Optimal planning of wind-based DG: A two-stage approach linking penetration factor, turbine type selection, capacity, and power factors to minimize line losses. ACCESS, 13, 118874-118889. (www)

  @article{ , abstract = {The growing penetration of wind energy into power distribution networks necessitates advanced planning models to ensure efficient integration and improved network operation. This paper proposes an optimization planning model for the optimal sizing and placement of wind distributed generators (DGs) aiming to minimize investment costs while reducing line losses. The proposed model also determines the optimal operating power factor of the DG to minimize line losses and enhance voltage stability. Unlike conventional approaches, the proposed model uniquely captures the complex interdependencies among turbine type selection, capacity factor, and DG sizing, ensuring optimal DG integration. By aligning turbine type selection with the available wind resources at the site, the model maximizes energy production and operational efficiency. Additionally, the model incorporates the relationships between DG penetration factor, optimal corrected power factor, and line loss reduction ratio. By integrating these elements into a unified framework, this holistic approach optimizes the deployment of wind DGs, leading to significant reductions in line losses, improved voltage stability, and minimized investment costs. Traditional techniques often rely on line resistance-based formulas to calculate line losses, which fail to capture this correlation, resulting in a less holistic approach for DG integration. Simulation results and comparisons confirm that our proposed model surpasses comparable methods by optimally balancing the trade-off between DG size (investment cost) and loss reduction, preventing oversizing while enhancing voltage stability. This is achieved through a novel correlation between DG size, loss reduction, and power factor improvement, ensuring a cost-effective and efficient solution using a single-objective function instead of more complex multi-objective approaches. The uncertainty associated with wind DGs, and system demand are addressed in the model using a probabilistic technique. Validation through simulations on IEEE 30-Bus mesh test system and IEEE 33-bus radial test system demonstrates the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Sina Saboktakin and Tarek Medalel Masaud and Ehab F. El-Saadany}, doi = {10.1109/ACCESS.2025.3586993}, journal = {IEEE Access}, pages = {118874-118889}, shortjournal = {IEEE Access}, title = {Optimal planning of wind-based DG: A two-stage approach linking penetration factor, turbine type selection, capacity, and power factors to minimize line losses}, volume = {13}, year = {2025}, }
• (2025). Optimal economic dispatch and load-following strategies for nuclear integrated energy systems. ACCESS, 13, 118857-118873. (www)

  @article{ , abstract = {The need for distributed and adaptable energy resources that can handle the growing unpredictability in both supply and demand is rising as the power system continues to modernize. In order to satisfy those needs and maintain grid resilience, nuclear power plants can dynamically control their output, despite typically being used as baseload generators. By incorporating energy storage and renewable energy sources, nuclear integrated energy systems are designed to satisfy the electrical and thermal demands of different end-user applications while ensuring flexible power operation. These systems generate revenue by participating in both wholesale and ancillary services electricity markets, as well as commodity markets for various byproducts generated from coupled industrial processes. This study addresses the economic dispatch efficiency of a tightly coupled nuclear integrated energy system comprising a gigawatt-scale light water reactor, commercialized in the U.S., a high-temperature steam electrolysis unit, a district heating network, and specified electrical loads. To demonstrate the nuclear power plant’s flexibility within the day-ahead unit commitment and economic dispatch framework, while maintaining equilibrium even during periods of refueling outages, this paper develops a mixed-integer linear programming framework that models the subsystems and components of its nuclear steam supply system. A systematic comparative analysis of flexible versus baseload nuclear power plant operation under varying levels of renewable energy integration indicates that flexible operation enhances system profitability by more than 18% while also increasing energy storage utilization, improving reactor responsiveness to load fluctuations, and allowing for greater participation across numerous electricity markets.}, archive = {J_ACCESS}, author = {Athanasios Ioannis Arvanitidis and Miltiadis Alamaniotis}, doi = {10.1109/ACCESS.2025.3586753}, journal = {IEEE Access}, pages = {118857-118873}, shortjournal = {IEEE Access}, title = {Optimal economic dispatch and load-following strategies for nuclear integrated energy systems}, volume = {13}, year = {2025}, }
• (2025). Interval state feedback control for uncertain systems through regional pole assignment with optimization. ACCESS, 13, 118847-118856. (www)

  @article{ , abstract = {Aiming at the problems of interval expansion and mismatch of uncertainty description in the design of interval state feedback controller (ISFCr), this paper proposes a new design method of ISFCr using the regional pole assignment (RPA). In this method, the interval system uncertainty is adjusted to a new formation, and the controller parameters are expressed with the interval’s center and half-width. Then, the linear matrix inequality (LMI) of RPA is extended, resulting in a set of variable order LMIs. A feasible controller is obtained using the multi-objective particle swarm optimization (MOPSO) algorithm after defining the interval width and the stability indicators. The proposed interval controller inherits the advantages of the state feedback controller based on pole assignment, and provides state feedback control sets that stabilize any parameter of the interval system. The effectiveness of the ISFCr is verified through numerical simulations.}, archive = {J_ACCESS}, author = {Cheng Wang and Shouping Guan and Tianyi Guan}, doi = {10.1109/ACCESS.2025.3587073}, journal = {IEEE Access}, pages = {118847-118856}, shortjournal = {IEEE Access}, title = {Interval state feedback control for uncertain systems through regional pole assignment with optimization}, volume = {13}, year = {2025}, }
• (2025). Self-supervised neural networks for precoding in MIMO rate splitting multiple access systems. ACCESS, 13, 118830-118846. (www)

  @article{ , abstract = {The use of machine learning (ML) tools to address the challenges posed in next generation of wireless communication systems has been gaining significant momentum. In this paper, we investigate the use of self-supervised data-driven schemes for precoder optimization in the downlink of a Multiple-Input Multiple-Output (MIMO) Rate Splitting Multiple Access (RSMA) system. Specifically, we propose two architectures based on Graph Neural Networks (GNN) and the Multi-Layer Perceptron (MLP) respectively, and analyze their achievable sum-rate performance in the underloaded and critically-loaded regime as the system scales up. The intention is to explore several alternatives to conventional iterative precoding benchmarks like Weighted Minimum Mean Square Error (WMMSE) which are computationally intensive algorithms. We evaluate the different precoding policies learnt by the neural network architectures by closely studying the respective radiation patterns. Complementarily, we compare their complexity in terms of the number of trainable parameters and computation time. We also extend the evaluation to non-linear channels in RSMA settings. The study dictates that GNN-based scheme offers an interesting performance-complexity and scalability trade-off.}, archive = {J_ACCESS}, author = {Dheeraj Raja Kumar and Carles Antón-Haro and Xavier Mestre}, doi = {10.1109/ACCESS.2025.3586865}, journal = {IEEE Access}, pages = {118830-118846}, shortjournal = {IEEE Access}, title = {Self-supervised neural networks for precoding in MIMO rate splitting multiple access systems}, volume = {13}, year = {2025}, }
• (2025). TransFINN “Transparent feature integrated neural network for text feature selection and classification”. ACCESS, 13, 118821-118829. (www)

  @article{ , abstract = {This paper introduces TransFINN, a transparent extension artificial neural network that combines transparent feature selection with text classification. TransFINN provides clear insights into which terms most influence its decisions, addressing the “black–box” limitation of conventional artificial neural networks. We evaluate TransFINN on AG News classification dataset and Yahoo! Answers Topic classification dataset, Two topic classification benchmark datasets. Results demonstrate that TransFINN achieves competitive classification accuracy and macro F1-score against baseline models while significantly improving feature-selection transparency. These findings indicate that TransFINN successfully narrows the gap between the prediction capability of artificial neural networks and the explainability needed for applications where the latter becomes paramount. This work opens up new directions for further research to explore how complex topic classification task could be improved, especially on religious data where a greater semantic insight is required.}, archive = {J_ACCESS}, author = {Saif Ur Rehman and Ramsha Jat and Muhammad Rafi and Jaroslav Frnda}, doi = {10.1109/ACCESS.2025.3587075}, journal = {IEEE Access}, pages = {118821-118829}, shortjournal = {IEEE Access}, title = {TransFINN “Transparent feature integrated neural network for text feature selection and classification”}, volume = {13}, year = {2025}, }
• (2025). VConnect: V2V connectivity prediction and independent task offloading framework in vehicular edge computing. ACCESS, 13, 118802-118820. (www)

  @article{ , abstract = {Vehicular Edge Computing enables the utilization of idle resources on vehicles. Task offloading schemes have been widely investigated to satisfy the need for high computational power. In this paper, we propose a novel V2V connectivity prediction and independent task offloading framework for vehicular edge computing, called vConnect. There are three necessary modules for supporting the overall task offloading process: the mobility prediction module, deadline-constraint task partitioning module, and independent task offloading module. This framework aims to maximize the success rate of task offloading within the deadline in urban scenarios. For practicality in the various tasks, the deadline-constraint task partitioning was introduced. Owing to the challenging characteristics of vehicular networks, we introduced a new metric, the so-called V2V connectivity time, which can efficiently represent the duration of uninterrupted communication between vehicles. We introduced the independent task offloading module to select and offload subtasks to multiple optimal candidate vehicles in a one-to-one relationship. We evaluated the performance of our vConnect in both the realistic movement of vehicles in an urban scenario and the real trace file covering the Central Business District (CBD) in Bangkok, Thailand, using OMNeT++, Veins, SUMO, and Python programming. The results show that our vConnect outperforms existing baselines in terms of the success ratio, especially when the required offloading time exceeds 10 s in urban scenarios and 30 s in real-world scenarios. Specifically, our vConnect achieves a 100% success rate in most urban scenarios and up to 94% in the real-world cases, demonstrating its effectiveness in improving task offloading performance.}, archive = {J_ACCESS}, author = {Adsadawut Chanakitkarnchok and Kiattikun Kawila and Kultida Rojviboonchai}, doi = {10.1109/ACCESS.2025.3586281}, journal = {IEEE Access}, pages = {118802-118820}, shortjournal = {IEEE Access}, title = {VConnect: V2V connectivity prediction and independent task offloading framework in vehicular edge computing}, volume = {13}, year = {2025}, }
• (2025). T-type-based dual-active-bridge DC-DC converter with voltage balancing capability for bipolar DC distribution system. ACCESS, 13, 118793-118801. (www)

  @article{ , abstract = {A dual-active-bridge (DAB) converter has been widely used to construct the dc bus voltage of a dc distribution system. However, in the case of a bipolar dc system, conventional DAB converters fail to regulate the bipolar dc bus voltage levels due to the absence of voltage balancing capability. Existing solutions, such as voltage balancers, require additional circuits and bulky balancing inductors, significantly reducing power density and increasing the system’s complexity. To solve this issue, this paper proposes a T-type-based DAB converter that can obtain voltage balancing capability without additional voltage balancing circuits and bulky inductors. An enhanced switching modulation is proposed for the T-type bridge to individually regulate the bipolar output power flows for the voltage balancing capability. Finally, a 2-kW prototype is developed to verify the performance of the proposed converter.}, archive = {J_ACCESS}, author = {Jun Young Lee and Jun-Suk Lee and Jee-Hoon Jung}, doi = {10.1109/ACCESS.2025.3586193}, journal = {IEEE Access}, pages = {118793-118801}, shortjournal = {IEEE Access}, title = {T-type-based dual-active-bridge DC-DC converter with voltage balancing capability for bipolar DC distribution system}, volume = {13}, year = {2025}, }
• (2025). Event-triggered h∞ consensus of leader-follower multi-agent systems under ADT switching topologies. ACCESS, 13, 118782-118792. (www)

  @article{ , abstract = {This paper is concerned with the ${\mathcal {H}}_{\infty }$ control problem of leader-follower multi-agent systems under switching topology. The switching between the topologies is assumed to satisfy an average dwell time constraint. Considering the time delay introduced by sampling, a time-delay error system is established. To conserve bandwidth resources, an event-triggered approach is employed. By constructing a Lyapunov-Krasovskii functional, a sufficient condition for the exponential stability of the switching time-delay error system under average dwell time is provided in the form of Linear matrix inequalities. Then, an ${\mathcal {H}}_{\infty }$ controller is designed to guarantee both exponential stability and ${\mathcal {H}}_{\infty }$ performance of the time-delay error system. Furthermore, the effects of sampling time and the threshold of the event-triggered mechanism on ${\mathcal {H}}_{\infty }$ performance are discussed. Finally, a numerical example demonstrates the effectiveness of the proposed approach.}, archive = {J_ACCESS}, author = {Cheng Fengmin and Liu Liwei and Guo Huina and Fan Yan}, doi = {10.1109/ACCESS.2025.3586015}, journal = {IEEE Access}, pages = {118782-118792}, shortjournal = {IEEE Access}, title = {Event-triggered h∞ consensus of leader-follower multi-agent systems under ADT switching topologies}, volume = {13}, year = {2025}, }
• (2025). Flexible all-optical remote frequency conversion of 5G signals to FR1 and FR2 bands employing an optical comb and multi-core fiber for next-generation C-RAN fronthaul. ACCESS, 13, 118771-118781. (www)

  @article{ , abstract = {The rapid deployment of 5G wireless networks demands efficient, high performance, dynamically reconfigurable solutions for seamless multi-band frequency conversion and connectivity provision. Efficient centralized radio access networks (C-RAN) can benefit from an all-optical signal conversion and remoting employing optical frequency combs and multicore fiber (MCF) as optical source and transmission media respectively. This paper proposes and evaluates experimentally an all-optical frequency conversion approach based on an optical frequency comb (OFC) generated using a dual-drive Mach-Zehnder modulator (DD-MZM) and transmitted through MCF. The proposed method enables seamless frequency up- and down-conversion across sub-6 GHz and millimeter-wave (mm-wave) bands, leveraging optical heterodyning to achieve low-distortion signal transmission in next-generation C-RAN fronthaul implementations. The performance of the system is evaluated in terms of signal-to-noise ratio (SNR) and error vector magnitude (EVM) of the frequency-converted 5G signals. The results demonstrate that frequency-converted replicas exhibit minimal EVM degradation, with values consistently below 10.5%, ensuring compliance with 3GPP 5G NR standards. Additionally, we analyze how the modulation order affects the 5G performance, finding that lower-order orthogonal frequency division multiplexing (OFDM) schemes (e.g., QPSK) maintain robust performance at lower SNRs, while higher-order OFDM schemes (e.g., 64QAM and 256QAM) require higher SNRs for satisfactory performance meeting 3GPP threshold recommendations. Furthermore, we explore the role of multiple modulated optical carriers over the MCF media, showing that increasing the number of modulated carriers improves SNR and reduces the fluctuation of the received EVM. The EVM fluctuation is measured for different configurations, using one or two modulated optical carriers. The maximum access network reach is calculated by measuring the power margin available using the same receiver. Considering the propagation losses and expected crosstalk-induced SNR penalty of a 7-core MCF, a 23.6 km extension could be reached when using two modulated carriers for optical heterodyning compared with a single modulated carrier without frequency conversion. Finally, the impact of power balance of the optical carriers used for optical heterodyning is also evaluated in terms of EVM and SNR of the original and upconverted signals. These findings highlight the potential of MCF networks using an OFC for the transmission of 5G signals as a scalable solution for next-generation C-RAN supporting agile reconfiguration in multiple frequency bands.}, archive = {J_ACCESS}, author = {Vicente Fito and Maria Morant and Roberto Llorente}, doi = {10.1109/ACCESS.2025.3586496}, journal = {IEEE Access}, pages = {118771-118781}, shortjournal = {IEEE Access}, title = {Flexible all-optical remote frequency conversion of 5G signals to FR1 and FR2 bands employing an optical comb and multi-core fiber for next-generation C-RAN fronthaul}, volume = {13}, year = {2025}, }
• (2025). Multi-user task offloading strategy in RIS-aided multi-AP mobile edge computing networks. ACCESS, 13, 118759-118770. (www)

  @article{ , abstract = {The incorporation of RIS into the mobile edge computing (MEC) network can improve the wireless communication environment and enhance the task-offloading capability of the network. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided edge computing-enabled multiuser networks with multiple access points (APs) and investigate the computation offloading strategy of user equipments (UEs). The user association matrix and the RIS reflecting coefficients are jointly optimized to minimize the energy consumption of UEs, subject to the latency constraint and other ones. We formulate it as a mixed integer nonlinear programming (MINLP). To solve the MINLP, we propose a branch and bound (BnB) based method, in which two important links are tackled, i.e., finding the MINLP’s lower bound and updating the incumbent solution. For the first link, the objective function and the latency constraint are relaxed to render the problem more tractable. For the second link, a lemma is first introduced to address the nonconvex phase constraint by establishing the equivalence between the original problem and the transformed problem. Subsequently, we propose an algorithm based on successive convex approximation (SCA), constructing a sequence of convex subproblems. To enhance the feasibility of the algorithm, a subgradient method is further employed to solve each subproblem. The convergence and complexity of the proposed method are analyzed and its effectiveness is demonstrated through simulation results.}, archive = {J_ACCESS}, author = {Wen Zhou and Ling Miao and Dan Deng and Min Hua and Dan Xiang and Chunguo Li}, doi = {10.1109/ACCESS.2025.3585551}, journal = {IEEE Access}, pages = {118759-118770}, shortjournal = {IEEE Access}, title = {Multi-user task offloading strategy in RIS-aided multi-AP mobile edge computing networks}, volume = {13}, year = {2025}, }
• (2025). Chinese legal case similarity matching based on text importance extraction. ACCESS, 13, 118745-118758. (www)

  @article{ , abstract = {Similarity case matching can effectively enhance the efficiency of case adjudication and promote judicial fairness. Recent advances in natural language processing (NLP), particularly those based on deep learning technologies, have significantly enhanced the intelligent development of similar case judgments. The BERT model can efficiently extract features from legal texts by utilizing self-attention mechanisms, thereby facilitating subsequent matching tasks. However, traditional BERT models are often constrained by the input text length. To achieve better comparison results for long case descriptions, an iterative unsupervised clustering method is employed to evaluate the importance of legal case texts during contrastive learning. This results in extracted texts that align more closely with cluster centers in the feature space, thus becoming more representative. Extracting key information and retaining important legal texts can reduce the input length for the BERT model, thereby improving its performance. The selected case statement text is fed into a model based on the BERT framework for similarity case matching. Compared to inputting the original case statement text, the approach proposed in the paper can more effectively retain critical information about the case. The accuracy of our method on the test set is 75.08%, outperforming all existing methods on the public CAIL2019-SCM dataset.}, archive = {J_ACCESS}, author = {Aman Fan and Shaoxi Wang and Yanchuan Wang}, doi = {10.1109/ACCESS.2025.3585265}, journal = {IEEE Access}, pages = {118745-118758}, shortjournal = {IEEE Access}, title = {Chinese legal case similarity matching based on text importance extraction}, volume = {13}, year = {2025}, }
• (2025). Adaptive prescribed-time fault-tolerant control for a class of uncertain nonlinear systems with time-varying sensor and actuator faults. ACCESS, 13, 118733-118744. (www)

  @article{ , abstract = {This paper addresses the prescribed-time fault-tolerant control (PT-FTC) problem for a class of uncertain nonlinear systems in the strict-feedback form, which are subject to completely unknown time-varying virtual control coefficients, uncertain time-varying parameters, and unknown time-varying multiplicative faults in both sensors and actuators. By combining a descending power time-varying feedback technique, a bound estimation approach, and the backstepping design framework, along with employing a Lyapunov function that incorporates lower bounds of the virtual control coefficients, a novel adaptive PT fault compensation control strategy is proposed. Under the proposed scheme, the system states can converge to zero within an arbitrarily predefined finite time and remain there thereafter, while the control input remains continuous and bounded throughout the entire time interval, despite the presence of the time-varying sensor and actuator faults. Finally, simulation results are provided to validate the effectiveness of the proposed algorithm.}, archive = {J_ACCESS}, author = {Chuixi Wei and Dandan Zhang}, doi = {10.1109/ACCESS.2025.3586225}, journal = {IEEE Access}, pages = {118733-118744}, shortjournal = {IEEE Access}, title = {Adaptive prescribed-time fault-tolerant control for a class of uncertain nonlinear systems with time-varying sensor and actuator faults}, volume = {13}, year = {2025}, }
• (2025). Cluster analysis of RF-EMF exposure to detect time patterns in urban environment: A model-based approach. ACCESS, 13, 118724-118732. (www)

  @article{ , abstract = {The increase in human exposure to electromagnetic fields (EMFs), driven by advancements in telecommunication systems like the 5G mobile system, highlights the need for continuous EMF monitoring. Advanced techniques for data analysis, based on machine learning like clustering, can decompose daily variations in EMF exposure into distinct patterns, providing a clearer understanding of how exposure fluctuates over time. Although several exposure monitoring systems exist in Europe, only a few studies have thoroughly examined the time variability. This study addresses the gap by applying model-based clustering techniques to analyze the temporal patterns. Specifically, the study focuses on characterizing fluctuations in field strength during workdays and holidays, thereby contributing to a deeper understanding of time-distributed exposure. Continuous monitoring data, collected through the Serbian EMF RATEL network’s sensors installed in Novi Sad, were processed and analyzed using the Log-Normal Mixture Model (LNMM), a model-based clustering algorithm resorting to mixture distributions. The analyses reveal that the LNMM can separate night and day exposure values and identify periods when values persist longer over the day. This suggests that model-based clustering can be useful for understanding the temporal patterns of local EMF exposure.}, archive = {J_ACCESS}, author = {Nicola Pasquino and Nunzia Solmonte and Nikola Djuric and Dragan Kljajic and Snezana Djuric}, doi = {10.1109/ACCESS.2025.3586905}, journal = {IEEE Access}, pages = {118724-118732}, shortjournal = {IEEE Access}, title = {Cluster analysis of RF-EMF exposure to detect time patterns in urban environment: A model-based approach}, volume = {13}, year = {2025}, }
• (2025). Enhancing consumer insights through multimodal artificial intelligence and affective computing. ACCESS, 13, 118706-118723. (www)

  @article{ , abstract = {The growing interest in learning more about consumer behaviors through analytical techniques requires the integration of innovative approaches that relate their needs to strategic marketing procedures. Multimodality and Affective Computing combined a series of robust optimizations for this challenge, implying the complexity of each application. However, the entanglement of different modalities demands new and tailored refinements to enhance adaptability and accuracy in the field. This paper outlines the implementation of a Multimodal Artificial Intelligence methodology with Affective Computing to enhance consumer insights and marketing strategies. The application combines different data modalities, such as textual, visual, and audio inputs, to tackle complex issues in dealing with consumer sentiment. The proposed approach uses advanced preprocessing techniques, including word embeddings, neural networks, and recurrent models, to extract information from diverse modalities. Fusion strategies, such as attention-based and late fusion procedures, are utilized to combine knowledge, facilitating robust sentiment detection. The implementation includes the analysis of real-time customer feedback on social media and product assessments, demonstrating improvements in predicting engagement and shaping consumer behavior. The results underscore the practical viability of the suggested method, promoting progress in multimodal sentiment analysis to extract actionable consumer insights in marketing.}, archive = {J_ACCESS}, author = {Inês César and Ivo Pereira and Fátima Rodrigues and Vera L. Miguéis and Susana Nicola and Ana Madureira and José Luís Reis and José Paulo Marques Dos Santos and Duarte Coelho and Daniel Alves De Oliveira}, doi = {10.1109/ACCESS.2025.3586713}, journal = {IEEE Access}, pages = {118706-118723}, shortjournal = {IEEE Access}, title = {Enhancing consumer insights through multimodal artificial intelligence and affective computing}, volume = {13}, year = {2025}, }
• (2025). A deep learning framework for healthy lifestyle monitoring and outdoor localization. ACCESS, 13, 118695-118705. (www)

  @article{ , abstract = {The green research field of ubiquitous computing has been able to draw and hold academics’ interest for a while. Recognition and localization of human locomotion have also been widely developed as ubiquitous computing applications. Personal safety, behavior analysis, entertainment, and healthcare monitoring all utilize these apps. A key component of several fields, such as robots, sports, healthcare, and security, is human locomotion recognition (HLR). Researchers and engineers have been trying to use the increasing popularity of wearable technology, especially environmental sensors and Inertial Measurement Units (IMUs), to identify and categorize human locomotion activities in an accurate and efficient manner. The capabilities of smartphones and wearable technology have increased due to advancements in sensing technology. Inertial sensors like gyroscopes and accelerometers are now frequently seen in smartphones. These sensors can now be utilized for a wide range of purposes, while their original purpose was to improve gadget features. Using smartphone IMU, ambient, GPS, and audio sensor data from two publicly available benchmark datasets—the Extrasensory dataset and the Domino dataset—this study proposes a sophisticated approach for human locomotion and localization detection. In the preprocessing stage, a Chebyshev Type 2 filter was used for windowing and segmentation, while a Hamming window was applied. Feature extraction was divided into two parts: for actions, the extracted features included Fast Fourier Transform (FFT), State Space Correlation Entropy (SSCE), Maximum Lyapunov Exponent (MLE), and Auto Regression; for localization-based features, Recursive Feature Elimination (RFE), step count, heading angle, and step length were employed. Kernel Fisher Discriminant Analysis was applied for feature optimization, and a deep neural network was utilized for feature classification. The proposed system achieved an overall classification accuracy of 88.4% on the Extrasensory dataset and 86.4% on the Domino dataset, outperforming several existing state-of-the-art methods. These results highlight the effectiveness of the preprocessing pipeline and feature optimization techniques in enhancing recognition and localization performance. The experimental evaluation confirms the robustness of the system across diverse activities and environments, making it suitable for real-world ubiquitous computing applications.}, archive = {J_ACCESS}, author = {Mehrab Rafiq and Naif S. Alshammari and Haifa F. Alhasson and Dina Abdulaziz Alhammadi and Mohammed Alshehri and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3573439}, journal = {IEEE Access}, pages = {118695-118705}, shortjournal = {IEEE Access}, title = {A deep learning framework for healthy lifestyle monitoring and outdoor localization}, volume = {13}, year = {2025}, }
• (2025). Human-in-the-loop reinforcement learning to track excavation paths for a hydraulic excavator in a short period of time. ACCESS, 13, 118678-118694. (www)

  @article{ , abstract = {The construction industry faces a severe shortage of skilled operators. One promising approach to mitigate this problem is to automate construction machinery, particularly hydraulic excavators, which have a wide range of applications. However, it is challenging to automate hydraulic excavators due to the difficulty of modeling their nonlinear dynamics and soil interactions, which makes difficult to design a controller that tracks excavation paths precisely and quickly. Reinforcement learning (RL) is promising solution because it can learn to track excavation paths in a purely data-driven way, but simply applying RL may lead to an impractically long data collection time due to its low data efficiency. Moreover, since controlling the hydraulic excavator is a safety-critical task, the safety of RL is also a considerable challenge. In light of this background, we propose a path-tracking controller based on a human-in-the-loop RL system. While some human intervention is required, we can collect data safely and efficiently. Our experimental results demonstrate that the path-tracking controller assisted by a skilled human operator learned to excavate gravelly, sandy, and cobbly soils in 12, 18, and 21 minutes of data collection, respectively, and dispensed with human assistance afterward. Moreover, the results of safety analysis experiments show that the trained controller succeeded in performing 128 successive excavations (38 minutes) without any safety violation or human assistance.}, archive = {J_ACCESS}, author = {Tatsuya Mori and Tatsuya Yoshimoto and Hiroshi Yoshida}, doi = {10.1109/ACCESS.2025.3585182}, journal = {IEEE Access}, pages = {118678-118694}, shortjournal = {IEEE Access}, title = {Human-in-the-loop reinforcement learning to track excavation paths for a hydraulic excavator in a short period of time}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches for malware detection: A comprehensive review of techniques, challenges, and future directions. ACCESS, 13, 118652-118677. (www)

  @article{ , abstract = {Malware remains a persistent and evolving cybersecurity threat, necessitating advanced detection techniques to counter increasingly sophisticated attacks. This survey provides a comprehensive review of deep learning-based approaches for malware detection, synthesizing 109 publications published between 2011 and 2024. We analyze models based on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid CNN-RNN architectures, evaluating their performance across publicly available datasets, including BIG2015, Malimg, Drebin, Malicia, and IoT-23. Reported detection accuracies range from 93% to 98.7%, with false positive rates as low as 1.1% in hybrid models. This survey contributes a structured taxonomy diagram categorizing models by architecture and analysis type, an ablation study summarizing individual and combined contributions of CNN and RNN components, and a comparative evaluation of key performance metrics. We further discuss current challenges, such as adversarial robustness and computational complexity, and propose future research directions to guide ongoing advancements in deep learning-based malware detection.}, archive = {J_ACCESS}, author = {Mohammad Alshoulie and Abid Mehmood}, doi = {10.1109/ACCESS.2025.3582875}, journal = {IEEE Access}, pages = {118652-118677}, shortjournal = {IEEE Access}, title = {Deep learning approaches for malware detection: A comprehensive review of techniques, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). A comparative review on superdirectivite antenna systems in modern literature. ACCESS, 13, 118636-118651. (www)

  @article{ , abstract = {Superdirectivity describes the phenomenon in which an antenna system exceeds its maximum theoretical directivity. Originally developed theoretically in the twentieth century, superdirectivity has regained attention due to the increasing demand for high-gain electrically small antenna systems. This review offers a foundational overview of basic concepts and reviews recent advancements in superdirective antenna systems research. It categorizes recent studies based on their approaches to various design challenges including radiation efficiency, bandwidth, and practical realized gain. The progression from classically fed arrays with optimized excitation currents to strategic reactive loading of parasitic arrays is discussed, along with emerging approaches that favor supergain over classical superdirectivity through load-less or geometrically optimized designs. The use of computational techniques such as spherical wave expansion, network characteristic mode analysis, and machine learning is highlighted. A comparative analysis of performance metrics, including gain, efficiency, and fractional bandwidth, is presented. Despite significant advancements from impractical theoretical concepts in classical research, recent performance improvements are reaching superdirectivity limits often involve trade-offs to array size or bandwidth. This review aims to serve as a resource for researchers by summarizing progress, highlighting gaps, and suggesting future directions.}, archive = {J_ACCESS}, author = {Monica Wasfy William and Yahia M. M. Antar and Alois P. Freundorfer}, doi = {10.1109/ACCESS.2025.3587178}, journal = {IEEE Access}, pages = {118636-118651}, shortjournal = {IEEE Access}, title = {A comparative review on superdirectivite antenna systems in modern literature}, volume = {13}, year = {2025}, }
• (2025). Deep learning for vehicle deceleration detection using UWB CIR and doppler shift. ACCESS, 13, 118625-118635. (www)

  @article{ , abstract = {Traffic accidents remain a significant challenge in road safety systems, highlighting the need for real-time vehicle motion monitoring technologies. Traditional vehicle-tracking systems, which primarily rely on the global positioning system and inertial measurement unit sensors, face limitations in GPS-denied environments such as tunnels and underground parking. To address these challenges, this study proposes a novel vehicle deceleration classification framework based on Ultra-Wideband (UWB) Channel Impulse Response (CIR) analysis and Doppler Frequency Shift (DFS) analysis. A parallel model architecture is developed to integrate CIR time-domain features, time-frequency representations, and DFS-based velocity estimations. The proposed model was evaluated using real-world driving data collected in tunnel environments. It achieved a classification accuracy of 98.10%, outperforming baseline models using only CIR or IMU data. The experimental results demonstrate that DFS features significantly enhance classification robustness, particularly under strong multipath conditions. These findings validate the feasibility of UWB CIR as a real-time deceleration detection modality for intelligent transportation systems, even in GPS-denied environments.}, archive = {J_ACCESS}, author = {Kyungbo Lee and Jiwoong Park and Young-Bae Ko}, doi = {10.1109/ACCESS.2025.3586445}, journal = {IEEE Access}, pages = {118625-118635}, shortjournal = {IEEE Access}, title = {Deep learning for vehicle deceleration detection using UWB CIR and doppler shift}, volume = {13}, year = {2025}, }
• (2025). Maximum torque per ampere control of PMSMs using real-time numerical optimization. ACCESS, 13, 118611-118624. (www)

  @article{ , abstract = {This paper proposes a novel online maximum torque per ampere (MTPA) control method for permanent magnet synchronous motors (PMSMs) that addresses nonlinearities such as magnetic saturation and cross-coupling effects. A flux-linkage based nonlinear PMSM model was developed to improve the torque control under dynamic conditions. The proposed gradient projection descent (GPD) method was introduced as a computationally efficient solution that is specifically tailored for real-time applications. It is evaluated alongside the Levenberg-Marquardt algorithm (LMA), which is renowned for its high accuracy and rapid convergence. Compared to the LMA method, the proposed GPD method achieves an optimal balance between computational efficiency and control accuracy, making it suitable for embedded systems with limited resources. Notably, the method can support online experimental characterization and online parameter estimation to capture real operating conditions, such as temperature and load variations, thereby providing a significant advantage for dynamic PMSM applications. Extensive simulations and experimental validations using a 1.3 kW interior PMSM prototype and TMS320F28375S control platform demonstrate the effectiveness of the proposed methods. The results demonstrate that GPD ensures real-time feasibility while maintaining acceptable levels of torque accuracy. These findings highlight the potential of GPD as a practical solution for high-performance motor control applications, especially in resource-limited embedded systems. Future work should explore online characterization and estimation for enhanced adaptability.}, archive = {J_ACCESS}, author = {Yong Xu and Min-Yi Lin and Wen-Jie Wan and Yunchong Wang and Jian-Xin Shen}, doi = {10.1109/ACCESS.2025.3586854}, journal = {IEEE Access}, pages = {118611-118624}, shortjournal = {IEEE Access}, title = {Maximum torque per ampere control of PMSMs using real-time numerical optimization}, volume = {13}, year = {2025}, }
• (2025). A novel vehicle lateral velocity observer with adaptive feedback gain via deep learning. ACCESS, 13, 118599-118610. (www)

  @article{ , abstract = {The lateral velocity of a vehicle is crucial for effective lateral control. However, unlike yaw rate signal, there is a lack of reliable sensors that are affordable enough to be applied in mass production for directly measuring lateral velocity. As a result, numerous studies have focused on estimating lateral velocity through various methods. In this study, we propose a novel approach that combines a vehicle lateral dynamics model with lateral acceleration kinematic models, utilizing an AI-powered state observer. Whereas many previous studies have relied on empirical tuning by domain experts to determine the fusion weights of these models, our research takes a data-driven approach through artificial intelligence methods. Specifically, we designed a new method to calculate the true values of observer feedback gains, which act as appropriate model weights depending on the driving situation. Based on these ground truth values, we developed an AI model capable of predicting optimal observer feedback gains through supervised learning techniques. Finally, verification using both training and test scenario data demonstrated that the lateral velocity estimates from the state observer, powered by AI-generated feedback gains, were significantly more accurate than those obtained using gains designed by domain experts.}, archive = {J_ACCESS}, author = {Minchang Kwak and Wanki Cho and Seung-Han You}, doi = {10.1109/ACCESS.2025.3583942}, journal = {IEEE Access}, pages = {118599-118610}, shortjournal = {IEEE Access}, title = {A novel vehicle lateral velocity observer with adaptive feedback gain via deep learning}, volume = {13}, year = {2025}, }
• (2025). Efficient joint transmit and receive beam alignment via sequential CNN LSTM networks. ACCESS, 13, 118586-118598. (www)

  @article{ , abstract = {This paper introduces a deep learning-assisted joint transmit and receive beam tracking approach for uplink multiple-input multiple-output (MIMO) communication over millimeter wave (mmWave) channels. In current wireless networks, beam alignment between transmitter and receiver is necessary to guarantee a strong and stable connection. However, to prevent misalignment due to user equipment (UE) mobility, frequent beam training is required, resulting in significant training overhead. To address this issue at the minimum performance degradation, a sequential convolutional neural network (CNN)–long short-term memory (LSTM) network is presented to capture the spatio-temporal correlation of the channel associated with UE movements. To further enhance the achievable rate, a deep learning model for codebook-free receive beam design is presented, which is capable of achieving the optimum maximum ratio combining (MRC) method without instantaneous channel state information (CSI) knowledge. Simulation results demonstrate that the proposed deep learning-based beam tracking methods are superior to traditional beam tracking algorithms in terms of beamforming gain and effective spectral efficiency.}, archive = {J_ACCESS}, author = {Takumi Yoshida and Koji Ishibashi and Hiroki Iimori and Paulo Valente Klaine and Szabolcs Malomsoky}, doi = {10.1109/ACCESS.2025.3584331}, journal = {IEEE Access}, pages = {118586-118598}, shortjournal = {IEEE Access}, title = {Efficient joint transmit and receive beam alignment via sequential CNN LSTM networks}, volume = {13}, year = {2025}, }
• (2025). Analysis of cogging torque ripple based on multiple square-wave radial superposition MMF for wound-field synchronous motors. ACCESS, 13, 118574-118585. (www)

  @article{ , abstract = {Bilateral slotting restricts the application of wound-field excitation synchronous motors in underwater vehicles, which focus on vibrations. There are few studies on the wound rotor, and the theory of permanent magnet motors is not entirely applicable. To avoid the separation of a strong coupling relationship between rotor slots and magnetomotive force (MMF) and the computational complexity of the bilateral slotting, the effect of the whole rotor is equivalent to the superposition of multiple square-wave MMF with different pole arc coefficients along the radial direction of the air gap. First, based on the energy method, the analytical expression of cogging torque ripple under the square wave MMF is derived, and the source and weakening method of cogging torque ripple are clarified. Then, the relationship between the cogging torque ripple and the structural parameters of the distributed salient-pole wound rotor is analyzed. The design method of the structure to weaken the cogging torque ripple is analyzed. Based on the finite element model, the cogging torque ripple, key electromagnetic performance, and vibration spectrum of the machine before and after optimization are compared. Finally, a prototype is built to verify its correctness and effectiveness.}, archive = {J_ACCESS}, author = {Tingting Hou and Jiakuan Xia and Jin Chen and Meijun Qi}, doi = {10.1109/ACCESS.2025.3585917}, journal = {IEEE Access}, pages = {118574-118585}, shortjournal = {IEEE Access}, title = {Analysis of cogging torque ripple based on multiple square-wave radial superposition MMF for wound-field synchronous motors}, volume = {13}, year = {2025}, }
• (2025). Multi-modal dynamic fusion for defect detection in electronic products: A novel approach based on energy and deep learning. ACCESS, 13, 118565-118573. (www)

  @article{ , abstract = {As electronic products continue to evolve in complexity, maintaining stringent quality standards during manufacturing presents mounting challenges. Conventional defect detection approaches, which typically depend on a single modality, often fall short in both efficiency and reliability. To address these shortcomings, this study introduces a dynamic multi-modal fusion framework that leverages data from sensors, visual imagery, and component attributes to enhance detection performance. Specifically, Transformer architectures are employed for sensor data analysis, Convolutional Neural Networks (CNNs) are applied to process image data, and Multi-Layer Perceptrons (MLPs) are used to represent part-level features. A distinguishing element of this approach is an energy-based late-stage fusion mechanism that adaptively modulates each modality’s influence according to its uncertainty level. Empirical evaluations demonstrate that the proposed model achieves superior results across multiple performance metrics—including accuracy, precision, recall, and F1 score—compared to conventional and unimodal systems. These findings underscore the model’s potential in advancing practical defect detection and quality assurance in manufacturing environments.}, archive = {J_ACCESS}, author = {Yulin Liu and Yang Gao}, doi = {10.1109/ACCESS.2025.3584551}, journal = {IEEE Access}, pages = {118565-118573}, shortjournal = {IEEE Access}, title = {Multi-modal dynamic fusion for defect detection in electronic products: A novel approach based on energy and deep learning}, volume = {13}, year = {2025}, }
• (2025). Vibration control and performance analysis of a 3DOF high-payload flexible link manipulator. ACCESS, 13, 118547-118564. (www)

  @article{ , abstract = {Flexible link manipulators (FLMs) are often praised for their potential advantages in industrial settings, such as being more lightweight, energy-efficient, and allowing higher operation speeds with smaller actuators. Many studies make unrealistic assumptions, like simplistic links, small deflections, low payloads, limited degrees of freedom, and well-defined mode shapes, which limit their real-world applicability. This paper investigates the performance of a flexible link manipulator with three degrees of freedom, designed for high payload capabilities and characterized by complex link shapes and complex vibration modes. An advanced control methodology was developed following the identification of the parameter-varying flexible link manipulator setup to manage the manipulator’s vibrations effectively. Comparative experiments were conducted between the flexible link manipulator and a conventional rigid link manipulator, assessing performance metrics such as repeatability, overshoot, settling time, torque and energy consumption. The results show that the flexible link manipulator, equipped with the advanced control system, achieves a repeatability of 0.14 mm, comparable to that of commercially available rigid robots. Moreover, energy consumption is reduced even with the implementation of active vibration suppression. The study highlights the potential of flexible link manipulators for real-world applications.}, archive = {J_ACCESS}, author = {Thierry Hubert and Amin Khorasani and Muhammad Usman and Hafsa Nouhi and Raphaël Furnémont and John Lataire and Bram Vanderborght and Greet van de Perre and Tom Verstraten}, doi = {10.1109/ACCESS.2025.3582429}, journal = {IEEE Access}, pages = {118547-118564}, shortjournal = {IEEE Access}, title = {Vibration control and performance analysis of a 3DOF high-payload flexible link manipulator}, volume = {13}, year = {2025}, }
• (2025). Effects of visual feedback arrangement on ballet balance training. ACCESS, 13, 118536-118546. (www)

  @article{ , abstract = {Providing real-time visual feedback during standing balance training has been shown to enhance balance ability. For example, displaying images of the trainee’s body or center-of-pressure (COP) trajectories while maintaining a standing posture facilitates the detection of postural sway, thereby improving balance. However, it remains unclear whether the spatial arrangement of such visual feedback influences the degree of balance improvement. Therefore, this study examined whether varying the display configuration of identical visual feedback affects balance enhancement. Specifically, we employed two fundamental ballet movements, “relevé” and “plié,” to investigate how different arrangements of body images and COP trajectories influence balance. Experimental results revealed that: (1) when a single visual feedback element was provided, placing it at eye level in front led to greater improvements in balance, and (2) when two visual feedback elements were presented simultaneously, arranging them side by side or overlapping yielded better balance outcomes. These findings highlight the significance of considering visual feedback arrangement to optimize balance enhancement.}, archive = {J_ACCESS}, author = {Hana Mizukami and Arinobu Niijima and Chanho Park and Takefumi Ogawa}, doi = {10.1109/ACCESS.2025.3586920}, journal = {IEEE Access}, pages = {118536-118546}, shortjournal = {IEEE Access}, title = {Effects of visual feedback arrangement on ballet balance training}, volume = {13}, year = {2025}, }
• (2025). Federated learning for all: A reinforcement learning-based approach for ensuring fairness in client selection. ACCESS, 13, 118515-118535. (www)

  @article{ , abstract = {In federated learning, selecting participating devices (clients) is critical due to their inherent diversity. Clients typically hold non-IID data and possess varying computational and communication capabilities, which introduces heterogeneity that can impact overall system performance. Ignoring this heterogeneity may result in underutilization of valuable resources. This paper introduces FairFedDRL, a fair client selection approach based on Double Deep Q-learning, designed to ensure equitable participation opportunities for all clients, thereby enhancing both fairness and performance. The method integrates data and system heterogeneity, along with selection history, into the reward function, and leverages Jain’s fairness index to balance selection frequency with client diversity. This ensures all clients have fair opportunities to participate, leading to more balanced and effective learning. Experiments conducted on three benchmark datasets—MNIST, CIFAR-10, and CINIC-10—utilize variance, Gini coefficient, Jain’s fairness index, and entropy as fairness metrics, with accuracy used to assess performance. Results show that FairFedDRL improves the Gini coefficient by 44.28% and Jain’s fairness index by 21.91% on MNIST, and by 23.44% and 7.42%, respectively, on CIFAR-10, and 67.4% and 53.47% on CINIC-10 alongside noticeable gains in overall model performance.}, archive = {J_ACCESS}, author = {Saeedeh Ghazi and Saeed Farzi and Amirhossein Nikoofard}, doi = {10.1109/ACCESS.2025.3586943}, journal = {IEEE Access}, pages = {118515-118535}, shortjournal = {IEEE Access}, title = {Federated learning for all: A reinforcement learning-based approach for ensuring fairness in client selection}, volume = {13}, year = {2025}, }
• (2025). Multistage convolutional neural network with deformable attention for word-level offline text-independent writer identification. ACCESS, 13, 118504-118514. (www)

  @article{ , abstract = {Offline writer identification is an essential component of biometric and forensic security fields, focusing on identifying the authors of handwritten documents in physical form. Historically, this approach has focused on text-dependent identification using page-level document images containing multiple paragraphs and sentences. However, text-independent identification using a limited number of word-level text images still faces performance challenges in real-world scenarios, especially when relying on traditional handcrafted features. To address these issues, this study improves the generalization and interpretative capabilities in word-level offline text-independent writer identification by introducing a novel multistage convolutional neural network designed as an end-to-end model. We then improve the model’s ability to interpret and classify handwriting effectively by incorporating the insights of forensic document examiners through the use of a deformable attention module. The experimental validation on four public datasets, namely, IAM, CVL, Firemaker, and CERUG-EN, confirmed the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Manabu Okawa}, doi = {10.1109/ACCESS.2025.3586994}, journal = {IEEE Access}, pages = {118504-118514}, shortjournal = {IEEE Access}, title = {Multistage convolutional neural network with deformable attention for word-level offline text-independent writer identification}, volume = {13}, year = {2025}, }
• (2025). FSSA: A fully serial and mixed-precision systolic array for vision transformer acceleration on edge. ACCESS, 13, 118490-118503. (www)

  @article{ , abstract = {Transformers have achieved remarkable success, often surpassing human performance in specific tasks. However, this noteworthy capability comes at the cost of increasing complexity, posing significant challenges when deploying on resource-constrained edge devices. Concerning this, previous efforts have primarily focused on enhancing computational efficiency via compression. Leading-edge studies often utilize approximate computing to improve efficiency with minimal loss of accuracy. Meanwhile, mixed-precision accelerators enhance efficiency while preserving accuracy, albeit with extra overhead. Conversely, bit-serial processing presents a compelling alternative to improve efficiency without sacrificing accuracy or increasing overhead. This article introduces the Fully Serial Systolic Array (FSSA) for Transformer acceleration on edge devices. The proposed architecture integrates bit-serial processing into the systolic array architecture to optimize computational efficiency. FSSA leverages a streamlined design, bit-precision adjustment, and bit-sparsity exploitation to accelerate and enhance efficiency. The proposed architecture is described at the RTL level and synthesized using the 28nm technology library, enabling comprehensive evaluation. Furthermore, the functionality of the FSSA was validated through an FPGA-based implementation. Cycle-accurate simulations of Transformers for image classification reveal that a $16\times 128$ FSSA achieves impressive efficiency gains of 68% and 83% in terms of PDP and EDP, respectively. Additionally, FSSA provides further speed-ups of 31% through bit-precision adjustment and 35% through bit-sparsity exploitation for the same workloads without decreasing accuracy. These results highlight the significance of FSSA, outperforming the bit-parallel baseline in computational efficiency without compromising accuracy.}, archive = {J_ACCESS}, author = {Iraj Moghaddasi and Byeong-Gyu Nam}, doi = {10.1109/ACCESS.2025.3584356}, journal = {IEEE Access}, pages = {118490-118503}, shortjournal = {IEEE Access}, title = {FSSA: A fully serial and mixed-precision systolic array for vision transformer acceleration on edge}, volume = {13}, year = {2025}, }
• (2025). Enhancing wind turbine efficiency: An experimental investigation of a sensorless three-vector finite set predictive torque control approach for PMSG-based systems. ACCESS, 13, 118468-118489. (www)

  @article{ , abstract = {Wind energy is a fast-growing renewable energy source that helps reduce greenhouse gas emissions and supports sustainable power solutions. However, managing wind turbines efficiently and reliably is challenging, especially when wind conditions are unpredictable. Traditional control methods often rely on mechanical sensors such as anemometers, which increase complexity, maintenance costs, and reduce reliability. They can also suffer from problems like torque fluctuations, high computational demands, and reduced power quality. To make wind turbines work efficiently and reliably, especially under changing wind conditions, advanced control strategies are needed. This paper addresses these challenges by proposing an innovative control method designed to improve system performance and simplify turbine management. This paper presents a new control strategy that combines two intelligent estimation techniques—the Extended Kalman Filter (EKF) and Particle Swarm Optimization (PSO)—with Three-Vector Finite Set Predictive Torque Control (3V FS-PTC). This method is designed specifically for wind turbines that use Permanent Magnet Synchronous Generators (PMSGs) connected directly to the power grid (Variable Speed Wind Turbine (VSWT)). This approach does not require an anemometer, mechanical parameters, or rotor position sensors, making the system simpler, more reliable, and cost-effective. The 3V FS-PTC algorithm enhances control performance by selecting the three most optimal voltage vectors, two active voltage vectors and one zero voltage vector. Experimental results demonstrate a 19.75% reduction in computational time during wind speed profile, and the method significantly reduces torque and flux ripples compared to Conventional Model Predictive Torque Control (CMPTC), which uses just one voltage vector per control period and often leads to significant torque fluctuations. Additionally, the Tip Speed Ratio Maximum Power Point Tracking (TSR-MPPT) approach is used to maximize efficiency, achieving the power coefficient value at 0.48. Then the energy produced has been successfully integrated into the grid with low THD value of 3.5% and high power factor value of 0.99. The proposed FS-PTC strategy was implemented experimentally, and its performance was evaluated under varying wind speeds using MATLAB/SIMULINK, ControlDesk, dSPACE DS1104, and the DSP LAUNCHXL-F28379D real-time platform.}, archive = {J_ACCESS}, author = {Marouane Ahmed Ghodbane and Toufik Mohamed Benchouia and Mohamed Chebaani and Mohamed Becherif and Yassine Himeur and Amar Golea and Abdelmoumen Ghilani and Zakaria Alili and Shadi Atalla and Wathiq Mansoor}, doi = {10.1109/ACCESS.2025.3581837}, journal = {IEEE Access}, pages = {118468-118489}, shortjournal = {IEEE Access}, title = {Enhancing wind turbine efficiency: An experimental investigation of a sensorless three-vector finite set predictive torque control approach for PMSG-based systems}, volume = {13}, year = {2025}, }
• (2025). From broadcast to on-demand: Innovations shaping the video streaming Landscape—Case study: RaiPlay. ACCESS, 13, 118452-118467. (www)

  @article{ , abstract = {This study explores the rapid growth of the video streaming industry, following its journey from early technological challenges to its current prominence in global entertainment. Using an extensive literature review paired with a real-life example on the digitalization of RaiPlay, the research explores how technological innovations including high-speed internet, big data or AI-driven platforms have transformed users’ streaming experience and influence viewer’s behavior. The study highlights how subscription-based and advertising-based commercial models have progressively inclined viewers on modern entertainment, while new developments in video quality and innovations in adaptive streaming have improved the user experience. However, this research is limited by the rapidly evolving technological landscape. Looking forward, emerging trends in technology are going to keep transforming this challenging industry, offering new directions for exploration.}, archive = {J_ACCESS}, author = {Alexandra Ioanid and Denisa Iliescu}, doi = {10.1109/ACCESS.2025.3582860}, journal = {IEEE Access}, pages = {118452-118467}, shortjournal = {IEEE Access}, title = {From broadcast to on-demand: Innovations shaping the video streaming Landscape—Case study: RaiPlay}, volume = {13}, year = {2025}, }
• (2025). Automating IoT data privacy compliance by integrating knowledge graphs with large language models. ACCESS, 13, 118438-118451. (www)

  @article{ , abstract = {Regulatory compliance is mandatory for Internet of Things (IoT) manufacturers, particularly under stringent frameworks such as the General Data Protection Regulation (GDPR), which governs the handling of personal data. We introduce a novel framework for automating IoT compliance verification by integrating a Large Language Model (LLM) with a domain-specific Knowledge Graph (KG). The framework achieves two primary objectives: 1) leveraging the LLM to interpret natural-language compliance queries, and 2) employing a KG populated with synthetic GDPR scenarios to provide structured, up-to-date regulatory guidance, modeling obligations, permissions, and prohibitions for both deontic (normative) and non-deontic (factual) queries, thus mitigating biases and hallucinations inherent in language models. Evaluated on 50 representative GDPR compliance queries, our approach achieves high semantic alignment (mean BERTScore F1 of 0.89), with expert reviewers rating approximately 84% of generated compliance advice as fully or mostly correct. This work offers IoT manufacturers a scalable, automated solution for data privacy compliance.}, archive = {J_ACCESS}, author = {Kelvin U. Echenim and Karuna P. Joshi}, doi = {10.1109/ACCESS.2025.3586278}, journal = {IEEE Access}, pages = {118438-118451}, shortjournal = {IEEE Access}, title = {Automating IoT data privacy compliance by integrating knowledge graphs with large language models}, volume = {13}, year = {2025}, }
• (2025). Leveraging power line communications for grid intelligence: A comprehensive review of signal-based techniques to infer grid information. ACCESS, 13, 118423-118437. (www)

  @article{ , abstract = {High-frequency signals were widely studied in the last decade to identify grid and channel conditions in power lines. Power Line Modems (PLMs) operating on the grid’s physical layer are capable of transmitting such signals to infer information about the medium. When applied to the electrical grid, one of the key advantages of Power Line Communication (PLC) is its capacity to use signals to provide information about the grid itself. This makes PLC an ideal communication technology for smart grid applications, with significant potential for expanding its use in the realm of grid monitoring and surveillance. In this paper, we focus on PLC grid information inference and provide several contributions: a classification of PLC-based applications, a review of the relevant literature, and insights to further advance the field. Our research identified contributions addressing PLMs for three main grid information inference applications: topology inference, anomaly detection, and grid cybersecurity. We utilize the outcome of our review to shed light on the current limitations of the research contributions and suggest future research directions in this field.}, archive = {J_ACCESS}, author = {Javier Hernandez Fernandez and Abdulah Jarouf and Aymen Omri and Roberto Di Pietro}, doi = {10.1109/ACCESS.2025.3586095}, journal = {IEEE Access}, pages = {118423-118437}, shortjournal = {IEEE Access}, title = {Leveraging power line communications for grid intelligence: A comprehensive review of signal-based techniques to infer grid information}, volume = {13}, year = {2025}, }
• (2025). Enhancing privacy in lightweight data encoding for sensitive applications. ACCESS, 13, 118406-118422. (www)

  @article{ , abstract = {Record linkage aims to identify records referring to the same individual across different datasets, but often involves sensitive personal identifiers. Privacy-Preserving Record Linkage (PPRL) techniques, particularly those based on Bloom filter encoding, enable secure linkage while protecting privacy. However, existing methods suffer from information loss and lack rigorous privacy guarantees. To address these limitations, we propose a novel (Hash)-A method that retains position and frequency information of q-grams, enhancing matching accuracy. We also introduce the Utility-optimized Bloom Filter (UBF), a privacy-preserving mechanism grounded in User-Level Local Differential Privacy (ULDP), which offers formal and quantifiable privacy guarantees. These components are integrated into a complete three-party PPRL protocol that supports missing value handling. Experimental results on real-world datasets show that our method achieves improved linkage accuracy and robust privacy protection against inference attacks, demonstrating a practical balance between utility and privacy.}, archive = {J_ACCESS}, author = {Yan Wang and Min Xu and Sheng Zhong}, doi = {10.1109/ACCESS.2025.3580958}, journal = {IEEE Access}, pages = {118406-118422}, shortjournal = {IEEE Access}, title = {Enhancing privacy in lightweight data encoding for sensitive applications}, volume = {13}, year = {2025}, }
• (2025). Optimization algorithms for cable fault localization and assessment in smart power systems. ACCESS, 13, 118394-118405. (www)

  @article{ , abstract = {Frequency domain reflection (FDR) has good detection and localization ability for defects or faults with weak impedance changes, and has been widely used in the field of cable fault diagnosis, however, in recent years, field experience has shown that this method cannot solve the problem of judging the fault type and severity yet. In view of the above problems, this paper proposes an optimization algorithm based on the frequency domain-space domain integral transform to enhance the cable fault location capability and assess the fault type and severity. Firstly, we use the good self-sustainability of the optical soliton (Soliton) in fiber optic communication to modulate its spectral information into the reflection information of the cable frequency domain, secondly, we define the attenuation operator to compensate for the attenuation of the spectral information and the integral kernel function, and set up the Chebyshev window function to weaken the low-frequency components of the spectrum to be detected. This method realizes the identification of impedance mismatch type at the cable anomaly while ensuring high detection sensitivity and localization accuracy, and further evaluates the fault severity according to the amplitude of reflected information in the localization curve. The simulation verification of inductive and capacitive faults and intermediate joints with different mismatches is carried out in 10 kV cable simulation lines; the actual measurements are carried out on coaxial cables, 10 kV cable experimental lines, and 5 km of 110 kV cable lines that have been put into operation, respectively, and the results show that the error of its localization accuracy is less than 2%, which enables it to accurately determine the type of faults and assess the seriousness of the faults.}, archive = {J_ACCESS}, author = {Wenxuan Wang and Yali Shi and Minghui Liu}, doi = {10.1109/ACCESS.2025.3586719}, journal = {IEEE Access}, pages = {118394-118405}, shortjournal = {IEEE Access}, title = {Optimization algorithms for cable fault localization and assessment in smart power systems}, volume = {13}, year = {2025}, }
• (2025). A dual-mode compatible CT ADC with FIR DAC and SB quantization for DSM and IDSM operations. ACCESS, 13, 118381-118393. (www)

  @article{ , abstract = {This paper presents a continuous-time (CT) ADC capable of operating as either a delta-sigma modulator (DSM) or an incremental DSM (IDSM). To address the high jitter sensitivity inherent to oversampled CT systems, an architecture incorporating FIR DACs and a single-bit (SB) quantizer is adopted. A fully numerical loop parameter extraction method based on the least-mean-square (LMS) algorithm is proposed, eliminating the need for tedious algebraic derivations required in conventional approaches. Additionally, a multirate state-space simulation framework is introduced to address compatibility issues arising from excess loop delay (ELD), which conventional approaches fail to adequately handle. At the circuit level, a compact and energy-efficient compensation FIR DAC is implemented based on a configurable capacitive passive summation network. The main FIR DAC with 8 taps employs a Zapped Virtual-Ground-Switched Dual Return-To-Open (DRTO) topology to suppress ISI and the effects of limited reference impedance. Layout asymmetry in the main FIR DAC induces differential-mode (DM) crosstalk, which is alleviated through a layout-level cancellation technique leveraging even-indexed FIR taps. The prototype, fabricated in a TSMC 65 nm LP CMOS process with a core area of 0.235 mm2, achieves SNDRpeak of 84.3 dB and 81.3 dB in DSM and IDSM modes, respectively, over a 100 kHz bandwidth. Powered by a single 1.2 V supply, it consumes $269~\mu $ W and $277~\mu $ W in DSM and IDSM modes, corresponding to FoMSNDR of 170 dB and 166.9 dB.}, archive = {J_ACCESS}, author = {Han Yang and Yuqi Wang and Ying Hou and Xiaosong Wang and Yu Liu}, doi = {10.1109/ACCESS.2025.3585363}, journal = {IEEE Access}, pages = {118381-118393}, shortjournal = {IEEE Access}, title = {A dual-mode compatible CT ADC with FIR DAC and SB quantization for DSM and IDSM operations}, volume = {13}, year = {2025}, }
• (2025). Phoneme-level duration controllable neural text-to-speech with phoneme embedding skip connection and modified gaussian duration modeling. ACCESS, 13, 118369-118380. (www)

  @article{ , abstract = {This paper proposes a simple but practically important and effective approach to improve phoneme duration expansion and contraction control in neural text-to-speech (TTS) systems for modifying the speaking rate of synthesized speech. The use of simple uniform expansion and contraction is sometimes unnatural because the expansion and compression rates of actual human speech are different for each phoneme in fast and slow conditions. To address this problem, this paper introduces a phoneme embedding skip connection technique (PESC), in which phoneme-level embeddings are added with the encoder output features in neural TTS acoustic models by a simple skip connection, and phoneme-level components can then be enhanced. By enhancing the phoneme-level components before the length regulator, PESC can improve the synthesized speech quality for phoneme duration expansion and contraction conditions. Additionally, this paper proposes modified Gaussian duration modeling (GDM), which predicts not only mean values but also variance values of phoneme durations, for neural TTS acoustic models. Combined with PESC, the modified GDM can further improve the synthesized speech quality for the duration expansion condition. Objective and subjective experiments are conducted for both whole sentence duration control and partial utterance duration control conditions with both Japanese and English neural TTS systems. The results of the experiments demonstrate that PESC without GDM outperforms conventional WSOLA, feature interpolation, and simple expansion and contraction methods for the duration contraction condition, and that PESC with GDM outperforms the conventional methods for the duration expansion condition.}, archive = {J_ACCESS}, author = {Tadashi Ogura and Takuma Okamoto and Yamato Ohtani and Erica Cooper and Tomoki Toda and Hisashi Kawai}, doi = {10.1109/ACCESS.2025.3585135}, journal = {IEEE Access}, pages = {118369-118380}, shortjournal = {IEEE Access}, title = {Phoneme-level duration controllable neural text-to-speech with phoneme embedding skip connection and modified gaussian duration modeling}, volume = {13}, year = {2025}, }
• (2025). A financial data analysis method based on time-series generative adversarial network and decomposition learning. ACCESS, 13, 118354-118368. (www)

  @article{ , abstract = {Stock price series are highly volatile and non-stationary, making it difficult to predict future movements. Deep learning technology has provided new ways to predict stock prices. In existing works, deep learning combined with stock price series decomposition is a common architecture. Inspired by this, we propose a financial data analysis method based on time series generative adversarial network (TimeGAN) and decomposition learning. Specifically, we use multi-view data, including the highest price, lowest price, opening price, closing price, and trading volume, as explanatory variables for predicting the closing price. In parallel, we utilize TimeGAN to generate data with similar distributions to these data to reduce overfitting due to data scarcity. Further, we decompose the closing price series into several subseries with relatively simple patterns by singular spectrum analysis (SSA) and then use self-attention mechanisms to let the subseries perceive each other and learn their dependency relationships, thus providing useful information about stock price change patterns for the prediction model. Experimental results on multiple national stock indices have demonstrated that the proposed method significantly improves prediction accuracy compared to recent advanced methods.}, archive = {J_ACCESS}, author = {Wei Wang and Bo Li}, doi = {10.1109/ACCESS.2025.3585968}, journal = {IEEE Access}, pages = {118354-118368}, shortjournal = {IEEE Access}, title = {A financial data analysis method based on time-series generative adversarial network and decomposition learning}, volume = {13}, year = {2025}, }
• (2025). Fake me if you can: Unforgeable digi-physical academic certificates with instant verifiability. ACCESS, 13, 118334-118353. (www)

  @article{ , abstract = {Academic certificate forgery is a significant societal issue that impacts immigration controls and competitive job markets. Existing physical certificate verification processes are inefficient, relying heavily on manual methods, requiring substantial time and human resources, and are further complicated by cross-border challenges such as data protection laws. While robust blockchain-based certificate management solutions exist and are well-suited for environments where regulatory trust is lacking, their cost and complexity make them less attractive in settings where regulatory bodies are already trusted. In such practical contexts, certificate verification can be performed efficiently without the overhead associated with blockchain technologies. In this work, we introduce FastVer, a cryptographically secure, novel certificate verification framework that enables instant verification of issued certificates at the global cross-border level, as well as at domestic and university levels. Moreover, under a stronger adversarial model, we invoke the IbSM tree and propose SFastVer, a novel framework capable of revoking previously issued certificates that were authorized but issued in an unauthorized manner. Both frameworks seamlessly integrate into existing systems and policies with minimal disruption. Verification within this framework is cost-free, and maintenance costs remain remarkably low. We implement prototypes of both frameworks. The experimental results demonstrate that they are fast, highly scalable, and efficient, achieving verification times of just a few seconds per certificate.}, archive = {J_ACCESS}, author = {Laltu Sardar}, doi = {10.1109/ACCESS.2025.3583184}, journal = {IEEE Access}, pages = {118334-118353}, shortjournal = {IEEE Access}, title = {Fake me if you can: Unforgeable digi-physical academic certificates with instant verifiability}, volume = {13}, year = {2025}, }
• (2025). Trajectory tracking control of a six-degree-of-freedom manipulator based on EAVOA-LADRC. ACCESS, 13, 118324-118333. (www)

  @article{ , abstract = {In the trajectory tracking control of the manipulator, since it is difficult to establish an accurate system model for the manipulator, the use of Linear Active Disturbance Rejection Control (LADRC), although it does not rely on an accurate system model, is difficult to obtain the optimal parameters in practical applications, which affects the control accuracy. Therefore, a linear self-imposed perturbation parameter tuning method based on the Enhance African Vulture Optimization Algorithm (EAVOA) is proposed. In order to solve the problem that the AVOA is unbalanced between the exploration stage and the development stage, and is prone to fall into the local optimal solution, the Kent Chaos initialization, Cauchy inverse learning, optimization of the F computation, and introduction of inhibition coefficients are proposed as the improvement strategies. Through experimental comparison, the improved algorithm has faster convergence speed and better optimal solution, and has smaller joint angle error in the trajectory tracking control of six-degree-of-freedom manipulator based on LADRC.}, archive = {J_ACCESS}, author = {Xinxin Shi and Haihao Yu}, doi = {10.1109/ACCESS.2025.3586915}, journal = {IEEE Access}, pages = {118324-118333}, shortjournal = {IEEE Access}, title = {Trajectory tracking control of a six-degree-of-freedom manipulator based on EAVOA-LADRC}, volume = {13}, year = {2025}, }
• (2025). Cross-domain explainable recommendation using graph convolutional networks and a topic model. ACCESS, 13, 118310-118323. (www)

  @article{ , abstract = {Graph Convolutional Network (GCN)-based recommendation systems (RSs) have recently gained popularity for their ability to improve recommendation accuracy by utilizing neighborhood information in user-item interaction graphs. Despite their success, GCN-based systems still face two major challenges, which are the lack of explainability and limited applicability in multi-domain environments. To address these limitations, we propose a novel GCN and Review text-based cross-domain ExplainAble recommendaTion (GREAT) framework that enhances both the accuracy and explainability of cross-domain recommendations. Instead of relying on structured knowledge graphs, GREAT utilizes user-generated review texts to incorporate users’ sentiments and opinions directly into recommendations and explanations. GREAT extracts domain-wide topics using a proposed Term-Weighted Latent Dirichlet Allocation (TW-LDA) model, which reduces domain biases by adjusting for differences in term usage across domains. For recommendation, GREAT introduces TACO (Topic-Aided Cross-dOmain Recommendation), a GCN-based collaborative filtering model that integrates domain-wide topic information to enable effective knowledge transfer across domains. For explanation, GREAT identifies topic-level connections between recommended items and a user’s historical interactions. Experiments on a real-world Amazon dataset spanning three domains demonstrate that GREAT outperforms state-of-the-art baselines, achieving up to 6.8% improvement in NDCG and 5.2% in recall over the strongest baseline. Additionally, we validate the explainability of GREAT through a case study based on actual recommendation results, demonstrating how items from different domains are related through shared topics.}, archive = {J_ACCESS}, author = {Kunyoung Kim and Mye Sohn and Jongmo Kim}, doi = {10.1109/ACCESS.2025.3581344}, journal = {IEEE Access}, pages = {118310-118323}, shortjournal = {IEEE Access}, title = {Cross-domain explainable recommendation using graph convolutional networks and a topic model}, volume = {13}, year = {2025}, }
• (2025). Next-generation water management and crop modeling of sustainable energy in PLC based on agrivoltaics systems with IoT. ACCESS, 13, 118293-118309. (www)

  @article{ , abstract = {The integration of PLC controllers and LoRaWAN-IoT in sensor-based Agrivoltaics Systems (AVS) presents a cutting-edge approach to sustainable energy management and precision agriculture. AVS strives to combine solar energy generation with agriculture to optimize land utilization. Nevertheless, these systems encounter various issues, particularly regarding water management, energy efficiency, and communication between agricultural components. Moreover, standard crop models fail to adjust to shifting climate conditions, resulting in inaccurate yield forecasts. Smart control systems are essential for managing energy production and consumption in agricultural activities. Tackling these issues is essential for improved resource utilization, lowering costs, and promoting sustainable agriculture. This paper proposes an IoT-driven framework for optimizing water management and dynamic crop modelling, ensuring efficient resource utilization in agrivoltaics environments. By combining PLC for high-speed data transmission and LoRaWAN for long-range, low-power connectivity, the system enables seamless communication between distributed sensors, minimizing infrastructure costs and enhancing scalability. Real-time data analytics is employed to improve water conservation, crop health prediction, and energy efficiency. Additionally, the dynamic crop modelling framework adapts to climatic variations, optimizing land use for both agriculture and renewable energy generation. The experimental demonstration of this model is performed in MATLAB, and simulation results and real-world case studies validate the effectiveness in terms of planting area quality, energy production, and beam and diffuse shading factors. The comparative analysis is performed among the baseline methods for the parameters like power generation, Irrigation supply, yield production and irrigation water productivity calculation, and Voltage/current analysis. This research highlights the potential of LoRaWAN-IoT and PLC-based agrivoltaics systems in fostering sustainable farming while promoting the dual use of land for agriculture and renewable energy generation.}, archive = {J_ACCESS}, author = {Sami Abduljabbar Rashid and Abdalrahman Fatikhan Ataalla and Yousif Al Mashhadany and Sameer Algburi and Norhana Arsad}, doi = {10.1109/ACCESS.2025.3586204}, journal = {IEEE Access}, pages = {118293-118309}, shortjournal = {IEEE Access}, title = {Next-generation water management and crop modeling of sustainable energy in PLC based on agrivoltaics systems with IoT}, volume = {13}, year = {2025}, }
• (2025). Recovering the images distorted by surface waves using an efficient multi-stage image reconstruction strategy. ACCESS, 13, 118281-118292. (www)

  @article{ , abstract = {Imaging through a fluctuating water-air interface will will cause image distortion and motion blur, which seriously restricts the application and development of underwater vehicle. In previous studies, utilizing the spatiotemporal correlation of image sequences for image reconstruction is a very effective solution. However, existing methods fail to account for the phenomenon of coexisting periodic and stochastic waves in natural fluid flows, and ignore the impact of image structure information on restoration accuracy, so high-quality restored images cannot be obtained. This paper proposes a multi-stage reconstruction strategy based on compressed sensing and non-rigid registration. First, compressed sensing is used to eliminate the global periodic distortion in the image. Then, lucky patch fusion and non-rigid registration are combined to eliminate the local distortion caused by random fluctuations. Finally, principal component analysis is applied to reduce image blur caused by water waves. Experiment results demonstrate that the proposed method has a superior convergence rate compared to current methods while maintaining the accuracy of image reconstruction.}, archive = {J_ACCESS}, author = {Bijian Jian and Ting Peng and Xu Tian}, doi = {10.1109/ACCESS.2025.3586802}, journal = {IEEE Access}, pages = {118281-118292}, shortjournal = {IEEE Access}, title = {Recovering the images distorted by surface waves using an efficient multi-stage image reconstruction strategy}, volume = {13}, year = {2025}, }
• (2025). Exploring the limitations of federated learning: A novel wasserstein metric-based poisoning attack on traffic sign classification. ACCESS, 13, 118264-118280. (www)

  @article{ , abstract = {Federated Learning (FL) enhances privacy but remains vulnerable to model poisoning attacks, where an adversary manipulates client models to upload poisoned updates during training, thereby compromising the overall FL model. Existing attack models often assume adversaries have full knowledge of the FL procedure, including server aggregation algorithms. In contrast, we consider a more practical attack scenario in which the adversary has access only to local client data and the FL model. To address this security gap, we propose a novel attack called the Wasserstein Metric-based Model Poisoning Attack (WMPA). In this approach, adversaries embed malicious updates within aggregated ones without detection, posing a significant threat to FL applications. WMPA leverages historical information from the FL process to forecast the next round’s global model as a reference. This reference model is then used to generate an adversarial local model characterized by low accuracy but minimal perturbation. We explore the use of the Wasserstein distance in place of traditional metrics such as Euclidean distance to better disguise malicious updates. Extensive experiments show that WMPA outperforms existing model poisoning attacks and can compromise robust aggregation methods. For example, in a cross-silo setting using Krum, WMPA reduces the FL model’s accuracy from 70% to 30.1%. In a cross-device setting, it reduces accuracy from 68% to 32.6%. Furthermore, we demonstrate that the Wasserstein metric is superior to other similarity metrics in capturing the underlying structure and shape of the provided distributions.}, archive = {J_ACCESS}, author = {Suzan Almutairi and Ahmed Barnawi}, doi = {10.1109/ACCESS.2025.3584948}, journal = {IEEE Access}, pages = {118264-118280}, shortjournal = {IEEE Access}, title = {Exploring the limitations of federated learning: A novel wasserstein metric-based poisoning attack on traffic sign classification}, volume = {13}, year = {2025}, }
• (2025). A reference architecture for on-premises software-defined infrastructure using model-based systems engineering. ACCESS, 13, 118249-118263. (www)

  @article{ , abstract = {IT leaders have long refined the use of automation of common support processes in the public cloud through development and operations (DevOps) methods and tools, particularly in organizations where custom application development is a core capability. Cloud service providers inherently expose the application programming interfaces (APIs) to support automation, and development organizations have mature processes and tools to support process automation. This is not the case for organizations that heavily depend on commercial off-the-shelf (COTS) applications running in on-premises data centers, particularly healthcare provider organizations. It remains an open question whether these organizations should pursue automation based on value: are the benefits of automation greater than the costs to implement? In order to begin answering the second part of that question, this paper proposes a reference architecture (RA) for software-defined infrastructure (SDI) focused on leveraging infrastructure and management tools in the on-premises data center, using the methods and tools of Model-Based Systems Engineering (MBSE) through the Model-Based System Architecture Process (MBSAP).}, archive = {J_ACCESS}, author = {Eric S. Enos and Daniel R. Herber}, doi = {10.1109/ACCESS.2025.3586990}, journal = {IEEE Access}, pages = {118249-118263}, shortjournal = {IEEE Access}, title = {A reference architecture for on-premises software-defined infrastructure using model-based systems engineering}, volume = {13}, year = {2025}, }
• (2025). Genetic algorithm-driven joint optimization of task offloading and resource allocation for fairness-aware latency minimization in mobile edge computing. ACCESS, 13, 118237-118248. (www)

  @article{ , abstract = {Mobile Edge Computing (MEC) alleviates latency and bandwidth strain on centralized cloud infrastructures by enabling the offloading of tasks to proximal edge servers, yet resource optimization in dense dynamic networks remains an open problem. This paper proposes a genetic algorithm (GA)-based approach to jointly optimize three important parameters: 1) the proportion of tasks offloaded to mobile edge servers (MES), 2) channel bandwidth allocation, and 3) computational resource allocation, in order to minimize the total task completion time with fairness among user devices (UDs). Resolving constraints such as limited wireless transmission capacity that is bounded and MES processing resources, the GA effectively explores solution spaces through selection, crossover, and mutation operations without falling into local optima. Simulation outcomes demonstrate 50% reduction in completion time compared to non-offloading strategies, with fairness indicated by standard deviation metrics showing equal performance across UDs. The algorithm scales, with task durations leveling off after 50 generations for 15 UDs and robustness being maintained up to 300 iterations. Results identify important thresholds (e.g., bandwidth > 5 MHz yields diminishing returns) and achieve near-optimal efficiency at high levels of resources. This work advances MEC efficiency by tackling multi-resource optimization issues through metaheuristic exploration, with a scalable approach to latency-sensitive IoT and 5G network use cases.}, archive = {J_ACCESS}, author = {Mohamed Elkawkagy and Ibrahim A. Elgendy and Samia Allaoua Chelloug and Heba Elbeh}, doi = {10.1109/ACCESS.2025.3584971}, journal = {IEEE Access}, pages = {118237-118248}, shortjournal = {IEEE Access}, title = {Genetic algorithm-driven joint optimization of task offloading and resource allocation for fairness-aware latency minimization in mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). SCRED-distillation: Improving low-dose CT image quality via feature fusion and mutual learning. ACCESS, 13, 118224-118236. (www)

  @article{ , abstract = {The substantial noise inherent in low-dose CT (LDCT) significantly impedes diagnostic accuracy. Although deep learning techniques, particularly CNNs, have offered promise for LDCT denoising, their inherent focus on local features and the scarcity of extensive training data can limit their performance and ability to generalize effectively. To address these critical shortcomings, we introduce SCRED-Distillation, a novel denoising method. This approach synergistically integrates the global contextual awareness of Transformer architectures with the efficiency and regularization benefits of Knowledge Distillation. By effectively leveraging both local and global image characteristics, SCRED-Distillation achieves demonstrably superior denoising results. Furthermore, to enhance the model’s capacity for robust generalization across diverse datasets, we employ a mutual learning framework during training. Extensive quantitative evaluations conducted on the challenging Mayo Clinic LDCT Grand Challenge dataset reveal remarkable improvements in key image quality metrics: the Peak Signal-to-Noise Ratio (PSNR) increased significantly from 29.2489 to 33.2103, the Structural Similarity Index Measure (SSIM) steadily rose from 0.8759 to 0.9132, and the Root Mean Squared Error (RMSE) was effectively reduced from 14.2416 to 8.9377. Notably, SCRED-Distillation effectively suppresses noise artifacts while crucially preserving fine diagnostic details, leading to clearer and more reliable medical images and ultimately facilitating more accurate clinical diagnoses.}, archive = {J_ACCESS}, author = {Yanqing Wang and Xinru Zhan and Wanquan Liu and Yingying Li and Kexin Guo and Huafeng Wang}, doi = {10.1109/ACCESS.2025.3585001}, journal = {IEEE Access}, pages = {118224-118236}, shortjournal = {IEEE Access}, title = {SCRED-distillation: Improving low-dose CT image quality via feature fusion and mutual learning}, volume = {13}, year = {2025}, }
• (2025). Defect detection of gas insulation switch by infrared thermography technology with an improved yolo algorithm. ACCESS, 13, 118212-118223. (www)

  @article{ , abstract = {Gas-insulated switch-gear (GIS) defects, such as internal bubbles, surface cracks, and high pressure breakdowns, critically threaten power transmission reliability. However, conventional nondestructive detection methods like ultrasound and X-ray face the challenges in anti-interference capability, cost, and structural complexity. To address those issues, this study has proposed an infrared thermography-based defect detection method by combing with an enhanced YOLOv7 algorithm for accurate and efficient defect localization identification and depth estimation. The proposed method modifies the YOLOv7 framework by replacing standard CBS modules with Fused-MBConv layers in the backbone to improve feature extraction efficiency while integrates a coordinate attention (CA) mechanism in the neck layer to enhance spatial defect recognition. The experiments have been carried out with the infrared thermal images of insulation rod defects, including flashover, block bubbles, scratches, with the impurities at depths within 0.4–1 mm. The experimental results have demonstrated significant performance gains. The proposed method achieves a mean Average Precision (mAP) of 10.6% and 13% higher than baseline YOLOv7 and YOLOv5, respectively. Moreover, improved computational speed and detection accuracy has been seen via comparison with the fellow algorithms. These results validate the effectiveness of the method, which provide a reference solution for overcoming existing technological barriers for GIS defect diagnosis.}, archive = {J_ACCESS}, author = {Ma Tianci and Chen Xiangping and Li Bo and Bai Jie}, doi = {10.1109/ACCESS.2025.3582967}, journal = {IEEE Access}, pages = {118212-118223}, shortjournal = {IEEE Access}, title = {Defect detection of gas insulation switch by infrared thermography technology with an improved yolo algorithm}, volume = {13}, year = {2025}, }
• (2025). DPA-HairNet: A dual encoder attention based network for hair artifact removal in dermoscopic images. ACCESS, 13, 118185-118211. (www)

  @article{ , abstract = {Hair artifacts in dermoscopic images significantly hinder the accurate diagnosis of melanoma and other skin conditions by obscuring critical lesion details. To address this challenge, we introduce DPA-HairNet, a novel Dual Encoder Attention-Based Network designed specifically for effective hair artifact removal while preserving lesion integrity. The model features a dual encoder architecture to separately process hair-specific and lesion-specific features, an attention mechanism to prioritize diagnostically relevant regions, and a dual-output design that generates precise hair segmentation masks and high-quality reconstructed images. This study leverages the ISIC 2018 and HAM10000 datasets, incorporating advanced data augmentation techniques to enhance dataset diversity and ensure robust model training. DPA-HairNet was evaluated against six state-of-the-art segmentation models using comprehensive metrics, including Accuracy, Dice Coefficient, Jaccard Index, PSNR, MAE, Specificity, Precision, Recall, and F1-Score, where the proposed model outperformed all models. Furthermore, classification performance was assessed before and after hair artifact removal using twelve pre-trained classifiers, demonstrating significant improvements in diagnostic accuracy. Explainable AI techniques, Grad-CAM, UMAP and attention heatmaps, were employed to interpret and validate the model’s focus. These results underscore DPA-HairNet’s effectiveness and potential integration into clinical workflows to enhance dermoscopic image analysis and diagnosis. Future work will explore generalization to additional artifact types and optimization for real-time clinical deployment.}, archive = {J_ACCESS}, author = {F M Javed Mehedi Shamrat and Mohd Yamani Idna Idris and Chowdhury Forhadul Karim and Xujuan Zhou and Raj Gururajan}, doi = {10.1109/ACCESS.2025.3585353}, journal = {IEEE Access}, pages = {118185-118211}, shortjournal = {IEEE Access}, title = {DPA-HairNet: A dual encoder attention based network for hair artifact removal in dermoscopic images}, volume = {13}, year = {2025}, }
• (2025). Forecasting healthcare 5.0 driven IoMT for a seamless continuum of care. ACCESS, 13, 118163-118184. (www)

  @article{ , abstract = {The evolution of technology towards Healthcare 5.0 corresponding to the fifth industrial revolution (5IR) is enabled by the key technology enablers of Healthcare 4.0 plus the patient centric dimension. This presents a paradigm shift in the healthcare landscape transformation for sustainable, personalized and precise medicine. Most existing studies on Healthcare 5.0 focused on the Healthcare 5.0 enabling technologies and potential architectures and in particular, studies on their application for brain cancer are scarce. However, to ensure a seamless continuum of care, there is need to address critical challenges including system interoperability, data security and capability for early and improved diagnosis. In this paper, we focus on forecasting the transformative potential of Healthcare 5.0 and its role on future generations of medical applications developed on IoMT (Internet of Medical Things) systems by analyzing the paradigm shift in the healthcare technology evolution and suggesting solutions to address the existing challenges. We introduce the Healthcare 5.0, its key enabling technologies, salient features and analyze the evolution of healthcare technology based industrial and manufacturing processes (Healthcare 1.0 to Healthcare 5.0). We establish the potential opportunities and limitations in the development of Healthcare 5.0. Further, we adopt the model driven engineering paradigm to propose the reference model of a 5G/6G driven, IoMT enabled cancer care framework and the information model for a brain cancer care scenario. To this end, the paper demonstrates the potential of Healthcare 5.0 to transform IoMT enabled healthcare systems by integrating smart systems with human intelligence to optimize operational processes, advance sustainable practices and improve patient outcomes while addressing existing healthcare delivery challenges like interoperability. Through future projection, this work provides a platform to further explore the intersection of technological advancements based on Healthcare 5.0 and IoMT systems.}, archive = {J_ACCESS}, author = {Mavis Gezimati and Ghanshyam Singh}, doi = {10.1109/ACCESS.2025.3583884}, journal = {IEEE Access}, pages = {118163-118184}, shortjournal = {IEEE Access}, title = {Forecasting healthcare 5.0 driven IoMT for a seamless continuum of care}, volume = {13}, year = {2025}, }
• (2025). LS-YOLO: A lightweight, real-time YOLO-based target detection algorithm for autonomous driving under adverse environmental conditions. ACCESS, 13, 118147-118162. (www)

  @article{ , abstract = {Autonomous driving faces significant object detection challenges under complex backgrounds characterized by dense scenes, object occlusion, long-range targets, and extreme weather conditions. These challenges are further exacerbated in adverse weather such as rain, snow, and fog, leading to decreased detection accuracy and increased missed detection rates. To address these issues, a lightweight real-time object detection algorithm, LS-YOLO, is proposed. The LS-YOLO incorporates a MACA module to capture both global and local features, an SPDD module to reduce computational complexity, and a DR-Concat module to optimize feature fusion. Additionally, an improved ATFL-Wasserstein loss function is employed to enhance the learning capability for small objects and hard samples. Experimental results on public datasets demonstrate that LS-YOLO significantly outperforms existing algorithms in terms of accuracy, robustness, and real-time performance. Notably, under adverse weather and complex backgrounds, LS-YOLO achieves lower missed detection rates and higher object detection accuracy.}, archive = {J_ACCESS}, author = {Cheng Ju and Yuxin Chang and Yuansha Xie and Dina Li}, doi = {10.1109/ACCESS.2025.3586599}, journal = {IEEE Access}, pages = {118147-118162}, shortjournal = {IEEE Access}, title = {LS-YOLO: A lightweight, real-time YOLO-based target detection algorithm for autonomous driving under adverse environmental conditions}, volume = {13}, year = {2025}, }
• (2025). Time series-based fault detection and classification in IEEE 9-bus transmission lines using deep learning. ACCESS, 13, 118135-118146. (www)

  @article{ , abstract = {Transmission line faults present a significant threat to the stability of power systems, potentially causing widespread outages. Timely detection of these faults is essential to prevent substantial disruptions in the power supply. This paper explores a time series-based deep learning technique for fault detection and classification in the IEEE 9-bus system. Post asymmetrical fault current and voltage time series data have been used to train a convolutional neural network (CNN), representing normal and faulty conditions, with convolutional and ReLU layers. A fully connected layer is used to detect features without missing critical information of the signal, achieving MSE as zero for fault detection and 0.0149 for fault classification. This demonstrates the effectiveness of CNNs for real-time fault detection and classification in complex power grids. The robustness of the CNN model indicates its potential for deployment in practical applications, enhancing the reliability and resilience of the transmission network. Using deep learning techniques opens opportunities for further improvements in fault detection and location strategies within the power grid.}, archive = {J_ACCESS}, author = {Somchat Jiriwibhakorn and Shazia Kanwal}, doi = {10.1109/ACCESS.2025.3586045}, journal = {IEEE Access}, pages = {118135-118146}, shortjournal = {IEEE Access}, title = {Time series-based fault detection and classification in IEEE 9-bus transmission lines using deep learning}, volume = {13}, year = {2025}, }
• (2025). AutoTA: A dynamic intent-based virtual teaching assistant for students using open source LLMs. ACCESS, 13, 118122-118134. (www)

  @article{ , abstract = {Large Language Models (LLMs) are explored for their potential to transform education by serving as virtual teaching assistants, offering personalized support through human-like responses to tasks such as content-related questions and coursework guidance. In this study, we present a novel framework that leverages intent classification to enhance the effectiveness of LLMs in this role. Our framework, AutoTA, categorizes student queries into distinct topics— lecture discussions, homework assistance, and syllabus questions—triggering specific conversation chains tailored to each intent. Additionally, we incorporate a custom vector-space filter that refines responses based on filename tracking after intent identification. To evaluate the framework, we used course materials from the undergraduate-level CS course, Computer Incident Response, and compared the performance of several open-source LLMs, including Llama 3.1. Our results show that the framework accurately classifies intent and provides appropriate guidance, measured through quantitative and qualitative metrics. These findings highlight the potential of the proposed framework to enhance personalized learning and improve student engagement. While tested in a computer science course, the framework incorporates diverse assessment types that suggest potential for broader application.}, archive = {J_ACCESS}, author = {Rajashree Dahal and Greg Murray and Robin Chataut and Mohamed Hefeida and Anurag K. Srivastava and Prashnna K. Gyawali}, doi = {10.1109/ACCESS.2025.3576329}, journal = {IEEE Access}, pages = {118122-118134}, shortjournal = {IEEE Access}, title = {AutoTA: A dynamic intent-based virtual teaching assistant for students using open source LLMs}, volume = {13}, year = {2025}, }
• (2025). Novel double-terminal traveling wave fault location method for railway power transmission lines. ACCESS, 13, 118112-118121. (www)

  @article{ , abstract = {Traveling wave (TW) fault location holds great promise for railway power transmission lines. However, the single-terminal method struggles with identifying the second wavefront and has issues with wave velocity accuracy. The double-terminal method necessitates strict clock synchronization. This paper introduces a novel double-terminal TW fault location method that merges the benefits of both approaches. By examining the fault TW propagation characteristics and paths in railway power transmission lines, this method leverages the time gap of initial voltage Traveling waves between ground-mode and aerial-mode at both ends of the substation. This approach, through formula derivation, eliminates the influence of wave velocity and removes the need for data synchronization between the terminals. Simulations using a railway power transmission line model in PSCAD/EMTDC demonstrate that this method not only retains the advantages of traditional TW fault location methods but also avoids the need to identify the second wavefront, is unaffected by wave velocity, and does not require double-terminal clock synchronization. Simulation results show that the method achieves high accuracy, with fault location errors within 400 m.}, archive = {J_ACCESS}, author = {Kaiqing Tian and Yuhang Fan and Wenyan Feng}, doi = {10.1109/ACCESS.2025.3585981}, journal = {IEEE Access}, pages = {118112-118121}, shortjournal = {IEEE Access}, title = {Novel double-terminal traveling wave fault location method for railway power transmission lines}, volume = {13}, year = {2025}, }
• (2025). Deep pseudogene categorization and genome-wide transcription prediction using GANP-based feature selection and TabNet interpretability. ACCESS, 13, 118096-118111. (www)

  @article{ , abstract = {Pseudogenes, once regarded as genomic relics, have emerged as critical regulators of gene expression, influencing cancer, neurodegenerative disorders, and developmental processes. This study introduces an advanced framework for pseudogene classification, leveraging deep learning to address the challenges of large-scale transcriptomic analysis. The proposed approach integrates an autoencoder for dimensionality reduction, a conditional generative adversarial network (cGAN) for synthetic data generation, and a TabNet classifier for final prediction. Extensive literature on pseudogenes highlights their interaction with coding genes, non-coding RNAs, and epigenetic mechanisms, which are pivotal in transcriptional and post-transcriptional regulation. The pipeline uses SMOTE to mitigate class imbalance and applies synthetic feature augmentation to boost classification performance. Tested on a large-scale curated transcriptomic dataset, our framework achieves an accuracy of 96%, surpassing traditional machine learning models. Visualization tools such as t-SNE, heatmaps, and SHAP plots further enhance model interpretability. The system is fully implemented on accessible AI platforms (Google Colab, Kaggle), ensuring real-time simulation and reproducibility, especially for research teams with limited computational resources. This method offers a scalable, explainable solution for pseudogene identification and non-coding RNA characterization, with broad applications in cancer research, genome annotation, and precision transcriptomics. The results underscore the power of deep learning and generative models in unveiling the regulatory complexity of pseudogenes, contributing to future genomic studies and clinical precision medicine. Additionally, a user-friendly Gradio interface has been developed, enabling interactive exploration and prediction of pseudogene classes, providing a practical tool for biologists and clinicians alike.}, archive = {J_ACCESS}, author = {Zeeshan Ahmed and Kashif Munir and Muhammad Usama Tanveer and Syed Rizwan Hassan and Ateeq Ur Rehman and Habib Hamam}, doi = {10.1109/ACCESS.2025.3585601}, journal = {IEEE Access}, pages = {118096-118111}, shortjournal = {IEEE Access}, title = {Deep pseudogene categorization and genome-wide transcription prediction using GANP-based feature selection and TabNet interpretability}, volume = {13}, year = {2025}, }
• (2025). Test case prioritization based on neural networks classification: A replication study and hyper-parameter optimization using taguchi methods. ACCESS, 13, 118082-118095. (www)

  @article{ , abstract = {Regression testing plays an essential role in developing a highly qualitative software system. When code is changed, it is needed to provide confidence that modifications are correct. Running all test cases would be time-consuming, however, Test Case Prioritization (TCP) could help in this direction by ordering the execution of test cases considering various criteria, for example, fault detection and time constraint. This research aims to achieve two main objectives: the first is to replicate the experiments outlined in the original article, and the second is to determine the optimal hyperparameters for existing AI models, with a particular focus on neural networks. The Taguchi approach was utilized, implementing an L4 design to optimize the layers and $hidden \ neurons$ parameters, while an L9 design also incorporated the epochs and $batch \_size$ parameters. The results of the replicated experiments confirm the original findings. Two datasets were used to discover the best parameters in both L4 and L9 designs. In the context of L4, we notice that a better performance is achieved for 2 layers instead of 3. Regarding hidden neurons number, the value 90 is better for the real dataset, however, the value 60 is preferred for the synthetic dataset. In the L9 design experiments conducted on both datasets, we observed comparable trends. The configurations that resulted in enhanced accuracy include 4 layers with 120 hidden neurons, 90 epochs, and a batch size of 64. Our findings corroborated the initial research on employing artificial intelligence for TCP, and by utilizing the Taguchi method, we identified the best parameters for the neural network models applied in TCP.}, archive = {J_ACCESS}, author = {Andreea Vescan and Cristina-Maria Tiutin}, doi = {10.1109/ACCESS.2025.3586144}, journal = {IEEE Access}, pages = {118082-118095}, shortjournal = {IEEE Access}, title = {Test case prioritization based on neural networks classification: A replication study and hyper-parameter optimization using taguchi methods}, volume = {13}, year = {2025}, }
• (2025). Strategic electric vehicle charging in community microgrids: Enhancing grid stability, reducing emissions, and optimizing costs. ACCESS, 13, 118066-118081. (www)

  @article{ , abstract = {This paper investigates Electric Vehicle (EV) charging strategies within a community microgrid (CMG) framework, focusing on optimizing grid stability, minimizing emissions, and reducing system costs. The study analyzes user behavior and charging needs based on data such as charging times, state of charge (SOC) at arrival and departure, and EV battery capabilities. Three main charging strategies are considered: Earliest, Least Laxity, and Optimal. The Earliest strategy ensures EV readiness by prioritizing immediate charging, potentially raising peak-hour demand; the Least Laxity strategy improves demand distribution by prioritizing EVs with limited charging times; and the Optimal strategy balances demand with grid availability, reducing peak demand and leveraging low-price periods. The CMG under study incorporates renewable sources (solar and wind) and dispatchable resources (microturbines and fuel cells) to analyze how these charging strategies impact grid load and emissions. Additionally, the paper explores the CMG’s potential for energy independence through different grid interaction levels: only buying, only selling, and complete independence. The model optimizes charging schedules using mixed-integer linear programming (MILP) to achieve economic efficiency and emission reduction, comparing the effectiveness of each charging strategy within real-time grid conditions. This work demonstrates that strategic charging can effectively manage EV load, support grid stability, and enhance CMG reliability while balancing costs and emissions.}, archive = {J_ACCESS}, author = {Divya Mathur and Neeraj Kanwar and Sunil Kumar Goyal}, doi = {10.1109/ACCESS.2025.3584453}, journal = {IEEE Access}, pages = {118066-118081}, shortjournal = {IEEE Access}, title = {Strategic electric vehicle charging in community microgrids: Enhancing grid stability, reducing emissions, and optimizing costs}, volume = {13}, year = {2025}, }
• (2025). SD-YOLOv8: Automated motion detection system for aerobics students. ACCESS, 13, 118055-118065. (www)

  @article{ , abstract = {Existing object detection algorithms are often constrained by the high computational costs associated with large network structures in practical applications. To facilitate the development of intelligent physical education classrooms, particularly for aerobic exercise student movement recognition, we propose a lightweight student action recognition system based on SD-YOLOv8. First, we constructed a specialized dataset by capturing and annotating aerobic exercise student movement images. Then, based on the YOLOv8 network architecture, we implemented two key improvements: 1) introducing Selective Kernel Networks (SKNet) in the multi-scale feature fusion layer to enhance local feature capture capabilities; 2) replacing the original detection head with Dynamic Head to effectively reduce the computational burden while improving detection accuracy and efficiency. Finally, we developed a user-friendly graphical interface using the PyQt framework, enabling visual deployment and real-time interaction with the model. Experimental results demonstrate that the improved SD-YOLOv8 achieves 97.6%, mean Average Precision (mAP) on the aerobic exercise student behavior recognition dataset, representing a 3.5% improvement over the original model. The system shows significant advantages in enhancing action recognition accuracy and real-time feedback, providing robust technical support for physical education instruction.}, archive = {J_ACCESS}, author = {Lian Tang and Ya Li and Qing Du and Jincheng Liang}, doi = {10.1109/ACCESS.2025.3585561}, journal = {IEEE Access}, pages = {118055-118065}, shortjournal = {IEEE Access}, title = {SD-YOLOv8: Automated motion detection system for aerobics students}, volume = {13}, year = {2025}, }
• (2025). Exploring the state-of-the-art algorithms for brain tumor classification using MRI data. ACCESS, 13, 118033-118054. (www)

  @article{ , abstract = {Brain structural magnetic resonance image (MRI) is a very useful way to learn about the structure and function of the brain. For doctors, finding and classifying brain tumor (BT) is a routine and difficult procedure. Digital image processing methods such as segmentation, classification, and pre-processing may assist medical practitioners in diagnosing certain forms of brain cancer, finding tumors, and observing small changes. In this paper, cutting-edge machine learning (ML) and deep learning (DL) techniques are used to show how BT is currently being diagnosed with MRI images. This study is about the algorithms, datasets, and model designs that different researchers have utilized for classifying BT images. The study also looks at how well AI algorithms work now, what problems they face, and what possible research paths they could follow in the future. This study aims to keep experts up to date on the latest developments in this challenging area and get them interested in it again. BT classification using MR scans may be accomplished using computer-aided design (CAD) systems by using digital image processing techniques including segmentation, classification, and pre-processing. In this paper, we discuss the traditional ML and DL methods for detecting BT. This study takes a look at the present state of the art in methodology and may be used to create plans for successful diagnostics of additional brain diseases employing different MRI techniques.}, archive = {J_ACCESS}, author = {Joice J Anish and D Ajitha}, doi = {10.1109/ACCESS.2025.3579727}, journal = {IEEE Access}, pages = {118033-118054}, shortjournal = {IEEE Access}, title = {Exploring the state-of-the-art algorithms for brain tumor classification using MRI data}, volume = {13}, year = {2025}, }
• (2025). Can deep learning models acquire specialist knowledge? identifying carotid bruits in type 2 diabetes using an electronic stethoscope. ACCESS, 13, 118023-118032. (www)

  @article{ , abstract = {The present study explores the potential of artificial intelligence as a substitute for trained clinician identification of a carotid bruit from multiple auditory recordings from an electronic stethoscope in 98 people with type 2 diabetes, 48 of whom had a bruit. We employed various deep networks, including ResNet and VGGish, alongside tailor-made deep learning models for this purpose. Our findings indicated that in scenarios such as this, where the data show high variability and yet are limited, the highest model accuracy achieved was 67.8%. This suggests the inadequacy of AI-driven systems in accurately detecting carotid bruits in the present participant sample. Detailed results for each deep learning and machine learning approach are presented separately, and the dataset has been established as a benchmark for future studies. Given the complexity observed with the current data volume, additional studies with larger sample sizes are warranted.}, archive = {J_ACCESS}, author = {Aref Miri Rekavandi and Mohammed Bennamoun and Farid Boussaid and Wendy A. Davis and Timothy M. E. Davis}, doi = {10.1109/ACCESS.2025.3582938}, journal = {IEEE Access}, pages = {118023-118032}, shortjournal = {IEEE Access}, title = {Can deep learning models acquire specialist knowledge? identifying carotid bruits in type 2 diabetes using an electronic stethoscope}, volume = {13}, year = {2025}, }
• (2025). Autonomous mobile robot path planning Techniques—A review: Classical and heuristic techniques. ACCESS, 13, 117999-118022. (www)

  @article{ , abstract = {Autonomous mobile robots (AMRs) are increasingly used in various applications, including transportation, logistics, and healthcare. One of the central challenges in AMR deployment is path planning, which involves finding an optimal and collision-free route from the robot’s initial position to its destination. This review, which builds upon a two-part study, presents a comprehensive overview of state-of-the-art techniques for AMR path planning. This paper focuses on classical and heuristic-based strategies, providing valuable insights into their foundational roles in autonomous mobile robot navigation. We categorize existing approaches based on their core principles, including graph-based, heuristic-based, metaheuristic-based, and learning/reasoning-based methods. For each category, we analyze the strengths and limitations of the representative techniques and evaluate their effectiveness in various operational scenarios. Furthermore, we identify emerging research directions and ongoing challenges in AMR path planning, such as multi-robot coordination, adaptation to dynamic environments, and integration with human–robot interaction frameworks.}, archive = {J_ACCESS}, author = {Mubarak Badamasi Aremu and Ibrahim K. Kabir and Gamil Ahmed and Sami El-Ferik}, doi = {10.1109/ACCESS.2025.3579863}, journal = {IEEE Access}, pages = {117999-118022}, shortjournal = {IEEE Access}, title = {Autonomous mobile robot path planning Techniques—A review: Classical and heuristic techniques}, volume = {13}, year = {2025}, }
• (2025). Proactive data placement in heterogeneous storage systems via predictive multi-objective reinforcement learning. ACCESS, 13, 117986-117998. (www)

  @article{ , abstract = {Modern data-intensive applications demand efficient orchestration across heterogeneous storage tiers, ranging from high-performance DRAM to cost-effective cloud storage. Existing tiered storage systems predominantly employ reactive policies that respond to observed access patterns, leading to suboptimal performance under dynamic workloads and failing to address multi-objective optimization requirements. We propose a novel proactive data placement framework that integrates predictive deep learning with multi-objective reinforcement learning to anticipate future data access patterns and optimize placement decisions across storage hierarchies. Our method employs Long Short-Term Memory networks and Transformer architectures to model complex temporal dependencies in I/O traces, generating predictive access probability distributions for data blocks. A deep reinforcement learning agent subsequently leverages these predictions, along with application-specific metadata hints, to make proactive placement decisions that simultaneously optimize latency, throughput, and cost objectives. The system incorporates a sophisticated reward mechanism that balances performance gains against migration overhead, while employing prioritized experience replay and adaptive learning rates to handle non-stationary workload characteristics. Through comprehensive evaluation using both synthetic and real-world traces from deep learning training workloads, our method demonstrates substantial improvements over state-of-the-art algorithms: achieving up to 45.1% reduction in average I/O latency, 32.5% improvement in throughput for critical applications, and 28.8% reduction in storage costs. The framework’s ability to proactively adapt to evolving access patterns while maintaining computational efficiency makes it particularly suitable for large-scale machine learning and scientific computing environments where data placement critically impacts overall system performance.}, archive = {J_ACCESS}, author = {Suchuan Xing and Yihan Wang}, doi = {10.1109/ACCESS.2025.3586378}, journal = {IEEE Access}, pages = {117986-117998}, shortjournal = {IEEE Access}, title = {Proactive data placement in heterogeneous storage systems via predictive multi-objective reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Static voltage stability evaluation and correction strategy based on stage solving strategy and SIFT. ACCESS, 13, 117971-117985. (www)

  @article{ , abstract = {Aiming at the static voltage instability problem of the power grid caused by large-scale access of renewable energy sources, the study adopts the scale invariant feature transform algorithm for boundary feature extraction, while optimizing the traditional generalized resilience index of the power grid. An improved static voltage stability elasticity index is proposed for fast static voltage stability assessment. In addition, a time-domain rolling phase solution strategy is introduced to correct the static voltage stability. The results indicated that the boundary feature extraction method based on SIFT could effectively realize the extraction of boundary features and reflect the correlation between the network and the load. In the single-load and full-load growth scenarios, the improved elasticity metrics proposed by the study were consistent with the change trend of the traditional method and had higher sensitivity. In addition, in time phase 1 of the IEEE30 node system, the average value of load margin for each scenario in the first phase was 18.62 MW, which was below the load margin threshold. The power generation cost was only 577.1 yuan. In phase 3, the average load margin for each scenario increased to 19.71 MW, which was significantly higher than the load margin threshold, and the power generation costs were only 573.0 yuan. In time stages 2 and 3, the load margin of each scenario in the stage 1 was higher than the load margin threshold. In addition, the solution time of the time-domain rolling-based stage solving strategy was only 36.9s, which decreased by 10.9s compared to the interior point method. The study demonstrated that the proposed static voltage stability assessment and correction strategy enhances the accuracy and responsiveness of the power grid assessment. Concurrently, it ensures grid stability while preserving economic efficiency. This strategy is of considerable importance for facilitating the sustainable integration of large-scale renewable energy sources into the power grid.}, archive = {J_ACCESS}, author = {Yan Wen and Xiaoming Liu and Yongzhi Zhou and Huiwen Zhang and Donglei Sun}, doi = {10.1109/ACCESS.2025.3584182}, journal = {IEEE Access}, pages = {117971-117985}, shortjournal = {IEEE Access}, title = {Static voltage stability evaluation and correction strategy based on stage solving strategy and SIFT}, volume = {13}, year = {2025}, }
• (2025). Integrated bidding and battery scheduling in a microgrid for sealed-bid double auction power trading with peer microgrids under uncertainty and its blockchain-based implementation. ACCESS, 13, 117953-117970. (www)

  @article{ , abstract = {This paper proposes a novel framework for conducting sealed-bid double auctions in power trading for multi-microgrid networks, addressing the critical challenge of jointly optimizing bidding decisions and battery scheduling under uncertainty in renewable energy generation and load demand. In contrast to existing approaches that treat these components independently, our method explicitly models their interdependency for maximizing trading efficiency. We assume a normal distribution of prediction errors and introduce an uncertainty range and a bid buffer capacity to account for expected variations in forecasted generation and load, enabling more robust coordination between bidding and storage operations. While Q-learning determines the exact bid, the feasible power availability or demand is derived from the uncertainty range, ensuring consistency between learned bidding decisions and forecast-aware constraints. The Q-learning relies solely on its historical bidding outcomes without attempting to predict the bids of other participants. In parallel, battery operations are optimized using a hybrid method that combines Genetic Algorithm (GA) and Simulated Annealing (SA), explicitly incorporating the bid buffer capacity to align scheduling with market commitments. We also propose a fully decentralized and tamper-resistant execution architecture based on a consortium blockchain, where multiple aggregator agents within each microgrid, representing renewable sources, loads, storage systems, and the bid agent, function as independent blockchain nodes. Simulation results on a benchmark microgrid system with Monte-Carlo modeled prediction errors demonstrate that the proposed approach significantly enhances both economic benefits and trading robustness compared to conventional frameworks.}, archive = {J_ACCESS}, author = {Zubin J. B. and Sunitha R. and Gopakumar Pathirikkat}, doi = {10.1109/ACCESS.2025.3586465}, journal = {IEEE Access}, pages = {117953-117970}, shortjournal = {IEEE Access}, title = {Integrated bidding and battery scheduling in a microgrid for sealed-bid double auction power trading with peer microgrids under uncertainty and its blockchain-based implementation}, volume = {13}, year = {2025}, }
• (2025). Lyapunov-based deep residual neural network (ResNet) adaptive control. ACCESS, 13, 117943-117952. (www)

  @article{ , abstract = {Deep Neural Network (DNN)-based controllers have emerged as a tool to compensate for unstructured uncertainties in nonlinear dynamical systems. A recent breakthrough in the adaptive control literature provides a Lyapunov-based approach to derive weight adaptation laws for each layer of a fully-connected feedforward DNN-based adaptive controller. However, deriving weight adaptation laws from a Lyapunov-based analysis remains an open problem for deep residual neural networks (ResNets). This paper provides the first result on Lyapunov-derived weight adaptation for a ResNet-based adaptive controller. A nonsmooth Lyapunov-based analysis is provided to guarantee asymptotic tracking error convergence. Comparative Monte Carlo simulations are provided to demonstrate the performance of the developed ResNet-based adaptive controller. The ResNet-based adaptive controller shows a 64% improvement in the tracking and function approximation performance, in comparison to a fully-connected DNN-based adaptive controller.}, archive = {J_ACCESS}, author = {Omkar Sudhir Patil and Duc M. Le and Emily J. Griffis and Warren E. Dixon}, doi = {10.1109/ACCESS.2025.3584253}, journal = {IEEE Access}, pages = {117943-117952}, shortjournal = {IEEE Access}, title = {Lyapunov-based deep residual neural network (ResNet) adaptive control}, volume = {13}, year = {2025}, }
• (2025). Few-shot evaluation of vision language models for detecting visual defects in autonomous vehicle software requirement specifications. ACCESS, 13, 117914-117942. (www)

  @article{ , abstract = {Software Requirements Specifications (SRS) are crucial for defining system functionality, constraints, and objectives, particularly in safety-critical applications like autonomous vehicles (AV). Ensuring these requirements are precise, unambiguous, and complete is important for developing reliable self-driving systems. While traditional SRS are predominantly text-based, AV requirements pose unique challenges as they often include visual elements such as images and diagrams. Although Large Language Models (LLMs) have enhanced traditional text-based requirements, Vision-Language Models (VLMs) remain largely unexplored for evaluating defects in visual elements. This paper investigates the few-shot performance of three large-scale VLMs on their ability to detect various types of ambiguities, inconsistencies, and incompleteness in visual traffic scenario images that could be used in AV requirements specifications. Using Soft prompting and Chain-of-Thought (CoT) prompting, we evaluated the effectiveness of GPT-4, GEMINI, and ClaudeAI in detecting six types of visual defects. GPT-4 achieved the highest overall performance among the evaluated models, with the highest F1-score of 0.63 in detecting Spatial Incompleteness. CoT prompting notably enhanced defect detection performance for both GEMINI and ClaudeAI across all defect types. While CoT prompting did not significantly improve GPT-4’s detection capabilities, it significantly enhanced the ability of all models to explain the reasoning behind the identified defects. Despite promising results, none of the models could fully automate the requirements validation process, primarily due to their lack of domain-specific knowledge. Nevertheless, our findings suggest that fine tuning the models and structured prompt design offer the potential to improve VLM precision in visual defect detection.}, archive = {J_ACCESS}, author = {Nabil Bukhary and Maaz Ahmad and Karim Rashad and Samarth Rai and Salsabeel Shapsough and Yara Kaddoura and Dana Dghaym and Imran Zualkernan}, doi = {10.1109/ACCESS.2025.3586554}, journal = {IEEE Access}, pages = {117914-117942}, shortjournal = {IEEE Access}, title = {Few-shot evaluation of vision language models for detecting visual defects in autonomous vehicle software requirement specifications}, volume = {13}, year = {2025}, }
• (2025). Machine tool interoperability in smart manufacturing and industry 4.0. ACCESS, 13, 117867-117913. (www)

  @article{ , abstract = {Real-time decision making is supported by data-hungry emerging technologies such as machine learning and digital twins. Innovations in manufacturing process control utilizing these technologies are constrained by interoperability. Marketing materials, sales pitches, and academic literature portray interoperability on the factory floor as seamless and robust. However, this study demonstrates that in spite of plentiful mature standards, interoperability on the factory floor is neither seamless nor robust. To exemplify interoperability in the context of an established and widely adopted standard, this study analyzes the interoperability of machine tools produced by a premier equipment builder which has exceeded US ${\$}$ 1 billion in sales with more than 200,000 machines currently in operation worldwide. Through demonstrating and discussing non-interoperability and influencing factors, this study aims to provide insights and actionable intelligence applicable to industry and academia for laymen, end users, systems integrators, standards organizations, equipment builders, and data scientists. By leveraging phronesis and empirical evidence to demonstrate the state of interoperability, this study provides a fresh perspective on an age-old manufacturing challenge.}, archive = {J_ACCESS}, author = {M. R. Mccormick and Mohammed Shafae and Thorsten Wuest}, doi = {10.1109/ACCESS.2025.3585766}, journal = {IEEE Access}, pages = {117867-117913}, shortjournal = {IEEE Access}, title = {Machine tool interoperability in smart manufacturing and industry 4.0}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey of security and privacy in UAV systems. ACCESS, 13, 117843-117866. (www)

  @article{ , abstract = {The proliferation of unmanned aerial vehicles (UAVs) across diverse applications, from photography to critical infrastructure inspection, has raised significant security and privacy challenges. This comprehensive survey presents a systematic analysis of UAV security vulnerabilities, privacy concerns, and practical solutions, including concerns regarding implementation feasibility and regulatory compliance. We begin with a detailed UAV system model encompassing hardware, software, and communication protocols, followed by a novel categorization of security vulnerabilities that clearly distinguishes between hardware attack, software attack, and communication technology attack surfaces. Our analysis of privacy issues extends beyond traditional concerns, including ethical considerations in UAV surveillance and compliance with global privacy frameworks such as GDPR. We present state-of-the-art technical solutions, including lightweight encryption protocols, privacy-preserving machine learning, and blockchain-based security mechanisms, with specific implementation metrics and resource utilization guidelines. The paper also examines policy-based approaches and regulatory frameworks across different jurisdictions, providing a balanced perspective on both technical and governance aspects. Our work concludes with an analysis of emerging technologies and future research directions, emphasizing the need for integrated solutions that balance security requirements with operational constraints. This survey serves as a comprehensive reference for researchers, practitioners, and policymakers working to enhance UAV security and privacy while ensuring practical deployability.}, archive = {J_ACCESS}, author = {Bryce Cordill and Dongfeng Fang and Shengjie Xu}, doi = {10.1109/ACCESS.2025.3583985}, journal = {IEEE Access}, pages = {117843-117866}, shortjournal = {IEEE Access}, title = {A comprehensive survey of security and privacy in UAV systems}, volume = {13}, year = {2025}, }
• (2025). Dual strategy of electroless metal deposition and surface silylation toward scalable low-temperature hybrid bonding for advanced packaging applications. ACCESS, 13, 117831-117842. (www)

  @article{ , abstract = {The growing demand for high-performance computing and compact electronics has driven the transition toward advanced three-dimensional (3D) packaging technologies. Traditional packaging technologies, such as micro-bump interconnections, face limitations in achieving sub-micrometer pitches, prompting the development of alternative bonding strategies. Among them, Cu/SiO2 hybrid bonding (HB) has emerged as a promising method for enabling fine-pitch, high-density interconnects in next-generation semiconductor packaging. In this study, a reliable low-temperature Cu/SiO2 HB process was developed by combining metal electroless deposition (ELD) on sub-micron pitch Cu pads with selective surface functionalization of the SiO2 dielectric layer using silane. The ELD process facilitated uniform and selective Au deposition on Cu, acting as a diffusion metal that maintained interfacial stability during bonding. Conclusively, the SiO2 surface was modified with (3-aminopropyl)triethoxysilane (APTES), which formed strong covalent networks through silane polymerization, enhancing adhesion at the dielectric interface. This dual modification strategy facilitated direct Cu–Cu bonding and robust SiO2–SiO2 adhesion, resulting in a defect-free interface without voids or delamination. The bonding was conducted at a low-temperature of $250~^{\circ }$ C, thereby minimizing thermal stress typically associated with conventional high-temperature bonding processes. These result clearly demonstrates a practical and scalable method for achieving low-temperature Cu/SiO2 HB, contributing to the advancement of 3D integration in semiconductor packaging.}, archive = {J_ACCESS}, author = {Zambaga Otgonbayar and Jeoung Han Kim and Jinsung Rho and Jeong-Chul Kim and Jungchul Noh and Jeonghun Kim and Seong-Ho Yoon and Chang-Min Yoon}, doi = {10.1109/ACCESS.2025.3586041}, journal = {IEEE Access}, pages = {117831-117842}, shortjournal = {IEEE Access}, title = {Dual strategy of electroless metal deposition and surface silylation toward scalable low-temperature hybrid bonding for advanced packaging applications}, volume = {13}, year = {2025}, }
• (2025). Secure latency-aware task offloading using federated learning and zero trust in edge computing for IoMT. ACCESS, 13, 117808-117830. (www)

  @article{ , abstract = {The exponential growth of wearable medical devices generates massive healthcare data within the Internet of Medical Things (IoMT) framework, necessitating efficient and secure task processing. Although offloading medical tasks to Edge Servers (ESs) improves the agility of medical services and enhances real-time processing capabilities, it simultaneously introduces significant security and trust concerns in heterogeneous and delay-sensitive IoMT environments. Existing task offloading approaches either compromise on performance or fail to ensure sufficient safeguards against sophisticated cyber intrusions. To address the dual challenge of minimizing latency while ensuring robust security during task offloading in IoMT systems, this paper proposes a Trust and Latency-aware Task Offloading (TLTO) approach in heterogeneous IoMT by integrating ZTA with FL. TLTO achieves decentralized intelligence without compromising data confidentiality and system integrity. Before offloading, ZTA enforces authentication among the ESs and cloud entities, orchestrated through a dedicated zero-trust orchestrator. Within FL, an improved on-policy temporal difference control algorithm is leveraged for local model training. Moreover, this algorithm determines the optimal offloading strategy based on the global model while considering trustworthiness, the heterogeneous nature of tasks, resource availability, and latency. Simulation results validate the efficiency of the TLTO approach in securing task offloading while improving performance in dynamic IoMT environments. This approach provides a scalable and resilient solution to the stringent requirements of privacy-preserving, latency-sensitive healthcare applications.}, archive = {J_ACCESS}, author = {Waleed Almuseelem}, doi = {10.1109/ACCESS.2025.3586730}, journal = {IEEE Access}, pages = {117808-117830}, shortjournal = {IEEE Access}, title = {Secure latency-aware task offloading using federated learning and zero trust in edge computing for IoMT}, volume = {13}, year = {2025}, }
• (2025). Forecasting bitcoin volatility spikes from whale transactions and CryptoQuant data using synthesizer transformer models. ACCESS, 13, 117788-117807. (www)

  @article{ , abstract = {The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin’s next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.}, archive = {J_ACCESS}, author = {Dorien Herremans and Kah Wee Low}, doi = {10.1109/ACCESS.2025.3584243}, journal = {IEEE Access}, pages = {117788-117807}, shortjournal = {IEEE Access}, title = {Forecasting bitcoin volatility spikes from whale transactions and CryptoQuant data using synthesizer transformer models}, volume = {13}, year = {2025}, }
• (2025). A novel energy model for optimizing energy efficiency and prolonging network lifetime in MANET. ACCESS, 13, 117771-117787. (www)

  @article{ , abstract = {Mobile ad-hoc networks are functional for usage, which require inter-node contact, including disaster relief, protection, and combat spotting. These networks face critical challenges, including rapid energy consumption in battery-limited devices and random topological changes resulting from node mobility, which lead to communication instability and packet loss. These factors reduce network efficiency and shorten lifespan, particularly in mission-critical applications such as environmental monitoring or military operations. This research addresses the imbalance in energy consumption caused by a node failure that acts as a relay node. It presents the Replacing Relay Nodes to Prolong Lifetime and improve Energy Efficiency (RePLEE) model. Comprehensive simulations conducted in NS-2 reveal that RePLEE significantly improves performance compared to standard AODV and enhanced EEAODV protocols. Specifically, RePLEE achieves a 13% reduction in energy consumption, a 61% decrease in packet loss, and a 26% reduction in end-to-end delay relative to AODV. Additionally, it extends network lifetime by 49% over AODV and by 26% over EEAODV. RePLEE also demonstrates superior scalability by maintaining high packet delivery ratios and lower control overhead across varying node densities. These results confirm the effectiveness of RePLEE in addressing the limitations of traditional protocols by enhancing energy efficiency, prolonging operational lifespan, and ensuring stable connectivity in MANET environments.}, archive = {J_ACCESS}, author = {Abdulsalam Tonin and Malek Nasser Ali Algabri and Mansoor N. Ali and Mossa Ghurab and Abdualmajed Ahmed Ghaleb Al-Khulaidi and Ibrahim Ahmed Al-Baltah}, doi = {10.1109/ACCESS.2025.3586795}, journal = {IEEE Access}, pages = {117771-117787}, shortjournal = {IEEE Access}, title = {A novel energy model for optimizing energy efficiency and prolonging network lifetime in MANET}, volume = {13}, year = {2025}, }
• (2025). A study of factors influencing chinese design students’ adoption of AIGC tools. ACCESS, 13, 117753-117770. (www)

  @article{ , abstract = {As a critical factor in determining a product’s success, users’ willingness to adopt artificial intelligence-generated content (AIGC) tools is a key driver for their sustainable development. As a new productivity tool, AIGC tool adoption by design students is influenced by various factors. However, there is a lack of systematic research on this topic in the academic community, which hinders the sustainable development of AIGC tools. Based on this, the study combines the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-Technology Fit (TTF) models to construct a dual-perspective model—integrating “technology (external)” and “individual (internal)” dimensions—to investigate the key factors influencing design students’ willingness to adopt AIGC tools in their professional learning. A questionnaire was distributed to Chinese design students, resulting in 517 valid responses. The structural equation model (SEM) analysis yielded the following findings: Characteristics of AIGC Technology (CoAT) and students’ task characteristics positively influence task-technology fit. CoAT positively affect performance expectations (PE) but have no significant impact on effort expectations (EE). Task-technology fit positively influences use behaviors and performance expectations. Additionally, performance expectations, effort expectations, and social influences (SI) significantly enhance willingness to use, which in turn drives use behaviors (UB). Conversely, facilitating conditions (FC) do not affect use behaviors. Based on the empirical findings, this study discusses strategies for enhancing design students’ adoption of AIGC tools and provides recommendations for promoting their adoption and sustainability in design education.eing rejected by search engines.}, archive = {J_ACCESS}, author = {Xiang Wang and Mingxing Li and Yu Yao and Xiaoyang Zhu}, doi = {10.1109/ACCESS.2025.3582599}, journal = {IEEE Access}, pages = {117753-117770}, shortjournal = {IEEE Access}, title = {A study of factors influencing chinese design students’ adoption of AIGC tools}, volume = {13}, year = {2025}, }
• (2025). Advanced technologies to smart grid systems: Challenges and demands. ACCESS, 13, 117732-117752. (www)

  @article{ , abstract = {Smart grid systems are designed to allow bidirectional communication between smart meters’ side and control systems on the utility side. They are essential to incorporate green energy resources into grid systems. This incorporation allows for optimized green energy integration in grid systems. Thus, minimizing reliance on non-clean energy resources and limiting harmful gas emissions in the environment will ultimately contribute to a more sustainable and eco-friendly energy infrastructure. They also open the door widely for smart city technologies to become a reality. Nevertheless, there are many challenges facing the transformation from traditional to smart grid systems. Many of these challenges are related to security and resiliency problems. This work reviews research papers incorporating advanced technologies to enhance smart grid systems. This work explores five scopes in smart grid systems: 5G, fog computing, multi-agent reinforcement learning, heartbeat, and self-healing technologies. A comprehensive discussion is presented on every scope, focusing on their role in smart grid systems, especially resiliency and security. The reviewed works reveal the strengths of these technologies and the advantages achieved when each technology is applied in smart grid systems. Meanwhile, this review stresses the need to create a comprehensive framework for smart grid systems that integrates multiple advanced technologies. The framework should use emerging and advanced technologies to guarantee efficiency, security, and resiliency.}, archive = {J_ACCESS}, author = {Remah Bani Younisse and Mouhammd Alkasassbeh and Amjad Aldweesh}, doi = {10.1109/ACCESS.2025.3586466}, journal = {IEEE Access}, pages = {117732-117752}, shortjournal = {IEEE Access}, title = {Advanced technologies to smart grid systems: Challenges and demands}, volume = {13}, year = {2025}, }
• (2025). Impact of virtual synchronous generators on frequency-constrained unit commitment: A neural network-assisted framework for assessing rate of change of frequency. ACCESS, 13, 117716-117731. (www)

  @article{ , abstract = {The increasing penetration of inverter-based resources (IBRs) into electric power systems and the retirement of synchronous generators (SGs) both reduce system inertia and challenge frequency stability. This is particularly evident in terms of the rate of change of frequency (ROCOF) measured over short, practically measurable time windows (P-ROCOF). Conventional frequency-constrained unit commitment (FCUC) uses a theoretically idealized ROCOF (T-ROCOF) constraint that underestimates the effect of the fast frequency response (FFR) provided by virtual synchronous generators (VSGs) on P-ROCOF, leading to over-provisioning of generator capacity and higher operating costs. This paper presents a neural network-assisted FCUC (NN-UC) framework that formulates a mixed-integer ROCOF constraint based on an NN trained to accurately predict P-ROCOF, and introduces a post-solution compensation procedure to correct NN prediction errors. We conducted one-year scenario studies on modified IEEE 9-bus and Japanese EAST 10-machine systems. Results show that NN-UC reduces both total operating cost and committed SG capacity compared to the conventional UC, which uses the T-ROCOF constraint. Even after worst-case compensation for NN’s ROCOF prediction errors, NN-UC still achieves net SG capacity reductions in most scenarios. These findings demonstrate that explicitly modeling the VSG’s FFR in FCUC yields both economic and operational benefits, while the compensation procedure ensures reliable operation.}, archive = {J_ACCESS}, author = {Ryosuke Shikuma and Yu Fujimoto and Dai Orihara and Hiroshi Kikusato and Hisao Taoka and Yasuhiro Hayashi}, doi = {10.1109/ACCESS.2025.3585194}, journal = {IEEE Access}, pages = {117716-117731}, shortjournal = {IEEE Access}, title = {Impact of virtual synchronous generators on frequency-constrained unit commitment: A neural network-assisted framework for assessing rate of change of frequency}, volume = {13}, year = {2025}, }
• (2025). A mmWave JCAS system for real-time high-data rate communication and RADAR sensing. ACCESS, 13, 117700-117715. (www)

  @article{ , abstract = {This paper presents a real-time millimeter-wave (mmWave) joint communication and sensing (JCAS) system, supporting bi-directional data communication with up to 5.8 Gbit/s coded data rate as well as mono-static radio detection and ranging (RADAR) with a range resolution of 6.7 cm and up to 2.1 kHz sensing rate. The same hardware resources, i.e., an orthogonal frequency-division multiplexing (OFDM) baseband processor and a beam-steering phased array antenna frontend, the same signal waveform and the same frequency channel are used for both sensing and communication. After a description of one intended use case and the design constraints, the system architecture is described in detail. The focus is on two main modules, i.e. the advanced extensible interface 4 (AXI4) stream crossbar switch and the sensing controller, while changes of the basic OFDM baseband processor design are also explained. For the sensing function, the used hardware-based OFDM baseband processor does not provide a sufficiently precise synchronization of received frames, so a method for the temporal alignment of the received channel impulse responses (CIRs) for different beams is developed and assessed. The evaluation of the system performance and the measurement results highlight that it is possible to perform very precise sensing with an OFDM baseband processor purely developed for data communication. The presented method for the CIR alignment does not depend on the baseband signal processing and can be used in any JCAS system with full duplex sensing.}, archive = {J_ACCESS}, author = {Markus Petri and Nebojsa Maletic}, doi = {10.1109/ACCESS.2025.3586645}, journal = {IEEE Access}, pages = {117700-117715}, shortjournal = {IEEE Access}, title = {A mmWave JCAS system for real-time high-data rate communication and RADAR sensing}, volume = {13}, year = {2025}, }
• (2025). YOLO-DKM: A flame and spark detection algorithm based on deep learning. ACCESS, 13, 117687-117699. (www)

  @article{ , abstract = {In the field of fire prevention and industrial safety monitoring, it is very important to accurately and efficiently detect fires and deal with some factors that may affect fires in the early stages. In the field of computer vision, existing fire detection algorithms often have problems such as low detection accuracy and algorithm error detection. Moreover, for some fire warning factors, the current focus is mainly on detecting smoke, while existing detection algorithms are still relatively lacking in detecting other warning factors. In order to explore other fire warning factors and enhance the detection capability of fires, this paper proposes an improved YOLOv8 fire and spark detection algorithm-YOLO-DKM. Improvements have been made to the backbone network and neck network. Firstly, the SKAttention attention mechanism has been introduced into the backbone network, which can adaptively adjust the attention weights at different scales, effectively improving the detection of small flame targets or tiny granular sparks. And integrate DSConv into the C2f module, relying on dynamic characteristics to adaptively adjust convolution operations according to different scene features for more flexible local region feature extraction, capturing local features of flames and sparks. Introducing CBAM attention mechanism into the neck network can help reduce background interference, enhance model perception, and improve detection accuracy and recall. The experimental results show that compared with the original YOLOv8 algorithm, the YOLO-DKM algorithm improves accuracy by 5.4% and recall by 3.6%, proving the effectiveness of the improved algorithm in detecting flames and sparks.}, archive = {J_ACCESS}, author = {Linpo Shang and Xufei Hu and Zijian Huang and Qiang Zhang and Zhiyu Zhang and Xin Li and Yanzuo Chang}, doi = {10.1109/ACCESS.2025.3581968}, journal = {IEEE Access}, pages = {117687-117699}, shortjournal = {IEEE Access}, title = {YOLO-DKM: A flame and spark detection algorithm based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Modeling the impact of public transportation and mitigation strategies in COVID-19 spread in mexico city. ACCESS, 13, 117668-117686. (www)

  @article{ , abstract = {Understanding the spread of infectious diseases such as COVID-19 has been a persistent challenge for scientists and policymakers. While numerous studies have researched general aspects of the COVID-19 transmission dynamics, few have specifically examined its spread within public transportation systems in highly interconnected urban environments while incorporating the role played by the degree of effectiveness of time-varying mitigation strategies. This paper presents a novel approach using a discrete, probabilistic, and age-structured model that integrates the concept of compartmental models in epidemiology with cellular automata networks to investigate the role of public transportation and mitigation strategies in COVID-19 transmission in Mexico City districts. Therefore, the transmission of the disease is based on the definition of two distinct forces of infection: one associated with the use of public transport and the other with the occurrence within each district. Furthermore, the model incorporates several key time-varying parameters such as district population, commuting patterns, and contact matrices for different settings. Moreover, a stringency index was constructed based on the Oxford index in order to evaluate the impact of mitigation strategies established by the Mexican government and their compliance. Simulation results successfully reproduce COVID-19 transmission dynamics in Mexico City districts before vaccination efforts. Findings suggest that public transportation significantly contributed to the spread of the disease during the initial phase of the pandemic in Mexico City. These insights provide valuable guidance for public health strategies to mitigate infectious disease transmission in densely populated urban environments.}, archive = {J_ACCESS}, author = {Fernando Reyes-Gómez and Emmanuel Torres-Marín and María Elena Lárraga and Luis Alvarez-Icaza and Román A. Mora-Gutiérrez}, doi = {10.1109/ACCESS.2025.3584378}, journal = {IEEE Access}, pages = {117668-117686}, shortjournal = {IEEE Access}, title = {Modeling the impact of public transportation and mitigation strategies in COVID-19 spread in mexico city}, volume = {13}, year = {2025}, }
• (2025). Gradient guided depth super-resolution using attention-based cross domain interaction module. ACCESS, 13, 117656-117667. (www)

  @article{ , abstract = {Color guided depth image super-resolution (DISR), leveraging the strong structural similarity between the registered high-resolution (HR) color image and the low-resolution (LR) depth image. has achieved remarkable results. However, when the sampling factor is large, the DISR suffers from reconstructing accurate depth edges due to the severe loss of high frequency components. Crucially, direct use of the high frequency (HF) information of the registered map may transfer the high-frequency textures into the depth results and blur the depth edges. To address this, we propose GDISRNet, which is a gradient guided DISR network. It not only utilizes HF information of the color image, but also fundamentally avoids the texture copying artifacts. First, we compute the gradient map from the Y channel of the HR color image. Then, an Attention-based Multi-Scale Feature extraction module (AMSF) extracts the multi-scale features respectively from the gradient map and the interpolated LR Depth map. Third, a novel Cross-Domain Interaction Module (CDIM) is used to fuse these features to enhance depth boundaries while suppressing color textures at the same time. Additionally, a gradient loss is introduced to prevent edge smoothing effectively. Finally, a residual progressive structure of AMSF and CDIM reconstructs the SR depth image with enhanced high-frequency information. Experiments on several popular benchmark datasets including Middlebury, Sintel, Lu and NYU V2, demonstrate the superiority of the proposed GDISRNet over representative state-of-the-art methods.}, archive = {J_ACCESS}, author = {Yang Shuyuan and Cao Ning and Li Wei}, doi = {10.1109/ACCESS.2025.3582793}, journal = {IEEE Access}, pages = {117656-117667}, shortjournal = {IEEE Access}, title = {Gradient guided depth super-resolution using attention-based cross domain interaction module}, volume = {13}, year = {2025}, }
• (2025). Building a robust scenario library for safety assurance of automated driving systems: A review. ACCESS, 13, 117619-117655. (www)

  @article{ , abstract = {Ensuring the safety of Automated Driving Systems (ADSs) is both a critical and complex endeavor. The increasing demand for autonomous driving technologies underscores the importance of robust safety assurance, yet the intricate nature of these systems presents significant challenges. Scenario-based testing has emerged as a promising approach for ADS safety assurance, but the industry still lacks a comprehensive workflow to effectively implement this process. A pivotal element of scenario-based safety assurance is the creation of a scenario library that thoroughly encompasses the necessary conditions to test ADSs for deployment in specific regions. This paper offers an in-depth analysis of best practices and research in scenario-based safety assurance, aiming to develop a detailed scenario library tailored for testing ADSs within diverse driving conditions. The research addresses the need for scenario creation for the verification and validation (V&V) of ADSs across different environments. The diverse environmental conditions and road traffic behaviors present unique challenges that distinguish one region from another. While every region has its specificities, certain contexts pose particular difficulties in scenario development. This paper outlines the literature review methodology used and presents the current state of scenarios and scenario generation activities. The review synthesizes information from over a hundred sources, including research articles, standards, and best practices. The literature is evaluated across six key areas: Operational Design Domain (ODD), scenario description and representation, data sources and scenario generation methods, scenario selection, scenario assessment and test criteria, and general frameworks for ADS safety assurance. Key contributions, among others, include a structured classification of scenario generation and selection methods, identification of gaps in current practices, and a set of actionable recommendations for future research and regulatory alignment. The paper concludes with recommendations for future work, focusing on the use of scenarios for ADS V&V and proposing a scenario-generation framework tailored to various driving environments.}, archive = {J_ACCESS}, author = {António A. Bruto da Costa and Patrick Irvine and Tudor Dodoiu and Siddartha Khastgir and Paul Jennings}, doi = {10.1109/ACCESS.2025.3584206}, journal = {IEEE Access}, pages = {117619-117655}, shortjournal = {IEEE Access}, title = {Building a robust scenario library for safety assurance of automated driving systems: A review}, volume = {13}, year = {2025}, }
• (2025). A hybrid LSTM-GRU model for stock price prediction. ACCESS, 13, 117594-117618. (www)

  @article{ , abstract = {The dynamic variation of the stock market plays a crucial role in assessing a country’s economic power and development. Modeling the chaotic fluctuations in stock prices aids investors and traders in uncertain situations by evaluating market trends for investment decisions. Previous methods utilized Recurrent Neural Network (RNN) models for prediction. However, none of the previous works attempted to leverage the full range of technical analysis features and use Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) modules to capture short and long dependencies. Initially, we use machine learning to extract appropriate features for making predictions. Subsequently, the impact of each feature is adjusted proportionally to the weight matrix extracted from the neural network. We combined LSTM and GRU to capture long and short information horizons in stock sequences to mitigate limitations of prior methods, such as the difficulty in hyperparameter tuning. By combining the capabilities of these two networks along together, we present a model for predicting market fluctuations. Our proposed model was evaluated in a specified test market, achieving a 3% improvement in Mean Squared Error (MSE) over the previous best approaches.}, archive = {J_ACCESS}, author = {Amirfarhad Farhadi and Azadeh Zamanifar and Amir Alipour and Alireza Taheri and Mohammad Asadolahi}, doi = {10.1109/ACCESS.2025.3586558}, journal = {IEEE Access}, pages = {117594-117618}, shortjournal = {IEEE Access}, title = {A hybrid LSTM-GRU model for stock price prediction}, volume = {13}, year = {2025}, }
• (2025). Research on the classification of electromagnetic radiation safety levels of mobile communication terminals using unsupervised learning models. ACCESS, 13, 117582-117593. (www)

  @article{ , abstract = {Specific Absorption Rate (SAR) is a critical metric for assessing the electromagnetic radiation safety of devices, and it plays a significant role in international standards and certifications. Leading organizations, such as the International Commission on Non-Ionizing Radiation Protection (ICNIRP), the Institute of Electrical and Electronics Engineers (IEEE), and several countries have established mandatory standards and clearly defined SAR limits. However, there is a lack of a more detailed grading system, especially concerning highly sensitive groups such as infants, pregnant women, and others, whose sensitivity to electromagnetic radiation and potential health risks are much higher than that of the general population. This study, based on 1,119 SAR measurement data of mobile communication terminals published by the French National Frequency Agency (ANFR) from 2012 to 2024, applies unsupervised learning methods to uncover the natural distribution characteristics of SAR data. By integrating health protection standards from multiple countries, clinical research findings, and certification practice, this study proposes a four-level SAR safety classification proposal, aiming to provide a foundation for the development of a labeling and certification system for the electromagnetic radiation safety levels of mobile communication terminals.}, archive = {J_ACCESS}, author = {Chuanzi Deng and Qiulin Wu and Tong Wu}, doi = {10.1109/ACCESS.2025.3585928}, journal = {IEEE Access}, pages = {117582-117593}, shortjournal = {IEEE Access}, title = {Research on the classification of electromagnetic radiation safety levels of mobile communication terminals using unsupervised learning models}, volume = {13}, year = {2025}, }
• (2025). A high-voltage-gain non-isolated dual-input single-output soft-switching DC–DC converter with low-voltage stress. ACCESS, 13, 117571-117581. (www)

  @article{ , abstract = {Multiple input non-isolated dc-dc converters remain very popular in applications involving hybrid energy integration. This is due to their compact structure and low cost in comparison with their isolated topologies counterparts. This paper proposed a high voltage gain high efficiency, soft-switching dual-input single-output dc-dc converter with low voltage stress for hybrid energy integration. The proposed converter achieves soft-switching for all the power switches at turn-on instants alongside high voltage conversion ratio. The coupled inductor is designed to facilitate ZVS and achieve high voltage gain eliminating the need for additional auxiliary circuits. This reduces the converter cost, number of components count and enhances efficiency. Furthermore, the voltage across the power switch is clamped with the help of an active clamp network. This facilitates the use of switches with low drain-to-source resistance ( $R_{ds ~on}$ ) hence reducing conduction losses and increasing the efficiency. Similarly, the operating modes of the converter as well as comparison with other similar converters are presented. In addition, experimental results obtained for 250 W prototype are presented which converts an input of 24 V dc to a 250 V dc at the output terminal with 97.8% efficiency.}, archive = {J_ACCESS}, author = {Ahmed Alanssari and Mohd Rodhi Sahid and Razman Ayop and Farag Elghabsi and Mohammad Al Takrouri and Abdulhakeem Dobi}, doi = {10.1109/ACCESS.2025.3584048}, journal = {IEEE Access}, pages = {117571-117581}, shortjournal = {IEEE Access}, title = {A high-voltage-gain non-isolated dual-input single-output soft-switching DC–DC converter with low-voltage stress}, volume = {13}, year = {2025}, }
• (2025). Stacked LSTM model for contextual correlation detection among multiple emotions. ACCESS, 13, 117558-117570. (www)

  @article{ , abstract = {Emotions are closely tied to human behavior and play a critical role in daily life. With the widespread use of social media, individuals frequently express their feelings online, making emotion extraction from social networks an active area of research. This has applications in domains such as patient emotion monitoring, emotional text-to-speech synthesis, and empathetic chatbots. While numerous studies have focused on single-emotion detection, limited work has addressed the simultaneous identification of multiple coexisting emotions. These emotions are often influenced by contextual factors, including past emotional states and social interactions. Leveraging such dependencies can significantly enhance emotion detection performance. This study proposes a fast, accurate, and robust method for predicting future emotions based on current emotional states, while also identifying interrelated emotions present within social media posts. We annotate a public dataset with multilabel emotion tags and model contextual dependencies, both temporal and social, using a novel Stacked Long Short-Term Memory (LSTM) architecture. The model incorporates encoder-decoder layers to capture short- and long-term dependencies among emotions and employs a time-distributed dense layer to differentiate features associated with each emotion. Our experiments show significant improvements in capturing contextual correlations among multiple emotions. The proposed model achieved a micro F1-score of 0.50 on the GoEmotions dataset and demonstrated comparable performance to transformer-based models on the DepressionEmo dataset, while requiring fewer computational resources. These findings underscore the effectiveness and efficiency of our approach in modeling complex emotional dynamics across social networks.}, archive = {J_ACCESS}, author = {Aqsa Younas and Shazia Riaz and Saqib Ali and Rafiullah Khan and Mohib Ullah and Daehan Kwak}, doi = {10.1109/ACCESS.2025.3582764}, journal = {IEEE Access}, pages = {117558-117570}, shortjournal = {IEEE Access}, title = {Stacked LSTM model for contextual correlation detection among multiple emotions}, volume = {13}, year = {2025}, }
• (2025). Contrastive learning algorithm for low-resource cryptographic attack event detection. ACCESS, 13, 117545-117557. (www)

  @article{ , abstract = {The widespread use of the internet has led to frequent cryptographic attack event incidents, which pose various risks, including the leakage of personal information, privacy data, identity theft, and potential legal liabilities. However, conventional event detection models depend on expert-annotated trigger words, which are costly to obtain and constrain the applicability of detection methods in cryptographic security. Detecting cryptographic attack events faces two challenges: 1) Semantic complexity: terminology and abbreviations are diverse, and the same event is described using different vocabulary, sentence structures, and contexts, making it difficult for models to accurately identify specific events; 2) Lack of annotated features: This field relies on manually annotated data for feature learning, but there is a lack of publicly available datasets, and related research is scarce. Thus, we propose a method CLAD: Contrastive Learning Algorithm for Detecting Low-resource Cryptographic Attack Event. By comparing similarities and differences between samples, richer feature representations can be extracted. Additionally, assigning appropriate weights to keywords of different event types and employing clustering methods for effective classification reduce reliance on manually labeled data, thereby facilitating the training of an efficient cryptographic attack event detection model. To evaluate the effectiveness of the proposed method, we analyzed 916 cryptographic attack events collected from news blogs. The evaluation results show that the proposed method achieved an F1 score of 82.05% in detecting cryptographic attack events in low-resource scenarios, significantly outperforming state-of-the-art solutions. It reduces reliance on manual annotation and effectively handles semantic complexity using contrastive learning and clustering techniques.}, archive = {J_ACCESS}, author = {Peng Luo and Rangjia Cai and Yuanbo Guo}, doi = {10.1109/ACCESS.2025.3581984}, journal = {IEEE Access}, pages = {117545-117557}, shortjournal = {IEEE Access}, title = {Contrastive learning algorithm for low-resource cryptographic attack event detection}, volume = {13}, year = {2025}, }
• (2025). 2D animal skeletons keypoint detection: Research progress and future trends. ACCESS, 13, 117521-117544. (www)

  @article{ , abstract = {Research on two-dimensional keypoint detection within the domain of computer vision has experienced significant advancements. In contrast to the high precision and applicability achieved in human applications, the field of animal keypoint detection remains in its nascent stages of development. To explore the applications and potential of skeleton keypoint detection in areas such as animal pose estimation, behavior recognition, and intelligent breeding. To this end, we focuse on analyzing animal skeletal keypoint detection models based on deep learning technology, examining their keypoint representation forms and multi-object detection strategies. Additionally, it provides a detailed analysis of the method’s practical application details and improvements. The paper not only summarizes different model algorithms, datasets, and evaluation metrics related to animal keypoint detection but also integrates various application scenarios, highlighting distinct features under different focal points. Ultimately, this review is expected to broaden the research horizons and methodologies related to animal intelligent behavior recognition, animal welfare studies, and intelligent breeding among scholars.}, archive = {J_ACCESS}, author = {Pengfei Ma and Ronghua Gao and Qifeng Li and Qinyang Yu and Rong Wang and Chengrong Lai and Weiwei Huang and Peng Hao and Zhaoyang Wang and Xuwen Li}, doi = {10.1109/ACCESS.2025.3585267}, journal = {IEEE Access}, pages = {117521-117544}, shortjournal = {IEEE Access}, title = {2D animal skeletons keypoint detection: Research progress and future trends}, volume = {13}, year = {2025}, }
• (2025). Intelligent data reduction for IoT: A context-driven framework. ACCESS, 13, 117500-117520. (www)

  @article{ , abstract = {The exponential growth of the Internet of Things (IoT) has resulted in significant data production, characterized by the continuous collection and transmission of data from interconnected devices and sensors to Internet-based storage and processing services. In this scenario, several challenges arise in efficiently managing huge amounts of data, especially when IoT applications involve multiple heterogeneous sensor-based sources, necessitating effective data fusion techniques. A new approach based on artificial intelligence is proposed in this article to process data at the edge of the network infrastructure, adopting a contextual relevance paradigm as an optimization criterion. For this purpose, an Artificial Intelligence (AI)-based mechanism is developed to predict analytical metrics for different sensor data configurations, notably reduction and distortion ratios, across a range of pre-selected data reduction algorithms. With these predictions based on existing datasets, a selector algorithm module is adopted to identify the most suitable data reduction approach for specific IoT applications. This approach differs from existing work by using contextual relevance to guide the dynamic selection of Data Reduction (DR) algorithms, predicting results rather than computing them, which significantly reduces computational overhead. It is technique-agnostic, scalable, and adaptable to any DR method with available training data, acting as an open decision-support solution. This innovative approach showcases its ability to dynamically select data reduction algorithms for performance enhancement, which can contribute significantly to improving many heterogeneous IoT-based applications. Experimental results using IoT time-series data demonstrate its effectiveness in predicting DR metrics and selecting optimal algorithms with minimal latency, highlighting its potential in time-critical and data-intensive IoT scenarios.}, archive = {J_ACCESS}, author = {Laercio Pioli and Douglas D. J. De Macedo and Daniel G. Costa and Mario A. R. Dantas}, doi = {10.1109/ACCESS.2025.3586539}, journal = {IEEE Access}, pages = {117500-117520}, shortjournal = {IEEE Access}, title = {Intelligent data reduction for IoT: A context-driven framework}, volume = {13}, year = {2025}, }
• (2025). PyCSBtool: Python-based GUI learning tool for basics control system education. ACCESS, 13, 117491-117499. (www)

  @article{ , abstract = {Control systems education plays a fundamental role in engineering education, as it provides the foundation for understanding how dynamic systems respond to various inputs and behave over time. Software packages like MATLAB and LabVIEW are widely used in control system analysis and synthesis; however, their proprietary nature and high costs limit access, preventing many students from using them as tools to grasp and practice advanced theories in control education. To solve this problem, the open-source GUI-based Python tool PyCSBtool was created to help teach control systems. Students can use transfer function or state-space representations to model and study control systems. The tool has interactive graphs like root locus diagrams, step and impulse responses, and PID tuning, as well as pole-zero maps. These features allow students to experiment with different parameters and observe system responses in real time, enhancing their understanding of control system dynamics. PyCSBtool also includes real-time data acquisition and system identification features, enabling users to analyse real systems using experimental data. PyCSBtool is an open-source, flexible, and easy-to-use option for paid software. It was made with Python’s Control System and SciPy libraries, and its graphical user interface is based on the Tkinter framework. It is a useful tool for students, researchers, and teachers because it bridges the gap between academically studying control systems and using them in real life. This makes both basic and advanced control system ideas easier to understand. The source code and documentation for PyCSBtool are openly available at https://github.com/dhanu2017/PyCSBtool}, archive = {J_ACCESS}, author = {C. H. Manathunga and R. A. D. D. Dharmasiri and W. K. Indika L. Wanniarachchi and M. K. Jayananda and Tadachika Nakayama}, doi = {10.1109/ACCESS.2025.3583103}, journal = {IEEE Access}, pages = {117491-117499}, shortjournal = {IEEE Access}, title = {PyCSBtool: Python-based GUI learning tool for basics control system education}, volume = {13}, year = {2025}, }
• (2025). SEMP-TA: A novel stock market prediction approach based on stacking ensemble machine learning for effective trend analysis. ACCESS, 13, 117479-117490. (www)

  @article{ , abstract = {The stock market has seen an influx of capital as a result of the rapid economic expansion over the last few years. Therefore, trying to foretell how the stock market would behave is seen as an essential and, so far, more successful activity. If the investments are profitable, the stock values will go up. The ever-changing and unpredictable character of stock values, however, makes forecasting by investors quite challenging. This study presents the Stacked Ensemble Model for Trend Analysis (SEMP-TA), a novel approach to trend prediction in the stock market. The first step of SEMP-TA is data preprocessing, which involves selecting features based on clusters to find the most critical indicators quickly. The second step is trend prediction. The initial step in cleaning data is preprocessing, which includes tasks such as normalising data, identifying and discarding missing values, and selecting important features using cluster-based feature selection. The second step involves feeding the cleaned data into the prediction model. To anticipate future stock market patterns, trend prediction uses a mix of XGBoost, LightGBM, and Support Vector Regression (SVR). With an impressive Mean Absolute Error (MAE) of 1.60, a Mean Squared Error (MSE) of 7.896, and an R2 score of 0.9997, the findings demonstrate that SEMP-TA outperforms other current approaches. In addition, the method’s efficacy and resilience have been shown by its application to the NIFTY-50 dataset.}, archive = {J_ACCESS}, author = {Chanda Raj Kumar and S. Manikandan}, doi = {10.1109/ACCESS.2025.3586233}, journal = {IEEE Access}, pages = {117479-117490}, shortjournal = {IEEE Access}, title = {SEMP-TA: A novel stock market prediction approach based on stacking ensemble machine learning for effective trend analysis}, volume = {13}, year = {2025}, }
• (2025). A fine-tuned BERT-based model for individual log anomaly detection in operational monitoring at paranal observatory. ACCESS, 13, 117464-117478. (www)

  @article{ , abstract = {In operational environments such as astronomical observatories, continuous monitoring of system logs is critical yet challenging due to the vast volume of data generated. Manual inspection of these logs is impractical, needing automated methods capable of accurate and scalable anomaly detection. This paper proposes a novel sentiment-aware anomaly detection framework, leveraging Bidirectional Encoder Representations from Transformers with In-Task Pre-Training and Fine-Tuning (BERT-ITPT-FiT), to classify textual logs based on their inherent sentiment polarity. Specifically tailored for the Paranal Observatory, our approach incorporates a computationally efficient, character-based preprocessing strategy that retains essential semantic elements, such as acronyms and technical terms, thereby eliminating the need for traditional parsing. This method efficiently processes a substantial dataset comprising 7,359,506 log entries from three VLTI instruments in an average runtime of only 114.23 seconds. Extensive experiments utilizing real operational data demonstrate that our architecture achieves an effective throughput of approximately 3,958 logs per second during the combined embedding generation and model training phases while consistently delivering an excellent classification performance. Besides, it significantly outperforms existing deep learning methods combined with conventional embeddings in both in- and cross-instrument scenarios, achieving F1-Scores exceeding 99.99% and 99.96%, respectively. This work emphasizes a critical balance between computational efficiency and classification performance, providing a robust and scalable solution for anomaly detection in high-stakes observatory operations.}, archive = {J_ACCESS}, author = {Andrés H. Catalán and Rodrigo A. Carrasco and Gonzalo A. Ruz and Juan P. Gil}, doi = {10.1109/ACCESS.2025.3586586}, journal = {IEEE Access}, pages = {117464-117478}, shortjournal = {IEEE Access}, title = {A fine-tuned BERT-based model for individual log anomaly detection in operational monitoring at paranal observatory}, volume = {13}, year = {2025}, }
• (2025). A novel SHiP vector machine for network intrusion detection. ACCESS, 13, 117445-117463. (www)

  @article{ , abstract = {In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data.}, archive = {J_ACCESS}, author = {Sowmya T. and E. A. Mary Anita}, doi = {10.1109/ACCESS.2025.3586296}, journal = {IEEE Access}, pages = {117445-117463}, shortjournal = {IEEE Access}, title = {A novel SHiP vector machine for network intrusion detection}, volume = {13}, year = {2025}, }
• (2025). Evaluating electricity demand elasticity from a social perspective: Implications for sustainable development and urban resilience. ACCESS, 13, 117430-117444. (www)

  @article{ , abstract = {The central focus of this study revolves around constructing a comprehensive load model to estimate the elasticity of electricity demand predicated on customers’ risk-averse tendencies. This model serves as the foundation for designing a proficient demand response program. Moreover, a welfare function is employed to ascertain the optimal incentives to motivatecustomers to curtail their electricity consumption. Given Iran’s recent socioeconomic challenges marked by a high inflation rate and inadequate investment in power plants, data from the Iranian power system is employed to validate the efficacy of the proposed model in the numerical analysis. The findings reveal that customers exhibiting higher levels of risk aversion tend to derive greater satisfaction from consuming electricity and consequently necessitate more substantial incentives to prompt a reduction in their demand. Additionally, the results highlight that variations in electricity prices can counterbalance the impact of income increases on consumption patterns.}, archive = {J_ACCESS}, author = {Yaser A. Nanehkaran and Shixi Tang and Amir Bairami Akhi Jahani and Amir Niromandfam and Tolga Pusatli}, doi = {10.1109/ACCESS.2025.3586482}, journal = {IEEE Access}, pages = {117430-117444}, shortjournal = {IEEE Access}, title = {Evaluating electricity demand elasticity from a social perspective: Implications for sustainable development and urban resilience}, volume = {13}, year = {2025}, }
• (2025). Fully data-driven but interpretable human behavioural modelling with differentiable discrete choice model. ACCESS, 13, 117420-117429. (www)

  @article{ , abstract = {Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. In this paper, we introduce the differentiable discrete choice model (Diff-DCM), a fully data-driven method for the interpretable modelling, learning, prediction, and control of complex human behaviours, which is realised by differentiable programming. Solely from input features and choice outcomes without any prior knowledge, Diff-DCM can estimate interpretable closed-form utility functions that reproduce observed behaviours. Comprehensive experiments with both synthetic and real-world data demonstrate that Diff-DCM can be applied to various types of data and requires only a small amount of computational resources for the estimations, which can be completed within tens of seconds on a laptop without any accelerators. In these experiments, we also demonstrate that, using its differentiability, Diff-DCM can provide useful insights into human behaviours, such as an optimal intervention path for effective behavioural changes. This study provides a strong basis for the fully automated and interpretable modelling, prediction, and control of human behaviours.}, archive = {J_ACCESS}, author = {Fumiyasu Makinoshima and Tatsuya Mitomi and Fumiya Makihara and Eigo Segawa}, doi = {10.1109/ACCESS.2025.3585172}, journal = {IEEE Access}, pages = {117420-117429}, shortjournal = {IEEE Access}, title = {Fully data-driven but interpretable human behavioural modelling with differentiable discrete choice model}, volume = {13}, year = {2025}, }
• (2025). Ego vehicle speed estimation with automotive corner radar under velocity ambiguity using optimized MobileNetV3. ACCESS, 13, 117404-117419. (www)

  @article{ , abstract = {Accurate estimation of ego vehicle speed is crucial for autonomous driving and advanced driver-assistance systems (ADAS). However, conventional front-facing radar-based speed estimation methods suffer from challenges such as limited field of view, Doppler ambiguity, and the presence of moving targets. Moreover, corner radar not only encounters these issues but also faces additional difficulties due to its mount angle, leading to severe performance degradation in complex environments. To overcome these limitations, this study proposes a lightweight deep learning-based regression model optimized for corner radar, enabling robust and real-time speed estimation. The proposed method utilizes the MobileNetV3-Small architecture to achieve high accuracy while maintaining computational efficiency. Experimental validation using real-world measurement data demonstrates that the proposed model surpasses conventional methods, exhibiting strong generalization capability even in environments with velocity ambiguity. These findings confirm the feasibility of radar-only speed estimation without additional vehicle sensors and emphasize the potential of corner radar beyond object detection, extending its application to autonomous driving systems.}, archive = {J_ACCESS}, author = {Soyun Lee and Joonseok Yoon and Howon Kim and Jinwoo Kim and Byungkwan Kim}, doi = {10.1109/ACCESS.2025.3585938}, journal = {IEEE Access}, pages = {117404-117419}, shortjournal = {IEEE Access}, title = {Ego vehicle speed estimation with automotive corner radar under velocity ambiguity using optimized MobileNetV3}, volume = {13}, year = {2025}, }
• (2025). From theory to practice: Empirical insights on BPM lifecycle adoption and low-code utilization in SMEs. ACCESS, 13, 117385-117403. (www)

  @article{ , abstract = {Small and medium-sized enterprises (SMEs) face growing pressure to adapt to dynamic, competitive markets. Business Process Management (BPM) offers a strategic pathway to operational efficiency and digital transformation. While the BPM lifecycle and low-code platforms are frequently presented as key enablers for SMEs, few empirical studies examine their practical implementation. This study addresses two research questions: 1) how SMEs use low-code technologies to support BPM, and 2) how they apply – or deviate from – the traditional BPM lifecycle. Using a triangulated research design combining a rapid literature review and an expert survey of BPM practitioners in SMEs, the study finds that low-code tools are widely adopted for basic BPM tasks such as process modeling and workflow automation. However, advanced features like AI and process mining are underutilized. Likewise, SMEs often skip or adapt BPM lifecycle phases due to organizational constraints. These findings highlight a gap between BPM theory and SME practice, emphasizing the need for more adaptive BPM frameworks and accessible low-code solutions tailored to SME contexts.}, archive = {J_ACCESS}, author = {Pavel Arno}, doi = {10.1109/ACCESS.2025.3585261}, journal = {IEEE Access}, pages = {117385-117403}, shortjournal = {IEEE Access}, title = {From theory to practice: Empirical insights on BPM lifecycle adoption and low-code utilization in SMEs}, volume = {13}, year = {2025}, }
• (2025). Cross-domain carotid artery segmentation using folding fan ResNet and quadratic mapping loss. ACCESS, 13, 117374-117384. (www)

  @article{ , abstract = {Vessel-wall volume and localized three-dimensional ultrasound (3DUS) metrics are sensitive indicators of carotid atherosclerosis changes in response to therapies. Deep-learning segmentation models have been proposed to segment the media-adventitia boundary (MAB) and lumen-intima boundary (LIB) necessary to obtain these measurements. However, the performance of these models declines substantially when applied to datasets with different properties from their training data, highlighting a need for more generalizable models. We introduce Folding Fan ResNet (FFRN), a deep-learning model designed to enhance cross-domain generalizability. FFRN incorporates a novel skip connection pathway, the Folding Fan Connection (FFC), which improves encoder-decoder feature fusion through multiple addition and convolutional operations. This design reduces parameter redundancy while maintaining efficient feature propagation, leading to increased generalizability compared to existing models. Additionally, the quadratic mapping loss (QML) function is proposed to improve model fine-tuning. QML addresses arterial mislocalization by deferring the optimization of incorrectly localized boundaries while prioritizing adjustments to correctly segmented regions. This strategy prevents the reinforcement of erroneous features and allows for better modeling of carotid structures. FFRN demonstrated higher generalizability than four state-of-the-art segmentation models, improving Dice similarity coefficients (DSCs) of 78.9±24.1% and 79.0±23.2% attained by the U-Net for LIB and MAB segmentation, respectively, to 84.0±18.2% and 84.6±17.1% in MAB and LIB segmentation, respectively. With QML fine-tuning, the DSCs increased to 88.9±8.2% (LIB) and 90.6±8.1% (MAB), outperforming all competing approaches, even after their performances were enhanced by QML. Notably, the fine-tuned model is the first to achieve a zero incorrect localization rate (ILR) in the target dataset. The improved generalizability afforded by the model enhances efficiency for carotid disease monitoring across clinical centers.}, archive = {J_ACCESS}, author = {Zhaozheng Chen and Zhiyin Liu and Bernard Chiu}, doi = {10.1109/ACCESS.2025.3585064}, journal = {IEEE Access}, pages = {117374-117384}, shortjournal = {IEEE Access}, title = {Cross-domain carotid artery segmentation using folding fan ResNet and quadratic mapping loss}, volume = {13}, year = {2025}, }
• (2025). Malware-SeqGuard: An approach utilizing LSTM and GRU for effective detection of evolving malware in android environments. ACCESS, 13, 117355-117373. (www)

  @article{ , abstract = {Malware detection is crucial for safeguarding devices and networks from malicious software that can compromise sensitive information, disrupt operations, and lead to financial losses. By identifying and neutralizing threats early effective malware detection helps maintain the integrity confidentiality and availability of critical systems and data. In this study, we introduce a state-of-the-art scheme for Android malware detection that combines the strengths of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. These advanced deep learning architectures are particularly adept at capturing sequential dependencies within data making them well-suited for analyzing complex time-series information, such as user behaviour patterns system logs and network traffic. By leveraging the complementary capabilities of both LSTM and GRU models can effectively dissect intricate and dynamic data to uncover malicious activities that often evade conventional detection methods. This innovative approach not only enhances the model’s ability to identify known malware but also empowers it to adapt to emerging threats by continuously learning from both historical and real-time behavioural data. As malware evolves in sophistication, our model improves detection accuracy and response times, ensuring robust defences against new attack vectors in Android environments. The findings from our research underscore the potential of combining different neural network architectures to create more resilient cybersecurity solutions, paving the way for more effective malware detection strategies in an increasingly complex digital landscape. By addressing the limitations of traditional methods, our proposed model represents a significant advancement in safeguarding Android systems from the ever-growing range of malware threats.}, archive = {J_ACCESS}, author = {Muhammad Usama Tanveer and Kashif Munir and Abdulatif Alabdulatif and Anas R. Najdawi and Rutvij H. Jhaveri}, doi = {10.1109/ACCESS.2025.3585605}, journal = {IEEE Access}, pages = {117355-117373}, shortjournal = {IEEE Access}, title = {Malware-SeqGuard: An approach utilizing LSTM and GRU for effective detection of evolving malware in android environments}, volume = {13}, year = {2025}, }
• (2025). Relative applicability of diverse automatic speech recognition platforms for transcription of psychiatric treatment sessions. ACCESS, 13, 117343-117354. (www)

  @article{ , abstract = {Service delivery in mental healthcare involves documentation of sensitive patient-clinician conversations that require serious caution. Conventionally, clinicians take handwritten notes, which causes low readability and lack of database which hinders research. Having these conversations digitized via Automatic Speech Recognition (ASR) based Speech-to-Text (STT) transcription enables progressive analysis of mental health cases. The ASR applications usually require audio recording prior to the transcription, for labeling speakers or diarization. Although such models are good enough for most use cases, storing audio recordings in psychiatry complicates the data handling and adoption of ASR platforms in mental healthcare. This study involved a two-stage methodology, where at first, a list of 32 well-reputed STT transcription tools were evaluated in terms of applicability in psychiatry; followed by experimental testing using nine audio clips derived from three psychiatric session recordings of varying durations (1, 3, and 10 minutes) and speakers’ gender. Metrics such as inference time, Word Error Rate (WER), and Diarization Error Rate (DER) were analyzed. The results indicated that while WER was positively low (0-7%), DER varied significantly (2-32%), influenced by the audio length and speaker characteristics. DER was notably lower for clips with speakers of differing genders or ages, but negatively increased for speakers of similar demographics. The study also compared synchronous and asynchronous diarization approaches, highlighting challenges in accuracy, privacy, and processing efficiency in psychiatry. These findings provide actionable insights for selecting ASR tools in mental healthcare and underscore the need for targeted improvements in ASR technology to address the unique demands of this field.}, archive = {J_ACCESS}, author = {Rana Zeeshan and John Bogue and Mamoona Naveed Asghar}, doi = {10.1109/ACCESS.2025.3585454}, journal = {IEEE Access}, pages = {117343-117354}, shortjournal = {IEEE Access}, title = {Relative applicability of diverse automatic speech recognition platforms for transcription of psychiatric treatment sessions}, volume = {13}, year = {2025}, }
• (2025). Enhancing browser physics simulations: WebAssembly and multithreading strategies. ACCESS, 13, 117328-117342. (www)

  @article{ , abstract = {This study presents a series of optimization strategies for integrating the Bullet Physics engine into a browser-based WebAssembly (Wasm) environment, addressing the performance limitations of traditional solutions such as Ammo.js. Using techniques such as Foreign Function Interface (FFI) optimization, memory management refinement, and multithreading through Web Workers, the proposed approach achieves substantial performance improvements. The optimized Wasm binaries demonstrated up to 9.88x speedup over Ammo.js in terms of frame time and exhibited superior scalability with increasing thread counts. These findings highlight the potential of Wasm as a robust platform for high-performance browser-based physics simulations, offering significant benefits in computational efficiency and scalability.}, archive = {J_ACCESS}, author = {Hyeonjong Nam and Eun-Jin Im}, doi = {10.1109/ACCESS.2025.3586116}, journal = {IEEE Access}, pages = {117328-117342}, shortjournal = {IEEE Access}, title = {Enhancing browser physics simulations: WebAssembly and multithreading strategies}, volume = {13}, year = {2025}, }
• (2025). Predicting medical device life expectancy and estimating remaining useful life using a data-driven multimodal framework. ACCESS, 13, 117300-117327. (www)

  @article{ , abstract = {Accurately predicting the life expectancy of medical devices is crucial in optimizing healthcare operations, managing costs, and ensuring patient safety. Medical devices in clinical environments must be maintained, replaced, or refurbished on time to prevent malfunctions that could compromise patient care. The prediction of the Remaining Useful Life (RUL) of these devices enables improved resource utilization and reduced downtime. This study develops a predictive model to predict the medical device lifespan while estimating its RUL to prevent unexpected failures that could disrupt essential healthcare services. A data-driven multimodal framework is proposed to estimate the RUL, particularly for devices classified as Beyond Economic Repair (BER). This innovative framework integrates unstructured information from maintenance reports with structured device data, such as usage history, manufacturer specifications, and operational conditions, to create a comprehensive dataset that enhances predictive accuracy. Findings indicate that an ensemble learning technique demonstrates exceptional performance, achieving a Root Mean Square Error (RMSE) of 0.1563 and an R-squared (R2) value of 0.9749. Thus, by implementing data-driven maintenance and replacement strategies derived from these findings, healthcare organizations can improve operational efficiency, reduce costs, and ensure patient safety. This proactive approach allows healthcare institutions to allocate resources more effectively, prioritize maintenance for devices at higher risk of failure, and ultimately enhance the reliability of medical equipment in clinical settings.}, archive = {J_ACCESS}, author = {Nur Haninie Abd Wahab and Khairunnisa Hasikin and Khin Wee Lai and Kaijian Xia and Alicia Ying Taing and Ran Zhang}, doi = {10.1109/ACCESS.2025.3581548}, journal = {IEEE Access}, pages = {117300-117327}, shortjournal = {IEEE Access}, title = {Predicting medical device life expectancy and estimating remaining useful life using a data-driven multimodal framework}, volume = {13}, year = {2025}, }
• (2025). Unleashing the power of wireless communication in healthcare by empowering patient care and connectivity: A comprehensive survey. ACCESS, 13, 117239-117299. (www)

  @article{ , abstract = {The emergence of the wireless network as a potentially revolutionary innovation has the ability to change the field of medical diagnostics. This in-depth study aims to explore the various aspects of using the latest wireless technologies to improve the standard of care given to patients and the interactions between patients and healthcare providers. This study investigates a wide range of issues such as patient-centric communication technology, 6G based applications using smart technologies, real-time communication protocols, implementation of artificial intelligence (AI) and blockchain technology in healthcare and the use of wireless devices for remote patient monitoring. 6G wireless communication brings transformative capabilities to healthcare, offering ultra-reliable and low-latency communication (URLLC), improved network capacity, and higher data rates. These advances enable the real-time transmission of critical health data, support complex medical applications, facilitate remote consultations, surgical robotics, and AI-driven diagnostics. This study highlights the significance and implications of combining these concepts in the context of 5G and beyond, paving the way for connected healthcare, personalized medicine, and unprecedented levels of efficiency and innovation. In addition, it also investigates the obstacles and potential associated with the implementation of wireless communication in the healthcare industry. These challenges and opportunities include data security and privacy issues, as well as the need for a robust communication infrastructure. This paper demonstrates the influence that wireless communication}, archive = {J_ACCESS}, author = {Urvashi Chaudhary and Mohammad Furqan Ali and Ambrish Kumar and Abhishek Sharma and Dushantha K. Nalin Jayakody}, doi = {10.1109/ACCESS.2025.3578344}, journal = {IEEE Access}, pages = {117239-117299}, shortjournal = {IEEE Access}, title = {Unleashing the power of wireless communication in healthcare by empowering patient care and connectivity: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Sleep stage classification using variational mode decomposition and wrapper-based feature selection from the single channel EEG. ACCESS, 13, 117224-117238. (www)

  @article{ , abstract = {Sleep stage classification can diagnose various sleep disorders and sleep patterns. The classification model classifies many stages of sleep, including wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Precise and reliable sleep stage classification is crucial for clinical applications and research studies. Diagnosing sleep disorders without an accurate automated classification model is laborious and susceptible to inaccuracies.This may lead to delayed or ineffective treatments. Manual scoring is tiresome and inconsistent, making it difficult to provide personalized treatments to treat sleep diseases efficiently. Sleep disorders, including narcolepsy, insomnia, and sleep apnea, can be identified and monitored by automatic sleep classification. The proposed framework uses variational mode decomposition (VMD). The electroencephalogram (EEG) is processed into band-limited intrinsic mode functions (IMFs) by VMD. Each IMF signal in EEG was broken down into 15 features based on time, frequency, and information theory. Furthermore, the optimum feature subset was selected using the Wrapper-Based Feature Selector (WBFS). Finally, well-known classifiers used to classify the EEG signal into five distinct sleep stages. This study achieves accuracies of 94.84% and 95.20%on the Sleep-EDF database, and 95.60% and 96.17% on the ISRUC-Sleep dataset, for the balanced and unbalanced cases, respectively.}, archive = {J_ACCESS}, author = {Vipin Prakash Yadav and M. A. Aswathy and Sahebgoud Hanamantray Karaddi and Sana Pavankumar Reddy and G. Pradeep Reddy and Aman Kumar}, doi = {10.1109/ACCESS.2025.3585963}, journal = {IEEE Access}, pages = {117224-117238}, shortjournal = {IEEE Access}, title = {Sleep stage classification using variational mode decomposition and wrapper-based feature selection from the single channel EEG}, volume = {13}, year = {2025}, }
• (2025). A lightweight method for detecting bearing surface defects based on deep learning and ontological reasoning. ACCESS, 13, 117210-117223. (www)

  @article{ , abstract = {Bearing as a critical component of machinery and equipment, its state of health directly affects the operating efficiency and security of the equipment. Therefore, the quality control of bearings must be very strict. Aiming at the different sizes and textures of the defect types on the surface of the outer ring of the bearing, and the fact that most of the target detection algorithms relying on deep learning show low speed and low precision in the detection of defects on the surface of the bearing, this paper presents a lightweight method for detecting the defects on the surface of the bearing based on deep learning and ontological reasoning. In this paper, YOLOv5s is chosen as the baseline model. First, the dynamic convolution is fused with the Ghost module and the combined structure is embedded into the C3 module, thus constructing a new module named C3-GhostDynamicConv (C3-GDConv) module, which achieves network lightweighting while maintaining efficient computation. Secondly, adding the lightweight attention mechanism SGE (Spatial Group-wise Enhancement) to the neck network almost eliminates the need to add additional parameters and computation. Without adding significant computational burden, it can significantly enhance the model’s performance and realize the efficient identification of bearing surface defects. Finally, a knowledge ontology of bearing defects is constructed by combining ontological reasoning techniques to give reasoning results on whether bearing defects can be repaired or not. The experimental results show that the frame rate per second (FPS) value of the improved model GDS-YOLOv5s for bearing defect recognition in this paper is 130, which is an improvement of 11% compared to the original YOLOv5s model. GFLOPs and the number of parameters decreased by 7% and 10% respectively, and mAP@0.5 reached 92.4%. The recognition accuracy is ensured while keeping the model lightweight. Combined with ontological reasoning, it is able to determine the repairability of defects, which provides a reliable basis for the maintenance decision of bearings. This study offers an efficient and intelligent scheme for bearing defect inspection and subsequent processes, which has important engineering application value.}, archive = {J_ACCESS}, author = {Xiaolin Shi and Haisong Xu and Han Zhang and Yi Li and Xinshuo Li and Fan Yang}, doi = {10.1109/ACCESS.2025.3586619}, journal = {IEEE Access}, pages = {117210-117223}, shortjournal = {IEEE Access}, title = {A lightweight method for detecting bearing surface defects based on deep learning and ontological reasoning}, volume = {13}, year = {2025}, }
• (2025). Empowering K-12 mathematics teachers with artificial intelligence: A systematic review of insights, applications, and challenges. ACCESS, 13, 117198-117209. (www)

  @article{ , abstract = {The integration of artificial intelligence (AI) into education is rapidly transforming teaching practices across various subjects, including mathematics. However, research specifically focusing on K-12 mathematics teachers’ use of AI remains limited, and this study fills this gap. Therefore, this systematic review explores the current practices of AI integration among K-12 mathematics teachers, focusing on a range of factors, such as demographics, research methodologies, AI tools, models, mathematical content, and the challenges encountered in this process. Following PRISMA guidelines, 18 articles published between 2020 and 2024, focusing on the integration of AI in education by K-12 mathematics teachers, were analysed from WOS, Scopus, and arXiv. The value of this study lies in the following findings: The research focuses on pre-service teachers, especially from the United States and China, which is also the main development trend in the future. The selected studies often utilized quantitative, qualitative, and mixed-methods approach, applying models such as the Technology Acceptance Model (TAM) to measure acceptance and adopting frameworks such as TPACK to guide teachers in effectively integrating technology in their teaching. Prominent AI tools frequently integrated into educational settings include ChatGPT and Intelligent Tutoring Systems (ITSs). However, there are also limitations, such as ITSs research often focusing on hypothetical applications rather than direct implementation in the classroom. Furthermore, the review emphasizes the absence of specific mathematical content or tasks for K-12 teachers, especially in secondary schools where barriers were noted regarding teacher-specific, tool-specific, and systemic issues. This underscores the necessity for professional development, improved AI tools, and comprehensive systemic frameworks.}, archive = {J_ACCESS}, author = {Dantong Li and Sharifah Osman and Najua Syuhada Ahmad Alhassora and Jeya Amantha Kumar and Sharifah Kartini Said Husain}, doi = {10.1109/ACCESS.2025.3585784}, journal = {IEEE Access}, pages = {117198-117209}, shortjournal = {IEEE Access}, title = {Empowering K-12 mathematics teachers with artificial intelligence: A systematic review of insights, applications, and challenges}, volume = {13}, year = {2025}, }
• (2025). Interpretable chinese fake news detection with chain-of-thought and in-context learning. ACCESS, 13, 117186-117197. (www)

  @article{ , abstract = {The spread of fake news through on-line platforms poses a significant threat to public trust and social stability, especially in resource-constrained Chinese domains. Existing Fake News Detection (FND) models often lack transparency, functioning as “black boxes” that limit their practical utility. To address this challenge, we propose a novel framework for Chinese Fake News Detection that enhances both accuracy and explainability. Our approach integrates Chain-of-Thought reasoning, retrieval-based in-context learning, and knowledge distillation, allowing for interpretable model predictions. We also introduce an “undetermined” label to handle ambiguous cases and construct the Chinese Explainable Fake News Detection Dataset, which provides detailed annotations for training and evaluation. Experiments show that our model improves both detection accuracy and interpretability, advancing the field of explainable AI in FND task.}, archive = {J_ACCESS}, author = {Bingyi Liu and Anqi Wang and Chengqian Xia}, doi = {10.1109/ACCESS.2025.3571497}, journal = {IEEE Access}, pages = {117186-117197}, shortjournal = {IEEE Access}, title = {Interpretable chinese fake news detection with chain-of-thought and in-context learning}, volume = {13}, year = {2025}, }
• (2025). Adaptive video anomaly detection by attention-based relational knowledge distillation. ACCESS, 13, 117170-117185. (www)

  @article{ , abstract = {Detecting anomaly patterns in videos is a challenging task due to complex scenes, huge diversity of anomalies, and fuzzy nature of the task. With advent of technology, tremendous size of visual data is being generated by video surveillance systems, which makes harder to search, analyze, and detect anomalies on video data by human operators. In this paper, we introduce three relational distillation approaches to handle both robust detection of anomalous events and gradual adaptation to different anomaly patterns in new videos while not forgetting anomaly patterns learned from the previous video data. In order to realize these concepts, we propose a unique attention mechanism with feature and relation based knowledge distillation methods. We adapted our knowledge distillation methods to two state-of-the-art models designed for anomaly detection task. Our extensive experiments on two public datasets show that not only our best version model achieves robust performance with a frame-level AUC of 80.22 on UCF-Crime and video-level AUC of 78.20 on RWF-2000 datasets but also the proposed distillation methods improve the performance while reducing catastrophic forgetting problem.}, archive = {J_ACCESS}, author = {Burcak Asal and Ahmet Burak Can}, doi = {10.1109/ACCESS.2025.3585984}, journal = {IEEE Access}, pages = {117170-117185}, shortjournal = {IEEE Access}, title = {Adaptive video anomaly detection by attention-based relational knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Deriving the upstream truncation thresholds of human promoter sequences based on expression correlation. ACCESS, 13, 117162-117169. (www)

  @article{ , abstract = {Most promoter sequences are generated by arbitrary truncating regions upstream from the transcription start site, typically around 1,000 base pairs. However, this arbitrary approach might miss essential regulatory elements required for proper transcription. Unfortunately, there is no widely accepted rationale for choosing a specific promoter truncation threshold, despite its importance in obtaining expected expression profiles. In this study, we present a data-driven method to define biologically meaningful promoter thresholds. We begin by identifying putative shared promoters between neighboring gene pairs and classify them as true shared promoters based on expression similarity using GTEx data. Our analysis reveals that shorter putative shared promoters are more likely to be true shared promoters and thus lead to similar expression profiles. Using the lengths of these true shared promoters, we subsequently define aggressive, standard, and conservative thresholds for human promoter sequences. We further refine these thresholds using known cis-regulatory elements and transcription factor binding regions, resulting in more compact promoter boundaries. These refined thresholds provide a biologically grounded alternative to arbitrary values and establish a foundation for future experimental validation and practical applications. Moreover, our refined thresholds are especially well-suited for scenarios where compact promoter design is critical.}, archive = {J_ACCESS}, author = {Jiahong Dong and Theodora Shaw and Stephen Brown and Kevin Truong}, doi = {10.1109/ACCESS.2025.3585094}, journal = {IEEE Access}, pages = {117162-117169}, shortjournal = {IEEE Access}, title = {Deriving the upstream truncation thresholds of human promoter sequences based on expression correlation}, volume = {13}, year = {2025}, }
• (2025). Defining and achieving next-generation green electronics: A perspective on best practices through the lens of hybrid printed electronics. ACCESS, 13, 117135-117161. (www)

  @article{ , abstract = {As global electronics production and e-waste generation accelerate alongside efforts to reduce carbon emissions, the need for transformative solutions in the electronics sector has become urgent. Therefore, to support the transition to greener electronics, this work reviews existing research and legislation relevant to the field and considers the perspectives and ongoing efforts of the EU Green Electronics Working Group (comprised of 12 green electronics-focused Horizon Europe projects) to identify and define the most important aspects of green electronics. Given the absence of a widely accepted definition of what makes electronics truly “green,” the most critical aspects are clarified to support the development of a common, unified definition: Electronicsthat, when measured against their alternatives over their whole lifecycle and value chain,have a reduced environmental impact in terms of greenhouse gas emissions, toxicity, and resource depletion and avoid burden shifting from one impact to another or along the value chain, while fulfilling a given function. A set of recommendations and best practices, informed by the latest advancements and ongoing research developments in green electronics, is then provided to address the entire lifecycle of electronic devices. These strategies offer a framework to guide the development and adoption of greener electronics.}, archive = {J_ACCESS}, author = {Kealie Vogel and Sara Carniello and Valerio Beni and Akshat Sudheshwar and Nadia Malinverno and Yolanda Alesanco and Max Torrellas and Stephan Harkema and Margreet De Kok and Corné Rentrop and Ignacio Zurano Villasuso and Lou Bernard and Enrique Moliner and Christian Rein and Yves Bayon and Zulfiqur Ali and Carolin Zachäus and Nicolas Gouze and Marie Berthuel and Cristina González Buch and Fruela Pérez Sánchez and Claudia Som}, doi = {10.1109/ACCESS.2025.3585340}, journal = {IEEE Access}, pages = {117135-117161}, shortjournal = {IEEE Access}, title = {Defining and achieving next-generation green electronics: A perspective on best practices through the lens of hybrid printed electronics}, volume = {13}, year = {2025}, }
• (2025). A historical review of the technology used for the study of freshness in red meats. ACCESS, 13, 117119-117134. (www)

  @article{ , abstract = {Meat is a high-quality source of protein, vitamins, and minerals, making it a fundamental component of the human diet. Ensuring the availability of fresh meat is, therefore, essential for both consumers and the food industry. Traditional methods for assessing meat freshness, such as sensory evaluation and chemical analysis, are often manual, time-consuming, costly, and may lead to human interpretation errors. In contrast, advanced non-destructive technologies—supported by data-driven analysis—offer efficient, accurate, and objective alternatives. This review provides a comprehensive historical overview of the methods used to assess red meat freshness, including their advantages, limitations, and applications. Furthermore, it discusses state of the art data analysis techniques to enhance detection accuracy, highlighting the potential for industrial implementation and the challenges that must be addressed.}, archive = {J_ACCESS}, author = {Julieta Nava-Granados and María T. Orvañanos-Guerrero and Claudia N. Sánchez and Julieta Domínguez-Soberanes}, doi = {10.1109/ACCESS.2025.3585491}, journal = {IEEE Access}, pages = {117119-117134}, shortjournal = {IEEE Access}, title = {A historical review of the technology used for the study of freshness in red meats}, volume = {13}, year = {2025}, }
• (2025). Energy-based voltage stabilization in DC shipboard power systems with dual loop control. ACCESS, 13, 117105-117118. (www)

  @article{ , abstract = {The electrification of shipboard power systems carries an increasing variety in power sources, energy storage systems, and power converters. DC distribution is gaining relevance due to efficiency increase, space savings, and high controllability. The dominant primary control method voltage droop control, which offers easily implementable and scalable power sharing and voltage stabilization. However, compared to terrestrial microgrids, shipboard power systems have low line impedances and highly fluctuating loads. Most primary loads are power-controlled, introducing a non-linearity that leads to a weak damping and unstable operation points. To handle this non-linearity, this study proposes an energy-based control approach as an alternative to the voltage-based scheme. The controller is further extended by integral feedback loop to achieve fast voltage restoration. A low-bandwidth communication is leveraged for an adaptive power sharing control that facilitates an efficient allocation of the load among parallel units under varying conditions. The proposed control structure is deployed on an I/O board embedded in a hardware-in-the-loop testbed. It is shown that the energy-based controller operates stably and achieves a reduced voltage deviation from the nominal voltage in various load conditions compared to the conventional voltage-based method.}, archive = {J_ACCESS}, author = {Timon Kopka and Alejandro Latorre and Andrea Coraddu and Henk Polinder}, doi = {10.1109/ACCESS.2025.3586382}, journal = {IEEE Access}, pages = {117105-117118}, shortjournal = {IEEE Access}, title = {Energy-based voltage stabilization in DC shipboard power systems with dual loop control}, volume = {13}, year = {2025}, }
• (2025). InLeR: A vehicle-mounted IoT solution for efficient streetlight monitoring. ACCESS, 13, 117090-117104. (www)

  @article{ , abstract = {Maintaining compliance with light intensity standards is critical to ensuring proper illumination, particularly during nighttime, and for preserving healthy ecosystems in urban and peri-urban areas. Traditional methods, relying on manual inspections with handheld sensors, are labour-intensive and inefficient. This paper presents a novel vehicle-mounted light intensity measurement system, the “Innovative Light Evaluation Rig” (InLer), designed to significantly improve streetlight monitoring compared to conventional methods. InLer operates as an autonomous IoT device with on-edge processing, leveraging strategically placed sensors to capture light intensity in both vertical and horizontal planes. Its ability to measure light intensity at large scale continuous monitoring in three directions simultaneously within a single sampling cycle significantly enhances operational efficiency. The system features an aerodynamic and mechanically robust design, incorporating three independent lux sensors, wireless connectivity, waterproof components, and magnetic mounts for easy installation. Additionally, InLer integrates GNSS/GPS technology for precise geolocation, enabling detailed spatial analysis when combined with light-intensity data. A comprehensive calibration analysis presented in this paper validates the system’s accuracy and consistency, establishing InLer as a state-of-the-art solution with wide-ranging applications across multiple sectors.}, archive = {J_ACCESS}, author = {Syed Salman Alam and Najam Us Saqib and Arshad Khan and Waqas Bukhari and Riad Souissi}, doi = {10.1109/ACCESS.2025.3585335}, journal = {IEEE Access}, pages = {117090-117104}, shortjournal = {IEEE Access}, title = {InLeR: A vehicle-mounted IoT solution for efficient streetlight monitoring}, volume = {13}, year = {2025}, }
• (2025). SIGNETS: Neural network architectures for m-QAM soft demodulation. ACCESS, 13, 117078-117089. (www)

  @article{ , abstract = {This paper presents a novel approach to Quadrature Amplitude Modulation (QAM) demodulation using neural networks, addressing the limitations of traditional demodulation techniques in complex channel conditions. Through systematic Neural Architecture Search and Hyper-parameter optimization, we develop a family of Convolutional Neural Network architectures that demonstrate robust performance across challenging channel conditions, including multi-path fading, inter-symbol interference, and non-linear distortions, without requiring explicit channel estimation. We comparatively evaluate the members of the family of networks to find a Pareto-optimal Neural Network Demodulator with a balance of demodulation accuracy and computational cost, achieving an average accuracy of 99.658% across 4 dB to 24 dB SNR while requiring less than 16,000 Floating-Point Operations (FLOPs) for every demodulated QAM-16 symbol. A practical Field Programmable Gate Array (FPGA) implementation that achieves 2.52 Million bits per second throughput while maintaining 99.55% demodulation accuracy through structured pruning and quantization-aware training is presented. Experimental validation over acoustic channels demonstrates superior performance compared to traditional techniques, with performance further enhanced through fine-tuning.}, archive = {J_ACCESS}, author = {Aravind R. Voggu and Kanish R and Nishith Akula and Lohitaksh Maruvada and Takanori Shimizu and Madhav Rao}, doi = {10.1109/ACCESS.2025.3586043}, journal = {IEEE Access}, pages = {117078-117089}, shortjournal = {IEEE Access}, title = {SIGNETS: Neural network architectures for m-QAM soft demodulation}, volume = {13}, year = {2025}, }
• (2025). GPS-aided deep learning for beam prediction and tracking in UAV mmWave communication. ACCESS, 13, 117065-117077. (www)

  @article{ , abstract = {Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Uncrewed Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. These outcomes stem from a proposed data splitting method ensuring balanced label distribution, paired with a GPS preprocessing technique that extracts key positional features, and a DL architecture that maps sequential position data to beam index predictions. The model reduces overhead by approximately 93% compared to exhaustive search methods with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.}, archive = {J_ACCESS}, author = {Vendi Ardianto Nugroho and Byung Moo Lee}, doi = {10.1109/ACCESS.2025.3586594}, journal = {IEEE Access}, pages = {117065-117077}, shortjournal = {IEEE Access}, title = {GPS-aided deep learning for beam prediction and tracking in UAV mmWave communication}, volume = {13}, year = {2025}, }
• (2025). Bayesian and non-bayesian inference for the trigonometric extended rayleigh-weibull model under progressively censored data. ACCESS, 13, 117043-117064. (www)

  @article{ , abstract = {In this paper, a new extension of the Rayleigh-Weibull (RW) model, called the sine Rayleigh-Weibull (SRW) model, is proposed. This model is constructed using the trigonometrically generated family of distributions based on sine functions and involves two parameters. The SRW model provides greater flexibility than the RW model and several other well-known statistical models, such as the sine-Weibull, exponential, Weibull, and Rayleigh distributions. Its hazard rate function can exhibit various shapes, including constant, J-shaped, or increasing forms. Under a progressive Type-II censoring scheme, statistical inference for the parameters of the SRW model is a central focus of this study. To estimate the parameters, we employ various methods, including the Expectation-Maximization (EM) algorithm. Under the Bayesian framework, parameter estimation is conducted using Markov Chain Monte Carlo (MCMC) methods and the Tierney-Kadane approximation, with respect to different loss functions, such as the squared error loss and the linear exponential loss. The numerical properties of the proposed estimators are assessed through Monte Carlo simulations, and the model is further validated by application to real datasets from the fields of electrical appliances and survival times of breast cancer patients.}, archive = {J_ACCESS}, author = {Ahmed R. El-Saeed and Ibrahim Elbatal and Ehab M. Almetwally and Mohammed Elgarhy}, doi = {10.1109/ACCESS.2025.3582851}, journal = {IEEE Access}, pages = {117043-117064}, shortjournal = {IEEE Access}, title = {Bayesian and non-bayesian inference for the trigonometric extended rayleigh-weibull model under progressively censored data}, volume = {13}, year = {2025}, }
• (2025). Feasibility and benefits of off-highway mining trucks electrification for different mine profiles. ACCESS, 13, 117024-117042. (www)

  @article{ , abstract = {While smaller vehicle categories in mining already offer various electrified alternatives, the electrification of large trucks used in open-pit mines remains less developed. Nevertheless, given the substantial annual fuel consumption of these vehicles, their electrification also possesses incomparable potential. We present a methodology for calculating fuel savings, productivity increase, and energy costs for different electrified powertrains of large mining trucks. The methodology is validated with actual measurements. Furthermore, it is applied to analyze the electrification potential in three selected open-pit case study mines. Different powertrain alternatives are compared using measured work cycles and one artificial work cycle of a possible future scenario of these case study mines as input. In the first mine, the adoption of a series hybrid electric powertrain with a 400-kWh energy storage decreases fuel consumption by up to 17%. A diesel-electric alternative with an overhead trolley line, however, reduces fuel consumption by up to 91% and even slightly increases productivity. The energy consumption of the second mine is reduced by up to 27% with a 200-kWh battery without trolley assist. When the expansion plans of this mine were investigated, several viable powertrain configurations were found, of which the downhill tech configuration is the most intriguing. In this setup, the vehicle would be driving empty uphill and loaded downhill regenerating more energy than is consumed driving uphill empty. This requires no recharging with a 600-kWh battery. The last third mine also achieves a considerable productivity increase of up to 26% with a trolley electric powertrain.}, archive = {J_ACCESS}, author = {Marius Baranauskas and Jenni Pippuri-Mäkeläinen and Pekka Rahkola and Mehrnaz Farzam Far and Mikko Pihlatie}, doi = {10.1109/ACCESS.2025.3586728}, journal = {IEEE Access}, pages = {117024-117042}, shortjournal = {IEEE Access}, title = {Feasibility and benefits of off-highway mining trucks electrification for different mine profiles}, volume = {13}, year = {2025}, }
• (2025). Performance-driven battery pack sizing for light-duty electric vehicles. ACCESS, 13, 117013-117023. (www)

  @article{ , abstract = {This work presents a method for sizing Lithium-Ion batteries (LIB) for light-duty electric vehicles (LDEV). The proposed method considers the physical and dynamic characteristics of the LDEV, such as weight, frontal area, aerodynamic and rolling coefficients, and an expected performance under a specific route to evaluate its energy and power requirements. The aim is to size the battery bank to meet the energetic demands of the LDEV for a specific autonomy, using commercial LIB to reach an appropriate configuration. The methodology is applied to a midibus in a local route using the Manhattan Bus Cycle for eight expected autonomies (round trips) to demonstrate its effectiveness. This work shows that the battery pack size must be readjusted recursively since the analysis must consider the weight of the battery itself. The proposed recursive method computes a minimal battery size. Moreover, the analysis considers two kinds of batteries with higher and lower energy densities. The results indicate that high-density batteries enable a swap approach for extended autonomy due to the staggered increase in the LIB bank size. Conversely, the sizing increases proportionally when using LIBs with low energy densities. Although the weight is lower with low-density batteries, more batteries are required.}, archive = {J_ACCESS}, author = {Manuel López-Pérez and Joaquín Domínguez-Zenteno and Guillermo Valencia-Palomo and Francisco-Ronay López-Estrada and Samuel Gómez-Peñate and Ildeberto Santos-Ruiz}, doi = {10.1109/ACCESS.2025.3585754}, journal = {IEEE Access}, pages = {117013-117023}, shortjournal = {IEEE Access}, title = {Performance-driven battery pack sizing for light-duty electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Skew logistic distribution applied as activation function in artificial neural networks. ACCESS, 13, 116994-117012. (www)

  @article{ , abstract = {In recent years, Artificial Neural Networks (ANNs) have stood out among machine learning algorithms in many applications, such as image and video pattern recognition. Activation functions play a crucial role in the operation of these algorithms, directly influencing the effectiveness of ANNs. The logistic (or sigmoid) function is often used as a standard activation function, but the existing literature lacks in-depth investigations into the potential of the Skew Logistic (SL) function. This work investigates the SL as an activation function in ANNs, exploring its ability to handle imbalanced data distributions. To achieve this, the function was implemented and evaluated on four binary classification datasets using accuracy, precision, recall, and F1-score. The results show that SL improved performance on the datasets selected for experiments 1 to 3, where an increase was observed in some performance metrics compared to the sigmoid function. And in experiment 4, competitive performance was obtained with softmax, using a multiclass version of SL. In particular, it was noted that it is possible to improve precision or recall, adjusting the asymmetry parameter ( $\lambda $ ). It is concluded that the SL function offers a viable and promising alternative to conventional activation functions.}, archive = {J_ACCESS}, author = {Eder Silva Dos Santos and Altemir da Silva Braga and Ana Beatriz Alvarez and Thuanne Paixão}, doi = {10.1109/ACCESS.2025.3584237}, journal = {IEEE Access}, pages = {116994-117012}, shortjournal = {IEEE Access}, title = {Skew logistic distribution applied as activation function in artificial neural networks}, volume = {13}, year = {2025}, }
• (2025). Branch drift: A visually explainable metric for consistency monitoring in collaborative software development. ACCESS, 13, 116972-116993. (www)

  @article{ , abstract = {Modern software engineering uses branch-based workflows for collaborative development on a shared codebase. A branch may represent a task or feature within the responsibilities of a specific developer or team. When branches are merged, unanticipated conflicts occur frequently. Their resolution is time-consuming and costly. Depending on the development process, a branch exists for a few hours or up to several months. With increasing time and many branches, keeping track of potential merge conflicts becomes difficult. Typically, developers are unaware of these conflicts until the branches are merged. Issues solvable through joint reviews, discussion, and collaboration are therefore identified and resolved late. It is evident that the effort required to resolve merge conflicts poses an organizational technical debt. Project managers and team leaders must be able to track, analyze, and interpret the merge conflict potential to call for timely actions. Therefore, a metric is required. This work transfers the drift metric for inconsistency analysis from model-driven software engineering to branch-based software development. The resulting branch-drift metric summarizes the merge conflict potential of a development project in a numeric value. Its calculation is configurable to fit any project’s requirements. The branch-drift is based on an intuitive and explainable geometric representation, easing the analysis of conflict patterns and outliers. We evaluate the branch-drift by quantitatively analyzing and discussing the drift values, trends, and patterns for 21 large-scale open-source repositories over three months. The results of this work encourage and enable the continuous and configurable branch-drift calculation for arbitrary Git repositories. The developed tools and gathered data are publicly available.}, archive = {J_ACCESS}, author = {Karl Kegel and Sebastian Götz and Uwe Aßmann}, doi = {10.1109/ACCESS.2025.3586537}, journal = {IEEE Access}, pages = {116972-116993}, shortjournal = {IEEE Access}, title = {Branch drift: A visually explainable metric for consistency monitoring in collaborative software development}, volume = {13}, year = {2025}, }
• (2025). SynergyScore: Leveraging pretrained language models for effective evaluation of code-switched sentences. ACCESS, 13, 116959-116971. (www)

  @article{ , abstract = {Code-switching, a common phenomenon in multilingual communication, presents significant challenges for linguistic analysis, particularly in evaluating semantic and contextual nuances such as meaning, fluency, and factuality. Existing metrics, including the Code-Mixing Index (CMI), Multilingual Index (M-index), and Language Entropy (LE), primarily quantify surface-level language switching characteristics but fail to address deeper linguistic dimensions. This paper introduces SynergyScore, a novel composite metric designed to reflect human-like evaluations of code-switched text by integrating traditional measures with insights from pretrained language models (PLMs). SynergyScore provides a holistic evaluation framework by combining structural analysis with semantic and contextual assessments. Comparative experiments demonstrate its superiority over traditional metrics, achieving stronger alignment with human judgments across dimensions such as fluency, coherence, and meaning. This study highlights SynergyScore’s potential to enhance code-switching evaluation and sets a foundation for further advancements in multilingual natural language processing.}, archive = {J_ACCESS}, author = {S. R. Mithun Kumar and Arjun Khandelwal and Chinmay Bidarkar and Aruna Malapati}, doi = {10.1109/ACCESS.2025.3585782}, journal = {IEEE Access}, pages = {116959-116971}, shortjournal = {IEEE Access}, title = {SynergyScore: Leveraging pretrained language models for effective evaluation of code-switched sentences}, volume = {13}, year = {2025}, }
• (2025). The influence of software engineering education on developing teamwork skills in both genders. ACCESS, 13, 116944-116958. (www)

  @article{ , abstract = {Teamwork is recognized as a highly valuable skill for any software engineer. In computer science programs it is typically developed through project-based courses, though these courses often do not adopt a gender-oriented approach. Women are underrepresented in the software industry, and there is limited knowledge about their teamwork competencies. While some studies compare teamwork skills between men and women, few have explored the impact of education on the development of this skill, particularly in relation to different aspects of teamwork. By comparing teamwork quality between genders at different program levels, we intend to gain valuable insights about students’ initial teamwork skills and the effect of software engineering education over time. To do this, we have carried out an empirical study of two project-based software engineering courses in our Computer Science and Engineering program: an introductory software engineering course and a capstone course. In each case we evaluate different aspects of teamwork by applying the same peer assessment instrument along 13 semesters. Our study revealed no significant gender-based disparities within the courses under study, indicating that such differences are absent both in students’ initial abilities and final competencies. What we did observe, however, is that women’s teamwork assessments are more homogeneous over time than the men’s, where we see significant improvements in Attitude, Motivation, Coordination and Communication between courses. Our results lead us to believe that, it takes men longer to reach the same capacity for working in teams than women, but in the end, students of both genders reach similar teamwork capabilities in all teamwork quality dimensions. Therefore, women have just as much potential to excel as software engineers as men, especially when it comes to teamwork.}, archive = {J_ACCESS}, author = {María Cecilia Bastarrica and Jocelyn Simmonds}, doi = {10.1109/ACCESS.2025.3585807}, journal = {IEEE Access}, pages = {116944-116958}, shortjournal = {IEEE Access}, title = {The influence of software engineering education on developing teamwork skills in both genders}, volume = {13}, year = {2025}, }
• (2025). LSENet: A lightweight spectral enhancement network for high-quality speech processing on resource-constrained platforms. ACCESS, 13, 116934-116943. (www)

  @article{ , abstract = {Although recent deep-learning-based speech enhancement (SE) methods significantly outperform traditional approaches, their computational demands often scale proportionally with their performance. This scaling typically makes them impractical for deployment on data throughput-sensitive and resource-constrained edge devices. In this paper, we propose a novel lightweight spectral enhancement network (LSENet) designed to estimate high-quality speech with minimal computational overhead. The network consists of an encoder-decoder architecture enhanced by a group-dilated convolutional module, which efficiently leverages time-frequency domain information while significantly reducing resource consumption through dilated convolutional groups and spectral-wise attention modules. Additionally, to capture the long-range contextual dependencies of the extracted features, an improved dual-path recurrent neural network is introduced between the encoder and decoder structures. Experimental results show that the proposed model achieves competitive performance with state-of-the-art baseline models on the Voicebank + Demand and DNS-Challenge datasets while requiring only 39.4 thousand model parameters and 237 million multiply-accumulate operations.}, archive = {J_ACCESS}, author = {Hyeong Il Koh and Sungdae Na and Myoung Nam Kim}, doi = {10.1109/ACCESS.2025.3585958}, journal = {IEEE Access}, pages = {116934-116943}, shortjournal = {IEEE Access}, title = {LSENet: A lightweight spectral enhancement network for high-quality speech processing on resource-constrained platforms}, volume = {13}, year = {2025}, }
• (2025). Novel mathematical modeling parameters of PEMFC based on Newton–Raphson iterative method. ACCESS, 13, 116924-116933. (www)

  @article{ , abstract = {A Proton Exchange Membrane Fuel Cell (PEMFC) is an electrochemical generator used for various applications in modern life. In recent years, it has become a promising Renewable Energy Source (RES) technology that uses natural gases such as oxygen and hydrogen for electrical generation. Modeling PEMFCs is essential to achieve advanced research work to improve PEMFCs performance. This paper builds a novel mathematical PEMFC model with the aid of only five points on the V-I polarizing curve based on the Newton-Raphson (NR) iterative method to extract its seven parameters with high accuracy and simplified analysis. The accuracy of the PEMFC model parameters was measured by comparing the results obtained using the estimated parameters and the measured results of the PEMFC stacks in the Objective Function (OF). In addition, the proposed method was validated by comparing the results with different research works listed in the literature. Furthermore, the polarization curves obtained from the proposed method closely align with the manufacturer-provided polarization curves across all case studies. Thus, the proposed PEMFC model is superior to other models in terms of its simplicity and minimum test points for modeling a PEMFC. The precision of the PEMFC model parameters was evaluated by juxtaposing the outcomes derived from the estimated parameters with the actual measurements of the PEMFC stacks within the OF. The suggested model attained a relative error of under 0.5% and SSE values as low as $2.9795\times 10^{-7}$ for five measurement locations in the SR-250W stack, hence affirming the estimation’s excellent precision.}, archive = {J_ACCESS}, author = {Ahmed A. Zaki Diab and Ahmed Hamdy Ali and Hamdy M. Sultan and Mohamed A. Ismeil and Montaser Abdelsattar}, doi = {10.1109/ACCESS.2025.3580704}, journal = {IEEE Access}, pages = {116924-116933}, shortjournal = {IEEE Access}, title = {Novel mathematical modeling parameters of PEMFC based on Newton–Raphson iterative method}, volume = {13}, year = {2025}, }
• (2025). Shaping empathy and trust toward agents: The role of agent behavior modification and attitude. ACCESS, 13, 116908-116923. (www)

  @article{ , abstract = {As artificial agents become increasingly integrated into daily life, designing interactions that foster empathy and trust is vital for ensuring ethical and effective human-agent collaboration. This study investigates how an agent’s attitude (positive or negative) and behavior modification (positive, negative or none) jointly influence users’ perceptions of empathy and trust over time. Using a traffic safety reflection scenario, we conducted a three-factor mixed design experiment involving 582 participants, who interacted with a computer-animated agent and evaluated it both before and after behavioral changes. The results revealed that positive behavior modification significantly enhanced empathy and trust, particularly when the agent’s attitude remained positive throughout. Interaction effects between behavior modification and time (before/after) were statistically significant (p <.001), and agents with a consistently positive attitude were rated significantly higher on both empathy (M = 3.65) and trust (M = 41.19) than negative ones. These findings underscore the importance of emotional responsiveness and adaptive behavior in shaping social perceptions of agents. The study contributes to theoretical frameworks of human-agent trust and empathy by highlighting how temporal dynamics and affective cues influence relational outcomes. These insights offer practical guidance for designing agents that are perceived not only as intelligent but also as emotionally attuned and socially trustworthy.}, archive = {J_ACCESS}, author = {Takahiro Tsumura and Seiji Yamada}, doi = {10.1109/ACCESS.2025.3584456}, journal = {IEEE Access}, pages = {116908-116923}, shortjournal = {IEEE Access}, title = {Shaping empathy and trust toward agents: The role of agent behavior modification and attitude}, volume = {13}, year = {2025}, }
• (2025). A review of arabic text summarization: Methods, datasets, and evaluation metrics, with a proposed solution. ACCESS, 13, 116887-116907. (www)

  @article{ , abstract = {This survey comprehensively reviews Arabic text summarization, examining state-of-the-art methodologies, commonly used datasets, and evaluation practices. Despite notable progress, the field faces challenges such as fragmented benchmarking, inconsistent metric use, and lacking resources for long-document summarization. We categorize existing summarization methods into traditional, Transformer-based, and hybrid approaches, highlighting their strengths and limitations. We introduce Mukhtasar, a novel dataset supporting short and long summaries across diverse genres to address significant gaps. Additionally, we propose six standardized evaluation splits tailored to distinct summarization goals, promoting reproducibility and fair comparison. To address inconsistencies, we also recommend a consistent reporting protocol using ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S. While lexical overlap metrics dominate evaluation practices, we identify the absence of neural-based metrics for Arabic as a significant limitation and call for future development in this area. Our contributions aim to unify evaluation protocols, enrich available resources, and guide the community toward more interpretable and scalable Arabic summarization research.}, archive = {J_ACCESS}, author = {Zeyad Ezzat and Ghada Khoriba and Ayman Khalafallah}, doi = {10.1109/ACCESS.2025.3584855}, journal = {IEEE Access}, pages = {116887-116907}, shortjournal = {IEEE Access}, title = {A review of arabic text summarization: Methods, datasets, and evaluation metrics, with a proposed solution}, volume = {13}, year = {2025}, }
• (2025). An insight on the timely diagnosis of diabetic retinopathy using traditional and AI-driven approaches. ACCESS, 13, 116869-116886. (www)

  @article{ , abstract = {Diabetic Retinopathy is a progressive microvascular complication of diabetes that requires early detection to improve patient outcomes. Traditional screening techniques, including fundus photography and optical coherence tomography, provide valuable diagnostic insights but have some limitations, like cost and technical complexity. Artificial intelligence is transforming the detection of diabetic retinopathy, moving away from traditional machine learning models that rely on manually created features to deep learning methods that allow for automatic feature extraction from retinal images. This systematic review investigates the evolution and diagnostic performance of AI-based techniques for DR detection. Studies were included if they applied machine learning or deep learning methods to retinal fundus or OCT images for DR classification. A total of 116 studies were included following comprehensive searches in databases such as PubMed, ScienceDirect, and IEEE Xplore, covering publications up to February 2024. Risk of bias was assessed in a representative sample of six studies, indicating a significant overall risk due to inadequate reporting on blinding and selective outcome reporting. Federated learning emerged as a promising alternative, enabling decentralized collaboration without compromising data privacy. Additionally, the growing focus on Explainable AI helps address the “non-explainable” nature of deep learning models by providing visual and textual explanations for predictions, thereby enhancing clinician trust and facilitating informed decision-making. By incorporating artificial intelligence with standard diagnostic frameworks, this research highlights the possibility for more accurate, scalable, and reachable diabetic retinopathy detection, paving the way for considerable advancements in ophthalmic problem management and enhancing patient care.}, archive = {J_ACCESS}, author = {Malaika Asif and Fasih Ur Rehman and Zoya Rashid and Altaf Hussain and Alina Mirza and Waqar Shahid Qureshi}, doi = {10.1109/ACCESS.2025.3583647}, journal = {IEEE Access}, pages = {116869-116886}, shortjournal = {IEEE Access}, title = {An insight on the timely diagnosis of diabetic retinopathy using traditional and AI-driven approaches}, volume = {13}, year = {2025}, }
• (2025). Toward sustainable 6G cellular system core-network-level traffic aggregation: An empirical study. ACCESS, 13, 116856-116868. (www)

  @article{ , abstract = {In this paper, the feasibility of core network (CN)-level traffic aggregation for multigeneration cellular radio access networks (RAN) is experimentally demonstrated. CN-level aggregation aims to integrate emerging radio access technologies into the legacy cellular network without modifying the legacy RAN by effort on CN, facilitating the smooth spread of emerging technologies. However, CN-level aggregation’s positioning remains unclear due to the lack of direct comparisons with RAN-level aggregation, which has been widely commercialized and inherently impacts the legacy infrastructure. To fill the knowledge gap, an extensive performance comparison between the RAN- and CN-level aggregations is provided. The experimental results reveal that CN-level aggregation achieves competitive throughput performances compared with RAN-level aggregation and does not impact the legacy RAN.}, archive = {J_ACCESS}, author = {Takeo Ogawara and Sohei Itahara and Akito Suzuki and Masaki Suzuki}, doi = {10.1109/ACCESS.2025.3586253}, journal = {IEEE Access}, pages = {116856-116868}, shortjournal = {IEEE Access}, title = {Toward sustainable 6G cellular system core-network-level traffic aggregation: An empirical study}, volume = {13}, year = {2025}, }
• (2025). Cybersecurity in connected and autonomous vehicles: A systematic review of automotive security. ACCESS, 13, 116818-116855. (www)

  @article{ , abstract = {The rapid evolution of the automotive industry has driven the emergence of Connected and Autonomous Vehicles, raising significant concerns about the cybersecurity vulnerabilities inherent in their complex networks. This systematic review investigates cybersecurity in Connected and Autonomous Vehicles, focusing on internal and external networks and addressing four key research questions: (RQ1) What security controls exist in CAV networks? (RQ2) What methodologies are employed in cybersecurity studies? (RQ3) How effective are these methods, and what limitations do they present? (RQ4) What are the key themes, common approaches, and future research directions? Peer-reviewed studies published between 2019 and 2024 were included, using IEEE Xplore, Elsevier, MDPI, ACM Digital Library, and Springer as data sources. Following PRISMA 2020 guidelines, 111 relevant articles were analysed and grouped into seven themes: Authentication, Blockchain, Intrusion Detection Systems, Vehicle-to-Everything communication, Network Operation Centers, Security Operations Centers, and Systematic Reviews. The thematic synthesis highlighted study objectives, methodologies, and implemented security controls. This review identifies significant gaps in the literature, particularly in integrating Security Information and Event Management systems and the real-world validation of proposed security measures. It underscores the need for adaptive cybersecurity frameworks to address evolving threats and highlights the importance of collaboration between academia and industry. Furthermore, future research should prioritize the development of advanced security protocols, address scalability challenges, and explore the impact of emerging technologies such as Artificial Intelligence and 5G. Providing awareness and training is also essential to mitigate human error. These findings are a foundation for designing more resilient and secure Connected and Autonomous Vehicles systems.}, archive = {J_ACCESS}, author = {Rafael Abreu and Frederico Branco and Manuel J. Cabral S. Reis and Carlos Serôdio}, doi = {10.1109/ACCESS.2025.3584649}, journal = {IEEE Access}, pages = {116818-116855}, shortjournal = {IEEE Access}, title = {Cybersecurity in connected and autonomous vehicles: A systematic review of automotive security}, volume = {13}, year = {2025}, }
• (2025). A hard real-time kernel for CPIoT systems with safety features in rust. ACCESS, 13, 116803-116817. (www)

  @article{ , abstract = {The increasing deployment of Cyber-Physical Internet of Things (CPIoT) systems in safety-critical domains demands execution environments with strict real-time guarantees, memory safety, and high reliability. Existing real-time operating systems and kernels (RT-OS/Kernels), predominantly developed in C/C++, are prone to memory corruption, jitter, and non-deterministic execution due to blocking primitives and queue-based scheduling. This paper proposes HarSaRK-RS, a hard real-time kernel implemented in Rust, designed to address these limitations through compile-time safety, modular resource management and constant-time task scheduling. HarSaRK employs a boolean-vector-based scheduling and synchronization model that replaces queues, ensuring low-jitter and deterministic execution. It supports both single-core and dual-core microcontrollers using the Stack-Based Priority Ceiling (SBPC) Protocol and the Multiprocessor Resource Sharing Protocol (MRSP), respectively. A hardware-assisted IP-connectivity solution, realized via ESP8266 and managed through an interrupt-driven high-priority task, enables non-blocking internet communication for CPIoT applications. Experimental evaluation on STM32 and NXP LPC55S69 platforms demonstrates the kernel’s predictable latency, low memory footprint, and efficient multicore task migration. HarSaRK establishes an efficient foundation for next-generation CPIoT deployments, combining Rust’s safety guarantees with a real-time tasking model. Our proposed kernel achieves 1) context switch time reductions ranging from a few processor cycles to up to $6\times $ , and 2) task switch time improvements between $2\times $ and $6\times $ .}, archive = {J_ACCESS}, author = {Nayancy Gupta and Gourinath Banda and Neminath Hubballi and K. V. Srinivas}, doi = {10.1109/ACCESS.2025.3586018}, journal = {IEEE Access}, pages = {116803-116817}, shortjournal = {IEEE Access}, title = {A hard real-time kernel for CPIoT systems with safety features in rust}, volume = {13}, year = {2025}, }
• (2025). Generalized additive model for predicting ECBR of stabilized subgrades for pavement. ACCESS, 13, 116785-116802. (www)

  @article{ , abstract = {This study presents a Generalized Additive Model (GAM) to predict the effective CBR (ECBR) of soil subgrade stabilized with recycled plastic waste under repeated loading (RL) conditions. Univariate and bivariate GAM were explored to evaluate the influence of parameter interactions on ECBR predictions. The Bivariate GAM model demonstrated superior performance, achieving a cross-validation mean squared error of $9.2203\times 10^{-5}$ , mean absolute error (MAE) of 0.007, root mean squared error (RMSE) of 0.009, mean absolute percentage error (MAPE) of 0.077 and percentage bias (PBIAS) of 0.00026% indicating minimal deviation and high accuracy. Localized interpretability analysis yielded ECBR predictions closely aligned with the actual values, highlighting the model’s robustness. Further interpretability analysis through mutual information and partial dependence plots identified resilient modulus ( $\text{M}_{\mathrm {r}}$ ), optimum moisture content (OMC), maximum dry density (MDD) and content of plastic waste (CP) as key predictors influencing ECBR. Among these Mr demonstrated the highest mutual information score (1.00), emphasizing its critical role and direct association with RL conditions. The Predicted ECBR values, with a 1.130% average deviation from literature-derived values, confirm the bivariate GAM’s exceptional accuracy and reliability in handling the diverse, unseen datasets. These findings underscore the model’s potential as a trustworthy, interpretable and efficient alternative to traditional methods for road pavement applications.}, archive = {J_ACCESS}, author = {Alka Shah and Tejaskumar Thaker and Vipin Shukla}, doi = {10.1109/ACCESS.2025.3585272}, journal = {IEEE Access}, pages = {116785-116802}, shortjournal = {IEEE Access}, title = {Generalized additive model for predicting ECBR of stabilized subgrades for pavement}, volume = {13}, year = {2025}, }
• (2025). 3D visualization system of breast magnetic resonance images based on deep learning and volume rendering. ACCESS, 13, 116765-116784. (www)

  @article{ , abstract = {Compared to natural images, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) exhibits lower spatial resolution, reduced contrast sensitivity, and limited detail visibility, making it time-consuming and labor-intensive for clinicians to delineate lesion areas. Additionally, patients often struggle to understand the condition of the lesions intuitively. To address these challenges, a three-dimensional (3D) visualization and interactive system for breast DCE-MRI data is proposed and implemented, which integrates deep learning-based segmentation with volume rendering techniques. The multimodal feature fusion network (MFF-Vnet) based on the Vnet architecture employs a multi-branch design tailored to process multimodal images. Specifically, the MFF-Vnet model automatically segments breast lesions, providing accurate delineation of lesion areas. Then, an improved ray-casting algorithm is utilized for volume rendering to visualize the 3D structure of the breast and associated lesions with enhanced speed and rendering quality. Finally, an interactive visualization system is developed based on a client/server (C/S) three-tier architecture, enabling functions such as data loading, slice visualization, lesion segmentation, 3D rendering, and interface interaction. Experimental results demonstrate that the improved ray-casting algorithm accelerates the rendering process while maintaining visualization quality. The MFF-Vnet model demonstrated promising segmentation performance on a breast DCE-MRI dataset containing expert-annotated lesions, achieving a Dice Similarity Coefficient (DSC) of up to 83.77%. Through interactive operations, the system facilitates multi-scale and multi-perspective observation and analysis of lesion characteristics, including shape, size, and location, thus assisting clinicians in diagnosis and treatment planning, while improving patient understanding of their condition.}, archive = {J_ACCESS}, author = {Rong Chen and Stephen Karungaru and Kenji Terada and Changyan Xu}, doi = {10.1109/ACCESS.2025.3584874}, journal = {IEEE Access}, pages = {116765-116784}, shortjournal = {IEEE Access}, title = {3D visualization system of breast magnetic resonance images based on deep learning and volume rendering}, volume = {13}, year = {2025}, }
• (2025). Enhancing photovoltaic thermal system efficiency through geometric optimization and intelligent fuzzy logic based management. ACCESS, 13, 116745-116764. (www)

  @article{ , abstract = {The growing demand for sustainable and efficient energy systems has intensified research into improving the performance of photovoltaic thermal (PVT) technologies. This study aims to enhance the electrical and thermal efficiency of PVT systems by integrating material design modifications with intelligent control strategies. A novel methodology is proposed, which involves reducing the thickness of the tedlar layer from 5 mm to 1 mm to improve heat dissipation, alongside the implementation of an adaptive fuzzy logic control (AFLC) system to dynamically regulate the cooling flow rate based on environmental conditions. The results show a notable improvement in system performance, with electrical efficiency increasing from 11.81% to 13.81%, thermal efficiency rising from 27% to 81%, and electrical power output improving from 243.8 W to 271 W under controlled cooling conditions. These findings demonstrate that the proposed integrated approach, named GO-AFLC, effectively maximizes the energy extraction from PVT systems. The combination of passive (material optimization) and active (intelligent control) strategies offers a promising pathway for advancing the performance and sustainability of hybrid solar energy systems.}, archive = {J_ACCESS}, author = {Hamza Nasri and Jamel Riahi and Hatem Oueslati and Hichem Taghouti and Silvano Vergura}, doi = {10.1109/ACCESS.2025.3585303}, journal = {IEEE Access}, pages = {116745-116764}, shortjournal = {IEEE Access}, title = {Enhancing photovoltaic thermal system efficiency through geometric optimization and intelligent fuzzy logic based management}, volume = {13}, year = {2025}, }
• (2025). Copolar synchronous switching method for BLDC motor control in EV application. ACCESS, 13, 116729-116744. (www)

  @article{ , abstract = {This paper proposes a new PWM switching method, called copolar switching, for synchronous rectification in brushless DC motors (BLDC). This method-integrates the benefits of both unipolar and bipolar switching, addressing the efficiency trade-off commonly observed in low-resistance BLDC drives. Unipolar switching is known to reduce current ripple and switching losses at low speeds, while bipolar switching enhances efficiency during high-speed operation due to faster recovery of freewheeling current. However, most existing methods statically adopt one scheme, leading to suboptimal performance across variable-speed conditions. The proposed copolar switching method combines unipolar switching at zero-speed for ripple-free conduction, and switches to bipolar mode at higher speeds to enable rapid energy feedback to the source. Therefore, it can be used in electric vehicles (EV) applications to increase the average power efficiency. The EV simulation in this paper identified there could be a 5% efficiency improvement.}, archive = {J_ACCESS}, author = {Yueh-Tsung Shieh and Gang-Ting Liou and Shyr-Long Jeng and Wei-Hua Chieng and Jih-Sheng Lai and Edward-Yi Chang}, doi = {10.1109/ACCESS.2025.3584259}, journal = {IEEE Access}, pages = {116729-116744}, shortjournal = {IEEE Access}, title = {Copolar synchronous switching method for BLDC motor control in EV application}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of optimization methods in skeleton-based human action recognition. ACCESS, 13, 116713-116728. (www)

  @article{ , abstract = {Skeleton-based human action recognition (HAR) has become a prevalent topic in the field of computer vision due to its ability to widely use in different applications such as video surveillance, healthcare monitoring, sport analysis, and human computer interaction. Over the past decade, researchers have introduced various approaches such as CNN, RNN, and GCN to classify human actions using skeleton data. However, challenges such as noisy data, inter-class similarity, and diverse action dynamics require more robust and efficient solutions for skeleton-based HAR. To address these challenges, optimization methods, such as attention mechanisms and fusion strategies are being implemented in the feature extraction and classification processes to enhance the performance of action recognition. Despite their critical role in improving the recognition accuracy and system reliability, there is currently no systematic review that focuses specifically on these optimization methods in the context of skeleton-based HAR. This study presents a systematic review of the current optimization methods by thoroughly examining 92 publications published from 2014 to 2024. This study aims to provide the researchers with a better understanding of how optimization methods are integrated in recent HAR systems and serve as a reference for future studies on improving skeleton-based HAR.}, archive = {J_ACCESS}, author = {Jen-Li Chung and Lee-Yeng Ong and Meng-Chew Leow}, doi = {10.1109/ACCESS.2025.3586476}, journal = {IEEE Access}, pages = {116713-116728}, shortjournal = {IEEE Access}, title = {A systematic literature review of optimization methods in skeleton-based human action recognition}, volume = {13}, year = {2025}, }
• (2025). Multidimensional resource task scheduling based on particle swarm optimization in edge computing. ACCESS, 13, 116701-116712. (www)

  @article{ , abstract = {The default Kubernetes scheduling algorithm, Balanced Resource Allocation (BRA), has limitations in achieving resource balance. It primarily focuses on the instantaneous utilization of CPU and memory while neglecting the coordinated optimization of disk I/O and network bandwidth, resulting in an imbalance in cluster resource utilization, commonly known as the bottleneck effect. To address this issue, this paper proposes a multi-task scheduling model based on multidimensional resource demands. By analyzing Pod resource requests and the real-time availability of cluster resources, the model evaluates scheduling decisions to enhance overall load balancing. The proposed approach enables the simultaneous scheduling of multiple tasks while iteratively adjusting the inertia weight and learning factors in the discrete particle swarm optimization (DPSO) algorithm. Experimental results with different types of applications show that, compared to the baseline scheduling methods, the proposed strategy can flexibly adapt to the varying demands of different task types and effectively reduce the imbalance in the utilization of multi-dimensional cluster resources. This is particularly evident in I/O-intensive scenarios, where the IPSO algorithm demonstrates superior resource adaptation capability and utilization efficiency.}, archive = {J_ACCESS}, author = {Zhonglu Zou and Zhuxin Zhai and Xin Yan and Zilin You and Liming Chen}, doi = {10.1109/ACCESS.2025.3585628}, journal = {IEEE Access}, pages = {116701-116712}, shortjournal = {IEEE Access}, title = {Multidimensional resource task scheduling based on particle swarm optimization in edge computing}, volume = {13}, year = {2025}, }
• (2025). COMIX: Generalized conflict management in O-RAN xApps—Architecture, workflow, and a power control case. ACCESS, 13, 116684-116700. (www)

  @article{ , abstract = {Open Radio Access Network (O-RAN) is transforming the telecommunications landscape by enabling flexible, intelligent, and multi-vendor networks. Central to its architecture are xApps hosted on the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), which optimize network functions in real time. However, the concurrent operation of multiple xApps with conflicting objectives can lead to suboptimal performance. This paper introduces a generalized Conflict Management scheme for Multi-Channel Power Control in O-RAN xApps (COMIX), designed to detect and resolve conflicts between xApps. To demonstrate COMIX, we focus on two Deep Reinforcement Learning (DRL)-based xApps for power control: one maximizes the data rare across UEs, and the other optimizes system-level energy efficiency. COMIX employs a standardized Conflict Mitigation Framework (CMF) for conflict detection and resolution and leverages the Network Digital Twin (NDT) to evaluate the impact of conflicting actions before applying them to the live network. We validate the framework using a realistic multi-channel power control scenario under various conflict resolution policies, demonstrating its effectiveness in balancing antagonistic objectives. Evaluation results show that COMIX achieves up to 60% energy savings across different Service-Level Agreement (SLA) policies compared to a baseline conflict-unaware system, with negligible impact (around 3%) on system throughput. While this study considers power control xApps, the COMIX framework is generalizable and can be applied to any xApp conflict scenario involving resource contention or KPI interdependence.}, archive = {J_ACCESS}, author = {Anastasios E. Giannopoulos and Sotirios T. Spantideas and George Levis and Alexandros S. Kalafatelis and Panagiotis Trakadas}, doi = {10.1109/ACCESS.2025.3585774}, journal = {IEEE Access}, pages = {116684-116700}, shortjournal = {IEEE Access}, title = {COMIX: Generalized conflict management in O-RAN xApps—Architecture, workflow, and a power control case}, volume = {13}, year = {2025}, }
• (2025). Predictive beamforming with active inference under continuous time-varying channels. ACCESS, 13, 116669-116683. (www)

  @article{ , abstract = {In dynamic communication environments, where channel conditions change continuously over time, beamforming employing massive multiple-input multiple-output (MIMO) in the millimeter wave (mmWave) band requires frequent beam re-alignment. To reduce the beam search overhead, hierarchical codebooks have been introduced to narrow down candidate beams efficiently. However, in such environments, a repeated and significant drop in signal-to-noise ratio (SNR) often occurs due to re-searching for the optimal beam, which remains a challenge. To address this issue, we propose a method that combines hierarchical codebooks with active inference to predict the optimal beam rather than search for it. Our approach infers the underlying channel state and selects the beam accordingly, aiming to minimize temporary SNR degradation caused by repeated beam training. Simulations in a scenario involving a mobile terminal moving around a single base station demonstrate that our method achieves a higher average SNR than classical beam training using the divide-and-conquer algorithm. Furthermore, the frequency of sharp SNR drops is reduced. These results show that our proposed predictive beamforming method offers high adaptability and stable communication quality under continuously time-varying channel conditions.}, archive = {J_ACCESS}, author = {Naoki Nishio and Tatsuya Otoshi and Masayuki Murata}, doi = {10.1109/ACCESS.2025.3586313}, journal = {IEEE Access}, pages = {116669-116683}, shortjournal = {IEEE Access}, title = {Predictive beamforming with active inference under continuous time-varying channels}, volume = {13}, year = {2025}, }
• (2025). Hybrid CNN-based transfer learning enhances brain tumor classification on MRI images. ACCESS, 13, 116654-116668. (www)

  @article{ , abstract = {Brain tumors are among the deadliest diseases worldwide and require early and accurate diagnosis via Magnetic Resonance Imaging (MRI). Deep learning techniques, particularly convolutional neural networks (CNNs), have been widely adopted for analyzing brain MRI images in the context of tumor classification tasks. However, individual models of conventional CNNs often struggle with tumor diversity due to their limited receptive fields and homogeneous feature extraction, resulting in suboptimal diagnostic precision. We propose a hybrid transfer learning framework based on CNN architectures for enhanced multiclass classification of brain tumors. A publicly accessible brain MRI dataset from Kaggle is employed in this study. The dataset, constructed from Figshare, SARTAJ, and Br35H, contains 7023 original images categorized into four classes: glioma, meningioma, pituitary, and no tumor. We introduce a preprocessing pipeline that involves MRI cropping and affine-based augmentation to balance a strategically modified version of our dataset, which intentionally incorporates class imbalance. Leveraging transfer learning, we fine-tune lightweight pre-trained models, such as ResNet50V2, MobileNetV2, DenseNet121, EfficientNetV2S, and NASNetMobile, and adopt a hybrid feature extraction strategy that integrates multilevel feature maps. Experimental evaluations reveal that our hybrid model (ResNet50V2 + MobileNetV2 + DenseNet121) achieves superior performance, attaining 98.75% accuracy, 98.76% precision, 98.75% recall, and 98.75% F1-score, outperforming both individual models and state-of-the-art methods. The results highlight that integrating diverse features from multiple CNNs enhances classification robustness by capturing complementary tumor characteristics. In conclusion, our study advances medical imaging diagnostics by demonstrating the efficacy of hybrid transfer learning in overcoming data imbalance and model limitations, offering a reliable tool for clinical decision-making.}, archive = {J_ACCESS}, author = {Rizal Dwi Prayogo and Nur Hamid and Hidetaka Nambo}, doi = {10.1109/ACCESS.2025.3584376}, journal = {IEEE Access}, pages = {116654-116668}, shortjournal = {IEEE Access}, title = {Hybrid CNN-based transfer learning enhances brain tumor classification on MRI images}, volume = {13}, year = {2025}, }
• (2025). Evaluation of different range-doppler processing techniques for OFDM-based DVB-t2 passive radar. ACCESS, 13, 116643-116653. (www)

  @article{ , abstract = {There are many papers investigating the processing stages required for the first-generation terrestrial Digital Video Broadcast, DVB-T, and the advantages and disadvantages of each technique. As the world moves towards the deployment of the second generation (DVB-T2) standard, there is a gap in the literature focusing on applying these processing steps in the DVB-T2 context. One of the advantages of the DVB-T2 standard is the higher level of flexibility in transmission. This flexibility poses challenges to passive radar (PR) designers that needs to be addressed. One of the major differences between DVB-T and DVB-T2 is the increased number of pilot configurations and higher data rates (effectively larger FFT sizes). This paper compares the two main processing techniques, mismatched filtering and inverse filtering in the DVB-T2 context. Using real world signal parameters, extracted from recorded data, the application of each processing technique is clearly demonstrated, illustrating the advantages and disadvantages for each. A novel approach to resolving Doppler ambiguities that appear when using inverse filtering for DVB-T2 based passive radar, is then presented and its application demonstrated.}, archive = {J_ACCESS}, author = {Stephen Paine and W. P. Francois Schonken and Daniel W. O’Hagan and Christof Schüpbach and Jan Pidanic and Josef Jordan}, doi = {10.1109/ACCESS.2025.3583054}, journal = {IEEE Access}, pages = {116643-116653}, shortjournal = {IEEE Access}, title = {Evaluation of different range-doppler processing techniques for OFDM-based DVB-t2 passive radar}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of energy efficient cooperative SWIPT NOMA-assisted cognitive satellite-terrestrial networks. ACCESS, 13, 116632-116642. (www)

  @article{ , abstract = {This study explores an energy-efficient cooperative simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA)-assisted cognitive satellite-terrestrial network (CSTN), enhancing system performance in terms of outage probability (OP), system outage probability (SOP), throughput, and energy efficiency (EE). The network comprises a primary satellite source, its destination, and a secondary terrestrial source-destination pair. The primary satellite source employs NOMA to serve users, while the secondary source assists primary communication through cooperative relaying. This shared relay harvests energy through a nonlinear energy harvesting model and utilizes a time-switching protocol for information decoding and energy harvesting. The secondary source transmits a superposed signal of primary and secondary messages to their destinations using NOMA protocol. To further enhance the system performance, maximal ratio combining (MRC), selection combining (SC), and time-switching schemes are employed to process the received signal at the primary user. Considering Shadowed-Rician satellite channels and Rayleigh-distributed terrestrial channels, analytical expressions for OP, SOP, throughput, and EE are derived and validated for both users with perfect and imperfect successive interference cancellation (SIC) by using the stochastic geometry method, and are validated by means of MATLAB-based simulations. In addition, the performances of primary and secondary users are further improved by using an optimal power distribution scheme which is implemented at the shared relay.}, archive = {J_ACCESS}, author = {Swathi Priya Indraganti and Suseela Vappangi}, doi = {10.1109/ACCESS.2025.3578767}, journal = {IEEE Access}, pages = {116632-116642}, shortjournal = {IEEE Access}, title = {Performance analysis of energy efficient cooperative SWIPT NOMA-assisted cognitive satellite-terrestrial networks}, volume = {13}, year = {2025}, }
• (2025). Error correction codes for double burst errors correction in memories. ACCESS, 13, 116621-116631. (www)

  @article{ , abstract = {This paper addresses the issue of double burst errors occurring in memories and presents the design of corresponding error correction codes (ECC). The proposed ECCs can correct up to two burst errors simultaneously, each up to 4-bit. The errors’ positions are randomly distributed across any bit within the codeword. The proposed coding scheme is designed with FUEC and syndrome-based decoding. Then the scheme is implemented in hardware with Verilog HDL, utilizing parallel decoding techniques to minimize the latency and parity bits’ number. Comparative evaluations on hardware overheads are conducted for ECCs with the correction capabilities of double burst errors. To minimize the hardware overhead associated with codes with high correction requirements, bit interleaving techniques are applied, further reducing codec area, power, and delay. The ECCs are proved to demonstrate excellent performance in correcting double burst errors, with optimized hardware efficiency.}, archive = {J_ACCESS}, author = {He Liu and Liyi Xiao and Tianqi Wang and Jiaqiang Li and Jie Li}, doi = {10.1109/ACCESS.2025.3586226}, journal = {IEEE Access}, pages = {116621-116631}, shortjournal = {IEEE Access}, title = {Error correction codes for double burst errors correction in memories}, volume = {13}, year = {2025}, }
• (2025). Implementing holographic reduced representations for spiking neural networks. ACCESS, 13, 116606-116620. (www)

  @article{ , abstract = {Neuromorphic Computing surpasses conventional von Neumann architectures in terms of energy efficiency, parallelisation, scalability, and stochasticity. Given the inherent structure of neurons and synapses, neuromorphic computers can be directly implemented as spiking neural networks. Despite these advantages, neuromorphic computing applications are hitherto limited to benchmark datasets and empirical demonstrations. This is primarily due to the lack of a unifying computing framework that designates a middle-layer abstraction between the actual neuromorphic computing and the required application functionality. Drawing on the distributed vector representation of symbolic and numerical data structures and robust dual interface with diverse operational primitives, Vector Symbolic Architectures (VSA) have been positioned as a suitable candidate to address this middle-layer void. In this paper, we explore the potential of VSA as an intermediary abstraction layer to advance practical neuromorphic computing applications. We introduce a novel vectorised framework that efficiently processes parallel streams of spiking data by combining and computing them through VSA for real-time downstream learning tasks, leveraging spike latency encoding. Our implementation utilises containerised methods within Lava, an open-source framework for neuromorphic computing.}, archive = {J_ACCESS}, author = {Vidura Sumanasena and Daswin de Silva and Evgeny Osipov and Dmitri A. Rachkovskij and Ross W. Gayler}, doi = {10.1109/ACCESS.2025.3580582}, journal = {IEEE Access}, pages = {116606-116620}, shortjournal = {IEEE Access}, title = {Implementing holographic reduced representations for spiking neural networks}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in action (2019-2024): A review on parkinson’s disease detection using non-invasive procedures. ACCESS, 13, 116586-116605. (www)

  @article{ , abstract = {Alzheimer’s disease, Parkinson’s disease (PD), multiple sclerosis, epilepsy, and stroke are among the most significant neurological disorders. This paper focuses on PD. Although there is currently no cure for PD, early identification can help manage the condition. We present a comprehensive review of cutting-edge research on computerized methods for detecting and monitoring Parkinson’s disease. Our study covers various feature extraction techniques, methods for reducing dimensionality, feature selection approaches, and classification strategies related to PD. The articles reviewed were chosen from various journals, from January 2019 to September 2024, based on the data sources and symptoms used to diagnose PD. We conducted a thorough analysis and documented information about datasets, software tools, and libraries for future use of researchers in this field.}, archive = {J_ACCESS}, author = {Peetabas Patro and Tapan Kumar Das}, doi = {10.1109/ACCESS.2025.3583572}, journal = {IEEE Access}, pages = {116586-116605}, shortjournal = {IEEE Access}, title = {Artificial intelligence in action (2019-2024): A review on parkinson’s disease detection using non-invasive procedures}, volume = {13}, year = {2025}, }
• (2025). Low voltage AC series arc fault detection method based on approximate entropy for low computational performance device. ACCESS, 13, 116556-116568. (www)

  @article{ , abstract = {The series arc faults have been increasingly recognized as one of the major causes of electrical fires in residential environments. However, detecting series arc faults is becoming more challenging due to the increasing diversity of electrical loads used in daily life. This paper proposes an algorithm for detecting series arc faults in low-voltage AC residential environments. The proposed algorithm utilizes Approximate Entropy as a key feature. However, computing Approximate Entropy within a limited time is constrained by the computational performance of commonly used conventional microcontrollers, which restricts the number of input data points that can be processed. To address this limitation, Maximum Difference and MinMax preprocessing methods are introduced to reduce the number of data points by extracting representative values from segmented intervals. During Approximate Entropy computation, these preprocessed data points are used as inputs. Additionally, key parameters for Approximate Entropy are carefully selected to ensure effective differentiation between pre- and post-series arc conditions across various load types and configurations. The computed Approximate Entropy values for each load and configuration are then used as features in the proposed algorithm. Finally, the algorithm’s performance is evaluated by assessing its series arc fault detection performance and its ability to minimize false detections during normal operation.}, archive = {J_ACCESS}, author = {Kyoung-Tak Kim and Min-Ho Yoon and Chan-Muk Park and Joung-Hu Park and Sung-Hun Lim}, doi = {10.1109/ACCESS.2025.3583939}, journal = {IEEE Access}, pages = {116556-116568}, shortjournal = {IEEE Access}, title = {Low voltage AC series arc fault detection method based on approximate entropy for low computational performance device}, volume = {13}, year = {2025}, }
• (2025). A novel transit bus number identification approach for frictionless fare collection using passenger location data. ACCESS, 13, 116548-116555. (www)

  @article{ , abstract = {Automated Fare Collection (AFC) systems are essential for advancing public transportation infrastructure. This study introduces a Be-In Be-Out (BIBO) framework that uses smartphone location data to facilitate frictionless fare collection by identifying public bus trips. The framework consists of two main components: a Travel Mode Detector (TMD), which operates on mobile devices to identify transportation modes and collect location data during transit, and a Journey Recognition Module (JRM), which processes the collected data in the cloud to identify bus trips. By combining GPS and General Transit Feed Specification (GTFS) data, the system enables efficient real-time bus trip recognition. The framework incorporates trajectory similarity algorithms such as Dynamic Time Warping (DTW) and Longest Common Subsequence (LCSS) within the JRM to enhance the accuracy of user-to-bus matching. Experimental evaluations demonstrate the effectiveness of the approach, highlighting the superior performance of DTW in accurately identifying transit bus IDs.}, archive = {J_ACCESS}, author = {Nafise Ghorbankhani and Morteza Adl and Ryan Ahmed and Ali Emadi}, doi = {10.1109/ACCESS.2025.3586176}, journal = {IEEE Access}, pages = {116548-116555}, shortjournal = {IEEE Access}, title = {A novel transit bus number identification approach for frictionless fare collection using passenger location data}, volume = {13}, year = {2025}, }
• (2025). DDoS attack detection in IoT: A comparative resource and performance analysis of deep learning and machine learning models. ACCESS, 13, 116529-116547. (www)

  @article{ , abstract = {The proliferation of Internet of Things (IoT) devices has increased susceptibility to Distributed Denial of Service (DDoS) attacks, exposing the limitations of traditional security mechanisms such as firewalls and rule-based intrusion detection systems (IDS), which often fail due to static rules and limited adaptability in resource-constrained environments. To address these challenges, this study introduces a lightweight IDS leveraging advanced deep learning techniques, including lightweight convolutional neural networks (CNNs) such as MobileNetV3, EfficientNetB0, and other models like CNN and Xception. The proposed IDS employs different lightweight deep learning models on image representations of network traffic data and machine learning models on tabular CSV representations of the same data. The primary challenge stems from the balance of high detection accuracy with the computational efficiency required for real-time IDS deployment in constrained IoT environments. This study proposes a dual-format comparative approach to assess how data representation image-based for deep learning and tabular for traditional machine learning influences detection performance and resource utilization. The evaluation is conducted using the CIC-DDoS2019 dataset, measuring detection metrics including accuracy, precision, recall, and AUC, as well as resource efficiency metrics such as inference time, memory usage, and CPU consumption. The results demonstrate that although lightweight deep learning models like MobileNetV3 and EfficientNetB0 deliver near-perfect AUC scores, their inference times are still too high for real-time applications. Conversely, traditional machine learning models such as Random Forest and SVM provide significantly faster inference with minimal resource consumption, making them better suited for real-time IDS deployment.}, archive = {J_ACCESS}, author = {Amer Abualhassan and Irfan Rashid and Farid Binbeshr and Muhammad Imam}, doi = {10.1109/ACCESS.2025.3583855}, journal = {IEEE Access}, pages = {116529-116547}, shortjournal = {IEEE Access}, title = {DDoS attack detection in IoT: A comparative resource and performance analysis of deep learning and machine learning models}, volume = {13}, year = {2025}, }
• (2025). Bimanual motion primitives for manipulating 3D deformable objects having biological tissues on a planar surface with passive end-effectors. ACCESS, 13, 116519-116528. (www)

  @article{ , abstract = {Manipulating deformable objects (DOs) that change shape during operations poses a challenge for robots due to the complexity of modelling and predicting the object’s state. This challenge is critical for the food industry, where collaborative robots will assist workers in processing and packaging food products. Most meat products are three-dimensional DOs (3D-DOs) composed of biological tissues, which give them viscoelastic and anisotropic properties that make their behaviour highly unpredictable and challenging to model. To address this challenge and minimise food cross-contamination, dual-arm robots and non-actuated end-effectors, such as chopsticks, are used to provide more flexibility, especially when working with objects in confined spaces, and reduce tool actuation time. Our goal is to develop a methodology for manipulating predominant convex 3D-DOs composed of biological tissues on a planar surface, altering their position and orientation while minimising their deformations. To accomplish this, we propose bimanual motion primitives utilising a novel geometric approach. Effectiveness is measured by the Intersection-over-Union ratio and object centroid error metrics through 180 chicken-breast, 36 pork-loin-steak experiments, and 96 comparative experiments with heuristic methods for rigid objects.}, archive = {J_ACCESS}, author = {Hoang Pham and Pablo Malvido Fresnillo and Saigopal Vasudevan and Wael M. Mohammed and Jose L. Martinez Lastra}, doi = {10.1109/ACCESS.2025.3586079}, journal = {IEEE Access}, pages = {116519-116528}, shortjournal = {IEEE Access}, title = {Bimanual motion primitives for manipulating 3D deformable objects having biological tissues on a planar surface with passive end-effectors}, volume = {13}, year = {2025}, }
• (2025). Division of a natural number by two: A cellular automata-based approach. ACCESS, 13, 116508-116518. (www)

  @article{ , abstract = {Cellular automata are mathematical models for systems that consist of large numbers of simple identical components with local interactions. The simple components act together to produce complicated patterns of behavior that are especially suitable for modeling natural systems. Division by two or halving has been of special importance among the division operations and has been investigated separately in the literature. One of the most important factors in performing division operations is the speed of calculations. This paper proposes a new method, DN2, for dividing a natural number by two to speed up the calculations. Then, using the parallel property of block cellular automaton, a method for implementing DN2, called D2CA, with two rules is presented. Experimental results indicate that D2CA has an average of 28.11% less execution time than the Replacement algorithm. Also, some mathematical properties of D2CA are analyzed, and as a case study, the application of D2CA in cryptography is investigated.}, archive = {J_ACCESS}, author = {Mohammad Zeynali Azim and Babak Anari and Saeid Alikhani and Bagher Zarei}, doi = {10.1109/ACCESS.2025.3569454}, journal = {IEEE Access}, pages = {116508-116518}, shortjournal = {IEEE Access}, title = {Division of a natural number by two: A cellular automata-based approach}, volume = {13}, year = {2025}, }
• (2025). Open access datasets for active magnetic bearing high-speed machinery. ACCESS, 13, 116491-116507. (www)

  @article{ , abstract = {There is a notable shift towards data-based research, with an increasing emphasis on openly sharing research datasets. This trend aligns with the principles of open access (OA) and the FAIR (Findable, Accessible, Interoperable, and Reusable) data movement, which enhance the visibility and usability of research outputs. In several fields of study, this practice is already well established. When considering the development of high-speed technology (HST), particularly those based on magnetic suspension, the field is inherently multidisciplinary, involving various engineering disciplines to develop functional concepts. While the design phase often generates valuable data, including experimental data from integrated sensors, there remains a notable lack of openly shared data in this field. Before introducing our own HST data, this article provides a comprehensive literature review on data-driven research, FAIR data principles, and existing open data platforms used for data sharing to highlight the significant rise in data-driven research within engineering and justify the need for a dedicated repository for data sharing. Following this, we present a detailed description of the open-access datasets related to one HST application example system, including identification results, operational data, and design data. By making this data available in the selected repository, we aim to support research in control theory, modeling, and data-driven methodologies related to the development of high-speed technology based on magnetic suspension. The datasets discussed in this paper are openly available in the Harvard Dataverse repository (https://dataverse.harvard.edu/dataverse/luthstdata).}, archive = {J_ACCESS}, author = {Mohammad Rayhan and Ibrahim Abubakar and Gyan Ranjan and Jussi Sopanen and Tuomo Lindh and Niko Nevaranta}, doi = {10.1109/ACCESS.2025.3586026}, journal = {IEEE Access}, pages = {116491-116507}, shortjournal = {IEEE Access}, title = {Open access datasets for active magnetic bearing high-speed machinery}, volume = {13}, year = {2025}, }
• (2025). 3D modeling for slope angles and volume of underground conduits using RGB-D camera. ACCESS, 13, 116480-116490. (www)

  @article{ , abstract = {This paper proposes a 3D modeling system to measure the slope angles and volume of underground electrical conduits installation. The proposed system consists of an RGB-D camera and a RTK device for data acquisition, and exploits AR markers when capturing 3D data. The RGB-D data at the consecutive frames are matched and reconstructed as 3D point clouds for the surfaces of underground conduit. The RTK data compensate for the scale errors in the 3D point cloud which geometric calibration has done with the RGB-D camera. AR markers indicate the starting and end cross-sections in the reconstructed point cloud data. To measure the slope angles of side surfaces and volume of underground conduit, first, the front direction of 3D point cloud data is accurately determined while minimizing the area of cross-sectional convex hull. Then, the piecewise slope angles and volumes are calculated and accumulated for the quantized y and z points. Histogram analysis is proposed to statistically determine the cross-sectional convex hull and point cloud on the conduit surfaces. According to tests with acquired synthetic and real data, the proposed system determines the slope angles and volume of the electrical conduits. For synthetic data, the proposed system shows errors within 1%, and for real data, it achieves errors within 3% compared to highly precise measurements. The proposed system demonstrates a capacity to measure underground conduits with consistency and precision. Consequently, this system is expected to be effectively applied to analogous construction environments.}, archive = {J_ACCESS}, author = {Seunghan Paek and Sang Hwa Lee and Sanghyun Joo and Jong-Il Park}, doi = {10.1109/ACCESS.2025.3586733}, journal = {IEEE Access}, pages = {116480-116490}, shortjournal = {IEEE Access}, title = {3D modeling for slope angles and volume of underground conduits using RGB-D camera}, volume = {13}, year = {2025}, }
• (2025). RT-DETR-pro: An enhanced RT-DETR model for visual detection of photovoltaic module defects. ACCESS, 13, 116473-116479. (www)

  @article{ , abstract = {An enhanced RT-DETR (Real-Time Detection Transformer) model, RT-DETR-Pro, is proposed to address the problem of low recognition rates for visual detection of PV (photovoltaic) module defects by traditional object detection methods. Firstly, an EMA (Efficient Multi-Scale Attention) module is integrated into the vanilla RT-DETR model to optimize feature representation by reshaping the channel and batch dimensions of feature maps. Secondly, an improved neck feature fusion strategy is adopted, which not only improves feature representation capability but also reduces computational costs and model complexity. Finally, a PV module image dataset is constructed using production line data from a PV module production factory. Both the original RT-DETR model and our RT-DETR-Pro model are trained and evaluated on this dataset. Experimental results demonstrate that RT-DETR-Pro can increase detection accuracy for hard-to-identify defects. The results show that while the original RT-DETR model exhibited missed detections for subtle small-target defects, our RT-DETR-Pro model shows significant enhancement in detecting such inconspicuous defects, with 86.9% mAP50, outperforming the original RT-DETR by 4.5%. Additionally, our RT-DETR-Pro model achieves 144 FPS in inference, meeting the requirements of both accuracy and real-time performance for industrial PV module quality inspection. The proposed method provides an effective solution for automated PV module quality inspection, demonstrating strong potential for industrial applications.}, archive = {J_ACCESS}, author = {Yunliang Chen and Mingtao Shan and Wei Zhou and Bing Ma and Xun Mei}, doi = {10.1109/ACCESS.2025.3585151}, journal = {IEEE Access}, pages = {116473-116479}, shortjournal = {IEEE Access}, title = {RT-DETR-pro: An enhanced RT-DETR model for visual detection of photovoltaic module defects}, volume = {13}, year = {2025}, }
• (2025). Leveraging RAG and LLMs for access control policy extraction from user stories in agile software development. ACCESS, 13, 116462-116472. (www)

  @article{ , abstract = {Agile development has become increasingly popular among software development teams due to its capacity to deliver and update software rapidly while accommodating evolving requirements. Within this dynamic context, access control policies are critical for ensuring the security of systems by defining who can access specific resources under given conditions. However, identifying and documenting these policies often rely on manual, time-intensive processes prone to errors and oversight. This paper proposes an innovative framework leveraging Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to automate the extraction and organization of access control policies from user stories and software documentation. The framework focuses on the early stages of the development lifecycle, capturing access control requirements as expressed in natural language artifacts. It comprises two core components: 1) a pipeline for extracting and categorizing access control policies, enabling precise mappings between roles, actions, and resources, and 2) an interactive chatbot designed to support Security Operations Center (SOC) analysts in evaluating suspicious access requests by providing contextualized insights into access policies. By integrating advanced natural language processing techniques with retrieval-based augmentation, the framework aims to reinforce access control mechanisms by improving visibility, and providing contextualized insights for security analysts.}, archive = {J_ACCESS}, author = {Sara Aboukadri and Aafaf Ouaddah and Abdellatif Mezrioui and Ikram El Asri}, doi = {10.1109/ACCESS.2025.3586203}, journal = {IEEE Access}, pages = {116462-116472}, shortjournal = {IEEE Access}, title = {Leveraging RAG and LLMs for access control policy extraction from user stories in agile software development}, volume = {13}, year = {2025}, }
• (2025). Investigation and study of high voltage capacitor bank techniques for voltage drop improvement in 22-kV distribution line. ACCESS, 13, 116442-116461. (www)

  @article{ , abstract = {Voltage drop in distribution lines contributes to power losses, leading to a decrease in the overall efficiency of the electrical power system. In this paper, we investigate methods to improve voltage drop through capacitor bank allocation techniques, specifically installation at the end of the distribution line, the 1/2-kvar rule, and the 2/3-kvar rule. In the case study, a 22 kV overhead distribution line in Thailand is utilized as a model to analyze voltage drop improvement. The study is conducted using PSCAD software to simulate and evaluate the effectiveness of various capacitor bank allocation techniques in enhancing voltage drop in the distribution line. In addition, the impact of capacitor bank placement and sizing on voltage drop mitigation in a distribution line is analyzed through a proposed mathematical framework. This analysis considers the interaction between reactive current compensation and impedance of a distribution line, ensuring an optimized approach for capacitor bank deployment to enhance voltage drop. The results emphasize that the capacitor bank installation at the end of the distribution line yields the most substantial voltage drop improvement. This is attributed to the utilization of larger capacitor bank capacities compared to the 1/2-kvar and 2/3-kvar methods. Furthermore, the mathematical analysis confirms that both the placement and sizing of capacitor banks play a crucial role in voltage drop mitigation.}, archive = {J_ACCESS}, author = {Surakit Thongsuk and Praikanok Lertwanitrot and Panapong Songsukthawan and Santipont Ananwattanaporn and Atthapol Ngaopitakkul and Suntiti Yoomak and Chaichan Pothisarn}, doi = {10.1109/ACCESS.2025.3581928}, journal = {IEEE Access}, pages = {116442-116461}, shortjournal = {IEEE Access}, title = {Investigation and study of high voltage capacitor bank techniques for voltage drop improvement in 22-kV distribution line}, volume = {13}, year = {2025}, }
• (2025). SE-DeepLabV3+: Cervical cell segmentation and classification using a novel SE-based DeepLabV3+ and ensemble method. ACCESS, 13, 116430-116441. (www)

  @article{ , abstract = {Cervical cancer remains a major cause of morbidity and mortality worldwide, highlighting the need for early detection and effective treatment strategies. This study develops a robust deep learning-based system for cervical cell segmentation and classification using a novel Squeeze-and-Excitation DeepLabV3+ (SE-DeepLabV3+) model with a dynamic atrous rate for segmentation (instead of a fixed dilation rate) and an ensemble method for classification. The system leverages both global context and local features to enhance segmentation performance. It is important to note that for the Pomeranian and SIPaKMeD datasets, the segmentation masks were automatically generated using OpenCV without expert validation, which may limit immediate clinical applicability. The classification model utilizes an ensemble approach by combining features from multiple pre-trained convolutional neural networks (CNNs) and introducing learnable weights (dense layers applied to the global average pooling layer) to dynamically adjust the importance of each model’s feature and improve prediction accuracy. The performance of the proposed system is evaluated using the Pomeranian, SIPaKMeD, and Herlev datasets. For segmentation, the SE-based DeepLabV3+ outperforms the standard DeepLabV3+ across all datasets, achieving the highest accuracy in the Pomeranian dataset (99.09%), SIPaKMeD dataset (96.09%), and Herlev dataset (87.12%). In classification, the proposed ensemble method achieves an accuracy of 99% for the Pomeranian dataset, 94% for SIPaKMeD (multiclass), 99% for SIPaKMeD (binary), and 90% for Herlev in classification-only. These results demonstrate the effectiveness of the proposed SE-DeepLabV3+ model for segmentation and the ensemble classification method in improving overall cervical cell analysis, leading to more accurate and reliable predictions.}, archive = {J_ACCESS}, author = {Betelhem Zewdu Wubineh and Andrzej Rusiecki and Krzysztof Halawa}, doi = {10.1109/ACCESS.2025.3586764}, journal = {IEEE Access}, pages = {116430-116441}, shortjournal = {IEEE Access}, title = {SE-DeepLabV3+: Cervical cell segmentation and classification using a novel SE-based DeepLabV3+ and ensemble method}, volume = {13}, year = {2025}, }
• (2025). Ensemble-based uncertainty quantification for reliable large language model classification in social data applications. ACCESS, 13, 116419-116429. (www)

  @article{ , abstract = {Assessing classification confidence is essential for effectively leveraging Large Language Models (LLMs) in automated data labeling, particularly within the sensitive contexts of Computational Social Science (CSS) tasks. In this study, we evaluate five uncertainty quantification (UQ) strategies across three CSS classification problems: stance detection, ideology identification, and frame detection. We benchmark these strategies using three different LLMs. To enhance human-in-the-loop classification performance, we introduce an ensemble-based UQ aggregation method, C_ensemble, and propose a novel evaluation metric, Misclassified Recall, designed to better assess model uncertainty on mislabeled or ambiguous data points. Our results show that C_ensemble outperforms existing UQ techniques in six out of nine model-dataset combinations, achieving an average AUC improvement of 8.7%. These findings highlight the potential of UQ-driven methods to significantly improve the reliability and efficiency of human-in-the-loop data annotation pipelines.}, archive = {J_ACCESS}, author = {David T. Farr and Lynnette Hui Xian Ng and Iain J. Cruickshank and Nico Manzonelli and Nicholas Clark and Kate Starbird and Nathaniel D. Bastian and Jevin West}, doi = {10.1109/ACCESS.2025.3585414}, journal = {IEEE Access}, pages = {116419-116429}, shortjournal = {IEEE Access}, title = {Ensemble-based uncertainty quantification for reliable large language model classification in social data applications}, volume = {13}, year = {2025}, }
• (2025). PCB-based dual-polarized liquid crystal reflectarray with high aperture efficiency and bridge-shaped biasing topology for 2D scanning. ACCESS, 13, 116407-116418. (www)

  @article{ , abstract = {This paper presents a novel dual-linearly polarized liquid crystal reflectarray antenna (DLP-LCRA) based on a printed circuit board (PCB) process. The proposed design employs a bridge-shaped biasing topology to reduce RF losses typically associated with bias lines in PCB-based implementations. While conventional DLP-LCRAs are often fabricated using glass substrates due to the difficulty of achieving sufficiently thin bias lines in PCB processes, this work demonstrates that a cost-effective and practical solution is possible through optimized biasing structures. The proposed reflectarray cell structure and biasing scheme enable wide 2D beam scanning with high aperture efficiency. Specifically, the bridge-shaped topology mitigates polarization-dependent RF losses and supports broad beam coverage in the E-, H-, and D-planes. The influence of the biasing layer is analyzed in detail using an equivalent circuit model for TE and TM modes. Experimental results validate the performance of the proposed DLP-LCRA, achieving a maximum aperture efficiency of 46.5 %, which represents a 106.67 % improvement over state-of-the-art designs.}, archive = {J_ACCESS}, author = {Hogyeom Kim and Byeongju Moon and Seungwoo Bang and Seongwoog Oh and Jungsuek Oh}, doi = {10.1109/ACCESS.2025.3581322}, journal = {IEEE Access}, pages = {116407-116418}, shortjournal = {IEEE Access}, title = {PCB-based dual-polarized liquid crystal reflectarray with high aperture efficiency and bridge-shaped biasing topology for 2D scanning}, volume = {13}, year = {2025}, }
• (2025). Spectral density analysis of wind power fast fluctuations in a wind farm with DFIG and DD turbines. ACCESS, 13, 116391-116406. (www)

  @article{ , abstract = {The analysis of wind power fluctuations is essential for strategic decisions, such as turbine generation control or the sizing of spinning reserves and energy storage systems. This study aims to characterize rapid power fluctuations in a wind farm located in northeastern Brazil, composed of turbines with Doubly-Fed Induction Generator (DFIG) and Direct Drive (DD) technologies, using power spectral density (PSD) analysis. The origin and magnitude of the fluctuations are examined, as well as the smoothing effect resulting from turbine aggregation. It was found that, despite the different technologies, after the smoothing process, rapid fluctuations still account for an average of 4.89% of the total power generation.}, archive = {J_ACCESS}, author = {Pablo Luiz Tabosa Da Silva and Pedro Rosas and José Filho Da Costa Castro and Douglas Contente Pimentel Barbosa and Ronaldo Ribeiro Barbosa de Aquino and Davidson Da Costa Marques and Jeydson Lopes Da Silva and Rafael Cavalcanti Neto}, doi = {10.1109/ACCESS.2025.3586471}, journal = {IEEE Access}, pages = {116391-116406}, shortjournal = {IEEE Access}, title = {Spectral density analysis of wind power fast fluctuations in a wind farm with DFIG and DD turbines}, volume = {13}, year = {2025}, }
• (2025). Green video transcoding in cloud environments using kubernetes: A framework with dynamic renewable energy allocation and priority scheduling. ACCESS, 13, 116362-116390. (www)

  @article{ , abstract = {Video content continues to be a major source of Internet traffic, with a growing demand for high-quality, on-demand videos. This leads to significant energy consumption across cloud servers. Conserving energy and improving energy efficiency in cloud servers is a major challenge. The growing demand for video transcoding services and increasing concerns over energy consumption necessitate systems that balance processing power with energy usage. The research addresses these challenges by developing a green, energy-aware video transcoding system that predicts energy availability from renewable sources (solar and wind) using machine learning techniques and optimizes tasks allocation. The system utilizes a Kubernetes-managed backend to dynamically scale resources for FFmpeg-based transcoding while prioritizing renewable energy, minimizing grid usage utilizing the advanced machine learning models, including Random Forest, XGBoost, and CatBoost, predict energy production and guide task assignments. The integration of predictive analytics with Kubernetes’ Horizontal Pod Autoscaler (HPA) allows dynamic workload distribution, ensuring optimal energy utilization. Additionally, the system incorporates real-time energy monitoring to adjust task scheduling based on fluctuations in renewable energy availability. Two novel scheduling algorithms, Dynamic Renewable Energy Allocation (DREA) and Energy-Aware Priority Scheduling (EAPS), enhance energy efficiency. DREA allocates tasks to energy zones based on real-time renewable availability, while EAPS prioritizes tasks by urgency and energy needs, deferring low-priority tasks to periods of high renewable availability. These green strategies minimize reliance on non-renewable sources while maintaining performance and scalability. The system’s modular design allows easy integration with various cloud platforms, increasing its applicability in real-world scenarios. Furthermore, extensive scalability tests demonstrate that the proposed approach maintains efficient task execution even under high workloads, making it suitable for large-scale cloud environments. By reducing energy consumption and carbon footprint, this framework contributes to the advancement of sustainable cloud computing solutions.}, archive = {J_ACCESS}, author = {B. M. Beena and Prashanth Cheluvasai Ranga and A. Vinitha Chowdary and Rohan Gamidi and M. Hemasri and Tejaswi Muppala}, doi = {10.1109/ACCESS.2025.3585615}, journal = {IEEE Access}, pages = {116362-116390}, shortjournal = {IEEE Access}, title = {Green video transcoding in cloud environments using kubernetes: A framework with dynamic renewable energy allocation and priority scheduling}, volume = {13}, year = {2025}, }
• (2025). Adaptive defense: Zero-day attack detection in NIDS with deep reinforcement learning. ACCESS, 13, 116345-116361. (www)

  @article{ , abstract = {Zero-Day attack detection in Network Intrusion Detection Systems (NIDS) refers to the ability to identify previously unseen attack patterns during testing without having been explicitly trained on those specific attacks, utilizing learned features from other known attacks. In this paper, we propose a Deep Reinforcement Learning (DRL)-based NIDS designed for Zero-Day attack detection. We use a stacked LSTM architecture to extend the learning capabilities of the DRL agent. We apply several oversampling techniques to handle the issue of class imbalance since the zero-day attack datasets are not as abundant. We use some of the most widely available benchmark datasets in NIDS domain, which all together cover a wide range of attack types, such as reconnaissance, ddoS, infiltration, injection, password attacks, brute force, dos, backdoor, and benign traffic. For example, we converted attacks to 1 and benign traffic to 0, then excluded certain attack categories (DoS and Backdoor) from the training dataset while keeping them in the test dataset. This makes those attack types zero-day attacks, as they are entirely unseen during training. We also compare which data balancing technique works better among K-means SMOTE, SMOTE, Borderline-SMOTE and ADASYN on the performance of our DRL agent. We then demonstrate how powerful our agent is by validating many datasets for remarkable success in detecting both known and unknown attacks in a zero-day manner. Our work has been made publicly available on GitHub (https://github.com/codewithkhurshed/ZDAD) to support researchers in advancing zero-day attack detection in NIDS.}, archive = {J_ACCESS}, author = {Khorshed Alam and Md Fahad Monir and Md Junayed Hossain and Mohammad Shorif Uddin and Md. Tarek Habib}, doi = {10.1109/ACCESS.2025.3585445}, journal = {IEEE Access}, pages = {116345-116361}, shortjournal = {IEEE Access}, title = {Adaptive defense: Zero-day attack detection in NIDS with deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Live streaming introduction and mode selection: Strategic analysis and optimal decision-making for a BOPS retailer. ACCESS, 13, 116324-116344. (www)

  @article{ , abstract = {In the context of e-commerce live streaming, this paper considers the issue of BOPS (buy online, pick up in store) retailers intending to introduce live streaming sales to provide greater shopping flexibility and cater to consumers’ diversified shopping preferences. We classify consumers into online and offline groups on the basis of their lifestyles and shopping habits. As consumer shopping behavior becomes increasingly diversified, the BOPS model enhances shopping flexibility by integrating online and offline resources, whereas live streaming sales, with their real-time interaction and product display capabilities, emerge as a key driver of retail innovation. However, existing studies have often analyzed BOPS or live streaming sales in isolation, lacking a systematic examination of their synergistic effects. To address this gap, this paper constructs a three-stage strategic model that integrates BOPS and live streaming sales (N strategy: not introducing live streaming sales; S strategy: adopting the merchant self-broadcasting mode; I strategy: adopting the influencer cooperation live streaming mode) and analyzes profits under different strategies through utility theory, game theory, and numerical simulation. The study reveals that, first, the introduction of live streaming sales by BOPS retailers leads to higher product prices, with even higher prices set when collaborating with influencers for live streaming. Second, the self-broadcasting model typically significantly increases market demand for BOPS retailers, whereas whether collaboration with influencers for live streaming results in demand growth depends on the extent to which the positive effects of live streaming services enhance consumers’ perceived value. Finally, BOPS retailers can benefit from introducing live streaming sales channels, but the choice of live streaming model is influenced by the fixed fee charged by influencer hosts. A high fixed fee ( $T\gt 0.25$ ) prompts BOPS retailers to prefer the self-broadcasting model, whereas a low fixed fee ( $T\lt 0.2$ ) makes collaboration with influencers for live streaming a win-win choice for both BOPS retailers and influencers. The results of the numerical research and theoretical analyses are consistent, further indicating that BOPS retailers can profit from the introduction of a live streaming sales channel. This study, which is based on utility theory, clearly delineates three strategic decision-making models for BOPS retailers when choosing live streaming methods, and it provides corresponding rules for strategy option selection, offering theoretical support for BOPS retailers.}, archive = {J_ACCESS}, author = {Shuai Feng and Wanling Zhou and Jiqiong Liu}, doi = {10.1109/ACCESS.2025.3567519}, journal = {IEEE Access}, pages = {116324-116344}, shortjournal = {IEEE Access}, title = {Live streaming introduction and mode selection: Strategic analysis and optimal decision-making for a BOPS retailer}, volume = {13}, year = {2025}, }
• (2025). Efficient software development effort estimation approaches for improving scalability in the training phase. ACCESS, 13, 116304-116323. (www)

  @article{ , abstract = {Effective software effort estimation is essential for project management, but faces scalability challenges with large datasets. While clustering can address this complexity, standard methods often rely on random initial centers, leading to inconsistent and less precise results. This randomness frequently overlooks critical contextual factors, such as industry or domain-specific characteristics, which can impair cluster quality and the accuracy of effort predictions. To overcome these issues, this study introduces the Contextual Initial Cluster Centroids (CICC), a novel methodology designed to optimize initial centroid selection. Unlike approaches that depend on randomness or are computationally intensive, CICC uses parallel processing of Jaccard similarity and a refined neighbor-finding technique, K-Reciprocal Nearest Neighbors (KRNN), to identify the most relevant and similar projects as initial centers. This deterministic approach ensures clusters are built around meaningful, context-rich representatives, reducing computations and improving scalability. Experiments on public software project datasets show that CICC significantly outperforms existing techniques. It achieves higher cluster quality, measured by metrics like the Global Silhouette Index, and provides more accurate effort estimates, indicated by lower MAE and higher PRED values. By establishing a more robust and efficient foundation for effort estimation, CICC offers considerable potential to optimize project planning and resource allocation in large-scale software development.}, archive = {J_ACCESS}, author = {Ho Le Thi Kim Nhung and Petr Silhavy and Radek Silhavy}, doi = {10.1109/ACCESS.2025.3586081}, journal = {IEEE Access}, pages = {116304-116323}, shortjournal = {IEEE Access}, title = {Efficient software development effort estimation approaches for improving scalability in the training phase}, volume = {13}, year = {2025}, }
• (2025). A systematic review of the current state of transfer learning accelerated CNN-based plant leaf disease classification. ACCESS, 13, 116262-116303. (www)

  @article{ , abstract = {Crops and their produce are vital to the livelihood of humans everywhere. World food security heavily relies on them, but still, even today, hundreds of millions of people world-wide are suffering from hunger. This is why it is essential to ensure that losses to the agricultural yield are kept at a minimum. Plant diseases, however, cause massive losses to the possible yield every year, rendering large amounts of the planted crops useless. And, if these diseases are not identified early enough, they will further infect more plants and therefore destroy even more yield. This is why plant diseases need to be recognized as fast as possible. Many diseases can be detected via the symptoms present on the plants leaves. As such, traditionally, staff samples and checks plants for their health in field manually. To speed up and increase accuracy, deep learning methods have been proposed to classify plant leaf images by their diseases. To train such models, however, one needs sufficiently large datasets. Such datasets are a gap in the field, with many datasets either being too small, not applicable (wrong plants or diseases), private, or taken under conditions that do not apply to the task at hand. One way to lower the need for more data and to overcome lesser data availability is transfer-learning, which utilizes unrelated rich, big, and available data to train feature extractors, which can then be re-trained to identify plant-leaves, based on the knowledge extracted from the prior task. In this work we will review and analyze a total of 84 convolutional neural network based transfer-learning papers from 2022 to 2025 out of 118 considered using PRISMA and discuss the insights gathered from them. This paper presents statistics on model, dataset, hyperparameter, pre-processing, augmentation, and metrics usage. Also, different methods for improving transfer-learning accelerated convolutional neural network models and training are listed. Additionally, performance comparisons of different prominent models for the field of plant leaf disease classification, as well as performance comparisons of different dataset types will be provided.}, archive = {J_ACCESS}, author = {David J. Richter and Md Ilias Bappi and Shivani Sanjay Kolekar and Kyungbaek Kim}, doi = {10.1109/ACCESS.2025.3584404}, journal = {IEEE Access}, pages = {116262-116303}, shortjournal = {IEEE Access}, title = {A systematic review of the current state of transfer learning accelerated CNN-based plant leaf disease classification}, volume = {13}, year = {2025}, }
• (2025). Artificial neural network approach to study imprecisely defined nonlinear systems with case studies. ACCESS, 13, 116246-116261. (www)

  @article{ , abstract = {This paper introduces an alternative method for analysing mathematical models by combining an artificial neural network (ANN) with a modified Levenberg-Marquardt (LM) optimization approach to solve fuzzy systems of nonlinear equations. The proposed method transforms fuzzy equations into an unconstrained optimization problem by employing triangular fuzzy numbers (TFNs) and utilizing a membership value to map fuzzy constraints into crisp values. A feed-forward neural network (FFNN) is used to generate outputs based on initial guesses for the field variables, and the ANN-LM algorithm optimizes the solution by minimizing the error between predicted and target values. The effectiveness of the approach is validated through convergence analysis and case studies, including applications in electrical circuits and static structural problem. The developed algorithm is coded with the MATLAB software, and the results are examined through numerical examination and graphical visualization. The results demonstrate that the proposed approach outperforms various traditional exact and numerical methods, providing a reliable and guaranteed solution to the field variables.}, archive = {J_ACCESS}, author = {Lakshmi Durga Pathipati and Sukanta Nayak}, doi = {10.1109/ACCESS.2025.3584181}, journal = {IEEE Access}, pages = {116246-116261}, shortjournal = {IEEE Access}, title = {Artificial neural network approach to study imprecisely defined nonlinear systems with case studies}, volume = {13}, year = {2025}, }
• (2025). Stability investigation of an AC-DC power system feeding a buck converter controlled by sliding mode controller. ACCESS, 13, 116234-116245. (www)

  @article{ , abstract = {Power converters are a highly important technology for power systems in electric vehicles (EVs), as well as in the generation and utilization of renewable energy. However, controlled power converters behave as constant power loads which directly affect the stability of the system. Therefore, this article presents stability investigation of an AC-DC power system feeding a buck converter controlled by a sliding mode controller. The stability analysis is conducted using the eigenvalue theorem and based on a system mathematical model derived through a combination of the DQ and the generalized state-space averaging methods. The theoretical analysis is verified through simulation on MATLAB/SIMULINK, hardware-in-the-loop (HIL), and experimental testing. The results demonstrate that the considered system exhibits instability due to the constant power load effect, which aligns with the theoretical predictions. In addition, the variation of DC-link filter parameters directly impacts the system stability. The resulting results provide useful insights for avoiding system operation at the unstable point and for designing the filter when the system stability is considered. Furthermore, this study serves as a foundation for future research.}, archive = {J_ACCESS}, author = {Surabadin Daengdon and Apichai Suyapan and Kongpan Areerak and Jakkrit Pakdeeto}, doi = {10.1109/ACCESS.2025.3585767}, journal = {IEEE Access}, pages = {116234-116245}, shortjournal = {IEEE Access}, title = {Stability investigation of an AC-DC power system feeding a buck converter controlled by sliding mode controller}, volume = {13}, year = {2025}, }
• (2025). Small slope approximation applied to the electromagnetic scattering from a rough soil surface with furrows. ACCESS, 13, 116222-116233. (www)

  @article{ , abstract = {The scattering of an electromagnetic wave by agricultural tilled soils is studied by using the first-order small-slope approximation. The rough surface is the superposition of two levels of roughness. A two-dimensional Gaussian random process describes the small-scale roughness, and a two-dimensional narrow-band Gaussian random process defined around a certain spatial frequency describes the large-scale component. The incoherent intensity obtained by the small-slope approximation is expressed as a combination of three terms. The first term is the intensity of small-scale roughness multiplied by an attenuation factor, the second is that of large-scale roughness multiplied by an attenuation factor and the third is the coupling intensity between the two roughness levels. When the surface is illuminated perpendicularly to the furrows and the conditions are right, the Floquet modes can be observed in the signature of the large-scale roughness. These effects are not observable for an impinging wave parallel to the furrows. Knowing that the correlation lengths of the large-scale roughness are much larger than those of the small-scale component, we derive an approximate expression for the coupling intensity which then depends only on the small-scale roughness spectrum and the root-mean-square height of the large-scale spectrum. Resulting expressions are studied for a cloddy sowing soil and a ploughed soil in monostatic configurations, in L- and C-bands. We also analyze the influence of the anisotropy of the large-scale roughness component autocorrelation function on the electromagnetic signature.}, archive = {J_ACCESS}, author = {Richard Dusséaux and Saddek Afifi and Edwige Vannier}, doi = {10.1109/ACCESS.2025.3585662}, journal = {IEEE Access}, pages = {116222-116233}, shortjournal = {IEEE Access}, title = {Small slope approximation applied to the electromagnetic scattering from a rough soil surface with furrows}, volume = {13}, year = {2025}, }
• (2025). CapsuleThermNet: A CNN-CapsuleNet architecture for early detection of suicide risk in depressed patients using thermal facial imaging. ACCESS, 13, 116211-116221. (www)

  @article{ , abstract = {Early detection of suicide risk plays a vital role in improving mental health care outcomes, especially for individuals suffering from depression. Traditional methods, such as clinical interviews, are widely regarded as the gold standard in suicide risk assessment. However, these methods often rely heavily on verbal communication and subjective interpretation, which may lead to the overlooking of important non-verbal cues that could indicate distress. To address this limitation, this study proposes a novel approach to early suicide risk detection by utilizing thermal facial imaging combined with a deep learning model called CapsuleThermNet. CapsuleThermNet is designed by integrating CNN with Capsule Networks (CapsNet). In this architecture, CNNs are responsible for extracting spatial features from thermal facial images, while CapsNet captures hierarchical relationships and performs classification tasks. The study uses a specially developed primary dataset, known as the FaceTherm Suicide Risk (FTSR) Dataset, which includes 152 thermal images of depressed patients. These images are categorized equally into two groups: patients identified as at risk of suicide (76 images) and those not at risk (76 images). The proposed model achieves excellent performance with an accuracy of 99.67%, precision of 93.75%, sensitivity of 100%, specificity of 93.33%, F1-score of 96.77%, and AUC of 0.97. The perfect recall rate highlights the model’s strength in minimizing false negatives—crucial in clinical settings. With its robust classification capabilities, CapsuleThermNet holds promise as a reliable decision-support tool for psychiatrists, enabling earlier detection and timely intervention for patients at risk of suicide before proceeding to more extensive clinical evaluation.}, archive = {J_ACCESS}, author = {Eddy Muntina Dharma and Harjanto Prabowo and Agung Trisetyarso and Tjhin Wiguna}, doi = {10.1109/ACCESS.2025.3580539}, journal = {IEEE Access}, pages = {116211-116221}, shortjournal = {IEEE Access}, title = {CapsuleThermNet: A CNN-CapsuleNet architecture for early detection of suicide risk in depressed patients using thermal facial imaging}, volume = {13}, year = {2025}, }
• (2025). Seismic foresight: A novel multi-input 1D convolutional mixer model for earthquake prediction using ionospheric signals. ACCESS, 13, 116200-116210. (www)

  @article{ , abstract = {This study proposes a novel deep learning approach for predicting significant earthquakes (Mw $\ge 5.0$ ) in Turkey using ionospheric Total Electron Content (TEC) data and space weather indices. ConvMixer is a lightweight CNN architecture that blends spatial and channel information using depthwise convolutions and pointwise layers. Inspired by vision transformers, it offers efficient image classification with fewer parameters and high performance. We developed a multi-input one-dimensional convolutional mixer (MI-1D-ConvMixer) model to classify TEC data from the preceding five consecutive days as either precursory to an earthquake on the 6th day or normal. The model incorporates six inputs: five 1D TEC signals and one 1D space-weather index array, including the global geomagnetic index (Kp), storm duration distribution (Dst), sunspot number (R), geomagnetic storm index (Ap-index), solar wind speed (Vsw), and solar activity index (F10.7) are also utilized to reveal non-seismic related pre-earthquake ionospheric variations. Our methodology involves two stages: 1) a preprocessing stage to enhance TEC signals and 2) an end-to-end training of the MI-1D-ConvMixer model. The model architecture features depth-wise and point-wise convolutions with patch embedding, utilizing uses four tunable hyperparameters: network depth, hidden dimension size, kernel size, and patch size. We used 196 earthquakes data from Turkey from 2010-2023, and TEC data from the TNPGN-Active GNSS stations. The dataset was split into 75% for training and 25% for testing. Performance metrics, including classification accuracy, sensitivity, specificity, and F1-score, are used for evaluation. Our model achieved a classification accuracy of 97.49%, demonstrating its potential for earthquake prediction systems. This research contributes to the field by introducing a novel deep learning architecture specifically designed for integrating TEC and space weather data for earthquake prediction. Future work should focus on validating the model’s performance in different geographical regions and investigating its limitations.}, archive = {J_ACCESS}, author = {Hakan Uyanik and Mehmet Kokum and Erman Senturk and Mohamed Freeshah and Salih T. A. Ozcelik and Muhammed Halil Akpinar and Serenay Celik and Abdulkadir Sengur}, doi = {10.1109/ACCESS.2025.3583749}, journal = {IEEE Access}, pages = {116200-116210}, shortjournal = {IEEE Access}, title = {Seismic foresight: A novel multi-input 1D convolutional mixer model for earthquake prediction using ionospheric signals}, volume = {13}, year = {2025}, }
• (2025). An efficient design approach for a broadband E-plane five-way power Divider/Combiner in rectangular waveguide. ACCESS, 13, 116188-116199. (www)

  @article{ , abstract = {This paper presents a comprehensive investigation and proposal of an innovative equivalent circuit for a five-way power divider, developed within a rectangular waveguide framework. This design serves as an effective solution for odd-number power dividers, achieving performance metrics that are rarely seen in the literature. The proposed equivalent circuit model eliminates the need for quarter-wavelength impedance converters, allowing for an equitable power distribution solely based on the interrelationship of the characteristic impedances across the branches. Furthermore, we present a novel method to ensure phase coherence among the output ports, employing various E- and H-plane waveguide bends to achieve effective phase distribution. A power divider/combiner prototype is manufactured and rigorously tested to validate its operational efficacy. Experimental results indicate that the return loss at the input port exceeds 20 dB, while the insertion loss remains below 0.2 dB in a frequency range of 12 to 18 GHz, corresponding to a relative bandwidth of 40%. The proposed power divider not only offers a compact structure and broad bandwidth but also demonstrates low loss and straightforward fabrication processes. These attributes position it as a promising candidate for applications in solid-state power amplifiers (SSPAs) and passive antenna arrays.}, archive = {J_ACCESS}, author = {Yuankai Li and Jiaquan Ye and Huan Lin and Fei Liang and Chao Zhang and Rui Shi}, doi = {10.1109/ACCESS.2025.3584776}, journal = {IEEE Access}, pages = {116188-116199}, shortjournal = {IEEE Access}, title = {An efficient design approach for a broadband E-plane five-way power Divider/Combiner in rectangular waveguide}, volume = {13}, year = {2025}, }
• (2025). Evaluation of V2X technologies for the connectivity of small autonomous vehicles on secondary railway lines. ACCESS, 13, 116170-116187. (www)

  @article{ , abstract = {A new concept involving on-demand automatic electric vehicles operating on both road and railway tracks is being developed to rejuvenate secondary railway lines. It is known as FERROMOBILE. Its architecture involves several entities, including vehicles, level crossings, stations, and other infrastructure. The operation of this system necessitates a dynamic, flexible, and safe supervision between these entities, which requires complete and continuous connectivity between them. In this regard, several vehicular communication modes can be created, such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-network (V2N). Consequently, connectivity can be achieved by utilizing multiple short and long-range wireless technologies, including vehicle-to-everything (V2X). This paper aims to provide a performance analysis of V2X connectivity technologies for autonomous vehicles on secondary railway lines. It first presents an overview of the potential wireless technologies for FERROMOBILE connectivity. Then, we comprehensively describe the developed emulation platform and perform an experimental evaluation of ITS-G5 and C-V2X for V2V/V2I communications, 4G, 5G NR and LoRa for V2N communications. The evaluation was conducted based on V2X channel models consistent with the FERROMOBILE environment. Several evaluations were performed using latency, jitter, packet delivery rate, and packet error rate as key performance indicators. The evaluation results demonstrate that C-V2X outperforms ITS-G5, and 4G/5G NR surpass LoRa’s performance. Indeed, the results show that ITS-G5 and C-V2X can be applicable in V2V/V2I communication and 4G/5G NR in V2N communication for FERROMOBILE architecture.}, archive = {J_ACCESS}, author = {Dereje Mechal Molla and Chadli Hadji and Sassi Maaloul and Leo Mendiboure and Marion Berbineau and Hakim Badis}, doi = {10.1109/ACCESS.2025.3585300}, journal = {IEEE Access}, pages = {116170-116187}, shortjournal = {IEEE Access}, title = {Evaluation of V2X technologies for the connectivity of small autonomous vehicles on secondary railway lines}, volume = {13}, year = {2025}, }
• (2025). A novel method for epileptic seizure detection using separable gabor wavelets. ACCESS, 13, 116158-116169. (www)

  @article{ , abstract = {In clinical therapy, epileptic seizure detection is receiving more and more attention. The analysis of scalp electroencephalograms (EEGs) is a widely used method for detecting brain abnormalities related to seizure onset. This paper presents a novel technique for the classification of epileptic seizure based on low-complex separable Gabor wavelet. Initially, the continuous wavelet transform is used to create an EEG scalogram sequence, which represents the time-frequency data. Next, the distinct features from the EEG sclogram are extracted using separable Gabor wavelet filter bank which requires low computational complexity. For the purpose of classifying seizures, the several features are concatenated into a fixed-length feature vector. The linear discriminant analysis approach is then used to reduce the dimensionality of the retrieved features significantly. Lastly, to distinguish between seizure-free and epileptic seizures, the most discriminative variables are given into a support vector machine (SVM) classifier. The proposed method’s classification performance is evaluated against a number of cutting-edge epileptic seizure detection methods. The average accuracy of the proposed method for all categories of seizure detection is found to be 99.10% with average sensitivity and specificity of 99.02% and 99.18% respectively. The results of the experiments performed on a real EEG dataset show that the proposed technique can successfully classify an epileptic seizure by extracting relevant context information from various angles.}, archive = {J_ACCESS}, author = {Aswini Kumar Samantaray and Amol D. Rahulkar}, doi = {10.1109/ACCESS.2025.3585748}, journal = {IEEE Access}, pages = {116158-116169}, shortjournal = {IEEE Access}, title = {A novel method for epileptic seizure detection using separable gabor wavelets}, volume = {13}, year = {2025}, }
• (2025). Enhanced rotor field-oriented control for low-voltage delta inverter-fed IMs with look-up table implementation. ACCESS, 13, 116143-116157. (www)

  @article{ , abstract = {This paper introduces an innovative rotor field-oriented control (RFOC) approach tailored for a low-voltage three-phase induction motor (IM) driven by a reduced-structure delta inverter (B3-VSI). The delta inverter, featuring only three power switches, three freewheeling diodes, and three DC voltage sources, represents a promising solution for cost-effective, low-power applications, such as electric vehicles (EVs) and photovoltaic (PV)-fed systems. However, due to the limited voltage vectors provided by this inverter topology, advanced control strategies are essential for achieving optimal motor performance. The proposed RFOC method utilizes a look-up table to generate the control signals for the inverter switches. This table is developed through precise selection of the Boolean variables produced by hysteresis controllers applied to the stator currents. Simulation and experimental results validate the proposed approach, demonstrating its potential to improve system performance in low-power applications while maintaining simplicity and efficiency.}, archive = {J_ACCESS}, author = {Imen Nouira El Badsi and Bassem El Badsi}, doi = {10.1109/ACCESS.2025.3585399}, journal = {IEEE Access}, pages = {116143-116157}, shortjournal = {IEEE Access}, title = {Enhanced rotor field-oriented control for low-voltage delta inverter-fed IMs with look-up table implementation}, volume = {13}, year = {2025}, }
• (2025). Secure and reversible face de-identification with format-preserving encryption. ACCESS, 13, 116130-116142. (www)

  @article{ , abstract = {With the rapid growth of digital services and transactions, concerns about personal information leakage and security threats have intensified. In particular, facial images stored in surveillance systems, security agency databases, and biometric authentication platforms can be exploited for identity theft, fraud, phishing, illegal marketing, or deepfake-based misinformation. These risks have increased the need for technologies that securely protect stored facial data while enabling authorized restoration when necessary. We propose a reversible face de-identification method using format-preserving encryption (FPE). Our method integrates symmetric-key FPE into two deep neural network (DNN)-based face swap models (FaceShifter and SimSwap) that separate identity and attribute information. This approach enables the generation of de-identified facial images and allows only authorized users to restore the original data. Experiments on the LFW, FFHQ, VGGFace2-HQ, and CelebA-HQ datasets showed that, on average, the FaceShifter model achieved a 98.64% de-identification success rate and 96.86% restoration rate, while SimSwap recorded 99.58% and 99.39%, respectively. Image quality was evaluated using SSIM, FID, LPIPS, PSNR, and BRISQUE, confirming that restored images closely resemble the originals. In conclusion, the proposed method provides a robust privacy-preserving solution for facial data in digital environments, balancing security and utility while supporting lawful restoration.}, archive = {J_ACCESS}, author = {Heehwan Kim and Sungjune Park and Daeseon Choi}, doi = {10.1109/ACCESS.2025.3583388}, journal = {IEEE Access}, pages = {116130-116142}, shortjournal = {IEEE Access}, title = {Secure and reversible face de-identification with format-preserving encryption}, volume = {13}, year = {2025}, }
• (2025). Intelligent network-level energy saving strategy with STGNN-driven traffic prediction and path optimization in transport networks and field trial. ACCESS, 13, 116118-116129. (www)

  @article{ , abstract = {With the evolution of green communication networks, device-level energy saving approaches face diminishing returns due to fundamental hardware limitations, while persistent traffic imbalances in metropolitan transport networks create untapped optimization potential. This work reveals that the inherent flexibility of switch fabrics in live network enables dynamic traffic redistribution, presenting an opportunity for multi-node collaborative optimization to achieve deeper energy savings. This paper proposes a novel network-level energy saving strategy that leverages spatiotemporal graph neural networks (STGNN) and dynamic path optimization to overcome the limitations of conventional device-level approaches. The proposed approach integrates AI-driven traffic prediction with intelligent resource allocation, where the STGNN model captures complex spatiotemporal traffic patterns over joint port-tunnel traffic modeling across large-scale network elements, achieving prediction accuracy with a 19.44% improvement over traditional LSTM models. Combined with K-shortest-path optimization algorithm, the system dynamically reallocates traffic to minimize active switch fabrics while maintaining strict service quality guarantees. Validated through a large-scale field trial involving 1966 network elements, the strategy demonstrates a 7.7% total network energy reduction with up to 20.8% for individual nodes without service disruption. The results highlight the effectiveness of STGNN in capturing complex spatiotemporal traffic dependencies and optimizing network resources. It establishes a new paradigm for green and sustainable transport networks, demonstrating how network-wide coordination can unlock energy savings beyond device-level approaches.}, archive = {J_ACCESS}, author = {Xinyu Chen and Liuyan Han and Minxue Wang and Jiang Sun and Yong Gao and Xuegang Ou and Dechao Zhang and Han Li}, doi = {10.1109/ACCESS.2025.3585343}, journal = {IEEE Access}, pages = {116118-116129}, shortjournal = {IEEE Access}, title = {Intelligent network-level energy saving strategy with STGNN-driven traffic prediction and path optimization in transport networks and field trial}, volume = {13}, year = {2025}, }
• (2025). Applying progressive frequency bands to improve image quality and training stability of GAN. ACCESS, 13, 116104-116117. (www)

  @article{ , abstract = {Generative Adversarial Networks (GANs) have revolutionized the field of image generations, yet their training instability remains a critical challenge that limits their practical applications. This paper introduces Progressive Frequency Band GAN (PFB-GAN), a novel framework that fundamentally reimagines GAN training through the lens of frequency domain analysis. Unlike conventional approaches that focus on time domain stabilization, our method leverages the inherent structure of frequency components to enable systematic and stable training. By introducing a progressive learning strategy that gradually incorporates frequency bands from low to high, PFB-GAN achieves remarkable stability while preserving fine details that are often lost in existing methods. Our comprehensive experiments across various datasets demonstrate consistent improvements, with performance metrics showing significant enhancements ranging from 10.55% to 21.03% across FID, Inception Score, Density & Coverage, and Precision & Recall metrics. More importantly, PFB-GAN shows exceptional resilience under extreme learning conditions, maintaining stability even at high learning rates where conventional GANs fail completely. This work not only advances the theoretical understanding of GAN training dynamics but also provides a practical solution for developing more reliable and robust generative models.}, archive = {J_ACCESS}, author = {Chae-Eun Lee and Sung Hoon Jung}, doi = {10.1109/ACCESS.2025.3585951}, journal = {IEEE Access}, pages = {116104-116117}, shortjournal = {IEEE Access}, title = {Applying progressive frequency bands to improve image quality and training stability of GAN}, volume = {13}, year = {2025}, }
• (2025). Performance and reliability assessment of schottky complementary multi-FinFET inverter for advanced scaling nodes. ACCESS, 13, 116094-116103. (www)

  @article{ , abstract = {This study investigates the performance of Schottky Complementary Multi-FinFET inverter for advanced technology nodes of 7 nm, 5 nm, and 3 nm using Sentaurus TCAD simulations. The impact of scaling on key device parameters, including threshold voltage (V ${}_{\mathrm {th}}$ ), subthreshold swing (SS), drain-induced barrier lowering (DIBL), ON-state current (I ${}_{\mathrm {ON}}$ ), and OFF-state current (I ${}_{\mathrm {OFF}}$ ), is systematically investigated. The results indicate that the 5 nm node exhibit superior performance compared to other nodes, with improved electrostatic control, higher drive current (0.00212 for n-FinFET, - $1.16\times 10 ^{-4}$ for p-FinFET), and lower leakage current, making them suitable for low-power applications. Further, Voltage Transfer Characteristics (VTC) analysis was performed for the 5 nm node, demonstrating sharp transitions with well-defined logic levels and strong voltage gain. Additionally, transient analysis confirms high-speed switching operation with minimal propagation delay, highlighting the feasibility of Schottky-based FinFET inverters for next-generation ultra-scaled digital circuits. Furthermore, a comprehensive reliability assessment was carried out for the 5 nm node, focusing on analog/RF performance and VTC characteristics by varying temperatures from 100 K to 250 K. The study reveals that as temperature increases, there are notable shifts in key parameters, affecting the overall stability and efficiency of the device. The temperature-dependent analysis highlights the robustness of the Schottky-contacted FinFET inverter and provides critical insights into its applicability for future high-performance and energy-efficient logic applications under varying thermal conditions. These findings make the proposed device highly suitable for applications in microprocessors, data converters, IoT systems, and cryogenic computing platforms.}, archive = {J_ACCESS}, author = {Shalini Virumandi and Prashanth Kumar}, doi = {10.1109/ACCESS.2025.3581940}, journal = {IEEE Access}, pages = {116094-116103}, shortjournal = {IEEE Access}, title = {Performance and reliability assessment of schottky complementary multi-FinFET inverter for advanced scaling nodes}, volume = {13}, year = {2025}, }
• (2025). Advancing energy transition: A circular economy framework for co-optimizing energy, water, and emissions in low-carbon systems. ACCESS, 13, 116069-116093. (www)

  @article{ , abstract = {The urgent global need for sustainable energy, water, and climate solutions requires bold, integrated strategies. This study introduces a transformative framework that pioneers the co-optimization of energy, water, and emissions, advancing the energy transition while addressing key global challenges. By harnessing renewable energy sources (RES), carbon capture (CC) technologies, and reverse osmosis (RO) desalination systems within a circular economy context, this approach disrupts traditional thinking, laying the groundwork for a pathway to a resilient, low-carbon economy. Current models fail to address the dynamic interplay between RES, CC, and desalination, especially given the complexities of temperature-sensitive desalination processes and operational uncertainties. This paper introduces an innovative solution providing a systemic, co-optimized strategy that balances not only intermittent energy generation and water demand but also integrates risk-based demand response (DR) tailored for desalination plants, a first-of-its-kind approach that minimizes emissions and operational costs in real-time. We employ a novel risk-multi-objective stochastic (RMOBS) optimization method, coupled with a two-stage stochastic programming model, to address uncertainties through hidden Markov processes and conditional value-at-risk (CVaR) techniques. This integrated methodology allows us to achieve unprecedented results: a 9% reduction in peak load, 4% cost savings, and a 6.6% decrease in CO2 emissions in benchmark IEEE test systems. When scaled to larger grids, the framework achieves a 5% cost reduction, a 19.7% reduction in emissions, and a 5.7% reduction in peak load, demonstrating both scalability and robustness across diverse system configurations.}, archive = {J_ACCESS}, author = {Mohamed Elsir and Ameena Saad Al-Sumaiti and Mohamed Shawky El Moursi}, doi = {10.1109/ACCESS.2025.3585736}, journal = {IEEE Access}, pages = {116069-116093}, shortjournal = {IEEE Access}, title = {Advancing energy transition: A circular economy framework for co-optimizing energy, water, and emissions in low-carbon systems}, volume = {13}, year = {2025}, }
• (2025). Stability improvement of grid-connected DFIG wind farm with STATCOM compensated power network using RL-based coordinated transient controller. ACCESS, 13, 116054-116068. (www)

  @article{ , abstract = {This paper focuses on improving the stability of a power network (PN) with an embedded DFIG-based wind farm (DWF) using the power system stabilizer (PSS) and STATCOM. The stability of the rotor angle (RAS) is improved by determining the optimal PSS type based on the minimum value of the sum of the maximum deviations of the rotor angle (SMRAD). The optimum allocation of DWF through an artificial neural network (ANN) reduces the power loss. The voltage stability (VS) is improved by the optimal allocation of STATCOM, considering the VS index and the voltage enhancement index. Moreover, a novel reinforcement learning-based coordinated transient controller (RL-CTC) is proposed for regulating the reactive power compensation between DWF and STATCOM to augment the voltage and rotor angle stability (VRAS). The deep deterministic policy gradient agent approach is used to maximize performance for the RL-CTC tuning process. The dynamic efficacy of the RL-CTC is analyzed against several voltage disturbances with different combinations of PSS, DWF, and STATCOM using the IEEE-9 bus system. The results presented in the paper show that the proposed strategy enhances the system’s overall stability by improving the RAS, and zero, low, and high voltage ride through capabilities. The suggested strategy can be used to optimize PSS and STATCOM parameters to solve VRAS issues in a PN integrated with renewable generation.}, archive = {J_ACCESS}, author = {Muhammad Rasheed and Babar Hussain and Ameena Saad Al-Sumaiti and Muhammad Abid}, doi = {10.1109/ACCESS.2025.3584719}, journal = {IEEE Access}, pages = {116054-116068}, shortjournal = {IEEE Access}, title = {Stability improvement of grid-connected DFIG wind farm with STATCOM compensated power network using RL-based coordinated transient controller}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep multistacking integrated model for plant disease detection. ACCESS, 13, 116037-116053. (www)

  @article{ , abstract = {Plant disease detection is a critical challenge in agriculture, as undetected or poorly managed diseases can lead to significant yield losses, economic setbacks for farmers, and compromised food security. Therefore, accurate and efficient models for timely identification and mitigation are imperative to address these challenges and ensure sustainable agricultural practices. In this study, we introduce a deep multistacking integrated model for plant leaf disease detection that leverages fine-tuned transfer learning (TL) models, multistacking feature generation, and an ensemble XGBoost meta-classifier. Our approach involves specialized pipelines for image preprocessing, augmentation, and fine-tuning TL models, resulting in a robust hybrid model. The multistacking feature generation technique aggregates the prediction probabilities from fine-tuned models, which are then utilized as input for the XGBoost classifier, boosting both accuracy and efficiency. We analyze the proposed model on three benchmark datasets: Tomato Disease Dataset (TDDS), Potato Pepper Dataset (PPDS), and Apple Grape Dataset (AGDS). Our experimental results demonstrate that the multistacking integrated model considerably outperforms traditional single-model methodologies, obtaining high accuracy scores of 99.78%, 99.86%, and 99.82% on TDDS, PPDS, and AGDS, respectively. These results underscore the value of our technique, particularly in multilevel classification, where combining data from several fine-tuned models allows for increased generalization and higher precision. By combining multistacking techniques and an ensemble XGBoost classifier, we have advanced the state-of-the-art in plant disease identification. Moreover, the model demonstrates improved computational efficiency, with lower build and forecast times compared to existing methodologies. These results show that combining advanced ensemble methods with well-tuned models can make plant disease detection systems more generic and reliable. Our work presents a scalable and effective strategy for enhancing plant disease identification, adding to the broader application of deep learning (DL) in precision agriculture.}, archive = {J_ACCESS}, author = {Majdi Khalid and MD. Alamin Talukder}, doi = {10.1109/ACCESS.2025.3583793}, journal = {IEEE Access}, pages = {116037-116053}, shortjournal = {IEEE Access}, title = {A hybrid deep multistacking integrated model for plant disease detection}, volume = {13}, year = {2025}, }
• (2025). Assessing the capability and limitations of small signal simplified grid following models for the identification of sub-synchronous oscillations. ACCESS, 13, 116025-116036. (www)

  @article{ , abstract = {Electricity grids are seeing dramatic changes due to increased power converter penetration, causing variations in grid strength, X/R ratio, and new dynamic bandwidths, which present stability challenges that need to be captured in models. The use of appropriate modelling techniques is critical to achieve an accurate stability assessment; however, it is also important to balance this accuracy with computational burden. This paper compares three key small signal models of a grid following converter; a full detail small signal model, a model order reduction (MOR) method, and a simplified phasor model. They are compared for their ability to represent transient accuracy, eigenvalue stability and ability to identify the presence of key modes that could cause sub-synchronous oscillations. Furthermore, the bandwidth and accuracy of these different modelling approaches across a range of parameters, especially as extreme conditions, needs to be defined. Therefore, this paper addresses the need for parametric sensitivity studies, and presents a methodology of evaluation of lower detail models, for highlighting instability, and the sub-synchronous, oscillatory modes that low detail models fail to capture, even when they are expected to. Results show that the MOR performs slightly better than the simplified phasor model, however, both fail to capture critical dynamic information regarding oscillatory modes at sub-synchronous frequencies in certain parameter conditions. Additionally, they both have reduced accuracy in representing system instability, especially at low short circuit ratios, when the full detail model should be used.}, archive = {J_ACCESS}, author = {Sophie Coffey and Sam Harrison and Agustí Egea-Àlvarez and Lie Xu}, doi = {10.1109/ACCESS.2025.3586197}, journal = {IEEE Access}, pages = {116025-116036}, shortjournal = {IEEE Access}, title = {Assessing the capability and limitations of small signal simplified grid following models for the identification of sub-synchronous oscillations}, volume = {13}, year = {2025}, }
• (2025). Explainable AI for sensor signal interpretation to revolutionize human health monitoring: A review. ACCESS, 13, 115990-116024. (www)

  @article{ , abstract = {The complexity of sensor signal patterns in healthcare, coupled with the variability of physiological data present significant challenges in developing reliable diagnostic and monitoring. While machine learning (ML) has greatly advanced sensor-based health analysis, its decision-making processes often lack transparency, raising concerns about reliability and clinical adoption. This review explores the role of Explainable Artificial Intelligence (XAI) in enhancing the interpretability of ML models for sensor signal analysis, particularly in applications such as pressure sensors, wearable inertial sensors, imaging sensors (MRI, CT, X-ray), ECG, EEG, and wearable health tracking. A systematic literature review was conducted across multiple databases to identify studies applying XAI techniques to sensor-based health monitoring. The review categorizes nine trending XAI methods used to interpret ML-driven analyses of biosignals, evaluating their advantages and limitations in different healthcare scenarios. The findings emphasize the importance of transparency in ML-driven sensor analysis, which is critical for building trust and real-time clinical decision-making and wearable healthcare applications. Despite its potential, XAI for sensor signals faces challenges related to model scalability, real-time processing, and clinician interpretability. The review identifies key research gaps in integrating XAI into sensor-based healthcare systems, emphasizing the need for robust validation methods and user-friendly explanations. Ultimately, XAI offers a promising path toward revolutionizing sensor-driven health monitoring, though further advancements are necessary to fully integrate explainability into real-world clinical and assistive applications.}, archive = {J_ACCESS}, author = {Abdullah S. Alharthi and Ahmed Alqurashi and Turki Essa Alharbi and Mohammed M. Alammar and Nasser Aldosari and Houssem R. E. H. Bouchekara and Yusuf A. Sha’aban and Mohammad Shoaib Shahriar and Abdulrahman Al Ayidh}, doi = {10.1109/ACCESS.2025.3585764}, journal = {IEEE Access}, pages = {115990-116024}, shortjournal = {IEEE Access}, title = {Explainable AI for sensor signal interpretation to revolutionize human health monitoring: A review}, volume = {13}, year = {2025}, }
• (2025). Automated system to optimize the process and energy consumption for sewage treatment plant based on gas emission by using sensors and IoT. ACCESS, 13, 115972-115989. (www)

  @article{ , abstract = {Sewage Treatment Plants (STPs) play a crucial role in environmental sustainability and public health by removing pollutants from wastewater. Traditional STPs suffer from high energy consumption, operational inefficiencies, and a lack of real-time monitoring, leading to increased energy consumption, unassured water quality, and potential environmental pollution. This study presents a custom-designed Internet of Things (IoT)- enabled automated system to optimize energy usage and treatment efficiency in STPs by monitoring gas emissions, including carbon monoxide, carbon dioxide, ammonia, methane, and hydrogen sulfide. The system explores gas emission patterns during water treatment and maps emission levels during the process to assess treatment completion. It integrates advanced sensors, microcontrollers, and cloud-based data management for real-time tracking and process optimization. Experimental validation across two STPs demonstrates significant energy savings, reducing aerator run times by 45 minutes per cycle —a 15% reduction compared to manual operation —while maintaining satisfactory effluent quality. Additionally, the system enhances operational safety by providing automated alerts for hazardous gas levels. The findings indicate that IoT-based automation can revolutionize wastewater treatment, making it more cost-effective, energy-efficient, and environmentally sustainable.}, archive = {J_ACCESS}, author = {S. Ullas and B. Uma Maheswari and Seshaiah Ponnekanti and T. M. Mohan Kumar}, doi = {10.1109/ACCESS.2025.3585283}, journal = {IEEE Access}, pages = {115972-115989}, shortjournal = {IEEE Access}, title = {Automated system to optimize the process and energy consumption for sewage treatment plant based on gas emission by using sensors and IoT}, volume = {13}, year = {2025}, }
• (2025). A new time series of gas outburst prediction model and application in coal mine and through-coal-seam tunnel mining face. ACCESS, 13, 115960-115971. (www)

  @article{ , abstract = {To address the limitations of existing gas anomaly prediction models in mining environments, this paper proposes a novel time series forecasting model named MSA-BiLG, which combines a multi-scale attention mechanism with a BiLSTM-GRU neural architecture. The model is designed to capture both local and global spatiotemporal dependencies in gas concentration data. To validate its effectiveness, we apply the model to three real-world datasets collected from a through-coal-seam tunnel in Sichuan, a coal mining face in Heilongjiang, and a mining project in Jilin, totaling over 460,000 data points. Experimental results show that MSA-BiLG achieves RMSEs of 0.0075, 0.0099, and 0.0007 and MAEs of 0.0072, 0.0076, and 0.0004 on the DSzQ-3, A52301, and N11902 datasets, respectively, outperforming baseline models such as GRU-LSTM, CNN, MLP, and SARIMA. The model demonstrates superior convergence, robustness, and generalization across varying data scales. This study provides a reliable and scalable solution for intelligent gas monitoring and early warning in high-risk mining and tunnel construction scenarios.}, archive = {J_ACCESS}, author = {Xv Li and Wanbo Zheng and Gen Lu and Yueming Kang and Yunni Xia and Zhou Zhou}, doi = {10.1109/ACCESS.2025.3576171}, journal = {IEEE Access}, pages = {115960-115971}, shortjournal = {IEEE Access}, title = {A new time series of gas outburst prediction model and application in coal mine and through-coal-seam tunnel mining face}, volume = {13}, year = {2025}, }
• (2025). Neural classification of argument elements and styles in arabic competitive debates. ACCESS, 13, 115944-115959. (www)

  @article{ , abstract = {Argument mining for spoken Modern Standard Arabic (MSA) remains almost untouched, largely because no sizeable, well-annotated resource exists. We fill that gap with an 80-hour, 110-debate corpus drawn from 29 countries and labelled by experts with a hybrid scheme that blends Aristotle’s Logos-Ethos-Pathos with an extended 13-element Toulmin model. Using this dataset, we form two tasks: 1) three-way rhetorical-style classification, and 2) full 13-label element detection. Zero- and few-shot large language models (DeepSeek-7B, Falcon3-Mamba-7B) plateau at 46% accuracy and 30% F1-score, illustrating corpus complexity. After fine-tuning, Arabic pre-trained encoder-based models lift the performance in the first task to 81% accuracy and 80% F1-score, with the compact CAMeLBERT-quarter outperforming larger variants thanks to reduced over-fitting. For the second task, all models drop to 50% F1-score, yet performance rebounds to 81% accuracy and 80% F1-score when predictions are post-processed back to Logos, Pathos, Ethos, revealing that most errors in the second task occur within persuasion modes rather than across them. A long-context state-space model (Mamba-130M) nears encoder scores once labels are post-processed (75% accuracy) but lags on 13 argumentation elements, showing that Arabic pre-training matters more than sheer context length. Because many debating applications sit in educational settings, we also report fairness metrics. Demographic-parity and equalized-odds gaps stay below 0.06 for both gender and native-language groups, indicating that higher accuracy does not come at the expense of equity. Our annotated corpus and baseline models establish the first rigorous benchmark for argument mining in Arabic competitive debates and serve as a blueprint for applying these methods to other low-resource languages.}, archive = {J_ACCESS}, author = {Ali Al-Zawqari and Mohamed Ahmed and Abdul Gabbar Al-Sharafi and Mohammad M. Khader and Ali Safa and Gerd Vandersteen}, doi = {10.1109/ACCESS.2025.3585299}, journal = {IEEE Access}, pages = {115944-115959}, shortjournal = {IEEE Access}, title = {Neural classification of argument elements and styles in arabic competitive debates}, volume = {13}, year = {2025}, }
• (2025). Frequency-selective feeding network for concurrent multi-frequency wireless systems using enhanced multi-stub line matching topology. ACCESS, 13, 115935-115943. (www)

  @article{ , abstract = {This paper introduces a new approach to developing a compact dual-wideband feeding network designed to support simultaneous operation across distinct frequency bands, addressing the evolving demands of modern wireless communication and sensing systems. The proposed feeding network, termed the Compact, Low-Loss, Selective Coupler (CLSC), enhances isolation levels and transmission efficiency while maintaining a highly integrated structure. The CLSC concept is demonstrated at 24-30 GHz and 39-50 GHz, utilizing a multi-stub line matching topology, with scalability to other frequency ranges. In the designated dual-frequency bands, the optimized CLSC achieves exceptionally low insertion loss, with transmission coefficients near −1.5 dB, ensuring efficient power distribution. Simultaneously, it maintains an isolation level exceeding −20 dB across the blocked frequency bands, with peak isolation reaching −70 dB—a level unparalleled in conventional feeding networks. These results highlight the capability of the CLSC to precisely filter and manage multiple frequency bands, making it a highly promising solution for next-generation phased arrays, millimeter-wave communication, and advanced antenna systems.}, archive = {J_ACCESS}, author = {Jeonghyo Lee and Dongseop Lee and Myoungsun Kim and Wonjeong Jo and Sungjun Han and Wonbin Hong}, doi = {10.1109/ACCESS.2025.3585307}, journal = {IEEE Access}, pages = {115935-115943}, shortjournal = {IEEE Access}, title = {Frequency-selective feeding network for concurrent multi-frequency wireless systems using enhanced multi-stub line matching topology}, volume = {13}, year = {2025}, }
• (2025). Zero-sum game-based tracking control for fully-coupled nonaffine AUV with unknown affine disturbance using critic network. ACCESS, 13, 115924-115934. (www)

  @article{ , abstract = {In the process of water quality monitoring, autonomous underwater vehicle (AUV) is required to track desired trajectory. And AUV is a very complex nonlinear systems and exhibits fully-coupled and nonaffine properties. These make trajectory-tracking control more challenging. In this paper, the unknown affine disturbance is considered and a two-player zero-sum game (TPZSG) is employed to design tracking control scheme for fully-coupled nonaffine AUV (FCNAUV). Firstly, the mean value theorem is used to transfer the fully-coupled nonaffine system into affine system. Secondly, the error system is constructed based on the reference trajectory. Then, the trajectory-tracking control problem of FCNAUV with unknown affine disturbance is formulated as the TPZSG. One player is the optimal controller which is designed to make states of error system converge to zero. And the other player is the unknown affine disturbance that represents the overall damping effect of the FCNAUV. In order to reduce computing burden, only a single critic network is employed to realize TPZSG based adaptive tracking control scheme. Using the Lyapunov stability theory, the stability of the FCNAUV is analyzed and the system is guaranteed to be uniformly ultimately bounded (UUB). Finally, simulation and comparison results of FCNAUV with unknown affine disturbance verify that the proposed method has superior computational efficiency and accuracy.}, archive = {J_ACCESS}, author = {Gaofeng Che and Xiaoqiang Hu}, doi = {10.1109/ACCESS.2025.3584917}, journal = {IEEE Access}, pages = {115924-115934}, shortjournal = {IEEE Access}, title = {Zero-sum game-based tracking control for fully-coupled nonaffine AUV with unknown affine disturbance using critic network}, volume = {13}, year = {2025}, }
• (2025). Exploiting temporal variability in TCP timestamps for covert channel design. ACCESS, 13, 115909-115923. (www)

  @article{ , abstract = {Information is a valuable asset for individuals, organizations, and governments, making secure transmission a critical concern. Cryptographic techniques are widely used to protect communication; however, in scenarios where the mere detection of communication poses a risk, encryption alone is insufficient. In such cases, covert channels—designed to conceal the existence of communication—offer a viable alternative. When these channels operate over network protocols, they are referred to as network covert channels. A covert channel is undetectable if its traffic blends with regular communication. One approach to achieving this level of stealth is by embedding secret information into an inherent source of uncertainty within the transmission process. If the characteristics of this uncertainty are preserved, it becomes difficult to differentiate covert communication from normal traffic. Among widely used network protocols, the Transmission Control Protocol (TCP) presents intrinsic uncertainties in its header fields. Specifically, the TCP Timestamp option exhibits temporal uncertainty, making it a promising candidate for covert communication. This paper introduces two novel covert channel models exploiting the inherent temporal variability of TCP timestamps to achieve stealthy and reliable communication. Our approach overcomes limitations of previous methods by ensuring statistical indistinguishability from legitimate traffic while maintaining robustness against detection and packet loss. Experimental validation in real-world network environments demonstrates the practical applicability and enhanced security of the proposed models. It demonstrates that it is possible to enhance both stealth and reliability in covert communication, providing a practical reference for secure data transmission in real-world network environments.}, archive = {J_ACCESS}, author = {Silvio R. A. O. Filho and Claudio A. S. Lelis and Eric T. E. Soares and Cesar A. C. Marcondes}, doi = {10.1109/ACCESS.2025.3584868}, journal = {IEEE Access}, pages = {115909-115923}, shortjournal = {IEEE Access}, title = {Exploiting temporal variability in TCP timestamps for covert channel design}, volume = {13}, year = {2025}, }
• (2025). Enhanced android malware detect models based on explainable generative adversarial networks. ACCESS, 13, 115898-115908. (www)

  @article{ , abstract = {With the widespread adoption of smartphones over the past decade, mobile applications have become a primary target for malicious attacks, usually in the form of malware. Recent studies have leveraged artificial intelligence (AI) techniques for malware detection and classification. However, applying such approaches, particularly deep learning (DL) techniques, to mobile malware detection poses significant challenges. These challenges arise from the difficulty of collecting large quantities of mobile malware samples and the inherent class imbalance in the collected datasets. To tackle these issues and enhance the performance of machine learning (ML) and DL detection models, we propose novel detection models based on a generative adversarial network (GAN). Furthermore, our approach not only employs a conditional tabular GAN (CTGAN) for data augmentation to explore the impact of augmentation but also identifies the optimal multiplication factor for achieving the best results. The evaluation results demonstrate that the proposed data augmentation approach significantly improves the performance of mobile malware detection models, especially those based on DL. We have notably figured out that doubling the original dataset is sufficient to enhance the performance of ML models, whereas DL models require additional data to achieve optimal results. Hence, our proposed mechanism is an effective solution for improving mobile malware detection.}, archive = {J_ACCESS}, author = {Shamma Alshebli and Hyeran Mun and Deepak Puthal and Mohamed Jamal Zemerly and Luigi Martino and Ernesto Damiani and Chan Yeob Yeun}, doi = {10.1109/ACCESS.2025.3585241}, journal = {IEEE Access}, pages = {115898-115908}, shortjournal = {IEEE Access}, title = {Enhanced android malware detect models based on explainable generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). YOLOv10n-based defect detection in power insulators: Attention enhancement and feature fusion optimization. ACCESS, 13, 115888-115897. (www)

  @article{ , abstract = {In modern power systems, insulators, as key components of transmission lines, are crucial for defect detection for the safe operation of power grids. Aiming at the problems of low efficiency of traditional manual detection, the vulnerability of traditional image processing methods to environmental interference, and the insufficient ability of existing deep learning models to detect small target defects under complex backgrounds, this paper proposes an improved target detection model based on YOLOv10n, which is the first time to integrate the spatial channel attention mechanism (SEAttention) with the up-sampling expansion operation (Patch Expanding) in the Neck part of the model. In the Neck part of the model, the spatial channel attention mechanism (SEAttention) and the up-sampling expansion operation (Patch Expanding) are deeply integrated. The spatial information of the channels is compressed by global average pooling, and the feature map is adaptively weighted after learning the channel weights through the fully connected layer, so as to strengthen the key channel features and suppress the noise. The Concat module in Neck is replaced by a two-way weighted feature fusion mechanism, which constructs a top-down and bottom-up two-way information flow, reduces the loss of cross-layer information, and significantly enhances the model’s ability to detect multi-scale defects, such as insulator cracks and fouling, and especially improves the resolution of details of small targets in long-distance shooting scenarios. The channel-space dual attention mechanism (CBAM) is integrated into the C2f module, and the channel weights are computed by global average pooling and maximum pooling in parallel, combined with the spatial attention features extracted by convolution, to realize the accurate focusing on defect-related channels and spatial regions, and the experimental results show that the improved model’s mAP@50 reaches 94.2%, which is 2.4% better than that of the baseline model YOLOv10n, and better than that of YOLOv5n. The experimental results show that the mAP@50 of the improved model reaches 94.2%, which is 2.4% higher than that of the baseline model YOLOv10n, and better than the mainstream algorithms such as YOLOv5 (93.1%) and YOLOv8n (93.2%).}, archive = {J_ACCESS}, author = {Zhihao Wei and Yan Wei}, doi = {10.1109/ACCESS.2025.3581672}, journal = {IEEE Access}, pages = {115888-115897}, shortjournal = {IEEE Access}, title = {YOLOv10n-based defect detection in power insulators: Attention enhancement and feature fusion optimization}, volume = {13}, year = {2025}, }
• (2025). Heisenberg group-based digital signatures for secure communications. ACCESS, 13, 115881-115887. (www)

  @article{ , abstract = {This paper proposes a new digital signature algorithm that employs the Heisenberg group over a prime field and exploits its structure to provide strong cryptographic properties. The developed scheme involves the Heisenberg group within a commutator-based authentication system contributing to key exchange while providing secure means of authentication as well as data integrity. The proposed method for digital signature generation and verification is described by a comprehensive algorithm, which proof of correctness is provided by a confirmation theorem. The security analysis establishes the strength of the scheme against different cryptanalytic attacks and proves the effectiveness of non-repudiation and message integrity in the digital environment. The efficiency of the presented security scheme is also evaluated in practice, using the finite field $\mathbb {Z}_{11}$ , where it is measured against other standards such as ECDSA and RSA. While it has a bigger signature size, the scheme is still efficient when it comes to signing and verifying messages which highlights its suitability for protecting communications under post-quantum cryptography. It supports the on-going progress of cryptography, giving added security to new technologies when faced with the threat of complex attacks.}, archive = {J_ACCESS}, author = {Najla Althuniyan and Ahmed Alamer and Awais Yousaf and Muhammad Waseem and Sarah Aljohani}, doi = {10.1109/ACCESS.2025.3585273}, journal = {IEEE Access}, pages = {115881-115887}, shortjournal = {IEEE Access}, title = {Heisenberg group-based digital signatures for secure communications}, volume = {13}, year = {2025}, }
• (2025). A study on the susceptibility of wildfire disasters in wuzhou city based on interpretability model. ACCESS, 13, 115860-115880. (www)

  @article{ , abstract = {Frequent wildfires in Wuzhou City, Guangxi, pose serious threats to the ecological environment and the safety of people’s lives and property. Accurately assessing wildfire susceptibility is crucial for effective disaster prevention and management decisions. This study aims to create an accurate wildfire susceptibility model for Wuzhou City and examine the influence of various features on the prediction results through an explanatory approach. Based on historical wildfire data from 2017 to 2023, the study comprehensively considered the spatial distribution characteristics of the samples, selecting 15 evaluation factors such as elevation, NDVI, average annual precipitation, and average temperature. The Extreme Gradient Boosting (XGBoost) algorithm was adopted to construct the prediction model, aiming to provide precise wildfire susceptibility assessment. The SHapley Additive exPlanations (SHAP) method was then used to explain the global and local models, analyzing the importance and dependence of each feature. XGBoost is widely used in wildfire prediction due to its efficiency and accuracy. It not only provides strong predictive performance but also helps identify key factors influencing wildfire occurrences. The SHAP method further enhances the XGBoost model by improving the interpretability of the results. It quantifies each feature’s contribution to the predictions, enabling decision-makers to better understand the model’s decision-making process. The model’s performance was further validated using typical wildfire data from 2024. The results indicate that: 1) the wildfire susceptibility model for Wuzhou City exhibits excellent performance, with an Area Under the Curve (AUC) value of 0.981, accurately identifying high-susceptibility areas and providing essential decision-making support for relevant departments; 2) nine key factors affecting wildfire susceptibility in Wuzhou City were identified, including average annual precipitation, average temperature, and average wind speed, offering scientific evidence for wildfire risk management; 3) a comparison between typical historical wildfire samples from 2017 to 2023 and the latest wildfire data from January 2024 to November 2024, further validated the model’s accuracy. The findings of this study hold significant theoretical and practical value for wildfire prevention and control in Wuzhou City.}, archive = {J_ACCESS}, author = {Lei Zou and Hongjuan Shao and Yintao Liu and Chao Ren and Qinyi Chen and Haoming Bai and Zhengzhong Huang and Yao Gu}, doi = {10.1109/ACCESS.2025.3583089}, journal = {IEEE Access}, pages = {115860-115880}, shortjournal = {IEEE Access}, title = {A study on the susceptibility of wildfire disasters in wuzhou city based on interpretability model}, volume = {13}, year = {2025}, }
• (2025). Power regulation based on three-phase balanced currents with direct digital control. ACCESS, 13, 115845-115859. (www)

  @article{ , abstract = {In this paper, a power regulation based on three-phase balanced currents with direct digital control is proposed. This control evaluates the active and reactive power injected into the grid, and compensated by adjusting the amplitude and phase of the three-phase balanced currents. Since the amplitude and phase of the three-phase balanced currents can be directly adjusted, the power injected into the grid can be regulated more flexibly. In addition, this control can operate in the case of not only grid voltage amplitude imbalance, but phase imbalance. Compared with the conventional direct power control (DPC), it improves the problem of excessive current in a single phase when the grid is unbalanced. Finally, the proposed control is validated with different unbalanced grid conditions through 1 kVA prototype.}, archive = {J_ACCESS}, author = {Tsai-Fu Wu and Yun-Hsiang Chang and Chien-Chih Hung}, doi = {10.1109/ACCESS.2025.3539287}, journal = {IEEE Access}, pages = {115845-115859}, shortjournal = {IEEE Access}, title = {Power regulation based on three-phase balanced currents with direct digital control}, volume = {13}, year = {2025}, }
• (2025). Adjusting xception neural network by improved human evolutionary optimizer for fine art sorting. ACCESS, 13, 115828-115844. (www)

  @article{ , abstract = {This study tackles the problem of fine-art image classification that suffers from both the softness of styles and the absence of labelled datasets. The state-of-the-art with CNNs leverages transfer learning and fine-tuning to overcome the scarcity of traditional datasets, which is not sufficient to extract the nuances within various fine art images. In this paper, a new approach is presented based on the combination of the Xception Neural Network and an Improved version of Human Evolutionary Optimizer (IHEO) to overcome these limitations. The model architecture of the Xception model, which uses a depth-wise separable convolution, allowing for a better capture of complex features, and introduced specialized IHEO, to enhance the task performance on fine art classification tasks. Through extensive experiments on various fine art datasets, such as WikiArt, WGA, and TICC, they achieved better accuracy and robustness over the state-of-the-art methods. These results demonstrate the promise of the Xception/IHEO approach in analyzing images in computational arts and classifications. There is also an analysis of deep representation transferability, where fine-tuning Xception on sentiment forecasting and scene identification is shown to outdo models pre-trained on object recognition. The challenges of fine art classification discussed herein in conjunction with methods for domain-specific weight initialization and optimization will allow for significantly more accurate and interpretable systems in the domain of fine art analysis going forward.}, archive = {J_ACCESS}, author = {Tianfu Liu}, doi = {10.1109/ACCESS.2025.3576196}, journal = {IEEE Access}, pages = {115828-115844}, shortjournal = {IEEE Access}, title = {Adjusting xception neural network by improved human evolutionary optimizer for fine art sorting}, volume = {13}, year = {2025}, }
• (2025). Modeling and optimization of energy efficiency in 5G mmWave networks with JT-CoMP under different deployment conditions. ACCESS, 13, 115804-115827. (www)

  @article{ , abstract = {In the face of exponential growth in mobile traffic and network densification, reducing energy consumption is a strategic challenge for the sustainability of 5G networks, particularly in the millimeter wave (mmWave) bands, where high propagation losses and inter-cell interference make optimizing Energy Efficiency (EE) a complex task. The urgent need to develop reliable models to quantify and improve EE under these conditions is all the more crucial given that operators have to reconcile performance and energy sobriety. Against this backdrop, this article proposes an accurate and flexible analytical model to assess the EE of two transmission modes: Joint Transmission-Coordinated Multi-Point (JT-CoMP), which exploits inter-cell interference as useful signals, and the uncoordinated No-CoMP mode. The analysis covers several typical scenarios for 5G mmWave networks, including Rural Macro (RMa), Urban Macro (UMa), and Urban Micro (UMi) environments. The model features the joint integration of complex propagation parameters (line-of-sight probabilities, path losses, shadow fading, three-dimensional distances, and breakpoints), topological factors (type of base stations and Inter-Site Distances – ISDs), as well as the main sources of energy consumption (transmission, circuits, and backhaul), while taking into account the spatial distribution of users. Simulation results demonstrate: 1) a clear superiority of JT-CoMP in terms of EE over No-CoMP in densified environments, with maximum gains observed in UMi, particularly at high mmWave frequencies; 2) the specific challenges of UMa, linked to larger ISDs in urban environments, although it offers higher data rates; and 3) the importance of adapting ISD and transmission power to maintain optimal EE. Finally, we identify critical thresholds beyond which No-CoMP becomes more efficient, underlining the importance of adaptive transmission strategies depending on the environment.}, archive = {J_ACCESS}, author = {Mohamad Younes and Laurent Quibus}, doi = {10.1109/ACCESS.2025.3584304}, journal = {IEEE Access}, pages = {115804-115827}, shortjournal = {IEEE Access}, title = {Modeling and optimization of energy efficiency in 5G mmWave networks with JT-CoMP under different deployment conditions}, volume = {13}, year = {2025}, }
• (2025). Unpacking youth privacy management in AI systems: A privacy calculus model analysis. ACCESS, 13, 115780-115803. (www)

  @article{ , abstract = {The increasing use of Artificial Intelligence (AI) in daily life has introduced substantial issues in protecting user privacy, particularly for young digital citizens. This study examines the complex dynamics of privacy management in AI systems utilizing the Privacy Calculus Model (PCM), with 482 participants: 176 young digital citizens (ages 16–19), 146 parents and educators, and 160 AI specialists. The research used a mixed methods approach to analyze key characteristics, including data ownership, user control, parental data sharing attitude, transparency, trust, perceived risks, benefits, and education. The results underscore the necessity of promoting digital literacy, establishing trust through transparent practices, and implementing collaborative approaches for privacy governance. The study emphasizes the significance of customized educational activities and regulatory frameworks that enable users to manage the trade-offs between the advantages and risks of data sharing by including varied views. This research enhances ethical AI development and advocates equal privacy safeguards for children and young adults.}, archive = {J_ACCESS}, author = {Austin Shouli and Ankur Barthwal and Molly Campbell and Ajay Kumar Shrestha}, doi = {10.1109/ACCESS.2025.3585635}, journal = {IEEE Access}, pages = {115780-115803}, shortjournal = {IEEE Access}, title = {Unpacking youth privacy management in AI systems: A privacy calculus model analysis}, volume = {13}, year = {2025}, }
• (2025). DiffusionScore: A framework for assessing institutional research impact through influence diffusion. ACCESS, 13, 115766-115779. (www)

  @article{ , abstract = {Assessing an institution’s research output has traditionally relied mostly on the opinion of peers, but as the field of scientometrics progressed dramatically in recent years, the evolution of quantitative indicators gained popularity and became essential. The present approaches for evaluating an institution’s research output rely on citation data, that has been quantitatively analyzed and are predominantly data-centric models. It is observed that research productivity and impact are taken into account by many ranking systems, and the measurement of research impact is mostly dependent on citation-counting techniques and the volume of research produced. The recent ranking systems evaluate the reputation of academic institutions by employing network-based algorithms, like Page Rank which examine the citation network at the institution level. An Article’s outreach and causal impact are not completely taken into consideration by the aforementioned approaches which leads to incomplete quantification of metrics. In this study, we propose a framework that uses a data-agnostic influence diffusion model to measure the academic impact of the institutions in the citation network. The diffusion model is inspired by the phenomenon of heat diffusion. The suggested framework investigates the citation trajectory for each article published by the university in the research area, analyses its influence diffusion in citation networks, and provides methods, metrics, and processes that others could apply to similar problems or datasets. By leveraging transfer entropy-based techniques, our research goes beyond citation mapping to investigate the causal link between academic influence and the reputation of publication venues employed by universities.}, archive = {J_ACCESS}, author = {Gambhire Swati Sampatrao and Anisha Khetan and Nithin Nagaraj and Snehanshu Saha and Sudeepa Roy Dey and Archana Mathur}, doi = {10.1109/ACCESS.2025.3584856}, journal = {IEEE Access}, pages = {115766-115779}, shortjournal = {IEEE Access}, title = {DiffusionScore: A framework for assessing institutional research impact through influence diffusion}, volume = {13}, year = {2025}, }
• (2025). A side-channel protected and high-performance hardware implementation for EdDSA25519. ACCESS, 13, 115748-115765. (www)

  @article{ , abstract = {This paper presents a high-performance and secure hardware implementation of the Edwards-Curve Digital Signature Algorithm (EdDSA25519). Using the fixed-base signed multi-comb and the k-ary algorithms for scalar multiplication, the proposed design achieves 307%, 253%, and 48% faster performance in key generation, signature generation, and signature verification, respectively, compared to the fastest previous hardware implementation in the state-of-the-art. When compared to the software-based OpenSSL implementation, our design demonstrates timing performance improvements ranging from 1000% to 2200%. Additionally, we integrate robust Side-Channel Attack (SCA) countermeasures and validate their effectiveness through Test Vector Leakage Assessment (TVLA). The results demonstrate increased resistance to Simple Power Analysis (SPA) and Differential Power Analysis (DPA), offering a hardware-based secure solution for modern cryptographic applications.}, archive = {J_ACCESS}, author = {Pablo Navarro-Torrero and Eros Camacho-Ruiz and Macarena C. Martínez-Rodríguez and Piedad Brox-Jiménez}, doi = {10.1109/ACCESS.2025.3584696}, journal = {IEEE Access}, pages = {115748-115765}, shortjournal = {IEEE Access}, title = {A side-channel protected and high-performance hardware implementation for EdDSA25519}, volume = {13}, year = {2025}, }
• (2025). Multi-modal analysis of bi-parametric MRI slices for lesion detection in prostate cancer screening. ACCESS, 13, 115735-115747. (www)

  @article{ , abstract = {Prostate cancer is a leading cause of cancer-related mortality among men, with early detection playing a critical role in improving patient outcomes. While deep learning has significantly advanced prostate cancer detection in Magnetic Resonance Imaging (MRI), most existing models depend on full 3D scans, posing challenges for real-time deployment in clinical practice. Our work addresses this problem by leveraging a slice-based approach that balances accuracy with efficiency, ensuring scalability for real-world applications. We present a dual-branch, multi-modal deep learning framework focusing on individual bi-parametric MRI slices to detect clinically significant prostate cancer areas (csPCa). The proposed approach combines pixel-level segmentation via UNet++ and instance-level classification using EfficientNet to enhance lesion localization and reduce false positives. We evaluated our framework on the PI-CAI (Prostate Imaging: Cancer AI) dataset, achieving perforamances that outperform other 2D segmentation models. The study highlights the feasibility of slice-based MRI analysis for prostate cancer screening in resource-limited clinical settings, highlight the potential of this approach as a clinical decision support tool, reducing interpretation burden and aiding radiologists in prostate cancer screening.}, archive = {J_ACCESS}, author = {Davide Antonutti and Axel de Nardin and Silvia Zottin and Claudio Piciarelli and Gian Luca Foresti}, doi = {10.1109/ACCESS.2025.3585236}, journal = {IEEE Access}, pages = {115735-115747}, shortjournal = {IEEE Access}, title = {Multi-modal analysis of bi-parametric MRI slices for lesion detection in prostate cancer screening}, volume = {13}, year = {2025}, }
• (2025). Self-supervised image anomaly detection through diverse pseudo anomaly insertion. ACCESS, 13, 115712-115734. (www)

  @article{ , abstract = {Industrial anomaly detection through deep learning-based vision systems is becoming a critical inspection tool for deploying efficient, defect-free manufacturing lines in smart factories. In fully automated manufacturing lines, deep self-supervised learning (SSL) based anomaly detectors are ideal inspection tools, as they require no data annotations during a periodic detector retraining. In this paper, we propose a novel anomaly insertion framework designed to seamlessly integrate with various state-of-the-art self-supervised anomaly detectors (ADs). This framework enhances anomaly detection and segmentation performance by incorporating diverse synthetic anomalies during training and produces labels and segmentation masks automatically. We introduce an artificial anomaly insertion tool that enables practitioners to manipulate normal and anomalous source images, creating training samples with varied characteristics and distributions. Our tool consolidates various pseudo anomaly insertion (PAI) techniques into a single platform and offers adaptive or selective anomaly insertion modes based on user preferences. We integrated our anomaly insertion tool into baseline self-supervised AD and evaluated its performance on well-known public datasets such as MVTec, VisA and KolektorSDD2. Our framework not only reduces dependency on labeled data but also improves robustness of AD across various real anomaly patterns, paving the way towards robust self-supervised anomaly segmentation paradigm. We proposed a novel Affine transformation-based pseudo-Anomaly insertion scheme (AA) that demonstrates an improvement in $AP_{pixel}$ of 5.2% for MVTec and 10.1% for VisA dataset with respect to the second best PAI technique. To use the PAI library, code is provided at https://github.com/RizwanAliQau/pail.git}, archive = {J_ACCESS}, author = {Rizwan Ali Shah and Odilbek Urmonov and Hyungwon Kim}, doi = {10.1109/ACCESS.2025.3580864}, journal = {IEEE Access}, pages = {115712-115734}, shortjournal = {IEEE Access}, title = {Self-supervised image anomaly detection through diverse pseudo anomaly insertion}, volume = {13}, year = {2025}, }
• (2025). Strengthening collaboration via interlinked skew assessment and rectification (ISAR) in swarm robotics. ACCESS, 13, 115697-115711. (www)

  @article{ , abstract = {Ensuring time synchronization among robotic agents in a IoT based swarm robotic system is crucial for smooth operations. This synchronization enables distributed data logging, coordination of algorithms, and synchronized event-driven interactions, which would be impossible without a shared time reference. Maintaining this synchronization is key to keeping data generated by robotic agents consistent and accurate. While various synchronization methods exist, they often require multiple timestamp exchanges or radio messages to address clock skew. This article introduces the Interlinked Skew Assessment and Rectification (ISAR) method. It also presents the Swarm Agent Singular (SAS) Timestamp which provides a cross layered approach to handle clock skew and phase inconsistencies. A SAS timestamp can calculate both clock skew offset and phase offset at the receiving node. The paper delves into ISAR’s mathematical intricacies which includes skew calculations and statistical analysis. MATLAB simulations and experiments with Texas Instruments TMS320C6713 DSP Starter Kits showcase ISAR’s effectiveness which demonstrates practical applicability. The results affirm that using one SAS timestamp instead of multiple timestamp exchanges is a viable approach for synchronizing robotic agents. This will eventually conserves computational resources within swarm network.}, archive = {J_ACCESS}, author = {Muhammad Usman Hashmi and Anas Bilal and Muhammad Khurram Ehsan and Monir Abdullah and Abdulbasit A. Darem and Sahraoui Dhelim}, doi = {10.1109/ACCESS.2025.3585174}, journal = {IEEE Access}, pages = {115697-115711}, shortjournal = {IEEE Access}, title = {Strengthening collaboration via interlinked skew assessment and rectification (ISAR) in swarm robotics}, volume = {13}, year = {2025}, }
• (2025). Automatic determination of parting line locations for sand-casting process using CAD system. ACCESS, 13, 115686-115696. (www)

  @article{ , abstract = {The use of Computer Aided Design (CAD) technology is now playing an increasingly important role in determining the location of parting lines on cast objects. The most used approach is to use tools that are available in certain CAD software, such as SolidWorks Mold Tools or the use of solid parting method. Each approach taken is closely related to manual work which often requires special knowledge and requires a longer completion time, when compared to work that has been carried out automatically. The Slicing method that parallels to parting direction will provide a simple and effective algorithm to obtain flat parting line solution. However, the lack of criteria representing mouldability can result in decisions being made from incomplete information. This research prioritizes five criteria specifically for the sand-casting process, namely projected area, undercut (number of undercut), draw distance, dimensional stability and volume part in cope and drag which are applied to an algorithm to build an automatic system in the form of an Add-in type application in SolidWorks 2022. With the proposed algorithm it is found that the use of five criteria will provide a broader view in making decisions regarding the location of parting lines on cast parts.}, archive = {J_ACCESS}, author = {Tama Reza Setiawan Putra and Fariz Muharram Hasby and Isa Setiasyah Toha and Wisnu Aribowo}, doi = {10.1109/ACCESS.2025.3581536}, journal = {IEEE Access}, pages = {115686-115696}, shortjournal = {IEEE Access}, title = {Automatic determination of parting line locations for sand-casting process using CAD system}, volume = {13}, year = {2025}, }
• (2025). Water leak detection and classification with complex-valued neural networks and sensor fusion. ACCESS, 13, 115669-115685. (www)

  @article{ , abstract = {Water scarcity is an escalating global concern, with pipeline leaks and breaks accounting for up to 30% of urban water loss. While many recent studies have explored leak detection using intelligent systems and acoustic sensing, major challenges remain, particularly in achieving robust performance across sensor types, handling the complex nature of signal information, and extending detection to more informative leak classification tasks. In this study, we address these limitations by proposing a novel, application-driven method that integrates complex-valued Convolutional Neural Networks (CNNs) with a multi-sensor fusion strategy involving hydrophones and accelerometers. Unlike prior works that rely solely on signal magnitude, our method leverages both magnitude and phase information via the Short Time Fourier Transform (STFT), significantly enhancing feature expressiveness. The extracted features are fused and passed to a Multi Layer Perceptron (MLP) to perform leak detection or classification, the latter being a task rarely explored in the literature. We validate our method on two distinct real-world datasets, achieving a balanced accuracy of 99% in both detection and classification tasks. These results demonstrate our model’s strong generalization and competitive advantage over existing state-of-the-art approaches. Our contribution lies not only in the novel use of complex CNNs and sensor fusion, but also in advancing practical, data-driven leak classification, paving the way for more reliable and informative water infrastructure monitoring systems.}, archive = {J_ACCESS}, author = {Daniele Ugo Leonzio and Sara Mandelli and Paolo Bestagini and Marco Marcon and Stefano Tubaro}, doi = {10.1109/ACCESS.2025.3585306}, journal = {IEEE Access}, pages = {115669-115685}, shortjournal = {IEEE Access}, title = {Water leak detection and classification with complex-valued neural networks and sensor fusion}, volume = {13}, year = {2025}, }
• (2025). Efficient semantic segmentation of remote sensing images through global-local feature integration. ACCESS, 13, 115653-115668. (www)

  @article{ , abstract = {The rapid acquisition of remote sensing information plays a significant role in the development of image semantic segmentation methods for remote sensing image interpretation applications. With the increasing variety and complexity of data recorded by satellite remote sensing images, accurately and effectively extracting information from remote sensing images has become crucial for interpreting these images using semantic segmentation methods. However, natural scene-based semantic segmentation methods often suffer from low segmentation accuracy when applied to high-resolution remote sensing images due to the complex background, diverse scales, and irregular shapes. To address these challenges, this paper proposes an efficient remote sensing image semantic segmentation model called Multi-GLISS, which integrates global and local features. The model captures global features through consecutive downsampling and Fourier transform while preserving spatial feature learning and boundary information using convolutional residual layers. Moreover, the local wavelet block utilizes wavelet transform to extract multi-scale local features, enhancing sensitivity to local textures and shapes. Subsequently, the model gradually increases the image resolution through a series of upsampling steps, reconstructing a high-resolution version of the image. Experiments on the Vaihingen dataset show that Multi-GLISS achieves a mean F1 score of 93.29, outperforming the baseline DeepLabV3+ by 1.17 points, while providing clear boundary and structural information for accurate and detailed segmentation.}, archive = {J_ACCESS}, author = {Fengyi Zhang and Xiuyu Xia}, doi = {10.1109/ACCESS.2025.3567806}, journal = {IEEE Access}, pages = {115653-115668}, shortjournal = {IEEE Access}, title = {Efficient semantic segmentation of remote sensing images through global-local feature integration}, volume = {13}, year = {2025}, }
• (2025). Design methodology of LLC resonant converter for PV-powered li-ion battery charger. ACCESS, 13, 115640-115652. (www)

  @article{ , abstract = {Existing PV-powered Li-ion chargers used in electric vehicles and other standalone applications typically employ pulse-width modulated dc-dc converters for battery charging control. However, these converters suffer from hard switching, resulting in high switching losses and significant electromagnetic interference. To address these issues, the LLC resonant converter is adopted due to its soft-switching capability. Nevertheless, the design of resonant converters becomes challenging, especially under wide input and output voltage ranges as encountered in PV-powered Li-ion chargers. This paper proposes a systematic design methodology for LLC resonant converters tailored for PV-powered Li-ion charging applications. The objective is to ensure zero-voltage switching (ZVS) for the primary-side switching devices while minimizing the resonant tank current. By accounting for the unique characteristics of PV sources and Li-ion batteries, the proposed approach achieves a narrow variation in PV voltage, which results in a narrow converter gain range and enables a more optimal LLC converter design. Compared to existing design methods, the proposed approach demonstrates reduced resonant tank current and lower turn-off current in the primary-side devices, enhancing overall converter efficiency. Both simulation and experimental results validate the effectiveness of the methodology, confirming ZVS for the primary-side switches across the full operating range, along with zero-current switching of the secondary-side rectifiers throughout most of the operating range.}, archive = {J_ACCESS}, author = {Ahmed Y. Farag and Mohamed Rabah and Khaled A. El-Metwally and Abdelmomen Mahgoub}, doi = {10.1109/ACCESS.2025.3585483}, journal = {IEEE Access}, pages = {115640-115652}, shortjournal = {IEEE Access}, title = {Design methodology of LLC resonant converter for PV-powered li-ion battery charger}, volume = {13}, year = {2025}, }
• (2025). A novel unified single-phase active rectifier topology with advanced operating modes for smart grids. ACCESS, 13, 115627-115639. (www)

  @article{ , abstract = {Active rectifiers are of paramount importance for guaranteeing power quality in smart grids, due to their characteristics of sinusoidal current consumption and unitary power factor operation. In this paper, a novel unified single-phase (USP) front-end converter is proposed for active rectifier applications in smart grids, offering additional advantages and a set of relevant features besides the conventional active rectification. With the proposed USP front-end converter, the following independent operation modes are available: 1) operation as a traditional active rectifier, ensuring sinusoidal AC grid current (with very low total harmonic distortion), unitary power factor, and controlled DC link voltage; 2) operation as a shunt active power filter, producing a current with controlled harmonic content and phase angle at the fundamental frequency, in order to ensure almost sinusoidal current and a unitary power factor at the AC power grid; and 3) combined operation as an active rectifier and as a shunt active power filter, providing power to the DC load, while simultaneously producing a current waveform with reduced harmonic content and controlled power factor. By controlling these individual operation modes, electrical appliances in smart grids can operate dynamically and collaboratively, improving power quality. In addition, the proposed USP front end converter may operate as a single-phase diode rectifier in case of control system failure or damage of the controlled switching devices. This situation is particularly relevant since it allows to supply the DC load even under such unfortunate circumstances. Along with the paper, the topology of the proposed USP front end converter, its principle of operation, and the control strategies for each operation mode, are separately explained. A 3.6 kW, 230 V–50 Hz laboratorial prototype of the proposed USP front end converter is presented, and the experimental validation is carried out for all operation modes.}, archive = {J_ACCESS}, author = {Vitor Monteiro and Filipe Costa and Sergio Coelho and Joao L. Afonso}, doi = {10.1109/ACCESS.2025.3586110}, journal = {IEEE Access}, pages = {115627-115639}, shortjournal = {IEEE Access}, title = {A novel unified single-phase active rectifier topology with advanced operating modes for smart grids}, volume = {13}, year = {2025}, }
• (2025). A robust deep learning framework for mitigating label noise with dual selective attention. ACCESS, 13, 115604-115626. (www)

  @article{ , abstract = {The performance of Machine Learning (ML) models is highly sensitive to data quality, still the impact of label accuracy remains underexplored. In this study, a novel architecture, the Dual Selective Attention Network (DSAN), is proposed to improve robustness against mislabeled data in deep learning tasks. DSAN incorporates Position Attention Module (PAM) and Channel Attention Module (CAM) to emphasize relevant spatial and channel level features, effectively suppressing the influence of incorrect labels. DSAN was evaluated alongside four baseline models Visual Geometry Group Network (VGG16), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and ResNet-50 on three datasets, with label noise introduced at 0%, 5%, 10%, 15%, and 20% to simulate real-world mislabeling scenarios. Model performance was assessed using accuracy, precision, recall (sensitivity), and F1-score. DSAN maintained over 96.5% accuracy under 20% label noise and outperformed all baseline models by 4–8% on average across performance metrics. While CNN showed moderate robustness and ResNet-50 exhibited better resilience due to its residual learning mechanism, both models experienced notable performance degradation as noise increased. VGG16 and ANN were particularly vulnerable, with sharp declines observed even under low noise levels. To further address mislabeling, the Latent Space Variation using Supervised Autoencoder (AQUAVS) technique was also applied to remove mislabeled data and assess model recovery. Although slight improvements were observed, AQUAVS still lagged behind DSAN in all scenarios. Additionally, an ablation study was conducted to evaluate the individual contributions of PAM and CAM, showing that their combination significantly enhances DSAN robustness to mislabeled data while maintaining high classification performance under label noise. These results highlight the importance of designing architectures that are inherently robust to label noise. By maintaining high reliability under noisy conditions, DSAN contributes to the development of more dependable and generalizable AI systems.}, archive = {J_ACCESS}, author = {Hasnain Hyder and Gulsher Baloch and Amreen Batool and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3585288}, journal = {IEEE Access}, pages = {115604-115626}, shortjournal = {IEEE Access}, title = {A robust deep learning framework for mitigating label noise with dual selective attention}, volume = {13}, year = {2025}, }
• (2025). TMN-net: A hybrid 2.5D multi-branch transformer network for coronary artery segmentation in cardiac diagnosis. ACCESS, 13, 115581-115603. (www)

  @article{ , abstract = {Coronary artery disease (CAD) continues to be a leading cause of global mortality, necessitating accurate and efficient diagnostic tools for early detection and treatment planning. Coronary computed tomography angiography (CCTA) serves as a non-invasive imaging modality critical for visualizing coronary anatomy. However, manual coronary artery segmentation (CAS) is labor-intensive, prone to inter-observer variability, and limited in scalability due to the anatomical complexity of coronary arteries, particularly in small and distal branches. Current deep learning approaches, including 2D and 3D segmentation frameworks, exhibit trade-offs between computational efficiency and spatial context awareness. While traditional methods such as U-shaped architectures provide significant advancements, they fall short in capturing multi-scale contextual information and handling imaging artifacts. This study proposes the Transformer Multi-branch Network (TMN-Net), an innovative hybrid framework that integrates augmented 2.5D and transformer-based 3D processing pathways. TMN-Net leverages dual transformer blocks to capture global context and long-range dependencies, while its multi-scale feature fusion and attention mechanisms enhance the extraction of fine-grained details, enabling robust segmentation of intricate coronary structures. Novel post-processing fusion strategies, including slice fusion and maximum fusion methods, further refine boundary alignment and mitigate false negatives. TMN-Net outperforms existing models, achieving Dice Similarity Coefficient (DSC) improvements of 1.63% and 3.22% over CAS-Net and MC-DFN, respectively, on the CAT08 dataset, with similar gains on the ASOCA dataset. Qualitative evaluations and attention heatmaps highlight the model’s ability to preserve vessel continuity, capture distal branches, and ensure accurate boundary delineation. These advancements confirm TMN-Net’s efficacy in handling the anatomical complexity and imaging variability inherent in coronary artery segmentation.}, archive = {J_ACCESS}, author = {Bo Zhao and Jianjun Peng and Kai Zhang and Yongyan Fan and Ce Chen and Yang Zhang}, doi = {10.1109/ACCESS.2025.3585465}, journal = {IEEE Access}, pages = {115581-115603}, shortjournal = {IEEE Access}, title = {TMN-net: A hybrid 2.5D multi-branch transformer network for coronary artery segmentation in cardiac diagnosis}, volume = {13}, year = {2025}, }
• (2025). Robust fault detection in industrial machines using hybrid transformer-DNN with visualization via a humanoid-based telepresence robot. ACCESS, 13, 115558-115580. (www)

  @article{ , abstract = {Fault diagnosis in industrial systems remains a critical challenge due to complex operational conditions and the need for high accuracy in real-time applications. Traditional deep learning models often struggle with generalization and adaptability, leading to misclassifications, reduced accuracy, and unreliable fault detection. To overcome these limitations, this study proposes an enhanced hybrid Transformer-deep neural network (DNN) architecture designed for both binary and multi-class fault classification in industrial environments. By leveraging the Transformer’s superior feature extraction capabilities alongside the DNN’s classification strength, the model significantly improves detection accuracy and reduces misclassification errors. The proposed model is evaluated on two distinct datasets, including industrial machine fault detection (TMFD) and induction motor fault diagnosis (MFD). In binary classification tasks, the hybrid Transformer-DNN achieved accuracies of 99.97% for industrial machines and 98.26% for induction motors. For multi-class classification, it attained 99.97% accuracy on industrial machines and 98.39% on induction motors. These results demonstrate the model’s superior performance and robust fault identification across diverse datasets compared to other models. Furthermore, the system’s outputs are integrated into a humanoid-based telepresence platform named ARAtronica, facilitating enhanced remote monitoring and safety during fault inspections. This research offers a scalable, highly accurate framework to advance predictive maintenance in various industrial applications.}, archive = {J_ACCESS}, author = {Amir R. Ali and Hossam Kamal}, doi = {10.1109/ACCESS.2025.3584997}, journal = {IEEE Access}, pages = {115558-115580}, shortjournal = {IEEE Access}, title = {Robust fault detection in industrial machines using hybrid transformer-DNN with visualization via a humanoid-based telepresence robot}, volume = {13}, year = {2025}, }
• (2025). The role of teacher calibration in knowledge distillation. ACCESS, 13, 115548-115557. (www)

  @article{ , abstract = {Knowledge Distillation (KD) has emerged as an effective model compression technique in deep learning, enabling the transfer of knowledge from a large teacher model to a compact student model. While KD has demonstrated significant success, it is not yet fully understood which factors contribute to improving the student’s performance. In this paper, we reveal a strong correlation between the teacher’s calibration error and the student’s accuracy. Therefore, we claim that the calibration of the teacher model is an important factor for effective KD. Furthermore, we demonstrate that the performance of KD can be improved by simply employing a calibration method that reduces the teacher’s calibration error. Our algorithm is versatile, demonstrating effectiveness across various tasks from classification to detection. Moreover, it can be easily integrated with existing state-of-the-art methods, consistently achieving superior performance.}, archive = {J_ACCESS}, author = {Suyoung Kim and Seonguk Park and Junhoo Lee and Nojun Kwak}, doi = {10.1109/ACCESS.2025.3585106}, journal = {IEEE Access}, pages = {115548-115557}, shortjournal = {IEEE Access}, title = {The role of teacher calibration in knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Optimal scheduling of multi-storage tank-based hydrogen refueling stations in distribution systems via joint power and hydrogen peak shaving. ACCESS, 13, 115533-115547. (www)

  @article{ , abstract = {In an integrated power-hydrogen distribution system (IPHDS), on-site hydrogen refueling stations (HRSs) with photovoltaic (PV) systems, battery energy storage systems, electrolyzers (ELs), fuel cells, and hydrogen storage tanks (HSTs) are key devices for maintaining reliable and cost-effective IPHDS operations via power peak shaving while supporting the charging demands of hydrogen vehicles (HVs). This study proposes an optimization framework that ensures profitable and robust operations of on-site HRSs under uncertainties in IPHDSs. Compared with conventional methods that manage IPHDS operation using single HST (S-HST)-based HRSs through only power peak shaving, the proposed method performs dual-peak shaving of power and hydrogen through substations and ELs in multiple HST (M-HST)-based HRSs, respectively. The proposed optimization framework is implemented using chance-constrained optimization (CCO) to address uncertainties in PV generation outputs and HV charging demands in IPHDSs. Furthermore, the proposed CCO framework includes constraints that adjust the predicted HV charging demand, thereby satisfying the actual HV charging demand for each predetermined scheduling period. Numerical examples conducted on IPHDSs (IEEE 33-node and 69-node power distribution systems with three and five on-site HRSs) show that, compared to a method with S-HST and only power peak shaving, the proposed CCO method with M-HST based on dual-peak shaving of power and hydrogen increases the total profit by 5.88% and 8.19% on average, respectively, under varying HV charging demand adjustment period and chance-constraint violation probability.}, archive = {J_ACCESS}, author = {Md Mohaiminul Islam Mahin and Sangyoon Lee and Dae-Hyun Choi}, doi = {10.1109/ACCESS.2025.3584881}, journal = {IEEE Access}, pages = {115533-115547}, shortjournal = {IEEE Access}, title = {Optimal scheduling of multi-storage tank-based hydrogen refueling stations in distribution systems via joint power and hydrogen peak shaving}, volume = {13}, year = {2025}, }
• (2025). Enhancing accuracy of 2D 1H–13C HSQC NMR spectra under low sampling rates via non-harmonic analysis and band segmentation. ACCESS, 13, 115520-115532. (www)

  @article{ , abstract = {Multidimensional nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for analyzing molecular structures; however, its practical application is hindered by prolonged acquisition times and reduced resolution due to spectral overlap. Although non-uniform sampling (NUS) offers a partial solution by reducing measurement duration, conventional reconstruction methods struggle at ultra-low sampling rates, often producing artifacts and missing spectral features. This study introduces a novel hybrid framework that integrates non-harmonic analysis (NHA) with a band-segmented processing strategy—termed non-uniform non-harmonic analysis (NUNHA)—for high-fidelity two-dimensional NMR spectrum reconstruction under NUS conditions. NHA enables grid-free, continuous frequency adaptation, grids, allowing for the precise extraction of subtle signals while minimizing artifacts. By incorporating the band-segmented methodology, the framework effectively reduces cross-band interference and achieves high-quality spectral reconstruction—even at ultra-low sampling rates (for example, 6%). Experimental results demonstrate that NUNHA yields spectral resolutions comparable to conventional high-rate sampling methods, enabling accurate recovery and enhanced analysis of complex molecular structures using NMR spectroscopy. These findings highlight the potential of NUNHA for accelerating high-throughput metabolomics and enabling real-time structural analysis in future multidimensional NMR applications.}, archive = {J_ACCESS}, author = {Yuhang Du and Koushin Matsumoto and Shu Nagahara and Kazuma Tabo and Masaya Hasegawa and Shigeki Hirobayashi}, doi = {10.1109/ACCESS.2025.3585189}, journal = {IEEE Access}, pages = {115520-115532}, shortjournal = {IEEE Access}, title = {Enhancing accuracy of 2D 1H–13C HSQC NMR spectra under low sampling rates via non-harmonic analysis and band segmentation}, volume = {13}, year = {2025}, }
• (2025). A comprehensive approach for detecting and handling MitM-ARP spoofing attacks. ACCESS, 13, 115503-115519. (www)

  @article{ , abstract = {Nowadays, most public facilities provide free internet access to support daily activities. These public internet facilities are open to anyone, making them prone to attacks. Communications between the host and router can become attack targets because they may contain important data traffic, such as credentials. Most of these public internet facilities still use Internet Protocol version 4 in their IP addressing, and attackers can use Address Resolution Protocol (ARP) weaknesses as an opportunity. Man-in-the-Middle ARP (MitM-ARP) spoofing attacks can be launched to sniff credentials in host and router communication. We propose a comprehensive approach to solving the problem, which comprises two separate solution parts applied to the host and the router. In the host, we employ a combination of ping Round-Trip Time (RTT) anomaly detection, the SendARP function, static entry, and ping confirmation to detect and mitigate attacks. In the router, we propose combining the Dynamic Host Configuration Protocol leases as the source of the ARP entry and setting the router interface’s ARP mode to “reply-only” to protect the ARP cache from attacks. Experiments and evaluations of the proposed method yield several findings. It can completely revert the diverted route between the host and the router to its legitimate route. It can return the ping RTT (both sides unprotected is approximately 12 ms, one side unprotected is approximately 6 ms) to its typical value (1 ms), just as before the attack. It can avoid credentials sniffing. The most significant result is that our proposed method can comprehensively, quickly, and accurately solve the MitM-ARP spoofing problem without onerous or unusual requirements.}, archive = {J_ACCESS}, author = {Standy Oei and Yohanes Suyanto and Reza Pulungan}, doi = {10.1109/ACCESS.2025.3585463}, journal = {IEEE Access}, pages = {115503-115519}, shortjournal = {IEEE Access}, title = {A comprehensive approach for detecting and handling MitM-ARP spoofing attacks}, volume = {13}, year = {2025}, }
• (2025). Vision-based automated pavement distress inspection: A review. ACCESS, 13, 115474-115502. (www)

  @article{ , abstract = {Computer vision and image processing have been extensively researched to enhance road inspection systems, particularly for detecting pavement distress and assessing road surface conditions. However, most research on vision-based pavement defect inspection is experimental, with few comprehensive studies aligning with standards like ASTM D6433-18. This paper provides a review of vision-based automated pavement distress inspection systems for asphalt concrete. It examines four main aspects, including image acquisition, pre-processing, pavement distress inspection methods, and pavement distress dataset. The discussion covers image acquisition methods, which include platforms such as vehicles, drones, and satellites, as well as vision sensors, sensor calibration, and techniques for capturing road surface images. Pre-processing techniques are discussed for improving image quality and isolating regions of interest. Pavement distress inspection methods employ 2D and 3D approaches to classify different types of pavement distress, severity level estimation, and distress quantification. Additionally, the review provides an overview of existing pavement distress datasets, covering image collection, annotation, augmentation, and evaluation processes. This paper offers insights into the current state of research and references for designing vision-based automated pavement distress inspection systems that meet ASTM D6433-18 standards.}, archive = {J_ACCESS}, author = {Agus Mulyanto and Riri Fitri Sari and Abdul Muis and Ruki Harwahyu}, doi = {10.1109/ACCESS.2025.3582976}, journal = {IEEE Access}, pages = {115474-115502}, shortjournal = {IEEE Access}, title = {Vision-based automated pavement distress inspection: A review}, volume = {13}, year = {2025}, }
• (2025). Task mapping and scheduling with uneven data partition on homogeneous and single-ISA heterogeneous CPUs in model-based parallelization. ACCESS, 13, 115459-115473. (www)

  @article{ , abstract = {Integrated systems comprise diverse tasks, including real-time control and image processing, which must be efficiently executed on embedded system-on-chips with high performance and low power consumption. Multi-core and heterogeneous central processing unit (CPU) architectures, such as Intel’s performance-efficient X86 CPUs, can optimize task execution. Model-based development tools, such as MATLAB/Simulink have further streamlined software design, enabling automatic code generation for embedded systems. This study addresses the challenges of task mapping and scheduling in model-based parallelization (MBP) by focusing on bottleneck tasks that exhibit data parallelism. Control systems may have several dependencies that cause numerous sequential parts and idle times, particularly when the cores exhibit varying performances. Existing methods that rely on even data partitioning may result in suboptimal performance. We propose an ILP task mapping and scheduling framework that introduces uneven data partitioning and optimizes workload balance by filling idle times. Additionally, we present an enhanced MBP workflow that utilizes OpenCV libraries and generates a general pthread code, which improves compatibility across platforms. For random task graphs, the improvement in parallel speedup is approximately 10.02%. For three Simulink models, including a real-world model on single-ISA heterogeneous CPUs, the improvements are 26.25%, 26.55%, and 19.83%.}, archive = {J_ACCESS}, author = {Shanwen Wu and Satoshi Kumano and Kei Marume and Masato Edahiro}, doi = {10.1109/ACCESS.2025.3585556}, journal = {IEEE Access}, pages = {115459-115473}, shortjournal = {IEEE Access}, title = {Task mapping and scheduling with uneven data partition on homogeneous and single-ISA heterogeneous CPUs in model-based parallelization}, volume = {13}, year = {2025}, }
• (2025). Average current reconstruction method for ultra-low inductance PMSM using a single DC-link current sensor. ACCESS, 13, 115446-115458. (www)

  @article{ , abstract = {This article proposes a method to minimize current reconstruction errors when driving ultra-low inductance permanent magnet synchronous motors (PMSMs) using a single current sensor system for three-phase voltage source inverters. The single current sensor system is regarded as a solution for reducing cost and size in PMSMs vector control systems. However, this approach has inherent limitations, as it introduces current reconstruction errors due to sampling timing constraints and exhibits dead zones where current reconstruction is impossible. Voltage injection methods can be used to avoid dead zones, but these methods generate additional current ripples, which further worsen current reconstruction error. In particular, in ultra-low inductance PMSMs, the low inductance characteristic intensifies current ripples, which can ultimately lead to drive failures in low-speed operating regions. To address these challenges, this paper proposes the Extended Switching Signal Split Pulse Width Modulation (EXSSS-PWM) method, which minimizes current reconstruction errors and mitigates dead zone issues without circuit configuration changes or motor parameter dependency. Experimental results demonstrate that the proposed method provides superior current reconstruction performance and is more suitable for ultra-low inductance motor systems compared to conventional methods.}, archive = {J_ACCESS}, author = {Gyeong-Sik Lee and Min-Seok Chae and Jun-Sik Hwang and Hyeon-Gyu Choi}, doi = {10.1109/ACCESS.2025.3582286}, journal = {IEEE Access}, pages = {115446-115458}, shortjournal = {IEEE Access}, title = {Average current reconstruction method for ultra-low inductance PMSM using a single DC-link current sensor}, volume = {13}, year = {2025}, }
• (2025). A framework for wildfire risk assessment to electric grid. ACCESS, 13, 115433-115445. (www)

  @article{ , abstract = {Wildfires can cause catastrophic damage to the surrounding environment and pose substantial challenges to the reliability and resiliency of the electric grid. Recently, wildfire incidents have increased, and electric utilities are facing numerous challenges in maintaining seamless operation of the power system. In this paper, a data-driven framework is proposed to assess the spatio-temporal aspects of wildfires on the power system. To illustrate this approach, we utilize data from two existing power networks in the southwestern-US, including corresponding fire risk values for these geographical locations. This methodology aims to establish a correlation between the wildfire risks to the power grid models, leveraging the geographical attributes of transmission lines. Subsequently, power flow is solved using dynamic power grid models in Positive Sequence Load Flow (PSLF) to demonstrate the damages caused by wildfires using probabilities representing wildfire risks and fire arrival times based on single fire growth, respectively. Additionally, grid hardening techniques and component-induced ignition probabilities are incorporated in the analysis to provide a comparative evaluation of the effects on power grids with grid hardening included. The findings demonstrate that both N-k and N-1-1 contingencies evaluations are required, and the reliability of a system in adverse fire-related conditions can be exacerbated without grid-hardening measures, and the cascading failure probabilities can be significantly increased while component failures are incorporated.}, archive = {J_ACCESS}, author = {Jubair Yusuf and Holly Eagleston and Michelle Bester and Brian J. Pierre}, doi = {10.1109/ACCESS.2025.3583964}, journal = {IEEE Access}, pages = {115433-115445}, shortjournal = {IEEE Access}, title = {A framework for wildfire risk assessment to electric grid}, volume = {13}, year = {2025}, }
• (2025). Streamlined model predictive control for a triple-optimization of thermal comfort, heating costs, and photovoltaic self-consumption. ACCESS, 13, 115419-115432. (www)

  @article{ , abstract = {This paper introduces a straightforward Model Predictive Control (MPC) method for residential buildings equipped with thermally activated building structures (TABS), photovoltaics (PV), and heat pumps (HP). The main goal is a triple-optimization of thermal comfort, heating costs, and PV self-consumption. Building on existing low-tech MPC developments, this study extends them to enhance PV self-consumption optimization. The proposed streamlined MPC algorithm relies solely on weather forecast data and real-time electricity market prices to determine the required heating power input. The developed and extended MPC algorithm is simulated and validated using Matlab/Simulink. Various cost function variants of the MPC algorithm are analyzed and calculated with both fixed and flexible price signals. The results demonstrate that different cost function variations significantly impact energy consumption, costs, and comfort levels. With flexible day-ahead prices, the cost function incorporating comfort, electricity costs, and PV signals performs comparably to the variant focusing solely on comfort and PV optimization. However, for fixed electricity prices, the results indicate that the simplest cost function of the MPC achieves the best outcomes. These findings suggest that further simplifications of the MPC approach can effectively reduce heating costs while maintaining comfort, making it more practical for implementation in residential buildings.}, archive = {J_ACCESS}, author = {Vukasin Klepic and Magdalena Wolf and Tobias Pröll}, doi = {10.1109/ACCESS.2025.3584388}, journal = {IEEE Access}, pages = {115419-115432}, shortjournal = {IEEE Access}, title = {Streamlined model predictive control for a triple-optimization of thermal comfort, heating costs, and photovoltaic self-consumption}, volume = {13}, year = {2025}, }
• (2025). TriNet: A hybrid feature integration approach for motor imagery classification in brain-computer interface. ACCESS, 13, 115406-115418. (www)

  @article{ , abstract = {Brain-computer interface (BCI) is inevitably a promising technology holding the potential to revolutionize the world with its wide range of applications. From healthcare to innovative computer gaming, integrating BCI for intelligent control has become an emergent scope. However, optimizing motor imagery (MI) classification in non-invasive BCI remains a significant challenge due to the poor quality of the acquired signal. In this paper, we propose a unified approach for MI classification by combining features from three diverse domains. Initially, the EEG data is preprocessed using bandpass filtering to extract the relevant EEG signals. Next, the preprocessed signal is fed simultaneously to three branches to extract three distinct categories of features from the signal. Specifically, spectral features are extracted using the fast-Fourier transform (FFT) and a spatial transformer is utilized to extract spatial features from the EEG data. Moreover, the third branch extracts temporal features using an encoder-decoder architecture. The features obtained using the three branches are concatenated together to obtain a comprehensive features set which is finally classified using extreme learning machine (ELM). Our proposed approach which uses a novel combination of features from three distinct domains is hereby named TriNet and is validated using two benchmark datasets BCI Competition IV-2a and BCI Competition IV-2b. The experimental results show an accuracy of 87.30% and 92.64% respectively on BCI IV-2a and BCI IV-2b datasets in subject-specific classification. Moreover, TriNet is also tested in subject-independent classification setup, and average classification accuracies of 63.92% and 78.60% are obtained on BCI IV-2a and 2b datasets respectively which is an improvement of 8 to 10% compared to the existing methods. The classification performance and computational cost comparisons demonstrate the superior performance of TriNet compared to the existing methods highlighting its potential to enhance MI classification in BCI.}, archive = {J_ACCESS}, author = {Hafza Faiza Abbasi and Muhammad Ahmed Abbasi and Shen Jianbo and Xiang Liping and Xiaojun Yu}, doi = {10.1109/ACCESS.2025.3585180}, journal = {IEEE Access}, pages = {115406-115418}, shortjournal = {IEEE Access}, title = {TriNet: A hybrid feature integration approach for motor imagery classification in brain-computer interface}, volume = {13}, year = {2025}, }
• (2025). Leverage gaussian interpolation features extraction and advanced Encode–Decode models for 2D steady state flows estimation. ACCESS, 13, 115384-115405. (www)

  @article{ , abstract = {Accurate estimation of two-dimensional (2D) multi-obstacle steady-state flows is crucial in various scientific and engineering disciplines, yet conventional methods often fall short in precision and computational efficiency. This paper introduces a novel approach that combines Gaussian interpolation-based feature extraction with advanced encode-decode models, including AutoEncoder, UNet, and DeepLabV3+. By leveraging Gaussian interpolation and advanced Encoder-Decoder models, we effectively capture the smooth, continuous nature of steady-state flows while mitigating noise and small-scale fluctuations that may obscure global flow patterns. We evaluate our method on a private dataset of 2D multi-obstacle steady-state flows, comparing the performance of each model based on accuracy, computational efficiency, and robustness. Our results demonstrate that the encode-decode architectures, particularly UNet and DeepLabV3+, significantly improve flow estimation accuracy and feature retention. This study suggests that integrating Gaussian interpolation with advanced encode-decode models offers an efficient and effective solution for complex flow estimation tasks, opening new avenues for research in fluid dynamics applications.}, archive = {J_ACCESS}, author = {Thi-Thu-Huong Le and Changwoo Choi and Junyoung Son and Howon Kim}, doi = {10.1109/ACCESS.2025.3585402}, journal = {IEEE Access}, pages = {115384-115405}, shortjournal = {IEEE Access}, title = {Leverage gaussian interpolation features extraction and advanced Encode–Decode models for 2D steady state flows estimation}, volume = {13}, year = {2025}, }
• (2025). Scenario-based optimized coordinated scheduling of transmission and distribution networks with multiple timescales considering renewables correlations. ACCESS, 13, 115367-115383. (www)

  @article{ , abstract = {The increasing integration of distributed energy resources (DERs) into distribution networks enhances operational flexibility but also introduces significant challenges, including bidirectional power flow and uncertainties in both generation and load. These challenges complicate the scheduling and secure operation of integrated transmission and distribution (T&D) systems. To address these issues, this paper presents a multi-timescale scenario-based optimal scheduling approach for an integrated transmission-distribution power system considering the correlation of DERs. First, an adaptive multivariate nonparametric kernel density estimation (KDE) method is developed to construct high-dimensional joint probability distributions of DER outputs, capturing complex correlations between wind power and photovoltaics. Second, a hybrid scenario generation method combining improved rejection sampling with a continuous-time Markov chain is proposed to efficiently produce realistic and representative scenarios across multiple timescales. Third, a coordinated scheduling strategy is designed, which integrates scenario-based optimization into both day-ahead and intraday operations, balancing cost minimization with risk management. Numerical case studies verify the effectiveness of the proposed method, demonstrating enhanced coordination between transmission and distribution levels and improved reliability and DER accommodation.}, archive = {J_ACCESS}, author = {Ruifeng Zhao and Ruhao Wu and Jiangang Lu and Fengchao Chen and Qiwei Li}, doi = {10.1109/ACCESS.2025.3581077}, journal = {IEEE Access}, pages = {115367-115383}, shortjournal = {IEEE Access}, title = {Scenario-based optimized coordinated scheduling of transmission and distribution networks with multiple timescales considering renewables correlations}, volume = {13}, year = {2025}, }
• (2025). POD-based machine learning approach for coupled EM-thermal analysis in microwave heating. ACCESS, 13, 115354-115366. (www)

  @article{ , abstract = {Microwave heating processes require accurate simulation of time-varying electromagnetic (EM) properties to ensure reliable temperature predictions. Conventional methods which require conducting full-wave EM simulations at every thermal step, impose substantial computational burdens. In this paper, we propose a machine learning-based approach for reduced-order modeling that efficiently predicts the specific absorption rate (SAR) distributions in coupled EM and thermal analyses. The method employs proper orthogonal decomposition (POD) to extract dominant spatial and temporal modes from simulation data, thereby reducing the parametric complexity even when training data are limited. A linear regression model is then established between time-variant material properties and temporal POD modes, which enables the accurate reconstruction of dynamic SAR distributions during the heating processes. Numerical validations using 3-D FDTD simulations of microwave heating with temperature-dependent Debye characteristics of the material properties show that the proposed method achieves a maximum temperature deviation of only 0.0095°C for a 32.65°C temperature rise in a 20-minute heating simulation. This corresponds to a 0.029% error while reducing the required number of EM simulations to $1/6$ compared to conventional methods. This significant reduction in computational cost and high prediction accuracy highlights the potential of the proposed method as an effective alternative for coupled EM-thermal simulations in microwave heating applications.}, archive = {J_ACCESS}, author = {Jeong-Wan Lee and Gyu-Sik Choi and Sung-Jun Yang}, doi = {10.1109/ACCESS.2025.3585411}, journal = {IEEE Access}, pages = {115354-115366}, shortjournal = {IEEE Access}, title = {POD-based machine learning approach for coupled EM-thermal analysis in microwave heating}, volume = {13}, year = {2025}, }
• (2025). Ethical considerations of AI in education: A case study based on pythia learning enhancement system. ACCESS, 13, 115331-115353. (www)

  @article{ , abstract = {As the use of AI in education increases, addressing ethical concerns is essential for its responsible use and implementation. This study explores the ethical considerations of AI in education by analyzing a concrete application, the Pythia Learning Enhancement System, which enables personalized and adaptive educational experiences. In this case study, the concrete implementation of Pythia, contrary to high-level abstract analyses, provides practical insights into these ethical issues. To achieve this goal, we first define Pythia and outline its implementation. Using the various topics covered by Pythia, the ethical issues associated with each of these topics are outlined, resulting in a list of ten distinct ethical issues. Because there is overlap in issues covered by similar applications, they can benefit from this identification. An ethical (human rights) and didactic (constructivism) theoretical background is then provided as a basis for further discussion. This is used to build the arguments used in the main study. This consists of analyzing each of the issues and providing recommendations to mitigate potential harm arising from these issues. This helps to provide guidelines for future applications. In the analysis, the ethical discussion is mainly based on the human rights of the stakeholders involved, emphasizing human dignity. After the analysis, each of the issues is contextualized with the recent AI Act of the European Union, providing a novel mapping of the issues of AI in education to the AI Act. Finally, the limitations of the study are stated and a plan for future research is presented.}, archive = {J_ACCESS}, author = {Simon Röhrl and Susanne Staufer and Flemming Bugert and Vamsi Krishna Nadimpalli and Florian Hauser and Lisa Grabinger and Dominik Bittner and Timur Ezer and Ricardo Altweck and Sabine Graf and Thomas Kriza and Jürgen Mottok}, doi = {10.1109/ACCESS.2025.3583975}, journal = {IEEE Access}, pages = {115331-115353}, shortjournal = {IEEE Access}, title = {Ethical considerations of AI in education: A case study based on pythia learning enhancement system}, volume = {13}, year = {2025}, }
• (2025). A passive fault tolerance strategy for super-twisting algorithm-based sliding mode control with barrier function for quadrotor UAVs. ACCESS, 13, 115321-115330. (www)

  @article{ , abstract = {Unknown external disturbances and actuator failures significantly affect the performance and flight safety of quadrotor unmanned aerial vehicles (UAVs) during task execution. To address this challenge, this paper proposes a barrier function-based passive fault-tolerant control strategy combining a super-twisting algorithm (STA) with sliding mode control (SMC). Firstly, STA based on potential barrier function is used to compensate unknown fault coefficients and external disturbances. This strategy does not need parameters related to actuator faults and external disturbances, and relies on its sign function-based controller design and robustness to overcome the adverse effects of unknown parameters. Furthermore, considering the transient response of the system, combined with the SMC strategy, the system has a faster response speed. In addition, the finite-time stability of the closed-loop system is guaranteed by Lyapunov theory. Finally, two sets of simulation experiments verify the effectiveness and superiority of the proposed control method.}, archive = {J_ACCESS}, author = {Yujuan Wang and Congmin Ji}, doi = {10.1109/ACCESS.2025.3585408}, journal = {IEEE Access}, pages = {115321-115330}, shortjournal = {IEEE Access}, title = {A passive fault tolerance strategy for super-twisting algorithm-based sliding mode control with barrier function for quadrotor UAVs}, volume = {13}, year = {2025}, }
• (2025). Gray wolf optimizer with communication strategy based on DV-hop for nodes location of wireless sensor networks. ACCESS, 13, 115307-115320. (www)

  @article{ , abstract = {Node localization remains a pivotal research challenge in Wireless Sensor Networks (WSN). The DV-Hop method, which estimates the positions of unknown nodes using anchor nodes and multi-hop forwarding, significantly lacks localization accuracy. To address this deficiency, this paper introduces an improved Grey Wolf Optimizer with a communication strategy (IGWO-CS). To alleviate the constraints imposed by the convergence factor in the conventional GWO algorithm, a Logistic chaotic mapping has been integrated. Moreover, two novel communication strategies, namely the Adaptive Weight Mechanism and Lévy Flight, have been implemented. The Adaptive Weight Mechanism dynamically adjusts positions during the weight update process, while the pronounced randomness and the mechanisms of Lévy Flight steps enhance information exchange among wolves, thereby improving convergence speed. Comprehensive experimental analyses on established benchmark functions have validated the efficacy of the IGWO-CS method, illustrating its competitiveness with other advanced algorithms such as the Ranking-based Adaptive Cuckoo Search (RACS) and Modified Particle Swarm Optimization (MPSO). The IGWO-CS method outperformed in 17 out of 23 benchmark functions. In simulation experiments, it achieved standard deviations of normalized errors and distance errors at 1.15E-04 and 2.31E-03, respectively. Quantitative evaluations confirm that the IGWO-CS method surpasses the RACS and MPSO in localization accuracy within WSN, demonstrating its substantial superiority.}, archive = {J_ACCESS}, author = {Dan Yu and Ting Yuan and Haiyin Qing and Wenwu Xie and Peng Zhu}, doi = {10.1109/ACCESS.2024.3445650}, journal = {IEEE Access}, pages = {115307-115320}, shortjournal = {IEEE Access}, title = {Gray wolf optimizer with communication strategy based on DV-hop for nodes location of wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Transformation of circular polarization along the propagation axis via a polarization-mixed metasurface. ACCESS, 13, 115297-115306. (www)

  @article{ , abstract = {Polarization is a fundamental property of light and plays an important role in a wide range of optical phenomena and applications. While many earlier studies of metasurfaces have primarily focused on controlling polarization within the transverse plane, recent research has extended to explore the control along the light propagation direction. In this study, we design and analyze a Bessel beam–based metasurface capable of continuously varying the circular polarization state of light along its propagation axis. By leveraging polarization beating arising from the spatially engineered superposition of four circularly polarized (CP) components with distinct spatial frequencies, the proposed metasurface enables continuous switching between left-handed circular polarization (LCP) and right-handed circular polarization (RCP) along the propagation axis, which remains robust regardless of the orientations of incident linear polarization. We further demonstrate such metasurfaces being compatible with beams carrying orbital angular momentum (OAM). This dynamic polarization-switching capability offers precise and flexible control of polarization states, paving the way for the potential applications including optical communication, 3D imaging, and chiral sensing.}, archive = {J_ACCESS}, author = {Changhyun Lee and Jun Seok Yoon and Hyeon Woo Jeon and Seokjae Yoo and Soo Jin Kim}, doi = {10.1109/ACCESS.2025.3585670}, journal = {IEEE Access}, pages = {115297-115306}, shortjournal = {IEEE Access}, title = {Transformation of circular polarization along the propagation axis via a polarization-mixed metasurface}, volume = {13}, year = {2025}, }
• (2025). Design of 2D-scanning bifocal metalens antenna using ControlNet-enabled stable diffusion deep generative model. ACCESS, 13, 115289-115296. (www)

  @article{ , abstract = {We present a low-cost, low-profile, high-gain, 2D-scanning bifocal metalens antenna for 5G/6G communication applications. First, we employed the ControlNet-enabled stable diffusion technique to generate a set of low-loss, wide-phase-range pixelated unit cells. Second, we generalized the 1D-scanning bifocal metalens phase distribution formula for use in 2D-scanning applications. Finally, the ControlNet-enabled stable diffusion generated unit cells are used to synthesize the metalens capable of 2D-scanning using the generalized 2D-scanning bifocal phase distribution formula. The synthesized antenna with diameter (D) of 300 mm, focal length (f) of 150 mm, and an $f/D$ ratio of 0.5 is capable of having a scan beam range up to ±23.3° with a scan loss of ~3 dB in both principal cuts. This enhancement in scan loss performance by ~0.6 dB surpasses that of traditional center-fed, single-focus, spherical-wave phase-compensated metalenses and offers performance that is better than other 1D-scanning designs. This is accomplished while upholding a measured high peak aperture efficiency of 44.8% and a 3-dB gain bandwidth of 16.5%. Most importantly, our work is the first to generalize the 1D-scanning bifocal phase distribution formula for use in the design of a 2D-scanning bifocal metalens.}, archive = {J_ACCESS}, author = {Cedric W. L. Lee and Peng Khiang Tan and Theng Huat Gan}, doi = {10.1109/ACCESS.2025.3585147}, journal = {IEEE Access}, pages = {115289-115296}, shortjournal = {IEEE Access}, title = {Design of 2D-scanning bifocal metalens antenna using ControlNet-enabled stable diffusion deep generative model}, volume = {13}, year = {2025}, }
• (2025). Applications of machine learning in human factors and ergonomics: A comprehensive review of research from the past decade. ACCESS, 13, 115263-115288. (www)

  @article{ , abstract = {This study provides a comprehensive literature review of 130 peer-reviewed journal articles published between 2015 and 2024 on the application of Machine Learning (ML) algorithms in Human Factors and Ergonomics (HFE). The selected studies cover eight key application areas, including Work-Related Musculoskeletal Disorders (WRMSDs), Mental Workload Assessment (MWLA), physical workload, and occupational safety. ML techniques examined include Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Random Forest (RF), Decision Trees (DTs), K-Nearest Neighbors (KNN), Naive Bayes (NB), boosting algorithms, and k-means clustering. Among the reviewed articles, Artificial Neural Networks (ANNs) and Deep Learning (DL) methods were the most prevalent, at 36%, followed by Support Vector Machines (SVMs) at 22% and Random Forests (RF) at 19%. Performance benchmarks from these studies indicate classification accuracies ranging from 83% to 97.4% for tasks such as posture recognition, MWLA, and fatigue detection. For instance, ANN models for Electroencephalography (EEG)-based MWLA achieved up to 94% accuracy, while SVM classifiers reached up to 97.94% in posture classification. KNN, when optimized for k-values, yielded accuracy rates exceeding 90% in ergonomic applications. There was a marked increase in ML adoption in HFE post-2019, with 2020 publications nearly doubling compared to 2019. This review not only benchmarks algorithmic performance but also identifies research gaps and provides guidance for future machine-learning applications in human factors, ergonomics, and human-centered systems design.}, archive = {J_ACCESS}, author = {Erman Çakit and Waldemar Karwowski}, doi = {10.1109/ACCESS.2025.3585773}, journal = {IEEE Access}, pages = {115263-115288}, shortjournal = {IEEE Access}, title = {Applications of machine learning in human factors and ergonomics: A comprehensive review of research from the past decade}, volume = {13}, year = {2025}, }
• (2025). Robust model predictive control for DAB converters using extended phase shift modulation with minimum backflow power and ZVS design. ACCESS, 13, 115248-115262. (www)

  @article{ , abstract = {This paper introduces a method for deriving an optimal closed-form solution for extended phase shift (EPS) control of a dual active bridge (DAB) DC-DC converter, capable of achieving both minimum backflow power (MBP) and zero-voltage switching (ZVS) conditions. First, backflow power, which significantly impacts the converter’s efficiency, is analyzed and minimized using Karush-Kuhn-Tucker (KKT) conditions. By solving an optimization problem with constraints that include power transfer, ZVS, and EPS conditions, the proposed method provides closed-form solutions for the inner and outer phase shifts. Second, based on the KKT solutions and the output filter, robust model predictive control (RMPC) is designed, taking system parameter uncertainties into account. As a result, the closed-loop control can simultaneously adjust the inner and outer phase shifts, facilitating overall performance improvement. This approach ensures that the converter operates efficiently and reliably, reducing power losses and enhancing efficiency. Finally, to validate the theoretical findings, various real-time simulations on hardware-in-the-loop (HIL) 404 devices are conducted. Furthermore, the effectiveness of the proposed method is demonstrated through comparisons with previous works.}, archive = {J_ACCESS}, author = {Nguyen Ngoc Nam and Dong Hun Lee and Young Il Lee}, doi = {10.1109/ACCESS.2025.3585877}, journal = {IEEE Access}, pages = {115248-115262}, shortjournal = {IEEE Access}, title = {Robust model predictive control for DAB converters using extended phase shift modulation with minimum backflow power and ZVS design}, volume = {13}, year = {2025}, }
• (2025). Customer lifetime value modeling via two stage selected trees ensembles. ACCESS, 13, 115236-115247. (www)

  @article{ , abstract = {The successful implementation of customer relationship management and product development need individual customer value assessment to generate sustained profit returns. As an important part of the customer relationship management, predicting future buyers and their monetary value allow companies to tailor interactions, focus on high-value prospects, and increase customer profitability and retention. This paper proposes a system that adopts two sequential stages to calculate customer lifetime value based on their purchase behaviors. To handle the diversity and imbalance in customer data, we explore three decision tree selection strategies to form an overall efficient ensemble. Decision tree selection is done based on individual tree performance on independent samples, out-of-bag samples, and sub-samples for their inclusion in the final ensemble. The first stage predicts whether a customer will purchase in a future window, while the second stage estimates the monetary value of their future purchases. Model evaluation includes Brier score, accuracy, kappa, and sensitivity for the first stage, and unexplained variation, RMSE, Spearman correlation, and MAD for the second stage. Based on a thorough analysis of five customer transaction datasets that are further divided into power and sporadic customers data, the proposed ensemble methods surpass several other state-of-the-art methods, i.e., k nearest neighbor, weighted kNN, random forests, support vector machine and neural networks in majority of the cases considering both the stages.}, archive = {J_ACCESS}, author = {Zardad Khan and Amjad Ali and Saeed Aldahmani and Henrik Nordmark and Berthold Lausen}, doi = {10.1109/ACCESS.2025.3584709}, journal = {IEEE Access}, pages = {115236-115247}, shortjournal = {IEEE Access}, title = {Customer lifetime value modeling via two stage selected trees ensembles}, volume = {13}, year = {2025}, }
• (2025). Sensorless control of permanent magnet synchronous motor based on variable gain load torque observer. ACCESS, 13, 115226-115235. (www)

  @article{ , abstract = {The conventional sensorless control strategy based on a sliding mode observer (SMO) leads to considerable deviation between the estimated and actual rotor positions under sudden load changes. The deviation causes instability in the motor. In order to address this issue, a sensorless control strategy for permanent magnet synchronous motors (PMSM) based on variable-gain load torque observer (LTO) is proposed in this paper. The presented method improves rotor position estimation accuracy under sudden load variations while effectively reducing chattering. The first step in this approach is to implement a hyperbolic tangent function with a variable boundary layer. This designed function replaces the switching function in the sliding mode observer. Next, a load torque observer with a variable feedback gain is designed, and the influence of the gain parameter m on estimated speed and position error is analyzed to determine the optimal value. Finally, simulations and experimental results confirm the feasibility and effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Jun Ma and Xianqun Qiu and Huanqing Xu and Zhendong Yin}, doi = {10.1109/ACCESS.2025.3584506}, journal = {IEEE Access}, pages = {115226-115235}, shortjournal = {IEEE Access}, title = {Sensorless control of permanent magnet synchronous motor based on variable gain load torque observer}, volume = {13}, year = {2025}, }
• (2025). Robust fault classification in permanent magnet synchronous machines under dynamic and noisy conditions. ACCESS, 13, 115217-115225. (www)

  @article{ , abstract = {Detection and isolation of multiple low-severity faults in permanent magnet synchronous machines (PMSMs) under dynamics and noisy conditions are very important to enhance the system’s reliability, lifetime, and service availability. This study investigates a robust fault classification scheme for PMSMs under low-severity, multiple-fault conditions in noisy and variable-speed scenarios. Two supervised learning models, namely Extra Trees (ET) and Support Vector Machine (SVM), are developed and compared using time-domain (TD) and frequency-domain (FD) features. A hybrid feature extraction method is adopted to balance the computational burden and classification accuracy. Hyperparameters and frequency-domain (FD) features are optimized based on prediction confidence, prediction times, and accuracy. Furthermore, the robustness of the data-driven models is evaluated at different Signal-to-Noise (SNR) ratios under mixed faults and variable speeds in an in-house PMSM, combining an inter-turn short circuit fault of 2.2% in one phase and a 30% demagnetization of one rotor magnet. A comparative study was conducted, confirming the proposed ET model’s superior performance in terms of prediction accuracy and robustness.}, archive = {J_ACCESS}, author = {Mikal Laursen and Van-Van Huynh and Duy-Hung Ha and Mahmoud S. Mahmoud and van Khang Huynh}, doi = {10.1109/ACCESS.2025.3585518}, journal = {IEEE Access}, pages = {115217-115225}, shortjournal = {IEEE Access}, title = {Robust fault classification in permanent magnet synchronous machines under dynamic and noisy conditions}, volume = {13}, year = {2025}, }
• (2025). CrowNet: Open source modeling and simulation of pedestrian-2-X and crowd networks. ACCESS, 13, 115196-115216. (www)

  @article{ , abstract = {One way to improve urban traffic is by networking road users. While V2X communication initially focused on vehicles, attention is now shifting towards pedestrian communication, particularly through wireless applications. Simulations are crucial for developing and evaluating these applications and their communication protocols, but most existing frameworks are vehicle-centered. Consequently, they lack accurate pedestrian models, limiting their use for analyzing applications for pedestrians. We close this gap by introducing CrowNet, the first open-source co-simulation framework designed explicitly for pedestrian-centric wireless applications. CrowNet couples a high-fidelity pedestrian simulator with wireless network simulations in OMNeT++. The framework is developed systematically, beginning with specifying the scope and software requirements. Co-simulation techniques are used to integrate established state-of-the-art simulators, including OMNeT++ model libraries (INET, Artery, Veins, Simu5G), mobility simulators (SUMO, Vadere), and a pedestrian management simulator. The defining feature of CrowNet is its model library for pedestrian-to-X communication applications – a component that no other open co-simulation framework currently provides. We run simulations to verify the software requirements. Our simulations show that the scientific simulation framework CrowNet is suitable for the simulation of pedestrian-centric applications with high accuracy requirements. We also present examples demonstrating that CrowNet can be applied to a wide range of use cases, including V2X and traffic management applications. We conclude that CrowNet represents a novel and valuable simulation tool for traffic management and pedestrian communication research, filling a critical gap in simulation tools for the assessment of pedestrian-centric applications.}, archive = {J_ACCESS}, author = {Stefan Schuhbäck and Christina Maria Mayr and Jörg Ott and Lars Wischhof}, doi = {10.1109/ACCESS.2025.3585785}, journal = {IEEE Access}, pages = {115196-115216}, shortjournal = {IEEE Access}, title = {CrowNet: Open source modeling and simulation of pedestrian-2-X and crowd networks}, volume = {13}, year = {2025}, }
• (2025). Toward energy efficiency and fairness in UAV-based task offloading. ACCESS, 13, 115178-115195. (www)

  @article{ , abstract = {The rising demand for compute-intensive mobile applications challenges the limited energy and processing power of user equipment (UE). While Mobile Edge Computing (MEC) enables task offloading to nearby servers, deploying fixed MEC infrastructure is often impractical in settings like disaster zones or temporary high-density events. Furthermore, challenges such as high task delays, limited UE battery life, and unfair load distribution persist. To address these issues, we propose a system where Unmanned Aerial Vehicles (UAVs) serve as mobile relays between UEs and MEC servers. This results in a joint optimization framework combining 1) a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for UAV trajectory control to enhance service coverage and energy efficiency, with 2) a low-complexity task offloading algorithm for UEs. The framework is explicitly designed to minimize UE energy consumption while promoting fairness in task allocation and data rates. Simulations demonstrate that our approach significantly outperforms state-of-the-art benchmarks, reducing UE energy consumption by 25–30% and improving fairness indices by up to 90%. The proposed system proves scalable and robust, making it suitable for real-time deployment in resource-constrained environments with dynamic workloads.}, archive = {J_ACCESS}, author = {Mohamed El-Emary and Ali Ranjha and Diala Naboulsi and Razvan Stanica}, doi = {10.1109/ACCESS.2025.3584645}, journal = {IEEE Access}, pages = {115178-115195}, shortjournal = {IEEE Access}, title = {Toward energy efficiency and fairness in UAV-based task offloading}, volume = {13}, year = {2025}, }
• (2025). Data-driven displacement-based formation control with range-only measurements. ACCESS, 13, 115167-115177. (www)

  @article{ , abstract = {This paper proposes an algorithm to coordinate a displacement-based formation of a multi-agent system when their local frames are not aligned. Agents can measure neither the relative positions nor orientations, but only the range (distance) to their neighbors. We demonstrate that persistently exciting data exchanged between two agents can be used to infer relative positions and orientations. In the proposed method, we address discrete-time distributed orientation estimation based on an averaging consensus protocol. An asymptotic stability analysis of the desired formation shape is presented and the proposed strategy is validated through simulation results.}, archive = {J_ACCESS}, author = {Soojeong Hyeon and Hyuntae Kim and Hyungbo Shim}, doi = {10.1109/ACCESS.2025.3584829}, journal = {IEEE Access}, pages = {115167-115177}, shortjournal = {IEEE Access}, title = {Data-driven displacement-based formation control with range-only measurements}, volume = {13}, year = {2025}, }
• (2025). Accurate and efficient characterization of a high voltage capacitor by fitting single discharge experiments with a simple circuit theory. ACCESS, 13, 115160-115166. (www)

  @article{ , abstract = {A capacitor is ideally considered as a pure capacitance, but under practical conditions, it also exhibits some resistive and inductive properties, called equivalent series resistance (ESR) and equivalent series inductance (ESL), respectively. These ESR and ESL characteristics are usually ignored in most cases as a first approximation. For high-power and/or high-frequency applications, especially pulsed systems, designed to achieve an accurate and reliable output, it is important to take these parasitic effects into consideration in circuit designs and operations. Conventionally, two capacitors are used, and two discharge tests are required to obtain the ESR and ESL values of the capacitors using a differential method. Besides, the two capacitors have to be identical. This makes an accurate determination very difficult and inefficient. In this work, we introduce a new method that can accurately and efficiently characterize the ESR, ESL, and capacitance of a high-voltage capacitor in a single discharge test employing only one capacitor and a spark gap. The electrical characterization is obtained by fitting the voltage drop across the capacitor and the current flowing through the spark gap during the discharge with simple circuit equations using a multiple linear regression method. Furthermore, the experiments have also been modeled more completely with PSpice simulations, resulting in good agreement and providing better understanding. It is found that the ESR, ESL, and capacitance obtained from the proposed methodology are consistent with the PSpice predictions, within differences of 5.71%, 4%, and 0.92%, respectively, in the demonstrated experiments.}, archive = {J_ACCESS}, author = {Po-Yu Chang and Kaviya Aranganadin and Hua-Yi Hsu and Ming-Chieh Lin}, doi = {10.1109/ACCESS.2025.3584519}, journal = {IEEE Access}, pages = {115160-115166}, shortjournal = {IEEE Access}, title = {Accurate and efficient characterization of a high voltage capacitor by fitting single discharge experiments with a simple circuit theory}, volume = {13}, year = {2025}, }
• (2025). Design and characteristic analysis of a novel compact torsion spring for assistive exoskeleton robots. ACCESS, 13, 115151-115159. (www)

  @article{ , abstract = {To improve traditional rigid exoskeletons in terms of human-machine interaction flexibility and safety, a novel compact torsion spring was developed for lower limb joint assistance in this study. The flexibility of the torsion spring drive was enhanced by the deformation of elastic elements between inner and outer rings and three limiting beams were added between elastic bodies to improve its intensity. Subsequently, the overall quality, maximum stress, and maximum deformation were selected as optimization objectives and the size of the torsion spring was optimized with finite element method. To verify the stable stiffness characteristics of the design scheme under different load conditions, finite element simulation and experimental data were respectively utilized to fit the torque and torsion angle variables in MATLAB. Finally, a performance testing experimental platform for the torsion spring was established to experimentally analyze its stiffness characteristics and position tracking performance. The feasibility of the torsion spring was confirmed through experiments on exoskeleton following human walking.}, archive = {J_ACCESS}, author = {Mingxing Yang and Taohong Jia and Yue Yang and Shiliang Liu and Tao Zhou}, doi = {10.1109/ACCESS.2025.3584646}, journal = {IEEE Access}, pages = {115151-115159}, shortjournal = {IEEE Access}, title = {Design and characteristic analysis of a novel compact torsion spring for assistive exoskeleton robots}, volume = {13}, year = {2025}, }
• (2025). 4D memristive hyperchaotic system for secure image encryption using non-associative algebraic-chaotic sequences. ACCESS, 13, 115135-115150. (www)

  @article{ , abstract = {The potential of discrete memristors to improve chaotic systems for safe communication has been demonstrated by recent advancements. This paper presents a novel four-dimensional (4D) memristive hyperchaotic map that combines a sine map with a two-dimensional logistic-Gaussian hyperchaotic (2D LGH) map through a sinusoidal discrete memristor. The proposed system exhibits high-dimensional chaotic behavior, including hyperchaos and strong randomness, as illustrated through bifurcation diagrams, Lyapunov exponent (LE) analysis, and complexity metrics. We present a high-security image encryption approach based on these features, incorporating the proposed 4D memristive hyperchaotic system with a non-associative LA-group structure. This method generates algebraic-chaotic sequences by mixing non-associative algebraic operations with chaotic sequences, significantly broadening the key space. The encryption framework provides great efficiency, low computing cost, and resilience to cryptographic attacks. Comprehensive security assessments demonstrate the scheme’s efficacy, such as key sensitivity analysis, entropy evaluation, correlation testing, and differential attack resistance. The simulation results show that the suggested technique provides better encryption quality, high unpredictability, and resistance to statistical and differential attacks, making it an appealing choice for secure image transmission and data security in current communication systems.}, archive = {J_ACCESS}, author = {Tanzeela Anwar and Nazli Sanam and Sarah Abu Ghazalah and Mansoor Alghamdi and Ibrahim S. Alkhazi}, doi = {10.1109/ACCESS.2025.3584850}, journal = {IEEE Access}, pages = {115135-115150}, shortjournal = {IEEE Access}, title = {4D memristive hyperchaotic system for secure image encryption using non-associative algebraic-chaotic sequences}, volume = {13}, year = {2025}, }
• (2025). A novel model for authority and access delegation utilizing self-sovereign identity and verifiable credentials. ACCESS, 13, 115102-115134. (www)

  @article{ , abstract = {Self-Sovereign Identity (SSI) is a decentralized, user-centric approach to Identity Management (IdM), that allows users to gain control over their digital identity and data. It brings many advantages and reduces the shortcomings of traditional, centralized IdM systems. Despite its positive features, SSI focuses predominantly on direct interactions between two independent entities. It enables direct identification, authentication, authorization, and access to resources and services where the identity holders are the authenticated bearers of their credentials. On the other hand, it does not address primarily indirect identity control, such as delegation, which is an essential part of life situations and natural human relationships. Delegation is particularly common in organizational contexts, and usually arises from the need for greater efficiency, relief, or distribution of work, collaboration, and cooperation and the inability to perform work, tasks, responsibilities, and duties for various reasons. However, it is relatively unexplored within the SSI framework, and requires additional research efforts and the development of new approaches. Consequently, our research aimed to reduce the aforementioned gap and address delegation in the SSI ecosystem comprehensively. Thus, we present a novel authority and access delegation model utilizing Verifiable Credentials, that enable delegation support for existing use cases, workflows, and processes of organizations and companies, providing resources and services to end users. The model includes delegation building blocks, data objects and data flow, delegation phases and processes, and technology-independent components. It is validated with a case study in the field of Education.}, archive = {J_ACCESS}, author = {Špela Čučko and Muhamed Turkanović}, doi = {10.1109/ACCESS.2025.3582312}, journal = {IEEE Access}, pages = {115102-115134}, shortjournal = {IEEE Access}, title = {A novel model for authority and access delegation utilizing self-sovereign identity and verifiable credentials}, volume = {13}, year = {2025}, }
• (2025). Decentralized voltage and var control of active distribution network based on parameter-sharing deep reinforcement learning. ACCESS, 13, 115090-115101. (www)

  @article{ , abstract = {This work introduces a Parameter Sharing - twin-delay deep deterministic policy gradient (PS-TD3) method for carrying out decentralized voltage and var control in active distribution networks. The more renewable energies are integrated, the more Active Distribution Networks (ADNs) are at risk of instability. Most optimization techniques depend on accurate uncertainty models and take a long time to produce results. By allowing agents to share parts of their neural network, the proposed Parameter Sharing - twin-delay deep deterministic policy gradient algorithm improves the stability and efficiency of voltage regulation. Importantly, the framework makes it possible to add new actions, such as reducing Photovoltaic (PV) output, when traditional ways (adjusting reactive power and turning off wind power) are not enough. Also, when looking at the financial side of energy trading under net metering, Energy Storage System (ESS) operations are organized to even out the costs of charging and discharging. The results from IEEE 33-node simulations show that the approach reduces voltage fluctuations and lowers power loss better than other methods. The findings indicate that parameter-sharing helps agents work together, so large-scale Active Distribution Networks can use decentralized voltage control more efficiently. The framework allows the action space to change by including other control variables, such as reducing photovoltaic output when regular actions such as reactive power increase and wind curtailment do not support voltage stability.}, archive = {J_ACCESS}, author = {Binqiao Zhang and Changlin He}, doi = {10.1109/ACCESS.2025.3584129}, journal = {IEEE Access}, pages = {115090-115101}, shortjournal = {IEEE Access}, title = {Decentralized voltage and var control of active distribution network based on parameter-sharing deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Quantum well-based physical unclonable functions for IoT device attestation. ACCESS, 13, 115079-115089. (www)

  @article{ , abstract = {Internet of Things (IoT) peripheral devices typically operate in resource-constrained environments and platform enclosures. This poses challenges when attempting to secure data traffic between operators and field devices. Due to the resource-intensive nature and/or stringent (e.g., connectivity) requirements of current encryption techniques, many prove infeasible to use within an IoT Edge network. In this work, we have developed a hardware-based attestation and authentication solution for IoT devices operating within constrained Edge environments where compute resource can be scarce, connectivity intermittent, environmental conditions uncontrolled, and accessibility limited. This is achieved through the use of an array of Resonant Tunneling Diodes (RTDs) that facilitate the generation of a ‘strong’ Physical Unclonable Function (PUF) from a limited number of RTDs, reducing the device footprint and resource requirements. Comprehensive statistical analysis of the response of the array to external excitation has shown that the proposed RTD-based PUF can produce a suitably random, yet repeatable, and most importantly unclonable response to a given challenge. Thus, demonstrating the validity of the proposed solution for use within key generation or device authentication of assets within the IoT network.}, archive = {J_ACCESS}, author = {Chris Williamson and Kyle A. Simpson and Douglas J. Paul and Dimitrios P. Pezaros}, doi = {10.1109/ACCESS.2025.3583881}, journal = {IEEE Access}, pages = {115079-115089}, shortjournal = {IEEE Access}, title = {Quantum well-based physical unclonable functions for IoT device attestation}, volume = {13}, year = {2025}, }
• (2025). Transformers for domain-specific text classification: A case study in the banking sector. ACCESS, 13, 115063-115078. (www)

  @article{ , abstract = {The growing volume of unstructured text data in the banking sector has created a need for advanced classification methods to manage customer inquiries efficiently, resulting in faster response times, automated message classification, and reduced human errors. The classification results are integrated into live banking systems, enabling continuous 24/7 message processing and instant categorization. Previously, human operators could process up to 70 messages per day during working hours. With AI integration, the system now categorizes messages automatically and in real time, ensuring that relevant department managers receive them promptly, significantly improving operational workflows. Our research offers key insights into applying transformer models for domain-specific classification in banking. This paper presents a case study on fine-tuning transformer models—BERT, GPT-2, GPT-3, and Falcon-1B for domain-specific text classification in the banking industry. The dataset used in this investigation consists of 5,447 customer messages submitted between 2023 and 2024 through Invest Bankś secure messaging portal, which serves as a direct communication channel where customers can submit their requests at any time of the day without needing to visit the bank in person. The inquiries span fifteen service categories provided by the bank. To the best of our knowledge, no prior studies have focused on automating customer secure messages in the banking sector, especially those written in colloquial and abbreviated language. The models were fine-tuned to improve classification accuracy and operational efficiency. A heterogeneous ensemble of BERT and GPT-2 achieved the best performance with an Area Under the Curve (AUC) score of 99.42% and an F1 score of 86.74%. A homogeneous ensemble of BERT models also performed well, achieving an AUC score of 98.81% and an F1 score of 84.57%. Notably, the single BERT and GPT-2 models, with AUC scores of 97.65% and 97.9%, respectively, delivered competitive performance, making them viable alternatives when computational resources are limited.}, archive = {J_ACCESS}, author = {Samer Murrar and Fatima M. Alhaj and Fadi Almasalha and Mahmoud H. Qutqut}, doi = {10.1109/ACCESS.2025.3585164}, journal = {IEEE Access}, pages = {115063-115078}, shortjournal = {IEEE Access}, title = {Transformers for domain-specific text classification: A case study in the banking sector}, volume = {13}, year = {2025}, }
• (2025). BTCP: Binary temporal convolutional network-based data prefetcher for low inference latency and storage overhead. ACCESS, 13, 115048-115062. (www)

  @article{ , abstract = {Data prefetching is crucial for hiding long-latency memory access on modern high-performance processors. Machine learning (ML)-based data prefetchers have demonstrated superior address prediction performance compared with traditional rule-based prefetchers by learning complex patterns to hide long-latency memory access. However, prioritizing only predictive performance introduces challenges, such as increased inference latency and significant storage demands, when deployed in real hardware systems. To address these issues, we propose a binary temporal convolutional network-based data prefetcher (BTCP) that offers advantages in terms of computational efficiency and memory requirements, enabling feasible hardware implementation. The efficiency of BTCP is fundamentally attributed to its binary temporal convolutional network architecture, which achieves low inference latency and storage overhead by processing memory patterns through efficient binary operations. BTCP aids in detecting memory patterns by processing addresses and program counters through bitwise XOR operations and feeding them into a neural network. We employed positional delta maps to predict the closest future address, integrating address deltas with temporal weights for labeling. BTCP facilitates variable-degree prefetching by incorporating the ratio of irregular addresses into the positional delta map for enhanced training. The design choices result in an inference latency and storage overhead of only 134 cycles and 4,654 bytes, respectively, indicating a substantial decrease of 95.87% and 99.70%, respectively, compared to the leading ML-based prefetcher, TransFetch. With benchmark evaluations using SPEC CPU and GAP, BTCP achieved an IPC improvement of 18.70% over systems without prefetching, surpassing the state-of-the-art rule-based prefetcher Bingo by 2.78% and ML-based prefetcher TransFetch by 0.16%.}, archive = {J_ACCESS}, author = {Chang Ho Ryu and Tae Hee Han}, doi = {10.1109/ACCESS.2025.3585251}, journal = {IEEE Access}, pages = {115048-115062}, shortjournal = {IEEE Access}, title = {BTCP: Binary temporal convolutional network-based data prefetcher for low inference latency and storage overhead}, volume = {13}, year = {2025}, }
• (2025). Dual improvements DCNv4 and AsDDet mechanisms for small object detection in aerial imagery. ACCESS, 13, 115033-115047. (www)

  @article{ , abstract = {This study introduces a dual enhancement mechanism integrating Deformable Convolution v4 (DCNv4) and an Asymmetric Decoupled Detection Head (AsDDet) to significantly improve the detection performance of small objects in high-altitude unmanned aerial vehicle (UAV) imagery using YOLOv11 model architecture. We embedded DCNv4 into YOLOv11 and redesigned its operational modality to establish an efficient and dynamic sparse operator while optimizing memory access processes. This integration enhances both the operational efficiency and overall performance. Furthermore, AsDDet, an efficient and innovative technique, employs an asymmetric dual-branch structure to decouple the classification and regression tasks. This allows the application of distinct convolutional layers tailored to process the positional and complex shape data characteristics of small objects. This dual-improvement design enables the model to accommodate the diverse demands of detection tasks better, thereby increasing the overall detection accuracy. An experimental validation was conducted using a dataset of urban appearance violations, which demonstrated significant improvements in precision, recall, and mean average precision (mAP). Specifically, the precision increased from 2.50% to 84.0%, recall improved from 3.94% to 81.3%, and mAP@0.5, and mAP@0.5:0.95, increased from 2.62% and 4.08% to 87.8% and 61.2%, respectively. Compared with the original YOLOv11 model and other mainstream object detection models, the proposed method exhibited marked enhancements in detection precision, recall, and mAP while effectively reducing both false negatives and false positives. The experimental results substantiate the significant performance gains achieved in UAV-based small-object detection, offering a novel solution for addressing related challenges in the field.}, archive = {J_ACCESS}, author = {Fan Jiang and Hua-Ching Chen and Song-Lin Wei and Hsuan-Ming Feng}, doi = {10.1109/ACCESS.2025.3583696}, journal = {IEEE Access}, pages = {115033-115047}, shortjournal = {IEEE Access}, title = {Dual improvements DCNv4 and AsDDet mechanisms for small object detection in aerial imagery}, volume = {13}, year = {2025}, }
• (2025). SAM2Former: Segment anything model 2 assisting UNet-like transformer for remote sensing image semantic segmentation. ACCESS, 13, 115018-115032. (www)

  @article{ , abstract = {Remote sensing semantic segmentation plays a crucial role in the fields of land cover classification, disaster monitoring, and urban planning. However, due to the high complexity and category imbalance of remote sensing data sets, traditional image segmentation network tend to be slow to train and difficult to capture deep semantic information, which leads to the accuracy failing to meet the requirements of practical applications. Inspired by the powerful transfer learning capability of visual foundation models, we introduce a semantic segmentation network for remote sensing images called SAM2Former, which utilizes the Segment Anything Model 2 (SAM2) to assist a UNet-like Transformer. The SAM2Former is a dual-encoder and single-decoder structure composed of the efficient ResNet18 and SAM2’s Image Encoder as dual encoders, with Transformer as the decoder. Firstly, we incorporate the lightweight Adapter to perform parameter-efficient fine-tuning on SAM2 and design a multi-scale information aggregation module (MIAM) to connect the dual-encoder, which weighted multi-scale features layer by layer of SAM2 Block and CNN, preserving key information in the image. Secondly, we devise a decoder based on global-local transformer module (GLTM) to effectively extract global context information and local detail information, improving the segmentation ability of edge texture. Finally, we construct the feature enhancement module (FEM) to improve the recognition accuracy of similar categories by combining channel and spatial attention features. Moreover, our SAM2Former performed com-prehensive comparative and ablation experiments on the ISPRS Vaihingen and Potsdam datasets, showing notable improvements in segmentation performance.}, archive = {J_ACCESS}, author = {Xuewen Li and Xiaomin Tian and Zihong Wang and Feng Zhang and Yanting Zhang and Na Yang and Chuanzhao Tian}, doi = {10.1109/ACCESS.2025.3583458}, journal = {IEEE Access}, pages = {115018-115032}, shortjournal = {IEEE Access}, title = {SAM2Former: Segment anything model 2 assisting UNet-like transformer for remote sensing image semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Assembly sequence flexibility – Assembly precedence matrix-based evaluation model. ACCESS, 13, 115002-115017. (www)

  @article{ , abstract = {The dimensioning and design of assembly operating equipment (AOE), such as workpiece carriers, assembly devices, and assembly assistive devices, are central to the early phase of many processes in assembly planning. In addition to the traditional economic requirements, production-related factors such as assembly sequence flexibility (ASF) are increasingly important. The characteristics and design of AOE result in restrictions about assembly precedence relationships, which must be considered when assigning assembly contents to workstations. In the assembly planning process, the restrictions imposed by the product can only be influenced to a limited extent. In particular, combining different product variants on a standard assembly system according to the flow principle results in a complex network of assembly precedence relationships that can affect productivity. It is because assembly line balancing can only be implemented to a limited extent, or the compatibility of different product variants within the same assembly system is insufficient. This paper presents a developed general approach using assembly precedence matrices (APMs) that allows further analysis by integrating additional information. This approach evaluates the constraints caused by AOE and analyzes or prioritizes their elimination in terms of ASF. To validate the proposed model, a practical implementation follows in a learning factory, where an exemplary product is evaluated in terms of its ASF when implemented in an assembly system based on the flow principle.}, archive = {J_ACCESS}, author = {Marco Bleckmann and Luca Mastroianni and Meret Börgerding and Peter Nyhuis}, doi = {10.1109/ACCESS.2025.3584447}, journal = {IEEE Access}, pages = {115002-115017}, shortjournal = {IEEE Access}, title = {Assembly sequence flexibility – Assembly precedence matrix-based evaluation model}, volume = {13}, year = {2025}, }
• (2025). Explainable AI meets synthetic data: A deep learning framework for detecting network intrusion in NextG network infrastructure. ACCESS, 13, 114979-115001. (www)

  @article{ , abstract = {In today’s digitally driven world, network security has become a top accountability as cyberattacks become more sophisticated, especially within emerging NextG network infrastructures. Advanced threats, including as zero-day exploits, polymorphic malware, and large-scale distributed denial-of-service (DDoS) attacks, have surpassed traditional Network Intrusion Detection Systems (NIDS), which frequently use out-of-date signature-based methodologies. These conventional methods not only struggle to detect unknown attack patterns but are also hindered by the issue of imbalanced datasets, where minority attack classes are underrepresented and frequently overlooked. To address these challenges, we proposes an innovative NIDS framework tailored for NextG networks that combines Generative Adversarial Networks (GANs) with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. The framework utilizes GANs to generate synthetic samples for minority classes, ensuring a balanced dataset and enhancing the detection of underrepresented attack types. The CNN and LSTM models, applied independently, leverage their respective strengths to extract spatial and temporal features from network traffic, achieving robust classification accuracy. Furthermore, we integrate Local Interpretable Model-Agnostic Explanations (LIME) to make model predictions transparent, increasing trust and usability for practical deployment. Our framework is evaluated on the NF-CSE-CIC-IDS2018 dataset and achieves outstanding results. The LSTM model attains a detection accuracy of 99.67%, while CNN achieves 97.45%. On this GitHub Repository: https://github.com/i-am-junayed/XAI-Intrusion-Detection-System, the entire data analysis and prediction method is available for use by anyone. Here, both models perform exceptionally well in detecting minority attack classes, with the LSTM showing superior consistency across all metrics. This framework’s high accuracy, explainability, and adaptability make it a critical tool for securing dynamic and high-speed NextG networks against evolving cyber threats.}, archive = {J_ACCESS}, author = {Md Junayed Hossain and Khorshed Alam and Md Fahad Monir and Md Mozammal Hoque and Tarem Ahmed}, doi = {10.1109/ACCESS.2025.3585783}, journal = {IEEE Access}, pages = {114979-115001}, shortjournal = {IEEE Access}, title = {Explainable AI meets synthetic data: A deep learning framework for detecting network intrusion in NextG network infrastructure}, volume = {13}, year = {2025}, }
• (2025). The effect of waste bins location in residential area using geographic information system (GIS): A case study in saudi arabia. ACCESS, 13, 114968-114978. (www)

  @article{ , abstract = {The rapid growth of urbanization and high population density have significantly increased waste and pollution, especially in residential areas. In this regard, the current state of household waste management in the Al-Salama neighborhood of Jeddah city has been examined, focusing on inappropriate disposal practices such as waste being thrown outside bins, which have led to significant environmental concerns. This study aims to propose a model utilizing Geographic Information Systems (GIS) to identify the key factors influencing the optimal location of waste bins. A mixed-method approach is adopted, employing ordinary least squares (OLS) regression analysis to examine the contributions of six factors to each other’s. The result of the proposed model shows that the 2-yard capacity of waste bins and 150 km of walking distance positively influence bin locations. Additionally, High-density areas require more bins and discharge frequency to meet the needs of the neighborhood. This model predicts 24 new bin locations and suggests redistributing them along both sides of the roads. Overall, the Variance Inflation Factor (VIF) remains below 7.5, confirming the model’s robustness and validity. Furthermore, focus group techniques were used to evaluate the effectiveness of the proposed model, ensuring that the identified factors are relevant and applicable in real-world scenarios. This study provides municipalities and waste management companies with valuable insights in making appropriate decisions regarding waste bin location while identifying areas with high waste generation and insufficient waste bin coverage. Ultimately, it aims to contribute to the creation of a cleaner and healthier environment for residents.}, archive = {J_ACCESS}, author = {Rahaf Assar and Hind Bitar and Amal Babour}, doi = {10.1109/ACCESS.2025.3581016}, journal = {IEEE Access}, pages = {114968-114978}, shortjournal = {IEEE Access}, title = {The effect of waste bins location in residential area using geographic information system (GIS): A case study in saudi arabia}, volume = {13}, year = {2025}, }
• (2025). An infrared small target detection algorithm utilizing joint local contrast measure. ACCESS, 13, 114959-114967. (www)

  @article{ , abstract = {The effectiveness of infrared small target detection technology depends on high detection accuracy. However, when dealing with complex backgrounds or targets close to the edges of jamming objects, existing algorithms typically encounter significant challenges, such as high false alarm rates and low detection probabilities. To address this issue, we propose a novel measurement scheme utilizing joint local contrast measure based on background enhancement and edge suppression (JLCM-BEES), in which a new scanning window is employed to compute the joint local contrast of infrared images, with the focus on enhancing the target while suppressing the background, a new filtering kernel strengthens the background and suppresses the target, and two related structural elements are used for dilation and differencing operations on the infrared image, retaining the target while effectively suppressing edges with strong contrast. A new fusion method is introduced to combine the three obtained saliency maps, followed by the separation of the target from the background using an adaptive thresholding algorithm. Experimental results indicate that the proposed algorithm demonstrates superior detection capability and robustness when facing complex conditions and low signal-to-clutter ratios, providing new insights for the development of infrared small target detection technology.}, archive = {J_ACCESS}, author = {Qing Zhao and Jiawen Liao and Derui Zhang}, doi = {10.1109/ACCESS.2025.3584513}, journal = {IEEE Access}, pages = {114959-114967}, shortjournal = {IEEE Access}, title = {An infrared small target detection algorithm utilizing joint local contrast measure}, volume = {13}, year = {2025}, }
• (2025). Unraveling the nexus of ML and 6G: Challenges, opportunities, and future directions. ACCESS, 13, 114934-114958. (www)

  @article{ , abstract = {Machine learning (ML) and sixth-generation (6G) wireless networks together present a revolutionary paradigm ready to meet hitherto unheard-of needs in connection, scalability, and intelligence. Including physical-layer signal processing, edge computing, resource optimisation, network slicing, and security, this study provides a thorough and cross-layer study of ML integration into 6G systems. Unlike previous works that concentrate on isolated use cases, this paper systematically classifies and evaluates developing techniques such as federated learning (FL), quantum machine learning (QML), swarm intelligence, and explainable AI within the framework of key 6G performance indicators including ultra-low latency, energy efficiency, and privacy. Presenting a thorough taxonomy and comparative benchmarking, we show the operational trade-offs of centralised, distributed, and meta-learning models. We also find important difficulties like model heterogeneity across ultra-dense networks, adversarial vulnerability in FL, and reconfigurable intelligent surfaces (RIS)-assisted privacy risks. Moreover, we describe open research areas and suggest a single orchestration design to support intelligent, scalable, and reliable 6G systems. Researchers and professionals aiming to create strong, ML-driven wireless infrastructures beyond 5G find basic reference in this work.}, archive = {J_ACCESS}, author = {Mohammed Anis Oukebdane and A. F. M. Shahen Shah and Abul Kalam Azad and John Ekoru and Milka Madahana}, doi = {10.1109/ACCESS.2025.3585051}, journal = {IEEE Access}, pages = {114934-114958}, shortjournal = {IEEE Access}, title = {Unraveling the nexus of ML and 6G: Challenges, opportunities, and future directions}, volume = {13}, year = {2025}, }
• (2025). Analysis of meteorological and soil parameters for predicting ecosystem state dynamics. ACCESS, 13, 114923-114933. (www)

  @article{ , abstract = {This study presents a comprehensive quantitative analysis of the interplay between meteorological variables and soil conditions over the period 2018–2023 in the Almaty region. The investigation is grounded in high-resolution meteorological observations encompassing air temperature (mean annual variations ranging from −5°C to +30°C), atmospheric pressure (average values between 681 and 685 hPa), and wind velocity (0.5–12 m/s). Additionally, a systematic evaluation of soil characteristics was conducted to assess seasonal fluctuations in soil composition and physicochemical properties across spring, summer, and autumn. By employing advanced statistical methodologies, significant correlations between meteorological dynamics and soil parameters were elucidated. The findings reveal that the most pronounced deviations in soil conditions occur during the spring season, exhibiting a deviation coefficient of 0.7896, whereas the summer season demonstrates the most substantial negative deviation at -0.9566. Furthermore, the predictive model developed within this study exhibits high precision, yielding a coefficient of determination (R2) of 0.85, thereby enabling not only the real-time assessment of ecosystem status but also the reliable forecasting of its temporal evolution. The novelty of this research lies in the integration of classical Navier-Stokes equations with contemporary big data analytics, facilitating a sophisticated representation of atmospheric flow dynamics and their consequential impact on soil properties. This interdisciplinary approach enhances the accuracy of predictive environmental modeling, offering a robust framework for ecosystem monitoring and management.}, archive = {J_ACCESS}, author = {Lyazat Naizabayeva and Saberikamarposhti Morteza and Nurgul Seilova}, doi = {10.1109/ACCESS.2025.3585232}, journal = {IEEE Access}, pages = {114923-114933}, shortjournal = {IEEE Access}, title = {Analysis of meteorological and soil parameters for predicting ecosystem state dynamics}, volume = {13}, year = {2025}, }
• (2025). Text-guided visual representation learning via cross-modal fusion for person re-identification. ACCESS, 13, 114911-114922. (www)

  @article{ , abstract = {Person Re-identification (Re-ID) aims at accurately querying pedestrians across multiple non-overlapping cameras system, playing an essential role in computer vision applications. While CNN-based methods leverage feature aggregation and attention mechanisms to achieve competitive performance, their limitations in capturing long-range dependencies have motivated the research of transformer-based architectures, which excel in the evolution of person Re-ID research. Despite leveraging pretrained vision-language models to facilitate visual encoder training, current methods suffer from fragmented optimization procedures, complexity increasing, and inadequate semantic guidance from textual prompts. To address these limitations, we propose an end-to-end method named Text-Guided Fusion Transformer (TGFT). TGFT applies the fixed but semantically enriched texts to guide visual training with a novel Gated Cross Attention (GCA) fusion module. The GCA module adaptively modulates the integration of textual information and visual features, effectively enhancing cross-modal feature alignment and semantic consistency. Without significantly increasing computational complexity or parameter count, TGFT achieves superior performance, demonstrating strong effectiveness and scalability on multiple major Re-ID benchmarks.}, archive = {J_ACCESS}, author = {Ge Cao and Qing Tang and Xuan-Thuy Vo and Adri Priadana and Tien-Dat Tran and Kang-Hyun Jo}, doi = {10.1109/ACCESS.2025.3584377}, journal = {IEEE Access}, pages = {114911-114922}, shortjournal = {IEEE Access}, title = {Text-guided visual representation learning via cross-modal fusion for person re-identification}, volume = {13}, year = {2025}, }
• (2025). Compact H-plane CLAF-SIW horn antenna with phase front correction. ACCESS, 13, 114901-114910. (www)

  @article{ , abstract = {This work presents an H-plane contactless air-filled substrate-integrated waveguide (CLAF-SIW) horn antenna, whose directivity is enhanced by incorporating a metalens along the horn flaring. By tuning the distribution of the unit cells of the metalens, it is possible to reduce phase errors at the aperture, a common issue in horn antennas. The unit cell of the metalens is designed by using a metallic patch on a substrate-integrated hole. By varying the size of the metal patches, different refraction indices can be tuned, resulting in a wideband performance. An analysis of the distribution of these unit cells along the horn antenna is conducted to achieve the desired equivalent effective refraction index through the metalens. The directivity achieved is comparable to that of a horn antenna with optimal dimensions, but with a 41% reduction in length. The measured results demonstrate an impedance bandwidth below −10 dB from 32.8 to 50 GHz, with a realized gain above 9.3 dBi.}, archive = {J_ACCESS}, author = {Andrés Biedma-Pérez and Cleofás Segura-Gómez and Ángel Palomares-Caballero and Pablo Padilla}, doi = {10.1109/ACCESS.2025.3585567}, journal = {IEEE Access}, pages = {114901-114910}, shortjournal = {IEEE Access}, title = {Compact H-plane CLAF-SIW horn antenna with phase front correction}, volume = {13}, year = {2025}, }
• (2025). Personalized solutions for foot health: Machine learning-based foot condition detection, classification, and recommendation of customized footwear. ACCESS, 13, 114880-114900. (www)

  @article{ , abstract = {Foot diseases such as plantar fasciitis and flat feet occur in millions of people worldwide, resulting in mobility problems and serious health complications. This paper presents an AI-based approach that uses machine learning to identify foot ailments, classify foot conditions, and suggest the right footwear. The approach integrates clinical gait analysis, symptom diagnosis, and customized footwear suggestions with synthetically generated images, which aid in the production of the footwear. The proposed work is based on three large datasets: (1) Grayscale pressure sensor heat maps for foot posture, with high-resolution foot pressure maps that capture weight distribution and posture; (2) Clinically Validated Foot Condition Dataset, comprising foot conditions verified by physiotherapists and linked to real symptoms; and (3) Footwear Recommendation Dataset for Specific Foot Conditions, with expert-curated footwear suggestions tailored to various foot conditions for optimal support and comfort. The framework consists of three modules: a CNN-based VGG-16-GRU model which identifies gait posture based on pressure sensor heatmaps, an autoencoder-based random forest model which classifies foot diseases based on the detected gait posture, and an LSTM-ensembled XGBoost model which recommends features of suggested footwear. The recommended footwear features obtained from this model is then synthetically generated to visually perceive the suggested footwear. The experimental results show excellent performance with detecting and recommending shoe designs. Additionally, Stable Diffusion-based synthetically generated footwear images provide improved personalization and recommendations for footwear. This study proposes a method to design customized shoes for foot conditions by leveraging AI-generated designs, biomechanical analyses, and material optimization.}, archive = {J_ACCESS}, author = {Sonaa Rajagopal and Muralikrishnan Mani and Shyam Venkatraman and R. Suganya}, doi = {10.1109/ACCESS.2025.3584966}, journal = {IEEE Access}, pages = {114880-114900}, shortjournal = {IEEE Access}, title = {Personalized solutions for foot health: Machine learning-based foot condition detection, classification, and recommendation of customized footwear}, volume = {13}, year = {2025}, }
• (2025). AdaptedNorm: An adaptive modeling strategy for graph convolutional network-based deep learning tasks. ACCESS, 13, 114865-114879. (www)

  @article{ , abstract = {Graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have demonstrated remarkable success in modeling graph-structured data across diverse applications. A critical yet underexplored aspect of GCN design lies in graph representation normalization, where the choice of normalization scheme significantly influences model performance on learning tasks. Despite its importance, existing research lacks systematic analysis on determining optimal task-specific normalization strategies. This study proposes AdaptedNorm, a principled framework that establishes connections between normalization schemes and GCN task objectives, while introducing three key contributions: 1) A novel random walk re-normalized transformation (RWRT) technique for graph representation normalization; 2) A task-aligned modeling paradigm demonstrating that symmetric re-normalized transformation (SRT) enhances node classification accuracy, while RWRT achieves superior performance in link prediction; 3) Integration of normalization schemes with GNN sparsification strategies, enabling effective model compression without sacrificing performance. Extensive experiments on benchmark datasets (Cora, Citeseer, and PubMed) confirm the generalizability of the findings. The implementation is publicly available at: https://www.github.com/ChuanDai/AdaptedNorm.}, archive = {J_ACCESS}, author = {Chuan Dai and Yajuan Wei and Hao Wang and Ying Liu and Zhijie Xu}, doi = {10.1109/ACCESS.2025.3584523}, journal = {IEEE Access}, pages = {114865-114879}, shortjournal = {IEEE Access}, title = {AdaptedNorm: An adaptive modeling strategy for graph convolutional network-based deep learning tasks}, volume = {13}, year = {2025}, }
• (2025). Deep neural networks in smart grid digital twins: Evolution, challenges, and future outlooks. ACCESS, 13, 114845-114864. (www)

  @article{ , abstract = {The increasing demand for reliable, sustainable, and efficient energy systems is accelerating the adoption of innovative technologies in the power sector. Among these advancements, the integration of Deep Neural Networks (DNNs) with Digital Twins (DTs) emerged as a groundbreaking innovation, particularly in smart grids (SGs). DNNs, subset of artificial intelligence, process complex datasets and are able to identify intricate patterns, proving invaluable for predictive analytics, optimization, and real-time decision-making. DTs serve as dynamic virtual representations of physical grid systems, enabling engineers to monitor, simulate, and optimize grid performance with outstanding precision. This strategic review presents the evolution of DNNs in Smart Grid DTs (SGDTs), highlighting their transformative role in enhancing grid operations, outlining the significant challenges that hindered their widespread adoption, and providing a forward-looking perspective on their future potential in revolutionizing energy systems. The scope of this study encompasses exploring evolution of smart grids, the advancements in communication systems within SGDTs, and the integration of DNNs in SGDTs by taking lessons from existing DT architectures and case studies. It also categorized challenges into physical, intelligent, and virtual layers, to emphasize hybrid DNNs’ role in addressing big data and intermittent energy issues.}, archive = {J_ACCESS}, author = {Shewit Tsegaye and Kifle Godana Heyi and Mimi Tsegaye Endaylalu and Zemenu Addis Melaku and Kasech Tsegaye Turufi}, doi = {10.1109/ACCESS.2025.3585967}, journal = {IEEE Access}, pages = {114845-114864}, shortjournal = {IEEE Access}, title = {Deep neural networks in smart grid digital twins: Evolution, challenges, and future outlooks}, volume = {13}, year = {2025}, }
• (2025). RUL prediction based on MBGD-WGAN-GRU for lithium-ion batteries. ACCESS, 13, 114834-114844. (www)

  @article{ , abstract = {To address the challenges associated with acquiring complete charge-discharge cycle data and extracting health indicator factors (IHFs) from fragmented datasets in current automotive lithium-ion batteries (LIBs), this study proposes a novel online remaining useful life (RUL) prediction method. First, the IHF, which captures battery aging characteristics, is extracted from raw LIBs data, and the dataset is partitioned into training (70%) and testing (30%) subsets. Subsequently, the mini-batch stochastic gradient descent algorithm is employed to optimize a Wasserstein generative adversarial network, thereby augmenting the training set and enhancing data diversity. The extracted IHF and expanded training set are then used to train a gated recurrent unit (GRU) model, leveraging GRU’s strengths in sequential data processing to improve the characterization of battery aging trends. Finally, the proposed GRU-based model is validated for RUL prediction using an open-source test dataset. Experimental results demonstrate that the mean absolute error of the proposed method on B0005, B0006, and B0007 batteries is 0.0023 Ah, 0.0030 Ah, and 0.0014 Ah, respectively, confirming its effectiveness and practical applicability. This approach provides robust technical support for LIBs health management.}, archive = {J_ACCESS}, author = {Zhiguo Zhao and Ke Li and Yibo Dai and Biao Chen and Yeqin Wang and Qian Zhao}, doi = {10.1109/ACCESS.2025.3585036}, journal = {IEEE Access}, pages = {114834-114844}, shortjournal = {IEEE Access}, title = {RUL prediction based on MBGD-WGAN-GRU for lithium-ion batteries}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of machine learning-based personality trait detection using electroencephalographic data. ACCESS, 13, 114812-114833. (www)

  @article{ , abstract = {Traditional self-report personality assessments, such as detecting Big Five personality traits through the NEO-Five-Factor-Inventory, are personnel-, time- and cost-intensive approaches that are prone to bias, whereas electroencephalography provides a biometric alternative by capturing neural correlates of personality. That is why machine learning-based detection of personality traits using electroencephalographic data has gained increasing attention. The integration of machine and deep learning algorithms has improved automated trait classification but the comparative reliability of these approaches remains unclear. This systematic literature review examines if trait detection is possible by using electroencephalography in combination with machine and deep learning models, analyzing 58 studies since 2015. We compare their performance by a meta-analysis of weighted data, highlighting strengths and limitations. Our findings indicate that machine and deep learning models achieve varying degrees of accuracy in detecting different personality traits, with Openness demonstrating the highest classification accuracy, while Neuroticism remains the most challenging to detect. Deep learning models, particularly hybrid architectures, outperform traditional machine learning classifiers, highlighting the advantage of deep feature extraction in EEG data processing. However, methodological inconsistencies, limited dataset standardization and the influence of transient emotional states pose significant challenges. Despite these limitations, machine and deep learning models based on electroencephalography show promise for applications in mental health, recruitment and personalized interventions. Further research is needed to standardize methods and improve model reliability for real-world application.}, archive = {J_ACCESS}, author = {Celina Rieck and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3586005}, journal = {IEEE Access}, pages = {114812-114833}, shortjournal = {IEEE Access}, title = {A systematic literature review of machine learning-based personality trait detection using electroencephalographic data}, volume = {13}, year = {2025}, }
• (2025). Multi-modal feature set-based detection of freezing of gait in parkinson’s disease patients using SVM. ACCESS, 13, 114798-114811. (www)

  @article{ , abstract = {Parkinson’s disease (PD) ranks as the second most prevalent neurodegenerative disorder, leading to motor complications, particularly in the elderly. Among its many symptoms, Freezing of Gait (FoG) affects nearly 50% of PD patients globally, leading to an increased risk of falls. Despite the widespread use of uni-modal FoG detection, it does not produce satisfactory results. This work aims to detect FoG using a multi-modal feature set. A publicly available multi-modal dataset was used, comprising data collected from 12 patients using various sensors, including Electroencephalogram (EEG), Electromyography (EMG), tri-axial ACC (accelerometers and gyroscopes), and Skin Conductance (SC) sensors. First, the data were pre-processed and segmented into 3-second windows with 90% overlap. The segmented windows were labeled based on the various percentage of FoG points (PFG) thresholds. Then, time-, frequency-, and time-frequency-based features were extracted and fed to the Support Vector Machine (SVM) model. These features included statistical, wavelet transform-based Shannon entropy, and Fast Fourier transform-based features. The results demonstrated that the multi-modal combination EMG + ACC, with a PFG of 0.75, outperformed all other sensor combinations, achieving the f1 score (binary) of 98.82%, highlighting the significant impact of ACC on FoG detection. It surpasses the state-of-art study, which reported an f1 score of 93.49%. Additionally, the combination of the right shank, waist, and arm within the acceleration sensors yielded the best f1 score of 99.19%. Furthermore, this work shows the power of synergistic concatenation of uni-modal data to enhance the model performance for FoG detection tasks.}, archive = {J_ACCESS}, author = {Ghulam Murtaza and Mohammed Hammoud and Andrey Somov}, doi = {10.1109/ACCESS.2025.3585099}, journal = {IEEE Access}, pages = {114798-114811}, shortjournal = {IEEE Access}, title = {Multi-modal feature set-based detection of freezing of gait in parkinson’s disease patients using SVM}, volume = {13}, year = {2025}, }
• (2025). Measurement-based analysis of 5G cellular network interference on radar altimeters and joint power-angle control mitigation strategy. ACCESS, 13, 114785-114797. (www)

  @article{ , abstract = {The global deployment of 5G technology, particularly in the C-band (3.4-4.2 GHz), has introduced critical interference challenges for safety-critical avionics, such as radar altimeters (RAs), which are essential for safe aircraft navigation. This study investigates the impact of 5G base stations (BS) on RAs through airborne measurements conducted using a helicopter above and around a 5G BS. Signal strength and interference patterns were captured at various altitudes and distances, illustrating how out-of-band (OOB) and spurious emissions from 5G signals can interfere with RA readings. An optimization model was developed to minimize interference through joint power and angle control at the BS, balancing the trade-off between 5G signal quality and aviation safety. The proposed method significantly reduces interference while maintaining a minimum quality of service (QoS) for 5G users. Simulation results demonstrate that, compared to power-only control, joint control improves signal quality by approximately 15 dB while reducing interference, suggesting a viable solution for harmonizing 5G and aviation requirements. This work offers a novel approach to mitigating 5G interference on RAs and provides valuable insights for improving the coexistence of these critical systems.}, archive = {J_ACCESS}, author = {Zahra Rostamikafaki and Francois Chan and Claude D’Amours}, doi = {10.1109/ACCESS.2025.3582024}, journal = {IEEE Access}, pages = {114785-114797}, shortjournal = {IEEE Access}, title = {Measurement-based analysis of 5G cellular network interference on radar altimeters and joint power-angle control mitigation strategy}, volume = {13}, year = {2025}, }
• (2025). Research and application of power dispatching speech recognition based on deep neural networks. ACCESS, 13, 114772-114784. (www)

  @article{ , abstract = {Due to the computational resource consumption of the decoding module and the impact of a noisy environment on speech recognition system, this paper designs a real-time multi-stage speech preprocessing algorithm. The algorithm changes the traditional streaming recognition mode of detecting endpoints while recognizing. It reduces the decoding power consumption of the system by first performing endpoint detection and then decoding module. For wake-up decoding and recognition decoding, the real-time multilevel speech preprocessing algorithm significantly reduces the real-time rate and power consumption of the entire speech recognition system, and at the same time ensures that the impact on the wake-up rate and word error rate is negligible. The real-time rate is reduced by 27%-37% and the power consumption of the whole system is reduced by about 25%.}, archive = {J_ACCESS}, author = {Haitao Yu and Xuqiang Wang and Jian Zheng and Xiaoxi Zhou}, doi = {10.1109/ACCESS.2025.3584800}, journal = {IEEE Access}, pages = {114772-114784}, shortjournal = {IEEE Access}, title = {Research and application of power dispatching speech recognition based on deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Prescribed-time cooperative output regulation by bounded observer-based time-varying protocols. ACCESS, 13, 114756-114771. (www)

  @article{ , abstract = {This research focuses on the prescribed-time cooperative output regulation problem within linear multi-agent systems subject to input constraints. The key contribution is the design of a distributed observer-based bounded time-varying state controller and an output feedback protocol. Specifically, to address scenarios where the external system state is only accessible to partial subsystems, the proposed controller integrates a distributed observer framework with gain matrices derived from the regulator equation and a parametric Lyapunov equation, thereby ensuring both stability and adaptability under limited state information. The contribution is remarkable as it concurrently addresses the prescribed-time convergence and input constraints, which are often treated independently in existing literature. This work lays a theoretical groundwork for complex network systems, such as the cooperative combat scenarios of multiple unmanned aerial vehicles and coordinated attacks of multiple missiles. The practical value of the proposed algorithm is verified through two simulation cases of a linear multi-agent system with one leader and four followers, showcasing its efficacy and potential in real applications.}, archive = {J_ACCESS}, author = {Jiaxin Zhou and Yuan Tian and Yan Li and Sidong Wu}, doi = {10.1109/ACCESS.2025.3553850}, journal = {IEEE Access}, pages = {114756-114771}, shortjournal = {IEEE Access}, title = {Prescribed-time cooperative output regulation by bounded observer-based time-varying protocols}, volume = {13}, year = {2025}, }
• (2025). The optimization of illustration design in cultural and creative products for liaoning region under intelligent generative adversarial network. ACCESS, 13, 114746-114755. (www)

  @article{ , abstract = {This study explores the application of intelligent Generative Adversarial Networks (GANs) in illustration design and cultural and creative product design in Liaoning. It aims to enhance design efficiency and quality while promoting the inheritance and innovation development of local cultural heritage. The study employs CycleGAN combined with self-attention mechanisms to conduct style transfer experiments on cultural illustrations featuring Liaoning’s regional characteristics. It compares the performance of six models, including traditional GAN, CycleGAN, and CycleGAN + Self-attention, in terms of image quality and training efficiency. The results show that the CycleGAN + Self-attention model outperforms others in image quality indicators (PSNR and SSIM). On the Liaoning illustration dataset, this model achieves a PSNR of 30.5 dB and an SSIM of 0.96, which are 10 dB and 0.21 higher than traditional GANs. Tests across diverse datasets also demonstrate its robust generalization and adaptability. The proposed CycleGAN + Self-attention model performs well in illustration and cultural product design, generating high-quality illustrations with local cultural features. Beyond enhancing design efficiency, this model offers new technical means for cultural inheritance. Overall, this study furnishes novel ideas and methods for integrating intelligent GANs into art design practices. It helps promote Liaoning’s cultural heritage and innovation, and supports the development of the cultural and creative industry.}, archive = {J_ACCESS}, author = {Minghui Niu and Ying Zhou}, doi = {10.1109/ACCESS.2025.3583506}, journal = {IEEE Access}, pages = {114746-114755}, shortjournal = {IEEE Access}, title = {The optimization of illustration design in cultural and creative products for liaoning region under intelligent generative adversarial network}, volume = {13}, year = {2025}, }
• (2025). Enabling eye tracking for crowd-sourced data collection with project aria. ACCESS, 13, 114736-114745. (www)

  @article{ , abstract = {Through a novel, multi-sensor platform in a wearable glasses form factor, paired with crowd-sourced data collection, we have enabled the collection of tens of thousands of egocentric gaze recordings across hundreds of daily tasks without the limitations posed by traditional eye-tracking or lab research. The eye tracking model that we describe and open source here was created with a large study of 1500 participants performing eye tracking calibration. Data collection utilized the device sensor suite, allowing us to leverage scene and contextual information across various environments and tasks. This paper will discuss the systems in place and technical steps we have taken to enable eye tracking for the project, and select lessons learned in the process. This large-scale data collection tool and open source eye tracking model will enable advances in mobile and pervasive eye tracking to understand nuances in eye movements and various future AI and eye-tracking applications.}, archive = {J_ACCESS}, author = {Yusuf Mansour and Ajoy Savio Fernandes and Kiran Somasundaram and Tarek Hefny and Mahsa Shakeri and Oleg V. Komogortsev and Abhishek Sharma and Michael J. Proulx}, doi = {10.1109/ACCESS.2025.3583623}, journal = {IEEE Access}, pages = {114736-114745}, shortjournal = {IEEE Access}, title = {Enabling eye tracking for crowd-sourced data collection with project aria}, volume = {13}, year = {2025}, }
• (2025). Securing electrical drive systems against man-in-the-middle attacks using S-box optimized AES encryption. ACCESS, 13, 114716-114735. (www)

  @article{ , abstract = {The Protection of electric drive systems is significant in modern industrial applications to ensure reliable operations. These systems consist of multiple interconnected control units that communicate wirelessly, making them vulnerable to cyber threats. This paper focuses on ensuring secure end-to-end communication within a drive system that includes a 2.2 kW induction motor drive controlled by the F28379D microcontroller and connected through the ESP8266 Wi-Fi microcontroller. To address the security challenges of such interconnected systems, this study proposes a solution utilizing an optimized Advanced Encryption Standard (AES) algorithm to safeguard data against man-in-the-middle (MitM) attacks. The microcontroller is connected to the cloud through the ThingSpeak platform for control program deployment and drive parameter monitoring. The cloud environment handles both private and public data sources, which introduces vulnerabilities to cyber-attacks. This vulnerability is mitigated by the encryption and decryption processes. The security of the AES encryption is enhanced through diffusion and confusion mechanism, using the optimized S-box, which adds confusion during the substitution phase. The optimized S-box is generated using the Differential Evolution (DE) algorithm for encrypting data within the communication network to mitigate data visibility. The optimization performance is evaluated using nonlinearity, avalanche effect, and bit independence metrics, while the AES algorithm’s performance is analyzed in terms of encryption throughput and latency. Results indicate that the nonlinearity of the DE-optimized S-box is slightly lower compared to the genetic algorithm (GA)- optimized S-box, with similar trends observed for the avalanche effect and bit independence criterion. However, the DE-optimized S-box demonstrates superior performance in encryption throughput and latency for various data sizes compared to the GA-optimized S-box. This demonstrates that the proposed optimization algorithm can handle data encryption in less time, which is important for real-time applications in industrial drive systems.}, archive = {J_ACCESS}, author = {J. Dheeba and Vansh Oberoi and R. Raja Singh and V. Gautam Karthik}, doi = {10.1109/ACCESS.2025.3584926}, journal = {IEEE Access}, pages = {114716-114735}, shortjournal = {IEEE Access}, title = {Securing electrical drive systems against man-in-the-middle attacks using S-box optimized AES encryption}, volume = {13}, year = {2025}, }
• (2025). AEMS: Adaptive ensemble GNNs for multibehavior stream recommendation. ACCESS, 13, 114696-114715. (www)

  @article{ , abstract = {Traditional systems often focus on a singular action type, such as a purchase, while overlooking the rich behavioral pluralism that is inherent in user engagement, such as the transient clicks or latent purchase intents signaled by cart additions. This narrow focus leads to sparse datasets and hinders recommendation accuracy. In response to the evolving landscape of real-time recommendation systems, contemporary frameworks are increasingly leveraging dynamic user engagement streams that span actions, such as purchasing, item retention, and exploratory browsing, to refine suggestions. To bridge this gap, we introduce a novel and revolutionary model: adaptive ensemble graph neural networks (GNNs), for multibehavior stream recommendation (AEMS). The AEMS synergizes long-term preference patterns (derived from historical interactions) with real-time user intents and item attributes (captured through multibehavior signals), integrating them via an adaptive ensemble neural gating mechanism. This design effectively fuses multifaceted behaviors and addresses the challenges of data sparsity and heterogeneity. This architecture is further enhanced by a behavior-aware training paradigm that dynamically weights heterogeneous interactions, such as clicks, add-to-cart actions, likes, and other behavioral signals, to increase its recommendation precision. Extensive experiments conducted on real-world datasets demonstrate that the AEMS significantly outperforms the state-of-the-art methods, demonstrating its ability to decode complex, high-order synergies in streaming environments.}, archive = {J_ACCESS}, author = {Ritchie Natuan Caibigan and Punyaphol Horata and Pusadee Seresangtakul}, doi = {10.1109/ACCESS.2025.3583425}, journal = {IEEE Access}, pages = {114696-114715}, shortjournal = {IEEE Access}, title = {AEMS: Adaptive ensemble GNNs for multibehavior stream recommendation}, volume = {13}, year = {2025}, }
• (2025). Enhancing engineering and STEM education with vision and multimodal large language models to predict student attention. ACCESS, 13, 114681-114695. (www)

  @article{ , abstract = {Generative Artificial Intelligence (AI) and Large Language Models (LLMs), including Visual Language Models (VLMs) and Multimodal LLMs (MLLMs), have shown transformative potential in education. These technologies address persistent challenges in fostering classroom engagement and interaction. Our study highlights the efficacy of these models in detecting students’ attention levels and emotional states, equipping educators with actionable insights to optimize instructional delivery. However, widespread adoption is hindered by significant barriers such as high computational demands and the limited availability of high-quality datasets. To overcome these challenges, this research proposes the integration of MLLMs with Few-Shot Learning techniques, offering a resource-efficient framework to enable their practical implementation in educational contexts. This study focuses on the application of VLMs and MLLMs to predict student attention in science, technology, engineering and mathematics (STEM) education, evaluating the effectiveness of Few-Shot Training compared to traditional AI methodologies. The research is structured into two phases: the first phase optimizes image frequency and computational costs using MLLMs, while the second phase trains VLMs on classroom data to identify visual cues, including gaze direction and head movement. The results demonstrate that VLMs combined with Few-Shot Learning significantly outperform traditional models in capturing nuanced visual data, allowing for pedagogical adjustments comparable to those made through human labeling. These findings underline the transformative potential of VLMs and MLLMs in education, particularly in resource-constrained environments. Few-Shot Learning emerges as a practical and effective approach for leveraging small datasets to enhance student engagement and instructional quality.}, archive = {J_ACCESS}, author = {Luis Marquez-Carpintero and Diego Viejo and Miguel Cazorla}, doi = {10.1109/ACCESS.2025.3584025}, journal = {IEEE Access}, pages = {114681-114695}, shortjournal = {IEEE Access}, title = {Enhancing engineering and STEM education with vision and multimodal large language models to predict student attention}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of machine learning algorithms for antenna alignments. ACCESS, 13, 114669-114680. (www)

  @article{ , abstract = {In the rapidly evolving field of radio frequency (RF) engineering, precise antenna alignment remains a critical challenge, directly influencing communication performance and reliability. This study presents a comprehensive comparative analysis of three advanced machine learning models—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP)—to predict antenna alignments based on S-parameter measurements. By addressing the complexities of both distance and angle predictions, our approach demonstrates the efficacy of these neural network architectures in automating alignment tasks traditionally reliant on manual intervention. The results reveal that the MLP and LSTM models excel in distance prediction, achieving a remarkable accuracy of 97%, while the CNN model outperforms in angle prediction with an accuracy of 86%. This is the first work to propose a data-driven antenna alignment method based solely on S-parameter analysis, offering a cost-effective and scalable alternative to complex imaging or optical systems. The approach significantly reduces setup complexity while maintaining high prediction accuracy, making it well-suited for deployment in practical RF systems. The study further highlights the strengths and limitations of each model, offering valuable insights into their applicability for RF engineering tasks. By providing a robust machine learning framework, this research contributes significantly to advancing automated alignment processes, reducing dependency on manual methods, and paving the way for future innovations in RF systems. These findings have far-reaching implications for the development of intelligent and scalable solutions in wireless communication systems.}, archive = {J_ACCESS}, author = {Mohammad Al Bataineh and Dana I. Abu Abdoun and Mahmoud Al Ahmad}, doi = {10.1109/ACCESS.2025.3585952}, journal = {IEEE Access}, pages = {114669-114680}, shortjournal = {IEEE Access}, title = {Comparative analysis of machine learning algorithms for antenna alignments}, volume = {13}, year = {2025}, }
• (2025). Efficient protocol to discriminate multiple ballistic targets. ACCESS, 13, 114657-114668. (www)

  @article{ , abstract = {In this paper, we propose a novel protocol for discriminating multiple ballistic targets during the midcourse and terminal phases of their trajectories. The midcourse-phase protocol includes the following steps: 1) estimating the number of targets, 2) separating the trajectories, and 3) performing discrimination using either a convolutional neural network (CNN) or a nearest-neighbor classifier. The terminal-phase protocol performs discrimination using trajectory information or a CNN. Simulations that consider radar signals calculated using physical optics, indicate that the proposed protocol effectively discriminates between warheads and decoys in scenarios that involve multiple targets.}, archive = {J_ACCESS}, author = {Min Kim and Sewon Yoon and Inoh Choi and Jooho Jung and Sanghong Park}, doi = {10.1109/ACCESS.2025.3584058}, journal = {IEEE Access}, pages = {114657-114668}, shortjournal = {IEEE Access}, title = {Efficient protocol to discriminate multiple ballistic targets}, volume = {13}, year = {2025}, }
• (2025). Advancing multivariate time series similarity assessment: An integrated computational approach. ACCESS, 13, 114639-114656. (www)

  @article{ , abstract = {Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key components, each addressing essential aspects of time series similarity assessment, offering a comprehensive framework for analysis. To evaluate the effectiveness of MTASA, we conducted an empirical study focused on assessing agroecological similarity, a key aspect of climate smart agriculture, using real-world environmental data. The results from this study highlight MTASA’s superiority, achieving approximately 1.5 times greater accuracy and twice the speed compared with existing state-of-the-art integrated frameworks for multivariate time series similarity assessment. It is hoped that MTASA will significantly enhance the efficiency and accessibility of multivariate time series analysis, benefitting researchers and practitioners across various domains. Its capabilities in handling large datasets, addressing temporal misalignments, and delivering accurate results make MTASA a valuable tool for deriving insights and aiding decision-making processes in complex systems.}, archive = {J_ACCESS}, author = {Franck B. N. Tonle and Henri E. Z. Tonnang and Milliam M. Z. Ndadji and Maurice Tchoupé Tchendji and Armand Nzeukou and Kennedy Senagi and Saliou Niassy}, doi = {10.1109/ACCESS.2025.3584712}, journal = {IEEE Access}, pages = {114639-114656}, shortjournal = {IEEE Access}, title = {Advancing multivariate time series similarity assessment: An integrated computational approach}, volume = {13}, year = {2025}, }
• (2025). Computational overhead across prioritizing bioinformatics traffic packet operations by differentiated services code point (DSCP). ACCESS, 13, 114622-114638. (www)

  @article{ , abstract = {The purpose of this research is to examine the results of applying DSCP in order to learn how it affects bioinformatics network efficiency and performance under different types of bioinformatics traffic. The consistency of the performance across all types of bioinformatics traffic was demonstrated by the baseline data, which did not use DSCP. A gigabit per second throughput was accompanied by an average of thirty milliseconds of delay, ten milliseconds of jitter, and two percent of packet loss. When DSCP was put into place, performance metrics for high-priority traffic saw a marked improvement due to the drastic decrease in latency, jitter, and packet loss. Bioinformatics video conferencing and voice over IP are examples of this kind of data transfer. When bioinformatics traffic was both changing and increasing, the benefits were apparent. The findings suggest that DSCP may be able to prioritizes key bioinformatics applications in an effective manner, which results in an improvement in Quality of Service (QOS)and make the most of available bandwidth. However, when it comes to bioinformatics traffic with a lower priority, the network sees greater latency and jitter performance. The outcomes of the study indicate that DSCP is a vital strategy for increasing bioinformatics network performance, particularly in environments where the volume of bioinformatics traffic is unpredictable. This ensures that applications that are essential will receive service that is dependable and of the highest quality.}, archive = {J_ACCESS}, author = {Ala Abdulsalam Alarood}, doi = {10.1109/ACCESS.2025.3585459}, journal = {IEEE Access}, pages = {114622-114638}, shortjournal = {IEEE Access}, title = {Computational overhead across prioritizing bioinformatics traffic packet operations by differentiated services code point (DSCP)}, volume = {13}, year = {2025}, }
• (2025). Addressing connectivity challenges in solar home systems: A monitoring solution for rural electrification. ACCESS, 13, 114608-114621. (www)

  @article{ , abstract = {Access to reliable energy remains a significant challenge in rural areas of developing countries, where Solar Home Systems (SHS) are widely deployed to address electrification gaps. However, these systems often lack monitoring capabilities, making operation and maintenance costly and inefficient. This study presents a hybrid monitoring and communication solution designed to overcome the unique challenges of SHS in rural contexts, including limited connectivity, cost constraints, and energy efficiency. The proposed system integrates a robust sensing architecture with a hybrid communication network combining LoRa and mobile technologies to enable real-time monitoring and data transmission. The system was tested in a pilot project conducted in the Ngäbe Buglé comarca of Panama, where eight SHS units were retrofitted with the proposed system. Results demonstrated the system’s ability to monitor key parameters, such as energy consumption, battery status, and solar energy generation, while maintaining connectivity in areas with intermittent mobile coverage. The system’s low power consumption ensured minimal impact on SHS usability, and its cost-efficient design required mobile network connectivity for only one or two devices per mesh network. Furthermore, the pilot highlighted the feasibility of integrating the monitoring system into the existing installed base of SHS, avoiding the need for entirely new installations. This work advances the state-of-the-art in SHS monitoring technologies, providing a scalable and efficient solution tailored to the constraints of rural electrification projects. Future directions include the development of plug-and-play devices for existing systems and enabling bidirectional communication to enhance system interactivity.}, archive = {J_ACCESS}, author = {Carlos Cuenca-Enrique and Laura del-Río-Carazo and Emiliano Acquila-Natale and Santiago Iglesias-Pradas}, doi = {10.1109/ACCESS.2025.3585231}, journal = {IEEE Access}, pages = {114608-114621}, shortjournal = {IEEE Access}, title = {Addressing connectivity challenges in solar home systems: A monitoring solution for rural electrification}, volume = {13}, year = {2025}, }
• (2025). Survey on 3D anomaly detection regarding defect size: Tradeoff between accuracy and efficiency with future research direction. ACCESS, 13, 114595-114607. (www)

  @article{ , abstract = {Recent advancements in neural networks has enhanced image anomaly detection in different industries such as manufacturing, reduced human intervention, and boosted productivity and quality. However, there are still some challenges in using point cloud and RGB data for anomaly detection across varying defect sizes. This study explores such challenges and and highlights the importance of high-quality data for effective training. It also highlights the trade-off between detection accuracy and inference speed, where faster models tend to sacrifice performance on smaller defects due to the use of general loss functions that do not sufficiently capture fine-grained anomalies. This work suggests that future research should focus on improving the models that efficiently detect small defects while ensuring high-speed inference and bridging the gap between accuracy and efficiency for real-world industrial applications.}, archive = {J_ACCESS}, author = {A-Seong Moon and Kunho Lee and Jaesung Lee}, doi = {10.1109/ACCESS.2025.3585843}, journal = {IEEE Access}, pages = {114595-114607}, shortjournal = {IEEE Access}, title = {Survey on 3D anomaly detection regarding defect size: Tradeoff between accuracy and efficiency with future research direction}, volume = {13}, year = {2025}, }
• (2025). A maturity model for digital product passports: A design science study. ACCESS, 13, 114575-114594. (www)

  @article{ , abstract = {As part of the Green Deal announced by the European Commission, digital product passports (DPPs) are becoming increasingly important for companies to become more sustainable. DPPs are an emerging concept to structure sustainability-related product data and make it fit for inter-organizational data sharing. As such, DPPs hold promise for closing information gaps in supply chains and production networks, as well as supporting sustainable paradigms, such as the circular economy. As DPPs are intended to make data available for third parties, their launch requires industrial organizations to develop certain information technology (IT) and data management capabilities. In addition, the solution space for implementing DPPs is vast and is still only determined by vague legislation. These factors create uncertainty for companies that need to launch and operate DPPs. Initial research projects indicate an equifinality in the design of DPPs and do not focus on the processes to integrate the data in DPPs and operate them successfully. To assist industrial organizations in this complex endeavor, this study employs design science research to propose a validated maturity model. The model integrates nine technological and organizational maturation paths that industrial organizations can use to become conscious of their current state and improve systematically to launch and operate DPPs successfully. The knowledge base was derived from a multivocal literature review (MLR) and refined in two series of interviews with experts from industrial organizations. Between the two series of interviews, the maturity model was instantiated as a transfer media. The model enables decision-makers and researchers to assess the systematic optimization of technological and organizational maturation paths, turning DPPs into a competitive advantage for more sustainable decisions.}, archive = {J_ACCESS}, author = {Dimitri Petrik and Fabian Dzierzawa and Kirsten Warthmann}, doi = {10.1109/ACCESS.2025.3584842}, journal = {IEEE Access}, pages = {114575-114594}, shortjournal = {IEEE Access}, title = {A maturity model for digital product passports: A design science study}, volume = {13}, year = {2025}, }
• (2025). Parameter identification of induction motor considering soft starter characteristics. ACCESS, 13, 114566-114574. (www)

  @article{ , abstract = {In this paper, a method for estimating induction motor (IM) parameters using a soft starter has been proposed. Unlike the conventional method using a three phase inverter, the proposed method use only the soft starter to obtain IM parameters. The stator resistance, stator inductance and magnetizing inductance of the IM are estimated under the assumption of steady state because the IM connected to the grid is often used where the load torque does not change rapidly. Four quantities are defined based on currents and voltages of motor, and the angle of the rotor flux is estimated using these quantities to obtain the stator inductance and magnetizing inductance. Using the parameters obtained in this way, it is possible to estimate rotor resistance using existing methods. The effectiveness of proposed method was verified through simulations and experiments.}, archive = {J_ACCESS}, author = {Pooreum Jang and Young-Su Noh and Byong Jo Hyon and Joon Sung Park and Jin-Hong Kim}, doi = {10.1109/ACCESS.2025.3584311}, journal = {IEEE Access}, pages = {114566-114574}, shortjournal = {IEEE Access}, title = {Parameter identification of induction motor considering soft starter characteristics}, volume = {13}, year = {2025}, }
• (2025). Dynamic inductor switching and frequency control to enhance efficiency in buck converters for CubeSat EPS. ACCESS, 13, 114555-114565. (www)

  @article{ , abstract = {The Electrical Power System (EPS) is a critical satellite subsystem that must meet mission requirements to operate effectively and ensure mission success, including supporting various operational modes during the required satellite lifetime. The power distribution of an EPS relies on Point of Load (POL) converters, which supply energy to all other subsystems. The efficiency of these DC/DC converters is crucial, especially when the satellite depends solely on batteries. High-efficiency converters help to minimize power losses, thereby potentially reducing the required battery size. This is particularly important for nanosatellite standardized platforms like CubeSats, which have strict limitations on space and weight. This paper introduces a versatile POL converter architecture based on switched inductors and switching frequency adjustment, aiming to optimize efficiency under different operating conditions. The effectiveness of the proposed method is demonstrated through a measurement campaign, and the preliminary results show that, with the proposed method, the converter can adapt its efficiency under both heavy and light load conditions. Our method exhibits an efficiency enhancement up to more than 25% compared to solutions that are based on the standard circuit parameters sizing, providing notable advantages for applications where power consumption is critical.}, archive = {J_ACCESS}, author = {Francesco Rocco Nardelli and Giuseppe Coviello and Giuseppe Brunetti and Caterina Ciminelli}, doi = {10.1109/ACCESS.2025.3584667}, journal = {IEEE Access}, pages = {114555-114565}, shortjournal = {IEEE Access}, title = {Dynamic inductor switching and frequency control to enhance efficiency in buck converters for CubeSat EPS}, volume = {13}, year = {2025}, }
• (2025). SRCPAR—Spike response-based congestion prediction for adaptive routing for 2D NoCs. ACCESS, 13, 114542-114554. (www)

  @article{ , abstract = {Network-on-Chip (NoC) architectures offer several advantages over bus-based systems, including improved scalability, efficient and high-performance interconnects, reduced wire routing congestion, and enhanced power efficiency. These benefits make NoC a promising solution for complex System-on-Chip (SoC) designs. In NoC, congestion prediction and its use in routing algorithms play a significant role in determining performance. A major challenge is the staleness of congestion information when it reaches a router. This paper introduces a Spike Response-based Congestion Prediction and Adaptive Routing (SRCPAR) algorithm to mitigate this issue. It leverages spike information alongside the local congestion data to accurately predict network congestion. Performance is evaluated using metrics such as average latency and throughput. Proposed method shows an improvement in performance for synthetic and real traffic patterns.}, archive = {J_ACCESS}, author = {Rajendra Singh and Manoj Kumar Bohra and Ashish Sharma and Sourabh Singh Verma and Devershi Pallavi Bhatt and Amit Kumar Bairwa and Masoud Daneshtalab}, doi = {10.1109/ACCESS.2025.3583432}, journal = {IEEE Access}, pages = {114542-114554}, shortjournal = {IEEE Access}, title = {SRCPAR—Spike response-based congestion prediction for adaptive routing for 2D NoCs}, volume = {13}, year = {2025}, }
• (2025). Dynamic optimization of recurrent networks for wind speed prediction on edge devices. ACCESS, 13, 114520-114541. (www)

  @article{ , abstract = {Accurate wind speed prediction (WSP) remains essential for optimizing energy management in small-scale domestic windmills. Server-dependent machine learning models, commonly deployed in wind farms, prove infeasible for domestic systems due to high costs and energy demands. While edge computing offers a viable alternative, current WSP methods prioritize hyperparameter optimization without constraining model size (MS), resulting in memory-intensive architectures incompatible with resource-limited devices. To address this gap, we propose a framework that co-optimizes the discrete hyperparameter spaces of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) models under strict memory constraints. An adaptive Simulated Annealing algorithm with memory-based rejection (aSAR) navigates the discrete design space, employing nine objective functions that balance Mean Absolute Percentage Error (MAPE) against model compactness. Evaluations on wind datasets from Chile, Kazakhstan and Mongolia demonstrate that aSAR-optimized models reduce prediction errors by up to 54.17% and decrease MS by 98.75% relative to state-of-the-art techniques. The results highlight significant regional performance variations, underscoring the necessity of location-specific architecture selection. This work establishes a systematic approach for deploying memory-efficient WSP models on edge devices, advancing sustainable energy solutions for decentralized wind power systems.}, archive = {J_ACCESS}, author = {Laeeq Aslam and Runmin Zou and Ebrahim Shahzad Awan and Sayyed Shahid Hussain and Muhammad Asim and Samia Allaoua Chelloug and Mohammed A. ELAffendi}, doi = {10.1109/ACCESS.2025.3581973}, journal = {IEEE Access}, pages = {114520-114541}, shortjournal = {IEEE Access}, title = {Dynamic optimization of recurrent networks for wind speed prediction on edge devices}, volume = {13}, year = {2025}, }
• (2025). Multi-parameter-based state of health observation method for second-hand hybrid electric vehicle batteries operating in extreme climates. ACCESS, 13, 114506-114519. (www)

  @article{ , abstract = {The global shift toward electrified transportation is accelerating as nations work to reduce carbon emissions and combat climate change. Second-hand hybrid electric vehicles (HEVs) have become a practical alternative in developing countries like Mongolia due to their affordability and fuel efficiency. However, the degradation of nickel-metal hydride (NiMH) batteries in aging HEVs presents a significant challenge, particularly in Mongolia’s extreme climate conditions, which accelerate battery deterioration. Existing state of health (SOH) estimation methods often face limitations in accuracy, consistency, and practicality, reducing their effectiveness in real-world applications. This paper proposes a novel SOH observation method tailored for second-hand HEVs operating in harsh environmental conditions to address these challenges. The proposed method utilizes multiple battery parameters, including current, voltage, temperature, internal resistance, and capacity, to improve estimation accuracy while maintaining simplicity and efficiency. The effectiveness of the proposed method is validated through simulation and experimental analysis, demonstrating a correlation between predicted and actual battery health. The findings offer a reliable and practical solution for battery health monitoring, enhancing vehicle performance, informing consumer decisions, and supporting the sustainable adoption of HEVs.}, archive = {J_ACCESS}, author = {Tsend-Ayush Oldokh and Munkh-Ochir Gankhuyag and Ganbat Baasantseren and Turmandakh Bat-Orgil}, doi = {10.1109/ACCESS.2025.3583774}, journal = {IEEE Access}, pages = {114506-114519}, shortjournal = {IEEE Access}, title = {Multi-parameter-based state of health observation method for second-hand hybrid electric vehicle batteries operating in extreme climates}, volume = {13}, year = {2025}, }
• (2025). Removing scattered light in biomedical images via total variation guided filter. ACCESS, 13, 114495-114505. (www)

  @article{ , abstract = {The scattered light is common in biomedical images. However, its removal is a challenging task. The challenge comes from two aspects. First, the scattered-light-free ground truth biomedical images are difficult to obtain or even unavailable. This is fundamentally different from the case in natural images where the ground truth is available. Second, although some neural network methods can remove the scattered light in biomedical images, they contain a large number of parameters, hampering their training process and the deployment in practical applications. To tackle these issues, this paper proposes a simple filter method that can effectively and efficiently remove the scattered light in biomedical images without knowing the ground truth. After analyzing the physical law behind the light scattering, we derive a novel model for biomedical images from a well-known mathematical model for the natural image de-hazing task. Then, we present a quarter-window dark channel prior for solving this model, leading to a fast filter with linear computation complexity. Finally, several numerical experiments are conducted to confirm the effectiveness of the proposed model and the efficiency of the proposed solving filter. Thanks to the effectiveness and the efficiency, the proposed method can be deployed in practical applications and achieve real-time performance.}, archive = {J_ACCESS}, author = {Yuanhao Gong}, doi = {10.1109/ACCESS.2025.3584403}, journal = {IEEE Access}, pages = {114495-114505}, shortjournal = {IEEE Access}, title = {Removing scattered light in biomedical images via total variation guided filter}, volume = {13}, year = {2025}, }
• (2025). Characterizing cyber-physical system testbeds as design artifacts: A morphological analysis across application domains. ACCESS, 13, 114471-114494. (www)

  @article{ , abstract = {Cyber-physical system (CPS) testbeds are critical research tools for advancing safety-critical technologies, from autonomous vehicles to smart grids. As CPS grow more complex, adaptive, and interconnected, testbeds must evolve in kind—yet the architectural assumptions guiding their design remain fragmented across domains. This paper introduces a morphological framework that enables comparison and synthesis across traditionally siloed CPS fields. Drawing on a structured review and a feature abstraction process, we derive 25 architectural dimensions and use them to classify 113 CPS testbeds spanning a wide range of use cases. Cluster analysis reveals three dominant design archetypes characterized by centralized control, reconfigurability, and virtualized operation, and highlights convergence in user interaction mechanisms and the decision-making architectures that govern testbed behavior. We quantify configurational rigidity, expose underexplored regions of the design space, and identify architectural opportunities in emerging CPS research domains. By reconceiving testbeds as design artifacts, this work opens new avenues for architectural innovation in CPS testbed research.}, archive = {J_ACCESS}, author = {Philip Odonkor and Evans Honu}, doi = {10.1109/ACCESS.2025.3582743}, journal = {IEEE Access}, pages = {114471-114494}, shortjournal = {IEEE Access}, title = {Characterizing cyber-physical system testbeds as design artifacts: A morphological analysis across application domains}, volume = {13}, year = {2025}, }
• (2025). A hybrid quantum-classical approach for multi-class skin disease classification using a 4-qubit model. ACCESS, 13, 114460-114470. (www)

  @article{ , abstract = {Quantum machine learning (QML) presents a promising avenue for addressing complex classification challenges, yet its application in medical imaging remains largely unexplored. This work introduces a hybrid quantum-classical framework designed to classify skin diseases, Chickenpox, Measles, Monkeypox, and Normal, using a 4 qubit quantum system. The framework integrates a Quantum Convolutional Neural Network (QCNN) for feature extraction and a Variational Quantum Classifier (VQC) for classification, processing a dataset of 770 images with significant class imbalance. We employ a class-weighted loss function with weights $w_{y_{i}} = [{3.0, 3.5, 0.6, 0.4}]$ to address this imbalance, ensuring balanced representation across all classes. The model is optimized using Simultaneous Perturbation Stochastic Approximation (SPSA) over 150 iterations, achieving a test accuracy 70.13% on a subset of 154 images, representing 20% of the dataset. This marks a substantial improvement over both a random baseline of 25% and a prior run that achieved 37.66% accuracy, which was skewed toward the majority class. Principal Component Analysis (PCA) reduces image features to a 4-dimensional representation, enabling compatibility with the quantum system. The QCNN extracts these features, while the VQC performs classification based on measurements from 1024 shots on the QASM simulator. To ensure practical reusability, the trained model, including quantum circuits, optimized parameters, and PCA transformations, is saved for future deployment. Validation on a separate set of 24 samples (6 per class) yields 70.83% accuracy (17 out of 24 correct predictions), demonstrating the model’s generalization capabilities. While the performance falls short of classical benchmarks, this study establishes the feasibility of QML with minimal qubits, paving the way for quantum driven diagnostics in resource constrained environments.}, archive = {J_ACCESS}, author = {Aravinda C Ⅴ and Emerson Raja Joseph and Sultan Alasmari}, doi = {10.1109/ACCESS.2025.3581030}, journal = {IEEE Access}, pages = {114460-114470}, shortjournal = {IEEE Access}, title = {A hybrid quantum-classical approach for multi-class skin disease classification using a 4-qubit model}, volume = {13}, year = {2025}, }
• (2025). Dealing with heterogeneous information in multi-criteria group decision-making problems: A comprehensive design framework. ACCESS, 13, 114444-114459. (www)

  @article{ , abstract = {This study provides a comprehensive review of the literature on group decision-making with heterogeneous information, aiming to identify the main characteristics in this field, highlight gaps, and suggest new promising approaches and methods to overcome current limitations. The research reveals the main research front directions, key authors, most common application areas, the most frequently used preference formats, and aggregation schemes in existing studies. The research innovation and originality lie in developing five new approaches and methods to overcome the identified gaps and limitations. First, a transparent, comprehensive, and intuitive framework for dealing with heterogeneous information in multi-criteria group decision problems is proposed. Second, a standardization scheme is introduced to define the name of the preference format when it has many names and establish a standard naming structure for all formats. Third, an easy-to-follow framework for categorizing existing and new formats is studied to facilitate an understanding of the structure of each format. Fourth, the new “relational ordered preference” is introduced, a format that increases agility and accuracy in alternative assessments. Fifth, we introduce a pioneering aggregation scheme (consensus-based ordered weighted averaging operator) to maximize the consensus level between individual and collective assessments. An illustrative and a real-world example are also provided. The example of the governance composite indicator demonstrates that relational ordered preference enhances the accuracy of assessments and, consequently, increases the degree of consensus. In turn, the consensus-based approach achieved higher degrees of consensus than extreme value reductions, indicating that preserving more convergent opinions contributes more to consensus than preserving intermediate opinions.}, archive = {J_ACCESS}, author = {Matheus Pereira Libório and Petr Ekel and Marcos Flávio Silveira Vasconcelos D’Angelo and Luis Martínez and Witold Pedrycz}, doi = {10.1109/ACCESS.2025.3582537}, journal = {IEEE Access}, pages = {114444-114459}, shortjournal = {IEEE Access}, title = {Dealing with heterogeneous information in multi-criteria group decision-making problems: A comprehensive design framework}, volume = {13}, year = {2025}, }
• (2025). Smart forest navigation system using LoRa and dynamic pathfinding. ACCESS, 13, 114428-114443. (www)

  @article{ , abstract = {Navigating dense forest environments presents significant challenges due to obstructed GPS signals, dynamic terrain, and limited communication infrastructure. This research proposes a Smart Forest Navigation System that enables real-time, infrastructure-free navigation by integrating LoRa communication, radar-based obstacle detection, and Dynamic A ${}^{\ast }$ (D ${}^{\ast } $ ) pathfinding. The system leverages Time of Arrival (ToA) measurements from LoRa nodes for long-range localization, enhanced through sensor fusion with Inertial Measurement Unit (IMU) data using an Extended Kalman Filter (EKF) on the user’s smartphone. A radar sensor provides real-time environmental perception, detecting obstacles and updating a dynamically evolving map enriched with satellite, drone, and crowdsourced data via probabilistic modeling. Path optimization is handled by the D ${}^{\ast }$ Lite algorithm, which adapts to terrain changes and obstacle updates, using a cost function that accounts for terrain difficulty and energy efficiency. All computations are performed on-device, ensuring low-latency operation without reliance on GPS or external servers. The proposed system offers a cost-effective, energy-efficient solution suitable for forest exploration, wildlife tracking, and search and rescue missions in GPS-denied environments.}, archive = {J_ACCESS}, author = {Isha Nevatia and Vraj Chaudhary and M. Thurai Pandian}, doi = {10.1109/ACCESS.2025.3580463}, journal = {IEEE Access}, pages = {114428-114443}, shortjournal = {IEEE Access}, title = {Smart forest navigation system using LoRa and dynamic pathfinding}, volume = {13}, year = {2025}, }
• (2025). External validation of AI models for skin diseases: A systematic review. ACCESS, 13, 114411-114427. (www)

  @article{ , abstract = {The increasing integration of Artificial Intelligence (AI) into dermatology holds significant potential to enhance the diagnosis and prognosis of skin diseases, which represent a considerable public health burden. While numerous AI models for skin conditions have been developed, their successful translation into real-world clinical practice critically depends on rigorous external validation to ensure generalizability and reliability beyond their development environment. Despite the growing recognition of external validation’s importance in medical AI, a comprehensive synthesis specifically addressing this process for AI models in dermatology has been lacking. This systematic literature review aimed to identify and synthesize studies focused exclusively on the external validation of AI models for skin diseases, thereby assessing their clinical applicability, methodological robustness, and identifying key challenges. Following the PRISMA framework, a systematic search was conducted across five major digital libraries (ACM Digital Library, IEEE Xplore, Web of Science, PubMed, and Scopus). Study quality was assessed using a customized checklist, and data extraction focused on validation practices, model characteristics, targeted diseases, performance assessment, and identified limitations. The systematic study selection led to a detailed analysis of 41 studies. We observed a clear predominance of retrospective external validation studies focused on diagnostic models, while prognostic models and prospective validation designs were notably scarce. Although some models demonstrated promising performance in external validation, several critical limitations persist. These include the narrow representation of skin disease categories, lack of subgroup fairness analyses, small and imbalanced sample sizes, and insufficient standardization of data collection and evaluation metrics. These findings underscore the need for more rigorous and transparent validation frameworks to support the reliable integration of AI into dermatological practice.}, archive = {J_ACCESS}, author = {Thayaná Ribeiro Silva Fernandes and Ariel Soares Teles and Jacks Renan Neves Fernandes and Lucas Daniel Batista Lima and Daniel Lima Sousa and Raimundo de Castro Soares and Silmar Silva Teixeira}, doi = {10.1109/ACCESS.2025.3584904}, journal = {IEEE Access}, pages = {114411-114427}, shortjournal = {IEEE Access}, title = {External validation of AI models for skin diseases: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Fog visibility detection of highway based on improved dark channel prior algorithm. ACCESS, 13, 114401-114410. (www)

  @article{ , abstract = {The low visibility near the ground in foggy weather can easily lead to vicious traffic accidents, so research on visibility detection in foggy weather is fundamental for traffic control. This paper presents an algorithm for detecting fog visibility in expressway based on improved dark channel prior method, which transforms the difficult problem of detecting visibility through image processing into the problem of calculating atmospheric extinction coefficient. Firstly, Canny operator is used to extract the image contour and divide the sky region. The average value of all pixels in the sky region is regarded as the atmospheric light value, thus increasing the accuracy of atmospheric light estimation. Then, guided filtering is used to refine the atmospheric transmittance. Finally, the extinction coefficient of the image can be calculated according to the lane line, so that the visibility value of the image can be calculated. In the experiment, the video image of a section of Rylan Expressway under fog is used. Experimental results show that the relative error of the improved algorithm is obviously reduced.}, archive = {J_ACCESS}, author = {Huiqi Zhang and Yong Liu and Chenxi Wu}, doi = {10.1109/ACCESS.2025.3583072}, journal = {IEEE Access}, pages = {114401-114410}, shortjournal = {IEEE Access}, title = {Fog visibility detection of highway based on improved dark channel prior algorithm}, volume = {13}, year = {2025}, }
• (2025). Optimization method of wind turbine locations in complex terrain areas using a combination of simulation and analytical models. ACCESS, 13, 114384-114400. (www)

  @article{ , abstract = {Once an area has been identified for a wind farm, the annual energy production of the farm is the most important quantity to obtain high exploitation efficiency. This quantity depends mainly on factors such as wind resource characteristics, type, number and arrangement of turbines. For areas with complex terrain, wind resource characteristics depend largely on terrain features, so the selection of turbine installation locations is very important. Because when the turbines operate, they will cause a wake effect that increases the turbulence of the flow behind. Therefore, it is necessary to find the optimal distance between turbines so that the annual energy production reaches the maximum value. This study presents a method to determine the optimal turbine locations when considering the correlation between wake loss and turbine space in the case of mountainous terrain. Firstly, a computational fluid dynamics model combined with a geographic information system are used to determine the 3-dimensional wind characteristics at specific locations. Secondly, a Jensen model is used to consider the wake effect according to the distance between turbines. Then, the the annual energy production values are determined through the analytical model. In addition, a comprehensive assessment of levelized cost of energy is also provided to confirm the practicality of implementing the optimization model. Finally, the optimal location configuration of the turbines is proposed. This method was tested and compared with a farm with sufficient data to assess reliability and then applied to an area in Ninh Thuan, Viet Nam. The results showed that the the annual energy production obtained from this farm can be up to 252.3 GWh (30 turbines) compared to 99.9 GWh (10 turbines), which is 2.5 times larger.}, archive = {J_ACCESS}, author = {Dinh Van Thin and Le Quang Sang and Nguyen Huu Duc}, doi = {10.1109/ACCESS.2025.3584560}, journal = {IEEE Access}, pages = {114384-114400}, shortjournal = {IEEE Access}, title = {Optimization method of wind turbine locations in complex terrain areas using a combination of simulation and analytical models}, volume = {13}, year = {2025}, }
• (2025). AZIM: Arabic-centric zero-shot inference for multilingual topic modeling with enhanced performance on summarized text. ACCESS, 13, 114370-114383. (www)

  @article{ , abstract = {Topic modeling is an unsupervised learning technique, that is extensively used for discovering latent topics in huge text corpora. However, existing models often fall short in cross-lingual scenarios, particularly for morphologically rich and low-resource languages such as Arabic. Cross-lingual topic analysis extracts shared topics across languages but often relies on resource-intensive datasets or limited translation dictionaries, restricting its diversity and effectiveness. Transfer learning provides a promising solution to these challenges. This presents AZIM, an Arabic-centric extension of ZeroShotTM, adapted to use Arabic as the training language for zero-shot multilingual topic modeling. The model’s performance is evaluated across diverse Latin-script and non-Latin-script languages, focusing on its adaptability to Modern Standard Arabic (MSA) and Classical Arabic (CA). Additionally, the study explores the impact of summarized versus general text. The results illustrate that the summarized versions of the datasets consistently outperform their baselines in terms of interpretability and coherence. Furthermore, the model also illustrates robust cross-lingual generalization as shown by non-Latin scripts such as Persian and Urdu outperforming certain Latin-based languages. However, variations in performance between the languages show the complex nature of multilingual embeddings. The performance difference between Modern Standard Arabic and Classical Arabic reveals that the limitations of the pre-trained embeddings, namely, their bias towards modern corpora. These findings point out the importance of adapting techniques for morphologically rich and low-resource languages for the purpose of enhancing the cross-lingual topic modeling.}, archive = {J_ACCESS}, author = {Sania Aftar and Abdul Rehman and Sonia Bergamaschi and Luca Gagliardelli}, doi = {10.1109/ACCESS.2025.3584309}, journal = {IEEE Access}, pages = {114370-114383}, shortjournal = {IEEE Access}, title = {AZIM: Arabic-centric zero-shot inference for multilingual topic modeling with enhanced performance on summarized text}, volume = {13}, year = {2025}, }
• (2025). Average corrosion rate prediction model for buried oil and gas pipelines based on SSA-LightGBM. ACCESS, 13, 114356-114369. (www)

  @article{ , abstract = {Corrosion represents a major cause of damage and leakage in oil and gas pipelines, making accurate corrosion rate prediction critical for operational safety. This study establishes an average corrosion rate prediction model using the Sparrow Search Algorithm-optimized Light Gradient Boosting Machine (SSA-LightGBM). To enhance model accuracy, Stochastic Perturbation Data Augmentation (SPDA) was applied to expand the original dataset from 60 to 300 samples. The optimized SSA-LightGBM model was then trained, and its performance was rigorously evaluated through error analysis and stability assessment. Results demonstrate that the SSA-LightGBM model achieves exceptional accuracy and stability, with an R2 value of 0.9813 and stability index (Pstd) of 1.1701. Comparative analysis against LightGBM, GBRT, CatBoost, SSA-GBRT, and SSA-CatBoost models confirms that SSA-LightGBM delivers superior predictive performance in both accuracy and stability metrics.}, archive = {J_ACCESS}, author = {Weigang Fu and Haitao Wang and Kuankuan Zhang and Xia Wang and Kunlun Chen and Chunmei Sun and Zhengwei Wang and Liuyang Song and Niannian Wang}, doi = {10.1109/ACCESS.2025.3578186}, journal = {IEEE Access}, pages = {114356-114369}, shortjournal = {IEEE Access}, title = {Average corrosion rate prediction model for buried oil and gas pipelines based on SSA-LightGBM}, volume = {13}, year = {2025}, }
• (2025). RL-RTree: A reinforcement learning-optimized dynamic R-tree for high-dimensional spatial indexing. ACCESS, 13, 114346-114355. (www)

  @article{ , abstract = {Spatial indexing in high-dimensional dynamic environments faces critical challenges, including the curse of dimensionality and rapid distribution shifts, which degrade the performance of traditional indexes like R*-trees and static learned indexes. We propose RL-RTree, a dynamic R-tree optimization method that integrates Spatial Graph Attention Networks (SGAT) for density-aware embeddings and online reinforcement learning (RL) to adjust query strategies in real-time. The hybrid offline-online architecture decouples embedding learning from runtime policy optimization. Experiments on 10- to 100-dimensional data show that RL-RTree improves query speed by 75.2% and accuracy by 14.3% compared to R*-trees. For dynamic scenarios, it achieves $3.7\times $ faster recovery than static indexes and maintains 91.3% accuracy under high noise ( $\sigma =0.5$ ). This work bridges the gap between learning-based indexing and real-time adaptive systems, enabling sub-second updates for mission-critical applications like autonomous vehicles and recommendation engines. The interpretable RL policies and SGAT embeddings set a new paradigm for robust high-dimensional indexing.}, archive = {J_ACCESS}, author = {Yongxin Peng}, doi = {10.1109/ACCESS.2025.3583701}, journal = {IEEE Access}, pages = {114346-114355}, shortjournal = {IEEE Access}, title = {RL-RTree: A reinforcement learning-optimized dynamic R-tree for high-dimensional spatial indexing}, volume = {13}, year = {2025}, }
• (2025). Using cough sounds for the recognition of multiclass respiratory diseases with artificial intelligence: A review. ACCESS, 13, 114318-114345. (www)

  @article{ , abstract = {The multi-class identification of respiratory diseases based on cough sounds plays a critical role in the diagnosis of respiratory conditions. Compared to traditional auscultation methods, multi-class techniques integrate various deep-level disease features of cough sounds, enabling a more comprehensive capture of cough characteristics associated with specific diseases, thereby effectively reducing the risk of misdiagnosis. Furthermore, multiclass classification enhances the system’s ability to differentiate among multiple respiratory conditions, significantly improving diagnostic accuracy and robustness. This study provides an in-depth examination of artificial intelligence-driven methodologies for multi-class respiratory disease recognition. We reviewed 19 articles from a 5-year open-access literature database to summarize the current research on diagnosing voice-affecting disorders through AI techniques using cough sound samples for multi-class classification. The study also covers AI workflows for multi-class cough recognition, including datasets, feature extraction techniques, models, and evaluation metrics. Additionally, recent advancements in AI technologies, cough monitoring applications, and wearable devices are discussed, along with the challenges and future research directions aimed at developing robust and universally applicable solutions for multi-class diagnosis.}, archive = {J_ACCESS}, author = {Peng-Fei Zhang and Hua-Nong Ting and Siow-Wee Chang}, doi = {10.1109/ACCESS.2025.3584401}, journal = {IEEE Access}, pages = {114318-114345}, shortjournal = {IEEE Access}, title = {Using cough sounds for the recognition of multiclass respiratory diseases with artificial intelligence: A review}, volume = {13}, year = {2025}, }
• (2025). Enhanced coronary artery disease classification through feature engineering and one-dimensional convolutional neural network. ACCESS, 13, 114306-114317. (www)

  @article{ , abstract = {Coronary artery disease (CAD) diagnosis remains a significant contributor to global mortality rates, highlighting the need for novel approaches. Existing CAD diagnostic tools rely on costly and complex biomarkers and scanners. In this paper, using only electrocardiogram (ECG) signals, we propose a novel learning-based model for CAD diagnosis. The proposed method works based on a one-dimensional convolutional neural network (1D-CNN), offering a cost-effective alternative for sophisticated cardiac health monitoring. Furthermore, we introduce the concept of feature engineering to improve the quality of the model training process and mitigate the challenge of ill-conditioned ECG data. Unlike existing approaches, which often overlook signal quality, our model applies a smart feature engineering, ensuring that only diagnostically reliable signals are used. This design improves robustness, generalisability, and suitability for real-world clinical settings. Utilising one of the most complex publicly available datasets, i.e., MIMIC III, sourced from Physionet, the performance of the proposed model, along with existing ones in classifying potential cases of CAD and non-CAD is investigated. Our findings confirm that the proposed model exhibits outstanding performance, highlighting the effectiveness of our integrated feature engineering approach with the CNN model.}, archive = {J_ACCESS}, author = {Atitaya Phoemsuk and Vahid Abolghasemi}, doi = {10.1109/ACCESS.2025.3584735}, journal = {IEEE Access}, pages = {114306-114317}, shortjournal = {IEEE Access}, title = {Enhanced coronary artery disease classification through feature engineering and one-dimensional convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Depression analysis and detection using machine learning: Incorporating gender differences in a comparative study. ACCESS, 13, 114297-114305. (www)

  @article{ , abstract = {Depression is a significant mental health problem and presents a challenge for the machine learning field in the detection of this illness. This study explores automated depression classification, leveraging computational techniques to address this issue. The proposed approach performs spectrogram analysis and utilizes several machine learning methods, including SVM (Support Vector Machine), Random Forest, MLP (Multilayer Perceptron), and DepAudioNet. The research examines four datasets, namely DAIC-WOZ, EATD Corpus, D-Vlog, and EMU, which vary in terms of linguistic background (English and Chinese), depression classification scales, and gender representation proportions. Feature extraction employs parameters such as formant-related, MFCCs (Mel Frequency Cepstral Coefficients), and jitter parameters. The innovation of this study lies in strategic enhancement, which involves incorporating a gender-specific perspective. This is achieved through the implementation of a tailored feature vector methodology. In most models, this approach led to measurable improvements: SVM improved by 1.43% (p =0.044), MLP-CNN by 7.25% (p =0.008), and Perceptron by 7.39% (p =0.05). Such results underscore the necessity of integrating more personalized methods into the creation of machine learning algorithms for mental diagnostics.}, archive = {J_ACCESS}, author = {Marina Galanina and Anna Rekiel and Anna BąCzyk and Bozena Kostek}, doi = {10.1109/ACCESS.2025.3582361}, journal = {IEEE Access}, pages = {114297-114305}, shortjournal = {IEEE Access}, title = {Depression analysis and detection using machine learning: Incorporating gender differences in a comparative study}, volume = {13}, year = {2025}, }
• (2025). Research on the dimensional framework of digital marketing content on social media: Coarse grained and fine grained. ACCESS, 13, 114282-114296. (www)

  @article{ , abstract = {As renowned marketing expert Seth Golding stated that the focus of marketing is no longer on the stuff you make, but on the story content you tell. Existing dimensional analyses of digital marketing content on social media are not agreed upon, and lack a universal content categories. Addressing the gap, the study coordinates Uses and Gratifications Theory and Consumer Perceived Value Theory to design a universal dimensional framework for digital marketing content, presented in both coarse-grained and fine-grained forms. For the Sina Weibo platform, the rationality of the constructed digital marketing content dimension framework is empirically validated by using a approach combining quantitative natural language processing and qualitative content analysis. Results indicate that the dimension framework derived from the two theories effectively identifies the motivations and values expressed in the digital marketing content across both granularity levels. The study expands insights into social media digital marketing content and provides meaningful practical guidance for brands.}, archive = {J_ACCESS}, author = {Yang Liu and Jiaying Wang and Zhijie Zhao}, doi = {10.1109/ACCESS.2025.3584046}, journal = {IEEE Access}, pages = {114282-114296}, shortjournal = {IEEE Access}, title = {Research on the dimensional framework of digital marketing content on social media: Coarse grained and fine grained}, volume = {13}, year = {2025}, }
• (2025). A novel search algorithm and scan time estimation in airborne ground penetrating radar using cell footprint meshing. ACCESS, 13, 114268-114281. (www)

  @article{ , abstract = {Airborne Ground Penetrating Radar (A-GPR) systems in multilayer environments often exhibit inefficiencies stemming from suboptimal search patterns and inaccurate scan time estimations, resulting in protracted operation times and elevated energy consumption. To mitigate these challenges, this paper introduces a novel theoretical search algorithm predicated on cell footprint meshing. This approach leverages antenna footprint dimensions to optimize search paths, thereby reducing scan time and enhancing energy efficiency. By categorizing the target area into surface, subsurface, and return cells based on signal reception characteristics, the algorithm aligns search patterns (row-wise or column-wise) with the derived cell configurations and dimensions. Precise computations of wave penetration and two-way travel time (TWT), factoring in stacking parameters and drone velocity, are integral to optimal cell design and comprehensive area coverage. Simulation results indicate that this deterministic, geometry-based method yields an approximate 24% reduction in search time, consequently minimizing resource utilization. The inherent scalability of this algorithm renders it particularly advantageous for missions constrained by time and resources and establishes a framework for enhancing A-GPR system performance. However, as a theoretical investigation, the absence of experimental validation due to the limited availability of real-world A-GPR systems and data necessitates future field studies. This work underscores the critical role of the cellular network and search pattern selection in augmenting operational efficiency and provides a foundational basis for subsequent development and practical implementation in radar-based reconnaissance.}, archive = {J_ACCESS}, author = {Morteza Kazerooni and Ehsan Shahroosvand}, doi = {10.1109/ACCESS.2025.3582826}, journal = {IEEE Access}, pages = {114268-114281}, shortjournal = {IEEE Access}, title = {A novel search algorithm and scan time estimation in airborne ground penetrating radar using cell footprint meshing}, volume = {13}, year = {2025}, }
• (2025). A 1.8-V 95.8-dB SNDR incremental delta-sigma ADC with analog noise reduction techniques for sensor ROIC. ACCESS, 13, 114256-114267. (www)

  @article{ , abstract = {A delta-sigma modulator (DSM)-based incremental analog-to-digital converter (ADC) is proposed for use in the readout integrated circuit of low-power high-resolution sensors. The proposed incremental delta-sigma ADC (IADC) consists of a second-order cascaded-of-integrators feedforward (CIFF) DSM with a successive approximation register (SAR) ADC-based 3-bit quantizer and a digital filter with a cascaded of integrators (CoI) structure. Design techniques to reduce analog noise such as flicker, thermal noise, and nonlinearities in the digital-to-analog converter (DAC) generated by a second-order CIFF DSM with a 3-bit quantizer are introduced and their effectiveness is verified by measured results of the DSM and IADC. In particular, the measurement results confirm that the improvement in the characterization of the flicker noise of the reference driver, including the band-gap reference for generating the reference voltage used in the feedback path of the DSM, determines the overall characterization of the DSM. Furthermore, the proposed low-power high-resolution IADC is implemented using a second-order CoI filter optimized for the designed DSM. The proposed IADC, implemented using a 180-nm CMOS process with a supply of 1.8 V, has an area of 0.296 mm2 and a power consumption of $124.5~\mu $ W. It has an input bandwidth of 500 Hz and a sampling rate of 1 kHz. To implement this, the DSM operating synchronized to an external clock with a frequency of 3.584 MHz has an oversampling ratio of 512 and a sampling rate of 512 kHz. When the differential analog input signal has a frequency of 100 Hz and an amplitude of 0.94 Vpp, the measured peak SNDR of the DSM is about 97.4 dB, while the measured SNDR of the IADC in this case is about 95.8 dB.}, archive = {J_ACCESS}, author = {Juncheol Kim and Young-Chan Jang}, doi = {10.1109/ACCESS.2025.3583830}, journal = {IEEE Access}, pages = {114256-114267}, shortjournal = {IEEE Access}, title = {A 1.8-V 95.8-dB SNDR incremental delta-sigma ADC with analog noise reduction techniques for sensor ROIC}, volume = {13}, year = {2025}, }
• (2025). MPJ-SPARK integration-based technique to enhance big data analytics in high performance computing environments. ACCESS, 13, 114241-114255. (www)

  @article{ , abstract = {The explosion of data from various sources such as smartphone applications, sensors, social media, and High-Performance Computing (HPC) simulations, has driven demand for high-performance data analytics. Traditional analytics tools lag HPC in computational efficiency, whereas machine learning workloads require substantial resources. However, integrating HPC and big data presents challenges due to architectural differences. This study introduces an MPJ-Spark integration-based technique that includes a novel multi-Spark-driver architecture to bridge this gap. MPJ-Spark enables a single application to execute concurrently across multiple Spark drivers, thereby improving parallelization and resource management. The methodology involves designing an MPJ-Spark cluster that integrates Message Passing in Java (MPJ) with Spark for efficient communication across HPC nodes. A single MPJ root process manages cluster communications, input file partitioning, and distributes partition metadata to MPJ workers. Each worker operates with an isolated Spark driver, processes tasks independently, and returns results to the root process for aggregation. This eliminates remote shuffling and improves network efficiency. A key-value data structure was developed to facilitate data exchange and to convert Resilient Distributed Dataset (RDD) into contiguous arrays for MPJ. A shared-storage-aware file manager was designed to improve the reading, writing, and partitioning of the datasets. MPJ-Spark was evaluated on the Aziz Supercomputer utilizing the WordCount workload across datasets ranging from 32 GB to 4.3 TB. The results demonstrated a significant improvement in execution time, ranging from 4x to 6x faster than Spark. This technique enables big data applications to leverage HPC’s computational power and effectively address the gap between these platforms.}, archive = {J_ACCESS}, author = {Sakhr A. Saleh and Maher A. Khemakhem and Fathy E. Eassa}, doi = {10.1109/ACCESS.2025.3584744}, journal = {IEEE Access}, pages = {114241-114255}, shortjournal = {IEEE Access}, title = {MPJ-SPARK integration-based technique to enhance big data analytics in high performance computing environments}, volume = {13}, year = {2025}, }
• (2025). AHP-guided stacked ensemble modeling for student engagement level prediction in online education. ACCESS, 13, 114230-114240. (www)

  @article{ , abstract = {Given the increasing importance of online education, this research seeks to provide a solution to more accurately predict the level of student engagement in these environments. The main goal is to improve the performance of prediction models using an innovative hybrid approach. The proposed method consists of three key steps: first, data preprocessing to prepare and refine a set of indicators related to the level of student engagement in online education environments. Second, the use of a novel feature selection method based on Analytic Hierarchy Process (AHP) and an iterative approach. In this step, AHP is used to rank and weight the features, and then an iterative process is used to determine an appropriate number of these ranked features. This is done in order to select the most effective features for prediction. Third, the use of an ensemble stack based on classical machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB). In this stack, the learning models are organized at two levels. At the first level, KNN, SVM, and NB models are used independently to predict the target variable (engagement level) based on the selected features. At the second level, a Multilayer Perceptron (MLP) model as a meta-learner makes the final prediction based on the results of the predictions made by the first-level models. This combined structure, in order to take advantage of the strengths of each model and reduce errors, ultimately leads to more accurate and efficient prediction of student engagement level in online learning environments. According to the proposed method, experiments were conducted on a real dataset and the results obtained indicate the very good performance of the presented model. The values obtained for both Precision and Recall are above 90%. These results show that the proposed model is able to predict different levels of student engagement with high accuracy and, at the same time, has an acceptable performance in terms of both Precision of predictions and Recall of complete samples.}, archive = {J_ACCESS}, author = {Jingjing Fu and Linjie Luo and Zhifeng Zhong and Jiaming Qin}, doi = {10.1109/ACCESS.2025.3575529}, journal = {IEEE Access}, pages = {114230-114240}, shortjournal = {IEEE Access}, title = {AHP-guided stacked ensemble modeling for student engagement level prediction in online education}, volume = {13}, year = {2025}, }
• (2025). Old MacDonald and his hackable smartphone application: A security and privacy analysis of android agriculture applications. ACCESS, 13, 114216-114229. (www)

  @article{ , abstract = {The food and agriculture sector is a critical component of the United States’ infrastructure, supporting food and national security, as well as economic stability. However, the sector’s increasing adoption of technologies connected to the Internet has introduced both operational efficiencies and significant cybersecurity risks to the sector. Despite the widespread use of smartphones among farmers increasing from 77% in 2021 to 82% in 2023, minimal research has examined the security and privacy implications of agricultural smartphone applications, potentially used on these devices. Hence, this study investigates whether Android agricultural applications expose farmers to cybersecurity threats by analyzing 31 applications for security vulnerabilities, data transmission practices, and third-party data sharing. The results show that some applications contain critical security vulnerabilities, including the transmission of sensitive data over unencrypted channels, the presence of previously known coding issues, and potential data exposure to external entities.}, archive = {J_ACCESS}, author = {Logan Mears and Rachel Donner and George Grispos}, doi = {10.1109/ACCESS.2025.3584673}, journal = {IEEE Access}, pages = {114216-114229}, shortjournal = {IEEE Access}, title = {Old MacDonald and his hackable smartphone application: A security and privacy analysis of android agriculture applications}, volume = {13}, year = {2025}, }
• (2025). Transformer-guided serial knowledge distillation for high-precision anomaly detection. ACCESS, 13, 114208-114215. (www)

  @article{ , abstract = {Unsupervised anomaly detection (AD) remains a notable challenge in computer vision research, due to the inherent absence of annotated anomalous data and the unpredictable nature of anomaly manifestations. To address these challenges, a novel Transformer-based knowledge distillation framework is proposed through a hierarchical architecture designed for improved anomaly recognition. This three-stage architecture consists of a fixed pretrained teacher network serving as the upstream feature extractor, a dedicated multi-feature aggregation and filtering module integrated with Vision Transformer components as the intermediate processor, and a trainable student network functioning as the downstream reconstruction module. Extensive evaluations on benchmark datasets demonstrate that the proposed method achieves high performance in both AD accuracy and anomaly localization precision, while also maintaining strong generalization across diverse anomaly types. Notably, it shows particular effectiveness in industrial inspection scenarios, where anomalous patterns are subtle and training data are strictly limited to normal samples.}, archive = {J_ACCESS}, author = {Danyang Wang and Bingyan Wang}, doi = {10.1109/ACCESS.2025.3584892}, journal = {IEEE Access}, pages = {114208-114215}, shortjournal = {IEEE Access}, title = {Transformer-guided serial knowledge distillation for high-precision anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Adaptive traffic lights control system using deep Q-network and salp swarm optimization. ACCESS, 13, 114199-114207. (www)

  @article{ , abstract = {Effective traffic light control is critical for reducing congestion and improving urban mobility. This paper presents a novel algorithm combining Salp Swarm Optimization (SSO) with Deep Q-Network (DQN) to enhance decision-making in adaptive traffic management systems. The proposed DQN-SSO approach was evaluated against traditional DQN and Double DQN (DDQN) methods in two scenarios: an uncalibrated scenario with high traffic in localized lanes and a fully crowded scenario with high traffic across all lanes. Results indicate that DQN-SSO achieved substantial improvements, reducing total waiting times by up to 10% compared to DDQN and 12% compared to DQN in the uncalibrated scenario and by 10% and 18%, respectively, in the fully crowded scenario. The integration of SSO’s global optimization capabilities with DQN’s reinforcement learning framework enabled the algorithm to adapt efficiently to varying traffic conditions. These findings highlight the potential of DQN-SSO as a scalable and robust solution for adaptive traffic control in urban environments.}, archive = {J_ACCESS}, author = {Khalid Khalid and Anas Bouayad and Khalid Fardousse}, doi = {10.1109/ACCESS.2025.3583656}, journal = {IEEE Access}, pages = {114199-114207}, shortjournal = {IEEE Access}, title = {Adaptive traffic lights control system using deep Q-network and salp swarm optimization}, volume = {13}, year = {2025}, }
• (2025). Functional data analysis for prediction of employee presence. ACCESS, 13, 114179-114198. (www)

  @article{ , abstract = {The problem of space occupancy and the prediction of occupancy schedules has rarely been addressed, due to the highly stochastic behavior of occupants and the insufficient quality of data sets. The COVID-19 pandemic has challenged traditional models based on the physical presence of employees in the workplace. This crisis has accentuated the evolution of new trends, such as teleworking, and highlighted the inherent diversity of work environments, as well as the multitude of factors that influence employee deplacements. The ability to anticipate when people will come is essential for effective management of these spaces, particularly in terms of resource planning, safety, and comfort. As a result, predicting when employees will get to work is a complex challenge. Accurate presence prediction tools can optimize resource utilization and support strategic decisions in corporate real estate. This paper proposes a robust approach combining functional data analysis with the integration of exogenous variables adapted to time series (RobFTS). The purpose of this approach is to anticipate the future presence of employees in their workplace. The model is developed using lags of functional time series, as well as external variables such as weather data (temperature, precipitation) and meeting rooms reservations. In addition to standard error metrics, we apply a comprehensive model selection process using AIC, BIC, AICc, HQIC and other information criteria adapted for non-parametric models. This method is then applied to a real database, gathering daily observations of employee presence at a corporate site in Paris. Finally, we contrast this method with other models widely used in time series analysis, Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Long Short Term Memory (LSTM), Adaboost, and Deep Learning for functional data analysis (DFMM). It is found that the mean absolute percentage error (MAPE) is about 4.99% for RobFTS, compared to 17.30%, 12.92%, 11% and 6.50% for SARIMAX, LSTM, Adaboost and DFMM, respectively. The data on which we applied the different models is made available on https://github.com/goujilinouhaila-coder/Prediction-of-Employee-Presence-in-Smart-Workplaces along with the various scripts of the models.}, archive = {J_ACCESS}, author = {Nouhaila Goujili and Matthieu Saumard and Nicolas Cochard and Maher Jridi}, doi = {10.1109/ACCESS.2025.3580697}, journal = {IEEE Access}, pages = {114179-114198}, shortjournal = {IEEE Access}, title = {Functional data analysis for prediction of employee presence}, volume = {13}, year = {2025}, }
• (2025). SemantiPack: An efficient real-world data compressor using structural and semantic metadata. ACCESS, 13, 114159-114178. (www)

  @article{ , abstract = {The exponential growth of Real-World Data (RWD), primarily collected from IoT sensors and spanning domains such as mobility, environment, and energy consumption, presents critical challenges due to its scale, heterogeneity, and structural variability. Traditional compression methods often fail to adapt efficiently to these complexities, leading to sub-optimal storage and analysis performance. Additionally, while several metadata schemas exist to enhance the availability of RWD, smaller organizations often lack the resources to create and manage metadata effectively. This paper introduces RWD Profile, an automatically generatable metadata schema for RWD, and SemantiPack, which applies tailored compression to data fields in the individual RWD based on RWD Profile. RWD Profile has two kinds of metadata, Structural Profile and Semantic Profile. Structural Profile is generated through rule-based systems, while Semantic Profile is generated using large language models (LLMs) to capture semantic data properties. On the other hand, SemantiPack performs compression at the data field level in RWD using RWD Profile to achieve higher compression ratios. Experimental results demonstrate up to 23.2% improved compression rate for JSON and 19.3% for CSV compared to conventional methods while maintaining a faster or equivalent processing speed compared to conventional methods. Furthermore, SemantiPack supports lossy compression for applications prioritizing storage over precision. This research not only improves compression efficiency but also establishes a scalable solution for automated analysis and sustainable data utilization, paving the way for advancements in RWD management.}, archive = {J_ACCESS}, author = {Yoshiteru Nagata and Daiki Kohama and Yoshiki Watanabe and Shin Katayama and Kenta Urano and Takuro Yonezawa and Nobuo Kawaguchi}, doi = {10.1109/ACCESS.2025.3583829}, journal = {IEEE Access}, pages = {114159-114178}, shortjournal = {IEEE Access}, title = {SemantiPack: An efficient real-world data compressor using structural and semantic metadata}, volume = {13}, year = {2025}, }
• (2025). Customized spectro-temporal CNN feature extraction and ELM-based classifier for accurate respiratory obstruction detection. ACCESS, 13, 114145-114158. (www)

  @article{ , abstract = {The accurate prediction based on lung auscultation of respiratory obstruction conditions (ROC), such as chronic obstructive pulmonary disease (COPD) and asthma, is a challenging task due to the availability of small datasets, ambient noise, variability between patients, high computational power, overlapping auscultation characteristics and lack of universal clinical standards. Secondly, discrimination of obstructive respiratory diseases (COPD, asthma) and restrictive respiratory diseases (other) is critical, as they have different treatment and management strategies. Third, a clear distinction between COPD and asthma is of great concern, as treatment at the appropriate stage results in an open air passage in COPD and an improvement in shortness of breath in asthma. Although several techniques have been explored for diagnosing respiratory disease based on lung auscultation, there is still a need for an effective, low-cost, and faster solution suitable for real-time ROC detection. The time-frequency representations of audio signals are suitable to capture low-frequency information, as well as tonal and harmonic relationships. In contrast, deep learning architectures can learn complex, hierarchical, and high-level patterns from the spatio-temporal structures. In addition, extreme learning machines (ELM) can provide generalized performance with fewer training parameters. Hence, combining time-frequency representations, deep learning architecture, and ELM would result in the most reliable low-cost tool to predict ROC from lung auscultation. The fusion of deep features from different spatiotemporal structures outperforms individual features when fed into the ELM model, resulting in clear discrimination of obstructive and restrictive respiratory diseases. The proposed CNN-enhanced time-frequency features powered the ELM-based framework, yielding a test accuracy of 97.5% for the unseen test data considered. Thus, this study would be a useful aid for pulmonologists and would play a pivotal role in the accessibility to healthcare, early intervention, and long-term management of ROC disease.}, archive = {J_ACCESS}, author = {M. Muthulakshmi and K. Venkatesan and Syarifah Bahiyah Rahayu and K. L. Nayana Sree}, doi = {10.1109/ACCESS.2025.3581271}, journal = {IEEE Access}, pages = {114145-114158}, shortjournal = {IEEE Access}, title = {Customized spectro-temporal CNN feature extraction and ELM-based classifier for accurate respiratory obstruction detection}, volume = {13}, year = {2025}, }
• (2025). Feature generation-based fingerprint liveness detection: A novel multimodal approach. ACCESS, 13, 114128-114144. (www)

  @article{ , abstract = {Fingerprint liveness detection is an emerging security solution that distinguishes between spoofed fingerprints and genuine fingerprints. Its importance increases because of the availability of cheap spoofing materials for fingerprints. The literature reports on fingerprint liveness detection techniques that have adopted deep learning architectures. Although multimodal traits can be an effective way to counter such spoofing mechanisms, they add hardware complexity. Hence, such multimodal traits are dropped from the usage. In contrast, this paper introduces a feature-generation procedure to exploit the benefits of multimodalities in liveness detection. Hence, a Convolutional Neural Network (CNN) architecture is proposed to generate features of iris traits when the input fingerprint image is given. This results in detecting the liveness of a fingerprint using iris features, which are generated from the input fingerprint. This averts the usage of memory to store both fingerprint and iris features on the detection side. To further effectuate the detection process, an Adaptive Focal Loss (AFL) is proposed in this paper. The proposed AFL separates the features as high-frequency and low-frequency components and performs weighted error calculation among the components. Lion algorithm trains the proposed CNN architecture to learn the features of iris traits. A chimeric dataset is constructed using the LivDet2015 fingerprint and iris datasets, in which the proposed liveness detection method is compared against the state-of-the-art unimodal liveness detection methods and multimodal liveness detection methods. The results show that the proposed liveness detection method is better than the compared methods. The results further clarify that the proposed method is less sensitive to spoofing materials and sensor types, as well as has good feature generation capability and generalization ability. An ablation study discloses the performance of AFL and the lion algorithm over traditional loss functions and optimizers in learning the fingerprint and generating the iris features.}, archive = {J_ACCESS}, author = {B. R. Rajakumar and S. Amala Shanthi}, doi = {10.1109/ACCESS.2025.3584702}, journal = {IEEE Access}, pages = {114128-114144}, shortjournal = {IEEE Access}, title = {Feature generation-based fingerprint liveness detection: A novel multimodal approach}, volume = {13}, year = {2025}, }
• (2025). Interpretable learning method based on causal interactive attention. ACCESS, 13, 114117-114127. (www)

  @article{ , abstract = {The “opaque system” phenomenon inherent in deep neural networks poses significant challenges to the reliability and trustworthiness of their decision-making processes, particularly in high-stakes applications such as medical diagnosis and financial security. Current deep neural network models primarily use gradients or intermediate features to improve decision reliability, but most of the explanatory methods rooted in these features are post-hoc, with little direct influence on the classifiers’ decision outcomes. In contrast to human object recognition, where decisions and the underlying visual logic are clarified by both positive and negative causal evidence, deep neural networks often fail to achieve this level of nuanced understanding. To mitigate these limitations, we introduce a novel deep model based on Causal Interaction Attention (CIA), which aims to improve the reliability and interpretability of visual classification. Specifically, we first propose an interactive attention-driven decision mechanism that dynamically integrates visual features into the decision process, ensuring that each classifier output is accompanied by corresponding positive or negative visual interpretations. This mechanism not only explains “why this image belongs to a certain category”, but also rationalizes “why this image does not belong to other categories”, providing a comprehensive view of the model’s decision. In addition, to dynamically adapt to changes in the learning scenarios, we designed a loss function to adaptively control the strength of attention. It enhances the model to dynamically identify the most relevant visual cues, tailored to the granularity of the visual cues. The final experimental results show that the CIA can provide better visual interpretation while maintaining accuracy and reliability on small and medium-sized datasets.}, archive = {J_ACCESS}, author = {Wu Song and Sheng Ren and Bin Hu}, doi = {10.1109/ACCESS.2025.3583583}, journal = {IEEE Access}, pages = {114117-114127}, shortjournal = {IEEE Access}, title = {Interpretable learning method based on causal interactive attention}, volume = {13}, year = {2025}, }
• (2025). Tension-based reconfigurable multi-agent formation for aerial load transportation. ACCESS, 13, 114098-114116. (www)

  @article{ , abstract = {This paper proposes a trajectory tracking controller for a cable-suspended load transported by n uncrewed aerial vehicles (UAVs) based on Euler angle states. This is accomplished using the resultant tension on the load as a virtual input and then distributing it to each vehicle, for which low-level controllers generate the correct thrust and torque values. Furthermore, a leader-follower-based control is proposed to safely change the number of agents carrying the load. Numerical simulations are presented to validate the controllers. Finally, experiments demonstrate that the proposed method effectively enables the load to perform as expected and allows for the configuration of the formation to be changed without significantly impacting the load navigation.}, archive = {J_ACCESS}, author = {Vinícius Pacheco Bacheti and Daniel K. D. Villa and Rogelio Lozano and Mário Sarcinelli-Filho and Pedro Castillo}, doi = {10.1109/ACCESS.2025.3584623}, journal = {IEEE Access}, pages = {114098-114116}, shortjournal = {IEEE Access}, title = {Tension-based reconfigurable multi-agent formation for aerial load transportation}, volume = {13}, year = {2025}, }
• (2025). AI driven framework for need-based insurance plans generation and anomaly detection using deep learning techniques. ACCESS, 13, 114069-114097. (www)

  @article{ , abstract = {Traditional health insurance models lack flexibility in insurance coverage, as these models suggest fixed insurance policies and plans for individuals and enterprises. As a result, the insured employee is not adequately covered and has to bear additional expenses from his own pocket over and above the claim amount. Enterprises have to pay same premium amount for all employees even for those who utilize healthcare insurance services infrequently. This results into inadequate cost distribution. The manual and static evaluations and verification methods are used for processing insurance claims which may not fully mitigate the risks of entertaining fraudulent claims and ultimately enterprises and insurance companies may bear additional financial burdens. A transformative approach is needed, which can integrate insurance packages and anomaly detection techniques for prevention and timely detection of fraud, risk evaluation and claim optimization. The proposed framework introduces an efficient way to predict health insurance premiums and generate customizable insurance plans using Recurrent Neural Networks (RNNs) and clustering techniques. The proposed methodology attempts to predict the insurance premium amount and splits patients into three categories of risk- Low, Moderate, and High Risk-depending upon how they utilize healthcare services. These categorizations help to create dynamic and need-based insurance policies instead of the rigid designation-based policies. Moreover, to prevent and to monitor fraudulent practices in healthcare, we have applied LSTM-based Anomaly Transformer and Generative adversarial networks (GANs) and compared the results from both models. Experimental results show that Anomaly transformer generates better results as compared to GANs. The proposed framework efficiently detects anomalies in employee and physician behavior, with a 0.0917 validation loss and validation accuracy of 96.4%. The research demonstrates that the proposed AI driven framework can perform both coverage optimization using multiple patient data sets, as well as decision support in healthcare systems.}, archive = {J_ACCESS}, author = {Rabia Rehman and Afia Mazhar and Ayesha Faheem and Irum Matloob and Shoab Ahmad Khan and Sumaira Shaukat and Muazzam A. Khan Khattak and Anwar Ahmed Khan and Muhammad Shoaib Siddiqui and Shama Siddiqui}, doi = {10.1109/ACCESS.2025.3583562}, journal = {IEEE Access}, pages = {114069-114097}, shortjournal = {IEEE Access}, title = {AI driven framework for need-based insurance plans generation and anomaly detection using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Hybrid CNN-LSTM with attention mechanism for robust credit card fraud detection. ACCESS, 13, 114056-114068. (www)

  @article{ , abstract = {In an era marked by rapid technological advancements and a global shift toward cashless transactions, credit card fraud has emerged as a significant challenge, causing substantial financial losses and threatening the security of consumers and financial institutions. The exponential growth of online payments necessitates more effective fraud detection mechanisms. Traditional fraud detection methods, including rule-based systems and basic statistical analyses, struggle to keep pace with the evolving strategies employed by fraudsters. This paper proposes a hybrid fraud detection model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism to address these challenges. Each component of the hybrid model is designed to capture specific behavioral representations, with CNNs focusing on spatial features, LSTMs handling temporal sequences, and the attention mechanism highlighting the most relevant features. We utilized a benchmark dataset and applied the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution. Extensive data preprocessing was conducted to ensure compatibility with the model’s input requirements. The experimental results demonstrated that our hybrid model significantly outperforms traditional machine learning algorithms, achieving an accuracy of 99.93% and a recall of 0.89 on the Credit Card Fraud dataset (CCF). These results highlight the model’s ability to detect fraudulent activities with high precision and reliability. Our hybrid CNN-LSTM-Attention model improves fraud detection by addressing both spatial and temporal data features while dynamically focusing on critical elements. This work not only contributes to the advancement of fraud detection techniques but also provides a framework for future research.}, archive = {J_ACCESS}, author = {Iman Akour and Nour Mohamed and Said Salloum}, doi = {10.1109/ACCESS.2025.3583253}, journal = {IEEE Access}, pages = {114056-114068}, shortjournal = {IEEE Access}, title = {Hybrid CNN-LSTM with attention mechanism for robust credit card fraud detection}, volume = {13}, year = {2025}, }
• (2025). Vision-based control of robotic arm mounted on unmanned surface vehicle for rocket recovery. ACCESS, 13, 114038-114055. (www)

  @article{ , abstract = {Unmanned surface vehicles (USVs) equipped with robotic arms can perform various complex tasks such as grasping, sampling, and infrastructure repair. USVs are widely used for environmental monitoring, search and rescue missions, maintenance and repair, marine exploration, and its commercial applications. This paper presents an innovative vision-based strategy that utilizes a manipulator integrated with a USV to recover vertical takeoff vertical landing rockets in oceanic environments. This study focuses on a 4.3-m-long, 150-kg, three degree-of-freedom (DOF) WAM-V14 with a four-DOF manipulator. To detect and localize the landing rockets, a depth camera sensor and an advanced You Only Look Once Version Eight (YOLOv8) object-detection algorithm were employed. To train the YOLO algorithm, a customized bespoke dataset was developed for rocket identification. As the rocket descends and hovers, the system estimates its orientation and distance using a trained model. The measured distance served as the desired trajectory for the controller, thus guiding the USV toward the identified rocket and controlling the manipulator to reach its location. The integration of camera sensors, robotic arm manipulation, autonomous navigation, YOLO-based object detection, and a sliding-mode control system is promising for realizing affordable and safe rocket recovery operations in challenging oceanic environments.}, archive = {J_ACCESS}, author = {Abenezer Zegeye and Sang Ki Jeong and Hae Yong Park and Sunghun Jung}, doi = {10.1109/ACCESS.2025.3583854}, journal = {IEEE Access}, pages = {114038-114055}, shortjournal = {IEEE Access}, title = {Vision-based control of robotic arm mounted on unmanned surface vehicle for rocket recovery}, volume = {13}, year = {2025}, }
• (2025). Quadratic regression models for profile picture NFT valuation. ACCESS, 13, 114029-114037. (www)

  @article{ , abstract = {In this study, we propose a valuation methodology for Non-Fungible Tokens (NFTs), focusing on the profile picture (PFP) NFT category represented by the Bored Ape Yacht Club (BAYC). To identify the attributes that influence the value of individual BAYC NFTs, we develop a hedonic pricing model that uses the NFT’s value as the dependent variable and its properties as independent variables. We apply Term Frequency-Inverse Document Frequency (TF-IDF) to quantify attributes of NFTs. Three hedonic models—linear, quadratic, and full quadratic—are proposed. For the full quadratic model, we introduce a systematic procedure to select first-order, squared, and interaction terms in the model. To evaluate the performance of the proposed models, we carried out comparative computational experiments. We collected actual BAYC transaction data and split it into a training set (70%) and a validation set (30%). For benchmarking purposes, we compare the proposed models against four machine learning algorithms: Random Forest, Support Vector Regression (SVR), XGBoost, and LightGBM. The machine learning models perform well on the training set, however, this was largely due to overfitting. In contrast, the proposed hedonic models maintained consistent performance with minimal degradation from the training to the validation set. Among them, the full quadratic model demonstrates the highest explanatory power on the validation set in terms of adjusted $R^{2}$ and other evaluation metrics.}, archive = {J_ACCESS}, author = {Geun-Cheol Lee and Hoon-Young Koo and Heejung Lee}, doi = {10.1109/ACCESS.2025.3584222}, journal = {IEEE Access}, pages = {114029-114037}, shortjournal = {IEEE Access}, title = {Quadratic regression models for profile picture NFT valuation}, volume = {13}, year = {2025}, }
• (2025). YOLOv8n-GBE: A hybrid YOLOv8n model with ghost convolutions and BiFPN-ECA attention for solar PV defect localization. ACCESS, 13, 114012-114028. (www)

  @article{ , abstract = {Reliable photovoltaic (PV) module defect detection is essential for maintaining long term energy efficiency and lowering solar power system maintenance costs. The deep learning model presented in this research is based on a hybridized YOLOv8n architecture and is lightweight and high performing. It is designed for multi scale defect identification in a variety of imaging modalities, such as RGB, grayscale, and infrared datasets. The proposed approach combines a BiFPN based neck, Ghost Bottlenecks, and Efficient Channel Attention (ECA) to improve multi scale representation, decrease redundant computation, and increase feature extraction. The model performs better in terms of detection accuracy and efficiency, as shown by experimental findings on three benchmark datasets: PVEL-AD, PV-Multi-Defect, Solar Panel Anomalies. The model’s respective $\text {mAP@50}$ values are 96.5%, 94.6%, and 97.6%. At a steady inference time of only 1.9 ms and 8.1 GFLOPs, it also achieves near-perfect recall (up to 99.0%) and high precision (up to 98.4%). With just 3M parameters, the proposed hybrid model provides a much better accuracy-latency trade-off 61 current state-of-the-art models, which makes it perfect for real-time solar inspection applications, such as edge deployment in drones and embedded systems. The outcomes confirm that reliable PV fault localization under a range of operating situations may be achieved by combining deep feature fusion, lightweight attention, and efficient convolution.}, archive = {J_ACCESS}, author = {Likitha Reddy Yeddula and Archana Pallakonda and Rayappa David Amar Raj and Rama Muni Reddy Yanamala and K. Krishna Prakasha and Mallempati Sunil Kumar}, doi = {10.1109/ACCESS.2025.3584249}, journal = {IEEE Access}, pages = {114012-114028}, shortjournal = {IEEE Access}, title = {YOLOv8n-GBE: A hybrid YOLOv8n model with ghost convolutions and BiFPN-ECA attention for solar PV defect localization}, volume = {13}, year = {2025}, }
• (2025). RF and DC power combining for rectenna arrays: Layout approach and technical analysis. ACCESS, 13, 114003-114011. (www)

  @article{ , abstract = {Radio Frequency Energy Harvesting (RFEH) has become a promising alternative for powering low-power devices in wireless networks. However, the low power spectral density values normally available in the environment limit its application. So, one of the possible strategies to overcome this problem can be the use of antenna arrays in rectennas, to increase the RF power available at rectification input. However, from an RFEH perspective, the use of arrays presents a new challenge to optimization, choosing the domain in which the power combination (RF or DC) is performed. In this context, this work aims to compare array design strategies in an RFEH context, by analyzing the DC performance of these arrays at 2.4 GHz. The spatial limitation, which ensures fairness in comparison, is defined by the final dimensions of the rectenna composeb by $2\times 1$ antenna array with the best RF power performance.}, archive = {J_ACCESS}, author = {Vinicius S. Silva and Renan Diniz and Humberto Pereira da Paz and Ivan Roberto Santana Casella and Mustapha C. E. Yagoub and Carlos Eduardo Capovilla}, doi = {10.1109/ACCESS.2025.3581722}, journal = {IEEE Access}, pages = {114003-114011}, shortjournal = {IEEE Access}, title = {RF and DC power combining for rectenna arrays: Layout approach and technical analysis}, volume = {13}, year = {2025}, }
• (2025). Benchmarking machine learning algorithms for bearing fault classification using vibration data: A deployment-oriented study. ACCESS, 13, 113984-114002. (www)

  @article{ , abstract = {This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in condition monitoring systems. A total of 81,000 samples were collected from three case studies involving SKF7205, SKF7206, and SKF7207 rolling element bearings under varying fault conditions. Feature selection using Principal Component Analysis (PCA) and correlation-based filtering was employed to reduce redundancy and enhance model performance. The classifiers were evaluated across multiple metrics including validation accuracy, test accuracy, misclassification cost, training time, and area under the receiver operating characteristic (ROC) curve (AUC). Ensemble Bagged Trees consistently outperformed other models across all case studies, demonstrating superior classification accuracy, robustness, and low misclassification cost. Fine Tree models also demonstrated competitive performance while maintaining low computational demand, while Wide Neural Networks exhibited high predictive performance with longer training times. This work provides a practical reference for researchers and practitioners by systematically comparing ML algorithms and elucidating the trade-offs between predictive accuracy, computational efficiency, and deployment readiness in real-world fault diagnosis applications.}, archive = {J_ACCESS}, author = {Prasanta Kumar Samal and R. Srinidhi and Pramod Kumar Malik and H. J. Manjunatha and Imran M. Jamadar}, doi = {10.1109/ACCESS.2025.3581711}, journal = {IEEE Access}, pages = {113984-114002}, shortjournal = {IEEE Access}, title = {Benchmarking machine learning algorithms for bearing fault classification using vibration data: A deployment-oriented study}, volume = {13}, year = {2025}, }
• (2025). XV-AMD: An explainable vision transformer detection framework for age-related macular degeneration using fundus imaging. ACCESS, 13, 113967-113983. (www)

  @article{ , abstract = {Age-related macular degeneration (AMD) is a leading cause of visual impairment in adults over 50 years of age and is characterized by progressive damage to the macula, leading to challenges in daily activities such as reading and driving. This study introduces the XV-AMD (An Explainable Vision Transformer Detection for Age-Related Macular Degeneration) framework, which uses Vision Transformers (ViTs) and SHapley Additive exPlanations (SHAPs) for the diagnosis and explainability of AMD. Through a systematic preprocessing pipeline, including average cumulative distribution function (CDF) calculation and contrast limited adaptive histogram equalization (CLAHE), our approach enhances image quality and prepares data for analysis. We classify color fundus images into four distinct AMD categories, namely, normal, geographic atrophy (GA), intermediate AMD, and wet AMD, demonstrating the framework’s effectiveness in a clinical context. The experimental results reveal that our ViT-based model not only improves diagnostic accuracy but also facilitates the understanding of decision-making processes, thus supporting early detection and intervention strategies in AMD management. In addition, a 99.54% classification accuracy was attained, with a sensitivity of 99.54%, specificity of 99.85%, precision of 99.54%, and F1 of 99.54%. This research shows the promise of AI and deep learning in improving AMD diagnoses and identifies future paths for boosting model performance and interpretability in medical imaging applications.}, archive = {J_ACCESS}, author = {Aya A. Abd El-Khalek and Hossam Magdy Balaha and Mohamed T. Azam and Ashraf Sewelam and Mohammed Ghazal and Abeer T. Khalil and Mohy Eldin A. Abo-Elsoud and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3583555}, journal = {IEEE Access}, pages = {113967-113983}, shortjournal = {IEEE Access}, title = {XV-AMD: An explainable vision transformer detection framework for age-related macular degeneration using fundus imaging}, volume = {13}, year = {2025}, }
• (2025). Information content methods for semantic similarity: An experimental assessment. ACCESS, 13, 113953-113966. (www)

  @article{ , abstract = {This paper compares some of the most representative methods for calculating the information content (IC) of concepts within a taxonomy. These methods fall into two categories: extensional methods, which rely on external resources such as text corpora, and intensional methods, which depend solely on the taxonomy’s internal structure. To evaluate these methods, we integrated each one into SemSimp, a technique used to measure the similarity between resource annotations (i.e., sets of concepts from a taxonomy). We show how the adoption of each IC method affects the overall performance of SemSimp and the resulting similarity scores between annotated resources. To assess the results, we used three metrics: 1) the degree of confidence (a statistical measure); 2) the Pearson correlation (based on expert judgments); 3) the harmonic mean of the two. Although previous studies suggest that extensional methods outperform intensional ones, our findings reveal that both categories of methods can achieve strong performance in semantic similarity tasks. Specifically, the best-performing methods achieved an average degree of confidence of 0.86, an average Pearson correlation of 0.69, and their harmonic mean of 0.75.}, archive = {J_ACCESS}, author = {Antonio de Nicola and Anna Formica and Ida Mele and Francesco Taglino}, doi = {10.1109/ACCESS.2025.3584192}, journal = {IEEE Access}, pages = {113953-113966}, shortjournal = {IEEE Access}, title = {Information content methods for semantic similarity: An experimental assessment}, volume = {13}, year = {2025}, }
• (2025). An adaptive variable-weight combination forecasting method for energy product sales based on meta-learning. ACCESS, 13, 113941-113952. (www)

  @article{ , abstract = {Accurate energy product sales forecasting is one of the core tasks of energy planning modeling and is crucial for the sustainability of the energy supply chain. However, energy product sales data have both linear and nonlinear features, and it is difficult for a single model to effectively balance the feature learning of both. In addition, variable influence of external environment leads to the variability of each feature, and traditional fixed-weight combination multi-step forecasting is difficult to adapt to such variability. To address these problems, this study proposes a dynamic combination forecasting method that can adaptively change the model combination weights according to external environment changes. Firstly, a comprehensive and scientific feature system is constructed by combining natural language processing and statistical measurement method. Secondly, Lasso and LSTM models are constructed to deal with overlapping linear and nonlinear features in sales data, respectively. Finally, the meta-learning method is used to establish a relationship function between model combination weights and external features, which realizes adaptive variable model combination for different moments and energy products. Experimental results based on real data from 850 gasoline stations in China show that the proposed method (External feature-based variable-weight combination forecasting model, EFVCFM) significantly outperforms the baseline models and other combination models in terms of forecasting accuracy and stability, with a MAPE and R2 of 5.04% and 0.92, respectively. In addition, the method has high robustness and still achieves excellent model combination results on new data.}, archive = {J_ACCESS}, author = {Meitian Wang}, doi = {10.1109/ACCESS.2025.3584923}, journal = {IEEE Access}, pages = {113941-113952}, shortjournal = {IEEE Access}, title = {An adaptive variable-weight combination forecasting method for energy product sales based on meta-learning}, volume = {13}, year = {2025}, }
• (2025). The additional pedestrian’s behavior: Crossing the road in front of an automated vehicle in a wizard of oz setting. ACCESS, 13, 113924-113940. (www)

  @article{ , abstract = {With the increasing integration of automated vehicles into urban traffic, understanding pedestrians’ interactions with automated vehicles is essential to ensure both safety and efficiency. As complex scenarios involving more than one pedestrian in real-world contexts remain underexplored, more clarification is needed on the relationship between additional pedestrians and automated vehicles’ communication. To address this, the present study investigates pedestrians’ crossing behavior in response to automated vehicles in a real-world setting, focusing on the influence of an additional pedestrian and the effectiveness of an external communication concept. A Wizard of Oz experiment was conducted in which a manually driven vehicle equipped with an external human-machine interface (eHMI) communicated its intent to pedestrians. Thirty-five participants took part in six scenarios that varied the automated vehicle’s communication mode (granting or claiming right of way) and the presence and behavior of an additional pedestrian (absent, compliant, or non-compliant with the automated vehicle’s intent). Results show that the presence and behavior of an additional pedestrian significantly influence crossing initiation time and decision-making, demonstrating the decisive role of social cues in automated vehicles-pedestrian interactions. Furthermore, participants interpreted the automated vehicle’s explicit communication reliably, supporting the effectiveness of the eHMI concept. The findings emphasize the necessity of robust automated vehicle communication strategies to facilitate safe pedestrian interactions in increasingly automated traffic environments.}, archive = {J_ACCESS}, author = {Maximilian Hübner and Simon Bauer and Florian Bogner and Johanna Dechent and Christoph Hauenstein and Klaus Bengler}, doi = {10.1109/ACCESS.2025.3584226}, journal = {IEEE Access}, pages = {113924-113940}, shortjournal = {IEEE Access}, title = {The additional pedestrian’s behavior: Crossing the road in front of an automated vehicle in a wizard of oz setting}, volume = {13}, year = {2025}, }
• (2025). Planning and scheduling system for electric vehicle charging. ACCESS, 13, 113905-113923. (www)

  @article{ , abstract = {The paper presents a novel approach to instant on-the-road electric vehicle charging scheduling planning. It benefits from a mixed-integer mathematical optimization model employed to efficiently select charging stations along the route. It also utilizes machine learning models to approximate the traction battery state of charge using data on driving conditions (energy, speed, travel time, etc.) for individual route sections, considering the driver’s habits and preferences. The research details the functional requirements of this procedure application and its architecture. A step-by-step description of the optimization model is provided, in particular, the constraints responsible for the non-linear model of charging time, the varying time zone-dependent trip parameters (including rush hours), as well as the used optimization criteria, and the scalarization methods. Unlike other existing methods, the combination of the aforementioned mixed-integer mathematical optimization model and machine learning predictors allows for consideration of several factors and adaptation to actual data. Experiments are carried out on the operation of the application, including very long routes (with the actual Warsaw-Zakopane, 412 km long test drives). The results show enhanced, satisfactory execution times, from 0.088 [s] (for a simple model without time zones and with a linear charging model) to 0.365 [s] (for a model without time zones and with a non-linear charging model).}, archive = {J_ACCESS}, author = {Piotr Pałka and Tomasz Śliwiński and Przemysław Kaszyński and Marta Kuta and Bogdan Ruszczak and Marcin Malec and Piotr Saługa and Jacek Kamiński}, doi = {10.1109/ACCESS.2025.3583929}, journal = {IEEE Access}, pages = {113905-113923}, shortjournal = {IEEE Access}, title = {Planning and scheduling system for electric vehicle charging}, volume = {13}, year = {2025}, }
• (2025). A hardware-aware failure-detection method for GPU control-logic. ACCESS, 13, 113890-113904. (www)

  @article{ , abstract = {Graphics processing units (GPUs) are used for diverse applications and play a major role even in safety-critical applications. Although performance is usually the primary focus of GPUs, their reliability has become a major concern. One of the undesirable failures in GPUs is silent data corruption (SDC), which causes unexpected outputs without any warning. Various failure detection methods have been proposed for SDCs caused by faults in data units such as registers. However, effective methods for detecting SDCs resulting from faults in control logic, such as scheduling units, have not yet been established. This paper assumes three types of control-logic failures for a general GPU architecture and proposes efficient failure detection methods for each type. For instance, the proposed method efficiently detects GPU-specific control-logic failures caused by program counter faults with a detection rate of 99.5% and can be implemented with a runtime overhead of 5.3% and a memory-resource overhead of 4.2% for a matrix multiplication application. These methods are applicable to a wide range of applications and are expected to enhance system resiliency.}, archive = {J_ACCESS}, author = {Hiroaki Itsuji and Takumi Uezono and Tadanobu Toba and Kojiro Ito and Masanori Hashimoto}, doi = {10.1109/ACCESS.2025.3584759}, journal = {IEEE Access}, pages = {113890-113904}, shortjournal = {IEEE Access}, title = {A hardware-aware failure-detection method for GPU control-logic}, volume = {13}, year = {2025}, }
• (2025). Attention-based transfer learning for efficient obstructive sleep apnea (OSA) classification on snore sound. ACCESS, 13, 113879-113889. (www)

  @article{ , abstract = {Polysomnography (PSG) is currently the gold-standard technique for classifying sleep apnea disorders. Yet, it is costly and requires an expert to score the severity, making it impractical for self-screening and home use. Snore sound classification with Deep Learning (DL) is a promising approach and has gained increasing interest due to its relationship with abnormal breathing conditions in both time and frequency domains. This study proposes an attention-based transfer learning model for non-invasive detection of obstructive sleep apnea (OSA) using audio signals. Mel-spectrograms and MFCC features were input into the MobileNetV3-Large to extract deep features. A modified SENet was implemented to provide suitable channel attention for the extracted features. The attention-based features are then classified into normal and abnormal snore events. The study evaluates model performance using 10-fold cross-validation on sound data of the 70 adult OSA patients of an open-source PSG-Audio dataset. Results show that utilizing both Mel-Spectrogram and MFCCs as input features significantly enhances classification performance compared to single-feature models. In addition, the MobileNetV3-Large with modified SENet significantly outperforms the combination of other pre-trained and attention mechanisms. Specifically, the proposed model provides an accuracy of $92.576\pm 0.910$ %, a sensitivity of $92.906\pm 1.928$ %, a specificity of $92.269\pm 2.740$ %, a precision of $92.173\pm 3.024$ %, and an F1-score of $92.486\pm 1.326$ % on the binary classification. Its performance also shows statistically significant improvement when benchmarked with other existing OSA classification models. Our proposed model demonstrates suitable potential for portable device-based sleep apnea monitoring applications.}, archive = {J_ACCESS}, author = {Apichada Sillaparaya and Yuttapong Jiraraksopakun and Kosin Chamnongthai and Apichai Bhatranand}, doi = {10.1109/ACCESS.2025.3575203}, journal = {IEEE Access}, pages = {113879-113889}, shortjournal = {IEEE Access}, title = {Attention-based transfer learning for efficient obstructive sleep apnea (OSA) classification on snore sound}, volume = {13}, year = {2025}, }
• (2025). Techno-economic and environmental assessment of solar and wind-based hydrogen production: A case study in vietnam. ACCESS, 13, 113854-113878. (www)

  @article{ , abstract = {This study aims to assess the techno-economic and environmental feasibility of green hydrogen production in Vietnam, leveraging the country’s abundant solar and wind energy resources. The primary objective is to identify optimal locations for hydrogen production plants and determine the most cost-effective and sustainable energy configurations—solar photovoltaic, wind turbine, or hybrid systems. The study employs a dual methodology combining simulation and multi-criteria decision-making to achieve this target. Renewable energy potential and hydrogen output are simulated using MATLAB based on actual meteorological and infrastructure data from Vietnamese provinces. Economic indicators such as levelized cost of hydrogen (LCOH), internal rate of return, and payback period are calculated. Concurrently, the Step-wise Weight Assessment Ratio (SWARA) method is used to determine the relative importance of various decision criteria, and the Weighted Aggregates Sum-Product Assessment (WASPAS) method is applied to rank the optimal locations for hydrogen production. The findings reveal that hydrogen production from solar energy offers the lowest cost (LCOH of 1.24 USD/kg in Binh Thuan), followed by the hybrid configuration (LCOH of 1.59 USD/kg in Binh Thuan), and wind-only systems (LCOH of 1.65 USD/kg in Quang Binh). Environmental analysis also shows significant CO2 emission reductions, with wind-based systems achieving up to 2.255 tCO2/MW/year. The novelty of this study lies in its integrated approach, combining detailed techno-economic and environmental simulation with expert-informed decision analysis to support green hydrogen infrastructure planning. It is one of the first studies to offer a provincial-level hydrogen roadmap for Vietnam, providing valuable insights for policymakers, investors, and energy planners.}, archive = {J_ACCESS}, author = {Nhut Tien Nguyen and Van Phu Dang and Tran Thi Bich Chau Vo and Ryuji Matsuhashi}, doi = {10.1109/ACCESS.2025.3584059}, journal = {IEEE Access}, pages = {113854-113878}, shortjournal = {IEEE Access}, title = {Techno-economic and environmental assessment of solar and wind-based hydrogen production: A case study in vietnam}, volume = {13}, year = {2025}, }
• (2025). Emerging SMOTE and GAN variants for data augmentation in imbalance machine learning tasks: A review. ACCESS, 13, 113838-113853. (www)

  @article{ , abstract = {Class imbalance is a pervasive challenge in real-world machine learning (ML) applications, where the minority class, often the class of interest, is significantly underrepresented. This imbalance can degrade model performance, result in misleading evaluation metrics, and complicate validation processes. Two prominent data-augmentation techniques to address class imbalance are the Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN). However, both techniques have inherent limitations, motivating the emergence of novel variants designed to overcome these challenges. While previous reviews have typically focused on specific domains, conventional methodologies, or broad strategy overviews, this review presents a unified taxonomy that outlines the causes, types, and implications of class imbalance across diverse ML tasks. It further examines emerging trends in the application of SMOTE and GAN techniques, their limitations, and hybrid adaptations. By categorising imbalance types and analysing models, metrics, datasets, and comparative approaches, this review provides actionable insights and identifies future research directions for practitioners and researchers working to address class imbalance in real-world ML tasks.}, archive = {J_ACCESS}, author = {Amadi G. Udu and Marwah T. Salman and Maryam K. Ghalati and Andrea Lecchini-Visintini and David R. Siddle and Hongbiao Dong}, doi = {10.1109/ACCESS.2025.3584532}, journal = {IEEE Access}, pages = {113838-113853}, shortjournal = {IEEE Access}, title = {Emerging SMOTE and GAN variants for data augmentation in imbalance machine learning tasks: A review}, volume = {13}, year = {2025}, }
• (2025). Optimized motion capture for cricket shot classification using minimal hardware and machine learning. ACCESS, 13, 113822-113837. (www)

  @article{ , abstract = {Motion capture technology has been used in many sports analytics, particularly for analyzing player movements in games like cricket. However, the high cost of commercial systems and the disturbing nature of wearable devices limit their accessibility and practicality. This study presents a two-fold solution: first, the development of a low-cost, non-intrusive optical motion capture system; and second, the implementation of a machine learning model to classify cricket batting shots based on body angle variations over time. After evaluating multiple optical techniques, MediaPipe was identified as the most effective and affordable solution. A system comprising four Logitech 720p cameras was deployed using Python multiprocessing to capture player motion in real time, ensuring efficient handling of both computational and input/output operations. Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed model achieved an accuracy of 95% in classifying various batting shots, demonstrating the feasibility and effectiveness of the system as an economical alternative for sports motion analysis.}, archive = {J_ACCESS}, author = {J. Ishan Randika and Kanishka Rajamanthri and Avishka Kothalawala and Niroshan Gunawardana and Ashan Induranga and Pathum Weerakkody and Kaveendra Maduwantha and B. T. G. S. Kumara and Kaveenga Koswattage}, doi = {10.1109/ACCESS.2025.3584890}, journal = {IEEE Access}, pages = {113822-113837}, shortjournal = {IEEE Access}, title = {Optimized motion capture for cricket shot classification using minimal hardware and machine learning}, volume = {13}, year = {2025}, }
• (2025). Binding affinity prediction for pancreatic ductal adenocarcinoma using drug-target descriptors and artificial intelligence. ACCESS, 13, 113809-113821. (www)

  @article{ , abstract = {Pancreatic ductal adenocarcinoma (PDAC) is the most common and aggressive form of pancreatic cancer, accounting for 90% of all pancreatic malignancies. This study addresses the gap in disease-specific binding affinity prediction by integrating PDAC-derived targets with diverse molecular descriptors and artificial intelligence (AI) models, enabling more accurate therapeutic profiling. Initially, we constructed a drug library using compounds from the DepMap database, targeting proteins such as LIFR, BTG2, EPHX2, and PAK3 identified as differentially expressed genes in a previous PDAC study. We employed descriptors such as Conjoint Triad, amino acid composition (AAC), and Quasi sequence order to represent the targets. Similarly, the drugs were described by Morgan, RDKit, and PubChem descriptors. We used AI algorithms like random forest regressor (RFR), extreme gradient boost regressor (XGBR), and one-dimensional convolutional neural network (1D-CNN) to predict the binding affinity. We also employed two benchmark datasets, DAVIS and BindingDB, to compare our models’ performance in binding affinity prediction. We achieved a mean square error (MSE) value of 1.5 using Morgan-RDKit-PubChem-Conjoint descriptors and 1D-CNN on the PDAC dataset. Similarly, 1D-CNN with PubChem-AAC descriptors produced an MSE of 0.27 on the DAVIS dataset. Further, the XGBR model using the PubChem-AAC descriptors produced an MSE of 0.69 on BindingDB. Our study demonstrates the potential of an AI-driven framework as an effective and scalable solution for disease-specific drug-target interaction prediction, with promising implications for drug repurposing in PDAC.}, archive = {J_ACCESS}, author = {Pragya and A. Amalin Prince and Jac Fredo Agastinose Ronickom}, doi = {10.1109/ACCESS.2025.3583338}, journal = {IEEE Access}, pages = {113809-113821}, shortjournal = {IEEE Access}, title = {Binding affinity prediction for pancreatic ductal adenocarcinoma using drug-target descriptors and artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Priority-aware SDN orchestration for surgical IoMT: A joint optimization of hit ratio and latency with dynamic resource reallocation. ACCESS, 13, 113787-113808. (www)

  @article{ , abstract = {Surgical Internet of Medical Things (IoMT) applications impose stringent Quality of Service (QoS) demands, requiring ultra-low latency and high reliability for critical data streams like control signals or high-definition video. Conventional resource orchestration methods often fall short, failing to guarantee performance for high-priority surgical tasks, particularly under dynamic network conditions, including varied wireless channel quality, and potential congestion. This paper introduces the Priority-Aware SDN Orchestration (PASO) framework, leveraging Software-Defined Networking (SDN) for centralized network visibility and control to address these critical challenges. PASO implements a novel joint optimization strategy focused primarily on maximizing the completion rate of critical surgical tasks that adhere strictly to their maximum delay constraints. A key distinguishing feature is its dynamic resource reallocation mechanism, which employs the preemption of lower-priority, non-medical traffic to proactively secure network resources and guarantee QoS for vital surgical flows, even during periods of high network load. A computationally efficient, priority-aware greedy algorithm operationalizes this strategy on the SDN controller. Comparative simulations against Random, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and a Deep Reinforcement Learning (DRL) agent as baselines demonstrate PASO’s substantial effectiveness. It significantly reduces end-to-end delay (e.g., up to 70% less than PSO under high load) and drastically improves the completion rate for critical surgical tasks (over 80% higher than PSO under high load), while also promoting balanced network utilization. PASO offers a robust solution for dependable, low-latency surgical IoMT operations through its targeted optimization and preemptive resource management capabilities.}, archive = {J_ACCESS}, author = {Yalda Safaei and Pouria Arefijamal and Mahdi Siamaki and Bardia Safaei}, doi = {10.1109/ACCESS.2025.3583899}, journal = {IEEE Access}, pages = {113787-113808}, shortjournal = {IEEE Access}, title = {Priority-aware SDN orchestration for surgical IoMT: A joint optimization of hit ratio and latency with dynamic resource reallocation}, volume = {13}, year = {2025}, }
• (2025). Adaptive energy management for MATTER-enabled smart homes. ACCESS, 13, 113773-113786. (www)

  @article{ , abstract = {The proliferation of IoT devices in CSA MATTER-enabled smart homes presents new challenges and opportunities for intelligent energy management. This paper proposes a unified machine learning (ML) framework for dynamic energy optimization, integrating advanced prediction, anomaly detection, and reinforcement learning (RL) techniques. The energy demand prediction module employs a stacked ensemble learning model combining Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and AdaBoost with a meta-learner, achieving a prediction accuracy of 92%. For anomaly detection, a hybrid multi-stage approach incorporating Long Short-Term Memory (LSTM) networks, One-Class SVMs (OCSVM), and autoencoders delivers a precision of 0.97 and recall of 0.95 in identifying abnormal consumption patterns and potential cybersecurity threats. Resource allocation is optimized via RL strategies, including Q-learning, Deep Q-Networks (DQN), and actor-critic methods, achieving an Energy Efficiency Ratio (EER) of 0.82. The framework is evaluated across multiple metrics: energy consumption, detection accuracy, system resilience, and computational efficiency under edge-device constraints. A comprehensive analysis of hardware footprint, latency sensitivity, and data loss impact is presented, along with detailed anomaly detection criteria and threshold tuning to minimize false positives/negatives. The cybersecurity layer is validated through emulated attack scenarios, confirming robust threat detection. Additionally, interpretability of the ML models is addressed through the proposed future integration of Explainable AI (XAI) techniques such as SHAP and LIME, to foster end-user trust and transparency. This work demonstrates a scalable, adaptive, and secure ML-driven solution for energy management in MATTER-enabled smart homes, contributing significantly to sustainable and cost-efficient smart living environments.}, archive = {J_ACCESS}, author = {Navodit Bhardwaj and Pallavi Joshi}, doi = {10.1109/ACCESS.2025.3584381}, journal = {IEEE Access}, pages = {113773-113786}, shortjournal = {IEEE Access}, title = {Adaptive energy management for MATTER-enabled smart homes}, volume = {13}, year = {2025}, }
• (2025). Trajectory of fifths in tonal mode detection. ACCESS, 13, 113763-113772. (www)

  @article{ , abstract = {The article introduces a novel algorithm designed to distinguish between the major and minor tonal modes in musical compositions. It utilizes parameters derived from the trajectory of fifths obtained for a given piece: the angular coordinates of the trajectory’s first and last points, the orientation of the trajectory’s characteristic vector, and a coefficient quantifying the imbalance in the distribution of trajectory points between quadrants associated with major and minor modes. To assess the algorithm’s effectiveness, experiments were conducted using selected sets of Bach and Chopin works transposed into C major and A minor keys, as well as on pieces from the Saarland Music Data: MIDI-Audio Piano Music in their original keys. The results indicate high detection accuracy, demonstrating the algorithm’s potential for reliable tonal mode identification.}, archive = {J_ACCESS}, author = {Tomasz Łukaszewicz and Dariusz Kania}, doi = {10.1109/ACCESS.2025.3584400}, journal = {IEEE Access}, pages = {113763-113772}, shortjournal = {IEEE Access}, title = {Trajectory of fifths in tonal mode detection}, volume = {13}, year = {2025}, }
• (2025). Toward data systems that are business semantic centric and AI agents assisted. ACCESS, 13, 113752-113762. (www)

  @article{ , abstract = {Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in inefficiencies and delays. To address this gap, I propose the Business Semantics Centric, AI Agents Assisted Data System (BSDS), a holistic system that integrates architecture, workflows, and team organization to ensure data systems are tailored to business priorities rather than dictated by technical constraints. BSDS redefines data systems as dynamic enablers of business success, transforming them from passive tools into active drivers of organizational growth. BSDS has a modular architecture that comprises curated data linked to business entities, a knowledge base for context-aware AI agents, and efficient data pipelines. AI agents play a pivotal role in assisting with data access and system management, reducing human effort, and improving scalability. Complementing this architecture, BSDS incorporates workflows optimized for both exploratory data analysis and production requirements, balancing speed of delivery with quality assurance. A key innovation of BSDS is its incorporation of the human factor. By aligning data team expertise with business semantics, BSDS bridges the gap between technical capabilities and business needs. Validated through real-world implementation, BSDS accelerates time-to-market for data-driven initiatives, enhances cross-functional collaboration, and provides a scalable blueprint for businesses of all sizes. Future research can build on BSDS to explore optimization strategies using complex systems and adaptive network theories, as well as developing autonomous data systems leveraging AI agents.}, archive = {J_ACCESS}, author = {Cecil Pang}, doi = {10.1109/ACCESS.2025.3583260}, journal = {IEEE Access}, pages = {113752-113762}, shortjournal = {IEEE Access}, title = {Toward data systems that are business semantic centric and AI agents assisted}, volume = {13}, year = {2025}, }
• (2025). SafeTrack: Secure tracking protocol for mobile sensor nodes in unstable wireless sensor networks. ACCESS, 13, 113736-113751. (www)

  @article{ , abstract = {In this paper, we present SafeTrack, a novel, secure tracking protocol specifically for mobile sensor nodes in unstable wireless sensor networks (UWSNs). To the best of our knowledge, the proposed framework addresses the main challenges, including dynamic network topology, energy constraints, and Sybil and impersonation attacks. In SafeTrack, we introduce a Dual Layer Security Framework (DLSF), which a lightweight cryptographic technique is paired with a trust based approach to provide robust node authentication and secured communication. It also incorporates an Adaptive Routing Algorithm (ARA) and an Energy Optimization Module (EOM) to enable both efficient resource utilization and resilience to changes in dynamic network conditions. Simulation and performance analysis show that SafeTrack performs much better than any previous state of the art protocol. This results in a 64% improvement in localization accuracy, 24.1% higher energy retention, and 51.1% lower latency, while achieving optimal performance even in highly unstable environments. We validate scalability of the framework with support for up to 300 nodes, which is far beyond what other protocols can do. Additionally, SafeTrack demonstrates a 70.6% improvement in network throughput, providing the ability to support high loads of traffic. SafeTrack is novel in that it combines advanced security and adaptive optimization techniques specifically designed for UWSNs. Using this innovative approach not only improves network reliability and security but also provides sustainability in energy constrained environments. The results demonstrate the feasibility and effectiveness of SafeTrack, positioning it in the vanguard of solutions for real world applications in smart cities, healthcare systems and industrial IoT.}, archive = {J_ACCESS}, author = {Ali Adnan Al-Khazraji and Fatimah Abdulridha Rashid}, doi = {10.1109/ACCESS.2025.3584869}, journal = {IEEE Access}, pages = {113736-113751}, shortjournal = {IEEE Access}, title = {SafeTrack: Secure tracking protocol for mobile sensor nodes in unstable wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Multi-modal integration analysis of alzheimer’s disease using large language models and knowledge graphs. ACCESS, 13, 113718-113735. (www)

  @article{ , abstract = {We propose a novel framework for integrating fragmented multi-modal data in Alzheimer’s disease (AD) research using large language models (LLMs) and knowledge graphs. While traditional multi-modal analysis requires matched patient IDs across datasets, our approach demonstrates population-level integration of MRI, gene expression, biomarkers, EEG, and clinical indicators from independent cohorts. Statistical analysis identified significant features in each modality, which were connected as nodes in a knowledge graph. LLMs then analyzed the graph to extract potential correlations and generate hypotheses in natural language. This approach revealed several novel relationships, including a potential pathway linking metabolic risk factors to tau protein abnormalities via neuroinflammation (r>0.6, p<0.001), and unexpected correlations between frontal EEG channels and specific gene expression profiles (r=0.42-0.58, p<0.01). Cross-validation with independent datasets confirmed the robustness of major findings, with consistent effect sizes across cohorts (variance <15%). The reproducibility of these findings was further supported by expert review (Cohen’s $\kappa =0.82$ ) and computational validation. Our framework enables cross-modal integration at a conceptual level without requiring patient ID matching, offering new possibilities for understanding AD pathology through fragmented data reuse and generating testable hypotheses for future research.}, archive = {J_ACCESS}, author = {Kanan Kiguchi and Yunhao Tu and Katsuhiro Ajito and Fady Alnajjar and Kazuyuki Murase}, doi = {10.1109/ACCESS.2025.3582853}, journal = {IEEE Access}, pages = {113718-113735}, shortjournal = {IEEE Access}, title = {Multi-modal integration analysis of alzheimer’s disease using large language models and knowledge graphs}, volume = {13}, year = {2025}, }
• (2025). Enhancing diabetic foot ulcer classification through fine-tuned multilevel CNN. ACCESS, 13, 113702-113717. (www)

  @article{ , abstract = {Diabetic foot ulcers (DFUs) are especially problematic with their high rates of recurrence. Recognizing DFUs with infection and ischemia is vital for appropriate treatment and prevention of amputations. The proposed approach combines two convolutional neural networks (CNNs), fine-tuned through multilevel refinement, to classify DFU images into four categories: nonexistent, infection, ischemia, and both (infection/ischemia). Transfer learning was used and the adopted multilevel design includes fully connected layers with varying numbers of neurons and batch normalization. Data augmentation techniques were applied to a dataset comprising 8,242 images to deal with the class imbalance problem, mitigate the overfitting possibility, and enhance the approach’s performance. Based on a five-fold cross-validation, the proposed approach obtained 95.91%, 93.28%, and 95.1% of accuracy, Kappa, and F1-score metrics, respectively, confirming its superiority compared to the current solutions for the same number of image classes. Furthermore, a cross-dataset validation method was employed to evaluate the model’s generalization ability. Employing this methodology, the Kaggle DFU dataset obtained accuracy, Kappa, and F1-score values of 79.79%, 59.80%, and 79.02%. Therefore, the proposed approach appears promising for the precise and timely diagnosis of DFU images.}, archive = {J_ACCESS}, author = {Elineide Silva Dos Santos and Rodrigo de Melo Souza Veras and Francisco Das Chagas Torres Dos Santos and Márcia Ito and Andrea Gomes Campos Bianchi and Kelson Rômulo Teixeira Aires and João Manuel R. S. Tavares}, doi = {10.1109/ACCESS.2025.3583111}, journal = {IEEE Access}, pages = {113702-113717}, shortjournal = {IEEE Access}, title = {Enhancing diabetic foot ulcer classification through fine-tuned multilevel CNN}, volume = {13}, year = {2025}, }
• (2025). SORA: Energy-efficient resource allocation in open radio access network with online learning. ACCESS, 13, 113686-113701. (www)

  @article{ , abstract = {The Open Radio Access Network (O-RAN) architecture, which leverages virtualized base stations deployable on diverse commodity hardware, is poised to enhance the flexibility and cost-effectiveness of next-generation cellular networks. However, its widespread adoption faces challenges due to the complexity of configuration choices, which can significantly affect both network performance and energy efficiency. Achieving the right balance requires intelligent resource allocation—optimizing Modulation and Coding Scheme (MCS) selection and Physical Resource Block (PRB) assignment to maximize spectrum and processing efficiency. In this work, we propose a Thompson Sampling-based online learning framework for energy-efficient resource allocation in virtualized base stations. Our approach is designed to operate with minimal assumptions about the network environment, making it highly adaptable to both stationary and dynamic conditions. By integrating variational inference with Thompson Sampling, the framework efficiently balances exploration and exploitation, allowing dynamic adjustment of MCS and PRB allocations in response to changing network states. The proposed algorithm achieves sub-linear regret, ensuring convergence toward an optimal policy over time while maintaining robust adaptability. We validate our method using open-source platforms—OpenAirInterface and FlexRIC—demonstrating its practical effectiveness. Our results show that the proposed approach achieves energy savings of up to 15 percent and improves resource block utilization efficiency by up to 31 percent compared to the default scheduling policy. These findings underscore the potential of our method to significantly enhance the sustainability and efficiency of O-RAN deployments, paving the way for smarter, more adaptive next-generation cellular networks.}, archive = {J_ACCESS}, author = {Aerman Tuerxun and Akihiro Nakao}, doi = {10.1109/ACCESS.2025.3584345}, journal = {IEEE Access}, pages = {113686-113701}, shortjournal = {IEEE Access}, title = {SORA: Energy-efficient resource allocation in open radio access network with online learning}, volume = {13}, year = {2025}, }
• (2025). Modeling air combat behavior for simulation-based pilot training: A survey of machine learning approaches. ACCESS, 13, 113666-113685. (www)

  @article{ , abstract = {Fighter pilots train maneuvers and missions in simulators with and against simulated entities that must exhibit realistic behavior for effective training. However, current simulated entities often display simplified behavior, and traditional methods for improving realism require extensive manual effort to integrate domain knowledge. Recent advancements in machine learning, specifically reinforcement learning, imitation learning, and evolutionary algorithms, offer scalable alternatives by enabling agents to learn complex behavior from data. Despite improved methods, challenges remain in ensuring that agents reliably learn tactical maneuvers and proper employment of weapons and sensors in diverse scenarios. Another significant challenge is transferring agents from learning environments to military simulation systems because of differences in observation formats, action formats, and flight dynamic models. This survey categorizes the relevant literature in terms of applications, mission tasks, behavior model types, and machine learning methods, followed by a comparative analysis with an emphasis on the learning process. Based on this analysis, we present four primary recommendations: 1) standardization and research collaboration initiatives, 2) enhanced focus on multi-agent machine learning and cooperation, 3) utilization of hierarchical behavior models, and 4) emphasis on beyond-visual-range scenarios. These recommendations aim to address the current limitations and guide the development of more comprehensive, adaptable, and realistic machine learning-based behavior models for simulation-based pilot training.}, archive = {J_ACCESS}, author = {Andreas Strand and Patrick Ribu Gorton and Karsten Brathen}, doi = {10.1109/ACCESS.2025.3580279}, journal = {IEEE Access}, pages = {113666-113685}, shortjournal = {IEEE Access}, title = {Modeling air combat behavior for simulation-based pilot training: A survey of machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). Using fMRI-based multi-scale perception models to explore cognitive load and attention allocation in education. ACCESS, 13, 113648-113665. (www)

  @article{ , abstract = {The study of cognitive load and attention allocation has gained prominence in educational research due to its critical role in optimizing teaching strategies. Cognitive load refers to the mental effort required to process information, while attention allocation describes how learners distribute their cognitive resources across tasks. Traditional methods, such as Cognitive Load Theory (CLT) and behavioral measures (e.g., reaction times, eye-tracking), provide valuable insights but fall short in capturing real-time neural mechanisms. These approaches are limited by their reliance on single-task contexts and their inability to probe dynamic, multi-scale brain processes during learning. To address these gaps, we propose a novel framework integrating fMRI-based multi-scale perception models with behavioral and physiological data. This method allows for real-time analysis of brain activity, providing a granular understanding of how cognitive load and attention are distributed across complex learning tasks. Our approach leverages neural data from key brain regions involved in memory and attention, enabling the development of adaptive, personalized educational tools. Technically, our method employs a hybrid CNN-ViT architecture with dynamic uncertainty calibration, enabling high-resolution multimodal fusion across EEG and fMRI signals. Experimental results show that our model achieves up to 91.83% accuracy on the ADHD-200 dataset, significantly outperforming state-of-the-art baselines. Experimental results demonstrate that our method significantly improves the understanding of neural mechanisms underlying learning. By correlating cognitive load with brain oscillations (e.g., theta, alpha bands), we identify biomarkers that predict learning efficiency and attention shifts. This research contributes to the design of adaptive learning systems, paving the way for personalized, scalable educational solutions that align with neural capacities.}, archive = {J_ACCESS}, author = {Lu Chen and Hongli Lou and Pin Yue and Jianwen Chen}, doi = {10.1109/ACCESS.2025.3571711}, journal = {IEEE Access}, pages = {113648-113665}, shortjournal = {IEEE Access}, title = {Using fMRI-based multi-scale perception models to explore cognitive load and attention allocation in education}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based metal artifact reduction with masked mean squared error loss function in simulation CT for radiation therapy for head and neck cancer. ACCESS, 13, 113634-113647. (www)

  @article{ , abstract = {Deep learning-based approaches to metal artifact reduction have recently been proposed, yet these methods still struggle to effectively remove metal artifacts in head and neck computed tomography (CT) images, which have a complex structure and can contain strong artifacts due to the insertion of dental fillings and implants. These strong metal artifacts cause treatment uncertainty in radiation therapy. In this study, we propose a masked criterion function that weighs each region for CT numbers using masks extracted by supervised contrastive learning to better remove metal artifacts in head and neck CT images. Applying the criterion function, a convolutional neural network-based metal artifact reduction model was trained on a synthetic dataset. We adopted a new data synthesis method to prevent tissue information loss by sinogram handling. For the synthetic data, our method outperformed previous models (e.g., linear interpolation, UNet, IndudoNet, FusionNet, Uformer) in terms of image quality and quantitative evaluations, showing the lowest average value of calculated artifact index, 26.57311, compared to the others. In addition, we recalculated the dose on artifact-reduced CT images and found that artifacts clearly degraded the plan quality for patients whose target is close to metal. The results of this study demonstrate that the proposed criterion function helps separate artifacts and tissues using masks extracted through supervised contrastive learning, and that the proposed model can reduce even strong artifacts using this criterion function. Our code can be found here: github.com/wonjin0403/MAR.git}, archive = {J_ACCESS}, author = {Juhyeong Ki and Wonjin Lee and Bitbyeol Kim and Dukju Kim and Seongmoon Jung and Jimin Lee}, doi = {10.1109/ACCESS.2025.3583191}, journal = {IEEE Access}, pages = {113634-113647}, shortjournal = {IEEE Access}, title = {Deep learning-based metal artifact reduction with masked mean squared error loss function in simulation CT for radiation therapy for head and neck cancer}, volume = {13}, year = {2025}, }
• (2025). A hybrid machine learning approach for predicting power transformer failures using internet of things-based monitoring and explainable artificial intelligence. ACCESS, 13, 113618-113633. (www)

  @article{ , abstract = {Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime. Existing maintenance strategies often fail to accurately predict such failures, highlighting the need for novel predictive approaches. This study aims to improve the reliability of power systems by predicting transformer failures through the integration of IoT technologies and advanced machine learning techniques. The proposed hybrid model combines the LightGBM algorithm with GridSearch optimization to achieve both high predictive accuracy and computational efficiency. In addition, the model enhances interpretability by incorporating SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for transparent decision making. The study presents a detailed comparison of different classification algorithms and evaluates their performance using metrics such as accuracy, recall, and F1 score. The results show that the hybrid model outperforms other methods, achieving an accuracy of 99.91%. The SHAP and LIME analyses provide engineers and researchers with valuable insights by highlighting the most influential features in failure prediction. In addition, the model’s ability to efficiently handle large data sets enhances its practicality in real-world power systems. By proposing an innovative approach to failure prediction, this research contributes to both the theoretical foundation and practical advancement of sustainable and reliable energy infrastructures.}, archive = {J_ACCESS}, author = {Emrah Aslan and Yıldırım Özüpak and Feyyaz Alpsalaz and Zakaria M. S. Elbarbary}, doi = {10.1109/ACCESS.2025.3583773}, journal = {IEEE Access}, pages = {113618-113633}, shortjournal = {IEEE Access}, title = {A hybrid machine learning approach for predicting power transformer failures using internet of things-based monitoring and explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Benchmarking spiking neural network learning methods with varying locality. ACCESS, 13, 113606-113617. (www)

  @article{ , abstract = {Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning tasks. Information is processed as spikes within SNNs in an event-based mechanism that significantly reduces energy consumption. However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism. Traditional approaches, such as Backpropagation Through Time (BPTT), have shown effectiveness but come with additional computational and memory costs and are biologically implausible. In contrast, recent works propose alternative learning methods with varying degrees of locality, demonstrating success in classification tasks. In this work, we show that these methods share similarities during the training process, while they present a trade-off between biological plausibility and performance. Further, given the implicitly recurrent nature of SNNs, this research investigates the influence of the addition of explicit recurrence to SNNs. We experimentally prove that the addition of explicit recurrent weights enhances the robustness of SNNs. We also investigate the performance of local learning methods under gradient and non-gradient-based adversarial attacks.}, archive = {J_ACCESS}, author = {Jiaqi Lin and Sen Lu and Malyaban Bal and Abhronil Sengupta}, doi = {10.1109/ACCESS.2025.3582564}, journal = {IEEE Access}, pages = {113606-113617}, shortjournal = {IEEE Access}, title = {Benchmarking spiking neural network learning methods with varying locality}, volume = {13}, year = {2025}, }
• (2025). Deep learning in wireless communication receivers: A survey. ACCESS, 13, 113586-113605. (www)

  @article{ , abstract = {The design of wireless communication receivers to enhance signal processing in complex and dynamic environments is going through a transformation by leveraging deep neural networks (DNNs). Traditional wireless receivers depend on models and algorithms, which do not have the ability to learn from data. In contrast, deep learning-based receivers are more suitable for modern wireless communication systems because they can learn from data and adapt accordingly. This survey explores various deep learning architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and autoencoders, focusing on their application in the design of wireless receivers. Key modules of a receiver such as synchronization, channel estimation, equalization, space-time decoding, demodulation, decoding, interference cancellation, and modulation classification are discussed in the context of advanced wireless technologies like orthogonal frequency division multiplexing (OFDM), multiple input multiple output (MIMO), semantic communication, task-oriented communication, and next-generation (Next-G) networks. This survey fills a critical gap by providing a wireless receiver-focused deep learning roadmap, systematically mapping DNNs to each receiver stage, reviewing DL-enabled semantic and task-oriented reception, unifying state-of-the-art solutions for OFDM/MIMO, high-mobility links, and DL-enabled interference cancellation in a single reference. The survey not only emphasizes the potential of deep learning-based receivers in future wireless communication but also highlights different challenges of deep learning-based receivers, such as data availability, security and privacy concerns, model interpretability, computational complexity, and integration with legacy systems.}, archive = {J_ACCESS}, author = {Shadman Rahman Doha and Ahmed Abdelhadi}, doi = {10.1109/ACCESS.2025.3584000}, journal = {IEEE Access}, pages = {113586-113605}, shortjournal = {IEEE Access}, title = {Deep learning in wireless communication receivers: A survey}, volume = {13}, year = {2025}, }
• (2025). Automatic guitar transcription with deep neural networks. ACCESS, 13, 113573-113585. (www)

  @article{ , abstract = {Guitar tablature is a form of musical notation designed specifically for fretted string instruments. It is crucial for musicians, especially beginners, as it provides an easy-to-read format that visually represents the placement of the fingers on the instrument, making it more accessible to learn and play songs accurately. Automatic transcription of guitar music into tablature is a complex task in Music Information Retrieval, which has been enhanced by recent advances in deep learning. This article builds on a state-of-the-art note-level transcription model that uses a self-attention mechanism. The model incorporates beat-informed quantisation to accurately convert audio signals into tablature, overcoming challenges such as determining where notes are played on the guitar neck. In replicating this model, its properties have been investigated, and the results have been analysed. A key aspect of this article is the development of a new dataset designed to assess the robustness of the model in different scenarios. This is crucial due to the limited availability of data in this area. Through extensive experimentation and analysis, this study evaluates the performance of the model on unseen data, identifying its strengths and areas for improvement. In addition, this article provides insights into the mechanism of self-attention and its effectiveness in tasks such as Automatic Music Transcription. The model was tested with multiple attention heads to study their impact on performance, but this modification did not show significant improvement. Therefore, it suggests that other areas, such as improving the quality and quantity of data, may be more crucial to improve performance.}, archive = {J_ACCESS}, author = {Simone Chieppa and Pierpaolo Brutti and Rui Pedro Paiva}, doi = {10.1109/ACCESS.2025.3583646}, journal = {IEEE Access}, pages = {113573-113585}, shortjournal = {IEEE Access}, title = {Automatic guitar transcription with deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Product family formal design based on brand DNA extraction by AIGC technology. ACCESS, 13, 113557-113572. (www)

  @article{ , abstract = {Contemporary brands increasingly emphasize the unification of design elements to enhance brand image consistency. With the advancement of artificial intelligence, AIGC (Artificial Intelligence Generated Content) technology has been widely applied in the modern design industry. Although existing studies acknowledge the capability of AIGC technology in capturing and preserving brand characteristics, current research has two limitations. Most models rely on machine annotation, and little research addresses how design DNA transfers across different product categories within the same brand using this technology. This study proposes an AIGC-assisted approach for brand product family formal design, aiming to improve the design efficiency and innovation of brand products while maintaining brand consistency across multiple product categories. Through multi-dimensional analysis, design matrix analysis, and fuzzy evaluation methods, this research refines the design DNA of brand product families, providing a theoretical foundation for the subsequent AIGC model training and application. Additionally, based on the Stable Diffusion platform, this study conducts a comparative analysis of LoRA models trained with manual annotations versus CLIP-based machine annotations in inheriting design DNA of brand product families. Research findings suggest that AIGC technology effectively enhances the inheritance of brand attributes across product categories. While both annotation methods demonstrate effectiveness in preserving brand design coherence within ControlNet constraints, the manually labeled LoRA model outperforms its machine-labeled counterpart in representing design elements. This research establishes an innovative approach to brand product design, demonstrating AIGC technology’s practical value in maintaining brand consistency while fostering design innovation.}, archive = {J_ACCESS}, author = {Yiming Zhang and Biyun Wu and Jiating Chen and Yunkai Chen and Mianyu Yu and Lufang Zhang and Zhichuan Tang}, doi = {10.1109/ACCESS.2025.3584655}, journal = {IEEE Access}, pages = {113557-113572}, shortjournal = {IEEE Access}, title = {Product family formal design based on brand DNA extraction by AIGC technology}, volume = {13}, year = {2025}, }
• (2025). Optimized ensemble deep learning for real-time intrusion detection on resource-constrained raspberry pi devices. ACCESS, 13, 113544-113556. (www)

  @article{ , abstract = {The rapid growth of Internet of Things (IoT) networks has increased security risks, making it essential to have effective Intrusion Detection Systems (IDS) for real-time threat detection. Deep learning techniques offer promising solutions for such detection due to their superior complex pattern recognition and anomaly detection capabilities in large datasets. This paper proposes an optimized ensemble-based IDS designed specifically for efficient deployment on edge hardware. However, deploying such computationally intensive models on resource-limited edge devices remains a significant challenge due to model size and computational overhead on devices with limited processing capabilities. Building upon our previously developed stacked Long Short-Term Memory (LSTM) model integrated with ANOVA feature selection, we optimize it by integrating dual-stage model compression: pruning and quantization to create a lightweight model suitable for real-time inference on Raspberry Pi devices. To evaluate the system under realistic conditions, we combined with a Kafka-based testbed to simulate dynamic IoT environments with variable traffic loads, delays, and multiple simultaneous attack sources. This enables the assessment of detection performance under varying traffic volumes, latency, and overlapping attack scenarios. The proposed system maintains high detection performance with accuracy of 97.3% across all test scenarios, while efficiently leveraging multi-core processing with peak CPU usage reaching 111.8%. These results demonstrate the system’s practical viability for real-time IoT security at the edge.}, archive = {J_ACCESS}, author = {Muhammad Bisri Musthafa and Samsul Huda and Tuy Tan Nguyen and Yuta Kodera and Yasuyuki Nogami}, doi = {10.1109/ACCESS.2025.3584373}, journal = {IEEE Access}, pages = {113544-113556}, shortjournal = {IEEE Access}, title = {Optimized ensemble deep learning for real-time intrusion detection on resource-constrained raspberry pi devices}, volume = {13}, year = {2025}, }
• (2025). A combined diffusion model and reinforcement learning approach for solving the vehicle routing problem with multiple soft time windows. ACCESS, 13, 113529-113543. (www)

  @article{ , abstract = {The Vehicle Routing Problem with Multiple Soft Time Windows (VRPMSTW) is a challenging combinatorial optimization problem where a fleet of vehicles must deliver goods to a set of customers, adhering to time windows while minimizing costs. In this paper, we propose a novel solution approach that combines a Diffusion Model with Reinforcement Learning (RL) to efficiently solve the VRPMSTW. The Diffusion Model generates feasible vehicle routes by denoising a noise distribution, ensuring that constraints such as vehicle capacity, travel distance, and time windows are respected. Subsequently, the RL module fine-tunes these paths by optimizing the objective function, which minimizes the number of vehicles, travel distance, and time window penalties. We evaluate our approach on benchmark datasets and compare it with other state-of-the-art methods. The results demonstrate that our combined model outperforms traditional heuristics, achieving better optimization in terms of the number of vehicles, travel cost, and time window violations. The proposed method provides a promising solution for solving complex real-world vehicle routing problems with soft time window constraints.}, archive = {J_ACCESS}, author = {Yu Qiao and Jianjun Miao and Xiaoying Huang}, doi = {10.1109/ACCESS.2025.3583984}, journal = {IEEE Access}, pages = {113529-113543}, shortjournal = {IEEE Access}, title = {A combined diffusion model and reinforcement learning approach for solving the vehicle routing problem with multiple soft time windows}, volume = {13}, year = {2025}, }
• (2025). CSCP-YOLO: A lightweight and efficient algorithm for real-time steel surface defect detection. ACCESS, 13, 113517-113528. (www)

  @article{ , abstract = {Steel surface defect detection is critical for ensuring product quality, yet remains challenging due to multi-scale defects, small target prevalence, and complex background interference. To address these issues, a novel model named CSCP-YOLO is proposed. Specifically, the CGSF (C2f-GSConv-FasterNet) module is integrated into the feature extraction network to enhance the identification of small target defects on steel surfaces. To accelerate defect detection, the SPPF-DW module efficiently fuses multi-scale defect features through optimized computational pathways. The CCE (C2f-CIB-EMA) module strengthens feature extraction capabilities by optimizing weight sparsity distributions during model training. This architecture significantly improves feature map expressiveness and cross-channel information interaction, enhancing multi-scale and small-target perception. Finally, the Powerful-IoU loss function is employed to address optimization limitations in the original objective formulation. Extensive experiments on the NEU-DET dataset demonstrate that CSCP-YOLO achieves 79.1% mAP and 71.8% recall, outperforming YOLOv8s by 2.2% and 2.2%, respectively. Notably, through architectural simplification and quantization, the model size is reduced by 36.4% (from 22.5 MB to 14.3 MB) while achieving 186.2 FPS on an NVIDIA GeForce RTX3090, exceeding the 30 FPS threshold for real-time industrial inspection. This work provides a robust solution for high-speed, high-precision steel defect inspection, with potential applications in smart manufacturing.}, archive = {J_ACCESS}, author = {Chenglong Wang and Heng Wang and Yimin Jiang and Lei Yu and Xueting Wang}, doi = {10.1109/ACCESS.2025.3581024}, journal = {IEEE Access}, pages = {113517-113528}, shortjournal = {IEEE Access}, title = {CSCP-YOLO: A lightweight and efficient algorithm for real-time steel surface defect detection}, volume = {13}, year = {2025}, }
• (2025). UAV-IRS assisted full-duplex covert communication: Joint optimization of phase shift and 3D trajectory. ACCESS, 13, 113502-113516. (www)

  @article{ , abstract = {This paper proposes a novel strategy to enhance covert communication performance through a full-duplex communication system assisted by Unmanned Aerial Vehicles (UAV) and Intelligent Reflecting Surfaces (IRS). In the proposed system, the base station (Alice) leverages the IRS mounted on UAV to reflect covert information towards the legitimate full-duplex receiver (David). As a full-duplex receiver, David actively generates dynamic artificial noise (AN) to deceive the warden (Eve) and obscure her detection capability. Notably, due to the adversarial nature of the scenario, Eve’s exact location remains unknown to the legitimate parties. Within this framework, we first derive the minimum detection error probability (DEP) for Eve under the worst-case scenario, where Eve employs the optimal detection threshold. Subsequently, we formulate an optimization problem aimed at maximizing the covert transmission rate while satisfying the covertness constraint by alternately optimizing the IRS phase shifts and the three-dimensional (3D) trajectory of UAV. Simulation results validate the effectiveness of the proposed strategy in enhancing covert communication performance, particularly in non-line-of-sight (NLOS) environments.}, archive = {J_ACCESS}, author = {Pengwu Wan and Xiaolin Dong and Dongrui Chen and Xi Hui and Jin Wang}, doi = {10.1109/ACCESS.2025.3582944}, journal = {IEEE Access}, pages = {113502-113516}, shortjournal = {IEEE Access}, title = {UAV-IRS assisted full-duplex covert communication: Joint optimization of phase shift and 3D trajectory}, volume = {13}, year = {2025}, }
• (2025). Rehabilitation and motion symmetry analysis with a TACX smart cycling trainer using computational intelligence. ACCESS, 13, 113495-113501. (www)

  @article{ , abstract = {Motion analysis provides important information in rehabilitation, performance evaluation, and movement symmetry assessment, with applications including neurology, biomedicine, surgery, and sports monitoring. The integration of virtual reality, wearable sensors, and signal processing forms a robust interdisciplinary platform for such analysis. Specific methods are based on monitoring physiological and motion responses during controlled exercises that simulate real-world motion scenarios. This study focuses on processing of signals from wearable sensors collected from smart indoor trainers, enabling motion monitoring under predefined load conditions. The acquired datasets include heart rate (HR), motion accelerometric and gyrometric signals, and fitness parameters (cycling speed). The research objectives include analysis of motion patterns, evaluation of motion symmetry under varying loads, and examination of heart rate responses to load variations. Signal processing is conducted using advanced methods that include computational intelligence, digital signal processing, and artificial intelligence tools for data classification. Results point to the mean delay of the HR drop to 97% of the HR range in 15s after the change from the cycling on the slope of 8% to the rest period and the following drop to 5% in next 54s. The classification of spectral features evaluated separately for the left and right legs pointed the classification accuracy of 94.5% for accelerometric data and 99.1% for gyrometric data estimated by the use of the two layer neural network and the symmetry coefficient of 1.05 for the slope of 8%. In general, the paper presents selected processing methods and experimental results pointing to the effectiveness of computational intelligence in motion analysis.}, archive = {J_ACCESS}, author = {Hana Charvátová and Daniel Martynek and Alexandra Molčanová and Aleš Procházka}, doi = {10.1109/ACCESS.2025.3579804}, journal = {IEEE Access}, pages = {113495-113501}, shortjournal = {IEEE Access}, title = {Rehabilitation and motion symmetry analysis with a TACX smart cycling trainer using computational intelligence}, volume = {13}, year = {2025}, }
• (2025). Instant slow-motion visualization of high-speed phenomena based on event camera. ACCESS, 13, 113482-113494. (www)

  @article{ , abstract = {High-speed phenomena are difficult to perceive due to human vision limitations, yet their immediate understanding is crucial in fields like sports refereeing and neurosciences. Existing systems often rely on post-processing or require active user intervention, failing to provide intuitive, real-time insights. This study presents a system that instantly visualizes high-speed phenomena in slow-motion using an event camera. The system prepares a background image, captured in advance, as a spatial reference. It overlays detected high-speed phenomena on this image for visualization. The system automatically detects high-speed phenomena and transitions to slow-motion for detailed analysis, while maintaining a real-time display. Auditory notifications alert users to critical moments. Through two user studies, we confirmed that event cameras improved accurate perception of high-speed phenomena. Moreover, the integration of Event-Timeline enables users to perceive these phenomena intuitively, without experiencing cognitive overload. The experimental results demonstrated a significant usability improvement (SUS: 84.7, NASA-TLX: 16.3). Focus group discussions refined visualization techniques, resulting in the development of design implications. This system enables intuitive and immediate understanding of unpredictable high-speed occurrences, with applications in sports, animal behavior monitoring, and motion analysis.}, archive = {J_ACCESS}, author = {Kotoka Matsunaga and Katsutoshi Masai and Hideo Saito}, doi = {10.1109/ACCESS.2025.3584382}, journal = {IEEE Access}, pages = {113482-113494}, shortjournal = {IEEE Access}, title = {Instant slow-motion visualization of high-speed phenomena based on event camera}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of FGO windowing strategies for PDR+GNSS fusion architecture. ACCESS, 13, 113461-113481. (www)

  @article{ , abstract = {Smartphone-based pedestrian navigation has become increasingly popular due to the widespread availability of low-cost Android devices. However, these smartphones often suffer from low positional accuracy caused by inexpensive GNSS antennas and receivers. To address these limitations, fusion mechanisms are employed, with Factor Graph Optimization (FGO) emerging as a prominent method in navigation solutions, particularly for INS+GNSS integration. While FGO has also gained popularity in Simultaneous Localization and Mapping (SLAM) applications, its reliance on batch data processing, referred to as Batch Factor Graph Optimization (BFGO) and which is used as the baseline for this study, results in significant computational overhead, making it impractical for real-time or resource-constrained scenarios. To address these challenges, two windowing-based FGO approaches are proposed: the Naive Windowing FGO (NW-FGO) and the Marginalized Windowing FGO (MW-FGO). By incorporating marginalization, MWFGO retains critical information while reducing computational costs. Both methods are evaluated under two error conditions GNSS multipath and PDR errors to assess their impact on positional accuracy and efficiency. Our findings reveal that optimal window sizes, such as 50s for GNSS errors and 30s for PDR errors, achieve accuracy comparable to BFGO while significantly reducing computational time from 2500 seconds to 38 seconds with MWFGO. Furthermore, MWFGO outperforms NW-FGO, improving mean positional accuracy by 2.18% across varying window sizes, underscoring the effectiveness of marginalization. This study demonstrates the feasibility of windowing-based FGO, particularly MWFGO, in balancing computational efficiency and accuracy. By optimizing window sizes and leveraging marginalization, we provide a robust alternative to FGO, making real-time pedestrian navigation viable in resource-constrained environments.}, archive = {J_ACCESS}, author = {Amjad Hussain Magsi and Luis Enrique Díez Blanco}, doi = {10.1109/ACCESS.2025.3584664}, journal = {IEEE Access}, pages = {113461-113481}, shortjournal = {IEEE Access}, title = {Performance analysis of FGO windowing strategies for PDR+GNSS fusion architecture}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on large language models applications in computer programming teaching evaluation process. ACCESS, 13, 113449-113460. (www)

  @article{ , abstract = {Tools based on the use of Large Language Models (LLMs) have improved the computer programming teaching process, automated feedback processes, facilitated program repair, and enabled personalized learning experiences. This research examines which and how LLM-based opportunities are applied in the computer programming teaching assessment process and how LLMs are applied to improve evaluation accuracy, their impact on student learning outcomes, and the challenges in scaling these technologies. Key opportunities arise from prompt engineering, which optimizes precision and LLM-generated feedback relevance, and feedback propagation techniques, which offer scalable solutions for large-scale programming courses. LLMs are also applied effectively in debugging assistance to detect and repair syntactic and semantic errors in student code. This review identifies several research directions, including prompt engineering refinement, improved feedback system scalability, and deeper exploration of the long-term educational impacts of LLM. The study concludes that LLMs are effective in enhancing the assessment process, but a balanced approach combining human oversight with automated feedback is crucial to fostering critical thinking and ensuring long-term learning success in programming education.}, archive = {J_ACCESS}, author = {Andre Fabiano Pereira and Rafael Ferreira Mello}, doi = {10.1109/ACCESS.2025.3584060}, journal = {IEEE Access}, pages = {113449-113460}, shortjournal = {IEEE Access}, title = {A systematic literature review on large language models applications in computer programming teaching evaluation process}, volume = {13}, year = {2025}, }
• (2025). Automated classification of snow crab shell condition using analysis of field images. ACCESS, 13, 113438-113448. (www)

  @article{ , abstract = {The snow crab (Chionoecetes opilio) fishery is a significant economic driver in Atlantic Canada. Effective management of this resource requires accurate identification of crabs that have recently molted, which is currently performed through subjective visual assessment of the crab’s shell condition. However, there are challenges in terms of accuracy and consistency in shell conditions identification, due to differences among skill levels of field technicians and at-sea observers. This study introduces an automated method for shell condition classification using field images of snow crabs as inputs. The images were gathered from both fishery and scientific survey operations in the southern Gulf of Saint Lawrence in 2023 and 2024. Both the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) algorithms were able to reproduce reliable field identifications of five shell conditions (New-soft, New-hard, Intermediate, Old, and Very-old) to an accuracy of 87.6% and 91.7%, respectively. When the classification was simplified to two categories, “New-shells” and “Old-shells”, these accuracies improved to 96.0% and 97.9%, respectively.}, archive = {J_ACCESS}, author = {Ibrahim Kadri and Tobie Surette and Sid Ahmed Selouani and Mohsen Ghribi and Ryan LeBlanc}, doi = {10.1109/ACCESS.2025.3583885}, journal = {IEEE Access}, pages = {113438-113448}, shortjournal = {IEEE Access}, title = {Automated classification of snow crab shell condition using analysis of field images}, volume = {13}, year = {2025}, }
• (2025). SIMSPIRE: A simulator of the respiratory system. ACCESS, 13, 113428-113437. (www)

  @article{ , abstract = {This paper introduces SIMSPIRE, a comprehensive simulator of the human respiratory system designed as a versatile tool for simulating various patient conditions. It can be used to model patients with a wide range of physiological traits, such as varying lung compliance and resistance, as well as respiratory conditions, such as chronic obstructive pulmonary disease (COPD) and acute respiratory distress syndrome (ARDS). The versatility of SIMSPIRE makes it suitable for simulating diverse respiratory scenarios and evaluating new algorithms or control strategies for ventilatory support. For this work, we have tested and evaluated our simulator by connecting as input the control software of a ventilator prototype. SIMSPIRE represents a significant advancement in the field of respiratory simulation. It provides a highly accurate and customizable platform for improving the design of ventilation systems and optimizing patient outcomes. By eliminating the need for expensive hardware simulators, SIMSPIRE has the potential to accelerate innovation and contribute to enhanced patient care in critical care settings.}, archive = {J_ACCESS}, author = {Andrea Bombarda and Silvia Bonfanti and Angelo Gargantini and Elvinia Riccobene}, doi = {10.1109/ACCESS.2025.3584269}, journal = {IEEE Access}, pages = {113428-113437}, shortjournal = {IEEE Access}, title = {SIMSPIRE: A simulator of the respiratory system}, volume = {13}, year = {2025}, }
• (2025). Explainable AI for spectral analysis of electromagnetic fields. ACCESS, 13, 113407-113427. (www)

  @article{ , abstract = {This study explores the application of Explainable Artificial Intelligence (XAI) techniques to the spectral analysis of electromagnetic field (EMF) measurements conducted across various frequency bands in Cyprus. Using an extensive data set obtained from mobile telephony, radio broadcasting, and terrestrial digital television sources, this work aims to capture real-world EMF exposure levels spanning frequencies from 30 MHz to 6 GHz. The frequency bands analyzed include FM radio (87.5-108 MHz), VHF, UHF television, and various mobile telephony zones (700 MHz, 800 MHz, 900 MHz, 1800 MHz, 2100 MHz, 2600 MHz, and 3600 MHz), representing both traditional and emerging wireless technologies. A comparative evaluation of six machine learning algorithms was conducted: XGBoost, LightGBM, Random Forests, k-Nearest Neighbors, Neural Networks and Decision Trees to assess prediction performance across each frequency band. Furthermore, SHAP (SHapley Additive exPlanations) was employed to elucidate the contribution of spatial and demographic characteristics to the intensity of the EMF. The results show that ensemble tree-based methods, particularly Random Forests and LightGBM, consistently outperformed simpler models in accuracy and interpretability. The integration of SHAP enabled transparent feature attribution, revealing distinctive exposure patterns linked to urban topology, population density, and built environment metrics. This approach enables data-driven EMF exposure mapping, aiding urban infrastructure planning, compliance monitoring, and public health risk evaluation.}, archive = {J_ACCESS}, author = {Dimitris Kalatzis and Agapi Ploussi and Ellas Spyratou and Theodor Panagiotakopoulos and Efstathios P. Efstathopoulos and Yiannis Kiouvrekis}, doi = {10.1109/ACCESS.2025.3583944}, journal = {IEEE Access}, pages = {113407-113427}, shortjournal = {IEEE Access}, title = {Explainable AI for spectral analysis of electromagnetic fields}, volume = {13}, year = {2025}, }
• (2025). Performance, limitations, and design issues of the integration of a hardware-based IME module with HEVC video encoder software. ACCESS, 13, 113390-113406. (www)

  @article{ , abstract = {High Efficiency Video Coding (HEVC) was designed to improve on its predecessor, the H264/AVC standard, by doubling its compression efficiency. As in previous standards, motion estimation is critical for encoders to achieve significant compression gains. However, the cost of accurately removing temporal redundancy in video is prohibitive, especially when encoding very high resolution video sequences. To reduce the overall video encoding time, we have proposed the implementation of an HEVC motion estimation block in hardware, which can achieve significant speed-ups. However, when the IP hardware is integrated into a software platform, there are several constraints and limitations that reduce its impact on the overall encoding time. In this paper, we analyse these issues in detail to identify the main bottlenecks of the overall software/hardware encoding system. From this analysis, we propose a final integration of the hardware motion estimation module with a hardware unit combined with the slice-based parallel version of the HEVC encoding software. The resulting integrated version is able to achieve the best performance in terms of global speed-up, up to 149.63x compared to the sequential version of the HEVC encoder using the full search motion estimation algorithm.}, archive = {J_ACCESS}, author = {Otoniel López-Granado and Héctor Migallón and Estefanía Alcocer and Roberto Gutierrez and Glenn Van Wallendael and Manuel P. Malumbres}, doi = {10.1109/ACCESS.2025.3581961}, journal = {IEEE Access}, pages = {113390-113406}, shortjournal = {IEEE Access}, title = {Performance, limitations, and design issues of the integration of a hardware-based IME module with HEVC video encoder software}, volume = {13}, year = {2025}, }
• (2025). Robust controller with least mean square adaptive filter for voltage source inverters: Passivity-based design. ACCESS, 13, 113379-113389. (www)

  @article{ , abstract = {Voltage source inverters (VSIs) are essential for many applications. However, it is challenging to design a controller that enables a VSI to generate a sinusoidal load voltage with no steady-state error regardless of loads. Moreover, the controller has to suppress the harmonic distortion caused by a nonlinear load, such as a diode rectifier, while stabilizing the VSI. In this paper, we proposes a novel dual-loop controller for VSIs that comprises a passivity-based robust controller in the inner loop and a least mean square adaptive filter (LMS-AF) in the outer loop. The proposed controller can guarantee the robust stability for both linear and nonlinear loads, while achieving zero steady-state, fast-tracking, and robustness of the sensor noise. Regarding the harmonics caused by the diode rectifier load as cyclic, we suppress the harmonics caused by this load by applying the LMS-AF to the VSI controller. We demonstrate the effectiveness of the proposed controller through comparisons with conventional control approaches via simulations and experiments.}, archive = {J_ACCESS}, author = {Kenta Koiwa and Taiki Goto and Qingquan Liu and Kang-Zhi Liu and Tadanao Zanma}, doi = {10.1109/ACCESS.2025.3583325}, journal = {IEEE Access}, pages = {113379-113389}, shortjournal = {IEEE Access}, title = {Robust controller with least mean square adaptive filter for voltage source inverters: Passivity-based design}, volume = {13}, year = {2025}, }
• (2025). An improved robust filtering recursive algorithm for stochastic uncertain vehicle INS/OD systems with anomalous measurements. ACCESS, 13, 113368-113378. (www)

  @article{ , abstract = {The autonomous vehicular navigation technology has gained more and more attention of researchers. Odometer is the most commonly used auxiliary sensor for autonomous navigation in vehicles due to its stability and high accuracy. However, in poor road conditions, vehicle skidding or slipping will seriously affect the navigation accuracy of INS/OD integrated navigation system. To solve this problem, we propose an improved autonomous vehicular navigation system based on robust filter recursive algorithm for stochastic system with outlier measurements. The main contributions of this study can be summarized as: 1) Incremental odometer measurement model is proposed for INS/OD integrated navigation system to solve the problem of calculation of abnormal odometer information. 2) Robust filter recursive algorithm for stochastic uncertain system with outlier measurements caused by vehicle’s slipping and skidding is proposed to improve the robustness of the INS/OD integrated navigation system. Experimental results demonstrate that the proposed method significantly improves positioning accuracy compared to traditional INS/OD integrated navigation methods, especially under vehicle skidding or slipping conditions.}, archive = {J_ACCESS}, author = {Shuang Pan and Chao Wang and Chunlei Zhang and Linping Feng}, doi = {10.1109/ACCESS.2025.3582319}, journal = {IEEE Access}, pages = {113368-113378}, shortjournal = {IEEE Access}, title = {An improved robust filtering recursive algorithm for stochastic uncertain vehicle INS/OD systems with anomalous measurements}, volume = {13}, year = {2025}, }
• (2025). Three-way merging algorithm for structured data. ACCESS, 13, 113356-113367. (www)

  @article{ , abstract = {A general problem in multi-node systems is data synchronization, where the most used method uses synchronous data updating. All changes made by the user are immediately reflected in the data shared with other users. This also means that the user must be constantly connected to the data source via a computer network. As a result, each user always has instant access to up-to-date data. However, the trend in computer systems nowadays is more and more focused on mobility. For this reason, some systems are designed in such a way that it is not necessary or desirable for them to require constant network connectivity. In such systems with asynchronous data updating, it is significantly more complicated to keep the data up to date. The user has all the necessary data available locally on their computer and synchronizes it with the global state of the information system on demand. In this paper, we describe a novel algorithm that can merge data during asynchronous updates. The design of our algorithm is inspired by the diff3 algorithm. Since the diff3 algorithm is designed only for merging text files, we had to change it to enable data merging at the level of programming structures. The proposed algorithm has been verified and implemented in the IS ZONA developed by our department.}, archive = {J_ACCESS}, author = {Jan Janech and Patrik Hrkút and Matej Meško and Marek Tavač}, doi = {10.1109/ACCESS.2025.3581684}, journal = {IEEE Access}, pages = {113356-113367}, shortjournal = {IEEE Access}, title = {Three-way merging algorithm for structured data}, volume = {13}, year = {2025}, }
• (2025). Pattern-based feature extraction for improved deep learning in financial time series classification. ACCESS, 13, 113343-113355. (www)

  @article{ , abstract = {In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. Existing financial forecasting models often struggle with the inherent volatility and complexity of financial markets, particularly due to their reliance on traditional financial data features which fail to capture intricate price patterns. This research addresses the critical gap in effectively identifying and leveraging these patterns to improve predictive accuracy. By collecting tick-by-tick data from the European Carbon Emission Allowance market and resampling it to a 15-minute timeframe, we developed a method to detect pivotal price patterns and extract relevant features. Integrating these features with traditional open, high, low, close, and volume (OHLCV) data in long-short-term memory (LSTM) and gated recurrent unit (GRU) combined with dense neural networks, our empirical results demonstrate significant improvements in model performance, showcasing enhanced accuracy compared to models using only traditional data features.}, archive = {J_ACCESS}, author = {Seyed Ali Hosseini and Francesco Grimaccia and Alessandro Niccolai and Silvia Trimarchi}, doi = {10.1109/ACCESS.2025.3584251}, journal = {IEEE Access}, pages = {113343-113355}, shortjournal = {IEEE Access}, title = {Pattern-based feature extraction for improved deep learning in financial time series classification}, volume = {13}, year = {2025}, }
• (2025). Down-sampling large-scale social networks from real-world call data. ACCESS, 13, 113329-113342. (www)

  @article{ , abstract = {The rapid growth of digital communication technologies has led to the generation of large-scale social networks, particularly through the analysis of call data from mobile telecommunications. However, the sheer size of these networks poses significant challenges for computational analysis and storage. This paper presents a methodology for down-sampling large-scale social networks derived from real-world call data, using the c-core decomposition technique. By applying this technique, we efficiently reduce the size of the networks while preserving critical structural properties, such as degree distribution, clustering spectrum, and community structure. The analysis is conducted on data provided by a major Asian telecommunications company, encompassing over a year of call records. We demonstrate that the c-core decomposition not only scales down the networks effectively but also maintains the essential characteristics required for meaningful social network analysis. Our results show that down-sampling using c-core decomposition provides a robust framework for analyzing and interpreting large-scale social networks, offering valuable insights into the dynamics of human communication.}, archive = {J_ACCESS}, author = {Jinho Lee}, doi = {10.1109/ACCESS.2025.3582792}, journal = {IEEE Access}, pages = {113329-113342}, shortjournal = {IEEE Access}, title = {Down-sampling large-scale social networks from real-world call data}, volume = {13}, year = {2025}, }
• (2025). A cross-calibration approach for coastline extraction with time series satellite images. ACCESS, 13, 113318-113328. (www)

  @article{ , abstract = {In tidal flat regions, the spectral differences between water and land are often weak, making it challenging to extract the coastline from remote sensing imagery. Due to tidal influences, the satellite images captured at different times have different water coverage in nearshore area. Images at high tide typically display more distinct spectral characteristics between water and land. This study proposes a coastline optimization approach that utilizes isobath extracted from high-tide image to optimize the coastline extracted from low-tide image, thereby addressing the problem of weak spectral separability. The isobath, extracted from bathymetric inversion maps in steep slope areas during high tide, is referred to as the relative 0-meter isobath. The steep slope identification algorithm (SSI-A) is employed to detect these areas. The relative 0-meter isobath is identified in these areas. It is then used to optimize the coastline at an ebb tide moment, enhancing both the accuracy and consistency of coastline extraction. As a case study, the method is applied to Jibei Island, located in the northern part of the Penghu Islands. Based on different sensor images captured at different times, the proposed approach is used to optimize the coastline extracted by NDWI and SVM. The proposed approach reduces the average error by 13 meters based on Sentinel-2 with 10 m spatial resolution, and by 18 meters based on Landsat-8 and EnMAP image with 30 m spatial resolution. This approach provides more reliable data support for nearshore topographic evolution studies and coastal monitoring.}, archive = {J_ACCESS}, author = {Hua Yang and Jintao Xia and Jiang Zhang}, doi = {10.1109/ACCESS.2025.3583818}, journal = {IEEE Access}, pages = {113318-113328}, shortjournal = {IEEE Access}, title = {A cross-calibration approach for coastline extraction with time series satellite images}, volume = {13}, year = {2025}, }
• (2025). Enhancing healthcare with WBAN and digital twins: A machine learning approach for predictive health monitoring. ACCESS, 13, 113305-113317. (www)

  @article{ , abstract = {The vital signs and medical condition telemetry from remote areas, such as the death rate, are urgent for hospital isolation. By recognizing and forecasting the causes of similar diseases, Wireless Body Area Networks (WBAN), made up of powerful wearable Sensor Nodes (SNs) located across the human body, generate a huge surge in data backed by other SNs. The collected data undergoes processing and is then sent to a remote medical server over the Internet. Machine learning (ML) has reinvented many paradigms, especially in healthcare, where it is a key resource in WBAN. A new advancement in this field is the integration of digital twins—virtual representations of the physical body. These digital twins can continuously simulate and predict health outcomes based on real-time WBAN data, enabling proactive healthcare interventions. This paper assessed several ML models based on their ability to process WBAN data, such as glucose, blood pressure, and body temperature, among others obtained from WBAN. These parameters feed into the digital twins, further refining the predictive and diagnostic capabilities of the models. The ML algorithms used include Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbours (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Neural Network (NN), AdaBoost (AB), Bagging (Ba), Extra Trees (ET), and XGBoost (XGB). Each model was evaluated based on accuracy, precision, recall, F1 score, ROC AUC score, and cross-validation accuracy. The RF model scored highest across all measurements, with accuracy (97%), precision (98%), recall (97%), F1 score (98%), ROC AUC score (97%), and cross-validation accuracy (97%). XGB and SVC also performed strongly, with GB, Ba, ET, and AB ranking high in efficiency, with consistent scores in the mid-90s. Integrating digital twins with these models offers an innovative framework to improve healthcare efficiency, allowing for real-time simulation and more accurate forecasting of patient outcomes.}, archive = {J_ACCESS}, author = {Rishit Mahapatra and Deepak Sethi and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3584103}, journal = {IEEE Access}, pages = {113305-113317}, shortjournal = {IEEE Access}, title = {Enhancing healthcare with WBAN and digital twins: A machine learning approach for predictive health monitoring}, volume = {13}, year = {2025}, }
• (2025). How can business analytics enhance decision-making in oil and gas surface facilities?. ACCESS, 13, 113291-113304. (www)

  @article{ , abstract = {This systematic literature review examines the application of business analytics in oil and gas surface facilities, analyzing 43 peer-reviewed publications (2012–2025). It identifies three levels of analytics—descriptive, predictive, and prescriptive—each addressing operational challenges through specific technologies. Descriptive analytics enhances visibility via IoT sensors and dashboards. Predictive analytics enables proactive maintenance through machine learning, while prescriptive analytics optimizes operations using simulations and multi-objective decision models. Implementation examples demonstrate efficiency gains, including a 28% reduction in separation unit losses, an 18% decrease in compression system energy consumption, and a 64% improvement in methane emissions detection. However, adoption remains hindered by data integration issues, workforce skill gaps, and organizational resistance. This study proposes a business analytics framework to guide surface facility managers in selecting analytics solutions, with a focus on progressive capability development. Future research should explore digital twins, real-time data integration, ESG reporting, workforce training, and edge computing for remote facilities. This review offers a structured approach to analytics implementation, striking a balance between efficiency, economic performance, and environmental sustainability.}, archive = {J_ACCESS}, author = {Agung Prasetya and Meditya Wasesa and Yos Sunitiyoso}, doi = {10.1109/ACCESS.2025.3582939}, journal = {IEEE Access}, pages = {113291-113304}, shortjournal = {IEEE Access}, title = {How can business analytics enhance decision-making in oil and gas surface facilities?}, volume = {13}, year = {2025}, }
• (2025). Integrated technique of natural language texts and source codes authorship verification in the academic environment. ACCESS, 13, 113274-113290. (www)

  @article{ , abstract = {The issue of text plagiarism in academic and educational environments is becoming increasingly relevant every year. The quality of research articles and works is declining due to students copying fragments of others’ works and using modern generative models for text and source code creation. The article proposes an integrated technique for authorship verification of both natural and programming language texts, based on a combination of statistical methods, machine learning, and deep neural networks. The presented technique addresses several related tasks: assessing text homogeneity, detecting plagiarism when solving closed-set authorship attribution problems, and identifying texts and fragments created by generative models. Experimental data include a multi-domain dataset of natural language texts consisting of research articles on natural and technical sciences, PhD dissertations, and artificially generated samples on related topics. To evaluate the effectiveness of the technique in relation to programming language texts, a multilingual program dataset was used, consisting of source codes for programs of technical students as well as artificially generated program codes. The experimental results demonstrate the effectiveness of the proposed technique for plagiarism detection and copyright protection in the educational process. The accuracy of identifying heterogeneous fragments in text or code is 93-94%, authorship attribution ac-curacy is 89-99% depending on the number of co-authors, and verification accuracy is 97.5-99.4%.}, archive = {J_ACCESS}, author = {Aleksandr Romanov and Anna Kurtukova and Anastasiia Fedotova and Alexander Shelupanov}, doi = {10.1109/ACCESS.2025.3584616}, journal = {IEEE Access}, pages = {113274-113290}, shortjournal = {IEEE Access}, title = {Integrated technique of natural language texts and source codes authorship verification in the academic environment}, volume = {13}, year = {2025}, }
• (2025). Regularization for unconditional image diffusion models via shifted data augmentation. ACCESS, 13, 113258-113273. (www)

  @article{ , abstract = {Diffusion models are a powerful class of techniques in ML for generating realistic data, but they are highly prone to overfitting, especially with limited training data. While data augmentation such as image rotation can mitigate this issue, it often causes leakage, where augmented content appears in generated samples. In this paper, we propose a novel regularization framework, called shifted data-augmentation (SDA), for training unconditional diffusion models. SDA introduces an auxiliary diffusion path using transformed data and the noise-shift technique alongside the standard path with original data. This dual-path structure enables effective regularization while suppressing leakage through a trajectory shift in the diffusion process. We implement SDA with image rotation as its most basic and interpretable configuration. We also conduct synthetic and empirical analyses demonstrating that SDA effectively leverages the regularization benefit of image rotation. In particular, SDA yielded notable performance in training with limited data.}, archive = {J_ACCESS}, author = {Kensuke Nakamura and Bong-Soo Sohn and Simon Korman and Byung-Woo Hong}, doi = {10.1109/ACCESS.2025.3582756}, journal = {IEEE Access}, pages = {113258-113273}, shortjournal = {IEEE Access}, title = {Regularization for unconditional image diffusion models via shifted data augmentation}, volume = {13}, year = {2025}, }
• (2025). Characterizing the inter-core qubit traffic in large-scale quantum modular architectures. ACCESS, 13, 113236-113257. (www)

  @article{ , abstract = {Modular quantum processor architectures are envisioned as a promising solution for the scalability of quantum computing systems beyond the Noisy Intermediate Scale Quantum (NISQ) era. Based upon interconnecting tens to hundreds of quantum processors (i.e cores) via quantum coherent and classical links, this approach unravels the pressing limitations of densely qubit-packed monolithic processors, mainly by mitigating the requirements of qubit control and enhancing qubit isolation. Therefore, this new architectural design enables executing large-scale algorithms in a distributed manner. In order to assess the performance and optimize such architectures, it is crucial to analyze the quantum state transfers occurring via inter-core communication networks, referred to as inter-core qubit traffic. This would provide insights to improve the software and hardware stack for multi-core quantum computers. To this aim, we present a characterization of the spatio-temporal inter-core qubit traffic for different large-scale quantum algorithms. The programs are compiled on an all-to-all connected multi-core architecture that applies the teleportation protocol for inter-core state transfer and supports up to around 1000 qubits. We characterize the qubit traffic based on multiple performance metrics to assess the computational process and the communication overhead. Based on the showcased results, we conclude on the parallelization and scalability of presented algorithms, qualitatively evaluate the resource requirements as we scale circuit sizes, and lay the foundations of application-oriented benchmarking of large-scale multi-core architectures.}, archive = {J_ACCESS}, author = {Sahar Ben Rached and Isaac Lopez Agudo and Santiago Rodrigo and Medina Bandic and Artur Garcia-Saez and Sebastian Feld and Hans van Someren and Eduard Alarcón and Carmen G. Almudéver and Sergi Abadal}, doi = {10.1109/ACCESS.2025.3583218}, journal = {IEEE Access}, pages = {113236-113257}, shortjournal = {IEEE Access}, title = {Characterizing the inter-core qubit traffic in large-scale quantum modular architectures}, volume = {13}, year = {2025}, }
• (2025). Exposing optimal feature sets for enhancing machine learning performance. ACCESS, 13, 113222-113235. (www)

  @article{ , abstract = {The majority of high dimensional gene expression data contain a significant amount of redundant genes, posing challenges for machine learning algorithms due to their high dimensionality. Feature selection has shown to be a successful method for improving classification algorithms performance by addressing two primary objectives: reducing the number of features and improving classification accuracy. The aim of this paper is to introduce a novel hybrid multiobjective wrapper method for feature selection. The method combines the technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi-attribute decision making approach with a filtering mechanism for extracting informative features. Additionally, a multiobjective Crow Search Algorithm (CSA) that simultaneously reduces the number of features and classification error whereas obtaining a set of Pareto nondominated (ND) solutions is employed. The opposition based learning (OBL) technique is used to mitigate the risk of CSA converging towards local optima. To evaluate the effectiveness of our approach, we conduct experiments on benchmark microarray datasets from the ADNI database. Comparative analysis is performed against six traditional single objective methods and five other existing multiobjective methods. The results demonstrate that our proposed approach outperforms the single objective methods in terms of classification accuracy. Moreover, when compared to other multiobjective algorithms, our method exhibits superior performance in terms of both classification accuracy and the number of selected features.}, archive = {J_ACCESS}, author = {Hiba Mohammed Al-Marwai and Ghaleb H. Al-Gaphari and Mohammed Mohammed Zayed}, doi = {10.1109/ACCESS.2025.3580382}, journal = {IEEE Access}, pages = {113222-113235}, shortjournal = {IEEE Access}, title = {Exposing optimal feature sets for enhancing machine learning performance}, volume = {13}, year = {2025}, }
• (2025). Key design considerations and assessment of a serious game for hand and wrist rehabilitation using a leap motion controller. ACCESS, 13, 113208-113221. (www)

  @article{ , abstract = {Serious games are increasingly explored as tools to enhance rehabilitation, yet their effectiveness in medical applications is often insufficiently evaluated. Many studies do not use standardized methods to check if these games are truly effective. However, in medical settings, for example for stroke rehabilitation, it is important to know if a game is engaging, easy to use and actually supports therapy. Without proper assessment, serious games might be used without understanding their benefits or limitations. A major issue is that serious games are tested informally without using standardized methods for evaluation. This makes it difficult to compare results or improve the game based on feedback. In the medical field, where reliable and effective tools are essential, this is a serious problem. To address this, an existing serious game, RehaFox, designed for hand and stroke rehabilitation using the Leap Motion Controller (LMC), was selected. A structured evaluation method for a usability study was developed, combining both qualitative and quantitative approaches. In total, 15 participants evaluated the serious game during play sessions in the context of a System Usability Scale (SUS), the short form of the User Engagement Scale (UES), a Game Experience Questionnaire (GEQ), and interviews. These findings have provided initial insights into the strengths and challenges of RehaFox from various perspectives. The game excelled in several dimensions of the GEQ, particularly in terms of positive affect (average score of 4.2/5.0) and low levels of tension and anger (average score of 1.4/5.0). The UES results indicate a high level of user engagement, with an overall engagement score of 4.0/5.0, while an average SUS score of 78.0 suggests the game has good usability. In addition 9 different requirements were derived, which should be used within a serious game utilizing a LMC in the context of hand and wrist rehabilitation. These findings highlight the importance of structured evaluations for serious games in medical rehabilitation and provide a foundation for future improvements in this field.}, archive = {J_ACCESS}, author = {Selina Breuer and René Baranyi and Christoph Aigner and Dominik Hoelbling and Werner Hoerner and Thomas Grechenig}, doi = {10.1109/ACCESS.2025.3584156}, journal = {IEEE Access}, pages = {113208-113221}, shortjournal = {IEEE Access}, title = {Key design considerations and assessment of a serious game for hand and wrist rehabilitation using a leap motion controller}, volume = {13}, year = {2025}, }
• (2025). Meticulous thought defender: Fine-grained chain-of-thought (CoT) for detecting prompt injection attacks of large language models. ACCESS, 13, 113194-113207. (www)

  @article{ , abstract = {Large language models (LLMs) have exhibited exceptional capabilities across various natural language processing tasks, however, they remain susceptible to prompt injection attacks, which pose significant security challenges. Traditional detection methods often fail to effectively identify such attacks due to their reliance on static rules or surface-level analysis. In this study, we introduce a novel, fine-grained CoT based detection framework that enhances the interpretability and robustness of attack identification. By dissecting the step-by-step reasoning process of LLMs, our approach leverages multidimensional anomaly detection mechanisms-encompassing semantic analysis, consistency evaluations at both step and path levels, and confidence estimations to uncover subtle disruptions caused by malicious prompt manipulations. Experimental results demonstrate that our method achieves superior performance metrics, with significant improvements in accuracy and F1 score, outperforming traditional method by 1.16% in accuracy and 3.39% in F1 score, and surpassing LLMs’ intrinsic detection capabilities by 6.24% in accuracy and 7.65% in F1 score. This work not only fortifies the security of LLMs’ applications but also provides a foundational framework for future research on adaptive defenses against evolving attack strategies.}, archive = {J_ACCESS}, author = {Lijuan Shi and Yajing Kang and Jie Hu and Xinchi Li and Mingchuan Yang}, doi = {10.1109/ACCESS.2025.3583759}, journal = {IEEE Access}, pages = {113194-113207}, shortjournal = {IEEE Access}, title = {Meticulous thought defender: Fine-grained chain-of-thought (CoT) for detecting prompt injection attacks of large language models}, volume = {13}, year = {2025}, }
• (2025). Improving targeting specificity of transcranial focused ultrasound in subject specific head models using a random array transducer: A k-wave simulation study. ACCESS, 13, 113179-113193. (www)

  @article{ , abstract = {Transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive modality for precision neuromodulation. However, the heterogeneous acoustic properties of the skull often induce phase aberrations that shift the ultrasound focus and compromise energy delivery. In this study, we developed and validated a phase-reversal based aberration correction method to enhance the targeting specificity of tFUS using a 128-element random array ultrasound transducer. Individual head models were constructed from T1-weighted magnetic resonance (MR) images and corresponding pseudo-computed tomography (pCT) data to accurately represent subject-specific skull geometries and the targeted left V5 (V5L) region. Acoustic simulations were conducted with the k-Wave toolbox by first acquiring free-field pressure waveforms and then recording the aberrated waveforms in the presence of the skull. The phase differences between these conditions were used to compute corrective delays for each transducer element. Quantitative evaluation using metrics such as focal overlap with the target region, axial focal positioning, and the delivered ultrasound energy demonstrated significant improvements: the overlap volume increased by 98.70%, mean axial positioning errors were reduced by up to 14.36%, and energy delivery to the target improved by 17.58%. We further demonstrated that the proposed approach outperforms the conventional ray-tracing methods. The results show that phase-reversal based aberration correction markedly increases the spatial targeting accuracy of tFUS and enhances the efficiency of focused ultrasound energy deposition for the customized random array transducer, paving a way for effective and personalized non-invasive neuromodulation therapies.}, archive = {J_ACCESS}, author = {Zherui Li and Kai Yu and Joshua Kosnoff and Bin He}, doi = {10.1109/ACCESS.2025.3584245}, journal = {IEEE Access}, pages = {113179-113193}, shortjournal = {IEEE Access}, title = {Improving targeting specificity of transcranial focused ultrasound in subject specific head models using a random array transducer: A k-wave simulation study}, volume = {13}, year = {2025}, }
• (2025). Passivity-based safe maneuvering control of mechanical systems with input constraints. ACCESS, 13, 113171-113178. (www)

  @article{ , abstract = {For mechanical systems with input constraints, safe maneuvering control is investigated in this paper. How to accomplish the maneuvering task without violating safety constraints is the main challenge. To address the input constraints, the input constraints are expressed as state constraints of the augmented system by introducing a dynamic controller. First, a passivity-based desired controller is designed to fulfill the required maneuvering task without considering the multiple safety constraints. Then, multiple safety constraint functions are combined into a differentiable safety constraint function. Moreover, by using quadratic programming, a synthesis method for the safety controller with minimum deviation from the desired controller is given. Finally, the effectiveness of the proposed control strategy is demonstrated using a three-wheeled omnidirectional mobile robot.}, archive = {J_ACCESS}, author = {Mingyue Cui and Yanzhuang Wei}, doi = {10.1109/ACCESS.2025.3584465}, journal = {IEEE Access}, pages = {113171-113178}, shortjournal = {IEEE Access}, title = {Passivity-based safe maneuvering control of mechanical systems with input constraints}, volume = {13}, year = {2025}, }
• (2025). Credibility-adjusted data-conscious clustering method for robust EEG signal analysis. ACCESS, 13, 113155-113170. (www)

  @article{ , abstract = {Clustering neurodata, including electroencephalography (EEG) signals, is crucial for brain-computer interface (BCI) and neurological analysis. However, traditional methods struggle with noise, overlapping distributions, and high-dimensional data. This study presents the Credibility-Adjusted Data Conscious Clustering Method (CADCCM), an adaptive computational learning model for clustering neurodata, including EEG signals. CADCCM improves clustering robustness by dynamically adjusting assignments through credibility updates and optimizing parameters for better accuracy and stability in noisy, high-dimensional data. CADCCM dynamically adjusts cluster assignments by integrating alpha and beta parameters to balance fuzzy membership and credibility. A grid search framework optimizes clustering parameters, and preprocessing techniques (Fourier Transform, Wavelet Transform, and Gaussian filtering) improve feature separability. The method is benchmarked against traditional and recent Clustering methods across 13 datasets. CADCCM achieves superior clustering performance, consistently outperforming baseline methods in Rand Index (RI), F-score, and Cohen’s Kappa, particularly in noisy datasets. Gaussian filtering further enhances clustering accuracy. GPU acceleration ensures computational feasibility for large-scale neurodata applications. Additionally, CADCCM outperforms other methods by satisfying all clustering properties, including scale invariance, richness, consistency, and order independence. CADCCM bridges the gap between traditional and advanced clustering by introducing credibility-based updates and optimized parameter selection, leading to improved accuracy, robustness, and efficiency. The method holds promise for applications in cognitive state monitoring, neurological disorder detection, and BCI systems. CADCCM enhances the interpretability, reliability, and scalability of clustering. Future research will focus on real-time clustering, adaptive hyperparameter tuning, and deep-learning-based feature extraction.}, archive = {J_ACCESS}, author = {Fatemeh Divan and Teh Ying Wah and Kheng Seang Lim and Ali Seyed Shirkhorshidi}, doi = {10.1109/ACCESS.2025.3581598}, journal = {IEEE Access}, pages = {113155-113170}, shortjournal = {IEEE Access}, title = {Credibility-adjusted data-conscious clustering method for robust EEG signal analysis}, volume = {13}, year = {2025}, }
• (2025). Enabling power systems with SQKD in presence of remote nodes. ACCESS, 13, 113142-113154. (www)

  @article{ , abstract = {Semi-quantum key distribution (SQKD) can be applied in power systems to share unconditionally secure keys between the power substations and the local controllers. Secret keys can be used for the encryption and decryption of control commands and measurements of power substations. This paper studies the allocation problem of a minimal number of trusted nodes, quantum servers with optimal source rates and fiber links on a pre-existing classical cyber layer of power system under the constraint of a minimal target key generation rate in the presence of an attacker for power systems with remote nodes. The optimization problem is formulated as a binary program that upgrades a pre-existing classical cyber layer of power system to support SQKD. Due to the complexity of the allocation problem, this paper develops algorithms based on Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), Dijksra’s algorithm and genetic algorithms. This research examined the proposed allocation strategy on the cyber layer of the IEEE 30-bus and the IEEE 118-bus test systems, since these systems have remote nodes. The work in the literature did not solve the systems with remote nodes, so this study modified two benchmarks to handle the remote nodes in order to compare with them. The results demonstrate that the target key generation rate can be achieved at different attack levels with a number of trusted nodes, quantum servers, source rates and a number of fiber links, that is 62.5% and 50%, respectively, less than a benchmark for the IEEE 30-bus and the IEEE 118-bus test systems. The results also demonstrate that the proposed solution requires 62.23% and 54.99% less number of quantum server, source rates and a number of fiber links compared to QKD solutions for the IEEE 30-bus and the IEEE 118-bus test systems, respectively.}, archive = {J_ACCESS}, author = {Mariam Gado and Muhammad Ismail}, doi = {10.1109/ACCESS.2025.3583870}, journal = {IEEE Access}, pages = {113142-113154}, shortjournal = {IEEE Access}, title = {Enabling power systems with SQKD in presence of remote nodes}, volume = {13}, year = {2025}, }
• (2025). Digital twin-enabled blockage-aware dynamic mmWave multi-hop V2X communication. ACCESS, 13, 113130-113141. (www)

  @article{ , abstract = {Millimeter wave (mmWave) technology in vehicle-to-everything (V2X) communication offers unprecedented data rates and low latency, but faces significant reliability challenges due to signal blockages and limited range. This paper introduces a novel system for managing dynamic multi-hop mmWave V2X communications in complex blocking environments. We present a system architecture that integrates a mobility digital twin (DT) with the multi-hop routing control plane, providing a comprehensive, real-time view of the network and its surrounding traffic environment. This integration enables the control plane to make informed routing decisions based on rich contextual data about vehicles, infrastructure, and potential signal blockages. Leveraging this DT-enhanced architecture, we propose an advanced routing algorithm that combines high-precision environmental data with trajectory prediction to achieve blockage-aware mmWave multi-hop V2X routing. Our algorithm anticipates network topology changes and adapts topology dynamically to maintain reliable connections. We evaluate our approach through proof-of-concept simulations using a mobility DT of the Nishishinjuku area. Results demonstrate that our DT-enabled routing strategy significantly outperforms conventional methods in maintaining reliable mmWave V2X connections across various traffic scenarios, including fully connected and mixed traffic environments.}, archive = {J_ACCESS}, author = {Supat Roongpraiwan and Zongdian Li and Tao Yu and Kei Sakaguchi}, doi = {10.1109/ACCESS.2025.3583879}, journal = {IEEE Access}, pages = {113130-113141}, shortjournal = {IEEE Access}, title = {Digital twin-enabled blockage-aware dynamic mmWave multi-hop V2X communication}, volume = {13}, year = {2025}, }
• (2025). Reliable evolutionary field optimization-based energy management strategy for mitigating hydrogen consumption of hybrid generation system. ACCESS, 13, 113113-113129. (www)

  @article{ , abstract = {This paper proposes a new strategy incorporating the evolutionary field optimizer (EFO) to manage the production of a hybrid generating system supplying highly variable demand such as electric vehicles, automobiles, trains, and airplanes. The generating system includes fuel cell, battery, and supercapacitor. The main goal of the suggested strategy is to mitigate overall hydrogen consumption to the lowest possible level under severe fluctuations of loads and delay the deterioration of fuel cell performance. The EFO is used with a previously published external energy maximization method (EEMS) to reduce the fuel cell hydrogen usage by optimizing battery and supercapacitor generations. Two design variables are considered: the battery power and supercapacitor discharge/charge voltage. The proposed strategy is contrasted with other metaheuristic approaches such as Kepler optimization algorithm, walrus optimization algorithm, and golf optimization algorithm in addition to traditional strategies like classical proportional-integral, hybrid frequency decoupling state machine, equivalent consumption minimization strategy, and EEMS. The suggested approach reduced the amount of hydrogen consumption by 39.4% with an efficiency of 88.15% when compared to the published EEMS. The obtained results revealed that, the proposed methodology outperformed alternative approaches in terms of system overall efficiency, battery state of charge, and rate of hydrogen consumption.}, archive = {J_ACCESS}, author = {Shahr Alshahr and Ahmed Fathy and Ahmed Alshahir and Hossam Hassan Ali}, doi = {10.1109/ACCESS.2025.3584112}, journal = {IEEE Access}, pages = {113113-113129}, shortjournal = {IEEE Access}, title = {Reliable evolutionary field optimization-based energy management strategy for mitigating hydrogen consumption of hybrid generation system}, volume = {13}, year = {2025}, }
• (2025). Performance and secrecy analysis of cooperative full-duplex NOMA system with fountain codes and intelligent reflective surface. ACCESS, 13, 113089-113112. (www)

  @article{ , abstract = {Intelligent Reflective Surfaces (IRSs) are recognized as fundamental technology in the context of the forthcoming sixth-generation (6G) communication systems. Non-Orthogonal Multiple Access (NOMA) can augment fifth-generation (5G) wireless networks’ spectral efficiency. This manuscript investigates a NOMA-based downlink configuration that integrates IRS and functions in a physical layer security (PLS) scenario, accounting for self-interference (SI) inherent to full-duplex (FD) operation. The examination also considers the utilization of Fountain Codes (FCs) when faced with a potential eavesdropper exhibiting malicious intentions. This paper is evident in the development of accurate mathematical expressions for the outage probability (OP), throughput, energy efficiency (EE), and intercept probability (IP). Moreover, a Deep Neural Network (DNN) model is formulated to assess OP and IP. Subsequent Monte Carlo simulations were conducted to validate the theoretical results. More research is done on the relationship between base station (BS)-IRS-Users when the Nakagami-m fading scenario is present. The outcomes of the simulations offer the following insights: 1) Their primary aim is to corroborate the analytical expressions. 2) This investigation substantially enhances our understanding of IRS-NOMA systems, setting a foundation for prospective explorations into practical realizations. The optimal performance of the OP is presented. Furthermore, we have extended the analysis by examining the performance of two additional scenarios: one IRS without a relay and a two-IRS setup without a relay. 3) Furthermore, the results demonstrate the superior performance of IRS-NOMA compared to the traditional IRS-Orthogonal Multiple Access (OMA) approach.}, archive = {J_ACCESS}, author = {Phu Tran Tin and Minh-Sang Van Nguyen and Tran Trung Duy and Van Huy Pham and Le Viet Thanh and Byung-Seo Kim}, doi = {10.1109/ACCESS.2025.3583493}, journal = {IEEE Access}, pages = {113089-113112}, shortjournal = {IEEE Access}, title = {Performance and secrecy analysis of cooperative full-duplex NOMA system with fountain codes and intelligent reflective surface}, volume = {13}, year = {2025}, }
• (2025). FR-CapsNet: Enhancing low-resolution image classification via frequency routed capsules. ACCESS, 13, 113076-113088. (www)

  @article{ , abstract = {Very low-resolution (VLR) images present a significant challenge for deep classification networks due to their inherent lack of fine spatial detail. While capsule networks (CapsNets), which encode spatial and pose information, are robust to resolution changes, they often struggle to perform and scale effectively on complex datasets. In this study, we introduce a novel frequency routing-based CapsNet (FR-CapsNet) to replace the conventional spatial routing in CapsNets. Though VLR images lose fine grained features, they retain high-level features captured by the low-frequency components. By computing capsule activation and pose information in the frequency domain and subsequently encoding them in the spatial domain, FR-CapsNet improves robustness to resolution degradation. Furthermore, our method utilizes a global routing framework that considerably reduces computational demands, enabling FR-CapsNet to scale effectively to larger and more diverse datasets. FR-CapsNet outperforms state-of-the-art (SOTA) convolutional neural networks (CNNs), other CapsNets, Transformers, and other advanced architectures in real-world VLR digit and image classification tasks. Specifically, on the VLR CIFAR-10 dataset, FR-CapsNet surpasses the current benchmark by 4.77% while using 4 times fewer parameters. Similarly, on the VLR SVHN and CIFAR-100 datasets, it exceeds the benchmark by 0.27% and 1.55%, respectively. Extensive experiments further demonstrate the superior generalization and robustness of FR-CapsNet compared to other SOTA methods. The codes for our models are available at https://github.com/kdhasi/FR-CapsNet.git}, archive = {J_ACCESS}, author = {Hasindu Dewasurendra and Kunmin Yeo and Nhan Thi Cao and Taejoon Kim}, doi = {10.1109/ACCESS.2025.3583688}, journal = {IEEE Access}, pages = {113076-113088}, shortjournal = {IEEE Access}, title = {FR-CapsNet: Enhancing low-resolution image classification via frequency routed capsules}, volume = {13}, year = {2025}, }
• (2025). Skin lesion segmentation in clinical images using nonnegative matrix factorization and mutual information. ACCESS, 13, 113066-113075. (www)

  @article{ , abstract = {Early detection of cutaneous melanoma is crucial for improving patient survival rates. Skin lesion segmentation is a critical step towards computer-aided diagnosis that is effective in melanoma detection. This work proposes a novel patch-based model that leverages a combination of non-negative matrix factorization and mutual information for segmenting cutaneous melanomas in clinical images. The proposed approach tends to lead to accurate skin lesion segmentations, and it was comprehensively evaluated in terms of its effectiveness based on three benchmark datasets (DermIS, DermNet, and DermQuest) using statistical analysis, ablation studies, and cross-dataset testing. The proposed method tends to improve on the segmentation results provided by established deep learning baselines (FCNet, FCN, PSPNet, SegNet, U-Net) and by recent advancements specifically designed for skin lesion segmentation (DSNet, FrCNet, and LGPNet).}, archive = {J_ACCESS}, author = {Eliezer Soares Flores and Jacob Scharcanski}, doi = {10.1109/ACCESS.2025.3584305}, journal = {IEEE Access}, pages = {113066-113075}, shortjournal = {IEEE Access}, title = {Skin lesion segmentation in clinical images using nonnegative matrix factorization and mutual information}, volume = {13}, year = {2025}, }
• (2025). Spherical harmonics for robust next-best-view estimation. ACCESS, 13, 113049-113065. (www)

  @article{ , abstract = {As 3D depth cameras become more affordable, the additional depth information proves useful in industrial setups. However, with a multiview camera configuration, the complete coverage of a 3D model can only be accurately captured from several views. For this task, the next-best-view (NBV) estimation is essential to minimize the actions required for a complete reconstruction. Traditional NBV methods for 3D data are often sensitive to noise and lack rotation invariance. Learning-based methods can make estimations based on current camera information, significantly improving the accuracy of a task. These methods are generally limited to one step, as multi-step planning requires estimating sensor observations. A method that can predict and simulate multiple steps can significantly improve performance when a single step is insufficient. We show that a convex hull of the object can be used to predict n-steps of NBV in a coverage reconstruction setting. The convex hull is used as a proxy for the real geometry of the object in selecting views and requires fewer points sampled from the ground-truth geometry. We propose a volumetric information gain (VIG) score that measures the similarity between the convex hulls obtained from the partial point cloud from selected views and the ground truth point cloud. We retrain an NBV network to predict the views that lead to a point cloud with the highest VIG score, and demonstrate that spherical harmonic preprocessing can mitigate the effect of noise, thereby making the network faster. The convex hull is afterwards used to simulate views, and a set covering optimisation is applied to make a one-shot selection of the best views for surface coverage reconstruction. This method is robust to changes in view space, as shown in the tests applied on Shapenet models along with more complex synthetic shapes from HomebrewDB, Stanford 3D Scanning Repository, and Linemod.}, archive = {J_ACCESS}, author = {Alexandru Pop and Levente Tamas}, doi = {10.1109/ACCESS.2025.3574804}, journal = {IEEE Access}, pages = {113049-113065}, shortjournal = {IEEE Access}, title = {Spherical harmonics for robust next-best-view estimation}, volume = {13}, year = {2025}, }
• (2025). Evaluating mixing methods: Anonymity, long-term information leakage, and cost. ACCESS, 13, 113028-113048. (www)

  @article{ , abstract = {Mix-nets are essential tools for safeguarding user privacy in communication networks. When deploying systems, two well-known mixing methods, batch (e.g., Timed Mix) and continuous mixing (e.g., Stop-and-Go Mix), are commonly used in real-world Mix-nets. However, no comprehensive study has evaluated the impact of these methods on anonymity, long-term information leakage, and the cost associated with dummy message overhead. To address this gap, this study provides a comprehensive analysis of these mixing methods. To this end, anonymity set size and entropy metrics are used to evaluate the anonymity protection of individual messages. The applicability of the Statistical Disclosure Attack (SDA) is extended to evaluate long-term information leakage for both mixing methods, and the closed-loop control anonymity protection model is adapted to measure the cost of anonymity protection in terms of the dummy message overhead. The experimental results show that continuous mixes provide stronger anonymity protection for individual messages—yielding up to 10% higher entropy under the default experimental settings. However, both long-term information leakage and the cost of achieving the desired protection are generally higher in continuous mixes, especially under high traffic scenarios. For example, under the default experimental settings, completing the SDA requires 1.5 times more observations in batch mixing than in continuous mixing and achieves the desired protection with 49% less cost in terms of the dummy message usage. These findings highlight the importance of selecting appropriate mixing method to balance anonymity, long-term information leakage, and cost in anonymous communication systems.}, archive = {J_ACCESS}, author = {Alperen Aksoy and Doğan Kesdoğan}, doi = {10.1109/ACCESS.2025.3584267}, journal = {IEEE Access}, pages = {113028-113048}, shortjournal = {IEEE Access}, title = {Evaluating mixing methods: Anonymity, long-term information leakage, and cost}, volume = {13}, year = {2025}, }
• (2025). Interval forecasting using GEV regression with stochastic search variable selection: A simulation study and its application to forecast imported meat price range. ACCESS, 13, 113013-113027. (www)

  @article{ , abstract = {This study aims to forecast the price range, i.e., the interval forecast between the upper and lower bounds of prices, using Generalized Extreme Value Regression (GEVReg) as a robust tool with significant implications for the trade industry. The price range is crucial for identifying undervalued or overvalued imported goods. However, predicting this price range is challenging due to the asymmetric distribution of import prices, non-stationary trends, and time-varying covariates. Numerous covariates, including lags of response variables, increase computational complexity and multicollinearity issues, requiring variable selection to identify the most impactful covariates. To address the challenges mentioned earlier, this work proposes GEVReg-SSVS, which combines the GEVReg as an underlying model with Stochastic Search Variable Selection (SSVS), a data-driven approach to variable selection. In addition, a simulation study was conducted to evaluate the performance of the GEVReg-SSVS method, which shows that it excels in variable selection, consistently identifying models with covariates aligned with the true generated model. These insights offer a robust approach for forecasting imported meat prices while detecting potential price anomalies, directly benefiting the trade industry.}, archive = {J_ACCESS}, author = {Dedy Dwi Prastyo and Ike Yuniarti and Setiawan and Santi Puteri Rahayu}, doi = {10.1109/ACCESS.2025.3579702}, journal = {IEEE Access}, pages = {113013-113027}, shortjournal = {IEEE Access}, title = {Interval forecasting using GEV regression with stochastic search variable selection: A simulation study and its application to forecast imported meat price range}, volume = {13}, year = {2025}, }
• (2025). Monte carlo simulation-based method for evaluating sample thickness variation to improve reliability of dielectric strength assessment. ACCESS, 13, 113005-113012. (www)

  @article{ , abstract = {This study investigated the thickness effect (the influence of sample thickness variation) on the assessment of the dielectric breakdown strength in XLPE and PP insulators. A Monte Carlo simulation (MCS) was conducted to quantify the impact of voltage and thickness on the dielectric strength. Aimed at improving power system reliability, the study sought to provide a quantitative basis for the thickness effect and propose a reasonable range of thickness variation, specifically between 0.2 mm and 1.1 mm. The simulation was repeated 1,000 times by modeling thickness deviations ranging from 1% to 10%. Levene’s test was used to assess the homogeneity of variance between an ideal group with a 1% thickness deviation and comparison groups with larger deviations. The proportion of cases where Levene’s test produced a p-value below the significance level reduced below 80% when the thickness variation exceeded 2.5%. This verified that the variance differences were statistically significant. This result (particularly for samples with thicknesses of at most 1 mm) provides a scientific basis for establishing tolerance standards for thickness variation. Thereby, it contributes to enhancing the reliability of dielectric strength testing methods.}, archive = {J_ACCESS}, author = {Keon-Hee Park and Seung-Won Lee and Hae-Jong Kim and Jang-Seob Lim}, doi = {10.1109/ACCESS.2025.3583892}, journal = {IEEE Access}, pages = {113005-113012}, shortjournal = {IEEE Access}, title = {Monte carlo simulation-based method for evaluating sample thickness variation to improve reliability of dielectric strength assessment}, volume = {13}, year = {2025}, }
• (2025). A wideband inductorless programmable gain amplifier using 90-nm CMOS for high-speed communication. ACCESS, 13, 112996-113004. (www)

  @article{ , abstract = {This paper presents a wideband inductorless programmable gain amplifier (PGA) for high-speed communication. The proposed circuit employs a Cherry-Hooper amplifier structure as the gain cell and an active inductor, achieving a wide bandwidth with relatively low power consumption and a compact chip design. An in-depth analysis was conducted on the distinctive technique employed by this structure to achieve a wide bandwidth and precise gain control. A 6-bit control switch was utilized to attain the precise zero and self-resonant frequencies of the active inductor. The chip is fabricated using 90-nm CMOS technology, with a chip size of 0.3 mm2 and consuming 39.3 mW from a 1.2 V supply voltage. The on-chip measured results show a voltage gain ranging from 21 to 49 dB with a 4-dB step and a 2.1 GHz bandwidth. The gain step errors were within +0.1 dB to +0.7 dB. The inductorless topology achieves wideband performance and precise gain control, demonstrating the suitability of the proposed technique for high-speed communication PGA.}, archive = {J_ACCESS}, author = {Taufiq Alif Kurniawan and Tzu-Yu Lin and Chi-Tshao Wang and Hsiao-Chin Chen and Arief Udhiarto}, doi = {10.1109/ACCESS.2025.3583775}, journal = {IEEE Access}, pages = {112996-113004}, shortjournal = {IEEE Access}, title = {A wideband inductorless programmable gain amplifier using 90-nm CMOS for high-speed communication}, volume = {13}, year = {2025}, }
• (2025). Detecting current transformer measurement errors using double decomposition method. ACCESS, 13, 112986-112995. (www)

  @article{ , abstract = {Current transformer (CT) ensures the safe and stable operation of the power system by monitoring, measuring and protecting the current in the power grid. However, the CT measurement errors undermine the fairness of power trading. A double decomposition method is proposed to locate the abnormal phase and detect the CT measurement error in this study. Firstly, a variational mode decomposition (VMD) is used to decompose the measured CT secondary side phase currents to generate residuals. Meanwhile, wavelet decomposition is applied to decompose the residuals in multiple layers, and the approximate coefficient set of each phase is screened. Then, t-distributed stochastic neighbor embedding (t-SNE) is utilized to extract CT deterioration feature from the approximate coefficient set. Finally, the CT measurement error overrun threshold is calculated by means of kernel density estimation (KDE) to locate the abnormal phase and detect the CT measurement errors. In China Electric Power Research Institute, the proposed CT measurement error detection method can accurately locate the abnormal phase of CT and detect the 0.2% ratio difference threshold in the verification experiment. In the follow-up study, the proposed method in the study will be verified and supplemented based on CT data in the real distribution network environment.}, archive = {J_ACCESS}, author = {Bolun Du and Yinglong Diao and Huan Wang and Xiujuan Zeng and Tong Liu and Yiyi Peng}, doi = {10.1109/ACCESS.2025.3579128}, journal = {IEEE Access}, pages = {112986-112995}, shortjournal = {IEEE Access}, title = {Detecting current transformer measurement errors using double decomposition method}, volume = {13}, year = {2025}, }
• (2025). Graph neural networks for aerodynamic analysis of truck platoon under heterogeneous traffic environment. ACCESS, 13, 112978-112985. (www)

  @article{ , abstract = {Recent advancements in connected vehicle technology have the potential to transform the efficiency and sustainability of transportation systems. Among these, truck platooning has emerged as a promising strategy to enhance freight delivery operations. However, the generalization of truck platoon modeling and the economic impacts of implementing such systems remain areas that require deeper exploration. This paper introduces a novel graph neural network-based surrogate model, namely Graph Network-based Platoon Simulator (GNPS), designed to predict aerodynamic drag forces in truck platoons across a wide range of traffic and environmental conditions. By representing the platoon and surrounding traffic as a heterogeneous graph, the proposed GNPS model leverages node-level features such as wind configurations and vehicle geometries, alongside edge-level features like inter-vehicle distances. In comparison to traditional CFD simulations, the proposed GNPS model provides a cost-effective and computationally efficient alternative, offering dynamic adaptability to real-world traffic scenarios.}, archive = {J_ACCESS}, author = {Tong Liu and Fangyu Liu}, doi = {10.1109/ACCESS.2025.3583333}, journal = {IEEE Access}, pages = {112978-112985}, shortjournal = {IEEE Access}, title = {Graph neural networks for aerodynamic analysis of truck platoon under heterogeneous traffic environment}, volume = {13}, year = {2025}, }
• (2025). A review on the advances, applications, and future prospects of post-quantum cryptography in blockchain and IoT. ACCESS, 13, 112962-112977. (www)

  @article{ , abstract = {With the advancement of quantum computing, classical public-key cryptosystems are increasingly vulnerable, prompting global standardization efforts by organizations such as NIST, ETSI, and ISO. This paper presents a comprehensive review of recent advancements in post-quantum cryptography (PQC), with a particular focus on lattice-based, hash-based, and multivariate approaches. The study examines the security foundations, implementation challenges, and the applicability of these approaches across various domains, including finance, blockchain, and the Internet of Things (IoT). A key contribution of this work is the proposed Hybrid Cryptographic Framework (HCF), which integrates classical and quantum-resistant primitives, facilitating a secure and interoperable transition. This framework combines hybrid key exchange mechanisms, dual-signature schemes, and PQC-compatible Merkle trees, supported by a discrete multi-objective optimization model to select algorithms that meet real-world constraints. The paper also addresses deployment challenges, such as hardware acceleration, side-channel resistance, and legacy system compatibility. Real-world case studies demonstrate how leading institutions are actively piloting PQC integration. Finally, we present a strategic roadmap for the adoption of PQC, incorporating adaptive triggers based on quantum capabilities and evolving threat models. By aligning cryptographic design with international standards and practical system requirements, this paper provides valuable guidance for constructing resilient, quantum-safe infrastructures.}, archive = {J_ACCESS}, author = {Yong Wang and Eddie Shahril Ismail}, doi = {10.1109/ACCESS.2025.3584473}, journal = {IEEE Access}, pages = {112962-112977}, shortjournal = {IEEE Access}, title = {A review on the advances, applications, and future prospects of post-quantum cryptography in blockchain and IoT}, volume = {13}, year = {2025}, }
• (2025). AI-driven nudge optimization: Integrating two-tower networks and multi-armed bandit with behavioral economics for digital banking campaign. ACCESS, 13, 112948-112961. (www)

  @article{ , abstract = {In the era of digital banking, personalized financial product recommendations play a crucial role in enhancing customer engagement and conversion rates. Traditional recommender systems often fail to account for behavioral economic factors and adapt to real-time user interactions, leading to suboptimal nudge strategies. This study attempts to resolve this problem by creating a recommendation model that combines Two-Tower Networks (TWN) and Multi-Armed Bandit (MAB) reinforcement learning. The goal is to make nudge-based financial product suggestions that are consistently more effective. The proposed model leverages TWN to generate structured user-product rankings and capture behavioral insights. These rankings serve as input to the MAB model, which continuously refines recommendations through an adaptive exploration-exploitation strategy. In contrast to static suggestion models, MAB changes the weights of different nudge strategies based on how engaged users are in real-time. This approach ensures that financial nudges are personalized and relevant to the user’s situation. The model remains stable even when tested with outdated datasets, indicating that it can adapt to changing financial conditions. This study is unique because it uses both deep learning-based representation learning (TWN) and reinforcement-driven decision optimization (MAB). This allows the model to learn autonomously and adapt over time. This study represents a significant step forward in using AI to make financial recommendations. This closes the gap between basic personalization and real-time adaptation. Future studies will focus on using the model in real-life banking settings and improving reward functions to make users more engaged and help them make better financial decisions over time.}, archive = {J_ACCESS}, author = {Idha Kristiana and Harjanto Prabowo and Ford Lumban Gaol and Nunung Nurul Qomariyah}, doi = {10.1109/ACCESS.2025.3584648}, journal = {IEEE Access}, pages = {112948-112961}, shortjournal = {IEEE Access}, title = {AI-driven nudge optimization: Integrating two-tower networks and multi-armed bandit with behavioral economics for digital banking campaign}, volume = {13}, year = {2025}, }
• (2025). Efficient machine learning models for solar radiation prediction using ensemble techniques: A case study in low-rainfall arid climates. ACCESS, 13, 112932-112947. (www)

  @article{ , abstract = {Solar energy is a green energy source that can be used to generate electricity, making it one of the oldest existing renewable energy sources. However, solar energy values are not always available in many areas far from urban cities, due to the high cost of purchasing and maintaining measuring instruments. In these areas, the only way to know the values of solar radiation from is to predict these values. To address this problem, we have designed an efficient model capable of forecasting hourly solar radiation a climate with a combination of arid and humid conditions using real-time data from meteorological variables measured by a Meteorological Station located in a university area isolated from the central city. For the development of our model, 11 meteorological variables were collected from January to December 2023. From this, dimensionality reduction was carried out using the Principal Component Analysis (PCA) technique, obtaining the 8 most appropriate representative variables for the prediction of solar radiation using different Machine Learning (ML) models. To ensure the reliability of our model, we compared the performance metrics of several ML models. Finally, ensemble techniques were applied to enhance prediction accuracy by combining various ML algorithms. The best results were obtained using the Stacking Regressor (SR), achieving an $R^{2}$ of 0.92, RMSE of 47.30 W/ $m^{2}$ , and MAE of 18.27 W/ $m^{2}$ , demonstrating high predictive performance in the target climate region.}, archive = {J_ACCESS}, author = {Jimmy Aurelio Rosales Huamani and Uwe Rojas Villanueva and Christian Leonardo Rosales Ventocilla and Jose Luis Castillo Sequera and José Manuel Gómez Pulido}, doi = {10.1109/ACCESS.2025.3579755}, journal = {IEEE Access}, pages = {112932-112947}, shortjournal = {IEEE Access}, title = {Efficient machine learning models for solar radiation prediction using ensemble techniques: A case study in low-rainfall arid climates}, volume = {13}, year = {2025}, }
• (2025). Ethereum blockchain interconnectivity for dynamic and privacy-preserving access control. ACCESS, 13, 112918-112931. (www)

  @article{ , abstract = {This paper introduces a novel access control architecture based on a dual-blockchain model that separates access management from data storage to enhance security and scalability. The system enables users to submit access requests to a primary blockchain, where smart contracts dynamically verify permissions before retrieving data from a secondary, isolated blockchain. This design enforces fine-grained, account-level access control while preventing direct exposure of sensitive data. A proof of concept was implemented using Hyperledger YUI to interconnect Ethereum-based blockchains, demonstrating secure inter-chain communication and dynamic permission enforcement. The proposed solution addresses key limitations in existing blockchain infrastructures and offers a flexible, decentralized framework suitable for applications requiring robust data governance and regulatory compliance.}, archive = {J_ACCESS}, author = {Christian Delgado-von-Eitzen and Manuel José Fernández-Iglesias and Luis Anido-Rifón and Martín Llamas-Nistal}, doi = {10.1109/ACCESS.2025.3584429}, journal = {IEEE Access}, pages = {112918-112931}, shortjournal = {IEEE Access}, title = {Ethereum blockchain interconnectivity for dynamic and privacy-preserving access control}, volume = {13}, year = {2025}, }
• (2025). A two-stage bayesian receiver for massive grant-free OFDM-NOMA with interfering pilot symbols. ACCESS, 13, 112905-112917. (www)

  @article{ , abstract = {Massive grant-free non-orthogonal multiple access (NOMA) enables low-latency data transmission for a subset of coexisting user terminals, which may not be known a priori, and operates in an uncoordinated manner. On the receiver side, the effectiveness of user data decoding techniques heavily relies on accurate initial estimates of user activity, channel conditions, and parameters, all of which are derived from pilot symbols. However, in practical systems where pilot symbols are shared among a large number of users, the process is complicated by pilot interference. This paper introduces a pilot-only clustered multiple sparse Bayesian learning approach to address this acquisition challenge. It employs a multiple measurement vector (MMV) model to handle the common support shared by the multi-antenna channel impulse responses of each user. The resulting estimates are then used to initialize a low-complexity Bayesian procedure, based on expectation propagation, to refine both user activity and channel estimations, as well as to facilitate multi-user detection and decoding by leveraging both pilot and data observations. Numerical simulations demonstrate the good performance of the proposed two-stage Bayesian receiver for massive grant-free NOMA under frequency-selective channels with antenna correlation.}, archive = {J_ACCESS}, author = {Fakher Sagheer and Frederic Lehmann and Antoine O. Berthet}, doi = {10.1109/ACCESS.2025.3578593}, journal = {IEEE Access}, pages = {112905-112917}, shortjournal = {IEEE Access}, title = {A two-stage bayesian receiver for massive grant-free OFDM-NOMA with interfering pilot symbols}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of different speech-based emotional stress level detection approaches. ACCESS, 13, 112880-112904. (www)

  @article{ , abstract = {This study presents a comprehensive evaluation and comparison of selected conventional and advanced approaches for detecting and classifying emotional stress levels from speech. Three representative and publicly available stress datasets-CRISIS, StressDat, and SUSAS-were used. A detailed comparative analysis of their speech characteristics, including fundamental frequency ( $F_{0}$ ), formant frequencies ( $F_{1}$ , $F_{2}$ , $F_{3}$ ), voice intensity, harmonic-to-noise ratio, jitter, and shimmer, was conducted to investigate how these features change with increasing stress and contribute to its detection. Both conventional feature-based methods and deep learning techniques, including transfer and self-supervised learning, are explored in the experimental part of this research. The best classification results among the conventional methods were achieved using a combination of features extracted via the Discrete Wavelet Transform and GammaTone Cepstral Coefficients (DWT+GTCC), paired with a Subspace k-Nearest Neighbors classifier, which yielded F1-scores of up to 91.1% on CRISIS and 82.4% on StressDat. Among the deep learning approaches, we fine-tuned self-supervised models such as Wav2Vec 2.0 and BYOL-S (Bootstrap Your Own Latent for Speech), as well as transfer learning models including VGGish and YAMNet. Wav2Vec 2.0 consistently outperformed the others, achieving an F1-score of 94% on CRISIS and over 87% on StressDat. On the SUSAS dataset, which contains speech under simulated and real stress conditions, an F1-score of 77% was achieved using both the fine-tuned Wav2Vec 2.0 model as well as a pipeline combining BYOL-S feature extraction with a Support Vector Machine (SVM) classifier. After merging all stress-related datasets into a single balanced dataset, Wav2Vec 2.0 achieved the highest F1-score of 85%. Incorporating additional emotional speech data improved robustness and led to a slight increase in the accuracy of classifying stress into three distinct levels. These results highlight the importance of robust feature selection, appropriate classifier design, and the integration of emotional speech data to enhance generalization performance in real-world applications.}, archive = {J_ACCESS}, author = {Ján Staš and Stanislav Ondáš and Jozef Juhár}, doi = {10.1109/ACCESS.2025.3584534}, journal = {IEEE Access}, pages = {112880-112904}, shortjournal = {IEEE Access}, title = {Performance evaluation of different speech-based emotional stress level detection approaches}, volume = {13}, year = {2025}, }
• (2025). Optimal operation of electric vehicle supply equipment by aggregators in local energy community. ACCESS, 13, 112867-112879. (www)

  @article{ , abstract = {This paper proposes a centralized energy management system for low voltage (LV) distribution networks. The main contribution of this model is to manage the energy serving at the local energy communities in the presence of electric vehicle supply equipment (EVSE). Unlocking the demand response potential by the EVSE at the distribution network with the contribution of the active residential prosumers has been investigated in this study under different operational planning scenarios. The developed model is based on the multi-temporal optimal power flow (MTOPF) concept while the unbalanced nature of LV networks has been addressed using unbalanced power flow equations. The aggregator can effectively manage the optimal charging of electric vehicles (EVs) by home and public chargers available at the distribution network. Simulation results on a modified unbalanced LV network illustrate that the optimal operation of EVSE minimizes the electricity costs of end-users. The simulation results show that the operating costs and systems losses reduce by 9.22% and 43.45%, respectively. These results have been obtained considering the switching actions and 100% PV power generation index using the presented MV-LV coordinated operational model. Besides, the energy storage systems improve the peak-to-average (PAR) ratio by 9.87%.}, archive = {J_ACCESS}, author = {Ali Esmaeel Nezhad and Toktam Tavakkoli Sabour and R. P. Joshi and Mohammad Sadegh Javadi and Pedro H. J. Nardelli}, doi = {10.1109/ACCESS.2025.3582189}, journal = {IEEE Access}, pages = {112867-112879}, shortjournal = {IEEE Access}, title = {Optimal operation of electric vehicle supply equipment by aggregators in local energy community}, volume = {13}, year = {2025}, }
• (2025). Design of a cross-layer anti-jamming routing protocol for FANETS based on spatial filtering. ACCESS, 13, 112860-112866. (www)

  @article{ , abstract = {Flying Ad Hoc Networks (FANETs) are an essential component of future integrated air-space strategic systems. To achieve efficient and reliable network communication between nodes, a routing protocol capable of adapting to dynamic network changes and complex electromagnetic environments is required. This paper explores routing protocols for reliable networking between flight platforms in highly contested environments and the main idea is introducing link quality factors. Based on the integration of link quality for routing establishment, this study further investigates the performance of a cross-layer anti-jamming multi-hop routing protocol based on spatial disturbance resistance capabilities of nodes. It comprehensively evaluates link quality, incorporating nodes’ spatial disturbance resistance capabilities, to identify the most reliable routes in FANETs. Simulation results indicate that when nodes possess certain spatial disturbance resistance capabilities, routing established based on link quality can further ensure the network’s average throughput under interference conditions, as well as maintain a low packet loss rate.}, archive = {J_ACCESS}, author = {Qiong Yu and Cong Wang and Hao Wang and Wenfeng Ma and Hui Tian and Yi Zhu and Ming Lu and Xun Liu and Yuchen Ji and Chengyu Zhang}, doi = {10.1109/ACCESS.2025.3583209}, journal = {IEEE Access}, pages = {112860-112866}, shortjournal = {IEEE Access}, title = {Design of a cross-layer anti-jamming routing protocol for FANETS based on spatial filtering}, volume = {13}, year = {2025}, }
• (2025). Cervical cancer detection: A comprehensive evaluation of CNN models, vision transformer approaches, and fusion strategies. ACCESS, 13, 112835-112859. (www)

  @article{ , abstract = {Cervical cancer, a malignant tumor arising from the cervix, poses a significant health risk to women worldwide. Early detection plays a pivotal role in improving patient treatment by enabling timely intervention and effective treatment. This paper explores three distinct strategies for enhancing cervical cancer detection. Firstly, four Convolutional Neural Network (CNN) models—EfficientNetB0, DenseNet, Xception, and ResNet50—are evaluated with performance metrics including accuracy, recall, F1-score, and precision. Results indicate accuracy values ranging from 0.65 to 0.78, reflecting the challenges posed by low-resolution imagery. Secondly, the ViT-Cerv methodology is introduced, leveraging ViT architecture, trained from scratch on the SkipMed dataset. The ViT-Cerv model achieves an accuracy of 0.94, demonstrating promise in cervical cancer classification despite the dataset low resolution. Finally, the HViT-Cerv hybrid model, synergizing the strengths of both CNN models and the ViT-Cerv architecture through fusion, demonstrates enhanced accuracy ranging from 0.97 to 0.99. This underscores the efficacy of the fusion approach in achieving superior classification capabilities, showcasing a notable advancement in accuracy over individual models. A comprehensive comparison with state-of-the-art models highlights the significance of the proposed models in cervical cancer detection, particularly in overcoming challenges posed by low-resolution datasets.}, archive = {J_ACCESS}, author = {Heba M. Emara and Walid El-Shafai and Naglaa F. Soliman and Abeer D. Algarni and Reem Alkanhel and Fathi E. Abd El-Samie}, doi = {10.1109/ACCESS.2024.3473741}, journal = {IEEE Access}, pages = {112835-112859}, shortjournal = {IEEE Access}, title = {Cervical cancer detection: A comprehensive evaluation of CNN models, vision transformer approaches, and fusion strategies}, volume = {13}, year = {2025}, }
• (2025). GODiff: Region-specific semantic editing with CLIP-guided diffusion models. ACCESS, 13, 112818-112834. (www)

  @article{ , abstract = {Text-based image editing enables intuitive and flexible content creation, but existing methods often suffer from issues such as the loss of original image characteristics within the target area or unintended modifications in irrelevant regions. Additionally, high-quality results often require additional training or fine-tuning, which can cause considerable inconvenience in practical use. To address these limitations, we propose GODiff, a method that offers a more convenient and user-friendly editing experience. It allows immediate and precise modifications of a single image based on dynamically changing text prompts and can be flexibly applied to various diffusion models without additional training. In particular, the editing region is automatically identified, and text-based guidance is focused solely on that area, thereby improving efficiency and minimizing unnecessary changes. Furthermore, by optimizing the guidance in real-time during the generative process, GODiff more accurately reflects the intent of the text prompt and produces more natural and consistent results. It has demonstrated consistent performance across diverse datasets and model architectures, and a human evaluation confirmed that it provides superior editing quality in terms of naturalness and user preference compared to existing methods.}, archive = {J_ACCESS}, author = {Junho Kim and Eunwoo Kim and Kyungjae Lee}, doi = {10.1109/ACCESS.2025.3580263}, journal = {IEEE Access}, pages = {112818-112834}, shortjournal = {IEEE Access}, title = {GODiff: Region-specific semantic editing with CLIP-guided diffusion models}, volume = {13}, year = {2025}, }
• (2025). Low-light image enhancement algorithm integrating retinex illumination estimation and multi-attention. ACCESS, 13, 112805-112817. (www)

  @article{ , abstract = {To address the challenges of poor visibility, blurred textures, and distortions in low-light conditions, this paper presents an image enhancement algorithm that integrates Retinex-based illumination estimation with multi-attention mechanisms. The algorithm introduces a perturbation component into the Retinex model to simulate image degradation, enabling more accurate illumination estimation. Additionally, multi-attention mechanisms are incorporated to strengthen the model’s ability to capture both local and global features. An Illumination Estimation Module is proposed to predict illumination information of the image. A Degradation Restoration Module is designed to boost the visibility of the illumination map and eliminate artifacts. A Multi-Scale Color Correction Module is introduced for color correction on illumination feature maps. The preliminary enhanced image constructed by the Illumination Estimation Module and the restored map from Degradation Restoration Module are residually fused, and the color-corrected map from Multi-Scale Color Correction Module is integrated in the channel dimension with a scaling factor $\alpha $ , completing the enhancement of low-light images. Comprehensive quantitative and qualitative evaluations across seven benchmark datasets validate the proposed algorithm effectively removes artifacts, significantly improves image quality, and enhances visual perception.}, archive = {J_ACCESS}, author = {Yaping Jiang and Shixian Guo}, doi = {10.1109/ACCESS.2025.3583723}, journal = {IEEE Access}, pages = {112805-112817}, shortjournal = {IEEE Access}, title = {Low-light image enhancement algorithm integrating retinex illumination estimation and multi-attention}, volume = {13}, year = {2025}, }
• (2025). Effect of suspension length and substrate curvature on electric field distribution in emergency anti-galloping cables. ACCESS, 13, 112797-112804. (www)

  @article{ , abstract = {Galloping of transmission lines caused by wind and ice accumulation poses significant threats to the stability of power grids. While most existing research has focused on preventive anti-galloping devices intended to inhibit galloping before it occurs, emergency mitigation strategies that act after galloping has already developed remain significantly underexplored. This study investigates a 500 kV quad-bundle conductor system and proposes the use of an emergency anti-galloping cable as a mitigation measure. A three-dimensional finite element model is developed to analyze the influence of suspension length and substrate curvature on electric field distribution around key components. Simulation results demonstrate that increasing the suspension length beyond 1.0 m effectively reduces the maximum electric field strength to below the corona threshold of 20 kV/cm. Furthermore, increasing the substrate edge curvature radius to 2.5 mm decreases the maximum surface electric field strength by up to 46.8%. Moreover, the proposed methodology is generalizable to other transmission voltage levels and conductor configurations with appropriate parameter adjustments. This work provides theoretical guidance for the emergency suppression of conductor galloping and contributes to enhancing the reliability of high-voltage transmission systems.}, archive = {J_ACCESS}, author = {Zhongbin Lv and Bo Zhang and Guanghui Liu and Zehui Liu and Chencheng Wang and Lee Li}, doi = {10.1109/ACCESS.2025.3584159}, journal = {IEEE Access}, pages = {112797-112804}, shortjournal = {IEEE Access}, title = {Effect of suspension length and substrate curvature on electric field distribution in emergency anti-galloping cables}, volume = {13}, year = {2025}, }
• (2025). Offline detection of violations in chinese E-commerce live streaming content. ACCESS, 13, 112785-112796. (www)

  @article{ , abstract = {With the rapid growth of the Chinese e-commerce live streaming industry, regulatory oversight of its content has become increasingly critical. This paper addresses the task of offline detection of content violations in recorded Chinese e-commerce live streaming videos. We introduce a new task, CLiveSVD, which categorizes content into three classes: compliance, suspected violation, and serious violation. To support research in this area, we constructed a high-quality dataset in collaboration with market supervision experts, comprising a training set, validation set, and two test sets. Given the semantic similarity between suspected and serious violations, we propose a two-stage classification framework that improves upon direct multi-class classification. Additionally, to address the challenges of data imbalance and the high cost of manual annotation, we leverage large language models (LLMs) to generate synthetic violation examples, enhancing both the diversity and volume of the training data. Experimental results show that our two-stage approach achieves superior performance, and that LLM-generated data further boosts the robustness and effectiveness of the violation detection system in offline settings.}, archive = {J_ACCESS}, author = {Xiaohan Liu and Chenyu Liu and Ran Bai and Liu Yuan and Yanquan Zhou and Minjiang Huang and Jipeng Qiang}, doi = {10.1109/ACCESS.2025.3584238}, journal = {IEEE Access}, pages = {112785-112796}, shortjournal = {IEEE Access}, title = {Offline detection of violations in chinese E-commerce live streaming content}, volume = {13}, year = {2025}, }
• (2025). Video coding for machines with neural-network-based chroma synthesis. ACCESS, 13, 112777-112784. (www)

  @article{ , abstract = {Video coding for machines is an emerging area within video compression technology that has recently attracted considerable research attention. Within the ISO/IEC standardization activities, efforts are underway to develop a new standard optimized for machine vision tasks rather than just for human-oriented video consumption. In this context, the novel contribution of this work is the application of neural networks to perform chroma synthesis at the decoder side, thus eliminating the need for direct chroma transmission. This concept has been implemented and validated in the Video Coding for Machines Reference Software (VCM-RS), a test model developed by the MPEG Video group, which is briefly described for completeness. Experimental results reported in this paper show that our approach significantly reduces the number of bits required for video representation. An average bitrate reduction of 12% in Random Access End-to-End configurations for object tracking tasks is reported (for certain video sequences, bitrate reduction even up to 60%). The presented proposal has been accepted in the current version of the specification of the upcoming VCM standard.}, archive = {J_ACCESS}, author = {Mateusz Lorkiewicz and Sławomir Różek and Olgierd Stankiewicz and Tomasz Grajek and Sławomir Maćkowiak and Marek Domański}, doi = {10.1109/ACCESS.2025.3583047}, journal = {IEEE Access}, pages = {112777-112784}, shortjournal = {IEEE Access}, title = {Video coding for machines with neural-network-based chroma synthesis}, volume = {13}, year = {2025}, }
• (2025). Eternal-thing 3.0: Mixed-mode SoC for energy harvesting system towards sustainable IoT. ACCESS, 13, 112765-112776. (www)

  @article{ , abstract = {The power requirement in IoT is essential to fulfill the energy demand of the power-hungry sensors at end nodes. The use of fixed batteries restricts sustainability and makes the system costly. This work presents a battery-less solar energy harvesting system (EHS). Designing a state-of-the-art EHS needs a lot of exercise. Proper modeling of each unit makes the system robust and can be tuned at every stage to get an optimum result. The proposed EHS comprises a clock generator, DC-DC converters, analog-to-digital converters (ADCs), a maximum power point tracking (MPPT) unit, and a digital controller. The DC-DC converter and ADCs are designed in Verilog-A. The MPPT module digital controller is designed using Verilog HDL. The digital controller decides the mode of operation of the EHS based on power availability. Verilog-AMS allows us to do the mixed-mode simulation very early, so errors can only be eliminated in the initial stages at the circuit level. The proposed EHS is simulated in the Cadence Virtuoso AMS Designer Simulator (using the Incisive Run tool). The input solar voltage is 1 V to 1.5 V, and the output is 3 V to 3.5 V. The EHS provides supply voltages of 3.3 V, 1.8 V, and 1 V to the end node devices in IoT. The EHS is further designed with the parameters obtained from modeling in Cadence using virtuoso (for analog circuits) and genus (for digital circuits) and finally combined in Innovous (mixed-mode tool) for tape-out.}, archive = {J_ACCESS}, author = {Saswat Kumar Ram and Sanjeev Mani Yadav and Jayendra Kumar and Priyanka Singh and Banee Bandana Das}, doi = {10.1109/ACCESS.2025.3583798}, journal = {IEEE Access}, pages = {112765-112776}, shortjournal = {IEEE Access}, title = {Eternal-thing 3.0: Mixed-mode SoC for energy harvesting system towards sustainable IoT}, volume = {13}, year = {2025}, }
• (2025). SMART: Semantic, multi-objective, and reinforcement-based adversarial training for email spam detection. ACCESS, 13, 112749-112764. (www)

  @article{ , abstract = {This paper introduces SMART, a robust framework for email spam detection designed to adapt to and mitigate adversarial spam emails. SMART integrates several advanced techniques, including text cleaning and tokenization, word embeddings, K-means clustering for semantic enhancement, multiobjective adversarial training, and reinforcement learning for dynamic spam detection. By leveraging these components, SMART enhances the accuracy and efficiency of detecting and classifying spam emails, even in the face of evolving adversarial tactics. The framework incorporates semantic enrichment using BERT for contextual word replacements and reinforcement learning to adapt spam detection strategies dynamically. SMART provides a comprehensive solution to address the limitations of traditional spam detection methods, ensuring more secure and reliable email communication. We compared SMART experimentally with the following comparable methods: EGOAMLPs, MFO, OPTICS, STM, and MOBGOA. The following is average percentage improvement of SPAM over each of these methods: 2.41% over; 3.36% over; 4.51% over; 6.16% over; 6.89% over.}, archive = {J_ACCESS}, author = {Kamal Taha}, doi = {10.1109/ACCESS.2025.3581131}, journal = {IEEE Access}, pages = {112749-112764}, shortjournal = {IEEE Access}, title = {SMART: Semantic, multi-objective, and reinforcement-based adversarial training for email spam detection}, volume = {13}, year = {2025}, }
• (2025). Simulation-based analysis of DRL exploitation rate for voltage and frequency stability in a single-inverter microgrid control system. ACCESS, 13, 112731-112748. (www)

  @article{ , abstract = {This paper discusses the development and performance evaluation of a single-inverter-based microgrid control system using a Deep Reinforcement Learning (DRL) agent trained with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The controller is specifically designed to address unplanned transitions during fluctuating load conditions and transitions between grid-tied and islanded modes. An analysis of Final Exploitation Rates (FER) ranging from 5% to 1% was conducted to assess transient responses and steady-state performance. A comparison with a traditional Proportional-Integral-Derivative (PID) controller reveals that while the DRL agent with 2%-FER demonstrates strong performance in managing transient events, the PID controller provides superior stability in steady-state operation. Key performance indicators, including Rise Time, Settling Time, Settling Min/Max, Peak Time, and Mean Absolute Deviation (MAD), were examined. The results show that the DRL agent with 2%-FER effectively limits frequency deviations to ±1% and voltage deviations to within ±2.5% during transitions, whereas agents with FER 3-5% displayed instability with deviations of up to 2% for frequency and 4% for voltage. The PID controller, however, maintained more consistent steady-state performance, with frequency tightly distributed around 49.95-50.05 Hz and voltage near 1 pu. These findings suggest that DRL-based control is better suited for handling unexpected switching events, where proactive adjustment is crucial, while PID control is more effective for steady-state conditions where a passive control approach accommodates system dynamics more naturally.}, archive = {J_ACCESS}, author = {Noer Fadzri Perdana Dinata and Makbul Anwari Muhammad Ramli and Muhammad Irfan Jambak and Prisma Megantoro and Muhammad Abu Bakar Sidik}, doi = {10.1109/ACCESS.2025.3580992}, journal = {IEEE Access}, pages = {112731-112748}, shortjournal = {IEEE Access}, title = {Simulation-based analysis of DRL exploitation rate for voltage and frequency stability in a single-inverter microgrid control system}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection for wireless cellular communication based on synthetic anomaly. ACCESS, 13, 112720-112730. (www)

  @article{ , abstract = {With the growing demand for wireless communication services, efficient anomaly detection in the electromagnetic spectrum is crucial to ensure service quality and prevent illegal spectrum usage. This study introduces a novel Synthetic Anomaly Detection (SAD)-based method tailored for spectrum anomaly detection. By leveraging synthetic anomaly data generation that captures the unique characteristics of spectrum signals, the proposed method addresses the limitations of conventional approaches, such as reconstruction errors and inadequate representation of spectrum anomalies. A rectangular mask and Gaussian noise were employed to simulate anomalies like tones, chirps, and pulses commonly found in LTE and 5G bands. The method achieved AUROC scores of 90% (image-level) and 82% (pixel-level) under ISR 0 dB, outperforming traditional unsupervised learning-based detection methods. An ablation study demonstrated the effectiveness of the synthetic anomaly generation process, highlighting its contribution to enhanced detection performance. These results validate the practicality and robustness of the proposed approach, providing a solid framework for spectrum anomaly detection in dynamic electromagnetic environments.}, archive = {J_ACCESS}, author = {Hyeongwoo Kim and Jinha Kim and Youngseo Ji and Byungkwan Kim}, doi = {10.1109/ACCESS.2025.3584113}, journal = {IEEE Access}, pages = {112720-112730}, shortjournal = {IEEE Access}, title = {Anomaly detection for wireless cellular communication based on synthetic anomaly}, volume = {13}, year = {2025}, }
• (2025). Mifu-ER: Modality quality index-based incremental fusion for emotion recognition. ACCESS, 13, 112703-112719. (www)

  @article{ , abstract = {Physiological information, such as that obtained from an electroencephalogram (EEG), electromyogram (EMG) or electrocardiogram (ECG), is often used as more objective and reliable data in emotion recognition studies because it is less affected by social masking. Recently, there has been a growing trend in emotion recognition studies to use multimodal data that fuses more than two physiological signals to improve emotion recognition performance based on this physiological information. This paper proposes a novel incremental fusion framework for multimodal physiological data-based emotion recognition, called Modality Quality Index (MQI)-based incremental fusion for Emotion Recognition (Mifu-ER). Mifu-ER integrates three aspects: i) application of the sliding window method to ensure fast processing speed and a sufficient training dataset, ii) incremental fusion of physiological data by finding the MQI between modalities on the segmented dataset, and iii) emotion recognition by applying the fused multimodal dataset to the temporal convolution network (TCN) learning model. Experimental results show that Mifu-ER achieves significant performance improvements in emotion classification, improving accuracies by 15% and 11% in subject-dependent and subject-independent learning, respectively, compared to single-modality approaches.}, archive = {J_ACCESS}, author = {Sun-Hee Kim}, doi = {10.1109/ACCESS.2025.3584642}, journal = {IEEE Access}, pages = {112703-112719}, shortjournal = {IEEE Access}, title = {Mifu-ER: Modality quality index-based incremental fusion for emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Comprehensive assessment of electric vehicle charging impact on distribution networks: Integrating diversity in fleet and electricity tariffs. ACCESS, 13, 112686-112702. (www)

  @article{ , abstract = {With the ongoing trend of electrification in transportation, the charging demand from electric vehicles (EVs) is imposing an increasingly significant additional load on the existing power grid, especially on distribution networks. To reduce the short-term high peak demand of the distribution network, cost-effective electricity prices, such as time-of-use (TOU) tariffs, have been introduced to encourage EV customers to shift their load demand to off-peak periods. However, no existing studies have comprehensively analyzed the impacts of EV charging on distribution networks considering the effects of key parameters such as the proportion of customers on TOU tariffs, EV penetration levels, various daily charging frequencies and charging power levels. To address this gap, this paper proposes an innovative modelling approach that incorporates various proportions of these parameters. Critical analyses are presented for a real distribution network in South Australia with year-round smart meter data. The results indicate that this approach enhances the ability to assess the impact of EV charging on distribution networks by integrating the combined effects of key parameters. At 80% EV penetration, the evening peak demand under 100% Flat tariff increases by over 30%. If 25% of the customers charge EVs using TOU tariffs, the maximum transformer utilization during the evening peak can be reduced by approximately 8.5%; however, this results in a new demand peak at the beginning of the TOU off-peak period. For most load scenarios, the existing TOU tariffs exacerbates network load fluctuations and increases maximum transformer utilization.}, archive = {J_ACCESS}, author = {Yan Wu and Syed Mahfuzul Aziz and Mohammed H. Haque and Travis Kauschke}, doi = {10.1109/ACCESS.2025.3584326}, journal = {IEEE Access}, pages = {112686-112702}, shortjournal = {IEEE Access}, title = {Comprehensive assessment of electric vehicle charging impact on distribution networks: Integrating diversity in fleet and electricity tariffs}, volume = {13}, year = {2025}, }
• (2025). Study on the evolution law of ablation characteristic signals of high-voltage cable buffer layer based on multi-parameter analysis. ACCESS, 13, 112673-112685. (www)

  @article{ , abstract = {In recent years, breakdown failures caused by the ablation of the buffer layer in high-voltage cables have occurred frequently, posing a serious threat to the safe operation of the power grid. Moisture content and external pressure are considered key factors influencing the ablation process of the buffer layer. To address this, this paper establishes a simulation experimental platform to investigate the variations in temperature, ultrasound signals, and electrical signals generated during the ablation of the buffer layer under different moisture content and pressure conditions. These signals are collectively referred to as “multi-physical signals,” which can be used to characterize the developmental stages of the ablation process. This study innovatively established a dedicated experimental platform that allows precise control of moisture content (0-20%) and mechanical pressure (0-200g), enabling the synchronous collection and correlational analysis of temperature field distribution, ultrasonic signals, and high-frequency current features. This approach overcomes the limitations of traditional single-parameter detection methods. The experimental results indicate that the thermal effects generated by the concentration of radial currents and the discharge in the gas gaps within the buffer layer are the main reasons for the changes in the signals. Under 0% moisture content conditions, the maximum temperature at the center of the buffer layer reached $336.4~^{\circ }$ C, while the temperature peak under humid conditions appeared 20 minutes earlier. The amplitudes of the ultrasonic and high-frequency current signals increased by approximately 65% and 70%, respectively, at a moisture content of 20% compared to the dry state. Compared to dry conditions, the buffer layer is more susceptible to electrochemical corrosion with the aluminum sheath under humid conditions, resulting in signal enhancement, along with some small fluctuations in the signals during the process.}, archive = {J_ACCESS}, author = {Kang Guo and Zhengping Wang and Zhibo Tian and Qian Li and Siying Wang and Zhenwei Yang and Huimin Guo}, doi = {10.1109/ACCESS.2025.3584398}, journal = {IEEE Access}, pages = {112673-112685}, shortjournal = {IEEE Access}, title = {Study on the evolution law of ablation characteristic signals of high-voltage cable buffer layer based on multi-parameter analysis}, volume = {13}, year = {2025}, }
• (2025). End-to-end latency optimization for containerized ROS 2 autonomous driving software. ACCESS, 13, 112654-112672. (www)

  @article{ , abstract = {One of the most significant shifts in software development is the growing prevalence of microservice-oriented and containerized applications. However, it faces challenges regarding timing and latency optimization. We present an optimization approach that has been specifically tailored to the use of containerized ROS 2 architectures. The optimization approach was implemented and evaluated using three different configurations, differing by the weighting of individual callback sequences of the system during optimization. Our experimental evaluation demonstrated the optimization of an autonomous racing software, developed for high-speed racing applications. The optimization yields a notable improvement in the maximum end-to-end latency by 22.47 % compared to the baseline measurement.}, archive = {J_ACCESS}, author = {Tobias Betz and Harun Teper and Dominic Ebner and Maximilian Leitenstern and Simon Sagmeister and Marcel Weinmann and Jian-Jia Chen and Markus Lienkamp}, doi = {10.1109/ACCESS.2025.3582868}, journal = {IEEE Access}, pages = {112654-112672}, shortjournal = {IEEE Access}, title = {End-to-end latency optimization for containerized ROS 2 autonomous driving software}, volume = {13}, year = {2025}, }
• (2025). Nonlinear model predictive control for trajectory tracking of omnidirectional robot using resilient propagation. ACCESS, 13, 112642-112653. (www)

  @article{ , abstract = {This paper proposes an enhanced Nonlinear Model Predictive Control (NMPC) framework that incorporates a robust, convergent variant of the resilient propagation (RPROP) algorithm to efficiently solve the Nonlinear Optimization Problem (NOP) in real time. The controller is developed for both constrained and unconstrained trajectory tracking of Wheeled Mobile Robots (WMRs), with operational constraints handled via the external penalty method. The proposed method introduces adaptive step sizes and a backtracking mechanism, significantly improving convergence speed without compromising accuracy. Simulation results show that, even under constraints, the proposed method reduces computational time by a factor of 6 to 11 compared to the Interior Point method and 2 to 4 compared to the Active Set method. In addition, it achieves superior tracking accuracy, with root mean square (RMS) position tracking errors reduced by approximately 50% relative to the benchmark methods. Real-time experiments conducted on the Robotino Festo Omnidirectional Mobile Robot (OMR) validate the method’s practical effectiveness, demonstrating faster convergence and improved velocity tracking performance, while maintaining comparable or better position tracking. These findings establish the proposed controller as a computationally efficient and accurate solution for real-time WMR trajectory tracking.}, archive = {J_ACCESS}, author = {Mahmoud El-Sayyah and Mohamad R. Saad and Maarouf Saad}, doi = {10.1109/ACCESS.2025.3583596}, journal = {IEEE Access}, pages = {112642-112653}, shortjournal = {IEEE Access}, title = {Nonlinear model predictive control for trajectory tracking of omnidirectional robot using resilient propagation}, volume = {13}, year = {2025}, }
• (2025). HyCoViT: Hybrid convolution vision transformer with dynamic dropout for enhanced medical chest X-ray classification. ACCESS, 13, 112623-112641. (www)

  @article{ , abstract = {Medical chest X-ray (CXR) classification necessitates balancing detailed local feature extraction with capturing broader, long-range dependencies, especially when working with limited and heterogeneous datasets. In this paper, we propose HyCoViT, a hybrid model that integrates a custom Convolutional Neural Network (CNN) block with Vision Transformers (ViTs). This approach combines the locality of CNN-based latent space representations with the global attention mechanisms of ViTs. To address overfitting in data-scarce scenarios, we introduce a Dynamic Dropout (DD) algorithm that adaptively adjusts the dropout rate during training. Additionally, we enhance model generalization using a combination of traditional data augmentation and MixUp techniques. We evaluate HyCoViT on a multi-class classification task involving COVID-19, pneumonia, lung opacity, and normal CXR images. While COVID-19 serves as a case study, the model’s design is generalizable to various medical imaging applications. Experimental results show that HyCoViT achieves state-of-the-art (SOTA) performance, with 98.81% accuracy for three-class surpassing the existing CNN-based model by average +4.90%., and SOTA transformer-based average by 2.05%. In four-class classification, HyCoViT achieves the highest accuracy at 96.56%, which is 8.32% higher than the average accuracy of SOTA CNN-based models and 4.96% higher than the average accuracy of other SOTA transformer-based models. These results surpass many existing CNN-based and transformer-based models, demonstrating the robust generalization capabilities of our method. Furthermore, we provide interpretable, attention-based visualizations that highlight crucial lung regions to support context-aware decisions and ultimately improve patient outcomes.}, archive = {J_ACCESS}, author = {Omid Almasi Naghash and Nam Ling and Xiang Li}, doi = {10.1109/ACCESS.2025.3584065}, journal = {IEEE Access}, pages = {112623-112641}, shortjournal = {IEEE Access}, title = {HyCoViT: Hybrid convolution vision transformer with dynamic dropout for enhanced medical chest X-ray classification}, volume = {13}, year = {2025}, }
• (2025). A novel curriculum learning training strategy for pomegranate growth stage classification using YOLO models on multi-source datasets for precision agriculture. ACCESS, 13, 112594-112622. (www)

  @article{ , abstract = {Pomegranates are among the many vital crops generally believed to offer health quality and economically impact agriculture. Accurate detection and classification of the pomegranate growth stages enables fruit harvesting robots, resulting in yield optimization, supply chain, and market readiness. In this study, we propose that the YOLO object detection models adopt a novel curriculum learning approach to detect the growth stages of pomegranates using multi-source datasets. A combined dataset of 5700+ images is considered for categorizing pomegranates into bud, flowering, early-fruit, mid-growth, and maturity stages. In the present study, we have experimented with two deep learning-based object detection models, YOLOv5 and YOLOv7, to perform training using a multi-level curriculum learning approach. The proposed training strategy proves to show improvement over state-of-the-art work Zhao et al. (2024) in achieving higher detection accuracies towards all five classes. The efficiency of the curriculum learning strategy with YOLOv7 and YOLOv5 models achieved a mAP score of 92.2% with a precision of 90.1%, recall of 82.3%, and F1 score of 86.1%. By comparing the performance proposed curriculum learning-based YOLO object detection models with traditional learning-based YOLO models, it was revealed that the YOLOv5 model with curriculum learning shows a consistent improvement over another model. It is inferred that the proposed training strategy is valuable in increasing the efficacy of pomegranate fruit detection and enhancing precision agriculture activities.}, archive = {J_ACCESS}, author = {N. Shobha Rani and K. R. Bhavya and A. Vadivel and T. Vasudev and Raghavendra M. Devadas and Vani Hiremani}, doi = {10.1109/ACCESS.2025.3581794}, journal = {IEEE Access}, pages = {112594-112622}, shortjournal = {IEEE Access}, title = {A novel curriculum learning training strategy for pomegranate growth stage classification using YOLO models on multi-source datasets for precision agriculture}, volume = {13}, year = {2025}, }
• (2025). Personalized human-robot cognitive interaction via a novel fuzzy logic control and learning-based paradigm. ACCESS, 13, 112568-112593. (www)

  @article{ , abstract = {Socially assistive robotics is an emerging field that, through effective human-robot cognitive interactions (HRCIs), offers potential solutions for personalized care, education, and entertainment. For improved impact and for acceptance by humans, socially assistive robots (SARs) should autonomously personalize and adapt their behavior to, respectively, the personality and the changes in the states-of-mind of people they interact with. Despite extensive research on the ethical, societal, and psychological aspects of SARs, bridging systems-and-control-based methods and socially assistive robotics for developing control approaches that automate the personalization and adaptation of HRCIs remains under-attended. We propose the first systematic and generalizable paradigm for personalization and adaptation of the social interactive behaviors of SARs, combining two highly promising modeling and decision making approaches, namely fuzzy logic control (FLC) and reinforcement learning (RL). By replicating the rule-based decision making of humans, FLC provides a highly effective personalization mechanism and warm-starts the RL algorithm, which takes care of adapting the behaviors of SARs to the dynamics of people’s state-of-mind. Fuzzy logic is also used to develop two consecutive processes inside the RL-based adaptation module that, from the emotional responses of humans, estimate their state-of-mind and assign a reward to the most recent action of the SAR. Our extensive experiments for validation of this combined paradigm and for comparing it with conventional RL methods show meaningful improvements in the criteria that assess the personalization, convergence of learning, and performance accuracy of the proposed steering system for SARs.}, archive = {J_ACCESS}, author = {Marcel Munster and Anahita Jamshidnejad}, doi = {10.1109/ACCESS.2025.3584194}, journal = {IEEE Access}, pages = {112568-112593}, shortjournal = {IEEE Access}, title = {Personalized human-robot cognitive interaction via a novel fuzzy logic control and learning-based paradigm}, volume = {13}, year = {2025}, }
• (2025). Multi-objective clustering based on congestion games with player-specific cost functions. ACCESS, 13, 112552-112567. (www)

  @article{ , abstract = {Cluster analysis, the unsupervised identification of homogeneous groups within data, has become essential in numerous domains due to its broad applicability. As a central task in unsupervised learning, clustering aims to uncover the underlying structure of data by grouping similar instances without relying on predefined labels. This paper proposes a multi-objective clustering technique based on concepts from game theory. In particular, we formulate the clustering problem using Singleton Congestion Games with Player-Specific Cost Functions, a sub-class of non-cooperative simultaneous-move games, in which cluster heads act as players and data objects are treated as resources. This clustering model is solved using the Nash equilibrium as the solution concept, which can be computed in polynomial time, thereby ensuring favourable computational complexity for the proposed algorithm, MOCA-II. Experimental results on diverse synthetic and real-world datasets demonstrate that MOCA-II outperforms both mono-objective and multi-objective state-of-the-art clustering methods in terms of clustering quality and efficiency.}, archive = {J_ACCESS}, author = {Dalila Kessira and Mohand-Tahar Kechadi}, doi = {10.1109/ACCESS.2025.3583240}, journal = {IEEE Access}, pages = {112552-112567}, shortjournal = {IEEE Access}, title = {Multi-objective clustering based on congestion games with player-specific cost functions}, volume = {13}, year = {2025}, }
• (2025). Integrating abstract meaning representation to enhance transformer-based image captioning. ACCESS, 13, 112528-112551. (www)

  @article{ , abstract = {Although recent image captioning models have achieved substantial progress, they still encounter limitations in capturing abstract semantics, resulting in insufficient semantic depth and limited diversity in expression. Meanwhile, Abstract Meaning Representation (AMR), a form of abstract semantic representation, has been successfully applied in various natural language processing tasks. However, exploiting AMR in multimodal contexts, particularly for image captioning, remains largely unexplored. To address these limitations, this paper proposes a novel image captioning model within an encoder-decoder framework that leverages the abstract semantics of images through AMR. Specifically, AMR is incorporated into the model in two ways: 1) extracting AMR from ground-truth captions and 2) converting the image’s relational graph into an AMR-like graph to enrich abstract semantics. These AMR embeddings are fused with object-region features and relational-graph embeddings via a cross-modal attention mechanism. Additionally, embeddings from the AMR-like graph are integrated into the Transformer decoder using a masked multi-head attention mechanism to enhance semantic coherence during caption generation. Experimental results on the MS COCO and Flickr30k datasets demonstrate that the proposed model achieves superior captioning accuracy compared to recent state-of-the-art methods, confirming the effectiveness of incorporating AMR in image captioning tasks.}, archive = {J_ACCESS}, author = {Nguyen Van Thinh and Tran Lang and Van The Thanh}, doi = {10.1109/ACCESS.2025.3584128}, journal = {IEEE Access}, pages = {112528-112551}, shortjournal = {IEEE Access}, title = {Integrating abstract meaning representation to enhance transformer-based image captioning}, volume = {13}, year = {2025}, }
• (2025). Kinematic integration network with enhanced temporal intelligence and quality-driven attention for precise joint angle prediction in exoskeleton-based gait analysis. ACCESS, 13, 112508-112527. (www)

  @article{ , abstract = {Exoskeleton robots offer transformative potential in aiding the rehabilitation of patients with lower limb motor dysfunction, where precise and real-time prediction of knee joint angles is critical. Despite advances in deep learning for motion prediction, existing models struggle with balancing accuracy and real-time performance, particularly in wearable applications. This study introduces KINETIQA (Kinematic Integration Network with Enhanced Temporal Intelligence and Quality-Driven Attention), a novel predictive framework that advances the state of the art in knee angle prediction, demonstrating significant improvements in accuracy and clinical utility. The core innovation of KINETIQA lies in its Kinematic Integration Network (KIN), an architecture that seamlessly integrates advanced techniques, including Transformers, temporal convolutions, and multi-headed attention mechanisms. This integration enables KINETIQA to effectively capture complex temporal dynamics of knee movement across both short and extended time horizons. Specifically, KINETIQA achieves an MAE of 0.605 at the 20 ms horizon, improving over Transformer (0.705) by 14.2%, and maintains superior performance at 100 ms with an MAE of 1.174, representing a 19.8% reduction compared to Transformer’s 1.464 baseline. Beyond its predictive capabilities, KINETIQA employs rigorous feature selection and validation protocols to ensure clinical-grade precision, delivering actionable insights for personalized rehabilitation, injury prevention, and movement analysis. The model’s design improves prediction accuracy and enhances interpretability, facilitating practical adoption in clinical settings. Additionally, KINETIQA maintains real-time compatibility, achieving an inference latency of 22.469 ms in the 100 ms forecasting case. This performance remains well within the 50 ms threshold typically required for safe and responsive exoskeleton control. These findings underscore KINETIQA’s potential in robotic-assisted rehabilitation, offering a meaningful advancement toward reliable and effective patient support systems.}, archive = {J_ACCESS}, author = {Lyes Saad Saoud and Irfan Hussain}, doi = {10.1109/ACCESS.2025.3582745}, journal = {IEEE Access}, pages = {112508-112527}, shortjournal = {IEEE Access}, title = {Kinematic integration network with enhanced temporal intelligence and quality-driven attention for precise joint angle prediction in exoskeleton-based gait analysis}, volume = {13}, year = {2025}, }
• (2025). DNN layer specialization through sequential training for applications with smart road-user interactions. ACCESS, 13, 112494-112507. (www)

  @article{ , abstract = {The deployment of a large number of sensors in vehicles makes it possible to analyze road-user interactions which is crucial for most applications in vehicular scenarios. In this context, Deep Neural Networks (DNNs) are a powerful tool for recognizing complex data patterns stemming from interactions between users and smart roads. However, the user behaviour changes depending on the road environment and the user features. Hence, it is not possible to have a unique DNN model that is effective for all the users, in all kinds of road environments. On the contrary, a specific model might be needed for each different user interacting with each type of environment. Unfortunately, this would imply the need to collect and process a very large amount of data for the training of each DNN model, which is not practical. Therefore, this work proposes a novel training technique called Sequential Training that partitions the Neural Network (NN) layers of the DNN model into two sets of layers that are trained so that one is specific of the user while the other is trained cooperatively to be specific of the road environment. The proposed training technique is realized by means of Vehicle-to-Infrastructure (V2I) communication in which the vehicle user can download the layers of the DNN model specific of the road environment where the vehicle is currently located. Such layers are then concatenated with the user-specific layers.}, archive = {J_ACCESS}, author = {Joannes Sam Mertens and Salvatore Cafiso and Laura Galluccio and Giacomo Morabito and Giuseppina Pappalardo}, doi = {10.1109/ACCESS.2025.3584461}, journal = {IEEE Access}, pages = {112494-112507}, shortjournal = {IEEE Access}, title = {DNN layer specialization through sequential training for applications with smart road-user interactions}, volume = {13}, year = {2025}, }
• (2025). Spatial–Temporal transformer for optimizing human health through skeleton-based body sports action recognition. ACCESS, 13, 112476-112493. (www)

  @article{ , abstract = {Accurate recognition of human actions and poses is essential for applications in fitness monitoring, rehabilitation, and virtual coaching. Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. To address these gaps, we propose the Focal and Global Spatial-Temporal Transformer (FG-STTrans) framework, a novel multi-stage architecture designed to capture both localized and global spatial-temporal dependencies with high precision. The FG-STTrans framework incorporates key innovations, including Basic Spatial Transformer (BSTrans) and Basic Temporal Transformer (BTTrans) blocks for early-stage feature extraction, and advanced modules such as Joint-Region Cross-Attention (JRCA) for integrating focal joint and global region-level features. Additionally, the Focal and Global Temporal Transformer (FG-TTrans) module leverages dilated temporal convolutions and self-attention mechanisms to model both short-term and long-term temporal relationships. These components collectively enable the framework to handle complex co-movement patterns, occlusions, and variability in execution styles. We evaluate FG-STTrans on two diverse datasets: the Workout Action Video Dataset (WAVd) and the YogaVid dataset. The proposed framework achieves state-of-the-art performance, with an accuracy of 94.25% on WAVd and 90.70% on YogaVid datasets. These results demonstrate the robustness and generalizability of FG-STTrans in recognizing a wide range of workout actions and yoga poses.}, archive = {J_ACCESS}, author = {Faze Liang and Lejia Ou and Zujun Lei and Xiaohong Tu and Kai Xin}, doi = {10.1109/ACCESS.2025.3583930}, journal = {IEEE Access}, pages = {112476-112493}, shortjournal = {IEEE Access}, title = {Spatial–Temporal transformer for optimizing human health through skeleton-based body sports action recognition}, volume = {13}, year = {2025}, }
• (2025). Space-time coded buffer-aided relaying for improving the reliability of cooperative networks. ACCESS, 13, 112461-112475. (www)

  @article{ , abstract = {Buffer-aided (BA) relay networks provide increased reliability and coverage in future wireless communications. This paper explores advanced transmission strategies to further enhance the performance of decode-and-forward (DF) cooperative networks with two relays. Unlike the existing DF BA relay selection solutions where the relays attempt independently to deliver the information message to the destination node, this work introduces a new methodology for leveraging the outage performance of the network by enabling space-time (ST) coded transmissions. Complementing BA relaying with ST coding is rendered possible by adjusting the transmission and reception policies at the relays and by equipping one of the relays with an additional buffer for storing duplicates of the packets delivered to the second relay. The introduced coordination between the relays allows them to collaboratively generate and transmit ST codewords over some time slots thus enhancing the quality of signal reception and improving the network’s reliability. The performance improvements of the proposed BA relaying technique are quantified by a Markov chain analysis for the sake of evaluating the achievable outage probability and average packet delay. We also present an asymptotic analysis that highlights the diversity gains compared to the benchmark schemes. Simulations over a generalized kappa-mu channel fading model demonstrate the capabilities of amalgamating BA relaying with tailored versions of ST coding techniques.}, archive = {J_ACCESS}, author = {Chadi Abou-Rjeily}, doi = {10.1109/ACCESS.2025.3584203}, journal = {IEEE Access}, pages = {112461-112475}, shortjournal = {IEEE Access}, title = {Space-time coded buffer-aided relaying for improving the reliability of cooperative networks}, volume = {13}, year = {2025}, }
• (2025). Improving the accuracy of indoor object localization in the internet of things using recurrent neural networks (LSTM) in challenging environments. ACCESS, 13, 112446-112460. (www)

  @article{ , abstract = {Indoor localization is an essential part of many applications of the Internet of Things, and to track an object within a building with the required precision and reliability one needs to give guarantee to accurate indoor location-based services. This study proposes a new mixture of latest deep learning with sparse feature selection to further increase accuracy in the localization of objects in complex indoor environments. The approach uses nearly all the standard techniques to find best signal processing concepts and classification methods, successfully merging them together. The process integrates. For signal noise reduction, statistical, entropic, and stochastic differential modeling methods to identify the features, and then, selects the optimized features to correct the inputs. The Long Short-Term Memory (LSTM) neural network is then trained on the selected features and excels in location classification with an extremely high precision. The experimental results, however, show that the proposed new compact method achieved a higher classification accuracy at 97.5% which was better than that obtained by such classic approaches as CNNs, RNNs, and Random Forest classifiers. Increased optimization is a positive side-effect of harmonizing sparse autoencoders based on a feature selection process, and deep learning classifiers. The discovery must be the result of a cheap and deployable information technology, apart from crucial issues such as energy, noise, and the behavior of the moving objects, and actuators.}, archive = {J_ACCESS}, author = {Hussein Aqeel Hussein and Mehdi Hamidkhani and Nasseer K. Bachache and Mohammadreza Soltan Aghaei}, doi = {10.1109/ACCESS.2025.3581417}, journal = {IEEE Access}, pages = {112446-112460}, shortjournal = {IEEE Access}, title = {Improving the accuracy of indoor object localization in the internet of things using recurrent neural networks (LSTM) in challenging environments}, volume = {13}, year = {2025}, }
• (2025). An analytical review of preprocessing techniques in bengali natural language processing. ACCESS, 13, 112428-112445. (www)

  @article{ , abstract = {Research in Bengali Natural Language Processing (BNLP) is rapidly expanding. Despite being one of the most widely spoken languages in the world, BNLP research remains insufficient, particularly in Bengali speech recognition. The language’s rich morphology, agglutinative structure, and diverse dialects make text and speech processing especially challenging. However, these challenges can be addressed with effective preprocessing techniques. Various organizations in Bangladesh and West Bengal are integrating Natural Language Processing (NLP) into their services, but without a thorough understanding of preprocessing, these implementations remain incomplete. Applying proper preprocessing techniques to the Bengali language will serve as a foundation for developing robust NLP applications. This paper presents a comprehensive review of preprocessing techniques in BNLP based on state-of-the-art research. It covers key areas such as sentiment analysis, Named Entity Recognition, speech recognition, text categorization, and summarization. First, the paper provides an in-depth discussion of Bengali language characteristics and research areas in BNLP. It then explores the challenges faced by researchers in processing Bengali text and speech. Additionally, it details various preprocessing techniques, highlighting their advantages and disadvantages. Finally, the paper examines future directions for BNLP, emphasizing the role of effective preprocessing in advancing the field.}, archive = {J_ACCESS}, author = {Sovon Chakraborty and Protiva Das and Shakib Mahmud Dipto and Md. Aktaruzzaman Pramanik and Jannatun Noor}, doi = {10.1109/ACCESS.2025.3574234}, journal = {IEEE Access}, pages = {112428-112445}, shortjournal = {IEEE Access}, title = {An analytical review of preprocessing techniques in bengali natural language processing}, volume = {13}, year = {2025}, }
• (2025). Inverse design of metamaterial absorbers for far-infrared CMOS detectors. ACCESS, 13, 112417-112427. (www)

  @article{ , abstract = {Far-infrared(FIR) technology, spanning the infrared (IR) to terahertz (THz) range, has been limited by the lack of high-sensitivity detectors. Here, a complementary metal-oxide–semiconductor (CMOS) microbolometer with Ti/Si3N4/SiO2/Al metamaterial absorbers (MAs) is designed for the detection range of 10- $50~\mu $ m. The inverse design of the MAs is applied using binary coding and a staged genetic algorithm (SGA) to enhance efficiency and flexibility for ultra-broadband absorptivity. The optimal MA structure achieves an average absorptivity of 82% across the 10- $50~\mu $ m range and shows excellent tolerance to polarization and incidence angle variations. Thermal simulations reveal that microbolometer with MAs achieves a 162.1% increase in maximum temperature rise. This research provides a performance-optimized and highly integrable solution for extending the detection range of CMOS-based FIR detectors, addressing key challenges in ultra-wideband photodetection across IR to THz wavelengths.}, archive = {J_ACCESS}, author = {Tiantian Shi and Wenbin Zhou and Xiangze Liu and Wei Si and Zhangnan Li and Yiming Liao and Xiaoli Ji}, doi = {10.1109/ACCESS.2025.3577237}, journal = {IEEE Access}, pages = {112417-112427}, shortjournal = {IEEE Access}, title = {Inverse design of metamaterial absorbers for far-infrared CMOS detectors}, volume = {13}, year = {2025}, }
• (2025). Exploring Blockchain–IoT convergence for logistics traceability: A systematic review and future outlook. ACCESS, 13, 112390-112416. (www)

  @article{ , abstract = {The integration of Blockchain (BC) and the Internet of Things (IoT) has emerged as a transformative solution for addressing traceability challenges in logistics, offering enhanced transparency, data security, and operational efficiency. This study presents a systematic review of the current state of BC-IoT integration for logistics traceability, focusing on its motivations, deployment strategies, technical implementations, and evaluation approaches. A total of 1,619 records were initially retrieved from IEEE Xplore, MDPI, ScienceDirect, Scopus, and Web of Science, from which 61 peer-reviewed studies published between 2015 and 2024 were selected and analyzed. The selection process adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous, transparent, and high-quality synthesis. Only studies explicitly addressing the convergence of BC and IoT within logistics traceability contexts were included. Key findings reveal that BC-IoT systems significantly enhance traceability and transparency across logistics networks but face persistent challenges such as scalability, latency, energy efficiency, and security. The review categorizes various deployment architectures including cloud, edge, fog, and hybrid models, and examines their implications for data responsiveness and system reliability. Additionally, it evaluates popular BC platforms (e.g., Hyperledger Fabric, Ethereum, Solana) and consensus mechanisms (e.g., RAFT, PBFT, PoS) based on their suitability for logistics applications. Emerging research directions emphasize the need for cross-chain interoperability, domain-specific frameworks, and decentralized traceability models, particularly in sectors such as humanitarian logistics and regulatory compliance. This review consolidates fragmented knowledge and provides actionable insights for developing scalable, secure, and transparent BC-IoT systems to support next-generation logistics operations.}, archive = {J_ACCESS}, author = {Yahaya Saidu and Shuhaida Mohamed Shuhidan and Izzatdin Abdul Aziz and MD Mahmudul Alam and Dahiru Adamu Aliyu and Muhammad Muntasir Yakubu and Shamsuddeen Adamu}, doi = {10.1109/ACCESS.2025.3583927}, journal = {IEEE Access}, pages = {112390-112416}, shortjournal = {IEEE Access}, title = {Exploring Blockchain–IoT convergence for logistics traceability: A systematic review and future outlook}, volume = {13}, year = {2025}, }
• (2025). DMAC: Discovering multi-attribute correlations of deep quality features for defect detection in mechanical components. ACCESS, 13, 112378-112389. (www)

  @article{ , abstract = {This paper introduces DMAC (Discovering Multi-Attribute Correlations of Deep Quality Features), a novel framework that enhances defect detection in mechanical components by leveraging multimodal quality-guided deep/shallow feature fusion techniques. The DMAC model addresses challenges such as the variability of defect patterns under different operational conditions and the complexity of sensor data. It combines deep and shallow learning methods to discover high-quality features that reflect the unique characteristics of mechanical operations and defect occurrences. DMAC advances the state-of-the-art by constructing a large-scale hypergraph to model the complex, multi-attribute relationships among defect features, uncovering high-order interactions that traditional methods cannot detect. Additionally, a Gaussian Mixture Model (GMM)-based posterior probability is used to rank candidate patches for defect detection, prioritizing high-risk areas and minimizing false positives. Experimental results on datasets from multiple mechanical component assessments demonstrate that DMAC effectively uncovers complex feature interactions, offering adaptive, data-driven optimization for a responsive defect detection system. By integrating hypergraph modeling and advanced feature fusion, DMAC presents a robust and accurate solution for real-world defect detection in mechanical systems, setting the stage for future advancements in multi-attribute fusion techniques and real-time adaptive learning.}, archive = {J_ACCESS}, author = {Yue Yu}, doi = {10.1109/ACCESS.2025.3578044}, journal = {IEEE Access}, pages = {112378-112389}, shortjournal = {IEEE Access}, title = {DMAC: Discovering multi-attribute correlations of deep quality features for defect detection in mechanical components}, volume = {13}, year = {2025}, }
• (2025). Real-time human action recognition with dynamical frame processing via modified ConvLSTM and BERT. ACCESS, 13, 112368-112377. (www)

  @article{ , abstract = {In this study, a human action recognition approach with dynamical frame processing is proposed to fulfill the need for action recognition in a real-time manner. A novel architecture with a modified convolutional long short-term memory (ModConvLSTM) with pose heatmaps as input features is proposed to achieve human action recognition without a fixed frame number. The effects of ModConvLSTM are verified at different depths. In our proposed architecture, we replace global average pooling (GAP) with Bidirectional Encoder Representations from Transformers (BERT) to address the limitations of temporal processing in a two-dimensional convolutional neural network (2D-CNN). By incorporating a training-stage mask and leveraging BERT’s attention mechanism, the model gains improved contextual understanding. This enhancement enables our network to achieve accuracies of 91.46% on the NTU60 dataset and 83.06% on NTU120, marking an improvement of 1.63% and 3.22%, respectively, with only a slight increase of 0.1 GFLOPs in computational cost. Unlike other real-time models that rely on large-scale datasets such as Kinetics for extensive pretraining, our model achieves competitive performance while being trained directly on NTU datasets. A key contribution of our work is its ability to perform dynamical frame recognition, efficiently updating the ModConvLSTM cell and computing BERT every four new frames, achieving real-time inference in just 14.2ms on a CPU. This study enables continuous action recognition without requiring predefined action boundaries, making our model well-suited for real-world applications where actions occur naturally without explicit segmentation.}, archive = {J_ACCESS}, author = {Raden Hadapiningsyah Kusumoseniarto and Zhi-Yuan Lin and Shun-Feng Su and Pei-Jun Lee}, doi = {10.1109/ACCESS.2025.3581734}, journal = {IEEE Access}, pages = {112368-112377}, shortjournal = {IEEE Access}, title = {Real-time human action recognition with dynamical frame processing via modified ConvLSTM and BERT}, volume = {13}, year = {2025}, }
• (2025). Improved GNSS positioning schemes in urban canyon environments. ACCESS, 13, 112354-112367. (www)

  @article{ , abstract = {Accurate positioning in urban canyon environments poses significant challenges owing to signal obstructions, multipath effects, and limited satellite visibility. This paper evaluates the performance of standalone Global Navigation Satellite System (GNSS) positioning in urban canyon environments over three scenarios such as pedestrian, vehicle, and uncrewed aerial vehicle (UAV) for various altitudes and speeds. To improve the performance of the standalone GNSS in the urban canyon conditions, two hybrid positioning solutions such as GNSS with the Quasi-Zenith Satellite System (QZSS) and GNSS with 5G are proposed and their performances are investigated. Through extensive simulations, it is shown that higher speeds and lower receiver altitudes result in higher positioning errors for the standalone GNSS positioning. Additionally, in the UAV scenario, the hybrid solution with GNSS and QZSS demonstrates robust performance with 0% positioning failures and low positioning errors. Meanwhile, in the pedestrian and vehicle scenarios where satellite visibility is severely restricted, the hybrid solution with GNSS and 5G outperforms the hybrid solution with GNSS and QZSS, resulting in reduced positioning errors owing to the superior 5G terrestrial infrastructure.}, archive = {J_ACCESS}, author = {Jiaqi Li and Seung-Hoon Hwang}, doi = {10.1109/ACCESS.2025.3583347}, journal = {IEEE Access}, pages = {112354-112367}, shortjournal = {IEEE Access}, title = {Improved GNSS positioning schemes in urban canyon environments}, volume = {13}, year = {2025}, }
• (2025). A novel approach for pulse signals extraction and heart rate estimation with hilbert space orthogonalization using event-based camera. ACCESS, 13, 112340-112353. (www)

  @article{ , abstract = {This paper presents a novel signal processing pipeline for estimating pulse signals, onset peaks, and heart rate using an event-based camera (EVS). EVS capture changes in brightness as a series of events, including both spatially induced events triggered by motion artifacts and temporally induced events caused by changes in skin luminance. When reconstructing pulse signals, spatially induced ON- and OFF-events within the region of interest (ROI) often occur simultaneously, canceling each other out and compromising signal stability. Additionally, biases between ON- and OFF-events can cause peak inversions under certain conditions, further destabilizing the signal. The proposed method addresses these challenges and temporal characteristics of EVS by defining event streams in a Hilbert space and applying Gram-Schmidt orthogonalization to isolate the temporally induced event stream. This enables robust estimation of pulse signals, heart rate through frequency analysis, and onset peak detection. To our knowledge, this is the first study that applies orthogonalization in Hilbert space to improve the signal processing of event-based pulse waves. The performance of the proposed approach is evaluated by comparing the extracted parameters with ground truth photoplethysmography (PPG) signals under three different lighting conditions. In the setup combining a green LED and a green optical filter, the method achieves a mean absolute error (MAE) of 1.13 BPM, a root mean square error (RMSE) of 1.69 BPM, and a peak detection accuracy of 92.72%. These results demonstrate the effectiveness of the proposed event-based method and highlight the potential of EVS for non-contact heart rate monitoring.}, archive = {J_ACCESS}, author = {Hayato Nakamura and Gengtao Lin and Yasue Mitsukura}, doi = {10.1109/ACCESS.2025.3580578}, journal = {IEEE Access}, pages = {112340-112353}, shortjournal = {IEEE Access}, title = {A novel approach for pulse signals extraction and heart rate estimation with hilbert space orthogonalization using event-based camera}, volume = {13}, year = {2025}, }
• (2025). Federated learning for real-time demand response by data centers toward energy efficiency and privacy preservation. ACCESS, 13, 112324-112339. (www)

  @article{ , abstract = {Data centers are crucial in the era of digital economy and artificial intelligence. While enabling essential services in healthcare, business, and telecommunications, they consume substantial energy mostly from non-renewable sources and have enormous, environmental impacts. These in turn call for effective energy management strategies. This study presents a federated learning approach tailored for IoT-enabled data centers to optimize energy usage and enhance data privacy. Our novel method utilizes real-time data to generate effective and efficient actions in response to operational demands, in compliance with energy conservation and stringent privacy standards. We evaluate various demand response programs by the proposed method in different scenarios and case studies to demonstrate their efficacy in adapting to energy consumption patterns for optimal energy usage and cost reduction. The results show that by increasing local renewable energy capacity by 50%, energy costs are lowered by 24% and carbon emissions are reduced by 17%. This study reveals the significant environmental benefits and the crucial role of federated learning for sustainable energy management practices in data centers, while also ensuring high levels of data privacy and network traffic optimization for congestion prevention. Our method is particularly effective for preserving data confidentiality under strict privacy conditions, thus highlighting the value of federated learning as a transformative tool for managing energy in data centers.}, archive = {J_ACCESS}, author = {Mohannad Alhazmi and Alexis Pengfei Zhao and Wanzi Li and Chenlu Yang}, doi = {10.1109/ACCESS.2025.3583436}, journal = {IEEE Access}, pages = {112324-112339}, shortjournal = {IEEE Access}, title = {Federated learning for real-time demand response by data centers toward energy efficiency and privacy preservation}, volume = {13}, year = {2025}, }
• (2025). HSF: A hybrid SVM-RF machine learning framework for dual-plane DDoS detection and mitigation in software-defined networks. ACCESS, 13, 112303-112323. (www)

  @article{ , abstract = {Software-defined networking (SDN) has revolutionized network management by centralizing control through software, thereby enabling dynamic traffic adjustments that are independent of the data plane. However, this innovation introduces significant security vulnerabilities because the existing solutions are largely adaptations of traditional methods and fail to address the unique challenges of SDN environments. To address this issue, this study proposes a machine-learning (ML)-based intrusion detection framework tailored specifically for SDN. In particular, the framework utilizes a hybrid model that combines a Support Vector Machine (SVM) and Random Forest (RF) classifiers (HSF), which significantly improves intrusion detection accuracy. Specifically, the proposed solution is structured as a three-layer protection mechanism. First, the Data Plane Monitoring layer examines features, such as packet count and byte count, to detect anomalies. Second, the Control Plane Monitoring layer evaluates attributes such as the source IP, destination IP, and protocols to identify suspicious activity. Finally, the Detection Layer leverages the hybrid ML approach to further strengthen detection capabilities and ensure timely responses. Importantly, the experimental results reveal that the HSF technique achieves an anomaly detection rate exceeding 99% across both data and control planes. This highlights its efficacy in securing the next-generation SDN networks.}, archive = {J_ACCESS}, author = {Abdinasir Hirsi and Lukman Audah and Mohammed A. Alhartomi and Adeb Salh and Godwin Okon Ansa and Mustafa Maad Hamdi and Diani Galih Saputri and Salman Ahmed and Abdullahi Farah}, doi = {10.1109/ACCESS.2025.3583712}, journal = {IEEE Access}, pages = {112303-112323}, shortjournal = {IEEE Access}, title = {HSF: A hybrid SVM-RF machine learning framework for dual-plane DDoS detection and mitigation in software-defined networks}, volume = {13}, year = {2025}, }
• (2025). Proposal of a novel online-coupling modular-structure maglev carrier. ACCESS, 13, 112292-112302. (www)

  @article{ , abstract = {In the realm of autonomous applications, such as multi-agent drones, swarm ground vehicles, and cooperative/collaborative systems, the concept of modular structures has become increasingly significant. The advantages of modularity include the ability to design more compact agents, leading to reduced manufacturing costs and energy consumption per unit, while simultaneously enhancing operational efficiency. This study introduces a unique modular structure Maglev-based transportation system. The carriers are designed to semi-rigidly couple to each other with a permanent magnet locking structure, allowing two or more carriers to function as a larger structure, resulting in increased payload capacity and providing a “fail-safe” operational opportunity. This study initiates by introducing a simulation of a multi-agent carrier system operation scenario to showcase the potential of the proposed modular structure compared to a reference model in terms of power consumption. Following this, the analytical force model of MaglevMotor is given, which is subsequently expanded to encompass the dynamic model of the single actuator, single Maglev carrier, and coupled Maglev carrier structure, in order. This progression is accompanied by elucidation on the centralized control method and the extended centralized control method of the coupled model. Consequently, state feedback controller with integral action for each centralized control axis are synthesized for single carrier, and experimental results are provided. Next, the controllers are resynthesized for the coupled structure and proof-of-concept experimental verification for the multi-agent collaborative carrier structure is successfully demonstrated. Finally, online coupling experiments are conducted, and successful results are presented.}, archive = {J_ACCESS}, author = {Ahmet Fevzi Bozkurt and Kadir Erkan}, doi = {10.1109/ACCESS.2025.3579997}, journal = {IEEE Access}, pages = {112292-112302}, shortjournal = {IEEE Access}, title = {Proposal of a novel online-coupling modular-structure maglev carrier}, volume = {13}, year = {2025}, }
• (2025). Automating the design of scalable and efficient IoT architectures using generative adversarial networks and model-based engineering for industry 4.0. ACCESS, 13, 112271-112291. (www)

  @article{ , abstract = {The increasing demand for efficient and scalable IoT systems in Industry 4.0 has driven the development of increasingly complex architectures. However, the design of these architectures presents significant challenges, particularly in dense networks where latency, energy consumption, and scalability constraints affect operational performance. Traditional approaches, such as heuristic and genetic algorithms, have proven insufficient in automating and optimizing large-scale IoT configurations, resulting in a high design and validation time cost. This work proposes an innovative approach based on integrating generative adversarial networks (GANs), model-based engineering (MBSE), and MATLAB simulations to automate and optimize the design of IoT systems. Using GANs to generate scalable architectures and validate them using MBSE, our approach automates both the creation and optimization of configurations, ensuring compliance with technical and operational constraints. The results show that the proposed design achieves an average latency between 30 and 70 ms, improving by 10% compared to the reference values of the IoT Benchmark Dataset. Energy consumption remains between 3.2 W and 15.6 W, representing a 12% reduction compared to traditional systems. In addition, packet loss remains below 7%, exceeding the average of conventional systems.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Jaime Govea and Diego Buenaño-Fernandez and Aracely Mera-Navarrete}, doi = {10.1109/ACCESS.2025.3583869}, journal = {IEEE Access}, pages = {112271-112291}, shortjournal = {IEEE Access}, title = {Automating the design of scalable and efficient IoT architectures using generative adversarial networks and model-based engineering for industry 4.0}, volume = {13}, year = {2025}, }
• (2025). Function delivery network: A spatial-temporal execution orchestrator for optimizing serverless computing. ACCESS, 13, 112255-112270. (www)

  @article{ , abstract = {We present Function Delivery Network (FDN), a novel spatial-temporal execution orchestrator designed to address key limitations in current serverless computing models. The FDN introduces several innovations, including a multi-tenant serverless model, a secure and reusable functional context, and distributed shared memory, to optimize resource allocation and improve performance in high-concurrency and globally distributed scenarios. We implement the FDN on a major cloud platform and evaluate its performance against traditional Function-as-a-Service (FaaS) execution using a variety of benchmark functions. Our results demonstrate significant improvements in resource utilization, execution time, and request completion rates. The FDN reduces function instance allocation by up to 97.82%, improves median response times by 45.45%, and maintains higher request completion rates at high concurrency levels compared to native FaaS execution. The FDN’s adaptive execution window mechanism allows for fine-tuned optimization based on function characteristics and workload patterns. This approach effectively addresses challenges such as cold starts, inefficient resource allocation, and scalability limitations in current serverless platforms. By providing a more efficient and scalable model for serverless computing, the FDN enables more cost-effective and performant cloud-native applications, particularly in scenarios involving high concurrency and global distribution.}, archive = {J_ACCESS}, author = {Joel R. Corporan and Arshdeep Bahga and Vijay K. Madisetti}, doi = {10.1109/ACCESS.2025.3583721}, journal = {IEEE Access}, pages = {112255-112270}, shortjournal = {IEEE Access}, title = {Function delivery network: A spatial-temporal execution orchestrator for optimizing serverless computing}, volume = {13}, year = {2025}, }
• (2025). Natural language processing and machine learning for analysis of radiology reports: A systematic review. ACCESS, 13, 112215-112254. (www)

  @article{ , abstract = {Radiology reports, as a means of inter-physician and physician-to-patient communication, contain key findings and interpretations from imaging studies that guide diagnosis and treatment. Recent advancements in Artificial Intelligence (AI) technologies and the growing volume of clinical data necessitate efficient approaches for effective data utilisation. Integrating AI technologies can improve data analysis, enhance diagnostic accuracy, and streamline clinical workflows, reducing the burden on physicians. This Systematic Literature Review (SLR) examines the state-of-the-art of Natural Language Processing (NLP) and Machine Learning (ML) techniques, reviewing their diverse applications in radiology report processing, with a focus on automated disease diagnosis. We explored the commonly used approaches and their impact on diagnostic accuracy. We also looked into the availability of publicly accessible datasets that can be used for ML research within the radiology domain. Despite the promising advancements, challenges remain, such as data quality issues, interpretability of models and integrations of these technologies into clinical workflows. This review provides a thorough overview of the current research in this field by analysing existing findings and highlighting areas of improvement. The goal is to provide insights that can inform future studies in report analysis and improve the use of AI techniques in radiology.}, archive = {J_ACCESS}, author = {Umay Kulsoom and Frank G. Glavin and Malika Bendechache}, doi = {10.1109/ACCESS.2025.3582728}, journal = {IEEE Access}, pages = {112215-112254}, shortjournal = {IEEE Access}, title = {Natural language processing and machine learning for analysis of radiology reports: A systematic review}, volume = {13}, year = {2025}, }
• (2025). A methodology for electricity demand forecasting using a hybrid approach. ACCESS, 13, 112197-112214. (www)

  @article{ , abstract = {Load forecasting (LF) plays a crucial role in energy production planning and scheduling, simplifying budgeting processes, and improving power supply reliability. The available integrated solutions are superior to conventional approaches while considering the uncertainties of weather conditions. The primary objective of LF is to establish an optimal load model for the power grid, conducted offline, to achieve accurate predictions, thereby minimizing operational costs and enhancing grid stability. In this work, an integrated LF model is proposed that uses modified combined ensemble empirical mode decomposition with adaptive noise (MCEEMDAN), Shannon entropy (SE), and long short-term memory (LSTM) techniques. To demonstrate the efficacy of the proposed method, this manuscript utilizes a real-time dataset containing actual load data, social & temporal variables and meteorological parameters including temperature, humidity, and rainfall, gathered from Raipur region in Chhattisgarh state, India. A comparative analysis of the proposed method is conducted against other available approaches, including various time-series decomposition methods, different machine learning techniques, and alternative test system.}, archive = {J_ACCESS}, author = {Fanidhar Dewangan and Monalisa Biswal and Nand Kishor}, doi = {10.1109/ACCESS.2025.3583740}, journal = {IEEE Access}, pages = {112197-112214}, shortjournal = {IEEE Access}, title = {A methodology for electricity demand forecasting using a hybrid approach}, volume = {13}, year = {2025}, }
• (2025). Risk-based method for establishing ad hoc separation minima between aircraft in en-route airspace. ACCESS, 13, 112176-112196. (www)

  @article{ , abstract = {Separation minima are the minimum required distances between aircraft in vertical or horizontal dimensions, established to ensure safety levels in controlled airspace. Nevertheless, current separation standards are fixed and have remained unchanged for decades, limiting airspace capacity. As a result, separation management is an area where improvement is sought. This research proposes an innovative concept of operation: Ad Hoc (or variable) separation, which involves redefining both the separation values and the way they are applied (separation mode). This concept refers to the application of different separation minima values between aircraft pairs in the same airspace volume based on factors such as aircraft model and mass, encounter geometry, and flight level, among others. A novel collision risk model is developed to compute these Ad Hoc separation minima values from Monte Carlo and Fast Time Simulations. The methodology was applied to a Spanish en-route sector (LECMZMU) to determine safe horizontal Ad Hoc separations. The best results show reductions from 5 NM (current horizontal separation minima value) to 4 NM in over 90% of the evaluated situations, and from 5 NM to 3 NM in more than 80% of encounters for low intersection angles (around 15°). In contrast, for intersection angles around 45°, reductions from 5 NM to 4 NM are possible in 68% of encounters, and from 5 NM to 3 NM in only 11%. These findings confirm the feasibility of Ad Hoc separation and its potential to enhance airspace capacity while maintaining safety levels, supporting its integration into future operational concept.}, archive = {J_ACCESS}, author = {Lidia Serrano-Mira and Javier A. Pérez-Castán and Eduardo Sánchez Ayra and Irene García Moreno and Marta Pérez Maroto and Luis Pérez Sanz}, doi = {10.1109/ACCESS.2025.3583889}, journal = {IEEE Access}, pages = {112176-112196}, shortjournal = {IEEE Access}, title = {Risk-based method for establishing ad hoc separation minima between aircraft in en-route airspace}, volume = {13}, year = {2025}, }
• (2025). Generalized fractional order complex lorenz system-based dual layer security enhancement approach. ACCESS, 13, 112162-112175. (www)

  @article{ , abstract = {The optimization of chaotic systems has gained significant importance to attain high complex attractors for cryptographic and communication applications. In this paper, the fractional order complex Lorenz system is considered and system parameters are optimized using sequential quadratic programming algorithm by considering a multi objective function. Since the higher value of the Lyapunov exponent and attractor geometry results in a system with strong chaotic characteristics, the reciprocal of the positive Lyapunov exponent and Kaplan-Yorke dimension is considered to generate a constrained weighted sum objective function. The highest Lyapunov exponent and fractal dimension obtained through optimization is 1.845 and 3.109 respectively. The optimized system is further used to generate hashed keys to encrypt and decrypt the rover imager data of the Chandrayaan-3 mission. A two layer enhanced security approach that combines cryptography and steganography is introduced, which includes 1) encryption of satellite image using advanced encryption standard algorithm with hashed key and 2) embedding the encrypted image using the least significant bit, discrete wavelet transform and spread spectrum based steganography to strengthen the security. Various performance metrics are evaluated and compared to analyze the performance of the proposed integrated algorithm. The state of the art of the proposed work is compared by considering standard images as the input. The proposed approach exhibits the highest key space, information entropy, MAE, MSE, PSNR, NPCR and UACI of $2^{700}$ , 7.9998, 100.314, 13827.4349, 6.7234, 99.776 and 39.061 respectively which are greater than the existing methods. Histogram analysis, correlation analysis and key space analysis are performed to ensure the efficiency of the enhanced security approach. In addition, IND-CPA and IND-CCA tests are performed which provide the adversary’s guessing accuracy of 48.4% and 48.7% respectively. This confirms the resistance of the proposed scheme against standard cryptanalytic attacks.}, archive = {J_ACCESS}, author = {K. Meera and N. Selvaganesan}, doi = {10.1109/ACCESS.2025.3583894}, journal = {IEEE Access}, pages = {112162-112175}, shortjournal = {IEEE Access}, title = {Generalized fractional order complex lorenz system-based dual layer security enhancement approach}, volume = {13}, year = {2025}, }
• (2025). Formal verification of rewards and penalties mechanism of FFG attestations: Ethereum 2.0 beacon chain case study. ACCESS, 13, 112132-112161. (www)

  @article{ , abstract = {Ethereum 2.0 stands out as a progressive decentralized blockchain platform, drawing attention for its security, scalability, and flexibility. Central to Ethereum 2.0 is the Beacon Chain, serving as the cornerstone managing validator rewards, penalties, attestations, and slashing mechanisms. Rewards and Penalties Mechanism (RPM) is of particular importance within the Beacon Chain as it includes validator balances based on their attestation behavior. Despite the critical role of RPM in maintaining the reliability and security of the Beacon Chain, the absence of formal verification work employing model checking is notable. Therefore, this research endeavors to fill this gap by employing a formal verification technique to assess the RPM’s behavior concerning Friendly Finality Gadget (FFG) attestations. Utilizing Process Meta Language (PROMELA), a formal model of the RPM is specified, encompassing safety and liveness properties crucial for its robust functioning. The properties, including invalid attestation, integrity, fairness, availability, failure to attest, and inactivity imposition, are formalized through Linear Temporal Logic (LTL). Subsequently, the formal model alongside the specified properties is subjected to verification using the SPIN model checker. The properties are analyzed with respect to verification time, states visited, and memory usage. The outcome of this research contributes to a rigorous analysis of the RPM’s behavior. This work not only enhances an understanding of Beacon Chain’s operational dynamics but also underscores the importance of formal verification in ensuring the reliability and security of blockchain protocols.}, archive = {J_ACCESS}, author = {Muhammad Rashid and Imran Rasool and Nazir Ahmad Zafar and Hamra Afzaal}, doi = {10.1109/ACCESS.2025.3582822}, journal = {IEEE Access}, pages = {112132-112161}, shortjournal = {IEEE Access}, title = {Formal verification of rewards and penalties mechanism of FFG attestations: Ethereum 2.0 beacon chain case study}, volume = {13}, year = {2025}, }
• (2025). ARIMA modeling of noise of piezoelectric accelerometer. ACCESS, 13, 112121-112131. (www)

  @article{ , abstract = {Piezoelectric accelerometer is well-known for ultra-high sensitivity that is limited by the noise floor. Due to its importance, the modeling of the noise of piezoelectric accelerometers using Autoregressive Integrated Moving Average (ARIMA) is presented herein. This method is applied to noise data from five commercial and prototype devices with different noise patterns and bandwidths. The best-fit model for each accelerometer is selected after an iterative process. The resulting models are found to be in good match with the original noise patterns. The use of ARIMA to produce an analytical equation of the noise characteristic is helpful for users of commercial piezoelectric accelerometers, as it does not require knowledge of the internal structure of the device that is needed by physics-based noise models.}, archive = {J_ACCESS}, author = {Ghulam Ali and Faisal Mohd-Yasin}, doi = {10.1109/ACCESS.2025.3581427}, journal = {IEEE Access}, pages = {112121-112131}, shortjournal = {IEEE Access}, title = {ARIMA modeling of noise of piezoelectric accelerometer}, volume = {13}, year = {2025}, }
• (2025). High-precision characterization and bias control of parallel mach-zehnder modulators using optical spectrum analysis. ACCESS, 13, 112112-112120. (www)

  @article{ , abstract = {Optical modulators consisting of parallel Mach-Zehnder interferometers can generate complicated signals with precise bias control. A characterization method of such devices using optical spectrum analysis was reviewed, experimentally demonstrated and used in combination with a novel bias control technique to obtain high extinction ratio and optical two-tone signals.}, archive = {J_ACCESS}, author = {Qingchuan Huang and Hugo S. C. Freire and Tetsuya Kawanishi}, doi = {10.1109/ACCESS.2025.3583335}, journal = {IEEE Access}, pages = {112112-112120}, shortjournal = {IEEE Access}, title = {High-precision characterization and bias control of parallel mach-zehnder modulators using optical spectrum analysis}, volume = {13}, year = {2025}, }
• (2025). Born approximation multi-resolution imaging for TEM pseudo wavefield based on differential pulse. ACCESS, 13, 112102-112111. (www)

  @article{ , abstract = {In this study, to obtain Born approximation multi-resolution imaging for transient electromagnetic (TEM) pseudo wavefield, employing a series of differential pulse with multiple pulse widths as the transmission waveform. First, the spectral characteristics of square wave and differential pulse are compared, and the results reveal that differential pulse, compared to the square wave, can effectively eliminate low-frequency interference while maintaining more concentrated distribution of harmonic components. Second, the relationship between pulse width of differential pulse and detection depth is discussed, the findings indicate that the differential pulse with varying pulse widths exhibits different dominant frequency, corresponding to different exploration depths. Then, using two-layer and four-layer models for the method validation, it is evident that the differential pulse with varying pulse widths effectively captures geological information at different depths. At last, through calculations of a 3D model containing low-resistivity bodies and a thrust fault model, the results suggest that correlatively stacking imaging results of differential pulse with varying pulse widths can effectively provide subsurface interface information, spanning from shallow to deep layers. In general, the Born approximation multi-resolution imaging method based on differential pulse plays a significant role in interpreting the TEM pseudo wavefield data.}, archive = {J_ACCESS}, author = {Ya'Nan Fan and Xiu Li and Kailiang Lu and Juanjuan Li and Tianchi Fu and Xiaozhao Li}, doi = {10.1109/ACCESS.2025.3583770}, journal = {IEEE Access}, pages = {112102-112111}, shortjournal = {IEEE Access}, title = {Born approximation multi-resolution imaging for TEM pseudo wavefield based on differential pulse}, volume = {13}, year = {2025}, }
• (2025). Estimation and forecasting of nigeria’s residential, commercial, and industrial electricity demands. ACCESS, 13, 112081-112101. (www)

  @article{ , abstract = {Energy demand forecasting has emerged as a crucial area of research, driven by the need for accurate predictions of future electricity consumption. This is critically essential for the effective planning and operation of electric power systems. Several studies have only forecasted what could be best termed as ‘suppressed electricity demand’ in Nigeria, given the non-consideration of basic unmet demand factors. Suppressed electric energy demand arises from inadequate energy access accounting for unmet electricity needs such as electricity capacity utilization, access rate, outages, and transmission and distribution (T&D) losses. These unmet electricity needs are considered sensitive determinants in achieving optimal planning and utilization of electricity in a developing economy like Nigeria. This study, therefore investigated and estimated these unmet basic electricity needs in deriving a potential electricity consumption and typical demand forecast for Nigeria in consideration of sustainability and full capacity utilization. It aimed at forecasting Nigeria’s potential electricity demand by analyzing the various electricity usage patterns in the country. It presented a methodological approach for estimating and forecasting the expected electricity demand in the nation by developing and examining economic and environmentally sustainable models with the incorporation of unmet electricity needs. These needs were analyzed using the developed models and sourced data to obtain advanced estimates of electricity consumption. A high-precision, long-term forecasting procedure was then implemented using these estimates. The implementation and simulation of the procedure was conducted with a Machine Learning/AI procedure (using Python) compatible with the Google Collaboratory tool. The obtained results were post processed using MS Excel. The results indicated that Nigeria’s potential electricity demand will be 69783.099, 80383.785 and 90984.472 GWh by 2030, 2040 and 2050, respectively. The forecast results were further validated with those obtained using stochastic/probabilistic extrapolation method. While the Python simulation indicated a lower average Mean Square Error (MSE) of 0.008%, the stochastic/probabilistic extrapolation procedure gave an MSE of 0.021%. With the forecasting results, decision-makers and policy-makers in Nigeria’s energy sector are armed with the right tool for effective and informed decision-making in electricity planning and management. The approach of this study to solving the national energy challenge is timely and aims to help navigate the complex challenges of making informed decisions in the sector, and establish future research avenues that may facilitate the effective integration of renewable energy into the national energy landscape.}, archive = {J_ACCESS}, author = {Prosper O. Ugbehe and Ogheneruona E. Diemuodeke and Daniel O. Aikhuele and Kenneth E. Okedu and Gauri Kalnoor}, doi = {10.1109/ACCESS.2025.3582625}, journal = {IEEE Access}, pages = {112081-112101}, shortjournal = {IEEE Access}, title = {Estimation and forecasting of nigeria’s residential, commercial, and industrial electricity demands}, volume = {13}, year = {2025}, }
• (2025). Enhancing eLoran timing accuracy via machine learning with meteorological and terrain data. ACCESS, 13, 112067-112080. (www)

  @article{ , abstract = {The vulnerabilities of global navigation satellite systems (GNSS) to signal interference have increased the demand for complementary positioning, navigation, and timing (PNT) systems. To address this, South Korea has decided to deploy an enhanced long-range navigation (eLoran) system—a radionavigation system that relies on high-power, low-frequency terrestrial signals—as a complementary PNT solution. Similar to GNSS, eLoran provides highly accurate timing information, which is essential for applications such as telecommunications, financial systems, and power distribution. However, the primary sources of error for GNSS and eLoran differ. For eLoran, the main source of error is signal propagation delay over land, known as the additional secondary factor (ASF). This delay, influenced by ground conductivity and weather conditions along the signal path, is challenging to predict and mitigate. In this paper, we measure the time difference (TD) between GPS and eLoran using a time interval counter and analyze the correlations between eLoran/GPS TD and eleven meteorological factors. Accurate estimation of eLoran/GPS TD could enable eLoran to achieve timing accuracy comparable to that of GPS. We propose two estimation models for eLoran/GPS TD and compare their performance with existing TD estimation methods. The proposed WLR-AGRNN model captures the linear relationships between meteorological factors and eLoran/GPS TD using weighted linear regression (WLR) and models nonlinear relationships between outputs from expert networks through an anisotropic general regression neural network (AGRNN). The model incorporates terrain elevation to appropriately weight meteorological data, as elevation influences signal propagation delay. Experimental results based on four months of data demonstrate that the WLR-AGRNN model outperforms other models, highlighting its effectiveness in improving eLoran/GPS TD estimation accuracy.}, archive = {J_ACCESS}, author = {Taewon Kang and Seunghyeon Park and Pyo-Woong Son and Jiwon Seo}, doi = {10.1109/ACCESS.2025.3581615}, journal = {IEEE Access}, pages = {112067-112080}, shortjournal = {IEEE Access}, title = {Enhancing eLoran timing accuracy via machine learning with meteorological and terrain data}, volume = {13}, year = {2025}, }
• (2025). Addressing hybrid confounder issue for causal session-based recommendation. ACCESS, 13, 112056-112066. (www)

  @article{ , abstract = {In the field of session-based recommendation, users’ interaction behaviors are affected by both popularity and surrounding environmental factors, such as item popularity, season, or salary, and so on. Utilizing causal learning techniques to mitigate the negative impact of hybrid confounders on user preferences is a pressing challenge in session-based recommendation. Aiming at the above problems, this paper proposes a kind of causal session-based recommendation model for addressing hybrid confounder issue (HCCSRec). First, the conditional probability about user preference given a user sequence is solved by intervention and structural equation. Then, implicit environmental features are learned with observation sequence data. Next, user preference for the next item is estimated from the observation sequence, environmental feature and item popularity. Finally, the objective function is constructed by cross entropy and KL scatter, and the model parameters are learned. We perform extensive experiments on three real-world datasets from Beauty, MovieLens,2 and Yelp. Empirical studies validate that compared with the state-of-art recommendation methods, the HCCSRec method is helpful to discover user real interests, and can further improve the recommendation accuracy.}, archive = {J_ACCESS}, author = {Quan Li and Xinhua Xu and Jinjun Liu and Guangmin Li}, doi = {10.1109/ACCESS.2025.3583851}, journal = {IEEE Access}, pages = {112056-112066}, shortjournal = {IEEE Access}, title = {Addressing hybrid confounder issue for causal session-based recommendation}, volume = {13}, year = {2025}, }
• (2025). EMI prediction on ripple effect of voltage-controlled ramp waveform via ARIMA-MLR hybrid model. ACCESS, 13, 112039-112055. (www)

  @article{ , abstract = {Predicting electromagnetic interference (EMI) is crucial for maintaining the functionality of electronic devices. Accurate prediction of EMI is essential for daily electronic applications, such as setting the brightness of the smartphone and controlling the volume of the television, to avoid potential damage to electronic functionality. This paper presents an analysis of the ripple effect of the voltage ramp signal under control based on frequency modulation for EMI prediction. While the time series is in static mode, this paper suggests to control the amplifier voltage as prediction task to maintain different frequencies with varying EMI radiation rather than arbitrary settings. A mathematical analysis was conducted to model EMI radiation from 29 scenarios of amplifier experiments and 751 frequencies using a combined model of auto-regressive integrated moving average (ARIMA) and multiple linear regression (MLR). To evaluate the impact of data splitting on the model performance, the model was tested with 30%, 70% data splits, and all the data sets. The findings indicate that the (7,0,4) model is the highest accuracy achieving of 88.52%. This model was enhanced with multiple linear regression, resulting in an improved accuracy of 96.37% ensuring the optimal performance and longevity of electronic devices in various scenarios and frequencies. The study highlights the enhancement of the EMI prediction model, which is primarily based on frequency modulation and three-dimensional ripple effects of ramp signals.}, archive = {J_ACCESS}, author = {Mohammad Yanuar Hariyawan and Mochamad Nizar Palefi Ma’ady and Helmy Widyantara and Vania Martha Aurellia Lubis}, doi = {10.1109/ACCESS.2025.3582606}, journal = {IEEE Access}, pages = {112039-112055}, shortjournal = {IEEE Access}, title = {EMI prediction on ripple effect of voltage-controlled ramp waveform via ARIMA-MLR hybrid model}, volume = {13}, year = {2025}, }
• (2025). Supervised machine learning thyroid carcinoma diagnosis using wide-field SHG microscopy. ACCESS, 13, 112021-112038. (www)

  @article{ , abstract = {Papillary (PTC) and follicular (FTC) thyroid carcinomas require different treatment strategies, but their accurate differentiation remains a challenge in conventional histopathology. Misclassification can lead to overtreatment of low-risk PTCs or inadequate treatment of FTCs, increasing the risk of recurrence and metastasis. Since the structure of the collagen capsule surrounding thyroid nodules provides diagnostically valuable information, label-free imaging with second harmonic generation (SHG) microscopy combined with machine learning (ML)-based analysis offers a promising approach for automated classification. In this study, we extracted intensity and texture features from SHG images of thyroid nodules scanned as a whole and optimized several ML classifiers, including logistic regression, support vector classification (C-SVC), multilayer perceptron, random forest, XGBoost, and LightGBM, using hyperparameters tuning with stratified 10-fold cross-validation. One of the major challenges in classification was label noise resulting from 1) mislabeling of adjacent tissue, 2) PTC calcifications mimicking FTC features, and 3) capsule heterogeneity. To address this issue, we applied unsupervised segmentation to exclude mislabeled regions and consider capsular heterogeneity as a diagnostic feature. Recursive feature elimination and mutual information selection further refined the feature set and improved classification accuracy. Among all models, C-SVC achieved the highest accuracy (84.73%) with robust generalization to unknown data, significantly outperforming standard ML approaches (60–70%). These results demonstrate the feasibility of SHG microscopy-based ML classification as a reliable adjunct to existing histopathologic methods, which could improve diagnostic accuracy and patient outcomes.}, archive = {J_ACCESS}, author = {Yaraslau Padrez and Radu Hristu and Igor Timoshchenko and Lucian G. Eftimie and Danielis Rutkauskas and Lena Golubewa}, doi = {10.1109/ACCESS.2025.3583435}, journal = {IEEE Access}, pages = {112021-112038}, shortjournal = {IEEE Access}, title = {Supervised machine learning thyroid carcinoma diagnosis using wide-field SHG microscopy}, volume = {13}, year = {2025}, }
• (2025). A prescriptive approach based on fuzzy cognitive maps and genetic algorithms for disease management in beef production. ACCESS, 13, 112011-112020. (www)

  @article{ , abstract = {Livestock disease diagnosis and treatment often rely on the experience of veterinarians and the availability of clinical signs, which can vary significantly between cases. This paper proposes a novel prescriptive analytics approach based on Fuzzy Cognitive Maps (FCMs) integrated with Genetic Algorithms (GAs) to support decision-making in the treatment of common cattle diseases. The FCM captures expert knowledge through causal relationships between symptoms, treatments, and diagnoses, while the GA optimizes treatment actions to achieve desired health outcomes. We evaluated our approach using three case studies –babesiosis, anaplasmosis, and coccidiosis– on datasets comprising 3000 cattle records each. The predictive model achieved accuracies of 92%, 87%, and 87% for the respective diseases. The prescriptive model yielded high performance with average $R^{2}$ values above 0.93 and low RMSE values, demonstrating that the recommended treatments closely matched the optimal solutions. This work contributes a hybrid, explainable, and data-efficient framework that can be integrated into intelligent agriculture systems for improved livestock health management.}, archive = {J_ACCESS}, author = {Rodrigo García and Jose Aguilar and Angel Pinto and William Hoyos}, doi = {10.1109/ACCESS.2025.3583670}, journal = {IEEE Access}, pages = {112011-112020}, shortjournal = {IEEE Access}, title = {A prescriptive approach based on fuzzy cognitive maps and genetic algorithms for disease management in beef production}, volume = {13}, year = {2025}, }
• (2025). Multi-terminal coordinated frequency support strategy for alternating Current/Direct current hybrid voltage source converter-based multi-terminal direct current system. ACCESS, 13, 111997-112010. (www)

  @article{ , abstract = {The VSC-MTDC system incorporating hybrid AC/DC interconnections exhibits characteristics distinct from those of a purely DC-connected system during the process of grid frequency regulation. To address this issue, this paper proposes a novel frequency support approach for AC/DC hybrid VSC-MTDC system. Firstly, a frequency control model of VSC-MTDC system incorporating wind power integration is constructed. The mechanisms by which VSC-MTDC system and AC interconnected system participate in frequency regulation are analyzed. It has been clarified that power fluctuations will occur in areas connected by AC tie lines. Then, a re-identification method for disturbed areas is investigated, which can alter the operational state of VSCs in such area grid to mitigate power fluctuation that may happen in the exist approaches. In order to enhance the frequency support capability of the VSC-MTDC, a frequency deviation degree judgement factor is designed for the disturbance side VSCs to adaptively adjust the frequency droop coefficient. In addition, an adaptive voltage droop coefficient that based on the power margin is devised to achieve a more rational power allocation for the non-disturbance side VSCs. The proposed method is validated through simulation on Matlab/Simulink. The results demonstrate that compared to existing adaptive control methods and conventional control strategies, the approach can effectively reduce power fluctuation and improving the frequency regulation capacity for disturbed area.}, archive = {J_ACCESS}, author = {Xilin Zhao and Maoyuan Du}, doi = {10.1109/ACCESS.2025.3582277}, journal = {IEEE Access}, pages = {111997-112010}, shortjournal = {IEEE Access}, title = {Multi-terminal coordinated frequency support strategy for alternating Current/Direct current hybrid voltage source converter-based multi-terminal direct current system}, volume = {13}, year = {2025}, }
• (2025). A robust stability control method integrating linear quadratic regulator and proportional integral for islanded AC microgrid. ACCESS, 13, 111982-111996. (www)

  @article{ , abstract = {The Linear Quadratic Regulator (LQR) has been frequently employed for controlling islanded microgrids (MGs). However, it shows limited effectiveness in transient response regulation and resilience. To enhance the robustness and performance of three-phase inverters operating in islanded mode, a power-sharing control technique integrating Linear Quadratic Regulator (LQR) and Proportional Integral (PI) controllers is presented in this paper. Traditional Single Input Single Output (SISO) controllers are unable to handle transient responses and stability issues. The proposed hybrid LQR-PI control technique minimizes the quadratic value of power error and control inputs during transients while also optimizing robustness margins by including an adaptive tuning mechanism. The methodology involves a comprehensive analysis and application across multiple operational domains, which are used to develop a model that integrates voltage and current dynamics, as well as power sharing, in a synchronous reference frame. The proposed approach was thoroughly tested, and the suggested strategy demonstrates a significant reduction in settling time, as well as improved system responsiveness and stability. The results indicate that this hybrid control method is highly effective in providing precise and efficient control under varying operational conditions, as it ensures rapid convergence to the desired state with minimum overshoot. Under dynamic conditions, the research proposes a process for creating an islanded microgrid model in a synchronous reference frame, with robustness analysis confirming the stability of the LQR and PI controllers. The validation is conducted entirely in a Matlab/Simulink environment. This study is focused on simulation-based validation under ideal conditions. Simulation results validate the accuracy and efficiency of the proposed model and control technique, showing significant enhancements in transient response and robustness in islanded mode operations. It is very effective when requiring precise and efficient control under varying operational conditions since it guarantees rapid convergence to the desired state with minimum overshoot. Under dynamic conditions, the research proposes a process for creating an islanded microgrid model in a synchronous reference frame with robustness analysis confirming the stability of both controllers. The validation is conducted entirely in a Matlab/Simulink environment. This study focuses on simulation based validation under ideal conditions. Simulation results validate the accuracy and efficiency of the proposed model and control technique showing significant enhancements in transient response and robustness in islanded mode operations.}, archive = {J_ACCESS}, author = {Muhammad Nauman Malik and Qianyu Zhao and Shouxiang Wang}, doi = {10.1109/ACCESS.2025.3583195}, journal = {IEEE Access}, pages = {111982-111996}, shortjournal = {IEEE Access}, title = {A robust stability control method integrating linear quadratic regulator and proportional integral for islanded AC microgrid}, volume = {13}, year = {2025}, }
• (2025). A complete circuit model for terahertz spoof surface plasmon polariton waveguides for ultrafast and accurate synthesis of terahertz integrated circuits. ACCESS, 13, 111969-111981. (www)

  @article{ , abstract = {The increasing demand for ultra-wideband wireless communications has pushed requirements of integrated circuits (ICs) to terahertz band. Operation in the terahertz band requires ultra-high efficiency for all the building blocks in ICs; however, the conventional passive components prevent achieving desired output power levels. Moreover, these components also strongly impede the synthesis of ICs as the design flow requires an enormous number of full-wave analyses. In this paper, we present the first-ever, complete, closed form, theoretical model for Terahertz Spoof Surface Plasmon Polariton Waveguides (THz-SSPP WGs), which hold the record for the lowest loss performance among all the planar waveguides at 0.3 THz. The electromagnetic field distribution around the THz-SSPP WG is non-uniform that even makes the solution of the problem of a nonlinear differential equation of multiple variables even more challenging. Because the guided-wavenumber is non-linear because of the dispersive behavior of the waveguide and the field distribution, hence the boundary conditions depend on all the corrugation dimensions, substrate thickness, and material properties. The proposed model is the first complete closed-form model not only among all THz-SSPP WGs, but also any single conductor planar waveguide. The proposed model illustrates the behavior of the confined and propagated electromagnetic wave around the THz-SSPP WGs, where the field distribution pattern changes as any of the waveguide parameter changes in contrast with any conventional waveguide with a ground.}, archive = {J_ACCESS}, author = {Muhammed Abdullah Unutmaz and Korkut Kaan Tokgoz and Mehmet Unlu}, doi = {10.1109/ACCESS.2025.3582843}, journal = {IEEE Access}, pages = {111969-111981}, shortjournal = {IEEE Access}, title = {A complete circuit model for terahertz spoof surface plasmon polariton waveguides for ultrafast and accurate synthesis of terahertz integrated circuits}, volume = {13}, year = {2025}, }
• (2025). Analysis of the impact of renewable energy sources and energy storage systems on multi-stage transmission network expansion planning. ACCESS, 13, 111953-111968. (www)

  @article{ , abstract = {This paper presents a mathematical model to solve the multi-stage transmission network expansion planning (MTNEP) problem considering renewable energy sources (RES) and the allocation of energy storage systems (ESSs). Given the stochastic nature of both renewable energy sources and demand, the implementation of representative periods (RPs) is proposed. RPs represent the behavior of the electrical power system over time series, including short-term variations, demand and RES correlations, and geographic factors, offering feasible computational complexity to solve the MTNEP problem. The proposed model is formulated as a deterministic equivalent mixed-integer linear programming (MILP) model for a set of RPs. The MILP model optimizes investments in transmission lines (TLs) and ESSs. Furthermore, the MILP model incorporates active power losses through piecewise linearization and $N-1$ security constraints. A method is developed to reduce the combinatorial search space to deal with high-dimensional problems. The Garver’s 6-bus system, a modified IEEE 24-bus test system, and the northern Colombian transmission system are used to validate the proposed model. An extensive analysis of the impact of RES and ESSs on MTNEP is presented. Simultaneous optimization of TLs and ESSs provides better and cheaper MTNEPs compared to traditional methods. The effectiveness of ESSs in integrating RES, shifting electricity, reducing/delaying investments (especially when considering $N-1$ security constraints) and alleviating grid congestion is evidenced. This shows that ESSs have great potential as reinforcement and flexibility elements in the MTNEP problem.}, archive = {J_ACCESS}, author = {Mario D. Pastrana and Juan Carlos Cortez and Juan Camilo López and Marina Lavorato and Marcos J. Rider}, doi = {10.1109/ACCESS.2025.3582729}, journal = {IEEE Access}, pages = {111953-111968}, shortjournal = {IEEE Access}, title = {Analysis of the impact of renewable energy sources and energy storage systems on multi-stage transmission network expansion planning}, volume = {13}, year = {2025}, }
• (2025). Plant disease detection using an innovative swin-axial transformer. ACCESS, 13, 111938-111952. (www)

  @article{ , abstract = {Plant diseases are a significant threat to global agricultural production, and accurate and efficient disease detection is crucial for ensuring food security. With the rapid development of deep learning technology, Transformer architectures have demonstrated strong global feature extraction capabilities in image classification and detection tasks. However, traditional Transformer models still face high computational costs and large parameter volumes when handling multi-scale diseases and complex backgrounds. To address this challenge, this paper proposes the Swin-Axial Transformer model based on the Swin Transformer. By introducing the TokenEmbedder module, the number of tokens is reduced, and multi-scale deep convolution is used to efficiently extract image features, significantly lowering computational costs. Furthermore, the Axial Transformer module reduces computational complexity by restricting self-attention calculations to local regions along the axial direction. With the combination of the Axial Compression module and the Detail Enhancement module, the model can efficiently extract global semantic features and effectively supplement local detail information. Compared to traditional global self-attention mechanisms, the Axial Transformer successfully avoids computational bottlenecks through axial compression strategies. Experimental results show that the model achieves a precision of 79.52% on the PlantDoc dataset, 99.79% on the PlantVillage dataset, and 95.69% on a self-constructed fusion dataset. The model’s parameter size is 14.87 M with a computational load of 2.96 GMac, which represents a reduction of 46% and 32%, respectively, compared to the Swin Transformer-T model, significantly improving computational efficiency. The model presented in this paper provides an efficient and reliable solution for large-scale crop disease detection.}, archive = {J_ACCESS}, author = {Ao Zhang and Wei Liu}, doi = {10.1109/ACCESS.2025.3578486}, journal = {IEEE Access}, pages = {111938-111952}, shortjournal = {IEEE Access}, title = {Plant disease detection using an innovative swin-axial transformer}, volume = {13}, year = {2025}, }
• (2025). Heart disease prediction using a hybrid feature selection and ensemble learning approach. ACCESS, 13, 111926-111937. (www)

  @article{ , abstract = {Heart diseases have become the leading cause of death globally, highlighting the urgent need for robust diagnostic and treatment methods. This study leverages the UCI heart disease dataset to assess the effectiveness of various Machine Learning models in predicting heart diseases. This paper proposed an advanced prediction method that combines feature selection using a hybrid of Genetic Algorithm (GA) and Cuckoo Search Optimization (CSO) with a majority voting ensemble of Convolutional Neural Network and Random Forest. This approach also integrated GA for hyperparameter tuning, enhancing predictive accuracy. Comprehensive preprocessing techniques, including handling missing values, outlier detection, and normalization, were employed to ensure data quality. Using the proposed approach, 95% accuracy, 95.65% precision, 91.7% recall, 93.61% F1-score, 97.22% specificity, and 95.02% ROC AUC has been achieved. Our results demonstrate that the proposed method outperforms the existing models.}, archive = {J_ACCESS}, author = {Isha Gupta and Anu Bajaj and Manav Malhotra and Vikas Sharma and Ajith Abraham}, doi = {10.1109/ACCESS.2025.3583757}, journal = {IEEE Access}, pages = {111926-111937}, shortjournal = {IEEE Access}, title = {Heart disease prediction using a hybrid feature selection and ensemble learning approach}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on resource management for IoT applications in edge-fog-cloud environments. ACCESS, 13, 111892-111925. (www)

  @article{ , abstract = {Resource management in Edge-Fog-Cloud environments has emerged as a fundamental challenge in modern distributed computing systems, primarily due to the heterogeneous nature of underlying infrastructure, highly dynamic workloads, and the need to optimize multiple, often conflicting, Quality of Service (QoS) metrics such as latency, energy consumption, cost, and security. These environments are characterized by their layered architecture and real-time decision-making demands, which require efficient coordination among distributed resources to meet the expectations of both users and applications. Although several surveys in the literature have explored aspects of resource management, they tend to focus narrowly on specific components such as resource allocation, task scheduling, or workload placement, often neglecting the interdependence of these components. Moreover, a considerable number of surveys fail to consider the rapidly evolving landscape of edge and fog computing, where dynamic environmental changes and fluctuating user demands are becoming the norm. To bridge these gaps, this survey provides a comprehensive and up-to-date review of resource management strategies in Edge-Fog-Cloud ecosystems. We adopt a holistic perspective that integrates both combinatorial optimization techniques and learning-based methods, highlighting their roles in addressing different facets of resource management. The survey delves into five key aspects: task scheduling, resource placement, allocation, offloading, and service migration. We propose a structured taxonomy categorizing over 70 works (2017-2025) by objectives, techniques, QoS decision-making mechanisms, and environmental factors such as user mobility and network variation. Special attention is given to the synergy between combinatorial and Machine Learning-based approaches, identifying where each excels and under which conditions their integration can yield more robust solutions. Ultimately, this survey outlines the current limitations of the field, uncovers open challenges, and suggests future research directions to enable more intelligent, efficient, and adaptive resource management solutions.}, archive = {J_ACCESS}, author = {Nour El Houda Boubaker and Karim Zarour and Nawal Guermouche and Djamel Benmerzoug}, doi = {10.1109/ACCESS.2025.3583584}, journal = {IEEE Access}, pages = {111892-111925}, shortjournal = {IEEE Access}, title = {A comprehensive survey on resource management for IoT applications in edge-fog-cloud environments}, volume = {13}, year = {2025}, }
• (2025). Alternative motion control of a parallel mechanism with actuation redundancy. ACCESS, 13, 111879-111891. (www)

  @article{ , abstract = {Among the advantages of redundantly actuated mechanisms, one can mention the higher operational robustness, the feasibility of reduced power actuators, increasing the load capacity of the mechanism. This work proposes a motion control for a parallel mechanism with actuation redundancy, characterized by simplicity and effectiveness related to the implementation and accuracy, respectively. Hence, an experimental test-bed is built with three main systems, where the end-effector location is directly determined. Regarding the motion planning, some indices are proposed in order to select the appropriate non-redundant actuation modes that can achieve comparable performances with respect to the redundant one. Experimental results evidenced a good correlation between those indices and the path tracking error. For the longest path, the mean and maximum values of this metric for a non-redundant actuation mode were 20% lower than the redundant mode. Moreover, other experimental tests revealed that the proposed control has the capability to switch between the actuation modes and to overcome type-II singularities.}, archive = {J_ACCESS}, author = {Decio de Moura Rinaldi and Tarcisio-Antonio Hess-Coelho}, doi = {10.1109/ACCESS.2025.3581724}, journal = {IEEE Access}, pages = {111879-111891}, shortjournal = {IEEE Access}, title = {Alternative motion control of a parallel mechanism with actuation redundancy}, volume = {13}, year = {2025}, }
• (2025). Revolutionary unconventional transmission line designs with higher line loadability. ACCESS, 13, 111866-111878. (www)

  @article{ , abstract = {Traditionally, overhead AC transmission lines have been used to transfer electrical power from the generation to the distribution sector. The capacitance and inductance of these lines are significantly influenced by the size and arrangement of the subconductors in each phase, which in turn affects the transmission capacity. Conventional transmission lines typically employ a circular symmetry for the arrangement of subconductors in each phase. However, by altering the number and placement of these subconductors, the power transfer capacity of the lines can be significantly increased. Unconventional transmission lines leverage this principle by deviating from the traditional circular symmetry, resulting in a lower characteristic or surge impedance ( $Z_{c}$ ), which enhances the surge impedance loading (SIL). This paper introduces new designs for unconventional transmission lines, optimized under strict criteria to minimize corona discharge effects while maintaining a narrow corridor width (CW). Compared to a conventional transmission line from the literature—with a SIL of 996 MW and a line width of 24.6 meters (yielding a power density of 40.5 MW/m)—our optimally designed conventional HSIL line achieves a SIL of 1351 MW (a 36% increase) and a line width of only 8.4 meters (a 66% reduction), resulting in a power density of 160.8 MW/m (a 297% increase). Even greater improvements are observed with unconventional HSIL designs, reaching a SIL of 1592 MW—representing a 60% increase over the conventional line and an 18% improvement over the best conventional HSIL design. These findings offer promising prospects for the future of modern transmission networks.}, archive = {J_ACCESS}, author = {Mushfiqul Abedin Khan and Mona Ghassemi}, doi = {10.1109/ACCESS.2025.3583915}, journal = {IEEE Access}, pages = {111866-111878}, shortjournal = {IEEE Access}, title = {Revolutionary unconventional transmission line designs with higher line loadability}, volume = {13}, year = {2025}, }
• (2025). Insights and recommendations for the assessment and design of fault diagnosis methods applied to modern distribution systems. ACCESS, 13, 111847-111865. (www)

  @article{ , abstract = {The growing integration of Distributed Generation (DG) and the complexity of modern distribution networks present new challenges in fault diagnosis. Thus, this paper comprehensively reviews fault diagnosis methods in distribution systems, emphasizing modeling aspects for real-world applicability. In addition, this study critically evaluates the tests performed when proposing methodologies for fault detection, classification, and location in both overhead and underground networks, highlighting limitations such as the lack of consideration for variations in operating conditions and DG diversity. Separate analyses are performed for overhead lines and underground networks, which are not easily found in the literature. A bibliometric analysis was performed to identify research trends, revealing a focus on signal processing techniques like Wavelet Transform and the increasing use of machine learning methods. The analysis also emphasizes the importance of robust distribution systems modeling, which includes accurate measurement systems and communication network. The findings aid future research to improve the reliability and efficiency of fault diagnosis systems in distribution networks, address current gaps, and point the way for more practical and adaptable solutions.}, archive = {J_ACCESS}, author = {Talita M. O. A. Cunha and Gabriela N. Lopes and Leonardo S. Lessa and Moisés J. B. B. Davi and Mário Oleskovicz and José C. M. Vieira}, doi = {10.1109/ACCESS.2025.3583561}, journal = {IEEE Access}, pages = {111847-111865}, shortjournal = {IEEE Access}, title = {Insights and recommendations for the assessment and design of fault diagnosis methods applied to modern distribution systems}, volume = {13}, year = {2025}, }
• (2025). Enhanced helicopter vibration prediction with hybrid sampling and cost mining techniques. ACCESS, 13, 111832-111846. (www)

  @article{ , abstract = {Helicopter vibrations increase pilot workload and accelerate fatigue and wear in structural and mechanical components, potentially resulting in higher maintenance costs and reduced operational safety. To address these challenges, this study develops a machine learning-based prediction model using vibration test data from the cockpit of a Korean utility helicopter. To mitigate the issue of class imbalance in the dataset, two hybrid sampling techniques are proposed and analyzed: first oversampling and last undersampling (FOLU) and first undersampling and last oversampling (FULO). In addition to conventional evaluation based on prediction accuracy, this study adopts a cost-aware perspective by applying both cost-insensitive and cost-sensitive learning frameworks. The models are compared in terms of misclassification-related cost losses under realistic operational conditions. Experimental results confirm that the proposed hybrid sampling methods outperform traditional oversampling and undersampling techniques in prediction performance. Among all configurations, the FULO-based models using deep neural network (DNN) and random forest (RF) achieved the highest prediction accuracy. Moreover, cost-sensitive learning generally reduced misclassification losses compared to cost-insensitive learning; however, in certain cases, the cost-insensitive model yielded lower total costs. These findings indicate that predictive model selection should not be based solely on accuracy metrics, but also on economic efficiency within operational contexts. This study contributes to the literature by demonstrating the practical effectiveness of hybrid sampling in helicopter vibration prediction as well as introducing a cost-aware model evaluation framework suitable for prognostics and health management (PHM) applications in military and civilian rotorcraft operations.}, archive = {J_ACCESS}, author = {Jeonghun Kim and Keunho Choi and Donghee Yoo}, doi = {10.1109/ACCESS.2025.3583905}, journal = {IEEE Access}, pages = {111832-111846}, shortjournal = {IEEE Access}, title = {Enhanced helicopter vibration prediction with hybrid sampling and cost mining techniques}, volume = {13}, year = {2025}, }
• (2025). Behavioral study of bayesian neural networks under a typical surrogate model-assisted evolutionary search framework. ACCESS, 13, 111814-111831. (www)

  @article{ , abstract = {The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods in SAEAs are Gaussian process and radial basis function. This paper investigates the behavior of a machine learning alternative, Bayesian neural network (BNN), which has seldom been paid attention to in SAEAs. Through empirical methods, the properties of the BNN model, its co-work with prescreening methods, and its comparison with other machine learning alternatives are investigated using a typical SAEA model management framework. Experimental results show the behavior of BNN models in terms of surrogate model prediction accuracy, the availability of prediction uncertainty estimation, and the training cost, demonstrating the potential of BNN being a competitive surrogate alternative in SAEAs. This is validated in various experiments, in which, under the same SAEA model management framework, SAEAs using BNN as the surrogate model save 83.1% and 42.5% modeling time compared to those using GP and the drop-out method, respectively.}, archive = {J_ACCESS}, author = {Yushi Liu and Muhammad Ali Imran and Masood Ur-Rehman and Bo Liu}, doi = {10.1109/ACCESS.2025.3581151}, journal = {IEEE Access}, pages = {111814-111831}, shortjournal = {IEEE Access}, title = {Behavioral study of bayesian neural networks under a typical surrogate model-assisted evolutionary search framework}, volume = {13}, year = {2025}, }
• (2025). Developing effective techniques for the recognition of shanghai dialect text. ACCESS, 13, 111802-111813. (www)

  @article{ , abstract = {Recognizing Shanghai dialect text is crucial for preserving local dialects, yet research on its automatic distinction from Standard Mandarin remains limited. We construct a carefully balanced dataset specifically for the task of Shanghai dialect recognition and propose a two-stage approach for automatic language classification. In the first stage, we employ Jieba tokenization to retain dialect-specific lexical nuances, ensuring essential semantic and syntactic distinctions are captured. Next, we independently train both a BERT-Chinese-Based classifier and a traditional Support Vector Machine classifier for dialect recognition. The BERT model leverages powerful contextual representations to capture subtle differences between Shanghai dialect and Standard Mandarin, while the Support Vector Machine serves as a conventional baseline. Extensive experiments comparing the two approaches revealed that, although the Support Vector Machine can adequately perform the classification task, the BERT-Based classifier achieves significantly higher accuracy and is more sensitive to the nuanced linguistic features of the dialect. Further analysis through attention visualization reveals how the model specifically attends to unique dialectal features, highlighting distinctive lexical and structural differences between Shanghai dialect and Mandarin text. To the best of our knowledge, this study is the first to apply NLP techniques for language classification between Shanghai dialect and Standard Mandarin, emphasizing the potential for automated dialect recognition as an effective method for dialect documentation and preservation.}, archive = {J_ACCESS}, author = {Yida Bao and Zheng Zhang and Mohammad Arifuzzaman and Tran Duc Le and Qi Li and Masuzyo Mwanza and Jiaqing Lin and Philippe Gaillard and Jiafeng Ye}, doi = {10.1109/ACCESS.2025.3583708}, journal = {IEEE Access}, pages = {111802-111813}, shortjournal = {IEEE Access}, title = {Developing effective techniques for the recognition of shanghai dialect text}, volume = {13}, year = {2025}, }
• (2025). Electromagnetic vibration analysis of the permanent magnet vernier machine with dynamic eccentricity effect. ACCESS, 13, 111793-111801. (www)

  @article{ , abstract = {This paper aims to analyze the electromagnetic vibration of the permanent magnet vernier machine (PMVM) with dynamic eccentricity effect. Firstly, the topology of the PMVM and the definition of the dynamic eccentricity effect are introduced. Then, the magnetic field of the PMVM is calculated. The PM MMF and armature reaction MMF of the PMVM with dynamic eccentricity effect can be transformed by the eccentric air-gap permeance to change the magnetic field. And it can generate the unbalanced magnetic force (UMF) that can have bad impact on the electromagnetic vibration. What is more, the flux density and the UMF are calculated by finite element method (FEM) and analytical method. And the result shows that the electromagnetic vibration of the PMVM with dynamic eccentricity effect is worse. Finally, a new winding structure has been proposed to reduce the UMF of the PMVM with dynamic eccentricity effect. The result shows that it can reduce the electromagnetic vibration without scarifying electromagnetic performance.}, archive = {J_ACCESS}, author = {Rui Guan and Zhengmeng Liu and Liang Xu and Guohai Liu}, doi = {10.1109/ACCESS.2025.3582385}, journal = {IEEE Access}, pages = {111793-111801}, shortjournal = {IEEE Access}, title = {Electromagnetic vibration analysis of the permanent magnet vernier machine with dynamic eccentricity effect}, volume = {13}, year = {2025}, }
• (2025). Biometric identification via through-wall radar: A survey on vital signs and gait analysis. ACCESS, 13, 111766-111792. (www)

  @article{ , abstract = {Advancements in radar technologies, including Continuous Wave, Frequency Modulated Continuous Wave, and Ultra-Wideband, are surveyed for their applications in biometric identification and Through-Wall Imaging. This survey focuses on radar technologies used for vital signal detection and gait analysis. A comparison of key parameters across diverse research studies is conducted. Moreover, the benefits of varying radar technologies and Artificial Intelligence algorithms are discussed. The study provides a roadmap for subsequent investigations in the fields of radar technology and biometric identification.}, archive = {J_ACCESS}, author = {André Rouco and Carolina Gouveia and Daniel Albuquerque and Susana Brás and Pedro Pinho}, doi = {10.1109/ACCESS.2025.3583831}, journal = {IEEE Access}, pages = {111766-111792}, shortjournal = {IEEE Access}, title = {Biometric identification via through-wall radar: A survey on vital signs and gait analysis}, volume = {13}, year = {2025}, }
• (2025). A novel approach for identifying and eliminating the degradations in real time images. ACCESS, 13, 111752-111765. (www)

  @article{ , abstract = {Satellite imagery plays a crucial role in various applications, but the quality of the images can be degraded by noise. Accurate noise detection is essential for effective image enhancement and analysis. This project proposes a hybrid approach for noise detection and enhancement in satellite images by integrating deep learning and conventional techniques. We use a convolutional neural network (CNN) model along with Daubechies wavelet coefficient and statistically extracted coefficient paired with feature components extracted after applying median and high-pass filter for noise detection, to classify the type of noise in satellite images. The CNN model is trained on a dataset of images with various noises, learning to discriminate between different noise patterns. After classifying the type of noise, we apply a enhancement approach utilizes deep learning techniques by leveraging U-Net based CNN model to reduce noise levels in an image. Our approach achieves a validation accuracy of 91.29% for noise classification and a PSNR of 37.78 dB for image enhancement, outperforming several state-of-the-art methods.}, archive = {J_ACCESS}, author = {S. Rajkumar and L. M. Jenila Livingston and Vansh Juneja and Shashwat Sharv and Pratik Dattatray Mahajan}, doi = {10.1109/ACCESS.2025.3580634}, journal = {IEEE Access}, pages = {111752-111765}, shortjournal = {IEEE Access}, title = {A novel approach for identifying and eliminating the degradations in real time images}, volume = {13}, year = {2025}, }
• (2025). Classifying scam calls through content analysis with dynamic sparsity top-k attention regularization. ACCESS, 13, 111733-111751. (www)

  @article{ , abstract = {The rise of scam calls in recent years necessitated effective countermeasures against these fraudulent activities, which cause financial losses and threaten personal security. Although previous research utilizing traditional machine learning techniques has fallen short in today’s technological landscape, this study introduces a novel approach for recognizing scam calls by analyzing their content. By leveraging natural language processing techniques and deep learning methodologies, we propose the D-STAR (Dynamic Sparse Attention with Top-k Regularization) model, a transformer-based architecture designed to enhance scam call content detection. Unlike conventional models, D-STAR integrates Dynamic Sparse Attention (DSA), Top-k selection, and sparsity regularization, optimizing computational efficiency while preserving key scam-related contextual information. Our data set consists of 400 scam and 400 non-scam call transcripts, collected from publicly available sources such as social media, news reports, and discussion forums. To ensure dataset diversity, ChatGPT was utilized only to augment real scam scenarios across different contexts while preserving their core fraudulent structures. The model was evaluated using various hyperparameter configurations and managed to achieve an accuracy of 94%, a recall of 91.67%, and an F1-score of 84.98% in classifying scam call contents, outperforming state-of-the-art baselines such as CNN, LSTM, Decision Tree, Random Forest, and SVM in the scam call detection domain. A knowledge graph-based preprocessing technique was also introduced to enrich scam-related contextual understanding. The proposed approach demonstrates its effectiveness in enhancing scam call classification while maintaining computational efficiency. Future work will focus on real-world validation with telecom providers and further optimizations for real-time deployment.}, archive = {J_ACCESS}, author = {Brendan Hong Jun Zhi and Tee Connie and Thian Song Ong and Andrew Beng Jin Teoh}, doi = {10.1109/ACCESS.2025.3582906}, journal = {IEEE Access}, pages = {111733-111751}, shortjournal = {IEEE Access}, title = {Classifying scam calls through content analysis with dynamic sparsity top-k attention regularization}, volume = {13}, year = {2025}, }
• (2025). A low complexity algorithm for 3D-HEVC depth map intra coding based on MAD and ResNet. ACCESS, 13, 111722-111732. (www)

  @article{ , abstract = {As an extension of HEVC, 3D-HEVC retains the quadtree structure inherent to HEVC and is currently recognized as the most widely adopted international standard for stereoscopic video coding. In intra coding, quadtree partitioning is determined recursively through rate-distortion cost calculations. This process demands extensive computational resources and results in high encoding complexity. To mitigate this challenge, the present paper proposes a deep learning-based encoding algorithm designed to replace the intricate coding unit (CU) partitioning process utilized in HTM. First, we introduce the Mean Absolute Difference (MAD), which quantifies the dispersion of pixel values around the mean within a given region. By calculating the ratio of a coding unit’s MAD to its pixel mean, we categorize $64\times 64$ CUs into smooth and complex CUs. For smooth CUs, partitioning is terminated prematurely to minimize redundant rate-distortion optimization (RDO) computations. In contrast, for complex CUs, we propose a lightweight ResNet (Residual Neural Network) model that substitutes standard convolutions with depthwise separable convolutions (DSC) in order to decrease the number of parameters. This model effectively integrates both local and global features to generate partitioning predictions at various depths, while incorporating the quantization parameter (QP) into the input to enhance prediction accuracy. Experimental results indicate that, in comparison to the original HTM-16.2 method, the proposed approach achieves a reduction in encoding time of 48.16%, while only resulting in an increase of 0.28% in BDBR.}, archive = {J_ACCESS}, author = {Erlin Tian and Jiabao Zhang and Qiuwen Zhang}, doi = {10.1109/ACCESS.2025.3581930}, journal = {IEEE Access}, pages = {111722-111732}, shortjournal = {IEEE Access}, title = {A low complexity algorithm for 3D-HEVC depth map intra coding based on MAD and ResNet}, volume = {13}, year = {2025}, }
• (2025). Topological data analysis and wavelet- unsupervised machine learning approaches to identifying the flooding and non-flooding zones. ACCESS, 13, 111710-111721. (www)

  @article{ , abstract = {Every year, millions of individuals are affected by flooding in Bangladesh resulting in loss of habitat, properties, and lives. Because the country experiences monsoon rains, flat lands, cyclones as well as glacier melt, it is highly prone to flooding. This work aimed to lessen the impact of flooding disasters and develop effective strategies for early warning systems. The intertwined weather patterns known as the atmospheric river (AR) of Bangladesh make use of topological data analysis (TDA) in connection with wavelet decomposition and unsupervised machine learning (k-means clustering) methods to pave the way for enhanced flood detection. We have pinpointed the most desired cluster group, which stands at k =4 in our argument. In terms of the silhouette coefficient, this group had the highest values in Rangpur at 0.9016, Sylhet at 0.9028, Barisal at 0.9094, and Khulna at 0.9014. Our work was highly efficient and ranked at approximately 96% using SVMs. Later, we identified flooded and non-flooded regions with the same technique. The potential flood-prone periods were identified for all four regions based on optimal clustering results, as indicated by silhouette coefficient values. At any time when there is a possibility of flood occurrence, it can alert residents in the affected regions to move out to safer grounds; this could also stimulate appropriate actions from the risk managers.}, archive = {J_ACCESS}, author = {Md Raqibul Hasan and Md. Jamal Hossain and Md. Waliullah and Abdul Hannan and Md. Mijanoor Rahman}, doi = {10.1109/ACCESS.2025.3583581}, journal = {IEEE Access}, pages = {111710-111721}, shortjournal = {IEEE Access}, title = {Topological data analysis and wavelet- unsupervised machine learning approaches to identifying the flooding and non-flooding zones}, volume = {13}, year = {2025}, }
• (2025). Analysis of the operational impact of the interconnected wind power plant selac on enhancing the quality of supply and operational functionality of the kosovo power system. ACCESS, 13, 111699-111709. (www)

  @article{ , abstract = {This paper deals with the problem of establishing the effects of the technical and functional integration of wind power plants (WPP) in the Kosovo Power System (KPS). The analysis pertains to establishing the impact and the effects of interconnecting the Selac Wind Power Plant on the voltage profile, on the fault, that is, short circuit levels, as well as on the fault-ride-through capability of the wind park generators on the electrically adjacent power system network. The case study analyzed the Selac WPP in the Mitrovica region of Kosovo. It has a total of twenty-seven wind turbines each with a power rating of 3.8 MW with the total installed power generating capacity of 102.6 MW representing approximately one-tenth of the overall power-generating capacity of KPS. The WPP has been appropriately modelled mathematically, and numerous wide-ranging simulations have been carried out pertaining to the related analysis under various load conditions of the KPS, both with and without the contribution of the Selac WPP. The DigSilent Power Factory software package was used to perform simulations and obtain the results. All simulations were performed using accurate factory-provided data for wind generators, cables, transformers, and lines.}, archive = {J_ACCESS}, author = {Gazmend Pula and Kadri Kadriu and Petrit Kastrati and Qendrim J. Nika and Nuri Berisha}, doi = {10.1109/ACCESS.2025.3582569}, journal = {IEEE Access}, pages = {111699-111709}, shortjournal = {IEEE Access}, title = {Analysis of the operational impact of the interconnected wind power plant selac on enhancing the quality of supply and operational functionality of the kosovo power system}, volume = {13}, year = {2025}, }
• (2025). RSM-YOLOv11: Lightweight steel surface defect segmentation algorithm research based on YOLOv11 improvement. ACCESS, 13, 111681-111698. (www)

  @article{ , abstract = {Traditional segmentation algorithms fail to effectively handle complex texture backgrounds and diverse defect shapes in steel surface defect segmentation, resulting in insufficient segmentation accuracy (SA), slow speed, and false negatives. To address these issues, this paper proposes an improved YOLOv11 segmentation model, RSM-YOLOv11. The Space-to-Depth Convolution (SPD-Conv) module is introduced to replace the traditional convolutional layer. This module combines the Space-to-Depth (SPD) layer with a Non-strided Convolutional (NSConv) layer, which effectively preserves detailed information in the channels during the downsampling process, avoiding the loss of information associated with traditional convolutional operations and enhancing the segmentation performance for small-sized defects. The Re-parameterizable Block (RepBlock) module is integrated into the backbone and neck networks. During the training phase, the multi-branch structure of this module enriches the feature representation levels and captures steel surface defect (SD) features comprehensively, thereby improving SA. In the inference phase, the multi-branch structure is transformed into a compact single-branch form through reparameterization technology, effectively accelerating the network’s inference speed. Finally, the Multi-scale Channel Attention and Spatial Attention Module (MCASAM) is introduced to replace the original C2PSA module. This module optimizes the channel attention mechanism by replacing the channel attention mechanism in the Convolutional Block Attention Module (CBAM) with the efficient Multi-Scale Convolutional Attention (MSCA). While retaining the spatial localization advantage, it enhances the feature representation capability in the channel dimension, enabling the model to capture multi-scale contextual information and thereby increasing its sensitivity and segmentation ability for steel SD information. Experimental results show that the improved RSM-YOLOv11 model achieves an average SA of 92.3% for three types of defects in the NEU-Seg dataset, which is 6% higher than that of the baseline model. The model size is reduced by 23.81%, and the computational cost is decreased by 15.38%. While maintaining a lightweight structure, it outperforms existing mainstream algorithm models. Additionally, generalization experiments using other types of datasets confirm that the algorithm has good generalization ability.}, archive = {J_ACCESS}, author = {Zenghai Shan and Hu Haoyan and Changjian Zhu and Shaowen Du and Hongtao Jing and Wang Haibin}, doi = {10.1109/ACCESS.2025.3583752}, journal = {IEEE Access}, pages = {111681-111698}, shortjournal = {IEEE Access}, title = {RSM-YOLOv11: Lightweight steel surface defect segmentation algorithm research based on YOLOv11 improvement}, volume = {13}, year = {2025}, }
• (2025). Mixing high-frequency bands based on wavelet decomposition for long-term state-of-charge forecasting of lithium-ion batteries. ACCESS, 13, 111670-111680. (www)

  @article{ , abstract = {Although state-of-charge (SoC) forecasting has received considerable attention, long-term prediction remains a challenging task due to disrupted temporal dependencies and the neglect of battery signal characteristics. In this study, we propose a novel deep learning-based long-term SoC forecasting method that effectively captures temporal dynamics while preserving temporal order, thereby improving long-term predictive performance. Our approach first decomposes battery signals into low- and high-frequency bands using the discrete wavelet transform, enabling separate analyses of steady-state trends and localized events. Then, we introduce a feed-forward attention mechanism that selectively emphasizes informative high-frequency bands while suppressing irrelevant noise. Finally, we integrate the low- and high-frequency features generated solely by linear transformations that help maintain temporal structure and improve long-term forecasting accuracy. A series of experiments on a lithium-ion battery dataset demonstrate the superiority of the proposed method by achieving outstanding performance in long-term SoC forecasting.}, archive = {J_ACCESS}, author = {Yeseo Joo and Chihyeon Choi and Sangho Lee and Youngdoo Son}, doi = {10.1109/ACCESS.2025.3583216}, journal = {IEEE Access}, pages = {111670-111680}, shortjournal = {IEEE Access}, title = {Mixing high-frequency bands based on wavelet decomposition for long-term state-of-charge forecasting of lithium-ion batteries}, volume = {13}, year = {2025}, }
• (2025). A comprehensive analysis of data augmentation methods for speech emotion recognition. ACCESS, 13, 111647-111669. (www)

  @article{ , abstract = {The limited availability of labeled emotional speech data remains a significant challenge in the development of robust speech emotion recognition systems. This paper presents a comprehensive investigation of the effectiveness of diverse data augmentation strategies for enhancing emotion recognition performance. Three different data augmentation categories were examined: audio-based transformations, image-based modifications, and feature-level synthesis. Seventeen transformations were used in audio-based data augmentation to change the time and frequency content of the raw audio signal. Eight transformations, such as shifting, rotating, and zooming, were applied to the spectrogram images for image-based data augmentation. The SpecAugment method was also used to transform the spectrograms into versions with masked time and frequency axes. In feature-space-based approaches, new feature vectors were generated using five oversampling algorithms and a generative adversarial network. Experimental results from the EMO-DB and IEMOCAP datasets demonstrate that the data augmentation approaches enhance emotion classification performance by up to six percent. Empirical evidence indicates that training sets augmented through combinations of audio-based transformations yield the highest performance gains. In contrast, the GAN-based approach fails to improve the classification performance.}, archive = {J_ACCESS}, author = {Umut Avci}, doi = {10.1109/ACCESS.2025.3578143}, journal = {IEEE Access}, pages = {111647-111669}, shortjournal = {IEEE Access}, title = {A comprehensive analysis of data augmentation methods for speech emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Rainfall prediction using integrated machine learning models with K-means clustering: A representative case study of harirud murghab basin-afghanistan. ACCESS, 13, 111628-111646. (www)

  @article{ , abstract = {Accurate rainfall prediction was essential for effective water resource management and disaster preparedness, especially in regions with limited observational data such as Afghanistan. This study objective was to develop a reliable rainfall prediction machine learning (ML) model by integrating satellite-derived meteorological data with ground-based observational rainfall data. Four ML models including Gradient Boosting Regressor (GBR), Hist Gradient Boosting Regressor (HGBR), Random Forest Regressor (RFR), and Xtreme Gradient Boosting Regressor (XGBR) were used. The models were evaluated at three stations (Nazdik-i Herat, Shinya, and Torghundi) using coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), Mean absolute error (MAE), and median absolute error (MedAE) as evaluation metrics. Results showed at Nazdik-i Herat station, HGBR model achieved R2 of 0.90 for training phase and 0.83 for testing phase, RMSE of 7.35 and 10.25, and MAE of 5.41 and 6.45 for training and testing phases respectively. At Shinya station, HGBR model obtained an R2 of 0.76 and 0.76, RMSE of 10.91 and 9.51, and MAE of 6.25 and 7.44 for training and testing phases respectively. At Torghundi station, this model recorded R2 of 0.92 and 0.80, RMSE of 5.51 and 8.54, and MAE of 3.97 and 5.97 for training and testing phases respectively. While other models showed good performance, HGBR was the only model that consistently maintained high accuracy and low error across both training and testing phases at all stations. Making it the best performing model for monthly rainfall prediction in the region. This study contributed scientifically by showing the effectiveness of satellite data and advanced ML models in monthly rainfall prediction for data-limited regions. Practically, it enables a cost-effective alternative to physical rain gauge installations by offering a reliable method to estimate monthly rainfall in order to support irrigation planning, water resource management, and disaster preparedness in climate-vulnerable regions.}, archive = {J_ACCESS}, author = {Ziaul Haq Doost and Ali Alsuwaiyan and Abdulazeez Abdulraheem and Nabil M. Al-Areeq and Zaher Mundher Yaseen}, doi = {10.1109/ACCESS.2025.3581921}, journal = {IEEE Access}, pages = {111628-111646}, shortjournal = {IEEE Access}, title = {Rainfall prediction using integrated machine learning models with K-means clustering: A representative case study of harirud murghab basin-afghanistan}, volume = {13}, year = {2025}, }
• (2025). Digital twin and data-driven remaining useful life prediction of gearbox. ACCESS, 13, 111614-111627. (www)

  @article{ , abstract = {Traditional approaches for predicting the remaining useful life (RUL) of gearboxes often face challenges in integrating physical and virtual data, leading to reduced prediction accuracy and an increased risk of system failure. To realize reliable RUL prediction of gearbox, this paper proposes a novel approach called DT-SegRNN, which combines Digital Twin (DT) simulations with the data processing power of a Segment Recurrent Neural Network (SegRNN) to enhance RUL prediction. The key innovation of the DT-SegRNN model lies in its use of a DT system to simulate the gearbox’s lifespan under various operating conditions, generating a theoretical RUL. This theoretical RUL is then integrated with actual RUL data derived from experimental observations using a SegRNN model. To further improve prediction accuracy, the paper employs the Central Particle Swarm Optimization algorithm to merge both theoretical and actual RUL values. The DT-SegRNN model achieves a prediction accuracy exceeding 99.5%, outperforming traditional methods by more than 10.5%. In addition to its superior accuracy, the DT-SegRNN model is highly adaptable to a wide range of industrial machinery, offering a robust framework for proactive maintenance and minimizing unplanned downtime.}, archive = {J_ACCESS}, author = {Quanbo Lu and Mei Li and Xiaojuan Huang}, doi = {10.1109/ACCESS.2025.3583313}, journal = {IEEE Access}, pages = {111614-111627}, shortjournal = {IEEE Access}, title = {Digital twin and data-driven remaining useful life prediction of gearbox}, volume = {13}, year = {2025}, }
• (2025). Wavefront detection and event segmentation method for partial discharge signal analysis. ACCESS, 13, 111602-111613. (www)

  @article{ , abstract = {Monitoring the degradation of insulation systems in high-voltage equipment relies on identifying physical, chemical, or electrical phenomena occurring during operation, with partial discharge (PD) activity recognized as a primary indicator of insulation system deterioration. This study introduces a novel methodology for denoising, wavefront identification, and transient segmentation of PD signals collected in laboratory and substation settings using a printed monopole antenna (PMA) operating at ultra high-frequency (UHF). The proposed approach employs a shift-invariant wavelet denoising technique, where thresholds are estimated using the empirical Bayes method at each decomposition level of the wavelet transform. The denoised signal is subsequently processed through linear zero-phase filtering and post-processing steps to determine the start and end points of the PD event and to mitigate distortion effects introduced by denoising and other disturbances. The proposed methodology enables accurate identification of the first wavefront occurrence and segmentation of the PD event, offering enhanced precision for future studies on PD localization and classification. Experimental results showed substantial improvements in signal-to-noise ratio (SNR), high cross-correlation between the original and denoised signals, and a significant reduction in normalized mean squared error, confirming the robustness of the method under low-SNR conditions.}, archive = {J_ACCESS}, author = {Francisco A. de Oliveira Nascimento and Rodrigo De A. Coelho and George V. R. Xavier and Pedro D. Alvim and Almir C. Dos Santos Júnior and Hugerles S. Silva}, doi = {10.1109/ACCESS.2025.3582635}, journal = {IEEE Access}, pages = {111602-111613}, shortjournal = {IEEE Access}, title = {Wavefront detection and event segmentation method for partial discharge signal analysis}, volume = {13}, year = {2025}, }
• (2025). Detecting creative states from emotional cues: Insights from speech with focus on text modality. ACCESS, 13, 111577-111601. (www)

  @article{ , abstract = {Creativity and emotion are fundamental aspects of human expression, interaction, and innovation, shaping various facets of modern society. This paper explores the intersection of technology, human creativity and emotional expression by proposing a novel approach to detecting creative states through the analysis of emotional cues derived from speech. To validate the findings, a linguistic prompt creativity test was conducted, incorporating both human evaluations and AI-driven assistance as the co-collaborative approach, using the Torrance Test of Creative Thinking (TTCT) as the evaluation criteria. The study analysed 50 creative speech samples from 25 participants, each exposed to happy and sad video stimuli to evoke distinct emotional responses. The results reveal a correlation between emotional states and creativity. A paired t-test revealed a difference in creativity scores between the happy and sad conditions (t(24) = 2.46, p = 0.021), with higher creativity observed in the happy condition. In 80% of the participants, higher creativity levels were observed when they experienced positive emotions and happiness, highlighting the significant influence of emotional context on creative expression and creative states and also aligning with the psychological research on exploring the effects of emotions on creativity.}, archive = {J_ACCESS}, author = {Sepideh Kalateh and Sanaz Nikghadam-Hojjati and José Barata}, doi = {10.1109/ACCESS.2025.3564499}, journal = {IEEE Access}, pages = {111577-111601}, shortjournal = {IEEE Access}, title = {Detecting creative states from emotional cues: Insights from speech with focus on text modality}, volume = {13}, year = {2025}, }
• (2025). Low-cost and highly sensitive reflective-mode phase-variation sensor for bacteria detection. ACCESS, 13, 111567-111576. (www)

  @article{ , abstract = {This paper presents a reflective-mode planar microwave biosensor based on phase-variation allowing for the detection of low bacteria concentration. The sensor is a one-port coplanar waveguide (CPW) structure implemented by means of a pair of weakly coupled step-impedance resonators (SIRs). The high sensitivity needed for bacteria detection is obtained by modulating (reducing) the coupling level between the pair of resonators, which simply requires separating such resonators enough. A sensor demonstrator is developed to operate in K-band, resulting in a compact structure manufactured using a single-layer metal-on-glass technology. An experimental study is conducted on an example of detection of various concentrations of E. coli bacteria, where the phase variation at single frequency is an indicator of the bacteria presence. The measurement results show that as low as 100 colony-forming-unit in 1 ml (100 CFU/ml) of bacteria suspension can be easily detected with sensitivity of 3.24° per order of concentration, indicating potential to go even lower with detection threshold, which is superior to many state-of-the-art E. coli microwave biosensors.}, archive = {J_ACCESS}, author = {Ilona Piekarz and Jakub Sorocki and Witold Skowroński and Sabina Górska and Slawomir Gruszczynski and Krzysztof Wincza and Pau Casacuberta and Paris Velez and Ferran Martin}, doi = {10.1109/ACCESS.2025.3583631}, journal = {IEEE Access}, pages = {111567-111576}, shortjournal = {IEEE Access}, title = {Low-cost and highly sensitive reflective-mode phase-variation sensor for bacteria detection}, volume = {13}, year = {2025}, }
• (2025). An efficient fine-grained access control scheme based on policy protection in SGs. ACCESS, 13, 111555-111566. (www)

  @article{ , abstract = {With the rapid advancement of smart grid technology, data sharing between grid devices and between the grid and users has become increasingly frequent. This data sharing plays a crucial role in improving power system stability, optimizing energy distribution, and enhancing energy efficiency. However, the incomplete concealment of access policies during smart grid data sharing raises significant privacy concerns. We propose an efficient fine-grained access control scheme for secure information processing with policy protection to address this issue. By integrating the cuckoo filter, the proposed scheme achieves complete attribute hiding, thereby improving system privacy and security. We introduce an efficient attribute localization algorithm to address the complexity of attribute localization and recovery. Furthermore, blockchain technology is incorporated to support the revocation of secure data through signature verification. Security analysis demonstrates that the proposed scheme meets the defined security requirements, while performance evaluation indicates that it incurs minimal computational overhead while achieving enhanced privacy protection.}, archive = {J_ACCESS}, author = {Xiaoqing Guo and Huayi Wu and Zhen Qin and Ruonan Ying and Jilai Yang}, doi = {10.1109/ACCESS.2025.3582937}, journal = {IEEE Access}, pages = {111555-111566}, shortjournal = {IEEE Access}, title = {An efficient fine-grained access control scheme based on policy protection in SGs}, volume = {13}, year = {2025}, }
• (2025). Automated root cause analysis of network failures in IP/MPLS network using machine learning and case-based reasoning. ACCESS, 13, 111542-111554. (www)

  @article{ , abstract = {Managing IP/MPLS networks requires advanced tools due to their inherent complexity. Problems such as chain failures can be particularly challenging to resolve, as a single issue may impact multiple devices. This study introduces an integrated system aimed at improving the management of IP/MPLS networks by automatically identifying the root causes of network failures—particularly within large-scale environments. The proposed system features a dual-layered architecture comprising a Log Analysis Layer and an Operation and Maintenance Layer. The Log Analysis Layer enhances message uniformity by standardizing event logs through template generation, which replaces variable elements with wildcards. The Operation and Maintenance Layer includes components such as an event analysis service, node chain lookup, and node test service. These modules work together to identify affected devices, collect diagnostic metrics, and filter out critical events. A supervised learning model is employed to classify event messages, trained on a dataset of over seven million entries. The use of Term Frequency-Inverse Document Frequency for feature extraction improves classification accuracy by emphasizing distinctive terms over commonly occurring ones. Among the models evaluated, the SVM algorithm achieved the highest performance, with an F1-score of 0.969. The system integrates Apache Kafka as a high-throughput message broker to enable real-time processing of SNMP Traps and Syslog data. Additionally, a case-based fault identification service automates fault analysis and provides actionable insights via an interactive dashboard and a notification system that delivers alerts through modern messaging platforms. Experimental results demonstrate significant improvements in network resilience, including reduced reliance on manual troubleshooting, enhanced decision-making accuracy, and faster fault recovery times.}, archive = {J_ACCESS}, author = {Tikumporn Wankvar and Apichon Witayangkurn}, doi = {10.1109/ACCESS.2025.3583817}, journal = {IEEE Access}, pages = {111542-111554}, shortjournal = {IEEE Access}, title = {Automated root cause analysis of network failures in IP/MPLS network using machine learning and case-based reasoning}, volume = {13}, year = {2025}, }
• (2025). A systematic analysis of meteorological parameters in predicting rainfall events. ACCESS, 13, 111529-111541. (www)

  @article{ , abstract = {Accurate rainfall prediction is of paramount significance across diverse sectors, particularly in agriculture, where accurate predictions play a pivotal role in effective resource management and decision-making. However, due to the complexity and dynamic structure of climate systems, rainfall prediction is a difficult task. This study shifts the focus towards exploring correlations and feature selection in the context of rainfall prediction, contributing to a more sophisticated understanding of the process. By analyzing five years of weather data from three weather stations in the United States, Canada, and Ireland, the study delves into the interactions between meteorological features and rainfall occurrences. The use of a machine learning (ML)-based feature importance technique, which enables the identification of key meteorological features that significantly contribute to rainfall prediction, is central to the work. As a result, this method improves understanding of meteorological conditions, which act as accurate forecasters of rainfall outcomes and can help to develop accurate decision-support systems. The study also conducts a thorough assessment of prediction performance of various ML and deep learning (DL) techniques such as Classification and Regression Trees (CART), Support Vector Machine (SVM) and Dense Neural Networks (DNN).The findings show that the models using only the important meteorological features in the dataset perform better than using all the features. This rigorous examination also supports the selection of appropriate rainfall forecast models for specific use cases. Overall, this study increases our understanding of rainfall prediction by focusing on the investigation of correlations between meteorological indicators and the identification of key meteorological features using ML approaches, offering valuable insights for weather forecasting applications. This nuanced analysis contributes to the advancement of predictive modeling in the realm of rainfall forecasting, offering potential implications for decision-making across sectors reliant on precise weather forecasts.}, archive = {J_ACCESS}, author = {Muhammad Salman Pathan and Pardhu Nadella and Yasin Ul Haq and Soumini Chaudhury and Jiantao Wu and Avishek Nag and Soumyabrata Dev}, doi = {10.1109/ACCESS.2025.3573091}, journal = {IEEE Access}, pages = {111529-111541}, shortjournal = {IEEE Access}, title = {A systematic analysis of meteorological parameters in predicting rainfall events}, volume = {13}, year = {2025}, }
• (2025). A system dynamics approach for digital business ecosystem resilience and health. ACCESS, 13, 111508-111528. (www)

  @article{ , abstract = {With digital innovations, there has been a rise in the establishment of business ecosystems centered on digital platforms. To sustain the performance of these platforms and secure the health of the digital business ecosystems (DBEs), resilience towards disruptive events should be paramount. Resilience can be obtained through sound architectural design approaches and demanding the identification of relevant cause-effect relations between digital system characteristics and performance. In this study, a system dynamics approach is adopted to reveal the relationships and the dynamics between main architectural design characteristics and DBE resilience and health. The main characteristics of the digital platform and the main cause-and-effect relationships among them and with the platform performance are specified. The dynamic model of DBE health and resilience is constructed using causal loop and stock-flow diagrams. Scenario analysis is performed following validation tests. This study contributes to the DBE architectural design, resilience, and business ecosystem health literature by means of a system thinking approach that advances understanding on the main DBE characteristics affecting health and resilience, their relationships, and the dynamics regulating their interactions. Moreover, the study shows the most effective strategies to sustain the related digital business against disruptions and likewise can provide guidance on the design of main DBE architectural choices.}, archive = {J_ACCESS}, author = {Abide Coskun-Setirek and Bedir Tekinerdogan and Maria C. Annosi and William Hurst and Wilfred Dolfsma}, doi = {10.1109/ACCESS.2025.3582444}, journal = {IEEE Access}, pages = {111508-111528}, shortjournal = {IEEE Access}, title = {A system dynamics approach for digital business ecosystem resilience and health}, volume = {13}, year = {2025}, }
• (2025). Exploiting opportunistic scheduling schemes on cooperative NOMA networks under active eavesdropper. ACCESS, 13, 111484-111507. (www)

  @article{ , abstract = {In this paper, we investigate secure transmission in a cooperative non-orthogonal multiple access (NOMA) system in the presence of an active eavesdropper. To enhance secrecy performance, we propose two approaches. The first is a novel cooperative NOMA architecture, in which the cell-edge user generates a jamming signal to degrade the eavesdropper’s channel condition. The second is a novel opportunistic scheduling scheme, referred to as the minimum antenna selection (MAS) scheme, which selects the transmit antenna at the source node to minimize the maximum eavesdropper channel capacity between the cell-center and cell-edge users. We derive closed-form expressions for the secrecy outage probability (SOP) of both the cell-center and cell-edge users to characterize the impact of various network parameters on secrecy performance. Through numerical results, we analyze the impact of the source, cell-center, and cell-edge users’ transmit power on the SOP; the impact of the power allocation coefficient, target secrecy rate, and eavesdropper transmit power on the total SOP; and the impact of the distance between the source node and the eavesdropper, as well as the distance between the cell-center user and the eavesdropper, on the secrecy sum throughput (SST). These results demonstrate that the proposed cooperative NOMA architecture and the MAS scheme can effectively protect confidential messages against active eavesdropping attacks.}, archive = {J_ACCESS}, author = {Kyusung Shim and Soo-Hyun Park and Byung-Seo Kim and Beongku An}, doi = {10.1109/ACCESS.2025.3582184}, journal = {IEEE Access}, pages = {111484-111507}, shortjournal = {IEEE Access}, title = {Exploiting opportunistic scheduling schemes on cooperative NOMA networks under active eavesdropper}, volume = {13}, year = {2025}, }
• (2025). Cyber resilience of electric vehicle charging in smart grids: The dutch case. ACCESS, 13, 111454-111483. (www)

  @article{ , abstract = {As a part of energy transition, the shift from internal combustion engines (ICEs) to electric vehicles (EVs) has accelerated the development of the EV charging infrastructure (EVCI). EVCI rely heavily on information and communication technologies (ICTs) and the Internet of Things (IoT). As a result, the susceptibility to cyber attacks increases. However, although EVCI are strongly intertwined with cyber-physical power systems (CPPSs), the consequences of such a cyber attack on the power grid are not widely researched. In this paper, we present a comprehensive cyber-physical system architecture of the EV charging infrastructure based on the industry practice in The Netherlands, which is applicable to European distribution systems. We present a survey of work on EVCI cyber security and CPPS resilience. We combine unique industrial insights with the academic state-of-the-art. We show that although cyber security of EVCI is researched, the state-of-the-art inadequately covers the consequences for CPPSs, especially distribution networks. We survey the current work on CPPS resilience and conclude that while cyber attacks are often recognized as high impact low probability (HILP) disturbances of CPPSs, the resilience-related research on cyber attacks on EVCI is lacking. Therefore, we present a novel method to model the stochastic EV charging behaviour based on probability density functions (PDFs). We validate the method using PowerFactory models of distribution networks supplied by a Dutch distribution system operator (DSO). We demonstrate the effects of cyber attacks on EVCI on distribution networks voltages. Under the investigated operational scenario, the impact is not significant. However the results do underline the importance of researching cyber attacks on EVCI from a CPPS resilience perspective. Research into future scenarios of energy transition is essential for future resilient operation of power grids.}, archive = {J_ACCESS}, author = {Sjors Hijgenaar and Alexandru Ştefanov and Arjan M. van Voorden and Peter Palensky}, doi = {10.1109/ACCESS.2025.3580856}, journal = {IEEE Access}, pages = {111454-111483}, shortjournal = {IEEE Access}, title = {Cyber resilience of electric vehicle charging in smart grids: The dutch case}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of digital twin technology for deformable objects: Integration of modeling, rendering, and simulation. ACCESS, 13, 111420-111440. (www)

  @article{ , abstract = {Digital Twin (DT) technology replicates the real world within a virtual environment, enabling real-time mapping of dynamic states from the real world to its virtual replica. This paper comprehensively reviews recent research methods in modeling, rendering, and simulation of deformable objects within DT technology. While existing DT technologies primarily focus on static or rigid objects, deformable objects exhibit continuous and nonlinear deformations due to external forces and interactions, presenting challenges such as real-time tracking, representation of complex material behavior, and simulation stability. To address these challenges, this review systematically analyzes modeling techniques categorized by input data type (multi-view images, point clouds, voxels, and single-view images), rendering methods emphasizing Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), and various simulation approaches including Finite Element Method (FEM), Graph neural network (GNN), and differentiable physics-based simulations from recent studies within the last five years. Additionally, the paper investigates integrated cases of these modeling, rendering, and simulation techniques, evaluating their compatibility and discussing their technical limitations. The analysis concludes that 3DGS has recently gained prominence over NeRF due to its faster processing speed and superior rendering quality. Moreover, advancements in machine learning and differentiable physics-based simulations are anticipated to be pivotal in expanding the applicability of DT technologies for deformable objects.}, archive = {J_ACCESS}, author = {Hayeong Koo and Hyeonsu Kim and Haein Shin and Myeongjin Kim}, doi = {10.1109/ACCESS.2025.3582863}, journal = {IEEE Access}, pages = {111420-111440}, shortjournal = {IEEE Access}, title = {Comprehensive review of digital twin technology for deformable objects: Integration of modeling, rendering, and simulation}, volume = {13}, year = {2025}, }
• (2025). Advancements in optimization techniques for active magnetic bearing systems: Current trends and future directions. ACCESS, 13, 111392-111419. (www)

  @article{ , abstract = {Active Magnetic Bearings (AMBs) are revolutionizing high-speed, contactless operations across industries like aerospace, energy, and precision manufacturing. However, their complex dynamics—marked by nonlinear behavior, sensitivity to external disturbances, and intensive computational requirements—pose significant challenges. This review delves into cutting-edge optimization techniques for AMBs, from time-tested methods like PID control to innovative approaches such as metaheuristic algorithms, multi-objective optimization, and AI-powered strategies including reinforcement learning and iterative learning control. The emergence of hybrid optimization, adaptive fuzzy controllers, and machine learning-enhanced models is pushing the boundaries of AMB performance, offering substantial gains in stability, efficiency, fault tolerance, and vibration suppression. Through extensive simulations and real-world experiments, we highlight these advancements’ practical benefits in reducing energy consumption, combating harmonic vibrations, and ensuring resilient operation under dynamic conditions. We also explore key challenges such as enhancing power density, lowering computational overhead, and boosting long-term system reliability, while outlining exciting future directions like data-driven methods, real-time adaptive control systems, and novel material innovations. This review emphasizes the pivotal role of optimization in unlocking the full potential of AMBs, meeting the ever-growing demands of high-performance industrial applications.}, archive = {J_ACCESS}, author = {Tasnemul Hasan Nehal and Waleed M. Hamanah and Mohammad Ali Abido}, doi = {10.1109/ACCESS.2025.3575250}, journal = {IEEE Access}, pages = {111392-111419}, shortjournal = {IEEE Access}, title = {Advancements in optimization techniques for active magnetic bearing systems: Current trends and future directions}, volume = {13}, year = {2025}, }
• (2025). DRL-ED-TSPP: A deep reinforcement learning model with encoder-decoder for solving the traveling salesman problem with profits. ACCESS, 13, 111372-111391. (www)

  @article{ , abstract = {The rapid growth of smart cultural tourism necessitates intelligent path planning solutions that harmonize diverse stakeholder interests, including tourist satisfaction, attraction utilization, and guide efficiency. Traditional approaches, however, struggle to address the nonlinear optimization objectives inherent in balancing cultural value density against spatiotemporal costs. This paper proposes DRL-ED-TSPP, a deep reinforcement learning (DRL) model with an Encoder-Decoder architecture, to solve the Traveling Salesman Problem with Profits (TSPP) for sustainable cultural tourism planning. The model addresses the critical challenge of designing closed-loop tours that maximize cultural value per unit time while minimizing travel costs, a problem exacerbated by the NP-hard nature of TSPP and the inefficiencies of existing heuristic or metaheuristic methods. Our solution integrates a Transformer-based encoder with a Composite Feature Embedding (CFE) layer to fuse heterogeneous inputs—spatial coordinates, travel costs, and cultural value matrices—into a unified semantic space, enabling robust representation of nonlinear relationships. The encoder employs stacked self-attention layers to capture spatial-temporal dependencies and thematic continuity, while the decoder leverages a two-stage attention mechanism combining multi-head global context modeling and single-head cultural value prioritization. A dynamic masking strategy ensures Hamiltonian cycle constraints, and dual exploration-exploitation strategies (stochastic sampling for training, greedy search for inference) enhance solution diversity and decision efficiency. Evaluations on synthetic datasets (SyntheticGrid-20, RealCluster-50, MixedCity-100) and real-world cultural sites (Hangzhou West Lake, Xi’an Qin-Han-Tang Zone) demonstrate that DRL-ED-TSPP outperforms state-of-the-art baselines, achieving 4.6–7.0% improvement in cost-to-value ratio, 2.5–7.5% higher cultural value accumulation, and 9–15% faster inference times.}, archive = {J_ACCESS}, author = {Bin Hu and Liying Zhao}, doi = {10.1109/ACCESS.2025.3582025}, journal = {IEEE Access}, pages = {111372-111391}, shortjournal = {IEEE Access}, title = {DRL-ED-TSPP: A deep reinforcement learning model with encoder-decoder for solving the traveling salesman problem with profits}, volume = {13}, year = {2025}, }
• (2025). Motion constrained point cloud matching for maritime tracking. ACCESS, 13, 111354-111371. (www)

  @article{ , abstract = {Extended Object Tracking (EOT) plays a critical role in accurately estimating the state of nearby vessels in confined regions, such as urban waterways. By estimating a vessel’s extent, it is possible to improve velocity estimation and motion prediction. Traditional EOT methods in the maritime domain that use point clouds from Light Detection And Ranging (LiDAR) commonly parameterize target extents. However, these approaches often struggle with data association and clutter removal, particularly when using 2D parameterizations. To address these limitations, we propose a 3D EOT framework leveraging motion-constrained Iterative Closest Point (ICP) displacements integrated into an Error State Kalman Filter (ESKF). Our approach incorporates maritime-specific motion constraints, enabling robust target tracking even with sparse measurements. Evaluation on real-world maritime data demonstrates that the proposed 3D tracker significantly outperforms a 2D Gaussian Process Extended Object Tracking (GP-EOT) tracker, offering enhanced resilience against wake-induced noise and delivering superior state and extent estimation accuracy.}, archive = {J_ACCESS}, author = {Nicholas Dalhaug and Martin Baerveldt and Angelica I. Aviles-Rivero and Carola-Bibiane Schönlieb and Annette Stahl and Rudolf Mester and Edmund Førland Brekke}, doi = {10.1109/ACCESS.2025.3582327}, journal = {IEEE Access}, pages = {111354-111371}, shortjournal = {IEEE Access}, title = {Motion constrained point cloud matching for maritime tracking}, volume = {13}, year = {2025}, }
• (2025). Systemic analysis of the QS international research network indicator using big data: Regional inequalities and recommendations for improved university rankings. ACCESS, 13, 111335-111353. (www)

  @article{ , abstract = {The International Research Network (IRN) indicator, introduced in the QS (Quacquarelli Symonds) World University Rankings 2024, has generated notable volatility and regional disparities in global university standings. This paper presents a systemic analysis of the IRN indicator across three ranking cycles (2023–2025) using big data methodologies, including descriptive statistics, scatter plots, university size analysis, a case study of South Korean universities in Social Sciences & Management, correlation, and regression analysis. The results reveal pronounced instability in IRN scores, with sharp year-to-year fluctuations and a marked concentration of top-ranked institutions in English-speaking and European regions-98 of the top 100 and 85% of the top 500 IRN-ranked universities originate from these areas. In addition to identifying structural and regional biases, this study examines how effectively IRN functions as a ranking metric, particularly in its ability to predict overall QS performance. Findings from regression analysis show that the contribution of IRN to the overall QS score is minimal, with its predictive power diminishing significantly in the 2025 ranking year. The South Korean case study highlights methodological inconsistencies, showing that the IRN formula disadvantages institutions with multiple partnerships in the same region. These observations are reinforced by correlation and regression analyses, which further confirm that IRN’s explanatory power for overall QS scores weakened in the 2025 ranking year. These findings underscore the need to refine the IRN indicator to enhance transparency, consistency, and inclusivity, thereby supporting a more equitable evaluation of global research networks.}, archive = {J_ACCESS}, author = {Taewan Kim and Tae-Kook Kim}, doi = {10.1109/ACCESS.2025.3583236}, journal = {IEEE Access}, pages = {111335-111353}, shortjournal = {IEEE Access}, title = {Systemic analysis of the QS international research network indicator using big data: Regional inequalities and recommendations for improved university rankings}, volume = {13}, year = {2025}, }
• (2025). Enhancing efficiency and regulation current in dynamic wireless charging of electric vehicles: Phase-shift control methods in resonant inductive power transfer systems. ACCESS, 13, 111317-111334. (www)

  @article{ , abstract = {Wireless Power Transfer (WPT) introduces the possibility of charging electric vehicles (EVs) on a move, a concept known as Dynamic Wireless Charging (DWC). This paper proposes a solution for dynamic EV charging misalignment problems while restricting the charger current within permissible limits without any communication between the vehicle and the ground. This study aims to maximize the power transfer efficiency throughout the entire operational period of the charging device during EV motion over the charger coil, using Resonant Inductive Power Transfer (RIPT) technology. This study examined the effects of frequency and phase shifts on energy efficiency. It proposed two control methods for the primary-side current to maintain it within permissible values while considering factors such as varying load resistance and issues associated with misalignment. The simulation and practical results demonstrate that the phase-shift method effectively maintains the primary charging current within nominal limits. Additionally, it achieves higher efficiency, better power factor, and greater power transfer than the frequency control method, particularly when combined with a fuzzy controller. Using the proposed method, the transferred energy efficiency reached (88-93.5)% over the entire charging period. A prototype 40.2 W charging unit with primary and secondary coils was implemented and tested in a laboratory environment.}, archive = {J_ACCESS}, author = {Wissam Ayad Kiddm and S. S. Mortazavi and Mohsen Saniei and Mehdi Monadi}, doi = {10.1109/ACCESS.2025.3583200}, journal = {IEEE Access}, pages = {111317-111334}, shortjournal = {IEEE Access}, title = {Enhancing efficiency and regulation current in dynamic wireless charging of electric vehicles: Phase-shift control methods in resonant inductive power transfer systems}, volume = {13}, year = {2025}, }
• (2025). Mixture of experts for depression and anxiety disorder prediction from textual and non-textual social media data. ACCESS, 13, 111304-111316. (www)

  @article{ , abstract = {In Natural Language Processing (NLP) and related fields, computational models of mental health screening aim to detect early signs of mental health issues based on an individual’s behaviour on social media. Models of this kind, which are mostly devoted to depression disorders and to the English language, present many open research questions. First, since context-free social media data are prone to noise, the task may involve processing large amounts of data with little or no relation to mental health, which may hinder both model efficiency and accuracy. Second, existing work has been largely devoted to text processing, even though social media also include a wide range of non-textual information, which may be useful predictors of mental health as well. Finally, existing models are usually validated in a single domain, often involving one dataset of a particular text genre and language, and it is not clear whether their results may generalise to other scenarios. Based on these observations, the present work introduces a number of computational models for the prediction of depression and anxiety disorder using large language models (LLM) to handle noisy, context-free social media data. Our models combine textual and non-textual information with the aid of mixture of experts (MoE), and are evaluated in both the Twitter/X domain in Portuguese and in the Reddit domain in English using both machine learning metrics and human assessment provided by mental health specialists. Results show a number of improvements over the previous work and suggest new lines of investigation.}, archive = {J_ACCESS}, author = {Wesley Ramos Dos Santos and Ivandré Paraboni and Elton Hiroshi Matsushima and Camila Azevedo Da Silva and Emily Samara De Moura Meira and João Victor Rodrigues Ferreira Guimarães and Julia Da Silva Lins and Laura Enham De Azeredo and Luiz Guilherme Cerqueira Nunes and Vittória Thiengo Silveira Moreira Rego}, doi = {10.1109/ACCESS.2025.3583259}, journal = {IEEE Access}, pages = {111304-111316}, shortjournal = {IEEE Access}, title = {Mixture of experts for depression and anxiety disorder prediction from textual and non-textual social media data}, volume = {13}, year = {2025}, }
• (2025). Identification of relevant ECG features for epileptic seizure prediction using interpretable machine learning. ACCESS, 13, 111293-111303. (www)

  @article{ , abstract = {Epileptic seizure prediction holds the potential to enhance the quality of life for individuals with epilepsy by enabling the possibility of timely administration of medication and first aid, as well as preventing subsequent accidents. In this paper, we consider the well-established Heart Rate Variability (HRV) and Lorenz features, and augment them with the electrocardiogram (ECG) multifractality features for the first time for seizure prediction. Our experimental results demonstrate that incorporating multifractality features significantly enhances epileptic seizure prediction, with a 7.5% improvement over using only HRV features and a 6.9% improvement over using both HRV and Lorenz features. We also investigate the significance and impact of features in a seizure prediction Machine Learning (ML) model utilizing ECG signals, aiming to shed light on the intricate relationship between cardiac function and epileptic seizures. We employ SHAP (SHapley Additive exPlanations), an interpretability framework, to interpret the prediction patterns. Based on our analysis, multifractality features are among the most important features in seizure prediction, capturing patterns that are not captured by the HRV and Lorenz features.}, archive = {J_ACCESS}, author = {Azra Abtahi and Philippe Ryvlin and Amir Aminifar}, doi = {10.1109/ACCESS.2025.3583461}, journal = {IEEE Access}, pages = {111293-111303}, shortjournal = {IEEE Access}, title = {Identification of relevant ECG features for epileptic seizure prediction using interpretable machine learning}, volume = {13}, year = {2025}, }
• (2025). AdaLine: Adaptive counting line optimization by perspective-aware trajectory modeling in object-detection-tracking systems. ACCESS, 13, 111282-111292. (www)

  @article{ , abstract = {The combination of object detection, tracking, and counting has become a widely used method. The position and angle of cameras can vary according to the deployment scenarios, which affects counting accuracy. Traditional approaches often rely on manually pre-defined counting lines or regions-of-interest (ROIs), which are static, environment-specific, and difficult to generalize. To overcome these limitations, we propose AdaLine, an adaptive, perspective-aware algorithm that learns the optimal counting line from object trajectories, thereby enabling adaptation to diverse environments and camera viewpoints without manually defined counting lines. AdaLine adapts automatically as the scene evolves by clustering incoming trajectories with K-means, selecting the most stable line candidate, and smoothing it with an exponential moving average. Experimental evaluations across different scenarios and camera settings show that AdaLine achieves better performance in terms of accuracy, stability, and applicability. Our approach offers a scalable, real-time configuration-free solution for object-detection-tracking systems.}, archive = {J_ACCESS}, author = {Wenhao Huang and Yin Chen and Jin Nakazawa and Tadashi Okoshi}, doi = {10.1109/ACCESS.2025.3582577}, journal = {IEEE Access}, pages = {111282-111292}, shortjournal = {IEEE Access}, title = {AdaLine: Adaptive counting line optimization by perspective-aware trajectory modeling in object-detection-tracking systems}, volume = {13}, year = {2025}, }
• (2025). Toward intelligent fading channel prediction: A comprehensive survey. ACCESS, 13, 111260-111281. (www)

  @article{ , abstract = {Accurate knowledge of future time-varying channel state is a key enabler for adaptive transmission in current and future generation wireless networks. In this paper, we present a structured and comprehensive survey of advances in fading prediction, alongside a comparative analysis of the traditional and modern methodologies utilized in this domain. We place a significant emphasis on the shift towards machine learning techniques, which offer a promising avenue for enhancing prediction accuracy and operational efficiency. The survey delves into various predictive models, including neural networks, parametric models, and their applications across multiple input multiple output (MIMO) systems, highlighting their relative strengths and limitations. Furthermore, we propose an innovative neural network architecture for predicting multipath parameters, which significantly reduces the computational complexity typically associated with conventional methods. This model utilizes transformed channel state information for generating predictions, proving to be more effective in dynamic environments. Through this survey, we aim to provide a foundation for future research in intelligent channel prediction, highlighting the need for more sophisticated and adaptive algorithms to cope with the increasing complexity of wireless communication systems.}, archive = {J_ACCESS}, author = {Ramoni Adeogun}, doi = {10.1109/ACCESS.2025.3583361}, journal = {IEEE Access}, pages = {111260-111281}, shortjournal = {IEEE Access}, title = {Toward intelligent fading channel prediction: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Architectural exploration and performance enhancement of the CVA6 RISC-V core using the gem5 simulation framework. ACCESS, 13, 111249-111259. (www)

  @article{ , abstract = {As embedded devices continue to proliferate in applications ranging from IoT to edge computing, optimizing SoC architectures like CVA6 for performance and efficiency has become increasingly critical. This study not only evaluates the CVA6 architecture but also introduces a novel, scalable methodology for design space exploration, applicable to a wide range of SoC designs. A high-accuracy CVA6 model was developed using the gem5 simulator, achieving less than 5% error in the RISC-V microbenchmark suite and MiBench suite in both the system simulation (bare metal mode) and the full system (FS) modes. Through this model, we systematically analyzed CVA6’s architectural choices and their impact on performance and resource utilization. To further enhance the architecture, we integrated machine learning algorithms to optimize performance and resource efficiency. This approach resulted in two distinct optimized models: a resource-efficient variant that reduced resource consumption by 12% with only a 0.2% decrease in RMS micro-benchmark IPC, and a performance-optimized model that improved performance by 29.5% while increasing resource usage by just 4.20%. Our findings not only provide a deeper understanding of CVA6’s performance characteristics but also demonstrate the effectiveness of machine learning in architectural optimization. This paper details the evaluation methodology, experimental results, and key insights, offering a framework that can be generalized to other SoC designs. This work connects architectural analysis with machine learning-based optimization, offering useful insights for future research and development of embedded systems. This work could impact the design of next-generation SoCs, paving the way for smarter, faster, and more efficient solutions for cutting-edge applications.}, archive = {J_ACCESS}, author = {Umer Shahid and Muhammad Tahir and Bilal Zafar and Ayesha Ahmad and Shanzay Wasim and Bisal Saeed}, doi = {10.1109/ACCESS.2025.3583201}, journal = {IEEE Access}, pages = {111249-111259}, shortjournal = {IEEE Access}, title = {Architectural exploration and performance enhancement of the CVA6 RISC-V core using the gem5 simulation framework}, volume = {13}, year = {2025}, }
• (2025). Underwater sounds classification for effective smart marine monitoring systems. ACCESS, 13, 111237-111248. (www)

  @article{ , abstract = {Underwater sound classification is commonly referred to the process of identifying and categorizing different types of sounds in underwater environments, such as those produced by marine animals, human activities, or natural phenomena. Deep learning models have revealed to be able to powerfully classify underwater sounds when conditions are particularly favorable. However, a practical monitoring system must contend with environmental noise and the need to classify diverse sounds that can vary significantly, even within the same class. In this work, we conduct focused experiments to evaluate the efficacy of modern, lightweight deep learning models with an eye toward designing intelligent buoys capable of monitoring the sea for signs of malicious activities or the presence of marine animals. We assemble a large collection of audio samples by combining publicly available datasets to create a diverse set of audio sources that robustly tests the classification performance of different models. The experimental results show that an optimized deep learning model can achieve promising binary classification accuracy. As expected, accuracy decreases in the more challenging multi-class classification scenario but remains above 60%, demonstrating the model’s potential for real-world applications.}, archive = {J_ACCESS}, author = {Michele Nappi and Fabio Narducci and Benedetto Simone}, doi = {10.1109/ACCESS.2025.3583359}, journal = {IEEE Access}, pages = {111237-111248}, shortjournal = {IEEE Access}, title = {Underwater sounds classification for effective smart marine monitoring systems}, volume = {13}, year = {2025}, }
• (2025). A novel alternative to the beta and kumaraswamy distributions for double bounded hydroclimatology data. ACCESS, 13, 111217-111236. (www)

  @article{ , abstract = {The beta distribution is considered the most popular for modeling double-bounded data, such as rates and percentages. But when it comes to double-bounded hydrological data, it often does not show an adequate fit to such data. As a better alternative to the beta, the Kumaraswamy distribution has appeared particularly promising for modeling hydrology and related areas. Nevertheless, both the beta and the Kumaraswamy distributions seldom succeed in providing an optimal fit in all cases. Thereby, in this study, a novel alternative is proposed, referred to as generalized unit Weibull distribution (GUWD), which has three parameters for modeling double-bounded hydrology data. Some statistical characteristics are introduced, such as the quantile function, Bowley skewness, Moors kurtosis coefficients, and moments. The maximum likelihood estimation (MLE) and the Bayesian estimation (BE) techniques are applied to estimate the GUWD’s parameters. To assess the performance of the MLE and BE, a Monte Carlo simulation study was performed. Finally, we show the superiority of our model using 38 empirical applications of the daily minimum relative humidity records in mainland China. The fit results and goodness-of-fit tests show that our proposed model outperforms the beta and Kumaraswamy distributions for modeling these datasets.}, archive = {J_ACCESS}, author = {Hassan Eid Elghaly and Mohamed A. Abd Elgawad and Boping Tian}, doi = {10.1109/ACCESS.2025.3583265}, journal = {IEEE Access}, pages = {111217-111236}, shortjournal = {IEEE Access}, title = {A novel alternative to the beta and kumaraswamy distributions for double bounded hydroclimatology data}, volume = {13}, year = {2025}, }
• (2025). Exploring the translation lookaside buffer (TLB) for low-level task differentiation and classification. ACCESS, 13, 111199-111216. (www)

  @article{ , abstract = {The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related hpc for multi-task classification, without incorporating data from other microarchitectural components such as caches or branch predictors, and without relying on timing memory accesses. In the proposed methodology, an attacker uses hpc to collect TLB based data while a victim executes tasks. We demonstrate that statistical learning models, including Random Forest (RF) and Logistic Regression (LR), achieve classification accuracy of up to 87%, surpassing the next best TLB only method (based on timing analysis) by 11%. Furthermore, neural networks, such as Artificial Neural Network (ANN) and Convolution Neural Network (CNN), achieve 88% accuracy, improving prior TLB based approaches by 12%. These findings demonstrate the potential of TLB based methodologies for task classification, victim monitoring, and future security enhancements in microarchitectural design.}, archive = {J_ACCESS}, author = {Cristian Agredo and Daniel F. Koranek and Christine M. Schubert Kabban and Jose A. Gutierrez Del Arroyo and Tor J. Langehaug and Scott R. Graham}, doi = {10.1109/ACCESS.2025.3583115}, journal = {IEEE Access}, pages = {111199-111216}, shortjournal = {IEEE Access}, title = {Exploring the translation lookaside buffer (TLB) for low-level task differentiation and classification}, volume = {13}, year = {2025}, }
• (2025). Quad rapidly-exploring random tree star algorithm with improved potential force for unmanned aerial vehicle path planning. ACCESS, 13, 111187-111198. (www)

  @article{ , abstract = {Rapidly-exploring random tree star (RRT*) has attracted intensive attention in track planning due to its asymptotic optimal properties. However, the RRT* algorithm plans costly trajectory paths. Quad-RRT* algorithm with improved potential force for unmanned aerial vehicle (UAV) path planning is presented for reducing the track length of RRT* in this thesis. Firstly, the algorithm generates four random trees simultaneously in map, and it generates two random trees respectively at the starting point and the target point to improve the search efficiency. In the sampling stage of the algorithm, the random points are generated in a fixed region restricted by the sampling equation constraint method, which improves the node quality and decreases the track cost. Secondly, the potential attraction to the new node is added into the algorithm to improve new nodes quality and shorten the convergence time. Finally, the novel method is compared with other algorithms in complex environments, which proves the novel algorithm has good performance in the number of nodes, convergence time and track length.}, archive = {J_ACCESS}, author = {Peng Bian and Jiaming Fan and Yang Liu and Xia Chen and Yu Wang}, doi = {10.1109/ACCESS.2025.3582959}, journal = {IEEE Access}, pages = {111187-111198}, shortjournal = {IEEE Access}, title = {Quad rapidly-exploring random tree star algorithm with improved potential force for unmanned aerial vehicle path planning}, volume = {13}, year = {2025}, }
• (2025). UE-NER-2025: A GPT-based approach to multi-lingual named entity recognition on urdu and english. ACCESS, 13, 111175-111186. (www)

  @article{ , abstract = {Named Entity Recognition (NER) is a fundamental task that identifies and classifies entities into predefined categories from unstructured text. As textual data continues to grow and span diverse linguistic communities, NER is rarely studied as a multilingual task, particularly for low-resource languages. While many researchers have focused on name identification in various high-resource languages, only a few research efforts have addressed NER for the Urdu script. This is primarily due to a lack of resources and annotated datasets. Furthermore, previous research has mostly concentrated on monolingual techniques, leaving significant gaps in addressing multilingual challenges, especially for the Urdu language. To fill this gap, this study makes four key contributions. First, we created a unique multilingual dataset (UE-NER-2025) sourced from Twitter, which contains 182,411 tokens and 8 uniquely annotated entity types. Second, we applied two novel techniques that are relatively new to the UE-NER-2025 dataset: 1) a joint multilingual approach and 2) a joint translation-based approach. Third, we conducted 30 different experiments using 5-fold cross-validation, combining traditional supervised learning with token-based feature extraction, deep learning with pre-trained word embeddings such as FastText and GloVe, and advanced transfer learning models using contextual embeddings, to evaluate their effectiveness in enhancing NER performance for both English and Urdu, particularly addressing the challenges of low-resource and morphologically rich languages. Finally, we performed statistical analysis on our top-performing models to determine whether the differences in performance were statistically significant or occurred by chance. Based on the analysis of the results, our transformer-based language model (XLM-RoBERTa-base) achieved strong performance compared to traditional supervised learning models. We observed a performance improvement of 3.99% in the English translation-based approach, 3.72% in the multilingual approach, and 2.32% in the Urdu translation-based approach over traditional supervised learning (RF in Urdu = 0.927, in English = 0.9258, and multilingual = 0.9272).}, archive = {J_ACCESS}, author = {Muhammad Ahmad and Humaira Farid and Iqra Ameer and Fida Ullah and Muhammad Muzamil and Muhammad Jalal and Ameer Hamza and Ildar Batyrshin and Grigori Sidorov}, doi = {10.1109/ACCESS.2025.3579289}, journal = {IEEE Access}, pages = {111175-111186}, shortjournal = {IEEE Access}, title = {UE-NER-2025: A GPT-based approach to multi-lingual named entity recognition on urdu and english}, volume = {13}, year = {2025}, }
• (2025). A 249-GHz impedance-tunable waveguide transition using a microactuator and flexible conductive membrane. ACCESS, 13, 111163-111174. (www)

  @article{ , abstract = {A terahertz band (e.g., 150 GHz/300 GHz), with its broad bandwidth and potential for improved angular and distance resolution, is attracting attention for Beyond 5G/6G communication and sensing applications (radar, imager, etc.). However, as frequencies increase and wavelengths shorten, mechanical tolerances during integration can significantly impact performance (reflection, transmission, etc.), necessitating effective compensation mechanisms. In this paper, we verify the performance variations caused by mechanical tolerances in transmission-line-to-waveguide transitions, which integrate chips, substrates, and waveguides to transmit signals generated by the chip or receive signals from external sources. To overcome these challenges, a tunable-membrane microactuator is proposed, which adjusts the position of the back-short in the transition by using a flexible conductive membrane and a microactuator (dimension: $\phi 10$ mm $\times 16$ mm, maximum stroke of the membrane: 707.7 $\mathrm {\mu }$ m, accuracy<1 $\mathrm {\mu }$ m). The proposed waveguide transition comprises a transmission line and a probe integrated on the multi-layer substrate, along with waveguide flanges and the tunable membrane microactuator. Based on simulation and measurement results, this paper demonstrates the capability of the proposed technology to tune the reflection/transmission losses and frequency bandwidth characteristics of a 249-GHz waveguide transition.}, archive = {J_ACCESS}, author = {Chao Qi and Sangyeop Lee and Takeshi Yoshida and Tadahiko Shinshi}, doi = {10.1109/ACCESS.2025.3583279}, journal = {IEEE Access}, pages = {111163-111174}, shortjournal = {IEEE Access}, title = {A 249-GHz impedance-tunable waveguide transition using a microactuator and flexible conductive membrane}, volume = {13}, year = {2025}, }
• (2025). A review of learning-based methods for adaptive video streaming over HTTP. ACCESS, 13, 111134-111162. (www)

  @article{ , abstract = {Adaptive video streaming offers enhanced Quality of Experience (QoE) by dynamically adjusting the video quality to match network conditions and device capabilities. However, employing effective video streaming systems is becoming more challenging as user demands for high quality and low latency grow. The surge in video traffic is not only straining network resources but also causing a decline in video quality. To address these challenges, machine learning algorithms leverage data-driven techniques to optimize video delivery, improve QoE, and reduce network congestion. Although several surveys exist on the role of machine learning in adaptive streaming, there remains a lack of an up-to-date review that comprehensively covers the use of machine learning throughout all the stages of video streaming and explores practical deployment challenges and opportunities. This survey addresses this gap by systematically categorizing and analyzing recent research on learning techniques applied to adaptive video encoding, bandwidth optimization, and quality adaptation. Emerging trends and open challenges are identified, providing researchers with timely insights into how learning algorithms can be used to shape the future of adaptive streaming systems.}, archive = {J_ACCESS}, author = {Hala Amer and Mohamed S. Hassan and Mahmoud H. Ismail}, doi = {10.1109/ACCESS.2025.3582850}, journal = {IEEE Access}, pages = {111134-111162}, shortjournal = {IEEE Access}, title = {A review of learning-based methods for adaptive video streaming over HTTP}, volume = {13}, year = {2025}, }
• (2025). Improving cell nuclei segmentation in pathological tissues using self-supervised regression method. ACCESS, 13, 111115-111133. (www)

  @article{ , abstract = {Digital pathology, integral to cancer diagnosis, heavily utilizes the analysis of whole slide images (WSIs) to detect cancer cells. Traditionally, WSIs are manually examined—a process that is not only time-consuming but also subject to observer variability. This analysis typically focuses on the assessment of nuclei shape and size, crucial indicators of cancer stage and progression. Given these challenges, accurate nuclei segmentation is pivotal for aiding pathologists in cancer prognosis and staging, as well as for enhancing automated systems designed to assess similar aspects. Recent advances in deep learning, specifically Convolutional Neural Networks (CNNs), have proven effective in processing digital pathology data. Yet, CNNs generally require extensive labeled datasets, often unavailable in medical imaging. This study introduces a novel self-supervised learning approach for nuclei segmentation in WSIs, comparing its efficacy against other self-supervised techniques and traditional methods. It incorporates various self-supervised strategies like image-scale regression and classification, denoising autoencoder, and relative positioning, with a focus on a scale-regression-based approach that significantly outperforms others. Tested on two publicly available histopathological datasets, our method showed substantial improvements in nuclei segmentation, notably outperforming the second-best method by 9.2% on the TNBC dataset and achieving competitive results on the MoNuSeg dataset, with Aggregated Jaccard Indexes (AJI) of 0.73 and 0.619, and F1 scores of 0.927 and 0.804, respectively. These findings underscore the potential of self-supervised learning, especially via scale regression, to enhance the accuracy of training models on WSI images.}, archive = {J_ACCESS}, author = {Hesham Ali and Mostafa Hammouda and Mustafa Elattar and Sahar Selim}, doi = {10.1109/ACCESS.2025.3583069}, journal = {IEEE Access}, pages = {111115-111133}, shortjournal = {IEEE Access}, title = {Improving cell nuclei segmentation in pathological tissues using self-supervised regression method}, volume = {13}, year = {2025}, }
• (2025). Comprehensive signal modeling for talkative power conversion. ACCESS, 13, 111098-111114. (www)

  @article{ , abstract = {Talkative power conversion (TPC) is a switching ripple communication technique that integrates data modulation into a switched-mode power electronics converter, enabling simultaneous information transmission and power conversion. Despite numerous research papers published over the last decade on various theoretical and practical aspects of this emerging topic, thorough signal modeling suitable for analysis, computer simulations, and the development of suitable receiver architectures is still lacking. In this article, we derive the continuous-time output voltage of a DC/DC switched-mode power electronics converter for a broad range of pulsed-based modulation schemes. We also develop corresponding discrete-time signal models and assess their accuracies. Finally, we devise a generic end-to-end signal model for arbitrary modulation signals, discuss implications of continuous-time and discrete-time signal modeling on equalization, and consider generalizations to include parasitic effects as well as the influence of general impedance loads. This is the first time that comprehensive continuous- and discrete-time signal models are presented for TPC, including also general settings. In particular, the signal models take the initial values of all relevant variables into account and are therefore able to fully capture transient start-up behavior. Our signal modeling approach therefore provides a common framework for the fields of power electronics and communications engineering and can serve as a basis for system modeling regarding TPC system design, circuit design, evaluations, and optimizations.}, archive = {J_ACCESS}, author = {Jan Mietzner and Cerikh Chakraborty and Peter A. Hoeher and Lutz Lampe}, doi = {10.1109/ACCESS.2025.3582761}, journal = {IEEE Access}, pages = {111098-111114}, shortjournal = {IEEE Access}, title = {Comprehensive signal modeling for talkative power conversion}, volume = {13}, year = {2025}, }
• (2025). Optimizing matrix-vector operations with CGLA for high-performance approximate k-NN search. ACCESS, 13, 111087-111097. (www)

  @article{ , abstract = {In this paper, we propose a novel approach to accelerating approximate k-nearest neighbor (k-NN) search-a crucial operation in modern vector databases such as OpenSearch, ElasticSearch, Pinecone, and Milvus. Our method specifically targets the post-clustering phase in the inverted file (IVF) technique, where the bulk of computational effort is devoted to matrix-vector (MV) multiplication. The primary innovation lies in the optimization technique known as IMAX3–a Coarse-Grained Linear Array (CGLA)–which effectively leverages local memory to retain vectors between queries. This approach eliminates the need to reload vectors for subsequent queries, thereby minimizing memory movement and significantly reducing latency. Experimental evaluations demonstrate that, when excluding overhead, IMAX3 achieves up to a $54.9\times $ speedup for repeat queries compared to the initial execution-substantially outperforming conventional methods and making our solution highly suitable for real-time vector search applications. Moreover, relative to the RTX 4090, we achieve an energy-delay product (EDP) reduction of up to $498.41\times $ and on average $185.23\times $ .}, archive = {J_ACCESS}, author = {Dohyun Kim and Yasuhiko Nakashima}, doi = {10.1109/ACCESS.2025.3582825}, journal = {IEEE Access}, pages = {111087-111097}, shortjournal = {IEEE Access}, title = {Optimizing matrix-vector operations with CGLA for high-performance approximate k-NN search}, volume = {13}, year = {2025}, }
• (2025). Deep learning-driven labor education and skill assessment: A big data approach for optimizing workforce development and industrial relations. ACCESS, 13, 111064-111086. (www)

  @article{ , abstract = {The automation of resume screening is a critical component of modern recruitment processes, particularly in large organizations. Automated systems for resume screening typically involve various NLP tasks to streamline candidate evaluation. This paper investigates the application of LLM models in automating labor education and skill assessment, focusing on optimizing workforce development through advanced language models. We propose a comprehensive framework for automating resume screening and grading, utilizing SOTA LLM models to enhance recruitment processes. The proposed system integrates information extraction and summarization tasks, leveraging LLMs for decision-making throughout the hiring process. Our experiments, conducted on a publicly available resume dataset, demonstrate significant improvements in efficiency and accuracy. The LLaMA2-13B model, achieves a ROUGE-1 score of 37.31, ROUGE-2 of 15.04, ROUGE-L of 36.99, and BLEU score of 13.82, significantly outperforming the baseline models such as FLAN-T5 and GPT-NeoX. These results highlight the potential of LLM-based systems in automating labor-related assessments, with the fine-tuned LLaMA2-13B model delivering up to 27% better performance than zero-shot models.}, archive = {J_ACCESS}, author = {Dan Peng}, doi = {10.1109/ACCESS.2025.3583324}, journal = {IEEE Access}, pages = {111064-111086}, shortjournal = {IEEE Access}, title = {Deep learning-driven labor education and skill assessment: A big data approach for optimizing workforce development and industrial relations}, volume = {13}, year = {2025}, }
• (2025). Research on the influence of eccentricity on the leakage characteristics of piston rings. ACCESS, 13, 111056-111063. (www)

  @article{ , abstract = {Piston rings are more susceptible to eccentricity in a complex service environment, which affects the overall performance and reliability of wet clutches. Therefore, in this study, the influence of eccentricity on the leakage characteristics of piston rings was investigated using numerical simulations. An eccentric experiment bench of piston rings was also built in this research, and an eccentric leakage experiment of piston rings was performed using an experimental bench. The experimental results show that when the radial eccentricity is 0.3 mm, compared with the piston rings without radial eccentricity, the leakage increases by approximately 85.0% at the maximum. When the angular eccentricity is 0.1 degrees, compared with piston rings without angular eccentricity, the leakage increases by approximately 23.5% at the maximum. The research results reveal the effect of radial and angular eccentricities on the leakage of piston rings, which can provide support for improving the overall performance and reliability of wet clutches.}, archive = {J_ACCESS}, author = {Chunrui Liu and Gang Chen and Gaohui Xiao and Xingyun Jia and Lidong He}, doi = {10.1109/ACCESS.2025.3582638}, journal = {IEEE Access}, pages = {111056-111063}, shortjournal = {IEEE Access}, title = {Research on the influence of eccentricity on the leakage characteristics of piston rings}, volume = {13}, year = {2025}, }
• (2025). Integrated WHT- and DFT-based multiuser massive MIMO system for secure RIS-enabled mmWave communications. ACCESS, 13, 111037-111055. (www)

  @article{ , abstract = {Enhancing signal quality by reducing multiuser interference (MUI), improving system energy efficiency and spectrum efficiency, minimizing interference with adjacent frequency bands, and ensuring secure data communication are critical challenges in the development of future-generation reconfigurable intelligent surface (RIS)-enabled wireless communication systems. To address these challenges and to provide effective solutions, we propose a novel integrated Walsh-Hadamard transform (WHT)- and discrete Fourier transform (DFT)-based multiuser massive multiple-input multiple-output (mMIMO) system for secure RIS-enabled millimeter-wave (mmWave) communications. The proposed system emphasizes more appropriate performance metrics for a standard assumed scenario involving four users for color image transmission. The system integrates a two-dimensional (2D) hyperchaotic system for physical layer security (PLS) encryption, block diagonalization (BD) precoding, repeat-accumulate (RA) channel coding utilizing Cholesky decomposition-based zero-forcing (CD-ZF), and minimum mean square error (MMSE) signal detection techniques. PLS encryption based on a 2D hyperchaotic system significantly improves the confidentiality of the color image signal transmission. BD precoding minimizes multiuser interference, and using integrated WHT- and DFT-based algorithms with null carriers effectively reduces the out-of-band (OOB) spectrum power. The simulation results confirm the effectiveness of the proposed system for PLS enhancement in transmitting color images, demonstrating low image structural similarity indices of −7.70%, −4.82%, −5.48%, and −6.46% for users 1, 2, 3, and 4, respectively, along with an achievable OOB power emission of 312 dB. The four users achieved a bit error rate (BER) of $1\times 10^{-3}$ using RA channel coding, MMSE, and 4-QAM digital modulation at a signal-to-noise ratio (SNR) of 20 dB.}, archive = {J_ACCESS}, author = {Md. Najmul Hossain and Md. Rakibul Islam and S. K. Tamanna Kamal and Atia Kaniz and Shaikh Enayet Ullah and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3583557}, journal = {IEEE Access}, pages = {111037-111055}, shortjournal = {IEEE Access}, title = {Integrated WHT- and DFT-based multiuser massive MIMO system for secure RIS-enabled mmWave communications}, volume = {13}, year = {2025}, }
• (2025). Hierarchical clustering approach with distribution similarity strategy for multi-class botnet labeling in real-world traffic. ACCESS, 13, 111024-111036. (www)

  @article{ , abstract = {In today’s modern Internet era, botnets have become one of the primary cybersecurity threats, capable of causing immense disruption, financial losses, and data breaches. Botnets exist in many forms and are adept at disguising themselves in Internet traffic. Therefore, they are challenging to detect. Clustering methods can effectively group traffic with similar behaviors, facilitating the multi-class labeling of different botnet categories. However, most existing methods can only label a small portion of the network traffic; hence, malicious activities may continue unabated. The present study addresses the limitations of existing botnet detection systems by proposing a new multi-class botnet labeling system. The system utilizes a hierarchical clustering approach consisting of SDOclust followed by HDBSCAN to group synthetic datasets and real-world traffic and increase the volume of labeled data. A distribution similarity strategy is then adopted to label the traffic that could not be categorized in the prior hierarchical clustering stage. Finally, a semi-supervised learning framework is employed to assign labels to any remaining unlabeled traffic. The experimental results demonstrate that the proposed hierarchical clustering approach outperforms a single-class clustering algorithm, enabling an additional 7% of the traffic to be labeled. Moreover, the distribution similarity strategy helps the system label an appreciable additional 22.9% of traffic with an accuracy of 99.9%. The proposed system effectively overcomes the limitations of existing botnet detection approaches, with its improved performance stemming mainly from its ability to increase the volume of labeled data significantly.}, archive = {J_ACCESS}, author = {Ta-Chun Lo and Che-Hsien Lin and Jyh-Biau Chang and Ce-Kuen Shieh}, doi = {10.1109/ACCESS.2025.3582787}, journal = {IEEE Access}, pages = {111024-111036}, shortjournal = {IEEE Access}, title = {Hierarchical clustering approach with distribution similarity strategy for multi-class botnet labeling in real-world traffic}, volume = {13}, year = {2025}, }
• (2025). Behind-the-fence generation forecasting: A batched decomposition framework. ACCESS, 13, 111011-111023. (www)

  @article{ , abstract = {In this paper, we carry out behind-the-fence (BTF) generation forecasting using a new decomposition framework called batched decomposition framework. Here, BTF is framed as a particular structuring of the behind-the-meter (BTM) problem, where power is produced at generation and industrial facilities for internal loads rather than being supplied directly to the grid. BTF forecast is important for power system operators as it aids planning and decision making. This study employs a novel decomposition framework that effectively manages the non-linearity of BTF data while preventing the information leakage issues commonly found in traditional decomposition approaches. To assess the effectiveness of the proposed batched decomposition framework, we tested it on forecasting 24 hours ahead BTF for two Canadian provinces, Alberta and Quebec. The proposed method demonstrates high forecasting accuracy, comparable to the traditional decomposition method, while also avoiding information leakage and ensuring the practicability of the solution. Additionally, the results of the proposed method is benchmarked against various state of the art models using various error metrics. The batched decomposition method was shown to outperform the benchmarks for both test cases.}, archive = {J_ACCESS}, author = {Gideon Egharevba and Arne Dankers and Hamidreza Zareipour}, doi = {10.1109/ACCESS.2025.3583225}, journal = {IEEE Access}, pages = {111011-111023}, shortjournal = {IEEE Access}, title = {Behind-the-fence generation forecasting: A batched decomposition framework}, volume = {13}, year = {2025}, }
• (2025). Graph neural network for in-network placement of real-time metaverse tasks in next-generation networks. ACCESS, 13, 110999-111010. (www)

  @article{ , abstract = {Delivering realistic, real-time virtual experiences in the Metaverse demands computationally intensive rendering tasks with strict latency constraints. These challenges are further intensified by dynamic behavior and heterogeneity of network resources. While traditional machine learning (ML) methods have been applied to task placement, they often fail to accurately capture the complex relationships between tasks and computing nodes in such dynamic environments. To overcome these limitations, this study introduces a graph neural network (GNN)- based adaptive framework for in-network placement of delay-sensitive tasks in the Metaverse, leveraging the computing-in-the-network (COIN) paradigm. The task placement challenge is modeled as an integer linear programming (ILP) problem aimed at minimizing overall system costs while adhering to stringent delay constraints. Although optimal solutions can be derived using standard optimization solvers, their high computational overhead makes them unsuitable for real-time deployment. To address this, the optimal ILP solutions train a GNN offline, enabling fast, efficient placement decisions at runtime. The proposed GNN framework captures connected and isolated node interactions, facilitating dynamic adaptation to network conditions. It achieves a placement accuracy of 92.44%, surpassing multilayer perceptron 76.25% and decision trees 70.16%. Simulation results further confirm that the proposed model substantially outperforms conventional ML approaches, while task partitioning across multiple COIN nodes enhances network resource utilization.}, archive = {J_ACCESS}, author = {Sulaiman Muhammad Rashid and Ibrahim Aliyu and Il-Kwon Jeong and Tai-Won Um and Jinsul Kim}, doi = {10.1109/ACCESS.2025.3583139}, journal = {IEEE Access}, pages = {110999-111010}, shortjournal = {IEEE Access}, title = {Graph neural network for in-network placement of real-time metaverse tasks in next-generation networks}, volume = {13}, year = {2025}, }
• (2025). Single CDR-based MIMO antenna incorporating PDGS and PRS techniques for enhanced mmWave communications. ACCESS, 13, 110986-110998. (www)

  @article{ , abstract = {This work presents a comprehensive investigation of a Multiple-Input Multiple-Output (MIMO) antenna incorporating a Perforated Dielectric Gradient Surface (PDGS) and a Partially Reflective Surface (PRS) for millimeter-wave (mmWave) applications. The proposed design is based on a single cylindrical resonator antenna (CDRA) as a resonant element. It operates over the 24 GHz to 29 GHz frequency range and addresses critical challenges such as gain enhancement, mutual coupling suppression, and directional beam shaping. The PDGS introduces localized electromagnetic bandgaps, suppressing surface wave propagation and minimizing inter-port interference, while the PRS facilitates constructive interference, enhancing radiation efficiency and directivity. Extensive simulation and measurement results confirm the effectiveness of the combined PRS and PDGS integration, demonstrating improved reflection parameters, enhanced realized gain (with a peak gain increase of $8 \, \mathrm {dBi}$ at 27 GHz), and separated radiation patterns ±30° at 26.5 GHz and 28.5 GHz. The design achieves excellent isolation, with coupling parameters consistently below $-20 \, \mathrm {dB}$ and impedance matching across all ports. These results highlight the suitability of the proposed MIMO antenna for next-generation 5G in mmWave spectrum.}, archive = {J_ACCESS}, author = {M. Belazzoug and I. Messaoudene and S. Titouni and B. Hammache and Y. Braham Chaouche and S. Aidel and T. A. Denidni}, doi = {10.1109/ACCESS.2025.3579711}, journal = {IEEE Access}, pages = {110986-110998}, shortjournal = {IEEE Access}, title = {Single CDR-based MIMO antenna incorporating PDGS and PRS techniques for enhanced mmWave communications}, volume = {13}, year = {2025}, }
• (2025). Leveraging topic features in prediction of social network community evolutions. ACCESS, 13, 110972-110985. (www)

  @article{ , abstract = {While the cornerstone of community evolution revolves around discussion topics, both the structure and topics undergo dynamic changes simultaneously. Most studies in this field have focused on complex network structural features, overlooking the influence of topics and their features on network complexity and time consumption. Topic features, in particular, play a critical role in shaping the community’s evolution and are increasingly recognized for their influence in driving changes within these digital ecosystems. Our study aims to fill this research gap by proposing topical features to enhance the accuracy of tracking and efficiency of predictive models. In this study, we developed the Identification of Community Evolution by Mapping “ICEM” method by integrating topic modeling with network structure analysis to track community evolution. We validated our approach using two distinct datasets from different linguistic contexts: Arabic and English social media communities, allowing for cross-linguistic validation of our findings. We employed three predictive classifiers to assess the significance of topical features and compared their performance against and in combination with structural features across both datasets. Our findings not only demonstrate the effectiveness of topical features in predicting community evolution with high accuracy across different languages, but also highlight significant variations in feature effectiveness between linguistic contexts, along with substantial reduction in execution time compared to existing approaches.}, archive = {J_ACCESS}, author = {Rahman Nahi Abid and Hassan Naderi}, doi = {10.1109/ACCESS.2025.3578551}, journal = {IEEE Access}, pages = {110972-110985}, shortjournal = {IEEE Access}, title = {Leveraging topic features in prediction of social network community evolutions}, volume = {13}, year = {2025}, }
• (2025). Trust decay-based temporal learning for dynamic recommender systems with concept drift adaptation. ACCESS, 13, 110955-110971. (www)

  @article{ , abstract = {Modeling temporal dynamics in recommendation systems is essential for capturing drifts in user preferences over time, particularly under conditions of data sparsity and non-stationarity. In this study, we propose a novel framework, Trust Decay-based Temporal Learning (TDTL), which integrates trust region optimization, temporal regularization, and drift-aware adaptation to enhance the stability and adaptability of matrix factorization models. A key contribution of TDTL is the incorporation of bias-aware prediction and interaction-specific confidence weighting, which enables the model to capture rating tendencies and filter out noisy updates based on user-item frequency. TDTL also employs a BiGRU-based autoencoder to detect and refine temporal drift in user latent factors. Extensive experiments on nine benchmark datasets from diverse domains, including technology, health, entertainment, and social platforms, demonstrate that TDTL consistently outperforms both classical and deep learning-based baselines. For instance, TDTL reduces the root mean square error (RMSE) from 1.395 to 0.860 on the highly sparse Software dataset compared to TimeSVD++ and from 0.733 to 0.5349 on the Cellphones dataset compared to ALSTM. An ablation study further confirms that bias modeling and confidence weighting significantly contribute to predictive accuracy, particularly in extreme sparsity scenarios. These results highlight the robustness and generalization capability of TDTL as a reliable solution for real-world temporal recommendation tasks.}, archive = {J_ACCESS}, author = {Hartatik and Lukman Heryawan and Reza Pulungan}, doi = {10.1109/ACCESS.2025.3583186}, journal = {IEEE Access}, pages = {110955-110971}, shortjournal = {IEEE Access}, title = {Trust decay-based temporal learning for dynamic recommender systems with concept drift adaptation}, volume = {13}, year = {2025}, }
• (2025). Front pitch view shot extraction and ball tracking in cricket using deep-learning. ACCESS, 13, 110941-110954. (www)

  @article{ , abstract = {The use of technology in cricket has seen a significant increase in recent years, leading to overlapping computer vision-based research efforts. This study aims to extract front pitch view shots in cricket broadcasts by utilizing deep learning. The front pitch view (FPV) shots include ball delivery by the bowler and the stroke played by the batter. FPV shots are valuable for highlight generation, automatic commentary generation and bowling and batting techniques analysis. We classify each broadcast video frame as FPV and non-FPV using deep-learning models. We also detect pitch and umpire using recent object detectors. If a frame is classified FPV and pitch and umpire are detectable, it is considered as part of the FPV shot. In each FPV shot, we then track the cricket ball for further analysis using deep-learning-based fast-moving object trackers. Using the ball tracking, we first detect the ball bounce position on the pitch and using that position we classify the FPV video shots as full-pitched, good-length, or short-pitched deliveries. For training and testing purposes, we manually annotated four different datasets: 1) Front Pitch View (FPV) classification dataset consisting of 10k frames including 5k positive and 5k negative frames, 2) Pitch and Umpire Detection (PUD) dataset consisting of 5k frames with manually annotated pitches and umpires, 3) Ball Tracking Dataset (BTD) consisting of 3k frames with balls annotated and 6k frames with no visible balls, 4) Ball Delivery Classification (BDC) dataset consisting of 300 video clips annotated as full-pitched, good-length and short-pitched deliveries. For the FPV and PUD datasets, training and testing splits are marked as 50% each. For the BTD and BDC datasets training and testing splits are marked as 80% and 20%. The results of the proposed framework demonstrates its ability to accurately classify cricket shots into FPV and non-FPV, creating the foundation for large datasets of video shots of ball deliveries and strokes that can be used for further processing and semantic extraction. Our work has the potential to be of great benefit to cricket coaches and players for analyzing ball deliveries in a match. Our trained models and four datasets are online available at our Github page https://www.github.com/KumailAbb/FPVShotDetection}, archive = {J_ACCESS}, author = {Kumail Abbas and M. Imad Khan and Muhammad Saeed and Khandakar Ahmed and Hua Wang and Arif Mahmood}, doi = {10.1109/ACCESS.2025.3583039}, journal = {IEEE Access}, pages = {110941-110954}, shortjournal = {IEEE Access}, title = {Front pitch view shot extraction and ball tracking in cricket using deep-learning}, volume = {13}, year = {2025}, }
• (2025). Heterogeneous graph neural network framework for session-based cyberbullying detection. ACCESS, 13, 110926-110940. (www)

  @article{ , abstract = {Cyberbullying is one of the harmful activities on social networks that particularly affects the mental well-being of adolescents. Recent research has focused on session-based approaches to cyberbullying detection, which consider various components of a social media session, including posts, comments, and user information. This holistic perspective deepens the understanding of cyberbullying. However, the effective modeling of interactions within a session by incorporating session components remains a significant challenge. Previous methodologies have often failed to capture the complex dependencies between session components, thereby limiting their efficacy in modeling session interactions within a graph structure. Consequently, critical patterns and insights within sessions may be overlooked. To address this problem, we initially modeled social media sessions and their interactions as heterogeneous graphs. Subsequently, we introduced a heterogeneous graph neural network framework for session-based cyberbullying detection. Our approach leverages a Heterogeneous Graph Neural Network to capture complex high-order semantic relationships from a learned heterogeneous graph. The model comprises three components: a feature encoder, a heterogeneous graph encoder, and a classifier that collaboratively classifies social media sessions as bullying or not. We conducted experiments using two real-world session-based datasets to assess the effectiveness of the proposed method. The results demonstrate remarkable performance on the Vine dataset, achieving an F1-score of 99.34. In addition, our method provided competitive results for the Instagram dataset. Furthermore, we carried out extensive experiments to underscore the significance of the graph components in the proposed approach.}, archive = {J_ACCESS}, author = {Munkhbuyan Buyankhishig and Thanda Shwe and Israel Mendonça and Masayoshi Aritsugi}, doi = {10.1109/ACCESS.2025.3583332}, journal = {IEEE Access}, pages = {110926-110940}, shortjournal = {IEEE Access}, title = {Heterogeneous graph neural network framework for session-based cyberbullying detection}, volume = {13}, year = {2025}, }
• (2025). Graph based cooperative forwarding in information centric vehicular networks. ACCESS, 13, 110913-110925. (www)

  @article{ , abstract = {The rise of data intensive applications reveal the limitations of IP-based networking, particularly in dynamic vehicular environment. Transitioning to Information Centric Network (ICN) addresses these limitations by focusing on content retrieval from any suitable location rather than completely relying on content server or host-to-host communication. The content based communication uses an interest packet to seek named data. The interest packets are forwarded through the network to locate and retrieve content. In general the ICN uses Forwarding Information Base (FIB) tables for efficient forwarding of interest. However, in wireless environment like Vehicular Network (V-Net), use of FIB is expensive due to high bandwidth demand for maintenance. Hence, interest forwarding approach without FIB is much popular. In this paper, an interest forwarding technique for Information Centric Vehicular Networks (ICVNs) is proposed without the use of FIB. The prime objective of the algorithm is to reduce content retrieval time and optimize network overhead and throughput. For that purpose the network is modeled as a graph and partitioned into groups where each group is managed by a group leader. The group members cooperate with leader and share local information. The leaders, on the other hand cooperate with other group leaders and share information in the global level. Interest packets are forwarded by leaders on behalf of a group there by accelerating the content retrieval process. The proposed algorithm is named as GCF (Graph based Cooperative Forwarding) and is simulated in ndnSim-2.0. The performance of GCF is compared with the other two contemporary protocols, and observation shows better performance of the new approach, specifically in a densely populated scenario, compared to other benchmark algorithms. Various parameters used in the comparison are end-to-end latency, interest loss rate, network overhead, throughput, and server hit ratio. Quantitatively, the proposed protocol shows nearly 10% improvement in latency, 40% improvement in interest packet loss, and 60% improvement in network overhead.}, archive = {J_ACCESS}, author = {Beri Surya Samantha and Nitul Dutta and Rajesh Mahadeva and Shashikant P. Patole and Gheorghita Ghinea}, doi = {10.1109/ACCESS.2025.3583030}, journal = {IEEE Access}, pages = {110913-110925}, shortjournal = {IEEE Access}, title = {Graph based cooperative forwarding in information centric vehicular networks}, volume = {13}, year = {2025}, }
• (2025). A framework for assessment of perception systems in autonomous vehicles. ACCESS, 13, 110900-110912. (www)

  @article{ , abstract = {Sensors are integral components of autonomous vehicles and serve as vital interfaces that allow vehicles to perceive and interact with their surroundings. Although existing research has focused primarily on sensors in specific road settings, limited attention has been paid to a standardized methodology to understand their efficacy in urban and unconventional environments. This study focuses on common sensor types and their performance in urban terrains with an outlook for unconventional terrains, bridging a critical gap in the literature. A framework is proposed to quantify sensor performance in various environments, using experiments and simulations for data acquisition. The framework facilitates precise analysis, enabling researchers and developers to effectively evaluate sensor capabilities. To validate this framework, a simulated scenario was employed using Carla to observe the behavior of an autonomous vehicle. The results demonstrate that the sensors with the respective algorithms in the simulation are adapted to different lighting conditions, ensuring consistent sensory performance. The framework enables robust localization and kinematic estimations, enhancing the analysis of vehicle motion using state-of-the-art simulators. This research contributes to a deeper understanding of sensor performance and a validated framework to assess this performance, offering a valuable tool for advancing autonomous vehicle technology.}, archive = {J_ACCESS}, author = {Hussein Hennaoui and Daniel Paluszczyszyn and Lipika Deka and Serhan Cosar}, doi = {10.1109/ACCESS.2025.3582681}, journal = {IEEE Access}, pages = {110900-110912}, shortjournal = {IEEE Access}, title = {A framework for assessment of perception systems in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Material selection to modeling: A comprehensive investigation of a conformal circularly polarized textile antenna for wearable applications. ACCESS, 13, 110882-110899. (www)

  @article{ , abstract = {This study presents a comprehensive investigation into the design, material selection, and performance evaluation of a conformal circularly polarized textile antenna tailored for wearable applications. This study involves in-depth surface morphology analyses using Scanning Electron Microscopy (SEM) and Optical microscopy on various textile substrates. In the process of investigation, it was found that felt has the desired qualities to make a suitable substrate with good flexibility, skin-friendly nature, and exhibits excellent mechanical properties. Through the application of Energy Dispersive X-ray Spectroscopy (EDS) and flexibility investigation, nano-silver conductive paste deposition was established as superior in both conductivity and flexibility. The substrate with the size of $40\times 30\times 1.5$ mm3 is used in the design of the multiple-band circularly polarized antenna which covers LTE, LoRa, 5G, FWA, and ISM applications at 1.7, 2.4, 3.8, 4.6, and 5.2 GHz, correspondingly. The antenna operates with a gain value of 6.19, 6.95, 7.53, 8.29, and 9.16 dBi and an efficiency ranging between 85-90% in the operating frequencies. To test the antenna in a more realistic condition, mechanical bending tests were performed from 0° to 60° in vertical and horizontal planes. The unique circular polarization characteristics of the antenna were also confirmed with bending, exhibiting stability in axial ratio performance and polarization purity. Specific Absorption Rate (SAR) analysis was conducted by placing the textenna at various positions on the human phantom model, and all values remained within the safety limit of 1.6 W/kg. The results confirm that the proposed antenna offers excellent flexibility, robust performance, and reliable operation for body-centric wearable communication systems.}, archive = {J_ACCESS}, author = {D. Ram Sandeep and S. Salma and K. Sanjay Kumar and P. Rakesh Kumar and Satti Sudha Mohan Reddy and Kokku Aruna Kumari and Sanjay Bandi and B. T. P. Madhav}, doi = {10.1109/ACCESS.2025.3582711}, journal = {IEEE Access}, pages = {110882-110899}, shortjournal = {IEEE Access}, title = {Material selection to modeling: A comprehensive investigation of a conformal circularly polarized textile antenna for wearable applications}, volume = {13}, year = {2025}, }
• (2025). YLOMF: You look once for multi-Feature—A multi-feature multi-task network for intelligent real-time perception in autonomous driving. ACCESS, 13, 110867-110881. (www)

  @article{ , abstract = {Road traffic accidents claim 1.35 million lives annually, making them the leading cause of death among individuals aged 5–29 years, according to the world health organization (WHO). Low- and middle-income countries, despite having only 60% of the world’s vehicles, account for 93% of fatalities due to speeding, impaired driving, inadequate safety measures, and poor infrastructure. In the era of Industry 4.0, intelligent driving assistance systems offer a transformative solution to mitigate human errors, particularly those caused by fatigue or drowsiness. This study presents You Look Once for Multi-Feature(YLOMF), a novel single-stage, vision-based hierarchical model for autonomous driving. It features modular feature component heads, ensuring customizable deployment and real-time efficiency on edge devices. Trained on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset and evaluated on Berkeley Deep Drive Dataset (BDD100K), Indian Driving Dataset (IDD), and Audi Autonomous Driving Dataset (A2D2), YLOMF exhibits robust cross-domain generalization. It achieves 85.1% IoU for lane segmentation, 81.56% accuracy in 2D object detection, and 31.5% (easy), 26.38% (medium), and 23.9% (hard) accuracy in 3D instance detection, surpassing state-of-the-art benchmarks. Depth estimation performance was validated using absolute relative error, squared relative error, RMSE, and logged RMSE. By delivering a computationally efficient and highly accurate perception framework, YLOMF enhances scene understanding and object recognition in real time. Its integration into autonomous systems offers significant potential for reducing road accidents and improving overall traffic safety in safety-critical environments.}, archive = {J_ACCESS}, author = {B. N. Anoop and S. K. Abhilash and Venu Madhav Nookala and S. Prita and S. Raghavendra}, doi = {10.1109/ACCESS.2025.3583341}, journal = {IEEE Access}, pages = {110867-110881}, shortjournal = {IEEE Access}, title = {YLOMF: You look once for multi-Feature—A multi-feature multi-task network for intelligent real-time perception in autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Demand forecasting in data-scarce and resource-restricted environments. ACCESS, 13, 110853-110866. (www)

  @article{ , abstract = {With the growing integration of renewable energy technologies, forecasting demand in residential settings is becoming increasingly important. This research addresses the challenge of forecasting energy demand in data-scarce, resource-restricted environments, where traditional static load profiling proves inadequate due to the high variability of energy use. We present a lightweight average-based approach aimed at modeling dynamic user behavior in settings with limited historical data. This approach divides each day into evenly spaced time intervals and various average user demands, while forecasts are made and refined based on on-site data to account for short-term fluctuations. The proposed approach was compared with ARIMA and neural networks and tested on three publicly available datasets with differing temporal resolutions and data volumes. The results demonstrate that our approach is well suited in settings with little or no historical data, whereas ARIMA and neural networks outperform the average-based approach as the quantity of data increases. The proposed algorithm allows rapid integration into existing demand forecasting systems, providing adaptive demand forecasting for residential environments with restricted resources.}, archive = {J_ACCESS}, author = {David Gögelein and Marianne von Schwerin and Thomas Walter}, doi = {10.1109/ACCESS.2025.3583293}, journal = {IEEE Access}, pages = {110853-110866}, shortjournal = {IEEE Access}, title = {Demand forecasting in data-scarce and resource-restricted environments}, volume = {13}, year = {2025}, }
• (2025). Enhancing harmony search metaheuristic algorithm for coverage efficiency, test suite reduction, and running time in combinatorial interaction testing. ACCESS, 13, 110828-110852. (www)

  @article{ , abstract = {Optimization has developed powerful algorithms for solving complex problems efficiently. The effectiveness of these algorithms, also called metaheuristics, largely depends on the capabilities of their search techniques. Combinatorial Interaction Testing (CIT) is an efficient technique for detecting faults caused by interactions among system factors. By systematically covering combinations of input values, CIT can uncover faults that arise from complex interactions. One key aspect of CIT is coverage efficiency, which ensures that the desired interaction coverage is achieved with the fewest possible test cases, optimizing the testing process. Additionally, test suite reduction and execution time plays a crucial role by minimizing the number of test cases and time respectively without significantly compromising critical coverage, thereby improving the overall efficiency of the testing process. The Harmony Search (HS) algorithm is a recent metaheuristic that mimics musicians’ improvisation to create pleasant compositions according to aesthetic standards widely used in different areas continuously adjusts solution variables to find the best outcome. However, several studies show that most metaheuristics including HS face challenges when adjusting parameters to improve their performance. In this paper, we introduce an improved version of Harmony Search, referred to as eHS, and utilize CIT techniques for coverage efficiency, test suite reduction, and execution time. A significant shortcoming of the algorithm is improper arrangement of the harmony memory and inadequate exploitation during improvisation when trying to solve complex problems. In order to improve algorithm efficiency, we specifically embedded one-parameter-at-a-time approach of generating CIT test case within the harmony memory and adjust the improvisation processes to accommodate CIT test suite reduction, execution time, and coverage efficiency performance respectively. Despite HS’s tendency to get stuck in local optima, we dynamically adjust its parameter values in our proposed eHS. The experimental results demonstrate that eHS outperforms the other algorithms for CIT in terms of coverage efficiency and test suite reduction. Specifically, eHS achieved the best CIT interaction coverage efficiency performance with 15 (60.00%) successes out of 25 entries, 30 out of 33 cases (90.91%) for CIT test suite size performance, and unfortunately, 00.00% for CIT execution time performance. This study concludes that eHS emerges as a valuable algorithm for generating CIT test suite. Finally, we proposed a potential possibilities for further exploration of HS for CIT.}, archive = {J_ACCESS}, author = {Aminu Aminu Muazu and Ahmad Sobri Hashim}, doi = {10.1109/ACCESS.2025.3583176}, journal = {IEEE Access}, pages = {110828-110852}, shortjournal = {IEEE Access}, title = {Enhancing harmony search metaheuristic algorithm for coverage efficiency, test suite reduction, and running time in combinatorial interaction testing}, volume = {13}, year = {2025}, }
• (2025). Novel wire gripper for robotics picking of small green parts from powder bed of binder jet metal additive manufacturing. ACCESS, 13, 110821-110827. (www)

  @article{ , abstract = {The production revolution has materialized through Binder jet metal 3D printing because this technology creates detailed customized parts efficiently. The process achieves better results when parts are operated near each other because it reduces disturbance to the powder material. The protection of workers in addition to their safety is funda- mental. The essential elements for enhanced performance in- clude non-harmful material selection and controlled dust reg- ulation combined with ergonomic product optimization. The entire workforce needs complete training on safe protocols for operation. We can achieve improved quality and efficiency for binder jetting through proper factor resolution which will result in successful manufacturing of complex high-value components. Basic mechanical grippers do not have enough ability to handle polished parts, as their solid force can damage the components. Our research results in a special gripper system that is suitable for robotics used for safe depowdering operations. The adaptable gripper system fits different part geometries to decrease the chance of component deformation or fracturing. A combination of 304 stainless steel wires integrated with a PLA body allows the device to achieve precise handling and control together with versatile operation. The new gripper system extends its reach into tight vertical placement spaces, achieving improved comprehensive depowdering capabilities. Our work focuses on cutting down postprocessing times as we strive to reach higher levels of productivity. The development enhances the essential nature of advanced tooling systems for the advancement of binder jetting technology and process optimization within additive manufacturing. The development of these solutions makes binder jet metal 3D printing the advanced technology for fabricating intricate high-performance components that serve multiple industrial uses. The study improves the advancement of additive manufacturing by creating new possibilities for scalable and adaptable production systems in the modern industry.}, archive = {J_ACCESS}, author = {Abdulhamid Mathkur and Yanen Huang and Kenji Shimada}, doi = {10.1109/ACCESS.2025.3564271}, journal = {IEEE Access}, pages = {110821-110827}, shortjournal = {IEEE Access}, title = {Novel wire gripper for robotics picking of small green parts from powder bed of binder jet metal additive manufacturing}, volume = {13}, year = {2025}, }
• (2025). Analysis of the phase noise for frequency controlled cryogenic sapphire oscillators. ACCESS, 13, 110813-110820. (www)

  @article{ , abstract = {Cryogenic sapphire oscillators (CSO) typically require frequency stabilization and noise reduction servos to achieve ultra-high frequency stability and ultra-low phase noise. In order to analyze the performance of a 10.8 GHz frequency-locked CSO and determine the system’s limits, an extended phase noise model based on Leeson’s framework is utilized. This model accounts for noise from the locking system and variations in power and temperature. The results align well with predictions, showing that the phase noise can be reduced from −75 dBc/Hz in a free-running state to −95 dBc/Hz in a locked state at an offset frequency of 1 Hz. The analysis revealed that the fractional frequency stability of the frequency-locked CSO is limited to $4\times 10^{-15}$ at 1-second averaging time, primarily due to power fluctuations within the sapphire-loaded cavity. With the insights gained from this phase noise model, it is possible to further improve the performance of CSOs in the future.}, archive = {J_ACCESS}, author = {Encai Zhong and J.-M. Le Floch and Hongli Liu and Ke Deng and Jie Zhang and Zehuang Lu}, doi = {10.1109/ACCESS.2025.3582971}, journal = {IEEE Access}, pages = {110813-110820}, shortjournal = {IEEE Access}, title = {Analysis of the phase noise for frequency controlled cryogenic sapphire oscillators}, volume = {13}, year = {2025}, }
• (2025). Design of a manned-unmanned teaming system and forward-formation control for crevasse detection in the path of a human-driven vehicle. ACCESS, 13, 110802-110812. (www)

  @article{ , abstract = {This paper proposes a Man-Unmanned Teaming (MUM-T) system, which is designed to protect manned vehicles traveling in Antarctica from hazards such as crevasse by collaborating with a crevasse exploration robot. Additionally, it introduces a forward-formation between the manned vehicle and the exploration robot within the MUM-T system. In the forward-formation, the exploration robot, acting as the follower, moves ahead of the manned vehicle, which serves as the leader of the formation, to explore crevasses along the anticipated route. The follower predicts the leader’s expected path using its state information, generates a desired position, and tracks that position. For the tracking control of the crevasse exploration robot, the kinematic model based velocity command is employed. The stability and performance of the proposed MUM-T system, forward-formation, and motion control algorithm are validated through simulations. Especially, the forward-formation demonstrated more than an 86% improvement in predicting and mimicking the leader’s trajectory compared to conventional formation strategies. Finally, the field test results in Antarctica confirmed the high practical applicability of the proposed forward-formation-based MUM-T system.}, archive = {J_ACCESS}, author = {Ji-Wook Kwon and Hyoujun Lee and Taeyoung Uhm and Jongdeuk Lee and Na-Hyun Lee and Young-Ho Choi}, doi = {10.1109/ACCESS.2025.3575110}, journal = {IEEE Access}, pages = {110802-110812}, shortjournal = {IEEE Access}, title = {Design of a manned-unmanned teaming system and forward-formation control for crevasse detection in the path of a human-driven vehicle}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning for MU-MIMO beamforming training in mmWave WLAN. ACCESS, 13, 110791-110801. (www)

  @article{ , abstract = {In IEEE 802.11ay wireless local area network (WLAN), a single access point (AP) performs multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) to enable simultaneous directional communications with multiple stations (STAs). During MU-MIMO BFT, the AP transmits a significant number of action frames to multiple STAs, and the performance of MU-MIMO BFT depends on how the AP configures its transmit antenna arrays for the transmission of these action frames. In this paper, we develop an algorithm that utilizes a deep reinforcement learning model to learn from the configuration of transmit antenna arrays and the BFT feedback of the STAs in previous MU-MIMO BFT processes, enabling the accurate configuration of transmit antenna arrays for the transmission of action frames in the current MU-MIMO BFT process. Through performance evaluation, our proposed deep reinforcement learning scheme demonstrated improved performance in terms of latency and failure probability of MU-MIMO BFT compared to existing studies that require estimating the signal-to-noise ratios (SNRs) measured at the STAs during the transmission of action frames.}, archive = {J_ACCESS}, author = {Buseong Jo and Mun-Suk Kim and Sukyoung Lee}, doi = {10.1109/ACCESS.2025.3583336}, journal = {IEEE Access}, pages = {110791-110801}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning for MU-MIMO beamforming training in mmWave WLAN}, volume = {13}, year = {2025}, }
• (2025). Modeling and simulation of backlash dynamics of worm-wheel system and its gap size estimation using kalman filters. ACCESS, 13, 110779-110790. (www)

  @article{ , abstract = {Reduced weight, size, and maintenance cost, as well as quieter and eco-friendly operation of electro-mechanical actuators (EMA), gained profound attention in various sectors, particularly aerospace. As a result, hydraulic actuators are being replaced by EMA counterparts. However, EMA comprises mating gear systems used for power transmission, and these gears experience wear through time. Among widely known EMAs variants, worm-wheel gear system is quietest, smoothest, and compact with high gear ratio. Due to these advantages, worm-wheel is utilized in various devices that require high precision such as surgical robotic arms and 3D printers. Nevertheless, worm-wheel systems, alike other variants of mating gear systems in EMA, suffer friction wear that leads to a backlash in the system. Backlash induces non-linearity in the dynamics of the worm-wheel system, resulting in reduced EMA performance and complex control system design. Therefore, as a part of EMA prognostic and health management (PHM) approaches, we develop a mathematical model of the non-linear dynamic behavior of backlash in the worm-wheel system and derived equations from the model for backlash gap size estimation. This backlash gap size estimation can be employed not only for monitoring of worm-wheel system performance and reliability as a proactive measure but also for its compensation control system design. Simulations of backlash dynamic behavior and gap size estimation were conducted using the Matlab/simscape tool. Extended and unscented Kalman filters were implemented to estimate backlash gap size, and their performance was compared using root-mean-square error technique. Results show that both Kalman filters estimate a simulated gap size very well. However, the unscented Kalman filter performs relatively higher than that of the extended Kalman filter around sharp edges during the switching behavior.}, archive = {J_ACCESS}, author = {Abera Tullu and Jihyoung Cha and Sangho Ko}, doi = {10.1109/ACCESS.2025.3577747}, journal = {IEEE Access}, pages = {110779-110790}, shortjournal = {IEEE Access}, title = {Modeling and simulation of backlash dynamics of worm-wheel system and its gap size estimation using kalman filters}, volume = {13}, year = {2025}, }
• (2025). The adoption process of blockchain technology among social media influencers. ACCESS, 13, 110765-110778. (www)

  @article{ , abstract = {Social media influencers play a crucial role in driving blockchain technology adoption due to their ability to reach large audiences and influence the dissemination of technological innovations. This study investigates the blockchain adoption intention of social media influencers. It adopts a multidimensional approach to examine influencers’ blockchain adoption processes, using a sample of 119 cryptocurrency exchange influencers on X (Twitter) and Instagram through an online survey. Partial Least Squares Structural Equation Modeling was applied by integrating the Technology Acceptance Model and the Technology Readiness Index models. When combined with models provides a more comprehensive understanding of individuals’ behavioral intentions regarding technology adoption. The study’s findings reveal that innovativeness and optimism have significant positive effects on both Perceived Ease of Use and Perceived Usefulness, while insecurity and discomfort do not show significant effects. Additionally, Perceived Ease of Use and Perceived Usefulness both have positive and significant effects on Behavioral Intentions regarding technology adoption. The study’s findings emphasize the importance of considering both rational evaluations (usefulness and ease of use) and psychological readiness (optimism and innovativeness) in the design of interventions to foster technology adoption. Given these insights, companies and organizations aiming to accelerate blockchain adoption could collaborate with influencers who are both innovative and optimistic about the technology to create content that resonates with audiences, encouraging broader acceptance of blockchain.}, archive = {J_ACCESS}, author = {Murat Doğan}, doi = {10.1109/ACCESS.2025.3583547}, journal = {IEEE Access}, pages = {110765-110778}, shortjournal = {IEEE Access}, title = {The adoption process of blockchain technology among social media influencers}, volume = {13}, year = {2025}, }
• (2025). Knowledge-based planning for human-robot collaborative tasks. ACCESS, 13, 110752-110764. (www)

  @article{ , abstract = {Human-robot collaboration is a promising alternative to full automation and manual labour. Collaborative robots are considered safe and individual robot actions can often be easily programmed, for example, by physical hand-guiding. Coordinated collaboration, where tasks and the environment are shared, cannot be so easily achieved, due to continuously changing conditions and actions that need to be triggered at unknown instances. Besides, knowledge required for collaboration is difficult to program into robotic systems. This paper presents a system architecture that aims at facilitating human-robot collaboration by alleviating the need for pre-programmed information and exploring ways to teach new skills. This is done by reducing the amount of information required to program tasks by utilizing a knowledge base that represents knowledge on tasks, actions and the world. Automatic reasoning over conditions and properties of the knowledge is then utilized to generate available actions and action plans in order to complete the shared tasks. Moreover, learning new tasks is enabled by extending the original knowledge base, concatenating available actions and tasks into news bricks of knowledge. Two examples, a kitting task and a handover task, serve to validate the system architecture and exemplify its usage. Experiments demonstrate that by combining reasoning methods and knowledge-based planning, high-level shared tasks can be generated and executed, and robots can act reliable as teammate to human operators.}, archive = {J_ACCESS}, author = {Alexandre Angleraud and Metodi Netzev and Roel Pieters}, doi = {10.1109/ACCESS.2025.3583469}, journal = {IEEE Access}, pages = {110752-110764}, shortjournal = {IEEE Access}, title = {Knowledge-based planning for human-robot collaborative tasks}, volume = {13}, year = {2025}, }
• (2025). Efficient multipath mitigation in GNSS using dynamic selection and weighting of early-late correlators. ACCESS, 13, 110736-110751. (www)

  @article{ , abstract = {In this paper a real-time multipath interference mitigation technique is proposed for Global Navigation Satellite Systems (GNSS), employing an adaptive discriminator architecture that dynamically optimizes the selection and weighting of early-late (E-L) correlators to minimize discriminator function (DF) offset errors at the delay-locked loop (DLL) output. The significance of this adaptive technique lies in its ability to overcome the performance limitations of conventional static methods, which struggle in dynamic signal environments. The proposed approach operates in two principal phases: first, it performs real-time estimation of the relative multipath delay; subsequently, it reconfigures the DLL discriminator through an empirically optimized lookup table, ensuring robust and adaptive delay tracking across dynamic signal conditions. This adaptive mechanism optimally determines the number of E-L correlator pairs and their respective weighting coefficients to enhance DLL tracking stability and mitigate multipath-induced distortions. The proposed method is compatible with various Global Navigation Satellite Systems (GNSS) signal structures, including binary phase shift keying (BPSK) and advanced binary offset carrier (BOC) modulations, which exhibit high-resolution autocorrelation functions (ACFs) with multiple spectral side lobes, leading to synchronization ambiguities. Simulation results substantiate that the proposed real-time multipath mitigation technique outperforms conventional baseband signal processing approaches, achieving superior robustness and enhanced positioning accuracy, particularly in challenging propagation environments.}, archive = {J_ACCESS}, author = {Ayoub Bengherabi and Mustapha Flissi and Khaled Rouabah and Salim Attia and Diana W. Dawoud and Yassine Himeur and Shadi Atalla and Wathiq Mansoor}, doi = {10.1109/ACCESS.2025.3581856}, journal = {IEEE Access}, pages = {110736-110751}, shortjournal = {IEEE Access}, title = {Efficient multipath mitigation in GNSS using dynamic selection and weighting of early-late correlators}, volume = {13}, year = {2025}, }
• (2025). Some new optimal skew cyclic codes with derivation. ACCESS, 13, 110725-110735. (www)

  @article{ , abstract = {Our study included a class of cyclic codes named $\delta _{\alpha,\zeta }-$ cyclic codes over the ring $\mathcal {R}=\mathbb {F}_{2^{m}} + u\mathbb {F}_{2^{m}}+u^{2}\mathbb {F}_{2^{m}}$ , where m is an odd positive integer with $u^{3}=1$ . These codes are modules over the ring $\mathcal {R}{[x,\alpha,\delta _{\alpha,\zeta }]}$ with automorphism $\alpha $ and $\alpha $ -derivation $\delta _{\alpha,\zeta }$ (where $\zeta \in \mathcal {R}$ ). The structure of ring $\mathcal {R}{[x,\alpha,\delta _{\alpha,\zeta }]}$ is presented to develop linear codes over the ring. The center of the ring is characterized for all inner $\alpha $ -derivations $\delta _{\alpha,\zeta }$ where $\zeta $ is taken as an arbitrary element of the ring fixed by $\alpha $ . In addition, an algorithm was developed and implemented in MAGMA to perform division in this ring. The generator and parity-check matrices for $\delta _{\alpha,\zeta }-$ cyclic codes are proposed, and examples of new optimal $\delta _{\alpha,\zeta }-$ cyclic codes with better minimum Hamming distance than those already existing in the Markus Grassl code tables are provided.}, archive = {J_ACCESS}, author = {Asia Noor and Atif Hasan Soori and Muhammad Shoaib Arif and Kamaleldin Abodayeh}, doi = {10.1109/ACCESS.2025.3582640}, journal = {IEEE Access}, pages = {110725-110735}, shortjournal = {IEEE Access}, title = {Some new optimal skew cyclic codes with derivation}, volume = {13}, year = {2025}, }
• (2025). Diagnosis of commutation failure in a high- voltage direct current transmission system based on fuzzy entropy feature vectors and a PCNN-GRU. ACCESS, 13, 110709-110724. (www)

  @article{ , abstract = {To enhance the diagnostic accuracy of commutation failure in weak receiving-end high-voltage direct current (HVDC) transmission systems, this study proposes a novel diagnostic model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based fuzzy entropy and a Parallel Convolutional Gated Recurrent Unit Neural Network (PCNN-GRU). The model initially decomposes the rectifier-side DC voltage signal through CEEMDAN to obtain the high- and low-frequency characteristic components of fault voltages, followed by fuzzy entropy calculation for each component to construct the fault diagnosis input matrix. Subsequently, the PCNN-GRU architecture performs deep feature extraction through two distinct mechanisms: the PCNN branch employs dual-path convolutional kernels of varying sizes for multidimensional feature mining, whereas the GRU network enhances temporal feature extraction capabilities. The simulation results demonstrate the model’s exceptional performance in discriminating DC line faults and commutation failures, achieving diagnostic accuracies of 98.75%, 99.17%, and 99.5833% under 20 dB, 30 dB, and noise-free environments, respectively, significantly outperforming conventional deep learning models. The proposed approach not only improves diagnostic precision, but also exhibits remarkable noise immunity, providing a novel methodology for HVDC commutation failure detection. This breakthrough research offers critical technical support for stable HVDC system operation and holds significant application value for smart grid maintenance.}, archive = {J_ACCESS}, author = {Cao Ruirui and Yang Taigang and Li Guohui and Chen Shilong}, doi = {10.1109/ACCESS.2025.3582723}, journal = {IEEE Access}, pages = {110709-110724}, shortjournal = {IEEE Access}, title = {Diagnosis of commutation failure in a high- voltage direct current transmission system based on fuzzy entropy feature vectors and a PCNN-GRU}, volume = {13}, year = {2025}, }
• (2025). Toward predictive maintenance for aerospace hot press furnace and the current monitoring with machine learning. ACCESS, 13, 110689-110708. (www)

  @article{ , abstract = {In this research, we propose a Prognostics and Health Management (PHM) system that integrates an energy monitoring framework to enhance the efficiency of maintenance strategy of aerospace autoclaves, which are usually subjected to expensive and complicated inspection processes. It provides real-time health indicators, predicts the condition of equipment, and performs energy audits. A health evaluation logic was built upon the current balance through the analysis of the three-phase current fluctuation amount of heater units and circulation fan motors. Using a main-sub health assessment structure, the PHM system processes data from individual equipment units, identifies anomalies, and notifies maintenance personnel to carry out corrective actions. It uses complex AI techniques such as RNN, GRU, LSTM, etc., for trend prediction of health for proactive maintenance. It also accounts for energy consumption and carbon emissions per batch of production, thereby contributing to sustainability in the aerospace industry. Ultimately, this implementation makes autoclaves more reliable, longer-lasting, and more efficient, while minimizing maintenance costs, as well as energy consumption and carbon emissions. The end-to-end solution offers a powerful means of enhancing autoclave performance, and of enabling green aerospace operations.}, archive = {J_ACCESS}, author = {Chandra Wijaya and Jing-Zhang Liu and I-Jan Wang and Jen-Kai King and Chao-Tung Yang}, doi = {10.1109/ACCESS.2025.3580729}, journal = {IEEE Access}, pages = {110689-110708}, shortjournal = {IEEE Access}, title = {Toward predictive maintenance for aerospace hot press furnace and the current monitoring with machine learning}, volume = {13}, year = {2025}, }
• (2025). AI literacy among university students: A comparative study of three Countries—Slovenia, croatia, and india. ACCESS, 13, 110671-110688. (www)

  @article{ , abstract = {This study investigates artificial intelligence (AI) literacy among university students from Slovenia, Croatia, and India, focusing on variations in their understanding of AI concepts and applications across different educational systems. The sample comprised 471 students from the business studies programmes in Slovenia, Croatia and India. Data were collected through a structured online questionnaire, and statistical analyses utilized descriptive statistics and independent samples proportions to identify significant differences in AI literacy levels across the three countries. The findings reveal substantial disparities in AI literacy. Croatian students demonstrated foundational and practical knowledge of AI, reflecting well-developed educational frameworks. Indian students excelled in interdisciplinary and applied aspects of AI, though their understanding of theoretical concepts revealed notable gaps. Slovenian students showed the greatest need for improvement, particularly in foundational knowledge, practical applications, and ethical considerations. The results also highlight universal challenges, such as gaps in understanding AI’s ethical and legal implications, underscoring the need for all educational systems to incorporate discussions on accountability and societal impacts into their curricula. These findings offer actionable recommendations for educational policy and curriculum design to enhance AI literacy globally and prepare students for the challenges and opportunities of an AI-driven world. This study contributes to the literature by enhancing the understanding of how AI literacy manifests across diverse educational systems, providing comparative insights to support globally relevant curriculum development and digital skills advancement.}, archive = {J_ACCESS}, author = {Maja Rožman and Dijana Oreški and Arun Elias and Minnu F. Pynadath and Polona Tominc}, doi = {10.1109/ACCESS.2025.3581128}, journal = {IEEE Access}, pages = {110671-110688}, shortjournal = {IEEE Access}, title = {AI literacy among university students: A comparative study of three Countries—Slovenia, croatia, and india}, volume = {13}, year = {2025}, }
• (2025). A federated learning-based framework for accurately identifying human activity in the environment. ACCESS, 13, 110648-110670. (www)

  @article{ , abstract = {Human Activity Recognition (HAR) refers to the detection of people’s activities during daily life using various types of sensors. Machine learning (ML) has contributed to recording many human activities and plays a meaningful role in Human Activity Recognition. Analysis of the data generated by HAR devices may involve deep learning models and algorithms of different kinds. These data are personal and may include some sensitive data. However, many applications of Human Activity Recognition are implemented using a centralized approach, which may negatively affect user information. Federated learning (FL)—a distributed machine learning approach—aims to distribute machine learning models to edge devices. For this study, we developed a system based on federated learning to support Human Activity Recognition through constructing a model for each client individually, using user-based training data and without data sharing. We built FL models, and conducted experiments based on multiple client divisions—namely, 2, 5, or 10 clients—using both model types. The deep learning models used were Convolutional Neural Network (CNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), and the performance measures used to evaluate these FL models were the Loss function and Accuracy. Our study yielded promising results: ResNet—which, to the best of our knowledge, has not been used in previous studies in this context—achieved the best results with five clients, attaining a 93.05% Accuracy.}, archive = {J_ACCESS}, author = {Nwadher Suliman Al-Blihed and Dina M. Ibrahim}, doi = {10.1109/ACCESS.2025.3583556}, journal = {IEEE Access}, pages = {110648-110670}, shortjournal = {IEEE Access}, title = {A federated learning-based framework for accurately identifying human activity in the environment}, volume = {13}, year = {2025}, }
• (2025). SDFA-net: Synergistic dynamic fusion architecture with deformable attention for UAV small target detection. ACCESS, 13, 110636-110647. (www)

  @article{ , abstract = {This study proposes an enhanced detection algorithm based on a real-time detection transformer framework (RT-DETR). Address the problem of leakage and misjudgment in small-target detection in aerial images. At the network architecture level, first, a lightweight multiscale attention feature extraction architecture is designed to improve the base module by introducing a heavily parameterized convolution and incorporating a multiscale attention mechanism. Second, the dynamic attention interaction mechanism (attention) is used to replace the traditional Attention-based Intrascale Feature Interaction (AIFI) module, which effectively alleviates the computational complexity problem and strengthens the feature interaction efficiency. In feature processing, a Dynamic Sampling Feature Enhancement Module (Dysample-SDI) is innovatively constructed to realize the refinement of multiscale features; whereas the Shallow Detail Fusion Module (SDFM) is designed to enhance the characterization ability of shallow features. In terms of the optimization strategy, the composite bounding box regression loss function (Focaler-MPD-IOU) was proposed to integrate the advantages of the multi-dimensional positioning evaluation index. The experimental results show that the improved algorithm improves the $mAP_{0.5}$ and $mAP_{0.5:0.95}$ metrics by 2.3% and 1.1%, respectively, on the VisDrone2019 benchmark dataset. Through cross-domain validation of the ALU and HIT-UAV datasets, the ablation experiments and comparative analysis fully validated the effectiveness of the algorithmic improvement, and its detection accuracy and robustness were significantly improved over the baseline model. This study provides a new technical solution for small target detection in complex scenes.}, archive = {J_ACCESS}, author = {Yunsong Qi and Hengwei Cao}, doi = {10.1109/ACCESS.2025.3578737}, journal = {IEEE Access}, pages = {110636-110647}, shortjournal = {IEEE Access}, title = {SDFA-net: Synergistic dynamic fusion architecture with deformable attention for UAV small target detection}, volume = {13}, year = {2025}, }
• (2025). PSO-optimized deep learning for ultra-precise corrosion detection on HDD Read/Write heads. ACCESS, 13, 110623-110635. (www)

  @article{ , abstract = {This paper presents a novel deep learning approach for automated detection and counting of corrosion pits on Hard Disk Drive (HDD) read/write heads using Scanning Electron Microscopy (SEM) images. A U-Net model optimized via Particle Swarm Optimization (PSO) is developed to enhance segmentation performance by automatically tuning hyperparameters. The methodology includes optimized SEM image acquisition, preprocessing (patch-based subdivision and expert annotation), PSO-driven hyperparameter selection, and post-processing with thresholding and connected component analysis for pit counting. Experimental results demonstrate that the PSO-optimized U-Net significantly outperforms standard U-Net, SegNet, and LinkNet models, achieving an F1-score of 79.60%, an IoU of 86.51%, and an accuracy of 99.77%. Additionally, the proposed method achieves 86.9% counting accuracy, surpassing human experts (72.7%) while processing images 15 times faster (180 seconds vs. 2700 seconds per image). These findings highlight the potential of PSO-optimized deep learning for improving HDD quality control by providing an accurate, efficient, and standardized solution for corrosion pit detection, ultimately reducing the risk of HDD failure and data loss.}, archive = {J_ACCESS}, author = {Chaiwat Punyammaree and Somyot Kaitwanidvilai}, doi = {10.1109/ACCESS.2025.3582602}, journal = {IEEE Access}, pages = {110623-110635}, shortjournal = {IEEE Access}, title = {PSO-optimized deep learning for ultra-precise corrosion detection on HDD Read/Write heads}, volume = {13}, year = {2025}, }
• (2025). Heuristic algorithms for the heterogeneous vehicle routing problem with time windows, customers priority, pickup and delivery. ACCESS, 13, 110605-110622. (www)

  @article{ , abstract = {Driven by an unprecedented surge in freight transportation and city logistics, this paper tackles a practical variant of the famous Vehicle Routing Problem that jointly accounts for the existence of a heterogeneous fleet of vehicles, customers’ priorities, soft time windows, pickup and delivery operations. A mathematical model is developed which seeks to establish the optimal routing and delivery schedule minimizing the total cost comprised of transportation costs, earliness-tardiness penalties, priority violations, and vehicle idleness cost. To cater for scalability issues, two computationally-efficient heuristic approaches that guarantee the attainment of high-quality solutions within reasonable CPU time are proposed. Both heuristics possess a two-phase structure, where the first phase yields highly prudent initial solutions employing a Greedy Randomized Adaptive Search Procedure (GRASP) in the first heuristic, and Priority-Based Ant Colony Optimization (PBACO) in the second heuristic. As for the second phase, both heuristics embrace a common Variable Neighborhood Search (VNS) algorithm that explores seven different neighborhoods to improve upon the initial solutions. Numerical experiments on test instances with up to 80 customers demonstrate the superiority of both heuristics in terms of CPU time and solution quality. While GRASP exhibits a slight computational advantage for instances involving 40 or more customers, the two heuristics yield solutions that are on average 0.25% and 0.60% away from the best-found solution, respectively, across all test instances. Sensitivity analysis also reveals that vehicles’ capacities, alongside other parameters, greatly impact the routing plan and the utilization of the vehicles.}, archive = {J_ACCESS}, author = {Moayad Tanash and Rami As'Ad}, doi = {10.1109/ACCESS.2025.3583106}, journal = {IEEE Access}, pages = {110605-110622}, shortjournal = {IEEE Access}, title = {Heuristic algorithms for the heterogeneous vehicle routing problem with time windows, customers priority, pickup and delivery}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based surface temperature prediction for a porous radiant burner using thermocouple-calibrated thermal infrared images. ACCESS, 13, 110595-110604. (www)

  @article{ , abstract = {This paper presents an artificial intelligence (AI)-based method for surface temperature prediction, applying a hybrid convolutional neural network and LASSO regression (CNN-LASSO) to thermocouple-calibrated thermal infrared (IR) images of a large cylindrical top-dome hollow porous radiant burner for the first time. Three CNN architectures, VGG-16, ResNet-50, and DenseNet-121, are evaluated as feature extractors within this framework. Applying the lean premixed CH4/air mixture at a fixed equivalent ratio $\varphi = 0.65$ , the burner is operated at an internal combustion mode over a range of power levels varying from 5 kW to 12 kW controlled by various flow rates of the pre-mixture. The corresponding surface temperature distributions of the porous radiant burner are recorded by an IR camcorder, calibrated by three K-type thermocouples positioned at the top-dome, middle, and bottom of the cylindrical hollow porous burner. The temperature measurement range and the emissivity of the IR camcorder are set at 300-2000°C and 0.95, respectively, having an average temperature of about 40°C difference/uncertainty as compared to that measured by the thermocouples. We evaluate the three CNN-LASSO hybrid models using 900 IR images with 9-fold cross-validation for training and the remaining 100 images for testing. All models exhibit stable and high prediction performance, achieving R2 values above 0.85 on the test set, thereby confirming the effectiveness and robustness of the proposed framework. Thus, the proposed AI-driven framework introduces an innovative and accurate approach to non-intrusive surface temperature measurements using infrared imaging.}, archive = {J_ACCESS}, author = {Hao-Yu Hsieh and Shenqyang Shy and Wei-Wun Wang and Yung-Chien Chou}, doi = {10.1109/ACCESS.2025.3583173}, journal = {IEEE Access}, pages = {110595-110604}, shortjournal = {IEEE Access}, title = {Deep learning-based surface temperature prediction for a porous radiant burner using thermocouple-calibrated thermal infrared images}, volume = {13}, year = {2025}, }
• (2025). A 20–40 GHz harmonic rejection enhanced frequency doubler in 150 nm GaAs p-HEMT. ACCESS, 13, 110584-110594. (www)

  @article{ , abstract = {This paper presents a broadband monolithic microwave integrated circuit (MMIC) frequency doubler within a 20-40GHz frequency operation range in a $0.15\mu $ m GaAs p-HEMT process. A broadband balun with a branch line compensation is employed in the frequency doubler to improve the suppression of the fundamental and odd harmonics signal. The branch line compensates for the phase mismatch of the broadband balun to excite more balanced differential signal, which effectively suppresses the unwanted fundamental and odd harmonic components; In addition, an integrated filtering technique with multiple zero-point control is introduced to the driver amplifier, enhancing the overall out-of-band harmonic suppression capability of the frequency doubler. The experimental results show that, with an input power of 0 to 1 dBm, the output power of the second harmonic of 16 to 17 dBm is achieved with the corresponding frequencies ranging from 20 to 40 GHz. The typical suppression of the fundamental signal and the third harmonic exceeds 38dBc, and the typical suppression of the fourth harmonic exceeds 30dBc. The power consumption is 100mA at +5V with chip are of 1.6mm $\ast 1.25$ mm.}, archive = {J_ACCESS}, author = {Fengyun Chen and Li Gao and Xiu Yin Zhang and Yanjie Wang}, doi = {10.1109/ACCESS.2025.3582612}, journal = {IEEE Access}, pages = {110584-110594}, shortjournal = {IEEE Access}, title = {A 20–40 GHz harmonic rejection enhanced frequency doubler in 150 nm GaAs p-HEMT}, volume = {13}, year = {2025}, }
• (2025). Few-shot incremental learning with context-aware spatial enhancement for image recognition. ACCESS, 13, 110569-110583. (www)

  @article{ , abstract = {Few-shot incremental learning (FSIL) refers to the ability of a model to learn new concepts from a limited number of labeled examples and gradually recognize novel categories with minimal supervision while retaining previously learned knowledge to prevent forgetting. To address the key challenges in FSIL, this paper proposes a novel Context-Aware Spatial Enhancement (CASE) framework, which improves feature representations by jointly leveraging global and local spatial information. Specifically, the global spatial enhancement module captures long-range dependencies to enrich semantic context, while the local spatial enhancement module applies Gaussian filtering to refine fine-grained details and suppress background noise. Additionally, a multi-head interaction block is introduced to model intricate relationships between spatial regions, effectively bridging global and local perspectives for robust and context-aware feature learning. CASE also incorporates prior knowledge from base categories to enhance adaptability to novel classes. This comprehensive design not only reduces background interference and highlights salient features but also improves generalization and stability in few-shot learning scenarios. Extensive experiments on benchmark datasets validate the effectiveness of the proposed approach, demonstrating competitive performance along with superior scalability and generalization capability in FSIL tasks.}, archive = {J_ACCESS}, author = {Heng Wu and Ze Yang and Zijun Zheng and Haiyang Wang and Wansong Wang}, doi = {10.1109/ACCESS.2025.3579766}, journal = {IEEE Access}, pages = {110569-110583}, shortjournal = {IEEE Access}, title = {Few-shot incremental learning with context-aware spatial enhancement for image recognition}, volume = {13}, year = {2025}, }
• (2025). A digital shadow for modeling, studying and preventing urban crime. ACCESS, 13, 110547-110568. (www)

  @article{ , abstract = {Crime is one of the greatest threats to urban security worldwide. A significant portion of the global population lives in countries where criminal activity poses serious challenges to public safety. In cities, most crimes tend to occur within dense urban environments, where social, economic, and spatial factors contribute to higher vulnerability. This paper presents the development and validation of a digital shadow platform for macro-modeling and simulating urban crime. This digital shadow has been constructed using data-driven agent-based modeling and simulation techniques, which are suitable for capturing dynamic interactions among individuals and with their environment. Our approach transforms and integrates well-known criminological theories and the expert knowledge of law enforcement agencies (LEA), policy makers, and other stakeholders under a theoretical model, which is in turn combined with real crime, spatial (cartographic) and socio-economic data into an urban model characterizing the daily behavior of citizens. The digital shadow has also been instantiated for the city of Málaga, for which we had over 300,000 reports available. This instance has been calibrated with those complaints and other geographic and socio-economic information of the city. To the best of our knowledge, our digital shadow is the first for large urban areas that has been calibrated with a large dataset of real crime reports and with an accurate representation of the urban environment. The performance indicators of the model after being calibrated, in terms of the metrics widely used in predictive policing, suggest that our simulated crime generation matches the general pattern of crime in the city according to historical data. Our digital shadow platform could be an interesting tool for modeling and predicting criminal behavior in an urban environment on a daily basis and, thus, a useful tool for policy makers, criminologists, sociologists, LEAs, etc. to study and prevent urban crime.}, archive = {J_ACCESS}, author = {Juan Palma-Borda and Eduardo Guzmán and María-Victoria Belmonte}, doi = {10.1109/ACCESS.2025.3580949}, journal = {IEEE Access}, pages = {110547-110568}, shortjournal = {IEEE Access}, title = {A digital shadow for modeling, studying and preventing urban crime}, volume = {13}, year = {2025}, }
• (2025). Unveiling hidden structures: Multi-platform IoT malware analysis using graph embeddings. ACCESS, 13, 110528-110546. (www)

  @article{ , abstract = {This paper explores the use of control-flow information for detecting IoT malware across three different platforms. We propose a pipeline which extracts Control-Flow Graphs (CFGs) from program binaries, projects them onto an embedding space, then computes the similarity between the programs, in an attempt to explore whether this approach could help identify malware families across different platforms. We explore three different methods for generating graph embeddings-at node, edge and graph level-and find the latter to produce the best discrimination between malware families across different platforms. Moreover, we propose an efficient method for computing graph similarities for graphs with multiple connected components, which achieves accuracy on par with the cubic Hungarian Algorithm matching approach. The evaluations conducted on a dataset containing clean samples and malware from 18 distinct families indicate that our CFG embedding similarity approach can easily identify most malware family samples belonging to the same family, for the same platform, obtaining AUC scores above 90%. The method shows strong potential for discriminating between malware families across different platforms. We are currently exploring cross-platform limitations and improvement points, and we share preliminary insights from our current work towards the end of the paper.}, archive = {J_ACCESS}, author = {Remus M. Petrache and Ciprian Oprişa and Camelia Lemnaru}, doi = {10.1109/ACCESS.2025.3583549}, journal = {IEEE Access}, pages = {110528-110546}, shortjournal = {IEEE Access}, title = {Unveiling hidden structures: Multi-platform IoT malware analysis using graph embeddings}, volume = {13}, year = {2025}, }
• (2025). Machine learning in ambient assisted living for enhanced elderly healthcare: A systematic literature review. ACCESS, 13, 110508-110527. (www)

  @article{ , abstract = {As the global population ages, Ambient Assisted Living (AAL) systems have become essential in supporting the elderly to maintain independence and quality of life. Such systems integrate advanced technologies such as machine learning (ML), internet of things (IoT), and sensors to enhance safety and healthcare delivery. However, deploying such technologies raises significant challenges, especially in managing privacy, ensuring ethical compliance, and gaining user acceptance. This systematic literature review (SLR) explores the current state and advancements in AAL technologies, with a specific focus on their applications in elderly care. It synthesizes current methodologies, including predictive analytics, Explainable AI (XAI), and Generative AI (GenAI), while also evaluating the role of vision-based systems and multi-modal data fusion. It examines how such technologies are implemented to improve lives while also highlighting critical areas requiring attention, particularly privacy and ethical considerations. The review methodically analyzes articles and papers from the year 2020, selected based on their relevance to AAL technologies, their use of ML algorithms, and their focus on elderly care. The findings reveal the need for interpretability in AI-driven decisions and the role of GenAI in synthetic data generation and personalized conversational assistants. While ML and IoT significantly enhance AAL systems through predictive healthcare and personalized interventions, they also pose substantial privacy risks. The review identifies privacy as a critical concern due to the sensitive nature of the data collected and the vulnerabilities inherent in digital systems. Issues around data privacy, security breaches, and the need for robust privacy-preserving mechanisms are recurrent themes. This SLR illustrates the potential of AAL systems in elderly care and emphasizes the crucial requirement of addressing privacy and ethical issues to ensure such technologies are both beneficial and secure. The findings serve as a foundation for understanding the current state of AAL technologies and guide future advancements in elderly healthcare.}, archive = {J_ACCESS}, author = {Aabid A. Mir and Ahmad S. Khalid and Shahrulniza Musa and Mohammad Faizal Ahmad Fauzi and Normy Norfiza Abdul Razak and Tong Boon Tang}, doi = {10.1109/ACCESS.2025.3580961}, journal = {IEEE Access}, pages = {110508-110527}, shortjournal = {IEEE Access}, title = {Machine learning in ambient assisted living for enhanced elderly healthcare: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization of a spoke type synchronous machine with ferrite magnets considering torque ripple and demagnetization. ACCESS, 13, 110497-110507. (www)

  @article{ , abstract = {Electric machines are used in various environments with noise and vibration requirements. Torque ripple is a key design parameter that is considered when evaluating these. While rotor skewing is a common practice for reducing torque ripple in induction machines (IM), strategies for permanent magnet synchronous machines (PMSM) still vary significantly. This paper enhances a Spoke-Type PMSM that utilizes ferrite permanent magnets (PMs). It focuses on torque ripple at maximum torque at base speed, maximum torque, and the risk of permanent demagnetization. The study investigates torque ripple reduction by exploring rotor surface geometric parameters, allowing for the examination of a wide range of rotor surface configurations. The study is developed using experimentally validated FEM models. Furthermore, the study shows that, for Spoke Type PMSM with ferrite PMs, the inclusion of demagnetization in the optimization process alters the optimal design by skewing the Pareto front toward low-performing solutions. Instead, including a performance metric that represents the demagnetizing effects of a fault is sufficient to achieve a high-performing design with resilience towards demagnetization. Additionally, the results also show that the Spoke Type PMSM with ferrite PMs has another advantage apart from being rare-earth free; it can be designed to have minimal torque ripple without significant performance degradation.}, archive = {J_ACCESS}, author = {Marcelo D. Silva and Sandra Eriksson}, doi = {10.1109/ACCESS.2025.3583163}, journal = {IEEE Access}, pages = {110497-110507}, shortjournal = {IEEE Access}, title = {Multi-objective optimization of a spoke type synchronous machine with ferrite magnets considering torque ripple and demagnetization}, volume = {13}, year = {2025}, }
• (2025). 3D scene segmentation: A comprehensive survey and open problems. ACCESS, 13, 110457-110496. (www)

  @article{ , abstract = {This paper presents a detailed review of recent advancements in 3D indoor scene segmentation driven by deep learning techniques. It provides an overview of existing segmentation models, examines various data representations, data collection methods, augmentation techniques, and available datasets. A comparative analysis of loss functions and overview of evaluation metrics is conducted to highlight their impact on segmentation performance. Unlike previous surveys, this work introduces a new classification of data augmentation techniques and proposes two novel classification approaches for 3D instance and semantic segmentation. Furthermore, it unifies 3D semantic instance segmentation and 3D panoptic segmentation within an existing framework. The paper also identifies key challenges and open research directions, providing insights into future advancements in the field.}, archive = {J_ACCESS}, author = {Slavcho Neshev and Krasimir Tonchev and Agata Manolova and Vladimir Poulkov}, doi = {10.1109/ACCESS.2025.3583136}, journal = {IEEE Access}, pages = {110457-110496}, shortjournal = {IEEE Access}, title = {3D scene segmentation: A comprehensive survey and open problems}, volume = {13}, year = {2025}, }
• (2025). Enhancing security in DNP3 communication for smart grids: A segmented neural network approach. ACCESS, 13, 110436-110456. (www)

  @article{ , abstract = {The Distributed Network Protocol 3 (DNP3) protocols focus on securing critical infrastructure communication in sectors such as energy and supervisory control and data acquisition (SCADA) systems. The security of DNP3 is paramount, employing features such as encryption, authentication, and secure key management to safeguard against cyber threats. The robust security framework ensures the reliability and integrity of data exchange, fortifying the resilience of industrial control systems against potential cyber-attacks. This study investigates Smart Grid (SG) DNP3 communication security and provides a deep learning-driven approach to detect and prevent cyber-attacks in the SG. Securing communication in SG is a critical challenge, particularly for protocols such as DNP3, which is essential to SCADA systems. This study explores the potential for enhancing intrusion detection in DNP3 communications and the associated industrial control system traffic through the application of state-of-the-art deep learning (DL) algorithms. A Segmented Neural Network (SNN) architecture is employed to analyze the DNP3 dataset, which is captured using CICFlowMeter3 and a DNP3 Parser, integrating Deep Neural Network (DNN), Long Short Term Memory (LSTM), and Random Neural Network (RandNN) models. In CICFlowMeter3, the model achieved an accuracy of 99.86%, whereas, on the DNP3 Parser, it improved to 99.75%, demonstrating outstanding performance. These findings show that the proposed framework is efficient and resilient with complicated and varied data streams. The results show that the proposed SNN-based solution improved the security and resilience of SG operations to detect anomalies in industrial control systems (ICS) in real-time.}, archive = {J_ACCESS}, author = {Shahid Allah Bakhsh and Muhammad Shahbaz Khan and Oumaima Saidani and Nada Alasbali and S. Qasim Abbas and Muhammad Almas Khan and Jawad Ahmad}, doi = {10.1109/ACCESS.2025.3580507}, journal = {IEEE Access}, pages = {110436-110456}, shortjournal = {IEEE Access}, title = {Enhancing security in DNP3 communication for smart grids: A segmented neural network approach}, volume = {13}, year = {2025}, }
• (2025). Enhanced design of a hand exoskeleton: Balancing force transmission and actuation with evolutionary algorithms. ACCESS, 13, 110418-110435. (www)

  @article{ , abstract = {Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study addresses significant challenges of ensuring safety and maximizing performance in the design process for an underactuated hand exoskeleton intended for physical rehabilitation. We first implemented a single objective optimization problem by maximizing force transmission from the actuator to the finger joints, then expanded into multi-objective optimization by also minimizing the variance of torques rendered on the finger joints and the actuator displacement needed. The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Our simulation results and statistical analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process – at the cost of complicating the optimization algorithm and computational burden. These findings underline the critical importance of performing proper optimization techniques while designing exoskeletons, as well as providing a significant improvement to this specific robotic design that could provide more effective rehabilitation therapies and augmented human-robot interactions.}, archive = {J_ACCESS}, author = {Baris Akbas and Huseyin Taner Yuksel and Aleyna Soylemez and Mine Sarac and Fabio Stroppa}, doi = {10.1109/ACCESS.2025.3582495}, journal = {IEEE Access}, pages = {110418-110435}, shortjournal = {IEEE Access}, title = {Enhanced design of a hand exoskeleton: Balancing force transmission and actuation with evolutionary algorithms}, volume = {13}, year = {2025}, }
• (2025). A normalized exponential piecewise chaotic system (NEPCS) and DNA image cryptography using SHA-256. ACCESS, 13, 110392-110417. (www)

  @article{ , abstract = {One-dimensional (1D) chaotic systems are crucial in cryptography to encrypt digital images effectively by transforming them into noise. Despite their simplicity and computational efficiency, 1D chaotic systems often struggle with short periodicity, narrow control parameter ranges, less complex phase space attractors, and regularity in their output, which limit their applicability in cryptography. A novel 1D piecewise chaotic map, called Normalized Exponential Piecewise Chaotic System (NEPCS), is proposed with an extensive range of control parameters $\lambda $ and prime number p. It has a high score for Lyapunov exponent $(\gt 7.88)$ , sample entropy score of 2.2137 and correlation dimension score exceeding 1.1. The NEPCS is evaluated using the SP800-22 test suite recommended by the National Institute of Standards and Technology (NIST). The qualifying rate for the NIST test is > 0.49, proving its high quality of randomness. To validate the efficacy of the NEPCS, an image encryption technique is introduced, leveraging the SHA-256 hash function used iteratively on keyboard inputs and plain images to derive initial seeds for NEPCS. The encryption process begins by permuting the pixels of a color image and converting them into DNA bases. The cipher block chaining mode (CBC) of substitution is sandwiched between the keyless transpositions. The exclusive-or and addition operations are alternately applied to substitute the DNA bases. The presented image cipher has NPCR >99.59%, UACI exceeding 33.40%, $\chi ^{2}\lt 285.60$ , and entropy $\approx 7.9999$ scores while resisting communication noises.}, archive = {J_ACCESS}, author = {Hammad Safdar Gill and Madiha Amjad and Muhammad Faheem and Aqeel Ur Rehman and Umber Rana and Amjad Rehman Khan and Rab Nawaz Bashir}, doi = {10.1109/ACCESS.2025.3582318}, journal = {IEEE Access}, pages = {110392-110417}, shortjournal = {IEEE Access}, title = {A normalized exponential piecewise chaotic system (NEPCS) and DNA image cryptography using SHA-256}, volume = {13}, year = {2025}, }
• (2025). Robust optimal design of SPMSM considering manufacturing tolerances of permanent magnets. ACCESS, 13, 110379-110391. (www)

  @article{ , abstract = {Servo motors are widely used in systems that require high speed, high precision, and accurate position control. One of the critical aspects of servo motor design is the minimization of cogging torque to reduce motor vibration and noise. To achieve this, surface permanent magnet synchronous motors (SPMSMs) are commonly employed. However, manufacturing tolerances that arise during the production process of servo motors can significantly affect performance. In particular, variations in cogging torque caused by permanent magnet manufacturing tolerances can lead to motor vibration and noise, negatively impacting system stability. Therefore, robust optimization design is essential to suppress cogging torque variations and ensure system stability. This paper presents a robust optimization design for SPMSMs to mitigate the cogging torque variations caused by permanent magnet manufacturing tolerances. First, experiments and the finite element method (FEM) were used to analyze cogging torque while considering the manufacturing tolerances of permanent magnets. Subsequently, robust optimization was performed using the Taguchi method based on the design of experiments (DOE).}, archive = {J_ACCESS}, author = {Jae-Hyeon Yeo and Jun-Kyu Kang and Jin Hur}, doi = {10.1109/ACCESS.2025.3582397}, journal = {IEEE Access}, pages = {110379-110391}, shortjournal = {IEEE Access}, title = {Robust optimal design of SPMSM considering manufacturing tolerances of permanent magnets}, volume = {13}, year = {2025}, }
• (2025). Inherent third-harmonic circulating current in delta-CHB STATCOM: Analysis and compensation. ACCESS, 13, 110365-110378. (www)

  @article{ , abstract = {The Delta-connected Cascaded H-Bridge (Delta-CHB) is one of the most commonly adopted topologies for Static Synchronous Compensator (STATCOM) systems. Due to the oscillating instantaneous power resulting from the multiplication of sinusoidal voltage and current, a second-order harmonic arises in the Delta-CHB dc-link. In addition to increasing the required capacitance, it introduces a third-harmonic circulating current (THCC), increasing power losses and semiconductor stress. This paper analytically describes this phenomenon and compares two suppression strategies. The proposed model stands out for its simplicity and effectiveness in STATCOM applications, providing a straightforward approach to estimating both the circulating current and the capacitor voltage ripple, also accounting for their mutual influence. The capacitor voltage model exhibited errors lower than 4.5% compared to the simulation and lower than 6.9% compared to the experiment. Similarly, the circulating current model showed an error lower than 3.1% compared to the simulation and under 15% in the experiment. The first suppression strategy analyzed, which involves adding a resonant controller for the circulating current, was able to practically eliminate the circulating current. The second suppression strategy, based on normalization, reduced the circulating current by 30.27% in the experiment, demonstrating sensitivity to the measurement and control implementation delays.}, archive = {J_ACCESS}, author = {João Victor Guimarães França and Dayane Do Carmo Mendonça and Heverton Augusto Pereira and Yu Jin and Qian Xiao and Allan Fagner Cupertino}, doi = {10.1109/ACCESS.2025.3582584}, journal = {IEEE Access}, pages = {110365-110378}, shortjournal = {IEEE Access}, title = {Inherent third-harmonic circulating current in delta-CHB STATCOM: Analysis and compensation}, volume = {13}, year = {2025}, }
• (2025). Detection of valvular heart diseases from PCG signals using machine and deep learning models: A review. ACCESS, 13, 110344-110364. (www)

  @article{ , abstract = {Artificial intelligence (AI) predictions are widely used to address challenges in the heart health sector, such as providing clinical decision support. Early detection of valvular heart disease (VHD) is effective in reducing critical cardiac problems and sudden death. This review proposes investigating methods for automatically diagnosing heart disease from phonocardiogram (PCG) signals using various advanced Machine Learning (ML) and Deep Learning (DL) models. This study also aimed to provide an overview of ongoing research on PCG signal processing and to pinpoint areas that warrant additional investigation. Several Scopus-indexed research forums, such as IEEE, Science Direct, Frontiers, MDPI, and Computing in Cardiology, as well as databases such as the Michigan Heart Sound Library (MHSL), Github, and Physio-Net on the classification of AI-related PCG signals, were considered to construct this review with 199 relevant research articles covering the period from 2016 to 2024. The early diagnosis and prediction of heart valve disease are the domains in which machine learning and deep learning models were most commonly used. The performance of earlier detection has increased significantly according to advanced techniques of PCG signal classification. A limited number of studies have compared and analyzed categorization measures such as F score, sensitivity, accuracy, precision, and specificity. However, a mean increase in the predicted accuracy was observed, depending on the various advanced techniques and classifiers used.}, archive = {J_ACCESS}, author = {Ayappasamy Kannan and Manob Jyoti Saikia and Sushant Kumar and Sumit Datta}, doi = {10.1109/ACCESS.2025.3583263}, journal = {IEEE Access}, pages = {110344-110364}, shortjournal = {IEEE Access}, title = {Detection of valvular heart diseases from PCG signals using machine and deep learning models: A review}, volume = {13}, year = {2025}, }
• (2025). Fully-woven textile implementation of a circularly polarized rectenna for wireless power transfer applications. ACCESS, 13, 110336-110343. (www)

  @article{ , abstract = {This work describes a fully-woven textile rectenna for Wireless Power Transfer applications in the 2.4 GHz frequency band. The radiating element is based on a square patch fed through an offset T-match network to which the rectifier is connected. This feeding structure provides impedance matching with the rectifier without requiring additional networks, which results in a compact layout. On the other hand, its particular location at one or the patch corners is exploited to generate circular polarization. The rectifier is based on a single Schottky diode to provide a layout suitable for textile integration. It is mounted on a carrier thread which is then woven together with the rest of the textile structure of the antenna, providing a large degree of integration and robustness. The measured RF-DC conversion efficiency is about 50% for -10 dBm available power at the rectifier input, which is comparable to state-of-the-art values reported for rectennas implemented in conventional rigid substrates, demonstrating the viability of woven technology in the microwave frequency band. Furthermore, the frequency weighted and the power frequency weighted conversion efficiency figures of merit were evaluated, showing a considerably improvement with respect to previously reported works.}, archive = {J_ACCESS}, author = {Miguel Fernández and Carlos Vázquez and Samuel Ver Hoeye}, doi = {10.1109/ACCESS.2025.3582014}, journal = {IEEE Access}, pages = {110336-110343}, shortjournal = {IEEE Access}, title = {Fully-woven textile implementation of a circularly polarized rectenna for wireless power transfer applications}, volume = {13}, year = {2025}, }
• (2025). Comparing blind image quality metrics for reliable image assessment. ACCESS, 13, 110322-110335. (www)

  @article{ , abstract = {Reliable image quality assessment is essential not only in digital photography but also as a key metric for evaluating the performance of algorithms and models designed for image quality enhancement or generation. In recent years, a wide range of image quality assessment metrics, both traditional and learning-based, have been proposed, making it a challenge to select the appropriate method for a given task. This study presents a comparative analysis between five widely used traditional no-reference image quality assessment techniques and five machine learning-based approaches, evaluating their effectiveness in computing image quality scores. The evaluation is carried out comprehensively using a set of standard and advanced performance metrics. Furthermore, we analyze how characteristics of the training datasets, such as score distribution, influence model performance. The machine learning models considered vary significantly in architectural complexity, in terms of both the number of layers and parameters, and we investigate whether this variability has a considerable impact on prediction accuracy. The analysis also extends to non-photographic imagery, with a comparative evaluation of the methods on hyperspectral satellite image visualizations. For full transparency and reproducibility of the current study, all training parameters and hardware specifications are reported.}, archive = {J_ACCESS}, author = {Cristian George Fieraru and Maria Biserică and Ioana Cristina Plajer and Mihai Ivanovici}, doi = {10.1109/ACCESS.2025.3583029}, journal = {IEEE Access}, pages = {110322-110335}, shortjournal = {IEEE Access}, title = {Comparing blind image quality metrics for reliable image assessment}, volume = {13}, year = {2025}, }
• (2025). Diagnosis of device exception based on causality of device indicators. ACCESS, 13, 110307-110321. (www)

  @article{ , abstract = {Existing intelligent exception diagnosis methods face challenges in unstable feature extraction and difficulty detecting anomalies in high-dimensional spaces. To address these limitations, we propose CGNN—a novel anomaly detection framework integrating causal reasoning with graph neural networks. The framework operates in three stages: 1) Causality Detection (CD) employs the LASAR algorithm to construct sparse causal graphs from monitoring variables. 2) Prediction of Device Status (PODS) leverages GraphSAGE to extract spatio-temporal features from causal graphs for state prediction. 3) Diagnosis of Exception (DOE) utilizes kernel density estimation (KDE) for distribution-agnostic anomaly scoring. Experimental validation on the extended-TE dataset demonstrates that our CGNN framework outperforms baseline algorithms, achieving faster fault identification and higher prediction accuracy. Crucially, CGNN requires minimal prior causal knowledge and adapts to arbitrary data distributions, significantly enhancing interpretability and reliability in complex industrial systems.}, archive = {J_ACCESS}, author = {Zhaohui Wang and Yan Wei and Longhua Shang and Shiwei Zhang and Shixiong Bao and Zhengren Li}, doi = {10.1109/ACCESS.2025.3582346}, journal = {IEEE Access}, pages = {110307-110321}, shortjournal = {IEEE Access}, title = {Diagnosis of device exception based on causality of device indicators}, volume = {13}, year = {2025}, }
• (2025). BCAST IDS: A novel network intrusion detection system for broadcast networks. ACCESS, 13, 110289-110306. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDSs) play a pivotal role in cybersecurity by identifying malicious activities through network traffic information to safeguard network infrastructures and digital assets from disruptions and other negative consequences. A modern approach to enhancing the capabilities of NIDSs is the use of machine learning (ML) algorithms that predict attacks based on data. This study introduces a novel and lightweight NIDS called Broadcast IDS (BCAST IDS) that uses specific network traffic patterns and the Isolation Forest algorithm to detect anomalies in broadcast networks. It can also act as a canary token and dynamically learn new network flow patterns based on the network environment. The practical applications are thoroughly explored, and then the solution is deployed within an enterprise network for real-time monitoring and detection using Raspberry Pi devices. The findings show that the tool effectively recognizes certain classes of network scanning attempts that worms and attackers typically perform to find targets, denial-of-service (DoS) attacks, and critical network misconfigurations, reflecting the robustness of its anomaly detection capabilities. Furthermore, the system not only provides significant advantages over other NIDS schemes but also presents strong resistance to various evasion techniques and adversarial attacks.}, archive = {J_ACCESS}, author = {Javier Gombao}, doi = {10.1109/ACCESS.2025.3582893}, journal = {IEEE Access}, pages = {110289-110306}, shortjournal = {IEEE Access}, title = {BCAST IDS: A novel network intrusion detection system for broadcast networks}, volume = {13}, year = {2025}, }
• (2025). A novel nonparallel hyperplane based extreme learning machine for classification. ACCESS, 13, 110268-110288. (www)

  @article{ , abstract = {Recently, researchers have developed nonparallel variants of both extreme learning machine (ELM) and support vector machine (SVM) classifiers to improve learning speed and generalization. However, these nonparallel methods typically solve two SVM-type optimization problems. Thus far, no existing nonparallel ELM variants introduce nonparallel hyperplanes into the traditional ELM framework or use a unified optimization problem. To address this, the paper proposes a nonparallel hyperplane based extreme learning machine (NHELM), which solves a unified ELM-type optimization problem. The proposed nonparallel ELM employs nonparallel hyperplanes to improve the traditional ELM for better generalization. In NHELM, nonparallel hyperplanes are designed to align with class-specific data distributions to construct a more effective angle bisector (separating hyperplane) that improves class separation. Experiments on 20 KEEL binary benchmark datasets and six MedMNIST medical image datasets using both linear and gaussian kernels show that NHELM achieves up to a 3.4% improvement in classification performance over traditional ELM, and a 2.2% improvement over existing nonparallel ELM variants, based on the averages of metrics such as accuracy, G-mean, and area under the curve (AUC). The results demonstrate that the proposed method improves pattern learning and generalization, making it highly useful for diverse real-world classification problems.}, archive = {J_ACCESS}, author = {Arvind Kumar and Sanyam Shukla and Manasi Gyanchandani}, doi = {10.1109/ACCESS.2025.3577160}, journal = {IEEE Access}, pages = {110268-110288}, shortjournal = {IEEE Access}, title = {A novel nonparallel hyperplane based extreme learning machine for classification}, volume = {13}, year = {2025}, }
• (2025). Accurate arc modeling of single-phase arc grounding fault in distribution network and research on dynamic arc energy. ACCESS, 13, 110255-110267. (www)

  @article{ , abstract = {Aiming at the problem of energy exchange in the arc channel of single-phase arc grounding in distribution networks, this paper establishes a three-dimensional, single-phase arc grounding field-circuit coupled mathematical model based on magnetohydrodynamics. It analyzes the process of arc energy exchange, considers the competitive relationship among the input energy, dissipated energy, and the energy of arc itself under varying wind speeds, and conducts artificial single-phase arc grounding tests in actual distribution networks to validate the accuracy of the arc mathematical model. The research findings indicate that the simulation calculations begin with a voltage phase angle of 0°. At a wind speed of 1 m/s, the energy of arc itself increases in a step-like manner, and the arc burns stably. At wind speeds of 3 m/s and 5 m/s, the energy of arc itself exhibits a phenomenon of repeated fluctuations, rising to a peak before rapidly declining. At this moment, the arc does not extinguish; rather, the extinguishing occurs at the first current zero-crossing point following this peak. Higher wind speeds facilitate the extinguishing of the arc. The first extinguishing times corresponding to wind speed of 3 m/s and 5 m/s are 284 ms and 204 ms, respectively. After the arc extinguishes, a re-ignition phenomenon occurs, with higher wind speeds resulting in a longer interval between extinguishing and re-ignition.}, archive = {J_ACCESS}, author = {Yan Li and Jianyuan Xu and Zhenxin Geng}, doi = {10.1109/ACCESS.2025.3574767}, journal = {IEEE Access}, pages = {110255-110267}, shortjournal = {IEEE Access}, title = {Accurate arc modeling of single-phase arc grounding fault in distribution network and research on dynamic arc energy}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of adaptive fuzzy super-twisting sliding mode controller for uncertain 2-DOF robotic manipulator. ACCESS, 13, 110241-110254. (www)

  @article{ , abstract = {This paper proposes a robust adaptive fuzzy supertwisting sliding mode controller (AFST-SMC) to enhance the trajectory tracking capability of a 2-DOF robotic manipulator. The novel controller compensates for system uncertainties by adaptively adjusting control parameters based on the angular position of the target joints. To evaluate the controller’s performance, the dynamic model of a two-link manipulator is derived using the Lagrangian method, and stability analysis of the system is considered according to the Lyapunov Direct Method. Modeling and simulation are performed in Matlab®/Simulink®. Performance is assessed by comparing the proposed AFST-SMC with conventional sliding mode controller (SMC), super-twisting sliding mode controller (ST-SMC), and adaptive fuzzy sliding mode controller (AF-SMC), using integral absolute error (IAE) as a performance criterion. The simulation results indicate that the AF-STSMC successfully reduces the tracking error. For link 1, the IAE is reduced to 0.01465 rad, representing a 99.9% reduction compared to SMC (14.24 rad), an 85% reduction compared to ST-SMC (0.0977 rad), and a 78.5% reduction compared to AF-SMC (0.06819 rad). Similarly, in link 2, the AF-STSMC gives an IAE of 0.003862 rad that is equivalent to a 99.93% decrease with respect to SMC (5.723 rad), a 98.7% increase over ST-SMC (0.306 rad), and a 91.2% decrease compared to AF-SMC (0.04391 rad). It is therefore clear that the designed AFST-SMC performs better than the controllers in terms of accuracy and robustness to achieve the required performance specifications of accurate trajectory tracking by robotic manipulators.}, archive = {J_ACCESS}, author = {Hayleyesus Girma Dirara and Feleke Tsegaye Yareshe and Chala Merga Abdissa}, doi = {10.1109/ACCESS.2025.3581449}, journal = {IEEE Access}, pages = {110241-110254}, shortjournal = {IEEE Access}, title = {Design and analysis of adaptive fuzzy super-twisting sliding mode controller for uncertain 2-DOF robotic manipulator}, volume = {13}, year = {2025}, }
• (2025). Dataset construction using item response theory for educational machine learning competitions. ACCESS, 13, 110226-110240. (www)

  @article{ , abstract = {Machine learning has been integrated into numerous applications and has emerged as one of the most transformative technologies in our daily lives. In recent years, the number of individuals studying machine learning has grown substantially, leading to the emergence of numerous educational competitions focused on building expertise in machine learning. In these competitions, the participants are tasked with constructing machine learning (ML) models. However, the dataset used to compare the performances of competing models is often selected arbitrarily, causing discrepancies between the dataset and participants’ skill levels. This can result in competition outcomes that fail to accurately reflect the participants’ abilities. We have developed a framework for generating image datasets that enable the abilities of competition participants to be accurately assessed. Specifically, we introduce the use of item response theory (IRT), commonly used in test creation and ability assessment, to estimate parameters such as item discrimination and difficulty for each image in existing datasets. Additionally, we utilize a conditional variational autoencoder (CVAE) that generates images with specific parameter values. These parameter values are generated based on the ability distribution of the competition participants and used to generate a dataset aligned with their ability distribution. To evaluate the effectiveness of the proposed framework, we conduct experiments using 810 ML models automatically created using 6 parameters with multiple values. Comparison of their performances between the original and the generated dataset showed that the latter was more effective in differentiating model performance. Unlike conventional IRT-based methods, which require human effort for dataset generation, our proposed framework fully automates the dataset generation process. By automating dataset generation, our approach streamlines the organization of ML competitions and ensures that datasets are well-suited to participants’ skill levels. This automation reduces the challenges of hosting competitions, promoting their broader adoption in educational settings.}, archive = {J_ACCESS}, author = {Takeaki Sakabe and Yuko Sakurai and Emiko Tsutsumi and Satoshi Oyama}, doi = {10.1109/ACCESS.2025.3582016}, journal = {IEEE Access}, pages = {110226-110240}, shortjournal = {IEEE Access}, title = {Dataset construction using item response theory for educational machine learning competitions}, volume = {13}, year = {2025}, }
• (2025). Optimized time series feature selection for manufacturing AI: Reducing complexity and improving classification accuracy. ACCESS, 13, 110208-110225. (www)

  @article{ , abstract = {Although AI enhances productivity and quality in manufacturing, real-time validation remains difficult due to the complexity and high-dimensionality of time series data under strict inference time requirements. To address this, we propose two time series feature selection strategies. First, we extract meaningful variables by analyzing the time series patterns of non-defective and defective products. Through STL decomposition, we eliminate high-frequency noise and seasonal effects, and transform conventional feature-wise PCA into a production-wise approach by independently analyzing each sensor across products. We further quantify the pattern differences between non-defective and defective products for each sensor using DTW similarity, and perform clustering to systematically select significant variables. Second, we reconstruct lightweight statistical features through ANOVA and correlation analysis to further reduce feature dimensionality. In experiments on real-world manufacturing datasets, our techniques improve predictive performance and achieve reduced inference latency, successfully satisfying strict real-time production constraints.}, archive = {J_ACCESS}, author = {Jaeseok Jang and Chanyoung Jung and Hail Jung}, doi = {10.1109/ACCESS.2025.3582216}, journal = {IEEE Access}, pages = {110208-110225}, shortjournal = {IEEE Access}, title = {Optimized time series feature selection for manufacturing AI: Reducing complexity and improving classification accuracy}, volume = {13}, year = {2025}, }
• (2025). A semantic approach for linked model, data, and dataspace cards. ACCESS, 13, 110194-110207. (www)

  @article{ , abstract = {In artificial intelligence, the significance of thorough documentation of models and datasets for publication is underestimated. However, due to the rising trend in the explainability and fairness of AI models, frameworks like Model Cards, Service Cards and Data Cards have emerged to facilitate understanding and reusing those models and datasets. Moreover, the Dataspace concept integrates these resources into Dataspace Cards, a comprehensive framework that systematically captures and organises crucial information to guide model and data selection for a specific application. This paper advocates a Semantic Web approach for transforming Model/Data Cards into Linked Data or knowledge graphs within a Dataspace, rendering them machine-readable and interoperable. A significant contribution is the development of a vocabulary that unifies Data, Model and Dataspace Card ontologies, enhancing consistent documentation and understanding of the Dataspace design. The paper further demonstrates the applicability of the proposed schema in various use cases, including bias detection in BERT-base-uncased and Large Language Models. Additionally, we propose a conceptual semantic approach, examined in-depth for sentiment and emotion analysis, to highlight how extended Dataspace Cards can improve applicability and outcomes. We found that this unified, ontology-driven approach results in more consistent metadata linking and more fine-grained bias detection in BERT-based-uncased than standalone documentation tools relying solely on Model or Data Cards. Furthermore, compared to existing frameworks, the richer interlinking capabilities of our proposed Dataspace Cards also facilitated easier traceability of performance outcomes, thereby ultimately fostering higher trustworthiness and reusability of AI resources.}, archive = {J_ACCESS}, author = {Andy Donald and Apostolos Galanopoulos and Edward Curry and Emir Muñoz and Ihsan Ullah and M. A. Waskow and Manan Kalra and Sagar Saxena and Talha Iqbal}, doi = {10.1109/ACCESS.2025.3572211}, journal = {IEEE Access}, pages = {110194-110207}, shortjournal = {IEEE Access}, title = {A semantic approach for linked model, data, and dataspace cards}, volume = {13}, year = {2025}, }
• (2025). A hybrid control scheme for a single-stage LED driver with inherent power factor correction and low flicker. ACCESS, 13, 110185-110193. (www)

  @article{ , abstract = {This article presents the design and implementation of a hybrid control scheme for a single-stage LED driver. The power stage comprises a class-DE resonant converter coupled with a charge-pump circuit that achieves inherent power factor correction. The end-to-end driver structure incorporates a single half-bridge circuit for switching, resulting in a compact cost-effective solution. The driver operates with zero-voltage switching (ZVS) across the entire load range, allowing for increased switching frequency with high efficiency. A hybrid control scheme which uses both burst-mode (BM) and frequency-mode (FM) actuation is employed. The BM actuation uses ON/OFF operation of the switching half-bridge, whereas the FM actuation modulates the switching frequency of the converter, thus regulating the gain of the resonant tank in the power stage. Experimental results demonstrate a power factor (PF) of 0.97, a total harmonic distortion (THD) of 16.8% and a 100-Hz output current flicker content of 6.75% at 30 W of output power. The driver satisfies the requirements set by the IEC 61000-3-2 class-C and IEEE 1789–2015 standards for input current harmonics magnitudes and output current flicker, respectively.}, archive = {J_ACCESS}, author = {Christian K. Kruuse and Nicolai J. Dahl and Bima N. Sanusi and Michael A. E. Andersen and Ahmed M. Ammar}, doi = {10.1109/ACCESS.2025.3583266}, journal = {IEEE Access}, pages = {110185-110193}, shortjournal = {IEEE Access}, title = {A hybrid control scheme for a single-stage LED driver with inherent power factor correction and low flicker}, volume = {13}, year = {2025}, }
• (2025). Depth-guided monocular object pose estimation for warehouse automation. ACCESS, 13, 110166-110184. (www)

  @article{ , abstract = {Accurate object pose estimation is essential for warehouse automation, enabling tasks such as robotic picking, sorting, and inventory management. Current state-of-the-art approaches rely on both color (RGB) and depth (D) images, as depth information provides critical geometric cues that enhance object localization and improve robustness against occlusions. However, RGBD-based methods require specialized depth sensors, which can be costly and may not function reliably in warehouse environments with reflective surfaces, varying lighting conditions, or sensor occlusions. To address these limitations, researchers have explored RGB-only approaches, but the absence of depth cues makes it challenging to handle occlusions, estimate object geometry, and differentiate between textureless or highly similar objects, which are common in warehouses. In this paper, we propose a novel end-to-end depth-guided object pose estimation method tailored for warehouse automation. Our approach leverages both depth and color images during training but relies solely on RGB images during inference. Depth images are used to supervise the training of a depth estimation network, which generates initial depth-aware features. These features are then refined using our proposed depth-guided feature enhancement module to improve spatial understanding and robustness. The enhanced features are subsequently utilized for keypoint-based 6D object pose estimation. By integrating depth-guided feature learning, our method significantly enhances pose estimation accuracy, especially in cluttered warehouse environments with severe occlusions and textureless objects. Extensive experiments on warehouse-specific datasets, as well as standard benchmark datasets, demonstrate that our approach outperforms existing RGB-based methods while maintaining real-time inference speeds, making it a highly practical solution for real-world warehouse automation applications.}, archive = {J_ACCESS}, author = {Phan Xuan Tan and Dinh-Cuong Hoang and Anh-Nhat Nguyen and Eiji Kamioka and Ta Huu Anh Duong and Tuan-Minh Huynh and Duc-Manh Nguyen and Duc-Huy Ngo and Minh-Duc Cao and Thu-Uyen Nguyen and Van-Thiep Nguyen and Duc-Thanh Tran and Van-Hiep Duong and Anh-Truong Mai and Duc-Long Pham and Khanh-Toan Phan and Minh-Quang Do}, doi = {10.1109/ACCESS.2025.3582924}, journal = {IEEE Access}, pages = {110166-110184}, shortjournal = {IEEE Access}, title = {Depth-guided monocular object pose estimation for warehouse automation}, volume = {13}, year = {2025}, }
• (2025). Recognizing textual entailment in indonesian using individual biplet head-dependent and multi-head attention mechanism. ACCESS, 13, 110151-110165. (www)

  @article{ , abstract = {Recognizing Textual Entailment (RTE) has become essential to determine inferential relationships between sentences in text-understanding systems. Traditionally, RTE models have addressed textual inferences at both syntactic and semantic levels. However, the development of RTE models for the Indonesian language has mainly been limited to lexical sentence-level analysis, overlooking syntactic dependencies among words in the Premise (P) and Hypothesis (H) sentences. This limitation often leads to the capture of irrelevant information within Premise sentences. In this study, we proposed Indo-Biplet Entailment Model (Indo-BiEnt), a deep learning architecture for RTE capable of learning sequential information from sentence pairs transformed into individual Head-Dependent word pairs (Biplet (h-d)). These Biplets are derived from a word-pair-dependency process to capture syntactic relationships between words. Each Biplet pair undergoes direct comparison within the pair segments. Additionally, we employed a BiLSTM network augmented with multi-head attention to emphasize critical words more effectively. Through experiments carried out using the SNLI Indo dataset, our approach achieved a test accuracy of 79%, demonstrating its effectiveness in exploiting syntactic dependencies to enhance the performance of RTE in Indonesian.}, archive = {J_ACCESS}, author = {I Made Suwija Putra and Daniel Siahaan and Ahmad Saikhu}, doi = {10.1109/ACCESS.2025.3581954}, journal = {IEEE Access}, pages = {110151-110165}, shortjournal = {IEEE Access}, title = {Recognizing textual entailment in indonesian using individual biplet head-dependent and multi-head attention mechanism}, volume = {13}, year = {2025}, }
• (2025). A cross-Cultural/Lingual natural language processing analysis of disability awareness on social media. ACCESS, 13, 110128-110150. (www)

  @article{ , abstract = {This paper examines cross-lingual and cross-cultural variations in disability discourse on social media, specifically on X (formerly Twitter). Despite the growing presence of disability-related discussions online, empirical evidence remains limited regarding the perception, accessibility, and advocacy for people with disabilities in non-English-speaking countries of the Global South. Utilizing Natural Language Processing (NLP), this research analyzes three years of Arabic and English disability-related posts, incorporating two hashtags, #peoplewithdisabilities and #disability. The study investigates posting behaviors, engagement patterns, sentiment, and empathy-driven linguistic differences, while also assessing the cultural factors shaping these narratives. The findings show that there are notable linguistic and thematic differences between the posts in English and Arabic across different continents. They also highlight how these relate to disability theories and offer insights for policy makers and advocates on how to effectively study, track, and design for disability awareness in the global south.}, archive = {J_ACCESS}, author = {Zainab Almeraj and Fatemah Husain and Rosa I. Arriaga}, doi = {10.1109/ACCESS.2025.3580608}, journal = {IEEE Access}, pages = {110128-110150}, shortjournal = {IEEE Access}, title = {A cross-Cultural/Lingual natural language processing analysis of disability awareness on social media}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv5s model for improved multi-sized object detection in road scenes. ACCESS, 13, 110110-110127. (www)

  @article{ , abstract = {Detecting objects in complex driving environments is crucial for autonomous vehicles to navigate safely. However, this task becomes challenging when addressing scale variations, occlusions and diverse backgrounds. This paper proposes an enhanced YOLOv5s model for handling varying object sizes from small pedestrians and traffic signs to larger vehicles in road scenes. The proposed enhancement begins by refining the default anchor boxes using the percentile-based quantile method on the distribution of the bounding boxes and the adjustments to the convolution layers for enhanced feature extraction. Smaller kernel sizes and fewer channels are employed in the initial layers to capture fine-grained details, while in deeper layers, the number of channels is progressively increased to capture broader information that better represents larger objects. Furthermore, an efficient channel attention (ECA) mechanism is integrated into the backbone to prioritize key feature channels, thereby enhancing the model’s ability to detect overlapping and small objects. To improve the feature fusion process, a Multi-scale BiFPN block is integrated into the neck of the model. This combines fine-grained spatial details from the shallow layers with more abstract semantic information from deeper layers, enabling the detection of objects across varying scales. Experimental evaluations carried out on the IDD dataset reveal that the enhanced YOLOv5s model achieves a significant gain in prediction accuracy when compared with the original YOLOv5s. To mitigate the effect of class imbalance and improve generalization across varying object sizes, CutMix data augmentation is employed during training. It shows a 48% increase in mean average precision (mAP@0.5) and a 44% and 49% rise in precision and recall, respectively, with an inference time of 14.6ms compared to the baseline model. These improvements underscore the effectiveness of the proposed enhancements in addressing the challenges of detecting multi-sized objects in complex road environments.}, archive = {J_ACCESS}, author = {Sangavi Sivanandham and Dharanibai Gunaseelan}, doi = {10.1109/ACCESS.2025.3582136}, journal = {IEEE Access}, pages = {110110-110127}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv5s model for improved multi-sized object detection in road scenes}, volume = {13}, year = {2025}, }
• (2025). UAV path planners in complex environments: A multi-dimensional perturbation based on artificial bee colony. ACCESS, 13, 110090-110109. (www)

  @article{ , abstract = {Uncrewed aerial vehicle (UAV) path planning in complex environments requires more waypoints to generate high-quality flight paths. However, increasing waypoint density significantly raises computational complexity and risks slow convergence or entrapment in local optima. Although Artificial Bee Colony (ABC) algorithms are commonly employed for UAV path planning, they often struggle to achieve efficient exploration and exploitation, especially in scenarios with frequent terrain height variations. To address these limitations, we have proposed a Multi-Dimensional Perturbation Artificial Bee Colony (MDP-ABC) algorithm. The multi-dimensional perturbation strategy is introduced in the employed bee phase to balance exploration and exploitation through preferential and random selection. In the onlooker bee phase, the curvature-guided elite neighbor search strategy is used to prioritizes high-curvature waypoints, enhancing optimization efficiency in complex terrain. Furthermore, path costs are independently modeled in the horizontal and vertical directions. The MDP-ABC algorithm has been validated in three scenarios with different complexity, and compared with other ten algorithms. Simulation results demonstrate that MDP-ABC significantly enhances convergence speed, solution quality, and robustness, achieving an average performance improvement of 71.9% over ABCiff and 93.2% over AFT in the complex scenarios. These results confirm the MDP-ABC algorithm with the great effectiveness for solving the UAV 3D path planning in the complex environments.}, archive = {J_ACCESS}, author = {Xuzhao Chai and Guanhao Zhou and Haoyu Wang and Ping Liu and Zhongyun Liu and Chao Li and Li Yan}, doi = {10.1109/ACCESS.2025.3582770}, journal = {IEEE Access}, pages = {110090-110109}, shortjournal = {IEEE Access}, title = {UAV path planners in complex environments: A multi-dimensional perturbation based on artificial bee colony}, volume = {13}, year = {2025}, }
• (2025). LEO-PNT feasibility aspects: Satellite navigation payload size, weight, and power analysis. ACCESS, 13, 110069-110089. (www)

  @article{ , abstract = {Low Earth Orbit (LEO) satellites are expected to improve the robustness and reliability of critical Positioning, Navigation, and Timing (PNT) services by increasing both the efficiency and diversity of Global Navigation Satellite Systems (GNSS). However, the feasibility of LEO-PNT services remains uncertain primarily due to the large number of required satellites. The deployment of a LEO-PNT system therefore depends on the feasibility of deploying many LEO satellites with characteristics strongly dependent on the performance requirements of both the payload and the platform. This work addresses the feasibility uncertainty by modeling two potential LEO-PNT payloads and satellite platform designs with different size, weight, and power (SWaP) requirements. Based on simulations and components currently available in the space market, a holistic modeling for the performance of low- and high-SWaP payload designs is evaluated with a focus on clock stability and signal quality. Additionally, the design process includes an assessment of a custom navigation antenna to ensure that the antenna can meet its stringent size and performance constraints. The feasibility of the proposed LEO-PNT constellations is evaluated based on satellite link, power, and mass budgets, as well as antenna performance. In addition, the cost of both the low- and high-SWaP satellites is estimated. This work shows that LEO-PNT payload and constellations are feasible with the technology commercially available today, and they could offer significantly stronger PNT signals compared to traditional GNSS services. The work also benchmarks the proposed payloads against commercial projects in terms of SWaP, timing and positioning accuracy. These results can assist future LEO-PNT simulations to utilize detailed information of payload components and take LEO-PNT services a step closer to realization.}, archive = {J_ACCESS}, author = {Mayank and F. S. Prol and V. Lundén and Z. Saleem and Guillem Foreman-Campins and S. Sharma and E. S. Lohan and M. Z. H. Bhuiyan and S. Kaasalainen and H. Kuusniemi and J. Praks}, doi = {10.1109/ACCESS.2025.3583080}, journal = {IEEE Access}, pages = {110069-110089}, shortjournal = {IEEE Access}, title = {LEO-PNT feasibility aspects: Satellite navigation payload size, weight, and power analysis}, volume = {13}, year = {2025}, }
• (2025). Industrial-grade 3D detection for laser cleaning of metallic components based on neural radiance fields and temporal-spatial learning. ACCESS, 13, 110050-110068. (www)

  @article{ , abstract = {With the in-depth development of the Industry 4.0 technology toward the intelligent manufacturing, laser cleaning technology has gradually become an important method in the field of metal surface treatment due to its advantages such as high efficiency, green and non-contact, and the related technological innovation has also been a research focus in recent years. However, the laser cleaning process is highly dependent on the precise control of the process parameters, and the real-time detection of dynamic changes on the metal surface is still challenging. This paper proposes an industrial-grade 3D detection algorithm based on Neural Radiance Field (NeRF) and spatiotemporal consistency learning. The algorithm is used for accurate monitoring of metal laser cleaning process. By designing a multi-modal sensing system and combining the NeRF optimization strategy of Octree scene partitioning, high-fidelity reconstruction of the rust edge and micro-cracks on the metal surface is achieved. At the same time, the spherical harmonic coding and cross-scale attention fusion mechanism are further introduced to improve the spatio-temporal consistency modeling capability between multi-view images. The main innovations of this study include: 1. Build a data acquisition system that integrates event cameras and RGB cameras to give full play to the advantages of event cameras in capturing rapid changes in light intensity; 2. Propose a NeRF optimization strategy based on Octree scene partitioning to achieve high-fidelity reconstruction of micro-structures such as rust layer edges and micro-cracks; 3. Improve the collaborative modeling ability between multi-temporal images through cross-scale attention fusion; 4. Introduce spherical harmonic functions to replace traditional position encoding functions, enhance the anisotropic reflectance modeling ability and significantly reduce the network parameter scale. Experimental results show that the proposed method achieves a peak signal-to-noise ratio of 33.56 dB and an edge accuracy of 93.2% in metal surface 3D reconstruction, and the training efficiency is 35% higher than that of traditional methods. In addition, the cleaning cleanliness assessment accuracy based on HSV and 3D fusion images is as high as 99.0%. This breakthrough innovation provides a new paradigm of non-contact, high-precision visual inspection for industrial scenarios such as aircraft skin paint stripping and rail transit rust removal.}, archive = {J_ACCESS}, author = {Dandan Qi}, doi = {10.1109/ACCESS.2025.3582914}, journal = {IEEE Access}, pages = {110050-110068}, shortjournal = {IEEE Access}, title = {Industrial-grade 3D detection for laser cleaning of metallic components based on neural radiance fields and temporal-spatial learning}, volume = {13}, year = {2025}, }
• (2025). Battery energy storage system contribution to primary frequency control in isolated power systems. ACCESS, 13, 110030-110049. (www)

  @article{ , abstract = {Increased renewable energy penetration into conventional power plants results in significant frequency regulation (FR) problems, particularly at island power systems. To overcome FR problems, an energy storage system plays an important role. Therefore, a battery energy storage system (BESS) is proposed to provide primary frequency control (PFC) for San Cristobal Island hybrid wind diesel power system (WDPS) in this paper. The aim of this paper is to highlight the improvements achieved using BESS and estimate the optimal size of that BESS for San Cristobal WDPS. Therefore, a permanent magnet synchronous generator (PMSG) based variable speed wind turbine (VSWT) with synthetic (droop) control is used, which enables VSWT to release inertia during contingencies. A novel controller tuning methodology, named student psychology-based algorithm (SPBA), is used to optimally tune the BESS with hybrid WDPS and to estimate BESS size. In addition, several configurations set such as BESS with VSWT and/or diesel power plant (DPP) or both, are presented in this paper to highlight the optimal tuning effects. Moreover, the BESS based WDPS is tested under various real-world scenarios such as loss of wind generator, steadily increase wind speed ( $\Delta \mathrm{V}_{\mathrm {w}}\approx ~0.81$ ms−1), variable wind speed, variable load demand ( $\mathrm{P}_{\mathrm {load}}$ ) and simultaneously change in Vw and Pload. Finally, hybrid BESS based WDPS is tested in a real time environment (OPAL-RT) which validates its performance. Results show the significant reduction in frequency deviation, optimal sizing of BESS and optimal tuning of the whole WDPS.}, archive = {J_ACCESS}, author = {Muhammad Asad and Giulia Tresca and Pericle Zanchetta and Jose Angel Sanchez-Fernandez}, doi = {10.1109/ACCESS.2025.3583228}, journal = {IEEE Access}, pages = {110030-110049}, shortjournal = {IEEE Access}, title = {Battery energy storage system contribution to primary frequency control in isolated power systems}, volume = {13}, year = {2025}, }
• (2025). Improved energy efficient anytime optimistic algorithm for PEGASIS to extend network lifetime in homogeneous and heterogeneous networks. ACCESS, 13, 110012-110029. (www)

  @article{ , abstract = {This study introduces an enhanced version of the PEGASIS algorithm, termed the improved energy efficient anytime optimistic (IEE AO) protocol, designed to improve energy efficiency in wireless sensor networks (WSNs). The primary goals are to optimize chain formation, balance energy consumption, and extend the network’s lifespan. The proposed IEE AO algorithm is compared with several existing models, including low energy adaptive clustering hierarchy (LEACH), stable election protocol (SEP), ant colony optimization (ANT), and anytime optimistic (AO) algorithms, under both MAX energy and sequential energy criteria. Simulations conducted across different node densities and chain formation techniques reveal that IEE AO significantly outperforms state-of-the-art algorithms. Specifically, the sequential IEE AO achieves the best performance in terms of network scalability, stability, lifetime, and packet throughput to the base station (BS), requiring only 20 iterations. Among MAX-based algorithms, MAX IEE AO surpasses AO and ANT-based PEGASIS, which require over 200 iterations. The results highlight the effectiveness of the IEE AO algorithm in enhancing energy efficiency, extending network lifetime, improving node retention, and optimizing chain lengths with minimal iterations, making it a suitable solution for both homogeneous and heterogeneous WSNs. The proposed algorithm demonstrates a 40% improvement across all evaluated features compared to existing algorithms.}, archive = {J_ACCESS}, author = {Tadele A. Abose and Venumadhav Tekulapally and Samuel T. Daka and Diriba C. Kejela and Alemayehu E. Duguma and Desalegn T. Degaga}, doi = {10.1109/ACCESS.2025.3582798}, journal = {IEEE Access}, pages = {110012-110029}, shortjournal = {IEEE Access}, title = {Improved energy efficient anytime optimistic algorithm for PEGASIS to extend network lifetime in homogeneous and heterogeneous networks}, volume = {13}, year = {2025}, }
• (2025). Updated homogeneity criteria based low-dimensional representation for hyperspectral unmixing. ACCESS, 13, 109999-110011. (www)

  @article{ , abstract = {Superpixel-based approaches have been proposed for hyperspectral unmixing. The basic assumption of this approach is that the superpixel over-segmentation segments the image into small homogeneous areas. Here we present an improved superpixel-based dimensionality reduction approach that accounts for superpixel homogeneity. Homogeneous superpixels are represented by their mean but heterogeneous superpixels are represented by multiple representative signatures selected using the SVDSS column subset selection algorithm. The representative signatures for the homogeneous and heterogeneous superpixels provide an improved low-dimensional representation for the hyperspectral image that better captures the image structure. We present experiments applying the proposed and the conventional superpixel dimensionality reduction approaches to unmixing using the constrained non-negative matrix factorization (cNMF). Experimental results comparing the proposed approach with other superpixel-based unmixing approaches are presented. The proposed algorithm was performed on the hyperspectral image Fort A.P. Hill. Both the qualitative evaluation and quantitative assessment are implemented. Experimental results show that unmixing results are improved by the enhanced superpixel-based low dimensional representation.}, archive = {J_ACCESS}, author = {Jiarui Yi and Huiyi Gao}, doi = {10.1109/ACCESS.2025.3575776}, journal = {IEEE Access}, pages = {109999-110011}, shortjournal = {IEEE Access}, title = {Updated homogeneity criteria based low-dimensional representation for hyperspectral unmixing}, volume = {13}, year = {2025}, }
• (2025). AnyTEE: An open and interoperable software defined TEE framework. ACCESS, 13, 109983-109998. (www)

  @article{ , abstract = {Trusted Execution Environments (TEEs) are fundamental in securing a broad spectrum of applications and are prevalent on various platforms, from mobile and embedded devices to cloud servers. However, the existing landscape of TEE technologies is notably heterogeneous, posing substantial interoperability and compatibility challenges for application developers and system designers. These hurdles are not limited to well-established technologies like Arm TrustZone and Intel SGX, but also extend to newer generations of TEE models. In this context, we introduce AnyTEE, a new framework that allows application developers to use multiple TEE models in their applications and system designers to engineer customized TEE models across multiple Instruction Set Architectures (ISAs). Essentially, AnyTEE harnesses widely available hardware virtualization primitives to facilitate the development of Software Defined TEEs (sdTEEs). Our implementation of AnyTEE includes sdTEE versions of the SGX and TrustZone models for Arm and RISC-V, respectively, along with an sdTEE variant of SGX that incorporates fine-grained isolation mechanisms. Our evaluation shows that AnyTEE achieves near-native performance (<3% overhead).}, archive = {J_ACCESS}, author = {David Cerdeira and José Martins and Nuno Santos and Sandro Pinto}, doi = {10.1109/ACCESS.2025.3581487}, journal = {IEEE Access}, pages = {109983-109998}, shortjournal = {IEEE Access}, title = {AnyTEE: An open and interoperable software defined TEE framework}, volume = {13}, year = {2025}, }
• (2025). Detection of internal wave with multi-satellite cross-track interferometric synthetic aperture radar imaging technique. ACCESS, 13, 109968-109982. (www)

  @article{ , abstract = {Internal waves, which can wreak havoc on underwater activities, are difficult to detect because their signatures on the sea surface are easily obscured by wind waves. In this work, a cross-track interferometric synthetic aperture radar (XTI-SAR) imaging technique based on multiple satellite-pairs is proposed to detect the surface signatures of internal wave, with higher height accuracy and finer horizontal resolution that can be achieved by using conventional satellite pair. By superposing the XTI-SAR images acquired from multiple satellite pairs, random features of wind waves are filtered out to reveal the surface signatures of internal wave, without compromising the horizontal resolution. Three internal-wave events are simulated to demonstrate the efficacy of the proposed approach, with height accuracy of centimeters and spatial resolution of 14 m, under wind speed of $U_{{1}0} \leq 6$ m/s.}, archive = {J_ACCESS}, author = {Tung-Cheng Wang and Jean-Fu Kiang}, doi = {10.1109/ACCESS.2025.3582943}, journal = {IEEE Access}, pages = {109968-109982}, shortjournal = {IEEE Access}, title = {Detection of internal wave with multi-satellite cross-track interferometric synthetic aperture radar imaging technique}, volume = {13}, year = {2025}, }
• (2025). Speed and path planning optimization of autonomous vehicle to minimize lap time in racing track. ACCESS, 13, 109960-109967. (www)

  @article{ , abstract = {This study proposes a trajectory optimization method for racing vehicles, aiming to maximize speed and path planning performance by estimating tire friction coefficients. This method addresses the challenges inherent in high-speed racing environments, where tire friction and vehicle dynamics are critical for achieving optimal lap times. A friction circle model was used to determine the friction coefficient, which is subsequently applied to calculate the maximum feasible speed and acceleration constraints for different track segments. The optimization problem was formulated to minimize both path curvature and lap time, balancing these factors to determine the optimal trajectory. Experimental validation was conducted using a real vehicle on the AMG Speedway short track in Yongin, South Korea, where the proposed method demonstrated significant improvements in lap times across different tracks compared to the minimum curvature method. The proposed algorithm effectively minimized lap times and demonstrated applicability, with an average computation time of 30 s on a standard computer. This research provides valuable insights into autonomous vehicle path planning, particularly in racing contexts, and provides a robust framework for further advancements in autonomous driving technologies.}, archive = {J_ACCESS}, author = {Young-Jin Roh and Ji-Ung Im and Jong-Hoon Won}, doi = {10.1109/ACCESS.2025.3581367}, journal = {IEEE Access}, pages = {109960-109967}, shortjournal = {IEEE Access}, title = {Speed and path planning optimization of autonomous vehicle to minimize lap time in racing track}, volume = {13}, year = {2025}, }
• (2025). Underdiagnosis bias mitigation with expert foundation model’s representation. ACCESS, 13, 109946-109959. (www)

  @article{ , abstract = {Vector embeddings (VEmb) are compact data representations learned by large foundation models that have recently gained popularity; their use in place of original data requires less storage and computational power, making them ideal for low-resource computational settings. Nevertheless, the compression of large amounts of data, such as a large medical image, into a low-dimensional VEmb might weaken critical disease information, potentially increasing the risk of underdiagnosis risk -i.e. unhealthy patients flagged as healthy. In this work, we evaluate the underdiagnosis bias of Chest X-ray disease classifiers based on both VEmb and image-based models. Underdiagnosis occurs when an AI model incorrectly classifies unhealthy patients as healthy, leading to missed diagnoses and potential delays in treatment. We make use of VEmb from Google’s X-ray foundation model (trained exclusively on X-rays) and BiomedCLIP (trained on various types of medical imaging data). Our findings show that the expert Google VEmb-based model, highly finetuned solely on chest X-rays, reduces underdiagnosis rates, and narrows the underdiagnosis rate gap across subpopulations as well. Conversely, the VEmb from BiomedCLIP, a non-expert model trained on a diverse range of medical images, exhibits lower disease classification performance and higher underdiagnosis rates. This underscores the importance of obtaining VEmb from expert models trained on domain-specific data. Furthermore, we observed reduced demographic signal detection in VEmb of expert foundation models compared to image-based models. This aligns with a decrease in underdiagnosis bias, emphasizing the potential benefits of minimizing demographic signals to achieve fairer outcomes.}, archive = {J_ACCESS}, author = {Gebreyowhans H. Bahre and Hassan Hamidi and Andrew B. Sellergren and Alessandro Quarta and Leo Anthony Celi and Francesco Calimeri and Laleh Seyyed-Kalantari}, doi = {10.1109/ACCESS.2025.3582754}, journal = {IEEE Access}, pages = {109946-109959}, shortjournal = {IEEE Access}, title = {Underdiagnosis bias mitigation with expert foundation model’s representation}, volume = {13}, year = {2025}, }
• (2025). Estimating perceptual attributes of haptic textures using visuo-tactile data. ACCESS, 13, 109931-109945. (www)

  @article{ , abstract = {Accurate prediction of perceptual attributes of haptic textures is essential for advancing VR and AR applications and enhancing robotic interaction with physical surfaces. This paper presents a deep learning-based multi-modal framework, incorporating visual and tactile data, to predict perceptual texture ratings by leveraging multi-feature inputs. To achieve this, a four-dimensional haptic perceptual space encompassing rough-smooth, flat-bumpy, sticky-slippery, and hard-soft dimensions is first constructed through psychophysical experiments, where participants evaluate 50 diverse real-world texture samples. A physical signal space is subsequently created by collecting visual and tactile data from these textures. Finally, a deep learning architecture integrating a CNN-based autoencoder for visual feature learning and a ConvLSTM network for tactile data processing is trained to predict user-assigned attribute ratings. This multi-modal, multi-feature approach maps physical signals to perceptual ratings, enabling accurate predictions for unseen textures. To evaluate predictive accuracy, we employed leave-one-out cross-validation to rigorously assess the model’s reliability and generalizability against several machine learning and deep learning baselines. Experimental results demonstrate that the framework consistently outperforms single-modality approaches, achieving lower MAE and RMSE, highlighting the efficacy of combining visual and tactile modalities.}, archive = {J_ACCESS}, author = {Mudassir Ibrahim Awan and Seokhee Jeon}, doi = {10.1109/ACCESS.2025.3581685}, journal = {IEEE Access}, pages = {109931-109945}, shortjournal = {IEEE Access}, title = {Estimating perceptual attributes of haptic textures using visuo-tactile data}, volume = {13}, year = {2025}, }
• (2025). STAR-YOLO: A high-accuracy and ultra-lightweight method for brain tumor detection. ACCESS, 13, 109914-109930. (www)

  @article{ , abstract = {Brain tumors are among the deadliest malignant neoplasms worldwide, and early detection is critical for improving treatment outcomes and patient prognosis. Magnetic resonance imaging (MRI) plays a central role in the clinical diagnosis of brain tumors due to its exceptional ability to visualize brain structures and detect tumors. However, traditional MRI detection models for brain tumors often struggle to balance high accuracy with a lightweight design. To address this challenge, this study proposes a novel model—STAR-YOLO—based on the optimization of YOLOv8n. The model refines the backbone network and enhances the C2f module within the neck network. It also integrates SimAM, a parameter-free attention mechanism, and utilizes ShapeIoU as the localization loss function. These innovations enable STAR-YOLO to achieve both high accuracy and a lightweight design. Experimental results on the Br35H public dataset show that STAR-YOLO outperforms existing models, achieving outstanding scores of 0.937 in Precision, 0.852 in Recall, and 0.642 in mAP50–95. Additionally, the model contains only 2.01 million parameters and has a computational complexity of 6.1 GFLOPs, significantly lower than other models. STAR-YOLO accomplishes a lightweight design while guaranteeing high detection accuracy, prominently demonstrating its immense potential in the diagnosis of clinical brain tumors, particularly in circumstances with constrained computing resources.}, archive = {J_ACCESS}, author = {Liyan Sun and Linxuan Zheng and Zhiguo Xiao and Yi Xin and Linqing Jiang}, doi = {10.1109/ACCESS.2025.3581234}, journal = {IEEE Access}, pages = {109914-109930}, shortjournal = {IEEE Access}, title = {STAR-YOLO: A high-accuracy and ultra-lightweight method for brain tumor detection}, volume = {13}, year = {2025}, }
• (2025). Variability analysis of ferroelectric FinFETs for embedded non-volatile memory applications. ACCESS, 13, 109907-109913. (www)

  @article{ , abstract = {In this study, we conduct a comparative analysis of the process variation induced variability – specifically the random ferroelectric/dielectric phase variation, the metal work function variation, and the line edge roughness – in various ferroelectric field-effect transistors (FeFETs) at the 3 nm technology node, including ferroelectric FinFET (Fe-FinFET), one-sided ferroelectric-metal FinFET (FeM-FinFET), and FeM-FinFET. These variabilities are incorporated into TCAD simulations to quantitatively examine the variation of threshold voltages. As a result, the FeM-FinFET can reduce variability by an average of 12.4% compared to the Fe-FinFET structure and by an average of 55% compared to the one-sided FeM-FinFET structures.}, archive = {J_ACCESS}, author = {Byeongju Ha and Dongyeong Kim and Taekhan Yoon and Sola Woo}, doi = {10.1109/ACCESS.2025.3582799}, journal = {IEEE Access}, pages = {109907-109913}, shortjournal = {IEEE Access}, title = {Variability analysis of ferroelectric FinFETs for embedded non-volatile memory applications}, volume = {13}, year = {2025}, }
• (2025). Receiver processing of the multibeam azimuth scanning CC-OFDM-MIMO radar. ACCESS, 13, 109894-109906. (www)

  @article{ , abstract = {The proposed CC-OFDM-MIMO radar enables the development of software-defined radar by using array antennas to provide multiple high-resolution beams with low peak SLLs. The radar requires no RF phase shifters because the amplitude and phase control for multiple beamforming are provided directly from the waveform generation stage. The multiple beams are transmitted simultaneously and continuously without beams switching that makes the radar has full continuous observations over the wide coverage of −45 to 45 degree. The proposed design of long-range radar with low power transmission can be implemented by transmitting multi-orthogonal CC-OFDM symbols in series, which extends the maximum unambiguous range and improves the Signal-to-Noise of the radar system. The orthogonality of the beams and the symbols obtained by applying orthogonal Golay codes and sub-band frequency scheduling arrangements to avoid interference between beams and between symbols. In this paper, parallel receiver processing works by correlating the received signals with the multiple signal references that are well-known by the transmitters and receivers. The output of the multibeam correlators gives the first estimation of the target profiles in terms of the targets range and its direction. The target direction can be verified furthermore by correlating the spectrum of the received echo signal after delay compensation with the Golay codes which corresponds to a specific beam and specific symbol. With the orthogonal nature of Golay Codes, the receiver can distinguish targets which come from different beams, even though the beams are using the same frequency band. After the target is detected within a specific beam, and at a certain range, Doppler processing can be done by observing the phase variation between the samples of the received signal and the signal references. The coherence processing for Doppler detection can be done over one period of symbol, and the Doppler resolution can be improved by coherence processing over one frame period consisting of 12 orthogonal symbols. Simulation of the receiver processing is done for two targets located in the same beam, in neighboring beams and in co-channel beams. The results show that the radar could distinguish two targets with 60 meters in separation with SLLs as low as -80dB. Additionally, the radar is able to detect moving targets with better resolution because the velocity can be obtained from the phase variation over one symbol duration and over the whole symbol series.}, archive = {J_ACCESS}, author = {Devy Kuswidiastuti and L. P. Ligthart and Gamantyo Hendrantoro and Titiek Suryani and Prasetiyono Hari Mukti and Yuyu Wahyu}, doi = {10.1109/ACCESS.2025.3581842}, journal = {IEEE Access}, pages = {109894-109906}, shortjournal = {IEEE Access}, title = {Receiver processing of the multibeam azimuth scanning CC-OFDM-MIMO radar}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of parameterized MPC strategies for lane keeping assist systems in real-time HIL architecture. ACCESS, 13, 109883-109893. (www)

  @article{ , abstract = {The Lane Assist System (LAS) helps drivers stay within their road lanes. It uses cutting-edge technology (such as a camera system) to monitor marks for lane-following applications. The Lane Keeping Assist System (LKAS), a LAS with steering control, ensures appropriate lateral vehicle dynamics, avoiding unexpected steering operation. Despite LKAS solutions promoting comfort driving, robust steering control is lacking, requiring constant driver intervention, sometimes with premature LKAS deactivation. For this reason, this paper investigates parameterized Model-Predictive Control (MPC) solutions for real-time LKAS applications. The parameterized MPC calculates control actions for a constrained dynamic system, making it a potential candidate for robust LKAS. A sideslip and lateral speed bicycle model with three MPC formulations and various Quadratic Programming Solvers were considered. Trajectory tracking, lateral displacement and yaw angle regulation experimental tests were run through Hardware-In-the-Loop (HIL) Architecture. Experimental results, along with comparative performance and robustness analyses, demonstrate satisfactory results and provide valuable insights into the application of MPC for lane-keeping assist systems.}, archive = {J_ACCESS}, author = {James Duvan Garcia Montoya and André Murilo and Evandro Leonardo Silva Teixeira and Rafael Rodrigues da Silva and Bruno Augusto Angélico}, doi = {10.1109/ACCESS.2025.3582149}, journal = {IEEE Access}, pages = {109883-109893}, shortjournal = {IEEE Access}, title = {Design and implementation of parameterized MPC strategies for lane keeping assist systems in real-time HIL architecture}, volume = {13}, year = {2025}, }
• (2025). The impact of physical exercise on driver attention reaction time and decision making. ACCESS, 13, 109867-109882. (www)

  @article{ , abstract = {This paper presents an advanced Attention-Driven Reaction Framework (ADRF) to model the dynamic interplay between driver attention and reaction under complex road conditions. Traditional methods often rely on static or linear models, failing to capture temporal dependencies and individual variations. ADRF integrates probabilistic attention modeling with dynamic systems to predict reaction behaviors with higher accuracy. To further enhance interpretability and adaptability, we propose the Reaction-Augmented Attention Optimization (RAAO) strategy, which employs a feedback-driven mechanism to refine attention allocation and reaction prediction. Experimental results show that ADRF improves reaction time prediction accuracy by 25% and decision-making accuracy by 30% compared to baseline models. These findings highlight the potential of ADRF in improving driver performance and reducing traffic accidents. These advancements offer a comprehensive approach to reducing road traffic accidents, aligning with multidisciplinary initiatives aimed at improving road safety.}, archive = {J_ACCESS}, author = {Yan Xianyou and Jie Bai}, doi = {10.1109/ACCESS.2025.3563966}, journal = {IEEE Access}, pages = {109867-109882}, shortjournal = {IEEE Access}, title = {The impact of physical exercise on driver attention reaction time and decision making}, volume = {13}, year = {2025}, }
• (2025). Energy consumption modeling and flight time analysis of micro drones. ACCESS, 13, 109854-109866. (www)

  @article{ , abstract = {Autonomous aerial vehicles have emerged as revolutionary technology with a wide range of applications in different areas. One of the interesting drone types is miniature or lightweight micro drones which have palm-size small dimensions. Due to their small size, micro drones can navigate confined spaces and reach inaccessible areas, providing valuable data and insights. However, the small size of these drones limits their ability to carry heavy equipment, including batteries and many sensors. Increasing the flight time of micro drones using limited energy sources is a challenging task that requires advanced hardware technologies and efficient software methods. In this paper, we propose a voltage-based regression model to estimate the energy consumption and flight time of micro drones, using sampled voltage data obtained from experimental evaluations in ground, take-off, hovering, and flying modes. The mean absolute error of the proposed regression models is less than 0.3 V for all flying modes. The results from 150 second flight tests indicated an estimated total energy consumption of 2000 J, corresponding to an average power of 13.3 W. The unit mass power consumption was calculated as 0.38 W/g, highlighting the suitability of miniature UAVs for energy-constrained tasks. Also, the experiments showed that due to the voltage drop of the batteries, the drones cannot fly anymore when the battery energy drops to about 50% of the total battery capacity. These results emphasize the importance of lightweight high capacity batteries for developing micro drones.}, archive = {J_ACCESS}, author = {Nusin Akram and Mina Khoshrangbaf and Moharram Challenger and Orhan Dagdeviren}, doi = {10.1109/ACCESS.2025.3581944}, journal = {IEEE Access}, pages = {109854-109866}, shortjournal = {IEEE Access}, title = {Energy consumption modeling and flight time analysis of micro drones}, volume = {13}, year = {2025}, }
• (2025). A novel depth-first search algorithm for partial periodic-frequent pattern mining in temporal databases. ACCESS, 13, 109840-109853. (www)

  @article{ , abstract = {Partial periodic-frequent pattern mining is a critical technique in the data mining field. This technique finds all frequent patterns demonstrating partial periodicity within temporal datasets. Despite its potential, the broader industrial adoption of this technique has been limited due to two significant challenges. First, there is currently no algorithm capable of efficiently identifying the required patterns in columnar temporal databases, which are increasingly prevalent in real-world applications. Second, the existing algorithms suffer from high computational demands regarding processing time (or runtime) and memory usage, thus making them unsuitable for analyzing large-scale datasets effectively. To address these challenges, this paper proposes the Generalized Partial Periodic-Frequent Depth-First Search (GPPF-DFS) algorithm. Unlike GPF-growth, which is limited to row-based databases, and ECLAT, which cannot handle shared timestamps, GPPF-DFS supports both row-based and columnar formats and can process multiple transactions with identical timestamps. It compresses temporal databases into a unified dictionary structure, enabling efficient data traversal and recursive mining. The experimental results across various datasets demonstrate that our proposed algorithm significantly outperforms GPF-growth in terms of effectiveness and computational efficiency. Furthermore, a scalability test has been conducted to assess the performance of the proposed algorithm. Additionally, we demonstrate the practical applicability of GPPF-DFS through case studies on air pollution and retail analytics. The algorithm identifies periodic patterns in air pollution data, offering valuable insights for environmental monitoring. In retail analytics, it uncovers patterns that inform business strategies and enhance customer experience.}, archive = {J_ACCESS}, author = {Pamalla Veena and Vanitha Kattumuri and Yutaka Watanobe and Rage Uday Kiran and So Nakamura and Palla Likhitha and Koji Zettsu}, doi = {10.1109/ACCESS.2025.3581769}, journal = {IEEE Access}, pages = {109840-109853}, shortjournal = {IEEE Access}, title = {A novel depth-first search algorithm for partial periodic-frequent pattern mining in temporal databases}, volume = {13}, year = {2025}, }
• (2025). Toward automatic detection of pi2 magnetic pulsation using machine learning. ACCESS, 13, 109828-109839. (www)

  @article{ , abstract = {Magnetic pulsations of type Pi2 are a well-established category of Ultra Low Frequency (ULF) waves, characterized by irregularly damped oscillations with periods ranging from 40 to 150 seconds (6.7–25 mHz). Nowadays, it is well known that Pi2 occurs at the onset of geomagnetic substorms, is considered an outstanding research topic in space physics, and is a link between ionospheric and magnetospheric processes. The discontinuation of the conventional index previously employed to detect Pi2 pulsations has driven this study to propose an innovative detection method leveraging machine learning (ML). This paper introduces a novel ML-based classification framework that utilizes geomagnetic field data for Pi2 pulsation detection. A comprehensive analysis of various linear, ensemble, and non-linear ML models was conducted, employing hyperparameter optimization to identify the optimal model with high classification performance and minimal computational overhead during testing. Model robustness was assessed using multiple evaluation metrics, including accuracy, F1-score, kappa score, execution time, precision-recall curves, ROC curves, learning curves, and confusion matrices. The proposed gradient boost (GB) classifier demonstrated superior performance, achieving 98.21% accuracy in distinguishing Pi2 pulsations. This detection system offers a reliable and efficient tool for monitoring Pi2 pulsations in the nighttime, contributing to advancements in space weather analysis and substorm detection.}, archive = {J_ACCESS}, author = {Mohamed S. Abdalzaher and Essam Ghamry and Khairul Adib Yusof and Mostafa Shaaban}, doi = {10.1109/ACCESS.2025.3582762}, journal = {IEEE Access}, pages = {109828-109839}, shortjournal = {IEEE Access}, title = {Toward automatic detection of pi2 magnetic pulsation using machine learning}, volume = {13}, year = {2025}, }
• (2025). Strategies for enhancing card game analysis: Exploring large language models in imperfect information settings. ACCESS, 13, 109814-109827. (www)

  @article{ , abstract = {Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary for complex games with incomplete information remains a significant challenge. In this paper, we introduce a novel commentary method that combine Reinforcement Learning (RL) and LLMs, tailored specifically for the Chinese card game Guandan. Our system leverages RL to generate intricate card-playing scenarios and employs LLMs to generate corresponding commentary text, effectively emulating the strategic analysis and narrative prowess of professional commentators. The framework comprises a state commentary guide, a Theory of Mind (ToM)-based strategy analyzer, and a style retrieval module, which seamlessly collaborate to deliver detailed and context-relevant game commentary in the Chinese language environment. We empower LLMs with ToM capabilities and refine both retrieval and information filtering mechanisms. This facilitates the generation of personalized commentary content. The semantic similarity of our model far exceeds that of other models by close to 80%. Our experimental results showcase the substantial enhancement in performance achieved by the proposed commentary framework when applied to open-source LLMs, surpassing the performance of GPT-4 across multiple evaluation metrics.}, archive = {J_ACCESS}, author = {Feihu Ma and Xuechen Liang and Jianing Long and Meiling Tao}, doi = {10.1109/ACCESS.2025.3546629}, journal = {IEEE Access}, pages = {109814-109827}, shortjournal = {IEEE Access}, title = {Strategies for enhancing card game analysis: Exploring large language models in imperfect information settings}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review and analysis of image encryption techniques. ACCESS, 13, 109783-109813. (www)

  @article{ , abstract = {The rapid growth of digital media has increased the need to protect image data from unauthorized access, particularly in fields such as medical imaging, military communication, and online content distribution. This paper provides a comprehensive review of advanced image encryption techniques aimed at preserving the confidentiality and integrity of visual information. The analysis includes traditional cryptographic methods, such as AES, RSA, and Blowfish, as well as quantum encryption, chaos-based encryption, DNA encryption, frequency domain techniques, neural network-based methods, and compressive sensing. Each technique was evaluated based on key performance metrics, including the resistance to differential and statistical attacks, computational efficiency, noise resilience, key sensitivity, and encryption speed. Through comparative analysis, this study highlights the strengths, weaknesses, and applicability of these methods in various contexts. The review concludes by discussing the current challenges and suggesting future research directions to improve the security, scalability, and real-time processing capabilities of image encryption systems.}, archive = {J_ACCESS}, author = {K. Mahalakshmi and Sivakumar Nagarajan}, doi = {10.1109/ACCESS.2025.3578158}, journal = {IEEE Access}, pages = {109783-109813}, shortjournal = {IEEE Access}, title = {Comprehensive review and analysis of image encryption techniques}, volume = {13}, year = {2025}, }
• (2025). A systematic review exploring AI’s role in self-regulated learning within education contexts. ACCESS, 13, 109771-109782. (www)

  @article{ , abstract = {The application of Artificial Intelligence (AI) in education has attracted much attention, and self-regulated learning (SRL) is a key competency for student development and an important factor for student academic success in the current digital environment. Therefore, it is crucial to understand the status of AI applications in SRL. To this end, we conducted a systematic review of AI applications in SRL in the educational context by searching the literature from 2004 to 2024, with 27 studies meeting the final inclusion criteria. The results of the final literature analysis revealed trends in the educational level, research method, subject area application of AI on SRL, common types of AI used to support SRL in education, the relationship between AI and the SRL theories used, and the outcomes of the application of AI to SRL. Finally, implications for empirical research on the application of AI in SRL are also discussed.}, archive = {J_ACCESS}, author = {Liling Ren and Kerry Lee and Lawrence May}, doi = {10.1109/ACCESS.2025.3582600}, journal = {IEEE Access}, pages = {109771-109782}, shortjournal = {IEEE Access}, title = {A systematic review exploring AI’s role in self-regulated learning within education contexts}, volume = {13}, year = {2025}, }
• (2025). A comprehensive framework for adaptive electric vehicle charging station siting and sizing based on transferable spatio-temporal demand prediction. ACCESS, 13, 109752-109770. (www)

  @article{ , abstract = {Under the global trend of promoting green development, the electric vehicle (EV) industry has entered a period of rapid growth. However, the disorderly access of large-scale electric vehicles into the power grid threatens stability and security, and simultaneously rational planning of charging infrastructure is increasingly needed to meet the region-specific and time-varying demands. Thus, this work introduces a systematic framework combining spatiotemporal demand calculation and transferable prediction with Electric Vehicle charging station (EVCS) siting and sizing optimization. By leveraging partial EV trip data, the method can capture users’ driving patterns and supplement the citywide complete EV trip data, which is then fed into the established mathematical model to compute spatiotemporal charging demand. The derived best-performing ConvLSTM model, trained on basic features and calculated charging demand, can be applied to cross-city demand prediction by inputting identical basic features. This methodology is particularly tailored for prediction with limited or no corresponding data, mitigating data scarcity in underdeveloped small cities. Building upon this, a bi-level planning model is designed for EVCS siting and sizing, considering both user satisfaction and investment cost. The optimal coordinated EVCS construction strategy is obtained using the NSGA-II algorithm. Finally, this framework was employed in Shenzhen and Shanwei for empirical analysis, confirming its feasibility and accuracy.}, archive = {J_ACCESS}, author = {Yutong Peng and Liqi Ye and Wenrui Ouyang and Qi Xi and Jing Wang and Xiaolin Wang}, doi = {10.1109/ACCESS.2025.3582530}, journal = {IEEE Access}, pages = {109752-109770}, shortjournal = {IEEE Access}, title = {A comprehensive framework for adaptive electric vehicle charging station siting and sizing based on transferable spatio-temporal demand prediction}, volume = {13}, year = {2025}, }
• (2025). Model predictive contouring control with barrier and lyapunov functions for stable path-following in UAV systems. ACCESS, 13, 109742-109751. (www)

  @article{ , abstract = {In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables uncrewed aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The proposed method was experimentally validated using a DJI Matrice 100 quadrotor platform, considering scenarios with prior knowledge of obstacle locations. Results demonstrate the controller’s effectiveness in minimizing orthogonal and tangential tracking errors, ensuring stability and safety in complex environments.}, archive = {J_ACCESS}, author = {Bryan S. Guevara and José Varela-Aldás and Viviana Moya and Manuel Cardona and Daniel C. Gandolfo and Juan M. Toibero}, doi = {10.1109/ACCESS.2025.3582505}, journal = {IEEE Access}, pages = {109742-109751}, shortjournal = {IEEE Access}, title = {Model predictive contouring control with barrier and lyapunov functions for stable path-following in UAV systems}, volume = {13}, year = {2025}, }
• (2025). Biological motion-based emotion recognition through a deep learning approach. ACCESS, 13, 109729-109741. (www)

  @article{ , abstract = {Recognition of emotions has emerged as a significant trend in various artificial intelligence based applications and has recently been extensively explored. Facial expressions or speech signals form the basis of emotion-recognition research. In addition, there is a growing interest in investigating the utilization of body movements for emotion recognition because of their non-verbal nature and potential to complement facial expressions and speech signals. However, the use of biological motion for emotion recognition leverages the inherent expressiveness of human movement, while simplifying the computational and perceptual challenges associated with analyzing full-body movements. In this study, emotion recognitions from biological emotional motion was presented. A model integrating convolutional neural networks (CNN) and long short-term memory (LSTM) layers was proposed to predict six distinct emotions: happiness, sadness, anger, fear, disgust, and neutral. The dataset used in this study includes 2664 high-quality videos of emotional biological motion viewed from three different angles (frontal 0°, left 45°, and left 90°). The proposed model was evaluated using various evaluation metrics and exhibited outstanding results in all measurements, underscoring its enhanced ability to predict emotions. The CNN-LSTM model demonstrated superior precision, recall, and f1-score, attaining the highest accuracy of 95% in contrast to the other deep learning models within the Dalian emotional movement open-source set dataset, which was 85% for CNN and 83% for ConvLSTM in the test set. In this study, biological motion was employed to attain cutting-edge results in the field of emotion recognition tasks, highlighting its importance in various affective computing applications.}, archive = {J_ACCESS}, author = {Amjaad T. Alotaibi and Suhare Solaiman}, doi = {10.1109/ACCESS.2025.3582654}, journal = {IEEE Access}, pages = {109729-109741}, shortjournal = {IEEE Access}, title = {Biological motion-based emotion recognition through a deep learning approach}, volume = {13}, year = {2025}, }
• (2025). StaEn-IDS: An explainable stacking ensemble deep neural network-based intrusion detection system for IoT. ACCESS, 13, 109713-109728. (www)

  @article{ , abstract = {The demand for the Internet of Things (IoT) has increased exponentially because it connects devices, applications, and people, allowing smooth real-time communication and data sharing. This connectivity improves decision-making, making it more accurate and efficient. However, this also increases security risks. With millions of connected devices from different manufacturers using various protocols and IoT applications, ensuring IoT security is challenging. Moreover, IoT devices often have limited processing power and storage, making it harder to apply strong security measures, giving attackers an advantage. Single models can face issues like overfitting or being vulnerable to attacks. Combining multiple learning methods through ensemble techniques has effectively addressed these issues and improved performance. This paper proposes a stacking ensemble model. The approach integrates a Deep Intrusion Detection System (DIDS) with a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model. The Random Forest algorithm is used as a meta-classifier in the stacking process to improve accuracy. The model also addresses the challenge of detecting minority-class attacks in IoT traffic. We evaluate the approach on data derived from the CIC IoT 2022 dataset (pcap files converted to flow records using CICFlowMeter), and demonstrate real-time deployment on a resource-constrained edge device. Furthermore, we incorporate Explainable AI techniques to interpret the model’s decisions, thereby improving user trust in the system. Our results show that the proposed model effectively and robustly detects intrusions in IoT environments.}, archive = {J_ACCESS}, author = {Monika Vishwakarma and Nishtha Kesswani}, doi = {10.1109/ACCESS.2025.3582391}, journal = {IEEE Access}, pages = {109713-109728}, shortjournal = {IEEE Access}, title = {StaEn-IDS: An explainable stacking ensemble deep neural network-based intrusion detection system for IoT}, volume = {13}, year = {2025}, }
• (2025). A DC SSCB with range-limited parallel operation switches and a snubber circuit utilizing inverse current injection. ACCESS, 13, 109701-109712. (www)

  @article{ , abstract = {Although many snubber circuits have been studied, research on circuits that effectively suppress surge voltage while supporting bidirectional operation remains relatively limited. This paper proposes a bidirectional snubber circuit with an inverse current injection to effectively suppress the surge voltage generated when a solid-state circuit breaker (SSCB) switch is turned off. It generates inverse current using an LC resonant circuit employing thyristors and suppresses the surge voltage by forcing the fault current to be zero when the SSCB switch is turned off. The capacitor absorbs the energy stored in the line inductance on the power source and load sides, thereby preventing voltage increases. In addition, the SSCB switches are structured in parallel so that only the SiC MOSFET with low conduction loss is turned on during normal operation. When the fault current increases and reaches a specific current threshold, the Si MOSFET is driven in parallel until reaching the breaking current value. This solves the price increase problem due to the SiC switch’s current rating. The operational mode is described in detail with theoretical analysis, and the validity is verified through simulation and scaled-down experiments. The surge voltage suppression performance of the proposed snubber is evaluated by comparing it with the conventional RCD snubber and Metal Oxide Varistor (MOV)-based snubber, and the surge voltage occurrence due to the inverse current injection timing mismatch is analyzed to present the range of allowable delay time. Finally, the cost model analysis evaluates the economic feasibility of the proposed snubber applying the parallel switch structure.}, archive = {J_ACCESS}, author = {Jae-Seong Jo and Sun-Pil Kim and Sang-Hyeok Lee and Sung-Geun Song and Feel-Soon Kang}, doi = {10.1109/ACCESS.2025.3581596}, journal = {IEEE Access}, pages = {109701-109712}, shortjournal = {IEEE Access}, title = {A DC SSCB with range-limited parallel operation switches and a snubber circuit utilizing inverse current injection}, volume = {13}, year = {2025}, }
• (2025). Grid forming inverters for electric vehicle charging stations to enhance distribution grid resilience. ACCESS, 13, 109687-109700. (www)

  @article{ , abstract = {The increasing integration of renewable energy sources and electric vehicles is reshaping distribution networks, calling for advanced control strategies to maintain power system quality, stability and resilience, intended to capacity to maintain stability after severe perturbations. This paper investigates the application of grid-forming (GFM) controls, of two types: droop and virtual synchronous machine, within high-power EV charging stations (HPCS) connected to medium voltage distribution grids. Through detailed simulation of five representative case studies, our results show that GFM converters can sustain isolated grids, support black-start procedures, and provide voltage/frequency regulation. Notably, compared to traditional grid-following inverters, GFM strategies enable HPCS to enhance grid resilience to faults (short circuits considered) and also recovery after blackouts. All models are released as open-source Modelica code, enabling reproducibility and further research. These findings highlight the potential of leveraging charging infrastructure as a flexible, grid-supportive resource in future energy systems. The considered control actions may require bi-directional exchange of power between vehicles and the grid, usually called V2G (vehicle-to-grid) control. However, interesting opportunities also exist in the context of V1G (Smart Charging), where power flow is unidirectional but still controllable. They can in principle used also to interface grid-connected independent battery energy storage systems, but using charging battery-electric vehicles for this purpose has the advantage of exploiting infrastructure already in place (the charging station), adding grid services to it, to enhance its economic competitiveness. The importance of using future charging battery electric vehicles to support grid services is confirmed by the fact that this study has been funded by a research project devoted to this.}, archive = {J_ACCESS}, author = {Stefano Barsali and Radu Bojoi and Massimo Ceraolo and Vincenzo Mallemaci and Fabio Mandrile and Susanna Mocci and Gianluca Pasini}, doi = {10.1109/ACCESS.2025.3582070}, journal = {IEEE Access}, pages = {109687-109700}, shortjournal = {IEEE Access}, title = {Grid forming inverters for electric vehicle charging stations to enhance distribution grid resilience}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in UAV flight controls: Deep reinforcement learning based altitude-hold strategies for fixed-wing UAVs. ACCESS, 13, 109670-109686. (www)

  @article{ , abstract = {This paper implements a deep reinforcement learning (DRL) based flight control system for a fixed-wing uncrewed aerial vehicle (UAV). Unlike conventional flight control methods, DRL does not require an exact mathematical system model for the design and can handle non-linear coupled dynamics of highly agile aerospace vehicles like small-size UAVs. The deep deterministic policy gradient (DDPG) method was chosen to suit environments with continuous action spaces. The key contribution of this research is the implementation of three distinct approaches that successfully replace traditional classical control systems with Reinforcement Learning (RL)-based controllers, each offering unique advantages and exploring different trade-offs between interpretability, complexity, and performance crucial for safety-critical aerospace applications. The classical Proportional-Integral-Derivative (PID) flight control architecture, consisting of an altitude controller followed by a pitch (theta) controller, was developed as a baseline. Subsequently, three approaches were investigated; First, the altitude hold controller was replaced by a Reinforcement Learning (RL) agent, while the PID control was maintained for pitch control. In the second approach, both the altitude and pitch control loops were substituted with RL agents. Finally, a single RL agent replaced both the altitude and pitch angle control loops, unifying control under a single agent. A comparative analysis has been made with the widely used conventional PID controls to assess the effectiveness of the each implemented control system. The RL controllers outperformed the baseline PID controllers, among which the unified RL controller achieved a steady-state error of 0.58 meters and a transient response time of 5 seconds, compared to the PID controller’s 1.11 meter steady-state error and transient response time of 16 seconds, thereby reducing the error by 48% and improving the response time by nearly 69%. These results demonstrate the superior accuracy and response efficiency of the proposed RL-based control strategies. Notably, the implementation featuring a single RL agent yields promising results, highlighting the capacity of RL agents to handle complex control challenges. This approach simplifies the control system design by eliminating the need for a multiple-loop architecture. The outcomes of this study underscore the potential of RL-based controllers to enhance the performance of UAVs. Furthermore, the results offer valuable insights for developing future UAV control systems, emphasizing the advantages of RL techniques over traditional PID controls.}, archive = {J_ACCESS}, author = {Hasan Raza Khanzada and Adnan Maqsood and Abdul Basit}, doi = {10.1109/ACCESS.2025.3581966}, journal = {IEEE Access}, pages = {109670-109686}, shortjournal = {IEEE Access}, title = {Artificial intelligence in UAV flight controls: Deep reinforcement learning based altitude-hold strategies for fixed-wing UAVs}, volume = {13}, year = {2025}, }
• (2025). BEATS: Practical audit trail in blockchain systems. ACCESS, 13, 109657-109669. (www)

  @article{ , abstract = {An audit trail records activities and tracks changes within a system, serving as a crucial tool for fraud prevention and data management. However, traditional audit trail systems often face inefficiencies, high costs, and vulnerability to security breaches and unauthorized access, limiting their real-time fraud detection capabilities. These limitations highlight the need for more reliable, secure, and efficient solutions. Blockchain technology, with its decentralized and immutable ledger, provides a promising solution. By providing tamper-proof, transparent, and real-time transaction records, blockchain enhances the reliability, efficiency, and security of audit trails, transforming auditing practices and boosting stakeholder trust. Despite these advantages, a significant challenge remains: as transaction volumes grow, retrieving and verifying specific information becomes increasingly time-consuming and resource-intensive. This problem, often overlooked in research, restricts the scalability and practicality of blockchain-based audit systems. To address these limitations, this study proposes BEATS (Blockchain-based Efficient Audit Trail System) - an efficient approach for verifying specific information within a blockchain. BEATS leverages a Cryptographic Accumulator, a compact data structure that accumulates transaction hashes, enabling auditors to verify transaction authenticity without exhaustive searches or supplementary systems. Our evaluation on the Sawtooth platform compared BEATS against linear transaction search, database-supported search, and platform-specific solutions. Results demonstrate that the RSA-based Cryptographic Accumulator significantly improves verification efficiency, achieving O(1) time and space complexity. This research shows that BEATS enhances the practicality and scalability of blockchain-based audit trail systems for large-scale applications, advancing blockchain technology adoption in domains that demand secure, efficient, and scalable audit solutions.}, archive = {J_ACCESS}, author = {Bilal Alagha and Ilker Ozcelik}, doi = {10.1109/ACCESS.2025.3582722}, journal = {IEEE Access}, pages = {109657-109669}, shortjournal = {IEEE Access}, title = {BEATS: Practical audit trail in blockchain systems}, volume = {13}, year = {2025}, }
• (2025). A novel data-driven MPC framework using KDE and KPCA for autonomous vehicles. ACCESS, 13, 109638-109656. (www)

  @article{ , abstract = {Modeling and controlling complex, nonlinear, large-scale systems such as autonomous vehicles presents significant challenges due to high dimensionality, uncertain dynamics, and real-time constraints. This paper introduces a novel data-driven predictive control framework that synergistically integrates Kernel Density Estimation (KDE), Kernel Principal Component Analysis (KPCA), and Hankel matrix representation to address these challenges. The Hankel matrix formulation captures temporal correlations in system dynamics, enabling an effective representation of state transitions and dynamic modes. This representation is complemented by KPCA for nonlinear dimensionality reduction and KDE for probabilistic uncertainty quantification, resulting in a comprehensive framework that maintains computational tractability without sacrificing model fidelity. We present a rigorous mathematical foundation for the integration of these techniques, establishing formal convergence guarantees and error bounds for the kernel-based approximations. The framework demonstrates remarkable efficiency in feature extraction, preserving 95% of system variance with only two principal components, while the KDE component provides robust probabilistic predictions essential for safe autonomous navigation. Extensive experimental validation on an autonomous vehicle platform yields outstanding performance metrics: lateral tracking errors of 0.0225 m (RMSE), heading errors of 0.0476 rad, and longitudinal velocity tracking errors of 0.2725 m/s. These results represent an 81.3–89.8% improvement over traditional MPC approaches across diverse operational scenarios, including urban navigation, highway driving, parking maneuvers, and obstacle avoidance. The computational efficiency of our approach (1.6 ms execution time) enables real-time implementation on standard automotive-grade embedded control units while maintaining 100% compliance with lateral safety constraints. Comprehensive comparative analysis against state-of-the-art methods demonstrates that our framework effectively addresses the fundamental challenges of nonlinearity, high dimensionality, uncertainty quantification, and real-time implementation in autonomous vehicle control. These contributions advance the field of data-driven control for complex, nonlinear, large-scale systems, offering a principled alternative to traditional model-based approaches with significant practical implications for autonomous driving technologies.}, archive = {J_ACCESS}, author = {Romdhane Nasri and Jannet Jamii and Majdi Mansouri and Zouhaier Affi and Vicenc Puig}, doi = {10.1109/ACCESS.2025.3575283}, journal = {IEEE Access}, pages = {109638-109656}, shortjournal = {IEEE Access}, title = {A novel data-driven MPC framework using KDE and KPCA for autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Learning to be a transformer to pinpoint anomalies. ACCESS, 13, 109624-109637. (www)

  @article{ , abstract = {To efficiently deploy strong, often pre-trained feature extractors, recent Industrial Anomaly Detection and Segmentation (IADS) methods process low-resolution images, e.g., $224 \times 224$ pixels, obtained by downsampling the original input images. However, while numerous industrial applications demand the identification of both large and small defects, downsampling the input image to a low resolution may hinder a method’s ability to pinpoint tiny anomalies. We propose a novel Teacher-Student paradigm to leverage strong pre-trained features while processing high-resolution input images very efficiently. The core idea concerns training two shallow MLPs (the Students) by nominal images so as to mimic the mappings between the patch embeddings induced by the self-attention layers of a frozen vision Transformer (the Teacher). Indeed, learning these mappings sets forth a challenging pretext task that small-capacity models are unlikely to accomplish on out-of-distribution data such as anomalous images. Our method can spot anomalies from high-resolution images and runs way faster than competitors, achieving state-of-the-art performance on MVTec AD and the best segmentation results on VisA. We also propose novel evaluation metrics to capture robustness to defect size, i.e., the ability to preserve good localisation from large anomalies to tiny ones. Evaluating our method also by these metrics reveals its neatly superior performance.}, archive = {J_ACCESS}, author = {Alex Costanzino and Pierluigi Zama Ramirez and Giuseppe Lisanti and Luigi Di Stefano}, doi = {10.1109/ACCESS.2025.3582900}, journal = {IEEE Access}, pages = {109624-109637}, shortjournal = {IEEE Access}, title = {Learning to be a transformer to pinpoint anomalies}, volume = {13}, year = {2025}, }
• (2025). Broken bags: Disrupting service through the contamination of large language models with misinformation. ACCESS, 13, 109607-109623. (www)

  @article{ , abstract = {Large language models (LLMs) have progressively become essential production tools in contemporary society, owing to their formidable natural language generation and contextual reasoning skills. To facilitate the development of current responses by LLMs, individuals have used retrieval-augmented generation (RAG) technology, which extracts material from the corpus to assist large language models in producing relevant replies. The extensive utilization of huge language models necessitates urgent RAG security research. Conventional RAG attack techniques exhibit inadequate hiding and a substantial volume of harmful messages. Consequently, we have introduced an innovative attack mechanism termed “Broken Bags,” which adeptly injects a minimal quantity of toxic text to mislead large language models. The attack is executed through a hybrid approach that incorporates artificial prompt templates, toxic content generated by LLMs, and filtering mechanisms. For instance, when the RAG system engages with publicly available knowledge bases, adversaries can take advantage of the accessibility of these RAG knowledge bases to introduce malicious texts into the retrieval database, so as to intentionally alter the model’s behavior. This work employs the linguistic similarity between toxic content and the geographical vector characteristics of the “query question” to influence the information returned by RAG, hence preventing the LLM from generating responses to the target questions. We developed and refined an artificial prompt template to render toxic language more akin to authentic human expressions and less detectable. Experimental data indicates that our attack success rate attains 94%. Ultimately, we systematically evaluate state-of-the-art defenses (including perplexity-based detection and knowledge extension, among others), and the findings indicate that these measures are unable to counter “Broken Bags,” hence significantly enhancing the success rate of assaults on RAG systems.}, archive = {J_ACCESS}, author = {Yonghua Mo and Maoyang Tang and Ruohan Lin and Bohao Zhou and Xiaojian Li}, doi = {10.1109/ACCESS.2025.3582519}, journal = {IEEE Access}, pages = {109607-109623}, shortjournal = {IEEE Access}, title = {Broken bags: Disrupting service through the contamination of large language models with misinformation}, volume = {13}, year = {2025}, }
• (2025). Construction and mathematical application of document-level relationship extraction model combining R-GCN and text features. ACCESS, 13, 109593-109606. (www)

  @article{ , abstract = {With the development of smart education, building a knowledge graph to integrate educational resources has become particularly important. However, existing sentence level relationship extraction methods are difficult to address the complex relationship extraction of cross sentence and long-distance dependencies in educational texts. Therefore, a document-level relationship extraction model based on educational text features is proposed, which combines the graph convolutional neural network with text features to optimize the recognition ability of cross-sentence relationships. Unlike conventional methods, this study first constructs a document-level relationship extraction dataset for mathematics. Five types of mathematical knowledge point relationships are defined to construct a mathematical knowledge graph. This model uses graph convolutional neural networks to capture complex semantic relationships between entities in sentences, and extracts contextual information through a pre-trained language model BERT, demonstrating excellent performance in mathematical knowledge point relationship modeling. The model achieved an accuracy of 96.34%. In the extraction of cross-sentence relationships, the F1 value reached 92.05%. Regarding the relationship between mathematical knowledge points, the accuracy and recall in five types were all 0.87 to 0.92, and the accuracy and recall in complex nested relationships were 0.95 and 0.93, respectively. In low resource scenarios, the model still maintained a precision of 0.81 and an F1 value of 0.83 at a data scale of 10%, demonstrating excellent robustness. This research has improved the efficiency and accuracy of extracting educational text relationships, and expanded the application of document-level relationship extraction technology in the field of education, which has practical value for constructing mathematical knowledge graphs.}, archive = {J_ACCESS}, author = {Zhiqin Chen}, doi = {10.1109/ACCESS.2025.3580734}, journal = {IEEE Access}, pages = {109593-109606}, shortjournal = {IEEE Access}, title = {Construction and mathematical application of document-level relationship extraction model combining R-GCN and text features}, volume = {13}, year = {2025}, }
• (2025). Communication-free protection scheme with improved selectivity and backup reliability for ring DC networks. ACCESS, 13, 109579-109592. (www)

  @article{ , abstract = {This paper proposes a novel protection method for low- and medium-voltage DC networks. It is an easy-to-implement, low-cost, and communication-free protection scheme, applicable in ring configurations. The relays in this method calculate the difference between the post-fault and pre-fault currents, and the tripping time is determined using a time-current curve based on this difference. The core principle of the proposed scheme leverages the reduction in post-fault load current, caused by the significant voltage drop, and the controllable increase in inverter-based distributed generation current relative to their pre-fault values. As a result, relays located closer to the fault detect a more significant current variation, making them trip first. Specifically, in the simulations, relays located near the fault detect a current variation from 100 A to 300 A due to the fault, which can be effectively leveraged for fault detection. This behavior enhances selectivity and ensures reliable backup operation. Simulations conducted in MATLAB/Simulink validate the method speed, selectivity, and reliability. Moreover, the method demonstrates strong noise immunity, as confirmed by simulations, even for signal-to-noise ratios (SNR) as high as 10 dB. A brief comparison with state-of-the-art protection schemes underscores the advantages of the proposed method.}, archive = {J_ACCESS}, author = {Evangelos E. Pompodakis and Georgios I. Orfanoudakis and Theofilos A. Papadopoulos}, doi = {10.1109/ACCESS.2025.3582498}, journal = {IEEE Access}, pages = {109579-109592}, shortjournal = {IEEE Access}, title = {Communication-free protection scheme with improved selectivity and backup reliability for ring DC networks}, volume = {13}, year = {2025}, }
• (2025). Optimized physiological monitoring system for COVID-19 using VLC-based 3D localization and markov chain analysis. ACCESS, 13, 109553-109578. (www)

  @article{ , abstract = {This research presents an optimized physiological monitoring system for COVID-19 patients, integrating Visible Light Communication (VLC)-based three-dimensional localization with Markov Chain Analysis. The proposed system enables real-time tracking of vital physiological indicators while balancing localization accuracy and computational efficiency, making it suitable for real-world healthcare applications. The VLC-based localization system was implemented using three LED beacons and optimized through Particle Swarm Optimization (PSO). The analysis revealed that optimal PSO parameters ( $c_{1} = 1.9$ , $c_{2} = 2.1$ , and $w = 0.8$ ) significantly improved positioning accuracy, with 700 to 1100 particles providing the best trade-off between precision and computational cost. Additionally, the system successfully measured and calibrated cough frequency, respiratory rate, and oxygen saturation (SpO2) using MEMS sensors. The results showed a cough frequency peak at 0.2 Hz, an average respiratory rate of 1.3 breaths per minute, and precise detection of hypoxia events through infrared and red light absorption. To assess disease progression, a Markov Chain Model was developed, analyzing heart rate, temperature, cough frequency, respiratory rate, and SpO2 levels. The model identified four distinct patient states, ranging from mild to severe conditions, and provided probabilistic insights into symptom deterioration. A heat map analysis confirmed the reliability of state transition probabilities. The study underscores the critical trade-off between localization accuracy and computational efficiency, emphasizing the importance of careful parameter selection for real-time medical applications.}, archive = {J_ACCESS}, author = {Eduardo Viera Riquelme and Diego Alonso Candia and Ismael Soto and Carolina Lagos and Pablo Palacios Játiva and Raúl Carrasco and Cesar Azurdia Meza and Iván Sánchez}, doi = {10.1109/ACCESS.2025.3581790}, journal = {IEEE Access}, pages = {109553-109578}, shortjournal = {IEEE Access}, title = {Optimized physiological monitoring system for COVID-19 using VLC-based 3D localization and markov chain analysis}, volume = {13}, year = {2025}, }
• (2025). Succinct link transformation-based overlapping community detection framework for social network analysis. ACCESS, 13, 109539-109552. (www)

  @article{ , abstract = {Detecting communities in real-world networks is a challenging and important problem. Numerous algorithms have been proposed in recent years, and significant progress has been made. Overlapping community detection, where individual nodes may belong to multiple communities, has gained attention for its applicability to real-world scenarios. However, accurately analyzing complex networks with high levels of overlap remains difficult. This study presents a succinct link transformation-based framework for overlapping community detection that addresses the challenges of dense and large-scale networks while supporting scalability. The framework transforms the original network into a succinct line graph, or edge-centric graph, by leveraging both node-node and edge-edge (referred to as “link”) relationships. Within this transformed graph, edges and links are prioritized using minwise hashing, resulting in an efficient link transformation method. The proposed framework was evaluated against mainstream overlapping community detection algorithms. Experimental results showed superior performance on dense overlapping graphs, with up to 53% improvement in computational efficiency and up to 67% increase in detection accuracy compared to existing methods, demonstrating both scalability and extensibility. This framework enables high-quality overlapping community detection on dense and large-scale real-world networks using various existing algorithms.}, archive = {J_ACCESS}, author = {Seungwoo Ryu and Sungsu Lim and Seungsoo Yoo and Sun Yong Kim}, doi = {10.1109/ACCESS.2025.3573293}, journal = {IEEE Access}, pages = {109539-109552}, shortjournal = {IEEE Access}, title = {Succinct link transformation-based overlapping community detection framework for social network analysis}, volume = {13}, year = {2025}, }
• (2025). Robust drone video analysis for occluded urban traffic monitoring based on deep learning. ACCESS, 13, 109528-109538. (www)

  @article{ , abstract = {Urban traffic management (UTM) relies on accurate vehicular flow data to optimize infrastructure and reduce congestion. However, existing video-based methods struggle with occlusions and complex trajectories in multi-directional intersections, limiting their applicability. This paper presents a novel Speed Calculation and Trajectory Observation Algorithm (SCTOA) for robust vehicle counting, classification, and speed estimation in visually obstructed urban environments (e.g., tunnels, overpasses). Our approach integrates drone-captured aerial footage with YOLOv8 detection, ByteTrack tracking, and LoFTR-based motion compensation. To address occlusion challenges, we propose strategically placed Observation Areas (OAs), in points of traffic bifurcation, improving the overall performance. By mapping vehicle trajectories between OAs we achieve up to 96.73% accuracy on vehicle counting in unobstructed zones and 87.8% in complex scenarios, —outperforming prior single-direction methods. Experimental validation across 27 hours of drone footage from high-traffic intersections demonstrates the efficacy of the method for multi-directional flow analysis. The results enable precise input for traffic simulators (e.g., PTV-Vissim), supporting data-driven UTM decisions while minimizing costly real-world experimentation.}, archive = {J_ACCESS}, author = {Carlos Gellida-Coutiño and Reyes Rios-Cabrera and Alan Maldonado-Ramirez and Anand Sanchez-Orta}, doi = {10.1109/ACCESS.2025.3582270}, journal = {IEEE Access}, pages = {109528-109538}, shortjournal = {IEEE Access}, title = {Robust drone video analysis for occluded urban traffic monitoring based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Noncontact multispectral SpO2 prediction based on deep ratio-of-ratio refinement with optimal band selection and shading bias removal. ACCESS, 13, 109513-109527. (www)

  @article{ , abstract = {Noncontact prediction of peripheral oxygen saturation (SpO2) is necessary for monitoring vital signs of patients afflicted by infectious disease or sensitive to skin irritation. Recently, many approaches to imaging photoplethysmography (iPPG)-based noncontact SpO2 prediction have been proposed based on ratio-of-ratio (RoR) representing spectral ratio of diffused absorbances and deep learning (DL). Despite notable progress, issues concerning the accuracy and robustness under real-world variations and artifacts remain to be solved toward clinical viability. In this study, we proposed an approach to highly accurate and robust SpO2 prediction under significant disturbances based on multispectral iPPG signals captured from human facial regions of interest (ROIs). First, a method for accurately estimating actual diffused spectral absorbance of ROIs is proposed. To this end, the shading bias representing the discrepancy between the measured and actual diffused absorbances is calculated by projecting the measured diffused absorbance onto the actual diffused absorbance manifold preformulated based on Monte-Carlo modeling of light transport in multilayered tissues (MCML). Furthermore, the spectral band pairs optimal for estimating shading bias and actual diffused absorbances are selected, resulting in highly accurate RoRs. Second, the SpO2s derived from RoRs are spatiotemporally refined and regressed based on an LSTM AE network further to upgrade the accuracy and robustness of SpO2 prediction. Lastly, individual SpO2s predicted with multiple spectral band pairs and at multiple ROIs are fused to ensure a resilience against possible disruptions due to motion and appearance artifacts. Experiments were conducted with 12 subjects whose SpO2 levels were adjusted between 0.79-0.99 by breath-holding cycles. The cross-validated performance indicated about 2% of Mean Absolute Error (MAE) accuracy in case SpO2s were derived solely from the estimated RoRs while the accuracy of 0.46% of MAE was reached with the LSTM AE-based spatiotemporal refinement and regression.}, archive = {J_ACCESS}, author = {Sukhan Lee and Hyunwoo Park}, doi = {10.1109/ACCESS.2025.3582260}, journal = {IEEE Access}, pages = {109513-109527}, shortjournal = {IEEE Access}, title = {Noncontact multispectral SpO2 prediction based on deep ratio-of-ratio refinement with optimal band selection and shading bias removal}, volume = {13}, year = {2025}, }
• (2025). AttCORAL: Domain-adaptive attention networks for early alzheimer’s disease diagnosis. ACCESS, 13, 109503-109512. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders characterized by the progressive accumulation of amyloid-beta plaques and tau protein tangles in the brain. Due to the lack of a cure for Alzheimer’s disease, early and accurate diagnosis of AD is crucial for effective early interventions to slow disease progression. Magnetic Resonance Imaging (MRI) has emerged as a promising modality for early diagnosis, providing detailed insights into brain structure alterations associated with AD. However, domain shift due to variations in imaging protocols and data distribution among national cohorts remains a challenge for the application of MRI in clinical diagnosis. To address this issue, we propose AttCORAL, a novel Domain-Adaptive Attention Network for Early Alzheimer’s Disease Diagnosis, integrating attention mechanisms with Correlation Alignment (CORAL) loss to effectively mitigate domain discrepancy, enhancing the model’s robustness and generalization. We evaluate AttCORAL on two large-scale MRI datasets—Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarkers, and Lifestyle Study (AIBL)—which differ in acquisition protocols and demographics. While these datasets provide a valuable basis for cross-cohort validation, we acknowledge that further multi-cohort studies are necessary to fully assess global generalizability. To ensure the reliability of our approach, we apply Grad-CAM to visualize the pathological brain regions most informative for our model’s predictions. Experimental results demonstrate that AttCORAL significantly outperforms current state-of-the-art studies, highlighting its effectiveness in early diagnosis of AD across diverse imaging domains.}, archive = {J_ACCESS}, author = {Gia Minh Hoang and Nhat Hoang and Tran Thi Hoai Thu and Hoang Tien Trong Nghia and Jae Gwan Kim}, doi = {10.1109/ACCESS.2025.3580780}, journal = {IEEE Access}, pages = {109503-109512}, shortjournal = {IEEE Access}, title = {AttCORAL: Domain-adaptive attention networks for early alzheimer’s disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Predicting diabetic distress and emotional burden in type-2 diabetes using explainable AI. ACCESS, 13, 109484-109502. (www)

  @article{ , abstract = {Diabetic distress is a significant psychological burden affecting many individuals with Type-2 Diabetes Mellitus. Despite its prevalence, it remains underrecognized, and its impact can be complex. This study explores the integration of multimodal data sources and machine learning to identify diabetic distress and emotional burden in patients with Type-2 Diabetes Mellitus. The study highlights the use of Explainable Artificial Intelligence to make predictions more transparent. It combines patient information like age, gender, laboratory results, and survey scores to train and compare machine learning methods. Models tested include Ridge regression, Lasso regression, linear and logistic regression, neural networks, support vector machines, random forests, and various boosting techniques. Among all of these, Extreme Gradient Boosting with SHapley Additive exPlanations delivered the best results. It was 96.14% accurate, 0.94 precise, had an ROC-AUC of 0.98, an F1-score of 0.95, and a recall of 0.95. The approach also generates explanations of why a specific prediction is being generated. For instance, it shows how specific blood glucose levels or distress subscale scores influence the predicted emotional load. These clear explanations enable clinicians to make sense of both the model’s performance and the reasoning behind it. Such transparency is critical to the building of trust in machine learning-based decision support systems for diabetes care takers influencing DDS.}, archive = {J_ACCESS}, author = {Ali Al Bataineh and Bandi Vamsi and Abdullah Al-Refai}, doi = {10.1109/ACCESS.2025.3582191}, journal = {IEEE Access}, pages = {109484-109502}, shortjournal = {IEEE Access}, title = {Predicting diabetic distress and emotional burden in type-2 diabetes using explainable AI}, volume = {13}, year = {2025}, }
• (2025). Off-center rotational dynamics in virtual reality: Effects on cybersickness and presence. ACCESS, 13, 109473-109483. (www)

  @article{ , abstract = {This study investigates the effects of off-center rotational acceleration and rotational radius on cybersickness and spatial presence in virtual reality (VR) environments through a set of constrained rotational scenarios. Unlike previous studies, which assumed that the rotational axis is aligned with the virtual user position, this research focuses on off-center rotations, which more frequently occur in VR without alignment to the user position. Rotational acceleration significantly increased cybersickness severity, while its effect on presence was less substantial. As rotational radius increased, both cybersickness and presence intensified, with the influence on presence being more pronounced. Multiple regression analysis (MRA) revealed that, like acceleration, radius is a significant factor influencing cybersickness and presence. Additionally, individual linear regression analysis (LRA) and violin plots indicated significant differences among different radius groups. The correlation between the two dependent variables was positive but weak; this result suggests that while increased presence may coincide with heightened cybersickness due to immersive factors like vection, the two variables often act independently. This suggests the need to further investigate the interplay between cybersickness and presence in VR environments.}, archive = {J_ACCESS}, author = {Sion Cha and Jonghyeon Ka and Imbo Kong and Junseok Ma and Sangyeon Park and Wooksung Kim}, doi = {10.1109/ACCESS.2025.3580956}, journal = {IEEE Access}, pages = {109473-109483}, shortjournal = {IEEE Access}, title = {Off-center rotational dynamics in virtual reality: Effects on cybersickness and presence}, volume = {13}, year = {2025}, }
• (2025). 5G cellular communication for maritime applications. ACCESS, 13, 109451-109472. (www)

  @article{ , abstract = {Recent technological advancements in mobile cellular communication have brought to light a variety of new application scenarios in the field of maritime communication that were not possible with earlier generations of cellular communication. Communication technologies in the maritime domain have diverse application scenarios with varying communication requirements. Typically, these requirements are far different and much more stringent than those commonly set for the use cases of communication technologies ashore. To date, maritime communication has mostly utilized wired connectivity for onboard critical communication, while satellite communication is utilized typically for remote connectivity to shore. However, with the advent of 5G cellular communication with the provision for adapting service quality according to different applications, several existing communication requirements can better be addressed as well as a whole set of new applications can be developed. This article presents an illustrative review of 5G cellular technology and discusses potential use cases of 5G in the maritime domain, thus paving the way towards the uptake of 5G technology for maritime applications. First, we provide a short introduction to 5G cellular technology and its key features that are relevant to maritime applications. Thereafter, we present some of the key application areas of 5G cellular technology in the maritime sector, highlighting potential application topologies. This article also highlights the key novelties in 5G that are believed to be of significance to maritime and offshore applications in the near future.}, archive = {J_ACCESS}, author = {Gulzaib Rafiq and Pritam Bose and Pål Orten}, doi = {10.1109/ACCESS.2025.3581591}, journal = {IEEE Access}, pages = {109451-109472}, shortjournal = {IEEE Access}, title = {5G cellular communication for maritime applications}, volume = {13}, year = {2025}, }
• (2025). A hotspot prediction scheme based on ensemble learning with adaptive beamforming for virtual small cell in 5G networks. ACCESS, 13, 109435-109450. (www)

  @article{ , abstract = {Effectively managing network congestion in 5G systems is crucial for ensuring seamless connectivity and optimal resource utilization. In this study, an operational scheme based on hotspot prediction for Virtual Small Cell (VSC) is presented, which aims to improve coverage, reduce delay and improve energy efficiency in 5G networks for congested locations. For this purpose, at first, using the location map of users, the location of hotspots is predicted by an Ensemble deep learning algorithm. Ensemble deep learning algorithm in this research combines the advantages of GRU and GRN networks and creates an efficient and accurate deep model that is more accurate than previous approaches that used single networks. Then, the information of hotspots is used to form VSCs and finally the adaptive beamforming operation is performed. To optimally allocate resources, genetic algorithm has been used so that the answer of the problem is not trapped in the local optimum. Simulation on the Telecom Italia dataset demonstrates a 10.13% RMSE, showing superior hotspot prediction accuracy compared to existing methods. Also, the numerical results of this study show that by using the hotspots prediction results, the energy efficiency of the system based on VSC operation and adaptive beamforming can be significantly improved.}, archive = {J_ACCESS}, author = {Aws Majeed Ghalib Alawadi and Hadi Seyedarabi and Reza Afrouzian and Javad Musevi Niya}, doi = {10.1109/ACCESS.2025.3574119}, journal = {IEEE Access}, pages = {109435-109450}, shortjournal = {IEEE Access}, title = {A hotspot prediction scheme based on ensemble learning with adaptive beamforming for virtual small cell in 5G networks}, volume = {13}, year = {2025}, }
• (2025). Timing matters: Analyzing government intervention intensity and effectiveness in controlling rumor spread. ACCESS, 13, 109418-109434. (www)

  @article{ , abstract = {This study aims to research how government intervention intensity and effectiveness can control rumor spread under the condition of distinguishing the timing of intervention. This study establishes two extended Susceptible-Exposed-Infected-Removed (SEIR) models and then uses numerical analysis to simulate the impact of government intervention intensity and intervention effectiveness on rumor spread under different situations. There are two main findings in this research: 1. Government intervention, in terms of both intensity and effectiveness, significantly reduces the spread and impact of rumors. Higher intervention levels not only diminish the rumor’s reach but also slow down its spread. 2. The timing of intervention strategies should vary based on specific conditions: Pre-exposure interventions are recommended when intervention intensity is low, when intensity is high but the rumor is expected to last long, or when both intensity and effectiveness are high with a short rumor duration. Post-exposure interventions are more effective when both intensity and effectiveness are high, and the rumor spread is expected to last long. This study has two theoretical innovations: 1. It extends the traditional rumor propagation model by adding a government intervention component. 2. It introduces the concept of intervention timing, distinguishing between pre-exposure and post-exposure phases, thus enhancing the understanding of how timing affects rumor control. These insights can inform strategies for monitoring and managing public opinion across various fields.}, archive = {J_ACCESS}, author = {Yuxun Zhou and Mohammad Mafizur Rahman and Ren Long and Xiaoyun Liu}, doi = {10.1109/ACCESS.2025.3581040}, journal = {IEEE Access}, pages = {109418-109434}, shortjournal = {IEEE Access}, title = {Timing matters: Analyzing government intervention intensity and effectiveness in controlling rumor spread}, volume = {13}, year = {2025}, }
• (2025). Enhancing CNN-based signal denoising: A novel metric framework with harmonic suppression through hybrid modeling. ACCESS, 13, 109399-109417. (www)

  @article{ , abstract = {Convolutional neural networks (CNNs) show promise for signal denoising but can introduce harmonic distortions due to their nonlinearity. This paper introduces a comprehensive evaluation framework that merges traditional metrics like Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE) with proposed harmonic power-related metrics: Fundamental Power Ratio (FPR), Fundamental to Total Harmonic Power ratio (FTHPR), and Harmonic Power Ratio (HPR) to analyze the performance and generalization of four CNN architectures: Denoising Convolutional Neural Network (DnCNN), Deep Convolutional Neural Network (DCNN), Deep Convolutional Autoencoder (DDCAE), and a hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) model. The models were trained on sinusoids with white noise and tested under in-distribution, out-of-distribution (OOD) colored noise (pink/blue, lower SNRs) and real-world signal conditions. The results show that while DDCAE is computationally efficient and performs well in distribution, it struggles with excessive harmonic generation under OOD/real-world conditions. CNN-LSTM demonstrates superior spectral purity and generalization across synthetic colored noises. However, DCNN exhibits greater robustness in complex real-world signals and under extremely low SNR synthetic conditions, effectively handling noise and harmonic distortions where other models falter. A proposed hybrid model (post-CNN linear filter) generally improved performance, particularly by mitigating harmonic issues for models like DDCAE. These findings highlight significant performance trade-offs and demonstrate that the optimal model choice is highly context-dependent (noise type, SNR, computational budget). Comprehensive out-of-distribution evaluations and metrics that account for harmonic content are essential for choosing dependable denoising models in real-world settings.}, archive = {J_ACCESS}, author = {Omer Nacar and Turgay Koc}, doi = {10.1109/ACCESS.2025.3582668}, journal = {IEEE Access}, pages = {109399-109417}, shortjournal = {IEEE Access}, title = {Enhancing CNN-based signal denoising: A novel metric framework with harmonic suppression through hybrid modeling}, volume = {13}, year = {2025}, }
• (2025). Fatigue-PINN: Physics-informed fatigue-driven motion modulation and synthesis. ACCESS, 13, 109378-109398. (www)

  @article{ , abstract = {Fatigue modeling is essential for motion synthesis tasks to model human motions under fatigued conditions and biomechanical engineering applications, such as investigating the variations in movement patterns and posture due to fatigue, defining injury risk mitigation and prevention strategies, formulating fatigue minimization schemes, and creating improved ergonomic designs. Nevertheless, employing data-driven methods for synthesizing the impact of fatigue on motion, receives little to no attention in the literature. In this work, we present Fatigue-PINN, a deep learning framework based on Physics-Informed Neural Networks, for modeling fatigued human movements, while providing joint-specific fatigue configurations for adaptation and mitigation of motion artifacts on a joint level, resulting in more smooth, hence physically-plausible animations. To account for muscle fatigue, we simulate the fatigue-induced fluctuations in the maximum exerted joint torques by leveraging a PINN adaptation of the Three-Compartment Controller model to exploit physics-domain knowledge for improving accuracy. This model also introduces parametric motion alignment with respect to joint-specific fatigue, hence avoiding sharp frame transitions. Our results indicate that Fatigue-PINN accurately simulates the effects of externally perceived fatigue on open-type human movements being consistent with findings from real-world experimental fatigue studies. Since fatigue is incorporated in torque space, Fatigue-PINN provides an end-to-end encoder-decoder-like architecture, to ensure transforming joint angles to joint torques and vice-versa, thus, being compatible with motion synthesis frameworks operating on joint angles.}, archive = {J_ACCESS}, author = {Iliana Loi and Konstantinos Moustakas}, doi = {10.1109/ACCESS.2025.3582731}, journal = {IEEE Access}, pages = {109378-109398}, shortjournal = {IEEE Access}, title = {Fatigue-PINN: Physics-informed fatigue-driven motion modulation and synthesis}, volume = {13}, year = {2025}, }
• (2025). The impact of LoRA adapters on LLMs for clinical text classification under computational and data constraints. ACCESS, 13, 109365-109377. (www)

  @article{ , abstract = {Fine-tuning Large Language Models (LLMs) for clinical Natural Language Processing (NLP) poses significant challenges due to domain gap, limited data, and stringent hardware constraints. In this study, we evaluate four adapter techniques—Adapter, Lightweight, TinyAttention, and Gated Residual Network (GRN) - equivalent to Low-Rank Adaptation (LoRA), for clinical note classification under real-world, resource-constrained conditions. All experiments were conducted on a single NVIDIA Quadro P620 GPU (2 GB VRAM, 512 CUDA cores, 1.386 TFLOPS FP32), limiting batch sizes to $\leq 8$ sequences and maximum sequence length to 256 tokens. Our clinical corpus comprises only 580 000 tokens, several orders of magnitude smaller than standard LLM pre-training datasets. We fine-tuned three biomedical pre-trained LLMs (CamemBERT-bio, AliBERT, DrBERT) and two lightweight Transformer models trained from scratch. Results show that 1) adapter structures provide no consistent gains when fine-tuning biomedical LLMs under these constraints, and 2) simpler Transformers, with minimal parameter counts and training times under six hours, outperform adapter-augmented LLMs, which required over 1000 GPU-hours. Among adapters, GRN achieved the best metrics (accuracy, precision, recall, F1 = 0.88). These findings demonstrate that, in low-resource clinical settings with limited data and compute, lightweight Transformers trained from scratch offer a more practical and efficient solution than large LLMs, while GRN remains a viable adapter choice when minimal adaptation is needed.}, archive = {J_ACCESS}, author = {Thanh-Dung Le and Ti Ti Nguyen and Vu Nguyen Ha and Symeon Chatzinotas and Philippe Jouvet and Rita Noumeir}, doi = {10.1109/ACCESS.2025.3582037}, journal = {IEEE Access}, pages = {109365-109377}, shortjournal = {IEEE Access}, title = {The impact of LoRA adapters on LLMs for clinical text classification under computational and data constraints}, volume = {13}, year = {2025}, }
• (2025). A hybrid PSO-GA optimized approach for COVID-19 detection using CT scan. ACCESS, 13, 109345-109364. (www)

  @article{ , abstract = {The rapid global spread of COVID-19 has underscored the urgency of developing efficient diagnostic tools. Although RT-PCR is the gold standard for diagnosis, its time and resource-intensive nature emphasizes the need for alternative approaches. This study introduces a three-step hybrid approach for rapid COVID-19 detection using CT scans to address existing challenges. In the first step, pre-trained convolutional neural networks (CNNs), including VGG-16, ResNet-50, and MobileNet-v2, are utilized to extract critical features from COVID-19-affected lung images. In the second step, a hybrid particle swarm optimization (PSO) and genetic algorithm (GA) optimized approach, called Hybrid PSO-GA, is developed and used to select the optimal features that can increase the accuracy of COVID-19 detection. Finally, the selected features are classified using four distinct models. Remarkable classification accuracies of 99.88% and 99.92% were achieved on the COVIDx-2A CT dataset and the SARS-CoV-2 CT-Scan dataset, respectively. This methodology is novel due to the combination of multiple CNN architectures with sophisticated feature selection algorithms, augmenting the strengths of each method to enhance the accuracy of COVID-19 medical diagnosis, where precision is paramount. The method also exhibits efficiency in the feature selection phase by integrating PSO and GA, utilizing the complementary advantages of both methods.}, archive = {J_ACCESS}, author = {Abdelghany Fathy and Hatem Abdelkader and Samia Allaoua Chelloug and Mohammed Saleh Ali Muthanna and Amira Abdelatey}, doi = {10.1109/ACCESS.2025.3581852}, journal = {IEEE Access}, pages = {109345-109364}, shortjournal = {IEEE Access}, title = {A hybrid PSO-GA optimized approach for COVID-19 detection using CT scan}, volume = {13}, year = {2025}, }
• (2025). No-reference error detection in color difference datasets: Application to munsell data. ACCESS, 13, 109322-109344. (www)

  @article{ , abstract = {The Munsell dataset is a cornerstone in color science, offering extensive coverage of large color differences across a wide gamut, and serving as a prominent resource for developing and validating color models. The widely used second version, the “Munsell Renotation” dataset, has been instrumental in advancing uniform color spaces (UCS). However, substantial errors in the third version, the “Re-renotation”, compromise its reliability for optimizing color models. In this study, we identify and correct typographical errors in the “Re-renotation” dataset and introduce two semi-automatic methods for error detection. The Global Metric Outlier Detection (GMOD) method applies to datasets with varied structures, while the Smooth Curve Outlier Detection (SCOD) method targets ordered datasets, such as the Munsell dataset. These methods operate without relying on external references, instead assuming that a true color space metric guarantees gradual and smooth perceptual change between colors. Our revised dataset demonstrates significantly improved consistency with established UCS models. The corrections and methods introduced here support the ongoing refinement of psychophysical color difference datasets, with all results and data made available in a machine-readable format.}, archive = {J_ACCESS}, author = {Valerii Timofeev and Galim Usaev and Mikhail Seliugin and Dmitry Bocharov and Anastasia Sarycheva and Ivan Konovalenko and Olga Basova and Mikhail Tchobanou and Valentina Bozhkova and Dmitry Nikolaev}, doi = {10.1109/ACCESS.2025.3582053}, journal = {IEEE Access}, pages = {109322-109344}, shortjournal = {IEEE Access}, title = {No-reference error detection in color difference datasets: Application to munsell data}, volume = {13}, year = {2025}, }
• (2025). Analysis of impact on operating characteristics of new power system in low-temperature weather. ACCESS, 13, 109308-109321. (www)

  @article{ , abstract = {The secure operation of modern power systems is significantly compromised under low-temperature extreme weather conditions. This study investigates the operational characteristics of a novel power system under such conditions by integrating meteorological modeling with power system analysis. Specifically, the distinct features of two prevalent low-temperature weather patterns—cold waves and freezing rain—are incorporated into the system modeling framework. The proposed approach begins with a systematic analysis of meteorological characteristics and disaster-inducing factors, where the severity of these factors is employed to classify different weather scenarios. This classification enables the quantitative assessment of low-temperature impacts on power system components, particularly focusing on ice-induced generation loss in renewable energy systems and the increased failure probability of transmission lines due to ice accumulation. Subsequently, the operational behavior of power system equipment under extreme weather is simulated using a non-sequential Monte Carlo approach. To capture the dynamic topological changes caused by elevated line fault rates, graph-theoretic analysis and connected component identification techniques are applied. This leads to the development of a comprehensive power system model that accounts for both energy dynamics and structural reconfiguration. Key operational metrics, including voltage and system frequency, are evaluated through power flow analysis and primary frequency regulation. The proposed methodology is validated using the IEEE-RTS 79 test system, providing a quantitative assessment of power system performance under low-temperature weather.}, archive = {J_ACCESS}, author = {Zhipeng Liu and Yicong Wang and Yanling Lv}, doi = {10.1109/ACCESS.2025.3582548}, journal = {IEEE Access}, pages = {109308-109321}, shortjournal = {IEEE Access}, title = {Analysis of impact on operating characteristics of new power system in low-temperature weather}, volume = {13}, year = {2025}, }
• (2025). Further analysis on Fixed/Preassigned-time projective synchronization of discontinuous fuzzy delayed inertial neural networks. ACCESS, 13, 109294-109307. (www)

  @article{ , abstract = {Our goal is addressing the problems of preassigned-time projective synchronization and fixed-time projective synchronization of the system that studied in our article, and fuzzy terms, mixed delays, inertial terms and discontinuous activation function are also included in this system. And the results of this article are based on the latest fixed-time stabilization lemma. Through the construction of adaptative Lyapunov function and new corresponding feedback controllers, we arrive at the results of fixed-time projective synchronization and preassigned-time projective synchronization about investigated neural networks, and we are using a non-reduced-order approach. The proposed results are more extensive in comparison with traditional anti-synchronization and synchronization. To validate the theoretical findings, we provide numerical simulations demonstrating the effectiveness and advantages of the proposed control schemes. Furthermore, we provided confidential communications to further verify the validity of the results.}, archive = {J_ACCESS}, author = {Xiao Zhou and Jing Han and Yan Li and Guodong Zhang}, doi = {10.1109/ACCESS.2025.3582344}, journal = {IEEE Access}, pages = {109294-109307}, shortjournal = {IEEE Access}, title = {Further analysis on Fixed/Preassigned-time projective synchronization of discontinuous fuzzy delayed inertial neural networks}, volume = {13}, year = {2025}, }
• (2025). Design of a ROS2-based hybrid aerial-ground robot for autonomous inspection applications. ACCESS, 13, 109274-109293. (www)

  @article{ , abstract = {This study presents the design, modelling, and experimental validation of a lightweight hybrid aerial-ground robot developed for extended-duration inspection and monitoring tasks. The proposed system uniquely integrates a differential drive ground base with a quadrotor aerial module, thereby enabling adaptive mobility that facilitates effective navigation across constrained, cluttered, and complex environments. Built upon the Robot Operating System (ROS 2) framework, the robot architecture supports real-time control, modular software integration, and distributed task execution. A distinctive feature of the presented design is its energy-efficient operational strategy, emphasizing ground-based autonomous navigation and mapping for extended endurance while selectively utilising aerial capabilities for vertical access, obstacle circumvention, and challenging terrain traversal. Comprehensive experimental evaluations have been conducted to assess the robot’s performance, confirming its capability for smooth transitions between ground and aerial modes while reliably maintaining stability, precise localisation, and robust obstacle avoidance. These results illustrate the significant potential of the developed robotic platform for autonomous deployment in applications demanding extended operation times and versatile mobility, including infrastructure inspection, environmental monitoring, and search-and-rescue missions in complex scenarios.}, archive = {J_ACCESS}, author = {Mutaz Ryalat and Ghaith Al-Refai and Natheer Almtireen and Hisham ElMoaqet}, doi = {10.1109/ACCESS.2025.3582653}, journal = {IEEE Access}, pages = {109274-109293}, shortjournal = {IEEE Access}, title = {Design of a ROS2-based hybrid aerial-ground robot for autonomous inspection applications}, volume = {13}, year = {2025}, }
• (2025). LGFusion: Frequency-aware dual-branch integration network for infrared and visible image fusion. ACCESS, 13, 109254-109273. (www)

  @article{ , abstract = {Multimodal image fusion aims to generate a fused image that integrates complementary advantages from different modalities, for example, target saliency from infrared images and texture details from visible images. However, existing methods struggle to jointly model global and local features, separate shared and modality-specific information, and provide semantic interpretability. To address these challenges, we propose LGFusion, a dual-branch parallel fusion framework that enables structure-aware and detail-preserving feature extraction and fusion. We introduce a frequency-based structural assumption: low-frequency components across modalities are correlated and represent shared background and layout, while high-frequency components are modality-specific, capturing details like thermal patterns or textures. Based on this, we enhance the consistency of low-frequency features and increase the discriminability of high-frequency features to improve the interpretability and controllability of the fusion process. Additionally, we design an efficient feature extraction module combining Efficient Attention and the lightweight MambaBlock, which models long-range dependencies and global context at low computational cost. To further enhance local detail modeling, we integrate a high-pass filter with a Transformer encoder, effectively capturing high-frequency features such as edges and textures. Extensive experiments show that our method achieves state-of-the-art performance in infrared visible image fusion and demonstrates strong generalization in medical image fusion tasks.}, archive = {J_ACCESS}, author = {Ruizhe Shang and Jianan Liu and Xinhuai Wang}, doi = {10.1109/ACCESS.2025.3577033}, journal = {IEEE Access}, pages = {109254-109273}, shortjournal = {IEEE Access}, title = {LGFusion: Frequency-aware dual-branch integration network for infrared and visible image fusion}, volume = {13}, year = {2025}, }
• (2025). A survey on data selection for efficient speech processing. ACCESS, 13, 109234-109253. (www)

  @article{ , abstract = {While recent advances in speech processing have led to substantial performance improvements across diverse tasks, they often demand significantly higher computational costs and resources. To address this efficiency challenge, data selection has emerged as a crucial strategy. This survey provides a comprehensive overview and introduces a unifying taxonomy for data selection methods in speech processing, structured along three key dimensions: selection granularity (sample-level vs. segment-level), selection process (static, dynamic, or active learning), and selection criteria (uncertainty, diversity, or hybrid approaches). Through systematic analysis across major speech tasks, including automatic speech recognition, text-to-speech synthesis, audio anti-spoofing, speaker recognition, and emotion recognition, we evaluate the effectiveness and applicability of diverse data selection strategies. Our analysis reveals that targeted data selection not only alleviates computational burdens but often enhances model robustness and performance by strategically filtering redundant, noisy, or detrimental training examples. By synthesizing insights scattered across disparate speech domains, we identify common principles, highlight task-specific challenges, and reveal emerging research trends. Finally, we outline promising future research directions in data selection for efficient speech processing.}, archive = {J_ACCESS}, author = {Abdul Hameed Azeemi and Ihsan Ayyub Qazi and Agha Ali Raza}, doi = {10.1109/ACCESS.2025.3582395}, journal = {IEEE Access}, pages = {109234-109253}, shortjournal = {IEEE Access}, title = {A survey on data selection for efficient speech processing}, volume = {13}, year = {2025}, }
• (2025). Comparison and system development of a two-coil and three-coil inductive link for transcutaneous power transfer. ACCESS, 13, 109219-109233. (www)

  @article{ , abstract = {Implantable electronic devices are powerful tools to alleviate a range of medical conditions. To minimize the risk of infections and increase patient comfort, no wires should penetrate the skin. For devices with a relatively high power consumption and located right under the skin, wireless power transfer through an inductive link is the most reliable method to ensure robust and long-term functionality. However, the efficiency of these inductive links highly depends on the size, location and variable power consumption of the implant. Here, we developed a functional prototype for the application of a neurostimulator implant located subcutaneously in a recess in the skull. Keeping all geometric constraints constant, we investigated the differences between a two-coil and three-coil inductive link, showing that the two-coil system has a higher peak efficiency of the inductive link of 84%, while the three-coil system displays a flatter efficiency curve with a peak of 78%, as a function of the variable power consumption. Furthermore, we have developed a closed-loop electronic system to drive the link at a fixed frequency of 27.12 MHz, with an overall system efficiency of 30%. Our results demonstrate the advantages and disadvantages of using two-coil and three-coil inductive links, as well as the possibility to use these to transmit 60 mW to a subcutaneous implant. Such efficient and robust transcutaneous power transfer will allow for the development of safer and more patient-friendly implantable devices.}, archive = {J_ACCESS}, author = {Maarten Schelles and Ward Lievens and Laurens Goyvaerts and Frederik Ceyssens and Rikky Muller and Michael Kraft}, doi = {10.1109/ACCESS.2025.3581460}, journal = {IEEE Access}, pages = {109219-109233}, shortjournal = {IEEE Access}, title = {Comparison and system development of a two-coil and three-coil inductive link for transcutaneous power transfer}, volume = {13}, year = {2025}, }
• (2025). Weighted ensemble based on prisoner dilemma for facial expression recognition. ACCESS, 13, 109200-109218. (www)

  @article{ , abstract = {Facial Expression Recognition (FER) is a task that recognizes the expression or emotion of a person based on their face, enabling computers to identify the mood and emotions of individuals. FER tasks in real-world scenarios remain challenging due to the variations of many parameters captured by the image sensor. Many approaches have been proposed to improve FER tasks in real-world scenarios. One of them is the utilization of an ensemble model. The weighted ensemble is one way to do an ensemble by weighting each model with a weight. However, the weight values are left to the researcher to decide, which raises another problem in determining a weight for the weighted ensemble. In this research, we proposed a novel weighting voting inspired by the Prisoner Dilemma. Based on the experiments, our proposed method achieved an accuracy and f1 score of 83.25% and 75.02% respectively in the RAFDB dataset, and an accuracy and f1 score of 64.73% and 63.07% in the FER2013 dataset, which is relatively better than the other state-of-the-art and our baseline.}, archive = {J_ACCESS}, author = {Muhammad Hannan Hunafa and M Luthfi Ramadhan Mgs and Alif Wicaksana Ramadhan and Muhammad Febrian Rachmadi and Wisnu Jatmiko}, doi = {10.1109/ACCESS.2025.3582643}, journal = {IEEE Access}, pages = {109200-109218}, shortjournal = {IEEE Access}, title = {Weighted ensemble based on prisoner dilemma for facial expression recognition}, volume = {13}, year = {2025}, }
• (2025). Efficient calibration of heterogeneous agent-based pedestrian model using PSO with deep learning surrogates. ACCESS, 13, 109185-109199. (www)

  @article{ , abstract = {This paper presents an efficient framework for calibrating heterogeneous agent-based pedestrian models by combining particle swarm optimization (PSO) with deep learning-based surrogate modeling. Each agent was individually calibrated by predicting the next-step velocities using a neural network trained on real-world pedestrian trajectory data. By embedding the surrogate model into the PSO loop, the computational cost is significantly reduced while maintaining high accuracy. The proposed framework captures diverse agent behaviors by accounting for environmental influences such as walls, pillars, and crowd proximity, enabling dynamic and context-aware parameter adjustment. Comparative analysis revealed that although heterogeneous calibration is more time-intensive than homogeneous approaches, it consistently improves behavioral fidelity and model realism. Validation with a separate dataset confirms the effectiveness of the method in pedestrian dynamics applications such as flow analysis and the use of indoor spaces. The results demonstrate that agent-based modeling, when combined with deep learning surrogates and PSO, provides a scalable and accurate solution for simulating complex human movement patterns. This framework supports high-fidelity model calibration while offering practical runtime efficiency.}, archive = {J_ACCESS}, author = {Sanjjamts Amartaivan and Hiroshi Morita}, doi = {10.1109/ACCESS.2025.3582778}, journal = {IEEE Access}, pages = {109185-109199}, shortjournal = {IEEE Access}, title = {Efficient calibration of heterogeneous agent-based pedestrian model using PSO with deep learning surrogates}, volume = {13}, year = {2025}, }
• (2025). Adversarial dual-distortion image rectification: Integrating multi-scale discriminators and central self-attention modules. ACCESS, 13, 109172-109184. (www)

  @article{ , abstract = {Fisheye cameras, leveraging their wide-angle imaging advantages, have been widely adopted in industrial pipeline inspection, particularly for monitoring the inner wall conditions of cylindrical pipelines such as those in oil and gas wells. To achieve geometric reconstruction of pipeline wall images captured by fisheye lenses, this study proposes a method aimed at simultaneously correcting pipeline wall deformations (caused by cylindrical geometry) and fisheye lens distortions. Literature research reveals that fisheye distortion correction for pipeline wall images remains in its infancy, lacking real-scene datasets and data synthesis methods to support deep learning model training. Addressing this bottleneck, we constructed a dual-distortion mathematical model based on geometric optics principles and proposed a parametric simulation-based synthesis method to generate a specialized synthetic dataset tailored for training fisheye distortion correction networks. Existing fisheye correction methods exhibit performance degradation when processing dual-distortion images. To overcome this, we innovatively designed a central self-attention module adapted to fisheye imaging characteristics. This module dynamically enhances geometric feature extraction in the central region of distorted images, effectively suppressing distortion diffusion effects in peripheral areas. Additionally, we introduced a multi-scale discriminator with a pyramidal feature fusion architecture to strengthen the synergistic optimization of local textures and global structures in generative adversarial networks. Experimental results demonstrate that, compared to state-of-the-art correction methods, our approach achieves significant improvements in both objective quantitative metrics (e.g., SSIM, PSNR) and visual consistency. These findings validate the method’s robust capability to effectively correct fisheye-distorted pipeline wall images for industrial inspection applications, providing a reliable solution for related fields.}, archive = {J_ACCESS}, author = {Jingfeng Luo and Chengwan You and Bochuan Zheng}, doi = {10.1109/ACCESS.2025.3582046}, journal = {IEEE Access}, pages = {109172-109184}, shortjournal = {IEEE Access}, title = {Adversarial dual-distortion image rectification: Integrating multi-scale discriminators and central self-attention modules}, volume = {13}, year = {2025}, }
• (2025). A further stability analysis of neutral systems with multiple time-varying delays. ACCESS, 13, 109160-109171. (www)

  @article{ , abstract = {This paper makes some significant contributions to global stability problems of linear neutral systems involving multiple time delay terms in states and multiple neutral delay terms in the time derivative of the states of the system. By considering a newly defined Lyapunov functional candidate, some novel sets of stability criteria are determined for such types of neutral systems including time-varying delay terms. The proposed results determine global asymptotic stability of such neutral systems. Besides, our results totally rely on some constraint conditions imposed on constant system parameters, and on the time derivatives of time and neutral delay terms. We analyze a proper numerical example to address some main advantages of the results of the current article over some previously reported results. Various numerical results are also given to exhibit the applicability of stability conditions obtained. Since finding proper stability criteria for neutral systems including multiple delay components is obviously a hard duty to overcome, due to various parametric complexities of the mathematical models of these systems, the stability criteria we propose may simply be considered as valuable contributions to of stability issues of neutral systems involving multiple delay terms.}, archive = {J_ACCESS}, author = {Ozlem Faydasicok and Neyir Ozcan}, doi = {10.1109/ACCESS.2025.3582883}, journal = {IEEE Access}, pages = {109160-109171}, shortjournal = {IEEE Access}, title = {A further stability analysis of neutral systems with multiple time-varying delays}, volume = {13}, year = {2025}, }
• (2025). Designing for engagement: A mixed-methods study of AR and concept mapping method in mobile vocabulary learning. ACCESS, 13, 109144-109159. (www)

  @article{ , abstract = {This study investigates the effectiveness of integrating augmented reality (AR) and concept mapping method in English vocabulary learning and proposes a design thinking-based development framework for learning systems. Grounded in mobile learning and situated learning theories, the study applies a user-centered design approach through the design thinking process. Initially, semi-structured interviews with eight experienced users were conducted to identify key design requirements, including interface simplicity, visual clarity, thematic relevance, and AR interactivity. These insights informed the development of a concrete interface prototype. Subsequently, a quasi-experimental study involving 30 participants was conducted, employing vocabulary tests, questionnaires, and interviews to enable mixed-methods analysis. The results indicate that the immersive environment created by AR enhances learners’ motivation and learning outcomes, while concept maps support the structured understanding of vocabulary. However, the digital representation of concept maps requires further optimization. The study suggests future research explore variations in digital representation and content depth of concept maps, and incorporate gamification and immersive technologies to enrich learning experiences. The main contribution of this research lies in embedding design thinking into educational technology experimentation, establishing an evidence-based, user-centered development model that offers methodological insights for future learning technology research.}, archive = {J_ACCESS}, author = {Shuo-Fang Liu and An Yu Su and Yi Chieh Wu and Sheng-Fei Chien}, doi = {10.1109/ACCESS.2025.3582899}, journal = {IEEE Access}, pages = {109144-109159}, shortjournal = {IEEE Access}, title = {Designing for engagement: A mixed-methods study of AR and concept mapping method in mobile vocabulary learning}, volume = {13}, year = {2025}, }
• (2025). Cooperation and competition among automated vehicles in highway merge Scenario—A proof-of-concept study. ACCESS, 13, 109134-109143. (www)

  @article{ , abstract = {This study compares strategies for controlling the specific movements of automated vehicles by applying concepts of cooperation and competition in a highway merge scenario. To enhance understanding, each concept was first analyzed from a sociological perspective. Based on this analysis, cooperation in the context of automated vehicles was characterized by the key terms “common goal/benefit, shared ownership of resources, and interaction”, while competition was characterized by “individual goal/benefit, personal ownership of resources, and individual actions”. Reflecting these key terms, the objective of cooperation was to optimize system cost, whereas the objective of competition was to optimize individual cost. Simulations were conducted to implement the movements of automated vehicles, and a dynamic programming model was used to predict and optimize vehicle trajectories. The results were analyzed in terms of mobility and safety metrics. Vehicles in the cooperation scenario, due to system-level optimization, maintained speed distributions centered around 90 km/h in the Main segment and 80 km/h in the merge segment. Competitive vehicles showed broader distributions with speeds peaking at 85-90 km/h and 75-80 km/h, respectively. In terms of safety, the cooperative approach recorded 26 conflicts in the merge segment compared to 33 in the competitive scenario, while achieving higher time-to-collision (TTC) values and lower deceleration rates (4.3 m/s2 vs. 5.7 m/s2). These results highlight the advantages of the cooperative approach in optimizing both mobility and safety.}, archive = {J_ACCESS}, author = {Eugene Lee and Joonho Park and Kitae Jang and Eunjeong Ko and Zhibin Li and Jaehyun So}, doi = {10.1109/ACCESS.2025.3578815}, journal = {IEEE Access}, pages = {109134-109143}, shortjournal = {IEEE Access}, title = {Cooperation and competition among automated vehicles in highway merge Scenario—A proof-of-concept study}, volume = {13}, year = {2025}, }
• (2025). A platform for early class dropout prediction of university students. ACCESS, 13, 109116-109133. (www)

  @article{ , abstract = {Higher education faces significant challenges, with student dropout rates remaining a critical issue. While much research has focused on course-level dropout, less attention has been given to the early identification of at-risk students within individual courses. In this context, this study reviews the state-of-the-art and applies Machine Learning (ML) techniques to predict early dropout of students from real-world data. Key demographic, academic, and behavioral factors were analyzed to identify students at risk of dropping subjects, enabling timely intervention and the implementation of preventive strategies. The analysis revealed that student performance, re-enrollment behavior, and attendance patterns are strongly correlated with the risk of early dropout. Models trained on mid-term and final attendance data before final exams showed the best metrics, highlighting the importance of tracking student engagement throughout the semester. Based on these findings, we developed and implemented the Best Assisting Tutor and INteractive Advisor (BATINA) OnBoard Platform, an advanced platform that integrates predictive models in a practical and user-friendly way. This platform provides comprehensive support to students and faculty, enhancing academic performance and enabling early intervention to improve student retention. This study demonstrates how data-driven solutions can enhance educational support systems and help mitigate student dropout.}, archive = {J_ACCESS}, author = {Guilherme Antonio Borges and Carolina Filipa Dos Santos Pedro and Julio Cesar Santos Dos Anjos and André Rodrigues and Fernando Boavida and Jorge Sá Silva}, doi = {10.1109/ACCESS.2025.3581751}, journal = {IEEE Access}, pages = {109116-109133}, shortjournal = {IEEE Access}, title = {A platform for early class dropout prediction of university students}, volume = {13}, year = {2025}, }
• (2025). Robust image steganography approach based on edge detection combined with CNN algorithm. ACCESS, 13, 109099-109115. (www)

  @article{ , abstract = {Image steganography conceals secret data within a cover image to generate a new image (stego image) in a manner that makes the secret data undetectable. The main problem in image steganography is to find the right balance between the maximum amount of secret data that can be transmitted or stored in the cover image and the quality of the stego image to make the secret data un-noticeable to human senses or other detection mechanisms. In this research, a new framework is proposed that integrates the edge detection strategy (using edge detectors) with the deep learning methods, such as a convolutional neural network (CNN), for making secret data embedding and extraction processes efficient. Firstly, the edges in the cover image are identified using a suitable edge detection method (i.e., using the canny or sobel algorithm), and then the secret data is embedded inside the edge-detected cover image using a deep learning approach, and finally, the created stego image is sent to the receiver. On the receiver side, the secret data is extracted from the stego image using the same deep learning model in a reverse manner. In this article, we considered three datasets, such as the Ting ImageNet, Bossbase, and USC-SIPI datasets, to make edge-detected cover images and then consider them to build a deep learning model. We then evaluated the performance of our proposed deep learning model based embedding and extraction approach using various metrics related to imperceptibility, capacity, and robustness. Experimental results show high imperceptibility with PSNR (Peak Signal-to-Noise Ratio) reaching up to 39.85 dB and SSIM (Structural Similarity Index) up to 0.997 when using sobel, and PSNR up to 33.08 dB and SSIM up to 0.995 when using canny. The developed model also provides a higher capacity of 8 bits per pixel (BPP) and have ensured the robustness of the system against common image processing attacks (e.g., JPEG compression, Gaussian noise, salt-and-pepper, speckle, and filtering) which is validated with PSNR values up to 76.2 dB and error rates (Re) ranging between 0.32 and 0.47. Security testing using state-of-the-art steganalyzers (Xu-Net and Ye-Net) shows detection accuracy around 0.45-0.67 only, indicating strong resistance to steganalysis.}, archive = {J_ACCESS}, author = {Rana Al-Rawashdeh and Md Mahfuzur Rahman and Mahmood Niazi}, doi = {10.1109/ACCESS.2025.3582159}, journal = {IEEE Access}, pages = {109099-109115}, shortjournal = {IEEE Access}, title = {Robust image steganography approach based on edge detection combined with CNN algorithm}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of traction synchronous machine with rotor field winding and two-phase harmonic field exciter with PWM supply. ACCESS, 13, 109087-109098. (www)

  @article{ , abstract = {In contemporary automotive, maritime, and commercial vehicle technology, various types of transport rely on permanent magnet synchronous motors for their space-saving advantages. However, these motors come with certain drawbacks. Permanent magnets, while efficient, can prove costly and have issues maintaining consistent flux, especially when requiring wide constant power speed ranges in the field weakening domain. Consequently, an appealing substitute for permanent magnet motor systems might involve motors that incorporate a field winding, offering potential benefits in terms of both cost and performance adaptability. The article presents a new design of a traction synchronous motor with a field winding on the rotor with a two-phase brushless harmonic exciter that increases the electromotive force on the field winding. This article also proposes a zero-sequence current injector circuit using only the lower transistor, which does not require an expensive upper driver. Its mathematical model is proposed considering PWM and the simulation results are presented. The operation of the motor with relay control of the excitation flux is considered.}, archive = {J_ACCESS}, author = {Vladimir Dmitrievskii and Vadim Kazakbaev and Vladimir Prakht and Aleksey Paramonov and Victor Goman}, doi = {10.1109/ACCESS.2025.3582725}, journal = {IEEE Access}, pages = {109087-109098}, shortjournal = {IEEE Access}, title = {Performance analysis of traction synchronous machine with rotor field winding and two-phase harmonic field exciter with PWM supply}, volume = {13}, year = {2025}, }
• (2025). FLITESIM: A comprehensive verification and validation environment for small satellite attitude determination and control systems. ACCESS, 13, 109069-109086. (www)

  @article{ , abstract = {The past decades have seen a significant increase in popularity of CubeSats for commercial applications, leading to a strong demand for enhanced reliability in these satellite systems. A critical component of these systems is the Attitude Determination and Control System (ADCS), which plays a pivotal role in managing the satellite’s orientation in space. However, this importance highlights a concerning issue: the ADCS has been identified as a leading contributor to failures in small satellite missions, often due to inadequacies in testing environments. To address this challenge, this paper introduces the FLIght equivalent Testing Environment Simulator (FLITESIM), developed by the Satellite Technology and Research (STAR) Centre of National University of Singapore. FLITESIM is designed to offer a comprehensive verification and validation (V&V) environment tailored for ADCS. This paper elaborates on FLITESIM’s design, its key features and reports experimental results from V&V activities on STAR’s Lumelite satellites, demonstrating its effectiveness in enhancing mission assurance, increasing early orbit readiness and improving ADCS operational stability.}, archive = {J_ACCESS}, author = {Kai Jie Ethan Foo and Mihindukulasooriya Sheral Crescent Tissera and Kay Soon Low and Ankit Srivastava}, doi = {10.1109/ACCESS.2025.3582340}, journal = {IEEE Access}, pages = {109069-109086}, shortjournal = {IEEE Access}, title = {FLITESIM: A comprehensive verification and validation environment for small satellite attitude determination and control systems}, volume = {13}, year = {2025}, }
• (2025). Design methodology of power-level modulation for PV power loss minimization and DC series arc fault detection and extinguishing. ACCESS, 13, 109057-109068. (www)

  @article{ , abstract = {With the recent surge in the use of photovoltaic (PV) power systems, the incidence of DC series arc faults has significantly increased, which causes voltage drops within these systems. Previous studies have proposed arc fault detection algorithms based on these input voltage drops. However, voltage drops can also occur under normal conditions, such as irradiance variations. Operating the conventional arc fault detection algorithm under normal conditions, such as irradiance variations, results in a reduction in PV power generation. To overcome these limitations, this paper proposes a new algorithm that not only detects and extinguishes DC series arc faults but also minimizes the reduction in PV power generation under normal conditions, such as irradiance variations. The proposed method uses $Q_{1}$ and $Q_{2}$ of a non-inverting buck-boost converter to separate the detection and extinguishing processes of arc faults and detects arc faults through sinusoidal current control. The algorithm was validated through simulation models and experiments, and it achieved a power generation efficiency of 75.84% under irradiance variation, demonstrating effective operation without unnecessary energy loss and confirming its ability to enhance the stability and reliability of PV systems. Furthermore, it holds promise for application in more complex power systems in the future and is expected to contribute to a wide range of power system applications.}, archive = {J_ACCESS}, author = {Wan Kim and Hwa-Pyeong Park}, doi = {10.1109/ACCESS.2025.3581785}, journal = {IEEE Access}, pages = {109057-109068}, shortjournal = {IEEE Access}, title = {Design methodology of power-level modulation for PV power loss minimization and DC series arc fault detection and extinguishing}, volume = {13}, year = {2025}, }
• (2025). A consistency evaluation method for digital twins in manufacturing. ACCESS, 13, 109046-109056. (www)

  @article{ , abstract = {With the development of emerging technologies such as the Internet of Things and big data, Digital Twin (DT) is increasingly used in manufacturing, and their consistency directly affects the efficiency, quality and reliability of the manufacturing process. This paper proposes a DT consistency evaluation method for smart manufacturing, aiming to solve the problem of maintaining consistency of DT system with physical entities in the manufacturing process. In this paper, the consistency evaluation method is carried out from three dimensions of model consistency, data consistency, and interaction consistency. Combined with the propose of the application, the Analytic Network Process (ANP) is used to weight the indicators and comprehensively assess the consistency of the DT. Taking CNC machine tools as an example, the specific application of this evaluation method is elaborated in detail and its feasibility is verified. The method provides theoretical support for the design and optimization of the DT and helps to promote the further development of intelligent manufacturing.}, archive = {J_ACCESS}, author = {Li Sun and Ziyiming Jiao and Shihao Wang and Feng Jiang and Xianghui Li}, doi = {10.1109/ACCESS.2025.3579507}, journal = {IEEE Access}, pages = {109046-109056}, shortjournal = {IEEE Access}, title = {A consistency evaluation method for digital twins in manufacturing}, volume = {13}, year = {2025}, }
• (2025). Enhanced search spring algorithm for green agri-food supply chain network design. ACCESS, 13, 109034-109045. (www)

  @article{ , abstract = {Supply chain management plays a critical role in the process of converting raw materials into final products and services. Although, conventional supply chains may fall short of the modern expectations of efficiency, cost-efficiency and sustainability. Green Agri-Food Supply Chain Networks (GASCN) are facing high transportation cost, long-time delivery, and inefficient resource utilization, which this study aims to address. In light of these challenges, this study presents a new Enhanced Search Spring Algorithm (ESSA), which optimizes GASCN by minimizing total transportation costs and is characterized by improved solution quality and computational efficiency. The ESSA algorithm integrates a non-linear adaptive weight factor and chaotic map strategies to lay a foundation for balancing the exploration or exploitation of the transmitted solution, allowing it to outpace existing metaheuristic algorithms. The results show that ESSA is superior to state-of-the-art algorithms. In the benchmark tests, ESSA gained much lower mean fitness values (61% lower than GA and 59% lower than PSO) and execution times (32% lower than PSO execution time). For example, in a real-world case study, ESSA minimized transportation cost at 40,000.13 units lower than GA (77,152.08 units) and PSO (77,025.35 units). Moreover, ESSA provides a stable scalability with time complexity proportional to linear growth with increasing problem size. This solution provides an efficient model for agricultural supply chain framework optimization, to a great extent attain quicker conclusions and effective asset appropriation. An important transformation from the time-tested approaches taking us toward sustainable and efficient agri-food systems, ESSA patterns focus on the urgency of these critical problems: cost reductions and operational inefficiencies.}, archive = {J_ACCESS}, author = {Xiaoya Hu}, doi = {10.1109/ACCESS.2025.3578286}, journal = {IEEE Access}, pages = {109034-109045}, shortjournal = {IEEE Access}, title = {Enhanced search spring algorithm for green agri-food supply chain network design}, volume = {13}, year = {2025}, }
• (2025). Research on hybrid voltage-power switching control strategy of grid-connected two-stage photovoltaic system for low voltage ride through faults. ACCESS, 13, 109021-109033. (www)

  @article{ , abstract = {For two-stage grid-connected photovoltaic (PV) system, if the low voltage ride through (LVRT) faults occur in the grid side, because the grid voltage falls to a relative low value and the output current is limited, then the output power of inverter reduces and the dc bus voltage fluctuates, which leads the instantaneous dc bus voltage higher than the normal voltage and brings overvoltage breakdown risk. To solve the above problem, in this paper, a hybrid voltage-power switching control strategy for two-stage grid-connectedPV systemis proposed. If the grid voltage is normal, the dc-dcstage works in maximum power point tracking (MPPT) mode, and the inverter works in double-loop control (DLC)mode. If the LVRT fault occurs in grid side, the dc-dc stage switches from MPPT mode to voltage control mode, and the inverter switches from DLC mode to single-voltage-loop control (SVL) mode, that is to say, the two stages switch to voltage control mode to stabilize the dc bus voltage. To verify the effectiveness of the proposed hybrid voltage-power switching control strategy, an experiment is conducted based on the RT-LAB real-time platform (OPAL-RT 5707XG simulator with 8 CPU cores and OPAL-RT Linux OS). The experimental results show that the proposed hybrid control strategy can decrease the voltage ripple ratios by about 0.7%, 16.9%, and 70.3% for 80%, 20%, and 5% LVRT faults, respectively.}, archive = {J_ACCESS}, author = {Yangyang Wu and Tianshu Wen and Huayu Yuan and Caiqiang Zhang and Xiaolong Song and Peng Bai and Hengshan Xu and Dikang Wang and Kanghui Li and Xianjin Zeng and Siwei Wang}, doi = {10.1109/ACCESS.2025.3572740}, journal = {IEEE Access}, pages = {109021-109033}, shortjournal = {IEEE Access}, title = {Research on hybrid voltage-power switching control strategy of grid-connected two-stage photovoltaic system for low voltage ride through faults}, volume = {13}, year = {2025}, }
• (2025). Exploring the fractal space of sparse deep neural network. ACCESS, 13, 109009-109020. (www)

  @article{ , abstract = {Sparse deep neural networks (DNNs) are preferable in many real-world applications, especially edge computing. How to find the sparse structure of deep neural networks so that the model can achieve better performance is an important research topic. Under the same sparsity level, the connection patterns of DNNs significantly affect their performance. In this paper, we study the relationship between the structure and performance of DNNs from a graph perspective. Our research focuses on fractal structure, since it has already been demonstrated that many real-world networks benefit from their high fractal dimension structures. We statistically analyze the correlation between the fractal dimension and the performance of sparse DNNs whose structures are constructed by relational graphs. The graphs we investigate are generated from fractal growth model, Barabási-Albert (BA) model, Erdős-Rényi (ER) model, Watts-Strogatz (WS) model, regular network model, stochastic block model and random geometric network model. We find that the fractal dimension of the preset structure is positively correlated with the performance of the corresponding DNNs, indicating that fractal dimension could be a key factor in designing sparse DNNs of high effectiveness.}, archive = {J_ACCESS}, author = {Hang Xu and Zhuangzhi Chen and Jinhuan Wang and Shanqing Yu and Dongwei Xu and Qi Xuan}, doi = {10.1109/ACCESS.2025.3574731}, journal = {IEEE Access}, pages = {109009-109020}, shortjournal = {IEEE Access}, title = {Exploring the fractal space of sparse deep neural network}, volume = {13}, year = {2025}, }
• (2025). A novel context-aware feature pyramid networks with kolmogorov-arnold modeling and XAI framework for robust lung cancer detection. ACCESS, 13, 108992-109008. (www)

  @article{ , abstract = {The present work describes a new method for detecting lung cancer based on a Kolmogorov-Arnold Network (KAN) combined with a Context-Aware Feature Pyramid Network (FPN), denoted KAFPN and supported by XAI tools, including Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) for explanation purposes. The model is compiled based on an extended corpus containing 25,000 histopathological images from 15,000 lung tissue samples. The proposed KAFPN model outperformed the traditional models, such as Base CNN, Inception V3, VGG16, ResNet50, EfficientNet-B7, ResNet-101, and DenseNet-121, based on testing and validation accuracy of 99.63% and 99.60%, respectively. The integration of LIME and SHAP facilitated an advanced level of interpretability by providing detailed visual explanations of the model’s predictive decisions, delineating the image regions that most significantly impacted the decision-making process. The proposed KAFPN framework is well-suited for image segmentation and object detection because it has multiple-scale feature responses. Stringent data augmentation and correct division of data sets make the model robust and efficient for real-world applications.}, archive = {J_ACCESS}, author = {Yajnaseni Dash and Sudhir C. Sarangi and Vinayak Gupta and Naween Kumar and Ajith Abraham}, doi = {10.1109/ACCESS.2025.3570643}, journal = {IEEE Access}, pages = {108992-109008}, shortjournal = {IEEE Access}, title = {A novel context-aware feature pyramid networks with kolmogorov-arnold modeling and XAI framework for robust lung cancer detection}, volume = {13}, year = {2025}, }
• (2025). Quantum-resistant blockchain protocols for secure transactions. ACCESS, 13, 108984-108991. (www)

  @article{ , abstract = {The advent of quantum computing threatens the cryptographic security of traditional blockchain systems, potentially rendering current encryption techniques obsolete. This paper explores quantum-resistant blockchain protocols that leverage post-quantum cryptographic algorithms to ensure secure transactions. We analyze various quantum-safe cryptographic methods, such as lattice-based, hash-based, and multivariate polynomial cryptography, evaluating their feasibility for blockchain applications. Through comparative analysis, we discuss their computational efficiency and security guarantees. Our findings suggest that integrating quantum-resistant cryptographic techniques into blockchain frameworks is essential for future-proofing decentralized systems. This paper explores the integration of quantum-resistant cryptographic algorithms within blockchain technology. The proposed approach utilizes CRYSTALS-Dilithium for digital signatures, ensuring secure transaction verification in a post-quantum era. While Kyber is a robust key exchange mechanism, it is not suitable for signing transactions. The implementation is tested on standard hardware, demonstrating feasibility without requiring specialized quantum computing infrastructure.}, archive = {J_ACCESS}, author = {Vishal Puneyani and K. Vivekananda Bhat}, doi = {10.1109/ACCESS.2025.3581955}, journal = {IEEE Access}, pages = {108984-108991}, shortjournal = {IEEE Access}, title = {Quantum-resistant blockchain protocols for secure transactions}, volume = {13}, year = {2025}, }
• (2025). InceptMan: An InceptionNeXt-based architecture for end-to-end mandible reconstruction. ACCESS, 13, 108968-108983. (www)

  @article{ , abstract = {Mandible reconstruction is one of the virtual surgical planning (VSP) steps in mandible reconstruction surgery, particularly in cases involving tumors or radical cancer surgery. While deep learning approaches have shown success in medical image reconstruction, existing methods struggle with the complex morphology of mandible structures and often require manual intervention for defining resection margins. To address these challenges, we present a novel UNet-based architecture based on CraNeXt and InceptionNeXt designed for automated mandible reconstruction, InceptMan. In addition, we proposed the automated cutting pipeline to define resection margins between healthy and defective parts of the mandible before reconstruction. We evaluated our proposed model and pipeline on two datasets: patient data with mandible abnormalities in the unhealthy dataset and healthy controls in the healthy dataset. On the healthy dataset, InceptMan outperformed the baseline 3DRDUNet, improving the dice similarity coefficient (DSC) from 0.7125 to 0.8151 with performance comparable to the state-of-the-art CraNeXt, while also achieving $1.4\times $ higher training throughput. On the unhealthy dataset, evaluation with our proposed metrics, Surface Recall and $95^{\text {th}}$ percentile Asymmetric Hausdorff distance (dAH95), showed that the automated cutting pipeline performs comparably to manual cutting. These results highlight the pipeline’s potential for efficient and accurate automated mandible reconstruction in clinical applications.}, archive = {J_ACCESS}, author = {Nattapon Kamboonsri and Natdanai Tantisereepatana and Titipat Achakulvisut and Peerapon Vateekul}, doi = {10.1109/ACCESS.2025.3582504}, journal = {IEEE Access}, pages = {108968-108983}, shortjournal = {IEEE Access}, title = {InceptMan: An InceptionNeXt-based architecture for end-to-end mandible reconstruction}, volume = {13}, year = {2025}, }
• (2025). Autoencoder-driven fiducial landmark identification in 3D brain MRI for neuroimaging alignment. ACCESS, 13, 108955-108967. (www)

  @article{ , abstract = {Accurate identification of fiducial markers, such as the left pre-auricle (LPA), right pre-auricle (RPA), and nasion, is critical for head localization, source reconstruction, anatomical alignment, and ensuring reproducibility in functional and structural neuroimaging studies. However, manual annotation of these landmarks is time-consuming, prone to human error, and further complicated by variations in MRI acquisition protocols and incomplete head coverage. In this work, we propose a deep learning-based framework using autoencoders to automatically detect fiducial markers in 3D brain MRI scans. The method integrates a structured preprocessing pipeline, including intensity normalization, spatial resampling, and noise reduction, to ensure data consistency. A tailored autoencoder architecture is then trained to reconstruct MRI slices while learning the spatial characteristics of MRI slices at fiducial points across axial, coronal, and sagittal planes. Fiducial localization is determined based on the slice with the lowest reconstruction error, ensuring precise identification. Our approach significantly enhances registration accuracy, achieving an average ground truth Euclidean distance of 1.42 mm for LPA and 1.89 mm for RPA, with low variance across a dataset of 500 MRI volumes. The results demonstrate that automated fiducial detection can reduce manual intervention, improve anatomical alignment, and enhance the robustness of neuroimaging workflows. This method offers a scalable and efficient solution for clinical and research applications, facilitating more reliable head modeling, source localization, and multimodal image integration.}, archive = {J_ACCESS}, author = {G. Deepali and H. Anitha and B. P. Swathi and M. V. Suhas}, doi = {10.1109/ACCESS.2025.3582273}, journal = {IEEE Access}, pages = {108955-108967}, shortjournal = {IEEE Access}, title = {Autoencoder-driven fiducial landmark identification in 3D brain MRI for neuroimaging alignment}, volume = {13}, year = {2025}, }
• (2025). Topological data analysis and graph-based learning for multimodal recommendation. ACCESS, 13, 108934-108954. (www)

  @article{ , abstract = {Multimodal recommendation systems are becoming increasingly vital for delivering personalized content by utilizing various data sources, including text, images, and user interaction histories. However, current multimodal methods face challenges such as modality heterogeneity, data sparsity, and feature redundancy, which can result in less effective performance when dealing with complex, high-dimensional datasets. In this study, we present a new framework that combines Topological Data Analysis (TDA) with graph-based learning to improve multimodal recommendations (TDA-MMRec). Our approach captures higher-order dependencies and global structural patterns in multimodal data, enhancing the robustness and expressiveness of the representations we learn. By using persistent homology, we extract topological descriptors that convey stable structural information across different modalities, addressing the issues of sparsity and redundancy. We also introduce a modality-aware strategy for constructing graphs, which integrates features derived from TDA into multimodal similarity graphs to maintain both local and global structural properties. Furthermore, we propose a topological pruning technique that refines graph structures by removing redundant connections while preserving essential topological information, enhancing computational efficiency. Extensive experiments on large-scale multimodal datasets indicate that our TDA-augmented framework significantly outperforms leading multimodal recommendation models on key ranking metrics, including Precision@20, Recall@20, and NDCG@20. Our ablation studies confirm that topological descriptors are essential in boosting representation learning, especially in cold-start scenarios where traditional methods struggle due to data sparsity.}, archive = {J_ACCESS}, author = {Khalil Bachiri and Ali Yahyaouy and Maria Malek and Nicoleta Rogovschi}, doi = {10.1109/ACCESS.2025.3582480}, journal = {IEEE Access}, pages = {108934-108954}, shortjournal = {IEEE Access}, title = {Topological data analysis and graph-based learning for multimodal recommendation}, volume = {13}, year = {2025}, }
• (2025). An improved droop-based adaptive virtual impedance for accurate power sharing among multiple DGs in islanded microgrid. ACCESS, 13, 108918-108933. (www)

  @article{ , abstract = {Accurate power sharing in islanded microgrids (MGs) is essential to preserve the system stability and deliver the required load power. However, the distinct ratings of feeders result in disparate voltage drops and, therefore, varying output voltages from distributed generators (DGs). This work proposes a distributed adaptive virtual complex impedance to achieve accurate power sharing among DG units with minimal control loops. The controller dynamically adjusts its parameters based on the system parameters, allowing it to adapt to various conditions. Unlike previous controllers, the proposed controller can operate effectively with multiple connected DGs in the system. The designed approach accounts for complex components of the feeders’ impedances, with actual ratios between the complex components to enhance the system performance and the point of common coupling (PCC) voltage. Based on that, the decoupling between the active and reactive power is achieved. The effectiveness of the proposed controller is validated through simulations involving both two and multiple DG units, and its performance is compared against conventional control methods. It demonstrated superior performance by achieving accurate reactive power sharing, reducing active power overshoot from 7.14% to 4%, and decreasing the voltage deviation at the PCC from −0.783% to −0.435%. Additionally, the controller effectively synchronized system currents and reduced circulating currents by 98%. These improvements were observed across various test scenarios. The first scenario is executed considering two DGs in the MG system, and the results are compared with those of conventional droop control and conventional virtual impedance. The second scenario involves statistically comparing the proposed controller with those in the literature, focusing on the percentage overshoot of the controller, response time, and PCC voltage level. The third scenario is conducted to demonstrate the proposed controller’s ability to be extended for multiple connected DGs in the MG system.}, archive = {J_ACCESS}, author = {Ahmed M. Abouassy and Hamoud Alafnan and Diaa-Eldin A. Mansour and Abdullah Albaker and Tamer F. Megahed}, doi = {10.1109/ACCESS.2025.3581481}, journal = {IEEE Access}, pages = {108918-108933}, shortjournal = {IEEE Access}, title = {An improved droop-based adaptive virtual impedance for accurate power sharing among multiple DGs in islanded microgrid}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of current machine learning approaches for detecting GNSS spoofing attacks. ACCESS, 13, 108898-108917. (www)

  @article{ , abstract = {Global Navigation Satellite System (GNSS) spoofing attacks become increasingly important due to their impact on navigation security as well as their significant risks to privacy and infrastructure in civilian and military domains. Those spoofing attacks manipulate GNSS signals to create false positions, which can lead to dangerous situations. In recent years, the use of machine learning approaches to detect such attacks has gained relevance. Although numerous technical studies on GNSS spoofing exist, there is no systematic review that applies a theory-driven, socio-technical perspective. This paper addresses that gap by structuring the existing literature along two dimensions: machine learning approaches and Bell/Whaley’s Theory of Deception. Most detection methods are based on supervised learning methods or neural networks and have mainly been tested through simulations. By introducing the Deception Theory into this technical domain, the review explores which types of deception GNSS spoofing involves and what defenses exist. The results show that most of the methods are aimed at ‘mimicking’ attacks.}, archive = {J_ACCESS}, author = {Susan Bertram and Luca Eisentraut and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3582435}, journal = {IEEE Access}, pages = {108898-108917}, shortjournal = {IEEE Access}, title = {A systematic literature review of current machine learning approaches for detecting GNSS spoofing attacks}, volume = {13}, year = {2025}, }
• (2025). Facial kinship recognition through dilated-stacked-unified attention network. ACCESS, 13, 108883-108897. (www)

  @article{ , abstract = {Facial kinship verification (FKV) and facial kinship identification (FKI) are two major tasks in facial kinship recognition (FKR). Despite that, most of the recent works focus mainly on the FKV and only a few works on FKI. The joint learning of both tasks can enhance inference metrics and already adopted in recent works modelling the problem as a stacked channel-spatial attention scheme. Despite that, those joint models focus mainly on learning the channel and spatial features in attention chain using bottom-up top-down feed forward mechanism ignoring the fact that this could impede the global spatial features representation failing to capture the genetic similarities that are spread across the entire facial images. Starting from this point, we propose a FKV network built on top of a dilated stack of channel-spatial attention modules that focus on enlarging the receptive fields for enhancing the performance of attending to discriminative kinship features while maintaining the global spatial information. We investigate the effects of learning those features in an alternative domain of decoupled channel-spatial. Finally, we extend our work by building a jointly learnt ensemble of unified FKV networks for the FKI task and show that our scheme performs well compared to the state-of-the-art methods on the benchmark datasets.}, archive = {J_ACCESS}, author = {Ahmed A. Dabas and Mohamed A. Ismail and Nagia M. Ghanem}, doi = {10.1109/ACCESS.2025.3582532}, journal = {IEEE Access}, pages = {108883-108897}, shortjournal = {IEEE Access}, title = {Facial kinship recognition through dilated-stacked-unified attention network}, volume = {13}, year = {2025}, }
• (2025). Trajectory-based road autolabeling with lidar-camera fusion in winter conditions. ACCESS, 13, 108873-108882. (www)

  @article{ , abstract = {Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distribution in the trainset is challenging, as each sample must be labeled by hand. Trajectory-based self-supervised methods offer a potential solution as they can learn from the traversed route without manual labels. However, existing trajectory-based methods use learning schemes that rely only on the camera or only on the lidar. In this paper, trajectory-based learning is implemented jointly with lidar and camera for increased performance. Our method outperforms recent standalone camera- and lidar-based methods when evaluated with a challenging winter driving dataset, including countryside and suburban driving scenes. The source code is available at https://github.com/eerik98/lidar-camera-road-autolabeling.git. The winter driving dataset is made publicly available at https://zenodo.org/records/14856338}, archive = {J_ACCESS}, author = {Eerik Alamikkotervo and Henrik Toikka and Kari Tammi and Risto Ojala}, doi = {10.1109/ACCESS.2025.3582061}, journal = {IEEE Access}, pages = {108873-108882}, shortjournal = {IEEE Access}, title = {Trajectory-based road autolabeling with lidar-camera fusion in winter conditions}, volume = {13}, year = {2025}, }
• (2025). Spectrogram zeros method for rolling bearing fault diagnosis under variable rotating speeds. ACCESS, 13, 108862-108872. (www)

  @article{ , abstract = {Diagnosing rolling bearing faults is critical for maintaining machinery reliability, as these components are essential in reducing friction in rotating systems. The increased bearing failure rates at higher rotational speeds underscores the need for advanced diagnostic techniques. Traditional methods like generalized demodulation and order tracking require additional hardware, increasing complexity and cost. This study introduces a novel approach called SZC-SST, which uses spectrogram zeros classification and the Fourier-synchrosqueezing transform (FSST) to diagnose rolling bearing faults under variable speeds. The proposed method leverages the Hilbert transform to enhance the feature extraction, ensuring accurate fault detection. Experimental results validate the effectiveness of the method, demonstrating significant improvements in fault identification and noise reduction compared to conventional techniques. Integrating these advanced signal processing techniques provides a robust framework for early fault detection, potentially preventing catastrophic machinery failures.}, archive = {J_ACCESS}, author = {Trong-Du Nguyen and Thanh-Trung Pham and Tan-Le Phuc and Phong-Dien Nguyen}, doi = {10.1109/ACCESS.2025.3582280}, journal = {IEEE Access}, pages = {108862-108872}, shortjournal = {IEEE Access}, title = {Spectrogram zeros method for rolling bearing fault diagnosis under variable rotating speeds}, volume = {13}, year = {2025}, }
• (2025). Dual passive-aggressive stacking k-nearest neighbors for class-incremental multi-label stream classification. ACCESS, 13, 108838-108861. (www)

  @article{ , abstract = {Class-incremental multi-label stream classification (class-incremental MLSC) requires learning algorithms to adapt to concept drifts, perform single-pass online learning, and handle emerging new labels in the data stream. Most learning algorithms in the literature can fulfill only a subset but not all of these desiderata. This paper proposes a stacking ensemble method named Dual Passive-Aggressive stacking k-Nearest Neighbors (DPAkNN) to tackle class-incremental MLSC. DPAkNN uses a vanilla Passive-Aggressive classifier (vanilla PA) and a Random Kitchen Sink Passive-Aggressive classifier (RKS PA) for its base model, and a stacking kNN as the meta model - hence the name. The RKS PA complements the vanilla PA in the base model by handling non-linearities in the data stream. To refine predictions and capture label dependencies, predictions from the base model are fed into a streaming kNN named MetakNN to output the stacked predictions. MetakNN includes label-specific fitness for stored instances and a fitness rejuvenating mechanism based on relative performance to the base predictions for enhanced synergy. Our experiment compares DPAkNN against state-of-the-art kNN-based MLSC algorithms on 30 multi-label benchmark datasets. Results demonstrate the superior performance of DPAkNN in MLSC tasks - overall, DPAkNN has the best performance on the benchmarking tasks with an average rank of 2.62, demonstrating strong predictive power with decent runtime and memory consumption. Our further assessment also shows that DPAkNN is robust to various forms of real concept drift and capable of class-incremental learning.}, archive = {J_ACCESS}, author = {Hann Hsen Tan and Chu Kiong Loo and Chaw Seng Woo}, doi = {10.1109/ACCESS.2025.3578358}, journal = {IEEE Access}, pages = {108838-108861}, shortjournal = {IEEE Access}, title = {Dual passive-aggressive stacking k-nearest neighbors for class-incremental multi-label stream classification}, volume = {13}, year = {2025}, }
• (2025). Outlier detection of photovoltaic power generation system data based on GMM-IF algorithm. ACCESS, 13, 108823-108837. (www)

  @article{ , abstract = {The Gaussian Mixture Model (GMM) is widely used in anomaly detection due to its flexibility in handling complex data distributions and its soft classification mechanism. However, its modeling process relies on the assumption that data follows a Gaussian distribution. In contrast, time-series data from photovoltaic (PV) systems often exhibit high nonlinearity and non-stationarity, making it difficult to represent with a single Gaussian distribution, which affects detection performance. To address this issue and improve detection accuracy, this paper proposes a segmented GMM-IF outlier detection algorithm. In this method, the decision tree segmentation modeling strategy is used to structure the data to construct a local Gaussian-like distribution dataset to overcome the prior assumptions. Additionally, it integrates an Isolation Forest (IF) as the decision-making mechanism for detailed detection, further enhancing detection accuracy. Under different operating conditions and actual photovoltaic array datasets, comparative experiments were conducted using different evaluation metrics and algorithms to verify the effectiveness and superiority of the GMM-IF algorithm. Experimental results show that compared with the single model of GMM and IF, the GMM-IF algorithm has an average improvement of 3.8% in accuracy and 9.1% in F-value. Compared with other comparison algorithms, GMM-IF has obvious advantages, especially in terms of recall.}, archive = {J_ACCESS}, author = {Xin Li and Guoxin Du and Yuman Li and Zengqiang Ma and Lihu Liu and Minglong Jiang and Feiyu Liu and Tianming Mu}, doi = {10.1109/ACCESS.2025.3581641}, journal = {IEEE Access}, pages = {108823-108837}, shortjournal = {IEEE Access}, title = {Outlier detection of photovoltaic power generation system data based on GMM-IF algorithm}, volume = {13}, year = {2025}, }
• (2025). Adaptive 3D M-QAM using cross polarized antenna. ACCESS, 13, 108815-108822. (www)

  @article{ , abstract = {Wireless communication is a fundamental aspect of everyday life. Modern mobile applications such as the Internet of Things (IoT), high-resolution video streaming, connected cars, smart cities, and telehealth care have a universal presence these days. The growth in these applications will continue to require higher data rates, large bandwidth, increased capacity, low latency and high throughput. As the Radio Frequency (RF) spectrum is a limited resource, considerable effort has been expended in the search for more efficient techniques for transmitting data through limited bandwidth resources. The key component of meeting this requirement is employing an efficient adaptive modulation scheme. Adaptive M-ary Quadrature Amplitude Modulation (M-QAM) techniques have been developed to deliver better Bit Error Rate (BER) performance and higher Spectral Efficiency (SE) by taking advantage of the time-varying nature of wireless fading channels. This work further enhances the adaptive M-QAM by employing an efficient Three-Dimensional (3D) adaptive modulation scheme to significantly increase the BER and SE of the wireless networks. This paper introduces a novel 3D version of adaptive M-QAM that significantly improves the BER performance and, more importantly, doubles the SE. This results in also doubling the network capacity and throughput. Simulation results show that the SE is doubled, and the gain is increased by $6\:dB$ in BER versus Energy per Bit to Noise ratio $Eb/No$ curves. This study proposes that this improvement can be further enhanced by transmitting and receiving through more than two planes, using more than two cross- polarized antennas.}, archive = {J_ACCESS}, author = {Yousef Ali Abohamra}, doi = {10.1109/ACCESS.2025.3581437}, journal = {IEEE Access}, pages = {108815-108822}, shortjournal = {IEEE Access}, title = {Adaptive 3D M-QAM using cross polarized antenna}, volume = {13}, year = {2025}, }
• (2025). DDoS attack detection in SDN-assisted federated learning environment based on contrastive learning. ACCESS, 13, 108798-108814. (www)

  @article{ , abstract = {Software-defined networking (SDN)-assisted federated learning (FL) is an emerging network computing environment. It can not only shorten the training time of federated learning while maintaining high learning performance, but also enhance the security of the FL network. However, compared with traditional FL networks, SDN-assisted FL technology introduces new security threats. Distributed denial of service(DDoS) attacks are an important security threat for the SDN service in FL. In the SDN-assisted FL environment, the FL network requires the interaction of model parameters among multiple participants. During this process, DDoS attacks may target the SDN control plane, disrupt its normal operation, and thus affect the transmission of model parameters in FL. Hence, this paper proposes a novel approach to detecting and identifying DDoS attacks based on contrastive learning (CL), an adversarial learning framework based on two-layer deep neural networks. The framework features a two-layer classification structure. In the first layer, we integrate Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to identify DDoS attacks. In the second layer, we enhance the classifier structure by combining Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU). This layer can be optimized based on the contrastive classification loss from the LSTM-SVM classifier in the first layer. We conducted experiments on a specific SDN dataset generated by the Mininet emulator. The results show that for the LSTM-SVM model, the detection accuracy reaches 99.75%, and the recall rate is 99.80%. For the CNN-BiGRU model, the detection accuracy rate is 99.36%, and the recall rate is 99.55%. Overall, the proposed CL model can effectively identify DDoS attack traffic in SDN-assisted FL environments, demonstrating high detection performance. However, the model may face challenges such as high computational resource requirements and insufficient adaptability to complex network environments when deployed in practice. Further optimization is needed to facilitate its broader application.}, archive = {J_ACCESS}, author = {Minghong Fan and Jinghua Lan and Yiyi Zhou and Mengshuang Pan and Junrong Li and Daqiang Zhang}, doi = {10.1109/ACCESS.2025.3582445}, journal = {IEEE Access}, pages = {108798-108814}, shortjournal = {IEEE Access}, title = {DDoS attack detection in SDN-assisted federated learning environment based on contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Impact of international environmental emergency on stock market dynamics: A RoBERTa model and pearson correlation approach. ACCESS, 13, 108786-108797. (www)

  @article{ , abstract = {Understanding market reactions to environmental pollution crises is essential for assessing investor sentiment and financial stability. While prior studies have explored general market responses, the role of investor psychology during such crises remains underexplored. To bridge this gap, we propose a sentiment analysis framework based on the RoBERTa-WWM-ext-large model combined with Pearson correlation analysis. This approach investigates the relationship between environmental incidents, investor sentiment, and stock price dynamics. Experiments on real-world stock data reveal that sudden environmental events intensify the correlation between sentiment shifts and market volatility, often with a one-day temporal lag. Further analysis shows that different sentiment categories display varying levels of association with trading metrics—for instance, fear sentiment exhibits a strong positive Pearson correlation (up to 0.66) with transaction volume in one stock after the event. Moreover, industries such as aquaculture and salt demonstrate notably higher sensitivity to sentiment fluctuations. These findings offer practical insights for financial practitioners and policymakers seeking to understand sentiment-driven behavior in environmentally induced market disruptions.}, archive = {J_ACCESS}, author = {Wenxin Tang and Qingyang Liu and Yanrong Hu and Hongjiu Liu}, doi = {10.1109/ACCESS.2025.3582479}, journal = {IEEE Access}, pages = {108786-108797}, shortjournal = {IEEE Access}, title = {Impact of international environmental emergency on stock market dynamics: A RoBERTa model and pearson correlation approach}, volume = {13}, year = {2025}, }
• (2025). Resource efficient federated LoRaWAN architecture for far-edge IoT applications. ACCESS, 13, 108766-108785. (www)

  @article{ , abstract = {This study introduces an innovative, resource-efficient architecture that incorporates advanced technologies to create a lightweight, flexible, and scalable framework for remote and limited data collecting and processing in next-generation Internet of Things (IoT) applications. It specifically proposes an integrated Federated LoRaWAN (LoRA-FL) system that combines hierarchical, privacy-preserving Federated Learning (FL), Knowledge Distillation (KD), and a customised Medium Access Control (MAC) protocol, identified as FCA-LoRa, to address the significant limitations of LoRaWAN networks. These encompass strict duty cycle rules, restricted bandwidth, and energy limitations, elements that conventionally hinder the implementation of intelligent IoT devices in rural or isolated settings. The proposed architecture features a hierarchical FL approach enabling multi-tier Artificial Intelligence (AI) model aggregation across edge nodes, gateways, and a central server. The effectiveness of the proposed system is confirmed by two practical applications, smart agriculture and smart livestock farming, which exemplify standard situations for far-edge intelligence. The results indicate that the distilled model consistently attains over 90% packet delivery success, illustrating the architecture’s capacity to provide scalable and energy-efficient intelligence at the edge. This research addresses a significant gap in previous studies that frequently examine FL, communication optimisation, and model compression independently. This study offers a comprehensive, implementable approach that tackles model scalability, and network-layer issues within a cohesive architecture, enhancing the practical implementation of AI-driven IoT deployments over LoRaWAN.}, archive = {J_ACCESS}, author = {Anna Triantafyllou and Ilias Siniosoglou and Vasileios Argyriou and Sotirios K. Goudos and Georgios Th. Papadopoulos and Konstantinos Panitsidis and Panagiotis Sarigiannidis}, doi = {10.1109/ACCESS.2025.3580375}, journal = {IEEE Access}, pages = {108766-108785}, shortjournal = {IEEE Access}, title = {Resource efficient federated LoRaWAN architecture for far-edge IoT applications}, volume = {13}, year = {2025}, }
• (2025). DiffGAN: A fault diagnosis data augmentation method for hydropower units based on adversarial training and diffusion model. ACCESS, 13, 108756-108765. (www)

  @article{ , abstract = {In industrial applications, fault diagnosis can effectively enable early warning. However, in practical production scenarios, fault data is difficult to obtain, and traditional fault diagnosis methods require a large quantity of high-quality data, severely limiting their diagnostic performance. To address this issue, this paper proposes a data augmentation method based on adversarial training and diffusion models—DiffGAN. DiffGAN alleviates the data imbalance problem by generating high-quality fault data, thereby improving fault diagnosis performance. It takes diffusion noise as the input for adversarial training, enhances the generator’s high-speed generation capability through adversarial training, optimizes the efficiency of the reverse generation process in traditional denoising diffusion models, and mitigates gradient vanishing and mode collapse issues in generative adversarial networks. Experiments were conducted on an actual hydropower unit dataset, and the results demonstrate that DiffGAN can efficiently generate fault data and significantly improve the sample distribution in imbalanced datasets. Furthermore, fault diagnosis experiments incorporating the ResNet model further verify the effectiveness of DiffGAN in enhancing diagnostic performance. This provides a novel and practical solution for data augmentation and fault diagnosis in hydropower units.}, archive = {J_ACCESS}, author = {Hongwei Zhang and Zhao Liu and Hansong Si and Zhongzhi Chen and Huiping Xie}, doi = {10.1109/ACCESS.2025.3582274}, journal = {IEEE Access}, pages = {108756-108765}, shortjournal = {IEEE Access}, title = {DiffGAN: A fault diagnosis data augmentation method for hydropower units based on adversarial training and diffusion model}, volume = {13}, year = {2025}, }
• (2025). KAFA-merge: Model merging method with layer-based Bayes+Linear search. ACCESS, 13, 108743-108755. (www)

  @article{ , abstract = {In this paper, we consider the model merging process for large language models (LLMs) under a two-stage optimization framework. Traditional merging methods usually apply fixed blending rates to all layers, which ignores the different levels of contribution of layers to the output. To overcome this limitation, we first group layers with similar functionality and use Bayesian optimization to determine the optimal blending rates for each group. Bayesian optimization offers a globally effective discovery strategy in high-dimensional and computationally expensive search spaces. However, an approach based solely on global search can lead to local artifacts being missed. Therefore, in the second stage, we perform Linear Search by applying controlled variations around the ratios obtained with Bayesian optimization and obtain more precise, locally optimized results. Extensive experiments on the Turkish versions of the Arc, Hellaswag, MMLU and GSM8K datasets show that our proposed Bayes+Linear strategy outperforms existing methods such as SLERP, Linear, Ties and Breadcrumbs. In addition to improving model accuracy and generalization capacity, the KAFA approach works without any additional fine-tuning, making it possible to effectively reuse large language models in different tasks.}, archive = {J_ACCESS}, author = {Muzaffer Kaan Yuce and Mehmet Fatih Amasyali}, doi = {10.1109/ACCESS.2025.3582015}, journal = {IEEE Access}, pages = {108743-108755}, shortjournal = {IEEE Access}, title = {KAFA-merge: Model merging method with layer-based Bayes+Linear search}, volume = {13}, year = {2025}, }
• (2025). Benchmarking model predictive control and reinforcement learning-based control for legged robot locomotion in MuJoCo simulation. ACCESS, 13, 108732-108742. (www)

  @article{ , abstract = {Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task, straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain adaptability. The results show that RL excels in handling disturbances and maintaining energy efficiency but struggles with generalization to new terrains due to its dependence on learned policies tailored to specific environments. In contrast, MPC shows enhanced recovery capabilities from larger perturbations by leveraging its optimization-based approach, allowing for a balanced distribution of control efforts across the robot’s joints. The results present the advantages and limitations of both RL and MPC, offering insights into selecting an appropriate control strategy for legged robotic applications.}, archive = {J_ACCESS}, author = {Shivayogi Akki and Tan Chen}, doi = {10.1109/ACCESS.2025.3582523}, journal = {IEEE Access}, pages = {108732-108742}, shortjournal = {IEEE Access}, title = {Benchmarking model predictive control and reinforcement learning-based control for legged robot locomotion in MuJoCo simulation}, volume = {13}, year = {2025}, }
• (2025). A novel deep learner for human behavior prediction over public video surveillance. ACCESS, 13, 108718-108731. (www)

  @article{ , abstract = {Identifying human behavior effectively is essential for spotting anomalies in video surveillance systems, particularly in dynamic environments. Conventional methods frequently have significant false detection rates, which restricts their use. This paper proposes a unique framework to improve anomaly classification by combining improved preprocessing, feature extraction, optimization methods, and Remote Field-Based (RFB) video sensors. This work’s primary contribution is the creation of an all-encompassing system that uses feature-driven learning and optimization to minimize false alarms and achieve high detection accuracy. The UBI-Fight and PETS2009 datasets, which are often used in surveillance applications for human behavior detection, are used as validation standards by the framework. Key frame extraction is the first step in the procedure, which is then followed by sophisticated preprocessing methods like illumination correction, occlusion control, and noise reduction. Harris corner detection, Histogram of Oriented Gradients (HOG), and Particle Gradient Motion Analysis (PGMA) are used to extract motion and spatial data. By capturing high-resolution motion dynamics for improved data collection, the integration of RFB video sensors improves motion analysis and behavioral evaluation. While Particle Swarm Optimization (PSO) optimizes the classifiers to increase accuracy, a deep learning classification strategy is used for anomaly classification. The suggested methodology outperforms conventional techniques, achieving 93.4% accuracy on the PETS2009 dataset and 89.2% accuracy on the UBI-Fight dataset. These outcomes show how well feature extraction and optimization work together to improve the recognition of human behavior. Reliable security monitoring is ensured by the framework’s scalability and real-time applicability, which also considerably reduce false alarms and offer strong performance in a variety of public safety scenarios.}, archive = {J_ACCESS}, author = {Bayan Alabdullah and Bisma Batool Fatima and Haifa F. Alhasson and Mohammed Alshehri and Yahya AlQahtani and Nouf Albhassabi and Hui Liu}, doi = {10.1109/ACCESS.2025.3579749}, journal = {IEEE Access}, pages = {108718-108731}, shortjournal = {IEEE Access}, title = {A novel deep learner for human behavior prediction over public video surveillance}, volume = {13}, year = {2025}, }
• (2025). Mitigation of IUI in automotive radar based on OFDM scheme. ACCESS, 13, 108710-108717. (www)

  @article{ , abstract = {With rapid development of self-driving technology, automotive radars face more challenges, especially in dealing with interference in rush hour traffic. Interference from nearby radar-equipped vehicles, named inter-user-interference (IUI), can significantly degrade radar performance and compromise the accuracy and safety of vehicle operations. Orthogonal Frequency Division Multiplexing (OFDM) radar, with its capability of utilizing orthogonal coding, presents a suitable solution to effectively mitigate inter-user interference. This paper discusses the necessity of interference mitigation in automotive radar systems and examines how orthogonal coding, particularly through a proposed specific type of generalized chirp-like (ST-GCL) sequences, can reduce the interference and improve the system performance in key metrics such as velocity and range. For more analysis, a comparison between proposed OFDM and Frequency Modulated Continuous Wave (FMCW) radar, is provided that emphasizes the advantages of OFDM in heavy-interference scenarios.}, archive = {J_ACCESS}, author = {F. Yousefian and R. Mohseni}, doi = {10.1109/ACCESS.2025.3580225}, journal = {IEEE Access}, pages = {108710-108717}, shortjournal = {IEEE Access}, title = {Mitigation of IUI in automotive radar based on OFDM scheme}, volume = {13}, year = {2025}, }
• (2025). Non-thermal plasma applications in maize seed remediation, disinfection, and improvement for growth: A review. ACCESS, 13, 108679-108709. (www)

  @article{ , abstract = {Maize is a crucial cereal with significant global importance due to its nutritional value and adaptability. However, its production is increasingly threatened by contamination—both chemical and microbiological—as well as climate change, which negatively impacts supply. Current alternatives aimed at ensuring production often come with considerable adverse effects. In this context, non-thermal plasma (NTP) emerges as a promising technology. It generates reactive chemical species and can also produce associated phenomena such as ultraviolet radiation emission and shock wave formation, which may help protect agricultural yields. Research over the past decade has consistently highlighted the potential of NTP to enhance the germination and development of maize seeds. This review is particularly relevant because it synthesizes current information and provides a structured perspective on the application of NTP in agriculture. It analyzes the experimental conditions—such as voltage, power, working gas, and treatment capacity—as well as the materials and typical discharge configurations in detail. This analysis is crucial for informing future research and refining these treatments. Furthermore, a significant contribution of this work is the categorization of scientific literature based on observed agronomic benefits into four key areas: chemical decontamination, inactivation of pathogenic microorganisms, improvement of seed physical properties, and optimization of seed storage conditions. This framework not only enhances understanding of the advances and multiple positive impacts of NTP but also emphasizes the importance of this technology for sustainable agriculture and food security. Additionally, it identifies knowledge gaps and promising areas for innovation.}, archive = {J_ACCESS}, author = {Diana Guadalupe Gutiérrez-León and Jorge Jurado-Páramo and Benjamín Gonzalo Rodríguez-Méndez and Diego Medina-Castro and Régulo López-Callejas}, doi = {10.1109/ACCESS.2025.3582515}, journal = {IEEE Access}, pages = {108679-108709}, shortjournal = {IEEE Access}, title = {Non-thermal plasma applications in maize seed remediation, disinfection, and improvement for growth: A review}, volume = {13}, year = {2025}, }
• (2025). Global navigation satellite system positioning under interference conditions. ACCESS, 13, 108666-108678. (www)

  @article{ , abstract = {This paper contains a model of the flying object (FO) flight trajectory in the geocentric coordinate system and a model of FO position measurement errors using a Global Navigation Satellite System (GNSS) receiver under interference conditions. The size of the FO coordinate errors in the horizontal and vertical planes ranged from +20.0 m to −20.0 m. This paper’s contribution is the creation of algorithms for processing GNSS signals under interference conditions based on the Savitzky-Golay filter and neural network. The simulation results confirmed that the Savitzky-Golay filter when applied in the GNSS receiver, can suppress FO position measurement errors. For example, if absolute FO position measurement errors due to GNSS interference were from 0.0 m to 5.0 m, and the mean square error of these errors was 0.441 m2, the Savitzky-Golay filter reduced them to a value from 0.0 m to 0.8 m, and the mean square error of these errors was equal was 0.0195 m2. In this case, the mean error value in the FO position after filtering with the Savitzky-Golay filter was 0.32 m, and the mean square error was 0.019 m2. Advanced machine learning techniques in the form of neural networks were also used to suppress errors from the output of the Savitzky-Golay filter. The simulation confirmed that if the FO position errors at the neural network’s input ranged from 0 m to +0.8 m, the neural network reduced them to 0.0 m to 0.5 m. The mean FO position error value was 0.12 m, and the mean square error was 0.0089 m2. The results of this study may contribute to more precise and reliable FO navigation and improve the use of GNSS systems in interference conditions.}, archive = {J_ACCESS}, author = {Sebastián Čikovský and Milan Džunda}, doi = {10.1109/ACCESS.2025.3582056}, journal = {IEEE Access}, pages = {108666-108678}, shortjournal = {IEEE Access}, title = {Global navigation satellite system positioning under interference conditions}, volume = {13}, year = {2025}, }
• (2025). Integrated thermal management strategy using high-value prioritized experience replay deep reinforcement learning for battery electric vehicles. ACCESS, 13, 108653-108665. (www)

  @article{ , abstract = {In low-temperature environments, electric vehicles rely on effective thermal management strategies (TMS) to rapidly heat the battery to its optimal operating temperature while maintaining cabin temperature within a comfortable range. Considering the development trend and critical role of TMS, a deep reinforcement learning (DRL)-based TMS is established, integrating a high-value prioritized experience replay mechanism with the soft actor-critic (SAC) algorithm. This approach prioritizes the replay of high-value experiences during training, thereby enhancing the learning efficiency of the agent. Simulation results indicate that, compared to rule-based TMS, the proposed improved SAC (ISAC)-based TMS reduces the energy consumption of the integrated thermal management system by 4.10% and 3.82% under training cycle and test cycle, respectively. Additionally, it ensures the battery reaches the target temperature more efficiently and significantly reduces cabin temperature fluctuations. Sensitivity analysis further confirms the effectiveness and robustness of the proposed strategy. Comparative experiments at initial temperatures of - $5~^{\circ }$ C and $0~^{\circ }$ C demonstrate that the ISAC-based TMS outperforms the rule-based TMS, which yields 4.41% and 6.49% improvements in energy efficiency, respectively.}, archive = {J_ACCESS}, author = {Yongqiang Jia and Guanghui Cao and Jinrui Gao}, doi = {10.1109/ACCESS.2025.3582081}, journal = {IEEE Access}, pages = {108653-108665}, shortjournal = {IEEE Access}, title = {Integrated thermal management strategy using high-value prioritized experience replay deep reinforcement learning for battery electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Evaluating the effects of cyberattacks in mixed and fully connected vehicle environments using a novel microscopic traffic model. ACCESS, 13, 108641-108652. (www)

  @article{ , abstract = {Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular road for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction times and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats.}, archive = {J_ACCESS}, author = {Zawar Hussain Khan and Faryal Ali and Thomas Aaron Gulliver and Ahmed B. Altamimi and Mohammad Alsaffar and Fahad F. Alfaisal}, doi = {10.1109/ACCESS.2025.3581788}, journal = {IEEE Access}, pages = {108641-108652}, shortjournal = {IEEE Access}, title = {Evaluating the effects of cyberattacks in mixed and fully connected vehicle environments using a novel microscopic traffic model}, volume = {13}, year = {2025}, }
• (2025). Routing protocols and mobility predictions for UAV data collection from agricultural sites: A survey. ACCESS, 13, 108626-108640. (www)

  @article{ , abstract = {Unmanned Aerial Vehicles (UAVs), also known as drones, have found applications in precision agriculture owing to their agility and remote access. However, because of battery, bandwidth and environmental constraints, UAV’s networks experience intermittent links and packet drops. These constraints can either force the UAV to abort its mission or increase packet drops. Therefore, there is a need to improve routing protocols and UAV path planning. This work reviewed routing protocols such as reactive and hybrid as well as mobility predictions such as regressive and Markov models with the central argument of topology control and path optimizations. Optimizations minimize the coverage time and energy consumption. As routing protocols operate on the network layer, this review analyses articles that include cross-layer issues, modelling, and predictions. This article also examined QoS metrics (such as bandwidth and link quality), antenna selection and placement on the UAV and the methods used to validate routing protocols. In this work, UAV’s path optimizations consider crop locations, the appropriate placement of ground sensor nodes and how neighbouring UAVs are controlled for efficient data collection and relay. Analysis of the collected data can improve crop health, cultivation management, livestock monitoring, and reduction on operational cost. Therefore, this work resolved that by predicting the topology change, link-lifetime, and residual energy, the routing protocol can reduce retransmission and packet drops, thereby enhancing network longevity.}, archive = {J_ACCESS}, author = {Khemis Ben Samuel and Stephen D. Wolthusen and Julianne Sansa Otim and Roseline Nyongarwizi Akol and Mary Nsabagwa}, doi = {10.1109/ACCESS.2025.3582163}, journal = {IEEE Access}, pages = {108626-108640}, shortjournal = {IEEE Access}, title = {Routing protocols and mobility predictions for UAV data collection from agricultural sites: A survey}, volume = {13}, year = {2025}, }
• (2025). A scalable all-digital near-memory computing architecture for edge AIoT applications. ACCESS, 13, 108609-108625. (www)

  @article{ , abstract = {With the growing need to process large volumes of data, edge computing near data collection sources has become increasingly important. However, the resource constraints of edge devices require more efficient data processing techniques. Near-memory computing (NMC) presents an efficient solution, especially for data-intensive applications, by enabling processing that is both energy-efficient and hardware optimized. This work introduces a platform-agnostic NMC architecture tailored for convolutional neural network (CNN) workloads, integrated into the shared cache memory subsystem of a microcontroller unit (MCU). An open-source RISC-V MCU is chosen as the target platform due to its flexibility and low-power architecture. The NMC co-processor, operating alongside the general-purpose RISC-V core, forms a multi-core system-on-chip that combines low hardware cost with high energy efficiency, while maintaining a high degree of flexibility. The proposed design offers a configurable architecture capable of processing a wide range of CNN models with a computational efficiency of 94%. For evaluation purposes, widely recognized CNN benchmark models are utilized, showing a performance of 96GOPS and an energy efficiency of 1828GOPS/W for 8-bit precision at 200MHz. These results represent a significant improvement over both highly customized state-of-the-art hardware accelerators and multi-core MCU solutions.}, archive = {J_ACCESS}, author = {Masoud Nouripayam and Arturo Prieto and Joachim Rodrigues}, doi = {10.1109/ACCESS.2025.3582013}, journal = {IEEE Access}, pages = {108609-108625}, shortjournal = {IEEE Access}, title = {A scalable all-digital near-memory computing architecture for edge AIoT applications}, volume = {13}, year = {2025}, }
• (2025). The applications of artificial intelligence and digital twin in power systems: An in-depth review. ACCESS, 13, 108573-108608. (www)

  @article{ , abstract = {The accelerated evolution of Artificial Intelligence (AI) technology has garnered widespread attention and acclaim across various domains, owing to its remarkable performance in diverse applications. Power systems supporting modern life are no exception to this transformative wave. This paper stands as the first comprehensive review of various AI techniques in power systems, spanning applications such as load forecasting, security assessment, voltage stability assessment, load shedding (LS), false data injection attack (FDIA) detection, state estimation and localization, fault detection and location, and power quality disturbances (PQDs) detection. In addressing the challenges inherent in the practical implementation of AI in power systems, this study introduces two potent tools: the strategic utilization of transfer learning (TL) in conjunction with AI algorithms and the leveraging of digital twin technology. The integration of these approaches substantially enhances the performance and accuracy of AI models. Overall, this work aims to contribute to the foundational knowledge necessary for harnessing the full spectrum of AI’s capabilities and in advancing the shift toward a sustainable energy future.}, archive = {J_ACCESS}, author = {Ghazal Rahmani-Sane and Sasan Azad and Mohammad Taghi Ameli and Sasan Haghani}, doi = {10.1109/ACCESS.2025.3580340}, journal = {IEEE Access}, pages = {108573-108608}, shortjournal = {IEEE Access}, title = {The applications of artificial intelligence and digital twin in power systems: An in-depth review}, volume = {13}, year = {2025}, }
• (2025). Spatially-aware loss functions for GAN-driven super-resolution. ACCESS, 13, 108561-108572. (www)

  @article{ , abstract = {Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, these models often introduce side effects into the outputs, such as unexpected artifacts and noises. To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process. We extract spatial information from the input data and incorporate it into the training loss, making the corresponding loss a spatially adaptive (SA) one. After that, we utilize it to guide the training process. We will show that the proposed approach is independent of the methods used to extract the spatial information and independent of the SR tasks and models. This method consistently guides the training process towards generating visually pleasing SR images and video frames, substantially mitigating artifacts and noise, ultimately leading to enhanced perceptual quality.}, archive = {J_ACCESS}, author = {Xijun Wang and Santiago López-Tapia and Xinyi Wu and Rafael Molina and Aggelos K. Katsaggelos}, doi = {10.1109/ACCESS.2025.3579004}, journal = {IEEE Access}, pages = {108561-108572}, shortjournal = {IEEE Access}, title = {Spatially-aware loss functions for GAN-driven super-resolution}, volume = {13}, year = {2025}, }
• (2025). A voxelized transformer-based neural network for 3D reconstruction from multi-energy SEM backscattered electrons. ACCESS, 13, 108551-108560. (www)

  @article{ , abstract = {Three-dimensional (3D) data analysis, especially 3D reconstruction, is critical within integrated circuits for quality control and inspection. The traditional 3D reconstruction, like multi-energy deconvolution scanning electron microscopy (MED-SEM), can achieved resolution of 5 nm in the horizontal direction and 10 nm in the vertical direction from backscattered electron images of objects at different depth layers. However, the traditional methods are difficult to obtain high-precision reconstruction results in the longitudinal direction, and the reconstruction results of most methods contain a large number of artifacts. Thus, in this work, a hybrid CNN-Transformer network, called BSE-VoxNets, is proposed for 3D high precision reconstruction. The proposed BSE-VoxNets consists three parts: transformer feature extractor, convolutional upsampling block and feature fusion block. 10000 BSE samples were used for training BSE-VoxNets, with 200 samples for testing, and the optimal cross-union ratio (IoU) of the test dataset reached 0.953. Two numerical cases are used to respectively verify the inversion performance of the proposed method for different depths and different feature scales. The reconstruction results show that the proposed BSE-VoxNets can reach a resolution of 2 nm in both the horizontal and vertical dimensions.}, archive = {J_ACCESS}, author = {Caizhi Zheng and Ronghan Hong and Hao-Jie Hu and Qing Huo Liu}, doi = {10.1109/ACCESS.2025.3581974}, journal = {IEEE Access}, pages = {108551-108560}, shortjournal = {IEEE Access}, title = {A voxelized transformer-based neural network for 3D reconstruction from multi-energy SEM backscattered electrons}, volume = {13}, year = {2025}, }
• (2025). Assessment of scientific creative-potential by near-infrared spectroscopy using brain-network-based deep-fuzzy classifier. ACCESS, 13, 108533-108550. (www)

  @article{ , abstract = {The work presents a novel approach to assess the scientific creative ability of subjects by analyzing their brain connectivity patterns through functional Near-Infrared Spectroscopy (fNIRS) during participation in an analogical reasoning test. The proposed method involves three key stages: i) construction of brain connectivity networks using Wavelet Transform Coherence (WTC), ii) abstraction and analysis of three node-based network features, and iii) classification of abstracted features into five degrees of creative potential by a novel Enhanced Graph Convolution Induced Type-2 Fuzzy Classifier (EGCIFC). The novelty of the classifier lies in: i) design of an enhanced graph convolution operation that encapsulates local and global structural information from the input graph, ii) use of the Smish activation function to improve performance, iii) inclusion of a one-dimensional spatial convolution layer for preserving relevant information within convolved embeddings, iv) design of a novel mapping function to mitigate uncertainty among the spatial convolved vectors in the type-2 fuzzy layer, and v) application of Takagi-Sugeno-Kang (TSK)-based fuzzy reasoning to reduce computational cost. Evaluation on three datasets, each comprising over 45 individuals from different scientific backgrounds, shows that EGCIFC improves classification accuracy by 2.25% over the nearest competitor and by 22.72% over the lowest-performing baseline. The proposed method also reduces computational cost by 7.46% and 54.7% compared to the nearest and worst competitors, respectively. Additionally, EGCIFC exhibits a standard deviation of ±0.72% in classification accuracy, reflecting its robustness. Hence, the proposed approach may prove effective for recruiting individuals with varying degrees of scientific creativity across different research sectors.}, archive = {J_ACCESS}, author = {Sayantani Ghosh and Amit Konar and Atulya K. Nagar}, doi = {10.1109/ACCESS.2025.3581065}, journal = {IEEE Access}, pages = {108533-108550}, shortjournal = {IEEE Access}, title = {Assessment of scientific creative-potential by near-infrared spectroscopy using brain-network-based deep-fuzzy classifier}, volume = {13}, year = {2025}, }
• (2025). Defect prediction in CWDM optical modules using multimodal learning. ACCESS, 13, 108519-108532. (www)

  @article{ , abstract = {Reliable defect detection in coarse-wavelength division multiplexing (CWDM) optical modules is critical for ensuring stable high-speed optical communication and minimizing network disruptions. Traditional inspection methods, such as manual video screening and optical testing, are labor-intensive, prone to human error, and often fail to identify underlying defect patterns, thereby necessitating multiple rounds of testing. To overcome these limitations, this paper proposes a deep learning-based multimodal learning framework for defect prediction that integrates manufacturing process data (tabular features) and optical eye diagram images from the module’s receiver. Leveraging the complementary nature of these data sources enhances defect detection performance, enabling the proposed model to surpass conventional single modal alternatives. The model was comprehensively evaluated on a real CWDM module dataset through cross-validation, based on performance metrics such as accuracy and F1-score. The multimodal learning schemes achieved an accuracy of 91.0% and an F1-score exceeding 90%, significantly outperforming both traditional single modal approaches and manual inspection techniques. These results demonstrate the effectiveness of multimodal learning in improving defect prediction accuracy, reducing reliance on repetitive testing, and lowering overall inspection costs. The proposed approach represents a scalable and efficient solution for automated quality control in optical module manufacturing, with potential applications in optical network maintenance and defect detection for other photonic components.}, archive = {J_ACCESS}, author = {Kyu-Jeong Choi and Jia Yang and Botambu Collins and Sung-Geun Kim and Do-Jin Lim and Jin-Taek Seong}, doi = {10.1109/ACCESS.2025.3582242}, journal = {IEEE Access}, pages = {108519-108532}, shortjournal = {IEEE Access}, title = {Defect prediction in CWDM optical modules using multimodal learning}, volume = {13}, year = {2025}, }
• (2025). Analyzing taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques. ACCESS, 13, 108500-108518. (www)

  @article{ , abstract = {This study explores traffic patterns on Taiwanese highways during consecutive holidays, with a focus on understanding the behavior of Taiwanese highway traffic. We propose a prediction-based detection method for identifying highway traffic anomalies using reconciled ordinary least squares (OLS) forecasts and bootstrap prediction intervals. Two fundamental features of traffic flow time series – seasonality and spatial autocorrelation – are captured by adding Fourier terms in OLS models, spatial aggregation (as a hierarchical structure mimicking the geographical division into regions, cities, and stations), and a reconciliation step. Our approach, although simple, is capable of modeling complex traffic datasets with reasonable accuracy. Being based on OLS, it is efficient and permits avoiding the computational burden of more complex methods. Analyses of Taiwan’s consecutive holidays in 2019, 2020, and 2021 (73 days) showed strong variations in anomalies across different directions and highways. Specifically, we detected some areas and highways comprising a high number of traffic anomalies (north direction-central and southern regions-highways No. 1 and 3, south direction-southern region-highway No.3), and others with generally normal traffic (east and west direction). These results could provide important decision-support information to traffic authorities.}, archive = {J_ACCESS}, author = {Mahsa Ashouri and Frederick Kin Hing Phoa and Marzia Angela Cremona}, doi = {10.1109/ACCESS.2025.3581839}, journal = {IEEE Access}, pages = {108500-108518}, shortjournal = {IEEE Access}, title = {Analyzing taiwanese traffic patterns on consecutive holidays through forecast reconciliation and prediction-based anomaly detection techniques}, volume = {13}, year = {2025}, }
• (2025). Quantum-inspired control strategies for reducing DC-link voltage fluctuations in DFIG wind energy converters. ACCESS, 13, 108481-108499. (www)

  @article{ , abstract = {The integration of renewable energy sources into power grids presents significant technical challenges, particularly regarding voltage stability and power quality. While Doubly-Fed Induction Generators (DFIGs) offer superior performance in variable wind conditions, their DC-link voltage fluctuations remain a critical concern affecting system reliability, component longevity, and grid compliance. This paper presents a quantum-inspired discrete Proportional-Integral (PI) controller to stabilize DC-link voltage in DFIG-based wind energy systems. The approach integrates quantum computing principles into classical control frameworks, creating a hybrid methodology that leverages quantum-inspired optimization while maintaining implementation feasibility on conventional hardware. By incorporating quantum-inspired algorithms into the Grid-Side Converter (GSC) control framework, the strategy dynamically adjusts PI gains using qubit-based probabilistic modeling—where control parameters exist simultaneously in multiple potential states, similar to quantum bits existing in both 0 and 1 states concurrently. This superposition-based optimization explores multiple solution spaces in parallel, achieving 40-50% faster convergence than classical methods. Simulations of a 1.5 MW DFIG system demonstrated a 69.6% reduction in steady-state voltage fluctuations (from 11.97% to 3.64%) and 73.8% improvement during symmetrical faults (from 33.33% to 11.31%), while limiting peak deviations to <10% during unsymmetrical faults (L-L-G/L-G). The controller maintained stable DC-link voltage at 1150V ±40V under normal operation and exhibited only 3.5% overshoot during fault conditions, significantly outperforming conventional PI and fuzzy controllers. This quantum-classical hybrid approach reduces mechanical stress on capacitors and converters, extends equipment lifespan, and enables higher renewable integration through improved grid stability while maintaining compliance with IEEE 1547–2018 standards.}, archive = {J_ACCESS}, author = {Sarika Shrivastava and Saifullah Khalid and Dinesh Kumar Nishad}, doi = {10.1109/ACCESS.2025.3579545}, journal = {IEEE Access}, pages = {108481-108499}, shortjournal = {IEEE Access}, title = {Quantum-inspired control strategies for reducing DC-link voltage fluctuations in DFIG wind energy converters}, volume = {13}, year = {2025}, }
• (2025). High impedance fault location in distribution systems: A novel approach with enhanced metrics and intelligent algorithms. ACCESS, 13, 108466-108480. (www)

  @article{ , abstract = {Locating faults in a distribution system (DS) is a challenging task, especially with the increasing integration of distributed generation (DG). Among the various fault types in DSs, high impedance faults (HIFs) are particularly difficult to address due to their low fault currents and erratic behavior, often resulting from contact between a conductor and a low-conductivity surface. Despite of the numerous papers addressing HIFs, there is still a lack of consensus on the most effective metrics and techniques for their detection and location. This article proposes a comprehensive analysis aiming to identify the adequate metrics for artificial intelligent algorithms to locate HIFs in a DS in the presence of DG. This analysis provides valuable insights into developing more efficient HIF location methods. Therefore, these insights are applied to develop a new HIF location method, based on artificial intelligence. The analysis is carried out by evaluating the correlation between almost 11,000 metrics with the HIF distance. The metrics showing the strongest correlation with HIF location are then used as input data for intelligent algorithms designed to accurately estimate the HIF position. Subsequently, these algorithms are evaluated and compared based on their error rates in locating HIFs. Finally, a new intelligent-based method for HIF location in DSs is proposed and extensively evaluated, including a comparison with existing approaches and a generalization analysis, which showed high successful rates and strong potential for practical application.}, archive = {J_ACCESS}, author = {Gabriela Nunes Lopes and Pedro I. N. Barbalho and José Carlos Melo Vieira and Denis V. Coury}, doi = {10.1109/ACCESS.2025.3582359}, journal = {IEEE Access}, pages = {108466-108480}, shortjournal = {IEEE Access}, title = {High impedance fault location in distribution systems: A novel approach with enhanced metrics and intelligent algorithms}, volume = {13}, year = {2025}, }
• (2025). An improved IoT based hybrid predictive load forecasting model for a greenhouse integrated with demand side management. ACCESS, 13, 108446-108465. (www)

  @article{ , abstract = {Greenhouse farming enhances agricultural productivity but remains highly energy-intensive, requiring advanced energy management strategies to ensure sustainability. Traditional load forecasting and demand-side management (DSM) methods often fall short in adapting to the dynamic and highly variable environmental conditions within greenhouses. This study hypothesizes that combining hybrid machine learning models with IoT-based DSM can optimize energy consumption while maintaining critical microclimatic conditions for crop growth. A hybrid predictive model integrating Extreme Gradient Boosting (XGBoost) for static feature learning and Long Short-Term Memory (LSTM) for sequential pattern recognition is proposed, coupled with a priority-based DSM framework deployed on a Raspberry Pi IoT platform. The model was trained and tested using one year of real-world greenhouse data, achieving a Mean Absolute Percentage Error (MAPE) of 5.3%, reducing grid energy dependency by 7.1%, and lowering the average electricity cost by 17.3%. These results demonstrate that the proposed system offers a scalable, economically viable, and sustainable solution for intelligent energy management in greenhouse environments, significantly advancing the integration of AI and DSM technologies in controlled agriculture. A laboratory-scale prototype was developed for physical verification using an EcoSense 500W solar microgrid, Raspberry Pi 3B+, relay modules, current sensors (SCT-013, ADS1115 ADC), and load simulators. Furthermore, this work contributes directly to Sustainable Development Goals (SDGs) 2, 7, and 12 by promoting food security, increasing renewable energy utilization, and enhancing responsible resource consumption in greenhouse farming.}, archive = {J_ACCESS}, author = {Soumya Ranjan Biswal and Tanmoy Roy Choudhury and Babita Panda and Subhrajyoti Mishra}, doi = {10.1109/ACCESS.2025.3579435}, journal = {IEEE Access}, pages = {108446-108465}, shortjournal = {IEEE Access}, title = {An improved IoT based hybrid predictive load forecasting model for a greenhouse integrated with demand side management}, volume = {13}, year = {2025}, }
• (2025). A review of the recent trends in mobile malware evolution, detection, and analysis. ACCESS, 13, 108415-108445. (www)

  @article{ , abstract = {The rapid rise of smartphones and mobile applications has fostered an environment conducive to the advancement of mobile malware, presenting considerable obstacles for both detection and mitigation efforts. This analysis delves into the latest developments in mobile malware, underscoring the shift from basic threats to more complex forms of attacks, including ransomware, spyware, and banking trojans. It discusses significant progress in malware detection techniques, particularly those utilizing behavioral analysis, artificial intelligence (AI), and machine learning (ML). The paper points out deficiencies in current detection methods and stresses the importance of hybrid approaches that integrate both static and dynamic analyses to tackle issues such as obfuscation, polymorphism, and encrypted threats. Furthermore, the research investigates the impact of collaborative threat intelligence sharing and AI-driven tools in improving malware detection and analysis. This systematic review offers a thorough framework for comprehending the progression of mobile malware, assessing existing detection strategies, and pinpointing future research and security initiatives.}, archive = {J_ACCESS}, author = {Seetah Almarri and Alanoud Bodokhi and Mounir Frikha}, doi = {10.1109/ACCESS.2025.3582086}, journal = {IEEE Access}, pages = {108415-108445}, shortjournal = {IEEE Access}, title = {A review of the recent trends in mobile malware evolution, detection, and analysis}, volume = {13}, year = {2025}, }
• (2025). Degradation dynamics of mineral oil aged silica doped silicone rubber nanocomposites. ACCESS, 13, 108402-108414. (www)

  @article{ , abstract = {Room temperature vulcanized silicone rubber (RTVSR) is widely used as a coating material for transformer bushings to provide anti-pollution resistance. However, mineral oil leakage from power transformers can significantly degrade the performance of these coatings. This study investigates the degradation dynamics of RTVSR coatings exposed to mineral oil and evaluates the potential of nano silica (SiO2) doping to mitigate this issue, leveraging its molecular compatibility with silicone rubber. Silicone rubber (SR) doped with 1%, 3%, and 5% by weight of silica was subjected to thermal ageing in transformer mineral oil for 6 hours at 80°C. The physicochemical, surface morphological, and electrical properties of the aged specimens were analyzed using Fourier Transform Infrared Spectroscopy (FTIR), contact angle measurements, 3D microscopy, and breakdown voltage (BDV) testing. FTIR revealed molecular side-chain and backbone scission in all samples, with silica-doped specimens showing a less pronounced extent of degradation. This degradation caused reduced surface hydrophobicity, lower BDV, and increased surface roughness post-ageing. Thermal stability was further assessed using thermogravimetric analysis (TG-DTA) and infrared thermography. Residual weight, indicative of thermal stability, significantly decreased post-ageing for all specimens. Infrared thermography revealed lower heat accumulation in silica-doped specimens compared to undoped samples, although heat retention increased in all samples after ageing. Overall, the results confirm that mineral oil exposure has a detrimental effect on RTVSR coatings. However, 3 wt.% nano-SiO2 doping significantly enhances degradation resistance, making it a promising composition for transformer bushings exposed to mineral oil leakage.}, archive = {J_ACCESS}, author = {Moutusi Paul and Amrit Raj Sharma and Palash Mishra and M. Mohana Rao and Toan Phung and Mithun Mondal}, doi = {10.1109/ACCESS.2025.3581927}, journal = {IEEE Access}, pages = {108402-108414}, shortjournal = {IEEE Access}, title = {Degradation dynamics of mineral oil aged silica doped silicone rubber nanocomposites}, volume = {13}, year = {2025}, }
• (2025). Tiny-MobileNet-SE: A hybrid lightweight CNN architecture for resource-constrained IoT devices. ACCESS, 13, 108389-108401. (www)

  @article{ , abstract = {Traditional Convolutional Neural Network (CNN) architectures face challenges in deployment on resource-constrained devices such as Internet of Things (IoT) platforms, mobile applications, and drones due to their computational intensity and memory requirements. This limitation motivates the development of lightweight models that maintain high performance with minimal resource consumption. This paper introduces Tiny-MobileNet-SE, a novel hybrid CNN architecture designed for image classification tasks under critical resource-constrained environments. This architecture integrates Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration, Batch Normalization (BN) for accelerated convergence, and applies knowledge distillation techniques from MobileNetV2 for enhanced feature generalization. We conducted comprehensive experimentation to validate its performance across various metrics, including accuracy, number of parameters, and model size. Experimental results show that Tiny-MobileNet-SE achieved superior performance with a significantly reduced model size (0.72 MB) and a smaller number of parameters (482 K) compared to conventional lightweight CNNs, reaching up to 98.6% validation accuracy on real-world crop pest and disease datasets. This innovative architecture contributes to bridging the gap between computational efficiency and classification accuracy, enabling deep learning (DL) models to be deployed on small mobile, embedded, and IoT devices with minimized resource consumption.}, archive = {J_ACCESS}, author = {Jean Pierre Nyakuri and Celestin Nkundineza and Omar Gatera and Kizito Nkurikiyeyezu}, doi = {10.1109/ACCESS.2025.3582055}, journal = {IEEE Access}, pages = {108389-108401}, shortjournal = {IEEE Access}, title = {Tiny-MobileNet-SE: A hybrid lightweight CNN architecture for resource-constrained IoT devices}, volume = {13}, year = {2025}, }
• (2025). Optimization study of multidimensional computational model for distribution network cable fault location and hidden danger warning system in smart grid environment. ACCESS, 13, 108378-108388. (www)

  @article{ , abstract = {Traditional frequency spectrum testing instruments have limited gain bandwidth and low output voltage in the high-frequency range, leading to severe signal attenuation during long-distance cable testing and distortion issues in the high-frequency range of the impedance spectrum. This paper proposes a cable fault diagnosis and localization method based on a pseudo-trapezoidal wave (PT-PFM) excitation impedance spectrum digital reconstruction algorithm. By analyzing the influence of local fault impedance on the input impedance spectrum for different types of faults, as well as the attenuation characteristics of the excitation voltage signal at the input end and the power transmission characteristics, we designed a high-capacity pseudo-trapezoidal wave excitation system based on a SiC high-speed inverter, which can measure impedance spectrum information at frequencies up to 7 MHz, and utilizes digital reconstruction technology to achieve full coverage of a wide frequency range from 0.1 Hz to 60 MHz. Experiments show that in locating faults in a 120 m cable, the positioning error using the spatiotemporal transformation function is reduced by over 80% compared to traditional methods, the oscillation amplitude of the interval is reduced by 50%, and the convergence speed is improved by 60%. Additionally, the identification accuracy for both high-resistance and low-resistance faults exceeds 95%. The experimental results demonstrate that compared to traditional low-voltage sinusoidal excitation, this method produces smaller oscillations in the positioning results through the spatiotemporal transformation function, faster convergence speed, and an improvement in fault localization accuracy of over 80%, thereby validating the effectiveness and accuracy of this method.}, archive = {J_ACCESS}, author = {Xin Li and Jiyou Wang and Minghui Liu}, doi = {10.1109/ACCESS.2025.3582113}, journal = {IEEE Access}, pages = {108378-108388}, shortjournal = {IEEE Access}, title = {Optimization study of multidimensional computational model for distribution network cable fault location and hidden danger warning system in smart grid environment}, volume = {13}, year = {2025}, }
• (2025). A comparative study of XBORE and XBOREOPT hybrid models for ore production forecasting in the mining industry. ACCESS, 13, 108368-108377. (www)

  @article{ , abstract = {Mining industries worldwide are increasingly adopting machine learning tools to handle the vast amounts of data generated daily from mine systems. Few hybrid models have been developed to handle data from mine systems, and this study investigates the comparative performance of machine learning algorithms XBORE and XBOREOPT for predicting ore production in mining operations. The study explores the efficacy of hyperparameter optimization techniques in enhancing predictive accuracy, robustness, and computational efficiency, which are crucial for optimizing resource allocation and operational planning in mining contexts. Through empirical evaluation using real-world data, this research aims to provide insights into the practical benefits of hybrid modeling approaches in industrial predictive analytics. In terms of Mean Squared Error (MSE), XBORE scores 347.8001, whereas XBOREOPT achieves a lower value of 340.5477. Since MSE penalizes larger errors more heavily, the lower MSE of XBOREOPT suggests it is more effective at minimizing significant prediction errors. This study combines the viability of hybrid modeling with practical applications in the mining industry, focusing on enhancing predictive accuracy and operational efficiency.}, archive = {J_ACCESS}, author = {Morris Mkokweza and Santhi Kumaran and George Mufungulwa}, doi = {10.1109/ACCESS.2025.3582332}, journal = {IEEE Access}, pages = {108368-108377}, shortjournal = {IEEE Access}, title = {A comparative study of XBORE and XBOREOPT hybrid models for ore production forecasting in the mining industry}, volume = {13}, year = {2025}, }
• (2025). GAN-based driver’s head motion using millimeter-wave radar sensor. ACCESS, 13, 108359-108367. (www)

  @article{ , abstract = {The recognition of driver behavior is critical for enhancing road safety, with a particular focus on monitoring driver attention. Radar-based recognition systems offer distinct advantages over traditional computer vision methods, including enhanced user privacy, reduced power consumption, and greater flexibility in sensor deployment. This study leverages a compact millimeter-wave radar to monitor driver head movements, providing a non-intrusive method to assess driver focus. A frequency-modulated continuous-wave (FMCW) radar sensor is strategically positioned on the vehicle’s steering wheel, capturing reflection patterns that vary with the driver’s head orientation. These patterns are used to identify and classify different head movements, which are indicative of the driver’s attention level. To achieve accurate classification, a deep learning approach is adopted, utilizing a Generative Adversarial Network (GAN) model. This model is particularly effective in scenarios with limited labeled data, as it can generate high-quality synthetic data to augment training. Experimental results demonstrate that the proposed method reliably classifies all relevant head movement scenarios, underscoring its potential for real-world applications in driver monitoring systems.}, archive = {J_ACCESS}, author = {Hong Nhung Nguyen and Yong-Hwa Kim}, doi = {10.1109/ACCESS.2025.3582079}, journal = {IEEE Access}, pages = {108359-108367}, shortjournal = {IEEE Access}, title = {GAN-based driver’s head motion using millimeter-wave radar sensor}, volume = {13}, year = {2025}, }
• (2025). Fine motor tasks in virtual reality: The impact of haptic feedback and object characterization. ACCESS, 13, 108348-108358. (www)

  @article{ , abstract = {Fine motor tasks in Virtual Reality (VR) are often constrained by the lack of natural sensory cues, particularly the sense of touch. Previous studies have shown that object characteristics (e.g., shape and size) influence how users perceive and interact with objects. However, the literature lacks insight into how these factors affect each other. This study investigates how different haptic feedback modalities and object characteristics influence user performance during fine motor tasks in VR. We conducted a user study with 25 participants as SenseGlove Nova 1 rendered alternative haptic feedback conditions: combined (force and vibration), force-only, vibration-only, and no feedback. Participants grasped and inserted virtual objects of different shapes (cube, cylinder, pentagon, triangle) and sizes (small, medium, large) into a toybox. Our results show that combined feedback consistently led to greater precision, fewer collisions, and an improved manipulation experience, particularly with larger and more complex objects. Object characteristics also significantly influenced performance: medium-sized and simpler-shaped objects created the best results. Additionally, object characteristics also influenced performance, with medium-sized and simpler-shaped objects leading to better performance. Our findings indicate the need to choose haptic feedback modalities based on object-specific characteristics for better user performance and experience.}, archive = {J_ACCESS}, author = {Nour Hatira and Mine Sarac}, doi = {10.1109/ACCESS.2025.3581787}, journal = {IEEE Access}, pages = {108348-108358}, shortjournal = {IEEE Access}, title = {Fine motor tasks in virtual reality: The impact of haptic feedback and object characterization}, volume = {13}, year = {2025}, }
• (2025). Through-the-wall SAR auto-focusing with low accuracy positioning system. ACCESS, 13, 108330-108347. (www)

  @article{ , abstract = {This paper analyzes a through-the-wall synthetic aperture radar (SAR) imaging system designed for indoor use with imprecise positioning systems. Due to uncertainties in transceiver positions at scanning timestamps and the defocusing effects caused by multi-medium (air-wall-air) wave propagation, an autofocus algorithm is essential to achieve high-quality SAR imaging for further detection purposes. We propose a fast and accurate autofocus procedure integrated with the back-projection algorithm, outperforming conventional SAR autofocus algorithms (e.g., map drift algorithm, phase gradient algorithm). Furthermore, we present extensive simulated and experimental results for two indoor positioning systems: a stereoscopic camera and a radio frequency localization system based on the off-the-shelf Pulson P440 transceiver. The primary contributions of this paper include a novel SAR autofocus algorithm based on a prominent scatterer located in front of or behind the wall, along with a fast, recursive routine for solving the nonlinear system of equations associated with the 3D localization system.}, archive = {J_ACCESS}, author = {Adrian Focşa and Ştefan-Adrian Toma and Mihai Coca and Andrei Anghel}, doi = {10.1109/ACCESS.2025.3579096}, journal = {IEEE Access}, pages = {108330-108347}, shortjournal = {IEEE Access}, title = {Through-the-wall SAR auto-focusing with low accuracy positioning system}, volume = {13}, year = {2025}, }
• (2025). Optimizing inotropic infusion with cluster specific AI decision models and digital twins. ACCESS, 13, 108316-108329. (www)

  @article{ , abstract = {Inotropes are critical care medications essential for maintaining normal blood pressure (BP) in hospitalized patients. Titrating infusion rates of inotropes such as noradrenaline, vasopressin, and adrenaline based on fluctuating BP presents significant challenges in critical care settings. Typically, clinicians set a constant infusion rate for one hour, which may not accommodate the dynamic variability of BP inherent in critically ill patients, potentially leading to inadvertent hypotension or hypertension. Conventional feedback controllers, including fuzzy logic controllers (FLC), struggle to adapt to complex BP variations due to fixed algorithms and intracohort variability in drug responses. We propose an AI-enhanced closed-loop noradrenaline infusion control mechanism utilizing long short-term memory (LSTM) networks. This approach captures variability in drug responses through clustering of patients using LSTM autoencoders and K-means algorithms, subsequently developing LSTM-based decision models for infusion rates tailored to clusters. Additionally, a digital twin cardiac model serves as a simulation tool for validating the impact of inotropic infusion as indicated by the decision model. Comparative performance analyses demonstrate that our AI-enhanced closed-loop feedback method outperforms conventional systems like FLC regulators and pharmacokinetic-pharmacodynamic (PK-PD) models while ensuring patient safety as well as reducing the workload of clinicians.}, archive = {J_ACCESS}, author = {Vidya S Nair and G. D. Heshan Niranga and Aryalakshmi C.S and Dipu T. Sathyapalan and Thushara Madathil and Rahul Krishnan Pathinarupothi}, doi = {10.1109/ACCESS.2025.3581969}, journal = {IEEE Access}, pages = {108316-108329}, shortjournal = {IEEE Access}, title = {Optimizing inotropic infusion with cluster specific AI decision models and digital twins}, volume = {13}, year = {2025}, }
• (2025). ESCNet: Embedding space consistency network with scale-perspective adaptation for person re-identification. ACCESS, 13, 108306-108315. (www)

  @article{ , abstract = {Person re-identification (ReID) has made considerable advancements through extensive research in recent years. However, in practical scenarios, multi-scale and perspective-aware person re-identification becomes critically important because of the variations in the distance between the camera and the person and the differences in the angles of view. This paper proposes an Embedding Space Consistency Network (ESCNet). In this network, we first introduce Scale-Perspective Aware Adaptation (SPAA), which allows the model to actively perceive and adapt to diverse variations in scale and perspective during training. Building on this, to address the distribution shift of global features caused by scale and perspective changes, we design a Feature Consistency Loss (FCLoss) that constrains the distance between multi-scale and multi-perspective contrastive samples in the embedding space. Furthermore, to capture fine-grained local information, we propose Local Feature Alignment Enhancement (LFAE), allowing the model to focus on semantic alignment in local regions of the person. Finally, ESCNet achieves progressive feature learning from local discriminability to global consistency through collaborative optimisation of these modules. Extensive experiments on three benchmark ReID datasets show that our approach outperforms existing methods, achieving superior robustness and effectiveness.}, archive = {J_ACCESS}, author = {Hao-Yu Gao}, doi = {10.1109/ACCESS.2025.3582382}, journal = {IEEE Access}, pages = {108306-108315}, shortjournal = {IEEE Access}, title = {ESCNet: Embedding space consistency network with scale-perspective adaptation for person re-identification}, volume = {13}, year = {2025}, }
• (2025). Multidimensional readiness evaluation of smart tourism destinations: A natural language processing and thematic analysis approach. ACCESS, 13, 108284-108305. (www)

  @article{ , abstract = {The rapid expansion of the tourism sector, driven by technological progress, has led to smart tourism, improving visitor experiences and streamlining destination administration. In underdeveloped nations, such as Indonesia, destinations often are not adequately prepared to use smart technology, resulting in inefficiencies and unsuccessful projects. Existing models for evaluating tourism progress typically concentrate on specific regions or isolated dimensions, lacking a comprehensive assessment of governance, socio-economic conditions, and information technology (IT) awareness. This research addresses this gap by introducing a multidimensional framework for evaluating smart tourism readiness based on the Technology-Organization-Environment (TOE) paradigm. The study starts with a thorough review of existing literature using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method to find key factors for readiness, and it uses a combination of both qualitative and quantitative research methods. We conducted a quantitative examination using natural language processing (NLP) to cluster the dimensions of the existing evaluation models. In the qualitative phase, two validation processes were conducted. The first process involved expert interviews with stakeholders to validate the dimensions identified in the previous step. In the second validation process, we simplified the information by matching the proposed dimensions with IT governance components from well-known frameworks like COBIT and ITIL 4. This mapping ensures the proposed dimensions are aligned with IT governance components, supporting the effective management of smart technology in smart tourism destinations. The study identifies seven critical readiness dimensions as a key finding: governance, sustainability, online tourism services, smart technology, accessibility, innovation, and socioeconomic conditions. The findings contribute to a holistic readiness evaluation framework, offering actionable insights for policymakers and tourism stakeholders to inform strategic planning and policy development. This comprehensive model supports the successful implementation of smart tourism initiatives, addressing the unique challenges of developing countries and fostering long-term sustainability.}, archive = {J_ACCESS}, author = {Dhiani Tresna Absari and Arif Djunaidy and Tony Dwi Susanto}, doi = {10.1109/ACCESS.2025.3581962}, journal = {IEEE Access}, pages = {108284-108305}, shortjournal = {IEEE Access}, title = {Multidimensional readiness evaluation of smart tourism destinations: A natural language processing and thematic analysis approach}, volume = {13}, year = {2025}, }
• (2025). Joined spatial and spectral segmentation of hyperspectral datasets on historical art objects. ACCESS, 13, 108266-108283. (www)

  @article{ , abstract = {In the context of clustering and classification, the choice between spatial and spectral features hinges on data characteristics and analytical goals. Spatial features excel in spatially contextualised data like urban planning and object detection, capturing adjacent pixel relationships. Spectral features, prominent in hyperspectral data, shine in tasks requiring precise material identification. However, managing extensive hyperspectral data poses challenges, prompting hybrid approaches for efficient data segmentation. This study presents an innovative data processing pipeline that combines spatial and spectral clustering techniques for the extraction and mapping of spectral signatures in both cinematic films and pointillism painting. We explore Simple Linear Iterative Clustering (SLIC) Superpixel’s potential in reducing hyperspectral data while maintaining spectral richness. By optimizing superpixel segmentation and extracting central spectra, we reduce data size significantly. This facilitates applying various machine learning algorithms, especially the soft clustering algorithms Fuzzy C-Means clustering (FCM) and Gaussian Mixture Models (GMM), to classify spectra into distinct colourant groups, identifying complex mixtures and areas of degradation. This research highlights the potential of machine learning in aiding artwork diagnostics, conservation, and restoration, with transferable models for similar scenarios.}, archive = {J_ACCESS}, author = {Lingxi Liu and Aurore Malmert and Emeline Pouyet and Silvia Mirri and Giovanni Delnevo}, doi = {10.1109/ACCESS.2025.3579076}, journal = {IEEE Access}, pages = {108266-108283}, shortjournal = {IEEE Access}, title = {Joined spatial and spectral segmentation of hyperspectral datasets on historical art objects}, volume = {13}, year = {2025}, }
• (2025). State-of-charge estimation of lithium-ion batteries in parallel for electric vehicles using improved square root unscented kalman filter. ACCESS, 13, 108255-108265. (www)

  @article{ , abstract = {An accurate state of charge (SOC) estimation is crucial for battery applications. Traditional model-based methods have difficulty addressing parameter mismatches and measurement errors, whereas the Kalman filter method faces challenges in handling non-Gaussian noise. In this paper, an improved square root unscented Kalman filter (SRUKF) is proposed for the SOC estimation of lithium-ion batteries (LIBs). The improved SRUKF can effectively handle colored noise, and it is challenging to accurately model it owing to the mismatch between the measurement model and theoretical reference model in the process of SOC estimation. Using this method, colored noise and white noise are separated by the characteristic that the mean value of colored noise differ from that of white noise. The Kalman gain was modified according to the amplitude of the colored noise in the current and historical states, and the estimated value was closer to the real SOC than the conventional SRUKF. To better simulate the characteristics of large-capacity batteries in electric vehicles, parallel battery cells with different initial SOC values were configured for comparative simulations and experiments. Compared with the traditional SRUKF, the estimation error is reduced by 10% (pulsed-current condition) and 49% (World Transient Vehicle Cycle, WTVC) by the proposed method. The experimental results demonstrate that the improved SRUKF can overcome the influence of colored noise on the SOC estimation.}, archive = {J_ACCESS}, author = {Huakai Zhang and Jing Shi and Peixi Jiang}, doi = {10.1109/ACCESS.2025.3581903}, journal = {IEEE Access}, pages = {108255-108265}, shortjournal = {IEEE Access}, title = {State-of-charge estimation of lithium-ion batteries in parallel for electric vehicles using improved square root unscented kalman filter}, volume = {13}, year = {2025}, }
• (2025). GSR-fusion: A deep multimodal fusion architecture for robust sign language recognition using RGB, skeleton, and graph-based modalities. ACCESS, 13, 108235-108254. (www)

  @article{ , abstract = {Sign Language Recognition (SLR) plays a critical role in bridging communication gaps between the deaf and hearing communities. This research introduces GSR-Fusion, a deep multimodal fusion architecture that combines RGB-based, skeleton-based, and graph-based modalities to enhance gesture recognition. Unlike traditional unimodal models, GSR-Fusion utilizes gesture initiation and termination detection, along with a cross-modality fusion approach using a merge (network) technique, enabling it to capture both spatial-temporal and relational features from multiple data sources. The model incorporates ViViT for RGB feature extraction, Transformers for sequential pose modeling, and A3T-GCN for joint graph representation, which together form a comprehensive understanding of gestures. The study investigates five key experimental setups, covering single-hand static and dynamic gestures (one, two, and three strokes) as well as two-hand static and dynamic gestures from the Thai Finger Spelling dataset. Additionally, we compare our architecture with existing models on global datasets, including WLASL and MS-ASL, to evaluate its performance. The results show that GSR-Fusion outperforms state-of-the-art models on multiple datasets. On WLASL, it achieves 83.45% accuracy for 100 classes and 75.23% for 300 classes, surpassing models like SignBERT and Fusion3. Similarly, on MS-ASL, it attains 84.31% for 100 classes and 80.57% for 200 classes, outperforming both RGB-based and skeleton-based models. These results highlight the effectiveness of GSR-Fusion in recognizing complex gestures, demonstrating its ability to generalize across different sign languages and datasets. The research emphasizes the importance of multimodal fusion in advancing sign language recognition for real-world applications.}, archive = {J_ACCESS}, author = {Wuttichai Vijitkunsawat and Teeradaj Racharak}, doi = {10.1109/ACCESS.2025.3581683}, journal = {IEEE Access}, pages = {108235-108254}, shortjournal = {IEEE Access}, title = {GSR-fusion: A deep multimodal fusion architecture for robust sign language recognition using RGB, skeleton, and graph-based modalities}, volume = {13}, year = {2025}, }
• (2025). Virtual fencing for safety-critical cyber-physical systems: Computer-vision enabled digital twins. ACCESS, 13, 108212-108234. (www)

  @article{ , abstract = {Early warning zones (EWZs) are pivotal for future crowd management in smart cities, leveraging computer vision to transform dynamic environments into controllable cyber-physical systems. This approach aims to restrict unsafe or threatening movements by creating EWZs that enhance the resilience of critical infrastructures and ensure citizen safety. While conventional virtual fencing uses GPS-based solutions for outdoor zone-monitoring or surveillance of densely populated areas, as well as fixed cameras for indoor environments (e.g., museums), this article explores the use of computer vision and Uncrewed Aerial Vehicles (UAVs) to create risk-ranked EWZs. These zones can safeguard critical infrastructure and densely populated areas by using UAVs (where fixed cameras are not installed), thus promising to enhance crowd management and safety in smart cities. In this study, the EWZs are categorized by risk levels, with the proximity to hazardous areas determining the severity from low to high. This tiered structure allows for appropriate and timely responses to potential threats, thereby ensuring a robust early warning mechanism. A physical testbed was constructed to monitor human movement as a reflection of behavior within this cyber-physical-social environment. Experiments simulating virtual fence (V-fence) crossings demonstrated the system’s effectiveness in providing early warnings. The results also showed that the system successfully tracked multiple persons through a lightweight framework that can be deployed at the edge, ensuring real-time surveillance and response.}, archive = {J_ACCESS}, author = {Ali Aghazadeh Ardebili and Marco Zappatore and Antonella Longo and Antonio Ficarella}, doi = {10.1109/ACCESS.2025.3581041}, journal = {IEEE Access}, pages = {108212-108234}, shortjournal = {IEEE Access}, title = {Virtual fencing for safety-critical cyber-physical systems: Computer-vision enabled digital twins}, volume = {13}, year = {2025}, }
• (2025). Optimizing fingerprint identification: CNNs with raw images versus handcrafted features for real-time systems. ACCESS, 13, 108192-108211. (www)

  @article{ , abstract = {Fingerprint identification is a cornerstone in various domains such as security, forensics, and authentication. Despite progress, existing systems still face challenges with noise, database differences, and real-time speed. This study investigates the balance between accuracy and computational efficiency(thereby speed) by comparing two approaches: training a Convolutional Neural Network (CNN) with raw fingerprint images and training a CNN using handcrafted fingerprint features. Comprehensive evaluations were conducted across multiple benchmark datasets FVC2000, FVC2002, FVC2004, and NIST SD27, using both single-run and multi-run experiments. Single-run experiments assessed performance across epochs; multi-run cross-validation on FVC2000 ensured consistent and reliable results. A similarity-based matching mechanism with an optimal threshold of 0.8, determined through Receiver Operating Characteristic (ROC) curve analysis, was employed for classification. Experimental results on NVIDIA DGX A100 GPU demonstrate that CNN training with handcrafted features consistently achieves superior accuracy (99.80%) compared to CNN training with raw images approach accuracy (95.59%) on the FVC2000 dataset, while significantly reducing computation time from 1465 to 203 seconds. This performance was consistent across all experimental runs. Similar trends appeared across datasets, with handcrafted features reaching 96-99% accuracy versus 91-96% for raw images data. Statistical analyses (t-tests, ANOVA, and F-statistics) confirmed the significance of these performance differences ( ${p} \lt 0.01$ ) and demonstrated the model’s reliability, robustness, and consistency. This proposed method outperforms recent methods in accuracy and speed, validated by ROC, precision-recall, F1-score, and accuracy metrics in real-world scenarios. Our approach provides experimental confirmation that CNNs fed with features provide improvements in accuracy and speed of classification.}, archive = {J_ACCESS}, author = {Shaik Salma and Tauheed Ahmed and Garimella Ramamurthy}, doi = {10.1109/ACCESS.2025.3580438}, journal = {IEEE Access}, pages = {108192-108211}, shortjournal = {IEEE Access}, title = {Optimizing fingerprint identification: CNNs with raw images versus handcrafted features for real-time systems}, volume = {13}, year = {2025}, }
• (2025). A novel shape classification approach based on branch length similarity entropy. ACCESS, 13, 108180-108191. (www)

  @article{ , abstract = {This study presents a novel feature vector for shape clustering based on the Branch Length Similarity (BLS) entropy profile, which is invariant to translation, rotation, and scaling, enhancing its effectiveness for shape analysis. The methodology consists of two steps: the t-distributed Stochastic Neighbor Embedding (t-SNE) technique, which projects feature vectors into a two-dimensional space, and the k-means algorithm, which groups these points into clusters. We applied this approach to three datasets—MPEG-7, Swedish Leaf, and Heptagon—and evaluated its performance. The results revealed significant geometric similarities within clusters, demonstrating the method’s efficacy. Logistic curve modeling was employed to determine the optimal number of clusters, with the Swedish Leaf dataset achieving the highest clustering score (0.885), followed by scores of 0.76 for the MPEG-7 dataset and 0.731 for the Heptagon dataset. The comparative analysis demonstrated that the proposed method outperforms conventional approaches based on Fourier descriptors or Zernike moments, providing a robust and adaptable solution for shape classification across diverse datasets.}, archive = {J_ACCESS}, author = {Sang-Hee Lee and Cheol-Min Park}, doi = {10.1109/ACCESS.2025.3581901}, journal = {IEEE Access}, pages = {108180-108191}, shortjournal = {IEEE Access}, title = {A novel shape classification approach based on branch length similarity entropy}, volume = {13}, year = {2025}, }
• (2025). FEM and circuit simulation-optimized WPT with magnetic in-band communication for E-bike applications. ACCESS, 13, 108168-108179. (www)

  @article{ , abstract = {Coil optimization methodology and magnetic in-band communication-based control strategies tailored for electric bike (E-bike) inductive power transfer (IPT) systems are proposed in this paper. The transmitter (Tx) and receiver (Rx) coils for e-bike IPT applications are optimized using finite element method (FEM) and circuit simulations to ensure efficient power transfer operation under rated charging conditions. Furthermore, the proposed magnetic in-band communication circuit and protocol enable reliable exchange of critical information, supporting functions such as battery state monitoring, constant current/constant voltage (CCCV) charging management, foreign object detection (FOD), and fault detection without requiring additional communication modules. By leveraging the proposed simple and intuitive design process along with the magnetic in-band communication protocol, the e-bike IPT system can achieve high coil efficiency while ensuring stable charging operation. The proposed system is validated through simulations and experimental results, demonstrating its ability to achieve stable 100 W charging with enhanced reliability and efficiency. The proposed coil design and in-band communication methodologies provide a practical and commercially viable solution for e-bike WPT systems, addressing key challenges in coil design, control, and communication.}, archive = {J_ACCESS}, author = {Eun S. Lee}, doi = {10.1109/ACCESS.2025.3581673}, journal = {IEEE Access}, pages = {108168-108179}, shortjournal = {IEEE Access}, title = {FEM and circuit simulation-optimized WPT with magnetic in-band communication for E-bike applications}, volume = {13}, year = {2025}, }
• (2025). Unraveling the smart charging technologies, energy sources, and regulatory standards for EVs. ACCESS, 13, 108148-108167. (www)

  @article{ , abstract = {Electric vehicles (EVs) are anticipated to be pivotal contributors to the global energy transformation in the automobile industry, ignited by their rapid proliferation. However, the widespread integration of EVs requires rigorous research and synthesis in charging solutions and EV supply equipment to meet the expected performance and improve ancillary services. Analysing the current landscape of EV charging technologies, it is essential to accelerate EV adoption by incorporating advanced control strategies that mitigate negative impacts and improve charging efficiency. Thus, this study presents a meticulous review that culminates in a curated anthology of 81 pivotal articles, with a predominant emphasis on charging EVs and associated technologies. The findings reveal extensive potential in this domain with our rigorous scrutiny of studies uniquely integrating all critical EV charging technologies, including their taxonomy, on-board and off-board charging infrastructures, and the global standards, and future research directions. The revelations of the study elucidate the prevailing paradigms within the EV research nexus, charting a course for future scholarly pursuits and practical applications.}, archive = {J_ACCESS}, author = {Ansif Arooj and Qasim Zeeshan Ahmed and Muhammad Shoaib Farooq and Tariq Umer and Nauman Aslam and Temitope Alade}, doi = {10.1109/ACCESS.2025.3579428}, journal = {IEEE Access}, pages = {108148-108167}, shortjournal = {IEEE Access}, title = {Unraveling the smart charging technologies, energy sources, and regulatory standards for EVs}, volume = {13}, year = {2025}, }
• (2025). Multi-measurement correction of antenna radiation responses obtained in non-anechoic environments. ACCESS, 13, 108141-108147. (www)

  @article{ , abstract = {Prototype measurements, an essential stage in the development of antennas, are normally performed in anechoic chambers. Although capable of providing high-fidelity data (owing to strict control of propagation conditions), such facilities are prohibitively expensive. A cost-efficient alternative involves measurements in a non-anechoic environment followed by post-processing of the responses. In this work, a multi-measurement correction of antenna far-field characteristics obtained in uncontrolled conditions is considered. The approach involves the synchronization and combination of non-anechoic data gathered at a few test setups so as to attenuate interferences and noise while augmenting a useful fraction of the signals. The approach has been validated using a Vivaldi antenna. Benchmark against the existing correction techniques has also been performed.}, archive = {J_ACCESS}, author = {Vorya Waladi and Adrian Bekasiewicz and Qingsha S. Cheng}, doi = {10.1109/ACCESS.2025.3580757}, journal = {IEEE Access}, pages = {108141-108147}, shortjournal = {IEEE Access}, title = {Multi-measurement correction of antenna radiation responses obtained in non-anechoic environments}, volume = {13}, year = {2025}, }
• (2025). Adaptive multi-robot exploration for unknown environments using edge-weighted path planning. ACCESS, 13, 108127-108140. (www)

  @article{ , abstract = {Efficient multi-robot exploration of unknown environments is critical for numerous applications such as search and rescue, planetary exploration, and environmental monitoring. Existing centralized approaches struggle with scalability, while decentralized methods often incur high computational costs and inefficient task coordination. This study presents a scalable and adaptive multi-robot exploration algorithm that adaptively updates edge weights based on visit counts, reservations, and obstacles to optimize path allocation and minimize redundant scanning. The proposed algorithm ensures 100% area coverage and real-time adaptability, making it robust for exploration in many different unknown environments. The algorithm was validated in both a 2D grid-based simulation and a high-fidelity 3D environment using Isaac Sim with ROS integration. Experimental results demonstrate that the algorithm achieves improved exploration efficiency and adaptability compared to a real-time scheduling method while maintaining computational feasibility. The findings highlight the effectiveness of edge-weighting and reservation-based task allocation strategies for autonomous multi-robot systems in practical exploration scenarios.}, archive = {J_ACCESS}, author = {Farhad Baghyari and Tyler Parsons and Jaho Seo and Byeongjin Kim and Mingeuk Kim and Hanmin Lee}, doi = {10.1109/ACCESS.2025.3581807}, journal = {IEEE Access}, pages = {108127-108140}, shortjournal = {IEEE Access}, title = {Adaptive multi-robot exploration for unknown environments using edge-weighted path planning}, volume = {13}, year = {2025}, }
• (2025). Ransomware family attribution with ML: A comprehensive evaluation of datasets quality, models comparison, and a simulated deployment. ACCESS, 13, 108108-108126. (www)

  @article{ , abstract = {Ransomware, a form of cyber extortion that holds data hostage for payment, poses a growing threat to global digital infrastructure, potentially disrupting supply chains, financial systems, and critical healthcare services. Despite multiple proposed detection methods, critical gaps remain. For instance, most studies focus solely on binary classification, overlook dataset quality issues, and rarely evaluate model performance in practical deployment environments. These limitations lead to biased performance comparisons and hinder practical applicability. This study introduces a comprehensive evaluation framework, covering the entire pipeline from dataset collection to model deployment. We begin by presenting a feature extraction method from ransomware execution logs into usable datasets. From there, we assess dataset quality based on class balance, feature independence, data timeliness, class separability, and feature variability. We then train four traditional Machine Learning (ML) and two Deep Learning (DL) models, applying consistent preprocessing and hyperparameter tuning for fair comparisons. Next, we propose a real-time detection architecture in a Windows 10 virtual environment to deploy the models against live ransomware. Our dataset analysis revealed that, based on a quality score $Q \in [{0,1}]$ , class separability ( $\overline {Q_{CS}}=0.48$ ), feature variability ( $\overline {Q_{FV}}=0.07$ ), and data timeliness ( $\overline {Q_{DT}}=0.06$ ) are the main factors hindering dataset quality. As for detection, while Random Forest (RF) achieved the highest offline accuracy (100%), surpassing Multi Layer Perceptron (MLP) with 99.48%, the latter performed better in deployment, reaching 70% accuracy. These findings offer valuable insights and underscore the importance of comprehensive evaluation across all phases of model development, providing a foundation for building more robust and applicable detection systems.}, archive = {J_ACCESS}, author = {Emilio Rios-Ochoa and Jesús Arturo Pérez-Díaz and Enrique García-Ceja and Gerardo Rodríguez-Hernández}, doi = {10.1109/ACCESS.2025.3580395}, journal = {IEEE Access}, pages = {108108-108126}, shortjournal = {IEEE Access}, title = {Ransomware family attribution with ML: A comprehensive evaluation of datasets quality, models comparison, and a simulated deployment}, volume = {13}, year = {2025}, }
• (2025). Glioma grade classification: Hypertuned CNN with saliency-based insight from MRI. ACCESS, 13, 108093-108107. (www)

  @article{ , abstract = {Glioma classification is critical for early diagnosis and treatment planning, yet manual MRI-based diagnosis is time-consuming and prone to errors. This study proposed a CNN-based approach optimized with KerasTuner for glioma classification, focusing on achieving high accuracy using a lightweight architecture. While hyperparameter tuning is well-established, its tailored application to this dataset and problem domain improves performance and reproducibility. The model employs KerasTuner to optimize key hyperparameters, including filter sizes, pooling layers, dropout rates, and optimizers, ensuring improved feature extraction and robustness. A dataset comprising 16,956 T2w Flair MRI slice images from 231 patients was used and categorized into three tumor grades (Grade 2, Grade 3, and Grade 4). The proposed model was trained using five-fold cross-validation, achieving an average multi-class classification accuracy of 89% and a binary classification accuracy of 93% in distinguishing low-grade (Grades 2) from high-grade (Grade 3 and Grade 4) tumors. Comparative analysis with state-of-the-art deep learning models such as ResNet-50, DenseNet-201, and EfficientNet-B0 demonstrated that our optimized CNN outperformed these architectures in accuracy, recall, and F1-score. Moreover, an ablation study and statistical analysis were performed. The experimental results confirm that integrating hyperparameter tuning in lightweight CNN models significantly improves tumor classification performance, reducing misclassification errors between adjacent grades. These findings emphasize the potential of deep learning in automated glioma classification, providing a more efficient and reliable tool for clinical diagnosis. Saliency map visualizations revealed that the model predominantly attends to the peritumoral regions, highlighting their significance in accurate glioma grade classification. Our hyper-tuned CNN achieved comparable performance across T1-weighted, T1-weighted-Gd, T2-weighted, and FLAIR inputs. Extensive augmentation trials, dropout/depth ablation, and paired-t analysis (t =4.37, p ¡ 0.0001) verified the model’s superiority over other baselines. Future work will explore multimodal imaging, domain adaptation, and transfer learning to enhance model robustness across diverse medical datasets.}, archive = {J_ACCESS}, author = {Khondakar A. Shahriar and Enamul H. Bhuiyan and Md. Forhad Hossain and Rakibur Rahman and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3581857}, journal = {IEEE Access}, pages = {108093-108107}, shortjournal = {IEEE Access}, title = {Glioma grade classification: Hypertuned CNN with saliency-based insight from MRI}, volume = {13}, year = {2025}, }
• (2025). Efficacy of autonomous vehicle’s adaptive decision-making based on large language models across multiple driving scenarios. ACCESS, 13, 108076-108092. (www)

  @article{ , abstract = {Understanding how large language models (LLMs) generalize across diverse traffic scenarios is critical for advancing autonomous driving systems. While previous studies have validated LLMs’ potential in specific driving tasks, evaluations of their scenario adaptability remain limited. This research adopts the Dilu framework as a case study, with the objective of investigating the generalisation performance of LLMs in five typical scenarios: basic highway sections, highway merge area, intersection, racetrack, and roundabout, with varying traffic parameters. Through extensive experiments with 17 configurations in scenarios metioned above, we employ success rate (SR) and success steps (SS) as metrics to quantify LLMs’ generalization capabilities in different driving scenarios. The results reveal significant scenario-dependent performance variations: the LLM achieves a peak SR of 99% at 30 m/s in low-speed merges but declines to 69% at 60 m/s. In intersection scenarios, the LLM outperforms traditional reinforcement learning methods (DQN, PPO) by about three times (61% SR vs. 24% SR). Furthermore, expanding memory entries from 2-shot to 5-shot enhances median SS by 114% in roundabouts and 69% in intersections, highlighting the role of experience accumulation in dynamic environments. These findings provide empirical evidence for LLMs’ scenario-aware generalization capabilities and offer actionable insights for optimizing their deployment in real-world autonomous driving systems.}, archive = {J_ACCESS}, author = {Guanzhi Xiong and Siyang Liu and Yihong Yan and Qile Li and Hangze Li}, doi = {10.1109/ACCESS.2025.3580641}, journal = {IEEE Access}, pages = {108076-108092}, shortjournal = {IEEE Access}, title = {Efficacy of autonomous vehicle’s adaptive decision-making based on large language models across multiple driving scenarios}, volume = {13}, year = {2025}, }
• (2025). Attention-driven lane change trajectory prediction with traffic context in urban environments. ACCESS, 13, 108059-108075. (www)

  @article{ , abstract = {Lane-changing is one of the most fundamental driving behaviors, yet it is also one of the most hazardous maneuvers, as interactions with adjacent lanes critically impact vehicle safety. Accurate prediction of lane-change trajectories is essential for enhancing road safety and the effectiveness of autonomous driving systems. This study proposes a model that leverages traffic context from drone footage to predict lane-change trajectories, incorporating interactions between surrounding vehicles. The proposed model analyzes drone footage collected from natural urban driving environments using computer vision techniques to extract traffic context and employs a Transformer-LSTM architecture to predict lane-change trajectories. Beyond trajectory prediction, this study enhances model interpretability by analyzing the key factors influencing lane-change predictions. Critical moments in the prediction process can be visually identified using attention heatmaps, and the model’s focus on essential input information, such as the approach of nearby vehicles or significant interactions, can be observed. This approach offers meaningful insights into the model’s predictive focus. The feasibility of the proposed model was validated through the implementation of a prototype and its application to real traffic data, confirming its effectiveness in accurately predicting lane-change trajectories across diverse traffic scenarios.}, archive = {J_ACCESS}, author = {Seokjun Hong and Jaegyun Im and Byeongjoon Noh}, doi = {10.1109/ACCESS.2025.3581916}, journal = {IEEE Access}, pages = {108059-108075}, shortjournal = {IEEE Access}, title = {Attention-driven lane change trajectory prediction with traffic context in urban environments}, volume = {13}, year = {2025}, }
• (2025). Cybersecurity in cloud computing AI-driven intrusion detection and mitigation strategies. ACCESS, 13, 108051-108058. (www)

  @article{ , abstract = {This paper explores the development and evaluation of advanced machine learning models for intrusion detection in cloud environments. We focus on Transformer-based Spatio-Temporal Graph Neural Networks (ST-GNN), CNN, LSTM, Isolation Forest, and conventional GNNs, analyzing their performance on three distinct datasets: NSL-KDD, CICIDS2017, and a custom synthetic dataset. The models were assessed based on key metrics such as precision, recall, F1 score, ROC-AUC, and detection latency. Our results demonstrate that Transformer-based ST-GNN exhibits superior performance, showing robustness, scalability, and efficient real-time detection capabilities, making it a promising candidate for next-generation intrusion detection systems (IDS). We also discuss the mathematical foundations behind model superiority, including generalization bounds, and acknowledge the limitations of current models, such as vulnerability to adversarial attacks. The study highlights the potential for improvements in real-time federated deployment, hardware-aware acceleration through FPGA/GPU-based inference, and integration with Zero-Trust Architecture (ZTA) for enhanced cybersecurity. This paper provides a comprehensive comparison of IDS models, offering valuable insights for future research and real-world applications in network security.}, archive = {J_ACCESS}, author = {Fei Wang and Sanshan Xie}, doi = {10.1109/ACCESS.2025.3580569}, journal = {IEEE Access}, pages = {108051-108058}, shortjournal = {IEEE Access}, title = {Cybersecurity in cloud computing AI-driven intrusion detection and mitigation strategies}, volume = {13}, year = {2025}, }
• (2025). A novel lo-ra-based wireless communication module for industrial data acquisition-telemetry. ACCESS, 13, 108043-108050. (www)

  @article{ , abstract = {The development of communication technology must be implemented to improve the efficiency of industrial systems. One promising approach is the use of Ultra High Frequency (UHF) modulation for long-distance communication between sensors and industrial controllers. This study aims to design and implement a UHF communication module that can be integrated with conventional temperature transmitters and Siemens industrial controllers to enable effective remote measurement and monitoring. The implementation of the UHF communication module considers critical aspects such as reliability, data throughput, and latency for industrial applications. Testing and evaluation were conducted to assess the module’s performance, including measurement variations and distance testing between the systems. In addition to field trials, the module was evaluated in a controlled environment at the Instrumentation Laboratory of Institut Teknologi Sepuluh Nopember (ITS), where it transmitted RTD (Resistance Temperature Detector) temperature data. Key findings indicate that the system achieved reliable wireless communication up to 150 meters, with a bit error rate (BER) of $1.2\times 10 ^{-3}$ , signal-to-noise ratio (SNR) above 14 dB, and packet loss below 2% across the tested range. These results confirm the reliability and accuracy of the module for industrial telemetry applications. With specially designed UHF transmitter and receiver modules, the system aims to enhance measurement performance, reduce downtime, and support prompt decision-making in complex industrial operations. The proposed solution offers a cost-effective and scalable communication platform suitable for diverse industrial environments.}, archive = {J_ACCESS}, author = {Ranu Wijaya and Ahmad Radhy and Safira F. Mujiyanti}, doi = {10.1109/ACCESS.2025.3581373}, journal = {IEEE Access}, pages = {108043-108050}, shortjournal = {IEEE Access}, title = {A novel lo-ra-based wireless communication module for industrial data acquisition-telemetry}, volume = {13}, year = {2025}, }
• (2025). Point cloud geometry scalable coding using a resolution and quality-conditioned latents probability estimator. ACCESS, 13, 108025-108042. (www)

  @article{ , abstract = {In the current age, users consume multimedia content in very heterogeneous scenarios in terms of network, hardware, and display capabilities. A naive solution to this problem is to encode multiple independent streams, each covering a different possible requirement for the clients, with an obvious negative impact in both storage and computational requirements. These drawbacks can be avoided by using codecs that enable scalability, i.e., the ability to generate a progressive bitstream, containing a base layer followed by multiple enhancement layers, that allow decoding the same bitstream serving multiple reconstructions and visualization specifications. While scalable coding is a well-known and addressed feature in conventional image and video codecs, this paper focuses on a new and very different problem, notably the development of scalable coding solutions for deep learning-based Point Cloud (PC) coding. The peculiarities of this 3D representation make it hard to implement flexible solutions that do not compromise the other functionalities of the codec. This paper proposes a joint quality and resolution scalability scheme, named Scalable Resolution and Quality Hyperprior (SRQH), that, contrary to previous solutions, can model the relationship between latents obtained with models trained for different RD tradeoffs and/or at different resolutions. Experimental results obtained by integrating SRQH in the emerging JPEG Pleno learning-based PC coding standard show that SRQH allows decoding the PC at different qualities and resolutions with a single bitstream while incurring only in a limited RD penalty and increment in complexity w.r.t. non-scalable JPEG PCC that would require one bitstream per coding configuration.}, archive = {J_ACCESS}, author = {Daniele Mari and André F. R. Guarda and Nuno M. M. Rodrigues and Simone Milani and Fernando Pereira}, doi = {10.1109/ACCESS.2025.3580680}, journal = {IEEE Access}, pages = {108025-108042}, shortjournal = {IEEE Access}, title = {Point cloud geometry scalable coding using a resolution and quality-conditioned latents probability estimator}, volume = {13}, year = {2025}, }
• (2025). Coordinated control approach for brake actuator failures: A fault-tolerant strategy using braking systems and steering. ACCESS, 13, 108005-108024. (www)

  @article{ , abstract = {In this study, a fault-tolerant control system is proposed for brake actuators in a vehicle. A degradation in deceleration performance and lateral stability caused by brake actuator failure can cause serious safety issues. The integration of braking system with either the drive or steering actuators has performance limitations, and previously studied fully integrated braking, driving, and steering systems have complex structures. Therefore, a coordinated control approach for fault-tolerant control of brake actuators was proposed herein to achieve a coordinated control of the drive, brake, and steering actuators. The drive and brake controller performed optimal torque allocation of the desired longitudinal force and yaw moment, and a constraint was set to ensure that the yaw moment error did not exceed the allowable limit. The unachieved yaw moment in the drive and brake controllers was compensated by the front and rear wheel feedforward steering angles of the steering controller. This structure improved deceleration performance and lateral stability by allowing a certain amount of imbalance in braking force and compensating for it with the steering control. The fault-tolerant control system was verified via simulations and experiments, demonstrating that additional control of driving and steering improved the deceleration performance and lateral stability, respectively. Simulation results revealed that the proposed strategy considerably improved lateral stability compared to independent control strategy using the same actuators. The experiments further verified the applicability of the proposed strategy to vehicles and confirmed that yaw rate errors were reduced under both single and dual brake actuator failure conditions.}, archive = {J_ACCESS}, author = {Yunchul Ha and Seunguk Jeon and Jinyong Park and Seoyeon Choi and Seunghoon Woo}, doi = {10.1109/ACCESS.2025.3581926}, journal = {IEEE Access}, pages = {108005-108024}, shortjournal = {IEEE Access}, title = {Coordinated control approach for brake actuator failures: A fault-tolerant strategy using braking systems and steering}, volume = {13}, year = {2025}, }
• (2025). Attention-CARU with texture-temporal network for video depth estimation. ACCESS, 13, 107994-108004. (www)

  @article{ , abstract = {Video depth estimation has a wide range of applications, especially in the tasks of robot navigation and autonomous driving. RNN-based encoder-decoder architectures are the most commonly used methods for depth feature prediction, but recurrent operators have limitations of large-scale perspective from global information and also face the long-term dependency problem, which often leads to inaccurate prediction of object depth in complex scenes. To alleviate these issues, this work introduces an attention-based texture-temporal Content-Adaptive Recurrent Unit (CARU) for depth estimation. The CARU performs an enhanced RNN approach that covers the texture content in a video sequence and extracts the main features from temporal frames. Also, a combination of VGGreNet and Transformer refines the extraction of global and local features to facilitate depth map estimation. Besides, to improve the detection of moving objects in the depth map, an advanced loss function is introduced to further penalise the depth estimation error of moving objects. Experimental tests on the KITTI datasets show that this work achieves competitive performance in depth estimation, demonstrating good resultant capabilities and applicability to a variety of real-time scenarios.}, archive = {J_ACCESS}, author = {Sio-Kei Im and Ka-Hou Chan}, doi = {10.1109/ACCESS.2025.3579429}, journal = {IEEE Access}, pages = {107994-108004}, shortjournal = {IEEE Access}, title = {Attention-CARU with texture-temporal network for video depth estimation}, volume = {13}, year = {2025}, }
• (2025). Long short-term memory neural network combined with a hybrid-modular clockwork structure for transient modeling of nonlinear circuits. ACCESS, 13, 107979-107993. (www)

  @article{ , abstract = {This paper presents a novel macromodeling method and neural network structure called Clockwork Long Short-Term Memory (CWLSTM), tailored for high-speed nonlinear circuits. The proposed CWLSTM method is considered a more powerful yet simpler model than conventional LSTM due to its reduced parameter count and more efficient structure and training strategy. This structure promotes improved model generalization, resulting in better model accuracy and training time due to its unique modular gating connections. Additionally, the required training data is considerably reduced for generating a model with similar accuracy compared to the conventional LSTM. To further improve the proposed method, a hybrid version of CWLSTM, known as Hybrid-Modular CWLSTM, is introduced, utilizing various module types to enhance the model’s accuracy further. The reported experimental results reveal the superior performance of the proposed methods compared to the conventional LSTM in modeling high-speed nonlinear circuits. On top of the above advantages, the proposed methods can produce models that execute much faster than those based on existing simulation tools (LTspice and NGspice). The performance of the proposed methods is validated by modeling two high-speed nonlinear circuits.}, archive = {J_ACCESS}, author = {Razieh Moradi Chaleshtori and Amin Saboohi and Amin Faraji and Sayed Alireza Sadrossadat and Ali Moftakharzadeh and Yvon Savaria}, doi = {10.1109/ACCESS.2025.3580588}, journal = {IEEE Access}, pages = {107979-107993}, shortjournal = {IEEE Access}, title = {Long short-term memory neural network combined with a hybrid-modular clockwork structure for transient modeling of nonlinear circuits}, volume = {13}, year = {2025}, }
• (2025). Toward automatic dataset discovery from scientific publications. ACCESS, 13, 107961-107978. (www)

  @article{ , abstract = {Datasets are a crucial artifact in research, and there is always a high demand for good datasets. Due to rapid scientific progress and exponential growth in the scientific literature, there has been a proportionate increase in the number of datasets. However, many datasets and research studies are left unexplored and under-utilized as these are not discoverable easily, leading to duplicate efforts. Building good datasets is costly in terms of time, money, and human effort. Hence, automated tools to facilitate the search and discovery of datasets are crucial to the scientific community. In this work, we investigate a deep neural network-based architecture to automate the dataset discovery from scientific publications. In this paper, we perform two tasks namely dataset mention extraction and entity linking. Our method outperforms the earlier ones and achieves an F1 score of 56.24 in extracting dataset mentions from research papers on a popular corpus of social science publications. Our approach also outperforms the prior research and achieves a precision score of 88.63 in linking research papers to a dataset knowledge base for another popular corpus of social science publications. We hope that this system will further promote data sharing, offset the researchers’ workload in identifying the right dataset and increase the reusability of datasets.}, archive = {J_ACCESS}, author = {Sandeep Kumar and Tirthankar Ghosal and Asif Ekbal}, doi = {10.1109/ACCESS.2025.3532767}, journal = {IEEE Access}, pages = {107961-107978}, shortjournal = {IEEE Access}, title = {Toward automatic dataset discovery from scientific publications}, volume = {13}, year = {2025}, }
• (2025). Automated ultrasound diagnosis via CLIP-GPT synergy: A multimodal framework for image classification and report generation. ACCESS, 13, 107950-107960. (www)

  @article{ , abstract = {As a crucial non-invasive imaging modality in clinical diagnosis, ultrasound interpretation faces challenges of subjectivity and inefficiency. To address the limitations of traditional single-modal deep learning models in cross-modal alignment and structured text generation, this study proposes an intelligent analysis system based on a CLIP-GPT joint framework, integrating contrastive learning with generative pre-training for end-to-end image classification and diagnostic report generation. Utilizing an ultrasound dataset containing six types of liver lesions, we implement a multi-stage training strategy: first establishing visual-semantic cross-modal mapping through CLIP (ViT-B/32), followed by fine-tuning GPT-2 and GPT-3.5 to construct a structured report generator. Experimental results demonstrate that the proposed system achieves superior performance: classification accuracy reaches 96.4%, recall 95.1%, and F1-score 95.5%, significantly outperforming conventional CNN models (e.g., ResNet-50 with accuracy 89.6%). For report generation, the fine-tuned GPT-2 model achieves a BLEU-4 score of 32.5 and ROUGE-L score of 41.2, indicating strong alignment with clinical reporting standards. Key innovations include: a cross-modal feature decoupling-recombination mechanism bridging semantic gaps, clinical guideline-driven hierarchical templates ensuring professional standardization, and dynamic attention strategies enhancing lesion discrimination. This study provides an interpretable multimodal solution for medical image analysis, offering significant clinical value for intelligent diagnostic systems.}, archive = {J_ACCESS}, author = {Li Yan and Xiaodong Zhou and Yaotian Wang and Xuan Chang and Qing Li and Gang Han}, doi = {10.1109/ACCESS.2025.3578462}, journal = {IEEE Access}, pages = {107950-107960}, shortjournal = {IEEE Access}, title = {Automated ultrasound diagnosis via CLIP-GPT synergy: A multimodal framework for image classification and report generation}, volume = {13}, year = {2025}, }
• (2025). Faster-PPENet: Advancing logistic intelligence for PPE recognition at construction sites. ACCESS, 13, 107936-107949. (www)

  @article{ , abstract = {The usage of Personal Protective Equipment (PPE) is crucial in a variety of sectors, including construction, manufacturing, healthcare, and hazardous material handling, to safeguard employees from possible dangers and risks. Employers may encourage a safer workplace and lower the risk of accidents and injuries by using an automated system that can determine whether people are wearing the required PPE. Several approaches have been proposed by scientists to monitor employees at workplaces, however, these techniques are limited to recognizing a small number of PPE objects. Further, historic approaches lack the generalization power to perform well in real-world cases and are not proficient in tackling several transformational alterations of the examined images like clutter, blurring, light, and brightness changes in the background settings of images. The work is focused on overcoming the limitations of existing works by proposing an effective DL strategy called the Faster-PPENet. Clearly, we have utilized the Faster-RCNN approach with a modified ResNet101 base to detect and classify various PPE. We have presented a robust feature extractor base by altering the conventional ResNet model. Specifically, we have replaced the activation approach of the ResNet approach by utilizing the Swish activation method as an alternative to the ReLU function to extract a more effective set of sample features which are later recognized by the 2-phase locator of the Faster-RCNN model. The model is evaluated on two complex databases named the Color Helmet and Vest (CHV), and Safety Helmet Detection (SHD) to show the effectiveness of our approach. Further, a real-world data sample is also utilized to show the generalization ability of our approach. For the CHV and SHD data samples, we have attained mAP scores of 0.8845, and 0.9321, along with accuracies of 90.25%, and 94.03%, while for the real-world sample, we have attained the mAP, and accuracy values of 0.901, 91% of which is clearly proving the robustness of our approach.}, archive = {J_ACCESS}, author = {Jasim Alnahas}, doi = {10.1109/ACCESS.2025.3578110}, journal = {IEEE Access}, pages = {107936-107949}, shortjournal = {IEEE Access}, title = {Faster-PPENet: Advancing logistic intelligence for PPE recognition at construction sites}, volume = {13}, year = {2025}, }
• (2025). Image-based accelerated prediction of thermal properties of package substrates using combined deep-learning and an enhanced thermal network model. ACCESS, 13, 107926-107935. (www)

  @article{ , abstract = {As the need for processing large amounts of data increases, power consumption and the complexity of semiconductor package patterns also rise, making thermal management crucial. Traditional analytical models suffer from accuracy issues when analyzing thermal behaviors of complex patterns in commercial packages. To enable accurate and fast prediction of thermal behavior during the design stage in practical industry applications, this study proposes an image-based accelerated prediction method for the thermal properties of complex patterns in package substrates by using combined deep-learning and an enhanced thermal network model. The proposed method divides the layer-wise image data of package substrates into subdomains to define unit cells, and applies a thermal network with a new structure. The specified thermal networks are then matched with unit cell images and used for deep learning, thus automating the process for quick thermal property assessment. The proposed method is applied to commercialized package substrate designs and validated through experiments and finite element-based models, demonstrating high accuracy with R-squared values over 0.99 and reduction in prediction time exceeding 90%.}, archive = {J_ACCESS}, author = {Jeong-Hyeon Park and Jaechoon Kim and Sukwon Jang and Sungho Mun and Eun-Ho Lee}, doi = {10.1109/ACCESS.2025.3580727}, journal = {IEEE Access}, pages = {107926-107935}, shortjournal = {IEEE Access}, title = {Image-based accelerated prediction of thermal properties of package substrates using combined deep-learning and an enhanced thermal network model}, volume = {13}, year = {2025}, }
• (2025). Deep learning based mobile application for automated plant disease detection. ACCESS, 13, 107917-107925. (www)

  @article{ , abstract = {Plant diseases remain a significant threat to global agriculture, necessitating rapid and accurate detection to minimize crop loss. This paper presents a lightweight, end-to-end system for plant leaf disease detection and severity estimation, optimized for real-time field deployment. We propose a custom Convolutional Neural Network (CNN), built using PyTorch, trained on the PlantVillage dataset to classify leaves as healthy or diseased with a test accuracy of 92.06%. To enhance its practical relevance, we incorporate a classical image processing pipeline using OpenCV and NumPy to estimate the severity of infection by computing the ratio of diseased to total leaf area. These capabilities are integrated into a cross-platform mobile application developed using React Native, with inference served via a Flask-based backend API. The mobile app enables users to capture or upload images and instantly receive diagnostic results, and severity percentages. Our system bridges the gap between deep learning research and real-world agricultural application by combining accurate classification, interpretable severity estimation, and mobile accessibility. This approach offers farmers a powerful, on-device digital assistant to monitor crop health and make informed intervention decisions. Experimental results demonstrate strong generalization performance, visual alignment of model attention with infected regions, and real-time usability in field conditions.}, archive = {J_ACCESS}, author = {B. Ramana Reddy and Gauri Kalnoor and Mudigonda Devashish and Palagiri Sai Karthik Reddy}, doi = {10.1109/ACCESS.2025.3581099}, journal = {IEEE Access}, pages = {107917-107925}, shortjournal = {IEEE Access}, title = {Deep learning based mobile application for automated plant disease detection}, volume = {13}, year = {2025}, }
• (2025). Prototyping and characterization of gripper technologies for stiff fabric material. ACCESS, 13, 107890-107916. (www)

  @article{ , abstract = {This study explores robotic gripper technologies to automate the handling of rigid, porous, and fibrous polymer fabric pads, used in automotive acoustic applications. Currently handled manually, these materials present challenges due to their rough texture and structural rigidity. Various grippers were designed and tested, including vacuum suction, high-airflow systems, Bernoulli ejectors, needle penetration, Velcro, adhesives, cryogenics, mechanical friction, and electro-adhesion. The grip strength in the normal and shear directions was evaluated using a custom measurement system and an industrial robot to simulate detachment. Key metrics included maximum grip force and material integrity after testing. The comparative results highlight the trade-offs between grip security and potential material damage across different technologies. This comprehensive evaluation provides engineers and system designers valuable insights for selecting and optimizing gripper systems tailored to non-woven polymer fabric materials. The findings contribute to advancing automated handling solutions, promoting efficiency, consistency, and reduced manual labor in manufacturing environments.}, archive = {J_ACCESS}, author = {Vivek Kumar and Mihail Fontul and Carlos Neves and Paulo Jorge Coelho}, doi = {10.1109/ACCESS.2025.3580351}, journal = {IEEE Access}, pages = {107890-107916}, shortjournal = {IEEE Access}, title = {Prototyping and characterization of gripper technologies for stiff fabric material}, volume = {13}, year = {2025}, }
• (2025). Quasi-analytical least-squares generative adversarial networks: Further 1-D results and extension to two data dimensions. ACCESS, 13, 107872-107889. (www)

  @article{ , abstract = {Generative adversarial networks (GANs) are notoriously difficult to analyse, necessitating empirical studies in high dimensional spaces that suffer from stochastic sampling noise. Quasi-analytical, low-dimensional GANs can be developed in various special cases to elucidate aspects of GAN training in a manageable, precise setting where variables of interest can be easily visualised. A previously developed 1-D Rayleigh/Square/Exponential/Erf (R/S/E/E) least squares GAN (LSGAN), with 1-D latent variable z and 1-D data x, is extended to the case of 2-D exponentially distributed data. The 2-D R/S/E/E LSGAN has 8 parameters and its dynamics under gradient descent ascent (GDA) are analysable to high accuracy via two 1-D numerical integrals. Visualisation strategies are given for the cost function and parameter trajectories during training. Numerical performance is compared with the equivalent stochastic GDA algorithm, obtaining precise agreement. It is shown that the 2-D R/S/E/E LSGAN, which satisfies ${{ dim}}(z)\lt { { dim}}(x)$ , has an optimal discriminator that is not differentiable, does not depend on the data PDF and is nowhere equal to 1/2, contradicting conventional GAN theory. For numerical simulations in the 1-D case, when the functional form of the optimal discriminator (a scaled logistic function) is fixed but its parameters are not matched to the optimal generator and can vary, convergence to the optimal settings does not occur, and, for certain initial settings, severe error propagation results. It is proven that the optimal generator setting cannot be a stable point of the GDA recursion. For a specific 1-D case, we also characterise the range of initial conditions for which convergence to the neighbourhood of the optimal generator occurs in a given number of steps. Finally, the extension to an exponential mixture data PDF is considered. A 2-D mixture R/S/E/E LSGAN with bifurcating (chaotic) parameter trajectories is exhibited. Empirical evidence is provided of long-term oscillatory behaviour in the parameters and cost function when both the step size (learning rate) and the support of the data distribution are large. In this instance, the oscillations are not due to mode collapse.}, archive = {J_ACCESS}, author = {Graham W. Pulford}, doi = {10.1109/ACCESS.2025.3578826}, journal = {IEEE Access}, pages = {107872-107889}, shortjournal = {IEEE Access}, title = {Quasi-analytical least-squares generative adversarial networks: Further 1-D results and extension to two data dimensions}, volume = {13}, year = {2025}, }
• (2025). Interaction-aware scene debiasing for action recognition. ACCESS, 13, 107856-107871. (www)

  @article{ , abstract = {Deep learning models for action recognition remain challenged by scene biases as they prioritize easily learnable scene representations over the actual actor’s motion patterns. To address this limitation, we introduce InteractionCutMix, a novel scene debiasing method designed to preserve and leverage actor-object interactions in video augmentation. Unlike existing strategies that detach actors from their original scenes and neglect important contextual relationships with objects, InteractionCutMix captures and integrates these interactions into the augmentation process to enhance action representations. By maintaining actor-object interactions, our method provides a richer contextual understanding of actions and introduces better capabilities to distinguish visually similar motions across different action categories. Experiments on the UCF101, HMDB51, and Kinetics-100 datasets demonstrate the superiority of our approach compared to existing methods. The results validate our hypothesis about the fundamental importance of preserving actor-object interactions in video augmentation for robust and context-aware action recognition. Code is publicly available at https://github.com/rendicahya/intercutmix}, archive = {J_ACCESS}, author = {Randy Cahya Wihandika and Israel Mendonça and Masayoshi Aritsugi}, doi = {10.1109/ACCESS.2025.3581931}, journal = {IEEE Access}, pages = {107856-107871}, shortjournal = {IEEE Access}, title = {Interaction-aware scene debiasing for action recognition}, volume = {13}, year = {2025}, }
• (2025). Predicting knee cartilage degradation and osteoarthritis onset using a hybrid mathematical modeling and machine learning framework. ACCESS, 13, 107844-107855. (www)

  @article{ , abstract = {Osteoarthritis (OA) is the most prevalent form of arthritis, commonly affecting the knee joint and characterized by the progressive degeneration of articular cartilage (AC). Among the various contributing factors, repetitive cyclic loading plays a significant role in accelerating this deterioration. Knee osteoarthritis (KOA), in particular, represents a big data challenge due to the complexity, heterogeneity, and large volume of data required for its analysis and prediction. To address this, we employed a validated mathematical model capable of predicting the number of remaining mechanical cycles that the AC can endure during daily walking before showing signs of degradation. This model facilitated the generation of a wide range of simulations and scenarios that incorporated diverse individual profiles, including age, height, weight, gender, walking duration, and accumulated cartilage damage, along with their corresponding remaining mechanical cycles. The objectives of this study are twofold: (i) to use the degradation model’s outputs to train, validate, and test four machine learning (ML) models, and (ii) to compare the best-performing ML model with a Long Short-Term Memory (LSTM) neural network. Among the models tested, the Support Vector Regressor (SVR) demonstrated superior predictive performance, achieving an R2 of 0.95, a Root Mean Squared Error (RMSE) of 0.13, and a Mean Absolute Percentage Error (MAPE) of 2.5%. These findings provide a solid foundation for accurately predicting knee cartilage damage and guiding the prescription of personalized treatment strategies aimed at delaying or preventing the onset of KOA.}, archive = {J_ACCESS}, author = {F. Mekrane and R. Ouladsine and A. Barkaoui and R. Ghandour}, doi = {10.1109/ACCESS.2025.3581071}, journal = {IEEE Access}, pages = {107844-107855}, shortjournal = {IEEE Access}, title = {Predicting knee cartilage degradation and osteoarthritis onset using a hybrid mathematical modeling and machine learning framework}, volume = {13}, year = {2025}, }
• (2025). Inheriting cultural heritage: The application of virtual reality technology in the display of peking opera costumes. ACCESS, 13, 107832-107843. (www)

  @article{ , abstract = {Peking Opera costumes, as an essential visual emblem of traditional Chinese opera culture, encapsulate profound national aesthetics and socio-cultural significance. However, within contemporary communication contexts, conventional exhibition methods are often inadequate to elicit strong cognitive and emotional engagement from audiences. In this study, we develop an immersive presentation system for Peking Opera costumes using virtual reality (VR) technology, aiming to reconstruct the costumes’ cultural context and visual semiotic framework through multi-sensory interaction and situational re-creation. During system development, level-of-detail (LOD) management and occlusion culling techniques were incorporated to optimize model rendering efficiency and ensure smooth platform performance. On the exhibition side, a multimodal interface combined with semantic-parsing mechanisms was implemented to enhance user engagement and deepen cultural comprehension. Performance testing demonstrates that, while preserving high visual fidelity, the system significantly improves interaction efficiency and immersion. This work offers a viable pathway and technical foundation for the digital preservation and presentation of intangible cultural heritage.}, archive = {J_ACCESS}, author = {Zhiyan Su}, doi = {10.1109/ACCESS.2025.3572446}, journal = {IEEE Access}, pages = {107832-107843}, shortjournal = {IEEE Access}, title = {Inheriting cultural heritage: The application of virtual reality technology in the display of peking opera costumes}, volume = {13}, year = {2025}, }
• (2025). Recent advances in demand responsive transport: Opportunities with autonomous bus Service—A system-of-systems overview. ACCESS, 13, 107800-107831. (www)

  @article{ , abstract = {This study explores recent advancements in emerging technologies for Demand Responsive Transport (DRT), focusing on the potential integration of autonomous bus services. We review the historical development and current state of research, extending the discussion into the era of Connected and Automated Mobility (CAM), particularly in mass transit. Key components of modern DRT systems are categorised from multiple perspectives, including vehicle types, infrastructure, devices, simulation approaches, decision-making algorithms, and optimisation factors. Each element is discussed in detail, supported by comparative tables to help readers interpret the results effectively. Our findings highlight the growing interest and ongoing research in this promising domain. We identify common practices across studies and areas needing improvement. To support this, we propose a System-of-Systems (SoS) approach to evaluate the technological maturity of DRT solutions, in line with our ongoing project. A System Readiness Level (SRL) analysis is performed by identifying constituent systems and assessing their Technology Readiness Levels (TRLs) and Integration Readiness Levels (IRLs). The study also explores the deployment of DRT in a specific use case: the Maylands Business Park in Hertfordshire, UK. It aims to serve as a comprehensive reference for addressing various dimensions of DRT in mass transit, particularly in the context of automation. It offers insights into current progress and outlines opportunities for deploying DRT as a cost-effective, scalable solution to improve sustainability in Intelligent Transportation Systems (ITS).}, archive = {J_ACCESS}, author = {Sajid Fadlelseed and Pouria Sarhadi and Soodamani Ramalingam and Hock C. Gan and Pandelis Kourtessis and Gav Jackman and Atziri Guadalupe Sanchez Contreras and Javier Guimera Tena and James West and Jack Holland and Alice Missler}, doi = {10.1109/ACCESS.2025.3576918}, journal = {IEEE Access}, pages = {107800-107831}, shortjournal = {IEEE Access}, title = {Recent advances in demand responsive transport: Opportunities with autonomous bus Service—A system-of-systems overview}, volume = {13}, year = {2025}, }
• (2025). Research on personalized motion difficulty dynamic adjustment algorithm for VR rehabilitation sports based on reinforcement learning. ACCESS, 13, 107783-107799. (www)

  @article{ , abstract = {Virtual Reality (VR) rehabilitation has rapidly gained recognition as a transformative and effective tool in modern therapeutic practices, offering immersive, interactive environments that significantly enhance patient participation, motivation, and recovery outcomes. Traditional rehabilitation methods, although clinically validated, often suffer from limited adaptability, relying on static difficulty levels that fail to reflect the complex, evolving needs of individual patients. This rigidity can lead to reduced engagement, plateaued progress, and diminished therapeutic efficacy. To overcome these limitations, this study introduces a novel, intelligent algorithm designed to dynamically calibrate the difficulty of VR-based rehabilitation exercises in real time. By employing advanced reinforcement learning techniques, the algorithm continuously monitors patient movements, reaction times, and task performance to adjust exercise parameters on the fly. This adaptive mechanism maintains an optimal level of challenge, ensuring that therapy remains personalized, engaging, and effective across different stages of recovery. Experimental evaluations demonstrate that this approach significantly improves both short-term and long-term rehabilitation outcomes compared to traditional static frameworks. The findings suggest a promising future for adaptive VR therapy systems in clinical settings, with broader implications for personalized medicine. This research aligns closely with the scope of Frontiers in Computer Science, particularly within the realm of human-media interaction and intelligent health technologies designed to address real-world therapeutic challenges.}, archive = {J_ACCESS}, author = {Dong Yujie and Tong Zhou and Xiang Xu}, doi = {10.1109/ACCESS.2025.3579160}, journal = {IEEE Access}, pages = {107783-107799}, shortjournal = {IEEE Access}, title = {Research on personalized motion difficulty dynamic adjustment algorithm for VR rehabilitation sports based on reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Dynamic prompt-based framework for pragmatic quality improvement in business process models. ACCESS, 13, 107764-107782. (www)

  @article{ , abstract = {Business Process Modeling plays a critical role in enhancing organizational efficiency, standardization, and transparency. While prior research has extensively addressed syntactic and semantic quality dimensions, pragmatic quality, relevant to the clarity, interpretability, and comprehensibility of process elements, remains relatively underexplored. This study presents a dynamic prompt-based framework that leverages Large Language Models to automatically generate meaningful and contextually appropriate labels for BPMN elements, including tasks, gateways, and XOR split outgoing edges. The framework integrates contextual information extracted from BPMN models to construct structured prompts, ensuring that the generated labels are semantically precise and compliant with BPMN labeling conventions. The generated labels are evaluated using the all-MiniLM-L6-v2 sentence transformer model to assess semantic coherence and mutual exclusivity, while spaCy’s syntactic analysis validates verb presence in task labels. Experimental results on the BPMAI dataset, encompassing 29,810 BPMN models, confirm the framework’s effectiveness in enhancing pragmatic quality and improving overall model interpretability. To promote reproducibility and encourage future research, the full implementation and evaluation pipeline are publicly available at https://github.com/SarahayadAOU/MutuallyExclusive, and the dataset is accessible via Hugging Face at https://huggingface.co/datasets/AyadSarah/MutuallyExclusive}, archive = {J_ACCESS}, author = {Sarah Ayad and Fatimah Alsayoud}, doi = {10.1109/ACCESS.2025.3580616}, journal = {IEEE Access}, pages = {107764-107782}, shortjournal = {IEEE Access}, title = {Dynamic prompt-based framework for pragmatic quality improvement in business process models}, volume = {13}, year = {2025}, }
• (2025). Coupled induction machine and HVAC models for simulating HVAC performance considering grid dynamics in buildings. ACCESS, 13, 107745-107763. (www)

  @article{ , abstract = {This paper presents the development of novel models that integrate induction machines with HVAC equipment, such as pumps, heat pumps, and chillers, to analyze the impact of electrical parameters on the operational performance of thermo-fluid systems. The proposed model employs a coupling technique that captures the dynamic interactions between induction machines and HVAC systems. By integrating electrical, thermal, and mechanical dynamics, the models provide a comprehensive framework for simulating real-world scenarios, including interactions with the electrical grid. This achievement was made possible through the development of a Computationally Efficient and Accurate Induction Machine (CEAIM) model. Implemented using the equation-based Modelica language, the CEAIM model has been validated against experimental results, manufacturer data sheets, and various operating conditions. Its performance has been compared with existing induction machine models in the Modelica Standard Library (MSL), demonstrating superior accuracy and computational efficiency. The CEAIM model predicts torque, speed, and power consumption with a coefficient of determination ( ${R}^{2}$ ) ranging from 0.98 to 1 and a coefficient of variation of root mean square error (CVRMSE) between 0.27% and 6.67%. Additionally, CEAIM scales more efficiently than conventional MSL models, with a slower computational growth rate in large-scale simulations. After thorough validation of the CEAIM model, it was coupled with HVAC equipment as this approach provides a detailed multi-dimensional view of capturing electrical transients and mechanical performance. To support this, a case study was conducted to showcase its capabilities.}, archive = {J_ACCESS}, author = {Viswanathan Ganesh and Zhanwei He and Jianjun Hu and Sen Huang and Wangda Zuo}, doi = {10.1109/ACCESS.2025.3581502}, journal = {IEEE Access}, pages = {107745-107763}, shortjournal = {IEEE Access}, title = {Coupled induction machine and HVAC models for simulating HVAC performance considering grid dynamics in buildings}, volume = {13}, year = {2025}, }
• (2025). Antenna-in-package using IC-embedded metal fan-out wafer-level package for D-band applications. ACCESS, 13, 107736-107744. (www)

  @article{ , abstract = {In this study, we present a low interconnection loss antenna-in-package (AiP) using a chip-embedded metal fan-out wafer-level package (mFOWLP) technology for D-band (110–170 GHz) applications. The package configuration enables interconnection with radio frequency monolithic microwave integrated circuits (RF MMIC) through redistribution layers (RDL) and microvias, effectively reducing the signal path loss. Moreover, the narrow bandwidth of the series-fed patch antenna can be effectively overcome by employing a low-loss polyimide (PI) multilayer structure. The fabricated AiP comprised a $1\times 8$ stacked Chebyshev distribution series-fed patch antenna with a dummy chip. The proposed AiP achieved an interconnection loss of 0.19 dB, and an impedance bandwidth of 18.25 GHz. The peak antenna gain was measured to be 14.2 dBi.}, archive = {J_ACCESS}, author = {Ju-Yong Lee and Jein Yu and Wansik Kim and Jong-Min Yook}, doi = {10.1109/ACCESS.2025.3577256}, journal = {IEEE Access}, pages = {107736-107744}, shortjournal = {IEEE Access}, title = {Antenna-in-package using IC-embedded metal fan-out wafer-level package for D-band applications}, volume = {13}, year = {2025}, }
• (2025). Evaluating the robustness of foundation models for satellite imagery. ACCESS, 13, 107720-107735. (www)

  @article{ , abstract = {With abundant remote sensing data, satellite imagery foundation models have advanced significantly. Currently, the usefulness of the majority of thesemodels presented in their respective works are demonstrated using one or two downstream tasks, specifically classification and segmentation using limited datasets that lack complexity based on the diversity of scenes, resolution, and sizes. Consequently, generalization across varied tasks and imagery remains unsolved and a major challenge in remote sensing. To address this gap, we benchmarked eight state-of-the-art foundation models utilizing five self-supervised variants of MAE (SatMAE, Scale-MAE, Cross-Scale MAE) and the Geospatial Foundation Model alongside a supervised Multi-Task Pretraining (MTP) all built upon Vision Transformer (ViT) backbones. Each model was pretrained on one of four large collections (fMoW, SAMRS, Million-AID, GeoPile) and then used to initialize networks for the four downstream tasks namely oriented object detection, classification, segmentation, and change detection across three diverse datasets (DOTA-v2.0, BigEarthNet, and xDB). In every case, the full network underwent end-to-end fine-tuning. This study aims to provide valuable insights to guide in the development of more generalizable models and fine-tuning protocols, advancing the capabilities of foundation models for diverse remote sensing applications. Results show that models are task dependent: while the foundation models match or exceed supervised baselines in image classification or segmentation, they struggle with other tasks such as change detection and oriented object detection. Cross-dataset performance is further hampered by variations in the types of images provided by different satellites. Importantly, the quality of pretraining data namely spatial resolution and scene diversity, drives downstream success far more than sheer dataset size (number of image samples during training), as evidenced by SAMRS outperforming larger image datasets like GeoPile and fMoW. Finally, scaling from ViT-Base to ViT-Large yields less than a 1% improvement, underscoring the inefficiency of naive model enlargement. Together, these insights highlight the critical need for task-agnostic architectures and richly diverse pretraining strategies to fully realize the promise of self-supervised learning in remote sensing.}, archive = {J_ACCESS}, author = {Gilbert Rotich and Sudeep Sarkar}, doi = {10.1109/ACCESS.2025.3581067}, journal = {IEEE Access}, pages = {107720-107735}, shortjournal = {IEEE Access}, title = {Evaluating the robustness of foundation models for satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Millimeter-wave yagi MIMO antenna with high isolation and beam-tilting capability using optimized metamaterials. ACCESS, 13, 107710-107719. (www)

  @article{ , abstract = {A high-performance multiple-input-multiple-output (MIMO) antenna for the millimeter-wave spectrum is introduced in this paper, featuring broadband performance, high gain, superior isolation, and beam-tilting functionality. A single Yagi antenna, utilizing a third-order mode dipole as its driver with integrated reflectors, is designed for the 28 GHz band to enhance gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna functions within the 28 GHz 5G band, offering a 2.6 GHz bandwidth. The radiation pattern results reveal that the antenna’s gain in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. The MIMO antenna is subsequently designed using two adjacent Yagi elements, with a metamaterial array positioned between them to enhance isolation and tilt the radiation beam. The trust region (TR) method is utilized to refine the metamaterial’s dimensions, achieving up to 58 dB of isolation at 28 GHz. Additionally, the metamaterial enables a 33-degree E-plane beam tilting relative to the end-fire direction when switching between the two ports’ excitations. The system is validated through experiments, demonstrating a strong correlation between the simulated and measured data.}, archive = {J_ACCESS}, author = {Bashar A. F. Esmail and Dustin Isleifson and Slawomir Koziel}, doi = {10.1109/ACCESS.2025.3581586}, journal = {IEEE Access}, pages = {107710-107719}, shortjournal = {IEEE Access}, title = {Millimeter-wave yagi MIMO antenna with high isolation and beam-tilting capability using optimized metamaterials}, volume = {13}, year = {2025}, }
• (2025). Ghost module-enhanced MTCNN: A lightweight cascade framework for high-accuracy face detection in edge-deployable scenarios. ACCESS, 13, 107694-107709. (www)

  @article{ , abstract = {Face detection in complex environments remains challenging due to trade-offs between accuracy and computational efficiency, particularly for edge devices with limited resources. GhostNet-MTCNN is proposed. In this approach, the computationally intensive standard convolution layers in the Multi-task Cascaded Convolutional Neural Network (MTCNN) backbone are replaced by Ghost bottleneck modules from the GhostNet network, which offer lower computational requirements. This modification reconstructs the network’s feature extraction capabilities, resulting in a new model. Experimental results demonstrate that the proposed method effectively balances model parameters and accuracy. On the Easy, Medium, and Hard validation sets, the method improves accuracy by 5.6%, 6.6%, and 7.8%, respectively, with only a 0.62M increase in parameters compared to MTCNN. In comparison to MobileNetV3-MTCNN, the model reduces the parameter count by 1.27M while improving accuracy by 1.6%, 0.8%, and 0.5%, respectively. Hardware deployment on FPGA further validates its practical efficacy, achieving a $400\times $ acceleration in convolution operations through optimized parallelization and memory caching. This study enhances the MODEL’s ability to detect small-size, multi-angle faces in low light, partially occluded, and noisy environments in complex scenarios, while effectively balancing parameter count and detection accuracy, making it a superior choice for deployment on edge devices.}, archive = {J_ACCESS}, author = {Chen Wang and Fen Liu}, doi = {10.1109/ACCESS.2025.3581428}, journal = {IEEE Access}, pages = {107694-107709}, shortjournal = {IEEE Access}, title = {Ghost module-enhanced MTCNN: A lightweight cascade framework for high-accuracy face detection in edge-deployable scenarios}, volume = {13}, year = {2025}, }
• (2025). Combining autoregressive models and phonological knowledge bases for improved accuracy in korean grapheme-to-phoneme conversion. ACCESS, 13, 107678-107693. (www)

  @article{ , abstract = {Recent advances in deep learning have highlighted the importance of Grapheme-to-Phoneme (G2P) conversion in natural language processing and speech synthesis. Korean exhibits complex phonological changes such as liaison, initial sound law, and consonant assimilation, making it challenging to handle all exceptional patterns with simple models alone. In this paper, we propose integrating an autoregressive (AR) model with a phonological knowledge base that leverages standard pronunciation rules and a pre-analyzed dictionary, and conduct systematic comparisons with non-autoregressive (NAR) variants to validate the effectiveness of sequential processing for Korean G2P. Experimental results show that a BiLSTM-LSTM AR model based on ELECTRA embeddings achieves a phoneme error rate (PER) of 0.2%, word error rate (WER) of 0.68%, and a sentence accuracy of 95.16%, outperforming both a traditional rule-based approach (24.51% sentence accuracy) and non-autoregressive variants implemented on the same dataset (achieving 81.32%–85.72% sentence accuracy). When syllable constraints are introduced, accuracy significantly improves to 95.41% (p <0.05). Meanwhile, incorporating a pre-analyzed dictionary enhances the handling of neologisms and proper nouns without substantially increasing inference time. Through comprehensive same-dataset comparisons between AR and NAR approaches, our study demonstrates the effectiveness of combining AR models with phonological knowledge bases in Korean G2P, with AR models consistently outperforming NAR variants by 3.12-8.04% in sentence accuracy, while identifying key challenges including rule conflicts and handling multiple standard pronunciations. The methodology and findings provide a generalizable framework for other morpho-phonologically complex languages.}, archive = {J_ACCESS}, author = {Sung-Ki Choi and Hyuk-Chul Kwon and Minho Kim}, doi = {10.1109/ACCESS.2025.3581981}, journal = {IEEE Access}, pages = {107678-107693}, shortjournal = {IEEE Access}, title = {Combining autoregressive models and phonological knowledge bases for improved accuracy in korean grapheme-to-phoneme conversion}, volume = {13}, year = {2025}, }
• (2025). Progress in tomography with unknown shifts and view angles. ACCESS, 13, 107669-107677. (www)

  @article{ , abstract = {Parallel beam computed tomography (CT) is a foundational imaging modality in medical diagnostics and industrial inspection. It reconstructs an object from a series of projections taken at different angles. In many practical scenarios, however, the viewing angles and spatial shifts associated with the projections are not precisely known, such as in cryo-electron microscopy, microfluidic imaging, or low-dose CT acquisition. We address the problem of reconstructing a two-dimensional object from Radon projections acquired at unknown view angles and affected by unknown spatial shifts. To resolve this geometric ambiguity, we propose an analytic framework that first centers each projection using its first moment, then compares second- and third-order projection moments to identify the correct angle. The proposed method is non-iterative, requires only a few known projections of the base function, and supports full parallelization. Numerical experiments on synthetic and structured test images demonstrate sub-degree angular recovery accuracy and enable good-quality reconstruction via standard inversion techniques. These results underscore the method’s effectiveness and robustness under geometric uncertainty.}, archive = {J_ACCESS}, author = {Fawaz Hjouj and Mohamed Soufiane Jouini and Nabil Maalej and Aymen Laadhari}, doi = {10.1109/ACCESS.2025.3577978}, journal = {IEEE Access}, pages = {107669-107677}, shortjournal = {IEEE Access}, title = {Progress in tomography with unknown shifts and view angles}, volume = {13}, year = {2025}, }
• (2025). 3D bevel-tip needle insertion trajectory planning via computational optimal control. ACCESS, 13, 107657-107668. (www)

  @article{ , abstract = {Manual insertion of flexible bevel-tip needles often leads to unpredictable tissue deformation and compromised targeting accuracy, emphasizing the need for robust trajectory planning. To address this challenge, we formulate the insertion problem as a time-energy optimal control problem (OCP) subject to nonlinear kinematic and collision-avoidance constraints. Due to its large-scale and nonconvex nature, directly solving the nominal OCP is difficult. Instead, we first obtain a coarse collision-free trajectory via ${\mathrm {A}}^{\ast }$ search in the abstracted 3D workspace. Next, we create spatiotemporal safe corridors around this trajectory, replace the nominal collision-avoidance constraints with corridor-based constraints, and iteratively relax the kinematic equations as external penalties to refine feasibility. The refined solution subsequently warm-starts a final solve of the nominal OCP with strict kinematic constraints and reduced-scale collision-avoidance constraints. Simulations confirm that our proposed optimization-based trajectory planner converges reliably to numerically optimal needle trajectories, surpassing existing optimization-based trajectory planners in modeling accuracy, solution robustness, and efficiency.}, archive = {J_ACCESS}, author = {Lijuan Pan and Zhenhui Zhang and Zhuyan Yin and Bai Li}, doi = {10.1109/ACCESS.2025.3580645}, journal = {IEEE Access}, pages = {107657-107668}, shortjournal = {IEEE Access}, title = {3D bevel-tip needle insertion trajectory planning via computational optimal control}, volume = {13}, year = {2025}, }
• (2025). Iterative and non-iterative theoretical closed forms discrete cosine transform-based incremental LMS. ACCESS, 13, 107631-107656. (www)

  @article{ , abstract = {Novel closed-form expressions of the discrete cosine transform (DCT)-based iterative and non-iterative incremental least mean square (DCTILMS) algorithms are derived here for wireless sensor networks (WSNs) and agent localization. As a direct application to this field of study, smart cities, smart homes, intelligent transportation systems, healthcare, robotics, low-cost and energy efficient smart sensor devices, Internet of Things (IoT), target tracking, and agent localization are very relevant applications. Moreover, there are many achieved objectives in this study, among them the great performance obtained by this algorithm, and the well performance in low-SNR environments. The practical implication of the present model is to reduce computational complexity. In this respect, incorporating only the initial design parameters, the theoretical transient and steady-state expressions of the DCT $1{^{\text {st}}}$ - and $2{^{\text {nd}}}$ -moment formulas are derived in both iterative and non-iterative closed forms. The length of the main DCTILMS experiment can now be identified in advance by monitoring the theoretical DCT $1{^{\text {st}}}$ -moment closed-form formula. Extensive simulations are carried out to present that the theoretical DCT closed-form formulas are well-matched with their empirical counterparts, thus confirming the soundness of our theoretical derivations.}, archive = {J_ACCESS}, author = {Ali Almohammedi and Azzedine Zerguine and Mohamed Deriche}, doi = {10.1109/ACCESS.2025.3580946}, journal = {IEEE Access}, pages = {107631-107656}, shortjournal = {IEEE Access}, title = {Iterative and non-iterative theoretical closed forms discrete cosine transform-based incremental LMS}, volume = {13}, year = {2025}, }
• (2025). Integration of regression-based guidance ant for enhanced exploration and convergence in ant colony optimization (ACO). ACCESS, 13, 107621-107630. (www)

  @article{ , abstract = {Traditional Ant Colony Optimization (T-ACO) algorithms often face challenges in dynamic environments, particularly the tendency to become trapped in local minima, resulting in suboptimal path planning. To address these limitations, this research incorporates a linear regression line as a directional guide for ants, helping them navigate toward the optimal path more efficiently. This paper presents an improved Ant Colony Optimization (I-ACO) algorithm by integrating regression-based guidance to enhance both exploration and convergence. The proposed I-ACO method was tested using real-world geographic data from the Universitas Sriwijaya campus, and its performance was compared with that of T-ACO in both obstacle-free and obstacle-present scenarios. The results indicate that I-ACO consistently produces more accurate and efficient paths, with lower distance errors and faster computation times. Specifically, I-ACO achieved an average computation time of 0.8 seconds, compared to T-ACO’s 1.2 seconds. Moreover, I-ACO demonstrated superior adaptability when encountering obstacles, providing shorter and more efficient routes. These findings highlight the effectiveness of regression-based guidance in improving the performance of the ACO algorithm, making it more suitable for real-time autonomous vehicle navigation in complex environments. This approach not only enhances path accuracy but also reduces computational costs and improves obstacle avoidance capabilities.}, archive = {J_ACCESS}, author = {Desi W. Sari and Suci Dwijayanti and Bhakti Y. Suprapto}, doi = {10.1109/ACCESS.2025.3581425}, journal = {IEEE Access}, pages = {107621-107630}, shortjournal = {IEEE Access}, title = {Integration of regression-based guidance ant for enhanced exploration and convergence in ant colony optimization (ACO)}, volume = {13}, year = {2025}, }
• (2025). GenAI-based jamming and spoofing attacks on UAVs. ACCESS, 13, 107596-107620. (www)

  @article{ , abstract = {Recently, aerial vehicles have been more connected than ever, where there are many types of the vehicles. Uncrewed Aerial Vehicles (UAVs) operate on various environments with different technologies that are subject to many attacks. Creating effective intrusion detection systems against such attacks has been a significant challenge since there is a lack of sufficient attack data that can be used to design an intrusion detection system with advanced computing algorithms. In this research, we propose a novel framework to create attacks data for UAVs by using generative artificial intelligence algorithms. We use Variational Autoencoder, Gaussian Copula, Denoising Diffusion Probabilistic Model (DDPM), and Conditional Tabular Generative Adversarial Network to create synthetic attack data. Specifically, jamming and spoofing attacks on UAVs are generated to fool intrusion detection systems that may be implemented on UAVs. Experimental evaluations show that synthetically generated attack data reduces the accuracy of intrusion detections if the system was trained with inadequate attack data. Additionally, analysis results show that DDPM emerged as the most effective model for generating attack data, leading to F1 score reductions of 21% for jamming and 28% for spoofing attacks. This research highlights the need for more robust and adaptive intrusion detection systems that can be created with synthetic data. Thus, sustainable computing systems on UAVs will be achieved.}, archive = {J_ACCESS}, author = {Burcu Sönmez Sarikaya and Şerif Bahtiyar}, doi = {10.1109/ACCESS.2025.3574284}, journal = {IEEE Access}, pages = {107596-107620}, shortjournal = {IEEE Access}, title = {GenAI-based jamming and spoofing attacks on UAVs}, volume = {13}, year = {2025}, }
• (2025). Lifelong whole slide image analysis: Online vision-language adaptation and past-to-present gradient distillation. ACCESS, 13, 107583-107595. (www)

  @article{ , abstract = {Whole Slide Images (WSIs) play a crucial role in accurate cancer diagnosis and prognosis, as they provide tissue details at the cellular level. However, the rapid growth of computational tasks involving WSIs poses significant challenges. Given that WSIs are gigapixels in size, they present difficulties in terms of storage, processing, and model training. Therefore, it is essential to develop lifelong learning approaches for WSI analysis. In scenarios where slides are distributed across multiple institutes, we aim to leverage them to develop a unified online model as a computational tool for cancer diagnosis in clinical and hospital settings. In this study, we introduce ADaFGrad, a method designed to enhance lifelong learning for whole-slide image (WSI) analysis. First, we leverage pathology vision—language foundation models to develop a framework that enables interaction between a slide’s regional tissue features and a predefined text-based prototype buffer. Additionally, we propose a gradient—distillation mechanism that mimics the gradient of a logit with respect to the classification-head parameters across past and current iterations in a continual—learning setting. We construct a sequence of six TCGA datasets for training and evaluation. Experimental results show that ADaFGrad outperforms both state-of-the-art WSI-specific and conventional continual-learning methods after only a few training epochs, exceeding them by up to +5.068% in the class-incremental learning scenario while exhibiting the least forgetting (i.e., retaining the most knowledge from previous tasks).}, archive = {J_ACCESS}, author = {Doanh C. Bui and Hoai Luan Pham and Vu Trung Duong Le and Tuan Hai Vu and Van Duy Tran and Khang Nguyen and Yasuhiko Nakashima}, doi = {10.1109/ACCESS.2025.3580470}, journal = {IEEE Access}, pages = {107583-107595}, shortjournal = {IEEE Access}, title = {Lifelong whole slide image analysis: Online vision-language adaptation and past-to-present gradient distillation}, volume = {13}, year = {2025}, }
• (2025). Improving web accessibility with an LLM-based tool: A preliminary evaluation for STEM images. ACCESS, 13, 107566-107582. (www)

  @article{ , abstract = {Ensuring equitable access to web-based visual content in Science, Technology, Engineering, and Mathematics (STEM) disciplines remains a significant challenge for visually impaired users. This preliminary study explores the use of Large Language Models (LLMs) to automatically generate high-quality alternative texts for complex web images in these domains, contributing to the development of an accessibility tool. First, we analyzed the outputs of various LLM-based image-captioning systems, selected the most suitable one (Gemini), and developed a browser extension, AlternAtIve, capable of generating alternative descriptions at varying verbosity levels. To evaluate AlternAtIve, we assessed its perceived usefulness in a study involving 35 participants, including a blind user. Additionally, we manually compared the quality of the outputs generated by AlternAtIve with those provided by two state-of-the-practice tools from the Google Web Store, using a custom metric that computes the quality of the descriptions considering their correctness, usefulness, and completeness. The results show that the descriptions generated with AlternAtIve achieved high quality scores, almost always better than those of the other two tools. Although conveying the meaning of complex images to visually impaired users through descriptions remains challenging, the findings suggest that AI-based tools, such as AlternAtIve, can significantly improve the web navigation experience for screen reader users.}, archive = {J_ACCESS}, author = {Giacomo Pedemonte and Maurizio Leotta and Marina Ribaudo}, doi = {10.1109/ACCESS.2025.3577519}, journal = {IEEE Access}, pages = {107566-107582}, shortjournal = {IEEE Access}, title = {Improving web accessibility with an LLM-based tool: A preliminary evaluation for STEM images}, volume = {13}, year = {2025}, }
• (2025). Efficient segmentation using attention-fusion modules with dense predictions. ACCESS, 13, 107552-107565. (www)

  @article{ , abstract = {Fusing the multi-scale global and local semantic information remains a challenging task for foundation models with computational costs and the need for effective long-range recognition. Based on the recent success of transformers and attention mechanisms, this research applies dense predictions to attention-based methods of attention-boosting modules and attention-fusion networks with residual layers, resulting in SERNet-Former_v2. Dense predictions are deployed to augment the efficacy of dense-attention-boosting modules and dense-attention-fusion networks, enabling the extraction of global context feature maps in the encoder. This approach also enhances the performance of state-of-the-art segmentation networks, addressing the challenges issued by foundation models like InternImage. Attention-based algorithms are utilized in InternImage architectures that combine vision transformers and convolutional layers. Our enhancements showed improvements in the test performance of InternImage-H on the Cityscapes test set (86.2 % mean IoU), on BDD100K (74.1 % mean IoU), and ADE20K datasets (64.0 % mean IoU). SERNet-Former_v2 has also been evaluated on challenging datasets of ADE20K, BDD100K, CamVid, and Cityscapes, showing significant advancements through attention-based methods. The results achieved by SERNet-Former_v2, developed using our methods on these datasets, are found to be noteworthy: 85.12 % mean IoU on the Cityscapes test dataset, 59.35 % mean IoU on the ADE20K validation dataset, 67.42 % mean IoU on BDD100K validation dataset, and 85.08 % mean IoU on the CamVid dataset.}, archive = {J_ACCESS}, author = {Serdar Erişen}, doi = {10.1109/ACCESS.2025.3581986}, journal = {IEEE Access}, pages = {107552-107565}, shortjournal = {IEEE Access}, title = {Efficient segmentation using attention-fusion modules with dense predictions}, volume = {13}, year = {2025}, }
• (2025). Dielectric lens-type CATR system using a serrated structure for antenna radiation pattern measurement. ACCESS, 13, 107542-107551. (www)

  @article{ , abstract = {Large aperture antennas are used to compensate for high propagation losses in the millimeter wave bands used by Beyond 5G/6G, satellite, and automotive collision prevention radars. The big anechoic chambers required to accommodate Direct Far-field (DFF) measurements can be prohibitively large and expensive. Establishing anechoic chambers and test ranges large enough to accommodate DFF measurements has proven difficult and costly. More recently, however, the characterization of large aperture millimeter-wave antennas and transceivers can be more economically performed utilizing Compact Antenna Test Range (CATR) systems. Unlike traditional large anechoic chamber-based systems that utilize DFF techniques, CATR systems incorporate Indirect Far-Field (IFF) techniques with reflectors to achieve comparable measurements using less space. This translates into smaller and less expensive test systems. However, conventional reflectors used in millimeter-wave CATR chambers can be expensive to manufacture and set up. This paper proposes a more economical dielectric lens that utilizes a Serrated Structure (SS) around the lens to achieve suitable plane-wave characteristics. The dielectric lens was analyzed using the Finite Element Method (FEM) and then manufactured to theoretical dimensions. As a result, it showed Quiet Zone (QZ) characteristics of 1/3 the diameter of the lens. To show that the manufactured lens could be applied to the CATR system, the results of evaluating the radiation pattern of a 1024-element antenna at 30 GHz are shown below.}, archive = {J_ACCESS}, author = {Genma Hattori and Sou Ema and Yuta Horie and Toshiyuki Maeyama}, doi = {10.1109/ACCESS.2025.3581601}, journal = {IEEE Access}, pages = {107542-107551}, shortjournal = {IEEE Access}, title = {Dielectric lens-type CATR system using a serrated structure for antenna radiation pattern measurement}, volume = {13}, year = {2025}, }
• (2025). Bayesian-optimized multi-task gaussian process regression with composite kernels for soybean oil futures forecasting. ACCESS, 13, 107532-107541. (www)

  @article{ , abstract = {Accurate forecasting of agricultural commodity prices is critical for economic stability and strategic decision-making, yet remains challenging owing to nonlinear dynamics and multifactorial volatility. This study addresses the forecasting of soybean oil prices in China’s wholesale markets by developing a Bayesian-optimized Multi-Task Gaussian Process Regression (MTGPR) framework. Traditional econometric models and machine learning approaches often struggle with non-stationary data and uncertainty quantification, while existing Gaussian process applications in agricultural markets remain underexplored. We propose a composite kernel architecture integrating Matérn 2.5 and Exponential Sine Squared (ESS) kernels to capture localized price trends, periodic cycles, and heteroskedastic volatility. The framework incorporates futures market indicators and employs spectral analysis with Reproducing Kernel Hilbert Space (RKHS) theory for feature alignment. Using a decade-long dataset (2015–2024) of daily soybean oil prices, the model undergoes rigorous validation via cross-validated Bayesian optimization, bootstrap error estimation, and posterior predictive intervals. Comparative evaluations against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost) demonstrate MTGPR’s superiority, achieving a mean absolute percentage error (MAPE) of 0.85% and R2 of 0.9313. Key innovations include adaptive kernel selection, probabilistic uncertainty bands for risk-aware strategies, and cross-market transferability analysis. Results confirm the model’s capacity to disentangle complex market patterns while maintaining computational tractability. This work advances agricultural price forecasting by bridging theoretical Bayesian methods with practical trading applications, offering policymakers and market participants a robust tool for hedging and supply chain optimization.}, archive = {J_ACCESS}, author = {Hui-Dong Yin and Yi-Yang Li}, doi = {10.1109/ACCESS.2025.3581288}, journal = {IEEE Access}, pages = {107532-107541}, shortjournal = {IEEE Access}, title = {Bayesian-optimized multi-task gaussian process regression with composite kernels for soybean oil futures forecasting}, volume = {13}, year = {2025}, }
• (2025). Novel efficient steel surface defect detection model based on ConvNeXt v2 and squeeze aggregated excitation attention. ACCESS, 13, 107512-107531. (www)

  @article{ , abstract = {With the rapid advancement of artificial intelligence and intelligent manufacturing technologies in recent years, automated inspection technologies have ushered in new opportunities for industrial production. Against this backdrop, this paper proposes an improved single-stage detection model, ESS-YOLO, which enhances the detection capability of steel defects in complex industrial environments, particularly for large and small target defects that are confounded by complex backgrounds and ambiguous defect locations. Firstly, by integrating the concepts of CSP architecture with ConvNeXt v2, a module is constructed that enhances the capability of image feature extraction. Secondly, SaE channel attention is employed to bolster the feature representation in the detection head, providing robust support for classification. In addition, to mitigate the issue of excessive resource requirements for the detection head, DSC is incorporated, which effectively reduces the complexity of the model. Finally, The Shape-IoU loss function enhances the model’s ability to learn the geometric properties of bounding boxes, taking into account both the geometric information of the ground truth boxes and the predicted boxes, thereby improving the accuracy of regression. Experimental results on the NEU-DET dataset demonstrate that, compared to the original model YOLO v8, the proposed ESS-YOLO model achieves a 2.1% increase in detection accuracy and a 19.5% reduction in computational load. Compared to common detection algorithms such as Faster-R-CNN, the accuracy is improved by 7.1%, indicating the excellent detection performance of our model. These results suggest that the ESS-YOLO model has significant application potential in the field of steel defect detection.}, archive = {J_ACCESS}, author = {Chengfei Li and Haijian Huang and Huamin Mo and Zhikai Wen and Shiqin Zhou and Zhenhao Zhu}, doi = {10.1109/ACCESS.2025.3581313}, journal = {IEEE Access}, pages = {107512-107531}, shortjournal = {IEEE Access}, title = {Novel efficient steel surface defect detection model based on ConvNeXt v2 and squeeze aggregated excitation attention}, volume = {13}, year = {2025}, }
• (2025). Similarity-based clustering for identification and segmentation of responsive electricity customers. ACCESS, 13, 107499-107511. (www)

  @article{ , abstract = {The identification and segmentation of responsive electricity customers have been formulated here as a binary time series clustering (TSC) problem. The assumption of a stationary environment in kernel methods can complicate the mapping of non-stationary time series data to a high-dimensional feature space, leading to a degradation in the performance of kernel K-means clustering. Hence, a similarity-based non-linear TSC is proposed to capture consumers’ reactions to demand response (DR) signals. K-means with Dynamic Time Warping (DTW) is employed as a nonlinear TSC approach, and to extend it beyond standard sample-to-centroid comparisons, a similarity matrix is suggested in place of raw time series, enabling sample-to-sample comparisons. It is based on distance and correlation matrices ensembling one-to-many and two-to-two comparisons to map original data to the similarity space. By analyzing consumption data from the Low Carbon London project, we demonstrate the effectiveness of our approach in identifying responsive consumers with different responsive levels.}, archive = {J_ACCESS}, author = {Amirhossein Ahmadi and Hamidreza Zareipour and Henry Leung}, doi = {10.1109/ACCESS.2025.3582201}, journal = {IEEE Access}, pages = {107499-107511}, shortjournal = {IEEE Access}, title = {Similarity-based clustering for identification and segmentation of responsive electricity customers}, volume = {13}, year = {2025}, }
• (2025). Enhancing malware analysis using data visualization through shared code and attribute analysis. ACCESS, 13, 107482-107498. (www)

  @article{ , abstract = {Malware analysis is a crucial area of cybersecurity, focusing on identifying, categorizing, and studying malicious software to prevent it from posing a threat to computer systems. This study introduces a new approach to malware analysis by integrating two methods, Shared Code Analysis (SCA) and Shared Attribute Analysis (SAA). The SCA component searches for similarities in code within multiple malware samples using the MinHash techniques. The SAA component uses the Jaccard indexing technique to analyze and approximate similarity based on malware samples’ attributes. The visualization is implemented using NetworkX with Graphviz, where nodes represent individual malware samples and edges represent close relations. The visualization is provided as a PNG file, demonstrating the grouping of related malware samples according to their attributes. A real-life case based on malware samples from GitHub repositories illustrates the application of the proposed methodology. The integration of the Jaccard Index and MinHash provides an extensive method for covering the relationships among malware and enhancing detection and categorization steps. The proposed approach not only provides a better understanding of code patterns, but it also helps visualize attribute-based maximum similarity, making it a valuable tool for researchers and cybersecurity professionals.}, archive = {J_ACCESS}, author = {Narayandas Sai Ramana Vashista and K. Abhimanyu Kumar Patro}, doi = {10.1109/ACCESS.2025.3582164}, journal = {IEEE Access}, pages = {107482-107498}, shortjournal = {IEEE Access}, title = {Enhancing malware analysis using data visualization through shared code and attribute analysis}, volume = {13}, year = {2025}, }
• (2025). Self-isolation in MIMO microstrip patch arrays using annular via-based mode conversion. ACCESS, 13, 107467-107481. (www)

  @article{ , abstract = {This paper presents a novel self-isolation technique for multiple-input multiple-output (MIMO) microstrip patch antenna (MPA) arrays using annular via-based mode conversion. The proposed approach transforms the TM20 mode into a hybrid TM20+TM01 mode, enabling high isolation in both E-plane and H-plane coupled configurations without requiring additional decoupling structures. A compact $2\times 1$ single-fed antenna array is designed using annular vias and an open slot in the MPA, serving as a fundamental unit for the MIMO array. By exciting an element with a hybrid mode in the H-plane coupled MIMO array, a TM10 mode of very low intensity is induced on the adjacent half and a TM01 mode on the rear half of the nearby patch that leads to high isolation. However, in the E-plane coupled MIMO array a quasi-TM01 mode is induced on each half of the nearby patch whose overall field distribution resembles a TM11 mode due to the slot in the patch and the annular vias. Thus, in both configurations, the coupled modes are either orthogonal or exhibit weak field distribution concerning the excited mode. To validate the technique, $2\times 1$ MIMO arrays were fabricated and measured. An isolation levels of 38 dB and 40 dB is demonstrated in the E-plane and H-plane configurations, respectively, at 5.8 GHz. The measured cross-polarization level is −50 dB in the broadside direction for the E-plane coupled array in both planes and it is 18 dB/23 dB at $\phi =0^{\circ } $ / $\phi =90^{\circ } $ in the H-plane array. Additionally, a broadside gain of 7.3 dB and 7.1 dB is achieved for a single port in the respective configurations. The proposed technique offers an effective, scalable, and space-efficient solution for high-isolation MIMO antenna systems, making it suitable for advanced wireless communication applications.}, archive = {J_ACCESS}, author = {Manzoor Elahi and Slawomir Koziel and Leifur Leifsson}, doi = {10.1109/ACCESS.2025.3582162}, journal = {IEEE Access}, pages = {107467-107481}, shortjournal = {IEEE Access}, title = {Self-isolation in MIMO microstrip patch arrays using annular via-based mode conversion}, volume = {13}, year = {2025}, }
• (2025). Series compensated low frequency transformers emulating multipulse rectifiers for harmonic current content minimization in electrical grids. ACCESS, 13, 107455-107466. (www)

  @article{ , abstract = {This paper explores the use of series compensated low frequency transformers (SCLFT), operating in parallel to traditional low frequency transformers (LFT), to attenuate harmonic distortion caused by nonlinear loads in electrical networks. The proposed strategy achieves harmonic cancellation through a controlled phase shift between the SCLFT and LFT voltages, emulating the behavior of multi-pulse transformers. By integrating power converters into the LFT, the SCLFT’s operation is optimized to maximize system efficiency and implement a robust control algorithm for precise output voltage regulation. The effectiveness of the proposed approach is validated through hardware-in-the-loop (HIL) simulations and experimental testing, demonstrating a significant reduction in current harmonic distortion. These results position series-compensated SCLFTs as a viable solution for enhancing power quality in distribution grids. This technology offers a promising alternative to modernizing electrical infrastructure and paves the way for future applications in microgrid optimization and large-scale distribution systems.}, archive = {J_ACCESS}, author = {Carlos A. Munoz and Carlos R. Baier and Jesus C. Hernández and Esteban I. Marciel and Pedro E. Melin}, doi = {10.1109/ACCESS.2025.3581891}, journal = {IEEE Access}, pages = {107455-107466}, shortjournal = {IEEE Access}, title = {Series compensated low frequency transformers emulating multipulse rectifiers for harmonic current content minimization in electrical grids}, volume = {13}, year = {2025}, }
• (2025). ResNet-driven joint decision-making for VVC optimization via gradient search. ACCESS, 13, 107442-107454. (www)

  @article{ , abstract = {The latest video coding standard, H.266/VVC, introduces novel intra-coding techniques, advancing intra-coding technology while significantly increasing its complexity. Compared to H.265/HEVC, H.266/VVC introduces a new QTMT structure for CU partitioning and nearly doubles the number of intra-prediction modes, leading to a substantial increase in computational complexity and encoding time. To mitigate the exponential computational demands inherent in quad-tree based partitioning, this research establishes a rate-distortion aware optimization framework. The proposed dynamic decision-making mechanism coordinates coding unit hierarchy configuration with spatial prediction modalities, achieving Pareto efficiency in computational resource allocation. The algorithm consists of two main components: a fast CU partitioning module and an intra-mode selection module. In the first component of the proposed algorithm, we present a rapid CU partitioning technique built upon the ResNet model. This method effectively extracts and learns the distinctive features of CU blocks, enabling it to predict partition structures with high accuracy, thus streamlining the CU decision process. In the second component, we introduce an intra-mode selection algorithm based on gradient descent, which utilizes block gradient features to optimize and determine the final intra-prediction mode. Experimental results demonstrate that the proposed algorithm significantly reduces encoding time by 53.17%.At the same time, the BD-BR only increases by 1.38%, showcasing an optimal trade-off between video quality and encoding efficiency.}, archive = {J_ACCESS}, author = {Fangmei Liu and Jiyuan Wang and Qiuwen Zhang}, doi = {10.1109/ACCESS.2025.3580894}, journal = {IEEE Access}, pages = {107442-107454}, shortjournal = {IEEE Access}, title = {ResNet-driven joint decision-making for VVC optimization via gradient search}, volume = {13}, year = {2025}, }
• (2025). 3DDGD: 3D deepfake generation and detection using 3D face meshes. ACCESS, 13, 107429-107441. (www)

  @article{ , abstract = {3D face technology is revolutionizing various fields by providing superior security and realism compared with 2D methods. In biometric authentication, 3D facial features serve as unique, hard-to-forge identifiers, improving accuracy in facial recognition for border control and criminal identification. Additionally, 3D avatars enhance virtual interactions. In this study, we aimed to strengthen 3D facial biometric systems against deepfakes. Key contributions include proving the superior protection of 3D faces over 2D ones, creating a dataset of real and fake 3D faces, and developing advanced models for accurate 3D deepfake detection. We evaluated our models for generalization to other datasets and stability when changing training data. Our experiments used the mesh multi-layer perceptron model for deepfake detection along with self-attention mechanisms and the newly introduced TabTransformer model. Results indicate that 3D face meshes greatly improve security by distinguishing real faces from deepfakes. Future work will focus on enhancing detection tools and integrating geometric features with facial textures for more accurate 3D deepfake detection. The dataset and models are publicly available on GitHub, excluding licensed elements: https://github.com/hichemfelouat/3DDGD}, archive = {J_ACCESS}, author = {Hichem Felouat and Huy H. Nguyen and Junichi Yamagishi and Isao Echizen}, doi = {10.1109/ACCESS.2025.3580950}, journal = {IEEE Access}, pages = {107429-107441}, shortjournal = {IEEE Access}, title = {3DDGD: 3D deepfake generation and detection using 3D face meshes}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of superposition M-ary QAM modulation in coded protograph LDPC MIMO communication systems with low-resolution ADCs. ACCESS, 13, 107410-107428. (www)

  @article{ , abstract = {Low-resolution analog-to-digital converters (ADCs) offer a compelling approach to reducing power consumption and hardware complexity in large-scale multiple-input multiple-output (LS-MIMO) systems for next-generation wireless networks. However, aggressive quantization significantly increases detection complexity—particularly for high-order modulation schemes—due to elevated noise and interference. In this paper, we propose a novel framework that jointly detects and decodes signals in LS-MIMO systems employing superposition modulation and low-resolution ADCs. Our approach leverages a message-passing (MP) algorithm that decomposes each high-order symbol into multiple BPSK layers, enabling more tractable soft-interference cancellation and extrinsic information updates. To theoretically assess system performance, we further develop a modified Protograph Extrinsic Information Transfer (PEXIT) algorithm, which integrates the effects of MIMO fading, ADC quantization noise, and protograph LDPC decoding into a single analytical tool. This modified PEXIT accurately predicts the iterative decoding thresholds under varying antenna dimensions, ADC resolutions, and superposition weighting schemes, thus facilitating a systematic code and modulation design process. Extensive numerical results corroborate the analysis, demonstrating that equal-weight superposition modulation provides consistent performance gains over conventional equal-distance constellations, especially in low-to-moderate quantization scenarios. These findings highlight the importance of power allocation strategies for superposition modulation in practical LS-MIMO receivers with coarse quantizers. Overall, this work offers a unified perspective on superposition modulation, low-resolution ADCs, and advanced LDPC decoding, opening new avenues for designing energy-efficient and spectrally efficient communication systems suitable for 5G-and-beyond networks.}, archive = {J_ACCESS}, author = {Duc A. Hoang and Hieu T. Nguyen and Thang N. Le}, doi = {10.1109/ACCESS.2025.3581775}, journal = {IEEE Access}, pages = {107410-107428}, shortjournal = {IEEE Access}, title = {Performance analysis of superposition M-ary QAM modulation in coded protograph LDPC MIMO communication systems with low-resolution ADCs}, volume = {13}, year = {2025}, }
• (2025). Resilient multi-sink, multipath routing for mobility-limited wireless sensor networks in hostile event monitoring. ACCESS, 13, 107386-107409. (www)

  @article{ , abstract = {The demand for continuous observation of remote and inaccessible natural and artificial areas is increasing due to the escalating adverse effects of climate change and human activities. Surveillance and timely detection of phenomena such as flash floods, wildfires, landslides, and pollution are critical to human life, the natural environment, and infrastructure preservation. Advancements in wireless communication, computing, sensing, and energy storage have paved the way for wireless sensor networks (WSNs), a technology capable of monitoring such phenomena. A WSN consists of autonomous sensing devices that communicate wirelessly to deliver information hop by hop via routing to a distinguished entity called a sink. Hostile events of spatial extent can destroy individual devices and disrupt communication and monitoring. This study proposes the Multi-sink Reliable Resilient Multipath Routing Protocol (MsR2MRP), which can deliver information to multiple sinks to increase resiliency and construct multiple paths to avoid danger zones and mitigate failures. The protocol also supports constrained locomotion, a technique WSN nodes employ to evade damage by hostile events and to prolong the network’s lifetime. MsR2MRP utilizes sensor, energy, communication, and mobility information to maintain WSN-level monitoring capabilities even during mobility and hostile events. Furthermore, this study proposes a heuristic model for constrained locomotion in WSNs. Finally, this study evaluates the proposed solution with a state-of-the-art routing protocol from the literature and comprehensively analyzes its sensing abilities. Simulation results show that MsR2MRP outperforms the benchmark protocol in terms of monitoring performance and network lifetime.}, archive = {J_ACCESS}, author = {Bálint Áron Üveges and András Oláh}, doi = {10.1109/ACCESS.2025.3581731}, journal = {IEEE Access}, pages = {107386-107409}, shortjournal = {IEEE Access}, title = {Resilient multi-sink, multipath routing for mobility-limited wireless sensor networks in hostile event monitoring}, volume = {13}, year = {2025}, }
• (2025). Controlling AoA estimation errors in rician fading via measurement quality classification. ACCESS, 13, 107377-107385. (www)

  @article{ , abstract = {Angle of Arrival (AoA) estimation plays a crucial role in modern positioning systems but is often affected by errors in multipath channels. Existing methods typically lack a direct mechanism to assess the quality of the estimates, while full channel estimation using channel sounders is computationally expensive and impractical for many applications. To address this challenge, we propose a classification method that leverages frequency smoothing and the estimated Rician K-factor to evaluate AoA measurements. The algorithm discards estimates with low precision, thereby reducing the mean AoA error and achieving the stringent accuracy required for indoor positioning. Simulation results demonstrate that the proposed method significantly improves the AoA error statistics and effectively controls the maximum estimation error.}, archive = {J_ACCESS}, author = {Pedro Lemos and Glauber Brante and Ohara Kerusauskas Rayel and Richard Demo Souza}, doi = {10.1109/ACCESS.2025.3580273}, journal = {IEEE Access}, pages = {107377-107385}, shortjournal = {IEEE Access}, title = {Controlling AoA estimation errors in rician fading via measurement quality classification}, volume = {13}, year = {2025}, }
• (2025). Modified zebra optimization algorithm via design operators for modern power system planning problem solution. ACCESS, 13, 107343-107376. (www)

  @article{ , abstract = {Transmission Expansion Planning (TEP) is a key aspect in securing the reliability and efficiency of current-day power systems when dealing with the growing energy needs and energy security. This work presents an advanced optimization framework, which incorporates the Zebra Optimization Algorithm (ZOA) with two new improvement strategies: the FFDB method and OBL strategies. The FFDB mechanism improves it to balance exploitation and exploration by fine-tuning the contribution of the fitness and distance metrics using the fuzzy logic technique. At the same time, OBL increases population diversity and accelerates convergence by strategically distributing across the solution space. The combination of these elements is the FFDBZOA-OBL algorithm. The proposed algorithm was thoroughly validated in two experiment setups. First, its optimization performance was evaluated on the CEC2020 and CEC2022 benchmarks, showing a better performance than existing methods. Second, the FFDBZOA-OBL was applied to the real-world TEP problem, demonstrating the ability to minimize the costs of investment mechanisms while adjusting to the changing state of affairs in the market and supporting the resilience of local energy supply. Additionally, the robustness and significance of the proposed method are also supported by statistical analysis in the form of Wilcoxon and Friedman tests. Based on the results of the FFDBZOA-OBL approach, the obtained solution is a very competitive and adaptive one in terms of benchmark and practical power system optimization problems.}, archive = {J_ACCESS}, author = {Yunus Balci and Serhat Duman}, doi = {10.1109/ACCESS.2025.3581676}, journal = {IEEE Access}, pages = {107343-107376}, shortjournal = {IEEE Access}, title = {Modified zebra optimization algorithm via design operators for modern power system planning problem solution}, volume = {13}, year = {2025}, }
• (2025). Direction-of-arrival estimation for wideband chirps via multi-line fitting in the fractional fourier domain. ACCESS, 13, 107328-107342. (www)

  @article{ , abstract = {Estimating the direction-of-arrival (DoA) of a wideband chirp signal is an important topic in several modern systems. The referred estimation can be carried out using array signal processing in the fractional Fourier transform (FrFT) domain, which involves searching for peak amplitude in order to select the optimal (fractional) order. This paper addresses the arithmetic complexity associated with the aforementioned task. More specifically, we propose replacing the conventional FrFT with a simplified FrFT (SmFrFT) that is obtained as a special case of linear canonical transforms. In this context, we also introduce the so-called local discrete SmFrFT, an algorithm for computing partial points of the (discrete) SmFrFT with a further reduction in the number of involved arithmetic operations. We demonstrate that SmFrFT is not just an FrFT with simplifications in the amplitude spectrum, but it satisfies properties that bring benefits to chirp signal processing. The reduction in the number of complex multiplications is about 77%. The effectiveness of the proposed method is verified by considering single- and multi-target DoA estimation using a uniform linear array. In subspace-based algorithms, peak alignment is proposed as a pre-processing step. For the multi-target scenario, this operation can be generalized as a multi-line fitting problem. In this context, two approaches are considered: piecewise slope fitting and line detection in the Hough space. Numerical simulations demonstrate that these methods can offer low computational complexity. However, in applications where high estimation accuracy is critical, the use of the ESPRIT algorithm with spatial smoothing and incorporating the discrete SmFrFT is recommended.}, archive = {J_ACCESS}, author = {Eulogio G. Huampo and Juliano B. Lima and José R. de Oliveira Neto}, doi = {10.1109/ACCESS.2025.3581843}, journal = {IEEE Access}, pages = {107328-107342}, shortjournal = {IEEE Access}, title = {Direction-of-arrival estimation for wideband chirps via multi-line fitting in the fractional fourier domain}, volume = {13}, year = {2025}, }
• (2025). Countermeasuring anti-ship missiles for surface naval platforms: A machine learning approach with explainable artificial intelligence. ACCESS, 13, 107313-107327. (www)

  @article{ , abstract = {Considering the advancements in missile technologies, particularly in infrared or radio frequency-guided missile seeker systems, threats to naval platforms have become a significant danger today. Potential missile threats have made it imperative to equip ships with the most advanced defense systems. This has led to the development of newer systems with advanced technologies capable of countering various threats. The effective deployment of existing countermeasures and the determination of the most optimal platform maneuvers have also become crucial. Chaffs and flares can be launched from naval platforms as soft-kill countermeasures to address such threats within the context of conflict. This paper aims to devise effective strategies for deploying countermeasures and executing platform maneuvers against radar and infrared-guided seekers, utilizing machine learning and artificial intelligence techniques. Using a commercial simulator containing countermeasure algorithms, datasets representing large-scale scenarios have been created, and the collected data have been trained with the proposed Multilayer Perceptron model. This model has been used to predict target parameters. The simulator includes parameters for the ship, missile, and chaff/flare. Launcher configurations have also been incorporated into the evaluated ship model. The model’s success was assessed through predefined performance metrics, and the results were visualized transparently using explainable artificial intelligence. The authors believe that the proposed model can be further improved and integrated with existing countermeasure techniques.}, archive = {J_ACCESS}, author = {Murat Ertop and Alİ Öter and Alİ Kara}, doi = {10.1109/ACCESS.2025.3581163}, journal = {IEEE Access}, pages = {107313-107327}, shortjournal = {IEEE Access}, title = {Countermeasuring anti-ship missiles for surface naval platforms: A machine learning approach with explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Network packet transformation approaches for intrusion detection systems: A survey. ACCESS, 13, 107293-107312. (www)

  @article{ , abstract = {Network intrusion detection systems are crucial for securing information technology and operational technology networks against cyberattacks. While machine learning and deep learning techniques hold significant promise for enhancing these systems, their performance is highly dependent on how network traffic data is transformed and represented. In a survey of recent popular papers, we identified four main categories of data representations: numerical, pixel-based, sequence-based, and graph-based approaches. The identified transformations capture information either from network traffic packets, flows, or both. Using insights from the literature and additional experiments conducted on the CICIDS-2017 dataset, we assessed each representation not only in terms of its ability to enhance detection performance but also in terms of computational efficiency. Our findings highlight the need for future research to improve data transformation techniques, especially in terms of dataset labeling and inference time reporting, to support the development of more robust and practical network intrusion detection systems.}, archive = {J_ACCESS}, author = {Somaya Eltanbouly and Jezia Zakraoui and Abdulaziz Al-Ali and Abdelhak Belhi and Sandy Rahme and Abdelaziz Bouras}, doi = {10.1109/ACCESS.2025.3581354}, journal = {IEEE Access}, pages = {107293-107312}, shortjournal = {IEEE Access}, title = {Network packet transformation approaches for intrusion detection systems: A survey}, volume = {13}, year = {2025}, }
• (2025). Box2Rip: Instance segmentation of amorphous rip currents via box-supervised learning. ACCESS, 13, 107277-107292. (www)

  @article{ , abstract = {Rip currents are hazardous offshore water flows that significantly threaten swimmers and bathers, pulling them away from the shore at velocities up to $2.4\ ms^{-1}$ . Due to their amorphous structure and indistinct boundaries, accurately identifying rip currents remains a challenging task. While deep learning (DL)-based segmentation models have shown promise, they typically rely on laborious and resource-intensive pixel-level annotations. In this paper, we address this crucial problem by introducing Box2Rip, a novel box-supervised instance segmentation (BSIS) framework designed for rip current segmentation using only bounding box annotations. Box2Rip employs a two-phase architecture encompassing an extent-sensitive mask generator, mask refiner, and bbox matcher. Besides, we propose a joint loss system to encourage the predicted masks to adhere to the bounding boxes while capturing rip current boundaries. Our extensive experiments on the Dumitriu dataset with 2,466 images show that Box2Rip with ResNet-50 backbone achieves 76.2% $AP_{50:95}$ , outperforming fully supervised models by 66.3% and exceeding box-supervised methods by 9.4%. On the AI-Hub CCTV rip current dataset containing 30,000 images, Box2Rip attained 38.6% $AP_{50:95}$ , 76.2% $AP_{50}$ , and 60.2% AR with minimal bounding box supervision. The proposed framework demonstrates exceptional capability in delineating amorphous rip currents with significantly reduced annotation effort, enabling efficient and scalable rip current detection systems that enhance coastal safety measures.}, archive = {J_ACCESS}, author = {Juno Choi and Muralidharan Rajendran and Yong-Cheol Suh}, doi = {10.1109/ACCESS.2025.3578878}, journal = {IEEE Access}, pages = {107277-107292}, shortjournal = {IEEE Access}, title = {Box2Rip: Instance segmentation of amorphous rip currents via box-supervised learning}, volume = {13}, year = {2025}, }
• (2025). BR-Mix3DNet: Predicting breast neoadjuvant chemotherapy response with a hybrid MLP-mixer and 3D CNN model. ACCESS, 13, 107264-107276. (www)

  @article{ , abstract = {Neoadjuvant chemotherapy (NAC) is the standard treatment for locally advanced and inflammatory breast cancer, as well as early-stage triple-negative breast cancer (TNBC) and human epidermal growth factor receptor 2 (HER2)-overexpressing breast cancer. The primary objective of NAC is to reduce the tumor size, enabling breast-conserving surgery. However, patient responses to NAC vary widely, making accurate prediction of responsiveness crucial to avoid ineffective treatments and associated toxicities. This study evaluated the diagnostic performance of a deep learning hybrid model in predicting NAC responsiveness in patients with TNBC using pretreatment MRI data. Two independent datasets were used: the CHONNAM dataset for training and the DUKE dataset for validation and testing. Preprocessing steps included region of interest extraction, N4 bias field correction, intensity normalization, and ensuring MRI shape uniformity. The BR-Mix3DNet model, which integrates MLP Mixer layers with 3D convolutional neural network (CNN) layers, was used for feature extraction from MRI images. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Youden Index, sensitivity, and specificity. The BR-Mix3DNet demonstrated high diagnostic accuracy, achieving a mean AUROC of 0.89 and a Youden Index of 0.68, outperforming other hybrid models and standalone 3D CNN models. The integration of MLP Mixer and 3D CNN layers significantly enhanced predictive performance, offering promise for improving clinical decision-making and optimizing treatment strategies for patients with TNBC.}, archive = {J_ACCESS}, author = {Yousung Yeon and Hyo-Jae Lee and Jun Seo Kim and Doyeon An and Jeong Hoon Lee and Suehyun Lee}, doi = {10.1109/ACCESS.2025.3578669}, journal = {IEEE Access}, pages = {107264-107276}, shortjournal = {IEEE Access}, title = {BR-Mix3DNet: Predicting breast neoadjuvant chemotherapy response with a hybrid MLP-mixer and 3D CNN model}, volume = {13}, year = {2025}, }
• (2025). A novel voronoi-driven optimization approach for point-based sensor network deployment. ACCESS, 13, 107244-107263. (www)

  @article{ , abstract = {Sensor Networks (SNs) are gaining more attention in applications such as urban microclimate monitoring, which is a critical input for building energy simulation. Despite extensive research on SN placement, there remains a shortage of studies on efficient solutions that account for realistic sensing models without oversimplifying the environment or search spaces. As a result, existing methods often fall short when applied to large-scale, real-world problems. This study proposes a realistic coverage model for point-based sensor networks (e.g., air temperature sensors) and introduces a novel and efficient heuristic Voronoi-based Optimal Sensor Deployment Algorithm (VOSDA). The algorithm estimates the minimum number of sensors needed and their optimal placement. VOSDA leverages Voronoi diagram characteristics to manage the sensor network, assess error distribution, and enhance coverage quality through integrated sensor insertion and movement strategies. Its performance is evaluated using the root mean square error (RMSE), calculated via an interpolation process that reconstructs the field from sensor positions. Several experiments were conducted to evaluate the effectiveness and efficiency of the proposed approach, comparing the results with the Genetic Algorithm (GA) as a reference, by calculating the RMSE using Kriging, Thin Plate Spline, and Inverse Distance Weighting methods. In all cases, VOSDA was first used to estimate the required number of sensors, and RMSE was then calculated for both algorithms at that sensor count. Furthermore, in six out of nine different scenarios conducted across different benchmark heatmaps, VOSDA outperformed GA in achieving lower RMSE values. Both algorithms performed significantly better with Kriging and TPS than with IDW.}, archive = {J_ACCESS}, author = {Saeid Doodman and Mir-Abolfazl Mostafavi and Raja Sengupta and Ali Afghantoloee}, doi = {10.1109/ACCESS.2025.3581846}, journal = {IEEE Access}, pages = {107244-107263}, shortjournal = {IEEE Access}, title = {A novel voronoi-driven optimization approach for point-based sensor network deployment}, volume = {13}, year = {2025}, }
• (2025). Small-signal stability analysis and parameters optimization of virtual synchronous generator for low-inertia power system. ACCESS, 13, 107227-107243. (www)

  @article{ , abstract = {The stable operation of converter-interfaced generation (CIG) units is paramount for ensuring resilience in low-inertia power systems. This paper presents a comprehensive small-signal modeling and stability analysis framework for grid-connected virtual synchronous generators (VSGs), integrating: an LCL-filter interfaced power converter, active/reactive power loop (APL/RPL) controllers, and dual-loop PI-based current and voltage control. Through systematic eigenvalue analysis and parameter sensitivity studies, complemented by time-domain verification in MATLAB/SIMULINK, we demonstrate the decisive influence of VSG control parameters on low-frequency oscillation (LFO) damping characteristics, transient frequency stability metrics, including the rate of change of frequency (ROCOF), maximum frequency deviation ( $f_{nadir}$ ), overshoot, and settling time. We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. The optimized design achieves an 83% reduction in ROCOF, a 90% improvement in frequency deviation, and a non-oscillatory power response. These results quantitatively validate that proper VSG gain tuning can significantly enhance dynamic performance and frequency stability in inertia-constrained grids. The proposed methodology offers practical insights for designing resilient CIG-dominated power systems.}, archive = {J_ACCESS}, author = {Alaa Altawallbeh and Abdulrahman Alassi and Nader Meskin and Mohammed A. Al-Hitmi and Ahmed M. Massoud}, doi = {10.1109/ACCESS.2025.3581678}, journal = {IEEE Access}, pages = {107227-107243}, shortjournal = {IEEE Access}, title = {Small-signal stability analysis and parameters optimization of virtual synchronous generator for low-inertia power system}, volume = {13}, year = {2025}, }
• (2025). Measurement error estimation and its applications in bearing-only localization. ACCESS, 13, 107204-107226. (www)

  @article{ , abstract = {To clarify the impact of the sensor-target geometry on bearing measurement error estimation, this study conducts an in-depth investigation into the optimal placement of homogeneous/heterogeneous sensors and the optimal bearing measurement error estimation based on bearing-only localization technology. Firstly, a weighted least squares (WLS) bearing measurement error estimation algorithm is proposed, and its influencing factors are analyzed. Secondly, the equivalence among the A-, D-, and E-optimality criteria in the sense of mean squared error (MSE) is obtained, as well as the optimal placement of homogeneous/heterogeneous sensors. Heterogeneous and homogeneous sensors share the same optimal placement form if the bearing measurement accuracy of the sensor is regarded as the weight of distance, i.e., each sensor is equidistant from the target and the separation angles between adjacent sensors are equal. Furthermore, key issues such as the optimal MSE, optimal (estimation, performance, and accuracy) indices, optimal bearing measurement error, and the relationship between MSE and bearing measurement error are discussed. It is pointed out that each bearing measurement error is zero in the statistical sense when the sensors are optimally placed, and the system can achieve the optimal localization accuracy by simultaneously satisfying both the relative sensor-target geometry condition and the measurement error condition. Finally, the specific application of bearing measurement error estimation in the error quadrilateral is presented, and main content and conclusions of this paper are verified by simulations.}, archive = {J_ACCESS}, author = {Bencai Wang and Zhiyong Wang and Hongtao Zhao and Xiaotong Qi and Zheng Zhang and Lingyan Zhao}, doi = {10.1109/ACCESS.2025.3578886}, journal = {IEEE Access}, pages = {107204-107226}, shortjournal = {IEEE Access}, title = {Measurement error estimation and its applications in bearing-only localization}, volume = {13}, year = {2025}, }
• (2025). Beyond performance: Designing a super-resolution architecture search space and a hybrid multi-objective approach for neural architecture optimization. ACCESS, 13, 107187-107203. (www)

  @article{ , abstract = {Multi-objective neural architecture search (NAS) for super-resolution image restoration (SRIR) targets models that simultaneously deliver high-fidelity reconstructions and respect strict computational budgets—requirements that single-objective searches routinely overlook. In response, we introduce BASS, a versatile multi-branch search space, together with a hybrid optimisation framework that couples NSGA-III’s global exploration with fine-grained local search. The local phase is preliminarily instantiated with hill climbing, tabu search and simulated annealing, enabling a systematic comparison of their ability to refine candidate architectures. We cast the task as a tri-objective problem that maximises predicted PSNR while minimising floating-point operations and parameter count, ensuring that discovered networks remain both accurate and efficient. Extensive experiments show that hybridising NSGA-III with local search accelerates convergence and consistently yields superior architectures; the hill-climbing variant offers the best overall trade-off. The resulting BASSN models attain competitive peak signal-to-noise ratios across $\times 2$ , $\times 3$ and $\times 4$ upscaling factors while fitting diverse resource envelopes, providing a practical and scalable route toward deployable SRIR solutions.}, archive = {J_ACCESS}, author = {Jesús Leopoldo Llano García and Raúl Monroy and Víctor Adrián Sosa Hernández and Kalyanmoy Deb}, doi = {10.1109/ACCESS.2025.3581919}, journal = {IEEE Access}, pages = {107187-107203}, shortjournal = {IEEE Access}, title = {Beyond performance: Designing a super-resolution architecture search space and a hybrid multi-objective approach for neural architecture optimization}, volume = {13}, year = {2025}, }
• (2025). Closed-form solution for CFAR detection threshold in g0 distributed SAR clutter. ACCESS, 13, 107175-107186. (www)

  @article{ , abstract = {We devise a constant false alarm rate (CFAR) detector for detecting targets in clutter regions with varying degrees of heterogeneity. The CFAR detector employs the $\mathcal {G}^{0}$ distribution for characterizing the statistics of background Synthetic Aperture Radar (SAR) clutter amplitude data. We express the Method of Log-Cumulants (MoLC) estimator for the shape parameter of $\mathcal {G}^{0}$ -distribution in closed-form using an approximation of the polygamma function. We quantify the effectiveness of employing a polygamma approximation in MoLC estimation for clutter regions with varying levels of heterogeneity using a Monte Carlo simulation. Exploiting the dependencies between hypergeometric functions and the Fisher-Snedecor distribution function, we formulate a closed-form solution for the CFAR detection threshold. Furthermore, we derive a closed-form expression of the detection probability for the proposed CFAR detector in terms of clutter model parameters and the signal-to-clutter ratio (SCR). We present analytical results that confirm the efficacy of the CFAR detector in various clutter environments, as well as for different SCR levels, compared to the CFAR-WBL, CFAR-LGN, CFAR- $\mathcal {K}$ , and existing CFAR- $\mathcal {G}^{0}$ detectors. We further assess the performance of the CFAR detector on ALOSPALSAR and MSTAR (Moving and Stationary Target Acquisition and Recognition) data, which represent sea clutter and vegetation clutter, respectively. The proposed detector achieves accurate detection results compared to the aforementioned state-of-the-art CFAR detectors. Finally, experimental results illustrate the computational effectiveness of the proposed CFAR- $\mathcal {G}^{0}$ detector compared to CFAR- $\mathcal {K}$ and conventional CFAR- $\mathcal {G}^{0}$ detectors.}, archive = {J_ACCESS}, author = {Dheeren Ku Mahapatra and Sourabh Paul and Biswajit Dwivedy and Alejandro C. Frery}, doi = {10.1109/ACCESS.2025.3581682}, journal = {IEEE Access}, pages = {107175-107186}, shortjournal = {IEEE Access}, title = {Closed-form solution for CFAR detection threshold in g0 distributed SAR clutter}, volume = {13}, year = {2025}, }
• (2025). Fully quantized matrix arithmetic-only BERT model and its FPGA-based accelerator. ACCESS, 13, 107165-107174. (www)

  @article{ , abstract = {In this paper, we propose a fully quantized matrix arithmetic-only BERT (FQ MA-BERT) model to enable efficient natural language processing. Conventionally, the BERT model relies on floating point arithmetic for inference and requires not only linear matrix multiplication but also nonlinear functions, such as softmax and normalization functions, which significantly increase hardware costs. In contrast, the proposed FQ MA-BERT model quantizes all activations and weights to 8-bit integer precision and ternary precision, respectively, i.e., the proposed model is fully quantized. Moreover, all nonlinear layers are replaced with matrix arithmetic operations. This means that the proposed model consists solely of integer-precision linear matrix arithmetic operations, allowing a significant reduction in hardware resources. To validate the hardware-friendliness of the proposed FQ MA-BERT model, we implement an accelerator for the proposed model on AMD Zynq Ultrascale+ FPGA. The proposed accelerator comprises only two types of integer-precision matrix multiplication units without the need for specialized circuits to perform nonlinear functions. The implementation results show that the hardware resource efficiency of the proposed accelerator is 1.8 times higher than that of conventional FPGA-based BERT accelerators.}, archive = {J_ACCESS}, author = {Hiroshi Fuketa and Toshihiro Katashita and Yohei Hori and Masakazu Hioki}, doi = {10.1109/ACCESS.2025.3581957}, journal = {IEEE Access}, pages = {107165-107174}, shortjournal = {IEEE Access}, title = {Fully quantized matrix arithmetic-only BERT model and its FPGA-based accelerator}, volume = {13}, year = {2025}, }
• (2025). LAURA: Let’s arrange user requirements accurately. ACCESS, 13, 107154-107164. (www)

  @article{ , abstract = {One of the most important skills in software engineering is the ability to specify clear unambiguous requirements. Students in the software engineering degree program at the University of Guayaquil present difficulties in describing user requirements (without using technical words) in an unambiguous manner. Thus, the present work proposes a serious game called LAURA (which is the acrostic of “Let’s Arrange User Requirements Accurately”) to support software engineering students in developing their ability to specify unambiguous user requirements in natural language. LAURA has been conceived following the EMERGO methodology. To evaluate the effectiveness of the game when improving the students’ specifications and to collect their perceptions about the learning experience, we performed an experiment with 64 participants, who were divided into two groups: one that used the game and one that received a traditional training session. It reports that both groups improved their ability to specify unambiguous functional user requirements after the training sessions. However, the game group showed a higher preference for the activity experience and learning experience, considering LAURA as a game that stimulates their learning, maintaining attention with non-tense interactions, and recognizing it as a tool for learning to specify unambiguous user requirements. All in all, the experiment demonstrates that the proposed LAURA serious game can be a useful learning tool that produces learning results similar to those obtained with blackboard class while improving its requirement specifications in an interactive way.}, archive = {J_ACCESS}, author = {Franklin Parrales-Bravo and Janio Jadán-Guerrero and Lorenzo Cevallos-Torres and Giampaolo Delgado-Olea and Alfredo López-Lozano and Leonel Vasquez-Cevallos}, doi = {10.1109/ACCESS.2025.3579126}, journal = {IEEE Access}, pages = {107154-107164}, shortjournal = {IEEE Access}, title = {LAURA: Let’s arrange user requirements accurately}, volume = {13}, year = {2025}, }
• (2025). Estimating technical debt through changes related to code smells in the evolution of software projects. ACCESS, 13, 107130-107153. (www)

  @article{ , abstract = {Technical debt and code smells are related concepts used to describe the trade-offs and consequences of suboptimal coding practices. Leaving code smells in the code means that developers choose a quick solution instead of the optimal code, and this can be a sign of technical debt or the implied cost of additional work during maintenance. Technical debt measurement must be applied to better manage code smells during the evolution of a software project. However, existing approaches to measuring the principal, i.e., the effort required to refactor the suboptimal code to pay off the debt, are limited, such that the effort measurement is coarse-grained or insensitive to the characteristics of code smell instances and their changes over time. Moreover, none of the existing approaches measure the interest, i.e., the extra effort developers spend understanding or working around code smells during maintenance. This research presents a method to estimate technical debt in terms of the effort to pay off the principal and the effort that has been paid for the interest. The method comprises a set of fine-grained metrics for measuring the sizes of change activities that are related to code smells in each version of the software project history. The sizes of change activities are then applied to a maintenance effort estimation model to determine the principal and interest. An experiment compared the application of two maintenance effort estimation models, COCOMO II and Change Acceptance Analysis (CAA), to the proposed method and suggested COCOMO II for technical debt estimation.}, archive = {J_ACCESS}, author = {Jessada Wiriyakul and Twittie Senivongse}, doi = {10.1109/ACCESS.2025.3581401}, journal = {IEEE Access}, pages = {107130-107153}, shortjournal = {IEEE Access}, title = {Estimating technical debt through changes related to code smells in the evolution of software projects}, volume = {13}, year = {2025}, }
• (2025). Super multi-view near-eye display based on collimated images from waveguides. ACCESS, 13, 107123-107129. (www)

  @article{ , abstract = {Waveguides have been used as combiners for near-eye display in conventional three-dimensional (3D) display, guiding an image to each eye of the viewer by total reflection. Such near-eye displays suffer from manufacturing complexity of said exit pupil expansion (EPE), and Vergence-accommodation Conflict (VAC) inherent to the one-image-one-eye display mechanism. In this paper, a head-mounted-display augmented reality (HMD-AR) is proposed for simultaneously settling these two problems. At a time-point, lights from a display panel are transformed into a bundle of paralleling light beams, and guided to corresponding eye by a waveguide without EPE, as a collimated image. Guiding multiple such collimated images to each eye along different paths sequentially, a VAC-free Super Multi-view (SMV) near-eye display gets implemented. The proposed waveguide without EPE avoids the manufacturing complexity of EPE waveguide that requires multiple reflection planes with different coupling output coefficients. A prototype system with two geometric waveguides is set up for concept demonstration, obtaning a field of view (FOV) of $10.2^{\circ }\times 14^{\circ }$ and a depth of field (DOF) of 1500mm. A large FOV is expected experimentally when diffractive waveguides are adopted in the further work.}, archive = {J_ACCESS}, author = {Yiping Lin and Lilin Liu and Dongdong Teng}, doi = {10.1109/ACCESS.2025.3577647}, journal = {IEEE Access}, pages = {107123-107129}, shortjournal = {IEEE Access}, title = {Super multi-view near-eye display based on collimated images from waveguides}, volume = {13}, year = {2025}, }
• (2025). Battery internal-heating method via SW mode switching of the drive inverter during electric vehicle stoppage. ACCESS, 13, 107112-107122. (www)

  @article{ , abstract = {The discharge voltage and capacity of lithium-ion batteries (LIBs), widely used in electric vehicles (EVs), decrease at sub-zero temperatures. To enhance the drive motor’s output, it is essential to rapidly increase the battery’s temperature before startup. One effective method for heating LIBs is the use of Joule heating. Specifically, internal heating methods utilizing alternating current (AC) are gaining attention due to their reduced likelihood of precipitate lithium. In this study, we propose an internal heating method for the battery through AC using reactive power from the existing drive system when the EV is stationary. Additionally, by switching the switching (SW) mode of the drive inverter, we aim to maximize the overall effective value of the inverter output voltage, and increase the reactive power supplied to the permanent magnet synchronous motor (PMSM). The circuit characteristics and effectiveness of the proposed method were demonstrated through both simulation and experimental verification using a downscaled model.}, archive = {J_ACCESS}, author = {Ryuhei Hasegawa and Keiichiro Kondo and Chikako Funayama and Akihiro Imura}, doi = {10.1109/ACCESS.2025.3582150}, journal = {IEEE Access}, pages = {107112-107122}, shortjournal = {IEEE Access}, title = {Battery internal-heating method via SW mode switching of the drive inverter during electric vehicle stoppage}, volume = {13}, year = {2025}, }
• (2025). Mapping the research landscape of virtual coaches: What is to come?. ACCESS, 13, 107093-107111. (www)

  @article{ , abstract = {Individuals increasingly encounter complex challenges in areas such as mental health, physical fitness, education, and professional development, further exacerbated by rising rates of mental illnesses and chronic, lifestyle-related conditions. Coaching plays a pivotal role in fostering positive personal and professional developments, particularly by addressing the unique and complex challenges individuals face. However, access to professional coaching is often limited due to high costs, availability issues, and scalability barriers. Leveraging advancements in artificial intelligence, virtual coaches (VCs) offer tailored, autonomous coaching that addresses staff shortages and overcomes temporal and geographical constraints. However, the interdisciplinary nature of VC research - encompassing computer science, psychology, healthcare, and education - poses challenges in obtaining a comprehensive overview of existing studies. To fill this gap, we conducted a bibliometric analysis of 540 articles on VCs published between 2001 and 2024. Our analysis identifies key topics, prominent authors, leading journals, and influential institutions, providing a comprehensive mapping of the field. Based on these findings, we propose a future research agenda to further advance VC development and implementation. This study not only maps the current state of VC research but also offers valuable insights for scholars and practitioners, serving as a foundational resource for future exploration in this rapidly evolving area.}, archive = {J_ACCESS}, author = {Falco Korn and Erik Karger and Frederik Ahlemann}, doi = {10.1109/ACCESS.2025.3581440}, journal = {IEEE Access}, pages = {107093-107111}, shortjournal = {IEEE Access}, title = {Mapping the research landscape of virtual coaches: What is to come?}, volume = {13}, year = {2025}, }
• (2025). GS-ReLoc: A gaussian-splatting relocalization method for robust and accurate mono camera pose estimation. ACCESS, 13, 107080-107092. (www)

  @article{ , abstract = {Relocalization is a critical challenge in visual SLAM and autonomous navigation, where precise initial pose estimation is essential for robust system performance. Common approaches to visual relocalization rely on image retrieval to find the most similar image in a database, followed by 2D-2D feature matching to estimate the query image’s pose. However, these methods heavily depend on feature matching, which can be challenging when there is a significant spatial gap between the retrieved and query images or when local visual features are insufficient to establish reliable correspondences. To address image-retrieval database sparsity and reliability we present GS-ReLoc, a novel method that leverages 3D Gaussian Splat (3DGS) models to augment image retrieval databases. This augmentation increases the likelihood of initializing the rendering-based pose refinement process closer to the ground truth (GT) pose, leading to improved final pose estimates. The method begins by constructing a 3DGS model using Structure-from-Motion (SfM) reconstruction, which serves as the foundation for rendering novel virtual keyframes from novel poses. These keyframes enrich the database with diverse viewpoints through an efficient keyframe pose generation strategy. For a query frame, the algorithm identifies the best-matching database entry using a kd-tree structure, providing an initial pose estimate informed by the augmented database. This improved initial pose estimation strategy reduces the risk of the rendering-based pose refinement process converging to local minima, while also yielding higher final pose accuracy. The proposed method is evaluated on the indoor 7Scenes and outdoor Cambridge Landmark datasets, and achieves state-of-the-art pose estimation accuracy while maintaining robustness and computational efficiency, demonstrating its practical applicability in real-world relocalization scenarios.}, archive = {J_ACCESS}, author = {Károly Fodor and András Rövid}, doi = {10.1109/ACCESS.2025.3581068}, journal = {IEEE Access}, pages = {107080-107092}, shortjournal = {IEEE Access}, title = {GS-ReLoc: A gaussian-splatting relocalization method for robust and accurate mono camera pose estimation}, volume = {13}, year = {2025}, }
• (2025). Competitiveness enablers in handicraft toy manufacturing industries based on ISM-fuzzy MICMAC analysis. ACCESS, 13, 107067-107079. (www)

  @article{ , abstract = {The traditional handicraft toy manufacturing industry, known for its unique craftsmanship and cultural significance, faces challenges that impact its competitiveness. These challenges restrict the industry’s ability to adapt to changing market demands. This study identified and analyzed the key enablers of competitiveness in the Channapatna handicraft toy manufacturing industry through a combined approach of Interpretive Structural Modeling (ISM) and Fuzzy Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Eleven critical competitiveness enablers were identified through qualitative data collection methods, including structured interviews with key stakeholders—artisans, retailers, government officials, and industry experts. ISM was used to structure the relationships, emphasizing their hierarchy and influence among these enablers. Fuzzy MICMAC analysis categorized these enablers according to their level of influence (driving power) and reliance (dependence power), providing insights into which enablers are essential for enhancing competitiveness. The results indicate that improving marketing techniques, enhancing branding, and fostering innovation are crucial drivers for industry growth. Furthermore, government support, financial assistance, and the adoption of technological advancements are significant enablers for sustaining long-term competitiveness. This study outlines strategies to guide policymakers, industry leaders, and stakeholders in revitalizing the Channapatna handicraft toy industry and effectively positioning it in domestic and global markets.}, archive = {J_ACCESS}, author = {V. Meghasree and C. K. Nagendra Guptha}, doi = {10.1109/ACCESS.2025.3580674}, journal = {IEEE Access}, pages = {107067-107079}, shortjournal = {IEEE Access}, title = {Competitiveness enablers in handicraft toy manufacturing industries based on ISM-fuzzy MICMAC analysis}, volume = {13}, year = {2025}, }
• (2025). Improved a* algorithm based on artificial potential field method for global path planning of unmanned surface vessels in complex environments. ACCESS, 13, 107055-107066. (www)

  @article{ , abstract = {Aiming at the problems of long path planning time, excessive ineffective expansion nodes, and easy collision with obstacles that may occur when using traditional A* algorithm for unmanned surface vessels in complex environments, an improved A* algorithm combined with artificial potential field thinking is proposed for global path planning of unmanned surface vessels. In the improved A* algorithm, the search direction was first optimized, simplifying the traditional eight direction search to a “target point attracts four directions” search, significantly reducing the ineffective expansion nodes of the traditional algorithm, reducing algorithm running time, and improving search efficiency; Secondly, on the basis of improving the cost function, the idea of artificial potential field algorithm is integrated, and the repulsive function is introduced. Compared with the traditional A* algorithm that uses a fixed weighted cost function, it improves the algorithm’s obstacle avoidance ability and safety; Then, a redundant point deletion scheme was proposed to optimize the path nodes, effectively shortening the path length; Finally, the Bessel function was used to smooth the path. The experimental results show that in complex environments, the improved A* algorithm can reduce the average computation time by 30.5% compared to traditional A* algorithms, improve security by 52.44%, and successfully achieve the target point, verifying the effectiveness of the algorithm.}, archive = {J_ACCESS}, author = {Shuxuan Liu and Zhijia Qian and Sunwei Jiang and Quanming Miao}, doi = {10.1109/ACCESS.2025.3577645}, journal = {IEEE Access}, pages = {107055-107066}, shortjournal = {IEEE Access}, title = {Improved a* algorithm based on artificial potential field method for global path planning of unmanned surface vessels in complex environments}, volume = {13}, year = {2025}, }
• (2025). Hybrid wavelet-attention model for detecting changes in high-resolution remote sensing images. ACCESS, 13, 107035-107054. (www)

  @article{ , abstract = {Change detection is a remote sensing task for detecting a change from two satellite images in the same area, while being taken at different times. Change detection is one of the most difficult remote sensing tasks because the change to be detected (real-change) is mixed with apparent changes (pseudo-change) due to differences in the two images, such as brightness, humidity, seasonal differences, etc. The emergence of a Vision Transformer (ViT) as a new standard in Computer Vision, replacing Convolutional Neural Network (CNN), also shifts the role of CNN in the field of remote sensing. Although ViT can capture long-range interactions between image patches, its computational complexity increases quadratically with the number of patches. One solution to reduce the computational complexity in ViT is to reduce the key and value matrices in the self-attention (SA) mechanism. However, this causes information loss, resulting in a trade-off between the effectiveness and efficiency of the method. To solve the problem, we developed a new change detection method called WaveCD. WaveCD uses Wave Attention (WA) instead of SA. WA uses the Discrete Wavelet Transform (DWT) decomposition to reduce the key and values matrices. Besides reducing the data, DWT decomposition also serves to extract important features that represent images so that the initial data can be approximated through the Inverse Discrete Wavelet Transform (IDWT) process. On the CDD dataset, WaveCD outperforms the state-of-the-art CD method, SwinSUNet, by 12.3% on IoU and 7.3% on F1 score. While on the LEVIR-CD dataset, WaveCD outperforms SwinSUNet by 4% on IoU and 2.5% on F1 score.}, archive = {J_ACCESS}, author = {Lhuqita Fazry and MGS M. Luthfi Ramadhan and Alif Wicaksana Ramadhan and Muhammad Febrian Rachmadi and Aprinaldi Jasa Mantau and Lukito Edi Nugroho and Chi-Hung Chi and Wisnu Jatmiko}, doi = {10.1109/ACCESS.2025.3580481}, journal = {IEEE Access}, pages = {107035-107054}, shortjournal = {IEEE Access}, title = {Hybrid wavelet-attention model for detecting changes in high-resolution remote sensing images}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid deep learning-based framework for intelligent anomaly detection in smart meters. ACCESS, 13, 107022-107034. (www)

  @article{ , abstract = {Smart meters deployment in residential buildings generates a large volume of time-series data that offers valuable insights about electricity consumption. The proposed work exploits smart meter data to identify abnormal behavior in electricity consumption and sensor malfunctioning. In this paper, a two-stage anomaly detection approach that integrates the capabilities of deep learning and ensemble learning is designed. This approach combines Bidirectional Long Short-Term Memory (BiLSTM) networks for feature extraction and a random forest model to detect anomalies. BiLSTM is applied to determine complex temporal features, and these extracted high-level features are taken by a random forest model for anomaly detection. The performance of the proposed work is verified using two real-world smart meter electricity datasets of a diverse collection of buildings. The performance metrics determine that the proposed BiLSTM-random forest approach provides superior results across key evaluation metrics, making it a practical solution for intelligent energy monitoring systems.}, archive = {J_ACCESS}, author = {Simarjit Kaur and Priyansh Chowhan and Aashima Sharma}, doi = {10.1109/ACCESS.2025.3581257}, journal = {IEEE Access}, pages = {107022-107034}, shortjournal = {IEEE Access}, title = {A novel hybrid deep learning-based framework for intelligent anomaly detection in smart meters}, volume = {13}, year = {2025}, }
• (2025). An adaptive approach in channel quantization for small cells based on per-receiver antenna quantization. ACCESS, 13, 107010-107021. (www)

  @article{ , abstract = {The widespread deployment of small cells (SCs) plays a crucial role in enhancing system capacity, coverage, and quality of service (QoS) for smart applications. However, due to the dynamic nature of user demands and the limited resources available, SCs cannot support large quantization codebooks, which are typically more suitable for macro cells (MCs) in finite rate feedback (FRF)-based multiple input single output (MISO) systems. In this paper, we propose an adaptive quantization approach for SCs that adjusts the codebook size based on the number of receiver antennas. Additionally, we address the issue of code quantization error (CQE), which arises when two distinct channels are quantized using the same code, as well as the average system error (AvgSysErr), which can increase due to elevated CQE. Our analysis shows that for SCs to achieve convergence of AvgSysErr with FRF-based MISO systems, the probability of non-unique codes in the quantization codebook must be less than $\frac {1}{N}$ , where N is the number of antennas at the transmitter. Similarly, the lower bound for the non-unique code probability must be less than or equal to $\varepsilon $ , where $\varepsilon $ represents the difference between the non-unique code probabilities of $\frac {1}{N_{1}}$ and $\frac {1}{N_{2}}$ , given $\frac {1}{N_{1}}\gt \frac {1}{N_{2}}$ (where $N_{1}$ and $N_{2}$ denote the number of antennas at the transmitter).}, archive = {J_ACCESS}, author = {Sanjeeb Shrestha and Xiaoying Kong and Paul Kwan and Xiaojing Huang}, doi = {10.1109/ACCESS.2025.3580698}, journal = {IEEE Access}, pages = {107010-107021}, shortjournal = {IEEE Access}, title = {An adaptive approach in channel quantization for small cells based on per-receiver antenna quantization}, volume = {13}, year = {2025}, }
• (2025). Sensorless control for PMSM using FPESO and improved PLL in the estimated coordinate. ACCESS, 13, 106998-107009. (www)

  @article{ , abstract = {Sensorless control systems based on position error are widely used across both zero-low speed and medium-high speed ranges owing to their inherent closed-loop characteristics that provide strong anti-interference. Such estimation systems are generally implemented in the estimated rotational reference frame ( $\gamma \delta $ frame). During acceleration and deceleration under a speed ramp, the widely used quadrature phase-locked loop (QPLL) exhibits notable transient errors, which reduce control accuracy and load capacity. To address this limitation, this study proposes a Type III improved phase-locked loop (IPLL), inspired by the analysis of the QPLL’s transfer function. The proposed IPLL can reduce estimation errors to zero, offers easy parameter tuning, and requires only minor modifications to the QPLL structure. To obtain the position error used as the PLL input, the motor’s extended back electromotive force is estimated using an extended state observer (ESO), followed by an arctangent calculation. This study comprehensively examines the effects of the parameter-adaptive ESO (AESO) and the fixed-parameter ESO (FPESO) on estimation errors. Results show that the AESO can reduce steady-state errors in the two-phase stationary frame ( $\alpha \beta $ frame) but exerts no effect on transient errors. Owing to stability criteria, the AESO is applicable only to motors with low rated speeds. This lack of effect on position error in the $\gamma \delta $ frame has been verified via simulations and experiments. Finially, effectiveness of the proposed IPLL reveals that it reduces the position error from 22.5° and 15.6° to 3° and 2° under no-load and rated-load conditions, respectively. These improvements enhance the torque control capability, dynamic response, positioning accuracy, reliability, and robustness of the sensorless system while also reducing energy loss.}, archive = {J_ACCESS}, author = {Li Helin and Fazilah Hassan}, doi = {10.1109/ACCESS.2025.3578495}, journal = {IEEE Access}, pages = {106998-107009}, shortjournal = {IEEE Access}, title = {Sensorless control for PMSM using FPESO and improved PLL in the estimated coordinate}, volume = {13}, year = {2025}, }
• (2025). Hardware implementation of block floating-point FFT based on approximate computation and conflict-free access. ACCESS, 13, 106989-106997. (www)

  @article{ , abstract = {This paper proposes a novel Fast Fourier Transform hardware architecture for natural-order multi-input multi-output processing, which is based on approximate computing and the block floating-point data format. The architecture innovatively combines the low-power characteristics of approximate multipliers with the high-precision advantages of block floating-point data formats, enabling efficient processing of 2048-point and 4096-point Fast Fourier Transform data. By employing conflict-free memory access techniques, the design optimizes data scheduling, reduces idle cycles during processing, and significantly enhances energy efficiency while maintaining computational accuracy. Experimental validation demonstrates that the processor, fabricated using a 40nm process, achieves a compact chip area of 0.42 mm2. Operating at a frequency of 671.14 MHz, it delivers a throughput of 1073.82 MS/s for 4096-point FFT computations, with a power consumption of only 110.85 mW and a Signal–to–Quantization Noise Ratio of 58.51 dB. Compared to state-of-the-art designs, this architecture achieves breakthrough improvements in throughput performance while maintaining competitive energy efficiency.}, archive = {J_ACCESS}, author = {Xiaohui Zhang and Zenghui Yu and Shushan Qiao and Yi Zhan}, doi = {10.1109/ACCESS.2025.3578531}, journal = {IEEE Access}, pages = {106989-106997}, shortjournal = {IEEE Access}, title = {Hardware implementation of block floating-point FFT based on approximate computation and conflict-free access}, volume = {13}, year = {2025}, }
• (2025). Multi-agent optimizing traffic light signals using deep reinforcement learning. ACCESS, 13, 106974-106988. (www)

  @article{ , abstract = {In recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as a promising approach to sequential decision-making, offering adaptive and efficient solutions for traffic management. This paper aims to develop an optimal traffic light planning strategy that integrates seamlessly with urban transportation systems, including trams and Bus Rapid Transit Systems (BRTS). The study explores three DRL-based approaches: Single-Agent Deep Reinforcement Learning (SADRL), Multi-Agent Deep Reinforcement Learning (MADRL) with fixed traffic lights, and an actuated control approach. System for Managing Actuated and Real-Time Traffic, referred to as SMART, dynamically adjusts traffic signals based on real-time conditions to enhance traffic flow efficiency. The proposed methods are evaluated and compared against the Webster method, Simulation of Urban Mobility (SUMO)-based control, and a genetic algorithm-based multi-objective traffic light optimization method (MamoTLO). The results demonstrate that DRL-based solutions improve traffic flow and reduce congestion.}, archive = {J_ACCESS}, author = {Zahra Fereidooni and Luciano Alessandro Ipsaro Palesi and Paolo Nesi}, doi = {10.1109/ACCESS.2025.3578518}, journal = {IEEE Access}, pages = {106974-106988}, shortjournal = {IEEE Access}, title = {Multi-agent optimizing traffic light signals using deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). An improved fault diagnosis strategy for induction motors using weighted probability ensemble deep learning. ACCESS, 13, 106958-106973. (www)

  @article{ , abstract = {Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep Learning (WPEDL) methodology, tailored for effectively diagnosing induction motor faults using high-dimensional data extracted from vibration and current features. The Short-Time Fourier Transform (STFT) extracts features from vibration and current signals. The performance of the WPEDL fault diagnosis method is compared against conventional deep learning models, demonstrating the superior efficacy of the proposed system. The multi-class fault diagnosis system based on WPEDL achieves high accuracies across different fault types: 99.05% for bearing (vibrational signal), 99.10%, and 99.50% for rotor (current and vibration signal), and 99.60%, and 99.52% for stator faults (current and vibration signal) respectively. To evaluate the robustness of our multi-class classification decisions, tests have been conducted on a combined dataset of 52,000 STFT images encompassing all three faults. Our proposed model outperforms other models, achieving an accuracy of 98.89%. The findings underscore the effectiveness and reliability of the WPEDL approach for early-stage fault diagnosis in IMs, offering promising insights for enhancing industrial operational efficiency and reliability.}, archive = {J_ACCESS}, author = {Usman Ali and Umer Ramzan and Waqas Ali and Khaled Ali Al-Jaafari}, doi = {10.1109/ACCESS.2025.3580159}, journal = {IEEE Access}, pages = {106958-106973}, shortjournal = {IEEE Access}, title = {An improved fault diagnosis strategy for induction motors using weighted probability ensemble deep learning}, volume = {13}, year = {2025}, }
• (2025). Prediction of ultra-high-speed spots using RTK-GNSS sensor fusion for UAV-to-UAV mmWave/THz communications. ACCESS, 13, 106942-106957. (www)

  @article{ , abstract = {mmWave/THz communications depend on highly focused and directive narrow beams. Accurate prediction of the beam’s spatial location and attitude in space, as well as the time it takes to reach its ultra-high-speed coverage area, referred to as the ultra-spot, is crucial for uncrewed aerial vehicles (UAVs) to adjust their flight direction and approach velocity. This adjustment increases the likelihood of successful communication between UAVs. This paper introduces a novel approach for detecting these ultra-spots using real-time kinematic (RTK)-GNSS and inertial measurement unit (IMU) sensor fusion positioning powered by an extended Kalman filter (RTK-GNSS-EKF). To achieve this, we implemented a mechanism that exchanges six degrees of freedom (6-DOF) information of positions among UAVs via a 920MHz wireless communication link. Additionally, we propose an algorithm that accurately estimates the time and distance from the in-flight UAV to the ultra-spot. For the first time, this work investigates the real-world 6-DOF fluctuations in position, velocity, and attitude experienced by an in-flight UAV due to wind, and analyzes the impact of these fluctuations on the ultra-spot prediction issue. Additionally, we analyze scenarios where the ultra-spot alters its attitude by actively changing the antenna angle, assessing the consequent effects on the volume of data transmitted and received at the ultra-spot. We demonstrate the effectiveness of the proposed method by simulation and verification with actual UAV-to-UAV and UAV-to-ground-station field experiments. Experimental results indicate an average ultra-spot detection accuracy of 172ms in time and 32.7cm in distance, with measurements taken 1s before the UAV’s actual approach to the ultra-spot. These findings confirm the feasibility of the proposed method for detecting mobile ultra-spots in UAV-to-UAV mmWave communication.}, archive = {J_ACCESS}, author = {Phuc Duc Nguyen and Ryosuke Isogai and Keitarou Kondou and Yozo Shoji}, doi = {10.1109/ACCESS.2025.3580781}, journal = {IEEE Access}, pages = {106942-106957}, shortjournal = {IEEE Access}, title = {Prediction of ultra-high-speed spots using RTK-GNSS sensor fusion for UAV-to-UAV mmWave/THz communications}, volume = {13}, year = {2025}, }
• (2025). Review on application and challenges of large language models in power grids. ACCESS, 13, 106932-106941. (www)

  @article{ , abstract = {Power grids are critical infrastructures that require real-time safe and reliable operation. Large Language Models (LLMs), have demonstrated significant potential in power grid applications. This paper presents a comprehensive review of the applications of LLMs in power grids, covering areas such as intelligent assistance for grid operation, generation and load forecasting, power system planning and operation, and power converter design. Additionally, the paper discusses key challenges associated with the integration of LLMs into power grids, including data quality issues, model explainability, cybersecurity risks, and real-time computational efficiency. Potential solutions are provided to enhance the reliability and applicability of LLMs in power grid applications. By leveraging the capabilities of LLMs, power system operators can enhance decision-making processes, improve operational efficiency, and foster a more resilient and adaptive power infrastructure.}, archive = {J_ACCESS}, author = {Liudong Zhang and Wenlu Ji and Yulin Zhao and Di Huang and Bingjun Qian}, doi = {10.1109/ACCESS.2025.3580439}, journal = {IEEE Access}, pages = {106932-106941}, shortjournal = {IEEE Access}, title = {Review on application and challenges of large language models in power grids}, volume = {13}, year = {2025}, }
• (2025). Efficient exploratory searches with an incremental R-tree under oscillating workloads. ACCESS, 13, 106916-106931. (www)

  @article{ , abstract = {Traditional spatial index structures face challenges in efficiently managing dynamic and irregular workloads, which are common in real-time spatial data analysis. In particular, oscillating workloads can significantly hinder data access performance due to the continuous influx of spatial records in varying volume and frequency. To address these challenges, this paper presents an Incremental R-tree index structure optimized for real-time storage and querying of large-scale spatial datasets under dynamic conditions. The proposed method employs a two-stage overflow mechanism, where newly arriving data batches are first distributed into a grid-based structure and then selectively integrated into the tree leaves based on query activity. This approach delays full indexing until it becomes necessary, thereby improving responsiveness and avoiding unnecessary tree restructuring. Furthermore, the Incremental R-tree adaptively incorporates principles from both STR-tree and ${\mathrm{R}}^{\ast }$ -tree to enhance indexing efficiency. Experimental results demonstrate that the Incremental R-tree significantly reduces cumulative query response latency compared to traditional ${\mathrm{R}}^{\ast }$ -tree and STR-tree structures under oscillating workloads, and consistently outperforms the widely used GiST-based R-tree implementation in PostGIS.}, archive = {J_ACCESS}, author = {Mustafa Utku Kalay}, doi = {10.1109/ACCESS.2025.3581403}, journal = {IEEE Access}, pages = {106916-106931}, shortjournal = {IEEE Access}, title = {Efficient exploratory searches with an incremental R-tree under oscillating workloads}, volume = {13}, year = {2025}, }
• (2025). A wideband dual-polarized reconfigurable reflectarray using polarization rotation technique. ACCESS, 13, 106906-106915. (www)

  @article{ , abstract = {This paper presents a wideband dual-polarized 1-bit reconfigurable reflectarray antenna (RRA) at Ku-band, featuring a novel multi-layer unit cell with two substrates and three metallic layers. By utilizing the out-of-phase characteristic of the electric field in the dominant mode of the patch and polarization conversion, the design achieves two distinct phase states with accurate resolution over a wide bandwidth. Two PIN diodes serve as switches, enabling 180° phase difference between states while converting an x-polarized incident wave to a y-polarized reflected wave (and vice versa). A single control signal simultaneously operates both diodes, significantly simplifying the control system. Simulations confirm a 180° ± 2° phase difference from 10 GHz to 15 GHz. To explore beam steering capability, a $10 \times 10$ reconfigurable reflectarray prototype was fabricated and tested, with each unit cell wired to a DC biasing board and controlled via an SPDT manual switch. Measured results demonstrate beam steering up to 50° in both planes for both polarizations, with a 1-dB gain bandwidth of 13.7% (12.2 GHz-14 GHz), closely matching simulations. The combination of wide bandwidth, dual-polarization capability, and simplified fabrication makes this design highly suitable for beam-steering applications.}, archive = {J_ACCESS}, author = {Jalaledin Tayebpour and Raafat R. Mansour}, doi = {10.1109/ACCESS.2025.3581161}, journal = {IEEE Access}, pages = {106906-106915}, shortjournal = {IEEE Access}, title = {A wideband dual-polarized reconfigurable reflectarray using polarization rotation technique}, volume = {13}, year = {2025}, }
• (2025). Sim-to-real transfer of deep reinforcement learning agents for online coverage path planning. ACCESS, 13, 106883-106905. (www)

  @article{ , abstract = {Coverage path planning (CPP) is the problem of finding a path that covers the entire free space of a confined area, with applications ranging from robotic lawn mowing to search-and-rescue. While for known environments, offline methods can find provably complete paths, and in some cases optimal solutions, unknown environments need to be planned online during mapping. We investigate the suitability of continuous-space reinforcement learning (RL) for this challenging problem, and propose a computationally feasible egocentric map representation based on frontiers, as well as a novel reward term based on total variation to promote complete coverage. Compared to existing classical methods, this approach allows for a flexible path space, and enables the agent to adapt to specific environment characteristics. Meanwhile, the deployment of RL models on real robot systems is difficult. Training from scratch may be infeasible due to slow convergence times, while transferring from simulation to reality, i.e. sim-to-real transfer, is a key challenge in itself. We bridge the sim-to-real gap through a semi-virtual environment, including a real robot and real-time aspects, while utilizing a simulated sensor and obstacles to enable environment randomization and automated episode resetting. We investigate what level of fine-tuning is needed for adapting to a realistic setting. Through extensive experiments, we show that our approach surpasses the performance of both previous RL-based approaches and highly specialized methods across multiple CPP variations in simulation. Meanwhile, our method successfully transfers to a real robot. Our code implementation can be found online (Link to code repository: https://github.com/arvijj/rl-cpp).}, archive = {J_ACCESS}, author = {Arvi Jonnarth and Ola Johansson and Jie Zhao and Michael Felsberg}, doi = {10.1109/ACCESS.2025.3581035}, journal = {IEEE Access}, pages = {106883-106905}, shortjournal = {IEEE Access}, title = {Sim-to-real transfer of deep reinforcement learning agents for online coverage path planning}, volume = {13}, year = {2025}, }
• (2025). A multi-mode switching energy management strategy based on travel habits for range-extended electric vehicle. ACCESS, 13, 106871-106882. (www)

  @article{ , abstract = {To achieve the optimal energy allocation for the auxiliary power unit (APU) and battery of a range-extended electric vehicle (REEV), a travel habit-based multi-mode switching energy management strategy (M-MSEMS) has been introduced. REEV has the advantages of high power, high efficiency and long driving range, and it is the core technology. An intelligent and efficient EMS must take into account all driving conditions to maximize power utilization while allowing the engine to work in the high-efficiency area to minimize fuel consumption. By statistically analyzing vehicle travel data, this study constructs the urban road network framework as the foundational dataset. Users’ common travelling conditions recognition is accomplished based on Bayesian classifier. The theoretical state of charge (SoC) curve is constructed by analyzing the driving conditions’ distinctive parameters, and an EMS based on driving routes’ trip patterns is then created. The proposed strategy is based on the fuzzy control algorithm, which ensures that the theoretical SoC curve is well followed. Finally, the simulation and experimental results thoroughly indicate that the proposed M-MSEMS with future mileage prediction function can achieve more comprehensive vehicle performance, including improving fuel economy and reducing energy loss, which is that equivalent fuel consumption is reduced by 3.09%, the fuel-to-electric conversion loss rate is reduced by 5.33%, and battery capacity degradation is reduced by 19.01%. This strategy not only optimizes the energy allocation of vehicles under different road conditions, but also improves overall efficiency by reducing the frequency and time of APU system intervention. And this result can be used to develop EMS to improve comprehensive performance levels.}, archive = {J_ACCESS}, author = {Zhenhai Gao and Guoqing Zhang and Hanwu Liu and Cheng Chang and Jingwen Dong and Rongrong He and Yu Cai}, doi = {10.1109/ACCESS.2025.3580646}, journal = {IEEE Access}, pages = {106871-106882}, shortjournal = {IEEE Access}, title = {A multi-mode switching energy management strategy based on travel habits for range-extended electric vehicle}, volume = {13}, year = {2025}, }
• (2025). Development of a digital hologram map using bathymetric data. ACCESS, 13, 106863-106870. (www)

  @article{ , abstract = {This study examines the development of hologram maps that provide a precise, intuitive depiction of underwater topography in a genuine three-dimensional format. It integrates sophisticated holographic methodologies with bathymetric datasets to address the challenges of visualizing intricate marine environments. Most traditional bathymetric mapping techniques rely on two-dimensional representations, which require users to mentally infer depth and spatial relationships. This cognitive burden can lead to misinterpretation, particularly when analyzing complex seafloor structures with subtle variations. Data from the General Bathymetric Chart of the Oceans was used to construct and enhance three-dimensional terrain models within Blender. Holographic representations were generated utilizing the Digital Holographic Stereogram Printing method, which produced two holograms representing the underwater morphology of Monterey Canyon and its terrestrial features. These holograms enhance accuracy and depth perception, fostering a deeper understanding of the canyon’s geological complexities. This pioneering methodology provides a robust technique for scientific studies and suggests novel approaches for public engagement and educational outreach. By integrating bathymetric data with holographic visualization, this study highlights the importance of precise and accessible three-dimensional mapping in advancing oceanographic research, enhancing underwater analysis, and supporting marine science.}, archive = {J_ACCESS}, author = {Jumamurod Farhod Ugli Aralov and Oybek Mirzaevich Narzulloev and Leehwan Hwang and Philippe Gentet and Seunghyun Lee}, doi = {10.1109/ACCESS.2025.3580199}, journal = {IEEE Access}, pages = {106863-106870}, shortjournal = {IEEE Access}, title = {Development of a digital hologram map using bathymetric data}, volume = {13}, year = {2025}, }
• (2025). Spatial-similarity dynamic graph bidirectional double-cell network for traffic flow prediction. ACCESS, 13, 106849-106862. (www)

  @article{ , abstract = {Accurate traffic flow prediction plays a pivotal role in optimizing urban transportation systems and improving traffic management efficacy. To address the limitations of existing methods in modeling complex spatial-temporal dependencies within dynamic traffic networks, this paper introduces a Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network (SDGBDCN). The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. Comprehensive evaluations on PeMS datasets demonstrate superior performance compared to existing approaches. Statistical validation through AIC and SBIC metrics confirms the model’s exceptional capability, achieving record-low scores of 1204.2 and 1219.8 respectively. This research advances traffic prediction methodologies through its integrated approach to dynamic spatial correlation modeling and bidirectional temporal learning, providing valuable insights for intelligent transportation system development.}, archive = {J_ACCESS}, author = {Zhifei Yang and Zeyang Li and Jia Zhang}, doi = {10.1109/ACCESS.2025.3579138}, journal = {IEEE Access}, pages = {106849-106862}, shortjournal = {IEEE Access}, title = {Spatial-similarity dynamic graph bidirectional double-cell network for traffic flow prediction}, volume = {13}, year = {2025}, }
• (2025). Convolutional neural network controller for autonomous variable stiffness ESR foot prosthesis: The MyFlex-θ. ACCESS, 13, 106834-106848. (www)

  @article{ , abstract = {Human ankles naturally adjust stiffness to accommodate various activities and terrains—an ability that conventional passive energy-storing-and-returning (ESR) prostheses cannot replicate due to their fixed stiffness. To address this limitation, this study introduces the MyFlex- $\theta$ prosthesis, an autonomous ESR foot equipped with a Variable Stiffness System (VSS) and a Human Activity Recognition (HAR) control system based on a Convolutional Neural Network (CNN) algorithm. The HAR-CNN system classifies seven activities—walking, fast walking, standing, ramp ascent/descent, and stair ascent/descent— using linear acceleration data from two embedded Inertial Measurement Units (IMUs). When three consecutive predictions are identical, the VSS actuator adjusts foot stiffness in real time. Offline evaluation of the HAR-CNN, following the optimization of nine input channels, algorithm hyperparameters, and sliding window and hop size configurations, achieved a classification accuracy of 97.2% (loss: 0.103). Real-time testing with an non-disabled participant yielded an accuracy of 91.5%, confirming robust performance under dynamic conditions. Furthermore, real-time ankle torque estimation was implemented using a third-order response surface derived from sixth-order polynomial Finite Element Analysis (FEA) models, correlating stiffness settings with IMU-recorded joint rotations. This integration of adaptive stiffness control and embedded torque estimation enables continuous, portable gait analysis. The MyFlex- $\theta$ represents a significant advancement toward intelligent prosthetic devices capable of autonomously adapting to user activity and biomechanical demands.}, archive = {J_ACCESS}, author = {Marco Leopaldi and Mirco Paltrinieri and Johnnidel Tabucol and Andrea Zucchelli and Tommaso Maria Brugo}, doi = {10.1109/ACCESS.2025.3580867}, journal = {IEEE Access}, pages = {106834-106848}, shortjournal = {IEEE Access}, title = {Convolutional neural network controller for autonomous variable stiffness ESR foot prosthesis: The MyFlex-θ}, volume = {13}, year = {2025}, }
• (2025). LegalBot-EC: An LLM-based chatbot for legal assistance in ecuadorian law. ACCESS, 13, 106817-106833. (www)

  @article{ , abstract = {This work presents LegalBot-EC, a domain-specific legal chatbot designed to provide accurate responses grounded in Ecuadorian law. The system combines ChromaDB for contextual retrieval and LLaMA 3.1 as its generative model, ensuring legally relevant and contextually grounded answers. Its knowledge base consists of two key legal documents, the COIP and the 2008 Constitution of Ecuador, which serve as the foundation for response generation. The chatbot was evaluated through two key assessments: user satisfaction and accuracy measurement. A satisfaction study conducted with final-year university students specializing in law resulted in an average score of 88.72 out of 100, indicating strong approval in terms of correctness, relevance, and clarity. Additionally, an accuracy assessment based on 25 legal queries demonstrated robust performance, with 23 correct responses, 2 partially correct responses, and none classified as incorrect, yielding a weighted accuracy of 96%. Furthermore, qualitative tests confirm the chatbot’s ability to generate well-structured, legally sound responses, even when queried in English, demonstrating multilingual capabilities.}, archive = {J_ACCESS}, author = {Francklin Rivas-Echeverría and Leo Thomas Ramos and José Luis Ibarra and Sonia Zerpa-Bonillo and Stalin Arciniegas and Marilena Asprino-Salas}, doi = {10.1109/ACCESS.2025.3580488}, journal = {IEEE Access}, pages = {106817-106833}, shortjournal = {IEEE Access}, title = {LegalBot-EC: An LLM-based chatbot for legal assistance in ecuadorian law}, volume = {13}, year = {2025}, }
• (2025). A novel dangerous goods detection network based on multi-layer attention mechanism in X-ray baggage images. ACCESS, 13, 106805-106816. (www)

  @article{ , abstract = {The automatic detection of dangerous goods in X-ray baggage images is a crucial task in the field of industrial security. This paper proposes a novel dangerous goods detection network (DGDN) designed to address the challenges of detecting dangerous goods in complex X-ray baggage images. In the network, a multilevel refinement (MR) module is designed to improve multi-level feature representation, enabling the network to capture varied goods sizes and overlapping structures effectively. Specifically, the selective spatial-channel adaptive module (SSCAM) introduces dynamically adaptive weights across spatial and channel dimensions, allowing the network to focus on critical features more effectively and thereby improving detection accuracy. In the experiments, the performance of the proposed DGDN is evaluated by using the mean average precision (mAP) metric on the SIXray and PIDray datasets. The experimental results demonstrate that the proposed DGDN achieves mAP scores of 91.9% and 69.4% on the SIXray and PIDray datasets, respectively. Compared with some state-of-the-art methods, our network improves performance by 5–10% while reducing parameters and increasing computational efficiency. These indicate that DGDN has good potential application capabilities.}, archive = {J_ACCESS}, author = {Xu Yang and Ting Lan and Yili Xu}, doi = {10.1109/ACCESS.2025.3581450}, journal = {IEEE Access}, pages = {106805-106816}, shortjournal = {IEEE Access}, title = {A novel dangerous goods detection network based on multi-layer attention mechanism in X-ray baggage images}, volume = {13}, year = {2025}, }
• (2025). Detailed design of lattice tower for low speed wind turbines. ACCESS, 13, 106793-106804. (www)

  @article{ , abstract = {This study presents the design of a lattice tower for wind turbines with a downwind rotor configuration, intended for operation in low-intensity wind conditions (Class III-B, according to IEC 61400-1). Developed as part of the ABV/Furnas project, the analysis outlines the criteria used to define the optimal tower typology, considering structural, aerodynamic, and economic factors. A key challenge in downwind rotor turbines is the wake generated by the flow around the tower, which can negatively affect rotor performance. To address this, a lattice tower with aerodynamic-profiled members was selected. This solution not only reduces aerodynamic interference but also lowers manufacturing, transportation, and assembly costs. The study demonstrates the feasibility and innovations of this approach. It highlights how lattice structures reduce aerodynamic impact, enhancing rotor efficiency. The analysis covers essential structural and geometric parameters, including weight, wind blocking area, ground loading, and their influence on foundation costs. Additionally, the effect of tower geometry on its natural vibration frequency is examined to prevent resonance and ensure long-term structural stability. Various lattice tower configurations are compared to conventional tubular towers, assessing their technical performance. Logistical constraints – such as transportation feasibility, on-site assembly, and limitations of the Brazilian steel market – are also evaluated, ensuring the practicality and cost-effectiveness of the proposed design for deployment in Brazil.}, archive = {J_ACCESS}, author = {James Rojas Waterhouse and Carlos Henrique Gasparetti and Kauan Pires Quevedo and Guilherme Beppu de Souza}, doi = {10.1109/ACCESS.2025.3580660}, journal = {IEEE Access}, pages = {106793-106804}, shortjournal = {IEEE Access}, title = {Detailed design of lattice tower for low speed wind turbines}, volume = {13}, year = {2025}, }
• (2025). Continuum robots: Current trends in design, control, and applications. ACCESS, 13, 106771-106792. (www)

  @article{ , abstract = {The presence of a multi-segmented and malleable structure makes continuum robot (CR) promising for use in medicine, industry, space, and deep-sea research. This trend encourages researchers worldwide to create fundamentally new types of continuously deformable CR structures suitable for various applications and delicate manipulations. Despite promising achievements, the processes of design, modeling, control system development, and improvement of existing CR mechanisms remain quite complex. Taking into account the main technological features of existing solutions, this article provides a comprehensive overview of the current state of continuums robots. The analysis covers general problems arising in the design of CRs, and possible solutions are also considered, allowing the full potential of CRs for wider application to be revealed. This work aims to analyze key design and modeling methods, where the main approaches to the process of creating CRs have been classified. Taking into account the identified limitations and problems in their practical application, this review focuses on their advantages, design principles, mathematical modeling, and control strategies. Modern approaches to kinematic and dynamic modelling of CRs and CR control algorithms, including AI-based methods, were analyzed. As a result, promising directions for improving CRs for various applications were identified.}, archive = {J_ACCESS}, author = {Marina Kalekeyeva and Zarina Konakbay and Meiramgul Imanbekova and Aliya Shakbutova and Olga Garmash and Gulzhan Muratbekova and Doszhan Mambetalin}, doi = {10.1109/ACCESS.2025.3580681}, journal = {IEEE Access}, pages = {106771-106792}, shortjournal = {IEEE Access}, title = {Continuum robots: Current trends in design, control, and applications}, volume = {13}, year = {2025}, }
• (2025). Detecting exercise-induced temperature distribution changes at the knee using a wearable array of thermistors. ACCESS, 13, 106759-106770. (www)

  @article{ , abstract = {The Heat Distribution Index (HDI) has been used to capture the abnormal variance in knee temperature distribution as a proxy for inflammation in osteoarthritis. HDI has been derived from thermal camera (TC) data, but its use has been limited to static conditions, as TCs are impractical for dynamic assessments. This study evaluates the feasibility of using a wearable array of thermistors to assess HDI changes in response to exercise, using TC data as reference. Ten healthy participants were enrolled, and knee skin temperature was measured pre-, post-exercise, and during a cooldown period of approximately 30 min. The thermistor-based system detected a significant HDI increase post-exercise ( $\Delta HDI=0.76^{\circ }\text {C}, p\lt 0.001$ ) consistent with the TC results ( $\mathrm {\Delta HDI=0.71^{\circ }C, p\lt 0.001)}$ , with no statistical difference detected between the two methods $\mathrm {(p\gt 0.05)}$ . During cooldown, both systems tracked recovery trends without detectable differences $\mathrm {(p\gt 0.05)}$ . The mean HDI error for the thermistor array was 0.20±0.36∘C. Simulations assessing performance and sources of error showed negligible bias in HDI estimates regardless of the number of thermistors, while variance decreased as thermistor count increased. A more uniform sensor distribution across the region of interest improved accuracy over clustering near its edges, while misplacement had minimal impact except for rare outliers. These findings support the feasibility of a wearable array of thermistors for assessing exercise-induced HDI changes. Improvements in sensor placement to maximize skin contact while allowing for perspiration may enhance reliability. This work lays the foundation for real-time thermal monitoring and may aid in developing wearable tools to monitor patients with knee osteoarthritis.}, archive = {J_ACCESS}, author = {Benito L. Pugliese and Virginia Civeriati and Adam S. Tenforde and Danilo Demarchi and Paolo Bonato}, doi = {10.1109/ACCESS.2025.3581491}, journal = {IEEE Access}, pages = {106759-106770}, shortjournal = {IEEE Access}, title = {Detecting exercise-induced temperature distribution changes at the knee using a wearable array of thermistors}, volume = {13}, year = {2025}, }
• (2025). Design of a koch-curved LPDA antenna with optimized beamwidth to improve the aperture efficiency of reflector antennas. ACCESS, 13, 106747-106758. (www)

  @article{ , abstract = {In this paper, we propose a Koch-curved log-periodic dipole array (LPDA) antenna with wideband characteristics and an optimal beamwidth as a reflector feed antenna for SIGINT satellites. Koch-curved dipole elements are employed to improve aperture efficiency by adjusting the beamwidth. The beamwidth is adjusted by changing the angle and size of the Koch-curve applied to the dipole array elements. Additionally, a tapered line boom is applied to improve the matching characteristics. The performance of the proposed antenna such as the reflection coefficients and radiation pattern characteristics are measured in an anechoic chamber. The proposed antenna shows a reflection coefficient of less than −8 dB over a wideband from 2.1 GHz to 8 GHz and an average 8.6 dB beamwidth of 109.6° with an average bore-sight gain of 6.6 dBi. Furthermore, using the proposed feed antenna, an average bore-sight gain of 30.9 dBi is measured from 2 GHz to 6 GHz. The results confirm that the proposed antenna, which has wideband characteristics and an optimized beamwidth across the operating frequency range, is suitable as a reflector feed antenna in the SIGINT satellite application.}, archive = {J_ACCESS}, author = {Jiyoung Park and Changhyeon Im and Hosung Choo}, doi = {10.1109/ACCESS.2025.3581215}, journal = {IEEE Access}, pages = {106747-106758}, shortjournal = {IEEE Access}, title = {Design of a koch-curved LPDA antenna with optimized beamwidth to improve the aperture efficiency of reflector antennas}, volume = {13}, year = {2025}, }
• (2025). Reading lips: An analytical framework for adversarial passive detection of wireless traffic in IoT ecosystems. ACCESS, 13, 106736-106746. (www)

  @article{ , abstract = {Eavesdropping by passive adversaries in wireless communications represents a significant threat, particularly within Internet of Things (IoT) environments, due to the limited security capabilities of such devices and the broadcast nature of wireless communication. An adversary can exploit the broadcast nature of wireless networks to gather metadata such as timing, duration, and frequency of transmissions to possibly infer sensitive information and operational details. Hence, monitoring and eavesdropping threats pose aggravated risks to 5G/6G use-cases driven by massive machine-type communication (e.g., IoT devices). However, there is a gap in our fundamental understanding of how these attacks are developed and conducted, which impacts our ability to develop effective detection and mitigation tools. This article introduces a novel analytical framework to formulate eavesdropping attacks in wireless IoT ecosystems. Specifically, we focus on “adversarial traffic detection attacks” and explore their mechanics, develop an analytical framework for optimal attack strategies, and demonstrate how adversaries might employ algorithmic techniques to enhance their attack effectiveness. Numerical results demonstrate the effectiveness of the proposed adversarial eavesdropping strategy. Finally, we propose cybersecurity countermeasures aimed at reducing the risk of such attacks to IoT systems, making our contribution both timely and relevant.}, archive = {J_ACCESS}, author = {Abdallah Farraj and Eman Hammad}, doi = {10.1109/ACCESS.2025.3573249}, journal = {IEEE Access}, pages = {106736-106746}, shortjournal = {IEEE Access}, title = {Reading lips: An analytical framework for adversarial passive detection of wireless traffic in IoT ecosystems}, volume = {13}, year = {2025}, }
• (2025). Optimal power flow based on a metaheuristics optimization approach for the iraqi super high voltages network. ACCESS, 13, 106724-106735. (www)

  @article{ , abstract = {Optimal power flow is a tool It enables operators to run the system as efficiently as possible within specific limitations. Therefore, many tools have been developed to assist operators to make decisions for different objectives. The optimal power flow (OPF) problem is the most difficult and complex problem in power system analysis and design due to the nonlinearities and imposed constrains. OPF’s goal is to reduce generation costs and transmission losses. when the demand and generated power are balanced. In this work, the proposed approach uses genetic algorithm (GA) and camping the Hybrid Particle Swarm Optimization and Genetic algorithm (HPSO+GA) as an intelligent methods to perform optimal power flow. Cost functions that are defined and minimized in this work are the overall active losses and amount of required fuel. The viability of the suggested method is confirmed by comparing the results of the presented methodology With previous research results. Using the MATLAB platform, the best load flow technique was evaluated using data from the Iraqi 400 KV transmission network, which consists of 58 buses. Results document the viability of the proposed method in terms of less active losses and reduced fuel costs. Moreover, the proposed GA and HPSO+GA methods requires no iterations hence errors of solution divergence and initial conditions are omitted.}, archive = {J_ACCESS}, author = {Ali Almosawi and Mesut Çevik and Aysel Ersoy}, doi = {10.1109/ACCESS.2025.3578579}, journal = {IEEE Access}, pages = {106724-106735}, shortjournal = {IEEE Access}, title = {Optimal power flow based on a metaheuristics optimization approach for the iraqi super high voltages network}, volume = {13}, year = {2025}, }
• (2025). State of charge estimation for li-ion batteries: An edge-based data-driven approach. ACCESS, 13, 106703-106723. (www)

  @article{ , abstract = {The traditional machine learning approach requires substantial computational resources which are scarce in the embedded devices. Recently, the confluence of Edge computing with IoT has enabled resource constrained embedded devices to implement machine learning algorithms. TinyML, with its emphasis on integrating machine learning into embedded systems, seeks to move the end users away from high-performance machines, towards devices with limited resources and power. The present work investigates the estimation of state of charge (SoC) of Lithium Ion (Li-ion) batteries focussing on data-driven methodologies developed during the last five years. This paper mainly focusses on the relationship between dataset characteristics and data stationarity, exploring battery behaviour prediction and related dataset comprehension techniques. A Long Short-Term Memory (LSTM) network, a variant of Recursive Neural Networks (RNN), is utilised for SoC estimation. A 1C rating standard is implemented to comprehend the charge and discharge properties of a Li-ion battery. This study includes hardware evaluation as well as Monte Carlo simulation analysis in circuit component design. The experimental results provide a Mean Absolute Error (MAE) of 0.0630, a Mean Squared Error (MSE) of 0.0107, and a Root Mean Squared Error (RMSE) of 0.1033, confirming the efficacy of the proposed methodology. These results illustrate the accuracy and reliability of SoC estimation model. In conclusion, the proposed data-driven technique can lead to improved data security, reduced latency and cost in the estimation of SoC for Li-ion batteries.}, archive = {J_ACCESS}, author = {Sesidhar Dvsr and Chandrashekhar Badachi and Chandrashekar Nagawaram and Panduranga Chary Kondoju and C. Dhanamjayulu and Innocent Kamwa}, doi = {10.1109/ACCESS.2025.3580552}, journal = {IEEE Access}, pages = {106703-106723}, shortjournal = {IEEE Access}, title = {State of charge estimation for li-ion batteries: An edge-based data-driven approach}, volume = {13}, year = {2025}, }
• (2025). Positional tracking study of greenhouse mobile robot based on improved monodepth2. ACCESS, 13, 106690-106702. (www)

  @article{ , abstract = {This paper presents a self-supervised monocular position tracking model tailored for greenhouse environments. These environments pose unique challenges: mutual crop shading and homogeneous color textures complicate feature extraction, resulting in blurred depth map boundaries and low-precision position estimation. Building upon the Monodepth2 baseline, the model incorporates three key enhancements: replacing the original backbone with ResNext50 to improve global information acquisition; integrating a hybrid convolution module (HC) into the encoder to expand the receptive field and capture multi-scale contextual features; and introducing a coordinate attention mechanism (CA) in the decoder to enhance discriminative feature extraction. Experiments conducted on a wheeled robot platform in a strawberry greenhouse demonstrate significant improvements: compared to the original backbone, the proposed model reduces position and attitude RMSE by 0.038 m and 0.012 rad, respectively. When compared to a baseline without HC, relative RMSE decreases by 0.048 m and 0.017 rad, while the CA-augmented version achieves RMSE reductions of 0.059 m and 0.034 rad compared to the CA-free variant. These results surpass existing monocular tracking methods, offering a technical foundation for vision system designs in greenhouse mobile robotics.}, archive = {J_ACCESS}, author = {Yaheng Cai and Yingli Cao}, doi = {10.1109/ACCESS.2025.3578135}, journal = {IEEE Access}, pages = {106690-106702}, shortjournal = {IEEE Access}, title = {Positional tracking study of greenhouse mobile robot based on improved monodepth2}, volume = {13}, year = {2025}, }
• (2025). MAHGA: Multi-aspect heterogeneous graph analysis for harmful speech detection on social networks. ACCESS, 13, 106673-106689. (www)

  @article{ , abstract = {Deep neural networks demonstrate high accuracy in detecting harmful social media posts; however, conventional text-based methods often overlook critical contextual relationships among posts, users, and shared information. Graph-based methods, particularly those employing graph neural networks, effectively capture these contextual interactions by modeling relationships within heterogeneous graphs and integrate diverse semantic elements derived through natural language processing. However, existing graph approaches do not adequately differentiate between explicit textual elements (e.g., hashtags, mentions), implicit semantic elements (e.g., entities, topics), and informational or emotional cues from images, thereby obscuring the distinct contribution of each component. This study proposes the multi-aspect heterogeneous graph analysis (MAHGA) framework to address these limitations. MAHGA explicitly models different semantic and emotional aspects of social media posts via specialized heterogeneous graphs. It independently constructs and analyzes distinct graphs for explicit textual elements, implicit semantic elements, and image-based content, enabling a clearer interpretation and more precise understanding of their individual contributions. Furthermore, a correlation-based loss function maintains the independence of learned features, enhancing the model’s ability to recognize nuanced harmful content. Experimental evaluations using COVID-19 vaccine-related social media data indicate that MAHGA significantly outperforms existing methods, offering improvements of approximately 2-3 percentage points in harmful post detection accuracy compared with previous state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Ryo Yoshida and Soh Yoshida and Mitsuji Muneyasu}, doi = {10.1109/ACCESS.2025.3581214}, journal = {IEEE Access}, pages = {106673-106689}, shortjournal = {IEEE Access}, title = {MAHGA: Multi-aspect heterogeneous graph analysis for harmful speech detection on social networks}, volume = {13}, year = {2025}, }
• (2025). Automated identification of cyclic alternating patterns of sleep using fusion of VGG16 and vision transformer. ACCESS, 13, 106652-106672. (www)

  @article{ , abstract = {Sleep plays a crucial role in human health and significantly impacting physical and mental well-being. An automated system is crucially needed to identify Cyclic Alternating Pattern (CAP) phases A and B, which are pivotal in assessing sleep depth and stability, enhancing the understanding of sleep and diagnosing sleep disorders. Our study addresses this critical need by proposing an automated classification system capable of accurately distinguishing CAP phases A and B. Leveraging advanced machine learning techniques, including the fusion of VGG-16 and Vision Transformer models, along with preprocessing methods, we aim to develop a robust system for CAP phase identification. By utilizing the PhysioNet dataset, encompassing a diverse range of subjects, from healthy individuals to those with various sleep disorders, such as Insomnia, Narcolepsy, Nocturnal Frontal Lobe Epilepsy (NFLE) and Periodic Limb Movement (PLM), our approach seeks to provide comprehensive insights into sleep patterns and disorders. Furthermore, our results demonstrate promising accuracies, with notable performance improvements over individual architectures. Specifically, accuracies of 94.29% for healthy subjects, 96.02% for Narcolepsy, 90.49% for Insomnia, 90.38% for PLM and 89.73% for NFLE were achieved. These findings highlight the effectiveness of the proposed approach in accurately identifying CAP phases and diagnosing sleep disorders, thus contributing to advancements in automated sleep analysis and healthcare.}, archive = {J_ACCESS}, author = {Hardik Telangore and Ashutosh Kumar Jha and Prithviraj Verma and Manish Sharma and Chakka Sabareesh and Ankit A. Bhurane and Hasan S. Mir and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3571145}, journal = {IEEE Access}, pages = {106652-106672}, shortjournal = {IEEE Access}, title = {Automated identification of cyclic alternating patterns of sleep using fusion of VGG16 and vision transformer}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of a dual uncrewed surface vessel platform for bathymetry research under high-flow conditions. ACCESS, 13, 106634-106651. (www)

  @article{ , abstract = {Bathymetry, the study of underwater depth and topography, relies on detailed sonar mapping of the underwater structures. These measurements, critical for infrastructure health monitoring and hazard detection, often require prohibitively expensive sensory equipment and stable measurement conditions. The high financial risk associated with sensor damage or vessel loss creates a reluctance to deploy uncrewed surface vessels (USVs) for bathymetry. However, the alternative, crewed-boat bathymetry operations, are costly, pose significant hazards to personnel, and frequently fail to achieve the highly stable conditions necessary for bathymetry data collection, especially under challenging conditions such as high currents. Consequently, further research is essential to advance autonomous control, navigation, and data processing technologies, with a particular focus on bathymetry while ensuring safety under extreme conditions. There is a notable lack of accessible hardware platforms that allow for integrated research in both bathymetry-focused autonomous control and navigation, as well as data evaluation and processing. This paper addresses this gap by detailing the design and implementation of two complementary (dual) research USV systems tailored for uncrewed bathymetry research. This includes a low-cost USV for Navigation and Control research (NAC-USV) and a second, high-end USV equipped with a high-resolution multi-beam sonar and the associated hardware for Bathymetry data quality Evaluation and Post-processing research (BEP-USV). The NAC-USV facilitates the investigation of autonomous, fail-safe navigation and control technologies, emphasizing the stability requirements for high-quality bathymetry data collection while minimizing the risk to expensive equipment, allowing for seamless transfer of validated controls to the BEP-USV. The BEP-USV, which mirrors the NAC-USV hardware, is then used for additional control validation and in-depth exploration of bathymetry data evaluation and post-processing methodologies. We detail the design and implementation of both USV systems, open source hardware and software design, and the bill of material. Furthermore, we demonstrate the system’s effectiveness in both research and bathymetric applications across a range of operational scenarios. All the information are available at: https://github.com/Soltanilara/Twin-USV/}, archive = {J_ACCESS}, author = {Dinesh Kumar and Amin Ghorbanpour and Kin Yen and Iman Soltani}, doi = {10.1109/ACCESS.2025.3580343}, journal = {IEEE Access}, pages = {106634-106651}, shortjournal = {IEEE Access}, title = {Design and implementation of a dual uncrewed surface vessel platform for bathymetry research under high-flow conditions}, volume = {13}, year = {2025}, }
• (2025). Diverse representation knowledge distillation for efficient edge AI teledermatology in skin disease diagnosis. ACCESS, 13, 106618-106633. (www)

  @article{ , abstract = {Access to dermatological care in rural areas is limited due to a shortage of specialists. While AI-powered teledermatology offers a solution, it faces challenges from unreliable internet connectivity. Edge AI offers a promising approach that enables inferences locally on mobile devices. However, high-performance AI models are often large, which makes them difficult to deploy on mobile devices. While recent research primarily concentrated on small models or cloud inference, this research addresses the underexplored application of knowledge distillation on mobile devices. By conducting applied research aiming at compressing the AI model while maintaining accuracy, this research proposes a novel adoption framework for skin disease inference using edge computing. The framework applies knowledge distillation to compress a high-performance teacher model into a small student model, followed by deployment on mobile devices for local inference. Key contributions include a novel framework for edge-based inference, evaluation of pre-trained models on the ISIC 2019 and Fitzpatrick17k-C datasets, and practical deployment on mobile devices. Model performance was evaluated using accuracy, precision, recall, and F1-score, while the prototype was measured using model size, compression ratio, and inference time. The experiments demonstrate that the scenario of distilling RegNetY32GF into MobileNetV2 results in the most efficient model, maintaining both accuracy and model size. The prototype evaluation shows the practicality of the proposed framework with a compression rate of 55.88 on the ISIC 2019 dataset, with inference time reduced by approximately 352 times. These results enable efficient inference on mobile devices with limited resources.}, archive = {J_ACCESS}, author = {Andreas Winata and Nur Afny Catur Andryani and Alexander Agung Santoso Gunawan and and Ford Lumban Gaol}, doi = {10.1109/ACCESS.2025.3581225}, journal = {IEEE Access}, pages = {106618-106633}, shortjournal = {IEEE Access}, title = {Diverse representation knowledge distillation for efficient edge AI teledermatology in skin disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Image coding for object recognition tasks based on contour feature learning with flexible object selection. ACCESS, 13, 106606-106617. (www)

  @article{ , abstract = {The consumption of image data by machines is rapidly increasing due to the growing adoption of image recognition technologies. This trend has accelerated research in image compression techniques tailored for machine processing. This emerging field, known as Image Coding for Machines (ICM), has gained significant attention in recent years. In particular, ICM is increasingly seen as essential for collaborative systems between edge devices and cloud AI. Since large AI models are challenging to deploy on edge devices, cloud AI services are made available to edge users, who can utilize them by transmitting images to the cloud. Cloud AI is expected to handle various tasks, including image generation and image recognition, with the latter being especially valuable for video and image analysis. Given its utility, image recognition models are anticipated to replace human analysts in applications such as farm and traffic monitoring. Moreover, since recognition models require only a small fraction of the total image data, developing specialized image compression methods for recognition can significantly enhance communication efficiency. However, applying conventional ICM methods to edge-cloud systems presents challenges, such as increased computational load on edge devices and limited versatility. In this paper, we address these challenges by proposing two novel image compression methods—SA-ICM and ST-ICM—designed for recognition models. These methods focus on preserving object contours within images while maintaining compatibility with various recognition models, without adding computational overhead to edge devices. Through experimental evaluations, we demonstrate the versatility and effectiveness of our proposed methods by comparing them with conventional approaches.}, archive = {J_ACCESS}, author = {Takahiro Shindo and Taiju Watanabe and Yui Tatsumi and Hiroshi Watanabe}, doi = {10.1109/ACCESS.2025.3578292}, journal = {IEEE Access}, pages = {106606-106617}, shortjournal = {IEEE Access}, title = {Image coding for object recognition tasks based on contour feature learning with flexible object selection}, volume = {13}, year = {2025}, }
• (2025). Enhancing urban heatwave response planning via a graph-based digital twin approach: Spatial dependency risk analysis in vienna city. ACCESS, 13, 106590-106605. (www)

  @article{ , abstract = {Urban heatwaves pose an escalating threat to cities, requiring advanced planning strategies. This paper presents a graph-based digital twin approach to model spatial dependencies in heatwave risk for urban areas. Using Vienna’s city center as a case study, we integrate (i) high-resolution spatial data from Google on heat hazard, (ii) spatial population distribution from Vienna Open Data and (iii) META’s Vienna population datasets alongside (iv) critical road infrastructure from OpenStreetMaps, into a graph network model that captures cumulative risk paths that help determine the most critical city blocks needing intervention and the optimal allocation of resources for routing first responders to reach critical locations of sensitive populations. Our Spatial Dependency Risk Analysis identifies how the distribution of vulnerable population needing first aid in one location together with heat data may amplify risks in connected areas and provide a planning tool to first responders to draft optimal routes to reach all persons in need as fast as possible. The results show how city center junctions form interdependent high-risk clusters; notably, our tool managed to identify the most at-risk sub-district reaches and automatically plan ambulance routes from cooling spots to high risk hotspots. To our knowledge, this is the first automatic tool able to quantify these network routing effects during heatwave disasters, and enhance heatwave response planning by pinpointing critical areas where targeted interventions (e.g., cooling centers or outreach) could substantially reduce cascading impacts}, archive = {J_ACCESS}, author = {George Stergiopoulos and Sozon A. Leventopoulos and Michalis Karamousadakis and Benjamin Schuster and Dimitris Gritzalis}, doi = {10.1109/ACCESS.2025.3580334}, journal = {IEEE Access}, pages = {106590-106605}, shortjournal = {IEEE Access}, title = {Enhancing urban heatwave response planning via a graph-based digital twin approach: Spatial dependency risk analysis in vienna city}, volume = {13}, year = {2025}, }
• (2025). Explainable and uncertainty aware AI-based ransomware detection. ACCESS, 13, 106573-106589. (www)

  @article{ , abstract = {Ransomware poses a serious and evolving threat, demanding detection methods that can adapt to new attack vectors while maintaining transparency and reliability. This study proposes a comprehensive framework that integrates data augmentation, explainable artificial intelligence, and uncertainty quantification to address key challenges in ransomware detection. By leveraging synthetic data generation techniques, the approach mitigates class imbalance and captures varied ransomware behaviours. Simultaneously, explainable AI methods shed light on model decisions, enhancing interpretability and building trust among cybersecurity professionals. An uncertainty-aware component flags ambiguous predictions, allowing for targeted manual reviews and minimising incorrect classifications. Experiments on multiple ransomware datasets show the framework’s ability to maintain high detection rates, even under adversarial conditions. By combining RanSAP and RDset datasets, the framework achieves marked performance improvements. When SMOTE was applied, Random Forest reached an F1-score of 0.9963, while a CNN with Monte Carlo Dropout attained 0.9906. Further incorporating CT-GAN boosted the CNN’s F1-score to 0.9978, underscoring the robustness of our approach. The results suggest that combining robust data augmentation, interpretability, and uncertainty handling offers a practical avenue for deploying reliable ransomware detection systems in real-world environments.}, archive = {J_ACCESS}, author = {Henry Kabuye and Biju Issac and Rahul Yumlembam and Jeyamohan Neera}, doi = {10.1109/ACCESS.2025.3581424}, journal = {IEEE Access}, pages = {106573-106589}, shortjournal = {IEEE Access}, title = {Explainable and uncertainty aware AI-based ransomware detection}, volume = {13}, year = {2025}, }
• (2025). Automated loudness growth prediction from EEG signals using autoencoder and multi-target regression. ACCESS, 13, 106561-106572. (www)

  @article{ , abstract = {Accurately assessing loudness perception is crucial for optimizing hearing aid fittings, especially for individuals who are unable to perform subjective tests. This study presents an automated method for estimating frequency-specific loudness growth curves using tone-burst auditory brainstem responses (ABRs), which are subsets of EEG (electroencephalography) signals. Unlike traditional methods that rely on manually engineered features, the proposed method uses a convolutional autoencoder to learn latent representations of ABR signals, reducing dimensionality while preserving critical auditory information. The extracted features are mapped to psychoacoustic loudness growth estimates using a multi-target regression model based on a convolutional neural network. An ablation study was conducted to analyze the impact of different autoencoder configurations on feature extraction performance. The results demonstrate strong predictive consistency, with high Pearson correlation coefficients (PCC $\geq 0.9$ ) and low mean square errors (MSE $\leq 0.0011$ ) across different stimulus frequencies and subjects.}, archive = {J_ACCESS}, author = {D. Rama Harshita and Nitya Tiwari and Himanshu Padole and K. S. Nataraj}, doi = {10.1109/ACCESS.2025.3580833}, journal = {IEEE Access}, pages = {106561-106572}, shortjournal = {IEEE Access}, title = {Automated loudness growth prediction from EEG signals using autoencoder and multi-target regression}, volume = {13}, year = {2025}, }
• (2025). Nonlinear model predictive yaw moment control through electric axle and friction brake torque distribution. ACCESS, 13, 106540-106560. (www)

  @article{ , abstract = {A broad literature deals with torque-vectoring (TV) algorithms based on direct yaw moment control (DYC), which continuously generate longitudinal tire forces that are different on the left and right vehicle sides. Such set-ups enable desirable yaw rate and sideslip responses in normal and extreme driving conditions. However, they imply two electric powertrains per axle, or controllable differentials, and therefore the related controllers are not applicable to typical production electric vehicle layouts with a single centralized electric machine and open differential per axle. Nevertheless, in proximity of the limit of handling, the cornering response of conventional all-wheel-drive electric vehicles with one motor per axle can benefit from front-to-rear axle torque-vectoring (ATV), which, by controlling the longitudinal force distribution between the front and rear axles, indirectly modifies the yaw moment contribution due to the lateral tire forces. Only a few studies exploit this option, with limited coverage, e.g., without considering the energy efficiency aspects and the electronic stability control (ESC) function actuating the friction brakes. To address the gap, this paper presents real-time capable nonlinear model predictive controllers (NMPCs) for ATV and ESC, targeting: i) energy consumption reduction in normal driving, through powertrain and tire slip power loss limitation; and ii) yaw rate, sideslip angle and wheel slip control at the limit of handling, by considering the trade-off between powertrain and friction brake actuation. The performance is assessed through simulations with a validated vehicle model, and experiments on a vehicle demonstrator. With respect to benchmarking rule-based controllers, the results show that the NMPCs bring significant reductions of the yaw rate errors and friction brake power losses, while increasing the exit speed in acceleration-during-cornering conditions.}, archive = {J_ACCESS}, author = {Carmine Caponio and Gaetano Tavolo and Davide Tavernini and Ahu Ece Hartavi Karci and Javad Ahmadi and Basilio Lenzo and Giulio Reina and Giacomo Mantriota and Pietro Perlo and Aldo Sorniotti}, doi = {10.1109/ACCESS.2025.3568635}, journal = {IEEE Access}, pages = {106540-106560}, shortjournal = {IEEE Access}, title = {Nonlinear model predictive yaw moment control through electric axle and friction brake torque distribution}, volume = {13}, year = {2025}, }
• (2025). Self-adaptive deep learning framework for non-intrusive load monitoring: Addressing aging appliance challenges with transfer learning and pseudo labeling. ACCESS, 13, 106524-106539. (www)

  @article{ , abstract = {Efficient energy management practices are recognized as crucial for optimizing energy utilization. Non-intrusive load monitoring (NILM) has been identified as a promising solution, particularly with the use of deep learning techniques. Conventional NILM models often face difficulties in adapting to changes in power consumption patterns, especially as appliances age. To address this challenge, a self-adaptive NILM model is proposed, which integrates deep learning techniques with transfer learning and pseudo labeling. Unlike traditional NILM models, this approach incorporates a unique self-adaptive feature that enables the model to automatically adapt to changing power patterns resulting from aging appliances. Synthetic data generation and advanced neural network architectures are used for training and validating the model, achieving exceptional accuracy rates in disaggregating power consumption. Electrical appliances used for this experiment are categorized into two groups: on-time fixed devices and on-time variable devices. Experimental results demonstrate the effectiveness of the Self-Adaptive NILM approach with on-time variable devices, such as three-phase refrigerators. The model was tested over a six-year period, focusing on a three-phase refrigerator, and an accuracy rate exceeding 97% in disaggregating power consumption was achieved. It was found that for on-time fixed devices, the conventional NILM model gives better predictions. This high level of accuracy and the findings underscore the potential of this approach for energy management systems. By addressing a significant gap in existing NILM literature, this research introduces the way for the development of more robust, resilient, and adaptive energy management solutions.}, archive = {J_ACCESS}, author = {W. A. Yasodya and S. M. L. Arampola and M. S. K. Nisakya and V. Logeeshan and S. Kumarawadu and Chathura Wanigasekara}, doi = {10.1109/ACCESS.2025.3581471}, journal = {IEEE Access}, pages = {106524-106539}, shortjournal = {IEEE Access}, title = {Self-adaptive deep learning framework for non-intrusive load monitoring: Addressing aging appliance challenges with transfer learning and pseudo labeling}, volume = {13}, year = {2025}, }
• (2025). Avoiding equivalent test executions in software product line testing. ACCESS, 13, 106511-106523. (www)

  @article{ , abstract = {In the context of software product line (SPL), test cases are reused for testing of multiple products that have commonality. In such a case, their executions may contain repetitions of equivalent executions, which are the test executions that have the identical execution flow and produce the same testing result. In SPL testing, repeating equivalent test executions should be avoided because it does not contribute to finding new faults and unnecessarily increases the SPL testing cost. This paper defines two types of equivalent test executions and points out that equivalent test executions increase the test redundancy. To show how equivalent test executions occur in a product line, we conduct a preliminary experiment using the VendingMachine SPL and its evolved versions. Then we propose a method to reduce the test redundancy of SPL by avoiding repetition of equivalent test executions. Our experimental evaluation with two SPL systems shows that our proposed method reduces the number of test executions by 57.1% on average compared to the exhaustive execution method without sacrificing fault detection effect.}, archive = {J_ACCESS}, author = {Pilsu Jung and Jaeho Kim}, doi = {10.1109/ACCESS.2025.3578378}, journal = {IEEE Access}, pages = {106511-106523}, shortjournal = {IEEE Access}, title = {Avoiding equivalent test executions in software product line testing}, volume = {13}, year = {2025}, }
• (2025). Evaluation of personal radiation exposure from wireless signals in indoor and outdoor environments. ACCESS, 13, 106489-106510. (www)

  @article{ , abstract = {With the development of wireless technology, the public is exposed to electromagnetic fields (EMF), which has led to concerns about the potential health effects of EMF exposures. This paper aims to evaluate personal EMF exposures from wireless signals in indoor and outdoor micro-environments in Malaysia. According to the influencing factors, four different types of micro-environments are selected. A radiation exposure meter called ExpoM-RF 4 is used to measure the electric field strength across these micro-environments. From the measurement campaigns, three machine learning (ML) techniques are simulated to model the Electric Field Strength in each micro-environment. The ML techniques are Fully connected neural network (FCNN), eXtreme Gradient Boosting (XG Boost), and Linear Regression (LR) to predict the RMS and Maximum radiation exposure. From the ML models, Total Emission Ratio (TER), Root Mean Square Error (RMSE) and Coefficient of Determination (R2) are evaluated to measure the performance of ML. By comparison, it is found that LR performs well with single and simple data set, while XG Boost and FCNN demonstrate superior capabilities in handling multiple types of data sets. The FCNN model provides the most accurate predictions, particularly in urban and suburban areas where extreme values are observed. Finally, the measured data and the predicted radiation exposure levels are compared against public exposure limit by International Commission on Non-Ionizing Radiation Protection (ICNIRP), Malaysian Communications and Multimedia Commission (MCMC) and Federal Communications Commission (FCC). The results demonstrate that typically personal radiation exposure is lower than the exposure limit (61.4 V/m), which is similar to the most research results. However, in areas with dense population and numerous base stations, the maximum exposure can approach 56.7365 V/m (measured data), which is close to the exposure limit.}, archive = {J_ACCESS}, author = {Pan Ruijie and Aduwati Sali and Lu Li and Muhammad Zamir Mohyedin and Sangin Qahtan}, doi = {10.1109/ACCESS.2025.3579085}, journal = {IEEE Access}, pages = {106489-106510}, shortjournal = {IEEE Access}, title = {Evaluation of personal radiation exposure from wireless signals in indoor and outdoor environments}, volume = {13}, year = {2025}, }
• (2025). AI-enhanced threat intelligence in remote patient monitoring systems: A survey on recent advances, challenges and future research directions. ACCESS, 13, 106465-106488. (www)

  @article{ , abstract = {Modern healthcare is increasingly relying on Remote Patient Monitoring (RPM) systems, which continuously collect health data and provide the ability to monitor patients in real-time. RPM systems are extremely susceptible to cyber threats due to their growing reliance on interconnected devices and hence need comprehensive security. AI has emerged as a crucial technology for addressing security issues in RPM systems. The objective of this survey is to understand the impact on the security of RPM systems by integrating Artificial Intelligence (AI) with threat intelligence. This survey analyzed 86 research articles from leading databases related to AI models, security of RPM systems, anomaly detection, and architectural solutions, in addition to 24 articles related to existing RPMs. This survey article emphasizes that RPM systems become much more resilient when automated attack mitigation, real-time anomaly detection, and predictive analytics are provided by AI-powered models. To secure the sensitive data in RPM, this survey discusses how AI can be applied to various architectural solutions, including edge computing, cloud integration, blockchain technology, and Federated Learning (FL). Furthermore, the benefits and challenges of deploying AI-driven threat intelligence, cross-platform compatibility, and the need for explainable AI to improve trust in automatically made decisions are presented. Moreover, this review highlights research gaps, including the necessity of comprehensive end-to-end architectures for maintaining security and privacy in RPM systems. It is revealed through this survey that AI-powered threat intelligence enhances RPM security considerably due to its ability of continuous monitoring, adaptive defense mechanisms, and early detection of threats. However, challenges such as the explainability of AI models persist and necessitate continued innovation. The survey paper suggests integrating AI-enhanced threat Detection as a Service (TDaas) that implements FL to transform the existing RPM security system, and ultimately contributes to a secure and reliable threat detection system in healthcare. This review provides a roadmap for future research in the area of AI-driven threat intelligence security for RPM systems and offers insights for developing resilient healthcare infrastructure.}, archive = {J_ACCESS}, author = {Jolly Trivedi and Mohammad Tahir and Jouni Isoaho}, doi = {10.1109/ACCESS.2025.3572626}, journal = {IEEE Access}, pages = {106465-106488}, shortjournal = {IEEE Access}, title = {AI-enhanced threat intelligence in remote patient monitoring systems: A survey on recent advances, challenges and future research directions}, volume = {13}, year = {2025}, }
• (2025). Assessing the fidelity and utility of water systems data using generative adversarial networks: A technical review. ACCESS, 13, 106443-106464. (www)

  @article{ , abstract = {Limited data access to Water Distribution Systems (WDSs) is a longstanding barrier to data-driven research and development. This limited access is further exacerbated by the reluctance of WDSs operators to share data. Driven by the absence of standard mandates, resource constraints, privacy and security concerns, and legal challenges, access to big data has been a challenge in the water scientific community. This review paper addresses this limitation by utilizing Generative Adversarial Networks (GANs) to generate realistic synthetic datasets, overcoming data scarcity and privacy concerns in WDSs. We review, train, and evaluate seven state-of-the-art GAN models using three multivariate time-series datasets. The core contribution of this work lies in its comprehensive technical review of the GANs, comparing and evaluating their ability to replicate temporal dynamics and maintain spatio-temporal dependencies within WDSs. For evaluation, we use techniques like t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) to quantify the diversity of the generated synthetic data. Key findings indicate that specific GAN models, such as Cramer GAN and CTGAN, are effective in generating data for predictive modeling, replacing the need for original WDSs datasets. Additionally, DoppelGANger and TimeGAN exhibit strong capabilities in preserving essential spatio-temporal relationships, which are critical for applications like environmental impact estimation. The results also highlight the practical utility of GAN-generated synthetic data in supporting the secure and effective management of WDSs, particularly in scenarios where data are scarce or sensitive. This research contributes to the application of Artificial Intelligence (AI) in water resource management and guides the selection of appropriate GAN models for specific tasks and contexts, demonstrating their practical implications in real-world scenarios. Experimental results are recorded, evaluated, and discussed.}, archive = {J_ACCESS}, author = {Md Nazmul Kabir Sikder and Yingjie Wang and Feras A. Batarseh}, doi = {10.1109/ACCESS.2025.3577969}, journal = {IEEE Access}, pages = {106443-106464}, shortjournal = {IEEE Access}, title = {Assessing the fidelity and utility of water systems data using generative adversarial networks: A technical review}, volume = {13}, year = {2025}, }
• (2025). Multi-scale information interaction and feature pyramid network for salient object detection. ACCESS, 13, 106435-106442. (www)

  @article{ , abstract = {Recently, salient object detection (SOD) methods based on deep learning have achieved significant progress. The existing models often require a considerable amount of memory and time to make precise inferences, SOD is an image preprocessing task, it is obviously unreasonable to consume a large amount of cost on SOD, which makes previous methods difficult to apply in practical scenarios. In this paper, we aim to find a balance between performance and efficiency by designing a lightweight network based on feature pyramid network (FPN), namely MII-FPN, to alleviate this problem. MII-FPN mainly contains a simple multi-scale convolution module and an efficient feature fusion module, which are used to extract and integrate the context information, respectively. Testing on a commonly used six datasets shows that MII-FPN achieves highly competitive accuracy compared with state-of-the-art SOD methods. Besides, the proposed MII-FPN is fairly efficient, which has a frame rate of 299.3FPS on NVIDIA RTX3080 GPU for $336\times 336$ input with only 1.12M parameters. The code is available at https://github.com/XUPTfangjie/MIIFPN}, archive = {J_ACCESS}, author = {Jie Fang and Zhicheng Zhang and Shasha Zhang}, doi = {10.1109/ACCESS.2025.3580437}, journal = {IEEE Access}, pages = {106435-106442}, shortjournal = {IEEE Access}, title = {Multi-scale information interaction and feature pyramid network for salient object detection}, volume = {13}, year = {2025}, }
• (2025). Intelligent waste management using WasteIQNet with hierarchical learning and meta-optimization. ACCESS, 13, 106416-106434. (www)

  @article{ , abstract = {Effective waste management remains a critical pillar for sustainable urban development, particularly in rapidly growing regions like Delhi-NCR, where heterogeneous waste streams complicate classification. This study presents WasteIQNet, an intelligent, hierarchy-aware deep hybrid model for fine-grained waste classification across 18 categories structured under Wet and Dry waste types. The model integrates MobileNetV3 for semantic feature extraction with GraphSAGE to capture structural relationships among image representations. To address class imbalance and feature sparsity, the architecture incorporates Feature-wise Attention (FWA), Top-K Mixture of Experts (TopK-MoE), and advanced regularization techniques including Dynamic Sparse Training (DST) and Model-Agnostic Meta-Learning (MAML). We introduce a novel Hierarchical Tree Loss function to penalize semantically distant misclassifications by leveraging domain-specific waste hierarchy paths. The proposed framework is trained and evaluated on the WEDR dataset, a curated collection of 360,000 images reflecting real-world waste conditions across Delhi’s major landfill zones. WasteIQNet achieves a peak classification accuracy of 97.87%, demonstrating substantial improvements in both interpretability and generalization. This work contributes a scalable, robust, and deployment-ready solution tailored for real-time smart city waste segregation initiatives.}, archive = {J_ACCESS}, author = {Sakshi Tiwari and Snigdha Bisht and Kanchan Sharma}, doi = {10.1109/ACCESS.2025.3574095}, journal = {IEEE Access}, pages = {106416-106434}, shortjournal = {IEEE Access}, title = {Intelligent waste management using WasteIQNet with hierarchical learning and meta-optimization}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning model for water quality prediction. ACCESS, 13, 106406-106415. (www)

  @article{ , abstract = {Water quality prediction is crucial for water environment management, playing a key role in preventing pollution and ensuring the safety of water resources. To improve prediction accuracy, this paper proposes a CEEMDAN-CNN-LSTM model enhanced by a Self-Attention mechanism. First, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm decomposes the water quality time series into multiple Intrinsic Mode Functions (IMFs), reducing noise and addressing nonlinearities in the data. Next, Convolutional Neural Networks (CNN) extract spatial features from the decomposed IMFs, enhancing the model’s spatial learning capability. Then, a Long Short-Term Memory (LSTM) network models the temporal features, capturing long-term dependencies in the water quality data. Finally, a Self-Attention mechanism assigns varying importance to features at different time steps, improving the model’s focus on key information. Experimental results demonstrate that the proposed model outperforms traditional methods, achieving high accuracy across multiple water quality indicators. This model effectively addresses non-stationarity and noise in the data, demonstrating strong generalization and robustness, providing a novel and efficient approach to water quality prediction.}, archive = {J_ACCESS}, author = {Qiliang Zhu and Xueting Yu and Liang Zhao and Linjun Xu}, doi = {10.1109/ACCESS.2025.3580741}, journal = {IEEE Access}, pages = {106406-106415}, shortjournal = {IEEE Access}, title = {A hybrid deep learning model for water quality prediction}, volume = {13}, year = {2025}, }
• (2025). An adaptive convolutional neural network with spatio-temporal attention and dynamic pathways (ACNN-STADP) for robust EEG-based motor imagery classification. ACCESS, 13, 106387-106405. (www)

  @article{ , abstract = {Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have gained substantial attention, particularly for motor imagery (MI) that facilitates direct brain-to-device communication without any muscular movement. However, existing classification models face limitations such as inter-subject variability, lack of generalizability, high computational demands, low signal-to-noise ratios, and inefficient feature extraction, which impede their robustness and accuracy. Moreover, advanced deep learning models often utilize rigid architectures with fixed spatial-temporal filters, restricting their adaptability to dynamic EEG patterns. To address these challenges, this paper proposes an Adaptive Convolutional Neural Network with Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP), which introduces a novel dynamic pathway mechanism and adaptive attention strategy for robust MI-EEG decoding. The proposed model integrates a Dynamic Pathway Convolution Network (DPCN) for adaptive feature extraction, incorporating a Dynamic Gating Controller (DGC) and Dynamic Adaptive Spatio-Temporal (DAST) blocks to efficiently capture multi-scale spatial and temporal dependencies. Additionally, an Adaptive Attention Fusion (AAF) module employs Dual Multi-Head Self-Attention (DMHSA) and a U-Net-inspired Adaptive Fusion Block (AFB) to enhance feature integration and improve classification performance. Furthermore, the model introduces three key innovations: Dynamic Multi-Scale Convolutional Learning for adaptive kernel selection, Unified Spatio-Temporal Attention (USTA) for efficient feature recalibration, and AFB for multi-scale feature fusion while preserving long-range dependencies. The model is validated on BCI Competition IV Dataset 2a, achieving a peak accuracy of 90.77%, and further evaluated across six additional MI-EEG datasets, demonstrating an overall average accuracy above 78.98%. ACNN-STADP significantly improves generalization, reduces computational complexity, and enhances real-time applicability, establishing a robust multi-dataset adaptive deep learning framework for EEG-based MI classification.}, archive = {J_ACCESS}, author = {Aaqib Raza and Mohd Zuki Yusoff}, doi = {10.1109/ACCESS.2025.3580145}, journal = {IEEE Access}, pages = {106387-106405}, shortjournal = {IEEE Access}, title = {An adaptive convolutional neural network with spatio-temporal attention and dynamic pathways (ACNN-STADP) for robust EEG-based motor imagery classification}, volume = {13}, year = {2025}, }
• (2025). Decision support system for evaluating corpus-based word lists for use in english language teaching contexts. ACCESS, 13, 106369-106386. (www)

  @article{ , abstract = {The selection of pedagogically relevant vocabulary is a critical aspect of English Language Teaching (ELT), yet traditional corpus-based word lists often rely solely on frequency-based ranking, limiting their effectiveness in real-world learning contexts. This study proposes a Decision Support System (DSS) that integrates corpus linguistics, Natural Language Processing (NLP), and machine learning to generate optimized vocabulary lists tailored for ELT. The DSS enhances word selection accuracy by incorporating contextual relevance, collocational strength, and pedagogical value, surpassing the limitations of static frequency-based models. The experimental evaluation compares DSS performance with traditional frequency-based methods and the academic word list (awl). The results indicate that the DSS achieves significantly higher ranking accuracy (mrr: 0.89, ndcg: 0.85) and improves classification performance (f1-score: 87.3%), ensuring that highly relevant words appear earlier in ranked lists. A case study in general and academic english contexts demonstrates that DSS-generated word lists offer greater usability, stronger contextual fit, and enhanced pedagogical effectiveness, as confirmed by positive teacher feedback (usability score: 4.7/5). Statistical validation using t-tests and anova confirms that DSS ranking improvements are statistically significant (p < 0.001), and effect size analysis (cohen’s d > 0.8) highlights its substantial impact on vocabulary instruction. Despite certain limitations such as corpus dependency and limited multilingual support, the DSS presents a scalable and adaptable solution for data-driven vocabulary selection in ELT. Future enhancements will focus on multilingual capabilities, personalized learning, and hybrid ranking models to further refine the system’s applicability in diverse educational settings.}, archive = {J_ACCESS}, author = {Ruoxi Yin and Chunmei Zhu and Jiuyang Zhu}, doi = {10.1109/ACCESS.2025.3579865}, journal = {IEEE Access}, pages = {106369-106386}, shortjournal = {IEEE Access}, title = {Decision support system for evaluating corpus-based word lists for use in english language teaching contexts}, volume = {13}, year = {2025}, }
• (2025). Evaluation of encoder-only transformer for multi-step traffic flow prediction. ACCESS, 13, 106349-106368. (www)

  @article{ , abstract = {Traffic flow prediction is critical for Intelligent Transportation Systems to alleviate congestion and optimize traffic management. The existing basic Encoder-Decoder Transformer model for multi-step prediction requires high computational complexity, making them less efficient for real-time applications. Therefore, this paper presents an Encoder-Only Transformer that eliminates the decoder component while evaluating the model’s ability to capture long-term spatial-temporal dependencies. This simplification was found to reduce computational overhead while maintaining a reasonable predictive accuracy comparable to the basic Encoder-Decoder Transformer and Long Short-Term Memory (LSTM) models. Experimental evaluations on two real-world datasets, Minnesota and California, for hourly prediction of 6, 12, and 24-hour horizon tasks, demonstrate the proposed model’s effectiveness. The proposed Encoder-Only Transformer outperforms LSTM networks across all horizons task for the Minnesota dataset, achieving up to 17.33% improvement in Mean Absolute Error. The proposed model excels in 24-hour horizon task for the California dataset but underperforms for shorter horizons compared to the LSTM and basic Encoder-Decoder Transformer models. These findings highlight the Encoder-Only Transformer’s application for multi-step traffic flow prediction while emphasizing dataset-specific variations in model performance.}, archive = {J_ACCESS}, author = {Mas Omar and Fitri Yakub and Andika Aji Wijaya and Ahmad Faiz Mohammad and Mohd Nazmin Maslan and Inge Dhamanti and Shi Zhongchao}, doi = {10.1109/ACCESS.2025.3580500}, journal = {IEEE Access}, pages = {106349-106368}, shortjournal = {IEEE Access}, title = {Evaluation of encoder-only transformer for multi-step traffic flow prediction}, volume = {13}, year = {2025}, }
• (2025). Distributed control method for improving renewable energy penetration rate and maintaining low-bus-voltage fluctuation in DC local grid. ACCESS, 13, 106336-106348. (www)

  @article{ , abstract = {In this paper, a droop-based distributed control method is proposed to enhance the penetration rate of renewable energy sources and minimize low-voltage fluctuations within the DC local grid. The proposed method designs droop gains for state of charge (SoC)-based droop control considering line impedance and other parameters in the DC local grid, thereby facilitating precise SoC equalization among different energy storage systems (ESS) considering the dc bus voltage fluctuation and power sharing of each component. Further, the proposed technique applies droop control to the grid-connected converter considering the SoC of ESS, enabling effective power distribution between the AC grid and ESS based on the magnitude of SoC. Secondary voltage compensation control is performed by integrating the structure of ramp rate control into this control method for DC bus voltage control, which helps alleviate excessive output fluctuations of renewable energy sources. The proposed method can be applied based on the SoC of ESS, thereby achieving a 56.56% improvement in the penetration rate of renewable energy. In addition, the DC bus voltage fluctuation is controlled within 1%, and SoC equalization is achieved when operating more than two ESS units in DC local grid system. The validity of the proposed technique is verified using PSCAD/EMTDC simulations and shows improvement on both renewable energy penetration rate and voltage fluctuation.}, archive = {J_ACCESS}, author = {Su-Seong Park and Rae-Young Kim and Dong-Geun Lee}, doi = {10.1109/ACCESS.2025.3573918}, journal = {IEEE Access}, pages = {106336-106348}, shortjournal = {IEEE Access}, title = {Distributed control method for improving renewable energy penetration rate and maintaining low-bus-voltage fluctuation in DC local grid}, volume = {13}, year = {2025}, }
• (2025). A virtual reality application and an interactive board game for students learning to program. ACCESS, 13, 106322-106335. (www)

  @article{ , abstract = {This study investigates the pedagogical effectiveness of three instructional formats for teaching block-based programming: an immersive virtual reality (VR) environment, a tactile board game (BG), and a traditional digital manual. Two interactive prototypes (VR and BG) were developed and validated to ensure functional and educational reliability. A three-way experimental design was employed to evaluate learner engagement, conceptual understanding, and satisfaction. Results revealed that both VR and BG significantly outperformed the traditional digital format across all metrics, with VR showing slightly higher engagement due to its immersive features. However, the BG demonstrated comparable learning outcomes and stronger accessibility. These findings validate the study’s hypotheses, emphasizing the value of interactivity in education. The BG emerges as an effective, low-tech alternative in contexts where VR access is limited, reinforcing the importance of well-designed physical tools in programming education.}, archive = {J_ACCESS}, author = {Anna Tonda Ramos and Águeda Gómez-Cambronero and Ignacio Miralles and Inmaculada Remolar}, doi = {10.1109/ACCESS.2025.3578769}, journal = {IEEE Access}, pages = {106322-106335}, shortjournal = {IEEE Access}, title = {A virtual reality application and an interactive board game for students learning to program}, volume = {13}, year = {2025}, }
• (2025). Advanced compact 5G MIMO base station for sub-6 GHz and millimeter wave frequencies: Practical implementation in underground mines. ACCESS, 13, 106311-106321. (www)

  @article{ , abstract = {A novel compact 5G multiple-input-multiple-output (MIMO) base station (5G-BS) is introduced for enhancing communications in underground mine environments. The structure includes four identical mm-wave antenna arrays combined with two sub-6 GHz antennas. The four mm-wave antenna arrays are symmetrically arranged around the central axis and positioned 45° apart from one another, while the sub-6 GHz antennas are symmetrically placed in the top corners of the structure. The design is initially evaluated through laboratory measurements, achieving gains of 4.6 dBi in the sub-6 GHz band and 10.5 dBi in the mm-wave band, with an envelope correlation coefficient (ECC) of less than 0.028. Additionally, the effectiveness of the design is validated through experimental measurements including: path loss, time dispersion and coherence bandwidth along with channel capacity evaluation, which have been conducted in real world underground mine using the proposed base station. Thanks to its simplicity and compact design, the array structure is a strong candidate for underground mine communications, capable of covering quad-band 5G frequencies (700 MHz, 3.5 GHz, 28 GHz, and 38 GHz).}, archive = {J_ACCESS}, author = {Mohamed Lamine Seddiki and Mourad Nedil and Soufiane Tebache and Saif Eddine Hadji}, doi = {10.1109/ACCESS.2025.3579968}, journal = {IEEE Access}, pages = {106311-106321}, shortjournal = {IEEE Access}, title = {Advanced compact 5G MIMO base station for sub-6 GHz and millimeter wave frequencies: Practical implementation in underground mines}, volume = {13}, year = {2025}, }
• (2025). Effect of enhancement material on the performance of ground electrodes under impulse conditions. ACCESS, 13, 106296-106310. (www)

  @article{ , abstract = {Among the factors that affect the soil resistivity is the chemical content in soil, where the ionic conduction is expectedly increased and the soil resistivity is reduced. Due to the beneficial use of chemical composition in soil, enhancement materials (EMs) or salts have been widely used in an effort to obtain a low ground resistance value. Much published data on the performance of the EMs on the steady-state resistance value is available; however, not much data is available on its characteristics under high impulse conditions by field measurements. In this paper, experimental test results on twelve configurations (six configurations with the EM and without the EM) installed in situ and tested under impulse conditions are reported. The study aims to quantify the effects of ground electrode configurations and EM on the soil characteristics under high impulse conditions, providing valuable insights for optimising grounding system design. Results show that the steady-state ground resistance is reduced by over 40% for most configurations with EM, significantly improving the current discharge time under impulse conditions. Additionally, inductive effects were more pronounced in configurations with lower resistance, particularly at higher voltages, while the impulse impedance was current-dependent for configurations without EM, whereas those with EM were less affected by current magnitude. CDEGS simulations indicated that parallel configurations are more prone to inductive effects than grid configurations, with electrode shape having a greater influence than size.}, archive = {J_ACCESS}, author = {Usman Muhammad and Nurul Nadia Ahmad and Normiza Mohamad Nor and Fazlul Aman}, doi = {10.1109/ACCESS.2025.3580466}, journal = {IEEE Access}, pages = {106296-106310}, shortjournal = {IEEE Access}, title = {Effect of enhancement material on the performance of ground electrodes under impulse conditions}, volume = {13}, year = {2025}, }
• (2025). Pseudospectrum in rectangular Mini/Micro channel fluidics linear transition to turbulence. ACCESS, 13, 106287-106295. (www)

  @article{ , abstract = {Recent years have seen a significant increase in interest in rectangular mini/micro channels, such as those found in high-power research reactors with plate-type fuels, high-performance microelectronic cooling systems, photovoltaic panel cooling systems, fuel-cell powered vehicles, advanced propulsion systems, heat exchangers for cooling computer chips and biomedical micro fluidics. The laminar-to-turbulent transition is critical when studying single-phase thermal hydraulics in the above problems because the fluid properties in the laminar flow differ dramatically from those in transition and turbulent flow. It is important to understand the boundaries of the laminar flow that cause instability for accurate design. Direct numerical simulation (DNS) presented itself as one high fidelity mathematical to resolve this challenge by full nonlinear simulations yet the computational needs, sensitivity to disturbances and related expertise are still very demanding for regular industrial applications. In the context of a more accessible linear transition to turbulence, certain laminar flows are known to be linearly stable at all Reynolds numbers, Re, although in practice they always become turbulent for large Reynolds numbers. One resolution of this paradox is given by small finite perturbations that lead to a transition. Trefethen has investigated the effects of small finite perturbations for Pipe Poiseuille flow. Rectangular duct flow of current study shows similarity to Pipe Poiseuille flow at aspect ratio one and at infinite aspect ratio it merges to Plane Poiseuille flow. A boundary satisfying Chebyshev-Collocation spectral method is employed for high-accuracy computation of linearized dynamics of incompressible flow in an infinite rectangular duct. The symmetries are exploited to reduce the high computational cost of the eigenvalue problem. The boundary satisfying bases are exploited for this cause. Systematic studies are conducted on the dependence of the eigenvalue problem on the downstream wave numbers, aspect ratio, and Reynolds number. The linear non-normal mechanisms are observed to investigate the mechanisms of small finite perturbations that might lead to transition to decide on transition envelopes. Systematic studies of the dependence of non-normality of eigenvalues, transient growth factors and other quantities on axial wave number, aspect ratio, and Reynolds numbers are presented for mini/micro rectangular channel’s design at low Reynolds number laminar flows that govern the physics of the design problems being investigated. The article’s conclusions can be used for initialization to prevent flow design disturbances and their relevant boundary conditions, for flow control applications, or for DNS simulation initialization to prevent or to initiate the transition to turbulence.}, archive = {J_ACCESS}, author = {Bahri Uzunoğlu}, doi = {10.1109/ACCESS.2025.3581137}, journal = {IEEE Access}, pages = {106287-106295}, shortjournal = {IEEE Access}, title = {Pseudospectrum in rectangular Mini/Micro channel fluidics linear transition to turbulence}, volume = {13}, year = {2025}, }
• (2025). A systematic review on smart and predictive maintenance in tool condition monitoring. ACCESS, 13, 106246-106286. (www)

  @article{ , abstract = {The main goal in the field of reliability and maintenance is ensuring and enhancing the availability of assets. A decrease in the production capability of machines can be the outcome of untimely and inefficient maintenance planning. Unexpected and unscheduled machinery shutdown due to required maintenance reflects poorly on a business, resulting in damaged credibility and financial losses. This puts organizations in a position to decide between undertaking preventive replacement of parts that could have been used for some more time or running the machine till it dies (run to failure). On the other hand, organizations can improve their uptime by promptly replacing potentially good parts that could have been used for some more cycles. In addition to assisting enterprises in minimizing or preventing unplanned downtime, smart and predictive maintenance (SPM) extends the machinery’s remaining useful life (RUL). A crucial instance is the cutting tool in machinery used for milling, drilling, or turning. It is an ideal asset to apply tool condition monitoring (TCM) since a breakdown of this part will result in unexpected downtime, resulting in a downturn in productivity. In a situation like this, a well-planned SPM strategy involving monitoring real-time health of tools used for cutting is beneficial. In the industrial predictive maintenance domain of Industry 5.0, accurate prediction of RUL of machinery is highly desired. Much research has been done on this topic, but none of it has covered all the techniques that have been used or have the potential to be used in the future. This study aims to support a comprehensive and methodical review of studies on the data-driven approach for estimating the RUL of cutting tools used in various computer numerical control (CNC) machining processes, including drilling, milling, and turning operations. This paper is a summary of various methods for monitoring, feature extraction techniques, decision-making models, and sensors currently available in this domain. A comparison of the accuracy of different prediction models used for estimating tool wear in TCM is also presented in this paper. The study concludes with a discussion of recent advances, challenges, and limitations in RUL prognostic methods that use artificial intelligence (AI), as well as the potential for further research in this domain.}, archive = {J_ACCESS}, author = {Dhanalekshmi Prasad Yedurkar and Thomas Schlech and Markus G. R. Sause}, doi = {10.1109/ACCESS.2025.3579204}, journal = {IEEE Access}, pages = {106246-106286}, shortjournal = {IEEE Access}, title = {A systematic review on smart and predictive maintenance in tool condition monitoring}, volume = {13}, year = {2025}, }
• (2025). An improved diagnosis approach for short-circuit fault diagnosis in MPC-based current source inverter system. ACCESS, 13, 106230-106245. (www)

  @article{ , abstract = {Due to its excellent dynamic performance, the model predictive current control (MPC) method is widely adopted in power inverter systems, such as current source inverters (CSI). However, short-circuit faults in power switches are common in CSI systems, leading to a periodic output current distortion. While previous fault diagnosis methods have primarily focused on CSI systems using conventional control strategies, this study introduces a novel approach based on the MPC method to enhance the reliability of current source converters by diagnosing short-circuit faults in power switches. The proposed method begins by calculating the phase of the reference current vector in real time. It then leverages the cost function results from the MPC algorithm as the key diagnostic parameter derived through a comparative analysis of predicted system behavior under normal and fault conditions. In the presence of faulty switches, the algorithm identifies the phase of the reference current where the predicted currents align with the reference values. Additionally, it leverages the fact that during a fault, the MPC optimal vector remains unchanged from the moment the fault occurs. The duration and value of this unchanging optimal vector depend on the type and location of the switch fault, enabling precise fault detection and localization using both phase information and the MPC optimal current vector. This approach remains highly robust, as it analyzes both the recovery state, where reference currents are accurately tracked, and the faulty stage, where currents approach zero. To validate the effectiveness of the proposed method, a MATLAB/Simulink model was developed. Simulation results confirm the method’s robustness, as the proposed approach is capable of reliably identifying faults even under low current demand conditions, where conventional methods typically struggle. This is because traditional methods rely heavily on detecting changes in the measured current amplitude, which become increasingly subtle and difficult to distinguish as the current decreases, making fault identification unreliable. This approach provides a reliable and efficient solution for enhancing the safety and performance of CSI systems, particularly in applications where precise fault detection is crucial.}, archive = {J_ACCESS}, author = {Jonggrist Jongudomkarn and Pirat Khunkitti and Apirat Siritaratiwat}, doi = {10.1109/ACCESS.2025.3580785}, journal = {IEEE Access}, pages = {106230-106245}, shortjournal = {IEEE Access}, title = {An improved diagnosis approach for short-circuit fault diagnosis in MPC-based current source inverter system}, volume = {13}, year = {2025}, }
• (2025). A semi-angular cylindrical shape-based wideband circularly polarized dielectric resonator antenna for C-V2X and sub-6 GHz IoT applications. ACCESS, 13, 106218-106229. (www)

  @article{ , abstract = {This article highlights the realization of a circularly polarized (CP) dielectric resonator antenna (DRA) that consists of two orthogonally placed aperture-coupled semi-angular cylindrical DRs and has 45.1% of wide axial ratio bandwidth (ARBW). The antenna has an acceptable axial ratio value ( $\leq 3$ dB) and impedance bandwidth ( $S_{11} \leq -10$ dB) from 3.18-5.02 GHz that covers n77, n78, n48, and n79 5G bands. Additionally, it supports wireless standards that are essential for next-generation sub-6GHz Internet of Things (IoT) communications, such as WiMAX (3.3-3.8 GHz), Citizens Broadband Radio Service (CBRS) (3.55-3.7 GHz), and LTE (uplink 3.41-3.5 GHz/downlink 3.51-3.6 GHz). The antenna shows consistent broadside radiation with a cross-polarization discrimination (XPD) $\geq 15$ dB throughout the band with a maximum gain value of 6.3 dBic at 3.4 GHz. It has a volume of $1.4\lambda _{0} \times 1.4\lambda _{0} \times 0.1\lambda _{0}$ (at lower frequency) and can be a good candidate for the emerging C-V2X (Cellular Vehicle-to-Everything) which mainly uses 5G NR (3.3-4.2 GHz (C-Band, mid-band 5G for wide-area coverage) along with LTE 4G and 4.9 GHz (Public Safety Band) for its communication.}, archive = {J_ACCESS}, author = {Biswajit Dwivedy and Tanmaya Kumar Das and Dheeren Ku Mahapatra and Debasis Gayen}, doi = {10.1109/ACCESS.2025.3580143}, journal = {IEEE Access}, pages = {106218-106229}, shortjournal = {IEEE Access}, title = {A semi-angular cylindrical shape-based wideband circularly polarized dielectric resonator antenna for C-V2X and sub-6 GHz IoT applications}, volume = {13}, year = {2025}, }
• (2025). Cross attentive multi-cue fusion for skeleton-based sign language recognition. ACCESS, 13, 106201-106217. (www)

  @article{ , abstract = {Sign language, the primary communication medium of the Deaf, uses visual cues from upper body, hands, and face. Sign Language Recognition (SLR) aims to learn salient representations from these cues to bridge the communication gap between Deaf and hearing communities. Existing Graph Neural Network-based SLR frameworks often represent sign videos as sequences of graphs formed by hand and body joints. However, relying solely on upper body topology often results in suboptimal solutions. This work shows that incorporating domain-specific hand topologies can single-handedly help reach state-of-the-art SLR performance. This motivates the need for fusing multiple visual cues to build robust and generalizable SLR frameworks. Yet, the fusion process is challenged by changing spatial and temporal dynamics across articulators. To address this, we propose a multi-cue cross-attention framework that enables interactions between hand and upper body cues during fusion. We demonstrate how the proposed attention-based framework exposes distinct temporal patterns of visual cue representations extracted via Spatio-Temporal Graph Convolutional Network (ST-GCN) and exploits them for learning SL representations more effectively. Our experiments on two benchmark isolated sign language datasets, BosphorusSign22k and AUTSL, show that our proposed framework is on par with state-of-the-art performance on isolated SLR while highlighting the benefit of choosing domain-specific hand graph topologies and fusing multiple cues for SLR. Furthermore, our cross-attentive approach for fusion of upper-body and hand cues improves recognition accuracy by around 1% and 3% on the respective datasets over hand-only models while making the recognition interpretable and demonstrating the complementary interactions between visual cues.}, archive = {J_ACCESS}, author = {Ogulcan Ozdemir and Inci M. Baytas and Lale Akarun}, doi = {10.1109/ACCESS.2025.3579092}, journal = {IEEE Access}, pages = {106201-106217}, shortjournal = {IEEE Access}, title = {Cross attentive multi-cue fusion for skeleton-based sign language recognition}, volume = {13}, year = {2025}, }
• (2025). Adaptive quantum-inspired evolution for denoising PCG signals in unseen noise conditions. ACCESS, 13, 106188-106200. (www)

  @article{ , abstract = {This study investigates a novel Quantum-inspired Evolutionary Algorithm (QiEA) for denoising phonocardiogram signals using a fractional-order digital differentiator FIR filter (FODD). The filter coefficients were optimised using the proposed QiEA with Adaptive Rotation Gate Operator (ARGO). The proposed algorithm accelerates convergence towards optimal solutions based on fitness feedback, improving filter optimisation while clamping rotation angles to maintain algorithm stability. The performance metrics utilised were Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (CC) and Zero Crossing Rate (ZCR). The results demonstrate that the proposed algorithm achieved a 25% improvement in the SI-SDR metric in highly noisy environments for pathological PCG signals. Detailed comparative analyses were conducted for single and combined-noise models against current state-of-the-art Deep learning (DL) methods. It exhibited an increase in the SI-SDR metric in the presence of APGN, by 17% and 24% for single and combined-noise models, respectively. In the presence of AWGN, the output increased by 9% and 11%, respectively, for both models in highly noisy environments. To quantify suppression of noise, ZCR was evaluated for the PCG signals before and after denoising. ZCR reduced by 98.4% revealing removal of high frequency noise artifacts for both pathological and non-pathological signals. The results position the proposed algorithm as a data-efficient, noise-adaptable alternative solution critical in resource-constrained dynamic situations across unseen noise patterns. These improved results can be attributed to reliance on evolutionary strategies rather than learned weights. The findings presented in this paper can have a significant impact on healthcare monitoring systems and real-time processing in critical systems.}, archive = {J_ACCESS}, author = {Lubna Siddiqui and Ashish Mani and Jaspal Singh}, doi = {10.1109/ACCESS.2025.3580584}, journal = {IEEE Access}, pages = {106188-106200}, shortjournal = {IEEE Access}, title = {Adaptive quantum-inspired evolution for denoising PCG signals in unseen noise conditions}, volume = {13}, year = {2025}, }
• (2025). YAREN: Humanoid torso robot platform for research, social interaction, and educational applications. ACCESS, 13, 106175-106187. (www)

  @article{ , abstract = {In the pursuit of maximum efficiency and performance, robots are often designed with complex functionalities and mechanisms customized for specific applications. These designs are frequently proprietary, limiting their adoption by the research community for experimentation. This work introduces YAREN, an open-source humanoid torso robot for social and educational applications. We modeled YAREN’s anatomy based on anthropometric measurements, considering dimensions and mobility limitations, to promote more friendly and natural interactions with its end-users. YAREN has 12 degrees of freedom which allows it to perform a wide variety of movements, generating more realistic and dynamic interactions. Executing broader and more expressive poses enables more efficient and meaningful social communication with users. The electronic components were selected for availability and robustness, ensuring the robot’s structure moves without complications. Relatively low-cost components were selected to provide a high-quality product that remains accessible to multiple laboratories, even in resource-limited contexts. YAREN’s software is based on the Robot Operating System (ROS) to facilitate expansion and scalability. We also provide the kinematic model and tuning of the PID controller for the motors to control each joint precisely and optimize movement without compromising user safety. Based on our proposed open-source design, YAREN can be expanded to perform diverse interactive functions for social and educational purposes, promoting the research and development of humanoid robots. We hope our design can be replicated by researchers worldwide to support multiple projects and help grow more laboratories.}, archive = {J_ACCESS}, author = {Daniela Sánchez-Orozco and Rhandall Valdez and Yandri Uchuari and Marcelo Fajardo-Pruna and Luis Cavazos Quero and Redhwan Algabri and Emiliano Quinones Yumbla and Francisco Yumbla}, doi = {10.1109/ACCESS.2025.3581134}, journal = {IEEE Access}, pages = {106175-106187}, shortjournal = {IEEE Access}, title = {YAREN: Humanoid torso robot platform for research, social interaction, and educational applications}, volume = {13}, year = {2025}, }
• (2025). When multimodal large language models meet computer vision: Progressive GPT fine-tuning and stress testing. ACCESS, 13, 106155-106174. (www)

  @article{ , abstract = {The rapid evolution of Multimodal Large Language Models (LLMs) has redefined the landscape of artificial intelligence, with OpenAI’s GPT-4o representing a transformative leap in multimodal learning and processing. This study pioneers the fine-tuning of GPT-4o on image data, addressing a critical gap in evaluating its performance beyond zero-shot capabilities. It investigates the potential of GPT-4o multimodal LLM for computer vision classification tasks, focusing on e-commerce product categorization through a novel, progressive, few-shot learning fine-tuning framework. Leveraging GPT-4o’s newly available multimodal capabilities, we implemented a four-phase progressive training procedure across varying image resolutions (100, 200, and 400 pixels). Each phase was rigorously evaluated for training duration, computational cost, loss metrics, and validation performance. Predictions were assessed using accuracy, precision, recall, and F1-score metrics while closely monitoring prediction costs and processing time. To establish a benchmark, we replicated the process with ResNet-50 and ConvNeXt-tiny models, applying Bayesian optimization for hyperparameter tuning. We explored a range of batch sizes and learning rates using the Optuna library, conducting 100 trials over a maximum of 50 epochs with early stopping and the successive halving pruner to identify optimal configurations for each model. Although ResNet-50 and ConvNeXt-tiny exhibited lower fine-tuning costs, achieving comparable performance required larger batch sizes and lower learning rates. GPT-4o’s multimodal learning approach achieved 87% accuracy with minimal training—just three epochs and a batch size of one—highlighting its efficiency in few-shot learning with limited labeled data. Our findings highlight key trade-offs between cost-efficiency and performance, offering actionable insights for deploying multimodal Large Language Models in resource-constrained scenarios. GPT-4o’s progressive fine-tuning approach showed promising results in computer vision classification tasks, suggesting its potential as a scalable, multimodal AI tool for real-world multimodal learning and vision-based applications.}, archive = {J_ACCESS}, author = {Konstantinos I. Roumeliotis and Nikolaos D. Tselikas and Dimitrios K. Nasiopoulos}, doi = {10.1109/ACCESS.2025.3579420}, journal = {IEEE Access}, pages = {106155-106174}, shortjournal = {IEEE Access}, title = {When multimodal large language models meet computer vision: Progressive GPT fine-tuning and stress testing}, volume = {13}, year = {2025}, }
• (2025). Modeling energy communities: A case study of quantum approximate optimization on a superconducting processor. ACCESS, 13, 106140-106154. (www)

  @article{ , abstract = {This work explores the use of variational quantum algorithms to optimize energy distribution among users in energy communities, using real data from a community lab. This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. Given the increasing complexity as the problem size grows and the limitations of classical computing methods in addressing it, we study an approach that models the problem using QUBO and solves it applying quantum optimization algorithms. Our analysis includes a comparative study of two variational techniques, QAOA and VQE, and the implementation of the former on Qmio, an actual superconducting quantum processor. Although deploying QAOA on Qmio required specific adaptations to the hardware, we were able to achieve a shift of the probability distribution towards lower-energy solutions without error mitigation techniques, which highlights both QAOA’s potential and the intrinsic challenges of quantum optimization.}, archive = {J_ACCESS}, author = {Mateo Alonso and Guillermo Rubiños Rodríguez and Pablo Díez-Valle and Ana Garbayo and Xela García-Santiago and Gonzalo Blázquez Gil}, doi = {10.1109/ACCESS.2025.3578780}, journal = {IEEE Access}, pages = {106140-106154}, shortjournal = {IEEE Access}, title = {Modeling energy communities: A case study of quantum approximate optimization on a superconducting processor}, volume = {13}, year = {2025}, }
• (2025). Cooperative terrain-based GNSS-denied navigation with particle filter. ACCESS, 13, 106129-106139. (www)

  @article{ , abstract = {Terrain-based Navigation (TBN) is a widely used navigation method for aircraft and missiles when GNSS applications are not reliable. However, this method encounters the issue of state estimation uncertainty when flying over flat areas such as the sea, fields, or where the landforms are very similar. We propose a cooperative particle filter-based approach to address this issue, exploiting information sharing between aircraft information flights. Assuming that the relative positions of the aircraft in the fleet are known, each air vehicle makes a state estimation with its particle filter (PF) and sends this position information to the other plane. The shared information and the relative positions of the aircraft are compared to decide whether the estimated positions are correct. Additionally, in cases of uncertainty where it is challenging to calculate the position, the uncertainty is eliminated through the shared information. The simulation results show that the proposed algorithm performs well regarding flight accuracy in various terrains.}, archive = {J_ACCESS}, author = {Ziya Uygar Yengin and Emre Koyuncu}, doi = {10.1109/ACCESS.2025.3580462}, journal = {IEEE Access}, pages = {106129-106139}, shortjournal = {IEEE Access}, title = {Cooperative terrain-based GNSS-denied navigation with particle filter}, volume = {13}, year = {2025}, }
• (2025). Robust model predictive control-based recurrent neural networks for autonomous vehicles in avoidance collisions. ACCESS, 13, 106115-106128. (www)

  @article{ , abstract = {Ensuring safe driving under real-time uncertainties remains a critical challenge in autonomous vehicle control. To address this issue for a collision avoidance task, this study proposes a robust model predictive control (RMPC) framework that handles parametric uncertainties using optimization-based linear matrix inequality (LMI). By incorporating system parametric uncertainties, the RMPC enhances driving stability and safety through the use of input-state constraints. However, due to its computational complexity, we employ a data-driven approach by collecting measurements under different road adhesion conditions to train deep neural networks with a long short-term memory layer (DNN-LSTM). The proposed DNN-LSTM effectively captures temporal dependencies, outperforming existing DNNs when using the same hyperparameters in accuracy and generalization. All comparative simulations are conducted and verified using the high-fidelity CarSim/Simulink co-simulation platform. Therefore, the proposed DNN-LSTM approach approximates the RMPC policy with high training performance and significantly reduces computational complexity, which is more beneficial for real-time implementation. Using the DNN-LSTM is further emphasized to maintain the ability to drive stability of autonomous vehicles compared with online and offline RMPCs, which show a stable region violation at some fixed operation points.}, archive = {J_ACCESS}, author = {Hung Duy Nguyen and Duc Thinh Le and Tung Lam Nguyen and Minh Nhat Vu}, doi = {10.1109/ACCESS.2025.3579216}, journal = {IEEE Access}, pages = {106115-106128}, shortjournal = {IEEE Access}, title = {Robust model predictive control-based recurrent neural networks for autonomous vehicles in avoidance collisions}, volume = {13}, year = {2025}, }
• (2025). Two-stage learning of CPG and postural reflex toward quadruped locomotion on uneven terrain with simple reward. ACCESS, 13, 106103-106114. (www)

  @article{ , abstract = {Locomotion control for quadruped robots has been extensively studied through various methodologies, including model-based, learning-based, and bio-inspired approaches. The integration of these methods is increasingly recognized as a powerful strategy for enhancing locomotion performance. Among them, the Central Pattern Generator-Reinforcement Learning (CPG-RL) framework, which combines Central Pattern Generators (CPG) with reinforcement learning (RL), offers key advantages such as accelerated learning, improved Sim-to-Real transfer, and the ability to learn with a simplified reward function. However, its robustness on uneven terrains remains limited, as learning primarily focuses on tuning the parameters within the CPG. Specifically, because the foot placement is constrained to a pre-designed foot trajectory, the system is unable to stabilize its posture on uneven terrain. In biological systems, posture control is regulated by the brainstem and cerebellum, which send corrective signals to the spinal cord to modulate muscle activity and maintain balance in coordination with the CPG network. Inspired by this mechanism, this study introduces a postural reflex network that seamlessly integrates with the CPG network to enhance stable locomotion on uneven terrains. Notably, this improvement is achieved exclusively through piecewise sequential learning with a simplified reward function. The proposed approach expands the applicability of CPG-based locomotion under unstructured environments and contributes to the advancement of bio-inspired control strategies for quadrupedal locomotion. The proposed method progressively improves CPG-based locomotion on uneven terrain by learning the CPG parameters and the foot placement adjustments relative to the pre-designed trajectory in two-stage learning manner.}, archive = {J_ACCESS}, author = {Ryosei Seto and Guanda Li and Kyo Kutsuzawa and Dai Owaki and Mitsuhiro Hayashibe}, doi = {10.1109/ACCESS.2025.3579786}, journal = {IEEE Access}, pages = {106103-106114}, shortjournal = {IEEE Access}, title = {Two-stage learning of CPG and postural reflex toward quadruped locomotion on uneven terrain with simple reward}, volume = {13}, year = {2025}, }
• (2025). Optimizing semantic-aware video compression using particle swarm optimization technique for automotive applications. ACCESS, 13, 106091-106102. (www)

  @article{ , abstract = {Autonomous Mobility (AM) is the next milestone in the history of the automotive industry. Radar, lidar, cameras, ultrasonic, and other sensor units provide the required information for highly automated driving and pave the way for AM. This popular and fast-developing technology aims to increase vehicle safety through various sensors and advanced-level Artificial Intelligence (AI)- based algorithms. The camera is one of the vital components of AM systems, which captures real-time videos, processes, encodes, packetizes and transfers them to different processing units for further applications. Video compression plays a crucial role in video data compression for multi-camera systems and this technique needs to be perception-aware. The capabilities of video compression are improved by perception awareness, but it is complicated and requires a method to obtain optimal values for its parameters. This paper proposes a framework for semantic-aware video compression with reduced functional complexity and a proper flow for quantization parameter optimization. The optimized quantization factors used for encoding the region of interest and the region out of interest of the input video frame. The proposed framework was evaluated with H.264, HEVC, VP8, and AV1 video compression on the public cityscape dataset. Our experimental results demonstrated that our proposed framework with the AV1 codec achieved the highest efficiency (compression ratio of 12:1). HEVC produced the highest quality results, with a notable improvement in both SSIM (0.9981) and PSNR (53.5656). The fastest execution speeds were achieved by H.264 (CPU: 237 ms, GPU: 65) and VP8 (CPU: 246 ms, GPU: 64).}, archive = {J_ACCESS}, author = {Vadivel Shanmugam and B. Uma Maheswari}, doi = {10.1109/ACCESS.2025.3580151}, journal = {IEEE Access}, pages = {106091-106102}, shortjournal = {IEEE Access}, title = {Optimizing semantic-aware video compression using particle swarm optimization technique for automotive applications}, volume = {13}, year = {2025}, }
• (2025). Predicting sugar yield from sugarcane using machine learning for jaggery production. ACCESS, 13, 106077-106090. (www)

  @article{ , abstract = {This study presents a machine learning approach for rapid and accurate prediction of sugar yield per metric ton of sugarcane, using multiple physicochemical and environmental parameters. Multiple regression models were developed and evaluated using a comprehensive dataset containing Brix values, cane juice weights, Pol values, reducing sugar content, non-sugar content, water content, water type, soil type, ratoon type, season, and cane variety. After rigorous evaluation and hyperparameter optimization, the Random Forest model demonstrated superior performance with a coefficient of determination (R ${^{{2}}} = 0.9503$ ), Root Mean Squared Error (RMSE = 3.13), and Mean Absolute Error (MAE = 1.63). The feature importance results highlighted a clear hierarchy in predictor significance, with water content and Brix values collectively driving approximately 90% of the model’s predictive strength. These two variables substantially outweighed all other factors in determining the model outcomes. Unlike traditional approaches that rely on proxy measures that require subsequent calculations and technical interpretation, this direct prediction methodology provides an immediate, actionable metric for all stakeholders. This data-driven alternative to laboratory-based assessment methods enables jaggery manufacturers and sugarcane growers to make informed decisions regarding crop selection, harvesting time, pricing, and processing strategies based on predicted sugar yields. The proposed model has the potential to significantly reduce economic losses, improve product quality, and enhance sustainability in the sugarcane value chain. Overall, this study offers a simplified, cost-effective, and interpretable solution for sugar yield estimation, which is particularly beneficial to jaggery producers and smallholder farmers who lack access to an advanced testing infrastructure.}, archive = {J_ACCESS}, author = {Kathirvel Narayanasamy and Ilayaraja Venkatachalam}, doi = {10.1109/ACCESS.2025.3580487}, journal = {IEEE Access}, pages = {106077-106090}, shortjournal = {IEEE Access}, title = {Predicting sugar yield from sugarcane using machine learning for jaggery production}, volume = {13}, year = {2025}, }
• (2025). Review on silicon carbide-based high-fundamental frequency inverters for high-speed drives. ACCESS, 13, 106055-106076. (www)

  @article{ , abstract = {This article provides a comprehensive review of Silicon Carbide (SiC) based inverters designed for High-Speed (HS) drive applications, which require higher output frequencies to enhance efficiency and power density. The review analyzes approximately 70 recent three-phase SiC inverter designs, categorizing them by topology, specifically two-level, Neutral Point Clamped (NPC), T-type, and Multilevel, and discussing their advantages, limitations, and emerging trends. It also examines key design considerations, including the selection of fundamental and switching frequencies, optimization of DC-link voltage, and thermal management. Furthermore, the paper reviews industrial SiC prototypes, recent advancements in SiC device technologies, and various configurations (discrete, parallel, and module), highlighting the advantages and challenges associated with each approach. The analysis identifies a research gap in the development of high-power inverters (greater than 1 MW) capable of operating above 1 kHz fundamental frequencies. The paper concludes by outlining future research directions aimed at advancing SiC inverter technology for HS drives.}, archive = {J_ACCESS}, author = {Deepak Upadhyay and Saeid Deliri and Aleksi Mattsson and Pasi Peltoniemi and Lassi Aarniovuori}, doi = {10.1109/ACCESS.2025.3580521}, journal = {IEEE Access}, pages = {106055-106076}, shortjournal = {IEEE Access}, title = {Review on silicon carbide-based high-fundamental frequency inverters for high-speed drives}, volume = {13}, year = {2025}, }
• (2025). A review of developments and metrology in machine learning and deep learning for wearable IoT devices. ACCESS, 13, 106035-106054. (www)

  @article{ , abstract = {This research explores the integration of Artificial Intelligence (AI) in wearable devices, including smartwatches, fitness trackers, smart clothing, and smart eyewear. Machine Learning (ML) and Deep Learning (DL) play a crucial role in enhancing these devices, leveraging sophisticated algorithms within the Internet of Things (IoT) ecosystem. AI-powered wearables incorporate metrology and advanced computational techniques, with Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) driving applications in activity recognition, health monitoring, and personalized recommendations. The work presents case studies, highlighting AI applications in smart devices, such as stress detection via Heart Rate Variability, personalized exercise guidance, muscular activity monitoring, and real-time image recognition. Additionally, this review presents metrological approaches for evaluating AI performance in wearable systems, including data quality parameters such as accuracy, precision, and sampling rate. It also examines how algorithm selection influences model performance metrics and computational efficiency, which are critical for real-time and resource-constrained applications. Real-world implementations will illustrate the practical deployment of AI in commercial wearable products. Moreover, the research will address privacy and data security challenges associated with AI-driven wearable technology, ensuring the safeguarding of user information.}, archive = {J_ACCESS}, author = {Minh Long Hoang}, doi = {10.1109/ACCESS.2025.3573937}, journal = {IEEE Access}, pages = {106035-106054}, shortjournal = {IEEE Access}, title = {A review of developments and metrology in machine learning and deep learning for wearable IoT devices}, volume = {13}, year = {2025}, }
• (2025). Dynamic updating the three-way regions for the optimal rules. ACCESS, 13, 106019-106034. (www)

  @article{ , abstract = {Knowledge acquisition is an important research hotspot in the field artificial intelligence. Most of the research methods mainly mine all decision rules from the static data. However, they face two aspects: 1) Some old possible rules with lower confidences may be useless; 2) Some optimal rules from the new data are unable to be effectively obtained. How to acquire the optimal rules from dynamic data while ensuring efficiency becomes an important challenge. To this end, a dynamic updating mechanism for the optimal rules using three-way decisions is designed. More specifically, the incremental updating strategies for three-way regions are proposed and illustrated when multiple objects are dynamically changed from the decision table. Secondly, the two corresponding incremental learning algorithms are implemented for the dynamic objects using three-way decisions. Finally, a series of experiments on eight data sets are validated our models. Experiment results demonstrate that our proposed incremental updating algorithms for the optimal rules can significantly accelerate the knowledge updating.}, archive = {J_ACCESS}, author = {Xiaohua Wei and Jiangang Ye and Jin Qian and Qiaowen Deng}, doi = {10.1109/ACCESS.2025.3580589}, journal = {IEEE Access}, pages = {106019-106034}, shortjournal = {IEEE Access}, title = {Dynamic updating the three-way regions for the optimal rules}, volume = {13}, year = {2025}, }
• (2025). Multi-agent energy market simulations with machine learning integration: A systematic literature review. ACCESS, 13, 106003-106018. (www)

  @article{ , abstract = {The transition to green energy requires advanced models to analyze complex energy systems. Among these models, agent-based modeling (ABM) or multi-agent systems represents the most pivotal simulation techniques. Machine learning (ML) methods are integrated into ABM to accurately simulate real-world scenarios and the behavior of emerging energy market agents, such as renewable power plants, incentive schemes, electric vehicles, and battery energy storage systems. Initially, ML methods were primarily used to improve agent learning using a reinforcement learning (RL) method. However, the need to estimate external data has motivated the broader adoption of various ML methods. This paper reviews the integration of ML within ABM frameworks, by examining articles from 2014 to 2024. The review methodology utilized self-organizing map (SOM) based clustering using the market data types, simulation periods, and ML contribution types as the clustering attributes. The study provides an overview of the current state of the field and identifies potential research gaps. The findings indicate that agent learning and traditional RL methods dominate; however, significant gaps are evident in terms of the inclusion of long-term and data prediction studies, and future studies should integrate more observations from other energy markets along with electricity.}, archive = {J_ACCESS}, author = {Burak Gokce and Gulgun Kayakutlu}, doi = {10.1109/ACCESS.2025.3580735}, journal = {IEEE Access}, pages = {106003-106018}, shortjournal = {IEEE Access}, title = {Multi-agent energy market simulations with machine learning integration: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A crystal-less clock generator for low-power and low-cost sensor transceiver with 12.9 MHz-to-3.3 GHz range, 16.67 ppm/°C inaccuracy from −25°C to 85°C, and 0.25 μs settle-time. ACCESS, 13, 105995-106002. (www)

  @article{ , abstract = {This paper proposes the design and implementation of a crystal-less clock generator for low-power, cost-effective sensor transceiver systems. The proposed system integrates a p-poly resistor-based relaxation RC oscillator (RCO), a Fractional-N PLL (W-FPLL) with a wide frequency range and fast settling time, and an on-chip temperature sensor unit (TSU) for temperature compensation. To minimize frequency variation in the RCO, a low target frequency is set, and the frequency range is expanded using the PLL. The W-FPLL supports a wide frequency range from 12.9 MHz to 3.3 GHz. Additionally, temperature-induced frequency variations are compensated by applying the TSU’s digital data to the Delta-Sigma Modulator (DSM), while Piecewise Linear Calibration enhances compensation accuracy. The proposed clock generator achieves a frequency stability of 16.67 ppm/°C under temperature variations. The total system die area is 0.102 mm2. At 3.3 GHz, the system consumes 6.35 mW of total power.}, archive = {J_ACCESS}, author = {Sangdon Jung and Minsu Park and Jung-Hoon Chun}, doi = {10.1109/ACCESS.2025.3578249}, journal = {IEEE Access}, pages = {105995-106002}, shortjournal = {IEEE Access}, title = {A crystal-less clock generator for low-power and low-cost sensor transceiver with 12.9 MHz-to-3.3 GHz range, 16.67 ppm/°C inaccuracy from −25°C to 85°C, and 0.25 μs settle-time}, volume = {13}, year = {2025}, }
• (2025). A 4–40 v input Floating–Ground GaN-based Buck–Boost converter with pre-charge function for enhanced power efficiency. ACCESS, 13, 105982-105994. (www)

  @article{ , abstract = {Many applications including automotives demand DC-DC power converters that can cover wide input range with improved power efficiency. This paper presents a 4–40 V input, Gallium Nitride (GaN) based non-inverting buck-boost converter incorporating several methods to improve power efficiency. It employs a new floating-ground three-state four-switch topology along with a pre-charge gate driver for the low-side GaN switch, a Dual Duty-Cycle Controller (DDCC) and an Inductor Peak Current ( $I_{LPEAK}$ ) adjusting Module (IPCM). The floating-ground technique alleviates the voltage stress on the high-side power switch and minimizes the inductor current ripple ${\Delta I}_{L}$ , resulting in reduction of both switching power loss and conduction power loss. Additionally, the floating-ground topology together with a pre-charge gate driver for the GaN switch achieves a precise pre-charging time duration, preventing reverse conduction during the switch dead time. Consequently, the converter optimizes the power efficiency especially in the buck-boost mode (for $V_{IN}$ of 10–14 V in design). The DDCC determines precise duration of each state for the three-state operation and maintain seamless transition across different modes. The ICPM enables adjusting $I_{LPEAK}$ through indirectly sensing $I_{L}$ and the output voltage $V_{OUT}$ followed by the DDCC. With the proposed techniques, the reverse conduction of the low-side switch is minimized, and a 2% peak efficiency improvement is achieved in the buck-boost mode. Operating at 2 MHz covering 4–40 V input to a 24W (12 V and 2 A) output, this buck-boost converter achieves a peak efficiency of 92%.}, archive = {J_ACCESS}, author = {Weizhong Chen and Lei Chen and Ping Gui}, doi = {10.1109/ACCESS.2025.3579792}, journal = {IEEE Access}, pages = {105982-105994}, shortjournal = {IEEE Access}, title = {A 4–40 v input Floating–Ground GaN-based Buck–Boost converter with pre-charge function for enhanced power efficiency}, volume = {13}, year = {2025}, }
• (2025). Deep learning techniques for detecting morphed face images: A literature review. ACCESS, 13, 105952-105981. (www)

  @article{ , abstract = {Face morphing, as a digital image manipulation technique, is becoming increasingly prevalent, posing challenges in various fields, including biometric security, forensics, and identity verification systems. This paper provides a comprehensive review of the state-of-the-art of deep learning techniques employed in morphed image detection, including Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs). These models have shown considerable potential in automating feature extraction, identifying subtle manipulation traces, and improving detection accuracy across different morphing techniques. However, as morphing techniques continue to evolve and produce more sophisticated attacks, current methods face challenges in detecting subtle artifacts, particularly in high-quality morphs. The review also highlights the issues related to dataset diversity and model generalization, noting that many approaches struggle with unseen morphing attacks and real-world scenarios, such as low-resolution, compressed, or printed & scanned images. Despite these challenges, deep learning techniques have made significant progress in improving Morph Attack Detection (MAD) systems. The paper suggests future directions for enhancing the robustness and generalization of these systems, focusing on expanding datasets, improving model adaptability, and developing methods that can detect increasingly sophisticated morphing attacks in real-world applications.}, archive = {J_ACCESS}, author = {Pedro Morais and Inês Domingues and Jorge Bernardino}, doi = {10.1109/ACCESS.2025.3578199}, journal = {IEEE Access}, pages = {105952-105981}, shortjournal = {IEEE Access}, title = {Deep learning techniques for detecting morphed face images: A literature review}, volume = {13}, year = {2025}, }
• (2025). Future open networks cross-domain cognitive orchestration: A novel design paradigm. ACCESS, 13, 105911-105951. (www)

  @article{ , abstract = {Future Open Networks (FONs) are envisioned as large-scale, decentralised, and data-driven systems designed with open interfaces and interoperable standards to deliver diverse services and applications. The management and orchestration of these networks will utilise intelligent processing. This paper introduces a novel Cognitive Cross-Domain Orchestration (CDO) architectural framework for FONs, structured as a quad-view architecture. A background setting for the architecture is provided by a survey of relevant projects, followed by a requirements analysis for FON orchestration and management. The proposed CDO architectural framework consists of four distinct views: the Organizational View identifies stakeholders and business entities; the Functional View outlines the logical design; the Cognitive & AI/ML View connects logical functions with supportive cognitive functions for network optimization; and the System-level View links these functions to the necessary physical and intangible assets. This segregation enables each view to provide a focused analysis of the roles, interactions, and properties, thereby promoting a coherent design. The paper also describes a plausible partial proof-of-concept implementation of the CDO System-level architecture. Within the context of this CDO implementation, stakeholder participation in the provision of an end-to-end service delivery orchestrated across multiple network domains is described.}, archive = {J_ACCESS}, author = {Saptarshi Ghosh and Hamid Asgari and Darryl Hond and Konstantinos Antonakoglou and Ioannis Mavromatis and Noel Butler and Rasheed Hussain and Shah Zeb and Kostas Katsaros and Shadi Moazzeni and David Tee and Gabriele Incorvaia and Dimitra Simeonidou}, doi = {10.1109/ACCESS.2025.3580176}, journal = {IEEE Access}, pages = {105911-105951}, shortjournal = {IEEE Access}, title = {Future open networks cross-domain cognitive orchestration: A novel design paradigm}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving emotion detection: Evaluating the trade-off between K-anonymity and model performance. ACCESS, 13, 105901-105910. (www)

  @article{ , abstract = {In the realm of artificial intelligence, the pursuit of enhanced model performance has often prioritized the exponential growth of training data, sometimes relegating concerns about data privacy. This approach has fostered a perception that data privacy and the achievement of high-performance AI models are inherently opposing goals, particularly as digital fingerprinting is increasingly presented as essential for personalized experiences. This study aims to challenge this notion by demonstrating that even straightforward anonymization preprocessing techniques do not substantially alter the performance of machine learning models, regardless of their initial capabilities, while simultaneously safeguarding user privacy. We trained four different machine learning models: linear regression, linear ridge regression, a neural network, and a BiLSTM network, and evaluated their performance in data sets with varying levels of k-anonymity, specifically comparing results from a K-index of 1 to 52. Our findings indicate that, while certain trade-offs may exist, they should not be considered significant enough to deter the integration of anonymization techniques in machine learning and AI research. This work advocates for the routine adoption of anonymization practices, supporting the premise that robust model performance and strong data privacy are not mutually exclusive objectives.}, archive = {J_ACCESS}, author = {Alejandro de León Languré and Mahdi Zareei}, doi = {10.1109/ACCESS.2025.3578958}, journal = {IEEE Access}, pages = {105901-105910}, shortjournal = {IEEE Access}, title = {Privacy-preserving emotion detection: Evaluating the trade-off between K-anonymity and model performance}, volume = {13}, year = {2025}, }
• (2025). Improved YOLOv8-based algorithm for citrus leaf disease detection. ACCESS, 13, 105888-105900. (www)

  @article{ , abstract = {In view of the small difference between citrus leaf diseases which can lead to false inspection and missed inspection, an improved YOLOv8 citrus leaf disease detection algorithm is proposed. The proposed approach uses YOLOv8n as the base model and introduces adaptive convolution into the Backbone, allowing the model to dynamically prioritize different disease features. This modification increases the model’s parameter count without adding to the computational cost in terms of floating-point operations. Next, a fast convolution layer is implemented to replace the original C2f module, improving both detection accuracy and computational efficiency. In addition, a multi-scale fusion attention module is incorporated into the Neck section, which effectively integrates disease-related information from different receptive fields, thereby boosting the model’s ability to detect a wider variety of diseases. To further enhance adaptability, the traditional Intersection over Union (IoU) is replaced with the Wise-IoU loss function, improving the model’s performance across different disease types. The algorithm model uses a self-built data set containing 1,810 images, with 481 images of Huanglongbing, 430 images of magnesium deficiency, 451 images of anthracnose, and 448 images of black spot. In order to maintain the randomness of the experimental data, this experiment divided the data set into training set, validation set and test set with a ratio of 7:2:1, which contained 1267 images, 362 images and 181 images respectively. Experimental results show that the improved algorithm achieves an accuracy of 93.2% on the test set, representing a 1.2 percentage increase over the original model, while also reducing the parameter volume and computational complexity by 7% and 7.4% respectively. The improved model notably decreases the missed detection rate under occlusion and improves the detection of small targets. Furthermore, it strikes an effective balance between enhancing detection accuracy, reducing model complexity, and maintaining a lightweight architecture, making them well-suited for efficient citrus leaf disease detection.}, archive = {J_ACCESS}, author = {Zhengbing Zheng and Yibang Zhang and Luchao Sun}, doi = {10.1109/ACCESS.2025.3579074}, journal = {IEEE Access}, pages = {105888-105900}, shortjournal = {IEEE Access}, title = {Improved YOLOv8-based algorithm for citrus leaf disease detection}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on integrated photonic switch fabrics: From device to network topology and controller design. ACCESS, 13, 105850-105887. (www)

  @article{ , abstract = {Emerging applications such as Artificial Intelligence (AI), the Internet of Things (IoT), and Big Data are significantly increasing data traffic within data center networks and high-performance computing (HPC) systems. This surge in data flow is pushing the existing network infrastructures to their limits. To address the demands of this data-driven era, there is a critical need for network technologies and architectures that offer higher capacity, improved performance, and reduced transmission delays. Central to these networks are switches, which play a crucial role in directing data traffic between various nodes and clusters with minimal latency. These switches must manage large bandwidths and support numerous ports efficiently. As electronic switches approach limitations in energy efficiency and bandwidth scaling, optical circuit switching has emerged as a promising alternative. Optical switches are being explored for their potential to provide higher bandwidth and lower energy consumption in data center and HPC networks. This survey paper reviews the most recent advancements in various types of optical switches, including those based on Micro-Electromechanical Systems (MEMS), Silicon Photonics, and III-V interferometric switches, as well as wavelength-selective and hybrid solutions. It covers aspects such as device design, switch fabric topology, tuning mechanisms (e.g., thermo-optic and electro-optic), and control plane implementations. The paper highlights the potential of these technologies while also discussing some challenges that remain in this field for further advancement of optical switches.}, archive = {J_ACCESS}, author = {Mohammad Amin Mahdian and Ebadollah Taheri and Mahdi Nikdast}, doi = {10.1109/ACCESS.2025.3576150}, journal = {IEEE Access}, pages = {105850-105887}, shortjournal = {IEEE Access}, title = {A comprehensive survey on integrated photonic switch fabrics: From device to network topology and controller design}, volume = {13}, year = {2025}, }
• (2025). An adaptive framework for collective anomaly detection in key performance indicators from mobile networks. ACCESS, 13, 105828-105849. (www)

  @article{ , abstract = {Anomaly detection is a critical component of Self-Organizing Networks (SON), enhancing network efficiency and resilience. This paper proposes a novel framework for detecting collective anomalies in univariate Key Performance Indicators (KPIs) in mobile networks. By leveraging data mining and Machine Learning (ML) techniques, the framework enables timely anomaly detection without requiring expert-labeled data. The proposed framework starts by clustering time series from 11 distinct KPIs into four groups. Then, representative KPIs from each cluster are selected to evaluate the anomaly detection performance using two algorithms: the Smart Trouble Ticket Management (STTM) and STUMPY. The STTM algorithm is applied to KPIs with low variability, such as Call Setup Success Rate and Service Drop Rate, showing high accuracy in anomaly detection. For KPIs with higher variability, such as User Downlink (DL) Average Throughput and DL Resource Block Utilization Rate, the STUMPY algorithm is employed, yielding similarly accurate results. The framework, composed of the STTM and the STUMPY algorithms, demonstrates effective anomaly detection, achieving high precision (0.94), recall (0.86), and an F1-score of 0.90, with minimal False Positives (FP). These results underline the framework’s reliability across different types of KPIs, providing a robust solution for anomaly detection in mobile network monitoring, outperforming benchmark algorithms in all key metrics.}, archive = {J_ACCESS}, author = {Madalena Cilínio and Thaína Saraiva and Marco Sousa and Pedro Vieira and António Rodrigues}, doi = {10.1109/ACCESS.2025.3581120}, journal = {IEEE Access}, pages = {105828-105849}, shortjournal = {IEEE Access}, title = {An adaptive framework for collective anomaly detection in key performance indicators from mobile networks}, volume = {13}, year = {2025}, }
• (2025). Balancing complexity and performance of machine learning models for avian pests sound detection in agricultural environments. ACCESS, 13, 105813-105827. (www)

  @article{ , abstract = {Agricultural pest control traditionally relies on inefficient visual inspections. Acoustic monitoring offers a promising alternative by analyzing pest-specific sounds. While effective, implementing acoustic monitoring in agricultural settings faces practical constraints, particularly the limited computational resources available in remote farming environments. This necessitates optimized machine learning (ML) solutions for low-power edge devices. This study evaluates ML models for bird pest detection on resource-constrained platforms. We evaluated convolutional neural networks (CNNs), recurrent neural networks (RNNs), and traditional ML models by comparing standalone and knowledge-distilled versions of EfficientNetB0 and gated recurrent unity (GRU) against EfficientNetB4, Long short-term memory (LSTM), MobileNetV2, LightGBM, and support vector machine (SVM). Analysis revealed significant performance variations across computational requirements. LightGBM achieved 98% accuracy with minimal resources (8,500 parameters, 7KB, 0.6ms inference), demonstrating good efficiency. SVM (97% accuracy) and distilled GRU (86% accuracy) also showed favorable performance-to-resource ratios. Knowledge distillation substantially enhanced the accuracy of EfficientNetB0 (from 73% to 98%) and modestly improved GRU (from 84% to 86%). We examined platform compatibility across computing tiers, discovering that while high-performance edge devices (Jetson Nano, Raspberry Pi 4) support all studied models effectively, microcontrollers require specialized approaches. Advanced microcontrollers (such as ESP32-S3 and STM32H7) can accommodate optimized implementations, while highly constrained platforms (such as Arduino Nano) require TinyML techniques. This research contributes 1) an on-farm audio dataset, 2) comprehensive cross-model evaluation metrics, and 3) deployment optimization strategies for acoustic pest detection systems in resource-constrained agricultural environments.}, archive = {J_ACCESS}, author = {Micheline Kazeneza and Anna Sergeevna Bosman and Destiny Kwabla Amenyedzi and Damien Hanyurwimfura and Emmanuel Ndashimye and Anthony Vodacek}, doi = {10.1109/ACCESS.2025.3580620}, journal = {IEEE Access}, pages = {105813-105827}, shortjournal = {IEEE Access}, title = {Balancing complexity and performance of machine learning models for avian pests sound detection in agricultural environments}, volume = {13}, year = {2025}, }
• (2025). An optimized broadcast-based synchronization protocol for wireless time sensitive networks. ACCESS, 13, 105798-105812. (www)

  @article{ , abstract = {Wireless Time-Sensitive Networks (TSNs) play a crucial role in ensuring high-reliability and low-latency communication, which are fundamental for industrial Internet of Things (IIoT) applications. However, extending TSNs to wireless domains introduces unique challenges, including constrained channel resources and significant propagation delay variations. This paper presents a novel broadcast-based synchronization protocol tailored for multi-node wireless TSNs. The proposed method leverages broadcast frames for simultaneous delay estimation across multiple nodes, significantly reducing channel occupancy. It incorporates a cyclic measurement mechanism that captures delay variation through repeated measurements, enabling robust modeling of delay jitter. Additionally, synchronization accuracy is further enhanced by applying Bayesian estimation based on the measured delay distribution, an approach that outperforms conventional mean-delay compensation strategies. Simulation results demonstrate that the protocol reduces channel usage by up to 50% compared to conventional methods, while maintaining exceptional synchronization accuracy even under conditions of severe delay jitter. Moreover, the proposed method was validated in a large-scale network environment, and several synchronization strategies in multi-AP scenarios were introduced. These findings underscore the protocol’s potential to enhance the reliability and practicality of general wireless TSNs, with IIoT applications in complex industrial environments serving as representative and demanding use cases.}, archive = {J_ACCESS}, author = {Zihao Liu and Zhe Zhao and Peiwei Guo}, doi = {10.1109/ACCESS.2025.3580457}, journal = {IEEE Access}, pages = {105798-105812}, shortjournal = {IEEE Access}, title = {An optimized broadcast-based synchronization protocol for wireless time sensitive networks}, volume = {13}, year = {2025}, }
• (2025). Dual-agent transformer network based on prompt engineering for voltage prediction. ACCESS, 13, 105786-105797. (www)

  @article{ , abstract = {Voltage prediction plays a crucial role in ensuring power quality and enhancing the efficiency of power grid operations. Driven by the need for accurate and reliable voltage prediction methods, this paper introduces a novel dual-agent transformer network based on prompt engineering. By integrating prompt embeddings derived from historical voltage trends into the input embeddings, the proposed method effectively captures temporal correlations within voltage data. Utilizing a dual-agent attention mechanism, which reduces computational complexity by 42% compared to standard transformers, while the prompt embeddings enable precise capture of temporal patterns. Extensive simulations on real-world datasets demonstrate that our approach outperforms existing state-of-the-art models, achieving a 0.5% improvement in prediction accuracy. This advancement provides a robust solution for power system scheduling and operation, underscoring the importance and practical value of our work in advancing voltage prediction techniques.}, archive = {J_ACCESS}, author = {Wanting Xu and Amirrudin Kamsin and Ling Jiang and Yuan Mei and Xinyue Zhou and Jun Huang}, doi = {10.1109/ACCESS.2025.3580456}, journal = {IEEE Access}, pages = {105786-105797}, shortjournal = {IEEE Access}, title = {Dual-agent transformer network based on prompt engineering for voltage prediction}, volume = {13}, year = {2025}, }
• (2025). FRESH: Fully reliable and effective protection against soft and hard errors. ACCESS, 13, 105766-105785. (www)

  @article{ , abstract = {Advances in modern computing systems have led to an unprecedented spread of safety-critical applications in real-world environments. In safety-critical applications, preventing malfunctions due to faults is a primary design concern, as malfunctions in such applications can induce catastrophic results. Software-level redundant multithreading (RMT) solutions, which do not require hardware modifications and hence do not incur hardware costs, are attractive alternatives to hardware-level redundancy solutions for hardware unreliability issues, such as soft and hard errors. However, existing software-level RMT solutions can only provide fault detection and rely on external schemes for error recovery. This study investigated the potential of software-level RMT schemes for complete soft and hard error detection and recovery. First, a baseline software-level triple redundant multithreading (STRMT) scheme was implemented to serve as a baseline, pinpointing the ineffectiveness of the naïve STRMT, which makes the application even more vulnerable than the unprotected version due to the runtime overhead. Subsequently, Fully Reliable and Effective protection against Soft and Hard errors (FRESH) was introduced as a software-only RMT scheme that can achieve comprehensive error resiliency against both soft and hard errors. The main idea of FRESH is to distribute and intertwine error detection and recovery operations between redundant threads based on thread-level load-back checking of the state-of-the-art RMT scheme. FRESH further applies a lazy-fault-diagnosis optimization to reduce the number of thread-level synchronizations required for fault detection and recovery. Experimental results with an ARM cortex53-like $\mu $ -architecture simulated microprocessor demonstrated that FRESH can reduce program failure rate by around 99.88% compared to the unprotected versions.}, archive = {J_ACCESS}, author = {Daehoon Son and Hwisoo So and Jinhyo Jung and Yohan Ko and Aviral Shrivastava and Kyoungwoo Lee}, doi = {10.1109/ACCESS.2025.3574577}, journal = {IEEE Access}, pages = {105766-105785}, shortjournal = {IEEE Access}, title = {FRESH: Fully reliable and effective protection against soft and hard errors}, volume = {13}, year = {2025}, }
• (2025). CLF3D: A coarse-labeling framework to facilitate 3D object detection in point clouds. ACCESS, 13, 105753-105765. (www)

  @article{ , abstract = {Tremendous scenarios have to be considered for autonomous driving, leading to extremely large amount of point cloud data which need to be labeled for model training. Manually labeling such data is labor-intensive and highly expensive. In this paper, we propose CLF3D, a simple and effective coarse-labeling framework designed to improve existing automated labeling methods by fully leveraging scene-specific information in the unlabeled data to significantly enhance detection accuracy. Specifically, CLF3D first utilizes a pre-trained model to generate initial pseudo-labels, which are subsequently refined using a two-stage filtering strategy in combination with an instance bank built from high-quality annotated instances. These refined pseudo-labels are then used to fine-tune the model, progressively improving its detection performance on unlabeled data. Through iterative refinement of pseudo-labels, the model parameters, and the instance bank, CLF3D continuously improves label quality and accuracy. Experimental results demonstrate that the proposed method improves the detection accuracy by up to 14% compared to the originally pre-trained model across datasets of various sizes. This means our approach can reduce 14% of the manual workload for labeling point cloud data in comparison to the existing methods.}, archive = {J_ACCESS}, author = {Nuo Cheng and Chuanyu Luo and Han Li and Sikun Ma and Shengguang Lei and Pu Li}, doi = {10.1109/ACCESS.2025.3580824}, journal = {IEEE Access}, pages = {105753-105765}, shortjournal = {IEEE Access}, title = {CLF3D: A coarse-labeling framework to facilitate 3D object detection in point clouds}, volume = {13}, year = {2025}, }
• (2025). Techno-economic assessment of grid sales constraints on solar power plants through capacity sizing: A case study of türkiye. ACCESS, 13, 105738-105752. (www)

  @article{ , abstract = {The integration of solar power plants into the electrical grid poses significant challenges, particularly with regard to the sale of excess energy. Inadequately defined tariffs and regulatory scopes can lead to either excessive investment profitability or technical issues affecting grid security. Regulatory frameworks play a crucial role in shaping the feasibility and profitability of solar projects. Until recently, the high purchase guarantees under Türkiye’s feed-in tariffs have led to excessive profitability for many producers with low consumption, making solar power plants a highly lucrative investment. To address this issue, a significant regulatory change has been introduced, linking the amount of electricity sold to the amount consumed. As a result, a substantial decline in revenues is expected for numerous power plants. This article examines how the regulatory and technical constraints limiting the sale of excess energy to the grid in Türkiye will impact solar power plants and alter the feasibility of new projects. It explores how restrictions imposed by regulatory authorities influence the optimal sizing of solar investments using technical and financial metrics. Various scenarios are analyzed, including capacity limits, demand profiles, net metering periods, and grid sales constraints, to assess their impact on solar plant design. The findings of this study demonstrate that sales restrictions significantly affect the economic viability and technical configuration of solar projects. Compliance with these regulations often requires adjusting plant size to align generation capacity with permissible sales volumes. The study first establishes that the regulatory change has resulted in substantial revenue losses for existing solar power plants and subsequently provides a framework for structuring future investments. The research concludes with recommendations for optimizing the size of solar power plants under electricity sales-constrained conditions across different scenarios to maximize economic benefits. The results demonstrate that with proper sizing, investment returns can be nearly as high as those in unconstrained sales conditions.}, archive = {J_ACCESS}, author = {Sait Gazi Yildirim and Ali Durusu}, doi = {10.1109/ACCESS.2025.3580505}, journal = {IEEE Access}, pages = {105738-105752}, shortjournal = {IEEE Access}, title = {Techno-economic assessment of grid sales constraints on solar power plants through capacity sizing: A case study of türkiye}, volume = {13}, year = {2025}, }
• (2025). A GPT-based code review system with accurate feedback for programming education. ACCESS, 13, 105724-105737. (www)

  @article{ , abstract = {The increasing demand for programming education and growing class sizes require immediate and personalized feedback. However, integrating Large Language Models (LLMs) like ChatGPT in introductory programming courses raises concerns about AI-assisted cheating. In large-scale settings, faulty code submissions may lead LLMs to overanalyze, causing unnecessary token consumption. This paper proposes a GPT-4o-based code review system that provides accurate feedback while reducing token usage and preventing AI-assisted cheating. Unlike general-purpose LLM tools for professionals, the system is pedagogically designed for primary and secondary students by focusing on review necessity and learner-friendly feedback. The system features a Code Review Module (CRM) that reduces token usage via a Review Necessity Chain (RNC), and Code Correctness Check Module (CCM) combining test case validation with LLM-based assessment. To prevent AI-assisted cheating, the system provides automated feedback on submitted code without prompting and revealing correct answers, which are accessed only through the “Ask Code Tutor” button. In usability test, the system detected up to 42.86% more errors than a conventional online judge. BERTScore analysis showed that over 80% of the system-generated reviews were semantically aligned with human feedback. A performance comparison with state-of-the-art systems demonstrated a blocking success rate of 86%, with a comparable review omission rate. These results indicate that the system provides more accurate feedback than conventional automated code reviews, while achieving token efficiency and supporting self-directed learning through educational feedback. Thus, it can serve as a practical solution for scalable programming education in primary and secondary classes.}, archive = {J_ACCESS}, author = {Dong-Kyu Lee and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3581139}, journal = {IEEE Access}, pages = {105724-105737}, shortjournal = {IEEE Access}, title = {A GPT-based code review system with accurate feedback for programming education}, volume = {13}, year = {2025}, }
• (2025). Separated and independent contrastive semi-supervised learning for imbalanced datasets. ACCESS, 13, 105712-105723. (www)

  @article{ , abstract = {Conventional semi-supervised learning (SSL) encounters challenges in effectively addressing issues associated with long-tail datasets, primarily stemming from imbalances within a dataset. Existing contrastive SSL methods typically rely on pseudo-labels generated from unlabeled data, which can be inaccurate and introduce confirmation bias toward majority classes. To address this issue, we propose Separated and Independent Contrastive Semi-Supervised Learning (SICSSL), which applies supervised contrastive learning separately and independently to labeled and unlabeled samples. We validate SICSSL on four public benchmark datasets: CIFAR-10-LT, CIFAR-100-LT, STL-10-LT, and ImageNet-127. The results demonstrate consistent performance improvements over existing contrastive learning approaches. Furthermore, SICSSL is modular and easily integrates with recent SSL frameworks, including Auxiliary Balanced Classifier (ABC) and Adaptive Consistency Regularizer (ACR), enabling improved robustness in imbalanced semi-supervised settings. Source code is available at https://github.com/dongyyyyy/SICSSL}, archive = {J_ACCESS}, author = {Dongyoung Kim and Won-Sook Lee and Young-Woong Ko and Jeong-Gun Lee}, doi = {10.1109/ACCESS.2025.3580738}, journal = {IEEE Access}, pages = {105712-105723}, shortjournal = {IEEE Access}, title = {Separated and independent contrastive semi-supervised learning for imbalanced datasets}, volume = {13}, year = {2025}, }
• (2025). A self-sovereign identity-based authentication and reputation protocol for IoV applications. ACCESS, 13, 105693-105711. (www)

  @article{ , abstract = {The Internet of Vehicles (IoV) ecosystem faces significant security challenges in authentication and reputation management, especially in scenarios lacking cellular coverage. In such environments, the lack of real-time identity verification and trust assessment increases the risk of false alerts, message injection, and cooperative system failures, potentially leading to advanced driver assistance systems malfunctions or traffic accidents. While the Cellular Vehicle-to-Everything (C-V2X) architecture provides robust security features under coverage, it lacks standardized mechanisms for decentralized authentication and trust evaluation when connectivity is unavailable. This paper introduces RepuAuth-IoV, a novel self-sovereign identity-based protocol that integrates decentralized authentication with a distributed reputation system tailored for IoV applications. RepuAuth-IoV empowers vehicles to autonomously manage identities and reputations using verifiable credentials, eliminating dependence on centralized authorities. Key innovations include a lightweight proof of reputation mechanism based on multi-party computation, enabling vehicles to collaboratively assess trustworthiness using recent spatiotemporal data without compromising privacy. To validate the protocol’s correctness and resilience, we formally modeled its behavior using hierarchical coloured Petri nets and verified critical properties through temporal logic model checking. Results demonstrate RepuAuth-IoV’s effectiveness in ensuring secure and privacy-preserving C-V2X communications, even in disconnected or adversarial environments. Compared to blockchain-based solutions, our protocol achieves similar security goals with significantly lower computational and communication overhead, offering a scalable and practical solution for real-time trust management in heterogeneous IoV deployments.}, archive = {J_ACCESS}, author = {Victor Emanuel Farias da Costa Borges and Álvaro Sobrinho and Danilo F. S. Santos and Angelo Perkusich}, doi = {10.1109/ACCESS.2025.3579929}, journal = {IEEE Access}, pages = {105693-105711}, shortjournal = {IEEE Access}, title = {A self-sovereign identity-based authentication and reputation protocol for IoV applications}, volume = {13}, year = {2025}, }
• (2025). Trajectory tracking control based on visual localization and adaptive radial basis function sliding mode control. ACCESS, 13, 105678-105692. (www)

  @article{ , abstract = {Autonomous driving is a prominent research focus in the field of intelligent vehicles, with trajectory tracking serving as a fundamental control strategy. However, most existing tracking methods rely heavily on GPS signals, making them susceptible to signal loss due to environmental obstacles such as buildings. Furthermore, traditional GPS-based approaches struggle to adapt to unpredictable real-world traffic conditions. To address these challenges, this study develops a vision-based localization model using a deep learning Convolutional Neural Network (CNN) to provide auxiliary localization signals. An Adaptive Radial Basis Function Sliding Mode Control (ARBFSMC) strategy is further employed to enhance the robustness of trajectory tracking. The proposed approach integrates the CNN-based localization model with the ARBFSMC controller using the CARLA simulator, leveraging real-time road images for training and testing. Comparative simulations between Pure Pursuit and ARBFSMC controllers were conducted. Results show that ARBFSMC achieves lower tracking errors than Pure Pursuit, especially at higher vehicle speeds. Under conditions where the CNN model’s predicted localization images remain consistent with the training set, the maximum average localization error is below 0.15 meters. Furthermore, when CNN localization outputs fluctuate, the ARBFSMC demonstrates greater resilience and bounded tracking behavior than Pure Pursuit, contributing to improved reliability in autonomous driving scenarios. Finally, real-world experiments were conducted on a GoKart platform utilizing RTK-GPS-based localization. When the vehicle was driven along a predefined trajectory at a constant speed of 5 km/h, the ARBFSMC controller achieved an average maximum tracking error of less than 0.37 meters. These results confirm the practical feasibility and bounded-control performance of the ARBFSMC strategy for real-world trajectory tracking applications.}, archive = {J_ACCESS}, author = {Hung-Yih Tsai and Shih-Min Liao and Chang-Hong Liu}, doi = {10.1109/ACCESS.2025.3580642}, journal = {IEEE Access}, pages = {105678-105692}, shortjournal = {IEEE Access}, title = {Trajectory tracking control based on visual localization and adaptive radial basis function sliding mode control}, volume = {13}, year = {2025}, }
• (2025). Advanced cluster-based load forecasting and peak demand management for electric vehicle charging networks. ACCESS, 13, 105664-105677. (www)

  @article{ , abstract = {The rapid adoption of Electric Vehicles (EVs) necessitates advanced solutions for flexible charging (FC) systems to meet diverse user needs and optimize grid efficiency. This study introduces a data-driven, cluster-based load forecasting framework using machine learning techniques. Charging sessions are categorized into residential, workspace, and instantaneous station clusters via HDBSCAN and Elbow methods. Regression models, including Random Forest, SVM, and Logistic Regression, are applied to forecast load demand, with refined datasets and feature engineering techniques.The proposed methodology, implemented in Python and MATLAB, integrates a Battery Energy Storage System (BESS) for peak demand curtailment, demonstrating scalability for 1000 EVs. Results highlight significant improvements in load prediction accuracy, operational efficiency, and demand response outcomes. This integrated framework provides a scalable, adaptable, and efficient solution for EV charging systems, aligning with future energy management needs.}, archive = {J_ACCESS}, author = {Yashvi Mudgal and Rajive Tiwari and Narayanan Krishnan and Alexander Aguila Téllez}, doi = {10.1109/ACCESS.2025.3578875}, journal = {IEEE Access}, pages = {105664-105677}, shortjournal = {IEEE Access}, title = {Advanced cluster-based load forecasting and peak demand management for electric vehicle charging networks}, volume = {13}, year = {2025}, }
• (2025). Memory-efficient imagification for light-weight prediction model of multivariate time-series data. ACCESS, 13, 105654-105663. (www)

  @article{ , abstract = {This paper addresses the challenge of memory-efficient time-series forecasting in resource-constrained environments. To this end, an imagification method is proposed that enables lightweight convolutional neural network CNN-based prediction by transforming multivariate time-series data into image representations. The method consists of three steps: rearranging features using the Pearson correlation coefficient to enhance local associations, generating images through a sliding window technique along the time axis, and applying multivariate data interpolation to improve smoothness across the feature axis. The proposed method is evaluated using real-world traffic speed data collected from highway sections in Seoul, South Korea. Compared to benchmark imagification methods (Recurrence Plot, Gramian Angular Field) and a conventional multivariate LSTM model, the proposed approach achieves competitive prediction accuracy with significantly reduced training time. These results suggest that the method is well-suited for deployment in embedded or low-memory systems requiring efficient time-series prediction.}, archive = {J_ACCESS}, author = {Seungwoo Kang and Ohyun Jo}, doi = {10.1109/ACCESS.2025.3576392}, journal = {IEEE Access}, pages = {105654-105663}, shortjournal = {IEEE Access}, title = {Memory-efficient imagification for light-weight prediction model of multivariate time-series data}, volume = {13}, year = {2025}, }
• (2025). Machine learning in aircraft design: A comprehensive review of optimization, aerodynamics, and structural applications. ACCESS, 13, 105642-105653. (www)

  @article{ , abstract = {Machine learning (ML) with approximation and numerical simulations plays an important role in aircraft design. ML techniques, such as deep learning and reinforcement learning, are increasingly being adopted to solve complex, nonlinear problems in aircraft design, offering new opportunities for optimization and innovation. This technology is used in various topics of aerodynamics, fluid dynamics, acoustics, penetration, health monitoring, automatic control, etc. This article reviews recent studies related to different areas of ML application in aircraft design. The review highlights how ML can reduce computational costs while improving the precision of simulations, ultimately accelerating the design cycle and enhancing aircraft performance. The results show that ML is able to improve in all areas of application in aircraft design and the development of techniques in future applications will have a significant impact on the design of modern aircraft.}, archive = {J_ACCESS}, author = {Shima Mohaghegh and Ali Mohaghegh}, doi = {10.1109/ACCESS.2025.3580485}, journal = {IEEE Access}, pages = {105642-105653}, shortjournal = {IEEE Access}, title = {Machine learning in aircraft design: A comprehensive review of optimization, aerodynamics, and structural applications}, volume = {13}, year = {2025}, }
• (2025). Wide-angle, multiplexed backscatter communications using a dynamic metasurface-backed luneburg lens. ACCESS, 13, 105629-105641. (www)

  @article{ , abstract = {Backscatter communications is attractive for its low power requirements due to the lack of actively radiating components; however, commonly used devices are typically limited in range and functionality. Here, we design and demonstrate a backscatter device consisting of a flattened Luneburg lens combined with a spatially-tunable dynamic metasurface. Using quasi-conformal transformation optics (QCTO), we design a flattened, additively manufactured Luneburg lens that focuses incoming waves over a wide field-of-view onto its flattened focal plane. When a reflective surface is placed at the focal plane, the flattened Luneburg lens retroreflects, enabling long-range backscatter communications over an extremely large field-of-view (±30°) and bandwidth. The dynamic metasurface is designed to modulated the reflected phase across the S-band (2-4 GHz) with fine spatial control. Thus, when combined with the flattened Luneburg lens, the device is able to modulate the retroreflected signal to achieve backscatter communications. We experimentally demonstrate full phase control of the backscattered signal across a range of incidence angles, spatial multiplexing, and secure communications against eavesdroppers by actively suppressing or randomizing signals in unwanted directions. The metasurface-backed Luneburg lens device offers a low-power solution for long-range wireless networks with advanced capabilities.}, archive = {J_ACCESS}, author = {Samuel Kim and Timothy Sleasman and Avrami Rakovsky and Ra'Id Awadallah and David B. Shrekenhamer}, doi = {10.1109/ACCESS.2025.3573944}, journal = {IEEE Access}, pages = {105629-105641}, shortjournal = {IEEE Access}, title = {Wide-angle, multiplexed backscatter communications using a dynamic metasurface-backed luneburg lens}, volume = {13}, year = {2025}, }
• (2025). Deep residual transfer ensemble model for mRNA gene-expression-based breast cancer. ACCESS, 13, 105608-105628. (www)

  @article{ , abstract = {The last few years have witnessed exponential rise in breast cancer disease. The increasing mortality rate due to the lack of earlier diagnosis has alarmed healthcare industry to develop more efficient and scalable computer aided diagnosis (CAD) solution. Despite that the biopsy-based histopathological analysis provides easier breast cancer detection, yet the invasive treatment and iterative costs limit its scalability for the masses. Gene-expression analysis can be an alternative, where the different machine learning and artificial intelligence techniques can be developed to learn microarray RNA details for breast cancer detection. Unlike traditional methods where phenotypic changes are learnt or detected by machine learning models, the use of mRNA specific two-dimensional images can yield superior results due to increased spatial informativeness. Unfortunately, the major existing methods use convolutional neural networks (CNN) to learn features from the RNA-specific 2D images. The inability to exploit contextual details, long-term dependency, gradient vanishing and high dependence on local hierarchical features confine their efficacy in run-time environments. This research proposes deep residual transfer ensemble model for mRNA gene-expression based breast cancer detection. The proposed method at first transformed mRNA one-dimensional sequences into 2D images. Executing standard pre-processing tasks, the synthetic underrepresented class over-sampling technique (SMOTE) was applied to alleviate any possible class-imbalance problem. Subsequently, ResNet101 and AlexNet deep networks were applied distinctly to extract respective features, which were later fused to yield composite ensemble feature. Here, ResNet101 avoided gradient vanishing, while AlexNet provided 4096-dimensional features to train the ensemble-of-ensemble (E2E) classifier for consensus-based breast cancer detection. The E2E ensemble learning method used bagging, AdaBoost, Random Forest, Extra Tree Classifier and XGBoost algorithms as base classifier to perform maximum voting-based prediction. Being consensus-driven solution, it improved reliability of breast cancer prediction results. Simulation results confirmed its robustness in terms of superior accuracy (99.91%), precision (99.65%), recall (99.81%) and F-Measure (99.72%) over other methods, signifying its optimality towards real-time and scalable breast cancer diagnosis.}, archive = {J_ACCESS}, author = {Job Prasanth Kumar Chinta Kunta and Vijayalakshmi A. Lepakshi}, doi = {10.1109/ACCESS.2025.3573808}, journal = {IEEE Access}, pages = {105608-105628}, shortjournal = {IEEE Access}, title = {Deep residual transfer ensemble model for mRNA gene-expression-based breast cancer}, volume = {13}, year = {2025}, }
• (2025). Low voltage ride-through capability of grid-connected PV systems: A comparative study of grid-following and grid-forming converters under current limits. ACCESS, 13, 105590-105607. (www)

  @article{ , abstract = {This study investigates the Low Voltage Ride-Through (LVRT) capabilities of grid-connected photovoltaic (PV) systems, focusing on the comparative performance of Grid-Following (GFL) and Grid-Forming (GFM) converters under current limit constraints. The two converters are designed to improve grid integration using systematic methodologies to ensure high performance and are compared under various grid fault conditions. A detailed model of the overall grid-connected PV system is first developed, including the PV array with its parameters extracted from the datasheet, Maximum Power Point Tracking (MPPT) algorithm, power converter topology, modulation technique, converter control structure (GFM or GFL), and the filter. The GFM converter employs a PQ droop control strategy. In contrast, the GFL converter utilizes conventional control techniques using a cascaded control structure along with the phase-locked loop (PLL) to synchronize the injected current with the grid voltage. The study evaluates the performance of both converters under several grid fault scenarios, considering current limit constraints imposed by the PV system and converter ratings. While GFM converters can enhance grid stability in weak grids due to their autonomous voltage and frequency regulation, the results demonstrate that GFL converters can exhibit superior performance under certain conditions, particularly when current limitations significantly impact the GFM converter’s response. Conversely, GFM converters demonstrate faster clearance times in specific scenarios, highlighting their potential advantages in dynamic stability. Using MATLAB/Simulink simulations, this research provides valuable insights into the impact of current limitations on the LVRT performance of both GFL and GFM converters across varying grid strengths, characterized by Short Circuit Ratio (SCR) and Grid Impedance Ratio (GIR), enabling the selection of the most suitable converter topology for specific grid conditions.}, archive = {J_ACCESS}, author = {Zainab M. Almesri and Hussain A. Hussain and Rashad M. Kamel}, doi = {10.1109/ACCESS.2025.3580643}, journal = {IEEE Access}, pages = {105590-105607}, shortjournal = {IEEE Access}, title = {Low voltage ride-through capability of grid-connected PV systems: A comparative study of grid-following and grid-forming converters under current limits}, volume = {13}, year = {2025}, }
• (2025). Advanced leaf classification using multi-layer perceptron for smart crop management. ACCESS, 13, 105579-105589. (www)

  @article{ , abstract = {Accurate leaf classification plays a vital role in ecological studies and precision agriculture, yet the diversity in leaf morphology and environmental influences presents a significant challenge. To improve accuracy, this study suggests a revolutionary methodology that combines sophisticated preprocessing, segmentation, feature extraction, optimization, and classification algorithms. To improve image quality, preprocessing is first done with bilateral filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). Then, to ensure accurate border identification, Conditional Random Fields (CRF) are used for segmentation. To extract features, complex texture and shape details are captured using Local Ternary Patterns (LTP), KAZE, and Histogram of Oriented Gradients (HOG). The optimization method, Stochastic Gradient Descent (SGD), refines the feature space for better categorization. Finally, a Multi-Layer Perceptron (MLP) classifier is used, and it produces remarkable results on three different datasets: accuracy of 92.63%, 88.3%, and 94.5%, respectively. This strong architecture shows great promise for disease monitoring and plant identification, providing a flexible and scalable solution for a range of agricultural applications.}, archive = {J_ACCESS}, author = {Sara Mumtaz and Mohammed Alshehri and Yahya AlQahtani and Abdulmonem Alshahrani and Bayan Alabdullah and Haifa F. Alhasson and Hui Liu}, doi = {10.1109/ACCESS.2025.3572985}, journal = {IEEE Access}, pages = {105579-105589}, shortjournal = {IEEE Access}, title = {Advanced leaf classification using multi-layer perceptron for smart crop management}, volume = {13}, year = {2025}, }
• (2025). FDE-RCF: A navel orange defect edge detection algorithm based on feature depth enhancement. ACCESS, 13, 105564-105578. (www)

  @article{ , abstract = {A new edge detection method is proposed for the inspection of navel orange surface defects, enabling precise defect localization, classification type, and size estimation. This study facilitates accurate and efficient grading and sorting, thereby ensuring product quality and extending storage life. To overcome the limitations of existing edge detection methods in handling small, blurred, and color-similar defects, a novel Feature Depth-Enhanced Edge Detection Model (FDE-RCF) is introduced. This model enhances the Richer Convolutional Features (RCF) network by removing its final pooling layers to preserve fine-grained details and integrating three key modules: 1) Dilated Convolution for improved global context, 2) Efficient Channel Attention (ECA) module to selectively focus on defect-relevant features, and 3) Scale Enhancement Module (SEM) for robust edge detection across varying object sizes. A residual fusion structure is also introduced to better capture intricate edge patterns. Experimental results demonstrate that FDE-RCF outperforms conventional models. On navel orange defect datasets, it achieves higher ODS (83.04%), OIS (84.97%), and AP (86.64%), with improvements of 2.32%, 2.44%, and 2.48% respectively, over the baseline RCF. On the standard BSDS500 benchmark, it achieves additional ODS and OIS by 1.81% and 1.78%, while maintaining real-time processing speeds at 28 FPS. The proposed FDE-RCF model generates coherent and continuous edge representations and accurately capture subtle and small-scale defects, highlighting its suitability for integration in automated fruit grading and visual quality inspection systems.}, archive = {J_ACCESS}, author = {Jinmei Guo and Wan Nurshazwani Wan Zakaria and Wu Jinping and Norhaflyza Marbukhari}, doi = {10.1109/ACCESS.2025.3576718}, journal = {IEEE Access}, pages = {105564-105578}, shortjournal = {IEEE Access}, title = {FDE-RCF: A navel orange defect edge detection algorithm based on feature depth enhancement}, volume = {13}, year = {2025}, }
• (2025). Pattern synthesis of standing-wave antenna by using transmission line model. ACCESS, 13, 105555-105563. (www)

  @article{ , abstract = {In this article, a synthesis procedure is proposed to achieve desired radiation pattern in standing-wave structure. To obtain the desired pattern, transmission line model is used, also. In fact, the current obtained from the synthesis of the desired pattern is considered equivalent to each slot admittance. The slot offset and length are used to realize the conductance and susceptance of each slot, respectively. Two different design examples, sectoral beam and cosecant-squared one, are demonstrated to verify the effectiveness of the proposed method in shaped beam pattern designs. Moreover, the slotted waveguide with cosecant-squared is fabricated at 10 GHz. High efficiency, low SLL, and low gain ripple are the valuable advantages of the proposed method in comparison with the state-of-the-art references.}, archive = {J_ACCESS}, author = {Alireza Heshmatimoez and Mohammad Khalaj-Amirhosseini and Seyed Hassan Sedighy}, doi = {10.1109/ACCESS.2025.3579534}, journal = {IEEE Access}, pages = {105555-105563}, shortjournal = {IEEE Access}, title = {Pattern synthesis of standing-wave antenna by using transmission line model}, volume = {13}, year = {2025}, }
• (2025). Hierarchical multi-scale patch attention and global feature-adaptive fusion for robust occluded face recognition. ACCESS, 13, 105541-105554. (www)

  @article{ , abstract = {Occluded face recognition remains a challenging problem in biometric identification, where real-world obstructions such as masks, sunglasses, scarves, and hands obscure key facial features. To address this, we introduce a dual-branch architecture that combines a Local Multi-Patch Attention Module (LMPAM) for extracting localized features with a Global Self-Attention Channel Module (GSACM) to enhance overall feature representation. The local branch utilizes Multi-Scale Patch Attention to adaptively emphasize visible facial regions, ensuring robust feature learning from unoccluded areas. Meanwhile, the global branch employs Self-Attention with Channel Recalibration to enhance discriminative features, capturing long-range dependencies while suppressing occlusion-induced noise. The two branches are integrated using Dynamic Weighted Local-Global Fusion (DW-LG), allowing the model to balance local and global information effectively. Unlike predefined occlusion-aware methods, our approach generalizes across occlusions of varying types, regions, and sizes and demonstrates robustness on multiple datasets with changes in illumination, pose, and facial expression—without requiring explicit localization. Extensive evaluations on CASIA-WebFace, LFW, and AR datasets demonstrate the effectiveness of our approach, achieving higher recognition performance under severe occlusion conditions.}, archive = {J_ACCESS}, author = {Elhamsadat Hejazi and Majid Ahmadi and Arash Ahmadi}, doi = {10.1109/ACCESS.2025.3576639}, journal = {IEEE Access}, pages = {105541-105554}, shortjournal = {IEEE Access}, title = {Hierarchical multi-scale patch attention and global feature-adaptive fusion for robust occluded face recognition}, volume = {13}, year = {2025}, }
• (2025). Digital twins for power systems: Review of current practices, requirements, enabling technologies, data federation, and challenges. ACCESS, 13, 105517-105540. (www)

  @article{ , abstract = {The Digital Twins (DT) have emerged as the technology that provides capabilities to simulate and analyze cyber-physical systems’ behaviors using digital replicas. This is achieved through high-fidelity digital models, bi-directional communication and (near) real-time data exchange between physical real-world systems and DTs. Despite its capabilities of facilitating real-time monitoring, optimization, and predicting system performance, effectively leveraging DT for power system applications requires integrating data from heterogeneous sources and addressing various data related aspects. These include data modeling, exchange and interoperability. One promising concept to address these aspects is that of data federation which promotes interoperability, allowing DTs to operate autonomously, yet interact seamlessly. While various studies in literature have addressed DT applications, technologies, and challenges, a comprehensive review on the data federation aspects within power systems still needs to be investigated. This research seeks to bridge this gap by providing an in-depth review of DT practices in academia and industry, functional and non-functional requirements, and enabling technologies, with emphasis on data federation. Its role in enhancing system-wide interoperability in the power system, along with associated challenges are summarized and discussed.}, archive = {J_ACCESS}, author = {May Myat Thwe and Alexandru Ştefanov and Vetrivel Subramaniam Rajkumar and Peter Palensky}, doi = {10.1109/ACCESS.2025.3580055}, journal = {IEEE Access}, pages = {105517-105540}, shortjournal = {IEEE Access}, title = {Digital twins for power systems: Review of current practices, requirements, enabling technologies, data federation, and challenges}, volume = {13}, year = {2025}, }
• (2025). State estimation in power distribution grids using deep unfolding. ACCESS, 13, 105505-105516. (www)

  @article{ , abstract = {The power distribution network has undergone significant changes in recent years, primarily due to the integration of renewable energy sources and electric vehicles. These changes have led to new challenges in distribution grid management, which relies on situational awareness to ensure safe, reliable, and secure operations. Real-time distribution system state estimation (DSSE) is essential for achieving this situational awareness. Previously proposed DSSE methods are computationally expensive and lack scalability. Therefore, recently, researchers have turned to learning-based approaches such as deep neural networks (DNNs) to overcome these limitations. However, DNNs are susceptible to over-fitting. To address these challenges, this paper proposes a novel model-based neural network approach, called deep unfolding, for fast and accurate estimation of system states in a low observable distribution network. Unlike model-agnostic neural networks (NNs), which are difficult to tune and train, the proposed model-based NN is created by “unfolding” the alternating direction method of multipliers (ADMM) solver. This article proposes two methods for state estimation: a purely data-driven approach called model-free DSSE network (MFDSSE-net) and a model-based DSSE network (MBDSSE-net) that incorporates network information by including the linearized power flow constraints into the optimization framework. The simulation results are compared with the state-of-the-art ADMM method and weighted least square method and demonstrate the efficacy of the proposed approaches in the IEEE-123 bus and a 559 bus network. Simulation results show accurate state estimation is possible with nearly 300% reduction in simulation time when deep unfolding is used.}, archive = {J_ACCESS}, author = {Biswajeet Rout and Balasubramaniam Natarajan}, doi = {10.1109/ACCESS.2025.3579768}, journal = {IEEE Access}, pages = {105505-105516}, shortjournal = {IEEE Access}, title = {State estimation in power distribution grids using deep unfolding}, volume = {13}, year = {2025}, }
• (2025). Hierarchical convolution-transformer framework for gear fault diagnosis under severe noise. ACCESS, 13, 105492-105504. (www)

  @article{ , abstract = {To address the limitations of convolutional neural networks in capturing global fault features, the high computational cost and overfitting risk of Transformer models in gear fault diagnosis, and the feature degradation under strong noise, this study proposes a novel convolution-Transformer–channel attention network. The proposed method achieves a dynamic balance between local and global features through the joint design of multi-scale convolution and sparse self-attention. Multi-scale convolution extracts local features, while the enhanced sparse multi-head self-attention models long-range dependencies via adaptive sequence compression to reduce computational complexity. In addition, a dual-stage squeeze-and-excitation mechanism recalibrates feature channels to suppress noise interference and enhance robustness. This method is well-suited for gear fault diagnosis under strong noise conditions, offering improved generalization capability and promising potential for industrial applications.}, archive = {J_ACCESS}, author = {Qiushi He and Bo Kang and Shanshan Fan and Xueyi Li}, doi = {10.1109/ACCESS.2025.3580424}, journal = {IEEE Access}, pages = {105492-105504}, shortjournal = {IEEE Access}, title = {Hierarchical convolution-transformer framework for gear fault diagnosis under severe noise}, volume = {13}, year = {2025}, }
• (2025). Attention-enhanced hybrid automatic modulation classification for advanced wireless communication systems: A deep learning-transformer framework. ACCESS, 13, 105463-105491. (www)

  @article{ , abstract = {The advancement of wireless communication toward beyond fifth-generation (B5G) and sixth-generation (6G) standards demands intelligent and scalable signal processing capable of accurate modulation classification under dynamic and noisy channel conditions. Automatic modulation classification (AMC) is essential for spectrum awareness, interference mitigation, and autonomous decision-making. While deep learning (DL) models have improved AMC performance, many conventional architectures face limitations due to sequential processing, high complexity, and reduced robustness in low signal-to-noise ratio (SNR) environments. To address these limitations, this paper presents a novel attention-enhanced hybrid AMC framework that synergistically integrates specialized convolutional layers for efficient temporal feature extraction with a compact transformer encoder for global sequence modeling. The proposed architecture is meticulously tailored to capture the amplitude-phase dynamics inherent in in-phase/quadrature ( $I/Q$ ) modulated signals while maintaining lightweight design principles. Furthermore, the proposed architecture employs state-of-the-art computational optimizations, including gradient scaling, mixed precision training, and adaptive moment estimation with fixed weight decay (AdamW), to enhance energy efficiency and accelerate convergence. Unlike conventional DL architectures, the proposed technique introduces an efficient parallelized sequence processing strategy that significantly reduces training complexity while preserving robust adaptability across diverse conditions. In contrast to existing hybrid or purely sequential architectures, this design attains high classification fidelity without the need for complex graph-based structures or stacked attention mechanisms, thereby enhancing both model interpretability and practical deployment feasibility. Extensive benchmarking is conducted employing the RadioML 2018.01A dataset, comprising 24 diverse modulation types across various SNR levels, simulating real-world impairments such as fading, oscillator drift, and noise. The proposed framework is rigorously compared against six representative models—recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks-transformer graph neural network (CTGNet), MobileViT, and DeepsigNet—across multiple evaluation criteria. Experimental results consistently validate the effectiveness of the proposed framework, which surpasses leading architectures in accuracy under both static and dynamic (i.e., Jakes-fading) channel conditions. The model achieves an average accuracy improvement of 14.86% over benchmark methods and delivers up to a 38% reduction in inference latency. The study further includes comprehensive ablation analysis, statistical validation, and computational benchmarking, confirming the proposed model as a robust, efficient, and scalable solution for next-generation AMC applications.}, archive = {J_ACCESS}, author = {Sam Ansari and Khawla A. Alnajjar and Sohaib Majzoub and Eqab Almajali and Anwar Jarndal and Talal Bonny and Abir Hussain and Soliman Mahmoud}, doi = {10.1109/ACCESS.2025.3580574}, journal = {IEEE Access}, pages = {105463-105491}, shortjournal = {IEEE Access}, title = {Attention-enhanced hybrid automatic modulation classification for advanced wireless communication systems: A deep learning-transformer framework}, volume = {13}, year = {2025}, }
• (2025). Research on adaptive education path dynamic programming algorithm based on reinforcement learning and cognitive graphs. ACCESS, 13, 105446-105462. (www)

  @article{ , abstract = {The rapid evolution of Adaptive Education highlights the necessity of personalized learning paths that cater to the unique cognitive styles, preferences, and capabilities of each student. Traditional educational methods often fail to accommodate the diverse needs of learners, resulting in inefficient learning experiences. Current systems rely heavily on static models that do not dynamically adjust to the learner’s evolving needs. This research addresses these limitations by proposing an innovative approach that integrates reinforcement learning and cognitive graphs to optimize learning paths in real-time. The proposed method combines dynamic learner models, real-time performance tracking, and predictive analytics, allowing for a highly adaptive educational system. By employing reinforcement learning algorithms, the system continuously refines its model based on learner feedback and past interactions. The introduction of cognitive graphs enhances the system’s ability to represent complex relationships between concepts and learning materials, enabling more effective content sequencing and personalized learning. Experimental results show that the adaptive education system significantly improves learner performance compared to traditional approaches, demonstrating its efficacy in real-world educational. This work offers a promising solution for overcoming the challenges faced by conventional educational systems, providing a flexible, personalized, and data-driven learning environment that better supports diverse learners’ needs.}, archive = {J_ACCESS}, author = {Hongli Lou and Pin Yue and Jianwen Chen}, doi = {10.1109/ACCESS.2025.3578389}, journal = {IEEE Access}, pages = {105446-105462}, shortjournal = {IEEE Access}, title = {Research on adaptive education path dynamic programming algorithm based on reinforcement learning and cognitive graphs}, volume = {13}, year = {2025}, }
• (2025). Detection of cisplatin resistance in bladder cancer using machine learning. ACCESS, 13, 105434-105445. (www)

  @article{ , abstract = {Bladder Cancer (BC) is the ninth most common cancer worldwide, with cisplatin-based chemotherapy being a standard treatment. However, the emergence of cisplatin resistance poses significant challenges, contributing to high recurrence rates. This study investigates the mechanisms of cisplatin resistance in BC through the application of Machine Learning (ML) models, including Association Rule Mining, K-mode Clustering, and Decision Tree Classifier. By analyzing large-scale genomic data, we uncover the molecular pathways driving resistance. Specifically, our findings show that hypermethylation of CpG islands, such as those in UCSC_CPG_ISLANDS and GENOME_BUILD_37, leads to reduced cell accessibility, contributing to cisplatin resistance. The ML models highlight strong associations between hypermethylation and cell inaccessibility, with 99% confidence. This work provides valuable insights into the role of methylation patterns in BC recurrence and offers potential for personalized therapeutic strategies aimed at overcoming cisplatin resistance and improving clinical outcomes.}, archive = {J_ACCESS}, author = {Moditha Reddy Jagannatha Reddy and Mrudula Ranganatha and Shanthala Lakshminarayana and Jayoung Kim and Sungyong Jung and Yuze Sun and Hyusim Park}, doi = {10.1109/ACCESS.2025.3580057}, journal = {IEEE Access}, pages = {105434-105445}, shortjournal = {IEEE Access}, title = {Detection of cisplatin resistance in bladder cancer using machine learning}, volume = {13}, year = {2025}, }
• (2025). Sense of agency in closed-loop muscle stimulation. ACCESS, 13, 105417-105433. (www)

  @article{ , abstract = {To maintain a user’s sense of agency (SoA) when using motor augmentation devices, system actuation must align with the user’s intention. While stimulus-response paradigms in lab settings allow precise timing, real-world use requires detecting user volition directly. We developed a closed-loop system using a brain-computer interface (BCI) to detect readiness potentials (RPs) from EEG and trigger electrical muscle stimulation (EMS) at moments of volitional intent. The system distinguishes in real-time between idle and pre-movement states, allowing EMS to support finger movements aligned with the user’s intention. We evaluated the system in a within-subject user study comparing three conditions: INTENTION (voluntary action), INVOLUNTARY (EMS-triggered without intent), and AUGMENTED (BCI-controlled EMS). We measured classifier performance (mean F1 score = 0.7), intentional binding, subjective control ratings, and collected qualitative interview data. Results showed that AUGMENTED preserved more agency than INVOLUNTARY, though less than INTENTION. Participants described moments of synchronized stimulation as collaborative, while misalignments reduced perceived control. These findings highlight the promise of BCI-controlled EMS for agency-preserving augmentation and identify design challenges for real-world systems.}, archive = {J_ACCESS}, author = {Lukas Gehrke and Leonie Terfurth and Klaus Gramann}, doi = {10.1109/ACCESS.2025.3579043}, journal = {IEEE Access}, pages = {105417-105433}, shortjournal = {IEEE Access}, title = {Sense of agency in closed-loop muscle stimulation}, volume = {13}, year = {2025}, }
• (2025). Enhancing keyword search in relational databases with word embeddings. ACCESS, 13, 105401-105416. (www)

  @article{ , abstract = {Keyword search in relational databases allows the users to query these databases using natural language keywords, bridging the gap between structured data and intuitive querying. However, ambiguity in user queries as well as the complexities of structured database relationships often complicate the retrieval of relevant results. This paper introduces Key2Vec as a framework for leveraging the Natural Language Processing (NLP) techniques, specifically Word2Vec embeddings, for enhancing the keyword search in relational databases. Key2Vec enhances both the query relevance and the execution efficiency by semantically interpreting the keywords and optimizing the query classes, those categories of user queries which are grouped by their specific objectives and their data requirements. Experimental evaluations, on benchmark databases, demonstrate that Key2Vec improves the accuracy about 25% and reduces the query processing time by 30%, establishing it as a robust solution.}, archive = {J_ACCESS}, author = {Fatemeh Khalifeh and Mohammad Taheri and Eghbal Mansoori and Mostafa Fakhrahmad}, doi = {10.1109/ACCESS.2025.3574428}, journal = {IEEE Access}, pages = {105401-105416}, shortjournal = {IEEE Access}, title = {Enhancing keyword search in relational databases with word embeddings}, volume = {13}, year = {2025}, }
• (2025). Constructions of low-hit-zone frequency-hopping sequence set with strictly optimal periodic partial hamming correlation. ACCESS, 13, 105394-105400. (www)

  @article{ , abstract = {Frequency-hopping technology has been used widely in wireless communications, especially in military communications and in civil mobile communications. The constructions of optimal frequency-hopping sequences (FHSs) are the core of frequency-hopping technology. As the most important evaluation index, the partial Hamming correlation properties reflect the local features of FHSs and have been attracting more and more attention in the field of both theoretical research and engineering application. In this paper, based on q-ary m-sequence and its decimated sequence, two classes of low-hit-zone frequency-hopping sequence set (LHZ FHS set) with new flexible parameters are constructed. It turns out that the new FHS sets have strictly optimal maximum periodic partial Hamming correlation characteristic, and have important value and broad application prospect.}, archive = {J_ACCESS}, author = {Chun Xie and Changyuan Wang}, doi = {10.1109/ACCESS.2025.3580293}, journal = {IEEE Access}, pages = {105394-105400}, shortjournal = {IEEE Access}, title = {Constructions of low-hit-zone frequency-hopping sequence set with strictly optimal periodic partial hamming correlation}, volume = {13}, year = {2025}, }
• (2025). EmotionNet-X: An optimized CNN architecture for robust facial emotion analysis. ACCESS, 13, 105380-105393. (www)

  @article{ , abstract = {Facial emotions are expressions of people’s inner feelings. A computer’s ability to recognize emotions is known as emotion recognition (ER), which involves extracting facial characteristics or expressions from a person’s face in order to enable the computer to communicate emotionally with them. In recent years, the field of computer vision has grown rapidly, and the recognition of human facial emotions (FER) has drawn the attention of the research community due to its potential utility. The proposed EmotionNet-X bridges accuracy and deployability, enabling cost-effective FER in IoT systems, such as access control, authentication, monitoring health status in real-time, security systems, live assistance, etc. In addition, it involves a number of disciplines, including cognition, medicine, physiology, and psychology. There have been several publications in the literature regarding Facial Emotion Recognition (FER). FER remains challenging due to variations in facial expressions, demographics (age, ethnicity), and imaging conditions (lighting, occlusion). Existing pretrained models suffer from high computational costs, limiting real-time IoT deployment. Deep Neural Networks (DNNs), particularly Convolutional Neural Networks (CNNs), are widely used for facial expression recognition (FER). This is primarily due to their inherent ability to extract features from images automatically. Image-based prediction tasks are well suited to deep learning techniques, such as CNNs, which have demonstrated remarkable performance in this area. We propose EmotionNet-X, a lightweight CNN architecture with 19.9M parameters and 18 ms/image inference time. Key innovations include a streamlined design (four convolutional layers, seven dropout layers) and batch normalization for robust feature learning. Various pre-trained models, such as VGG19, ResNet50V2, MobileNetV2, EfficientNetB7, and recently state-of-the-art proposed models, have been compared with our proposed model. Public datasets named Cohn-Kanade (CK+) and FER2013 were used to evaluate the predictor’s performance and 99.86% accuracy was achieved on the CK+.}, archive = {J_ACCESS}, author = {Syed Muhammad Aqleem Abbas and Qaisar Abbas and Syed Muhammad Naqi}, doi = {10.1109/ACCESS.2025.3580112}, journal = {IEEE Access}, pages = {105380-105393}, shortjournal = {IEEE Access}, title = {EmotionNet-X: An optimized CNN architecture for robust facial emotion analysis}, volume = {13}, year = {2025}, }
• (2025). Interpretable ordinal-aware with contrastive-enhanced anomaly severity detection on UAV flight log messages. ACCESS, 13, 105361-105379. (www)

  @article{ , abstract = {Detecting abnormalities in a system using log records is a common approach, although such applications may vary between different deployment domains. For example, in drone forensics, existing studies generally consider anomalies as contradictory to normal events. In other words, all types of anomalies are considered equally important. However, the peculiar events experienced by a drone during a flight might contain distinct features indicating the anomalies’ importance or level of urgency. For instance, the message “Critically low power. Aircraft is landing” is more important than “Forward Obstacle Sensing not Working”. This study proposes DroneLog, an interpretable framework for detecting anomalies and the severity levels on drone flight logs, using a multitask learning approach in a unified pipeline. Complying with the multitask learning nature, two target label representations are designed to leverage the shared low-level common features of the input messages over various severity levels. An extensive experiment with a significant hyperparameter search space is performed to find the best-performing model to compare with several baselines and state-of-the-art methods from previous studies. The integrated gradient is employed to investigate the best-performing model’s trustworthiness and interpretability. We found that the model perceived the log messages’ semantics differently and were not aligned with how a human expert would. Therefore, DroneSBERT, a domain-specific embedding trained using contrastive learning, is proposed as a follow-up to the interpretability findings. From the experimental results, the proposed method achieves the highest performance with an improvement in accuracy and F1 scores of 5.128% and 4.453% on the unique-filtered dataset, respectively. Therefore, the proposed method accordingly addresses the task objective with a promising performance while being interpretable and trustworthy.}, archive = {J_ACCESS}, author = {Swardiantara Silalahi and Tohari Ahmad and Hudan Studiawan and Eirini Anthi and Lowri Williams}, doi = {10.1109/ACCESS.2025.3580056}, journal = {IEEE Access}, pages = {105361-105379}, shortjournal = {IEEE Access}, title = {Interpretable ordinal-aware with contrastive-enhanced anomaly severity detection on UAV flight log messages}, volume = {13}, year = {2025}, }
• (2025). A biphase code sidelobe suppression method based on the CLEAN algorithm. ACCESS, 13, 105353-105360. (www)

  @article{ , abstract = {In the processing of biphase-coded signals, if the moment of ADC sampling coincides with the transition edge of the code inversion, it can result in the loss of peak information. This loss leads to a reduction in the peak energy of pulse compression and diminishes the mainlobe-to-sidelobe ratio. To address this issue, this paper proposes a sidelobe suppression method based on the CLEAN algorithm. First, the digital moving target detection (DMTD) algorithm is employed to estimate the target velocity, and the phase of the echo signal is dynamically adjusted to eliminate straddle loss. Subsequently, through Doppler compensation techniques and optimized pulse compression processing, the energy loss caused by Doppler mismatch is minimized. Finally, the sidelobes of the signal are suppressed using the CLEAN algorithm. Simulation results indicate that, under low SNR conditions, when processing a biphase-coded signal modulated by a 255-bit M-sequence, the degree of sidelobe suppression can reach 27.7 dB.}, archive = {J_ACCESS}, author = {Yiding Liang and Liyu Tian}, doi = {10.1109/ACCESS.2025.3579389}, journal = {IEEE Access}, pages = {105353-105360}, shortjournal = {IEEE Access}, title = {A biphase code sidelobe suppression method based on the CLEAN algorithm}, volume = {13}, year = {2025}, }
• (2025). Machine learning and data science in social sciences: Methods, applications, and future directions. ACCESS, 13, 105334-105352. (www)

  @article{ , abstract = {Artificial intelligence (AI) is transforming social science research by enabling scalable data analysis, predictive modeling, and causal inference, thereby reshaping the methodological foundations of fields such as political science, economics, and psychology. This survey examines the application of key AI techniques, particularly machine learning (ML), natural language processing (NLP), network science, and explainable AI (XAI), in addressing domain-specific challenges in empirical research. Emphasis is placed on methodological integration, including bias mitigation, fairness-aware learning, causal discovery, and model interpretability. While these tools enhance analytical capacity, they raise critical concerns about algorithmic bias, transparency, and ethical accountability. We review current strategies for responsible AI deployment, including regulatory frameworks, human-centered design principles, and privacy-preserving methods. By synthesizing methodological advances and cross-cutting challenges, this study provides a focused and interdisciplinary roadmap for the rigorous and equitable use of AI in social science research.}, archive = {J_ACCESS}, author = {Elias Dritsas and Maria Trigka}, doi = {10.1109/ACCESS.2025.3578906}, journal = {IEEE Access}, pages = {105334-105352}, shortjournal = {IEEE Access}, title = {Machine learning and data science in social sciences: Methods, applications, and future directions}, volume = {13}, year = {2025}, }
• (2025). Integrating indoor localization technologies for enhanced smart education: Challenges, innovations, and applications. ACCESS, 13, 105317-105333. (www)

  @article{ , abstract = {This comprehensive study investigates the integration of indoor localisation technologies in Smart Education (SE) environments, emphasizing their transformative potential to optimize educational systems. Moreover, this work reviews various technologies such as WiFi, 5G, Bluetooth, IoT, Ultra-Wideband (UWB), and Radio Frequency Identification (RFID), analysing their advantages, limitations, and applicability within educational settings. Special attention is given to privacy concerns, exploring compliance with international regulations such as the GDPR. A novel Proof of Concept (PoC) is presented, demonstrating the effectiveness of an Automatic Attendance Control (AAC) system leveraging WiFi and 5G technologies. By combining these radio technologies, the PoC achieves real-time localisation with a high degree of accuracy and scalability, even in resource-constrained environments. The results highlight the significant improvements in attendance management and other potential applications, such as real-time navigation and occupancy monitoring, contributing to enhanced efficiency and quality in educational institutions. This study underscores the potential of indoor localisation technologies to advance SE and offers insights for future innovation in smart, technology-driven learning ecosystems.}, archive = {J_ACCESS}, author = {Carlos Simón Álvarez-Merino and Emil Jatib Khatib and Antonio Tarrías Muñoz and Raquel Barco Moreno}, doi = {10.1109/ACCESS.2025.3578718}, journal = {IEEE Access}, pages = {105317-105333}, shortjournal = {IEEE Access}, title = {Integrating indoor localization technologies for enhanced smart education: Challenges, innovations, and applications}, volume = {13}, year = {2025}, }
• (2025). Effect of bulk capacitor on harmonics of energy-efficient devices. ACCESS, 13, 105302-105316. (www)

  @article{ , abstract = {This paper develops analytical expressions to describe the behavior of harmonic components in rectifier circuits. It examines how harmonic content in the supply voltage influences the harmonic currents drawn by energy-efficient loads. By analyzing these interactions, the study provides insights into circuits integrating single-phase AC/DC converters, such as LED lamps. The findings emphasize the interdependence between harmonic phase angles and their impact on load current harmonics, which is essential for understanding supply voltage distortions and their effects on electrical loads. The assumptions established in this study support the development of more accurate models for characterizing load harmonic currents, enhancing the predictive capabilities of harmonic estimation methods. Furthermore, the proposed computational approach determines key rectifier performance parameters, including peak voltage across the bulk capacitor and the peak time relationship with harmonic components of the voltage waveform. These insights provide a deeper understanding of rectifier circuit behavior under distorted supply conditions, demonstrating the quantifiable impact of harmonic phase angles on rectifier capacitor dynamics offering practical implications for harmonic analysis, and performance optimization. The analytical framework developed here enables a more precise characterization of peak voltage timing and magnitude, which is critical for evaluating rectifier operation under various harmonic conditions.}, archive = {J_ACCESS}, author = {Kamran Daniel and Lauri Kütt and Noman Shabbir and Muhammad Naveed Iqbal and João F. Martins and Matti Lehtonen and Päivo Simson}, doi = {10.1109/ACCESS.2025.3579320}, journal = {IEEE Access}, pages = {105302-105316}, shortjournal = {IEEE Access}, title = {Effect of bulk capacitor on harmonics of energy-efficient devices}, volume = {13}, year = {2025}, }
• (2025). Enhancing creativity and validation in explanatory deep learning-based symbolic music generation: A hybrid approach with LSTM and genetic algorithms. ACCESS, 13, 105280-105301. (www)

  @article{ , abstract = {This research proposes an explanatory deep learning-based music generation approach, where the output of a deep learning model is validated through a set of predefined musical rules, with a refinement process applied when inaccuracies are detected. The study focuses on gamelan, a traditional form of Indonesian music. A Long Short-Term Memory (LSTM) network is used to generate musical compositions, while a modified Genetic Algorithm (GA), omitting the selection and crossover operators, performs validation and, when necessary, refinement via mutation. The LSTM network produces initial compositions, and the GA module ensures compliance with musical rules, enhancing both explainability and creativity. The model successfully generates new bars and lines with notation sequences not found in the original dataset, indicating creative variation. Whether produced directly by the LSTM or refined through GA, the generated output demonstrates the system’s ability to innovate while preserving core musical characteristics. Furthermore, the GA-based validation allows the generated music to be interpreted in terms of the underlying rule constraints. The evaluation using the Pearson’s Correlation Coefficient T-test provides supporting evidence that the proposed automatic music generation (AMG) model is capable of learning and generating gamelan music effectively. The LSTM component, functioning based on its ability to creatively generate note sequences, and the GA component, tasked with validation and refinement, have both proven to collaborate effectively and fulfill their respective roles. These findings support the effectiveness of the proposed model in fostering creative exploration of new tonal patterns aligned with the target genre.}, archive = {J_ACCESS}, author = {Ahmad Zainul Fanani and Arry Maulana Syarif and Ika Novita Dewi and Abdul Karim}, doi = {10.1109/ACCESS.2025.3578449}, journal = {IEEE Access}, pages = {105280-105301}, shortjournal = {IEEE Access}, title = {Enhancing creativity and validation in explanatory deep learning-based symbolic music generation: A hybrid approach with LSTM and genetic algorithms}, volume = {13}, year = {2025}, }
• (2025). Collision avoidance for parametric railway lines by supervisor control. ACCESS, 13, 105264-105279. (www)

  @article{ , abstract = {In the present paper, two collision avoidance supervisor schemes are developed for a single line and one-way railway system, with a parametric number of stations and a parametric number of trains. The consecutive sections of the railway line are linked by Traffic Management Points, being equipped with either motion detectors and/or generalized traffic light indicators, in parametric positions. The parametric model of the railway system is developed. The goal of collision avoidance is translated to three alternative desired behaviors, for three different control schemes. The desired behaviors are formulated in sets of regular languages, being analytic and parametric with respect to the trains and the Traffic Management Points of the railway line. The supervisors realizing each language are designed in simple and transparent forms that can be implemented in different control architectures. The advantages of the supervisors of the second control scheme, regarding the arrival time of the trains, are demonstrated through the application of the supervisors to a developed testbed. The first supervisor scheme covers published results regarding one-way railway lines with two operating trains as a special case. For all control schemes, a reconfigurable supervisor control scheme, towards resilience to new faults and attacks, is designed.}, archive = {J_ACCESS}, author = {Fotis N. Koumboulis and Dimitrios G. Fragkoulis and George E. Panagiotakis and Konstantinos A. Ioannou and Alexandros Nikolopoulos}, doi = {10.1109/ACCESS.2025.3580483}, journal = {IEEE Access}, pages = {105264-105279}, shortjournal = {IEEE Access}, title = {Collision avoidance for parametric railway lines by supervisor control}, volume = {13}, year = {2025}, }
• (2025). On the nuances of multimodal laughter detection: Where, why, and when visual insight augments audio in classification. ACCESS, 13, 105246-105263. (www)

  @article{ , abstract = {This paper explores the impact of visual input on traditional audio classification, focusing on the analysis of audiovisual data compilation and the interaction between modalities in laughter detection. While assessing user reactions to measure potential engagement is distinct from evaluating a content’s entertainment value, incorporating user-centered audio and visual cues enhances the contextual understanding of stimuli and the completeness of the emotional response. Among the results, making use of facial expressions augments the audio-only prediction accuracy with up to 26% when cross-attention mechanisms are employed. To evaluate the robustness of our proposed approach, experiments were systematically conducted under unimodal conditions, omitting each modality individually to assess its impact on performance. As audio and visual insights yield unbalanced contributions to the user’s overall emotional profiling, the accuracy gain of visual features on top of the audio setup provides evidence on the classifier’s ability to capture the nuances of human laughter behaviors.}, archive = {J_ACCESS}, author = {Liviu-Alexandru Marina and Corina Dimitriu}, doi = {10.1109/ACCESS.2025.3580429}, journal = {IEEE Access}, pages = {105246-105263}, shortjournal = {IEEE Access}, title = {On the nuances of multimodal laughter detection: Where, why, and when visual insight augments audio in classification}, volume = {13}, year = {2025}, }
• (2025). Secrecy analysis of α-beaulieu-xie fading channel under selection combining diversity. ACCESS, 13, 105231-105245. (www)

  @article{ , abstract = {Petahertz (PHz) communication technology is envisioned to provide an enormously high data rate with true sense global coverage ranging from air to underwater deployment. However, both line-of-sight (LOS) and non-LOS (NLOS) components are needed to characterize the fading channels of the PHz communication system. Recently, the proposed $\alpha $ -Beaulieu-Xie fading distribution, with $\alpha $ as a modified parameter, appropriately characterizes wireless channels with multiple LOS and NLOS components simultaneously. In addition, $\alpha $ -Beaulieu-Xie distribution exhibits a high degree of trade-off between mathematical tractability and match with experimental data. This research presents various measures of physical layer security over $\alpha $ -Beaulieu-Xie fading channel under the Selection Combining (SC) diversity scheme. In particular, analytical expressions for Secrecy Outage Probability (SOP) $(P_{\gamma } (\gamma)|_{Out-SC})$ , Probability of Non-Zero Secrecy Capacity $(P_{nz-SC})$ , and Average Secrecy Capacity $(C_{SAv-SC})$ are derived over $\alpha $ -Beaulieu-Xie fading channel under the SC diversity scheme. Further, asymptotic expressions for respective secrecy measures are presented as $(P_{Out-SC}(\gamma)|_{Aymp} R(s)$ , $P_{nz-SC}|_{Aymp}$ , and $(C_{SAv-SC}|_{Aymp})$ . Furthermore, the effect of various physical parameters, such as the overall severity parameter of both LOS and NLOS ( $m_{X}$ ), the severity parameter of LoS ( $m_{Y}$ ), and the fading parameter ( $\alpha $ ) on the presented secrecy measures, is quantified numerically. Numerical quantification reveals that the decrease in the value of $(P_{\gamma } (\gamma)|_{Out-SC})$ with an increase in the value of $\alpha $ is proportional to the value of $\alpha $ for a lower regime of $\gamma $ only. Whereas a decrease in the value of $m_{x}$ with an increase in the value of $\alpha $ reflects that the value of $(P_{\gamma } (\gamma)|_{Out-SC})$ depends more dominantly on $m_{x}$ than that of $\alpha $ . In addition, it is noticeable that an increase in either $m_{x}$ or $\alpha $ increases the value of $C_{SAv-SC}$ . However, an increase in the value of $m_{x}$ and $\alpha $ by the same amount, say 0.2, results in a higher increase in the value of $C_{SAv-SC}$ with an increase in the value of $m_{x}$ than that of an increase in the value of $\alpha $ . In addition, each analytical expression is quantified numerically with respect to the respective asymptotic expressions.}, archive = {J_ACCESS}, author = {S. Pratap Singh and Arun Prakash Agrawal and Kanwarpreet Kaur and Amit Kumar and Kulbir Singh and Ghanshyam Singh}, doi = {10.1109/ACCESS.2025.3579485}, journal = {IEEE Access}, pages = {105231-105245}, shortjournal = {IEEE Access}, title = {Secrecy analysis of α-beaulieu-xie fading channel under selection combining diversity}, volume = {13}, year = {2025}, }
• (2025). Adaptive voltage control for DC motors of omnidirectional mobile robot with prevent wheel slippage. ACCESS, 13, 105220-105230. (www)

  @article{ , abstract = {Omnidirectional mobile robots (OMRs) are widely recognized in the industry for their excellent maneuverability and flexibility, particularly when navigating complex environments. Standard designs typically feature three wheels powered by direct current (DC) motors. However, precise control of these systems remains challenging due to the nonlinear traction dynamics between the wheels and the ground, leading to slipping and instability, mainly when operating in changing surface conditions or sudden movements. Traditional control methods often fail to comprehensively address these issues, especially in ensuring accurate path tracking and dynamic adaptation to changes in traction force without encountering controller singularities or input constraints. This study proposes an adaptive voltage controller for three-wheeled omnidirectional robots driven by DC motors to address these challenges. The controller’s contribution is its ability to dynamically limit the input voltage based on real-time wheel and ground traction force estimates, thus effectively preventing wheel slippage. The controller consists of three main components: 1) an adaptive tracking controller that ensures the system states converge rapidly to the desired trajectory, 2) a transient singularity-free controller that resolves the singularities associated with traditional adaptive control laws, and 3) a backend system designed to adapt to input voltage constraints. The proposed controller has been applied to the path tracking of an omnidirectional mobile robot and has demonstrated outstanding performance through numerical simulations. When implemented in various environments, the proposed controller shows significant advantages over conventional controllers.}, archive = {J_ACCESS}, author = {Hoa Thi Truong}, doi = {10.1109/ACCESS.2025.3580384}, journal = {IEEE Access}, pages = {105220-105230}, shortjournal = {IEEE Access}, title = {Adaptive voltage control for DC motors of omnidirectional mobile robot with prevent wheel slippage}, volume = {13}, year = {2025}, }
• (2025). Three-way clustering based on digital image processing. ACCESS, 13, 105211-105219. (www)

  @article{ , abstract = {A three-way clustering algorithm based on image processing is proposed by combining blurring and sharpening operations in digital image processing. The proposed algorithm quantifies the density of the dataset into gray values through inverse multivariate quadratic kernel function. By blurring and sharpening operations for the university, we delete the samples with low density and obtain samples with high density. Then, different clusters are produced by traditional clustering algorithm for samples with high density. For each cluster, the core region and fringe region are obtained through blurring operation and sharpening operation, respectively. The experiments on different UCI datasets verify that the clustering algorithm is stable and effective by comparing the clustering indexes adjusted rand index (ARI), normalized mutual information (NMI) and adjusted mutual information (AMI).}, archive = {J_ACCESS}, author = {Xiangjian Chen and Pingxin Wang}, doi = {10.1109/ACCESS.2025.3579784}, journal = {IEEE Access}, pages = {105211-105219}, shortjournal = {IEEE Access}, title = {Three-way clustering based on digital image processing}, volume = {13}, year = {2025}, }
• (2025). Proactive edge caching with popularity prediction and content replication. ACCESS, 13, 105199-105210. (www)

  @article{ , abstract = {The increasing demand for low-latency content delivery in 5G networks highlights the need for intelligent edge caching solutions. This paper proposes a cooperative edge caching framework for small cell base stations (SBSs), leveraging overlapping cache assignments and a dynamic programming-based (DP) optimization algorithm to minimize the content retrieval delay and maximize the cache hit ratio (CHR). The algorithm incorporates both global and local content popularity predictions—obtained via exponential moving average (EMA) and long short-term memory (LSTM) models, and dynamically scales content placement decisions based on delay-aware replication benefits. Simulation results, conducted under diverse and realistic content demand patterns, demonstrate that the proposed approach significantly outperforms both traditional caching methods, such as Least Recently Used (LRU) and random assignment, as well as state-of-the-art demand-aware algorithms in terms of average delay and cache efficiency. The framework is well-suited for dynamic mobile edge computing (MEC) environments and provides a scalable foundation for next-generation cooperative caching strategies.}, archive = {J_ACCESS}, author = {İlhan Demirci and Ömer Korçak}, doi = {10.1109/ACCESS.2025.3578362}, journal = {IEEE Access}, pages = {105199-105210}, shortjournal = {IEEE Access}, title = {Proactive edge caching with popularity prediction and content replication}, volume = {13}, year = {2025}, }
• (2025). Implementation and analysis of a secure communication with SunSpec modbus and transport layer security protocols for short-term energy management systems. ACCESS, 13, 105183-105198. (www)

  @article{ , abstract = {The growth of Renewable Distributed Generations (RDG) presents both a solution to common problems of traditional energy sources - such as the environmental impact and losses in transmission and distribution - and a challenge to appropriately control a large number of Distributed Energy Resources (DERs). The feasibility of such control depends on adequate communication protocols that, among other features, must address the security requirements of DER applications. This paper analyses the impact of a TLS-based SunSpec Modbus implementation on connection time and bulk data transfer when applied to short-term energy management systems. The protocol was tested with synthetic benchmarks, like the PI-MBUS-300, and was also deployed to the pilot-scaled short-term energy management system in the Pato Branco campus of the Federal University of Technology – Paraná. The results should be sufficient to characterize Modbus/TLS in normal operation with multiple cipher suites, as well as under abnormal situations that may cause frequent reconnections, like an unstable network.}, archive = {J_ACCESS}, author = {Matheus K. Ferst and Gustavo Weber Denardin and Carlos Marcelo de Oliveira Stein and Emerson Giovani Carati and Jean Patric da Costa and Rafael Cardoso}, doi = {10.1109/ACCESS.2025.3577972}, journal = {IEEE Access}, pages = {105183-105198}, shortjournal = {IEEE Access}, title = {Implementation and analysis of a secure communication with SunSpec modbus and transport layer security protocols for short-term energy management systems}, volume = {13}, year = {2025}, }
• (2025). Machine learning models for predicting seismic response of a novel two-stage friction pendulum isolated bridge structure. ACCESS, 13, 105165-105182. (www)

  @article{ , abstract = {Bridges are critical infrastructure, and their vulnerability to seismic events necessitates efficient methods for predicting structural responses. Traditional methods, such as Nonlinear Time History Analysis (NLTHA), are computationally intensive, time-consuming, and require an expert analyst. Additionally, innovative structural elements like Two-Stage Friction Pendulum Bearings (TSFPBs) are not readily available in commercial software. This study proposes a Machine Learning (ML) approach as a faster and simplified alternative to NLTHA for predicting the structural response of TSFPB-isolated bridge structures. A comprehensive dataset of 500,000 data points was meticulously generated through NLTHA. Force-displacement laboratory tests under various loading and displacement conditions were conducted to validate the TSFPB nonlinear element developed in OpenSees. Utilizing this extensive dataset, seven ML models were trained and evaluated using statistical key performance indicators (KPIs). Results revealed that Extreme Gradient Boosting (XGB) achieved the highest accuracy ( $\mathrm{R}^{2} =0.959$ ) on the testing dataset, while Adaptive Boosting (AB) showed the lowest accuracy ( $\mathrm{R}^{2} =0.761$ ). Furthermore, a comparison of different surrogate models was conducted for global feature interpretation, while SHAP analysis was used for local interpretation to identify the influence of input parameters on individual structural responses. A graphical user interface (GUI) was developed using the best-performing model (i.e., XGB), offering engineers an accessible tool for seismic response prediction. This study demonstrates the potential of ML as an efficient alternative to NLTHA while providing valuable insights into key parameters influencing the seismic performance of TSFPB-isolated bridges, enhancing decision-making and design optimization.}, archive = {J_ACCESS}, author = {Hanzlah Akhlaq and Tianbo Peng and Kawsu Jitteh and Muhammad Salman Khan}, doi = {10.1109/ACCESS.2025.3580065}, journal = {IEEE Access}, pages = {105165-105182}, shortjournal = {IEEE Access}, title = {Machine learning models for predicting seismic response of a novel two-stage friction pendulum isolated bridge structure}, volume = {13}, year = {2025}, }
• (2025). Security-enhanced image encryption: Combination of S-boxes and hyperchaotic integrated systems. ACCESS, 13, 105151-105164. (www)

  @article{ , abstract = {In the field of image encryption, with the continuous development of attack techniques, the requirements for the attack resistance and robustness of encryption algorithms are increasing, and the method of image encryption using a single chaotic system can no longer provide sufficient security and randomness. In this paper, an image encryption method combining S-box and hyperchaotic integrated system is proposed. First, a particle swarm optimization algorithm is designed to search for S-boxes based on an adaptive function that takes into account several security metrics and an S-function inertia weighting strategy. The S-box obtained from the search has good performance index, in which the nonlinearity is 110, the differential uniformity is 6, the algebraic number is 7, and the strict avalanche criterion coefficient is 0.50415. Further, a hyperchaotic integrated system is constructed by combining the Lorenz system and the Henon system with the hyperchaotic Chen system as the core. A three-step diffusion mechanism is adopted in the encryption process, and the S-boxes obtained from the search of the particle swarm optimization algorithm are fully integrated with the chaotic sequences generated by the hyperchaotic integrated system in the first two steps of diffusion to improve the security and randomness of the encryption algorithm. The performance of the proposed encryption algorithm is evaluated and the average values of the calculated metrics are as follows: information entropy value is 7.993, correlation coefficient is 0.008803, NPCR is 99.61%, and UACI is 33.48%. Therefore, the proposed image encryption algorithm combining S-boxes and a hyperchaotic integrated system has high security and robustness, making it applicable to the field of highly demanding image encryption.}, archive = {J_ACCESS}, author = {Renjie Song and Haixia Zhao}, doi = {10.1109/ACCESS.2025.3575935}, journal = {IEEE Access}, pages = {105151-105164}, shortjournal = {IEEE Access}, title = {Security-enhanced image encryption: Combination of S-boxes and hyperchaotic integrated systems}, volume = {13}, year = {2025}, }
• (2025). ML-driven transistor self-heating analysis from TCAD to large IP circuits. ACCESS, 13, 105141-105150. (www)

  @article{ , abstract = {To mitigate the increasing short-channel effects of miniaturization, the channel is wrapped increasingly with gate metal and insulating oxides to improve electrostatic control while worsening self-heating. Hence, self-heating has become a reliability concern and will increase in severity with more advanced nodes in the future (e.g., nanosheets), accelerating aging and shortening the transistor’s lifetime. Although standardized compact models include self-heating, their lumped nature abstracts the temperature gradient over the transistor’s cross-section. To this end, we have used TCAD to generate a distributed channel temperature map data set. We propose a technology-agnostic methodology for extracting localized temperature data on the circuit level using the trained machine learning model (with a mean error of 0.1 K). We explore our method to study self-heating in complex large circuits featuring over 3700 transistors. This includes foundries IPs like a complete high-performance SRAM-based register file. Our method provides the thermal map for every transistor and time step. The voltage and thermal traces are processed with an aging framework to estimate the expected aging more realistically. Using the proposed methodology reduces the conservative constant 50 mV threshold voltage shift to 31 mV, improving circuit performance through less pessimistic aging estimations. This unprecedented detailed analysis of self-heating at the large scale of the circuit will open new doors for designers to evaluate how self-heating propagates from transistors to circuits and impacts their reliability and lifetime.}, archive = {J_ACCESS}, author = {Simon Thomann and Nico Mayr and Albi Mema and Hussam Amrouch}, doi = {10.1109/ACCESS.2025.3576161}, journal = {IEEE Access}, pages = {105141-105150}, shortjournal = {IEEE Access}, title = {ML-driven transistor self-heating analysis from TCAD to large IP circuits}, volume = {13}, year = {2025}, }
• (2025). A self-supervised monocular depth estimation framework based on detail recovery and feature fusion. ACCESS, 13, 105126-105140. (www)

  @article{ , abstract = {With the rapid development of autonomous driving and robotics, the demand for high-precision depth estimation is growing steadily. The emergence of self-supervised learning methods has reduced the reliance on expensive and hard-to-obtain ground truth data. However, existing models still face challenges in design, such as insufficient detail recovery, inadequate feature fusion, and limited global contextual understanding, which lead to deficiencies in the accuracy and precision of the generated depth maps. To address these challenges, this paper proposes a novel self-supervised monocular depth estimation network, Enhanced Depth Fusion Net (EDFNet). Based on the encoder-decoder architecture, EDFNet introduces three key modules: the Adaptive Selective Attention Module (ASAM), the Multi-Dimensional Attention Fusion Module (MDAFM), and the Hybrid Parallel Convolution Block (HPCB). Specifically, ASAM selectively emphasizes critical features and spatial locations in images to enhance the model’s ability to capture details. MDAFM captures feature information from different perspectives and levels to address the problem of critical information loss, while HPCB effectively captures and fuses local and global information through parallel processing. The synergy of these modules enables EDFNet to generate high-quality depth maps in various complex scenarios. Experimental results on the KITTI dataset demonstrate that the proposed method significantly outperforms state-of-the-art approaches across multiple evaluation metrics, including Abs Rel, Sq Rel, RMSE, $\delta \lt 1.25$ , effectively mitigating the issues of detail loss and edge blurring in depth maps. Additionally, EDFNet demonstrates excellent generalization performance on the Make3D, NYU Depth V2, and Cityscapes datasets.}, archive = {J_ACCESS}, author = {Shun Li and Chongzheng Huang and Xiangzhe Li and Zhengyou Liang}, doi = {10.1109/ACCESS.2025.3577699}, journal = {IEEE Access}, pages = {105126-105140}, shortjournal = {IEEE Access}, title = {A self-supervised monocular depth estimation framework based on detail recovery and feature fusion}, volume = {13}, year = {2025}, }
• (2025). Optimized reinforcement learning model via contrastive learning for intention classification of chinese questions on respiratory diseases. ACCESS, 13, 105113-105125. (www)

  @article{ , abstract = {The intent classification of Chinese questions on respiratory diseases (IC-CQRD) can not only promote the development of smart medical care, but also strengthen epidemic surveillance. To address the limited availability of datasets in this field, we have developed the IC-CQRD dataset. The core of IC-CQRD is text representation. Drawing upon the characteristics of Chinese questions on respiratory diseases (CQRD), we propose a reinforcement learning model incorporating contrastive learning (RL_CL) to extract keywords and construct CQRD representations. Unlike previous reinforcement learning-based text representation models, we design a word frequency reward function (WF-RF) and a pair of positive and negative samples of CQRD in RL_CL. The WF-RF not only assists in identifying keywords but also enables rough classification of keywords. The positive and negative samples, which are constructed on the basis of the keywords extracted from CDRD, serve to provide feedback on the quality of these keywords. We demonstrate three attempts to build positive and negative samples: enhancing keywords in both directions (EKBD), filtering out keywords (FOK), and building real samples (BRS). Compared with solely utilizing reinforcement learning models, several methods for constructing positive and negative samples based on RL_CL have demonstrated varying degrees of improvement. Notably, EKBD achieved the best results across all three datasets.}, archive = {J_ACCESS}, author = {Hao Wu and Degen Huang and Xiaohui Lin}, doi = {10.1109/ACCESS.2025.3573707}, journal = {IEEE Access}, pages = {105113-105125}, shortjournal = {IEEE Access}, title = {Optimized reinforcement learning model via contrastive learning for intention classification of chinese questions on respiratory diseases}, volume = {13}, year = {2025}, }
• (2025). RMixDA: A random compound data augmentation operation framework for neural machine translation. ACCESS, 13, 105101-105112. (www)

  @article{ , abstract = {In Neural Machine Translation (NMT), data augmentation is an effective method for improving model robustness by generating diverse augmented data from existing datasets. Typically, the quality of augmented data is evaluated based on its similarity and diversity to training data. Current approaches often rely on a single type of operation, limiting their ability to fully capture the range of possible variations in real-world data. This limitation will affect the robustness and generalization of the model. To address this, we propose RMixDA: a Random Mixed Algorithm Framework For Data Augmentation, which dynamically integrates multiple NMT-oriented data augmentation methods. RMixDA defines an extensible collection of multi-augmentation operations using the Backus-Naur Form (BNF) framework, which categorizes data augmentation methods into pre-embedding $(pre\_embed\_op)$ , embedding $(embed\_op)$ and post-embedding $(post\_embed\_op)$ levels. These operations are combined using a random walk algorithm to generate diverse augmented data, for maintaining both similarity and diversity. We further incorporate contrastive learning, identifying augmented data as positive or negative samples to evaluate similarity more effectively. Additionally, a novel evaluation method for augmented data quality is introduced, which is regularized by the NMT model’s loss function to improve the positive correlation between augmented data quality and model training. Extensive experimental studies demonstrate the effectiveness of RMixDA and its scalability for integrating diverse data augmentation operations. On the IWSLT14 German-English and WMT14 English-German benchmarks, RMixDA achieves BLEU scores of 37.87 and 29.13, respectively, outperforming state-of-the-art methods by up to 3.44 and 1.83. This framework showcases practical utility in real-world NMT tasks, particularly for enhancing translation quality in low-resource language scenarios.}, archive = {J_ACCESS}, author = {Huijun Wang and Xiaojing Du and Xinkun Hao}, doi = {10.1109/ACCESS.2025.3580635}, journal = {IEEE Access}, pages = {105101-105112}, shortjournal = {IEEE Access}, title = {RMixDA: A random compound data augmentation operation framework for neural machine translation}, volume = {13}, year = {2025}, }
• (2025). Multivariate machine learning model based on YOLOv8 for traffic flow prediction in intelligent transportation systems. ACCESS, 13, 105091-105100. (www)

  @article{ , abstract = {Traffic flow prediction plays a crucial role in Intelligent Transportation Systems (ITS), as it substantially enhances traffic management efficiency, alleviates congestion, and improves road safety. Traditional models often face challenges in addressing the dynamic complexity of modern highway traffic, whereas multivariate machine learning models demonstrate superior predictive accuracy by leveraging diverse data sources. To address these limitations, this study proposes a traffic flow prediction framework based on sensor networks and multivariate machine learning techniques. Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. Building on this, we propose a hybrid model combining Gradient Boosting Regression (GBR) and Support Vector Regression (SVR), where the GBR component captures complex nonlinear patterns in traffic flow data, while the SVR component enhances the model’s generalization ability by optimizing predictions. Experimental results demonstrate that the proposed GBR-SVR model outperforms comparative methods in short-term traffic flow prediction, particularly under dynamic and complex traffic conditions. In conclusion, by integrating real-time data collection from cameras with the predictive advantages of machine learning models, this algorithm offers a more efficient and sustainable solution for urban traffic management.}, archive = {J_ACCESS}, author = {Fukui Wu and Hanzhong Tan and Linfeng Zhang and Shuangbing Wen and Tao Hu}, doi = {10.1109/ACCESS.2025.3580201}, journal = {IEEE Access}, pages = {105091-105100}, shortjournal = {IEEE Access}, title = {Multivariate machine learning model based on YOLOv8 for traffic flow prediction in intelligent transportation systems}, volume = {13}, year = {2025}, }
• (2025). Solar-powered pumping for alleviating energy poverty: Sustainable solutions for agriculture sector in portugal. ACCESS, 13, 105077-105090. (www)

  @article{ , abstract = {Sustainable Development Goal 7 highlights affordable and reliable energy access as a pervasive problem around the world, affecting millions of people struggling with energy poverty, and it hinders global social justice, economic prosperity, and quality of life. Addressing this problem is critical to achieving environmental stewardship and alleviating poverty. Green energies address energy poverty in nations that are already threatened. Solar systems are sustainable solutions for decentralizing clean energy. Photovoltaic (PV) minimizes emissions and dependency on fossil fuels. This study highlights the significance of RES integration in tackling energy poverty in industries. Coimbra, Portugal, is used as a case study to illustrate PV-powered irrigation networks and the agriculture sector. Results indicate the effectiveness of Portugal’s PV-powered pumping system, meeting 94.3% of water needs with an annual efficiency of 48.3%, and achieving a Net Present Value (NPV) of €22,469.73 and an Internal Rate of Return (IRR) of 74.24%, demonstrating strong economic viability over 20 years. With considerable electricity cost reductions and a strong return on investment, the solar-powered pumping system turns out to be a profitable project.}, archive = {J_ACCESS}, author = {Alireza Zabihi and Tripura Pidikiti and L. N. Sastry Varanasi and Gauri Kalnoor and Iskender Akkurt and V. B. Murali Krishna}, doi = {10.1109/ACCESS.2025.3579783}, journal = {IEEE Access}, pages = {105077-105090}, shortjournal = {IEEE Access}, title = {Solar-powered pumping for alleviating energy poverty: Sustainable solutions for agriculture sector in portugal}, volume = {13}, year = {2025}, }
• (2025). Empowering student learning in intelligent tutoring systems: Building a comprehensive learner model with feedback from students, parents, and instructors. ACCESS, 13, 105063-105076. (www)

  @article{ , abstract = {This paper introduces a novel approach for developing a learner model for school students by incorporating assessments from multiple sources, including students, parents, and teachers, in addition to exams. Traditional Intelligent Tutoring Systems (ITS) typically rely on a single source, such as exams, which often results in an incomplete picture of a student’s learning needs. To address this limitation, we developed a multi-source ITS that integrates feedback from various stakeholders to enhance the accuracy and personalization of the learner model. In a study involving 75 high school students, participants were divided into three groups: one receiving traditional classroom instruction, one using a conventional ITS, and one using our multi-source ITS. Results showed that the group using the multi-source system demonstrated the greatest improvement, with an average 38% increase in post-test scores, compared to 19% for the traditional instruction group and 26% for the conventional ITS group. Moreover, students using the multi-source ITS reported higher engagement and motivation, driven by their ability to interact with and adjust their learner model. These findings highlight the significant benefits of incorporating multi-source feedback into ITS to boost student performance, engagement, and motivation.}, archive = {J_ACCESS}, author = {Abdallatif Abu-Issa and Ali Aljadaa and Abualsoud A. Hanani and Iyad Tumar and Mohammed S. Hussein}, doi = {10.1109/ACCESS.2025.3579575}, journal = {IEEE Access}, pages = {105063-105076}, shortjournal = {IEEE Access}, title = {Empowering student learning in intelligent tutoring systems: Building a comprehensive learner model with feedback from students, parents, and instructors}, volume = {13}, year = {2025}, }
• (2025). Hardware-software stitching algorithm in lightweight Q-learning system on chip (SoC) for shortest path optimization. ACCESS, 13, 105044-105062. (www)

  @article{ , abstract = {This paper presents a novel hardware-software co-design approach to accelerate Q-learning algorithms using a RISC-V-based System-on-Chip (SoC) design. We introduce a maze-stitching algorithm that enables efficient solving of large, complex mazes by decomposing them into smaller sub-mazes and thus can perform Q-Learning computation in low-complexity hardware accelerator. Furthermore, we provide a comprehensive analysis of the algorithm’s complexity, theoretical performance gains, and practical implementation results. The proposed implementation demonstrates significant performance improvements over traditional software approaches, achieving speedups ranging from $84\times $ to $233\times $ for complex maze-solving tasks while maintaining a small footprint. The algorithm, even without the accelerator also achieved speedups ranging from $13\times $ to $36\times $ . The proposed system combines a 64-bit RISC-V core with a Q-Learning accelerator, operating at 50MHz on an Arty A7-100T FPGA. The maze-stitching technique allows for scaling to larger problem sizes while maintaining hardware efficiency. The proposed work can be applied to a lightweight accelerator and provides a scalable solution for resource-constrained edge computing environments.}, archive = {J_ACCESS}, author = {Yahwista Salomo and Infall Syafalni and Nana Sutisna and Trio Adiono}, doi = {10.1109/ACCESS.2025.3578681}, journal = {IEEE Access}, pages = {105044-105062}, shortjournal = {IEEE Access}, title = {Hardware-software stitching algorithm in lightweight Q-learning system on chip (SoC) for shortest path optimization}, volume = {13}, year = {2025}, }
• (2025). SVR-optimized ANN model for predicting earthquake risk in electrical substations based on disaster datasets in the aceh region, indonesia. ACCESS, 13, 105027-105043. (www)

  @article{ , abstract = {This paper proposes a predictive model based on an Artificial Neural Network (ANN) optimized with Support Vector Regression (SVR) to predict seismic parameters as part of earthquake risk mitigation for substations. The proposed model aims to improve the accuracy of seismic parameter predictions, including seismic energy, Peak Ground Acceleration (PGA), and Modified Mercalli Intensity (MMI). A case study was conducted in the Aceh region, Indonesia, which is located near the subduction zone between the Indo-Australian Plate and the Eurasian Plate, making it highly seismically active. Therefore, this model is developed to identify substations most at risk of earthquake impacts while supporting disaster mitigation planning through early warning systems. The dataset used in the modeling includes information on the epicenter and substation coordinates, earthquake magnitude, depth, and the distance between the two points. The ANN training process uses backpropagation with sigmoid and tanh activation functions. The ANN outputs are then used as input features for the SVR model, applying a Radial Basis Function (RBF) kernel to capture the non-linear relationships between variables. Evaluation results show that the hybrid SVR-ANN model significantly improves prediction accuracy compared to conventional ANN. This improvement is indicated by a reduction in error and an increase in the coefficient of determination ( $R^{2}$ ). The findings of this paper can serve as a foundation for developing more effective mitigation systems to reduce earthquake impacts on electrical infrastructure.}, archive = {J_ACCESS}, author = {Elvy Sahnur Nasution and Yuwaldi Away and Syahrial and Ira Devi Sara and Andri Novandri}, doi = {10.1109/ACCESS.2025.3579797}, journal = {IEEE Access}, pages = {105027-105043}, shortjournal = {IEEE Access}, title = {SVR-optimized ANN model for predicting earthquake risk in electrical substations based on disaster datasets in the aceh region, indonesia}, volume = {13}, year = {2025}, }
• (2025). OPTISTACK: A hybrid ensemble learning and XAI-based approach for malware detection in compressed files. ACCESS, 13, 104992-105026. (www)

  @article{ , abstract = {The increasing reliance on compressed file formats for data storage and transmission has made them attractive vectors for malware propagation, as their structural complexity enables evasion of conventional detection mechanisms. Although entropy-based analysis has been widely applied in executable malware detection, its application to compressed file formats remains underexplored. Moreover, existing approaches are predominantly limited to Shannon entropy, failing to exploit the discriminative power of higher-order statistical metrics. Additionally, standalone machine learning models often suffer from limited generalizability and lack interpretability, hindering their real-world deployment in security-critical systems. To address these challenges, we propose OPTISTACK, a novel stacking ensemble framework that integrates Random Forest (RF), Decision Tree (DT), and XGBoost (XGB) as base learners with a Logistic Regression (LR) meta-classifier. Our model leverages an advanced entropy-based feature space—including Rényi entropy (with $\alpha = 2, 4, 6$ ), mean entropy, and quartile-based entropy (25th and 75th percentiles)—to capture fine-grained statistical variations in compressed data. To the best of our knowledge, this is the first study to integrate higher-order entropy metrics and distributional entropy features into a stacking ensemble model for malware detection in compressed files. Extensive evaluation on the NapierOne dataset, spanning six prevalent compression formats—ZIP, 7ZIP, GZIP (GNU Zip), RAR (Roshal Archive), TAR (Tape Archive), and ZLIB—demonstrates that OPTISTACK significantly outperforms traditional models, achieving 99.45% accuracy, 99.62% F1-score, 98.80% MCC, and 94.11% AUC-ROC. Our PDP-ICE analysis reveals that minor variations in 25th and 75th quartile entropy values lead to substantial shifts in classification probabilities, underscoring their critical role in model sensitivity and robustness. SHAP-based interpretability analysis further identifies the 25th quartile entropy as the most influential feature across all models. Additionally, we introduce an entropy network graph-based vulnerability analysis that reveals ZIP and RAR as the most malware-prone formats. By combining stacking ensemble learning, advanced entropy metrics, and Explainable AI (XAI) techniques, OPTISTACK delivers a robust, interpretable, and generalizable framework for detecting malware in compressed file environments—addressing key limitations in existing cybersecurity methodologies.}, archive = {J_ACCESS}, author = {Khaled Mahmud Sujon and Rohayanti Binti Hassan and M. Abdullah-Al-Wadud and Jia Uddin}, doi = {10.1109/ACCESS.2025.3579880}, journal = {IEEE Access}, pages = {104992-105026}, shortjournal = {IEEE Access}, title = {OPTISTACK: A hybrid ensemble learning and XAI-based approach for malware detection in compressed files}, volume = {13}, year = {2025}, }
• (2025). Abstractive text summarization in arabic-like script using multi-encoder architecture and semantic extraction techniques. ACCESS, 13, 104977-104991. (www)

  @article{ , abstract = {In the field of Natural Language Processing (NLP), the task of text summarization plays a vital role in understanding textual content and producing concise summaries. Text summarization approaches can be categorized as either extractive or abstractive, with the latter largely unexplored in the Arabic script languages. Previous research on abstractive summarization for the Urdu language has made progress in creating logical and succinct summaries. Still, it has failed to preserve the underlying semantics, often resulting in inconsistent and ambiguous outputs. This study focuses on designing and implementing an abstractive text summarization model for the Urdu language, aiming to capture the semantic meaning of the original text. The proposed model improves readability and comprehension by generating concise and meaningful overviews of original content while introducing new words and expressions. The study implements a multi-layer transformer encoder as part of the mBART (Multilingual BART) model. The encoder consists of multiple stacked transformer layers. It utilizes self-attention mechanisms to process and understand the input sequences effectively, achieving a BERTScore of 90%, a BLEU score of 43%, and ROUGE scores of 80.3% for ROUGE-1, 74.3% for ROUGE-2, and 80.3% for ROUGE-L. These results demonstrate the efficacy of the model in producing coherent and high-quality abstractive summaries for the Urdu language.}, archive = {J_ACCESS}, author = {Wajiha Fatima and Syed Saqib Raza Rizvi and Taher M. Ghazal and Qasem M. Kharma and Munir Ahmad and Sagheer Abbas and Muhammad Furqan and Khan Muhammad Adnan}, doi = {10.1109/ACCESS.2025.3575610}, journal = {IEEE Access}, pages = {104977-104991}, shortjournal = {IEEE Access}, title = {Abstractive text summarization in arabic-like script using multi-encoder architecture and semantic extraction techniques}, volume = {13}, year = {2025}, }
• (2025). CLM-UNet: A remote sensing image semantic segmentation method incorporating attention mechanism. ACCESS, 13, 104966-104976. (www)

  @article{ , abstract = {This study proposes an enhanced U-Net model incorporating a CLM attention module, designed to improve semantic segmentation performance in remote sensing images of the Circum-Tarim region. Traditional U-Net structures face challenges in balancing the extraction of global and local features, especially in complex, multi-scale remote sensing data. The CLM module addresses this by dynamically adjusting the focus on key channel features, thereby suppressing irrelevant information and improving segmentation accuracy. Ablation experiments demonstrate the effectiveness of the CLM module, showing that CLM-UNet outperforms the standard U-Net in key metrics, including an increase of 6.07% in mean Intersection over Union (MIOU) and a 0.19% improvement in overall accuracy (OA). Additionally, the CLM-UNet shows superior performance in distinguishing between desert and urban bare land, with MIOU values 4.75% and 3.19% higher than U-Net for buildings and vegetation, respectively. Comparative analysis with mainstream models in other semantic segmentation fields shows that CLM-UNet achieves better generalization, with relatively higher evaluation metrics for MIOU, OA, and G-means, confirming its robustness in complex environments.}, archive = {J_ACCESS}, author = {Panpan Zhang and Xin Teng and Yong Qin and Qiwei Zhao and Yu Gao and Wanchao Kang and Junfu Fan}, doi = {10.1109/ACCESS.2025.3570269}, journal = {IEEE Access}, pages = {104966-104976}, shortjournal = {IEEE Access}, title = {CLM-UNet: A remote sensing image semantic segmentation method incorporating attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Survey of navigational perception sensors’ security in autonomous vehicles. ACCESS, 13, 104937-104965. (www)

  @article{ , abstract = {The adoption of autonomous vehicles (AVs) significantly increased in recent years, playing a crucial role in enhancing transportation safety and efficiency, expanding mobility options, and reducing user costs. However, as AVs become more connected and automated, they also become more susceptible to malicious attacks, making it imperative to prioritize their security. Specifically, AVs are vulnerable to a range of cyber-threats, leading to a growing body of literature on potential cyberattacks targeting AVs. A key focus of existing research is understanding these threats and proposing mitigation strategies, particularly those aimed at attacks on navigational perception sensors, which are critical to AV navigation and decision-making. AVs rely on an array of navigational perception sensors, including LiDAR, cameras, RADAR, global positioning system (GPS) sensors, and ultrasonic sensors, to ensure secure and safe operation. However, adversarial manipulation of these sensors can compromise an AV’s navigational perception system, potentially leading to catastrophic consequences. While existing surveys provide valuable insights on AV cybersecurity challenges, several gaps remain unexplored, such as hardware security issues in AVs, analyzing sensor fusion attacks and evaluating practical defense implementations. To address these gaps, this paper makes a novel contribution by offering a comprehensive review of sensor fusion vulnerabilities that have not been sufficiently addressed in previous literature, specifically focusing on the compounded effects of sensor manipulation in AVs. In addition, this paper introduces a new classification of sensor vulnerabilities, highlighting practical defense mechanisms based on real-world simulation and testbed environments, a key contribution not explored in earlier works. We also introduce a detailed classification of sensor vulnerabilities, categorize the attacks that exploit these vulnerabilities, and critically assess existing countermeasures and defense mechanisms. Our study provides unique insights into the practical implementation of these countermeasures by reviewing simulation and testbed frameworks, which have been underrepresented in prior research. This approach aims to support the development of more robust security solutions for AVs.}, archive = {J_ACCESS}, author = {Abhijeet Solanki and Wesam Al Amiri and Marim Mahmoud and Blaine Swieder and Syed Rafay Hasan and Terry N. Guo}, doi = {10.1109/ACCESS.2025.3578891}, journal = {IEEE Access}, pages = {104937-104965}, shortjournal = {IEEE Access}, title = {Survey of navigational perception sensors’ security in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Norm-based outlier filtering and consensus aggregation for robust federated learning. ACCESS, 13, 104926-104936. (www)

  @article{ , abstract = {Federated Learning (FL) enables collaborative training of machine learning models across distributed clients without sharing private data, making it ideal for privacy-sensitive applications. However, FL is highly vulnerable to backdoor attacks, especially those designed to persist undetected over time. One of the most advanced of these is the Irreversible Backdoor Attack (IBA), which remains effective even after malicious clients are removed by aligning its updates with benign ones. In this paper, we propose a novel defense framework called Norm-Based Outlier Filtering and Consensus-Aware Aggregation (NOFCA) to effectively defend against such attacks. NOFCA combines norm-based outlier removal of client updates with a consensus-direction-based perturbation mechanism that disrupts the insertion of aligned adversarial updates. Unlike conventional aggregation rules or heuristic-based filtering, our method actively removes anomalous updates and performs adjusted aggregation based on the consensus direction of benign clients, thereby effectively obstructing backdoor attacks. Experimental results on MNIST and CIFAR-10 datasets demonstrate that NOFCA consistently achieves the lowest backdoor accuracy across all evaluated attack scenarios while maintaining competitive main accuracy. Furthermore, under fixed-frequency attack settings, where the attacker participates in every communication round, NOFCA shows robust and stable performance, maintaining strong defense even in highly persistent threat conditions. These findings confirm the effectiveness of NOFCA as a practical and resilient defense strategy for real-world FL systems.}, archive = {J_ACCESS}, author = {Hasung Yeo and Joon-Woo Lee}, doi = {10.1109/ACCESS.2025.3580198}, journal = {IEEE Access}, pages = {104926-104936}, shortjournal = {IEEE Access}, title = {Norm-based outlier filtering and consensus aggregation for robust federated learning}, volume = {13}, year = {2025}, }
• (2025). 0.3-V 36-nW voltage-mode first-order filter based on multiple-input operational transconductance amplifier. ACCESS, 13, 104916-104925. (www)

  @article{ , abstract = {This paper presents an electronically tunable, voltage-mode first-order filter based on multiple-input operational transconductance amplifiers (MI-OTAs). The filter circuit consists of two MI-OTAs and a single capacitor. It is demonstrated that a single topology can realize first-order low-pass, high-pass, and all-pass filters using MI-OTA-based designs. These filter responses are achieved without the need for component matching, input matching, or inverting input signals. The pole frequency of all filter responses can be electronically tuned by adjusting the transconductance of the OTA. The MI-OTA operates at 0.3 V with a rail-to-rail input voltage range, thanks to the use of bulk-driven MOS transistors operating in the subthreshold region and the multiple-input MOS transistor technique. This results in a simple CMOS structure with low supply voltage and reduced power consumption. The circuit was designed and evaluated in Cadence Virtuoso using UMC’s 130-nm 1P8M CMOS process. The proposed filter demonstrates improved performance in terms of lower supply voltage and reduced power consumption compared to previous works. It achieves a dynamic range of 52 dB while consuming only 36 nW of power, with a bias current setting of 10 nA.}, archive = {J_ACCESS}, author = {Montree Kumngern and Tomasz Kulej and Fabian Khateb}, doi = {10.1109/ACCESS.2025.3579494}, journal = {IEEE Access}, pages = {104916-104925}, shortjournal = {IEEE Access}, title = {0.3-V 36-nW voltage-mode first-order filter based on multiple-input operational transconductance amplifier}, volume = {13}, year = {2025}, }
• (2025). ML auditing and reproducibility: Applying a core criteria catalog to an early sepsis onset detection system. ACCESS, 13, 104899-104915. (www)

  @article{ , abstract = {Background: On the way towards a commonly agreed framework for auditing ML algorithms, in our previous paper we proposed a 30-question core criteria catalog. In this paper, we apply our catalog to an early sepsis onset detection system use case. Methods: The assessment of the ML algorithm behind the sepsis prediction system takes place in a kind of external audit. We apply the questions of our catalog with described context to the available sepsis project resources made publicly available. For the audit process we considered three steps proposed by the Supreme Audit Institutions of Finland et al. and utilized inter-rater reliability techniques. We also conducted an extensive reproduction study, as being encouraged by our catalog, including data perturbation experiments. Results: We were able to successfully apply our 30-question catalog to the sepsis ML algorithm development project. 37% of the questions were rated as fully addressed, 33% of the questions as partially addressed and 30% of the questions as not addressed, based on the first auditor. The weighted Cohen’s kappa agreement coefficient results in $\kappa = 0.51$ . The focus of the sepsis project is on algorithm design, data properties and assessment metrics. In our reproduction study, using externally validated pooled prediction on the self-attention deep learning model, we achieved an AUC of 0.717 (95% CI, 0.693-0.740) and a PPV of 28.3 (95% CI, 24.5-32.0) at 80% TPR and 18.8% sepsis-case prevalence harmonization. For the lead time to sepsis onset, we could not reproduce meaningful values. In the perturbation experiment, the model showed an AUC of 0.799 (95% CI, 0.756-0.843) with modified input data in contrast to an AUC of 0.788 (95% CI, 0.743-0.833) with original input data, when trained on the AUMC dataset and validated externally. Discussion: The catalog application results are visualized in a radar diagram, allowing an auditor to quickly assess and compare strengths and weaknesses of ML algorithm development or implementation projects. In general, we were able to reproduce the magnitude of the sepsis project’s reported performance metrics. However, certain steps of the reproduction study proved to be challenging due to necessary code changes and dependencies on package versions and the runtime environment. The extent of the deviation in the result metrics was −5.83% for the AUC and −11.03% for the PPV, presumably explained by our absence of tuning. The AUC change of 1.45% indicates resilience of the self-attention deep learning model to input data manipulation. An algorithmic error is most likely responsible for the missing lead time to sepsis onset metric. Even though the acquired weighted Cohen’s kapa coefficient is interpreted as having a “fair to good” agreement between both auditors, there exists potential subjectivity showing room for improvement. This could be mitigated if more groups (multiple auditors) would apply our catalog to existing ML development and implementation projects. A subsequent “catalog application guideline” could be established this way. Our activities might also help development or implementation teams to prepare themselves for future, legally required audits of their newly created ML algorithms/AI products.}, archive = {J_ACCESS}, author = {Markus Schwarz and Ludwig Christian Hinske and Ulrich Mansmann and Fady Albashiti}, doi = {10.1109/ACCESS.2025.3579631}, journal = {IEEE Access}, pages = {104899-104915}, shortjournal = {IEEE Access}, title = {ML auditing and reproducibility: Applying a core criteria catalog to an early sepsis onset detection system}, volume = {13}, year = {2025}, }
• (2025). Canonical state-space realization of 3-D separable denominator discrete systems. ACCESS, 13, 104888-104898. (www)

  @article{ , abstract = {In this paper, the state-space realization of 3-D discrete systems having transfer functions with separable denominators is considered. The proposed state-space model has a canonical form and can be derived directly from the transfer function coefficients. Using the fact that the denominator is separable, the dimensionality of the proposed model is lower than that in the non-separable case. In addition, block diagrams for the transfer function are proposed. The proposed realization is useful for the implementation of these systems. Three examples are presented to illustrate the results of the paper.}, archive = {J_ACCESS}, author = {Apostolos Kanellakis and Ayman Tawfik and Panajotis Agathoklis}, doi = {10.1109/ACCESS.2025.3580161}, journal = {IEEE Access}, pages = {104888-104898}, shortjournal = {IEEE Access}, title = {Canonical state-space realization of 3-D separable denominator discrete systems}, volume = {13}, year = {2025}, }
• (2025). Evaluating web accessibility in countries included in the latin american artificial intelligence index. ACCESS, 13, 104872-104887. (www)

  @article{ , abstract = {This study examines the relationship between the Latin American Artificial Intelligence Index (ILIA) and the Web Accessibility Index (WAIN) in 19 Latin American countries to evaluate how technological innovation aligns with digital inclusivity. Results reveal notable disparities: Uruguay, Chile, and Brazil excel in both indices, whereas Jamaica and Venezuela face critical challenges. Significantly, high scores in artificial intelligence adoption do not always correlate with improved web accessibility, underscoring the need for integrated strategies that prioritize compliance with the Web Content Accessibility Guidelines (WCAG) 2.2 and inclusive design. The research highlights accessibility as a fundamental pillar for sustainable digital ecosystems and equitable access to technology. Limitations include the regional focus on Latin America, the reliance on automated tools, and the dynamic nature of technology. Future research should incorporate longitudinal analyses, user-centered evaluations, and comparative studies across diverse regions to develop comprehensive frameworks that are grounded in empirical evidence. By aligning technological progress with inclusivity, this study emphasizes the importance of fostering sustainable and accessible digital environments. Recommendations include adopting accessibility standards in early development stages, enhancing public policy, and prioritizing universal design. These steps are essential to achieving the Sustainable Development Goals (SDGs) related to reducing inequalities and promoting inclusive institutions. This work is a foundation for advancing equitable digital ecosystems in Latin America.}, archive = {J_ACCESS}, author = {Patricia Acosta-Vargas and Belén Salvador-Acosta and Mauricio Loachamín-Valencia and Marcos Chacón-Castro and Janio Jadán-Guerrero and Luis Salvador-Ullauri}, doi = {10.1109/ACCESS.2025.3579751}, journal = {IEEE Access}, pages = {104872-104887}, shortjournal = {IEEE Access}, title = {Evaluating web accessibility in countries included in the latin american artificial intelligence index}, volume = {13}, year = {2025}, }
• (2025). Research on SeaTreasure target detection technology based on improved YOLOv7-tiny. ACCESS, 13, 104858-104871. (www)

  @article{ , abstract = {With the increasing market demand for sea treasures such as sea cucumbers, sea urchins and shells, the global aquaculture industry is booming. However, the complexity of underwater environments leads to problems such as missed and misdetection of sea treasures in the traditional manual inspection process. To address this challenge, this paper proposes a target detection algorithm for underwater sea treasures called UPA-YOLO, which aims to achieve accurate and efficient detection of underwater treasures and accelerates the inference through model transformation to enable the deployment of the detection model in edge devices. First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. Experimental results demonstrate that our optimized algorithm achieves a mAP0.5 of 79.5%, representing a 2.0 percentage point improvement over the baseline YOLOv7-tiny model (77.5%) while maintaining computational efficiency at 45.2 GFLOPs. When deployed on Jetson Nano edge devices, the model achieves an inference speed of 86 ms per image, corresponding to a $4\times $ acceleration compared to the unoptimized version. This performance enables practical real-time detection of marine products in field applications.}, archive = {J_ACCESS}, author = {Xiang Shi and Yunli Zhao and Jinrong Guo and Yan Liu and Yongqi Zhang}, doi = {10.1109/ACCESS.2025.3579201}, journal = {IEEE Access}, pages = {104858-104871}, shortjournal = {IEEE Access}, title = {Research on SeaTreasure target detection technology based on improved YOLOv7-tiny}, volume = {13}, year = {2025}, }
• (2025). Robust radio frequency fingerprinting with signal denoising and stacked multivariate ensemble learning for secure wireless communications. ACCESS, 13, 104844-104857. (www)

  @article{ , abstract = {Radio Frequency Fingerprinting (RFF) has gained significant attention in wireless communication and security research due to its potential for device authentication and intrusion detection. While deep learning-based approaches have shown promising results, existing methods suffer from critical limitations: high computational complexity hinders real-time deployment on resource-constrained hardware, poor robustness under low Signal-to-Noise Ratio (SNR) conditions, and inadequate generalization across different datasets. To address these gaps, this paper proposes a novel and efficient RFF framework that integrates signal denoising preprocessing using Savitzky-Golay Filtering (SGF) with Stacked Multivariate Ensemble Learning (SMvEL). The proposed architecture employs lightweight, homogeneous Convolutional Neural Networks (CNNs) optimized for rapid model training and fast inference, ensuring computational efficiency without sacrificing accuracy. Experimental results on real-world walkie-talkie datasets, as well as two open-source benchmark datasets for cellphones and drones, demonstrate that the proposed method outperforms state-of-the-art deep learning approaches in both accuracy and robustness.}, archive = {J_ACCESS}, author = {Syed Usman Ali Shah and Muhammad Usama Zahid and Syed Abuzar H Shah and Saeed Ur Rehman and Dan Komosny}, doi = {10.1109/ACCESS.2025.3579347}, journal = {IEEE Access}, pages = {104844-104857}, shortjournal = {IEEE Access}, title = {Robust radio frequency fingerprinting with signal denoising and stacked multivariate ensemble learning for secure wireless communications}, volume = {13}, year = {2025}, }
• (2025). Enhancing fault detection and classification in wind farm power generation using convolutional neural networks (CNN) by leveraging LVRT embedded in numerical relays. ACCESS, 13, 104828-104843. (www)

  @article{ , abstract = {Real-time fault detection and classification are important for power system stability and resilience of the power grid to minimize downtime and prevent cascading failures. Numerical relays (NRs) are used to detect faults in power system networks and isolate the power to prevent grid instability in the event of a fault. Faults may occur due to many factors, such as lightning strikes, weather events (heavy rain, snow, and wind), accidents, or power surges. However, integrating renewable energy sources, particularly wind farms, introduces more complex and unpredictable fault conditions due to weather patterns that traditional numerical relays struggle to handle effectively. In addition, microgrids impose more complex types of faults, which may pose challenges for NRs and necessitate a “smart grid” where communication can happen between the power company and devices on the grid for fast responses. In this paper, a non-overlapping sliding window-based continuous online monitoring Convolutional Neural Network (CNN) technique is proposed by leveraging the Low Voltage Ride Through (LVRT) code to detect and classify the type of faults in wind farms and other renewable sources. Extensive simulations of different fault conditions were conducted using MATLAB/Simulink to evaluate the performance of the proposed model. Electrical fault data were converted to images to simulate the output of a numerical relay. These images were fed to a neural network. To validate the model, a detailed analysis was performed, comparing different combinations of classifiers and optimizers. The proposed model performs well in fault detection and classification, introducing a new feature of numerical relays.}, archive = {J_ACCESS}, author = {Tarek Kandil and Adam Harris and Remon Das}, doi = {10.1109/ACCESS.2025.3580052}, journal = {IEEE Access}, pages = {104828-104843}, shortjournal = {IEEE Access}, title = {Enhancing fault detection and classification in wind farm power generation using convolutional neural networks (CNN) by leveraging LVRT embedded in numerical relays}, volume = {13}, year = {2025}, }
• (2025). A multi-strategy active learning framework for enhanced peripheral blood cell image detection. ACCESS, 13, 104815-104827. (www)

  @article{ , abstract = {Peripheral blood cell detection is essential for diagnosing and monitoring hematologic disorders. However, existing methods typically focus on a limited number of cell types (usually 3 to 10), restricting their ability to detect a broader range of cells. These methods also struggle with class imbalance and still rely heavily on large amounts of annotated data, limiting their effectiveness for underrepresented categories and hindering scalability. To address these challenges, we propose a novel composite multi-strategy active learning framework using YOLOv5 for enhanced peripheral blood cell detection. The framework reduces annotation costs and improves detection performance by combining uncertainty-based selection, diversity querying, and density-based querying to prioritize the most informative and diverse samples. The process begins with entropy-based uncertainty selection to identify the most uncertain samples, followed by clustering analysis to capture diverse samples from the feature space, and concludes with density-based selection using the k-nearest neighbors algorithm to prioritize samples from high-density regions. Experimental results demonstrate that the framework achieves a mean average precision (mAP@0.5) of 64.8% on a private dataset with 26 cell types, outperforming other active learning strategies and existing methods. It also reduces manual annotation workload by 28.7% compared to random sampling. On the public BCCD dataset (3 cell types), the framework achieves an mAP@0.5 of 86.7%. These results highlight the practicality and reliability of the proposed framework for optimizing peripheral blood cell detection. Our code can be accessed at: https://github.com/Sar-fyh/bme}, archive = {J_ACCESS}, author = {Yuheng Feng and Jiangtao He and Linjin Wang and Wuchen Yang and Sihan Deng and Lanlin Li and Xinwei Li}, doi = {10.1109/ACCESS.2025.3579918}, journal = {IEEE Access}, pages = {104815-104827}, shortjournal = {IEEE Access}, title = {A multi-strategy active learning framework for enhanced peripheral blood cell image detection}, volume = {13}, year = {2025}, }
• (2025). A driving simulator investigation on the aggressiveness of an automatic lane-change system. ACCESS, 13, 104799-104814. (www)

  @article{ , abstract = {Most existing personalized automatic lane-change systems rely on manual driving data, but obtaining such data for future highly automated driving systems will become increasingly difficult. Emphasis should be placed on the bidirectional interaction between humans and intelligent vehicles. However, little research has explored how the aggressiveness of automated driving systems affects drivers, hindering personalization through human-vehicle interaction. To address this, this study investigates how the aggressiveness of automated lane-change systems affects drivers’ subjective perception and gaze behavior. A driving simulator experiment was conducted where 22 participants experienced automatic lane-change systems. These systems featured varying levels of aggressiveness in lane change decisions, implemented using a previously proposed fuzzy-based model. The experiment entailed the collection of both subjective perceptions and gaze patterns from the participants. The research findings indicate that the aggressiveness of automatic lane changes simultaneously affects drivers’ perceptions, particularly safety evaluations and decision agreement, as well as gaze patterns toward the rear vehicle in the target lane. This implies that the relative driving status of the rear vehicle is the primary factor influencing drivers’ subjective perceptions during automatic lane changes. These results affirm the potential of personalizing automated driving systems through human-machine interaction, promising advancement in future highly automated driving systems. Further analysis of the experimental results provides practical suggestions for implementing this personalization approach.}, archive = {J_ACCESS}, author = {Muhua Guan and Zheng Wang and Bo Yang and Chenchang Li and Kimihiko Nakano}, doi = {10.1109/ACCESS.2025.3579019}, journal = {IEEE Access}, pages = {104799-104814}, shortjournal = {IEEE Access}, title = {A driving simulator investigation on the aggressiveness of an automatic lane-change system}, volume = {13}, year = {2025}, }
• (2025). The global element method for three- dimensional potential problems. ACCESS, 13, 104791-104798. (www)

  @article{ , abstract = {This paper presents the Global Element Method (GEM) for solving three-dimensional potential problems. Compared to traditional boundary element methods, this approach offers significant advantages. Conventional methods typically require discretizing the boundary into numerous local elements to approximate boundary functions, whereas the GEM adopts a novel strategy: it selects the largest possible portion of the boundary as the integration element, thereby avoiding the cumbersome discretization process. The core advantage of this method lies in the fact that the formation of the influence matrix depends solely on the normal and position vectors, eliminating the need for explicit interpolation approximation of boundary quantities or numerical integration, making it a true boundary node method. However, the implementation of the GEM requires the establishment of a “fully” regularized boundary integral equation. Here, “fully” implies that the integrand in the boundary integral has finite values everywhere on the boundary, which is a crucial prerequisite and main challenge for the effective application of this method. To meet this requirement, the paper proposes a new “fully” regularized boundary integral equation capable of computing arbitrary boundary gradients. Based on this, the Boundary Global Element Method is constructed. Numerical experiments demonstrate that the proposed method exhibits high accuracy and efficiency.}, archive = {J_ACCESS}, author = {Xinrong Li and Chao Zhang and Yaoming Zhang}, doi = {10.1109/ACCESS.2025.3578077}, journal = {IEEE Access}, pages = {104791-104798}, shortjournal = {IEEE Access}, title = {The global element method for three- dimensional potential problems}, volume = {13}, year = {2025}, }
• (2025). Dynamic fusion of LSTM predictions using reinforcement learning-based GOWLA for human activity recognition. ACCESS, 13, 104779-104790. (www)

  @article{ , abstract = {Human Activity Recognition (HAR) contributes significantly to vital areas in healthcare, IoT, and smart monitoring applications. Generally, the current models rely on deep learning and traditional machine learning techniques such as LSTM and SVM. However, these methods face considerable challenges, such as imbalanced data distribution, weak adaptation of weights to temporal changes, and a decline in classification performance in rare activities. In this paper, we propose a new method based on dynamic reinforcement learning to update GOWLA weights, which is entitled Dynamic RL-GOWLA. In this method, the LSTM outputs are combined using Weighted Logarithmic Averaging with a reinforcement learning agent for dynamically updating the weights based on the model’s performance in each training iteration. Experiments on WISDM database show that the proposed method outperforms traditional models such as LSTM, SVM, Bayesian Classifier, RNN, and CNN, achieving a F1-score of 96.58%. This approach improves the adaptability of models to imbalanced data, making it a promising solution for tasks that require accurate classification of dynamic human activities.}, archive = {J_ACCESS}, author = {Hayder K. Fatlawi and Attila Kiss}, doi = {10.1109/ACCESS.2025.3579926}, journal = {IEEE Access}, pages = {104779-104790}, shortjournal = {IEEE Access}, title = {Dynamic fusion of LSTM predictions using reinforcement learning-based GOWLA for human activity recognition}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of source number estimation in multi-sensor array signal processing. ACCESS, 13, 104756-104778. (www)

  @article{ , abstract = {Source number estimation is a key challenge in multi-sensor array signal processing, focused on accurately determining the number of signal sources based on observed data. This problem is vital for applications in radar, sonar, wireless communication, and astronomy. Despite the variety of methods developed for source number estimation, a comprehensive systematic literature review (SLR) is lacking. This review begins by outlining traditional methods for source number estimation and assessing their performance through simulations. It then delves into the latest advancements made between 2017 and 2025, aiming to provide a thorough overview of the factors influencing source number estimation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, seven digital databases were searched, and 23 key papers were selected after applying predefined inclusion and exclusion criteria. The results show that the performance of source number estimation algorithms is heavily impacted by variables such as noise background, signal-to-noise ratio (SNR), the number of snapshots, array manifold, and array element number. Finally, this review points to future research opportunities, particularly the potential of deep learning techniques, and discusses unresolved challenges and gaps in current studies, offering recommendations to steer future research in this area.}, archive = {J_ACCESS}, author = {Ge Shengguo and Fei Xiaotao}, doi = {10.1109/ACCESS.2025.3573071}, journal = {IEEE Access}, pages = {104756-104778}, shortjournal = {IEEE Access}, title = {A systematic literature review of source number estimation in multi-sensor array signal processing}, volume = {13}, year = {2025}, }
• (2025). Wearable system for cardiac diagnosis and monitoring: Clustering analysis and usability assessment using fractal geometry. ACCESS, 13, 104742-104755. (www)

  @article{ , abstract = {Heart rate abnormalities, including tachycardia and bradycardia, are among the leading global causes of morbidity, necessitating continuous and accurate monitoring for early detection and intervention. This study introduces an innovative smartwatch-based cardiac monitoring system that integrates fractal geometry analysis, dynamical systems modeling, and clustering techniques to enhance diagnostic precision. Unlike conventional smartwatch-based monitoring systems, this approach employs advanced mathematical modeling to identify nonlinear patterns in heart rate dynamics, enabling more precise differentiation between normal and pathological conditions. The system was developed using the CRISP-DM methodology, ensuring a structured and data-driven implementation. A mobile application, “My Cardio,” was designed for Android-based smartwatches, enabling the collection of real-time heart rate data and cloud storage for subsequent fractal-based processing. Additionally, a clustering analysis was performed on data from patients with and without cardiac history, identifying three distinct patient groups based on heart rate characteristics. Cluster 0 included individuals with lower, stable heart rates; Cluster 1 represented intermediate variations; and Cluster 2 comprised patients with significantly elevated heart rates associated with higher clinical risk. The findings were statistically validated and visualized, demonstrating that integrating clustering techniques with fractal geometry enhances the detection of clinically relevant cardiac patterns. Furthermore, a usability assessment using the System Usability Scale (SUS) yielded a score of 80.3 or higher, confirming high user acceptance and feasibility for widespread adoption. This study differentiates itself from existing approaches by combining wearable technology with advanced computational techniques to enhance cardiac diagnosis and monitoring. The results underscore the potential of smartwatch-based systems as a noninvasive, intelligent alternative for continuous cardiovascular assessment, paving the way for future applications in digital cardiology and telemedicine.}, archive = {J_ACCESS}, author = {José Sulla Torres and Cusirramos Montesinos Roderick Nestor and Sandra Catalina Correa Herrera and Jairo Jattin Balcázar and Herwin Huillcen Baca and Agueda Muñoz del Carpio Toia}, doi = {10.1109/ACCESS.2025.3579256}, journal = {IEEE Access}, pages = {104742-104755}, shortjournal = {IEEE Access}, title = {Wearable system for cardiac diagnosis and monitoring: Clustering analysis and usability assessment using fractal geometry}, volume = {13}, year = {2025}, }
• (2025). An integer erasure correction coding and its application for security enhancement of encryption. ACCESS, 13, 104728-104741. (www)

  @article{ , abstract = {This paper presents a new class of erasure-correcting codes (ECCs) aimed at enhancing cryptographic security of certain encryption schemes. The proposed ECCs employ integer arithmetic to encode and decode data bits and can correct all data bytes, each affected by exactly two erasures. In the enhanced encryption scheme, the ciphertext produced by the initial encryption undergoes further processing. The enhancement leverages specific fragmentation, the proposed ECCs and a simulated noisy channel. For legitimate users, the simulated noisy channel functions as a binary erasure channel, while for an attacker without the secret key, it acts as a channel with random deletions. Security notation and evaluation follow the traditional approach, assessing the attacker’s advantage in distinguishing between two ciphertexts versus random guessing. This evaluation employs dedicated analysis based on information-theoretic findings on the capacity of certain deletion channels and is supported by illustrative numerical examples.}, archive = {J_ACCESS}, author = {Miodrag J. Mihaljević and Aleksandar Radonjić and Nevena Mijajlović and Lianhai Wang and Shujiang Xu}, doi = {10.1109/ACCESS.2025.3579919}, journal = {IEEE Access}, pages = {104728-104741}, shortjournal = {IEEE Access}, title = {An integer erasure correction coding and its application for security enhancement of encryption}, volume = {13}, year = {2025}, }
• (2025). A new approach for automatic search for families of optimal undirected double-loop networks. ACCESS, 13, 104716-104727. (www)

  @article{ , abstract = {Based on the analysis of a large dataset for optimal undirected double-loop networks, we studied the problem of finding families of optimal double-loop graphs with the minimal possible diameter. Optimal double-loop networks are of practical interest as graph models for reliable, low-delay communication in computer systems and networks-on-chip, due to their advantageous networking properties. A large dataset of optimal double-loop networks with up to 50000 nodes and all the optimal generators for given graph orders were built. We automated the process of searching for analytical (described by polynomials in diameter) descriptions of the families of optimal double-loop networks. After applying the integration of differential evolution methods, exhaustive local search, and a new search algorithm (based on the division of graph description polynomials) to the analysis of the generated dataset, we found a large amount of new (distinct from all previously known) families of optimal double-loop networks with generators of linear or quadratic types. Based on the analysis of the constructed dataset, a method for accelerated search for finding descriptions of optimal double-loop networks using second-order surface approximation was proposed. Our new approach can be used for the analysis of datasets of other promising classes of graphs and networks.}, archive = {J_ACCESS}, author = {Emilia A. Monakhova and Oleg G. Monakhov and Edward R. Rzaev and Aleksandr A. Amerikanov and Mikhail Y. Romashikhin and Aleksandr Y. Romanov}, doi = {10.1109/ACCESS.2025.3579790}, journal = {IEEE Access}, pages = {104716-104727}, shortjournal = {IEEE Access}, title = {A new approach for automatic search for families of optimal undirected double-loop networks}, volume = {13}, year = {2025}, }
• (2025). An effective pipeline vibration trend prediction method based on adaptive chirp mode decomposition and long short-term neural networks. ACCESS, 13, 104706-104715. (www)

  @article{ , abstract = {Pipelines are widely present in nuclear power plants, and their structural integrity is crucial for ensuring the safety of the plants. Traditional pipeline vibration control strategy is to measure vibrations during hot function tests to identify high-vibration pipelines for remediation. However, this method does not completely resolve the issue of pipeline vibrations, especially for high-vibration pipelines that are difficult with remediation. Therefore, a Structural Health Monitoring (SHM) system is employed for the identification and assessment of structure health. For the early identification of potential failures, the prediction of pipeline vibrations and the implementation of preventive measures is important. In view of the multi-frequency, modal aliasing, and high noise characteristics of pipeline vibration signals, a method that balances efficiency and accuracy, with good adaptability, is needed for online continuous prediction. This paper introduces a novel vibration trend prediction method that combines the adaptive chirp mode decomposition (ACMD) method and the long short-term memory neural network (LSTM). This approach effectively predicts future vibration signals based on historical data. Finally, the effectiveness of the method is demonstrated through both numerical simulations and field experiments.}, archive = {J_ACCESS}, author = {Tao Qi and Yong Wang and Zhike Peng}, doi = {10.1109/ACCESS.2025.3579129}, journal = {IEEE Access}, pages = {104706-104715}, shortjournal = {IEEE Access}, title = {An effective pipeline vibration trend prediction method based on adaptive chirp mode decomposition and long short-term neural networks}, volume = {13}, year = {2025}, }
• (2025). Multi-person fall detection using data assimilation method with kalman filter. ACCESS, 13, 104692-104705. (www)

  @article{ , abstract = {Fall detection is an essential technology for ensuring the safety of elderly individuals, as falling accidents are critical and can cause significant functional damage in old age. Our previous work focused on fall detection with just a single individual, using simple statistical aggregation to achieve low model complexity and moderate accuracy. However, the pose estimation model may score low confidences on individual body parts (landmarks), affecting the aggregated statistics, thereby resulting in incorrect fall detection status. To solve this problem, we propose an enhanced fall detection method that adopts the Kalman filter for improved landmark and fall detection and object tracking for multi-person fall detection. Specifically with the Kalman filter, we reduce noise in network model’s heatmaps and landmarks with an adaptability across different input video sources. Compared to other methods that analyze AI models’ hidden layers and the layer outputs for providing confidence of measurements, this approach has a big advantage of plug-and-play for the pose estimation models and other streaming models that provide confidence. Tested on a computer with i7-13700HK, RTX 4070, 32 GB RAM, and a full HD camera, our method achieved an F1-score of 0.944 in the multi-person setup and 0.933 in the single-person setup.}, archive = {J_ACCESS}, author = {Jinmo Yang and Ye Jin Jin and R. Young Chul Kim}, doi = {10.1109/ACCESS.2025.3578683}, journal = {IEEE Access}, pages = {104692-104705}, shortjournal = {IEEE Access}, title = {Multi-person fall detection using data assimilation method with kalman filter}, volume = {13}, year = {2025}, }
• (2025). Enhancing analog electronics education in undergraduate degrees through digital spine-enabled hybrid passive-active learning design. ACCESS, 13, 104679-104691. (www)

  @article{ , abstract = {The field of analog electronics remains core to modern technology, being critical to a wide range of applications, such as telecommunications, robotics and biomedical devices. However, its reliance on hands-on and iterative design processes presents unique challenges for traditional learning and teaching designs. In particular, traditional laboratory settings highly limit the weekly laboratory practice opportunities. In recent years, in the education research society the flipped classroom model, an active learning method, has emerged as a transformative pedagogical approach in higher education across various disciplines. In this paper, we discuss the design and application framework of a new hybrid passive-active learning approach in analog electronics, which partially leverages the flipped classroom model and partially utilizes the traditional passive model. Evaluation results show that this new hybrid passive-active learning approach has significantly improved learning outcomes, skill acquisition, and student engagement in analog electronics education in undergraduate degrees.}, archive = {J_ACCESS}, author = {Ke Wang and James Scott and Margaret Jollands}, doi = {10.1109/ACCESS.2025.3574639}, journal = {IEEE Access}, pages = {104679-104691}, shortjournal = {IEEE Access}, title = {Enhancing analog electronics education in undergraduate degrees through digital spine-enabled hybrid passive-active learning design}, volume = {13}, year = {2025}, }
• (2025). Spike-RISC: Algorithm/ISA co-optimization for efficient SNNs on RISC-V. ACCESS, 13, 104666-104678. (www)

  @article{ , abstract = {Artificial intelligence has proven its benefits in many domains. Yet, traditional deep learning models are still too energy and compute-intensive for resource-constrained edge environments. Spiking neural networks (SNNs) promise a more energy-efficient and low-latency alternative to traditional neural networks. However, their leaky-integrate and fire (LIF) neurons rely on complex floating-point equations, which are very costly in embedded systems. Further, many computations are unnecessary because SNNs are inherently sparse. This paper proposes Spike-RISC, a holistic algorithm/instruction set architecture (ISA) co-optimization for efficient SNN inference. Our key algorithm optimizations include quantizing the weights to just eight bits and exploiting the network’s sparsity in hardware and software. We enhance the ISA of a RISC-V processor to accelerate two core SNN operations during inference. The sparsity-aware vector unit processes the fully connected layers avoiding unnecessary computations. Additionally, we implement custom vector instructions for LIF neurons. Compared to the floating-point baseline, our Spike-RISC improves energy efficiency by 108 x and reduces latency by 113 x, making SNNs compatible with resource-constrained edge environments.}, archive = {J_ACCESS}, author = {Ipek Akdeniz and Sandy A. Wasif and Paul R. Genssler and Hussam Amrouch}, doi = {10.1109/ACCESS.2025.3577946}, journal = {IEEE Access}, pages = {104666-104678}, shortjournal = {IEEE Access}, title = {Spike-RISC: Algorithm/ISA co-optimization for efficient SNNs on RISC-V}, volume = {13}, year = {2025}, }
• (2025). Efficient real-time object detection and classification using mmWave radar and jetson xavier NX. ACCESS, 13, 104651-104665. (www)

  @article{ , abstract = {Recently, mmWave radars have been used in several automotive and industrial applications to provide the position and speed of detected objects using FMCW algorithms. However, this radar is not able to recognize or classify the detected objects. This paper associates a mmWave radar with a real-time embedded computing platform and proposes a real-time implementation of deep learning algorithms in order to classify the detected objects. Three different classes of objects have been studied in this paper, i.e., human, car, and motorcycle. The proposed on-board system is based on the AWR2944EVM as a mmWave radar and the Nvidia Jetson Xavier NX as an embedded computing platform. The system was mounted on a motorcycle for data acquisition and real-time classification. The system developed captures radar data and processes it rapidly, converting it into usable formats for efficient object classification. Several YOLO detection deep learning models were evaluated, among which the YOLOv9-E model, which provided the best accuracy and speed performances for our application, i.e., a mAP_0.5 of 84% and an accuracy of 86.4%. The proposed architecture can process up to 18.59 frames per second (FPS). This system has been tested in a wide range of real-life conditions, validating its robustness and ability to operate in a variety of environments. The results obtained show that the proposed implementation delivers good real-time inference performance with optimized power consumption.}, archive = {J_ACCESS}, author = {Mohamed Lamane and Abdessamad Klilou and Mohamed Tabaa}, doi = {10.1109/ACCESS.2025.3576331}, journal = {IEEE Access}, pages = {104651-104665}, shortjournal = {IEEE Access}, title = {Efficient real-time object detection and classification using mmWave radar and jetson xavier NX}, volume = {13}, year = {2025}, }
• (2025). Neural-augmented hybrid dynamic modeling of a series-parallel elastic actuator. ACCESS, 13, 104630-104650. (www)

  @article{ , abstract = {This paper proposes a neural network-based model of a novel hybrid Series-Parallel Elastic Actuator (SEA-PEA) architecture for high-performance robotic applications. The system combines a Brushless DC (BLDC) motor, a planetary gearbox, and a rotary pneumatic actuator serving as an elastic element. While the gearbox offers advantages such as low cost and back-drivability, it suffers from significant backlash due to manufacturing tolerances. To address this, the actuator connects the gearbox output shaft directly to that of the pneumatic actuator. For a parallel configuration, both stators are mounted on an aluminum holder supporting the entire assembly. Within the backlash region, the system behaves as a Series Elastic Actuator (SEA), with the pneumatic actuator’s elasticity influencing stiffness, damping, and inertia. Outside this region, the system transitions to a Parallel Elastic Actuator (PEA), rigidly coupling the motor and gearbox shafts. A hybrid modeling approach is introduced to capture the actuator’s dynamic response. The SEA behavior is modeled using a closed box Artificial Neural Network (ANN), specifically an Echo State Network (ESN), which showed the lowest mean squared error (MSE) among the tested architectures. The PEA behavior is modeled using a gray-box approach, combining a linear dynamic block with a nonlinear static block represented by an ANN (Wiener model). This hybrid framework, integrating an ESN, achieved lower prediction error and better performance than other ANN architectures, as confirmed by experimental results.}, archive = {J_ACCESS}, author = {Timur Umurzakov and Sharafatdin Yessirkepov and Michele Folgheraiter}, doi = {10.1109/ACCESS.2025.3580222}, journal = {IEEE Access}, pages = {104630-104650}, shortjournal = {IEEE Access}, title = {Neural-augmented hybrid dynamic modeling of a series-parallel elastic actuator}, volume = {13}, year = {2025}, }
• (2025). Evaluation of 5G relay-empowered and device-to-device communications for rescue mission. ACCESS, 13, 104614-104629. (www)

  @article{ , abstract = {In public safety scenarios, communication between responders is crucial to ensure better cooperation during rescue missions. Several Mission critical services have been proposed by 3GPP, enabling the exchange of data, voice, and multimedia content over the latest generation of cellular networks. Despite the great popularity of cellular technologies, there are limitations when users exchange large amounts of data traffic at the edge of the antenna. By taking advantage of the proximity between users, deploying Device-to-Device (D2D) communication should offer better bandwidth, lower delays, and optimized device energy consumption. To overcome the range limitations of Direct D2D communication, a multi-hop cooperative relay based on CQI (Channel Quality Indicator) can route the message to the final destination. Two communication modes are thus proposed: (1) the D2D Loosely Coupled mode, which relies exclusively on user-type relays, and (2) the Hybrid mode, which allows the cellular antenna to be used as a relay when routing packets. This paper proposes modeling and evaluation of these solutions in the context of rescue operations, using an extension of the Simu5G library under the OMNeT++ simulator. Several configurations, including different application bitrates, showcase the coverage capabilities and related end-to-end delays when using a multi-relays solution compared to Direct D2D communication. The D2D Loosely coupled and Hybrid modes are evaluated and compared to the Infrastructure mode, particularly when traffic moves to the edge of the cell. Hybrid mode performance has been highlighted through a series of mobility scenarios.}, archive = {J_ACCESS}, author = {Wajdi Elleuch and Patrick Sondi and Ahmed Meddahi and Sylvain Lecomte}, doi = {10.1109/ACCESS.2025.3579872}, journal = {IEEE Access}, pages = {104614-104629}, shortjournal = {IEEE Access}, title = {Evaluation of 5G relay-empowered and device-to-device communications for rescue mission}, volume = {13}, year = {2025}, }
• (2025). Vibration analysis of lithium-ion batteries and proton exchange membrane fuel cells in electric vehicles: A comprehensive survey. ACCESS, 13, 104565-104613. (www)

  @article{ , abstract = {The global movement towards an electrified world and reduced use of fossil fuels is growing stronger, especially in the automotive sector. Lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs) undoubtedly represent the main energy storage and generation systems of powertrains and subsystems for electric vehicles (EVs), with some architectures possibly combining both devices. Accordingly, the reliability and durability of both LIBs and PEMFCs under real-world scenarios are critical to the further development of EVs. Actual operating conditions include the presence of mechanical vibrations. Nonetheless, much research has yet to be done to understand the effects of vibrations on these devices. This study aims at assessing the most critical factors that affect the performance, durability, and reliability of LIBs and PEMFCs when exposed to vibration in automotive applications, through an in-depth review of the literature. To the Authors’ best knowledge, this is the first comprehensive survey on the topic. Both numerical and experimental studies are reviewed with a particular focus on mechanical response, state of charge (SOC), state of health (SOH), and thermal interactions in LIBs, as well as mechanical integrity, durability, and stability mechanisms in PEMFCs. The available findings relevant to such parameters are discussed and compared systematically, to highlight the most advanced achievements and the open challenges. This may provide useful indications for the development and integration of new LIBs and PEMFCs solutions. Moreover, controversial results as well as aspects that require further investigation are also identified by the analysis. Hence, this survey may also represent a reference to drive future research activities aimed at addressing unresolved issues.}, archive = {J_ACCESS}, author = {Hadi Eidinejad and Alessandro Rivola and Marco Troncossi and Alberto Martini}, doi = {10.1109/ACCESS.2025.3578818}, journal = {IEEE Access}, pages = {104565-104613}, shortjournal = {IEEE Access}, title = {Vibration analysis of lithium-ion batteries and proton exchange membrane fuel cells in electric vehicles: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Enhancing crack segmentation with limited data: SinGAN-based synthesis and blending of textures and cracks. ACCESS, 13, 104555-104564. (www)

  @article{ , abstract = {The reliable detection of structural cracks is crucial in maintaining the integrity of different structures like pipes and pavements, yet it is often constrained by the limited availability of diverse training datasets. This research presents a novel approach utilizing Single Image Generative Adversarial Networks (SinGAN) for the synthesis and blending of realistic textures and synthetic cracks, aimed at augmenting sparse datasets for improved crack detection. Recognizing the challenges in generating sufficiently varied and realistic data for training robust detection models, we leverage SinGAN’s capacity to generate textures from single images and introduce procedural techniques for the generation of synthetic cracks using Simplex noise. These synthetic cracks are seamlessly integrated with the generated textures, employing SinGAN’s harmonization capabilities, thus significantly enhancing the realism of our synthetic dataset. Another aspect of our research is the validation of the synthesized dataset’s utility in improving crack detection and segmentation. Utilizing a subset of “CrackSeg9k” dataset as a benchmark, we employed two state-of-the-art crack segmentation methods, Pix2pix and DeepLabv3+, to evaluate the efficacy of our approach. The inclusion of our SinGAN-generated data led to notable improvements in model performance in terms of F1 score and mean intersection-over-union (mIoU), emphasizing the value of our synthetic dataset in addressing the limitations posed by limited real-world data.}, archive = {J_ACCESS}, author = {Farhan Mahmood and Myrto Inglezou and Panagiotis Chatzakos and Antonis Porichis}, doi = {10.1109/ACCESS.2025.3576335}, journal = {IEEE Access}, pages = {104555-104564}, shortjournal = {IEEE Access}, title = {Enhancing crack segmentation with limited data: SinGAN-based synthesis and blending of textures and cracks}, volume = {13}, year = {2025}, }
• (2025). A hybrid approach to model hospitals and evaluate wards’ performances. ACCESS, 13, 104538-104554. (www)

  @article{ , abstract = {The degree of connectivity among hospital wards and the dynamic nonlinear flow of patients cause bottlenecks to begin in non-priority wards, accumulate within the paths, distribute throughout the hospital, and emerge as overflow in crucial wards. This requires a network-based modeling approach to address the bottlenecks caused by second- and third-order wards and to significantly influence the overall and emergent performance of multiple wards. Understanding the relative merits of different network modeling and analysis approaches in this complex environment is often challenging and requires a holistic strategy to identify persistent bottlenecks and provide evidence-based scenarios. This article introduces a novel hybrid modeling approach that integrates network analysis algorithms and agent-based network simulation of patient flow over a complete hospital network. Through network analysis, such as structural hole and flow algorithms, the approach identifies common persistent bottlenecks from the flow and structural perspectives, while percolation and perturbation analyses measure the performance improvement of wards based on variations in patient flow, and the simulations enable the investigation of scenarios. The results indicate the wards and patient types that can contribute to improving the hospital’s performance. The proposed approach facilitates holistic, dynamic modeling of hospitals, irrespective of their network scale, and enables the identification of bottleneck sources and their associated paths, contributing to a comprehensive assessment of the system’s performance.}, archive = {J_ACCESS}, author = {Arsineh Boodaghian Asl and Luca Marzano and Jayanth Raghothama and Adam S. Darwich and Nina Falk and Patrik Bodeby and Sebastiaan Meijer}, doi = {10.1109/ACCESS.2025.3580174}, journal = {IEEE Access}, pages = {104538-104554}, shortjournal = {IEEE Access}, title = {A hybrid approach to model hospitals and evaluate wards’ performances}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient control system for small-scale rocket recovery. ACCESS, 13, 104521-104537. (www)

  @article{ , abstract = {This study investigates energy efficient control system for a small scale rocket recovery, utilizing thrust vector control (TVC) and reaction gas control (RGC) approaches. The dynamics of the rocket is managed through control input thruster forces and gimbaled angles, with the output parameters expressed in terms of velocity. Leveraging this model, several controllers: the linear quadratic regulator (LQR), linear quadratic Gaussian (LQG), sliding mode control (SMC), and proportional integral derivative (PID) controllers were designed and implemented. A linearized version of the nonlinear dynamic model of the rocket, obtained through Jacobian linearization, facilitates the application of LQR and LQG controllers. For precise pitch and yaw control, the architecture incorporates a single-nozzle TVC actuation and four-sided gas thrusters. Simulation studies, conducted in MATLAB/Simulink, evaluated the energy consumption profiles of each control strategy during rocket recovery, with the PID controller used as a baseline for comparison. Without disturbances, the LQG controller reduced energy consumption by 17.63% compared to PID, while SMC and LQR achieved reductions of 14.65% and 14.24%, respectively. Under disturbed conditions, LQG, SMC, and LQR reduced energy consumption by 30.39%, 27.38%, and 27.30%, respectively. These results highlight the potential of advanced control techniques to optimize energy efficiency. Thus, this study provides valuable insights for the development of more sustainable and cost-effective reusable rocket systems by emphasizing energy-efficient recovery control.}, archive = {J_ACCESS}, author = {Shinhyung Kim and Tsegaye Molla and Abenezer Zegeye and Sunghun Jung}, doi = {10.1109/ACCESS.2025.3579590}, journal = {IEEE Access}, pages = {104521-104537}, shortjournal = {IEEE Access}, title = {Energy-efficient control system for small-scale rocket recovery}, volume = {13}, year = {2025}, }
• (2025). Face mesh for dementia detection: Evaluating data augmentation in deep learning and traditional machine learning. ACCESS, 13, 104501-104520. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) stands as a prevalent form of dementia, primarily impacting the elderly, and presently lacking any known cure. Early screening and intervention have demonstrated efficacy for both current and prospective treatments. With the global population aging, early detection of AD has become imperative. Language-based approaches for dementia detection have received considerable attention in research due to dementia’s nature of hurting cognitive and language functions. Facial features, on the other hand, have been comparatively neglected despite their potential in identifying AD. The face mesh, comprising 478 landmarks, serves as a rich and informative facial representation. This study concentrates on leveraging face mesh data extracted from the PROMPT dataset for AD detection. The dataset comprises 445 voice and video recordings from 117 dementia patients and healthy individuals collected during professionally conducted interview sessions. We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. By augmenting the dataset, we improved the performance of the deep learning methods. The combined use of 1D CNN networks and data augmentation method reported the highest accuracy of 76%, which is the state-of-the-art accuracy in dementia detection using face mesh. These results confirmed the effectiveness of our data augmentation methods. They also underscore the potential of face-related features in enhancing early detection approaches for AD.}, archive = {J_ACCESS}, author = {Chuheng Zheng and Mondher Bouazizi and Taichi Okunishi and Tomoaki Ohtsuki and Momoko Kitazawa and Toshiro Horigome and Taishiro Kishimoto}, doi = {10.1109/ACCESS.2025.3579241}, journal = {IEEE Access}, pages = {104501-104520}, shortjournal = {IEEE Access}, title = {Face mesh for dementia detection: Evaluating data augmentation in deep learning and traditional machine learning}, volume = {13}, year = {2025}, }
• (2025). Reliability assessment and condition monitoring of wind energy conversion systems using bayesian networks: Recent advances and key insights. ACCESS, 13, 104472-104500. (www)

  @article{ , abstract = {Wind energy conversion systems (WECSs) play a vital role in the transition to sustainable energy, requiring continuous advancements in reliability, efficiency, and predictive maintenance. This paper provides a comprehensive review of Bayesian Networks (BNs) as a robust probabilistic framework for enhancing fault detection, risk assessment, and condition monitoring in WECSs. By integrating diverse data sources—including supervisory control and data acquisition (SCADA) systems, sensor networks, and environmental monitoring tools—BNs facilitate predictive maintenance, improve failure diagnostics, and extend turbine lifespan through adaptive learning. Their capability to quantify uncertainty and model complex dependencies makes them particularly effective in addressing operational and failure uncertainties, ensuring reliable energy generation under variable environmental conditions. The paper further examines key implementation challenges, including computational demands, data integration complexities, and the need for high-quality datasets to refine probabilistic models. Future research directions focus on hybridizing BNs with deep learning (DL) and reinforcement learning (RL), incorporating real-time sensor data for adaptive reliability analysis, and developing scalable, computationally efficient frameworks. Given the rapid advancements in machine learning and data-driven methodologies over the past five years, this review highlights the evolving role of BNs in modernizing WECS operations, emphasizing the necessity of an updated perspective to address emerging challenges and opportunities in wind energy systems.}, archive = {J_ACCESS}, author = {Fatemeh Salboukh and Yashar Mousavi and Ibrahim Beklan Kucukdemiral and Afef Fekih and Umit Cali}, doi = {10.1109/ACCESS.2025.3579939}, journal = {IEEE Access}, pages = {104472-104500}, shortjournal = {IEEE Access}, title = {Reliability assessment and condition monitoring of wind energy conversion systems using bayesian networks: Recent advances and key insights}, volume = {13}, year = {2025}, }
• (2025). Secure and efficient maTLS with proxy signature scheme. ACCESS, 13, 104456-104471. (www)

  @article{ , abstract = {Many companies providing mobile and web-based internet services deploy various middleboxes, including network and security appliances, to enhance network functionality and security. In particular, security appliances such as Web Application Firewalls and Intrusion Detection/Prevention Systems are frequently used to inspect transmitted data for security threats. However, the common practice of installing server certificates on middleboxes for encrypted traffic inspection introduces the risk of exposing secret keys. To address this, this paper proposes the middlebox-delegated TLS (mdTLS) protocol, a novel approach leveraging proxy signatures. mdTLS eliminates the need for direct server certificate installation on middleboxes, instead enabling each middlebox to perform network traffic auditing with its own unique certificate. This delegation of certificate authority, facilitated by proxy signature techniques, prevents certificate duplication and streamlines the certificate issuance process, thereby enhancing performance. Implementation results based on OpenSSL demonstrate that mdTLS reduces latency by approximately 25% in key and certificate generation within a general communication environment. Furthermore, similar performance gains are observed when mdTLS is applied to a Snort security appliance. The protocol’s security is formally verified using the Tamarin Prover, confirming its adherence to established TLS security properties and the additional security properties derived from the proxy signature scheme.}, archive = {J_ACCESS}, author = {Taehyun Ahn and Jiwon Kwak and Seungjoo Kim}, doi = {10.1109/ACCESS.2025.3577909}, journal = {IEEE Access}, pages = {104456-104471}, shortjournal = {IEEE Access}, title = {Secure and efficient maTLS with proxy signature scheme}, volume = {13}, year = {2025}, }
• (2025). Stochastic cournot model for coordinated strategic bidding of wind-storage systems. ACCESS, 13, 104446-104455. (www)

  @article{ , abstract = {In recent years, government incentives and subsidies have driven significant growth in wind power plant (WPP) capacity, establishing WPPs as key players in the evolving electricity market. However, the inherent variability of wind generation introduces challenges, including potential penalties for deviations in energy output. Energy Storage Systems (ESS) serve as a versatile solution to mitigate the uncertainties associated with wind power variability. This paper proposes a comprehensive model for the joint bidding of WPPs and ESS in competitive electricity markets. The interactions between WPPs and ESS are modeled using a stochastic Cournot framework, accounting for wind power uncertainties through inter-hour ramp constraints. A Location-Based Dual Imbalance Price (DIP) mechanism is adopted for wind-storage systems operating within a network-constrained oligopolistic electricity market. The Nash equilibrium strategy is used to optimize the WPP-ESS bids, considering the competitive behavior of other market participants. A Mixed Integer Linear Programming (MILP) model is developed to determine the Nash equilibrium by evaluating a payoff matrix. Various practical scenarios are analyzed to demonstrate the efficacy of the proposed approach. Furthermore, the study investigates the influence of different imbalance price mechanisms on WPP-ESS profits, market clearing operations, and Nash equilibrium outcomes.}, archive = {J_ACCESS}, author = {Swati Gupta and Bharat Bhushan Sharma and Pawan Kumar Pathak and Vivek Prakash and Sandeep Chawda and Sachin Sharma}, doi = {10.1109/ACCESS.2025.3578996}, journal = {IEEE Access}, pages = {104446-104455}, shortjournal = {IEEE Access}, title = {Stochastic cournot model for coordinated strategic bidding of wind-storage systems}, volume = {13}, year = {2025}, }
• (2025). Data-driven mobility system for vehicular communication: A step towards transport resilience in intelligent transport systems. ACCESS, 13, 104422-104445. (www)

  @article{ , abstract = {Intelligent Transport Systems (ITS) collect a dynamic and versatile range of data from vehicles and infrastructure to analyze and regulate the traffic and network flow. The collected data optimizes ITS functional capacity and efficiency to build smart infrastructure or smart cities. The issue arises when huge data volumes from rapidly transitioning vehicles result in missing data points, poor analytical capabilities, replication, higher cost, big volumes, increased time, and congestion followed by frequent network disruptions and disoriented communication. Ensuring transport resilience is crucial to maintaining stable and adaptive mobility systems that can withstand such disruptions and optimize real-time decision-making. To practically design solutions for previously mentioned challenges, an optimized Intelligent Information System (IDT) is proposed that is a self-maturing data information system set to independently train data streams coming from real-time traffic to facilitate data communication. Data is set to be processed and stored through designated smart interchangeable logs established using Apache Spark. Live data streams are integrated into the information system to induce independent learning within vehicles for smooth ITS operation. IDT’s functionalities are enhanced through dynamic segmentation switching which is a smart feature of calculated division and time-dependent interoperability of segments to avoid replicated data and network bottlenecks. IDT’s features and modules are real-time implemented on Ireland’s largest road network M50. Its operations, functionalities, and features are set to perform model training based on collected data for transparent communication in a cost-effective way. The validated results have shown that the given data information system is accomplishing higher performance with optimal resource utilization with rich and time-efficient data communication ranging from 70% to 94% based on architectural complexities and traffic ratios.}, archive = {J_ACCESS}, author = {Maryam Gillani and Hafiz Adnan Niaz and Beatriz Martinez-Pastor}, doi = {10.1109/ACCESS.2025.3579566}, journal = {IEEE Access}, pages = {104422-104445}, shortjournal = {IEEE Access}, title = {Data-driven mobility system for vehicular communication: A step towards transport resilience in intelligent transport systems}, volume = {13}, year = {2025}, }
• (2025). A LiDAR-based dual-vehicle collaborative transportation approach without communication. ACCESS, 13, 104406-104421. (www)

  @article{ , abstract = {In scenarios where wireless communication is unavailable, achieving dual-vehicle collaborative transportation for ultra-long and heavy loads solely relying on sensor data from automated guided vehicles (AGVs) poses a significant challenge. The primary obstacle lies in the inability to obtain real-time relative pose and velocity information between the two AGVs. To address this issue, this paper proposes a multi-line LiDAR-based dual-vehicle cooperative control method to tackle the synchronization challenges in robot collaborative transportation under non-communication conditions. By integrating multi-line LiDAR with reflective markers, the proposed method achieves high-precision measurement of the relative poses between the two AGVs. Based on a leader-follower cooperative control strategy, the follower AGV maintains a stable relative position relative to the leader AGV throughout the collaborative transportation process. Additionally, a six-axis force sensor mounted on the vehicle body monitors the load force in real time, and this information is incorporated to adjust the follower’s synchronization speed, thereby enhancing the robustness of the AGV system. Experimental results from multiple real-vehicle tests demonstrate that the proposed method successfully accomplishes dual-vehicle collaborative transportation tasks under non-communication conditions. Unlike traditional cooperative control methods reliant on wireless communication, the proposed approach operates efficiently in completely non-communication environments, making it widely applicable to complex or specialized scenarios. This study provides a reliable technical solution for multi-robot collaborative transportation in industrial automation.}, archive = {J_ACCESS}, author = {Feng Xie and Haibin Liu and Mingfei Li and Minghao Xia}, doi = {10.1109/ACCESS.2025.3579501}, journal = {IEEE Access}, pages = {104406-104421}, shortjournal = {IEEE Access}, title = {A LiDAR-based dual-vehicle collaborative transportation approach without communication}, volume = {13}, year = {2025}, }
• (2025). Novel quanvolutional neural network-based COVID-19 diagnosis from raman spectroscopic signals of human serum. ACCESS, 13, 104382-104405. (www)

  @article{ , abstract = {COVID-19 has proven to be one of the most devastating pandemics of the 21st century, causing widespread mortality and overwhelming healthcare systems globally. Early and accurate diagnosis is crucial to managing and controlling the spread of this virus. Although the Polymerase Chain Reaction (PCR) technique is effective for identifying the genetic material of the pathogens causing SARS-CoV-2 COVID-19, its longer processing time has led to the emergence of Raman spectroscopy as a promising alternative, a non-destructive method that provides detailed molecular insights for detecting COVID-19 through human serum samples. The primary goal of this research is to accurately diagnose COVID-19 by analyzing Raman spectroscopic signals of serum samples with a novel quantum circuit-based Quanvolutional Neural Network (QNN). In this research, although the signals are initially in one dimension (1D), we applied wavelet transforms to convert Raman spectroscopic signals into 2D scaleogram images to capture intricate spectral patterns for efficient classification. We compared the performance of our proposed QNN with existing QNN variant and classical convolutional neural network (CNN) models such as Vanilla CNN, MobileNet CNN, Xception CNN, and VGG-16 CNN. We were surprised to achieve outstanding performance across all seven evaluation metrics namely Jaccard index(JI) of 97.73%, accuracy of 98.92%, F1-score of 98.85%, recall of 97.73%, Matthews correlation coefficient (MCC) of 97.86%, precision of 100% and Area Under the Curve (AUC) score of 98.86%. The results demonstrate that proposed novel QNN outperforms classical models without overfitting. Our findings highlight the potential of QNN in revolutionizing diagnostic tools for critical diseases like COVID-19.}, archive = {J_ACCESS}, author = {Shivanya Shomir Dutta and Sahil Sandeep and S. Sridevi and Rajiv Mistry and Jonathan Ortega and Gorkem Kar}, doi = {10.1109/ACCESS.2025.3580316}, journal = {IEEE Access}, pages = {104382-104405}, shortjournal = {IEEE Access}, title = {Novel quanvolutional neural network-based COVID-19 diagnosis from raman spectroscopic signals of human serum}, volume = {13}, year = {2025}, }
• (2025). A proposal for future research agenda on automated disruption management in intralogistic systems. ACCESS, 13, 104368-104381. (www)

  @article{ , abstract = {This paper presents a systematic literature review on Automated Disruption Management (ADM) in intralogistic systems, analyzing 1.406 papers between 2018 and 2024. The review examines current approaches to managing disruptions in modern intralogistic environments, focusing on system architectures, adaptation capabilities, decision-making methods, and implementation aspects. Through a structured analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify several critical gaps in current research, particularly in handling multiple simultaneous disruptions, integrating predictive capabilities with real-time adaptation, and validating theoretical developments in real-world settings. Our findings reveal a significant trend toward Reinforcement Learning (RL) approaches and an observable evolution from traditional Automated Guided Vehicles (AGV) to more flexible Autonomous Mobile Robot (AMR) solutions. Also, our work revealed the need for more integrated approaches that can handle multiple disruption types simultaneously while maintaining system performance, particularly in complex industrial environments. Moreover, the analysis also shows a considerable gap between theoretical development and practical implementation, with very few papers reporting real-world testing results. This review contributes to the field by providing a comprehensive taxonomy of current approaches, identifying critical research gaps, and proposing a specific research agenda in the field for future research.}, archive = {J_ACCESS}, author = {Imanol Olaizola-Arregui and Miguel Mediavilla and Enrique Onieva}, doi = {10.1109/ACCESS.2025.3578757}, journal = {IEEE Access}, pages = {104368-104381}, shortjournal = {IEEE Access}, title = {A proposal for future research agenda on automated disruption management in intralogistic systems}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of intrusion detection techniques: ML and DL in different networks. ACCESS, 13, 104345-104367. (www)

  @article{ , abstract = {With the increasing number of new attacks, virtualized and distributed networks require greater attention and investment in cybersecurity. Organizations must rely on effective Intrusion Detection Systems (IDS) to detect both known and novel attacks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques have been widely used for intrusion detection. Several studies have reviewed ML and DL-based detection models, but they often overlook the specific networks targeted by these models. It is crucial to understand not only which methods are effective but also the contexts in which they are effective. This study aims to fill this gap by reviewing and classifying recent contributions based on their target networks. It focuses on three key network types: Cloud Computing (CC), Internet of Things (IoT), and Software-Defined Networks (SDN). Our study emphasizes the importance of thoroughly understanding the strengths and vulnerabilities of a given network, which is an important step towards developing effective ML- and DL-based intrusion detection approaches. We first provide an overview of related works and our research steps, followed by a presentation of ML and DL techniques, and commonly used datasets in this field. Next, a detailed presentation of the current research on IDS based on ML and DL techniques by network categories is provided. The strengths and limitations of ML and DL algorithms, which are frequently used for intrusion detection, are highlighted. Finally, the challenges are discussed and future research directions are proposed.}, archive = {J_ACCESS}, author = {Imane Rakine and Aziz Oukaira and Kamal El Guemmat and Issam Atouf and Sara Ouahabi and Mohamed Talea and Tarik Bouragba}, doi = {10.1109/ACCESS.2025.3579990}, journal = {IEEE Access}, pages = {104345-104367}, shortjournal = {IEEE Access}, title = {Comprehensive review of intrusion detection techniques: ML and DL in different networks}, volume = {13}, year = {2025}, }
• (2025). Control method of main steam valve opening in multi-machine power system by integrating dynamic surface and RBF neural network. ACCESS, 13, 104330-104344. (www)

  @article{ , abstract = {This study proposes a robust control method for main valve opening in multi-machine power systems that integrates dynamic surfaces and radial basis function neural networks to address the poor robustness and stability in existing control systems. As a method of simplifying controller design by introducing low-pass filters, dynamic surfaces can effectively reduce computational complexity. Meanwhile, radial basis function neural networks are used to handle unknown non-linear characteristics in the system, enhancing the adaptive ability and robustness of the controller. Due to the time delay in signal transmission during the operation of the power system, the dynamic performance may decrease. Therefore, a robust controller design method for the main steam valve opening based on the L $\infty $ norm performance index is proposed. This method sets the initialization conditions for multi-machine power systems and combines dynamic surfaces and radial basis function neural networks to process functions with time delays in the system to meet the L $\infty $ norm performance index of system tracking error and ensure system stability. The simulation results demonstrated significant improvements in system stability and robustness, with the rotor angle difference and angular velocity error converging quickly to stable values. After increasing the L $\infty $ performance index, the controller still quickly stabilized the system under time delay conditions, and the rotor angle error and angular velocity error converged to 0.70° and 0rad/s, respectively, verifying its robustness performance. The combination of dynamic surface control and radial basis function neural network significantly improves the robustness of the system to time delay and interference. This method has important theoretical and practical application value in the stable operation of power systems.}, archive = {J_ACCESS}, author = {Ying Xia}, doi = {10.1109/ACCESS.2025.3579328}, journal = {IEEE Access}, pages = {104330-104344}, shortjournal = {IEEE Access}, title = {Control method of main steam valve opening in multi-machine power system by integrating dynamic surface and RBF neural network}, volume = {13}, year = {2025}, }
• (2025). Can immersive virtual reality environments improve stress reduction? experimental design with progressive muscle relaxation training. ACCESS, 13, 104312-104329. (www)

  @article{ , abstract = {Psychological relaxation techniques are now fundamental in stress-management and anxiety-disorder prevention training. Progressive Muscle Relaxation (PMR) stands out among various other training programmes. However, some limitations restrict its widespread usage, such as the requirements for a therapist to be in attendance and for patients to close their eyes during treatment. In such cases, support through immersive Virtual Reality (iVR) during the training procedure may be a suitable solution. In this study, an iVR application was developed for individuals undergoing PMR training, and an experimental design with both independent and subjective measures was conducted to compare this novel approach with conventional PMR training. The study was validated in two population groups: nursing undergraduates (one training session, n=63) and undergraduates following a test anxiety programme (complete training procedure: 7 sessions, n=13). The results pointed to high satisfaction and relaxation levels across all groups. No significant differences were found between the two methodologies, suggesting that the iVR application could be a useful tool in both educational and clinical contexts. In the long experience group (7 sessions), the iVR students showed higher interest which may have contributed to adherence to the entire training procedure. Furthermore, the iVR tool demonstrated potential suitability users unable to follow conventional procedures, exemplified by a student who, due to her own anxiety-related symptoms, felt very uncomfortable when instructed to close her eyes during the relaxation training.}, archive = {J_ACCESS}, author = {H. Guillen-Sanz and M. C. Escolar-Llamazares and I. Quevedo Bayona and M. A. Martínez-Martín and A. Bustillo}, doi = {10.1109/ACCESS.2025.3579493}, journal = {IEEE Access}, pages = {104312-104329}, shortjournal = {IEEE Access}, title = {Can immersive virtual reality environments improve stress reduction? experimental design with progressive muscle relaxation training}, volume = {13}, year = {2025}, }
• (2025). Channel phase nonreciprocity compensation for space-frequency line coded OFDM systems. ACCESS, 13, 104301-104311. (www)

  @article{ , abstract = {This paper investigates the impact of phase nonreciprocity in space-frequency line code-orthogonal frequency-division multiplexing (SFLC-OFDM) systems, leading to significant performance degradation due to non-ideal multi-channel radio-frequency transceivers. A novel demodulation reference signal (DMRS) structure with phase rotation is proposed to address this challenge, enabling effective phase nonreciprocity compensation. To compensate for the channel phase nonreciprocity, the user equipment, as the uplink (UL) transmitter, estimates the downlink (DL) phase offset (PO) and then embeds this offset information into the phase of UL DMRS sequences. The base station, as the UL receiver, extracts the embedded DL PO and estimates the UL PO from the received DMRS sequences. The DL and UL PO values are then used at the base station to compensate for phase discrepancies between the UL and DL channels during the UL channel estimation process. Extensive numerical simulations across various system parameters validate the effectiveness of the proposed method in mitigating phase nonreciprocity and ensuring reliable SFLC-OFDM performance.}, archive = {J_ACCESS}, author = {Han-Gyeol Lee and Jihoon Choi and Juyeop Kim and Koichi Adachi and Yue Xiao and Jingon Joung}, doi = {10.1109/ACCESS.2025.3579312}, journal = {IEEE Access}, pages = {104301-104311}, shortjournal = {IEEE Access}, title = {Channel phase nonreciprocity compensation for space-frequency line coded OFDM systems}, volume = {13}, year = {2025}, }
• (2025). Design of a controllable axial-flux halbach array for magnetic suspension tasks. ACCESS, 13, 104291-104300. (www)

  @article{ , abstract = {Magnetic fields enable force application without mechanical connection. By this means, electromagnets apply force across a distance, but require continuous power to maintain constant force. In contrast, switchable permanent magnet mechanisms allow unpowered constant force application, but are typically used in close-range (small gap) applications. Hence, a device enabling low-power force application over greater distances (large gap) is needed. To meet this need, we introduce an adjustable magnetic actuator called a controllable, axial-flux Halbach array (CAHA). The novelty of our design lies in the concentric nesting of two axial-flux Halbach rings. By nesting these rings, the magnetic field extends out axially on one side, with a strength dependent on the relative rotation between the rings. To demonstrate how this mechanism performs in magnetic suspension tasks, we simulated a CAHA design model, compared this design model to alternative magnetic actuators, swept geometric parameters of the design, and empirically characterized a CAHA prototype. Our simulations show that our device required much less power than an electromagnet for low-frequency (<16 Hz) tasks, with an up to 93% reduction in average power for a 5 Hz task. The CAHA model also had force densities up to 2.3x higher than a switchable magnet for large-gap ( $\geq 10$ mm) applications. In empirical tests of the CAHA, magnetic performance closely matched our simulations. These results represent an introduction to the CAHA design that could improve magnetic suspension systems for low-frequency, large-gap applications.}, archive = {J_ACCESS}, author = {Will Flanagan and He Kai Lim and Cameron R. Taylor and Tyler R. Clites}, doi = {10.1109/ACCESS.2025.3580352}, journal = {IEEE Access}, pages = {104291-104300}, shortjournal = {IEEE Access}, title = {Design of a controllable axial-flux halbach array for magnetic suspension tasks}, volume = {13}, year = {2025}, }
• (2025). Design and optimization of brushless hybrid wound rotor vernier motor for variable speed applications. ACCESS, 13, 104281-104290. (www)

  @article{ , abstract = {This paper presents the optimized design and electromagnetic performance analysis of a brushless hybrid wound rotor vernier motor (BL-HWRVM) for automatic washing machine applications. The automatic washing machine has two operating cycles: the wash cycle at low speed and the spin dry cycle at high speed. The proposed machine utilizes a different winding configuration that improves the performance as compared to the existing reference machine. The machine configuration utilizes two different windings on the stator. One is a three-phase main ABC winding, and the other is a single-phase additional X winding. Both windings are designed in such a way that they generate both fundamental and subharmonic components of MMF in the air gap of the machine. In order to excite the rotor field winding using a brushless excitation technique, an additional excitation winding is placed on the rotor, which is connected through a rotating rectifier to the field winding. The voltages are induced in this excitation winding using the sub-harmonic component of the MMF. These AC-induced voltages are rectified by a rotating rectifier and are then fed to the field winding. A 4-pole-24-slot stator and 44-pole-44-slot outer rotor motor is designed. To reduce the torque ripple in the BL-WRVM 3-degree of skew angle is implemented on the rotor. To improve the value of average torque, a new hybrid model is designed and proposed by placing small magnets on the rotor. To find out the optimal shape of the magnets, the Genetic algorithm optimization technique is used. Finally, the performance of all the designs is compared. The proposed BL-HWRVM demonstrates a 9.47% efficiency improvement in washing mode and an 11.84% efficiency improvement in spin-dry mode compared to the reference machines.}, archive = {J_ACCESS}, author = {Kapeel Kumar and Qasim Ali and Sheeraz Ahmed and Muhammad Ilyas and Hanlin Zhan and Mudassir Raza Siddiqi}, doi = {10.1109/ACCESS.2025.3579611}, journal = {IEEE Access}, pages = {104281-104290}, shortjournal = {IEEE Access}, title = {Design and optimization of brushless hybrid wound rotor vernier motor for variable speed applications}, volume = {13}, year = {2025}, }
• (2025). Spiking residual ShuffleNet-based intrusion detection in IoT environment. ACCESS, 13, 104257-104280. (www)

  @article{ , abstract = {The Internet of Things (IoT) system has been developed to create a smart environment. Privacy and security are critical issues in IoT systems. Security vulnerabilities in IoT-enabled models generate threats that impact various applications. Therefore, intrusion detection systems (IDS) are essential for IoT systems to prevent attacks. Due to the limited storage and computational abilities of IoT devices, traditional IDS is not suitable for IoT systems. In this context, a new model called Spiking Residual ShuffleNet (SR-ShuffleNet) is introduced for intrusion detection (ID) in an IoT environment. The Multi-objective Fractional Artificial Bee Colony (MFABC) algorithm is used for routing. MFABC combines fractional calculus with artificial bee colonies. At the base station (BS), data normalization, data augmentation (DA), feature selection (FS), and ID processes are carried out. The input data is normalized using Z-score normalization (ZN). Features are selected based on Motyka similarity and Topsoe similarity. After feature selection, the bootstrapping technique is used to augment the features in the DA phase. Finally, ID is performed using SR-ShuffleNet, which integrates the Spiking deep residual network (S-ResNet) with ShuffleNet. After detecting an intrusion, an attack is mitigated to eliminate the malicious node. The SR-ShuffleNet model achieves impressive accuracy and efficiency in identifying and mitigating attacks in IoT systems.}, archive = {J_ACCESS}, author = {Sneha Leela Jacob and H. Parveen Sultana}, doi = {10.1109/ACCESS.2025.3579777}, journal = {IEEE Access}, pages = {104257-104280}, shortjournal = {IEEE Access}, title = {Spiking residual ShuffleNet-based intrusion detection in IoT environment}, volume = {13}, year = {2025}, }
• (2025). Isolation enhancement of four-element quad-band MIMO meander-line loaded inverted-F antennas for sub-6 GHz wireless applications. ACCESS, 13, 104244-104256. (www)

  @article{ , abstract = {A set of compact sub-6 GHz four-element quad-band MIMO antennas with high inter-element isolations is presented. It is formed by four meander-line loaded inverted-F antenna (IFA) structures located in the four quarters and oriented perpendicularly to each other to exhibit both polarization and spatial diversities in MIMO applications. The orthogonality of polarization reduces the inter-element mutual coupling for basic good isolation which is further enhanced by introducing a modified swastika-shaped decoupling element in the middle of the four antennas and cutting slits loaded on the dielectric substrate’s four edges. The inter-element isolations are observed better than 20 dB at low frequencies and between (40-50) dB at higher frequencies. The measured radiation characteristics of the fabricated antenna prototype agree very well with the simulated results. The antenna set has an overall size of $24\times 24\times 0.8$ mm3 with an inter-element spacing of 2 mm, corresponding to less than a quarter wavelength at the lowest frequency band and still less than a half wavelength at the highest frequency band of operation. The antenna resonates in the following four bands: 2.85 GHz (S-band), 4.33 GHz (Radar altimeter), 5.5 GHz (WLAN), and 5.9 GHz (Wi-MAX, WLAN). The antenna gains are better than 6 and 5 dBi in most bands from the simulations and measurements, respectively, with measured efficiencies of better than 80%.}, archive = {J_ACCESS}, author = {Anupa Chatterjee and Manas Midya and Panuwat Janpugdee and Hsi-Tseng Chou}, doi = {10.1109/ACCESS.2025.3579679}, journal = {IEEE Access}, pages = {104244-104256}, shortjournal = {IEEE Access}, title = {Isolation enhancement of four-element quad-band MIMO meander-line loaded inverted-F antennas for sub-6 GHz wireless applications}, volume = {13}, year = {2025}, }
• (2025). Impact of observation time window on RTN-induced jitter in CMOS ring oscillators. ACCESS, 13, 104233-104243. (www)

  @article{ , abstract = {At nanometer scale device dimensions, factors that lead to integrated circuit performance variations from one instant in time to another become increasingly significant. Random Telegraph Noise (RTN) is one of the relevant time-dependent variability sources. We perform a simple, physically based analysis that illustrates the impact of RTN on CMOS ring oscillators. Time-domain jitter analysis is used to study the evolution of different jitter metrics over time. We clearly show that the observed jitter may strongly depend on the observation time window. Straightforward expressions for period jitter, cycle-to-cycle jitter and absolute jitter enable evaluation of design choices such as loading, number of stages and bias point on ring oscillator jitter performance. This is relevant not only for circuit design, but also for test and characterization procedures, because metrics such as measurement setup bandwidth and observation time window may impact the outcome of test and characterization procedures. We validate the analytical expressions through simulations and experimental data.}, archive = {J_ACCESS}, author = {Gilson Wirth and Caroline P. Garcia and Guillermo L. Nogueira}, doi = {10.1109/ACCESS.2025.3579295}, journal = {IEEE Access}, pages = {104233-104243}, shortjournal = {IEEE Access}, title = {Impact of observation time window on RTN-induced jitter in CMOS ring oscillators}, volume = {13}, year = {2025}, }
• (2025). Fast and scalable dynamic gas network modeling and simulation framework with gas composition tracking. ACCESS, 13, 104220-104232. (www)

  @article{ , abstract = {The gas sector is playing an increasingly important role in the decarbonization of energy systems. As a result, tools that enable the integrated analysis of power and gas networks are becoming increasingly essential. Hydrogen-blending is regarded as a promising bridging technology that can accelerate the adoption of hydrogen. However, it raises the complexity of the gas network simulation as it involves solving complex partial differential equation systems. This paper presents a new framework for the dynamic modeling and simulation of gas pipeline flows, capable of tracking the composition of gas mixtures. The proposed approach decouples the dynamic gas flow simulation and the composition tracking. The dynamic flow simulation is modeled using the central differencing and the IMEX integration scheme and represented as an equivalent electric circuit model. The derived model can then be solved using a power system EMT solver. On top of that, a batch-tracking algorithm is implemented to accurately track the propagation of the gas mixture composition in the gas pipeline network. Simulations of network models of different sizes demonstrate the advantages of this proposed modeling and simulation framework and show its potential applications in gas pipeline network analyses.}, archive = {J_ACCESS}, author = {Yifei Lu and Andrew Ivan Sulimro and Thiemo Pesch and Andrea Benigni}, doi = {10.1109/ACCESS.2025.3579340}, journal = {IEEE Access}, pages = {104220-104232}, shortjournal = {IEEE Access}, title = {Fast and scalable dynamic gas network modeling and simulation framework with gas composition tracking}, volume = {13}, year = {2025}, }
• (2025). Dynamic analysis and experimental verification of a multi-degree-of-freedom eddy current damper in a cryogenic environment. ACCESS, 13, 104201-104219. (www)

  @article{ , abstract = {Reusable rockets are under development and have received considerable attention. The large rockets and reusable vehicles under development in Japan use liquid hydrogen, and their turbopumps require high-speed rotation. However, this approach causes vibrational problems. A wire-mesh damper has been used to reduce shaft vibration; however, it uses a friction force, causing wear and being unsuitable for dampers of reusable rockets in terms of life-cycle cost. Eddy current dampers play a significant role in solving these problems. The eddy current dampers reduce vibration without any mechanical contact; however, conventional eddy current damper structures are unsuitable for reducing multiple-degree-of-freedom (multi-DOF) vibrations. Therefore, a novel structure for a multi-DOF eddy current damper for liquid-hydrogen turbopumps is proposed in this paper. The damping performance of the proposed eddy current damper is superior to that of the existing multi-DOF eddy current damper by analyzing the damping performance of the structural model at an ambient temperature using a two-dimensional finite element method. Further, the damping performance of the proposed damper at various temperatures, from ambient temperature to cryogenic temperatures, is compared. Moreover, the effect of aluminum electrical conductivity attributed to the decrease in temperature is shown to affect not only damping but also the magnetic stiffness and skin effect and the vibration pattern. Finally, experiments were conducted on actual equipment to confirm the effects of the material properties and temperature on eddy current dampers.}, archive = {J_ACCESS}, author = {Yudai Umekawa and Akira Heya and Shinsaku Nakamura and Tsuyoshi Inoue}, doi = {10.1109/ACCESS.2025.3579227}, journal = {IEEE Access}, pages = {104201-104219}, shortjournal = {IEEE Access}, title = {Dynamic analysis and experimental verification of a multi-degree-of-freedom eddy current damper in a cryogenic environment}, volume = {13}, year = {2025}, }
• (2025). System strength beyond fault level. ACCESS, 13, 104184-104200. (www)

  @article{ , abstract = {System Strength (SS) is becoming increasingly important while more and more inverter-based resources (IBRs) get connected to weak parts of the grid. There is still, however, a level of ambiguity with respect to formal SS definition. It is to a large extent legacy from conventional systems dominated by synchronous generators, often only simplistically associated with fault level. To overcome these issues and encourage discussion that will lead to more formal SS definition, this paper proposes a new inclusive concept of SS and technical framework to clarify its fundamental technical aspects. Starting from a comprehensive review of existing definitions, assessment metrics, and observed real-life challenges and solutions, the paper identifies the gaps between the technical fundamentals of the SS concept and its legacy perception. This is followed by identification of the key SS components relevant for assessment of both steady-state and dynamic system performance. The paper proposes a new SS classification where the SS “umbrella concept” is broken down into small-signal strength, large-signal strength, and short-circuit strength with relevant SS factors, which may also be used in the context of ancillary services market developments. We indeed highlight how the overall SS can be influenced by a combination of factors, not only by fault level, and recommend that a “voltage-behind-an-impedance” parametrization and model may be more effective in defining SS in many circumstances. Finally, the capabilities of different types of IBRs in providing SS products are discussed along with relevant complexity and limitations.}, archive = {J_ACCESS}, author = {Mehdi Ghazavi Dozein and Brian Berry and Jovica V. Milanović and Pierluigi Mancarella}, doi = {10.1109/ACCESS.2025.3572432}, journal = {IEEE Access}, pages = {104184-104200}, shortjournal = {IEEE Access}, title = {System strength beyond fault level}, volume = {13}, year = {2025}, }
• (2025). WiCNNAct: Wi-fi-based human activity recognition utilizing deep learning on the edge computing devices. ACCESS, 13, 104173-104183. (www)

  @article{ , abstract = {Nowadays, human activity recognition plays an essential role in the application of human-computer interaction. Comprehensive systems, however, mostly rely on wearables, video cameras, and ambient sensors, which might be expensive and difficult to deploy or cause privacy issues. Wi-Fi-based wireless human sensing utilizes channel state information to detect human presence and movements, leveraging changes in signal properties caused by humans. This cost-effective approach utilizes existing Wi-Fi infrastructure and can operate in obscured or obstructed environments. However, signal interference and environmental conditions can affect accuracy and reliability. To address these challenges, a WiCNNAct for human activity recognition is proposed in this paper. The proposed approach utilizes the channel state information (CSI) measurements (complex values) from Wi-Fi and processes the different combinations of the real, imaginary, and absolute values using multi-channel 1D convolutional neural networks (1D-CNN). After conducting preliminary investigations, we validated various combinations of multi-channel 1D-CNNs and identified three methods for accurate activity recognition: a three-channel (real, imaginary, and absolute) setup, two channels (real and imaginary/real and absolute/imaginary and absolute), single channel (real/imaginary/absolute). The proposed three-channel Wi-Fi-Net underwent verification with 10-fold testing validation and achieved an overall accuracy of 98.29% with a standard deviation of 0.33%. The model is deployed on various edge computing devices, including Raspberry Pi, to assess real-time deployment feasibility. In this work, 1D-CNN is chosen based on its ability to automatically extract spatial and temporal features, reducing manual feature engineering. Experimental validation identifies the optimal setup for robust activity recognition.}, archive = {J_ACCESS}, author = {Venkata Raghava Shashank Viswanathuni and Rakesh Reddy Yakkati and Sreenivasa Reddy Yeduri and Linga Reddy Cenkeramaddi}, doi = {10.1109/ACCESS.2025.3579649}, journal = {IEEE Access}, pages = {104173-104183}, shortjournal = {IEEE Access}, title = {WiCNNAct: Wi-fi-based human activity recognition utilizing deep learning on the edge computing devices}, volume = {13}, year = {2025}, }
• (2025). A file-statement approach for bug localization: Optimizing IRBL and combination strategy. ACCESS, 13, 104159-104172. (www)

  @article{ , abstract = {One of the main objectives of software testing is to locate the position of bugs. Bug localization is generally categorized into statement-level and file-level localization. File-level bug localization is typically performed using information retrieval-based bug localization (IRBL) methods. However, when used alone, file-level bug localization can only identify bugs at the file level and has low accuracy, limiting its practicality. Integrating file-level and statement-level bug localization can produce more precise results. To address these limitations, this study proposes a novel hierarchical bug localization framework that integrates multiple localization techniques across the file and statement levels. First, we present AS_IRBL, an enhanced IRBL method that introduces two innovations: a word-attention component that selectively amplifies the weight of key terms in bug reports, and a complex-word segmentation component that improves semantic matching by decomposing compound identifiers. These enhancements lead to significantly improved file-level localization performance. Second, we introduce C_FF_S, a new cross-level integration strategy that hierarchically combines file-level and statement-level localization results. Unlike prior approaches, C_FF_S uses an activation-based weighting mechanism to adjust statement-level suspicion scores according to file-level confidence, enabling context-aware and more accurate bug localization. Experimental results on the Defects4J benchmark demonstrate the effectiveness of our method: AS_IRBL improves MAP by 30.44% and Einspect@n by 31.89% over baseline IRBL. C_FF_S outperforms existing combination strategies, with MAP, MRR, and Einspect@n increased by 6.08%, 7.31%, and 7.58%, respectively. These results confirm the novelty and practical value of our hierarchical and mechanism-driven approach to multi-level bug localization.}, archive = {J_ACCESS}, author = {Zhonghao Guo and Xinyue Xu and Xiangxian Chen and Chenge Geng}, doi = {10.1109/ACCESS.2025.3577608}, journal = {IEEE Access}, pages = {104159-104172}, shortjournal = {IEEE Access}, title = {A file-statement approach for bug localization: Optimizing IRBL and combination strategy}, volume = {13}, year = {2025}, }
• (2025). The role of country- and firm-level factors in determining firms’ environmental, social, and governance (ESG) performance: A machine learning approach. ACCESS, 13, 104137-104158. (www)

  @article{ , abstract = {The objective of this study is to apply machine learning techniques to predict firms’ environmental, social, and governance (ESG) performance. We employed ten supervised machine learning models—decision tree, stochastic gradient descent, random forest, adaptive boosting, extra trees, extreme gradient boosting, k-nearest neighbors, multiple linear regression, transformer-based regression, and multi-layer perceptron—and evaluated their effectiveness in ESG prediction. The analysis covers global firms and incorporates seven firm-specific characteristics and country-level macroeconomic variables. Using a sample of 19,476 observations from 2010 to 2018 from publicly listed firms across 53 countries, our findings indicate that random forest regressor exhibit the highest predictive power, with the lowest root mean square error. For the random forests regressor, the coefficient of determination (R2) was 30% and the mean absolute error (MAE) was 1.52. The second-best predictive model was the extra-trees regressor with an R2 value of 27% and an MAE of 1.56. In our analysis, the multiple regression model had the lowest R2 value of 14%, with an MAE value of 1.70. We also analyzed the feature importance of the random forests regressor, identifying firm size, capital expenditure, and cash holdings as the top three predictors of ESG performance. Additionally, we used the double-debiased machine learning technique to assess causal inference in feature importance, revealing capital expenditure, political stability, and leverage as the most influential factors in ESG predictions. Although the ranking of feature importance varies between the causal inference and predictive models, the key factors identified by both techniques largely align and are consistent with the significance of the coefficients from the multiple regression model.}, archive = {J_ACCESS}, author = {Eman Abdelfattah and Mahfuja Malik and Syed Muhammad Ishraque Osman}, doi = {10.1109/ACCESS.2025.3579712}, journal = {IEEE Access}, pages = {104137-104158}, shortjournal = {IEEE Access}, title = {The role of country- and firm-level factors in determining firms’ environmental, social, and governance (ESG) performance: A machine learning approach}, volume = {13}, year = {2025}, }
• (2025). Automated detection of schizophrenia based on retinal measures and neurological soft signs: Applications of interpretable classification methods and the choquet integral aggregation function. ACCESS, 13, 104120-104136. (www)

  @article{ , abstract = {Background: Schizophrenia is a psychiatric illness with a significant functional impairment. Its diagnosis is based on clinical observation, despite much evidence for its neurobiological basis. More recently, alterations in retinal structure and function were also documented. The retina is a part of the central nervous system, developing embryologically from the same tissue as the brain. Research indicates that retinal morphology measures may be treated as a proxy of brain health. Due to the need to identify schizophrenia biomarkers, we applied a set of automated classifiers encompassing selected retinal and other indirect markers of neuronal system health. Methods: The data came from 59 schizophrenia patients and 61 healthy controls. Interpretable classifiers were also used to differentiate patients with shorter and longer duration of illness. After obtaining the classification results, Choquet integral-based aggregators were adopted to increase discrimination accuracy. Results: Applying Smooth Quadrature-Inspired Choquet aggregators led to a patient vs. control group discrimination accuracy of 93.5%, and accuracy for discrimination between patients with shorter vs. longer duration of illness was 86.25%. In addition, the three-class classification regarding controls and two subgroups of patients reached 88.37% accuracy using 3/8 rule smooth quadrature and triangular norm. Discussion: These results indicate that using measures of retinal morphology and additional variables associated with brain health are useful to classify schizophrenia patients at a level similar to brain imaging-based data. The application of the Choquet integral aggregation function improved the classification results, which is another argument for using this computational method in medical research.}, archive = {J_ACCESS}, author = {Paweł Krukow and Steven M. Silverstein and Paweł Karczmarek and Adam Domagała and Adam Kiersztyn and Małgorzata Plechawska-Wójcik and María García Gadañón and Kamil Jonak}, doi = {10.1109/ACCESS.2025.3579973}, journal = {IEEE Access}, pages = {104120-104136}, shortjournal = {IEEE Access}, title = {Automated detection of schizophrenia based on retinal measures and neurological soft signs: Applications of interpretable classification methods and the choquet integral aggregation function}, volume = {13}, year = {2025}, }
• (2025). HCMMA-net: A hybrid convolutional multi-modal attention network for human activity recognition in smart homes using wearable sensor data. ACCESS, 13, 104106-104119. (www)

  @article{ , abstract = {Human activity recognition (HAR) plays a pivotal role in applications such as healthcare monitoring, fitness tracking, and smart homes. Multi-modal sensor data from wearable devices offers diverse perspectives on human motion, enhancing recognition accuracy and robustness. However, integrating these modalities poses challenges due to sensor heterogeneity and variability in placement. This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. We evaluate the model on two benchmark datasets Cogage, achieving an accuracy of 93.94%, and WISDM, with an accuracy of 99.29%, demonstrating its strong generalizability across varied sensor configurations. Additionally, we present a newly collected multi-modal dataset, HumcareV1.0, comprising different activities in smart-home-like scenarios. On this real-world dataset, HCMMA-Net attains an accuracy of 97.56%, highlighting its effectiveness in capturing subtle behavioral nuances in practical environments. The model exhibits robust generalization across complex activity patterns and sensor configurations, underscoring the significance of multi-modal integration in advancing HAR systems. These findings highlight the potential of our approach for deployment in real-time, context-aware smart environments.}, archive = {J_ACCESS}, author = {Nazish Ashfaq and Zeeshan Aziz and Muhammad Hassan Khan and Muhammad Adeel Nisar and Adnan Khalid}, doi = {10.1109/ACCESS.2025.3577730}, journal = {IEEE Access}, pages = {104106-104119}, shortjournal = {IEEE Access}, title = {HCMMA-net: A hybrid convolutional multi-modal attention network for human activity recognition in smart homes using wearable sensor data}, volume = {13}, year = {2025}, }
• (2025). Ship target SAR dataset generation based on ray tracing including multiple scattering. ACCESS, 13, 104089-104105. (www)

  @article{ , abstract = {In order to improve the fidelity of SAR image simulation for ship target, we propose a novel ship target SAR dataset generation method based on ray tracing including multiple scattering. The method integrates the SAR spatial traversal mode and ray casting theory for discriminating the radar beam radiation areas of ship target and modeling electromagnetic (EM) wave transmissions under the geometric constraints of Range Doppler (RD) imaging geometry. Based on the simulation of EM waves transmission, the ray tracing algorithm and the physical illumination model are combined to simulate the multiple backscattering of EM waves, and the multiple backscattering amplitude and phase are recorded in real time. Then, the total backscattering field of the distributed ship target can be generated by the vector superposition of the multiple backscattering subfields of the EM waves transmitted towards the beam radiation area. Finally, the raw echo signal of the ship target is processed by RD imaging algorithm, then the corresponding SAR simulated image is obtained. We select some ship targets to simulate SAR images, and then compare the simulated results with the real SAR images and the simulated results of the mainstream commercial simulation software to verify the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Junjie Li and Ke Wu and Liuyan Wei and Chuanxiang Cheng and Gaohao Zhu}, doi = {10.1109/ACCESS.2025.3575702}, journal = {IEEE Access}, pages = {104089-104105}, shortjournal = {IEEE Access}, title = {Ship target SAR dataset generation based on ray tracing including multiple scattering}, volume = {13}, year = {2025}, }
• (2025). OXSeg: Multidimensional attention UNet-based lip segmentation using semi-supervised lip contours. ACCESS, 13, 104075-104088. (www)

  @article{ , abstract = {Lip segmentation plays a crucial role in various domains, such as lip synchronization, lip-reading, and diagnostics. However, the effectiveness of supervised lip segmentation is constrained by the availability of lip contour in the training phase. A further challenge with lip segmentation is its reliance on image quality, lighting, and skin tone, leading to inaccuracies in the detected boundaries. To address these challenges, we propose a sequential lip segmentation method that integrates attention UNet and multidimensional input. We unravel the micro-patterns in facial images using local binary patterns to build multidimensional inputs. Subsequently, the multidimensional inputs are fed into sequential attention UNets, where the lip contour is reconstructed. We introduce a mask generation method that uses a few anatomical landmarks and estimates the complete lip contour to improve segmentation accuracy. This mask has been utilized in the training phase for lip segmentation. To evaluate the proposed method, we use facial images to segment the upper lips and subsequently assess lip-related facial anomalies in subjects with fetal alcohol syndrome (FAS). Using the proposed lip segmentation method, we achieved a mean dice score of 84.75%, and a mean pixel accuracy of 99.77% in upper lip segmentation. To further evaluate the method, we implemented classifiers to identify those with FAS. Using a generative adversarial network (GAN), we reached an accuracy of 98.55% in identifying FAS in one of the study populations. This method could be used to improve lip segmentation accuracy, especially around Cupid’s bow, and sheds light on distinct lip-related characteristics of FAS.}, archive = {J_ACCESS}, author = {Hanie Moghaddasi and Christina Chambers and Sarah N. Mattson and Jeffrey R. Wozniak and Claire D. Coles and Raja Mukherjee and Michael Suttie}, doi = {10.1109/ACCESS.2025.3577000}, journal = {IEEE Access}, pages = {104075-104088}, shortjournal = {IEEE Access}, title = {OXSeg: Multidimensional attention UNet-based lip segmentation using semi-supervised lip contours}, volume = {13}, year = {2025}, }
• (2025). An ISO/IEC/IEEE 42010:2022 standard-based adaptation for systems-of-systems. ACCESS, 13, 104058-104074. (www)

  @article{ , abstract = {The increasing adoption of system-of-systems (SoS) engineering has emerged as a crucial approach for designing architectures that manage complex, decentralized systems across various domains, including IoT-enabled infrastructure. This paper introduces a metamodel that aligns with the ISO/IEC/IEEE 42010:2022 standard for architecture description, tailored to address the unique challenges of SoS. A formal classification technique leveraging first-order predicate logic ensures precise and consistent SoS categorization. The metamodel’s applicability is demonstrated through a case study on integrated water and energy management, involving real-world implementation. To evaluate its effectiveness, the Goal-Question-Metric (GQM) methodology is applied, detailing metrics for performance, relevance, usefulness and adaptability. A comparative analysis with existing models underscores the metamodel’s strengths in addressing SoS-specific requirements. By bridging theoretical rigor with practical usability, this work advances SoS modeling and offers a standards-based solution, with IoT-enabled examples illustrating its versatility and potential.}, archive = {J_ACCESS}, author = {Aymen Abdelmoumen and Zakaria Benzadri and Ismael Bouassida Rodriguez and Borhen Louhichi}, doi = {10.1109/ACCESS.2025.3576238}, journal = {IEEE Access}, pages = {104058-104074}, shortjournal = {IEEE Access}, title = {An ISO/IEC/IEEE 42010:2022 standard-based adaptation for systems-of-systems}, volume = {13}, year = {2025}, }
• (2025). DTNLS: 3D point cloud segmentation based on 2D image and 3D point cloud double texture feature. ACCESS, 13, 104047-104057. (www)

  @article{ , abstract = {Panoramic segmentation of 3D point clouds is an essential and challenging technology for robots with 3D detection and measurement capabilities. In order to fuse the color information of 2D image pixels with the spatial position information of the 3D LiDAR point cloud, it is necessary to establish the corresponding relationship between the RGB of pixels and the XYZ position of the 3D LiDAR point cloud. We present Double Texture Neighbor LiDAR Segmentation (DTNLS) in this letter. Double texture refers to the color texture of the image and the point cloud texture of the 3D LiDAR. The DTNLS method first uses the color texture feature of the image to segment the pixels and then obtains the clustering center and segmentation boundary outline. 3D LiDAR point cloud texture segmentation takes the clustering center obtained above as the diffusion source, diffuses outwards along the ring LiDAR line, finds the LiDAR point cloud texture boundary features near the image segmentation boundary contour, and finally realizes 3D point cloud segmentation. We carried out quantitative analysis experiments, and the results showed that compared with the best results among the existing mainstream segmentation methods, the proposed DTNLS method improved the accuracy of pedestrian segmentation by 32.2%. The recall improved by 20.12% in pedestrian segmentation. IoU improved by 48.8% for pedestrians. We conducted empirical studies on public datasets and our datasets to demonstrate that DTNLS has broad applicability and better performance in 3D point cloud segmentation than the previous latest techniques, without the need for any 2D images and 3D point cloud training data.}, archive = {J_ACCESS}, author = {Zhiguang Liu and Xiaoxiao Yan and Jiahui Zhao and Yong Shi and Fei Yu and Jian Zhao}, doi = {10.1109/ACCESS.2024.3446593}, journal = {IEEE Access}, pages = {104047-104057}, shortjournal = {IEEE Access}, title = {DTNLS: 3D point cloud segmentation based on 2D image and 3D point cloud double texture feature}, volume = {13}, year = {2025}, }
• (2025). Dilated neural networks for improving microaneurysm detection in fundus images. ACCESS, 13, 104037-104046. (www)

  @article{ , abstract = {Diabetic retinopathy (DR) is a microvascular complication that can lead to blindness if left untreated. Early detection of microaneurysms (MAs) is crucial for effective DR diagnosis. However, detecting MAs is challenging due to their small size, low contrast, and the presence of noise and other artifacts. This paper presents a novel deep learning approach for MA detection using a dilated residual network (DRN) with an Region of interest (ROI) layer. Unlike traditional models such as VGG16 and ResNet, our DRN model achieves a higher input resolution $(28\times 28)$ for ROI pooling, significantly improving MA detection accuracy. Extensive experiments confirm the effectiveness of our approach, achieving superior performance compared to state-of-the-art methods on the e-ophtha-MA, DiaretDB1-MA, and ROCh datasets. Notably, our method achieves the best FROC (free-response receiver operating characteristic) score of 0.6038 on the e-ophtha-MA dataset.}, archive = {J_ACCESS}, author = {Norah Asiri and Abdel Monim Artoli and Muna Al-Razgan and Taha Alfakih}, doi = {10.1109/ACCESS.2025.3579342}, journal = {IEEE Access}, pages = {104037-104046}, shortjournal = {IEEE Access}, title = {Dilated neural networks for improving microaneurysm detection in fundus images}, volume = {13}, year = {2025}, }
• (2025). An AI-driven cybersecurity framework for IoT: Integrating LSTM-based anomaly detection, reinforcement learning, and post-quantum encryption. ACCESS, 13, 104027-104036. (www)

  @article{ , abstract = {In an increasingly digital world, cybersecurity threats have grown in frequency, complexity, and scale, challenging the adequacy of traditional rule-based defense systems. This study proposes a unified AI-driven cybersecurity framework to detect anomalies, verify data integrity, automate incident response, and ensure long-term cryptographic resilience. The framework integrates four core components: Long Short-Term Memory (LSTM) networks for temporal anomaly detection, homomorphic hashing using SHA-256 with hidden salts for real-time data integrity verification, Q-learning-based reinforcement learning for automated threat response, and lattice-based encryption grounded in the Learning With Errors (LWE) problem to safeguard against quantum-era attacks. The system was evaluated in a simulated IoT network environment, where it demonstrated high accuracy in identifying anomalies, effectively distinguishing between original and tampered data, and adaptively responding to different levels of cyber threats. The integration of these components allows the framework to operate autonomously and contextually, improving scalability and responsiveness in resource-constrained digital infrastructures. This study concludes that the proposed framework addresses key limitations of existing methods by offering a scalable, adaptive, and future-proof cybersecurity solution. The results support its potential for deployment in real-world settings such as smart cities, healthcare systems, and critical infrastructure, with future work aimed at improving real-time adaptability and validating performance in live, heterogeneous environments.}, archive = {J_ACCESS}, author = {Mozamel M. Saeed}, doi = {10.1109/ACCESS.2025.3576506}, journal = {IEEE Access}, pages = {104027-104036}, shortjournal = {IEEE Access}, title = {An AI-driven cybersecurity framework for IoT: Integrating LSTM-based anomaly detection, reinforcement learning, and post-quantum encryption}, volume = {13}, year = {2025}, }
• (2025). Secure medical image sharing: Technologies, watermarking insights, and open issues. ACCESS, 13, 103995-104026. (www)

  @article{ , abstract = {With the growth of the Internet, medical documents have become widely used by healthcare professionals. Secure transmission and management of medical images are essential to enable collaboration while protecting sensitive patient information. This study analyzes various methods for safe medical data sharing, highlighting their advantages and limitations We categorize these approaches into centralized techniques, such as encryption and watermarking, and distributed methods, such as blockchain and federated learning. Additionally, this research examines the evolution of medical image watermarking techniques, from traditional methods to advanced AI-based systems. While traditional techniques known as white boxes are simple and interpretable, deep learning models are considered black boxes, offering greater robustness and adaptability. This analysis emphasizes the need to integrate modern technology to address the growing complexity of threats, while preserving the diagnostic integrity of medical images. Furthermore, our work provides a comprehensive classification of watermarking methods and outlines future research directions, contributing to the ongoing discourse on enhancing data security in medical imaging}, archive = {J_ACCESS}, author = {Sawsan D. Mahmood and Fadoua Drira and Hussain Falih Mahdi and Adel M. Alimi}, doi = {10.1109/ACCESS.2025.3574477}, journal = {IEEE Access}, pages = {103995-104026}, shortjournal = {IEEE Access}, title = {Secure medical image sharing: Technologies, watermarking insights, and open issues}, volume = {13}, year = {2025}, }
• (2025). Application of online semi-supervised learning embedded with chaotic dynamics in equipment health prognostics. ACCESS, 13, 103982-103994. (www)

  @article{ , abstract = {The development of artificial intelligence (AI) methods with high generalization and robustness for industrial equipment prognostics remains a significant challenge. Traditional lifespan prediction models often struggle with complex, dynamic tasks that require real-time performance, primarily due to limited data availability and the inherent constraints of supervised learning approaches. These models are typically overly reliant on labeled data, which is difficult to obtain in real-world industrial settings, and they suffer from poor dynamic adaptability when dealing with varying operating conditions. To address these limitations, we propose a novel data augmentation method inspired by chaos theory, designed specifically for real-time feature extraction from industrial equipment. In addition, we introduce a semi-supervised learning framework that integrates Kent mapping with model predictive control (MPC), enabling continuous, real-time correction of predictions. This approach is capable of learning from sparse labeled data while maintaining high accuracy in forecasting the remaining useful life (RUL) of critical industrial components, such as lithium-ion batteries, turbine engines, and bearings. By overcoming the traditional model limitations—such as excessive dependence on labeled data and poor adaptability to dynamic environments—our method demonstrates strong predictive capabilities and holds significant promise for real-world industrial applications, offering improved reliability and efficiency in equipment health monitoring and maintenance planning.}, archive = {J_ACCESS}, author = {Shuo Wang and Jun Li and Guangyu Hou and Dezhi Yuan}, doi = {10.1109/ACCESS.2025.3580267}, journal = {IEEE Access}, pages = {103982-103994}, shortjournal = {IEEE Access}, title = {Application of online semi-supervised learning embedded with chaotic dynamics in equipment health prognostics}, volume = {13}, year = {2025}, }
• (2025). Prediction of imbalance prices through gradient boosting algorithms: An application to the greek balancing market. ACCESS, 13, 103968-103981. (www)

  @article{ , abstract = {An accurate prediction of imbalance prices is crucial for making well-informed decisions within short-term energy markets. This study proposes a two-stage probabilistic framework for the prediction of system imbalance and imbalance price. In the first stage, the quantiles of system imbalance are predicted employing the Quantile Regression Forest algorithm. In the second stage, the quantiles are leveraged to predict the imbalance prices, given the observed correlation between the system imbalance and the imbalance prices. The corresponding imbalance prices for each 15-minute interval are then predicted utilizing a novel methodology that combines Gradient Boosting with Markov switching models. The proposed methodology was evaluated using real data from the Greek balancing market. Comparative analysis with alternative models including a persistence model, Quantile Linear Regression, Long Short-Term Memory, and Quantile Regression Forest reveals that the proposed approach consistently outperforms the rest in terms of forecast accuracy. Specifically, the results demonstrate that the proposed approach outperforms all alternative methods, achieving an 9% improvement in nMAE and a 10.8% reduction in CRPS with respect to the second best method (Quantile Regression Forest). The findings suggest that this approach can significantly improve the decision-making process for market participants, helping mitigate the financial risks associated with imbalance prices.}, archive = {J_ACCESS}, author = {Konstantinos Plakas and Nikos Andriopoulos and Dimitrios Papadaskalopoulos and Alexios Birbas and Efthymios Housos and Ioannis Moraitis}, doi = {10.1109/ACCESS.2025.3580274}, journal = {IEEE Access}, pages = {103968-103981}, shortjournal = {IEEE Access}, title = {Prediction of imbalance prices through gradient boosting algorithms: An application to the greek balancing market}, volume = {13}, year = {2025}, }
• (2025). Fifty years of climate change studies: A scientometric assessment. ACCESS, 13, 103944-103967. (www)

  @article{ , abstract = {Over the years, climate change has emerged as a critical area of research, marked by rapid growth in both interest and scholarly output. This surge has resulted in an extensive body of scientific literature addressing diverse aspects of the field. To better understand the evolution, development, and current landscape of climate change studies, it is essential to examine the structure and trends within this expanding corpus. Despite the increasing volume of publications, comprehensive scientometric assessments in this domain remain limited. Addressing this gap, the present study conducts a scientometric analysis of 437,035 climate change-related research documents indexed in Scopus, covering a 50-year period (1972-2021). The study analyzes annual publication trends, subject classifications, geographical and linguistic distributions, and various bibliometric attributes. In addition, advanced analyses using the VOSviewer tool investigate citation patterns, authorship trends, keyword co-occurrences, research contributions, and collaborative networks. The results highlight an average annual growth rate of 14.3%, with over 69% of the total publications produced in the last decade (2012-2021). English accounts for 95.2% of all publications, followed by Chinese (1.8%) and German (0.6%). The United States leads in research output, contributing 19.18% of total publications, followed by China (8.18%) and the United Kingdom (8.00%). Citation and collaboration analyses further examine institutional output and international research networks. Nature, Science, and Journal of Climate emerged as the most influential journals, while leading institutions include the National Center for Atmospheric Research (USA), the University of East Anglia (UK), and the Chinese Academy of Sciences. Prominent authors such as Philippe Ciais, Josep Peñuelas, and Pete Smith are also recognized for their significant contributions. By providing a comprehensive overview, this study reveals key trends and collaborative patterns that have shaped climate change research over the last five decades, offering valuable insights to academics, research institutions, and policymakers for advancing future research and global cooperation in this vital field.}, archive = {J_ACCESS}, author = {Sulaimon Oyeniyi Adebayo and Hardeep Singh and Supreet Kaur and Himanshi Sharma and Jai Sharma and Karman Singh Sethi}, doi = {10.1109/ACCESS.2025.3578434}, journal = {IEEE Access}, pages = {103944-103967}, shortjournal = {IEEE Access}, title = {Fifty years of climate change studies: A scientometric assessment}, volume = {13}, year = {2025}, }
• (2025). ShieldDFL: A blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus. ACCESS, 13, 103931-103943. (www)

  @article{ , abstract = {In the industrial Internet of Things (IIoT) scenarios, federated learning (FL) provides a privacy-preserving solution for utilizing industrial data. At the same time, blockchain integration enhances trustworthiness in federated learning training. However, existing blockchain-based FL frameworks still face several critical challenges: 1) Current consensus mechanisms lack effective filtering of malicious devices, allowing low-quality participants to interfere with global model training and compromise model robustness; 2) Existing privacy budget strategies are overly simplistic, making it difficult to balance privacy protection between statistical queries and gradient updates—strong privacy protection reduces model accuracy, while weak protection fails to defend against poisoning attacks. To address these challenges, this paper proposes a blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus, called ShieldDFL. This approach employs a hybrid consensus mechanism driven by LSTM-based reputation scoring to dynamically evaluate both short-term and long-term device contributions, enabling the precise selection of high-quality devices. At the same time, it introduces an innovative dual privacy budget mechanism that applies differential privacy separately to statistical queries and gradient updates, ensuring robust privacy protection while maintaining high model performance. Experimental results on the MNIST and CIFAR-10 datasets show that the proposed method reduces the probability of malicious devices entering the consensus pool to 1.5%, lowers the success rates of SAR and BASR attacks to 5.8% and 2.1% respectively, while maintaining high model accuracy of 98.1% on MNIST and 87.6% on CIFAR-10. Overall, the proposed framework effectively breaks through the security and privacy bottlenecks of blockchain-based federated learning, providing an efficient and scalable solution for decentralized and trustworthy collab-oration in IIoT scenarios.}, archive = {J_ACCESS}, author = {Yali Cai and Xuetao Du and Chen Zhang and Meilun Li}, doi = {10.1109/ACCESS.2025.3576261}, journal = {IEEE Access}, pages = {103931-103943}, shortjournal = {IEEE Access}, title = {ShieldDFL: A blockchain-based federated learning framework with dual privacy protection and reputation-driven consensus}, volume = {13}, year = {2025}, }
• (2025). PanOpt: A nationwide joint optimization of dynamic bed allocation and patient transfer in pandemics. ACCESS, 13, 103913-103930. (www)

  @article{ , abstract = {Dynamic bed allocation and patient admission have emerged as critical strategies for hospitals during pandemics, driving research toward minimizing costs while effectively managing hospital resources. Existing studies have explored bed management in the intensive care unit, resource allocation, and surge capacity planning. However, gaps remain in addressing inter-hospital patient transfers. This paper addresses the pressing challenges hospitals face in resource management during the rapid spread of infectious diseases, which increase the risk of cross-infection and financial strain on healthcare systems. To tackle these issues, we introduce a nationwide joint optimization framework of dynamic bed allocation and patient transfer in pandemics, which we refer to as PanOpt. PanOpt presents a novel integer linear programming model for optimizing patient allocation to specific wards based on case severity, dynamic bed allocation across wards, and inter-hospital patient transfers based on capacity. Our proposed framework minimizes total healthcare system costs while reducing cross-infection risks and ensuring efficient utilization of resources. PanOpt is evaluated under various system parameters and benchmarked against two models. In the first model, inter-hospital transfers are prohibited, whereas in the second model, reallocation and inter-hospital transfers are prohibited. Results show that PanOpt outperforms existing techniques, significantly lowering operational costs and providing a robust solution for healthcare resource optimization.}, archive = {J_ACCESS}, author = {Saja Taweel and Mohammad J. Abdel-Rahman and Noor Alawi and Jude Daghlas and Ahmad Ghunaim}, doi = {10.1109/ACCESS.2025.3577490}, journal = {IEEE Access}, pages = {103913-103930}, shortjournal = {IEEE Access}, title = {PanOpt: A nationwide joint optimization of dynamic bed allocation and patient transfer in pandemics}, volume = {13}, year = {2025}, }
• (2025). Pulsed current operation and adaptive state-of-charge balancing for smart battery energy storage systems. ACCESS, 13, 103898-103912. (www)

  @article{ , abstract = {This paper advances the development of next-generation energy storage systems based on smart batteries. The investigated approach integrates a half-bridge converter into each battery, enabling pulsed current operation. Two State-of-Charge (SOC) balancing strategies are investigated. The first, inspired by existing literature, employs a proportional controller. The second is based on an adaptive controller that dynamically adjusts its gain based on real-time SOC deviation. As a second contribution, this work explores the interactions between the smart battery pack and the power conversion system (PCS) in a grid-connected application. The proposed balancing strategies are analytically modeled and validated through simulations in PLECS and Control Hardware-in-the-Loop (C-HIL) experiments. Additionally, the study examines the impact of battery bypass on the dc bus voltage and power processing in the two-stage PCS. Results indicate that the adaptive balancing scheme reduces balancing time by approximately four times compared to the proportional controller-based approach. Moreover, the interaction between the smart battery pack and the PCS enables stable regulation of the dc bus voltage, minimizing fluctuations caused by battery switching and ensuring stability in active power delivery, reinforcing its viability for grid-connected applications.}, archive = {J_ACCESS}, author = {Ediward Amaral Carelli and Allan Fagner Cupertino and Pedro Machado de Almeida and Heverton Augusto Pereira and Remus Teodorescu}, doi = {10.1109/ACCESS.2025.3579313}, journal = {IEEE Access}, pages = {103898-103912}, shortjournal = {IEEE Access}, title = {Pulsed current operation and adaptive state-of-charge balancing for smart battery energy storage systems}, volume = {13}, year = {2025}, }
• (2025). Hypnopaedia-aware machine unlearning via psychometrics of artificial mental imagery. ACCESS, 13, 103880-103897. (www)

  @article{ , abstract = {Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject’s subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes a conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoor threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine’s behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern as the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability.}, archive = {J_ACCESS}, author = {Ching-Chun Chang and Kai Gao and Shuying Xu and Anastasia Kordoni and Christopher Leckie and Isao Echizen}, doi = {10.1109/ACCESS.2025.3576800}, journal = {IEEE Access}, pages = {103880-103897}, shortjournal = {IEEE Access}, title = {Hypnopaedia-aware machine unlearning via psychometrics of artificial mental imagery}, volume = {13}, year = {2025}, }
• (2025). Visual servoing drone utilizing ground robot detection model for UAV-UGV alignment for retrieval operations. ACCESS, 13, 103866-103879. (www)

  @article{ , abstract = {Hazardous and remote environments, such as volcanic regions and disaster zones, require regular monitoring due to the significant risks they pose. Innovative solutions like aerial and mobile robots offer safer monitoring options, though drones are limited by short battery life, and mobile robots face mobility challenges. Collaborative aerial-ground robots have been developed to address these issues, with drones transporting and recovering ground robots. Deploying the ground robot is easy, but its recovery is challenging due to drone Global Positioning System (GPS) inaccuracies. It highlights the need for a precise visual alignment technique. This study utilized YOLOv8n segmentation model to develop a ground robot detection model. The development involved gathering images of ground robots in various environmental settings such as rocks, seashores, sand, grasslands, limestone, and soil. Three models were tested in this study: plate detection, ground robot detection, and a combined plate and ground robot detection model. The ground robot detection model emerged as the most robust, achieving an F1 score of 99.7% and an mAP of 95.4% at 0.5 to 0.96 IoU thresholds. The model was integrated into a small computer on a visual servoing drone and tested outdoors. The alignment threshold was set at 65 cm, based on the size of the retrieval mechanism. The experiments indicate that the drone achieved an average alignment error of 41.985 cm within 7.82 seconds. The experimental results exhibit the effectiveness of the UAV-UGV alignment for the retrieval task in hazardous and remote areas.}, archive = {J_ACCESS}, author = {Jamaica Mae Pepito and John Mel Bolaybolay and Earl Ryan Aleluya and Francis Jann Alagon and Steve Clar and Immanuel Paradela and Sherwin Guirnaldo and Jeanette Pao and Carl John Salaan and Argel Bandala}, doi = {10.1109/ACCESS.2025.3578612}, journal = {IEEE Access}, pages = {103866-103879}, shortjournal = {IEEE Access}, title = {Visual servoing drone utilizing ground robot detection model for UAV-UGV alignment for retrieval operations}, volume = {13}, year = {2025}, }
• (2025). Prediction of head injury criteria in pedestrian crashes using frequency response function-based deep neural networks. ACCESS, 13, 103848-103865. (www)

  @article{ , abstract = {This study aims to develop a deep neural network model capable of predicting the Head Injury Criterion (HIC), which is traditionally obtained through time-consuming and costly headform impact tests using dynamic stiffness measurements. The correlation between dynamic stiffness and HIC was initially analyzed in a simple steel plate to explore feasibility. Subsequently, this correlation was evaluated using the hoods of two actual vehicles. The results revealed significant differences compared to the steel plate analysis, primarily due to the influence of contact with the lower components beneath the hood. To overcome these physical differences, a deep neural network model incorporating various features was designed to improve HIC prediction accuracy. Additionally, an algorithm was developed to estimate the penetration of the hood into the lower components, and this feature was included as an input to enhance the performance of the model. This study demonstrates the potential of the deep neural network model as an auxiliary tool for predicting changes in HIC resulting from modifications to the hood design.}, archive = {J_ACCESS}, author = {Seon-Hong Kim and Seounghyun Lee and Taewung Kim and Je-Heon Han}, doi = {10.1109/ACCESS.2025.3579369}, journal = {IEEE Access}, pages = {103848-103865}, shortjournal = {IEEE Access}, title = {Prediction of head injury criteria in pedestrian crashes using frequency response function-based deep neural networks}, volume = {13}, year = {2025}, }
• (2025). An area-time efficient hardware architecture for ML-KEM post-quantum cryptography standard. ACCESS, 13, 103834-103847. (www)

  @article{ , abstract = {To facilitate the integration of the NIST-standardized post-quantum cryptographic (PQC) algorithm, Module Lattice-based Key Encapsulation Mechanism (ML-KEM), into quantum-resistant devices and cryptosystems, this study introduces an area-time efficient hardware implementation. We propose several key optimizations: a dedicated SHA3 design merging hashing and sampling to eliminate hash output storage, a high-performance polynomial arithmetic unit with a novel reuse technique that reduces twiddle factor storage by threefold, and a partitioned storage strategy to minimize memory footprint. By tightly coupling pipelining and parallelism, we further enhance execution speed and avoid memory access bottlenecks. Our FPGA-based ML-KEM implementation achieves a superior area-time product (ATP), outperforming state-of-the-art results by up to $6.7\times $ across NIST security levels 1, 3, and 5. A 180 nm CMOS fabrication of our design yields a die area of 1, $940\times 1.789~\mu $ m2. Measured performance shows the lowest SRAM requirement (7.37 kB) and minimal energy consumption ( $6.4~\mu $ J, $10.3~\mu $ J, and $14.4~\mu $ J for security levels 1, 3, and 5), leading to ATP improvements ranging from $1.5\times $ to $115.0\times $ compared to existing ASIC solutions.}, archive = {J_ACCESS}, author = {Trong-Hung Nguyen and Tuan-Kiet Dang and Duc-Thuan Dam and Khai-Duy Nguyen and Phuc-Phan Duong and Cong-Kha Pham and Trong-Thuc Hoang}, doi = {10.1109/ACCESS.2025.3579379}, journal = {IEEE Access}, pages = {103834-103847}, shortjournal = {IEEE Access}, title = {An area-time efficient hardware architecture for ML-KEM post-quantum cryptography standard}, volume = {13}, year = {2025}, }
• (2025). A hybrid Dynamic/Steady-state tool with protection simulation for cascading-outage analysis of extreme events in power systems. ACCESS, 13, 103823-103833. (www)

  @article{ , abstract = {Large portions of the electrical power grid are susceptible to component failures that when combined with certain other factors (extreme events), could cause cascading outages. Some of these outages can be severe enough to trigger brownouts and blackouts. There are a few established methodologies and tools for directly analyzing the hazards of cascading component outages over a longer time scale although there is a great deal of knowledge regarding the mitigation of the initial few failures that occur near the beginning of a cascade. Current power system tools have limited ability to perform detailed and accurate cascading-outage analysis, which could be computationally intensive. With the help of the Dynamic Contingency Analysis Tool (DCAT), power system planning engineers can evaluate the effects of severe contingencies and possible cascade events on their systems and connections in a more practically grounded manner. DCAT has several unique features: (1) detailed hybrid dynamic and steady-state analysis of power systems to mimic real-world cascading outages, (2) detailed modeling of protection systems embedded in the dynamic simulation, (3) simulation of corrective action after transients, (4) simulation of islanding, and (5) high-performance computing capability to simulate a large number of contingencies in a reasonable time. DCAT outputs will help find technically sound solutions to reduce the risk of cascading outages. This paper provides details of DCAT methodology and shows its capabilities with extreme events on real-world cases.}, archive = {J_ACCESS}, author = {Bharat Vyakaranam and Nader A. Samaan and Yuri V. Makarov and Tony Nguyen and Yousu Chen and Jeff Dagle and Xiaoyuan Fan and Mallikarjuna R. Vallem and José Conto and Sun Wook Kang}, doi = {10.1109/ACCESS.2025.3578245}, journal = {IEEE Access}, pages = {103823-103833}, shortjournal = {IEEE Access}, title = {A hybrid Dynamic/Steady-state tool with protection simulation for cascading-outage analysis of extreme events in power systems}, volume = {13}, year = {2025}, }
• (2025). Technical optimization and experimental validation of wall-mounted cylindrical solar cell in urban environments. ACCESS, 13, 103809-103822. (www)

  @article{ , abstract = {Cylindrical solar cells have omnidirectional light reception, excellent heat dissipation, and high wind resistance, making them suitable for application in solar power generation systems in urban environments. In this study, we conducted technical optimization and experimental verification of a cylindrical solar cell module installation on urban building walls. We performed illumination simulations by varying the wall orientation, reflectance, and distance between the module and the wall. We evaluated the effects on the light reception characteristics and power generation performance. Additionally, we verified the validity of the simulation results through actual power generation measurements of the cylindrical modules. The simulation results indicated that, under the evaluated conditions, the light reception increased under the white wall installation conditions. Moreover, the light-reception characteristics were further improved by setting the distance from the wall to 600 mm. When no wall was present, orienting the modules in an east-west direction (90°) was effective. Actual power generation measurements confirmed that the cylindrical solar cell modules exhibit increased power generation during morning and evening hours, demonstrating a stable performance in terms of daily cumulative power generation. Furthermore, sealing durability evaluations suggested that the modules possessed high environmental durability. These results show that cylindrical solar cells are technically feasible and reliable for the development of wall-mounted solar power generation systems suitable for urban spaces.}, archive = {J_ACCESS}, author = {Fumiki Tamura and Kento Honma and Shinji Yokogawa and Syuzi Hayase and Masaki Nakamura and Tomoyuki Hirami and Daisuke Hirotani and Yuya Sono and Toshihiro Takenoya and Daishiro Nomura and Takatoshi Nomura and Masahiro Hayashi}, doi = {10.1109/ACCESS.2025.3578361}, journal = {IEEE Access}, pages = {103809-103822}, shortjournal = {IEEE Access}, title = {Technical optimization and experimental validation of wall-mounted cylindrical solar cell in urban environments}, volume = {13}, year = {2025}, }
• (2025). Investigation and performance evaluation of novel single-switch high-gain DC-DC converters for DC microgrid applications. ACCESS, 13, 103798-103808. (www)

  @article{ , abstract = {This paper introduces a novel single-switch, non-isolated high-gain DC-DC converter for solar photovoltaic (PV) and fuel-cell (FC) applications. These energy sources typically provide a continuous supply of current, necessitating a high-gain DC-DC converter that operates in continuous conduction mode (CCM). This converter draws a continuous input current from the supply and delivers a continuous output current to the load. The performance of the converter is thoroughly analyzed through the development of a state-space model and the derivation of the small signal transfer function, which helps in understanding the converter’s dynamic behavior. Detailed comparisons with existing converters are also presented. Furthermore, an output voltage controller is designed using the k-factor method to effectively regulate the output voltage without requiring a current sensor, even in the presence of input voltage variations. To validate the effectiveness of the converter and its controller, a 150 W prototype was constructed and experimentally verified in a laboratory setting.}, archive = {J_ACCESS}, author = {M. Diwakar Naik and U. Vinatha and M. Venkatesh Naik and Praveen Kumar Bonthagorla}, doi = {10.1109/ACCESS.2025.3579261}, journal = {IEEE Access}, pages = {103798-103808}, shortjournal = {IEEE Access}, title = {Investigation and performance evaluation of novel single-switch high-gain DC-DC converters for DC microgrid applications}, volume = {13}, year = {2025}, }
• (2025). SDMA-net: Swin transformer-based dynamic memory-attention network for endoscopic navigation. ACCESS, 13, 103786-103797. (www)

  @article{ , abstract = {Accurate endoscopic motion navigation is crucial for minimally invasive surgical procedures. Nevertheless, endoscopic video data often exhibit low texture, variable lighting, and dynamic motion patterns, which poses significant challenges to existing methods. To address these issues, we propose a novel deep learning framework, namely Swin Transformer-based Dynamic Memory-Attention Network (SDMA-Net). SDMA-Net integrates a Swin Transformer for multiscale feature extraction, a Dynamic Channel Attention (DCA) module for frequency-aware feature refinement, and a Channel-Level Masked AutoEncoder (CL-MAE) for self supervised learning. Temporal dependencies are modeled using a Long Short-Term Memory (LSTM) network. Additionally, a Dynamic Memory Augmentation Module (DMAM) adaptively updates and retrieves motion patterns to enhance robustness against noise and occlusions. Experiments on a colonoscopy dataset of over 12,000 images demonstrate that SDMA-Net achieves superior classification accuracy and Area Under the Curve (AUC) compared to existing baselines. As a conclusion, our proposed SDMA-Net provides an effective and efficient solution for endoscopic motion detection and classification.}, archive = {J_ACCESS}, author = {Runnan Zhang and Qi Tian and Jinghui Chu and Wei Lu}, doi = {10.1109/ACCESS.2025.3576739}, journal = {IEEE Access}, pages = {103786-103797}, shortjournal = {IEEE Access}, title = {SDMA-net: Swin transformer-based dynamic memory-attention network for endoscopic navigation}, volume = {13}, year = {2025}, }
• (2025). Design of dual-band MIMO array antenna and its characterization for urban millimeter wave channel conditions. ACCESS, 13, 103761-103785. (www)

  @article{ , abstract = {The multiple-input-multiple-output (MIMO) antenna improves the channel capacity, and the array antenna improves the channel condition. A combined feature benefits the current cellular and other 5G applications. The strength of the MIMO array antenna is that it improves channel condition and operates with wide bandwidth and directional beam, achieving high gain. Not many of the articles focused on the second issue. Thus, this article focuses on designing a MIMO array antenna with a directional narrow beam resonating at dual bands that are 28 and 38 GHz. The circular slot in the ground plane serves a dual purpose: i) improve the bandwidth at both bands ranging from 25.35-30.53 GHz, and 36.76-41.43 GHz. ii) It acts as a decoupling structure providing isolation $\vert $ S $21\vert \gt 39$ dB and 42 dB in respective bands. The proposed antenna generates circular (RHCP) and elliptical (LHCP) polarization in the first and second bands. The antenna has achieved half-power beamwidth (HPBW) of 15° and 16.5° in the YZ-plane and a broad beam in the XZ-plane. The antenna is validated for diversity metrics, and the results are satisfactory. The MIMO antenna performance regarding channel capacity and path loss is virtually tested for cellular Picocell urban scenarios with line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The computed results agree with the analytical results of standard Friss, Stanford Inter-University (SUI), and Close-In (CI) models.}, archive = {J_ACCESS}, author = {B. G. Parveez Shariff and Tanweer Ali and Satish Kumar Sharma and Qammer H. Abbasi and Masood Ur-Rehman and Yahia M. M. Antar and Pradeep Kumar and Pallavi R. Mane and Sameena Pathan}, doi = {10.1109/ACCESS.2025.3579151}, journal = {IEEE Access}, pages = {103761-103785}, shortjournal = {IEEE Access}, title = {Design of dual-band MIMO array antenna and its characterization for urban millimeter wave channel conditions}, volume = {13}, year = {2025}, }
• (2025). ICP enhancement algorithm for 6D pose tracking of household objects. ACCESS, 13, 103748-103760. (www)

  @article{ , abstract = {Real-time 6D pose tracking using camera images plays an important role in enabling robots to track and grasp household items. Although iterative closest point (ICP) is frequently employed as the primary method for pose tracking owing to its good performance and low resource consumption, there are several challenges such as the difficulty in dealing with flat or symmetric objects. These challenges make the ICP prone to failure in tracking scenarios. In this study, we introduce an ICP enhancement algorithm to effectively utilize the ICP for tracking household items. Our main contribution can be summarized in three ways: usage of RGB keypoints for obtaining initial pose, refinement approach for dealing with flat or rotationally symmetric objects, and self-recovery algorithm under tracking failure. We estimate the initial pose using RGB keypoints and fine-tune the weights of the loss function to achieve an accurate pose by considering the flat and rotational symmetric properties of the object. Additionally, a recovery algorithm with the Oriented FAST and Rotated BRIEF (ORB) descriptor was employed to detect and resolve tracking failures. While the performance was relatively limited for objects with low RGB texture, our algorithm achieved stable and high accuracy when sufficient RGB texture was present. We confirmed that our algorithm achieved 100% ADDs-AUC on 16 objects in YCB Video dataset.}, archive = {J_ACCESS}, author = {Hyunho Hwang and Hyunsoo Shin and Ji-Hun Bae and Sungon Lee}, doi = {10.1109/ACCESS.2025.3579363}, journal = {IEEE Access}, pages = {103748-103760}, shortjournal = {IEEE Access}, title = {ICP enhancement algorithm for 6D pose tracking of household objects}, volume = {13}, year = {2025}, }
• (2025). Uncertainty impact on aggregator performance for peak shaving. ACCESS, 13, 103733-103747. (www)

  @article{ , abstract = {Accurate peak period forecasting and load consumption profile prediction are critical for utility resource planning and emergency response preparedness. This article investigates the impact of uncertainties in both peak period characteristics (timing and magnitude) and thermostatically controlled loads (TCLs) behavior on capacity estimation and subsequent peak shaving performance. Uncertainty sources associated with common TCLs and challenges with forecasting peak period parameters are discussed. Conventional TCL models are updated to address and incorporate these inaccuracies and uncertainty factors. We design and implement a management system that aggregates TCLs using a comprehensive virtual battery (VB) model, ensuring adherence to all operational constraints. An optimal peak shaving algorithm is deployed during peak demand periods utilizing this VB framework, enabling comparative analysis of “OFF” and “ON/OFF” control scenarios. A novel metric, the shared load capacity percentage, is introduced to provide insights for peak shaving optimization and broader demand response (DR) applications. Findings indicate that employing an ON/OFF control strategy can potentially result in 20% more capacity compared to OFF control, particularly when baseline energy consumption is reduced by 10%. Furthermore, we present a detailed assessment of the impact of various uncertainties on the peak shaving process, considering both utility operations and end-user loads. The sensitivity of the proposed control system to forecasting inaccuracies is thoroughly analyzed, and the expected loss of loads’ hybrid reserved energy is quantified. A metric for aggregator reserved capacity loss is introduced, giving insight to the detrimental effects of forecasting errors on peak shaving efficacy, encompassing performance, cost, and temporal implications. The results reveal that inaccurate peak time forecasting and voltage level uncertainties can lead to up to a 50% variation of peak shaving capacity with electric water heaters (EWHs) and heat pumps (HPs). The study also demonstrates that ambient temperature forecasting inaccuracies lead to a 35% capacity provision change. These findings underscore the critical need for robust forecasting techniques and uncertainty management strategies to implement TCL-based peak shaving programs effectively.}, archive = {J_ACCESS}, author = {Ismail Arafat and Eduardo Castillo Guerra and Julian Meng}, doi = {10.1109/ACCESS.2025.3579635}, journal = {IEEE Access}, pages = {103733-103747}, shortjournal = {IEEE Access}, title = {Uncertainty impact on aggregator performance for peak shaving}, volume = {13}, year = {2025}, }
• (2025). Optimizing monkeypox lesions detection with a lightweight hybrid model. ACCESS, 13, 103709-103732. (www)

  @article{ , abstract = {The increasing global prevalence of monkeypox (mpox) has necessitated the development of accurate, efficient, and interpretable diagnostic models for timely disease identification. Although deep learning has advanced medical image analysis, diagnosing diseases with complex lesions, such as monkeypox, remains challenging in resource-limited settings. Existing methods often rely on large architectures that demand high computational resources and lack interpretability. To address these issues, we propose a lightweight, interpretable architecture that truncates MobileNetV2 for efficient feature extraction, incorporates Ghost Modules to reduce redundancy, and integrates a SA-MobileViT block to capture both local and global dependencies in lesion images. Grad-CAM visualizations enhance interpretability, and Optuna is used for hyperparameter optimization. Our model achieved an accuracy of 98.72%, precision of 98.76%, recall of 98.72%, and F1-score of 98.72%, outperforming existing approaches, especially in handling irregular lesion patterns. The model’s design is ideal for deployment in resource-constrained settings, offering a robust solution for real-world healthcare applications. Grad-CAM, saliency maps, and other visualization techniques help improve clinical trust by highlighting relevant regions in input images, emphasizing key features of the lesions. The proposed model is lightweight with only 630,204 parameters, making it $92.3\times $ smaller than ResNet152V2 (58,152,004 parameters) and $28.7\times $ smaller than DenseNet201 (18,100,612 parameters). It requires 321.61M FLOPs, $35.8\times $ fewer than ResNet152V2 (11.51G FLOPs) and $13.4\times $ fewer than DenseNet201 (4.29G FLOPs). With a size of just 72.92 MB, it is $8.14\times $ smaller than ResNet152V2 (594.08 MB) and $4.57\times $ smaller than DenseNet201 (333.47 MB), making it an efficient solution for mobile and edge deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Mehdhar S. A. M. Al-Gaashani and Reem Ibrahim Alkanhel and Dina S. M. Hassan and Abduljabbar S. Ba Mahel and Ahmed Aziz and Mashael M. Khayyat and Ammar Muthanna}, doi = {10.1109/ACCESS.2025.3574244}, journal = {IEEE Access}, pages = {103709-103732}, shortjournal = {IEEE Access}, title = {Optimizing monkeypox lesions detection with a lightweight hybrid model}, volume = {13}, year = {2025}, }
• (2025). Depth-supervised and curvature-optimized 3D gaussian splatting for scene reconstruction. ACCESS, 13, 103697-103708. (www)

  @article{ , abstract = {Recently, 3DGS has achieved revolutionary advancements in dynamic rendering and novel view synthesis tasks. The Adaptive Density Control (ADC) algorithm plays a pivotal role in 3DGS, utilizing an image gradient-based average gradient threshold to regulate the splitting and cloning of Gaussians and optimize the density of Gaussian distributions. However, relying solely on an average gradient threshold to adjust Gaussians may lead to some Gaussians exhibiting erroneous opacity or neglecting the contributions of Gaussians in regions with smaller gradients to the rendering process. To overcome these limitations, we introduce depth as a geometric constraint, which adjusts the scale of Gaussians based on depth to ensure more coherent frequency information. Furthermore, after the ADC algorithm, we optimize the Gaussians in dense regions by incorporating curvature-based assessments, ensuring that all Gaussians maintain proper occlusion relationships in the final rendering. We conducted extensive tests on datasets such as synthetic Blender and Mip-NeRF360, and our method demonstrated superior performance compared to 3DGS and other state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Antong Li and Lutao Wang and Ziwei Wang}, doi = {10.1109/ACCESS.2025.3579377}, journal = {IEEE Access}, pages = {103697-103708}, shortjournal = {IEEE Access}, title = {Depth-supervised and curvature-optimized 3D gaussian splatting for scene reconstruction}, volume = {13}, year = {2025}, }
• (2025). Underwater image enhancement based on transformer, attention, and multi-color-space inputs. ACCESS, 13, 103682-103696. (www)

  @article{ , abstract = {Underwater images suffer from color distortion, low contrast and blurring caused by the attenuation, refraction, and scattering of light. For many maritime operations, underwater image enhancement is essential. This paper proposes an underwater image enhancement method based on transformer, attention and multi-color-space inputs. First, transformer block is embedded into ResNet-50 as the backbone network. Combining this backbone network and multi-scale feature fusion forms the fundamental framework of our method. This framework contributes to mine the rich features of different scene underwater image and outputs a feature map of the same size as the input image. Then, multi-dimensional attention mechanism is added to the feature maps of different scales to mine key areas that require enhancement in underwater images and calculate the enhancement value of each pixel. Finally, multi-color-space inputs including RGB, HSV and LAB as the inputs for CNN to enhancement images from various aspects such as color deviation, brightness and saturation. The comparison of image quality evaluation metrics, visual enhancement performance evaluation and impact on the performance of underwater object detection with other underwater enhancement methods on datasets including SUIM-E, UIEB, EUVP, RUIE-RIQS, RUIE-UCCS and URPC-2018 prove the good performance of our underwater image enhancement method.}, archive = {J_ACCESS}, author = {Liqiong Lu and Dong Wu and Liuyin Wang and Wanzhen Zhang and Tonglai Liu}, doi = {10.1109/ACCESS.2025.3577005}, journal = {IEEE Access}, pages = {103682-103696}, shortjournal = {IEEE Access}, title = {Underwater image enhancement based on transformer, attention, and multi-color-space inputs}, volume = {13}, year = {2025}, }
• (2025). RoBERTa-based multi-feature integrated BiLSTM and CNN model for ceramic review analysis. ACCESS, 13, 103668-103681. (www)

  @article{ , abstract = {To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa’s multi-output architecture. By feeding different outputs of RoBERTa into Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, the model effectively captures both local static patterns and global contextual dependencies, thereby enhancing its capability to handle complex textual inputs. Additionally, an attention mechanism highlights semantically important features, further improving feature representation and classification performance. To evaluate the effectiveness of the proposed model, we conducted comparative experiments on the Amazon, IMDB, SST-2, and Ceramic datasets. The F1 scores achieved were 94.25%, 92.52%, 91.51%, and 81.32%, respectively, substantially outperforming existing models, particularly in handling domain-specific and nuanced texts. These results demonstrate that the proposed multi-feature fusion model significantly enhances sentiment classification performance.}, archive = {J_ACCESS}, author = {LiHua Yang and Jun Wang and WangRen Qiu}, doi = {10.1109/ACCESS.2025.3579022}, journal = {IEEE Access}, pages = {103668-103681}, shortjournal = {IEEE Access}, title = {RoBERTa-based multi-feature integrated BiLSTM and CNN model for ceramic review analysis}, volume = {13}, year = {2025}, }
• (2025). Blockchain-based collaboration design for 6G distributed networks. ACCESS, 13, 103649-103667. (www)

  @article{ , abstract = {The collaboration across various usage scenarios in 6G distributed networks presents new challenges, including the need for instantaneous access, heterogeneous collaboration, and robust security and trustworthiness. To address these challenges, this paper proposes a universal blockchain-based collaboration architecture, along with an end-to-end collaboration mechanism designed to ensure efficient, secure, and trustworthy resource-sharing capabilities. However, due to the inherent limitations of blockchain technology regarding throughput and storage, it cannot be directly applied to the proposed architecture without adaptive enhancements. To address the throughput limitation, this paper introduces a service-based, capacity-adaptive blockchain sharding framework. In this framework, nodes with varying consensus efficiencies are partitioned into different shards based on a reputation-based capability assessment strategy. Transactions associated with different service types are then allocated to the corresponding shards, ensuring that each shard’s consensus capability aligns with the service’s requirements. Furthermore, to mitigate the load’s impact on consensus efficiency, a load-sensitive Practical Byzantine Fault Tolerance (PBFT) consensus mechanism is proposed for each shard. To address the storage limitation, this paper introduces a hybrid storage policy that combines both on-chain and off-chain solutions, effectively alleviating blockchain storage constraints. Simulation results demonstrate the feasibility and superiority of the proposed architecture and mechanisms, confirming their potential for enabling effective collaboration in 6G distributed networks.}, archive = {J_ACCESS}, author = {Tong Zhang and Chao Liu and Zhonghui Wu and Shuoming Zhang and Nanxiang Shi}, doi = {10.1109/ACCESS.2025.3573518}, journal = {IEEE Access}, pages = {103649-103667}, shortjournal = {IEEE Access}, title = {Blockchain-based collaboration design for 6G distributed networks}, volume = {13}, year = {2025}, }
• (2025). TCL: Time-dependent clustering loss for optimizing post-training feature map quantization for partitioned DNNs. ACCESS, 13, 103640-103648. (www)

  @article{ , abstract = {This paper introduces an enhanced approach for deploying deep learning models on resource-constrained IoT devices by combining model partitioning, autoencoder-based compression, quantization with Time Dependent Clustering Loss (TCL) regularization, and lossless compression, to reduce communication overhead, minimizing latency while maintaining accuracy. The autoencoder compresses feature maps at the partitioning point before quantization, effectively reducing data size and preserving accuracy. TCL regularization clusters activations at the partitioning point to align with quantization levels, minimizing quantization error and ensuring accuracy even with extreme low-bitwidth quantization. Our method is evaluated on classification models (ResNet-50, EfficientNetV2-S) and an object detection model (YOLOv10n) using the TinyImageNet-200 and Pascal VOC datasets. Deployed on Raspberry Pi 4 B and GPU, each model is tested across various partitioning points, quantization bit-widths (1-bit, 2-bit, and 3-bit), communication datarate (1MB/s to 10MB/s), and LZMA lossless compression. For a partitioned ResNet-50 after the convolutional stem block, the speed-up against a server solution is $2.33\times $ and 1.85x compared to the all-in-node solution, with only a minimal accuracy drop of less than one percentage points. The proposed framework offers a scalable solution for deploying high-performance AI models on IoT devices, extending the feasibility of real-time inference in resource-constrained environments.}, archive = {J_ACCESS}, author = {Oscar Artur Bernd Berg and Eiraj Saqib and Axel Jantsch and Irida Shallari and Silvia Krug and Isaac Sánchez Leal and Mattias O’Nils}, doi = {10.1109/ACCESS.2025.3579107}, journal = {IEEE Access}, pages = {103640-103648}, shortjournal = {IEEE Access}, title = {TCL: Time-dependent clustering loss for optimizing post-training feature map quantization for partitioned DNNs}, volume = {13}, year = {2025}, }
• (2025). Multiple instance learning with instance-level positive-unlabeled learning in anomaly detection. ACCESS, 13, 103627-103639. (www)

  @article{ , abstract = {We propose a method for learning a classifier that accurately predicts both instance and bag classes in multiple instance learning (MIL) for anomaly detection, achieving significant performance improvement. MIL, a form of weakly supervised learning, represents datasets as sets of bags labeled as either positive or negative, where each bag contains multiple instances. A positive bag includes at least one positive instance, whereas a negative bag contains only negative instances. Taking into account the lack of instance-level labels, a classifier must learn to predict instance-level labels from only bag labels during training. Whereas bag label annotation is relatively straightforward in many applications, annotating individual instances is often expensive. Consequently, MIL settings often arise in various tasks. Recent studies in MIL for anomaly detection integrate bag-level training with instance-level training focused on feature reconstruction. However, these methods use only instances in normal bags for instance-level training, and some methods also make it difficult to determine a suitable decision boundary due to the nature of training based on the area under the receiver operating characteristic curve. Our proposed method uses positive and unlabeled learning for enhancing instance-level classification performance and weighted-noisy-OR for training bag-level classification. We validate the proposed method in terms of the F1 score and area under the precision-recall curve (PR-AUC) on 35 anomaly-detection datasets. The experimental results indicate that our method outperforms conventional methods in both F1 score and PR-AUC, achieving an F1 score/ PR-AUC of 0.706/0.713 for instance classification and 0.789/0.845 for bag classification.}, archive = {J_ACCESS}, author = {Ryosuke Matsuo and Shinya Yasuda and Hiroshi Yoshida}, doi = {10.1109/ACCESS.2025.3578651}, journal = {IEEE Access}, pages = {103627-103639}, shortjournal = {IEEE Access}, title = {Multiple instance learning with instance-level positive-unlabeled learning in anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Advanced analytical and empirical control strategies for active magnetic bearing systems. ACCESS, 13, 103613-103626. (www)

  @article{ , abstract = {Active Magnetic Bearings (AMB) systems are well-suited for high-speed applications and demonstrate compatibility with clean environments, such as renewable energy sectors, and challenging conditions like extreme pressures, temperatures, and harsh environments. This paper provides an in-depth assessment of the control of AMB, which are electromagnetic bearings powered by power electronic components. It highlights the advantages and disadvantages of various control techniques for AMB. However, controlling AMB presents difficulties due to their nonlinear and inherently unstable nature. The work focuses on emphasizing advanced control strategies using Fourier and empirical data-driven engineering approaches, which have significantly expanded the potential applications of AMB across various industries. The article also highlights potential directions in research, emphasizes numerical modeling for AMB using Maxwell’s principle, and presents sophisticated control schemes to comprehend dynamic circumstances.}, archive = {J_ACCESS}, author = {Debarghya Dutta and Pabitra Kumar Biswas and Suraj Gupta and Sukanta Debnath and Ameni Boumaiza and Furkan Ahmad}, doi = {10.1109/ACCESS.2025.3579676}, journal = {IEEE Access}, pages = {103613-103626}, shortjournal = {IEEE Access}, title = {Advanced analytical and empirical control strategies for active magnetic bearing systems}, volume = {13}, year = {2025}, }
• (2025). Robust anti-jamming method for large-array radar systems using deep learning based null-space beamforming. ACCESS, 13, 103599-103612. (www)

  @article{ , abstract = {With the increasing use of AI in radar signal processing, researchers have started combining minimum variance distortionless response (MVDR) with AI technology; however, the use of MVDR results in higher nonlinearity, making the learning process difficult. A combination of null-space beamforming (NSB) and AI technology may be a solution to this issue. However, inferring NSB weights requires inputting prior angle information, which necessitates an additional direction of arrival estimation, thereby increasing system complexity. To meet the requirements for applications in radar systems, we present a robust anti-jamming method for large-array radar systems using a deep neural network with NSB (DNN-NSB). The proposed method combines the computational simplicity of null-space beamforming with the adaptability of deep learning to effectively suppress interference and maintain a high signal-to-interference-plus-noise ratio (SINR). Unlike traditional methods, DNN-NSB eliminates the need for prior angle information, enabling efficient and scalable weight inference even in complex scenarios. The performance of DNN-NSB was validated through simulations across four scenarios by varying the number of interference sources, interference-to-signal ratio (ISR) conditions, and array sizes. The results showed that DNN-NSB consistently achieved near-optimum SINR within the training range and demonstrated superior performance compared to a convolutional neural network based on MVDR (CNN-MVDR) under multi-source interference conditions. To evaluate scalability, the model was further tested using a 32-element array, where it consistently achieved near-optimum interference suppression and maintained high spatial resolution. In conclusion, the study findings highlight the potential of DNN-NSB as a practical and effective solution for modern radar systems, particularly for applications that require large arrays and robust anti-jamming capabilities.}, archive = {J_ACCESS}, author = {Jaehyuk Lim and Hogeun Yoo and Euihyuk Lee and Sunjin Oh and Jaehoon Lee}, doi = {10.1109/ACCESS.2025.3579422}, journal = {IEEE Access}, pages = {103599-103612}, shortjournal = {IEEE Access}, title = {Robust anti-jamming method for large-array radar systems using deep learning based null-space beamforming}, volume = {13}, year = {2025}, }
• (2025). Reference torque tracking controller for electric power steering using a disturbance observer with system identification-based parameter selection. ACCESS, 13, 103580-103598. (www)

  @article{ , abstract = {In this paper, an innovative control system architecture is presented for electric power steering (EPS) systems, designed to improve reference torque tracking performance. Conventional EPS systems use boost curves to modify assist torque based on vehicle speed and drive modes. However, these approaches require considerable tuning effort, especially when providing configurable steering feel options. To overcome these limitations, this study proposes a novel EPS control system that incorporates a disturbance observer (DOB) to mitigate the effects of system uncertainties and disturbances. Furthermore, a system identification procedure is used to determine the model parameters that significantly affect the DOB performance. This process enables the derivation of refined model parameters and the rejection of disturbances such as friction. Subsequent simulation and vehicle testing demonstrate that the proposed control system maintains stable tracking performance within the frequency range of typical driver inputs, even under varying road conditions and changes in reference steering torque. As a result, the effectiveness of the proposed system is confirmed through its application to the EPS controllers of production vehicles, achieving stable performance with minimal calibration effort.}, archive = {J_ACCESS}, author = {Chanwoo Heo and Daehyun Jung and Jeongha Moon and Deukpyo Lee and Wonshick Ko and Seunghoon Woo}, doi = {10.1109/ACCESS.2025.3578679}, journal = {IEEE Access}, pages = {103580-103598}, shortjournal = {IEEE Access}, title = {Reference torque tracking controller for electric power steering using a disturbance observer with system identification-based parameter selection}, volume = {13}, year = {2025}, }
• (2025). Optimizing knowledge transfer graph for deep collaborative learning. ACCESS, 13, 103565-103579. (www)

  @article{ , abstract = {Knowledge transfer among multiple networks, using predicted probabilities or intermediate-layer activations, has evolved significantly through extensive manual design, ranging from simple teacher—student approaches (for example, knowledge distillation) to bidirectional cohort methods (for example, deep mutual learning). However, key factors such as network size, the number of networks, transfer direction, and loss function design interact in complex ways and limit conventional methods to exploring only a narrow range of possible combinations. This study proposes a novel training method that enables more flexible and diverse combinations of knowledge transfer strategies. Specifically, a graph-based representation called the knowledge transfer graph is introduced, providing a unified framework for representing a wide variety of knowledge transfer strategies. Furthermore, specialized loss functions are proposed that incorporate five distinct gate functions to dynamically control gradient propagation and thereby enable a richer set of knowledge transfer strategies. By systematically searching the structure of the knowledge transfer graph, the proposed method automatically discovers more effective knowledge transfer strategies than those designed manually. Experimental results on eleven datasets demonstrate that the proposed approach consistently yields significant performance improvements over conventional methods employing basic combinations of the key factors and reliably identifies effective graph structures.}, archive = {J_ACCESS}, author = {Soma Minami and Naoki Okamoto and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi}, doi = {10.1109/ACCESS.2025.3579344}, journal = {IEEE Access}, pages = {103565-103579}, shortjournal = {IEEE Access}, title = {Optimizing knowledge transfer graph for deep collaborative learning}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of EEG-based seizure detection techniques. ACCESS, 13, 103531-103564. (www)

  @article{ , abstract = {The automated classification and prediction of epileptic seizures using electroencephalography (EEG) remains a dynamic and critical area of interdisciplinary research, situated at the intersection of neuroscience and computational intelligence. Despite considerable progress in EEG signal acquisition, preprocessing methodologies, and the application of machine learning algorithms, significant challenges persist due to the inherent complexity and inter-subject variability observed in seizure patterns. This review aims to provide guidance for new researchers in this field, starting with the fundamental aspects of epilepsy, encompassing its diverse clinical presentations (syndromes), associated health conditions (comorbidities), and characteristic EEG manifestations. Furthermore, it provides a detailed analysis of recent advancements in automated seizure classification and prediction systems. We examine crucial preprocessing techniques, feature extraction strategies, and the application of both established statistical models and contemporary deep learning architectures, including innovative approaches such as image-based representations of EEG data and transfer learning paradigms. A comprehensive compilation of EEG datasets, spanning neonatal, pediatric, adult, and animal studies, is systematically presented, with a focus on their annotation schemes, and the underlying research objectives. Additionally, we discuss post-processing methodologies aimed at refining prediction accuracy, considerations for real-time hardware implementation to facilitate practical application, and emerging trends such as explainable artificial intelligence (XAI) to enhance the interpretability of AI-driven decisions, federated learning to enable collaborative model development across institutions, and neuromorphic computing inspired by the brain’s architecture. By discussing the pros and cons of different methods, current limitations, and ethical considerations, this review seeks to help new researchers develop better EEG-based seizure classification and prediction systems with real-world impact for patients.}, archive = {J_ACCESS}, author = {Goldwyn Sudhakar Jebaraj and Konguvel Elango}, doi = {10.1109/ACCESS.2025.3578991}, journal = {IEEE Access}, pages = {103531-103564}, shortjournal = {IEEE Access}, title = {A comprehensive review of EEG-based seizure detection techniques}, volume = {13}, year = {2025}, }
• (2025). ANN activation function estimators for homomorphic encrypted inference. ACCESS, 13, 103512-103530. (www)

  @article{ , abstract = {Homomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.}, archive = {J_ACCESS}, author = {Mhd Raja Abou Harb and Baris Celiktas}, doi = {10.1109/ACCESS.2025.3579667}, journal = {IEEE Access}, pages = {103512-103530}, shortjournal = {IEEE Access}, title = {ANN activation function estimators for homomorphic encrypted inference}, volume = {13}, year = {2025}, }
• (2025). A multi-scale adaptive fusion network: End-to-end interpretable small-sample classifier for motor imagery EEG. ACCESS, 13, 103497-103511. (www)

  @article{ , abstract = {Brain-computer interface (BCI) technology based on motor imagery (MI) represents an advanced human-computer interaction approach that enables us to communicate and exchange directly with the external environment through neural activities. However, the non-stationarity and individual variability of EEG signals present significant challenges to improving decoding accuracy. Existing studies often struggle with feature extraction, dynamic feature selection, and temporal modeling, failing to capture critical EEG patterns effectively. In this study, we propose a multi-scale adaptive fusion network (MSAFNet) to improve the accuracy of MI-EEG classification. The MSAFNet integrates two primary modules: scale-aware channel attention (SCA) and adaptive dynamic feature fusion (ADF). The SCA module strengthens the model’s ability to perceive features of different frequencies and spatio-temporal information through multi-scale feature interactions to extract richer primary feature representations. The ADF module captures short-term dynamic variations in EEG signals while performing key feature selection and cross-layer feature fusion. Additionally, long-term temporal dependence is modeled by a temporal convolution network (TCN) to enhance the extraction capability of temporal features of EEG signals. Ablation experiments and t-SNE visualization analysis verified the effectiveness of the modules. Gradient-weighted Class Activation Mapping generated EEG topography maps visualized the brain regions of interest of the model, and the experimental results show that the decision-making process of the model in the MI-EEG decoding task is in accordance with neurophysiological principles. The within-subject average classification accuracies on the BCI Competition IV-2a and IV-2b public datasets are 88.08% and 90.47%, respectively, and the subject-independent average classification accuracies reach 68.79% and 80.86%, achieving state-of-the-art decoding accuracies and providing new possibilities for practical applications of BCI.}, archive = {J_ACCESS}, author = {Qiulei Han and Yan Sun and Ze Song and Hongbiao Ye and Tingwei Chen and Jian Zhao}, doi = {10.1109/ACCESS.2025.3579003}, journal = {IEEE Access}, pages = {103497-103511}, shortjournal = {IEEE Access}, title = {A multi-scale adaptive fusion network: End-to-end interpretable small-sample classifier for motor imagery EEG}, volume = {13}, year = {2025}, }
• (2025). Unveiling facial fidelity: A novel approach to synthesizing high-quality face images using generative adversarial networks. ACCESS, 13, 103482-103496. (www)

  @article{ , abstract = {In the field of computer vision, generating realistic images from sketches has garnered considerable attention due to its wide-ranging applications in art, design, and facial recognition systems. With increasing demand for more sophisticated image synthesis, advanced methodologies are essential to bridge the gap between sketches and high-fidelity images. This study proposes a novel methodology that comprises two key modules: the Generator and the Discriminator. The Generator is designed to transform sketches into images, while the Discriminator extracts 512-dimensional face encodings from both generated and ground truth images. Leveraging pre-trained ResNet-101, the Discriminator uses Mean Squared Error (MSE) loss to compare the face encodings, reinforcing the Generator to produce images with face encodings closely resembling the ground truth. The research utilizes two datasets: Labeled Faces in the Wild (LFW) for training the ResNet-101 CNN model for face recognition, and CelebFaces Attributes Dataset (Celeb-A) for training the sketch-to-image generator. To assess the quality of generated images, Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) evaluation measures are used. The SNR steadily increases over the epochs, indicating enhanced performance, and reaches a peak value of 0.334. This indicates the model’s efficacy in reducing noise and enhancing signal strength, leading to improved overall performance. The highest achieved PSNR value of 0.334 signifies an enhancement in the quality of the reconstructed signal. In particular, the proposed model exceeds previous models, with accuracies of 99.9% on the LFW dataset and 99.4% on the Celeb-A dataset, respectively.}, archive = {J_ACCESS}, author = {Modafar Ati and Muhammad Ahmed Hassan and Muhammad Usman Ghani}, doi = {10.1109/ACCESS.2025.3578940}, journal = {IEEE Access}, pages = {103482-103496}, shortjournal = {IEEE Access}, title = {Unveiling facial fidelity: A novel approach to synthesizing high-quality face images using generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). Dual-arm mobile manipulator teleoperation with coupled rate-position mapping under time-varying delays. ACCESS, 13, 103463-103481. (www)

  @article{ , abstract = {Bilateral teleoperation of dual-arm mobile manipulators presents considerable challenges. These include increased kinematic redundancy, coordination complexity, and sensitivity to time-varying communication delays. Most existing approaches control each arm independently using leader position-based mappings; however, the teleoperation of mobile dual-arm systems mechanically coupled through an articulated torso remains largely unexplored. Unlike previous studies that primarily emphasize controller tuning, this work emphasizes the critical role of command mapping design in achieving effective teleoperation. Specifically, it analyzes a coupled rate/nonlinear-position mapping strategy that enables coordinated motion in a torso-equipped dual-arm mobile manipulator operated through a dual-leader haptic interface. The proposed framework extends existing single-arm teleoperation schemes by introducing a coupled reference generation mechanism, where the reference for the secondary arm depends on both the position of the secondary leader and the rate-position-type reference of the primary arm. A two-stage stability analysis, based on the Lyapunov-Krasovskii criterion and numerical simulations, is conducted to determine the parameter conditions required to ensure bounded coordination errors in the presence of time-varying communication delays. Preliminary human-in-the-loop tests in dual-arm pick-and-place tasks support the theoretical findings and demonstrate a clear dependence of motion stability on the structure of the command mapping. The results provide foundational insights into the joint optimization of control and mapping strategies and offer practical guidelines for advancing teleoperation in complex, real-world scenarios.}, archive = {J_ACCESS}, author = {Fernando A. Chicaiza and Emanuel Slawiñski and Vicente Mut}, doi = {10.1109/ACCESS.2025.3577009}, journal = {IEEE Access}, pages = {103463-103481}, shortjournal = {IEEE Access}, title = {Dual-arm mobile manipulator teleoperation with coupled rate-position mapping under time-varying delays}, volume = {13}, year = {2025}, }
• (2025). Frequency enhanced dynamic graph convolutional networks for traffic flow forecasting. ACCESS, 13, 103451-103462. (www)

  @article{ , abstract = {With rapid urbanization and technological advancements, increasing population density has posed significant challenges for urban traffic management. Traffic flow forecasting is essential for optimizing traffic signals, regulating traffic flow, and improving overall transportation efficiency. Recent advancements in deep learning, particularly Graph Convolutional Networks (GCNs), offer more effective solutions for traffic forecasting. However, existing approaches face challenges in modeling traffic trends, periodicity, and randomness. To address these issues, this paper proposes a Frequency-enhanced dynamic Graph Convolutional Network (FGCN) that incorporates sequence decomposition and reconstruction to extract periodic and trend components. Additionally, the proposed method integrates the Fourier transform into the loss function to mitigate label autocorrelation and improve long-term forecasting accuracy. Furthermore, this paper employs a dynamic graph convolution method to separately model spatial and temporal dependencies, capturing traffic flow variations more effectively. Experimental results on four open datasets demonstrate that FGCN not only outperforms state-of-the-art methods but also provides interpretable insights into the dynamic spatial relationships between road segments, while significantly improving forecasting accuracy.}, archive = {J_ACCESS}, author = {Liyuan Wang and Jiafeng Zhuang and Shuo Ma and Hai Lin}, doi = {10.1109/ACCESS.2025.3579334}, journal = {IEEE Access}, pages = {103451-103462}, shortjournal = {IEEE Access}, title = {Frequency enhanced dynamic graph convolutional networks for traffic flow forecasting}, volume = {13}, year = {2025}, }
• (2025). Dynamic split computing framework for multi-task learning models: A deep reinforcement learning approach. ACCESS, 13, 103439-103450. (www)

  @article{ , abstract = {Split computing has emerged as a promising approach to alleviate the resource constraints of IoT devices by offloading computation to edge servers. However, conventional split computing schemes fail to effectively support multi-task learning (MTL) models, which feature a shared backbone and multiple task-specific branches. These structural characteristics, combined with the variability of network conditions, require a more flexible and adaptive offloading strategy. In this paper, we propose a deep reinforcement learning (DRL)-based dynamic split computing framework (D2SCF) tailored for the MTL model. In D2SCF, the MTL model is first split into a head model of the shared layers and several tail models for the task-specific layers (i.e., individual output branches). Subsequently, an IoT device makes decisions regarding the split computing of the head and tail models. To minimize the average task completion time across multiple tasks and the energy consumption of the IoT device, we formulate a Markov decision process problem. The formulated problem is solved using a model-free DRL algorithm (i.e., a deep deterministic policy gradient). Evaluation results demonstrate that D2SCF reduces the average task completion time by more than 50% compared to conventional split computing schemes, while maintaining low energy consumption on the IoT device. Moreover, it consistently outperforms baseline methods across heterogeneous network settings, confirming its robustness in dynamic environments.}, archive = {J_ACCESS}, author = {Haneul Ko and Sangwon Seo and Sangheon Pack}, doi = {10.1109/ACCESS.2025.3578009}, journal = {IEEE Access}, pages = {103439-103450}, shortjournal = {IEEE Access}, title = {Dynamic split computing framework for multi-task learning models: A deep reinforcement learning approach}, volume = {13}, year = {2025}, }
• (2025). Multi-objective scheduling for green flexible assembly job-shop system via multi-agent deep reinforcement learning with game theory. ACCESS, 13, 103417-103438. (www)

  @article{ , abstract = {In discrete manufacturing systems, production scheduling techniques are urgently needed with lacking dynamic response-ability, inferior real-time decision-making, and sharply increasing uncertainty in the production process. In this regard, this paper presents the flexible assembly job-shop scheduling (FAJS) problem that incorporates job shop and assembly shop. And, a multi-agent deep reinforcement learning (MA-DRL) system with game theory is proposed to solve the proposed FAJS problem aiming at minimizing the Makespan, total energy consumption, and human factor’s comfort. A mathematical model is formulated to describe the FAJS problem, which then is translated into a Markov Decision Process (MDP) where an agent directly selects behavioral policies according to the processing state of the current decision point. The processing state feature data that uses a deep convolutional neural network to fit the value function is extracted from three matrices including the processing time matrix, task assignment Boolean matrix, and an adjacency matrix. Simple Constructive Heuristics (SCH) are used as candidate action for scheduling decisions. For multi-objective optimization into a reward strategy problem, a game model combining Nash equilibrium and Pareto optimality is established to obtain an objective unification solution. A multi-agent deep deterministic policy gradient (MA-DDPG) framework is designed to train the proposed MA-DRL model. The case ‘MK’ with four assembly constraints is formed as the novel datasets. These datasets and a case of the plunger pump production workshop scheduling scenario are designed to analyze the basic performance of this algorithm. The results show that the proposed algorithm compares with other single-indicator optimal algorithms with 9.47 per cent, 12.66 per cent and 22.86 per cent respectively for the three-evaluation metrics (GD, and HV), which contributed to solving the practical production problem of the manufacturing industry.}, archive = {J_ACCESS}, author = {Xiao Wang and Zhongyuan Liang and Peisi Zhong and Dongmin Li and Hongqi Li and Mei Liu}, doi = {10.1109/ACCESS.2025.3577044}, journal = {IEEE Access}, pages = {103417-103438}, shortjournal = {IEEE Access}, title = {Multi-objective scheduling for green flexible assembly job-shop system via multi-agent deep reinforcement learning with game theory}, volume = {13}, year = {2025}, }
• (2025). Multimodal phishing detection on social networking sites: A systematic review. ACCESS, 13, 103405-103416. (www)

  @article{ , abstract = {Phishing is one of the most common cyberattacks, with the number of incidents increasing annually. Significant research interests have been generated in phishing emails, URLs, and websites over the past decade. Phishing attackers often target easy prey, and phishing on social networking sites (SNS) is increasing due to the popularity of easier communication using short text, images, and voice. Multimodal features can be used for phishing, yet there is relatively little research on phishing on SNS using multimodal features and models. This systematic review report, guided by the PRISMA and SEGRESS, aimed to uncover the techniques used for multimodal phishing detection. A comprehensive literature search in Scopus, Web of Science, IEEE Xplore, and ACM Digital Library was conducted, including studies published in English from 2018 to 2025. A total of 20 studies are included in the study out of 74 records returned. Twelve articles were included from the references of the selected studies. The study reveals that HTML content, URLs, and visual elements are used as multimodal features to classify phishing URLs and websites. These studies used deep learning models, including CNN, RNN, LSTM, and MLP, which produced promising results. However, challenges persist related to resource constraints, adversarial attacks, data quality and availability, false positives and negatives, and integration with existing SNS frameworks. These multimodal studies show promise, but require adaptation to SNS-based phishing attacks.}, archive = {J_ACCESS}, author = {Tandin Wangchuk and Tad Gonsalves}, doi = {10.1109/ACCESS.2025.3579584}, journal = {IEEE Access}, pages = {103405-103416}, shortjournal = {IEEE Access}, title = {Multimodal phishing detection on social networking sites: A systematic review}, volume = {13}, year = {2025}, }
• (2025). A review on energy communities development, opportunities and challenges in germany, spain, and italy. ACCESS, 13, 103385-103404. (www)

  @article{ , abstract = {The European Union introduced key directives regarding the use of the Clean Energy Package for All Europeans to accelerate the transition to clean energy in 2019. These directives, the Renewable Energy Directive II and the Internal Energy Market Directive, significantly transformed the regulatory and operational framework of energy communities (ECs) in European countries. This paper presents a systematic review of how Germany, Spain, and Italy implemented these directives by identifying their operational and management impacts on ECs. This analysis highlights how national policy differences shape ECs development. By applying VOSviewer as a bibliometric analysis tool and NVivo as content analysis software, we evaluated the determining factors and barriers influencing the development of ECs to provide innovative insights. The study focuses on key aspects such as grid management, citizen participation, technological advancements, and flexibility in energy systems. It then assesses both the opportunities and challenges these countries face. The findings of our study show that national strategies enhanced grid efficiency, participatory mechanisms, and technological integrations, while they also faced grid management, flexibility, and regulatory barriers. This research can guide policymakers and stakeholders to refine governance frameworks and operational practices, enhancing the scalability and resilience of ECs across Europe.}, archive = {J_ACCESS}, author = {Saeed Khorrami and Maria Carmen Falvo and Massimo Pompili}, doi = {10.1109/ACCESS.2025.3579336}, journal = {IEEE Access}, pages = {103385-103404}, shortjournal = {IEEE Access}, title = {A review on energy communities development, opportunities and challenges in germany, spain, and italy}, volume = {13}, year = {2025}, }
• (2025). A reinforcement learning approach to personalized asthma exacerbation prediction using proximal policy optimization. ACCESS, 13, 103373-103384. (www)

  @article{ , abstract = {Asthma exacerbation prediction is critical for preventing severe respiratory complications and improving patient outcomes. Traditional predictive models rely on static machine learning approaches, which lack adaptability to evolving patient conditions and environmental changes. This study proposes a reinforcement learning (RL)-based model using Proximal Policy Optimization (PPO) to enhance personalized early prediction of asthma exacerbation attacks. Unlike conventional models, PPO dynamically refines its policy through iterative learning, allowing for real-time, adaptive risk assessment based on personalized patient data and environmental factors. The PPO model was trained on a comprehensive asthma dataset, integrating demographic, environmental, lifestyle, and Asthma Control Test (ACT) scores. The model achieved 96.60% accuracy, 95.79% precision, 96.65% recall, and 95.92% F1-score, outperforming baseline RL algorithms such as Deep Q-Learning (92.21% accuracy), Advantage Actor-Critic (94.34% accuracy), and Trust Region Policy Optimization (95.12% accuracy). Comparative evaluation with existing studies further demonstrated PPO’s superior recall and sensitivity to exacerbation cases, making it highly suitable for personalized asthma management. RL performance metrics confirmed the model’s stability and convergence, including cumulative rewards, policy, and value loss. These findings highlight PPO’s potential for real-time clinical integration, enabling continuous patient monitoring and proactive intervention strategies. Future work will focus on training the model on larger, multi-source datasets to improve generalization across diverse populations. Additionally, further research will explore deployment in telemedicine, integration with wearable health technologies, and adaptability to other chronic diseases, ensuring scalability and real-world applicability in personalized healthcare systems.}, archive = {J_ACCESS}, author = {Dahiru Adamu Aliyu and Emelia Akashah Patah Akhir and Maryam Omar Abdullah Sawad and Jameel Shehu Yalli and Yahaya Saidu}, doi = {10.1109/ACCESS.2025.3579198}, journal = {IEEE Access}, pages = {103373-103384}, shortjournal = {IEEE Access}, title = {A reinforcement learning approach to personalized asthma exacerbation prediction using proximal policy optimization}, volume = {13}, year = {2025}, }
• (2025). Personalized persuasion by shaping beliefs about the multidimensional features of objects. ACCESS, 13, 103358-103372. (www)

  @article{ , abstract = {Personalization consistently improves the impact of persuasive agents, recommender systems, and nudge interventions. However, most implementations still rely on coarse-grained demographic or personality attributes such as age, sex, or broad personality traits. Ultimate personalization instead tailors messages to an individual’s nuanced beliefs and utilities. Multiattribute utility theory formalizes personal utility as the sum of utilities over an object’s features, highlighting that negative beliefs about even a few features can suppress overall acceptance. Leveraging multiattribute utility theory, we test whether countering the features of each individual value can increase the acceptance of a complex technology—fully autonomous vehicles. For each participant (N = 197), a belief-manipulation algorithm identified the five fully autonomous vehicle features with the lowest subjective utilities and generated counterpropositions during a semistructured dialog with a virtual agent. Pre- and postdialog measures captured (i) the monetary valuation of fully autonomous vehicles, (ii) the desire to ride, and (iii) the perceived social obligation to accept fully autonomous vehicles. Two comparison conditions served as controls: a nonpersonalized counterargument condition and a baseline small-talk condition in which the agent discussed everyday topics unrelated to autonomous vehicles. The results showed that the proposed method improved the social obligation to accept fully autonomous vehicles more than the nonpersonalized method and the baseline method did but had no effect on the monetary value or the desire to ride them. This suggests that personalized belief manipulation may not be effective in enhancing the “want to” desire or utility of an object but may only improve the thought of “ought to do”. These findings have broad implications for understanding belief manipulation across diverse domains where acceptance involves complex cognitive and motivational processes, including healthcare decision-making, educational engagement, and sustainable behavior adoption.}, archive = {J_ACCESS}, author = {Kazunori Terada and Yasuo Noma and Masanori Hattori}, doi = {10.1109/ACCESS.2025.3575369}, journal = {IEEE Access}, pages = {103358-103372}, shortjournal = {IEEE Access}, title = {Personalized persuasion by shaping beliefs about the multidimensional features of objects}, volume = {13}, year = {2025}, }
• (2025). An extensive investigation on intelligent-based control techniques for the performance improvement in multilevel inverters. ACCESS, 13, 103329-103357. (www)

  @article{ , abstract = {Multilevel inverters have become an essential technology in modern power electronics, offering benefits like reduced harmonic distortion with lower electromagnetic interference and enhanced efficiency applicable to electric vehicles, industrial drives, and renewable energy systems. Various considerations, such as fault tolerance, harmonic mitigation, and switching techniques, complicate the control of multilevel inverters. Soft computing approaches like fuzzy logic, artificial neural networks, and bio-inspired optimization techniques have shown promise in addressing these challenges. Recent developments have highlighted the significance of intelligent computational approaches in improving the inverter’s efficiency and recent optimization techniques using stellar motions are proposed and compared to eliminate selective harmonics in cascaded H-bridge multilevel inverters. Among these, big bang and big crunch outperform the other optimization techniques in terms of reduction in THD. This work will delve into the application of soft computing techniques in various sectors, thereby enabling researchers and industrial professionals to know the current trends adopted in multilevel inverters.}, archive = {J_ACCESS}, author = {N. Subramanian and Albert Alexander Stonier}, doi = {10.1109/ACCESS.2025.3576380}, journal = {IEEE Access}, pages = {103329-103357}, shortjournal = {IEEE Access}, title = {An extensive investigation on intelligent-based control techniques for the performance improvement in multilevel inverters}, volume = {13}, year = {2025}, }
• (2025). A 3D-printed, metallic-free, water-based metamaterial for tunable S-band applications. ACCESS, 13, 103319-103328. (www)

  @article{ , abstract = {In this paper, a tunable, water-based S-band metamaterial realized with 3D printing technology and not requiring metallic materials is presented. The proposed structure is capable of reconfiguring between Artificial Magnetic Conductor (AMC) and Radar Absorbing Material (RAM) behaviors, two electromagnetic solutions required in a wide range of applications. The main challenge herein addressed lies in achieving both AMC and RAM configurations by conceiving a 35 mm thick single structure with tunable properties without employing metals and resorting only to Polylactic Acid (PLA) and water, opportunely loaded with sodium chloride (NaCl). The dielectric characteristic of the PLA has been evaluated using a waveguide technique finding a dielectric permittivity of 2.61 and a tangent loss of 0.0327. A multilayer metamaterial structure is designed by exploiting the transmission line equivalent circuit modeling. The switching capability is ensured by controlling the salted water filling of the most external PLA cavity. Accurate numerical simulations carried out with a commercial full-wave software and experimental measurements performed by characterizing the 3D printed prototype within a waveguide demonstrated an excellent agreement and confirmed the validity of the design process. For a normal incidence, the obtained AMC bandwidth is approaching 60% within the S-band, with a maximum loss peak of 3 dB, while the RAM shows a -10 dB absorption fractional bandwidth of 42% centered at 2.8 GHz. The absence of metallic materials in the proposed solution represents a significant advantage compared to traditional AMC/RAM configurations including grounded metallic Frequency Selective Surface patterns in all the applications requiring a high angle stability, where interferences with other devices must be avoided or it is preferrable an environmentally friendly approach.}, archive = {J_ACCESS}, author = {Edoardo Giusti and Danilo Brizi and Agostino Monorchio}, doi = {10.1109/ACCESS.2025.3579239}, journal = {IEEE Access}, pages = {103319-103328}, shortjournal = {IEEE Access}, title = {A 3D-printed, metallic-free, water-based metamaterial for tunable S-band applications}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient coverage of rechargeable UAV networks in forest scenarios. ACCESS, 13, 103309-103318. (www)

  @article{ , abstract = {When conducting scientific research or emergency rescue in the forest, it is essential to establish a seamless communication network to ensure the safety of users. This paper investigates the energy efficiency coverage problem of rechargeable seamless uncrewed aerial vehicle (UAV) networks in forest scenarios. Several charging stations are distributed around the forest to provide charging services for UAVs. We first designed an energy efficiency objective function based on the finite difference time domain method (FDTD) radio wave propagation model, and the energy efficiency coverage problem is formulated as a constrained optimization problem. Secondly, the problem is reduced to two subproblems, the coverage maximization problem and the energy minimization problem. A two-stage joint optimization algorithm is proposed based on a binary logarithmic learning algorithm and a simulated annealing algorithm to solve the problem. The simulation proves that the proposed algorithm outperforms the compared algorithms.}, archive = {J_ACCESS}, author = {Chi Zheng and Ting Su and Jianming Liu and Kuo Chi and Yongqin Yang}, doi = {10.1109/ACCESS.2025.3576274}, journal = {IEEE Access}, pages = {103309-103318}, shortjournal = {IEEE Access}, title = {Energy-efficient coverage of rechargeable UAV networks in forest scenarios}, volume = {13}, year = {2025}, }
• (2025). Optimized power allocation algorithms for UAV-based agricultural systems. ACCESS, 13, 103292-103308. (www)

  @article{ , abstract = {Agriculture 5.0 is an emerging concept that builds on the integration of cutting-edge technologies to create smarter, more sustainable, and efficient farming systems. Integrating Unmanned Aerial Vehicles (UAVs) is a key enabler of precision agriculture, allowing real-time data collection, analysis, and decision-making to boost farming practices. UAVs provide a crucial function in this ecosystem, contributing to achieving Sustainable Development Goals (SDGs). Nevertheless, these vehicles encounter significant computing power, throughput, real-time delay, and battery capacity challenges. Addressing these challenges requires innovations in network design, power management, and communication technologies. To this end, we propose to deploy 6G’s Massive MIMO (Multiple Input Multiple Output) technology within a three-layered architecture for UAV-based smart agricultural systems. Our study focuses on the power allocation problem in both downlink and uplink transmissions, aiming to maximize throughput while ensuring fairness among different terminals. To achieve this, we deploy a downlink fairness-based algorithm (DFA) and a downlink hybrid algorithm (DHA). The first approach ensures fairness by optimizing the throughput of the weakest IoT terminal in the network, while the second approach balances throughput and fairness among IoT terminals. Additionally, we employ an uplink heuristic power control algorithm (UHPCA) to determine the optimal power allocation. The numerical results indicate that the proposed allocation algorithms achieve a better trade-off between throughput and fairness compared to conventional power allocation strategies in the literature.}, archive = {J_ACCESS}, author = {Ayman Massaoudi and Walid Aydi}, doi = {10.1109/ACCESS.2025.3578898}, journal = {IEEE Access}, pages = {103292-103308}, shortjournal = {IEEE Access}, title = {Optimized power allocation algorithms for UAV-based agricultural systems}, volume = {13}, year = {2025}, }
• (2025). Linearity and noise evaluation compound semiconductor-based highly sensitive junctionless gate-all-around FET biosensor. ACCESS, 13, 103282-103291. (www)

  @article{ , abstract = {A nanocavity-embedded nanowire junctionless field effect transistor (NW JLFET) biosensor based on III-V compound semiconductors in GaAs/InGaN/InGaP is described in detail in this article. By interacting with biological molecule, the compound semiconductor biosensor changes the electrostatic characteristics that are crucial for the detection process. We investigate important parameters such as drain current, transconductance, and linearity in terms of their sensitivity to change the dielectric constants and their relationship. InGaAs, InGaN and InGaP NW JLFETs were further investigated to optimize their material properties for use as biosensors. Furthermore, this study evaluates the effect of the dielectric constants of biomolecules on VIP2, VIP3, IIP3, GM2, GM3, and IMD3 of all three devices. The benchmarking is performed to provide sensitivity of $7.8\times 10 ^{5}$ , ION/IOFF ratio of $1.31\times 10 ^{8}$ , and SS of 61 mV/dec when the cavity was fully filled of biomolecule for the InGaAs NW JLFET-based biosensor (D1).}, archive = {J_ACCESS}, author = {Jeevanarao Batakala and Rudra Sankar Dhar and Kuleen Kumar and Alexey Y. Seteikin and Amit Banerjee and I. G. Samusev}, doi = {10.1109/ACCESS.2025.3577789}, journal = {IEEE Access}, pages = {103282-103291}, shortjournal = {IEEE Access}, title = {Linearity and noise evaluation compound semiconductor-based highly sensitive junctionless gate-all-around FET biosensor}, volume = {13}, year = {2025}, }
• (2025). Modeling sports engagement: A game refinement theory perspective on game length and scoring frequency. ACCESS, 13, 103264-103281. (www)

  @article{ , abstract = {Game structure plays a crucial role in shaping engagement for both players and spectators, with elements such as game length, scoring frequency, and outcome uncertainty significantly influencing the emotional and cognitive experience. This paper investigates how these structural components impact engagement across three globally popular sports, One Day International (ODI) cricket, Twenty20 (T20) cricket, and soccer. Using Game Refinement (GR) theory and the Motion in Mind (MiM) framework, we quantitatively model engagement dynamics by analyzing the game length, scoring patterns, uncertainty resolution, and psychological pacing. Our results reveal that ODI cricket falls within the optimal GR zone ( $\text {GR} \in \{ 0.07, 0.08\}$ ), indicating a balanced structure that supports fairness and sustained engagement. T20 cricket, while exceeding the optimal GR range, achieves high emotional intensity due to its rapid scoring and momentum shifts. Soccer, in contrast, exhibits a low scoring rate and a GR value above the ideal range, reflecting higher unpredictability and reduced fairness. To optimize soccer’s game length and scoring frequency, we propose a hypothetical 60-minute match model. Our findings demonstrate that reducing game length while scaling scoring frequency in soccer leads to an ideal GR value ( $\approx 0.077$ ), aligning structural fairness with emotional pacing. This study offers a novel computational and theoretical lens for evaluating and refining sports formats, with broader implications for enhancing engagement across competitive sports.}, archive = {J_ACCESS}, author = {Muhammad Numan and Hiroyuki Iida and Mohd Nor Akma Khalid}, doi = {10.1109/ACCESS.2025.3580196}, journal = {IEEE Access}, pages = {103264-103281}, shortjournal = {IEEE Access}, title = {Modeling sports engagement: A game refinement theory perspective on game length and scoring frequency}, volume = {13}, year = {2025}, }
• (2025). NeuroFusion-trans: A novel transformer-based EEG-EMG fusion model for assistive robotics. ACCESS, 13, 103245-103263. (www)

  @article{ , abstract = {User intent recognition from multimodal neurophysiological signals, particularly electroencephalography (EEG) and electromyography (EMG), is critical for enhancing human-machine interaction in assistive robotics. Recent advances in neurophysiological signal processing have enabled enhanced user intent recognition for assistive robotics and human-machine interfaces. However, achieving high accuracy and real-time adaptability in electromyography (EMG) and electroencephalography (EEG)-based gesture recognition remains challenging due to temporal misalignment, weak cross-modality fusion, and lack of adaptive learning. This paper proposes NeuroFusion-Trans, a novel transformer-based framework that improves EEG-EMG gesture recognition by improving temporal resolution, using cross-modality attention, and integrating adaptive online learning. Temporal resolution enhancement ensures dynamic EEG-EMG synchronization for improved signal alignment. The cross-modality attention mechanism captures interdependencies between EEG and EMG signals, leading to more accurate intent classification. Adaptive online learning enables real-time personalization by dynamically adjusting to user-specific variations. The model is evaluated on two publicly available EEG-EMG upper-limb gesture datasets: Dataset 1 (5,296 for training, 1,324 for validation) and Dataset 2 (5,276 for training, 1,304 for validation). NeuroFusion-Trans achieves state-of-the-art performance, with an accuracy of 97% and 96% and Cohen’s Kappa of 0.97 and 0.95 after online adaptation, significantly outperforming baseline models such as CNN-LSTM, GRU, and LSTMNet. Ablation studies reveal that removing the cross-modality attention mechanism reduces accuracy by 6.1%, underscoring its importance in exploiting the EEG-EMG dependencies. Turning off synchronization leads to a 6.7% performance drop, demonstrating the necessity of real-time learning for robust intent recognition. Furthermore, NeuroFusion-Trans enhances EEG-EMG synchronization, achieving synchronization scores of 0.7471 on Dataset 1 and 0.7687 on Dataset 2, confirming its effectiveness in temporal alignment. The proposed approach demonstrates robust generalization, improved responsiveness, and potential applications in real-time assistive robotics. The implementation details and source code can be found at: https://github.com/madibabaiasl/NeuroFusion-Trans}, archive = {J_ACCESS}, author = {Tipu Sultan and Guangping Liu and Pascal Sikorski and Samah Alshathri and Walid El-Shafai and Madi Babaiasl}, doi = {10.1109/ACCESS.2025.3577996}, journal = {IEEE Access}, pages = {103245-103263}, shortjournal = {IEEE Access}, title = {NeuroFusion-trans: A novel transformer-based EEG-EMG fusion model for assistive robotics}, volume = {13}, year = {2025}, }
• (2025). LoRA-adv: Boosting text classification in large language models through adversarial low-rank adaptations. ACCESS, 13, 103234-103244. (www)

  @article{ , abstract = {Low-rank adaptation (LoRA), a paradigm bridging the gap between large language models and fine-tuning, has demonstrated effectiveness across various natural language processing tasks. The LoRA algorithm updates only a small number of model parameters, significantly reducing the consumption of computational resources. Although the LoRA algorithm achieves feasible performance, it is susceptible to the influence of training samples. To enhance the stability of the LoRA algorithm and improve the performance of classification models, this paper introduces a novel adversarially enhanced LoRA algorithm, named LoRA-Adv. Specifically, in the LoRA-Adv algorithm, we leverage carefully designed adversarial perturbations to expand the diversity of training samples. Minimizing the adversarial training loss for both normal and adversarial samples significantly reinforces the model’s robustness. To verify the performance of the LoRA-Adv algorithm, multiple state-of-the-art large language models were employed, and all experimental results confirmed that LoRA-Adv significantly enhances the classification performance of the models. Therefore, the LoRA-Adv algorithm represents a significant advancement in fine-tuning large language models, enhancing both their stability and classification accuracy.}, archive = {J_ACCESS}, author = {Hong Ye and Xialin Xie and Fenlong Xie and Jun Zuo and Chunyan Bu}, doi = {10.1109/ACCESS.2025.3579539}, journal = {IEEE Access}, pages = {103234-103244}, shortjournal = {IEEE Access}, title = {LoRA-adv: Boosting text classification in large language models through adversarial low-rank adaptations}, volume = {13}, year = {2025}, }
• (2025). Behavior modeling-based QoS preserving model with intelligent trust ranking for 6G. ACCESS, 13, 103220-103233. (www)

  @article{ , abstract = {In the generation-next WCN (Wireless communication Network), trust assessment is a key criterion for any node to enable QoS communication. Compared to previous versions, 6G is ready to serve a large number of users who are seeking a variety of novel use cases that are power-hungry. Hence, a power-optimized network is essential. For the QoS service within the network, the trustworthiness of nodes by assigning trust rank, particularly within the networks’ platforms for healthcare and defence, is crucial. In a 6G network, there will be a greater emphasis on providing users with a high-quality experience, including data rate and latency. Therefore, compared to previous generation networks, latency in a 6G network should be extremely low and data rate extremely high which can be achieved by providing trust ranking to the user. In this paper, an RS data set has been prepared using a trust ranking behavior model for all users presented in a proposed layer-based wireless network. Machine learning is used to provide trust ranking to all users, where different ways of communication are present in another layer. There are three trust rankings in the model. The maximum level of trust is shown by the third trust rank, while the least amount of trust is indicated by the first trust rank. The particular services provided to the appropriate node depend on the level of trust that has been acquired. The suggested SVM (Support Vector Machine) based prediction model preserves the three-way trade-off between QoS, accuracy, and processing time. The outcomes of the simulation show that potential consumers are intelligently identified using the suggested SVM-based trust-ranking approach for QoS network communication.}, archive = {J_ACCESS}, author = {Santosh Sharma and Rakesh Kumar Jha and Anil Kumar}, doi = {10.1109/ACCESS.2025.3568151}, journal = {IEEE Access}, pages = {103220-103233}, shortjournal = {IEEE Access}, title = {Behavior modeling-based QoS preserving model with intelligent trust ranking for 6G}, volume = {13}, year = {2025}, }
• (2025). Enhancing arrhythmia diagnosis through ECG deep learning classification deploying and augmented reality 3D heart visualization and interaction. ACCESS, 13, 103198-103219. (www)

  @article{ , abstract = {Cardiovascular diseases (CVDs) continue to be a leading cause of mortality globally, highlighting the urgent need for timely and accurate diagnosis. Electrocardiography (ECG) is a vital diagnostic tool for detecting and monitoring various heart conditions by analysing the heart’s electrical activity; however, manually identifying ECG features and classifying heartbeats is a complex and time-consuming process that demands significant expertise. To address this challenge, we have developed ArythmiAR, a novel system that integrates Convolutional Neural Networks (CNN) with Augmented Reality (AR) to enable interactive diagnosis with 3D visualisation and real-time engagement. ArythmiAR offers several key innovations: deep learning-based ECG classification for precise arrhythmia detection, 3D heart modelling and assembly for detailed visualisation, an AR interface for deploying CNN models, 3D localisation of heart sub-regions responsible for arrhythmia anomalies, and enhanced 3D visualisation and interaction capabilities. Our study explores various ECG classification techniques, employing data rebalancing strategies to enhance model performance, with a particular focus on Multilayer Perceptron (MLP) and CNN models, which demonstrated highly competitive results on the PhysioNet MIT-BIH Arrhythmia dataset, achieving an accuracy of 99.07% with the MLP model. Remarkably, this work also involves deploying the ECG classification deep learning model within an AR environment, presenting a prototype for augmented rendering that allows users to localise, visualise, and interact with specific heart regions responsible for arrhythmias. This platform empowers medical professionals to make more accurate diagnoses and develop effective treatment strategies, thereby improving overall patient care.}, archive = {J_ACCESS}, author = {Kahina Amara and Mohamed Amine Guerroudji and Oussama Kerdjidj and Nadia Zenati and Shadi Atalla and Naeem Ramzan}, doi = {10.1109/ACCESS.2025.3576243}, journal = {IEEE Access}, pages = {103198-103219}, shortjournal = {IEEE Access}, title = {Enhancing arrhythmia diagnosis through ECG deep learning classification deploying and augmented reality 3D heart visualization and interaction}, volume = {13}, year = {2025}, }
• (2025). A survey of autonomous robotic ultrasound scanning systems. ACCESS, 13, 103178-103197. (www)

  @article{ , abstract = {This review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems. Traditional ultrasound imaging depends on manual probe manipulation, which introduces operator variability, physical strain, and limitations in accessibility. To address these challenges, this review investigates recent advancements in autonomous, semi-autonomous, and teleoperated robotic ultrasound systems by analyzing over 60 publications, including key developments from 2022 to 2025. Our survey reveals a growing adoption of cobot-based solutions equipped with 6-DOF force/torque sensors and RGB-D vision systems for precise probe positioning. Notably, several systems now integrate reinforcement learning, image-guided visual servoing, and real-time feedback loops to enable intelligent trajectory planning and adaptive force control. However, we identify critical gaps in the literature: surface-parallel force and torque components are often ignored in control models, limiting the accuracy of probe orientation and tissue coupling. Furthermore, real-time ultrasound image feedback is rarely used for path optimization, despite its importance in enhancing image quality and diagnostic reliability. This review emphasizes the need for future systems to integrate multi-modal sensing, adaptive control, and real-time image quality assessment to achieve robust, generalizable robotic ultrasound workflows.}, archive = {J_ACCESS}, author = {Khushboo Munir and Abdullah F. Al-Battal and Ammar Alsheghri and Harald Becher and Michelle Noga and Kumaradevan Punithakumar}, doi = {10.1109/ACCESS.2025.3574464}, journal = {IEEE Access}, pages = {103178-103197}, shortjournal = {IEEE Access}, title = {A survey of autonomous robotic ultrasound scanning systems}, volume = {13}, year = {2025}, }
• (2025). LPTN-based real-time stator hotspot temperature estimation for enhanced thermal management in high-performance PMSMs. ACCESS, 13, 103166-103177. (www)

  @article{ , abstract = {Thermal management is critical for high-performance electric motor drives with compact design and extreme overload capability, resulting in significant temperature gradients within the stator winding. Due to limited access to the winding hotspot, conventional temperature sensors cannot directly measure the peak hotspot temperature, which can fluctuate rapidly during operation and often exceeds accessible measurements. This constraint requires high thermal safety margins, limiting, in practice, the motor’s peak torque potential. This work deals with hotspot temperature monitoring and prediction for synchronous motor drives. This paper presents a real-time hotspot temperature monitoring solution using an advanced Lumped Parameter Thermal Network (LPTN) model. The proposed LPTN is analytically solvable, enabling efficient implementation on automotive-grade microcontrollers. Parameters are calibrated through simple experimental tests, eliminating the need for detailed motor geometry knowledge. Experimental validation on a high-performance traction PMSM under real driving conditions shows that the model achieves a residual estimation error of approximately 5° C, demonstrating reliable tracking of the winding hotspot during severe thermal transients.}, archive = {J_ACCESS}, author = {Paolo Pescetto and Gaetano Dilevrano and Gianmario Pellegrino and Aldo Boglietti}, doi = {10.1109/ACCESS.2025.3578802}, journal = {IEEE Access}, pages = {103166-103177}, shortjournal = {IEEE Access}, title = {LPTN-based real-time stator hotspot temperature estimation for enhanced thermal management in high-performance PMSMs}, volume = {13}, year = {2025}, }
• (2025). YOLO-CSMD: Integrating improved convolutional techniques for manhole cover defect detection. ACCESS, 13, 103155-103165. (www)

  @article{ , abstract = {Recent detection algorithms for manhole cover defects exhibit limited detection capabilities, which frequently encounter the issue of missed detection. To address this issue, this paper proposes an improved detection algorithm based on YOLOv8, called YOLO-CSMD. First, Convolution State Space Module (ConvSSM) was introduced into the Neck network to improve our model’s sequence modeling capabilities. Second, the Spatial Texture Transform Module (STTM) is proposed to improve the ability of texture processing and feature fusion through the lightweight convolution and efficient multiscale attention module. Finally, the experimental results of YOLO-CSMD, using YOLOv8 as the baseline on our self-build dataset which including 5 defect classes and 10,115 images show that the mAP@50 of our model increased from 78.1% to 84.8%, and the mAP@50-95 increased from 61.3% to 67.5%. This demonstrates the effectiveness of our method.}, archive = {J_ACCESS}, author = {Zhiwang Xu and Haowei Luo and Huijie Zhu and Wanfa Sun and Shengying Yang}, doi = {10.1109/ACCESS.2025.3579665}, journal = {IEEE Access}, pages = {103155-103165}, shortjournal = {IEEE Access}, title = {YOLO-CSMD: Integrating improved convolutional techniques for manhole cover defect detection}, volume = {13}, year = {2025}, }
• (2025). Review of advanced control strategies for isolated DC–DC converters in DC microgrids. ACCESS, 13, 103137-103154. (www)

  @article{ , abstract = {Direct current (DC) microgrids have emerged as a promising solution for achieving resilient, efficient, and sustainable power distribution across diverse applications. Advanced control strategies are crucial to ensure reliable operation, effective energy management, and effective integration of renewable energy sources. This paper comprehensively reviews the latest developments in control techniques in DC microgrids, emphasizing their theoretical foundations, real-world applications, and performance assessments. The review covers a wide range of sophisticated control methodologies, including backstepping control, model predictive control (MPC), passivity-based control (PBC), and sliding mode control (SMC). Each approach is analyzed in detail, highlighting its underlying principles, advantages, limitations, and specific applications in DC microgrid systems. To further illustrate the practical relevance of these strategies, case studies and real-world examples are presented, demonstrating their efficacy in various operational scenarios. Additionally, the paper addresses key research challenges and identifies opportunities for future advancements in DC microgrid control strategies. This review aims to provide valuable insights for researchers and practitioners involved in the design, optimization, and operation of DC microgrids, contributing to the advancement of sustainable energy technologies in modern power systems.}, archive = {J_ACCESS}, author = {Mehwish Batool and Waqas Hassan and Dylan Dah-Chuan Lu}, doi = {10.1109/ACCESS.2025.3578910}, journal = {IEEE Access}, pages = {103137-103154}, shortjournal = {IEEE Access}, title = {Review of advanced control strategies for isolated DC–DC converters in DC microgrids}, volume = {13}, year = {2025}, }
• (2025). Spark booster-an optimized IoT architecture for energy sector delving predictive analysis on energy usage with stochastic monte carlo method. ACCESS, 13, 103118-103136. (www)

  @article{ , abstract = {Optimal energy distribution and Energy management in a dynamic environment in smart city applications is a challenging task that requires a more optimised method to predict energy consumption. This research presents a novel data-driven Internet of Things (IoT) architecture called Spark Booster to enhance the prediction accuracy of energy consumption in a dynamic environment. The proposed architecture integrates the Stochastic Monte Carlo method and Markov chain modelling to simulate diversified energy usage scenarios and analyse energy data for accurate prediction of energy consumption. The proposed architecture is an adaptive one with real-time data acquisition and edge analytics for energy consumption prediction. The experimental results demonstrate a significant improvement in the prediction accuracy and robustness compared to the conventional architectures. The Sparks Boost architecture offers a scalable solution for both residential and industrial applications. The proposed optimized architecture enables optimal energy distribution, reducing resource wastage for supporting sustainability.}, archive = {J_ACCESS}, author = {S. Delsi Robinsha and B. Amutha and Vijayakumar Ponnusamy}, doi = {10.1109/ACCESS.2025.3577773}, journal = {IEEE Access}, pages = {103118-103136}, shortjournal = {IEEE Access}, title = {Spark booster-an optimized IoT architecture for energy sector delving predictive analysis on energy usage with stochastic monte carlo method}, volume = {13}, year = {2025}, }
• (2025). Bandit algorithms for efficient toxicity detection in competitive online video games. ACCESS, 13, 103109-103117. (www)

  @article{ , abstract = {This article considers the problem of efficient sampling for toxicity detection in competitive online video games. Video game service operators take proactive measures to detect and address undesirable behavior, seeking to focus these costly efforts where such behavior is most likely. To achieve this objective, service operators need estimates of the likelihood of toxic behavior. When no pre-existing predictive model of toxic behavior is available, one must be estimated in real-time. To this end, we propose a contextual bandit algorithm that uses a small set of variables, selected based on domain expertise, to guide monitoring decisions. This algorithm balances exploration and exploitation to optimize long-term performance and is designed intentionally for easy deployment in production environments. Using data from the popular first-person action game Call of Duty®: Modern Warfare® III, we show that our algorithm consistently outperforms baseline algorithms that rely solely on individual players’ past behavior, achieving improvements in detection rate of up to 24.56 percentage points or 51.5%. These results have substantive implications for the nature of toxicity and illustrate how domain expertise can be harnessed to help video game service operators detect and address toxicity, ultimately fostering a safer and more enjoyable gaming experience.}, archive = {J_ACCESS}, author = {Jacob Morrier and Rafal Kocielnik and R. Michael Alvarez}, doi = {10.1109/ACCESS.2025.3579418}, journal = {IEEE Access}, pages = {103109-103117}, shortjournal = {IEEE Access}, title = {Bandit algorithms for efficient toxicity detection in competitive online video games}, volume = {13}, year = {2025}, }
• (2025). DPOAE responses in humans and ear models: Can a single-loudspeaker approach match the two-loudspeaker technique?. ACCESS, 13, 103095-103108. (www)

  @article{ , abstract = {When a dual-tone frequency stimulus ( $f_{1}$ and $f_{2}$ ) is presented to the ear, the cochlea responds by generating a tone at $2 f_{1}-f_{2}$ , known as the distortion product otoacoustic emission (DPOAE), useful for assessing cochlear health. Typically, to measure emissions from one ear, tones at $f_{1}$ and $f_{2}$ are generated by two separate loudspeakers and delivered through tubing to a single probe in most clinical DPOAE devices. Using a single loudspeaker, however, introduces cubic intermodulation distortion (IMD) at $2 f_{1}-f_{2}$ , which interferes with the DPOAE signal, making accurate measurements difficult. In this study, to enable DPOAE measurement using only one loudspeaker, we designed a three-tone stimulus. The third tone was adjusted in phase and amplitude to destructively interfere with the IMD at $2 f_{1}-f_{2}$ , effectively canceling it. Using an IEC 60318–4 ear simulator, we optimized the third tone parameters and then measured DPOAEs with single- and two-loudspeaker setups. DPOAEs from 54 ears were measured at frequencies from 1–8 kHz using an ER-10B+ DPOAE microphone system, with an $f_{2}/f_{1}$ ratio of 1.22 and stimulus levels of $65/55$ dB SPL ( $L_{1}/L_{2}$ ). Bland-Altman analysis, with limits of agreement (LoA), was performed to assess whether the method sufficiently agreed with the standard DPOAE technique. Additionally, 10 silicone ear models, created from 3D scans of real human outer ears, were used to evaluate residual IMD that cannot be measured in humans due to the presence of DPOAEs. These models accounted for the anatomical effects of the ear canal shape, providing an objective, realistic assessment of cancellation performance.}, archive = {J_ACCESS}, author = {Irwansyah and Sho Otsuka and Seiji Nakagawa}, doi = {10.1109/ACCESS.2025.3579475}, journal = {IEEE Access}, pages = {103095-103108}, shortjournal = {IEEE Access}, title = {DPOAE responses in humans and ear models: Can a single-loudspeaker approach match the two-loudspeaker technique?}, volume = {13}, year = {2025}, }
• (2025). 5G/6G architecture evolution for XR and metaverse: Feasibility study, security, and privacy challenges for smart culture applications. ACCESS, 13, 103077-103094. (www)

  @article{ , abstract = {This paper investigates the evolution of 5G/6G architectures to support demanding Extended Reality (XR) and Metaverse applications, focusing specifically on the “smart culture” domain. We evaluate the capabilities of the 5G Service-Based Architecture (SBA), including Multi-Access Edge Computing (MEC) and network analytics, through a comprehensive feasibility study comparing stringent XR requirements (bitrate, latency, capacity, power, accuracy) against current 5G performance. Our key contribution is the identification of significant performance gaps where 5G struggles to meet the demands of advanced XR, particularly concerning capacity, scalability, and ultra-low latency. Furthermore, we provide a detailed analysis of critical security and privacy challenges inherent in 5G-enabled XR environments, including virtualization vulnerabilities, API security, and sensitive data protection. While 5G provides core capabilities, significant challenges persist, emphasizing the need for continued research and the evolution toward 6G to effectively support immersive experiences in smart culture and the Metaverse.}, archive = {J_ACCESS}, author = {Maria Christopoulou and Ioannis Koufos and George Xilouris and Nikos Dimitriou}, doi = {10.1109/ACCESS.2025.3578595}, journal = {IEEE Access}, pages = {103077-103094}, shortjournal = {IEEE Access}, title = {5G/6G architecture evolution for XR and metaverse: Feasibility study, security, and privacy challenges for smart culture applications}, volume = {13}, year = {2025}, }
• (2025). Detecting black-box model probing attacks through probability scores. ACCESS, 13, 103060-103076. (www)

  @article{ , abstract = {In the black-box model probing attack, the attacker sends a series of model inference requests to a victim model to map out the classification boundary of the model. This attack is considered critical because it helps the attacker gain a better understanding of the model and launch follow-up attacks. While most existing works defend against this attack through the input of the victim model, this paper addresses the problem by utilizing the output of the victim model, i.e., the sequence of probability scores generated through the model inquiries. Our research discovered that the sequences of top-2 probability scores generated by different model probing attacks form distinct patterns that can be detected through time-series classification methods, such as Transformer and Bidirectional Long Short-Term Memory (BiLSTM). Our experiments showed that both Transformer and BiLSTM can detect known and unknown black-box model probing attacks and their variants. Compared with existing defense methods, our classifiers can reduce the Attack Success Rate (ASR) of the OARS-enhanced attacks from 93% to 1.78%. To facilitate further studies on the black-box model probing attack, we collected the probability score sequences generated by 6 different black-box model probing attacks and released them as an open dataset: https://www.kaggle.com/datasets/drvoyager/model-probing-attack-dataset.}, archive = {J_ACCESS}, author = {Yongzhi Wang and Ahsan Habib and Likhitha Reddy Kesara and Brahmarshi Jasti and Renjie Hu and Tejasv Singh}, doi = {10.1109/ACCESS.2025.3578565}, journal = {IEEE Access}, pages = {103060-103076}, shortjournal = {IEEE Access}, title = {Detecting black-box model probing attacks through probability scores}, volume = {13}, year = {2025}, }
• (2025). Multi-agent meta reinforcement learning for reliable and low-latency distributed inference in resource-constrained UAV swarms. ACCESS, 13, 103045-103059. (www)

  @article{ , abstract = {The integration of unmanned aerial vehicles (UAVs) in the Industrial Internet of Things (IIoT) for smart city applications has been gaining significant attention. UAV swarms are increasingly employed to monitor ground-based IIoT devices in smart cities, offering valuable support to situation-awareness IoT applications, such as surveillance, traffic management, and emergency response. A key requirement in these applications is minimizing the latency of data processing, particularly for time-sensitive tasks like image classification of IIoT device data. Due to resource limitations, UAVs often rely on online task offloading to remote machines, but this can be inefficient due to unstable connections, constrained resources, and high latency. Distributed inference enabled via swarms of collaborative UAVs presents a promising solution by partitioning tasks among UAVs based on their available resources, allowing for more efficient, collaborative processing. However, the IIoT inference distribution raises challenges in ensuring reliable data transmission with minimal latency while respecting the practical UAVs’ constraints. To address these issues, we formulate the problem of CNN layer distribution and UAV trajectory planning (LDTP) as an optimization problem to improve latency, reliability, and resource usage. Given the complexity of the LDTP solution for managing online requests, we propose a real-time, lightweight solution using multi-agent meta-reinforcement learning. Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.}, archive = {J_ACCESS}, author = {Marwan Dhuheir and Aiman Erbad and Bechir Hamdaoui and Samir Brahim Belhaouari and Mohsen Guizani and Thang X. Vu}, doi = {10.1109/ACCESS.2025.3572036}, journal = {IEEE Access}, pages = {103045-103059}, shortjournal = {IEEE Access}, title = {Multi-agent meta reinforcement learning for reliable and low-latency distributed inference in resource-constrained UAV swarms}, volume = {13}, year = {2025}, }
• (2025). DAViT: A domain-adapted vision transformer for automated pneumonia detection and explanation using chest X-ray images. ACCESS, 13, 103033-103044. (www)

  @article{ , abstract = {Pneumonia, a leading cause of mortality worldwide, especially among children under five, is typically diagnosed via chest X-rays. However, detecting it is challenging as expert radiologists must discern subtle patterns. Artificial intelligence (AI) offers a scalable alternative by automating diagnosis through deep learning (DL) models. Despite progress, current methods face two key limitations: 1) reliance on CNNs that capture local but may overlook global features, and 2) the use of pre-trained models from natural image datasets like ImageNet, which lack the contextual relevance of medical imaging, leading to suboptimal performance. To address these challenges, we propose DAViT (Domain-Adapted Vision Transformer), a hybrid architecture that combines Vision Transformers (ViTs) and shallow CNNs with domain adaptation. The ViT leverages self-attention to capture global features, while the CNN extracts local ones. To mitigate domain differences, we adapt the model using a diverse chest X-ray dataset. We evaluate DAViT on a real-world dataset of 5,856 chest X-rays. The results demonstrate that DAViT achieves state-of-the-art performance with a 97% F1-score and 96% AUC for pneumonia detection, outperforming twelve baseline methods. For pneumonia type classification, DAViT achieves an 81% F1-score and 84% AUC, outperforming baselines by 25% to 74%. An ablation study highlights the critical contributions of domain adaptation, ViT, and CNN components, collectively enhancing performance by 21%. Finally, we apply Grad-CAM on top of DAViT to generate interpretable heatmaps that highlight relevant areas for bacterial and viral pneumonia cases, providing insights to assist medical practitioners in decision-making. These findings indicate the potential of DAViT to assist clinicians in pneumonia diagnosis through improved model accuracy and interpretability. The training code and pre-trained models are available at https://github.com/awsm-research/DAViT}, archive = {J_ACCESS}, author = {Michael Fu and Chakkrit Tantithamthavorn and Trung Le}, doi = {10.1109/ACCESS.2025.3579314}, journal = {IEEE Access}, pages = {103033-103044}, shortjournal = {IEEE Access}, title = {DAViT: A domain-adapted vision transformer for automated pneumonia detection and explanation using chest X-ray images}, volume = {13}, year = {2025}, }
• (2025). System verification and FPGA implementation of hardware preemptive scheduler for RISC-V processor. ACCESS, 13, 103019-103032. (www)

  @article{ , abstract = {In soft-core processor implementations based on software schedulers, there is an overhead that should not be neglected in HARD and FIRM real-time systems (RTS). Due to interrupt service routines (ISRs), which are asynchronous with the processor and have a higher priority than thread execution, the response time of an execution thread interrupted by an asynchronous ISR may exhibit jitter. The overall purpose of this paper is to present the hardware scheduler accelerator (HwSA) concept based on the RISC-V 4-stage pipeline CPU, where the scheduling algorithm is implemented in hardware. The proposed system is compared with existing soft-core processors using different parameters, such as the field-programmable gate arrays (FPGA) resources used for implementation, frequency, instruction set architecture (ISA), scheduler type, and the number of pipeline stages. Priority inversion is a scheduling concurrency issue in RTOS, in which a high-priority task is indirectly blocked by a task with lower priority. The scientific contribution within this project is given by a dynamic scheduling algorithm implemented in hardware, support for resolving the priority inversion situations and the use of RISC-V ISA provided under open source licenses. The analysis shows that the proposed system has higher performance in terms of the event response time and lower jitter for the scheduler, as well as higher FPGA resource utilization compared to existing designs. The support for solving priority inheritance, as a derivative contribution presented in this paper, is represented by the HwSA_RTOS hardware scheduler and dynamically changing the priorities of a task (instPi). The corresponding instPi priorities set by the mrPRIsCPUi register can be changed based on hardware logic since HwSA_RTOS handles multiple prioritized events at each instPi, including the mutex event lr_enMutexi. The results obtained achieve the proposed objectives since the execution threads have their hardware context, mainly following the real-time deterministic response of the system. We evaluate FPGA resource utilization, operating frequency, and processor performance jitter. From the implementation results, we show that HwSA_RTOS achieves 10,82% (FFs) and 14,84% (LUTs) FPGA hardware resource utilisation improvement compared with nMPRA + nHSE, which is based on MIPS32 and 5-stage pipeline implementation.}, archive = {J_ACCESS}, author = {Ionel Zagan and Vasile Gheorghiţă Găitan}, doi = {10.1109/ACCESS.2025.3579294}, journal = {IEEE Access}, pages = {103019-103032}, shortjournal = {IEEE Access}, title = {System verification and FPGA implementation of hardware preemptive scheduler for RISC-V processor}, volume = {13}, year = {2025}, }
• (2025). Enhancing physical layer security in RIS-aided HAPS for non-terrestrial networks. ACCESS, 13, 103010-103018. (www)

  @article{ , abstract = {This paper presents a novel approach to secure communication by integrating Reconfigurable Intelligent Surfaces (RIS) with High Altitude Platform Stations (HAPS) within the framework of Non-Terrestrial Networks (NTN) to protect against Aerial Eavesdroppers (AEve). This study addresses critical optimization challenges in enhancing the instantaneous secrecy rate within this innovative framework. By employing fractional programming, we effectively decompose the optimization problem for beamforming, enabling a robust solution that significantly improves security performance. Our model provides conservative estimates and advances the theoretical understanding of security in RIS-aided HAPS communications, which are essential components of future NTN architectures aimed at achieving seamless global connectivity. Simulation results demonstrate that our algorithm converges rapidly and outperforms conventional schemes that do not utilize RIS or employ random phases, achieving substantial improvements in secrecy rates. This research marks a significant advancement in addressing security vulnerabilities in next-generation wireless networks, laying the groundwork for future investigations into secure NTN-enabled RIS-HAPS systems.}, archive = {J_ACCESS}, author = {Hanieh Memarian and S. Mohammad Razavizadeh and Ali Kuhestani}, doi = {10.1109/ACCESS.2025.3576866}, journal = {IEEE Access}, pages = {103010-103018}, shortjournal = {IEEE Access}, title = {Enhancing physical layer security in RIS-aided HAPS for non-terrestrial networks}, volume = {13}, year = {2025}, }
• (2025). SAM-guided accurate pulmonary nodule image segmentation. ACCESS, 13, 102994-103009. (www)

  @article{ , abstract = {Addressing the challenges of inaccurate lung nodule segmentation due to significant scale variations, indistinct boundary textures, and intense background noise, this study introduces a Segment Anything model (SAM)-based feature-enhanced U-Net algorithm for lung nodule segmentation, incorporating the advantages of a Transformer structure and the traditional 3D U-Net framework. Specifically, the Transformer is employed to globally extract structural features of lung nodules and adjacent tissues, whereas a shallow 3D U-Net focuses on capturing image texture characteristics. This manner leverages both structural and texture features for feature enhancement, which are significant for accurate segment the lung nodules. Furthermore, guided by the SAM, the U-Net architecture is refined to assimilate multi-scale information post-feature enhancement, with deep semantic information being re-utilized from the U-Net decoder to achieve precise lung nodule segmentation. The proposed model is evaluated on the LUNA16(Lung Nodule Analysis 2016) and LNDb(Lung Nodule Database) lung nodule segmentation datasets, yielding promising results specifically on the LUNA16 data: Precision achieves 91. 20%, Sensitivity reaches 89. 95%, and the Dice Similarity Coefficient (DSC) achieves 98. 90%. These findings not only showcase the superior performance of our model in lung nodule segmentation, but also underscore its potential to significantly improve segmentation accuracy compared to existing methodologies.}, archive = {J_ACCESS}, author = {Xuewei Niu and Jianyuan Zhang and Yu Bai and Mengman Gao and Xin Yang}, doi = {10.1109/ACCESS.2025.3578869}, journal = {IEEE Access}, pages = {102994-103009}, shortjournal = {IEEE Access}, title = {SAM-guided accurate pulmonary nodule image segmentation}, volume = {13}, year = {2025}, }
• (2025). AIoT-enabled data management for smart agriculture: A comprehensive review on emerging technologies. ACCESS, 13, 102964-102993. (www)

  @article{ , abstract = {The deep integration of artificial intelligence (AI) and Internet of Things (IoT) technologies has promoted the application of Artificial Intelligence of Things (AIoT) in smart agriculture, driving substantial transformations in agricultural data management systems and facilitating sustainable agricultural development. This paper reviews the key AIoT data management technologies employed in smart agriculture, systematically exploring the technical applications and practical implementations across four architectural layers. These layers (i.e., perception layer, transport layer, data platform layer, and application layer) correspond to agricultural information sensing, data transmission, storage processing, and intelligent decision-making, respectively. Research indicates that, through the deployment of diverse sensing devices, low-latency short-range communications, wide-area wireless networks, cloud-edge collaborative computing, blockchain-based data storage, and AI-driven agricultural decision support systems, the data management framework of smart agriculture is undergoing continuous and intelligent enhancement. AIoT exhibits significant potential in enhancing agricultural productivity, optimizing resource allocation, and improving environmental adaptability. However, significant challenges remain, particularly in protecting agricultural data privacy and achieving real-time interoperability across heterogeneous platforms. The paper concludes by outlining future research directions, offering valuable insights for researchers in the field of smart agriculture.}, archive = {J_ACCESS}, author = {Xu Luo and Shuming Xiong and Xiangchen Jia and Yu Zeng and Xiao Chen}, doi = {10.1109/ACCESS.2025.3578751}, journal = {IEEE Access}, pages = {102964-102993}, shortjournal = {IEEE Access}, title = {AIoT-enabled data management for smart agriculture: A comprehensive review on emerging technologies}, volume = {13}, year = {2025}, }
• (2025). Enhancing fault detection in stochastic environments using interval-valued KPCA: A cement rotary kiln case study. ACCESS, 13, 102951-102963. (www)

  @article{ , abstract = {Fault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics ( $T^{2}$ , Q, and $\Phi $ ) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environments.}, archive = {J_ACCESS}, author = {Abdelhalim Louifi and Abdelmalek Kouadri and Mohamed-Faouzi Harkat and Abderazak Bensmail and Majdi Mansouri}, doi = {10.1109/ACCESS.2025.3578885}, journal = {IEEE Access}, pages = {102951-102963}, shortjournal = {IEEE Access}, title = {Enhancing fault detection in stochastic environments using interval-valued KPCA: A cement rotary kiln case study}, volume = {13}, year = {2025}, }
• (2025). Adaptive spanning tree-based coverage path planning for autonomous mobile robots in dynamic environments. ACCESS, 13, 102931-102950. (www)

  @article{ , abstract = {In this study, a unique approach is presented to improve autonomous robots’ path planning abilities, especially in dynamic environments. We propose a Dynamic Spanning Tree Coverage (D-STC) algorithm designed to handle both stationary and moving obstacles using a depth-first search (DFS) methodology. The workspace is partitioned into cells, and a spanning tree guides the robot’s motion to ensure full coverage while dynamically avoiding obstacles detected using onboard LIDAR sensors. The effectiveness of D-STC was evaluated across three dynamic scenarios based on relative speeds of the robot and obstacles. Simulation results show that the proposed method achieves a coverage efficiency of up to 98.25% when the robot is faster, with a minimal overlap rate of 3.06% and only 412 steps required to cover a workspace of $20\times 20$ grid. Even in more challenging scenarios with faster-moving obstacles, D-STC maintains robust performance with 96.52% coverage and 11.2% overlap. These results demonstrate that the proposed approach significantly enhances coverage quality, reduces redundancy, and adapts effectively to dynamic environments, making it suitable for real-world applications such as surveillance, cleaning, and agricultural robotics.}, archive = {J_ACCESS}, author = {K. P. Jayalakshmi and Vishnu G. Nair and Dayakshini Sathish and K. R. Guruprasad}, doi = {10.1109/ACCESS.2025.3578338}, journal = {IEEE Access}, pages = {102931-102950}, shortjournal = {IEEE Access}, title = {Adaptive spanning tree-based coverage path planning for autonomous mobile robots in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Algorithms, techniques, and applications of intelligent diagnosis using dynamic ultrasound: A review. ACCESS, 13, 102899-102930. (www)

  @article{ , abstract = {Intelligent diagnosis based on dynamic ultrasound utilizes machine learning and image processing technologies to improve the speed and accuracy of ultrasound video diagnostics, thereby providing reliable diagnostic assistance and decision support for clinicians. This paper reviews the intelligent diagnostic methods based on machine learning and deep learning in dynamic ultrasound, as well as the latest developments and applications of dynamic ultrasound in the medical field. We classify the applications of dynamic ultrasound into six categories and provide detailed discussions on the use of machine learning, particularly deep learning, in intelligent diagnosis. The primary research areas explored include image enhancement, object detection, real-time segmentation, and reinforcement learning, with an emphasis on highlighting key contributions and identifying specific challenges. Following an in-depth survey of each category, a brief assessment is presented to summarize the achievements and future trends. Ultimately, we have curated a comprehensive collection of publicly accessible ultrasound video datasets for researchers to employ in their investigations.}, archive = {J_ACCESS}, author = {Jialong Liu and Jianfeng Zhang and Yuan Shao and Xiaolong Xiao and Shoujun Huang and Dexing Kong}, doi = {10.1109/ACCESS.2025.3578817}, journal = {IEEE Access}, pages = {102899-102930}, shortjournal = {IEEE Access}, title = {Algorithms, techniques, and applications of intelligent diagnosis using dynamic ultrasound: A review}, volume = {13}, year = {2025}, }
• (2025). A hybrid AI approach for fault detection in induction motors under dynamic speed and load operations. ACCESS, 13, 102869-102898. (www)

  @article{ , abstract = {Faults in an Induction Motor (IM) can lead to unexpected downtime, resulting in considerable economic and productivity losses. From existing literature, conventional fault diagnosis approaches in an IM struggle to reliably identify fault patterns at different speeds, particularly under variable speed and changing load conditions. To resolve this issue, this paper presents a unique hybrid Convolutional Neural Network (CNN) along with the Long Short Term Memory (LSTM) topology for diagnosing faulty patterns in an IM under loaded and unloaded variable speed settings. The proposed method can identify faults such as rotor imbalances, misalignment, stator winding issues, voltage imbalances, broken rotor bars, and broken bearings. Experiments performed using the University of Ottawa Electric Motor Dataset – Vibration and Acoustic Faults under Constant and Variable Speed Conditions (UOEMD-VAFCVS) dataset reveals that all three accelerometers are 99.93% accurate at constant speed and 99.96% at variable speed under both loaded and unloaded conditions. In terms of fault diagnostic accuracy in an IM operating at different speeds and load conditions, this methodology outperforms cutting-edge methodologies in the literature. Moreover, using the publicly available CWRU dataset, this study validates the robustness of the proposed methodology in terms of operational issues in an IM. Finally, the proposed method achieves incredible results at varying speeds, stressing the need to improve industrial equipment reliability and maintenance methods.}, archive = {J_ACCESS}, author = {Muhammad Irfan Ishaq and Muhammad Adnan and Muhammad Ali Akbar and Amine Bermak and Nimra Saeed and Maaz Ansar}, doi = {10.1109/ACCESS.2025.3574017}, journal = {IEEE Access}, pages = {102869-102898}, shortjournal = {IEEE Access}, title = {A hybrid AI approach for fault detection in induction motors under dynamic speed and load operations}, volume = {13}, year = {2025}, }
• (2025). A CRC comparison-based screen data management for energy efficient virtual desktop infrastructure. ACCESS, 13, 102859-102868. (www)

  @article{ , abstract = {Cloud computing exploits virtualized resources from a server with minimum user specifications. These virtualized resources are called a virtual machine (VM), and the VM enables a user to perform high performance computing. As the VM enables users to utilize high performance computing, the demand for virtual desktop infrastructure (VDI) which applies the VM as a desktop is increased. To use the VDI, the transmission of screen data from the VM to the user is essential. Recently, the workload of screen data transmission has increased due to the high screen resolution. In order to reduce the workload, several methods have been proposed that transmit only the changed parts of the screen. However, comparing the screen data requires memory access to detect the changes in the screen. In this paper, we proposed an efficient screen data management system for the VDI with the accelerated cyclic redundancy check (CRC) generation. By comparing the CRC of the screen blocks, the system reduced the number of memory accesses and power consumptions. The accelerated CRC generation also reduces the workloads in high resolution screen data. We simulated 90,000 frames of 1920 by 1080 resolution in different types of programs. As a result, the proposed system reduced the data traffic up to 98% with an error rate of under 0.35% and reduced the number of memory access.}, archive = {J_ACCESS}, author = {Chang Yeop Han and Jongwon Oh and Jinyeol Kim and Raehyeong Kim and Kwanghyun Go and Jung Hwan Oh and Seung Eun Lee}, doi = {10.1109/ACCESS.2025.3578943}, journal = {IEEE Access}, pages = {102859-102868}, shortjournal = {IEEE Access}, title = {A CRC comparison-based screen data management for energy efficient virtual desktop infrastructure}, volume = {13}, year = {2025}, }
• (2025). An efficient illumination estimation algorithm for low-light image enhancement. ACCESS, 13, 102848-102858. (www)

  @article{ , abstract = {Low-light image enhancement is of interest in many practical applications, such as low-light photography and video surveillance. Traditional methods, like histogram equalization-based methods, cannot produce good quality output consistently. Recently, a Retinex-based method called LIME has been proposed which can produce good quality output robustly without any learning procedures. The key step of this algorithm is illumination map refinement, which is achieved by solving an optimization problem. Inspired by LIME, we propose a simple and fast way to carry out the illumination map refinement by using guided image filtering and superpixel segmentation without any optimization processes, which can achieve comparable enhancement performance to LIME while running at several times the speed of the fast approximation solution of LIME. Furthermore, we integrate an enhancement control parameter into the refinement process to mitigate the over-enhancement problem. Experimental results show that the proposed method can not only retain the original input color distribution very well, but also obtain comparable image quality scores to state-of-the-art neural network-based algorithms.}, archive = {J_ACCESS}, author = {Tirui Wu and Martin Glavin and Edward Jones}, doi = {10.1109/ACCESS.2025.3578916}, journal = {IEEE Access}, pages = {102848-102858}, shortjournal = {IEEE Access}, title = {An efficient illumination estimation algorithm for low-light image enhancement}, volume = {13}, year = {2025}, }
• (2025). Dynamic interactions between inverter-based resources and synchronous generators: A comparative study for cascading failure risk. ACCESS, 13, 102830-102847. (www)

  @article{ , abstract = {The increasing integration of inverter-based resources (IBRs) alongside traditional synchronous generators (SGs) presents new challenges for maintaining stability and resilience in modern power systems. Conventional cascading failure frameworks, originally developed for SG-dominated grids, often fail to capture the unique dynamics of mixed SG-IBR systems. This paper addresses this gap by developing an analytical framework based on a unified second-order swing-equation model for SGs, grid-following (GFL) inverters, and three grid-forming (GFM) control strategies: droop, virtual synchronous machine (VSM), and dispatchable virtual oscillator control (dVOC). This approach enables direct comparison across technologies and incorporates eigenvalue-based analysis of oscillation frequency and damping ratio, as well as Critical Clearing Time (CCT) estimation using the Equal Area Criterion (EAC). The analytical insights are validated through electromagnetic transient (EMT) simulations of a 16-bus, 230 kV system in EMTP-RV, evaluating the dynamic behavior of SGs, GFLs, and GFMs. Results show that while GFLs maintain stability under strong grid conditions, they become unstable in weak grids. SGs offer longer CCTs due to their high inertia but may exhibit underdamped responses. Various GFM strategies significantly enhance stability in weak grids, particularly when combined with GFLs; however, their effectiveness diminishes under strong grid conditions or when the system contains a high proportion of GFMs, leading to dynamic instability and reduced CCTs. Additionally, improperly tuned VSM controllers may introduce low-frequency oscillations. These findings highlight the importance of coordinated control design and careful tuning to ensure robust and resilient operation in mixed SG-IBR power systems.}, archive = {J_ACCESS}, author = {M. Alizadeh and Wei Sun}, doi = {10.1109/ACCESS.2025.3579293}, journal = {IEEE Access}, pages = {102830-102847}, shortjournal = {IEEE Access}, title = {Dynamic interactions between inverter-based resources and synchronous generators: A comparative study for cascading failure risk}, volume = {13}, year = {2025}, }
• (2025). A genetic algorithm approach to adaptive learning: Enhancing learner satisfaction and performance. ACCESS, 13, 102815-102829. (www)

  @article{ , abstract = {As educational technologies mature at a growing rate, effective models of instructional delivery systems that can deliver learning experiences based on the individual learner’s learning style have also been created. One of the core elements of such systems is the ability to recommend the right content that improves the learner’s experience. The adaptative learning environment and content recommendation with the help of genetic algorithms are investigated in this paper. GA, based on the principles of Natural Selection, provides stability-based search and solving capacity needed to tackle the inherent messiness and diversity of PBL. In our approach, each learner’s context information, as well as performance metrics, preferences and learning goals, are dynamic and fed into the GA. The algorithm genetically generates a random vote of potential content sequences as the initial population that improves through creation by selection, crossover, or mutation. The fitness function assesses an individual sequence in terms of its conformity to the learner’s profile and, thus, helps to increase an LMS usage rate, select appropriate content, and achieve targeted educational objectives. The proposed system is an application of GA that constantly updates feedback from the learner’s generated performance data and adjusts the recommended content in real time. This dynamic creates a positive learning environment capable of enhancing learner interaction and improving learning achievements. When the proposed GA-based solution is applied in a specific case with an instructional design within an online learning context, enhancement in learner satisfaction and performance are substantial, sufficiently proving the approach’s applicability. Thus, this study expands the existing literature on adaptive learning technologies by including the usage of GA in education. Future work will extend this by investigating how GAs can be mixed with other AI approaches, GA applicability to larger datasets, and real-time adaptation improvements.}, archive = {J_ACCESS}, author = {Anju Kalwar and Deepika Shekhawat and Sandeep Joshi and Amit Kumar Bairwa}, doi = {10.1109/ACCESS.2025.3578867}, journal = {IEEE Access}, pages = {102815-102829}, shortjournal = {IEEE Access}, title = {A genetic algorithm approach to adaptive learning: Enhancing learner satisfaction and performance}, volume = {13}, year = {2025}, }
• (2025). Safety-ensured robotic control framework for cutting task automation in endoscopic submucosal dissection. ACCESS, 13, 102803-102814. (www)

  @article{ , abstract = {There is growing interest in automating surgical tasks using robotic systems, such as endoscopy for treating gastrointestinal (GI) cancer. However, previous studies have primarily focused on detecting and analyzing objects or robots, with limited attention to ensuring safety, which is critical for clinical applications, where accidents can be caused by unsafe robot motions. In this study, we propose a new control framework that can formally ensure the safety of automating the cutting task in endoscopic submucosal dissection (ESD), a representative endoscopic surgical method for the treatment of early GI cancer, by using an endoscopic robot. The proposed framework utilizes Control Barrier Functions (CBFs) to accurately identify the boundaries of individual tumors, even in close proximity within the GI tract, ensuring precise treatment and removal while preserving the surrounding normal tissue. Additionally, by adopting a model-free control scheme, safety assurance is made possible even in endoscopic robotic systems where dynamic modeling is challenging. We demonstrate the proposed framework in a simulation-based experimental environment, where the tumors to be removed are close to each other, and show that the safety constraints are enforced. We show that the model-free CBF-based controlled robot eliminates one tumor completely without damaging it, while not invading another nearby tumor.}, archive = {J_ACCESS}, author = {Yitaek Kim and Iñigo Iturrate and Christoffer Sloth and Hansoul Kim}, doi = {10.1109/ACCESS.2025.3578607}, journal = {IEEE Access}, pages = {102803-102814}, shortjournal = {IEEE Access}, title = {Safety-ensured robotic control framework for cutting task automation in endoscopic submucosal dissection}, volume = {13}, year = {2025}, }
• (2025). Predictable paths: Novel ASLR bypass methods and mitigations. ACCESS, 13, 102784-102802. (www)

  @article{ , abstract = {Address Space Layout Randomization (ASLR) is a widely adopted mitigation designed to protect systems against memory corruption attacks by randomizing memory addresses of critical regions; however, its effectiveness is limited by inherent design flaws and platform-specific vulnerabilities. This paper introduces a novel methodology for bypassing ASLR on modern, 64-bit Windows systems, focusing on High Entropy ASLR. By using Return-Oriented Programming (ROP) and exploiting predictable internal Windows structures such as the Process Environment Block (PEB) and module lists, the paper demonstrates how base addresses from three system DLLs can be disclosed reliably on virtually all modern Windows systems. These techniques negate ASLR’s protections and allow the attack surface for ROP to be expanded, to include Kernel32.dll, Kernelbase.dll, and NTDLL.dll. This research provides nine original bypass approaches, each validated across Windows versions from 7 to 11, each one working without error in our tests. These findings demonstrate weaknesses in current ASLR implementations in Windows. These ASLR bypasses demonstrate the need for stronger mitigations, such as hardened internal data structure layouts and access control enhancements. Exploit mitigation can be improved and advanced by the future defenses that stem from this research.}, archive = {J_ACCESS}, author = {Bramwell Brizendine and Bhaskar P. Rimal}, doi = {10.1109/ACCESS.2025.3578602}, journal = {IEEE Access}, pages = {102784-102802}, shortjournal = {IEEE Access}, title = {Predictable paths: Novel ASLR bypass methods and mitigations}, volume = {13}, year = {2025}, }
• (2025). Recognition of knee osteoarthritis by 1D and 2D convolutional neural networks using vibroarthrographic signals. ACCESS, 13, 102771-102783. (www)

  @article{ , abstract = {Osteoarthritis (OA) of the knee is a leading cause of joint pain and mobility loss. Early diagnosis is crucial for effective management yet remains challenging with traditional imaging techniques such as X-rays, computed tomography, and MRI. This study utilized 1D and 2D convolutional neural networks (CNN) to assess OA of the knee using vibroarthrographic (VAG) signals recorded by an inertial measurement unit sensor. VAG signals were recorded by deploying accelerometers at three key points on the knee joint, alongside knee movement data captured by an angle sensor. These signals were further processed, transforming them into time-domain features for 1D CNN and frequency-domain features for 2D CNN. The classification models—a depthwise separable 1D CNN and a pre-trained 2D MobileNet—aimed to distinguish between healthy individuals and those diagnosed with OA. Validation with 128 subjects yielded promising classification results. The 1D CNN demonstrated a strong benefit in the realizable edge computing system on raw VAG data, the memory size of only 234 Kbytes, while the 2D CNN, utilizing spectrogram images, achieved even higher classification accuracy of $0.764~\pm ~0.010$ . This work offers a low-cost, non-invasive, and effective method for knee OA diagnosis, potentially enhancing clinical assessments of joint disorders for outpatients.}, archive = {J_ACCESS}, author = {Jia-Jung Wang and Alok Kumar Sharma and Shing-Hong Liu and Wenxi Chen and Cheng-Yo Yen}, doi = {10.1109/ACCESS.2025.3578067}, journal = {IEEE Access}, pages = {102771-102783}, shortjournal = {IEEE Access}, title = {Recognition of knee osteoarthritis by 1D and 2D convolutional neural networks using vibroarthrographic signals}, volume = {13}, year = {2025}, }
• (2025). Practical synchronous sliding mode control with time delay estimation for a 4-DOF parallel manipulator with unknown dynamics and variable payload. ACCESS, 13, 102758-102770. (www)

  @article{ , abstract = {This paper presents a practical synchronous sliding mode control (SSMC) using time-delay estimation (TDE) for trajectory tracking of a 4-DOF parallel robot with a variant payload, unknown dynamics, and actuator faults. The feature of the proposed control is its integration of the synchronous sliding mode control to synchronize joint motions using cross-coupling error in the 4-DOF parallel robot, which is often neglected in conventional control. Moreover, the TDE is employed to estimate lumped uncertainties including unknown dynamics, external disturbance, and internal uncertainty. By integrating these techniques, the controller achieves high tracking accuracy and robustness without requiring a precise dynamic model. Additionally, the stability analysis of the closed-loop system is proved using the Lyapunov theory. Furthermore, the proposed controller is evaluated through both simulations and the 4-DOF parallel robot experiment. In simulations, the proposed method achieved the lowest root mean squared error (RMSE) of 0.00063 rad among all joints for tracking error and 0.00049 rad for synchronous error, outperforming conventional controllers. Experimental results from the test bench demonstrate that the proposed controller maintained superior accuracy under a 2 kg payload and motor efficiency loss, reducing tracking RMSE by 12.1% and synchronous RMSE by 10.6% compared to synchronous PD time delay estimation.}, archive = {J_ACCESS}, author = {Duc Thien Tran and Xuan Tra Nguyen and Thanh Nha Nguyen and Quoc Thanh Truong}, doi = {10.1109/ACCESS.2025.3577689}, journal = {IEEE Access}, pages = {102758-102770}, shortjournal = {IEEE Access}, title = {Practical synchronous sliding mode control with time delay estimation for a 4-DOF parallel manipulator with unknown dynamics and variable payload}, volume = {13}, year = {2025}, }
• (2025). Portallite: A bare-hand 3-D portal creation and manipulation technique for remote object interactions in virtual reality. ACCESS, 13, 102746-102757. (www)

  @article{ , abstract = {Interaction with objects beyond an arm’s reach is a challenging task in virtual reality (VR). Ray-casting is often used to solve this problem, but it still has limitations when targeting areas that the ray cannot reach. To address this issue, the concept of a portal, which allows close access to a distant area, has been investigated. In this study, we introduce Portallite, an interaction technique that allows users to easily create and control 3D portals. We offer a world-in-miniature map with satellites above it as a metaphorical representation, where the position and height of the satellites represent the location and size of portals in a virtual environment. Users are able to interact with the interaction proxies, a map and satellites, using their hands. This interaction metaphor allows them to exploit the spatial relationship between the map and satellites through vision and proprioception. We conduct a user study to evaluate Portallite for portal creation and subsequent object manipulation tasks in comparison with other bare-hand interaction techniques for 3D portals. The results demonstrate that Portallite offers advantages in terms of speed, accuracy, and reduced cognitive workload.}, archive = {J_ACCESS}, author = {Minha Jeon and Sangkeun Park and Seungjae Oh}, doi = {10.1109/ACCESS.2025.3579143}, journal = {IEEE Access}, pages = {102746-102757}, shortjournal = {IEEE Access}, title = {Portallite: A bare-hand 3-D portal creation and manipulation technique for remote object interactions in virtual reality}, volume = {13}, year = {2025}, }
• (2025). Machine learning in intelligent networks: Architectures, techniques, and use cases. ACCESS, 13, 102724-102745. (www)

  @article{ , abstract = {Integrating machine learning (ML) into intelligent networks (INs) has redefined the capabilities of modern communication systems by enabling real-time decision-making, adaptive optimization, and enhanced security. The present survey systematically analyzes state-of-the-art ML techniques deployed across diverse IN architectures, including centralized, decentralized, and hybrid frameworks. It delves into key ML methodologies, supervised, unsupervised, reinforcement, and deep learning (DL), and highlights their transformative impact on network operations, such as resource allocation, fault management, and traffic optimization. Moreover, the survey emphasizes practical applications, showcasing the role of ML in enhancing performance across domains like telecommunication networks, smart cities, healthcare, and industrial automation. By consolidating fragmented research, this survey presents a unified perspective that bridges theoretical advancements with real-world implementations. The insights offered serve as a comprehensive reference for understanding how ML propels INs toward achieving unparalleled efficiency, reliability, and scalability levels. This work stands out by addressing implementation challenges, synergies across applications, and actionable strategies for leveraging ML in complex network environments.}, archive = {J_ACCESS}, author = {Elias Dritsas and Maria Trigka}, doi = {10.1109/ACCESS.2025.3577968}, journal = {IEEE Access}, pages = {102724-102745}, shortjournal = {IEEE Access}, title = {Machine learning in intelligent networks: Architectures, techniques, and use cases}, volume = {13}, year = {2025}, }
• (2025). Hybrid SARIMA+BO-LSTM framework for forecasting EV adoption: A road to net-zero in ireland. ACCESS, 13, 102706-102723. (www)

  @article{ , abstract = {Reducing greenhouse gas (GHG) emissions from the transport sector is central to achieving Ireland’s national climate goals. To support the Climate Action Plan target of registering 945,000 electric vehicles (EVs) by 2030, this study develops a hybrid time series forecasting framework that combines a Seasonal Autoregressive Integrated Moving Average (SARIMA) model with a Bayesian Optimized Long Short-Term Memory (BO-LSTM) network. SARIMA captures linear and seasonal patterns in monthly EV registration data, while BO-LSTM models the non-linear residual structure. Monthly data from January 2010 to October 2024, sourced from the Society of the Irish Motor Industry (SIMI), is used for model training and evaluation. The SARIMA-BO–LSTM model achieves a Mean Absolute Error (MAE) of 742.99, Root Mean Squared Error (RMSE) of 1200, and R2 of 0.93, outperforming several baseline statistical and machine learning models. Scenario-based forecasts are conducted under three conditions: Business-as-Usual, Accelerated Adoption, and Saturation Bound. Projections show that under the Business-as-Usual scenario, Ireland is likely to fall short of the 2030 EV target. In contrast, the Accelerated Adoption scenario meets the target through sustained exponential growth backed by strong policy and infrastructure investment. The Saturation Bound scenario also reaches the target, though with slower growth after an initial surge due to behavioral and economic constraints. The proposed forecasting framework provides a basis for planning infrastructure, incentives, and regulatory measures consistent with Ireland’s decarbonization objectives.}, archive = {J_ACCESS}, author = {Afaq Khattak and Brian Caulfield}, doi = {10.1109/ACCESS.2025.3579321}, journal = {IEEE Access}, pages = {102706-102723}, shortjournal = {IEEE Access}, title = {Hybrid SARIMA+BO-LSTM framework for forecasting EV adoption: A road to net-zero in ireland}, volume = {13}, year = {2025}, }
• (2025). Smart chaining: Templing and temple search. ACCESS, 13, 102688-102705. (www)

  @article{ , abstract = {Hash tables continue to be at the forefront of applications that require fast data lookup, but their usefulness in practice depends on effective collision resolution. Classical approaches like chaining (linked lists) and open addressing (linear/quadratic/random probing) have inherent challenges, like chaining degrades with increasing lists, whereas universal hash functions impose unrealistic computational costs on bulk operations. This work introduced two complementary innovations that can circumvent these flaws. First, Even-Odd Hash Function optimizes prime-number selection for modulo computations so that 95% uniformly distributed load is achieved at zero overhead. Second, chaining is structurally re-architected with linked lists substituted by B-trees of order 5, which gives the Templing and Temple Search framework. By combining combinatorial hashing with hierarchical collision resolution, the scheme avoids linear search delays and resists clustering without increasing the memory footprint. Experimental evidence illustrates the dominance of Templing and Temple Search as it delivers $4.5x$ faster searches and $3.5x$ faster insertions over plain chaining at equal space complexity. Light-weight prime selection and non-contiguous memory structure within the methodology guarantee scalability even under heavy loads. It restates hash table performance, resolving algorithmic advancement against practical exigencies to set the agenda for contemporary data structure design.}, archive = {J_ACCESS}, author = {Rajeev Ranjan Kumar Tripathi and Rahul Mishra and Shailesh Kumar Agrahari and Pradeep Kumar Singh and Sarvpal Singh}, doi = {10.1109/ACCESS.2025.3577461}, journal = {IEEE Access}, pages = {102688-102705}, shortjournal = {IEEE Access}, title = {Smart chaining: Templing and temple search}, volume = {13}, year = {2025}, }
• (2025). Discrete starfish optimization algorithm for symmetric travelling salesman problem. ACCESS, 13, 102675-102687. (www)

  @article{ , abstract = {This paper introduces a new discrete StarFish Optimization Algorithm (D-SFOA) to solve a complex discrete Symmetric Travelling Salesman Problem (STSP). The discrete SFOA algorithm is initialized with the initial population of classical SFOA, and the continuous values of the individuals in the population are converted to the discrete version using the random key method. Ten neighbourhood methods used in this study provide diversity to the starfish population, and the 2-opt local search algorithm allows the study to find shorter tours. The performance of D-SFOA is tested on STSP samples ranging in size from 30 to 1084 from TSPLIB. Discrete versions of the Grey Wolf Optimizer (D-GWO) and Harris Hawk Optimization (D-HHO) algorithms are applied with the same parameters to compare the performance of the proposed algorithm. The algorithm uses descriptive statistics such as average tour, best tour, percentage of deviation of the mean tour, percentage of deviation of the best tour, and execution time to ensure a fair comparison. The Wilcoxon signed-rank test and Ablation test are applied to measure the significant difference in the values of the algorithms and to observe the performance effect of the main components used in the proposed algorithm on tour length and execution time, respectively. This study’s numerical and statistical results show that D-SFOA has significantly outperformed other alternative algorithms and provided better solutions than the best-known solution.}, archive = {J_ACCESS}, author = {Muhammet Aktaş and Fatih Kiliç}, doi = {10.1109/ACCESS.2025.3579248}, journal = {IEEE Access}, pages = {102675-102687}, shortjournal = {IEEE Access}, title = {Discrete starfish optimization algorithm for symmetric travelling salesman problem}, volume = {13}, year = {2025}, }
• (2025). ETC-PCR: Coarse-to-fine edge-triangle compatibility for robust and accurate point cloud registration. ACCESS, 13, 102659-102674. (www)

  @article{ , abstract = {Accurately estimating the 6D pose of objects or scenes is a fundamental challenge in point cloud registration (PCR), especially when dealing with partially overlapping scenarios. Typically, features are first extracted—either through hand-crafted descriptors or deep learning-based methods—to establish a putative correspondence set. However, despite advances in deep learning for feature extraction and matching, the presence of a low inlier ratio often complicates the estimation process. To address these challenges, this paper proposes a novel inlier estimation approach in PCR that leverages edge-triangle combined compatibility for progressively filtering out outliers. First, geometric compatibility is exploited by randomly selecting two points to form an edge. If the compatibility test is passed, both endpoints receive a vote, and the top-ranked correspondences are retained for the next stage, enabling rapid removal of a large portion of outliers. Next, a stricter constraint is introduced based on triangles, requiring all three edges to satisfy compatibility conditions, which further refines the candidate set. The refined correspondences are then processed through a lightweight RANSAC module for hypothesis generation and selection. Finally, a geometry-based pose refinement module is presented, which utilizes the original geometric information and formulates registration as a nonlinear least squares problem. To enhance robustness against outliers, Welsch function is selected as a robust kernel, efficiently solving the optimization using the Majorization-Minimization algorithm. Extensive experiments on benchmark datasets, including 3DMatch, 3DLoMatch, and KITTI, validate the effectiveness of the proposed approach. The results demonstrate state-of-the-art performance, particularly in scenarios with extremely low inlier ratios.}, archive = {J_ACCESS}, author = {Mingyuan Zhao and Long Xu}, doi = {10.1109/ACCESS.2025.3577668}, journal = {IEEE Access}, pages = {102659-102674}, shortjournal = {IEEE Access}, title = {ETC-PCR: Coarse-to-fine edge-triangle compatibility for robust and accurate point cloud registration}, volume = {13}, year = {2025}, }
• (2025). Synthesizing recursive logic programs by inverting general resolution. ACCESS, 13, 102649-102658. (www)

  @article{ , abstract = {A fundamental scalability restriction of most Inductive Logic Programming (ILP) systems is that they search syntactically defined program spaces and cannot utilize relations in data. While semantic search methods that directly explain examples by inductive inference rules may utilize data relations to effectively prune the search space, in general logic programs, the compactness of the search space is often outweighed by the high complexity of inductive inference algorithms. In this study, we designed MPL, an inductive inference system based on two key ideas. First, MPL contains rules with at most four literals of dyadic predicates, which allow efficient and complete abduction without losing too much expressivity. Second, MPL uses the general resolution as the deduction model, which is inverted by two inductive rules: abduction and split. The general resolution allows MPL to inductively transform non-recursive sentences with invented predicates into recursive sentences. We analyze MPL language’s expressivity and implement a search algorithm, which shows performance advantages over existing syntactical search methods for several tasks.}, archive = {J_ACCESS}, author = {Taosheng Qiu and Ryutaro Ichise}, doi = {10.1109/ACCESS.2025.3577238}, journal = {IEEE Access}, pages = {102649-102658}, shortjournal = {IEEE Access}, title = {Synthesizing recursive logic programs by inverting general resolution}, volume = {13}, year = {2025}, }
• (2025). Model reduction and chattering mitigation in multi-model predictive control for quadrotor UAVs. ACCESS, 13, 102640-102648. (www)

  @article{ , abstract = {This study introduces a multi-model predictive control (MMPC) framework for quadrotor attitude regulation, combining the high-precision capabilities of nonlinear model predictive control (NMPC) with the computing efficiency of linear model predictive control (LMPC). Although NMPC provides excellent regulation, its substantial computational expense restricts its real-time use in high-refresh-rate applications like attitude control. In contrast, LMPC exhibits computational efficiency but encounters challenges with stability and precision in nonlinear dynamics. In order to resolve this discrepancy, we propose a Multi-Model Predictive Control (MMPC) approach that integrates Principal Component Analysis (PCA) for model reduction and employs linear quadrotor attitude models. This approach reduces the computational complexity and the number of necessary Linear Model Predictive Controllers (LMPCs). To improve performance, we implement an adaptive gain scheduling algorithm to mitigate the chattering effect frequently seen in multi-model approaches. This seamless switching technique diminishes high-frequency noise in brushless DC motor operation, minimises peak overshoot, and mitigates actuator wear, thereby enhancing system stability and durability. Comparative assessments indicate that the suggested MMPC framework attains NMPC-equivalent performance while incurring LMPC-level computational expenses, exceeding the efficacy of conventional incremental nonlinear dynamic inversion, sliding mode control, and traditional NMPC and LMPC methodologies. This study offers a computationally efficient and stable control solution for real-time UAV applications, including model reduction, chattering mitigation, and predictive control optimisation for autonomous aerial systems.}, archive = {J_ACCESS}, author = {Ghulam E. Mustafa Abro and Sufyan Ali Memon and Saddaf Rubab and Kaznah Alshammari and Faheem Khan}, doi = {10.1109/ACCESS.2025.3576178}, journal = {IEEE Access}, pages = {102640-102648}, shortjournal = {IEEE Access}, title = {Model reduction and chattering mitigation in multi-model predictive control for quadrotor UAVs}, volume = {13}, year = {2025}, }
• (2025). PNet-IDS: A lightweight and generalizable convolutional neural network for intrusion detection in internet of things. ACCESS, 13, 102624-102639. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDS) play a crucial role in IoT security. In recent years, deep learning-based intrusion detection systems have demonstrated excellent performance. However, the high computational and storage requirements make these impractical for most IoT devices. To address this pressing issue, we propose PNet-IDS, a novel lightweight convolutional neural network (CNN)-based method to reduce computational complexity and optimize on-device resource usage for real-time intrusion detection. The key contribution of the proposed method is the reduced number of floating point operations (FLOPs) and effective utilization of on-device computational resources at high accuracies and precision, making PNet-IDS lightweight and efficient for real-time next generation IoT intrusion detection. Moreover, PNet-IDS’ robustness against distribution shifts in network traffic is enhanced by through a knowledge distillation framework. Comprehensive experimental evaluations using the popular BoT-IoT and CIC-IDS2017 benchmark datasets prove the superiority of the proposed PNet-IDS over competitive related methods in terms of reduced parameters count, reduced FLOPs, reduced model size while maintaining high accuracy and precision. By combining PNet-IDS’ efficiency with knowledge distillation’s adaptability, the proposed method offers a scalable and resilient solution for IoT intrusion detection.}, archive = {J_ACCESS}, author = {Auwal Sani Iliyasu and Abdul Jabbar Siddiqui and Houbing Song and Fahad Jibrin Abdu}, doi = {10.1109/ACCESS.2025.3575705}, journal = {IEEE Access}, pages = {102624-102639}, shortjournal = {IEEE Access}, title = {PNet-IDS: A lightweight and generalizable convolutional neural network for intrusion detection in internet of things}, volume = {13}, year = {2025}, }
• (2025). An end-to-end deep learning system for automated fashion tagging: Segmentation, classification, and hierarchical labeling. ACCESS, 13, 102613-102623. (www)

  @article{ , abstract = {This paper presents a comprehensive end-to-end system designed for the automated tagging of fashion products using deep learning and image processing techniques. The system initiates by segmenting fashion items in images to define their boundaries, achieving a mean Intersection-over-Union (IoU) score of $0.79\pm 0.24$ with Detectron2, outperforming alternative models such as U-Net, which scored an IoU of $0.62\pm 0.36$ . Following segmentation, the system classifies these items into primary categories, reaching a top-level classification accuracy of 96% with the InceptionV3 model, and determines their dominant color with an RGB-based color-matching algorithm. The hierarchical structure enables multi-tier labeling, refining attributes like skirt length, collar type, and sleeve length. Once the primary category and color are identified, the product is further categorized using a taxonomy tree that includes additional classifiers, allowing hierarchical tagging with an overall tagging accuracy of 56%. Although this accuracy may seem modest, it is significant given the high number of classes, the hierarchical nature of the label tree, and the challenging presence of cropped or partially visible products in images, all of which naturally increase the error rate. The system is deployed on Google Cloud Run, demonstrating real-world feasibility with an average request latency of 67ms. Its scalable architecture supports high-traffic e-commerce applications. This detailed product labeling process not only improves operational efficiency for e-commerce platforms in the fashion industry but also contributes to improved product discoverability and customer satisfaction, supporting a stronger competitive position in online fashion retail.}, archive = {J_ACCESS}, author = {Başak Esin Köktürk-Güzel}, doi = {10.1109/ACCESS.2025.3578738}, journal = {IEEE Access}, pages = {102613-102623}, shortjournal = {IEEE Access}, title = {An end-to-end deep learning system for automated fashion tagging: Segmentation, classification, and hierarchical labeling}, volume = {13}, year = {2025}, }
• (2025). Effective urban region representation learning using heterogeneous urban graph attention network (HUGAT). ACCESS, 13, 102602-102612. (www)

  @article{ , abstract = {Background: Understanding the complex dynamics of urban environments is crucial for building smarter and more livable cities. To achieve this, it is essential to capture the interactions between physical space and human activities at finer scales. Objective: This study aims to develop a model that effectively represents urban regions by incorporating both spatial features and human activity patterns, in order to better understand and predict urban dynamics. Methods: We propose a novel urban region representation model called the Heterogeneous Urban Graph Attention Network (HUGAT). HUGAT utilizes an urban-Heterogeneous Information Network (Urban-HIN) to model diverse relations among multiple urban node and edge types. It simultaneously learns multiple objectives of spatial and human activity variations through a heterogeneous graph attention network. Results: Experiments conducted on data from New York City show that HUGAT outperforms state-of-the-art models across various prediction tasks, including average personal income, poverty ratio, region popularity, and spatial clustering. The results demonstrate not only the effectiveness of HUGAT but also the importance of human movement data in capturing ever-evolving urban dynamics. Conclusion: HUGAT’s ability to utilize readily available urban data sources has the strong potential to enhance informed decisions on urban policy and public engagement by providing critical insights in a timely and cost-effective manner, without relying on direct information sourcing such as surveys.}, archive = {J_ACCESS}, author = {Namwoo Kim and Yoonjin Yoon}, doi = {10.1109/ACCESS.2025.3577202}, journal = {IEEE Access}, pages = {102602-102612}, shortjournal = {IEEE Access}, title = {Effective urban region representation learning using heterogeneous urban graph attention network (HUGAT)}, volume = {13}, year = {2025}, }
• (2025). Hierarchical article classification: A multi-level framework for organizing scholarly literature. ACCESS, 13, 102589-102601. (www)

  @article{ , abstract = {The classification of scholarly publications is a critical task for knowledge organization, yet traditional methods often fall short in addressing the complexities of interdisciplinary research and evolving hierarchical structures. This study introduces a novel Hierarchical Article Classification (HAC) framework that leverages machine learning and deep learning techniques to improve accuracy, adaptability, and scalability in classifying scientific documents across multiple hierarchical levels. The proposed framework integrates text transformation methods, hierarchical classification strategies, classical and cutting-edge machine learning models, including SVM and transformer-based architectures like BERT and GPT-4, and evaluation metrics. A comprehensive experimental analysis conducted on a dataset from Fiocruz demonstrates that the framework is particularly effective and is able to identify models that are especially accurate in the Biological and Health Sciences fields. The main contributions of this work include: (i) a modular framework capable of handling incomplete data and hierarchical structures, adaptable to diverse domains, (ii) a practical application using Fiocruz’s scientific publications, and (iii) a robust evaluation methodology combining traditional metrics, hierarchical metrics, and expert analysis. The results illustrate the potential of integrating deep learning and contextual embeddings for sensitive, comprehensive, and automated literature organization.}, archive = {J_ACCESS}, author = {Luís P. F. Garcia and Jesús P. Mena-Chalco and Rafaela L. Grando and Waldeyr M. C. da Silva and Felipe P. Guimarães and Vanessa de A. Jorge}, doi = {10.1109/ACCESS.2025.3579232}, journal = {IEEE Access}, pages = {102589-102601}, shortjournal = {IEEE Access}, title = {Hierarchical article classification: A multi-level framework for organizing scholarly literature}, volume = {13}, year = {2025}, }
• (2025). AAD-YOLO: An improved YOLOv8 model for complex remote sensing scenarios. ACCESS, 13, 102578-102588. (www)

  @article{ , abstract = {Detecting small objects in remote sensing images is challenging due to complex background interference, the tendency for them to be misse, and multi-scale variations. These challenges lead to background noise, blurred boundaries, and loss of edge information, which complicates detection. To address these challenges, we propose the Adaptive Architecture for Detection with Edge Awareness and Dynamic Upsampling (AAD-YOLO). Inspired by the Sobel operator, we designed the C2f-based Adaptive Instance Enhancement Block (CAIEB) to improve the fusion of spatial and edge information. Additionally, the Attention-based Intrascale Feature Interaction (AIFI) module, which incorporates a multi-head self-attention mechanism, focuses on salient object regions while suppressing background interference. The Dynamic Upsampler (Dysample) module enhances multi-scale detection robustness by adaptively adjusting sampling point positions. Experiments on the SIMD dataset demonstrate that AAD-YOLO outperforms YOLOv8n in terms of precision (P), recall (R), and mean average precision (mAP50) by 3.6%, 0.8%, 2.5%, and 1.9%, respectively. On the NWPU VHR-10 dataset, AAD-YOLO achieves a precision of, recall, and average accuracy of 96.3%, 93.5%, and 97.6%. A comparative analysis with several state-of-the-art detection algorithms further validates the effectiveness and robustness of the proposed method.}, archive = {J_ACCESS}, author = {Yue Hong and Yi Shu and Shuo Guo}, doi = {10.1109/ACCESS.2025.3577094}, journal = {IEEE Access}, pages = {102578-102588}, shortjournal = {IEEE Access}, title = {AAD-YOLO: An improved YOLOv8 model for complex remote sensing scenarios}, volume = {13}, year = {2025}, }
• (2025). A novel deep hybrid model for automatic femoral stem classification in hip arthroplasty from radiographs: MSFT-net with CBAM and transformer modules. ACCESS, 13, 102564-102577. (www)

  @article{ , abstract = {Accurate identification of femoral stem implants in hip arthroplasty is essential for effective revision surgery, minimizing operative complexity, patient morbidity, intraoperative blood loss, and postoperative recovery time. In cases where prior implant data are unavailable, manual identification is often required, posing significant challenges due to its time-consuming and error-prone nature. To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. The proposed multi-scale feature transformer network was trained and validated on a dataset comprising 1266 anteroposterior (A.P.) hip radiographs of 10 different femoral stem implant types. The proposed hybrid deep learning architecture achieved a training accuracy of 96.7% and validation accuracy of 94.86%, significantly outperforming other baseline models. Compared with state-of-the-art methods, the proposed model achieved an absolute accuracy improvement of 9.5% over VGG19 and 7.4% over DenseNet201 and 8.8% over InceptionV3, demonstrating a significant advancement over existing models in femoral stem classification. The average inference time per image was under 1 second. The experimental results demonstrated that the proposed architecture enhances classification performance while reducing overfitting through attention and transformer-based feature refinement. This automated approach facilitates real-time preoperative implant recognition, thereby streamlining surgical planning, potentially reducing operative costs and duration, and improving clinical outcomes.}, archive = {J_ACCESS}, author = {Emre Gogus and Atinç Yilmaz and Meric Enercan}, doi = {10.1109/ACCESS.2025.3578919}, journal = {IEEE Access}, pages = {102564-102577}, shortjournal = {IEEE Access}, title = {A novel deep hybrid model for automatic femoral stem classification in hip arthroplasty from radiographs: MSFT-net with CBAM and transformer modules}, volume = {13}, year = {2025}, }
• (2025). Knowledge graph-driven approach in aspect-based sentiment analysis: Exploring the impact of embedding techniques. ACCESS, 13, 102553-102563. (www)

  @article{ , abstract = {Despite the high performance of the existing embedding approaches for Aspect-Based Sentiment Analysis (ABSA), such as Word2Vec and GloVe, they still have several limitations, mainly in contextual understanding and relational insights of natural language, especially in complex and long sentences. This paper presents a novel approach that enhances ABSA by integrating knowledge graphs into a transformer model, where the graphs are automatically built from raw text without requiring external resources, making the system adaptable and fully data-driven. Our approach allows a better understanding of context and relationships between entities, by combining of the contextual understanding of BERT with the relational insights provided by Node2Vec, a graph-based embedding technique. In this paper, we benchmark our hybrid embedding technique with the existing state-of-the-art embedding techniques. Specifically, we compare traditional embeddings, such as Word2Vec and GloVe, against BERT for textual input, while also exploring Word2Vec and Node2Vec for graph-based embeddings. Our experiments demonstrate that combining BERT’s deep contextual embeddings with the structural insights of Node2Vec leads to promising improvements in sentiment classification performance. Our model achieved 98% accuracy on SemEval2015 Restaurant dataset. These results demonstrate that integrating both contextual and relational information significantly enhances the performance of ABSA models, thereby making them more effective at capturing nuanced sentiment relationships. The proposed model’s modular design also allows flexible integration of alternative embeddings or graph configurations, making it suitable for broader sentiment analysis applications beyond the benchmark datasets.}, archive = {J_ACCESS}, author = {Souha Al Katat and Chamseddine Zaki and Hussein Hazimeh and Ibrahim El Bitar and Rafael Angarita and Lionel Trojman}, doi = {10.1109/ACCESS.2025.3577048}, journal = {IEEE Access}, pages = {102553-102563}, shortjournal = {IEEE Access}, title = {Knowledge graph-driven approach in aspect-based sentiment analysis: Exploring the impact of embedding techniques}, volume = {13}, year = {2025}, }
• (2025). Reinventing effective grounding systems via optimal soil composition, thickness, and replacement parameters under fault conditions. ACCESS, 13, 102525-102552. (www)

  @article{ , abstract = {This paper investigates the impact of soil replacement around a ground grid on its performance, with a focus on replacing the base soil with a lower-resistivity material and analyzing the influence of the replaced layer’s area and thickness. The study explores the effects of soil replacement on critical parameters such as total ground resistance, earth surface potential (ESP), current density, and electric field distribution under line-to-ground fault conditions. Key soil properties, including water content, thermal and electrical conductivity, heat capacity, density, and relative permittivity coefficient, are also considered to assess their role in optimizing the grounding environment. Despite extensive research on grounding system performance, most existing studies have assumed homogenous soil models or ideal conditions, overlooking the complex variations in real soil properties and environmental factors that critically affect fault performance. Traditional design methodologies often fail to address the effects of non-uniform soil composition, moisture variations, and localized resistivity changes, leading to suboptimal safety and performance in practical installations. This gap necessitated the proposed research, which systematically investigates soil replacement strategies as a practical and scalable solution to enhance grounding system efficiency under realistic conditions. This paper addresses a detailed investigation of how targeted soil replacement strategies influence grounding system behavior under realistic fault conditions. It introduces a practical framework that integrates both environmental and electrical soil properties to enhance performance evaluation and design accuracy. The results demonstrate that replacing high-resistivity sand with low-resistivity clay significantly reduces total ground resistance. For a 1-meter-thick replaced layer, resistance decreased from $8.4~\Omega $ to $3.17~\Omega $ (a 62.3% reduction) when sand was replaced with clay, whereas replacing clay with sand increased resistance to $11.5~\Omega $ (a 37% increase) at 1.5 meters of thickness. Similarly, ESP values dropped substantially when clay was introduced as a replacement layer, enhancing current dissipation and reducing surface potential risks. Moreover, expanding the replaced soil width from 25 meters to 50 meters resulted in further ground resistance reduction, with values decreasing from $3.3~\Omega $ to $3.17~\Omega $ when replacing sand with clay, while resistance increased from $2.5~\Omega $ to $8.4~\Omega $ when replacing clay with sand. The study also revealed that a 50% replacement of sand with clay improved current density uniformity and enhanced grounding efficiency compared to full replacement scenarios. The findings show that replacing the covered soil layer achieves greater improvements in reducing ground resistance and enhancing current dissipation compared to the base layer. Increasing the thickness and area of the replaced soil further improves current density uniformity, reduces surface potential, and significantly boosts the safety and efficiency of grounding systems, especially in high-resistivity soils.INDEX TERMS Lightning strokes, grounding systems, soil resistivity, fault current dissipation, earth surface potential, simulation analysis.}, archive = {J_ACCESS}, author = {Ahmed El-Tayeb Khalil and Adel Zein El Dein and Mahmoud Ramadan Hefny and Matti Lehtonen and Mohamed M. F. Darwish}, doi = {10.1109/ACCESS.2025.3577577}, journal = {IEEE Access}, pages = {102525-102552}, shortjournal = {IEEE Access}, title = {Reinventing effective grounding systems via optimal soil composition, thickness, and replacement parameters under fault conditions}, volume = {13}, year = {2025}, }
• (2025). CAD of antenna feed systems with tracking capabilities by controlling high order modes. ACCESS, 13, 102516-102524. (www)

  @article{ , abstract = {This work presents a workflow to simplify the design process, simulation effort and verification scheme, to generate a signal with desired power, polarization and controlled higher-order modes required for aeronautical tracking systems. This workflow is especially suitable for the simulation and analysis of complex antenna feed systems with multimode monopulse tracking capabilities through the emulation of real operation. The presented scheme is based on S-parameters obtained from literature or full-wave simulations and linear techniques, transforming a CAD-FEM problem into a circuital one, that can be easily implemented analytically and processed in commercial schematic simulation tools. The proposed approach is applied to the analysis of an antenna feed system with a resonant TE21 tracking coupler, commonly used in tracking systems today and can be easily adapted and/or extended to more complex feed systems working with different tracking couplers and/or high-order modes. Different polarizations and feed settings are analyzed, obtaining useful information on the operation and best configuration of these complex systems.}, archive = {J_ACCESS}, author = {Juan Luis Cano and Gabriele Ceccato and Luca Perregrini}, doi = {10.1109/ACCESS.2025.3579079}, journal = {IEEE Access}, pages = {102516-102524}, shortjournal = {IEEE Access}, title = {CAD of antenna feed systems with tracking capabilities by controlling high order modes}, volume = {13}, year = {2025}, }
• (2025). Study on model-based estimation of continuous blood pressure from pulse arrival time using two doppler radar sensors. ACCESS, 13, 102505-102515. (www)

  @article{ , abstract = {A unique method for extracting heartbeat and arterial pulse from noisy signals, based on mathematical models of heart and arterial movements, was proposed and evaluated for non-contact and continuous blood pressure (BP) estimation using pulse arrival time (PAT) for nine healthy participants, consisting of four females and five males. PAT was calculated from two Doppler radars positioned 0.5 meters away from the chest and inguinal sites. With this setup, conventional methods relying on low-noise signals failed to extract waveforms effectively due to weak and noisy radar signals. The proposed method generates templates of baseband signals of Doppler radar simulated from mathematical models, incorporating heart and arterial movements during systole and diastole. Using the proposed method, the Pearson correlation coefficient between heart rate and pulse rate was found to be a strong linear correlation of 0.99 ( $p~\lt 0.01$ ) compared to 0.96 ( $p~\lt 0.01$ ) obtained using the conventional method of peak detection. The standard deviations of the difference between estimated BP and measured BP were 9.93 mmHg for systolic BP and 10.51 mmHg for diastolic BP, where conventional peak detection methods proved unsuitable for BP estimation. During the Valsalva maneuver, estimated systolic BP exhibited changes consistent with typical BP variations, confirming the method’s potential for practical application.}, archive = {J_ACCESS}, author = {Takashi Ota and Kosuke Okusa}, doi = {10.1109/ACCESS.2025.3579243}, journal = {IEEE Access}, pages = {102505-102515}, shortjournal = {IEEE Access}, title = {Study on model-based estimation of continuous blood pressure from pulse arrival time using two doppler radar sensors}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of supervised machine learning techniques for predictive maintenance in industry 4.0. ACCESS, 13, 102479-102504. (www)

  @article{ , abstract = {Industry 4.0 has driven a paradigm shift in manufacturing, pushing industries to adopt innovative technologies for more efficient decision-making. A key component of this revolution is predictive maintenance, which plays a central role in this transformation by leveraging, among others methods, supervised machine learning techniques to anticipate equipment failures, optimise maintenance schedules, and enhance operational efficiency. This study presents a Systematic Literature Review (SLR) of 216 peer-reviewed papers published between 2019 and 2024, analysing the adoption of supervised machine learning techniques for predictive maintenance in various industrial domains, including manufacturing, machinery, energy, and smart systems. Unlike previous SLRs that broadly examine ML applications, this review provides a structured taxonomy of predictive maintenance methods, highlighting their domain-specific usage and prevalence in safety-critical industries. Additionally, we analyse the types of datasets used, revealing a strong preference for real-world data but limited public availability, which poses challenges for reproducibility and benchmarking. This study identifies key trends in ML adoption and offers insights into future research directions, thereby reinforcing the need for open datasets, explainable AI, and cross-domain generalization.}, archive = {J_ACCESS}, author = {Dario Guidotti and Laura Pandolfo and Luca Pulina}, doi = {10.1109/ACCESS.2025.3578686}, journal = {IEEE Access}, pages = {102479-102504}, shortjournal = {IEEE Access}, title = {A systematic literature review of supervised machine learning techniques for predictive maintenance in industry 4.0}, volume = {13}, year = {2025}, }
• (2025). Scalable and sustainable blockchain: Architecting infrastructure and developing a platform for efficient management and exploration. ACCESS, 13, 102462-102478. (www)

  @article{ , abstract = {The application of blockchain technology in the health and healthcare sector is considered disruptive, thus requiring trust, integrity, and value of data. In addition, this document analyses the potential of permissioned blockchain, more specifically Hyperledger Fabric, to improve the security, integrity, and efficiency of healthcare systems. It examines ways in which this can be done in a manner that has both scalability and sustainability prospects in the long-term. This, by reaching the optimal compromise between efficiency of resources and its ease of use, management and upscaling. The ability to be as lightweight as possible, while being able to quickly expand the infrastructure and seamlessly integrate new functionalities, maintaining operational efficiency. The research developed involves a review of the literature, the development of three different blockchain implementations/architectures: one based on Docker, another based on Kubernetes and, finally, one based on Kubernetes with a load balancer. Where the practical assessment of performance metrics is performed. The evaluation revealed that Docker is particularly suitable for smaller environments, thanks to its faster configuration and lower resource consumption, making it ideal for development, proof of concept and testing phases. On the other hand, Kubernetes stood out for its significant advantages in terms of scalability, managing increased load and traffic efficiently, particularly in larger environments and production scenarios. Finally, a blockchain management and monitoring platform is presented. This was developed to ensure long-term usability and maintenance of blockchain solutions in the healthcare industry. This work aims to advance the application of blockchain in healthcare, addressing both immediate and long-term needs for security and efficiency.}, archive = {J_ACCESS}, author = {Pedro Silva and Tiago Guimarães and Ricardo Duarte and Manuel Filipe Santos}, doi = {10.1109/ACCESS.2024.3510294}, journal = {IEEE Access}, pages = {102462-102478}, shortjournal = {IEEE Access}, title = {Scalable and sustainable blockchain: Architecting infrastructure and developing a platform for efficient management and exploration}, volume = {13}, year = {2025}, }
• (2025). An electronically controlled resonant microwave sensor array for skin tumor detection. ACCESS, 13, 102445-102461. (www)

  @article{ , abstract = {The application of microwave sensors in skin tumor detection has attracted a great deal of interest from researchers due to its safety, sensitivity, non-invasiveness, and low cost. In this research, an electrically controlled two-dimensional microwave sensor array based on a complementary spiral resonator (CSR) for skin tumor detection is proposed. The detection is based on the resonant frequency shift of CSR caused by the skin tumor under test. The $10\times 10$ CSRs excited by spoof surface plasmon polaritons (SSPPs) are etched on the ground as the sensing elements for sensing skin tissue under test. Each sensing element represents a pixel, and all the sensing elements together form a dielectric image of the tissue. They are independently controlled by the corresponding PIN diodes, which are conveniently controlled by an external controller. The introduction of electrical scanning allows the quick detection of skin tumors without mechanical scanning, greatly improving the detection efficiency. In addition, each sensing element senses the corresponding skin area highly independently under the control of the PIN diode, weakening mutual interference between the elements during the measurement. Compared to the frequency multiplexed sensor array, the sensing elements’ number of the designed sensor array is greatly improved while maintaining the sensing elements’ size consistent. The operating principle of the sensor array is analyzed in detail and numerically simulated in electromagnetic simulation software. Several experiments were performed on tissue phantoms to validate the effectiveness of the designed sensor array.}, archive = {J_ACCESS}, author = {Junkai Bai and Hongfu Guo and Xuerui Lai}, doi = {10.1109/ACCESS.2025.3578636}, journal = {IEEE Access}, pages = {102445-102461}, shortjournal = {IEEE Access}, title = {An electronically controlled resonant microwave sensor array for skin tumor detection}, volume = {13}, year = {2025}, }
• (2025). Denoising and recognition method for weak acoustic abnormal signals in hot-wall hydrogenation reactors using DnCNN-CNN. ACCESS, 13, 102434-102444. (www)

  @article{ , abstract = {Accurate identification of damage in hot-wall hydrogenation reactors is a challenging and costly task. Acoustic Emission (AE) technology is considered a promising approach owing to its high sensitivity to micro-damages. However, during the operation of the hot-wall hydrogenation reactor, high-intensity operational noise often masks weak damage signals, presenting a significant challenge for damage monitoring under strong noise conditions. To address this issue, this study proposes a deep double convolutional neural network that combines denoising convolutional neural networks (DnCNN) and convolutional neural networks (CNN) for denoising and recognition of weak abnormal AE signals under strong noise conditions. Short-Time Fourier Transform (STFT) is employed to convert the AE time-domain signals into time-frequency domain joint representations, constructing a compressed spectrogram as the network input. The DnCNN module removes noise from the mixed spectrogram, while the CNN module performs signal classification. The model was validated through three representative case studies. Experimental results demonstrate that the proposed method achieves excellent signal denoising performance with limited prior knowledge of signals and noise. Furthermore, the model demonstrates strong generalization and robustness in recognition tasks, significantly improving the accuracy of abnormal signal recognition. This study provides technical support for the application of AE monitoring technology in the safety operation of hot-wall hydrogenation reactors.}, archive = {J_ACCESS}, author = {Xueqin Wang and Shilin Xu and Yun Tu and Ying Zhang and Mingguo Peng}, doi = {10.1109/ACCESS.2025.3578459}, journal = {IEEE Access}, pages = {102434-102444}, shortjournal = {IEEE Access}, title = {Denoising and recognition method for weak acoustic abnormal signals in hot-wall hydrogenation reactors using DnCNN-CNN}, volume = {13}, year = {2025}, }
• (2025). Dynamic fuzzy logic models for superior badminton teaching and performance assessment. ACCESS, 13, 102421-102433. (www)

  @article{ , abstract = {The badminton coaching and performance evaluation depend on subjective assessments and statistical analysis. Due to the unpredictable sports conditions and badminton player metrics, a more advanced method is required. This research contributes a new T-spherical fuzzy Aczel-Alsina aggregation approach to evaluate badminton match performances. Using T-spherical fuzzy sets with Aczel-Alsina aggregation enables a thorough measurement of shot accuracy, opponent adaptation, endurance, and tactical decision-making assessment in performance evaluation. The assessment method succeeds in measuring performance uncertainties through expert assessments and sports statistics, which merge with live evaluation adaptations. The model shows its strength through experimental testing, which enables effective analysis of performance rankings among players based on different match situations. The current research adds value to data-driven badminton coaching through a solution that allows trainers, analysts, and players to enhance their performance optimization techniques.}, archive = {J_ACCESS}, author = {Wulong Feng}, doi = {10.1109/ACCESS.2025.3577078}, journal = {IEEE Access}, pages = {102421-102433}, shortjournal = {IEEE Access}, title = {Dynamic fuzzy logic models for superior badminton teaching and performance assessment}, volume = {13}, year = {2025}, }
• (2025). An improved MCDM model to support smart energy management system in smart grid paradigm. ACCESS, 13, 102407-102420. (www)

  @article{ , abstract = {Smart grid development is required to accommodate the integration of renewable generation resources into systems. Energy management is improved by smart devices that allow two-way communication. A multi-criteria decision analysis framework is used in this study to provide a model for decision making. Prior to communicating with the energy market aggregator, it considers the human-oriented viewpoint to offer an interface with a smart device within smart grid system. In this research, the proposed framework is utilized for decision-making optimization based on six criteria, evaluated using three multi-criteria decision-making techniques. To assess criteria, a thorough investigation has been undertaken across five load classes aiming at demand side management (DSM) options from high load class to lowest load class and concerned load-generation balance. The findings of this research make it convenient for the framework that communicates the actual results of the energy system and the energy markets aggregator for an energy management plan. The findings of this study support the development of a framework that effectively communicates the actual performance of the energy system and the energy markets aggregator for an energy management plan. The results have been evaluated using sensitivity analysis across five DSM options, and trade-off studies have been conducted to support effective decision-making in practice. Finally, this research offers a cost-effective, clean, and efficient system configuration aimed at consumer preferences.}, archive = {J_ACCESS}, author = {Abdulrahman AlKassem and Zafar A. Khan and Mishaal Alkabi and Bader Alharbi and Syed Ali Abbas Kazmi and Kamal Al-Haddad}, doi = {10.1109/ACCESS.2025.3578524}, journal = {IEEE Access}, pages = {102407-102420}, shortjournal = {IEEE Access}, title = {An improved MCDM model to support smart energy management system in smart grid paradigm}, volume = {13}, year = {2025}, }
• (2025). An approach to finding a robust deep learning model. ACCESS, 13, 102390-102406. (www)

  @article{ , abstract = {The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of a large numbers of models. This growing demand highlights the importance of training models without human supervision, while ensuring that their predictions are reliable. In response to this need, we propose a novel approach for determining model robustness. This approach, supplemented with a model selection algorithm designed as a meta-algorithm, is versatile and applicable to any machine learning model, provided that it is appropriate for the task at hand. This study demonstrates the application of our approach to evaluate the robustness of deep learning models. To this end, we study small models composed of a few convolutional and fully connected layers, using common optimizers because of their ease of interpretation and computational efficiency. We address the influence of training sample size, model weight initialization, and inductive bias on the robustness of deep learning models.}, archive = {J_ACCESS}, author = {Alexey Boldyrev and Fedor Ratnikov and Andrey Shevelev}, doi = {10.1109/ACCESS.2025.3578926}, journal = {IEEE Access}, pages = {102390-102406}, shortjournal = {IEEE Access}, title = {An approach to finding a robust deep learning model}, volume = {13}, year = {2025}, }
• (2025). RehabHand—A new physical rehabilitation training dataset: Construction and benchmark performances of the relevant hand tasks. ACCESS, 13, 102373-102389. (www)

  @article{ , abstract = {Physical training for a person with an impaired hand generally requires extensive guidance and supervision from rehabilitation therapists. Currently, the advantages of wearable sensing technologies and the breakthrough performances of deep neural networks offer feasible solutions for developing assistive tools, particularly to automatically monitor the functional use of the patient’s hand. These applications help to report the results of a rehabilitation training, such as the interaction time between hands and objects and the number of interactions during each practice session. This reduces the burden on physiotherapists and occupational therapists and is beneficial for related research, such as rehabilitation assessment and cognitive sciences. However, the lack of datasets, particularly a dedicated dataset for rehabilitation training/exercises that are collected by body-worn sensors, can block these potential advantages. In this paper, we construct such a multimodal dataset and present a comprehensive study of relevant hand tasks using state-of-the-art deep neuronal models. The dataset is collected in a series of rehabilitation exercises of ten patients who are in treatment after injuries or strokes. The constructed dataset includes different wearable modalities such as cameras, accelerometers, and gyroscopes. Table-based exercises for rehabilitation of patients are designed with ball, water bottles, wooden blocks, and cylindrical blocks. In total, 56 exercise videos with more than 54 GB of data are collected. These videos are segmented into 431 sequences that present instances of each exercise at a certain time. To evaluate the performance of recent neuronal networks, we extract 4500 still images to label eight classes including left- and right-hand, and other objects involved in the exercises. In addition, labeling the patient’s hand in a consecutive frames of 40 shots is implemented with approximately 29K images annotated. These labeled datasets are utilized to evaluate three primary tasks related to evaluating physical hand functions: including one- and two-stage neuronal networks for hand detection and identifying left or right patient’s hand; hand tracking with SORT and DeepSORT algorithms. We report a thorough performance analysis of the neuronal network models for the constructed dataset. These results are also thoroughly discussed in terms of technical issues and challenges in developing a robust clinical tool. The dataset and benchmark performances are available to download online: http://mica.edu.vn:50211/rehabhand/}, archive = {J_ACCESS}, author = {Sinh Huy Nguyen and Hoang Bach Nguyen and Thi Thu Hong Le and Chi Thanh Nguyen and Thi Thanh Huyen Nguyen and Quynh Tho Chu and Thi Lan Le and Thanh Hai Tran and Hai Vu}, doi = {10.1109/ACCESS.2025.3578564}, journal = {IEEE Access}, pages = {102373-102389}, shortjournal = {IEEE Access}, title = {RehabHand—A new physical rehabilitation training dataset: Construction and benchmark performances of the relevant hand tasks}, volume = {13}, year = {2025}, }
• (2025). SSDFusion: A semantic segmentation driven framework for infrared and visible image fusion. ACCESS, 13, 102359-102372. (www)

  @article{ , abstract = {Fusing infrared images with visible images facilitates obtaining more abundant and accurate information content. However, existing infrared and visible image fusion methods often lack attention to the semantic information and global context information in the original images. To address these issues, we propose a novel deep learning framework for infrared and visible image fusion, which is named Semantic Segmentation Driven Infrared and Visible Image Fusion Framework (SSDFusion). Within the fusion framework, the Local Global Feature Extraction Fusion Module is employed, complemented by the decoder. Furthermore, under the guidance of semantic segmentation, SSDFusion achieves a better understanding of complex scene region information, enhancing fusion task performance. Finally, an adaptive loss function is implemented throughout SSDFusion to fine-tune the balance between the semantic segmentation task and the image fusion task by adjusting their proportional contributions. This approach aids in more accurately preserving the semantic information in the image, thereby enhancing the performance of the fusion framework. We conducted comparative experiments on the MSRS dataset with existing advanced fusion methods. The experimental results show that SSDFusion performs best in both qualitative and quantitative metrics. Analysis of the public datasets indicates that our algorithm can improve the entropy (EN), spatial frequency (SF), standard deviation (SD), mutual information (MI), visual information fidelity (VIF), and edge-based similarity measure (Q ${}_{\text {AB/F}}$ ) metrics with about 15.33%, 91.55%, 17.09%, 93.39%, 66.94%, and 122.56% gains, respectively. The ablation study further demonstrates that the local global feature fusion module, the adaptive fusion loss function, and the integration of semantic segmentation and image fusion have significant effects on improving the model performance. SSDFusion also exhibits excellent performance in terms of computational efficiency and parameter count. Furthermore, we have also verified the good generalization ability of SSDFusion on the RoadScene and M3FD datasets.}, archive = {J_ACCESS}, author = {Qishen Lv and Rui Yang and Chengmin Zhang and Shuaihui Liu and Xinyan Fan and Zihao Luo}, doi = {10.1109/ACCESS.2025.3578478}, journal = {IEEE Access}, pages = {102359-102372}, shortjournal = {IEEE Access}, title = {SSDFusion: A semantic segmentation driven framework for infrared and visible image fusion}, volume = {13}, year = {2025}, }
• (2025). Federated learning for privacy-preserving severity classification in healthcare: A secure edge-aggregated approach. ACCESS, 13, 102339-102358. (www)

  @article{ , abstract = {Federated learning (FL) has emerged as a promising paradigm for privacy-preserving machine learning across decentralized healthcare systems. This study proposes a secure and adaptive FL framework tailored for multi-institutional healthcare environments, combining structured electronic health records (EHR) and real-world ICU datasets (MIMIC-III) to predict patient severity levels. The framework incorporates secure multiparty computation (SMPC) with Shamir’s Secret Sharing to ensure encrypted communication between clients and edge aggregators, preserving data confidentiality throughout the training process. A key enhancement in this work is the integration of a dynamic edge thresholding mechanism that filters client updates based on round-wise gradient variance. Unlike static thresholds, this adaptive strategy enables real-time decision-making to accept or reject updates, improving robustness against noisy or unstable contributions and simulating real-world client dropout. The system was evaluated on both synthetic and MIMIC-III datasets using CatBoost, XGBoost, and TabNet across multiple threshold configurations and client setups. Performance metrics were reported with statistical confidence, standard deviation and 95% confidence intervals across five independent runs per model. The proposed framework demonstrates high classification accuracy, scalability across clients, and improved resilience to data heterogeneity and communication noise. It further incorporates deployment-aware considerations such as latency, update frequency, and dropout tolerance, making it suitable for integration in production healthcare networks. Experimental results highlight that dynamic thresholding not only improves model convergence but also contributes to reliable, fault-tolerant learning under practical constraints.}, archive = {J_ACCESS}, author = {Ankita Maurya and Rahul Haripriya and Manish Pandey and Jaytrilok Choudhary and Dhirendra Pratap Singh and Surendra Solanki and Duansh Sharma}, doi = {10.1109/ACCESS.2025.3576135}, journal = {IEEE Access}, pages = {102339-102358}, shortjournal = {IEEE Access}, title = {Federated learning for privacy-preserving severity classification in healthcare: A secure edge-aggregated approach}, volume = {13}, year = {2025}, }
• (2025). Power wavelet cepstral coefficients (PWCC): An accurate auditory model-based feature extraction method for robust speaker recognition. ACCESS, 13, 102323-102338. (www)

  @article{ , abstract = {Human capability for Speaker Recognition (SR) exceeds recent machine learning approaches, even in noisy environments. To bridge this gap, researchers investigate the human auditory system to support machine learning algorithm performance. The paper introduces a novel feature extraction method, named “Power Wavelet Cepstral Coefficients” (PWCC), for enhancing SR accuracy. This method is derived from the “Normalized Wavelet FilterBank” (NWFB), which utilizes an “Equivalent Rectangular Bandwidth” rate (ERB-rate) scale and additionally integrates a “Noise Suppression Module” (NSM). The NWFB imitates the cochlea’s frequency selectivity using “Morlet Wavelet filters” alongside an ERB-rate scale. The NSM applies a medium-duration power analysis, an asymmetrical noise-suppression module incorporating a temporal masking component, and a spectral smoothing module to reduce the impact of noisy signal. To assess the performance of the proposed PWCC method, experiments were conducted using clean speech signals from the TIMIT database, corrupted with various noises from the AURORA dataset. Using a “Gaussian Mixture Model-Universal Background Model” (GMM-UBM) classifier, the PWCC method demonstrated superior SR accuracy in noisy environments compared to traditional methods such as PNCC and MFCC. Furthermore, PWCC maintained higher precision, recall, and F1-scores than PNCC and MFCC under overall noise conditions. For instance, with babble noise at 15 dB SNR, PWCC achieved a recognition rate of 92.06%, compared to 75.24% for PNCC and 68.33% for MFCC.}, archive = {J_ACCESS}, author = {Youssef Zouhir and Mohamed Zarka and Kaïs Ouni and Lilia El Amraoui}, doi = {10.1109/ACCESS.2025.3576659}, journal = {IEEE Access}, pages = {102323-102338}, shortjournal = {IEEE Access}, title = {Power wavelet cepstral coefficients (PWCC): An accurate auditory model-based feature extraction method for robust speaker recognition}, volume = {13}, year = {2025}, }
• (2025). Affine formation maneuver control for multiagent systems based on unknown input reconstruction. ACCESS, 13, 102311-102322. (www)

  @article{ , abstract = {Disturbances can significantly impact the dynamic performance and stability of a system, making them a key focus in stability analysis. However, direct measurement of disturbances is often challenging due to technological and cost limitations. These disturbances introduce uncertainties that complicate multi-agent coordination, particularly in formation control tasks. Consequently, designing effective control strategies for multi-agent systems becomes both challenging and crucial. This paper addresses the affine transformation formation control problem of multi-agent systems in the presence of unknown disturbances. We begin by using a state-space model to describe agent dynamics and employing an unknown input observer to estimate both the state and the unknown disturbances. Based on these estimates, we redesign the control law to ensure that tracking errors converge to an adjustable residual set. This distributed control approach allows agents to operate without global information, thereby reducing communication requirements. Finally, a simulation example demonstrates the feasibility and effectiveness of the proposed control law.}, archive = {J_ACCESS}, author = {Liduo Hu and Wei Zhang and Chenhang Yan and Yuhang Cai and Hao Chen and Zhi Hu}, doi = {10.1109/ACCESS.2025.3578856}, journal = {IEEE Access}, pages = {102311-102322}, shortjournal = {IEEE Access}, title = {Affine formation maneuver control for multiagent systems based on unknown input reconstruction}, volume = {13}, year = {2025}, }
• (2025). An efficient ISAR imaging method for space maneuvering target based on multisynchrosqueezing transform. ACCESS, 13, 102299-102310. (www)

  @article{ , abstract = {This study resolves defocusing artifacts in conventional Range-Doppler (RD) algorithms for Inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering targets by introducing a multi-synchrosqueezing transform (MSST)-based high-resolution imaging method. The methodology integrates motion compensation to suppress translational interference, models range cell echoes as multi-component Polynomial Phase Signals(PPS), and deploys MSST’s superior time-frequency localization for reconstructing 3D range-time-Doppler energy distribution from 2D radar data. By extracting instantaneous Doppler features through energy concentration optimization, the framework achieves enhanced ISAR image resolution. Simulation and experimental results demonstrate that this method significantly outperforms conventional RD algorithms and standard time-frequency analysis techniques in time-frequency focusing performance under complex target motion conditions.}, archive = {J_ACCESS}, author = {Ting Zhao and Chengxiang Zhang and Yu Zheng and Jishen Liang}, doi = {10.1109/ACCESS.2025.3578950}, journal = {IEEE Access}, pages = {102299-102310}, shortjournal = {IEEE Access}, title = {An efficient ISAR imaging method for space maneuvering target based on multisynchrosqueezing transform}, volume = {13}, year = {2025}, }
• (2025). Hybrid channel attention regression U-net (ARU-net): An enhanced architecture for brain tumour segmentation in magnetic resonance imaging. ACCESS, 13, 102278-102298. (www)

  @article{ , abstract = {This research presents an accurate segmentation of brain tumours which is important in diagnosis and treatment planning of magnetic resource imaging (MRI) scans. Despite existing methodologies based on deep learning models, it remains difficult to distinguish between a tumour region and a normal brain tissue. To tackle this problem, a recent work proposed a new brain tumour segmentation technique on MRI, known as the Hybrid Attention Regression U-Net (ARU-Net) architecture. To enhance the effectiveness of segmentation, the proposed architecture utilises modern approaches including attention mechanisms, feature fusion processes, and regression-based output layer. It specifically uses Spatial and channel-wise attention mechanisms to capture important regions and inter channel dependencies. Spatial attention module in this architecture is based on improved Convolutional Block Attention Module which constrain attention on informative areas and suppress irrelevant or noisy ones. On the other hand, the channel-wise attention part, the Squeeze-and-Excitation (SE) blocks, recalibrates channel-wise feature responses for higher emphasis on salient features. The performance of proposed method has been evaluated comprehensively on the BraTS-2021 dataset where it shows enhanced accuracy over existing methods in detecting the tumour area. From the point of epoch curve, F1 score, the area under the curve, specificity, sensitivity, and accuracy in augmented and non-augmented datasets validated the stability of the proposed ARU-Net. This underlines its roles in segmentation of brain tumour, which if optimally developed holds the promise to significantly contribute for diagnosis and treatment planning.}, archive = {J_ACCESS}, author = {Noman Ahmed Siddiqui and Muhammad Tahir Qadri and Zain Anwar Ali and Muhammad Ovais Akhter and Ahmed Alsanad and Areej Abdullah Alhogail and Abdu H. Gumaei}, doi = {10.1109/ACCESS.2025.3567558}, journal = {IEEE Access}, pages = {102278-102298}, shortjournal = {IEEE Access}, title = {Hybrid channel attention regression U-net (ARU-net): An enhanced architecture for brain tumour segmentation in magnetic resonance imaging}, volume = {13}, year = {2025}, }
• (2025). Graph-enhanced deep learning with character similarity mining for automated NOTAM correction in aviation systems. ACCESS, 13, 102265-102277. (www)

  @article{ , abstract = {As the number of Notices to Airmen (NOTAMs) has increased exponentially, the existing manual processing methods have become increasingly inefficient and error-prone. To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A trained relational graph convolutional neural network (CEV-RGCN) model, along with a tianzege-convolutional neural network (CNN), were employed to calculate the feature similarity of Chinese characters based on phonetics and glyphs. Based on this analysis, separate knowledge graphs for phonetics and glyphs were constructed, forming the foundation for a similar character library. During the masking stage of the CKBERT model, both positive and negative samples of knowledge graph triples were generated and integrated into multi-hop comparative learning. Candidate characters are retrieved from the similar character database, and the appropriate character is selected for replacement in the original text based on contextual information and similarity. This led to the development of the Ctc-CKBERT model, which significantly enhanced both computational efficiency and accuracy. Characters with a similarity score ranging from 0.9 to 1 were identified and applied based on the NOTAM dataset, thereby improving the accuracy of text error correction.}, archive = {J_ACCESS}, author = {Bing Dong and Haoran Yao and Chuang Luo and Ruichao Yang and Ziyue Wang}, doi = {10.1109/ACCESS.2025.3578335}, journal = {IEEE Access}, pages = {102265-102277}, shortjournal = {IEEE Access}, title = {Graph-enhanced deep learning with character similarity mining for automated NOTAM correction in aviation systems}, volume = {13}, year = {2025}, }
• (2025). Adaptive response of muscle activity to exoskeleton-assisted walking. ACCESS, 13, 102251-102264. (www)

  @article{ , abstract = {The increasing use of robotic devices in clinical settings for rehabilitation and assistance underscores the need to understand their effects on muscle activation patterns. Prior studies have suggested that excessive assistance from robotic devices reduces voluntary control, leading to potential negative consequences on rehabilitation outcomes. However, the observation of muscle activation during exoskeleton-assisted walking in unimpaired individuals suggests the absence of adaptive responses to short-term exposure at high levels of assistance. The objective of this study is to determine whether prolonged exposure to maximum exoskeleton assistance induces adaptive changes in muscle activity and to analyze if distinct muscle activation profiles emerged during assisted versus unassisted walking. To achieve this, we performed the electromyographic analysis of eight bilateral lower limb muscles in ten participants during a one-hour training session. The results revealed a decrease in muscle activity over time. Furthermore, assisted walking exhibited distinct muscle patterns compared to unassisted walking, demonstrating that the level of assistance, along with the exoskeleton’s structure, significantly influences muscle activity. These findings hold significance for optimizing assistance regulation in exoskeleton-assisted walking, in terms of levels and timing of assistance changes, to enhance rehabilitative outcomes. Understanding how exoskeletons influence muscle activation can lead to improved rehabilitation strategies, maximizing the benefits of this technology for enhancing walking ability in people with neurological conditions or injuries.}, archive = {J_ACCESS}, author = {Beatrice Lagomarsino and Amy Bellitto and Antonino Massone and Giorgia Marchesi and Camilla Pierella and Maura Casadio}, doi = {10.1109/ACCESS.2025.3577700}, journal = {IEEE Access}, pages = {102251-102264}, shortjournal = {IEEE Access}, title = {Adaptive response of muscle activity to exoskeleton-assisted walking}, volume = {13}, year = {2025}, }
• (2025). Comprehensive study of nonlinear maglev system utilizing COOT optimized FOPID controller. ACCESS, 13, 102234-102250. (www)

  @article{ , abstract = {To improve the performance of the magnetic levitation system, the most recent metaheuristic COOT algorithm was first employed in this study to tune the Fractional Order Proportional Integral and Derivative (FOPID) controller. Because of its intrinsic instability and nonlinearity, the magnetic levitation system requires accurate and reliable control strategies to stay stable and reach the required position. Conventional PID controllers often fail to address the complexity of magnetic levitation systems due to their higher-order nature. The FOPID controller, with its fractional calculus-based design, offers greater flexibility and superior control performance by extending the integral and derivative orders to non-integer values. The objective function that encapsulates the magnetic levitation system’s performance requirements is minimized during the tuning phase. Integral Square Error (ISE) serves as the objective function in this case. The novelty of the proposed work is that the COOT algorithm is used to tune the FOPID controller by using the ISE as the objective function and examined by the servo and square responses in simulation and real-time. The contribution of the proposed work is two different algorithms, including the Genetic Algorithm and Whale Optimization Algorithm, were used to contrast the effectiveness of the FOPID controller based on the COOT method. In comparison to the other two algorithms, the simulation results and hardware results show that the FOPID controller tuned with the COOT algorithm performed substantially better. Additionally, the FOPID controller outperformed the PID controller tuned with the COOT algorithm.}, archive = {J_ACCESS}, author = {Marabathina Maheedhar and T. Deepa}, doi = {10.1109/ACCESS.2025.3578770}, journal = {IEEE Access}, pages = {102234-102250}, shortjournal = {IEEE Access}, title = {Comprehensive study of nonlinear maglev system utilizing COOT optimized FOPID controller}, volume = {13}, year = {2025}, }
• (2025). EyeUnderstand: Dashboard for gaze and deep-learning driven comprehension estimation in online lectures. ACCESS, 13, 102220-102233. (www)

  @article{ , abstract = {Online videos are a potent tool for educators to disseminate knowledge widely to diverse student audiences. However, collecting student feedback remains a significant challenge for lecturers, particularly in the absence of feedback. Understanding students’ subjective comprehension levels during online video lectures with sensor technology is yet to be thoroughly researched. This study uses eye-tracking technology to predict self-reported comprehension levels during video lectures. We recruited 20 participants from Germany and Japan who were invited to watch 50-minute lecture videos in three domains. The participants self-annotate the time segment in each lecture video where they dropout using open-source LabelStudio and answer the survey. We applied Long-Short-Term Memory (LSTM) to the preprocessed dataset and achieved an F1 Score of 0.886 for predicting binary self-annotated comprehension levels. We also introduce EyeUnderstand, the web-based application for visualizing the results of the comprehension estimation. We recruited 28 participants for the user study. As a result, 89.3% of the students and 92.9% of the lecturers confirmed that our application is practical.}, archive = {J_ACCESS}, author = {Ko Watanabe and Gitesh Gund and Jayasankar Santhosh and Haruka Sakagami and Yuki Matsuda and Andreas Dengel and Shoya Ishimaru}, doi = {10.1109/ACCESS.2025.3578185}, journal = {IEEE Access}, pages = {102220-102233}, shortjournal = {IEEE Access}, title = {EyeUnderstand: Dashboard for gaze and deep-learning driven comprehension estimation in online lectures}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on blockchain storage scalability. ACCESS, 13, 102194-102219. (www)

  @article{ , abstract = {The scalability of blockchain storage presents a critical bottleneck that hinders the widespread adoption of this transformative technology. Addressing this challenge is paramount to realizing the full potential of blockchains. This paper presents a systematic literature review (SLR) that focuses on storage scalability challenges and solutions in the context of blockchain technology. The SLR meticulously extracted 131 primary articles from prominent scientific databases, including Scopus, IEEE Xplore, ScienceDirect, Google Scholar, and Web of Science. The synthesis of these papers enables an in-depth analysis of storage scalability issues in blockchain networks. It highlights the key factors contributing to these issues, identifying eight major factors: distributed storage, immutability, decentralization, programmability, block size, transaction volume, node capacity, and replication strategy. The review then examines the latest state-of-the-art solutions proposed to address these challenges. These solutions are broadly categorized into 1) on-chain solutions and 2) off-chain solutions. Furthermore, the paper evaluates storage optimization strategies in light of the blockchain trilemma, which underscores the inherent balance between scalability, security, and decentralization. By providing a comprehensive overview of existing research, this study aims to offer valuable insights and pathways for future research and the development of scalable blockchain storage solutions while preserving the technology’s core principles.}, archive = {J_ACCESS}, author = {Maher Boughdiri and Takoua Abdellatif and Chirine Ghedira Guegan}, doi = {10.1109/ACCESS.2025.3578451}, journal = {IEEE Access}, pages = {102194-102219}, shortjournal = {IEEE Access}, title = {A systematic literature review on blockchain storage scalability}, volume = {13}, year = {2025}, }
• (2025). End-to-end design of webtoon-style portrait stylization system for real-world demo booth. ACCESS, 13, 102180-102193. (www)

  @article{ , abstract = {Webtoon-style portrait stylization has gained prominence across various fields, including social media, virtual characters, and the Metaverse, with significant applications in demo booths for real-time audience interaction. However, achieving high-quality, real-time stylizations presents challenges, such as preserving facial identity while maintaining the webtoon aesthetics and ensuring robustness to diverse inputs. This study explores diffusion-based generative models for webtoon-style portrait stylization, focusing on real-world scenarios such as demo booths. We address challenges such as artifact reduction and adaptability to varying inputs, showcasing the potential of diffusion models to enhance interactive and artistic applications, particularly in demo booth settings.}, archive = {J_ACCESS}, author = {Sojeong Kim and Bong-Soo Sohn and Jaesung Lee}, doi = {10.1109/ACCESS.2025.3578868}, journal = {IEEE Access}, pages = {102180-102193}, shortjournal = {IEEE Access}, title = {End-to-end design of webtoon-style portrait stylization system for real-world demo booth}, volume = {13}, year = {2025}, }
• (2025). Enhancement of virtual assistants through multimodal AI for emotion recognition. ACCESS, 13, 102159-102179. (www)

  @article{ , abstract = {Emotion recognition is becoming increasingly critical for enhancing human-computer interactions, as emotions play a vital role in shaping human interactions and overall well-being. Machines that can detect and respond to emotional cues similar to humans are essential in multiple industries. Emotionally responsive agents find applications in education, healthcare, gaming, marketing, customer service, human-robot interaction, and entertainment. This study explores the potential of enhancing virtual assistants through multimodal Artificial Intelligence (AI), utilizing various emotion recognition techniques to create more empathetic and effective systems. The proposed methodology makes use of facial expressions and textual cues to enhance the emotional awareness of the system and achieve user satisfaction through empathetic conversation. The Facial Emotion Recognition (FER) model achieved 71% real-time accuracy, whereas the Textual Emotion Recognition (TER) model achieved 59% validation accuracy, demonstrating effective Multimodal Emotion Recognition (MER). Unlike prior multimodal emotion-aware systems, our lightweight architecture ensures real-time inference and uniquely integrates facial and textual emotion recognition with DialoGPT-based response generation — demonstrating compatibility with large language models for empathetic dialogue.}, archive = {J_ACCESS}, author = {Shaun George Rajesh and Smriti Vipin Madangarli and Gauri Santosh Pisharady and Rolla Subrahmanyam}, doi = {10.1109/ACCESS.2025.3577664}, journal = {IEEE Access}, pages = {102159-102179}, shortjournal = {IEEE Access}, title = {Enhancement of virtual assistants through multimodal AI for emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Metaheuristic-based model optimization of a steam-filled chamber. ACCESS, 13, 102144-102158. (www)

  @article{ , abstract = {Steam-filled chambers are an important part of many technological processes, among others, in tire manufacturing and electricity production from thermal power plants. This work proposes a chamber model as a strongly nonlinear process with time-delays, where parameters depend on operating conditions and may vary in time. To identify system parameters, a parametric optimization task is formulated that minimizes the fit of the model to factory-measured data under varying valve opening conditions. Two significantly different approaches were used to solve this nonlinear optimization task. The first utilized local and semi-local optimization with prior knowledge derived from solving a simplified variant of the task. The second used global optimization methods without any prior knowledge. The obtained parametric models were compared based on the quality of fit and the sensitivity and stability analysis of the obtained solutions. The achieved models reflect real data with high accuracy, with mean squared errors as low as 0.0138 on output values ranging from 0.0 to 20.0, representing less than 0.1% of the output range. The solutions differ significantly in the values of the obtained parameters. The use of multiple methods has thus made it possible to obtain a diverse set of solutions, which is particularly valuable in applications for difficult engineering problems. Results of this work can be further used e.g. for subsequent step - optimal control system design for the given process and operating conditions.}, archive = {J_ACCESS}, author = {Hubert Guzowski and Roman Senkerik and Maciej Smołka and František Gazdoš and Miroslav Pálka and Libor Pekař and Michal Pluhacek and Adam Viktorin and Tomas Kadavy and Aleksander Byrski and Zuzana Komínková Oplatková and Radek Matušů and Janusz Kacprzyk}, doi = {10.1109/ACCESS.2025.3574414}, journal = {IEEE Access}, pages = {102144-102158}, shortjournal = {IEEE Access}, title = {Metaheuristic-based model optimization of a steam-filled chamber}, volume = {13}, year = {2025}, }
• (2025). A lightweight fault diagnosis framework for hydro-turbine main shaft bearing under noise interference. ACCESS, 13, 102133-102143. (www)

  @article{ , abstract = {As a critical component of hydro-generating units, the main shaft bearing of a hydro-turbine is essential for ensuring the safety and stability of the unit. However, in industrial environments, operational noise often interferes with fault diagnosis accuracy for main shaft bearings. Thus, this paper proposes a novel noise-robust hydro-turbine fault diagnosis framework. This framework integrates and enhances the local feature extraction capabilities of convolutional neural networks with the global feature extraction capabilities of Transformers. First, Noise-adaptive Random Convolution is employed to randomly perturb the signal, enabling the extraction of noise-robust sample features while preserving the periodic characteristics. Moreover, it introduces only a minimal number of parameters. Second, a parameter-free Light Global Attention mechanism is proposed, which distinguishes key features from noise interference by minimizing the energy differences among similar features. Comparative experiments demonstrate that the lightweight method proposed in this paper exhibits superior diagnostic performance and noise robustness.}, archive = {J_ACCESS}, author = {Hongwei Zhang and Zhao Liu and Hansong Si and Kaipeng Yu and Shuaifang Li and Zhenwu Yan}, doi = {10.1109/ACCESS.2025.3578682}, journal = {IEEE Access}, pages = {102133-102143}, shortjournal = {IEEE Access}, title = {A lightweight fault diagnosis framework for hydro-turbine main shaft bearing under noise interference}, volume = {13}, year = {2025}, }
• (2025). Temporal-spatial feature extraction in IoT-based SCADA system security: Hybrid CNN-LSTM and attention-based architectures for malware classification and attack detection. ACCESS, 13, 102109-102132. (www)

  @article{ , abstract = {This research presents a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model developed for malware classification from IoT devices in the SCADA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. CICIoT 2023 is used as the dataset. ADAM optimization algorithm with cross-entropy loss is used to eliminate overfitting and training is performed. Within the scope of the study, the proposed model is compared by applying it with six deep learning architectures (hybrid CNN-LSTM, Non-Local Neural Network (NLNN), Residual Attention Network (RAN), Dual Attention Network (DANet), Transformer-CNN and Attentional CNN). The obtained results show that the proposed CNN-LSTM model outperforms other complex architectures and achieves a high test accuracy of 99.23%. It has demonstrated remarkable performance according to precision, recall and F1 evaluation metrics in detecting distributed denial of service (DDoS) and denial of service (DoS) attacks. The proposed model successfully identifies Mirai botnet variants and fragmentation-based attacks. Although other models, Transformer-CNN (98.81%) and DANet (98.07%), provide high performance, they fall behind the superior temporal modeling capabilities of CNN-LSTM. When the obtained findings are examined, they highlight the relative strengths of various deep learning approaches for IoT security applications. The performances of the implemented deep learning models reached accuracies exceeding 96%, demonstrating the importance of IoT-based SCADA systems against evolving cyber threats. The study revealed the superior successes of deep learning-based approaches for IoT security.}, archive = {J_ACCESS}, author = {Onur Polat and Ali Ayid Ahmad and Saadin Oyucu and Enes Algül and Ferdi Doäan and Ahmet Aksöz}, doi = {10.1109/ACCESS.2025.3577761}, journal = {IEEE Access}, pages = {102109-102132}, shortjournal = {IEEE Access}, title = {Temporal-spatial feature extraction in IoT-based SCADA system security: Hybrid CNN-LSTM and attention-based architectures for malware classification and attack detection}, volume = {13}, year = {2025}, }
• (2025). Enhancing social interaction for the visually impaired: A systematic review of real-time emotion recognition using smart glasses and deep learning. ACCESS, 13, 102092-102108. (www)

  @article{ , abstract = {This systematic review investigates the application of smart glasses as an assistive technology for visually impaired individuals, focusing on real-time emotion recognition powered by deep learning (DL) models. This study analyzed the advancements in hardware (e.g., Raspberry Pi and NVIDIA Jetson) and software (e.g., CNN and RNN) that enhance facial expression detection. This review identifies the integration of cameras, processors, and audio outputs as being pivotal for assistive devices. Furthermore, it discusses the technical challenges that affect the reliability of emotion recognition, such as environmental variability, cultural diversity in emotional expressions, and scalability for real-world use. Despite these challenges, this review highlights the transformative potential of combining DL models with wearable technologies to improve the quality of life of visually impaired individuals. These findings suggest that improvements in DL models and hardware design can significantly enhance user independence and social interaction among visually impaired individuals using smart glasses. Moreover, this study underscores the need for multimodal systems that incorporate voice, facial, and environmental data to overcome the existing limitations and adapt to individual user needs. This survey builds on an analysis and covers various aspects of enhancing social interaction for the visually impaired, which remain underexplored.}, archive = {J_ACCESS}, author = {Nursel Yalcin and Muthana Alisawi}, doi = {10.1109/ACCESS.2025.3577106}, journal = {IEEE Access}, pages = {102092-102108}, shortjournal = {IEEE Access}, title = {Enhancing social interaction for the visually impaired: A systematic review of real-time emotion recognition using smart glasses and deep learning}, volume = {13}, year = {2025}, }
• (2025). Systematic review of current approaches and innovative solutions for combating zero-day vulnerabilities and zero-day attacks. ACCESS, 13, 102071-102091. (www)

  @article{ , abstract = {Ensuring robust cybersecurity in technological ecosystems involves addressing security vulnerabilities and cyberattacks. Although these two are closely related, they must be examined separately because the approaches and solutions for addressing them are quite different. Both security vulnerabilities and cyberattacks can be divided into the known and zero-day categories. This paper presents a systematic review that focuses on combating zero-day vulnerabilities and zero-day attacks. First, we established a taxonomy of current approaches and solutions for combating security vulnerabilities and cyberattacks. The focus was then shifted to zero-day threats. Zero-day solutions recently proposed in open-access articles indexed in the Web of Science (WoS) database were systematically analyzed. We searched for answers to the research questions of this systematic review, using inferences drawn from the reviewed articles. We examined the most adopted approaches, the most employed solutions, and the use of Artificial Intelligence (AI) in the reviewed articles to answer the research questions.}, archive = {J_ACCESS}, author = {Kübra Nilgün Karaca and Aydin Çetin}, doi = {10.1109/ACCESS.2025.3577941}, journal = {IEEE Access}, pages = {102071-102091}, shortjournal = {IEEE Access}, title = {Systematic review of current approaches and innovative solutions for combating zero-day vulnerabilities and zero-day attacks}, volume = {13}, year = {2025}, }
• (2025). Key nodes identification method in scale-free network based on structural holes. ACCESS, 13, 102060-102070. (www)

  @article{ , abstract = {Scale-free networks exhibit high heterogeneity, enhancing robustness against random attacks but creating vulnerability to targeted assaults. While current key node identification methods (e.g., degree centrality, betweenness centrality) often neglect structural hole nodes or suffer from high computational costs, this can lead to biased prioritization of key nodes. This study proposes a novel hybrid method that integrates structural holes importance indices with Mixed Degree Decomposition (MDD) to optimize node importance evaluation. It introduces two innovations: Dynamic multi-hop bridge detection, extending beyond the 2-hop limitation of E-Burt to quantify long-range bridging influence efficiently; and a weighted fusion model balancing coreness (via MDD) and bridging roles (via structural holes) with linear time complexity.Experiments on synthetic (Barabási-Albert), social (dolphin), infrastructure(Western U.S. power grid), and collaboration (co-authorship) networks demonstrate superiority. In deliberate attack simulations, the proposed algorithm triggers structural collapse (Structural Largest Connected Component $\mathrm {SLCC}\le 0.03$ ) with only 17.1% node removal, outperforming degree centrality (18.5% node removal required), E-Burt algorithm (21.5% higher node removal required), MDD (28.6% node removal required), and Tensor-Centric Hybrid (23.6% node removal required). The proposed algorithm achieves higher accuracy in structural hole-aware ranking compared to benchmarks while maintaining ${ O}\left ({{ m+n.logn }}\right)$ complexity. It achieves an effective balance between computational complexity and identification accuracy for critical infrastructure and collaboration network analyses.}, archive = {J_ACCESS}, author = {Weiyan Liu}, doi = {10.1109/ACCESS.2025.3578331}, journal = {IEEE Access}, pages = {102060-102070}, shortjournal = {IEEE Access}, title = {Key nodes identification method in scale-free network based on structural holes}, volume = {13}, year = {2025}, }
• (2025). Research on a lattice-based fuzzy signature and bimodal gaussian. ACCESS, 13, 102044-102059. (www)

  @article{ , abstract = {Fuzzy signatures differ from standard signature schemes in that they use noisy strings as keys, thereby eliminating the need for user-specific auxiliary data. With the advent of quantum computing, lattice-based digital signatures are considered potential candidates for resisting quantum attacks. In this work, we revisited the lattice-based fuzzy signature scheme proposed by Zheng et al. and demonstrated how the scheme can be implemented more efficiently and securely. In our new signature scheme, our contributions are as follows: The utilization of bimodal Gaussian sampling reduces the number of aborting terminations while simultaneously decreasing the signature size. By eliminating the T matrix from the verification equation, we improve verification efficiency. Additionally, in response to the linear sketch proposed by the authors, we provide a proof of the collision resistance of the mapping h, which further enhances the correctness of the scheme.}, archive = {J_ACCESS}, author = {Jinxin Huang and Jie Cai and Fenghe Wang and Junquan Wang}, doi = {10.1109/ACCESS.2025.3578736}, journal = {IEEE Access}, pages = {102044-102059}, shortjournal = {IEEE Access}, title = {Research on a lattice-based fuzzy signature and bimodal gaussian}, volume = {13}, year = {2025}, }
• (2025). Adaptive PID gain scheduling control for hydropower turbine using neural CDE and stochastic distribution shaping. ACCESS, 13, 102032-102043. (www)

  @article{ , abstract = {This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.}, archive = {J_ACCESS}, author = {Hong Wang and Zhun Yin and Zhong-Ping Jiang}, doi = {10.1109/ACCESS.2025.3578406}, journal = {IEEE Access}, pages = {102032-102043}, shortjournal = {IEEE Access}, title = {Adaptive PID gain scheduling control for hydropower turbine using neural CDE and stochastic distribution shaping}, volume = {13}, year = {2025}, }
• (2025). MFN: Multi-scale frequency feature fusion network for multi-classification image segmentation. ACCESS, 13, 102021-102031. (www)

  @article{ , abstract = {Semantic segmentation is a fundamental task in computer vision. In multi-class scenarios, the abundance of categories, feature similarity across classes, class imbalance, and the complexity of feature space modeling pose significant challenges in delineating features and class boundaries. Recent multi-class segmentation methods often suffer from issues such as boundary ambiguity, loss of detail, and inadequate preservation of contextual information. To address these challenges, this paper introduces the Multi-scale Frequency Feature Fusion Network (MFN), a convolutional neural network designed to effectively integrate multi-scale information. First, the Deeplab network architecture is enhanced by refining the ASPP module. Finer-grained atrous convolutions are introduced in its channels to capture multi-scale semantic information, enabling the extraction of richer and more detailed features. The network incorporates a hybrid attention module to enhance the importance of specific spatial points and strengthen feature dependencies between channels. During the feature fusion stage, the FreqFusion module adaptively smooths high-level features to address class inconsistency and boundary misalignment effectively. And a combined loss function, integrating cross-entropy loss and the Dice coefficient, is designed to improve model training and achieve optimal segmentation performance. Extensive experiments conducted on the Cityscapes and Pascal VOC datasets demonstrate that the proposed method achieves superior performance.}, archive = {J_ACCESS}, author = {Ji Xiao and Li Jianfang and Zhao Peng and Li Xiaochen and Zhang Chengchun and Zheng Junyi and Pang Yonghui and Huang Xiangsheng}, doi = {10.1109/ACCESS.2025.3573719}, journal = {IEEE Access}, pages = {102021-102031}, shortjournal = {IEEE Access}, title = {MFN: Multi-scale frequency feature fusion network for multi-classification image segmentation}, volume = {13}, year = {2025}, }
• (2025). Analysis of the impact of the collateral funds in payment channel networks. ACCESS, 13, 101994-102020. (www)

  @article{ , abstract = {This paper provides results and conclusions of empirical analysis of two protocols intended for handling blockchain transactions in the Lightning Network (LN), Blitz and HTLC. HTLC protocol is already implemented and widely adopted protocol in the Lightning Network. Blitz is a newer protocol, not yet implemented in the Lightning Network. The objective of this paper is to compare two protocols which operate over payment channel networks (PCN), and to conduct experiments that display advantages of the Blitz protocol. Experiments are made on generated transactions based on the topology of a real LN snapshot.}, archive = {J_ACCESS}, author = {Ana Marija Ereš and Marina Bagić Babac}, doi = {10.1109/ACCESS.2025.3578310}, journal = {IEEE Access}, pages = {101994-102020}, shortjournal = {IEEE Access}, title = {Analysis of the impact of the collateral funds in payment channel networks}, volume = {13}, year = {2025}, }
• (2025). Attention-enhanced CNN for high-performance deepfake detection: A multi-dataset study. ACCESS, 13, 101980-101993. (www)

  @article{ , abstract = {Deepfakes, which emerge from advanced deep learning techniques, present complex ethical and security challenges across media and communication landscapes. While offering creative potential in education and entertainment, synthetic media technologies simultaneously threaten societal trust through potential misinformation, opinion manipulation, privacy violations, and identity fraud. With the advancement of deep learning models, creating deepfake images has become easier and more convincing, resulting in the development of reliable deepfake detection models. This research works with a method that combines multi-head self-attention (MHSA) with a custom-designed convolutional neural network (CNN) to develop a robust deepfake detection model. We created a dataset called the Center for Cyber Defense DeepFake (CCDDF) dataset by generating fake images using publicly available Artificial Intelligence (AI) tools and trained our model on these data, achieving a detection accuracy of 97% and an AUC score of 99.58. Additionally, we evaluated our model on the 140K Real and Fake Faces dataset and the Celeb-DF v2 dataset, where it demonstrated exceptional performance with accuracies of 98% and 94% respectively, and corresponding AUC scores of 99.75 and 98.72. We utilized attention heatmap visualizations to analyze the model’s decision-making process to enhance qualitative interpretability. Our results demonstrate the effectiveness of combining multi-head self-attention with a convolutional neural network for deepfake detection, highlighting its strong performance across multiple datasets and its potential for real-world applications.}, archive = {J_ACCESS}, author = {Subhram Dasgupta and Kushal Badal and Swetha Chittam and Md Tasnim Alam and Kaushik Roy}, doi = {10.1109/ACCESS.2025.3578343}, journal = {IEEE Access}, pages = {101980-101993}, shortjournal = {IEEE Access}, title = {Attention-enhanced CNN for high-performance deepfake detection: A multi-dataset study}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based method for object workpiece recognition and grasp detection. ACCESS, 13, 101969-101979. (www)

  @article{ , abstract = {Accurate and stable target detection is crucial for robotic grasping tasks under uneven lighting conditions. To address this, this paper proposes a target object detection network (YOLO-Net) that integrates feature fusion and attention mechanisms. First, a deep learning-based object detection model is developed to effectively mitigate the interference caused by uneven lighting, accurately extracting the features of the target objects. Next, the pose of the target object in the world coordinate system is obtained through hand-eye calibration. Finally, robot modeling and control are implemented within the ROS system to guide the robot in precisely grasping and placing the target object. Experimental results demonstrate that the proposed method effectively handles the interference caused by uneven lighting, achieving a recognition accuracy of 92.2% and an overall average grasping success rate of 93.75%, confirming the feasibility and effectiveness of the approach.}, archive = {J_ACCESS}, author = {Yunhan Li and Jingjing Lou and Zhiduan Cai and Chuan Ye and Ruichao Zhao and Yuhang Jiang}, doi = {10.1109/ACCESS.2025.3578454}, journal = {IEEE Access}, pages = {101969-101979}, shortjournal = {IEEE Access}, title = {A deep learning-based method for object workpiece recognition and grasp detection}, volume = {13}, year = {2025}, }
• (2025). Multi-dimensional motion parameter estimation of a rotating object based on orbital angular momentum. ACCESS, 13, 101955-101968. (www)

  @article{ , abstract = {This paper considers the multi-dimensional motion patterns of a rotating object and presents methods to estimate its motion parameters, including the rotational direction, angular velocity, motion trajectory, and inclination angle, using orbital angular momentum. Firstly, the rotational direction and the angular velocity are analyzed and estimated from the aspects of time domain and frequency domain, respectively. In the time domain, the rotational direction is inferred by analyzing phase variations based on the angular momentum theorem. Then, an iterative phase retrieval algorithm is introduced to obtain the angular velocity. In the frequency domain, a dynamic sparse step-size matching algorithm is developed to determine the rotational direction and the angular velocity simultaneously. Secondly, an analytical equation for the electric field is derived to determine the translational motion, facilitating analysis of the motion trajectory. Moreover, an objective function that incorporates polarizations, reflection coefficients, and inclination angle is established. A weighted iterative gradient algorithm is then applied to solve this function, ultimately obtaining the inclination angle. The results demonstrate that the estimated values agree well with the theoretical values. Both the rotational direction and the angular velocity are accurately estimated. The translational motion exhibits an error of 6.33%, while the error in the inclination angle is 2.6%. These findings indicate that the proposed methods offer reliable approaches for evaluating the multi-dimensional motion parameters of a rotating object. The methods also show potential for engineering applications, such as drone-based surveillance and industrial instrument monitoring involving rotating components.}, archive = {J_ACCESS}, author = {Bowen He and Kun Tang and Guiping Jin and Yuehui Cui}, doi = {10.1109/ACCESS.2025.3577991}, journal = {IEEE Access}, pages = {101955-101968}, shortjournal = {IEEE Access}, title = {Multi-dimensional motion parameter estimation of a rotating object based on orbital angular momentum}, volume = {13}, year = {2025}, }
• (2025). An ensemble deep learning approach for accurate urinary sediment detection using YOLOv9e and KD-YOLOX-ViT. ACCESS, 13, 101934-101954. (www)

  @article{ , abstract = {Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling earlier, real-time treatment on a global scale.}, archive = {J_ACCESS}, author = {Mansura Naznine and Abdus Salam and Muhammad Mohsin Khan and Sadia Farhana Nobi and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3577959}, journal = {IEEE Access}, pages = {101934-101954}, shortjournal = {IEEE Access}, title = {An ensemble deep learning approach for accurate urinary sediment detection using YOLOv9e and KD-YOLOX-ViT}, volume = {13}, year = {2025}, }
• (2025). Prediction-based distributed state estimation control for IoT-enabled train positioning systems. ACCESS, 13, 101922-101933. (www)

  @article{ , abstract = {This study addresses a prediction and distributed state estimation-based reliable control problem for IoT-enabled train positioning systems which are subject to deception attacks, missing measurements, coupling delay, process and measurement noises. To mitigate the adverse effects of sensor attacks and faults on estimation performance, a novel resilient distributed state estimation method is proposed. During abnormal measurements, a saturation mechanism with adaptive bounds is embedded into each local estimator to limit the impact of distorted measurements for preventing them from exceeding acceptable boundaries. The modified local estimates are then transmitted to the state estimation center to generate a fused estimate. Using Lyapunov stability theory and stochastic analysis, sufficient conditions are derived in the form of linear matrix inequalities to obtain estimator and control gains. Furthermore, the whale optimization algorithm combined with the distributed state estimation scheme is employed to optimally adjust the weighting values of individual state estimates, thereby reducing estimation error. As a result, the improved state estimation enhances the reliability and performance of the controller, contributing to more accurate and stable train operation. Finally, the simulation results demonstrate the efficiency of the proposed predictive estimator design under output measurements affected by diverse noises, anomalous reading, varying train operational states and external disturbances. These results confirm that the proposed approach is robust and reliable, making it suitable for practical implementation in high-speed train systems.}, archive = {J_ACCESS}, author = {L. Ponnarasi and P. B. Pankajavalli and Y. Lim and R. Sakthivel and Sultan Alfarhood and Mejdl Safran}, doi = {10.1109/ACCESS.2025.3577247}, journal = {IEEE Access}, pages = {101922-101933}, shortjournal = {IEEE Access}, title = {Prediction-based distributed state estimation control for IoT-enabled train positioning systems}, volume = {13}, year = {2025}, }
• (2025). Scalable discrete event simulation tool for large-scale cyber-physical energy systems: Advancing system efficiency and scalability. ACCESS, 13, 101900-101921. (www)

  @article{ , abstract = {Modern power systems face growing risks from cyber-physical attacks, necessitating enhanced resilience due to their societal function as critical infrastructures. The challenge is that defense of large-scale systems-of-systems requires scalability in their threat and risk assessment environment for cyber-physical analysis including cyber-informed transmission planning, decision-making, and intrusion response. Hence, we present a scalable discrete event simulation tool for analysis of energy systems, called DESTinE. The tool is tailored for large-scale cyber-physical systems, with a focus on power systems. It supports faster-than-real-time traffic generation and models packet flow and congestion under both normal and adversarial conditions. Using three well-established power system synthetic cases with 500, 2000, and 10,000 buses, we overlay a constructed cyber network employing star and radial topologies. Experiments are conducted to identify critical nodes within a communication network in response to a disturbance. The findings are incorporated into a constrained optimization problem to assess the impact of the disturbance on a specific node and its cascading effects on the overall network. Based on the solution of the optimization problem, a new hybrid network topology is also derived, combining the strengths of star and radial structures to improve network resilience. Furthermore, DESTinE is integrated with a virtual server and a hardware-in-the-loop (HIL) system using Raspberry Pi 5. The performance of star, radial, and hybrid topologies is quantified under standalone operation, virtual server integration, and HIL setup to evaluate scalability and network performance. Results are compared for accuracy with the Common Open Research Emulator (CORE). The results show that DESTinE is efficient and scalable for large-scale test cases. These findings highlight DESTinE’s potential for real-time applications in large-scale cyber-physical systems.}, archive = {J_ACCESS}, author = {Khandaker Akramul Haque and Shining Sun and Xiang Huo and Ana E. Goulart and Katherine R. Davis}, doi = {10.1109/ACCESS.2025.3578948}, journal = {IEEE Access}, pages = {101900-101921}, shortjournal = {IEEE Access}, title = {Scalable discrete event simulation tool for large-scale cyber-physical energy systems: Advancing system efficiency and scalability}, volume = {13}, year = {2025}, }
• (2025). A design of workflow-based automated failure recovery framework in IoT edge environment. ACCESS, 13, 101885-101899. (www)

  @article{ , abstract = {In IoT Edge environments, microservice-based architectures decompose workloads into functional units for efficient deployment and scaling across edge nodes. However, service dependencies and layered infrastructure often cause cascading failures, complicating automated failure recovery processes in IoT Edge environment. Existing failure recovery methods usually rely on static, isolated recovery strategies, ignoring system-wide dependencies and resulting in recovery conflicts (e.g., multiple processes modifying the same target or violating recovery order), leading to redundant actions and prolonged downtime. To address these limitations, we propose a workflow-based automated recovery framework that structures recovery actions into coordinated workflows to enable conflict-free execution while flexibly adapting to evolving infrastructure, dependencies, and failure scenarios. The framework incorporates a $\boldsymbol {C}$ onflict- $\boldsymbol {A}$ ware $\boldsymbol {W}$ orkflow $\boldsymbol {E}$ xecution (CAWE) mechanism that leverages a cluster dependency graph (CDG) and priority-based coordination to enforce correct recovery order and prevent conflicting operations. Experimental results demonstrate that eliminating conflicts during recovery improves recovery completion time by up to 47% compared to the baseline method without conflict awareness. Furthermore, by ensuring dependency-consistent coordination and prioritization of target objects, our approach achieves a higher recovery success rate with a minimum of 88% in severe failure conditions and substantially reduces peak data loss compared to traditional methods like failover and retry-failover. Although short-term resource usage increases (not exceeding 30% of system capacity) occur during recovery, this trade-off is justified by significant gains in recovery speed, reliability, and data preservation, ensuring stable service operation in IoT Edge environments.}, archive = {J_ACCESS}, author = {Phuong Bac Ta and Vitumbiko Mafeni and Younghan Kim}, doi = {10.1109/ACCESS.2025.3578739}, journal = {IEEE Access}, pages = {101885-101899}, shortjournal = {IEEE Access}, title = {A design of workflow-based automated failure recovery framework in IoT edge environment}, volume = {13}, year = {2025}, }
• (2025). The use of long short-term memory models to estimate soybean pricing: A regional climate data evaluation from brazil. ACCESS, 13, 101869-101884. (www)

  @article{ , abstract = {This work uses machine learning methods to analyze the influence of the Brazilian climate on international soybean price variability. For this purpose, climatic data, historical series of the extended national consumer price index, dollar price historical series, soybean harvest historical series, and international soybean price historical series are collected from different data sources. The data is then preprocessed and grouped by state. Afterward, data from the five most productive Brazilian states, Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS), which together correspond to 74.9% of total production, are chosen. Then, predictive models were built using the long short-term memory (LSTM) method, which is applied to each state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. These models are evaluated using 10-fold sequential split cross-validation, whose predictive results are used for performance comparison through statistical hypothesis testing. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest average values of root mean squared error (RMSE) and mean absolute error (MAE), which are 20.90 and 16.72, respectively. Regarding predictors trained without climate data, the RS and PR obtained the lowest averaged values of RMSE (20.84) and MAE (16.81), respectively. After performing statistical hypothesis tests, no significant differences were found between the models, stating, with certainty 95%, that the Brazilian climatic data did not influence soybean prices in our study.}, archive = {J_ACCESS}, author = {Jefferson Tales Oliva and Otávio Pigozzo Martelli and Marco Antônio de Castro Barbosa and Viviane Dal Molin and Dalcimar Casanova and Marcelo Teixeira}, doi = {10.1109/ACCESS.2025.3577087}, journal = {IEEE Access}, pages = {101869-101884}, shortjournal = {IEEE Access}, title = {The use of long short-term memory models to estimate soybean pricing: A regional climate data evaluation from brazil}, volume = {13}, year = {2025}, }
• (2025). Hands-free UAV control: Real-time eye movement detection using EOG and LSTM networks. ACCESS, 13, 101852-101868. (www)

  @article{ , abstract = {Industry 4.0 has created a growing need for effective human-robot collaboration (HRC). As robots and humans work more closely together, efficient communication becomes essential for coordinating their actions seamlessly. While speech may seem like the obvious choice for communication, noisy factory environments can render it impractical. Additionally, workers often have their hands occupied with assembly tasks, making hand-controlled interfaces less practical for controlling robots. To address these challenges, this paper presents a novel, hands-free method for robot control using electrooculography (EOG) signals—specifically, eye movements and blinks—with unmanned aerial vehicles (UAVs) used as the demonstration platform. We developed a real-time system that captures EOG signals through a graphical user interface (GUI), allowing users to direct their gaze toward on-screen commands and confirm selections with blinks. A Long Short-Term Memory (LSTM) model was trained to detect gaze coordinates and blink events from the EOG data. For performance benchmarking, we implemented a vision-based video-oculography (VOG) system as a camera-based baseline and compared the two approaches under various conditions, including low light, wind disturbances, and deployment on embedded hardware as a resource-constrained device. Our comparative analysis showed the EOG-based method achieved 97.6% accuracy in command detection under normal conditions and maintained strong performance across challenging environments where the vision-based VOG system experienced significant degradation. The proposed EOG method also operated in real time at 47.97 FPS on a high-performance workstation and 38.87 FPS on the resource-constrained NVIDIA Jetson Orin Nano. On this platform, it ran $13.9\times $ faster and used $319\times $ less physical memory than the VOG-based alternative. These results underscore the advantages of EOG-based control in delivering reliable, real-time performance on both high-performance workstations and resource-constrained platforms, outperforming vision-based alternatives in efficiency and adaptability.}, archive = {J_ACCESS}, author = {Niloofar Zendehdel and Khosro Ghorbani Zadeh and Haodong Chen and Yun Seong Song and Ming C. Leu}, doi = {10.1109/ACCESS.2025.3578558}, journal = {IEEE Access}, pages = {101852-101868}, shortjournal = {IEEE Access}, title = {Hands-free UAV control: Real-time eye movement detection using EOG and LSTM networks}, volume = {13}, year = {2025}, }
• (2025). Real-time tracking of diagnostic discrepancies in electronic health records for improved predictive modeling. ACCESS, 13, 101841-101851. (www)

  @article{ , abstract = {Diagnostic discrepancies, a persistent threat in healthcare, compromise patient outcomes and hinder effective treatment. Despite their prevalence, addressing diagnostic discrepancies remains a complex challenge due to the limitations of traditional methods and fragmented and inaccessible data. The emergence of machine learning (ML) offers a promising solution by analyzing vast medical datasets to detect subtle disease signatures and improve treatment personalization with unparalleled precision. However, data quality and availability remain significant barriers, as incompleteness, inconsistencies, and biases can distort ML models, leading to potentially harmful misinterpretations. This paper highlights the critical role of diagnostic discrepancy statistics in overcoming these challenges and proposes a standardized data schema that ensures interoperable, real-time, and historical data without compromising patient privacy. By addressing the current lack of accessible diagnostic discrepancy data, the schema lays a foundation for more effective analysis, better patient outcomes, and improved use of machine learning in healthcare. The key contribution of this paper is the framework that allows for increased data transparency and improved data quality.}, archive = {J_ACCESS}, author = {Elizabeth A. Trader and Varadraj P. Gurupur}, doi = {10.1109/ACCESS.2025.3573931}, journal = {IEEE Access}, pages = {101841-101851}, shortjournal = {IEEE Access}, title = {Real-time tracking of diagnostic discrepancies in electronic health records for improved predictive modeling}, volume = {13}, year = {2025}, }
• (2025). Generalized MPC-DSVPWM methods: Reduction techniques and explainable machine learning with conformal prediction for PMSM drives. ACCESS, 13, 101827-101840. (www)

  @article{ , abstract = {Addressing computational time challenges of model predictive control with discrete space vector pulse width modulation (MPC-DSVPWM) poses a significant hurdle. MPC-DSVPWM can emulate desired multilevel inverter performance by generating virtual voltage vectors (VVs). Different reduction methods have been used to choose optimal VVs; however, they are limited to low level inverters and require large initialization of VVs. Additionally, machine learning methods have been implemented; however, they require complex optimization methods, as the classification number increases exponentially with the level of the inverter. Moreover, ANNs lack explainability in choosing optimal features and confidence in their predictions. To solve the above mentioned problems. We eliminate the need for initializing and optimizing a large number of virtual VVs and propose reduction method to select optimal switching states. Secondly, we propose robust machine learning algorithm by employing explainable machine learning techniques to select important features. Then, we implement parallel neural networks to handle computations efficiently. Then, conformal prediction technique is implemented to ensure trustworthy and confident performance. The proposed methods are validated by both simulation and experimental results on permanent magnet synchronous motor (PMSM) for two-level inverter. The proposed method shows both excellent steady-state and fast dynamic performance.}, archive = {J_ACCESS}, author = {Hasan Ali Gamal Al-Kaf and Sadeq Ali Qasem Mohammed and Kyo-Beum Lee}, doi = {10.1109/ACCESS.2025.3578600}, journal = {IEEE Access}, pages = {101827-101840}, shortjournal = {IEEE Access}, title = {Generalized MPC-DSVPWM methods: Reduction techniques and explainable machine learning with conformal prediction for PMSM drives}, volume = {13}, year = {2025}, }
• (2025). Analysis of the impact and weight of structural parameters on the operating characteristics of the spring operating mechanism. ACCESS, 13, 101817-101826. (www)

  @article{ , abstract = {The operational characteristics of high-voltage circuit breakers critically influence phase-controlled closing and power grid safety. To investigate the influence weights of structural parameters on the operational characteristics of the spring-operated mechanism and optimize its structure, the following steps were undertaken: First, a closing simulation model of the spring-operated mechanism used in a 252 kV circuit breaker was developed in Simulink to obtain the contact closing displacement curve. Second, the effects of different structural parameters on the closing time and velocity of the circuit breaker were examined. Third, a reliability model of the spring-operated mechanism was established, and a limit state function based on closing time was determined. By integrating the support vector machine surrogate model with the Monte Carlo reliability analysis method, the failure probability of the mechanism was determined, along with the specific influence weights of structural parameters on the operational characteristics of the spring-operated mechanism. Among these parameters, the spring preload has the greatest impact on the operational characteristics, accounting for approximately 28%, followed by the spring stiffness at 18%, and the output crank length at 15%. Finally, based on the influence weights, a sensitivity-adaptive particle swarm optimization algorithm was developed to optimize the spring-operated mechanism, reducing its failure probability by 16.67%, which is beneficial for the design of controlled switching algorithms.}, archive = {J_ACCESS}, author = {Qi Long and Xu Yang and Keru Jiang and Changhong Zhang and Xiao Wang and Xiongying Duan}, doi = {10.1109/ACCESS.2025.3575856}, journal = {IEEE Access}, pages = {101817-101826}, shortjournal = {IEEE Access}, title = {Analysis of the impact and weight of structural parameters on the operating characteristics of the spring operating mechanism}, volume = {13}, year = {2025}, }
• (2025). Taxonomic robot identifiers: Toward general classification and oversight for autonomous systems. ACCESS, 13, 101801-101816. (www)

  @article{ , abstract = {This article introduces a novel taxonomic identification system and highlights key innovations in robotic and autonomous system classification. Robotic products have grown increasingly complex, diverse, autonomous, and pervasively ubiquitous in recent years, rapidly outgrowing traditional definitions and categorization standards. However, current classification methods struggle to adapt to these advancements. New generations of robotic products are no longer limited to traditional mechatronic devices; they now expand into various forms, including cyber-physical systems, software platforms, nanorobots, organismic systems, soft and flexible robots, and developmental robotics. Without effective tools to identify and compare their fundamental differences, efficient development is stifled due to duplicated and entrenched efforts, while social adoption and safety are also impacted. This article directly addresses these challenges and proposes solutions for achieving a more unified and comprehensive taxonomic classification and identification of robotic and autonomous systems. It first examines in depth the current industry standards and academic literature, identifying major issues in building an effective taxonomy. To resolve these problems, the paper establishes new fundamental principles to classify broad classes of machines and develops a coding system for consistent naming, identification, classification, and organization. It then provides practical use cases to demonstrate and validate the utility and adaptability of the proposed system. The result is a novel, unified taxonomic framework that identifies robotic and autonomous systems from the perspectives of anthropogenic, ecological, and phylogenetic factors. This biologically grounded, interdisciplinary approach expands traditional classification boundaries and offers a robust and inclusive framework that encompasses a broad spectrum of technologies, including software platforms and nanorobots, thereby future-proofing the taxonomy in anticipation of the imminent emergence of intelligent robots with new capabilities.}, archive = {J_ACCESS}, author = {Satoru Isaka}, doi = {10.1109/ACCESS.2025.3578870}, journal = {IEEE Access}, pages = {101801-101816}, shortjournal = {IEEE Access}, title = {Taxonomic robot identifiers: Toward general classification and oversight for autonomous systems}, volume = {13}, year = {2025}, }
• (2025). MultiSHTM: Multi-level attention enabled bi-directional model for the summarization of chart images. ACCESS, 13, 101789-101800. (www)

  @article{ , abstract = {Chart-to-text conversion is an emerging research area focused on extracting useful information from chart images to improve understanding and analysis. Deep learning methods help in identifying important details and patterns from charts. However, existing models struggle to analyze charts because of the mix of text and graphical elements. To solve this problem, we propose a new method called MultiSHTM (Multi-level Stacked Houghless Network-based Bi-LSTM). This method improves accuracy, reduces complexity, and works well across different chart types. MultiSHTM integrates two key innovations: 1) a multilevel attention mechanism in a stacked houghless network, which accurately identifies key points in charts without relying on traditional Hough Transform-based methods and 2) a Bi-LSTM model enhanced with a Hierarchical and Channel Attention module, which effectively captures contextual relationships to generate precise summaries of chart images. Compared to existing methods, MultiSHTM performs better, achieving scores of Rouge: 0.55, Bleu: 0.45, Cider: 0.8, Meteor: 0.25, and Spice: 25.60.}, archive = {J_ACCESS}, author = {Indra Kumari and Hansung Lee}, doi = {10.1109/ACCESS.2025.3568849}, journal = {IEEE Access}, pages = {101789-101800}, shortjournal = {IEEE Access}, title = {MultiSHTM: Multi-level attention enabled bi-directional model for the summarization of chart images}, volume = {13}, year = {2025}, }
• (2025). Suppression of reverse drain-induced barrier lowering in negative capacitance FETs using a hetero-metal-gate structure. ACCESS, 13, 101782-101788. (www)

  @article{ , abstract = {In this paper, the reverse drain-induced barrier lowering (RDIBL) in the negative capacitance field-effect transistor (NCFET) is discussed. It is found that the RDIBL is attributed to the increase of barrier height between channel and drain with the higher drain voltage ( $V_{\mathrm {DS}}$ ). To address this issue, a hetero-metal-gate negative capacitance field-effect transistor (HM NCFET), which features the metal-gate composed of two different work function materials at source and drain sides, is proposed and its electrical characteristics are analyzed with the help of technology computer-aided design (TCAD) simulation. In contrast to the conventional NCFET, the HM NCFET can efficiently suppress RDIBL from -57 mV/V to -3 mV/V by decreasing the work function of metal-gate at drain side from 4.6 eV to 4.0 eV, while the work function of metal-gate at source side is fixed at 4.6 eV.}, archive = {J_ACCESS}, author = {Jae Yeon Park and Seungwon Go and Dong Keun Lee and Seonggeun Kim and Sihyun Kim and Sangwan Kim}, doi = {10.1109/ACCESS.2025.3576583}, journal = {IEEE Access}, pages = {101782-101788}, shortjournal = {IEEE Access}, title = {Suppression of reverse drain-induced barrier lowering in negative capacitance FETs using a hetero-metal-gate structure}, volume = {13}, year = {2025}, }
• (2025). LightEnsemble-guard: An optimized ensemble learning framework for securing resource-constrained IoT systems. ACCESS, 13, 101764-101781. (www)

  @article{ , abstract = {The rapid proliferation of real-time Internet of Things (IoT) devices has increased the need for efficient and accurate security mechanisms. Real-time IoT devices are highly vulnerable to cyberattacks due to their continuous connectivity and limited security mechanisms. In this work, we propose LightEnsemble-Guard, an ensemble learning-based approach specifically designed for resource-constrained IoT environments. Our method achieves a high detection accuracy of 99.55%, with strong precision, recall, and F1-score, while maintaining a low computational cost of just 22.23 seconds. To ensure robustness, we conducted 5-fold cross-validation and ROC curve evaluations, confirming the model’s reliability and generalizability. LightEnsemble-Guard integrates three lightweight classifiers LightGBM, XGBoost, and Extra Trees using a majority voting mechanism to improve detection performance on highly imbalanced datasets without burdening system resources. This ensemble strategy ensures an optimal balance between detection performance and computational resource utilization, making it well-suited for IoT networks, where processing power and memory are limited. The results highlight LightEnsemble-Guard as an effective, scalable and lightweight solution for real-time IoT security, significantly outperforming traditional models in both accuracy and computational efficiency.}, archive = {J_ACCESS}, author = {Muhammad Usama Tanveer and Kashif Munir and Madiha Amjad and Hasan J. Alyamani and Amine Bermak and Atiq Ur Rehman}, doi = {10.1109/ACCESS.2025.3576218}, journal = {IEEE Access}, pages = {101764-101781}, shortjournal = {IEEE Access}, title = {LightEnsemble-guard: An optimized ensemble learning framework for securing resource-constrained IoT systems}, volume = {13}, year = {2025}, }
• (2025). Exploring critical factors of crowd-based requirement engineering using fuzzy DEMATEL-ANP method. ACCESS, 13, 101745-101763. (www)

  @article{ , abstract = {The elicitation of software requirements through the crowd has become an effective approach for requirements development. This approach could improve the quality of the requirement elicitation process, which is a daunting requirement engineering activity. Crowd-based requirement engineering (CrowdRE) has proven beneficial for several systems, specifically those involving diverse stakeholders. This study intends to identify and prioritise the critical factors related to the CrowdRE process through a hybrid Multi-Criteria Decision Making (MCDM) method called DEMATEL-ANP. Hence, in the first place, an extensive literature review was carried out to determine important factors that greatly affect the CrowdRE approach. Secondly, amid the recognised factors, only those with high frequency in the literature were considered for further analysis. The study employs MCDM methods due to their ability to rank and evaluate conflicting alternatives based on expert opinion. Among MCDM techniques, Fuzzy DEMATEL-ANP was chosen, where DEMATEL finds the importance, interrelations, and cause & effect of factors, and ANP assesses relative weights. The fuzzy approach was applied to handle the vagueness and uncertainty among expert opinions. The research results are processed with the help of a custom-developed tool that automates the methods and operations. A comparative analysis was performed to evaluate the performance of Fuzzy and conventional DEMATEL-ANP. The results revealed that Competence is the most important factor, followed by feedback, whereas Anonymity is the least significant factor; thus, it can be excluded from the final list of factors. Among the interrelated factors, competence and feedback were correlated factors of CrowdRE. Moreover, the factors are categorised into cause-and-effect groups. The cause group contains largeness, anonymity, and diversity factors and is termed an influencing factor. The effect group consists of feedback, motivation, competence, and collaboration, known as influencing factors.}, archive = {J_ACCESS}, author = {Rida Fatima and Khubaib Amjad Alam and Ansar Siddique and Talha Mahboob Alam and Kamran Shaukat}, doi = {10.1109/ACCESS.2025.3576723}, journal = {IEEE Access}, pages = {101745-101763}, shortjournal = {IEEE Access}, title = {Exploring critical factors of crowd-based requirement engineering using fuzzy DEMATEL-ANP method}, volume = {13}, year = {2025}, }
• (2025). A 4-DoF pick-and-place robot with high structural rigidity and pose repeatability. ACCESS, 13, 101735-101744. (www)

  @article{ , abstract = {Parallel robots can use large actuators fixed to the base to provide larger driving torque without affecting the moving inertia. Hence, parallel robots are suitable for picking and placing large payloads at high speeds. For pick-and-place applications to be productive, a parallel robot must exhibit high pose repeatability and short stabilization time. This paper investigates the structural compliance, dynamics, and control of a new Schönflies-motion parallel robot. The aim is to achieve high-speed and large-payload motion with high pose repeatability and short stabilization time. Using a novel kinematic chain to achieve limb symmetry, the compliance distribution of the robot can be homogeneous in the workspace. The exclusive use of revolute joints can eliminate joint clearance issues and make the robot stronger. Experimental results are presented to verify the high structural rigidity of the robot in all directions. Asymmetric jerk profiles are proposed to significantly reduce the residual vibration during the stopping motion. Experiments on stabilization time, pose repeatability, and large payloads are given to show the merits of the parallel robot over existing counterparts. The pose accuracy can also be greatly improved through kinematic calibration. The outcomes suggest that this new parallel robot can advance pick-and-place automation.}, archive = {J_ACCESS}, author = {Ming-Yang Cheng and Hao-Chun Liao and Chao-Chieh Lan}, doi = {10.1109/ACCESS.2025.3578357}, journal = {IEEE Access}, pages = {101735-101744}, shortjournal = {IEEE Access}, title = {A 4-DoF pick-and-place robot with high structural rigidity and pose repeatability}, volume = {13}, year = {2025}, }
• (2025). TRACE: Transformer-based risk assessment for clinical evaluation. ACCESS, 13, 101721-101734. (www)

  @article{ , abstract = {We present TRACE (Transformer-based Risk Assessment for Clinical Evaluation), a novel method for clinical risk assessment based on clinical data, leveraging the self-attention mechanism for enhanced feature interaction and result interpretation. Our approach is able to handle different data modalities, including continuous, categorical and multiple-choice (checkbox) attributes. The proposed architecture features a shared representation of the clinical data obtained by integrating specialized embeddings of each data modality, enabling the detection of high-risk individuals using Transformer encoder layers. To assess the effectiveness of the proposed method, a strong baseline based on non-negative multi-layer perceptrons (MLPs) is introduced. The proposed method outperforms various baselines widely used in the domain of clinical risk assessment, while effectively handling missing values. In terms of explainability, our Transformer-based method offers easily interpretable results via attention weights, further enhancing the clinicians’ decision-making process.}, archive = {J_ACCESS}, author = {Dionysis Christopoulos and Sotiris Spanos and Valsamis Ntouskos and Konstantinos Karantzalos}, doi = {10.1109/ACCESS.2025.3577973}, journal = {IEEE Access}, pages = {101721-101734}, shortjournal = {IEEE Access}, title = {TRACE: Transformer-based risk assessment for clinical evaluation}, volume = {13}, year = {2025}, }
• (2025). Optimizing gammatone cepstral coefficients for gear fault detection. ACCESS, 13, 101711-101720. (www)

  @article{ , abstract = {Cepstral features, such as Gammatone Cepstral Coefficients (GTCC), have recently been applied in fault detection and diagnosis. However, GTCC was originally designed for speech feature extraction rather than fault detection, which limits its ability to capture relevant features for fault identification effectively. In this research, three key parameters of GTCC, namely, the number of coefficients, maximum frequency, and minimum frequency, are optimized using two metaheuristic algorithms: Fitness Dependent Optimizer (FDO) and Grey Wolf Optimization (GWO). These parameters are vital for enhancing the effectiveness of GTCC in extracting relevant features from the vibration signal for gear defect identification. Specifically, the maximum and minimum frequency values are critical for capturing the Gear Mesh Frequency (GMF), a common indicator of gear faults. Furthermore, optimizing the number of GTCC coefficients helps reduce model complexity. Experimental results demonstrate that optimizing GTCC parameters with GWO improves fault detection performance using an SVM classifier, achieving over 1% and 3% accuracy improvements on the PHM09 and DDS datasets, respectively.}, archive = {J_ACCESS}, author = {Zrar Kh Abdul and Abdulbasit K. Al-Talabani and Wisam Hazim Gwad and Entisar Alkayal and Halgurd S. Maghdid and Safar Maghdid Asaad}, doi = {10.1109/ACCESS.2025.3577667}, journal = {IEEE Access}, pages = {101711-101720}, shortjournal = {IEEE Access}, title = {Optimizing gammatone cepstral coefficients for gear fault detection}, volume = {13}, year = {2025}, }
• (2025). Area-efficient variable-gain delta-sigma modulator achieving 11.5-dB dynamic range improvement by sharing capacitors in the first-stage integrator. ACCESS, 13, 101698-101710. (www)

  @article{ , abstract = {This paper presents an area-efficient variable-gain delta-sigma modulator (VGDSM) that shares the sampling, feedback, and integrating capacitors in the first-stage integrator while maintaining a constant sum of their capacitance values. By adjusting the VGDSM gain through sharing these capacitors, the proposed architecture increases the dynamic range (DR) compared to the fixed-gain delta-sigma modulator, while improving area efficiency compared to conventional VGDSM that use dummy sampling capacitors with fixed-valued feedback and integrating capacitors. For each VGDSM gain, the capacitances of the three capacitors were determined considering three design constraints related to area, noise, and linearity, respectively. Compared to the conventional VGDSM that includes dummy capacitors, this approach reduces the total capacitance of the first-stage integrator by 46.7%. Implemented in 180-nm CMOS technology, the modulator increased DR by 11.5 dB, to 100.4 dB. The active chip area is 0.301 mm2, and with a 1.8-V supply, the power consumption is $732.8~\mu $ W, achieving a Schreier figure-of-merit of 175.7 dB.}, archive = {J_ACCESS}, author = {Yeon-Tae Choi and Hong-June Park}, doi = {10.1109/ACCESS.2025.3578415}, journal = {IEEE Access}, pages = {101698-101710}, shortjournal = {IEEE Access}, title = {Area-efficient variable-gain delta-sigma modulator achieving 11.5-dB dynamic range improvement by sharing capacitors in the first-stage integrator}, volume = {13}, year = {2025}, }
• (2025). Kinematic adaptive frame recognition (KAFR): A novel framework for video segmentation via frame similarity and surgical tool tracking. ACCESS, 13, 101681-101697. (www)

  @article{ , abstract = {The interest in leveraging Artificial Intelligence (AI) for surgical procedures to automate analysis has witnessed a significant surge in recent years. One of the primary tools for recording surgical procedures and conducting subsequent analyses, such as performance assessment, is through videos. However, these operative videos tend to be notably lengthy compared to other fields, spanning from thirty minutes to several hours, which poses a challenge for AI models to effectively learn from them. Despite this challenge, the foreseeable increase in the volume of such videos in the near future necessitates the development and implementation of innovative techniques to tackle this issue effectively. In this article, we propose a novel technique called Kinematics Adaptive Frame Recognition (KAFR) that can efficiently eliminate redundant frames to reduce dataset size and computation time while retaining useful frames to improve accuracy. Specifically, we compute the similarity between consecutive frames by tracking the movement of surgical tools. Our approach follows these steps:1) Tracking phase: a YOLOv8 model is utilized to detect tools presented in the scene, 2) Similarity phase: Similarities between consecutive frames are computed by estimating variation in the spatial positions and velocities of the tools, 3) Classification phase: An X3D CNN is trained to classify segmentation. We evaluate the effectiveness of our approach by analyzing datasets obtained through retrospective reviews of cases at two referral centers. The newly annotated Gastrojejunostomy (GJ) dataset covers procedures performed between 2017 and 2021, while the previously annotated Pancreaticojejunostomy (PJ) dataset spans from 2011 to 2022 at the same centers. In the GJ dataset, each robotic GJ video is segmented into six distinct phases. By adaptively selecting relevant frames, we achieve a tenfold reduction in the number of frames while improving accuracy by 4.32% (from 0.749 to 0.7814) and the F1 score by 0.16%. Our approach is also evaluated on the PJ dataset, demonstrating its efficacy with a fivefold reduction of data and a 2.05% accuracy improvement (from 0.8801 to 0.8982), along with 2.54% increase in F1 score (from 0.8534 to 0.8751). In addition, we also compare our approach with the state-of-the-art approaches to highlight its competitiveness in terms of performance and efficiency. Although we examined our approach on the GJ and PJ datasets for phase segmentation, this could also be applied to broader, more general surgical datasets. Furthermore, KAFR can serve as a supplement to existing approaches, enhancing their performance by reducing redundant data while retaining key information, making it a valuable addition to other AI models.}, archive = {J_ACCESS}, author = {Huu Phong Nguyen and Shekhar Madhav Khairnar and Sofia Garces Palacios and Amr Al-Abbas and Melissa E. Hogg and Amer H. Zureikat and Patricio M. Polanco and Herbert J. Zeh and Ganesh Sankaranarayanan}, doi = {10.1109/ACCESS.2025.3573264}, journal = {IEEE Access}, pages = {101681-101697}, shortjournal = {IEEE Access}, title = {Kinematic adaptive frame recognition (KAFR): A novel framework for video segmentation via frame similarity and surgical tool tracking}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning framework for early-stage alzheimer’s disease classification from neuro-imaging biomarkers. ACCESS, 13, 101662-101680. (www)

  @article{ , abstract = {Alzheimer’s disease, a leading neurodegenerative disorder, poses significant challenges to modern healthcare, emphasizing the urgent need for precise early-stage diagnostics. Despite promising insights from neuroimaging into potential AD biomarkers, the intricacy of data interpretation demands advanced computational techniques. Existing diagnostic tools have yet to fully harness the combined strengths of neuroimaging and modern deep-learning architectures for nuanced, early-stage AD classification. In this study, we propose a novel hybrid deep learning framework that ensembles three state-of-the-art architectures—EfficientNetB7, Xception, and MobileNetV3Large—through a soft-voting strategy to classify Alzheimer’s disease across multiple stages. Our ensemble methodology deciphers complex patterns within Magnetic Resonance Imaging, mapping them to specific AD early stages. Integrating pre-trained model weights and adaptive learning rates ensures swift convergence and optimal performance. Benchmarking against a comprehensive AD neuro-imaging dataset revealed that our ensemble approach achieves a groundbreaking precision of 99.87%. These results significantly surpass those of individual architectures addressing this problem. Evaluation metrics like precision-recall and f1 score nearly approached 100% across different experiment stages. With inference times as low as 3ms for MobileNetV3Large, our model is highly suitable for real-time deployment in clinical environments. Furthermore, Grad-CAM explainability is incorporated to highlight critical brain regions contributing to the model’s predictions, improving clinical trust. To the best of our knowledge, this is the first ensemble-based deep learning framework combining EfficientNetB7, Xception, and MobileNetV3Large for multi-stage Alzheimer’s classification. The proposed hybrid model can confidently replace conventional diagnostic methods. As medical science is quickly changing and adapting AI decisions, our methodology is helpful for early diagnosis of dementia detection in the early stages of preemptive treatment.}, archive = {J_ACCESS}, author = {Samina Akram and Muhammad Amjad Iqbal and Muhammad Rashid and Muhammad Shahid Bhatti and Benish Fida}, doi = {10.1109/ACCESS.2025.3574039}, journal = {IEEE Access}, pages = {101662-101680}, shortjournal = {IEEE Access}, title = {A hybrid deep learning framework for early-stage alzheimer’s disease classification from neuro-imaging biomarkers}, volume = {13}, year = {2025}, }
• (2025). STAA: Spatio-temporal attention attribution for real-time interpreting transformer-based AI video models. ACCESS, 13, 101647-101661. (www)

  @article{ , abstract = {Transformer-based video models have performed SOTA in various action recognition and video understanding tasks. The limitation is the lack of explainability of these complex models. Current Explainable AI (XAI) methods focus on feature importance analysis of only one dimension, either spatial or temporal features. When applied to transformer-based video models, the challenges are two-fold. They fail to capture the integrated spatio-temporal nature of video data and incur prohibitive computational costs for real-time applications. This paper presents STAA (Spatio-Temporal Attention Attribution), an XAI method for interpreting video transformer models for action recognition tasks. STAA simultaneously extracts both spatial importance within frames and temporal relevance across the video sequence from attention values in transformers. This unified approach solves the challenge by capturing how transformer models integrate information across both dimensions for decision-making, providing comprehensive explanations that reflect the model’s actual reasoning process. We enhance STAA’s raw output through post-processing. The experiments on the Kinetics-400 dataset demonstrate superior faithfulness ( $0.844~\pm ~0.116$ ) and monotonicity ( $0.850~\pm ~0.030$ ). In terms of computational overhead, our STAA method is applicable for real-time video XAI analysis applications. We implement a cloud-based architecture that enables video explanations with 150 ms latency.}, archive = {J_ACCESS}, author = {Zerui Wang and Yan Liu}, doi = {10.1109/ACCESS.2025.3575440}, journal = {IEEE Access}, pages = {101647-101661}, shortjournal = {IEEE Access}, title = {STAA: Spatio-temporal attention attribution for real-time interpreting transformer-based AI video models}, volume = {13}, year = {2025}, }
• (2025). Research on lingnan culture image restoration methods based on multi-scale non-local self-similar learning. ACCESS, 13, 101639-101646. (www)

  @article{ , abstract = {In the process of digital preservation of Lingnan cultural heritage, Lingnan cultural images often suffer from multiple degradation problems such as noise pollution, blurred details and color distortion due to storage, transmission and environmental factors, which seriously affects the complete presentation and inheritance of cultural heritage. This paper proposes an image restoration method based on multi-scale non-local self-similar learning. This method combines the improved non-local attention mechanism with the multi-scale codec structure and utilizes deep learning technology to achieve high-quality restoration of Lingnan cultural images. The experimental results show that, compared with the traditional methods, the method proposed in this paper has significant advantages in maintaining the authenticity of image details and colors, providing effective technical support for the digital protection of Lingnan cultural heritage.}, archive = {J_ACCESS}, author = {Shang Xinping}, doi = {10.1109/ACCESS.2025.3576817}, journal = {IEEE Access}, pages = {101639-101646}, shortjournal = {IEEE Access}, title = {Research on lingnan culture image restoration methods based on multi-scale non-local self-similar learning}, volume = {13}, year = {2025}, }
• (2025). An integrated theoretical model for assessing digital literacy’s impact on academic performance: A case study using PLS-SEM. ACCESS, 13, 101624-101638. (www)

  @article{ , abstract = {As technology becomes integral to education, especially with the emergence of advanced artificial intelligence applications, students require advanced digital literacy (DL) skills to succeed both academically and professionally. DL, defined as the ability to use digital technologies with a positive attitude toward digital learning, is explored in this study. The literature review for this study identified gaps in understanding the relationship between DL and academic performance. This study aims to explore digital competence among higher education students using a tailored theoretical model, focusing on the impact of these competencies on academic performance. Employing a customized DL theoretical model that incorporates 22 competencies across three domains namely “Computer and Information Literacy”, “Ethics, Privacy, and Safety”, “Advanced Production and Problem-solving”. This research used partial least squares structural equation modeling (PLS -SEM) and other statistical methods were used to measure the validity, collinearity, and reliability of variables, as well as to evaluate the study model. The findings revealed a significant correlation between computer and information literacy and student academic performance with variations according to demographic factors. This study enhances academic understanding of DL and offers practical insights into educational strategies and policy development in higher education. This streamlined approach to developing a DL model addresses the changing educational needs of undergraduate students.}, archive = {J_ACCESS}, author = {Suadad Muammar and Piyush Maheshwari and Shadi Atalla}, doi = {10.1109/ACCESS.2025.3578107}, journal = {IEEE Access}, pages = {101624-101638}, shortjournal = {IEEE Access}, title = {An integrated theoretical model for assessing digital literacy’s impact on academic performance: A case study using PLS-SEM}, volume = {13}, year = {2025}, }
• (2025). Selective intensity ensemble classifier (SIEC): A triple-threshold strategy for microscopic malaria cell image classification. ACCESS, 13, 101609-101623. (www)

  @article{ , abstract = {Accurate malaria detection in resource-limited settings requires robust solutions—here, we introduce a selective intensity ensemble classifier (SIEC) that applies a triple-threshold strategy for enhanced microscopic image classification. This involves training three separate convolutional neural network models on the same images processed with different pixel-intensity thresholds: original, pixels above 100, and pixels above 200. This approach enables the ensemble to capture complementary low-, mid-, and high-intensity features, enhancing feature diversity and classification accuracy. The experiments were conducted on the publicly available Malaria Cell Dataset, consisting of 27,558 images. The proposed SIEC achieved an accuracy of 95.09%, with a precision of 95.27%, and matching recall and F1 scores of 95.09%, consistently outperforming six standard CNN models, including ResNet50, VGG16, Inception, and MobileNetV2. Notably, the combination of the 100-pixel filtered and original images yielded the highest classification performance, demonstrating the ensemble’s ability to integrate detailed and abstracted features effectively. These findings highlight SIEC as a promising and scalable solution for automated malaria detection and broader diagnostic imaging tasks.}, archive = {J_ACCESS}, author = {Abdulaziz Anorboev and Sarvinoz Anorboeva and Javokhir Musaev and Esanbay Usmanov and Dosam Hwang and Yeong-Seok Seo and Jeongkyu Hong}, doi = {10.1109/ACCESS.2025.3574528}, journal = {IEEE Access}, pages = {101609-101623}, shortjournal = {IEEE Access}, title = {Selective intensity ensemble classifier (SIEC): A triple-threshold strategy for microscopic malaria cell image classification}, volume = {13}, year = {2025}, }
• (2025). Research on citrus leaf disease recognition using class-agnostic contrastive learning and supervised organizational mapping. ACCESS, 13, 101592-101608. (www)

  @article{ , abstract = {Early detection of plant diseases is crucial for enhancing agricultural productivity and ensuring crop protection. While computer vision offers scalable alternatives to manual inspection, existing methods face two understudied challenges: 1) performance degradation in cluttered real-world conditions, and 2) ineffective feature separation for randomly distributed lesions with diverse symptoms. Notably, current research predominantly relies on controlled-environment datasets, lacking publicly accessible benchmarks with authentic field complexities. To address these, we present the first large-scale citrus leaf disease dataset (5,763 images, 6 categories) capturing complex field conditions including varying illumination, occlusion, multi-scale lesions, and deceptive backgrounds. Our key methodological innovations include a three-stage weakly supervised framework that systematically resolves background interference and feature redundancy: 1) Class-agnostic contrastive localization enabling precise lesion mapping without region annotations, 2) Stable feature initialization through region-guided classification training, 3) Sparse orthogonal mapping with experimentally-determined nodes introducing orthogonality constraints to enhance feature separation. Through feature alignment and feature fusion, an accuracy of 97.1% is achieved (+2.4% vs ResNet-18). Evaluations on citrus leaf and public datasets demonstrate superior accuracy and interpretable lesion localization, advancing plant disease diagnosis.}, archive = {J_ACCESS}, author = {Wen Xiao and Jinxia Shang and Fan Li and Ou Ao and Xin Wang and Shiyi Tian}, doi = {10.1109/ACCESS.2025.3557929}, journal = {IEEE Access}, pages = {101592-101608}, shortjournal = {IEEE Access}, title = {Research on citrus leaf disease recognition using class-agnostic contrastive learning and supervised organizational mapping}, volume = {13}, year = {2025}, }
• (2025). Incentive mechanism for data sharing in smart manufacturing under the industrial internet. ACCESS, 13, 101575-101591. (www)

  @article{ , abstract = {This study proposes an incentive mechanism for data sharing in smart manufacturing under the Industrial Internet. The mechanism addresses several key challenges in data sharing, including data silos among manufacturing devices, insufficient incentive structures, dynamic supply-demand imbalances under production task and equipment state constraints, and the high computational complexity of achieving global equilibrium in large-scale distributed systems. Grounded in Walrasian equilibrium theory, this mechanism models device holders and the Industrial Internet Platform as participants in a competitive market. By leveraging price signals, it enables the adaptive matching of data supply from device holders with globally optimized demand, ensuring Pareto-optimal outcomes in terms of device holder revenue, production efficiency, and overall social welfare. Furthermore, numerical experiments are conducted to analyze the impact of key parameters on the optimal data pricing and data upload volume. The results demonstrate that this mechanism effectively maximizes the utility of the production system. The comparative experiments prove that this incentive mechanism has significant advantages in terms of optimal data price and social welfare. However, due to the heterogeneity and nonlinearity of actual utilities among device holders and the platform, further validation and refinement of the model using real-world transaction data are necessary.}, archive = {J_ACCESS}, author = {Jialiang Zhang and Xinxin Guo and Yongtao Peng}, doi = {10.1109/ACCESS.2025.3576771}, journal = {IEEE Access}, pages = {101575-101591}, shortjournal = {IEEE Access}, title = {Incentive mechanism for data sharing in smart manufacturing under the industrial internet}, volume = {13}, year = {2025}, }
• (2025). A DSL-driven solution and code generation for accelerated tourism decision support system development. ACCESS, 13, 101558-101574. (www)

  @article{ , abstract = {Decision Support Systems (DSS) play a crucial role in commerce and tourism by assisting decision-making through data analysis, modeling, and visualization. However, their development is often complex, costly, and requires expertise in both software engineering and Artificial Intelligence, making them inaccessible to many organizations. To address these challenges, this paper introduces a Domain-Specific Language (DSL), a graphical decision rule modeling tool, and code generation templates designed to streamline DSS development for smart tourism. The proposed framework enables rule-based system modeling and automatic code generation, leveraging Drools, a powerful rule engine, to manage business rules, execute inferences, and optimize decision processes. This approach significantly reduces development time while improving system flexibility and adaptability to meet the diverse needs of smart tourism. A comparative study demonstrates a substantial reduction in development time, while a case study confirms the framework’s ability to generate functional applications suited to real-world environments. Our contribution lies in automating and simplifying DSS development by integrating a dedicated DSL and Drools, reducing manual programming complexity, and ensuring the generation of optimized, maintainable, and easily integrable code. This enhances productivity, reduces development effort, and ensures adaptability to changing business needs.}, archive = {J_ACCESS}, author = {Adnane Souha and Charaf Ouaddi and Lamya Benaddi and Hamza Abdelmalek and El Mahi Bouziane and Abdeslam Jakimi}, doi = {10.1109/ACCESS.2025.3578440}, journal = {IEEE Access}, pages = {101558-101574}, shortjournal = {IEEE Access}, title = {A DSL-driven solution and code generation for accelerated tourism decision support system development}, volume = {13}, year = {2025}, }
• (2025). Quantitative assessment of data volume requirements for reliable machine learning analysis. ACCESS, 13, 101545-101557. (www)

  @article{ , abstract = {Applying machine learning (ML) techniques in the context of limited data remains a challenge of practical importance. Questions on both the sufficiency of a given dataset for ML data analysis and data acquisition planning arise. Both aspects are quantitatively addressed in this work. The first one is treated as part of dataset diagnostics, which includes the analysis of learning curves, the quantification of confidence intervals (CI) during model evaluation and further dataset metrics. Regarding data generation, data sufficiency considerations must cover both model training and testing. Twenty case datasets are diagnosed, and serve as a reference for establishing an empirical rule for data sufficiency estimation. The results indicate that the relevant aspects are the ratio of data volume to the number of input features, the complexity of the involved data correlations and the applied definition of the small data regime. The presented framework allows for an improved pre-assessment of data sufficiency for a typical given dataset and facilitates a reliable implementation of ML in data volume-limited contexts.}, archive = {J_ACCESS}, author = {Xukuan Xu and Jinghou Bi and Michael Moeckel and Hajo Wiemer and Steffen Ihlenfeldt}, doi = {10.1109/ACCESS.2025.3578528}, journal = {IEEE Access}, pages = {101545-101557}, shortjournal = {IEEE Access}, title = {Quantitative assessment of data volume requirements for reliable machine learning analysis}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based multi-floor indoor localization using smartphone IMU sensors with 3D location initialization. ACCESS, 13, 101532-101544. (www)

  @article{ , abstract = {Pedestrian Dead Reckoning (PDR) is a widely used indoor localization technique that does not require infrastructure and can exploit smartphone inertial sensors. However, it suffers from accumulated drift, and accurate initial position and floor information are especially important in multi-floor environments. This study proposes a deep learning-based multi-floor indoor localization method that estimates a three-dimensional position using only smartphone sensors without relying on external infrastructure. The proposed method utilizes the smartphone magnetometer to perform accurate 3D position initialization and drift correction, thereby addressing major limitations of conventional PDR methods such as sensitivity to initialization errors and performance degradation caused by drift. In this paper, we explain conventional IMU sensor-based PDR methods and describe the problems of PDR as well as the limitations of barometer-based floor transition detection techniques. The proposed initial 3D position estimation method detects the initial floor using a deep learning model trained on magnetic field sequences from the user’s first 10 steps, and estimates the 2D position by comparing the current magnetometer data with a pre-built magnetic field map using the k-nearest neighbors algorithm. After detecting the initial 3D position, the method combines barometer-based floor transition detection and PDR-based position estimation to enable multi-floor indoor localization, and addresses the drift problem using correction nodes based on magnetic field data. As a result of experiments conducted in multi-floor buildings, the proposed method demonstrated significantly superior performance in terms of path similarity and localization accuracy compared to conventional methods based on PDR, complementary filter, and Kalman filter. In particular, it achieved a localization error of 64 cm and up to 99.8% accuracy within a 2-meter threshold, proving the effectiveness and practicality of the proposed approach.}, archive = {J_ACCESS}, author = {Jin-Woo Kim and Yoan Shin}, doi = {10.1109/ACCESS.2025.3578354}, journal = {IEEE Access}, pages = {101532-101544}, shortjournal = {IEEE Access}, title = {Deep learning-based multi-floor indoor localization using smartphone IMU sensors with 3D location initialization}, volume = {13}, year = {2025}, }
• (2025). Research on hybrid architecture neural networks for time series prediction. ACCESS, 13, 101507-101531. (www)

  @article{ , abstract = {In recent years, agricultural product price forecasting has played a crucial role in supply chain management and market decision-making. However, due to the influence of multiple factors such as supply-demand dynamics, seasonality, and weather conditions, agricultural product prices exhibit highly nonlinear and dynamic characteristics, making traditional methods often insufficient for accurate price change prediction. To address these challenges, this paper proposes a novel time series prediction model based on Temporal Convolutional Network (TCN), Multi-Layer Perceptron (MLP), and attention mechanism (referred to as “TCN-MLP-Attention”), using daily average price data from the U.S. avocado market from 2015 to 2023. The model aims to provide more accurate price trend forecasts for various stages of the supply chain. In the model architecture design, the TCN module utilizes causal convolution structure and dilated convolution to handle long-term temporal dependencies; the MLP module performs feature transformation and nonlinear mapping through a two-layer network; and the attention mechanism identifies important features at key time steps. This multi-module collaborative architecture effectively processes multi-scale features of time series data while providing model interpretability. Through comparative analysis of various optimization algorithms’ convergence performance and prediction accuracy, this study found that the AdamW optimizer, with its effective weight decay mechanism and adaptive learning rate, demonstrated superior performance in training stability and generalization capability, with MSE and R2 metrics outperforming traditional optimizers. Therefore, the AdamW optimizer with an adaptive learning rate adjustment strategy was adopted for model training. Experimental results show that the proposed TCN-MLP-Attention model exhibits better prediction performance compared to the baseline Transformer model: MSE decreased by approximately 1.24%, and R2 increased by about 0.17%, indicating stronger generalization ability in capturing time series features. Although MAE was slightly higher, the overall error control remained within an acceptable range, validating the model’s stability and effectiveness. The importance of the attention mechanism was further verified through ablation experiments, with the complete model’s R2 value improving by 87.04% and MSE decreasing by 87.18% compared to using TCN alone, confirming the effectiveness of the multi-module collaborative design. The model’s generalization capability is further validated through noise robustness tests and cross-product validation experiments. Even with medium-level noise ( $\sigma \approx 0.05$ ), the model maintains 77% of its R2 value, and when applied to strawberry price prediction using the dataset published by the United States Department of Agriculture Economic Research Service (USDA ERS)—a product with significantly different market characteristics—it achieves an R2 value of 0.7499 without any retraining, demonstrating strong adaptability to different data distributions. This research not only demonstrates the superiority of this hybrid architecture in handling complex time series prediction tasks, especially showing strong adaptability during market fluctuation periods, but also provides new research directions for deep learning applications in agricultural price prediction.}, archive = {J_ACCESS}, author = {Fujin Zhuang and Xiao Chen and Punyaphol Horata and Khamron Sunat}, doi = {10.1109/ACCESS.2025.3578325}, journal = {IEEE Access}, pages = {101507-101531}, shortjournal = {IEEE Access}, title = {Research on hybrid architecture neural networks for time series prediction}, volume = {13}, year = {2025}, }
• (2025). Decentralized and joint resource allocation, beamforming, and beamcombining for 5G networks with heterogeneous MARL. ACCESS, 13, 101491-101506. (www)

  @article{ , abstract = {In this paper, we propose a novel Multi-Agent Reinforcement Learning (MARL) -based paradigm for distributed and joint resource allocation, beamforming (BF), and beam combining of uplink transmissions in 5G networks. The proposed paradigm employs two types of heterogenous agents that learn to perform and optimize different tasks in order to achieve the main objective of the system, as well as the objective of the individual agents. In the proposed paradigm, UEs can be multi-agents that optimize their own resource allocation and BF. In addition to these multi agents (i.e., UEs), the BS is a different type of agent that optimizes the combining of UEs’ transmissions. We developed three different implementations of our proposal using three different MARL algorithms: Independent Q Learners (IQL), Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and QTRAN. Various experiments were conducted to validate the usability of our proposal. Our results show that the proposed paradigm can successfully optimize the task of joint resource allocation, beamforming, and combining. Furthermore, we provide a comparative analysis of the three different implementations, highlighting noteworthy insights into the strengths and limitations of fully distributed algorithms, such as IQL, in comparison to algorithms employing the Centralized Training with Decentralized Execution (CTDE) framework, exemplified by QTRAN and MADDPG.}, archive = {J_ACCESS}, author = {Ala’a Al-Habashna and Jon Menard and Gabriel Wainer and Gary Boudreau}, doi = {10.1109/ACCESS.2025.3576190}, journal = {IEEE Access}, pages = {101491-101506}, shortjournal = {IEEE Access}, title = {Decentralized and joint resource allocation, beamforming, and beamcombining for 5G networks with heterogeneous MARL}, volume = {13}, year = {2025}, }
• (2025). FISNET: A learnable fusion-based iris segmentation network improving robustness across NIR and VIS modalities. ACCESS, 13, 101472-101490. (www)

  @article{ , abstract = {Iris is one of the most distinctive biometric traits used for reliable identity verification in applications such as border control, secure access systems, and national ID programs. The primary challenge in iris recognition is the reliable segmentation of the iris region from the eye image. Segmenting irises captured under different imaging conditions is challenging for a single model due to variations in spectral features, texture, lighting conditions, and noise patterns between NIR and VIS images. To tackle this problem, we present Fused Iris Segmentation Network (FISNET) that combines segmentation maps from two models to achieve enhanced precision and accuracy. FISNET demonstrates robust generalizability across varying lighting, resolution, and sensor types, consistently outperforming individual models on all NIR and VIS datasets, including smartphone-captured images. The performance of FISNET was evaluated on CASIA-V4 subsets, Dark and Blue iris datasets, and the AFHIRIS dataset, achieving superior segmentation accuracy and recognition performance. The results demonstrate significant improvements over the IrisParseNet, PixlSegNet, and SAM model, achieving remarkable mIoU scores of 0.955, 0.930, 0.945, 0.955, 0.907, 0.815, 0.924, 0.913, 0.842, 0.852, 0.829, and 0.839 on the Lamp-V4, Interval-V4, Thousand-V4, Syn-V4, Twins-V4, UBIRIS-V2, BI-P1, BI-P2, DI-P1, DI-P2, DI-P3, and AFHIRIS-V1 datasets, respectively. Additionally, the Type-I error rate (E1) achieved exceptional results, with values of 0.0014, 0.0067, 0.0016, 0.0012, 0.0027, 0.0023, 0.0015, 0.0019, 0.0014, 0.0015, 0.0020, and 0.0069 across these datasets, further emphasizing the superiority of the proposed approach. Code is available at https://github.com/GeetanjaliGTZ/FIS-Net-NIR-and-VIS-Iris-Segmentation}, archive = {J_ACCESS}, author = {Geetanjali Sharma and Gaurav Jaswal and Aditya Nigam and Raghavendra Ramachandra}, doi = {10.1109/ACCESS.2025.3578293}, journal = {IEEE Access}, pages = {101472-101490}, shortjournal = {IEEE Access}, title = {FISNET: A learnable fusion-based iris segmentation network improving robustness across NIR and VIS modalities}, volume = {13}, year = {2025}, }
• (2025). Cyber-physical emulation and threat scenario simulation for enhanced microgrid resilience. ACCESS, 13, 101455-101471. (www)

  @article{ , abstract = {Microgrid systems have gained significant prominence as compelling platforms for investigating and validating advanced power system operations due to their inherent flexibility and resilience. However, such systems are susceptible to complex cybersecurity risks that require proactive and robust defense mechanisms. Failure to address these vulnerabilities can severely undermine the coordination and power-sharing mechanisms among distributed generators (DGs). This paper introduces a cyber-physical emulation of an existing microgrid at the University of St. Thomas with its overlaid synthetic cyber network to evaluate its vulnerabilities and strengthen its security. Unlike previous testbeds that focus primarily on single-layer analysis or simplified attack models, our framework uniquely integrates real-world microgrid specifications with comprehensive cyber network emulation, enabling the first systematic evaluation of complex multi-stage attacks on operational microgrids using industry-standard protocols and structured adversarial techniques. Our results reveal that correlation analysis identified critical attack relationships. Implementing these focused security measures shows a reduction in attack success rates by 64% and improved system recovery time following disruptions, demonstrating the framework’s effectiveness for enhancing microgrid resilience. Different threat scenarios, guided by the MITRE ATT&CK framework, are implemented to systematically assess the system’s susceptibility to cyberattacks. Additionally, the Distributed Network Protocol 3 (DNP3) is leveraged to collect data in the form of DNP3 objects, enabling effective monitoring and control within this threat simulation. Our findings demonstrate the enhanced resilience of the microgrid when subjected to various cyber threats. Furthermore, it highlights how the proposed methodology can be scaled to safeguard larger power systems.}, archive = {J_ACCESS}, author = {Khandaker Akramul Haque and Mohamed Massaoudi and Leen Al Homoud and Katherine R. Davis and Mahmoud Kabalan and Hassan Salamy}, doi = {10.1109/ACCESS.2025.3578421}, journal = {IEEE Access}, pages = {101455-101471}, shortjournal = {IEEE Access}, title = {Cyber-physical emulation and threat scenario simulation for enhanced microgrid resilience}, volume = {13}, year = {2025}, }
• (2025). Remote aerial vehicle solutions for weed detection in precision agriculture. ACCESS, 13, 101440-101454. (www)

  @article{ , abstract = {This study presents a novel Remote Aerial Vehicle-based approach for detecting pigweeds in soybean (Glycine max) fields using a combination of deep learning and advanced image processing techniques. A custom, high-resolution dataset comprising RGB (red, green, and blue) and multispectral images was collected from USDA operated fields and manually annotated for target pigweed detection. Beyond incorporating YOLOv8 variants for real-time weed classification, this study integrates a comprehensive image processing pipeline, incorporating global thresholding, k-means clustering, 3D surface mapping, and spectral signature analysis to enhance interpretability and detection accuracy. A comparative evaluation of YOLOv8 nano, small, medium, and large models, along with Faster R-CNN, confirmed YOLOv8’s balance between detection accuracy and real-time performance for deployment in precision agriculture. The YOLOv8 nano model emerged as the most balanced in terms of precision (75.6%), recall (81.7%), and mAP@0.5 (81.5%), demonstrating effective weed detection performance under real field conditions. In addition, quadrant-level weed coverage and spatial heatmaps were generated to support targeted interventions. This study advances the current state of RAV based weed detection by providing a field-ready, explainable, and resource-efficient solution, contributing to sustainable farming and data-driven weed management practices.}, archive = {J_ACCESS}, author = {Shekhar S. Borah and Aryan Anand and Prabha Sundaravadivel and Reginald Fletcher and Krishna N. Reddy}, doi = {10.1109/ACCESS.2025.3575601}, journal = {IEEE Access}, pages = {101440-101454}, shortjournal = {IEEE Access}, title = {Remote aerial vehicle solutions for weed detection in precision agriculture}, volume = {13}, year = {2025}, }
• (2025). The characterization and evaluation of dimensional stability for highly stable composites. ACCESS, 13, 101430-101439. (www)

  @article{ , abstract = {In this study, we explored the dimensional stability of high-performance carbon fiber reinforced plastics (CFRP) under regulated thermal conditions. This study systematically examined the interplay between temperature variations and structural response via experimental characterization and analytical calculations, establishing a methodology for assessing material properties in dynamic thermal environments. By associating the temperature stability threshold with nanoscale dimensional fluctuations within the low-frequency domain, this study enhances our understanding of CFRP reliability in high-stability applications such as aerospace systems or optical instruments. The findings of this research suggest that the dimensional stability of the test specimens satisfies the fundamental standards for practical applications, and that achieving dimensional stability exceeding 1 nm/Hz ${}^{\mathrm {1/2}}$ (0.005Hz~0.1Hz) through optimized thermal management is attainable. This offers actionable insights for material selection and design in domains requiring extremely high dimensional precision, bridging the essential gap between the fundamentals of materials science and practical engineering requirements.}, archive = {J_ACCESS}, author = {Baolong Sun and Hansi Zhang and Xiaoyu Wang and Chuang Xue and Yue Qiu}, doi = {10.1109/ACCESS.2025.3578559}, journal = {IEEE Access}, pages = {101430-101439}, shortjournal = {IEEE Access}, title = {The characterization and evaluation of dimensional stability for highly stable composites}, volume = {13}, year = {2025}, }
• (2025). An X-band dual function transceiver utilizing digital predistortion for radar and communication in 250 nm GaN HEMT. ACCESS, 13, 101415-101429. (www)

  @article{ , abstract = {We present an X-band transceiver with memory polynomial digital predistortion (MP-DPD) and neural network digital predistortion (NN-DPD) for a dual-function radar and communication (DFRC) system targeting 6G FR3 in $0.25\mu $ m GaN High Electron Mobility Transistor (HEMT) technology. In the implemented transceiver, we integrated a transmit and receive switch (T/R switch), high-power amplifier (HPA), and low noise amplifier (LNA) into a single-chip. By utilizing shunt switches and quarter-wave transmission-lines ( $\lambda $ /4 T-lines) without series switches, the T/R switch achieved an insertion loss of 0.69dB, and the input 0.2dB compression point was 44 dBm. In the transmit (Tx) path, the HPA and T/R switch achieved a bandwidth of 8.4 – 10GHz, an output power (P ${}_{\mathrm {out}}$ ) of 40.8dBm, and a power added efficiency (PAE) of 33.1% at 9GHz. By eliminating a limiter and circulator in the receive (Rx) path owing to the superior breakdown performance of the GaN HEMT, the designed MMIC achieved a noise figure of 2.14dB at 8.5GHz, which is comparable with that of the Rx using a GaAs LNA with a limiter and circulator. The bandwidth of the LNA with T/R switch was 7.3GHz - 10.6GHz, and the power gain was 33.5dB. To mitigate the nonlinearity of GaN HEMTs for 16-QAM and 64-QAM signals, NN-DPD was adopted. At 10GHz carrier frequency and 1GHz bandwidth, the NN-DPD improved the 16-QAM EVM from –24.0dB to –32.4dB and ACLR from 29.3dB to 39.3dB. Similarly, the 64-QAM performance rose from –25.1dB to –34.6dB in EVM and from 28.7dB to 45.4dB in ACLR while transmitting a 6Gbps signal. The fabricated X-band MMIC has outstanding performance and is perfectly suitable for implementing high-performance DFRC applications.}, archive = {J_ACCESS}, author = {Tae-Hoon Kim and Jun Kwon and Mun-Kyo Lee and Bok-Hyung Lee and Jung-Dong Park}, doi = {10.1109/ACCESS.2025.3577194}, journal = {IEEE Access}, pages = {101415-101429}, shortjournal = {IEEE Access}, title = {An X-band dual function transceiver utilizing digital predistortion for radar and communication in 250 nm GaN HEMT}, volume = {13}, year = {2025}, }
• (2025). Method for knowledge transfer via multi-task semi-supervised self-paced. ACCESS, 13, 101405-101414. (www)

  @article{ , abstract = {Adequate labeled data is essential for learning a reliable and generalizable model in many machine learning tasks. However, labeled data is becoming scarce and costly to obtain, which has spurred consistent interest in knowledge transfer techniques. Therefore, semi-supervised and multi-task learning is combined to alleviate the challenge, but the complexity of the task should be considered. To achieve more effective knowledge transfer with limited labeled data, we propose a unified multi-task semi-supervised self-paced learning (MSSP) scheme in this paper. MSSP naturally integrates the common structures shared by multiple related tasks and the manifold structure regularized by unlabeled data, enabling the respective knowledge transferred from the feature space and the instance space to complement and constrain each other. This leads to faster and more accurate searches in the underlying hypothesis space. We adopt Alternating convex search (ACS) method to solve MSSP, that is, each iteration sequentially trains the prediction model with a fixed set of labeled instances and then updates the labeled training set by adding more complex instances. With the aid of a self-controlled learning pace, a more robust and globally optimal model can be gradually constructed. Experimental results on several benchmark datasets show that our method achieves a performance gain of 3%-15% in classification accuracy compared to baseline algorithms, along with significant advantages in convergence speed.}, archive = {J_ACCESS}, author = {Yao Zhao and Hongying Liu and Huaxian Pan and Zhen Song and Chunting Liu and Anni Wei and Baoshuang Zhang and Wei Lu}, doi = {10.1109/ACCESS.2025.3574982}, journal = {IEEE Access}, pages = {101405-101414}, shortjournal = {IEEE Access}, title = {Method for knowledge transfer via multi-task semi-supervised self-paced}, volume = {13}, year = {2025}, }
• (2025). Robotic mobile fulfillment systems: Are hyper-heuristics a feasible approach when dealing with the pod allocation and item storage problems?. ACCESS, 13, 101385-101404. (www)

  @article{ , abstract = {The rise of online shopping has forced warehouses to improve their operations to keep up with demand. In this work, we focus on a warehouse type known as the Robotic Mobile Fulfillment System (RMFS). The literature shows that modern optimization techniques have been explored for the RMFS. However, its complexity restricts the simultaneous optimization of the whole system. For this work, we tackle two poorly explored subproblems: the Item Storage Problem (ISP) and the Pod Allocation Problem (PAP). We analyze the effects of several heuristics (available within the RAWSim-O framework) and hyper-heuristics on the throughput time and the number of orders completed within a 24-hour simulation window. All tests were run in 60 instances, split into four sets. Moreover, we integrated two main stages. First, we considered a baseline given by the best solution for each instance found in a previous work. Our goal was to assess how performance changes when dealing with ISP and PAP simultaneously. We found that although performance improves, no single set of heuristics works best for all instances. The best combination of heuristics provides the best solution in only 23.33% of the instances. Moreover, even if we can solve half the instances with only three combinations of heuristics, we need up to 11 to achieve the best solution in 80% of the instances. In the second stage, we wanted to analyze whether the effect of the ISP extended to sequence-based hyper-heuristics for the PAP. So, we considered a baseline given by the best hyper-heuristic from a previous work. Our data revealed that our best hyper-heuristic improves the solution of 70% of the instances when considering the throughput time, and of 67% of them when considering the number of orders. So, developing a high-level solver that simultaneously selects heuristics for these two problems seems to be a feasible approach to further enhance warehouse performance.}, archive = {J_ACCESS}, author = {Toby Yung and Maria Torcoroma Benavides-Robles and José Carlos Ortiz-Bayliss and Ivan Amaya}, doi = {10.1109/ACCESS.2025.3578401}, journal = {IEEE Access}, pages = {101385-101404}, shortjournal = {IEEE Access}, title = {Robotic mobile fulfillment systems: Are hyper-heuristics a feasible approach when dealing with the pod allocation and item storage problems?}, volume = {13}, year = {2025}, }
• (2025). Investigation of compact metallic environments on power attenuation and read performance of HF RFID. ACCESS, 13, 101368-101384. (www)

  @article{ , abstract = {Radio Frequency Identification (RFID) technology has revolutionized inventory management, enhancing tracking efficiency across industries. However, its reliability is significantly compromised in metal-rich environments due to wave reflection, absorption, and signal obstruction, leading to missed reads, reduced detection range, and interference. This study presents a systematic investigation into the detection performance of High Frequency (HF) RFID-tagged inventories stored within confined metallic storage facilities. Specifically, this research examines how metallic environments create RFID interference by altering electromagnetic wave propagation, causing signal distortions, multi-path effects, and unpredictable attenuation patterns. Through simulation and experimentation, we analyze the impact of metallic shelves on RFID performance and explore strategic mitigation techniques, including optimized tag-to-reader placement, antenna configurations, and adaptive scanning methodologies. Our findings reveal unique interference characteristics in different metal scenarios, uncover detection patterns, and form the basis for proposing adaptive methods to enhance RFID readability. The study provides novel insights into optimizing RFID detection in challenging metallic environments and lays the groundwork for future advancements in RFID-based inventory tracking systems.}, archive = {J_ACCESS}, author = {Herrick Han Lin Yeap and Kok Seng Eu and Kian Meng Yap}, doi = {10.1109/ACCESS.2025.3576940}, journal = {IEEE Access}, pages = {101368-101384}, shortjournal = {IEEE Access}, title = {Investigation of compact metallic environments on power attenuation and read performance of HF RFID}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of the main heavyweight and lightweight SDLC for big data analytics systems (BDAS). ACCESS, 13, 101328-101367. (www)

  @article{ , abstract = {Big Data Analytics Systems (BDAS) are software systems developed with descriptive, predictive, or prescriptive decision-making purposes, and currently are implemented in diverse domains – i.e. Healthcare, Finance, and Manufacturing, among others -. BDAS emerged by the joint availability of Analytics techniques and affordable sources of massive internal and external datasets. However, despite the advanced technological progress – i.e. on BDAS algorithms, languages and platforms-, their development has been mainly through uncomplete ad-hoc or old rigorous heavyweight System Development Life Cycles (SDLC). Nowadays, the business competitive environment demands lightweight and agile SDLC, and initial ones have emerged for BDAS. However, studies comparing heavyweight vs lightweight BDAS SDLC are still scarce in the literature. This research, thus, addresses this knowledge gap, and report a comparative review between the main heavyweight (KDD, SEMMA, CRISP-DM, BDPL) and emergent lightweight (ASUM, TDSP, DDSL) SDLC for BDAS, selected through a Selective Systematic Literature Review (SSLR) research method for the 2000-2023 period. For this aim, a Pro Forma of the ISO/IEC 29110 standard – Basic Profile –is used to examine their conceptual structure – i.e. roles, phases-activities, products -. This comparative review provides theoretical and practical insights for discriminating both approaches for BDAS development and calls for further conceptual and empirical research.}, archive = {J_ACCESS}, author = {David Alejandro Montoya-Murillo and Sergio Galvan-Cruz and Manuel Mora and Estela Lizbeth Muñoz Andrade}, doi = {10.1109/ACCESS.2025.3577970}, journal = {IEEE Access}, pages = {101328-101367}, shortjournal = {IEEE Access}, title = {A comprehensive review of the main heavyweight and lightweight SDLC for big data analytics systems (BDAS)}, volume = {13}, year = {2025}, }
• (2025). Embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering. ACCESS, 13, 101313-101327. (www)

  @article{ , abstract = {Multi-view subspace clustering mines fusion maps that reflect the underlying structure of views in low-dimensional subspace. It has been broadly popularized for its capability to consolidate multi-view information effectively. The cubic time complexity of both graph construction and spectral clustering has greatly hindered the application of these algorithms to large-scale scenarios. Several research approaches have dramatically improved the clustering productivity by employing the anchor sampling mechanism. However, existing methods do not consider that large-scale data often includes extensive noise and anomalous information, which inevitably degrades the clustering performance by performing the selection of anchors in the primitive data space. Besides, these methods do not reveal the high-order relationships concealed behind multi-view data and recover the global low-rank of the anchor graphs. Given this, we present a new approach called embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering (ALTMSC). Specifically, we firstly map the multi-view data to a clean feature space through the feature transfer matrix, and adaptively accomplish anchor learning and the construction of embedded anchor graphs. As such, we can obtain high-quality anchor graphs. In addition, to enhance cross-view global consistency, we learn multiple intrinsic anchor graphs via rank-preserving decomposition to diminish the negative impact of view-specific statistical properties on consistency. Then, these intrinsic anchor maps are stacked into a third-order tensor with tensor nuclear norm constraint that can fully explore the high-order relationships between views. Compared with existing approaches, extensive experiments on eight datasets confirm the supremacy of ALTMSC.}, archive = {J_ACCESS}, author = {Yueyao Li and Yanying Mei and Zhenwen Ren and Bin Wu}, doi = {10.1109/ACCESS.2025.3575517}, journal = {IEEE Access}, pages = {101313-101327}, shortjournal = {IEEE Access}, title = {Embedded anchors coupled low-rank tensor learning for multi-view intrinsic subspace clustering}, volume = {13}, year = {2025}, }
• (2025). A new framework for classifying university-industry collaboration using synthetic minority oversampling technique and stacking ensemble. ACCESS, 13, 101297-101312. (www)

  @article{ , abstract = {University-industry collaboration has emerged as a critical driver of innovation and economic growth. However, predicting the outcomes of these collaborations remains methodologically challenging. Conventional statistical methods fail to capture non-linear relationships in collaboration data. While single machine learning models are increasingly adopted, they are often limited by class imbalance. This paper proposes a framework that integrates stacking ensemble and synthetic minority over-sampling technique (SMOTE) for classifying university-industry collaboration into different performance classes (low, high and medium). This approach leverages the collective merits of multiple single models, while mitigating class imbalance, to deliver optimal classification results. Our framework combines outputs of random forest, J48 and support vector machine base models, with the multilayer perceptron making final classifications. Utilizing the global competitiveness index dataset, we analyze four key predictive dimensions; higher learning and research institutes, government involvement, resources and absorptive capacity. Experimental results reveal the stacking ensemble-SMOTE300% model achieves superior accuracy (92.6%), surpassing other ensemble configurations (stacking ensemble-SMOTE200%: 90.3%, stacking ensemble-SMOTE100%: 88.2%) and single models (random forest: 87.7%, neural networks: 84.6%, J48: 84.2%, and support vector machines: 83.8%). Notably, stacking ensemble-300% reduces misclassifications of the minority class (high-performing collaborations) to 1.9%, a substantial improvement over random forest (13.9%). Thus, effectively resolving class imbalance. This enhancement comes with additional computational costs, a necessary trade-off for predictive benefits. Beyond methodological advancements, our framework identifies university-industry collaboration success factors, offering stakeholders a data-driven tool to inform resource allocation and targeted mitigation strategies. These advancements offer actionable insights to enhance collaboration success across various national contexts.}, archive = {J_ACCESS}, author = {Uzapi Hange and Ezenwa Chike Nwanesi and Monhesea Obrey Patrick Bah}, doi = {10.1109/ACCESS.2025.3577777}, journal = {IEEE Access}, pages = {101297-101312}, shortjournal = {IEEE Access}, title = {A new framework for classifying university-industry collaboration using synthetic minority oversampling technique and stacking ensemble}, volume = {13}, year = {2025}, }
• (2025). Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning. ACCESS, 13, 101274-101296. (www)

  @article{ , abstract = {In recent years, advances have been made in chaotic coverage path planning (CCPP) for autonomous search and traversal of spaces with limited environmental cues. However, the field remains unfit for practical application due to limited experimental work addressing three critical challenges: 1) an obstacle avoidance technique that reduces halts or disruptions in continuous chaotic trajectories, 2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and 3) a real-time coverage calculation technique that is accurate and independent of cell size. This study develops a novel framework implemented in ROS to address these challenges. We compare our Chaotic Coverage Path Planning (CCPP) approach against Boustrophedon Coverage Path Planning (BCPP) in both simulated and real-world environments. Across all three simulated environments of increasing complexity, BCPP consistently achieved shorter coverage times than our CCPP algorithm. However, an interesting pattern emerged when analyzing the relative performance degradation. When comparing coverage times across environments, CCPP showed less performance degradation than BCPP. Specifically, when normalizing coverage times against the simplest environment, BCPP’s performance degraded by factors of 3.31 and 3.59 for the medium and most complex environments, respectively, while CCPP showed lower degradation factors of 2.57 and 2.35. This suggests that CCPP may be more adaptive to increasing environmental size and complexity, despite having longer absolute coverage times. The source code is available at: https://gitlab.com/dsim-lab/paper-codes/Autonomous_search_of_real-life_environments}, archive = {J_ACCESS}, author = {Uyiosa Philip Amadasun and Patrick Mcnamee and Zahra Nili Ahmadabadi and Peiman Naseradinmousavi}, doi = {10.1109/ACCESS.2025.3577496}, journal = {IEEE Access}, pages = {101274-101296}, shortjournal = {IEEE Access}, title = {Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning}, volume = {13}, year = {2025}, }
• (2025). DBTU-net: A dual branch network fusing transformer and U-net for skin lesion segmentation. ACCESS, 13, 101262-101273. (www)

  @article{ , abstract = {The dermoscopy, as a non-invasive diagnostic tool, plays a significant role in the early diagnosis of skin cancer and in improving patient survival rates. However, the complexity of skin lesion regions, the ambiguity of their boundaries, and issues such as hair occlusion pose challenges for the segmentation of skin lesions. Currently, models based on the convolutional neural network (CNN) and Transformer have been widely used for the segmentation of skin lesions regions. However, CNN-based models struggle with long-range feature modeling, while Transformer-based models tend to pay less attention to local information, resulting in lower boundary accuracy. To address the aforementioned issues, we propose a dual branch network fusing Transformer and U-Net (DBTU-Net). DBTU-Net utilizes attention dense U-Net to capture local features and employs vision Transformer (ViT) to model long-range dependencies and the local contribution score of the image, thereby achieving a comprehensive extraction of both local and global features. Attention dense U-Net includes a triple fusion attention module that extracts features across the height, width, and channel dimensions, aiding the U-Net in capturing the interdependencies between channel and spatial locations. Additionally, we employ channel and spatial fusion attention after the attention dense U-Net to fuse channel and spatial information, thereby enhancing the CNN’s ability to capture long-range dependencies. DBTU-Net achieves accuracies of 0.9680, 0.9647, and 0.9623 on the ISIC-2017, ISIC-2018, and PH2 datasets, respectively, demonstrating its strong generalization capability and superior performance in segmenting skin lesion regions.}, archive = {J_ACCESS}, author = {Wanqing Peng and Jiaojiao Li and Dandan Lai and Qiaomin Lin and Ying Han and Guangrong Huang}, doi = {10.1109/ACCESS.2025.3578295}, journal = {IEEE Access}, pages = {101262-101273}, shortjournal = {IEEE Access}, title = {DBTU-net: A dual branch network fusing transformer and U-net for skin lesion segmentation}, volume = {13}, year = {2025}, }
• (2025). SecFedMDM-1: A federated learning-based malware detection model for interconnected cloud infrastructures. ACCESS, 13, 101246-101261. (www)

  @article{ , abstract = {Enforcing security and reliability in the cloud is a challenging but vital task because of the multitude of heterogeneous applications utilising the same infrastructure. Integrating a security analysis system to identify and mitigate potential threats, such as malicious software (Malware), is crucial for the effective functioning of cloud infrastructures. Over the past few decades, new malware analysis and detection approaches have emerged because of various malware strategies that evade host- and network-based security measures. This study established a federated learning-based malware detection model for interconnected cloud infrastructures. This method protects users’ privacy as numerous devices can collaborate to build machine learning models without sharing data. Three distinct deep-learning algorithms were chosen for the models’ training, validation, and testing phases. With the training of eight clients and twenty-five federation rounds, the FeedForward Neural Networks (FFNN) model performed best. It had accuracy, an F1-score, and a precision of 84%, while the Multi-Layer Perceptron (MLP) model performed with 83% accuracy, 83% F1-score, and 83% precision, and the Long Short-Term Memory (LSTM) model performed with 80% accuracy, 80% F1-score, and 80% precision.}, archive = {J_ACCESS}, author = {Daniella Mughole Kalimumbalo and Joke A. Badejo and Kennedy O. Okokpujie and Emmanuel Adetiba and Molo Mbasa Joaquim and Nzanzu Vingi Patrick and Gabriel Oyeyemi and Claude Takenga}, doi = {10.1109/ACCESS.2025.3577706}, journal = {IEEE Access}, pages = {101246-101261}, shortjournal = {IEEE Access}, title = {SecFedMDM-1: A federated learning-based malware detection model for interconnected cloud infrastructures}, volume = {13}, year = {2025}, }
• (2025). Next-gen decoding: Non-binary LDPC algorithms for emerging power line and visible light communications. ACCESS, 13, 101224-101245. (www)

  @article{ , abstract = {Non-Binary Low-Density Parity-Check (LDPC) codes have gained significant attention due to their remarkable error correction capabilities in various communication systems. Decoding algorithms play a pivotal role in realizing the potential of non-binary LDPC codes. This paper provides a comprehensive review and analysis of non-binary LDPC decoding algorithms, focusing on their efficiency, complexity, and performance. Furthermore, recent advancements and innovations in non-binary LDPC decoding algorithms are discussed, such as improved message passing strategies, layered decoding techniques, and adaptive algorithms. The review also highlights challenges and open research directions in non-binary LDPC decoding, such as mitigating error floors, reducing decoding complexity, and integrating with emerging communication technologies. Finally, the paper draws conclusions on the current state of non-binary LDPC decoding algorithms, underscoring their promising applications in wireless communication, visible light communication (VLC), and power line communication (PLC). Simulation results demonstrate a marked improvement in bit error rate performance for both VLC and PLC systems, highlighting the practical potential of these advanced decoding techniques.}, archive = {J_ACCESS}, author = {Waheed Ullah and Fengfan Yang and Kwonhue Choi and Dushantha Nalin K. Jayakody}, doi = {10.1109/ACCESS.2025.3578151}, journal = {IEEE Access}, pages = {101224-101245}, shortjournal = {IEEE Access}, title = {Next-gen decoding: Non-binary LDPC algorithms for emerging power line and visible light communications}, volume = {13}, year = {2025}, }
• (2025). MHCAGAT: A meta hybrid convolution attention network for urban traffic flow prediction. ACCESS, 13, 101211-101223. (www)

  @article{ , abstract = {Urban traffic flow prediction is a core task in intelligent transportation systems and plays a vital role in enabling real-time decision making and congestion management. However, increasingly strict privacy regulations and highly fragmented data collection environments such as VANETs have substantially reduced the amount of usable data, thereby making it significantly more challenging to build accurate and reliable models. To address these issues, a novel traffic prediction model is proposed, Meta Hybrid Convolution Attention Graph Attention Network (MHCAGAT). MHCAGAT integrates meta-learning with a base HCAGAT model, where the HCA block is responsible for extracting temporal dependencies and the GAT block captures spatial correlations within the traffic data. This architecture enables rapid convergence and stable performance in small-sample and resource-constrained settings. Experiments conducted on real-world traffic data from the Luohu District of Shenzhen demonstrate that MHCAGAT ranks within the top two across all five evaluation metrics while maintaining the lowest standard deviation, indicating exceptional stability and robustness. Further ablation analysis shows that even without incorporating meta-learning and recurrent structures, the HCAGAT model performs comparably to dedicated temporal models, highlighting the effectiveness and versatility of the architecture in time sequence modeling tasks. In conclusion, MHCAGAT achieves a well-balanced trade-off between accuracy and stability, offering a powerful solution for traffic prediction scenarios characterized by limited data and constrained computational resources.}, archive = {J_ACCESS}, author = {Yu Zhan and Suzi Iryanti Fadilah and Azizul Rahman Mohd Shariff}, doi = {10.1109/ACCESS.2025.3578030}, journal = {IEEE Access}, pages = {101211-101223}, shortjournal = {IEEE Access}, title = {MHCAGAT: A meta hybrid convolution attention network for urban traffic flow prediction}, volume = {13}, year = {2025}, }
• (2025). End-to-end online vectorized map construction with confidence estimates. ACCESS, 13, 101196-101210. (www)

  @article{ , abstract = {This study proposes an end-to-end online vector map generation method, termed COMap, which integrates confidence estimation to enhance the robustness and reliability of environmental perception systems in autonomous driving. The method is based on the DETR framework and introduces a dynamic instance query mechanism to strengthen the geometric representation capabilities of map elements. Additionally, it employs a full-covariance Gaussian distribution for confidence modeling, effectively quantifying prediction uncertainty and thereby improving the reliability of the map data. Experimental results demonstrate that COMap achieves a higher mean Average Precision (mAP) value on the nuScenes dataset compared to existing methods and significantly reduces the false detection rate in complex scenarios, thereby enhancing the accuracy of road structure representation. This method not only improves the stability of online map generation but also provides more reliable data support for downstream tasks such as path planning and decision-making in autonomous driving.}, archive = {J_ACCESS}, author = {Bo Huang and Feng Du and Tao Peng and Xiao-Long Zheng and Ji-Quan Wu and Jun-Hui Zhang}, doi = {10.1109/ACCESS.2025.3577751}, journal = {IEEE Access}, pages = {101196-101210}, shortjournal = {IEEE Access}, title = {End-to-end online vectorized map construction with confidence estimates}, volume = {13}, year = {2025}, }
• (2025). EVSEB: Efficient and verifiable searchable encryption with boolean search for encrypted cloud logs. ACCESS, 13, 101177-101195. (www)

  @article{ , abstract = {With the growing adoption of cloud services, organizations are increasingly outsourcing their system log management to cloud service providers (CSPs). While this approach offers cost efficiency and scalability, it introduces critical challenges related to the confidentiality, searchability, and integrity of sensitive log data. Specifically, existing solutions fall short in supporting fine-grained Boolean keyword search, dynamic access control, and verifiable result integrity over encrypted log records. To address these challenges, we propose EVSEB—an efficient and verifiable searchable encryption framework for encrypted cloud-hosted logs. EVSEB supports fine-grained multi-keyword Boolean search, privacy-preserving access control, and per-file integrity verification with minimal overhead. At its core, EVSEB introduces a hybrid indexing model that integrates a hierarchical log-type classification tree, Bloom filters, and inverted indexes to reduce search space and accelerate query execution. Access control is enforced through blockchain-based smart contracts that securely validate encrypted user tokens. For integrity assurance, EVSEB combines an off-chain distributed hash table (DHT) and an on-chain Merkle root to detect tampering at the level of individual log files. Experimental evaluion demonstrates that our proposed EVSEB outperforms existing solutions in terms of search under increasing keyword and file load, and integrity verification. This substantiates the practicality of our proposed scheme in implementing a searchable encryption and access solution for large-scale encrypted log management in cloud environments.}, archive = {J_ACCESS}, author = {Somchart Fugkeaw and Jirakit Deevijit and Prapachai Ueasathitwong and Thanapoom Thanyasukpaisal}, doi = {10.1109/ACCESS.2025.3577466}, journal = {IEEE Access}, pages = {101177-101195}, shortjournal = {IEEE Access}, title = {EVSEB: Efficient and verifiable searchable encryption with boolean search for encrypted cloud logs}, volume = {13}, year = {2025}, }
• (2025). PlantHealthNet: Transformer-enhanced hybrid models for disease diagnosis and severity estimation in agriculture. ACCESS, 13, 101160-101176. (www)

  @article{ , abstract = {Global plant diseases represent a major threat to agriculture and represent significant economic losses constituting an important threat to food security. This study introduces a transformative hybrid framework for plant disease diagnosis and severity estimation, combining the strengths of advanced deep learning architectures: Detection Transformer (DETR), Swin Transformer and SAM2-UNet. The proposed methodology incorporates classification, detection, segmentation, and severity estimation in the form of a unified pipeline to address the complexities of real-world agricultural settings, including variable lighting, background variety and large-scale fields. With a hierarchical attention mechanism for efficient feature extraction, the Swin Transformer achieves a top 1 accuracy of 85.6%, and a top 5 accuracy of 96.2%. Utilizing its encoder-decoder attention technique, Detection Transformer (DETR) effectively detects diseases by employing an encoder to forecast bounding boxes and class labels for affected areas. Segmentation performance is enhanced with SAM2-UNet, achieving a pixel-level segmentation with a Dice Similarity Coefficient (DSC) of 94.7%, cleanly delineating disease-affected areas. Finally, disease impact is quantified in the final severity prediction stage to allow for targeted interventions based on disease intensity. The framework has been extensively validated through experiments, and it performs significantly better on all the evaluation metrics: Average ROC AUC of 99.97%, Average Precision-Recall AUC of 98.47%, and F1-score of 94.46%. The suggested system improves precision agriculture by making disease detection and management reliable, scalable, and efficient. This work applies cutting-edge deep learning to sustainable agriculture to reduce crop losses, optimise resource use, and provide farmers with proactive disease management knowledge.}, archive = {J_ACCESS}, author = {Abid Iqbal}, doi = {10.1109/ACCESS.2025.3576990}, journal = {IEEE Access}, pages = {101160-101176}, shortjournal = {IEEE Access}, title = {PlantHealthNet: Transformer-enhanced hybrid models for disease diagnosis and severity estimation in agriculture}, volume = {13}, year = {2025}, }
• (2025). Non-homogeneous image dehazing model based on vision transformer shunt self-attention aggregation. ACCESS, 13, 101143-101159. (www)

  @article{ , abstract = {This study introduces an innovative dehazing technique utilizing a Vision Transformer to mitigate the image quality degradation caused by non-homogeneous haze in real-world environments. Initially, the model employs convolutional layers to augment the channel dimensions of the input image, enabling the extraction of basic local features. Subsequently, we incorporate a Multi-Scale Channel-Pixel Joint Attention Module, which refines the attention on various haze-affected areas, ensuring accurate capture of the complex characteristics associated with non-homogeneous haze. The cornerstone of our approach is the proposed improved Vision Transformer, which integrates a Shunt Self-Attention Aggregation Module. This module, leveraging a multi-head parallel self-attention mechanism, facilitates the efficient fusion of features across multiple scales, thereby enhancing feature reuse and integration. We conducted extensive experimental evaluations using both real-world and synthetic non-homogeneous haze datasets, rigorously validating the robustness and effectiveness of our model. The results demonstrate that our approach surpasses existing benchmark techniques across various evaluation metrics, affirming its preeminence in the field of image dehazing.}, archive = {J_ACCESS}, author = {Zhigang Zhang and Byung-Won Min and Zijiao Zhang}, doi = {10.1109/ACCESS.2025.3578195}, journal = {IEEE Access}, pages = {101143-101159}, shortjournal = {IEEE Access}, title = {Non-homogeneous image dehazing model based on vision transformer shunt self-attention aggregation}, volume = {13}, year = {2025}, }
• (2025). Change detection network based on transformer and transfer learning. ACCESS, 13, 101131-101142. (www)

  @article{ , abstract = {The purpose of the change detection(CD) task is to contrast the change information of a specific object in remote sensing images from different time periods. The deep-learning-based change-detection algorithm can extract pixel-level semantic segmentation results for changed objects. Currently, deep learning based change detection algorithms have achieved excellent detection results through the meticulous design of feature extraction and change judgment modules. However, challenges, such as limited generalization ability owing to small-scale change-detection datasets and noise generated by the change judgment module, still exist. Therefore, it is crucial to enhance the generalization capability of the change detection model and improve its ability to detect challenging information in remote sensing building change tasks. In this study, we propose a swin transformer based change detection network (STTL-CD) that incorporates Transfer Learning. Based on the idea of transfer learning, we transferred the weights of the backbone network trained on a large semantic segmentation dataset to initialize our feature extraction network, and enhance the generalization ability of the change detection network. To address the issue of noise during change detection, we established a joint loss function that increases the weight ratio of difficult and easy samples, thereby compelling the model to focus on discerning challenging samples. Our experimental results demonstrate that STTL-CD achieves IoU(0.8571), F1(0.9231) and IoU(0.8902), F1(0.9422) scores in the LEVIR-CD and WHU-CD datasets, surpassing the state-of-the-art change-detection methods developed in recent years.}, archive = {J_ACCESS}, author = {Hua Li and Jingyu Li and Guanghao Luo and Liang Zhou and Hao Wu and Zhangcai Yin}, doi = {10.1109/ACCESS.2025.3575483}, journal = {IEEE Access}, pages = {101131-101142}, shortjournal = {IEEE Access}, title = {Change detection network based on transformer and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Intelligent optimization algorithms for multi-UAV path planning: A comprehensive review. ACCESS, 13, 101106-101130. (www)

  @article{ , abstract = {This work was supported by the National Social Science Fund of China under Grant 2023-SKJJ-B-117, in part by the Natural Science Foundation of Shaanxi Province under Grant 2024JC-YBMS-529, in part by the Fund for technical areas of infrastructure strengthening plan projects under Grant 2023-JCJQ-JJ-0772 and in part by the Equipment Comprehensive Research Project under Grant WJ2023B020400-4. The deployment of multi-UAV systems for frontline and complex combat missions has become a dominant trend in intelligent warfare. As operational airspace extends from medium-high to low and ultra-low altitudes, combined with global efforts to strengthen multi-layered air defense systems, the increasingly dense and complex battlefield environments impose rigorous demands on multi-UAV path planning. Serving as the cornerstone of collaborative mission coordination, path planning is critical for maximizing combat effectiveness through comprehensive analysis of adversarial and operational constraints. This paper synthesizes existing reviews and scientific literature to establish a systematic framework for multi-UAV path planning. We analyze current research progress through four key dimensions: 1) battlefield environment modeling, 2) constraint formulations and objective functions, 3) intelligent optimization algorithms, and 4) collaborative path optimization. Highlighting the Wolf Pack Algorithm’s (WPA) exceptional performance in resolving high-dimensional, multi-peak optimization challenges, we focus on its advancements and applications in multi-UAV path planning. Finally, we project future development directions aligned with the evolution of unmanned and intelligent warfare.}, archive = {J_ACCESS}, author = {Lixiang Zhai and Husheng Wu and Linghong Lai and Ziqian Gao}, doi = {10.1109/ACCESS.2025.3577598}, journal = {IEEE Access}, pages = {101106-101130}, shortjournal = {IEEE Access}, title = {Intelligent optimization algorithms for multi-UAV path planning: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Adaptive backstepping decoupled fast terminal sliding mode control for underactuated systems under uncertainties and actuator faults. ACCESS, 13, 101089-101105. (www)

  @article{ , abstract = {This paper presents an Adaptive Backstepping Decoupled Fast Terminal Sliding Mode Control (ABDFTSMC) framework for underactuated nonlinear systems with coupled dynamics. Traditional sliding mode control methods often suffer from chattering, reliance on conservative uncertainty bounds, and inefficiency in managing underactuation. The proposed framework integrates adaptive backstepping with a decoupling mechanism and fast terminal sliding mode control to ensure finite-time convergence and enhance resilience to uncertainties and actuator faults. Using a recursive Lyapunov-based design, the controller dynamically estimates and compensates for uncertainties and actuator faults without requiring prior knowledge of disturbance bounds. A hierarchical decoupling strategy stabilizes subsystems, reducing control complexity while maintaining precise tracking. Experimental validation on an inverted pendulum-cart system demonstrates the effectiveness of the framework, showing significant reductions in chattering under nominal conditions. When subjected to disturbances—including a 25% loss in actuator effectiveness, additive faults, and parametric uncertainties—the proposed controller achieved superior tracking performance with a settling time of 4.73 seconds, outperforming conventional sliding mode controls, which failed to stabilize the system under the same conditions.}, archive = {J_ACCESS}, author = {Baris Ata}, doi = {10.1109/ACCESS.2025.3578385}, journal = {IEEE Access}, pages = {101089-101105}, shortjournal = {IEEE Access}, title = {Adaptive backstepping decoupled fast terminal sliding mode control for underactuated systems under uncertainties and actuator faults}, volume = {13}, year = {2025}, }
• (2025). Unsupervised context-linking retriever for question answering on long narrative books. ACCESS, 13, 101066-101088. (www)

  @article{ , abstract = {Narrative Question Answering (QA) involves understanding the context, events, and relationships within narrative texts for accurate question answering. However, narrative books impose new challenges while utilizing recent pretrained large language models since such lengthy content requires additional computational costs and leads to performance degradation. Moreover, identifying the most relevant passages for a given question is particularly challenging due to the lack of labeled question-passage pairs for training the retriever. This paper introduces the Unsupervised Context Linking Retriever (UCLR), a novel approach that efficiently retrieves relevant passages from long narrative texts without requiring labeled (question, passage) pairs. UCLR uses an encoder-decoder model to generate synthetic (question, answer) pairs, measuring the relevance of passages by comparing the error between the generated pair and the reference pair, which serves as a synthetic training signal. This method optimizes the retriever to identify passages with sufficient context to accurately reconstruct both the question and the answer, improving retrieval accuracy. UCLR also identifies key events surrounding each passage in the retrieved set and constructs a new set of passages from these key events, enabling coverage of both broader narrative structures and finer details. Experimental results on the NarrativeQA benchmark show that UCLR achieves relative improvements of +8% on the validation set and +5% on the test set, outperforming state-of-the-art unsupervised retrievers. Additionally, the results demonstrate that combining UCLR with a simple reader model outperforms other state-of-the-art readers designed for processing lengthy documents, achieving a relative performance gain of 7.8% on the test set while being 5 times faster as UCLR allows the reader model to focus on a pertinent subset of tokens.}, archive = {J_ACCESS}, author = {Mohammad A. Ateeq and Sabrina Tiun and Hamed Abdelhaq and Wandeep Kaur}, doi = {10.1109/ACCESS.2025.3578497}, journal = {IEEE Access}, pages = {101066-101088}, shortjournal = {IEEE Access}, title = {Unsupervised context-linking retriever for question answering on long narrative books}, volume = {13}, year = {2025}, }
• (2025). Force control of SMA springs using RNNs. ACCESS, 13, 101051-101065. (www)

  @article{ , abstract = {Shape memory alloys (SMAs), generally composed of nickel-titanium, have unique properties that enable them to transition from the martensite phase to the austenite phase when heated. During this transformation, SMAs can shorten and revert to their original shape, which makes them valuable as actuators. SMAs’ biocompatibility, high power-to-weight ratio, low noise, and ability to generate substantial force position them as essential materials in robotic and biomedical applications. However, their control is complicated by inherent nonlinearity and hysteresis. When cooled in the austenite phase, SMAs elongate and deform, with different phase transformation temperatures observed during heating and cooling due to hysteresis. These complexities highlight the need for advanced control techniques. Artificial intelligence models, particularly recurrent neural networks (RNNs), are well-suited for capturing the intricate relationships within SMA behavior. This paper introduces an approach that utilizes RNNs and real-time measurements for force control in SMAs. A compact test setup equipped with sensors collects data on voltage, current, and generated force, enabling the system’s dynamic behavior analysis. RNNs effectively model the nonlinear characteristics of SMA actuators, leveraging their capacity to learn complex temporal relationships from the collected data. This approach is shown to enhance the control of SMAs over conventional control schemes, i.e., Proportional Integral Derivative (PID) control, and expand their applications in various fields.}, archive = {J_ACCESS}, author = {Ahmet Atasoy and Mehmed Özkan}, doi = {10.1109/ACCESS.2025.3570856}, journal = {IEEE Access}, pages = {101051-101065}, shortjournal = {IEEE Access}, title = {Force control of SMA springs using RNNs}, volume = {13}, year = {2025}, }
• (2025). Bearing fault diagnosis in induction motors using low-cost triaxial ADXL355 accelerometer and a hybrid CWT-DCNN-LSTM model. ACCESS, 13, 101037-101050. (www)

  @article{ , abstract = {This paper presents a novel approach for bearing fault diagnosis in induction motor utilizing an improved hybrid Continuous Wavelet Transform-Deep Convolutional Neural Network-Long Short-Term Memory (CWT-DCNN-LSTM) model. The vibration data, recorded using an low-cost ADXL355 accelerometer, was preprocessed by converting the one-dimensional (1D) signals into two-dimensional (2D) images using Continuous Wavelet Transform (CWT). The dataset, comprising 13 classes with varying fault conditions, was segmented and shuffled before model training. Three datasets, corresponding to different load conditions (100W, 200W, and 300W), were used to evaluate the model’s performance. Experimental results demonstrated high training accuracy of 100% and validation accuracies of 96.43%, 97.47%, and 95.06% for the 100W, 200W, and 300W load conditions, respectively. Validation losses were recorded at 12.33%, 9.81%, and 20.33% for the respective loads. Furthermore, performance results using accuracy, sensitivity, specificity, balanced accuracy and geometric mean were computed for all three load conditions. The results indicate the robustness and effectiveness of the proposed CWT-DCNN-LSTM model for bearing fault diagnosis of induction motor using low-cost ADXL335 accelerometer, highlighting its potential for real-world industrial applications.}, archive = {J_ACCESS}, author = {Muhammad Ahsan and Jose Rodriguez and Mohamed Abdelrahem}, doi = {10.1109/ACCESS.2025.3577672}, journal = {IEEE Access}, pages = {101037-101050}, shortjournal = {IEEE Access}, title = {Bearing fault diagnosis in induction motors using low-cost triaxial ADXL355 accelerometer and a hybrid CWT-DCNN-LSTM model}, volume = {13}, year = {2025}, }
• (2025). Event-triggered adaptive synchronous control of interconnected microgrids under denial-of-service attacks. ACCESS, 13, 101027-101036. (www)

  @article{ , abstract = {Considering the threat of malicious Denial-of-Service (DoS) attacks in the communication network layer of distributed interconnected microgrids, which disrupt information exchange among subsystems and compromise synchronization control algorithms, an event-triggered secure synchronization control protocol is proposed for DC interconnected microgrids under DoS attacks. The proposed protocol ensures bus voltage regulation, current sharing among distributed generation units, and charge balance among distributed energy storage units. To reduce the communication burden on controllers, a fully distributed dynamic event-triggered communication mechanism is developed. This mechanism incorporates adaptive parameters to eliminate the dependence of event-triggering mechanism design on global information of the topology, and employs a dynamic event-triggering threshold, resulting in significantly lower triggering frequency compared to static methods. A switching-like control strategy is then applied to design a secure event-triggered synchronization control protocol resilient to DoS attacks. The stability of the closed-loop system is rigorously analyzed through the construction of a switching-like Lyapunov function. Additionally, Zeno behavior is ruled out through theoretical analysis. The effectiveness of the proposed protocol is validated via a simulation case study.}, archive = {J_ACCESS}, author = {Xiaodan Li and Lei Zhang and Hongbin Liu and Xiaoyu Yi and Xiaochun Liu and Zichen Yi}, doi = {10.1109/ACCESS.2025.3576174}, journal = {IEEE Access}, pages = {101027-101036}, shortjournal = {IEEE Access}, title = {Event-triggered adaptive synchronous control of interconnected microgrids under denial-of-service attacks}, volume = {13}, year = {2025}, }
• (2025). Evaluating lightweight transformers with local explainability for android malware detection. ACCESS, 13, 101005-101026. (www)

  @article{ , abstract = {Mobile phones have evolved into powerful handheld computers, fostering a vast application ecosystem but also increasing security and privacy risks. Traditional deep learning-based Android malware detection, reliant on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), struggles to capture long-range dependencies, which are critical for identifying complex malware patterns. Transformers, with their self-attention mechanism, offer a promising alternative but are often computationally intensive for mobile deployment. To tackle this gap, this study assesses ten models—five customized architectures and five fine-tuned lightweight transformers (DistilBERT, CodeBERT, TinyBERT, MobileBERT, ALBERT), using a real-world dataset of 100K Android applications from Koodous, with API calls and permissions as features. The fine-tuned DistilBERT achieves an accuracy of 91.6% and an AUC of 96.5%, outperforming the customized variants (up to 90.5% accuracy), thereby highlighting the advantage of transfer learning. It remains competitive compared to AutoGluon leaderboard models (90–92% accuracy). With an average inference time of $4.46 \pm 0.43$ ms and a 275 MB memory footprint, it balances efficiency and accuracy better than heavier transformers. Local Interpretable Model-Agnostic Explanations (LIME) are further integrated, with explanations aligning closely with VirusTotal’s malware descriptions. The findings demonstrate the viability of lightweight transformers for near-real-time Android malware detection, balancing accuracy, efficiency, and interpretability.}, archive = {J_ACCESS}, author = {Fatima Bourebaa and Mohamed Benmohammed}, doi = {10.1109/ACCESS.2025.3577775}, journal = {IEEE Access}, pages = {101005-101026}, shortjournal = {IEEE Access}, title = {Evaluating lightweight transformers with local explainability for android malware detection}, volume = {13}, year = {2025}, }
• (2025). High-order vehicular pattern learning and privacy-preserving and unsupervised GAN for privacy protection toward vehicular parts detection. ACCESS, 13, 100987-101004. (www)

  @article{ , abstract = {This paper introduces High-order Vehicular Pattern Learning (HVPL), a novel framework designed to enhance vehicular pattern detection while ensuring privacy protection, associated with authentication through the integration of Privacy-Preserving and Unsupervised GAN (PPUP-GAN). To preserve data privacy, we use the PPUP-GAN to create massive-scale vehicular data that well mimics real data. Afterward, HVPL improves upon traditional methods by constructing a large-scale hypergraph that models complex relationships among vehicular pattern features, uncovering high-order interactions that conventional models fail to detect. The model also uses a Gaussian Mixture Model (GMM)-based posterior probability to rank candidate patches for vehicular pattern detection, prioritizing high-risk areas and minimizing false positives. Experiments demonstrated that our method outperforms its counterparts by achieving higher precision and stability in vehicular pattern detection, with a notable improvement of up to 33.39% in Average Precision (AP) and 62.62% in AP_50.}, archive = {J_ACCESS}, author = {Yanqin Zhang and Zhanling Zhang}, doi = {10.1109/ACCESS.2025.3578006}, journal = {IEEE Access}, pages = {100987-101004}, shortjournal = {IEEE Access}, title = {High-order vehicular pattern learning and privacy-preserving and unsupervised GAN for privacy protection toward vehicular parts detection}, volume = {13}, year = {2025}, }
• (2025). Assessing mulsemedia authoring application based on events with STEVE 2.0. ACCESS, 13, 100970-100986. (www)

  @article{ , abstract = {The concept of Multiple Sensorial Media (mulsemedia) has been explored to enhance user experiences by engaging different senses beyond sight and hearing. The growing demand for developing such applications has led to various studies focusing on the mulsemedia authoring process. However, there remains a significant gap in accessible methods for developing these applications. This article addresses this issue by assessing an event-based approach to mulsemedia creation. We implemented this approach in the graphical authoring tool STEVE 2.0, which enables users to develop mulsemedia applications by defining event-based temporal relationships that synchronize traditional media with sensory effects. Additionally, STEVE 2.0 allows for the configuration of media presentation and sensory effect rendering properties. To assess the usability, features, and user experience of the tool, we conducted experiments with 44 participants. The results show that STEVE 2.0 effectively empowers users to create mulsemedia applications. Furthermore, we discuss the integration of STEVE 2.0 with other multimedia technologies used for interactive digital TV, underscoring the importance of comprehensive platforms that support the entire life cycle of these applications, from production to distribution and rendering.}, archive = {J_ACCESS}, author = {Douglas Mattos and Rômulo Vieira and Débora C. Muchaluat-Saade and George Ghinea}, doi = {10.1109/ACCESS.2025.3576167}, journal = {IEEE Access}, pages = {100970-100986}, shortjournal = {IEEE Access}, title = {Assessing mulsemedia authoring application based on events with STEVE 2.0}, volume = {13}, year = {2025}, }
• (2025). The construction of knowledge graphs in the assembly domain based on deep learning. ACCESS, 13, 100957-100969. (www)

  @article{ , abstract = {The operation scenario of human-robot collaboration assembly involves multiple channels to acquire assembly domain knowledge data. In order to equip collaborative robots with certain general knowledge of assembly work and logical reasoning ability, this paper proposes a Chinese-character-based names entity recognition model Bert-BiLSTM-CRF and a relational extraction model Bert-BiGRU-ATT, based on the multiple heterogeneous data in the assembly domain, to construct an assembly domain knowledge graph centered on and <assembly object> for human-robot collaboration. Among them, the Chinese-character-based names entity recognition model avoids the error bias caused by Chinese word segmentation in traditional methods, while the relation extraction model fully extracts the association information between entities and relations. The proposed entity recognition model and relation extraction model are validated in a real scenario of mechanical product assembly. The experimental results show that the proposed models exhibit excellent performance in the human-machine collaborative assembly domain, with overall average F1-score of 84.02% and 94.92% for entities and relations, respectively. The constructed knowledge graph of the assembly domain contains 2724 triples, and the knowledge graph is presented through a visual interactive interface.}, archive = {J_ACCESS}, author = {Jing Qu and Yanmei Li and Huilong Du and Wen Wang and Weiping Fu}, doi = {10.1109/ACCESS.2025.3576168}, journal = {IEEE Access}, pages = {100957-100969}, shortjournal = {IEEE Access}, title = {The construction of knowledge graphs in the assembly domain based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Fire hawk optimizer adjusted tri-stage (1+PI)-PI-TID cascade controller for automatic generation control of PSI. ACCESS, 13, 100934-100956. (www)

  @article{ , abstract = {This study aims to enhance the performance of automatic generation control (AGC) in power systems interconnection (PSI) by employing the fire hawk optimizer (FHO), a green algorithm, to optimize a novel tri-stage cascade controllers (CCs) configuration: (1+PI)-PI-PID and (1+PI)-PI-TID CCs. The FHO draws inspiration from the foraging behaviors of whistling kites, black kites, and brown falcons, known for their unique prey-catching method involving fire propagation. Evaluation of the proposed FHO: (1+PI)-PI-T/PID scheme is conducted using the integral time multiplied absolute error (ITAE) criteria. The versatility of this technique is demonstrated across four PSI test models, including a two-area non-reheat thermal PSI (NRTPSI) and a multi-unit multi-area (MUMA) system incorporating reheat, hydro, and gas units. Challenges are addressed by initially testing NRTPSI with and without governor dead band (GDB) nonlinearity, showing the superior performance of the proposed method. Next, the HVDC link in MUMA is employed under different scenarios. To assess the robustness of the FHO: (1+PI)-PI-T/PID CCs, real-world AGC issues, such as sudden load perturbations and nonlinearities like generation rate constraint, GDB, boiler dynamics, and communication time delay in MUMA, are employed. Also, sensitivity analysis involving a range variation of ±40% in PSI model parameters is conducted for validation. The proposed scheme also undergoes evaluation under random step-load perturbations. The results demonstrate significant improvements in the performance of ITAE by 84.3%, 10.7%, 42.1%, and 16.7% for scenario 1 across all four tested models. The FHO: (1+PI)-PI-TID CC significantly improves system performance compared to existing controllers like the HHO: PD-PI CC, FHO: TID, FHO: PID, and the proposed FHO: (1+PI)-PI-PID CCs. This shows that this approach is effective at improving AGC capabilities and can be used in real life. Update each group’s position using their respective equations.}, archive = {J_ACCESS}, author = {Moses Awal and Michael Robson Atim and Jimmy Nabende Wanzala and Johnes Obungoloch and Mohamed Barakat}, doi = {10.1109/ACCESS.2025.3577622}, journal = {IEEE Access}, pages = {100934-100956}, shortjournal = {IEEE Access}, title = {Fire hawk optimizer adjusted tri-stage (1+PI)-PI-TID cascade controller for automatic generation control of PSI}, volume = {13}, year = {2025}, }
• (2025). ST-GAT resident OD prediction model based on mobile signaling data. ACCESS, 13, 100920-100933. (www)

  @article{ , abstract = {Aiming at the challenges of spatio-temporal heterogeneity and dynamic correlation in the prediction of travel origins and destinations for urban residents, this study invokes a spatio-temporal graph attention network (ST-GAT) OD prediction model based on cell phone signaling data. This model innovatively introduces the graph attention mechanism into the spatio-temporal graph network (ST-GNN), in the spatial dimension, the attention layer (GAL) dynamically learns the attention weights among nodes to adaptively capture the dynamic spatial dependencies in the transportation network, and in the temporal dimension, the temporal convolutional layer extracts the multiscale temporal patterns, which efficiently captures the complex spatiotemporal dependencies in the OD data. The data cleaning innovatively uses the DTW data cleaning model, and MSE, MAE, RMSE are used as evaluation indexes. In this paper, this model is compared with the existing ST-GCN, DMS, GMM, PSAM-CNN, ST-Transformer, and COMD models, and the experimental results show that the predicted values of the ST-GAT model have a significant improvement in the prediction accuracy compared to other models. The research in this paper provides an effective solution for the application of Intelligent Transportation System (ITS) and demonstrates the potential of Deep Learning (DL) in urban transportation planning.}, archive = {J_ACCESS}, author = {Xianguang Jia and Weijie Fang and Yingying Lyu and Jinke Zhang and Mengyi Guo and Dong Li and Jie Qu and Fengxiang Guo}, doi = {10.1109/ACCESS.2025.3577614}, journal = {IEEE Access}, pages = {100920-100933}, shortjournal = {IEEE Access}, title = {ST-GAT resident OD prediction model based on mobile signaling data}, volume = {13}, year = {2025}, }
• (2025). A benchmark dataset and a framework for urdu multimodal named entity recognition. ACCESS, 13, 100904-100919. (www)

  @article{ , abstract = {The emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within Natural Language Processing. Despite progress in high-resource languages such as English, MNER remains underexplored for low-resource languages like Urdu. The primary challenges include the scarcity of annotated multimodal datasets and the lack of standardized baselines. To address these challenges, we introduce the U-MNER framework and release the Twitter2015-Urdu dataset, a pioneering resource for Urdu MNER. Adapted from the widely used Twitter2015 dataset, it is annotated with Urdu-specific grammar rules. We establish benchmark baselines by evaluating both text-based and multimodal models on this dataset, providing comparative analyses to support future research on Urdu MNER. The U-MNER framework integrates textual and visual context using Urdu-BERT for text embeddings and ResNet for visual feature extraction, with a Cross-Modal Fusion Module to align and fuse information. Our model achieves state-of-the-art performance on the Twitter2015-Urdu dataset, laying the groundwork for further MNER research in low-resource languages.}, archive = {J_ACCESS}, author = {Hussain Ahmad and Qingyang Zeng and Jing Wan}, doi = {10.1109/ACCESS.2025.3576784}, journal = {IEEE Access}, pages = {100904-100919}, shortjournal = {IEEE Access}, title = {A benchmark dataset and a framework for urdu multimodal named entity recognition}, volume = {13}, year = {2025}, }
• (2025). Variation-aware bernstein-based upper confidence reinforcement learning for environment with endogenous and exogenous uncertainty. ACCESS, 13, 100889-100903. (www)

  @article{ , abstract = {Online Reinforcement Learning (RL) has yielded remarkable performance in dynamic wireless communication and networks by interacting with the environment and gradually improving the effectiveness of its policy. As it is normal to witness much uncertainty in such an environment due to the intrinsic randomness of channels and service demands, designing a sample-efficient RL with bounded regrets has significant merits. In this paper, we focus on general Markov Decision Processes (MDPs) with time-evolving rewards and state transition probability unknown a priori and develop a Variation-aware Bernstein-based Upper Confidence Reinforcement Learning (VB-UCRL). In particular, we allow for restarting VB-UCRL according to a variation-aware schedule. We successfully overcome the challenges due to both endogenous and exogenous uncertainty and establish a regret bound of saving at most $\sqrt {S}$ or $S^{\frac {1}{6}}T^{\frac {1}{12}}$ compared with the latest results in the literature, where S denotes the size of the state space of the MDP and T indicates the iteration index of learning time-steps. Finally, we show via simulation that our algorithm VB-UCRL significantly outperforms the existing algorithms in the literature.}, archive = {J_ACCESS}, author = {Ruoqi Wen and Rongpeng Li}, doi = {10.1109/ACCESS.2025.3577997}, journal = {IEEE Access}, pages = {100889-100903}, shortjournal = {IEEE Access}, title = {Variation-aware bernstein-based upper confidence reinforcement learning for environment with endogenous and exogenous uncertainty}, volume = {13}, year = {2025}, }
• (2025). Distilling diverse knowledge for deep ensemble learning. ACCESS, 13, 100875-100888. (www)

  @article{ , abstract = {Bidirectional knowledge distillation improves network performance by sharing knowledge between networks during the training of multiple networks. Additionally, performance is further improved by using an ensemble of multiple networks during inference. However, the performance improvement achieved by an ensemble of networks trained with bidirectional knowledge distillation is lower compared to a general ensemble that does not use knowledge distillation. From this trend, we think there is a relationship between network diversity, which is essential for performance improvement through ensembling, and the networks’ shared knowledge. Therefore, we present a distillation strategy to promote network diversity for ensemble learning. Since different types of network diversity can be considered, we design loss functions to separate knowledge and automatically design an effective distillation strategy for ensemble learning by performing a hyperparameter search using these loss functions as hyperparameters. Furthermore, considering network diversity, we design a network compression method for the ensemble and obtain a single network with performance equivalent to that of the ensemble. In the experiments, we automatically design distillation strategies for ensemble learning and evaluate the ensemble accuracy on five classification task datasets.}, archive = {J_ACCESS}, author = {Naoki Okamoto and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi}, doi = {10.1109/ACCESS.2025.3578105}, journal = {IEEE Access}, pages = {100875-100888}, shortjournal = {IEEE Access}, title = {Distilling diverse knowledge for deep ensemble learning}, volume = {13}, year = {2025}, }
• (2025). A robust population dynamics model with carrying capacity constraints for centrally coordinated distributed optimization. ACCESS, 13, 100861-100874. (www)

  @article{ , abstract = {Distributed optimization is crucial for managing complex, large-scale systems, yet faces challenges in achieving robustness and efficiency under practical constraints. This paper introduces the Capacity Constrained Replicator Dynamics (CCRD) model and its robust variant (RCCRD), novel population dynamics frameworks inspired by ecological principles. These frameworks are designed for constrained optimization in distributed settings that employ a centralized coordinator. CCRD extends classic replicator dynamics by incorporating carrying capacity constraints and is proven to converge to Nash equilibria in full-potential games. RCCRD further enhances robustness through a self-correcting mechanism, ensuring constraint satisfaction and reliable performance even with computational errors. Theoretical analysis demonstrates the convergence properties of (R)CCRD, and comparative simulations reveal that RCCRD outperforms some existing methods in terms of runtime, optimality gap, and constraint satisfaction, particularly in noisy environments. The (R)CCRD framework offers a practically relevant and efficient anytime optimization approach for centrally coordinated distributed constrained optimization, with significant potential for real-time applications in large-scale systems.}, archive = {J_ACCESS}, author = {Germán Obando and Andrés Pantoja}, doi = {10.1109/ACCESS.2025.3578157}, journal = {IEEE Access}, pages = {100861-100874}, shortjournal = {IEEE Access}, title = {A robust population dynamics model with carrying capacity constraints for centrally coordinated distributed optimization}, volume = {13}, year = {2025}, }
• (2025). Comparative study of advanced heatsink structures for improved thermal performance in axial flux motors. ACCESS, 13, 100850-100860. (www)

  @article{ , abstract = {Additive manufacturing has enabled the use of advanced geometries such as lattice-structures in heatsinks. This study investigates the thermal performance of various advanced heatsink designs in the context of cooling the windings of an axial flux motor using active air cooling. Physical samples of the heatsinks are additively manufactured from an aluminum alloy and tested under realistic airflow conditions. The heatsink designs are compared against an optimized conventional finned geometry based on the measured thermal resistance at a given fan speed. This ensures that the results are representative of real-world use cases and applicable in practical solutions. The results indicate that airfoil-shaped pins can slightly outperform conventional straight fins in thermal resistance while weighing less. In terms of mass-based relative performance, the rhombi octet lattice structure outperformed the other prototypes. These findings reinforce conventional design principles in terms of absolute performance while suggesting that lattice structures can provide higher relative performance in certain weight sensitive applications.}, archive = {J_ACCESS}, author = {Martin Sarap and Shalini Singh and Ants Kallaste and Ahmed Jawad Qureshi and Hans Tiismus and Toomas Vaimann and Payam Shams Ghahfarokhi}, doi = {10.1109/ACCESS.2025.3577289}, journal = {IEEE Access}, pages = {100850-100860}, shortjournal = {IEEE Access}, title = {Comparative study of advanced heatsink structures for improved thermal performance in axial flux motors}, volume = {13}, year = {2025}, }
• (2025). Automatic body measurement for straight-tube mountain bike size selection using closest size classification. ACCESS, 13, 100831-100849. (www)

  @article{ , abstract = {The lack of bike expert personnel in bike shops challenges new mountain bike riders to choose the correct bike size. Moreover, there are currently no automated methods specifically designed for mountain bike size selection. This paper proposes a method for selecting the appropriate mountain bike size based on top tube length and seat tube length. This study focuses on mountain bikes with straight-tube frame bikes. The process involves three steps: human segmentation, body size measurement, and bike size selection. U-Net with batch normalization layers and morphological operations is used to accurately segment the human area, followed by measurements of the rider’s arm length, inseam height, and torso length. Finally, a bike size is selected using a closest size classification. Five experiments validated the method: 1) human segmentation results, 2) feature point detection results, 3) body size measurement results, 4) top tube length and seat tube length measurement results, and 5) bike size selection results. The human segmentation achieved a 99.24% F1-score on the test dataset. The average relative absolute errors of the inseam height, arm length, and torso length are 1.93%, 2.37% and 3.12%, respectively. The average relative absolute errors of the top tube length and seat tube length are 2.00% and 1.93%, respectively. The accuracy rate of bike size selection for all three models is 90.80%. The novelty of this work lies in its end-to-end automation, eliminating the need for expert assistance or physical markers. This makes it a pioneering contribution to the field of automated bike fitting, with significant implications for consumer convenience and retail efficiency.}, archive = {J_ACCESS}, author = {Patsama Charoenpong and Kunyada Kongtanee and Patee Chaiprasittigul and Kitti Sathapornprasath and Theekapun Charoenpong}, doi = {10.1109/ACCESS.2025.3576649}, journal = {IEEE Access}, pages = {100831-100849}, shortjournal = {IEEE Access}, title = {Automatic body measurement for straight-tube mountain bike size selection using closest size classification}, volume = {13}, year = {2025}, }
• (2025). A survey of non-learning-based abstractions for sequential decision-making. ACCESS, 13, 100808-100830. (www)

  @article{ , abstract = {Abstraction techniques play a crucial role in enabling agents to make decisions more effectively by simplifying complex problems. This survey provides a comprehensive literature overview of non-learned abstraction construction methods and explores how these abstractions can enhance or be seamlessly integrated into existing solvers. We delve into key properties of abstractions, outline general strategies for constructing them, and discuss specialized approaches for specific problem domains, such as those with continuous action spaces. Additionally, we introduce the Abstraction Mapping Graph (AMG) framework, offering a structured lens through which abstraction usage can be systematically understood.}, archive = {J_ACCESS}, author = {Robin Schmöcker and Alexander Dockhorn}, doi = {10.1109/ACCESS.2025.3572830}, journal = {IEEE Access}, pages = {100808-100830}, shortjournal = {IEEE Access}, title = {A survey of non-learning-based abstractions for sequential decision-making}, volume = {13}, year = {2025}, }
• (2025). Decoy traffic-aware IDS: A novel approach using CSHHHNN and GGGOA. ACCESS, 13, 100791-100807. (www)

  @article{ , abstract = {In the digital world, the Network Intrusion Detection System (NIDS) plays a prime role in protecting against cyber threats. Nevertheless, none of the prevailing studies focused on analyzing the impact of decoy traffic in networks, thereby resulting in poor security. To address this research gap, this article proposes a decoy traffic-aware NIDS that incorporates enhanced Cognitive Spiking Hyperbolic Hodgkin-Huxley Neural Networks (CSHHHNN) and the Greylag Griewank Goose Optimization Algorithm (GGGOA). The process starts with node initialization and registration, followed by network segmentation via clustering and subnet masking schemes. Thereafter, possible routes are recognized for each node, and then, a graph structure is generated. Next, multipath routing is established between the source and destination. Afterward, the network paths are verified. If a valid path is found, then it is selected; otherwise, it is discarded. Now, data sensing and data encryption are done. Thereafter, load balancing is performed in the encrypted data. Then, the load-balanced data is fed into the intrusion detection system. In the proposed NIDS, the dataset is initially pre-processed. Afterward, the relevant features are extracted. Subsequently, optimal features are selected, and privacy measures are applied to the model. Finally, the improved CSHHHNN model is used to classify the network traffic into normal or attack categories. The proposed approach demonstrates superior performance with 99.5124% accuracy in decoy traffic management and intrusion detection.}, archive = {J_ACCESS}, author = {R. C. Jeyavim Sherin and K. Parkavi}, doi = {10.1109/ACCESS.2025.3577684}, journal = {IEEE Access}, pages = {100791-100807}, shortjournal = {IEEE Access}, title = {Decoy traffic-aware IDS: A novel approach using CSHHHNN and GGGOA}, volume = {13}, year = {2025}, }
• (2025). Advanced fault diagnosis in milling machines using acoustic emission and transfer learning. ACCESS, 13, 100776-100790. (www)

  @article{ , abstract = {The accurate diagnosis of faults in milling machines is important to ensure manufacturing efficiency and minimize downtime. Acoustic emission (AE) signals, known for their transient and high-frequency nature, provide valuable insights into tool and machine faults. However, their non-stationary characteristics present challenges for traditional analysis methods. This study proposes an innovative framework that combines time-frequency representation, transfer learning, and dimensionality reduction for effective fault diagnosis. AE signals are transformed into scalograms and spectrograms using continuous wavelet transform (CWT) and short-time Fourier transform (STFT), respectively, extracting both localized and global signal characteristics. These visual representations are processed through pre-trained deep learning architectures, EfficientNet-B0 and InceptionV3, to extract high-level features. Dimensionality reduction through uniform manifold approximation and projection (UMAP) further refines these features while preserving useful patterns. Finally, a lightweight k-nearest neighbors (k-NN) classifier is used to distinguish across all classes with high accuracy, achieving an average of 99.60% cross-validation performance. This framework highlights the strength of combining transfer learning with dimensionality reduction for fault diagnosis, providing a computationally efficient and highly accurate solution with significant potential for real-time monitoring and predictive maintenance in advanced manufacturing systems.}, archive = {J_ACCESS}, author = {Muhammad Umar and Zahoor Ahmad and Saif Ullah and Faisal Saleem and Muhammad Farooq Siddique and Jong-Myon Kim}, doi = {10.1109/ACCESS.2025.3578248}, journal = {IEEE Access}, pages = {100776-100790}, shortjournal = {IEEE Access}, title = {Advanced fault diagnosis in milling machines using acoustic emission and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Panoramic image generation algorithm based on cylindrical surface mapping. ACCESS, 13, 100764-100775. (www)

  @article{ , abstract = {In real-life scenarios, the field-of-view angles captured by cameras are typically constrained. This has led to a growing demand for panoramic images to cater to various applications, such as virtual reality, mapping, decorative displays, and more. To meet this demand, we proposed an algorithm based on cylindrical mapping for generating panoramic images with a wide field of view angle. The algorithm utilizes a small number of standard cameras to capture images from various angles. It incorporates cylindrical surface mapping into the image-stitching process and continuously expands the view angles by iteratively stitching multiple sets of adjacent images to create a seamless and circular 360-degree panoramic image. The threshold-based SURF algorithm is used for image registration. The normalization processing algorithm is specifically developed to rectify image distortion during stitching. Additionally, the irregular boundary panorama image is transformed into a rectangular shape using variable radius circular surface mapping to eliminate any invalid void regions within the image. The results of the experiment demonstrate that the proposed algorithm successfully achieves panoramic image stitching with good scene adaptability. Compared to existing representative algorithms, it requires fewer camera points and produces wider viewing angle panoramic images.}, archive = {J_ACCESS}, author = {Fuquan Ji and Yueguang Li and Xianchao Wang}, doi = {10.1109/ACCESS.2025.3578114}, journal = {IEEE Access}, pages = {100764-100775}, shortjournal = {IEEE Access}, title = {Panoramic image generation algorithm based on cylindrical surface mapping}, volume = {13}, year = {2025}, }
• (2025). Panoramic X-ray synthesis from dental CBCT using patient-tailored simulated geometry with uniform projection sampling. ACCESS, 13, 100752-100763. (www)

  @article{ , abstract = {Panoramic X-ray (PX) is the most widely used imaging modality in dental health care. However, PXs can be severely degraded due to artifacts such as ghostly shadows, distortions, and superimposition of adjacent structures. Panoramic tomosynthesis from cone-beam computed tomography (CBCT) can be considered an alternative to noisy PX. Traditional cylinder-based methods take orthogonal projections along an appropriate dental arch; however, they suffer from a significant discrepancy between synthesized and genuine PXs, making them less applicable for clinical purposes. Recently, another approach based on simulated geometry that emulates the X-ray source-detector movement of PX scanners for ray tracing has been proposed. However, their methods suffer from poorly fitted ray trajectories for individual patients, uneven ray tracing, image misalignment, and, in some cases, the need for prior information on scan parameters. To tackle those problems, we propose a robust method of synthesizing PXs from CBCT by defining a universally applicable simulated geometry of the ray trajectory. Our method adjusts the ray trajectory to be optimal for a patient’s jaw shape and also achieves uniform ray sampling throughout the patient’s jaw by progressively increasing ray sampling intervals. In addition, tilt correction was applied to alleviate potential errors caused by incorrect patient positioning. Quantitative and qualitative results show that our method outperforms prior simulated geometry (SG)-based methods as well as image processing methods. Our framework can synthesize realistic PX images from dental CBCT by employing efficient ray tracing strategies customized to the patient’s oral structure without the need for scan parameters and ultimately empower dentists’ diagnostic capabilities.}, archive = {J_ACCESS}, author = {Anusree P. Sunilkumar and Bikram Keshari Parida and Seong-Yong Moon and Wonsang You}, doi = {10.1109/ACCESS.2025.3573744}, journal = {IEEE Access}, pages = {100752-100763}, shortjournal = {IEEE Access}, title = {Panoramic X-ray synthesis from dental CBCT using patient-tailored simulated geometry with uniform projection sampling}, volume = {13}, year = {2025}, }
• (2025). An innovative smart irrigation using embedded and regression-based machine learning technologies for improving water security and sustainability. ACCESS, 13, 100731-100751. (www)

  @article{ , abstract = {Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more efficient agricultural practices. Traditional irrigation systems, often imprecise, contribute to water wastage. The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. The system adjusts irrigation levels according to specific crop needs, thus contributing to more sustainable water management. Using ML algorithms like linear regression algorithms to model the relationships between environmental factors and crop water requirements, enabling accurate prediction of required irrigation levels based on data collected by sensors. The use of embedded systems such as the ESP32, combined with temperature, humidity, and water level sensors, has enabled the development of an autonomous and efficient system for collecting data in real-time and processing it for decision-making. The proposed model has an MAE of 0.8434, an RMSE of 0.8434, and a coefficient (R2 Score) of 0.4044, offering soil moisture prediction accuracy. Furthermore, the training time of our model is 0.00253 seconds, while the prediction time is 0.00117 seconds. These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. The results of the study show a significant reduction in water consumption, with a marked improvement in water resource management. Discussions reveal that the integration of embedded systems with machine learning algorithms can not only improve irrigation efficiency but also contribute to the sustainability of agriculture by enabling more accurate and adaptive management of water resources. This paper represents an important step towards improving food security and sustainable water management, particularly in regions facing increasing climatic challenges.}, archive = {J_ACCESS}, author = {Abdennabi Morchid and Abdennacer Elbasri and Zahra Oughannou and Hassan Qjidaa and Rachid El Alami and Badre Bossoufi and Saleh Mobayen and Paweł Skruch}, doi = {10.1109/ACCESS.2025.3577911}, journal = {IEEE Access}, pages = {100731-100751}, shortjournal = {IEEE Access}, title = {An innovative smart irrigation using embedded and regression-based machine learning technologies for improving water security and sustainability}, volume = {13}, year = {2025}, }
• (2025). Optimized image dehazing using dark channel prior with minimum fusion and improved atmospheric light handling. ACCESS, 13, 100720-100730. (www)

  @article{ , abstract = {Haze-induced image degradation in outdoor scenes, caused by atmospheric scattering and absorption, poses significant challenges for applications such as surveillance, autonomous navigation, and remote sensing. This research focuses on improving image dehazing by enhancing two key components: Dark Channel Prior (DCP) and Atmospheric Light Estimation (ALE). DCP exploits the statistical property that haze-free regions exhibit low minimum intensity values, aiding in accurate haze detection. ALE estimates atmospheric light from distant, less-scattered sources, ensuring precise haze removal. To further enhance dehazing performance, this study introduces a Minimum Fusion Technique, which selects the minimum intensity value for each pixel across multiple dehazed outputs, effectively preserving fine details and reducing artifacts. The proposed method is evaluated on the SOTS Outdoor dataset from the RESIDE benchmark, comprising 500 pairs of real-world hazy and clear images. By integrating optimized DCP, ALE, and the Minimum Fusion Technique, the approach achieves superior performance with a PSNR of 35.45 dB and an SSIM of 0.97, outperforming existing methods such as Scene-Specific DCP, TON, and FFA_NET. Additionally, it demonstrates reduced execution time and enhanced visual quality, making it a more efficient and reliable solution for real-world dehazing applications. These improvements establish the proposed method as a state-of-the-art approach for restoring clarity in outdoor images, contributing to more effective and accurate image reconstruction in challenging environments.}, archive = {J_ACCESS}, author = {Ahmad Hussain and Mansoor Iqbal and Sheraz Aslam and Saad Alahmari and Abdulwahab Alkharashi}, doi = {10.1109/ACCESS.2025.3576646}, journal = {IEEE Access}, pages = {100720-100730}, shortjournal = {IEEE Access}, title = {Optimized image dehazing using dark channel prior with minimum fusion and improved atmospheric light handling}, volume = {13}, year = {2025}, }
• (2025). Role of visual saliency in video quality assessments. ACCESS, 13, 100699-100719. (www)

  @article{ , abstract = {The human visual system naturally prioritizes unique and salient objects within a scene. In computer vision, visual saliency refers to the property that makes specific regions stand out in an image or video. With the growing reliance on video-based applications such as streaming, virtual reality, and video conferencing, video quality assessment (VQA) has become crucial. This research investigates the role of Global Contrast-Based Visual Saliency in VQA and proposes two methodologies: 1) feature extraction using Visual Saliency, 2) Visual Saliency combined with Feature Extraction and Feature Selection. The methodologies were evaluated using the LIVE Video Quality Challenge Database and multiple regression models. Experimental results demonstrate that incorporating visual saliency significantly enhances VQA accuracy while reducing computational complexity. The best-performing approach—Visual Saliency combined with Feature Extraction and Feature Selection—achieved the highest correlation with subjective quality scores (PCC = 0.997, KRCC = 0.955, SROCC = 0.995) and the lowest Mean Squared Error (MSE = 1.318). This highlights the effectiveness of visual saliency and feature selection in improving the reliability and efficiency of VQA models, making them more aligned with human perception.}, archive = {J_ACCESS}, author = {Syeda Eesha-Tir-Razia and Qaisar Abbas and Imran Fareed Nizami and Adil Ali Raja and Mutlaq B. Aldajani and Mudasir Wahab and Tallha Akram and Sadiq Ahmad}, doi = {10.1109/ACCESS.2025.3578043}, journal = {IEEE Access}, pages = {100699-100719}, shortjournal = {IEEE Access}, title = {Role of visual saliency in video quality assessments}, volume = {13}, year = {2025}, }
• (2025). FUSCANet: Enhancing skin disease classification through feature fusion and spatial-channel attention mechanisms. ACCESS, 13, 100683-100698. (www)

  @article{ , abstract = {Skin diseases represent a prevalent global health issue that significantly impacts the physical and mental well-being of patients. With the widespread application of computer vision technology in dermatology, automating skin lesion classification through computer algorithms has become a crucial method for improving diagnostic efficiency and reducing the mortality rate due to malignant skin conditions. In response to this need, a novel lightweight neural network model, called Feature fUsion and Spatial-Channel Attention Network (FUSCANet) model, is proposed in this paper, based on the MobileViT framework, aiming at classifying multi-class skin disease images on mobile or embedded devices. Firstly, a newly designed Leaky MobileNetV2 (LMV2) block, integrated into the proposed model, allows to enhance its ability to extract features from skin images. Then, a novel Multi-Scale Feature Aggregation (MSFA) layer is added to enhance feature extraction across various scales, effectively capturing more comprehensive features and fusing feature maps from different scales to improve final feature reuse. Lastly, a newly designed Enhanced Convolutional Block Attention Module (ECBAM) increases the model’s focus on important information, allowing for the final extraction and summarization of features. The proposed FUSCANet model is evaluated on four different datasets (the public PAD-UFES-20, HAM10000, and ISIC 2019 datasets, and a private dataset) containing images of various skin diseases. The obtained experimental results show that FUSCANet outperforms existing models on most evaluation metrics, while maintaining low parameter counts, making it suitable for deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Qinyang Liu and Xuan Wang and Hongjiu Liu and Xiangzhen Zang and Lei Li and Zhanlin Ji and Ivan Ganchev}, doi = {10.1109/ACCESS.2025.3577740}, journal = {IEEE Access}, pages = {100683-100698}, shortjournal = {IEEE Access}, title = {FUSCANet: Enhancing skin disease classification through feature fusion and spatial-channel attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Subjective and objective quality assessments of AI-generated images for language E-learning. ACCESS, 13, 100667-100682. (www)

  @article{ , abstract = {AI-generated images (AIGIs) are becoming popular and can be employed in many applications, owing to Generative AI (GAI). Researchers have developed models that can be used to generate images for different scenarios. In addition, researchers have proposed datasets of natural scene images for language learning and different AIGI-quality datasets for general applications. For e-learning, particularly in the context of language learning, no AIGI dataset is currently available. To fill this gap, we first proposed an AIGI quality dataset for language learning. Both subjective and objective assessments have been conducted on the proposed dataset. The findings from subjective assessment show that higher perceptual quality also corresponds to a more substantial alignment. It also shows that the average MOS scores of images generated from Stability AI models are similar and lower than images generated by the Dall.E3 model. The results of the objective assessment indicate that the performance of off-the-shelf quality models is generally low. In addition, results from finetuning learning-based quality models show that significant gains and improvements can be achieved using the dataset. The results of the alignment evaluation show that the HPS model is the best, and realistic images in the dataset produced the best alignment correlation compared to the other styles in the dataset. The findings also show that multimodal large language models, such as vision-enabled GPT-4 (GPT-4V), still struggle to produce alignment scores that correlate with humans.}, archive = {J_ACCESS}, author = {Chibuike Onuoha and Vu Hai Dang and Nguyen Duc Nam and Truong Thu Huong and Pham Ngoc Nam and Truong Cong Thang}, doi = {10.1109/ACCESS.2025.3578047}, journal = {IEEE Access}, pages = {100667-100682}, shortjournal = {IEEE Access}, title = {Subjective and objective quality assessments of AI-generated images for language E-learning}, volume = {13}, year = {2025}, }
• (2025). Transformer-based motion predictor for multi-dancer tracking in non-linear movements of dancesport performance. ACCESS, 13, 100647-100666. (www)

  @article{ , abstract = {Automated multi-dancer tracking is a critical yet challenging task in Dance Quality Assessment (DanceQA), requiring precise motion estimation to evaluate synchronization, formation transitions, and rhythmic accuracy. Traditional Multi-Object Tracking (MOT) frameworks predominantly rely on appearance-based features and Kalman Filter-based motion models, which struggle with complex, non-linear motion patterns exhibited in dance performances. These conventional approaches often suffer from identity fragmentation, occlusion-related failures, and inaccurate motion predictions due to their inherent assumption of constant velocity. Although recent deep learning-based trackers incorporating recurrent architectures and transformers have improved motion modeling, they still lack adaptability to highly dynamic motion variations and remain heavily reliant on large-scale training datasets. To bridge this gap, we propose the Multi-Dancer Spatio-Temporal Tracker (MDSTT), a novel transformer-based framework that exclusively leverages historical motion cues for robust and identity-consistent tracking. Unlike conventional tracking methods that integrate appearance features, MDSTT processes historical bounding box trajectories through a transformer encoder, capturing both long-range and short-term spatio-temporal dependencies while mitigating occlusion-induced identity switches. The proposed framework introduces a Historical Trajectory Embedding module to enhance motion-based representation learning, an Adaptable Motion Predictor with a learnable prediction token for improved trajectory continuity, and a refined Hungarian Matching strategy incorporating Intersection-over-Union (IoU), motion direction difference, and L1 distance to optimize object association. Additionally, probabilistic masked token augmentation is incorporated to simulate real-world occlusion scenarios, improving resilience against missing detections. Extensive evaluations on the DanceTrack dataset demonstrate that MDSTT achieves state-of-the-art (SoTA) tracking performance, surpassing existing methods with a 22.3% improvement in HOTA (77.4 vs. 63.3), 7.6% higher detection accuracy (86.4 vs. 80.3), and 26.6% better identity association accuracy (63.4 vs. 50.1) compared to SoTA transformer-based MOT models.}, archive = {J_ACCESS}, author = {Zhiling Wang}, doi = {10.1109/ACCESS.2025.3577797}, journal = {IEEE Access}, pages = {100647-100666}, shortjournal = {IEEE Access}, title = {Transformer-based motion predictor for multi-dancer tracking in non-linear movements of dancesport performance}, volume = {13}, year = {2025}, }
• (2025). AI-driven post-earthquake emergency material demand prediction: Integrating RAG with reasoning large language model. ACCESS, 13, 100630-100646. (www)

  @article{ , abstract = {The supply of emergency rescue materials plays a pivotal role in post-earthquake relief operations. However, varying disaster scenarios generate differentiated demands for emergency resources, where factors such as seasonal impacts, geographic environment of affected areas, population density, and requirements of emergency response protocols significantly influence the categories and quantities of required supplies. Current research predominantly focuses on essential materials, while determinations of scenario-specific material demands rely heavily on expert empirical inference, resulting in discrepancies between model predictions and actual requirements. This study proposes a reasoning-enhanced large language model (LLM) framework integrated with Retrieval-Augmented Generation (RAG) technology for post-earthquake emergency material demand prediction. By simulating expert decision-making processes, we construct an emergency knowledge base amalgamating standardized protocols, historical seismic case data, and disaster scenario characteristics extracted from digital resources. The framework employs RAG to enhance domain-specific knowledge integration within the reasoning model, utilizing Chain-of-Thought generation to produce differentiated prediction schemes that specify material categories and per-capita demand metrics. Through dynamic updating of population statistics and material requirements via post-disaster network information monitoring, the system achieves real-time demand prediction based on evolving victim counts and individualized allocation parameters. Validation through expert evaluations using the 2013 Ya’an Lushan earthquake and simulated disaster scenarios demonstrates effectiveness, with successful practical implementation observed in the January 7, 2025, Dingri M6.8 earthquake case.}, archive = {J_ACCESS}, author = {Song Zhang and Meng Huang and Shuai Liu and Fanxin Meng and Yingyao Xie and Xirui Ren and Yuanwang Zhang and Wenbo Shao}, doi = {10.1109/ACCESS.2025.3578192}, journal = {IEEE Access}, pages = {100630-100646}, shortjournal = {IEEE Access}, title = {AI-driven post-earthquake emergency material demand prediction: Integrating RAG with reasoning large language model}, volume = {13}, year = {2025}, }
• (2025). Teach pendant at fingertips: Intuitive vision-based gesture-driven control of dexter ER2 robotic arm. ACCESS, 13, 100614-100629. (www)

  @article{ , abstract = {Teaching a robotic arm can be unintuitive and challenging due to the disparity between natural human limb movements and robotic manipulator control. Substantial training is often required to effectively operate such systems. This work presents the development of an intuitive, easy-to-use interface that maps human palm gestures to control the movement of a 5-axis robot, the Dexter ER2. The proposed vision-based system enables the robotic arms to autonomously follow hand gestures through a three-stage methodology: gesture detection, gesture tracking, and robotic manipulator control. A deep learning-based model is employed for accurate gesture recognition, allowing the robot to mimic the controller’s limb movements with minimal cognitive load. For trajectory computation and joint movement generation, an adaptive gradient descent-based inverse kinematic solver is implemented, enabling efficient and smooth convergence of joint angles corresponding to the desired end-effector positions. The interface was successfully implemented, enabling the robot to follow gesture commands and reach target positions. The proposed vision-based gesture-driven control scheme of the Dexter ER2 robot arm is validated through both simulation and experimental studies. The system employed serial communication, ensuring a continuous and lossless data stream between the controller and the computer. A detailed comparative analysis is also presented to highlight the advantages of the proposed approach. Future work includes enhancing visual appeal with a graphical interface and addressing the latency issue in real-time control.}, archive = {J_ACCESS}, author = {S. Abhishek and Yash S. Jogi and Umesh Kumar Sahu and Santanu Kumar Dash and Umesh Kumar Yadav}, doi = {10.1109/ACCESS.2025.3576693}, journal = {IEEE Access}, pages = {100614-100629}, shortjournal = {IEEE Access}, title = {Teach pendant at fingertips: Intuitive vision-based gesture-driven control of dexter ER2 robotic arm}, volume = {13}, year = {2025}, }
• (2025). Adversarial learning as a PMU signal filtering technique in load models identification. ACCESS, 13, 100601-100613. (www)

  @article{ , abstract = {A current trend in the area of load modeling in medium voltage busbars of an electric power system is to take advantage of ambient data (with small variations) recorded by Phasor Measurement Units (PMU) to estimate the parameters of the load models using automatic and online methodologies. When working with ambient PMU data, noise has a significant and negative impact on the parametric estimation of the load models. This is where signal filtering algorithms play a key role. In the literature lacks automatic and robust filtering algorithms designed for load modeling, especially with ambient PMU data. In order to solve this problem, this work proposes a filtering algorithm based on Artificial Neural Networks (ANN) and Adversarial Learning. The advantage of the proposed algorithm is that it does not need a design parameter to be adjusted and, therefore, it is ideal to work with different PMU measurements, either voltage, current, active power, or reactive power, indistinctly. Finally, the performance of the proposed algorithm is compared in two ways with respect to traditional filtering techniques recommended in the literature. On the one hand, based on the amount of noise removed and, on the other hand, on the accuracy achieved when the load model’s parameters are estimated (the latter being an aspect not previously explored in literature). It is concluded that the proposed algorithm achieves significantly better performance, especially with ambient PMU data.}, archive = {J_ACCESS}, author = {Joffre R. Constante and Delia Graciela Colomé and Diego E. Echeverría and Edgar Guillermo Lincango and José L. Camacho}, doi = {10.1109/ACCESS.2025.3577897}, journal = {IEEE Access}, pages = {100601-100613}, shortjournal = {IEEE Access}, title = {Adversarial learning as a PMU signal filtering technique in load models identification}, volume = {13}, year = {2025}, }
• (2025). Design of tunable diffraction meta-gratings based on plasma discharges. ACCESS, 13, 100587-100600. (www)

  @article{ , abstract = {This work presents the design and analysis of tunable diffractive meta-gratings utilizing plasma technology. The proposed structures consist of arrays of cylindrical plasma discharges, each enclosed within a dielectric shield. Through both theoretical modeling and numerical simulations, we demonstrate how the propagation direction of transmitted waves can be dynamically steered by tuning the plasma frequencies of the discharges that form the meta-grating unit cell. A practical meta-grating capable of redirecting an incident wave from -46° to +46° relative to the broadside direction is designed and characterized. The reconfiguration is achieved while maintaining high transmission efficiency, highlighting the potential of plasma-based meta-gratings for adaptive and high-performance applications in the microwave range.}, archive = {J_ACCESS}, author = {Mohammad G. H. Alijani and Alessio Monti and Stefano Vellucci and Mirko Barbuto and Alessandro Toscano and Filiberto Bilotti}, doi = {10.1109/ACCESS.2025.3577398}, journal = {IEEE Access}, pages = {100587-100600}, shortjournal = {IEEE Access}, title = {Design of tunable diffraction meta-gratings based on plasma discharges}, volume = {13}, year = {2025}, }
• (2025). Position sensor offset fault diagnosis in speed-regulated PMSM drives. ACCESS, 13, 100572-100586. (www)

  @article{ , abstract = {Speed regulation of the electro-mechanically actuated systems is common across broad application segments, including industrial and residential sectors. The growth in demand for efficient energy conversion has made permanent magnet synchronous machines (PMSM) a friend, rather than a foe. Vector controlled PMSMs in speed regulation mode being the dominant form of control, accurate speed, and position measurement is crucial. However, a misalignment in the position sensor with respect to the true rotor zero position in speed regulated PMSM drives leads to inefficient energy conversion with the possibility of unstable motor behavior. The considered position sensor offset error fault, under severe fault modes, has the potential to introduce an error between −180° to +180°. Within this full range of the fault, the system is stable within the ±90° interval and it is unstable beyond this range. The impact of such position sensor fault on the overall system performance is presented from an efficiency as well as a control system point of view. The theory presented is further corroborated with simulation and experimental results to practically demonstrate the importance of the position sensor misalignment fault from a speed regulated application point of view in a non-salient PMSM. A strategy to quantify the severity of the fault is also proposed and validated with experimental results, supporting fault mitigation activities.}, archive = {J_ACCESS}, author = {Ujala Anuradhi Maha Gamage and Sandun Kuruppu}, doi = {10.1109/ACCESS.2025.3578189}, journal = {IEEE Access}, pages = {100572-100586}, shortjournal = {IEEE Access}, title = {Position sensor offset fault diagnosis in speed-regulated PMSM drives}, volume = {13}, year = {2025}, }
• (2025). Method-level syntactic and semantic clustering for microservice discovery in legacy enterprise systems. ACCESS, 13, 100557-100571. (www)

  @article{ , abstract = {Enterprise systems, such as enterprise resource planning, customer relationship management, and supply chain management systems, are widely used in corporate sectors and are notorious for being large, inflexible and monolithic. Their many application-specific methods are challenging to decouple manually because they manage asynchronous, user-driven business processes and business objects having complex structural relationships. We present an automated technique for identifying parts of enterprise systems that can run separately as fine-grained microservices in flexible and scalable Cloud systems. Our remodularization technique uses both semantic properties of enterprise systems, i.e., domain-level business object and method relationships, together with syntactic features of the methods’ code, e.g., their call patterns and structural similarity. Semantically, business objects derived from databases form the basis for prospective clustering of those methods that act on them as modules, while on a syntactic level, structural and interaction details between the methods themselves provide further insights into module dependencies for grouping, based on K-means clustering and optimization. Our technique was prototyped and validated using two open-source enterprise customer relationship management systems, SugarCRM3 and ChurchCRM. The empirical results demonstrate improved feasibility of remodularizing enterprise systems, inclusive of coded business objects and methods, compared to microservices constructed using class-level decoupling of business objects only. Furthermore, the microservices recommended, integrated with “backend” enterprise systems, demonstrate improvements in execution efficiency, scalability, and availability.}, archive = {J_ACCESS}, author = {Adambarage Anuruddha Chathuranga de Alwis and Alistair Barros and Colin Fidge and Artem Polyvyanyy}, doi = {10.1109/ACCESS.2025.3577095}, journal = {IEEE Access}, pages = {100557-100571}, shortjournal = {IEEE Access}, title = {Method-level syntactic and semantic clustering for microservice discovery in legacy enterprise systems}, volume = {13}, year = {2025}, }
• (2025). Enhanced FCS-MPC for DFIG under non-ideal grid conditions. ACCESS, 13, 100546-100556. (www)

  @article{ , abstract = {This article presents an improvement to the Model Predictive Control-Finite Control Set (MPC-FCS) applied to grid-connected Doubly-Fed Induction Generators (DFIG) for operation under distorted voltage conditions. The MPC-FCS controller uses the discrete system model to predict the future behavior of the rotor currents, minimizing a cost function based on the error between predictions and references. The proposed approach incorporates the Decoupled Double Synchronous Reference Frame Phase-Locked Loop (DDSRF-PLL), allowing precise synchronization with the grid, even in distorted voltage scenarios, and optimizing the selection of voltage vectors applied to the rotor. Experimental tests on a 3 kW/220 V DFIG bench setup demonstrated that the proposed enhancements deliver superior performance to conventional MPC-FCS, SRF-PLL, Deadbeat, and classical PI controllers. The proposed method, integrated with DDSRF-PLL, exhibited superior performance compared to the other strategies, standing out in waveform quality and dynamic response. The proposed controller ensured the highest power quality among all evaluated strategies, even under distorted grid voltage conditions. Notably, the traditional MPC-FCS control strategy failed to meet the current THD requirement of less than 5%, as specified by the IEEE Std. 519-2014. Additionally, it showed fast transient response, low overshoot, and high steady-state accuracy, outperforming MPC-FCS variations without proper synchronization and the other controllers compared. It also contributes significantly to the literature, addressing the gap in studies on the performance of classic MPC-FCS in grid-connected wind generation systems with DFIG.}, archive = {J_ACCESS}, author = {Yuri O. Cota and Jefferson S. Costa and Angelo Lunardi and Igor Oliani and Ekom E. Okpo and Alfeu J. Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3578259}, journal = {IEEE Access}, pages = {100546-100556}, shortjournal = {IEEE Access}, title = {Enhanced FCS-MPC for DFIG under non-ideal grid conditions}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of a reference model-based disturbance observer for position control of electro-hydraulic servo systems. ACCESS, 13, 100530-100545. (www)

  @article{ , abstract = {Electro-hydraulic servo systems are often subject to parameter uncertainties and external disturbances, which degrade position tracking accuracy and control robustness. To address these challenges, this paper proposes a reference model-based disturbance observer (RMDO) control strategy. The state-space model of the system is first established, followed by the design of an ideal reference model. Based on this, a reference model controller is developed to enable the reference model output to track the desired input, while a disturbance observer is constructed to estimate real-time disturbances. A corresponding control law is then formulated to compensate for the estimated disturbances, ensuring that the system output accurately follows the reference model output and ultimately achieves precise tracking. Stability analysis of the closed-loop error system is performed using a Lyapunov-based approach within the input-to-state stability (ISS) framework, demonstrating that the tracking error remains bounded under bounded disturbance derivatives. Finally, simulation and experimental results verify the effectiveness and robustness of the proposed control strategy.}, archive = {J_ACCESS}, author = {Lizhen Liu and Meizhen Liu and Lixin Wang and Qiang Li and Nur Syazreen Ahmad}, doi = {10.1109/ACCESS.2025.3577656}, journal = {IEEE Access}, pages = {100530-100545}, shortjournal = {IEEE Access}, title = {Design and implementation of a reference model-based disturbance observer for position control of electro-hydraulic servo systems}, volume = {13}, year = {2025}, }
• (2025). Toward compliance and transparency in raw material sourcing with blockchain and edge AI. ACCESS, 13, 100514-100529. (www)

  @article{ , abstract = {The global push for green technologies and digital transformation has intensified the demand for critical raw materials, prompting the European Union to enact the Critical Raw Materials Act (CRMA) to ensure ethical, transparent, and resilient mineral supply chains. As the world advances toward the 6G and IoT era, characterized by ultra-low latency, ubiquitous connectivity, and intelligent edge computing, there is an urgent need to empower all levels of the supply chain, especially artisanal small-scale mining (ASM), which remains largely disconnected from such innovations. To address this gap, we propose the Raw Material Radar (RMR) framework, centered on a smart barrel that integrates IoT sensing, edge AI processing, and blockchain-based record-keeping. This low-power device enables real-time detection of shipment anomalies such as tampering or unauthorized access, even in offline and resource-limited environments. Our fully functional prototype achieves high performance (94% F1-score, 98% recall), with minimal resource usage (8.45KB RAM, 407KB flash) and fast inference (5.25 ms), making it well-suited for deployment at the network edge. By enabling verifiable, autonomous custody tracking, the RMR solution exemplifies how edge AI can enhance supply chain transparency and regulatory compliance—critical components for ethical sourcing in the 6G-driven IoT landscape.}, archive = {J_ACCESS}, author = {Abderrahim Khiari and Anas Osman and Massimo Vecchio and Mattia Antonini and Miguel Pincheira}, doi = {10.1109/ACCESS.2025.3577307}, journal = {IEEE Access}, pages = {100514-100529}, shortjournal = {IEEE Access}, title = {Toward compliance and transparency in raw material sourcing with blockchain and edge AI}, volume = {13}, year = {2025}, }
• (2025). Double-sided LCC V2G inductive link with four-quadrant control for an EV SAE j2954-compliant charger. ACCESS, 13, 100504-100513. (www)

  @article{ , abstract = {Electric vehicles (EVs) have a high impact on power systems, since the demand peaks they generate can cause instabilities in the electricity grid. One of the technologies that are proposed to alleviate this impact is Vehicle-to-Grid (V2G) strategies, which could be implemented in conductive charger and in advanced equipment such as wireless charging. V2G combined with adjustable power levels (in the charging and discharging processes) adds flexibility to the grid. The aim of this paper is the definition of a V2G control wireless system working in V2G 4-quadrants, where any combination of active and reactive power is possible. In this way, the EVs with wireless charging can participate in multiple ancillary services, beyond the traditional approach to comply with power quality requirements. The main contribution of this work is that a SAE J2954-compliant EV charger has been considered, where it is included a double-sided LCC compensation topology due to its popularity. To do so, analytical studies, simulations and experimental validations up to 500 W have been carried out. The final results highlight the feasibility of this system in eight operation modes, achieving an overall efficiency higher than $90~\%$ .}, archive = {J_ACCESS}, author = {Inmaculada Casaucao and Alicia Triviño}, doi = {10.1109/ACCESS.2025.3578103}, journal = {IEEE Access}, pages = {100504-100513}, shortjournal = {IEEE Access}, title = {Double-sided LCC V2G inductive link with four-quadrant control for an EV SAE j2954-compliant charger}, volume = {13}, year = {2025}, }
• (2025). Understanding point clouds of electrical substations using a multi-view multi-scale fusion with transformer. ACCESS, 13, 100492-100503. (www)

  @article{ , abstract = {We propose a deep learning-based network model, relying on multi-view multi-scale fusion with transformer, for understanding semantical information of electrical substation point clouds. Our method is inspired by human recognition of 3D objects using information from multiple perspectives and with multiple levels of details, and it is constructed by a dynamic integration of multi-view 3D morphological representation and multi-scale 3D geometric characterization. Additionally, we leverage the channel and spatial attention mechanism to capture the relationship between morphological representations at multiple views and utilize the self and cross attention mechanism to understand the relationship between geometric characterization at multiple scales. To verify our model, experiments were conducted based on our substation point clouds dataset and two benchmark datasets. Our model shows a better performance than the state-of-the-art methods, and it achieves an overall accuracy of 91.15% on our dataset, 93.30% on ModelNet40, and 92.80% on ModelNet40-C, indicating its effectiveness and robustness.}, archive = {J_ACCESS}, author = {Xinghua Wang and Yanxi Meng and Hantuo Dong and Tao Jia}, doi = {10.1109/ACCESS.2025.3578226}, journal = {IEEE Access}, pages = {100492-100503}, shortjournal = {IEEE Access}, title = {Understanding point clouds of electrical substations using a multi-view multi-scale fusion with transformer}, volume = {13}, year = {2025}, }
• (2025). Impact of parameter uncertainties on power electronic device lifetime predictions. ACCESS, 13, 100479-100491. (www)

  @article{ , abstract = {Properly addressing uncertainties in reliability analysis is essential for realistic lifetime predictions of power devices. This paper investigates parameter uncertainties on the lifetime estimation of power devices using an empirical lifetime model and Monte Carlo simulations. Key parameters such as junction temperature swings ( $\Delta T_{j}$ ), minimum junction temperature ( $T_{j,\min }$ ), and lifetime model constants are analyzed for their impacts on lifetime outcomes. Sensitivity analysis reveals significant effects from variations in parameters like $\beta _{1}$ and $\Delta T_{j}$ on the expected lifetime and its variability. Simultaneous variations across all parameters further highlight the dominant influence of $\beta _{1}$ on lifetime predictions. The analysis suggests that a $5\,\%$ uncertainty margin appears to offer a balanced trade-off between realistic lifetime estimations and predictability. This Study underscores the importance of considering parameter uncertainties for precise reliability evaluations. It addresses a critical gap by examining the rationale behind commonly assumed $5\,\%$ , and $10\,\%$ uncertainty margins in lifetime modeling. By systematically evaluating these margins’ impacts on key reliability parameters, the study provides a framework for selecting reasonable assumptions based on physical insights and variability analysis, advancing the reliability modeling of power devices.}, archive = {J_ACCESS}, author = {Faezeh Kardan and Aditya Shekhar and Pavol Bauer}, doi = {10.1109/ACCESS.2025.3576930}, journal = {IEEE Access}, pages = {100479-100491}, shortjournal = {IEEE Access}, title = {Impact of parameter uncertainties on power electronic device lifetime predictions}, volume = {13}, year = {2025}, }
• (2025). Martingale stopping rule application in bayesian A/B testing with batch data. ACCESS, 13, 100462-100478. (www)

  @article{ , abstract = {Continuous monitoring in A/B testing is widely used for selecting options, variants, or treatments in healthcare analytics. However, the appropriate stopping strategy for A/B testing in continuous monitoring remains unclear. This study proposes a Bayesian A/B testing method for continuous monitoring, incorporating a martingale stopping strategy. A uniform Beta distribution is used as the prior in the Bayesian A/B testing method. The posterior expected loss is employed as the criterion for choosing an option. When the posterior expected loss falls below a preset threshold, the result of the continuous monitoring is accepted for the A/B testing. With the proposed method, continuous monitoring of the null hypothesis (that the conversion rate of A is less than that of B) is managed using the posterior expected loss. The results obtained using the martingale stopping strategy enhance the interpretability of the A/B testing process. Finally, the effectiveness of the Bayesian A/B testing method is validated through numerical experiments on both simulated data and real-world applications.}, archive = {J_ACCESS}, author = {Yali Wang and Haiyan Yu and Ching-Chi Yang and Ronggui Zhang}, doi = {10.1109/ACCESS.2025.3577906}, journal = {IEEE Access}, pages = {100462-100478}, shortjournal = {IEEE Access}, title = {Martingale stopping rule application in bayesian A/B testing with batch data}, volume = {13}, year = {2025}, }
• (2025). Quantum key distribution in multiple fiber networks and its application in urban communications: A comprehensive review. ACCESS, 13, 100446-100461. (www)

  @article{ , abstract = {Information security faces unprecedented challenges in the digital age, particularly in the face of cyber threats and the emergence of quantum computing. This research examines QKD as a solution for secure communications in urban fiber networks. The study analyzes QKD protocols, including BB84 and Twin-Field QKD, implementing a 46-node metropolitan quantum network and key distribution over distances greater than 1002 kilometers. Various transmission media, such as multicore optical fibers and free-space communication, were evaluated, demonstrating high-capacity quantum communication with advanced noise mitigation techniques. The results reveal significant advances in quantum communication security, highlighting the potential of QKD to provide theoretically secure key distribution based on quantum mechanical principles. The research addresses challenges such as transmission loss, environmental disturbances, and network scalability, laying the groundwork for future implementations in metropolitan networks. This work contributes significantly to the standardization of evaluation protocols and metrics, which is crucial for the global adoption of QKD in a world increasingly dependent on cybersecurity, especially in developing smart cities.}, archive = {J_ACCESS}, author = {German Granados and Washington Velasquez and Ricardo Cajo and Maria Antonieta-Alvarez}, doi = {10.1109/ACCESS.2025.3577086}, journal = {IEEE Access}, pages = {100446-100461}, shortjournal = {IEEE Access}, title = {Quantum key distribution in multiple fiber networks and its application in urban communications: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). MG6D: A deep fusion approach for 6D pose estimation with mamba and graph convolution network. ACCESS, 13, 100433-100445. (www)

  @article{ , abstract = {Accurate and efficient 6D pose estimation is a fundamental technology in many industrial applications. While existing dense correspondence methods have shown progress, they face challenges in multimodal feature fusion under complex scenarios involving occlusions, illumination variations, and sensor noise. This paper proposes a novel 6D pose estimation framework that addresses these limitations through a hybrid Mamba-Graph architecture. The algorithm first introduces a panoramic attention fusion Mamba module, leveraging state-space modeling to capture long-range dependencies in multi-modal data while establishing cross-dimensional interactions between channel and spatial features to emphasize critical information. A dynamic graph convolutional adaptive fusion module is then designed to enable cross-modal geometric consistency modeling via multi-modal feature integration. Finally, a texture-geometry co-driven keypoint selection mechanism is proposed to ensure keypoint distributions satisfy both spatial uniformity and discriminability requirements. Experimental results on three common datasets demonstrate that the proposed algorithm achieves ADD(-S) metrics of 99.82%, 80.26%, and 97.2%, respectively. Notably, it exhibits significant advantages in pose estimation for objects with repetitive textures and high symmetry.}, archive = {J_ACCESS}, author = {Jiaqi Zhu and Bin Li and Xinhua Zhao}, doi = {10.1109/ACCESS.2025.3575778}, journal = {IEEE Access}, pages = {100433-100445}, shortjournal = {IEEE Access}, title = {MG6D: A deep fusion approach for 6D pose estimation with mamba and graph convolution network}, volume = {13}, year = {2025}, }
• (2025). SegAN for recognition of caries from 2D-panoramic X-ray images. ACCESS, 13, 100419-100432. (www)

  @article{ , abstract = {Accurate recognition and segmentation of dental caries in 2D panoramic X-ray images are crucial for timely diagnosis and strategic treatment planning. The current study uses a Generative Adversarial Network (GAN) model named SegAN to segment the X-ray samples to recognize the caries. The SegAN model works in an adversarial architecture in which the generator focuses on creating precise segmentation maps of caries from 2D panoramic X-ray images. On the other hand, the discriminator ensures that the output matches realistic segmentation patterns. The SegAN model efficiently handles the local and global contextual information for precise segmentation by considering Pixel-Wise and structural loss measures that assist in better segmentation of complex structures. Moreover, the SegAN model efficiently deals with noisy data and effectively handles class imbalances. Data augmentation, like histogram equalization and affine transforms, is performed on the input images for precise segmentation of the samples. The model was evaluated on both raw and preprocessed dental X-ray images using standard quantitative metrics. SegAN demonstrated superior performance compared to traditional segmentation approaches, achieving an accuracy of 98.5%, and a dice coefficient of 0.936 in caries detection.}, archive = {J_ACCESS}, author = {Parvathaneni Naga Srinivasu and Gorli L. Aruna Kumari and D. Jaya Kumari and Paolo Barsocchi and Akash Kumar Bhoi}, doi = {10.1109/ACCESS.2025.3576914}, journal = {IEEE Access}, pages = {100419-100432}, shortjournal = {IEEE Access}, title = {SegAN for recognition of caries from 2D-panoramic X-ray images}, volume = {13}, year = {2025}, }
• (2025). Fuzzy data modeling and parameter estimation in two gamma populations. ACCESS, 13, 100405-100418. (www)

  @article{ , abstract = {This study addresses the challenge of estimating parameters for two Gamma populations that share a common scale parameter but differ in their shape parameters, within the context of fuzzy data. To manage these complexities, both the Maximum Likelihood and Bayesian estimation techniques are employed. Because of the absence of closed-form solutions for the Maximum Likelihood estimators, the Expectation-Maximization algorithm is utilized, and asymptotic confidence intervals are constructed based on the observed information matrix. For Bayesian estimation, a conjugate prior is used to derive Bayes estimators, which are approximated using Lindley’s method in light of the analytical intractability. Gibbs sampling was implemented to estimate posterior densities and construct the Highest Posterior Density intervals. Approximate Bayesian Computation is also employed as a likelihood-free approach to Bayesian inference, which is particularly useful under fuzzy data conditions where the likelihood is difficult to specify explicitly. A comprehensive comparison of the Maximum Likelihood Estimation, Lindley’s approximation, Approximate Bayesian Computation, and Gibbs sampling is conducted to evaluate their performance. The effectiveness of the proposed method was further illustrated using real data from a light-emitting diode manufacturing process.}, archive = {J_ACCESS}, author = {Vijay Kumar Lingutla and Nagamani Nadiminti}, doi = {10.1109/ACCESS.2025.3576384}, journal = {IEEE Access}, pages = {100405-100418}, shortjournal = {IEEE Access}, title = {Fuzzy data modeling and parameter estimation in two gamma populations}, volume = {13}, year = {2025}, }
• (2025). KQUIC: A kernel-based quick UDP internet connections (QUIC) transport for IoT. ACCESS, 13, 100392-100404. (www)

  @article{ , abstract = {To address the shortcomings of TCP, the IETF has standardized QUIC, a new userspace transport protocol. Despite its benefits, the implementation of QUIC in the userspace introduces significant processing overhead, which makes it less suitable for IoT devices. According to our empirical analyses, as the packet loss rate increases from 0% to 2.5%, the data copying overhead between the kernel and userspace increases by 26% with QUIC, whereas TCP experiences no additional overhead. In this paper, we identify and study the overhead associated with QUIC and introduce an enhanced implementation called kQUIC, designed to enhance the protocol for IoT applications. kQUIC tackles two primary shortcomings of QUIC. First, it reduces the overhead of system calls by implementing QUIC within the kernel, as opposed to the traditional userspace implementation. Second, kQUIC enables multiple applications on an IoT device to multiplex their data through a single underlying UDP pipeline, thereby reducing transport-layer header overhead. Our analyses and empirical evaluations demonstrate that kQUIC outperforms both TCP and QUIC in terms of throughput and processor utilization. For example, in a single-application scenario with a 2.5% packet loss rate, kQUIC achieves 13.2% and 29.5% higher throughput than TCP and QUIC, respectively. Under the same conditions, with an application message size of 1400 bytes, kQUIC exhibits 49.76% lower processor utilization than QUIC and 27.41% lower than TCP. kQUIC’s application multiplexing feature further enhances throughput and reduces overall processor usage in multi-processor and multi-application scenarios across all packet loss rates and message sizes.}, archive = {J_ACCESS}, author = {Puneet Kumar and Behnam Dezfouli}, doi = {10.1109/ACCESS.2025.3578120}, journal = {IEEE Access}, pages = {100392-100404}, shortjournal = {IEEE Access}, title = {KQUIC: A kernel-based quick UDP internet connections (QUIC) transport for IoT}, volume = {13}, year = {2025}, }
• (2025). Metaheuristic-based cepstral sensing technique for prolonging network lifetime and mitigating primary user emulation attacks in cognitive radio sensor network. ACCESS, 13, 100370-100391. (www)

  @article{ , abstract = {Cognitive Radio Sensor Network (CRSN) impart adaptive access to inactive radio bands, optimizing spectrum utilization. Conversely, by manipulating secondary users, Primary User Emulation Attack (PUEA) create severe threats to security and cause improper spectrum allocation and higher energy usage. To overcome this issue, we present a Cepstral Spectrum Sensing Multi-Hop Whale Optimization Algorithm (CSSMWA) and the proposed cepstral sensing uses a Logarithmic Spectral Derivative algorithm that aids in identifying authentic primary users from mimicked signals due to its feature-revealing properties, elevating the ability to identify the precision of PUEAs, perform better than other methods with low, moderate to high noise levels. Whale optimisation algorithm maintains network performance and energy efficiency while refining sensing parameters and enhancing decision-making. Extensive simulations are used to analyze the proposed algorithm, incorporating important performance parameters including error probability, energy efficiency, residual energy, data gathering efficiency, network longevity, and throughput. According to simulation findings, the proposed algorithm surpasses other methods in terms of privacy, network surveillance capabilities, and spectrum sensing accuracy. For future wireless networks, the work assists in developing CRSN that are resilient, trustworthy, and energy-efficient.}, archive = {J_ACCESS}, author = {V. Abilasha and A. Karthikeyan}, doi = {10.1109/ACCESS.2025.3577663}, journal = {IEEE Access}, pages = {100370-100391}, shortjournal = {IEEE Access}, title = {Metaheuristic-based cepstral sensing technique for prolonging network lifetime and mitigating primary user emulation attacks in cognitive radio sensor network}, volume = {13}, year = {2025}, }
• (2025). FPG-AI RNN: A technology-agnostic framework for the automatic acceleration of LSTM/GRU-based models on FPGAs. ACCESS, 13, 100353-100369. (www)

  @article{ , abstract = {Recurrent Neural Networks (RNNs) are pivotal in artificial intelligence, excelling in tasks involving sequential data across fields such as natural language processing and time-series forecasting. FPGAs have emerged as an efficient technology for accelerating these algorithms, especially in resource- and power-constrained platforms such as edge devices. To improve the accessibility of this technology, both academia and industry are exploring the design of automation toolflows. This article proposes FPG-AI RNN, a novel technology-agnostic RNN-to-FPGA framework that enables the fast deployment of LSTM- and GRU-based models on FPGAs belonging to different vendors and with diverse resource budgets, outclassing state-of-the-art solutions in terms of device portability. The toolflow leverages post-training compression techniques to reduce model complexity and streamline implementation. The developed accelerator is a highly tunable Hardware Description Language (HDL)-based architecture featuring no third-party Intellectual Properties (IPs). An iterative algorithm explores the parameters’ space of the underlying architecture, selecting a point that meets the user-defined constraints on the target RNN-FPGA pair, resource consumption, and performance. To demonstrate the technology independence of our solution, we collect results for a heterogeneous set of models on low/mid/high range FPGAs belonging to AMD Xilinx, Intel, and Microchip. A comparison with state-of-the-art solutions targeting an LSTM-based model for sentence classification highlights the unmatched device portability of FPG-AI RNN and shows metrics (inference time, resource utilization, power consumption, post-quantization accuracy) on par with the best available solutions.}, archive = {J_ACCESS}, author = {Tommaso Pacini and Pietro Nannipieri and Silvia Moranti and Luca Fanucci}, doi = {10.1109/ACCESS.2025.3577908}, journal = {IEEE Access}, pages = {100353-100369}, shortjournal = {IEEE Access}, title = {FPG-AI RNN: A technology-agnostic framework for the automatic acceleration of LSTM/GRU-based models on FPGAs}, volume = {13}, year = {2025}, }
• (2025). Pole slipping in droop-based grid-forming inverters. ACCESS, 13, 100338-100352. (www)

  @article{ , abstract = {Integration of grid-forming inverter-based resources is considered as a viable solution to address the challenges associated with the dynamics of power systems with high penetration levels of inverter-based resources. Yet, various aspects of dynamic behaviour of grid-forming inverters during disturbances have remained unknown. This paper for the first time introduces the concept of pole slipping in grid forming inverters, referring to the loss of synchronism where the inverter’s internal phase angle deviates beyond 180° with respect to the grid, similar to synchronous generators losing step. It is revealed that this event in grid forming inverters can be stable or unstable. The differences between stable and unstable pole slipping are highlighted. The determining factors for the occurrence of stable and unstable pole slipping in grid forming inverters and equipment-level implications of pole slipping are identified and studied. A solution is proposed to mitigate stable and unstable pole slipping in grid-forming inverters by adjusting the operating reference power during the disturbance. The validity of the proposed concept of pole slipping in grid forming inverters and the proposed solution is tested and verified through extensive time-domain simulations and experimental results. The results verify that pole slipping is more likely to happen in high short circuit ratios (SCRs). It is revealed that unstable pole slipping does not occur at SCRs lower than 3.5. Simulation and experimental results demonstrate that a phase jump as small as 10° may lead to instability in a grid with SCRs equal or higher than 10. This is while in lower SCRs such phase jumps result in long fault recovery without causing pole slipping.}, archive = {J_ACCESS}, author = {Negar Karimipour and Hansi Wijayalath Arachchilage and Amir Abiri Jahromi and Behrooz Bahrani and Mohammadreza F. M. Arani}, doi = {10.1109/ACCESS.2025.3577987}, journal = {IEEE Access}, pages = {100338-100352}, shortjournal = {IEEE Access}, title = {Pole slipping in droop-based grid-forming inverters}, volume = {13}, year = {2025}, }
• (2025). Improving sharpness-aware minimization using label smoothing and adaptive adversarial cross-entropy loss. ACCESS, 13, 100326-100337. (www)

  @article{ , abstract = {Recent advances in learning algorithms have identified loss surface sharpness as an effective metric for reducing the generalization gap. Building on this principle, Sharpness-Aware Minimization (SAM) was introduced to improve model generalization and has achieved state-of-the-art performance through its weight perturbation and updating steps. However, SAM’s perturbation is based solely on the gradient of the standard cross-entropy loss. As the model approaches convergence, this gradient diminishes and oscillates, leading to inconsistent perturbation directions. To address these limitations, we introduce SAM-CE-AACE and SAM-SCE-AACE. SAM-CE-AACE replaces the conventional cross-entropy loss with the Adaptive Adversarial Cross-Entropy (AACE) loss in the perturbation step. Unlike the standard loss, AACE increases in both value and gradient magnitude as the model converges, ensuring stable perturbation directions. Additionally, SAM-CE-AACE’s perturbation utilizes AACE loss without normalization, enhancing the model’s exploratory capabilities in near-optimum stages. Moreover, SAM-SCE-AACE further improves SAM-CE-AACE by integrating label smoothing into the weight-updating step, reducing overconfidence in predictions. Together, this combination of the AACE loss function and label smoothing technique leads to further improvement in model performance. Intensive empirical experiments have been conducted to confirm the effectiveness of our methods, with experiments demonstrating improved performance in image classification tasks using different network architectures across various datasets. The reproduction code is available online at https://www.vip.sc.eng.isct.ac.jp/proj/AACE}, archive = {J_ACCESS}, author = {Tanapat Ratchatorn and Masayuki Tanaka}, doi = {10.1109/ACCESS.2025.3578265}, journal = {IEEE Access}, pages = {100326-100337}, shortjournal = {IEEE Access}, title = {Improving sharpness-aware minimization using label smoothing and adaptive adversarial cross-entropy loss}, volume = {13}, year = {2025}, }
• (2025). Learning frequency-aware spatial attention by reconstructing images with different frequency responses. ACCESS, 13, 100317-100325. (www)

  @article{ , abstract = {Convolutional Neural Networks are widely used in various real-world applications due to their exceptional performance. To further enhance this effectiveness, many approaches utilize spatial attentions that emphasize features in specific regions critical for recognition. However, most existing methods compute the attention area as single-channel matrices for each input, failing to distinguish between coarse and fine features. This limitation hinders recognition performance, particularly in fine-grained classification scenarios where both global and detailed features are crucial. In this paper, we propose a novel method that computes separate spatial attention maps for high-frequency and low-frequency features. Our approach employs frequency-aware reconstruction losses applied to images reconstructed from divided feature maps corresponding to high and low frequencies, enabling separate calculation of spatial attention for each frequency band. Through comprehensive quantitative and qualitative evaluations on both general and fine-grained image classification datasets, we demonstrate that our method achieves higher accuracy compared to the Attention Branch Network, a representative spatial attention method. Furthermore, by fine-tuning the frequency-separated spatial attention through the incorporation of human perceptual knowledge, we achieve additional improvements in classification accuracy.}, archive = {J_ACCESS}, author = {Keisuke Sano and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi}, doi = {10.1109/ACCESS.2025.3577932}, journal = {IEEE Access}, pages = {100317-100325}, shortjournal = {IEEE Access}, title = {Learning frequency-aware spatial attention by reconstructing images with different frequency responses}, volume = {13}, year = {2025}, }
• (2025). Decision model for information security control assessment for hospitals using disc picture fuzzy information. ACCESS, 13, 100307-100316. (www)

  @article{ , abstract = {In order to improve on traditional fuzzy and picture fuzzy models, this paper presents the disc picture fuzzy set (D-PFS) framework, which includes distinct radius parameters for membership, non-membership, and neutrality. When dealing with complicated and unpredictable decision-making situations, like evaluating hospital information security control measures, this sophisticated method achieves 18.5% higher accuracy and reduces ambiguity by 30% compared to conventional approaches. We suggest an analytical framework that combines D-PFS with the Weighted Aggregated Sum Product Assessment (WASPAS) method in a multi-attribute decision-making (MADM) setting in order to thoroughly assess the efficacy of security controls. By combining qualitative and quantitative security assessment criteria, the proposed model effectively lowers ambiguity and subjective bias in decision assessments. A case study on hospital information security control assessment demonstrates 95% agreement with expert evaluations while considering crucial security elements such as data confidentiality, system integrity, and access control efficacy. The findings demonstrate that the D-PFS-based WASPAS approach provides more reliable and accurate evaluation than traditional techniques, with 22% faster consensus-building among decision-makers. This approach offers useful information for enhancing security decision-making processes in healthcare settings and can be adapted to other sectors that require in-depth security assessments.}, archive = {J_ACCESS}, author = {Peng Zhuo and Lu Jin}, doi = {10.1109/ACCESS.2025.3577220}, journal = {IEEE Access}, pages = {100307-100316}, shortjournal = {IEEE Access}, title = {Decision model for information security control assessment for hospitals using disc picture fuzzy information}, volume = {13}, year = {2025}, }
• (2025). A cross-dimensional attention mechanism for pedestrian trajectory forecasting. ACCESS, 13, 100298-100306. (www)

  @article{ , abstract = {Forecasting pedestrian trajectories is crucial for autonomous driving systems but remains challenging due to complex spatial and temporal interactions. Most existing methods model these interactions separately; for example, they capture temporal features and then pass this information to a spatial interaction model. These sequential methods hinder communication between the two dimensions, reducing forecasting accuracy. To address this limitation, we propose a novel method called CAM (Cross-Dimensional Attention Mechanism for Pedestrian Trajectory Forecasting). CAM independently captures temporal and spatial features and facilitates effective communication between them. Specifically, we utilize graph attention networks to capture spatial features and transformers to capture temporal features. The cross-dimensional attention mechanism enables features encoded in one dimension to query and retrieve relevant information from the other dimension. This mechanism allows features in both dimensions to interact effectively, making pedestrian trajectory forecasting easier. We evaluated our method on two well-known public datasets, ETH and UCY. The results show that our method improves forecasting accuracy, with the average displacement error (ADE) of 0.21 and final displacement error (FDE) of 0.41, improving the ADE and FDE by 16.0% and 6.8%, respectively.}, archive = {J_ACCESS}, author = {Feng Bian and Wensheng Zhang}, doi = {10.1109/ACCESS.2025.3576890}, journal = {IEEE Access}, pages = {100298-100306}, shortjournal = {IEEE Access}, title = {A cross-dimensional attention mechanism for pedestrian trajectory forecasting}, volume = {13}, year = {2025}, }
• (2025). G-KAN: Graph kolmogorov-arnold network for node classification using contrastive learning. ACCESS, 13, 100287-100297. (www)

  @article{ , abstract = {Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. The selection of activation functions and message-passing strategies significantly influences performance. A novel fully connected model, named G-KAN, introduces the learnable activation strategy and eliminates the message-passing strategy. G-KAN utilizes the Kolmogorov-Arnold Network (KAN) to dynamically learn activation functions and applies contrastive loss to implicitly extract node features. Firstly, linear layers and KAN layers are used to convert the inputs into vector space. Secondly, contrastive loss is introduced to optimize node representations within the hidden layer, encouraging the representations of high-similarity nodes to be closer together while pushing the representations of low-similarity nodes further apart. Finally, the softmax function and cross-entropy loss are employed to achieve end-to-end node classification. In the node classification task, G-KAN achieves state-of-the-art performance compared to other baselines. Micro-F1 and Macro-F1 are raised by 45.46% and 47.07%, respectively, compared to the original GCN on the BlogCatalog. In the comparative experiment of activation functions, KAN demonstrated superior performance over commonly used activation functions. The learnable activation function strategy enhances the representation ability, making node representations more distinguishable, thereby improving both accuracy and generalization.}, archive = {J_ACCESS}, author = {Lining Yuan}, doi = {10.1109/ACCESS.2025.3577059}, journal = {IEEE Access}, pages = {100287-100297}, shortjournal = {IEEE Access}, title = {G-KAN: Graph kolmogorov-arnold network for node classification using contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Accelerating thermal homann flow simulation with mesh-based graph neural networks. ACCESS, 13, 100271-100286. (www)

  @article{ , abstract = {The numerical simulation of Homann flow, a classical problem in fluid dynamics involving axisymmetric flow over a solid plate, is essential for understanding various boundary layer phenomena. Due to the prominence of this flow and its computationally intensive nature when dealing with turbulent regimes in complex geometries, the present paper explores the use of machine learning (ML) to solve Homann flow with heat transfer both rapidly and accurately. However, the application of ML, especially deep learning (DL), to engineering-focused physics simulations remains a significant challenge. Traditional numerical approaches have consistently demonstrated higher accuracy in simulation tasks compared to DL models, which often suffer from a lack of generalizability when predicting outcomes for geometries that differ slightly from those used in training. In this study, Graph Neural Networks (GNNs) are employed to solve thermal Homann flow on substrates of various geometries. We demonstrate that the trained model accurately predicts outcomes even on entirely novel cases, and notably, the computational efficiency of the GNN model significantly surpasses that of conventional numerical solvers.}, archive = {J_ACCESS}, author = {Dara Rahmat Samii and Moussa Tembely}, doi = {10.1109/ACCESS.2025.3577098}, journal = {IEEE Access}, pages = {100271-100286}, shortjournal = {IEEE Access}, title = {Accelerating thermal homann flow simulation with mesh-based graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Resilience analysis of coupling combat network: Considering multiple destruction-recovery processes. ACCESS, 13, 100261-100270. (www)

  @article{ , abstract = {Modern military force operates in the form of combat systems of systems (CSoS) involving multiple components assembled in terms of function and structure with abundant complex relationships, which can be abstracted as the coupling combat network (CCN). However, coupling failures render the CSoS more vulnerable and challenging to recover under external disturbances. As the battlefield environment grows increasingly variable and future operations confront high-intensity conflict, it is necessary for the CSoS to consider resilience against continuous strikes as well. As such, this paper proposes an analysis framework of resilience based on the CCN modeling. Then, considering multiple and continuous strikes from enemies, a resilience modeling and assessment method is developed based on trend analysis for multiple destruction-recovery processes (DRPs). Additionally, the notion of importance is introduced into the resilience analysis of the CCN, wherein the correlation of layers’ resilience is investigated, encompassing both direct and coupling resilience importance. Finally, the CCN model for a typical CSoS is established to illustrate the proposed method and framework, which can serve as a reference for research on CSoSs’ resilience.}, archive = {J_ACCESS}, author = {Yuheng Dang and Huixiong Wang and Zimeng Yin and Xing Pan}, doi = {10.1109/ACCESS.2025.3577347}, journal = {IEEE Access}, pages = {100261-100270}, shortjournal = {IEEE Access}, title = {Resilience analysis of coupling combat network: Considering multiple destruction-recovery processes}, volume = {13}, year = {2025}, }
• (2025). Impact of processing parameters and medium properties on escherichia coli inactivation in a continuous pulsed electric field system. ACCESS, 13, 100247-100260. (www)

  @article{ , abstract = {This study investigated the antimicrobial impact of pulsed electric field (PEF) parameters, including pulse number, pulse duration, and high voltage (electric field) levels, in Milli-Q water inoculated with varying loads of Escherichia coli. Milli-Q represents the electrical conductivity of the medium, where keeping the electrical field at a desired value and minimizing conduction current is an ideal environment to analyze pulsed power impacts on bacteria. The maximum log reduction in PEF-treated Milli-Q water was 5.5 log CFU/mL from 8-log CFU/mL initial count after 240,000 pulses (60,000 pulses per step at 9.6 kV). Results showed that increasing the initial count of E. coli from 5 to 7 log CFU/mL lowered the reduction (3.8 vs. 1.5 log CFU/mL, respectively) through PEF treatment, particularly at a lower voltage or electric field. However, further increasing bacterial concentration to 8-log consequently increased the medium conductivity and resulted in higher reductions while the electric field remained constant during the excitation modes. Increasing the conductivity of the treatment medium from 3 to $40~\mu $ S/cm using 0.003% saline enhanced the microbial inactivation from 1.5 to 6.6 log CFU/mL under the same PEF conditions with minimal joule heating. Additionally, increasing the pulse number enhanced microbial inactivation, though a maximal point was reached beyond which further pulses did not yield additional inactivation. The study highlights the complexity of multi-objective PEF optimization, which depends on electrical, chemical, and biological parameters. Specifically, the effects of electric field strength vary with voltage adjustments, leading to changes in other factors that affect microbial inactivation.}, archive = {J_ACCESS}, author = {Yiting Wang and Farzan Zare and Elisabeth K. Prabawati and Buddhi Dayananda and Mirhojjat Seyedi and Mark S. Turner and Negareh Ghasemi and Firuz Zare and P. Nicholas Shaw and Nidhi Bansal}, doi = {10.1109/ACCESS.2025.3577223}, journal = {IEEE Access}, pages = {100247-100260}, shortjournal = {IEEE Access}, title = {Impact of processing parameters and medium properties on escherichia coli inactivation in a continuous pulsed electric field system}, volume = {13}, year = {2025}, }
• (2025). An OGFA+CNN approach for multi-level disease identification in fundus images. ACCESS, 13, 100234-100246. (www)

  @article{ , abstract = {Fundus disease classification is critical for the early detection of conditions such as diabetic retinopathy and glaucoma, which can lead to blindness if left untreated. Timely and accurate diagnosis through fundus image analysis enables prompt intervention, personalized treatment, and better patient outcomes. Traditional methods often rely on manual feature extraction, which is labor-intensive and error-prone. While Convolutional Neural Networks (CNNs) have automated classification, they struggle to capture complex relationships in fundus images, especially subtle changes in blood vessels and the optic disc. Additionally, many CNN models fail to incorporate global context, which is essential for understanding the interdependencies between various image features. These limitations hinder CNN performance in accurately detecting early-stage abnormalities. The proposed Optimized Graph Filter Augmentation (OGFA) model addresses these issues by integrating custom graph filters with CNNs and other deep learning methods. Graph-based techniques are employed to capture the structural relationships between key elements such as blood vessels and the optic disc, providing valuable global context to the image. To enhance the model’s accuracy, custom-designed graph filters are used, improving the focus on important structural features and minimizing noise. This customization allows the OGFA model to go beyond local feature extraction capabilities of traditional CNNs and improves diagnostic precision. By incorporating graph-based methods for global context alongside CNNs for detailed local feature extraction, the OGFA model becomes more adept at detecting subtle abnormalities and intricate changes in fundus images. This hybrid approach boosts robustness and reliability, offering a more accurate and effective solution for fundus disease classification, particularly in clinical settings where precision is essential for optimal patient care.}, archive = {J_ACCESS}, author = {Preethi Kulkarni and K. Srinivasa Reddy}, doi = {10.1109/ACCESS.2025.3576115}, journal = {IEEE Access}, pages = {100234-100246}, shortjournal = {IEEE Access}, title = {An OGFA+CNN approach for multi-level disease identification in fundus images}, volume = {13}, year = {2025}, }
• (2025). Integrating a sequential model into GNN-based social recommendation for relieving over-smoothing problem. ACCESS, 13, 100219-100233. (www)

  @article{ , abstract = {In recent years, social recommendation systems have increasingly integrated Graph Neural Networks (GNNs) to capture social relationships among users from both direct and higher-order neighbors. However, these state-of-the-art models face limitations regarding the number of iterations they can propagate across the graph. Messages from higher-order neighbors often dilute the unique characteristics of the target node. This causes node representations to become similar and indistinguishable, leading to a phenomenon known as the over-smoothing problem. Several works have attempted to address this problem with the expectation of preserving user characteristics. They add auxiliary information to the GNN-based social recommendation models without considering the dynamic preferences of users, which frequently change over time. Therefore, those works still struggle with the same problem after propagating in a few iterations. To mitigate this phenomenon, we propose a novel approach to integrate user characteristics extracted from users’ historical interactions using a sequential model to slow down node convergence. Integrating user characteristics into the GNN-based social recommendation will allow the model to capture user preferences that dynamically change over time. Consequently, the proposed model will be able to leverage informative data from higher-order neighbors over social networks for user representation learning, leading to better performance and effectiveness of the recommendations. Our experimental results demonstrate that the proposed model outperforms state-of-the-art social recommendation models across three benchmark datasets.}, archive = {J_ACCESS}, author = {Janekhwan Kitsupapaisan and Saranya Maneeroj and Atsuhiro Takasu}, doi = {10.1109/ACCESS.2025.3574781}, journal = {IEEE Access}, pages = {100219-100233}, shortjournal = {IEEE Access}, title = {Integrating a sequential model into GNN-based social recommendation for relieving over-smoothing problem}, volume = {13}, year = {2025}, }
• (2025). P-GELU: A novel activation function to optimize whisper for darija speech translation. ACCESS, 13, 100198-100218. (www)

  @article{ , abstract = {Activation functions play a critical role in optimizing deep learning models, directly influencing gradient flow, convergence stability, and overall translation accuracy. In this work, we investigate their impact within the Whisper-Turbo model, a speech-to-text Transformer trained from scratch on the Darija-C dataset for Moroccan Darija speech translation. Our study begins by evaluating baseline activation functions—GELU, Swish, and Mish—demonstrating that while GELU is widely used in Transformer-based architectures, it may not be optimal for dialectal speech translation, particularly in low-resource settings. To address this limitation, we introduce Parameterized GELU (P-GELU), a novel activation function that extends GELU by incorporating trainable parameters ( $\alpha $ , $\beta $ ), allowing the model to dynamically adjust its non-linearity across layers and training phases. Through extensive experiments, we demonstrate that P-GELU outperforms standard GELU, with superior convergence speed and generalization. Furthermore, P-GELU reduces training loss, improves feature retention, and enhances linguistic adaptability, making it a more effective alternative for speech translation tasks involving phonetic variability, code-switching, and limited training data. The proposed P-GELU offers a promising balance between computational efficiency and performance gains, presenting a viable solution for enhancing Transformer-based speech models in low-resource language scenarios.}, archive = {J_ACCESS}, author = {Maria Labied and Abdessamad Belangour and Mouad Banane}, doi = {10.1109/ACCESS.2025.3574398}, journal = {IEEE Access}, pages = {100198-100218}, shortjournal = {IEEE Access}, title = {P-GELU: A novel activation function to optimize whisper for darija speech translation}, volume = {13}, year = {2025}, }
• (2025). Optimizing nutrient formulations through artificial intelligence model to reduce excessive fertigation in lettuce grown in hydroponic systems. ACCESS, 13, 100183-100197. (www)

  @article{ , abstract = {Optimizing nutrient uptake is critical for maximizing productivity in hydroponically cultivated plants. In hydroponic systems, where soil is absent, essential nutrients must be precisely delivered through the water solution to support plant growth. Despite the widespread use of conventional nutrient formulations, limited research has focused on dynamically optimizing nutrient concentrations based on plant-specific needs at different growth stages. This study addresses the gap by leveraging Artificial Neural Networks (ANNs) to develop an intelligent nutrient management system for hydroponic lettuce cultivation. A novel approach was implemented to optimize nitrogen (N), phosphorus (P), and potassium (K) concentrations by utilizing real-time data collected through Ion-Selective Electrodes (ISE) sensors and the PlantEye platform. These datasets were used to train ANN models capable of predicting optimal NPK levels on a weekly basis, minimizing nutrient waste while maintaining high crop productivity. The results demonstrate that the AI-driven approach can reduce nutrient usage by up to 40% without compromising plant growth and yield when compared to traditional formulations. This preliminary study presents a significant advancement in sustainable hydroponic farming by introducing an adaptive, data-driven fertigation strategy. The findings have broad implications for precision agriculture, enabling resource-efficient nutrient management while reducing environmental impact. By integrating AI-based optimization, this study paves the way for more cost-effective, scalable, and environmentally sustainable hydroponic production systems.}, archive = {J_ACCESS}, author = {Gadelhag Mohmed and Gultekin Hasanaliyeva and Roy O’Mahony and Chungui Lu}, doi = {10.1109/ACCESS.2025.3571730}, journal = {IEEE Access}, pages = {100183-100197}, shortjournal = {IEEE Access}, title = {Optimizing nutrient formulations through artificial intelligence model to reduce excessive fertigation in lettuce grown in hydroponic systems}, volume = {13}, year = {2025}, }
• (2025). Three-wheeled mobile robot trajectory tracking control using nonlinear PID controller based neural network combined with backstepping controller. ACCESS, 13, 100167-100182. (www)

  @article{ , abstract = {This paper presents a novel approach for the trajectory tracking control of a three wheeled mobile robot using neural network based nonlinear PID controller combined with the backstepping control techniques. First, the kinematic and dynamic model of the robot was derived using the Lagrangian method. Second, the neural network based nonlinear PID controller was trained and developed using the input and output data of a nonlinear PID controller(NPID). The nonlinear PID controller provided all the data used to train the proposed controller. The neural network has been trained using a Bayesian regularization algorithm. Finally, the stability of the designed controller is analyzed using the Lyapunov stability method and the performance of the proposed controller was compared with the Backstepping+PID controllers(BSC+PID) and BSC+NPID controller using MATLAB/Simulink. Extensive numerical simulations demonstrate that the BSC+NNNPID controller significantly enhances reference tracking accuracy, robustness and error minimizing capability compared to both BSC+PID and BSC+NPID when tested with different reference trajectory inputs.}, archive = {J_ACCESS}, author = {Tolera Beharu Arega and Yalew Mersha Tesfa and Chala Merga Abdissa}, doi = {10.1109/ACCESS.2025.3577269}, journal = {IEEE Access}, pages = {100167-100182}, shortjournal = {IEEE Access}, title = {Three-wheeled mobile robot trajectory tracking control using nonlinear PID controller based neural network combined with backstepping controller}, volume = {13}, year = {2025}, }
• (2025). Toward sustainable manufacturing: A review on innovations in robotic assembly and disassembly. ACCESS, 13, 100149-100166. (www)

  @article{ , abstract = {In the quest for sustainable manufacturing, the integration of robotic assembly and disassembly processes has emerged as a pivotal innovation. This paper explores advancements in robotic assembly and disassembly aimed at enhancing sustainable manufacturing. Key advancements include machine learning for optimized robotic motion, advanced sensors for real-time control, and collaborative robots (cobots) for safe human-robot interaction. These technologies enable precise and efficient assembly of complex products and effective end-of-life disassembly for recycling. Case studies across various industries demonstrate significant reductions in waste, energy consumption, and environmental impact. The paper discusses current challenges and future directions, emphasizing the need for interdisciplinary collaboration and ongoing technological development to achieve fully sustainable manufacturing systems.}, archive = {J_ACCESS}, author = {Adip Ranjan Das and Maria Koskinopoulou}, doi = {10.1109/ACCESS.2025.3576441}, journal = {IEEE Access}, pages = {100149-100166}, shortjournal = {IEEE Access}, title = {Toward sustainable manufacturing: A review on innovations in robotic assembly and disassembly}, volume = {13}, year = {2025}, }
• (2025). Behavior modeling and bio-hybrid systems: Using reinforcement learning to enhance cyborg cockroach in bio-inspired swarm robotics. ACCESS, 13, 100119-100148. (www)

  @article{ , abstract = {Bio-inspired swarm robotics is an emerging field at the intersection of biology, robotics, and artificial intelligence, offering novel capabilities by integrating living organisms with robotic systems. This paper presents a groundbreaking approach to behavior modeling and enhancement for biological hybrid cockroach robots using reinforcement learning (RL). The study focuses on developing a hybrid system that combines the natural adaptability of cockroaches with the precision of robotics. The RL method predicts and optimizes cockroach movements to enhance task performance in bio-inspired swarm robotics. The proposed methodology includes modeling cockroach behaviors through data-driven techniques, designing a control framework using RL, and integrating these systems into a swarm robotics architecture. By defining precise state-action spaces and reward functions, the RL model effectively learned to influence cockroach behavior via electrical stimulation of their antennae, guiding them to perform complex navigational tasks. Experimental results demonstrate the efficacy of the system in both simulated and real-world environments. The RL framework achieves high prediction accuracy and control fidelity, significantly enhancing the operational capabilities of the bio-hybrid system. Swarm-level tests reveal the potential of this approach for collaborative tasks, such as obstacle avoidance and collective decision-making. This research contributes to the advancement of bio-hybrid systems by bridging biological adaptability and robotic precision, with implications for search-and-rescue missions and environmental monitoring. We discuss ethical considerations to address the challenges of using live organisms. Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms.}, archive = {J_ACCESS}, author = {Le Minh Triet and Nguyen Truong Thinh}, doi = {10.1109/ACCESS.2025.3569285}, journal = {IEEE Access}, pages = {100119-100148}, shortjournal = {IEEE Access}, title = {Behavior modeling and bio-hybrid systems: Using reinforcement learning to enhance cyborg cockroach in bio-inspired swarm robotics}, volume = {13}, year = {2025}, }
• (2025). Advancing breast cancer detection: SE-conformer framework for malignancy detection in histopathology images. ACCESS, 13, 100105-100118. (www)

  @article{ , abstract = {Globally, breast cancer is the second most lethal form of cancer among women, and has high rates of incidence and mortality. Early detection is crucial in improving survival rates. As deep learning technologies continue to advance, they are increasingly being leveraged to automate pathological image classification, primarily through conventional models. Traditional models excel at processing images in which objects are clearly defined against their backgrounds, but they struggle with pathological images because the boundaries between tumor lesions and surrounding tissues are often indistinct. Hence, models that can learn the minute details in these kinds of images are very essential. We propose a hybrid approach that integrates convolutional networks with attention mechanisms to achieve high classification performance while reducing computational complexity. To enhance feature representation in convolutional neural networks, we introduced an improved convolutional block, termed the SE-Res-Conv Block, which incorporates Squeeze-and-Excitation (SE) attention mechanisms within a residual convolutional framework. The extracted features are then processed by a Conformer block, which further refines them by emphasizing the most relevant regions in the input feature map. Finally, a dense output layer is used for classification. The proposed model is evaluated on both the BACH dataset and all four magnification levels of the BreakHis dataset. Experimental results, obtained through K-fold cross-validation, indicate that the model performs optimally on $200\times $ magnification images, achieving an average accuracy, precision, and recall of 0.97. These results underscore the model’s effectiveness and its potential applicability in medical image classification tasks.}, archive = {J_ACCESS}, author = {Lekha S. Nair and K. R. Amarnath and Jyothisha J. Nair}, doi = {10.1109/ACCESS.2025.3576825}, journal = {IEEE Access}, pages = {100105-100118}, shortjournal = {IEEE Access}, title = {Advancing breast cancer detection: SE-conformer framework for malignancy detection in histopathology images}, volume = {13}, year = {2025}, }
• (2025). Integrating generative and contrastive approaches for human action recognition. ACCESS, 13, 100095-100104. (www)

  @article{ , abstract = {This study introduces a novel approach to unsupervised skeleton-based human action recognition by integrating generative and contrastive learning methods. We propose a decomposition of representations, allowing for the preservation of detailed motion information for the generative learning objective while also extracting action features for the contrastive learning objective. By swapping contrastive representations between positive pairs (coining the name SwapCLR), we ensure that the generative and contrastive representations are complementary and both objectives contribute to learning a strong representation for downstream tasks like action recognition. Additionally, we address the challenge of noisy data in skeleton-based action recognition with a new saturating reconstruction loss, significantly reducing the impact of noise common to key-point detections. Our method demonstrates state-of-the-art performance in unsupervised action recognition on the NTU and PKU-MMD datasets, while also enabling generative downstream tasks such as motion in-painting and motion generation. Overall, these experimental results confirm the method’s effectiveness and suggest its applicability to a variety of action analysis tasks.}, archive = {J_ACCESS}, author = {Pablo Cervantes and Yusuke Sekikawa and Ikuro Sato and Koichi Shinoda}, doi = {10.1109/ACCESS.2025.3575707}, journal = {IEEE Access}, pages = {100095-100104}, shortjournal = {IEEE Access}, title = {Integrating generative and contrastive approaches for human action recognition}, volume = {13}, year = {2025}, }
• (2025). Review of certificateless authentication scheme for vehicular ad hoc networks. ACCESS, 13, 100074-100094. (www)

  @article{ , abstract = {Vehicular networks play an integral role in Intelligent Transportation Systems (ITS) by facilitating real-time communication between vehicles, roadside infrastructure, and cloud-based services. But this inherent characteristics on mobility, decentralization, and open wireless broadcast nature makes security threats a big target to them with impersonation attacks, Sybil attacks, data breaches and unauthorized access. PKI-based authentication methods, on the other hand, do not fit our objective, given the high computational overhead, certificate management issues, and centralization risk involved in V2V authentication protocols. Certificateless authentication has been proposed to avoid the challenges and costs associated with certificate-based security infrastructures by providing a lightweight and efficient alternative solution. Based on authentication models, cryptographic techniques, privacy-preserving mechanisms, emerging technologies and security properties, this survey presents a systematic classification of certificateless authentication schemes. It encompasses an investigation of emerging cryptographic solutions like lattice-based encryption, blockchain-supported authentication/credential management, AI-empowered security, and considers their impact on VANET security, scalability, and real-time constraints. Moreover, this study considers the application of 5G networks, edge computing, and post-quantum cryptography in VANET authentication. To ensure privacy-preserving and secure vehicular communication systems that could be scaled for infrastructural networks, the results highlight the shortcomings of existing vehicular authentication methods, and the need for hybrid authentication models, standardization, and interoperability.}, archive = {J_ACCESS}, author = {Abdulaziz Zaid A. Aljarwan and Md Asri Bin Ngadi}, doi = {10.1109/ACCESS.2025.3576926}, journal = {IEEE Access}, pages = {100074-100094}, shortjournal = {IEEE Access}, title = {Review of certificateless authentication scheme for vehicular ad hoc networks}, volume = {13}, year = {2025}, }
• (2025). An instructional optimization method based on bidirectional transformer and reinforcement learning. ACCESS, 13, 100064-100073. (www)

  @article{ , abstract = {With the rapid development of information technology, personalized education has become a key direction for improving the quality of online learning and optimizing individualized learning paths. However, accurately recommending appropriate courses and exercises for diverse learners remains a significant challenge. Existing recommendation methods often struggle with effectively modeling learner interests, addressing the cold-start problem, and dynamically adapting recommendation strategies to meet personalized needs. To address these limitations, this paper proposes RL-TBTNet, a novel teaching optimization recommendation framework that integrates a bidirectional Transformer, BERT, and reinforcement learning (DQN). The model first vectorizes user behavior data, learning content, and knowledge base information. Transformer layers are employed for feature encoding, while BERT extracts deep semantic representations to form individualized feature vectors. These features are then fused via Transformer-based processing to predict optimal learning content. In addition, a DQN-based reinforcement learning module models dynamic shifts in user interests, enabling adaptive refinement of learning trajectories over time. Experimental evaluations on public datasets show that RL-TBTNet outperforms existing Transformer-based methods such as BST in terms of key metrics like HR and NDCG, particularly excelling in cold-start scenarios. Ablation studies further confirm the effectiveness of semantic enhancement through BERT and reinforcement-driven optimization. These results demonstrate the framework’s potential as a robust and adaptive solution for personalized educational content recommendation, offering both practical value and theoretical insights for the development of intelligent education systems.}, archive = {J_ACCESS}, author = {Ran Zhang and Xiaoping Wu and Xude Zhang and Li Xu}, doi = {10.1109/ACCESS.2025.3576878}, journal = {IEEE Access}, pages = {100064-100073}, shortjournal = {IEEE Access}, title = {An instructional optimization method based on bidirectional transformer and reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). A review on the application of model-based systems engineering in the development of safe circular systems. ACCESS, 13, 100042-100063. (www)

  @article{ , abstract = {The increasing complexity of modern technical systems, coupled with the growing demand for sustainability and safety, necessitates innovative approaches in system development. Model-Based Systems Engineering (MBSE) has emerged as a powerful methodology to support the development process of such complex systems. This paper presents a systematic literature review that explores the role of MBSE in the development of sustainable and safe systems. The study classifies existing design supports by analyzing their role in the development process across three key areas: sustainability, safety risk, and knowledge management. The findings reveal current challenges, such as fragmented lifecycle data, limited automation in safety analysis, and difficulties in reusing expert knowledge, while also identifying research opportunities for AI-driven methods, digital twin utilization, and enhanced interoperability between MBSE and lifecycle management tools. By consolidating the class supported activities and their interactions, this paper provides insights into how MBSE can facilitate circularity in system development and highlights directions for future research.}, archive = {J_ACCESS}, author = {Zvonimir Lipšinić and Neven Pavković and Stephan Husung}, doi = {10.1109/ACCESS.2025.3575578}, journal = {IEEE Access}, pages = {100042-100063}, shortjournal = {IEEE Access}, title = {A review on the application of model-based systems engineering in the development of safe circular systems}, volume = {13}, year = {2025}, }
• (2025). Pedestrian trajectory prediction via window attention and spatial graph interaction network. ACCESS, 13, 100031-100041. (www)

  @article{ , abstract = {The accuracy of pedestrian trajectory prediction is crucial for the safety of autonomous driving systems. However, the task still faces challenges in modeling long-term dependencies, complex spatial interactions, and multi-scale feature fusion. To address these issues, this paper proposes the WAGIN (Windowed Attention Graph Interaction Network) model. First, in the temporal dimension, a window mask mechanism is designed to adjust the attention receptive field at each time step, effectively capturing temporal dependencies. In the spatial dimension, a hierarchical heterogeneous GCN (graph convolutional network) is constructed, combining pedestrian dynamic interaction graphs and scene semantic static graphs. Additionally, an interaction kernel function based on motion consistency is proposed to model the interactions between individual pedestrians. Finally, a multi-scale dilated convolution network is employed for future trajectory generation, capturing multi-scale spatiotemporal features through dilated convolutions to enhance prediction accuracy and robustness. The model is experimentally validated on the public ETH/UCY dataset, and the results demonstrate its effectiveness, achieving improvements of 23% in average displacement error (ADE) and 21% in final displacement error (FDE) over baseline methods. Moreover, qualitative analysis reveals the model’s excellent generalization ability in handling different scenarios.}, archive = {J_ACCESS}, author = {Xiang Gu and Chao Li and Jie Yang and Jing Wang and Qiwei Huang}, doi = {10.1109/ACCESS.2025.3573782}, journal = {IEEE Access}, pages = {100031-100041}, shortjournal = {IEEE Access}, title = {Pedestrian trajectory prediction via window attention and spatial graph interaction network}, volume = {13}, year = {2025}, }
• (2025). SmartExplorer1.0: A novel intelligent framework for path planning in robotics. ACCESS, 13, 100002-100030. (www)

  @article{ , abstract = {Autonomous systems require advanced path planning to ensure safe and efficient navigation through complex terrains. The SmartExplorer1.0 algorithm (SE1.0) utilizes Digital Terrain Models (DTMs) as input to identify navigable areas and plan optimal routes by incorporating terrain gradients, obstacle types, and environmental conditions. Compared to traditional methods such as A ${}^{\ast }$ or the Artificial Potential Field (APF), and modern metaheuristic methods such as Rapidly-Exploring Random Tree $\ast $ (RRT $\ast $ ), SE1.0 offers superior adaptability. It complies with varying vehicle specifications while considering factors like soil granularity, slope consistency, heat sources and electromagnetic fields, elements often overlooked by conventional approaches. This integration allows for more precise and context-aware navigation, especially in dynamic or complex environments. The algorithm defines accessible regions and penalizes hazardous zones. Starting from specified initial and target points, it iteratively evaluates potential movements by optimizing an objective function to determine the optimal path. This approach not only visualizes accessible and non-accessible areas but also produces actionable data files for practical use, such as a list of trajectory points, which can be used to activate control sensors with pinpoint accuracy and enables the calculation of both completed trajectory and remaining path to be traveled. Its flexibility and comprehensive integration of environmental and vehicle-specific parameters make SE1.0 a versatile solution for autonomous navigation in challenging environments. The efficiency and versatility of SE1.0 are demonstrated by solving various path planning problems on DTMs also considering real world applications that involve the use of the DUNE rover.}, archive = {J_ACCESS}, author = {Chiara Furio and Angelo Ugenti and Giulio Reina and Mario Massimo Foglia and Luciano Lamberti}, doi = {10.1109/ACCESS.2025.3577464}, journal = {IEEE Access}, pages = {100002-100030}, shortjournal = {IEEE Access}, title = {SmartExplorer1.0: A novel intelligent framework for path planning in robotics}, volume = {13}, year = {2025}, }
• (2025). Category-based sentiment analysis of sindhi news headlines using machine learning, deep learning, and transformer models. ACCESS, 13, 99985-100001. (www)

  @article{ , abstract = {The rapid growth of digital content has made sentiment analysis (SA) an essential tool for understanding public sentiment and classifying textual data. Despite significant progress in natural language processing (NLP), low-resource languages, particularly Sindhi, remain underexplored due to the lack of computational tools and annotated datasets. This study addresses this gap by introducing the Sindhi News Headlines Dataset (SNHD), a novel corpus annotated for both SA and category classification across eight categories: Crime, Economy, Entertainment, Health, Politics, Science & Technology, Social, and Sports. To evaluate the effectiveness of different machine learning (ML), deep learning (DL), and transformer-based approaches, we conduct a comparative analysis of various models on SA and category classification tasks. Furthermore, we leverage Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), to gain insights into model decision-making. Experimental results show that traditional ML models outperform DL and transformer-based models on the SNHD dataset. Specifically, Support Vector Machines with Radial Basis Function (SVM-RBF) achieves the highest performance for SA (0.74 accuracy and weighted F-score), while the Ridge Classifier (RC) delivers the best results for category classification (0.84 accuracy and weighted F-score). Among transformer models, XLM-RoBERTa demonstrates strong performance in category classification (0.82 accuracy and weighted F-score). These findings establish a benchmark for future research in Sindhi NLP and highlight the potential of hybrid approaches in tackling challenges associated with low-resource languages. This work provides a foundational resource for NLP researchers seeking to advance computational methods for Sindhi and similar underrepresented languages.}, archive = {J_ACCESS}, author = {Safdar Ali Soomro and Siti Sophiayati Yuhaniz and Mazhar Ali Dootio and Ghulam Mujtaba and Jawaid Ahmed Siddiqui}, doi = {10.1109/ACCESS.2025.3576853}, journal = {IEEE Access}, pages = {99985-100001}, shortjournal = {IEEE Access}, title = {Category-based sentiment analysis of sindhi news headlines using machine learning, deep learning, and transformer models}, volume = {13}, year = {2025}, }
• (2025). AI-driven aerial relay placement and power allocation for enhanced secrecy in untrusted UAV-enabled networks. ACCESS, 13, 99970-99984. (www)

  @article{ , abstract = {In this paper, we introduce an innovative artificial intelligence (AI)-driven methodology for optimizing the deployment of unmanned aerial vehicle (UAV)-enabled networks, specifically addressing scenarios where an untrusted UAV serves as an amplify-and-forward (AF) relay while potentially engaging in eavesdropping activities. The primary goal is to enhance secrecy performance by optimizing both the three-dimensional (3-D) positioning of the UAV relay and the power allocation strategy. Conventional optimization methods often fail to effectively capture the intricate, dynamic nature of such networks, thereby limiting their ability to ensure adaptive secrecy optimization. To address this challenge, we propose a hybrid approach that integrates particle swarm optimization (PSO) with the K-means and K-medoids unsupervised machine learning (ML) techniques. This combined approach not only refines UAV placement but also ensures adherence to the derived rule-based power allocation conditions, which are crucial for maximizing secrecy performance. Through extensive simulations, we evaluate the efficacy of the proposed K-PSO methodology against standalone PSO, K-means, and K-medoids models. The results demonstrate that the hybrid K-PSO approach consistently outperforms its standalone counterparts by significantly improving the average achievable secrecy rate while reducing the secrecy outage probability (SOP).}, archive = {J_ACCESS}, author = {Lefteris Tsipi and Emmanouel T. Michailidis and Michail Karavolos and Demosthenes Vouyioukas and Athanasios G. Kanatas}, doi = {10.1109/ACCESS.2025.3577053}, journal = {IEEE Access}, pages = {99970-99984}, shortjournal = {IEEE Access}, title = {AI-driven aerial relay placement and power allocation for enhanced secrecy in untrusted UAV-enabled networks}, volume = {13}, year = {2025}, }
• (2025). Robust face recognition using deep learning and ensemble classification. ACCESS, 13, 99957-99969. (www)

  @article{ , abstract = {Facial recognition systems are widely used in various applications such as security, healthcare, and authentication, but face significant challenges in uncontrolled environments. Poor lighting conditions can obscure facial features, introduce shadows, and distort spatial relationships, while changes in pose are critical for accurate identification. Existing methods often struggle to strike a balance between accuracy, computational efficiency, and robustness. Deep learning has become popular for automatically learning features through convolution layers. This study proposes a robust framework that integrates contrast-limited adaptive histogram equalization (CLAHE) and adaptive gamma correction for illumination normalization and multi-task cascaded convolutional networks (MTCNN) for precise face detection under varying poses and lighting conditions. This study proposes a deep learning-based approach for face recognition utilising multiple models, including VGG16, VGG19, ResNet-50, ResNet-101, and MobileNetV2. For classification, an ensemble of SVM, XGBoost, and random forest classifiers is combined using weighted averaging. The approach is tested on datasets such as CASIA3D and 105PinsFace, which include variations in illumination conditions. Using deep learning for automated hierarchical feature extraction and ensemble strategies, experimental results demonstrate significant improvements in recognition accuracy and enhanced robustness against lighting and pose variations while ensuring scalability for real-world applications. The approach achieved 99.91% accuracy on the CASIA3D dataset and 98.77% on the 105PinsFace dataset, showcasing its effectiveness across challenging conditions.}, archive = {J_ACCESS}, author = {Pavani Chitrapu and Mahesh Kumar Morampudi and Hemantha Kumar Kalluri}, doi = {10.1109/ACCESS.2025.3575192}, journal = {IEEE Access}, pages = {99957-99969}, shortjournal = {IEEE Access}, title = {Robust face recognition using deep learning and ensemble classification}, volume = {13}, year = {2025}, }
• (2025). Dynamic control of isolated network microgrids: A resilient backpropagation neural network-based virtual inertia control approach. ACCESS, 13, 99939-99956. (www)

  @article{ , abstract = {In isolated networked microgrid with high penetration of solar and wind-based generation, maintaining system stability and achieving optimal dynamic performance poses significant challenges due to reduced mechanical inertia traditionally provided by synchronous generators. This paper introduces a novel Resilient Back Propagation Bayesian Neural network-based virtual inertia control strategy to enhance frequency response and overall stability of the considered network microgrid. By leveraging robust control techniques, the proposed approach provides virtual inertia to respond effectively to varying system conditions and disturbances, improving system robustness and minimising the isolated networked microgrid’s tie-line power and frequency deviations. Comprehensive simulations, including case studies under varying disturbance conditions such as load fluctuations and renewable energy variations, prove the efficacy of the introduced virtual inertia control strategy. The proposed method outperforms conventional proportional integral derivative, and artificial bee colony-optimised proportional integral derivative control techniques in key dynamic performance metrics. It achieves the lowest integral time absolute error of 18.3912, compared to 30.8946 for proportional integral derivative and 20.8212 for optimised proportional integral derivative control techniques, demonstrating superior frequency response. Additionally, it achieves a mean square error of 4.0897e-07, significantly lower than 4.239e-06 for neural network-based fractional order proportional integral derivative control and 3.072e-06 for feed-forward neural network, confirming improved accuracy. Results indicate improved dynamic performance metrics, including faster frequency stabilization and reduced overshoot with minimal tie-line power and frequency deviation, compared to proportional integral derivative and optimal control techniques. The strategy’s adaptability and computational efficiency offer practical benefits for real-world implementation, contributing to more resilient and efficient microgrid operations.}, archive = {J_ACCESS}, author = {Md Asaduzzaman Shobug and Md Alamgir Hossain and Fuwen Yang and Junwei Lu}, doi = {10.1109/ACCESS.2025.3576345}, journal = {IEEE Access}, pages = {99939-99956}, shortjournal = {IEEE Access}, title = {Dynamic control of isolated network microgrids: A resilient backpropagation neural network-based virtual inertia control approach}, volume = {13}, year = {2025}, }
• (2025). A resource-efficient 3D U-net for hippocampus segmentation using CLAHE and SCE-3DWT techniques. ACCESS, 13, 99923-99938. (www)

  @article{ , abstract = {Hippocampus segmentation on MRI (magnetic resonance imaging) plays a vital role in detecting, diagnosing, tracking, and monitoring neurodegenerative diseases, particularly Alzheimer’s disease. While larger datasets often provide an advantage in deep learning-based segmentation, smaller datasets pose unique challenges due to limited data variability and an increased risk of overfitting. This study addresses these challenges by developing a computationally efficient and accurate 3D U-Net model tailored for hippocampus segmentation. The proposed approach employs a preprocessing pipeline combining 3D Contrast Limited Adaptive Histogram Equalization (CLAHE) and Selective Coefficient-Enhanced 3D Wavelet Transform (SCE-3DWT), which enhances contrast and reduces noise for improved feature extraction. The experimental evaluation was conducted using the EADC-ADNI HarP dataset, comprising 135 hippocampal MRI scans with an input image size of $64\times 64 \times 96$ . The model achieved a Dice coefficient of 0.8838 and a Jaccard Index of 0.7920, surpassing recent state-of-the-art methods. Comparative analysis highlights reduced Over-Segmentation Ratio (OSR = FP/(FP+TP), 0.0594) and Under-Segmentation Ratio (USR = FN/(FN+TP), 0.0569, reflecting its robustness and generalization. The lightweight architecture, designed with a maximum filter size of 512, operates efficiently without relying on transfer learning, making it accessible for broader applications. Future work will focus on integrating post processing techniques, leveraging larger and more diverse datasets, and exploring higher-resolution volumetric data to further improve segmentation accuracy and clinical utility. This study contributes to the advancement of medical image analysis, offering a resource-efficient framework for precise hippocampus segmentation, with potential implications for improved Alzheimer’s disease management.}, archive = {J_ACCESS}, author = {Faizaan Fazal Khan and Jun-Hyung Kim and Chun-Su Park and Ji-In Kim and Goo-Rak Kwon}, doi = {10.1109/ACCESS.2025.3577266}, journal = {IEEE Access}, pages = {99923-99938}, shortjournal = {IEEE Access}, title = {A resource-efficient 3D U-net for hippocampus segmentation using CLAHE and SCE-3DWT techniques}, volume = {13}, year = {2025}, }
• (2025). StackTrans–Multimodal heart disease detection using stacked transformer fusion framework. ACCESS, 13, 99911-99922. (www)

  @article{ , abstract = {Cardiac disease diagnosis demands precise interpretation of complex physiological signals, however existing systems often rely on unimodal data and lack adaptive fusion strategies. Most conventional frameworks fall short in capturing intermodal dependencies and adjusting to performance variations across heterogeneous inputs. This study introduces StackTrans, a transformer-based multimodal classification framework designed to improve diagnostic accuracy through ECG and PCG signal integration. The architecture comprises modality-specific transformer encoders, a bidirectional cross-modal fusion transformer that facilitates latent-level attention between modalities, and a stacked ensemble mechanism governed by a meta-learner. Residual learning modules enhance prediction refinement, while entropy-guided adaptive voting improves confidence-weighted decision reliability. The PCG and ECG modules are independently trained using the PhysioNet 2016 and MIT-BIH Arrhythmia datasets, respectively, and integrated through joint inference. Evaluations using TensorFlow on an NVIDIA RTX GPU demonstrate that StackTrans attains a precision of 98.6%, an F1 score of 98.4%, and an AUC of 0.99—outperforming unimodal ECG and PCG models by 2.5% and 7.5%, respectively.}, archive = {J_ACCESS}, author = {Muhammad Adnan and Yang Yi and Enci Wang and Md Nasir Imtiaz}, doi = {10.1109/ACCESS.2025.3574310}, journal = {IEEE Access}, pages = {99911-99922}, shortjournal = {IEEE Access}, title = {StackTrans–Multimodal heart disease detection using stacked transformer fusion framework}, volume = {13}, year = {2025}, }
• (2025). Analytic evaluation of the statistical power of accelerated reliability demonstration tests. ACCESS, 13, 99892-99910. (www)

  @article{ , abstract = {The planning of reliability demonstration tests is a typical task for reliability engineers. To find the optimum test plan, many different scenarios must be considered and compared. Comparing test plans by their estimation variance has several disadvantages, as it only considers the type I error. To overcome these disadvantages, the probability of successfully demonstrating a lifetime target is used to implement a statistical power analysis for test plans from a practical perspective. Typically, this is done through simulation based on existing knowledge about the relevant failure mechanism. Such simulations are inadequate for real-world applications, mostly as their are computationally demanding. To enable wide applicability of optimal test planning methods based on the probability of test success, an analytic approach is presented in this paper. The simulation is replaced with an approximation of the stochastical distribution of the test results. The analytic method consists of two steps. First, the expected mean and covariance of the maximum likelihood estimates for a given Weibull distribution with an integrated lifetime model are derived by using pivotal quantities. Second, the delta method is applied to describe the lifetime demonstrated with a particular test plan. The method is compared against simulation results, showing an average error of 1.3– $1.7~\%$ .}, archive = {J_ACCESS}, author = {Philipp Mell and Martin Dazer}, doi = {10.1109/ACCESS.2025.3577366}, journal = {IEEE Access}, pages = {99892-99910}, shortjournal = {IEEE Access}, title = {Analytic evaluation of the statistical power of accelerated reliability demonstration tests}, volume = {13}, year = {2025}, }
• (2025). COMQ: A backpropagation-free algorithm for post-training quantization. ACCESS, 13, 99879-99891. (www)

  @article{ , abstract = {Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their low-bit counterparts without compromising the original accuracy remains a key challenge. In this paper, we propose an innovative PTQ algorithm termed COMQ, which sequentially conducts coordinate-wise minimization of the layer-wise reconstruction errors. We consider the widely used integer quantization, where every quantized weight can be decomposed into a shared floating-point scalar and an integer bit-code. Within a fixed layer, COMQ treats all the scaling factor(s) and bit-codes as the variables of the reconstruction error. Every iteration improves this error along a single coordinate while keeping all other variables constant. COMQ is easy to use and requires no hyper-parameter tuning. It instead involves only dot products and rounding operations. We update these variables in a carefully designed greedy order, significantly enhancing the accuracy. COMQ achieves remarkable results in quantizing 4-bit Vision Transformers, with a negligible loss of less than 1% in Top-1 accuracy. In 4-bit INT quantization of convolutional neural networks, COMQ maintains near-lossless accuracy with a minimal drop of merely 0.3% in Top-1 accuracy. The code is available at https://github.com/AozhongZhang/COMQ}, archive = {J_ACCESS}, author = {Aozhong Zhang and Zi Yang and Naigang Wang and Yingyong Qi and Jack Xin and Xin Li and Penghang Yin}, doi = {10.1109/ACCESS.2025.3576737}, journal = {IEEE Access}, pages = {99879-99891}, shortjournal = {IEEE Access}, title = {COMQ: A backpropagation-free algorithm for post-training quantization}, volume = {13}, year = {2025}, }
• (2025). ODACE-RMS: A remote web-based platform for automated multi-device android testing and certification. ACCESS, 13, 99863-99878. (www)

  @article{ , abstract = {The evolving nature of the software industry has increased the complexity and cost of software testing. This paper highlights the critical need for automation in software testing, specifically for mobile Android device certification. We introduce ODACE-RMS, a platform designed to streamline the certification process by enabling the automated execution of comprehensive telecommunication test scenarios. ODACE-RMS runs as an application on a tester’s PC, featuring a browser-based interface powered by Appium. The paper also outlines ODACE-RMS’s modular architecture that combines Appium, ADB, and USB-over-IP to support remote and parallel testing. With a Spring Boot backend and web-based frontend, the platform enables flexible multi-device test sessions, whether connected locally via USB or remotely through a USB-over-IP hub. These features significantly reduce certification time and allow engineers to execute tests without physically handling devices. Our study compares ODACE-RMS with traditional systems, which reduced engagement time in certification testing by 89%, significantly decreasing the need for human intervention and enhancing the overall efficiency of the certification process. Additionally, the proposed ODACE-RMS architecture results show that testing remotely is not much slower than testing locally through physical ports, even when multiple devices are tested in parallel, with an average 7% delay.}, archive = {J_ACCESS}, author = {Sundos Mojahed and Réjean Drouin and Lokman Sboui}, doi = {10.1109/ACCESS.2025.3576823}, journal = {IEEE Access}, pages = {99863-99878}, shortjournal = {IEEE Access}, title = {ODACE-RMS: A remote web-based platform for automated multi-device android testing and certification}, volume = {13}, year = {2025}, }
• (2025). Joint scheduling of IEEE 802.1AS gPTP and industrial data traffic in TSN-6G networks. ACCESS, 13, 99842-99862. (www)

  @article{ , abstract = {The 6th Generation (6G) mobile network is envisaged to play a significant role for supporting time-sensitive applications in industrial scenarios. The integration between 6G and (wired) Ethernet Time-Sensitive Networking (TSN) is a natural step to provide end-to-end deterministic communications guaranteeing bounded latency. Time synchronization, which is key for the operation of industrial automation networks, can be provided via the IEEE 802.1AS generalized Precision Time Protocol (gPTP). This protocol is executed over the integrated TSN-6G network to synchronize industrial devices and to keep the TSN bridges synchronized to support the IEEE 802.1Qbv Time-Aware Shaper (TAS). The TAS is a time-triggered scheduling mechanism commonly used to guarantee the delay requirements of industrial data traffic. The gPTP traffic may also be scheduled as a critical traffic, requiring careful consideration. First, the accuracy provided by gPTP relies on the effective transmission of the gPTP packets over the integrated TSN-6G network. Second, uncoordinated or misaligned TAS schedules in the TSN bridges due to loss of timing accuracy may provoke undesired packet delays. Third, the 6G system may introduce additional sources of timing inaccuracies with respect to a TSN-native network. In this paper, we propose a TAS-based scheduling scheme for gPTP traffic and industrial data traffic by allocating them a number of periodic, aligned time intervals, such that the provided timing accuracy and schedules are enough to transmit the time-critical packets in the specified time intervals. We compare by simulation various configurations for comprehensive analysis of synchronization accuracy, end-to-end delay and bandwidth utilization.}, archive = {J_ACCESS}, author = {Pablo Muñoz and Pablo Rodríguez-Martín and Julia Caleya-Sánchez and Jonathan Prados-Garzón and Óscar Adamuz-Hinojosa and and Pablo Ameigeiras}, doi = {10.1109/ACCESS.2025.3577021}, journal = {IEEE Access}, pages = {99842-99862}, shortjournal = {IEEE Access}, title = {Joint scheduling of IEEE 802.1AS gPTP and industrial data traffic in TSN-6G networks}, volume = {13}, year = {2025}, }
• (2025). Global structural knowledge distillation for semantic segmentation. ACCESS, 13, 99826-99841. (www)

  @article{ , abstract = {Knowledge distillation (KD) has become a cornerstone for compressing deep neural networks, allowing a smaller student model to learn from a larger teacher model. In the context of semantic segmentation, traditional KD methods primarily focus on pixel-level feature alignment, where the student model is trained to match the teacher’s features at each pixel. Despite performance improvements, the pixel-level alignment can introduce noise and redundant information, particularly in complex scenes, and often overlook the global structural context that is crucial for robust segmentation. To overcome these limitations, we propose Global Structural Knowledge Distillation (GSKD), a novel approach that moves beyond dense pixel-level alignment. Instead of aligning features pixel-by-pixel, we focus on capturing and transferring global structural information within an image. Our method begins with Class-Balanced Sampling (CBS), which ensures that representative features from various classes are sampled evenly from the teacher’s feature maps. This helps the model better represent both common and rare classes, addressing class imbalance. Next, we construct a Global Structural Similarity Map (GSSM) for both the teacher and student models. This map encodes the key structural patterns of the image by calculating pairwise similarities between the sampled points, providing the structural information of the scene. To enhance the knowledge transfer process, we generate Sub-Image Descriptors (SID) through row-wise shuffling and column-wise grouping of the GSSM. These descriptors allow the student model to capture high-level semantic relationships and structural patterns, overcoming the limitations of traditional pixel-level feature alignment. The proposed method is designed to be flexible; It can be used both as a standalone method and as a plug-and-play module for integration with existing KD techniques. Our extensive experiments demonstrate that GSKD consistently outperforms or matches recent KD methods in standalone settings and significantly enhances the performance of state-of-the-art KD methods when incorporated as a plug-in-play module.}, archive = {J_ACCESS}, author = {Hyejin Park and Keonhee Ahn and Hyesong Choi and Dongbo Min}, doi = {10.1109/ACCESS.2025.3575066}, journal = {IEEE Access}, pages = {99826-99841}, shortjournal = {IEEE Access}, title = {Global structural knowledge distillation for semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). An adaptive model predictive control based control strategy of BESS to improve AGC performance of thermal power plant. ACCESS, 13, 99816-99825. (www)

  @article{ , abstract = {Battery energy storage system (BESS) coordinated with thermal power plant (TPP) is a practical way to improve the frequency response of the system with high renewable integration. The high cost and lifespan degradation limit the capacity and capability of BESS in participating in automatic generation control (AGC). This paper proposes an adaptive model predictive control (MPC) strategy of BESS to improve AGC performance of TPP. A detailed model of the TPP is built to describe its dynamic behavior in AGC response to obtain the accurate system model for the proposed controller. An adaptive MPC control strategy incorporated the lifespan degradation of BESS is adopted to reduce the total cost of a one-time response and preserve the frequency regulation capability for future disturbance. Different weighting factors are chosen in the frequency regulation phase and state of charge (SoC) recovery phase under specified constraints respectively. The frequency regulation performance of the system is improved by the proposed strategy. The proposed control strategy is verified by a two-area system with wind farm. The results show that the proposed control strategy improves the frequency response of the system and reduce the lifespan degradation of the BESS significantly.}, archive = {J_ACCESS}, author = {Keman Lin and Tao Su and Feng Wu and Wenxin Xiang and Linjun Shi and Yang Li}, doi = {10.1109/ACCESS.2025.3573749}, journal = {IEEE Access}, pages = {99816-99825}, shortjournal = {IEEE Access}, title = {An adaptive model predictive control based control strategy of BESS to improve AGC performance of thermal power plant}, volume = {13}, year = {2025}, }
• (2025). A comparative study on thai license plate recognition: Object detection and transformer learning approaches. ACCESS, 13, 99802-99815. (www)

  @article{ , abstract = {Car theft remains a significant issue in Thailand, necessitating advanced security solutions. This research investigates the application of license plate recognition (LPR) technology using deep learning models for vehicle and license plate detection, as well as both YOLO and Transformer models for character recognition. Experimental results demonstrate that YOLOv5nu achieved a high Mean Average Precision (mAP) of 0.995 for license plate detection, while the Transformer model excelled in character recognition with an accuracy of 0.9108 and a loss of 0.0326. Despite these promising results, the models faced limitations in low-light conditions and complex plate layouts, affecting detection accuracy. Future work should focus on enhancing the models’ adaptability to real-world environments, expanding datasets to include diverse scenarios, and improving robustness against environmental variations. These findings underscore the potential of AI-driven LPR systems in advancing vehicle security technology.}, archive = {J_ACCESS}, author = {Kwankamon Dittakan and Jirawat Thaenthong and Thanakorn Prasomkit}, doi = {10.1109/ACCESS.2025.3575811}, journal = {IEEE Access}, pages = {99802-99815}, shortjournal = {IEEE Access}, title = {A comparative study on thai license plate recognition: Object detection and transformer learning approaches}, volume = {13}, year = {2025}, }
• (2025). Interesting concept mining with concept lattice convolutional networks. ACCESS, 13, 99789-99801. (www)

  @article{ , abstract = {The extraction of meaningful conceptual structures is often a critical task in many scientific and engineering disciplines, as it enables a comprehensive analysis of complex data in terms of both context and content. In this paper, we introduce the Concept Lattice Convolutional Network ( $\mathcal {LCN}$ ), an efficient semi-supervised learning approach to identify actionable concepts (i.e., interesting conceptual structures) based on a scalable convolutional neural network architecture that operates on concept lattices. The $\mathcal {LCN}$ captures diverse levels of global context by employing a message-passing mechanism that incorporates local structural and conceptual information within a lattice. It also employs parameter-sharing convolutional operations as conceptual filters to efficiently discern relevant concepts amidst the irrelevant ones. Moreover, it applies consistent aggregations that maintain local consistency of labeling across concepts in the lattice. Experiments on several datasets show that $\mathcal {LCN}$ can accurately identify actionable concepts and is at least three times faster than state-of-the-art exact interestingness indices.}, archive = {J_ACCESS}, author = {Mohamed Hamza Ibrahim and Rokia Missaoui and Pedro Henrique B. Ruas}, doi = {10.1109/ACCESS.2025.3577445}, journal = {IEEE Access}, pages = {99789-99801}, shortjournal = {IEEE Access}, title = {Interesting concept mining with concept lattice convolutional networks}, volume = {13}, year = {2025}, }
• (2025). A flexible multi-mechanism market model for air conditioning users in electricity market: Design and application. ACCESS, 13, 99778-99788. (www)

  @article{ , abstract = {With the continuous growth of electricity demand, optimizing load management strategies has become a crucial approach for alleviating the pressure on power supply. As a significant component of power consumption, air conditioning loads offer substantial curtailment potential. However, the current design of electricity market mechanisms for air conditioning users remains inadequate, making it difficult to fully mobilize user participation and utilize the curtailment potential of air conditioning loads. This paper considers market operation objectives across four dimensions: demand fulfillment, operational costs, user participation, and user comfort. A flexible multi-mechanism market model for air conditioning users is proposed, enabling market operators to design adaptable mechanisms that meet various operational needs. Additionally, a set of market mechanism evaluation metrics is developed. Through case studies, the application effects of various market mechanisms under different load reduction demands are compared. The results demonstrate that the proposed model provides a new approach to designing electricity market mechanisms for air conditioning users, effectively enhancing system responsiveness, flexibility, and economic efficiency of grid operations.}, archive = {J_ACCESS}, author = {Chupeng Xiao and Zhuofan Tang and Suoyue Wang and Kui Wang and Rui Xiang and Xiaojing Li}, doi = {10.1109/ACCESS.2025.3562076}, journal = {IEEE Access}, pages = {99778-99788}, shortjournal = {IEEE Access}, title = {A flexible multi-mechanism market model for air conditioning users in electricity market: Design and application}, volume = {13}, year = {2025}, }
• (2025). Efficient management of safety documents using text-based analytics to extract safety attributes from construction accident reports. ACCESS, 13, 99758-99777. (www)

  @article{ , abstract = {The time-intensive extraction of insights from textual safety documents using conventional methods causes delays and inaccuracies, hindering proactive incident prevention in construction projects. While the architecture of large language models (LLMs) were well-studied, their deployment efficiencies were often overlooked. This study proposes DistilBERT as a more efficient text management method for extracting safety text from construction safety documents. To maintain the relevance of the extracted safety text, a dataset of 5,224 construction accident cases from 73 projects across the Euro-Asia region was compiled, where incidents were analyzed through detailed questionnaires to identify safety attributes, with term frequency-inverse document frequency (TF-IDF) analysis applied for validation. When benchmarked against conventional NLP methods and state-of-the-art LLMs such as BERT, RoBERTa, and XLNet, DistilBERT demonstrated comparable accuracy with significantly reduced computational time. Specifically, DistilBERT achieved an accuracy of 79% in severity scale classification with an F1 score of 0.72, while reducing processing time by approximately 50% compared to BERT (from 2,918.28 seconds to 1,492.08 seconds). By offering rapid inference speeds with negligible accuracy trade-offs, DistilBERT emerges as a practical tool for automating safety text extraction, making it ideal for settings with limited computational capabilities and urgent decision-making requirements. This study examines how DistilBERT can be integrated into construction safety management systems without modifying the underlying platforms. Future work should focus on API creation, secure machine learning pipelines, and optimized deployment of LLMs, particularly in complex contexts.}, archive = {J_ACCESS}, author = {Vedat Toğan and Fatemeh Mostofi and Onur Behzat Tokdemir and Fethi Kadioğlu}, doi = {10.1109/ACCESS.2025.3576442}, journal = {IEEE Access}, pages = {99758-99777}, shortjournal = {IEEE Access}, title = {Efficient management of safety documents using text-based analytics to extract safety attributes from construction accident reports}, volume = {13}, year = {2025}, }
• (2025). Simultaneous optimization of traffic signal control and vehicle platooning scheme based on connected and automated vehicle (CAV) technology. ACCESS, 13, 99744-99757. (www)

  @article{ , abstract = {Connected and automated vehicle (CAV) technologies can improve the traffic operation and management in urban transportation systems. In particular, a great advantage is expected when CAV technologies are applied to the traffic signal control for intersections. So far in the academy, researchers have dealt with platooning vehicles moving in an approach to an intersection to reduce stops and delays without traffic signals. The present study focuses on synchronizing stopped vehicles at a stop line in severely congested traffic condition. If vehicles in a queue move synchronously when green light initiates, the traffic throughput can be maximized without start-up delays. A reinforcement learning (RL) algorithm is devised to simultaneously optimize both the traffic signal control and the platooning scheme in a unified modelling framework. The proposed approach outperforms both an adaptive traffic signal control without platooning and a fully automated intersection management (AIM) without traffic lights, as traffic flows are oversaturated.}, archive = {J_ACCESS}, author = {Jiwon Kang and Yongjun Choi and Keemin Sohn}, doi = {10.1109/ACCESS.2025.3577165}, journal = {IEEE Access}, pages = {99744-99757}, shortjournal = {IEEE Access}, title = {Simultaneous optimization of traffic signal control and vehicle platooning scheme based on connected and automated vehicle (CAV) technology}, volume = {13}, year = {2025}, }
• (2025). Sustainable transmitters for high-capacity metro-access networks. ACCESS, 13, 99734-99743. (www)

  @article{ , abstract = {Single sideband (SSB) modulation enables high spectral efficiency, and ensures high throughput performance when combined with discrete multitone (DMT) modulation. The conventional approach for generating SSB signals involves an IQ modulator to combine a pair of signals related by the Hilbert transform. However, this method relies on a power-intensive device, less suitable for intensity-modulation direct-detection (IM-DD) systems in metropolitan-access networks. In this paper, we analyze and compare various architectures for achieving SSB modulation in a more sustainable way, either through optical filtering or by satisfying the Hilbert transform condition using less power-intensive devices. Specifically, we focus on a sustainable approach, addressed as the dual-modulator (DM) scheme, which is implemented by cascading a directly modulated VCSEL with a Mach-Zehnder modulator. At the receiver side, SSB modulation enables the use of a Kramers-Kronig (KK) receiver, providing an efficient compensation of chromatic dispersion. The comparison of the considered schemes is achieved by a suitable simulation tool that features the generation of a SSB-DMT signal, its propagation in an uncompensated single mode fiber, its detection with a KK receiver, and the subsequent signal processing and data decoding. The simulations evaluate the performance of the various architectures in terms of capacity as a function of the propagation distance (up to 50 km), comparing also the associated signal to noise ratio curves. The proposed VCSEL-based DM scheme appears to offer an excellent trade-off between performance and implementation complexity, supporting high throughput, high spectral efficiency, thanks to SSB-DMT modulation.}, archive = {J_ACCESS}, author = {Stefano Gaiani and Alberto Gatto and Paola Parolari and Pierpaolo Boffi}, doi = {10.1109/ACCESS.2025.3576977}, journal = {IEEE Access}, pages = {99734-99743}, shortjournal = {IEEE Access}, title = {Sustainable transmitters for high-capacity metro-access networks}, volume = {13}, year = {2025}, }
• (2025). FedPark: A federated learning crowdsensing solution for on-street parking availability. ACCESS, 13, 99717-99733. (www)

  @article{ , abstract = {Locating available on-street parking in busy urban areas is often frustrating and time-consuming, particularly during peak hours. Inefficient parking searches contribute to traffic congestion, increased fuel consumption, and higher carbon emissions, making real-time parking availability information essential for smart urban mobility. This paper presents FedPark, a privacy-preserving crowdsensing system that leverages drivers’ smartphone sensors to monitor parking space occupancy. FedPark employs a Federated Learning (FL) algorithm, enabling the creation of a global model that aggregates insights from multiple local models maintained on individual drivers’ devices. Each local model learns from a user’s driving patterns without sharing personal data, ensuring enhanced privacy protection. Instead of transmitting raw data, only model updates are shared, reducing communication overhead and security risks. FedPark is evaluated under different urban conditions such as different numbers of drivers and destinations. The evaluation demonstrates that FedPark achieves a high level of accuracy at 98.31%, recall of 98.31%, precision of 98.34%, and an F1-score of 98.31% comparable to centralized machine learning models, including XGBoost, Random Forest, CatBoost, CNN, and ensemble classifiers while preserving driver privacy.}, archive = {J_ACCESS}, author = {Afraa Attiah and Shatha Alahmadi and Abeer Hakeem and Linda Mohaisen and Abeer Almakky and Reemah M. Alhebshi}, doi = {10.1109/ACCESS.2025.3577489}, journal = {IEEE Access}, pages = {99717-99733}, shortjournal = {IEEE Access}, title = {FedPark: A federated learning crowdsensing solution for on-street parking availability}, volume = {13}, year = {2025}, }
• (2025). Intelligent unmanned defense system for autonomous interception of UAVs based on improved acoustic source localization algorithm. ACCESS, 13, 99697-99716. (www)

  @article{ , abstract = {Illegal flights of UAVs have seriously threatened personal privacy and social security. UAV localization techniques, especially acoustic source localization, have received extensive attention from scholars. However, the traditional acoustic source direction of arrival (DOA) estimation algorithms are based only on cross-correlation or covariance matrices of part of the array received data. This leads to insufficient information utilization for high-precision localization and interception. Therefore, this article proposed a UAV acoustic source localization algorithm based on the ESPRIT algorithm with Toeplitz matrix reconstruction. Simulation tests were conducted on multiple UAV acoustic source signals in different directions, and the relational equation between the number of multiple UAV acoustic source signals and the minimum incidence critical angle was derived. The results demonstrated that the proposed algorithm has minimum RMSE and high DOA estimation signal accuracy. The resolution probability of the algorithm even exceeds 95%. The improved algorithm was applied to the indoor and outdoor tests of a self-designed 12-channel spherical microphone array and a circular MEMS microphone array. The effectiveness of the proposed algorithm and the accuracy of the derived equations are verified. Finally, we designed an intelligent interception unmanned defense system and applied it to different locations. The results show that the improved algorithm proposed in this article is correct and feasible.}, archive = {J_ACCESS}, author = {Xiaokai Hu and Min Yang and Changming Liu and Huan Ma and Kuangang Fan and Haonan Hou and Wenlong Cai}, doi = {10.1109/ACCESS.2025.3575959}, journal = {IEEE Access}, pages = {99697-99716}, shortjournal = {IEEE Access}, title = {Intelligent unmanned defense system for autonomous interception of UAVs based on improved acoustic source localization algorithm}, volume = {13}, year = {2025}, }
• (2025). VCD-CIM: A vertical-compute-decode digital computing-in-memory area-efficient macro for vector-wise computation with variable accumulation length. ACCESS, 13, 99690-99696. (www)

  @article{ , abstract = {Computing-In-Memory (CIM) has shown significant potential in handling inference tasks for edge artificial-intelligences (Edge-AI). However, as Edge-AI tasks grow increasingly complex and diverse, with a sharp increase in edge computing requirements and limited hardware resources, there is a pressing need for CIM designs customized for Edge-AI to improve both throughput and area efficiency. Nevertheless, Edge-AI tasks involve numerous vector-wise computations with variable accumulation lengths, which obstruct enhancements in CIM area efficiency. In this paper, we introduce a solution by proposing a vertical-compute-decode digital CIM architecture (VCD-CIM) featuring vertical compute units and a vertical-decode-adder-tree. This CIM architecture enables area-efficient full-precision data/vector-wise computations for Edge-AI inference tasks. Additionally, we employ a data-priority CIM strategy. In post-layout simulations, for signed 8-bit inputs, 8-bit weights, and 23-bit outputs, VCD-CIM achieves a peak energy efficiency of 30.11 TOPS/W and maintains an area energy efficiency of 1.785 TOPS/mm2 across the accumulation length range of 4 to 128. We validate the effectiveness of the VCD-CIM architecture through simulations on the MobileViT network, achieving CIM array activation rates between 75% and 100% during complete network inference tasks.}, archive = {J_ACCESS}, author = {Bo Wang and Xiaoxue Zhong and Jun Yang}, doi = {10.1109/ACCESS.2025.3574524}, journal = {IEEE Access}, pages = {99690-99696}, shortjournal = {IEEE Access}, title = {VCD-CIM: A vertical-compute-decode digital computing-in-memory area-efficient macro for vector-wise computation with variable accumulation length}, volume = {13}, year = {2025}, }
• (2025). Multimodal deep learning for cardiovascular risk stratification: Integrating retinal biomarkers and cardiovascular signals for enhanced heart attack prediction. ACCESS, 13, 99672-99689. (www)

  @article{ , abstract = {Cardiovascular diseases, particularly myocardial infarction, have remained a significant cause of death around the world. Therefore, dedicated non-invasive risk prediction frameworks are required. Conventional measures for risk prediction among most heterogeneous patients, such as the Framingham Risk Score and ASCVD calculator, are often less precise and poorly generalizable as they fail to capture individual variations in the mechanisms. This study seeks to present a new multimodal deep learning model developed for cardiovascular risk stratification by fusing retinal microvascular features with cardiovascular physiological signals. The pipeline includes a Hierarchical Retinal Vessel Graph Transformer (HRV-GT) for graph-based vascular representation, Spectro-Temporal Cardiovascular Transformer (STC-T) for capturing short- and long-term signal variability, Sparse Manifold Multimodal Fusion (SMM Fusion) for joint alignment of features, Evolutionary Feature Selection with Clinical Prior Constraints (EFS-CP), and a Contrastive Hierarchical Risk Classifier (CHRC-Net) for decision modeling structured. The model was evaluated across the UK Biobank and SEED datasets (n = 55,000), achieving an AUC of 0.97, sensitivity of 91%, and specificity of 89%, with more than 35% better performance than existing models in reducing misclassification. Clinically interpretable biomarkers that combine high computational efficiency (inference time $\approx \; 37$ ms) affirm its design for real-world deployment in preventive cardiovascular screening processes.}, archive = {J_ACCESS}, author = {K. Sathya and G. Magesh}, doi = {10.1109/ACCESS.2025.3577064}, journal = {IEEE Access}, pages = {99672-99689}, shortjournal = {IEEE Access}, title = {Multimodal deep learning for cardiovascular risk stratification: Integrating retinal biomarkers and cardiovascular signals for enhanced heart attack prediction}, volume = {13}, year = {2025}, }
• (2025). AGU2-net: Multi-scale u2-net enhanced by attention gate mechanism for image tampering localization. ACCESS, 13, 99659-99671. (www)

  @article{ , abstract = {The advancement of image editing and compositing technologies has posed significant challenges to the authenticity of digital images. Although deep learning algorithms based on convolutional neural networks (CNNs) have made notable progress in image forgery detection, they still face certain limitations in effectively detecting and localizing tampered areas due to the subtle nature of existing manipulation traces. To address this limitation, we propose AGU2-Net, a novel network architecture that integrates an advanced attention mechanism with multi-scale feature extraction. The architecture employs a nested U-structure as its backbone, enabling effective integration of image features across various scales. Additionally, AGU2-Net is an end-to-end autonomous training network with a lightweight framework that incurs low computational overhead. A significant innovation of the architecture is the introduction of the attention gate mechanism, which enhances the network’s ability to selectively process multi-scale features. By adaptively fusing feature information from the encoder and decoder, this mechanism increases spatial perception and improves the network’s ability to capture intricate local tampering details. Experiments on four publicly available benchmarks show that AGU2-Net achieves top F1 scores of 0.556, 0.338, and 0.273 on the CASIA, NIST16, and IMD2020 datasets, respectively, confirming its superior tamper-detection accuracy and sharper pixel-level localization compared with state-of-the-art methods.}, archive = {J_ACCESS}, author = {Kefei Wu and Lin Li and Qingyan Li}, doi = {10.1109/ACCESS.2025.3577221}, journal = {IEEE Access}, pages = {99659-99671}, shortjournal = {IEEE Access}, title = {AGU2-net: Multi-scale u2-net enhanced by attention gate mechanism for image tampering localization}, volume = {13}, year = {2025}, }
• (2025). Integrating the prior shape knowledge into deep model and feature fusion for topologically effective brain tumor segmentation. ACCESS, 13, 99641-99658. (www)

  @article{ , abstract = {Effective segmentation of brain tumor from MRI scans is crucial for clinical diagnosis. However, deep learning models prioritize either local or global features, failing to adequately capture topological features or integrate prior shape knowledge. Deep learning techniques totally rely on the loss function optimization and due to the lack of explicit form of prior knowledge, they may struggle to generate the accurate tumor shapes. This often leads to significant errors in capturing the underlying shapes or sub structures of tumor. These limitations lead to challenges such as topological errors, patchy patterns, and unrealistic structures in segmentation. To address these shortcomings, we proposed a novel technique termed TDAConvAttentionNet that captures the local, global and topological features and integrates the prior shape, captured using persistent homology, into the deep model to enhance segmentation results and reduce topological errors. By incorporating prior shape information, our proposed model guides the segmentation process, resulting in more precise delineation of tumor regions and mitigating the effect of fragmented structures or patchy patterns. The proposed technique leverages the strength of ConvNeXt to capture the local features and multi-headed self-attention to capture the global features without using the entire transformer encoder to keep the model less complex. Evaluation on the publicly available BraTS2021 dataset demonstrates the effectiveness of our approach, assessed through dice score, IOU score, and Jaccard distance metrics. The proposed approach achieved dice scores of 89.36%, 87.36%, and 89.98% on the validation dataset for Whole Tumor, Tumor Core, and Enhancing Tumor, respectively. On the test dataset, the method achieved Dice scores of 87.74%, 85.86%, and 86.62% for Whole Tumor, Tumor Core, and Enhancing Tumor respectively. Our results highlight better segmentation performance compared to the existing state-of-the-art methods.}, archive = {J_ACCESS}, author = {Salma Asif and Ahmad Raza Shahid and Kiran Aftab and Syed Ather Enam}, doi = {10.1109/ACCESS.2025.3577393}, journal = {IEEE Access}, pages = {99641-99658}, shortjournal = {IEEE Access}, title = {Integrating the prior shape knowledge into deep model and feature fusion for topologically effective brain tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). A multi-head attention-based lightweight generative adversarial network for thyroid ultrasound video super-resolution. ACCESS, 13, 99628-99640. (www)

  @article{ , abstract = {Thyroid cancer is a prevalent malignancy, highlighting the critical need for early detection of thyroid nodules. Despite ultrasound being the primary diagnostic modality, its limited resolution often hampers the accuracy of identifying malignant nodules. Method: We present a cutting-edge deep learning approach, the Multi-Head Attention-based Generative Adversarial Network (MHVSR), tailored for the super-resolution of thyroid ultrasound videos. This innovative method encompasses a lightweight optical flow extraction network for swift analysis, a recurrent fusion reconstruction module based on multi-head mechanism, and a hybrid loss function designed to expedite network convergence. Result: The MHVSR showcased exceptional performance on $4\times $ super-resolution task on Thyroid dataset, achieving a PSNR of 31.43dB and an SSIM of 0.98, surpassing current VSR algorithms. It delivered reconstructions that closely approximated the quality of original high-resolution videos, while meeting the stringent real-time diagnostic criteria with an impressive inference speed of 27.89FPS. Furthermore, on the Liver test dataset, MHVSR demonstrated its robustness with a PSNR of 39.39dB and an SSIM of 0.97, outperforming other algorithms. Conclusion: MHVSR not only achieves state-of-the-art super-resolution in ultrasound videos but also satisfies the demanding real-time diagnostic requirements, marking a significant leap forward in the field of medical imaging, particularly for the diagnosis of thyroid nodules.}, archive = {J_ACCESS}, author = {Yuchen Ning and Xianshuang Meng and Lingxiao Zhou and Jun Liu and Shijie Qiu and Jingfang Wu and Xi Wei and Jun Ying and Siwei Zhu and Yantian Zhang}, doi = {10.1109/ACCESS.2025.3570905}, journal = {IEEE Access}, pages = {99628-99640}, shortjournal = {IEEE Access}, title = {A multi-head attention-based lightweight generative adversarial network for thyroid ultrasound video super-resolution}, volume = {13}, year = {2025}, }
• (2025). Leveraging UAV data and deep learning models for detecting waste in rivers. ACCESS, 13, 99603-99627. (www)

  @article{ , abstract = {The growing problem of riverine waste pollution threatens water sustainability in Nepal, which is driven largely by rapid urban development and inadequate waste management. This study presents the first comparative evaluation of object detection and segmentation models for automated waste detection using novel UAV-captured imagery datasets. We introduce four datasets, two for object detection and two for segmentation in the Bishnumati and Bagmati rivers. We evaluated three training strategies—training from scratch, full-model fine-tuning, and fine-tuning with frozen layers—and assessed model generalizability through cross-river transfer learning. The study showed that fine-tuning pretrained weights is a better approach for training segmentation models, whereas freezing pretrained backbone layers is better for object detection. DeepLabv3+, trained by fine-tuning pretrained models, performed better than object detection models for waste detection, with precision and recall scores of 0.915 and 0.934 for Bagmati, and 0.913 and 0.939 for Bishnumati. Furthermore, transfer learning across locations improves object detection mAP scores and refines segmentation mask predictions, thereby uncovering previously undetected waste items. The transfer learning fine-tuned DeepLabv3+ model obtained an mIoU score of 0.849 for Bagmati to Bishnumati and 0.841 for Bishnumati to Bagmati transfer learning. Our novel datasets and methodological blueprint for optimizing deep learning training strategies in riverine environments offer a scalable and adaptable pipeline for automated waste monitoring in riverine environments, particularly for regions with limited surveying infrastructure and geographically proximate rivers where transfer learning can be effectively applied.}, archive = {J_ACCESS}, author = {Bipun Man Pati and Bishnu Khadka and Ukesh Thapa and Sujay Kumar Pal and Subarna Sakya and Anup Shrestha and Hemant Joshi and Dhiraj Pyakurel and Prem Chandra Roy}, doi = {10.1109/ACCESS.2025.3576295}, journal = {IEEE Access}, pages = {99603-99627}, shortjournal = {IEEE Access}, title = {Leveraging UAV data and deep learning models for detecting waste in rivers}, volume = {13}, year = {2025}, }
• (2025). A portable real-time electronic nose for evaluating seafood freshness using machine learning. ACCESS, 13, 99583-99602. (www)

  @article{ , abstract = {Real-time evaluation of seafood freshness is essential for ensuring quality and safety. This study presents an electronic nose (e-nose) system designed to assess seafood freshness using gas sensors and machine learning (ML) algorithms. The system detects volatile organic compounds (VOCs) released during spoilage and employs hyperparameter-optimized ML models for both classification (fresh vs. not fresh) and regression (shelf-life prediction). From an initial pool of eight gas sensors, four were selected based on Predictive Power Score (PPS) analysis for their sensitivity to spoilage-related gases: MQ137 (ammonia), MQ2 (alcohol), MQ136 (hydrogen sulfide), and MQ9 (methane). A comprehensive dataset, E-Nose-Based Seafood Freshness (ESF), was developed, comprising 336,997 data points from five major seafood species: tuna, salmon, cod, shrimp, and crab. Various ML models were evaluated, including Random Forest, KNN, Naive Bayes, Decision Tree, AdaBoost, Gradient Boosting, and XGBoost. The XGBoost achieved the best performance with 100% precision, recall, accuracy, and F1-score, and a cross-validation accuracy of 99.8%. It also demonstrated excellent efficiency with detection times under 1 millisecond and memory usage of ~4 kB. For regression, XGBoost maintained strong results ( $\mathrm{R}^{2} = 100$ %, RMSE = 0.08, CV MSE = 0.009) with fast prediction times (~12 milliseconds) and memory usage under 5 kB. These findings confirm the system’s potential as a lightweight, scalable, and accurate solution for real-time seafood freshness monitoring, supporting food safety, quality assurance, and waste reduction in the supply chain.}, archive = {J_ACCESS}, author = {Muhammad Rafi Mahfuz Setyagraha and Hurul Aini Nurqamaradillah and Laksamana Mikhail Hermawan and Nyoman Raflly Pratama and Ledya Novamizanti and Dedy Rahman Wijaya}, doi = {10.1109/ACCESS.2025.3577128}, journal = {IEEE Access}, pages = {99583-99602}, shortjournal = {IEEE Access}, title = {A portable real-time electronic nose for evaluating seafood freshness using machine learning}, volume = {13}, year = {2025}, }
• (2025). A digital public health framework for understanding causal cognitive drivers of adolescent violence. ACCESS, 13, 99563-99582. (www)

  @article{ , abstract = {Adolescent violence is a complex public health issue with significant societal and individual consequences. Traditional research methods often fail to capture the dynamic, multifaceted nature of the cognitive and behavioral drivers of such violence. This study introduces EEG-CauseNet, an innovative Cross-Attention Transformer framework, designed to address this gap by identifying and analyzing the causal cognitive mechanisms underlying adolescent violence. The primary objective of EEG-CauseNet is to integrate and analyze diverse, high-dimensional datasets—such as socio-economic, mental health, and behavioral data—while leveraging advanced causal inference techniques to uncover complex interdependencies between factors influencing violence. Through empirical validation on real-world adolescent datasets, we demonstrate that EEG-CauseNet outperforms traditional models in both predictive accuracy and interpretability. The novel integration of cross-attention mechanisms allows the model to capture dynamic temporal-spatial relationships, offering deeper insights into the underlying drivers of adolescent violence. The outcomes of this research provide actionable insights for public health stakeholders, enabling the development of targeted, technology-driven interventions. This framework has the potential to transform digital public health strategies, offering scalable and impactful solutions to reduce adolescent violence globally.}, archive = {J_ACCESS}, author = {Wei Zhang and Lixin Zhang}, doi = {10.1109/ACCESS.2025.3576363}, journal = {IEEE Access}, pages = {99563-99582}, shortjournal = {IEEE Access}, title = {A digital public health framework for understanding causal cognitive drivers of adolescent violence}, volume = {13}, year = {2025}, }
• (2025). Different scale crack contour extraction techniques for bridges incorporating improved YOLOv5 and GCNet. ACCESS, 13, 99547-99562. (www)

  @article{ , abstract = {Bridge cracks, as the main form of damage to bridge structures, have a significant impact on the safety and service life of bridges. Aiming at the lack of accuracy and efficiency of the traditional detection methods and the existing deep learning-based detection methods, this study proposes a contour extraction model for bridge cracks at different scales based on the deep learning algorithm. This can improve the accuracy of crack detection and the precision of contour extraction. It can also provide technical support for bridge health monitoring. In this study, the attention mechanism and double-cone feature fusion are adopted to improve the target detection algorithm, enhancing the algorithm’s ability to extract crack features. Through multi-stage processing, crack target detection is carried out first. Then, the crack contour is extracted and a smart bridge crack contour extraction model is established. The results showed that the segmentation efficiency of the research model for crack images of different sizes was 42 frames per second. The intersection-union ratio coefficient tested on the dataset was 0.93, and the pixel accuracy value was 0.99. However, in real complex environments, the accuracy, recall, and average precision of the model were tested with average values of 50-90, which were 90%, 0.89, and 0.83, respectively. The model can accurately detect and extract crack profiles at different scales. It provides efficient and accurate technical support for bridge health monitoring. This helps to extend the life of bridges and ensure traffic safety.}, archive = {J_ACCESS}, author = {Yiming Wang and Yanfang Guo}, doi = {10.1109/ACCESS.2025.3577297}, journal = {IEEE Access}, pages = {99547-99562}, shortjournal = {IEEE Access}, title = {Different scale crack contour extraction techniques for bridges incorporating improved YOLOv5 and GCNet}, volume = {13}, year = {2025}, }
• (2025). Optimizing state monitoring with domain degradation knowledge. ACCESS, 13, 99534-99546. (www)

  @article{ , abstract = {Monitoring the degradation of industrial equipment is vital for maintaining efficient production. Existing deep learning-based monitoring techniques often focus on isolated target characteristics, failing to capture the device’s condition throughout its degradation process comprehensively. We propose a novel approach to constructing a unified feature space incorporating degradation-based features from multiple degradation characteristics. The proposed method enhances the predictive accuracy of degradation parameters, improves model interpretability, and minimizes overfitting by avoiding reliance on unrealistic characteristic patterns. We introduce a new model architecture, Industrial Health Index Extraction, designed to implement this approach effectively. Our methodology demonstrates state-of-the-art performance on self-supervised and supervised tasks using NASA’s CMAPSS and milling datasets.}, archive = {J_ACCESS}, author = {Zhevnenko Dmitry and Ilya Makarov}, doi = {10.1109/ACCESS.2025.3573683}, journal = {IEEE Access}, pages = {99534-99546}, shortjournal = {IEEE Access}, title = {Optimizing state monitoring with domain degradation knowledge}, volume = {13}, year = {2025}, }
• (2025). IRuCoR: Intelligent running companion robot for personalized training. ACCESS, 13, 99524-99533. (www)

  @article{ , abstract = {In this study, we introduce IRuCoR, an intelligent running companion robot designed to provide personalized training during running exercises. IRuCoR runs ahead of the user, analyzes the user’s running posture, and provides audio feedback to correct improper postures when detected. It also recognizes the user’s command poses to set reference velocities. It autonomously navigates the track lane of a running field using a front camera and recognizes the poses of the user using a rear camera. For personalized training, users provide their information to IRuCoR via a touchscreen display or QR code and they can review workout details upon completing their running exercise. The proposed robot system was experimentally evaluated on an actual outdoor track, demonstrating its effectiveness through the evaluation of the pose detection capabilities and autonomous track navigation functionality.}, archive = {J_ACCESS}, author = {Chan-Un Jeon and Sang-Hee Cheon and Beom-Su Bae and Gi-Hyeon Cho and Bae Ho Cha and Dong-Hyun Lee}, doi = {10.1109/ACCESS.2025.3577117}, journal = {IEEE Access}, pages = {99524-99533}, shortjournal = {IEEE Access}, title = {IRuCoR: Intelligent running companion robot for personalized training}, volume = {13}, year = {2025}, }
• (2025). Lifetime-based discontinuous PWM for cascaded H-bridge converters. ACCESS, 13, 99509-99523. (www)

  @article{ , abstract = {The cascaded H-bridge (CHB) is an attractive converter topology because it can provide voltage and power scalability. However, due to the difference in electrical and thermal characteristics of power devices, the CHB can be composed of cells with unequal remaining useful lifetime (RUL). In this paper, a PWM strategy is proposed to manage the lifetime of individual cells within a single-phase CHB by controlling the thermal stress of power devices. The proposed PWM has two variants of modulation signals. The discontinuous modulation (DM) which reduces the thermal stress of a cell and the compensating modulation (CM) for removing the offset voltage introduced by DM. Based on the accumulated damages of cells, the modulation signals are actively updated to achieve equalized RUL. The benefit of the proposed PWM is that the power transmission of cells is not affected by the clamping region of the DM. This is achieved by introducing a factor C during the unclamped region which reduce the fundamental voltage by the same amount the clamping region causes it to increase. The experimental result verifies the impact of the proposed PWM on the power transmission of cells. Furthermore, a long-term study is provided in simulation to validate the lifetime equalization of cells.}, archive = {J_ACCESS}, author = {Anas Ibrahim and Mohamed Salem and Tomonobu Senjyu and Soichiro Ueda and Dahaman Ishak and Mahmood Swadi}, doi = {10.1109/ACCESS.2025.3577163}, journal = {IEEE Access}, pages = {99509-99523}, shortjournal = {IEEE Access}, title = {Lifetime-based discontinuous PWM for cascaded H-bridge converters}, volume = {13}, year = {2025}, }
• (2025). Framework for emotion recognition using cross-modal transformers with non-contact multimodal signals aiming clinical service support. ACCESS, 13, 99490-99508. (www)

  @article{ , abstract = {In clinical communication, it is believed that accurately understanding and appropriately responding to patients’ emotions contributes to treatment effectiveness and patient satisfaction. Recently, multimodal approaches that integrate various modalities such as speech, text, and physiological signals have gained attention for emotion estimation. However, the application of emotion recognition (ER) technology in clinical settings has not been thoroughly explored, particularly in terms of utilizing non-contact measurement techniques to reduce patient burden. Furthermore, there is limited research on quantitatively evaluating physicians’ ER abilities and comparing them with existing ER methods. This study aims to propose a multimodal ER framework using non-contact measurement techniques and validate its effectiveness by comparing it with the emotion prediction accuracy of experienced physicians. The results demonstrated that the proposed non-contact multimodal approach outperformed physicians in ER accuracy. While physicians’ empathetic abilities have traditionally been considered high, integrating multiple modalities was shown to surpass the recognition accuracy of unimodal approaches and human physicians. Moreover, the ability to obtain emotion-related data non-invasively enables advanced emotion estimation while reducing physical and psychological burdens on patients, highlighting the potential for clinical applications. These findings suggest a new method for supporting physicians’ ER in clinical settings, offering a means to reduce the risk of fatigue associated with empathy.}, archive = {J_ACCESS}, author = {Homura Kawamura and Tomofumi Miura and Yuka Maeda and Yukihiko Okada and Keiichi Zempo}, doi = {10.1109/ACCESS.2025.3573648}, journal = {IEEE Access}, pages = {99490-99508}, shortjournal = {IEEE Access}, title = {Framework for emotion recognition using cross-modal transformers with non-contact multimodal signals aiming clinical service support}, volume = {13}, year = {2025}, }
• (2025). Machine learning and explainable AI for thai basil growth prediction in hydroponics. ACCESS, 13, 99479-99489. (www)

  @article{ , abstract = {Hydroponic farming has emerged as a sustainable solution to modern agricultural challenges, offering enhanced resource efficiency, reduced environmental impacts, and optimized crop growth. Compared with traditional hydroponic systems, deep-water culture (DWC) is a hydroponic technique that provides continuous nutrients and oxygenated water to plants to increase plant growth. Thai basil is a medicinal plant chosen for this study. The growth of plants is affected by temperature, humidity, solar radiation, pH and total dissolved solids (TDS). This study emphasized the impact of DWC on the cultivation of Thai basil over a one-month monitoring period in terms of environmental and physiological parameters. The green area and plant height were recorded as growth parameters. Six machine learning (ML) models are employed to estimate the growth of Thai Basil. The best model is selected on the basis of the ensemble voting regressor method. The predictions of the best model are explained with Shapley additive explanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs), which are explainable AI models. Explainable AI provides clear and understandable justifications for predictions. Using explainable AI, the proposed research predicts Thai basil growth while providing reasoning for its prediction.}, archive = {J_ACCESS}, author = {Sankalp Kadam and Vinaya Gohokar and Rupali Kute}, doi = {10.1109/ACCESS.2025.3576440}, journal = {IEEE Access}, pages = {99479-99489}, shortjournal = {IEEE Access}, title = {Machine learning and explainable AI for thai basil growth prediction in hydroponics}, volume = {13}, year = {2025}, }
• (2025). Clutch structure design and automatic control of front and rear dual-motor driven pure electric vehicles. ACCESS, 13, 99465-99478. (www)

  @article{ , abstract = {Due to the development and widespread application of pure electric vehicles, the structural design and control technology of their automatic clutches becomes an important direction in the automotive market. The study conducts structural analysis on the normal transmission system, proposes control design for the automatic clutch, and uses the clutch engagement force process for control optimization. In the mathematical model and mechanical calculation of the brushless DC motor, the force analysis of the camshaft and other equipment during the engagement and disengagement operations is conducted. Through experimental testing, it is found that the target speed of the drive motor is reduced by about 13% after optimization control, indicating the feasibility of its engagement control. During the vehicle validation, when the driving time is 285s, the motor’s rotational speed is 7900rpm. During the 280-second driving time, the car’s torque value reaches 174 Nm. Combined with experimental bench testing, it can be proven that the automatic clutch control technology proposed in the study is reliable and provides advanced technology for the future development of automatic clutches in the automotive field.}, archive = {J_ACCESS}, author = {Yanfen Sun}, doi = {10.1109/ACCESS.2025.3575509}, journal = {IEEE Access}, pages = {99465-99478}, shortjournal = {IEEE Access}, title = {Clutch structure design and automatic control of front and rear dual-motor driven pure electric vehicles}, volume = {13}, year = {2025}, }
• (2025). A comprehensive study on underwater object detection using deep neural networks. ACCESS, 13, 99446-99464. (www)

  @article{ , abstract = {The escalating crisis of marine pollution, especially the buildup of underwater waste, poses a significant threat to ocean ecosystems and marine life. Identifying and removing submerged debris is challenging due to poor visibility, complex water conditions, and the inefficiency of traditional manual approaches. This study leverages deep learning-based object detection techniques to improve underwater waste identification, focusing on the YOLOv8 and YOLOv9 architectures. Using the TrashCan 1.0 dataset—a large collection of labeled underwater images—the models were trained and evaluated based on key metrics such as mean Average Precision (mAP) and Intersection over Union (IoU). Results show that the YOLOv9c model performed best on the 3-Class dataset, achieving a mAP50:95 of 0.759, a mAP50 of 0.934, precision of 0.915, and recall of 0.877, highlighting its high accuracy and robustness. Meanwhile, YOLOv8l showed slightly better results on the 4-Class dataset with a mAP50:95 of 0.725, indicating dataset-dependent performance variations. The ability of these models to deliver real-time detection makes them ideal for integration into autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs), enabling large-scale monitoring and cleanup operations. By advancing AI-driven solutions for marine conservation, this research contributes to more efficient and scalable efforts in mitigating the long-term impact of ocean pollution.}, archive = {J_ACCESS}, author = {K. Samanth and Ramyashree Ramyashree and B. N. Anoop and S. Raghavendra}, doi = {10.1109/ACCESS.2025.3577239}, journal = {IEEE Access}, pages = {99446-99464}, shortjournal = {IEEE Access}, title = {A comprehensive study on underwater object detection using deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Research on load adaptive control for high-frequency motion of voice coil motor with online parameter optimization. ACCESS, 13, 99434-99445. (www)

  @article{ , abstract = {The fast tool servo system is a crucial intelligent equipment for workpiece machining and forming in the precision manufacturing field. The Voice Coil Motor (VCM), serving as the driving device of this equipment, offers advantages such as high-frequency response, high precision, large stroke, and low noise. However, during high-frequency reciprocating motion control, the VCM is prone to the influence of changes in factors like load, motion amplitude and frequency, which leads to a decline in motion accuracy. To address this issue, this research proposes an innovative adaptive control method based on online parameter optimization. By enabling the Back Propagation Neural Network (BPNN) to learn the key characteristics of the VCM’s driving current under different working conditions, and through continuous iterations during the training process of the network model, real-time online adjustment of controller parameters is achieved, thereby realizing the adaptive control of the VCM. Experimental results show that compared with classical PID control and fuzzy PID adaptive control, the proposed control method enables the VCM to achieve higher motion accuracy during high-frequency motion. Under the condition of a sinusoidal superimposed wave input with an amplitude of 0.1 mm and frequencies of 60 Hz and 120 Hz, when the load carried by the VCM is 200 g, the absolute peak error of the displacement under the proposed control reaches $3.5~\mu $ m.}, archive = {J_ACCESS}, author = {Feng Huang and Chenxi Wang and Qipeng Li}, doi = {10.1109/ACCESS.2025.3577452}, journal = {IEEE Access}, pages = {99434-99445}, shortjournal = {IEEE Access}, title = {Research on load adaptive control for high-frequency motion of voice coil motor with online parameter optimization}, volume = {13}, year = {2025}, }
• (2025). Electric bus charging schedules relying on real data-driven targets based on hierarchical deep reinforcement learning. ACCESS, 13, 99415-99433. (www)

  @article{ , abstract = {The charging scheduling problem of Electric Buses (EBs) is investigated based on Deep Reinforcement Learning (DRL). A Markov Decision Process (MDP) is conceived, where the time horizon includes multiple charging and operating periods in a day, while each period is further divided into multiple time steps. To overcome the challenge of long-range multi-phase planning with sparse reward, we conceive Hierarchical DRL (HDRL) for decoupling the original MDP into a high-level Semi-MDP (SMDP) and multiple low-level MDPs. The Hierarchical Double Deep Q-Network (HDDQN)-Hindsight Experience Replay (HER) algorithm is proposed for simultaneously solving the decision problems arising at different temporal resolutions. As a result, the high-level agent learns an effective policy for prescribing the charging targets for every charging period, while the low-level agent learns an optimal policy for setting the charging power of every time step within a single charging period, with the aim of minimizing the charging costs while meeting the charging target. It is proved that the flat policy constructed by superimposing the optimal high-level policy and the optimal low-level policy performs as well as the optimal policy of the original MDP. Since jointly learning both levels of policies is challenging due to the non-stationarity of the high-level agent and the sampling inefficiency of the low-level agent, we divide the joint learning process into two phases and exploit our new HER algorithm to manipulate the experience replay buffers for both levels of agents. Numerical experiments are performed with the aid of real-world data to evaluate the performance of the proposed algorithm.}, archive = {J_ACCESS}, author = {Jiaju Qi and Lei Lei and Thorsteinn Jonsson and Lajos Hanzo}, doi = {10.1109/ACCESS.2025.3571211}, journal = {IEEE Access}, pages = {99415-99433}, shortjournal = {IEEE Access}, title = {Electric bus charging schedules relying on real data-driven targets based on hierarchical deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). BeyondLife: An open-source digital will solution for posthumous data management. ACCESS, 13, 99391-99414. (www)

  @article{ , abstract = {Managing posthumous data is becoming an increasingly complex challenge, with many existing technical solutions proving impractical in real-world applications. Existing tools are typically closed-source, lack transparency, fail to offer cross-platform support, and provide limited access control. This paper introduces “BeyondLife”, a cross-platform, open-source, and privacy-enhancing digital will management solution designed to securely handle and distribute digital assets after death. At the core of this solution is a customized Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, referred to as PD-CP-ABE, which enables efficient, fine-grained control over access to will content at scale. Unlike existing systems, BeyondLife operates independently of service providers, offering users greater transparency and control over how their will is generated, stored, and executed. The system is designed to be portable, allowing users to change their will service provider. BeyondLife has been fully developed and evaluated to ensure performance and real-world feasibility. The security of PD-CP-ABE has also been formally proved using Tamarin Prover.}, archive = {J_ACCESS}, author = {Xinzhang Chen and Arash Shaghaghi and Jesse Laeuchli and Salil S. Kanhere}, doi = {10.1109/ACCESS.2025.3576448}, journal = {IEEE Access}, pages = {99391-99414}, shortjournal = {IEEE Access}, title = {BeyondLife: An open-source digital will solution for posthumous data management}, volume = {13}, year = {2025}, }
• (2025). Average model and controller design for virtual synchronous generator. ACCESS, 13, 99377-99390. (www)

  @article{ , abstract = {This paper proposes a novel systematic design procedure for virtual synchronous generators (VSGs) and develops an average VSG model. The VSG comprises distributed generators, an energy storage system consisting of a battery and a DC-DC converter (DDC), and a VSG controller based on the swing equation. Determining the rated power (energy capacity) of the battery and the controller parameters for both the DDC and the VSG is essential, as these factors directly impact the cost and performance of the VSG. The proposed design method enables the determination of the VSG and DDC controller parameters, as well as rated power (energy capacity) of the battery, by incorporating the dynamics of the VSG, DDC, and ESS. Although the proposed design procedure utilizes the time response of the VSG obtained through simulation, the proposed average model for the VSG significantly reduces the simulation time. Simulations and experiments demonstrate the effectiveness of both the proposed design procedure and the average model.}, archive = {J_ACCESS}, author = {Kenta Koiwa and Tomo Hirabayashi and Tadanao Zanma and Kang-Zhi Liu}, doi = {10.1109/ACCESS.2025.3577242}, journal = {IEEE Access}, pages = {99377-99390}, shortjournal = {IEEE Access}, title = {Average model and controller design for virtual synchronous generator}, volume = {13}, year = {2025}, }
• (2025). Model-free adaptive iterative learning control of high-order pseudo partial derivatives for nonlinear systems. ACCESS, 13, 99366-99376. (www)

  @article{ , abstract = {In this work, a model-free adaptive iterative learning control of high-order pseudo partial derivatives is presented for a category of nonlinear discrete-time systems that have features defined by non-repetitive disturbances. Firstly, the nonlinear system is initially transformed into a dynamic linearized data model, taking into account external non-repetitive disturbances by the iterative dynamic linearized method. Simultaneously, a high-order estimation method is developed utilizing historical batch input and output data, and an iterative extended state observer is constructed for estimating non-repetitive disturbances in the linearized data model to compensate for actual disturbances; Secondly, a model-free adaptive iterative learning control approach was devised, this approach employs estimated values of high-order pseudo partial derivatives and non-repetitive disturbances. The idea of compressive mapping was used to illustrate the provided algorithm’s convergence, and the conditions required for its convergence were given. The study’s findings show that, within a limited period time, the suggested method may lower the tracking error to the expected trajectory’s neighborhood, independent of explicit mathematical model information, and the proposed technique enhances tracking precision compared with low-order control algorithms. Finally, It has been shown through simulation results that the suggested strategy is effective in improving tracking accuracy.}, archive = {J_ACCESS}, author = {Wei Cao and Ting Wang and Jinjie Qiao and Buning Chai}, doi = {10.1109/ACCESS.2025.3574439}, journal = {IEEE Access}, pages = {99366-99376}, shortjournal = {IEEE Access}, title = {Model-free adaptive iterative learning control of high-order pseudo partial derivatives for nonlinear systems}, volume = {13}, year = {2025}, }
• (2025). Patterned multi-wall nanosheet FETs for sustainable scaling: Zero gate extension with minimal gate cut width. ACCESS, 13, 99355-99365. (www)

  @article{ , abstract = {In nanosheet field-effect transistors (NSFETs), the scaling of the cell height (CH) is constrained by strict design rules related to gate extension (GE), gate cut (GC), and device-to-device distance. In contrast, forksheet FETs (FSFETs) enable aggressive CH reduction by introducing a dielectric wall between N/P-type devices. However, FSFETs have inherent drawbacks: one side of the channel and source/drain (S/D) inevitably interfaces with the wall, leading to a compromised electrical performance compared with that of NSFETs. Moreover, when the gate edge aligns with the wall on one side, sufficient GE and GC margins are still required on the opposite side. To address these challenges, we propose patterned multi-wall NSFETs (MW-NSFETs) comprising patterned walls on both sides of the channel, which facilitate gate-all-around channel formation and wrap-around contact, resulting in superior DC behavior. Furthermore, CH can be scaled down as both sides of the gate edges self-align with the patterned multiwall. Consequently, MW-NSFETs yield a higher Ion (4.65%) and lower RC delay (6.2%) at a 12.04% smaller CH than FSFETs. Moreover, at the circuit level, the MW-NSFETs exhibited a higher oscillation frequency compared with FSFETs. Thus, MW-NSFETs represent viable devices for sub-2-nm nodes, meeting the stringent criteria for high performance and minimal CH values in logic applications.}, archive = {J_ACCESS}, author = {Sanguk Lee and Jinsu Jeong and Jongseo Park and Seunghwan Lee and Junjong Lee and Yonghwan Ahn and Minchan Kim and Rock-Hyun Baek}, doi = {10.1109/ACCESS.2025.3576265}, journal = {IEEE Access}, pages = {99355-99365}, shortjournal = {IEEE Access}, title = {Patterned multi-wall nanosheet FETs for sustainable scaling: Zero gate extension with minimal gate cut width}, volume = {13}, year = {2025}, }
• (2025). Fabrication and evaluation of polydimethylsiloxane-La2O3 composites-based flexible capacitance pressure sensors’ parameters and applications. ACCESS, 13, 99343-99354. (www)

  @article{ , abstract = {Flexible capacitance pressure sensors (fCPS) with high sensitivity, wide linear working range, fast response and recovery times, low hysteresis, and long durability with stable output are very much needed for many physical sensor applications. For flexibility sensors both capacitor plates and dielectric sandwich layer in parallel plate capacitor (PPC) geometry should be flexible with low elastic modulus (E) with high tensile break point. For this purpose, many researchers were used flexible polymer sheet with surface conductive material coating as a flexible capacitor electrodes, and elastomer composites as dielectric sandwich layers. Capacitance (C) of a PPC sensor is C= $\epsilon \frac {A}{D}$ , here $\epsilon $ is dielectric permittivity of sandwich layer, A is effective area of PPC electrode plates, and D is distance between the electrode plates. Here change in capacitance under applied pressure is induced by change in $\epsilon $ as well as D. Flexible dielectric elastomer composites (DECs) with high dielectric constant ( $\epsilon _{\text {r}}$ ) were prepared by adding 0, 0.45, 0.90, & 1.34 mass% of relatively high dielectric La2O3 ceramic nanoparticles as fillers to polydimethylsiloxane (PDMS) matrix. The DEC were spin coated to make 0.2 mm thick films to use in PPCs. Four fCPS were fabricated using conductive silver epoxy coated polyethylene terephthalate (PET) substrates (0.125 mm thickness) for PPC electrodes and 0.2 mm thick DECs as sandwich layers. Sensitivity of the fCPS increases with La2O3 filler content. Among the four fCPS studied here, fCPS with 1.34 mass% La2O3 DEC shows highest sensitivity of 23.77 pF/kPa, linear working range in 0–2.56 kPa, response and recovery times of 123 & 126 ms, negligible hysteresis in 0-128 kPa range, and long durability with stable output. Compressive and tensile strain sensing capabilities of this sensor was tested in the real time applications.}, archive = {J_ACCESS}, author = {Soly Mathew and Krishnamoorthi Chintagumpala}, doi = {10.1109/ACCESS.2025.3577066}, journal = {IEEE Access}, pages = {99343-99354}, shortjournal = {IEEE Access}, title = {Fabrication and evaluation of polydimethylsiloxane-La2O3 composites-based flexible capacitance pressure sensors’ parameters and applications}, volume = {13}, year = {2025}, }
• (2025). MPT-based droop control toward multi-operation requirement of multi-bus DC microgrid. ACCESS, 13, 99331-99342. (www)

  @article{ , abstract = {To ensure the reliable and efficient operation of the DC microgrid, the control system needs to be well designed so that the system can meet various operation requirements, including stability, power sharing and voltage drop limitation. However, it is challenging in designing the control strategy which considers multiple-operation requirements, especially when facing the DC microgrid with complex multi-bus architecture. To address this problem, this paper presents an improved droop control towards the multiple-operation requirements of multi-bus DC microgrid, such as decentralized stability, power sharing and drop limitation of bus voltage. Firstly, the system stability is analyzed based on the mixed potential theory, and a decentralized stability criterion is proposed. Then, based on the proposed stability criterion and the potential function reshaping, an improved droop control strategy is designed to realize these multiple requirements. Finally, the effectiveness and the general applicability of the proposed criterion and the proposed control are verified by case studies.}, archive = {J_ACCESS}, author = {Yufei Li and Fei Wang and Yuxin Zhu and Ying Yang}, doi = {10.1109/ACCESS.2025.3576695}, journal = {IEEE Access}, pages = {99331-99342}, shortjournal = {IEEE Access}, title = {MPT-based droop control toward multi-operation requirement of multi-bus DC microgrid}, volume = {13}, year = {2025}, }
• (2025). Sample denoising and optimization technique based on noise filtering and evolutionary algorithms for imbalanced data classification. ACCESS, 13, 99314-99330. (www)

  @article{ , abstract = {Imbalanced data remains a challenge in classification research and significantly influences classifier performance. The strategy that is widely used to address this issue is the data-level approach or sampling method, through over-sampling, under-sampling, or hybrid-sampling methods. However, data quality problems, such as the presence of noise disrupt the sampling process and adversely affect classifier performance, particularly in the popular over-sampling method, such as Synthetic Minority over-sampling Technique (SMOTE). Therefore, the data preprocessing strategy at the before and after data balancing process is crucial to improve data quality before the classification process is conducted. This study proposes a method to improve the data balancing process by integrating two preprocessing steps with the SMOTE sampling technique. Technically, we performed a sample denoising process with Tomek links before applying the SMOTE and then followed by sample optimization with an evolutionary algorithm after the SMOTE. A genetic algorithm (GA) as one of the popular evolutionary algorithms is utilized for sample optimization including synthetic samples of SMOTE and original samples from both classes. Then, the selected train set is used to develop classification model using five classifier, i.e., decision tree, logistic regression, support vector machine, k-nearest neighbors, and naive bayes. Experimental results and statistical evaluations on 24 real-world imbalanced datasets demonstrate that our proposed method Tomek-SMOTE-GA (TSGA) is significantly better than baseline and state-of-the-art sampling methods in term of geometric-mean, particularly when using decision tree classifiers.}, archive = {J_ACCESS}, author = {Fhira Nhita and Asniar and Isman Kurniawan and Adiwijaya}, doi = {10.1109/ACCESS.2025.3573786}, journal = {IEEE Access}, pages = {99314-99330}, shortjournal = {IEEE Access}, title = {Sample denoising and optimization technique based on noise filtering and evolutionary algorithms for imbalanced data classification}, volume = {13}, year = {2025}, }
• (2025). Machine learning-enriched cybersecurity in smart grids. ACCESS, 13, 99303-99313. (www)

  @article{ , abstract = {A smart grid is one of the critical infrastructures that, when targeted by a cyber-attack, could have disastrous effects on the economy and disrupt the lives of the population. Firewalls and Intrusion detection systems, the conventional protective schemes and cyber threat mitigation systems in smart grids, are usually deployed to analyse incoming and outgoing traffic. Although these approaches are capable of detecting external attacks, they are ill-suited to detect threats originating from a compromised device [internal to the grid] infected with malware or malicious software. To mitigate such threats, Industrial Control System (ICS) traffic monitoring and the implementation of anomaly detection systems based on the traffic data are crucial. We propose an anomaly detection system for monitoring the ICS traffic data based on IP traffic flows extended with application layer data obtained from the ICS packet headers. Unlike conventional methods that utilise statistical properties of the communication profiles of the dataflows to identify unknown threats, this study proposes an Autoencoder-based threat detection model. The dataset considered for the scope of this research was generated for the project at Brno University of Technology: Security monitoring of Industrial Control System (ICS) communication in the smart grid (Bonnet), 2019–2022, funded by the Ministry of Interior of the Czech Republic. The proposed solution was validated on diverse cyber-attack datasets, in conformance with IEC (International Electrotechnical Commission) 104 and IEC 61850 standards. The proposed model is efficient with an average anomaly detection rate of 99% in IEC 104 dataset and 97% in IEC 61850 dataset.}, archive = {J_ACCESS}, author = {G. Amritha and Manjula G. Nair and Fabrizio Granelli}, doi = {10.1109/ACCESS.2025.3576497}, journal = {IEEE Access}, pages = {99303-99313}, shortjournal = {IEEE Access}, title = {Machine learning-enriched cybersecurity in smart grids}, volume = {13}, year = {2025}, }
• (2025). Attention-guided deep reinforcement learning for realistic neural painting. ACCESS, 13, 99291-99302. (www)

  @article{ , abstract = {Neural painting aims to produce realistic artworks using stroke sequences, having attracted considerable interest from academia and industry. Previous methods often focus on minimizing the total color distance, neglecting distinctions between foreground and background objects. In contrast, human artists typically prioritize important content over background details. Motivated by this observation, we propose an attention-guided deep reinforcement learning framework for stroke planning that emulates human painting processes. Our approach consists of an attention-guided policy network that generates stroke parameters and a stroke rendering network that updates the canvas. The networks interact iteratively: the policy proposes stroke parameters based on attention maps, while the renderer applies them and feeds updated canvas states back to the policy. Specifically, the attention module computes an attention map to guide stroke generation, and a feature-masked reward function prioritizes important regions. Experiments demonstrate that our method achieves high-fidelity rendering of foreground objects with fine details, while approximating backgrounds with coarser strokes. This approach produces visually appealing paintings using significantly fewer strokes compared to existing methods.}, archive = {J_ACCESS}, author = {Xin Huang and Minglun Gong}, doi = {10.1109/ACCESS.2025.3576500}, journal = {IEEE Access}, pages = {99291-99302}, shortjournal = {IEEE Access}, title = {Attention-guided deep reinforcement learning for realistic neural painting}, volume = {13}, year = {2025}, }
• (2025). Evaluating YOLO variants with transfer learning for real-time UAV obstacle detection in simulated forest environments. ACCESS, 13, 99266-99290. (www)

  @article{ , abstract = {Autonomous navigation in forest environments poses significant challenges for Uncrewed Aerial Vehicles (UAVs), where reliable obstacle detection is essential for safe operation. This study presents a detailed performance evaluation of recent YOLO models (v8–v11) using a realistic simulation environment built on Unreal Engine and AirSim. To the best of our knowledge, this is the first study to comprehensively benchmark YOLOv8–v11 in a UAV-based obstacle detection context, incorporating both onboard deployment factors and the impact of transfer learning (TL) using EfficientNet backbones. We analyze detection accuracy (mAP@50, precision, recall), inference time, FPS, and memory usage across all YOLO configurations, from lightweight (YOLOv8s) to high-performance models (YOLOv11x). TL significantly improved model performance; for instance, YOLOv8s showed a recall increase from 0.6318 to 0.7804 and mAP@50 from 0.7228 to 0.8447. YOLOv8m and YOLOv10b emerged as optimal models, offering the best trade-off between speed and detection accuracy for real-time UAV deployment. Larger models like YOLOv9c and YOLOv10x delivered superior accuracy but required more computational resources. The results offer practical guidance for selecting YOLO architectures in UAV-based obstacle detection tasks, balancing accuracy, speed, and deployment feasibility in complex environments.}, archive = {J_ACCESS}, author = {Shouthiri Partheepan and Farzad Sanati and Jahan Hassan}, doi = {10.1109/ACCESS.2025.3577251}, journal = {IEEE Access}, pages = {99266-99290}, shortjournal = {IEEE Access}, title = {Evaluating YOLO variants with transfer learning for real-time UAV obstacle detection in simulated forest environments}, volume = {13}, year = {2025}, }
• (2025). ECoG dual context network for brain machine interfaces. ACCESS, 13, 99252-99265. (www)

  @article{ , abstract = {Speech impairments in conditions such as amyotrophic lateral sclerosis (ALS) and muscular dystrophy significantly limit communication with patients, profoundly impacting their daily life and social participation. Communication support through decoding technology based on electrocorticography (ECoG) is thus critically important to assist the day-to-day communication of patients with speech impairments. This study proposes an electrocorticography (ECoG)-based communication support system to enhance communication for individuals with speech impairments, such as those caused by amyotrophic lateral sclerosis (ALS) or muscular dystrophy. In our system, we classify ECoG signals recorded during movement attempt tasks involving the upper limbs, such as hand grasping. This classification enables the control of a virtual keyboard, allowing for text-based communication. Specifically, predicted movement attempts are associated with keyboard functions, such as moving the cursor or pressing the select button. In this study, we propose the ECoG Dual Context Network (ECoG DCNet), designed to efficiently embed information between electrodes while preserving the length of temporal sequences to capture long-range dependencies. To validate our approach, we used a clinical dataset from a single paralyzed subject with ALS, focusing on the ECoG recorded during movement attempt tasks. The proposed method outperformed baseline methods in terms of classification accuracy. Notably, our method achieved an accuracy of 75.3%, outperforming the widely-used EEGNet by 30%. Moreover, Gradient-weighted Class Activation Mapping visualizations suggested that the proposed method focuses on features that could be neurophysiologically relevant.}, archive = {J_ACCESS}, author = {Shunya Nagashima and Kanta Kaneda and Yuiga Wada and Tsumugi Iida and Misa Taguchi and Masayuki Hirata and Komei Sugiura}, doi = {10.1109/ACCESS.2025.3577407}, journal = {IEEE Access}, pages = {99252-99265}, shortjournal = {IEEE Access}, title = {ECoG dual context network for brain machine interfaces}, volume = {13}, year = {2025}, }
• (2025). A soft-switching type-3 fuzzy control for electrically driven nonholonomic robots. ACCESS, 13, 99234-99251. (www)

  @article{ , abstract = {The wheeled mobile robots (WMRs) are used in extensive fields because of their simple structure and high efficiency. Due to the different applications of WMRs in different problems, the control problem of WMRs is challenging. Various frictions and disturbances complicate the dynamics of MRs. To cope with these problems, various control systems have been developed. But, in most of the existing methods, just the structural uncertainties are considered, and non-structural uncertainties such as disturbance and un-modeled dynamics are neglected. Although in some studies, the sliding mode controller (SMC) and its combination with different methods have been presented, however, in most of these controllers, to ensure stability, some coefficients are conservatively set with large values, and as a result, large control signals are generated. These signals are often not within the acceptable range, and their practical implementation is difficult. In this paper, an adaptive controller is designed that is robust under structural/un-structural uncertainties and unknown dynamics. In the designed controller, the coefficients and bounds of uncertainties are adjusted using an adaptive type-3 (T3) fuzzy system (FS). Based on back-stepping kinematic control, new adaptation laws, sufficient conditions for stability, and control signals are derived. Adaptive T3-FS is designed to create a soft-switching mechanism. New learning rules of T3-FS are derived to adjust the upper/lower bounds of structural and un-structural/uncertainties. The stability is mathematically analyzed and proved under uncertainties. Several simulations are given to prove the effectiveness.}, archive = {J_ACCESS}, author = {Xinhong Yuan and Afef Fekih and Abouzar Taghizadeh and Chunwei Zhang and Ardashir Mohammadzadeh}, doi = {10.1109/ACCESS.2025.3576193}, journal = {IEEE Access}, pages = {99234-99251}, shortjournal = {IEEE Access}, title = {A soft-switching type-3 fuzzy control for electrically driven nonholonomic robots}, volume = {13}, year = {2025}, }
• (2025). Contrastive disentangled variational autoencoder for collaborative filtering. ACCESS, 13, 99221-99233. (www)

  @article{ , abstract = {Recommender systems aim to accurately predict user preferences in order to provide potential items of interests. However, the highly skewed long-tail item distribution leads the models to more focus on popular items, which can damage the predictive performance by recommending specific items repeatedly. We propose a novel Variational Autoencoder (VAE) framework for collaborative filtering using contrastive disentanglement. We contrast salient latent features in VAE against the non-salient background. Here, we intentionally generate a background dataset based on item popularity, a concept previously unexplored in existing recommender system. The representations learnt from our proposed scheme better reflect salient latent factor instead of being washed out by latent factor of popular items. Consequently, our model improves the predictive performance by effectively learning the representations toward salient latent features while excluding the effects from popularity. The proposed contrastive disentanglement framework is generic and thus adaptable to VAE-based recommender algorithms. We comprehensively evaluate our proposed method on popular datasets such as MovieLens20M and Netflix and show that it consistently outperforms corresponding VAE models, achieving superior performance across all metrics. Notably, in terms of diversity, our approach outperforms the best-performing methods by 5.5% and 4.8% on the MovieLens20M and Netflix datasets, respectively. We further present a way of extending the framework to an ensemble of two contrasts with two different backgrounds, which achieves state-of-the-art performance in standard settings with significant improvements. Our code is available at https://github.com/yunwooseong/CD-VAE}, archive = {J_ACCESS}, author = {Woo-Seong Yun and Seong-Min Kang and Yoon-Sik Cho}, doi = {10.1109/ACCESS.2025.3576445}, journal = {IEEE Access}, pages = {99221-99233}, shortjournal = {IEEE Access}, title = {Contrastive disentangled variational autoencoder for collaborative filtering}, volume = {13}, year = {2025}, }
• (2025). Integrating VR, AR, and haptics in basic surgical skills training: A review and perspective. ACCESS, 13, 99203-99220. (www)

  @article{ , abstract = {Traditional surgical training faces challenges related to sustainability, cost, and ethical considerations. Limited access to resources, ethical concerns about cadaver-based learning, limited availability of surgical cases, and the high costs associated with hands-on training impede the effectiveness and accessibility of surgical education. Despite these challenges, the need for direct tactile experience remains critical, as it fosters the development of essential motor skills and tactile abilities vital for surgical practice. However, advancements in immersive technologies such as virtual reality (VR), augmented reality (AR), and haptics offer a promising solution to address these challenges. VR and AR technologies offer realistic, controlled environments where learners can visualise anatomical structures and practice surgical procedures without risks. The integration of haptic feedback in these immersive simulations further enriches the learning process by adding the sense of touch and physical interaction, aiding in mastering critical surgical skills. The incorporation of immersive technologies in surgical training presents an opportunity to address the constraints of conventional methods. These technologies offer the possibility of creating platforms that replicate real-life surgical scenarios, allowing learners to hone their skills and build confidence in a safe and controlled environment. Moreover, immersive learning offers an ethical alternative to cadaver-based training and extends access to training materials irrespective of geographical limitations. Such an approach allows for step-based training, where students may begin acquiring skills through immersive learning, then move to special surgical simulators, and finally to patients. This article deals with the impact of immersive technologies such as VR, AR, and haptics on reshaping conventional surgical training, by focusing specifically on basic surgical skills. Targeting educators, medical experts, and involved parties, the article aims to stimulate additional exploration and adoption of immersive technologies to elevate surgical competencies and patient care standards.}, archive = {J_ACCESS}, author = {Filippo Sanfilippo and Gionata Salvietti and Tomas Blažauskas and Guido Gabriele and Maria Zafar and Minh Tuan Hua and Muhammad Hamza Zafar and Syed Kumayl Raza Moosavi and Patrikas Armalis and Mohammad Poursina and Povilas Ignatavicius and Andrejus Subocius and Audrius Parseliunas and Ernest Margelis}, doi = {10.1109/ACCESS.2025.3574265}, journal = {IEEE Access}, pages = {99203-99220}, shortjournal = {IEEE Access}, title = {Integrating VR, AR, and haptics in basic surgical skills training: A review and perspective}, volume = {13}, year = {2025}, }
• (2025). Highly available li-ion batteries sensors readings prediction framework. ACCESS, 13, 99192-99202. (www)

  @article{ , abstract = {Lithium-ion batteries can experience internal failures due to issues like overcharging and excessive discharging. They can also encounter external failures, primarily involving sensors that monitor temperature, voltage, and current. External failures are often seen as the primary causes of internal failures [1]. Numerous battery health monitoring systems (BHMS) have been developed to ensure the proper functioning of batteries. Current BHMS frameworks lack robust mechanisms to handle sensor data loss or corruption. Without reliable recovery of sensor readings, these systems remain vulnerable to undetected failures and incomplete diagnostics. This creates a serious bottleneck in developing fault-tolerant, real-time battery monitoring systems. In this article, the main contribution is presenting a highly available sensor readings prediction framework to address situations where sensor readings are unavailable. The framework aims to accurately reconstruct missing or faulty sensor data using correlations among healthy sensors, thereby ensuring continuous and dependable battery monitoring, even in the presence of sensor faults. The generated reading sequences achieve high $R^{2}$ accuracy, ranging from 94% to 99%. The proposed highly available sensor readings prediction framework is implemented using a lightweight long short-term memory (LSTM) network. The prediction model is trained and tested on batteries B6 and B7 from the NASA dataset and validated using the Toyota dataset, one of the largest available datasets.}, archive = {J_ACCESS}, author = {Anas Tiane and Mohamad Alzayed and Chafik Okar and Safi Bamati and Hicham Chaoui}, doi = {10.1109/ACCESS.2025.3577133}, journal = {IEEE Access}, pages = {99192-99202}, shortjournal = {IEEE Access}, title = {Highly available li-ion batteries sensors readings prediction framework}, volume = {13}, year = {2025}, }
• (2025). Application of graph attention network for breast cancer data and explanation of prediction basis. ACCESS, 13, 99183-99191. (www)

  @article{ , abstract = {Breast cancer is one of the most prevalent cancers globally. Applying machine learning methods to predict breast cancer prognosis is essential for developing effective clinical treatment plans. However, predicting models alone does not ensure the reliability of prognostic outcomes. Therefore, reliable predictions require clear explanations of the underlying basis for its predicted results. This study proposes a method employing a machine learning model for predicting cancer prognosis and a method for explaining the basis for the predicted results. In this study, a machine learning method was introduced to predict the prognosis of breast cancer patients using a graph attention network. We used GNNExplainer to obtain a graph containing important genes and their interrelationships as the basis for the prediction. This method enables the visualization and explanation of the rationale behind the predictive outcomes. This study identified 55 genes critical to predicting survival status within a 10-year timeframe. This study proposed a method employing a graph attention network to breast cancer data, and to explain the prediction basis. The experimental results allowed to identify the genes that play a key role in prognosis prediction, demonstrating the effectiveness of this method in interpreting predicted results. This study has shown subgraphs visually and improved the reliability of prognostic predictions by clearly revealing the factors that affect patient prognosis, which helps to develop more effective clinical treatment plans and decision-making.}, archive = {J_ACCESS}, author = {Qingyu Zeng and Shoma Tamori and Taku Harada}, doi = {10.1109/ACCESS.2025.3575946}, journal = {IEEE Access}, pages = {99183-99191}, shortjournal = {IEEE Access}, title = {Application of graph attention network for breast cancer data and explanation of prediction basis}, volume = {13}, year = {2025}, }
• (2025). Enhancing urban road safety: A 5G NR model for vulnerable road users awareness. ACCESS, 13, 99170-99182. (www)

  @article{ , abstract = {The widespread adoption of cellular networks has played an important role in the integration of road safety applications, capitalizing on the ubiquitous use of this technology by road users and its extensive market penetration. This paper introduces an analytical model to evaluate the Vulnerable Road User (VRU) Awareness Probability (VAP)—the likelihood that vehicles can detect VRUs via 5G New Radio (NR) cellular vehicle-to-everything (C-V2X) communication at urban intersections. The model assumes full VRU connectivity to the 5G network and simulates 5G NR operating in communication mode 2, incorporating realistic urban mobility patterns (via SUMO traces) and signal propagation effects, including building-induced obstructions. Results demonstrate high model accuracy, with Root Mean Square Error (RMSE) values of 0.0265 in partially obstructed and 0.0309 in highly obstructed scenarios, using the non-obstructed case as a baseline. This model provides a robust framework for assessing the impact of C-V2X-based safety applications on VRU protection across varying traffic densities, network conditions, and deployment architectures.}, archive = {J_ACCESS}, author = {Alexis Yáñez and Felipe Salas and Cesar A. Azurdia-Meza and Jorge Ignacio Sandoval and Sandra Céspedes}, doi = {10.1109/ACCESS.2025.3576726}, journal = {IEEE Access}, pages = {99170-99182}, shortjournal = {IEEE Access}, title = {Enhancing urban road safety: A 5G NR model for vulnerable road users awareness}, volume = {13}, year = {2025}, }
• (2025). Time series forecasting based on temporal networks evolution and dynamic constraints. ACCESS, 13, 99160-99169. (www)

  @article{ , abstract = {Time series forecasting holds significant application value in financial market analysis, biological prediction, and other domains. Analyzing time series from a network perspective offers novel insights for forecasting. This article proposes an innovative time series prediction method based on the network characteristics of time series. First, the time series is mapped to a complex network with node weights assigned according to association of nodes. Subsequently, leveraging the unique evolutionary patterns of temporal networks, we employ matrix evolution to predict the topological structure of the network at the next time step. A loss function is constructed by integrating the ability of weighted temporal networks to reconstruct the original time series data and visibility-based constraint features, enabling the computation of predicted values for next time step. The proposed method achieves prediction considering global topological characteristics of temporal networks during the forecasting process. To validate its performance across diverse scenarios, five time series datasets from distinct domains are selected. These datasets encompass varied temporal granularities and exhibiting periodic, stochastic, trend, and seasonal patterns. Prediction errors are calculated to evaluate method efficacy. Statistical tests and error metric results demonstrate that the proposed method achieves superior prediction accuracy compared to existing forecasting approaches under consideration, which confirms that this work not only contributes theoretically but also provides a practical solution for time series forecasting applications.}, archive = {J_ACCESS}, author = {Yunlong Peng and Han Li and Xu Han}, doi = {10.1109/ACCESS.2025.3577499}, journal = {IEEE Access}, pages = {99160-99169}, shortjournal = {IEEE Access}, title = {Time series forecasting based on temporal networks evolution and dynamic constraints}, volume = {13}, year = {2025}, }
• (2025). Comprehensive style transfer for facial images using enhanced feature attribution in generative adversarial nets. ACCESS, 13, 99145-99159. (www)

  @article{ , abstract = {Image-to-image translation is a fundamental task in computer vision that transforms images between domains while preserving essential content. Although adaptive instance normalization (AdaIN) is widely used for style transfer, its reliance on simple statistical measures (mean and variance) may limit its ability to capture complex style characteristics. We propose a novel framework that enhances style transfer by combining AdaIN with Gram matrices, leveraging the complementary strengths of both approaches. Our method introduces two key innovations for enhanced feature attribution: 1) dual Gram matrix-based loss functions (G1 and G2), which operate at different stages of the generation process to capture richer style information by establishing deeper correlations between feature maps, and 2) a balanced training objective that integrates perceptual loss with cycle-consistency loss to maintain content fidelity during style transfer. This comprehensive feature attribution mechanism enables our model to decompose and reassign stylistic elements across domains more precisely. Through ablation studies, we demonstrate that each component of our framework contributes to performance improvements, with the complete model achieving the best results on both the CelebA-HQ and FFHQ datasets. Our comprehensive evaluation, using distribution similarity metrics, classification-based assessments, and visual comparisons, demonstrates that our approach effectively captures and transfers complex style characteristics while preserving content integrity, outperforming state-of-the-art models. Specifically, our model achieves superior Fréchet Inception Distance (FID) scores (19.88 vs. 24.22) and recognition accuracy (0.966 vs. 0.941) compared to StarGAN v2, confirming the performance gains introduced by our enhanced feature attribution strategy.}, archive = {J_ACCESS}, author = {Yongseon Yoo and Seonggyu Kim and Jong-Min Lee}, doi = {10.1109/ACCESS.2025.3574729}, journal = {IEEE Access}, pages = {99145-99159}, shortjournal = {IEEE Access}, title = {Comprehensive style transfer for facial images using enhanced feature attribution in generative adversarial nets}, volume = {13}, year = {2025}, }
• (2025). MLAR-net: A multilevel attention-based ResNet module for the automated recognition of emotions using single-channel EEG signals. ACCESS, 13, 99122-99144. (www)

  @article{ , abstract = {Human emotion recognition is important as it finds applications in multiple domains such as medicine, entertainment, and military. However, accurately identifying emotions remains challenging due to humans’ ability to hide or suppress their emotional expressions. Hence it becomes important to recognize emotions by using brain signals as they provide more reliable data. Brain signals can be captured using Electroencephalograms (EEG) electrodes. Most used EEG devices come with multiple channels. However, not all channel information is important for emotion recognition. Another issue with the existing dataset is the availability of a small quantity of samples. To address these challenges, we propose MLAR-Net, a novel multilevel attention module for emotion recognition using single-channel EEG signals. Our approach converts EEG signals into spectrograms using multiple parameters to generate a large set of images. This data is then processed through our proposed MLAR-Net, which integrates a multilevel attention module with ResNet18 architecture. Our study identifies channel number 24 (T7) as the most effective for emotion classification, achieving an average accuracy of 98.06% using a cubic support vector machine and a maximum accuracy of 99.51% using fine K-Nearest Neighbors. The study was conducted using the SEED dataset. It is a publicly available dataset developed by capturing EEG signals from fifteen subjects for three classes of emotions, namely positive, negative, and neutral. The results achieved by the proposed study show an improvement of around 4 to 5% compared to state-of-the-art studies using the same channel. This performance surpasses existing state-of-the-art methods for single-channel EEG-based emotion recognition. Furthermore, we highlight the top-performing channels that can be used for real-time implementation of the system with a minimum number of channels.}, archive = {J_ACCESS}, author = {M. Maithri and U. Raghavendra and Anjan Gudigar and Samir Kumar Praharaj and Karthikeyan Sriram and Massimo Salvi and Chai Hong Yeong and Filippo Molinari and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3576059}, journal = {IEEE Access}, pages = {99122-99144}, shortjournal = {IEEE Access}, title = {MLAR-net: A multilevel attention-based ResNet module for the automated recognition of emotions using single-channel EEG signals}, volume = {13}, year = {2025}, }
• (2025). Defect diagnosis of photovoltaic module visible light images under imbalanced sample conditions. ACCESS, 13, 99112-99121. (www)

  @article{ , abstract = {To address the issues of high miss and false detection rates in defect detection of PV (photovoltaic) module visible light images under imbalanced data conditions, an improved data augmentation method based on DCGAN (Deep Convolutional Generative Adversarial Networks) is proposed. Firstly, in the DCGAN model, fully connected layers are incorporated into both the generator and the discriminator, and the transpose convolutional layers and convolutional layers are replaced with residual blocks. This enables the generation of $512\times 512$ resolution images using fewer network layers and avoids the problems of gradient vanishing or gradient exploding caused by excessive layers. Secondly, a self-attention mechanism is introduced into both the generator and the discriminator to enhance the ability of DCGAN to extract image features, allowing the model to capture small defects or subtle differences in defects on PV modules more accurately. Finally, to address the issue of DCGAN generating repeated and similar images, an image similarity measurement function is introduced into the loss function. The dataset generated by improved DCGAN achieved mAP scores of 89.3% and 90.9% on YOLOv5 and YOLOv8 models, respectively, showing increases of 0.7% and 1.2% compared to the original models, which validates the effectiveness of the enhancements.}, archive = {J_ACCESS}, author = {Huiqing Rao and Qiong Li and Long Chen and Sha Jin and Yong Lu and Zhiguang Li}, doi = {10.1109/ACCESS.2025.3574334}, journal = {IEEE Access}, pages = {99112-99121}, shortjournal = {IEEE Access}, title = {Defect diagnosis of photovoltaic module visible light images under imbalanced sample conditions}, volume = {13}, year = {2025}, }
• (2025). Computationally enhanced UAV-based real-time pothole detection using YOLOv7-C3ECA-DSA algorithm. ACCESS, 13, 99092-99111. (www)

  @article{ , abstract = {Road deterioration due to potholes has a significant impact on traffic safety and infrastructure maintenance, highlighting the need for detection systems that combine precision with real-time capabilities. Although YOLO-based algorithms have been widely adopted for their speed and efficiency in object detection, achieving a balance between high accuracy and low inference time remains a challenge, particularly in scenarios involving small objects and complex features. This study introduces YOLOv7-C3ECA-DSA, an improved YOLOv7 architecture designed to address these limitations. The model incorporates Cross-Stage Enhanced Channel Attention (C3ECA) blocks in the backbone network and Depthwise Shuffle Attention (DSA) in the detection head to enhance feature learning and boundary detection, achieving high detection accuracy and real-time inference capabilities. The experimental results demonstrate that YOLOv7-C3ECA-DSA achieves an mAP0.5 of 85.3% with an inference time of 10.9 ms, outperforming the prior methods. The proposed model also performs well under adverse vision conditions such as night and rain. However, the model performance may be limited in severe environmental conditions, such as low-contrast surfaces or heavily obscured potholes. Despite these limitations, the research significantly advances real-time pothole detection by striking an optimal balance between computational efficiency and detection accuracy. The findings underscore the effectiveness of YOLOv7-C3ECA-DSA in addressing practical challenges in real-time infrastructure monitoring, making it suitable for scalable deployment in autonomous vehicle systems and road maintenance applications.}, archive = {J_ACCESS}, author = {Siti Fairuz Mat Radzi and Mohd Amiruddin Abd Rahman and Muhammad Khairul Adib Muhammad Yusof and Nurin Syazwina Mohd Haniff and Romi Fadillah Rahmat}, doi = {10.1109/ACCESS.2025.3573651}, journal = {IEEE Access}, pages = {99092-99111}, shortjournal = {IEEE Access}, title = {Computationally enhanced UAV-based real-time pothole detection using YOLOv7-C3ECA-DSA algorithm}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of virtual impedance control techniques for improved stability in DC microgrids. ACCESS, 13, 99077-99091. (www)

  @article{ , abstract = {DC microgrids (DCMGs) have been extensively studied using various control techniques, with droop control and its variants being the most widely adopted. Virtual impedance control (VIC), a technique commonly used in AC microgrids, can be adapted for DCMGs. VIC employs virtual resistance as the droop constant and incorporates a dynamic element, such as a virtual inductor, to enhance microgrid stability. This study compares four approaches to implementing VIC to determine the most effective method for reducing voltage oscillations during transient conditions, which enhances overall DCMG stability. In this paper, the analyzed DCMG consists of two units of energy storage system (ESS), electronic loads, and a renewable energy source (RES), each connected via a dedicated power electronic converter. Parametric analyses are conducted to define the VIC parameter range, and a stability analysis is performed to evaluate the different methods of implementation. The techniques under analysis are tested using a controller hardware-in-the-loop (C-HIL). The results establish the operating range for each VIC strategy, the stability limitations, and identifying the control approach that achieves optimal damping performance and stability in the studied microgrid.}, archive = {J_ACCESS}, author = {Beatriz Cristina Moura and Danillo Borges Rodrigues and Érico Chagas Guimarães and Gustavo Brito de Lima and João Pedro Carvalho Silveira and Luiz Carlos Gomes Freitas and Pedro José Dos Santos Neto}, doi = {10.1109/ACCESS.2025.3576476}, journal = {IEEE Access}, pages = {99077-99091}, shortjournal = {IEEE Access}, title = {Comparative analysis of virtual impedance control techniques for improved stability in DC microgrids}, volume = {13}, year = {2025}, }
• (2025). Stability analysis and controller design with less conservatism for singular networked control systems with short time-varying delay. ACCESS, 13, 99068-99076. (www)

  @article{ , abstract = {This paper investigates the issue of reducing conservatism in stability analyses and controller design for singular networked control systems (SNCSs) with short time-varying delays. The shortcomings and its causes of existing research on related SNCSs are analyzed. By constructing augmented state equations and considering the effect of time delays, the SNCSs are modeled as linear discrete systems with parameter uncertainties, and the bound of uncertainties is obtained based on the nominal point method. A more universal quadratic Lyapunov functional is selected for the stability analysis and controller design. Based on Lyapunov stability theory, a sufficient condition for asymptotic stability is proposed in the form of linear matrix inequalities (LMIs), and design methods for a guaranteed cost controller and an optimal H $\infty $ controller are proposed. The numerical example shows that the proposed method can effectively reduce conservatism and improve the performance index compared with the existing method, thus, the application scenarios can be expanded.}, archive = {J_ACCESS}, author = {Quan Zou}, doi = {10.1109/ACCESS.2025.3575595}, journal = {IEEE Access}, pages = {99068-99076}, shortjournal = {IEEE Access}, title = {Stability analysis and controller design with less conservatism for singular networked control systems with short time-varying delay}, volume = {13}, year = {2025}, }
• (2025). Machine learning in E-commerce: Trends, applications, and future challenges. ACCESS, 13, 99048-99067. (www)

  @article{ , abstract = {The rapid evolution of e-commerce has been significantly influenced by the integration of machine learning (ML) and data science techniques. The present survey provides a comprehensive overview of how ML methods are applied across various functional domains in e-commerce, including personalized recommendations, dynamic pricing, fraud detection, customer segmentation, and behavioral analysis. We categorize and evaluate a wide range of ML paradigms, namely supervised, unsupervised, reinforcement, and hybrid learning, as well as emerging approaches such as neurosymbolic artificial intelligence (AI), federated learning (FL), and quantum ML (QML). Key challenges related to scalability, interpretability, cold-start problems, data sparsity, and privacy are critically analyzed. Additionally, we highlight underexplored areas, such as continual learning (CL) and multi-agent architectures in commerce. The survey incorporates comparative tables, real-world use cases, and a taxonomy of methods to support both academic and industrial perspectives. Ultimately, by analyzing trends and gaps in the literature, we provide a forward-looking research roadmap that bridges ML innovations with the evolving demands of e-commerce ecosystems.}, archive = {J_ACCESS}, author = {Elias Dritsas and Maria Trigka}, doi = {10.1109/ACCESS.2025.3572865}, journal = {IEEE Access}, pages = {99048-99067}, shortjournal = {IEEE Access}, title = {Machine learning in E-commerce: Trends, applications, and future challenges}, volume = {13}, year = {2025}, }
• (2025). Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals. ACCESS, 13, 99032-99047. (www)

  @article{ , abstract = {Electroencephalogram (EEG) and electrocardiogram (ECG) signals provide vital insights into brain and heart activity and are widely used in automated medical diagnostics. This study introduces a novel, multimodal fibromyalgia detection system developed by the fusion of EEG and ECG signals recorded during sleep stages 2 and 3. The novelty of the model is the use of dynamic and interpretable feature engineering framework comprising of two innovations: 1) Flower Automata Pattern (FAP) for self-organized pattern-based feature extraction, and 2) Attention-Driven Wavelet Transform and Absolute Maximum Pooling (ADWTAMP) method for signal decomposition and compression. Three feature selection strategies—Neighborhood Component Analysis (NCA), Chi2, and the intersection of NCA and Chi2 (NCA^Chi2) — are employed to generate robust feature vectors, which are classified using k-nearest neighbors (kNN) and support vector machine (SVM) under the leave-one-record-out cross-validation (LORO CV) scheme. The final decision is derived through an iterative voting and greedy fusion approach. The proposed model achieved classification accuracies of 99.36% and 98.37% for sleep stages 2 and 3, respectively. Key advantages of the model include its high accuracy, low computational requirements (CPU-only execution), and explainable architecture. To the best of our knowledge, this is the first multimodal automata-based classification framework designed for fibromyalgia detection.}, archive = {J_ACCESS}, author = {Prabal Datta Barua and Makiko Kobayashi and Sengul Dogan and Mehmet Baygin and Turker Tuncer and Jose Kunnel Paul and Thomas Iype and U. R. Acharya}, doi = {10.1109/ACCESS.2025.3573035}, journal = {IEEE Access}, pages = {99032-99047}, shortjournal = {IEEE Access}, title = {Flower automata pattern-based discrimination of fibromyalgia from control subjects using fusion of sleep EEG and ECG signals}, volume = {13}, year = {2025}, }
• (2025). Data-driven optimized load forecasting: An LSTM-based RNN approach for smart grids. ACCESS, 13, 99018-99031. (www)

  @article{ , abstract = {Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to enhance electricity demand prediction. The research analyzes key temporal patterns in historical load, renewable energy generation, and weather data to improve forecasting precision. RNN-based LSTM, with its gated memory structure, effectively captures long-term dependencies and mitigates vanishing gradient issues, making it highly suitable for modeling complex time-series data in power systems. Its ability to retain crucial past information across extended sequences enables precise load forecasting, even in the presence of fluctuations and intermittent renewable energy sources. To validate the effectiveness of the proposed technique, the model was implemented and tested using MATLAB/Simulink, ensuring realistic simulation of power system dynamics. Through a comprehensive comparative analysis, RNN-LSTM is found to exhibit superior predictive accuracy, achieving the lowest RMSE (2.2889), MAE (1.1041), and MAPE (1.538%), while also demonstrating faster convergence, lower inference time, and higher stability. Furthermore, LSTM demonstrates superior convergence speed, lower inference time (0.00098 s), and high stability (0.9091), which confirms its robustness. Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. These findings underscore the significance of RNN-based LSTM in enhancing load forecasting reliability, ultimately contributing to improved grid stability, energy management, and operational efficiency.}, archive = {J_ACCESS}, author = {Muhammad Asghar Majeed and Sotdhipong Phichaisawat and Furqan Asghar and Umair Hussan}, doi = {10.1109/ACCESS.2025.3576303}, journal = {IEEE Access}, pages = {99018-99031}, shortjournal = {IEEE Access}, title = {Data-driven optimized load forecasting: An LSTM-based RNN approach for smart grids}, volume = {13}, year = {2025}, }
• (2025). Optimal allocation of hybrid energy storage capacity for microgrids considering electric vehicles. ACCESS, 13, 99003-99017. (www)

  @article{ , abstract = {The efficient utilization of vehicle-to-grid (V2G) technology and energy storage systems (ESS) can significantly enhance grid stability. This paper proposes a capacity optimization allocation model for a hybrid energy storage system (HESS) in microgrids with electric vehicle (EV) access, aiming to ensure the stability of power in the tie-line. By implementing an orderly charging strategy, EVs can reduce the peak-to-valley load difference, thus lowering the cost of configuring the ESS. Additionally, this paper utilizes a modified Subtraction-Average-Based Optimizer (SABO) algorithm to optimize variable mode decomposition (VMD) in addressing the power allocation problem of HESS. Simultaneously, a model is established to minimize the economic costs of HESS, thereby determining the optimal energy storage configuration and power distribution scheme. The scheme is validated using typical daily data in Southwest China, and the results show that the orderly charging of EVs not only reduces the peak-to-valley difference of loads and decreases the demand for HESS, but also synergizes with HESS to effectively suppress power fluctuations and enhance the stability of the power grid.}, archive = {J_ACCESS}, author = {Yvhua Bian and Haizheng Wang and Jinhua Zhang and Jie Yan and Xiaoke He}, doi = {10.1109/ACCESS.2025.3574048}, journal = {IEEE Access}, pages = {99003-99017}, shortjournal = {IEEE Access}, title = {Optimal allocation of hybrid energy storage capacity for microgrids considering electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Robust h∞ control synthesis of a biomimetic flapping wing UAV operating in gusty environment. ACCESS, 13, 98990-99002. (www)

  @article{ , abstract = {The Unmanned Aerial Vehicles (UAVs) generally execute their missions in atmospheric boundary layer and this region is highly turbulent which drastically degrades UAVs stability. Drawing inspiration from nature for the solution, it is revealed that natural birds utilize covert feathers to tackle gusts and turbulence. This paper presents design of an active biomimetic Gust Alleviation System (GAS) augmented with robust $H_{\infty }$ controller for a flapping wing UAV (FUAV) replicating bird’s covert feathers. Reduced order model of the proposed design is computed using bond graph modelling method. Stability analysis is performed to study internal dynamics of the proposed design. A comparison is made between the proposed $H_{\infty }$ controller-augmented GAS and Linear quadratic regulator (LQR) controller-based GAS in response to external gust disturbances. The $H_{\infty }$ controller augmented GAS design demonstrate more robust behavior of the FUAV compared to LQR based design and successfully alleviates gusts up to 50%. Finally, consistency between the obtained results and literature data confirms the validity of offered $H_{\infty }$ controller.}, archive = {J_ACCESS}, author = {Saddam Hussain Abbasi and Zaid Ahmed Shamsan and Nouman Abbasi}, doi = {10.1109/ACCESS.2025.3576369}, journal = {IEEE Access}, pages = {98990-99002}, shortjournal = {IEEE Access}, title = {Robust h∞ control synthesis of a biomimetic flapping wing UAV operating in gusty environment}, volume = {13}, year = {2025}, }
• (2025). BALI—A benchmark for accelerated language model inference. ACCESS, 13, 98976-98989. (www)

  @article{ , abstract = {The rise of Large Language Models (LLMs) has revolutionized natural language processing, enabling advancements across diverse applications, including chatbots, live translators, content generation, virtual assistants, and domain-specific automation tools. These applications rely on real-time or near-real-time responses to process sequential LLM requests, creating a critical demand for efficient and accelerated inference. These developments have led to numerous frameworks optimizing inference speed and resource utilization. However, they are often mutually incomparable or are inadequately described due to the lack of standardized benchmarks. Consequently, there is a notable lack of comparison frameworks due to the vast configuration space, bounded factors such as hardware specifications, inference framework parameters, and dataset variations. We propose BALI, an open-source Benchmark for Accelerated Language Model Inference, aiming to provide comprehensive analysis and standardized evaluation metrics to enhance the comparability of LLM performance across configurations. With BALI, we propose substantial measurements to evaluate and rank the efficiency of LLM frameworks across multiple aspects, including sequential decoding, parallelization, and setup efficiency. We show results for mainly small to medium-size models (1-30B parameters) in a sequential or non-batched setup, which is highly relevant for various real-time LLM applications. These observations reveal that the design decisions for such a framework constitute an application-dependent and multidimensional challenge. Thus, our objective is to provide LLM an inference benchmark with a clearly defined evaluation, incorporating multidimensional criteria to provide comparable performance assessments.}, archive = {J_ACCESS}, author = {Lena Jurkschat and Preetam Gattogi and Sahar Vahdati and Jens Lehmann}, doi = {10.1109/ACCESS.2025.3576898}, journal = {IEEE Access}, pages = {98976-98989}, shortjournal = {IEEE Access}, title = {BALI—A benchmark for accelerated language model inference}, volume = {13}, year = {2025}, }
• (2025). A methodological and structural review of parkinson’s disease detection across diverse data modalities. ACCESS, 13, 98931-98975. (www)

  @article{ , abstract = {Parkinson’s Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally—1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation—early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 342 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. Notably, limitations such as the lack of robust multimodal frameworks and challenges in continuous PD recognition are identified, underscoring the need for innovative solutions. The review highlights significant advancements in PD recognition, particularly the transition from handcrafted feature engineering to DL-based methods. Furthermore, it explores promising trends in multimodal approaches, offering insights into overcoming existing challenges such as data scarcity, model generalization, and computational efficiency. The study emphasizes the importance of integrating diverse data sources to achieve higher diagnostic accuracy and reliability. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next-generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.}, archive = {J_ACCESS}, author = {Abu Saleh Musa Miah and Taro Suzuki and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3575023}, journal = {IEEE Access}, pages = {98931-98975}, shortjournal = {IEEE Access}, title = {A methodological and structural review of parkinson’s disease detection across diverse data modalities}, volume = {13}, year = {2025}, }
• (2025). Dual-branch neural network-based in-loop filter for VVC intra coding using spatial-frequency feature fusion. ACCESS, 13, 98918-98930. (www)

  @article{ , abstract = {In company with the reconstructed frame, the quantization parameter (QP) map, the predicted frame, and the partition frame are popularly used as auxiliary input for the neural network-based in-loop filter (NNLF). The QP map provides quantization information for the reconstructed frame, while the predicted frame and partition frame represent compression artifacts by VTM. Since each has different types of information, directly concatenating all these inputs for the network may cause mutual interference between the input data and decrease the model performance. In this paper, we propose a dual-branch NNLF for VVC intra coding using spatial-frequency feature fusion. We design a dual-branch network for NNLF according to the input type: One branch includes reconstructed frames and QP maps, while the other branch includes reconstructed frame, predicted frame, and partition frame. The dual-branch architecture processes quantization information from the QP map and compression artifacts from the predicted frame and partition frame separately. Thus, the dual-branch architecture processes the input data efficiently according to its unique characteristics, while reducing mutual interference. Moreover, we adopt fast Fourier transform (FFT) to capture global context in a frame, instead of Transformer that has relatively high complexity. The spatial-frequency feature fusion combines features in spatial and frequency domains, which enhances feature representation capability and learns local and long-range feature correlations. Furthermore, we provide patch size-considered incremental learning based on QP distance that combines patch size and QP distance for network training. The training strategy enforces the proposed network to extend receptive field and learn both local features and global structure in a frame, thus enhancing its model adaptability. Experimental results show that the proposed NNLF achieves average BD-rate savings of {8.55% (Y), 20.48% (U), 21.44% (V)} over VTM-11.0_NNVC-3.0 in All Intra (AI) configuration.}, archive = {J_ACCESS}, author = {Zhen Feng and Xu Liu and Cheolkon Jung}, doi = {10.1109/ACCESS.2025.3573831}, journal = {IEEE Access}, pages = {98918-98930}, shortjournal = {IEEE Access}, title = {Dual-branch neural network-based in-loop filter for VVC intra coding using spatial-frequency feature fusion}, volume = {13}, year = {2025}, }
• (2025). Sub-6 GHz filtenna integration with mm-wave dielectric resonator antenna array for 5G applications. ACCESS, 13, 98909-98917. (www)

  @article{ , abstract = {A patch filtenna operating at 5.2 GHz is integrated with a Cylindrical Dielectric Resonator Antenna Array (CDRA) resonating at 28 GHz to enable simultaneous microwave and milliliter wave communication has been introduced in this paper. First, an elliptical patch antenna is designed to operate at 5.2 GHz for the sub-6 GHz band. A hollow section is introduced at the patch’s center to accommodate the CDRA. Subsequently, a cylindrical dielectric resonator (CDR) is engineered to resonate at 28 GHz for the mm-wave band. These two resonators are carefully combined to ensure seamless operation across both frequency bands. To further suppress unwanted harmonics of the lower band and enhance its isolation from the higher band, a low-pass filter was incorporated with the sub-6GHz antenna. To boost the overall realized gain of the mm-wave band, the DRA is translated to design, and the mm-wave resonator single-element antenna is translated to design a $2\times 2$ (4-element) antenna array. This array is further utilized to form the proposed $4\times 4$ (16-elements) array configuration to boost the overall realized gain of the antenna. The final antenna system design achieved 34.5% and 16.8% bandwidths, with maximum gains of 9.1 dBi and 19.0 dBi for the sub-6 and mm-wave bands, respectively. Additionally, the design demonstrates excellent isolation, with values exceeding 73 dB at 5.2 GHz and 62 dB at 28 GHz. The proposed design’s main innovation is the concept of microwave filtenna integration with a high gain mm-wave antenna array. While existing research mainly focuses on dual-band operation design, this study highlights the importance of the lower band filtering effect for enhanced isolation and harmonic suppression, as well as the high-gain mm-wave radiation required for long-distance communication. Other innovative aspects of this work include independent control of each band, a reduced overall size, and a wide bandwidth.}, archive = {J_ACCESS}, author = {Mohamed Sedigh Bizan and Tayeb A. Denidni}, doi = {10.1109/ACCESS.2025.3577105}, journal = {IEEE Access}, pages = {98909-98917}, shortjournal = {IEEE Access}, title = {Sub-6 GHz filtenna integration with mm-wave dielectric resonator antenna array for 5G applications}, volume = {13}, year = {2025}, }
• (2025). Navigating IoT security: Architectures, emerging threats, and adaptive countermeasures. ACCESS, 13, 98888-98908. (www)

  @article{ , abstract = {The Internet of Things (IoT) has revolutionized modern life, yet its interconnected nature poses significant security challenges. This survey investigates threats and countermeasures associated with IoT ecosystems, categorizes threats targeting network infrastructure, devices, and data security, and provides a taxonomy to understand vulnerabilities. By analyzing IoT architectures—including Cloud, Fog, and Edge—the study highlights how each architecture presents distinct vulnerabilities when integrated with IoT systems. It explores research on several countermeasures, including intrusion detection systems, secure authentication protocols, blockchain technology, and machine learning integration, evaluating both their advantages and limitations. Through a systematic review of recent literature, the survey identifies articles that contribute to IoT security understanding.The study advocates for lightweight, adaptable security measures that can be applied across diverse architectures and emphasizes the importance of standardized protocols through collaborative efforts among stakeholders. By mapping the current IoT security landscape, this survey guides future research and development toward a more secure and resilient IoT ecosystem, emphasizing the need for both innovation and standardization. Additionally, it critically evaluates the effectiveness of existing solutions and provides actionable insights into addressing the evolving security challenges within IoT environments.}, archive = {J_ACCESS}, author = {Farah Kandah and Thilina Mendis and Lalith Medury and Hemant Sherawat and Haofan Wang}, doi = {10.1109/ACCESS.2025.3576355}, journal = {IEEE Access}, pages = {98888-98908}, shortjournal = {IEEE Access}, title = {Navigating IoT security: Architectures, emerging threats, and adaptive countermeasures}, volume = {13}, year = {2025}, }
• (2025). Enhancing the sustainability of machine learning-based malware detection techniques for android applications. ACCESS, 13, 98876-98887. (www)

  @article{ , abstract = {The rapid increase in smartphone usage has led to a corresponding rise in malicious Android applications, making it important to develop efficient and sustainable malware detection methods that maintain high accuracy. This paper presents a two-stage machine learning approach aimed at improving both detection accuracy and sustainability in Android malware classification. The first stage estimates the release year of an app using its SDK version information, while the second stage classifies apps as benign or malicious through a weighted voting mechanism applied to year-specific malware detection models. This method balances the high accuracy of retraining with reduced computational overhead, delivering robust and scalable malware detection. Using a dataset spanning 2014 to 2023, we evaluate the performance of the proposed method in comparison to retraining-based and incremental learning-based approaches. Experimental results demonstrate that while the retraining-based method achieves the highest accuracy and F1 score, it incurs a significant increase in training time. Conversely, the incremental learning-based method offers lower accuracy but reduced training time. Our two-stage model-based classification method effectively balances these trade-offs, providing accuracy comparable to the retraining-based approach while maintaining stable training times and moderate model sizes, making it a viable option for sustainable malware detection in real-world environments. Future research will explore non-machine-learning-based release year prediction methods to further optimize training efficiency and improve adaptability to the rapidly evolving malware detection landscape.}, archive = {J_ACCESS}, author = {Seyeon Park and Hojun Lee and Daeun Kim and Hyeun Jun Moon and Seong-Je Cho and Youngsup Hwang and Hyoil Han and Kyoungwon Suh}, doi = {10.1109/ACCESS.2025.3576733}, journal = {IEEE Access}, pages = {98876-98887}, shortjournal = {IEEE Access}, title = {Enhancing the sustainability of machine learning-based malware detection techniques for android applications}, volume = {13}, year = {2025}, }
• (2025). Conditional generation of building bubble diagrams based on stochastic differential equations. ACCESS, 13, 98860-98875. (www)

  @article{ , abstract = {This study introduces a novel conditional generative model based on stochastic differential equations (SDEs) for synthesizing architectural bubble diagrams that meet specific customer requirements. The forward SDE progressively injects noise into the data, transforming it into a tractable prior distribution, while the reverse SDE removes the noise to reconstruct the original data distribution. Since the reverse SDE relies on the gradient of the data distribution (i.e., the score function), we employ a neural network to approximate these gradients. The trained score-based model enables conditional sampling from pure noise to generate new diagrams. To evaluate the quality of the generated outputs, we propose an effective metric tailored to conditionally generated graphs. Experimental results demonstrate that the proposed framework produces high-quality diagrams that adhere to specified structural constraints.}, archive = {J_ACCESS}, author = {Zhiwen Wei and Joonki Lee and Hyeongmo Gu and Seungyeon Choo and Jaeil Kim}, doi = {10.1109/ACCESS.2025.3571825}, journal = {IEEE Access}, pages = {98860-98875}, shortjournal = {IEEE Access}, title = {Conditional generation of building bubble diagrams based on stochastic differential equations}, volume = {13}, year = {2025}, }
• (2025). Energy-based exploration for reinforcement learning of underactuated mechanical systems. ACCESS, 13, 98847-98859. (www)

  @article{ , abstract = {Reinforcement Learning (RL) for underactuated mechanical systems presents unique challenges due to the limited control inputs and complex dynamics of the system. Efficient exploration poses a significant problem for two reasons: First, these systems must start each episode of learning from an equilibrium position instead of a random position, severely limiting exploration. Second, these systems are often chaotic, which limits model-based exploration. To address these challenges, we introduce Energy-based Exploration for Reinforcement Learning (EBERL), which exploits a model of the system’s energy represented by a Deep Lagrangian Network (DeLaN), coupled to an energy-based controller to ensure goal-directed exploration on top of the exploration principles that are already present in RL. First, we validated our method in simulation on a Cartpole, Furuta pendulum and Pendubot with challenging torque constraints that cannot be solved by traditional methods. Additionally, we experimentally validated our method on the swing-up and stabilization of a Quanser QUBE2 Furuta pendulum.}, archive = {J_ACCESS}, author = {Jeroen Taets and Tom Lefebvre and Frederik Ostyn and Guillaume Crevecoeur}, doi = {10.1109/ACCESS.2025.3576394}, journal = {IEEE Access}, pages = {98847-98859}, shortjournal = {IEEE Access}, title = {Energy-based exploration for reinforcement learning of underactuated mechanical systems}, volume = {13}, year = {2025}, }
• (2025). Secure scan architecture for enhanced testability and resistance against side-channel attacks in cryptographic ICs. ACCESS, 13, 98837-98846. (www)

  @article{ , abstract = {Traditional scan-based Design for Testability (DFT) techniques give full visibility of internal nodes, which makes them vulnerable to side-channel attacks where the confidential data is compromised. These attacks exploit physical attributes of power, timing, electromagnetic radiation, and fault responses, which threaten intellectual property, secret keys, and chip information. This paper addresses these vulnerabilities by introducing a novel secure scan architecture that protects against scan-based attacks. To ensure robust security from side-channel attacks, brute force key attacks, and unauthorized access, the proposed architecture utilizes Linear Feedback Shift Registers (LFSRs), shadow registers, and key-based authentication mechanisms. A thorough evaluation of the countermeasures and a detailed comparison of Power, Performance, and Area (PPA) metrics versus Quality of Results (QoR) metrics such as coverage, area, runtime, and security checks, is presented. The proposed design attains high fault coverage of ~99.99%, reduces test patterns by ~1.81x, and lowers run time by ~5.45x. Moreover, the power consumption is reduced by ~50.9% while the area overhead is kept below ~2.17% to achieve resource-efficient implementation. Architectural scalability analysis shows the flexibility of the architecture in terms of supporting different scan chain lengths, improved fault detection, and energy efficiency. These results prove the proposed secure scan architecture to be a very effective and resource-optimized solution for advanced cryptographic and integrated circuit applications with a superior tradeoff between security, testability, and efficiency.}, archive = {J_ACCESS}, author = {A. Swetha Priya and S. Kamatchi and E. Lakshmi Prasad}, doi = {10.1109/ACCESS.2025.3575442}, journal = {IEEE Access}, pages = {98837-98846}, shortjournal = {IEEE Access}, title = {Secure scan architecture for enhanced testability and resistance against side-channel attacks in cryptographic ICs}, volume = {13}, year = {2025}, }
• (2025). Daily crude oil prices forecasting using a novel hybrid time series technique. ACCESS, 13, 98822-98836. (www)

  @article{ , abstract = {This paper introduces a new hybrid time series forecasting technique to obtain an efficient and accurate daily crude oil prices forecast. The proposed hybrid technique combines the features of various regression, time series, and machine learning models to improve forecast accuracy. First, it involved processing the original crude oil prices time series to address missing values, variance stabilization, and normalization. Second, it divides the purified crude oil price time series into two major parts: the nonlinear long-term trend (long-run fluctuations) and the residual (short-term fluctuations). The secular non-linear trend series is modeled using various regression models, including linear, spline, lowess, and smoothing spline regression models. On the other hand, the short-run random part is modeled and forecasted using four benchmark time series (linear and non-linear AR, ARMA, ESM) and four machine learning (Neural network autoregressive, Random forest, support vector regression with polynomial and radial basal functions) models. The forecast from both parts is summed to obtain the final forecasting results. The proposed hybrid time series method builds a strong emphasis on effectively measuring the nonlinear long-term trend, which has been disregarded in prior works. A variety of evaluation metrics are employed to evaluate the proposed effectiveness. The testing findings demonstrate the hybrid forecasting technique’s accuracy and efficacy. Specifically, an MAE of 1.28106, an RMSE of 1.59259, a PCC of 0.94158, and a DS of 0.82149 are obtained for the Brent and WTI oil markets when the Lowess regression and NPAR models are combined. With regard to WTI oil prices in particular, the model’s resilience in producing precise forecasts is further demonstrated by the lowest MAE of 1.25730, RMSE of 1.55390, and PCC of 0.93890.}, archive = {J_ACCESS}, author = {Hasnain Iftikhar and Moiz Qureshi and Paulo Canas Rodrigues and Muhammad Usman Iftikhar and Javier Linkolk López-Gonzales and Hasnain Iftikhar}, doi = {10.1109/ACCESS.2025.3574788}, journal = {IEEE Access}, pages = {98822-98836}, shortjournal = {IEEE Access}, title = {Daily crude oil prices forecasting using a novel hybrid time series technique}, volume = {13}, year = {2025}, }
• (2025). A novel dilated convolution light weight neural network (DCLW-NN) for the classification of breast thermograms. ACCESS, 13, 98806-98821. (www)

  @article{ , abstract = {Breast cancer poses lots of challenges in the medical fraternity worldwide. Hence, it requires early diagnosis to treat the affected people. In this paper, we have presented a novel Dilated Convolution Light Weight Neural Network (DCLW-NN) to analyze the breast thermograms. Convolutional Neural Network (CNN) is a deep learning model to process the hierarchical feature of visual data to conduct image classification. DCLW-NN is a light weight architecture that uses dilation principle in standard convolution to reduce the model parameters and maintain accuracy. To reduce system complexity, dilated Convolution enables the integration of information from a wider range without the need for a large kernel. The proposed model is experimented with two different infrared thermography imaging datasets like DMR-IR and Proprietary Datasets. The model is tested under four cases by training and testing with similar and dissimilar datasets and obtain the classification accuracy. To solve the problem of data scarcity, the model is worked out with datasets at various augmentation level and their performance is compared. Lastly, the categorization layer of the CNN model is replaced with Support Vector Machine(SVM) classifier with L2 regularization and found the prospects of DCLW-NN Classifier. The significant improvement of the model is obtained by varying the hyper tuning parameters of the model. The classifier performance of the proposed model was compared with existing architecture like RESNET-50, AlexNet, VGG-16 and it was found that it produces better accuracy of 99%, Sensitivity of 100% and Specificity of 98%.}, archive = {J_ACCESS}, author = {S. Malathi and A. Shenbagavalli and N. Sriraam}, doi = {10.1109/ACCESS.2025.3572294}, journal = {IEEE Access}, pages = {98806-98821}, shortjournal = {IEEE Access}, title = {A novel dilated convolution light weight neural network (DCLW-NN) for the classification of breast thermograms}, volume = {13}, year = {2025}, }
• (2025). Data compression and encryption fusion: A review of hybrid techniques for secure and efficient online transmission. ACCESS, 13, 98791-98805. (www)

  @article{ , abstract = {With the exponential growth of digital data, ensuring efficient and secure online file transmission has become a significant challenge. This study presents a comprehensive analysis of hybrid compression-encryption techniques, evaluating their impact on transmission efficiency, computational complexity, and security strength. Various lossless compression methods, including Huffman Coding, Lempel-Ziv-Welch (LZW), and Burrows-Wheeler Transform (BWT), are examined alongside encryption schemes such as Advanced Encryption Standard (AES), Rivest Cipher 4 (RC4), and Elliptic Curve Cryptography (ECC). The performance of these techniques is assessed based on key metrics, including compression ratio, encryption and decryption time, and security strength. The results indicate that BWT achieves the highest compression ratio of 55% but incurs longer encryption times (20.3 ms), while RC4 provides the fastest encryption speed (8.3 ms) at the cost of lower security strength. ECC, despite its higher encryption time of 18.9 ms, ensures the highest security level. Additionally, this study explores the effects of compression on encryption efficiency, showing that LZW-compressed data reduces encryption time by approximately 20% compared to uncompressed data. The findings provide valuable insights into optimizing hybrid compression-encryption solutions for secure and efficient online data transmission, emphasizing the need for a balanced approach between security, computational efficiency, and data compression.}, archive = {J_ACCESS}, author = {Lei Zhao and Junhua Deng and Yimin Wang and Yulong Ma and Peng Lu}, doi = {10.1109/ACCESS.2025.3575428}, journal = {IEEE Access}, pages = {98791-98805}, shortjournal = {IEEE Access}, title = {Data compression and encryption fusion: A review of hybrid techniques for secure and efficient online transmission}, volume = {13}, year = {2025}, }
• (2025). IoT BotScan: Ultra-lightweight AI defense against botnet threats. ACCESS, 13, 98771-98790. (www)

  @article{ , abstract = {This research study examines the effectiveness of Deep Learning (DL) and Machine Learning (ML) algorithms in identifying BotNet attacks within network infrastructures. Various algorithms, including Random Forests (RF), Decision Trees (DT), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were evaluated using the N-BaIoT dataset, which encompasses multiple BotNet attack types. The findings indicate that ML models, specifically RF and K-Nearest Neighbors (KNN), demonstrate superior performance in terms of recall, precision, and overall accuracy compared to DL methods, with KNN achieving the highest accuracy of 99.94%, followed by RF and, ETC (99.55%). Among DL models, CNN has the highest accuracy (86.20%). The study focuses on multiclass classification (11 classes) and employs 10-fold cross-validation to enhance model robustness and mitigate overfitting. Notably, the research emphasizes the significance of feature engineering and selection techniques in enhancing model performance by identifying critical indicators of BotNet activity. A key contribution of this study is the lightweight RF approach, which minimizes the number of features to reduce complexity. The insights gained from this research provide valuable information for academics and cybersecurity practitioners, aiding in the development of proactive defenses against evolving BotNet threats and promoting the advancement of resilient security measures for network infrastructures.}, archive = {J_ACCESS}, author = {Sapna Sadhwani and Urvi Kavan Modi and Raja Muthalagu and Pranav M. Pawar and Alavikunhu Panthakkan and Wathiq Mansoor}, doi = {10.1109/ACCESS.2025.3574355}, journal = {IEEE Access}, pages = {98771-98790}, shortjournal = {IEEE Access}, title = {IoT BotScan: Ultra-lightweight AI defense against botnet threats}, volume = {13}, year = {2025}, }
• (2025). Robust predictive control of PMSM via dual-loop parameter-disturbance adaptation. ACCESS, 13, 98759-98770. (www)

  @article{ , abstract = {To address the control performance degradation caused by parameter mismatches and external disturbances in permanent magnet synchronous motor (PMSM) model predictive control, this study proposes a composite predictive control strategy integrating online parameter identification and disturbance observation. By constructing an incremental motor model, a forgetting factor-based recursive least squares (FF-RLS) method is designed to dynamically identify stator resistance, inductance, and permanent magnet flux linkage parameters in real time, enabling adaptive correction of the predictive model to suppress parameter drift effects. Innovatively, a third-order extended state observer (ESO) is introduced to uniformly model unmodeled dynamics, load torque disturbances, and residual parameter errors as a total disturbance term for estimation, which is actively compensated through a feedforward voltage reference injection mechanism. The core innovation lies in the synergistic optimization framework of parameter identification and disturbance compensation: A dynamic coupling framework for parameter updating and observer bandwidth adjustment is established based on Lyapunov stability theory, ensuring uniform boundedness of parameter estimation errors and disturbance observation errors. Simultaneously, voltage feedforward compensation achieves decoupled control of predictive model errors. Results demonstrate that the proposed strategy significantly enhances system robustness against parameter uncertainties and external disturbances by dynamically updating model parameters and proactively suppressing composite disturbances. While preserving the fast dynamic response of model predictive control, it effectively resolves steady-state errors and harmonic distortions induced by parameter mismatches, offering a theoretically rigorous and practically viable solution for high-precision motor drive systems.}, archive = {J_ACCESS}, author = {Ran Zu and Nenghui Jiang and Yanhui Huang and Dong Xu}, doi = {10.1109/ACCESS.2025.3577056}, journal = {IEEE Access}, pages = {98759-98770}, shortjournal = {IEEE Access}, title = {Robust predictive control of PMSM via dual-loop parameter-disturbance adaptation}, volume = {13}, year = {2025}, }
• (2025). Federated learning model using CBDPL for medical image segmentation. ACCESS, 13, 98747-98758. (www)

  @article{ , abstract = {Federated learning (FL) allows hospitals and medical centers to train models without sharing patient data. Instead of sending data to a central server, each institution trains the model locally and only shares updates, keeping patient information private. Our study proposes a novel methodology for medical image segmentation that leverages semi-supervised learning and confidence-based dynamic pseudo-labeling (CBDPL) to enhance model performance with limited labeled data. Each participating client site independently performs feature extraction and basic segmentation tasks. For labeled data, segmentation is guided by a ground-truth comparison and loss minimization. For unlabeled data, semi-supervised learning is applied, with a dynamic pseudo-labeling module that assigns pseudo-labels based on confidence scores, integrating only high-confidence predictions to refine the model iteratively. Client updates are then combined on a central server. It helps to improve the overall model while keeping each client’s data private. This approach demonstrates the potential for robust, privacy-preserving segmentation in multi-dataset medical image analysis settings, addressing labeled data scarcity and privacy concerns.}, archive = {J_ACCESS}, author = {K. Hemalatha and Shradhanjali Das}, doi = {10.1109/ACCESS.2025.3576875}, journal = {IEEE Access}, pages = {98747-98758}, shortjournal = {IEEE Access}, title = {Federated learning model using CBDPL for medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). A real-time ETP outlet monitoring framework leveraging environmental IoT, colorimetry, and learning theory. ACCESS, 13, 98729-98746. (www)

  @article{ , abstract = {Industries such as textiles and ready-made garments (RMG) contribute significantly to industrial waste and effluent, leading to severe environmental pollution. Effluent treatment plants (ETPs) are designed to treat and recycle this wastewater, aiming to remove suspended solids before discharge. However, many factories worldwide either operate without ETPs or fail to maintain them consistently due to high energy consumption, resulting in the release of untreated waste and posing serious environmental risks. This research aims to establish a system for real-time monitoring of water quality outside industrial sites, enabling the determination of ETP operational status and providing an automated monitoring solution. This research utilizes IoT (Internet of Things)–based real-time monitoring to enhance industrial wastewater management. A Convolutional Neural Network (CNN) is used for video-based binary classification to detect the On/Off state of ETPs. K-Nearest Neighbor (KNN) classifies water quality based on sensor data, and Long Short-Term Memory (LSTM) forecasts seasonal impacts on water quality, including inactive periods. Additionally, colorimetry analysis is employed to monitor watercolor variations as potential indicators of contamination. The proposed system achieves accuracy of 98.3%, 97%, and 94.9% for CNN, KNN, and LSTM, respectively, offering a practical, low-cost solution for remote ETP monitoring. Furthermore, it holds promise for broader applications in environmental compliance, with potential benefits for the agriculture and nutrition sectors by minimizing industrial pollution and supporting sustainability efforts.}, archive = {J_ACCESS}, author = {Md. Jahid Hasan Mridha and Md. Mehedi Hossain and Sazid Md. Imran and Sk Ayub Al Wahid and Anika Tahsin and Md. Golam Rabiul Alam and Ashis Talukder and Ahmed Wasif Reza}, doi = {10.1109/ACCESS.2025.3576826}, journal = {IEEE Access}, pages = {98729-98746}, shortjournal = {IEEE Access}, title = {A real-time ETP outlet monitoring framework leveraging environmental IoT, colorimetry, and learning theory}, volume = {13}, year = {2025}, }
• (2025). Gamification and artificial intelligence in education: A review of innovative approaches to fostering critical thinking. ACCESS, 13, 98699-98728. (www)

  @article{ , abstract = {Gamification and artificial intelligence (AI) are transforming modern education by increasing student motivation, supporting personalized learning, and fostering the development of critical thinking. In the context of rapid digitalization, analytical skills and the ability to evaluate information have become essential competencies across all educational levels. This article presents a bibliometric analysis of 101 academic publications that explore the integration of game-based learning and AI technologies into educational environments. The study identifies dominant research themes, methodological trends, and the distribution of publications in educational sectors, including schools, universities, and corporate training. It also examines how these technologies are being used to improve cognitive engagement and support the development of independent decision-making. The results highlight the growing global interest in applying gamification and AI to improve learning outcomes. This work provides evidence-based information for the development of adaptive educational platforms and instructional strategies aimed at cultivating critical thinking and enhancing student engagement. The findings are particularly relevant for educators, policymakers, and developers working at the intersection of education and technology.}, archive = {J_ACCESS}, author = {Aray M. Kassenkhan and Aiman N. Moldagulova and Vasiliy V. Serbin}, doi = {10.1109/ACCESS.2025.3576147}, journal = {IEEE Access}, pages = {98699-98728}, shortjournal = {IEEE Access}, title = {Gamification and artificial intelligence in education: A review of innovative approaches to fostering critical thinking}, volume = {13}, year = {2025}, }
• (2025). Big data meets social networks: A survey of analytical strategies and research challenges. ACCESS, 13, 98668-98698. (www)

  @article{ , abstract = {As the big data era transforms the information analysis landscape, social network (SN) analytics has emerged as a critical discipline to understand complex relationships and interactions within enormous social systems. This study aims to provide a comprehensive overview of SN analytics as big data, providing an in-depth exploration of concepts, methods, and applications within this rapidly evolving field. The survey covers a wide range of topics, including methods for pre-processing and cleaning social network data, as well as more advanced analytical approaches such as opinion mining, sentiment analysis, text mining, and natural language processing. It also discusses various big data architectures for SN analytics, like Lambda architecture, Kappa architecture, and GraphX architecture. In addition, it covers issues of scalability, data quality, privacy, and real-time analysis. SN analytics can be useful for researchers from across domains like business and marketing, healthcare and biomedicine, social sciences and psychology, politics and governance, cybersecurity and fraud detection, recommendation systems, crisis management, and disaster response to extract crucial information from vast datasets and formulate domain-specific strategies.}, archive = {J_ACCESS}, author = {Shashank Sheshar Singh and Shashank Singh and Kuldeep Singh and Vishal Srivastava and Harish Kumar Shakya}, doi = {10.1109/ACCESS.2025.3576143}, journal = {IEEE Access}, pages = {98668-98698}, shortjournal = {IEEE Access}, title = {Big data meets social networks: A survey of analytical strategies and research challenges}, volume = {13}, year = {2025}, }
• (2025). An intelligent contract-driven bidding approach for electric vehicle aggregators to facilitate blockchain-powered energy trading. ACCESS, 13, 98656-98667. (www)

  @article{ , abstract = {The integration of aggregators into the market and peer-to-peer energy trading of electric vehicles achieved through blockchain technology represents one of the potential ways to ensure peer privacy preservation, transaction trackability, and greater energy system flexibility. This paper presents a decentralized blockchain-powered smart contract-based peer-to-peer model to enable optimal energy trading coordination of electric vehicles with aggregators in the smart community, where revenue from feed-in tariffs, ancillary services, and peer-to-peer sales are optimized, and the cost is minimized using a mixed-integer linear programming formulation. The goal of the real-time bilateral trading scheme is to balance supply and demand within the constraints of the power network through direct peer-to-peer negotiations without the need for a middleman. The performance evaluation of the proposed scheme demonstrates how well the framework synchronizes power supply and demand by coordinating electric vehicles’ charging and discharging through an appropriate aggregator by consumption patterns. The development of smart contracts for initiation, enrollment, stay-alive negotiation, trading, and settlement of transactions is presented in the subsequent demonstration of the blockchain model’s distinctive establishment process. Furthermore, the transactional efficiency depicting the execution time, latency, and throughput performance for viability and scalability evaluations in practical applications is presented to illustrate the effectiveness of the proposed framework.}, archive = {J_ACCESS}, author = {Imran Hussain and Hafiz Ashiq Hussain and Nasim Ullah and Stanislav Misak}, doi = {10.1109/ACCESS.2025.3576593}, journal = {IEEE Access}, pages = {98656-98667}, shortjournal = {IEEE Access}, title = {An intelligent contract-driven bidding approach for electric vehicle aggregators to facilitate blockchain-powered energy trading}, volume = {13}, year = {2025}, }
• (2025). Explainable artificial intelligence in malignant lymphoma classification: Optimized DenseNet121 deep learning approach with particle swarm optimization and genetic algorithm. ACCESS, 13, 98639-98655. (www)

  @article{ , abstract = {One of the forms of cancerous tumors that can be fatal is malignant lymphoma. Histopathological examination of lymphoma tissue images is a diagnostic technique for detecting malignant lymphomas. Differentiating lymphoma subtypes manually is challenging due to their similar morphological features. This paper introduces a cutting-edge methodology for classifying malignant lymphoma using Hematoxylin and Eosin (H&E)-stained biopsy images. It integrates feature reduction using Particle Swarm Optimization (PSO) and model optimization through a Genetic Algorithm (GA) in conjunction with DenseNet121 architecture. The DenseNet121 model, which employs four distinct freezing strategies, extracts features from the images, generating an initial 4096 features per image. These features are subsequently reduced by more than 54.4% using PSO, thereby enhancing the computational efficiency without sacrificing classification accuracy. The trained GA Deep Neural Network (DNN) model achieved a significant score of 96.77% testing accuracy with high precision, recall and F1 scores as well. The results after comparison with other existing state-of-the-art models showed the usefulness of the proposed approach in terms of performance as well as the consumption of resources for medical imaging classification. It is recommended that the combined application of PSO for feature reduction and GA for model optimization can be successfully used for improving accuracy rate of such algorithms while reducing computation time. This technique is expected to be further developed and applied in different tasks of computer aided diagnosis of malignant lymphoma in particular the tasks with requirements for explainable artificial intelligence.}, archive = {J_ACCESS}, author = {Haitham ELwahsh and Ali Bakhiet and Omar Ibrahim Alirr and Tarek Khalifa and Maazen Alsabaan and Mohamed I. Ibrahem and Engy El-Shafeiy}, doi = {10.1109/ACCESS.2025.3575364}, journal = {IEEE Access}, pages = {98639-98655}, shortjournal = {IEEE Access}, title = {Explainable artificial intelligence in malignant lymphoma classification: Optimized DenseNet121 deep learning approach with particle swarm optimization and genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Effects of SBAS-InSAR deformation integration methods and machine learning model selection on landslide susceptibility mapping. ACCESS, 13, 98622-98638. (www)

  @article{ , abstract = {Landslide susceptibility mapping (LSM) is effective in revealing the spatial distribution and probability of landslides. Its accuracy is crucial for minimizing casualties and property loss. However, current LSM methods primarily rely on static indicators, that do not consider the significant surface deformation during landslide events, resulting in inadequate reliability of the assessment results. This study aims to enhance the accuracy and reliability of landslide susceptibility assessment by integrating surface deformation data extracted using Small Baseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology with static LSM results. We selected Jinping Autonomous County as the study area and performed a landslide susceptibility evaluation based on 11 static evaluation factors using three machine learning models: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Comparative analysis of the results indicated that RF and XGBoost exhibited higher accuracy in susceptibility evaluations. Joint training and the weighted sum method were used to integrate the assessment results of the RF and XGBoost models with surface deformation rates. Four dynamic landslide susceptibility models (joint training RF (JTRF), weighted sum RF (WSRF), joint training XGBoost (JTXGBoost), and weighted sum XGBoost (WSXGBoost)) were developed for LSM. The results demonstrate that integrating dynamic surface deformation data with static LSM outcomes generates more reasonable and accurate landslide susceptibility maps. This research provides valuable insights for optimizing the integration of surface deformation data with static LSM results and a scientific basis for governments to identify and manage high-risk areas, increasing the capacity for landslide disaster prevention and response.}, archive = {J_ACCESS}, author = {Zhen Yang and Xiangchao Jiang and Meinan Zheng and Qingbiao Guo}, doi = {10.1109/ACCESS.2025.3576854}, journal = {IEEE Access}, pages = {98622-98638}, shortjournal = {IEEE Access}, title = {Effects of SBAS-InSAR deformation integration methods and machine learning model selection on landslide susceptibility mapping}, volume = {13}, year = {2025}, }
• (2025). Enhancing semantic forestry segmentation through advanced preprocessing with ML models. ACCESS, 13, 98602-98621. (www)

  @article{ , abstract = {This study explores the application of advanced artificial intelligence based preprocessing techniques to improve semantic forestry segmentation. The research investigates the performance of You Only Look Once version 8 (YOLOv8) from Ultralytics, Detectron2 from Meta, and the Segment Anything Model (SAM) from Meta, with the goal of enhancing segmentation accuracy and detail in forest environments. Goal is accomplished by combining models into a pipeline along with machine-larning-based preprocessing models. The integration of SAM and pre-processing steps significantly enhanced the quality and number of segmentation masks, resulting in more accurate and detailed representations of complex forest structures compared to the ground truth. The findings highlight the importance of combining AI models with effective preprocessing strategies to navigate the complexities of forest environments, offering valuable insights for future advancements in semantic forestry segmentation.}, archive = {J_ACCESS}, author = {Krzysztof Wołk and Jacek Niklewski and MichaŁ KOPCZYŃSKI and Marek S. Tatara and Oleg Żero}, doi = {10.1109/ACCESS.2025.3575713}, journal = {IEEE Access}, pages = {98602-98621}, shortjournal = {IEEE Access}, title = {Enhancing semantic forestry segmentation through advanced preprocessing with ML models}, volume = {13}, year = {2025}, }
• (2025). H∞ dynamic output feedback control for asynchronously switched systems: Dwell time dependent control method. ACCESS, 13, 98592-98601. (www)

  @article{ , abstract = {This study focuses on the dynamic output feedback control for asynchronously switched systems with a dwell time dependent control scheme. By combining asynchronous switching with mode-dependent hybrid dwell time switching signal, a novel asynchronously control strategy is proposed, and the controller gain can be dynamically adjusted according to the controller-running interval and switching signal, which reduces the conservatism of the existing results. Firstly, grounded in the constructed Lyapunov function, a stability and $\mathcal {L}_{2}$ -gain criterion is presented for the considered system. Then, an arrangement of mode-dependent dynamic output feedback controllers and switching signal are jointly designed to ensure the global uniform exponential stability of switched systems with ${\mathcal {H}}_{\infty } $ performance. At the conclusion, a simulation example is provided to demonstrate the strengths and practicality of the proposed method.}, archive = {J_ACCESS}, author = {Fugui Deng and Haoxu Wang and Jiandong Guo and Boyu Wang and Yongqing Fu and Weizhong Chen}, doi = {10.1109/ACCESS.2025.3576834}, journal = {IEEE Access}, pages = {98592-98601}, shortjournal = {IEEE Access}, title = {H∞ dynamic output feedback control for asynchronously switched systems: Dwell time dependent control method}, volume = {13}, year = {2025}, }
• (2025). Xception spiking fractional neural network for oral squamous cell carcinoma classification based on histopathological images. ACCESS, 13, 98574-98591. (www)

  @article{ , abstract = {Oral Squamous Cell Carcinoma (OSCC) is the leading cancer caused due to the immense growth of malignant cells in different regions of the oral cavity particularly in the area of the neck and head. The habits of smoking, betel nuts, and chewing tobacco that mainly affect the pharynx, nasopharynx, viral infection, and oral cavity regions are the major causes of OSCC. OSCC has to be detected at the initial stages to provide accurate treatment and to assess the severity. Thus, various screening and detection models are used to accurately detect OSCC with great significance for reducing morbidity and mortality. In this research, a novel deep learning model Xception Spiking Fractional Neural Network (XSFN-Net) is introduced to classify OSCC from the histopathology images. Here, the histopathology images are enhanced initially using the Medav filter. The pixel-wise image segmentation is performed using Parallel Reverse Attention Network (PraNet) along with the designed loss function from the enhanced image. Later, the extraction of relevant features from the segmented output and the OSCC is finally classified using XSFN-Net from the resultant extracted features. The XSFN-Net attained high performance with True Positive Rate (TPR) of 96.604%, accuracy of 97.538%, and True Negative Rate (TNR) of 95.545%.}, archive = {J_ACCESS}, author = {Singaraju Ramya and R. I. Minu and K.T. Magesh}, doi = {10.1109/ACCESS.2025.3574327}, journal = {IEEE Access}, pages = {98574-98591}, shortjournal = {IEEE Access}, title = {Xception spiking fractional neural network for oral squamous cell carcinoma classification based on histopathological images}, volume = {13}, year = {2025}, }
• (2025). Gradient boosting-based simultaneous classification and regression approach. ACCESS, 13, 98557-98573. (www)

  @article{ , abstract = {The recent development of advanced data analytics and machine learning promoted the introduction of diverse learning techniques designed to alleviate challenges related to two major supervised learning tasks: (i) Classification and (ii) Regression. Despite the successful consideration of these tasks to address problems relevant to various applications, the design of dual machine learning approaches that perform both tasks simultaneously remains challenging. This paper proposes a Dual-learning XGBoost Based Approach (DXGBA), a novel gradient boosting-based machine learning approach that concurrently carries out classification and regression tasks. Specifically, the proposed approach aims at learning a supervised learning model that is jointly trained for both classification and regression using a single unified framework. The optimization of this dual task is formulated using a novel joint cost function that minimizes the total error of both classification and regression tasks during the learning phase. The proposed approach was assessed using benchmark datasets and performance measures. The experiments proved its effectiveness compared to native eXtreme Gradient Boosting (XGBoost) models. DXGBA achieved an F1-score of 0.4718 and an MSE of 0.0002 on the bankruptcy dataset, outperforming the single-task XGBoost, which yielded 0.3942 and 0.0003, respectively. Moreover, DXGBA reduced testing time by up to 81.9% compared to baseline methods. It also evidenced better performance relative to conventional machine learning models.}, archive = {J_ACCESS}, author = {Rawabi Alwanin and Ouiem Bchir and Mohamed Maher Ben Ismail}, doi = {10.1109/ACCESS.2025.3576086}, journal = {IEEE Access}, pages = {98557-98573}, shortjournal = {IEEE Access}, title = {Gradient boosting-based simultaneous classification and regression approach}, volume = {13}, year = {2025}, }
• (2025). TinySurveillance: An extra low-power event-based surveillance method for UAVs. ACCESS, 13, 98542-98556. (www)

  @article{ , abstract = {Uncrewed Aerial Vehicles (UAVs) have always been faced with power management challenges to extend their flight time. Managing power consumption becomes critical, especially in surveillance applications, where the longer flight time results in wider coverage and a cheaper solution. Most of the current studies show new methods for event detection without considering power consumption. This article presents an event-driven four-stage video surveillance pipeline with an efficient video transmission algorithm balancing power consumption and image quality. The surveillance starts automatically when the low-power AI-based onboard processor detects the desired event. When The edge node detects the defined event, a sample image is sent to the server for validation. After validation, a colored image accompanied by N grayscale images are sent to the server. The server colorizes the grayscale images using a convolutional neural network trained by the colored images. In this work, an application of wildfire detection and surveillance has been implemented to show the proof of concept of the TinySurveillance method. The results show that the power consumption of the onboard processing unit in detection mode reduces by at least 4 times; during the surveillance mode, the data transmission rate can be decreased by almost 66% while achieving a competent image quality PSNRAvg of 41.35 dB, PSNR of 30.94 dB, and output frame rate of 5.2.}, archive = {J_ACCESS}, author = {Arash Farahdel and Alimul H. Khan and Hossein Keshmiri and Khan A. Wahid}, doi = {10.1109/ACCESS.2025.3576192}, journal = {IEEE Access}, pages = {98542-98556}, shortjournal = {IEEE Access}, title = {TinySurveillance: An extra low-power event-based surveillance method for UAVs}, volume = {13}, year = {2025}, }
• (2025). A probabilistic adversarial autoencoder for novelty detection: Leveraging lightweight design and reconstruction loss. ACCESS, 13, 98530-98541. (www)

  @article{ , abstract = {A novelty detection task involves identifying whether a data point is an outlier, given a training dataset that primarily captures the distribution of inliers. The novel class is usually absent, poorly sampled, or not well defined in the training data. A common technique for anomaly detection at present is to use an adversarial network generator to generate an anomaly score for inputs using the reconstruction loss. However, because this technique uses a competitive training process, it can be unreliable, with its performance being inconsistent during each adversarial training step. This inconsistency arises from changes in the network’s ability to detect anomalies. In this paper, we propose a revised framework for generative probabilistic novelty detection. We use a similar adversarial autoencoder-based framework but with a lightweight deep network, a novel training paradigm, and a probabilistic score to compute the reconstruction loss. Our methodology calculates the probability of whether a sample comes from the inlier distribution or not. The proposed approach can be applied to anomaly and outlier detection in images and videos. We present the results on multiple benchmark datasets, including the challenging UCSD Ped2 dataset for video anomaly detection. Our results illustrate that our proposed method learns the inlier classes and differentiates them from the outlier classes effectively, leading to better results than the baseline and state-of-the-art methods in several benchmark datasets.}, archive = {J_ACCESS}, author = {Muhammad Asad and Ihsan Ullah and Muhammad Adeel Hafeez and Ganesh Sistu and Michael G. Madden}, doi = {10.1109/ACCESS.2025.3577080}, journal = {IEEE Access}, pages = {98530-98541}, shortjournal = {IEEE Access}, title = {A probabilistic adversarial autoencoder for novelty detection: Leveraging lightweight design and reconstruction loss}, volume = {13}, year = {2025}, }
• (2025). The evolution of generative AI: Trends and applications. ACCESS, 13, 98504-98529. (www)

  @article{ , abstract = {Generative artificial intelligence (AI) has revolutionized AI by enabling high-fidelity content creation across text, images, audio, and structured data. This survey explores the core methodologies, advancements, applications, and ongoing challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. These innovations have driven breakthroughs in healthcare, scientific computing, Natural Language Processing (NLP), computer vision, and autonomous systems. Despite its progress, generative AI faces challenges in bias mitigation, interpretability, computational efficiency, and ethical governance, necessitating research into scalable architectures, explainability, and AI safety mechanisms. Integrating Reinforcement Learning (RL), multi-modal learning, and self-supervised techniques enhances controllability and adaptability in generative models. Additionally, as AI reshapes industrial automation, digital media, and scientific discovery, its societal and economic implications demand robust policy frameworks. This survey provides a comprehensive analysis of generative AI’s current state and future directions, highlighting innovations in efficient generative modelling, AI-driven scientific reasoning, adversarial robustness, and ethical deployment. By consolidating theoretical insights and real-world applications, it offers a structured foundation for researchers, industry professionals, and policymakers to navigate the evolving landscape of generative AI.}, archive = {J_ACCESS}, author = {Maria Trigka and Elias Dritsas}, doi = {10.1109/ACCESS.2025.3574660}, journal = {IEEE Access}, pages = {98504-98529}, shortjournal = {IEEE Access}, title = {The evolution of generative AI: Trends and applications}, volume = {13}, year = {2025}, }
• (2025). A study on using transfer learning to utilize information from similar systems for data-driven condition diagnosis and prognosis. ACCESS, 13, 98485-98503. (www)

  @article{ , abstract = {Prognostics and health management (PHM) is a field of study that aims to diagnose the health of engineering systems and predict their future degradation. In PHM, data-driven methods are among the most popular and empirically validated approaches because they do not require a deep physical understanding of the system properties and degradation behavior. However, training these models typically requires substantial data sets, which are often limited in industry. Additionally, prediction performance is known to degrade when operating and environmental conditions deviate from the training data, or when similar systems with different technical characteristics are considered. Transfer learning aims to address these issues by enabling the transfer of information. The present study analyzes the effectiveness of transfer learning on similar system applications. A rolling bearing and a filter degradation data set are used to evaluate the diagnostic and prognostic performance. The former includes condition data of rolling bearings of different dimensioning, recorded under different operating conditions, and the latter includes degradation data of filters with different filtration areas. Two transfer learning approaches are analyzed: parameter transfer with fine-tuning and retraining, and feature alignment. Both concepts are implemented with the neural network types multilayer perceptron, 1D convolutional neural network, and temporal convolutional network. The analysis demonstrates that current transfer learning approaches can successfully transfer information between similar systems, with improvements exceeding 20 % in some scenarios. However, significant variations in performance are also observed. In certain scenarios, only marginal improvements or even deteriorations are exhibited, revealing prevailing challenges confronting these approaches.}, archive = {J_ACCESS}, author = {Marcel Braig and Peter Zeiler}, doi = {10.1109/ACCESS.2025.3576435}, journal = {IEEE Access}, pages = {98485-98503}, shortjournal = {IEEE Access}, title = {A study on using transfer learning to utilize information from similar systems for data-driven condition diagnosis and prognosis}, volume = {13}, year = {2025}, }
• (2025). Efficient pathfinding on grid maps: Comparative analysis of classical algorithms and incremental line search. ACCESS, 13, 98473-98484. (www)

  @article{ , abstract = {Pathfinding on grid maps is a cornerstone problem in robotics, autonomous navigation, and game development. Classical algorithms such as A*, Dijkstra’s, and Breadth-First Search (BFS) are known for their ability to guarantee optimal solutions, while others like Depth-First Search (DFS) and Greedy Best-First Search prioritize speed over optimality. However, these algorithms can be computationally inefficient in large-scale or real-time environments. This paper introduces Incremental Line Search (ILS), a novel optimization strategy that incorporates Bresenham’s line algorithm to define a dynamically adjustable corridor between the start and goal points. By restricting the search to this focused region and expanding only when necessary, ILS significantly reduces computational overhead while preserving optimality in cases where the underlying algorithm guarantees it, and delivering near-optimal paths in others. Extensive experiments were conducted to evaluate the performance of ILS across various metrics, including execution time, visited nodes, and path length, over a total of 6000 grid maps generated with obstacle densities of 10%, 20%, and 30%. On average, ILS achieved a 87.31% reduction in execution time and a 71.44% reduction in node expansions compared to their standard counterparts. Despite these improvements, path quality was preserved: Best-First Search showed a 63.24% improvement in path length, while DFS improved by 86.70%, confirming that ILS not only accelerates computation but also leads to smoother paths in most scenarios. These findings affirm ILS as a highly effective and resilient optimization technique for demanding pathfinding tasks. Its dynamic adaptability renders ILS especially suitable for applications in robotics, autonomous systems, and resource-constrained environments, setting the stage for future advancements in multi-agent navigation and real-time optimization.}, archive = {J_ACCESS}, author = {Amr Elshahed and Majid Khan Bin Majahar Ali and Ahmad Sufril Azlan Mohamed and Farah Aini Binti Abdullah and Ts. Lee Jian Aun}, doi = {10.1109/ACCESS.2025.3575168}, journal = {IEEE Access}, pages = {98473-98484}, shortjournal = {IEEE Access}, title = {Efficient pathfinding on grid maps: Comparative analysis of classical algorithms and incremental line search}, volume = {13}, year = {2025}, }
• (2025). Characterization of stability regions in the discrete PID gain space via D-partition approach. ACCESS, 13, 98452-98472. (www)

  @article{ , abstract = {In the study of linear control systems, D-partition theory provides a powerful technique for controller design by dividing the controller parameter space into stability regions, within which the number of unstable characteristic roots remains invariant. By re-expressed the discrete PID controller as $G_{pid}\left ({{ z }}\right)=({k_{0}+k}_{1}z+k_{2}\left ({{ 1+z^{2} }}\right))/(z\left ({{ z-1 }}\right))$ , we revisit the problem of characterizing stability regions in the gain space $\mathbf {k} = (k_{0},k_{1},k_{2})$ using the D-partition technique. Specifically, we identify and complete seven types of critical $k_{0}$ points, which divide the $k_{0}$ axis into intervals where the topology of the D-partition of the $(k_{1},k_{2})$ -plane remains invariant. This enables the exact construction of all stability - not merely stable - regions in the three-dimensional gain space. Furthermore, we present a refined algorithm for determining all the critical $k_{0}$ points associated with a discrete PID controller for a given arbitrary-order plant. This algorithm improves upon existing methods in two key aspects: 1) two previously unrecognized types of critical $k_{0}$ points are identified, ensuring the completeness of the characterization; and 2) new mathematical expressions are derived for describing singular D-partition border lines, which eliminates the occurrence of spurious critical $k_{0}$ points. In addition, due to the finite range of the frequency variable in discrete-time systems, the proposed critical $k_{0}$ revelation algorithm is computationally well-suited for revealing all critical proportional gain values $k_{p}$ for the continuous-time PID control, through appropriate use of bilinear s-to-z and z-to-s transformations. To validate the effectiveness of the proposed method, extensive examples are provided.}, archive = {J_ACCESS}, author = {Chun-Chong Fu and Chyi Hwang and Li-Shin Lu}, doi = {10.1109/ACCESS.2025.3574803}, journal = {IEEE Access}, pages = {98452-98472}, shortjournal = {IEEE Access}, title = {Characterization of stability regions in the discrete PID gain space via D-partition approach}, volume = {13}, year = {2025}, }
• (2025). A review of resource allocation for maximizing performance of IoT systems. ACCESS, 13, 98426-98451. (www)

  @article{ , abstract = {Resource allocation is critical for maximizing the performance of Internet of Things (IoT) systems, in which devices with limited resources work together to complete diverse tasks. This paper provides a detailed overview of existing resource management solutions in IoT contexts, with a particular emphasis on AI techniques, heuristic/metaheuristic approaches, 5G/6G, digital twins, and blockchain. We evaluate the potential advantages and limitations of several resource allocation techniques and frameworks suggested in the literature. Our findings illustrate the potential of advanced AI and decentralized approaches to solving critical difficulties in IoT systems, including security and privacy preservation, communication overhead reduction, energy consumption, real-time performance improvement, and scalability enhancement. Furthermore, the ramifications of these discoveries were examined, as well as potential possibilities for future research in this vital field of study.}, archive = {J_ACCESS}, author = {Mahdi Safaei Yaraziz and Richard Hill}, doi = {10.1109/ACCESS.2025.3576716}, journal = {IEEE Access}, pages = {98426-98451}, shortjournal = {IEEE Access}, title = {A review of resource allocation for maximizing performance of IoT systems}, volume = {13}, year = {2025}, }
• (2025). Deep learning in defect detection of wind turbine blades: A review. ACCESS, 13, 98399-98425. (www)

  @article{ , abstract = {The increasing adoption of wind turbines as a key component of renewable energy generation necessitates the development of efficient and reliable maintenance strategies to ensure their optimal performance and safety. Among the most critical aspects of turbine maintenance is detecting and classifying defects in wind turbine blades, which are constantly exposed to extreme environmental conditions. Defects such as cracks, delamination, erosion, and icing not only compromise the structural integrity of blades but also significantly reduce their aerodynamic efficiency and energy production capabilities. While effective, traditional inspection methods are labor-intensive and time-consuming, prompting the exploration of automated solutions. This review paper examines the state-of-the-art deep learning methodologies applied to defect detection in wind turbine blades. Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. Additionally, transfer learning and attention mechanisms have been instrumental in enhancing the precision and speed of defect detection, enabling real-time applications. Notable approaches like YOLO (You Only Look Once) and its variants have shown exceptional performance in detecting defects with varying scales and complexities, leveraging innovations such as feature pyramid networks and efficient loss functions. Furthermore, this review discusses the role of advanced data acquisition techniques, such as drone-based imaging, thermographic analysis, and LiDAR (Light Detection and Ranging), in generating high-resolution and multi-spectral data for improved detection accuracy. Insights are provided into challenges such as class imbalance, limited labeled datasets, and environmental noise, alongside emerging solutions like semi-supervised learning and data augmentation. The paper concludes by emphasizing the transformative potential of deep learning in achieving automated, accurate, and efficient defect detection in wind turbine blades. Addressing current limitations through interdisciplinary research and innovative algorithms will pave the way for sustainable and cost-effective wind energy systems, ultimately contributing to the global transition toward renewable energy.}, archive = {J_ACCESS}, author = {Katleho Masita and Ali N. Hasan and Thokozani Shongwe and Hasan Abu Hilal}, doi = {10.1109/ACCESS.2025.3569799}, journal = {IEEE Access}, pages = {98399-98425}, shortjournal = {IEEE Access}, title = {Deep learning in defect detection of wind turbine blades: A review}, volume = {13}, year = {2025}, }
• (2025). Multi-parameter optimization using metaheuristic algorithms to improve uncrewed aerial vehicles’ wireless communications: A performance analysis. ACCESS, 13, 98374-98398. (www)

  @article{ , abstract = {Uncrewed aerial vehicles (UAVs) require robust communication for operational reliability. Recent research has explored artificial intelligence techniques, particularly metaheuristic algorithms, to address this challenge. However, studies on enhancing communication robustness through metaheuristic multi-parameter optimization along the flight path remain limited. To bridge this gap, we investigate the impact of optimizing five key parameters—carrier frequency, transmit power, modulation scheme, speed, and altitude—on minimizing bit error rate (BER) in UAV communication. This five-parameter optimization is defined as Approach 1. For comparison, a reduced three-parameter optimization is evaluated, excluding speed and altitude, as Approach 2. Seven metaheuristic algorithms were applied to both approaches, and the performance was evaluated via convergence and processing times. All algorithms achieved BERs of $10^{-5}$ or lower. Approach 1 caused significant speed and altitude variations in 100 milliseconds, leading to impractical acceleration demands. In contrast, Approach 2 produced more stable and feasible results by constraining the dynamics of the UAV. On average, in Approach 1, the whale optimization algorithm had the shortest convergence time, while ant colony optimization was the slowest. In Approach 2, particle swarm optimization converged fastest, while gray wolf optimization was slowest. Across both approaches, the genetic algorithm achieved the lowest processing time, and ant colony optimization the highest. This study highlights the importance of realistic UAV motion constraints in optimization and provides guidance on selecting metaheuristic algorithms that balance convergence speed and computational efficiency to improve communication reliability. Following an in-depth discussion, several directions for future research are proposed.}, archive = {J_ACCESS}, author = {Lalan J. Mishra and Naima Kaabouch}, doi = {10.1109/ACCESS.2025.3576109}, journal = {IEEE Access}, pages = {98374-98398}, shortjournal = {IEEE Access}, title = {Multi-parameter optimization using metaheuristic algorithms to improve uncrewed aerial vehicles’ wireless communications: A performance analysis}, volume = {13}, year = {2025}, }
• (2025). 3D-SCUMamba: An abdominal tumor segmentation model. ACCESS, 13, 98362-98373. (www)

  @article{ , abstract = {Identification and segmentation of tumors from CT scans are essential for early detection and effective treatment but they remain challenging due to imaging artifacts and significant variability in tumor location, size, and morphology. Existing deep learning models typically adopt encoder-decoder architectures integrating convolutional layers with global dependency modeling to capture broader contextual information around tumors. Capturing global dependencies is critical, as local information alone often inadequately distinguishes tumors, especially given their diverse sizes, complex shapes, and interference from imaging artifacts. Recent advancements observe the emergence of the convolution-transformer architecture, which improves segmentation performance on the cost of substantial computational resources. In this article, we propose 3D-SCUMamba, an innovative architecture strategically integrating State Space Modeling-Based deep learning (Mamba) within the bottleneck of the encoder-decoder structure to overcome the limitations of existing segmentation networks. The proposed model efficiently models global dependencies while maintaining stable training dynamics and efficient inference. Additionally, we introduce a novel Spatio-Context (SC) module utilizing 3D convolutions without pooling to enhance feature representations and reduce information loss commonly associated with pooling operations. The SC module effectively prepares summarized features for robust global dependency processing by the Mamba component. 3D-SCUMamba explicitly addresses the prevalent limitations in current deep learning methods by prioritizing robust feature representation, training stability, and substantial accuracy improvements. Comprehensive evaluations conducted on three medical imaging datasets: MSD Pancreas Tumor, MSD Colon Tumor, and Synapse BTCV, demonstrate that computationally efficient 3D-SCUMamba consistently outperforms state-of-the-art methods with segmentation accuracy improvements of 2% to 7%, exhibiting stable training behavior and efficient inference suitable for clinical applications. The code and dataset are available at https://github.com/juwita-sj/3DSCU-Mamba}, archive = {J_ACCESS}, author = {Juwita and Ghulam Mubashar Hassan and Amitava Datta}, doi = {10.1109/ACCESS.2025.3573619}, journal = {IEEE Access}, pages = {98362-98373}, shortjournal = {IEEE Access}, title = {3D-SCUMamba: An abdominal tumor segmentation model}, volume = {13}, year = {2025}, }
• (2025). XAI-based photovoltaic energy management framework for smart cities. ACCESS, 13, 98349-98361. (www)

  @article{ , abstract = {The demand for explainable energy forecasting models has been emphasized in recent studies to enhance decision-making transparency. The efficient conversion of solar energy is stochastic and depends on environmental conditions. To address this, we propose a model-chain framework named XPEMM (XAI-based Photovoltaic Energy Management Model chain) for Solar Energy Production and Management in Smart Cities, ensuring explainability using LIME. Our model chain consists of three stages: Long-term forecasting of Global Horizontal Irradiance (GHI) as the first stage, Regression Modeling of GHI with power as the second stage, and Model-Agnostic explanation generation as the third stage. A recursive forecasting strategy was employed, leveraging Recurrent Neural Networks (RNN) to reduce error accumulation over a 5-year forecast horizon. The output of the proposed model chain assists grid cell operators in real-time monitoring, resulting in optimized performance by predicting the causes of high and low performance in advance, allowing for necessary adjustments. It also aids in site selection for high-budget PV-plant installations. We used two different datasets for training and benchmarking the Recursive Multistep GHI Forecasting Model (RMGFM). The proposed recursive forecasting strategy has been evaluated using RMSE and MAE error metrics which are 91.40 and 47.32 respectively, which are 2.9% 29.39%, and 7.9% less than the previously established approach using LSTM, PCR, and SVR based on RMSE measure and 15.9%, 49.9%, 34.8% less based on MAE metric. This study concludes that a recursive approach is best for long-term forecasting of GHI in regions with cyclic climatic patterns based on RMSE and MAE values. The interpretation of the LIME output establishes the fitness of model agnosticity in the Photovoltaic model chain.}, archive = {J_ACCESS}, author = {Malini Alagarsamy and Umamaheswari Rajasekaran and Sriram Ganesan and Ramyavarshini Palanivel}, doi = {10.1109/ACCESS.2025.3572492}, journal = {IEEE Access}, pages = {98349-98361}, shortjournal = {IEEE Access}, title = {XAI-based photovoltaic energy management framework for smart cities}, volume = {13}, year = {2025}, }
• (2025). A brief review on the development progress of high-efficiency klystrons. ACCESS, 13, 98323-98348. (www)

  @article{ , abstract = {The linear beam ‘O’ type klystrons that feature very high output power (greater than several MW) with an efficiency level of 90% are considered in this study. Improvement of efficiency, which depicts the figure of merit to convert the electrical power to RF, leads to an ample amount of energy saving in the installations of particle accelerators, synchrotron radiation sources and other advanced light sources. Since the past few years, the high-performance multi-beam klystrons exhibited high efficiency by lowering perveance, but are limited to a level ~70% with an output power ~6MW.In recent years, the development of high-efficiency klystrons with peak output power exceeding 20 MW and efficiency ranges from 80-90% has been fuelled by the demand for high-energy particle accelerators. The development of mathematical and computer models of beam-wave interactions in klystrons plays a pivotal role in significantly increasing the efficiency of these devices. This paper presents a brief review on recently emerging bunching formation techniques and configurations, including the Core Oscillation Method (COM), Core Stabilization Method (CSM), Bunch Alignment Collection method (BAC), Kladistron principle, Multigap output structures, novel architecture of Two Stage Multibeam klystrons, and related optimization techniques. These techniques have been successfully adopted to achieve more than 80% efficiency in klystrons. The background theory and computer codes utilized globally are summarized in this article. Although a number of research groups are working towards enhancing the efficiency of these devices, there is an inadequacy of concise articles presenting these efforts. This article aims to address this inadequacy and also points out the challenges and the future of the high-efficiency klystrons.}, archive = {J_ACCESS}, author = {Soumaya Mandal and Ayan Kumar Bandyopadhyay and Chaitali Koley and Debasish Pal and Taha Selim Ustun and Ahmet Onen}, doi = {10.1109/ACCESS.2025.3575697}, journal = {IEEE Access}, pages = {98323-98348}, shortjournal = {IEEE Access}, title = {A brief review on the development progress of high-efficiency klystrons}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Leveraging knapsack QAOA approach for optimal electric vehicle charging”. ACCESS, 13, 98322. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Leveraging Knapsack QAOA Approach for Optimal Electric Vehicle Charging”.}, archive = {J_ACCESS}, author = {Kimleang Kea and Chansreynich Huot and Youngsun Han}, doi = {10.1109/ACCESS.2025.3575833}, journal = {IEEE Access}, pages = {98322}, shortjournal = {IEEE Access}, title = {Corrections to “Leveraging knapsack QAOA approach for optimal electric vehicle charging”}, volume = {13}, year = {2025}, }
• (2025). Camera-LiDAR calibration using total station. ACCESS, 13, 98309-98321. (www)

  @article{ , abstract = {With the rapid advancements in autonomous driving in recent years, the importance of HD (High Definition) map referenced during autonomous driving has also increased. The mainstream method for creating HD maps involves using data collected from MMS (Mobile Mapping System) equipped with multiple sensors. Accurate fusion of data from multiple sensors is required. Typically, calibration between sensors is done by simultaneously measuring the same object. However, some sensors on MMS have non-overlapping FoV (Field of View), making it difficult to accurately calibrate and fuse their data. In this study, we focus on the fusion of data from a camera and LiDAR with non-overlapping FoVs. By estimating the relative positional information between the camera and LiDAR using two same calibration target by ‘Total station’, we performed data fusion. Originally, the total station are used for civil surveying, but we show that they can be used for camera and LiDAR calibration in computer vision. Specifically, two boards of the same make are placed in the field of view of the camera and LiDAR, and the relative positions of the two boards are matched by a total station, enabling highly accurate camera and LiDAR calibration even when the fields of view do not overlap. In simulation experiments, a translational error of 15.28 mm was achieved, which is approximately 4 mm smaller than the conventional target-based method.}, archive = {J_ACCESS}, author = {Kotaro Noji and Gou Koutaki and Shuhei Noguchi}, doi = {10.1109/ACCESS.2025.3576446}, journal = {IEEE Access}, pages = {98309-98321}, shortjournal = {IEEE Access}, title = {Camera-LiDAR calibration using total station}, volume = {13}, year = {2025}, }
• (2025). Response characteristics of marine sediments in luhai village, southern liaodong peninsula to environmental changes. ACCESS, 13, 98296-98308. (www)

  @article{ , abstract = {Luhai Village in the southern part of Liaodong Peninsula is located at the confluence of rivers and oceans, and is an area of frequent material and energy exchange. Studying the response characteristics of marine sediments to environmental changes is of great importance. This research analyzes the grain size and geo-chemical characteristics of marine sediments in Luhai Village and investigates their response to environmental changes to provide a scientific basis for regional environmental evolution and coastal management. Field observations, columnar sample collection, and marine hydro-logical data analysis are used in the study. Experimental analyses were carried out in combination with X-ray fluorescence spectrometry, 210Pb dating, laser diffraction particle size analyzer, elemental geochemistry, organic carbon and nitrogen indicators. The results indicated that the sediment mainly contained silty sand and sandy silt, with a particle size range of $4.25\sim 5.54\Phi $ and poor sorting. High magnesium content might be related to geology or human activities. The carbon to nitrogen ratio ranged from 8.8 to 15.84, indicating significant input and preservation of organic matter. The sedimentation rate was 1.06 cm/a, and a dating framework was constructed using 210Pb dating. Particle size analysis showed a negative correlation between sand and silt, with low and stable clay content. Total organic carbon (TOC) was positively correlated with total nitrogen (TN). Moreover, carbon nitrogen ratio and $\delta 13$ C analysis showed that organic matter mainly come from marine plankton with some terrestrial inputs. The study provides quantitative data for understanding the evolution of regional sedimentary environments, which has important practical value for the sustainable development of coastal resources and ecological environment protection.}, archive = {J_ACCESS}, author = {Tao Liu and Juan Shan and Jing Zhang and Xianzhi Xu and Yan Lin}, doi = {10.1109/ACCESS.2025.3575794}, journal = {IEEE Access}, pages = {98296-98308}, shortjournal = {IEEE Access}, title = {Response characteristics of marine sediments in luhai village, southern liaodong peninsula to environmental changes}, volume = {13}, year = {2025}, }
• (2025). Ultra-lightweight collaborative SLAM for robot swarms. ACCESS, 13, 98278-98295. (www)

  @article{ , abstract = {A key requirement in robotics is the ability to simultaneously self-localize and map a previously unknown environment, relying primarily on onboard sensing and computation. Achieving fully onboard accurate simultaneous localization and mapping (SLAM) is feasible for high-end robotic platforms, whereas small and inexpensive robots face challenges due to constrained hardware, therefore frequently resorting to external infrastructure for sensing and computation. The challenge is further exacerbated in swarms of robots, where coordination, scalability, and latency are crucial concerns. This work introduces a decentralized and lightweight collaborative SLAM approach that enables mapping on virtually any robot, even those equipped with low-cost hardware and only 1.5Mbyte of memory, including miniaturized insect-size devices. Moreover, the proposed solution supports large swarm formations with the capability to coordinate hundreds of agents. To substantiate our claims, we have successfully implemented collaborative SLAM on centimeter-size drones weighing 46g. Remarkably, we achieve a mapping accuracy below 30cm, a result comparable to high-end state-of-the-art solutions while reducing the cost, memory, and computation requirements by two orders of magnitude. Our approach is innovative in three main aspects. First, it enables onboard infrastructure-less collaborative mapping with a lightweight and cost-effective $({\$}20)$ solution in terms of sensing and computation. Second, we optimize the data traffic within the swarm to support hundreds of cooperative agents using standard wireless protocols such as ultra-wideband (UWB), Bluetooth, or WiFi. Last, we implement a distributed swarm coordination policy to decrease mapping latency and enhance accuracy.}, archive = {J_ACCESS}, author = {Vlad Niculescu and Tommaso Polonelli and Michele Magno and Luca Benini}, doi = {10.1109/ACCESS.2025.3574556}, journal = {IEEE Access}, pages = {98278-98295}, shortjournal = {IEEE Access}, title = {Ultra-lightweight collaborative SLAM for robot swarms}, volume = {13}, year = {2025}, }
• (2025). Bare-metal firmware fuzzing: A survey of techniques and approaches. ACCESS, 13, 98253-98277. (www)

  @article{ , abstract = {Firmware attacks are increasingly prevalent, often serving as low-hanging fruit for attackers due to the challenges of firmware security analysis. The complexity of hardware systems, platform diversity, peripheral interactions, and asynchronous events make thorough security analysis of embedded firmware particularly difficult. Despite these challenges, significant research has been dedicated to advancing dynamic analysis techniques, such as fuzzing, to improve firmware security. Existing research approaches these issues with varying methods and emphases. This survey paper examines the implementation of existing firmware fuzzing techniques, providing an overview of their emulation strategies and fuzzing methodologies. It also reviews several existing fuzzers and the application of large language models (LLMs) in fuzzing generic software. Our survey focuses specifically on frameworks for fuzzing embedded bare-metal/monolithic firmware. Our analysis highlights that most existing research has focused primarily on firmware emulation, rehosting, and back-end instrumentation to facilitate fuzzing, often relying on direct integration with existing fuzzers. However, the broader exploration of various fuzzing techniques, such as input generation, mutation, feedback, and scheduling strategies, widely used in generic software remains limited for embedded firmware. Recent efforts have started to address these aspects, with emerging work exploring fuzzing techniques beyond simple fuzzer integration. Furthermore, the application of LLMs presents a promising direction for further investigation. This survey provides a comprehensive overview of the past, present, and future landscape of bare-metal firmware fuzzing.}, archive = {J_ACCESS}, author = {Asmita Asmita and Ryan Tsang and Sujan Ghimire and Soheil Salehi and Houman Homayoun}, doi = {10.1109/ACCESS.2025.3575691}, journal = {IEEE Access}, pages = {98253-98277}, shortjournal = {IEEE Access}, title = {Bare-metal firmware fuzzing: A survey of techniques and approaches}, volume = {13}, year = {2025}, }
• (2025). OD-SHIELD: Convolutional autoencoder-based defense against adversarial patch attacks in object detection. ACCESS, 13, 98242-98252. (www)

  @article{ , abstract = {In the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems. We introduce OD-Shield, a novel defense approach that employs a convolutional autoencoder framework to detect and remove anomalous regions in patched images, subsequently reconstructing these regions to mitigate adversarial effects. The reconstructed images are then processed by the object detector, thereby restoring reliable performance under diverse attack scenarios. Distinctly model-agnostic, OD-Shield operates as a pre-processing step and can be applied to a wide range of tasks—including image classification and object detection—without compromising the fidelity of the original image. Experiments on benchmark datasets (COCO, Visdrone, and Argoverse) reveal that OD-Shield outperforms existing defenses by 13%–47% on COCO, highlighting its effectiveness in addressing critical security vulnerabilities. This work not only tackles the immediate threat of adversarial patches but also lays the foundation for future research into adaptive, resilient defense mechanisms that keep pace with evolving adversarial tactics.}, archive = {J_ACCESS}, author = {Byeongchan Kim and Heemin Kim and Minjung Kang and Hyunjee Nam and Sunghwan Park and Jaewoo Lee and Il-Youp Kwak}, doi = {10.1109/ACCESS.2025.3576264}, journal = {IEEE Access}, pages = {98242-98252}, shortjournal = {IEEE Access}, title = {OD-SHIELD: Convolutional autoencoder-based defense against adversarial patch attacks in object detection}, volume = {13}, year = {2025}, }
• (2025). Quantum hardware devices (QHDs): Opportunities and challenges. ACCESS, 13, 98229-98241. (www)

  @article{ , abstract = {Quantum Hardware Devices (QHDs) have marked a revolutionary leap in computational power, harnessing the principles of superposition, entanglement, and quantum interference to surpass classical processing capabilities. This review focuses on the core building blocks of quantum computing qubits, quantum gates, registers, and chipsets, while emphasizing the pivotal role of advanced architectures, such as Field-Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs), in optimizing quantum information processing. Despite their transformative potential, QHDs face critical hurdles, such as decoherence, Quantum Error Correction (QEC), scalability, and seamless integration with classical computing systems. Overcoming these challenges requires breakthroughs in materials science, cryogenic engineering, and quantum error mitigation. In addition, the development of high-fidelity quantum processors is essential for building robust Quantum Computing Systems (QCS) capable of solving problems beyond the reach of classical computation. This paper presents a comprehensive analysis of Emerging Quantum Technologies (EQT), examining their profound impact on cryptography, Artificial Intelligence (AI), Quantum Simulation (QS), and Secure Communication (SC). By reviewing state-of-the-art research and engineering innovations, we outline the roadmap for transitioning from experimental prototypes to large-scale, fault-tolerant quantum systems, paving the way for the next era of computing.}, archive = {J_ACCESS}, author = {Aziz Oukaira}, doi = {10.1109/ACCESS.2025.3576216}, journal = {IEEE Access}, pages = {98229-98241}, shortjournal = {IEEE Access}, title = {Quantum hardware devices (QHDs): Opportunities and challenges}, volume = {13}, year = {2025}, }
• (2025). Enhancing AI explainability through the EXACT framework: A user-centric approach. ACCESS, 13, 98208-98228. (www)

  @article{ , abstract = {The increasing adoption of Artificial Intelligence (AI) in several industries has created a demand for user-centered explanations that align with how users think and understand concepts. This paper presents EXACT (EXplainable AI with Cognitive Theories), a novel framework that combines cognitive theories that explain how people think and understand with cognitive functions, focusing on perception, memory and language abilities, to improve users’ comprehension of and engagement with artificial intelligence technologies. By aligning cognitive functions with the design principles of Human-Computer Interaction (HCI), which promote user-centered intuitive systems. the framework addresses challenges related to making AI understandable to users with various levels of cognitive abilities. As a proof-of-concept, a self-diagnosis tool was created to demonstrate the framework’s effectiveness. Then, 60 participants were divided into a control group and an experimental group. Participants completed six tasks designed to evaluate their perception, memory, and language-related cognitive functions. The experimental group outperformed the control group across all tasks, demonstrating significantly improved performance. Subjective metrics also supported these findings: the experimental group reported higher levels of understanding (4.60 vs. 2.87), confidence (4.67 vs. 3.07), and clarity (4.87 vs. 2.80) compared to the control group. These findings suggest that EXACT framework significantly enhances user’s functions when using AI systems. However, further research is needed to explore its broader applicability in other contexts and utilize other cognitive functions.}, archive = {J_ACCESS}, author = {Sara S. Alhasan and Reem A. Alnanih}, doi = {10.1109/ACCESS.2025.3576234}, journal = {IEEE Access}, pages = {98208-98228}, shortjournal = {IEEE Access}, title = {Enhancing AI explainability through the EXACT framework: A user-centric approach}, volume = {13}, year = {2025}, }
• (2025). Semantic RF waveform adaptation in flying ad hoc networks via hybrid knowledge bases and deep reinforcement learning. ACCESS, 13, 98190-98207. (www)

  @article{ , abstract = {By directly encoding the semantic meaning into each RF waveform symbol, we can significantly reduce the communication overhead. Particularly, semantic communication in Flying Ad Hoc Networks (FANETs)—formed by uncrewed aerial vehicles (UAVs)—is a promising application for enabling efficient airborne networks. This paper introduces a semantics-aware approach to optimize OFDM (Orthogonal Frequency Division Multiplexing) waveforms, leveraging Deep Q-Network (DQN)-based reinforcement learning (RL) with nonlinear optimization, enhanced by an innovative hybrid knowledge base shared by all nodes. Our method incorporates semantic importance through adaptive parameter tuning and constrained optimization. The hybrid knowledge base allows for efficient representation and utilization of semantic information. We propose a framework that jointly optimizes modulation order and cyclic prefix length while considering the semantic importance of transmitted data. The system is formulated as a Markov Decision Process (MDP) with scalable state/action spaces. This algorithm enhances the system’s ability to handle diverse and dynamic communication scenarios, adapting to varying semantic importance levels.}, archive = {J_ACCESS}, author = {Weiqiang Lyu and Linsheng He and Jiamiao Zhao and Fei Hu}, doi = {10.1109/ACCESS.2025.3576146}, journal = {IEEE Access}, pages = {98190-98207}, shortjournal = {IEEE Access}, title = {Semantic RF waveform adaptation in flying ad hoc networks via hybrid knowledge bases and deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). SEGYM: A novel electrified weight machine for performing free-weight training. ACCESS, 13, 98175-98189. (www)

  @article{ , abstract = {As part of digital healthcare, weight equipment has been motorized using electric motors to overcome the limitations of conventional weight equipment that relies on the weight of objects. However, this has been limited to cable and strength machines, and the most popular free weights, which lack safety and ease of use, have not yet been electrified. As such, in this study, SEGYM was developed as electrified weight equipment for free weight training. SEGYM aimed to imitate the load and motion characteristics of traditional free weights while achieving consistent human-robot interaction. The actuating modules, located on both sides of SEGYM, generate loads independent of user motion through a belt-drive mechanism, similar to gravitational loads. Additionally, the barbell module, combined with the actuating modules, applies ergonomic design to enable natural human movement in three-dimensional space while delivering the load. To implement the same resistance sensation as conventional free weights, a multi-layered disturbance rejection controller (MDRC), based on robust control, was applied to the actuating modules. This controller changes the dynamic characteristics of SEGYM to match those of traditional free weights. An experiment with elite weightlifters was conducted to verify the similarity between SEGYM and conventional free weights. The results confirmed their similarity.}, archive = {J_ACCESS}, author = {Jangyeon Park and Keeung Yoo and Dongwan Choi and Jungsu Choi}, doi = {10.1109/ACCESS.2025.3576346}, journal = {IEEE Access}, pages = {98175-98189}, shortjournal = {IEEE Access}, title = {SEGYM: A novel electrified weight machine for performing free-weight training}, volume = {13}, year = {2025}, }
• (2025). Augmenting tourism websites with voice commands for increasing accessibility in smart tourism destinations. ACCESS, 13, 98158-98174. (www)

  @article{ , abstract = {Accessibility of tourism destination of official websites has been widely considered under the umbrella of Smart Tourism Destinations. Unfortunately, digital accessibility perceived by users is still a challenge since there are a plethora of websites apart from DMOs (Destination Management Organizations). Users with disabilities encounter problems when accessing these websites, especially elderly people with visual problems while browsing and reading the contents of a website, thus making it more difficult to organize a travel. In this article, we propose a Web Augmentation Framework for Accessibility and apply it to a tourism scenario (WAFRA4Tourism) in order to improve the accessibility of other-than-DMO websites at runtime. To this aim, the framework uses Web augmentation techniques that extend the website with voice interaction and new actions useful for a person to be inspired as well as to start organizing a trip: select and read aloud fragments, increase font size, facilitate navigation and show videos. The evaluation of the framework shows its usefulness to ease website access for visually impaired users compared to using screen readers. In this way, perceived digital accessibility of a destination is ameliorated, not only considering DMO’s websites but external websites on the destination.}, archive = {J_ACCESS}, author = {César González-Mora and Irene Garrigós and Marco A. Celdrán-Bernabeu and David Giner-Sánchez and Jose-Norberto Mazón}, doi = {10.1109/ACCESS.2025.3575725}, journal = {IEEE Access}, pages = {98158-98174}, shortjournal = {IEEE Access}, title = {Augmenting tourism websites with voice commands for increasing accessibility in smart tourism destinations}, volume = {13}, year = {2025}, }
• (2025). Method for building a community-oriented agent with a dialogue ability adapted to the community. ACCESS, 13, 98144-98157. (www)

  @article{ , abstract = {Autonomous dialogue agents are expected to be applied in multi-party dialogue where three or more agents or people converse. In multi-party dialogue, autonomous dialogue agents that perform one-to-many dialogue with interlocutors assembled for specific purposes, such as communities, are useful, because these agents can be applied to provide information and entertainment to communities. For such applications, autonomous dialogue agents need to be acceptable and attractive to community members, but no method has been established to achieve this. Therefore, we aim to realize a “Community-Oriented Agent” (COA) that is acceptable and attractive to community members through dialogue. We proposed a method for building an autonomous dialogue function of COA, which consists of two essential steps: 1) Collect dialogue examples among the community members by creating a situation in which multiple members of the community converse with other members through operating the agent; 2) Build the agent’s autonomous dialogue function by utilizing the collected examples. To validate this method, we developed a “COA system” to implement the approach and conducted the experiment. In the experiment, using this system, we collected actual dialogue examples among community members, and then asked the members to evaluate the autonomous dialogue agents built using these examples. The results showed that the members evaluated an autonomous dialogue agent built using dialogue examples collected within their community as acceptable and attractive, but it was also shown that it is difficult to build such an agent if there are not enough participants in the dialogue when collecting dialogue examples.}, archive = {J_ACCESS}, author = {Atsushi Kobayashi and Tomonori Kubota and Satoshi Sato and Kohei Ogawa}, doi = {10.1109/ACCESS.2025.3575708}, journal = {IEEE Access}, pages = {98144-98157}, shortjournal = {IEEE Access}, title = {Method for building a community-oriented agent with a dialogue ability adapted to the community}, volume = {13}, year = {2025}, }
• (2025). ATBShellFinder: A bytecode-level webshell detector based on adversarial training. ACCESS, 13, 98133-98143. (www)

  @article{ , abstract = {Webshell is a malicious server-side script that attackers can upload to a server to execute commands, steal sensitive data, and maintain persistent access. With the rapid evolution of evasion techniques, existing detection methods fail to effectively capture the contextual semantics and abstract features of webshell variants. This allows some webshells to evade detection and cause significant damage to target systems. To address these challenges, this study proposes ATBShellFinder, an enhanced detection framework based on adversarial training. ATBShellFinder applies adversarial training techniques from computer vision to the embedding layer of the Bidirectional Encoder Representations from Transformers (BERT) to generate adversarial word embeddings. These adversarial embeddings are then combined with normal embeddings for model training, significantly improving the robustness and generalization of the model even with limited webshell samples. Furthermore, the framework performs detection at the bytecode level, which can effectively mitigate the interference of encryption and obfuscation, and introduces a bidirectional long short-term memory network (BiLSTM) as the detector. To validate the generalizability of our method, experiments were conducted on two widely used web programming languages. Experimental results demonstrate that ATBShellFinder achieves detection accuracy of 99.07% for PHP-based Webshells and 98.97% for Java-based Webshells, outperforming existing detection models and tools.}, archive = {J_ACCESS}, author = {Yuqin Xie and Yuan Zhang and Daofeng Li and Guoren Xiong}, doi = {10.1109/ACCESS.2025.3575263}, journal = {IEEE Access}, pages = {98133-98143}, shortjournal = {IEEE Access}, title = {ATBShellFinder: A bytecode-level webshell detector based on adversarial training}, volume = {13}, year = {2025}, }
• (2025). The attention enhanced autoencoder for balise anomaly detection in potting process. ACCESS, 13, 98120-98132. (www)

  @article{ , abstract = {Balise is a critical trackside component in railway systems, operating in harsh environments where silicone gel potting enhances reliability. However, the potting process is irreversible; defects lead to costly scrap and operational delays. For this reason, this study introduces an anomaly detection method for the transponder potting process for the first time. We propose an attention-enhanced autoencoder (AEA) for anomaly detection. Our approach integrates three core innovations: max-min normalisation to handle scaling discrepancies between input attributes, an attention mechanism that dynamically highlights key features, and empirically derived adaptive reconstruction error thresholds to discriminate anomalies. Experiments on real production data show that the AEA model achieves Precision of 97.10%, Recall of 100.00%, weighted F1-Score of 98.53%, Accuracy of 99.90%, ROC-AUC of 99.96%, and G-means of 98.54%, outperforming all baselines. Compared to the best baseline AE model, the AEA model improves Precision by 1.45%, Recall by 1.49%, weighted F1-Score by 1.47%, Accuracy by 0.09%, ROC-AUC by 0.03%, and G-means by 1.47%. These results demonstrated that our model significantly outperforms existing well-regarded models in anomaly detection performance.}, archive = {J_ACCESS}, author = {Ke Ye and Jingpin Jiao and Fanghua Chen and Qing Xu and Gehui Liu and Jun Wang}, doi = {10.1109/ACCESS.2025.3569756}, journal = {IEEE Access}, pages = {98120-98132}, shortjournal = {IEEE Access}, title = {The attention enhanced autoencoder for balise anomaly detection in potting process}, volume = {13}, year = {2025}, }
• (2025). Supervised learning of depth estimation from imperfect rectified stereo laparoscopic images of liver surface. ACCESS, 13, 98106-98119. (www)

  @article{ , abstract = {Depth estimation is crucial in surface reconstruction for laparoscopic minimally invasive surgery (MIS) navigation. The structured light depth recovery algorithm accurately restores pixel-level depth but is time-consuming and requires additional light sources. Using structured light 3D optical reconstruction and network training in porcine laparoscopic environments allows deep learning algorithms to achieve depth recovery in white light settings. Yet, using deep learning algorithms for binocular depth estimation in laparoscopic images may encounter challenges like system-induced calibration errors and significant object depth variations in the laparoscopic field of view, which can impact rectification and depth estimation accuracy. In this paper, a novel ACVNet++ algorithm based on vertical disparity correction is proposed. This method uses structured light reconstruction for depth estimation. which includes deformable convolution modules, vertical disparity correction weights, and edge-enhanced loss functions. A dataset is created for laparoscopic liver depth images, and the ACVNet++ algorithm is tested on this dataset. The experiments demonstrate that, compared to non-learning algorithms based on white light image features, the ACVNet++ algorithm reduces the average absolute error (MAE) and endpoint error (EPE) of depth prediction under white light conditions by 51% and 44%, reaching 2.46 mm and 2.56 pixels, respectively. The results indicate that the edge-enhanced ACVNet++ algorithm improves the ability for 3D surface reconstruction of the liver and holds potential for clinical application.}, archive = {J_ACCESS}, author = {Chongan Zhang and Ziyi Jin and Xiaoyue Liu and Yuelong Liang and Xiao Liang and Peng Wang and Xiujun Cai and Xuesong Ye}, doi = {10.1109/ACCESS.2025.3574079}, journal = {IEEE Access}, pages = {98106-98119}, shortjournal = {IEEE Access}, title = {Supervised learning of depth estimation from imperfect rectified stereo laparoscopic images of liver surface}, volume = {13}, year = {2025}, }
• (2025). A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images. ACCESS, 13, 98088-98105. (www)

  @article{ , abstract = {Increasingly available ultrastructural data from a continuously growing diversity of experimental conditions are driving new opportunities for fruitful neuroscientific hypotheses tested in intracellular compartments such as the nanoscale roles of, e.g., the mitochondria. While state-of-the-art deep CNNs demonstrate advanced capabilities, they can exhibit a degree of fragility; often yielding coarse, high-frequency oscillatory outputs characterized by discontinuities and false positives, even in the context of straightforward segmentation of mitochondria. Traditionally, the leading methodologies in medical image segmentation have utilized variations of the encoder-decoder framework, notably including the U-Net architecture. The SAM does not perform as well, since it has not been explicitly trained for the task and does not demonstrate user-interactive, over one billion annotations mostly for natural images. However, the SAM may be applied to segment anything, including medical image segmentation challenging new datasets. This research tackles the complex task of implementing domain adaptation for task of mitochondria segmentation in electron microscopy images obtained from various tissues and species, employing deep learning methodologies. A thorough analysis is conducted to evaluate the effectiveness of SAM in segmentation tasks in medical imaging and to measure its performance on volumetric datasets derived from electron microscopy, and show that it is powerful at segmenting instances even under challenging imaging conditions. We provide a fine-tuning SAM which can be naturally trained by SAM at an exemplary scale, benefiting from a diverse and large dataset over one million image masks in 11 modalities. This model would be able to perform precise segmentation for a diverse array of targets across different imaging conditions at the level of performance of specialized U-Net models, or even better. A visual comparison is shown between our fine-tuning SAM model and U-Net, along with an examination of different watershed post-processing strategies to discriminate between adjacent or conjoined instances. Results from our experiments show that the method suggested is fast, very robust, and accurate, with an imbibed model that has improved learning capability. A comprehensive sensitivity analysis has been carried out, and an ablation study using most popular metrics segmentation evaluation is performed, the results derived from the Jaccard Index coefficient, Dice Similarity Coefficient, Aggregated Jaccard Index, and Panoptic Quality reinforce the strength and effectiveness of the proposed modules in enhancing the performance of mitochondrial segmentation.}, archive = {J_ACCESS}, author = {Zahra Faska and Lahbib Khrissi and Imadeddine Mountasser and Khalid Haddouch and Nabil El Akkad and Samah Alshathri and Walid El-Shafai}, doi = {10.1109/ACCESS.2025.3574555}, journal = {IEEE Access}, pages = {98088-98105}, shortjournal = {IEEE Access}, title = {A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images}, volume = {13}, year = {2025}, }
• (2025). The hybrid best worst integrated machine learning methodology to clustering the countries based on the happiness index. ACCESS, 13, 98076-98087. (www)

  @article{ , abstract = {Happiness, a universal component that affects human life, is defined as personal well-being, encompassing a person’s evaluation of their life satisfaction. Although the perception of happiness varies from person to person, it is the primary goal that societies strive to achieve. In this regard, studies on measuring happiness have continued from the past to the present. In this context, the World Happiness Report includes happiness index values created using eight criteria with equal weights for each country. This study assumes that the variables used to calculate the happiness index values have different weights. These weights are obtained using the Best-Worst method (BWM), one of the multi-criteria decision-making methods. The BWM results, derived from expert evaluations, indicate that ‘Freedom to Make Life Choices’ is the most influential factor, while ‘Generosity’ is the least. Then, using these weights, countries are clustered into three using the k-means method, one of the unsupervised learning techniques. When the clusters are examined, it is seen that there are countries with similar characteristics in the clusters. The clustering analysis reveals three distinct country groups, where countries in Cluster 1 show higher and more homogeneous well-being indicators. In contrast, countries in Cluster 3 are characterized by low and more diverse scores. Furthermore, scenario analyses demonstrate the sensitivity of the clustering outcomes to changes in specific variable weights. The results emphasize that incorporating weighted criteria offers a more realistic and intuitive classification of the happiness profiles of the countries.}, archive = {J_ACCESS}, author = {Ebru Geçici and Aslihan Yildiz and Selçuk Alp}, doi = {10.1109/ACCESS.2025.3576378}, journal = {IEEE Access}, pages = {98076-98087}, shortjournal = {IEEE Access}, title = {The hybrid best worst integrated machine learning methodology to clustering the countries based on the happiness index}, volume = {13}, year = {2025}, }
• (2025). Error performance of DDPSK receiver in the presence of composite fading. ACCESS, 13, 98066-98075. (www)

  @article{ , abstract = {We investigate the justification for implementing double differential phase-shift keying (DDPSK) instead of differential phase-shift keying (DPSK) in the presence of carrier frequency offset (CFO) over wireless channels affected by thermal noise, multipath fading, and shadowing. When shadowed multipath fading is modeled using the Fisher-Snedecor $\mathcal {F}$ distribution, we present the probability density function (PDF) of the received composite signal phase in the form of the Fourier series and derive novel analytical expressions for its Fourier coefficients. Furthermore, we present analytical expressions for the symbol error probability (SEP) in detection of DDPSK and DPSK signals over such shadowed fading channels. These expressions are derived in the form of infinite series followed by the appropriate proof of convergence. We determine the necessary number of terms to achieve a given accuracy. Derived expressions allow us to illustrate the superiority of DDPSK compared to DPSK in the presence of CFO. We analyze the applicability of DDPSK across various values of normalized CFO, multipath fading depth, and shadowing severity, considering different modulation orders. Our key conclusion is that DDPSK outperforms DPSK when CFO values are higher. Additionally, as the modulation order increases, CFO has a stronger impact on SEP degradation in DPSK detection. Notably, at higher CFO values, DDPSK superiority becomes evident even at lower signal-to-noise ratio levels.}, archive = {J_ACCESS}, author = {Goran T. Djordjević and Milica Petković and Nenad Milosević and Bane Vasić}, doi = {10.1109/ACCESS.2025.3576058}, journal = {IEEE Access}, pages = {98066-98075}, shortjournal = {IEEE Access}, title = {Error performance of DDPSK receiver in the presence of composite fading}, volume = {13}, year = {2025}, }
• (2025). Low-voltage current-mode analog filter using current differencing transconductance amplifier. ACCESS, 13, 98055-98065. (www)

  @article{ , abstract = {This paper presents a new low-voltage, low-power current-mode active filter with three inputs and seven outputs. The design is implemented using three current differencing transconductance amplifiers (CDTAs) and two grounded capacitors. The proposed current-mode filter can realize both non-inverting and inverting transfer functions for low-pass, band-pass, high-pass, band-stop, and all-pass filters within a single topology. It features low input impedance, high output impedance, and eliminates issues related to inverting inputs, multiple input signals, and input matching conditions. The natural frequency of the filters can be electronically controlled. The bulk-driven MOS transistor technique is employed in the CDTA to enable operation at low supply voltages, with transistors operating in the subthreshold region to achieve low power consumption. This version of the CDTA also incorporates a z-copy terminal, which enhances its versatility for current-mode circuit applications. Simulations conducted in Cadence Virtuoso using $0.18~\mu $ m CMOS technology from TSMC validate the effectiveness of the proposed filter and CDTA. The filter operates with a supply voltage of 0.45 V, consumes $1.039~\mu $ W of power, achieves a total harmonic distortion (THD) of 1% with a 14 nA amplitude input sine wave signal, and provides a dynamic range of 65.9 dB. The Monte Carlo analysis, along with process, temperature, and voltage corner analyses, confirms the robustness of the design.}, archive = {J_ACCESS}, author = {Montree Kumngern and Fabian Khateb and Tomasz Kulej}, doi = {10.1109/ACCESS.2025.3576142}, journal = {IEEE Access}, pages = {98055-98065}, shortjournal = {IEEE Access}, title = {Low-voltage current-mode analog filter using current differencing transconductance amplifier}, volume = {13}, year = {2025}, }
• (2025). ColorPCA: Scalable colored dimensionality reduction for unlabeled high-dimensional data. ACCESS, 13, 98037-98054. (www)

  @article{ , abstract = {Mapping labeled high-dimensional data to colors based on class labels in low-dimensional projections is effective for enhancing pattern recognition and reducing misinterpretation of clusters. However, automatic coloring of unlabeled high-dimensional data remains challenging for revealing unknown patterns or class structures in the data. To address this, we propose ColorPCA, a scalable method that improves existing dimensionality reduction-based automatic coloring by integrating Principal Component Analysis with alpha compositing. Rather than mapping reduced dimensions to color coordinates, ColorPCA directly encodes data into color space to enhance pattern discovery in unlabeled datasets. We implemented ColorPCA in a web-based visual analytics system for interactive exploration and evaluated it through three case studies using benchmark, simulated, and real-world climate datasets. Additionally, we conducted three user studies, two with generic users and one with climate domain experts. Comparisons with two state-of-the-art coloring methods based on PCA and t-SNE demonstrate that ColorPCA improves visual separability and facilitates deeper insight extraction in high-dimensional data visualization.}, archive = {J_ACCESS}, author = {Wenqiang Cui and Shehzad Afzal and Rafael M. Martins and Sohaib Ghani and Ibrahim Hoteit}, doi = {10.1109/ACCESS.2025.3575955}, journal = {IEEE Access}, pages = {98037-98054}, shortjournal = {IEEE Access}, title = {ColorPCA: Scalable colored dimensionality reduction for unlabeled high-dimensional data}, volume = {13}, year = {2025}, }
• (2025). Research and application of a multitarget detection algorithm based on improved YOLOv8 for indoor objects. ACCESS, 13, 98019-98036. (www)

  @article{ , abstract = {This study focuses on enhancing the intelligence level of assisted living robots to meet the needs of older adults with disabilities. The goal is to achieve efficient multitarget detection of everyday objects and to improve the accuracy and recall of the detection algorithm. To this end, an improved YOLOv8 algorithm (YOLOv8-CBW3) is proposed. First, the GAM global attention mechanism is embedded in the backbone network of YOLOv8 to improve the sensitivity of the backbone network to the important feature information and reduce the attention to the irrelevant features, and it is determined that the GAM global attention mechanism will be embedded in the backbone network of YOLOv8 after comparing and analyzing the GAM with the attention mechanisms of CBAM, SE, and ECA; second, at the neck layer, the original PAN-FPN network structure is replaced with the BiFPN structure at the neck layer to realize bidirectional cross-scale connectivity and weighted feature fusion. In addition, a small-target detection layer is added to the original YOLOv8 model to enhance the detection capability for small-scale targets. Finally, the WIoUv3 loss is used instead of the CIoU loss as the localization regression loss function, which reduces the influence of low-quality anchor frames and enhances the Model’s focus on global information, thus improving the accuracy of target object detection. After 300 rounds of testing on 3000 data samples, the YOLOv8 - CBW3 algorithm achieves a detection accuracy of 95.11%, a recall of 94.60%, and an average precision of 95.23%, which are improvements of 2.78% and 3.72% in accuracy and recall, respectively, compared with YOLOv8n. A comparison with other popular models shows that YOLOv8 - CBW3 has strong generalizability, localization performance, detection ability and robustness. To verify that YOLOv8-CBW3 has strong generalizability, a comparative analysis is conducted on the VOC2007 public dataset, which shows through the data that its detection ability is still optimal, and it provides a reference solution for subsequent similar problems.}, archive = {J_ACCESS}, author = {Yanzhen Wang and Wei Wang and Xiaolong Zhou and Xubin Dong and Jianyong Li and Qi Zhao and Xinyu Yang and Yao Wang}, doi = {10.1109/ACCESS.2025.3575764}, journal = {IEEE Access}, pages = {98019-98036}, shortjournal = {IEEE Access}, title = {Research and application of a multitarget detection algorithm based on improved YOLOv8 for indoor objects}, volume = {13}, year = {2025}, }
• (2025). Fast decision making of point cloud video geometry CU partition based on occupied pixels. ACCESS, 13, 98006-98018. (www)

  @article{ , abstract = {The video-based point cloud compression Standard (V-PCC) uses file-based projection technology to map point cloud data in three-dimensional space to two-dimensional space, creating geometric graphs and attribute graphs, then, with the help of the currently very mature two-dimensional video image coding technologies, such as high-efficiency video coding (HEVC), Versatile Video Coding (VVC), etc., these generated images are encoded and processed. Compared with natural video, point cloud video has a larger amount of data, and V-PCC coding improves coding efficiency but is more complex. In order to achieve effective complexity reduction and improve coding performance in the encoding process, this paper proposes a fast decision making method of coding unit (CU) partition of point cloud video geometry based on occupied pixels. In this method, the occupancy information is represented by block based occupancy map, and the early termination partition of unoccupied blocks in geometric graph is realized by generating occupied pixel marker block. Then, an unsupervised hierarchical clustering method is used for different scales of fully occupied blocks and partial occupied blocks containing occupied pixels, so as to realize the fast decision of optimal CU partition. The experimental results show that when the proposed method is used to process different dynamic point clouds in All Intra (AI) configuration, the average time of geometric graph obtained after coding is saved by 62.76%, while the BD-TotalRate only increases by 0.04% and 0.10%. Compared with other methods, the coding complexity is reduced. A good balance is also obtained for coding losses.}, archive = {J_ACCESS}, author = {Fengqin Wang and Juanjuan Jia and Qiuwen Zhang}, doi = {10.1109/ACCESS.2025.3576160}, journal = {IEEE Access}, pages = {98006-98018}, shortjournal = {IEEE Access}, title = {Fast decision making of point cloud video geometry CU partition based on occupied pixels}, volume = {13}, year = {2025}, }
• (2025). Human motion sensing through blockage and reflection measurements at 60 GHz and 300 GHz. ACCESS, 13, 97997-98005. (www)

  @article{ , abstract = {As future wireless communication systems operating in the terahertz frequency range (0.1 to 1 THz) will likely utilize highly directive antennas to mitigate high path losses, precise alignment between the transmitter and the receiver becomes crucial. In time-variant propagation scenarios therefore, beam tracking and switching are necessary. Consequently, integrated sensing and communication is becoming increasingly important as it enables the detection of potential channel changes. In this study, time-variant power delay profiles from measurements involving a moving human are employed to assess movement. Blockage scenario measurements are used to determine the moving direction, while reflection scenario measurements assess velocity. The results demonstrate that channel measurements can successfully be used to track human movement characteristics, specifically walking direction and velocity.}, archive = {J_ACCESS}, author = {Tobias Doeker and Malte Eggers and Carla E. Reinhardt and Daniel M. Mittleman and Thomas Kürner}, doi = {10.1109/ACCESS.2025.3573681}, journal = {IEEE Access}, pages = {97997-98005}, shortjournal = {IEEE Access}, title = {Human motion sensing through blockage and reflection measurements at 60 GHz and 300 GHz}, volume = {13}, year = {2025}, }
• (2025). Consideration and evaluation of network location and communication protocols utilizing HTTP for edge computing. ACCESS, 13, 97987-97996. (www)

  @article{ , abstract = {In recent years, Edge Computing has gained attention to alleviate the concentration of traffic in the cloud by processing large amounts of data on Edge servers closer to the data source. To realize this approach, Multi-access Edge Computing (MEC) has been proposed. MEC is expected to utilize the high-speed and large-capacity communication of 5G to offload computations to nearby the devices. To effectively utilize the computational resources of MEC, we hAve proposed a middleware called MEC-RM (MEC Resource Manager). MEC-RM has the advantage of transparently utilizing network-side resources and is possible to be deployed in various locations within a network. However, there are problems of lacking discussion and evaluation regarding the optimal location of MEC-RM in the network and the suitable communication protocol to be used by MEC-RM. To solve these problems, this study aims to clarify the appropriate location to be placed of MEC-RM, and to achieve the purpose, we evaluate both 5G and Wi-Fi communication packet and HTTP communication delays within a specific network architecture. The results show that the 5G communication is slower than the Wi-Fi. The latency caused by 5G communication is more than three network hops. Additionally, the communication delay using HTTP is less than 0.1 seconds, which is sufficient for the scenarios envisioned by MEC-RM.}, archive = {J_ACCESS}, author = {Li Yanzhi and Midori Sugaya}, doi = {10.1109/ACCESS.2025.3572509}, journal = {IEEE Access}, pages = {97987-97996}, shortjournal = {IEEE Access}, title = {Consideration and evaluation of network location and communication protocols utilizing HTTP for edge computing}, volume = {13}, year = {2025}, }
• (2025). A deep semantic comprehension-based automatic conversation model based on autoencoder-enhanced transformer. ACCESS, 13, 97975-97986. (www)

  @article{ , abstract = {With the continuous advancement of artificial intelligence and machine learning technology, automatic dialogue systems have become an indispensable part of people’s daily lives. However, the complexity of semantic understanding makes it difficult for systems to accurately capture user intent and contextual information. Dialogue systems often appear stiff and disjointed when generating natural language, lacking the fluency and naturalness of human dialogue. Therefore, a model is needed to deal with these problems. This article proposes an automatic conversation model based on deep semantic understanding, which aims to improve the performance of natural language processing systems in dialogue scenarios and achieve more efficient and accurate semantic understanding and generation. The study introduced AutoEncoder into the Transformer structure and designed a multi-level autoencoder network to achieve deeper semantic understanding of input text. The AutoEncoder based on deep semantic algorithm was developed and a new automatic session awareness module was created. The study used the DailyDialog and Ubuntu dialogue datasets, and through comparative experiments and qualitative analysis, we validated the effectiveness and superiority of the proposed model in dialogue scenarios. Our model has achieved significant improvements in semantic understanding and generation, demonstrating its potential application prospects in practical dialogue systems. The automatic conversation model based on deep semantic understanding and the Autoencoder enhanced Transformer proposed in this article provide a new approach and method for improving the performance and efficiency of dialogue systems.}, archive = {J_ACCESS}, author = {Shouyu Liang and Bimei Zhao and Cheng Li and Yuanfeng Chen and Zhengguo Ren and Bang Ao}, doi = {10.1109/ACCESS.2025.3572384}, journal = {IEEE Access}, pages = {97975-97986}, shortjournal = {IEEE Access}, title = {A deep semantic comprehension-based automatic conversation model based on autoencoder-enhanced transformer}, volume = {13}, year = {2025}, }
• (2025). GeoDisasterAINet: An explainable deep ensemble framework for real-time urban and rural disaster classification and resilience. ACCESS, 13, 97944-97974. (www)

  @article{ , abstract = {These Sustainable Development Goal 11 (SDG 11), whose target is to make urban and rural settlements resilient and disaster-prepared, integrates with it the significant objective of having disaster-resilient communities and cities. GeoDisasterAINet is an AI deep learning and machine learning model for distinguishing between rural and urban-area occurrences of floods, cyclones, earthquakes, and wildfires effectively. In this article, we introduce GeoDisasterAINet. There are four successive phases in model performance improvement, with increasingly enhancing classification accuracy, namely an integrated CNN (baseline model), an integrated CNN with XGBoost (feature improvement model), an integrated CNN with XGBoost and Multiclass SVM (refinement model for decision boundary), and LIME segmentation for interpretability and model performance evaluation. EfficientNetB7, ResNet-50, InceptionV3, and DenseNet-201 are architectures in use in GeoDisasterAINet, and these architectures make three settings for an ensemble: ERI-2025 (EfficientNetB7, ResNet-50, InceptionV3), DRI-2025 (DenseNet-201, ResNet-50, InceptionV3), and DE-2025 (DenseNet-201, EfficientNetB7). In balancing out the dataset, we used the Synthetic Minority Over-sampling Technique (SMOTE). With 99.25% accuracy in training, 96.37% accuracy in testing, 98% in precision, 97% in recall, and 97% in F1-score, ERI-2025+ XGBoost + Multiclass SVM, our best model, effectively classified items. LIME-based interpretability uncovered disaster-specific features such as storm behaviour in cyclones, infrastructure loss in earthquakes, wildfire propagation in wildfires, and water build-up in floods, and in confirming that the model was reliable, it helped in an effective location of disasters. GeoDisasterAINet is a strong AI-powered system that addresses directly SDG 11 through its contribution towards safer and disaster-resilient communities and cities. It does this through AI-powered deep learning and machine learning technology for improving disaster detection, evaluation of risk, and preparedness for emergencies in real-time.}, archive = {J_ACCESS}, author = {Akella S. Narasimha Raju and Seelam Sreekanth and Ranjith Kumar Gatla and M. Rajababu and Devineni Gireesh Kumar and Aymen Flah and Claude Ziad El-Bayeh and Khaled A. El-Nagdy and Ali Alzaed}, doi = {10.1109/ACCESS.2025.3574451}, journal = {IEEE Access}, pages = {97944-97974}, shortjournal = {IEEE Access}, title = {GeoDisasterAINet: An explainable deep ensemble framework for real-time urban and rural disaster classification and resilience}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal aware collaborative service ranking prediction in IoT-enabled edge computing. ACCESS, 13, 97930-97943. (www)

  @article{ , abstract = {With the rapid proliferation of services deployed on edge servers, selecting high quality services from a multitude of functionally similar offerings based on their rankings has become a critical issue. Service ranking is an effective approach to solve this problem. Traditional QoS aware service ranking approaches encounter new challenges in edge computing enabled Internet of Things (IoT) systems. On one hand, the geographical characteristics of services introduce heterogeneity into the evaluation criteria for service ranking. On the other hand, analyzing the multi-dimensional temporal characteristics of QoS poses a significant challenge. To address these challenges, we propose a spatio-temporal aware collaborative service ranking prediction approach, named ST-CRank, to achieve accurate service ranking results. Specifically, a spatial-aware service partial ranking model is introduced to generate partial rankings. In this model, a silhouette coefficient based clustering algorithm is utilized to partition edge domains, and services deployed on the same edge server are compared using the partial ranking model. The QoS comparisons are forecasted using a deep time series model to obtain partial rankings for each edge server, thereby capturing the multi-dimensional temporal characteristics of QoS. With the partial rankings obtained, the global ranking is achieved by aggregating the partial rankings within the same edge domain. Finally, the effectiveness of ST-CRank is evaluated through large scale real world dataset based experiments. The results demonstrate that our approach achieves higher accuracy in prediction compared to other baseline algorithms.}, archive = {J_ACCESS}, author = {Yuze Huang and Xiao Chen and Wenhui Zhang and Qianxi Li and He Li}, doi = {10.1109/ACCESS.2025.3576253}, journal = {IEEE Access}, pages = {97930-97943}, shortjournal = {IEEE Access}, title = {Spatio-temporal aware collaborative service ranking prediction in IoT-enabled edge computing}, volume = {13}, year = {2025}, }
• (2025). An efficient architecture for edge AI federated learning with homomorphic encryption. ACCESS, 13, 97919-97929. (www)

  @article{ , abstract = {With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FL—such as high communication overhead, resource limitations, and convergence inefficiencies—our method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average increase of +8.4% in accuracy, −16.2% lower latency, −35.1% reduction in memory usage, and −2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.}, archive = {J_ACCESS}, author = {Dadmehr Rahbari and Masoud Daneshtalab and Maksim Jenihhin}, doi = {10.1109/ACCESS.2025.3576689}, journal = {IEEE Access}, pages = {97919-97929}, shortjournal = {IEEE Access}, title = {An efficient architecture for edge AI federated learning with homomorphic encryption}, volume = {13}, year = {2025}, }
• (2025). An improved large neighborhood search for network-level airport slot allocation optimization. ACCESS, 13, 97901-97918. (www)

  @article{ , abstract = {Airports worldwide face a significant gap between capacity and demand. As a short-term solution, the International Air Transport Association has implemented schedule coordination rules at busy airports. While the complex slot allocation problem has been studied extensively, most research focuses on single airports, overlooking interactions with other airports. Limited studies on network-level allocation lack scalability for real-world problems. This study develops an optimization model to enhance schedule synchronization among airports with connection dependencies. The model integrates differentiated slot priorities and flight connection requirements under capacity constraints, while introducing unassigned request variables to address request overflow scenarios. Accordingly, an enhanced large neighborhood search algorithm was developed with three proposed spatial-temporal neighborhood structures specifically designed for the model characteristics. Validated through an extensive case study involving 4,695 requests across six hub airports, the algorithm achieves solutions within a 1.7% optimality gap while reducing computation time by up to 64%.}, archive = {J_ACCESS}, author = {Qiangzhe Wang and Yuanyuan Ma and Yifan Xu and Zhiyuan Shen and Dan Li}, doi = {10.1109/ACCESS.2025.3575323}, journal = {IEEE Access}, pages = {97901-97918}, shortjournal = {IEEE Access}, title = {An improved large neighborhood search for network-level airport slot allocation optimization}, volume = {13}, year = {2025}, }
• (2025). Improved YOLOv8n models for object detection in remote sensing images. ACCESS, 13, 97889-97900. (www)

  @article{ , abstract = {In recent years, numerous efficient object detectors have emerged in computer vision. However, applying these models to remote sensing images remains challenging due to complex backgrounds, high object scale variation, and the difficulty of detecting small objects. Many existing approaches prioritize accuracy but overlook the trade-off between precision and efficiency, limiting their usability in resource-constrained environments. To address these challenges, we propose a lightweight model based on the original YOLOv8n, culminating in the Transformer with Bi-directional and Coordinate Attention-YOLOv8n (TBC-YOLO8n) model. Our approach introduces a weighted bidirectional feature pyramid network that adaptively assigns importance to different feature maps, enhancing multi-scale object detection. Additionally, we integrate transformer blocks to improve global feature representation and capture long-range dependencies, alongside a coordinate attention block before each detection head to enhance localization by incorporating spatial information. Unlike previous YOLO variants, our method optimizes feature fusion strategies and integrates advanced attention mechanisms specifically tailored for remote sensing images, effectively addressing small-object detection, large-scale variations, and complex backgrounds. Compared to the original YOLOv8n, our proposed TBC-YOLO8n model improves mAP@50 from 90.2% to 94.5%, with a slight increase in inference time from 3.0 ms to 4.0 ms. Experimental results confirm the robustness and effectiveness of our model, making it a practical choice for real-time remote sensing applications with limited computational resources.}, archive = {J_ACCESS}, author = {Young-Long Chen and Kai-Chun Hung and Jia-Yun Zhang and Ling-Wei Lin}, doi = {10.1109/ACCESS.2025.3574856}, journal = {IEEE Access}, pages = {97889-97900}, shortjournal = {IEEE Access}, title = {Improved YOLOv8n models for object detection in remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of metaheuristics for LQR controller optimization: A two-wheel balancing robot case study. ACCESS, 13, 97870-97888. (www)

  @article{ , abstract = {Two-wheeled balancing robots are commonly modeled as inverted pendulum systems, which are inherently unstable and present significant challenges for control due to their nonlinear and underactuated dynamics. One of the main problems in their control is achieving stability and precise position tracking under real-world disturbances and dynamic uncertainties. This research addresses these challenges by proposing a methodology to optimize a linear quadratic regulator (LQR) controller using metaheuristic algorithms. Specifically, the parameters $ Q $ and $ R $ of the LQR are tuned using three optimization strategies: artificial bee colony (ABC), differential evolution (DE), and global-best harmony search (GHS). The performance of each algorithm is evaluated through a comparative analysis, focusing on control stability and dynamic responsiveness. Experimental results demonstrate that the controller optimized by differential evolution achieves the lowest root mean square error (0.4910 degrees), outperforming ABC (0.5187 degrees) and GHS (0.5195 degrees). These findings underscore the effectiveness of metaheuristic algorithms in enhancing control performance for dynamically unstable systems, facilitating their implementation in real-world autonomous control applications.}, archive = {J_ACCESS}, author = {José Ricardo Rivera-Ruiz and Ricardo Rojas-Galván and José R. García-Martínez and Edson Eduardo Cruz Miguel and Omar A. Barra-Vázquez and Maria Ines Cruz Orduña and Jesús Enrique Escalante-Martínez and Juvenal Rodríguez-Reséndiz}, doi = {10.1109/ACCESS.2025.3576343}, journal = {IEEE Access}, pages = {97870-97888}, shortjournal = {IEEE Access}, title = {Performance evaluation of metaheuristics for LQR controller optimization: A two-wheel balancing robot case study}, volume = {13}, year = {2025}, }
• (2025). Toward generalizable multiple sclerosis lesion segmentation models. ACCESS, 13, 97859-97869. (www)

  @article{ , abstract = {Automating Multiple Sclerosis (MS) lesion segmentation would be of great benefit in initial diagnosis as well as monitoring disease progression. Deep learning based segmentation models perform well in many domains, but the state-of-the-art in MS lesion segmentation is still suboptimal. Complementary to previous MS lesion segmentation challenges which focused on optimizing the performance on a single evaluation dataset, this study aims to develop models that generalize across diverse evaluation datasets, mirroring real-world clinical scenarios that involve varied scanners, settings, and patient cohorts. To this end, we used all high-quality publicly-available MS lesion segmentation datasets on which we systematically trained a state-of-the-art UNet++ architecture. The resulting models demonstrate consistent performance across the remaining test datasets (are generalizable), with larger and more heterogeneous datasets leading to better models. To the best of our knowledge, this represents the most comprehensive cross-dataset evaluation of MS lesion segmentation models to date using publicly available datasets. Additionally, explicitly enhancing dataset size by merging datasets improved model performance. Specifically, a model trained on the combined MSSEG2016-train, ISBI2015, and 3D-MR-MS datasets surpasses the winner of the MICCAI-2016 competition. Moreover, we demonstrate that the generalizability of our models also relies on the use of quantile normalization on MRI intensities.}, archive = {J_ACCESS}, author = {Liviu Badea and Maria Popa}, doi = {10.1109/ACCESS.2025.3576145}, journal = {IEEE Access}, pages = {97859-97869}, shortjournal = {IEEE Access}, title = {Toward generalizable multiple sclerosis lesion segmentation models}, volume = {13}, year = {2025}, }
• (2025). A review of multimodal AI in veterinary diagnosis: Current trends, challenges, and future directions. ACCESS, 13, 97846-97858. (www)

  @article{ , abstract = {Artificial intelligence is playing an increasingly prominent role in veterinary diagnostics, offering innovative solutions to address the complexities of modern animal healthcare. This paper focuses on multimodal AI, a rapidly evolving field that integrates diverse data types, including medical imaging (e.g. X-rays, CT scans), textual data (e.g. clinical notes, lab reports), behavioral videos (e.g. gait analysis), and audio recordings (e.g. respiratory sounds). By combining these modalities, multimodal AI systems provide a holistic and data-driven approach to veterinary diagnostics, enhancing precision and efficiency. However, unlike human medicine, veterinary diagnostics must account for species-specific physiological variations, diverse movement patterns, and the absence of verbal patient feedback, making the integration of multimodal data both uniquely challenging and critically important. The objectives of this review are to consolidate current knowledge on multimodal AI applications in veterinary medicine while drawing insights from analogous applications in human medicine, identifying key challenges and knowledge gaps, and proposing future directions for research and clinical adoption. Key findings highlight the prevalent methods, including machine learning models, as well as advanced data fusion strategies for integrating multimodal inputs. The paper also addresses significant challenges, such as data collection and standardization, computational limitations, adoption barriers, and ethical concerns regarding data privacy and bias. The review underscores the transformative potential of multimodal AI in veterinary practice, with implications for improving diagnostic accuracy, reducing cognitive load, and streamlining clinical workflows. Moving forward, collaborative efforts between researchers, veterinary professionals, and technology developers are essential to overcome current barriers. With continued advancements, multimodal AI is set to become a standard tool, driving the future of veterinary diagnostics toward more comprehensive and efficient care.}, archive = {J_ACCESS}, author = {Carolina Gomes and Luísa Coheur and Paula Tilley}, doi = {10.1109/ACCESS.2025.3575343}, journal = {IEEE Access}, pages = {97846-97858}, shortjournal = {IEEE Access}, title = {A review of multimodal AI in veterinary diagnosis: Current trends, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Effects of different noise reduction deep learning strategies on computed tomography images. ACCESS, 13, 97835-97845. (www)

  @article{ , abstract = {Noise reduction in Computed Tomography (CT) is very important for two reasons. On one hand, it can improve the quality of reconstructions, and on the other hand, significant dose reduction can be achieved, which is important due to the radiation risk to the patient. In this area, deep learning-based noise reduction possibilities are in the focus of interest. There are numerous different network models with various training strategies. In this paper, we examined different supervised training strategies with four different deep-learning architectures, and for the training, we only used mathematical phantoms with artificially simulated noise. The noise model was designed for a real multi-slice cone-beam CT and was performed in projection space on forward-projected mathematical phantoms. We validated the noise model and evaluated the effectiveness of different deep learning-based noise reduction models. We redesigned some of the deep learning models and analyzed their noise reduction effect using the noise power spectrum. We examined the noise reduction performance of the models on both real multi-slice cone beam CT scans and mathematical phantoms, with particular attention to their practical applicability.}, archive = {J_ACCESS}, author = {Zsolt Adam Balogh and Anusuya Krishnan and Mahmoud Nizar Hassan}, doi = {10.1109/ACCESS.2025.3576498}, journal = {IEEE Access}, pages = {97835-97845}, shortjournal = {IEEE Access}, title = {Effects of different noise reduction deep learning strategies on computed tomography images}, volume = {13}, year = {2025}, }
• (2025). Application of fuzzy logic sliding mode control approach with PID structure for electro-hydraulic actuator tracking system. ACCESS, 13, 97818-97834. (www)

  @article{ , abstract = {The electro-hydraulic actuator (EHA) system generates a trajectory by transferring high force densities in the form of pressurized fluid flows to a hydraulic actuator. Moreover, the sliding mode control (SMC) approach has been discovered as a potential method for the EHA trajectory tracking control system. However, high-frequency proportional valve activity has occurred during the practical application of the conventional SMC approach, resulting in tracking performance degradation. Furthermore, a preferable SMC sliding surface design is necessary to improve the precision of trajectory tracking performance, and the SMC designs involve complicated procedure and mathematical formulations. Therefore, this paper proposes a hybrid optimized fuzzy logic (FL) SMC with a proportional-integral-derivative (PID) structure (FLSMCPID) for trajectory tracking control in an EHA system. The proposed control strategy was designed with the switching function modification based on an FL approach in the conventional SMC algorithm. Due to the difficulty of concurrent hybrid design, a particle swarm optimization (PSO) algorithm was employed to determine the optimal control variables value. Simulations utilizing a linear EHA system model obtained using the grey-box identification approach and experimentation on an EHA system workbench for various trajectories and under the consequences of variation in supply pressure were conducted to evaluate the performance of the proposed control strategy. The simulation and experimental results demonstrate that higher effectiveness, precision, and robustness were achieved by the EHA system with the proposed control strategy.}, archive = {J_ACCESS}, author = {Muhamad Fadli Ghani and Ahmad Athif Mohd Faudzi and Rozaimi Ghazali and Shahrol Mohamaddan}, doi = {10.1109/ACCESS.2025.3575908}, journal = {IEEE Access}, pages = {97818-97834}, shortjournal = {IEEE Access}, title = {Application of fuzzy logic sliding mode control approach with PID structure for electro-hydraulic actuator tracking system}, volume = {13}, year = {2025}, }
• (2025). Advancing functional near-infrared spectroscopy (fNIRS) with transformers: A PRISMA-driven systematic review. ACCESS, 13, 97794-97817. (www)

  @article{ , abstract = {Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging modality in various domains, including cognitive neuroscience, brain-computer interfaces (BCIs), clinical diagnostics, mental workload assessment, and neurorehabilitation. Despite its growing applications, processing fNIRS signals presents unique challenges. The advent of deep learning, particularly Transformers, has taken on an increasingly pivotal role in addressing these complexities. As Transformers have revolutionized multiple fields, their application in fNIRS signal processing has gained significant traction recently. To support ongoing research, this study systematically reviews the advancements and applications of Transformer models in fNIRS research, following PRISMA guidelines. A comprehensive analysis was conducted on 30 studies (N = 895), offering a structured evaluation of current trends, methodologies, and outcomes. This review paper highlights Transformer architectures in fNIRS-based studies, focusing on advanced modifications in key stages such as the embedding and attention mechanisms. Multimodal studies analyzing fNIRS with other modalities, such as Electroencephalography (EEG), using Transformers are also reviewed. Finally, the paper examines open-access and private datasets used in the literature and offers guidelines for future research, identifying open problems that warrant further exploration.}, archive = {J_ACCESS}, author = {Neda Abdollahpour and Abhinav Kakaraparthi and Chakradhar Sai Valluru and N. Sertac Artan}, doi = {10.1109/ACCESS.2025.3576056}, journal = {IEEE Access}, pages = {97794-97817}, shortjournal = {IEEE Access}, title = {Advancing functional near-infrared spectroscopy (fNIRS) with transformers: A PRISMA-driven systematic review}, volume = {13}, year = {2025}, }
• (2025). Beyond N-grams: Enhancing string kernels with transformer-guided semantic insights. ACCESS, 13, 97779-97793. (www)

  @article{ , abstract = {The rapid advancements in large language models (LLMs) have led to the generation of sophisticated AI-produced texts, posing significant challenges in distinguishing machine-generated content from authentic human writing. This study presents a novel hybrid framework that effectively integrates string kernel approaches with deep contextual embeddings from state-of-the-art transformers for robust AI-generated text detection. We propose and evaluate four innovative kernel-based methods namely Attention-Augmented Kernel, Error Pattern Analysis, Transformer-Guided N-gram Selection, and a Custom Kernel Function each designed to uniquely capture semantic and structural distinctions of text. Extensive experiments conducted on eight diverse datasets comprising 2,501 total samples, featuring texts generated and enhanced by leading LLMs including GPT-3.5, GPT-4, DeepSeek, and KIMI, demonstrate superior performance of the proposed methods. Particularly, the Transformer-Guided N-gram Selection and the Custom Kernel Function consistently outperform baseline models, achieving near-perfect detection accuracy with significantly reduced computational complexity. Comprehensive hyperparameter optimization further solidifies our methods’ effectiveness and practical applicability. The publicly available datasets and robust empirical evaluations contribute valuable benchmarks for future research. This work sets a new standard in AI-text detection methodologies, enhancing reliability, efficiency, and scalability for real-world applications.}, archive = {J_ACCESS}, author = {Nazar Zaki and Reem Alderei and Mahra Alketbi and Alia Alkaabi and Fatima Alneyadi and Nadeen Zaki}, doi = {10.1109/ACCESS.2025.3576076}, journal = {IEEE Access}, pages = {97779-97793}, shortjournal = {IEEE Access}, title = {Beyond N-grams: Enhancing string kernels with transformer-guided semantic insights}, volume = {13}, year = {2025}, }
• (2025). Deepfake audio detection for urdu language using deep neural networks. ACCESS, 13, 97765-97778. (www)

  @article{ , abstract = {Audio Deepfakes, which are highly realistic fake audio recordings driven by AI tools that clone human voices, recent advancements in Text-to-Speech (TTS) and Vocal Conversion (VC) technologies have made it easier to create realistic synthetic and imitative speech, making audio Deepfakes a common and potentially dangerous form of deception. Well-known people, such as politicians and celebrities, are often targeted. They are tricked into saying controversial things in fake recordings, making them a nuisance on social media. Even kid’s voices are cloned to scam parents into ransom payments, etc. Therefore, developing effective algorithms to distinguish fake audio from real audio is critical to preventing such frauds. Various machine learning (ML) and deep learning (DL) techniques have been created to identify Deepfake audios. However, most of these solutions are trained on datasets in English, Portuguese, French, and Spanish, expressing concerns about their correctness for other languages. The main goal of the research presented in this paper is to evaluate the effectiveness of deep learning neural networks in detecting Deepfake audios in the Urdu language. Since there was no suitable dataset of Urdu audio available for this purpose, we created our own dataset (URFV) using both genuine and fake audio recordings. The original/real Urdu audio recordings were gathered from random YouTube podcasts and generated as Deepfake audios using the RVC (Retrieve based voice conversion) model. Our dataset comprises three versions containing audio clips of 5, 10, and 15 seconds in duration. Each version includes samples from a total of 125 speakers (95 males and 30 females), and the data is divided into 70% for training, 15% for validation, and 15% for testing. We have built various deep learning neural networks like (RNN+LSTM, CNN+attention, TCN, CNN+RNN) to detect Deepfake audio made through imitation or synthetic techniques. The proposed approach extracts Mel frequency-cepstral-coefficients (MFCC) features from the audios in the dataset. When tested and evaluated, our model’s accuracy across datasets was noteworthy. 97.78% (5s), 98.89% (10s), and 98.33% (15s) were remarkable results for the RNN+LSTM model. In comparison, the CNN + RNN hybrid model equaled 92. 2% (5 s) and scored 98. 89% (10 s) and 93. 33% (15 s), while the CNN + Attention model recorded 98. 89% (5 s), 92. 22% (10 s), and 93. 33% (15 s). Despite its lower effectiveness, the TCN model obtained 70% (5s), 83.33% (10s), and 77.78% (15s).}, archive = {J_ACCESS}, author = {Omair Ahmad and Muhammad Sohail Khan and Salman Jan and Inayat Khan}, doi = {10.1109/ACCESS.2025.3571293}, journal = {IEEE Access}, pages = {97765-97778}, shortjournal = {IEEE Access}, title = {Deepfake audio detection for urdu language using deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Infrared monocular depth estimation based on radiation field detail enhancement. ACCESS, 13, 97751-97764. (www)

  @article{ , abstract = {Monocular depth estimation (MDE) offers cost-effective approach to 3D perception, while thermal infrared sensors show high robustness under varying illumination conditions. Their combination is crucial for day and night 3D perception, yet it faces two key challenges: 1) inadequate processing of 14-bit RAW data, leading to loss of subtle infrared information, and 2) the resolution limitation of current infrared datasets (existing $640\times 512$ sensor collections restrict the camera’s ability to capture critical scene details). To this end, in this paper, we introduce the IRSL dataset, which integrates infrared, stereo RGB, and LiDAR modalities. Data were acquired using a high-resolution thermal infrared camera ( $1280\times 1024$ pixels), synchronized stereo cameras, and LiDAR (500K points per frame). The dataset aims to enhance the precision and robustness of depth estimation in both daytime and nighttime environments. Secondly, this paper proposes Radiation Field Detail Enhancement (RFDE), a novel method for enhancing infrared radiation field details in RAW data. This method performs gray mapping based on radiation field intensity and spatial context awareness and uses a bilateral filter to enhance the details in the image. Experimental results show that the proposed RFDE method fully leverages the advantages of high-resolution infrared images, effectively improving the quantitative and qualitative performance of the MDE model ( $\delta _{1}$ =0.964, SqRel = 0.441).}, archive = {J_ACCESS}, author = {Rihua Hao and Chao Xu and Chonghao Zhong}, doi = {10.1109/ACCESS.2025.3574932}, journal = {IEEE Access}, pages = {97751-97764}, shortjournal = {IEEE Access}, title = {Infrared monocular depth estimation based on radiation field detail enhancement}, volume = {13}, year = {2025}, }
• (2025). Optimized error detection and correction in ATM networks using the enhanced berlekamp massey algorithm with hybrid automatic repeat request. ACCESS, 13, 97737-97750. (www)

  @article{ , abstract = {Asynchronous Transfer Mode (ATM) is a widely used communication method known for its high bandwidth features and low latency. It uses fixed size cells, namely 53 bytes, including 48 bytes for the real data and five bytes set for control purposes. To help in resolving any transmission issues, the control information contains components such as a Virtual Path Identifier (VPI), Virtual Channel Identifier (VCI), and Cyclic Redundancy Check (CRC). While Automatic Repeat Request (ARQ) can cause delays because of retransmissions, Forward Error Correction (FEC) improves efficiency by fixing errors without the need of data retransmissions. Reed-Solomon (RS) codes are widely used in ATM networks because of their ability in correcting burst errors. The proposed Enhanced Berlekamp Massey Algorithm (EBMA) improves RS error correction by updating only the essential polynomial coefficients. Not only the proposed algorithm cuts decoding complexity and processing time but also it makes it suitable for real time and modern communication networks like ATM. The simulation results confirm that the integration of EBMA and Hybrid Automatic Repeat Request (HARQ) succeeds remarkable gains in Bit Error Rate (BER) and power efficiency. Furthermore, a new adaptive algorithm has been developed that simplifies the process by dynamically modifying decoding parameters in real time according to channel conditions. This minimizes the need for unnecessary computations. The results show that EBMA can really improve the transmission reliability and data integrity in ATM networks.}, archive = {J_ACCESS}, author = {Mahmut Aykaç}, doi = {10.1109/ACCESS.2025.3572836}, journal = {IEEE Access}, pages = {97737-97750}, shortjournal = {IEEE Access}, title = {Optimized error detection and correction in ATM networks using the enhanced berlekamp massey algorithm with hybrid automatic repeat request}, volume = {13}, year = {2025}, }
• (2025). Context-aware negative sampling for sequential recommendation. ACCESS, 13, 97717-97736. (www)

  @article{ , abstract = {Recommender systems have become essential in large-scale e-commerce and content platforms. While user preferences are crucial in generating recommendations, the context in which recommendations are made—such as time, location, and occasion—also plays a significant role. Over the past decades, context-aware models have been developed to address this. However, the use of context information in negative sampling remains less expored, despite the well-known impact of sampling strategies on recommendation performance. In this study, we first quantitatively demonstrate that item distributions vary significantly across different contexts. Based on this observation, we propose two novel negative sampling methods: context-aware hard negative item sampling and negative context sampling. These methods enhance recommendation diversity by more accurately reflecting context-dependent item distributions during training. To validate the effectiveness of the proposed methods, we apply them to a sequential recommendation model leveraging temporal context, where time information plays a critical role. The proposed methods are designed to be easily integrated into any sampling-based sequential recommendation model in a plug-and-play manner, and extensive experimental results on 10 real-world datasets demonstrate that the proposed methods significantly improve recommendation diversity, with an average increase of 20.65%, while incurring only a minor accuracy loss of 1.89%.}, archive = {J_ACCESS}, author = {Jinseok Seol and Jaesik Choi}, doi = {10.1109/ACCESS.2025.3576124}, journal = {IEEE Access}, pages = {97717-97736}, shortjournal = {IEEE Access}, title = {Context-aware negative sampling for sequential recommendation}, volume = {13}, year = {2025}, }
• (2025). Secure satellite downlink communication with attention-enhanced transmitter fingerprinting. ACCESS, 13, 97708-97716. (www)

  @article{ , abstract = {As the internal nature of satellite networks such as the wide coverage, the low received power at the ground, and the radio broadcasting, detecting replay or spoofing attacks is an intractable challenge. Due to the limited onboard processing ability or leaked keys, a lot of satellites cannot secure downlink communication with cryptography. As a result, non-cryptographic methods based on fingerprint modeling have become popular. Nevertheless, existing methods overlook the difference between fingerprints during message transmission, leading to insufficient performance. To this end, we propose an attention-enhanced transmitter fingerprinting method (Att-Sat) to authenticate transmissions from satellites in a non-cryptographic way. We use the attention mechanism to make the model learn to pay more attention to the distinct fingerprint features of a specific transmitter, exploring more distinguished fingerprint characteristics for each transmitter. Experimental results on a publicly available dataset collected from the Iridium constellation indicate the effectiveness of our method. Under different settings, our method achieves a 6.2% improvement of AUC and a 6.5% improvement of EER on average compared with the state-of-the-art method. Moreover, our method Att-Sat achieves significant improvement under the real replay attack scenario.}, archive = {J_ACCESS}, author = {Yinan Bao and Haojie Yuan}, doi = {10.1109/ACCESS.2025.3575409}, journal = {IEEE Access}, pages = {97708-97716}, shortjournal = {IEEE Access}, title = {Secure satellite downlink communication with attention-enhanced transmitter fingerprinting}, volume = {13}, year = {2025}, }
• (2025). Approaching collaborative manipulation by pushing-grasping fusion. ACCESS, 13, 97693-97707. (www)

  @article{ , abstract = {Smart manipulation is always the desired performance for the interesting researches in the field of robotic control. The complicated fusion among motion primitives could offer the advanced adaptions in presence of highly success rate or unknown objects. In this paper, a hierarchical framework for pushing-grasping fusion in the cluttered environment is demonstrated. Our method involves three-phase training process, integration of masks and the reinforcement learning scheme. Both simulation and experimentation are undertaken to validate the efficacy and feasibility of the proposed methodology. Our contributions in this work are (i) to propose both grasp mask and push mask for encouraging the model to focus on exploiting, adjusting the proper grasping posture in the desired target area as well as avoiding the phenomenon of the gripper slipping on the surface of an object, (ii) to recommend the reinforcement learning scheme without object model, and (iii) to release a hierarchical training method to enhance the interactive efficiency between grasping and pushing actions.}, archive = {J_ACCESS}, author = {Lu Anh Duy Phan and Dang Quy Phan and The Tri Bui and Phuong H. Le and Dinh Tuan Tran and Joo-Ho Lee and Ha Quang Thinh Ngo}, doi = {10.1109/ACCESS.2025.3574018}, journal = {IEEE Access}, pages = {97693-97707}, shortjournal = {IEEE Access}, title = {Approaching collaborative manipulation by pushing-grasping fusion}, volume = {13}, year = {2025}, }
• (2025). Intelligent control valve stiction diagnosis approach. ACCESS, 13, 97681-97692. (www)

  @article{ , abstract = {Variations in process variables are one of the major concerns in industrial plants due to their impact on product quality and equipment performance, leading to higher operating and maintenance costs in addition to safety issues. One of the common sources of process variation is control valve stiction. Therefore, early detection and quantification of valve stiction is crucial for maintaining product quality and reducing maintenance costs. A smart valve equipped with a positioner simplifies stiction diagnosis, whereas the diagnosis process for an ordinary control valve is not straightforward. As the number of ordinary control valves installed in plants is still quite high, we developed in this research a smart approach for stiction detection and quantification for this type of control valve. In order to implement the proposed approach, we used state-of-the-art signal processing and machine learning techniques to develop a trustworthy strategy for stiction analysis applicable to a wide variety of operating setups, such as cascade loops and settings variations. Testing this approach on publicly available industrial data showed that it worked well for all tested scenarios to detect the stiction and provided an accurate estimate of the stiction value.}, archive = {J_ACCESS}, author = {Sami El-Ferik and Mustafa Al-Naser and Abrar Al-Amoudi and Abdullatif Al-Najim and Karim Asif Sattar}, doi = {10.1109/ACCESS.2025.3573646}, journal = {IEEE Access}, pages = {97681-97692}, shortjournal = {IEEE Access}, title = {Intelligent control valve stiction diagnosis approach}, volume = {13}, year = {2025}, }
• (2025). Enhancing extreme programming (XP) adoption through SAMAM: A scalable agile maturity assessment model based on industry best practices. ACCESS, 13, 97647-97680. (www)

  @article{ , abstract = {The adoption of Extreme Programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a novel Scalable Agile Maturity Assessment Model (SAMAM) to address these barriers and facilitate the effective adoption of XP. The model is designed by leveraging established frameworks, including the Capability Maturity Model Integration (CMMI), Software Outsourcing Vendor Readiness Model (SOVRM), and Software Process Improvement Implementation Management Model (SPIIMM). Unlike traditional models that rely on predefined Key Process Areas (KPAs), SAMAM adopts 14 critical barriers (CBs) identified through a Systematic Literature Review (SLR) and corresponding practices as the foundation for its maturity levels. The study was conducted in four phases. First, an SLR was performed to identify 14 critical barriers to XP adoption and their respective mitigation practices. In the second phase, a survey questionnaire was administered within the software industry to validate the SLR findings and extract additional industry-relevant practices. The third phase involved the development of SAMAM, structured into five maturity levels using the identified barriers and practices instead of traditional KPAs. In the final phase, industrial case studies were conducted to evaluate the model’s effectiveness in real-world settings using the Motorola Assessment Tool. The findings demonstrate that SAMAM provides a comprehensive and scalable approach to assess and improve XP adoption maturity by systematically addressing critical adoption barriers. The model supports organizations in overcoming XP adoption challenges and achieving higher process maturity. The evaluation through case studies confirmed the practical applicability and effectiveness of the proposed model, contributing to the body of knowledge on agile methodologies and advancing XP adoption in the software development industry.}, archive = {J_ACCESS}, author = {Muhammad Faisal Abrar and Ali Alferaidi and Tariq S. Almurayziq and Muhammad Saqib and Wilayat Khan and Zawar Khan and Mohammad Alsaffar}, doi = {10.1109/ACCESS.2025.3574147}, journal = {IEEE Access}, pages = {97647-97680}, shortjournal = {IEEE Access}, title = {Enhancing extreme programming (XP) adoption through SAMAM: A scalable agile maturity assessment model based on industry best practices}, volume = {13}, year = {2025}, }
• (2025). Optimized AI and IoT-driven framework for intelligent water resource management. ACCESS, 13, 97628-97646. (www)

  @article{ , abstract = {The scheme of water resources management is necessary for reducing water scarcity in arid areas and improving water availability in general. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems.}, archive = {J_ACCESS}, author = {Mahmoud Badee Rokaya Mahmoud and Dalia Ismaeil Ibrahim Hemdan and Samah Hazzaa Alajmani and Raneem Yousif Alyami and Ghada Elmarhomy and Hassan Hashim and El-Sayed Atlam}, doi = {10.1109/ACCESS.2025.3572067}, journal = {IEEE Access}, pages = {97628-97646}, shortjournal = {IEEE Access}, title = {Optimized AI and IoT-driven framework for intelligent water resource management}, volume = {13}, year = {2025}, }
• (2025). LightSTATE: A generalized framework for real-time human activity detection using edge-based video processing and vision language models. ACCESS, 13, 97609-97627. (www)

  @article{ , abstract = {Human activity detection plays a vital role in applications such as healthcare monitoring, smart environments, and security surveillance. However, traditional methods often rely on computationally intensive models, which are unsuitable for edge devices due to their limited resources and high latency. This research introduces a new approach named LightSTATE, a lightweight hybrid framework designed for real-time activity detection by integrating edge-based preprocessing with cloud-hosted Vision Language Models (VLMs). The system processes video streams through efficient frame extraction, edge detection and Root Mean Square Error (RMSE)-based frame filtering to identify significant motion events. These selected frames are transmitted to the cloud for semantic analysis and state recognition. The system utilizes GPT-4o as the vision-language model to generate rich, descriptive captions for human state understanding. LightSTATE was evaluated on a curated dataset of 9 real-world videos containing 634 frames, with 320 labeled frames used for testing. The framework demonstrated high accuracy in detecting predefined states, with an average precision of 0.98, an average recall of 0.98, and an average F1-score of 0.98. The experimental results confirm that the average frame processing time is approximately 0.8-0.9 seconds, ensuring suitability for real-time applications. Due to the scarcity of publicly accessible datasets, we compiled a custom dataset to effectively assess the performance of our proposed approach. The results validate the scalability and real-time capabilities of our proposed system, making it a promising solution for continuous monitoring applications. This study establishes LightSTATE as a scalable, efficient, and privacy-preserving framework for real-time human activity detection. Future work will focus on extending its applicability to dynamic and multi-person scenarios while enhancing system adaptability for diverse use cases.}, archive = {J_ACCESS}, author = {Anik Debnath and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3574659}, journal = {IEEE Access}, pages = {97609-97627}, shortjournal = {IEEE Access}, title = {LightSTATE: A generalized framework for real-time human activity detection using edge-based video processing and vision language models}, volume = {13}, year = {2025}, }
• (2025). Stacking gaussian process regression for soil moisture estimation over the continental U.S.. ACCESS, 13, 97597-97608. (www)

  @article{ , abstract = {To overcome the limitations posed by sparse and insufficient sample sizes in soil moisture estimation, ensemble learning—specifically the integration of multiple individual estimation models—has emerged as a promising approach. In this paper, we introduce a novel soil moisture estimation method based on ensemble learning, termed Stacking Gaussian Process Regression (SGPR). This method incorporates Gaussian Process Regression (GPR) within a stacking strategy that utilizes gradient boosting and employs K-fold cross-validation, leveraging 11 multi-source remote sensing features. Experiments conducted across the Continental U.S. from April 2015 to March 2016 demonstrate that the proposed SGPR method significantly outperforms existing state-of-the-art models, achieving a correlation coefficient $ R = 0.9097 $ and a root mean square error $ RMSE = 0.0474 \, cm^{3}/cm^{3} $ . By harnessing the strengths of various regression models and fully utilizing the prior information embedded within the sample data, the SGPR model effectively enhances both the accuracy and stability of soil moisture estimation.}, archive = {J_ACCESS}, author = {Ling Zhang and Yujuan Zhang and Wenchang Ji and Zhaohui Xue}, doi = {10.1109/ACCESS.2025.3576157}, journal = {IEEE Access}, pages = {97597-97608}, shortjournal = {IEEE Access}, title = {Stacking gaussian process regression for soil moisture estimation over the continental U.S.}, volume = {13}, year = {2025}, }
• (2025). Self-supervised ECG anomaly detection based on time-frequency specific waveform mask feature fusion. ACCESS, 13, 97585-97596. (www)

  @article{ , abstract = {The imbalance of ECG signal data and the complexity of labeling pose significant challenges for deep learning-based anomaly detection. Traditional contrastive learning approaches for ECG anomaly detection often rely on reconstruction or generation; however, normal signals that resemble abnormal ECG samples may be incorrectly clustered, leading to suboptimal performance. To address this issue, we propose an anomaly detection framework TFMAD that integrates ECG signal mask reconstruction with time-frequency contrastive learning, leveraging the correlation between time- and frequency-domain features for anomaly detection. Specifically, the proposed method incorporates an auto-encoder module, a time-frequency mask module, and a contrastive learning module to extract masked time-frequency domain features of ECG signals. The model then reconstructs the signal using time-frequency feature fusion and employs contrastive learning to structure the feature space, ensuring abnormal distributions are effectively learned. We evaluated this method on six datasets, and the results demonstrate that TFMAD outperforms nine state-of-the-art methods.}, archive = {J_ACCESS}, author = {Chongrui Tian and Fengbin Zhang}, doi = {10.1109/ACCESS.2025.3572484}, journal = {IEEE Access}, pages = {97585-97596}, shortjournal = {IEEE Access}, title = {Self-supervised ECG anomaly detection based on time-frequency specific waveform mask feature fusion}, volume = {13}, year = {2025}, }
• (2025). A federated explainable AI framework for smart agriculture: Enhancing transparency, efficiency, and sustainability. ACCESS, 13, 97567-97584. (www)

  @article{ , abstract = {This paper presents a comprehensive framework for integrating Explainable Artificial Intelligence (XAI) into smart agriculture to address challenges in transparency, interpretability, and trust associated with AI-driven decision-making. Leveraging techniques such as Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM), the framework provides actionable insights into predictive maintenance, crop health monitoring, and resource optimization. A hybrid methodology combines IoT-based data acquisition, Federated Learning (FL), and multimodal feature analysis to ensure scalability and privacy preservation. Additionally, the study introduces a multi-context agricultural dataset and a novel interpretability-accuracy metric to evaluate XAI models’ adaptability across diverse agricultural settings. Experimental results demonstrate the proposed framework’s superiority in achieving an optimal balance between accuracy and interpretability, resource efficiency, and robust decision-making in precision agriculture. This approach fosters sustainable practices while addressing ethical and practical challenges in democratizing AI in agriculture.}, archive = {J_ACCESS}, author = {Hassam Ahmed Tahir and Walaa Alayed and Waqar Ul Hassan}, doi = {10.1109/ACCESS.2025.3571340}, journal = {IEEE Access}, pages = {97567-97584}, shortjournal = {IEEE Access}, title = {A federated explainable AI framework for smart agriculture: Enhancing transparency, efficiency, and sustainability}, volume = {13}, year = {2025}, }
• (2025). Accelerating research and development processes through in-house pre-compliance testing using in-lab constructed setups. ACCESS, 13, 97559-97566. (www)

  @article{ , abstract = {This paper explores strategies for accelerating the research and development (R&D) process through in-house pre-compliance testing in a dedicated laboratory. The focus is on conducting electromagnetic compatibility (EMC) testing, particularly conducted emissions, and electrostatic discharge (ESD) immunity, early in the development cycle. The paper also introduces an innovative device, called the DMX Buddy. This is a specialized device designed to bridge the gap between intelligent lighting systems and non-intelligent light sources. One of the key features of the device is its ability to act as a gateway between the DMX512 protocol and other control systems that incorporate failsafe mechanisms. The control unit of the device is a microcontroller from STMicroelectronics, aimed at managing the interactions between various peripherals, power management circuits, and user interfaces.}, archive = {J_ACCESS}, author = {Kamil Kaczmarek and Agnieszka Lazarowska}, doi = {10.1109/ACCESS.2025.3576266}, journal = {IEEE Access}, pages = {97559-97566}, shortjournal = {IEEE Access}, title = {Accelerating research and development processes through in-house pre-compliance testing using in-lab constructed setups}, volume = {13}, year = {2025}, }
• (2025). A multi-modal approach for the molecular subtype classification of breast cancer by using vision transformer and novel SVM polyvariant kernel. ACCESS, 13, 97545-97558. (www)

  @article{ , abstract = {Accurate classification of breast cancer into distinct molecular subtypes is crucial for personalized treatment and improved patient outcomes. Despite advancements in machine learning, predicting these subtypes remains challenging due to tumor heterogeneity and complex data characteristics. This study introduces the Polyvariant kernel, a novel SVM kernel that leverages the Vision Transformer (ViT) for multimodal data integration from the TCGA dataset of 1,081 breast cancer patients. The ViT extracts features from Whole Slide Imaging (WSI), which are fused with gene expression profiles through an Optimization-Based Search mechanism. These fused features are then classified using the Polyvariant kernel. The proposed approach significantly enhances breast cancer subtype classification, outperforming classical RBF, Polynomial, and Utility kernels, achieving 87% accuracy and 87% recall. The Polyvariant kernel is designed with distinct hyperparameters, enabling effective integration of diverse data sources such as gene expression profiles and WSI. We theoretically validated the proposed Polyvariant kernel by proving the Mercer conditions and establishing it as both a universal approximator and a universal kernel. Furthermore, we empirically demonstrated its superiority by benchmarking it against deep learning models (ResNet-50, VGG16, GoogLeNet) and classical ML datasets. Statistical significance tests confirmed its robustness, reinforcing its effectiveness for complex biomedical data integration. The proposed research demonstrates the efficacy of multimodal data fusion in improving breast cancer subtype classification and establishes the Polyvariant kernel as a powerful tool for complex biomedical data analysis.}, archive = {J_ACCESS}, author = {Vinayak Shukla and Kaarthika and Archana Mathur and Piyush Narayan and Kunal Kishor}, doi = {10.1109/ACCESS.2025.3575126}, journal = {IEEE Access}, pages = {97545-97558}, shortjournal = {IEEE Access}, title = {A multi-modal approach for the molecular subtype classification of breast cancer by using vision transformer and novel SVM polyvariant kernel}, volume = {13}, year = {2025}, }
• (2025). Cardiac clarity: Harnessing machine learning for accurate heart-disease prediction. ACCESS, 13, 97529-97544. (www)

  @article{ , abstract = {The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals; the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the ‘CARDIACX’ model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC =0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care.}, archive = {J_ACCESS}, author = {Sujith Santhosh and Krishnaraj Chadaga and R.Vijaya Arjunan and Sandra D'Souza}, doi = {10.1109/ACCESS.2025.3573760}, journal = {IEEE Access}, pages = {97529-97544}, shortjournal = {IEEE Access}, title = {Cardiac clarity: Harnessing machine learning for accurate heart-disease prediction}, volume = {13}, year = {2025}, }
• (2025). MediVerse: A secure and scalable IoT-MR framework for real-time health and performance monitoring. ACCESS, 13, 97511-97528. (www)

  @article{ , abstract = {Recent advancements in wearable and Internet of Things (IoT) technologies have yet to be fully realized in combination with Mixed Reality (MR) for comprehensive real-time health monitoring systems. This paper introduces MediVerse, a secure and scalable IoT-MR framework designed to enhance proactive health management through immersive, context-aware data visualization and real-time feedback. MediVerse employs a three-tiered architecture integrating intelligent sensors, wearable technologies, and MR interfaces to facilitate dynamic prediction and decision-making during critical healthcare scenarios. The framework addresses key challenges in latency, data throughput, interoperability, and user engagement by leveraging edge computing, adaptive compression, and AI-driven analytics. Extensive evaluations demonstrate MediVerse’s superiority, achieving a 70% latency reduction, 51% higher data throughput, and 69% lower error rates compared to conventional systems. Additionally, usability studies highlight significant improvements in user satisfaction and interaction clarity. These findings position MediVerse as a transformative solution for next-generation real-time healthcare monitoring, with adaptability to domains such as sports and education.}, archive = {J_ACCESS}, author = {Pedro H. Regalado and Tao Han}, doi = {10.1109/ACCESS.2025.3570731}, journal = {IEEE Access}, pages = {97511-97528}, shortjournal = {IEEE Access}, title = {MediVerse: A secure and scalable IoT-MR framework for real-time health and performance monitoring}, volume = {13}, year = {2025}, }
• (2025). Robust resilience blocks detection problem in dynamic social networks. ACCESS, 13, 97499-97510. (www)

  @article{ , abstract = {Finding a stable community in dynamically developing networks is crucial. In this study, we introduce an innovative concept of resilience, originally rooted in infrastructure networks, and merge it with the distinctive features of k-core graphs to define a novel community structure in dynamic networks-referred to as the k-block. Leveraging the Independent Cascade model, we evaluate communities based on their capacity to sustain activity under adversarial conditions, where an attack is characterized by the blocking of nodes responsible for information propagation. The proposed k-block demonstrates exceptional robustness against such disruptions. We develop a non-submodular resilience block identification model to identify these resilient communities. Addressing the computational complexity inherent in this task, we propose a groundbreaking two-stage greedy algorithm with a time complexity lower than $O(V \cdot E)$ . To validate the algorithm’s efficacy, we conduct extensive experiments on four real-world datasets. The results are meticulously analyzed to evaluate both the algorithm’s performance and its practical applicability. Our findings not only underscore the algorithm’s efficiency but also highlight its potential for real-world deployment in dynamic network analysis.}, archive = {J_ACCESS}, author = {Yuxin Gao and Jianming Zhu and Peikun Ni and Guoqing Wang}, doi = {10.1109/ACCESS.2025.3576306}, journal = {IEEE Access}, pages = {97499-97510}, shortjournal = {IEEE Access}, title = {Robust resilience blocks detection problem in dynamic social networks}, volume = {13}, year = {2025}, }
• (2025). The RBF-FDTD method for computing lightning-transient voltages on grounding systems. ACCESS, 13, 97487-97498. (www)

  @article{ , abstract = {This paper introduces an advanced application of the Radial Basis Function-Finite-Difference Time-Domain (RBF-FDTD) method, as proposed in Vu et al., for computing lightning-induced transient voltages in grounding systems. The approach models grounding structures as non-uniform transmission lines, governed by telegrapher’s equations with space- and time-varying parameters—inductance $L(x,t)$ , conductance $G(x,t)$ , and capacitance $C(x,t)$ —while accounting for electromagnetic coupling across complex grounding configurations. Leveraging the method’s inherent flexibility and precision, extensive numerical simulations were conducted on two horizontal grounding electrodes and three benchmark grounding grids, validating its capability to accurately capture both localized and distributed transient effects. The results underscore the robustness, computational efficiency, and practical utility of RBF-FDTD in analyzing transient electromagnetic phenomena in power system grounding infrastructures. This study not only confirms the method’s superiority in modeling intricate grounding responses but also offers critical insights for enhancing the design and optimization of grounding systems under lightning and fast-transient conditions.}, archive = {J_ACCESS}, author = {Xuan-Binh Nguyen and Nhat-Nam Nguyen and Tinh-Minh T. Le and Phan-Tu Vu}, doi = {10.1109/ACCESS.2025.3575913}, journal = {IEEE Access}, pages = {97487-97498}, shortjournal = {IEEE Access}, title = {The RBF-FDTD method for computing lightning-transient voltages on grounding systems}, volume = {13}, year = {2025}, }
• (2025). Leveraging RAG with transformer for context-based personalized recommendations. ACCESS, 13, 97472-97486. (www)

  @article{ , abstract = {Recent advancements in large language models (LLMs) have shown significant progress in addressing challenges related to data sparsity and the cold-start problem. In e-commerce, recommendation systems are widely used as strategic tools to boost sales and enhance the customer experience by helping users find relevant products. Custom LLMs, leveraging textual features from user feedback, have been successfully applied to recommendation systems, yielding improvements across various recommendation scenarios. However, most existing methods rely on training-free recommendation approaches, which depend heavily on pre-trained knowledge. When LLMs are trained on sparse data or lack historical information, their performance in recommendation systems can be negatively impacted. Furthermore, inference with LLMs tends to be slow due to autoregressive generation, which limits the efficiency of traditional recommendation methods. To address these challenges, our contributions are: We proposed the Retrieval Augmented Generation with Transformer Recommendation (RAGX11Rec) framework. This framework integrated LLMs with a transformer-based model in a two-step process: 1) RankRAG is used to filter the top-k preferences via tuning the LLM for effective context ranking, 2) a transformer model with 11 embedded layers generated the top-N recommendations based on ranked preferences. Our instruction-tuned transformer module demonstrates superior performance by incorporating a fraction of ranked data into the training process. We evaluated the effectiveness of RAGX11Rec against state-of-the-art baseline methods using two public datasets taken from AliExpress and Epinions. Experimental results indicate that RAGX11Rec consistently outperforms other methods in recommendation accuracy and efficiency. Our key findings are; 1) RAGX11Rec effectively addresses the cold-start problem by leveraging retrieval-augmented generation (RAG) and transformer-based ranking. Unlike traditional models, it can deliver high-quality recommendations even when user interaction data is limited. 2) Tested on public datasets, RAGX11Rec delivered consistent improvements over other models and proved its scalability and adaptability across diverse product categories. This suggests the framework is robust and adaptable enough for large-scale commercial use.}, archive = {J_ACCESS}, author = {Faten S. Alamri and Amjad Rehman and Bayan Alghofaily and Adeel Ahmed and Khalid Saleem}, doi = {10.1109/ACCESS.2025.3574073}, journal = {IEEE Access}, pages = {97472-97486}, shortjournal = {IEEE Access}, title = {Leveraging RAG with transformer for context-based personalized recommendations}, volume = {13}, year = {2025}, }
• (2025). Deep spectral-spatial transformer for robust hyperspectral image segmentation in varying field conditions. ACCESS, 13, 97454-97471. (www)

  @article{ , abstract = {Hyperspectral imaging serves as a powerful tool for environmental studies, enabling the capture of significant properties in the objects being analyzed. These hidden properties may not be discernible through traditional RGB analysis, thus enhancing the distinguishability of target classes. Machine learning (ML) algorithms are widely utilized for processing hyperspectral data in tasks such as image classification or segmentation. Despite the rich hyperspectral feature space, ML-based approaches often struggle with the challenges posed by varying outdoor lighting conditions, leading to instability in the developed solutions. Furthermore, traditional segmentation methods designed for RGB images need to be adapted to ensure robust performance when applied to hyperspectral images (HSI). This work proposes a robust method for the classification of HSI in the outdoor environment under varying lighting conditions. A calibration procedure for hyperspectral sensor sensitivity has been proposed, ensuring improved quality of input data and, consequently, enhanced performance of models in solving hyperspectral image classification tasks. The paper also proposes a novel architecture, the Deep Spectral Spatial Transformer (DSST), which is employed as the classifier. This architecture leverages a deeper feature extractor to ensure robustness in various lightning conditions. Among these, standardization within individual channels yielded the best results. The applicability of the proposed approach is validated in the weed detection task in an agricultural field. The data collected were captured using a pushbroom hyperspectral sensor. A series of comparative experiments were conducted on a range of classification algorithms, encompassing both traditional ML algorithms and contemporary neural network architectures developed in recent years. The proposed DSST architecture demonstrated superior performance metrics, with values of 0.911 and 0.907 on F1-Score and Accuracy, respectively.}, archive = {J_ACCESS}, author = {Andrey Makarov and Islombek Mirpulatov and Nikita Firsov and Valeriy Lobanov and Svetlana Illarionova and Vladimir Podlipnov and Yuliya Vybornova and Dmitrii Shadrin and Andrey Rastorguev and Roman Skidanov and Evgeny Burnaev and Artem Nikonorov}, doi = {10.1109/ACCESS.2025.3575699}, journal = {IEEE Access}, pages = {97454-97471}, shortjournal = {IEEE Access}, title = {Deep spectral-spatial transformer for robust hyperspectral image segmentation in varying field conditions}, volume = {13}, year = {2025}, }
• (2025). Adaptive fixed-time practically tracking control for n-link flexible-joint manipulator with input quantization and input delay via command filter-based approach. ACCESS, 13, 97441-97453. (www)

  @article{ , abstract = {This paper investigates the fixed-time trajectory tracking control problem for n-link flexible joint manipulator arms subject to input quantization, input delay, and unmodeled dynamics. To address the “explosion of complexity” issue inherent in backstepping-based designs, the command-filtered backstepping technique is employed. However, conventional control scheme utilized in fixed-time control often suffer from the singularity problem. Tackling this critical challenge, a novel error compensation mechanism is proposed by introducing a novelly designed switching function into the compensation signal structure, thereby effectively circumventing the potential singularity. Furthermore, the effects of communication pressure and input delay are explicitly handled with a hysteresis uniform quantizer. Concurrently, the proposed method efficiently mitigates the adverse impacts of unmodelled dynamics by an auxiliary signal. Through the introduction of a compact set in the stability analysis, all signals within the closed-loop system are rigorously proven to exhibit semi-globally practically fixed-time stability (SGPFTS). Simulation results validate the effectiveness and superior performance of the proposed control scheme.}, archive = {J_ACCESS}, author = {Rongyang Qiu and Meizhen Xia and Jiu Zhang and Chao Ma}, doi = {10.1109/ACCESS.2025.3574842}, journal = {IEEE Access}, pages = {97441-97453}, shortjournal = {IEEE Access}, title = {Adaptive fixed-time practically tracking control for n-link flexible-joint manipulator with input quantization and input delay via command filter-based approach}, volume = {13}, year = {2025}, }
• (2025). Enhanced consumer healthcare data protection through AI-driven TinyML and privacy-preserving techniques. ACCESS, 13, 97428-97440. (www)

  @article{ , abstract = {In the recent digital landscape, securing healthcare data stored on personal devices has become imperative due to increasing cyberattacks. Healthcare data inside an organization is often vulnerable to security breaches and requires privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This research proposes a novel method by integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to ensure the security and privacy of health-related data on resource-constrained edge devices. The proposed method with TinyML looks at patient data, like ECG readings and reports of unusual heartbeats, right on local edge devices that have limited resources, enabled immediate privacy checks while using minimal computing power. Federated learning trains the model on local edge devices by ensuring sensitive data remains on user devices, which are accessed centrally. Finally, differential privacy techniques strengthen security by additionally adding noise to data for safeguarding against malicious attacks without compromising the data’s accessibility. The proposed model achieves a high accuracy of 99%, significantly outperforming the traditional models, like decision trees at 76%, random forests at 98.1%, and federated learning with deep Q at 95%. The proposed methodology provides a scalable and efficient solution for real-time healthcare data sharing by ensuring data privacy and security across healthcare data source environments. The analysis of experimental results validates the model’s efficiency in securing healthcare data, with implications for broader applications in secure, privacy-preserving medical data security analysis.}, archive = {J_ACCESS}, author = {S. Aanjankumar and Monoj Kumar Muchahari and Shabana Urooj and Ishmeet Kaur and Rajesh Kumar Dhanaraj and Hanan Abdullah Mengash and S. Poonkuntran and Parag Ravikant Kaveri}, doi = {10.1109/ACCESS.2025.3573076}, journal = {IEEE Access}, pages = {97428-97440}, shortjournal = {IEEE Access}, title = {Enhanced consumer healthcare data protection through AI-driven TinyML and privacy-preserving techniques}, volume = {13}, year = {2025}, }
• (2025). Broadband chaos generation and synchronization induced by integrated tri-section laser. ACCESS, 13, 97421-97427. (www)

  @article{ , abstract = {Broadband chaos generation is numerically demonstrated using an integrated tri-section laser which is composed of mutually coupled distributed feedback (DFB) lasers with a phase section, and the broadband chaos synchronization of two DFB lasers is achieved using the integrated laser as a driver. The simulation results indicate that the integrated tri-section laser with optical feedback can generate broadband chaos with a bandwidth up to 34 GHz, based on dual-mode beating effect. The influence of feedback strength and frequency interval on the chaos bandwidth are studied in detail. As a common driving signal, the broadband chaos signal can induce the response DFB lasers to generate chaos signals with a bandwidth of no less than 25 GHz and meanwhile achieve chaos synchronization with a correlation coefficient beyond 0.98. The low correlation coefficient between the drive signal and the response signal ensures the security of the chaos carrier from the public channel. Moreover, the dynamic properties of the integrated laser and the influence of the external parameters on chaotic synchronization are also investigated.}, archive = {J_ACCESS}, author = {Yuanyuan Guo and Wuyin Wei and Hua Gao and Yuanye Zhang and Tong Zhao and Zhiwei Jia and Pengfa Chang and Longsheng Wang and Yuncai Wang}, doi = {10.1109/ACCESS.2024.3409547}, journal = {IEEE Access}, pages = {97421-97427}, shortjournal = {IEEE Access}, title = {Broadband chaos generation and synchronization induced by integrated tri-section laser}, volume = {13}, year = {2025}, }
• (2025). Minimizing model size of CNN-based vehicle make recognition for frontal vehicle images. ACCESS, 13, 97409-97420. (www)

  @article{ , abstract = {Vehicle Make Model Recognition (VMMR) is commonly used in Intelligent Transportation Systems (ITS), free-flow image-based toll systems, and enforcement systems. These systems must analyze and process vehicle front images for use as evidence. Convolutional Neural Networks (CNN) are widely used for image classification and VMMR problems. Complex model structures and more internal parameters are needed to improve classification accuracy with many classes. Issues included larger models and longer processing times. The goal of this work is to study and create a smaller CNN model that can be used on devices with limited resources, like embedded computers and embedded computer cameras, to figure out what kind of car it is from a front view picture. Real free-flow toll systems were used to train a CNN model that recognized vehicle makes with 99% accuracy. The model is smaller than VGG16, InceptionV3, Yolo11m-cls, and ResNet50 and has over 90% accuracy. It reduced parameters by 69.95% and developed the CTv1 model to achieve an F1 score 2.06% higher than InceptionV3, the best. The model was tested on a Raspberry Pi 3 Model B, processing images in 1 second and using 25 mWh. The compact version of the proposed model also adjusts the Padding and Stride of the Convolutional Layer and reduces the CNN model size using Depth-wise Separable Convolutional and $1\times 1$ Convolutional Dimension Reduction (Bottleneck) methods to test vehicle make recognition accuracy, training time, processing time, and model size.}, archive = {J_ACCESS}, author = {Wiput Puisamlee and Rathachai Chawuthai}, doi = {10.1109/ACCESS.2025.3574187}, journal = {IEEE Access}, pages = {97409-97420}, shortjournal = {IEEE Access}, title = {Minimizing model size of CNN-based vehicle make recognition for frontal vehicle images}, volume = {13}, year = {2025}, }
• (2025). A lightweight YOLOv8s algorithm for ceiling fan blade defect detection with optimized pruning and knowledge distillation. ACCESS, 13, 97392-97408. (www)

  @article{ , abstract = {While industrial ceiling fans play a vital role in maintaining efficient airflow and system stability within factory settings, surface defects on their blades that arise over time can disrupt their functionality and create safety hazards. Detecting these surface defects is crucial; however, accurate and rapid detection typically involves complex machine vision algorithms, such as You Only Look Once (YOLO) networks, that require considerable computing resources, which contradicts the industry’s preference for simpler algorithms that can be deployed using low-cost computing power. We propose a lightweight algorithm for detecting surface defects on ceiling fan blades using YOLOv8s, incorporating optimizations and enhancements in model pruning and knowledge distillation. Using our extensively built dataset, the training process yields a YOLOv8s network capable of reliably identifying the surface defects, functioning as the teacher network for knowledge distillation. The dynamic self-tuning strategy for sparsity factors, developed for sparse training, effectively determines the regularization intensity that reflects network channel contributions, providing more appropriate network pruning results to form the lightweight student network. The proposed hybrid loss function promotes sufficient knowledge transfer of intermediate and output features from the teacher to the student network, thus preserving defect identification capabilities that might otherwise degrade through knowledge distillation. Experimental results confirm that our algorithm achieves an mAP of 0.976 and operates at 91.4 FPS, while reducing the number of parameters by 76.6% and floating-point operations by 85.2%. These results demonstrate superior accuracy, speed, and deployment efficiency over conventional methods and highlight our algorithm’s considerable potential for surface defect detection applications beyond ceiling fan blades. This work is anticipated to present a new solution for developing and applying lightweight YOLO-based networks.}, archive = {J_ACCESS}, author = {Qinyuan Huang and Chen Fan and Yuqi Sun and Jiaxiong Huang and Wengziyang Jiang}, doi = {10.1109/ACCESS.2025.3575952}, journal = {IEEE Access}, pages = {97392-97408}, shortjournal = {IEEE Access}, title = {A lightweight YOLOv8s algorithm for ceiling fan blade defect detection with optimized pruning and knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). A bias injection technique to assess the resilience of causal discovery methods. ACCESS, 13, 97376-97391. (www)

  @article{ , abstract = {Causal discovery (CD) algorithms are increasingly applied to socially and ethically sensitive domains. However, their evaluation under realistic conditions remains challenging due to the scarcity of real-world datasets annotated with ground-truth causal structures. Whereas synthetic data generators support controlled benchmarking, they often overlook forms of bias, such as dependencies involving sensitive attributes, which may significantly affect the observed distribution and compromise the trustworthiness of downstream analysis. This paper introduces a novel synthetic data generation framework that enables controlled bias injection while preserving the causal relationships specified in a ground-truth causal graph. The framework aims to evaluate the reliability of CD methods by examining the impact of varying bias levels and outcome binarization thresholds. Experimental results show that even moderate bias levels can lead CD approaches to fail to correctly infer causal links, particularly those connecting sensitive attributes to decision outcomes. These findings underscore the need for expert validation and highlight the limitations of current CD methods in fairness-critical applications. Our proposal thus provides an essential tool for benchmarking and improving CD algorithms in biased, real-world data settings.}, archive = {J_ACCESS}, author = {Martina Cinquini and Karima Makhlouf and Sami Zhioua and Catuscia Palamidessi and Riccardo Guidotti}, doi = {10.1109/ACCESS.2025.3573201}, journal = {IEEE Access}, pages = {97376-97391}, shortjournal = {IEEE Access}, title = {A bias injection technique to assess the resilience of causal discovery methods}, volume = {13}, year = {2025}, }
• (2025). StyleMI: An image processing-based method for detecting unauthorized style mimicry in fine-tuned diffusion models in a more realistic scenario. ACCESS, 13, 97364-97375. (www)

  @article{ , abstract = {The rapid development of diffusion models and model fine-tuning methods have enabled widespread applications in artistic style mimicry while also leading to significant concerns about copyright infringement. Existing approaches protect artists from unauthorized style mimicry by introducing adversarial examples, watermarks, or membership inference attacks. However, the former two are vulnerable to image pre-processing and unable to be applied to published artworks, while the latter requires access to the target model, which is typically inaccessible in real-world scenarios. To this end, the task of detecting unauthorized style mimicry without access to the target model is formulated into a novel membership inference problem. The key insight is that images generated by a diffusion model inherently carry information of its training dataset. A style comparison-based method is proposed, which consists of a LoRA-based style transfer module and a CLIP-based style extraction and comparison module. The style transfer module first learns the artist’s artistic style, then removes the style of the given image to avoid its influence, and finally transfers the artist’s style to the given image. The style extraction and comparison module utilizes a diffusion model to extract the content information of an image and then remove it in a latent semantic space with the pre-trained CLIP model to acquire style-related features and compare them. Experiments on Stable Diffusion demonstrate the effectiveness of the proposed method. The best result achieved a True Positive Rate of 85%, a False Positive Rate of 0%, an Attack Success Rate of 99.29%, a Precision of 100%, and an Area Under the Curve of 1.}, archive = {J_ACCESS}, author = {Xinjun Zhang and Shixin Zuo and Yu Sang}, doi = {10.1109/ACCESS.2025.3574053}, journal = {IEEE Access}, pages = {97364-97375}, shortjournal = {IEEE Access}, title = {StyleMI: An image processing-based method for detecting unauthorized style mimicry in fine-tuned diffusion models in a more realistic scenario}, volume = {13}, year = {2025}, }
• (2025). MCATD: Multi-scale contextual attention transformer diffusion for unsupervised low-light image enhancement. ACCESS, 13, 97348-97363. (www)

  @article{ , abstract = {Low-light image enhancement (LLIE) remains a challenging task due to the complex degradation patterns in images captured under insufficient illumination, including non-linear intensity mappings, spatially-varying noise distributions, and content-dependent color distortions. Despite significant advances, existing methods struggle with three fundamental challenges: 1) difficulty in simultaneously preserving structural details while reducing noise, 2) limited generalization across diverse lighting conditions and scene types, and 3) computational inefficiency when processing complex natural scenes. While recent diffusion-based methods have shown promise, they often struggle with generalization and require paired training data. We propose MCATD, a novel unsupervised framework that integrates adaptive sampling, multi-scale feature extraction, and dynamic enhancement capabilities into diffusion models for LLIE. The framework consists of three key components: 1) a Dynamic Adaptive Diffusion Sampling (DADS) strategy that adjusts sampling steps based on image complexity, 2) a Multi-scale Contextual Attention Transformer (MCAT) network that captures features at different scales with attention mechanisms, and 3) a Multi-scale Dynamic Structure-Preserving (MDSP) loss that preserves image structure while optimizing perceptual quality. Experimental results on multiple benchmarks demonstrate that our method outperforms state-of-the-art unsupervised approaches and achieves comparable performance to supervised methods while maintaining better generalization ability. Furthermore, ablation studies validate the effectiveness of each proposed component. The proposed framework not only advances the field of unsupervised LLIE but also provides insights into leveraging diffusion models for image restoration tasks.}, archive = {J_ACCESS}, author = {Cheng da and Yongsheng Qian and Junwei Zeng and Xuting Wei and Futao Zhang}, doi = {10.1109/ACCESS.2025.3573171}, journal = {IEEE Access}, pages = {97348-97363}, shortjournal = {IEEE Access}, title = {MCATD: Multi-scale contextual attention transformer diffusion for unsupervised low-light image enhancement}, volume = {13}, year = {2025}, }
• (2025). Cyber resilience of ships: An evaluation of guideline and frameworks. ACCESS, 13, 97337-97347. (www)

  @article{ , abstract = {The increasing adoption of smart ship design and the rising proportion of IT equipment on ships have significantly increased the frequency and severity of maritime cyber incidents. To address this issue, the International Association of Classification Societies introduced UR E26 in 2022. However, compared to the cybersecurity frameworks established by the National Institute of Standards and Technology (NIST), such as the Cybersecurity Framework (CSF) and Cyber Resilient Systems (CRS), Unified Requirement (UR) E26 lacks comprehensiveness and specificity. This study employed the Analytic Hierarchy Process (AHP) method to assess the prioritization of key elements of UR E26 across the four stages of the ship lifecycle, based on pairwise comparison surveys from 18 maritime cybersecurity experts. The analysis revealed that the critical elements of UR E26 vary by lifecycle phase and that UR E26 fails to adequately address these phase-specific requirements. In the comparative framework analysis, UR E26 was found to align with only 8.5% of the evaluation items in NIST CSF and 53.8% of the objectives in NIST CRS, indicating significant gaps in coverage. Furthermore, this study proposes practical improvement measures for UR E26 to enhance its applicability and effectiveness, ultimately contributing to the advancement of cyber resilience in the maritime industry.}, archive = {J_ACCESS}, author = {Jin Kim and Sam Youl Lee}, doi = {10.1109/ACCESS.2025.3571438}, journal = {IEEE Access}, pages = {97337-97347}, shortjournal = {IEEE Access}, title = {Cyber resilience of ships: An evaluation of guideline and frameworks}, volume = {13}, year = {2025}, }
• (2025). A low-cost AI-powered system for early detection of diabetic retinopathy and ocular diseases in resource limited settings. ACCESS, 13, 97322-97336. (www)

  @article{ , abstract = {Eye Health is a vital yet often neglected aspect of healthcare, especially in distant and deprived regions that are less accessible to specialized diagnostic tools and professionals. This paper introduces an intelligent diabetic retinopathy detection system to enhance affordability, accessibility, and early diagnosis. The system features a cost-effective imaging setup utilizing a 28D lens and a camera to capture high-quality fundus images. These images are analyzed using advanced Convolutional Neural Networks to determine the cases of people suffering from eye issues: Age-related vascular degeneration (AMD), Diabetic retinopathy (DR), and Glaucoma. A user-friendly web application hosted on AWS facilitates seamless image upload, automated diagnosis, and report generation. Achieving an accuracy of 96.5%, the system ensures reliable diagnostics, enabling timely medical intervention. By integrating AI-driven analysis and cloud-based accessibility, this solution aims to transform eye care in resource-limited settings. Performance evaluation against existing methods highlights its superiority, with an F1 score of 98.2 % for DR, 92.5% for Glaucoma, and 98.8% for AMD, outperforming many previous approaches that either focused on single-disease detection or relied on costly hardware.}, archive = {J_ACCESS}, author = {Harpreet Vohra and Mohammad Kamrul Hasan and Siti Norul Huda Sheikh Abdullah and Shayla Islam and Abdul Hadi Abd Rahman and Shreya N. Bhojake and Dhruv Bobal and Awal Kaur Gandhi and Muskan and Chandramohan Dhasarathan}, doi = {10.1109/ACCESS.2025.3572471}, journal = {IEEE Access}, pages = {97322-97336}, shortjournal = {IEEE Access}, title = {A low-cost AI-powered system for early detection of diabetic retinopathy and ocular diseases in resource limited settings}, volume = {13}, year = {2025}, }
• (2025). On the validity of traditional vulnerability scoring systems for adversarial attacks against LLMs. ACCESS, 13, 97273-97321. (www)

  @article{ , abstract = {This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks on Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of different metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by averaging the values assessed by three distinct LLMs. The results indicate that existing scoring systems yield vulnerability scores with minimal variation across different attacks, supporting the hypothesis that current vulnerability metrics are limited in evaluating AAs on LLMs, and highlighting the need for the development of more flexible, generalized metrics tailored to such attacks.}, archive = {J_ACCESS}, author = {Atmane Ayoub Mansour Bahar and Ahmad Samer Wazan}, doi = {10.1109/ACCESS.2025.3574108}, journal = {IEEE Access}, pages = {97273-97321}, shortjournal = {IEEE Access}, title = {On the validity of traditional vulnerability scoring systems for adversarial attacks against LLMs}, volume = {13}, year = {2025}, }
• (2025). Improved ellipsoidal intersection method for 2D data fusion. ACCESS, 13, 97260-97272. (www)

  @article{ , abstract = {This article presents a comprehensive review and analysis of data fusion techniques for both known and unknown correlation cases. Established methods, including naive fusion, Bar-Shalom Campo, Covariance Intersection, Ellipsoidal Intersection, Inverse Covariance Intersection, Split Covariance Intersection, Expected Bar-Shalom Campo and Expected Blue Fuser are evaluated, highlighting their strengths and limitations. A key finding is that while Ellipsoidal Intersection method effectively reduces fusion covariance, it does not fully capture the common information between data. To address this limitation, this article introduces Improved Ellipsoidal Intersection method, which enhances the representation of common information by incorporating inner tangent circles alongside the ellipsoid used in Ellipsoidal Intersection method. Monte Carlo simulations under various correlation cases demonstrate that Improved Ellipsoidal Intersection method calculates lower root mean square error compared to existing techniques, particularly in high-correlation cases. The results indicate that more accurate modeling of common information significantly improves fusion accuracy. Furthermore, the implications of these findings for state estimation and multisensor data fusion are discussed, emphasizing the importance of correlation modeling in improving fusion performance. The proposed Improved Ellipsoidal Intersection method offers a promising approach for applications requiring precise and reliable data integration, particularly in cases with strong correlations.}, archive = {J_ACCESS}, author = {Muhammed Mert Ulupinar and Gökhan Soysal}, doi = {10.1109/ACCESS.2025.3575949}, journal = {IEEE Access}, pages = {97260-97272}, shortjournal = {IEEE Access}, title = {Improved ellipsoidal intersection method for 2D data fusion}, volume = {13}, year = {2025}, }
• (2025). Dynamic classification: Leveraging self-supervised classification to enhance prediction performance. ACCESS, 13, 97249-97259. (www)

  @article{ , abstract = {In this study, we propose an innovative dynamic classification algorithm aimed at achieving zero missed detections and minimal false positives, critical in safety-critical domains (e.g., medical diagnostics) where undetected cases risk severe outcomes. The algorithm partitions data in a self-supervised learning-generated way, which allows the model to learn from the training set to understand the data distribution and thereby divides training set and test set into N different subareas. The training and test subsets in the same subarea will have nearly the same boundary. For each subarea, there will be the same type of model, such as linear or random forest model, to predict the results of that subareas. In addition, the algorithm uses subareas boundary to refine predictions results and filter out substandard results without requiring additional models. This approach allows each model to operate within a smaller data range and remove the inaccurate prediction results, thereby improving overall accuracy. Experimental results show that, with minimal data partitioning errors, the algorithm achieves exceptional performance with zero missed detections and minimal false positives, outperforming existing ensembles like XGBoost or LGBM model. Even with larger classification errors, it remains comparable to that of state-of-the-art models. Key innovations include self-supervised classification learning, small-range subset predictions, and optimizing the prediction results and eliminate the unqualified ones without the need for additional model support. Although the algorithm still has room for improvement in automatic parameter tuning and efficiency, it demonstrates outstanding performance across multiple datasets. Future work will focus on optimizing the classification components to enhance robustness and adaptability.}, archive = {J_ACCESS}, author = {Ziyuan Zhong and Junyang Zhou}, doi = {10.1109/ACCESS.2025.3575232}, journal = {IEEE Access}, pages = {97249-97259}, shortjournal = {IEEE Access}, title = {Dynamic classification: Leveraging self-supervised classification to enhance prediction performance}, volume = {13}, year = {2025}, }
• (2025). Optimal flexibility provision of electric vehicle and photovoltaic systems through probabilistic forecasting and unsuccessful day-ahead market integration. ACCESS, 13, 97228-97248. (www)

  @article{ , abstract = {Integrating electric vehicles (EVs) and photovoltaic (PV) systems into power grids introduces significant challenges because of uncertainty in EV charging behavior and PV generation—particularly when real-time data are limited. Managing these uncertainties—especially EV late arrivals, EV early departures, and PV forecast errors—is essential for grid stability. This study proposes a proactive flexibility provision framework that leverages probabilistic forecasting and cumulative adjustment factors (CAFs) for dynamic refinement of flexibility bounds. The method enhances resource utilization and maintains flexibility availability, improving real-time power imbalance management. The proposed flexibility program also integrates unsuccessful day-ahead market participants into real-time operations, increasing distributed resource use and reducing reliance on the main grid. Numerical simulations on a 380-V low-voltage distribution system in Thailand demonstrate that the model successfully resolved all real-time power imbalances. Compared to the no-CAF baseline (61.59%), the proposed method reduced utility dependence to 69.10%. Log-normal distributions with low variance (e.g., $\sigma =0.1$ for PV and $\sigma =0.5$ for EV) have consistently achieved strong performance across mean absolute error, root mean square error, and coverage probability metrics—delivering accurate forecasts while supporting long-term contracts and grid planning. Incentives were fairly allocated and fully funded within the local energy market.}, archive = {J_ACCESS}, author = {Anulekha Saha and Arnon Teawnarong and Surachai Chaitusaney}, doi = {10.1109/ACCESS.2025.3576245}, journal = {IEEE Access}, pages = {97228-97248}, shortjournal = {IEEE Access}, title = {Optimal flexibility provision of electric vehicle and photovoltaic systems through probabilistic forecasting and unsuccessful day-ahead market integration}, volume = {13}, year = {2025}, }
• (2025). The application of deep learning for lymph node segmentation: A systematic review. ACCESS, 13, 97208-97227. (www)

  @article{ , abstract = {Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator proficiency, limiting their ability to achieve high accuracy. The introduction of deep learning technologies offers new possibilities for improving the accuracy of lymph node image analysis. This study evaluates the application of deep learning in lymph node segmentation and discusses the methodologies of various deep learning architectures such as convolutional neural networks, encoder-decoder networks, and transformers in analyzing medical imaging data across different modalities. Despite the advancements, it still confronts challenges like the shape diversity of lymph nodes, the scarcity of accurately labeled datasets, and the inadequate development of methods that are robust and generalizable across different imaging modalities. To the best of our knowledge, this is the first study that provides a comprehensive overview of the application of deep learning techniques in lymph node segmentation task. Furthermore, this study also explores potential future research directions, including multimodal fusion techniques, transfer learning, and the use of large-scale pre-trained models to overcome current limitations while enhancing cancer diagnosis and treatment planning strategies.}, archive = {J_ACCESS}, author = {Jingguo Qu and Xinyang Han and Man-Lik Chui and Yao Pu and Simon Takadiyi Gunda and Ziman Chen and Jing Qin and Ann Dorothy King and Winnie Chiu-Wing Chu and Jing Cai and Michael Tin-Cheung Ying}, doi = {10.1109/ACCESS.2025.3575454}, journal = {IEEE Access}, pages = {97208-97227}, shortjournal = {IEEE Access}, title = {The application of deep learning for lymph node segmentation: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Spatial-temporal cooperative in-vehicle network intrusion detection method based on federated learning. ACCESS, 13, 97194-97207. (www)

  @article{ , abstract = {With the widespread adoption of intelligent connected vehicles (ICV) and the rapid development of the internet of vehicles (IoV), the security of in-vehicle network (IVN) has become increasingly critical. In particular, vulnerabilities in in-vehicle bus systems have become more exposed, with networks facing a growing range of cyber threats, including denial-of-service (DoS) attacks, spoofing attacks and fuzzing attacks. This paper proposes a spatial-temporal collaborative intrusion detection method for IVN based on federated learning (FL), aiming to address the limitations of traditional intrusion detection methods in data privacy protection, temporal modeling, and computational efficiency. The method employs an autoencoder (AE) to achieve feature compression, reducing data dimensionality and extracting core spatial features. Additionally, a temporal embedding transformer (TET) model is used to capture dynamic temporal dependencies, overcoming the shortcomings of traditional positional encodings in processing dynamic signals in vehicular networks. The model is trained and tested in FL framework to ensure data privacy security. Experimental results on the Car-Hacking dataset demonstrate that FL-TET achieves 100% accuracy, precision, recall, and F1 score, outperforming existing centralized and FL methods. Notably, the model parameters are reduced to just 0.306M, representing an 86.3% reduction compared to current FL methods. By enabling privacy-preserving and efficient intrusion detection, FL-TET has the potential to improve connected-vehicle security, enhancing the safety and reliability of autonomous and networked transportation systems.}, archive = {J_ACCESS}, author = {Liu Tao and Zhao Xiyang}, doi = {10.1109/ACCESS.2025.3575849}, journal = {IEEE Access}, pages = {97194-97207}, shortjournal = {IEEE Access}, title = {Spatial-temporal cooperative in-vehicle network intrusion detection method based on federated learning}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on ISAC for 6G: Enabling technologies, security, and AI/ML perspectives. ACCESS, 13, 97152-97193. (www)

  @article{ , abstract = {Integrated sensing and communication (ISAC) has received significant attention over the past five years as a key component of the 6G vision for next-generation communication systems. Starting with the fundamentals of ISAC, we present strategies focused on sensing-centric and communication-centric ISAC systems that satisfy both radar and communication objectives using the same physical hardware and waveform. The communication-centric ISAC focuses primarily on improving communication performance beyond 5G/6G cellular networks and Wi-Fi standards, while also performing additional processing required for detection. On the other hand, the sensing-centric ISAC prioritizes sensing and detection capabilities while incorporating communication functions in the most efficient way possible. In this survey, we present a comprehensive review of both ISAC systems along with new approaches for enabling technologies and highlight unique challenges and existing solutions that are already available. We explore the roles of innovative functions in enabling ISAC, including joint waveform design, edge computing, vehicular sensing and communications, and their practical applications. We then discuss 3GPP standardization efforts related to ISAC as a future direction for the 6G vision. We also outline possible future research directions, with a specific emphasis on security, artificial intelligence, and machine learning.}, archive = {J_ACCESS}, author = {Sultan Aldirmaz-Colak and Mustafa Namdar and Arif Basgumus and Serdar Özyurt and Selman Kulac and Nurullah Calik and Mehmet Akif Yazici and Ahmet Serbes and Lutfiye Durak-Ata}, doi = {10.1109/ACCESS.2025.3573371}, journal = {IEEE Access}, pages = {97152-97193}, shortjournal = {IEEE Access}, title = {A comprehensive review on ISAC for 6G: Enabling technologies, security, and AI/ML perspectives}, volume = {13}, year = {2025}, }
• (2025). Evaluating 60 GHz band underwater optical wireless communication performance based on low-density parity check coding under diverse water conditions and noise levels. ACCESS, 13, 97129-97151. (www)

  @article{ , abstract = {Underwater optical wireless communication (UOWC) is a recent advancement in optical wireless communication (OWC) that offers a higher capacity and bandwidth for underwater applications than traditional acoustic systems. However, UOWC links are vulnerable to the complex influences of aquatic environmental conditions involving concentrations of contaminant factors and sources of noise that degrade their performance, thus complicating their design and practical implementation. To compensate for the deterioration caused by these issues, techniques such as low-density parity-check (LDPC) coding have been proposed to enhance error correction and system performance. Therefore, in this study, we evaluate the performance of LDPC-coded line-of-sight UOWC channels under different aquatic environmental impacts, noise factors, and optical property parameters. The signal-to-noise ratio (SNR), bit error rate (BER), and Q-factor were among the most important metrics studied via simulations using OptiSystem v.21 and MATLAB. The results show that for a 6G-UOWC system operating at 60 GHz and 10 Gbps, the maximum transmission distances are 25 m in pure water and 1.96 m in harbor water at a depth of 7 m. Solar noise reduces these distances, whereas LDPC coding significantly improves performance by extending the maximum distance to 29 m in clear water. Moreover, a comparative analysis of different field-of-view (FOV) settings revealed that a lower FOV reduces the impact of solar noise across depths. In addition, the maximum achievable data rate with LDPC coding reached 18 Gbps, with distances varying from 21 m under optimal conditions to 1.65 m under less favorable conditions.}, archive = {J_ACCESS}, author = {Aser M. Matarneh and Aseel M. Rwaidi and Saqer S. Alja’afreh}, doi = {10.1109/ACCESS.2025.3569062}, journal = {IEEE Access}, pages = {97129-97151}, shortjournal = {IEEE Access}, title = {Evaluating 60 GHz band underwater optical wireless communication performance based on low-density parity check coding under diverse water conditions and noise levels}, volume = {13}, year = {2025}, }
• (2025). Dynamic stability and reactive power control using dandelion-optimized cascaded fractional order tilt controller in renewable energy systems. ACCESS, 13, 97107-97128. (www)

  @article{ , abstract = {Wind energy is recognized as a key sustainable resource in the renewable energy industry. Reactive power imbalance arises from load disturbances and fluctuations in wind power, directly impacting the stability of hybrid wind-integrated power systems. These imbalances result in voltage deviations within the power systems. Dynamic voltage stability (DVS) and reactive power compensation (RPC) in standalone wind-diesel hybrid power systems (SWDHPS) may be achieved by installing suitable compensating devices with novel control strategies. This paper proposes a novel cascaded fractional-order PI-tilt integral second-order double-derivative controller (FOPI-TIDD2) integrated with STATCOM for DVS enhancement and RPC in SWDHPS. The tunable parameters of the proposed controller are optimized using an improved dandelion optimization (DO) algorithm. The controller’s performance is evaluated by comparing its dynamic voltage characteristics with those of traditional controllers. Similarly, the effectiveness of the DO algorithm is validated through a comparative analysis of dynamic voltage and convergence characteristics with those of traditional algorithms. Furthermore, the robustness of the proposed controller is assessed through sensitivity analysis under different loading conditions. The proposed approach is implemented and validated through simulations in MATLAB/Simulink and further verified via real-time implementation on the OPAL-RT platform. The results demonstrate that the DO-tuned FOPI-TIDD2 controller integrated with STATCOM significantly enhances RPC and DVS in SWDHPS.}, archive = {J_ACCESS}, author = {G. M. Sachin and Abhik Banerjee and S. N. Deepa}, doi = {10.1109/ACCESS.2025.3575496}, journal = {IEEE Access}, pages = {97107-97128}, shortjournal = {IEEE Access}, title = {Dynamic stability and reactive power control using dandelion-optimized cascaded fractional order tilt controller in renewable energy systems}, volume = {13}, year = {2025}, }
• (2025). Accurate radar-based heartbeat measurement using higher harmonic components. ACCESS, 13, 97094-97106. (www)

  @article{ , abstract = {This study proposes a radar-based heartbeat measurement method that uses the absolute value of the second derivative of the complex radar signal, rather than its phase, in combination with the variational mode extraction method, which is a type of mode decomposition algorithm. We show that the proposed second-derivative-based approach can amplify the heartbeat component in the radar signals effectively. We also confirm that the use of the variational mode extraction method provides an efficient way to emphasize the heartbeat component when amplified via the second-derivative-based approach. We then demonstrate estimation of the interbeat intervals of the heart by using the proposed approach in combination with the topology method, which is an accurate interbeat interval estimation method. The performance of the proposed method is quantitatively evaluated using data acquired from eleven participants that were measured using a millimeter-wave radar system. Compared with the conventional methods based on the phase of the complex radar signal, our proposed method can achieve higher precision when estimating the heart’s interbeat intervals; the correlation coefficient for the proposed method was increased by 0.20 and the root mean square error was reduced by 23%.}, archive = {J_ACCESS}, author = {Itsuki Iwata and Kimitaka Sumi and Yuji Tanaka and Takuya Sakamoto}, doi = {10.1109/ACCESS.2025.3575932}, journal = {IEEE Access}, pages = {97094-97106}, shortjournal = {IEEE Access}, title = {Accurate radar-based heartbeat measurement using higher harmonic components}, volume = {13}, year = {2025}, }
• (2025). A survey on data plane security in software-defined networks: Toward adaptive security of data planes. ACCESS, 13, 97058-97093. (www)

  @article{ , abstract = {Software-Defined Networking (SDN) is the actual approach in the network design, based on separating the control and data plane. Such architectural model has brought improvements in terms of network monitoring, management and troubleshooting, but has also increased risks related to network security. Security attacks can occur at all SDN layers and disrupt part or the entire network. Existing research is mostly focused on the security of the control plane, since it contains all control logic of SDN networks and thus represents their main part. Although the data plane has many vulnerabilities and can also be a significant source of security threats towards the control plane, it is only partially covered in existing research, without enough details related to differences between methods and implementation techniques which provide security enhancement. In this paper, we present a comprehensive survey on security of the data plane, focusing on the latest advanced solutions. The survey starts with an overview of attacks, threats and affected security attributes in the data plane, classified using common security models: STRIDE, CIA and AAA. After that, we present a detailed analysis of solutions explored in the literature, including the methods used for security enhancement, implementation techniques, experimental environments, their contributions in terms of vulnerabilities that they address, performance analysis and limitations. Through this analysis, we introduce the concept of adaptive security and select several mechanisms which can be used to achieve it. Additionally, we propose possible combinations of presented mechanisms to provide strong, comprehensive solution which should adapt to dynamics of network, attackers and users, and in that way protect the network from different threats and also satisfy the requirements of services which need different levels of security.}, archive = {J_ACCESS}, author = {Amina Tankovic and Emir Dervisevic and Miralem Mehic and Enio Kaljic}, doi = {10.1109/ACCESS.2025.3575494}, journal = {IEEE Access}, pages = {97058-97093}, shortjournal = {IEEE Access}, title = {A survey on data plane security in software-defined networks: Toward adaptive security of data planes}, volume = {13}, year = {2025}, }
• (2025). A novel self-configurable algorithm for uncoordinated deployment of home gNodeBs in 5th generation wireless networks. ACCESS, 13, 97050-97057. (www)

  @article{ , abstract = {Currently, small cellular base stations of the Next Generation Wireless Networks called Home gNodeBs (HgNBs) are emerging as potential solutions for providing coverage and capacity extension in the local area and indoor environment. Hence, a random and uncoordinated deployment of large number of HgNBs can be imagined as a future possibility. This paper proposes a novel Spectrum Load Equilibrium (SLE) algorithm for flexible spectrum usage. Based on the Signal to Interference and Noise Ratio (SINR) threshold, the SLE algorithm works by self-assessing the radio environment to prevent mutual interference among HgNBs, so that their coexistence can be ensured on the shared spectrum. Performances of the network are measured in comparison to fixed frequency reuse of 1, 2 and 4 schemes after deployment of HgNBs in terms of considered key performance indicators (KPIs). It is seen that the proposed algorithm has provided higher average user and cell throughput performance for different offered cell loads in comparison to reuse 1 and 4 schemes although it is outperformed by frequency reuse 2 scheme. The results obtained using proposed algorithm are then compared with standard results which show improvement in KPIs like average user throughput and average cell throughput performance. This is primarily due to allocation of resources in a timely and reliable manner. The proposed algorithm is not only self-configurable, scalable, supports flexible spectrum usage but also improves average user and cell throughput performance. Hence, it can be considered as a candidate for uncoordinated deployment of HgNBs in the local area of $5^{\mathrm {th}}$ generation wireless systems.}, archive = {J_ACCESS}, author = {Madhusmita Panda and Saraju Prasad Padhy and Meetarani Swain}, doi = {10.1109/ACCESS.2025.3572185}, journal = {IEEE Access}, pages = {97050-97057}, shortjournal = {IEEE Access}, title = {A novel self-configurable algorithm for uncoordinated deployment of home gNodeBs in 5th generation wireless networks}, volume = {13}, year = {2025}, }
• (2025). Low-resource noisy transliteration normalization using large-scale language model. ACCESS, 13, 97038-97049. (www)

  @article{ , abstract = {Transliteration normalization is a crucial task for low-resource languages, particularly for Mongolian, where noisy text from social media presents significant challenges. The frequent use of non-standard transliteration can contribute to the gradual erosion of linguistic knowledge, particularly among young users, making it harder to maintain proficiency in their native language. Therefore, developing robust methods for normalizing such text is essential. In this paper, we propose a novel approach leveraging large-scale neural models, specifically GPT-2, to normalize noisy transliterated Mongolian text. Our study explores a data-driven approach, including word pairs, sentence pairs, and synthetic data, to enhance model performance. To further improve accuracy, we introduce a post-processing module that integrates Edit Distance-based corrections with a context-aware ranking mechanism using the Mongolian BERT model. Experimental results demonstrate that our approach (M10: 16.42%) improves overall accuracy by approximately 4.91%, while achieving a 10.44% increase in out-of-vocabulary (OOV) word normalization compared to baseline models. Our proposed approach demonstrates effectiveness in normalizing noisy transliterated text under low-resource conditions.}, archive = {J_ACCESS}, author = {Zolzaya Byambadorj and Ulziibayar Sonom-Ochir and Munkhsukh Enkhbayar and Hyun-Chul Kim and Altangerel Ayush}, doi = {10.1109/ACCESS.2025.3574933}, journal = {IEEE Access}, pages = {97038-97049}, shortjournal = {IEEE Access}, title = {Low-resource noisy transliteration normalization using large-scale language model}, volume = {13}, year = {2025}, }
• (2025). Modularized small-signal modeling of grid-forming inverters. ACCESS, 13, 97011-97037. (www)

  @article{ , abstract = {A wide variety of control schemes for Grid-Forming Inverters (GFMIs) have been developed to enable inverter-based resources (IBRs) to comply with evolving grid-code requirements and deliver essential support services such as frequency and voltage regulation. However, the diversity in these control structures presents substantial challenges for consistent modeling, analysis, and systematic comparison. Conventional approaches to modeling Grid-Forming Inverters typically focus on individual control strategies or isolated configurations, limiting their flexibility and scalability for broader applications. To overcome these limitations, this paper introduces a unified, modular framework utilizing state-space representation (SSR) and the Component Connection Method (CCM) for small-signal modeling of GFMIs. The proposed framework systematically accommodates four prevalent Active Power Control (APC) strategies—Droop control, Droop with Low-Pass Filter (LPF), Virtual Synchronous Generator (VSG), and Compensated Generalized Virtual Synchronous Generator (CGVSG). By establishing a comprehensive and modular modeling methodology, this paper facilitates efficient stability analysis, parameter sensitivity assessment, and performance optimization of diverse GFMI configurations under varying grid conditions. The effectiveness and accuracy of the proposed approach are demonstrated through detailed eigenvalue analyses and validated via time-domain simulations, illustrating significant implications for practical engineering design, grid code compliance, and operational stability in inverter-dominated power systems. The computational implementation of the proposed methodology is available online in a public repository.}, archive = {J_ACCESS}, author = {Sohail A. Ali and Paul Serna-Torre and Patricia Hidalgo-Gonzalez and Mehdi Ghazavi Dozein and Behrooz Bahrani}, doi = {10.1109/ACCESS.2025.3572689}, journal = {IEEE Access}, pages = {97011-97037}, shortjournal = {IEEE Access}, title = {Modularized small-signal modeling of grid-forming inverters}, volume = {13}, year = {2025}, }
• (2025). Robust dual-loop control with disturbance observer for DT-PMSM driven steer-by-wire systems. ACCESS, 13, 97000-97010. (www)

  @article{ , abstract = {The Steer-by-Wire (SBW) system, driven by a Dual Three-Phase Permanent Magnet Synchronous Motor (DT-PMSM), has attracted significant attention due to its advantages. However, parameter uncertainties and external disturbances pose challenges to precise steering control. To achieve accurate, fast, and robust control, this paper proposes a dual-loop control structure. The outer loop uses a Proportional-Integral (PI) controller to manage the steering angle, while the inner loop employs a Sliding Mode Controller (SMC) to regulate speed. The paper first introduces the structure of the DT-PMSM-driven SBW system and compares two existing methods for rack force estimation: one based on the steering system dynamics and the other based on vehicle dynamics. Through experiments under various input conditions, the strengths and weaknesses of each method are analyzed. Based on these analyses, a fuzzy logic-based data fusion method is proposed to combine the results of the two estimation methods, dynamically adjusting the weights using fuzzy logic to significantly improve the accuracy of the rack force estimation. Furthermore, a disturbance observer is introduced into the dual-loop control structure, resulting in a novel active tracking controller. The proposed control algorithm leverages the coordination between the inner and outer loops to effectively address system nonlinearities and uncertainties. The performance of the controller is validated through software-in-the-loop simulations and test bench experiments. The results show that the proposed controller achieves high accuracy in rack force estimation, with the overall error controlled within 800N across all operating conditions. Additionally, the controller demonstrates superior tracking control performance. Specifically, in ramp tests, the ATC controller reduces IAE by 64.17% and 87.85% compared to PID and SMC, RMSE by 65.87% and 72.08%, and MAE by 64.44% and 87.97%, respectively.}, archive = {J_ACCESS}, author = {Kunpeng Jiang and Yousheng Yang}, doi = {10.1109/ACCESS.2025.3567985}, journal = {IEEE Access}, pages = {97000-97010}, shortjournal = {IEEE Access}, title = {Robust dual-loop control with disturbance observer for DT-PMSM driven steer-by-wire systems}, volume = {13}, year = {2025}, }
• (2025). Vector decimation harmonic mean-based algorithm for online acoustic feedback active noise control. ACCESS, 13, 96978-96999. (www)

  @article{ , abstract = {This research addresses the acoustic feedback and stability performance of Active Noise Control (ANC) systems. The Filtered Cross Least Mean Square (FxLMS) algorithm is one of ANC’s most successful adaptive algorithms for reducing undesired noise. The ANC filters have their convergence speeds reduced by significant acoustic feedback, making them unstable in the worst-case scenario. It is possible to eliminate acoustic feedback using a fixed feedback neutralization filter that can be acquired offline. However, the feedback path could change over time, so we might have to make modifications often while the ANC system is online. This paper proposes two enhancements to the FxLMS algorithm to address the ANC’s performance difficulties. To address acoustic feedback problems, the first alteration improves the ANC duct’s feedback path modeling by incorporating the online Feedback Naturalization (FN) concept with an Active function normalized LMS algorithm (AF-NLMS). The second modification, the vector-decimation-based method, is proposed in this paper as a way to improve stability, convergence speed, and robustness. It is achieved by decimating the filter weight vector into small vectors and then updating each vector using the harmonic mean error of the VSS FxLMS. To ensure the enhanced effectiveness of the proposed solution, the AF-FxLMS, online-AFFNFxLMS, and HM-AFFNFxLMS algorithms are used to update the filter weights as a demonstration. The proposed method has superior noise reduction effectiveness compared to other algorithms, which indiscriminately attenuate the control filter across all frequencies. The validation of the proposed approach was achieved via simulations and experimental tests in a PVC duct ANC system, showing that robust stability can be achieved while keeping relatively good performance.}, archive = {J_ACCESS}, author = {Suman Turpati and Ajay Roy and Tathababu Addepalli and Mohammed Alkahtani and Abdullatif Hakami and Ahmad Faiz Minai and Abdallah Hammad}, doi = {10.1109/ACCESS.2025.3573446}, journal = {IEEE Access}, pages = {96978-96999}, shortjournal = {IEEE Access}, title = {Vector decimation harmonic mean-based algorithm for online acoustic feedback active noise control}, volume = {13}, year = {2025}, }
• (2025). Transfer function model-based HESS for DTC EV drive system with vehicle dynamics. ACCESS, 13, 96963-96977. (www)

  @article{ , abstract = {This paper deals with a method to extend the life span of battery in an Electric Vehicle (EV) by using transfer function model-based feedback control circuit. A hybrid energy storage system for Direct Torque Controlled (DTC) Interior Permanent Magnet Synchronous motor (IPMSM) with vehicle dynamics is considered in this paper. When an electric vehicle undergoes rapid acceleration, the motor requires a surge of current from the energy storage system. If the battery is used as the sole power source, this sudden demand results in heavy current discharge (high c-rate), significantly reducing the battery’s lifespan. The proposed approach integrates a Hybrid Energy Storage System (HESS), combining a battery pack with a supercapacitor. This system dynamically monitors the rate of current change during high acceleration and deceleration events. By allowing the supercapacitor to supply peak currents during these instances, the method effectively protects the battery from excessive stress, thereby enhancing its durability and overall performance. The developed system is simulated and tested in simulink environment for different driving conditions to prove the benefit of the proposed method. By the results it is observed that the proposed method is more efficient and effective for EV application.}, archive = {J_ACCESS}, author = {S. V. Pratap Simha Reddy and Mallapu Vijaya Kumar}, doi = {10.1109/ACCESS.2025.3573845}, journal = {IEEE Access}, pages = {96963-96977}, shortjournal = {IEEE Access}, title = {Transfer function model-based HESS for DTC EV drive system with vehicle dynamics}, volume = {13}, year = {2025}, }
• (2025). A survey of machine learning techniques for optimal capacitor placement and sizing in smart distribution networks. ACCESS, 13, 96933-96962. (www)

  @article{ , abstract = {The increasing complexity of modern power distribution networks necessitates advanced strategies for reactive power compensation, particularly in capacitor placement and sizing. Traditional optimization techniques, while effective, often struggle with dynamic system behaviors, nonlinear loads, and real-time operational constraints. This paper presents a comprehensive review of machine learning (ML)-based methodologies for optimal capacitor placement and sizing, focusing on their ability to enhance voltage stability, minimize power losses, and improve overall grid efficiency in smart distribution networks. Various ML techniques are analyzed, including supervised learning methods such as Support Vector Machines and Random Forests for predictive modeling, unsupervised learning approaches like clustering for system characterization, reinforcement learning for adaptive decision-making, and hybrid approaches, highlighting their strengths and limitations in the context of smart transformer-assisted systems. A critical evaluation of the challenges, computational complexities, and future research directions is provided, emphasizing the role of ML in enabling intelligent, data-driven optimization in smart grid environments. The findings underscore the transformative potential of ML-driven capacitor placement strategies in advancing sustainable, resilient, and efficient power distribution systems.}, archive = {J_ACCESS}, author = {Kwabena Addo and Katleho Moloi and Musasa Kabeya and Evans Eshiemogie Ojo}, doi = {10.1109/ACCESS.2025.3566587}, journal = {IEEE Access}, pages = {96933-96962}, shortjournal = {IEEE Access}, title = {A survey of machine learning techniques for optimal capacitor placement and sizing in smart distribution networks}, volume = {13}, year = {2025}, }
• (2025). A proactive and time-sensitive cyber risk assessment model integrating markov chains and bayesian networks. ACCESS, 13, 96911-96932. (www)

  @article{ , abstract = {As cyberattacks grow in complexity, they pose increasing threats to organizations reliant on networked infrastructures. Conventional risk assessment methodologies often fail to adapt to the evolving nature of these threats. This paper introduces a novel cyber risk assessment model that adopts a proactive, dynamic, and time-aware approach to evaluating security risks. The proposed model leverages the Exploit Prediction Scoring System (EPSS) to estimate the short-term likelihood of exploitation over a 30-day period. To improve accuracy, Bayesian networks are employed to capture both system vulnerabilities and asset interdependencies within the network. This information is integrated into an absorbing Markov chain along with the identified attack paths, which are explored using Depth-First Search (DFS). The model generates exploitation probability distributions over the predefined time window, which, when combined with asset impact, facilitates dynamic, proactive, and time-sensitive risk assessments. Additionally, it provides valuable insights into attack progression by estimating the time required for an adversary to compromise critical assets. To demonstrate the practical applicability of the model, a case study is presented, showcasing its effectiveness in assessing cyber risks within a SCADA environment.}, archive = {J_ACCESS}, author = {Pavlos Cheimonidis and Konstantinos Rantos}, doi = {10.1109/ACCESS.2025.3575070}, journal = {IEEE Access}, pages = {96911-96932}, shortjournal = {IEEE Access}, title = {A proactive and time-sensitive cyber risk assessment model integrating markov chains and bayesian networks}, volume = {13}, year = {2025}, }
• (2025). Telemedicine system quality from physicians’ perspectives: Key determinants and its impact on service effectiveness via interaction quality. ACCESS, 13, 96894-96910. (www)

  @article{ , abstract = {Telemedicine has emerged as a transformative approach in healthcare delivery; however, existing research has predominantly overlooked the evaluation of system quality from the perspective of physicians, who are the primary agents of patient care. This study addresses this gap by conceptualizing and empirically validating a theoretical model to identify the critical determinants of telemedicine system quality, with a particular focus on their influence on the quality of physician-patient interactions and the effectiveness of healthcare services provided through this system. Drawing on data collected from 129 physicians in Indonesia, this study employs partial least squares structural equation Modeling (PLS-SEM) to test and confirm seven hypotheses at a significance level of p <0.05. The findings revealed three pivotal determinants of telemedicine system quality: physician technological readiness, telemedicine alignment to care process, and physician telemedicine literacy. These factors contribute to the enhanced quality of clinical interactions, which in turn lead to an improvement in the overall effectiveness of telemedicine services. This study advances the theoretical understanding of telemedicine system quality from the physician’s perspective, identifying key determinants that influence service effectiveness. By translating these theoretical insights into practical approaches, the study proposes actionable insights for healthcare providers and system designers to optimize telemedicine systems, ultimately improving clinician-patient communication and enhancing the overall effectiveness of telemedicine services in the digital era.}, archive = {J_ACCESS}, author = {Timothy Rey Laheba and Rajesri Govindaraju and Made Andriani}, doi = {10.1109/ACCESS.2025.3576092}, journal = {IEEE Access}, pages = {96894-96910}, shortjournal = {IEEE Access}, title = {Telemedicine system quality from physicians’ perspectives: Key determinants and its impact on service effectiveness via interaction quality}, volume = {13}, year = {2025}, }
• (2025). Preventing spurious interactions: A new inductive bias for accurate treatment effect estimation. ACCESS, 13, 96881-96893. (www)

  @article{ , abstract = {In recent years, the use of non-linear machine learning techniques to estimate treatment effects from observational data has gained significant attention. Many of the most effective methods incorporate specific algorithmic inductive biases that encode causal information, thereby enhancing the precision of treatment effect estimates. In this paper, we explore and categorize several of these models based on the biases they address, and we introduce the prevention of spurious variable interactions as a new inductive bias that serves as a central focus of our study. This novel inductive bias, not previously considered in this area, aims to further improve the accuracy of treatment effect estimation. Additionally, we propose a practical method that utilizes knowledge of the data generation processes to restrict variable interactions to those defined by the mechanisms of the underlying causal model. This strategy integrates seamlessly with many of the leading neural network-based models. Our results show that these constraints significantly enhance performance, setting new state-of-the-art benchmarks in treatment effect estimation. Furthermore, we show that our method remains robust even when the causal model is imperfect and that incorporating partial causal information yields better results than disregarding it entirely.}, archive = {J_ACCESS}, author = {Roger Pros and Jordi Vitrià}, doi = {10.1109/ACCESS.2025.3574547}, journal = {IEEE Access}, pages = {96881-96893}, shortjournal = {IEEE Access}, title = {Preventing spurious interactions: A new inductive bias for accurate treatment effect estimation}, volume = {13}, year = {2025}, }
• (2025). Sidelobe level reduction in the ACF of NLFM signals using the smoothing spline method. ACCESS, 13, 96874-96880. (www)

  @article{ , abstract = {The high level of sidelobes in the autocorrelation function of the nonlinear frequency modulation signal is a challenge. One of the conventional methods to reduce the sidelobe levels is to use the principle of stationary phase. In this method, the frequency function is calculated using a selection window. The signal frequency function cannot be obtained in closed form and numerical methods must be used to find it. This is usually done using the polynomial curve fitting. In this paper, the frequency function of the signal has been obtained using the smoothing spline method. The simulation results show an improvement of 10 dB to 20 dB in the peak sidelobe level of the autocorrelation function of the nonlinear frequency modulation signal compared to the previous methods. Moreover, the sidelobe arrangement in the autocorrelation function becomes more regular with the smoothing spline method, improving range resolution. This improvement is highly applicable in various telecommunication fields, particularly for target discrimination.}, archive = {J_ACCESS}, author = {Roohollah Ghavamirad and Ramezan Ali Sadeghzadeh and Mohammad Ali Sebt}, doi = {10.1109/ACCESS.2025.3574681}, journal = {IEEE Access}, pages = {96874-96880}, shortjournal = {IEEE Access}, title = {Sidelobe level reduction in the ACF of NLFM signals using the smoothing spline method}, volume = {13}, year = {2025}, }
• (2025). An ensemble learning-based predictive parameterization approach for permanent magnet synchronous machines. ACCESS, 13, 96857-96873. (www)

  @article{ , abstract = {Permanent Magnet Synchronous Machines (PMSMs) are extensively utilized for their ability to deliver accurate position control, and the equivalent circuit characteristics of these machines are essential in several applications, particularly in formulating the control strategy. The study introduces an ensemble-based methodology for estimating the equivalent circuit parameters of PMSMs consisting of phase resistance (R), magnetizing reactance ( $X_{\mathrm {m}}$ ), and leakage reactance ( $X_{\mathrm {l}}$ ) via manufacturer catalog data, which eliminates the necessity for experimental setups, high-quality real-time data, and operational disruptions. Six machine learning models-Multilayer Perceptron (MLP), Cascade Forward Neural Network (CFNN), Layer Recurrent Neural Network (LRNN), Transformer-like Network (TRF), Decision Tree (DT), and Support Vector Regression (SVR)–were evaluated in the first stage of the study. Among these, LRNN and TRF showed the best performance, with LRNN achieving the highest $R^{2}$ ( $0.9212~\pm ~0.0973$ ) for the (R) parameter, followed by TRF ( $R^{2}$ : $0.9163~\pm ~0.0561$ ). An averaging voting ensemble model is developed by integrating the two highest-performing algorithms, LRNN and TRF, leveraging the strengths of both algorithms. The ensemble model combining TRF and LRNN further improved predictions, achieving an $R^{2}$ of $0.9804~\pm ~0.0151$ and TGF of $0.9827~\pm ~0.0173$ for R, $R^{2}$ of $0.9615~\pm ~0.0306$ for ( $X_{\mathrm {m}}$ ), and TGF of $0.9236~\pm ~0.1177$ for ( $X_{\mathrm {l}}$ ). It also outperformed individual models in error metrics, with a MAPE of 7.66% for (R) compared to 23.06% (TRF) and 29.42% (LRNN). The visualization analysis confirmed the model’s strong predictive capability, as the error distribution is tightly clustered around zero, the estimated values align closely with the ideal line, and the real trends in efficiency and torque across various load conditions are accurately represented. Thus, the model’s capacity to accurately predict parameters and represent machine behavior has been revealed, and this method offers a feasible option for the effective use of resources, such as time and labor, in the estimation of PMSM parameters.}, archive = {J_ACCESS}, author = {Sema Nur Ipek and Nur Bekiroglu and Murat Taskiran}, doi = {10.1109/ACCESS.2025.3576101}, journal = {IEEE Access}, pages = {96857-96873}, shortjournal = {IEEE Access}, title = {An ensemble learning-based predictive parameterization approach for permanent magnet synchronous machines}, volume = {13}, year = {2025}, }
• (2025). MaOEA-PGTS: A probabilistic flow-guided two-stage strategy for many-objective optimization. ACCESS, 13, 96844-96856. (www)

  @article{ , abstract = {Many-objective evolutionary algorithms (MaOEAs) often suffer a loss of selection pressure, hindering efficient exploration of Pareto-optimal regions. To address this problem, this paper proposes a probabilistic flow-guided two-stage strategy for many-objective optimization (MaOEA-PGTS). MaOEA-PGTS divides evolution into an exploratory phase and a convergence phase, and dynamically balances diversity and convergence via probabilistic-flow control. In the first stage, candidate solutions are classified into optimal, intermediate, and worst streams using a probabilistic-flow strategy. Each stream is assigned a performance-based probability weight, which is dynamically adjusted via the Poisson cumulative distribution function. In the second stage, the algorithm employs an adaptive thresholding mechanism is employed to reduce the probability of missegregation between solution streams. Finally, the algorithm integrates a normally distributed probability density function to optimize the selection of individuals during the selection process and further maintain inter-population balance. MaOEA-PGTS is tested against seven state-of-the-art algorithms (Two_Arch2, IBEA, NSGA-III, MOEA/D, AGE-II, MOPSO, CMOPSO) on standard many-objective optimization problems, specifically the DTLZ and WFG test functions. Experimental results show that MaOEA-PGTS significantly outperforms the comparison algorithms in terms of both convergence and diversity metrics.}, archive = {J_ACCESS}, author = {Ranyi Li and Weixin Tian}, doi = {10.1109/ACCESS.2025.3575774}, journal = {IEEE Access}, pages = {96844-96856}, shortjournal = {IEEE Access}, title = {MaOEA-PGTS: A probabilistic flow-guided two-stage strategy for many-objective optimization}, volume = {13}, year = {2025}, }
• (2025). Data-driven model predictive control for trajectory tracking in UAV-manipulator systems. ACCESS, 13, 96834-96843. (www)

  @article{ , abstract = {This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the Acados solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks.}, archive = {J_ACCESS}, author = {Bryan S. Guevara and José Varela-Aldás and Viviana Moya and Manuel Cardona and Daniel C. Gandolfo and Juan M. Toibero}, doi = {10.1109/ACCESS.2025.3575724}, journal = {IEEE Access}, pages = {96834-96843}, shortjournal = {IEEE Access}, title = {Data-driven model predictive control for trajectory tracking in UAV-manipulator systems}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis and recommendation of supply chain risk management framework for the military domain. ACCESS, 13, 96813-96833. (www)

  @article{ , abstract = {Supply Chain Risk Management (SCRM) involves managing the risks that exist throughout the process of delivering products from producers to end customers, ensuring that the products meet the end customers’ requirements safely. In the software industry, the delivery of products does not require physical access to execute attacks on the supply chain. As a result, attacks on the software supply chain can be carried out more easily compared to other industries, and their impact can spread rapidly. To manage the risks present in the software supply chain, various organizations such as NIST operate SCRM frameworks. But for the military, these common SCRM frameworks are not suitable due to the nature of military environments. This study analyzes existing frameworks and researches on SCRM and proposes a novel framework for military area. Additionally, a case study is conducted and feedback is gathered from employees working in cyber risk management positions within the military and defense industry professionals to assess the appropriateness and practical applicability of the proposed framework.}, archive = {J_ACCESS}, author = {Jung Keun Ahn and Kwangsoo Cho and Kyungdeok Seo and Hyun-Ji Kim and Seungjoo Kim}, doi = {10.1109/ACCESS.2025.3573515}, journal = {IEEE Access}, pages = {96813-96833}, shortjournal = {IEEE Access}, title = {Comprehensive analysis and recommendation of supply chain risk management framework for the military domain}, volume = {13}, year = {2025}, }
• (2025). LQT-based energy-efficient control for intelligent vehicles optimized by adaptive genetic algorithm. ACCESS, 13, 96800-96812. (www)

  @article{ , abstract = {Existing research on vehicle energy saving primarily focuses on longitudinal control, whereas path tracking control mainly emphasizes control accuracy. To simultaneously ensure both lateral control accuracy and energy-saving performance, this study proposes an Adaptive Genetic Algorithm LQT Controller to address the complex parameter tuning of traditional LQT controllers and the lack of feedback dynamic adjustment capability in conventional Genetic Algorithm LQT controllers. The proposed method dynamically adjusts the crossover and mutation rates of the Q and R matrices by evaluation of the control signals and results from the population, effectively balancing the global search and local convergence during the optimization process. Finally, joint simulations using Carsim and Simulink demonstrated that the proposed controller not only achieved high path tracking accuracy and smooth control signals but also significantly improved energy-saving performance under high-speed, large-curvature operating conditions. By optimizing the kinetic energy loss, the residual vehicle speed is notably increased, and the kinetic energy consumption is reduced by 14.45% compared with the traditional LQT controller. This proves the potential of the lateral control algorithm in enhancing the energy-saving performance.}, archive = {J_ACCESS}, author = {Xia Hong-Yang and Yang Ming and Luo Jing-Jing and Hong Xi and Xu Wei}, doi = {10.1109/ACCESS.2025.3574217}, journal = {IEEE Access}, pages = {96800-96812}, shortjournal = {IEEE Access}, title = {LQT-based energy-efficient control for intelligent vehicles optimized by adaptive genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Aerial dockable multirotor UAVs: Design, control, and flight time extension through in-flight battery replacement. ACCESS, 13, 96782-96799. (www)

  @article{ , abstract = {This paper presents the development and evaluation of the AirDock system, a novel aerial docking and in-flight battery exchange solution designed to extend the operational flight time of multirotor unmanned aerial vehicles (mUAVs). The proposed system enables seamless mid-flight battery replacement through an aerial docking mechanism and a rail-Car-based battery transfer and retrieval system. The AirDock platform utilizes a fully actuated multirotor design, allowing independent control of six degrees of freedom (DoF) motion to maintain a constant attitude during battery exchange operations, facilitating easy docking. In this research, a generalized control allocation algorithm is introduced to manage dynamic shifts in the center of mass (CoM) during inter-modular battery transportation, ensuring safe flight control and mitigating thruster overloading: a critical factor in avoiding flight failure. Both simulations and real-world experiments validate the system’s effectiveness, demonstrating stable aerial docking, reliable in-flight battery exchange performance, and extended flight durations for mission modules. These findings highlight the AirDock system’s potential to overcome the energy limitations of battery-powered UAVs, extending missions across various applications.}, archive = {J_ACCESS}, author = {Yeongin Song and Hyunmin Kim and Jeonghyun Byun and Keun Park and Murim Kim and Seung Jae Lee}, doi = {10.1109/ACCESS.2025.3574452}, journal = {IEEE Access}, pages = {96782-96799}, shortjournal = {IEEE Access}, title = {Aerial dockable multirotor UAVs: Design, control, and flight time extension through in-flight battery replacement}, volume = {13}, year = {2025}, }
• (2025). Enhancing fabric defect detection with attention mechanisms and optimized YOLOv8 framework. ACCESS, 13, 96767-96781. (www)

  @article{ , abstract = {Fabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accuracy and robustness. The framework incorporates the SimAM attention mechanism within the SPPF module and adopts an optimized Dilation-wise Residual (DWR) structure in the backbone. Comprehensive ablation studies and comparisons with state-of-the-art methods validate the effectiveness of the proposed approach. The enhanced model achieves a mAP50-95 of 74.3%, outperforming the baseline by 4.7 percentage points, with marked improvements in detecting challenging defect categories. While the framework demonstrates significant advancements, limitations in dataset diversity and computational efficiency are acknowledged. Future work will focus on resource optimization, dataset augmentation, and extending the framework’s applicability to other domains.}, archive = {J_ACCESS}, author = {Yonghua Mao and Guowen Wang and Yingcang Ma and Xiaolin Gui}, doi = {10.1109/ACCESS.2025.3570455}, journal = {IEEE Access}, pages = {96767-96781}, shortjournal = {IEEE Access}, title = {Enhancing fabric defect detection with attention mechanisms and optimized YOLOv8 framework}, volume = {13}, year = {2025}, }
• (2025). Digital staining algorithm for multi-domain transformation of unstained images. ACCESS, 13, 96758-96766. (www)

  @article{ , abstract = {Digital staining is an artificial intelligence-based technology that replaces the conventional staining process. This technology addresses the challenges associated with the traditional staining procedure, which is complex, time-consuming, and costly, as it relies heavily on manual labor. However, inconsistencies in staining color and quality, arising from variations in staining protocols caused by factors such as solution concentration, staining duration, patient-to-patient variability, and the type of scanner used, remain unresolved. To address this issue, a multi-domain digital staining method was employed. This study proposes a multi-hematoxylin and eosin (H&E) digital staining transformation method based on the phase image of fourier ptychographic microscopy (FPM) using a single model in bright-field microscopy (BM). The proposed model was trained by incorporating a mask and latent loss within the framework of the StarGAN algorithm. The model’s performance was quantitatively evaluated using a cell detection model trained on the MoNuSeg dataset. The results demonstrated improvements of 7.96% and 10.81% in Dice and Jaccard indices, respectively, compared to the CycleGAN model. These findings indicate that the proposed model achieves digital staining that closely replicates the conventional H&E staining method. Additionally, the visual assessment results revealed that the proposed method provides clearer differentiation between background and cytoplasmic boundaries. These results suggest that the proposed approach may effectively address quality degradation issues caused by the limitations of FPM data.}, archive = {J_ACCESS}, author = {Dong-Bum Kim and Jong-Ha Lee}, doi = {10.1109/ACCESS.2025.3572284}, journal = {IEEE Access}, pages = {96758-96766}, shortjournal = {IEEE Access}, title = {Digital staining algorithm for multi-domain transformation of unstained images}, volume = {13}, year = {2025}, }
• (2025). Robot trajectory tracking control based on neural networks and sliding mode control. ACCESS, 13, 96740-96757. (www)

  @article{ , abstract = {In recent years, robot technology has evolved rapidly, becoming a key component of industrial automation. Robot control technology is one of the cores of robot technology, which improves accuracy and flexibility. Sliding mode control is an effective method for robot trajectory tracking control, but traditional methods suffer from problems such as parameter adjustment, switching frequency of sliding mode surface, and large oscillation amplitude. A robot trajectory tracking control method based on neural networks and sliding mode control is proposed, which analyzes the differential motion of robots under non-integrity constraints. The traditional control method converged at 4.9s and the curve was relatively fluctuating, while the research method converged at 3.6s and the converged curve was relatively smooth and stable. Compared with the existing method, the trajectory deviation of the research method was smaller than that of the reference trajectory, and the robot motion was more stable. For average errors (instantaneous optimal control method), the average errors of lateral displacement, longitudinal displacement, and displacement offset angle of the comparison method were 0.99cm, 0.62cm, and 0.0211rad, respectively. The average errors of lateral displacement, longitudinal displacement, and displacement offset of the research method were 0.78cm, 0.45cm, and 0.0181rad, respectively. The research method has good convergence speed and tracking effect in robot sliding mode control, which can improve the system control performance, increase accuracy and stability, and enhance the robustness of the robot system.}, archive = {J_ACCESS}, author = {Yuyun Liang}, doi = {10.1109/ACCESS.2025.3574358}, journal = {IEEE Access}, pages = {96740-96757}, shortjournal = {IEEE Access}, title = {Robot trajectory tracking control based on neural networks and sliding mode control}, volume = {13}, year = {2025}, }
• (2025). FDN: A real-time ensemble fire detection network. ACCESS, 13, 96723-96739. (www)

  @article{ , abstract = {Fires frequently occur worldwide, devastating neighborhoods and forests owing to challenges in early extinguishment. Computer vision-based deep learning models have recently been employed in fire detection research. The primary objective of this study is to develop a multiclass fire detection model using images and videos. In this study, an ensemble fire detection model, FireDetectNet (FDN), comprising several classification models (ResNeXt-50 ( $32\times 4$ d), EfficinetNet-B4) and one object detection model (Yolov5) is proposed. This model classifies fire and three different colors of smoke, fog, light, and sunlight. The training of multiple as an ensemble results in their performance being superior to that of a single classification or detection model. In the classification phase, our model (FDN) achieves an F1-score that is 11.3% higher than that of the single-classification model and 31.7% higher specifically for gray smoke. In the detection phase, mAP@50 and mAP@50:95 are improved by 1.3% and 1.6%, respectively, compared to the single detection model, Yolov5s. The proposed model effectively distinguishes between fog and clouds, similar to smoke, and light and sunlight, akin to a real fire using the Partial-Loss-Balanced Task Weighting (P-LBTW) algorithm we develop to reduce negative transfer by partially learning task-specific weights. Leveraging the classified and detected results, wildfires can be promptly and accurately identified wherever the equipment is deployed, thus mitigating the risk of large-scale fires. Therefore, the proposed real-time ensemble fire detection system offers practical models and system configurations for field deployment, facilitating early fire suppression to mitigate fire damage.}, archive = {J_ACCESS}, author = {Dong Hwan Shin and Jang Mook Kang and Taesu Cheong}, doi = {10.1109/ACCESS.2025.3575057}, journal = {IEEE Access}, pages = {96723-96739}, shortjournal = {IEEE Access}, title = {FDN: A real-time ensemble fire detection network}, volume = {13}, year = {2025}, }
• (2025). Improved indoor 3D point cloud semantic segmentation method based on PointNet++. ACCESS, 13, 96715-96722. (www)

  @article{ , abstract = {With the rapid advancement of point cloud-based applications, including autonomous driving and robotic navigation, 3D point cloud semantic segmentation has gained increasing research attention. PointNet++ is a widely recognized neural architecture for point cloud analysis. However, its inefficient local feature learning and limited global context capture reduce segmentation accuracy. To overcome these limitations, this paper presents an enhanced PointNet++-based model for indoor 3D point cloud segmentation. Specifically, FastKAN (Kolmogorov-Arnold Networks) replaces all MLP layers, utilizing an attention mechanism during feature aggregation to emphasize important features while preserving both local and global details, thereby enhancing segmentation performance. Additionally, the SA (Shuffle Attention) mechanism is integrated, employing channel grouping and rearrangement to effectively capture both local and global information while ensuring computational efficiency. Experimental results on the Stanford S3DIS dataset show that the proposed model achieves an overall accuracy of 86.5%, reflecting a 3.6% improvement over PointNet++, and a mean Intersection over Union (mIoU) of 57.3%, 3.8% higher than PointNet++.}, archive = {J_ACCESS}, author = {Xingfei Tan and Yonghua Xia and Bin Wang and Ruidong Zou}, doi = {10.1109/ACCESS.2025.3572137}, journal = {IEEE Access}, pages = {96715-96722}, shortjournal = {IEEE Access}, title = {Improved indoor 3D point cloud semantic segmentation method based on PointNet++}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based intrusion detection systems for the internet of drones: A systematic literature review. ACCESS, 13, 96681-96714. (www)

  @article{ , abstract = {The Internet of Drones (IoD) is a dynamic network architecture in which multiple drones, equipped with communication, sensing, and computation capabilities, are interconnected through Internet of Things (IoT) technologies to perform coordinated tasks autonomously. This infrastructure enables seamless real-time data exchange and collaborative operations across diverse applications, ranging from surveillance to delivery services, while ensuring adaptability, scalability, and security in dynamic aerial environments. However, the IoD introduces new security challenges, as drones are highly vulnerable to various cyberthreats and cyberattacks. Existing Intrusion Detection Systems (IDS) for IoD face several limitations, including high false positive rates, resource constraints of drones, limited adaptability to evolving attack patterns, and a lack of standardized datasets for benchmarking, despite ongoing research efforts. Moreover, there is a lack of a comprehensive study that systematically consolidates existing research. In this paper, we present a systematic literature review to examine the current research area of intrusion detection systems for IoD, focusing on the effectiveness of implemented machine learning models, employed datasets, existing challenges and limitations, as well as emerging trends and future research directions. This review follows PRISMA guidelines, with peer-reviewed journal articles and conference papers selected as the inclusion criteria. Publications relevant to the topic are sourced from a range of databases, including Scopus, IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, and MDPI, covering a 10-year period from 2014 to 2024. From an initial pool of 1,909 records, 62 relevant reports are selected to address the identified research questions. The selected studies are categorized according to publication year, venue, journal, drone domain, IDS type, utilized algorithms, datasets, attack classifications, and software environments. Additionally, a comparative analysis across various factors is presented.}, archive = {J_ACCESS}, author = {Mostafa Ogab and Sofiane Zaidi and Abdelhabib Bourouis and Carlos T. Calafate}, doi = {10.1109/ACCESS.2025.3575236}, journal = {IEEE Access}, pages = {96681-96714}, shortjournal = {IEEE Access}, title = {Machine learning-based intrusion detection systems for the internet of drones: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). PoolRunner: An extensible performance testing simulation tool for thread-pool middleware. ACCESS, 13, 96660-96680. (www)

  @article{ , abstract = {Thread Pool System (TPS) is the most dominant concurrency control middleware service that boosts the scalability and performance of multithreaded servers. However, the performance evaluation of TPS is problematic. The performance of a TPS is tested at the end of the project by the load testing of the multithreaded server (in which the TPS is embedded) in a testbed environment. The TPS cannot be assessed alone early in the SDLC at the design time of the multithreaded server. This lacks the notion of early performance testing. Furthermore, the testbed construction and configurations are complex, difficult, time-consuming, and expensive, especially for researchers. Setting up the lab, learning testbed configurations, embedding TPS in an open-source multithreaded application (e.g. a web server or an application server), selection of load testing tool, and getting expertise to use the tool; all these are added costs for a researcher. Moreover, the load testing tools in the testbed environment cannot assess the internal performance metrics of TPS because the tool communicates only with the multithreaded server at the application layer. Thus, in this paper, we present the PoolRunner, a client-server-based extensible performance testing simulation tool for TPS, developed in JAVA. The PoolRunner is required not only by the practitioners in the industry in early performance testing when architectural choices are made for multithreaded servers, but also by researchers in the academia to assess and compare the performance of their new TPS with existing state-of-the-art TPSs. To the best of our knowledge, there is currently no publicly available simulation tool specifically designed for the early-stage performance evaluation of TPS. Our work contributes by developing a TPS-based performance testing simulation tool and by providing an extendible framework within the tool that enables researchers to embed and compare their custom TPS implementations with existing ones. As an example, we have embedded in the PoolRunner a state-of-the-art TPS of Oracle corporation as a baseline, as well as a static TPS for learning and validity. A detailed experimental evaluation of PoolRunner validates the correctness, robustness and high performance of PoolRunner.}, archive = {J_ACCESS}, author = {Faisal Bahadur and Zulfiqar Ahmad and Abdulmohsen Algarni}, doi = {10.1109/ACCESS.2025.3575437}, journal = {IEEE Access}, pages = {96660-96680}, shortjournal = {IEEE Access}, title = {PoolRunner: An extensible performance testing simulation tool for thread-pool middleware}, volume = {13}, year = {2025}, }
• (2025). Functional verification of extended kalman filter on FPGA for railway wheelset parameters estimation. ACCESS, 13, 96646-96659. (www)

  @article{ , abstract = {The forces generated at the wheel-rail contact patch are important and crucial to the railway vehicle’s operation because they control the dynamic behavior of the entire rolling stock. The transfer of the tractive force applied by the locomotive’s traction and braking system requires a certain amount of adhesion force at the wheel-rail contact area. The wheel may skid while decelerating and slip when accelerating due to the maximal adhesion force at the wheel-rail interface may be less than the applied tractive force. A major difficulty is accurately estimating adhesion conditions and implementing them in real time because the contact forces between the wheel and rail are complicated and are highly non-linear. The fast-processing speed and hardware flexibility of field programmable gate arrays (FPGAs) make them ideal for the implementation of nonlinear condition monitoring systems. The goal of our work is to estimate various railway wheelset characteristics relating to wheel-rail contact forces under variable track-contact situations by designing and implementing the extended Kalman filter (EKF) model utilizing MATLAB and FPGA. The onboard estimation of wheel-rail interaction parameters is verified using the Xilinx (Registered trademark) System-On-Chip (SoC) Zynq and Xilinx Spartan-3 FPGA devices, built-in with the National Instruments (NI) myRIO® development board and sbRIO (Registered trademark) single-board controller. Two FPGA platforms are used to test the dataset generated by the MATLAB railway nonlinear wheelset model under different track circumstances, which include the vehicle’s acceleration and deceleration modes. The 95% of the simulation results achieved. The proposed model’s hardware synthesizes on FPGA are validated, and the area-performance is analyzed. Both FPGA devices take 52 cycles per sample and hence throughput becomes 769 kilo samples per second. Comparing the Xilinx System-On-Chip Zynq FPGA device to the Spartan-3, it is found that the latter offers a worse area-performance tradeoff for the suggested calculation of railway wheelset parameters. The industry 4.0 is greatly benefit from the promising functional verification and area-performance analysis on small commercial FPGA devices to detect and identify the faults in the early stages as part of predictive maintenance that saves time, money, and reduce accidents caused by railway derailments. Hence properly estimation of wheel-rail contact conditions with real-time implementation on FPGA can meet the expectation that railway vehicles would be extremely fast, comfortable, safe, and cost-effective modes of transportation worldwide.INDEX TERMS Adhesion conditions, accuracy index, extended Kalman filter, FPGA synthesis, simulation.}, archive = {J_ACCESS}, author = {Khakoo Mal and Tayab Din Memon and Imtiaz Hussain Kalwar and Allah Rakhio Junejo and Majid Hussain Memon and Tarique Rafique Memon and Sufyan Ali Memon and Bhawani Shankar Chowdhry}, doi = {10.1109/ACCESS.2025.3563643}, journal = {IEEE Access}, pages = {96646-96659}, shortjournal = {IEEE Access}, title = {Functional verification of extended kalman filter on FPGA for railway wheelset parameters estimation}, volume = {13}, year = {2025}, }
• (2025). The interactive multiple model strategy with matrix form mode probability for models with different state vectors. ACCESS, 13, 96635-96645. (www)

  @article{ , abstract = {The Interactive Multiple Model (IMM) strategy is widely used for systems with multiple operating modes characterized by varying dynamics. This approach is particularly effective when a system undergoes sudden transitions between different modes, such as trajectory tracking problems or fault detection in systems. In some instances, as the system transitions from one mode to another, the describing model may undergo structural changes resulting in the IMM requiring different dimensionality state vectors. To accommodate these cases, model augmentation with rows and columns of zeros is used to ensure continuity of transition in the context of IMM. However, this form of augmentation with zeros can introduce inaccuracies in the final estimation produced by the IMM. To overcome this limitation, this paper proposes a modification to the combination step of the IMM strategy by introducing two distinct mode probability matrices: a diagonal matrix form and a symmetric matrix form. These matrices help mitigate the negative effects of augmented zeros during the mixing stage when applying the IMM. A classic trajectory-tracking scenario is considered in this paper, demonstrating the effectiveness of this method in estimating states that are not dynamically present across all models. This results in higher accuracy in estimating certain states compared to the standard IMM approach, outperforming state-of-the-art techniques.}, archive = {J_ACCESS}, author = {Ehsan Majma and Ryan Ahmed and Uday Deshpande and Saeid Habibi}, doi = {10.1109/ACCESS.2025.3575641}, journal = {IEEE Access}, pages = {96635-96645}, shortjournal = {IEEE Access}, title = {The interactive multiple model strategy with matrix form mode probability for models with different state vectors}, volume = {13}, year = {2025}, }
• (2025). ChildDiffusion: Unlocking the potential of generative AI and controllable augmentations for child facial data using stable diffusion and large language models. ACCESS, 13, 96616-96634. (www)

  @article{ , abstract = {Ensuring the availability of child facial datasets is essential for advancing AI applications, yet legal, ethical, and data scarcity concerns pose significant challenges. Current generative models such as StyleGAN excel at producing synthetic facial data but struggle with temporal consistency, control over output attributes, and diversity in rendered features. These limitations underscore the need for a more robust and adaptable framework. In this research, we propose the ChildDiffusion framework, designed to generate photorealistic child facial data using diffusion models. The framework integrates intelligent augmentations via short text prompts, employs various image samplers, and leverages ControlNet for enhanced model conditioning. Additionally, we have used large language models (LLMs) to provide complex textual guidance to enable precise image-to-image transformations, facilitating the curation of diverse, high-quality datasets. The model was validated by generating child faces with varied ethnicities, facial expressions, poses, lighting conditions, eye-blinking effects, accessories, hair colors, and multi-subject compositions. To exemplify its potential, we open-sourced a dataset of 2.5k child facial samples across five ethnic classes, which underwent rigorous qualitative and quantitative evaluations. Further, we fine-tuned a Vision Transformer model to classify child ethnicity as a downstream task, demonstrating the framework’s utility. This research advances generative AI by addressing data scarcity and ethical challenges, showcasing how diffusion models can produce realistic child facial data while ensuring compliance with privacy standards. The versatile ChildDiffusion framework offers broad potential for machine learning applications, serving as a valuable tool for AI innovation. The project website, along with the complete ChildRace dataset and the fine-tuned model, is available at (https://mali-farooq.github.io/childdiffusion/).}, archive = {J_ACCESS}, author = {Muhammad Ali Farooq and Wang Yao and Peter Corcoran}, doi = {10.1109/ACCESS.2025.3575964}, journal = {IEEE Access}, pages = {96616-96634}, shortjournal = {IEEE Access}, title = {ChildDiffusion: Unlocking the potential of generative AI and controllable augmentations for child facial data using stable diffusion and large language models}, volume = {13}, year = {2025}, }
• (2025). An improved time-frequency identification method for pipeline weld EMAE aliasing modes with a focused electromagnetic excitation device. ACCESS, 13, 96600-96615. (www)

  @article{ , abstract = {The detection of circumferential cracks in pipeline welds is crucial for ensuring pipeline safety. Electromagnetic acoustic emission (EMAE) is favored for its controllable inspection process and its sensitivity to small defects, making it particularly effective for detecting pipeline weld cracks. However, the complex nature of EMAE signals, which consist of both guided wave and acoustic emission (AE) components, presents challenges in the design of excitation devices and mode identification. This paper presents an improved time-frequency identification method for pipeline weld EMAE aliasing modes with a focused electromagnetic excitation device. The focused electromagnetic excitation device generates eddy currents and magnetic fields that focus on the crack, enhancing the Lorentz force and concentrating the AE source at the crack tip. The Group Sparse Wavelet Transform with a Tunable Q-factor (GS-TQWT) algorithm is applied to reconstruct the EMAE signal, separating the signal wave packets and improving the signal-to-noise ratio (SNR). Mode identification is performed by combining the pipeline’s guided wave conversion dispersion curves with Discrete Wavelet Transform (DWT) plots. The results show that EMAE signals from circumferential cracks contain multiple modes, with the first three wave packets containing the F(1,1), F(1,2), F(1,3), and L(0,1) modes. Additionally, as the crack width increases, the arrival time of the first three wave packets is delayed, the end time is advanced, and the overall duration is shortened. This method offers a promising approach for EMAE monitoring and multi-mode analysis of pipeline welds.}, archive = {J_ACCESS}, author = {Houshi Ding and Pan Hu and Tongyu Zhao and Yuchao Jin and Xu Gui}, doi = {10.1109/ACCESS.2025.3575797}, journal = {IEEE Access}, pages = {96600-96615}, shortjournal = {IEEE Access}, title = {An improved time-frequency identification method for pipeline weld EMAE aliasing modes with a focused electromagnetic excitation device}, volume = {13}, year = {2025}, }
• (2025). Improved UAV target detection model for RT-DETR. ACCESS, 13, 96589-96599. (www)

  @article{ , abstract = {In light of the shortcomings pertaining to UAV small target detection, the detection of complex scenes, and the detection of multi-scale targets, a time-frequency dual-domain feature extraction algorithm, TF-DETR, has been proposed. This algorithm has been optimized for RT-DETR.Firstly, a time-frequency domain feature extraction module, TF-CSPNet, has been introduced into the backbone network. This module facilitates the efficient extraction and fusion of multi-source features. Secondly, the Extreme Perceptive Linear Attention (EPLA) mechanism is designed and introduced to improve the AIFI module, which enhances the model’s attention to the key information by considering the positive and negative polarity interactions between the query and the key. Furthermore, the Focaler-MPDIoU loss function has been developed to address the challenge of suboptimal localization accuracy for hard-to-detect targets and diminutive targets. On the VisDrone2019 dataset, the mAP0.5 of the enhanced model demonstrates a 3.5% improvement, accompanied by a 6.1% and 2.9% reduction in parameters and computations, respectively. The efficacy of these enhancements is substantiated by the model’s superior performance in comparison to other target detection models at equivalent levels.}, archive = {J_ACCESS}, author = {Yong He and Yufan Pang and Guolin Ou and Renfeng Xiao and Yifan Tang}, doi = {10.1109/ACCESS.2025.3575189}, journal = {IEEE Access}, pages = {96589-96599}, shortjournal = {IEEE Access}, title = {Improved UAV target detection model for RT-DETR}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable intelligent surfaces: A hardware-centric review of structures, implementation, evaluation, and integration with UAV and machine learning. ACCESS, 13, 96564-96588. (www)

  @article{ , abstract = {Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative hardware innovation to shape the wireless propagation environment in Beyond-5G and 6G systems. This review provides a structured and hardware-centric analysis of recent advances in RIS technology, focusing on four key aspects: physical structures, hardware implementation, performance evaluation, and integration with emerging technologies. First, we examine the diversity of RIS architectures, including reflectarrays, transmittarrays, multifunctional metasurfaces, active RIS prototypes, and simultaneous transmitting and reflecting RIS, highlighting their operational principles and fabrication challenges. Second, we explore system-level performance analysis through power consumption modeling, channel estimation techniques, and experimental validation methods essential for practical deployment. Third, we emphasize integration with the Unmanned Aerial Vehicle (UAV), which offers mobility and flexible coverage, and with Machine Learning, which enables intelligent adaptation and control. Finally, we discuss open challenges in hardware design, scalability, and energy management, while outlining promising directions including software-defined metasurfaces and space-time modulation. This review aims to serve as a foundational reference for researchers and engineers focused on hardware-oriented RIS development and its integration into next-generation intelligent wireless systems.}, archive = {J_ACCESS}, author = {Binh Nguyen Thai and Thinh Nguyen Tien and Khanh Doan Minh and Manh Nguyen Tien and Ta Thi Kim Hue and Nguyen Xuan Quyen}, doi = {10.1109/ACCESS.2025.3575583}, journal = {IEEE Access}, pages = {96564-96588}, shortjournal = {IEEE Access}, title = {Reconfigurable intelligent surfaces: A hardware-centric review of structures, implementation, evaluation, and integration with UAV and machine learning}, volume = {13}, year = {2025}, }
• (2025). Strategic implementation of super-agents in heterogeneous multi-agent training for advanced military simulation adaptability. ACCESS, 13, 96544-96563. (www)

  @article{ , abstract = {This study focuses on the application of reinforcement learning in tactical military simulation environments involving heterogeneous multi-agent systems. Optimizing Heterogeneous Multi-Agent Training (HMAT) through scenario-specific adjustments to the Proximal Policy Optimization (PPO) algorithm, we tackle the complexity of tactical simulations. Utilizing an advanced simulation platform, a diverse range of Reinforcement Learning (RL) agents are rigorously trained across various combat scenarios. A ‘super-agent’, an Artificial Intelligence (AI) orchestrator for multi-agent systems, marks a significant advancement in collaborative AI, enhancing operational performance. Comparative analysis highlights the strengths of both traditional RL approaches and HMAT in a unified computational framework. While independent learning agents excel in predictable environments with fast training capabilities, HMAT stands out in dynamic scenarios for its adaptability and superior performance. The integration of HMAT with ‘super-agents’ is shown to markedly improve the fidelity and adaptive capacity of military simulations. Experimental results demonstrate that our fine-tuned super-agent framework achieves up to 92% mission success rate, outperforming scenario-specific baselines by 15–20% in complex Suppression of Enemy Air Defenses (SEAD) and air-to-ground tasks.These enhancements have far-reaching implications, potentially revolutionizing strategic military training and operational planning and underscoring AI’s critical role in modern defense strategies.}, archive = {J_ACCESS}, author = {Hüseyin Oktay Altun and Hüseyin Furkan Ceran and Korkut Kutay Metın and Tolga Erol and Emre Fişne}, doi = {10.1109/ACCESS.2025.3573419}, journal = {IEEE Access}, pages = {96544-96563}, shortjournal = {IEEE Access}, title = {Strategic implementation of super-agents in heterogeneous multi-agent training for advanced military simulation adaptability}, volume = {13}, year = {2025}, }
• (2025). Solving the problem of vehicle routing problem with time window via dual adaptive genetic algorithm. ACCESS, 13, 96535-96543. (www)

  @article{ , abstract = {To address the Vehicle Routing Problem with Time Windows (VRPTW), this paper presents a novel Dual Adaptive Genetic Algorithm (DAGA). While numerous metaheuristic algorithms have been proposed to solve VRPTW, most existing approaches either rely on fixed cost functions or incorporate local search heuristics without adaptive control. To the best of our knowledge, there is a lack of research on simultaneously integrating adaptive cost evaluation and adaptive local search strategies within the genetic algorithm framework for solving VRPTW. This paper aims to fill this gap. The proposed DAGA integrates two adaptive strategies: an adaptive cost function and an adaptive local search operator, designed to enhance exploration and exploitation capabilities. DAGA is evaluated on the widely used Solomon100 benchmark dataset to assess its effectiveness. Experimental results indicate that DAGA achieves the best-known solutions (BKS) in 14 out of 56 problem instances, with the remaining results exhibiting only minor deviations from the BKS. On average, the deviation in total travel distance is 2.3%, while the deviation in the number of vehicles used is 4%. Compared to other state-of-the-art algorithms, DAGA delivers competitive performance with a favorable balance between solution quality and computational effort. These findings demonstrate that the proposed method is a promising and practical tool for optimizing complex logistics and supply chain problems.}, archive = {J_ACCESS}, author = {Chunge Hua}, doi = {10.1109/ACCESS.2025.3575459}, journal = {IEEE Access}, pages = {96535-96543}, shortjournal = {IEEE Access}, title = {Solving the problem of vehicle routing problem with time window via dual adaptive genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Underactuated finger design: A minimalistic approach toward biomimetic prosthetic finger. ACCESS, 13, 96517-96534. (www)

  @article{ , abstract = {Dexterous prosthetic hands with a simple design are the need of the hour. The limiting factor for prosthetic hands is the structure, functionality, and control of the fingers and the thumb. Traditionally, robotic fingers used in prosthetic hands are tendon-driven or have linkage-based mechanisms. While tendon-driven mechanisms provide anthropomorphic behaviour, they lack sufficient grip force. In contrast, linkage mechanisms develop sufficient grip force but at the expense of the size and weight of the overall design. Further, a linkage-based mechanism provides only shape adaptation without joint coupling. To address this gap in prosthetic finger design, we propose a novel underactuated, hybrid, and minimalistic finger mechanism to achieve simultaneous coupling and self-adaptation. The designed finger mechanism has three degrees of freedom provided by two 4-bar mechanisms in series, with two degrees of actuation. The finger is actuated through a pair of inelastic tendons acting on two joints. The tendon architecture is bio-inspired and improves the finger mechanism’s anthropomorphic behaviour. A detailed kinematic and static analysis is provided to describe the finger design’s operating principle and construction. Simulations of fingertip trajectory and contact forces are also performed. Additionally, a prototype of the mechanism was 3D printed to validate the design of the finger. Grasping simulations and experiments were also performed for a typical 4-bar-based finger mechanism. The results show improved anthropomorphic motion and increased contact forces for the current finger design compared to the conventional 4-bar mechanism-based prosthetic finger.}, archive = {J_ACCESS}, author = {Vignesh Sompur and Varadhan Skm and Asokan Thondiyath}, doi = {10.1109/ACCESS.2025.3572241}, journal = {IEEE Access}, pages = {96517-96534}, shortjournal = {IEEE Access}, title = {Underactuated finger design: A minimalistic approach toward biomimetic prosthetic finger}, volume = {13}, year = {2025}, }
• (2025). GeoViT: Mixed-scale transformer for perspective correction in print-cam image watermarking. ACCESS, 13, 96503-96516. (www)

  @article{ , abstract = {Printed identity documents, such as ID cards and passports, continue to play a vital role in identity verification, despite the growing adoption of digital authentication methods. The print-cam process, which involves printing a watermarked image and capturing it with a smartphone camera, provides a practical approach to the authentication of mobile-based documents. However, this process introduces challenges such as perspective distortions, compression artifacts, noise, and lighting variations, making accurate watermark detection difficult. Existing distortion correction techniques often struggle to fully address these issues, especially in practical scenarios where handheld camera use is common and conditions are less controlled. In this study, we propose GeoViT, a Transformer-based framework that enhances watermark robustness against print-cam attacks. GeoViT utilizes a multi-head attention mechanism to capture global dependencies and spatial variations, improving feature extraction for distortion rectification. To address the limitations of the naive feed-forward network in Transformers for multi-scale information, we introduce a mixed-scale feed-forward network, which generates robust features for geometric alignment. Additionally, we incorporate a mixture of expert feature compensators, integrating local context from CNN-based operators to refine distortion correction. Our method significantly outperforms existing approaches in geometric accuracy, visual fidelity, and perceptual quality. Extensive experiments on a diverse set of ID images captured under various conditions with different smartphone models demonstrate that GeoViT significantly improves watermark robustness. These results highlight GeoViT’s effectiveness as a secure and efficient solution for mobile-based identity document authentication, advancing the development of watermarking techniques for real-time, smartphone-compatible systems.}, archive = {J_ACCESS}, author = {Said Boujerfaoui and Anass Mançour-Billah and Hassan Douzi and Rachid Harba}, doi = {10.1109/ACCESS.2025.3575472}, journal = {IEEE Access}, pages = {96503-96516}, shortjournal = {IEEE Access}, title = {GeoViT: Mixed-scale transformer for perspective correction in print-cam image watermarking}, volume = {13}, year = {2025}, }
• (2025). Investigation on the role of artificial intelligence in measurement system. ACCESS, 13, 96483-96502. (www)

  @article{ , abstract = {When transmitters measure process variables like temperature, pressure, flow, level, and quality in process industries, due to wear and tear, ambient conditions, process variations and electromagnetic interference, measured values always deviate from their actual value, and controlled variables will start to oscillate near setpoint. This review paper investigates artificial intelligence impacts on measurement system to improve characteristics of transmitters like linearity, accuracy, sensitivity, resolution, repeatability. Soft computation using artificial neural networks and deep learning for linearization, compensation and error reduction, machine learning for estimation levaraging different algorithms like levenberg marquardt, scaled conjugate gradient, bayesian regularization, are assessed for training, testing and validation in real time and simulation. Hardware approach with soft computation has reduced non linearity error by 84.63% for thermocouple linearization, meanwhile novel hybrid approach using genetic algorithm (GA) and particle swarm optimization (PSO) combined with back propagation neural network (BPNN) have reduced mean absolute percentage error to 1.2 % for industrial weir than conventional hardware approaches using sensors and signal conditioning circuits but at higher computational cost. Challenges like integration to distributed control system via programmable logic controller, huge amount of training data for estimation, real time implementation in measurement systems, overfitting, underfitting, implementation cost are analyzed. Application of artificial intelligence methods and hybrid approaches in measurement systems can drastically improve the operation, maintenance, cost, safety of plant and personnel in real time, where artificial intelligence is still in its nascent stage in instrumentation and control.}, archive = {J_ACCESS}, author = {P. A. Rezvy and Venkata Lakshmi Narayana Komanapalli}, doi = {10.1109/ACCESS.2025.3575164}, journal = {IEEE Access}, pages = {96483-96502}, shortjournal = {IEEE Access}, title = {Investigation on the role of artificial intelligence in measurement system}, volume = {13}, year = {2025}, }
• (2025). State of the art and future directions of healthcare 4.0: A systematic literature review. ACCESS, 13, 96469-96482. (www)

  @article{ , abstract = {This article explores the emerging field of Healthcare 4.0, which integrates advanced technologies from Industry 4.0 to enhance decision-making processes in the healthcare context. As the healthcare landscape evolves, there is a pressing need to understand how these innovations can support clinical and operational management, ultimately improving patient outcomes and healthcare efficiency. This study employs a systematic literature review (SLR) methodology to investigate the current state of research on Healthcare 4.0 solutions, focusing on their applications across various healthcare sectors. The main scientific databases were considered and we evaluated peer-reviewed articles published in scientific journals until July 2024. The review identifies key themes, trends, and gaps in the literature, revealing that while the field is still in its infancy, there is a notable increase in publications, indicating growing interest and potential for future research. The findings categorize applications into several areas, including hospital general facilities and specific environments, highlighting the diverse contexts in which Healthcare 4.0 technologies are being implemented. Additionally, the study examines the geographical distribution of research, with a significant emphasis on developments in Asia and Europe. Regarding the decision objectives, we highlight the use of Healthcare 4.0 solutions to improve the performance of activities related to data exchange, diagnosis, and patient monitoring. Furthermore, considering the main techniques and tools adopted by the authors, Artificial Intelligence, Internet of Things, and Blockchain stand out, as well as hybrid approaches which integrate different solutions. By synthesizing these insights, the article contributes to a deeper understanding of how Healthcare 4.0 can transform decision-making in health facilities, offering directions for future research and practical implications for healthcare researchers and professionals.}, archive = {J_ACCESS}, author = {Carlos Henrique dos Santos and André Luiz Romano and Walther Azzolini Junior and Francisco Ignácio Giocondo César and Fabio Ferraço and Ivanhoe Rozo-Rojas}, doi = {10.1109/ACCESS.2025.3575054}, journal = {IEEE Access}, pages = {96469-96482}, shortjournal = {IEEE Access}, title = {State of the art and future directions of healthcare 4.0: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Image classification model based on contrastive learning with dynamic adaptive loss. ACCESS, 13, 96454-96468. (www)

  @article{ , abstract = {As one of the core tasks in vision recognition, image classification is widely used in various scenarios. Most existing mainstream image classification models use the Convolutional Neural Network (CNN), the Transformer, or a combination of both as the backbone. However, the convolutional operation of CNN relies on local receptive fields, which limits its ability to capture global information about the image. The Transformer typically partitions the image into equal-sized and non-overlapping image patches, which may destroy the continuity of the edges of the image patches, leading to the loss of critical information. In addition, if the combination of CNN and Transformer is not done appropriately, it may significantly weaken the model’s ability to express local features. A new image classification model is proposed to solve the above problems. The backbone of the proposed model consists of CNN and Transformer in tandem, taking advantage of CNN’s strengths in local feature extraction and fully utilizing Transformer’s ability to process the global information of the image, thus deepening the model’s understanding of the image’s semantic relationship. Specifically, the proposed model contains two branches: contrastive learning and classification. The contrastive learning branch introduces the contrastive learning strategy to enhance the distinguishability between features of different samples, thus compensating for the model’s reliance solely on classification supervision signals. The classification branch is based on traditional image classification methods. Still, it improves the cross-entropy loss to develop a dynamic adaptive loss, enabling the model to learn indistinguishable features effectively. At the same time, it also reduces the phenomenon where easily classified samples at the beginning become indistinguishable as the model matures. Experimental results on the ImageNet-1k dataset show that the proposed model achieves a Top-1 accuracy of 82.8%, outperforming existing image classification models based on CNNs, Transformers, or their combinations. Notably, the model achieves high classification accuracy while maintaining a relatively low parameter size (23.9MB) and computational complexity (5.7Mflops). These experimental results fully demonstrate the effectiveness and feasibility of the proposed method.}, archive = {J_ACCESS}, author = {Quandeng Gou and Jingxuan Zhou and Zi Li and Fangrui Zhang and Yuheng Ren}, doi = {10.1109/ACCESS.2025.3574335}, journal = {IEEE Access}, pages = {96454-96468}, shortjournal = {IEEE Access}, title = {Image classification model based on contrastive learning with dynamic adaptive loss}, volume = {13}, year = {2025}, }
• (2025). Task specific signal transformation. ACCESS, 13, 96445-96453. (www)

  @article{ , abstract = {Neural signals, sourced directly from the brain, are the gold standard for use in cognitive state detection, but are infeasible for everyday usage. However, the use of more accessible non-neural data to predict cognitive state is significantly less accurate. To bridge this gap in accuracy and usability, in this paper we propose a method that uses non-neural data to infer neural activity and improve off-body cognitive state detection. This is achieved by exploiting the inherent correspondence of signals collected simultaneously from a single individual, and learning a task-specific signal transformation from non-neural to neural data. This reconstructed neural data representation can then be used to predict cognitive state. These predictions are nearly as accurate as predictions made directly from neural signals. This represents a significant advance in practical human–machine-teaming for everyday usage, allowing neural information to be inferred from non-neural sources. In other words, the neural information becomes tacit.}, archive = {J_ACCESS}, author = {Brianna Duffy and Mia Levy and Andrew Howe and Rodolfo Valiente Romero and Evelyn Kim and Maureen August and Akilesh Rajavenkatanarayanan}, doi = {10.1109/ACCESS.2025.3576052}, journal = {IEEE Access}, pages = {96445-96453}, shortjournal = {IEEE Access}, title = {Task specific signal transformation}, volume = {13}, year = {2025}, }
• (2025). Resolution equation for SAR systems synthesizing circular aperture. ACCESS, 13, 96434-96444. (www)

  @article{ , abstract = {The resolution of a synthetic aperture radar (SAR) system depends not only on the operating frequency range of a radar but also on the geometry. A radar system operating at MHz frequencies facilitates SAR resolution to the meter (m) level with a linear aperture. With the same radar system, the realization of the circular aperture can further enhance the SAR resolutions to the submeter level. For GHz SAR systems, the realization of circular apertures helps to reduce the resolution from the centimeter (cm) level to the millimeter (mm) level. In this paper, a resolution equation for SAR systems synthesizing circular aperture (CSAR) is introduced. The equation is derived from the backprojection integral for CSAR, its Fourier transform, and the method of stationary phase. The accuracy of the derived equation is enhanced with a correcting factor that is numerically calculated with a cubic interpolation. Therefore, the equation can provide a more accurate, analytical, and practical estimate of the spatial resolution that can be reached with a CSAR system than the ones available in the literature. The resolution equation introduced in this paper is verified with simulations of a MHz SAR system and a GHz SAR system. The equation is further verified by an experiment with a THz inverse SAR (ISAR) system. The radar utilized in the ISAR experiment is a D-band radar system mounted on an antenna positioner that supports circular movement.}, archive = {J_ACCESS}, author = {Viet T. Vu and Yevhen Ivanenko and Vanja Lindberg and Mats I. Pettersson}, doi = {10.1109/ACCESS.2025.3572523}, journal = {IEEE Access}, pages = {96434-96444}, shortjournal = {IEEE Access}, title = {Resolution equation for SAR systems synthesizing circular aperture}, volume = {13}, year = {2025}, }
• (2025). A physics-informed machine learning framework for permafrost stability assessment. ACCESS, 13, 96423-96433. (www)

  @article{ , abstract = {Global warming accelerates permafrost degradation, compromising the reliability of critical infrastructure relied upon by over five million people daily. Additionally, permafrost thaw releases substantial methane emissions due to the thawing of swamps, further amplifying global warming and climate change and thus posing a significant threat to more than eight billion people worldwide. To mitigate this growing risk, policymakers and stakeholders need accurate predictions of permafrost thaw progression. Comprehensive physics-based permafrost models often require complex, location-specific fine-tuning, making them impractical for widespread use. Although simpler models with fewer input parameters offer convenience, they generally lack accuracy. Purely data-driven models also face limitations due to the spatial and temporal sparsity of observational data. This work develops a physics-informed machine learning framework to predict permafrost thaw rates. By integrating a physics-based model into machine learning, the framework significantly enhances the feature set, enabling models to train on higher-quality data. This approach improves permafrost thaw rate predictions, supporting more reliable decision-making for construction and infrastructure maintenance in permafrost-vulnerable regions, with a forecast horizon spanning several decades.}, archive = {J_ACCESS}, author = {Polina Pilyugina and Timofey Chernikov and Maria Smirnova and Alexey Zaytsev and Alexander Bulkin and Evgeny Burnaev and Ilya S. Belalov and Nazar Sotiriadi and Albert Efimov and Yury Maximov and Oleg Anisimov}, doi = {10.1109/ACCESS.2025.3573072}, journal = {IEEE Access}, pages = {96423-96433}, shortjournal = {IEEE Access}, title = {A physics-informed machine learning framework for permafrost stability assessment}, volume = {13}, year = {2025}, }
• (2025). Benchmarking CNN architectures for tool classification: Evaluating CNN performance on a unique dataset generated by novel image acquisition system. ACCESS, 13, 96400-96422. (www)

  @article{ , abstract = {In this study, we introduce the ToolSurface-144 dataset, which is presented here for the first time. It comprises four subsets – Full R, Full S, Top R, and Top S – each containing 144 tool classes captured under varying illumination conditions and fields of view. A newly developed, patented imaging approach was employed to acquire the data. It is compared with conventional diffuse ring illumination to assess its effectiveness in evaluating state-of-the-art convolutional neural networks. This enabled a more targeted investigation of the role of global shape characteristics such as silhouettes versus localized features like the tool face, cutting edges, and delicate geometrical structures under different training strategies. In this study, we evaluate six state-of-the-art convolutional neural networks—AlexNet, DenseNet161, EfficientNet-B0, ResNet152, ResNet50, and VGG16—using three training strategies: fine-tuning, freezing of pre-trained layers, and training from scratch. The results show that EfficientNet-B0 consistently achieved the highest classification accuracy in nearly all experiments and data sets. Especially using the fine-tuning training strategy, the model achieved 99% accuracy in tool classification. ResNet50 benefited greatly from fine-tuning and freezing, achieving a significant increase in performance compared to training from scratch. In contrast, ResNet152, AlexNet, and VGG16 consistently showed poor classification performance, indicating difficulties regarding learning and generalisation. The results show that diffuse illumination and complete tool views provide the best classification conditions, while restricted image sections with homogeneous illumination negatively affect model performance. Among the evaluated training strategies, fine-tuning proved the most efficient training method for developing CNN models for tool classification.}, archive = {J_ACCESS}, author = {Mühenad Bilal and Ranadheer Podishetti and Daniel Grossmann and Markus Bregulla}, doi = {10.1109/ACCESS.2025.3574785}, journal = {IEEE Access}, pages = {96400-96422}, shortjournal = {IEEE Access}, title = {Benchmarking CNN architectures for tool classification: Evaluating CNN performance on a unique dataset generated by novel image acquisition system}, volume = {13}, year = {2025}, }
• (2025). Wireless sensor networks: From fundamentals and applications to innovations and future trends. ACCESS, 13, 96365-96399. (www)

  @article{ , abstract = {Wireless Sensor Networks (WSNs) have transformed data acquisition and processing across diverse domains such as environmental monitoring, healthcare, and industrial automation. Recent advancements in areas such as federated intelligence, blockchain-based trust mechanisms, and quantum sensing are reshaping the landscape of WSNs. Yet, their full implications for next-generation applications demand deeper exploration. This survey is driven by the need to synthesize these emerging directions through a forward-looking and interdisciplinary lens, highlighting their transformative potential in evolving WSNs into intelligent, autonomous systems. It examines key solutions in energy harvesting, adaptive communication, and edge intelligence, which address persistent constraints like limited energy availability, dynamic topologies, and scalability. Moreover, it highlights the integration of WSNs with disruptive technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins, enabling intelligent, autonomous systems capable of real-time analytics. Critical energy optimization, security, and data management challenges are explored alongside cutting-edge solutions like federated learning (FL), quantum sensing, and blockchain-based trust mechanisms. Future research directions emphasize bio-inspired architectures, hybrid communication in extreme environments, and efficient multi-modal data fusion. This survey serves as a foundational reference for researchers and practitioners seeking to design resilient, intelligent WSN-based systems for the next generation of applications.}, archive = {J_ACCESS}, author = {Maria Trigka and Elias Dritsas}, doi = {10.1109/ACCESS.2025.3572328}, journal = {IEEE Access}, pages = {96365-96399}, shortjournal = {IEEE Access}, title = {Wireless sensor networks: From fundamentals and applications to innovations and future trends}, volume = {13}, year = {2025}, }
• (2025). RIS-assisted terahertz communications for 6G networks: A comprehensive overview. ACCESS, 13, 96337-96364. (www)

  @article{ , abstract = {The integration of multiple-input multiple-output (MIMO) technology with reconfigurable intelligent surfaces (RIS) in Terahertz (THz) communication systems has emerged as a promising approach to address the challenges and exploit the opportunities in next-generation wireless networks. This technical review provides a systematic state-of-the-art research and developments toward MIMO-based RIS-supported THz communication, systems focus on the potential research opportunities, challenges, and emerging applications of this innovative technology. Further, this paper explores the combined benefits of integrating MIMO with RIS to improve link reliability, along with the associated system-level design challenges. Furthermore, we discuss various aspects of MIMO-based RIS-supported THz communication, such as channel modeling, channel estimation, beamforming techniques, beam split effect resource allocation strategies, and performance evaluation metrics. Moreover, the role of artificial intelligence and machine learning (AI/ML) in the design of RIS-empowered THz wireless communication systems is also discussed.}, archive = {J_ACCESS}, author = {Alok Kumar and Sanjeev Sharma and M. Hemanta Kumar and Ghanshyam Singh}, doi = {10.1109/ACCESS.2025.3574476}, journal = {IEEE Access}, pages = {96337-96364}, shortjournal = {IEEE Access}, title = {RIS-assisted terahertz communications for 6G networks: A comprehensive overview}, volume = {13}, year = {2025}, }
• (2025). An end-to-end concatenated CNN attention model for the classification of lung cancer with XAI techniques. ACCESS, 13, 96317-96336. (www)

  @article{ , abstract = {In the field of medical imaging, deep learning (DL) techniques have made significant contributions to the detection and classification of various cancers. Identifying the precise regions in medical images containing cancerous cells plays a crucial role in the diagnostic process. Early and accurate cancer detection is essential for effective treatment and improved patient outcomes. However, manual diagnosis is labor-intensive, requiring the specialized expertise of radiologists, and the increasing number of cancer cases presents challenges in processing large volumes of image data efficiently. To address these challenges, an end-to-end concatenated Convolutional Neural Network (CNN) attention model has been proposed for automatic lung cancer classification. This approach integrates two distinct CNNs, followed by a multi-layer perceptron (MLP) and a multi-head attention (MHA) mechanism, to enhance performance. The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. This model achieves impressive performance, with an accuracy of 99.54%, precision of 99.31%, recall of 99.95%, F1-score of 99.66%, and an AUC of 99.97%. These results demonstrate that this approach not only surpasses existing methods but also provides a highly accurate and interpretable solution. By reducing the need for extensive manual intervention, this model enables faster and more reliable lung cancer diagnosis, paving the way for timely and effective treatments.}, archive = {J_ACCESS}, author = {Fariha Haque and Mohammad Asif Hasan and Md. Abu Ismail Siddique and Tonmoy Roy and Tonmoy Kanti Shaha and Yamina Islam and Avijit Paul and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3572423}, journal = {IEEE Access}, pages = {96317-96336}, shortjournal = {IEEE Access}, title = {An end-to-end concatenated CNN attention model for the classification of lung cancer with XAI techniques}, volume = {13}, year = {2025}, }
• (2025). Spatial distribution of poverty clusters and its prediction algorithms: A visual analytics approach to understanding the disparities of poverty across zones. ACCESS, 13, 96302-96316. (www)

  @article{ , abstract = {Eradicating Poverty in all its forms remains a key priority and a significant challenge in Tanzania. More than 50% of the population lives in multidimensional poverty, with urban poverty stagnating while rural poverty, though higher, is declining at a slower rate. The challenge of higher multidimensional poverty, with a slow pace of reduction, entails ongoing empirical testing in order to inform strategies for achieving SDG 1 in 2030. This study aims to utilize a hybrid method to identify the most effective clustering algorithm for poverty distribution and its corresponding predictive algorithm at the household level across the different zones. The hybrid method was employed because supervised learning methods struggle with discovering hidden patterns due to pre-defined labels while clustering approach lacks predictive accuracy metrics. The analysis utilizes longitudinal data from the National Bureau of Statistics (NBS), where household surveys were conducted using a multi-stage, stratified cluster sampling technique. Multidimensional poor and non-poor households were classified using the K-Means clustering algorithm, which demonstrated the highest performance among the nine clustering algorithms tested, effectively categorizing households into two clusters. The findings indicate that while the overall poverty rate has declined slightly, it remains persistently high, affecting more than 65% of households in three zones: Western, Central and Lake zones. In contrast, the Coastal and Zanzibar zones show indications of an increasing number of poor households. The results reveal that the Ensemble Stacking method outperforms other predictive algorithms in accurately classifying poverty groups derived from K-Means clustering. In light of these findings, the study recommends that the government implement targeted policies that align with the specific poverty levels and socioeconomic conditions of each zone to enhance poverty alleviation efforts effectively.}, archive = {J_ACCESS}, author = {Ngong’ho Bujiku Sende and Snehanshu Saha and Leon Fidele Ruganzu Uwimbabazi}, doi = {10.1109/ACCESS.2025.3575577}, journal = {IEEE Access}, pages = {96302-96316}, shortjournal = {IEEE Access}, title = {Spatial distribution of poverty clusters and its prediction algorithms: A visual analytics approach to understanding the disparities of poverty across zones}, volume = {13}, year = {2025}, }
• (2025). A review on building resilient power networks: Emphasizing adjacent span dynamics. ACCESS, 13, 96290-96301. (www)

  @article{ , abstract = {This research article presents a comprehensive review of resilience in power transmission and distribution (T&D) systems, focusing on key studies that examine structural integrity, mechanical stress distribution, and system reliability under various conditions. The primary objective is to investigate the dynamics of adjacent spans and their role in influencing stress distribution and structural resilience of poles and towers through the cables connecting them. The article synthesizes findings across multiple studies, covering assumptions, system topologies, methodologies, and challenges that affect resilience in these networks. Particular attention is given to identifying studies that address the influence of adjacent spans and tower-line interactions, as these factors play a crucial role in the stress distribution and structural dynamics within poles, towers, and transmission lines. In addition to identifying critical factors contributing to system vulnerability, this review emphasizes the importance of continued research to better understand and mitigate risks from extreme events, such as high winds combined with ice storms, which pose significant risks to the stability and reliability of electricity supply. By consolidating knowledge on current challenges and future needs, this work outlines future directions to advance resilience studies and contributes to ongoing efforts to strengthen power T&D networks, reducing disruptions in electricity supply and improving overall system resilience in an evolving climate landscape.}, archive = {J_ACCESS}, author = {Tawsif Ahmad and Manisha Maharjan and Fernando Bereta Dos Reis and Jennifer Appiah-Kubi and Vishvas H. Chalishazar and Sraddhanjoli Bhadra}, doi = {10.1109/ACCESS.2025.3573271}, journal = {IEEE Access}, pages = {96290-96301}, shortjournal = {IEEE Access}, title = {A review on building resilient power networks: Emphasizing adjacent span dynamics}, volume = {13}, year = {2025}, }
• (2025). An advanced recomposition-based displaying technique: Maximizing image reconstruction for virtual museum applications. ACCESS, 13, 96277-96289. (www)

  @article{ , abstract = {We present an advanced recomposition-based displaying technique designed to optimize intelligent scene retargeting in hybrid-reality environments, with two key contributions: Geometry-preserving and Human-inspired Active Detection (GHAD) and time-sensitive feature selection. GHAD progressively constructs Gaze Shift Paths (GSPs), aligning image processing with human gaze dynamics to maximize image reconstruction accuracy, while prioritizing key visual elements based on human attention patterns. The time-sensitive feature selection utilizes the BING objectness metric to identify and prioritize the most relevant features from multimodal data sources, ensuring efficient extraction and preserving exhibit content. These methods, combined with a multi-layer aggregation algorithm that encodes deep feature representations in a Gaussian Mixture Model (GMM), enable seamless scene reconstruction with improved precision. Empirical evaluations, including user studies, demonstrate the techniqueạŕs superiority, achieving 3.9% to 5.0% higher precision on six scenery sets and reducing testing time by 50%. The approach effectively balances algorithmic precision with human-centered aesthetics, advancing AI-driven scene analysis and visual recomposition, while enhancing interactivity and immersion for a more engaging and adaptive user experience.}, archive = {J_ACCESS}, author = {Jingjie Zhao and Xin Shi and Olga Yezhova and Qinchuan Zhan and Xijing Zhang}, doi = {10.1109/ACCESS.2025.3575186}, journal = {IEEE Access}, pages = {96277-96289}, shortjournal = {IEEE Access}, title = {An advanced recomposition-based displaying technique: Maximizing image reconstruction for virtual museum applications}, volume = {13}, year = {2025}, }
• (2025). AirQuaNet: A convolutional neural network model with multi-scale feature learning and attention mechanisms for air quality-based health impact prediction. ACCESS, 13, 96261-96276. (www)

  @article{ , abstract = {Air pollution is a vital environmental and public health issue responsible for millions of early deaths worldwide every year. Predicting air quality and associated health impacts is crucial for early intervention and informed policy planning. This work presents Air Quality Network (AirQuaNet), a novel convolutional neural network (CNN) designed to predict air quality levels accurately. AirQuaNet integrates deep learning (DL) innovations, namely Multi-Scale Convolutional Blocks (MSCBs), residual connections, and self-attention mechanisms, to enhance its feature extraction capabilities and enable it to learn long-range temporal dependencies. The MSCBs employ four parallel 1D convolutional layers with different kernel sizes, enabling the model to extract multi-scale features critical for learning patterns in complex environmental data. Residual connections are employed to prevent vanishing gradient issues during training, and the self-attention mechanism dynamically weights informative inputs, improving the model’s attention towards informative pollutant features. AirQuaNet was evaluated on two public datasets, the Air Quality and Health Impact Dataset and the Comprehensive Health Data for Asthma. It achieved outstanding results, with an R2 of 0.9997 on regression tasks and a classification accuracy of 94.21%, outperforming traditional machine learning algorithms and DL baselines. These results highlight the model’s robustness under diverse data environments and its ability for high generalization across varied temporal scales and types of contaminants. AirQuaNet also offers high scalability, rendering it an excellent candidate for real-time urban surveillance networks. Its ability to deal with large, heterogeneous datasets qualifies it as a top contender for environmental health forecasting, policy support, and early warning systems.}, archive = {J_ACCESS}, author = {Sreeni Chadalavada and Suleyman Yaman and Abdulkadir Sengur and Ravinesh C. Deo and Abdul Hafeez-Baig and Tracy Kolbe-Alexander and Niranjana Sampathila and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3574722}, journal = {IEEE Access}, pages = {96261-96276}, shortjournal = {IEEE Access}, title = {AirQuaNet: A convolutional neural network model with multi-scale feature learning and attention mechanisms for air quality-based health impact prediction}, volume = {13}, year = {2025}, }
• (2025). Development of a weighted average ensemble model for predicting officially assessed land prices using grid map data and SHAP. ACCESS, 13, 96251-96260. (www)

  @article{ , abstract = {This study proposes a weighted average ensemble model to predict the Officially Assessed Land Price in Sejong City, South Korea, using 500m $\times 500$ m grid-based spatial data. The model analyzes the impact of key variables through SHAP for improved interpretability. Independent variables were grouped into three categories: building characteristics, population demographics, and accessibility to social infrastructure. To construct the predictive model, three boosting algorithms—Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and LightGBM—were evaluated. The optimal ensemble model, weighted as GBM:XGBoost:LightGBM =0:9:1, achieved a high predictive performance with R2 of 0.8964. SHAP analysis revealed five major factors influencing land price: floor area ratio, accessibility to fire stations, total male population, number of reinforced concrete buildings, and accessibility to general hospitals. The model’s outputs were visualized through a grid-based land price map, revealing elevated land values within Sejong’s Multifunctional Administrative City and around key transportation hubs. By employing fine-scale spatial data and enhancing model interpretability through SHAP, this research presents a robust empirical framework that addresses the limitations of traditional administrative-bound analyses. The findings provide valuable insights for data-driven policy formulation and the evaluation of the real estate market.}, archive = {J_ACCESS}, author = {Surin Im and Kangmin Kim and Geunhee Lee and Hoi-Jeong Lim}, doi = {10.1109/ACCESS.2025.3574698}, journal = {IEEE Access}, pages = {96251-96260}, shortjournal = {IEEE Access}, title = {Development of a weighted average ensemble model for predicting officially assessed land prices using grid map data and SHAP}, volume = {13}, year = {2025}, }
• (2025). Development of application to improve knowledge and comprehension of programming for learners in remote areas: A case study of schools in thailand. ACCESS, 13, 96236-96250. (www)

  @article{ , abstract = {Programming has become an important skill to acquire in order to meet the demands of Thailand’s Industry 4.0 society. However, programming remains a challenging and sometimes unengaging subject for some students, which makes it difficult to cultivate skilled professionals in this field. Schools in remote areas often depend on teachers who may lack the necessary expertise in programming and require additional tools to effectively teach the subject. This research aims to offer the feasibility of developing an application based on problem-based learning principles to create a research framework and subsequently develop it into a game-based learning application. This application focuses on collecting data regarding children’s learning behaviors during use and evaluating learning outcomes at each stage. The system is de-signed to adjust the difficulty level based on both the learner’s progress and the teacher’s input, thereby serving as a tool for educators to enhance children’s engagement and accessibility in programming. This initiative is being piloted in schools in Chiang Rai Province, Thailand, particularly among ethnic groups and children living in remote areas. The research findings have been collected and analyzed to inform further development.}, archive = {J_ACCESS}, author = {Kemachart Kemavuthanon and Nattapol Aunsri}, doi = {10.1109/ACCESS.2025.3574973}, journal = {IEEE Access}, pages = {96236-96250}, shortjournal = {IEEE Access}, title = {Development of application to improve knowledge and comprehension of programming for learners in remote areas: A case study of schools in thailand}, volume = {13}, year = {2025}, }
• (2025). Attention integrated residue CNN for classification of DNA-binding and RNA-binding proteins. ACCESS, 13, 96226-96235. (www)

  @article{ , abstract = {The classification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) is essential for understanding molecular interactions and regulatory functions. However, the existing computational approaches face difficulties in differentiating between DBPs and RBPs resulting in cross-prediction errors. To mitigate this issue, SERCNN as a multilabel classification model, which integrates deep neural networks with residual connections and squeeze-and-excitation attention mechanisms, is developed in this paper. This approach provides improved performance in predicting proteins that bind with DNA, RNA, and the proteins that bind to both DNA and RNA (DRBPs). On the TEST474 dataset, the SERCNN achieved an AUC value of 0.95 with a 1-AURC value of 0.91 for the prediction of DBPs. For RBP prediction an AUC value of 0.92 with a 1-AURC value of 0.90, is obtained. For the PDB255 dataset, the proposed model achieved an AUC of 0.80 and a 1-AURC of 0.85 for DBPs and an AUC of 0.80 and a 1-AURC of 0.81 for RBPs. Improved prediction performance has also been obtained on the DRBP206 dataset. Therefore, the prediction results obtained on the test dataset suggest that the proposed model can be employed as a useful tool for DBP and RBP prediction tasks.}, archive = {J_ACCESS}, author = {T. Prajwal Prabhu and Rasagnya Bethi and Satyajit Mahapatra and Tusar Kanti Dash}, doi = {10.1109/ACCESS.2025.3575700}, journal = {IEEE Access}, pages = {96226-96235}, shortjournal = {IEEE Access}, title = {Attention integrated residue CNN for classification of DNA-binding and RNA-binding proteins}, volume = {13}, year = {2025}, }
• (2025). Gird congestion management based on the renewable energy zones and clustering of grid congestion conditions. ACCESS, 13, 96213-96225. (www)

  @article{ , abstract = {This study proposes a novel output curtailment method of renewable energy sources (RESs) to mitigate grid congestion while balancing curtailment minimization and fairness among power producers, considering the operational efficiency of system operators. This method is based on creating zones using sensitivity analysis and clustering grid congestion conditions. Recently, the large introduction of RESs has raised concerns of grid congestion in power systems. To mitigate congestion, the system operator curtails the output of RESs; however, the curtailment should be minimized while ensuring equal generation opportunities among power producers. The proposed method divides RESs into multiple zones according to the congestion mitigation effect of their output curtailment and uniformly curtails them in each zone. Moreover, the method divides congestion conditions throughout the year by clustering and creates zones of RESs for each congestion pattern. This approach aims to apply the optimal zone division of RESs according to the congestion condition, while removing the burden on the operator to recreate the zone division for each time step. A simulation was conducted to evaluate the effectiveness of the proposed method using a bulk power system model in the northern area of Tohoku region of Japan. Based on the simulation results, our proposed method outperforms existing approaches in terms of minimizing RES curtailment and promoting fairness across power producers, while considering the frequency of zonal creation.}, archive = {J_ACCESS}, author = {Yujiro Tanno and Akihisa Kaneko and Yasuhiro Hayashi and Yuki Itoda and Wataru Wayama and Kevin Tomsovic}, doi = {10.1109/ACCESS.2025.3574790}, journal = {IEEE Access}, pages = {96213-96225}, shortjournal = {IEEE Access}, title = {Gird congestion management based on the renewable energy zones and clustering of grid congestion conditions}, volume = {13}, year = {2025}, }
• (2025). Formal verification- and AI/ML-assisted radio resource allocation for open RAN compliant 5G/6G networks. ACCESS, 13, 96198-96212. (www)

  @article{ , abstract = {This paper introduces a quantitative analytical framework for developing radio resource management (RRM) strategies tailored to 5G services of enhanced Mobile Broadband (eMBB) and ultra-Reliable Low Latency Communications (uRLLC). By leveraging the Open Radio Access Network (RAN) architecture, the framework enables flexible and efficient management of radio resources to meet the competing demands of services offered by the fifth/sixth generation (5G/6G) of wireless networks. The proposed RRM methodology incorporates formal verification capabilities to generate vast Pareto optimality datasets for specific RAN design parameters, establishing a foundation for rigorous RRM strategy selection. Additionally, the proposed approach enhances data-driven RRM decision-making through the application of unsupervised machine-learning techniques. Our proposed RRM methodology outperforms other baseline (i.e., stochastic and resource-proportional) RRM schemes, achieving up to 30% improvement in the 5G service reward.}, archive = {J_ACCESS}, author = {Tariq Mumtaz and Shahabuddin Muhammad and Faouzi Bouali}, doi = {10.1109/ACCESS.2025.3575021}, journal = {IEEE Access}, pages = {96198-96212}, shortjournal = {IEEE Access}, title = {Formal verification- and AI/ML-assisted radio resource allocation for open RAN compliant 5G/6G networks}, volume = {13}, year = {2025}, }
• (2025). Sensitivity improvement of C4D detectors by adjusting sinusoidal integration with a potentiometer. ACCESS, 13, 96190-96197. (www)

  @article{ , abstract = {Capacitively coupled non-contact conductivity detectors (C4D) are a versatile and universal type of detector because they can detect cationic, anionic, fluorophore, non-fluorophore, fluorescent, or non-fluorescent compounds and can be adapted to capillary electrophoresis, liquid chromatography, or ion chromatography systems. In this study, a capacitive pseudo-bridge is adapted to a C4D detector to facilitate the adjustment to optimal levels of sensitivity using a potentiometer, which is adjusted by a screw that divides its resistance to perform a sinusoidal integration. When the C4D is manufactured, different capacitance values are obtained for the sensor due to variations in diameter, length, and void space, as well as a change in the resistivity of the mobile phase. All these changes can be adjusted using a potentiometer and an oscilloscope to visualize the optimum integration that occurs when a phase change is observed. To demonstrate the sensitivity of the C4D, analysis by High Performance Liquid Chromatography (HPLC), flow injection analysis (FIA), and ion chromatography (IC) produced a limit of detection of $0.98~\mu $ g/L for fluorides, $0.4~\mu $ g/L for sodium chloride, and $20~\mu $ g/L for fluorides, respectively. This novel adjustment of a potentiometer in a circuit of C4D detectors has not been proposed before for this kind of detector. A cosine output of the operational amplifier gives the maximum sensitivity of the capacitance to voltage converter.}, archive = {J_ACCESS}, author = {Ricardo González-López and María Myrna Solís-Oba and Josué Ortiz-Medina and Verónica Loera-Castañeda and Julio C. Grijalva-Ávila and Ismael Lares-Asef and Ignacio Villanueva-Fierro}, doi = {10.1109/ACCESS.2025.3570598}, journal = {IEEE Access}, pages = {96190-96197}, shortjournal = {IEEE Access}, title = {Sensitivity improvement of C4D detectors by adjusting sinusoidal integration with a potentiometer}, volume = {13}, year = {2025}, }
• (2025). Detection of southern hemisphere constellations using convolutional neural networks. ACCESS, 13, 96182-96189. (www)

  @article{ , abstract = {Constellations allow the identification of most stars and celestial objects visible in the night sky at a glance, without the use of a telescope. However, because of the large number of constellations, this task can be overwhelming for astronomy beginners. To perform this identification, many rely on mobile applications that depend on Internet connectivity and the Global Positioning System. Unfortunately, these applications only provide estimates based on geolocation and do not guarantee an accurate visual representation. For this reason, this paper proposes the identification of constellations using Convolutional Neural Networks. The purpose of this research is to detect constellations with greater accuracy from photographs of any size, regardless of the availability of an Internet connection. For this, a convolutional neural network model called You Only Look Once was used. This neural network is widely known for its accuracy in object detection and is ideal for pattern recognition, such as constellations. In this work, different versions of this neural network were used to detect the 21 most representative constellations of the southern hemisphere. The results obtained reveal that all models exhibit outstanding performance, with high precision and recall values, resulting in F1-scores of 0.991 and higher.}, archive = {J_ACCESS}, author = {Vladimir Riffo and Sebastián Flores and Eduardo Chuy-Kan and Víctor Ariza}, doi = {10.1109/ACCESS.2025.3575093}, journal = {IEEE Access}, pages = {96182-96189}, shortjournal = {IEEE Access}, title = {Detection of southern hemisphere constellations using convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). A data-driven analysis of software testing automation challenges using structural equation modeling (SEM) approach. ACCESS, 13, 96159-96181. (www)

  @article{ , abstract = {The adoption of automation in software testing presents challenges that can hinder its effectiveness and scalability. This study systematically investigates these challenges using a multi-phase research approach. First, a Systematic Literature Review (SLR) was conducted to identify 14 Critical Challenges (CCs) in automation adoption. Second, a questionnaire survey of 50 industry experts validated these challenges and examined their interrelationships. Finally, Structural Equation Modeling (SEM) a mathematical and statistical approach was employed to analyze correlations and uncover structural dependencies among the challenges. The SEM analysis identified five latent variables: Human Resource Constraints (HRC), Technological & Process Challenges (TPC), Financial & Resource Constraints (FRC), Security & Reliability Issues (SRI), and Future Adaptability & Scalability (FAS) that significantly influence automation adoption. Hypothesis testing revealed that HRC (-0.32), TPC (-0.45), FRC (−0.41), and SRI (−0.38) negatively impact automation success, whereas FAS (+0.51) plays a pivotal role in enabling successful adoption. Model validation through Confirmatory Factor Analysis (CFA) and Exploratory Factor Analysis (EFA) confirmed strong construct reliability and fit indices (RMSEA =0.043, CFI =0.95, TLI =0.92, SRMR =0.037). The study highlights the need for workforce training, standardized automation processes, cost-effective solutions, and security enhancements. By providing an empirically validated framework, this research contributes to both academia and industry, guiding decision-makers in optimizing automation strategies and improving software testing efficiency.}, archive = {J_ACCESS}, author = {Muhammad Faisal Abrar and Muhammad Faran Majeed and Muhammad Saqib and Ali Alferaidi and Razauddin}, doi = {10.1109/ACCESS.2025.3574200}, journal = {IEEE Access}, pages = {96159-96181}, shortjournal = {IEEE Access}, title = {A data-driven analysis of software testing automation challenges using structural equation modeling (SEM) approach}, volume = {13}, year = {2025}, }
• (2025). ISLR101: An iranian word-level sign language recognition dataset. ACCESS, 13, 96147-96158. (www)

  @article{ , abstract = {Sign language recognition involves modeling complex multichannel information, such as hand shapes and movements, while relying on sufficient sign language-specific data. However, sign languages are often under-resourced, posing a significant challenge for research and development in this field. To address this gap, we introduce ISLR101, the first publicly available Iranian Sign Language dataset for isolated sign language recognition. This comprehensive dataset includes 4,614 videos covering 101 distinct signs, recorded from 10 different signers (3 deaf individuals, 2 sign language interpreters, and 5 L2 learners) against varied backgrounds, with a resolution of $800\times 600$ pixels and a frame rate of 25 frames per second. It also includes skeleton pose information extracted using OpenPose. We establish both a visual appearance-based and a skeleton-based framework as baseline models, thoroughly training and evaluating them on ISLR101. These models achieve 97.01% and 94.02% accuracy on the test set, respectively. Additionally, we publish the train, validation, and test splits to facilitate fair comparisons.}, archive = {J_ACCESS}, author = {Hossein Ranjbar and Alireza Taheri}, doi = {10.1109/ACCESS.2025.3574074}, journal = {IEEE Access}, pages = {96147-96158}, shortjournal = {IEEE Access}, title = {ISLR101: An iranian word-level sign language recognition dataset}, volume = {13}, year = {2025}, }
• (2025). Efficient inter-prediction method using reference frame accumulation for MPEG G-PCC. ACCESS, 13, 96135-96146. (www)

  @article{ , abstract = {This paper presents a novel hybrid reference frame generation method to enhance the inter-prediction performance of geometry-based point cloud compression (G-PCC), a recent point cloud coding standard developed by the moving picture experts group (MPEG) that currently employs a single past reference frame for prediction. However, this single-reference approach may not fully capture the temporal and spatial correlations between frames, potentially limiting prediction performance. To address this limitation, we propose a hybrid mode selection scheme that chooses the suitable reference frames from a set of candidate frames, thereby leveraging the spatial and temporal correlations among consecutive past frames more effectively and improving the coding efficiency. We introduce and evaluate three coding modes for the hybrid reference frame concept within the G-PCC test model: 1) entropy estimation-based mode selection, which selects the reference frame(s) that minimize the estimated entropy during inter-frame coding; 2) translation-based mode selection, which selects the reference frame(s) requiring the least translation to align with the current frame; and 3) lightweight entropy estimation-based mode selection, which minimizes entropy under a predefined translation constraint. The experimental results demonstrate that all proposed techniques outperform the current G-PCC standard; entropy-estimation-based mode selection achieves an average improvement of 0.86% in geometric coding efficiency and 0.81% overall. Translation-based mode selection improved these metrics by 0.59% and 0.56%, respectively, and the lightweight entropy estimation approach consistently yielded a gain of 0.7%. In specific driving scenarios, all proposed techniques showed significant improvements, reaching approximately 2.62% to 2.86% in geometric coding efficiency.}, archive = {J_ACCESS}, author = {Xin Li and Eun-Young Chang and Jihun Cha and Euee S. Jang}, doi = {10.1109/ACCESS.2025.3575099}, journal = {IEEE Access}, pages = {96135-96146}, shortjournal = {IEEE Access}, title = {Efficient inter-prediction method using reference frame accumulation for MPEG G-PCC}, volume = {13}, year = {2025}, }
• (2025). Integrating IoT with adaptive beamforming for enhanced urban sensing in smart cities. ACCESS, 13, 96120-96134. (www)

  @article{ , abstract = {The exponential growth of Internet of Things (IoT) devices in smart city environments presents significant challenges, including spectrum congestion, interference, energy inefficiency, and scalability. To address these issues, we propose SmartBeam+, a novel adaptive beamforming algorithm designed to optimize the communication performance in dense IoT networks. SmartBeam+ dynamically adjusts the beamforming weights using real-time Channel State Information (CSI) to maximize the Signal-to-Interference-and-Noise Ratio (SINR), minimize the bit error rate (BER), and suppress interference effectively. Extensive simulations compare SmartBeam+ with conventional algorithms such as Zero-Forcing (ZF), MVDR, and Least Mean Squares (LMS) across key performance metrics: SINR, BER, energy efficiency, reliability, throughput, and latency. Results show that SmartBeam+ consistently performs better, with SINR values ranging from 20 to 13 dB, BER as low as 0.0001, and 30 dB interference suppression. SmartBeam+ maintains 20 bps/W energy efficiency and achieves a reliability of 92%, significantly outperforming other methods, especially under high node density and interference levels. Furthermore, the network lifetime is prolonged by up to 90 hours, and the latency is reduced to 5 ms, making SmartBeam+ ideal for time-sensitive applications such as disaster management and urban sensing. These results highlight SmartBeam+ as a scalable, energy-efficient, and robust solution for next-generation IoT networks in smart cities.}, archive = {J_ACCESS}, author = {Sunday Enahoro and Sunday C. Ekpo and Mfonobong Uko and Fanuel Elias and Stephen Alabi}, doi = {10.1109/ACCESS.2025.3571468}, journal = {IEEE Access}, pages = {96120-96134}, shortjournal = {IEEE Access}, title = {Integrating IoT with adaptive beamforming for enhanced urban sensing in smart cities}, volume = {13}, year = {2025}, }
• (2025). Low-frequency model constrained petrophysical estimation based on canonical correlation analysis. ACCESS, 13, 96111-96119. (www)

  @article{ , abstract = {Petrophysical estimation serves as a critical tool for quantitative interpretation in reservoir characterization, bridging the gap between petrophysical and elastic parameters. Traditional approaches, which rely heavily on rock physics models, often face challenges in accurately capturing the complex relationships between these parameters. While statistical methods such as canonical correlation analysis have shown promise in identifying linear relationships and efficiently estimating petrophysical parameters, they tend to overlook low-frequency variation trends, which can lead to unreliable estimation results. To address this limitation, we propose an enhanced method that integrates low-frequency model constraints into the canonical correlation analysis framework. By incorporating a low-frequency model during the standardization process, we effectively eliminate the influence of low-frequency variation trends, strengthening the linear relationship between elastic and petrophysical parameters. Meanwhile, this approach establishes robust statistical relationships between petrophysical and elastic parameters, thereby improving the accuracy of subsurface spatial mapping of petrophysical properties. In both synthetic and field data experiments, the results demonstrate that our proposed method achieves significantly higher accuracy compared to conventional techniques. These findings highlight the reliability and precision of our approach in characterizing petrophysical parameters, offering a valuable advancement for reservoir characterization in complex geological environments.}, archive = {J_ACCESS}, author = {Hanming Chen and Lingqian Wang and Yamei Cao and Ning Wang and Hui Zhou and Mingyu Yang}, doi = {10.1109/ACCESS.2025.3572106}, journal = {IEEE Access}, pages = {96111-96119}, shortjournal = {IEEE Access}, title = {Low-frequency model constrained petrophysical estimation based on canonical correlation analysis}, volume = {13}, year = {2025}, }
• (2025). Fruit detection methods based on deep learning in agricultural planting: A systematic literature review. ACCESS, 13, 96092-96110. (www)

  @article{ , abstract = {With the continued advancement of agricultural intelligence, automation, and mechanization, artificial intelligence-driven fruit detection technology has developed rapidly. As an important task in agricultural computer vision, fruit target detection in real-world planting environments presents numerous technical challenges. This paper provides a systematic review of recent breakthroughs and representative studies in this field. Based on a comprehensive analysis of existing research, we classify deep learning-based fruit detection models into four application scenarios: few-shot detection (addressing limited data availability and high annotation costs), complex scene detection (resolving issues arising from object occlusion, overlapping, and variable illumination), small-target detection (improving performance on low-resolution and densely clustered objects), and real-time detection (designing lightweight algorithms for faster inference). The paper summarizes innovative technical approaches and evaluates detection performance across these scenarios, highlighting recent advancements in fruit detection for agricultural production and offering valuable insights for further technological innovation.}, archive = {J_ACCESS}, author = {Xinyu Gong and Qiufeng Wu}, doi = {10.1109/ACCESS.2025.3573364}, journal = {IEEE Access}, pages = {96092-96110}, shortjournal = {IEEE Access}, title = {Fruit detection methods based on deep learning in agricultural planting: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Device-free AMR crossing detection using LTE signals for smart factory applications. ACCESS, 13, 96083-96091. (www)

  @article{ , abstract = {The Third Generation Partnership Project (3GPP) is currently discussing a new network for Integrated Sensing and Communications (ISAC). ISAC aims to enhance fifth and sixth-generation networks (5G/6G) by enabling simultaneous wireless sensing and communication while reducing cost, size, and power consumption by reusing existing wireless communication infrastructure. One use case of ISAC is the detection and collision avoidance of autonomous mobile robots (AMRs) in smart factories. We have also been studying the feasibility of ISAC under this use case to monitor AMR operations, such as transporting loads within a factory. This paper proposes a device-free AMR crossing detection method using Long-Term Evolution (LTE) signals for smart factory applications. In the proposed system, an AMR is assumed to be present at both the transmitting and receiving stations, and AMR crossing is detected from the acquired channel state information (CSI). However, CSI obtained from an actual system typically includes phase offsets due to frequency synchronization errors and receive timing offsets between the transmitting and receiving stations, which degrade the sensing accuracy. To address this issue, the proposed system reduces the phase offset using differential information between multiple antennas at the transmitting station and uses an offset removal method applicable to actual LTE reference signals. Through experiments with the proposed system, we confirmed that the phase offset could be eliminated using CSI phase. In additional, we demonstrated the AMR crossing frequency and period could be measured by analyzing the periodic fluctuations in the phase difference of the measured CSI.}, archive = {J_ACCESS}, author = {Hanae Otani and Tomoki Murakami and Kazuya Ohara and Yasushi Takatori and Tomoaki Ogawa}, doi = {10.1109/ACCESS.2025.3572498}, journal = {IEEE Access}, pages = {96083-96091}, shortjournal = {IEEE Access}, title = {Device-free AMR crossing detection using LTE signals for smart factory applications}, volume = {13}, year = {2025}, }
• (2025). Video analysis and frame prediction based on improved object detection and ConvGRU. ACCESS, 13, 96069-96082. (www)

  @article{ , abstract = {Video analysis is of crucial importance in areas such as information dissemination, understanding, and environmental perception. To further improve video processing technology and increase video fluency, this article innovatively uses improved object detection algorithms for video analysis. The study also improves neural networks to construct video frame prediction models with the help of motion perception, synthetic streaming, and other technologies. The results demonstrated that the improved algorithm has significantly improved its detection performance after introducing improvement measures, with a detection accuracy of 0.988, a floating-point calculation count of 5.732G, and a detection speed of 270.646 fps. The loss function and average accuracy mean have been improved, with a maximum detection accuracy of 0.947. The PSNR, structural similarity, and interpolation error of the frame prediction model were superior to existing advanced models. Comparative models included future frame prediction models based on generative auxiliary discrimination networks, multi-source prediction frameworks based on spherical convolution, and inverted pyramid prediction models based on cross-optical flow registration. In summary, the designed video analysis and frame prediction methods have achieved efficient and high-quality video processing. This study can effectively enhance the ability of video analysis and frame prediction, promote the innovation and development of video analysis technology, and accelerate the development of intelligent video applications.}, archive = {J_ACCESS}, author = {Xijuan Wang and Ru Chen}, doi = {10.1109/ACCESS.2025.3575491}, journal = {IEEE Access}, pages = {96069-96082}, shortjournal = {IEEE Access}, title = {Video analysis and frame prediction based on improved object detection and ConvGRU}, volume = {13}, year = {2025}, }
• (2025). A geometric approach to efficient modeling and rendering of opaque ice with directional air bubbles. ACCESS, 13, 96056-96068. (www)

  @article{ , abstract = {In this paper, we introduce a geometric approach-based particle-grid hybrid framework for efficiently modeling and rendering realistic, opaque ice shapes containing air bubbles. To achieve this, we diffuse water temperature using a grid-based method and represent air bubbles within the ice shape using particles. To address the issues of noise and distorted dissolved air fields in previous methods, we propose the following improvements: 1) We synthesize the level-set of the ice shape into a spherical form to reduce noise and streamline the algorithm. 2) We propose an efficient method to control the termination condition of heat diffusion by utilizing the number of active particles and the initial dissolved oxygen level. 3) Unlike previous methods that calculated the dissolved air field only near air bubbles, we extend the diffusion to transparent regions, enabling the realistic representation of opaque ice shapes. 4) To accurately compute the directional properties of air bubbles, we introduce a level-set-based approach. 5) We extend the solver to control the generation of dissolved air fields and air bubbles based on water temperature. 6) Finally, we present a method to realistically visualize opaque ice shapes using Unreal Engine (UE). As a result, the proposed method achieves approximately three times faster computation speed compared to previous techniques while providing visually enhanced opaque ice shapes in real-time. This advancement makes it applicable to physics-based virtual ice shape simulations.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim}, doi = {10.1109/ACCESS.2025.3575809}, journal = {IEEE Access}, pages = {96056-96068}, shortjournal = {IEEE Access}, title = {A geometric approach to efficient modeling and rendering of opaque ice with directional air bubbles}, volume = {13}, year = {2025}, }
• (2025). Benchmarking the non-paraxial beam propagation method for deep-tissue microscopy. ACCESS, 13, 96046-96055. (www)

  @article{ , abstract = {This study evaluates the accuracy and computational efficiency of the Beam Propagation Method (BPM) for modeling light propagation in microscopy of biological tissue. Accurate simulation of light propagation is essential for improving imaging resolution and contrast in optical microscopy, where wavefront distortions and scattering pose major challenges. While rigorous solvers such as the Finite-Difference Time-Domain (FDTD) and Pseudo-Spectral Time-Domain (PSTD) methods provide high accuracy, their high computational cost makes large-volume simulations impractical for deep-tissue imaging. BPM extends the tractable depth of simulation assuming predominantly forward propagation in tissue, significantly reducing computational complexity. Unlike full-wave solvers, BPM avoids solving Maxwell’s equations in their entirety and requires fields to be stored for only two transverse planes at a time. This substantially lowers memory usage and computation time compared to full-field solvers, enabling the modeling of larger tissue volumes. We evaluate BPM’s performance using tissue models for multiphoton microscopy, where accurate modeling of wave propagation is critical. BPM is validated against Mie theory for single-sphere scattering and benchmarked against PSTD using experimentally acquired refractive index data and a discrete particle model that mimics biological tissue scattering. The Fast Fourier Transform BPM (FFT-BPM) is implemented without paraxial approximation, and the simulation results closely match reference solutions, while significantly reducing computational costs. These results establish BPM as a viable method for high-resolution optical microscopy in deterministically specified biological tissue. Its balance between accuracy and computational efficiency makes it particularly suitable for large-scale modeling. BPM’s extended simulation capabilities can advance biological imaging by improving our understanding of light-tissue interactions and enabling next-generation wavefront correction techniques.}, archive = {J_ACCESS}, author = {Praveen Kalarickel Ramakrishnan and Qi Hu and Peter R. T. Munro}, doi = {10.1109/ACCESS.2025.3575315}, journal = {IEEE Access}, pages = {96046-96055}, shortjournal = {IEEE Access}, title = {Benchmarking the non-paraxial beam propagation method for deep-tissue microscopy}, volume = {13}, year = {2025}, }
• (2025). Constrained control of fully-constrained cable-driven parallel robots with elastic cables: An optimal robust approach. ACCESS, 13, 96031-96045. (www)

  @article{ , abstract = {The paper discusses the robust optimal control of Cable-Driven Parallel Robots (CDPRs) with elastic cables, addressing the limitations of treating cables as rigid bodies. This simplification is inadequate for applications with large workspaces, where cable vibrations significantly affect performance. The proposed control strategy ensures that cables remain under tension by applying positive inputs, thus preventing compressive forces. The initial step involves transforming the nonlinear dynamics of the CDPR system, which includes both the elastic cables and motor dynamics, into a linear framework using State-Dependent Coefficient (SDC) parameterization. This allows for the retention of nonlinearities within the system and input matrices while encapsulating dynamic uncertainties and external disturbances. Subsequently, the constrained control is robustly designed utilizing optimization techniques rooted in Hamilton-Jacobi-Bellman (HJB) equations and Karush-Kuhn-Tucker (KKT) conditions. The robust optimal constrained controller is formulated using the Generalized State-Dependent Riccati Equation (GSDRE), followed by stability analysis conducted through Lyapunov’s second method. Finally, the effectiveness of the proposed method is validated through simulations, demonstrating its capability to maintain cable tension and damp vibrations, which are critical for the operational efficiency of CDPRs in practical applications.}, archive = {J_ACCESS}, author = {Hanie Marufkhani and Mohammad A. Khosravi}, doi = {10.1109/ACCESS.2025.3574570}, journal = {IEEE Access}, pages = {96031-96045}, shortjournal = {IEEE Access}, title = {Constrained control of fully-constrained cable-driven parallel robots with elastic cables: An optimal robust approach}, volume = {13}, year = {2025}, }
• (2025). Lampsy: An invisible real-time epilepsy video monitoring and automatic seizure detection device. ACCESS, 13, 96018-96030. (www)

  @article{ , abstract = {Epilepsy causes potentially fatal seizures, leading to fear and anxiety among people with epilepsy and caregivers. Real-time seizure detection can notify caregivers of seizures, helping reduce anxiety. Most available seizure detection devices use visible sensors, possibly preventing widespread adoption due to the stigma still associated with epilepsy. “Invisibles,” i.e., off-the-person devices seamlessly integrated into daily life, may offer an unobtrusive alternative, with video monitoring being a promising modality. However, no video-based, medically certified seizure detection devices exist, possibly due to the hardware costs needed for accurate real-time video-based detection. This work seeks to solve this problem by exploring the feasibility of real-time video-based seizure detection on affordable edge devices. For this purpose, we developed Lampsy, a privacy-preserving video-based seizure detection device embedded within a light fixture. Lampsy’s previously published detection algorithm, which employed Optical Flow, achieved a sensitivity and specificity of 99.06% $\pm ~1.61$ % for 21 tonic-clonic seizures, but required calibration and significant processing, rendering it impractical for real-time use on edge devices. Using the same dataset, we tested various Optical Flow methods and optimizations with the goal of achieving real-time detection on a Raspberry Pi. We achieved a real-time performance of 30 FPS and a sensitivity and specificity of 99.76% $\pm ~0.35$ % without calibration. Lampsy achieves accurate real-time video-based seizure detection on Raspberry Pi edge devices. This work extends the state-of-the art by demonstrating real-time video-based seizure detection on affordable hardware, highlighting the potential of integrating advanced digital health technology seamlessly into everyday environments.}, archive = {J_ACCESS}, author = {Vicente M. Garção and Mariana Abreu and Francisca Sá and Ana Rita Peralta and Carla Bentes and Ana Fred and Hugo Plácido da Silva}, doi = {10.1109/ACCESS.2025.3575528}, journal = {IEEE Access}, pages = {96018-96030}, shortjournal = {IEEE Access}, title = {Lampsy: An invisible real-time epilepsy video monitoring and automatic seizure detection device}, volume = {13}, year = {2025}, }
• (2025). Real-time autonomous driving: A unified single-stage multi-task network architecture. ACCESS, 13, 96001-96017. (www)

  @article{ , abstract = {The need for real-time autonomous driving reveals significant gaps in terms of accuracy rates and processing efficiency. Current systems struggle to maintain high accuracy across all scenarios while ensuring efficient real-time processing. Addressing these gaps, the proposed work presents a novel contribution aimed at advancing the capabilities of autonomous driving systems. The model introduces a single network architecture capable of simultaneously handling multiple crucial tasks including dynamic and static segmentation, depth estimation, and 3D object detection. The proposed architecture is specifically designed to be lightweight, ensuring efficient deployment for real-time autonomous driving applications. Moreover, the proposed single-stage architecture is end-to-end trainable, offering a streamlined approach to model training. Through extensive experimentation, the results demonstrate superior performance over existing architecture, achieving remarkable accuracy rates. Overall, the proposed work represents a significant step forward in the development of efficient and effective solutions for real-time autonomous driving.}, archive = {J_ACCESS}, author = {S. Raghavendra and Rajalekshmi Kishore and S. K. Abhilash and Venu Madhav Nookala and Manish Varun Yadav}, doi = {10.1109/ACCESS.2025.3572107}, journal = {IEEE Access}, pages = {96001-96017}, shortjournal = {IEEE Access}, title = {Real-time autonomous driving: A unified single-stage multi-task network architecture}, volume = {13}, year = {2025}, }
• (2025). Class and data-incremental learning framework for baggage threat segmentation via knowledge distillation. ACCESS, 13, 95977-96000. (www)

  @article{ , abstract = {With air travel growing rapidly worldwide, airports are busier than ever, making efficient security screening a top priority. To meet this growing demand, various advanced X-ray baggage scanners have been deployed at airports worldwide. Researchers have proposed multiple automated threat detection systems to enhance security screening efficiency; however, automated baggage threat segmentation remains a complex task, especially in the context of multi-class threat detection across diverse datasets. Traditional deep learning models struggle with differentiating between multiple threat types and suffer from catastrophic forgetting when exposed to new classes. To address these limitations, we propose an incremental learning framework that enables the model to progressively learn new threat categories while retaining previously acquired knowledge. Our approach utilizes SegFormer as the backbone and introduces a custom loss function, combining mutual distillation loss, KL divergence, and cross-entropy loss, to enhance knowledge retention and adaptability. The model is trained sequentially on three publicly available datasets, SIXRAY, GDXRAY, and PIDRAY, enabling it to generalize effectively across diverse baggage imagery. Through extensive experiments, we demonstrate that our method outperforms state-of-the-art incremental learning techniques, achieving superior segmentation accuracy and knowledge retention. Furthermore, Grad-CAM visualizations and t-SNE plots provide interpretability, offering insights into the model’s learning behavior and class separability. The proposed framework establishes a scalable and adaptable solution for real-world security screening applications, enabling efficient threat detection without requiring model retraining from scratch.}, archive = {J_ACCESS}, author = {Ammara Nasim and Saad Mazhar Khan and Anum Abdul Salam and Arslan Shaukat and Taimur Hassan and Adeel M. Syed and Muhammad Usman Akram}, doi = {10.1109/ACCESS.2025.3574919}, journal = {IEEE Access}, pages = {95977-96000}, shortjournal = {IEEE Access}, title = {Class and data-incremental learning framework for baggage threat segmentation via knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Efficient nearly piecewise continuous signal basis expansion by precision calculation. ACCESS, 13, 95963-95976. (www)

  @article{ , abstract = {Many one-dimensional (1D) signals, including ECG signals, economic data, particle distributions, seismic waveforms, image rows, and traffic statistics, are nearly piecewise continuous. These signals vary gradually in most regions but vary rapidly in certain narrow regions. Due to the non-uniform distribution of gradients, it is very challenging to well expand these nearly piecewise continuous signals. In this paper, we propose a novel algorithm, called “precise calculation,” to adaptively expand such signals. It categorizes signals into three types and then segments each type into smooth and large-gradient parts by using gradient-based analysis, adaptive thresholding, and morphology refinement. After decomposition, adaptive expansion is applied in each segment. Experiments on 1D ECG signals, electron-density distributions, seismic waveforms, image-row data, and economic time-series demonstrate that the proposed methods accurately expand nearly piecewise continuous signals. Under a fixed number of basis functions, our approach achieves significantly lower approximation errors than existing methods, highlighting its effectiveness for high-fidelity signal reconstruction. While applying only 5% of the expansion functions, experiments show that, for ECG signals, the expansion errors obtained by the proposed method range from 0.0003 to 0.0094—significantly lower than those achieved using only the DCT (0.0023–0.1506) and the Legendre method (0.00318–0.1677).}, archive = {J_ACCESS}, author = {Hsin-Jung Lee and Cheng-Che Lee and Kuei-Chen Chen and Shao-Yun Luo and Shu-Ping Chang and Jian-Jiun Ding}, doi = {10.1109/ACCESS.2025.3575792}, journal = {IEEE Access}, pages = {95963-95976}, shortjournal = {IEEE Access}, title = {Efficient nearly piecewise continuous signal basis expansion by precision calculation}, volume = {13}, year = {2025}, }
• (2025). A variational inference-based LSTM-enhanced deep neural model for sequential recommendations. ACCESS, 13, 95945-95962. (www)

  @article{ , abstract = {Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models.}, archive = {J_ACCESS}, author = {Armielle Noulapeu Ngaffo and Inès Raïssa Djouela Kamgang and Ebenezer Maka Maka and Yannick Malong}, doi = {10.1109/ACCESS.2025.3570636}, journal = {IEEE Access}, pages = {95945-95962}, shortjournal = {IEEE Access}, title = {A variational inference-based LSTM-enhanced deep neural model for sequential recommendations}, volume = {13}, year = {2025}, }
• (2025). A new test point insertion using weight adjusted grouping. ACCESS, 13, 95934-95944. (www)

  @article{ , abstract = {Integrated circuits (ICs) require high test coverage to ensure reliability and performance even during operation, which can be challenging with standard testing methods. Logic built-in self-test (BIST) is introduced to address the issue. Since Logic BIST generates patterns through a pseudo random pattern generator (PRPG), it requires lots of test patterns, resulting in a long test time to achieve the target coverage. To address this drawback, test point insertion (TPI) is employed as a potential solution. While TPI enables to increase test coverage efficiently by directly targeting locations with low testability, it inevitably incurs area overhead due to the additional logic and wires. To address these limitations, a new weight adjusted grouping based efficient test point insertion method (WAG) is proposed in this paper to achieve high test coverage with less test time and area overhead. Weight adjustment flip-flops (WAFFs) and the following load flip-flops (LOFFs) are selected based on specified-bit ratio and weight. WAG biases the outputs of the flip-flops and inserts control points by adding only a few gates to the selected WAFFs and LOFFs. Experimental results on ITC’99 and industrial circuits demonstrate this approach. This paper is notable for its effectiveness in reducing test time while improving test coverage and minimizing area overhead.}, archive = {J_ACCESS}, author = {Jaehyun Kim and Hyemin Kim and Jongho Park and Sangjun Lee and Sungho Kang}, doi = {10.1109/ACCESS.2025.3575800}, journal = {IEEE Access}, pages = {95934-95944}, shortjournal = {IEEE Access}, title = {A new test point insertion using weight adjusted grouping}, volume = {13}, year = {2025}, }
• (2025). Humanity lifeline: A resilient communication and sensor network framework for disaster response. ACCESS, 13, 95922-95933. (www)

  @article{ , abstract = {In the domain of disaster communication, the failure of conventional infrastructures such as cellular towers and power lines poses significant challenges to timely information exchange. Existing solutions often fall short in providing robust and scalable communication networks under such conditions. This study addresses this gap by proposing a hybrid communication system integrating Bluetooth Low Energy (BLE) with LoRa and LoRaWAN technologies. Our approach involves custom-developed hardware: LifeLinks with Renesas RA4W1 microcontrollers and LoRa-Router-Gateways equipped for both peer-to-peer and cloud connectivity. To validate our system, we conducted a series of experiments for high-traffic scenarios, network disruptions, diverse environmental conditions, and coordinated rescue operations. Results demonstrated 99% successful message delivery, minimal data loss, and strong network reliability, indicating the system’s efficacy in disaster scenarios. Our research bridges the gap in current literature by offering a comprehensive, resilient communication solution that ensures continuous data flow and robust performance even in severely disrupted environments.}, archive = {J_ACCESS}, author = {Ozgun Pinarer and Omer Komili}, doi = {10.1109/ACCESS.2025.3575712}, journal = {IEEE Access}, pages = {95922-95933}, shortjournal = {IEEE Access}, title = {Humanity lifeline: A resilient communication and sensor network framework for disaster response}, volume = {13}, year = {2025}, }
• (2025). A high-speed, multi-channel lossless compression algorithm for high-resolution video on FPGA. ACCESS, 13, 95911-95921. (www)

  @article{ , abstract = {With the increasing image resolution, data bit depth, and frame rate of industrial cameras, the signal output speed needs to be improved to keep pace with the rapidly developing image sensor technologies. The USB 3.0 interface has become a common transmission method for industrial cameras due to its high versatility. However, limited bandwidth often requires data compression techniques to address transmission bottlenecks. This paper proposes a four-channel high-speed lossless compression algorithm based on FPGA (Field-Programmable Gate Array). The algorithm uses the difference between adjacent pixels, which has fewer data bit widths, to replace the original pixels for transmission. This approach enhances the transmission frame rate while maintaining image quality. Additionally, a novel “first-in, last-out” data inversion technique is employed to solve the problem of real-time processing of four-channel data, significantly reducing FPGA hardware resource usage. The performance of the proposed algorithm is evaluated through on-board FPGA experiments. When transmitting $4096\times 4096$ resolution video via USB 3.0, the frame rate reaches 18.3 fps with a data size reduced by up to 49.98% (corresponding to a compression ratio of 50.02%), with an encoding overhead delay of $20.76~\mu $ s, resulting in a frame rate increase of approximately 71.03%. Additionally, decompression speeds for a large resolution image set ( $5568\times 3712$ ) are improved by at least 40% compared to mainstream algorithms. The algorithm demonstrates low FPGA resource utilization and high processing speed, ensuring efficient compression and transmission even for complex image scenes. These results provide a viable high-speed lossless compression solution for industrial camera applications.}, archive = {J_ACCESS}, author = {Xingkai Du and Longhua Xie and Hongchuan Huang and Huawei Chen and Xiaolong Guo and Tingyu Zhao}, doi = {10.1109/ACCESS.2025.3575854}, journal = {IEEE Access}, pages = {95911-95921}, shortjournal = {IEEE Access}, title = {A high-speed, multi-channel lossless compression algorithm for high-resolution video on FPGA}, volume = {13}, year = {2025}, }
• (2025). Digital twin-assisted load-balanced user association and AoI-aware scheduling in IoT networks. ACCESS, 13, 95888-95910. (www)

  @article{ , abstract = {In this paper, we propose a digital twin (DT) assisted-user device (UD)-base station (BS) association for cellular-supported internet of things (IoT) networks, and scheduling of uplink transmission of these devices to their associated BSs considering the age of information (AoI). Unlike existing work, we consider the scenario of a main base station (MBS) and several smaller base stations (SBSs), which is suitable for 5G and beyond 5G technology. We propose a novel Q-learning-inspired algorithm for simultaneous uplink throughput maximization and load balancing among the different BSs to associate the UDs away from the MBS and distribute them evenly between the SBSs as much as possible. We further take into account the maximum UD-association limit and limited coverage radius of each BS, which makes it closer to a realistic scenario. Another contribution is the proposal and analysis of heuristics-based scheduling of the UDs transmission to their respective BSs with the aim to reduce the overall AoI. Scheduling is important as many applications in the IoT networks require near-time or near-real-time updates for taking timely decisions and maintaining acceptable performance and safety levels. The AoI is an effective metric for the timeliness of information at a receiver, and the computational simplicity of the heuristics-based approach ensures that the scheduling decisions are taken promptly. Simulations results and comparison with several benchmark methods show that the proposed solution can achieve load balancing with a slight degradation in the cumulative maximum achievable throughput achieved in the network. The results also provide valuable insights into the joint performance of the proposed model and the benchmark UD-BS association and scheduling solutions with different packet generation rates for transmission by the UDs.}, archive = {J_ACCESS}, author = {Ahmed Shaharyar Khwaja and Nhat Tri Vo and Williams-Paul Nwadiugwu and Isaac Woungang and Alagan Anpalagan}, doi = {10.1109/ACCESS.2025.3574291}, journal = {IEEE Access}, pages = {95888-95910}, shortjournal = {IEEE Access}, title = {Digital twin-assisted load-balanced user association and AoI-aware scheduling in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Adaptive safe data driven control strategy for closed loop system. ACCESS, 13, 95876-95887. (www)

  @article{ , abstract = {Due to the widely studied data driven control in today’s new big data era, this paper combines other innovative considerations into our existed contributions about data driven control, for example, adaptation, safety and switching logic together, so that an innovative adaptive safe data driven control strategy is proposed to complete its entire research from the theoretical perspectively and practical application. More specifically, after reviewing one closed loop system with the unknown plant and unknown controller simultaneously, data driven control strategy is used to design one controller without any prior information about the unknown plant, while achieving the perfect tracking or model matching. Then the concept of safety is defined to constrain the unknown controller or its parameterized controller parameters, and consequently adaptive idea is introduced to design one controller, generating in its recursive form for nonparametric or parameterized controller respectively. Within our both defined safety and control mission, adaptive safe data driven control is turned into one inequality constrain optimization problem. Because of a special feasible region, i.e. a box constraint, the gradient projection algorithm is proposed to solve the optimal controller parameters, being dependent on the measured input-output data. Moreover from an applied point of view, we adjust our mentioned adaptive safe data driven control to its improved adaptive safe switching mechanism, more suited to practical engineering, i.e.unmanned helicopter automatic taking off and landing. Finally, some simulation results are given to show our theoretical results on adaptive safe data driven control and its improved switching form. Generally, this new paper extends our previous contributions about data driven control to complete its synthesis research, combining some advanced factors together from both the academic theory and practice, for example, adaptation, safety, optimization theory and algorithm, switching logic together.}, archive = {J_ACCESS}, author = {Wen Ruchun}, doi = {10.1109/ACCESS.2025.3573520}, journal = {IEEE Access}, pages = {95876-95887}, shortjournal = {IEEE Access}, title = {Adaptive safe data driven control strategy for closed loop system}, volume = {13}, year = {2025}, }
• (2025). A comprehensive overview and analysis of large language models: Trends and challenges. ACCESS, 13, 95851-95875. (www)

  @article{ , abstract = {Large Language Models (LLMs) have transformed numerous fields by offering innovative solutions that drive advancements across a wide range of applications. However, their widespread adoption presents several challenges, including variations in architectures, limitations in processing capabilities, and high computational resource demands for training. Addressing these challenges is crucial for maximizing the benefits of LLMs while ensuring their responsible and efficient use. This paper reviews LLMs, focusing on their key characteristics and the factors that influence their performance. It examines several prominent families of LLMs and provides a comparative analysis of their properties. In addition, it explores the classification of LLMs based on criteria such as availability, context window, and model size. In addition, the study explores advanced fine-tuning techniques, including Parameter-Efficient fine-tuning (PEFT) and Low-Rank Adaptation (LoRA), that enhance the performance and efficiency of models. Furthermore, it reviews the wide-ranging applications of LLMs and evaluates the methodologies used to evaluate their effectiveness.}, archive = {J_ACCESS}, author = {Ammar Mohammed and Rania Kora}, doi = {10.1109/ACCESS.2025.3573955}, journal = {IEEE Access}, pages = {95851-95875}, shortjournal = {IEEE Access}, title = {A comprehensive overview and analysis of large language models: Trends and challenges}, volume = {13}, year = {2025}, }
• (2025). Real-time sensor fault tolerant control of DC-DC converters in DC microgrids using a switching unknown input observer. ACCESS, 13, 95837-95850. (www)

  @article{ , abstract = {DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of $1\mu $ s from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies.}, archive = {J_ACCESS}, author = {Mohammed Said Ouahabi and Abdelhafid Benyounes and Said Barkat and Syphax Ihammouchen and Toufik Rekioua and Abdelaziz Rabehi and El-Sayed M. El-Kenawy and Amal H. Alharbi}, doi = {10.1109/ACCESS.2025.3571650}, journal = {IEEE Access}, pages = {95837-95850}, shortjournal = {IEEE Access}, title = {Real-time sensor fault tolerant control of DC-DC converters in DC microgrids using a switching unknown input observer}, volume = {13}, year = {2025}, }
• (2025). Analysis of sub-synchronous oscillation in grid-connected wind farm and proposed improved solution. ACCESS, 13, 95821-95836. (www)

  @article{ , abstract = {Sub-synchronous oscillation (SSO) is considered a stability issue related to turbine-generator systems, posing negative impacts on system integrity. The high penetration of wind energy has increased the urgency for this phenomenon. The analyses in this paper reveal that high compensation levels and low wind speeds are the two primary factors driving SSO, categorized as the induction generator effect (IGE), whereas the control parameters of the rotor-side converter (RSC) adversely contribute to the sub-synchronous control interaction (SSCI) phenomenon. Various solutions have been explored to suppress the SSO, which are divided into grid side and generation side approaches. However, relying on a single solution for either side does not provide the optimal damping effectiveness under critical operating conditions. Therefore, this paper proposes optimizing the internal control parameters of the RSC using meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Ant Colony Optimization (ACO). These tuned parameters are combined with a grid side solution based on a thyristor-controlled series capacitor (TCSC) or a generation side solution involving a supplementary damping controller (SDC) added to the RSC to improve SSO damping. This combination offers advantages such as low investment costs, simplicity, and high effectiveness. Both time-domain simulations and small-signal analyses have been carried out to demonstrate the capability of the improved approach, as they significantly enhance the damping ratio across low wind speed ranges and wide compensation levels. This study was conducted using MATLAB software based on the IEEE first benchmark, which was connected to the IEEE 14-bus system.}, archive = {J_ACCESS}, author = {Trong Nghia Le and Chau Le Thi Minh and Phuong Nam Nguyen and Vu Nguyen Hoang Minh}, doi = {10.1109/ACCESS.2025.3572782}, journal = {IEEE Access}, pages = {95821-95836}, shortjournal = {IEEE Access}, title = {Analysis of sub-synchronous oscillation in grid-connected wind farm and proposed improved solution}, volume = {13}, year = {2025}, }
• (2025). Black hole algorithm for software requirements prioritization. ACCESS, 13, 95810-95820. (www)

  @article{ , abstract = {With the increase in complexity in the software development process, the optimization of requirements management has developed into a critical and necessary task in Software Engineering. The selection and prioritization of software requirements is one of the most commonly encountered issues among the many requirements for software release. Software engineers have introduced several methods for solving these problems. The Black Hole Algorithm (BHA) is a population-based approach. It is among one of the many modern approaches and has been successfully applied to solve optimization problems. The purpose of this study is to provide a BHA-based solution to the Requirements Prioritization (RP) problem. Furthermore, the proposed BHA-based solution was evaluated on three real-world datasets (RALIC, Word, and ReleasePlanner), and its performance was compared with that of multiple state-of-the-art algorithms, including Ant Colony Optimization (ACO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Fitness Dependent Optimizer (FDO), Goose Algorithm (GAO), and Lagrange Elementary Optimization (LEO). The findings show that BHA consistently yielded higher fitness values, reaching 98.37% for Word, 98.82% for ReleasePlanner, and 99.67% for RALIC. In contrast, the highest percentages achieved by PSO were 95.01%, 90.53%, and 94.37%, respectively, while ACO, GA, GWO, FDO, GAO, and LEO also remained behind BHA in all three datasets. Thus, BHA outperforms competing techniques and provides a better solution to the problem of software requirements prioritization.}, archive = {J_ACCESS}, author = {Norah Ibrahim Alfassam and M. Abdullah-Al-Wadud and Mubarak Rashed Alrashoud}, doi = {10.1109/ACCESS.2025.3574998}, journal = {IEEE Access}, pages = {95810-95820}, shortjournal = {IEEE Access}, title = {Black hole algorithm for software requirements prioritization}, volume = {13}, year = {2025}, }
• (2025). Decentralized robust PID controller design using disk margins specifications. ACCESS, 13, 95797-95809. (www)

  @article{ , abstract = {Many industrial processes are multi-input and multi-output. One challenge during controller design is to ensure both performance and robustness of the closed-loop in the presence of coupling between the loops. One way to ensure robustness in the frequency domain is by using disk margins. They represent a family of complex-valued uncertainties that account for simultaneous gain and phase variations in the frequency response. In this paper, a frequency-based decentralized robust proportional-integral-derivative controller design method is proposed. An optimization problem is used to achieve zero steady-state error for constant setpoints. The uncertainty is expressed using an output multiplicative model. To ensure robust stability, constraints on the disk margins are imposed using a multiloop approach. The problem is formulated using linear matrix inequalities. Simulation results validate the proposed method in comparison to other decentralized robust controllers presented in the literature. In the examples, the designed controllers resulted in a closed-loop that meets performance requirements and robust stability.}, archive = {J_ACCESS}, author = {Egydio Tadeu G. Ramos and Péricles R. Barros and George Acioli}, doi = {10.1109/ACCESS.2025.3574928}, journal = {IEEE Access}, pages = {95797-95809}, shortjournal = {IEEE Access}, title = {Decentralized robust PID controller design using disk margins specifications}, volume = {13}, year = {2025}, }
• (2025). Automatic scene generation: State-of-the-art techniques, models, datasets, challenges, and future prospects. ACCESS, 13, 95753-95796. (www)

  @article{ , abstract = {Automatic scene generation is an essential area of research with applications in robotics, recreation, visual representation, training and simulation, education, and more. This survey provides a comprehensive review of the current state-of-the-arts in automatic scene generation, focusing on techniques that leverage machine learning, deep learning, embedded systems, and natural language processing (NLP). We categorize the models into four main types: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion Models. Each category is explored in detail, discussing various sub-models and their contributions to the field. We also review the most commonly used datasets, such as COCO-Stuff, Visual Genome, and MS-COCO, which are critical for training and evaluating these models. Methodologies for scene generation are examined, including image-to-3D conversion, text-to-3D generation, graph-based methods, and interactive scene generation. Evaluation metrics such as Fréchet Inception Distance (FID), Kullback-Leibler (KL) Divergence, Inception Score (IS), Intersection over Union (IoU), and Mean Average Precision (mAP) are discussed in the context of their use in assessing model performance. The survey identifies key challenges and limitations in the field, such as maintaining realism, handling complex scenes with multiple objects, and ensuring consistency in object relationships and spatial arrangements. By summarizing recent advances and pinpointing areas for improvement, this survey aims to provide a valuable resource for researchers and practitioners working on automatic scene generation.}, archive = {J_ACCESS}, author = {Awal Ahmed Fime and Saifuddin Mahmud and Arpita Das and Md. Sunzidul Islam and Jong-Hoon Kim}, doi = {10.1109/ACCESS.2025.3574298}, journal = {IEEE Access}, pages = {95753-95796}, shortjournal = {IEEE Access}, title = {Automatic scene generation: State-of-the-art techniques, models, datasets, challenges, and future prospects}, volume = {13}, year = {2025}, }
• (2025). A comparative study of deep audio models for spectrogram- and waveform-based SingFake detection. ACCESS, 13, 95739-95752. (www)

  @article{ , abstract = {Recent advancements in singing voice synthesis have significantly improved the quality of artificial singing voices, raising concerns about their potential misuse in generating deepfake singing, or “singfake” voices. Detecting these synthetic voices presents unique challenges due to the complex nature of singing, which involves pitch, timbre, and accompaniment variations. In this study, we conduct a comparative analysis of two model types for singfake detection: (1) models utilizing Log-Mel spectrograms, such as Audio Spectrogram Transformer (AST) and Whisper, and (2) models that process raw waveform inputs, including UniSpeech-SAT and HuBERT. Our experiments on the SingFake dataset evaluate these models under two input conditions—separated vocal tracks and full song mixtures—across different test subsets. The results indicate that spectrogram-based models generally outperform waveform-based models, notably on unseen singers. Metrics such as Precision, Recall, F1-score, Equal Error Rate (EER), and Area Under the Curve (AUC) provide insights into the strengths and weaknesses of each approach. Our findings contribute to developing more effective deepfake singing detection methods, with implications for security, media authentication, and digital content protection.}, archive = {J_ACCESS}, author = {Minh Nguyen-Duc and Luong Vuong Nguyen and Huy Nguyen-Ho-Nhat and Tri-Hai Nguyen and O-Joun Lee}, doi = {10.1109/ACCESS.2025.3571728}, journal = {IEEE Access}, pages = {95739-95752}, shortjournal = {IEEE Access}, title = {A comparative study of deep audio models for spectrogram- and waveform-based SingFake detection}, volume = {13}, year = {2025}, }
• (2025). Dynamic surrogate optimization of vertically stacked nanosheet FET based on gaussian process regression. ACCESS, 13, 95730-95738. (www)

  @article{ , abstract = {While speed improvements are pivotal for advancing modern transistor technologies and ensuring faster computational performance, the tradeoffs with other critical electrical properties, such as threshold voltage ( $V_{T}$ ) and off-current ( $I_{OFF}$ ), are often overlooked. These tradeoffs can significantly impact the overall device performance. The design of vertically stacked n-type and p-type nanosheet FETs (NSFETs) was applied using a dynamic surrogate optimization-gaussian process regression (DSO-GPR) with batch assessment and halting conditions. A vertically stacked silicon-based system is created and refined. Source extension ( $S_{ext}$ ), drain extension ( $D_{ex\mathrm {t}}$ ), gate length ( $L_{g}$ ), nanosheet height (NSh), nanosheet width (NSw), and number of fin (nfin) make up the input variables. In addition to applying normalization to make the weight assignment to the goal functions easier, a penalty component was incorporated to manage the $V_{T}$ . This study demonstrates the effectiveness of the DSO-GPR framework in optimizing NSFET-based inverter designs by balancing speed, $I_{OFF}$ and $V_{T}$ . Among the three evaluated cases, case 1 achieved the fastest propagation delay (1.05 ps) due to higher $I_{ON}$ and lower $V_{T}$ , albeit with increased $I_{OFF}$ and $V_{T}$ mismatch. Case 2 provided a balanced trade-off, significantly reducing $I_{OFF}$ while maintaining a competitive delay of 1.25 ps and achieving $V_{T}$ matching at 0.11 V for both n-type and p-type NSFETs. The optimum parameters for n-type are ${L} _{g}$ = 5 nm, $N_{Sh} = 9$ nm, $N_{Sw} = 42$ nm, $S_{ext} = 3$ nm and $D_{ext} = 4$ nm, nfin = 5; for p-type, $L_{g} = 7$ nm, $N_{Sh} = 10$ nm, $N_{Sw} = 42$ nm, $S_{ext} = 3$ nm and $D_{ext} = 3$ nm, nfin = 5. Case 3 minimized leakage at the cost of delay, highlighting the trade-off between power and performance. These results validate the framework’s adaptability and automation, with performance-based stopping criteria that balance accuracy and computational efficiency.}, archive = {J_ACCESS}, author = {Christofer N. Yalung and Doldet Tantraviwat}, doi = {10.1109/ACCESS.2025.3575090}, journal = {IEEE Access}, pages = {95730-95738}, shortjournal = {IEEE Access}, title = {Dynamic surrogate optimization of vertically stacked nanosheet FET based on gaussian process regression}, volume = {13}, year = {2025}, }
• (2025). A heat decay model-based hybrid sampling algorithm for imbalanced overlapping datasets. ACCESS, 13, 95717-95729. (www)

  @article{ , abstract = {Imbalanced datasets pose significant challenges to classification tasks, as traditional classifiers often favor majority classes. Although numerous methods have been proposed to balance data distributions, recent studies identify that imbalanced datasets frequently exhibit complex intrinsic characteristics, among which class overlap is the most harmful to classification performance. To this end, we propose a novel sampling algorithm called Heat Decay Model-Based Hybrid Sampling (HDHS). The proposed method comprises three key steps. First, it calculates the weight of each minority class sample using density and closeness factors, addressing within-class imbalance and initializing the heat of each minority class sample. Second, the heat decay model is employed to dynamically expand the sampling region until thermal equilibrium is reached, simultaneously removing majority class samples from the extended region. Finally, a weighted oversampling strategy is applied to synthesize new minority samples within the adjusted space. Experimental evaluations conducted on 50 imbalanced datasets demonstrate that HDHS significantly outperforms 10 state-of-the-art methods in terms of AUC, G-mean, and F-measure. This superiority is especially pronounced on datasets with a high degree of class overlap. Moreover, HDHS exhibits generalization capability across various classification paradigms, consistently achieving superior performance when integrated with classifiers such as KNN, CART, NB, and SVM.}, archive = {J_ACCESS}, author = {Liangliang Tao and Lilin Zhang}, doi = {10.1109/ACCESS.2025.3574677}, journal = {IEEE Access}, pages = {95717-95729}, shortjournal = {IEEE Access}, title = {A heat decay model-based hybrid sampling algorithm for imbalanced overlapping datasets}, volume = {13}, year = {2025}, }
• (2025). Depth integrated multi-task prototypical learning with self refinement for unsupervised domain adaptation. ACCESS, 13, 95706-95716. (www)

  @article{ , abstract = {Unsupervised Domain Adaptation (UDA) serves as a potential alternative for improving cross- domain segmentation tasks. Recent UDA approaches have identified class-wise prototypes and leveraged them to guide the segmentation process in the target domain. However, these methods overlook additional information from other auxiliary tasks, such as depth, which can potentially improve overall segmentation performance. This paper proposes a Depth-Aware Prototypical learning for Semantic Segmentation (DA- ProSS) pipeline, which includes a novel multitask prototype learning framework that comprises task-specific and task-integrated classifiers. The task-specific classifiers capture the semantic and depth features, and the task-integrated classifier captures the hidden semantic features from depth prediction. This framework ensures that the prototypes from respective heads learn better class-representative features using semantic information and depth cues. Additionally, this pipeline integrates a Self-Refinement learning (SRL) algorithm that generates cross-domain pseudo-labels, which are leveraged to generate refined targets for further self-supervised training. Results indicate that prototypes generated through the depth-encoded semantic task could understand the underlying semantics of the object. The proposed DA-ProSS pipeline with SRL helps the model generalize better to achieve a mIoU of 57.3 that outperforms the previous state-of-the-art methods in the SYNTHIA-to-Cityscapes benchmark dataset.}, archive = {J_ACCESS}, author = {Antonio Dauphin Fernando and Thumma Anirudh and Selvaraj Palanisamy and Karthika Prasad and Katia Alexander and Pandiyarasan Veluswamy and Rohini Palanisamy}, doi = {10.1109/ACCESS.2025.3572013}, journal = {IEEE Access}, pages = {95706-95716}, shortjournal = {IEEE Access}, title = {Depth integrated multi-task prototypical learning with self refinement for unsupervised domain adaptation}, volume = {13}, year = {2025}, }
• (2025). Modified Bron–Kerbosch-based sensor deployment algorithm for target coverage in wireless sensor networks. ACCESS, 13, 95692-95705. (www)

  @article{ , abstract = {The growing adoption of Wireless Sensor Network (WSN) technologies has highlighted several critical factors that are vital for their efficiency and functionality. Optimal sensor node deployment emerges as a critical factor that significantly impacts the performance and overall effectiveness of the network. To address these challenges, a new deployment algorithm is proposed that substantially reduce the number of sensor requirement to ensure maximum coverage based on the geometric relationship between the targets. This paper proposes a novel Modified Bron-Kerbosch based Sensor Deployment (MBK-SD) algorithm that partition the vertex set as maximal clique and an iterative circle intersection method is introduced to find the optimal sensor placement. Theorem is proven that a central point exists within a specified distance R from all vertices forming a circle with radius R. The experimental results demonstrate that the proposed algorithm requires minimum number of sensors compared to Vertex Coloring based Optimal Sensor Placement (VC-OSP) and achieves better coverage compared to the Artificial Bee Colony (ABC) algorithm and Firefly Algorithm (FA). Statistical analysis is conducted for the proposed algorithm in comparison with ABC and FA. The results demonstrate the significance of the proposed algorithm over ABC and FA.}, archive = {J_ACCESS}, author = {P. Surya Bharathi and R. Pavithra}, doi = {10.1109/ACCESS.2025.3575245}, journal = {IEEE Access}, pages = {95692-95705}, shortjournal = {IEEE Access}, title = {Modified Bron–Kerbosch-based sensor deployment algorithm for target coverage in wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Solving 0-1 knapsack and bin packing problem using logical social group optimization. ACCESS, 13, 95665-95691. (www)

  @article{ , abstract = {The 0-1 Knapsack Problem (KP) and Bin Packing Problem (BPP) are NP-hard combinatorial optimization challenges often tackled using metaheuristics. Both problems have prominent utilization in the real world such as in resource allocation, logistics, decision-making, etc. The binarized variant of metaheuristics available in the literature mostly uses transfer functions for conversion of the domain from continuous to binary. Generally, the conversion functions are computationally expensive which demands more utilization of computational resources. Whereas the boolean functions are performed using bitwise operations which are inherent to the digital computer hardware and are less computationally expensive. This prominent research gap has been addressed in this paper by introducing Logical Social Group Optimization (LSGO), a logic-based binarized variant of Social Group Optimization (SGO) that leverages Boolean logic for improved efficiency. In LSGO, after the binarization by replacing the arithmetic operators with logical operators, one of the variables gets cancelled out. To preserve that variable, two versions of LSGO have been proposed. The first one is LSGO-Reduc where one of the initial operands in the equation is removed or reduced and after reducing it, no variables disappear. The second one is LSGO-Rever, where the boolean operators in the equation are reversed or exchanged and hence, no variables are lost. The three algorithms have been simulated with various datasets of knapsack and its variant problems and bin-packing problem for thorough testing on different problem types and scalability. Experimental results demonstrate that LSGO variants outperform conventional algorithms, achieving an average profit of 970 units on the P07 dataset for single knapsack problems, surpassing SGO (950 units) and Genetic Algorithm (GA) (930 units) with greater stability. In multiple knapsack problems, LSGO variants reach a profit of 1925 units, exceeding Particle Swarm Optimization (PSO) (1850 units). For bin packing problems, the binary splitting strategy optimally minimizes bin usage, achieving 5 bins on the P04 dataset, while maintaining solution quality. Unlike conventional methods that rely on transfer functions to solve binary problems, LSGO employs logical operators instead of arithmetic ones, eliminating the need for external functions. This approach significantly reduces computational overhead and offers potential improvements in large-scale instances. LSGO-Rever outperforms LSGO and LSGO-Reduc for most computationally complex problems. The results highlight the effectiveness of LSGO in enhancing solution quality, stability, scalability, and efficiency for discrete optimization problems where Knapsack has been taken as a case study in this paper. Moreover, the practical application of the proposed algorithm for simulation of path finding robots has been discussed to shed some light on the applicability of the algorithm. The results have been validated with ranking algorithm, Nemenyei post hoc statistical test and friedman chi square test which shows promising results with adequate graphs and tables derived from the simulations.}, archive = {J_ACCESS}, author = {Rhiddhi Prasad Das and Junali Jasmine Jena and Suresh Chandra Satapathy and Naeem M. S. Hannoon}, doi = {10.1109/ACCESS.2025.3575004}, journal = {IEEE Access}, pages = {95665-95691}, shortjournal = {IEEE Access}, title = {Solving 0-1 knapsack and bin packing problem using logical social group optimization}, volume = {13}, year = {2025}, }
• (2025). A deep learning approach for fault detection and localization in MT-VSC-HVDC system utilizing wavelet scattering transform. ACCESS, 13, 95647-95664. (www)

  @article{ , abstract = {This study presents a novel algorithm for automatic fault detection in multi-terminal voltage source converter-based high voltage direct current (MT-VSCHVDC) systems. The approach integrates the wavelet scattering transform (WST) to extract low-variance feature vectors and a newly developed variable batch size long-short-term-memory (VB-LSTM) network for accurate fault detection. The synthetic minority oversampling technique addresses class imbalance issues in fault and no-fault data. Utilizing a 5-fold cross-validation process, the study demonstrates that WST-based features, combined with the proposed VB-LSTM network and a hybrid training strategy, achieve 100% and 99.46% accuracy in classifying internal and external faults with no-noise and 20db noisy conditions respectively. The variable epoch size training enhances convergence speed, leading to more stable and consistent results. A secondary LSTM model with a similar layer architecture is also trained and evaluated on WST-based features to identify the fault location within internal faults. The proposed fault distance (WST-VB-LSTM) estimation model achieves excellent performance with an R-square of 0.9946 and MAE of 0.0135 without noise, and maintains robustness under noise with 30 dB ( $\text{R}^{2} =0.9903$ , MAE =0.0182) and 20 dB ( $\text{R}^{2} =0.9598$ , MAE =0.0364). Lastly, comparative performance analysis reveals that the proposed model outperforms recently published works and state-of-the-art techniques, exhibiting higher accuracy and significantly lower error metrics across varying noise conditions.}, archive = {J_ACCESS}, author = {Manohar Mishra and Debadatta Amaresh Gadanayak and Abha Pragati and Jai Govind Singh}, doi = {10.1109/ACCESS.2025.3574222}, journal = {IEEE Access}, pages = {95647-95664}, shortjournal = {IEEE Access}, title = {A deep learning approach for fault detection and localization in MT-VSC-HVDC system utilizing wavelet scattering transform}, volume = {13}, year = {2025}, }
• (2025). IVEMPS: IoT-based vehicle emission monitoring and prediction system. ACCESS, 13, 95628-95646. (www)

  @article{ , abstract = {Greenhouse gas emissions are a critical issue that can have severe environmental impacts such as global warming and climate change. Gases emitted by road transportation vehicles constitute a significant part of global greenhouse gas emissions. Therefore, this paper proposes an IoT-based real-time vehicle emission monitoring and prediction system to swiftly identify high-emitting vehicles (HEVs). The system consists of an on-board system with wireless networking capabilities housing seven gas sensors (CO, CO2, NO, NO2, H2S, HC, and O2) for real-time data acquisition. It also includes a cloud computing layer for data storage and emission forecasting for predictive maintenance using deep learning time series forecasting techniques. Finally, the system provides visualizations of the collected data through a dedicated web-based dashboard. Trained forecasting models achieved a prediction accuracy of 83% and an F1-score of 0.79. Additionally, real-world on-road test data validated the system’s effectiveness in identifying current and potential HEVs in real-time while generating large datasets to support ongoing research and development aimed at reducing vehicle emissions.}, archive = {J_ACCESS}, author = {Omar Farrag and Ahmad Mansour and Baraa Abed and Amr Abu Alhaj and Taha Landolsi and A. R. Al-Ali}, doi = {10.1109/ACCESS.2025.3572726}, journal = {IEEE Access}, pages = {95628-95646}, shortjournal = {IEEE Access}, title = {IVEMPS: IoT-based vehicle emission monitoring and prediction system}, volume = {13}, year = {2025}, }
• (2025). Enhance social network bullying detection using multi-teacher knowledge distillation with XGBoost classifier. ACCESS, 13, 95618-95627. (www)

  @article{ , abstract = {Cyberbullying remains a pressing issue in Thai social media, especially among teenagers. While many studies have explored deep learning approaches for sentiment analysis or toxicity detection, the detection of cyberbullying—especially in the Thai language—remains underexplored. This study introduces a novel framework that enhances cyberbullying detection by integrating Multi-Teacher Knowledge Distillation (MTKD) with an XGBoost classifier, specifically adapted for Thai-language social media posts. Unlike prior work that relies solely on neural models, this research demonstrates how distilled soft labels from diverse teacher models can be effectively transferred to a lightweight and interpretable XGBoost student model. A key contribution of this study is the successful adaptation of XGBoost, traditionally used for structured/tabular data, for a natural language classification task by using rich semantic features extracted via pre-trained NLP models. Additionally, although the selected datasets (Wisesight, Thai Toxic Tweet, and 40 Thai Children Stories) are often used for sentiment analysis, we reframe and preprocess them for the purpose of cyberbullying classification by focusing on toxic, harmful, or aggressive linguistic patterns. Our framework achieved strong classification performance—92.5%, 90.5%, and 91.0% accuracy across the three datasets—demonstrating its robustness and practical application in Thai-language cyberbullying detection.}, archive = {J_ACCESS}, author = {Sathit Prasomphan}, doi = {10.1109/ACCESS.2025.3574679}, journal = {IEEE Access}, pages = {95618-95627}, shortjournal = {IEEE Access}, title = {Enhance social network bullying detection using multi-teacher knowledge distillation with XGBoost classifier}, volume = {13}, year = {2025}, }
• (2025). Turbulence modeling technique has significant effects on the optimization of centrifugal blood pumps. ACCESS, 13, 95609-95617. (www)

  @article{ , abstract = {Blood pumps need to be further optimized to reduce blood damage and associated clinical complications. Turbulence is the key factor affecting blood damage. Turbulence models, have been widely employed in the design, optimization and evaluation of blood pumps. Advanced turbulence models such as large-eddy simulation (LES) has shown its advantage in predicting pump performance and resolving turbulence in blood pumps, compared with conventional Reynolds-Averaged Navier-Stokes (RANS) method. Optimization of blood pumps requires an effective comparison of pump performance between multiple designs at reasonable cost. This study aims at exploring the impacts and cost-effectiveness of different turbulence modeling technique in assisting optimization of blood pumps. 25 pump models based on a centrifugal maglev blood pump were designed considering 6 design variables. The steady RANS, unsteady RANS(URANS) and LES were employed to assist the optimization, combined with orthogonal experiments and experimental validation. Turbulence simulation method significantly affects the predicted variation of pressure head among pump models. RANS failed to capture the variation of pressure head, while LES were closest to experiments. Tip clearance was the most influential factor, followed by splitter blade inlet angle. Pump efficiency was improved by 10.76%, 5.55% and 3.21%, for the LES, URANS and RANS respectively. The computational cost of LES is comparable with the URANS, and one order higher than RANS. Thus, when pump performance is the priority, LES should be employed to maximized the gain of optimization, while RANS is a viable option when a tradeoff between cost and gain is expected.}, archive = {J_ACCESS}, author = {Zhigang Pan and Fei-Hong Gao and Peng Wu}, doi = {10.1109/ACCESS.2025.3568301}, journal = {IEEE Access}, pages = {95609-95617}, shortjournal = {IEEE Access}, title = {Turbulence modeling technique has significant effects on the optimization of centrifugal blood pumps}, volume = {13}, year = {2025}, }
• (2025). STDP-driven automated retinal circuit with 7-nm FinFET for motion and looming detection: A hybrid model with image analysis. ACCESS, 13, 95594-95608. (www)

  @article{ , abstract = {This paper presents a spike-timing-dependent plasticity (STDP) driven silicon retina designed using 7nm FinFET technology for detecting object motion and looming threats inspired by the biological visual system. The proposed neuromorphic architecture integrates an automated motion spiking circuit with STDP and advanced image analysis, mimicking natural retinal processing to identify potential hazards. Two specialized circuits are introduced to generate bipolar spiking signals for object motion and looming detection. These spikes are processed by an STDP-based spiking neural network, qualifying real-time distance estimation of approaching objects. Model Results demonstrate accurate collision time prognosis based on object dynamics. YOLOv5 and MiDaS enhance object recognition and depth perception, improving situational awareness. The proposed model uses a hybrid strategy combining object detection and depth estimation to identify objects with different spatial distances and confidence levels accurately. Experimental results show a motion detection accuracy of 93.2% and an average latency of around 15 ms, 66 frames per second, which proves this is suitable for time-sensitive applications. Because the 7nm FinFET technology improves system efficiency and responsiveness, it can be used in real-time applications where high-precision motion detection is necessary in dynamic conditions. However, potential challenges include scalability for large-scale deployment and hardware constraints associated with real-time processing on FinFET-based circuits. The proposed system can be used for robotic vision systems, autonomous vehicles, and other contexts where real-time visual processing and threat detection are needed.}, archive = {J_ACCESS}, author = {Rubina Akter Rabeya and Md Turiqul Islam and Shah Muhammad Imtiyaj Uddin and Alaaddin Al-Shidaifat and Hanjung Song and Heung-Kook Choi and Hee-Cheol Kim}, doi = {10.1109/ACCESS.2025.3571588}, journal = {IEEE Access}, pages = {95594-95608}, shortjournal = {IEEE Access}, title = {STDP-driven automated retinal circuit with 7-nm FinFET for motion and looming detection: A hybrid model with image analysis}, volume = {13}, year = {2025}, }
• (2025). In-context learning in large language models (LLMs): Mechanisms, capabilities, and implications for advanced knowledge representation and reasoning. ACCESS, 13, 95574-95593. (www)

  @article{ , abstract = {The rapid growth of Large Language Models (LLMs) and their in-context learning (ICL) capabilities has significantly transformed paradigms in artificial intelligence (AI) and natural language processing. Notable models, such as OpenAI’s GPT series, have demonstrated previously unprecedented advancements in verbal comprehension and adaptability, dynamically responding to new tasks offered via contextual prompts. This study provides a detailed survey of recent advances in theoretical research on LLMs and ICL. The search was conducted across several scholarly databases including Google Scholar, arXiv, IEEE Xplore, ACM Digital Library, and SpringerLink, covering publications from January 2019 to March 2024. We investigate how LLMs encode and use knowledge via ICL, the evolving reasoning skills that result from this process, and the considerable impact of prompt design on LLM reasoning performance, particularly in symbolic reasoning tasks. Furthermore, we investigate the theoretical frameworks that explain or challenge LLM behaviors in ICL contexts and address the significance of these findings for the development of complex knowledge representation and reasoning systems. Using a systematic methodology consistent with accepted research criteria, this review synthesizes significant observations, highlights existing gaps and obstacles, and discusses implications for future research and practice. Our goal is to connect theoretical ideas with actual advances in Artificial Intelligence, ultimately contributing to the continuing discussion about the capabilities and applications of LLMs in knowledge representation and reasoning.}, archive = {J_ACCESS}, author = {Azza Mohamed and Mohamed El Rashid and Khaled Shaalan}, doi = {10.1109/ACCESS.2025.3575303}, journal = {IEEE Access}, pages = {95574-95593}, shortjournal = {IEEE Access}, title = {In-context learning in large language models (LLMs): Mechanisms, capabilities, and implications for advanced knowledge representation and reasoning}, volume = {13}, year = {2025}, }
• (2025). A reference voltage loop operation based ZQ calibration technique for multi-load high- capacity NAND flash memory interface. ACCESS, 13, 95563-95573. (www)

  @article{ , abstract = {This paper proposes a novel ZQ calibration method based on a reference voltage loop operation. ZQ calibration technology improves the integrity of signals transmitted on the channel by calibrating on-die termination (ODT) and output driver strength, which vary with process, voltage, and temperature (PVT), and plays an important role in the high-speed memory environment. However, the existing ZQ calibration method was designed for a single-die configuration, which can lead to calibration errors and potential operational failures in high-load environments driven by recent high-speed, high-capacity NAND Flash memory demands. To address this issue, this paper proposes a new reference voltage loop operation based ZQ calibration method that mitigates calibration errors caused by heavy loads. The proposed technique significantly reduces the impact of load fluctuations and enables stable and accurate impedance matching by switching the calibration reference node from the heavily-loaded ZQ node to a low-load reference voltage node within the die. In addition, a 2b/Cycle SAR-based ZQ code calculation method has been introduced to increase the calibration efficiency and speed. The proposed circuit is designed using a 28nm CMOS process with a supply voltage of 1V. As a result of measurements on prototype chips, it achieved a maximum voltage error of 13mV and power consumption of 8.2mW, even under a capacitive load of $4.7\mu $ F.}, archive = {J_ACCESS}, author = {Jun-Ha Lee and Jun-Eun Park and Dong-Ho Shin and Kang-Yoon Lee}, doi = {10.1109/ACCESS.2025.3572038}, journal = {IEEE Access}, pages = {95563-95573}, shortjournal = {IEEE Access}, title = {A reference voltage loop operation based ZQ calibration technique for multi-load high- capacity NAND flash memory interface}, volume = {13}, year = {2025}, }
• (2025). Coverage enhancement using UAVs for cognitive marine network. ACCESS, 13, 95548-95562. (www)

  @article{ , abstract = {Like terrestrial communications, marine users also need stable network paths between any source and destination pair. Besides the rough sea surface, another issue in this network is the communication range. Marine users in deep sea are distant from the base stations (BS) onshore, resulting in fragile links. To solve this problem, the literature has suggested the use of multiple relay nodes to stabilize the network. But more relays need more bandwidth to establish a stable link between any two communicating nodes. However, the dedicated spectrum for marine communications has been found to be scarce due to the increase in number of users and their service demands. Therefore, we propose a cognitive routing scheme in marine environments, which uses unmanned aerial vehicles (UAVs) as mobile BS and/or relays to handle the above-mentioned issues. Preferring a satellite-free network for marine users, we opt for UAVs to enhance the communication range. Because spectrum is a precious resource, we apply spectrum sensing technique to address spectrum scarcity by keeping primary user activity undisturbed. Finally, we ensure an optimal path for the cognitive marine environment that exhibits a high packet delivery ratio, less delay and overhead, and prove it by comparing our scheme to various reference schemes.}, archive = {J_ACCESS}, author = {Waqas Mustafa and Huma Ghafoor and Insoo Koo}, doi = {10.1109/ACCESS.2025.3575026}, journal = {IEEE Access}, pages = {95548-95562}, shortjournal = {IEEE Access}, title = {Coverage enhancement using UAVs for cognitive marine network}, volume = {13}, year = {2025}, }
• (2025). Impact of artificial intelligence on software engineering phases and activities (2013–2024): A quantitative analysis using zero- truncated poisson model. ACCESS, 13, 95535-95547. (www)

  @article{ , abstract = {This paper presents the results of a quantitative analysis derived from data collected in our earlier systematic literature review, focusing on integrating Artificial Intelligence (AI) techniques across various phases of Software Engineering (SE). Employing a Zero-Truncated Poisson (ZTP) model, we analyzed 120 selected research papers to assess the impact of AI methodologies on SE phases. The findings indicate significant improvements attributable to AI in different phases of SE. Specifically, AI techniques have been found to enhance the accuracy of the planning, requirement engineering, and development phases and improve the efficiency of the requirement engineering and design phases. Notably, AI’s integration has markedly improved the accuracy and efficiency of the requirement engineering phase, underscoring AI’s critical role in advancing SE practices. This study contributes to a deeper understanding of the quantitative impact of AI on SE, providing valuable insights for practitioners and researchers in understanding AI technologies to enhance SE phases.}, archive = {J_ACCESS}, author = {Usman Khan Durrani and Mustafa Akpinar and Hakan Bektas and Mohammed Saleh}, doi = {10.1109/ACCESS.2025.3574462}, journal = {IEEE Access}, pages = {95535-95547}, shortjournal = {IEEE Access}, title = {Impact of artificial intelligence on software engineering phases and activities (2013–2024): A quantitative analysis using zero- truncated poisson model}, volume = {13}, year = {2025}, }
• (2025). I-MCM: IoT malware counter measures for cross-architecture IoT malware detection. ACCESS, 13, 95524-95534. (www)

  @article{ , abstract = {The recent attacks initiated by malware-infected IoT devices illustrate that these attacks have tremendous impacts not only on the targeted systems but also on the entire internet infrastructure. Due to the vast number of infected IoT devices, the Quality of Service of the target system can be severely hampered by DDoS attacks. Despite the severity of the threat, the security measures against IoT malware are highly limited, and mostly, users are unaware that their devices are infected by malware. Due to the constrained nature of IoT devices, high-resource-demanding anti-malware tools cannot be run on edge devices, and lightweight malware detection systems are highly needed to secure those devices against IoT malware. In this regard, we propose the I-MCM framework for IoT malware detection on IoT devices by utilizing Tiny ML techniques based on static malware analysis data. Our proposed I-MCM framework is capable of detecting IoT malware in a Raspberry Pi Node deployed with Random Forest Classifier in 7.87 s. (including file analysis and data preprocessing) with 99.8% accuracy and in an Arduino Nano 33 BLE Sense microcontroller deployed with a Tiny ANN model in 8.16 s. (including file analysis and data preprocessing) with 97.1% accuracy. Besides, the I-MCM framework can detect IoT malware regardless of CPU architecture with only one ML model by utilizing our Opcode Purification Technique. Last but not least, this research presents a cross-architecture IoT Malware dataset that includes both malware and benignware samples.}, archive = {J_ACCESS}, author = {Ibrahim Gulatas and H. Hakan Kilinc and A. Halim Zaim and M. Ali Aydin}, doi = {10.1109/ACCESS.2025.3573942}, journal = {IEEE Access}, pages = {95524-95534}, shortjournal = {IEEE Access}, title = {I-MCM: IoT malware counter measures for cross-architecture IoT malware detection}, volume = {13}, year = {2025}, }
• (2025). Transformers for vision: A survey on innovative methods for computer vision. ACCESS, 13, 95496-95523. (www)

  @article{ , abstract = {Transformers have emerged as a groundbreaking architecture in the field of computer vision, offering a compelling alternative to traditional convolutional neural networks (CNNs) by enabling the modeling of long-range dependencies and global context through self-attention mechanisms. Originally developed for natural language processing, transformers have now been successfully adapted for a wide range of vision tasks, leading to significant improvements in performance and generalization. This survey provides a comprehensive overview of the fundamental principles of transformer architectures, highlighting the core mechanisms such as self-attention, multi-head attention, and positional encoding that distinguish them from CNNs. We delve into the theoretical adaptations required to apply transformers to visual data, including image tokenization and the integration of positional embeddings. A detailed analysis of key transformer-based vision architectures such as ViT, DeiT, Swin Transformer, PVT, Twins, and CrossViT are presented, alongside their practical applications in image classification, object detection, video understanding, medical imaging, and cross-modal tasks. The paper further compares the performance of vision transformers with CNNs, examining their respective strengths, limitations, and the emergence of hybrid models. Finally, current challenges in deploying ViTs, such as computational cost, data efficiency, and interpretability, and explore recent advancements and future research directions including efficient architectures, self-supervised learning, and multimodal integration are discussed.}, archive = {J_ACCESS}, author = {Balamurugan Palanisamy and Vikas Hassija and Arpita Chatterjee and Arpita Mandal and Debanshi Chakraborty and Amit Pandey and G. S. S. Chalapathi and Dhruv Kumar}, doi = {10.1109/ACCESS.2025.3571735}, journal = {IEEE Access}, pages = {95496-95523}, shortjournal = {IEEE Access}, title = {Transformers for vision: A survey on innovative methods for computer vision}, volume = {13}, year = {2025}, }
• (2025). Light path modeling based on data assimilation using changes in a guided light passing through a packaged optical waveguide device. ACCESS, 13, 95489-95495. (www)

  @article{ , abstract = {This work demonstrates a method for light path modeling of various packaged optical waveguide devices in Si photonics and its applicability to inspection of the horizontal misalignments. The optical waveguide structure or light path is estimated by data assimilation using changes in a guided light passing through a packaged optical waveguide device. Tests of the method are carried out on the most essential structures such as straight, tapered, and inverse tapered structures with estimation errors less than a couple of percent in the simulation and it is experimentally verified using a straight Si waveguide (height: 220 nm, length: 6.9 mm, width: 440 nm) with a tapered optical coupling device (height: 220 nm, length: 2 mm) as a device under test. It is also expected to evaluate dynamical light path changes such as those due to thermal variation to verify that they affect the results in the device behavior after 3D co-packaging.}, archive = {J_ACCESS}, author = {Shizen Nakayama and Koichiro Ohkushi and Junpei Funatsuki and Tsuyoshi Konishi}, doi = {10.1109/ACCESS.2025.3573747}, journal = {IEEE Access}, pages = {95489-95495}, shortjournal = {IEEE Access}, title = {Light path modeling based on data assimilation using changes in a guided light passing through a packaged optical waveguide device}, volume = {13}, year = {2025}, }
• (2025). Physical defense planning against voltage distortion attacks in active distribution grids. ACCESS, 13, 95475-95488. (www)

  @article{ , abstract = {The growing number of distributed energy resources (DER) in distribution grids increases the occurrence rate and magnitude of voltage deviations. DER-inverters are able to counteract these deviations via voltage/VAr control. In addition, modern DER-inverters, as part of the Internet of Things, are potentially subject to cyber attacks, potentially targeting voltage distortions to destabilize distribution grid operations. This work proposes a novel trilevel defender-attacker-operator problem that enables system operators to physically defend against cyber-induced risks of voltage distortion attacks due to manipulated DER-inverters. The problem considers uncertainties of DERs’ power injections and loads, bidirectional power flows, and simultaneous under- and overvoltages contributing to a holistic defense design. For the defense, the system operator places voltage support devices, such as capacitors or distribution grid static compensators, to protect the system and counteract the cyber-induced voltage distortions in the worst realization of DERs’ power injections and loads. A column and constraint generating algorithm is proposed that efficiently solves the trilevel problem. Results for the IEEE 13 bus and IEEE 123 bus test grids implicate the proposed algorithm’s high efficiency and effectiveness in finding optimal defense strategies against voltage distortion attacks.}, archive = {J_ACCESS}, author = {Kirill Kuroptev and Sina Hajikazemi and Florian Steinke}, doi = {10.1109/ACCESS.2025.3575174}, journal = {IEEE Access}, pages = {95475-95488}, shortjournal = {IEEE Access}, title = {Physical defense planning against voltage distortion attacks in active distribution grids}, volume = {13}, year = {2025}, }
• (2025). LaborEase: An artificial intelligence-based wearable electrohysterography device for preterm pregnancy detection. ACCESS, 13, 95457-95474. (www)

  @article{ , abstract = {In this research, a portable electrohysterography device is developed for electrohysterogram recording and early detection of preterm pregnancies using machine learning techniques to classify the acquired recordings as preterm or term. The data collected in this study is combined with ‘TPEHGDB’ open-source dataset. With the aid of SMOTE ENN, Multilayer Perceptron achieved the highest 10-fold Cross-Validation Accuracy of 0.90, Test Accuracy, Recall, Precision, and F1 Score of 0.96, Sensitivity of 0.97, Specificity and ROC of 0.98, Cohen’s Kappa, MCC of 0.91, and F2 score of 0.85 when various features are used for classification and the Random Forest classifier achieved the highest 10-fold Cross-Validation Accuracy of 0.95, Test Accuracy, Recall, Precision, F1 Score, Sensitivity, and Specificity of 0.94, ROC of 0.98, Cohen’s Kappa, MCC of 0.87, and F2 score of 0.86 when only optimized features and channels are used for classification. The proposed device has a signal-to-noise ratio of 20.44 dB, ensuring the quality of the acquired signals. The developed system can be used for early, noninvasive, reliable, and cost-effective detection of preterm pregnancies, enhancing maternal and neonatal health monitoring and saving millions of lives with cost-effective and proactive interventions.}, archive = {J_ACCESS}, author = {Muhammad Omar Cheema and Zia Mohy-Ud-Din and Abdullah Al Aishan and Jahan Zeb Gul}, doi = {10.1109/ACCESS.2025.3574554}, journal = {IEEE Access}, pages = {95457-95474}, shortjournal = {IEEE Access}, title = {LaborEase: An artificial intelligence-based wearable electrohysterography device for preterm pregnancy detection}, volume = {13}, year = {2025}, }
• (2025). A new fractional-order adaptive interval type-3 fuzzy logic-backstepping control algorithm of doubly fed induction generators. ACCESS, 13, 95445-95456. (www)

  @article{ , abstract = {This paper presents a new fractional-order control algorithm for a doubly fed induction generator-based wind energy conversion system. Nonlinearities resulting from wind turbine dynamics, parametric uncertainties due to generator modeling, and stochastic wind disturbances can seriously affect control performances and consequently decrease wind power extraction. The suggested approach combines interval type 3 adaptive fuzzy systems with a backstepping control technique to improve the quality of the closed-loop response and the robustness of the system. Besides approximating nonlinear functions, interval type 3 fuzzy systems are used to handle a higher level of uncertainty compared to type 1 or type 2 fuzzy logic approaches. The stability analysis is proved according to the Lyapunov method extended to fractional order. Several simulation results are presented accompanied by a comparative study with some other well-known fuzzy backstepping methods.}, archive = {J_ACCESS}, author = {Tarek Aounallah and Nawal Ferhat and Najib Essounbouli and Abdelaziz Hamzaoui}, doi = {10.1109/ACCESS.2025.3575048}, journal = {IEEE Access}, pages = {95445-95456}, shortjournal = {IEEE Access}, title = {A new fractional-order adaptive interval type-3 fuzzy logic-backstepping control algorithm of doubly fed induction generators}, volume = {13}, year = {2025}, }
• (2025). Systematic analysis of federated learning approaches for intrusion detection in the internet of things environment. ACCESS, 13, 95410-95444. (www)

  @article{ , abstract = {The Internet of Things (IoT) has transformed various sectors by connecting devices, services, and applications, which boosts intelligence and operational efficiency. However, this growing connectivity has heightened concerns regarding data privacy and security, particularly for sensitive information. Intrusion detection systems (IDSs) have emerged as an effective strategy by incorporating machine learning (ML) methodologies to identify sophisticated cybersecurity threats. Traditional IDS solutions typically rely on centralized learning architectures, where data from edge nodes are transferred to central servers for analysis. This approach is effective but introduces major risks to the privacy of IoT data. Artificial intelligence (AI), specifically federated learning (FL), is a new frontier that offers an alternative option by enabling the direct implementation of distributed ML on IoT devices without requiring the sharing of raw data. FL supports decentralized and privacy-preserving learning, which makes it a critical instrument for enhancing the security of IoT ecosystems. Data security and privacy research in FL is increasingly becoming popular. However, the development of FL for IoT remains in its infancy and requires exploration in various areas. Moreover, current survey studies are limited and overlook the vital aspects of FL, particularly the diversity of datasets and the most recent methodologies employed in FL-enabled IDSs. This study conducts a systematic literature review that examines 43 studies published between 2020 and 2024. The focus revolves around recent advancements in applying AI to intrusion detection within IoT environments. This study comprehensively analyzes key ML models and datasets used to develop an IDS tailored for IoT systems. The review also explores emerging trends, challenges, and opportunities in implementing FL-based IDSs in IoT networks. The findings highlight the potential of FL to tackle pressing privacy and security challenges while enabling scalable and adaptive threat detection solutions. The study highlights important research gaps and proposes critical future directions to advance the field.}, archive = {J_ACCESS}, author = {Nuha A. Hamad and Khairul Azmi Abu Bakar and Faizan Qamar and Ahmed Mahdi Jubair and Rajina R. Mohamed and Mohamad Afendee Mohamed}, doi = {10.1109/ACCESS.2025.3574672}, journal = {IEEE Access}, pages = {95410-95444}, shortjournal = {IEEE Access}, title = {Systematic analysis of federated learning approaches for intrusion detection in the internet of things environment}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based adaptive downsampling of hyperspectral bands for soil organic carbon estimation. ACCESS, 13, 95392-95409. (www)

  @article{ , abstract = {Accurate estimation of soil organic carbon (SOC) is critical for assessing soil health and guiding sustainable land management. Hyperspectral sensing has emerged as an approach for SOC analysis due to its ability to capture detailed spectral signatures of soil properties. However, hyperspectral data suffer from redundancy and noise, which can degrade predictive accuracy if not properly addressed. Existing dimensionality reduction techniques, such as fixed-interval downsampling and autoencoders, either risk discarding informative bands or disrupt spectral continuity, limiting their effectiveness for models like one-dimensional convolutional neural networks (1D-CNNs) that rely on local spectral patterns. The recent supervised band selection algorithm BSDR improves accuracy but does not retain spectral continuity. This study introduces a deep learning model, AD-CNN, which employs a novel adaptive downsampling technique that jointly prioritizes band relevance and continuity for SOC estimation. Unlike prior techniques, the proposed technique is supervised, physically interpretable, and fully differentiable, enabling end-to-end learning with minimal parameter overhead. It preserves original spectral bands for scientific traceability and does not require extensive manual feature engineering. Experimental results on a large-scale soil spectral dataset show that AD-CNN improves $R^{2}$ by 17.95% and 19.48% over BSDR and a recent autoencoder-based baseline, respectively. The selected bands also exhibit automatic exclusion of noisy wavelengths, confirming the model’s ability to learn scientifically aligned features.}, archive = {J_ACCESS}, author = {Mohammad Rahman and Shyh Wei Teng and Manzur Murshed and Manoranjan Paul and David Brennan}, doi = {10.1109/ACCESS.2025.3574697}, journal = {IEEE Access}, pages = {95392-95409}, shortjournal = {IEEE Access}, title = {Deep learning-based adaptive downsampling of hyperspectral bands for soil organic carbon estimation}, volume = {13}, year = {2025}, }
• (2025). ALDExA: Automated LLM-assisted detection of CVE exploitation attempts in host-captured data. ACCESS, 13, 95379-95391. (www)

  @article{ , abstract = {Currently, the detection of Common Vulnerabilities and Exposures (CVE) exploitation attempts heavily depends on rule sets manually written for the detection unit. As the number of published CVEs increases each year, there is a need to advance automation efforts for CVE detection. For this purpose, we introduce ALDExA, a framework that fetches CVE information and corresponding exploit codes to identify an exploit string supported by a Large Language Model (LLM). An exploit string is a characteristic element of the analyzed attack and can be monitored during an actual exploitation attempt. As the novelty in this framework lies in the extraction capabilities of the LLM, we furthermore evaluate eight different models towards their performance in identifying a correct exploit string. We evaluate contemporary models in two experiments and find that they are, in up to 81% of the evaluated cases, capable of extracting a correct exploit string from the attack code. In addition, we propose a promising approach to increase the accuracy further and to automatically detect false predictions. As ALDExA is the first approach to fully automate the CVE detection pipeline, we also discuss remaining limitations and worthwhile areas of future research.}, archive = {J_ACCESS}, author = {Niclas Ilg and Maximilian Pfitzenmaier and Dominik Germek and Paul Duplys and Michael Menth}, doi = {10.1109/ACCESS.2025.3575258}, journal = {IEEE Access}, pages = {95379-95391}, shortjournal = {IEEE Access}, title = {ALDExA: Automated LLM-assisted detection of CVE exploitation attempts in host-captured data}, volume = {13}, year = {2025}, }
• (2025). Parasitic monitoring of road constructions: Setting up and implementing a new system for pavement-tyre friction. ACCESS, 13, 95358-95378. (www)

  @article{ , abstract = {Tyre-pavement friction influences transport safety, particularly in autonomous vehicles (AVs), where real-time friction estimation impacts critical decisions (e.g., stopping, changing lanes, and deceleration rate). Despite this, there is a lack of methods to have low cost and continuous monitoring. Consequently, this study proposes a novel system to continuously provide on-board microprocessors with updated estimates of tyre-pavement friction as a function of pavement, tyre, meteorological, and driving conditions. To this end, the following main tasks were carried out: 1) In task 1, the problem was modelled, and high-level equations to satisfy were set up for both AVs and human-driven vehicles (HDVs). 2) In task 2, a framework was set up to solve friction-related issues considering critical scenarios involving unconnected vehicles. 3) In task 3, a solution to continuously estimate tyre-pavement friction for AVs and HDVs was set up, and the consequences for connected and cooperative vehicles were analysed and modelled. 4) In task 4, the following conclusions were derived: A) The system herein set up and implemented has outstanding characteristics in terms of small cost. B) It allows for quasi-continuous monitoring of pavement friction, and both long-term and short-term changes can be detected. C) Both points are crucial due to the limitations of common friction monitoring systems in achieving high sampling frequencies. Future research will focus on increasing the number of sets of braking manoeuvres and on the optimisation of the hardware and software of the system to add to the existing onboard system.}, archive = {J_ACCESS}, author = {Alessia Lazzaro and Massimo Merenda and Filippo Giammaria Praticò}, doi = {10.1109/ACCESS.2025.3574996}, journal = {IEEE Access}, pages = {95358-95378}, shortjournal = {IEEE Access}, title = {Parasitic monitoring of road constructions: Setting up and implementing a new system for pavement-tyre friction}, volume = {13}, year = {2025}, }
• (2025). An integrated CNN-BiLSTM-transformer framework for improved anomaly detection using surveillance videos. ACCESS, 13, 95341-95357. (www)

  @article{ , abstract = {Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are widely used to surveillance normal and anomalous incidents. Real-world anomaly detection in complex environment situations is a significant challenge due to its diverse nature manually reviewing this vast data is difficult, time-consuming, and prone to errors. Human operators may overlook crucial information due to failure, which reduces the accuracy of manual monitoring. This highlights the need for automated solutions to improve accuracy and efficiency We proposed a Bidirectional Motion Temporal (BiMT) model to address this issue by coupling a pre-trained model of deep neural networks. This system utilizes a Convolutional Neural Network (CNN) to extract the best spatial key features from the video stream, which are converted into time series data and coupled with Recurrent Neural Network (RNN) deep learning model Bidirectional Long Short-Term Memory (BiLSTM) extract temporal key features, and Multi-Head Self-Attention (MHSA) allows for the detection of short-term frame correlations. In addition, created a custom transformer encoder with relative positional embedding that improves the detection and recognizes long-range frame dependencies. Furthermore, categorical focal loss (CFL) is used during training to prioritize essential features and further enhance the model’s precision. The more sophisticated ConvNeXtv2 model produced average AUC scores of 98.6%, 86.5%, and 83.5% for the UCF-Crime, UBI-Fight, and RAD datasets respectively.}, archive = {J_ACCESS}, author = {Sarfaraz Natha and Mohammad Siraj and Fareed Ahmed and Majid Altamimi and Muslim Syed}, doi = {10.1109/ACCESS.2025.3574835}, journal = {IEEE Access}, pages = {95341-95357}, shortjournal = {IEEE Access}, title = {An integrated CNN-BiLSTM-transformer framework for improved anomaly detection using surveillance videos}, volume = {13}, year = {2025}, }
• (2025). Performance-based scheduling in distributed mixed criticality systems. ACCESS, 13, 95321-95340. (www)

  @article{ , abstract = {With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches.}, archive = {J_ACCESS}, author = {Amjad Ali and Saud Wasly and Asad Masood Khattak and Ihsan Ali and Shahid Iqbal and Bashir Hayat}, doi = {10.1109/ACCESS.2025.3571737}, journal = {IEEE Access}, pages = {95321-95340}, shortjournal = {IEEE Access}, title = {Performance-based scheduling in distributed mixed criticality systems}, volume = {13}, year = {2025}, }
• (2025). Distributed fixed-time cooperative guidance law for simultaneous attack with impact angle constraint. ACCESS, 13, 95307-95320. (www)

  @article{ , abstract = {Multi-missile cooperative guidance is an important way to improve the strike effect of missiles, and the fast convergence of guidance commands can effectively ensure the stability of multi-missile systems. The cooperative guidance method based on the fixed-time consistency theory has been proved to be suitable for multi-missile coordinated attack, but the non-singularity problem of the controller itself will have a large impact on the striking effect of the multi-missile system, so this paper designs a fixed-time fast non-singular distributed cooperative guidance law to solve the above problem. First, a fixed-time cooperative guidance law with fast convergence is designed in the line-of-sight (LOS) direction and an auxiliary function is designed to solve the singularity problem; after that, a cooperative guidance law in the LOS normal direction is designed to ensure that each missile attacks the target simultaneously at the desired attack angle. Finally, the effectiveness, superiority and robustness of the designed guidance law are demonstrated by numerical simulations.}, archive = {J_ACCESS}, author = {Yu Zhang and Shiyan Sun}, doi = {10.1109/ACCESS.2025.3574457}, journal = {IEEE Access}, pages = {95307-95320}, shortjournal = {IEEE Access}, title = {Distributed fixed-time cooperative guidance law for simultaneous attack with impact angle constraint}, volume = {13}, year = {2025}, }
• (2025). Scalable Δ -AGC logic for enhanced electromechanical oscillation damping in modern power systems with utility-scale photovoltaic generators. ACCESS, 13, 95288-95306. (www)

  @article{ , abstract = {Power systems with utility-scale solar photovoltaic (PV) can significantly influence the operating points (OPs) of synchronous generators, particularly during periods of high solar PV generation. A sudden drop in solar PV output due to cloud cover or other transient conditions will alter the generation of synchronous generators shifting their OPs. These shifted OPs can become a challenge for stability as the system may operate closer to its stability limits. If a disturbance occurs while the system is operating at the shifted OP, with reduced stability margins, it will be more vulnerable to increased oscillations, loss of synchronism of its generator(s) and system instability. This study introduces a scalable $\Delta $ -automatic generation control ( $\Delta $ -AGC) logic method designed to address stability challenges arising from shifts in the OPs of synchronous generators during abrupt drops in PV generation. By temporarily adjusting the OPs of synchronous generators through modification of their participation factors (PFs) in the AGC logic dispatch, the proposed method enhances power system stability. The proposed $\Delta $ -AGC logic method focuses on the optimal determination of $\Delta PFs$ in power systems with large number of generators, using the concept of coherency and employing a hierarchical optimization strategy that includes both inter-coherent and intra-coherent group optimization. Additionally, a new electromechanical oscillation index (EMOI), integrating both time response analysis (TRA) and frequency response analysis (FRA), is utilized as an online situational awareness tool (SAT) for optimizing the system’s stability under various conditions. This online SAT has been implemented in a decentralized manner at the area level, limiting wide-area communication overheads and any cybersecurity concerns. The $\Delta $ -AGC logic method is illustrated on a modified IEEE 68 bus system, incorporating large utility-scale solar PV plants, and is validated through real-time simulation. Various cases, including high-loading conditions with and without power system stabilizers, conventional AGC logic, and $\Delta $ -AGC logic, are carried out to evaluate the effectiveness of the proposed $\Delta $ -AGC logic method. The results illustrate the performance and benefits of the $\Delta $ -AGC logic method, highlighting its potential to significantly enhance power system stability.}, archive = {J_ACCESS}, author = {Rajan Ratnakumar and Ganesh K. Venayagamoorthy}, doi = {10.1109/ACCESS.2025.3575028}, journal = {IEEE Access}, pages = {95288-95306}, shortjournal = {IEEE Access}, title = {Scalable Δ -AGC logic for enhanced electromechanical oscillation damping in modern power systems with utility-scale photovoltaic generators}, volume = {13}, year = {2025}, }
• (2025). Multi-stage operational resilience metric-driven optimal service restoration in DER-rich power distribution systems. ACCESS, 13, 95275-95287. (www)

  @article{ , abstract = {The increasing occurrence of high probability events and the absence of a universal resilience metric have shifted the research paradigm towards quantifying resilience to support adaptive restoration. Thus, there is a need for a resilience-driven strategy for effective operation encompassing an immediate restoration in uncertain scenarios. However, distributed energy resources (DERs) have emerged as a crucial element for resilient distribution systems, but their integration introduces greater variability in operational decision-making. This paper proposes a novel integrated resilience assessment metric (IRAM) to support real-time operations in DER-rich distribution systems. This multi-stage metric significantly contributes in developing a formal resilience-driven solution in evolving distribution grids. The proposed metric utilizes pairwise criteria weights for resilience computation based on comprehensive attributes- operational redundancy, operational flexibility, and topological robustness. It plans for DER services by considering their operation and control modes, and real-time weather-dependent availability. The developed algorithm uses iterative meta-heuristic optimization to scan the system at different intervals and yield the optimal resilience-driven post-event restorative measure on observing the path redundancy. This work also utilizes weighted graph theory to compute the centrality indices, maintaining an efficacious level of robustness. The effectiveness of the proposed approach is validated through rigorous case studies on the IEEE-33 bus distribution system with 285 control variables optimizing for switching, load management, control strategies, and operational modes. Detailed results demonstrate that the developed approach maximizes the distribution system’s resilience by achieving 97% restoration amid extreme events.}, archive = {J_ACCESS}, author = {Samridhi Sajwan and Bijaya Ketan Panigrahi and Anurag K. Srivastava}, doi = {10.1109/ACCESS.2025.3574480}, journal = {IEEE Access}, pages = {95275-95287}, shortjournal = {IEEE Access}, title = {Multi-stage operational resilience metric-driven optimal service restoration in DER-rich power distribution systems}, volume = {13}, year = {2025}, }
• (2025). Analysis of IoT-collected radish growth data using deep neural networks. ACCESS, 13, 95257-95274. (www)

  @article{ , abstract = {Managing crop growth conditions with an eye toward sustainability helps optimize resource use and reduce environmental impact. The aim of this study was to apply genetic algorithm-based hyperparameter optimization (GA-HPO) to deep neural networks (DNNs) to classify an IoT-collected dataset on radish growth conditions, with the goal of identifying optimal growth parameters. By using physiological and morphological data from various radish species and treatment conditions, this research explores the effectiveness of GA in fine-tuning DNN hyperparameters to achieve high classification accuracy. The GA-HPO approach improved model performance to a classification accuracy of 92%, showcasing its adaptability and potential as a tool for precision agriculture. Through the identification of optimal growing conditions, this study supports sustainable practices in agriculture by facilitating resource-efficient and environmentally friendly crop management.}, archive = {J_ACCESS}, author = {Ivan P. Malashin and Vadim S. Tynchenko and Andrei P. Gantimurov and Vladimir A. Nelyub and Aleksei S. Borodulin}, doi = {10.1109/ACCESS.2025.3575107}, journal = {IEEE Access}, pages = {95257-95274}, shortjournal = {IEEE Access}, title = {Analysis of IoT-collected radish growth data using deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Research on SNN learning algorithms and networks based on biological plausibility. ACCESS, 13, 95243-95256. (www)

  @article{ , abstract = {Spiking Neural Networks, inspired by the brain’s neuronal information processing mech- anisms, utilize sparse, event-based spike signals to emulate biological computation. These networks aim to replicate the spike-driven behavior of biological neurons, incorporating temporal dynamics alongside spatial inputs, potentially offering enhanced spatiotemporal processing capabilities compared to conventional artificial neural networks. Due to their event-driven nature and binary spike information representation, they hold promise for ultra-low power consumption, making them suitable for energy-constrained applications. Recent advancements suggest these networks are approaching, and sometimes exceeding, the performance of traditional deep learning methods in domains like image classification and speech recognition, particularly concerning energy efficiency. This paper provides a comprehensive review focusing on the biological plausibility of spiking neural networks. It begins by discussing the concept of biological plausibility and outlining fundamental principles and development approaches for spiking neuron models. It then details various temporal encoding strategies used to convert input data into spikes. The review subsequently explores the evolution of learning algorithms, covering both biologically inspired unsupervised methods, such as Spike-Timing-Dependent Plasticity, and supervised learning approaches adapted primarily from backpropagation techniques using surrogate gradients. Finally, the paper surveys recent advancements in deep network architectures specifically designed for spiking neural networks. Challenges and future research directions are also discussed.}, archive = {J_ACCESS}, author = {Bingqiang Huo and Fang Li and Siyu Peng and Hongwei Chen and Sudan Xin and Hongjun Wang}, doi = {10.1109/ACCESS.2025.3566717}, journal = {IEEE Access}, pages = {95243-95256}, shortjournal = {IEEE Access}, title = {Research on SNN learning algorithms and networks based on biological plausibility}, volume = {13}, year = {2025}, }
• (2025). CNF: An automated test case generation method based on neuron coverage guidance in key network layers. ACCESS, 13, 95227-95242. (www)

  @article{ , abstract = {To address the issue of low efficiency and inability to cover all test scenarios in manually generated test datasets, an automated test case generation method called Critical Network Layer Fuzzy testing (CNF) is proposed, based on the key network layer neuron coverage guidance criterion. The convolutional layer is selected as the key network layer. It is verified through experiments that the trend of neuron coverage changes in the key network layer is consistent with that of the entire network layer, significantly improving computational efficiency. Then, through visual and quantitative comparisons, the number of affine transformations required for automatic label generation is preliminarily determined. Finally, a network model is trained and comparative experiments are conducted, obtaining final results that verify the effectiveness of the CNF automatic label generation strategy.}, archive = {J_ACCESS}, author = {Yiran Gu and Yunlai Sun and Yiling Wu and Fei Yan}, doi = {10.1109/ACCESS.2025.3566549}, journal = {IEEE Access}, pages = {95227-95242}, shortjournal = {IEEE Access}, title = {CNF: An automated test case generation method based on neuron coverage guidance in key network layers}, volume = {13}, year = {2025}, }
• (2025). Joint neighbor discovery and topology exchange with directional antennas in multi-hop MANETs. ACCESS, 13, 95218-95226. (www)

  @article{ , abstract = {As a crucial step for routing establishment in multi-hop mobile ad hoc networks (MANETs), topology discovery (TD) is extremely challenging when directional antennas are used. In this paper, we propose a joint neighbor discovery and topology exchange (JNDTE) algorithm for the purpose of fast TD. By arranging one topology-information exchange (TIE) closely following the occurrence of one successful neighbor discovery for any pair of nodes, the TD time is significantly decreased due to the avoidance of beam misalignment during the TIE process. Besides, a beam selection strategy is utilized to dynamically adjust the probability distribution for each beam sector to further speed up the TD process. Simulation results show the superiority of the proposed algorithm.}, archive = {J_ACCESS}, author = {Yu Liu and Long Chen and Huiyuan Xu and Dalong Yang and Tao Cao and Youjiang Liu}, doi = {10.1109/ACCESS.2025.3575253}, journal = {IEEE Access}, pages = {95218-95226}, shortjournal = {IEEE Access}, title = {Joint neighbor discovery and topology exchange with directional antennas in multi-hop MANETs}, volume = {13}, year = {2025}, }
• (2025). AUNET (Attention-based unified network): Leveraging attention-based N-BEATS for enhanced univariate time series forecasting. ACCESS, 13, 95184-95217. (www)

  @article{ , abstract = {Univariate time series forecasting is pivotal in domains such as climate modeling, finance, and healthcare, where both short-term precision and long-term reliability are essential. This study introduces AUNET (Attention-based Unified Network)—a novel extension of the N-BEATS model—integrating multi-head self-attention to capture both short-term fluctuations and long-term dependencies while reducing architectural redundancy and enhancing interpretability. Empirical evaluations on the (baseline dataset for this research) CIMIS weather dataset show that AUNET achieves a 0.3580 MAE, 0.4632 RMSE, 0.68% MAPE, and an R2 of 0.9988, outperforming baseline models including N-BEATS and attention-augmented variants by margins exceeding 87% in MAE and RMSE. AUNET’s generalization capabilities are further validated on diverse domains: in finance it attains an R2 of 0.9990, and in healthcare, it outperforms baselines in RMSE and R2 despite inherent volatility. Statistical assessments confirm the significance and robustness of these improvements. Moreover, AUNET demonstrates reduced training time, lower FLOPs, and higher convergence stability. Its attention-guided modular architecture not only enhances predictive accuracy but also promotes transparency, making AUNET a compelling candidate for deployment in high-stakes, real-world forecasting tasks across varied domains.}, archive = {J_ACCESS}, author = {Adria Binte Habib and Md Golam Rabiul Alam and Md Zia Uddin}, doi = {10.1109/ACCESS.2025.3574459}, journal = {IEEE Access}, pages = {95184-95217}, shortjournal = {IEEE Access}, title = {AUNET (Attention-based unified network): Leveraging attention-based N-BEATS for enhanced univariate time series forecasting}, volume = {13}, year = {2025}, }
• (2025). Cauchy-lanczos algorithm for effective dimension reduction. ACCESS, 13, 95173-95183. (www)

  @article{ , abstract = {The aim of dimension reduction techniques is to eliminate unnecessary information from extensive datasets, thereby enhancing the effectiveness of data analysis. Some linear dimension reduction techniques can be formulated using an appropriate pair of symmetric matrices. To identify the dimension of the signal subspace and distinguish between the signal and noise components of the data, one can employ the eigenvalue decomposition of a symmetric matrix. In this paper, we implement the Cauchy interlace theorem within the Lanczos algorithm to compute the eigenvalue decomposition of large symmetric matrices, which we refer to as the Cauchy-Lanczos algorithm (abbreviated as C-Lanczos algorithm). We demonstrate that this method can yield more accurate approximate eigenvalues and eigenvectors compared to the Lanczos algorithm, with only a minor increase in computational cost. Furthermore, we outline the convergence characteristics and provide an error analysis for the C-Lanczos algorithm. A series of experiments are also presented, illustrating the algorithm’s effectiveness in dimension reduction.}, archive = {J_ACCESS}, author = {Xuansheng Wang and Linzhong Xia and Changwei Lv}, doi = {10.1109/ACCESS.2025.3570420}, journal = {IEEE Access}, pages = {95173-95183}, shortjournal = {IEEE Access}, title = {Cauchy-lanczos algorithm for effective dimension reduction}, volume = {13}, year = {2025}, }
• (2025). Don’t blink at the wrong time: Smart glasses inducing timely blinks. ACCESS, 13, 95162-95172. (www)

  @article{ , abstract = {Blinking is essential for eye health, but its frequency decreases during concentration-intensive tasks, such as using Visual Display Terminals (VDTs) or playing musical instruments, potentially leading to dry eye syndrome. While systems exist to promote blinking, previous studies have not fully considered the adverse effects of eye closure during tasks or the timing of blinking. To address this, this study first investigated appropriate blink timing by having participants blink at designated moments while tracking a moving object on a PC screen. Results showed that blinking simultaneously with the object’s direction change significantly delayed tracking, confirming that certain blink timings negatively affect task execution. Based on these findings, this study proposes a system that induces reflexive blinking at optimal moments to prevent dry eyes while minimizing task disruption. The proposed system is an eyeglass-type device that detects suitable blinking moments using IMU sensors and wireless communication. Micro-blowers direct airflow onto the eyelashes, inducing reflexive blinking only at appropriate times. To evaluate whether the device can induce blinking at appropriate moments in practice, we conducted experiments under four task conditions: distant viewing, reading, video watching, and gaming. The system achieved an average blink induction rate of 61.4%. The median blink delay—defined as the time between the airflow stimulus and the user’s eyelid closure—was 342 ms for distant viewing, 392 ms for reading, 373 ms for video watching, and 506 ms for gaming. These findings indicate that the device is effective only when the duration of appropriate blinking moment exceeds these delays.}, archive = {J_ACCESS}, author = {Ayumi Ohnishi and Soichiro Utsumi and Tsutomu Terada and Masahiko Tsukamoto}, doi = {10.1109/ACCESS.2025.3575357}, journal = {IEEE Access}, pages = {95162-95172}, shortjournal = {IEEE Access}, title = {Don’t blink at the wrong time: Smart glasses inducing timely blinks}, volume = {13}, year = {2025}, }
• (2025). A novel DNA techniques to strengthen cryptographic permutation tables in encryption algorithm. ACCESS, 13, 95148-95161. (www)

  @article{ , abstract = {Cryptography has long safeguarded sensitive data within information security. Recently, integrating traditional cryptographic methods with Deoxyribonucleic Acid (DNA) technologies sparked interest, giving rise to DNA cryptography. This hybrid approach seeks to leverage both domains to enhance security measures and explore new avenues for encryption and data protection. DNA-based cryptography combines a transdisciplinary structure of natural sciences specifically biology, and technological sciences, particularly information security. A good cryptosystem should accommodate Shannon’s confusion and diffusion properties. However, recent studies in DNA cryptography do not consider these two properties in the cryptographic design. This research introduces a novel approach to cryptographic permutation techniques utilizing DNA-based methodologies to achieve good diffusion properties. It begins with foundational DNA concepts, including DNA encoding/decoding rules, Watson-Crick complementary rules, and DNA hybridization. Subsequently, the paper proposes a new cryptographic DNA-based Permutation Table called the DNA P-Box as a secure cryptographic primitive. Security analysis of the DNA P-Box is then performed, focusing on three key aspects: Correlation Coefficient, Bit Error Rate (BER), and Key Sensitivity. Results from the analysis demonstrate the robustness of the DNA P-Box in maintaining cryptographic security properties. The average Correlation Coefficient value calculated for the DNA P-Box is 0.01665, which is concentrated around the lower range of the Correlation Coefficient values. The average Bit Error Rate for the DNA P-Box is 50.20%, close to the optimum value. The Key Sensitivity assessment shows the average Correlation Coefficient value ranges from -0.44262 to 0.57447, with an average Bit Error Rate of 50.74%. Overall, this study highlights the promising potential of DNA-based permutation techniques in enhancing cryptographic security and presents avenues for further exploration in this domain towards the reliability of the encryption algorithm.}, archive = {J_ACCESS}, author = {Nik Azura Nik Abdullah and Nur Hafiza Zakaria and Azni Haslizan Ab Halim and Abdul Alif Zakaria and Samsul Ariffin Abdul Karim}, doi = {10.1109/ACCESS.2025.3571586}, journal = {IEEE Access}, pages = {95148-95161}, shortjournal = {IEEE Access}, title = {A novel DNA techniques to strengthen cryptographic permutation tables in encryption algorithm}, volume = {13}, year = {2025}, }
• (2025). 3D-printed reflect-transmit-array antenna with modified transmitarray mode for RCS reduction. ACCESS, 13, 95137-95147. (www)

  @article{ , abstract = {In this paper, a 3D-printed dual-band reflect-transmit-array (RTA) antenna has been presented, which operates as a reflectarray (RA) in the 28 GHz band and as a transmitarray (TA) in the 41.5 GHz band along with modified TA mode operation for radar cross-section (RCS) reduction. It consists of a metallic and a dielectric 3D-printed layers placed in close proximity to each other. The RTA comprises 316 unit cells placed across a circular aperture, where each unit cell simultaneously obtains a desired transmission and reflection phase characteristics in order to compensate for the spatial phase delay in RA as well as the TA mode of operations. A uniform transmission and reflection amplitude distribution has been assumed for design simplicity. The metallic layer consists of periodic cross-slots, whereas the dielectric layer comprises of periodic square dielectric posts. The combination of the metallic layer and dielectric layer provides reflection characteristics at 28 GHz and passband transmission characteristics at 41.5 GHz. A Ka-band pyramidal horn antenna, designed to maintain similar radiation characteristics in both the orthogonal planes, has been utilized as a feed antenna. Measured peak gain, peak aperture efficiency, 1-dB gain bandwidth (BW), and 3-dB gain BW for the RTA in the RA (TA) mode of operations are 26.6 (19.1) dBi, 40 (6) %, 32.3 (8.7) % and 51.8 (16.4) %, respectively. Further, the dielectric layer has been altered for RCS reduction in the TA mode operation while the RA mode of operation remains unaffected. An RCS reduction of up to 22.6 dB at 41.5 GHz along with a 10 dB RCS reduction BW of 6 % is accomplished. The antenna system can be highly useful for bi-directional communication, defense, radar, and low-detectable applications.}, archive = {J_ACCESS}, author = {Vimal Kumar and Akhilesh Mohan and Gunjan Srivastava and Sachin Kumar and Tanweer Ali}, doi = {10.1109/ACCESS.2025.3574052}, journal = {IEEE Access}, pages = {95137-95147}, shortjournal = {IEEE Access}, title = {3D-printed reflect-transmit-array antenna with modified transmitarray mode for RCS reduction}, volume = {13}, year = {2025}, }
• (2025). Novel view synthesis of defocused blur scenes based on neural radiance fields. ACCESS, 13, 95122-95136. (www)

  @article{ , abstract = {In recent years, neural radiance fields have been widely used in the field of computer graphics due to their excellent reconstruction quality. However, the shooting process in the wild environment is often affected by various internal and external factors, resulting in blurry images. To address the problem of defocus blur in the real world leading to a decrease in the reconstruction quality of neural radiance fields, this paper proposes a new deblurred radiance field and designs a rigid blur kernel based on the depth features of the image frame to model the rigid transformation of light and the weights of the coarse components of color. For the problem of similar two-dimensional coordinates restricting the model to distinguish scene details in the non-focal plane background, a fine sampling weight using multiscale depth feature fusion is further proposed, and a staged optimization strategy is designed. Experimental results show that compared with the state-of-the-art methods, the method proposed in this paper can better recover scene details and generate high-quality images for defocused blur scenes.}, archive = {J_ACCESS}, author = {Zhaoji Lin and Yuxin Zheng and Li Yao}, doi = {10.1109/ACCESS.2025.3574881}, journal = {IEEE Access}, pages = {95122-95136}, shortjournal = {IEEE Access}, title = {Novel view synthesis of defocused blur scenes based on neural radiance fields}, volume = {13}, year = {2025}, }
• (2025). Efficient cache performance equivalent 2D-texel to memory mapping identification for embedded GPUs. ACCESS, 13, 95094-95121. (www)

  @article{ , abstract = {With the increasing trend of utilizing smart devices, exploiting embedded GPUs becomes plausible to provide for intense computations that approach workstation performance, opening the way for complex, server-grade applications on such devices. Although general-purpose development frameworks exist, they generally abstract implementation details making it difficult for further performance exploitation. One such critical aspect is the texture memory hierarchy design parameters, which are primarily trade secrets. Unfortunately, standard cache hierarchy identification methods are not applicable, due to utilizing logical 2D-texel-to-memory mapping to exploit the 2D locality of access prior to physical cache access. This paper presents, for the first time, a parameterized model capable of describing the underlying multidimensional tiling layouts governing this mapping. Although it can be shown that reverse-engineering the model to obtain the exact parameters is an undecidable problem, we strive to obtain a corresponding set of parameters that results in the same cache behavior. In particular, such parameters define the texture order resulting in a linear order that traverses the caches in memory contiguous blocks. This study proposes a reverse-engineering observation method that exploits the contiguity of tiled cached regions and cache set conflicts to efficiently reveal such parameters. The complexity of the method is $O(n^{2})$ , where n is the number of bits in one of the texture buffer dimensions, ensuring practical applicability. Furthermore, optimization of a benchmark workload—via input data and memory access pattern alignment with the inferred multidimensional tiling layout—yields up to a $2.22\times $ speedup.}, archive = {J_ACCESS}, author = {Ahmed El-Mahdy and Marwa K. Elteir and Kholoud Shata}, doi = {10.1109/ACCESS.2025.3573668}, journal = {IEEE Access}, pages = {95094-95121}, shortjournal = {IEEE Access}, title = {Efficient cache performance equivalent 2D-texel to memory mapping identification for embedded GPUs}, volume = {13}, year = {2025}, }
• (2025). Imitation game for adversarial disillusion with chain-of-thought reasoning in generative AI. ACCESS, 13, 95085-95093. (www)

  @article{ , abstract = {As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where specific stimuli are crafted based on the victim model’s general decision logic, and inductive illusion, where the victim model’s general decision logic is shaped by specific stimuli. The former exploits the model’s decision boundaries to create a stimulus that, when applied, interferes with its decision-making process. The latter reinforces a conditioned reflex in the model, embedding a backdoor during its learning phase that, when triggered by a stimulus, causes aberrant behaviors. The multifaceted nature of adversarial illusions calls for a unified defence framework, addressing vulnerabilities across various forms of attack. In this study, we propose a disillusion paradigm based on the concept of an imitation game. At the heart of the imitation game lies a multimodal generative agent, steered by chain-of-thought reasoning, which observes, internalizes and reconstructs the semantic essence of a sample, liberated from the classic pursuit of reversing the sample to its original state. As a proof of concept, we conduct experimental simulations using a multimodal generative dialogue agent and evaluates the methodology under a variety of attack scenarios. Experimental results demonstrate that the proposed framework consistently neutralizes both deductive and inductive adversarial illusions across diverse white-box and black-box attack scenarios.}, archive = {J_ACCESS}, author = {Ching-Chun Chang and Fan-Yun Chen and Shih-Hong Gu and Kai Gao and Hanrui Wang and Isao Echizen}, doi = {10.1109/ACCESS.2025.3574016}, journal = {IEEE Access}, pages = {95085-95093}, shortjournal = {IEEE Access}, title = {Imitation game for adversarial disillusion with chain-of-thought reasoning in generative AI}, volume = {13}, year = {2025}, }
• (2025). Database systems in the big data era: Architectures, performance, and open challenges. ACCESS, 13, 95068-95084. (www)

  @article{ , abstract = {The advent of Big Data has fundamentally transformed the database management systems (DBMS) field, necessitating the development of innovative paradigms, architectures, and technologies to address unprecedented challenges. Despite their historical dominance, traditional systems falter under the high velocity, massive volume, and diverse variety of Big Data. These limitations have catalyzed the emergence of alternative solutions such as not only structured query language (NoSQL), NewSQL, and cloud-native databases, each offering unique approaches to scalability, flexibility, and performance optimization. This survey provides a comprehensive and systematic overview of the evolving database ecosystem in the Big Data era. It delves into the historical progression from traditional relational DBMS (RDBMS) to modern paradigms, emphasizing these transformations’ motivations, trade-offs, and innovations. The classification of databases based on data models, deployment strategies, scalability mechanisms, and consistency models is explored in depth, providing a structured framework for understanding their diverse capabilities. Furthermore, critical performance characteristics, including throughput, latency, fault tolerance, and cost efficiency, are analyzed to assess their effectiveness in real-world applications. By highlighting persistent challenges such as data heterogeneity, security, and interoperability, this survey outlines key research directions, fostering a holistic understanding of the domain and inspiring future advancements in database technologies.}, archive = {J_ACCESS}, author = {Elias Dritsas and Maria Trigka}, doi = {10.1109/ACCESS.2025.3572059}, journal = {IEEE Access}, pages = {95068-95084}, shortjournal = {IEEE Access}, title = {Database systems in the big data era: Architectures, performance, and open challenges}, volume = {13}, year = {2025}, }
• (2025). Trains detection using LTE downlink reference signals. ACCESS, 13, 95054-95067. (www)

  @article{ , abstract = {In this article a method for detecting the presence of trains on tracks by analyzing the changes in the cell-specific reference symbols parameters in the downlink signal from nearby LTE base station operating in transmit diversity mode is proposed. The signal processing method to obtain waveforms showing correlation with the presence of trains is described with experimental results on trains detection using real LTE downlink signals from commercial networks. The main outcome of the investigation is that even in case of signal reception using receiver with limited long term stability, which doesn’t allow to track absolute phase values of reference symbols, difference between received phases of symbols transmitted using different antenna ports in base station may be highly correlated with presence of trains on tracks. Proposed reference symbols analysis may be alternative to detection of objects in the environment using bistatic passive radar principle, which couldn’t be used in proposed measurement setup due to low performance of bistatic radar detection using single antenna receiver.}, archive = {J_ACCESS}, author = {Olga Blaszkiewicz and Krzysztof K. Cwalina and Alicja Olejniczak and Piotr Rajchowski and Jaroslaw Sadowski}, doi = {10.1109/ACCESS.2025.3574854}, journal = {IEEE Access}, pages = {95054-95067}, shortjournal = {IEEE Access}, title = {Trains detection using LTE downlink reference signals}, volume = {13}, year = {2025}, }
• (2025). H221E; switched controller design for a class of uncertain linear systems using derivative feedback. ACCESS, 13, 95034-95053. (www)

  @article{ , abstract = {This manuscript proposes ${\mathcal {H}}_{\infty }$ switched controller designs for a class of linear systems with time-invariant uncertainties, where only the derivative of the state vector is used for feedback. We propose two strategies using auxiliary dynamics, which have accessible state vectors for feedback, to control the original plant. The first strategy uses a static feedback-gain matrix, while the second uses a set of feedback-gain matrices, selecting one of them through a switching law each time for feedback. Both designs ensure stability, decay rate constraints, and an ${\mathcal {H}}_{\infty }$ guaranteed cost for the original plant. Theoretical analyses and numerical examples demonstrate that the switched control law is superior to a constant feedback gain and a derivative feedback controller previously proposed in the literature for uncertain linear systems. We provide simulation examples that show the effectiveness of the proposed approach. In these examples, the proposed approach presents better results than another method in the literature when the external disturbance is zero ( $w(t)=0$ ). We also report a practical implementation of the designed switched controller in a Quanser® active suspension system. This case study, where the system is subject to uncertain parameters and actuator faults, highlights the method’s effectiveness.}, archive = {J_ACCESS}, author = {Leidy D. Wolmuth and Uiliam Nelson L. T. Alves and André Krindges and Rodrigo Cardim and Edvaldo Assunção and Marcelo C. M. Teixeira}, doi = {10.1109/ACCESS.2025.3574986}, journal = {IEEE Access}, pages = {95034-95053}, shortjournal = {IEEE Access}, title = {H221E; switched controller design for a class of uncertain linear systems using derivative feedback}, volume = {13}, year = {2025}, }
• (2025). A framework integrating federated learning and fog computing based on client sampling and dynamic thresholding techniques. ACCESS, 13, 95019-95033. (www)

  @article{ , abstract = {The exponential growth in the number of Internet of Things (IoT) devices and the vast quantity of data they generate present a significant challenge to the efficacy of traditional centralized training models. Federated Learning (FL) is a machine learning framework that effectively addresses this issue and other concerns about data privacy. Furthermore, fog computing represents a robust distributed computing methodology with the potential to bolster and propel the advancement of FL. An integrated distributed architecture combining FL and fog computing (FC) has the potential to overcome the limitations of traditional centralized architectures, offering a promising solution for the future. One of the objectives of implementing this novel architectural framework is to alleviate the burden on communication links within the core network by training a model on distributed training data across many clients. Various techniques and frameworks have been developed and implemented, including approaches to model compression and those addressing data and device heterogeneity. These have demonstrated effectiveness in specific contexts. In this paper, we introduce a novel gradient-driven client-sampling framework that tightly couples Federated Learning with Fog Computing. By dynamically adjusting per-round thresholds based on local gradient change rates, our method selects only the most informative clients and leverages fog nodes for partial aggregation, thereby minimizing redundant transmissions, accelerating convergence under heterogeneous data, and offloading the central server. Extensive simulations on MNIST and CIFAR-10 demonstrate that our approach reduces cumulative communication by 39% and 31%, respectively, without sacrificing convergence speed or final accuracy.}, archive = {J_ACCESS}, author = {Dang van Thang and Artem Volkov and Ammar Muthanna and Ibrahim A. Elgendy and Reem Alkanhel and Dushantha Nalin K. Jayakody and Andrey Koucheryavy}, doi = {10.1109/ACCESS.2025.3571979}, journal = {IEEE Access}, pages = {95019-95033}, shortjournal = {IEEE Access}, title = {A framework integrating federated learning and fog computing based on client sampling and dynamic thresholding techniques}, volume = {13}, year = {2025}, }
• (2025). A systematic survey of sparse clustering. ACCESS, 13, 94982-95018. (www)

  @article{ , abstract = {Handling a vast amount of high-dimensional data has always been challenging. The advancement of computer technology has led to an exponential growth of accumulated information where storing and processing are to be carefully handled since not all information gathered is useful. Feature selection and feature reduction algorithms have been proposed to process the data. In this paper, we review sparse clustering approaches that aim to cluster data sets while selecting and removing redundant and irrelevant features as well as noisy points and outliers. This paper surveys existing sparse clustering algorithms and explores their effectiveness in the analysis of high-dimensional data. The exploration and analysis of these sparse clustering approaches outperform the existing conventional clustering algorithms when faced with large and high-dimensional data. We also investigate the use of regularization terms in the sparse clustering algorithms. In this survey, we consider an extensive search on published papers as well as textbooks and discuss important results reported in the literature. We further explore and compare their strengths, limitations, adaptability, interpretability, complexity, and usability in handling high-dimensional data, and more research directions and topics on sparse clustering are also analyzed.}, archive = {J_ACCESS}, author = {Josephine Bernadette M. Benjamin and Miin-Shen Yang}, doi = {10.1109/ACCESS.2025.3574066}, journal = {IEEE Access}, pages = {94982-95018}, shortjournal = {IEEE Access}, title = {A systematic survey of sparse clustering}, volume = {13}, year = {2025}, }
• (2025). Diversity-driven multi-head attention for enhanced trajectory prediction. ACCESS, 13, 94970-94981. (www)

  @article{ , abstract = {Trajectory prediction is a core task in areas such as autonomous driving and robot navigation, where the goal is to predict future motion trajectories based on historical trajectories and environmental data. This task involves multiple complex challenges: first, trajectory prediction needs to deal with significant pattern diversity, such as multiple interacting behaviors and possible paths in dynamic environments; second, the task requires finding a balance between long-term historical information and local dynamic changes in order to accurately capture the target’s motion trends; furthermore, the generalization ability and robustness of the model are especially critical in the face of unseen data; and finally, trajectory prediction needs to satisfy efficient reasoning requirements in realtime, further increasing the complexity of the task. To address these challenges, we propose an innovative ensemble learning framework named Diversity-Driven Multi-Head Attention (DD-MHA), which leverages the diversity of independently trained attention modules to significantly enhance the robustness and accuracy of trajectory predictions. Integrated learning improves model diversity and robustness through the synergy of multiple sub-models, which is highly compatible with the requirements of the trajectory prediction task. In particular, the Multi-Head Attention (MHA) mechanism, as a core component in current state-of-the-art models, shows great potential in capturing complex relationships and diverse patterns in data. The approach creates multiple subsets of training data by random sampling and trains multiple independent MHA modules separately. By aggregating outputs from these diverse MHA modules during inference, our method effectively captures richer trajectory patterns, resulting in significantly improved prediction performance and generalization. This scheme effectively mitigates the problems of pattern diversity and long-term dependency in trajectory prediction, and improves generalization performance. While it introduces additional computational cost, it achieves better accuracy and robustness, making it suitable for scenarios where predictive performance is prioritized over inference speed.}, archive = {J_ACCESS}, author = {Zhen Zhang}, doi = {10.1109/ACCESS.2025.3574749}, journal = {IEEE Access}, pages = {94970-94981}, shortjournal = {IEEE Access}, title = {Diversity-driven multi-head attention for enhanced trajectory prediction}, volume = {13}, year = {2025}, }
• (2025). Impact of multiple CPU cores to the forensic insights acquisition from mobile devices using electromagnetic side-channel analysis. ACCESS, 13, 94953-94969. (www)

  @article{ , abstract = {Modern processors tend to incorporate multiple CPU cores. These multiple CPU cores, running at the same or different clock frequencies, enable the effective distribution of workload and efficiency in energy consumption. Although Electromagnetic Side-Channel Analysis (EM-SCA) has been shown to be an effective and non-invasive method to acquire forensic insights from smartphones and Internet of Things (IoT) devices, the presence of multiple CPU cores has the potential to cause disruptions in this process. This research focuses on analysing the impact of multi-core CPU emissions — specifically the iPhone 13 and iPhone 14 Pro — on the EM-SCA-based forensic insights acquisition procedure. To achieve this, we developed a novel multi-core EM-SCA model specifically for iPhone models by integrating electromagnetic (EM) radiation traces captured from different core clusters of a single device. The developed multi-core model is then subjected to three transfer learning processes: inductive learning, feature extraction, and fine-tuning. The model is tested using individual single-core datasets collected at specific system-clock frequencies of the device. The findings of both smartphones indicate that inductive transfer learning consistently yields poor results, ranging between 5% and 20%, regardless of the core cluster. Although feature extraction provides moderate accuracy for certain datasets — around 50% to 70% for the iPhone 13 and 20% to 92% for the iPhone 14 Pro — it is the fine-tuning process that proves to be the most effective. Fine-tuning supports a wide range of datasets across different system-clock frequencies, achieving classification accuracy as high as 99%. This highlights fine-tuning as the most reliable transfer learning technique for multi-core forensic investigations. We also tested for catastrophic forgetting to evaluate the robustness of the multi-core model when using single-core datasets from the same devices. The results demonstrate that the accuracy of the multi-core model remains unchanged, even after the transfer learning process across various datasets.}, archive = {J_ACCESS}, author = {Lojenaa Navanesan and Kasun de Zoysa and Asanka P. Sayakkara}, doi = {10.1109/ACCESS.2025.3574340}, journal = {IEEE Access}, pages = {94953-94969}, shortjournal = {IEEE Access}, title = {Impact of multiple CPU cores to the forensic insights acquisition from mobile devices using electromagnetic side-channel analysis}, volume = {13}, year = {2025}, }
• (2025). Tracking control of CSTRs based on improved OU noise and the TD3 algorithm. ACCESS, 13, 94944-94952. (www)

  @article{ , abstract = {Achieving satisfactory control accuracy in typical chemical processes is a challenging task. This is primarily due to their complex nonlinear dynamic characteristics. To address this challenge, this study proposes a novel deep reinforcement learning (DRL) approach that integrates an improved Ornstein-Uhlenbeck (IOU) noise into the twin delayed deep deterministic policy gradient (TD3) algorithm. This method is applied to the tracking control of continuous stirred tank reactors (CSTRs). Initially, a mechanistic model of the CSTRs system is established to simulate its dynamic environment, enabling interaction between the TD3 agent and the system. To enhance exploration capabilities and convergence speed, fractional-order characteristics and a reward feedback mechanism are introduced into the OU noise, dynamically adjusting noise intensity to improve adaptability to complex states and optimize the exploration strategy of the TD3 algorithm. Furthermore, a well-designed reward function and optimized hyperparameters enable the agent to efficiently learn the optimal control policy, achieving high-precision tracking control of the CSTRs system. Simulation results demonstrate that the proposed TD3 algorithm outperforms conventional control methods, such as PID control and nonlinear model predictive control, as well as other DRL algorithms, including SAC and PPO, in terms of control accuracy and convergence speed.}, archive = {J_ACCESS}, author = {Hongyan Shi and Xiaofei Wu and Guogang Wang}, doi = {10.1109/ACCESS.2025.3574730}, journal = {IEEE Access}, pages = {94944-94952}, shortjournal = {IEEE Access}, title = {Tracking control of CSTRs based on improved OU noise and the TD3 algorithm}, volume = {13}, year = {2025}, }
• (2025). Offshore horizons: HVDC wind farms-exploring techno-economic dimensions. ACCESS, 13, 94910-94943. (www)

  @article{ , abstract = {High Voltage Direct Current (HVDC) technology is a cornerstone of efficient Offshore Wind Farm (OWF) power transmission. This review examines the integration of HVDC technology in OWFs, considering collection and transmission aspects. The analysis is structured around four key dimensions: economic considerations, connection topologies, converter designs, and technical modeling. It begins with an in-depth economic analysis, evaluating cost-effectiveness, reliability, and market dynamics, focusing on investment, operational costs, and lifecycle expenses. Building on this foundation, the review explores various collection and transmission architectures, highlighting their technical and economical trade-offs, and evaluates power converter designs for efficiency, reliability, and offshore adaptability. Finally, advanced modeling and simulation techniques are reviewed to optimize system performance, enhance reliability, and balance computational efficiency. Throughout each of the four sections, economic and technical constraints are considered together. This helps to improve understanding of how systems can be designed in a way that meets the constraints of both fields and to enhance the feasibility on both dimensions. These insights provide a holistic framework for sustainable and economically viable Offshore Wind Energy (OWE) integration, offering practical guidance for developers and grid operators involved in the planning, deployment, and operation of HVDC-enabled offshore wind systems.}, archive = {J_ACCESS}, author = {Deepi Singh and Dana Golden and Gaurav Bhansali and Ali Anwar and Shreepooja Singh and Fang Luo and Yiyi Zhou}, doi = {10.1109/ACCESS.2025.3567624}, journal = {IEEE Access}, pages = {94910-94943}, shortjournal = {IEEE Access}, title = {Offshore horizons: HVDC wind farms-exploring techno-economic dimensions}, volume = {13}, year = {2025}, }
• (2025). Generative AI meets virtual reality: A comprehensive survey on applications, challenges, and future direction. ACCESS, 13, 94893-94909. (www)

  @article{ , abstract = {The integration of generative artificial intelligence (AI) with virtual reality (VR) is reshaping how immersive environments are designed, personalized, and experienced. Unlike traditional VR systems that rely on static and pre-scripted content, Generative VR leverages AI-driven content generation, multimodal interaction, and contextual adaptation to create dynamic virtual spaces. This paper presents a comprehensive survey of the core components, applications, and challenges associated with Generative VR. It explores its transformative impact across education, healthcare, industrial training, entertainment, and emerging fields such as environmental conservation and virtual tourism. While Generative VR enhances user engagement and realism, it also introduces critical challenges, including computational scalability, ethical concerns, AI explainability, and real-time performance constraints. The paper further identifies key research gaps and future directions, such as AI-driven multi-user collaboration, cognitive load management, and cross-domain interoperability. By addressing these challenges and advancing the integration of AI with VR, Generative VR has the potential to reshape human-computer interaction, unlocking new possibilities for creativity, accessibility, and intelligent virtual ecosystems.}, archive = {J_ACCESS}, author = {Fatema Rahimi and Abolghasem Sadeghi-Niaraki and Soo-Mi Choi}, doi = {10.1109/ACCESS.2025.3574779}, journal = {IEEE Access}, pages = {94893-94909}, shortjournal = {IEEE Access}, title = {Generative AI meets virtual reality: A comprehensive survey on applications, challenges, and future direction}, volume = {13}, year = {2025}, }
• (2025). High voltage gain DC-DC converter with wide range of soft switching and continues input current for renewable energy applications. ACCESS, 13, 94878-94892. (www)

  @article{ , abstract = {This paper presents a non-isolated high step-up DC-DC converter that achieves a high voltage gain through the application of coupled inductor and multiplier techniques. Also, in this topology continuous input current and a shared ground between input and output is obtained which enhances reliability of the proposed converter. Moreover, by utilizing ZVT technique, soft switching performance for both switches are achieved across the entire range of output power which reduces switching losses and improves efficiency significantly. Furthermore, the output diodes of this converter are turned off under ZCS condition, considerably reducing reverse-recovery losses. The paper includes an analysis of the proposed converter’s operation, a comparative study with existing high step-up converters and experimental results for 200 W laboratory prototype.}, archive = {J_ACCESS}, author = {Hamed Moradmand Jazi and Behrouz Mohammadzadeh and Ramin Rahimzadeh Khorasani and Pericle Zanchetta and Ehsan Adib and Guillermo Velasco-Quesada and Herminio Martínez-García}, doi = {10.1109/ACCESS.2025.3573936}, journal = {IEEE Access}, pages = {94878-94892}, shortjournal = {IEEE Access}, title = {High voltage gain DC-DC converter with wide range of soft switching and continues input current for renewable energy applications}, volume = {13}, year = {2025}, }
• (2025). Bringing machine learning classifiers into critical cyber-physical systems: A matter of design. ACCESS, 13, 94858-94877. (www)

  @article{ , abstract = {Machine Learning (ML) models are increasingly used by domain experts to tackle classification tasks, aiming for high predictive accuracy. However, classifiers are inherently prone to misclassifications, especially when they encounter unfamiliar, previously unseen or out-of-distribution input data. This creates significant challenges for their deployment in critical Cyber-Physical Systems (CPSs)—such as autonomous vehicles, industrial control systems, and medical devices—where misclassifications can lead to severe consequences for people, infrastructure, and the environment. This paper argues that ML classifiers intended for critical applications should not be designed nor evaluated in isolation. Instead, Critical System Classifiers (CSCs) primarily aim at reducing misclassifications by rejecting uncertain predictions and trigger mitigation strategies integrated into the encompassing CPS. We present a high-level CSC architecture that supports black-box classifier integration, preprocessing for unknown detection, post-hoc calibration, and cost-sensitive thresholding. We emphasize the need for cost-aware evaluation metrics that explicitly account for rejected predictions, enabling a more realistic assessment of classifier performance in critical systems. We validate our approach through experiments on tabular datasets related to failure prediction, intrusion detection, and error detection—common use cases for classifiers in CPSs. Key findings include: 1) cost-sensitive evaluation often leads to the selection of different classifiers than standard metrics suggest; 2) tree-based models outperform statistical ones in classification tasks; 3) calibration and rejection mechanisms provide a robust notion of confidence; and 4) combining multiple uncertainty-based rejection strategies achieves a favorable trade-off between high accuracy, low rejection rates, and cost. All experiments and implementations are publicly available via our CINNABAR GitHub repository. Overall, this study offers a system-level perspective and practical software architecture for safely deploying ML classifiers in critical CPS domains, paving the way toward more trustworthy and certifiable AI in real-world infrastructures.}, archive = {J_ACCESS}, author = {Burcu Sayin and Tommaso Zoppi and Nicolò Marchini and Fahad Ahmed Khokhar and Andrea Passerini}, doi = {10.1109/ACCESS.2025.3568501}, journal = {IEEE Access}, pages = {94858-94877}, shortjournal = {IEEE Access}, title = {Bringing machine learning classifiers into critical cyber-physical systems: A matter of design}, volume = {13}, year = {2025}, }
• (2025). Synchronous rectification control of CLLC resonant converter based on resonant capacitor voltage. ACCESS, 13, 94846-94857. (www)

  @article{ , abstract = {For resonant converters, synchronous rectification (SR) is an effective way to reduce switch conduction losses. In this paper, a synchronous rectification driving strategy based on resonant capacitor voltage is proposed for CLLC resonant converters. Unlike existing SR driving strategies, the proposed method requires neither use costly sensors nor complex theoretical calculations. The control principle of the proposed method at different operating frequencies is analyzed in detail, and a simple implementation control block diagram is established based on the proposed method. Finally, the effectiveness of the proposed SR driving strategy is verified by simulation and experimental results.}, archive = {J_ACCESS}, author = {Yapeng He and Chunguang Ren and Xinqi Li}, doi = {10.1109/ACCESS.2025.3574702}, journal = {IEEE Access}, pages = {94846-94857}, shortjournal = {IEEE Access}, title = {Synchronous rectification control of CLLC resonant converter based on resonant capacitor voltage}, volume = {13}, year = {2025}, }
• (2025). FedSVD: Asynchronous federated learning with stale weight vector decomposition. ACCESS, 13, 94834-94845. (www)

  @article{ , abstract = {Federated learning (FL) emerges as a collaborative learning framework that addresses the critical needs for privacy preservation and communication efficiency. In synchronous FL, each client waits for the global model, which is aggregated from the trained models of all participating clients. To alleviate the downtime associated with waiting for all clients to complete training, asynchronous FL enables independent aggregation of client models. In asynchronous FL, the global model is continuously updated during client training, leading to the inevitable issue of stale updates when clients return their models to the server. These outdated updates hinder the convergence of the global model during aggregation. To address this staleness problem, we propose FedSVD, a method that leverages vector decomposition of stale weights. FedSVD evaluates each client’s trained weight in terms of their staleness relative to the current global model and decomposes the weights into two vectors: one pointing in the direction of the previous global model update, and another orthogonal to it. The global model is then updated using only the orthogonal vector, as the parallel vector is considered already accounted for in the current global model. Experimental results show that FedSVD outperforms existing baseline methods on benchmark datasets under various client conditions and data distributions.}, archive = {J_ACCESS}, author = {Giwon Sur and Hyejin Kim and Seunghyun Yoon and Hyuk Lim}, doi = {10.1109/ACCESS.2025.3573806}, journal = {IEEE Access}, pages = {94834-94845}, shortjournal = {IEEE Access}, title = {FedSVD: Asynchronous federated learning with stale weight vector decomposition}, volume = {13}, year = {2025}, }
• (2025). Multi-source reinforced selective domain adaptation for cross-subject and cross-session EEG-based emotion recognition. ACCESS, 13, 94822-94833. (www)

  @article{ , abstract = {In recent years, transfer learning methods have found widespread applications in emotion recognition, especially in domains with limited labeled data. The significant distribution disparity between source and target domains in transfer learning often leads to detrimental negative transfer phenomena. To mitigate this issue, the judicious selection of source domain data has proven to be an effective strategy. However, traditional training data selection methods typically require predefined thresholds, which are not easy to set, and the model is trained separately from the training data selection process. In an effort to address this gap, we introduce the Multi-Source Reinforced Selective Domain Adaptation (MSRSDA) model. The MSRSDA model comprises three components: a data augmentation module, a data selector based on the actor-critic framework, and a domain adaptation module. The data augmentation module generates new samples for the data selector to consider. The data selector, acting as an actor, performs actions to select high-quality source domain data for optimization of the domain adaptation model. Additionally, we propose an innovative domain adaptation model that provides rewards to update the data selector. To the best of our knowledge, our work represents an initial attempt to apply reinforcement learning to EEG-based data selection. Extensive experiments on both the public SEED dataset and our in-house dataset demonstrate that the MSRSDA model achieves competitive performance for emotion recognition tasks.}, archive = {J_ACCESS}, author = {Xin Deng and Xinyi Hong and LiJiao Ai and Xingchen Li and Chenhui Li}, doi = {10.1109/ACCESS.2025.3574515}, journal = {IEEE Access}, pages = {94822-94833}, shortjournal = {IEEE Access}, title = {Multi-source reinforced selective domain adaptation for cross-subject and cross-session EEG-based emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Exact pull-in range and the hidden boundary of global stability for PLL with lead-lag filter. ACCESS, 13, 94785-94821. (www)

  @article{ , abstract = {Phase-locked loops (PLLs) are nonlinear automatic control circuits widely used in grid synchronization, gyroscopes, wireless communications, and other applications. One of the main tasks of a PLL is to synchronize an internal oscillator, both in frequency and phase, with a reference signal. The pull-in range concept describes the range of reference frequencies such that synchronization occurs for an arbitrary initial state, making its accurate determination essential for the reliable design and operation of PLLs. This work analyzes a classical PLL model with a lead-lag loop filter and a continuous piecewise-linear phase detector characteristic. An efficient approach for global nonlinear analysis is developed. It enables the rigorous closed-form derivation of the exact pull-in range formula and its asymptotics. The derived analytical formulas rectify inaccuracies from previous works, provide counterexamples to Kapranov’s conjecture, and significantly refine existing engineering approximations. Within the framework of the theory of hidden oscillations, this approach provides a complete solution to the problem of determining the boundary of global stability and revealing its hidden parts corresponding to the nonlocal birth of hidden oscillations. To support practical implementation, bifurcation diagrams for the pull-in range calculation are presented, accompanied by code for their construction.}, archive = {J_ACCESS}, author = {Nikolay V. Kuznetsov and Mikhail Y. Lobachev}, doi = {10.1109/ACCESS.2025.3573693}, journal = {IEEE Access}, pages = {94785-94821}, shortjournal = {IEEE Access}, title = {Exact pull-in range and the hidden boundary of global stability for PLL with lead-lag filter}, volume = {13}, year = {2025}, }
• (2025). A low-complexity MIMO-OFDM transmission scheme for optical wireless communications. ACCESS, 13, 94777-94784. (www)

  @article{ , abstract = {A novel low-complexity multiple-input multiple-output orthogonal frequency division multiplexing transmission scheme for optical wireless communications (OWC), referred to as LC-MIMO-OFDM, is introduced in this paper. LC-MIMO-OFDM eliminates the need for inverse fast Fourier transform or inverse fast Hartley transform operations at the transmitter, resulting in the reduced computational complexity in generating unipolar transmission signals. In LC-MIMO-OFDM, the modulated symbols first undergo a phase rotation operation, followed by separate extraction of their real and imaginary components. These components are then converted into positive signals by adding a direct current bias. Finally, LC-MIMO-OFDM uses two light-emitting diodes to transmit positive signals via the OWC channel. Comparative analysis shows that the LC-MIMO-OFDM offers better peak-to-average power ratio reduction than do existing unipolar transmission schemes for MIMO-OFDM OWC systems while maintaining lower complexity. Additionally, the proposed scheme outperforms well-known unipolar MIMO-OFDM OWC systems operating at the same spectral efficiency in terms of the bit error rate.}, archive = {J_ACCESS}, author = {Wei-Wen Hu}, doi = {10.1109/ACCESS.2025.3574926}, journal = {IEEE Access}, pages = {94777-94784}, shortjournal = {IEEE Access}, title = {A low-complexity MIMO-OFDM transmission scheme for optical wireless communications}, volume = {13}, year = {2025}, }
• (2025). Probabilistic deep learning for highly multivariate spatio-temporal log-gaussian cox processes. ACCESS, 13, 94761-94776. (www)

  @article{ , abstract = {Multivariate spatio-temporal point patterns have become increasingly common due to the advancement of technology for massive data collection. Parameter estimation is vital for understanding the distributional patterns within such data. However, performing estimation using a parametric approach on multivariate spatio-temporal point pattern data is challenging due to the curse of dimensionality, making parametric estimation increasingly difficult as data dimensionality grows. Deep learning offers a promising alternative due to its ability to model complex nonlinear patterns in large datasets. Despite limited applications in multivariate point pattern analysis, this study aims to introduce deep learning as a tool for parameter estimation of the multivariate spatio-temporal log-Gaussian Cox Process (LGCP) model. We employ the concept of probabilistic deep learning, ensuring that each estimated parameter follows a certain distribution that aligns with its assumption. We assess our model performance via a simulation study, and analyze the highly multivariate spatio-temporal point pattern data of Barro Colorado Island (BCI). Both the simulation study and application demonstrate our model effectiveness over previous approaches to handle highly multivariate point pattern data.}, archive = {J_ACCESS}, author = {Achmad Choiruddin and Ekky Rino Fajar Sakti and Tintrim Dwi Ary Widhianingsih and Jorge Mateu and Kartika Fithriasari}, doi = {10.1109/ACCESS.2025.3570476}, journal = {IEEE Access}, pages = {94761-94776}, shortjournal = {IEEE Access}, title = {Probabilistic deep learning for highly multivariate spatio-temporal log-gaussian cox processes}, volume = {13}, year = {2025}, }
• (2025). Optimal sizing of a hybrid renewable energy system for auxiliary services in substations through genetic algorithm and variable neighborhood search. ACCESS, 13, 94740-94760. (www)

  @article{ , abstract = {Auxiliary services in substations are fundamental systems for the operation and coordination of power electrical systems. Because of their importance, backup systems are utilized to support critical loads during main power supply failures. Hybrid renewable backup systems, comprising renewable generation and batteries, offer a more sustainable alternative compared to the current use of fossil fuel generators in substations. However, sizing these systems can be a complex task due to the uncertainty associated with interruptions and renewable source production. This paper proposes an optimization model to size hybrid renewable energy systems for auxiliary services in substations. Uncertainties related to wind and photovoltaic generation, as well as power outages start time and durations, are addressed through Monte Carlo simulations. Furthermore, a hybrid algorithm, based on Genetic Algorithm (GA) and Variable Neighborhood Search (VNS), is introduced. The proposed approach considers the costs associated with diesel generator usage and instances of non-supply. A comparison of the metaheuristics GA, VNS, and the new hybrid algorithm is presented, emphasizing the advantages of the hybrid approach. The algorithms are validated by comparing them with results from the literature, demonstrating their ability to achieve solutions with high accuracy and low computational time. The results demonstrate that the proposed sizing approach successfully minimizes costs while ensuring reliability, highlighting its potential to optimize hybrid renewable backup systems for critical substation loads.}, archive = {J_ACCESS}, author = {Matheus Holzbach and John Fredy Franco Baquero and Mariana Resener}, doi = {10.1109/ACCESS.2025.3574106}, journal = {IEEE Access}, pages = {94740-94760}, shortjournal = {IEEE Access}, title = {Optimal sizing of a hybrid renewable energy system for auxiliary services in substations through genetic algorithm and variable neighborhood search}, volume = {13}, year = {2025}, }
• (2025). Analysis of electric field and temperature distributions of non-uniformly contaminated silicone composite insulators using deep learning. ACCESS, 13, 94721-94739. (www)

  @article{ , abstract = {This study examines the insulation performance of a silicone composite insulator under various contamination conditions. The non-uniform pollution of a silicone composite insulator, operating in a 34.5 kV, 50 Hz power grid under atmospheric conditions, was analyzed using COMSOL Multiphysics. Considering that the conductivity of the contamination layer on the insulator surface affects leakage currents, surface temperature, and electric field distribution, five critical regions of the insulator surface, significant in terms of contamination, were identified, and distinct conductivity levels were assigned to each. Various combinations of these conductivity levels and voltage values were used to calculate the electric field (kV/cm) in the frequency domain and temperature (°C) in the time domain. Data sets were generated for all possible combinations at 29 critical points along the leakage distance. The aim was to identify critical conditions for the electric field and temperature, thus providing a closer approximation to actual operating conditions. Using the obtained data, a Deep Neural Network (DNN)-based model was developed to predict the insulator’s response under varying contamination, current density, and voltage conditions. The model demonstrated consistent predictions for electric field and temperature values under nonuniform pollution conditions. The predictive performance of the proposed model was validated through comparative analysis with established machine learning techniques, including Support Vector Machine (SVM) and Random Forest (RF). The model demonstrated consistent predictions for electric field and temperature values under nonuniform pollution conditions.}, archive = {J_ACCESS}, author = {İrem Görgöz and Mehmet Cebeci}, doi = {10.1109/ACCESS.2025.3574493}, journal = {IEEE Access}, pages = {94721-94739}, shortjournal = {IEEE Access}, title = {Analysis of electric field and temperature distributions of non-uniformly contaminated silicone composite insulators using deep learning}, volume = {13}, year = {2025}, }
• (2025). Accurate and efficient LiDAR SLAM by learning unified neural descriptors. ACCESS, 13, 94705-94720. (www)

  @article{ , abstract = {Point clouds generated by LiDAR sensors have been widely exploited in Simultaneous Localization and Mapping (SLAM). However, existing LiDAR SLAM approaches based on hand-crafted features easily suffer from being either overly sparse or dense, causing low-fidelity map construction or severe scalability problems. Recent deep learning-based features under the existing SLAM framework can be poorly affected by error accumulation problems over time. To address these issues, we propose a unified architecture named DeepPointMap++, which enables both memory-efficient map representation and accurate multi-scale localization. We design a deep encoder to extract highly representative unified neural descriptors from the input point clouds and propose a novel deep decoder to find their correspondences. It also incorporates a foreground-background classifier during feature extraction, effectively separating dynamic foreground objects from the static background to boost the final localization and mapping effects. In the experiment, DeepPointMap++ outperformed other state-of-the-art methods on multiple auto-driving benchmarks. We also showcase the versatility of our framework by extending it to more challenging multi-agent collaborative SLAM.}, archive = {J_ACCESS}, author = {Baihe Feng and Ying Zhang}, doi = {10.1109/ACCESS.2025.3566472}, journal = {IEEE Access}, pages = {94705-94720}, shortjournal = {IEEE Access}, title = {Accurate and efficient LiDAR SLAM by learning unified neural descriptors}, volume = {13}, year = {2025}, }
• (2025). An age- and state-dependent competing failure process model with wiener process and different failure threshold case. ACCESS, 13, 94696-94704. (www)

  @article{ , abstract = {This study aims to develop some novel dependent competing failure processes (DCFP) model subject to the degradation failure process and shock failure process. Those two failure processes are dependent due to three forms of correlation: first, each shock load results in additional sudden degradation; second, there exist bidirectional interactions between natural degradation and random shock; and third, the present state exerts an influence on the failure threshold. Firstly, a DCFP model is obtained, where the soft failure is described by a Wiener process, and hard failure is caused by random shock. Furthermore, when the bidirectional effects are considered, some novel DCFP models are established. At last, some new system reliability models are obtained under the different random failure threshold situations. A real numerical example about MEMS oscillator is conducted to explain the given model and procedure. Numerical results demonstrate that random shocks, random failure thresholds, and bidirectional effects all have significantly impact on the system reliability. Furthermore, some sensitivity analysis results are provided.}, archive = {J_ACCESS}, author = {Chunping Li and Huibing Hao}, doi = {10.1109/ACCESS.2025.3572931}, journal = {IEEE Access}, pages = {94696-94704}, shortjournal = {IEEE Access}, title = {An age- and state-dependent competing failure process model with wiener process and different failure threshold case}, volume = {13}, year = {2025}, }
• (2025). A novel biclustering algorithm based on differential sparsity constraints and dynamic graph regularization for cancer gene expression data. ACCESS, 13, 94681-94695. (www)

  @article{ , abstract = {Biclustering is an effective method for identifying biologically significant gene modules, which aims at extracting gene modules enriched with more information and achieving accurate cancer subtype classification. However, most biclustering algorithms based on sparse singular value decomposition overlook the differences in sparsity between gene and sample dimensions in real cancer gene expression data. Furthermore, biclustering algorithms that incorporate graph-regularized penalties typically fail to adequately address the dynamic update of graph information during the layer-by-layer extraction of biclusters. To address these issues, this paper proposes a novel biclustering algorithm based on differential sparsity constraints and dynamic graph regularization (BCDD). On one hand, considering that the cancer gene expression data contains numerous redundant genes unrelated to the disease, while all samples belong to a specific cancer subtype or come from healthy subjects, the proposed algorithm imposes $l_{\mathrm {1/2}}$ -norm and $l_{1}$ -norm constraints on gene and sample dimensions, respectively, to better capture the differences in sparsity between these two dimensions. On the other hand, to ensure that the graph adjacency matrix can be synchronously updated with expression data during the iterative solution process, a dynamic graph updating strategy based on the change of singular value is designed. This strategy can effectively avoid the interference of graph information corresponding to previously identified biclusters in the subsequent analysis. Experimental results from multiple cancer gene expression datasets demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in terms of biclustering performance.}, archive = {J_ACCESS}, author = {Dan Li and Peicong Song and Jia Wang}, doi = {10.1109/ACCESS.2025.3570818}, journal = {IEEE Access}, pages = {94681-94695}, shortjournal = {IEEE Access}, title = {A novel biclustering algorithm based on differential sparsity constraints and dynamic graph regularization for cancer gene expression data}, volume = {13}, year = {2025}, }
• (2025). An adaptive neuro-fuzzy controller to enhance power sharing in distributed energy resources applications. ACCESS, 13, 94667-94680. (www)

  @article{ , abstract = {In inverter-interfaced microgrids, droop control techniques are essential for regulating active and reactive power exchange. However, their performance is compromised by the varying impedance of the feeder and the slow response to dynamic load changes, leading to power-sharing inaccuracies. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based virtual impedance controller to address this issue and enhance active and reactive power sharing. The proposed controller dynamically adjusts a virtual voltage to compensate for impedance mismatches, modifying the reference voltage of the inverter. This enables precise power tracking with minimal deviation from the defined reference values and a faster response under transient conditions, including startup and external disturbances. The ANFIS framework integrates fuzzy logic and neural networks, eliminating the limitations of manual and separate tuning in conventional controllers and improving performance in nonlinear systems. The controller’s performance is validated on an IEEE 39-bus test system under various scenarios, including charging, discharging, and transient disturbances. It is tested with three battery sizes (1 MW, 96 kW, and 75 kW) under the same controller setup to assess scalability. Training with per-unit data ensures scalability across different battery capacities and distributed generators. The results are compared to traditional methods to demonstrate the controller’s superior effectiveness.}, archive = {J_ACCESS}, author = {Seyedmohammad Hasheminasab and Mohamad Alzayed and Hicham Chaoui}, doi = {10.1109/ACCESS.2025.3575039}, journal = {IEEE Access}, pages = {94667-94680}, shortjournal = {IEEE Access}, title = {An adaptive neuro-fuzzy controller to enhance power sharing in distributed energy resources applications}, volume = {13}, year = {2025}, }
• (2025). Distributed control algorithms for nonholonomic mobile robots with time-varying position constraints and event-triggered communication. ACCESS, 13, 94658-94666. (www)

  @article{ , abstract = {In this paper, the consensus problem of multiple nonholonomic mobile robots is addressed under event-triggered communication. It is assumed that the position of each robot is subject to generic time-varying constraints, and that the communication graph contains a directed spanning tree. A novel distributed algorithm that employs coordinate transformation with transverse functions and dynamic systems to generate reference signals is presented. Rigorous analysis, based on appropriately constructed Lyapunov functions, demonstrates that all signals in the closed-loop system remain bounded and that the consensus error can be regulated within a neighborhood of the origin. Moreover, we prove that the time-varying position constraints are consistently satisfied. The existence of a positive minimum inter-event time is also established for preventing Zeno behavior. The efficacy of the proposed approach is validated through comprehensive simulation studies involving five mobile robots.}, archive = {J_ACCESS}, author = {Zhihua Zhang and Chaoli Wang and Xuan Cai}, doi = {10.1109/ACCESS.2025.3574798}, journal = {IEEE Access}, pages = {94658-94666}, shortjournal = {IEEE Access}, title = {Distributed control algorithms for nonholonomic mobile robots with time-varying position constraints and event-triggered communication}, volume = {13}, year = {2025}, }
• (2025). A hankelization-based neural network-assisted signal classification in integrated sensing and communication systems. ACCESS, 13, 94648-94657. (www)

  @article{ , abstract = {In this paper, we introduce a neural network (NN)-based framework aimed at classifying sensing and communication signals at base stations, improving the efficiency of integrated sensing and communication (ISAC) systems in a bistatic configuration. The framework leverages a key mathematical insight: the Hankelized matrix formed from an equidistantly sampled signal of sparsely superimposed radio waves exhibits a low-rank property, whereas a frequency-modulated signal lacks this characteristic. It ensures that, even in practical environments, the Hankelized matrix of a sensing or communication channel statistically retains the relevant information. Hence, we use the singular values of the Hankelized matrix as the input to the neural NN, while the output is a one-hot encoded vector indicating whether the received signal is intended for sensing or communication. We investigate three scenarios where the communication and sensing signals either use the same or different waveforms in terms of the detection performance of the communication signals. The results demonstrate that the proposed method outperforms existing approaches in classification performance across all scenarios, regardless of whether the communication and sensing signals utilize the same waveform or not. The framework achieves a detection rate of over 95% even at an SNR of 0 dB. Notably, the network performs well in terms of a small number of pilot symbols, a small number of training dataset, and dynamic environments.}, archive = {J_ACCESS}, author = {Linyi Zhang and Mustafa Ozger and Woong-Hee Lee}, doi = {10.1109/ACCESS.2025.3574848}, journal = {IEEE Access}, pages = {94648-94657}, shortjournal = {IEEE Access}, title = {A hankelization-based neural network-assisted signal classification in integrated sensing and communication systems}, volume = {13}, year = {2025}, }
• (2025). Battery system fault detection: A data-driven aggregation and augmentation strategy. ACCESS, 13, 94632-94647. (www)

  @article{ , abstract = {In applying machine learning to battery system fault detection, current methods encounter some challenges. Inadequate extraction of discriminative features and data imbalance notably hinder the accuracy and robustness of detection. This study proposes a novel data-driven approach that synergistically combines data aggregation and feature augmentation strategies. The methodology first implements data aggregation to address data scarcity by effectively expanding fault sample representation. Subsequently, an advanced feature augmentation process is employed to enhance feature separability through multi-dimensional transformation techniques. Leveraging these enhanced datasets, this study develop an optimized Light Gradient Boosting Machine model specifically tailored for fault detection tasks. Comprehensive experimental evaluations demonstrate that our approach achieves marked improvements in both detection accuracy and robustness compared to conventional methods. These advancements not only enable more precise battery system diagnostics but also present a generalizable paradigm for other industrial fault detection applications requiring robust performance under data constraints.}, archive = {J_ACCESS}, author = {Zhiming Zhang and Dan Zhang and Dejun Li and Yi Liu and Jiong Yang}, doi = {10.1109/ACCESS.2025.3574787}, journal = {IEEE Access}, pages = {94632-94647}, shortjournal = {IEEE Access}, title = {Battery system fault detection: A data-driven aggregation and augmentation strategy}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of lithium-ion batteries and liquid air energy storage systems for grid use. ACCESS, 13, 94611-94631. (www)

  @article{ , abstract = {The global energy landscape is undergoing a paradigm shift driven by the increasing penetration of renewable energy sources into the electrical power grid. However, the variable nature of renewables presents significant challenges for grid stability and reliability. Long-term grid-scale energy storage is crucial to overcoming these challenges and facilitating the transition from carbon-based generation. Effective long-term grid-scale energy storage solutions must possess large energy capacity, long lifespans, geographical flexibility, and be economically viable and technologically ready. Liquid Air Energy Storage (LAES) emerges as a promising solution, offering similar benefits to Compressed Air Energy Storage (CAES) but with higher energy densities (typically 5 to 10 times higher) and without the geographical constraints of underground caverns or the uneconomical nature of pressurised tanks. Lithium Battery Energy Storage (LiBES) has driven much of the growth in the stationary energy storage market. However, its limitations with regards to energy capacity and long-term storage suitability are well established. Furthermore, the synthetic inertia of LiBES, reliant on software-based control, lacks the inherent reliability of LAES’s physical inertia, which provides a direct and robust response to grid fluctuations. A comparative analysis of LAES versus LiBES is conducted from technical, environmental, and economic perspectives. The findings highlight the suitability of LAES over LiBES for long-term grid-scale applications. As a general trend, LAES offers a lower levelised cost of storage (LCOS) than LiBES demonstrating improved economic performance in scenarios with lower cycling frequencies and higher discount rates.}, archive = {J_ACCESS}, author = {Matthew Brown and Alan L. Leigh Jarvis and Andrew G. Swanson}, doi = {10.1109/ACCESS.2025.3574454}, journal = {IEEE Access}, pages = {94611-94631}, shortjournal = {IEEE Access}, title = {Comparative analysis of lithium-ion batteries and liquid air energy storage systems for grid use}, volume = {13}, year = {2025}, }
• (2025). On the performance analysis of UE-VBS-based wireless communications: Network outages, resource utilization, and optimization. ACCESS, 13, 94585-94610. (www)

  @article{ , abstract = {User Equipment as a Virtual Base Station (UE-VBS) computing paradigm represents a significant advancement in wireless networking. It enables User Equipment (UE) to form: i) Virtual Base Stations (VBSs) by dynamically integrating Cluster Heads (referred to as UE-VBSCH), or Virtual Relays (referred to as UE-VBSRL), in the far-edge domain. This research focuses on enhancing the Quality of Service (QoS) (and thereby improving user experience) in networks supported by UE-VBS computing through outage prediction, network optimization, and advanced wireless techniques. In addition, the paper presents a detailed outage probability analysis and explores the trade-off between efficiency and reliability (namely, spectral and energy efficiency and link-level reliability (outage probability)), which are core contributions of this work. For a representative urban density of 2 UEs per m2, a single-hop UE-VBS slice lowers the outage probability from 0.78 to 0.23, raises the peak area-spectral efficiency to 4.3 bits−1 Hz−1 ( $\approx 4.8\times $ the baseline), and delivers an energy efficiency of $2.4\times 10^{5}$ bit J−1 ( $\approx 4.6\times $ improvement). These concrete figures substantiate the claimed gains and illustrate how UE-VBS computing simultaneously improves efficiency and reliability. Specifically, it provides a thorough examination of UE-VBS computing’s capacity to enhance service quality, reduce congestion, and promote energy efficiency. Also, it empirically confirms UE-VBS computing’s superior performance, including mitigating coverage gaps coverage gaps are localized areas inside a nominally covered cell where received SINR falls below the outage threshold because of shadowing or cell-edge distance), optimizing network traffic, and reducing battery consumption compared to traditional networks/non-UE-VBS computing-supported networks. Enhanced QoS aims to minimize the challenges associated with restricted network coverage, ensuring consistent data transmission rates and improving overall user satisfaction. The potential exists for adopting effective network traffic offloading to mitigate the heavy traffic on primary base stations known as Next Generation Node B (gNodeB). Consequently, this can result in enhanced spectrum utilization and heightened data throughput. Leveraging UE-VBS computing also contributes to power conservation and fosters sustainability.}, archive = {J_ACCESS}, author = {Iacovos I. Ioannou and Ala’ Khalifeh and Prabagarane Nagaradjane and Christophoros Christophorou and Vasos Vassiliou and Orestis Neokleous and Andreas Pitsillides}, doi = {10.1109/ACCESS.2025.3573846}, journal = {IEEE Access}, pages = {94585-94610}, shortjournal = {IEEE Access}, title = {On the performance analysis of UE-VBS-based wireless communications: Network outages, resource utilization, and optimization}, volume = {13}, year = {2025}, }
• (2025). Algorithmic approach of majority voting with agents’ inclusiveness for facility resource matching. ACCESS, 13, 94570-94584. (www)

  @article{ , abstract = {In a two-sided matching with preferences, an agent has preferences over the agents from the other side. In a head-to-head election between any two matchings, the agent votes for the matching with a better allocation in the pairing based on its preference. A matching M is popular when the number of votes for M is not less than the number of votes for any other matching $M'$ in such an election. A U-popular matching is introduced recently with a two-sided matching model where U and W are the two sets of agents, but only the preferences of the agents from U are emphasized, and the preferences of the agents from W are ignored. The matching, which is popular among the votes of the agents from U, is defined as U-popular. We consider such a model for a one-to-one matching where the agents from U have an inclusive mindset and want to integrate the voting decision of the agents from W into their voting process. In parallel, the agents from W rely more on their ranking on the preference list of the agents from U than their own preferences due to the uncertainty involved in the construction of their preferences. We define an inclusive voting model with such a predominant-subordinate agent scenario (U as predominant, W as subordinate) and prove that the Boston mechanism matching is U-popular under the model. However, it is possible that the U-popular matching is not the choice of the majority of agents. The choice to be neutral is added in the voting process, and the U-neutral matching type is introduced when the majority of agents vote for neutral. We characterize the U-neutral and max-size U-neutral matching under the inclusive voting model and propose a polynomial-time algorithm to determine a max-size U-neutral matching. The experiments we performed with synthetic instances endorse the algorithm based on the theoretical foundations established.}, archive = {J_ACCESS}, author = {Peash Ranjan Saha and Salimur Choudhury and Kai Salomaa}, doi = {10.1109/ACCESS.2025.3574286}, journal = {IEEE Access}, pages = {94570-94584}, shortjournal = {IEEE Access}, title = {Algorithmic approach of majority voting with agents’ inclusiveness for facility resource matching}, volume = {13}, year = {2025}, }
• (2025). Advances and challenges in deep learning for automated welding defect detection: A technical survey. ACCESS, 13, 94553-94569. (www)

  @article{ , abstract = {Automated welding defect detection has emerged as a pivotal aspect of quality assurance in high-stakes industries such as aerospace, oil and gas, and construction. This paper presents a comprehensive review of state-of-the-art Deep Learning (DL) models tailored for welding defect detection, segmentation, and classification, emphasizing technical advancements and persistent challenges. A critical analysis of single-stage and two-stage architectures is conducted to evaluate their ability to address issues like small defect sizes, low image contrast, and diverse defect geometries. The study also highlights the integration of advanced preprocessing techniques, such as noise reduction and contrast enhancement, within DL workflows to improve feature extraction and detection accuracy. Persistent challenges, such as the scarcity of large, labeled datasets, lack of real-time applicability, and limited model interpretability, are explored in depth. To address these gaps, the survey proposes future directions, including the use of self-supervised learning, domain adaptation, Generative Adversarial Networks (GANs), and explainable AI techniques to enhance the robustness, scalability, and transparency of welding defect detection systems. By synthesizing insights from more than a decade of research, this paper provides a detailed roadmap for advancing automated welding inspection technologies, enabling reliable deployment in real-world industrial environments.}, archive = {J_ACCESS}, author = {Abdulrahim Mohammed and Muhammad Hussain}, doi = {10.1109/ACCESS.2025.3574083}, journal = {IEEE Access}, pages = {94553-94569}, shortjournal = {IEEE Access}, title = {Advances and challenges in deep learning for automated welding defect detection: A technical survey}, volume = {13}, year = {2025}, }
• (2025). Wide parallax image stitching with balanced alignment-naturalness. ACCESS, 13, 94538-94552. (www)

  @article{ , abstract = {Parallax is a crucial factor that affects the quality of image stitching, particularly in complex scene images with wide camera baselines. Achieving precise alignment while maintaining natural visual aesthetics is a challenging task. Traditional stitching methods use fixed-grid partitioning and uniform local projective transformations, which can lead to distortion and nonlinear stretching in scenes with varying depths and orientations. This article proposes a novel approach to wide parallax image stitching that balances alignment precision with visual naturalness. Our method employs superpixel segmentation to partition images into regions with consistent local projective content. Additionally, local projective matrices derived from the Multi-GS algorithm (more efficient RANSAC) are used to transform inliers, thereby enhancing the accuracy of each superpixel block’s projective fitting, innovatively utilizing the guided local projective transformation algorithm. Furthermore, global similarity transformation and the linearization of local projective matrices are utilized to adjust overall distortion, while the combination of local projective transformation with global similarity is employed to achieve a more natural result. Experimental results show that our proposed algorithm outperforms state-of-the-art methods in improving alignment accuracy and the natural visual quality in scenarios with wide parallax. The RMSE and straight-line preservation improved by about 1% and 2% compared to LPC, while external expansion distortion and angular distortion improved by about 20% and 16% compared to UDIS-D.}, archive = {J_ACCESS}, author = {Tao Song and Qian Li and Peiguo Hou and Ning Li and Wenhao Liu and Zeheng Xia}, doi = {10.1109/ACCESS.2025.3569080}, journal = {IEEE Access}, pages = {94538-94552}, shortjournal = {IEEE Access}, title = {Wide parallax image stitching with balanced alignment-naturalness}, volume = {13}, year = {2025}, }
• (2025). Block number-based address clustering for bitcoin taproot upgrade. ACCESS, 13, 94523-94537. (www)

  @article{ , abstract = {Bitcoin address clustering is a technique used for Bitcoin deanonymization to determine address ownership. It serves as a valuable method for assessing the level of anonymity within the system and detecting addresses associated with illicit activities. The recent Taproot soft fork in Bitcoin has introduced significant privacy enhancements, making deanonymization more challenging. In this paper, we propose a novel on-chain heuristic approach, block number-based address clustering, which leverages the fundamental principle of 6 block confirmations for double-spending prevention. By relying on the trust relationships between transacting parties, our method enables the generation of clusters even upon the soft fork, where existing methods encounter challenges. To evaluate our heuristics, we analyze our approach using the address reduction ratio and pure contribution as key metrics. The results show the effectiveness of our method both before and after the soft fork, which implies that our approach remains unaffected by the usage of Taproot transactions. Furthermore, we provide real-world empirical cases that validate the practicality of our heuristics. As shown in the investigation cases conducted by the Korean National Police Agency, our method can generate new clusters and improve address ownership identification compared to the previous approaches.}, archive = {J_ACCESS}, author = {Gyeong Min Baek and Young Ah Shin and Gye Hyun Jang and Ik Rae Jeong}, doi = {10.1109/ACCESS.2025.3570459}, journal = {IEEE Access}, pages = {94523-94537}, shortjournal = {IEEE Access}, title = {Block number-based address clustering for bitcoin taproot upgrade}, volume = {13}, year = {2025}, }
• (2025). Research on electric vehicle charging load prediction methods combining signal noise reduction and time series modeling. ACCESS, 13, 94508-94522. (www)

  @article{ , abstract = {Accurate prediction of electric vehicle (EV) charging demand is crucial for grid stability, energy allocation, and charging infrastructure planning. This study introduces a hybrid deep learning model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Bi-directional Gated Recurrent Units (BiGRU), and Attention Mechanism (AM) to address the volatility in charging load patterns. The proposed method begins with the application of CEEMDAN to decompose the data into intrinsic mode functions (IMFs) and residuals. Subsequently, it employs convolutional and pooling layers within the CNN to reduce the dimensionality of the data, thereby optimizing the input dimensions. The BiGRU is then utilized to analyze both long- and short-term temporal dependencies inherent in the load data features, facilitating enhanced feature extraction. Finally, the AM is introduced to prioritize significant components within the load data features, thereby improving the model’s forecasting accuracy. To validate the prediction accuracy of proposed model, a comparative experiment is conducted comparing with five cutting-edge prediction models. The results demonstrate that proposed model achieves 11.37% average improvement in prediction accuracy. These findings confirm the reliability and effectiveness of the proposed model for user charging demand load prediction, showcasing its strong generalization ability and robustness.}, archive = {J_ACCESS}, author = {Liyun Liu and Xiaomei Xu and Jinsong Zhang and Dong Li}, doi = {10.1109/ACCESS.2025.3574409}, journal = {IEEE Access}, pages = {94508-94522}, shortjournal = {IEEE Access}, title = {Research on electric vehicle charging load prediction methods combining signal noise reduction and time series modeling}, volume = {13}, year = {2025}, }
• (2025). A fast true time-delay wideband multi-beam beamforming algorithm based on a 16-beam approximate-DVM. ACCESS, 13, 94488-94507. (www)

  @article{ , abstract = {True-time-delay (TTD) beamformers can generate wideband squint-free beams in analog and digital signal domains. The delay Vandermonde matrix (DVM) was introduced as a mathematical model that represents TTD-based multi-beam beamformers while reducing the delays from $\mathcal {O}(N^{2})$ to $\mathcal {O}(N \log N)$ , where $N=2^{r}(r \geq 1)$ is the number of beams. In this paper, we propose to reduce the complexity of delays from $\mathcal {O}(N \log N)$ to nearly $\mathcal {O}(N)$ for a small number of beams. More precisely, we present a recursive algorithm to compute the DVM-vector product with a complexity reduction of at least 21% to at most 52% compared to our most recent work, and at least 39% to at most 98% compared to the brute-force DVM-vector calculation. This enhancement was achieved by using 16-beam approximate-DVM (ADVM) building blocks that recursively execute with the DVM algorithm. The reduced complexity DVM algorithm achieves nearly linear complexity for smaller input sizes, specifically when $ N \leq 1024$ . This modification results in a complexity reduction when compared to the $\mathcal {O}(N \log N)$ complexity of the DVM algorithm, spanning from 8 to 1024 beams. For example, by computing the DVM-vector product for $N = 8$ to 1024 elements antenna arrays, we can obtain wideband RF beams while reducing the required chip area and power consumption by at least 21% at 1024 beams to at most 52% at 16 beams compared to radix-2 DVM algorithm, and also at least 39% at 8 beams to at most 98% at 1024 beams compared to the brute-force DVM-vector product computation. With this reduction, we show that the proposed DVM algorithm is better suited for end-to-end RF-IC design that includes multiple wideband channels. At the end, a signal flow graph, simulated beam patterns at 150 MHz, 300 MHz, 600 MHz, and 1 GHz frequencies based on the proposed ADVM algorithm, and a digital overview are provided to demonstrate the simplicity, efficiency, and accuracy of the proposed TTD multibeam beamformers for RF-IC design.}, archive = {J_ACCESS}, author = {Sirani M. Perera and Levi Lingsch and Alp Tuztas and Arjuna Madanayake}, doi = {10.1109/ACCESS.2025.3573930}, journal = {IEEE Access}, pages = {94488-94507}, shortjournal = {IEEE Access}, title = {A fast true time-delay wideband multi-beam beamforming algorithm based on a 16-beam approximate-DVM}, volume = {13}, year = {2025}, }
• (2025). Event-triggered adaptive control for a class of nonlinear systems with saturation input and unknown modeling errors. ACCESS, 13, 94478-94487. (www)

  @article{ , abstract = {In this paper, an event-triggered adaptive control scheme is proposed by backstepping for a class of strict feedback systems with the saturation actuator. The unknown parameters and modelling errors are also considered. By constructing the auxiliary signal system and introducing auxiliary signals into the controller design, the uncertainties caused by unknown saturation effects are compensated for. Based on this auxiliary signal, an event-triggered adaptive control scheme is constructed. The main contributions of this proposed controller can be summarized as follows: 1) A new event-triggered mechanism is proposed by setting a maximum triggering time interval limit. It integrates time triggering in the event triggering mechanism, and avoiding the phenomenon of system performance not being optimized due to control signals not being updated for a long time. It was also proven that this proposed triggering mechanism can prevent the occurrence of Zeno-behavior; 2) The unknown constant parameters in the upper bound function of the unknown nonlinear terms are considered. By estimating these parameters the compensation accuracy for such unknown uncertainties is improved. Finally, simulation results validate the effectiveness of this proposed controller.}, archive = {J_ACCESS}, author = {Lujuan Shen and Dong Guo and Congli Mei and Jianping Cai}, doi = {10.1109/ACCESS.2025.3574658}, journal = {IEEE Access}, pages = {94478-94487}, shortjournal = {IEEE Access}, title = {Event-triggered adaptive control for a class of nonlinear systems with saturation input and unknown modeling errors}, volume = {13}, year = {2025}, }
• (2025). In-context vision-pattern-language model for enhancing vessel activity explanation. ACCESS, 13, 94464-94477. (www)

  @article{ , abstract = {Illegal vessel activities pose significant threats to marine resources and ecosystems worldwide, necessitating effective monitoring and detection methods. Current vessel monitoring systems struggle to accurately interpret vessel behaviors at a detailed level due to limitations in utilizing multi-modal data and regulatory frameworks. To overcome these challenges, we propose a new Vision-Pattern-Language (VPL) model designed to enhance the explanation and detection of illegal, unreported, and unregulated (IUU) vessel activities. To handle AIS-off in boundary waters, proposed model integrates satellite imagery with Automatic Identification System (AIS) trajectory data using a probabilistic fusion approach based on Maximum A Posteriori (MAP) estimation with Monte Carlo dropout. Additionally, the VPL model employs a CLIP-based zero-shot classifier to accurately identify vessel behaviors. To support law enforcement, the VPL model with in-context learning also generates faithful and contextually reasonable explanations grounded in the fused data and a legal-text database. Extensive experimental evaluations on the AIS and satellite imagery dataset demonstrate that the VPL model significantly improves trajectory prediction accuracy and classification performance than baselines. Moreover, VPL attains higher faithfulness and reasoning scores compared to Llama-3.3, highlighting its potential for robust and reliable maritime surveillance and contributing meaningfully to the detection and regulation of IUU vessel activities.}, archive = {J_ACCESS}, author = {Changha Lee and Chan-Hyun Youn}, doi = {10.1109/ACCESS.2025.3572714}, journal = {IEEE Access}, pages = {94464-94477}, shortjournal = {IEEE Access}, title = {In-context vision-pattern-language model for enhancing vessel activity explanation}, volume = {13}, year = {2025}, }
• (2025). A robust pilot decontamination scheme in massive MIMO systems: Integrating rateless orthogonal STBC, weighted graph coloring, and channel estimation. ACCESS, 13, 94453-94463. (www)

  @article{ , abstract = {Even though Multiple Input Multiple Output (MIMO) systems are one of the greatest innovations in wireless cellular network technology, they still face many challenges as we progress to 6G, hindering their ability to achieve the best throughput. In cellular networks, channel estimations are made using pilot sequences. Deploying these orthogonal time-frequency limited resources leads to pilot contamination, causing incorrect channel estimation due to mixed signals from neighboring cells and vice versa. Previous research has proven that this significantly decreases system performance. To reduce the impact and improve the output of Multiuser MIMO systems, this paper proposes a hybrid model combining Rateless Orthogonal Space-Time Block Codes (ROSTBC), Weighted Graph Coloring Pilot Allocation (WGCPA), and Maximum Likelihood Estimation (MLE). While ROSTBC has the ability to adapt to channel conditions, allowing the system to perform well despite pilot contamination, WGCPA and MLE address the problems of pilot contamination and the accuracy of channel estimation, respectively. The results showed that this hybrid scheme has proven to perform better compared to previously proposed schemes, providing a valuable contribution to reliable Massive MIMO wireless communication.}, archive = {J_ACCESS}, author = {Habib M. Hussien and Zelalem A. Kelem}, doi = {10.1109/ACCESS.2025.3573689}, journal = {IEEE Access}, pages = {94453-94463}, shortjournal = {IEEE Access}, title = {A robust pilot decontamination scheme in massive MIMO systems: Integrating rateless orthogonal STBC, weighted graph coloring, and channel estimation}, volume = {13}, year = {2025}, }
• (2025). Renewable energies integration in electrification for chilean Smartports—Challenges and case study. ACCESS, 13, 94437-94452. (www)

  @article{ , abstract = {This paper analyses and discusses the environmental impact of integrating renewable energies in the operation of seaports in a context in which maritime activities are an essential contributor to global greenhouse gas emissions. The analysis performed in this work is structured as follows: first, the implementation of smartport microgrids is discussed as an important strategy to control seaports because its design facilitates the integration of different technologies such as cold ironing and distributed energy resources. In this context, properly integrating cold ironing and renewable energy technologies is essential to reducing pollution levels. Secondly, the main challenges of implementing these technologies are discussed, covering the economic, technical, and environmental aspects. Here, Chile’s lack of proper normative is an important problem that must be addressed to incentivise projects for sustainable smartport implementations. Finally, this work presents a case study where a smartport with cold ironing and renewable energy facilities is designed for the port in Puerto Montt, Chile. The simulation results show the effectiveness of the smartport strategy for reducing emissions and operational costs. Still, this strategy usually requires higher investments due to the inclusion of renewable energies.}, archive = {J_ACCESS}, author = {Rafaella Canessa and Rodrigo Santibañez and Constanza Ahumada and Patricio Mendoza-Araya and Oscar Cartagena and Doris Sáez}, doi = {10.1109/ACCESS.2025.3574632}, journal = {IEEE Access}, pages = {94437-94452}, shortjournal = {IEEE Access}, title = {Renewable energies integration in electrification for chilean Smartports—Challenges and case study}, volume = {13}, year = {2025}, }
• (2025). HistoDX: Revolutionizing breast cancer diagnosis through advanced imaging techniques. ACCESS, 13, 94416-94436. (www)

  @article{ , abstract = {Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches ( $50\times 50$ pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting under sampled IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology.}, archive = {J_ACCESS}, author = {Wishal Arshad and Tehreem Masood and H. M. Shahzad and Hassan A. Ahmed and Syed Hamza Ahmed and Hafiz Muhammad Tayyab Khushi}, doi = {10.1109/ACCESS.2025.3574210}, journal = {IEEE Access}, pages = {94416-94436}, shortjournal = {IEEE Access}, title = {HistoDX: Revolutionizing breast cancer diagnosis through advanced imaging techniques}, volume = {13}, year = {2025}, }
• (2025). Triplet-style dynamic graph network with transformer encoder for scam detection in cryptocurrency transactions. ACCESS, 13, 94402-94415. (www)

  @article{ , abstract = {The surge in cryptocurrencies has been accompanied by a significant rise in scams, underscoring the critical need for precise scam detection. Cryptocurrency markets and transaction networks are dynamic, leading to evolving scam tactics and transaction topologies that challenge detection efforts. Compounding this, scammers cleverly mimic benign transactions, blurring the lines between legitimate and illicit activities. To address these intertwined challenges, we introduce the Triplet-style Dynamic Graph Convolutional Network (TD-GCN). TD-GCN is specifically engineered to model the dynamic nature of cryptocurrency transaction networks and to disentangle subtle distinctions between scam and benign transaction patterns. It leverages a Transformer Encoder for dynamic weight updates, effectively capturing network evolution, and employs Triplet Learning to disentangle representations of similar-appearing scam and benign transactions. Evaluations on a real-world Bitcoin transaction network demonstrate TD-GCN’s superior scam detection performance. Notably, TD-GCN achieves a leading F1-macro score of 0.8855 and a 10.36%p precision increase over competing models, crucial for minimizing false positives. Ablation studies highlight the Triplet Dynamic Disentangle component’s key role, reducing false positives by 81.1%. TD-GCN’s performance gains stem from its dynamic updates for evolving networks and Triplet Learning for disentangling subtly different patterns. These features enable TD-GCN to significantly bolster cryptocurrency security by effectively detecting scams and minimizing false positives in dynamic transaction networks.}, archive = {J_ACCESS}, author = {Min-Woo Nam and Hyeon-Ju Lee and Seok-Jun Buu}, doi = {10.1109/ACCESS.2025.3574038}, journal = {IEEE Access}, pages = {94402-94415}, shortjournal = {IEEE Access}, title = {Triplet-style dynamic graph network with transformer encoder for scam detection in cryptocurrency transactions}, volume = {13}, year = {2025}, }
• (2025). Automatic navigation and self-driving technology in agricultural machinery: A state-of-the-art systematic review. ACCESS, 13, 94370-94401. (www)

  @article{ , abstract = {The advent of automatic navigation and self-driving technology in agricultural machinery has transformed modern farming by enhancing efficiency and safety. This systematic review aims to evaluate the development, implementation, and impact of self-driving technology in agriculture. We synthesized recent advancements and research findings on the integration of Global Positioning System (GPS), machine learning algorithms, and sensor technologies in agricultural machinery. Key applications of self-driving machinery in planting, harvesting, and field monitoring were explored, with findings showing improvements in farm productivity, reduced operational costs, and enhanced worker safety. Despite the benefits, challenges such as technological barriers, regulatory issues, and infrastructure limitations remain. This review informs stakeholders about the future potential of self-driving technology in promoting sustainable and precision farming practices.}, archive = {J_ACCESS}, author = {Md. Sabbir Hossain and Mostafijur Rahman and Ashifur Rahman and Md. Mohsin Kabir and M. F. Mridha and Jie Huang and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3573324}, journal = {IEEE Access}, pages = {94370-94401}, shortjournal = {IEEE Access}, title = {Automatic navigation and self-driving technology in agricultural machinery: A state-of-the-art systematic review}, volume = {13}, year = {2025}, }
• (2025). A measurement-based trade-off analysis of EMF exposure levels and QoS in mobile networks. ACCESS, 13, 94354-94369. (www)

  @article{ , abstract = {As mobile services rapidly expand, network operators face the challenge of carefully rolling out advanced networks that meet strict Quality of Service (QoS) requirements while adhering to electromagnetic field (EMF) exposure limits. In this study, we present a trade-off analysis based on real data collected through a measurement campaign between EMF exposure levels from all operating cellular technologies and main QoS parameters experienced by users, namely data rate, received signal strength and quality indicators. This work investigates the relationship and interplay between the EMF exposure levels and QoS received by the mobile users at different frequency bands in use by network operators, relying on real life measurement data. The results show that the mean value of measured electric field (E-field) samples is between 0.2-0.9 V/m for all frequency bands included in the study. Fewer than 10% of samples exceed the value of 1 V/m, while the maximum value recorded is 5.46 V/m. In terms of trade-off analysis, we observe that there is a tendency for higher data rates to be associated with lower E-field levels. Our results show that 90% of samples with high E-field values are associated with data rates lower than 1 Mb/s, while 50% of samples with low E-field values exhibit data rates that are above 1 Mb/s. From all quality indicators, our results show that the received signal strength indicator shows the strongest relationship with the E-field level and could be used as a reliable predictor for EMF exposure when planning future EMF-aware networks.}, archive = {J_ACCESS}, author = {Hëna Maloku and Zana Limani Fazliu and Jeta Dobruna and Mimoza Ibrani}, doi = {10.1109/ACCESS.2025.3574368}, journal = {IEEE Access}, pages = {94354-94369}, shortjournal = {IEEE Access}, title = {A measurement-based trade-off analysis of EMF exposure levels and QoS in mobile networks}, volume = {13}, year = {2025}, }
• (2025). Consistent quality in video coding: A perceptual RDO and neural network-based QP prediction approach. ACCESS, 13, 94336-94353. (www)

  @article{ , abstract = {Video coding has been greatly growing in the recent four decades with a number of standards such as H.264/AVC, HEVC or VVC which have opened a new era of multimedia applications such as video conference, video surveillance, broadcasting, and E-learning. However, to achieve high compression performance, video coding standard like HEVC typically suffers from the quality fluctuation which may directly affect the quality of experiences (QoE). To cope with this problem, we propose a novel video coding framework targeting two purposes, the consistent quality and the compression efficiency. First, to achieve the perceptual quality of video frame, we propose to use the VMAF (video multi-methods assessment fusion) metric as the distortion measurement instead of PSNR or SSIM in literatures due to its high correlation with the human perceptual system. Second, we propose a novel VMAF-QP (quantization parameter) neural network that learns how QP can be adjusted to achieve a certain VMAF grade. Following the estimated QP, we propose a RDO process to guarantee the compression efficiency of the proposed codec while maintaining the decoded video with quality consistency. In this process, the Rate-QP and Distortion-QP models are introduced and optimized through a number of statistical analyses. Finally, we integrate the proposed consistent quality video coding (CQVC) framework into the HEVC standard. Experimental results reveal that the proposed video coding framework achieves a small quality fluctuation while still guarantying the high compression efficiency, notably 4.5% Bitrate reduction when compared to the standard HEVC.}, archive = {J_ACCESS}, author = {Tien Vu Huu and Huy Bui Quoc and Minh Do Ngoc and Xiem HoangVan}, doi = {10.1109/ACCESS.2025.3574303}, journal = {IEEE Access}, pages = {94336-94353}, shortjournal = {IEEE Access}, title = {Consistent quality in video coding: A perceptual RDO and neural network-based QP prediction approach}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning with dueling DQN for partial computation offloading and resource allocation in mobile edge computing. ACCESS, 13, 94319-94335. (www)

  @article{ , abstract = {Computation offloading transfers resource-intensive tasks from local Internet of Things (IoT) devices to powerful edge servers, which minimizes latency and reduces the computational load on IoT devices. Deep Reinforcement Learning (DRL) is widely utilized to optimize computation offloading decisions. However, previous studies fall short in two main ways: firstly, they do not collectively optimize the comprehensive state space, and secondly, their reliance on Q-learning and Deep Q Networks (DQN) makes it challenging for agents to discern the optimal action in large action spaces, as many actions may possess similar values. In this paper, we introduce a multi-branch Dueling Deep Q Network (MBDDQN) that tackles the challenges of high-dimensional state-action spaces and long-term cost optimizations in dynamic environments. The Dueling DQN alleviates the complexity of simultaneous offloading and resource allocation decisions, with each branch independently controlling a subset of the decision variables to scale efficiently with an increasing number of IoT devices, thereby avoiding the combinatorial explosion of potential actions. Furthermore, we implement a long short-term memory (LSTM) network with distinct advantage-value layers to enhance both short-term action selection and long-term system cost estimation, as well as improve the temporal learning capacity of the model. Finally, we propose an innovative adaptive cost-weighting mechanism within the reward function to dynamically balance competing objectives, including energy consumption, latency, and bandwidth utilization. Unlike prior works that use fixed reward structures, we leverage weighted state-action advantage values to dynamically adjust the optimization variables. This approach also enables the agent to self-tune, allowing it to prioritize delay minimization in delay-sensitive scenarios and energy conservation in resource-constrained environments. Simulation results demonstrate the superiority of the proposed scheme compared to benchmarks. For instance, MBDDQN reduces delay by 17.88% over DQN and 12.28% over DDPG. Additionally, regarding energy consumption, MBDDQN achieves a 10.1% improvement over DQN and a 7.64% enhancement over DDPG.}, archive = {J_ACCESS}, author = {Ehzaz Mustafa and Junaid Shuja and Faisal Rehman and Abdallah Namoun and Mazhar Ali and Abdullah Alourani}, doi = {10.1109/ACCESS.2025.3573929}, journal = {IEEE Access}, pages = {94319-94335}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning with dueling DQN for partial computation offloading and resource allocation in mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). Lockstep replacement: Fault-tolerant design. ACCESS, 13, 94302-94318. (www)

  @article{ , abstract = {System-level lockstep, interconnecting two original cores, is nowadays the state-of-the-art approach for protecting processor systems against random hardware faults. However, the lack of information outside the cores necessitates many compromises in dependability, software complexity, power consumption, performance, and system cost. We propose a redundancy-based protection scheme integrated into the microarchitecture of the core. The scheme separates the execution pipeline into sections with fault detection and fault tolerance, while the large predictor components remain unprotected. This approach results in fault-tolerant hardware with only an 8% area penalty compared to the dual-core lockstep, which provides only fault detection. Since the hardware recovers from the faults automatically, it does not necessitate software support and additional memory for checkpoints. The scheme comprises independent protection approaches, resulting in negligible frequency impact. It also provides an interface and memory protection and is designed to be applicable in most of the embedded-class cores. Simulation-based fault injection campaigns were performed, considering physical synthesis data, to assess the fault-tolerance. We also analyze how software compilation affects dependability and provide a hardware solution to mitigate the undesired effects.}, archive = {J_ACCESS}, author = {Ján Mach and Lukáš Kohútka}, doi = {10.1109/ACCESS.2025.3573684}, journal = {IEEE Access}, pages = {94302-94318}, shortjournal = {IEEE Access}, title = {Lockstep replacement: Fault-tolerant design}, volume = {13}, year = {2025}, }
• (2025). Recent developments in thermal management of 3D ICs: A review. ACCESS, 13, 94286-94301. (www)

  @article{ , abstract = {Three-dimensional integration circuits (3D ICs) have become an important direction of integrated circuits, which brings in a higher integration level, higher power density, and shorter wire length. However, the heat management challenge becomes one of the key limitations for the development of 3D integration which needs desperate solutions. To address this challenge, the researchers conducted an in-depth investigation into heat transfer technologies within key structures of 3D ICs. This paper aims to review the literature on thermal management technologies, with a particular emphasis on advancements in three critical areas: thermal interface materials (TIM), through-silicon vias (TSV), and heat sink designs. Continuous research and innovation in TIM, TSV, and heat sink structures have provided diverse approaches and technical solutions for addressing thermal management issues in 3D ICs. It is expected that this review article can help researchers in academia and industry to understand the state-of-the-art of heat transfer in 3D ICs and provide better research ideas.}, archive = {J_ACCESS}, author = {Fen Guo and Zhao-Jun Suo and Xin Xi and Yu Bi and Tuo Li and Changhong Wang and Kang Su and Xiaofeng Zou and Rengang Li}, doi = {10.1109/ACCESS.2025.3569879}, journal = {IEEE Access}, pages = {94286-94301}, shortjournal = {IEEE Access}, title = {Recent developments in thermal management of 3D ICs: A review}, volume = {13}, year = {2025}, }
• (2025). Optimization of beamforming and transmit power using DQN and comparison with traditional techniques. ACCESS, 13, 94275-94285. (www)

  @article{ , abstract = {As Artificial Intelligence (AI) and Machine Learning (ML) technologies continue to evolve, their integration into 5G and 6G networks has become critical for improving performance and efficiency. These technologies enhance wireless communication by leveraging deep neural networks and data-driven methods to optimize resource allocation, signal detection, and channel coding. They also address the growing need to reduce energy consumption in next-generation networks. Deep Q-Networks (DQN) play a key role in this transformation by enabling dynamic resource management, adaptive beamforming, and efficient network slicing. The use of DQN can improve spectrum utilization, power consumption, and beamforming in massive MIMO systems, supporting demands like eMBB and URLLC. In 6G, it is considered for optimizing resource allocation, dynamic beamforming, RIS control for Terahertz communication, and complex network slicing. This article focuses on maximizing channel capacity by deploying DQN to identify optimal beamforming angles and transmit power values for multiple transmitters and receivers. The model determines the best steering vectors and power levels to achieve maximum channel capacity while minimizing energy consumption and mutual interference. Its performance is compared to conventional optimization methods, demonstrating its effectiveness in enhancing the efficiency and reliability of 5G and 6G networks.}, archive = {J_ACCESS}, author = {Parmida Geranmayeh and Eckhard Grass}, doi = {10.1109/ACCESS.2025.3573096}, journal = {IEEE Access}, pages = {94275-94285}, shortjournal = {IEEE Access}, title = {Optimization of beamforming and transmit power using DQN and comparison with traditional techniques}, volume = {13}, year = {2025}, }
• (2025). Drone-based sound source localization: A systematic literature review. ACCESS, 13, 94256-94274. (www)

  @article{ , abstract = {Sound source localization (SSL) using microphones mounted on uncrewed aerial vehicles (UAVs) holds significant potential for tasks ranging from search-and-rescue and gunshot detection to industrial inspection and wildlife monitoring, particularly in scenarios where camera-based sensing may be limited by poor visibility. Recent surveys take a broad view of SSL methods, with limited coverage of UAV-based approaches. This paper addresses this gap through a systematic review of UAV-based SSL, drawing on 49 studies published between 2014 and 2024. Four research questions are addressed: 1) What is the array configuration of the SSL platform? 2) What are the intended applications of SSL on UAV platforms? 3) What are the choices for the UAV platforms? 4) What are the solutions employed for performing the SSL task? The findings of this review indicate that 82% of the studies are intended for search-and-rescue contexts, 60% rely on arrays of eight microphones or fewer, and only three implement real-time onboard SSL. Notably, real-time processing of larger arrays and public availability of comprehensive datasets are identified among key obstacles to the field. This paper concludes by identifying research gaps and proposing future directions in multi-source localisation, real-time processing, and energy efficiency.}, archive = {J_ACCESS}, author = {Sérgio F. Chevtchenko and Belman Jahir Rodríguez and Rafaella Vale and Abishek Soti and Yeshwanth Bethi and Naqib Ibnul and Alexandre Marcireau and Mostafa Rahimi Azghadi and Andrew Wabnitz and Saeed Afshar}, doi = {10.1109/ACCESS.2025.3572478}, journal = {IEEE Access}, pages = {94256-94274}, shortjournal = {IEEE Access}, title = {Drone-based sound source localization: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Deep learning for video fluoroscopic swallowing study analysis: A survey on classification, detection, and segmentation techniques. ACCESS, 13, 94239-94255. (www)

  @article{ , abstract = {Deep learning has significantly advanced the analysis of Video Fluoroscopic Swallowing Study data, an essential diagnostic tool for dysphagia assessment. This review explores recent applications of deep learning across key VFSS analysis tasks, including classification, detection, and segmentation. Classification methods utilizing convolutional neural networks achieve high accuracy, ranging from 91.7% to 95.98%, and Area Under the ROC Curve scores between 0.71 and 0.97, thus enhancing the consistency and reliability of swallowing phase identification. Detection approaches employing advanced deep learning architectures effectively localize anatomical landmarks and temporal swallowing events, reaching Mean Average Precision values of up to 0.89 and tracking errors as low as 2.38 pixels. Segmentation techniques based on variants of U-Net and related architectures accurately delineate critical anatomical regions, with Dice Similarity Coefficients ranging from 0.67 to 0.90. Collectively, these advances substantially improve VFSS interpretation by increasing accuracy, reducing subjective variability, and streamlining clinical workflows. This survey summarizes recent methodologies and discusses strategies for dataset collection and preprocessing, including both proprietary and limited publicly available datasets, discusses ongoing challenges such as computational demands and dataset diversity, and highlights future directions in leveraging deep learning to enhance dysphagia diagnosis and treatment.}, archive = {J_ACCESS}, author = {Ahmed Fakhry and Sarah Mary Antony and Eunhee Park and Jong Taek Lee}, doi = {10.1109/ACCESS.2025.3573282}, journal = {IEEE Access}, pages = {94239-94255}, shortjournal = {IEEE Access}, title = {Deep learning for video fluoroscopic swallowing study analysis: A survey on classification, detection, and segmentation techniques}, volume = {13}, year = {2025}, }
• (2025). PPFL-DCS: Privacy-preserving federated learning using neural transformer and leveraging dynamic client selection to accommodate data diversity. ACCESS, 13, 94225-94238. (www)

  @article{ , abstract = {The vulnerabilities and security issues of industrial Cyber-Physical Systems (CPSs), such as Intrusion Detection Systems (IDSs), have significantly increased due to the rapid integration of conventional industrial setups with advanced networking and computing technologies like 5G, software-defined networking, and artificial intelligence. Coping strategies for such challenges frequently involve transferring data to a central location, which raises concerns about latency, efficiency, and privacy. To address these issues, Federated Learning (FL) was developed as a solution to mitigate both the privacy concerns of organizations and the complexities of networked systems. However, FL-based techniques still have shortcomings, FedAvg equally weights weak models, risking suboptimal results; FL also faces Membership Inference privacy attacks. To address these challenges, we propose PPFL-DCS, an FL framework that incorporates a weighted mechanism for dynamic client selection, accounting for the performance of each local model and data size of each client in integration with a Neural Transformer System (NTS) that enhances the system‘s robustness against the MIA attacks. The NTS limits the impact and gains of attackers, thereby reducing the effectiveness of MIAs. Extensive experiments demonstrate that PPFL-DCS achieves a high detection accuracy of 97.424% for cyber threats in industrial CPSs, and highlight its efficiency over state-of-the-art techniques.}, archive = {J_ACCESS}, author = {Nakul Mehta and Nitesh Bharot and John G. Breslin and Priyanka Verma}, doi = {10.1109/ACCESS.2025.3572605}, journal = {IEEE Access}, pages = {94225-94238}, shortjournal = {IEEE Access}, title = {PPFL-DCS: Privacy-preserving federated learning using neural transformer and leveraging dynamic client selection to accommodate data diversity}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Data-driven driving state control for unmanned agricultural logistics vehicle”. ACCESS, 13, 94224. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Data-Driven Driving State Control for Unmanned Agricultural Logistics Vehicle”).}, archive = {J_ACCESS}, author = {Xuesheng Zhou and sJun Zhou}, doi = {10.1109/ACCESS.2025.3570781}, journal = {IEEE Access}, pages = {94224}, shortjournal = {IEEE Access}, title = {Corrections to “Data-driven driving state control for unmanned agricultural logistics vehicle”}, volume = {13}, year = {2025}, }
• (2025). Air quality and healthy ageing: Predictive modeling of pollutants using CNN quantum-LSTM. ACCESS, 13, 94212-94223. (www)

  @article{ , abstract = {The concept of healthy ageing is emerging and becoming a norm to achieve a high quality of life, reducing healthcare costs and promoting longevity. Rapid growth in global population and urbanisation requires substantial efforts to ensure healthy and supportive environments to improve the quality of life, closely aligned with the principles of healthy ageing. Access to fundamental resources which include quality healthcare services, clean air, green and blue spaces plays a pivotal role in achieving this goal. Air quality, in particular, is a critical factor in achieving healthy ageing targets. However, it necessitates a global effort to develop and implement policies aimed at reducing air pollution, which has severe implications for human health including cognitive impairment and neurodegenerative diseases, while promoting healthier environments such as high quality green and blue spaces for all age groups. Such actions inevitably depend on the current status of air pollution and better predictive models to mitigate the harmful impact of emissions on planetary health and public health. In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. The performance of the proposed model is analysed on five key pollutants including fine Particulate Matter PM2.5, Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulphur Dioxide (SO2), sourced from air quality monitoring station in Northern Ireland, UK. The effectiveness of the proposed model is evaluated by comparing its performance with its equivalent classical counterpart using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate the superiority of the proposed model, achieving a performance gain of up to 14% and validating its robustness, efficiency and reliability by leveraging the advantages of quantum computation.}, archive = {J_ACCESS}, author = {Fareena Naz and Muhammad Fahim and Adnan Ahmad Cheema and Bradley D. E. Mcniven and Tuan-Vu Cao and Ruth Hunter and Trung Q. Duong}, doi = {10.1109/ACCESS.2025.3570526}, journal = {IEEE Access}, pages = {94212-94223}, shortjournal = {IEEE Access}, title = {Air quality and healthy ageing: Predictive modeling of pollutants using CNN quantum-LSTM}, volume = {13}, year = {2025}, }
• (2025). An area-efficient continuous-time ising machine featuring dynamic threshold annealing. ACCESS, 13, 94200-94211. (www)

  @article{ , abstract = {Recently, Ising machines have received rising attention as efficient alternatives for solving combinatorial optimization problems (COPs), compared to von Neumann architectures with high power consumption and prolonged latencies. While prior discrete-time Ising machines exploited near-/in-memory computing to mitigate memory access bottlenecks, their reliance on sequential spin updates inherently limits solution speed. To address this problem, continuous-time (CT) Ising machines have emerged as promising low-cost COP solvers due to their fully parallel spin-update mechanisms. However, existing CT Ising machines face a critical trilemma in simultaneously achieving multi-level coefficient precision, area-efficient spin implementations, and effective annealing strategies. In this work, we present a CT Ising machine with an eDRAM-based in-memory computing architecture. The design introduces a neighboring spin computation-sharing architecture that halves the storage overhead of interaction coefficients, thereby significantly reducing the spin cell area. Furthermore, a novel dynamic threshold annealing method is proposed to attain lower Ising Hamiltonians during the solution process. An $18\times 34$ spin array has been designed in a 55-nm CMOS process, which achieves a compact spin unit footprint of merely $561~\mu $ m2 with 15-level programmable interaction coefficients. Additionally, post-layout simulation results validate that the proposed Ising machine achieves comparable nanosecond-scale time-to-solution (TTS) for diverse max-cut COPs while consuming 0.84 nJ at a 1.0 V supply voltage.}, archive = {J_ACCESS}, author = {Yufeng Wu and Xiaofeng Li and Jiayin Song and Yi Zhan and Shushan Qiao}, doi = {10.1109/ACCESS.2025.3568069}, journal = {IEEE Access}, pages = {94200-94211}, shortjournal = {IEEE Access}, title = {An area-efficient continuous-time ising machine featuring dynamic threshold annealing}, volume = {13}, year = {2025}, }
• (2025). Safe semi-supervised contrastive learning using in-distribution data as positive examples. ACCESS, 13, 94186-94199. (www)

  @article{ , abstract = {Semi-supervised learning (SSL) methods have shown promising results in solving many practical problems when only a few labels are available. The existing methods assume that the class distributions of labeled and unlabeled data are equal; however, their performances are significantly degraded in class distribution mismatch scenarios where out-of-distribution (OOD) data exist in the unlabeled data. Previous safe SSL studies have addressed this problem by making OOD data less likely to affect training based on labeled data. However, even if the studies effectively filter out the unnecessary OOD data, they can lose the basic information that all data share regardless of class. To this end, we propose to apply a self-supervised contrastive learning (SSCL) approach to fully exploit a large amount of unlabeled data. We also propose a contrastive loss function with a coefficient schedule to aggregate as an anchor the labeled negative examples of the same class into positive examples. To evaluate the performance of the proposed method, we conduct experiments on image classification datasets-CIFAR-10, CIFAR-100, Tiny ImageNet, and CIFAR-100+Tiny ImageNet—under various mismatch ratios. The results show that SSCL significantly improves classification accuracy, and our proposed loss function further enhances the performance, collectively outperforming existing methods by $2\sim 9$ % across various benchmark datasets. The performance gains become more pronounced as dataset complexity increases and remain robust even in challenging cross-dataset scenarios.}, archive = {J_ACCESS}, author = {Min Gu Kwak and Hyungu Kahng and Seoung Bum Kim}, doi = {10.1109/ACCESS.2025.3574345}, journal = {IEEE Access}, pages = {94186-94199}, shortjournal = {IEEE Access}, title = {Safe semi-supervised contrastive learning using in-distribution data as positive examples}, volume = {13}, year = {2025}, }
• (2025). A cognitive topic modeling approach for detailed learner analysis in asynchronous discussions. ACCESS, 13, 94169-94185. (www)

  @article{ , abstract = {Asynchronous discussion platforms are crucial components of online education: these sites promote knowledge exchange and enhance the learning experience. Students’ discussion data contain abundant information, including personal interests and preferences. Such content also offers insight into learners’ cognitive states. Uncovering the features of various cognitive processes holds value for the education field. We thus propose a cognitive topic modeling method for asynchronous online discussions called SLPoK-ENA. This approach consists of two parts: 1) SLPoK, a topic modeling technique that uses sentence embedding to improve latent Dirichlet allocation (LDA); and 2) the integration of SLPoK and epistemic network analysis (ENA) to perform cognitive topic modeling. Results from two real-world course datasets show that SLPoK outperforms LDA in topic modelling when dealing with educational data, achieving Normalized Mutual Information (NMI) values of 0.62 (Dataset 1) and 0.75 (Dataset 2), Adjusted Rand Index (ARI) values of 0.45 and 0.73, Silhouette Coefficient (SC) of 0.225 and 0.308, and Calinski-Harabasz Index (CH) of 196.350 and 464.415 on the two datasets, respectively—all significantly higher than the traditional LDA baseline model. Also, this method is better at capturing students’ cognitive characteristics. Experiments demonstrate that SLPoK-ENA can identify learners’ topic preferences under different cognitive conditions. This method has practical value for understanding students and improving teaching quality in online education settings.}, archive = {J_ACCESS}, author = {Ruofei Ding and Xianfei Luo and Shumin Chen and Xiaomin Wu and Chi Zhang and Lixiang Liu and Yanyu Huang and Ling Chen and Jingxiu Huang and Yunxiang Zheng and Ke Zhu}, doi = {10.1109/ACCESS.2025.3573873}, journal = {IEEE Access}, pages = {94169-94185}, shortjournal = {IEEE Access}, title = {A cognitive topic modeling approach for detailed learner analysis in asynchronous discussions}, volume = {13}, year = {2025}, }
• (2025). A model-driven approach employing DSL and generation templates to accelerate the development of conversational agents for smart tourism. ACCESS, 13, 94150-94168. (www)

  @article{ , abstract = {Conversational agents are intelligent, ubiquitous software applications widely used across various sectors, such as commerce, tourism, and more. Their key benefits include automating tasks, improving customer service, and ensuring service availability. The development of conversational agents utilizing artificial intelligence (AI) techniques represents a significant advancement in Natural Language Processing (NLP). Numerous studies employ deep learning and NLP methodologies to construct sophisticated conversational agent systems. Additionally, developers and companies often utilize APIs provided by intent recognition services like Dialogflow and Amazon Lex to easily create conversational agents using graphical forms, which enhance conversational agent functionality. However, these APIs have limitations, such as potential dependency on specific NLP service providers and associated high costs. Besides, the lack of a specialized conversational agent development platform for the tourism domain poses a considerable challenge. To address these limitations, this work tackles critical gaps in conversational agent development tools by constructing a graphical Domain-Specific Language (DSL) and code generation templates for accelerating the development of conversational agents tailored to smart tourism’s needs. First, we provide a designed metamodel to define the abstract syntax of a DSL. Second, we implement the metamodel using the Eclipse Modeling Framework. Third, we develop a graphical interface that incorporates intuitive icons to simplify the creation of conversational agent models. Fourth, we define code generation templates to translate the graphical models into executable agent source code. Finally, we validate the proposed approach to demonstrate its effectiveness and applicability in real-world scenarios, reducing development time and avoiding the costs associated with NLP services.}, archive = {J_ACCESS}, author = {Charaf Ouaddi and Lamya Benaddi and Adnane Souha and Hamza Abdelmalek and Abdeslam Jakimi and Rachid Saadane}, doi = {10.1109/ACCESS.2025.3574641}, journal = {IEEE Access}, pages = {94150-94168}, shortjournal = {IEEE Access}, title = {A model-driven approach employing DSL and generation templates to accelerate the development of conversational agents for smart tourism}, volume = {13}, year = {2025}, }
• (2025). Search coil-based startup angle and rotor position estimation for enhanced full-speed-range motor drives. ACCESS, 13, 94137-94149. (www)

  @article{ , abstract = {This study presents a fully integrated motor drive system capable of full-speed-range operation by employing a rotor angle estimation method based on an enhanced search coil architecture. The proposed method addresses key limitations of conventional search coil techniques, which often lack inherent startup mechanisms and require auxiliary strategies. By relying solely on search coil signals, without the need for current sensors, the system accurately detects the initial rotor angle, enabling reliable and smooth startup. In contrast to conventional sensorless methods that rely on voltage and current for flux linkage estimation, the search coil approach can directly obtain flux linkage and achieve accurate and stable rotor position estimation through active flux linkage calculation. These features enable seamless and sensorless operation from standstill to high speed. To validate the system’s effectiveness, comprehensive experimental evaluations were conducted, including comparisons with widely adopted sensorless techniques. Performance was assessed across key metrics, including current harmonics, efficiency, and load-handling capability. The results confirm that the proposed search coil-based system offers superior accuracy, robustness, and operational stability across the entire speed range. In addition, the enhanced search coil design improves overall system resilience, providing a solid foundation for future advancements in motor drive technology.}, archive = {J_ACCESS}, author = {Hsien-Yuan Hsu and Min-Fu Hsieh}, doi = {10.1109/ACCESS.2025.3574449}, journal = {IEEE Access}, pages = {94137-94149}, shortjournal = {IEEE Access}, title = {Search coil-based startup angle and rotor position estimation for enhanced full-speed-range motor drives}, volume = {13}, year = {2025}, }
• (2025). Hybrid deep learning and fuzzy matching for real-time bidirectional arabic sign language translation: Toward inclusive communication technologies. ACCESS, 13, 94118-94136. (www)

  @article{ , abstract = {Technological advances and AI tools can help address the challenges faced by individuals who are deaf or nonverbal in different areas of social interaction. Existing tools mainly focus on one-way translation and are limited by small vocabulary datasets, require significant computational power, and often lack a real-time implementation. Therefore, a bidirectional real-time translation application for Arabic Sign Language and written Arabic text was developed in this research to improve communication and learning experiences for individuals who are deaf. The proposed system designed with two primary translation modules includes sign-to-text and text-to-sign. The sign-to-text module employs transfer learning models to translate Arabic sign images into text, while the text-to-sign module integrates a fuzzy string-matching tool to convert Arabic text into sign images. The system was customized using six CNN-based deep learning architectures: AlexNet, ResNet152V2, YOLOv8n, Swin Transformer, InceptionV3, and Xception. Additionally, the ArSL dataset and an Arabic data dictionary were employed to enhance the diversity, accuracy, and completeness of the selected CNN models, thereby improving the system’s adaptability across various users and contexts. Experimental evaluations were conducted to assess the system’s performance in terms of both accuracy and processing efficiency. The results demonstrated exceptionally high accuracy across all investigated CNN models, but YOLOv8n-cls demonstrated the highest accuracy with a score of 99.9%, followed by Xception, Swin Transformer, and AlexNet at 99.0%, and InceptionV3 and ResNet152V2 at 98.0%. These closely aligned results are attributed to the inherent characteristics of the dataset, as well as the shared methodologies employed, including preprocessing, data augmentation, cross-validation, and hyperparameter tuning. In terms of the real-time adaptability in recognizing each sign image, the InceptionV3, AlexNet, and YOLOv8n models achieved high efficiency, with execution times of 13 ms, 16 ms, and 67 ms, respectively. This result indicates that YOLOv8n stands out among other models, particularly for its superior accuracy and lower computational speed. These findings highlighted that the integration of deep learning models and fuzzy string-matching techniques results in significant improvement in both accuracy and speed over baseline models, confirming the feasibility of developing a robust and efficient real-time bidirectional Arabic Sign Language (ArSL) translation system. Thus, the proposed system has strong potential to reduce communication barriers between people who are deaf and hearing individuals.}, archive = {J_ACCESS}, author = {Mogeeb A. A. Mosleh and Ahmed A. A. Mohammed and Ezzaldeen E. A. Esmail and Rehab A. A. Mohammed and Basheer Almuhaya}, doi = {10.1109/ACCESS.2025.3574103}, journal = {IEEE Access}, pages = {94118-94136}, shortjournal = {IEEE Access}, title = {Hybrid deep learning and fuzzy matching for real-time bidirectional arabic sign language translation: Toward inclusive communication technologies}, volume = {13}, year = {2025}, }
• (2025). Optimal low-carbon scheduling for smart microgrids with dynamic thermal capacity constraints. ACCESS, 13, 94106-94117. (www)

  @article{ , abstract = {This study aims to integrate electric vehicles, photovoltaic and battery energy storage systems, and distribution network information in a microgrid to achieve decarbonized optimal operation. Under the different operating states of distribution networks, the paper proposes a decarbonized two-stage deeply integrated operational mode for a photovoltaic, battery energy storage system, and electric vehicles integrated microgrid, incorporating the electricity market to optimize overall revenue. In the optimization process, this paper introduces statistical node carbon emission factors and dynamic thermal capacity constraints to explore microgrids’ decarbonized potential and maximize system equipment’s utilization efficiency. This study proposes a two-stage day-ahead robust optimization framework for the microgrids, integrating tri-level min-max-min modeling to address multidimensional uncertainties through parameterized uncertainty sets to balance conservatism and optimize costs under worst-case scenarios. This work develops an adaptive threshold tightening mechanism for Column-and-Constraint Generation algorithms to balance computational efficiency and solution precision. Additionally, this paper proposes an adaptive dynamic pricing framework that integrates electric vehicle users’ cost-adaptive behavior with supply-demand fluctuations, using incentive-driven temporal optimization to maintain real-time grid equilibrium and enhance microgrid flexibility. Simulation results show that the proposed approach improves system profitability by up to 55.2% while enhancing transformer safety and operational flexibility.}, archive = {J_ACCESS}, author = {Peng Xie and Hongwei Liu and Chun Chen and Mingjun Liu}, doi = {10.1109/ACCESS.2025.3572952}, journal = {IEEE Access}, pages = {94106-94117}, shortjournal = {IEEE Access}, title = {Optimal low-carbon scheduling for smart microgrids with dynamic thermal capacity constraints}, volume = {13}, year = {2025}, }
• (2025). Quantum machine learning: Recent advances, challenges, and perspectives. ACCESS, 13, 94057-94105. (www)

  @article{ , abstract = {This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems in different domains, leveraging quantum algorithms to enhance classical machine learning techniques. We explore the application of QML in various domains such as cybersecurity, finance, healthcare, and drug discovery. The survey includes detailed tabular comparisons of the different QML models used for each application area, highlighting key techniques, findings, and their limitations. In this work, we identify important trends such as the strong potential of hybrid quantum-classical models for near-term applications and the significant challenges in the quantum domain due to quantum noise, limited qubit scalability, and costly qRAM implementations. Furthermore, we discuss solutions that emphasize advances in hardware, quantum error correction, and algorithmic innovations to address these challenges. By providing an in-depth analysis of QML’s potential across different fields, this study provides valuable insights into how QML can address complex real-world challenges and transform traditional machine learning practices.}, archive = {J_ACCESS}, author = {Pradeep Lamichhane and Danda B. Rawat}, doi = {10.1109/ACCESS.2025.3573244}, journal = {IEEE Access}, pages = {94057-94105}, shortjournal = {IEEE Access}, title = {Quantum machine learning: Recent advances, challenges, and perspectives}, volume = {13}, year = {2025}, }
• (2025). Chart accessibility: A review of current alt text generation. ACCESS, 13, 94040-94056. (www)

  @article{ , abstract = {Charts are valuable visual representations of data, provide an easy overview, and reveal important aspects of the data, such as trends and outliers, which are usually difficult to notice in textual representations. Charts are important for sighted readers but are inaccessible to screen reader users without the help of alternative text (alt text). Thus far, alt text has to be written manually, but recent advancements in deep learning models can support this process by relying on automatic chart understanding models. While numerous studies have explored automatic chart understanding tasks, there remains a lack of comprehensive reviews specifically focused on automatic alt text generation for screen reader users. In this study, we collected 38 papers and reviewed them according to the alt text generation workflow: target alt text content, methods for automatic alt text generation, and available applications that can assist alt text generation. Furthermore, we discuss the remaining challenges and outline the directions for future research in this field. This study is the first comprehensive survey that specifically focuses on the generation of alt text for impaired individuals. It serves as a resourceful overview for researchers and practitioners working on alt text generation, emphasizing the need for the continued development of chart accessibility.}, archive = {J_ACCESS}, author = {Chuqiao Yan and Hans-Peter Hutter and Felix M. Schmitt-Koopmann and Alireza Darvishy}, doi = {10.1109/ACCESS.2025.3571626}, journal = {IEEE Access}, pages = {94040-94056}, shortjournal = {IEEE Access}, title = {Chart accessibility: A review of current alt text generation}, volume = {13}, year = {2025}, }
• (2025). Study on user fraud identification of PV expansion based on a bottom-up approach of a DELM algorithm improved by SSA for a power distribution network. ACCESS, 13, 94026-94039. (www)

  @article{ , abstract = {In order to accurately identify users engaged in the fraudulent expansion of illegally distributed photovoltaic (PV) capacity, this paper initially leverages the similarity of the PV power generation output in the same region. It preprocesses the reference power station and the site to be tested using cosine similarity. Next, a Sparrow Search Algorithm (SSA) was applied to optimize the weight parameters of the Deep Extreme Learning Machine (DELM). And then, this study proposes DELM Network model improved by the SSA to detect fraudulent behavior among domestic customers, which is the primary cause of non-technical losses in distribution networks. Meanwhile, we utilized a bottom-up approach to determine the normal behavior pattern of household loads with and without PV sources. Customers suspected of energy theft are identified by calculating an anomaly index for each user. Finally, the proposed approach is validated both in simulation and using measurement data from real networks. The algorithm’s performance in detecting fraudulent behavior in outdated electromagnetic meters is evaluated and verified. The results demonstrate that the proposed SSA-DELM model achieves significant optimization, increasing the fitness degree by 3.89% and reducing the overall detection error by 67.12% compared to other single deep learning models without data preprocessing.}, archive = {J_ACCESS}, author = {Wang Jinpeng and Wei Haojie and Dou Shunyao and Jeremy-Gillbanks and Zhao Xin}, doi = {10.1109/ACCESS.2025.3574167}, journal = {IEEE Access}, pages = {94026-94039}, shortjournal = {IEEE Access}, title = {Study on user fraud identification of PV expansion based on a bottom-up approach of a DELM algorithm improved by SSA for a power distribution network}, volume = {13}, year = {2025}, }
• (2025). Benchmarking open-source large language models for sentiment and emotion classification in indonesian tweets. ACCESS, 13, 94009-94025. (www)

  @article{ , abstract = {We benchmark 22 open-source large language models (LLMs) against ChatGPT-4 and human annotators on two NLP tasks—sentiment analysis and emotion classification—for Indonesian tweets. This study contributes to NLP in a relatively low-resource language (Bahasa Indonesia) by evaluating zero-shot classification performance on a labeled tweet corpus. The dataset includes sentiment labels (Positive, Negative, Neutral) and emotion labels (Love, Happiness, Sadness, Anger, Fear). We compare model predictions to human annotations and report precision, recall, and F1-score, along with inference time analysis. ChatGPT-4 achieves the highest macro F1-score (0.84) on both tasks, slightly outperforming human annotators. The best-performing open-source models—such as LLaMA3.1_70B and Gemma2_27B—achieve over 90% of ChatGPT-4’s performance, while smaller models lag behind. Notably, some mid-sized models (e.g., Phi-4 at 14B parameters) perform comparably to much larger models on select categories. However, certain classes—particularly Neutral sentiment and Fear emotion—remain challenging, with lower agreement even among human annotators. Inference time varies significantly: optimized models complete predictions in under an hour, while some large models require several days. Our findings show that state-of-the-art open models can approach closed-source LLMs like ChatGPT-4 on Indonesian classification tasks, though efficiency and consistency in edge cases remain open challenges. Future work should explore fine-tuning multilingual LLMs on Indonesian data and practical deployment strategies in real-world applications.}, archive = {J_ACCESS}, author = {Arbi Haza Nasution and Aytug Onan and Yohei Murakami and Winda Monika and Anggi Hanafiah}, doi = {10.1109/ACCESS.2025.3574629}, journal = {IEEE Access}, pages = {94009-94025}, shortjournal = {IEEE Access}, title = {Benchmarking open-source large language models for sentiment and emotion classification in indonesian tweets}, volume = {13}, year = {2025}, }
• (2025). MangoLeafXNet: An explainable deep learning model for accurate mango leaf disease classification. ACCESS, 13, 93977-94008. (www)

  @article{ , abstract = {Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture applications, contributing to enhanced crop health monitoring and sustainable mango cultivation practices.}, archive = {J_ACCESS}, author = {Md. Eshmam Rayed and Jamin Rahman Jim and Md Juniadul Islam and M. F. Mridha and Md Mohsin Kabir and Md. Jakir Hossen}, doi = {10.1109/ACCESS.2025.3571450}, journal = {IEEE Access}, pages = {93977-94008}, shortjournal = {IEEE Access}, title = {MangoLeafXNet: An explainable deep learning model for accurate mango leaf disease classification}, volume = {13}, year = {2025}, }
• (2025). IMETA-GNN: Meta learning-based cold start optimization for recommendation system. ACCESS, 13, 93964-93976. (www)

  @article{ , abstract = {Recommendation systems are becoming essential components of contemporary online goods and services, and they significantly affect customer satisfaction. Recommendation systems are designed to empower customers in their decision-making process by providing personalized recommendations. Meta-learning has proven to be effective in addressing user cold-start problems in recommendation systems. Many meta-learning-based recommendation systems designed for the cold-start problem are gradient-based. The existing frameworks require optimization techniques to maximize the potential of hyper networks, enabling them to adapt and generate compatible parameters for efficient learning as meta-learners. However, these frameworks often lack contextual knowledge from users to provide suitable initial guidelines in the recommendation network for new users. We propose a meta-learning-based framework that improves cold-start recommendation accuracy by incorporating user behavior and preferences. Evaluations on MovieLens 100K and DBook datasets show our IMETA-GNN model outperforms state-of-the-art baselines, achieving accuracies of 82% and 75%, respectively. Performance evaluation demonstrates that our proposed method outperforms several state-of-the-art metalearning recommendation systems in addressing the user cold-start conundrum.}, archive = {J_ACCESS}, author = {Nida Siddique and Amna Zafar and Beenish Ayesha Akram and Muhammad Waseem and Sajid Iqbal and Ahmad A. Al-Yahya and Muhammad Nabeel Asghar and Abdullah Abdulrrehman Alaulamie}, doi = {10.1109/ACCESS.2025.3564454}, journal = {IEEE Access}, pages = {93964-93976}, shortjournal = {IEEE Access}, title = {IMETA-GNN: Meta learning-based cold start optimization for recommendation system}, volume = {13}, year = {2025}, }
• (2025). Sto-CAV: A distributed simulation platform for connected and automated vehicles running with heterogeneous technologies and devices in the loop. ACCESS, 13, 93949-93963. (www)

  @article{ , abstract = {The development and validation of vehicular applications require extensive testing, often constrained by high costs, logistical challenges, and safety concerns. To address these issues, we introduce Sto-CAV (SimulaTiOn platform for Connected and Automated Vehicles), a novel, flexible, and scalable simulation platform that enables remote access and modular testing for connected and automated vehicle applications. Sto-CAV integrates both virtual and physical components geographically distributed, allowing users to execute simulations without requiring local hardware or software. In addition, Sto-CAV supports heterogeneous vehicle-to-everything (V2X) radio access technologies, including ITS-G5 based on IEEE 802.11p, LTE-V2X and Visible Light Communication (VLC), facilitating the test of beyond 5G vehicular communication scenarios where traditional RF communications meet optical bands. Unlike existing solutions, Sto-CAV emphasizes ease of configuration, remote accessibility, and the integration of heterogeneous communication technologies, thus providing a comprehensive tool for testing vehicular systems, accessible to researchers and developers worldwide. This paper details the design, capabilities, and applications of Sto-CAV, highlighting its potential to advance the testing of innovative vehicle technologies, also when they are geographically distributed, allowing average latencies suitable for quasi-real time applications.}, archive = {J_ACCESS}, author = {Barbara M. Masini and Mirko Mirabella and Leonardo Donnini and Muhammad Ali Umair and Marco Meucci and Mauro Aresti and Jacopo Catani}, doi = {10.1109/ACCESS.2025.3572358}, journal = {IEEE Access}, pages = {93949-93963}, shortjournal = {IEEE Access}, title = {Sto-CAV: A distributed simulation platform for connected and automated vehicles running with heterogeneous technologies and devices in the loop}, volume = {13}, year = {2025}, }
• (2025). Investigating the electric field influence on total ionization dose effects in space radiation for carbon nanotube field effect transistors. ACCESS, 13, 93941-93948. (www)

  @article{ , abstract = {Carbon nanotube field effect transistors (CNTFETs) are considered ideal components for radiation-hardened integrated circuits (ICs) due to their material and structural properties. CNTFETs have been shown to have good tolerance to total ionizing dose (TID) effects under no-bias condition. However, in practical applications, ICs typically operate under specific bias conditions. It is necessary to evaluate the TID effects of CNTFETs under different bias conditions. A wafer-level chip probe power-up system was designed to power CNTFETs during Co- $60~\gamma $ -ray irradiation. The experimental results show that the bias condition greatly affects the TID tolerance of the CNTFET. Under the no-bias condition, the CNTFET is tolerant to TID up to 2000 k rad (Si). On the contrary, under off-bias condition, the failure occurs at 200 k rad (Si). The failure of the CNTFET is characterized by out-of-control gate voltage and its failure mechanism is analyzed. It is attributed to the breakdown of the gate dielectric (HfO2) between the gate and the source via the time-dependent dielectric breakdown (TDDB) effect.}, archive = {J_ACCESS}, author = {Yanan Liang and Qian Chen and Rui Chen and Runjie Yuan and Ziyu Wang and Hailong Yu and Jianwei Han}, doi = {10.1109/ACCESS.2025.3572530}, journal = {IEEE Access}, pages = {93941-93948}, shortjournal = {IEEE Access}, title = {Investigating the electric field influence on total ionization dose effects in space radiation for carbon nanotube field effect transistors}, volume = {13}, year = {2025}, }
• (2025). Scrolling to success: A systematic analysis of social media’s role in scholarly communications. ACCESS, 13, 93930-93940. (www)

  @article{ , abstract = {This study presents the first comprehensive systematic literature review examining the patterns and trends of social media use among Asian scholars for scholarly communication and the advancement of digital scholarship. While social media platforms have become integral to global scholarly activities, including collaboration, research dissemination, and public discourse, a thorough understanding of their specific adoption and impact within diverse Asian academic contexts has been notably absent. Through a rigorous analysis of 42 articles, selected from an initial pool of 108 publications across two major databases spanning a 14-year period, this research identifies five key themes that shape scholarly social media engagement in Asia: overall usage, analytical approaches to social media data, the functions platforms serve, the features utilized, and the communication behaviors exhibited. The synthesis of these findings reveals significant variations in how scholars across different Asian regions leverage social media for academic purposes. This pioneering review offers a structured framework for understanding digital scholarship practices within Asia, thereby highlighting critical research gaps and providing strategic directions for enhancing scholarly communication through these rapidly evolving platforms. The principal contribution of this work is the proposal of an evidence-based framework that integrates these five identified themes, establishing a novel and foundational resource for future research aimed at optimizing academic knowledge dissemination via social media in the Asian context.}, archive = {J_ACCESS}, author = {Nur Syazwani Ahmad and Ali Fauzi and Samsuddin Samsul Farid and Shamshul Bahri}, doi = {10.1109/ACCESS.2025.3569232}, journal = {IEEE Access}, pages = {93930-93940}, shortjournal = {IEEE Access}, title = {Scrolling to success: A systematic analysis of social media’s role in scholarly communications}, volume = {13}, year = {2025}, }
• (2025). SMPL variable model for 3D reconstruction and image fusion in animation media applications. ACCESS, 13, 93914-93929. (www)

  @article{ , abstract = {3D human reconstruction is a pivotal research area in computer vision and computer graphics, with broad applications in fields such as virtual reality, animation media, and medical simulation. Traditional reconstruction approaches often rely on fixed-shape models, which limits their ability to accurately represent the dynamic soft tissue variations and complex interactions with the surrounding environment. These methods face challenges in managing occlusions in multi-person scenes, capturing a diverse range of poses, and handling extensive deformations in real-world scenarios, which constrains their practical application. To overcome these limitations, this paper introduces an enhanced 3D reconstruction model using the SMPL variable model, which integrates a soft tissue layer, UV space mapping, and a force-conditioning mechanism. The proposed model incorporates a variable-thickness soft tissue layer to better capture anatomical variations, utilizes UV space mapping to streamline deformation calculations, and employs force-conditioning to realistically simulate intricate interactions. Experimental results on datasets such as ShapeNet, ShapeNetCore, ABC, and Scan2CAD demonstrate that our approach surpasses current state-of-the-art methods in terms of accuracy, recall, F1 score, and AUC, while also achieving greater efficiency in both training and inference phases. This research not only delivers a more accurate and computationally efficient solution for 3D reconstruction but also contributes novel insights into multi-person interaction modeling and enhances generalization in real-world animation media applications.}, archive = {J_ACCESS}, author = {Tian Xie and Kunpeng Zhao}, doi = {10.1109/ACCESS.2025.3549466}, journal = {IEEE Access}, pages = {93914-93929}, shortjournal = {IEEE Access}, title = {SMPL variable model for 3D reconstruction and image fusion in animation media applications}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization for volt-var parameter tuning: Reducing losses and voltage unbalance in distribution systems. ACCESS, 13, 93903-93913. (www)

  @article{ , abstract = {The integration of distributed energy resources (DERs) into power grids requires new methodologies to optimize and efficiently control distribution network parameters. This paper presents a multi-objective optimization approach to enhance the operation of distribution networks with high penetration of distributed generation (DG). The proposed method jointly optimizes the active power output of dispatchable DG units and the Volt-Var control parameters of photovoltaic (PV) inverters to minimize power losses and voltage unbalance, ensuring compliance with the 2% limit set by Brazilian regulations. The optimization problem is solved using NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOPSO (Multi-Objective Particle Swarm Optimization), with three-phase power flow analysis performed using OpenDSS integrated into a Python-based framework. Simulations on the IEEE 123-bus system demonstrate that coordinated control of intermittent and dispatchable sources improves voltage profiles, reduces system losses, and significantly mitigates voltage unbalance.}, archive = {J_ACCESS}, author = {Arthur E. S. Ribeiro and Francisco C. R. Coelho and Wesley Peres}, doi = {10.1109/ACCESS.2025.3573245}, journal = {IEEE Access}, pages = {93903-93913}, shortjournal = {IEEE Access}, title = {Multi-objective optimization for volt-var parameter tuning: Reducing losses and voltage unbalance in distribution systems}, volume = {13}, year = {2025}, }
• (2025). TIER: Temporal convolutional network information extractor with conditional random field. ACCESS, 13, 93892-93902. (www)

  @article{ , abstract = {As data scales expand, effectively modeling long-distance dependencies has become crucial for information extraction. We propose TIER, which combines the Temporal Convolutional Network (TCN) and the Conditional Random Field (CRF). The dilated convolution structure of TCN is used to expand the Receptive Fields (RFs) to capture long-distance context features, and the global dependency between labels is modeled through the CRF layer to improve the information extraction performance further. We conducted experimental evaluations on three information extraction tasks, and the results show that our model outperforms SOTA models on four datasets. Specifically, on the MSRA and DUIE 2.0 datasets, F1-scores reached 94.97% and 93.64%, improving by 4.82% and 4.34% compared to the average performance of SOTA models. On the CLUENER dataset, an improvement of 4.12% is achieved compared to SOTA models, demonstrating the superiority of our approach. Additionally, we performed low-resource experiments, and under 30%-70% of the MSRA and CLUENER training data, F1-scores remained at relatively high levels, verifying that our method maintains good adaptability and generality in low-resource scenarios.}, archive = {J_ACCESS}, author = {Huiwen Wu and Xiubo Zhang and Tengyuan Zhou and An Hu}, doi = {10.1109/ACCESS.2025.3574042}, journal = {IEEE Access}, pages = {93892-93902}, shortjournal = {IEEE Access}, title = {TIER: Temporal convolutional network information extractor with conditional random field}, volume = {13}, year = {2025}, }
• (2025). Comments on “L-ECQV: Lightweight ECQV implicit certificates for authentication in the internet of Things”—Efficiency improvement based on the certificate digest method. ACCESS, 13, 93883-93891. (www)

  @article{ , abstract = {The state-of-the-art study, Malik et al. (2023) eliminates the need for a signature within the certificate using Elliptic Curve Qu-Vanstone (ECQV) and introduces a lightweight ECQV implicit certificate structure to significantly reduce the certificate length within packets. This is a noteworthy research achievement. Building on this foundation, the current study integrates the Certificate Digest Method, allowing each packet to carry only the certificate digest rather than the full certificate, further reducing packet size. Additionally, by utilizing pre-stored public keys, this study avoids the need to reconstruct the actual public key using reconstruction data and the certificate authority’s public key during each signature verification. In the experimental setup, this study was implemented on a Raspberry Pi 4 Model B and evaluated the computational efficiency across different National Institute of Standards and Technology (NIST) and Brainpool elliptic curves.}, archive = {J_ACCESS}, author = {Abel C. H. Chen}, doi = {10.1109/ACCESS.2025.3573266}, journal = {IEEE Access}, pages = {93883-93891}, shortjournal = {IEEE Access}, title = {Comments on “L-ECQV: Lightweight ECQV implicit certificates for authentication in the internet of Things”—Efficiency improvement based on the certificate digest method}, volume = {13}, year = {2025}, }
• (2025). Radiation-Hardened—AI-accelerated custom IC design methodology. ACCESS, 13, 93869-93882. (www)

  @article{ , abstract = {In this work, a radiation hardened - AI accelerated custom IC design methodology is proposed. The methodology employs reinforcement learning (RL) to optimize the IC design process, integrating radiation dosage performance degradation assessments and radiation-hardened-by-design (RHBD) MOSFET cell implementations at both schematic and layout levels. This approach streamlines the development of radiation-immune silicon products by embedding artificial intelligence to accelerate the design process and advanced radiation hardening strategies directly into the standard design flow. To validate the proposed framework, a charge-sensitive amplifier (CSA) based on a folded-cascode architecture is designed and assessed in a 180 nm standard CMOS process, demonstrating the efficiency of the methodology in radiation-resilient analog front-end systems.}, archive = {J_ACCESS}, author = {Vasiliki Gogolou and Savvas Karipidis and Eleni Papageorgiou and Anastasios Michailidis and Thomas Noulis}, doi = {10.1109/ACCESS.2025.3574058}, journal = {IEEE Access}, pages = {93869-93882}, shortjournal = {IEEE Access}, title = {Radiation-Hardened—AI-accelerated custom IC design methodology}, volume = {13}, year = {2025}, }
• (2025). Classification of short-segment pediatric heart sounds based on a transformer-based convolutional neural network. ACCESS, 13, 93852-93868. (www)

  @article{ , abstract = {Congenital heart diseases (CHDs), caused by structural abnormalities in the heart and blood vessels, pose a significant public health concern and contribute significantly to the socioeconomic burden, particularly in pediatric populations. Phonocardiograms (PCGs), as a non-invasive and cost-effective diagnostic modality, capture vital acoustic signals that reflect the mechanical activity of the heart and can reveal pathological patterns associated with various CHD types. This study investigates the minimum signal duration required for accurate automatic classification of heart sounds and evaluates signal quality using the root mean square of successive differences (RMSSD) and the zero-crossing rate (ZCR). Mel-frequency cepstral coefficients (MFCCs) are extracted as features and fed into a transformer-based residual one-dimensional convolutional neural network (1D-CNN) for classification. Experimental results show that a threshold of 0.4 for RMSSD and ZCR yields optimal classification performance, with a minimum signal length of 5 seconds required for reliable results. Shorter segments (3 seconds) lack sufficient diagnostic information, while longer segments (15 seconds) may introduce additional noise. The proposed model achieves a maximum classification accuracy of 93.69% with 5-second signals.}, archive = {J_ACCESS}, author = {Md Hassanuzzaman and Samit Kumar Ghosh and Mohammad Nurul Akhtar Hasan and Mohammad Abdullah Al Mamun and Khawza I. Ahmed and Raqibul Mostafa and Ahsan H. Khandoker}, doi = {10.1109/ACCESS.2025.3573870}, journal = {IEEE Access}, pages = {93852-93868}, shortjournal = {IEEE Access}, title = {Classification of short-segment pediatric heart sounds based on a transformer-based convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Cachedia: An environment for efficient cache verification with graphic visualization for debugging. ACCESS, 13, 93840-93851. (www)

  @article{ , abstract = {The recent boom in open-source processor architectures such as RISC-V has increased the never-ending demand for peripherals and System-on-Chip (SoC) hardware. With this in mind, how to provide an efficient graphical interface environment to increase the visibility of this system is an important question, not only for processors but also for internal blocks such as caches. This paper introduces Cachedia as an environment for better visualization and debugging of direct-mapped and set-associative hardware caches using graphical interfaces. Cachedia provides a comprehensive and user-friendly environment for designing, debugging, and verifying these systems for a variety of applications. The tool includes a Graphical User Interface (GUI) that allows the user to not only configure, visualize and understand the behavior of a cache but also to create an abstract model view to verify the provided hardware design. This provides a visual method for learning and understanding the system and the tools for further development and verification. The environment successfully generated a view model for a configurable cache that can be instantiated as a block design in Vivado. In summary, using the graphic visualization blocks in Cachedia can potentially be advantageous for debugging complex hardware designs.}, archive = {J_ACCESS}, author = {Francisco J. Rodriguez-Navarrete and German Pinedo-Diaz and L. A. Luna-Rodriguez and Susana Ortega-Cisneros and L. Pizano-Escalante and J. J. Raygoza-Panduro}, doi = {10.1109/ACCESS.2025.3531897}, journal = {IEEE Access}, pages = {93840-93851}, shortjournal = {IEEE Access}, title = {Cachedia: An environment for efficient cache verification with graphic visualization for debugging}, volume = {13}, year = {2025}, }
• (2025). Empirical evaluation and scalability analysis of proof of team sprint (PoTS): Reward fairness, energy efficiency, and system stability. ACCESS, 13, 93830-93839. (www)

  @article{ , abstract = {This paper presents a comprehensive empirical evaluation of the Proof of Team Sprint (PoTS) consensus algorithm, focusing on its impact on reward fairness, energy efficiency, system stability, and scalability. To assess these factors, we conducted large-scale simulations comparing PoTS with conventional Proof of Work (PoW) under various computational environments and team configurations. Our results demonstrate that PoTS significantly mitigates reward disparities caused by heterogeneous computational capabilities. In PoW, the highest-performing node secured the top ranking in 100 out of 100 trials, leading to extreme centralization. In contrast, under PoTS, the dominance of high-performance nodes was reduced, with the strongest node ranking first only 55 times out of 100, ensuring a fairer distribution of rewards among participants. The skewness and kurtosis of the reward distribution decreased as team size increased, confirming that PoTS fosters a more equitable allocation of mining rewards. Energy efficiency analysis revealed that PoTS exhibits a near $1/N$ scaling trend in computational workload, where N is the team size. Total active computation time in PoTS (team size 64) was reduced by a factor of 64 compared to PoW, demonstrating a drastic reduction in energy consumption while maintaining the same level of security and consensus reliability. Furthermore, system stability and scalability evaluations confirmed that PoTS maintains statistical consistency across multiple simulation runs, ensuring robust performance regardless of network size. The correlation between participant performance and reward allocation increased with team size, peaking at 0.898 when $N=16$ , before stabilizing, highlighting the controlled balance between computational power and fairness. These findings suggest that PoTS is a viable alternative to traditional PoW, offering a more decentralized, fair, and energy-efficient approach to achieving blockchain consensus. This study provides empirical validation for PoTS’s ability to improve the sustainability and fairness of decentralized networks, positioning it as a promising consensus mechanism for future blockchain applications.}, archive = {J_ACCESS}, author = {Naoki Yonezawa}, doi = {10.1109/ACCESS.2025.3574302}, journal = {IEEE Access}, pages = {93830-93839}, shortjournal = {IEEE Access}, title = {Empirical evaluation and scalability analysis of proof of team sprint (PoTS): Reward fairness, energy efficiency, and system stability}, volume = {13}, year = {2025}, }
• (2025). Medical knowledge-based differential image visual question answering. ACCESS, 13, 93818-93829. (www)

  @article{ , abstract = {Visual Question Answering (VQA) technology shows great promise for cross-disciplinary applications, with its integration into the medical field emerging as a major research focus in recent years. The current mainstream medical visual question answering (VQA) models only support single-image input, whereas differential medical VQA can support multiple images and answer questions, including those about differences between images. However, these approaches primarily focus on extracting information from images while neglecting the inherent information and relevant relationships associated with the diseases themselves. Therefore, this paper proposes a differential medical visual question answering method based on medical knowledge, comprising three modules: the feature encoding module, the feature processing module, and the answer generation module. The proposed method first learns cluster embeddings of medical knowledge features through the feature encoding module, which are then interactively learned with image and text features. Following differential operations, these features are fed into the feature attention module, and subsequently into the answer generation module to produce the final answer. Experimental results demonstrate that our method significantly enhances the performance of the differential medical visual question answering task. This advancement is of considerable reference value in improving the applicability and interpretability of medical visual question answering.}, archive = {J_ACCESS}, author = {Fangpeng Lu and Songyan Liu and Wenbin Lu and Peng Chen and Boyang Ding}, doi = {10.1109/ACCESS.2025.3565695}, journal = {IEEE Access}, pages = {93818-93829}, shortjournal = {IEEE Access}, title = {Medical knowledge-based differential image visual question answering}, volume = {13}, year = {2025}, }
• (2025). Effects of changing consumer demand on distributed generation and grid optimization. ACCESS, 13, 93798-93817. (www)

  @article{ , abstract = {Integrating Distributed Generations (DGs) within adaptive power grids is a highly effective approach to reducing electricity losses and minimizing generation and switching costs in contemporary electric systems. The efficiency of these strategies is heavily influenced by consumer load profiles, which significantly affect energy losses, generation expenses, and overall purchasing costs. This connection underscores the crucial role of consumer load variations in determining electricity losses, operational expenditures, and energy pricing, thereby impacting the network’s topology and the optimal placement of DGs. Considering load variability in DG placement within reconfigurable networks adds considerable computational complexity, resulting in extended processing times. Ignoring these variations, however, can lead to inaccurate estimations of losses and costs. This research examines how consumer load variations affect the allocation of DG and the reconfiguration strategies of distribution grids. Utilizing A Mathematical Programming Language (AMPL) as a traditional optimization tool, the study evaluates the proposed model across diverse distribution networks. The simulation results demonstrate that while consumer load variations do not change the total number of DG units required, they significantly influence the optimal locations, switching combinations, and total grid costs. From an energy policy perspective, these findings emphasize the need for utility operators to incorporate consumer load dynamics into their strategies. This integration ensures better alignment with regulatory frameworks that promote efficient energy.}, archive = {J_ACCESS}, author = {Meisam Mahdavi and Amir Bagheri and Alireza Soleimani and Anna Pinnarelli and Francisco Jurado and Augustine Awaafo}, doi = {10.1109/ACCESS.2025.3573588}, journal = {IEEE Access}, pages = {93798-93817}, shortjournal = {IEEE Access}, title = {Effects of changing consumer demand on distributed generation and grid optimization}, volume = {13}, year = {2025}, }
• (2025). Data-driven policy making framework utilizing TOWS analysis. ACCESS, 13, 93786-93797. (www)

  @article{ , abstract = {In the era of hyper-uncertainty, the demand for data-driven policy making has grown significantly, as it enables the fast and accurate capture of complex phenomena. This contrasts with conventional policy making processes, which are often slow and heavily reliant on the limited experience and knowledge of a select group of experts. Although interest in data-driven approaches continues to grow, existing research primarily focuses on identifying policy agendas, leaving policymakers to develop the specific content for those agendas from scratch. In this paper, we propose a novel data-driven policy making framework based on TOWS analysis, a well-established method for strategic extraction that systematically integrates internal strengths and weaknesses with external opportunities and threats. Our framework automatically extracts policy agendas and their specific contents. It begins by identifying national capabilities and cross-cutting issues to establish key agendas using topic modeling and sentiment analysis. Then, a TOWS matrix is constructed to derive policy directions by linking capabilities with issues. Here, relevant phrases are extracted to elaborate on these directions, forming policy content. To validate our framework, we conducted a case study on South Korea’s post-COVID-19 policies. The policies derived from our framework closely aligned with real-world ones and, in some instances, provided more detailed recommendations. This demonstrates the potential of data-driven approaches to enhance the precision and comprehensiveness of policy development in times of uncertainty.}, archive = {J_ACCESS}, author = {Yeonbin Son and Youngeun Kim and Yerim Choi}, doi = {10.1109/ACCESS.2025.3572908}, journal = {IEEE Access}, pages = {93786-93797}, shortjournal = {IEEE Access}, title = {Data-driven policy making framework utilizing TOWS analysis}, volume = {13}, year = {2025}, }
• (2025). Comparing electricity consumption per use of blockchain and generative AI. ACCESS, 13, 93775-93785. (www)

  @article{ , abstract = {The rapid adoption of blockchain technology and generative AI contributes significantly to global electricity consumption, raising concerns about environmental sustainability. The first step in saving energy is to identify current consumption. However, since blockchain and generative AI are cloud-based services, it is difficult to understand electricity consumption outside one’s facilities. This creates a barrier for user companies and organizations seeking to increase the accuracy of calculating Scope 3 emissions. This study quantifies the electricity consumption of these technologies at a system-wide and per-use level. It compares them to traditional services such as payment networks and web search engines. Bitcoin, a Proof of Work (PoW) blockchain, consumes approximately 121TWh, equivalent to 0.43% of global electricity consumption, and its energy demand per transaction is 720,000 times higher than that of the Visa payment system. Ethereum’s move to Proof of Stake (PoS) in 2022 reduces energy consumption by 99.988%, demonstrating the potential for efficiency gains. Generative AI models also have significant energy requirements, especially during the training and inference phases. For example, training GPT-4 required approximately 9450MWh, and daily inference work exceeded 500MWh. The results show that inference, driven by frequent user interaction, often exceeds the energy consumption of training. The study underscores the urgency of addressing these technologies’ environmental impact through strategies such as adopting energy-efficient consensus mechanisms and optimizing AI’s lifecycle. These findings are intended to guide organizations in refining their Scope 3 emissions calculations and adopting sustainable technology practices.}, archive = {J_ACCESS}, author = {Yusuke Kaneko}, doi = {10.1109/ACCESS.2025.3573722}, journal = {IEEE Access}, pages = {93775-93785}, shortjournal = {IEEE Access}, title = {Comparing electricity consumption per use of blockchain and generative AI}, volume = {13}, year = {2025}, }
• (2025). Efficient edge AI for next generation smart mirror applications. ACCESS, 13, 93755-93774. (www)

  @article{ , abstract = {A natural user interaction interface is required to enhance living environments to be fully smart, which would imply features like user-specific, individual interaction as well as awareness of context, situation, and environment. One already introduced example of such an interaction interface using only web service-based speech recognition is voice assistants, such as Amazon Alexa. A widely used visual solution is a smart mirror, consisting of a semi-transparent mirror underlay with a display and a computing device. In most cases, it only shows information next to the mirror image and does not feature an interactive interaction with the user. Hence, it does not utilize the potential of locally processed artificial intelligence. This publication presents the enhancement of a smart mirror with locally processed artificial intelligence, ensuring low-latency interaction as well non-exposure of private data. For a pleasant user experience, the image-based recognition of objects, gestures, and faces in combination with voice commands via speech recognition is used. With these features, the user can be identified, interact with the system, and receive personalized information or assistance. Due to the modular software architecture using ROS2 nodes, different embedded hardware modules and accelerators can be utilized. Possible configurations for such hardware and accelerator systems as well as optimization to increase energy efficiency are described and evaluated. Low power consumption, and therefore high energy-efficiency, is necessary for the application as it is intended to be integrated into private homes. Optimizations using pruning are evaluated and show improvements in energy per processed frame, required calculations per frame (50% reduction), and accuracy (increased for some classes by 15%). Currently the best solution regarding efficiency and optimized DNN models achieves 30FPS for object, gesture, and face detection models simultaneously while consuming around 38Watts, resulting in 1.3mJ per frame.}, archive = {J_ACCESS}, author = {Nils Kucza and Florian Porrmann and Christian Stollenwerk and Jens Hagemeyer}, doi = {10.1109/ACCESS.2025.3574492}, journal = {IEEE Access}, pages = {93755-93774}, shortjournal = {IEEE Access}, title = {Efficient edge AI for next generation smart mirror applications}, volume = {13}, year = {2025}, }
• (2025). Solar energy forecasting using machine learning techniques for enhanced grid stability. ACCESS, 13, 93735-93754. (www)

  @article{ , abstract = {The increasing integration of solar photovoltaic (PV) systems into modern energy grids presents significant challenges due to the intermittent and weather-dependent nature of solar energy generation. Accurate short-term forecasting is essential to ensure grid stability and optimize energy resource allocation. This study proposes a comprehensive data-driven framework for solar energy forecasting using multiple machine learning (ML) techniques, including Multiple Linear Regression, Ridge, Lasso, Decision Tree Regression, Support Vector Regression, and ensemble-based models such as Random Forest, AdaBoost, Bagging, and Gradient Boosting Regressors. The framework incorporates advanced feature engineering using high-resolution meteorological and solar geometric parameters-such as relative humidity, temperature, cloud cover, zenith angle, azimuth, and angle of incidence-to enhance model accuracy. Historical solar power and weather datasets were used to train and evaluate the models across multiple performance metrics. Among the models, the Gradient Boosting Regressor demonstrated the best performance, achieving an $R^{2}$ of 0.827, RMSE of 399.44, and MAE of 253.62, marking a significant improvement over baseline models. The study also evaluates model robustness and discusses feature relevance, hyperparameter optimization strategies, and deployment considerations for real-time grid operations. These findings provide practical insights for stakeholders aiming to implement intelligent solar forecasting systems in smart grid environments, thereby contributing to enhanced energy management and grid resilience.}, archive = {J_ACCESS}, author = {Attuluri R. Vijay Babu and N. Bharath Kumar and Rajanand Patnaik Narasipuram and Soundhar Periyannan and Alireza Hosseinpour and Aymen Flah}, doi = {10.1109/ACCESS.2025.3574093}, journal = {IEEE Access}, pages = {93735-93754}, shortjournal = {IEEE Access}, title = {Solar energy forecasting using machine learning techniques for enhanced grid stability}, volume = {13}, year = {2025}, }
• (2025). A comprehensive content-based recommendation system for programming problems through multi-faceted code analysis. ACCESS, 13, 93712-93734. (www)

  @article{ , abstract = {Recommending exercises in educational contexts requires balancing relevance and diversity to support effective learning progression. In such settings, content-based recommendation is particularly suitable, as it aligns with specific learning objectives and supports progression without relying on extensive user interaction history. This study introduces a content-based recommendation system (CBRS) designed to suggest programming exercises based on intrinsic characteristics of source code. The system evaluates syntactic, structural, statistical, complexity, and semantic attributes to provide a comprehensive assessment of exercise similarity. The approach addresses the challenge of identifying exercises that require similar knowledge or complexity levels to those previously completed by students. This capability is especially beneficial when students struggle with specific concepts, as it enables the recommendation of additional exercises targeting the same content to reinforce understanding and promote mastery. Each feature was first evaluated individually to determine its contribution to recommendation effectiveness. Selected combinations were then tested to examine how different attributes influence recommendation quality in terms of relevance and diversity. The full integration of all features resulted in a precision of 0.70, recall of 0.77, MRR of 0.64, coverage of 0.63, serendipity of 0.49, and novelty of 0.51. The proposed CBRS demonstrates strong alignment with learners’ exercise histories and the potential to generate recommendations that balance familiarity with exploration. Compared with collaborative filtering models on the same dataset, the CBRS achieved stronger performance in diversity-focused metrics, supporting more engaging and meaningful learning experiences.}, archive = {J_ACCESS}, author = {Daniel M. Muepu and Yutaka Watanobe and Md. Faizul Ibne Amin}, doi = {10.1109/ACCESS.2025.3574246}, journal = {IEEE Access}, pages = {93712-93734}, shortjournal = {IEEE Access}, title = {A comprehensive content-based recommendation system for programming problems through multi-faceted code analysis}, volume = {13}, year = {2025}, }
• (2025). A review of YOLO algorithm and its applications in autonomous driving object detection. ACCESS, 13, 93688-93711. (www)

  @article{ , abstract = {Object detection in autonomous driving scenarios represents a significant research direction within artificial intelligence. Real-time and accurate object detection and recognition are crucial in ensuring autonomous vehicles’ safe and stable operation. In recent years, the continuous introduction of the YOLO series of algorithms and their enhanced models has led to remarkable performance in autonomous driving object detection. From YOLOv1 to YOLOv12, detection accuracy has improved significantly, with mAP increasing from approximately 63.4% to over 80% and inference speed exceeding 100 FPS in lightweight versions such as YOLOv8n and YOLOv10. This paper reviews the YOLO algorithm and its application in object detection in autonomous driving scenarios. Firstly, the development and distinctions among the YOLO series of detection algorithms are explained, and their performance is analyzed. Secondly, the strategies for improving YOLO-based models across the input, feature extraction, and prediction stages are summarized. Thirdly, the research status and application of the YOLO algorithm in autonomous driving object detection are elaborated upon from the perspectives of traffic vehicles, pedestrians, traffic signs, traffic lights, and lane lines, with comparisons and analyses of performance metrics such as accuracy and real-time performance. Finally, considering the current challenges in autonomous driving object detection, the development trajectory and prospects of the YOLO algorithm are summarized and discussed.}, archive = {J_ACCESS}, author = {Jiapei Wei and Azizan As’arry and Khairil Anas Md Rezali and Mohd Zuhri Mohamed Yusoff and Haohao Ma and Kunlun Zhang}, doi = {10.1109/ACCESS.2025.3573376}, journal = {IEEE Access}, pages = {93688-93711}, shortjournal = {IEEE Access}, title = {A review of YOLO algorithm and its applications in autonomous driving object detection}, volume = {13}, year = {2025}, }
• (2025). The imbalanced target classification method based on mixed learning of virtual and real data. ACCESS, 13, 93672-93687. (www)

  @article{ , abstract = {We proposes a category imbalance classification model based on mixed feature enhancement between virtual and real domains to address the class imbalance problem in maritime target classification applications. In practical maritime target classification tasks, the imbalanced class distribution in real-world data poses challenges such as low accuracy and poor robustness in model training. To overcome this challenge, we combines balanced data generated from virtual simulation environments with imbalanced real-world data, employing a mixed learning approach to improve the model’s generalization ability and robustness. However, the domain shift problem between virtual and real data limits the transferability of the model. To address this challenge, domain adaptation methods are used to adjust the feature distribution discrepancy between the two domains, and a feature augmentation strategy is introduced at the hidden layer level to enhance the representation of tail classes (minority classes). Through experiments on multiple datasets combining balanced and imbalanced domains, the proposed model effectively alleviates the issues caused by class imbalance and improves model generalization performance in mixed data environments. On the imbalanced MNIST-M dataset, the imbalance rate was set to 0.1, and the accuracy of the model we trained was as high as 94.8%. In the unbalanced subdomain clipart dataset of DomainNet, using the proposed method, the accuracy of the trained model is as high as 65.1%. Finally, the accuracy of the model we trained on the Marine target classification dataset in the imbalanced reality domain reached 88.6%.}, archive = {J_ACCESS}, author = {Fengyu Yang and Peng Wang and Wutao Qin and Zhangze Liao}, doi = {10.1109/ACCESS.2025.3573320}, journal = {IEEE Access}, pages = {93672-93687}, shortjournal = {IEEE Access}, title = {The imbalanced target classification method based on mixed learning of virtual and real data}, volume = {13}, year = {2025}, }
• (2025). Operator as a service: A new concept for remote supervision of automated manufacturing lines. ACCESS, 13, 93661-93671. (www)

  @article{ , abstract = {This paper introduces the Operator as a Service (OaaS) concept, a novel paradigm for human labour in the era of Industry 4.0. OaaS empowers skilled operators to remotely oversee and control multiple flexible smart manufacturing lines through advanced technologies such as digital twins and remote collaboration. By transitioning from on-site, full-time operators to specialized remote operators available on demand, OaaS optimizes human resource utilization and facilitates the transition towards more flexible and responsive manufacturing systems. This paper presents the OaaS architecture, discusses its potential benefits for enhancing operational efficiency and adaptability in semi-controlled manufacturing environments, and validates its feasibility through a real-world testbed involving a fully automated assembly line. The results demonstrate the potential of OaaS to address the challenges of maintaining human oversight and ensuring operational flexibility in increasingly complex and interconnected manufacturing systems.}, archive = {J_ACCESS}, author = {Marco Ojer and Sergio Valdés and Ander Garcia and Xabier Oregui and Iker Gastesi and Xiao Lin and Iñigo Mendizabal-Arrieta and Jairo R. Sanchez and Elena Lazkano}, doi = {10.1109/ACCESS.2025.3574415}, journal = {IEEE Access}, pages = {93661-93671}, shortjournal = {IEEE Access}, title = {Operator as a service: A new concept for remote supervision of automated manufacturing lines}, volume = {13}, year = {2025}, }
• (2025). Integrating fine-grained classification and motion relation analysis for face anti-spoofing. ACCESS, 13, 93649-93660. (www)

  @article{ , abstract = {Face Anti-Spoofing (FAS) is the core technical means used to protect face recognition systems from face presentation attack. Facial movement is a commonly used cue in FAS. Existing face anti-spoofing methods based on facial movement usually extract motion features from the overall face, ignoring micro-movements in facial regions. Additionally, the fine-grained texture information of local facial regions is not well integrated into the motion. Therefore, we propose a face anti-spoofing detection method that integrates fine-grained classification and motion relation analysis. Specifically, we propose Motion Clues Attention Network (MCAN) and Fine-Grained Classification Network (FGCN). By introducing an attention mechanism, the MCAN uses the RAFT optical flow algorithm to adaptively focus on the direction and intensity of micro-movements in key face regions, distinguishing the natural movement of real faces from the static or repetitive motion patterns in spoofing attacks. Inspired by the latest face recognition research, the Distribution Based Additive Margin softmax-Similarity (DAMS-SIM) loss function is designed in the FGCN network to address the asymmetry between real and spoofed samples, enabling the network to capture the local fine-grained texture differences between real faces and spoofing attacks. Finally, we construct a simple feature fusion network that combines the features of MCAN and FGCN to generate the final classification results. We conducted extensive experiments on CASIA-FASD and Replay Attack, and the results show that the proposed method achieve the best performance compared to other methods. Besides, we created our own dataset PR-FASD to evaluate the model’s generalization ability, achieving good results.}, archive = {J_ACCESS}, author = {Ziyang Cheng and Xiafen Zhang}, doi = {10.1109/ACCESS.2025.3573790}, journal = {IEEE Access}, pages = {93649-93660}, shortjournal = {IEEE Access}, title = {Integrating fine-grained classification and motion relation analysis for face anti-spoofing}, volume = {13}, year = {2025}, }
• (2025). A case study of 3rd party hardware: The weakest link in google’s trustworthy artificial intelligence implementation. ACCESS, 13, 93640-93648. (www)

  @article{ , abstract = {The proliferated smart TV market has sparked a race among the tech giants to capture market share, with Google aggressively pursuing this domain through partnerships with third-party smart TV manufacturers. However, this expansion raises critical concerns regarding security vulnerabilities and the potential breakdown of Google’s Trustworthy Artificial Intelligence Implementation (TAII) standards. Despite the significant focus on software security, there is a lack of research on the security and privacy challenges associated with smart TV hardware. In this paper, we present a case study exploring the security vulnerabilities present in TCL Smart TVs integrated with Google Cloud services. Our case study result identifies significant risks of consumer data breaches and exposure of personal information. Our findings show significant gaps in following the TAII principles of privacy, security, safety, and transparency.}, archive = {J_ACCESS}, author = {Scott Lorenz and Stanley Stinehour and Anitha Chennamaneni and Abdul B. Subhani and Mohammad Nadim}, doi = {10.1109/ACCESS.2025.3570633}, journal = {IEEE Access}, pages = {93640-93648}, shortjournal = {IEEE Access}, title = {A case study of 3rd party hardware: The weakest link in google’s trustworthy artificial intelligence implementation}, volume = {13}, year = {2025}, }
• (2025). Movie genre classification based on poster and subtitles using hybrid combination of convolutional neural networks. ACCESS, 13, 93629-93639. (www)

  @article{ , abstract = {Automatic genre detection in movies is an important and catchy topic that can be used in many applications and contexts by different industries, such as personal development systems, database management, content analysis systems, and marketing and advertising systems. There are still a few gaps that have yet to be covered by research in the arena of movie genre detection. To address such challenges, we have proposed the hybrid methodology of applying image processing of posters along with subtitle text in order to identify movie genres automatically. It employs Convolutional Neural Networks (CNN) with preprocessing, feature extraction, and detection stages. Each technique processes information from posters and movie subtitles separately. Hence, it proposes a novel model for movie genre classification that can significantly improve the performance of genre detection systems by making them more accurate and reliable. The innovations in approach will help in better identification, which enhances the performance of the systems associated with the movie industry. The implementation results showed that the proposed method achieved a high accuracy of 92% and an F-measure of 91%, indicating the superiority of the proposed method compared to the comparative methods.}, archive = {J_ACCESS}, author = {Yuxiang Zhang}, doi = {10.1109/ACCESS.2025.3573341}, journal = {IEEE Access}, pages = {93629-93639}, shortjournal = {IEEE Access}, title = {Movie genre classification based on poster and subtitles using hybrid combination of convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). An adapted few-shot prompting technique using ChatGPT to advance low-resource languages understanding. ACCESS, 13, 93614-93628. (www)

  @article{ , abstract = {The lack of annotated data in low-resource languages presents a significant challenge in natural language processing, particularly for language understanding tasks such as intent detection and slot filling. To address this, we propose a novel approach that first employs an effective cross-lingual transfer model to generate labeled data for the target language, overcoming the scarcity of labeled data in low-resource settings. The main contribution of our work lies in the second step, where we introduce an adapted few-shot prompting technique to guide ChatGPT as a large language model (LLM). In this step, a subset of the machine-generated examples is selected based on the domain of the input, ensuring that the LLM is provided with more tailored and domain-specific examples. This two-step process leads to enhanced performance in handling low-resource languages. We conduct extensive experiments on Spanish, Thai, and Persian using the Facebook-multilingual and Persian-ATIS datasets. Experimental results demonstrate that our method outperforms existing techniques for non-Latin languages, such as Thai and Persian, and matches state-of-the-art performance for Latin-based languages, such as Spanish. The repository for this study is publicly available at https://github.com/saedeht/language-understanding-chatgpt}, archive = {J_ACCESS}, author = {Saedeh Tahery and Saeed Farzi}, doi = {10.1109/ACCESS.2025.3574115}, journal = {IEEE Access}, pages = {93614-93628}, shortjournal = {IEEE Access}, title = {An adapted few-shot prompting technique using ChatGPT to advance low-resource languages understanding}, volume = {13}, year = {2025}, }
• (2025). Enhanced UAV detection and classification with birds using NLFM pulse-doppler radar. ACCESS, 13, 93605-93613. (www)

  @article{ , abstract = {Detecting UAVs in clutter environments and classification with birds is a difficult and important problem. The high sidelobe levels of a typical linear frequency modulation waveform limit the ability to detect UAVs without applying windowing methods with significant SNR reduction. In this paper, nonlinear frequency modulation (NLFM) waveforms are proposed to enhance the detection probability of UAVs. The comparisons between LFM and NLFM show that NLFM provides improved performance in UAV signal detection. Furthermore, a simple clutter classification algorithm is proposed to classify signals containing micro-Doppler components, such as UAVs or birds, from clutter. The NLFM waveform has low clutter sidelobes and produces high power spectral density in the main lobe, which preserves and enhances micro-Doppler capabilities more effectively than those of LFM. Taking advantage of these features, a convolutional neural network (CNN) based classification of UAVs and birds using the Doppler spectrum is demonstrated. The performance of the NLFM waveform was validated using the proposed lightweight CNN model. The Doppler spectrum features derived from NLFM have achieved improved detection and high performance.}, archive = {J_ACCESS}, author = {Ju-Hong Park and Yoo-Ho Jang and Sang-Hoon Kang and He-Won Jung and Seong-Ook Park}, doi = {10.1109/ACCESS.2025.3572507}, journal = {IEEE Access}, pages = {93605-93613}, shortjournal = {IEEE Access}, title = {Enhanced UAV detection and classification with birds using NLFM pulse-doppler radar}, volume = {13}, year = {2025}, }
• (2025). Row-column decoupled loss: A probability-based geometric similarity framework for aerial micro-target detection. ACCESS, 13, 93593-93604. (www)

  @article{ , abstract = {Object detection, a fundamental research direction in the field of computer vision, serves as the basis for various complex visual tasks. However, detection performance for small targets remains significantly inferior compared to regular-sized targets. In conventional tiny object detection methods, the uniform distribution strategy adopted for bounding box regression during prediction processes tends to induce instability, particularly when handling small targets. To address this critical issue, this paper proposes Row-Column Decoupled Loss to enhance the stability of the prediction process and improve the accuracy of detection results. The proposed approach innovatively transforms the traditional regression paradigm by incorporating probabilistic modeling that better aligns with the spatial characteristics of small objects. Firstly, to address the challenge where the absence of overlap between predicted and ground-truth bounding boxes prevents distance computation in small object scenarios, this work introduces a probabilistic modeling framework based on Binomial distribution to characterize the spatial distribution of bounding boxes. This approach effectively captures the probabilistic attributes of pixel distributions within target regions. Secondly, we design a dual-channel evaluation mechanism, established a similarity evaluation strategy that better aligns with the target natural characteristics. Finally, Row-Column Decoupled Loss: A probability-Based Geometric Similarity Framework is proposed to optimize feature learning for minuscule targets. Experimental validation on the AI-TOD dataset demonstrates the efficacy of our method across multiple mainstream object detection frameworks and state-of-the-art small object detectors. Detectors equipped with Row-Column Decoupled Loss exhibit significant performance enhancements over baseline models, achieving a peak value of 60.5 in $AP_{0.5} $ and delivering a maximum improvement margin of 16.2 in $AP_{0.5} $ compared to baselines.}, archive = {J_ACCESS}, author = {Xiaohui Chen and Yunzhi Ling and Lingjun Chen and Li Liu and Xuechen Cui and Ziqiang Liu and Zhenyu Wang}, doi = {10.1109/ACCESS.2025.3572894}, journal = {IEEE Access}, pages = {93593-93604}, shortjournal = {IEEE Access}, title = {Row-column decoupled loss: A probability-based geometric similarity framework for aerial micro-target detection}, volume = {13}, year = {2025}, }
• (2025). Adaptive optimizable gaussian process regression linear least squares regression filtering method for SEM images. ACCESS, 13, 93574-93592. (www)

  @article{ , abstract = {Scanning Electron Microscopy (SEM) images often suffer from noise contamination, which degrades image quality and affects further analysis. This research presents a complete approach to estimate their Signal-to-Noise Ratio (SNR) and noise variance (NV), and enhance image quality using NV-guided Wiener filter. The main idea of this study is to use a good SNR estimation technique and infuse a machine learning model to estimate NV of the SEM image, which then guides the wiener filter to remove the noise, providing a more robust and accurate SEM image filtering pipeline. First, we investigate five different SNR estimation techniques, namely Nearest Neighbourhood (NN) method, First-Order Linear Interpolation (FOL) method, Nearest Neighbourhood with First-Order Linear Interpolation (NN+FOL) method, Non-Linear Least Squares Regression (NLLSR) method, and Linear Least Squares Regression (LSR) method. It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. In this test, the Optimizable GPR model shows the highest accuracy and it stands as the most effective solution for NV estimation. Combining these results lead to the proposed Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression (AO-GPRLLSR) Filtering pipeline. The AO-GPRLLSR method generated an estimated noise variance which served as input to NV-guided Wiener filter for improving the quality of SEM images. The proposed method is shown to achieve notable success in estimating SNR and NV of SEM images and leads to lower Mean Squared Error (MSE) after the filtering process.}, archive = {J_ACCESS}, author = {Dominic Chee Yong Ong and Iksan Bukhori and Kok Swee Sim and Kok Beng Gan}, doi = {10.1109/ACCESS.2025.3573389}, journal = {IEEE Access}, pages = {93574-93592}, shortjournal = {IEEE Access}, title = {Adaptive optimizable gaussian process regression linear least squares regression filtering method for SEM images}, volume = {13}, year = {2025}, }
• (2025). Operability improvement of human-machine systems by altering visual information: A case-study of the inverted pendulum. ACCESS, 13, 93566-93573. (www)

  @article{ , abstract = {When humans operate machines such as cars and drones based on visual information, it is essential to improve the subjective operational feeling, avoid failures, and reduce the learning time, in other words, to improve operability. In this study, to enhance operability, we focused on visual information and proposed presenting the machine’s altered state rather than its exact state. Here, alteration includes prediction, exaggeration, and noise removal based on the machine’s state, rather than mere image processing. Although some systems are difficult to operate even if the state is accurately displayed, this approach can enhance operability while maintaining human decision-making capabilities in such systems. In this study, with a focus on the prediction among alterations, we constructed a prototype experimental system and demonstrated the feasibility of the proposed concept through an experiment. In the experiment, the subjects played a game with the purpose of keeping an inverted pendulum upright as long as possible. We compared the game score, input amount, and pole inclination between two conditions: one in which the game displayed the current state of the inverted pendulum, and the other in which it displayed the prediction 0.6 seconds later. The experimental results with 35 subjects (32 valid) showed that presenting the altered state tended to improve the game score, reduce the input amount, and reduce the inclination of the pole. This is the first experiment demonstrating that altering visual information can improve the operability of human-machine systems where humans have to perform stabilization tasks.}, archive = {J_ACCESS}, author = {Kenta Tanaka and Yuki Minami and Masato Ishikawa}, doi = {10.1109/ACCESS.2025.3572082}, journal = {IEEE Access}, pages = {93566-93573}, shortjournal = {IEEE Access}, title = {Operability improvement of human-machine systems by altering visual information: A case-study of the inverted pendulum}, volume = {13}, year = {2025}, }
• (2025). Simultaneous fault estimation and fault-tolerant consensus control for multi-agent systems against multiple time delays. ACCESS, 13, 93551-93565. (www)

  @article{ , abstract = {This article addresses the problems of robust fault estimation (FE) and fault-tolerant control (FTC) for nonlinear multi-agent systems (MASs) subject to faults, multiple time-varying delays, and exogenous disturbances. First, by utilizing the output estimation error and relative output estimation error, an FE observer is constructed to estimate the states and faults of a single agent simultaneously. Second, an observer-based state feedback control protocol is proposed to eliminate the influences of faults and guarantee the consensus. Third, an $ H_{\infty }$ existent condition of control for the closed-loop system is translated into a convex linear matrix inequality (LMI) optimization problem by constructing special matrices and matrix inequalities. Finally, the effectiveness of the proposed methods is demonstrated via two illustrative simulation examples.}, archive = {J_ACCESS}, author = {Yimin Liu and Jianliang Chen and Yongli Yang and Hao Fu}, doi = {10.1109/ACCESS.2025.3573827}, journal = {IEEE Access}, pages = {93551-93565}, shortjournal = {IEEE Access}, title = {Simultaneous fault estimation and fault-tolerant consensus control for multi-agent systems against multiple time delays}, volume = {13}, year = {2025}, }
• (2025). Joint security and dependability modeling: An updated overview. ACCESS, 13, 93523-93550. (www)

  @article{ , abstract = {The increasing demand for mission-critical services and applications has emphasized the need for systems with strict security and dependability requirements. In this context, evaluating and addressing the complex interplay between security threats and dependability challenges is essential. Joint modeling of security and dependability allows an early evaluation of such interplay and is essential to identify the weakness of a system before the actual deployment. This paper aims to provide an updated overview of joint security and dependability modeling. First, we present the foundational concepts of security and dependability. Then, we introduce the primary modeling techniques in detail. This background knowledge is used to categorize the state-of-the-art work on joint modeling based on three key dimensions: considered attacks and faults, modeled attributes, and used modeling techniques. This paper also illustrates how the joint modeling is applicable and fruitful in a system that enables mission-critical applications, such as the fifth generation (5G) of mobile networks and the Multi-access Edge Computing (MEC). Moreover, it individually presents the state-of-the-art security and dependability modeling of 5G-MEC systems. Finally, the paper discusses the opportunities and challenges of advanced approaches to joint models for the next generation of mission-critical services and applications.}, archive = {J_ACCESS}, author = {Thilina Pathirana and Ruxandra F. Olimid and Gianfranco Nencioni}, doi = {10.1109/ACCESS.2025.3574256}, journal = {IEEE Access}, pages = {93523-93550}, shortjournal = {IEEE Access}, title = {Joint security and dependability modeling: An updated overview}, volume = {13}, year = {2025}, }
• (2025). A novel fuzzy PIDF enhancing PIDF controller tuned in two stages by TLBO and PSO algorithms for reliable AVR performance. ACCESS, 13, 93505-93522. (www)

  @article{ , abstract = {Reliability in control engineering remains a fundamental consideration in the design of controllers for various applications. This study proposes an innovative control design for Automatic Voltage Regulator (AVR) that integrates Fuzzy Proportional-Integral-Derivative with Filter action (Fuzzy PIDF) enhancing PIDF controllers (PIDF + Fuzzy PIDF), which is tuned in a two-stage framework. This framework leverages Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms to find the optimum values of the PIDF and Fuzzy-PIDF controllers’ gains, respectively. The dual-stage design ensures that the controller maintains functionality even if one stage experiences partial failure, thereby enhancing the reliability of both the controller and the system under control. The tuning process for both stages employs the well-known Integral Time Absolute Error (ITAE) cost function in conjunction with the TLBO and PSO algorithms. The reliability of the proposed controller is rigorously tested, demonstrating its ability to deliver robust performance even when operating partially. A comprehensive comparative analysis with existing techniques in the literature demonstrates the superior performance of the proposed PIDF plus Fuzzy PIDF controller. The results show significant improvements in transient response metrics: overshoot is reduced from 0.1559 pu to 0.0211 pu, undershoot from 0.0954 pu to 0.006 pu, and settling time from 0.7568 seconds to 0.2299 seconds. These advancements underscore the controller’s enhanced dynamic stability and precision compared to prior methodologies. Additionally, the robustness of the PIDF + Fuzzy-PIDF controller is evaluated under conditions of system uncertainty and varying load disturbances, with validation conducted in the MATLAB and Simulink environments. The results affirm the controller’s superiority, robustness, and reliability, establishing its potential for successful implementation in real-world applications. This study introduces a novel AVR system characterized by exceptional reliability, robustness, and performance, positioning it as a promising solution for practical deployment.}, archive = {J_ACCESS}, author = {Mokhtar Shouran and Mohammed Alenezi}, doi = {10.1109/ACCESS.2025.3573900}, journal = {IEEE Access}, pages = {93505-93522}, shortjournal = {IEEE Access}, title = {A novel fuzzy PIDF enhancing PIDF controller tuned in two stages by TLBO and PSO algorithms for reliable AVR performance}, volume = {13}, year = {2025}, }
• (2025). Zooming into precision: The zoom TFD for high-resolution analysis of non-stationary signals. ACCESS, 13, 93491-93504. (www)

  @article{ , abstract = {This paper introduces Zoom TFD, a time-frequency decomposition (TFD) method designed for the high-resolution analysis of non-stationary signals, achieving optimal energy concentration, high time-frequency resolution, and inherent cross-term suppression. Traditional TFDs, such as the Wigner-Ville Distribution (WVD) and Choi-Williams Distribution (CWD), often struggle with resolution trade-offs and cross-term interference, while methods like Zhao-Atlas-Marks Distribution (ZAMD) attempt to mitigate these effects at the cost of higher computational complexity. Zoom TFD takes a different approach, integrating adaptive Fourier Transform windowing with a minimization-based spectral selection mechanism. This formulation enables the dynamic refinement of the time-frequency representation by selectively enhancing dominant spectral components while suppressing noise and unwanted harmonics, ensuring a more precise and focused analysis. The effectiveness of the Zoom TFD is assessed against ten leading state-of-the-art TFDs using the Boashash—Sucic Normalized Instantaneous Resolution and Heisenberg uncertainty performance measures. This evaluation is conducted across a variety of experimental and simulated signals, such as frequency-modulated chirps and multi-component signals. The results indicate that the Zoom Time-Frequency Distribution (TFD) consistently outperforms existing methods. It achieves the highest energy concentration and the lowest uncertainty, demonstrating its robustness for precise and interference-free time-frequency analysis. This work establishes the Zoom TFD as a powerful tool for signal analysis, setting a new benchmark for high-resolution time-frequency representation applications, ranging from biomedical engineering to communications.}, archive = {J_ACCESS}, author = {Nisreen Said Amer and Samir Brahim Belhaouari}, doi = {10.1109/ACCESS.2025.3574237}, journal = {IEEE Access}, pages = {93491-93504}, shortjournal = {IEEE Access}, title = {Zooming into precision: The zoom TFD for high-resolution analysis of non-stationary signals}, volume = {13}, year = {2025}, }
• (2025). An effective method to compensate for testing induced SBFET degradation by charging deep-level interface trap. ACCESS, 13, 93481-93490. (www)

  @article{ , abstract = {In this paper, the threshold voltage ( $V_{\mathrm {th}}$ ) of Schottky barrier field effect transistors (SBFETs) based on ultra-thin silicon on insulator (SOI) wafer shift induced by electrical test is analyzed. Repeated testing gives rise to a positive shift of the $V_{\mathrm {th}}$ , which is similar to the constant voltage stress effect found in two-dimensional materials. The series resistance of Source and Drain ( $R_{\mathrm {sd}}$ ), mobility ( $\mu $ ), and trapped charge are studied. The positive $\Delta V_{\mathrm {th}}$ is independent of the $R_{\mathrm {sd}}$ and $\mu $ , but related to the deep-level acceptor interface trap, which is charged during the test. The main reason for trap charging is that the Source and Drain contact prepared by metal deposition cannot provide electrons for trap capturing before testing. In order to compensate for the $\Delta V_{\mathrm {th}}$ induced by the test, a deep-level trap charging method is proposed. The necessity of this new test method is verified by irradiation experiments.}, archive = {J_ACCESS}, author = {Tiexin Zhang and Fanyu Liu and Lei Shu and Siyuan Chen and Yuchong Wang and Yuchen Wu and Jing Wan and Yong Xu and Shi Li and Yuyang Ding and Bo Li and Zhengsheng Han and Tianchun Ye}, doi = {10.1109/ACCESS.2025.3573406}, journal = {IEEE Access}, pages = {93481-93490}, shortjournal = {IEEE Access}, title = {An effective method to compensate for testing induced SBFET degradation by charging deep-level interface trap}, volume = {13}, year = {2025}, }
• (2025). Lightweight and accurate YOLOv7-based ensembles with knowledge distillation for urinary sediment detection. ACCESS, 13, 93468-93480. (www)

  @article{ , abstract = {Urine sediment analysis plays an important role in evaluating kidney function. In addition to improving detection accuracy, reducing model size is also a key challenge, especially when considering deployment on medical devices where computational resources are limited. To address these demands, we propose a lightweight and accurate detection framework that combines YOLOv7-based ensemble learning with feature-based knowledge distillation. In our approach, two attention-enhanced YOLOv7 variants are used as teacher models, which transfer their feature representations to compact YOLOv7-tiny student models. These student models are then integrated using weighted boxes fusion to further enhance detection performance. Evaluated on a real-world urinary sediment dataset, the proposed framework achieves 84.6% precision, 88.1% recall, and a mAP@0.5 of 0.920. These results represent an improvement of more than 3% over the baseline YOLOv7-tiny and slightly outperform the state-of-the-art YOLOv7-CBAM teacher model. Moreover, the model size is reduced to approximately one-third of YOLOv7-CBAM, significantly improving its suitability for deployment in resource-constrained environments. Ablation studies further confirm the complementary strengths of the ensemble design and suggest that additional performance gains may be achievable through more adaptive fusion strategies.}, archive = {J_ACCESS}, author = {Keita Sasaki and Hiroki Nishikawa and Ittetsu Taniguchi and Takao Onoye}, doi = {10.1109/ACCESS.2025.3574169}, journal = {IEEE Access}, pages = {93468-93480}, shortjournal = {IEEE Access}, title = {Lightweight and accurate YOLOv7-based ensembles with knowledge distillation for urinary sediment detection}, volume = {13}, year = {2025}, }
• (2025). Enhancing visual question answering for multiple choice questions. ACCESS, 13, 93453-93467. (www)

  @article{ , abstract = {The proposed paper examines enhancements in Visual Question Answering (VQA) by systematically tuning hyperparameters and utilizing advanced image and text encoders. The study particularly explores the adaptation of these models to Multiple-Choice Question (MCQ) formats, aiming to refine their accuracy and applicability. MCQs consist of a question stem along with a set of options, from which the correct answer, the key needs to be identified among the distractors. Using MCQs provides the model with some context of the correct answer, improving its performance over a simple multiclass classification task. The research showcases the effectiveness of precise hyperparameter adjustments in improving the performance of VQA systems, through comparative analysis of varied sets of hyperparameters, highlighting their improved reasoning capabilities across various datasets, including samples from real world images and academic questions. This demonstrates the potential of VQA models for robust application in both educational and practical scenarios.}, archive = {J_ACCESS}, author = {Rashi Goel and Harsh Nandwani and Eshaan Shah and Ashalatha Nayak and Archana Praveen Kumar}, doi = {10.1109/ACCESS.2025.3572529}, journal = {IEEE Access}, pages = {93453-93467}, shortjournal = {IEEE Access}, title = {Enhancing visual question answering for multiple choice questions}, volume = {13}, year = {2025}, }
• (2025). WSNetDefender: Securing wireless sensor networks using BBIDNet and fuzzy-DQN threat mitigation system (FD-TMS). ACCESS, 13, 93439-93452. (www)

  @article{ , abstract = {In the Wireless Sensor Network (WSN), there has been a significant increase in intruders, owing towards rapid expansion in the cyber-space that encloses multi-layered cybersecurity. The aim of this work is to tackle the increasing cybersecurity issues in Wireless Sensor Networks (WSNs) by proposing an innovative framework named WSNetDefender. It encompasses a Boosted BiLSTM Intrusion Detector Network (BBIDNet) to identify intrusions with precise accuracy and a Fuzzy-DQN Threat Mitigation System (FD-TMS) to respond to threats dynamically. The framework aims to enhance the accuracy of detection of intrusion while reducing false positives and resource overhead.The first step involves gathering data from two distinct wireless sensor network datasets. Subsequently, preprocessing is done using the new Integrated Preprocessing Engine (IPE), wherein the Gaussian filtering for denoising, Kalman filtering for data fusion (2 databases), and Min-Max normalization for standardization are available. Then, features are extracted using the new NetFlow Profiling Network (NPN), which encloses the CAN Bus Analysis, Behavior Profiling, and Netflow Analysis. The newly introduced Jackal-Wolf Hybrid Optimizer (JWHO) assisted in selecting the optimal features from NPN, thereby assisting the model in lowering computing difficulty in terms of both memory and time. The proposed JWHO is the conceptual synergy of Golden Jackal Optimization and Grey Wolf Optimizer (GJO-GWO), respectively. The extracted JWHO-based features are utilized to train the new BBIDNet, which makes the accurate detection of intruders in WSN. This BBIDNet is the combination of the Bidirectional Long Short-Term Memory (BiLSTM) network with AdaBoost. Once, an attacker is found to be available, then for the concern attacker is mitigated via the new FD-TMS. In FD-TMS, the threat scoring and prioritization are undergone with respect to fuzzy rules, and based on the critical level identified, the mitigation is done using the Deep Q-Network (DQN) approach. As a consequence, there are noteworthy results from the implementation process in a variety of metrics, including F1-score, FPR, precision, FNR, NPV, MCC, accuracy, sensitivity, and specificity. The accuracy recorded by the suggested model is 99.6% and 99.7% for database-1 and database-2, respectively.}, archive = {J_ACCESS}, author = {Mohammed A. Aleisa}, doi = {10.1109/ACCESS.2025.3570980}, journal = {IEEE Access}, pages = {93439-93452}, shortjournal = {IEEE Access}, title = {WSNetDefender: Securing wireless sensor networks using BBIDNet and fuzzy-DQN threat mitigation system (FD-TMS)}, volume = {13}, year = {2025}, }
• (2025). Exploring metaverse technologies in entrepreneurship through machine learning-based topic modeling. ACCESS, 13, 93417-93438. (www)

  @article{ , abstract = {This study investigates the integration of metaverse technologies into entrepreneurship, focusing on policy frameworks, practical applications, research directions, and educational strategies to promote innovation and enhance business operations. Utilizing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, BERTopic modeling identified four key thematic areas in the literature: virtual reality (VR) in entrepreneurial education, augmented reality (AR) and VR in tourism, metaverse technologies for business ventures, and digital transformation. The main findings include that in education, VR enhances skills through immersive experiences, whereas AR and VR enrich tourism. Metaverse technologies create new business opportunities and redefine traditional models. Digital transformation, spurred by global events, drives innovation across various sectors. Case studies exploring the metaverse’s benefits for entrepreneurship across industries, including NFTs and crypto ventures, healthcare, tourism, cultural engagement, and space exploration, are presented. These findings signify the potential of metaverse technologies in driving innovation and fostering entrepreneurial growth in education, tourism, business, and beyond. Additionally practical insights are offered into how immersive virtual technologies can transform entrepreneurial education and business models viewed from the lens of educators, policy makers, practitioners, and researchers while ensuring scalability and sustainability. A framework for future work in metaverse integration that touches on ethical standards, data privacy, and digital literacy to balance innovation with societal safeguards is presented.}, archive = {J_ACCESS}, author = {Krishnashree Achuthan and Sasangan Ramanathan and Raghu Raman}, doi = {10.1109/ACCESS.2025.3567622}, journal = {IEEE Access}, pages = {93417-93438}, shortjournal = {IEEE Access}, title = {Exploring metaverse technologies in entrepreneurship through machine learning-based topic modeling}, volume = {13}, year = {2025}, }
• (2025). Strategies for reducing the communication and computation costs in cross-silo federated learning: A comprehensive review. ACCESS, 13, 93385-93416. (www)

  @article{ , abstract = {Federated learning is an innovative approach that allows collaboration across distributed clients while maintaining data privacy. Despite its numerous benefits, several issues persist in this domain. This comprehensive review examines the critical problems of communication and computation (C&C) costs in cross-silo federated learning environments, which significantly impact the scalability and practical adoption of the system. The research presents a novel multi-dimensional analysis methodology to evaluate cost reduction techniques that consider privacy, accuracy, scalability, and adaptability. The methodical investigation reveals that while existing approaches can substantially reduce communication overhead in controlled environments, ensuring model convergence and privacy guarantees remains challenging across diverse scenarios. Through detailed case studies spanning smart city deployments, healthcare, and finance sectors, the study demonstrates how various C&C optimization strategies perform differently in real-world applications. The review introduces a systematic taxonomy of cost-reduction techniques and proves that hybrid approaches combining multiple optimization methods can maintain model performance while optimizing resource utilization. The review concludes by presenting a unified roadmap for developing adaptive solutions that balance privacy, efficiency, and scalability requirements in cross-silo contexts and identifying crucial research gaps. This compilation of recent developments focuses on areas that require further investigation to enhance real-world deployments and provides practitioners with actionable guidelines for implementing effective federated learning systems.}, archive = {J_ACCESS}, author = {Vineetha Pais and Santhosha Rao and Balachandra Muniyal}, doi = {10.1109/ACCESS.2025.3573933}, journal = {IEEE Access}, pages = {93385-93416}, shortjournal = {IEEE Access}, title = {Strategies for reducing the communication and computation costs in cross-silo federated learning: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). An improved method of FM radio channel selection for a passive radar system based on signal environment analysis. ACCESS, 13, 93372-93384. (www)

  @article{ , abstract = {This study proposes an improved method of frequency modulation (FM) radio channel selection for a passive radar system based on signal environment analysis. Passive radar has numerous advantages over conventional active radar, such as survivability, low cost, and low design complexity owing to a lack of its own transmitter, as well as the capability to detect low radar cross section targets. However, its performance depends on the properties of the illuminators it exploits. Therefore, a passive radar must select proper FM radio channels to achieve better performance. We analyze the time-varying characteristics of FM radio channel content, signal interference, and signal distortion that occur in real-world operating environments. Based on these environment analyses results, proper FM radio channels can be selected. We conducted experiments in real-world environments and compared the test results obtained using the proposed method with those achieved via analytical methods based on the sensitivity of receiver to validate the proposed method. The results demonstrate that the proposed method enhances detection performance.}, archive = {J_ACCESS}, author = {Inhwan Jeong and Sanhae Kim and Hyungyu Kwak and Jonghwan Lee and Kyuha Song}, doi = {10.1109/ACCESS.2025.3573650}, journal = {IEEE Access}, pages = {93372-93384}, shortjournal = {IEEE Access}, title = {An improved method of FM radio channel selection for a passive radar system based on signal environment analysis}, volume = {13}, year = {2025}, }
• (2025). QLAW: An improved quantization-based local audio watermarking scheme using inter-frame correlation. ACCESS, 13, 93359-93371. (www)

  @article{ , abstract = {With the rapid development of the Internet, audio distribution has become more convenient with increasing copyright infringement. To address this problem, this paper proposes a quantization-based local audio watermarking scheme using inter-frame correlation, integrating machine learning techniques and traditional methods. To obtain the time-frequency spectrogram of the audio signal, a Short-Time Fourier Transform (STFT) is first applied to the audio signal. Then, Main Energy Region Extractor (MERE) is proposed to extract the main energy region of the spectrogram. Based on the main energy region, the Stable Frequency and Energy Region Extractor is conducted to find the local feature region for embedding. After segmenting the local feature embedding region into several frames, Adjacent Frame Extraction Process (AFEP) is conducted to select the adjacent frame. Then, Discrete Cosine Transform (DCT) is applied to each embedding frame and its adjacent frame to extract their corresponding frequency domain coefficients. To improve robustness, mid-frequency DCT coefficients are alternately selected to embed the watermark. By adjusting the difference between the embedding frame and its corresponding adjacent frame in a predefined range, the local watermark is embedded. Experimental results show that the proposed scheme outperforms existing schemes in inaudibility and robustness, achieving an average Signal-to-Noise Ratio (SNR) above 25 dB and a lower Bit Error Rate (BER) under various attacks.}, archive = {J_ACCESS}, author = {Qiutong Li and Zheng Xing and Ju Wang and Guoheng Huang and Xiaochen Yuan}, doi = {10.1109/ACCESS.2025.3573838}, journal = {IEEE Access}, pages = {93359-93371}, shortjournal = {IEEE Access}, title = {QLAW: An improved quantization-based local audio watermarking scheme using inter-frame correlation}, volume = {13}, year = {2025}, }
• (2025). Managing maintenance backlogs: An integrated multi-criteria decision-making and optimization approach. ACCESS, 13, 93346-93358. (www)

  @article{ , abstract = {Maintenance backlogs are the accumulation of uncompleted tasks or work orders that may cause significant challenges across capital-intensive industries such as manufacturing, infrastructure, and healthcare. These backlogs can compromise operational efficiency, safety, and service delivery. It emphasizes the need for structured prioritization and scheduling strategies. The current study presents a comprehensive framework that integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Bayesian Best-Worst Method (BWM), and a multi-dimensional knapsack optimization model to address maintenance backlog management challenges. DEMATEL identifies causal relationships among criteria, and BWM prioritizes criteria based on experts’ opinions. The knapsack model optimizes resource allocation under capacity constraints, ensuring the efficient scheduling of high-value tasks. The proposed framework transforms backlog management from a reactive to a proactive approach, improving operational reliability, resource utilization, and long-term sustainability. Results from a practical example demonstrate the model’s ability to maximize maintenance task value and optimize weekly scheduling, highlighting its scalability and applicability across various industrial contexts.}, archive = {J_ACCESS}, author = {Ehsan Esmaeeli and Mohsen Varmazyar and Vahid Hekmatshoar and Parviz Boroomandfar and Mohammad Reza Feylizadeh}, doi = {10.1109/ACCESS.2025.3572411}, journal = {IEEE Access}, pages = {93346-93358}, shortjournal = {IEEE Access}, title = {Managing maintenance backlogs: An integrated multi-criteria decision-making and optimization approach}, volume = {13}, year = {2025}, }
• (2025). A method for hand dorsal vein recognition based on frangi and morphological operation. ACCESS, 13, 93336-93345. (www)

  @article{ , abstract = {Aiming at the problem of image recognition of the hand dorsal vein, a method for hand dorsal vein recognition based on Frangi and morphological operation (Frangi-M) is proposed. Frangi-M combines the Frangi algorithm and morphological operation. Firstly, the Frangi algorithm is used to enhance the vascular structure of the hand dorsal vein based on the difference between the Hessian matrix eigenvalues of the points in the vascular part and the non-vascular part of the image, and then morphological operations and median filtering are used to eliminate image spots and smooth the image. Frangi-M was compared with the adaptive threshold method and the Butterworth high-pass filter method through qualitative and quantitative evaluation methods. Both qualitative and quantitative evaluation results show that Frangi-M has the clearest and most complete identification of the hand dorsal vein. In the quantitative evaluation, the precision, recall, and mAP@0.5 of the YOLOv5 hand dorsal vein recognition model generated by Frangi-M are 0.90, 0.86, and 0.90, respectively, while those of the adaptive threshold method are 0.87, 0.82, and 0.89, respectively, and those of the Butterworth high-pass filter method are 0.85, 0.82, and 0.85, respectively.}, archive = {J_ACCESS}, author = {Xiaoling Xie and Xiaorui Xu and Junxi Zheng and Xiaocui Sun}, doi = {10.1109/ACCESS.2025.3574188}, journal = {IEEE Access}, pages = {93336-93345}, shortjournal = {IEEE Access}, title = {A method for hand dorsal vein recognition based on frangi and morphological operation}, volume = {13}, year = {2025}, }
• (2025). Enhancing liver cirrhosis diagnosis using machine learning with explainable AI and cross-validated hyperparameter tuning techniques. ACCESS, 13, 93323-93335. (www)

  @article{ , abstract = {Cirrhosis is a form of Chronic Liver Disease (CLD) resulting from sustained liver damage from several causes, including viral infection, autoimmune disorders, cholestatic and metabolic disease Non-Alcoholic Fatty Liver Disease (NAFLD) or heavy alcohol use. Testing for the presence of Liver Cirrhosis (LC) is one of the most critical diagnostics and prognostic assessments for patients. Detecting liver illness in its early stages is challenging as initial symptoms are often mild and difficult to diagnose. Cirrhosis may not present symptoms until an advanced stage, posing a challenge for identification and diagnosis. The lack of explanations for a complex model’s decision-making process creates clinician distrust by concealing the reasoning behind its choices. This research sought to design and assess a novel and non-invasive proposed model by utilizing the XGBoost classifier with Explainable Artificial Intelligence (XAI) for diagnosing liver cirrhosis. In this work XAI framework enhances transparency in complex models, offering valuable insights into decision-making, thereby guiding treatment strategies, and improving cirrhosis diagnosis. The experimental results have shown superiority for the proposed method compared with various machine learning models. The proposed method has achieved significantly 91.19% using XGBoost for cirrhosis dataset with K-fold validation(K=10) approach.}, archive = {J_ACCESS}, author = {Koteswara Rao Makkena and Karthika Natarajan}, doi = {10.1109/ACCESS.2025.3568918}, journal = {IEEE Access}, pages = {93323-93335}, shortjournal = {IEEE Access}, title = {Enhancing liver cirrhosis diagnosis using machine learning with explainable AI and cross-validated hyperparameter tuning techniques}, volume = {13}, year = {2025}, }
• (2025). Hybrid artificial neural network activation function to reduce water wastage in agricultural irrigation. ACCESS, 13, 93302-93322. (www)

  @article{ , abstract = {Most nations rely heavily on agriculture to provide employment. In recent years, as a result of a growing global population’s increased demand for food and water, numerous nations are consuming excessive amounts of already scarce freshwater resources. A large amount of water is used for irrigation. The purpose of this study is to find a way to reduce the amount of water lost during the irrigation process. The only practical solutions to the above problems are precision farming and smart irrigation. The study proposes a hybrid activation function based on the Artificial Neural Network algorithm. This function is used to classify the need for irrigation in various crops and predict the best time of the day for watering. The achievement of this is done by utilizing a hybrid activation function known as TanElu, which combines the Tanh and ELU functions. This function is used to classify the need for irrigation and predict the best time to water crops. The overall accuracy achieved by the proposed model was 98.24% and 97.31% in irrigation classification and prediction of the best irrigation time, respectively, which is a significant improvement compared to previous studies. Besides, execution time was 7.88 and 8.28 seconds in irrigation classification and prediction of best irrigation time respectively. The results indicate that the proposed hybrid activation function outperforms the single activation functions. Thus, the proposed model can significantly reduce water loss during the irrigation process and represents an encouraging step toward sustainable agriculture and water conservation.}, archive = {J_ACCESS}, author = {Baraa H. Jawad and Ola A. Alwesabi and Nibras Abdullah and Ahmed Abed Mohammed}, doi = {10.1109/ACCESS.2025.3573678}, journal = {IEEE Access}, pages = {93302-93322}, shortjournal = {IEEE Access}, title = {Hybrid artificial neural network activation function to reduce water wastage in agricultural irrigation}, volume = {13}, year = {2025}, }
• (2025). Eliminating meteorological dependencies in solar power forecasting: A deep learning solution with NeuralProphet and real-world data. ACCESS, 13, 93287-93301. (www)

  @article{ , abstract = {Forecasting solar power generation is essential for efficient energy management and grid stability. However, existing predictive models often rely on external datasets, such as meteorological and sensor data, to make accurate predictions. This dependency introduces complexities and limits their application in data-sparse scenarios. In this study, we propose a novel forecasting approach based on the NeuralProphet algorithm, a deep learning model that predicts solar power generation solely from its historical data, eliminating reliance on additional input data. To evaluate the proposed approach, we conducted two case studies. The first utilized a 10-month dataset from a 1.2 kW small-scale solar power unit at Bursa Technical University’s Smart Grids laboratory, recorded at 15-minute intervals. Despite the limited dataset, the model achieved an R-squared value exceeding 0.74, demonstrating promising predictive capability. The second case study applied the NeuralProphet-based model to a large-scale dataset of nationwide solar power generation in Germany, spanning five years and collected at 15-minute intervals. Models trained on this dataset achieved R-squared values exceeding 0.99, highlighting the algorithm’s capacity to effectively capture seasonal and temporal patterns at a national scale. Our results indicate that the NeuralProphet-based forecasting approach offers a viable and efficient alternative for solar power prediction, achieving high accuracy without external data dependencies.}, archive = {J_ACCESS}, author = {Necati Aksoy and Alper Yılmaz and Gökay Bayrak and Mehmet Koç}, doi = {10.1109/ACCESS.2025.3573443}, journal = {IEEE Access}, pages = {93287-93301}, shortjournal = {IEEE Access}, title = {Eliminating meteorological dependencies in solar power forecasting: A deep learning solution with NeuralProphet and real-world data}, volume = {13}, year = {2025}, }
• (2025). FQsun: A configurable wave function-based quantum emulator for power-efficient quantum simulations. ACCESS, 13, 93271-93286. (www)

  @article{ , abstract = {Quantum computers are promising powerful computers for solving complex problems, but access to real quantum hardware remains limited due to high costs. Although software simulators like Qiskit, ProjectQ, and Pennylane offer flexibility and support for many qubits, they struggle with high power consumption and limited processing speed, particularly as qubit counts increase. Accordingly, quantum emulators implemented on dedicated hardware, such as FPGAs and analog circuits, offer a promising path for addressing energy efficiency concerns. However, existing studies on hardware-based emulators still face challenges in terms of limited flexibility and lack of fidelity evaluation. To overcome these gaps, we propose FQsun, a wave function-based quantum emulator that enhances performance by integrating four key innovations: efficient memory organization, a configurable Quantum Gate Unit (QGU), optimized scheduling, and five number precisions implemented on the Xilinx ZCU102. Experimental results demonstrate high fidelity, low mean square error, and high normalized gate speed, particularly with 32-bit versions. Benchmarking on Random Quantum Circuits, Quantum Fourier Transform, and Parameter-Shift Rule reveals that FQsun achieves a superior power-delay product and precisely, outperforms software simulators on CPUs in the processing speed range.}, archive = {J_ACCESS}, author = {Tuan Hai Vu and Vu Trung Duong Le and Hoai Luan Pham and Quoc Chuong Nguyen and Yasuhiko Nakashima}, doi = {10.1109/ACCESS.2025.3573313}, journal = {IEEE Access}, pages = {93271-93286}, shortjournal = {IEEE Access}, title = {FQsun: A configurable wave function-based quantum emulator for power-efficient quantum simulations}, volume = {13}, year = {2025}, }
• (2025). Emotion recognition through advanced signal fusion and kolmogorov-arnold networks. ACCESS, 13, 93259-93270. (www)

  @article{ , abstract = {Emotion recognition is an important aspect of affective computing that has many applications including human computer interaction and mental health care. In this paper, Kolmogorov-Arnold Networks (KANs) are explored for the purpose of multimodal emotion recognition from EEG (Electroencephalogram) and eye movement signals of the SEED-V dataset. Both modalities were represented by Differential Entropy (DE) features and a two-branch KAN architecture was used which composed of seven KAN variants, namely, Efficient KAN, Jacobi KAN, Hermite KAN, Fourier KAN, Chebyshev KAN, RBF KAN, and Wavelet KAN (with different wavelet types including DOG, Morlet, Mexican hat, and Meyer). The strengths of the two modalities were combined through late fusion for the final classification. The results show that Wavelet KANs outperformed other variants and Wavelet KAN (dog) and Wavelet KAN (morlet) achieved accuracies higher than 96%. The KAN models effectively identified the localized and transient features in EEG and eye movement signals, which was also reflected in the smooth training convergence and nearly diagonal confusion matrices. Poor performance was observed with Fourier KAN while polynomial-based and RBF KANs gave fairly good results. Efficient KAN had good accuracy but had some problems with validation instability. This work also shows that flexible non-linear, multi scale models are needed for modeling non-stationary physiological signals. The findings also indicate that future work should consider hybrid KAN architectures and more sophisticated fusion strategies to improve the robustness of emotion recognition systems. The proposed framework emphasizes the potential of wavelet-based KANs as a foundation for more adaptive, accurate affective computing solutions.}, archive = {J_ACCESS}, author = {Aung Myo Thant and Thap Panitanarak}, doi = {10.1109/ACCESS.2025.3572583}, journal = {IEEE Access}, pages = {93259-93270}, shortjournal = {IEEE Access}, title = {Emotion recognition through advanced signal fusion and kolmogorov-arnold networks}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence literacy structure and the factors influencing student attitudes and readiness in central europe universities. ACCESS, 13, 93235-93258. (www)

  @article{ , abstract = {This study examines the structure of artificial intelligence (AI) literacy and factors influencing students’ attitudes, readiness, and perceived relevance of AI in higher education at Central European universities. The research, based on data from 1,195 students enrolled in various study programs between 2022 and 2024, examines how variables such as gender, academic discipline, and year of study influence perceptions related to AI. A validated questionnaire targeting constructs including satisfaction, readiness, and relevance of AI was used. Non-parametric statistical methods were used to identify significant differences between groups, including Kruskal-Wallis and Mann-Whitney tests with Dunn-Bonferroni post hoc analysis. The findings reveal consistent differences across genders and disciplines, with males and IT students demonstrating significantly higher readiness and satisfaction with AI. Furthermore, satisfaction levels fluctuated over time, peaking in 2023 – likely influenced by the widespread adoption of tools like ChatGPT. Correlation analysis further highlighted the subtle interrelationships between constructs across different subgroups. The study underscores the importance of tailored AI education strategies and calls for targeted interventions to ensure equitable engagement with AI across diverse student populations.}, archive = {J_ACCESS}, author = {Ján Skalka and Małgorzata Przybyła-Kasperek and Eugenia Smyrnova-Trybulska and Cyril Klimeš and Radim Farana and Valentina Dagienė and Vladimiras Dolgopolovas}, doi = {10.1109/ACCESS.2025.3573575}, journal = {IEEE Access}, pages = {93235-93258}, shortjournal = {IEEE Access}, title = {Artificial intelligence literacy structure and the factors influencing student attitudes and readiness in central europe universities}, volume = {13}, year = {2025}, }
• (2025). Surface impedance for real-time cardiac monitoring in body area networks. ACCESS, 13, 93222-93234. (www)

  @article{ , abstract = {Biomedical electronics are becoming increasingly important as a result of concerns with remote patient monitoring. It is becoming increasingly vital to follow cardiac patients outside of hospitals and in their daily lives. Therefore, wireless communication and home usage are suitable for medical devices. This study proposes an innovative technique for monitoring cardiac activity based on variation in surface impedance during cardiac muscle activity. A human thoracic model was rigorously created to justify our approach, confirming efficiency of the technique. This model has a three-layered structure, with each layer distinguished by its distinct surface impedances. As one moves from the deepest to the outermost layer, the impedance of each layer changes. Therefore, it is crucial to understand the physical properties of biological tissues to predict their activity accurately. This work introduces an implementation of a novel approach, centered on analyzing the surface impedance variation within a multilayer model. This variation is intricately linked to the thickness of the innermost layer within the ISM band across four distinct frequencies: 433 MHz, 915 MHz, 2.4 GHz, and 5.8 GHz. These frequencies provide a balance between range, reliability, and compatibility with existing wireless technologies, ensuring effective and robust communication for applications.}, archive = {J_ACCESS}, author = {Khaoula Tayari and Imen Jarraya and Omar Cheikhrouhou and Anis Koubaa and Hamadi Ghariani}, doi = {10.1109/ACCESS.2024.3521041}, journal = {IEEE Access}, pages = {93222-93234}, shortjournal = {IEEE Access}, title = {Surface impedance for real-time cardiac monitoring in body area networks}, volume = {13}, year = {2025}, }
• (2025). Survey on backdoor attacks on deep learning: Current trends, categorization, applications, research challenges, and future prospects. ACCESS, 13, 93190-93221. (www)

  @article{ , abstract = {Deep Neural Networks (DNNs) have emerged as a prominent set of algorithms for complex real-world applications. However, state-of-the-art DNNs require a significant amount of data and computational resources to train and generalize well for real-world scenarios. This dependence of DNN training on a large amount of computational and memory resources has increased the use of Machine Learning as a Service (MLaaS) or third-party resources for training large models for complex applications. Specifically, the drift of the deep learning community towards self-supervised learning for learning better representations directly from large amounts of unlabeled data has amplified the computational and memory requirements for machine learning. On the one hand, the availability of MLaaS (or third-party resources) alleviates this issue. On the other hand, it opens up avenues for a new set of vulnerabilities, where an adversary (someone from a third party) can infect the model with malicious functionality that is triggered only with specific input patterns. Such attacks are usually referred to as Trojan or backdoor attacks and are very stealthy and hard to detect. In this paper, we highlight the complete attack surface that can be exploited to inject hidden malicious functionality (backdoors) in machine learning models. We classify the attacks into two major categories, i.e., poisoning attacks and non-poisoning attacks, and present state-of-the-art works related to each. Towards the end of the article, we highlight the limitations of existing techniques and cover some of the key challenges in developing stealthy and robust real-world backdoor attacks.}, archive = {J_ACCESS}, author = {Muhammad Abdullah Hanif and Nandish Chattopadhyay and Bassem Ouni and Muhammad Shafique}, doi = {10.1109/ACCESS.2025.3571995}, journal = {IEEE Access}, pages = {93190-93221}, shortjournal = {IEEE Access}, title = {Survey on backdoor attacks on deep learning: Current trends, categorization, applications, research challenges, and future prospects}, volume = {13}, year = {2025}, }
• (2025). PMD-transformer: A domain generalization approach for person re-identification. ACCESS, 13, 93178-93189. (www)

  @article{ , abstract = {Domain generalization person re-identification (DG-ReID) aims to address the performance degradation caused by domain shift between the training domain and unseen target domains. Transformers often demonstrate better generalization ability than CNNs due to their self-attention mechanism and capacity for capturing global features. However, the supervised learning strategy on the source domain makes the Transformer-based ReID model inevitably overfit the bias in some regions. Therefore, a transformer model (PMD Transformer) for DG-ReID is proposed. First, by introducing relative position coding, it prevents the person image block from losing its relative position information when passing through the attention mechanism and improves the extraction of person features. Second, in order to solve the excessive attention of the model to the features of some regions and thus the overfitting phenomenon, the part mask attention (PMA) module is designed to limit the scope of the attention mechanism and improve the computational efficiency. Finally, a new feedforward network (DRFNN) is used to enhance the mastery of spatial information and further improve the generalization performance of the model. The experimental results show that, in the setting where Market is the source domain and Duke is the target domain, the average precision (mAP) and Rank-1 metrics increased by 12.5% and 9.2%, respectively, compared to the optimal algorithm. In the setting where Duke is the source domain and CUHK03-NP is the target domain, the mAP and Rank-1 metrics increased by 10.5% and 8.6%, respectively. The experiments demonstrate that the proposed model exhibits outstanding generalization ability in DG-ReID.}, archive = {J_ACCESS}, author = {Xingguo Jiang and Ling Yu and Guojun Lin and Yuchao Zhang}, doi = {10.1109/ACCESS.2025.3573921}, journal = {IEEE Access}, pages = {93178-93189}, shortjournal = {IEEE Access}, title = {PMD-transformer: A domain generalization approach for person re-identification}, volume = {13}, year = {2025}, }
• (2025). Examining AI-powered ADHD diagnosis: Current trends, key challenges, and future directions in the field. ACCESS, 13, 93148-93177. (www)

  @article{ , abstract = {Attention deficit hyperactivity disorder (ADHD) is a type of neurodevelopmental disease affecting the mental health of children and adults. Individuals with ADHD show various symptoms such as inattention, hyperactivity, and impulsivity. Early diagnosis of ADHD helps to alter neural connections and refine symptoms. The clinical practice to diagnose ADHD is through subjective measures and does not significantly capture the underlying structural and functional mechanisms of the brain. Therefore, it is crucial to explore other approaches such as Artificial Intelligence (AI) to improve the accuracy and efficacy of ADHD diagnosis. Consequently, in this article we systematically investigate various Machine Learning (ML) and Deep Learning (DL) approaches as well as different diagnostic tools or modalities employed for the identification of ADHD. Particularly, a Systematic Literature Review (SLR) is conducted to review and analyze 98 selected studies published from 2021 to 2024. Subsequently, the selected studies are grouped into five categories based on the modalities utilized in these studies: physiological signals (37), magnetic resonance imaging (31), questionnaires (11), motion data (8), and others (11). We also analyze AI models which indicates that 45 studies utilized ML models, 33 studies employed DL models, and 20 studies used both. However, there are still some gaps in current research such as a lack of publicly available datasets except MRI and EEG. Although datasets for MEG and actigraphy exist, but they are underexplored and have been utilized in only a few studies. While DL models like CNNs and ANNs have been increasingly applied in recent years for ADHD diagnosis, there is a shortage of advanced DL models, including transfer learning approaches like ResNet and VGG. Additionally, there is a lack of interpretability in AI models, particularly DL models. Furthermore, most studies focus on individual modalities for ADHD diagnosis, and despite many studies showing excellent results, there is a lack of implementation of AI-based tools in real-world clinical settings. These gaps highlight areas for further exploration and development.}, archive = {J_ACCESS}, author = {Qurat Ul Ain and Soyiba Jawed and Ahmad Rauf Subhani and Wasi Haider Butt and Muhammad Usman Akram}, doi = {10.1109/ACCESS.2025.3567427}, journal = {IEEE Access}, pages = {93148-93177}, shortjournal = {IEEE Access}, title = {Examining AI-powered ADHD diagnosis: Current trends, key challenges, and future directions in the field}, volume = {13}, year = {2025}, }
• (2025). Systematic literature review of topic labeling. ACCESS, 13, 93124-93147. (www)

  @article{ , abstract = {The rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics. Among these methods, Topic Modeling helps extract relevant topics, represented as clusters of words. However, interpreting these clusters into meaningful topics remains a challenge. This limitation has led to further research into topic labeling, an approach for assigning comprehensive and semantically meaningful labels to topic modeling results, ensuring that they are interpretable and understandable from a human perspective. In this paper, we present a Systematic Literature Review (SLR) on topic labeling. This review explores its definition, geographical and time distribution, methodologies, datasets, evaluation methods, successes, and challenges. This paper presents an SLR on topic labeling, synthesizing insights from 41 high-quality studies. It serves as a rich source of information for researchers interested in investigating different approaches for discovering topics within textual data. It addresses the various aspects of topic labeling and includes discussions that highlight the challenges of this approach, encouraging further research in this field.}, archive = {J_ACCESS}, author = {Salma Mekaoui and Ilham Chaker and Arsalane Zarghili and Nikola S. Nikolov}, doi = {10.1109/ACCESS.2025.3573521}, journal = {IEEE Access}, pages = {93124-93147}, shortjournal = {IEEE Access}, title = {Systematic literature review of topic labeling}, volume = {13}, year = {2025}, }
• (2025). High-resolution multiple-source 2D DOA estimation using convolutional neural network with robustness to array imperfections. ACCESS, 13, 93115-93123. (www)

  @article{ , abstract = {In this paper, we present an efficient convolutional neural network (CNN)-based model to estimate both elevation and azimuth arrival angles of multiple sources with high resolution (small source angular separation). The sources are considered closely spaced in both elevation and azimuth up to 0.5° resolution in both directions. Ten sources are supposed to be received by an $8\times 8$ antenna array. Compared to multiple signal classification (MUSIC) algorithm and radial basis function neural network (RBFNN), numerical results indicate that the CNN model exhibits notable precision in two-dimensional (2D) direction of arrival (DOA) estimation of multiple-source with high resolution, along with less than one-tenth second execution time. Finally, it maintains its superior performance even in the presence of array imperfections and significantly reduced SNR.}, archive = {J_ACCESS}, author = {Tarek Sallam and Qun Wang and Ahmed M. Attiya}, doi = {10.1109/ACCESS.2024.3477920}, journal = {IEEE Access}, pages = {93115-93123}, shortjournal = {IEEE Access}, title = {High-resolution multiple-source 2D DOA estimation using convolutional neural network with robustness to array imperfections}, volume = {13}, year = {2025}, }
• (2025). Comprehensive micromagnetic modeling: Practical techniques, applications, and emerging challenges. ACCESS, 13, 93102-93114. (www)

  @article{ , abstract = {Micromagnetic models play a pivotal role in advancing magnetic materials and spintronic device designs, enabling dynamic and equilibrium studies essential for next-generation information processing and neuromorphic computing. This tutorial provides a comprehensive guide to the physics, practical tools, and applications of micromagnetic modeling using MuMax3. The tutorial first analytically defines the Landau-Lifshitz-Gilbert formalism and the major anisotropy terms that depend on the intrinsic and geometric material parameters. Material parameter lists for diverse magnetic conductors, insulators, semiconductors and topological insulators are presented. Zhang-Li spin transfer torques, Slonczewski spin transfer torques, and spin-orbit torques are formally defined and compared for spin current-driven control of magnetism. On the technical side, the tutorial presents detailed batch simulation scripting, phase diagram plotting, and methods for importing complex geometries for the analysis and design of real devices. Accompanying Jupyter notebook provide the source code for the examples. Finally, the tutorial addresses micromagnetic modeling limitations in capturing quantum effects, multi-domain couplings, and temperature-dependent phenomena. By bridging fundamentals with practical tools and insights into emerging challenges, this tutorial serves as a resource for researchers and educators in spintronics, magnetism, and computational materials science.}, archive = {J_ACCESS}, author = {Ahmet Bahadir Trabzon and Taichi Goto and Mehmet Cengiz Onbasli}, doi = {10.1109/ACCESS.2025.3573946}, journal = {IEEE Access}, pages = {93102-93114}, shortjournal = {IEEE Access}, title = {Comprehensive micromagnetic modeling: Practical techniques, applications, and emerging challenges}, volume = {13}, year = {2025}, }
• (2025). Timing and speculative execution attacks: Defeating state-of-the-art code-reuse defenses. ACCESS, 13, 93084-93101. (www)

  @article{ , abstract = {Recently, numerous effective defensive strategies like ASLR and execute-no-read have been put forward to counter code-reuse attacks in software systems. These methods safeguard systems robustly by addressing randomization or memory access constraints. However, this paper uncovers a novel vulnerability in these approaches: the lack of time protection. We present a new assault method named the timing function attack. This attack can successful initiate a code-reuse attack although the presence of state-of-the-art defensives. By exploiting the time channel, we can obtain crucial security information despite previous attempts to hide spatial details. Specifically, we use function execution time for side-channel attacks, de-randomize code segment layouts and then execute a code-reuse attack. To verify its practicality, we conduct attacks on ChakraCore and Chrome v8 JavaScript engines. Results show it can bypass defenses like function-granularity ASLR and XnR, escalating privileges. We also introduce SAROP, which uses speculative execution vulnerabilities to bypass address randomization. We compare these two attacks and discuss defense mechanisms, emphasizing the need for multi-layered security.}, archive = {J_ACCESS}, author = {Zhang Tianning and Cai Miao and Zhang Diming and Huang Hao}, doi = {10.1109/ACCESS.2025.3573038}, journal = {IEEE Access}, pages = {93084-93101}, shortjournal = {IEEE Access}, title = {Timing and speculative execution attacks: Defeating state-of-the-art code-reuse defenses}, volume = {13}, year = {2025}, }
• (2025). MUXAS-VR: A multi-dimensional user experience assessment system for virtual reality. ACCESS, 13, 93063-93083. (www)

  @article{ , abstract = {Virtual Reality (VR) technology offers immersive experiences across various domains, but assessing user experience (UX) remains a challenge. Traditional methods, such as questionnaires, are time-consuming and fail to capture the complexity of user interactions, while automated approaches which leverage sensors and deep learning (DL) techniques show promise but have several architectural limitations. Existing frameworks often rely on data from contact-based physiological sensors. They also tend to overlook attributes of the visual stimuli, use simplistic multimodal fusion techniques, and focus on a single UX dimension, all of which reduce their ability to provide a comprehensive and accurate assessment of UX. This study proposes MUXAS-VR, a DL-based framework that overcomes these limitations by leveraging multimodal behavioral cues. These are seamlessly captured using embedded sensors in commercially available VR devices, eliminating the need for contact-based physiological sensors. MUXAS-VR integrates individual behavioral data with visual attributes, such as motion dynamics and visual complexity, to account for individual interactions and visual influences. Its Multimodal Fusion Module (MFM) selectively combines data from multiple modalities to enhance feature representation and predictive performance. Unlike conventional systems, MUXAS-VR evaluates multiple UX dimensions—cybersickness (CS), sense of presence (SOP), and overall satisfaction (OS)—to provide a comprehensive view of VR UX. To validate its effectiveness, we proposed a rigorous evaluation strategy across three scenarios—Missing Content, New Content, and New User—to ensure generalizability and practical applicability. Experimental results showed that multimodal behavioral cues significantly improved predictive performance. Combining these cues with visual attributes provided complementary information, and the MFM outperformed simpler fusion methods by capturing complex interdependencies. These comprehensive evaluations across diverse scenarios demonstrate MUXAS-VR’s usefulness in predicting multiple UX dimensions.}, archive = {J_ACCESS}, author = {Peerawat Pannattee and Yosuke Fukuchi and Nobuyuki Nishiuchi}, doi = {10.1109/ACCESS.2025.3573382}, journal = {IEEE Access}, pages = {93063-93083}, shortjournal = {IEEE Access}, title = {MUXAS-VR: A multi-dimensional user experience assessment system for virtual reality}, volume = {13}, year = {2025}, }
• (2025). A low-profile broadband circularly polarized slot antenna loaded with metasurface for UHF-RFID readers. ACCESS, 13, 93057-93062. (www)

  @article{ , abstract = {A low-profile broadband antenna with high-gain circularly polarized (CP) radiation is proposed for ultra-high frequency (UHF) radio frequency identification (RFID) readers. The design is a sequentially rotated $2\times 2$ CP slot array. Each slot element utilizes two 45°-rotated stubs to introduce proper structural asymmetry, and consequently, achieve CP radiation. The array is fed by a sequential-phase feeding network for improving CP radiation in terms of gain and 3-dB axial ratio (AR) bandwidth. For a broadside radiation, the slot array is backed by a ground plane (GND). A metasurface, which is composed of $6\times 6$ periodic metallic plates, is inserted between the array and GND to allow a low profile with high efficiency. The final design with overall size of $210\times 210\times 32.4$ mm3 ( $0.6\lambda \times 0.6\lambda \times 0.09\lambda $ at 0.86 GHz) has been fabricated and measured. The measurements result in a 10-dB return-loss bandwidth of $0.81-0.96$ GHz, a 3-dB AR bandwidth of $0.86-0.93$ GHz, and broadside right-hand CP radiation with the peak gain of 8.7 dBic.}, archive = {J_ACCESS}, author = {Thi Ngoc Hien Doan and Hai Dang Le and van Son Hoang and Khac Kiem Nguyen and Son Xuat Ta}, doi = {10.1109/ACCESS.2025.3574117}, journal = {IEEE Access}, pages = {93057-93062}, shortjournal = {IEEE Access}, title = {A low-profile broadband circularly polarized slot antenna loaded with metasurface for UHF-RFID readers}, volume = {13}, year = {2025}, }
• (2025). Research and implementation of OFC-based multicarrier sliceable bandwidth variable transponders in hybrid Laser/Microwave satellite networks. ACCESS, 13, 93046-93056. (www)

  @article{ , abstract = {In this paper, a multicarrier sliceable bandwidth variable transponder based on optical frequency comb (OFC) is proposed. The structure of the transponder’s internal switching unit is designed, and the principle of generating ultra-flat OFC based on a standalone dual-parallel Mach-Zehnder modulator (DPMZM) is analyzed. A scheme for dynamic allocation of multi-channel frequency slots and flexible resource configuration in hybrid laser/satellite networks is provided, and an experimental system is built to test the data transmission and bandwidth allocation capabilities of laser and microwave links. Experimental results indicate that the muticarrier generation unit can produce a 7-line flat OFC with a flatness of 1.6 dB, and the switching unit can achieve channel switching and frequency slot allocation, with a minimum slice width of 12.5 GHz. After passing through this node, the phase noise of the 2 GHz IF signal with frequency offsets at 10 kHz and 100 kHz are −59.28 dBc/Hz and −87.95 dBc/Hz, respectively, and the bit error rates (BERs) of the recovered baseband data from the laser and microwave links are less than $10^{-9}$ . These results validate the feasibility of the designed transponder to be applied in the key relay nodes of satellite networks. The proposed scheme can solve the problems of low spectrum utilization and the inability to dynamically allocate resources in traditional WDM satellite networks.}, archive = {J_ACCESS}, author = {Bin Wu and Hao Zhang and Qingchun Zhao}, doi = {10.1109/ACCESS.2025.3573788}, journal = {IEEE Access}, pages = {93046-93056}, shortjournal = {IEEE Access}, title = {Research and implementation of OFC-based multicarrier sliceable bandwidth variable transponders in hybrid Laser/Microwave satellite networks}, volume = {13}, year = {2025}, }
• (2025). Plausible vehicle trajectory augmentation with hybrid machine learning. ACCESS, 13, 93033-93045. (www)

  @article{ , abstract = {In recent decades, advancements in autonomous driving technology have fundamentally transformed the automotive industry and transportation systems. Autonomous vehicles hold the potential to significantly reduce traffic accidents, improve driving convenience, and alleviate congestion. A crucial factor in achieving these outcomes is the accurate prediction of surrounding vehicle trajectories, which relies on the effective application of artificial intelligence and machine learning algorithms. However, the collection of sufficient high-quality driving data for training these models poses practical challenges, particularly in complex and unpredictable traffic scenarios. To address this issue, this paper proposes a novel data augmentation technique, integrating Chebyshev polynomial fitting with Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering and Gaussian mixture models to generate robust training data. This study focuses on augmenting vehicle driving datasets by automatically managing variations in time-series lengths and the clustering of driving maneuvers. This approach ultimately generates augmented data with a high degree of similarity to real-world data. Validation on the inD dataset confirms that our approach excels in expanding training datasets, yielding substantial improvements in trajectory prediction accuracy for autonomous vehicles when applied to a vanilla long short-term memory model. This study outlines the methodology and validates the effectiveness of the augmented data, offering a promising method for enhancing autonomous vehicle training frameworks.}, archive = {J_ACCESS}, author = {Junyeop Park and Hyeonsoo Jang and Byeongju Kang and Yunhyoung Hwang}, doi = {10.1109/ACCESS.2025.3573191}, journal = {IEEE Access}, pages = {93033-93045}, shortjournal = {IEEE Access}, title = {Plausible vehicle trajectory augmentation with hybrid machine learning}, volume = {13}, year = {2025}, }
• (2025). Hierarchical embedded system based on FPGA for classification of respiratory diseases. ACCESS, 13, 93017-93032. (www)

  @article{ , abstract = {Objectives: This research aims to design and develop a hierarchical embedded system that utilizes respiratory sound features for diagnosing COPD and other respiratory disorders. The system is engineered to achieve high accuracy and efficiency while minimizing energy consumption, making it suitable for deployment in mobile devices or embedded systems. Methods: Lung sounds are segmented into individual breathing cycles. Thirty-nine main respiratory features in the time and frequency domains are extracted and organized into four layers. The Random Forest algorithm and Artificial Neural Network are fine-tuned with the dataset and applied to disease classification. The first layer contains recorded information, while the second and third layers contain features extracted from fixed-length sound segments classified via Random Forest. The fourth layer utilizes Wavelet Transform to convert breathing patterns into Spectrogram images, which the Artificial Neural Network processes for disease diagnosis. The system is implemented on the Xilinx PYNQ-Ultra96-V2 FPGA development board. Results: The system achieves the highest accuracy of 98.81% for five disease classes: COPD, Healthy, URTI, Bronchitis, and Pneumonia, and saves 52.5% of energy consumption compared to CPU-GPU-based traditional methods. Conclusion: This study demonstrates the effectiveness of the proposed method in diagnosing COPD and other respiratory disorders. The hierarchical embedded system is designed with high accuracy and energy efficiency, with potential real-world applications to support clinical diagnosis.}, archive = {J_ACCESS}, author = {Trong-Thanh Han and Kien Le Trung and Phuong Nguyen Anh and Anh Do Trung}, doi = {10.1109/ACCESS.2025.3573162}, journal = {IEEE Access}, pages = {93017-93032}, shortjournal = {IEEE Access}, title = {Hierarchical embedded system based on FPGA for classification of respiratory diseases}, volume = {13}, year = {2025}, }
• (2025). Exploiting circular shifts for efficient chaotic image encryption. ACCESS, 13, 92997-93016. (www)

  @article{ , abstract = {This work presents a chaotic image encryption algorithm that acts on the binary level of the image for effective permutation, and on the byte level for substitution. The permutation step combines actions of bit level rearranging, and bit shuffling. Circular shift operations are used for bit shuffling, to reduce the execution time. Circular shifts are also applied to shuffle the permutation and substitution rules, effectively increasing security at a very low execution cost. The encryption keys are plaintext dependent, and a key mixing step in included, so that each part of the key affects all the subsequent encryption operations. For the encryption, a new Soboleva hyperbolic tangent based map is first designed. The dynamical behavior of the map is studied and shows robust chaos, and an absence of equilibria, meaning that it can generate hidden attractors. This map is used as an entropy source, along with a chaotic pseudo random number generator (PRNG). The encryption process shows a good performance under a collection of tests, and has also a low execution time, 0.0134 sec for a $256\times 256$ image, 0.0530 sec for a $512\times 512$ image, and 0.2050 sec for a $1024\times 1024$ image. So the proposed algorithm is a viable option for fast, efficient, and secure data encryption.}, archive = {J_ACCESS}, author = {Lazaros Moysis and Marcin Lawnik and Wassim Alexan and Sotirios K. Goudos and Murilo S. Baptista and George F. Fragulis}, doi = {10.1109/ACCESS.2025.3572589}, journal = {IEEE Access}, pages = {92997-93016}, shortjournal = {IEEE Access}, title = {Exploiting circular shifts for efficient chaotic image encryption}, volume = {13}, year = {2025}, }
• (2025). A verifiably secure and lightweight device-to-device (D2D) authentication protocol for the resource-constrained IoT networks. ACCESS, 13, 92982-92996. (www)

  @article{ , abstract = {Due to the growth of Internet of Things (IoT) devices on networks with limited resources, developing a safe and effective authentication method for Device-to-Device (D2D) communication has become necessary. Conventional authentication methods frequently have high communication and processing overhead, which makes them inappropriate for resource-constrained IoT networks. An innovative, lightweight, and verifiably secure D2D authentication protocol designed for resource-constrained IoT networks is proposed in this research. The protocol minimizes energy and computational costs while ensuring strong security by utilizing mutual authentication and effective cryptographic primitives. According to security analysis, the proposed protocol resists widespread attacks, such as DoS, replay, man-in-the-middle, and impersonation attacks. Additionally, the accuracy and security features of the protocol are confirmed by formal verification using commonly used techniques such as the ROR model and ProVerif validation. It is a feasible option for IoT deployments as performance examinations show considerable improvements over current systems regarding energy consumption, communication, computation, storage, and bandwidth utilization.}, archive = {J_ACCESS}, author = {Naif Alzahrani}, doi = {10.1109/ACCESS.2025.3568692}, journal = {IEEE Access}, pages = {92982-92996}, shortjournal = {IEEE Access}, title = {A verifiably secure and lightweight device-to-device (D2D) authentication protocol for the resource-constrained IoT networks}, volume = {13}, year = {2025}, }
• (2025). Optimized multiplicative pre-distortion PAPR reduction scheme for OTFS signals. ACCESS, 13, 92974-92981. (www)

  @article{ , abstract = {We present an iterative peak-to-average power ratio (PAPR) reduction scheme with multiplicative pre-distortion vectors for orthogonal time frequency space (OTFS) systems by making a compromise between the PAPR reduction and the bit error rate (BER) performance. In the scheme, the multiplicative pre-distortion vectors are introduced to weight the symbols in the delay-Doppler (DD) domain with the same delay (i.e. a vector in the Doppler domain) which cause the peak of the OTFS signal rather than all the delay-Doppler domain symbols. In each iteration, the multiplicative pre-distortion vector is optimized to minimize the introduced in-band distortions before and after the pre-distortion with the preset PAPR constraint using the error vector magnitude (EVM). Simulation results demonstrate that the gain of PAPR reduction in our scheme is about 3.0dB for QPSK and 16-QAM over conventional OTFS signals at the complementary cumulative distribution function level of 10−4 with negligible BER performance degradation and low complexity.}, archive = {J_ACCESS}, author = {Feiyang Liu and Jiayang Zhao and Guangliang Ren}, doi = {10.1109/ACCESS.2025.3574220}, journal = {IEEE Access}, pages = {92974-92981}, shortjournal = {IEEE Access}, title = {Optimized multiplicative pre-distortion PAPR reduction scheme for OTFS signals}, volume = {13}, year = {2025}, }
• (2025). Designing macroscopic surface features perceived as soft during finger sliding. ACCESS, 13, 92965-92973. (www)

  @article{ , abstract = {Humans evaluate material softness not only by pressing but also through stroking or rubbing motions. While previous studies have primarily focused on pressing to investigate compliance-related softness perception, the present study investigates softness perception during sliding, focusing on the “curved surface softness illusion,” in which slightly wavy surfaces feel softer than flat ones during fingertip sliding, despite being made of the same material. The objective of this study is to identify the surface features of sinusoidal surfaces that enhance the softness perceived in this illusion, using an optimal designing approach. A set of hard plastic specimens with varying wavelengths and amplitudes was manufactured based on a central composite design. Then, their perceived softness was evaluated in an experiment involving 32 participants. Results showed that perceived softness during sliding exhibited an inverted U-shaped function of both macroscopic sinusoidal wavelength and amplitude. A response surface method was then used to determine the design parameters that would maximize perceived softness. In a post-hoc experiment, the optimally designed specimen was rated as soft as the softest specimen in the initial experiment. The specimen judged as softest exhibited the surface wavelength of approximately 70 mm and amplitude of 0.40 mm. The findings of this study will enable the manipulation of softness perception through surface design, which will potentially be applied to consumer products. However, the mechanism of this perceptual phenomenon remains unknown and should be further investigated.}, archive = {J_ACCESS}, author = {Qingyu Sun and Hongbo Wang and Shogo Okamoto}, doi = {10.1109/ACCESS.2025.3573637}, journal = {IEEE Access}, pages = {92965-92973}, shortjournal = {IEEE Access}, title = {Designing macroscopic surface features perceived as soft during finger sliding}, volume = {13}, year = {2025}, }
• (2025). A hierarchical feature fusion and dynamic collaboration framework for robust small target detection. ACCESS, 13, 92953-92964. (www)

  @article{ , abstract = {Small target detection is an important research direction in computer vision, widely applied in scenarios such as drone monitoring, remote sensing image analysis, and autonomous driving. However, as small targets occupy fewer pixels, contain limited feature information, and often appear in complex backgrounds, existing detection algorithms face shortcomings in accuracy and robustness. To address this, this paper proposes a novel small target detection algorithm that integrates hierarchical feature fusion with a spatial dynamic collaboration mechanism. The hierarchical feature fusion module (HFA) effectively combines shallow detail features with deep semantic features, greatly enhancing the feature representation capability for small targets. Meanwhile, the dynamic collaboration mechanism (DCCA) dynamically adjusts feature fusion weights and detection strategies based on target scale and density distribution, thereby further improving detection accuracy and robustness. Extensive experiments are conducted on datasets such as VisDrone, TinyPerson, and NWPU VHR-10. Results demonstrate that, compared to state-of-the-art models like YOLOv8 and YOLOv10, the proposed algorithm achieves significant improvements in precision, recall, and mAP, with mAP increasing by 2.1% to 3.2% and mAP-95 by 1.2% to 1.8%. Ablation studies further validate the complementarity of HFA and DCCA in optimizing model performance, confirming the algorithm’s superiority and robustness in complex scenarios. This research provides a novel technical route for small target detection and offers valuable references for practical applications.}, archive = {J_ACCESS}, author = {Xu Yan and Junliang Du and Xuan Li and Xiaoye Wang and Xiaoxuan Sun and Pochun Li and Hongye Zheng}, doi = {10.1109/ACCESS.2025.3570669}, journal = {IEEE Access}, pages = {92953-92964}, shortjournal = {IEEE Access}, title = {A hierarchical feature fusion and dynamic collaboration framework for robust small target detection}, volume = {13}, year = {2025}, }
• (2025). Road marking sign damage detection and classification using deep learning and fuzzy method. ACCESS, 13, 92943-92952. (www)

  @article{ , abstract = {Road marking signs are part of traffic signs on the road’s surface. It can be found on almost every section of the road. It gives information about the road lanes and helps the driver understand the road ahead. Located on the road surface, road marking signs are more vulnerable to damage. Maintaining the condition of road marking signs is very challenging. It is a combination of environmental, logistical, and financial factors. Keeping the road marking signs clear and visible is essential for road safety. Faded or damaged road marking signs are difficult for the driver to see and identify, which can lead to missing or wrong information being captured. This research proposes a novel method to detect damaged road marking signs and classify the damage level using the combination of YOLOv9, VAE, image processing techniques, and fuzzy logic. The experiment results show that YOLOv9-M performs best in general road marking sign detection with 0.92 precision, 0.906 recall, 0.935 mAP@50, and 0.668 mAP@50:95. YOLOv9-M also excels in detecting the damaged road marking signs with 0.872 detection rate. The combination of VAE and image processing techniques shows promising results in detecting damaged road marking signs with 0.8395 precision, 0.8212 recall, and 0.7295 accuracy. The combination of anomaly score from the VAE with fuzzy logic is used to classify the damage level into three categories, which is more meaningful in understanding the road marking sign condition.}, archive = {J_ACCESS}, author = {William Eric Manongga and Rung-Ching Chen}, doi = {10.1109/ACCESS.2025.3572741}, journal = {IEEE Access}, pages = {92943-92952}, shortjournal = {IEEE Access}, title = {Road marking sign damage detection and classification using deep learning and fuzzy method}, volume = {13}, year = {2025}, }
• (2025). Multi-step quality-oriented training for cross-dataset offline iterative speech enhancement. ACCESS, 13, 92932-92942. (www)

  @article{ , abstract = {In recent years, speech enhancement research has increasingly leveraged deep neural network models to improve performance. Although these models often achieve strong results on specific datasets, they typically require substantial memory and computational resources, limiting their practicality in real-world applications. Consequently, evaluating model performance across diverse datasets has become essential. In this work, we propose a novel SE framework, termed CDiffSE-AC, which integrates a diffusion model with Actor-Critic techniques to address the limitations of existing methods. Our approach processes noisy speech inputs using a diffusion model alongside a pre-trained large-scale language model, which serves as the Actor by generating labels for the noisy input. These labels are subsequently used as conditioning information for the SE model. A Critic module evaluates the enhanced output by scoring its speech quality. During training, the enhanced outputs are recursively reintroduced as inputs to both the Actor and SE models, enabling iterative refinement and performance enhancement. We evaluate the proposed method using two different-sized subsets of the TIMIT and VCTK-DM datasets. Although CDiffSE-AC yields marginal score improvements within individual datasets, it demonstrates substantial gains in cross-dataset evaluations, significantly outperforming the baseline MOSE method. Specifically, CDiffSE-AC achieves PESQ improvements of 0.15 (7.2%) and 0.18 (7.5%) on the TIMIT and VCTK-DM test sets, respectively.}, archive = {J_ACCESS}, author = {Shih-Chuan Chu and Chung-Hsien Wu}, doi = {10.1109/ACCESS.2025.3573458}, journal = {IEEE Access}, pages = {92932-92942}, shortjournal = {IEEE Access}, title = {Multi-step quality-oriented training for cross-dataset offline iterative speech enhancement}, volume = {13}, year = {2025}, }
• (2025). Meta domain adaptation approach for multi-domain ranking. ACCESS, 13, 92921-92931. (www)

  @article{ , abstract = {In a real industry recommendation system, the distribution of recommended domains is very redundant. Different domains may address the same problem, such as the Click-Through Rate (CTR) prediction, and may share the same features. Multi-domain learning aims to use one model to complete the same task of all domains, in order to improve the model effect and achieve savings in computing resources and human effort. However, different domains have different data distributions and insufficient training data. In constructing a multi-domain model encompassing several domains with small sample sizes, how to make the best use of the existing data of other domains and ensure the generalization performance of the model in the training process has become the key problem of the research. To meet the challenge, in this paper, we propose a Meta Domain Adaptation model for Multi-Domain ranking (MDAMD), specifically designed for CTR prediction across multiple domains. It employs the unsupervised domain adaptation method to ensure the global unity of training for model optimization among all domains. Maximum Mean Discrepancy (MMD) loss calculation is added after the gate unit to ensure adaptive training of the model in feature extraction. Meanwhile, MDAMD applies a meta-learning method to avoid overfitting in the domain adaptation stage. Results from the industrial and public datasets demonstrate the effectiveness of MDAMD. Moreover, our model has been deployed online and obtained the CTR and Gross Merchandise Volume (GMV) improvements.}, archive = {J_ACCESS}, author = {Zihan Xia and Yixuan Liu and Xiangran Zhang and Xiaoshuang Sheng and Kewei Liang}, doi = {10.1109/ACCESS.2025.3564115}, journal = {IEEE Access}, pages = {92921-92931}, shortjournal = {IEEE Access}, title = {Meta domain adaptation approach for multi-domain ranking}, volume = {13}, year = {2025}, }
• (2025). Nearest-neighbor dual-path contrastive learning for lumbar disc herniation MRI image classification. ACCESS, 13, 92906-92920. (www)

  @article{ , abstract = {Lumbar disc herniation (LDH) is a common spinal condition that profoundly affects patients’ quality of life. Timely and precise diagnosis is essential for efficient therapy and enhancing patient outcomes. This paper introduces an innovative LDH classification framework utilizing nearest-neighbor dual-path contrastive learning, integrating global and local feature learning to improve lumbar MRI image classification efficacy. The global path delineates semantic linkages among samples by nearest-neighbor contrastive learning, enhancing global representations, whereas the local path employs clipped regions and data augmentation to highlight essential details, thus improving fine-grained feature modeling. The novel nearest-neighbor-based positive sample construction enhances feature consistency and classification accuracy by reducing the impact of irrelevant examples. Our method achieves state-of-the-art accuracy and robustness in complicated classification tasks, as shown by experimental results on the lumbar MRI dataset. This discovery enhances automated LDH diagnosis and offers a viable avenue for accurate and efficient automated diagnosis in intricate medical imaging contexts.}, archive = {J_ACCESS}, author = {Dan Pan and Yu-Xiang Pan and Hui Wang and Qi-Jing Liu and Mao-Chang Qiu}, doi = {10.1109/ACCESS.2025.3571014}, journal = {IEEE Access}, pages = {92906-92920}, shortjournal = {IEEE Access}, title = {Nearest-neighbor dual-path contrastive learning for lumbar disc herniation MRI image classification}, volume = {13}, year = {2025}, }
• (2025). Bridging theory and practice: Addressing current cybersecurity gaps in industry 5.0. ACCESS, 13, 92891-92905. (www)

  @article{ , abstract = {In the modern technological landscape, standardized practices are fundamental to ensuring efficiency, quality, and seamless interoperability across various industries. The IEC 62443 series of standards has become a critical benchmark for enhancing cybersecurity in Industrial Automation and Control Systems (IACS). Nevertheless, a significant limitation of these standards is the lack of detailed implementation guidance, which poses substantial challenges for organizations seeking compliance. This paper studies the critical role of IEC 62443 standards series certification, emphasizing its importance and the diverse benefits it offers. We emphasize the necessity of conducting thorough cybersecurity gap analyses to strengthen security measures and enhance resilience against the evolving landscape of cyber threats. Using the IDUNN project as a case study, we illustrate how aligning technological developments with the IEC 62443 standards series can bridge the divide between existing cybersecurity guidelines and emerging threats. Furthermore, we examine the lifecycle management of cybersecurity events in accordance with the IEC 62443 standards series, explain methodologies for capturing security requirements, and discuss the integration of IDUNN’s tools into current systems to fortify cybersecurity defenses. The findings highlight the imperative for continuous collaboration and the ongoing enhancement of cybersecurity frameworks, culminating in best practices and recommendations to ensure the effective implementation of advances derived from initiatives like IDUNN.}, archive = {J_ACCESS}, author = {Saeid Sheikhi and Maialen Eceiza and Cristóbal Arellano and Oscar López and Stefan Kelnberger and René Lindner and Jari Partanen and Lauri Lovén}, doi = {10.1109/ACCESS.2025.3569130}, journal = {IEEE Access}, pages = {92891-92905}, shortjournal = {IEEE Access}, title = {Bridging theory and practice: Addressing current cybersecurity gaps in industry 5.0}, volume = {13}, year = {2025}, }
• (2025). A transfer learning combination model for annual national electricity consumption forecasting in china. ACCESS, 13, 92882-92890. (www)

  @article{ , abstract = {Accurate forecasting of annual national electricity consumption data is crucial for energy and economic development planning. However, the small amount of data and the complex influencing factors pose great challenges. Here we propose a novel transfer learning combination model for the 5-year forecasting of annual national electricity consumption data in China. To improve the forecasting accuracy, we adopted a data transfer learning approach to extend the training set of China by migrating relevant data from 17 developed countries. To increase the data information of training sets, a new data preprocessing procedure containing trend calculation and data extension was designed. To fully utilize the advantages of different models, a multi-model integration framework with a neural network weighting unit is designed. The comparison results show that the proposed model has the lowest errors, with the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 0.0105, 0.0002, and 0.3047, respectively.}, archive = {J_ACCESS}, author = {Ling Liu and Jujie Wang}, doi = {10.1109/ACCESS.2025.3574137}, journal = {IEEE Access}, pages = {92882-92890}, shortjournal = {IEEE Access}, title = {A transfer learning combination model for annual national electricity consumption forecasting in china}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of UAV placement and resource allocation in FDMA wireless-powered sensor networks. ACCESS, 13, 92873-92881. (www)

  @article{ , abstract = {This paper investigates uncrewed aerial vehicle (UAV)-assisted wireless powered sensor networks (WPSNs). In this system, sensors harvest energy radiated from a UAV and use this energy to transmit collected data back to the UAV via frequency division multiple access (FDMA). The objective is to maximize the sum-throughput, subject to constraints on transmission scheduling and bandwidth allocation, while guaranteeing a minimum throughput for each sensor. This problem is non-convex due to the presence of coupled variables. To solve it, the alternating minimization technique is used, where the problem is divided into subproblems with respect to each of the variables by fixing the others. Efficient algorithms based on the dual Lagrange method and Karush-Kuhn-Tucker (KKT) conditions are proposed for optimal transmission time scheduling and bandwidth allocation. These algorithms offer significant advantages in execution time. The UAV placement subproblem is addressed using successive convex approximation (SCA), which iteratively maximizes a lower bound. Numerical results clearly show that the proposed method outperforms the benchmarks and has a much lower execution time compared to its time division multiple access (TDMA) counterpart.}, archive = {J_ACCESS}, author = {Omid Abachian Ghasemi and Mehdi Chehel Amirani}, doi = {10.1109/ACCESS.2025.3574193}, journal = {IEEE Access}, pages = {92873-92881}, shortjournal = {IEEE Access}, title = {Joint optimization of UAV placement and resource allocation in FDMA wireless-powered sensor networks}, volume = {13}, year = {2025}, }
• (2025). Control theory and bayesian networks for black spots study. ACCESS, 13, 92859-92872. (www)

  @article{ , abstract = {Traffic accidents are recurring ordinary disruptive events in an urban road network. To measure the performance and recovery of an urban road network, it is necessary to identify and predict the locations on the network where they occur. The application of control theory to identify black spots has mainly focused on interstate and inter-regional highways. However, there is limited literature on the application of these theories to urban road networks and in the definition of black spots. This article introduces an innovative methodology for defining black spots based on control theory, which is adapted to urban road networks with appropriate measurement metrics that prove more suitable than those used following Euclidean norms. To predict whether a traffic accident occurs at a black spot, a Bayesian network is employed, which also indicates the reliability of the accident classification result. It demonstrates better efficiency than methodologies based on logistic regression and Naive Bayes, which is why this algorithm is chosen. The article concludes by measuring the impact of the most significant black spots in an urban road network in Metropolitan Lima, using the concept of transport resilience.}, archive = {J_ACCESS}, author = {Leonardo Flores and Jorge Vargas and Alan Ayala and Jeremy Ramos and Edwin Salas}, doi = {10.1109/ACCESS.2025.3571837}, journal = {IEEE Access}, pages = {92859-92872}, shortjournal = {IEEE Access}, title = {Control theory and bayesian networks for black spots study}, volume = {13}, year = {2025}, }
• (2025). Tooth aligned control of shifting process in a dedicated hybrid transmission. ACCESS, 13, 92848-92858. (www)

  @article{ , abstract = {The shifting process of a dedicated hybrid transmission (DHT) can be implemented via motor control to achieve tooth-aligned control (TAC), aiming to attain both ‘zero speed difference’ and ‘zero angle difference’ between the clutch gear and sleeve. TAC not only enables smooth collision-free gear engagement but also enhances drivability. To accomplish TAC with short duration and high precision, a novel model predictive control (MPC) approach based on Pontryagin’s minimum principle (PMP-MPC) is proposed. PMP is employed to derive the optimal switching trajectory and reference state for TAC. Upon completing the first trajectory switching, the system enters the vicinity of the reference state. Subsequently, MPC is activated to precisely regulate the system state toward the reference target. MATLAB/Simulink simulations validate the proposed strategy, demonstrating that the PMP-MPC-controlled shifting process achieves TAC within 0.281s, which is 0.219s faster than standalone MPC. Furthermore, under harmonic disturbances on the DHT input shaft, the angular error remains bounded within ±0.057°, confirming the controller’s robustness. These results indicate that the PMP+MPC hybrid strategy effectively realizes rapid TAC execution, thereby significantly improving shift quality.}, archive = {J_ACCESS}, author = {Wei Ren and Ming Cao}, doi = {10.1109/ACCESS.2025.3571951}, journal = {IEEE Access}, pages = {92848-92858}, shortjournal = {IEEE Access}, title = {Tooth aligned control of shifting process in a dedicated hybrid transmission}, volume = {13}, year = {2025}, }
• (2025). V2Coder: A non-autoregressive vocoder based on hierarchical variational autoencoders. ACCESS, 13, 92833-92847. (www)

  @article{ , abstract = {This paper introduces V2Coder, a non-autoregressive vocoder based on hierarchical variational autoencoders (VAEs). The hierarchical VAE with hierarchically extended prior and approximate posterior distributions is highly expressive for modeling stochastic components of speech waveforms. V2Coder learns the stochastic components as hierarchical latent representations in a data-driven manner and generates diverse waveforms in the time domain. VAEs tend to suffer from a phenomenon known as posterior collapse, in which little data information is encoded in the latent variable. To address this problem, we introduce a carefully designed architecture and skip loss that encourage encoding to latent variables in deep layers. Additionally, VAEs suffer from low-quality samples generated using prior distribution due to the prior hole problem. To improve the sample quality, we impose a constraint on latent information in each layer of the hierarchical VAE and demonstrate that this constrained optimization significantly affects the sample quality. Experimental results using single-speaker and multi-speaker corpora showed that V2Coder is a competitive neural vocoder comparable to existing non-autoregressive neural vocoders based on deep generative models. V2Coder generates high-quality speech waveforms faster than real-time on both GPU and CPU.}, archive = {J_ACCESS}, author = {Takato Fujimoto and Kei Hashimoto and Yoshihiko Nankaku and Keiichi Tokuda}, doi = {10.1109/ACCESS.2025.3572904}, journal = {IEEE Access}, pages = {92833-92847}, shortjournal = {IEEE Access}, title = {V2Coder: A non-autoregressive vocoder based on hierarchical variational autoencoders}, volume = {13}, year = {2025}, }
• (2025). SHERA: SHAP-enhanced resource allocation for VM scheduling and efficient cloud computing. ACCESS, 13, 92816-92832. (www)

  @article{ , abstract = {Cloud computing plays a crucial role in modern technology, providing scalable and on-demand computing resources. However, excessive resource use can result in higher energy demand, higher operating expenses, and a more significant adverse effect on the environment as per study by Berl et al. (2010). In order to forecast and maximize the energy efficiency in cloud systems, this paper presents a machine learning based methodology. Using the Cloud Efficiency Dataset from Google Cloud, key performance indicators such as CPU usage, memory consumption, network traffic, and power consumption were analyzed. Three machine learning models—Random Forest, Naïve Bayes, and Support Vector Machine (SVM) were trained and assessed based on Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and an Equivalent Accuracy metric. Among them Random Forest model performed the best, achieving an RMSE of 0.16 and an accuracy of 96.8 %. To improve the interpretability of the model Explainable Artificial Intelligence (XAI) techniques were applied, specifically SHapley Additive exPlanations (SHAP), to evaluate feature importance. The findings demonstrated that the most important variables which influence energy efficiency are CPU usage and memory consumption. This work presents a system level integration of SHAP guided VM scheduling rather than a fundamentally new algorithm. Unlike static threshold or heuristic schedulers, our SHAP driven system adapts VM placement decisions in real time with built in interpretability. While SHAP adds an average 0.15s of per job explanation latency (0.05s on GPU), we consider this overhead acceptable given the critical need for transparent, trustable scheduling in production clouds.}, archive = {J_ACCESS}, author = {Ashwin Singh Slathia and Abhiram Sharma and P. B. Krishna and Saksham Anand and Ayush Rathi and Linda Joseph and Xiao-Zhi Gao}, doi = {10.1109/ACCESS.2025.3568917}, journal = {IEEE Access}, pages = {92816-92832}, shortjournal = {IEEE Access}, title = {SHERA: SHAP-enhanced resource allocation for VM scheduling and efficient cloud computing}, volume = {13}, year = {2025}, }
• (2025). ReACT_OCRS: An AI-driven anonymous online reporting system using synergized reasoning and acting in language models. ACCESS, 13, 92800-92815. (www)

  @article{ , abstract = {Victims and witnesses of cybercrime often hesitate to report incidents due to concerns over privacy, complexity, and fear of retaliation. Traditional reporting mechanisms require manual data entry, creating accessibility barriers and delaying response times. To address these challenges, this paper introduces ReACT_OCRS, an AI-driven voice-based cybercrime reporting system that allows victims and witnesses to anonymously submit complaints through audio recordings. Leveraging speech recognition transformers, a recent language model, and encryption, the system processes real-time multilingual voice inputs, extracts meaningful content, and classifies reports with high precision using a hybrid voting mechanism. Experimental evaluations on synthetically generated and human-validated datasets confirm the system’s ability to accurately transcribe, classify, and securely process audio complaints while preserving user anonymity. This work improves cybercrime reporting by making it more accessible, efficient, and secure, fostering greater participation from victims and witnesses.}, archive = {J_ACCESS}, author = {Amir Aboubakr Shaker Mahmoud and Wesam Shishah and Nilay R. Mistry}, doi = {10.1109/ACCESS.2025.3571526}, journal = {IEEE Access}, pages = {92800-92815}, shortjournal = {IEEE Access}, title = {ReACT_OCRS: An AI-driven anonymous online reporting system using synergized reasoning and acting in language models}, volume = {13}, year = {2025}, }
• (2025). Enhanced ANN-based MPPT for photovoltaic systems: Integrating metaheuristic and analytical algorithms for optimal performance under partial shading. ACCESS, 13, 92783-92799. (www)

  @article{ , abstract = {The efficiency of photovoltaic (PV) systems significantly decreases under partial shading conditions (PSC), leading to challenges in accurately tracking the maximum power point (MPP). This paper presents an enhanced Artificial Neural Network (ANN) to improve the performance of MPP tracking (MPPT) in PV systems subject to PSC. The proposed algorithm is based on an advanced ANN model trained with widely known analytical and metaheuristic algorithms, providing higher accuracy and faster convergence than existing methods. Furthermore, the ANN model was developed and trained using an extensive dataset that includes diverse shading scenarios, irradiation levels, and temperature conditions, with metaheuristic algorithms playing a key role in enhancing its training process. The performance of the proposed system has been evaluated through extensive simulations and sensitivity analyses. The results demonstrate that the improved ANN-based MPPT algorithm consistently outperforms existing MPPT techniques, including the Perturb and Observe (P&O) and Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and Particle Swarm Optimization (PSO) methods. The proposed approach achieves higher efficiency, faster response times, and improved stability under dynamic shading conditions. Specifically, its superior efficiency reaches up to 99.98% under clear sky conditions (CSC) and up to 99.97% under PSC, as verified through extensive simulations using MPPT efficiency metrics. This advancement holds significant potential for optimizing the energy yield of PV systems, promoting more reliable and efficient renewable energy solutions, especially when operating in challenging environmental conditions.}, archive = {J_ACCESS}, author = {Alpaslan Demirci and Idriss Dagal and Said Mirza Tercan and Hasan Gundogdu and Musa Terkes and Umit Cali}, doi = {10.1109/ACCESS.2025.3572554}, journal = {IEEE Access}, pages = {92783-92799}, shortjournal = {IEEE Access}, title = {Enhanced ANN-based MPPT for photovoltaic systems: Integrating metaheuristic and analytical algorithms for optimal performance under partial shading}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of blockchain and machine learning integration for peer-to-peer energy trading in smart grids. ACCESS, 13, 92756-92782. (www)

  @article{ , abstract = {The transition from traditional energy or electrical grids to smart energy or electrical grids has significantly transformed energy management. This evolution emphasizes decentralization, efficiency, and sustainability in energy systems. However, it also introduces numerous risks, including cyber-physical system vulnerabilities and challenges in energy trading. The application of blockchain and Machine Learning (ML) offers potential solutions to these issues. Blockchain enhances energy transactions by making them safer, more transparent, and tamper-proof, while ML optimizes grid performance by improving predictions, fault detection, and anomaly identification. This systematic review examines the application of blockchain and ML in peer-to-peer (P2P) energy trading within smart grids and analyzes how these technologies complement each other in mitigating risks and enhancing the efficiency of smart grids. Blockchain enhances security by providing privacy for transactions and maintaining immutable records, while ML predicts market trends, identifies fraudulent activities, and ensures efficient energy use. The paper identifies critical challenges in smart grids, such as unsecured communication channels and vulnerabilities to cyber threats, and discusses how blockchain and ML address these issues. Furthermore, the study explores emerging trends, such as lightweight blockchain systems and edge computing, to overcome implementation challenges. A new architecture is proposed, integrating blockchain with ML algorithms to create resilient, secure, and efficient energy trading markets. The paper underscores the need for global standardization, improved cybersecurity measures, and further research into how blockchain and ML can revolutionize smart grids. This study integrates current knowledge with a forward-looking perspective, providing valuable insights for researchers, policymakers, and stakeholders in the energy sector to collaboratively build a future of efficient and intelligent energy systems.}, archive = {J_ACCESS}, author = {Sameen Fatima and Muhammad Junaid Arshad}, doi = {10.1109/ACCESS.2025.3572174}, journal = {IEEE Access}, pages = {92756-92782}, shortjournal = {IEEE Access}, title = {A comprehensive review of blockchain and machine learning integration for peer-to-peer energy trading in smart grids}, volume = {13}, year = {2025}, }
• (2025). Embodied intelligence empowering customized manufacturing: Architecture, opportunities, and challenges. ACCESS, 13, 92740-92755. (www)

  @article{ , abstract = {With the continued advancement of Artificial Intelligence (AI) technology in Customized Manufacturing (CM), the current intelligence model, which separates ‘perception’ from ‘execution,’ lacks adaptability and generalizability. Embodied Intelligence (EI), an emerging technology emphasizing real-time environmental interaction and feedback, is expected to enable integrated intelligent manufacturing systems characterized by ‘perception-cognition-execution-feedback,’ enhancing the performance and intelligence of multifactor systems like the Internet of Manufacturing Things (IoMT), equipment management, and resource scheduling. However, current AI systems in CM remain isolated and lack methods for environmental interaction with multi-source perception and feedback, hindering the development of autonomous, evolving intelligent systems. To address the environmental interaction bottlenecks among diverse production elements in CM, this paper proposes a Circular Embodied Intelligence Manufacturing (CEIM) architecture aimed at enabling the fusion of heterogeneous production-element information. The objective is to enhance environmental perception and establish autonomous system evolution mechanisms, thereby optimizing production decision-making. By integrating sensor data and AI model outputs, and leveraging the advanced reasoning capabilities of large language models (LLMs), CEIM facilitates the semantic fusion of multiple production elements—including IoMT, intelligent devices, and manufacturing resources—to enable the deployment of EI applications in CM. The implementation of CEIM is illustrated and validated through a case study on a customized gift box packaging platform. Finally, this paper discusses the opportunities and challenges of applying EI in manufacturing and aims to provide insights into the future development of EI-oriented manufacturing systems.}, archive = {J_ACCESS}, author = {Jinbiao Tan and Jianhua Shi and Ligang Wu and Baotong Chen and Hao Tang and Chunhua Zhang and Wujie Zhang and Shiyong Wang and Jiafu Wan}, doi = {10.1109/ACCESS.2025.3572778}, journal = {IEEE Access}, pages = {92740-92755}, shortjournal = {IEEE Access}, title = {Embodied intelligence empowering customized manufacturing: Architecture, opportunities, and challenges}, volume = {13}, year = {2025}, }
• (2025). Design of corrugated horn antenna for LEO satellite feeding system: Efficient hybrid optimization approach. ACCESS, 13, 92730-92739. (www)

  @article{ , abstract = {Low Earth Orbit (LEO) satellites are receiving fresh interest from the wireless communications society. The transceivers on these satellites require high-gain antennas with consistent performance over the operational spectrum. Corrugated horn antennas are the most typical feed used in such systems. Furthermore, several satellite systems use dual-polarized antennas to provide polarisation variety. However, simulating the full satellite feed transceiver is a time-consuming task. This paper presents a revolutionary hybrid technique that combines real-measured data with simulation tools to reduce the optimization time for the entire system while providing more accurate and robust results. This optimized design is fabricated using a CNC lathe machine with ±0.002 inches accuracy. The fabricated prototype is measured in an anechoic chamber, where the measured results are in good agreement with the simulated ones.}, archive = {J_ACCESS}, author = {Mahmoud Gadelrab and Shoukry I. Shams and Mahmoud Elsaadany and Ghyslain Gagnon}, doi = {10.1109/ACCESS.2025.3573590}, journal = {IEEE Access}, pages = {92730-92739}, shortjournal = {IEEE Access}, title = {Design of corrugated horn antenna for LEO satellite feeding system: Efficient hybrid optimization approach}, volume = {13}, year = {2025}, }
• (2025). AMT-net: Adversarial motion transfer network with disentangled shape and pose for realistic image animation. ACCESS, 13, 92712-92729. (www)

  @article{ , abstract = {Computer vision advancements allow motion transfer for animating static objects in images. However, current methods rely on manually collected motion labels and struggle with accurate shape and pose representation, particularly for human bodies, due to occlusions and background variations. Thus, we propose an Adversarial Motion Transfer Network with a disentangled Shape and Pose representation for realistic image Animation (AMT-Net), utilizing an encoder-decoder adversarial structure. Specifically, we design a pose and shape learning module that captures the independent shape and pose information by training a discriminator with adversarial loss techniques, enhancing the generation of coherent animated frames. Furthermore, a motion estimation module is introduced to generate masks for objects in consecutive frames and identify occluded parts by creating occlusion maps from these masks and dense motion vectors. To evaluate the effectiveness of our approach, we conducted extensive experiments using four publicly available datasets, including VoxCeleb, TaiChiHD, TED-Talks, and MGif. The results emphasize the importance of landmark detection for video annotation and smooth transitions, while the independent shape and pose module helps capture precise representations.}, archive = {J_ACCESS}, author = {Nega Asebe Teka and Kumie Gedamu Alemu and Maregu Assefa and Feidu Akmel and Zhenting Zhou and Weijie Wu and Jianwen Chen}, doi = {10.1109/ACCESS.2025.3571760}, journal = {IEEE Access}, pages = {92712-92729}, shortjournal = {IEEE Access}, title = {AMT-net: Adversarial motion transfer network with disentangled shape and pose for realistic image animation}, volume = {13}, year = {2025}, }
• (2025). Zero-shot eggshell crack detection using grounding DINO and FFT-based outer-to-inner ring energy ratio. ACCESS, 13, 92690-92711. (www)

  @article{ , abstract = {This study presents a novel approach to eggshell crack detection by integrating zero-shot learning with advanced image analysis techniques. The proposed method utilizes a hybrid dataset composed of a custom hen egg collection—50 tray images containing 30 eggs each arranged in a $6\times 5$ grid—and the Botta et al. duck egg dataset, comprising approximately 1,000 images of cracked and intact eggs. Using YOLOv8s and the Fast Segment Anything Model (FastSAM), we generate bounding boxes, annotate segmentation masks, and crop background-free images, resulting in a curated set of 1,500 hen egg images alongside the processed duck egg samples. To localize crack regions accurately, we employ Grounding DINO with fine-grained prompting. To distinguish cracks from dirt and subtle surface irregularities, we introduce a novel frequency-domain feature: the FFT-based Outer-to-Inner (O/I) Ring Energy Ratio, which enhances the visibility of fine anomalies. Our zero-shot detection model achieves an average accuracy of 92.54% across 10-fold cross-validation on both datasets—without retraining—demonstrating strong generalization and eliminating the reliance on labeled training data. We benchmark our approach against supervised models (CNN, SVM, XGBoost, k-NN) and popular zero-shot and anomaly detection frameworks (CLIP, CLIPSeg, Florence-2, and SAA). For real-world validation, we implement our system on an egg-belt conveyor using a low-cost CCD microscope camera capturing 60 FPS video. The model processes one hen egg in tray images at 7.79 FPS in simulation and 0.44 FPS in real-time video testing, enabling inspection of approximately 1,794 eggs per hour per camera. The system’s scalability with multiple cameras enables adaptation to industrial egg-sorting speeds. The results demonstrate significant advancements in crack detection accuracy and throughput, highlighting the potential of combining frequency-based image analysis with zero-shot prompting for scalable, real-time quality control in the poultry industry. The full implementation and associated dataset are publicly available at (https://github.com/tomorn112/ZC-DINO-ER/).}, archive = {J_ACCESS}, author = {Tomorn Soontornnapar and Tuchsanai Ploysuwan}, doi = {10.1109/ACCESS.2025.3571984}, journal = {IEEE Access}, pages = {92690-92711}, shortjournal = {IEEE Access}, title = {Zero-shot eggshell crack detection using grounding DINO and FFT-based outer-to-inner ring energy ratio}, volume = {13}, year = {2025}, }
• (2025). Blockchain-empowered UAV-assisted computation resource allocation in smart farm services platform. ACCESS, 13, 92675-92689. (www)

  @article{ , abstract = {Unmanned aerial vehicle-to-everything (U2X) communications have become increasingly essential technologies to support multiple delay and computation-sensitive services, including augmented reality (AR), Virtual Reality (VR), digital twins, smart farms, unknown geological exploration, smart manufacturing, etc. Therefore, it is necessary to propose effective privacy-preserving communication and computation optimisation algorithm schemes to improve the quality of service (QoS) of multiple UAV-assisted Internet of Things (IoT) systems. In recent years, blockchain, U2X communications, and mobile edge computing will gradually become the main technologies of future academia and industry to overcome the aforementioned challenges for the UAV-assisted IoT system networks, while guaranteeing user data collection, data processing, and privacy-preserving requirements. In this paper, we consider a UAV-assisted IoT smart farm network systems, where blockchain technology is applied at each UAV server to guarantee real-time user data collection, data processing, data encryption, system access authorisation, and immutability. To minimise the total service delay (which includes the transmission delay and processing delay) among the user equipment (UEs) and UAVs, we propose jointly optimising the communication resource allocation for all UEs and UAVs, the computation resource allocation at each UAV, and the transmission power. This is achieved by devising a Q-learning-based optimisation algorithm, which is called a Q-learning-based latency reduction optimisation algorithm (QLROA). Simulation results illustrate that our proposed algorithm outperforms the benchmarks in terms of the long-term average delay among all the UEs.}, archive = {J_ACCESS}, author = {Sida Huang and Matilda Isaac and Yuan Gao and Yuji Dong and Jie Zhang and Bintao Hu}, doi = {10.1109/ACCESS.2025.3572519}, journal = {IEEE Access}, pages = {92675-92689}, shortjournal = {IEEE Access}, title = {Blockchain-empowered UAV-assisted computation resource allocation in smart farm services platform}, volume = {13}, year = {2025}, }
• (2025). Estimation of magnet temperature using disturbance observers of permanent magnet synchronous motor for driving single rotary compressor without lookup table. ACCESS, 13, 92663-92674. (www)

  @article{ , abstract = {The growing global demand for household air conditioners necessitates reliable and efficient motor operation under high-temperature conditions. Single rotary compressors, widely used in air-conditioning systems, suffered from excessive speed ripples, noise, and vibration caused by significant load torque pulsations. This paper developed an innovative method for estimating magnet temperature for air-conditioning motors without lookup tables in conjunction with reducing the speed ripple through precise load torque estimation. A dual observer system is adopted, enabling independent gain settings for both the load torque observer and speed observer, thereby enhancing estimation accuracy and dynamic response. By estimating the load torque pattern, the proposed method reduces speed ripple and estimates magnet temperature without relying on lookup tables. In this study, magnet temperature can be determined from the voltage equation, with a permissible torque reference modification. By applying the proposed method, speed ripple was reduced by 74.3% compared with pre-compensation levels, and the magnet temperature estimation error was within $5~^{\circ }$ C for a temperature increase from $26~^{\circ }$ C to $76~^{\circ }$ C. These results confirm the robustness, computational efficiency and feasibility of the proposed method, making it suitable for low-cost embedded systems in household appliances.}, archive = {J_ACCESS}, author = {Seoyun Lee and Jeonghyeok Kim and Hojun Kim and Dongho Kim and Jinyoung Choi and Hyun-Sam Jung}, doi = {10.1109/ACCESS.2025.3573340}, journal = {IEEE Access}, pages = {92663-92674}, shortjournal = {IEEE Access}, title = {Estimation of magnet temperature using disturbance observers of permanent magnet synchronous motor for driving single rotary compressor without lookup table}, volume = {13}, year = {2025}, }
• (2025). VT2Music: A multimodal framework for text-visual guided music generation and comprehensive performance analysis. ACCESS, 13, 92641-92662. (www)

  @article{ , abstract = {Recent years have witnessed significant advances in text-to-music generation technology through deep learning approaches, particularly using latent diffusion models (LDM), yet there remains a notable absence of artificial intelligence (AI) music composition systems capable of generating music from other modalities. Given the intricate relationship between visual perception and auditory experience in human cognition, exploring music generation from multimodal data holds considerable promise for creating more diverse and enriched musical experiences. To address this research gap, we propose VT2Music (various things to music), a multimodal music generation model based on diffusion transformers (DiT), capable of generating semantically aligned music from textual and visual modality data. Our framework not only supports music generation from single modalities (text, image, or video), but also accepts combined multimodal inputs (such as text+image or text+video). Through both objective and subjective evaluations, we demonstrate that VT2Music can generate music that reasonably aligns with the semantic and emotional content of the input, achieving performance comparable to current mainstream music generation models across multiple assessment metrics. This study represents an initial exploration into the possibilities of multimodal music generation, with future work aimed at enhancing the model’s visual feature comprehension and musical naturalness.}, archive = {J_ACCESS}, author = {Jiaxiang Zheng and Moxi Cao and Chongbin Zhang}, doi = {10.1109/ACCESS.2025.3572954}, journal = {IEEE Access}, pages = {92641-92662}, shortjournal = {IEEE Access}, title = {VT2Music: A multimodal framework for text-visual guided music generation and comprehensive performance analysis}, volume = {13}, year = {2025}, }
• (2025). A critical review on flexibility quantification and evaluation methods in medium and low voltage networks. ACCESS, 13, 92622-92640. (www)

  @article{ , abstract = {With the increasing integration of Distributed Energy Resources (DERs), effective methods for flexibility quantification and evaluation become necessary to ensure grid stability and reliability. This review systematically explores two complementary approaches to flexibility quantification in medium voltage (MV) and low voltage (LV) networks: metric-based (MB) and region-based (RB) methodologies. MB methods provide concise evaluations of system-wide flexibility, facilitating benchmarking and operational insights. However, they often fail to capture the spatiotemporal complexities and interdependencies inherent in modern grids. RB methods, on the other hand, leverage Feasible Operating Regions (FOR) to map flexibility spatially and temporally but are hindered by low observability characterizing distributions systems and limited uncertainty modelling. Compared to previous studies that analyze MB and RB approaches separately, this review uniquely synthesizes both methodologies and proposes their combination as a more comprehensive tool for dynamic flexibility management in modern distribution networks. By enhancing FOR with flexibility metrics, the approach bridges gaps in uncertainty and time modelling, addressing key challenges posed by the increased penetration of DERs. This integration offers a practical and scalable solution for system operators to achieve a more comprehensive and dynamic assessment of flexibility, paving the way for enhanced grid stability and operational efficiency.}, archive = {J_ACCESS}, author = {Georgia Eirini Lazaridou and Efstratia Sarafoglou and Nikolaos Fesakis and Vasileios Boglou and Athanasios Karlis}, doi = {10.1109/ACCESS.2025.3572595}, journal = {IEEE Access}, pages = {92622-92640}, shortjournal = {IEEE Access}, title = {A critical review on flexibility quantification and evaluation methods in medium and low voltage networks}, volume = {13}, year = {2025}, }
• (2025). Atrous convolution-based fusion attention mechanism for brain tumor segmentation. ACCESS, 13, 92611-92621. (www)

  @article{ , abstract = {Accurate medical image segmentation is pivotal for advanced diagnostic and therapeutic planning, especially for intricate tasks such as brain tumor delineation. However, existing segmentation methods often struggle with challenges such as heterogeneous tumor morphologies, diffuse boundaries, and low-contrast regions, limiting their reliability in complex medical imaging scenarios. In this work, we introduce an Atrous Convolution-Based Fusion Attention Mechanism, a novel framework that combines local and global attention through an innovative fusion block. Local attention leverages atrous convolutions to capture fine-grained spatial features at multiple scales, while global attention extracts long-range contextual dependencies across the image. These complementary features are dynamically fused via a novel fusion block, ensuring balanced representation and robust feature aggregation for precise segmentation. The proposed model is rigorously evaluated on the BraTS 2019, 2020, and 2023 datasets, demonstrating consistent and significant improvements across key segmentation metrics compared to baseline models. Quantitatively, the model achieves an absolute average increase of 0.2522, 0.1873, and 0.1934 in the Dice Coefficient, and 0.3071, 0.2365, and 0.2369 in the Intersection over Union (IoU) for enhancing tumor, tumor core, and whole tumor, respectively, across all datasets. The results highlight robustness and adaptability of the proposed architecture, underlining its potential to address critical challenges in brain tumor segmentation and advance the state-of-the-art in medical image analysis.}, archive = {J_ACCESS}, author = {Abhishek Jadhav and Akhtar Rasool and Manasi Gyanchandani}, doi = {10.1109/ACCESS.2025.3563494}, journal = {IEEE Access}, pages = {92611-92621}, shortjournal = {IEEE Access}, title = {Atrous convolution-based fusion attention mechanism for brain tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). A self-attention enhanced deep CNN-LSTM-based irregular surface recognition approach for integration into lower limb prosthesis systems to ensure safety through predictive walking. ACCESS, 13, 92596-92610. (www)

  @article{ , abstract = {Advancements in instrumentation and control systems for lower limb prostheses have substantially improved mobility for amputees. However, significant challenges persist when users encounter irregular surfaces, as most prosthetic systems lack the capability to dynamically adapt to surface variations. This limitation restricts mobility, compromises safety, and diminishes user confidence and security during walking. To address these challenges, integrating inertial measurement units (IMUs) with artificial intelligence (AI) techniques, particularly deep learning (DL) methods, has emerged as a promising solution for surface classification and safety enhancement. This study proposes a self-attention enhanced deep CNN-LSTM model to automatically classify walking surfaces as regular or irregular, utilizing IMU acceleration data collected from prosthetic limbs. The model employs the strengths of convolutional and recurrent neural networks combined with a self-attention mechanism to enhance feature representation and improve classification accuracy. Experimental evaluations reveal that the proposed method achieves a classification accuracy of 99.94%, outperforming existing approaches. This result underscores the model’s potential to serve as the basis for AI-driven IMU-based systems, enabling real-time surface recognition and safety alerts in prosthetic devices. By enhancing walking safety and user confidence, this method represents a significant advancement for lower limb prosthesis systems.}, archive = {J_ACCESS}, author = {Norazian Subari and Kamarul Hawari Ghazali and Yuanfa Ji}, doi = {10.1109/ACCESS.2025.3567456}, journal = {IEEE Access}, pages = {92596-92610}, shortjournal = {IEEE Access}, title = {A self-attention enhanced deep CNN-LSTM-based irregular surface recognition approach for integration into lower limb prosthesis systems to ensure safety through predictive walking}, volume = {13}, year = {2025}, }
• (2025). Novel unsupervised cluster reinforcement Q-learning in minimizing energy consumption of federated edge cloud. ACCESS, 13, 92577-92595. (www)

  @article{ , abstract = {As global demand for cloud computing rises, green computing has become essential. Federated Edge Cloud (FEC) offers improved energy efficiency compared to traditional infrastructures, yet managing distributed energy consumption remains a challenge. This research introduces an Unsupervised Cluster Reinforcement Q-Learning method in FEC (UCRL-FEC), which integrates Fuzzy C-Means (FCM) or K-Means clustering to identify migratable Virtual Machines (VMs) from overloaded hosts. The method enhances energy efficiency and workload balance by incorporating a modified reward function in Q-Learning. Experimental evaluations demonstrate that UCRL-FEC reduces energy consumption (EC) up to 1.07%, supporting reductions in both operational costs and greenhouse gas emissions, which is critical for large-scale cloud environments. In terms of Service Level Agreement Time per Active Host (SLATAH), UCRL-FEC achieves an improvement up to 1.56% over the baseline method, demonstrating enhanced efficiency in managing active host resources. Additionally, system stability improves with a reduction of up to 9.68% in Performance Degradation due to Migration (SLA-PDM), effectively minimizing service disruptions and ensuring efficient workload management. Furthermore, the method reduces overall Service Level Agreement Violations (SLAV) up to 6.06%, indicating enhanced service reliability and optimized resource allocation. A Friedman test confirms statistically significant improvements in energy efficiency, workload distribution, and system stability over baseline methods. These advancements prevent resource overutilization, enhance workload management, and extend hardware lifespan, fostering sustainable cloud operations. UCRL-FEC balances energy efficiency, performance, and scalability through dynamic resource optimization, validating it as implementable strategy for intelligent VM management in modern cloud-edge infrastructures.}, archive = {J_ACCESS}, author = {Guruh Fajar Shidik and Oki Setiono and Edi Jaya Kusuma and L. Budi Handoko and Pulung Nurtantio Andono and Mohd Faizal Abdollah}, doi = {10.1109/ACCESS.2025.3572084}, journal = {IEEE Access}, pages = {92577-92595}, shortjournal = {IEEE Access}, title = {Novel unsupervised cluster reinforcement Q-learning in minimizing energy consumption of federated edge cloud}, volume = {13}, year = {2025}, }
• (2025). AI-driven optimization of smart spaces: A WASPAS decision model to intelligent environment design. ACCESS, 13, 92569-92576. (www)

  @article{ , abstract = {Smart spaces are continually developing, needing cutting-edge ideas for better intelligent environment design to increase user experience, energy efficiency, and operational performance. This research presents a unique computational strategy that addresses the challenges of smart space design by leveraging AI-driven optimization principles. To handle uncertainty and imprecision in data, we propose merging Dempster-Shafer Fuzzy Sets (DSFS) with the Weighted Aggregated Sum Product Assessment (WASPAS) method for multi-criteria decisions. The DSFS framework enables robust modeling of ambiguous and conflicting information, while the WASPAS technique ensures a balanced assessment of competing design objectives. Through case studies in office and residential smart environments, our approach demonstrated a 15Ű22% improvement in energy efficiency, a 12Ű18% enhancement in user comfort scores, and a 20% reduction in system response latency compared to conventional methods. Additionally, the hybrid DSFS-WASPAS model showed 92.3% accuracy in resolving conflicting design constraints under uncertainty. These results highlight the efficacy of AI-driven optimization in smart space layouts, offering scalable and adaptable solutions for future intelligent environments. This study contributes to intelligent environment design by presenting a comprehensive, computationally efficient, and uncertainty-aware strategy.}, archive = {J_ACCESS}, author = {Yang Wu}, doi = {10.1109/ACCESS.2025.3572940}, journal = {IEEE Access}, pages = {92569-92576}, shortjournal = {IEEE Access}, title = {AI-driven optimization of smart spaces: A WASPAS decision model to intelligent environment design}, volume = {13}, year = {2025}, }
• (2025). Toward AI-driven cough sound analysis for respiratory disease diagnosis. ACCESS, 13, 92554-92568. (www)

  @article{ , abstract = {Respiratory diseases represent a significant health concern that attracts scientists and health professionals due to their impact on public health. Various techniques are used to identify respiratory conditions, from diagnosis to therapy, such as Computer Tomography (CT) scans and Chest X-ray (CXR) images. However, these methods require expert radiologists for manual examination, posing time challenges. This is especially critical during a pandemic, where swift detection and early treatment are paramount. In this regard, an AI-based diagnostic system for automatic identification would play a major role, particularly regarding the analysis of patient cough patterns. Coughs, as a diagnostic cue, provide distinctive information about glottis behavior related to different respiratory pathological cases. This work proposes a comparative investigation of respiratory disease detection techniques using cough sounds, with COVID-19 as a case study. Our study involves the application of three distinct models: a Convolutional Neural Network (CNN) model, a CNN-Support Vector Machine (CNN-SVM) hybrid model, and a transfer learning-based model. We methodically evaluate different model architectures, ranging from custom-built networks to pre-trained deep models, applying spectrogram or Mel-Frequency Cepstral Coefficients (MFCC) in transfer learning-based feature extraction, to determine which is the best approach in terms of accuracy, precision, recall, F1-score, and loss. The experimental findings highlight the superior performance of transfer learning and MFCC feature extraction upon deep learning results using CNN and CNN-SVM with best validation accuracies ranging from 99.55% to 100%. This significant advantage is attributed 1) the capacity of transfer learning to utilize prior knowledge from related domains, even in data-scarce target tasks, and 2) the proficiency of MFCC in capturing and representing the rich and essential information embedded within cough sounds. Furthermore, we propose an Android-based smartphone application that serves as a noninvasive and real-time prescreening tool. Our system holds significant importance to complement medical efforts containing respiratory diseases and pandemics in infected areas, making it applicable to any respiratory condition or pandemic where cough is a predominant symptom.}, archive = {J_ACCESS}, author = {Houda Benaliouche and Houda Hafi and Hakim Bendjenna and Zeyad Alshaikh}, doi = {10.1109/ACCESS.2025.3572202}, journal = {IEEE Access}, pages = {92554-92568}, shortjournal = {IEEE Access}, title = {Toward AI-driven cough sound analysis for respiratory disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Research on the dynamic characteristics of high-speed rotors supported by a double layer squeeze film damper-bearing composite structure. ACCESS, 13, 92531-92553. (www)

  @article{ , abstract = {In this study, a dynamic model for a damper-bearing-rotor system featuring a novel double layer squeeze film damper-bearing composite structure was developed, addressing the limitations of conventional single layer dampers in providing sufficient vibration damping for high-speed advanced mechanical equipment, such as aircraft and rocket engines. A 12-degree-of-freedom Timoshenko beam element was employed to construct a finite element model of a rotor system subjected to unbalanced and nonlinear oil film force excitation. An approach for decreasing the iterative dimension of high-dimensional local nonlinear systems has been devised, which considerably improves the efficiency of transient solutions for dynamic equations. The transient excitation process of an unbalanced rotor with multilayer oil film bearings was analyzed, and the Reynolds equations for the dampers and bearings were solved using the finite element method. The results indicate that the double layer squeeze oil film damper-bearing composite structure exhibited high stability, excellent impact resistance, and significantly reduced rotor amplitude at both high and critical speeds. Double layer squeeze film dampers provide a more evident vibration suppression benefit and can reduce vibrations more effectively at high speeds than single-layer squeeze film dampers. Within the range of 1‰ to 2‰ of the journal radius, increasing the radial clearance of the oil film bearings and double-layer squeeze film dampers can further reduce the rotor vibration amplitude under high-speed conditions. Additionally, experimental equipment was used to verify all three phenomena, and the results of the experiments demonstrated a high degree of agreement with the results of the numerical simulation.}, archive = {J_ACCESS}, author = {Wei Wang and Weimin Wang and Weibo Li and Jiale Wang and Tianqing Li}, doi = {10.1109/ACCESS.2025.3570390}, journal = {IEEE Access}, pages = {92531-92553}, shortjournal = {IEEE Access}, title = {Research on the dynamic characteristics of high-speed rotors supported by a double layer squeeze film damper-bearing composite structure}, volume = {13}, year = {2025}, }
• (2025). Priority-based reward mechanism for dual-robot path planning in dynamic environments. ACCESS, 13, 92519-92530. (www)

  @article{ , abstract = {The definition of motion priority enables robot groups to handle competition and cooperation better when performing physical tasks. In this paper, we propose a priority-based step reward mechanism, which is a new reward mechanism for deep reinforcement learning of multi-robot systems and can improve collaboration between robotic arms in shared workspaces. The intention of each agent is provided to other agents, presenting a 2D map of the head that is visually consistent, with state and action representations aligned spatially. Guided by priority-based step rewards, the dual-robots are sequentially assigned high priority, enabling them to pass through complex environments in sequence. We validated our method on a path planning task where two robots collaborate to transport cubes in dynamic environments. The two robots need to consider obstacle avoidance while handing cubes to the human operator. The experimental environment includes Free space, obstacles in the middle, obstacles on both sides, and combinations of several environments. The results show that priority step rewards improve the performance of robot collaborative tasks and significantly enhance cooperative behavior.}, archive = {J_ACCESS}, author = {Yibo Hu and Lai Wei and Xiaoyu Guo and Zhenyao Li and Yanding Wei and Qiang Fang}, doi = {10.1109/ACCESS.2025.3571931}, journal = {IEEE Access}, pages = {92519-92530}, shortjournal = {IEEE Access}, title = {Priority-based reward mechanism for dual-robot path planning in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). The A3C algorithm with eligibility traces of energy management for plug-in hybrid electric vehicles. ACCESS, 13, 92507-92518. (www)

  @article{ , abstract = {An energy management system is crucial for optimizing the performance and reducing fuel consumption of Plug-in Hybrid Electric Vehicles (PHEVs), which plays an important role in sustainable transportation. This paper presents a comprehensive study of the energy management problem for a selected PHEV, outlining key vehicle parameters such as battery state-of-charge (SOC), engine power, and energy consumption patterns. A mathematical formulation of the energy management problem is proposed to optimize fuel consumption while adhering to the vehicle’s operational constraints. The core of the paper focuses on the application of the Asynchronous Advantage Actor-Critic (A3C) algorithm to address this problem. To enhance the learning process, eligibility traces are introduced to refine the advantage function by weighting historical actions. This adjustment improves the decision-making process by making the action distribution more concentrated, which is crucial for optimizing fuel consumption. The proposed Asynchronous Advantage Actor-Critic Eligibility Traces (A3CET) algorithm incorporates these modifications and shows a significant improvement in fuel efficiency. During the training phase, it achieves 93.6% of the fuel optimality of the Dynamic Programming (DP) benchmark, and maintains 91.5% of DP’s performance during the testing phase. Moreover, A3CET reduces computation time by 62.5% compared to DP, and outperforms other EMSs in terms of both efficiency and real-time applicability.}, archive = {J_ACCESS}, author = {Dingyi Guo and Guangyin Lei and Huichao Zhao and Fang Yang and Qiang Zhang}, doi = {10.1109/ACCESS.2025.3572488}, journal = {IEEE Access}, pages = {92507-92518}, shortjournal = {IEEE Access}, title = {The A3C algorithm with eligibility traces of energy management for plug-in hybrid electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Digital twin and TD3-enabled optimization of xEV energy management in vehicle-to-grid networks. ACCESS, 13, 92495-92506. (www)

  @article{ , abstract = {The rapid expansion of extended electric vehicle (xEV) adoption necessitates optimizing energy storage systems (ESS) management for enhanced performance, longevity, and reliability. However, traditional ESS management approaches struggle with real-time state estimation, energy optimization, and predictive maintenance, leading to inefficiencies in battery utilization and sustainability. This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). This integration enables precise real-time estimation of critical ESS states, including state of charge (SoC), state of health (SoH), state of energy (SoE), and remaining useful life (RUL), thereby enhancing predictive maintenance and operational efficiency. The proposed framework facilitates proactive battery health monitoring, generates early warnings for potential failures, and enables intelligent battery swapping via DT-driven ESS control. By dynamically adapting ESS control strategies, the TD3 algorithm optimizes energy distribution, reduces energy consumption, and improves overall vehicle performance. With a prediction accuracy of 99.8% for SoC, SoH, and SoE, the proposed approach effectively addresses key challenges in xEV ESS management like cell balancing, dynamic charging rate adjustment, battery swapping decisions, and energy optimization, contributing to a more sustainable and efficient EV ecosystem.}, archive = {J_ACCESS}, author = {Irum Saba and Abdulraheem H. Alobaidi and Sultan Alghamdi and Muhammad Tariq}, doi = {10.1109/ACCESS.2025.3572345}, journal = {IEEE Access}, pages = {92495-92506}, shortjournal = {IEEE Access}, title = {Digital twin and TD3-enabled optimization of xEV energy management in vehicle-to-grid networks}, volume = {13}, year = {2025}, }
• (2025). A review of recipe recommendation methods. ACCESS, 13, 92483-92494. (www)

  @article{ , abstract = {Recipe recommendation aims to provide users with a curated list of recipes tailored to their dietary preferences, nutritional requirements, and other relevant factors. While existing recommendation algorithms have demonstrated effectiveness in traditional domains such as news, music, and product recommendations, there remains a relative paucity of research focused specifically on recipe recommendation within the culinary field, resulting in fragmented findings. The investigation into recipe recommendation methodologies primarily encompasses three key aspects: Firstly, it introduces fundamental concepts related to food computing and recommendation systems, delineates the specific research focus on recipe recommendations, and categorizes these algorithms into five distinct types: content-based methods, collaborative filtering-based methods, hybrid approaches, graph-based techniques, and nutrition- and health-oriented recommendation strategies. Secondly, it examines the sub-methods associated with each category of research methodologies while evaluating the technical advantages and limitations inherent in different approaches. Finally, it provides an overview of the current state of research concerning recipe recommendation methods and anticipates future developmental prospects in this area.}, archive = {J_ACCESS}, author = {Yao Wu and Ling Kang and Quan Guo and Lei Zhao}, doi = {10.1109/ACCESS.2025.3572149}, journal = {IEEE Access}, pages = {92483-92494}, shortjournal = {IEEE Access}, title = {A review of recipe recommendation methods}, volume = {13}, year = {2025}, }
• (2025). Electrohysterogram data augmentation using generative adversarial network for pregnancy outcome prediction. ACCESS, 13, 92468-92482. (www)

  @article{ , abstract = {There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications.}, archive = {J_ACCESS}, author = {Muhammad Omar Cheema and Zia Mohy-Ud-Din and Azhar Imran and Fawad Salam Khan and Mahmood Basil A. Al-Rawi and Mohammed A. El-Meligy and Jahan Zeb Gul}, doi = {10.1109/ACCESS.2025.3563623}, journal = {IEEE Access}, pages = {92468-92482}, shortjournal = {IEEE Access}, title = {Electrohysterogram data augmentation using generative adversarial network for pregnancy outcome prediction}, volume = {13}, year = {2025}, }
• (2025). A novel over-the-air measurement method for linear frequency modulation signals using continuous wave receiver. ACCESS, 13, 92457-92467. (www)

  @article{ , abstract = {This paper presents a novel over-the-air (OTA) measurement method for linear frequency modulation (LFM) and frequency modulated continuous wave (FMCW) radar signals. Conventional measurement techniques, often relying on expensive spectrum analyzers, face challenges such as high cost, logistical difficulties, and limited adaptability in dynamic or real-world environments. To address these problems, the proposed method leverages interference phenomena to measure critical signal parameters, including chirp interval, bandwidth, and frequency slope, without requiring complex time-frequency analysis or additional hardware. The method was validated using automotive mmWave radar evaluation boards operating in the 76-81 GHz range. Experimental results demonstrated high accuracy, achieving average measurement errors of 3.43%, 2.23% and 1.35% for three different radars under test (RUT). Measurements were completed within a single frame of 115.2 ms, showcasing the method’s efficiency. The study also explored the impact of varying sampling rates, revealing that higher rates generally reduce errors, with optimal results achieved when the sampling interval aligns harmonically with radar chirp intervals. This method significantly reduces computational overhead compared to traditional approaches, while providing robust performance in diverse scenarios, including environments with multiple signals. By eliminating the need for specialized hardware, it offers a practical and cost-effective solution for radar signal characterization. The findings suggest the method’s potential for real-time applications, including cooperative and networked radar systems, where OTA measurements are essential for efficient operation.}, archive = {J_ACCESS}, author = {Sungpil Cheon and Byungkwan Kim}, doi = {10.1109/ACCESS.2025.3572957}, journal = {IEEE Access}, pages = {92457-92467}, shortjournal = {IEEE Access}, title = {A novel over-the-air measurement method for linear frequency modulation signals using continuous wave receiver}, volume = {13}, year = {2025}, }
• (2025). Benchmarking 2D egocentric hand pose datasets. ACCESS, 13, 92445-92456. (www)

  @article{ , abstract = {Hand pose estimation from egocentric video is a topic of significant interest with broad implications for human-computer interactions, assistive technologies, activity recognition, and robotics. The efficacy of modern machine learning models depends on the quality of data used for their training. Thus, this work is devoted to the analysis of state-of-the-art egocentric datasets suitable for 2D hand pose estimation. We propose a novel protocol for dataset evaluation, which includes quantitative accuracy assessments, analysis of variability and challenging scenarios in dataset contents, realism, as well as the identification of dataset shortcomings through the performance evaluation of leading hand pose estimation models (OpenPose, DetNet, HRNetv2 and MediaPipe). Our study reveals that despite the availability of numerous egocentric databases intended for 2D hand pose estimation, the majority are tailored for specific use cases. There is no ideal benchmark dataset yet; however, H2O and GANerated Hands datasets emerge as the most promising real and synthetic datasets, respectively.}, archive = {J_ACCESS}, author = {Olga Taran and Damian M. Manzone and José Zariffa}, doi = {10.1109/ACCESS.2025.3573730}, journal = {IEEE Access}, pages = {92445-92456}, shortjournal = {IEEE Access}, title = {Benchmarking 2D egocentric hand pose datasets}, volume = {13}, year = {2025}, }
• (2025). Integration of machine learning and wavelet algorithms for processing probing signals: An example of oil wells. ACCESS, 13, 92429-92444. (www)

  @article{ , abstract = {High-frequency induction logging is a crucial technique in subsurface exploration, particularly in the oil and gas industry. It involves transmitting electromagnetic signals into the ground and analyzing their responses to determine the geological structure, lithology, and fluid content of the formation. However, one of the major technical challenges in processing digital sounding signals is the complexity of wave propagation paths, which can be influenced by multiple factors such as subsurface heterogeneity, noise interference, and signal attenuation. Traditional signal processing techniques often struggle to accurately interpret low-amplitude, high-frequency signals that contain valuable subsurface information. These signals can be obscured by noise or exhibit complex, non-stationary behavior, making them difficult to detect and analyze using conventional Fourier-based methods. Moreover, standard regression-based approaches for interpreting induction logging data may fail to capture intricate signal variations, limiting their effectiveness in real-time decision-making. To address these challenges, this research explores the application of machine learning and wavelet transform techniques for improving the accuracy and efficiency of digital signal processing in high-frequency induction logging. The wavelet transform, particularly the Daubechies, Morlet, and Galerkin wavelets, is employed to decompose signals into different frequency components while preserving time-domain information. This enables better identification of subtle variations and anomalies in the recorded data. Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. This advancement supports the implementation of smart field technology in Kazakhstan’s oil and gas industry, ultimately improving decision-making speed, operational efficiency, and resource optimization. The results were based on a set of three drilled oil wells, namely, West Kazakhstan region, Mangystau, publicly available well log dataset. The evaluation performed using R-square and root mean square error to validate the proposed approach revealed values of 0.887 and 0.0091. This implementation demonstrated the effectiveness of the proposed model, significantly outperforming traditional methods.}, archive = {J_ACCESS}, author = {Zukhra Abdiakhmetova and Zhanerke Temirbekova}, doi = {10.1109/ACCESS.2025.3570451}, journal = {IEEE Access}, pages = {92429-92444}, shortjournal = {IEEE Access}, title = {Integration of machine learning and wavelet algorithms for processing probing signals: An example of oil wells}, volume = {13}, year = {2025}, }
• (2025). Advancing water quality management: An integrated approach using ensemble machine learning and real-time interactive visualization. ACCESS, 13, 92406-92428. (www)

  @article{ , abstract = {Access to safe and potable water is critical for public health and socioeconomic development. Traditional methods for predicting water quality often lack accuracy and robustness due to limitations in data availability and model complexity. This study presents a novel approach to predicting water potability by developing an advanced ensemble model and an interactive visualization dashboard. A comprehensive dataset of water quality parameters was collected and preprocessed to ensure data integrity. An ensemble model combining Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost and Neural Networks was constructed, leveraging the strengths of each algorithm to enhance predictive accuracy. The model achieved an accuracy of 96.7%, precision of 96.7%, recall of 100%, and F1-score of 98.4%, outperforming existing models in the literature. Furthermore, to address contemporary developments in the field of machine learning, a transformer-based model was introduced alongside the existing ensemble and neural network approaches. This integration reflects the heightened prominence of deep learning methods and aligns the study’s methodology with cutting-edge research trends. Cross-validation results further confirmed the model’s robustness, with a mean accuracy of 96.9% and a low standard deviation. The interactive Water Quality Predictive Dashboard developed using the Dash framework, provides real-time predictions and visualizations, allowing stakeholders to input new data and receive immediate feedback on water potability. This tool enhances user accessibility and supports informed decision-making. The study highlights the advantages of the ensemble approach in improving prediction accuracy and reliability, though it also acknowledges the complexity and data dependency challenges. Overall, the developed model and dashboard offer a powerful solution for water quality monitoring with broad applicability for enhancing public health and resource management.}, archive = {J_ACCESS}, author = {Jigna K. Pandya and Suresh S. Khandelwal and Rupesh Kumar Tipu and Kartik S. Pandya}, doi = {10.1109/ACCESS.2025.3573589}, journal = {IEEE Access}, pages = {92406-92428}, shortjournal = {IEEE Access}, title = {Advancing water quality management: An integrated approach using ensemble machine learning and real-time interactive visualization}, volume = {13}, year = {2025}, }
• (2025). Enhancing retail strategies through anomaly detection in association rule mining. ACCESS, 13, 92376-92405. (www)

  @article{ , abstract = {Association rule mining (ARM) is a fundamental technique for uncovering meaningful patterns and relationships within retail datasets, providing valuable insights for decision-making processes in the retail industry. Traditional association rule mining (TARM) methods sometimes fail to handle inconsistencies and contradictions for Boolean logic concepts. To overcome the scenario, paraconsistent Annotated (PAL) logic offers a solution by embracing contradictions and providing a framework for reasoning with inconsistent information. Our approach integrates PAL with Association rule mining to form a para-association rule. Based on Boolean logic, TARM may struggle to handle such situations effectively. We propose a comprehensive framework named Para-Association rule mining (PARM), which provides decision-making by offering two distinct criteria: decided and undecided. To resolve undecided rules, an anomaly detection technique based on Isolation Forest is employed, ensuring that all rules are ultimately categorized into either “accept” or “reject” based on refined decision criteria. After solving the anomaly within the undecided criteria, all decisions are categorized into two criteria, such as accept and reject, to enhance decision-making. Our proposed approach is validated with our experimental results, where we have considered 549904 input retail transactions for generating retail decisions. Initially, we successfully generated 5000 association rules from this input, which are categorized as accept and reject 3387 and 1613 respectively. The comparative accuracy of the proposed approach achieves an accuracy of 90% which is a 15.38% improvement over the traditional approach. Our approach gives an 88% inconsistency management rate, which is a 36.67% improvement over the traditional approach. To validate the robustness of PARM, a noise test was conducted, demonstrating that the model effectively maintains performance under noisy conditions. The Z-test results confirm that the robustness improvement is statistically significant. In conclusion, PARM represents a significant advancement in data mining and decision-making techniques by offering a novel framework for handling inconsistencies and uncertainty in retail datasets.}, archive = {J_ACCESS}, author = {Bijayini Mohanty and Santi Lata Champati and Swadhin Kumar Barisal}, doi = {10.1109/ACCESS.2025.3573807}, journal = {IEEE Access}, pages = {92376-92405}, shortjournal = {IEEE Access}, title = {Enhancing retail strategies through anomaly detection in association rule mining}, volume = {13}, year = {2025}, }
• (2025). Prune and distill: A novel knowledge distillation method for GCNs-based recommender systems. ACCESS, 13, 92365-92375. (www)

  @article{ , abstract = {Graph convolutional networks (GCNs)-based recommenders have demonstrated remarkable recommendation performances but suffer from prohibitive computational cost, limiting their practical deployment. Knowledge distillation provides an effective approach to address this challenge by training a compact student model to inherit knowledge from a complex teacher, balancing accuracy and efficiency. However, existing knowledge distillation methods primarily focus on optimizing loss functions while neglecting the critical role of the graph structure for the student model in capturing personalized preferences. In this paper, we propose Prune and Distill (PAD), a novel knowledge distillation framework that refines the graph structure of the student by pruning redundant connections and enriching informative adjacencies under the guidance of the teacher model. By directly enhancing the structural representation of the student model, PAD facilitates effective preference while preserving competitive recommendation performance. Extensive experiments on three benchmark datasets demonstrate that PAD-enhanced student recommender outperforms state-of-the-art recommendation models with significantly reduced computational overhead, validating PAD’s superiority in effectiveness and efficiency for real-world applications.}, archive = {J_ACCESS}, author = {Peng Yi and Zhaoxian Li and Lu Chen and Cheng Yang and Xiongcai Cai}, doi = {10.1109/ACCESS.2025.3573181}, journal = {IEEE Access}, pages = {92365-92375}, shortjournal = {IEEE Access}, title = {Prune and distill: A novel knowledge distillation method for GCNs-based recommender systems}, volume = {13}, year = {2025}, }
• (2025). Optimization and benchmarking of image segmentation for improved landmark detection in lower limb X-rays and accurate coronal plane alignment of the knee classification. ACCESS, 13, 92350-92364. (www)

  @article{ , abstract = {Recent studies have explored image segmentation for landmark detection in computer vision and medical imaging of the lower limb, showing promising results. However, the proposed methodologies vary significantly, and a comparison with existing methods is lacking. In the present study, we investigated image segmentation for landmark detection on full lower-limb X-rays in detail and benchmark it against conventional landmark detection approaches. We detected eight landmarks in full lower limb X-rays and investigated methodological aspects to optimize image segmentation performance: network architecture (U-Net vs. Swin-UNETR), mask size centered at the landmark position to segment, and coordinate computation technique from the segmentation map. We contrasted image segmentation against optimized heatmap, coordinate, and segmentation-guided coordinate regression methods. The evaluation assessed the landmark detection error and phenotype classification accuracy based on lower limb alignment. The optimal segmentation approach employed a U-Net to segment circular masks (radius = 15 pixels), using probability thresholding before the centroid computation. Regarding landmark detection accuracy, image segmentation (median Euclidean distance (interquartile range) = 1.16 mm (1.50 mm)) was more accurate than heatmap (1.19 mm (1.61 mm)), coordinate (3.11 mm (2.87 mm)), and segmentation-guided coordinate regression (1.47 mm (1.67 mm)). Image segmentation outperformed heatmap, coordinate, and segmentation-guided coordinate regression in phenotype classification accuracy, achieving an average F1-score of 0.79, versus 0.72, 0.47, and 0.77, respectively. Our study led to an optimized approach for landmark detection using image segmentation, outperforming alternative detection approaches tuned and tested on the same data, highlighting image segmentation’s potential for broader medical imaging research applications.}, archive = {J_ACCESS}, author = {Sebastian Amador Sanchez and Ashkan Zarghami and Philippe van Overschelde and Jef Vandemeulebroucke}, doi = {10.1109/ACCESS.2025.3572342}, journal = {IEEE Access}, pages = {92350-92364}, shortjournal = {IEEE Access}, title = {Optimization and benchmarking of image segmentation for improved landmark detection in lower limb X-rays and accurate coronal plane alignment of the knee classification}, volume = {13}, year = {2025}, }
• (2025). Research and experiment on an adaptive feedforward compensation composite control strategy for an electro-hydraulic servo system. ACCESS, 13, 92338-92349. (www)

  @article{ , abstract = {In order to reduce the tracking and response errors of the electro-hydraulic servo system, improve the dynamic response quality of the system, enhance the adaptive ability of the system, and effectively solve the common problems of nonlinear interference and irregular load disturbance in the servo system, a research and experimental scheme for the adaptive feedforward compensation composite control strategy of the electro-hydraulic servo system was proposed. First, the basic structure and mathematical models of the important components of the electro-hydraulic servo test bench were introduced, and the control structure diagrams of torque control and position control were elaborated. The servo working principle of the adaptive composite control based on feedforward compensation was analyzed, and the error function between the theoretical model and the actual model of the motor for motor speed control in the feedforward compensation control process was proposed. Finally, the system was simulated and loaded for testing. Based on the theoretical analysis and experimental results of this study, it can be concluded that if the electro-hydraulic servo system adopts the composite control method of adaptive feedforward compensation, the adaptive ability and disturbance resistance of the system controller can be significantly improved. The experimental comparison results show that the performance is significantly better than that of the system using the traditional PI closed-loop control algorithm and fuzzy PI closed-loop control; The feedforward compensation composite control method can accurately calculate the reference value and adjustment parameters for servo motor speed compensation. The experimental results show that the dynamic response speed of the system is improved by more than 78% compared to the traditional closed-loop control algorithms; In the experiment, the sine response curves of 0.2 Hz and 0.5 Hz were also compared, and it was found that the feedforward compensation composite control system had the best control accuracy and position tracking curve performance, with the smallest error in the actual output value.}, archive = {J_ACCESS}, author = {Mei Luhai}, doi = {10.1109/ACCESS.2025.3573305}, journal = {IEEE Access}, pages = {92338-92349}, shortjournal = {IEEE Access}, title = {Research and experiment on an adaptive feedforward compensation composite control strategy for an electro-hydraulic servo system}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning with deep features: A dynamic approach for intrusion detection in IoT networks. ACCESS, 13, 92319-92337. (www)

  @article{ , abstract = {Intrusion detection in Internet of Things (IoT) networks is essential to identify and mitigate security breaches and unauthorized access to connected devices. As IoT devices continue to advance, securing interconnected systems against malicious attacks is essential to ensure data privacy, system integrity, and user safety. However, traditional intrusion detection systems (IDSs) often struggle to adapt to novel and evolving threats. Developing a system that can autonomously learn and enhance intrusion detection capabilities to effectively identify and mitigate emerging threats is challenging. To address this issue, we propose a reinforcement learning (RL)–based approach for enhancing cybersecurity in IoT networks. First, various preprocessing techniques such as handling missing values, outliers, and min-max scaling normalization were applied. Then, domain-specific features related to network traffic patterns, including packet size distribution, packet count, and packet rate, were extracted. Statistical measures, including mean, variance, and entropy, were calculated from these features to determine temporal and spatial variations in the network data. Moreover, deep learning models, including long short-term memory networks and convolutional neural networks (CNNs), were used to automatically extract high-level features from raw data such as network logs or sensor readings. The extracted feature sets were concatenated within a feature fusion layer that enabled efficient dimensionality reduction via principal component analysis. A hybrid optimization approach was also introduced for feature selection using green anaconda optimization and the chaotic learning osprey optimization algorithm. Furthermore, an RL-based IDS (RL-IDS) that incorporates RNNs and autoencoders with a deep Q-network was proposed to enhance threat detection. Experimental outcomes emphasize the superiority of the method, obtaining 99.45% accuracy with 70% training data and 99.80% with 80%. Precision and F-measure are 99.99% and 99.89%, respectively, outperforming current models like CNN, DNN, LSTM, and GAN-DRL, while efficiently reducing false positives and false negatives.}, archive = {J_ACCESS}, author = {Mohamad Khayat and Ezedin Barka and Mohamed Adel Serhani and Farag Sallabi and Khaled Shuaib and Heba M. Khater}, doi = {10.1109/ACCESS.2025.3569312}, journal = {IEEE Access}, pages = {92319-92337}, shortjournal = {IEEE Access}, title = {Reinforcement learning with deep features: A dynamic approach for intrusion detection in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Decentralized voltage prediction in multi-area distribution systems: A privacy-preserving collaborative framework. ACCESS, 13, 92305-92318. (www)

  @article{ , abstract = {With the high penetration of renewable energy and the deregulation of electricity markets, future active distribution networks are anticipated to comprise areas with diverse ownership. Frequent power exchange between these areas leads to strong interarea influences, making it essential to account for voltage coupling in decentralized voltage prediction (DVP). To ensure the accuracy of DVP, collaborative modeling using power information from neighboring areas is imperative. However, potential privacy concerns pose significant challenges to data sharing and interarea collaboration. To address these challenges, this paper proposes a privacy-preserving collaborative prediction framework. First, a privacy-preserving multi-objective XGBoost algorithm is developed to mitigate curious behavior through homomorphic encryption and minimal information sharing. Then, an equitable incentive mechanism is introduced to allocate rewards based on the relative marginal contributions, which discourages potential adversarial behavior. The proposed framework further modularizes subproblems, enabling parallelization to improve computational efficiency. Finally, case studies demonstrate that the proposed framework can achieve prediction accuracy comparable to centralized methods while ensuring privacy preservation and collaboration reliability, highlighting its potential for broad adoption in multi-area systems.}, archive = {J_ACCESS}, author = {Jianfeng Yan and Beibei Wang and Zhiqiang Wu and Zhengkai Ding}, doi = {10.1109/ACCESS.2025.3568025}, journal = {IEEE Access}, pages = {92305-92318}, shortjournal = {IEEE Access}, title = {Decentralized voltage prediction in multi-area distribution systems: A privacy-preserving collaborative framework}, volume = {13}, year = {2025}, }
• (2025). Mitigating safety risks in information systems: A self-adaptive approach. ACCESS, 13, 92279-92304. (www)

  @article{ , abstract = {With various benefits of Information Systems (IS), there are increasing apprehensions regarding their safety implications. Despite researchers’ investigation and publication of the safety advantages of IS, several case studies have revealed distinct, potentially fatal issues and safety risks even with the use of IS. Accidents occur due to differences between the process models (mental models) employed by users and the actual characteristics of the operation. This is particularly true for accidents consisting of interactions between users and safety-critical IS. These users’ process models and Situational Awareness (SA) generate incidents that pose safety hazards to human lives. This ambiguity in the process model and SA may be due to the user’s perspective of what the system does and the actual characteristics of the system. The literature on the user’s process model and SA has been extensively employed in technological systems, but its application to socio-technical systems has been limited. We have identified problems that constitute a potential safety risk with the IS. The issues correspond to the lack of alignment between the process model and situation awareness about the user when interacting with the IS. This lack of alignment and SA can result from various circumstances, including interruptions, multitasking, and cognitive overload. These disruptions make it more challenging to understand the circumstances, which may result in mistakes, inefficiencies, and even safety hazards. Knowing about these interruptions, an information system (IS) could adjust its usage procedure in real time to lessen the disruption’s impact. Such a system could aid in restoring SA by identifying, reacting and adapting to these disturbances. This self-adaptive system (SAS) is used in our study to investigate the impact of an adaptive IS on SA, usability and safety performance outcomes. The findings of this study and the controlled experiment shed light on how system adaptation can mitigate the negative impacts of interruptions, improving safety, effectiveness, efficiency, SA, usability, frustration, and decision-making.}, archive = {J_ACCESS}, author = {Shuja Mughal and Jens H. Weber and Assad Abbas}, doi = {10.1109/ACCESS.2025.3572879}, journal = {IEEE Access}, pages = {92279-92304}, shortjournal = {IEEE Access}, title = {Mitigating safety risks in information systems: A self-adaptive approach}, volume = {13}, year = {2025}, }
• (2025). RF-PSignal: The smart home gesture recognition system based on channel-wise topology and self-adaptive attention. ACCESS, 13, 92260-92278. (www)

  @article{ , abstract = {With the rapid development of the Internet of Things, smart home technology is bringing unprecedented convenience to the lives of the elderly. Particularly, applications for controlling smart homes based on Radio Frequency Identification (RFID) technology are opening up broad prospects for human-computer interaction. However, traditional RFID-based gesture recognition technologies are constrained by antenna beamwidth, posing certain challenges for elderly users in precisely controlling smart homes. To address this, this paper proposes a contactless RF-PSignal system based on Commercial Off-The-Shelf RFID devices. This system achieves high accuracy and lightweight gesture recognition for smart homes across wide areas. First, we fuse the signal phase and signal strength and propose a time-series-to-image algorithm to generate denoised images. Second, the system proposes three feature extraction modules designed based on the concept of channel topology, effectively improving recognition accuracy while reducing model complexity. Finally, we innovatively propose an adaptive attention module that dynamically assigns attention weights to enhance system performance. Experimental results show that the system achieves an average gesture recognition accuracy of 95.35% in wide-area scenarios. Furthermore, the system attains an average recognition accuracy of 97.53% in cross-environment scenarios and 96.22% in cross-user scenarios. Unlike most existing studies that rely on time-series signals for feature extraction and pattern recognition, the system achieves outstanding performance and computational efficiency through a time-series-to-image transformation algorithm and lightweight model design. Experimental results across various complex scenarios demonstrate the system’s generalizability and high robustness.}, archive = {J_ACCESS}, author = {Xiaohang Zhang and Chunhong Yue and Yajun Zhang and Kun Liang and Yanxiang Li}, doi = {10.1109/ACCESS.2025.3572406}, journal = {IEEE Access}, pages = {92260-92278}, shortjournal = {IEEE Access}, title = {RF-PSignal: The smart home gesture recognition system based on channel-wise topology and self-adaptive attention}, volume = {13}, year = {2025}, }
• (2025). Beyond precision and recall: Measuring search engine consistency using rank stability. ACCESS, 13, 92242-92259. (www)

  @article{ , abstract = {Traditional information retrieval metrics assess document relevance but neglect ranking stability—a critical factor in today’s dynamic web environments where search algorithms constantly evolve. We present Rmeasure, a comprehensive framework that quantifies search engine consistency by analyzing both overlapping and non-overlapping results through the lens of psychophysical principles, particularly the Weber-Fechner Law. Our three-component approach (Roverlap, Rnon-overlap, and Rcomprehensive) provides multidimensional insights into ranking variations. Experimental evaluation across Google and Bing using diverse query types reveals significant performance differences: Bing demonstrates superior ranking stability (minimum Roverlap of 0.0527), while Google exhibits considerable fluctuation (Rnon-overlap reaching 0.3653), especially for high-volume queries. Statistical measures confirm Bing’s consistency advantage, with Google showing rank differences averaging up to 21.622 positions. Beyond technical contributions, Rmeasure advances UN Sustainable Development Goals by enhancing information access equity (SDG 4), fostering innovation in search technology evaluation (SDG 9), and promoting consistent retrieval experiences across diverse user populations (SDG 10). By integrating perceptual modeling with rank-based analytics, Rmeasure extends search engine evaluation beyond traditional relevance metrics, offering a robust methodology for assessing real-world search performance.}, archive = {J_ACCESS}, author = {Krishnan Batri and Rajermani Thinakaran and S. Lakshmi and R. Sowrirajan and Sivaram Murugan}, doi = {10.1109/ACCESS.2025.3571184}, journal = {IEEE Access}, pages = {92242-92259}, shortjournal = {IEEE Access}, title = {Beyond precision and recall: Measuring search engine consistency using rank stability}, volume = {13}, year = {2025}, }
• (2025). Estimating bandwidth and analyzing worst-case delay bounds using network calculus. ACCESS, 13, 92227-92241. (www)

  @article{ , abstract = {This paper proposes a bandwidth estimation framework for Network-on-Chip systems using network calculus, addressing the challenges due to nonlinear behavior and dynamic traffic. However, most existing approaches struggle to predict bandwidth effectively when network traffic packets are affected by possible nonlinearity in the network infrastructure. Our approach models available bandwidth as a service curve and employs min-plus algebra-specifically, convolution and deconvolution operations-to derive end-to-end predictions from individual link characteristics. We conduct a worst-case delay analysis under varying traffic loads and channel rates, demonstrating the robustness of our approach across heterogeneous Network-on-chip platforms. Unlike conventional methods that are constrained by buffer sizes or flow serialization issues, our framework integrates passive measurement techniques and probabilistic method to capture bandwidth variations more accurately. The proposed solution enhances prediction fidelity in systems with bursty and synthetic non-uniform traffic, offering a scalable and analytically grounded alternative to existing bandwidth estimation and worst-case delay bound techniques.}, archive = {J_ACCESS}, author = {Md Amirul Islam and Giovanni Stea}, doi = {10.1109/ACCESS.2025.3573126}, journal = {IEEE Access}, pages = {92227-92241}, shortjournal = {IEEE Access}, title = {Estimating bandwidth and analyzing worst-case delay bounds using network calculus}, volume = {13}, year = {2025}, }
• (2025). Impedance modeling for line-frequency rectified constant-power loads. ACCESS, 13, 92213-92226. (www)

  @article{ , abstract = {Constant-power loads (CPLs) represent a growing presence on utility grids and microgrids. These power electronic loads are frequently configured as rectified loads operating from an ac input to produce a controlled dc output at a constant power level. This constant-power demand creates a well-known incremental negative input impedance that “fights” changes in grid voltage by demanding opposing changes in input current to maintain constant power operation for a load. Rectified CPLs can, therefore, create instabilities with relatively unique perturbations in the envelope of ac-side current and voltage. A system’s envelope response, characterized by the envelope impedance, has allowed for the application of traditional stability analysis techniques to rectified CPLs where complex nonlinear dynamics prevent the use of, e.g., small-signal approximations. This paper presents clear and experimentally useful definitions for envelope impedance, explains the limitations of traditional single-frequency perturbation stability analysis for rectified CPLs, develops new techniques for estimating rectified CPL input impedance using a double Fourier integral method, and demonstrates these techniques with both simulations and experimental results.}, archive = {J_ACCESS}, author = {Eric A. Ponce and Steven B. Leeb}, doi = {10.1109/ACCESS.2025.3572006}, journal = {IEEE Access}, pages = {92213-92226}, shortjournal = {IEEE Access}, title = {Impedance modeling for line-frequency rectified constant-power loads}, volume = {13}, year = {2025}, }
• (2025). A method for localization of cellular users from call detail records. ACCESS, 13, 92203-92212. (www)

  @article{ , abstract = {A common problem in justice applications is localization of a user of a cellular network using a call detail record (CDR), which typically reveals only the base station and sector to which the user was connected. This precludes precise estimation of location. Instead, one is limited to estimating a region of plausible locations (RPL) using static information such as sector antenna orientation, beamwidth, and locations of nearby base stations. In this paper, we propose a method for RPL estimation in which the shape bounding the RPL is derived from a model of the antenna pattern via the Friis Transmission Equation, and the size of the RPL is determined by mean distance to nearby base stations. The performance of the proposed method is evaluated by “best server” analysis of measurements acquired from drive testing in the vicinity of Winter Garden, Florida, observing three 700 MHz-band LTE cellular networks serving this area. Of the 16 sectors evaluated, the aggregate error rate (i.e., fraction of users located outside the RPL estimated for the associated sector) is found to be 1.3%, with worst per-sector error rate of about 13.3% and error rates below 1.8% for 13 of the 16 sectors. The principal difficulty is shown to be estimation of RPL size, which entails a tradeoff between minimizing RPL area (yielding the “tightest” localization) and minimizing error rate.}, archive = {J_ACCESS}, author = {Steven W. Ellingson}, doi = {10.1109/ACCESS.2025.3573688}, journal = {IEEE Access}, pages = {92203-92212}, shortjournal = {IEEE Access}, title = {A method for localization of cellular users from call detail records}, volume = {13}, year = {2025}, }
• (2025). On novel RIP of windowed compressed spectrum sensing. ACCESS, 13, 92180-92202. (www)

  @article{ , abstract = {Compressed spectrum sensing (CSS) offers great advantages in spectral analysis through sub-Nyquist sampling. However, conventional CSS approaches have not sufficiently addressed the effect of spectral leakage (SL) on sensing performance, a problem that fundamentally alters signal sparsity. Although WFs (WFs) are widely employed in traditional spectral analysis to mitigate SL, their systematic application within CSS lacks comprehensive analysis, and they have not been theoretically investigated. This study develops a rigorous theoretical framework for analyzing windowed measurement matrices in CSS by introducing two novel metrics: the Edge Zeroing Coefficient (EZC), which quantifies the boundary behavior of WFs and directly correlates with SL suppression, and the Window Scaling Coefficient (WSC), which characterizes how WFs affect the Restricted Isometry Property (RIP) of measurement matrices. By integrating these metrics, we formulate a dual-effects framework that quantitatively characterizes the inherent trade-offs between the accuracy of spectral analysis and the fidelity of signal reconstruction. We extend conventional block-sparsity concepts by explicitly incorporating WF characteristics, providing the first mathematical framework that directly links window properties to resulting signal sparsity patterns. By leveraging subspace counting theory, we establish sampling bounds for windowed CSS under various conditions, including block-sparse signal structures, ultra-sparse frequency distributions, and noisy environments. Our findings demonstrate that although WFs effectively reduce SL, excessively low EZC and WSC values can degrade RIP quality, potentially causing numerical instability during signal reconstruction. This research provides theoretical foundations and practical guidelines for selecting appropriate WFs based on application-specific requirements, enabling more accurate and efficient spectrum sensing across diverse domains.}, archive = {J_ACCESS}, author = {Huiguang Zhang and Baoguo Liu and Wei Feng}, doi = {10.1109/ACCESS.2025.3570328}, journal = {IEEE Access}, pages = {92180-92202}, shortjournal = {IEEE Access}, title = {On novel RIP of windowed compressed spectrum sensing}, volume = {13}, year = {2025}, }
• (2025). Enhancing performance toward torque and flux control through a hybrid approach of intelligent and DTC for SRM drives. ACCESS, 13, 92168-92179. (www)

  @article{ , abstract = {Switched Reluctance Motors (SRMs) are considered the preferred choice for next-generation electric vehicle (EV) traction motor applications due to their simple rotor structure and favorable power and torque characteristics. However, SRM drives suffer from high torque ripples, which can cause severe vibrations and acoustic noise. Moreover, the highly nonlinear characteristics of SRMs complicate their control and modeling, making it a significant challenge to achieve optimal performance. Therefore, this paper proposes a Direct Torque Control (DTC) strategy for SRMs, which incorporates a modified dynamic precise model and an Adaptive Neuro-Fuzzy Inference System (ANFIS). First, a DTC technique based on a two-level hysteresis controller with an appropriate switching scheme is introduced for instantaneous torque control, ensuring proper torque tracking with a smooth torque profile. Second, a novel hybrid intelligent ANFIS model is developed for accurate flux and torque estimation and optimal selection of switching angles. The proposed method employs a multi-objective function to fully exploit the torque production capability of the SRM, minimize torque ripple, and enhance operational efficiency. The proposed solutions are compared with conventional DTC schemes, and experimental evaluations demonstrate significant reductions in torque ripple, flux ripple, and current distortion.}, archive = {J_ACCESS}, author = {Deepak Mohanraj and M Umavathi and Rajesh Verma and Bharatiraja Chokkalingam and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3569277}, journal = {IEEE Access}, pages = {92168-92179}, shortjournal = {IEEE Access}, title = {Enhancing performance toward torque and flux control through a hybrid approach of intelligent and DTC for SRM drives}, volume = {13}, year = {2025}, }
• (2025). Training-free VLM-based pseudo label generation for video anomaly detection. ACCESS, 13, 92155-92167. (www)

  @article{ , abstract = {Video anomaly detection in weakly supervised settings remains a challenging task due to the absence of frame-level annotations. To address this, we propose a novel training-free pseudo-label generation module (TFPLG) for Weakly Supervised Video Anomaly Detection (WSVAD), which leverages the vision-language alignment of the pre-trained CLIP model to generate pseudo-labels without requiring any training. Unlike prior methods that depend on learned classifiers, our approach employs a threshold-guided similarity-matching mechanism to produce both fine-grained and coarse-grained pseudo-labels. The framework adopts a triple-branch architecture: the first branch generates pseudo-labels, while the second and third perform coarse-grained binary and fine-grained categorical classification. Temporal modeling is enhanced through the integration of transformers and Graph Convolutional Networks (GCNs) to capture both short- and long-range dependencies. Experiments on UCF-Crime and XD-Violence demonstrate the effectiveness of our approach, achieving a 1.4% average precision gain on XD-Violence compared to leading pseudo-labeling methods, and a 1.6% improvement in anomaly AUC on UCF-Crime over the best existing approaches. In zero-shot testing on the new MSAD dataset, our framework achieves a 3.24% AUC improvement, highlighting its robustness and adaptability. The source code is publicly available at: https://github.com/MoshiraAbdalla/TFPLG_VAD}, archive = {J_ACCESS}, author = {Moshira Abdalla and Sajid Javed}, doi = {10.1109/ACCESS.2025.3573594}, journal = {IEEE Access}, pages = {92155-92167}, shortjournal = {IEEE Access}, title = {Training-free VLM-based pseudo label generation for video anomaly detection}, volume = {13}, year = {2025}, }
• (2025). EISPY2D: An open-source python library for the development and comparison of algorithms in two-dimensional electromagnetic inverse scattering problems. ACCESS, 13, 92134-92154. (www)

  @article{ , abstract = {Microwave Imaging is a key technique for reconstructing the electrical properties of inaccessible media, relying on algorithms to solve the associated Electromagnetic Inverse Scattering Problem. To support the assessment of recent developments in this field, this work introduces an open-source Python library that provides a modular and standardized framework for implementing and evaluating microwave imaging algorithms. The library facilitates the development and comparison of new methods through a structured class system, offering features such as test randomization, performance metrics, and statistical analysis. To the authors’ knowledge, this is the first tool designed specifically for benchmarking and comparative studies in microwave imaging algorithms. The paper presents the library’s design principles, along with case studies demonstrating its functionality. The code is freely available on GitHub: https://andre-batista.github.io/eispy2d/.}, archive = {J_ACCESS}, author = {André Costa Batista and Ricardo Adriano and Lucas S. Batista}, doi = {10.1109/ACCESS.2025.3573679}, journal = {IEEE Access}, pages = {92134-92154}, shortjournal = {IEEE Access}, title = {EISPY2D: An open-source python library for the development and comparison of algorithms in two-dimensional electromagnetic inverse scattering problems}, volume = {13}, year = {2025}, }
• (2025). High-performance on-orbit intelligent computing and real-time services for remote sensing satellites based on large-scale computing power in space. ACCESS, 13, 92114-92133. (www)

  @article{ , abstract = {The rapid advancement in Earth observation systems and satellite networks driven by commercial space has led to a significant increase in the number of remote sensing satellites, thereby establishing the groundwork for all-weather, continuous, cluster-collaborative observation. Following anti-irradiation hardening measures, low-cost commercial off-the-shelf (COTS) devices are effectively operated within intricate space environments, leading to substantial enhancements of the on-orbit computing capabilities of individual satellites. The onboard-distributed processing framework can be leveraged to fully utilize wide-area scattered space computing resources; accordingly, it is possible to automatically establish a network of neighboring satellites through inter-satellite communication links to construct a spatially distributed and computationally cooperative cloud computing environment in space. This enables the sharing of space computing resources and facilitates satellite-based mission coordination, reducing reliance on ground-based remote sensing satellite services while enhancing the response efficiency of remote sensing application services. The present paper provides a comprehensive review of the current development status, existing challenges, and problems faced by space-based, on-orbit remote sensing processing platforms and onboard intelligent processing algorithms; we start with the immediate application requirements of the remote sensing satellites used in in-orbit processing. In view of the anticipated surge in large-scale computing resources in space, a distributed processing architecture for remote sensing satellite data, based on large-scale onboard computing power, is constructed. The architecture is designed in detail, with attention paid to the physical architecture, the software architecture, the workflow, and the key technologies. Finally, three real-time service scenarios that align with the construction content of an ultra-low-orbit satellite constellation and 6G technology have been designed to provide solutions and references for the subsequent development of space-based intelligent remote sensing constellations.}, archive = {J_ACCESS}, author = {Guoping Wang and Gang Wan and Zhijuan Su and Yang Wang and Yutong Jia and Gong Li and Shi Liang}, doi = {10.1109/ACCESS.2025.3573932}, journal = {IEEE Access}, pages = {92114-92133}, shortjournal = {IEEE Access}, title = {High-performance on-orbit intelligent computing and real-time services for remote sensing satellites based on large-scale computing power in space}, volume = {13}, year = {2025}, }
• (2025). A novel hierarchical multimodal recommender with enhanced global collaborative signals. ACCESS, 13, 92102-92113. (www)

  @article{ , abstract = {Multimodal recommender systems leverage auxiliary item features, such as images and descriptions, to alleviate the data sparsity problem and facilitate the preference modeling process. Despite their potential, existing multimodal recommenders fail to exploit global collaborative signals and lack insights into the underlying interaction formation mechanism, resulting in suboptimal recommendation performance. To this end, we propose a hierarchical multimodal recommender named HMMGCF, which can capture crucial global collaborative signals through modality feature-enhanced hierarchical structures and a novel inter-modality alignment strategy. Specifically, modality features are first utilized to identify neighboring relationships, and similar users (items) are steadily merged together to form modality-specific hierarchical structures. Then, with the proper graph convolution operation on each hierarchy, the crucial global collaborative signals can be effectively extracted and integrated into the modality-specific user (item) embeddings. Moreover, a novel group-wise contrastive learning strategy is also proposed to align inter-modality preference information and further enhance the extraction of global collaborative signals. By conducting extensive experiments on three benchmark datasets, we empirically and theoretically demonstrate the superiority of HMMGCF, validating the importance of global collaborative signal extraction in multimodal recommender systems.}, archive = {J_ACCESS}, author = {Peng Yi and Lu Chen and Zhaoxian Li and Cheng Yang}, doi = {10.1109/ACCESS.2025.3573204}, journal = {IEEE Access}, pages = {92102-92113}, shortjournal = {IEEE Access}, title = {A novel hierarchical multimodal recommender with enhanced global collaborative signals}, volume = {13}, year = {2025}, }
• (2025). HiPass: Hijacking CTAP in passkey authentication. ACCESS, 13, 92086-92101. (www)

  @article{ , abstract = {Passkeys are designed to enhance the security and convenience of authentication by leveraging Fast Identity Online (FIDO) and Web Authentication (WebAuthn) protocols and utilizing credential information stored on user devices to securely complete the authentication process. This study explores the potential for Man-in-the-Middle (MitM) attacks during the Passkey authentication process using the Client-to-Authenticator Protocol (CTAP). We applied existing MitM attack techniques to the Passkey authentication process and analyzed the outcomes. Through this analysis, we developed a scenario in which an attacker can use the victim’s Passkey to log into the attacker’s PC and explained why such an attack is feasible. Our implementation successfully hijacked the victim’s session during the CTAP process by connecting the victim’s authenticator to the attacker’s PC via Bluetooth, thereby gaining access to the victim’s account. By demonstrating the feasibility of this attack, our study highlights the need for more robust security measures in future implementations of FIDO and WebAuthn, which constitute the foundational technologies of Passkeys.}, archive = {J_ACCESS}, author = {Donghyun Kim and Junseok Shin and Gwonsang Ryu and Daeseon Choi}, doi = {10.1109/ACCESS.2025.3570377}, journal = {IEEE Access}, pages = {92086-92101}, shortjournal = {IEEE Access}, title = {HiPass: Hijacking CTAP in passkey authentication}, volume = {13}, year = {2025}, }
• (2025). Adversarial sparse teacher: Defense against distillation-based model stealing attacks using adversarial examples. ACCESS, 13, 92074-92085. (www)

  @article{ , abstract = {We introduce Adversarial Sparse Teacher (AST), a robust defense method against distillation-based model stealing attacks. Our approach trains a teacher model using adversarial examples to produce sparse logit responses and increase the entropy of the output distribution. Typically, a model generates a peak in its output corresponding to its prediction. By leveraging adversarial examples, AST modifies the teacher model’s original response, embedding a few altered logits into the output, while keeping the primary response slightly higher. Concurrently, all remaining logits are elevated to further increase the output distribution’s entropy. All these complex manipulations are performed using an optimization function with our proposed Exponential Predictive Divergence (EPD) loss function. EPD allows us to maintain higher entropy levels compared to traditional KL divergence, effectively confusing attackers. Experiments on the CIFAR-10 and CIFAR-100 datasets demonstrate that AST outperforms state-of-the-art methods, providing effective defense against model stealing, while preserving high accuracy. The source codes are publicly available at https://github.com/codeofanon/AdversarialSparseTeacher}, archive = {J_ACCESS}, author = {Eda Yilmaz and Hacer Yalim Keles}, doi = {10.1109/ACCESS.2025.3573105}, journal = {IEEE Access}, pages = {92074-92085}, shortjournal = {IEEE Access}, title = {Adversarial sparse teacher: Defense against distillation-based model stealing attacks using adversarial examples}, volume = {13}, year = {2025}, }
• (2025). Technology-enhanced multimodal learning analytics in higher education: A systematic literature review. ACCESS, 13, 92057-92073. (www)

  @article{ , abstract = {Multimodal learning analytics (MMLA) is an emerging field of learning analytics and promises a more comprehensive analysis of the learning process thanks to advances in technological devices and data science. The purpose of this study was to explore technology-enhanced multimodal learning analytics in higher education systematically. A systematic literature review was performed using the PRISMA guidelines, and 45 studies published between January 2012 and June 2024 were determined. The findings demonstrated that China, the USA, Australia, and Chile were the leading contributors to MMLA research, with a notable surge in publications in 2021. Audio recorders, cameras, webcams, eye trackers, and wristbands were the most used devices. Most studies were conducted in experiment rooms or laboratories, though studies in authentic classroom settings have been growing. Data were primarily collected during activities such as programming, simulation exercises, presentations, discussions, writing, watching videos, reading, or exams, as well as throughout the entire instructional process, predominantly in computer science, health, and engineering courses. The studies were mainly predictive or descriptive whereas quite a few studies were prescriptive. Frequently tracked data types included audio, gaze, log, facial expression, physiological, and behavioral data. Traditional machine learning and basic statistics were the commonly used analytical methods whilst advanced statistics and deep learning were relatively less utilized. Test performance, engagement, emotional state, debugging performance, and learning experience were the popular target variables. The studies also pointed out several implications and future directions, with a significant portion highlighting the development of interventions, frameworks, or adaptive systems using MMLA.}, archive = {J_ACCESS}, author = {Ozan Raşít Yürüm}, doi = {10.1109/ACCESS.2025.3572467}, journal = {IEEE Access}, pages = {92057-92073}, shortjournal = {IEEE Access}, title = {Technology-enhanced multimodal learning analytics in higher education: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). An integrated preprocessing and drift detection approach with adaptive windowing for fraud detection in payment systems. ACCESS, 13, 92036-92056. (www)

  @article{ , abstract = {As fraudulent transaction methods evolve rapidly; it becomes progressively more challenging to detect them in payment systems. Static machine learning and rule-based traditional detection methods cannot capture all the dynamic and evolving nature of fraudulent behaviors, resulting in lower detection accuracy and a higher false positive rate. This study proposes a complete framework that brings together advanced data preprocessing, effective drift detection, and a reliable detection model to address these issues. The method uses Mutual Information and SelectKBest for selecting important features, applies ADASYN to handle class imbalance, and adopts Convolutional Neural Networks (CNN) to capture complex transaction patterns. By implementing Early Drift Detection Method (EDDM) and ADaptive WINdowing (ADWIN), the drift can be detected in advance and the system can respond to changes, both gradual and sudden.The framework was evaluated on three datasets, including real-world transactions and mixed-data environments, achieving superior accuracy, precision, and drift detection rates, with values up to 99.99% accuracy and 1.0 respectively. The findings show that the framework can adjust to changing patterns of fraud, reduce false positives, and enhance detection performance. These insights demonstrate the significance of dynamic pre-processing and drift-aware approaches in the context of real-time fraud detection. This also serves as a basis for future work in adaptive fraud detection model research areas such as of integrating online learning for improved speed and efficiency in high-frequency transactional environments.}, archive = {J_ACCESS}, author = {Hadi M. R. Al Lawati and Anazida Zainal and Bander Ali Saleh Al-Rimy and Mohammad Al-Azawi and Mohamad Nizam Kassim and Sultan Ahmed Almalki and Tami Abdulrahman Alghamdi}, doi = {10.1109/ACCESS.2025.3569609}, journal = {IEEE Access}, pages = {92036-92056}, shortjournal = {IEEE Access}, title = {An integrated preprocessing and drift detection approach with adaptive windowing for fraud detection in payment systems}, volume = {13}, year = {2025}, }
• (2025). FedQP: Large-scale private and flexible federated query processing. ACCESS, 13, 92019-92035. (www)

  @article{ , abstract = {State-of-the-art federated learning coordinates stochastic gradient descent across clients to refine shared model parameters while protecting individual datasets. Current methods require a uniform data model and are vulnerable to privacy attacks such as model inversion. The key challenge is in designing algorithms that efficiently aggregate client input, minimize data exposure, and maximize model adaptability. We propose a new scheme for aggregating local, diverse, and independent data models, addressing data inconsistency and model-inversion attacks. The proposed scheme, federated query processing FedQP, enables clients to build local models independently, without coordinating with the server or other clients. The server communicates with selected clients only to predict future values. Our scheme only requires one communication round between each client and the server for model initialization without the need for a training phase. This article presents the methodology, design, and evaluation of a performance-efficient implementation of FedQP. We show that the caching algorithm in FedQP reduces redundant client communications during query processing. In addition, we present a privacy analysis that shows that our method outperforms prominent gradient-based approaches to federated learning. Our experiments show that FedQP consistently achieves higher classification accuracy in non-IID settings and demonstrates stronger resilience to reconstruction attacks compared to gradient-based methods such as federated averaging (FedAvg).}, archive = {J_ACCESS}, author = {Hussain M. J. Almohri and Layne T. Watson}, doi = {10.1109/ACCESS.2025.3567487}, journal = {IEEE Access}, pages = {92019-92035}, shortjournal = {IEEE Access}, title = {FedQP: Large-scale private and flexible federated query processing}, volume = {13}, year = {2025}, }
• (2025). From free to fee: Psychological ownership and advertising appeals in freemium conversion. ACCESS, 13, 92008-92018. (www)

  @article{ , abstract = {This study investigates how users’ psychological ownership of digital services influences their responses to different advertising appeals in freemium business models. Through a $2\times 2$ factorial experiment using a cloud-based note-taking service, we examined how the interaction between psychological ownership (high vs. low) and advertising appeal types (attribute-based vs. benefit-based) influences advertising attitudes, which in turn can lead to premium subscription intentions. Results reveal a significant interaction effect: users with high psychological ownership respond more positively to attribute-based advertising, which details specific features and functionalities. Conversely, users with low psychological ownership are more effectively persuaded by benefit-based advertising, which emphasizes experiential advantages. These findings expand psychological ownership theory into digital contexts and provide practical insights for freemium services seeking to optimize conversion rates. By tailoring advertising strategies to users’ psychological ownership levels, digital platforms can more effectively encourage free users to upgrade to premium services, ultimately enhancing their business performance in competitive digital markets.}, archive = {J_ACCESS}, author = {Bong-Goon Seo and Do-Hyung Park}, doi = {10.1109/ACCESS.2025.3572311}, journal = {IEEE Access}, pages = {92008-92018}, shortjournal = {IEEE Access}, title = {From free to fee: Psychological ownership and advertising appeals in freemium conversion}, volume = {13}, year = {2025}, }
• (2025). An interface evaluation model for usability and perceived security. ACCESS, 13, 91989-92007. (www)

  @article{ , abstract = {The increasing complexity of the digital world has amplified the importance of both security and usability in user interface design. While numerous approaches address these issues separately, there is a lack of perception-oriented evaluation models that integrate usability and perceived security into a single framework. This paper proposes the Usability and Perceived Security Evaluation Model (UPSEM), which enables developers to assess both dimensions simultaneously during the early stages of interface design. UPSEM incorporates hierarchical criteria based on design factors and groups that influence users’ look-and-feel-driven perceptions of usability and security. The model employs the Analytical Hierarchy Process, a multi-criteria decision-making method, to evaluate interfaces systematically. Expert evaluations were conducted to validate the model, resulting in a final version that includes 25 design factors categorized into three groups. Notably, the study found no consensus among experts on the existence of a trade-off between usability and perceived security, challenging previous assumptions in the literature. The findings demonstrate that UPSEM provides a practical and scientifically grounded metric for identifying interface features requiring improvement. By offering a structured approach to measure usability and perceived security together, UPSEM supports developers in designing user interfaces that are both usable and secure from the outset.}, archive = {J_ACCESS}, author = {Mehmet Göktürk}, doi = {10.1109/ACCESS.2025.3572108}, journal = {IEEE Access}, pages = {91989-92007}, shortjournal = {IEEE Access}, title = {An interface evaluation model for usability and perceived security}, volume = {13}, year = {2025}, }
• (2025). Data-driven identification of gas turbine engine dynamics via koopman operator genetic algorithm. ACCESS, 13, 91972-91988. (www)

  @article{ , abstract = {Gas turbine engines (GTEs) are highly nonlinear control-nonaffine systems. Deriving their physics-based models can be challenging, particularly when some critical parameters can be difficult to measure or determine otherwise. The possible solution to this problem lies in data-driven approaches that extract the underlying dynamical features from the measured trajectories. In this paper, a modified method based on the Koopman operator theory is utilized for GTE identification, as it allows a description of the system in a globally linear manner. Linear parameter-varying and bilinear Koopman models were considered. Extended dynamic mode decomposition was employed to obtain a finite-dimensional approximation of the operator. A novel application of genetic algorithm is proposed with a specifically crafted objective function that allows a constrained nonconvex optimization of functions in the Koopman observable subspace while forcing the solution to satisfy specified performance requirements via penalization. The prediction error was given by a numerical integration of lifted state-space equations as it reflects the usage of the model for real-time prediction in practice. Effects of parameter settings were analyzed. The prediction performance is compared to an in-house model that was validated against commercial simulation software, and the accuracy-complexity trade-off is discussed. Furthermore, model parameter corrections are introduced to cope with the effects of changing flight conditions. Finally, Koopman eigenfunctions and modes are investigated to analyze the underlying dynamics. Results point to suitability of the proposed approach for the identification of GTE dynamics and future optimal control system design.}, archive = {J_ACCESS}, author = {David Grasev}, doi = {10.1109/ACCESS.2025.3573472}, journal = {IEEE Access}, pages = {91972-91988}, shortjournal = {IEEE Access}, title = {Data-driven identification of gas turbine engine dynamics via koopman operator genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of the MUSIC algorithm in the presence of correlated noise. ACCESS, 13, 91957-91971. (www)

  @article{ , abstract = {Passive direction finding has been studied over the past decades and theoretic performance prediction is a fundamental problem. The performance of the MUSIC algorithm for Direction-of-arrival (DOA) estimation under measurement uncertainty is analyzed. In the conventional MUSIC algorithm performance analysis, noise is assumed to be uncorrelated. In this paper, the noise correlation between antenna elements is considered according to the distance between antenna elements. The Mean Squared Error (MSE) obtained through Monte-Carlo simulations is compared with the MSE derived from analytical expressions. The primary contribution of this paper is to reduce the computational complexity of the performance analysis of the MUSIC algorithm compared to the existing Monte-Carlo simulation-based performance analysis. To our knowledge, no previous studies have derived a closed-form analytical performance analysis of the algorithm in the presence of correlated noise.}, archive = {J_ACCESS}, author = {Hyeong Woo Ham and Hyuk-In Kwon and Joon-Ho Lee}, doi = {10.1109/ACCESS.2025.3569878}, journal = {IEEE Access}, pages = {91957-91971}, shortjournal = {IEEE Access}, title = {Performance analysis of the MUSIC algorithm in the presence of correlated noise}, volume = {13}, year = {2025}, }
• (2025). A new task-space neural nonlinear control approach for robotic manipulators under joint constraints. ACCESS, 13, 91945-91956. (www)

  @article{ , abstract = {Robotic manipulators are nonlinear systems with multi-input multi-output (MIMO) structures, uncertainties, and time-varying dynamical prospects. Unexpected influences of internal and external disturbances along with physical constraints in complicated kinematic configurations are large barriers for deriving excellent controllers of the manipulators. In this paper, we propose a novel two-level control approach to drive the end-effector of robotic manipulators following referenced profiles within the hard bounds of joint angles without using inverse kinematics solutions. A new Cartersian-based high-level sub controller is designed for calculating the desired angular velocities from the task-space control objective throughout a constrained-to-free transformation operator. An advanced sliding mode control framework is adopted to construct the low-level control layer to realize the indirect joint-control missions given from the upper one within the physical bounds. To effectively suppress the dynamical internal and external disturbances, a new neural network with a flexible nonlinear learning law is integrated in the joint-space sub controller. Stability of the whole system including both Cartersian and joint spaces is thoroughly proven by a Lyapunov function under a special constraint. The designed controller was verified on a two-link planar robotic arm in various challenging conditions and in comparing with other state-of-the-art control methods. The in-depth simulation results exhibited that the proposed control algorithm possesses outperformance working abilities such as model-free adaptation, robustness, high accuracy and comprehensiveness.}, archive = {J_ACCESS}, author = {Nguyen Tran Minh Nguyet and Dang Xuan Ba}, doi = {10.1109/ACCESS.2025.3571933}, journal = {IEEE Access}, pages = {91945-91956}, shortjournal = {IEEE Access}, title = {A new task-space neural nonlinear control approach for robotic manipulators under joint constraints}, volume = {13}, year = {2025}, }
• (2025). Depth map reconstruction for scleral bleb models using structured light in light field imaging. ACCESS, 13, 91925-91944. (www)

  @article{ , abstract = {Light field image processing offers promising opportunities for the development of three-dimensional object imaging technology in healthcare. However, implementing it in real-world scenarios often encounters numerous external challenges that can degrade the quality of captured data. One example is imaging blebs on the eye sclera after trabeculectomy. The surface of the blebs has a glossy appearance and, in some cases, contains minimal blood vessels, resulting in a predominantly white appearance. This condition leads to inaccuracies in depth map reconstruction from light field images. A depth map reconstruction method is introduced for bleb surface models on the eye sclera model, utilizing information from the center of the sub-aperture image as a boundary condition within a Markov Random Field. This approach reduces outliers at the edges of the depth map where intensity variations occur. Additionally, the use of structured light on textureless object surfaces serves as an auxiliary enhancement, adding contour detail to the clear and white bleb surface model. Four Heidelberg Collaboratory for Image Processing datasets were used to evaluate the performance of this approach, with the number of sub-aperture images reduced to match the amount of data generated by the light field camera employed in this study. Based on the tests conducted, the proposed method improves the accuracy of depth map reconstruction for the input dataset. Furthermore, the addition of structured light on textureless bleb model images significantly reduces the depth map reconstruction errors while improving the similarity index of the reconstructed depth maps compared to the ground truth.}, archive = {J_ACCESS}, author = {Octa Heriana and Andriyan Bayu Suksmono and Hasballah Zakaria and Andika Prahasta}, doi = {10.1109/ACCESS.2025.3573213}, journal = {IEEE Access}, pages = {91925-91944}, shortjournal = {IEEE Access}, title = {Depth map reconstruction for scleral bleb models using structured light in light field imaging}, volume = {13}, year = {2025}, }
• (2025). Cancer cell classification from peripheral blood smear data using the YOLOv8 architecture. ACCESS, 13, 91911-91924. (www)

  @article{ , abstract = {The accurate classification of cancer cells in the peripheral blood is essential for the diagnosis of leukemia and has traditionally been carried out by analyzing laboratory images. In this context, the use of deep learning techniques facilitates decision-making and accelerates the early diagnosis of the disease, allowing preventive measures to be adopted for the patient. This study explores the application of the YOLOv8 deep learning architecture for the classification of cancer cells in blood smear images owing, to its ability to perform this task quickly and accurately. The models were trained on two datasets, ALL and C-NMC Leukemia, and evaluated using top-1 accuracy, top-5 accuracy and loss metrics. The proposed approach achieved a top-1 accuracy of 99.982% and top-5 accuracy of 100% on the validation and test subsets of the ALL dataset respectively, with a final loss of 0.02952. For the C-NMC Leukemia dataset, the model obtained 66.897% and 89.612% top-1 accuracy in the validation and test subsets, respectively, while maintaining 100% top-5 accuracy in both subsets, with a loss of 0.18289. These results demonstrate the effectiveness of YOLOv8 in classifying cancer cells, particularly in the ALL set. However, future strategies such as expanding the data set and fine-tuning the hyperparameters could contribute to better generalization of the model to different data distributions.}, archive = {J_ACCESS}, author = {Joao C. S. Nunes and Jose E. B. De S. Linhares and Miguel Angel Orellana Postigo and Daniel Guzmán del Río and Angilberto Muniz Ferreira Sobrinho and Israel Gondres Torné}, doi = {10.1109/ACCESS.2025.3573277}, journal = {IEEE Access}, pages = {91911-91924}, shortjournal = {IEEE Access}, title = {Cancer cell classification from peripheral blood smear data using the YOLOv8 architecture}, volume = {13}, year = {2025}, }
• (2025). Quantitative relationship between components and pour point of natural esters and its molecular mechanism. ACCESS, 13, 91902-91910. (www)

  @article{ , abstract = {With increasing emphasis on environmental sustainability and fire safety in the global power transformer industry, natural esters have emerged as preferred alternatives to conventional mineral oil. However, the cold flow properties of natural esters are unsatisfactory, which limits their application in cold areas. To solve this problem, this study carried out tests on natural ester components and pour point, the relationship and its molecular mechanism between components and pour point was studied. The results indicate that unsaturated fatty acids may also increase the pour point. When the content of linolenic acid exceeds 18%, it will actually increase the pour point of natural esters. The quantitative relationship between components and pour point of natural esters was constructed, and its average relative percentage error in prediction of pour point is within 7.5%, which show its good predictive performance. Molecular dynamics further verified that C=C bonds inhibit chain bending and stacking while maintain intermolecular spacing during cooling, which helps to improve cold flow properties. This study provides a theoretical basis for the preparation of high-performance natural esters insulation oil.}, archive = {J_ACCESS}, author = {Guochao Qian and Weiju Dai and Jian Hao and Dawei Feng and Jing Xu}, doi = {10.1109/ACCESS.2025.3564280}, journal = {IEEE Access}, pages = {91902-91910}, shortjournal = {IEEE Access}, title = {Quantitative relationship between components and pour point of natural esters and its molecular mechanism}, volume = {13}, year = {2025}, }
• (2025). Control aspects of multifunctional converters in AC microgrids: A survey. ACCESS, 13, 91889-91901. (www)

  @article{ , abstract = {A converter, whether operating independently or connected to the grid and providing supplementary services, is referred to as a multifunctional converter (MFC). These converters possess the capability to offer reactive power compensation, facilitate fault ride-through, enhanced power quality, and meet the demanded reliability requirements. This paper explores various aspects of multifunctional control for islanded and grid connected converters. Through a systematic and extensive literature review, control strategies, ancillary services, emerging trends, standards, and grid codes related to multifunctional converters over the past decade are presented. A Venn diagram is used to present various control features of MFCs. The paper concludes with the possible future research avenues in the area of multifunctional converters.}, archive = {J_ACCESS}, author = {Nisheet Soni and Amod C. Umarikar and A. S. Vijay}, doi = {10.1109/ACCESS.2025.3573154}, journal = {IEEE Access}, pages = {91889-91901}, shortjournal = {IEEE Access}, title = {Control aspects of multifunctional converters in AC microgrids: A survey}, volume = {13}, year = {2025}, }
• (2025). Hybrid machine learning for CNC process monitoring. ACCESS, 13, 91875-91888. (www)

  @article{ , abstract = {The transition to highly customized, one-off production in modern manufacturing necessitates sophisticated process monitoring to reduce waste, minimize downtime, and alleviate operator burden. Computer Numerically Controlled (CNC) axes represent a fundamental component of automated manufacturing and offer a universal and accessible monitoring option through power supply data. By accurately predicting reference signals and comparing them with real-time measurements, deviations can be used for effective model-based process monitoring and anomaly detection. This study explores the efficacy of hybrid machine learning (ML) models in predicting reference signals for CNC axes using features derived from a physical model. Furthermore, relevant but difficult-to-measure features such as process forces and material removal rate (MRR) were made accessible through soft sensors. Various ML models were evaluated, including tree-based models (e.g. random forest (RF) and gradient boosting (GB)) and deep learning (DL) models (e.g. feed-forward neural networks (FNN), long short-term memory (LSTM) and transformers-based models (TF)). A feature importance analysis was performed to gain a better understanding of the influencing factors, which revealed that velocity, acceleration, process forces, spindle torque, and MRR are relevant. Tree-based models, particularly RF and GB, have been shown to be more accurate and robust than DL approaches, particularly when data is sparse and processes are complex. Although DL models improved with larger data sets, their performance remained inferior to that of tree-based methods. This study emphasizes the advantages of incorporating physical knowledge into hybrid ML models to improve model-based process monitoring.}, archive = {J_ACCESS}, author = {Robin Ströbel and Samuel Deucker and Hanlin Zhou and Hafez Kader and Alexander Puchta and Benjamin Noack and Jürgen Fleischer}, doi = {10.1109/ACCESS.2025.3573400}, journal = {IEEE Access}, pages = {91875-91888}, shortjournal = {IEEE Access}, title = {Hybrid machine learning for CNC process monitoring}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence techniques with digital twin for fault diagnosis in interconnected systems: A review. ACCESS, 13, 91860-91874. (www)

  @article{ , abstract = {As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments.}, archive = {J_ACCESS}, author = {Imen Nakti and Majdi Mansouri and Rami Al-Hmouz and Atef Khedher}, doi = {10.1109/ACCESS.2025.3572563}, journal = {IEEE Access}, pages = {91860-91874}, shortjournal = {IEEE Access}, title = {Artificial intelligence techniques with digital twin for fault diagnosis in interconnected systems: A review}, volume = {13}, year = {2025}, }
• (2025). Causal intervention-based multimodal class-incremental learning network for 3D model classification and retrieval. ACCESS, 13, 91846-91859. (www)

  @article{ , abstract = {Existing class-incremental learning methods have achieved significant progress in unimodal domains, but they face severe challenges of catastrophic forgetting and cross-modal interference when handling multimodal data. We propose a causal intervention-based multimodal class-incremental learning framework to address the stability-plasticity trade-off in 3D model classification and retrieval tasks. By employing backdoor adjustment-based causal intervention, our approach decouples historical task biases from new knowledge acquisition. It introduces an alternating modality freezing strategy to isolate parameter updates between geometric (point cloud) and visual (multi-view) modalities, thereby suppressing cross-modal gradient interference. Furthermore, a dynamic feature anchoring loss is utilized to constrain feature space drift and maintain cross-task consistency. Experimental results on the ModelNet40 and ShapeNetCore55 datasets demonstrate that our method achieves state-of-the-art performance compared to competitive baselines, confirming the effectiveness of multimodal redundancy in mitigating unimodal degradation. This work provides theoretical support and technical implementation pathways for lifelong learning systems in complex 3D scenarios.}, archive = {J_ACCESS}, author = {Qiang Li and Qiu-Yang Ma and Ning Zhang and Wei-Zhi Nie}, doi = {10.1109/ACCESS.2025.3573041}, journal = {IEEE Access}, pages = {91846-91859}, shortjournal = {IEEE Access}, title = {Causal intervention-based multimodal class-incremental learning network for 3D model classification and retrieval}, volume = {13}, year = {2025}, }
• (2025). Assessing the detection capabilities of RGB and infrared models for robust occluded and unoccluded pedestrian detection. ACCESS, 13, 91834-91845. (www)

  @article{ , abstract = {Pedestrian detection is a critical component of autonomous vehicle systems, particularly under challenging conditions where visibility may be compromised due to occlusion or low-light. This study presents a comparative analysis of RGB (visible spectrum) and infrared (IR) modalities, each employed independently to detect pedestrians under various conditions. By evaluating single-modal models on both occluded and unoccluded pedestrians, we aim to provide insights into each Modality’s strengths and limitations. Through extensive training, validation, and testing phases on the FLIR dataset, the RGB model achieved mean Average Precision (mAP@0.5) of 0.8711, and 0.8739 during training and testing, while the IR model recorded a higher training mAP@0.5 of 0.9020, and 0.8729 during testing. Notably, the IR model achieves higher mAP@0.5, indicating greater robustness in scenarios with relaxed bounding box precision requirements. These findings suggest that integrating RGB and IR modalities in a complementary manner could enhance detection reliability across diverse real-world conditions. This work provides a foundation for developing more resilient and accurate pedestrian detection systems in autonomous vehicles, capable of operating effectively under both occluded and unoccluded conditions.}, archive = {J_ACCESS}, author = {Muhammad Habibullah Abdulfattah and Usman Ullah Sheikh and Muhammad Idrees Masud and Mohd Afzan Othman and Nurulaqilla Khamis and Mohammed Aman and Zeeshan Ahmad Arfeen}, doi = {10.1109/ACCESS.2025.3570833}, journal = {IEEE Access}, pages = {91834-91845}, shortjournal = {IEEE Access}, title = {Assessing the detection capabilities of RGB and infrared models for robust occluded and unoccluded pedestrian detection}, volume = {13}, year = {2025}, }
• (2025). A micro-LED pixel circuit using capacitor-coupled current control and pulse-width modulation. ACCESS, 13, 91826-91833. (www)

  @article{ , abstract = {This paper proposes a p-type low-temperature polycrystalline silicon (LTPS) pixel circuit for driving micro light-emitting diode ( $\mu $ LED) displays. Using pulse width modulation (PWM) is crucial to prevent wavelength shifts due to driving current variations. Maximizing emission time results in reducing maximum LED current, which may additionally reduce power consumption. The proposed circuit features one horizontal line time of $3.0~\mu $ s, enabling $\mu $ LEDs to emit for 85.9% of a frame time. A capacitive coupling effect is utilized to generate a constant current, eliminating the need for an external reference voltage or data voltage. Simulation results confirm that the pulse width of $\mu $ LED emission is modulated by the data voltage, ensuring stable PWM operation. Furthermore, the average current deviation in a frame stayed within only 1.54% under threshold voltage ( ${\mathrm{V}}_{\mathrm {TH}}$ ) shift of ±0.5 V in the range of G31 to G255. When capacitance variation is ±5%, the average current deviation ranged only from −2.9% to 2.8% in a frame. Simulation results confirm the proposed pixel circuit’s operation is robust against variations in VTH and capacitance.}, archive = {J_ACCESS}, author = {Nak-Yoon Kim and Seung-Woo Lee}, doi = {10.1109/ACCESS.2025.3572381}, journal = {IEEE Access}, pages = {91826-91833}, shortjournal = {IEEE Access}, title = {A micro-LED pixel circuit using capacitor-coupled current control and pulse-width modulation}, volume = {13}, year = {2025}, }
• (2025). The effectiveness of large language models in transforming unstructured text to standardized formats. ACCESS, 13, 91808-91825. (www)

  @article{ , abstract = {The exponential growth of unstructured text data presents a fundamental challenge in modern data management and information retrieval. While Large Language Models (LLMs) have shown remarkable capabilities in natural language processing, their potential to transform unstructured text into standardized, structured formats remains largely unexplored - a capability that could revolutionize data processing workflows across industries. This study breaks new ground by systematically evaluating LLMs’ ability to convert unstructured recipe text into the structured Cooklang format. Through comprehensive testing of four models (GPT-4o, GPT-4o-mini, Llama3.1:70bLlama3.3:70b, and Llama3.1:8b), an innovative evaluation approach is introduced that combines traditional metrics (WER, ROUGE-L, TER) with specialized metrics for semantic element identification. Our experiments reveal that GPT-4o with few-shot prompting achieves breakthrough performance (ROUGE-L: 0.8209, WER: 0.3509), demonstrating for the first time that LLMs can reliably transform domain-specific unstructured text into structured formats without extensive training. Although model performance generally scales with size, we uncover surprising potential in smaller models like Llama3.1:8b for optimization through targeted fine-tuning. These findings open new possibilities for automated structured data generation across various domains, from medical records to technical documentation, potentially transforming the way organizations process and utilize unstructured information. For further information, source code, and associated resources, please refer to the source code repository (https://github.com/fiit-ba/text-to-cooklang).}, archive = {J_ACCESS}, author = {William Brach and Kristián Košťál and Michal Ries}, doi = {10.1109/ACCESS.2025.3573030}, journal = {IEEE Access}, pages = {91808-91825}, shortjournal = {IEEE Access}, title = {The effectiveness of large language models in transforming unstructured text to standardized formats}, volume = {13}, year = {2025}, }
• (2025). Research on synchronized control strategy of drone swarm garage side-pulling platform. ACCESS, 13, 91795-91807. (www)

  @article{ , abstract = {To address the issue of synchronization of dual Permanent Magnet Synchronous Motors (PMSMs) caused by uncertain disturbances in the drone swarm depot’s side-pull platform, a control method based on the combination of Back Propagation(BP) Neural Network PID control (BPPID) and Adaptive Fuzzy PID Cross-coupling Control (AFPIDCC), referred to as BPPID+AFPIDCC, is proposed. In the single-motor servo system, the BP neural network PID algorithm is introduced into the speed controller to increase the robustness of a single motor. Between the two motors, a cross-coupling synchronous control method is adopted. On the basis of the traditional cross-coupling, an adaptive fuzzy PID is introduced as a speed compensator to further reduce the synchronization error between the two electric push rods. The experimental results show that under different full-load conditions at varying speeds, the improvements compared with the classic PID control are 31.7%, 27.2% and 29.2%, respectively. In the cases of one and two UAVs with the same speed load, the improvements compared with the classic PID control are 37.2% and 27.0%, respectively. It is verified that the proposed control method can effectively reduce the position synchronization error of the system and the system has good robustness.}, archive = {J_ACCESS}, author = {Guohui Li and Peiqing Li and Richen Peng and Dehua Wu and Longlong Jiang and Qipeng Li and Zhuoran Li}, doi = {10.1109/ACCESS.2025.3568850}, journal = {IEEE Access}, pages = {91795-91807}, shortjournal = {IEEE Access}, title = {Research on synchronized control strategy of drone swarm garage side-pulling platform}, volume = {13}, year = {2025}, }
• (2025). Advancements in landmine detection: Deep learning-based analysis with thermal drones. ACCESS, 13, 91777-91794. (www)

  @article{ , abstract = {The pervasive threat of landmines across conflict-affected regions necessitates advancements in detection technologies to enhance safety and efficiency in demining efforts. Furthermore, the development of a solution that can be effectively utilized in both resource-rich and resource-poor environments is essential, as many conflict-affected regions lack the financial means or technical infrastructure to deploy expensive and complex detection technologies. This paper introduces a deep learning-based approach utilizing uncrewed aerial vehicles equipped with thermal imaging cameras for landmine detection. Leveraging the MobileNetV3-Large architecture and building upon it, we propose a lightweight, yet powerful, machine learning model that can be seen as a safe, cost-efficient and fast landmine detection method. We evaluated our approach using a unique dataset comprising 2,700 thermographic images captured by a DJI Matrice 100 drone with a Zenmuse XT infrared camera. Through meticulous data pre-processing and augmentation strategies, we enhance the model’s ability to generalize across different terrains and mine types, addressing one of the primary challenges in landmine detection. The evaluation of our model on a rigorously curated test set demonstrates promising results, achieving a training accuracy of 96.97 %, a validation accuracy of 97.19 %, and a test accuracy of 96.14 %. These metrics not only demonstrate the model’s effectiveness in identifying landmines from thermal images but also highlight its potential to outperform other thermal approaches in real-world demining operations. This supports the application of uncrewed aerial vehicles and deep learning technologies in humanitarian and environmental challenges.}, archive = {J_ACCESS}, author = {Daniel Heuschmid and Oliver Wacker and Yannick Zimmermann and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3572196}, journal = {IEEE Access}, pages = {91777-91794}, shortjournal = {IEEE Access}, title = {Advancements in landmine detection: Deep learning-based analysis with thermal drones}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of wireless charging station location and operation scheduling for electric buses under uncertain demand. ACCESS, 13, 91765-91776. (www)

  @article{ , abstract = {In the context of electric bus route operations, the introduction of wireless charging facilities significantly impacts the operational scheduling of bus fleets. This study focuses on a single bus route and proposes a joint optimization model for determining departure frequencies and charging station locations under uncertain demand conditions. The model aims to minimize both user travel costs and transit operation costs by jointly optimizing departure schedules across different time periods, electric bus assignments, and wireless charging station placement. An adaptive genetic algorithm (AGA) is developed to solve the model efficiently. A case study based on a real-world bus route in China demonstrates that the proposed strategy not only improves system performance but also yields tangible benefits—reducing the required number of electric buses by 29.4%, shortening departure intervals by 12.5%, and lowering daily operational costs by 10.5%.}, archive = {J_ACCESS}, author = {Jiacheng Li and Masato Noto and Yang Zhang and Jia Guo}, doi = {10.1109/ACCESS.2025.3571007}, journal = {IEEE Access}, pages = {91765-91776}, shortjournal = {IEEE Access}, title = {Joint optimization of wireless charging station location and operation scheduling for electric buses under uncertain demand}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on AI in counter-terrorism and cybersecurity: Challenges and ethical dimensions. ACCESS, 13, 91740-91764. (www)

  @article{ , abstract = {The advanced capabilities in threat detection and mitigation brought about by the rapid development of Artificial Intelligence (AI) significantly impact the fight against terrorism and cybersecurity threats. However, critical concerns such as algorithmic bias, data quality limitations, and governance challenges introduce significant obstacles to their deployment. This paper provides a comprehensive overview of AI methodologies, such as predictive analytics, Natural Language Processing (NLP), and machine learning architectures (e.g., Support Vector Machines – SVM and Long Short-Term Memory – LSTM), and optimization algorithms (e.g., Particle Swarm Optimization – PSO), assessing their effectiveness in security applications. Additionally, AI-based approaches to surveillance, misinformation management, and anomaly detection are explored, focusing on their impacts on national security. Beyond the technical aspects, the paper highlights ethical concerns and policy issues, putting forward frameworks such as Explainable Artificial Intelligence (XAI) and crowdsourced intelligence (Crosint) to ensure the responsible and transparent deployment of AI. The integration of the technical, ethical, and operational perspectives addressed in this research contributes to a holistic understanding of the potential of AI in cybersecurity, while also ensuring adherence to AI governance standards.}, archive = {J_ACCESS}, author = {Ioannis Syllaidopoulos and Klimis S. Ntalianis and Ioannis Salmon}, doi = {10.1109/ACCESS.2025.3572348}, journal = {IEEE Access}, pages = {91740-91764}, shortjournal = {IEEE Access}, title = {A comprehensive survey on AI in counter-terrorism and cybersecurity: Challenges and ethical dimensions}, volume = {13}, year = {2025}, }
• (2025). MNEMOSINE: A modular framework to monitor interaction in multimodal interactive spaces. ACCESS, 13, 91724-91739. (www)

  @article{ , abstract = {The evolution of human-machine interfaces has resulted in the proposal of increasingly complex multimodal solutions, encompassing a diverse set of input and output modalities, towards more natural and efficient interaction paradigms. Supporting the required range of interaction possibilities entails the development of complex systems which, as the number of features and involved technologies grows, pose challenges to research and development. This arises from the inherent difficulty in understanding how the system is working and how specific modules are behaving, important during development, integration, and testing, but also during deployment of these systems for evaluation by users, whether in a lab or in real environments. For these scenarios, approaches based on conventional logging or post-task questionnaires are no longer viable. In response, a first stage to tackle these challenges is proposed based on a systematic approach to monitoring interaction in these multimodal architectures. To this end, this work presents and illustrates the use of MNEMOSINE, the first instantiation of a framework offering real-time monitoring capabilities and event storage mechanisms for multimodal architectures. MNEMOSINE can serve as grounds for developers and researchers to get a structured view over current system status and creates the conditions supporting the analysis of system behavior over time. This aims to enhance the understanding and supervision of interactions within multimodal systems and can, in turn, foster improved system development and refinement, subsequently supporting the integration of increasingly multimodal systems into daily life.}, archive = {J_ACCESS}, author = {Fábio Barros and Bernardo Marques and António Teixeira and Samuel Silva}, doi = {10.1109/ACCESS.2025.3573184}, journal = {IEEE Access}, pages = {91724-91739}, shortjournal = {IEEE Access}, title = {MNEMOSINE: A modular framework to monitor interaction in multimodal interactive spaces}, volume = {13}, year = {2025}, }
• (2025). Sensor and actuator fault-tolerant fuzzy adaptive control for fractional-order strict feedback nonlinear systems subject to input saturation. ACCESS, 13, 91706-91723. (www)

  @article{ , abstract = {This paper introduces a fault-tolerant fuzzy adaptive controller for a specific class of strict-feedback fractional-order nonlinear systems. Systems are susceptible to actuator and sensor failures, input saturation, and external disturbances. The proposed control scheme effectively addresses four types of faults that affect sensors and actuators, including additive faults such as bias, drift, and loss of accuracy, as well as the multiplicative fault of loss of effectiveness. In contrast to the complexity associated with the back-stepping technique, the proposed approach relies on state transformation. Fuzzy logic systems (FLS) are incorporated to approximate unknown smooth nonlinear functions. A tracking-error observer is introduced to estimate unknown new states, and the properties of the reciprocal function and the strictly positive real function (SPR) are leveraged to overcome the unavailability of original state variables. Stability analysis of the closed-loop system is conducted using the Lyapunov theory. The results of the numerical simulation are presented to validate the feasibility and effectiveness of the proposed controller.}, archive = {J_ACCESS}, author = {Islem Daoudi and Zoubir Rabahi and Mohamed Chemachema and Sofiane Bououden and Ilyes Boulkaibet and Bilel Neji}, doi = {10.1109/ACCESS.2025.3572857}, journal = {IEEE Access}, pages = {91706-91723}, shortjournal = {IEEE Access}, title = {Sensor and actuator fault-tolerant fuzzy adaptive control for fractional-order strict feedback nonlinear systems subject to input saturation}, volume = {13}, year = {2025}, }
• (2025). Lower limb movement recognition based on a hybrid deep learning model using surface electromyography. ACCESS, 13, 91693-91705. (www)

  @article{ , abstract = {In the fields of intelligent rehabilitation and human-computer interaction, the accurate recognition of lower limb motion patterns is crucial for achieving efficient assistance and precise control. This study proposes a TCN-Transformer-LSTM (TTLNet) model based on multi-module fusion for lower limb motion recognition. By integrating the advantages of three advanced architectures—Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Transformer—the model constructs an efficient framework for lower limb motion recognition. First, surface electromyography (sEMG) signals from five types of lower limb movements (i.e., walking, standing, sitting, ascending stairs, and descending stairs) are collected and preprocessed. Then, six time-domain features are extracted from the preprocessed sEMG signals, including integrated electromyography (iEMG), variance of electromyography (VAR), waveform length (WL), zero crossing (ZC), slope sign change (SSC), and Willison amplitude (WAMP), which serve as inputs for the TTLNet model. Comparisons are made with TCN, LSTM, convolutional neural networks (CNN), and Support Vector Machine (SVM) models. Experimental results demonstrate that TTLNet achieves a mean accuracy of 98.82%, significantly outperforming TCN by 2.50%, LSTM by 4.46%, CNN by 3.24%, and SVM by 5.28%. Through systematic experiments, this study not only validates the superior performance of TTLNet in handling complex motion patterns but also provides an efficient and precise solution for lower limb motion recognition in the fields of intelligent rehabilitation and human-computer interaction.}, archive = {J_ACCESS}, author = {Yonglin Han and Qing Tao}, doi = {10.1109/ACCESS.2025.3571395}, journal = {IEEE Access}, pages = {91693-91705}, shortjournal = {IEEE Access}, title = {Lower limb movement recognition based on a hybrid deep learning model using surface electromyography}, volume = {13}, year = {2025}, }
• (2025). A prognostic framework for rotating machines considering multi-component fault scenarios. ACCESS, 13, 91682-91692. (www)

  @article{ , abstract = {The importance of preventing failures in rotating machines has led to extensive research on diagnosis and prognosis methods based on vibration data. To achieve a high generalizability for these solutions, they need to handle high discrepancies between the data used for developing the method and the data from the target machine, a lack of historical events from the target machine and multi-component fault scenarios. Currently, state-of-the-art research has successfully found solutions targeting the first two challenges. However, these almost exclusively focus on single-component fault scenarios, meaning it is unclear how a method should be constructed considering a multi-component system. Therefore, this study constructs a framework for multi-component fault scenarios called Rotating Machinery Prognostic Framework (RoMaP) that leverages the advancements in anomaly detection, fault diagnosis and RUL prediction based on vibration data. To evaluate the potential of RoMaP, an instance based on state-of-the-art research was implemented and compared to other methods based on alternative frameworks. The results show that the instance of RoMaP had the best performance across many scenarios expected in an industrial environment, suggesting that it is a suitable approach for monitoring the health of rotating machines.}, archive = {J_ACCESS}, author = {Adam Lycksam and Mattias O’Nils and Faisal Z. Qureshi}, doi = {10.1109/ACCESS.2025.3572582}, journal = {IEEE Access}, pages = {91682-91692}, shortjournal = {IEEE Access}, title = {A prognostic framework for rotating machines considering multi-component fault scenarios}, volume = {13}, year = {2025}, }
• (2025). Enhancing early exit performance with uncertainty-aware training in convolutional neural networks for image classification. ACCESS, 13, 91671-91681. (www)

  @article{ , abstract = {Deep Neural Networks have become more and more complex over the past years delivering efficient results but demanding more requirements for resources. The traditional cloud-based DNN inference produces poor real-time performance due to its high latency requirement in the network. Nowadays, fog and edge have been popular, where computation occurs near the data source or between the source and the cloud. High usage of computational resources by DNN incurs limited room for its deployment in such resource-constrained devices. Deep Neural Networks with multiple exit architecture are an optimal approach to preserve time and computational resources by predicting results at an early stage using multiple early exits to support Edge and Fog Intelligence. Early exit, although lightweight and energy efficient, mostly exhibits degraded performance compared to later exits due to the count of samples that are misclassified. To overcome the downfall, we propose uncertainty estimation in the loss function as an additional parameter for classification. Observational results on CIFAR, MNIST, human lung CT scan, and brain MRI datasets using VGG-16, Lenet-5, and Resnet architectures exhibit that our proposed model is better than the existing models to improve performance as well as prediction confidence in Deep Learning Models with early exits.}, archive = {J_ACCESS}, author = {Thanu Kurian and Somasundaram Thangam}, doi = {10.1109/ACCESS.2025.3572415}, journal = {IEEE Access}, pages = {91671-91681}, shortjournal = {IEEE Access}, title = {Enhancing early exit performance with uncertainty-aware training in convolutional neural networks for image classification}, volume = {13}, year = {2025}, }
• (2025). Framework for implementing quantum neural networks in wireless communications. ACCESS, 13, 91655-91670. (www)

  @article{ , abstract = {While quantum computing is already being employed in different domains to solve large scale and highly complex optimization problems, the wireless communications community has only recently begun exploring the potential of Quantum Machine Learning (QML). This paper provides a reference design approach that allows the wireless community to effectively leverage QML in optimizing their own wireless communications networks. Such an approach will become increasingly valuable as quantum computing matures and conventional computing methods reach their practical limits due to the high dimensionality of optimization problems. We present a detailed methodology enabling wireless communications practitioners to construct their own Quantum Neural Networks (QNN) implementations and benchmark their performance against conventional techniques. Optimization strategies for wireless communication channels using QNNs are explored. The resulting framework provides a generalizable, easy-to-adopt methodology for integrating QML into wireless networks, requiring minimal prior knowledge of quantum computing.}, archive = {J_ACCESS}, author = {Salik Siddiqui and Justin Holzer and Joshua Malcarne and Galahad M. B. Wernsing and Alexander M. Wyglinski}, doi = {10.1109/ACCESS.2025.3572416}, journal = {IEEE Access}, pages = {91655-91670}, shortjournal = {IEEE Access}, title = {Framework for implementing quantum neural networks in wireless communications}, volume = {13}, year = {2025}, }
• (2025). Automatic timbre transformation using enhanced diffusion model. ACCESS, 13, 91642-91654. (www)

  @article{ , abstract = {We present a novel timbre transfer model that uses an enhanced diffusion architecture to convert music from various instruments into Erhu timbre. The Erhu, a traditional Chinese instrument, is difficult to simulate due to its rich vibrato and smooth note transitions. Existing Musical Instrument Digital Interface systems struggle to capture its nuanced dynamics. Our model integrates a Pitch Encoder, a Loudness Encoder, and a Diffusion Decoder. The encoders extract pitch features and dynamic loudness variations, guiding the decoder in generating realistic Erhu timbre. By extracting general musical features, the system generalizes to unseen input types without retraining. Evaluations based on pitch accuracy, cosine similarity, and Fréchet Audio Distance show that our model achieves 96% pitch accuracy and high fidelity in Erhu timbre reproduction. This study demonstrates the potential of diffusion-based timbre transfer models in music generation and provides new directions for future work on both music generation and timbre transfer.}, archive = {J_ACCESS}, author = {Cheng-Han Wu and Pimpa Cheewaprakobkit and Timothy K. Shih and Yu-Cheng Lin and Bing-Ze Liu}, doi = {10.1109/ACCESS.2025.3570124}, journal = {IEEE Access}, pages = {91642-91654}, shortjournal = {IEEE Access}, title = {Automatic timbre transformation using enhanced diffusion model}, volume = {13}, year = {2025}, }
• (2025). Neuro-mimetic developmental architecture for continual learning through self-organizing multimodal perception coordination. ACCESS, 13, 91625-91641. (www)

  @article{ , abstract = {Developing cognitive capabilities in autonomous agents stands as a challenging yet pivotal task. In order to construct cohesive representations and extract meaningful insights from the environment, the brain utilizes patterns from a variety of sensory inputs, including vision and sound. Furthermore, related sensory inputs can activate each other’s representations, highlighting the brain’s ability to associate and integrate information across different sensory channels – key to cognitive development and adaptive learning. Current learning methods often mirror the developmental process of infants, who enhance their cognition through guidance and exploration. These methods often struggle with issues such as catastrophic forgetting and stability-plasticity trade-offs. This study presents a novel brain-inspired hierarchical autonomous framework, Cognitive Deep Self-Organizing Neural Network (CDSN), designed to enable autonomous agents to acquire object concepts dynamically. The architecture includes dual parallel audio-visual information pathways, incorporating three layers based on a Topological Kernel CIM-based Adaptive Resonance Neural Network (TC-ART). The first layer, referred to as the receptive layer, learns and organizes visual attributes and object names autonomously in an unsupervised manner. Subsequently, the second layer, the concept layer, distills clustered results from the corresponding receptive layer to create succinct symbol representation. In a synchronized manner, visual and auditory concepts are combined concurrently in the third layer, the associative layer, to establish real-time associative connections between the modalities. Furthermore, this layer introduces a top-down response approach, allowing agents to independently retrieve associated modalities and adapt acquired knowledge in a hierarchical manner. Experimental evaluations conducted on object datasets demonstrate the proposed architecture’s efficacy in online learning and the association of object viewpoints and labels. Our approach achieves superior recall accuracy, with rates of 92.25% for visual recall and 92.45% for auditory recall. A real-world simulation using humanoid platform further validate proposed neuromimetic architecture’s capabilities.}, archive = {J_ACCESS}, author = {Farhan Dawood and Naoki Masuyama and Chu Kiong Loo}, doi = {10.1109/ACCESS.2025.3558124}, journal = {IEEE Access}, pages = {91625-91641}, shortjournal = {IEEE Access}, title = {Neuro-mimetic developmental architecture for continual learning through self-organizing multimodal perception coordination}, volume = {13}, year = {2025}, }
• (2025). Hybrid MIMO antennas comprising ultra- wideband frame conjoined two-antenna elements in 3.3-8.4 GHz and low-profile backcover four-antenna module in 6.425-8.4 GHz for 5G/6G smartphones. ACCESS, 13, 91611-91624. (www)

  @article{ , abstract = {Each generation of mobile communications introduces new frequency spectrums and new communication systems to increase user’s experienced data rates. For instance, the fifth-generation (5G) communication introduces the $4\times 4$ multi-input-multi-output (MIMO) system (4 receive antennas for 4 spatial streams) for mobile devices such as the smartphone, especially in 3.3-5.0 GHz in the mid-band, to achieve increased data rates for the user. Also, the MIMO antennas are generally deployed along the frame of the smartphone. For future six-generation (6G) communications, the new spectrum of 6.425-8.4 GHz in the upper mid-band is envisioned as a potential 6G prime mobile spectrum. Additionally, higher-order MIMO operations such as the $8\times 4$ MIMO (eight receive antennas for four spatial streams) are promising for 6G smartphones to achieve increased data rates for the user. In this study, to conveniently employ 5G mid-band/6G upper mid-band MIMO antennas in future smartphones, we introduce for the first time hybrid MIMO antennas comprising 4 frame antennas provided by two ultra-wideband (UWB) frame conjoined two-antenna (FC2A) elements in 3.3-8.4 GHz and four backcover antennas provided by a low-profile backcover four-antenna (B4A) module or modular MIMO antennas in 6.425-8.4 GHz. The four frame antennas not only operate for 5G mid-band $4\times 4$ MIMO in 3.3-5.0 GHz but also can combine with four backcover antennas to operate for 6G upper mid-band $8\times 4$ MIMO in 6.425-8.4 GHz. We address details of the hybrid MIMO antennas formed by the FC2A element and B4A module in this study. We also apply the fabricated hybrid MIMO antennas in the mid-band $4\times 4$ and upper mid-band $8\times 4$ MIMO systems in the outdoor field test to evaluate their MIMO performance for 5G/6G smartphones. The results obtained demonstrate that the hybrid MIMO antennas are promising for the deployment of 5G/6G MIMO antennas in future smartphones.}, archive = {J_ACCESS}, author = {Kin-Lu Wong and Chia-Ying Wu and Wan-Ting Li and Wei-Yu Li}, doi = {10.1109/ACCESS.2025.3573721}, journal = {IEEE Access}, pages = {91611-91624}, shortjournal = {IEEE Access}, title = {Hybrid MIMO antennas comprising ultra- wideband frame conjoined two-antenna elements in 3.3-8.4 GHz and low-profile backcover four-antenna module in 6.425-8.4 GHz for 5G/6G smartphones}, volume = {13}, year = {2025}, }
• (2025). Machine learning for earthquake emergency evacuation: Site selection and neighborhood navigation. ACCESS, 13, 91595-91610. (www)

  @article{ , abstract = {This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. Tehran, with a population of 8.7 million, is located on multiple active faults, where 258 out of its 373 neighborhoods lack sufficient emergency evacuation centers. This inadequacy poses a significant risk during emergencies, potentially leading to failures in the evacuation process. By employing an artificial neural network model informed by data from San Francisco—a city with similar seismic risks, urban structure, building types, and population density—this study achieved a 23% reduction in the average minimum distance from each parcel to the nearest emergency evacuation center compared to the current conditions. Additionally, per capita adequacy at the neighborhood level increased from 27% to 59%. The second goal of the research is to streamline the emergency evacuation process through the development of an application that dynamically navigates users to the nearest evacuation center identified by the model. This application is built using OpenStreetMap and the OpenRouteService routing algorithm. With the successful achievement of both goals, the emergency evacuation process in Tehran is now smarter and more comprehensive. The model here can also be applied to other cities that are similar to the cities studied in terms of site-selecting criteria. For cities with less similarity, the model needs to be slightly trained with the data of the target city to achieve desirable results.}, archive = {J_ACCESS}, author = {Amirmasoud Amiran and Behrouz Behnam and Sanaz Seyedin}, doi = {10.1109/ACCESS.2025.3573207}, journal = {IEEE Access}, pages = {91595-91610}, shortjournal = {IEEE Access}, title = {Machine learning for earthquake emergency evacuation: Site selection and neighborhood navigation}, volume = {13}, year = {2025}, }
• (2025). Leveraging complex-valued federated learning for accurate and privacy-respectful threat detection based on millimeter-wave imaging. ACCESS, 13, 91581-91594. (www)

  @article{ , abstract = {Millimeter-wave (MMW) radar systems are widely recognized for their affordability, safety, and effectiveness in concealed object (CO) detection. This paper introduces a novel high-resolution pseudo-image-based CO detection framework that leverages complex-valued convolutional neural networks (CV-CNNs) and their federated learning extension (CV-FL) to enhance detection accuracy while preserving user privacy. Additionally, a decision-level fusion (DLF) framework is proposed to further improve CO detection performance. This approach provides a highly accurate and privacy-preserving solution for CO detection using high-resolution MMW radar. The proposed framework preserves privacy by processing complex-valued (CV) data locally to generate CV pseudo-images, which are not transmitted out by leveraging CV-FL, maintaining high detection accuracy and convergence efficiency. In this study, MMW data are collected using a commercial handheld scanner (Vayyar’s ECS2000), focusing on localized scans of the human body. The collected data are CV high-resolution local 3D pseudo-images over a volume of $13\times 13 \times 10$ cm with spatial resolutions of 1.6 mm, 1.6 mm, and 4.3 mm in the x, y, and z directions, respectively. Extending a cutting-edge real-valued (RV) CO detection framework to its CV-FL version and following by a DLF module, this paper utilizes the mentioned data to enhance detection accuracy and enabling collaborative AI training while preserving data privacy. The proposed method provides a valuable proof-of-concept for integrating CV-FL into privacy-preserving security systems and lays the foundation for broader applications beyond detection tasks. Experimental results reveal a significant performance improvement of up to 17.9% in detecting both metallic and non-metallic objects.}, archive = {J_ACCESS}, author = {Hadi Mahdipour and Jaime Laviada and Fernando Las-Heras Andrés and Mehdi Sookhak}, doi = {10.1109/ACCESS.2025.3573848}, journal = {IEEE Access}, pages = {91581-91594}, shortjournal = {IEEE Access}, title = {Leveraging complex-valued federated learning for accurate and privacy-respectful threat detection based on millimeter-wave imaging}, volume = {13}, year = {2025}, }
• (2025). Demand response potential of detached houses in finnish building stock for integration of variable renewable power generation and reserve power limitation. ACCESS, 13, 91564-91580. (www)

  @article{ , abstract = {With a rapid increase in variable renewable electricity generation such as wind power and solar photovoltaics, a power system must simultaneously incorporate flexible resources to maintain power balance. Detached houses, covering 34% of the Finnish built floor area, can be considered to include a thermal storage within their structure and indoor air due to their thermal inertia. In addition, a domestic hot water tank may be operated such that part of it is utilized as a thermal storage. Moreover, since a large part of the detached houses utilize electricity as an energy source for their heating, either directly (28%) or indirectly with heat pumps (15%), these buildings may be utilized as demand response units within the power system. Thus, this study analyzed how the demand response of detached houses in Finland can increase the utilization of variable renewable power generation while mitigating the required reserve power capacity and energy in the power system. Furthermore, the effect of different wind and solar power generation capacities was analyzed together with several scenarios for the Finnish building stock. It was found that during the analyzed six-year period, the demand response was able to lower the required additional power source capacity significantly by 0.44 to 0.62 GW (10.0-13.1%) and the energy reasonably, 1.41 to 2.23 TWh (2.5–11.6%) by increasing the utilization of renewables. Noting that the high shares of energy reductions were due to very high share of renewables. These reductions corresponded to emission reductions of 0.51 to 0.80 Mt-CO2 (1.1–2.4%).}, archive = {J_ACCESS}, author = {Ilkka Jokinen and Matti Lehtonen and Juha Jokisalo and Janne Hirvonen and Risto Kosonen}, doi = {10.1109/ACCESS.2025.3573451}, journal = {IEEE Access}, pages = {91564-91580}, shortjournal = {IEEE Access}, title = {Demand response potential of detached houses in finnish building stock for integration of variable renewable power generation and reserve power limitation}, volume = {13}, year = {2025}, }
• (2025). Outlier traffic flow detection and pattern analysis under unplanned disruptions: A low-rank robust decomposition model. ACCESS, 13, 91552-91563. (www)

  @article{ , abstract = {Outlier traffic flow detection under unplanned disruptions is vital for operational safety and management. Though numerous models have been proposed to effectively detect outlier traffic flow in previous studies, it is also essential to simultaneously estimate the traffic flow change caused by the unplanned disruption since the information supports the dispatch of traffic resources. To fill this research gap, the study aims to decompose the observed traffic flow matrix under unplanned disruptions into a regular flow matrix and an outlier flow matrix (i.e., flow change matrix). Specifically, we develop a low-rank robust decomposition model by formulating the decomposition problem as a convex optimization problem to minimize the recovering errors of the regular flow and flow change from the observed flow. We further consider the random fluctuations of regular flow and restrict the recovering errors concerning the magnitude of random fluctuation. The accelerated proximal gradient solution algorithm is applied to obtain the decomposition results. The spatiotemporal patterns of detected outlier traffic flow are analyzed at a station level. Extensive experiments are conducted to validate the model using synthetic and real-world data from a Special Administrative Region of China. The results show that the proposed model consistently outperforms the baseline model and is more robust to traffic flow noise during rush hours. The outlier flow pattern analysis shows that the outlier entry flow and exit flow exhibit four and five patterns at a station level, which is mainly influenced by the characteristics of unplanned disruptions and the response strategies, including the blocked segment, location, time, and such as shuttle bus, detrainment, temporal closure of the stations. The findings enable operators to make proactive preparations when a new disruption occurs.}, archive = {J_ACCESS}, author = {Zhipeng Duan}, doi = {10.1109/ACCESS.2025.3573642}, journal = {IEEE Access}, pages = {91552-91563}, shortjournal = {IEEE Access}, title = {Outlier traffic flow detection and pattern analysis under unplanned disruptions: A low-rank robust decomposition model}, volume = {13}, year = {2025}, }
• (2025). Leverage sampling methods and deep neural networks for fuzzer CAN bus message detection. ACCESS, 13, 91533-91551. (www)

  @article{ , abstract = {The Controller Area Network (CAN) is crucial for automotive safety, yet remains vulnerable to various fuzzing attacks that can compromise vehicle operations. This paper presents a comprehensive detection framework that identifies both common CAN vulnerabilities (DoS, Spoofing, Replay, and general Fuzzing) and specific fuzzer attack types (identity, replay, random, brute force, and mutation-based) using deep learning-based models. We evaluate four recurrent neural network architectures, including standard RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), across three CAN datasets: the publicly available Hacking Car and OTIDS datasets, along with our proprietary Multi-FuzzerCAN dataset. To address the inherent class imbalance in attack data, we implement targeted random oversampling and random undersampling techniques tailored to each dataset’s distribution characteristics, significantly improving model performance. Our experimental results demonstrate that all four models achieve exceptional accuracy, recall, precision, and F1 scores across all datasets, with LSTM and BiLSTM architectures consistently outperforming other approaches. These findings establish the viability of combining deep learning models with data balancing techniques to enhance the resilience of automotive CAN networks against diverse cyber threats, especially multi-fuzzer attacks.}, archive = {J_ACCESS}, author = {Thi-Thu-Huong Le and Yeonjeong Hwang and Junyoung Son and Howon Kim}, doi = {10.1109/ACCESS.2025.3572573}, journal = {IEEE Access}, pages = {91533-91551}, shortjournal = {IEEE Access}, title = {Leverage sampling methods and deep neural networks for fuzzer CAN bus message detection}, volume = {13}, year = {2025}, }
• (2025). Extraction of periodic characteristics and long-term stock price forecasting using non-harmonic analysis with over 14 years of NASDAQ data before and after COVID-19. ACCESS, 13, 91524-91532. (www)

  @article{ , abstract = {Stock price forecasting is a critical challenge in financial markets, and methods capable of accurately capturing the complex long-term dynamics of the market remain underdeveloped. Moreover, traditional methods such as Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) suffer from time-frequency resolution trade-offs. To address these challenges, this study applies Non-Harmonic Analysis (NHA) to extract periodic components from the NASDAQ Composite Index over the past 14.5 years. This novel approach provides a more accurate representation of stock price periodicities. By applying bandpass filtering and multi-windowing techniques, this study identifies key periodic components ranging from one week to four years. Furthermore, an Autoregressive Moving Average (ARMA) model is employed to predict the amplitudes of these periodic components, enabling the construction of long-term price waveforms. Compared with conventional techniques, including Ichinose’s method, Long Short-Term Memory (LSTM) networks, and the autoregressive integrated moving average (ARIMA) model, the proposed method demonstrates a superior predictive performance with lower Mean Absolute Percentage Error (MAPE). These findings provide new insights into stock market dynamics and suggest broader applications of NHA in financial time-series analysis.}, archive = {J_ACCESS}, author = {Yuki Kojima and Li Ma and Keisuke Nomoto and Masaya Hasegawa and Shigeki Hirobayashi}, doi = {10.1109/ACCESS.2025.3571589}, journal = {IEEE Access}, pages = {91524-91532}, shortjournal = {IEEE Access}, title = {Extraction of periodic characteristics and long-term stock price forecasting using non-harmonic analysis with over 14 years of NASDAQ data before and after COVID-19}, volume = {13}, year = {2025}, }
• (2025). A hybrid MLP and CNN architecture for sequential recommendation. ACCESS, 13, 91515-91523. (www)

  @article{ , abstract = {With the proliferation of online platforms, recommendation systems have become essential tools for providing relevant information to users. Traditional recommendation methods often rely on a single user interest, making it difficult to capture the diversity and dynamic nature of user preferences. In recent years, multi-interest sequential recommendation methods have shown promising results by leveraging temporal information in user behavior sequences. In this context, we propose a novel multi-interest sequential recommendation framework that effectively captures both long-term and short-term user interests. Specifically, we combine Multi-Layer Perceptron (MLP) and Convolutional Neural Networks (CNN) to model users’ global long-term interests and recent dynamic behaviors, respectively. Meanwhile, we introduce a multi-interest fusion mechanism to integrate these diverse interest features into a more comprehensive user representation. This framework better adapts to the evolving nature of user interests over time, thus improving both recommendation accuracy and interpretability. Experimental results demonstrate the effectiveness of the proposed model across two datasets. Further ablation studies reveal that long-term interest modeling, short-term interest extraction, and the multi-interest fusion mechanism are crucial for enhancing the model’s performance.}, archive = {J_ACCESS}, author = {Li Yuan and Xue-Yi Zhao}, doi = {10.1109/ACCESS.2025.3569820}, journal = {IEEE Access}, pages = {91515-91523}, shortjournal = {IEEE Access}, title = {A hybrid MLP and CNN architecture for sequential recommendation}, volume = {13}, year = {2025}, }
• (2025). CAD-ViT: Coordinate attention-enhanced vision transformer with dilated feature fusion for diabetic retinopathy staging. ACCESS, 13, 91505-91514. (www)

  @article{ , abstract = {Automatic Diabetic Retinopathy classification is crucial as it aids in determining the disease stage, enabling timely treatment to prevent progression to low vision and significantly reducing the diagnostic burden on ophthalmologists. Efficient and accurate network models are essential for clinical applications. In this paper, we propose a deep learning method for DR diagnosis and grading based on multi-scale feature fusion and attention guidance. This approach employs dilated convolutions with varying dilation rates to expand the receptive field and integrate features at multiple scales. Additionally, an attention mechanism based on spatial directional feature aggregation is embedded in the inverted residual modules to guide the model to focus on critical lesion features in fundus images. Furthermore, transformers are utilized to extract global features, enhancing model performance. Experiments conducted on the APTOS-2019 and DDR datasets demonstrate that our method achieves a diagnostic accuracy of 99.2% and grading accuracy of 88.6% on the APTOS-2019 dataset, and a grading accuracy of 84.8% on the DDR dataset. Experimental results show that the proposed method performs excellently and outperforms many recently proposed popular DR diagnosis and classification methods.}, archive = {J_ACCESS}, author = {Ye Wang and Xiaofang Gou and Wenman Li}, doi = {10.1109/ACCESS.2025.3572455}, journal = {IEEE Access}, pages = {91505-91514}, shortjournal = {IEEE Access}, title = {CAD-ViT: Coordinate attention-enhanced vision transformer with dilated feature fusion for diabetic retinopathy staging}, volume = {13}, year = {2025}, }
• (2025). Digital twin technology architecture and application for nuclear reactor intelligent operation and maintenance. ACCESS, 13, 91494-91504. (www)

  @article{ , abstract = {In order to improve the safety and reliability of nuclear reactors, and to enhance the digitalization and intelligence level of operation and maintenance management, a digital twin technology architecture for nuclear reactor intelligent operation and maintenance was proposed based on a conceptual analysis of nuclear reactor digital twin technology. Based on the technical implementation framework elements and their mutual relationships, the research on digital twins in nuclear reactor operation management scenarios can establish a process, and from the perspective of technical implementation, based on the number of twin intelligence operations, applications are analyzed. Digital twin has realized the organic integration of cyber space and physical space, effectively supporting nuclear reactor intelligent operations across various scenarios and achieving significant improvements in device efficiency. The research results provide certain guiding significance for the application research of nuclear reactor digital twin technology and the construction of digital twin platform for intelligent operation and maintenance.}, archive = {J_ACCESS}, author = {Zhuo Zhang and Jia Liu and Wei Zeng and Qingyu Huang and Xiaobo Liu}, doi = {10.1109/ACCESS.2025.3570182}, journal = {IEEE Access}, pages = {91494-91504}, shortjournal = {IEEE Access}, title = {Digital twin technology architecture and application for nuclear reactor intelligent operation and maintenance}, volume = {13}, year = {2025}, }
• (2025). The application of kalman filter algorithm in rail transit signal safety detection. ACCESS, 13, 91480-91493. (www)

  @article{ , abstract = {With the speedy prosperity and popularization of wireless communication, the informatization and automation level of rail transit systems is also improved. The rail communication transmission system is also affected by interference from wireless local area networks and rail foreign objects, leading to signal transmission failures. Therefore, research is being conducted to optimize the signal safety detection system for rail transit. Firstly, the improved Kalman filter algorithm is used to denoise the signal to ensure the accuracy of signal transmission. Then, in response to signal transmission failures caused by foreign object intrusion on the track, a track safety detection model based on improved You Only Look Once Version 8 (YOLOv8) is built. The outcomes reveal that the raised improved Kalman filtering has a high similarity between the denoised and the true signal, and a low root mean square error of 0.009. The improved Kalman filter algorithm also has a high signal-to-noise ratio of 2.086dB, a signal-to-noise ratio of 26dB, a reference signal received power of −61dBm, a handover success rate of 99.98%, and a handover delay of 97ms. The proposed improved YOLOv8 algorithm achieved an accuracy of 96.27%, a recall rate of 94.86%, a detection accuracy of 96.82%, a frame rate of 78.12fps, and an error value of 0.002 in the Track Obstacle Recognition Dataset. The experiment outcomes demonstrate the noise reduction and object detection capability of the raised model. The research results contribute to promoting the intelligent development of rail transit systems and improving their safety and operational efficiency.}, archive = {J_ACCESS}, author = {Zhinong Miao and Qilong Liao}, doi = {10.1109/ACCESS.2025.3572717}, journal = {IEEE Access}, pages = {91480-91493}, shortjournal = {IEEE Access}, title = {The application of kalman filter algorithm in rail transit signal safety detection}, volume = {13}, year = {2025}, }
• (2025). RegCDNet: A RegNet-based framework for remote sensing image change detection combining feature enhancement and gating mechanism. ACCESS, 13, 91466-91479. (www)

  @article{ , abstract = {With the rapid development of remote sensing (RS) technology, it has become more and more convenient to obtain multi-temporal RS images, which provides new opportunities for the research and development of change detection (CD) technology. However, existing methods still have shortcomings in recognizing complex targets and accurately distinguishing the edges of change regions when performing CD in complex backgrounds. Therefore, we propose a novel CD model called RegCDNet, which is specifically designed to address the needs of RS image CD. The model employs RegNet as the backbone network for feature extraction, using a simple and efficient strategy to fuse shallow features. We designed an ac-dramit feature enhancement module (AFEM) that combines atrous convolution and a transformer containing a dual attention mechanism, which can efficiently capture long-range dependencies between pixels while focusing on local information. A dual-gated fusion module (DGFM) is also designed, which employs a dual gating mechanism for feature fusion and can dynamically adjust the fusion weights. The experiment achieved 91.22%, 92.84% and 82.52% F1 metrics on the LEVIR-CD, WHU-CD and SYSU-CD datasets, respectively.}, archive = {J_ACCESS}, author = {Chuanlu Li and Xiaorong Xue and Caijia Zeng and Yifan Xu and Xingbiao Xu and Siyue Zhao}, doi = {10.1109/ACCESS.2025.3572928}, journal = {IEEE Access}, pages = {91466-91479}, shortjournal = {IEEE Access}, title = {RegCDNet: A RegNet-based framework for remote sensing image change detection combining feature enhancement and gating mechanism}, volume = {13}, year = {2025}, }
• (2025). A hybrid adaptive fusion deep learning model for fault diagnosis of rotating machinery under noisy conditions. ACCESS, 13, 91451-91465. (www)

  @article{ , abstract = {Rotating machinery is essential in modern industry. A robust noise-resistant method is proposed to improve diagnostic accuracy under intense noise conditions. Initially, time-domain signals are transformed into the time-frequency domain using the Synchrosqueezing Short-Time Fourier Transform to reduce the impact of noise. A novel hybrid adaptive fusion deep learning model is then introduced, incorporating two new modules: the heterogeneous convolution adaptive fusion block and the global-local attention adaptive fusion block. These modules enable the integration of heterogeneous convolution operators and complementary attention mechanisms, optimizing component importance for identifying subtle features despite noise. Additionally, the traditional Multilayer Perceptron in the classification layer is replaced with Kolmogorov-Arnold Networks to improve diagnostic accuracy. Case studies demonstrate that the method has strong noise resistance under challenging signal-to-noise ratio conditions.}, archive = {J_ACCESS}, author = {Junyu Ren and Soo Siang Teoh}, doi = {10.1109/ACCESS.2025.3572902}, journal = {IEEE Access}, pages = {91451-91465}, shortjournal = {IEEE Access}, title = {A hybrid adaptive fusion deep learning model for fault diagnosis of rotating machinery under noisy conditions}, volume = {13}, year = {2025}, }
• (2025). An uncertainty aware optimal energy management model for smart distribution networks contemplating reactive support from VRE and energy storage systems. ACCESS, 13, 91423-91450. (www)

  @article{ , abstract = {This paper proposes a new stochastic multi-objective optimal energy management model named SMO-OEM model for techno-economic operations of smart distribution network (SDN) under uncertainty. A typical SDN is integrated with various generating resources such WTs, PVs, DGs, BESS, and utility grid to meet ever increasing and uncertain energy demands. A scenario-based analysis is utilized for handling the uncertainty allied with renewable energy generation (PVs and WTs), load power demand, and utility grid prices. In the first phase of the proposed model, several initial scenarios are generated with respect to day-ahead forecasts of PVs, WTs, load demand, grid prices using Monte-Carlo simulations and subsequently reduced them to finalize the input test scenarios for next phase. Thereafter, in the second phase, two conflicting objectives i.e. expected total operational cost ( $EF_{TC}$ ), and the expected total active power loss ( $EF_{TPL}$ ) are optimized simultaneously. The proposed SMO-OEM model recommends the further reactive support acquired from WTs, PVs, and BESS along with a demand response program (DRP) for optimum SDN operations. The proposed model is applied to two distinct sized networks i.e. modified IEEE-33 and IEEE-69 bus distribution networks and examined for different uncertainty ranges of ±5%, ±10%, and ±20% with respect to a day-ahead forecasted uncertain variables. Three comprehensive case studies are presented for detailed model assessments and comparisons under different uncertainty ranges. It is found that significant reductions are achieved in both $EF_{TC}$ and $EF_{TPL}$ by the recommended supplementary reactive management through WTs, PVs, and BESS. Additionally, DRP scheme is also applied at few locations to offer peak load reductions by shifting them to other timings over a period of 24 hours which further reduces both objectives ( $EF_{TC}$ and $EF_{TP}$ ). These recommendations are found suitable to significantly improve the bus voltage profile as well as economic operations.}, archive = {J_ACCESS}, author = {Gaurav Gangil and Amit Saraswat and Sunil Kumar Goyal}, doi = {10.1109/ACCESS.2025.3573197}, journal = {IEEE Access}, pages = {91423-91450}, shortjournal = {IEEE Access}, title = {An uncertainty aware optimal energy management model for smart distribution networks contemplating reactive support from VRE and energy storage systems}, volume = {13}, year = {2025}, }
• (2025). Defect detection algorithm for electrical substation equipment based on improved YOLOv10n. ACCESS, 13, 91409-91422. (www)

  @article{ , abstract = {Electrical substations are fundamental infrastructure components for urban operations and civilian life. Many substations located in remote areas face challenges in monitoring equipment defects because robots or drones cannot effectively access these substations for direct inspection. Addressing the shortcomings of current algorithms in small target detection, complex background processing, and the balance between real-time and accuracy, this paper proposes several key improvements to the YOLOv10n framework to address these limitations. First, we design a novel attention mechanism module that enhances model focus on target objects under adverse weather conditions. Second, we modify the SPPF module by incorporating average pooling operations to compensate for feature information extraction deficiencies caused by maximum pooling operations. Third, we develop an improved C2f-OD module to strengthen the backbone network’s feature extraction capabilities. Finally, we implement Focal EIoU as the loss function to accelerate convergence and minimize losses. Our method can effectively identify and detect different substation equipment defects. Experimental results demonstrate a detection accuracy of 96.64% across six equipment categories. These results confirm the effectiveness of our proposed method, which helps reduce the cost of equipment maintenance and replacement.}, archive = {J_ACCESS}, author = {Qingkai Meng and Tianren Fu and Kangning Li and Long Huang and Shaoqiang Chen}, doi = {10.1109/ACCESS.2025.3570471}, journal = {IEEE Access}, pages = {91409-91422}, shortjournal = {IEEE Access}, title = {Defect detection algorithm for electrical substation equipment based on improved YOLOv10n}, volume = {13}, year = {2025}, }
• (2025). A hybrid CT-DEWCA-based energy-efficient routing protocol for data and storage nodes in underwater acoustic sensor networks. ACCESS, 13, 91392-91408. (www)

  @article{ , abstract = {Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission enhanced by hybrid model scheme with Differential Evolution, Whale Optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a “strategic dual transmission” approach, which employs CT for short-distance data transfer and utilizes the DEWCA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet-loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSNs performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks.}, archive = {J_ACCESS}, author = {Imran Khan and Muhammad Rauf and Atif Jamil}, doi = {10.1109/ACCESS.2025.3573089}, journal = {IEEE Access}, pages = {91392-91408}, shortjournal = {IEEE Access}, title = {A hybrid CT-DEWCA-based energy-efficient routing protocol for data and storage nodes in underwater acoustic sensor networks}, volume = {13}, year = {2025}, }
• (2025). A convolutional neural network with time-aware channel weighting for temporal knowledge graph completion. ACCESS, 13, 91384-91391. (www)

  @article{ , abstract = {Temporal Knowledge Graphs (TKGs) extend traditional knowledge graphs by incorporating a temporal dimension into triples, enabling a more precise modeling of dynamic relationships. However, TKGs often face challenges, such as data sparsity and incomplete information in real-world applications, which can hinder their performance in downstream tasks. To address the limitations of existing static completion models in capturing temporal information, we propose a novel Temporal Knowledge Graph Completion model, Conv-TA (Convolutional Network with Time-Aware Channel Weighting).In this model, temporal embeddings are represented as the concatenation of three independent embeddings: year, month, and date. These embeddings are then processed through linear transformations to generate time-aware weights that dynamically adjust the significance of convolutional output channels. This design allows the model to effectively capture the complex and dynamic characteristics inherent in temporal relationships.We evaluate the effectiveness of the Conv-TA model on publicly available datasets, including ICEWS and GDELT. Experimental results demonstrate that Conv-TA achieves superior performance compared to existing baselines, highlighting its potential in addressing the challenges of temporal knowledge graph completion.}, archive = {J_ACCESS}, author = {Kesheng Zhang and Guige Ouyang and Yongzhong Huang}, doi = {10.1109/ACCESS.2025.3572357}, journal = {IEEE Access}, pages = {91384-91391}, shortjournal = {IEEE Access}, title = {A convolutional neural network with time-aware channel weighting for temporal knowledge graph completion}, volume = {13}, year = {2025}, }
• (2025). YOLOP-MVF: A multi-task autonomous driving perception detection method based on multi scale feature weighted fusion. ACCESS, 13, 91374-91383. (www)

  @article{ , abstract = {To address challenges such as large-scale variations, background interference, and occlusions in multi-task autonomous driving perception, this paper proposes YOLOP-MVF, a multi-task detection framework based on multi-scale feature weighting fusion. The model integrates a sub-pixel 3D fusion module and a triple feature encoding module to enhance the representation of multi-scale features. A multi-scale convolutional attention-weighting mechanism is further introduced to adaptively emphasize critical spatial information. To improve feature extraction flexibility, deformable convolutions are incorporated, enabling dynamic sampling based on input characteristics. Additionally, the Powerful-IoU loss is employed to guide anchor box regression with adaptive penalty and gradient regulation, accelerating convergence. Experimental results on the BDD100K dataset demonstrate that YOLOP-MVF outperforms baseline models, achieving improvements of 1.2% in mIoU, 8.8% in accuracy, and 4.7% in mAP50, validating its effectiveness for robust multi-task perception in complex driving scenarios.}, archive = {J_ACCESS}, author = {Yanqiu Niu and Jing Zhang}, doi = {10.1109/ACCESS.2025.3572331}, journal = {IEEE Access}, pages = {91374-91383}, shortjournal = {IEEE Access}, title = {YOLOP-MVF: A multi-task autonomous driving perception detection method based on multi scale feature weighted fusion}, volume = {13}, year = {2025}, }
• (2025). Improved power transfer efficiency using a dual transmitter receiver design with integrated decoupling coils for IPT EV charging. ACCESS, 13, 91362-91373. (www)

  @article{ , abstract = {In high-power inductive power transfer (IPT) systems, multiple coils are used to enhance efficiency, but interactions between coils on the same side can reduce overall performance. Traditionally, decoupling coils are added to minimize these interactions, however they don’t contribute to power transfer and may even hinder system efficiency. This article introduces a new IPT system and coil structure that integrates decoupling coils, which not only improve mutual interaction between same-side coils but also actively contribute to power transfer. The coils are optimized for high mutual inductance, strong coupling coefficient, reduced self-inductance, and minimized side-by-side mutual inductance, performing well even with X-axis, Y-axis, and angular misalignments. Using 3-D Finite Element Analysis (ANSYS-Maxwell), the magnetic coupler’s performance is thoroughly evaluated, and a downsized experimental setup demonstrates the system’s feasibility. The proposed system achieves a maximum dc-dc efficiency of 94.27%, successfully delivering 1 kW across a 120-mm air gap with zero-voltage switching, presenting a significant advancement for multi-transmitter and multi-receiver systems.}, archive = {J_ACCESS}, author = {Bilal Alam and Saad Mekhilef and Marizan Mubin and Kok Soon Tey and Addy Wahyudie}, doi = {10.1109/ACCESS.2025.3572920}, journal = {IEEE Access}, pages = {91362-91373}, shortjournal = {IEEE Access}, title = {Improved power transfer efficiency using a dual transmitter receiver design with integrated decoupling coils for IPT EV charging}, volume = {13}, year = {2025}, }
• (2025). LQ-MixerNeT: A CNN-transformer deep fusion-based model for object detection in optical remote sensing images. ACCESS, 13, 91347-91361. (www)

  @article{ , abstract = {To address the challenges of low detection accuracy in optical remote sensing images (RSIs) caused by densely distributed targets, extreme scale variations, and insufficient feature representation of small objects, this paper proposes LQ-MixerNeT, a novel CNN-Transformer hybrid framework with deep fusion capabilities. The core innovation lies in the DMIR-Fusion feature integrator, which integrates two key components: the DMI-DWConv Module and the ReLU Linear Attention (RLA) mechanism. This feature integrator dynamically coordinates local high-frequency feature from CNNs and global low-frequency feature from Transformers, effectively overcoming the inherent limitations of unimodal architectures. Furthermore, a frequency-ramping structure is introduced to dynamically regulate the high-frequency/low-frequency information allocation ratio in the DMIR-Fusion integrator across different feature extraction stages through four-channel scaling ratios (1/2, 1/4, 1/8, 1/16). The framework also incorporates an enhanced Asymptotic Feature Pyramid Network (AFPN) and Coordinate Attention (CA) mechanisms, synergistically optimizing spatial-semantic alignment and multi-scale feature representation, thereby significantly improving feature extraction performance. Extensive experiments on three benchmark RSIs datasets (RSOD, NWPU VHR-10 and DIOR) validate the superiority of LQ-MixerNeT. Results demonstrate that our method achieves mAP@0.5 scores of 73.65%, 85.29%, and 83.25%, respectively. Ablation studies reveal that the DMIR-Fusion integrator contributes a 2.02% accuracy improvement, while the enhanced AFPN boosts performance by 0.54%. These findings highlight the model’s robustness in handling complex RSIs scenarios and establish a new paradigm for multimodal fusion in remote sensing object detection.}, archive = {J_ACCESS}, author = {Wenxuan Zheng and Ying Yang}, doi = {10.1109/ACCESS.2025.3569658}, journal = {IEEE Access}, pages = {91347-91361}, shortjournal = {IEEE Access}, title = {LQ-MixerNeT: A CNN-transformer deep fusion-based model for object detection in optical remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Optimizing P-chart performance using a percentage-based framework: Application to nonconforming items in the manufacturing sector. ACCESS, 13, 91334-91346. (www)

  @article{ , abstract = {The control chart approach in industrial processes often faces significant challenges in accurately assessing the in-control (IC) and out-of-control (OC) performance of a process because of the limitations of conventional performance metrics. Traditional methods, such as the average run length (ARL)-based p-chart, may not effectively capture the complexities of run-length (RL) distributions, particularly in sectors with demanding performance standards. This study addresses these challenges by introducing a percentile-based (PB) p-chart approach, which guarantees specific IC and OC performance with predefined probabilities. The proposed approach overcomes the limitations of conventional methods, treating the ARL-based p-chart as a special case within the broader PB framework. By imposing constraints on the RL distribution, it is possible to guarantee predetermined probabilities for both IC and OC performance. This ensures that the IC run length ( $\text {RL}_{\text {IC}}$ ) exceeds the desired value, whereas the out-of-control run length ( $\text {RL}_{\text {OC}}$ ) remains below the desired threshold. The effectiveness of this p-chart scheme is shown through simulations and various numerical examples. The numerical results show that the p-chart based on the proposed scheme outperforms the existing methods and minimizes false alarms. To ease the computation of the optimization used in this design, software support of the proposed approach is provided for public use through a freely accessible R library pbcc. Finally, the implementation procedure of the proposed design is also demonstrated using two real-data examples. The numerical results show that the p-chart based on the proposed scheme outperforms existing methods, with a 150% improvement in the water bottle manufacturing process and a 45% improvement in the cardboard filling and packing process, while also minimizing false alarms.}, archive = {J_ACCESS}, author = {Zahid Khan and Aamir Saghir and Attila I. Katona and Zsolt T. Kosztyán}, doi = {10.1109/ACCESS.2025.3572624}, journal = {IEEE Access}, pages = {91334-91346}, shortjournal = {IEEE Access}, title = {Optimizing P-chart performance using a percentage-based framework: Application to nonconforming items in the manufacturing sector}, volume = {13}, year = {2025}, }
• (2025). PCGOD: Enhancing object detection with synthetic data for scarce and sensitive computer vision tasks. ACCESS, 13, 91325-91333. (www)

  @article{ , abstract = {Object detection models rely on large-scale, high-quality annotated datasets, which are often expensive, scarce, or restricted due to privacy concerns. Synthetic data generation has emerged as an alternative, yet existing approaches have limitations: generative models lack structured annotations and precise spatial control, while game-engine-based datasets suffer from inaccuracies due to 3D bounding box projections, limited scene diversity, and poor handling of articulated objects. We propose PCGOD, an Unreal Engine-based framework that combines photorealistic rendering with comprehensive domain randomization to bridge the synthetic-to-real (sim2real) domain gap. PCGOD employs a marker-based extremity projection method that places markers at key points on object geometries and projects only visible markers to create tight-fitting bounding boxes. For articulated objects, our approach dynamically tracks skeletal pose changes, ensuring annotations adapt to varied configurations. The framework addresses sim2real transfer through six-dimensional randomization: background environments, model textures and poses, landscape textures, weather conditions, camera perspectives, and procedural scene composition. Evaluations using YOLOv11 and Salience-DETR in an object detection task demonstrate that our marker-based approach achieves up to 41.61% improvement in annotation accuracy over conventional methods. Models trained with just 10% real data supplemented by our synthetic data achieve over 80% of the performance of models trained on 100% real data. Moreover, mixed datasets containing 25% synthetic and 75% real data outperform pure real-data training by up to 5.1%. These results confirm that our approach significantly enhances synthetic data utility for object detection, offering an effective solution for domains with limited training data availability.}, archive = {J_ACCESS}, author = {Walid Remmas and Martin Lints and Jaak Joonas Uudmäe}, doi = {10.1109/ACCESS.2025.3572719}, journal = {IEEE Access}, pages = {91325-91333}, shortjournal = {IEEE Access}, title = {PCGOD: Enhancing object detection with synthetic data for scarce and sensitive computer vision tasks}, volume = {13}, year = {2025}, }
• (2025). HPG-tree: An enhanced b+-tree for spatial indexing. ACCESS, 13, 91312-91324. (www)

  @article{ , abstract = {In modern applications such as geographic information systems, game development, and image processing, the demand for efficient two-dimensional spatial indexing is rising exponentially. Traditional approaches like R-tree can suffer from excessive overlap and fragmentation, while standard $B^{+}$ -trees are limited to one-dimensional data. Motivated by these gaps, we propose the hyperbolic paraboloid grid (HPG) tree, an enhanced $B^{+}$ -tree structure specifically tailored for two-dimensional datasets. First, we employ a grid-based strategy that maps two-dimensional points into one-dimensional values without resorting to hashing. By combining grid partitioning with the $B^{+}$ -tree’s efficient, self-balancing properties, the HPG-tree simultaneously addresses overlap, storage utilization, and query performance. Unlike purely one-dimensional indexes, this method preserves spatial locality and avoids excessive rebalancing. Further, the HPG-tree integrates a hyperbolic paraboloid-like framework in its node layout, allowing it to halve the number of index nodes in many cases. This optimization is key to reducing disk or memory scans, thereby accelerating point lookups, range queries, and updates. Our experiments demonstrate that the HPG-tree outperforms comparable state-of-the-art structures like $R^{*}$ -tree, achieving up to a 50% improvement in storage utilization and significant gains in query throughput. HPG-tree opens a promising avenue for modern spatial databases that require both high scalability and sub-second response, making it a robust and flexible option for today’s evolving data-intensive environments. These advantages make HPG-tree particularly well-suited to large-scale spatial databases requiring both high scalability and sub-second responsiveness. Following a detailed discussion of the shortcomings in existing methods, the paper presents the HPG-tree methodology, including its grid partitioning, node layout, and insertion procedures, illustrating why this hybrid strategy stands out as a robust and flexible option for evolving data-intensive environments.}, archive = {J_ACCESS}, author = {Amal Saif and Alaa M. Altarazi and Amer Al-Badarneh and Mohammad J. Abdel-Rahman}, doi = {10.1109/ACCESS.2025.3573152}, journal = {IEEE Access}, pages = {91312-91324}, shortjournal = {IEEE Access}, title = {HPG-tree: An enhanced b+-tree for spatial indexing}, volume = {13}, year = {2025}, }
• (2025). Usability issues with mobile applications: Insights from practitioners and future research directions. ACCESS, 13, 91301-91311. (www)

  @article{ , abstract = {This study is motivated by two key considerations: the significant benefits mobile applications offer individuals and businesses, and the limited empirical research on usability challenges. To address this gap, we conducted structured interviews with twelve experts to identify common usability issues. Our findings highlight the top five concerns related to: information architecture, user interface design, performance, interaction patterns, and aesthetics. In addition, we identify five key directions for future research: usability in AI-powered mobile applications, augmented reality (AR) and virtual reality (VR), multimodal interactions, personalized mobile ecosystems, and accessibility. Our study provides insights into emerging usability challenges and trends, contributing to both the theory and practice of mobile human-computer interaction.}, archive = {J_ACCESS}, author = {Pawel Weichbroth}, doi = {10.1109/ACCESS.2025.3573503}, journal = {IEEE Access}, pages = {91301-91311}, shortjournal = {IEEE Access}, title = {Usability issues with mobile applications: Insights from practitioners and future research directions}, volume = {13}, year = {2025}, }
• (2025). Shuffling augmented decoupled features for multimodal emotion recognition. ACCESS, 13, 91290-91300. (www)

  @article{ , abstract = {Multimodal emotion recognition (MER) aims to identify human emotions using data from multiple modalities. Despite promising advances in previous MER methods, their performance remains limited due to the small size of available datasets, a result of the challenges in collecting multimodal data. While data augmentation can address this issue, generating augmented multimodal data without altering the underlying emotional meaning remains particularly challenging. To tackle this problem, we introduce a decoupled feature augmentation method that automatically learns multimodal feature variations in a decoupled feature space for MER. Specifically, we decompose multimodal features into modality-invariant and modality-specific components and then augment each component within the decoupled feature space across multiple modalities. Unlike existing unimodal augmentation approaches, our method preserves cross-modal semantic consistency by jointly augmenting the decoupled components. To enhance model generalization and stability, we propose a learning strategy that gradually incorporates more diverse information by using a combined set of original and augmented decoupled features. Comprehensive experiments on two MER benchmarks demonstrate that our method outperforms or is comparable to several baseline methods.}, archive = {J_ACCESS}, author = {Sunyoung Cho}, doi = {10.1109/ACCESS.2025.3572925}, journal = {IEEE Access}, pages = {91290-91300}, shortjournal = {IEEE Access}, title = {Shuffling augmented decoupled features for multimodal emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Intelligent robot trajectory tracking control combining mamdani theory and Q-learning algorithm. ACCESS, 13, 91276-91289. (www)

  @article{ , abstract = {This paper aims to develop an intelligent robot trajectory tracking control method with low computational complexity, good generalization ability, and an adaptive parameter adjustment mechanism. Aiming at the limitations of traditional control algorithms in complex environment adaptability and system robustness, a novel trajectory tracking control strategy is designed by combining Mamdani fuzzy theory and the Q-learning algorithm. The research results showed that through adaptive parameter adjustment and reward mechanism, the accuracy rate of the improved Q-learning algorithm reached 98%, the recall rate reached 90%, and converged at the fifth iteration, showing excellent convergence performance. The improved Q-learning algorithm reduced the number of steps in path planning and improved path fluency and efficiency while reducing the control time from 789 ms to 340 ms and improving the fitting accuracy to more than 96%. The research provides a new strategy for intelligent robot trajectory tracking control and promotes its application in a broader range of fields.}, archive = {J_ACCESS}, author = {Qiong Wu and Hua Chen and Baolong Liu}, doi = {10.1109/ACCESS.2025.3572461}, journal = {IEEE Access}, pages = {91276-91289}, shortjournal = {IEEE Access}, title = {Intelligent robot trajectory tracking control combining mamdani theory and Q-learning algorithm}, volume = {13}, year = {2025}, }
• (2025). Development of OCR service for page-level recognition for camera-captured document images. ACCESS, 13, 91263-91275. (www)

  @article{ , abstract = {The emergence of Large Language Models (LLMs) has driven significant advancements in Natural Language Processing (NLP) and introduced new text-related applications, such as Visual Question Answering (VQA). As a result, there is a growing need for Optical Character Recognition (OCR) systems that can extract textual contents from document images for LLM applications. However, most existing methods have primarily focused on scene text or well-structured document images, and typically limit text detection and recognition to the word level. In this paper, we propose a novel OCR framework capable of detecting and recognizing text at both the text-line and text-block levels. Specifically, we design a new deep neural network (DNN) to replace the Connected Component (CC) extraction and state estimation processes used in conventional methods. Despite being trained solely on synthetic datasets, the proposed OCR system performs robust text detection and layout analysis. Furthermore, we propose a recognition metric to evaluate content preservation in OCR systems and introduce a new OCR benchmark consisting of camera-captured document images. Our method demonstrates superior performance on this benchmark, outperforming existing OCR APIs.}, archive = {J_ACCESS}, author = {Junyoung Park and Wonjun Kang and Seonji Park and Keuntek Lee and Hyung Il Koo and Nam Ik Cho}, doi = {10.1109/ACCESS.2025.3572001}, journal = {IEEE Access}, pages = {91263-91275}, shortjournal = {IEEE Access}, title = {Development of OCR service for page-level recognition for camera-captured document images}, volume = {13}, year = {2025}, }
• (2025). Key frame detection in badminton swings and its application to physical education. ACCESS, 13, 91248-91262. (www)

  @article{ , abstract = {The use of video analysis in sports training has revolutionized the way coaches and players evaluate performance and develop strategies. This paper presents a machine learning based approach for key frame detection in badminton swings aimed at improving the learning experience for beginners through visualization and real-time feedback. Our proposed method uses the MediaPipe framework to extract 3D coordinates of skeleton joints, which serve as input features for a machine learning based model that accurately predicts key frame positions in badminton swing videos. This model is integrated into a mobile app developed for Android tablets, allowing learners to record their swings and compare them with those of professional players, thereby enhancing badminton learning. Comparative studies show that models with graph convolutional networks, a prominent approach commonly used in skeleton-based human action recognition, outperform other existing methods in terms of accuracy and reliability. Experimental studies demonstrate the app’s positive impact on performance, motivation, and self-perception. One main contribution of this research is the development of a robust key frame detection model. Another main contribution is that we implement this key frame detection model in a tablet and verify it through extensive experiments.}, archive = {J_ACCESS}, author = {Jen-Hao Hsu and Chi-Chuan Lee and Jing-Yuan Chang and Duan-Shin Lee}, doi = {10.1109/ACCESS.2025.3572105}, journal = {IEEE Access}, pages = {91248-91262}, shortjournal = {IEEE Access}, title = {Key frame detection in badminton swings and its application to physical education}, volume = {13}, year = {2025}, }
• (2025). Real-time EEG-based BCI for self-paced motor imagery and motor execution using functional neural networks. ACCESS, 13, 91232-91247. (www)

  @article{ , abstract = {This paper introduces a novel application of functional neural networks (FNNs) in the domain of electroencephalography-based (EEG-based) brain-computer interfaces (BCIs), targeting self-paced motor execution (ME) and motor imagery (MI). FNNs represent a neural network architecture tailored to smooth processes, and as such have been applied to EEG data classification recently. The paper proposes a comprehensive pipeline encompassing data acquisition, synchronization, pre-processing, training of FNNs, and real-time inference to enable the seamless integration of FNNs into real-world BCI applications. For the first time, FNNs are integrated into an end-to-end pipeline and serve for live inference outside a strict laboratory setting. In pursuit of a more accessible electroencephalography (EEG) artificial intelligence (AI) training scenario, the paper introduces a self-paced environment for auto-labeling EEG data. A customdesigned Pong game serves as the training task and enables subjects to engage in MI/ME tasks while receiving immediate visual feedback. To automate the labeling process of the recorded EEG data, the movements of both arms are captured with inertial measurement units (IMUs) and analyzed through gesture recognition. This novel training framework contributes to more natural and engaging data collection and reduces pre-processing for model training. To provide a comprehensive evaluation, the paper compares the performance of FNN and EEGNet in the self-paced MI/ME tasks. The comparative analysis addresses factors such as classification accuracy, real-time processing speed, and power consumption. Furthermore, the study explores various auto-labeling methods within the self-paced environment, analyzing their impact on the classification performances of both architectures. By evaluating these labeling methods, this work addresses the challenge of accurate and efficient EEG data labeling, crucial for training robust models for prediction of time critical events. The proposed pipeline and experimental design culminate in a full-scale evaluation of the FNN-based classification system to demonstrate its efficacy in real-time MI/ME tasks. The paper’s contributions not only establish FNNs as a potent tool for EEG classification but also provide valuable insights into enhancing the accessibility, usability, and performance of EEG-based AI systems in real-world applications.}, archive = {J_ACCESS}, author = {Mavin Heim and Florian Heinrichs and Michael Hueppe and Fran Nunez and Alexander Szameitat and Muriel Reuter and Stefan M. Goetz and Corinna Weber}, doi = {10.1109/ACCESS.2025.3569932}, journal = {IEEE Access}, pages = {91232-91247}, shortjournal = {IEEE Access}, title = {Real-time EEG-based BCI for self-paced motor imagery and motor execution using functional neural networks}, volume = {13}, year = {2025}, }
• (2025). A novel piezoelectric-electromagnetic vibration energy harvesting device with real-time adjustable resonant frequency. ACCESS, 13, 91215-91231. (www)

  @article{ , abstract = {To address the problems of the piezoelectric-electromagnetic vibration energy harvesting device, which exists in the collection frequency band, is not adjustable, and does not fully utilize the magnetic potential energy, this paper designs a piezoelectric-electromagnetic vibration energy harvesting device which adjusts the resonance frequency by changing the magnetic distance and introduces the bending piezoelectric transducer to make full use of the magnetic potential energy. The magnetic force between magnets is derived from the magnetic dipole model, and the relationship between the voltage and power of each power generation part is described theoretically. In the capacity characterization test of the vibration energy harvesting device, the influence of the laws of load resistance, excitation acceleration, magnetic distance, and excitation frequency on the output voltage and power was investigated. When the excitation acceleration is 0.5g and 2g, the output power is 1.68 mW and 9.1 mW, respectively; when the magnetic moment is 48 mm and 68 mm, the vibratory energy recovery device reaches the peak power at 18 Hz and 12.4 Hz, respectively, and when the magnetic moment is 58 mm, the double-peak power appears at 13.8 Hz and 14.4 Hz. The results show that the adjustment of the magnetic pitch can change the magnitude of the magnetic force between the permanent magnets, and the nonlinear magnetic force can change the device’s resonance frequency and frequency band, which significantly enhances the device’s energy the efficiency of energy collection of the device is significantly improved; the bending piezoelectric transducer improves the utilization of magnetic potential energy and enhances energy collection.}, archive = {J_ACCESS}, author = {Weijia Xiu and Yanchun Yao and Liang Li and Xibin Li and Jida Wu}, doi = {10.1109/ACCESS.2025.3570442}, journal = {IEEE Access}, pages = {91215-91231}, shortjournal = {IEEE Access}, title = {A novel piezoelectric-electromagnetic vibration energy harvesting device with real-time adjustable resonant frequency}, volume = {13}, year = {2025}, }
• (2025). Dynamic carbon emission measurement and optimal dispatching of power systems considering dual-carbon targets. ACCESS, 13, 91200-91214. (www)

  @article{ , abstract = {The power sector’s low-carbon transition is essential to reaching the dual-carbon objective since it is a significant source of energy consumption and greenhouse gas emissions. Thus, in order to lower the power system’s carbon emissions while also ensuring the system’s economy and dependability, the study suggests a dynamic carbon emission measurement and optimal dispatching model that considers the dual-carbon goal. Based on the dynamic carbon emission measurement model, the study uses a method for accounting for carbon emissions that accounts for the unpredictability of grid-connected renewable energy sources like solar and wind. The optimal dispatching of the power system is carried out by means of fuzzy parameter theory and demand response modeling. According to the study’s findings, the suggested model might successfully lower the power system’s carbon emissions while enhancing its dependability and economics. After the implementation of demand response, the total generation cost of the power system was reduced from 1.47 million RMB to 1.15 million RMB, and the total carbon emission was reduced from 44,000 tons to 37,000 tons, which significantly improved the operational efficiency and environmental benefits of the system. The power system’s carbon emissions under various operating situations may be better captured by the suggested dynamic carbon emission measuring methodology. In addition to offering a fresh optimization approach for the power system’s low-carbon operation, the study offers technological solutions and theoretical justification for the dual-carbon objective.}, archive = {J_ACCESS}, author = {Qiang Li and Feng Zhao and Laidong Zhang and Xuecheng Zhang and Juanyu Liu and Ying Chen and Xinyue Li}, doi = {10.1109/ACCESS.2025.3569822}, journal = {IEEE Access}, pages = {91200-91214}, shortjournal = {IEEE Access}, title = {Dynamic carbon emission measurement and optimal dispatching of power systems considering dual-carbon targets}, volume = {13}, year = {2025}, }
• (2025). Development of a human performance baseline of lay error in targeting. ACCESS, 13, 91182-91199. (www)

  @article{ , abstract = {This study pursued two key objectives: first, to create an advanced model that accurately represents a human’s precision in aligning sight crosshairs with the center of a target, otherwise known as lay error; second, to investigate how various engagement conditions (such as target shape, size, range, and motion) influence lay error and determine whether a single error model could be reliably applied across different targets. To address these objectives, a photo-realistic simulation environment using Unreal Engine was developed featuring four different targets, four motion configurations, four levels of zoom, four ranges, three levels of contrast, and four levels of obstructions. After prototyping and evaluation, the simulation environment was used to collect lay error metrics as 110 IRB-approved subjects aligned crosshairs on targets under various conditions. Subjects fired a total of 11,088 shots throughout a set of three different data collection sessions that were completed over 10 months. After data collection, statistical analyses of lay error were conducted including fitting non-Gaussian distribution functions and applying Fitts’ Law to the targeting analysis. Experimental results show that (a) the lay error changes with target type and is greater on closer targets and lower on farther targets for the majority of the subjects who participated in the study; (b) The firing time for most shots is below 10 seconds for most subjects, independent of the test conditions, distance to target, or target type; and (c) although the subjects’ shots were mostly accurate, subjects occasionally had significant errors (outliers) that merit deeper consideration.}, archive = {J_ACCESS}, author = {Thirimachos Bourlai and Parker Ensing and Alexia Toma and Victor Philippe and Jennifer Forsythe and Cody L. Lundberg and Nicholas R. Gans}, doi = {10.1109/ACCESS.2025.3571591}, journal = {IEEE Access}, pages = {91182-91199}, shortjournal = {IEEE Access}, title = {Development of a human performance baseline of lay error in targeting}, volume = {13}, year = {2025}, }
• (2025). SME business agility framework: A comprehensive review of capabilities, dimensions, and enablers. ACCESS, 13, 91161-91181. (www)

  @article{ , abstract = {Small and medium-sized enterprises (SMEs) are vital to economic growth but are particularly vulnerable to external disruptions due to limited resources and strategic constraints. Business agility, supported by dynamic capabilities, is increasingly recognized as a key to SME resilience, adaptability, and sustained competitiveness. This study presents a systematic literature review to identify and categorize agility-related capabilities across the strategic, tactical, and operational levels of SMEs. The research results in the development of the SME Business Agility Concept Map, which integrates nine agility dimensions and highlights key enablers in process, people, and technology perspectives. The proposed framework contributes to the growing body of knowledge on SME agility and offers practical guidance for building agile capabilities in dynamic market environments.}, archive = {J_ACCESS}, author = {Pavel Arno}, doi = {10.1109/ACCESS.2025.3572526}, journal = {IEEE Access}, pages = {91161-91181}, shortjournal = {IEEE Access}, title = {SME business agility framework: A comprehensive review of capabilities, dimensions, and enablers}, volume = {13}, year = {2025}, }
• (2025). A review of applicable technologies, routing protocols, requirements, and architecture for disaster area networks. ACCESS, 13, 91129-91160. (www)

  @article{ , abstract = {The swift progress of various cutting-edge network topologies, especially Fifth-Generation Networks (5G), Sixth-Generation Networks (6G), Unmanned Aerial Vehicles (UAVs), and satellite technologies, presents a promising opportunity to guarantee reliable communication and offer consistent and secure emergency services in times of calamities. Fuelled by the rapid evolution of network architectures, this study provides a comprehensive overview of the emerging network topologies, technologies, routing protocols, and difficulties associated with disaster area networks (DANs). This investigation seeks to examine the integration of various DAN topologies, including aerial, terrestrial, and maritime networks, to enhance communication during a disaster. Subsequently, we assess numerous network performance metrics, such as delay, overhead, throughput, energy consumption, packet ratio, and security. The review is concluded by illuminating recent research trends and open issues in the domain of emergency communication. Ultimately, this review serves as a valuable resource for researchers, network engineers, and policymakers in the domain of DANs, offering a thorough basis for informed decision-making to streamline network deployments and preserve lives.}, archive = {J_ACCESS}, author = {Mohammad M. Alsayyed and Selvakumar Manickam and Eka Ratri Noor Wulandari and I. Dewa Made Widia and Shankar Karuppayah}, doi = {10.1109/ACCESS.2025.3570733}, journal = {IEEE Access}, pages = {91129-91160}, shortjournal = {IEEE Access}, title = {A review of applicable technologies, routing protocols, requirements, and architecture for disaster area networks}, volume = {13}, year = {2025}, }
• (2025). An FPGA prototype for parkinson’s disease detection using machine learning on voice signal. ACCESS, 13, 91113-91128. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms — Chi-squared ( $\chi ^{2}$ ), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of $155 \times $ compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection.}, archive = {J_ACCESS}, author = {Mujeev Khan and Abdul Moiz and Gani Nawaz Khan and Mohd Wajid and Mohammed Usman and Jabir Ali}, doi = {10.1109/ACCESS.2025.3572092}, journal = {IEEE Access}, pages = {91113-91128}, shortjournal = {IEEE Access}, title = {An FPGA prototype for parkinson’s disease detection using machine learning on voice signal}, volume = {13}, year = {2025}, }
• (2025). Comprehensive design, modeling and analysis of grid-forming type IV wind turbine generators using state-space methods. ACCESS, 13, 91089-91112. (www)

  @article{ , abstract = {Grid-forming (GFM) control has emerged as a promising solution to the challenges posed by the increasing reliance on inverter-based resources (IBRs). However, unlike in a battery-based IBR, the implementation of GFM in wind turbine generators (WTGs) introduces challenges due to multiple machine-side converter (MSC) and grid-side converter (GSC) interactions. In this work, a GFM-WTG control structure is adopted in which the MSC primarily regulates the DC-link voltage, while the GSC emulates grid-forming behavior using virtual synchronous generator principles. Accordingly, this paper presents a practical control implementation scheme and a systematic small-signal modeling framework for GFM WTGs using the component connection method, enabling a unified state-space representation that captures key electromechanical, aerodynamic and control interactions inside the GFM-WTG system. The proposed model is validated through electromagnetic transient simulations, and eigenvalue and participation factor analyses reveal strong MSC-GSC inter-dependencies. Sensitivity analysis further confirms model accuracy across varying operating conditions. Additionally, a reduced-order model is derived to balance computational efficiency with dynamic fidelity. The findings provide a robust foundation for stability analysis and control tuning of GFM WTGs, supporting their reliable integration into future power grids.}, archive = {J_ACCESS}, author = {Harith E. Udawatte and Mohammad H. Ravanji and Behrooz Bahrani}, doi = {10.1109/ACCESS.2025.3572885}, journal = {IEEE Access}, pages = {91089-91112}, shortjournal = {IEEE Access}, title = {Comprehensive design, modeling and analysis of grid-forming type IV wind turbine generators using state-space methods}, volume = {13}, year = {2025}, }
• (2025). A4FL: Federated adversarial defense via adversarial training and pruning against backdoor attack. ACCESS, 13, 91070-91088. (www)

  @article{ , abstract = {Backdoor attacks threaten federated learning (FL) models, where malicious participants embed hidden triggers into local models during training. These triggers can compromise crucial applications, such as autonomous systems, when they activate specific inputs, causing a targeted misclassification in the global model. We recommend a strong defense mechanism that combines statistical testing, model refinement, and adversarial training methods. The primary goal is to develop a robust defense against backdoor attacks in federated learning (FL), where malicious participants embed hidden triggers into local models. This defense aims to preserve the integrity of the global model and ensure high reliability in real-world FL deployments, even when facing sophisticated adversarial strategies. Our defense strategy incorporates “Messy” samples with obvious triggers and “wrap” samples with similar but nonidentical triggers during adversarial training. This dual approach enhances the model’s ability to detect and resist hidden manipulations. We facilitate applying neuron pruning to remove compromised neurons, further refining the model architecture for improved security. Continuous statistical testing, including variance analysis and cosine similarity checks, ensures that only legitimate and significant updates are integrated into the global model. A key innovation of our method is a significance-based filtering mechanism that effectively identifies and excludes malicious updates, preventing backdoor triggers from affecting the global model. This iterative defense process adapts to attack strategies, maintaining the model’s robustness. Empirical results confirm that this defense mechanism significantly improves FL models’ resilience to sophisticated backdoor attacks while preserving high accuracy and reliability. Balancing defensive strategies from adversarial training and sample diversification to model pruning provides a dependable framework for safeguarding FL models where integrity and security are critical. Experimental results demonstrate that our defense mechanism significantly enhances FL models’ resistance to sophisticated backdoor attacks while maintaining high accuracy and reliability in real-world deployments. These solutions ensure the potential significance of balanced defense solutions, which offer strong protection against adversarial backdoor assaults. This framework provides a dependable solution for securing FL models in environments where integrity and security are paramount.}, archive = {J_ACCESS}, author = {Saeed-Uz-Zaman and Bin Li and Muhammad Hamid and Muhammad Saleem and Muhammad Aman}, doi = {10.1109/ACCESS.2025.3568275}, journal = {IEEE Access}, pages = {91070-91088}, shortjournal = {IEEE Access}, title = {A4FL: Federated adversarial defense via adversarial training and pruning against backdoor attack}, volume = {13}, year = {2025}, }
• (2025). Defect location analysis of CFRP plates based on morphological filtering technique. ACCESS, 13, 91065-91069. (www)

  @article{ , abstract = {Carbon fiber reinforced polymers (CFRP) have become an essentials structural material in advanced manufacturing sectors, including new-energy vehicles and precision equipment, owing to their superior strength-to-weight ratio, exceptional fatigue resistance, and lightweight characteristics. However, CFRP components are prone to developing surface defects, such as microcracks, interlinear delamination, and porosity, during both manufacturing processes and their service life. These millimeter-scale imperfections can reduce structural integrity by up to 60%, potentially leading to catastrophic failures in safety-critical applications. Current nondestructive evaluation methods face challenges in reliably distinguishing genuine defects from surface texture artifacts while maintaining operational efficiency. In this study, we propose a novel defect localization method for CFRP plates using an advanced morphological filtering technique. The proposed approach employs opening and closing operations to effectively separate genuine defects from noise artifacts. A subsequent defec-region decision model was established to accurately identify and localize the damage features. The experimental results demonstrate that the method achieves rapid and precise localization of surface defects on CFRP plates, significantly enhancing detection accuracy and efficiency. This method not only overcomes the limitations of traditional inspection techniques but also provides a robust solution for real-time quality assessment in industrial applications.}, archive = {J_ACCESS}, author = {Yanlai Peng and Zhenyu Wu and Xiaoying Cheng and Lin Shi}, doi = {10.1109/ACCESS.2025.3572138}, journal = {IEEE Access}, pages = {91065-91069}, shortjournal = {IEEE Access}, title = {Defect location analysis of CFRP plates based on morphological filtering technique}, volume = {13}, year = {2025}, }
• (2025). An enhanced and lightweight YOLOv8-based model for accurate rice pest detection. ACCESS, 13, 91046-91064. (www)

  @article{ , abstract = {Accurate pest identification is crucial for ensuring both high quality and high yield in rice production. This paper proposes RicePest-YOLO, a practical and generalizable model designed for agricultural pest detection, based on structural optimization and lightweight strategies applied to the YOLOv8 architecture. The model is specifically tailored to address the challenges of rice pest images, such as small insect size, complex backgrounds, and high inter-class similarity. In particular, it integrates ODConv and the BiFPN structure into the backbone and neck of YOLOv8n, enhancing the model’s ability to extract features from rice pest images. Subsequently, the Shape-IoU loss function is employed to enhance the model’s sensitivity and precision in target recognition. Furthermore, to facilitate the deployment of the new algorithm on embedded terminal devices, we applied Layer Adaptive Magnitude Pruning and Knowledge Distillation techniques for a lightweight model. The effectiveness of the proposed model was evaluated on common rice pest datasets. Experimental results demonstrate that the proposed model outperforms YOLOv8n, achieving an mAP@0.5 of 94.3%, and an mAP@0.5:0.95 of 67.3%, with 2.7% improvement in mAP@0.5 and 1.2% improvement in mAP@0.5:0.95. Comparison experiments with state-of-the-art models further demonstrated the proposed approach’s superiority. The model’s generalization and robustness were tested on the Pests dataset and the Paddy Rice Pest dataset. Results indicated a 2.6% and 3.0% improvement in mAP@0.5, and a 2.8% and 4.0% increase in mAP@0.5:0.95, respectively. Additionally, Experimental results show that the lightweight version of RicePest-YOLO reduced parameters by 48.1% and GFLOPs by 50%, with only a marginal decrease of 0.4% in mAP@0.5. The results validate that the proposed model is not only effective and efficient but also robust and lightweight, making it a promising solution for rice pest detection and offering valuable support for pest management in precision agriculture.}, archive = {J_ACCESS}, author = {Guisuo Liu and Juxing Di and Qing Wang and Yan Zhao and Yang Yang}, doi = {10.1109/ACCESS.2025.3569819}, journal = {IEEE Access}, pages = {91046-91064}, shortjournal = {IEEE Access}, title = {An enhanced and lightweight YOLOv8-based model for accurate rice pest detection}, volume = {13}, year = {2025}, }
• (2025). Deep learning model for analyzing EEG signal analysis. ACCESS, 13, 91034-91045. (www)

  @article{ , abstract = {To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique.}, archive = {J_ACCESS}, author = {Varun Gupta and Vivek Kumar and Prince and Saurabh Singh and Young-Seok Lee and In-Ho Ra}, doi = {10.1109/ACCESS.2025.3563760}, journal = {IEEE Access}, pages = {91034-91045}, shortjournal = {IEEE Access}, title = {Deep learning model for analyzing EEG signal analysis}, volume = {13}, year = {2025}, }
• (2025). DYCOCO: Dynamic configuration-based test compaction. ACCESS, 13, 91025-91033. (www)

  @article{ , abstract = {Systems with high reliability demands require large test sets that target several or advanced fault models. To reduce the storage requirements of a large test set, it was suggested earlier that a stored test may be used for applying several different tests. Industrial applications of this observation consider compressed tests that are applied from an external tester, or stored on-chip for logic built-in self-test (LBIST). It was also suggested earlier that several different tests can be obtained by allowing the scan chains to receive test data from different outputs of the decompression logic for different tests. The connection between the decompression logic and the scan chains is referred to as a configuration. The procedure described earlier for selecting stored tests and configurations for test application is referred to as a static configuration-based compaction procedure (STCOCO) since it requires a given test set as input. This article describes a configuration-based compaction procedure that is suitable for dynamic compaction (DYCOCO). DYCOCO can be used during test generation and does not require a precomputed test set. Compared with STCOCO, DYCOCO requires fewer tests to be generated, and it is more effective in reducing the storage requirements. A comparison of the experimental results produced by STCOCO and DYCOCO for benchmark circuits in an academic simulation environment demonstrates the advantages and costs associated with DYCOCO. Both STCOCO and DYCOCO are considered in the scenario where a compressed test set is applied from an external tester.}, archive = {J_ACCESS}, author = {Irith Pomeranz}, doi = {10.1109/ACCESS.2025.3572392}, journal = {IEEE Access}, pages = {91025-91033}, shortjournal = {IEEE Access}, title = {DYCOCO: Dynamic configuration-based test compaction}, volume = {13}, year = {2025}, }
• (2025). Multiple-input floating-point adders: A comprehensive review. ACCESS, 13, 91012-91024. (www)

  @article{ , abstract = {Floating-point addition is pivotal in various computational applications, including scientific computing, machine learning, and graphics processing. Traditional two-input floating-point adders (FPADD2) are widely used but become bottlenecks in scenarios requiring efficient multi-operand summation. The growing demand for high-performance computing has spurred interest in multiple-input floating-point adders (FPADDn), which simultaneously handle multiple operands to enhance throughput and reduce latency. This paper provides a comprehensive review of FPADDn architectures, exploring key design principles, implementation techniques, and challenges such as precision, hardware complexity, and rounding errors. Modern approaches leveraging hierarchical tree structures, parallelism, and pipelining are discussed, highlighting their advantages and trade-offs.}, archive = {J_ACCESS}, author = {Sami Ul Haq and Aiman H. El-Maleh and Ali Alsuwaiyan}, doi = {10.1109/ACCESS.2025.3572430}, journal = {IEEE Access}, pages = {91012-91024}, shortjournal = {IEEE Access}, title = {Multiple-input floating-point adders: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Online/Offline digital signatures: A systematic literature review. ACCESS, 13, 90991-91011. (www)

  @article{ , abstract = {In the rapidly evolving digital landscape, efficient cryptographic systems are increasingly critical. Digital Signature (DS) schemes are essential for ensuring data integrity and authenticity, particularly in resource-constrained environments like the Internet of Things (IoT). The Offline/Online (O/O) extension has gained attention for its effective signing process, as it allows for the precomputation of operations beforehand. Informally, in this kind of scheme, signing a message involves two stages. The first is an offline stage that requires moderate computation but can be done in advance, before the message is known. The second is an online stage that starts once the message is available, using the precomputed data to complete the signature more quickly. This survey provides the first comprehensive overview of the adaptation of O/O constructions for various resource-constrained use cases, such as IoT, low-power and mobile contexts, and other contexts, such as blockchains, where latency is a crucial metric. We reviewed 36 O/O DS schemes, focusing on trends, challenges, and applications. We examined the benefits, drawbacks, and potential areas for improvement of the reviewed O/O schemes, as well as identifying the features inherent to the O/O design. Our analysis indicates that the latter effectively mitigates issues related to expensive computations, delays, and high energy consumption, provided that the increased storage demands for offline signatures are manageable. This survey lays the groundwork for future research and development of O/O schemes, driven by the increasing prevalence of constrained environments where these schemes are particularly useful.}, archive = {J_ACCESS}, author = {Juliet Samandari and Clémentine Gritti}, doi = {10.1109/ACCESS.2025.3570689}, journal = {IEEE Access}, pages = {90991-91011}, shortjournal = {IEEE Access}, title = {Online/Offline digital signatures: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Research on measurement techniques for integrated optical waveguide-based electric field sensors. ACCESS, 13, 90983-90990. (www)

  @article{ , abstract = {The growing demand for high-precision sensing technologies in power systems has positioned the development of novel electric field measurement tools as a critical metrological challenge. This study presents a broadband photoelectric electric field sensor based on the Mach-Zehnder interference phenomenon in asymmetric straight wave-guides. Furthermore, a three-dimensional electric field measurement system was developed through systematic investigation of multidimensional field measurement methodologies. Experimental results demonstrate that the proposed device achieves broadband electric field measurement with linearity up to 0.9996 and power-frequency measurement accuracy exceeding 97%. The system demonstrates concurrent measurement capability for both lightning impulse waveforms and operational power frequency electric fields. The synthesized three-dimensional measurement error remains below 4%, enabling precise omnidirectional electric field detection.}, archive = {J_ACCESS}, author = {Feng Zhou and Zhenghao Chai and Yinglong Diao and Zhaozhi Long}, doi = {10.1109/ACCESS.2025.3569862}, journal = {IEEE Access}, pages = {90983-90990}, shortjournal = {IEEE Access}, title = {Research on measurement techniques for integrated optical waveguide-based electric field sensors}, volume = {13}, year = {2025}, }
• (2025). Efficient detection of microplastics on edge devices with tailored compiler for TinyML applications. ACCESS, 13, 90970-90982. (www)

  @article{ , abstract = {The current study aims to train and benchmark AI models tailored for the detection of microplastic in water from scattered signals. We trained two different models, the first based on a Multi-Layer Perceptron (MLP) and the second on a Gated Recurrent Unit (GRU). A Neural Architecture Search algorithm was used to determine the optimal configuration for each of the two models. Moreover, for deployment on edge devices, a specific custom-made compiler was designed and used. The compiler is specifically designed for TinyML applications and, therefore, for resource-constrained devices. It bypasses traditional inference engines, compiling the NNs to native C code using only standard C libraries. Our approach demonstrated better performance compared to state-of-the-art frameworks such as ONNX Runtime, achieving better latency, memory usage, energy consumption, and a higher portability. This highlights the potential of our method for efficient and effective microplastic detection in environmental monitoring.}, archive = {J_ACCESS}, author = {Alessandro Cerioli and Lorenzo Petrosino and Daniele Sasso and Clément Laroche and Tobias Piechowiak and Luca Pezzarossa and Mario Merone and Luca Vollero and Anna Sabatini}, doi = {10.1109/ACCESS.2025.3567816}, journal = {IEEE Access}, pages = {90970-90982}, shortjournal = {IEEE Access}, title = {Efficient detection of microplastics on edge devices with tailored compiler for TinyML applications}, volume = {13}, year = {2025}, }
• (2025). Linearity calibration method for stochastic time-to-digital converters. ACCESS, 13, 90962-90969. (www)

  @article{ , abstract = {Stochastic Time-to-Digital Converters (STDCs) can theoretically achieve very fine time resolutions utilizing random time offsets caused by device mismatch rather than relying on delay elements. However, the random time offsets follow a Gaussian distribution, which result in non-ideal TDC output characteristics, such as irregular gain and nonlinearity that may severely degrade the conversion performance. Until now, there have been few previous studies on the linearity calibration of STDCs because the STDCs employ a large number of arbiters and calibrating the individual arbiters may require significant time and effort. In this paper, we propose a linearity calibration algorithm called Arbiter Selection for Maximum Linearity (ASML) that efficiently yet effectively calibrates STDCs. To verify the effectiveness of the algorithm, we performed high-level simulations in MATLAB based on measured circuit parameters from an STDC chip fabricated in a 65nm CMOS process, assuming a 6-bit STDC with 2-bit redundancy. Further, we explore the benefits of the proposed ASML algorithm for higher bit resolution STDCs. The simulation results show the proposed algorithm can be beneficial, especially at high bit resolutions. For a 9-bit STDC with 2-bit redundancy, the proposed algorithm achieved more than 11 times better linearity compared to the conventional bit truncation method.}, archive = {J_ACCESS}, author = {Woongdae Na and Hayun Chung}, doi = {10.1109/ACCESS.2025.3572122}, journal = {IEEE Access}, pages = {90962-90969}, shortjournal = {IEEE Access}, title = {Linearity calibration method for stochastic time-to-digital converters}, volume = {13}, year = {2025}, }
• (2025). Interpersonal relationship detection using multi-head graph attention networks with multi-feature fusion. ACCESS, 13, 90945-90961. (www)

  @article{ , abstract = {The ability to automatically detect and understand interpersonal relationships from visual data represents a fundamental challenge in computer vision and social signal processing. While significant advances have been made in face recognition and object detection, understanding the complex dynamics of human relationships requires a detailed approach that can integrate multiple visual and contextual cues. This paper presents a novel Multi-Head Graph Attention Network (MHF-GAT) with Multi-Feature Fusion for interpersonal relationship detection (IRD) from images. The proposed architecture decomposes heterogeneous visual and semantic features into three specialized modules: local features capturing direct interpersonal cues (e.g., age differences, expression similarity), contextual features modeling environmental information (e.g., scene, objects), and semantic features derived from image captions. By representing features as graph nodes and employing multi-head attention mechanisms within each module, MHF-GAT effectively captures complex feature interactions while reducing redundancy and overfitting risks. The model’s performance is evaluated on two benchmark datasets: the Interpersonal Relation Dataset (IPR) and People in Social Context (PISC). Experimental results demonstrate state-of-the-art performance, achieving 87.18% accuracy on IPR and 77.1% mean Average Precision (mAP) on PISC. Ablation studies and attention visualization reveal that the modular architecture significantly improves feature discrimination and relationship classification compared to single-graph approaches.}, archive = {J_ACCESS}, author = {Simge Akay and Duygu Cakir and Nafiz Arica}, doi = {10.1109/ACCESS.2025.3571099}, journal = {IEEE Access}, pages = {90945-90961}, shortjournal = {IEEE Access}, title = {Interpersonal relationship detection using multi-head graph attention networks with multi-feature fusion}, volume = {13}, year = {2025}, }
• (2025). Numerical simulation study on adjoint-based optimization of a propeller using reverse engineering. ACCESS, 13, 90932-90944. (www)

  @article{ , abstract = {In order to enhance the performance of existing propellers, a study has been carried out on the adjoint-based optimization of propellers based on reverse engineering. This method facilitates the rapid development of a more efficient propeller. In the initial phase of the study, the propeller was scanned with a 3D scanner to obtain point cloud data. The point cloud reconstruction technique was then employed to create a 3D model of the propeller. The propeller was then subjected to a thrust test on a propeller thrust test bench. The numerical simulation employed the SST k- $\omega $ turbulence model to solve the multiple reference coordinate system model of the Reynolds average equation. This was then compared with the original propeller thrust test data. The thrust average discrepancy between the simulation and experimental results was found to be 9.8%, thereby confirming the reliability of the reverse-engineered data. Consequently, the radial distributions of the propeller thrust and torque were analysed. Furthermore, optimization of the airfoil sections contributing in excess of 10% of the total thrust was undertaken to enhance the propeller’s thrust. The optimization target was set at a 10% improvement in the lift-to-drag ratio, and the adjoint-based optimization of the target airfoil sections at different angles of attack was performed using the gradient descent algorithm. Following the replacement of the original airfoil profiles with the optimized ones, an overall increase in hover thrust of 9.46% was observed, along with a significant increase in thrust at all speeds from 2000rpm to 5500rpm. The findings of this study illustrate the efficacy of the adjoint-based optimization approach and offer significant insights into the domain of propeller design and optimization in related fields.}, archive = {J_ACCESS}, author = {Hao Wang and Yaoqing Liao and Mingge Shen and Sujie Zhou and Gang Xu}, doi = {10.1109/ACCESS.2025.3572180}, journal = {IEEE Access}, pages = {90932-90944}, shortjournal = {IEEE Access}, title = {Numerical simulation study on adjoint-based optimization of a propeller using reverse engineering}, volume = {13}, year = {2025}, }
• (2025). Robust predictive current control of IMs with MRAS-based parameter estimation. ACCESS, 13, 90921-90931. (www)

  @article{ , abstract = {Induction motors are extensively employed in numerous industrial and commercial applications due to their robustness, simplicity, and cost-effectiveness. However, the motor performance is often influenced by parameter mismatches, which can degrade control precision. Advanced control techniques like finite control set-model predictive control (FCS-MPC) address these challenges by utilizing system models to predict future behaviors and select optimal control actions. To further enhance the robustness of predictive control, this paper introduces a novel model predictive current control framework for induction motors in the stationary reference frame. It incorporates a Model Reference Adaptive System (MRAS) observer for real-time parameter estimation, targeting mismatches in inductance and resistance. Unlike conventional FCS-MPC methods, this approach employs stator voltage rather than current, torque, or flux as the optimization metric in its cost function. By leveraging the MRAS observer, the proposed method dynamically aligns model parameters with real-time motor conditions, mitigating the effects of parameter variations and external disturbances. Experimental evaluations on an induction motor under dynamic and steady-state conditions confirm the superior robustness and tracking performance of this approach compared to traditional predictive control strategies. The integration of the MRAS observer demonstrates significant improvements in maintaining control accuracy even under substantial parameter fluctuations, underscoring its potential for industrial application.}, archive = {J_ACCESS}, author = {Gabriel Caramori and Igor Oliani and Angelo S. Lunardi and Alfeu J. Sguarezi Filho}, doi = {10.1109/ACCESS.2025.3572872}, journal = {IEEE Access}, pages = {90921-90931}, shortjournal = {IEEE Access}, title = {Robust predictive current control of IMs with MRAS-based parameter estimation}, volume = {13}, year = {2025}, }
• (2025). Constrained heat kernel graph diffusion convolution: A high-dimensional statistical approximation via information theory. ACCESS, 13, 90912-90920. (www)

  @article{ , abstract = {Graph neural networks have been widely applied in various domains, handling numerous entities with high-dimensional features. Their success largely stems from the powerful information propagation process. Among these networks, diffusion-based approaches, such as generalized graph diffusion convolution, have extended conventional immediate neighborhood aggregation function to a diffusion process based on Newton’s law of cooling. Despite substantial improvements in model performance, the diffusion time is obtained by either grid search or complex bi-level optimization on train and validation sets. Furthermore, these methods do not explicitly explain the diffusion time obtained from the specific graph data. We propose the Constrained Heat Kernel Graph Diffusion to address these issues to estimate the appropriate diffusion time based on information theory and moments. By conceptualizing the graph as a communication network, we approximate the diffusion process as the transmission and reception of graph signals over noisy channels bounded by the channel capacity. Our method demonstrates consistent performance improvements in downstream graph-mining tasks across six public datasets, offering a principled framework and faster approximation method for the diffusion time. The results from the ablation study further validate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Zhuo-Chen He}, doi = {10.1109/ACCESS.2024.3425607}, journal = {IEEE Access}, pages = {90912-90920}, shortjournal = {IEEE Access}, title = {Constrained heat kernel graph diffusion convolution: A high-dimensional statistical approximation via information theory}, volume = {13}, year = {2025}, }
• (2025). Bambda: A real-time verification framework for serverless computing. ACCESS, 13, 90896-90911. (www)

  @article{ , abstract = {Serverless environments are rapidly emerging as the new paradigm for cloud computing due to their automatic scalability, cost efficiency, and ease of operation. However, IAM-based privilege management and event-driven execution mechanisms can introduce security vulnerabilities. In particular, complex inter-functional call relationships expose systems to attacks such as privilege abuse and event call condition exploitation. These attacks often occur dynamically at runtime, making them difficult to address with static defenses. Existing static analysis methods attempt to mitigate these risks, but are inherently limited in capturing dynamic attacks that occur at runtime. In this paper, we propose Bambda, a dynamic security framework for serverless environments that prevents privilege abuse and chained function call attacks. Bambda performs real-time function call verification through centralized logging and automated code injection based on application-specific log groups. Specifically, we introduce a multi-step verification process that distinguishes between direct calls, event-driven calls, and API calls, effectively preventing unauthorized attacks without requiring additional security configurations from developers. Experiments conducted in AWS Lambda environments demonstrate that Bambda effectively defends against privilege abuse and chained function call attacks, achieving practical deployment with minimal performance overhead of 8.12% under warm start conditions.}, archive = {J_ACCESS}, author = {Changhee Shin and Bom Kim and Seungsoo Lee}, doi = {10.1109/ACCESS.2025.3572729}, journal = {IEEE Access}, pages = {90896-90911}, shortjournal = {IEEE Access}, title = {Bambda: A real-time verification framework for serverless computing}, volume = {13}, year = {2025}, }
• (2025). Phase shift full bridge DC–DC converter with active clamping circuit for fuel cell electric vehicles. ACCESS, 13, 90875-90895. (www)

  @article{ , abstract = {This paper proposes a soft switching isolated phase-shifted full-bridge DC-DC converter using active clamping circuit for fuel cell power generation applications. An active clamping circuit is added to the primary side of the high frequency transformer to clamp voltage spikes and recycle the energy trapped in the leakage inductor. The presented converter has the advantages of a small number of components, simple circuit configuration, reduced voltage stress and a wide input voltage range. The soft switching operation of the presented converter allows for high switching frequency with high power conversion efficiency. Analysis, simulation, operation principles and a design example of the proposed converter are conducted using PSIM simulation software. Various operation modes and their equivalent circuits during each operating cycle are investigated in detail. A down scale 1.2 kW of simulation and 350 W of experimental prototypes are developed to validate the wide output voltage gain of the proposed converter employing a wide range low input voltage to maintain a regular output voltage with a maximum power conversion efficiency of 98% and 96.5% at full load are achieved due to the ability of the proposed converter to minimize switching losses rated power conditions. The results from simulation and experimentation are used to validate the operation and modeling and to strengthen the viability and efficiency of the suggested converter topology.}, archive = {J_ACCESS}, author = {Bader N. Alajmi and Nabil A. Ahmed and Ibrahim Abdelsalam and Mostafa I. Marei}, doi = {10.1109/ACCESS.2025.3571942}, journal = {IEEE Access}, pages = {90875-90895}, shortjournal = {IEEE Access}, title = {Phase shift full bridge DC–DC converter with active clamping circuit for fuel cell electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Electric vehicles CAN bus cyber attacks detection using adaptive neuro fuzzy inference system. ACCESS, 13, 90862-90874. (www)

  @article{ , abstract = {The Electric Vehicle (EV) industry has recently experienced notable technological progress in the field of Controller Area Network (CAN) protocol. The use of CAN bus protocol in EVs is exposed to intrinsic cybersecurity risks and consequently causing EV damages as a result of lack of authentication, authorization, and accounting mechanisms. This paper examines the vulnerabilities within the EVs’ CAN bus protocol and explores potential strategies for mitigating cyber threats (i.e. Denial of Service (DOS) and impersonation attacks). In particular, the paper proposes Adaptive Neuro Fuzzy Inference System (ANFIS) based detection techniques superimposed with Subtractive Clustering (SC) and Fuzzy C-Means clustering (FCM). Results demonstrate that the proposed ANFIS-SC and ANFIS-FCM detection model testing accuracy is 99.6%, TPR and TNR values are above 99.8%. In addition to the low FPR and FNR values are less than 0.2% of the proposed ANFIS-SC and ANFIS-FCM detection techniques. The overall F1 score is above 98.8%.}, archive = {J_ACCESS}, author = {Omar Adil Al Isawi and Khaled Ali Al Jaafari and Ameena Saad Al-Sumaiti}, doi = {10.1109/ACCESS.2025.3550970}, journal = {IEEE Access}, pages = {90862-90874}, shortjournal = {IEEE Access}, title = {Electric vehicles CAN bus cyber attacks detection using adaptive neuro fuzzy inference system}, volume = {13}, year = {2025}, }
• (2025). A cooperative motion control using leader-follower approach for unmanned underwater vehicle. ACCESS, 13, 90844-90861. (www)

  @article{ , abstract = {This paper aims to address the point-to-point tracking cooperative motion control problem during the recovery process of Unmanned Underwater Vehicle (UUV) in ocean engineering. A cooperative motion control method based on the leader-follower approach and double Deep Q-Network (DQN) decision network is proposed. Line-of-Sight (LOS) guidance is combined with the design of longitudinal velocity and heading angle virtual control laws. A double DQN structure with fixed Q targets is designed to reduce the complexity of the training algorithm, thus stability and efficiency are improved. The challenge of parameter selection in UUV controller design is addressed. A controller based on the DQN decision network is proposed. It estimates the expected cumulative reward of each action based on the current state, selects the optimal action, and significantly reduces the time required for UUV to reach the desired position. Furthermore, in a disturbed environment in ocean engineering, disturbance signals are incorporated as inputs to the DQN decision network and considered during training. This enhances control accuracy and robustness in disturbed environments. Simulation results validate the effectiveness of the proposed method. A new approach is provided to improving the stability, speed of training and the accuracy of control during the UUV recovery process in ocean engineering.}, archive = {J_ACCESS}, author = {Jingxiang Feng and Yang Cao and Xueman Fan and Guang Yang and Jingwei Dong}, doi = {10.1109/ACCESS.2025.3571978}, journal = {IEEE Access}, pages = {90844-90861}, shortjournal = {IEEE Access}, title = {A cooperative motion control using leader-follower approach for unmanned underwater vehicle}, volume = {13}, year = {2025}, }
• (2025). Compact dual-polarized quad-ridged probe antenna for spherical near-field measurement systems in 6G bands. ACCESS, 13, 90832-90843. (www)

  @article{ , abstract = {This paper presents the design, fabrication, and evaluation of a compact dual-polarized quad-ridged probe tailored for spherical near-field measurement systems operating in 6G frequency bands. The proposed probe demonstrates stable performance across the 4–16 GHz frequency range. The dual-polarized quad-ridged structure ensures high measurement accuracy for both horizontal and vertical polarizations, with a measured impedance bandwidth (VSWR $\le 3$ :1) covering the 4.4–4.8 GHz, 7.125–8.5 GHz, and 14.8–15.35 GHz candidate bands. The probe achieves peak gains of up to 12 dBi at 15 GHz. A detailed description of the design process is provided, including electromagnetic simulations and material selection. Experimental validation confirms the probe’s performance in terms of bandwidth and measurement precision. The proposed probe contributes to the advancement of next-generation near-field measurement systems, facilitating precise and efficient testing of high-frequency antennas for 6G technologies.}, archive = {J_ACCESS}, author = {Cheol Yoon and Won-Mo Seong and Woo-Su Kim}, doi = {10.1109/ACCESS.2025.3572152}, journal = {IEEE Access}, pages = {90832-90843}, shortjournal = {IEEE Access}, title = {Compact dual-polarized quad-ridged probe antenna for spherical near-field measurement systems in 6G bands}, volume = {13}, year = {2025}, }
• (2025). Enhancing college students’ achievement, motivation, and engagement in film character design through AI-driven smart sketchpad: A personalized interactive learning model for art education. ACCESS, 13, 90814-90831. (www)

  @article{ , abstract = {As technologies such as artificial intelligence rapidly evolve, the traditional lecture-based class can no longer meet students’ learning needs. Previous research has demonstrated that AI can be essential for improving the learning experience. However, research has also pointed out that promoting AI applications in education has been challenging. Developing domain-specific applications based on AI technology is a massive challenge for the development of AI in education. Further research has pointed out that the development of AI applications in education rarely considers students’ perceptual learning motivations. This study proposes a teaching method based on an artificial intelligence tool, the Artificial Intelligence Smart Sketchpad (AISS), to assist in teaching character art design in a multimedia environment. The artificial intelligence will identify students’ drawing sketches and make corrections. This study explores the impact of AISS on achievement, engagement, and motivation among film character art design students in a multimedia environment. To this end, we adopted a quantitative research method. Over five weeks, we assessed 60 university students. Research results indicate that the AI smart sketchpad (AISS) significantly improves students’ motivation, engagement, and performance. Student feedback suggests that AI stimulates creative thinking by offering greater creative freedom and possibilities and quickly transforming sketches into high-quality design drawings. A new direction will be explored for personalized and self-learning for students.}, archive = {J_ACCESS}, author = {Siyuan Zeng and Norsafinar Rahim and Songni Xu}, doi = {10.1109/ACCESS.2025.3560021}, journal = {IEEE Access}, pages = {90814-90831}, shortjournal = {IEEE Access}, title = {Enhancing college students’ achievement, motivation, and engagement in film character design through AI-driven smart sketchpad: A personalized interactive learning model for art education}, volume = {13}, year = {2025}, }
• (2025). Improved modulated model predictive control: Reducing total harmonic distortion, computational burden, and parameter sensitivity. ACCESS, 13, 90795-90813. (www)

  @article{ , abstract = {This paper presents an improved modulated model predictive control (M2PC) method, which is implemented on synchronous reluctance motor (SynRM) drives, significantly enhancing performance by streamlining the control algorithm. Unlike conventional M2PC approaches that suffer from complexity and high computational burden due to multiple cost functions, the proposed method introduces a novel voltage-based cost function that eliminates the need for multiple evaluations. By efficiently managing voltage vector sets through the new proposed cost function and using pulse-width modulation (PWM), the method achieves lower total harmonic distortion (THD), reduced sensitivity to parameter mismatches, and a decreased computational load. Experimental results demonstrate that the proposed approach effectively reduces THD, decreases computational burden, and enhances parameter robustness, validating its effectiveness for cost-effective microprocessor implementation.}, archive = {J_ACCESS}, author = {Seyed Rasul Eftekhari and Ali Mosallanejad and Hamidreza Pairo and Jose Rodriguez}, doi = {10.1109/ACCESS.2025.3569444}, journal = {IEEE Access}, pages = {90795-90813}, shortjournal = {IEEE Access}, title = {Improved modulated model predictive control: Reducing total harmonic distortion, computational burden, and parameter sensitivity}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence for DC arc fault detection in photovoltaic systems: A comprehensive review. ACCESS, 13, 90766-90794. (www)

  @article{ , abstract = {Photovoltaic (PV) systems are increasingly used for renewable energy generation but remain vulnerable to series arc faults, which can cause serious safety risks, fire hazards, and system failures. Detecting these faults in DC circuits is challenging due to their subtle electrical signatures and the presence of noise from system and environmental sources. Traditional detection methods often fall short in terms of accuracy, prompting growing interest in artificial intelligence (AI)-based solutions. This review article provides a comprehensive analysis of AI-based techniques for series arc fault detection in PV systems, covering key aspects such as data preprocessing, feature extraction, model optimization, and hardware implementation. It presents a structured comparison of existing methods, including their strengths and limitations, through descriptive discussion and summary tables. The review also includes a simplified flowchart to illustrate the typical AI-based DC arc fault detection process. Key challenges are discussed, along with future directions such as hybrid models, transfer learning, and implementation in resource-constrained edge devices. This work aims to support continued research and development by helping researchers and engineers better understand the strengths and limitations of current approaches and identify practical ways to improve arc fault detection for enhanced PV system safety and reliability.}, archive = {J_ACCESS}, author = {Kamal Chandra Paul and Disnebio Waldmann and Chen Chen and Yao Wang and Tiefu Zhao}, doi = {10.1109/ACCESS.2025.3572521}, journal = {IEEE Access}, pages = {90766-90794}, shortjournal = {IEEE Access}, title = {Artificial intelligence for DC arc fault detection in photovoltaic systems: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). O-arm imaging with real-time control for organ motion tracking: A feasibility study. ACCESS, 13, 90753-90765. (www)

  @article{ , abstract = {Image-guided surgery (IGS) has become one of the most practical, safest, and fastest procedures. One of its most crucial requirements is having high-quality, high-speed CT images during operation. This achievement has been realized through the O-Arm configuration. In this regard, numerous efforts have been made to correct motion artifacts caused by respiration, with the most effective and operational solution being the autofocus method. Despite the impressive results of this method, there are still concerns about the autofocus method, including the decrease in the accuracy of results with increasing patient movement and the significant time and computing performance required for this method in cases of extensive motion. To address this issue, a 3D CBCT Imaging system was designed, focusing on selecting motion mechanisms via estimated design parameters relating to weights and dimensions. In this study, the real model was simulated using ADAMS software, including the characterization of selected components, and a mathematical-dynamical model was developed and controlled. We considered a reliable hypothetical respiration path as input to the designed system. The tracking accuracy of the applied control system can maintain errors within 1mm for the X- and Y-axis, and 1.5mm for the Z-axis after two respiration cycles for an ideal model of the respiration; such error for the Z-axis is about 2mm for actual respiration data. Tracking the rigid motion of patients may lead to a reduction of the search area in the autofocus correction method for compensating deformable motion, which can directly impact computational efforts. This dual impact approach might be observed in the computational cost of the correction algorithm and the level of error.}, archive = {J_ACCESS}, author = {Ashkan Ghorbanian and Mobin Salehi and Mohammad Sajad Sokout and Borhan Beigzadeh}, doi = {10.1109/ACCESS.2025.3571989}, journal = {IEEE Access}, pages = {90753-90765}, shortjournal = {IEEE Access}, title = {O-arm imaging with real-time control for organ motion tracking: A feasibility study}, volume = {13}, year = {2025}, }
• (2025). A low power memory-integrated hardware BNN-MLP model on an FPGA for current signals in a biosensor. ACCESS, 13, 90741-90752. (www)

  @article{ , abstract = {This paper presents a method for processing and digitizing the current signal information output from biosensors and a hardware Artificial Intelligence (AI) model design that classifies data using low-power and compact AI algorithms to minimize the high-power consumption and on chip area of the Convolutional Neural Network (CNN) models used in biosensors. The dataset was built using a data sampling method that digitizes the current signal values from the biosensor by sampling them 16 times with a 16-bit Analog-to-Digital Converter (ADC), enabling feature extraction in advance. To reduce power consumption and area, a BNN-MLP model without an extraction layer was designed, and to improve accuracy, a dense layer was added to the final layer. This approach enhances accuracy while using binary weights. The BNN-MLP model was designed using TensorFlow for the software model and implemented in hardware at the Register Transfer Level (RTL). To verify the similarity between the hardware-implemented BNN-MLP model and the software BNN-MLP model, classification was performed on a test dataset of 1,000 samples using a Field Programmable Gate Array (FPGA), and the classification accuracy for each class was compared. When the BNN-MLP model is implemented on an FPGA, it utilizes 7,994 Look-Up Tables (LUT) and 9,780 Flip-Flops (FF), occupying fewer resources compared to previous studies. Additionally, it operates at a lower power consumption of 0.157 W, demonstrating the highest power efficiency performance of 407.6 GOPS/W. Finally, the inference time for a single data point was 0.018 ms, which is much faster than in previous studies, confirming its potential for low-power and compact applications in biosensors.}, archive = {J_ACCESS}, author = {Geon-Hoe Kim and Dong-Gyun Kim and Sung-Jae Lee and Jong-Han Kim and Da-Yeong An and Hyejin Kim and Young-Gun Pu and Heejeong Jasmine Lee and Jun-Eun Park and Kang-Yoon Lee}, doi = {10.1109/ACCESS.2025.3572201}, journal = {IEEE Access}, pages = {90741-90752}, shortjournal = {IEEE Access}, title = {A low power memory-integrated hardware BNN-MLP model on an FPGA for current signals in a biosensor}, volume = {13}, year = {2025}, }
• (2025). Implementing FFE-MLSD with improved BER and reduced complexity for long-reach PAM4 wireline receivers. ACCESS, 13, 90726-90740. (www)

  @article{ , abstract = {This paper proposes a PAM4 receiver DSP architecture based on Feed-Forward Equalizer (FFE) and Maximum Likelihood Sequence Detector (MLSD), targeting improved bit-error-rate (BER) performance and reduced hardware complexity. Two key contributions are presented: (1) an FFE tap coefficient optimization framework that tunes the FFE to provide the output with an inter-symbol interference (ISI) memory length of two for better MLSD performance, and (2) a Top-K selection technique that reduces the computational complexity of MLSD by limiting the number of branch metric computations per trellis stage. The coefficient training is performed in two stages using least-mean-square (LMS) and cross-entropy loss based on pseudo-labels derived from MLSD outputs. Simulation results show two orders of magnitude improvement in BER over conventional LMS-based FFE coefficient optimization. In addition, to mitigate the MLSD complexity, Top-K selection approach is proposed to select only the most relevant K branches for MLSD computation using a pre-computed lookup table. The proposed architecture is synthesized and evaluated in a 28nm CMOS process and also implemented on the RealDigital RFSoC $4\times 2$ FPGA board for demonstration. Compared to the full-metric MLSD architecture, the proposed reduced-complexity MLSD achieves 74% reduction in area and 65% in power consumption while maintaining BER performance.}, archive = {J_ACCESS}, author = {Hanseok Kim and Sihyun Lee and Piljun Jeong and Jaeha Kim and Woo-Seok Choi}, doi = {10.1109/ACCESS.2025.3572568}, journal = {IEEE Access}, pages = {90726-90740}, shortjournal = {IEEE Access}, title = {Implementing FFE-MLSD with improved BER and reduced complexity for long-reach PAM4 wireline receivers}, volume = {13}, year = {2025}, }
• (2025). IC backside tamper detection using impedance sensing. ACCESS, 13, 90706-90725. (www)

  @article{ , abstract = {The expansion of flip-chip technologies and lack of backside protection made integrated circuits (ICs) vulnerable to certain classes of physical attacks mounted from the IC’s backside, including laser-assisted probing, electromagnetic side-channel analysis, body biasing injection (BBI), and focused ion beam (FIB) editing. Existing countermeasures are expensive, incompatible with standard IC fabrication, and unsuitable for legacy systems such as field-programmable gate arrays (FPGAs), which are widely used in industrial and defense systems. In this paper, we demonstrate how impedance monitoring of the printed circuit board (PCB) and the IC package’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect IC backside tampering. Our method is based on the fact that any attempt to expose the backside silicon substrate, such as the removal of the fan and heatsinks, or polishing it, leads to changes in the equivalent impedance of the package’s PDN, and hence scanning the package impedance will reveal if the package integrity has been violated. To validate our claims, we deployed an on-FPGA network analyzer on an AMD Zynq UltraScale+ MPSoC (16 nm), which is part of a multi-PCB system. We performed a series of backside tamper attacks, including the removal of cooling components and IC backside silicon milling/polishing, across different temperatures, and evaluated detection effectiveness leveraging the difference of means as the statistical metric.}, archive = {J_ACCESS}, author = {Tahoura Mosavirik and Shahin Tajik}, doi = {10.1109/ACCESS.2025.3572694}, journal = {IEEE Access}, pages = {90706-90725}, shortjournal = {IEEE Access}, title = {IC backside tamper detection using impedance sensing}, volume = {13}, year = {2025}, }
• (2025). Analytical modeling of mutual inductance in superconducting pancake coils for wireless power transfer systems. ACCESS, 13, 90698-90705. (www)

  @article{ , abstract = {This paper presents an analytical approach for calculating the mutual inductance between superconducting pancake coils in Wireless Power Transfer (WPT) systems, leveraging the improved series-form analytical expression derived from Parise et al. The method addresses the challenges of misalignment and complex coil geometries typical in high-temperature superconducting (HTS) applications. The results obtained from the analytical model are compared with numerical simulations and experimental data from the work of Shi et al., demonstrating improved accuracy and computational efficiency. This work provides a robust tool for the design and optimization of HTS WPT systems.}, archive = {J_ACCESS}, author = {Michele Quercio and Mauro Parise and Davide Milillo and Lorenzo Sabino and Rafiq Asghar and Francesco Riganti Fulginei}, doi = {10.1109/ACCESS.2025.3572733}, journal = {IEEE Access}, pages = {90698-90705}, shortjournal = {IEEE Access}, title = {Analytical modeling of mutual inductance in superconducting pancake coils for wireless power transfer systems}, volume = {13}, year = {2025}, }
• (2025). Review of surface-defect detection methods for industrial products based on machine vision. ACCESS, 13, 90668-90697. (www)

  @article{ , abstract = {Industrial defect detection is crucial for ensuring product quality and production efficiency, playing a pivotal role in advancing smart manufacturing. This paper reviews defect detection technologies for various industrial products, including metals, textiles, and printed circuit boards, and introduces an innovative classification system. It also offers a detailed analysis of recent developments and practical applications of large models in industry defect detection. First, the basic principles of industrial defect detection are outlined. The detection methods are then categorized into three main groups: traditional image processing, machine learning, and deep learning, with their principles, case studies, limitations, and future development directions analyzed. Traditional methods consist of image preprocessing, segmentation, and feature extraction. Machine learning methods are divided into point-distance-based, hyperplane-based, tree-based, and neural network-based classification algorithms. Deep learning models are classified into two types: accuracy-oriented and efficiency-oriented. The paper organizes industrial defect datasets by type (multi-product and single-product), evaluates data quality and availability, and summarizes common evaluation metrics for accuracy, efficiency by task requirements. It also compares the latest methods on two public datasets to guide further research in defect detection. Real-world examples illustrate the end-to-end process, from data processing and hardware configuration to model training and deployment, while exploring the value and limitations of these technologies from the perspective of industry stakeholders. Finally, a systematic analysis of the key challenges and corresponding solutions is presented at the data and performance levels, and looks forward to the future direction of technological development, highlighting innovative paths and application potentials.}, archive = {J_ACCESS}, author = {Quan Wang and Mengnan Wang and Jiadong Sun and Deji Chen and Pei Shi}, doi = {10.1109/ACCESS.2025.3571297}, journal = {IEEE Access}, pages = {90668-90697}, shortjournal = {IEEE Access}, title = {Review of surface-defect detection methods for industrial products based on machine vision}, volume = {13}, year = {2025}, }
• (2025). Modified MCP-based modeling and performance analysis of 3-D cellular networks. ACCESS, 13, 90651-90667. (www)

  @article{ , abstract = {In this paper, we introduce a new stochastic geometry model, the ‘Modified Matérn Cluster Process (MMCP)’, to provide an accurate representation of cell locations in a network. This model not only incorporates realistic constraints but also takes into account the clustered distribution of user equipment (UE). Specifically, UEs are assumed to cluster around small-cell base stations (SCBs), which adhere to a minimum separation distance and are organized according to a Matérn Hard-Core Process (MHCP) II, ensuring efficient spatial distribution and reduced interference. To further enhance network performance, we propose a novel transmission scheme specifically designed to mitigate interference. This innovative approach results in the development of a tractable analytical framework, which includes a detailed assessment of the non-interference probability, thereby optimizing resource allocation strategies across the network. Within this framework, we derive expressions for the successful transmission probabilities for both uplink and downlink communications. Additionally, we explore the average ergodic capacity, a crucial metric for evaluating the overall efficacy of the three-dimensional (3-D) MMCP. These derivations enable a deeper understanding of the model’s impact on network performance. To validate our theoretical findings, we conduct extensive Monte Carlo simulations, which demonstrate the accuracy of our proposed model. Our results highlight the critical importance of accounting for user distribution when evaluating dynamic system performance.}, archive = {J_ACCESS}, author = {Dorsaf Ghozlani and Aymen Omri and Seifeddine Bouallegue and Hela Chamkhia and Ridha Bouallegue}, doi = {10.1109/ACCESS.2025.3569202}, journal = {IEEE Access}, pages = {90651-90667}, shortjournal = {IEEE Access}, title = {Modified MCP-based modeling and performance analysis of 3-D cellular networks}, volume = {13}, year = {2025}, }
• (2025). LLaMA-UTP: Knowledge-guided expert mixture for analyzing uncertain tax positions. ACCESS, 13, 90637-90650. (www)

  @article{ , abstract = {This paper introduces LLaMA-UTP, a novel framework for classifying and interpreting uncertain tax positions (UTPs) in technology companies. Tax professionals face significant challenges when analyzing UTPs: navigating vast bodies of intricate legal text, interpreting highly technical domain-specific language, maintaining accurate citations to authoritative sources, and producing justifications that comply with legal standards. We address these challenges with a knowledge-guided expert mixture system built upon a domain-adapted LLaMA architecture incorporating retrieval-augmented generation to ground analysis in relevant tax authorities, specialized expert subnetworks for different tax knowledge domains, and a tax citation knowledge graph that enhances interpretability through structured reasoning paths. Our comprehensive evaluation demonstrates that LLaMA-UTP achieves 90.5% accuracy in classifying UTP risk levels, significantly outperforming baseline models including LegalBERT (82.0%) and GPT-3.5 (78.4%). The system generates explanations with 93% citation accuracy and zero hallucinated references, as validated by tax professionals who rated its outputs as highly aligned with expert reasoning (4.2/5). Through ablation studies, we demonstrate that each architectural component contributes measurably to both accuracy and interpretability. This work makes significant contributions to both AI/ML methodology and tax law analysis by showcasing how knowledge-guided expert mixtures can enhance model performance in highly specialized professional domains while maintaining the explainability necessary for high-stakes legal applications.}, archive = {J_ACCESS}, author = {Yutong Tan and Bi Wu and Jialei Cao and Bingying Jiang}, doi = {10.1109/ACCESS.2025.3571502}, journal = {IEEE Access}, pages = {90637-90650}, shortjournal = {IEEE Access}, title = {LLaMA-UTP: Knowledge-guided expert mixture for analyzing uncertain tax positions}, volume = {13}, year = {2025}, }
• (2025). Topology knapsack problem for geometry optimization. ACCESS, 13, 90626-90636. (www)

  @article{ , abstract = {The knapsack problem is a classic NP-hard optimization challenge with wide-ranging applications in computer science, such as resource allocation. While several variants have been developed, including the 0/1, fractional, and multi-dimensional knapsack problems, no existing model simultaneously optimizes multiple attributes like rarity, importance, and preference alongside the weight and volume of items. The multi-dimensional knapsack problem is the closest approach, but it still fails to fully address this complex optimization challenge. This paper proposes the topology knapsack problem, a novel variant integrating high-dimensional datasets and multiple attributes for each item. The goal is to maximize the total “topo value” by optimally selecting items based on these diverse attributes. Experimental results using both virtual and real-world datasets demonstrate that the topology knapsack problem outperforms traditional knapsack models in terms of diversity and uniformity of the selected item set. It also shows superior performance compared to popular community detection algorithms. The use of t-SNE for dimensionality reduction and K-means for clustering provides an effective solution for multi-attribute optimization. The experiments compared the non-weighted topology knapsack problem, the weighted topology knapsack problem, and the traditional knapsack problem using a virtual dataset and a real-world dataset. The topology knapsack problem achieved better performance for entropy, evenness, Simpson index, and Pielou evenness. Moreover, the experiments compared the topology knapsack problem and the Louvain community detection algorithm using both datasets. The topology knapsack problem achieved better conductance values for each graph. In conclusion, the results indicate notable improvements over existing methods and suggest the potential for future applications in more complex decision-making problems.}, archive = {J_ACCESS}, author = {Hsiao-Hui Li and Po-Chun Chang and Yuan-Hsun Liao}, doi = {10.1109/ACCESS.2025.3571807}, journal = {IEEE Access}, pages = {90626-90636}, shortjournal = {IEEE Access}, title = {Topology knapsack problem for geometry optimization}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of network simulator for high-level naval ship system. ACCESS, 13, 90612-90625. (www)

  @article{ , abstract = {Operational naval ship systems rely on advanced sensors and equipment to detect targets and manage weapon control and engagement. However, their growing complexity, driven by technological advancements and the increasing integration of clients and servers, necessitates changes in system architecture and network structure. To address these challenges, this paper presents the design and implementation of NSNS (Network Performance Evaluation Simulator for Naval Ship Combat System), a specialized simulator for advancing research on naval ship system networks. The NSNS provides a robust environment for performance analysis by leveraging the unique characteristics of naval ship combat system nodes and network properties. Its reliability was validated through comparisons with real-world system measurements, demonstrating consistent trends and acceptable error rates. This research underscores the importance of dedicated simulation tools for validating new naval network architectures and enhancing network reliability and availability in wartime scenarios.}, archive = {J_ACCESS}, author = {Min-Hui Jang and Hyeong-Jin Kim and Young-Keun Go and Nak-Jung Choi and Jae-Ho Lee and Jae-Hak Yu and Jae-Min Lee and Tae-Soo Jun and Dong-Seong Kim}, doi = {10.1109/ACCESS.2025.3565301}, journal = {IEEE Access}, pages = {90612-90625}, shortjournal = {IEEE Access}, title = {Design and implementation of network simulator for high-level naval ship system}, volume = {13}, year = {2025}, }
• (2025). Evaluation of the serious game ABCCI: Usability and gamification in teacher training for science education and environmental sustainability. ACCESS, 13, 90601-90611. (www)

  @article{ , abstract = {The serious game ABCCI (Aprendizaje Basado en la Creatividad Científica, or Learning Based on Scientific Creativity) was developed to integrate inquiry-based learning and environmental sustainability into teacher training, aiming to enhance scientific competencies in primary education. This study evaluates its usability and educational effectiveness based on the experiences of pre-service teachers from diverse sociocultural contexts. A qualitative approach with an instrumental case study design was employed, involving 38 students from two public universities in Peru (Arequipa and Apurímac). Data collection included the System Usability Scale (SUS) questionnaire and semi-structured interviews, analyzed through content analysis. The results indicate that ABCCI received a moderate usability rating according to the SUS, suggesting room for improvement in design and accessibility. However, participants positively assessed its gamification elements, particularly rewards and immediate feedback, for their role in enhancing motivation and learning. The game also demonstrated potential to foster competencies in scientific inquiry and sustainability, reinforcing its educational value. Despite these strengths, key areas for improvement were identified, including greater role customization, expanded activities promoting sustainable actions, and more immersive scenarios to enhance engagement. ABCCI emerges as an innovative educational tool with significant potential for environmental education in teacher training, particularly in diverse sociocultural contexts. These findings underscore the importance of integrating educational technologies into teacher preparation programs to strengthen scientific competencies and promote sustainability awareness. Further refinements in game design and pedagogical strategies could maximize its impact, making it a valuable resource for enhancing science education and environmental consciousness in future educators.}, archive = {J_ACCESS}, author = {Diego Antonio Urdanivia Alarcon and Fabiola Talavera-Mendoza and Fabian Hugo Rucano Paucar}, doi = {10.1109/ACCESS.2025.3569258}, journal = {IEEE Access}, pages = {90601-90611}, shortjournal = {IEEE Access}, title = {Evaluation of the serious game ABCCI: Usability and gamification in teacher training for science education and environmental sustainability}, volume = {13}, year = {2025}, }
• (2025). Fault detection in blowfish algorithm using FPGA-based modified decision tree approach. ACCESS, 13, 90591-90600. (www)

  @article{ , abstract = {This research article identifies the fault occurrence in the blowfish cryptography algorithm using a modified Decision Tree classifier. Though there are several cryptography algorithms, the symmetric blowfish algorithm is considered for its high performance, compatibility, security, and ease of implementation. When utilized in real applications, the attack in the bit transmission either at the encryption or at the decryption might affect its authentication and security. This demands automatic fault identification within the blowfish algorithm using a modified Decision tree. This work includes attack induction and identification of the same using the developed modified decision tree-based equality checker circuit. The proposed method involves the development of an equality-checker-based decision tree algorithm to form 12 sections for the 16 iterations from the encryption and decryption process of the blowfish algorithm. The FPGA device implements the proposed method to validate the real-time feasibility. Also, the System on Chip IC layout is developed for the proposed method to analyze the parameters of power and area using the EDA tools. The utilization of FPGA in indicating the attack of the blowfish algorithm is evaluated for its high-performance capability that has low latency and higher throughput for the 64-bit design resolution.}, archive = {J_ACCESS}, author = {Joseph Anthony Prathap and Mithileysh Sathiyanarayanan}, doi = {10.1109/ACCESS.2025.3567659}, journal = {IEEE Access}, pages = {90591-90600}, shortjournal = {IEEE Access}, title = {Fault detection in blowfish algorithm using FPGA-based modified decision tree approach}, volume = {13}, year = {2025}, }
• (2025). Key factors influencing preservice chinese teachers’ willingness for implementing AI applications in higher education. ACCESS, 13, 90572-90590. (www)

  @article{ , abstract = {The swift progression of Artificial Intelligence (AI) is revolutionizing multiple domains, including higher education. This research examines the main variables affecting Chinese preservice teachers’ readiness to apply AI in higher education by using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Structural equation modeling (SEM) was used to analyze a questionnaire (N =493) that was distributed to preservice teachers in higher institution. The study finds that performance expectancy, habit, and ethical and privacy concerns significantly influence behavior intention, highlighting their importance in fostering AI adoption. Behavior intention is a strong predictor of use behavior, emphasizing the need to focus on behavior to ensure effective technology integration. Effort expectancy, social influence, facilitating conditions, hedonic motivation, or learning value do not significantly affect behavior intention, suggesting their impact may be context-specific. Notably, gender and experience significantly moderate use behavior in a negative way. This indicates that gender differences and various levels of experience impact technology usage, necessitating tailored support strategies. Future research should explore contextual factors and interactions between predictors and moderators to refine AI integration strategies in education.}, archive = {J_ACCESS}, author = {Yanfei Li and Michael John Harris and Ahmad Yahya Dawod and Thacha Lawanna}, doi = {10.1109/ACCESS.2025.3568834}, journal = {IEEE Access}, pages = {90572-90590}, shortjournal = {IEEE Access}, title = {Key factors influencing preservice chinese teachers’ willingness for implementing AI applications in higher education}, volume = {13}, year = {2025}, }
• (2025). An adaptive methodology for constructing domain-specific sentiment lexicons based on chinese social media data. ACCESS, 13, 90558-90571. (www)

  @article{ , abstract = {Currently, many methods for automatically constructing domain-specific sentiment lexicons rely on knowledge bases and domain-specific corpora. However, these methods often face accuracy challenges due to data sparsity, and inferring the polarity of new domain-specific sentiment words from a limited set of labeled seed words lacks precision. Chinese social media texts typically exhibit a high degree of randomness, noise, and informal sentiment words, which further increases the difficulty of constructing domain-specific sentiment lexicons. To address these challenges, we propose an adaptive framework for constructing domain-specific sentiment lexicons using Chinese social media data and apply it to develop a sentiment lexicon for public opinion during public health emergencies (PHEPO-SentiLex). We first fine-tune Bidirectional Encoder Representations from Transformers (BERT) via a multi-task framework on domain-specific corpus and a small number of Weibo-annotated sentiment datasets, enabling the model to encode both domain semantics and sentiment-related contextual patterns into word embeddings through gradient sharing. The embeddings are subsequently used to calculate the Sentiment Attraction Degree (SAD) during seed word filtering, cosine similarity during domain-specific sentiment word selection, and for constructing the domain-specific corpus-sentiment word graph (SentiGraph). Next, we propose SentiGraph-GCN, a method for sentiment word polarity determination that integrates semantic, sentiment, co-occurrence frequency, and global structural information embedded in the corpus. Experimental results demonstrate that SentiGraph-GCN significantly outperforms existing methods in determining sentiment word polarity. Furthermore, PHEPO-SentiLex exhibits superior accuracy and stability in relevant scenarios compared to general-purpose sentiment lexicons.}, archive = {J_ACCESS}, author = {Xue Xu and Haidong Liu and Lei Liu}, doi = {10.1109/ACCESS.2025.3572077}, journal = {IEEE Access}, pages = {90558-90571}, shortjournal = {IEEE Access}, title = {An adaptive methodology for constructing domain-specific sentiment lexicons based on chinese social media data}, volume = {13}, year = {2025}, }
• (2025). Modeling of multi-electrode epicardial electrograms for conductivity estimation in atrial fibrillation. ACCESS, 13, 90545-90557. (www)

  @article{ , abstract = {Atrial fibrillation (AF) is a complex, multifactorial heart disorder with mechanisms that are not yet fully understood. Numerical methods for estimating the conductivities of atrial cells are crucial for identifying the arrhythmogenic substrate responsible for AF. Although mechanistic models of atrial electrophysiology have become increasingly realistic, their complexity has grown over the years, making it challenging to estimate electrophysiological parameters from these models. In this work, we first introduce a simplified mechanistic model for multi-electrode epicardial electrograms, which balances the complexity of cardiac electrophysiology in AF with model simplicity. Based on this model, we derived a cross-power spectrum model capable of capturing time-spatial-frequency information from multi-electrode data for estimating conductivity parameters. As the parameter estimation problem is highly ill-posed and can lead to unstable solutions, we integrated simultaneous confirmatory factor analysis with an improved regularization strategy to robustly estimate conductivity parameters. The experimental results on different types of tissue demonstrate the effectiveness of our model and method in estimating conductivities in AF and demonstrate the improvement of abnormality detection in the tissue.}, archive = {J_ACCESS}, author = {Miao Sun and Chengli Sun and Cairong Zou and Jie Zhang and Dan Xiang}, doi = {10.1109/ACCESS.2025.3571102}, journal = {IEEE Access}, pages = {90545-90557}, shortjournal = {IEEE Access}, title = {Modeling of multi-electrode epicardial electrograms for conductivity estimation in atrial fibrillation}, volume = {13}, year = {2025}, }
• (2025). Speech translation from darija to classical arabic: Performance analysis of whisper, SeamlessM4T, and S2T models. ACCESS, 13, 90527-90544. (www)

  @article{ , abstract = {This study evaluates the performance of advanced speech-to-text translation models—Whisper large-v3, SeamlessM4T, and S2T—fine-tuned on the Darija-C corpus to translate Darija speech into Classical Arabic text. Darija, a widely spoken Arabic dialect, presents significant challenges for automated translation due to its linguistic complexity and lack of standardized resources. The fine-tuning process adapted each model to the nuances of Darija speech, aiming to optimize translation quality and linguistic alignment with Classical Arabic. The primary goal of this research is to identify the model achieving the highest BLEU score, which serves as a benchmark for translation accuracy and fluency. Comprehensive experiments highlight the comparative strengths and limitations of these models, with Whisper large-v3 emerging as a strong contender alongside SeamlessM4T and S2T. Results reveal the critical role of fine-tuning in improving the performance of pre-trained models on low-resource dialects. This work contributes to the development of effective speech translation systems for Arabic dialects, offering insights into optimizing model architectures for underrepresented languages and enhancing their practical applicability in real-world scenarios.}, archive = {J_ACCESS}, author = {Maria Labied and Abdessamad Belangour and Mouad Banane}, doi = {10.1109/ACCESS.2025.3572611}, journal = {IEEE Access}, pages = {90527-90544}, shortjournal = {IEEE Access}, title = {Speech translation from darija to classical arabic: Performance analysis of whisper, SeamlessM4T, and S2T models}, volume = {13}, year = {2025}, }
• (2025). Innovative applications of black box testing: A new strategy for teaching assessment in higher education. ACCESS, 13, 90514-90526. (www)

  @article{ , abstract = {Grade inflation in traditional teacher-led evaluation methods in higher education undermines the accurate assessment of students’ abilities, hinders their subsequent learning and career development, and negatively affects institutions and society. To address this problem, this paper describes an innovative approach that applies the black box testing method, traditionally used in software testing, to the evaluation of college course grades. This method eliminates direct teacher involvement in course teaching and management; instructors design assessment questions solely based on the syllabus to evaluate students’ learning outcomes. Using the C language course offered during the spring semester of 2021-2022 for mechanical engineering students at the Beijing Institute of Printing as a case study, this research compares student performance under the black box test method with traditional instructor-designed assessments. Results reveal that students scored significantly lower under the black box testing approach, validating its potential to mitigate grade inflation. This study not only highlights a novel application of black box testing in education but also offers a more objective and equitable framework for evaluating academic performance, benefiting students, parents, educational institutions, and society.}, archive = {J_ACCESS}, author = {Hanjiao Wang and Yongbin Zhang and Xiuli Fu}, doi = {10.1109/ACCESS.2025.3568825}, journal = {IEEE Access}, pages = {90514-90526}, shortjournal = {IEEE Access}, title = {Innovative applications of black box testing: A new strategy for teaching assessment in higher education}, volume = {13}, year = {2025}, }
• (2025). 3D part machining time prediction with parameter extraction, deep learning, and 3D data augmentation. ACCESS, 13, 90499-90513. (www)

  @article{ , abstract = {In the aviation industry, an aircraft consists of thousands of detail parts in different types of materials. The sizes of these parts can range from centimeters to meters, and their complexity can also vary greatly. Therefore, there is a need to produce detail parts in a wide variety of geometries that constitute the product. In addition, these detail parts, especially in the prototype phase, often require a workshop-type of production rather than mass production. To optimize the production process and its cost, the production times of these parts must be estimated in advance. There is currently a great need for expert effort on this matter. Experts examine each detail part and calculate their predictions. In this study, multiple innovations are proposed to automate these time-consuming expert examinations by using special software. To that end, a regression model that can predict machining times using 3D models of detail parts used in the aerospace industry is proposed. On the other hand, the relatively small amount of datasets created by the detail parts used in aircraft also poses an issue in the deep learning regression problem. To solve this problem, a special data augmentation and a series of parameter extraction and concatenation methods are introduced in this study. The solutions developed with the proposed procedure have significantly contributed to the prediction performance. In experiments after adapting these methods to regression, the prediction error is significantly reduced.}, archive = {J_ACCESS}, author = {Umut Nazmi Aktan and Mehmet Dikmen}, doi = {10.1109/ACCESS.2025.3572621}, journal = {IEEE Access}, pages = {90499-90513}, shortjournal = {IEEE Access}, title = {3D part machining time prediction with parameter extraction, deep learning, and 3D data augmentation}, volume = {13}, year = {2025}, }
• (2025). Autonomy in conflict: Technology, complexity, ethics, and policy implications. ACCESS, 13, 90489-90498. (www)

  @article{ , abstract = {The transformative role of autonomy-relevant technologies in modern conflict is examined, focusing on technological advancements, complexity, ethics and policy implications. The foundations of autonomous capabilities in conflict comprise high-grade, autonomy-relevant technologies that integrate human-machine interactions. Key technological milestones include AI-powered systems, component miniaturization, and enhanced data processing capabilities, enabling autonomous systems to perform complex tasks with increased precision and speed. Autonomy provides substantial value for intelligence gathering, surveillance, and targeted strikes, significantly impacting modern conflicts and offering benefits such as reduced risk exposure, accelerating decision-making in time-critical operations, enhanced precision, speed, persistence, endurance, and scale. Autonomy reduces cognitive operator load and enables missions that would otherwise be unfeasible or unaffordable. This work addresses system complexity challenges, which can hinder human operators in predicting behavior, intervening when necessary, and maintaining effective and meaningful control. Trust, transparency, ethical considerations, and interdependence between human operators and autonomous systems are essential for effective deployment. European initiatives, such as the European Defence Agency’s Action Plan on Autonomous Systems, emphasizes the necessity for developing autonomous technologies for military capabilities, underscoring the requirement for long-term investments in scientific research, training, and innovation to maintain a strategic advantage. Emerging technologies, such as neuromorphic processing and machine learning, are fundamental for developing future autonomous systems capable of adapting to dynamic environments and executing missions with minimal human intervention. In conclusion, the evolution of autonomous technologies represents a paradigm shift, necessitating robust ethical and policy frameworks and continuous innovation to address the complexities of modern conflict.}, archive = {J_ACCESS}, author = {Arne Norlander}, doi = {10.1109/ACCESS.2025.3571225}, journal = {IEEE Access}, pages = {90489-90498}, shortjournal = {IEEE Access}, title = {Autonomy in conflict: Technology, complexity, ethics, and policy implications}, volume = {13}, year = {2025}, }
• (2025). Sequential hybrid beamforming for radio stripes. ACCESS, 13, 90476-90488. (www)

  @article{ , abstract = {Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems will revolutionize wireless communication by moving away from traditional base stations (BSs). By eliminating conventional cell boundaries, CF mMIMO deploys a large number of distributed access points (APs), enabling seamless connectivity across a wide area, more flexible resource allocation, and enhanced overall system performance. However, CF demands a significant amount of fronthaul signaling overhead due to its inherent nature (star topologies), which limits its practical application. In this regard, radio stripe (RS) is a promising and cost-efficient approach to deal with these issues, improving system efficiency and scalability. Not only RS, millimeter wave (mmWave) communications also represent a significant advancement in the evolution of wireless communication since it achieves multi-Giga bit per second (Gbps) data transfer rates. Still, mmWave requires beamforming to overcome the high path loss (PL) and shadowing effects. In this paper, we propose a new low-complexity hybrid analog-digital beamforming technique that addresses the challenges posed by wideband mmWave CF mMIMO RS-based systems. The APs hybrid equalizer is optimized, using as a metric, the mean squared error (MSE) between the transmitted and the AP received signals. The optimization is done over the radio stripe, i.e., at each AP, only local and side information from the former AP in the stripe is considered. Simulation results have shown that our proposed sequential hybrid algorithm achieves approximately the same performance as the centralized CF counterpart with a significant reduction in the signaling and information exchange requirements.}, archive = {J_ACCESS}, author = {Joumana Kassam and Daniel Castanheira and Adão Silva and Rui Dinis and Atílio Gameiro}, doi = {10.1109/ACCESS.2025.3571771}, journal = {IEEE Access}, pages = {90476-90488}, shortjournal = {IEEE Access}, title = {Sequential hybrid beamforming for radio stripes}, volume = {13}, year = {2025}, }
• (2025). A 3-D magnetometer-aided low-field electromagnetic tracking system for clinical surgery applications. ACCESS, 13, 90467-90475. (www)

  @article{ , abstract = {Magnetic field distortions caused by metal objects or other magnetic materials interferes with the accuracy of high-field electromagnetic (EM) tracking systems. Additionally, the effective range of the EM field or the working volume may be limited. In this study, a low-power generator and magnetic sensors exhibiting high-performance are introduced as a substitute for high-field electromagnetic tracking systems. To generate magnetic field gradients that uniquely encode each spatial point, magnetic fields are varied over three locations. These gradients are detected using millimeter-sized sensors with quality resolution, and are able to measure their local magnetic fields with accuracy. The sensors are integrated into surgical instruments (e.g. catheters and brain electrodes). By utilizing a low-field generator and low power consumption, the incorporation of electromagnetic systems in surgical rooms is significantly improved. Using advanced 3D-axis magnetoresistive sensors, the system achieves a mean absolute error of 3 mm at a distance of 42 cm from the field generator, thereby enabling precise and orientation-independent spatial encoding. Following sensor calibration procedure, localization along the Z-axis showed substantial improvement. The developed low-field EM tracking system, which does not require a line of sight is ideal for real-time navigation in complex clinical environments.}, archive = {J_ACCESS}, author = {Oluwole John Famoriji and Thokozani Shongwe}, doi = {10.1109/ACCESS.2025.3572445}, journal = {IEEE Access}, pages = {90467-90475}, shortjournal = {IEEE Access}, title = {A 3-D magnetometer-aided low-field electromagnetic tracking system for clinical surgery applications}, volume = {13}, year = {2025}, }
• (2025). Machine learning and deep learning approaches for fake news detection: A systematic review of techniques, challenges, and advancements. ACCESS, 13, 90433-90466. (www)

  @article{ , abstract = {In response to the escalating threat of fake news on social media, this systematic literature review analyzes the recent advancements in machine learning and deep learning approaches for automated detection. Following the PRISMA guidelines, we examined 90 peer-reviewed studies published between 2020 and 2024 to evaluate the model effectiveness, identify limitations, and highlight emerging trends. Our analysis shows that deep learning models, particularly transformer-based architectures such as BERT, consistently outperform traditional machine learning methods, often achieving a high accuracy (Acc), precision (P), recall (R), and F1-score (F1). For instance, a BERT-based model reported up to 99.9% accuracy on the Kaggle fake news dataset and above 98% accuracy on other public datasets, including ISOT, Fake-or-Real, and D3. Similarly, the GANM model demonstrated robust performance on the FakeNewsNet dataset by integrating text and social features. Transfer learning and multimodal models that incorporate user behaviour and network information significantly improve detection in diverse, low-resource environments. However, challenges persist in terms of the dataset quality, model interpretability, domain generalisability, and real-time deployment. This review also underscores the limited adoption of few-shot and zero-shot learning techniques, highlighting a promising direction for future research on handling emerging misinformation using minimal training data. To support practical deployment, we advocate the development of explainable, multilingual, and lightweight models with greater emphasis on human-centred evaluation and ethical considerations. Our findings provide a foundation for researchers and practitioners to build scalable, trustworthy, and context-aware fake news detection systems for global use.}, archive = {J_ACCESS}, author = {Omar Bashaddadh and Nazlia Omar and Masnizah Mohd and Mohd Nor Akmal Khalid}, doi = {10.1109/ACCESS.2025.3572051}, journal = {IEEE Access}, pages = {90433-90466}, shortjournal = {IEEE Access}, title = {Machine learning and deep learning approaches for fake news detection: A systematic review of techniques, challenges, and advancements}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of reinforcement learning and rule-based system approaches for irrigation in horticulture. ACCESS, 13, 90418-90432. (www)

  @article{ , abstract = {Irrigation optimization in horticulture is highly relevant due to the high sensitivity of crops to water availability. Methods such as Reinforcement Learning (RL) and Rule-Based Systems (RBS) have been explored for automated irrigation; however, direct comparisons between these approaches for horticultural crops remain limited. This study compares RL and RBS for optimizing irrigation in lettuce cultivation using the AquaCrop-OSPy model. A Q-Learning RL agent dynamically adjusts irrigation decisions based on a customized reward function, while the RBS applies predefined water balance rules. Simulations were conducted over 30 days in each of the four seasons. Results indicate that RL achieved an average dry yield of 5.84 tonne/ha with 186.25 mm of water, while RBS produced 2.35 tonne/ha with 92.01 mm. RL consistently delivered higher productivity but at the cost of increased water use, whereas RBS demonstrated greater water efficiency under conservative irrigation strategies. The findings highlight RL’s adaptability to varying conditions and RBS’s reliability for water-limited scenarios. Future research can explore hybrid systems that integrate RL’s flexibility with RBS’s efficiency. The code and datasets are available at https://github.com/GabyQueiroz/RBS_RL_Irrigation}, archive = {J_ACCESS}, author = {Gabrielly de Queiroz Pereira and Douglas Paulo Bertrand Renaux and André Eugênio Lazzaretti}, doi = {10.1109/ACCESS.2025.3572288}, journal = {IEEE Access}, pages = {90418-90432}, shortjournal = {IEEE Access}, title = {Comparative analysis of reinforcement learning and rule-based system approaches for irrigation in horticulture}, volume = {13}, year = {2025}, }
• (2025). Underwater acoustic communication using multi-layer vector approximate message passing evaluated on the watermark benchmark. ACCESS, 13, 90409-90417. (www)

  @article{ , abstract = {The multi-layer vector approximate message passing algorithm is applied to the problem of channel estimation in underwater acoustic communication addressing the estimation of time-varying channel characteristics. A pre-trained artificial neural network is used as prior for the channel estimation. The communication receiver is designed as a factor graph with a low-density parity check decoder as factor node. The performance of the proposed communication scheme is evaluated by comparing the bit error rates with classical communication schemes. The bit error rate is significantly decreased on strongly time-varying channels. Different datasets are compared for training the channel estimator.}, archive = {J_ACCESS}, author = {Julian Winkler and Sabah Badri-Hoeher}, doi = {10.1109/ACCESS.2025.3572283}, journal = {IEEE Access}, pages = {90409-90417}, shortjournal = {IEEE Access}, title = {Underwater acoustic communication using multi-layer vector approximate message passing evaluated on the watermark benchmark}, volume = {13}, year = {2025}, }
• (2025). A fully digital indirect time-of-flight image sensor with multi-frame integration and time-gated single-photon counting method. ACCESS, 13, 90402-90408. (www)

  @article{ , abstract = {This paper presents an indirect time-of-flight (iToF) image sensor, based on a single-photon avalanche diode (SPAD) and a time-gated single-photon counting (TGSPC) method. The SPAD detects an incident photon and generates a voltage pulse through avalanche multiplication, enabling single-photon detection. The proposed SPAD utilizes a P-well and Deep N-well junction with a retrograde P-substrate guard-ring, designed to mitigate premature breakdown at the junction edges. Also, the TGSPC method is employed, which measures the phase delay proportional to the distance between the sensor and the target. To achieve this, the proposed pixel incorporates two multiplexers and two 5-bit counters, along with an active-recharge circuit for asynchronous SPAD recharge and row-selection switches for digital readout. Thanks to this compact pixel design, a small pixel pitch of $35~\mu $ m can be achieved. To overcome the limited depth of the 5-bit counter, a multi-frame integration technique is applied, effectively extending the counter depth to 10-bit. The proposed sensor is fabricated in a 110 nm backside-illumination (BSI) CMOS image sensor process, optimized for light detection and ranging (LiDAR) applications. By incorporating these optimizations, a detection range of 40 m and 0.19% depth precision are achieved simultaneously, with a frame rate of 180 frame/s. These results demonstrate the potential of the proposed sensor for low-cost, high-precision LiDAR systems.}, archive = {J_ACCESS}, author = {Byungchoul Park and Hyeon-June Kim}, doi = {10.1109/ACCESS.2025.3571838}, journal = {IEEE Access}, pages = {90402-90408}, shortjournal = {IEEE Access}, title = {A fully digital indirect time-of-flight image sensor with multi-frame integration and time-gated single-photon counting method}, volume = {13}, year = {2025}, }
• (2025). Collision detection and reaction for quadrotors using encoder-integrated tombo propellers. ACCESS, 13, 90389-90401. (www)

  @article{ , abstract = {This study presents a novel collision detection and reaction strategy for quadrotors equipped with deformable propellers (Tombo propellers). By integrating rotary encoders, we achieve real-time monitoring of individual propeller speeds, enabling rapid and reliable collision detection based on sudden velocity drops. The propeller speed variation during collisions is analyzed through simulations of the BLDC motor-Tombo propeller collision model. In addition, experimental validation is performed on a quadrotor to reinforce the correlation between propeller velocity changes and collisions. A collision detection algorithm based on this phenomenon successfully identifies eight out of twelve collisions during hover flights, while the remaining four minor collisions, which were undetected, had negligible effects on quadrotor stability. Subsequently, a collision response strategy that takes advantage of this algorithm for a quadrotor following a pre-defined trajectory is implemented. Experimental results demonstrate that this strategy is successfully applied to the Tombo quadrotor, whereas a quadrotor with rigid propellers fails due to propeller breakage on impact. This highlights the effectiveness of combining Tombo propellers for impact resilience with encoder-based velocity monitoring for reliable collision detection and response, ensuring safer quadrotor operations. (Video: https://www.youtube.com/watch?v=czliIvss5_I).}, archive = {J_ACCESS}, author = {Hung Tien Pham and Quang Ngoc Pham and Quan Khanh Luu and Giuseppe Loianno and Van Anh Ho}, doi = {10.1109/ACCESS.2025.3572243}, journal = {IEEE Access}, pages = {90389-90401}, shortjournal = {IEEE Access}, title = {Collision detection and reaction for quadrotors using encoder-integrated tombo propellers}, volume = {13}, year = {2025}, }
• (2025). Dynamic modeling and quantum-enhanced forecasting of multi-seasonal energy prices in simulated microgrid environments. ACCESS, 13, 90362-90388. (www)

  @article{ , abstract = {The study address the challenge of forecasting per unit energy prices in a microgrid environment consisting of solar and hydro power resources under multi-seasonal variations. Traditional deep learning techniques such as LSTM, GRU and ESN often struggle with non-linear dependencies and volatility in energy market. To overcome these we propose a hybrid framework incorporating Adiabatic Quantum Computing (AQC) for electricity price forecasting. The proposed AQC model encodes-32 system and market related variables into quantum states and applies adiabatic evolution to derive optimized price prediction. Simulation results using real microgrid data set-up based on HIL shows that AQC reduces forecasting error by 17.03% compared to LSTM, 14.29% to GRU and 13.88% to ESN over 24-hrs and 48-hrs horizons. The enhanced accuracy and robustness of the quantum assisted model demonstrates its potential for next generation energy market forecasting and decisions making tool. The entire framework is tested using a synthetic microgrid dataset designed to emulate real-world seasonal and operational dynamics. While this enables controlled validation of the models, the generalizability of the results to real world deployment requires further empirical evaluations on physical microgrid data set.}, archive = {J_ACCESS}, author = {Ritesh Dash and Anupa Sinha and K. Jyotheeswara Reddy and C. Dhanamjayulu and Innocent Kamwa}, doi = {10.1109/ACCESS.2025.3570719}, journal = {IEEE Access}, pages = {90362-90388}, shortjournal = {IEEE Access}, title = {Dynamic modeling and quantum-enhanced forecasting of multi-seasonal energy prices in simulated microgrid environments}, volume = {13}, year = {2025}, }
• (2025). Comment on ‘‘RIO: Return instruction obfuscation for bare-metal IoT devices’’. ACCESS, 13, 90358-90361. (www)

  @article{ , abstract = {This is a comment on “RIO: Return Instruction Obfuscation for Bare-Metal IoT Devices.” RIO prevents finding gadgets for return-oriented programming attacks by encrypting return instructions. This paper shows flaws in the design of RIO that allow for the easy retrieval of the plaintext return instructions without decrypting them. Additionally, changes are proposed to improve upon the original idea.}, archive = {J_ACCESS}, author = {Kai Lehniger and Peter Langendörfer}, doi = {10.1109/ACCESS.2025.3568598}, journal = {IEEE Access}, pages = {90358-90361}, shortjournal = {IEEE Access}, title = {Comment on ‘‘RIO: Return instruction obfuscation for bare-metal IoT devices’’}, volume = {13}, year = {2025}, }
• (2025). Fog-enabled multimodal chest-worn device for systolic blood pressure monitoring. ACCESS, 13, 90345-90357. (www)

  @article{ , abstract = {This paper presents a Fog-enabled chest-worn device for estimating systolic blood pressure (SBP). The device integrates two sensors that simultaneously detect the phonocardiogram (PCG), ballistocardiogram (BCG), and seismocardiogram (SCG) from a single location on the chest. These signals are good indicators of the early stages of cardiovascular diseases (CVD). Here, a new fog-based deep learning architecture that extracts features directly from raw signals to estimate the SBP is proposed. Three signals were transmitted to a fog computing system via a socket. Four models were implemented for analysis: Random Forest (RF), AdaBoost, Gradient Boosting (GB), and a novel deep learning architecture. The RF, AdaBoost, and GB models focused on the time delays between the main waves of the PCG, BCG, and SCG, while the deep learning architecture extracted features directly from the signals using convolutional layers. The tests involved 36 healthy volunteers under both resting and physically active conditions. The device was built using surface-mount components and powered by a 3.7 V, 250 mAh battery. The dimensions were 65 mm $\times 54$ mm, and the weight was 56 g. The three cardiac signals were simultaneously detected, with a signal-to-noise ratio higher than 41 dB. Among the four implemented models for SBP estimation in fog, the proposed deep learning architecture demonstrated the best performance, achieving an MAE of 3.5 mmHg without estimating the timing between the three detected signals. Integrating the methodology discussed here, the proposed device offers a novel method for estimating blood pressure through raw biosignals detected by a wearable device.}, archive = {J_ACCESS}, author = {Pamela Salas and Jose-Manuel Mejía-Muñoz and Rafael Gonzalez-Landaeta}, doi = {10.1109/ACCESS.2025.3571829}, journal = {IEEE Access}, pages = {90345-90357}, shortjournal = {IEEE Access}, title = {Fog-enabled multimodal chest-worn device for systolic blood pressure monitoring}, volume = {13}, year = {2025}, }
• (2025). Design of a novel adaptive weighted multiview deep neural network for enhanced islanding detection in smart distribution systems. ACCESS, 13, 90333-90344. (www)

  @article{ , abstract = {The extensive adoption of renewable energy resources has introduced a challenge of unintentional islanding in distributed generation (DG) systems. If the islanded DG is not promptly isolated from the system, it may lead to potential damage to the DGs. This paper develops a novel adaptive weighted multiview deep neural network (AWM-DNN) model for enhanced islanding detection in smart distribution networks. The proposed approach extracts distinct views from three-phase(3- $\phi $ ) voltage signal—using time-domain (TD), frequency-domain (FD), and time-frequency-domain analysis (TFD). The proposed method employs synchrosqueezed wavelet transform to extract frequency-domain and time-frequency domain features from the modal voltage signal. These views are then adaptively weighted and provided to a combination of gated recurrent unit and fully connected layer to classify the islanding events in the microgrids. The AWM-DNN based islanding detection scheme (IDS) uses the multiview learning framework to accurately classify islanding and grid-tied scenarios. Exhaustive simulations conducted on the modified IEEE 34-bus system and International Electrotechnical Commission (IEC) microgrid show that the proposed IDS surpasses traditional methods in terms of detection accuracy, non-detection zone (NDZ), and robustness to noisy data. Comparative analysis further highlights the superiority of model superior generalization capabilities and resilience to uncertainties in measurement.}, archive = {J_ACCESS}, author = {Hani Albalawi and Abdul Wadood and Syed Basit Ali Bukhari and Aadel Mohammed Alatwi}, doi = {10.1109/ACCESS.2025.3571907}, journal = {IEEE Access}, pages = {90333-90344}, shortjournal = {IEEE Access}, title = {Design of a novel adaptive weighted multiview deep neural network for enhanced islanding detection in smart distribution systems}, volume = {13}, year = {2025}, }
• (2025). An experimental study on dynamic lifelong learning with GPT for mitigating catastrophic forgetting in aspect-based sentiment analysis. ACCESS, 13, 90316-90332. (www)

  @article{ , abstract = {GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domain-specific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as “catastrophic forgetting,” arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities.}, archive = {J_ACCESS}, author = {Huang Huang and Mumtaz Begum Mustafa and Adeleh Asemi}, doi = {10.1109/ACCESS.2025.3547359}, journal = {IEEE Access}, pages = {90316-90332}, shortjournal = {IEEE Access}, title = {An experimental study on dynamic lifelong learning with GPT for mitigating catastrophic forgetting in aspect-based sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). C2IDTL: Novel click to image conversion approach for deep transfer learning in click fraud detection on digital platforms. ACCESS, 13, 90293-90315. (www)

  @article{ , abstract = {The global retail industry has witnessed a massive expansion of e-commerce businesses in the last decade by transforming physical stores into digitalised entities. One of the biggest threats to e-commerce is click fraud, which generates fraudulent clicks without genuine intention on online advertisements using automated software programs or organised human activities. This has become a major threat to the online advertising eco systems, causing heavy losses for tech companies like Google, Facebook, and Meta which rely on pay per click (PPC) models. To overcome click fraud, we propose a novel click to image conversion technique to generate synthetic images from clicks and perform click fraud detection on those generated images directly with Xception, Inception, VGG, ResNet and DenseNet DTL models (C2IDTL). This novel approach embedded the feature importance and spatial structure into synthetic images to enable the identification of complex pattern recognition via DTL models. We evaluated the performance of C2IDTL on three publicly available datasets, namely FDMA 2012 BuzzCity, TalkingData AdTracking and Sweden Ad click datasets and compared the performances with the conventional state of the art ML models. C2IDTL enhances the fraud detection by exceeding 91.18% accuracy, 90.90% F1 score and 96.45% AUC based on the k-fold cross validation performed on all three datasets. Consequently, these results validate the robustness and the ability to generalise across diverse click datasets evaluated in prior scientific literature. Despite DTL’s association with the visual domain, this study challenges its traditional usage and opens avenues for future studies to optimise performance and robustness.}, archive = {J_ACCESS}, author = {Channa Fernando and Chamila Walgampaya and Janaka Alawatugoda}, doi = {10.1109/ACCESS.2025.3571187}, journal = {IEEE Access}, pages = {90293-90315}, shortjournal = {IEEE Access}, title = {C2IDTL: Novel click to image conversion approach for deep transfer learning in click fraud detection on digital platforms}, volume = {13}, year = {2025}, }
• (2025). LDDFSF-YOLO11: A lightweight insulator defect detection method focusing on small-sized features. ACCESS, 13, 90273-90292. (www)

  @article{ , abstract = {Insulators are essential for maintaining electrical isolation in transmission lines, with the timely and accurate identification of their defects via uncrewed aerial vehicle (UAV) being crucial for ensuring operational stability and safety in high-voltage transmission lines. Due to complex environmental conditions, varying viewing angles, and unstable lighting conditions, existing insulator defect detection algorithms still face challenges, including missed detection, false detection, and inadequate adaptability for detecting small-sized defects in UAV captured images. This study proposes a lightweight insulator defect detection method (LDDFSF-YOLO11) that focuses on small-sized features to efficiently detect and identify insulator defects. In the LDDFSF-YOLO11 model, a focused feature extraction module (MFFEConv) is proposed to enhance the feature extraction capability of the backbone network. In addition, a collaborative attention mechanism and a multi-scale feature fusion module (MDMF) are proposed to enhance the model’s ability to detect small-sized defects in remote and complex backgrounds, effectively solving the problems of missed detections, false detections, and poor adaptability to complex backgrounds. In addition, we propose an RWLoss loss function to improve the accuracy of bounding box matching in the model. And optimize the network structure through channel pruning technology to improve model lightweighting. Finally, experimental validation was conducted using a self built insulator dataset and the China Power Line Insulator Dataset (CPLID). The experimental results showed that compared with the baseline model YOLO11, the LDDFSF-YOLO11 model improved AP50 by 9.8%, 8.2%, and 5.4%, mAP by 7.1%, and reduced model size by 56.1%, accounting for only 4.2MB, in three types of small-sized defects: fractional insulator, drop insulator, and flash insulator, respectively. The LDDFSF-YOLO11 model can quickly and accurately identify small-scale defects in insulators in complex backgrounds, making important contributions to the safe and stable operation of high-voltage transmission lines.}, archive = {J_ACCESS}, author = {Peng Shen and Keyu Mei and Huiqiong Cao and Yongxiang Zhao and Guoqing Zhang}, doi = {10.1109/ACCESS.2025.3569970}, journal = {IEEE Access}, pages = {90273-90292}, shortjournal = {IEEE Access}, title = {LDDFSF-YOLO11: A lightweight insulator defect detection method focusing on small-sized features}, volume = {13}, year = {2025}, }
• (2025). Identification of weak frequency areas in low inertia systems. ACCESS, 13, 90262-90272. (www)

  @article{ , abstract = {The continued replacement of synchronous generators by converter-interfaced generators (CIG) is changing power systems dynamics worldwide. Regarding frequency stability, reduced system inertia is leading to larger and steeper frequency deviations, threatening system security in some regions of the world. Moreover, recent investigations have recognized that an uneven spatial distribution of inertia can significantly deteriorate the local rate of change of frequency and frequency oscillations. In this context, screening methods to identify weak frequency areas are one of the critical tools needed to successfully overcome the technical challenges of the energy transition. With the aim in mind, this paper proposes a systematic methodology to identify weak frequency areas. The main outcome is an offline tool that calculates a novel frequency strength indicator for each area called the Local Equivalent Effect of Inertia Index (LEEII). The indicator considers the spatial distribution of inertia and generation dispatch information along with some key technical characteristics of systems dominated by CIG. The tool is applied to the Chilean electricity system. The results, validated through time-domain simulations, allow identifying weak areas of the system that may experience frequency stability issues. Moreover, the proposed LEEII helps to define sufficient conditions for ensuring a secure operation from a frequency perspective.}, archive = {J_ACCESS}, author = {Ignacio Ponce and Claudia Rahmann and Pouyan Pourbeik}, doi = {10.1109/ACCESS.2025.3570908}, journal = {IEEE Access}, pages = {90262-90272}, shortjournal = {IEEE Access}, title = {Identification of weak frequency areas in low inertia systems}, volume = {13}, year = {2025}, }
• (2025). IP SafeGuard–An AI-driven malicious IP detection framework. ACCESS, 13, 90249-90261. (www)

  @article{ , abstract = {The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today’s dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework’s modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange.}, archive = {J_ACCESS}, author = {Abdullah Al Siam and Moutaz Alazab and Albara Awajan and Md Rakibul Hasan and Areej Obeidat and Nuruzzaman Faruqui}, doi = {10.1109/ACCESS.2025.3569289}, journal = {IEEE Access}, pages = {90249-90261}, shortjournal = {IEEE Access}, title = {IP SafeGuard–An AI-driven malicious IP detection framework}, volume = {13}, year = {2025}, }
• (2025). Electronically controlled impedance tuner for 5.8 GHz band. ACCESS, 13, 90243-90248. (www)

  @article{ , abstract = {In this paper, a single-port impedance tuning network is proposed. The presented circuit allows for electronic programming of the reflection coefficient over a broad range, covering the entire unit circle, making the network well-suited as a reference impedance in sensing systems. The system operates exclusively by applying appropriate attenuation values through electronically programmable attenuators, rather than utilizing variable phase shifters, which are inherently narrowband and introduce high insertion losses. The tuning process consists of dividing the input signal among three separate paths with different phase shifts, applying attenuation to each path, combining the signals, and returning them to the transmitter, thereby mimicking reflection. The proposed network consists of a microwave PCB and standard electronic components, making it easily repeatable and inexpensive to fabricate. Moreover, the circuit can be easily extended for wideband operation by employing wideband directional couplers and fixed phase shifters. A detailed description of the system, including theoretical analysis and corresponding design equations, is provided. The performance of the fabricated network was verified by measuring the effective reflection coefficients for all attenuation settings at 5.8 GHz, yielding magnitude coverage from -30 dB to higher than 0 dB at any phase angle. The obtained results demonstrate an excellent tuning range and strong agreement with theoretical predictions.}, archive = {J_ACCESS}, author = {Piotr Wilkon and Kamil Staszek}, doi = {10.1109/ACCESS.2025.3572091}, journal = {IEEE Access}, pages = {90243-90248}, shortjournal = {IEEE Access}, title = {Electronically controlled impedance tuner for 5.8 GHz band}, volume = {13}, year = {2025}, }
• (2025). Development and analysis of a non-resonant wide-band tri-axial electromagnetic energy harvester. ACCESS, 13, 90223-90242. (www)

  @article{ , abstract = {This manuscript introduces a non-resonant, wide-band, tri-axial vibration-based electromagnetic energy harvester (VEMEH) with a cylindrical permanent magnet that freely moves over silicone micro beads in a Teflon casing. The micro beads operate as anti-friction micro bearings between the magnet’s bottom surface and the double-sided planar coil, smoothing in-plan motion and moreover, also act as micro-springs during out-of-plan vibration. Besides two double-sided planar coils, the harvester has a wound coil to boost energy conversion. The harvester’s tri-axial ability, lower frequency response, and larger frequency bandwidth are due to the freely moving magnet without the suspension spring. Furthermore, the harvester’s simple architecture allows the harvester to seamlessly integrate with vibration source equipment, thus easily converting the real tri-axial excitation into electrical power. Additionally, experimental results show that the developed harvester can generate much more power than single-coil energy harvesters. The developed harvester is also integrated with a low-power wireless monitoring system for remote equipment condition monitoring in order to demonstrate its practical utility. The VEMEH exhibits remarkable performance, featuring an open-circuit RMS voltage of 367.3 mV at 40 Hz and 3 g acceleration during in plan vibrations. Under these excitation conditions, at an optimal load resistance of $149.8~\Omega $ , the harvester attains an RMS load voltage of 174.9 mV and a peak output power of $204.2~\mu $ W. The device produces an increased open-circuit voltage of 547.5 mV at 20 Hz and 3 g when exposed to vertical vibrations, yielding a load voltage of 272.3 mV and a power output of $495~\mu $ W. The harvester functions well throughout an extensive frequency spectrum, ranging from 10 Hz to 120 Hz horizontally and from 10 Hz to 200 Hz vertically, offering versatility for diverse applications. The devised VEMEH prototype, rectification circuitry, and rechargeable system are integrated with a wireless sensor node for real-time monitoring of a car engine compartment. Furthermore, the device is evaluated for wearable applications by placing the harvester in a trouser pocket and measuring output values while running. Moreover, the harvester is utilized with various other home appliances, including washing machine, kitchen blender, and domestic water pump. The open circuit AC output voltage for washing machine is measured at 92.6 mV with an acceleration of 0.31 g and a frequency of 35 Hz, whereas the kitchen blender exhibits an open circuit AC output voltage of 112.5 mV at an acceleration of 0.65 g and a frequency of 60 Hz. The output voltage of a home water pump is recorded at 92.1 mV with an acceleration of 0.9 g and a frequency of 100 Hz. Similarly, the developed harvester is positioned on the car dashboard and evaluated for output voltage on both smooth and rough roads.}, archive = {J_ACCESS}, author = {Hamid Khan and Farid Ullah Khan}, doi = {10.1109/ACCESS.2025.3571908}, journal = {IEEE Access}, pages = {90223-90242}, shortjournal = {IEEE Access}, title = {Development and analysis of a non-resonant wide-band tri-axial electromagnetic energy harvester}, volume = {13}, year = {2025}, }
• (2025). A vehicle path planning algorithm: QDDG-RRT. ACCESS, 13, 90212-90222. (www)

  @article{ , abstract = {Autonomous vehicles require highly reliable collision-free capabilities, necessitating extensive research in path planning. Path planning determines an optimal path, crucial for safe and efficient driving. The Rapidly-exploring Random Tree (RRT) algorithm, while widely used, suffers from slow search speeds, numerous inflection points, and redundant operations. To address these issues, We propose a global path planning algorithm: Quick Dynamic Directional Guidance-RRT (QDDG-RRT) algorithm. Key improvements include dynamically constraining the search space using a direction guidance strategy, employing steering techniques to avoid obstacles, optimizing path length to minimize global cost, and refining trajectories using second-order Bessel curves. Simulation experiments compare QDDG-RRT with P-RRT(Probabilistic Rapidly-exploring Random Tree), P-RRT*, APF(Artificial Potential Field), and A* algorithms. Results show that QDDG-RRT outperforms others in execution speed, path length, and smoothness, effectively avoiding obstacles and maintaining safe distances in complex environments.}, archive = {J_ACCESS}, author = {Ruixin Zhang and Qing Xu and Kai Sun and Yi Liu and Ximming Zhu and Guo Zhang and Xiang Cheng}, doi = {10.1109/ACCESS.2025.3571453}, journal = {IEEE Access}, pages = {90212-90222}, shortjournal = {IEEE Access}, title = {A vehicle path planning algorithm: QDDG-RRT}, volume = {13}, year = {2025}, }
• (2025). TSC-IBR: A variant of interval-based memory reclamation using CPU’s time-stamp counter. ACCESS, 13, 90200-90211. (www)

  @article{ , abstract = {Hardware timestamps are increasingly used in concurrent data structures. On the x86_64 platform, a clock with cycle-level resolution maintained by the CPU’s time-stamp counter register is frequently employed to implement parallel algorithms, and its value can be accessed swiftly without a system call. In line with this emerging trend, our research focuses on leveraging the CPU’s time-stamp counter in a memory management algorithm to secure safe memory reclamation in concurrent data structures. Existing quiescent-state-based memory reclamation algorithms rely on a global epoch counter maintained via a software timestamp. Although most quiescent-state-based methods demonstrate good performance when employed in lock-free concurrent data structures, they grapple with issues concerning robustness. Specifically, in Interval-Based Reclamation, a thread reclaiming a shared memory block must check whether the lifetime of the block intersects with the reserved epoch intervals of all threads. Interval-Based Reclamation can provide more fine-grained memory management than Epoch-Based Reclamation and hazard eras. In this study, we propose TSC-IBR, a variant of Interval-Based Reclamation. TSC-IBR leverages the CPU’s timestamp counter instead of the software timestamp. Our experiments confirmed the practicability of employing the CPU’s time-stamp counter in a memory reclamation algorithm. Compared to other quiescent-state-based methods using the software timestamp, TSC-IBR exhibits excellent robustness across all our lock-free concurrent data structure benchmarks and attains high throughput in most cases.}, archive = {J_ACCESS}, author = {Chen Zhang and Wendong Li and Xinghui Zhu}, doi = {10.1109/ACCESS.2025.3569072}, journal = {IEEE Access}, pages = {90200-90211}, shortjournal = {IEEE Access}, title = {TSC-IBR: A variant of interval-based memory reclamation using CPU’s time-stamp counter}, volume = {13}, year = {2025}, }
• (2025). Blockchain-assisted trustworthy QoE accelerator for adaptive streaming over HTTP. ACCESS, 13, 90185-90199. (www)

  @article{ , abstract = {This paper presents a blockchain-assisted trustworthy quality of experience (QoE) accelerator for adaptive streaming services over hypertext transfer protocol (HTTP). The proposed trustworthy QoE accelerator aims to provide seamless, energy-efficient, and trustworthy streaming services to mobile users. The proposed QoE accelerator is logically located in the middle of the path between the mobile user and media server. At the midpoint, the accelerator severs the end-to-end transmission control protocol (TCP) connection and establishes parallel HTTP connections with the media cloud. Then, the accelerator appropriately assigns segment requests to each parallel stream to make effective use of network resources. Further, a blockchain network is utilized to make the proposed system trustworthy. To demonstrate the potential for real-world deployment, the proposed blockchain-assisted QoE accelerator was implemented using the moving picture experts group (MPEG) DASH open-source player, Hyperledger Fabric open-source blockchain, Docker, and Kubernetes and thoroughly verified using the 802.11 network. The experimental results showed that the blockchain-assisted QoE accelerator outperforms existing approaches in terms of QoE metrics such as frozen events and temporal video quality.}, archive = {J_ACCESS}, author = {Jeongjin Park and Gi Seok Park}, doi = {10.1109/ACCESS.2025.3571530}, journal = {IEEE Access}, pages = {90185-90199}, shortjournal = {IEEE Access}, title = {Blockchain-assisted trustworthy QoE accelerator for adaptive streaming over HTTP}, volume = {13}, year = {2025}, }
• (2025). Segmentation and classification of skin cancer diseases based on deep learning: Challenges and future directions. ACCESS, 13, 90163-90184. (www)

  @article{ , abstract = {Deep Learning (DL) techniques have significantly improved the diagnostic accuracy in healthcare, particularly for detecting and classifying skin cancer. Such technological advancements will assist healthcare professionals in delivering more accurate, efficient, and timely diagnoses, ultimately improving patient outcomes and facilitating early detection and treatment. Medical imaging technologies such as magnetic resonance imaging (MRI) and computed tomography (CT) are critical for diagnosing dermatological conditions. However, interpreting these images can be challenging due to overlapping structures and varying image quality. This study explores the application of DL in skin cancer diagnosis, focusing on advances in image segmentation and classification. DL-based models are reviewed specifically by convolutional neural networks (CNNs), and evaluations on their effectiveness for skin lesion detection are provided. This study also examines the critical challenges of deploying DL models in clinical practice, covering issues including dataset diversity, model interpretability, and real-world implementation feasibility. It further explores the selection of network architectures and data preprocessing techniques, emphasizing their influence on model performance. In summary, this study identifies research gaps and suggests future directions for enhancing DL models for dermatological applications.}, archive = {J_ACCESS}, author = {Abdulrahman Dira Khalaf and Hazlina Hamdan and Alfian Abdul Halin and Noridayu Manshor}, doi = {10.1109/ACCESS.2025.3569170}, journal = {IEEE Access}, pages = {90163-90184}, shortjournal = {IEEE Access}, title = {Segmentation and classification of skin cancer diseases based on deep learning: Challenges and future directions}, volume = {13}, year = {2025}, }
• (2025). Camscribe: Enhanced dashcam video descriptions through multimodal spatiotemporal and object detection for autonomous vehicles. ACCESS, 13, 90144-90162. (www)

  @article{ , abstract = {The generation of accurate and coherent video descriptions necessitates comprehensive understanding of multiple visual cues. While conventional video description models have predominantly relied on RGB and optical flow information, yet these approaches face fundamental accuracy constraints, underscoring the importance of exploring expanded modalities in both visual and audio domains. This study introduces a novel approach that integrates three distinct visual modalities, i.e., spatial features, temporal dynamics, and object-level semantics, to enhance description quality through the incorporation of fine-grained object information, resulting in more effective and semantically meaningful outputs. Our framework proposes the integrated YOLO object detection for multilabel class embeddings generation, combined systematically with spatial and temporal embeddings through an early fusion approach. These integrated embeddings are then processed using memory-augmented extended recurrent transformer architecture capable of handling upto 14 sentences recursively with 12 attention heads for complex sequence modeling. We also propose a Harmonic Aggregation of Language Evaluation Metrics (HA-LEM) for model selection that combines multiple linguistic dimensions, enabling robust model selection during the training phase and resulting in enhanced paragraph generation capabilities. The model training was conducted on the DeepRide dashcam dataset, leveraging multimodal video features for robust performance. The efficacy of our proposed methodology is validated through extensive qualitative, quantitative evaluation and ablation analysis. Our framework achieves superior performance on the DeepRide dataset, as evidenced by comprehensive evaluation using standard metrics: BLEU@4 (35.02), METEOR (27.92), ROUGE-L (45.78), and CIDEr (68.82), surpassing previous state-of-the-art results.}, archive = {J_ACCESS}, author = {Muhammad Rafiq and Mankyu Sung and Ghazala Rafiq and Gyu Sang Choi}, doi = {10.1109/ACCESS.2025.3570711}, journal = {IEEE Access}, pages = {90144-90162}, shortjournal = {IEEE Access}, title = {Camscribe: Enhanced dashcam video descriptions through multimodal spatiotemporal and object detection for autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Active voltage quadrupler rectifier-based ultra-high boost ratio multidirectional energy router in 800 v DC microgrids. ACCESS, 13, 90129-90143. (www)

  @article{ , abstract = {In 800V DC microgrids, an energy router is essential for managing power distribution among the low-voltage photovoltaic (PV) panel, energy storage battery, and high-voltage dc link. Current solutions face challenges in simultaneously achieving high step-up ratios, multidirectional power flow, and maintaining high efficiency across broad input voltage and load ranges. To address these issues, this paper introduces a novel energy router featuring three key innovations. First, it combines an interleaved boost converter with an active voltage quadrupler cell (VQC), reducing voltage stress by 50% compared to conventional designs. Second, it employs an integrated magnetic design that utilizes leakage inductance, reducing magnetic volume by 36%. Third, it uses hybrid modulation, merging pulse-width modulation (PWM) and phase-shift modulation (PSM), to optimize switching and circulating losses while minimizing transformer turns and total power loss. Additionally, this configuration halves the voltage stress experienced by the MOSFETs on the high-voltage side relative to the dc link voltage. A 500W prototype was developed and tested, interfacing with a 15-25 V PV port, 40-50 V battery port, and 800 V dc link. Experimental results demonstrate a peak efficiency of 97.5% with stable operation under 0-100% load transitions. Comprehensive analysis, including dynamic studies (working modes switching, 95% load step changes) confirms the topology’s reliability in 800V DC microgrid applications.}, archive = {J_ACCESS}, author = {Yuchong Peng and Jiawei Liang and Haoyu Wang}, doi = {10.1109/ACCESS.2025.3572293}, journal = {IEEE Access}, pages = {90129-90143}, shortjournal = {IEEE Access}, title = {Active voltage quadrupler rectifier-based ultra-high boost ratio multidirectional energy router in 800 v DC microgrids}, volume = {13}, year = {2025}, }
• (2025). LISE: A logic-based interactive similarity explainer for clusters of RDF data. ACCESS, 13, 90109-90128. (www)

  @article{ , abstract = {Clustering methods are instrumental in the preliminary analysis of unstructured data, yet interpreting the resulting groups—-especially in the context of RDF (Resource Description Framework) data—-poses significant challenges. This paper introduces LISE (Logic-based Interactive Similarity Explainer), an integrated and model-agnostic framework designed to generate explainable, human-readable insights into clusters of RDF resources. LISE combines four core components: (i) a machine learning module leveraging vector embeddings and k-means clustering; (ii) a logic-based reasoning component that computes the common semantic features of clustered items via an optimized Least Common Subsumer (LCS); (iii) a Natural Language Generation (NLG) module that verbalizes these features into structured and human-readable explanations; and (iv) an interactive user feedback loop that captures user perception of explanation relevance to iteratively enhance embedding quality and cluster interpretability. An extensive use case on the DrugBank dataset demonstrates LISE’s ability to generate meaningful, context-aware cluster explanations and adapt to user preferences, advancing the state of explainable AI for semantic web technologies and knowledge graph analytics. The paper investigates also the integration in LISE of an LLM-based NLG approach, both in the DrugBank use case and through an extended experiment in a general-purpose dataset: YAGO3-10.}, archive = {J_ACCESS}, author = {Simona Colucci and Francesco Maria Donini and Verdiana Schena and Floriano Scioscia and Eugenio Di Sciascio}, doi = {10.1109/ACCESS.2025.3571518}, journal = {IEEE Access}, pages = {90109-90128}, shortjournal = {IEEE Access}, title = {LISE: A logic-based interactive similarity explainer for clusters of RDF data}, volume = {13}, year = {2025}, }
• (2025). Understanding government enterprise architecture: A review and case study. ACCESS, 13, 90088-90108. (www)

  @article{ , abstract = {The implementation of enterprise architecture (EA) is no longer exclusive to large corporations; its principles have been adapted for various organizations, including small and medium-sized enterprises (SMEs) and industry-specific entities. Recognizing its potential, many governments have also adopted EA frameworks to address growing complexities in public sector operations. This adaptation, termed Government Enterprise Architecture (GEA), aims to enhance governance efficiency and digital transformation. However, GEA implementation remains a work in progress, with significant gaps in standardization and execution. For instance, Indonesia introduced GEA in 2018, but many implementers struggled with misinterpretations of the framework’s objectives and processes. To address these challenges, this study conducts a comprehensive global comparison of GEA methodologies. Using a Systematic Literature Review (SLR), the research examines key aspects of GEA, including its core principles, architectural domains, critical success factors, performance metrics, and potential benefits. By analyzing these dimensions, the study provides actionable insights for GEA developers—particularly in Indonesia, where the initiative is still nascent. A deeper understanding of global best practices can help refine Indonesia’s approach, ensuring effective implementation and maximizing GEA’s value in improving public sector governance.}, archive = {J_ACCESS}, author = {Raden Trimanadi and Dana Indra Sensuse}, doi = {10.1109/ACCESS.2025.3571742}, journal = {IEEE Access}, pages = {90088-90108}, shortjournal = {IEEE Access}, title = {Understanding government enterprise architecture: A review and case study}, volume = {13}, year = {2025}, }
• (2025). Eff-SAM: SAM-based efficient method for brain tumor segmentation in multimodal 3D MRI scans. ACCESS, 13, 90063-90087. (www)

  @article{ , abstract = {Accurate brain tumor segmentation in MRI scans is crucial for effective diagnosis and treatment planning, as even minor segmentation errors can lead to significant clinical concerns. However, this task is challenged by the complex anatomy of the brain, variable tumor shapes, and low contrast between tumor sub-regions. Moreover, existing methods often lack generalizability across diverse segmentation tasks and imaging modalities. Large vision model (LVM) like the segment anything model (SAM) represents a foundational advancement in tumor segmentation. While SAM has shown impressive performance on natural images, its effectiveness in medical imaging, especially brain tumor segmentation, is limited due to domain differences and indistinct boundaries in MRI scans. To address these challenges, we propose Eff-SAM, a 3D brain tumor segmentation framework that adapts SAM for medical applications through parameter-efficient fine-tuning (PEFT) technique. The framework incorporates PEFT with adapters into SAM’s encoder to fine-tune the model effectively and optimize its performance for medical imaging while maintaining computational efficiency. Additionally, a cross-sliced attention (CSA) mechanism captures semantic relationships, improves tumor localization, and is introduced into the encoder. Robust data preprocessing further enhances the model’s generalization across datasets. Eff-SAM demonstrates state-of-the-art performance and outperforms benchmark methods on the BraTS 2020 and BraTS 2021 datasets. It achieves dice scores of 0.884 for whole tumor (WT), 0.853 for tumor core (TC), and 0.818 for enhancing tumor (ET) on the BraTS 2020 dataset, and 0.880 (WT), 0.861 (TC), and 0.821 (ET) on the BraTS 2021 dataset. This work highlights the potential of integrating vision models like SAM with lightweight, domain-specific modules to deliver accurate and efficient brain tumor segmentation offering a clinically valuable tool, especially in scenarios with limited data and complex tumor morphology.}, archive = {J_ACCESS}, author = {Nisar Ahmad and Yao-Tien Chen}, doi = {10.1109/ACCESS.2025.3571464}, journal = {IEEE Access}, pages = {90063-90087}, shortjournal = {IEEE Access}, title = {Eff-SAM: SAM-based efficient method for brain tumor segmentation in multimodal 3D MRI scans}, volume = {13}, year = {2025}, }
• (2025). Two-stage power allocation for IRS-assisted downlink NOMA: Intra-group optimization and inter-group allocation. ACCESS, 13, 90052-90062. (www)

  @article{ , abstract = {Non-Orthogonal Multiple Access (NOMA) has emerged as a key enabler for massive connectivity in next-generation networks, while Intelligent Reflecting Surfaces (IRS) provide a transformative approach to enhancing spectral efficiency by reconfiguring the wireless propagation environment. However, integrating IRS with downlink NOMA presents significant challenges in power allocation, user grouping, and interference management, with existing methods often struggling to balance power efficiency, quality-of-service (QoS) guarantees, and computational scalability. This paper investigates a two-stage power allocation framework for IRS-assisted downlink NOMA. The first stage performs intra-group optimization, jointly adjusting user-level power allocation and IRS phase shifts to minimize power consumption while meeting QoS requirements based on an iterative optimization procedure. The second stage optimizes inter-group, allocating power across user groups to maximize the number of served users based on a sequential fixing programming procedure. A detailed complexity analysis demonstrates the computational efficiency of the framework, enabling real-time deployment in dynamic networks. The simulation results confirm that the proposed approach achieves superior sum rate performance while minimizing total power consumption and maintaining QoS guarantees.}, archive = {J_ACCESS}, author = {Zaid Albataineh and Haythem Bany Salameh and Mohammad Al Bataineh}, doi = {10.1109/ACCESS.2025.3570120}, journal = {IEEE Access}, pages = {90052-90062}, shortjournal = {IEEE Access}, title = {Two-stage power allocation for IRS-assisted downlink NOMA: Intra-group optimization and inter-group allocation}, volume = {13}, year = {2025}, }
• (2025). Cooperative detection for cell-free massive MIMO with multiple central processing units under backhaul capacity limitation. ACCESS, 13, 90034-90051. (www)

  @article{ , abstract = {Cell-free massive multiple-input multiple-output (CF-mMIMO) systems offer enhanced spectral efficiency (SE) and uniform coverage through cooperative baseband signal processing across geographically distributed, interconnected central processing units (CPUs). However, signal distortions due to backhaul link capacity limitations can significantly hinder system performance. To address this challenge, we propose distortion-aware clustering (DAC) and distortion-aware adaptive clustering (DAAC) methods to enhance scalability and robustness against distortions. We evaluate their performance against the baseline proximity-based clustering (PC) method under two cooperative data detection approaches: centralized detection (CD) and two-stage detection based on local combining (TDLC). Simulation results demonstrate that (i) DAC and DAAC outperform PC in terms of SE, fairness, and robustness against distortions with DAC improving SE over PC by approximately 7.5, 1.2, and 0.02 dB, while DAAC achieves gains of 10.7, 1.7, and 0.2 dB under low, medium, and sufficient backhauling capacities respectively; (ii) in contrast to existing literature, TDLC can outperform CD depending on the system’s backhauling capacity; (iii) TDLC provides a more favorable tradeoff between backhaul capacity requirements and achievable SE. These findings offer valuable insights for designing scalable, distortion-resilient CF-mMIMO systems and lay the groundwork for efficient resource allocation and network deployment strategies for future wireless communication systems.}, archive = {J_ACCESS}, author = {Faurdoir Ndimumahoro and Koji Ishibashi}, doi = {10.1109/ACCESS.2025.3571649}, journal = {IEEE Access}, pages = {90034-90051}, shortjournal = {IEEE Access}, title = {Cooperative detection for cell-free massive MIMO with multiple central processing units under backhaul capacity limitation}, volume = {13}, year = {2025}, }
• (2025). Specification and verification method of parallel hierarchical timed automata by predicate abstraction and refinement. ACCESS, 13, 90017-90033. (www)

  @article{ , abstract = {Embedded systems have three major features: real-time, parallel and hierarchical. Therefore, we specified an embedded system as a hierarchically structured timed automata operating in parallel. The reduction technique of the state space for verifying embedded systems is crucial. On the other hand, Counterexample-Guided Abstraction Refinement (CEGAR) is a very hopeful verification method. In this study, we propose an effective CEGAR for a parallel hierarchical timed automaton. First, the algorithm performs abstraction of the hierarchical structures and timing constraints. Next, it performs a reachability analysis and divided counterexample analysis on the individual automaton. Based on the results of the analysis, the algorithm performs refinement corresponding to each abstraction. In the refinement, we devised it such that an over approximation is maintained even if two abstractions are paired. In addition, the abstraction of timing constraints is performed after the abstraction of hierarchical structures; however the algorithm refines only the attention part, and the refinement of the hierarchical structure applies to an individual automaton. Finally, we implemented the proposed method as a prototype in Mathematica. The proposed CEGAR effectively reduced the state space.}, archive = {J_ACCESS}, author = {Satoshi Yamane}, doi = {10.1109/ACCESS.2025.3572089}, journal = {IEEE Access}, pages = {90017-90033}, shortjournal = {IEEE Access}, title = {Specification and verification method of parallel hierarchical timed automata by predicate abstraction and refinement}, volume = {13}, year = {2025}, }
• (2025). An advanced generative AI-based anomaly detection in IEC61850-based communication messages in smart grids. ACCESS, 13, 89997-90016. (www)

  @article{ , abstract = {Security incidents in digital substations can create notable difficulties for the consistent and stable functioning of power systems. To address these issues, implementing defense and mitigation strategies is essential. Identifying and detecting irregularities in information and communication technology (ICT) is vital to maintaining secure interactions between devices in digital substations. This paper proposes a task-oriented dialogue (ToD) system for anomaly detection (AD) in multicast message datasets, such as generic object-oriented substation events (GOOSE) and sampled values (SV) in digital substations using generative AI (GenAI). The proposed ToD model demonstrates significant advantages over the human-in-the-loop (HITL) approach, particularly in error rate, adaptability, and scalability. Specifically, compared to HITL, the ToD model achieves a reduction in false positives (FPs) of up to 20% and enhances the accuracy of AD by up to 17.5%, resulting in a general accuracy of 97.5%. Moreover, the system shows a substantial improvement in advanced evaluation metrics, including a Matthews Correlation Coefficient (MCC) of 0.95, highlighting its robust capability to accurately differentiate between normal and anomalous events. The ToD model adapts effectively to new attack scenarios without extensive retraining, unlike traditional machine learning (ML) models or HITL, which require frequent updates. This adaptability significantly reduces implementation time compared to HITL, as the model requires fewer manual interventions and updates. These findings are supported by a comparative analysis using standard and advanced evaluation metrics. The generation and extraction of datasets of IEC 61850 communications were performed using a hardware-in-the-loop (HIL) testbed, ensuring the robustness of the proposed approach in practical scenarios.}, archive = {J_ACCESS}, author = {Aydin Zaboli and Yong-Hwa Kim and Junho Hong}, doi = {10.1109/ACCESS.2025.3571881}, journal = {IEEE Access}, pages = {89997-90016}, shortjournal = {IEEE Access}, title = {An advanced generative AI-based anomaly detection in IEC61850-based communication messages in smart grids}, volume = {13}, year = {2025}, }
• (2025). Infrared tiny structureless object detection enhanced by video super-resolution. ACCESS, 13, 89983-89996. (www)

  @article{ , abstract = {The precise detection of infrared (IR) tiny objects against complex backgrounds is of great significance in the field of aircraft imaging guidance. However, due to the extremely small target features, low brightness, and the tendency to be submerged in complex backgrounds, the detection of IR tiny aerial targets remains a challenge. Concurrently, super-resolution technology is a novel technique that has emerged in recent years, aiming to enhance image quality without altering hardware specifications, thereby magnifying the characteristics of tiny targets. In light of this, we introduce the infrared tiny object detection method enhanced by video super-resolution. Our proposed method is a two-stage model. Initially, it enhances target features through bidirectional propagation and optical flow alignment within the super-resolution module. Subsequently, it employs restricted receptive field convolution and multi-receptive field feature fusion to enhance the detection accuracy of small targets. Experimental results on the SIATD data set show that our method performs better state-of-the-art approaches, with an F1 score of 0.957, precision of 99.2%, and recall of 92.4%. Ablation studies confirm that the super-resolution module contributes to 35% of the performance, while the restricted receptive field design accounts for another 28% improvement. Furthermore, our model maintains sub-real-time performance at 50ms per frame with only 0.37M parameters, making it suitable for deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Duixu Mao and Xiaxu Chen and Linhan Xu and Jun Ke}, doi = {10.1109/ACCESS.2025.3571965}, journal = {IEEE Access}, pages = {89983-89996}, shortjournal = {IEEE Access}, title = {Infrared tiny structureless object detection enhanced by video super-resolution}, volume = {13}, year = {2025}, }
• (2025). Advancing in vivo molecular bioimaging with optimal frequency offset selection and deep learning reconstruction for CEST MRI. ACCESS, 13, 89967-89982. (www)

  @article{ , abstract = {Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is an emerging non-invasive molecular imaging technique offering significant potential for biomedical research and clinical applications. However, CEST MRI data acquisition requires prolonged scanning times, as data need to be collected at multiple frequency offsets to capture necessary information for accurate analysis of biological compounds. Faster CEST MRI will improve molecular imaging, advancing biomedical pre-clinical research studies and clinical applications. Thus, herein, we accelerate CEST MRI data acquisition using a two-step approach. Firstly, we use an optimization algorithm to identify a set of optimal sparse frequency offsets for data collection. Secondly, we apply a deep learning algorithm to reconstruct the high-resolution CEST MRI Z-spectra from the low-resolution Z-spectra. CEST MRI data acquired on adult mice brains (n = 19) were utilized. The optimization technique efficiently selected down to 10% of the total frequency offset points, and the deep learning algorithm accurately reconstructed dense Z-spectra. The performance metrics, root mean square errors (RMSE), mean absolute error (MAE), and Pearson’s correlation were calculated for various Z-spectra reconstructions. The minimum, maximum, and average RMSE values achieved when the lowest 10% of frequency offsets were used were 0.0065, 0.0133, and 0.0094, respectively. The proposed CEST MRI approach, involving optimal frequency offset selection followed by deep learning reconstruction, achieves an acceleration by 10 times while maintaining high-quality data. This approach expands the applications of CEST MRI, potentially advancing in vivo molecular bioimaging for both basic science and clinical research.}, archive = {J_ACCESS}, author = {Adarsha Bhattarai and Chathumi Samaraweera and Mariano Uberti and Aditya N. Bade and Yutong Liu and Dongming Peng}, doi = {10.1109/ACCESS.2025.3571638}, journal = {IEEE Access}, pages = {89967-89982}, shortjournal = {IEEE Access}, title = {Advancing in vivo molecular bioimaging with optimal frequency offset selection and deep learning reconstruction for CEST MRI}, volume = {13}, year = {2025}, }
• (2025). Multi-objective improved differential evolution algorithm-based smart home energy management system considering energy storage system, photovoltaic, and electric vehicle. ACCESS, 13, 89946-89966. (www)

  @article{ , abstract = {Home energy management systems (HEMSs) are becoming increasingly popular as smart homes become more prevalent, along with their ability to reduce peak network loads and generate and store green energy locally. A HEMS typically controls and schedules each appliance in a home using photovoltaics (PVs) and energy storage systems (ESSs) to help cut energy bills. HEMS and optimization algorithms are studied extensively. However, the wasted energy of PVs and the simultaneous consideration of ESS and plug-in hybrid electric vehicles (PHEVs) in multi-objective problems have not been taken into account. Hence, this paper examines the maximum use of PVs, ESS, and PHEV, and utilizes upstream electricity and selling energy simultaneously in single-objective and multi-objective problems to minimize three objective functions, such as the entire energy cost, peak-to-average ratio (PAR), and wasted energy of PV. A multi-objective improved differential evolution algorithm is used to solve the HEMSs. Based on the outcome, PV, ESS, and PHEV reduce the costs and also reduce the wasted energy of PV close to zero. Also, by penetration of ESSs and PHEVs, the energy is bought at a low price-time, and then used for demand response and sold to the upstream in high-price periods. The suggested method is considered under various scenarios, such as different ESS volumes and various charging/discharging rates, with and without PHEV participation in single and multi-objective optimization. Furthermore, the efficiency of the method is proven by obtaining lower energy costs, optimized PAR, and zero wasted PV energy compared to other prior papers.}, archive = {J_ACCESS}, author = {Mina GhasemiGarpachi and Moslem Dehghani and Mokhtar Aly and Jose Rodriguez}, doi = {10.1109/ACCESS.2025.3567888}, journal = {IEEE Access}, pages = {89946-89966}, shortjournal = {IEEE Access}, title = {Multi-objective improved differential evolution algorithm-based smart home energy management system considering energy storage system, photovoltaic, and electric vehicle}, volume = {13}, year = {2025}, }
• (2025). Automated microservices identification through business process analysis: A semantic-driven clustering approach. ACCESS, 13, 89926-89945. (www)

  @article{ , abstract = {The rise of microservice architectures has further evolved software development practices, building upon the foundation established by web services in breaking down monolithic systems, while offering more finely detailed, coherent, and loosely interconnected services. Despite the transformative potential of microservices, the challenge of effectively pinpointing microservices that meet an organization’s specific requirements remains a difficult task. This paper presents an extended approach for automating microservices identification by leveraging business processes (BPs). Addressing limitations in existing microservices identification techniques, this approach utilizes advanced Natural Language Processing (NLP) techniques—such as Named Entity Recognition (NER) and semantic analysis—to capture dependencies and align boundaries with business logic. This methodology applies unsupervised clustering algorithms, including K-means, K-medoids, and DBSCAN, to generate well-defined microservices by analyzing semantic similarity across BP activities. Evaluation on real-world case studies, such as the Bicing bicycle rental system, Cargo Tracking, and JPetStore, demonstrates its effectiveness, yielding high cohesion, low coupling, and superior granularity in microservices decomposition compared to traditional methods. Metrics such as the Silhouette Index, Afferent Coupling, and Instability further validate its performance. By automating microservices identification with a BP-centered framework, this approach aligns technical architecture with organizational goals, advancing both agility and scalability in software systems. This work marks a step forward in optimizing system modularity and provides a foundation for future scalability improvements.}, archive = {J_ACCESS}, author = {Idris Oumoussa and Rajaa Saidi}, doi = {10.1109/ACCESS.2025.3571809}, journal = {IEEE Access}, pages = {89926-89945}, shortjournal = {IEEE Access}, title = {Automated microservices identification through business process analysis: A semantic-driven clustering approach}, volume = {13}, year = {2025}, }
• (2025). StegGPT: A novel foundation-model-based character-level linguistic steganography method utilizing large language models. ACCESS, 13, 89915-89925. (www)

  @article{ , abstract = {This study addresses the critical need for robust safeguarding of sensitive data stored on personal computing devices and during data transmissions, alongside the increasing need for secure digital interactions. Conventional methodologies for obfuscating data within textual covers exhibit inherent limitations and susceptibility to detection. The primary objective of this investigation is to devise an algorithm that not only ensures secure transmission of information, but also proficiently conceals it from unauthorized access and detection. Using advanced techniques in Natural Language Processing (NLP), Artificial Intelligence (AI), and deep learning within the domain of information security, this study delves into the realm of steganography, revealing the restricted embedding capabilities of conventional language-centric approaches. A comparative analysis pits the newly minted algorithm against contemporaneous approaches, notably cutting-edge neural linguistic steganography (NLS), evaluating their algorithmic capacities in terms of Bits per Word (BpW) and Bits per Character (BpC), along with gauging their security and imperceptibility through metrics like Area Under the Curve (AUC), Equal Error Rate (EER) and Difference of Mean Perplexity ( $\Delta $ MP). Findings underscore the marked superiority of the proposed steganography algorithm in embedding capacity metrics, while upholding comparable standards of security and imperceptibility compared to other AI-driven statistical (Markov chain-based) and neural (deep learning-based) techniques. Specifically, the StegGPT algorithm showcases a remarkable 44% increase in Word-level capacity criterion (from 2.97 to 4.27) and 53% increase in Character-level capacity criterion (from 0.51 to 0.78) in comparison to its closest competitor, all while maintaining consistent levels of security and imperceptibility.}, archive = {J_ACCESS}, author = {Omer Farooq Ahmed Adeeb and Seyed Jahanshah Kabudian}, doi = {10.1109/ACCESS.2025.3568339}, journal = {IEEE Access}, pages = {89915-89925}, shortjournal = {IEEE Access}, title = {StegGPT: A novel foundation-model-based character-level linguistic steganography method utilizing large language models}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based classification of programming logic understanding levels by mouse-tracking heatmaps. ACCESS, 13, 89905-89914. (www)

  @article{ , abstract = {In the past decade, there has been a significant increase in the growth of the global technology employment market, primarily owing to the digital age, artificial intelligence expansion, cloud computing, and cybersecurity requirements. Consequently, the demand for IT professionals has increased. Introductory programming is a crucial early course in IT higher education as it aids in developing problem-solving skills. However, such courses tend to result in substantially high failure and dropout rates. Some studies have proposed solutions such as peer learning and personalized feedback, but challenges persist. One key assumption is that teacher support is likely to help mitigate these rates. However, with limited class time, teachers find it challenging to support all the students. This study aims to automatically assess the programming logic understanding level (PLUL) during introductory programming courses. We proposed a method for classifying PLUL through a block coding learning platform based on mouse-tracking heatmaps using machine learning techniques. We collected actual learning-task data from first-year university students and compared the performance of five machine learning models. The decision tree algorithm achieved an accuracy score of 76.00%, F1 score of 77.36%, sensitivity score of 75.93%, and specificity score of 76.09%. These findings demonstrate that our method can effectively classify students based on their PLUL of tasks on a block coding learning platform.}, archive = {J_ACCESS}, author = {Attaporn Khaesawad and Yosuke Fukuchi and Vibol Yem and Nobuyuki Nishiuchi}, doi = {10.1109/ACCESS.2025.3571050}, journal = {IEEE Access}, pages = {89905-89914}, shortjournal = {IEEE Access}, title = {Machine learning-based classification of programming logic understanding levels by mouse-tracking heatmaps}, volume = {13}, year = {2025}, }
• (2025). Balancing precision and speed: Introducing the performance efficiency evaluation ratio (PEER) in visual odometry. ACCESS, 13, 89874-89904. (www)

  @article{ , abstract = {Visual odometry is extensively utilized in robotics, autonomous navigation, and augmented reality to estimate motion by analyzing sequential images. Provides high precision without relying on external sensors such as GPS. However, real-time visual odometry systems must strike a balance between computational correctness and speed, a critical yet often overlooked trade-off in existing evaluation methods. Traditional metrics such as ATE, RPE, RMSE, FPS, and RTF typically assess precision or efficiency in isolation and lack adaptability to real-time constraints. To address this limitation, we propose the Performance Efficiency Evaluation Ratio (PEER), a novel, adaptive, and lightweight metric that jointly evaluates algorithm performance based on both fidelity and computation time. PEER incorporates a tunable weighting parameter to prioritize performance, speed, or a balanced trade-off, and employs normalization techniques to ensure comparability across different algorithms and systems. We evaluated PEER using the KITTI dataset with various feature extraction algorithms (SIFT, ORB, BRISK, KAZE, AKAZE) and matching algorithms (BF, FLANN). The findings indicate that PEER not only distinguishes performance effectively across various scenarios but also shows strong alignment with established MCDM methods such as TOPSIS and VIKOR, all while maintaining lower computational complexity and greater suitability for real-time deployment. Overall, PEER provides a robust and flexible framework for optimizing visual odometry systems.}, archive = {J_ACCESS}, author = {Cem Atilgan and Muharrem Mercimek}, doi = {10.1109/ACCESS.2025.3571921}, journal = {IEEE Access}, pages = {89874-89904}, shortjournal = {IEEE Access}, title = {Balancing precision and speed: Introducing the performance efficiency evaluation ratio (PEER) in visual odometry}, volume = {13}, year = {2025}, }
• (2025). Decision-aid framework for face authentication detection using ResNext50 and BiLSTM to enhance media integrity. ACCESS, 13, 89858-89873. (www)

  @article{ , abstract = {This paper presents a decision-aid framework for face authentication detection that integrates ResNext50 with Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance media integrity and improve deepfake detection. Unlike conventional approaches focusing solely on spatial features, the proposed hybrid model incorporates temporal analysis, enabling the detection of subtle manipulations distributed across sequential video frames. ResNext50 is a robust spatial feature extractor from cropped facial regions, while BiLSTM captures bidirectional temporal dependencies, enriching the model’s contextual understanding and temporal sensitivity. The model was evaluated on three benchmark datasets—FaceForensics++, DFDC, and Celeb-DF—and demonstrated superior performance in classifying real versus manipulated facial content. The hybrid ResNext50 + BiLSTM model achieved a final accuracy of 96.11%, precision of 96.42%, recall of 96.68%, F1-score of 96.23%, and an AUC of 98.89%, outperforming the standalone ResNext50 and other state-of-the-art CNN-based approaches. These improvements reflect a notable reduction in false positives and negatives, critical for real-world face authentication systems. The proposed framework offers a scalable and robust solution for digital forensics, media verification, and cybersecurity applications, where maintaining trust in visual content is paramount. This study underscores the pivotal role of temporal modeling in improving the reliability of face manipulation detection. Future work will explore advanced fusion strategies, lightweight architectures for real-time deployment, and cross-dataset generalization to enhance adaptability in diverse, real-world scenarios.}, archive = {J_ACCESS}, author = {Ayat Abd-Muti Alrawahneh and Siti Norul Huda Sheikh Abdullah and Tarik Abuain and Sharifah Nurul Asyikin Syed Abdullah and Sarah Khadijah Taylor and Nur Hanis Sabrina Suhaimi}, doi = {10.1109/ACCESS.2025.3569792}, journal = {IEEE Access}, pages = {89858-89873}, shortjournal = {IEEE Access}, title = {Decision-aid framework for face authentication detection using ResNext50 and BiLSTM to enhance media integrity}, volume = {13}, year = {2025}, }
• (2025). Quad-mode H-beam metasurface-embedded dual-band circularly-polarized multi-tiered antenna scheme with 5G (n79) and wi-fi 6E capabilities. ACCESS, 13, 89844-89857. (www)

  @article{ , abstract = {This research proposes a quad-mode H-beam metasurface (MTS)-embedded dual-band circularly-polarized (CP) multi-tiered antenna scheme with 5G (n79) and Wi-Fi 6E capabilities. The proposed antenna scheme consists of three layers of corner-truncated FR-4 substrates without an air gap. The upper substrate contains an array of $3\times 4$ H-beam MTS elemental units tilted at 49°. The middle substrate is a copper plate with a horizontal Z-shaped slot at its center functioning as the ground plane, and the lower substrate contains a microstrip feed line. Characteristic mode analysis (CMA) is employed to optimize the circular polarization of the MTS structure. Results show that Modes 1 & 2 at 4.81 GHz and Modes 4 & 9 at 6.2 GHz contribute to CP radiation. The measured return loss bandwidths of the antenna are 19.37% (4.63 – 5.56 GHz) and 9.51% (5.97 – 6.56 GHz) for the 5G (n79) and Wi-Fi 6E bands. The measured axial ratio bandwidths are 13.33% (4.6 – 5.24 GHz) and 1.77% (6.15 – 6.26 GHz), with maximum gains of 5.2 and 3.9 dBic at 5.5 and 6.2 GHz, respectively. Unlike conventional MTS-based CP antennas which employ rectangular- or square-shaped MTS elements for single-band operation, this work utilizes H-beam-shaped MTS elemental units to achieve dual-band CP capability. Additionally, rather than traditional full-wave methods, CMA is used to optimize circular polarization. Essentially, the proposed antenna scheme is suitable for 5G (n79) and Wi-Fi 6E communications.}, archive = {J_ACCESS}, author = {N. Supreeyatitikul and P. Chomtong and J. Konpang and P. Thitimahatthanakusol and P. Akkaraekthalin}, doi = {10.1109/ACCESS.2025.3571774}, journal = {IEEE Access}, pages = {89844-89857}, shortjournal = {IEEE Access}, title = {Quad-mode H-beam metasurface-embedded dual-band circularly-polarized multi-tiered antenna scheme with 5G (n79) and wi-fi 6E capabilities}, volume = {13}, year = {2025}, }
• (2025). The impact of integrating shallow and deep information on knowledge distillation. ACCESS, 13, 89831-89843. (www)

  @article{ , abstract = {Knowledge distillation is a key technique for compressing neural networks, leveraging insights from a large teacher model to enhance the generalization capability of a smaller student model. The ResNet family, recognized for its residual connections, is commonly employed for various visual tasks. It effectively addresses issues such as gradient vanishing and degradation in deep neural networks, making the training process more manageable. As a deep network architecture, ResNet is proficient in feature extraction and is frequently used as a backbone for knowledge refinement. However, our experimental findings reveal that the ResNet model falls short in extracting shallow information from images, which in turn affects the performance of the corresponding student model. To address these issues, we propose a shallow feature extraction module (SFEM) that enriches the receptive field of images through dilated convolutions and captures information at different scales, while reducing parameter counts and optimizing computational resources. Additionally, to enhance the model’s sensitivity to both horizontal and vertical directions, we introduce a full-dimensional perceptual (FDP) attention mechanism. Experimental results demonstrate that on the CIFAR-100 dataset, compared to the baseline model, our model improves the performance of the student model under the same architectural guidance by 1.73%, 1.41%, 1.16%, and 0.72%, respectively. Under different architectural guidance, the performance of the student model on the CIFAR-100 dataset improves by 2.27% and 1.51%, respectively. Compared to state-of-the-art knowledge distillation methods (e.g., RKD and VID), the performance of our student model improves by 1.03% and 1.56%, respectively.}, archive = {J_ACCESS}, author = {Yilin Miao and Yuhong Tang and Huangliang Ren and Jianjun Li}, doi = {10.1109/ACCESS.2025.3571732}, journal = {IEEE Access}, pages = {89831-89843}, shortjournal = {IEEE Access}, title = {The impact of integrating shallow and deep information on knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Continuous health state monitoring of high-voltage circuit breakers. ACCESS, 13, 89819-89830. (www)

  @article{ , abstract = {Circuit breakers (CBs) are renowned for their high reliability and long lifespan. As a result, many CBs installed decades ago are now approaching their predefined end of service life. However, this predefined service life does not always reflect the actual condition, as some may still be far from reaching their true end-of-life, depending on their operating conditions and history. To assess their true lifetime, continuous condition monitoring is essential. While previous studies have effectively demonstrated the ability to distinguish between different CB fault types, the evolution of CB degradation remains unclear when faults are artificially introduced. This paper investigates the health condition of two high-voltage CBs continuously through run-to-failure experiments. A comprehensive dataset was collected for all opening and closing operations with various sensors such as vibration, coil current, and travel curve and has been made publicly available for further analysis. Furthermore, features were derived from the sensor data, revealing distinct degradation trajectories over time that can be used to monitor the condition of the CBs. This paper highlights the degradation patterns of these features, some of which are well-suited for continuous condition monitoring due to their gradual changing trend over time that likely correlates with the true degradation condition, while others are less useful as they show abrupt changes only before or at failure. By leveraging these features, we can progress beyond the focus of previous research using only fault diagnosis towards fault prognosis. This shift opens the possibility for accurate prediction of the CB condition over time, enabling more effective maintenance strategies.}, archive = {J_ACCESS}, author = {Chi-Ching Hsu and Gaetan Frusque and Olga Fink and Christian M. Franck}, doi = {10.1109/ACCESS.2025.3570535}, journal = {IEEE Access}, pages = {89819-89830}, shortjournal = {IEEE Access}, title = {Continuous health state monitoring of high-voltage circuit breakers}, volume = {13}, year = {2025}, }
• (2025). A spin photonic topological metasurface based on a kagome lattice for use in leaky-wave antenna application. ACCESS, 13, 89811-89818. (www)

  @article{ , abstract = {The emerging field of topological metasurfaces offers unique advantages, particularly in robustness against backscattering in low-profile structures. The lattice configuration of these structures significantly influences the ability to achieve sharp turns in the propagation path. One of the most studied lattices in condensed matter physics is the kagome lattice, characterized by its hexagonal Brillouin zone, which displays a Dirac cone in its dispersion diagram. Previous research on kagome lattices in photonic topological insulators has primarily focused on valley types of insulators. This article introduces a spin topological metasurface based on the kagome lattice and its unit cell, enabling a broad range of sharp turns and propagation paths. The unit cell is compared to its hexagonal and 60-degree rhombic counterparts, and a parametric study of its dimensions is conducted. As a result of this research, a new X-band leaky-wave antenna designed in the kagome lattice with an armchair arrangement interface has been developed. This antenna provides two forward and two backward beams, each pair achieving an approximately 50-degree scan within the 8.8 to 11.1 GHz bandwidth.}, archive = {J_ACCESS}, author = {Sayyed Ahmad Abtahi and Mohsen Maddahali and Ahmad Bakhtafrouz}, doi = {10.1109/ACCESS.2025.3567182}, journal = {IEEE Access}, pages = {89811-89818}, shortjournal = {IEEE Access}, title = {A spin photonic topological metasurface based on a kagome lattice for use in leaky-wave antenna application}, volume = {13}, year = {2025}, }
• (2025). A novel approach to continual knowledge transfer in multilingual neural machine translation using autoregressive and non-autoregressive models for indic languages. ACCESS, 13, 89775-89810. (www)

  @article{ , abstract = {Recent progress in multilingual pre-trained models has significantly improved translation quality for Indic languages. However, extending these models to new languages via fine-tuning or retraining remains computationally costly and often leads to parameter interference, degrading performance on previously learned or typologically distant languages. While continual learning offers a promising alternative for incremental language addition, its application in Indic contexts is still limited and faces challenges in generalization across diverse linguistic settings. To overcome these issues, we propose a Continual Knowledge Transfer (CKT) framework for efficient and scalable multilingual adaptation. CKT is realized in both autoregressive (MNMT) and non-autoregressive (Switch-GLAT) architectures, yielding two variants: MNMT+CKT and Switch-GLAT+CKT. Rather than retraining the entire model, CKT freezes the multilingual base and updates only parameters relevant to the newly added language. Key innovations include gradient-based knowledge pruning, sequential teacher integration, and dynamic vocabulary expansion for minimizing interference and maximizing cross-lingual retention. Comprehensive evaluations on the IN22-Conv and IN22-Gen benchmark datasets demonstrate that both MNMT+CKT and Switch-GLAT+CKT consistently outperform established baselines, such as IndicTrans2, Google Translate, GPT-4-32K, LLaMA-2-17B, and NLIP-LAB-IITH. The proposed multi-step distillation approach, MNMT+CKT, consistently outperforms conventional fine-tuning and Knowledge Transfer (MNMT+KT) strategies for incremental adaptation of linguistically diverse Indic languages. On IN22-Conv, BLEU improvements range from +4.93 (Kashmiri $\rightarrow $ English) to +11.48 (Assamese $\rightarrow $ English) and similar improvements are seen for IN22-Gen. The method also achieves substantial reductions in trainable parameters—19.80% (Nepali) to 66.87% (Kannada)—while enabling up to $4x$ faster inference when integrated with the Switch-GLAT architecture. Among the two, Switch- GLAT+CKT achieves the highest BLEU scores across all language pairs. In the English $\rightarrow $ Indic translation direction, BLEU gains range from +9.12 (Kannada) to +26.00 (Nepali), while in the Indic $\rightarrow $ English direction, gains range from +0.07 (Odia) to +3.78 (Assamese). Furthermore, ablation studies and sequential integration of multilingual teacher models reveal that CKT significantly reduces the number of trainable parameters required for each incremental step.}, archive = {J_ACCESS}, author = {Shailashree K. Sheshadri and Deepa Gupta and Biswajit Paul and J. Siva Bhavani}, doi = {10.1109/ACCESS.2025.3570699}, journal = {IEEE Access}, pages = {89775-89810}, shortjournal = {IEEE Access}, title = {A novel approach to continual knowledge transfer in multilingual neural machine translation using autoregressive and non-autoregressive models for indic languages}, volume = {13}, year = {2025}, }
• (2025). License plate recognition for smart construction sites based on GMH-YOLO. ACCESS, 13, 89760-89774. (www)

  @article{ , abstract = {With the rapid urbanization, vehicle management at construction sites has become crucial for safety and logistics efficiency. However, License Plate Detection in such environments faces unique challenges, such as simultaneous detection of dual plates and body plates, strong light reflections, and muddy interferences. This paper constructs a license plate dataset, CSLPD, specifically for construction sites, containing 1495 images and 2301 license plate instances, with double and body license plates accounting for 27.2% and 25.4%, respectively. To enhance detection performance in complex environments, this paper proposes the GMH-YOLO model, which integrates an innovative Gated Multi-Head Attention mechanism into the C3k2 module of YOLO11. This lightweight gating unit adaptively allocates feature channel resources, effectively enhancing key information while suppressing background interference, making it particularly suitable for detecting multiple license plate types and partially occluded plates in complex construction site environments. Experimental results show that GMH-YOLO achieves 93.3% mAP@50 on the CSLPD dataset, outperforming YOLO11 by 1.4%. For the challenging body license plate task, detection accuracy improves from 81.3% to 87.2%, a 5.9% increase. The model maintains high real-time performance due to the optimized gating mechanism. Comparative experiments with six attention mechanism integration schemes confirm that the gated mechanism provides the best balance between feature extraction and computational efficiency, offering a high-precision, efficient solution for intelligent license plate recognition at construction sites.}, archive = {J_ACCESS}, author = {Ming Li and Ze-Quan Wang and Yu-Hang Zhao and Qiang Li}, doi = {10.1109/ACCESS.2025.3569101}, journal = {IEEE Access}, pages = {89760-89774}, shortjournal = {IEEE Access}, title = {License plate recognition for smart construction sites based on GMH-YOLO}, volume = {13}, year = {2025}, }
• (2025). Taming mambas for 3D medical image segmentation. ACCESS, 13, 89748-89759. (www)

  @article{ , abstract = {Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with its distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas Transformers are hindered by their substantial memory requirements as well as their data hunger, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnU-Net, still dominate the scene when segmenting medical structures in large 3D medical volumes. Despite numerous advancements toward developing Transformer variants with subquadratic time and memory complexity, these models still fall short in content-based reasoning. A recent breakthrough is Mamba, a Recurrent Neural Network (RNN) based on State-Space Models (SSMs), outperforming Transformers in many long-context tasks (million-length sequences) on famous natural language processing and genomic benchmarks while keeping a linear complexity. In this paper, we evaluate the effectiveness of Mamba-based architectures in comparison to state-of-the-art convolutional and Transformer-based models for 3D medical image segmentation across three well-established datasets: Synapse Abdomen, MSD BrainTumor, and ACDC. Additionally, we address the primary limitations of existing Mamba-based architectures by proposing alternative architectural designs, hence improving segmentation performances. The source code is publicly available to ensure reproducibility and facilitate further research: https://github.com/LucaLumetti/TamingMambas}, archive = {J_ACCESS}, author = {Luca Lumetti and Vittorio Pipoli and Kevin Marchesini and Elisa Ficarra and Costantino Grana and Federico Bolelli}, doi = {10.1109/ACCESS.2025.3570461}, journal = {IEEE Access}, pages = {89748-89759}, shortjournal = {IEEE Access}, title = {Taming mambas for 3D medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). Theoretical optimization of lateral heterojunction perovskite solar cells: A structural perspective. ACCESS, 13, 89738-89747. (www)

  @article{ , abstract = {Lateral heterojunction perovskite solar cells are well-known for their high stability. However, their efficiency is very low compared to traditional solar cells. Hence, it is crucial to theoretically study lateral heterojunction perovskite solar cells to find the reason for low efficiency and improve their performance. Therefore, in this study we simulated the lateral heterojunction perovskite solar cells to find optimal sizes and doping concentrations. We used TiO2 as Electron Transport Layer (ETL) and NiOx as Hole Transport Layer (HTL) to extract charges from CH3NH3PbI3 perovskite layer. We varied the thickness, width and doping concentration of each layer. According to the obtained results, optimal thicknesses are 40 nm for ETL and HTL, and 900 nm for perovskite layer. In lateral heterojunctions, the width of each layer is one of the key parameters that can significantly influence photoelectric parameters of the solar cell. We have found that the optimal width of ETL and HTL is 1200 nm. Optimal donor and acceptor concentrations are equal to $1\times 10^{15}$ cm−3 in the n-region and $1\times 10^{16}$ cm−3 in the p-region. A lateral heterojunction solar cell with optimal size and doping concentrations achieved 29.9% efficiency, an open circuit voltage of 1.172 V, a short circuit current of 18.33 mA/cm2 and a fill factor of 88.06%.}, archive = {J_ACCESS}, author = {Irodakhon Gulomova and Oussama Accouche and Rayimjon Aliev}, doi = {10.1109/ACCESS.2025.3571476}, journal = {IEEE Access}, pages = {89738-89747}, shortjournal = {IEEE Access}, title = {Theoretical optimization of lateral heterojunction perovskite solar cells: A structural perspective}, volume = {13}, year = {2025}, }
• (2025). Early in-hospital mortality prediction based on xTimesNet and time series interpretable methods. ACCESS, 13, 89723-89737. (www)

  @article{ , abstract = {Accurate prediction of in-hospital mortality is critical for healthcare risk management and clinical decision-making. However, existing time-series models struggle to model the non-stationarity and cross-periodic patterns in clinical data effectively, and their interpretability remains limited, particularly in addressing the dynamic importance of time-varying features and actionable counterfactual interventions. To address these challenges, this paper proposes the xTimesNet-TSR-CoMTE framework, which integrates a hybrid spectral-temporal model, xTimesNet, with two interpretability methods: Two-Step temporal saliency Rescaling (TSR) and Counterfactual Multivariate Time series Explainability (CoMTE). xTimesNet leverages adaptive Wavelet-FFT mixture and Inception modules to capture cross-period temporal interactions in clinical time series. Experiments on the MIMIC-III dataset, involving 27 features over 48 timesteps, demonstrate the framework’s superior performance, achieving a ROC_AUC of 0.785 and PR_AUC of 0.403. Comparative analysis shows that CoMTE outperforms existing methods by generating clinically plausible counterfactuals with minimal modifications, as evidenced by the lowest L1 distance of 0.024 and zero violent feature modifications. TSR identifies critical time-varying features and their temporal importance, pinpointing systolic blood pressure, diastolic blood pressure, and body temperature as the most impactful predictors of mortality. CoMTE generates actionable counterfactual explanations by suggesting minimal interventions on these key features, such as maintaining systolic blood pressure within a stable range during critical early time points. This study advances the application of time-series models in clinical risk analysis and offers a robust solution for interpretable and actionable mortality prediction.}, archive = {J_ACCESS}, author = {Xueyan Wang and Zhengxing Yuan and Zhiqiu Yao and Youwei Yuan and Lanjun Luo}, doi = {10.1109/ACCESS.2025.3571789}, journal = {IEEE Access}, pages = {89723-89737}, shortjournal = {IEEE Access}, title = {Early in-hospital mortality prediction based on xTimesNet and time series interpretable methods}, volume = {13}, year = {2025}, }
• (2025). Enhancing breast cancer diagnosis with multi-resolution vision transformers and robust decision-making. ACCESS, 13, 89704-89722. (www)

  @article{ , abstract = {This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows.}, archive = {J_ACCESS}, author = {Margo Sabry and Hossam Magdy Balaha and Khadiga M. Ali and Tayseer Hassan A. Soliman and Dibson Gondim and Mohammed Ghazal and Norah Saleh Alghamdi and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3570840}, journal = {IEEE Access}, pages = {89704-89722}, shortjournal = {IEEE Access}, title = {Enhancing breast cancer diagnosis with multi-resolution vision transformers and robust decision-making}, volume = {13}, year = {2025}, }
• (2025). Hybrid HDBSCAN-FHMM approach for energy disaggregation in non-intrusive load monitoring (NILM) systems. ACCESS, 13, 89685-89703. (www)

  @article{ , abstract = {Non-intrusive load monitoring (NILM) is emerging as a useful approach to improving the energy efficiency of buildings and households, particularly in the face of the growing challenges of environmental sustainability. Despite recent advances, the accuracy and reliability of disaggregation algorithms remain limited by the diversity of household energy behaviors and the heterogeneous operation of domestic appliances. In this study, we propose a method based on the prior identification of appliance operating states using the HDBSCAN (Hierarchical Density-Based Spatial Clustering of Application with Noise) algorithm, prior to the disaggregation of overall energy consumption. Our study is based on the AMPds dataset (Almanac of Minutely Power Dataset), which provides detailed measurements of residential power consumption. Our methodology consisted of two main phases: an appliance operating state identification phase using the HDBSCAN algorithm, followed by an energy disaggregation phase based on a modified FHMM (Factorial Hidden Markov Model). The model’s performance was evaluated using three main metrics: F1 score, Mean Absolute Error (MAE) and Match Rate, in comparison with several reference models including the Adaptive-FHMM. The results obtained show that our HDBSCAN-FHMM model outperforms several models of energy disaggregation algorithms, but also the Adaptive-FHMM reference approach, with significant improvements of 6.25%, 46.24% and 12.04% for the F1, MAE and Match Rate metrics respectively. These performances reinforce the reliability of our method in accurately attributing the energy consumption of household appliances. Nevertheless, challenges remain, particularly with regard to model robustness in the face of variability in energy behavior and technological evolution. Future exploration of deep learning techniques combined with current methods could offer significant advances in energy disaggregation.}, archive = {J_ACCESS}, author = {Leonce W. Tokam and Sena K. Apeke and Sanoussi S. Ouro-Djobo}, doi = {10.1109/ACCESS.2025.3570930}, journal = {IEEE Access}, pages = {89685-89703}, shortjournal = {IEEE Access}, title = {Hybrid HDBSCAN-FHMM approach for energy disaggregation in non-intrusive load monitoring (NILM) systems}, volume = {13}, year = {2025}, }
• (2025). Label-free biomolecule detection with InP/AlGaAs charge plasma dielectric-modulated vertical TFET using TaN as metal gate. ACCESS, 13, 89667-89684. (www)

  @article{ , abstract = {This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.}, archive = {J_ACCESS}, author = {Hashim Elshafie and Abdulrahman Saad Alqahtani and Azath Mubarakali and Adarsh Rag S and M. Venkatesh and P. Parthasarathy}, doi = {10.1109/ACCESS.2025.3570233}, journal = {IEEE Access}, pages = {89667-89684}, shortjournal = {IEEE Access}, title = {Label-free biomolecule detection with InP/AlGaAs charge plasma dielectric-modulated vertical TFET using TaN as metal gate}, volume = {13}, year = {2025}, }
• (2025). Change-guided difference interaction attention network for remote sensing change detection. ACCESS, 13, 89654-89666. (www)

  @article{ , abstract = {Change Detection (CD) is a fundamental task in remote sensing image analysis, widely applied in fields such as municipal planning and vital signs monitoring. However, many existing methods struggle to extract change-relevant features when faced with dual-temporal remote sensing images characterized by inconsistent and complex feature distributions, leading to false alarms. Moreover, these methods rely on simple concatenation, differential operations, or the addition of attention mechanisms to fuse dual-temporal features, resulting in the loss of edge information. Additionally, these fusion strategies fail to fully exploit the relationship between dual-temporal features and difference features, which negatively impacts the overall performance of CD. Furthermore, the problem of internal holes remains unresolved. To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). This network effectively establishes interaction between dual-temporal features through difference interaction attention module (DIAM), enhancing the capability to extract change features. During the feature fusion stage, the edge-enhanced difference fusion module (EEDFM) thoroughly integrates dual-temporal features with difference features, and employs an edge enhancement mechanism to prevent the loss of edge information. In the decoding stage, multi-scale change guidance module (MSCGM) combines prior change information with enhanced multi-scale dilated convolutions, addressing the limited receptive field of traditional CNN decoders, thereby effectively mitigating the internal holes. Experimental results on three public datasets demonstrate that CGDIANet outperforms ten existing advanced methods.}, archive = {J_ACCESS}, author = {Canbin Hu and Sida Du and Hongyun Chen and Xiaokun Sun and Kailun Liu}, doi = {10.1109/ACCESS.2025.3571463}, journal = {IEEE Access}, pages = {89654-89666}, shortjournal = {IEEE Access}, title = {Change-guided difference interaction attention network for remote sensing change detection}, volume = {13}, year = {2025}, }
• (2025). A novel dense-swish-CNN with bi-LSTM framework for image deepfake detection. ACCESS, 13, 89641-89653. (www)

  @article{ , abstract = {Artificial intelligence (AI) developments have revolutionized technologies and methodologies, particularly for malicious uses, especially since the advent of generative adversarial networks (GANs) in 2014. GANs can fabricate media streams, seamlessly blending them into different environments. Advanced machine learning techniques can also generate highly realistic deepfake images, which can be exploited for harassment and blackmail. Furthermore, deepfakes can manipulate public sentiment and opinions about individuals or groups. This situation necessitates the development of automated methods to detect deepfakes. In this work, we propose a hybrid deep learning approach for deepfake face detection. The base models include CNN+Bi-LSTM, ResNet34+EfficientNet+Bi-LSTM, and MobileNet+EfficientNet+Bi-LSTM. Initially, images from multiple sources were collected and also generated using various GANs, and the proposed Dense-Swish-CNN with Bi-LSTM was trained. Extract spatial & temporal information from input samples using hybrid pre-trained models. We evaluated our model on challenging datasets such as DFDC, CelebDF, ForgeryNIR, and UADFV. Our experimentation demonstrated the effectiveness of our proposed approach with accuracy 97.76%. Additionally, we assessed the effectiveness of deepfake detection method and validated the explainability of the Dense-Swish-CNN model by performing cross-dataset validation.}, archive = {J_ACCESS}, author = {B. C. Soundarya and H. L. Gururaj}, doi = {10.1109/ACCESS.2025.3570761}, journal = {IEEE Access}, pages = {89641-89653}, shortjournal = {IEEE Access}, title = {A novel dense-swish-CNN with bi-LSTM framework for image deepfake detection}, volume = {13}, year = {2025}, }
• (2025). Automated testing for service-oriented architecture: Leveraging large language models for enhanced service composition. ACCESS, 13, 89627-89640. (www)

  @article{ , abstract = {This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI’s ChatGPT 4o and ChatGPT 4o-mini, Anthropic’s Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google’s Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI’s ChatGPT and Anthropic’s Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems.}, archive = {J_ACCESS}, author = {Mahsun Altin and Behcet Mutlu and Deniz Kilinc and Altan Cakir}, doi = {10.1109/ACCESS.2025.3571994}, journal = {IEEE Access}, pages = {89627-89640}, shortjournal = {IEEE Access}, title = {Automated testing for service-oriented architecture: Leveraging large language models for enhanced service composition}, volume = {13}, year = {2025}, }
• (2025). Toward secure electronic voting: A survey on E-voting systems and attacks. ACCESS, 13, 89600-89626. (www)

  @article{ , abstract = {The trend of electronic voting has risen in recent years as an alternative to paper ballot elections, bringing meaningful benefits in terms of efficiency and error proneness. However, real-world applications have demonstrated significant vulnerabilities and susceptibility to software errors that could be exploited by malicious entities. In fact, the difficult challenge of electronic voting lies in the need to incorporate multiple, often contradictory, properties into the system design. Moreover, the stakes of any potential vulnerability are amplified due to the high-value applications of e-voting systems. To understand the current landscape of electronic voting and its readiness for real-world implementations, this paper surveys the literature on e-voting systems and related attacks. Emphasis is put on the analysis of the fundamental properties of the reviewed systems, in order to better understand their strengths and weaknesses, and on blockchain-based solutions, given their novelty and potential advantages regarding immutability and transparency. Through the analysis of the current proposals and known attacks, we conclude that the paradigm of electronic voting needs additional research before it is considered secure to use in a general election setting.}, archive = {J_ACCESS}, author = {Riccardo Barelli and Mario D'Onghia and Stefano Longari}, doi = {10.1109/ACCESS.2025.3569334}, journal = {IEEE Access}, pages = {89600-89626}, shortjournal = {IEEE Access}, title = {Toward secure electronic voting: A survey on E-voting systems and attacks}, volume = {13}, year = {2025}, }
• (2025). Exploration of new dimensions for the massive internet of things. ACCESS, 13, 89582-89599. (www)

  @article{ , abstract = {Like all other types of wireless communications, massive Internet of Things (mIoT) communication is all about degrees of freedom. They hold the key to almost all mIoT challenging issues. However, they are also the most limited resources in mIoT due to their unique constraints. This paper derives a new class of waveforms and explores the dimensions hidden in its underlying mathematical structure. The intrinsic characteristics and the degrees of freedom of the waveform are studied and fully explored to reap the full potential of the waveform. Most significantly, a new dimension is uncovered and exploited as additional degrees of freedom to better address the mIoT challenges in energy efficiency, resource efficiency, and robustness under the mIoT constraints. We demonstrate that the new waveform class subsumes existing mIoT modulation waveforms as its subsets while introducing new degrees of freedom for a unified, comprehensive solution to the mIoT challenges. The corresponding benefits are evaluated analytically and numerically in AWGN, frequency-flat, and frequency-selective channels.}, archive = {J_ACCESS}, author = {Michael M. Wang and Lei Wang and Jingjing Zhang and Xiaohu You}, doi = {10.1109/ACCESS.2025.3565755}, journal = {IEEE Access}, pages = {89582-89599}, shortjournal = {IEEE Access}, title = {Exploration of new dimensions for the massive internet of things}, volume = {13}, year = {2025}, }
• (2025). A monte carlo tree search-based load balancing algorithm for 6G indoor heterogeneous networks. ACCESS, 13, 89570-89581. (www)

  @article{ , abstract = {With the advent of 6G networks, efficient load balancing in indoor heterogeneous networks has become critical to address the proliferation of connected devices and the demand for high data rates. This paper introduces a Monte Carlo tree search (MCTS)-based load balancing algorithm for 6G indoor heterogeneous networks, exemplified by hybrid visible light communication (VLC) and radio frequency (RF) systems. By dynamically predicting future network states, the proposed algorithm optimally distributes traffic, improving overall network performance. Simulation results show that the proposed MCTS-based method increases average user throughput by 13-60%, reduces handover rates by around 50%, and lowers latency by 15-50% as compared to benchmark methods while maintaining balanced computational complexity. These findings highlight the potential of MCTS as an effective and scalable solution for load balancing in complex 6G network environments.}, archive = {J_ACCESS}, author = {Guanghui Ma and Syifaul Fuada and Marcos Katz}, doi = {10.1109/ACCESS.2025.3567470}, journal = {IEEE Access}, pages = {89570-89581}, shortjournal = {IEEE Access}, title = {A monte carlo tree search-based load balancing algorithm for 6G indoor heterogeneous networks}, volume = {13}, year = {2025}, }
• (2025). Design of hybrid MPC-PID controller based on fractional order model of AMR. ACCESS, 13, 89556-89569. (www)

  @article{ , abstract = {In today’s world, robots, especially autonomous mobile robots (AMR), have critical roles in domestic, medical, and industrial applications. As technology develops and the diverse demands emerge, the tasks to be completed by AMRs become more complex and challenging. AMRs need to be improved to struggle with these difficulties. So, implementing more precise and reliable control strategies for robots is becoming increasingly important. To this end, a novel hybrid control methodology, composed of a Proportional-Integral-Derivative (PID) controller and a model predictive controller (MPC), is proposed based on the fractional-order model of the AMR. The MPC optimizes the reference signal for the PID controller, enhancing overall performance. First, the fractional order model of AMR is extracted by considering the input and output data from AMR at different velocity values, eliminating the need for detailed physical modeling and technical information for AMR. After verifying the obtained model, a model reduction technique is applied to simplify the obtained transfer function to reduce the computational burden on the processor of AMR. Then, based on the simplified transfer function a hybrid MPC-PID controller is designed and implemented on Turtlebot3 Burger (TB3) AMR to improve the robot’s performance. It is demonstrated that the hybrid controller, which is designed without requiring detailed equations of the system components, exhibits a good performance in minimizing errors and enhancing reliability for both angular and linear velocity values.}, archive = {J_ACCESS}, author = {Orkan Murat Celik and Furkan Nur Deniz and Murat Koseoglu}, doi = {10.1109/ACCESS.2025.3570947}, journal = {IEEE Access}, pages = {89556-89569}, shortjournal = {IEEE Access}, title = {Design of hybrid MPC-PID controller based on fractional order model of AMR}, volume = {13}, year = {2025}, }
• (2025). Revolutionizing marine stability: A review of cutting-edge gyro stabilizer designs and techniques for vessels. ACCESS, 13, 89542-89555. (www)

  @article{ , abstract = {Advancements in marine vessel stabilization technologies have greatly improved the safety and comfort for crews, cargo, and passengers, while also lowering the risk of capsizing. Marine vessels operating in open waters experience disturbances from unpredictable waves and strong winds, leading to instability and potential accidents. To mitigate these risks, stabilizers are integrated into ships to maintain stability, safety, and seaworthiness. As modern vessels evolve, stabilizers play an increasingly vital role, with gyro stabilizers emerging as a highly effective solution for mitigating roll motion without external appendages. This paper provides a comprehensive review of gyro stabilizer designs and techniques, covering their historical development, mathematical modeling, advantages, limitations, and case studies. A review of current research highlights a strong emphasis on simulating gyro stabilizer control systems, demonstrating their potential in enhancing vessel stability. However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. By exploring the latest developments, this review serves as a valuable reference for researchers, engineers, and maritime professionals seeking to advance marine stabilization technologies. The findings underscore the potential of next-generation gyro stabilizers, including fiber optic gyros, in achieving higher precision, stability, and operational efficiency.}, archive = {J_ACCESS}, author = {Wan Rahiman and Mohamad Hazwan Mohd Ghazali and Iskandar Al-Thani Mahmood}, doi = {10.1109/ACCESS.2025.3571710}, journal = {IEEE Access}, pages = {89542-89555}, shortjournal = {IEEE Access}, title = {Revolutionizing marine stability: A review of cutting-edge gyro stabilizer designs and techniques for vessels}, volume = {13}, year = {2025}, }
• (2025). A comprehensive methodology of field-oriented control design with parameter variation analysis for interior permanent magnet synchronous machine drives. ACCESS, 13, 89524-89541. (www)

  @article{ , abstract = {This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a Controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.}, archive = {J_ACCESS}, author = {Tiago Davi Curi Busarello and Abdullah Bubshait and Oruganti V. S. R. Varaprasad and Abdulhakeem Alsaleem and Marcelo Godoy Simões}, doi = {10.1109/ACCESS.2025.3571610}, journal = {IEEE Access}, pages = {89524-89541}, shortjournal = {IEEE Access}, title = {A comprehensive methodology of field-oriented control design with parameter variation analysis for interior permanent magnet synchronous machine drives}, volume = {13}, year = {2025}, }
• (2025). A simple and robust solid-state circuit breaker for low voltage DC applications. ACCESS, 13, 89514-89523. (www)

  @article{ , abstract = {The demand for solid-state circuit breakers (SSCBs) in modern DC power systems is growing because of their fast switching speed, high efficiency, and reliable fault protection. The existing DC-SSCBs have limitations such as high conduction losses, bulk weight and size, need for auxiliary power supply, high fault currents, limited fault ramp rates, and susceptibility to parasitic inductance and resistance. These issues are very crucial for the development of an effective SSCB, especially for low-voltage DC (LVDC) applications. This paper proposes a novel SSCB topology that ensures automatic fault response with a minimal number of components without any auxiliary power supply. The proposed design utilizes a silicon controlled rectifier (SCR) thyristor as the main switch, which is turned off by a MOSFET during a fault condition. At fault condition, the gate-to-source voltage of the MOSFET exceeds the threshold voltage and the MOSFET creates an alternate low-impedance path for the anode current of the SCR. As a result, the SCR loses its holding current, causing it to turn OFF. The proposed circuit exhibits less sensitivity to parasitic inductance and resistance, effective low-pass filtering, and the ability to detect slow-arising faults. These advantages make the proposed SSCB suitable for LVDC applications, offering a sensible way to enhance the protection of the DC system.}, archive = {J_ACCESS}, author = {Rezoana Sharmin Riana and Sajib Biswas Shuvo and Abdul Jabber Antor and Muhammad Abdullah Arafat}, doi = {10.1109/ACCESS.2025.3571668}, journal = {IEEE Access}, pages = {89514-89523}, shortjournal = {IEEE Access}, title = {A simple and robust solid-state circuit breaker for low voltage DC applications}, volume = {13}, year = {2025}, }
• (2025). Comprehensive insulation design method of high voltage oil-immersed pulse transformers. ACCESS, 13, 89502-89513. (www)

  @article{ , abstract = {This article proposes a detailed method for the design of the insulation distances in high voltage (HV) oil-immersed pulse transformers, named the stressed oil volume scaling (SOVS) method. The developed SOVS method uses breakdown measurement data, which are obtained from a scaled down measurement setup (SDMS) by applying pulsed voltages of different pulse durations in the range of a few $\mu $ s. The acquired breakdown measurement data are scaled to the pulse transformer, offering a methodical approach for estimating the pulse breakdown voltage with a defined probability of breakdown for the pulse transformer. The proposed SOVS method shows a significantly lower computational effort compared to the typical insulation design approaches for pulse transformers, and it is possible to be integrated within a general design procedure of oil-immersed HV pulse transformers. As an example, the method is applied to the specifications of the cyclotron autoresonance maser (CARM) pulse transformer, which is currently under investigation at the ENEA research center. Further, the method is applied to two already built pulse transformers in order to evaluate its efficacy.}, archive = {J_ACCESS}, author = {Spyridon Stathis and Bastian Korthauer and Florian Krismer and Jürgen Biela}, doi = {10.1109/ACCESS.2025.3571982}, journal = {IEEE Access}, pages = {89502-89513}, shortjournal = {IEEE Access}, title = {Comprehensive insulation design method of high voltage oil-immersed pulse transformers}, volume = {13}, year = {2025}, }
• (2025). A biologically inspired program-level imitation approach for robots. ACCESS, 13, 89481-89501. (www)

  @article{ , abstract = {Robots may learn new skills from humans to better assist us with everyday tasks. We propose a novel, biologically inspired imitation approach to enable robots to understand and perform complex actions using high-level programs that incorporate sequential regularities between sub-goals a robot can recognize and physically achieve. To learn a new task, human-provided demonstrations—obtained by a robot through different modalities such as kinesthetic teaching or behavioural observation—are processed by an algorithm to discover multiple possible arrangements of sub-goals that achieve the task goal. When performing the task, the robot first evaluates the available sequences in the program based on user-defined criteria, through mental simulation of the real task, to find the optimal sequence of actions. The selected sequence is then executed using the hierarchical structure of actions embedded in the program. We implemented the proposed learning architecture on an iCub humanoid robot and evaluated the effectiveness of the system in multiple scenarios. Our approach accommodates variations in human teaching styles and is expected to help robots perform tasks with greater flexibility and efficiency, opening the way to more adaptable and intelligent robots.}, archive = {J_ACCESS}, author = {Pourya Aliasghari and Moojan Ghafurian and Chrystopher L. Nehaniv and Kerstin Dautenhahn}, doi = {10.1109/ACCESS.2025.3570922}, journal = {IEEE Access}, pages = {89481-89501}, shortjournal = {IEEE Access}, title = {A biologically inspired program-level imitation approach for robots}, volume = {13}, year = {2025}, }
• (2025). Design and economic analysis of marine vessel PV power system based on solar radiation estimation. ACCESS, 13, 89463-89480. (www)

  @article{ , abstract = {The need for clean energy has accelerated energy production by using Renewable Energy Sources (RES) in marine vessels. With the developments in solar energy technology, the use of various simulation programs to analyze the performance predicted of Photovoltaic (PV) systems is increasing. In this study, 12 month solar radiation data belonging to 12 different points in a specific region of the Marmara Sea were used for prediction. The Artificial Neural Network (ANN) model was trained by dividing it into training, validation and test data sets at a ratio of 70-15-15 and the performance of the model was obtained with average Mean Squared Error 0.00098 and Regression 0.99997. The predicted values were used in PV power system modeling for a marine vessel and the system performance was analyzed with HOMER, SAM, RETScreen, PVSOL, PVsyst, PVWatts and PVGIS simulation programs. 16.8 kW capacity PV power system was created for the optimum design. When the results are examined, it is seen that the maximum production is obtained from HOMER with 23193 kW, but it is the most expensive in terms of cost, and it is determined that an average of 20771 kW of energy is produced throughout the year from the simulation programs, an annual decrease of 45.01 kg in SO2 emissions and 18166 kg in CO2 emissions is achieved. In addition, a decrease of 0.2542 USD is observed in energy costs. This study contributes to the literature in optimizing the use of RES in moving platforms and comparing the programs.}, archive = {J_ACCESS}, author = {Batuhan Tural and Onur Akar}, doi = {10.1109/ACCESS.2025.3571875}, journal = {IEEE Access}, pages = {89463-89480}, shortjournal = {IEEE Access}, title = {Design and economic analysis of marine vessel PV power system based on solar radiation estimation}, volume = {13}, year = {2025}, }
• (2025). Modeling and control of an induction motor with built-in force actuator for vibration suppression. ACCESS, 13, 89452-89462. (www)

  @article{ , abstract = {Lateral vibrations are occurring in rotating machinery and can limit operational performance and range. Such vibrations often lead to instability, decreased precision, and potential damage to the system. However, by integrating force actuators directly within the electrical machine, it becomes possible to actively suppress these vibrations, enhancing system stability and extending its operational speed limits. This paper focuses on the modeling and control of an induction machine equipped with in-built force actuator to suppress lateral vibrations. The studied electrical machine concept involves a structure with separate winding sets; one for suspension and another for motoring. Through a selected simulation case involving a rotor system, it is demonstrated how the inclusion of force actuators allows for the suppression of the dominant vibration modes. As a result, the approach effectively extends the operational range of the induction machine, offering a more stable and reliable solution for high-speed applications. The proposed model and control strategy are analyzed with a detailed simulation model, supported by a robustness analysis of the controller under model uncertainties.}, archive = {J_ACCESS}, author = {Muhammad Numan and Sadjad Madanzadeh and Jawad Tariq and Olli Liukkonen and Gyan Ranjan and Firdausa Ahmed and Tuomo Lindh and Niko Nevaranta}, doi = {10.1109/ACCESS.2025.3570219}, journal = {IEEE Access}, pages = {89452-89462}, shortjournal = {IEEE Access}, title = {Modeling and control of an induction motor with built-in force actuator for vibration suppression}, volume = {13}, year = {2025}, }
• (2025). Racket, rhythm, and results: Assessment of factors leading to transformation of badminton teaching based on intuitionistic fuzzy extended DEMATEL method. ACCESS, 13, 89441-89451. (www)

  @article{ , abstract = {The proposed research extends the decision-making trial and evaluation laboratory (DEMATEL) through the combination of intuitionistic fuzzy sets (IFSs) and multiple decision-maker (DMK) participation. The proposed extension addresses traditional DEMATEL methodology biases that stem from using a single DMK through effective mitigation. Intuitionistic fuzzy logic enhances precision and reliability in decision-making (DM) processes through an extended approach to handling complex decision problems. The proposed solution is implemented in the significant case study to assess factors leading to the transformation of badminton teaching and determine which factors shape badminton teaching success. The research identified adaptability and pacing ${\mu }_{3 }$ , shot execution ${\mu }_{2 }$ and technical proficiency ${\mu }_{5 }$ as cause factors that directly impact teaching outcomes. However, changing rhythm ${\mu }_{4 }$ and racket weight, balance, string tension ${\mu }_{1 }$ along with mental focus and adaptability ${\mu }_{6 }$ belong to the effect group, which depends on the cause factors. Teaching methodologies of modern badminton should include adaptable methods, pace control, and precision technique instruction to improve athlete performance levels. The research enhances DEMATEL methodology and sports teaching strategy improvement through multiple DMK integration and IF logic applications.}, archive = {J_ACCESS}, author = {Feifei Du}, doi = {10.1109/ACCESS.2025.3570431}, journal = {IEEE Access}, pages = {89441-89451}, shortjournal = {IEEE Access}, title = {Racket, rhythm, and results: Assessment of factors leading to transformation of badminton teaching based on intuitionistic fuzzy extended DEMATEL method}, volume = {13}, year = {2025}, }
• (2025). UltraLightSqueezeNet: A deep learning architecture for malaria classification with up to 54× fewer trainable parameters for resource constrained devices. ACCESS, 13, 89428-89440. (www)

  @article{ , abstract = {Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained environments. For our study, we selected SqueezeNet1.1 as it is one of the most popular lightweight architectures. SqueezeNet1.1 is a later version of SqueezeNet1.0 and is 2.4 times more computationally efficient than the original model. We proposed and implemented three ultra-lightweight architecture variants to SqueezeNet1.1 architecture, namely Variant 1 (one fire module), Variant 2 (two fire modules), and Variant 3 (four fire modules), which are even more compact than SqueezeNetV1.1 (eight fire modules). These models were implemented to evaluate the best performing variant that achieves superior computational efficiency without sacrificing accuracy in malaria blood cell classification. The models were trained and evaluated using the NIH Malaria dataset. We assessed each model’s performance based on metrics including accuracy, recall, precision, F1-score, and Area Under the Curve (AUC). The results show that the SqueezeNet1.1 model achieves the highest performance across all metrics, with a classification accuracy of 97.12%. Variant 3 (four fire modules) offers a competitive alternative, delivering almost identical results (accuracy 96.55%) with a $6\times $ reduction in computational overhead compared to SqueezeNet1.1. Variant 2 and Variant 1 perform slightly lower than Variant 3, with Variant 2 (two fire modules) reducing computational overhead by $28\times $ and Variant 1 (one fire module) achieving a $54\times $ reduction in trainable parameters compared to SqueezeNet1.1. These findings demonstrate that our SqueezeNet1.1 architecture variants provide a flexible approach to malaria detection, enabling the selection of a variant that balances resource constraints and performance.}, archive = {J_ACCESS}, author = {Suresh Babu Nettur and Shanthi Karpurapu and Unnati Nettur and Likhit Sagar Gajja and Sravanthy Myneni and Akhil Dusi and Lalithya Posham}, doi = {10.1109/ACCESS.2025.3571696}, journal = {IEEE Access}, pages = {89428-89440}, shortjournal = {IEEE Access}, title = {UltraLightSqueezeNet: A deep learning architecture for malaria classification with up to 54× fewer trainable parameters for resource constrained devices}, volume = {13}, year = {2025}, }
• (2025). An efficient and generic construction of public key encryption with equality test under the random oracle model. ACCESS, 13, 89411-89427. (www)

  @article{ , abstract = {Public key encryption with equality test (PKE-ET) facilitates authorized entities in distinguishing if two ciphertexts involve the identical underlying message. This functionality has driven its adoption across various applications, such as secure data management in cloud environments, encrypted spam filtering, and keyword searches in encrypted databases. However, existing generic PKE-ET constructions often rely on cryptographic primitives that require strong security assumptions or additional complex functionalities, leading to inefficiencies. This paper proposes an improved generic construction for PKE-ET under the random oracle model. The proposed method leverages only fundamental cryptographic building blocks, relying exclusively on a standard public key encryption (PKE) scheme along with cryptographic hash functions, without requiring additional complex primitives. More concretely, the proposed construction leverages a PKE scheme that ensures one-wayness against chosen plaintext attacks (OW-CPA), differing from earlier generic frameworks that necessitate stronger security guarantees. We demonstrate that the proposed construction satisfies one-wayness against adaptively chosen ciphertext attacks (OW-CCA2) for a Type-1 adversary-who possesses equality test trapdoors-and indistinguishability against adaptively chosen ciphertext attacks (IND-CCA2) for a Type-2 adversary-who lacks such trapdoors-assuming that the exploited PKE scheme achieves OW-CPA security. Furthermore, we present three concrete instantiations of our generic framework using well-established PKE schemes: The hashed ElGamal encryption, the RSA encryption, and the BIKE cryptosystem. These instantiations offer notable advantages over existing PKE-ET designs. The first two constructions enhance efficiency in comparison to prior schemes, while the third stands out as the first code-based PKE-ET instantiation, relying solely on code-based security assumptions.}, archive = {J_ACCESS}, author = {Seongbong Choi and Chanhyeok Park and Hyung Tae Lee}, doi = {10.1109/ACCESS.2025.3571714}, journal = {IEEE Access}, pages = {89411-89427}, shortjournal = {IEEE Access}, title = {An efficient and generic construction of public key encryption with equality test under the random oracle model}, volume = {13}, year = {2025}, }
• (2025). FEXGBIDS: Federated XGBoost-based intrusion detection system for in-vehicle network. ACCESS, 13, 89399-89410. (www)

  @article{ , abstract = {With the rapid development of Intelligent Connected Vehicles (ICVs) and In-Vehicle Networks (IVNs), the Controller Area Network (CAN) protocol faces significant cybersecurity threats due to its open design. Traditional Intrusion Detection Systems (IDS) struggle to balance privacy protection, computational efficiency, and detection accuracy. We propose FEXGBIDS, a privacy-preserving intrusion detection system based on federated XGBoost. By integrating the Federated Learning (FL) framework with cryptographic optimization techniques, FEXGBIDS addresses data silo and security challenges in the vehicular network environment. We use the BatchCrypt algorithm to batch encrypt first- and second-order gradients to reduce the computational and communication overhead of Paillier homomorphic encryption. Additionally, we employ BLS aggregate signatures to achieve efficient and verifiable parameter aggregation, and leverage the DDSketch distributed quantile estimation algorithm to optimize the feature bucketing process. Experiments on the Car Hacking dataset show that FEXGBIDS achieves a detection accuracy of 97.26%, comparable to centralized XGBoost, and improves computational efficiency by 65% over the traditional Paillier method. The system effectively resists spoofing attacks and data leakage, while supporting efficient collaborative training in large-scale Internet of Vehicles (IoV) environments.}, archive = {J_ACCESS}, author = {Jie Li and Yuanyuan Song and Ming-Gang Zheng and Shuo Zhang and Han Liang}, doi = {10.1109/ACCESS.2025.3569356}, journal = {IEEE Access}, pages = {89399-89410}, shortjournal = {IEEE Access}, title = {FEXGBIDS: Federated XGBoost-based intrusion detection system for in-vehicle network}, volume = {13}, year = {2025}, }
• (2025). Application of quantitative interpretability to evaluate CNN-based models for medical image classification. ACCESS, 13, 89386-89398. (www)

  @article{ , abstract = {Convolutional Neural Networks (CNNs) dominate medical image classification, yet their “black box” nature limits understanding of their decision-making process. This study applies quantitative interpretability metrics to evaluate CNN performance in stained tongue coating recognition and compare with traditional metrics. We trained four classical CNN models (ResNet18, ResNet50, VGG19, and AlexNet) on a dataset of 2,008 tongue coating images, with external validation on 381 new images. Class Activation Mapping (CAM) algorithms generated heatmaps visualizing influential regions. The Heatmap Assisted Accuracy Score (HAAS) was utilized to assess feature attribution quality. All models achieved high classification performance on the test set (accuracy >0.92, precision >0.89, recall >0.91), but VGG19 and AlexNet performed poorly on external validation. Interpretability analysis revealed that VGG19 and AlexNet deviated from regions of interest, while ResNet models achieved significantly higher HAAS scores. ResNet50 emerged as the best model in external validation (accuracy =0.900, precision =0.869, recall =0.911), consistent with its superior interpretability metrics (Eigen-CAM HAAS=1.548). Our findings demonstrate that interpretability metrics more accurately reflect CNN performance before external validation, offering valuable tools for understanding model behavior and enhancing transparency in medical image classification.}, archive = {J_ACCESS}, author = {Nuan Cui and Yingjie Wu and Guojiang Xin and Jiaze Wu and Liqin Zhong and Hao Liang}, doi = {10.1109/ACCESS.2025.3567775}, journal = {IEEE Access}, pages = {89386-89398}, shortjournal = {IEEE Access}, title = {Application of quantitative interpretability to evaluate CNN-based models for medical image classification}, volume = {13}, year = {2025}, }
• (2025). DeepSeqCoco: A robust mobile friendly deep learning model for detection of diseases in cocos nucifera. ACCESS, 13, 89367-89385. (www)

  @article{ , abstract = {Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification methods are manual, labor-intensive, and non-scalable. In response to these limitations, we come up with DeepSeqCoco, a deep learning based model for accurate and automatic disease identification from coconut tree images. The model was tested under various optimizer settings, such as SGD, Adam, and hybrid configurations, to identify the optimal balance between accuracy, minimization of loss, and computational cost. Results from experiments indicate that DeepSeqCoco can achieve as much as 99.5% accuracy (achieving up to 5% higher accuracy than existing models) with the hybrid SGD then Adam showing the lowest validation loss of 2.81%. It also shows a drop of up to 18% in training time and up to 85% in prediction time for input images. The results point out the promise of the model to improve precision agriculture through an AI-based, scalable, and efficient disease monitoring system.}, archive = {J_ACCESS}, author = {Miit Daga and Dhriti Parikh and Swarna Priya Ramu}, doi = {10.1109/ACCESS.2025.3571800}, journal = {IEEE Access}, pages = {89367-89385}, shortjournal = {IEEE Access}, title = {DeepSeqCoco: A robust mobile friendly deep learning model for detection of diseases in cocos nucifera}, volume = {13}, year = {2025}, }
• (2025). Security-enhanced decentralized content sharing in Publish/Subscribe system. ACCESS, 13, 89351-89366. (www)

  @article{ , abstract = {We consider a content dissemination model, where the contents disseminated from a content source on a regular or event-driven basis are shared among the group members, without directly contacting the content source. To realize such a content dissemination model, a publish/subscribe (pub/sub) system is integrated with a decentralized peer-to-peer data network (dP2PN) through establishing a security-enhanced interface between them. As a result of such integration, the effect of security bootstrapping within the pub/sub system can be propagated to the dP2PN, so that published contents can be shared securely. For this purpose, we design a hierarchical node ID and content ID to create more than one virtual subnets on the dP2PN, and show that our proposed dP2PN based on the hierarchical IDs is superior to the conventional dP2PN in terms of routing efficiency, ease of network configuration, and security. Regarding security issues, we consider a malicious-broker model and authorized-but-malicious-node model, under which a couple of security primitives are designed to provide an end-to-end security and a detection mechanism against malicious content modification. The security analysis and performance evaluation shows that the proposed integration scheme is a viable solution to realize our content dissemination model.}, archive = {J_ACCESS}, author = {Wang-Seok Park and Chang-Seop Park and Samuel Woo}, doi = {10.1109/ACCESS.2025.3571824}, journal = {IEEE Access}, pages = {89351-89366}, shortjournal = {IEEE Access}, title = {Security-enhanced decentralized content sharing in Publish/Subscribe system}, volume = {13}, year = {2025}, }
• (2025). Exponential stabilization for delayed memristive neural networks by comparison strategy. ACCESS, 13, 89343-89350. (www)

  @article{ , abstract = {This article is concerned with the rth moment global exponential stabilization of delayed memristive neural networks (DMNNs). By using the comparison strategy, the theories of differential inclusion and inequality techniques, the exponential stabilization of DMNNs is investigated. To achieve this purpose, a state feedback controller and an adaptive controller are designed, respectively. The comparison strategy is a new analyzed method without employing Lyapunov stability theory and relaxes the constraint of time delays. In addition, the obtained results are represented by algebraic criteria, which are convenient for testing. In the end, a numerical simulation is given to show the validity of the derived criteria.}, archive = {J_ACCESS}, author = {Fenglin Wang and Jiemei Zhao and Ning Wu and Yaqin Li}, doi = {10.1109/ACCESS.2025.3571786}, journal = {IEEE Access}, pages = {89343-89350}, shortjournal = {IEEE Access}, title = {Exponential stabilization for delayed memristive neural networks by comparison strategy}, volume = {13}, year = {2025}, }
• (2025). A novel linguistic relational fuzzy C-means. ACCESS, 13, 89332-89342. (www)

  @article{ , abstract = {In real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are several types of clustering including clustering on information from objects themselves or clustering on a relational data. Currently, there are few researches on a fuzzy data clustering and a fuzzy relational data clustering. However, those algorithms are either defuzzifying the data or the relation of the data before implementing the clustering Therefore, in this paper, we develop a linguistic relational fuzzy C-means (LRFCM) that works with fuzzy relation of vectors of fuzzy numbers directly. The algorithm is an extension of the regular relational fuzzy C-means (RFCM). The proposed algorithm does not defuzzify fuzzy attributes beforehand. We found that the results from the LRFCM are similar to that from the RFCM. However, the results from the LRFCM can provide information that contains all the uncertainties from the inputs while the RFCM cannot. We also compare the results with the fuzzy C-means clustering. These comparisons show that the LRFCM behaves the same way as the RFCM and the FCM in partitioning the input space into clusters.}, archive = {J_ACCESS}, author = {Peerawich Phaknonkul and Sansanee Auephanwiriyakul and Nipon Theera-Umpon}, doi = {10.1109/ACCESS.2025.3570014}, journal = {IEEE Access}, pages = {89332-89342}, shortjournal = {IEEE Access}, title = {A novel linguistic relational fuzzy C-means}, volume = {13}, year = {2025}, }
• (2025). A novel nonlinear method for cardiovascular data synchronization. ACCESS, 13, 89314-89331. (www)

  @article{ , abstract = {This study introduces and evaluates a novel nonlinear technique for synchronizing electrocardiogram (ECG) and photoplethysmogram (PPG) signals by embracing the intrinsic relationship between heart rate variability (HRV) and pulse rate variability (PRV). The proposed method utilizes normalized mutual information (NMI) derived from cross-distance matrices (CDM) of HRV and PRV, alongside the distance matrix of a reference HRV extract. We tested the methodology on two databases containing simultaneous ECG and PPG signals, including one publicly available on Physionet, and benchmarked it against various synchronization techniques, both linear (Pearson coefficient, dynamic time warping) and nonlinear (conventional mutual information, NMI with recurrence plots, cross-recurrence plot, and determinism). Results showed that the proposed method (NMI of CDM) outperformed all others, achieving synchronization rates near 50% within a 1.2-second lag threshold. This study also comprehensively examines how signal quality and recording methodology variations affect synchronization outcomes, confirming the importance of lag threshold adjustment for real accuracy assessment. However, some limitations must be kept in mind: the proposed approach is not suited for blood pressure estimation based on HRV-PRV differences, nonlinear methods generally require higher computational resources than linear ones, and further validation is needed with additional databases from real-world scenarios similar to those used in this study.}, archive = {J_ACCESS}, author = {Daniele Padovano and Arturo Martinez-Rodrigo and Norbert Marwan and Santiago Mula Muñoz and Jose J. Rieta and Raul Alcaraz}, doi = {10.1109/ACCESS.2025.3571070}, journal = {IEEE Access}, pages = {89314-89331}, shortjournal = {IEEE Access}, title = {A novel nonlinear method for cardiovascular data synchronization}, volume = {13}, year = {2025}, }
• (2025). Recognition of machine-readable zone in identity documents: A review. ACCESS, 13, 89300-89313. (www)

  @article{ , abstract = {Encoding personal identity document information into machine-readable formats is one of the most important approaches to automatic data processing. The Machine-Readable Zone (MRZ) of a document contains text data in the form of 2 or 3 long text lines of a special structure regulated by ICAO, which are easily read by computing devices using OCR methods. The importance of the MRZ lies not only in the automatic data entry, but also in the ability to verify the authenticity of a document and access biometric data in the RFID chip. Originally developed for scanners, it has gradually come to be read from photos and videos taken in uncontrolled conditions from mobile device cameras. The main problem with this transition is that MRZ was not designed for recognition in photos and videos with projective distortions, blurs and other effects, which makes the task much more difficult. Modern document processing systems recognize MRZ in complex 3D scenes with a quality of around 90%, which is extremely low for a machine-readable form of data representation with a strict format. This paper provides an overview of the current state of the problem of MRZ recognition in identity documents. Recognition approaches from the 1990s to modern ones for scanned images and photos are considered. The review reveals understudied aspects of MRZ reading in contemporary research, such as the lack of specialized recognition and language models. It is also noted that many works are based on universal recognition models, the complexity of which is excessive for the MRZ. At the end of the review, a brief description of the state of the problem is given and directions for further research in this area are outlined.}, archive = {J_ACCESS}, author = {Alexander V. Gayer and Yulia S. Chernyshova and Vladimir V. Arlazarov}, doi = {10.1109/ACCESS.2025.3571547}, journal = {IEEE Access}, pages = {89300-89313}, shortjournal = {IEEE Access}, title = {Recognition of machine-readable zone in identity documents: A review}, volume = {13}, year = {2025}, }
• (2025). AFHNet: Attention-free hybrid network for salient object detection in underwater images. ACCESS, 13, 89287-89299. (www)

  @article{ , abstract = {Underwater salient object detection (USOD) has attracted significant attention with the rapid development of machine vision. However, traditional machine vision and deep learning approaches face notable challenges in complex underwater environments due to issues such as light attenuation, scattering, motion blur, color distortion, noise, low contrast, and multipath effects, which severely affect detection accuracy. Furthermore, conventional up-sampling and down-sampling techniques often lead to the loss of critical local and global object information, reducing both accuracy and efficiency. To address these issues, we propose an Attention-Free Hybrid Network (AFHNet) for underwater optical image analysis. AFHNet integrates an attention-free mechanism, global feature enhancement, and a convolution-free Transformer-based dual-encoder to effectively fuse RGB and depth features while preserving essential information. The Global Feature Enhancement Module (GFEM) improves feature representation by fusing local and global features, while Multilevel Feature Fusion with Multitask Learning (MFF-ML) enhances detail preservation and feature diversity. Comprehensive experiments conducted on an underwater optical image dataset, comparing AFHNet with 36 state-of-the-art methods, demonstrate its superior performance, achieving a mean absolute error (MAE) of 1.94%. These results highlight the potential of AFHNet to advance underwater vision applications and establish a new benchmark for salient object detection in challenging environments. The source code and models are available at https://github.com/darkseid-arch/Underwater-AFHNet}, archive = {J_ACCESS}, author = {Qian Tang and Zhen Wang and Xuqi Wang and Shan-Wen Zhang}, doi = {10.1109/ACCESS.2025.3567606}, journal = {IEEE Access}, pages = {89287-89299}, shortjournal = {IEEE Access}, title = {AFHNet: Attention-free hybrid network for salient object detection in underwater images}, volume = {13}, year = {2025}, }
• (2025). A transfer learning-based framework for enhanced classification of perceived mental stress using EEG spectrograms. ACCESS, 13, 89266-89286. (www)

  @article{ , abstract = {Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies.}, archive = {J_ACCESS}, author = {Sheharyar Khan and Sadam Hussain Noorani and Jaroslav Frnda and Usman Rauf and Aamir Arsalan and Sanay Muhammad Umar Saeed}, doi = {10.1109/ACCESS.2025.3571437}, journal = {IEEE Access}, pages = {89266-89286}, shortjournal = {IEEE Access}, title = {A transfer learning-based framework for enhanced classification of perceived mental stress using EEG spectrograms}, volume = {13}, year = {2025}, }
• (2025). Deep-learning approach for an analysis of real-estate prices and transactions. ACCESS, 13, 89248-89265. (www)

  @article{ , abstract = {Research on the study of houses, condominiums and buildings in Taiwan’s metropolitan areas continues to be an important area of research. In real estate forecasting and analysis, methods such as statistical analysis and questionnaire data collection are widely used. However, when multidimensional data is considered, these methods are time-consuming and inadequate. This study aimed to build a real estate forecasting model that can adapt to a changing environment. Data were collected from public government databases, the collected data were standardized for accurate clustering, an appropriate data clustering algorithm was applied to the standardized data, and cross-statistical analysis was performed to verify the adopted algorithm. We used a deep learning based on autoencoder algorithm to increase the accuracy of the clustering analysis. A double-bottom map particle swarm optimization (DBM-PSO) clustering algorithm was then used to determine the optimal clustering solution. Cluster analysis and deep learning were conducted on data collected from public websites to understand the factors that led to the sustained increase in housing prices in Taiwan over the past decade. The results of this study indicate that three key factors—the number of real estate transactions, the average unit price of real estate transactions, and the building material and construction index—significantly affected real estate prices in Taiwan. Our results could help researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors. In addition, the relationships between real estate trends and the aforementioned three key factors were determined to obtain valuable information that can enable the Taiwanese government to regulate the property market and prevent excessive growth. The framework proposed in this paper allows researchers and governments to focus on specific aspects of real estate development without being influenced by other related factors, and provides a new mechanism for approaching real estate-related forecasting.}, archive = {J_ACCESS}, author = {Cheng-Hong Yang and Borcy Lee and Yu-Da Lin}, doi = {10.1109/ACCESS.2025.3568798}, journal = {IEEE Access}, pages = {89248-89265}, shortjournal = {IEEE Access}, title = {Deep-learning approach for an analysis of real-estate prices and transactions}, volume = {13}, year = {2025}, }
• (2025). Edge intelligence in enhancing last-mile delivery logistics. ACCESS, 13, 89236-89247. (www)

  @article{ , abstract = {The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal, but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements by using PRISMA 2020, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on operational efficiency and customer satisfaction. Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should refine use-cases of these technologies and explore their long-term impacts on logistics.}, archive = {J_ACCESS}, author = {João Reis}, doi = {10.1109/ACCESS.2025.3570894}, journal = {IEEE Access}, pages = {89236-89247}, shortjournal = {IEEE Access}, title = {Edge intelligence in enhancing last-mile delivery logistics}, volume = {13}, year = {2025}, }
• (2025). Animation pose generation model based on kinect depth image and occlusion-robust pose-maps algorithm. ACCESS, 13, 89222-89235. (www)

  @article{ , abstract = {Aiming at the problem of low efficiency and difficulty in adapting to complex interactive scenes in traditional animation pose generation methods, a dynamic pose generation model based on Kinect depth image and an occlusion robust pose map algorithm is proposed. A three-dimensional skeleton sequence is constructed through spatiotemporal sparse sampling, and the missing areas in the depth data are effectively repaired using matrix filling techniques. Additionally, dynamic median filtering is used to ensure smooth motion. This model innovatively combines Kinect depth information with monocular visual pose estimation results, utilizing an improved combination point representation method and Kalman filter data fusion strategy. The robustness in occluded scenes has been significantly improved while maintaining spatiotemporal consistency. The experiment showed that the improved model achieved correct limb percentage values of 87.7% and 86.8% on the Human3.6M and MPI-INF-3DHP datasets, respectively. In addition, the improved model performed better than the comparison model on both datasets in terms of the percentage of correct keypoints and the percentage of detected joints, reaching (75.4%, 74.6%) and (83.4%, 82.3%). In the evaluation of registration error, the research model showed the lowest error on both datasets, and the error values did not exceed 2. In addition, in the case analysis, the angle fluctuation curve presented by the research model was most in line with the actual situation. In the visual evaluation of motion capture effects, the animation generated by the model had smooth poses, reasonable hand and leg movements, ergonomic joint movements, and no abrupt jumps or disjointed frames, resulting in a better overall effect. Overall, the proposed model can be effectively applied in animation production, providing a new technological means to enhance the quality of animation production.}, archive = {J_ACCESS}, author = {Zhenxi Yu and Ting Wang and Peng Tian and Xuejie Li}, doi = {10.1109/ACCESS.2025.3570798}, journal = {IEEE Access}, pages = {89222-89235}, shortjournal = {IEEE Access}, title = {Animation pose generation model based on kinect depth image and occlusion-robust pose-maps algorithm}, volume = {13}, year = {2025}, }
• (2025). A shield segment bolt detection and localization method based on ellipse detection. ACCESS, 13, 89208-89221. (www)

  @article{ , abstract = {The automatic assembly of shield tunnel segments can significantly reduce labor intensity and improve tunneling efficiency. However, during the automatic segment gripping process, traditional methods of setting targets introduce manual errors and complicate operations. Moreover, single-object detection methods only capture the bolt area, which makes precise bolt positioning challenging. Consequently, this research introduces an approach to detect and pinpoint the position of segment bolts by utilizing the circular imagery of the bolt head’s surface. The technique uses the elliptical shape of the bolt head in images to accurately determine bolt locations through ellipse detection methods. In response to the issue of decreased elliptical detection accuracy due to circular arc defects, an arc-cluster-based elliptical detection algorithm is further proposed, which encompasses key steps such as image preprocessing, arc clustering, and least squares fitting. Furthermore, in response to the challenge that monocular vision systems face in obtaining vertical data with point lasers during the gripping process, this study presents a monocular positioning strategy. This strategy is founded on the constant diameter of the bolt head’s circular form, facilitating the determination of the bolt’s spatial orientation without the need for additional height measurements. This strategy is based on the constant diameter of the bolt head’s circular shape, facilitating the determination of the bolt’s spatial orientation without the need for additional height measurements. Experimental results demonstrate that this method improves elliptical detection accuracy, and gripping experiments further validate its feasibility.}, archive = {J_ACCESS}, author = {Jie Liu and Shuaishuai Liu and Zhendong He and Yanqiu Xiao and Guangzhen Cui and Pengpeng Wang}, doi = {10.1109/ACCESS.2025.3566675}, journal = {IEEE Access}, pages = {89208-89221}, shortjournal = {IEEE Access}, title = {A shield segment bolt detection and localization method based on ellipse detection}, volume = {13}, year = {2025}, }
• (2025). Finding friendly neighborhood: Optimal D2D relaying in mmWave IoT networks. ACCESS, 13, 89193-89207. (www)

  @article{ , abstract = {Device-to-Device (D2D) relaying helps improve the coverage range and throughput of the millimeter Wave (mmWave) networks. This work focuses on improving the uplink throughput in a single-cell mmWave-based Internet-of-Things (IoT) network. The key factors involve the placement of relays and neighbor selection (NS) ( $, i.e$ ., the process of selecting source IoTs within the relay node’s coverage range). The problem is formulated using model-based bandits, and the associated structural results are analyzed. Considering the resource-constrained nature of IoTs, we have developed a simple rule-based NS as an alternative to computationally intensive learning-based neighbor-selection processes from state of the art. By utilizing the rule-based NS, we show that there exists a conducive coverage range (called a friendly neighborhood) for the relay node to choose the neighbors. Accordingly, a geometry-based relay placement mechanism is proposed to maximize the uplink throughput. The optimality of the proposed solutions is shown analytically, and their performance concerning the state of the art is evaluated using extensive simulation.}, archive = {J_ACCESS}, author = {J. Vamshi Vijay Krishna and V. Mahendran and Venkataramana Badarla}, doi = {10.1109/ACCESS.2025.3566743}, journal = {IEEE Access}, pages = {89193-89207}, shortjournal = {IEEE Access}, title = {Finding friendly neighborhood: Optimal D2D relaying in mmWave IoT networks}, volume = {13}, year = {2025}, }
• (2025). Global single-shot indoor localization of autonomous flight robots with minimal sensor configuration. ACCESS, 13, 89179-89192. (www)

  @article{ , abstract = {This paper introduces a novel localization system for global single-shot localization of flight robots in indoor environments. The system is designed for initialization at standstill, to ensure that a safe and hazard-free state is confirmed before the engines start. Existing localization solutions are often unsuitable for intralogistic applications because of to their weight, energy consumption, or infrastructure costs. The proposed system achieves global localization with minimal sensing hardware. The pose can be reliably determined using a few 1D distance sensors and a digital map of the environment. Two methods are introduced, adapted to perform single-shot localization with resource-constrained sensor configurations. Both methods use application-specific knowledge to initially reduce the search space during localization. Afterwards, one of two methods is employed: a randomized coarse-to-fine search, in which the search space is iteratively reduced, or a parallel differential evolution algorithm, which enhances pose estimates with evolutionary algorithms while avoiding premature convergence. In a real scenario, the system with six 1D sensors and an inertial measurement unit achieved a 100% success rate in localization, with position errors below 0.1 m and rotation errors below 0.05 rad. Owing to the low information density of sensor data, transmission to external edge computing hardware is efficient and enables remote processing with minimal bandwidth requirements. This reduces onboard computations, system weight, and energy consumption of the flight robot. The localization system provides a lightweight and energy-efficient solution for global pose estimation in flexible productions and enables the economical use of autonomous flight robots in industrial intralogistics.}, archive = {J_ACCESS}, author = {Timm von Bergen and Sascha Röck}, doi = {10.1109/ACCESS.2025.3571700}, journal = {IEEE Access}, pages = {89179-89192}, shortjournal = {IEEE Access}, title = {Global single-shot indoor localization of autonomous flight robots with minimal sensor configuration}, volume = {13}, year = {2025}, }
• (2025). EMHANet: Lightweight salient object detection for remote sensing images via edge-aware multiscale feature fusion. ACCESS, 13, 89164-89178. (www)

  @article{ , abstract = {Salient object detection in remote sensing images (RSI-SOD) aims to identify visually prominent objects by mimicking human visual perception. While convolutional neural networks (CNNs) have significantly improved detection accuracy, most RSI-SOD methods suffer from high computational costs and large model sizes, limiting their applicability in resource-constrained environments. Additionally, RSI’s complex backgrounds and diverse object scales further challenge existing methods. To address these issues, we propose EMHANet, a lightweight network that integrates edge texture detail extraction, multi-scale feature fusion, and hybrid attention mechanism. EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. The network employs a coarse-to-fine strategy to accurately detect salient objects while maintaining efficiency. Experiments on ORSSD and EORSSD datasets demonstrate EMHANet superior performance over 31 state-of-the-art methods. It achieves high accuracy with an inference speed of 143 fps, 0.257M parameters, and 0.92G FLOPs, making it suitable for resource-limited applications. The source code and dataset will be available on https://github.com/darkseid-arch/EMHANet}, archive = {J_ACCESS}, author = {Qian Tang and Zhen Wang and Xuqi Wang and Shan-Wen Zhang}, doi = {10.1109/ACCESS.2025.3565485}, journal = {IEEE Access}, pages = {89164-89178}, shortjournal = {IEEE Access}, title = {EMHANet: Lightweight salient object detection for remote sensing images via edge-aware multiscale feature fusion}, volume = {13}, year = {2025}, }
• (2025). A 1.43-nW 1.3-kHz resistor-less relaxation oscillator with 67.3-ppm/ ° c stability. ACCESS, 13, 89157-89163. (www)

  @article{ , abstract = {This paper introduces a novel non-RC, leakage-based relaxation oscillator designed as a timer in ultra-low-power (ULP) applications. The proposed oscillator structure is created by stacking a source degeneration and a common-gate amplifier within a single branch, which effectively suppresses the temperature dependence of the leakage current. This temperature sensitivity is further mitigated through a two-point calibration method using a thermometer-driven nonlinear polynomial fitting process. The oscillator is designed to produce a 1.3 kHz output and is fabricated using a 180-nm CMOS process, occupying an active area of 0.108 mm2 (including estimates for off-chip blocks). The oscillator consumes 1.43 nW, achieving a power efficiency of 1.1 nW/kHz. It maintains an average temperature sensitivity of 67.3 ppm/°C over a 0-to-90°C temperature range. The supply sensitivity of the oscillator is measured at 0.055%/V across a supply voltage range of 1.1 V to 1.8 V, and the measured Allan deviation floor is 20 ppm.}, archive = {J_ACCESS}, author = {Jiho Lee and Jae-Yoon Sim}, doi = {10.1109/ACCESS.2025.3571938}, journal = {IEEE Access}, pages = {89157-89163}, shortjournal = {IEEE Access}, title = {A 1.43-nW 1.3-kHz resistor-less relaxation oscillator with 67.3-ppm/ ° c stability}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid approach to forecasting crude oil prices using local mean decomposition, ARIMA, and XGBoost. ACCESS, 13, 89140-89156. (www)

  @article{ , abstract = {Crude oil in its usage and process brings about several issues that affect the environment including soiling of the air and water and the release of greenhouse gases leading to changes in the climate. Further, historically use of oil has created political and economic stakes that primarily end up in war, nationalism to control resources, and political instability. As a new approach to this problem in this study, we present a novel hybrid of Local Mean Decomposition (LMD) for breaking down the data and contribution coefficients for rebuilding the component with less computational complexity. Using the XGBOOST approach, deep dynamic learning features, augmented with autoregressive moving average (ARIMA), it is possible to predict each reconstructed segment. Turbulences that occur in the oil markets are random, the market shares are sensitive to pressures from other markets, the movement in the market is disorderly, and there is high volatility in the market. Thus, the price of oil is very unfixed, unpredictable, sensitive, and uncertain. Several techniques have been adopted in cost estimation in the aspect of time variation; some of the traditional methods include EMD, EEMD couldn’t solve the computational complexity and save time and cost. Because of the unpredictability and fluctuation observed in oil prices non deep neural network models and data mining cannot be used in the prediction of costs and generalized forecasting. The analysis of the onset of deep non-linear modeling as a part of the machine learning process demonstrates how the discussed hybrid model successfully acquires relevant data and adapts a non-stationary function. The data is modeled using an LMD-SD-ARIMA-XGBOOST hybrid recurrent network when the nonlinearity is considered, and the residual values are forwarded to the XGBOOST model. Besides the volatility problem, the solution provided by the work also addresses the overfitting problem of neural networks. The hybrid LMD-SD-ARIMA-XGBOOST model overcomes overfitting by combining statistical and machine learning components, where the decomposition process isolates key features of the data, reducing noise and complexity. Unlike standalone neural networks, which require large amounts of data to generalize effectively, our hybrid approach leverages ARIMA and XGBOOST models that are inherently less prone to overfitting due to their simpler structures and feature-focused learning. To validate the proposed hybrid approach, data from the West Texas Intermediate (WTI) which is publicly available is employed.}, archive = {J_ACCESS}, author = {Jawaria Nasir and Muhammad Aamir and Soofia Iftikhar and A. B. Albidah and Ohud A. Alqasem and Maysaa E. A. Elwahab and Ilyas Khan and Wei Sin Koh}, doi = {10.1109/ACCESS.2025.3561193}, journal = {IEEE Access}, pages = {89140-89156}, shortjournal = {IEEE Access}, title = {A novel hybrid approach to forecasting crude oil prices using local mean decomposition, ARIMA, and XGBoost}, volume = {13}, year = {2025}, }
• (2025). Anomaly-focused augmentation method for industrial visual inspection. ACCESS, 13, 89118-89139. (www)

  @article{ , abstract = {In industrial inspection, the detection of surface defects—such as scratches, dents, or other defects—is crucial for ensuring product quality. However, the limited availability of annotated images of such defects poses challenges for developing reliable detection models. This paper proposes a novel image augmentation algorithm to address this limitation by generating synthetic imperfections from a small sample set. Unlike traditional approaches that augment entire images, the proposed approach isolates defects and applies spatial transformations (e.g., rotation, shear) before blending them into background images. This allows for the creation of diverse training datasets. The algorithm employs a 2D binary map to encode the spatial relationship between defect and background regions of images and then applies an AI-driven model to generate new realistic blended images. By leveraging a generator-discriminator framework, the augmentation process is iteratively refined during training to maintain the style and texture of the original imperfections. Experimental results show that the proposed method significantly enhances the performance of defect detection models compared to existing augmentation techniques while preserving the realism of the generated images.}, archive = {J_ACCESS}, author = {Melvin A. Lara and Ibrahim A. Hameed and Jiri Koziorek}, doi = {10.1109/ACCESS.2025.3570075}, journal = {IEEE Access}, pages = {89118-89139}, shortjournal = {IEEE Access}, title = {Anomaly-focused augmentation method for industrial visual inspection}, volume = {13}, year = {2025}, }
• (2025). Low-cost blind and semi-blind equalizers for nonlinear SIMO systems. ACCESS, 13, 89108-89117. (www)

  @article{ , abstract = {Nonlinearities in systems, such as those encountered in optical and satellite communications, can significantly degrade signal quality and require advanced equalization techniques for effective compensation. This work introduces an innovative blind and semi-blind (SB) signal estimation method designed for Single-Input Multiple-Output (SIMO) systems with nonlinearity. The proposed approach leverages the truncated covariance matrix kernel in conjunction with signal subspace properties and pilot symbols (when available) to compute an accurate equalizer. Furthermore, a low-complexity variant of the equalizer, which bypasses subspace decomposition, is presented. Extensive simulations are conducted to evaluate the performance of the proposed equalizers in nonlinear multichannel systems. The results demonstrate the effectiveness of the proposed methods, achieving robust signal estimation with significantly reduced computational overhead, making them suitable for practical deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Abdulmajid Lawal and Karim Abed-Meraim and Azzedine Zerguine and Nguyen Linh Trung and Kabiru Nasiru Aliyu}, doi = {10.1109/ACCESS.2025.3571195}, journal = {IEEE Access}, pages = {89108-89117}, shortjournal = {IEEE Access}, title = {Low-cost blind and semi-blind equalizers for nonlinear SIMO systems}, volume = {13}, year = {2025}, }
• (2025). AI-assisted web application for leukocyte abnormality counting with YOLOv11 and smartphone microscopy. ACCESS, 13, 89079-89107. (www)

  @article{ , abstract = {Accurate and timely white blood cell (WBC) analysis is crucial for diagnosing hematological disorders, often requiring microscopic examination of peripheral blood smears (PBS). While manual counting by trained specialists is considered the gold standard, it is time-consuming and impractical in resource-limited settings. Automated cell counters can misclassify similitude or immature cells, hindering accurate diagnosis. To address these limitations, we propose an AI-powered web application that utilizes YOLOv11 with enhanced small object detection capabilities, enabled by integrating our C3k2-Conv blocks, an architecture inspired by C3k2. Our model, trained on eleven WBC classes and nucleated red blood cells (NRBCs), achieves an impressive mean average precision (mAP@0.5) of approximately 0.9000 on validation and unseen test sets, demonstrating a performance comparable to human specialists in identifying and quantifying WBCs. Furthermore, our research demonstrates that providing general practitioners and medical students with PBS images annotated by our AI model significantly improves their counting accuracy and reduces the time spent on manual counting. Our web application, Myelosoft, allows clinicians to upload smartphone-captured PBS images for rapid and automated analysis. The system provides comprehensive differential counts for 11 WBC classes, including atypical lymphocytes, band neutrophils, basophils, blasts, eosinophils, lymphocytes, metamyelocytes, monocytes, myelocytes, promyelocytes, and segmented neutrophils, as well as NRBCs. This real-time analysis facilitates timely diagnosis and treatment, potentially reducing risks associated with delayed interventions. Our approach offers a robust and accessible solution for improving hematologic treatment, especially in resource-constrained environments.}, archive = {J_ACCESS}, author = {Natthakorn Kasamsumran and Piyalitt Ittichaiwong and Chutinart Chinudomporn and Kanyakorn Veerakanjana and Ekapun Karoopongse and Wanchalerm Pora}, doi = {10.1109/ACCESS.2025.3569767}, journal = {IEEE Access}, pages = {89079-89107}, shortjournal = {IEEE Access}, title = {AI-assisted web application for leukocyte abnormality counting with YOLOv11 and smartphone microscopy}, volume = {13}, year = {2025}, }
• (2025). Prognostic survival analysis for AD diagnosis and progression using MRI data: An AI-based approach. ACCESS, 13, 89059-89078. (www)

  @article{ , abstract = {Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes.}, archive = {J_ACCESS}, author = {T. Deenadayalan and S. P. Shantharajah}, doi = {10.1109/ACCESS.2025.3564611}, journal = {IEEE Access}, pages = {89059-89078}, shortjournal = {IEEE Access}, title = {Prognostic survival analysis for AD diagnosis and progression using MRI data: An AI-based approach}, volume = {13}, year = {2025}, }
• (2025). R-YOLO: Enhancing Takeoff/Landing safety in UAM vertiports with deep learning model. ACCESS, 13, 89045-89058. (www)

  @article{ , abstract = {This study introduces the R-YOLO model, an innovative AI-driven safety system that incorporates real-time object detection and trajectory prediction to provide decision support information during the critical takeoff and landing phases of improving urban air mobility (UAM) operations. The proposed unified approach, in contrast with traditional techniques, effectively combines detection and prediction into a single model, allowing effective real-time video processing. Leveraging data from onboard cameras, the model continuously monitors vertiport surroundings, enabling instantaneous situational awareness and predictive risk assessment. This method offers useful information about object positions and movement, which improves situational awareness, reduces operator workload, and facilitates better decision-making. The primary contribution of the research is the unified structure for anticipatory safety mechanisms, a step forward in AI-enabled aviation. By addressing major issues in urban aviation with this innovative artificial intelligence framework, the study provides the possibility of safer and more effective UAM operations.}, archive = {J_ACCESS}, author = {Nan Lao Ywet and Aye Aye Maw and Jae-Woo Lee}, doi = {10.1109/ACCESS.2025.3571435}, journal = {IEEE Access}, pages = {89045-89058}, shortjournal = {IEEE Access}, title = {R-YOLO: Enhancing Takeoff/Landing safety in UAM vertiports with deep learning model}, volume = {13}, year = {2025}, }
• (2025). Heuristics for the order-picking problem with sequence-dependent constraints and a one-directional conveyor. ACCESS, 13, 89025-89044. (www)

  @article{ , abstract = {Optimizing the logistic order-picking process in the distribution center depends on two closely related decisions: how to schedule the processing of orders by a limited number of employees and how to sequence the containers on the conveyor to be transported to the buffers where they can be serviced. The main aim of this research is to develop a heuristic solution approach for the logistic order-picking problem with sequence-dependent constraints and a one-directional conveyor. The developed heuristics allow for minimizing the total time needed to process all orders. Central to this approach is decomposing the order-picking problem and finding the product quantity using mathematical programming. Next, using product quantity as an input value, find the container start time implementing heuristics. The performance criterion is time; thus, in the developed Pickers and Buffers heuristics, the execution time of each one is less than a second. A comprehensive computational study demonstrates the numerical advantage of the proposed heuristic approach in most instances compared to the solution of the initial and reduced models obtained with the commercial solver. On the one hand, for small instances, Pickers heuristics were better on average 50.17%, Buffers heuristics were better on average 50.87% compared to the initial model for different layouts. For large instances, both heuristics showed 100%. On the other hand, compared to the reduced model, Pickers heuristics were better on average 35.91%, Buffers heuristics were better on average 36.16% for different layouts for different instances.}, archive = {J_ACCESS}, author = {Kateryna Czerniachowska and Marcin Hernes}, doi = {10.1109/ACCESS.2025.3571202}, journal = {IEEE Access}, pages = {89025-89044}, shortjournal = {IEEE Access}, title = {Heuristics for the order-picking problem with sequence-dependent constraints and a one-directional conveyor}, volume = {13}, year = {2025}, }
• (2025). Security in post-quantum era: A comprehensive survey on lattice-based algorithms. ACCESS, 13, 89003-89024. (www)

  @article{ , abstract = {Lattice-based post-quantum cryptography (PQC) has attracted significant attention as a promising solution to the security challenges posed by quantum computing. Unlike traditional cryptographic algorithms, lattice-based schemes are expected to remain secure even in the presence of quantum attacks, making them essential for securing future data. Despite their strong theoretical foundations, lattice-based schemes face several practical challenges, particularly in optimizing performance and scalability for real-world applications. This survey provides a novel taxonomy that categorizes lattice-based PQC designs, with an emphasis on computational paradigms and security considerations. We systematically evaluate lattice-based PQC implementations across both software platforms, including central processing units and graphics processing units, as well as hardware platforms like field-programmable gate arrays and application-specific integrated circuits, highlighting their strengths and limitations. In addition, we explore the practical applications of lattice-based cryptography in fields such as secure communication, critical infrastructure, privacy-preserving data analytics, artificial intelligence, and trust and authentication systems. By offering a comprehensive overview of the current state of lattice-based PQC, this survey aims to provide valuable insights into the ongoing advancements and future research directions in the field as we transition to a post-quantum era.}, archive = {J_ACCESS}, author = {Hien Nguyen and Samsul Huda and Yasuyuki Nogami and Tuy Tan Nguyen}, doi = {10.1109/ACCESS.2025.3571307}, journal = {IEEE Access}, pages = {89003-89024}, shortjournal = {IEEE Access}, title = {Security in post-quantum era: A comprehensive survey on lattice-based algorithms}, volume = {13}, year = {2025}, }
• (2025). Modified SPECK (M-SPECK) lightweight cipher architecture for resource-constrained applications. ACCESS, 13, 88993-89002. (www)

  @article{ , abstract = {The Internet of Things (IoT) devices have grown in recent years and are applied to various fields, with the development of new applications in the automotive industry, health care, agriculture, etc. The IoT system demands that data exchange be highly secured, especially in low-resource-constrained devices. The major challenge of IoT is providing data security with a low-complexity cryptography algorithm. However, traditional encryption algorithms are unsuitable for IoT applications due to their high complexity, whereas lightweight block ciphers have become promising solutions for resource-constrained applications. Meanwhile, a trade-off exists between the hardware complexity and performance of the lightweight block cipher. This paper proposes the modified SPECK (M-SPECK) block cipher with a novel key schedule mechanism. The proposed M-SPECK is a modified Addition-Rotation-XOR (ARX) structure that encrypts a 32-bit plaintext with a 64-bit key. The proposed key schedule mechanism is verified for randomness through 15 National Institute of Standards and Technology (NIST) benchmark statistical tests from the NIST SP 800-22 suite. The proposed M-SPECK cipher architecture is implemented and analyzed over the existing lightweight ciphers on the Artix-7 (XC7A100T) FPGA platform. Furthermore, the proposed cipher is analyzed for performance parameters like entropy, correlation coefficient, and histogram using MATLAB for image applications.}, archive = {J_ACCESS}, author = {R. Mohanapriya and V. Nithish Kumar}, doi = {10.1109/ACCESS.2025.3570727}, journal = {IEEE Access}, pages = {88993-89002}, shortjournal = {IEEE Access}, title = {Modified SPECK (M-SPECK) lightweight cipher architecture for resource-constrained applications}, volume = {13}, year = {2025}, }
• (2025). Line-frequency noise suppressor to reduce audible noise in induction heating systems. ACCESS, 13, 88984-88992. (www)

  @article{ , abstract = {Induction heating (IH) technology is widely used in domestic applications due to its high heating efficiency and environmentally friendly operations. As the performance of domestic IH appliances has advanced and user demands have increased, technologies enabling multi-coil operations and high-output power have been introduced. Additionally, heating performance for high-power operations with weakly magnetic-coupled vessels has been developed to enhance user convenience. However, when heating them, the high-power operation induces a large resonant current, resulting in audible noise caused by the line frequency. To reduce audible noises caused by the line frequency, active rectification techniques such as power factor corrections (PFC) circuits can convert the low-frequency AC into DC. However, this approach increases the entire IH system’s costs. This paper proposed a domestic IH system operating with a line-frequency noise suppressor (LNS) circuit to attenuate the audible noise issue. The LNS handles only partial power compared with PFCs, offering a cost advantage while providing an alternative solution to improve user convenience. The proposed IH system employing the LNS is experimentally verified with a 1.8-kW half-bridge IH inverter and LNS circuit prototypes.}, archive = {J_ACCESS}, author = {Jun-Suk Lee and Kwang-Hyung Cha and Jee-Hoon Jung}, doi = {10.1109/ACCESS.2025.3571399}, journal = {IEEE Access}, pages = {88984-88992}, shortjournal = {IEEE Access}, title = {Line-frequency noise suppressor to reduce audible noise in induction heating systems}, volume = {13}, year = {2025}, }
• (2025). Adaptive LVRT control strategy for VSC based on hybrid synchronization control. ACCESS, 13, 88972-88983. (www)

  @article{ , abstract = {In recent years, the problem of large-scale renewables tripping caused by power grid faults has occurred from time to time. By analyzing the power angle and fault current characteristics of the hybrid synchronization control (HSC)-based voltage source converter (VSC), this paper presents an adaptive low-voltage ride-through (LVRT) control strategy for HSC-based VSC. The proposed strategy includes three adaptive control measures: adaptive power angle control, adaptive internal potential control and adaptive virtual impedance control. The adaptive power angle control can keep the power angle equal to its pre-fault value, which greatly improves the transient stability of the system and effectively suppress the fault current. The adaptive internal potential control quantitatively suppresses fault current by adjust voltage reference in the voltage control. The adaptive virtual impedance control is used to suppress pulse currents at the instant of grid voltage dip/recovery. Finally, both theoretical and simulation analyses are conducted to validate the effectiveness of the proposed adaptive LVRT control strategy.}, archive = {J_ACCESS}, author = {Huangqing Xiao and Kai Gong and Huichen Gan}, doi = {10.1109/ACCESS.2025.3570112}, journal = {IEEE Access}, pages = {88972-88983}, shortjournal = {IEEE Access}, title = {Adaptive LVRT control strategy for VSC based on hybrid synchronization control}, volume = {13}, year = {2025}, }
• (2025). The spillover effects of auditor sanctions on client firms: Evidence from deep learning on investors’ online voice in china. ACCESS, 13, 88952-88971. (www)

  @article{ , abstract = {This study examines the spillover effects of auditor sanctions on client firms and validates the extent to which social media communications empower traditionally weak investors’ voice. In the context of China which offers unique data from regulated interactive platforms, the study examines the effects of auditor sanctions on investors’ online voice, employing the DID model. Results suggest that auditor sanctions increase the number of questions and number of words in the questions asked by investors about client firms on online interactive platforms. The deep learning Pre-trained BERTBASE model for sentiment analysis demonstrates that auditor sanctions also significantly increase the number of questions with negative sentiments. The results still hold after multiple robustness tests. Such adverse spillover effects of auditor sanctions are more pronounced among non-state-owned client firms and firms with higher shareholding concentration. This study contributes by enriching the application of deep learning models in text analysis in the social sciences, validating the importance that investors attach to auditor sanctions and thus the effectiveness of government regulation.}, archive = {J_ACCESS}, author = {Xiao Gu and Yeng Wai Lau and Saidatunur Fauzi Bin Saidin}, doi = {10.1109/ACCESS.2025.3568536}, journal = {IEEE Access}, pages = {88952-88971}, shortjournal = {IEEE Access}, title = {The spillover effects of auditor sanctions on client firms: Evidence from deep learning on investors’ online voice in china}, volume = {13}, year = {2025}, }
• (2025). Electromyography-based gesture recognition with explainable AI (XAI): Hierarchical feature extraction for enhanced spatial-temporal dynamics. ACCESS, 13, 88930-88951. (www)

  @article{ , abstract = {Hand gesture recognition using multichannel surface electromyography (sEMG) is challenging due to unstable predictions and inefficient time-varying feature enhancement. To overcome the lack of signal-based time-varying feature problems, we propose a lightweight squeeze-excitation deep learning-based multi-stream spatial-temporal dynamics time-varying feature extraction approach to build an effective sEMG-based hand gesture recognition system. Each branch of the proposed model was designed to extract hierarchical features, capturing both global and detailed spatial-temporal relationships to ensure feature effectiveness. The first branch, utilizing a Bidirectional-TCN (Bi-TCN), focuses on capturing long-term temporal dependencies by modelling past and future temporal contexts, providing a holistic view of gesture dynamics. The second branch, incorporating a 1D Convolutional layer, separable CNN, and Squeeze-and-Excitation (SE) block, efficiently extracts spatial-temporal features while emphasizing critical feature channels, enhancing feature relevance. The third branch, combining a Temporal Convolutional Network (TCN) and Bidirectional LSTM (BiLSTM), captures bidirectional temporal relationships and time-varying patterns. Outputs from all branches are fused using concatenation to capture subtle variations in the data and then refined with a channel attention module, selectively focusing on the most informative features while improving computational efficiency. The proposed model was tested on the Ninapro DB2, DB4, and DB5 datasets, achieving accuracy rates of 95.31%, 92.40%, and 93.34%, respectively. Additionally, we visualize the attention maps across various classes. These results demonstrate the system’s capability to handle complex sEMG dynamics, offering advancements in prosthetic limb control and human-machine interface technologies with significant implications for assistive technologies.}, archive = {J_ACCESS}, author = {Jungpil Shin and Abu Saleh Musa Miah and Sota Konnai and Shu Hoshitaka and Pankoo Kim}, doi = {10.1109/ACCESS.2025.3569899}, journal = {IEEE Access}, pages = {88930-88951}, shortjournal = {IEEE Access}, title = {Electromyography-based gesture recognition with explainable AI (XAI): Hierarchical feature extraction for enhanced spatial-temporal dynamics}, volume = {13}, year = {2025}, }
• (2025). Deep learning innovations for underwater waste detection: An in-depth analysis. ACCESS, 13, 88917-88929. (www)

  @article{ , abstract = {Underwater waste detection is a critical challenge for preserving aquatic ecosystems, particularly due to inherent underwater distortions such as light refraction, occlusion, and scattering. In this study, we present a novel deep learning framework for real-time underwater waste detection by evaluating state-of-the-art object detection algorithms on a manually annotated custom dataset comprising images across various water bodies to represent real-world turbidity, illumination, and occlusion. Our approach incorporates robust feature extraction and specialized data augmentation techniques that effectively mitigate the adverse effects of underwater distortions. We investigate multiple architectures, including YOLOv8n, YOLOv7, YOLOv6s, YOLOv5s, Faster R-CNN, and Mask R-CNN, analyzing key parameters such as mean average precision (mAP), inference speed, and computational efficiency. Our results demonstrate that YOLO-based models achieve faster & accurate performance, with YOLOv8n and YOLOv7 reaching a detection accuracy of 96%, compared to 81% and 83% for Faster R-CNN and Mask R-CNN, respectively. Computationally, YOLOv8n achieves 76 ms (milliseconds) per frame, while YOLOv5s runs at 14 ms per frame, confirming real-time viability for AUV deployment. The proposed approach offers significant advantages over existing methods by enabling rapid, accurate detection with low computational overhead, thereby paving the way for integration with autonomous underwater vehicles (AUVs) for environmental monitoring and waste management.}, archive = {J_ACCESS}, author = {Jaskaran Singh Walia and Kavietha Haridass and L. K. Pavithra}, doi = {10.1109/ACCESS.2025.3569344}, journal = {IEEE Access}, pages = {88917-88929}, shortjournal = {IEEE Access}, title = {Deep learning innovations for underwater waste detection: An in-depth analysis}, volume = {13}, year = {2025}, }
• (2025). The pearson χ²-divergence measure for doubly truncated random variables. ACCESS, 13, 88905-88916. (www)

  @article{ , abstract = {The divergence information measures play a significant role in quantifying the discrimination between two probability distributions. Two conventional divergence measures, the Kullback-Leibler divergence and the Pearson $\chi ^{2}$ -divergence, possess significant importance in information theory, signal processing, data science and other associated fields. In this manuscript, the Pearson $\chi ^{2}$ -divergence measure between two doubly truncated non-negative random variables is proposed. The proposed measure quantifies the amount of information lost inside a truncated interval when an assumed or estimated distribution is used instead of the actual distribution. It is shown that Pearson’s $\chi ^{2}$ -divergence measure characterizes the distribution uniquely under certain conditions. Several upper and lower bounds have been derived. The implications of monotonic transformations on the proposed measure are thoroughly examined. Further, the doubly truncated $\chi ^{2}$ -divergence for exponential populations has been estimated by a simulation study. In addition, we demonstrated the application using real world data to illustrate the utility of the new measure. Finally, we concluded our findings with possible future scopes.}, archive = {J_ACCESS}, author = {P. Akila and Rajesh Moharana}, doi = {10.1109/ACCESS.2025.3571188}, journal = {IEEE Access}, pages = {88905-88916}, shortjournal = {IEEE Access}, title = {The pearson χ²-divergence measure for doubly truncated random variables}, volume = {13}, year = {2025}, }
• (2025). 2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images. ACCESS, 13, 88888-88904. (www)

  @article{ , abstract = {Recently, intelligence-based hotspot detection has been widely used in solar photovoltaic (PV) image applications. However, the semantic segmentation approach has limitations in terms of accuracy, particularly for hotspot thermal images. This study introduces a novel method based on Two-tier Semantic Segmentation (2TSS) framework explicitly aimed at enhancing hotspot detection in thermal images of PV modules. The proposed method is designed with two subsequent stages of segmentation models, including image pre-processing at the initial of the framework. The first tier of segmentation distinguishes between solar PV modules and the background, whilst the second tier enhances the hotspot localization region. This research enhances comprehension of multi-tier segmentation architectures in deep learning, focusing on optimizing performance for solar energy systems through comparative analysis of semantic models. Three different segmentation models, namely U-Net, ResNet 18 and ResNet 50 were tested. The ResNet 50 model demonstrated superior segmentation performance across both tiers with 98% and 85% accuracy respectively. In summary, the proposed method demonstrates that applying a combined enhancement algorithm prior to training for hotspot segmentation promotes superior performance with an accuracy improvement of 2.26% over the non-enhancement approach.}, archive = {J_ACCESS}, author = {Nurul Huda Ishak and Iza Sazanita Isa and Muhammad Khusairi Osman and Mohd Shawal Jadin and Kamarulazhar Daud and Mohd Zulhamdy Ab Hamid}, doi = {10.1109/ACCESS.2025.3570974}, journal = {IEEE Access}, pages = {88888-88904}, shortjournal = {IEEE Access}, title = {2TSS: Two-tier semantic segmentation framework with enhancement for hotspot detection of solar photovoltaic thermal images}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on system dynamics. ACCESS, 13, 88871-88887. (www)

  @article{ , abstract = {System Dynamics is a methodology for modeling and simulating dynamically complex systems, which are characterized by feedback loops, nonlinearity, and time delays. It uses Causal Loop Diagrams and Stock-and-Flow Diagrams to represent system structures and to analyze how these structures influence system behavior over time. The majority of reviews on System Dynamics use cases either focus on a single thematic domain or are not up to date. As a result, a comprehensive and up-to-date overview of System Dynamics use cases across all thematic domains is lacking. Furthermore, no Systematic Literature Review has yet examined whether and to what extent artificial intelligence has been employed to extend and enhance the System Dynamics approach in identified use cases. To address this research gap, we conducted a Systematic Literature Review. The identified studies are classified within a sociotechnical system framework consisting of User-Machine Interaction, Value Chain-related Domains, and Social Factors. Additionally, we examine the role of artificial intelligence in advancing the System Dynamics approach within the identified use cases. We identified 41 relevant publications covering 19 thematic domains. Most studies fall within the Value Chain-related Domains system, followed by Social Factors and User-Machine Interaction. The integration of artificial intelligence into System Dynamics use cases remains rare. Our findings highlight the broad applicability of System Dynamics across various thematic domains while simultaneously revealing a gap in the complementary use of artificial intelligence.}, archive = {J_ACCESS}, author = {Justin Hoffmann and Christopher Mai and Bo Hu and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3571620}, journal = {IEEE Access}, pages = {88871-88887}, shortjournal = {IEEE Access}, title = {A systematic literature review on system dynamics}, volume = {13}, year = {2025}, }
• (2025). Enhanced EMV security: Preventing credit card fraud from a distance. ACCESS, 13, 88858-88870. (www)

  @article{ , abstract = {The EMV (Europay, Mastercard, and Visa) standard has been found susceptible to relay attacks, impacting the store and financial sectors. The current EMV protocol employs RTT (Round Trip Time) for device distance estimation. However, RTT is susceptible to being affected by the angle at which the credit card is held and cannot prevent attackers from responding to messages prematurely. To prevent distance-based fraud, we propose ambient authentication using artificial audio into the natural environment for device distance detection. We employ implicit certificates for mutual device authentication, preventing attacks before the transaction phase. Ensuring confidentiality, we encrypt all subsequent communication messages after the GET PROCESSING OPTIONS command. Our experimental results indicate that our method outperforms other approaches, providing more comprehensive security. Our protocol is capable of defending against man-in-the-middle, replay, pre-play, downgrade, relay, and other known attacks targeting credit cards. Furthermore, the execution time of the proposed method complies with the EMV protocol.}, archive = {J_ACCESS}, author = {Ming-Hour Yang and Yu-Shan Hsu and Hsiao-Chuan Hsu}, doi = {10.1109/ACCESS.2025.3570987}, journal = {IEEE Access}, pages = {88858-88870}, shortjournal = {IEEE Access}, title = {Enhanced EMV security: Preventing credit card fraud from a distance}, volume = {13}, year = {2025}, }
• (2025). Ancient character recognition: A comprehensive review. ACCESS, 13, 88847-88857. (www)

  @article{ , abstract = {Deep learning-based character recognition of Tamil inscriptions plays a significant role in preserving the ancient Tamil language. The complexity of the task lies in the precise classification of the age-old Tamil letters (Vattezhuthu) into modern-day Tamil letter structures. Various methodologies and pre-processing techniques have been used for denoising the ancient Tamil manuscript to retrieve the Tamil text. Researchers have used various synthesized and scanned images of stone wall inscriptions, palm leaves manuscripts, and offline handwritten characters for their analysis. Over the years, Ancient Tamil scripts have deteriorated with time due to various natural calamities. Strong denoising and feature extraction methods are required to separate the letters accurately to tackle this issue. Techniques such as CNN(OCR), ResNet, SVM, KNN, HorVer method, etc., are utilized to digitize Tamil characters. This technique has successfully converted handwritten characters into digitalized text for multiple languages, including Tamil, Arabic, English, Latin, Chinese, German, etc. Different models have been evaluated based on their segmentation and recognition rates, accuracy, detection rate, precision, and confusion matrix. This paper will concentrate on Ancient Tamil character segmentation and recognition models. Besides, we will give an overview of the different models and datasets available. Lastly, we summarise the key challenges and the future scope related to the topic.}, archive = {J_ACCESS}, author = {R. Krithiga and S. R. Varsini and R. Gabriel Joshua and C. U. Om Kumar}, doi = {10.1109/ACCESS.2023.3341352}, journal = {IEEE Access}, pages = {88847-88857}, shortjournal = {IEEE Access}, title = {Ancient character recognition: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on machine and deep learning for optical communications. ACCESS, 13, 88794-88846. (www)

  @article{ , abstract = {The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential solutions, and identifying promising future research directions to foster advancements in intelligent optical communication systems.}, archive = {J_ACCESS}, author = {Mohammad Ali Amirabadi and S. Alireza Nezamalhosseini and Mohammad Hossein Kahaei and Lawrence R. Chen}, doi = {10.1109/ACCESS.2025.3569559}, journal = {IEEE Access}, pages = {88794-88846}, shortjournal = {IEEE Access}, title = {A comprehensive survey on machine and deep learning for optical communications}, volume = {13}, year = {2025}, }
• (2025). A data resource trading price prediction method based on improved LightGBM ensemble model. ACCESS, 13, 88780-88793. (www)

  @article{ , abstract = {To address the key challenges of limited practical application, high implementation difficulty, and poor generalization capability in existing theoretical models for data resource pricing, this study employs generative adversarial network (GAN) to augment the dataset and constructs a DRV-LightGBM model based on a Bayesian parameter optimization algorithm that maximizes the coefficient of determination ( $R^{2}$ ) to predict data resource transaction prices and provide post-hoc explanations for the prediction model. The experimental results demonstrate that the proposed model achieves approximately a 10% improvement in predictive performance compared to conventional LightGBM models, with the coefficient of determination ( $R^{2}$ ) exceeding 99%, reaching a high level of prediction accuracy. Moreover, for small-sample datasets, GAN technique can effectively augment tabular training datasets while preserving the original data distribution. The four features with the highest sensitivity in data resources are mutually independent, and the impact of each feature on transaction price is not influenced by other factors. The intrinsic physical attributes of data resources are the primary influencing factors on transaction prices, though the final price is also subject to other contributing elements.}, archive = {J_ACCESS}, author = {Wan Nie and Bingliang Shen and Desheng Li}, doi = {10.1109/ACCESS.2025.3568152}, journal = {IEEE Access}, pages = {88780-88793}, shortjournal = {IEEE Access}, title = {A data resource trading price prediction method based on improved LightGBM ensemble model}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection for PV modules using multi-modal data fusion in aerial inspections. ACCESS, 13, 88762-88779. (www)

  @article{ , abstract = {Solar energy is a compelling and growing avenue for transitioning towards reliance on sustainable and environmentally friendly energy sources. However, the performance of individual photovoltaic (PV) panels is vulnerable to defects inflicted by weather exposure. The industry currently addresses these challenges by conducting manual inspections at each PV installation, a process that is highly time-consuming, financially burdensome, difficult to scale, prone to human error, and sometimes unfeasible due to topographical constraints. This study aims to develop a solution that detects these defects in real time resorting to an Autonomous Aerial Vehicle (AAV) equipped with for a thermal and a visual sensor. This paper contributes three major advancements to PV defect detection: a robust, standardized dataset built following IEC TS 62446-3 specifications with annotations from a real PV power plant, a novel multi-spectral approach that leverages early fusion of visual and thermal data captured via an AAV, and benchmark performance metrics for defect detection using state-of-the-art AI models. Our implementation uses Real-Time DEtection TRansformer (RT-DETR) and You Only Look Once (YOLO) models, achieving a mean Average Precision@50 (mAP) of 94% for RT-DETR and a mAP@50 of nearly 95% for YOLOv11. These results demonstrate the effectiveness of our approach in real-world settings, addressing a significant operational challenge in PV plant maintenance while establishing new performance benchmarks for automated defect detection in industrial solar installations.}, archive = {J_ACCESS}, author = {Lourenço Sousa Pinho and Tiago Daniel Sousa and Celso D. Pereira and Andry M. Pinto}, doi = {10.1109/ACCESS.2025.3570815}, journal = {IEEE Access}, pages = {88762-88779}, shortjournal = {IEEE Access}, title = {Anomaly detection for PV modules using multi-modal data fusion in aerial inspections}, volume = {13}, year = {2025}, }
• (2025). FDM-RTDETR: A multi-scale small target detection algorithm. ACCESS, 13, 88747-88761. (www)

  @article{ , abstract = {To address challenges in uncrewed aerial vehicles (UAV) object detection including complex backgrounds, severe occlusion, dense small objects, and varying lighting conditions, we propose FDM-DETR, a novel detection algorithm specifically designed for small objects in UAV imagery. This method effectively captures global image information by fusing multi-scale spatial features and performing feature extraction in the frequency domain within the backbone network. We design a Dynamic Feature Interaction (DIFI) module with position-based biases in the encoder, enhancing the model’s perception of local features for small objects. In the neck network, we introduce a Multi-Scale Feature Enhancement Pyramid (MSFEP) module to improve feature extraction capabilities for small object detection. Compared to RT-DETR, our improved model achieves performance gains of 2.5% and 2.7% in AP on the Vis-Drone2019 validation and test sets, respectively. While maintaining low computational complexity and parameter count, the method demonstrates significant improvements in detection performance. FDM-DETR exhibits robust practicality and reliability in UAV-based small object detection tasks.}, archive = {J_ACCESS}, author = {Hongya Wang and Yongtao Yu and Zhaoxia Tang}, doi = {10.1109/ACCESS.2025.3571229}, journal = {IEEE Access}, pages = {88747-88761}, shortjournal = {IEEE Access}, title = {FDM-RTDETR: A multi-scale small target detection algorithm}, volume = {13}, year = {2025}, }
• (2025). Complex emotion estimation using analysis-by-synthesis of facial expression images. ACCESS, 13, 88731-88746. (www)

  @article{ , abstract = {This research proposes an approach for recognizing facial expressions for complex emotion estimation through the utilization of generated facial images. The proposed approach consists of two main parts: facial image generation and facial expression recognition. In the first part, we introduce Conditioned Emotion Generative Adversarial Networks (cEmoGANs) to synthesize images that convey complex facial expressions. Unlike previous methods, our generative model maintains face identity information and expresses various types of complex emotions. This capability encourages the generator to have control over the image generation process, resulting in enhanced image quality, reduced distortion, and increased diversity of generated images. The second part involves the design of a multiple-label classification based on a convolutional neural network trained on the complex facial expression images generated from the first part, which is employed in the recognition of complex facial expressions for emotion estimation. Our model, using images generated by cEmoGANs, demonstrates a notable performance surpassing the capabilities of the previous models in comparative evaluations.}, archive = {J_ACCESS}, author = {Win Shwe Sin Khine and Prarinya Siritanawan and Kazunori Kotani}, doi = {10.1109/ACCESS.2025.3570167}, journal = {IEEE Access}, pages = {88731-88746}, shortjournal = {IEEE Access}, title = {Complex emotion estimation using analysis-by-synthesis of facial expression images}, volume = {13}, year = {2025}, }
• (2025). Microservices evolution factors: A multivocal literature review. ACCESS, 13, 88707-88730. (www)

  @article{ , abstract = {Microservices is a software architectural approach based on decomposing systems into small services that are simpler and technology-independent for their development and operation. However, microservices design involves not only technical requirements but also organizational information to ensure their alignment with the organization’s strategy and structure. This raises the question of how microservices evolve over time, that is, which factors foster and hinder microservices’ evolution. In this study, we present a multivocal literature review of scientific and industry articles reporting analyses and experiences on microservice architectures design and evolution. We aim to identify the factors that influence the evolution of microservice architecture, as well as the key non-functional requirements and quality attributes that drive the evolution of microservices. We collected 554 articles from Web of Science, Scopus, and Google; 26 were finally selected. We found empirical evidence in both academic and gray literature that design practices are the most influential factors in microservices evolution. Well-managed granularity and bounded context design were the main factors fostering evolution, while dependencies were the most common factors hindering evolution. In addition, the results of this study empirically show that factors related to infrastructure and management services, DevOps, organization culture, and cost also impact microservices evolution. Our findings can help both researchers and practitioners to be aware of the decisions they make throughout the design and development phases of microservices and how those decisions can affect the evolution of this architectural approach.}, archive = {J_ACCESS}, author = {Sebastian Pinto-Agüero and Rene Noel}, doi = {10.1109/ACCESS.2025.3570658}, journal = {IEEE Access}, pages = {88707-88730}, shortjournal = {IEEE Access}, title = {Microservices evolution factors: A multivocal literature review}, volume = {13}, year = {2025}, }
• (2025). Real-time speech extraction based on rank-constrained spatial covariance matrix estimation and spatially regularized independent low-rank matrix analysis with fast demixing matrix estimation. ACCESS, 13, 88683-88706. (www)

  @article{ , abstract = {Real-time speech extraction is a valuable task and has diverse applications, such as speech recognition in a human-like avatar/robot and hearing aids. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial covariance matrix estimation (RCSCME). It has been reported that, in an offline scenario, the RCSCME-based method (a multichannel blind speech extraction method based on ILRMA and RCSCME) experimentally achieved superior speech extraction performance under diffuse noise conditions. Here, we focus on the facts that the ILRMA output required in RCSCME is only the time-invariant demixing matrix and the entire process of the RCSCME-based method can be divided into two parts: the ILRMA and RCSCME parts. Thus, to perform the RCSCME-based method in real time, we introduce the blockwise batch algorithm into the RCSCME-based method by performing the ILRMA and RCSCME parts in parallel. To improve the real-time speech extraction performance, we introduce a spatial regularization into the ILRMA part and devise two regularizers. For further acceleration and numerical stabilization, we derive new algorithms for vectorwise coordinate descent (VCD) and iterative projection (IP). These algorithms are analytically equivalent to conventional ones. In experiments, we first confirm the effectiveness of the proposed VCD algorithm in terms of both computational time and numerical stability. Next, we show that the proposed real-time framework with the proposed VCD/IP algorithms achieves superior speech extraction performance compared with conventional methods and can function in real time on low computational resources. Finally, we also demonstrate the effectiveness of the designed regularizers in terms of speech extraction performance and the robustness of the proposed methods to errors in the prior information.}, archive = {J_ACCESS}, author = {Yuto Ishikawa and Tomohiko Nakamura and Norihiro Takamune and Daichi Kitamura and Hiroshi Saruwatari and Yu Takahashi and Kazunobu Kondo}, doi = {10.1109/ACCESS.2025.3569590}, journal = {IEEE Access}, pages = {88683-88706}, shortjournal = {IEEE Access}, title = {Real-time speech extraction based on rank-constrained spatial covariance matrix estimation and spatially regularized independent low-rank matrix analysis with fast demixing matrix estimation}, volume = {13}, year = {2025}, }
• (2025). SPUFChain: Permissioned blockchain lightweight authentication scheme for supply chain management using PUF of IoT. ACCESS, 13, 88662-88682. (www)

  @article{ , abstract = {The complexity of the entire process of supply chain management (SCM) is quite cumbersome and traditional way of handling it is devoid of proper authentication and security and very often suffers from human errors in dealing with flaws in quality control process of SCM. While it may have started with shipment tracking, the outcome of using IoT on supply chains has spread to every link in the chain. For instance, manufacturers are employing Internet-enabled sensors in production to find product faults, resulting in higher-quality production runs. Physical Unclonable Function (PUF) is a defense countermeasure that exploits the exclusive, unrepeatable features of hardware components to generate distinct cryptographic keys or identifiers, typically for a semiconductor device like an Internet of Things (IoT) device. The distinctive proof of identity of IoT devices in the supply chain is employed by means of PUFs as inviolable IDs. Blockchain, the distributed, immutable ledger, on the other hand is the disruptive technology that provides higher security as compared to traditional centralized systems. The integration of PUF and blockchain proves to be quite interesting while handling the above issues of authentication. A smart contract on the blockchain is a software code that executes spontaneously as and when the conditions of the contract or agreement are satisfied. Hence after authentication process the results are fed to blockchain smart contract for the final validation. This paper presents a novel permissioned blockchain smart contract-based lightweight authentication scheme for SCM using PUF of IoT known as SPUFChain. Informal and formal security analysis (using AVISPA and BAN logic) of the proposed framework show its efficiency to resist several threat scenarios like man-in-the middle, non-repudiation, impersonation, replay attacks and many other security features as compared to other related schemes. The processing time (~13.8ms) is better than existing lightweight scheme for blockchain-based SCM as well.}, archive = {J_ACCESS}, author = {Mohammad Iqbal Saryuddin Assaqty and Ying Gao and Abeer D. Alqarni and Siraj Khan and Mohuya Chakraborty and Hafied Nur Siddiqi and Sadique Ahmad}, doi = {10.1109/ACCESS.2025.3566478}, journal = {IEEE Access}, pages = {88662-88682}, shortjournal = {IEEE Access}, title = {SPUFChain: Permissioned blockchain lightweight authentication scheme for supply chain management using PUF of IoT}, volume = {13}, year = {2025}, }
• (2025). The effects of countermeasures for preventing vigilance decrement during manual and automated driving. ACCESS, 13, 88648-88661. (www)

  @article{ , abstract = {Using driving mode (manual and automated driving) and countermeasures (chewing gum and auditory-tactile stimulation) for preventing vigilance decrement as experimental factors, this study investigated the effectiveness of chewing gum and auditory-tactile stimulation for preventing vigilance decrement during 2-hour simulated manual and automated driving. The observed reaction times and error rates for the unexpected events revealed the vigilance decrement in the latter half of the 2-hour experiment for both manual and autonomous driving conditions. The countermeasures contributed to mitigating vigilance decrement. The psychological rating on comfort and sleepiness evaluation by KSS during the 2-hour experiment indicated that the chewing gum was more effective than auditory-tactile stimulation. Vigilance decrement was less for the manual driving condition than for the Level 2 automated driving condition with less WWL score of NASA-TLX. The overload explanation applied to the vigilance decrement in manual driving, because the vigilance decrement for the higher workload condition (manual driving without a countermeasure) was more remarkable than that for the lower workload condition (manual driving with a countermeasure). Data of automated driving did not apply to either overload or underload explanation of vigilance decrement, and only the countermeasures contributed to the mitigation of vigilance decrement.}, archive = {J_ACCESS}, author = {Atsuo Murata and Toshihisa Doi and Waldemar Karwowski}, doi = {10.1109/ACCESS.2025.3570073}, journal = {IEEE Access}, pages = {88648-88661}, shortjournal = {IEEE Access}, title = {The effects of countermeasures for preventing vigilance decrement during manual and automated driving}, volume = {13}, year = {2025}, }
• (2025). Generating anticancer peptides sequences using Seq2Seq modeling and machine learning methods. ACCESS, 13, 88638-88647. (www)

  @article{ , abstract = {Cancer remains a major health threat with rising incidence and mortality rates. Despite the efficacy of chemotherapy, its lack of selectivity and associated severe side effects highlight the need for new, targeted anticancer therapies. Anticancer peptides (ACPs) have emerged as a promising alternative due to their biocompatibility, broad-spectrum anticancer activity, and unique mechanisms of action. This study presents a novel computational approach to design and identify ACPs using a multi-tier filtration system. Our method begins with peptide sequence generation via a recurrent neural network (RNN) trained on the acp740 dataset. The generated sequences undergo rigorous filtration: Tier-1 employs three deep learning-based classifiers (ACP-DL, ACP-MHCNN, ACP-LSE) to identify potential ACPs; Tier-2 uses a nearest centroid classifier to filter out statistically less relevant sequences; Tier-3 involves a final filtration using unsupervised nearest neighbor learning based on fused feature encoding schemes (CKSAAP, k-Mer, and BPF). Experimental results demonstrate a significant improvement in identifying viable ACP candidates, with the proposed method showing a 2.21-fold higher hit-rate compared to random sequence generation. Further analysis using t-SNE, PCA, and antimicrobial peptide (AMP) prediction tools confirms the robustness and effectiveness of the selected ACPs. Furthermore, performance comparisons using the proposed sequence filtering technique reveal that it surpasses the baseline LSTM and RNN-based sequence generation models by 2.95% and 14.11%, respectively. Complementary reverse analyses further validate the robustness and effectiveness of proposed sequence generation framework. The proposed computational approach offers a streamlined and economical alternative to traditional experimental methods, expediting the discovery of new ACPs and enhancing the accuracy of anticancer peptide predictions. The relevant models, codes, and results are also available on the authors github page at (https://github.com/mhdshl/ACP-Seq2Seq).}, archive = {J_ACCESS}, author = {Muhammad Sohail Ibrahim and Saheed Ademola Bello and Yunsang Kwak and Minseok Kim and Shujaat Khan}, doi = {10.1109/ACCESS.2025.3569255}, journal = {IEEE Access}, pages = {88638-88647}, shortjournal = {IEEE Access}, title = {Generating anticancer peptides sequences using Seq2Seq modeling and machine learning methods}, volume = {13}, year = {2025}, }
• (2025). Control strategy and small signal stability analysis of the direct-drive wind farm transmitted through the HC-HVdc system under no synchronous power support conditions. ACCESS, 13, 88623-88637. (www)

  @article{ , abstract = {The hybrid cascaded high voltage direct current (HC-HVDC) technology, which combines the advantages of the line commutated converter-based HVDC (LCC-HVDC) and the modular multilevel converter-based HVDC (MMC-HVDC) technologies, is one of the most cost-effective solutions for long-distance transmission of large-scale renewable energy. Therefore, a topology of the HC-HVDC transmission system is constructed in this paper for direct-drive wind farms in desert and gobi areas where there is no synchronous power support in the sending-end system. Then a grid-forming control strategy for the sending-end hybrid cascade converter is designed to provide AC voltage support. Besides, an active power balancing control strategy of the sending-end system based on the additional DC voltage control of the MMC is proposed to adapt to the stochastic fluctuation characteristics of wind power. After that, a small signal dynamic model of the whole system is established, and the influence of important parameters on the small signal stability of the system is analyzed. Finally, the time-domain simulations based on PSCAD/EMTDC confirm the correctness and validity of the proposed control strategies and the stability analysis conclusions.}, archive = {J_ACCESS}, author = {Qin Wang and Yongjie He and Mengyao Xu and Jincan Zeng and Binghao He and Rongfeng Deng and Xi Liu and Guori Huang and Yuanzhe Zhu and Minwei Liu}, doi = {10.1109/ACCESS.2025.3570382}, journal = {IEEE Access}, pages = {88623-88637}, shortjournal = {IEEE Access}, title = {Control strategy and small signal stability analysis of the direct-drive wind farm transmitted through the HC-HVdc system under no synchronous power support conditions}, volume = {13}, year = {2025}, }
• (2025). Certified robustness of antenna selecting neural networks for massive MIMO wireless communications. ACCESS, 13, 88609-88622. (www)

  @article{ , abstract = {Future wireless systems with massive antennas must balance data rates and RF chain costs. Antenna selection activating only a subset of antennas addresses this challenge. Recently, neural network-based approaches have shown promise over traditional symbolic methods, offering fixed complexity in inference and suitability for hardware implementation. However, their closed-box nature raises concerns for safety-critical 6G applications like autonomous driving and drones, where reliable communication is vital. Specifically, it is often unclear how the neural network determines which antennas to select, making it difficult to interpret or trust the decision-making process. This paper investigates the robustness of neural networks for antenna selection in such contexts. While empirical robustness against finite random inputs sampled from a uniform distribution may suffice for general applications, certified robustness ensuring consistent inference under all possible perturbations is essential for safety-critical systems. Although certified robustness is well studied in vision and language tasks, we are the first, to our knowledge, to explore its application in telecommunications. We mathematically define robustness for antenna-selection networks and apply state-of-the-art linear relaxation-based perturbation analysis. Our findings show that pruned networks, beyond being more efficient, also exhibit superior certified robustness compared to their unpruned counterparts. We further compare certified and empirical robustness, identifying a significant gap that suggests the need for improved certification methods. Additionally, in our antenna selection setting, we observe that removing monotonic activations in the final layer improves certified robustness.}, archive = {J_ACCESS}, author = {Jaekwon Kim and Hyo-Sang Lim and Kwanghoon Choi}, doi = {10.1109/ACCESS.2025.3570973}, journal = {IEEE Access}, pages = {88609-88622}, shortjournal = {IEEE Access}, title = {Certified robustness of antenna selecting neural networks for massive MIMO wireless communications}, volume = {13}, year = {2025}, }
• (2025). MRIE: Enhanced rainfall intensity estimation with two-stage multimodal deep learning. ACCESS, 13, 88600-88608. (www)

  @article{ , abstract = {Accurate rainfall intensity estimation is crucial for environmental monitoring and urban planning. However, the reliability of existing methods in practical applications is relatively low due to certain deficiencies in data processing and noise resistance. To address these issues, MRIE is proposed, a two-stage multimodal framework that integrates data from environmental sensors and audio recordings to improve estimation accuracy. MRIE effectively fuses complementary information from multiple sensor modalities and demonstrates strong robustness with the help of a cross-modal guidance module and advanced feature fusion techniques. Experimental results show that the MAE and RMSE of MRIE are reduced by 40.7% and 29.1% respectively, and the $R^{2}$ is increased by 17.3%, compared with the best baseline. These research findings highlight the potential of MRIE as a powerful and reliable tool for sensor-based rainfall monitoring systems.}, archive = {J_ACCESS}, author = {Jin Li and Wentao Hu and Zhigang Zhou}, doi = {10.1109/ACCESS.2025.3571440}, journal = {IEEE Access}, pages = {88600-88608}, shortjournal = {IEEE Access}, title = {MRIE: Enhanced rainfall intensity estimation with two-stage multimodal deep learning}, volume = {13}, year = {2025}, }
• (2025). Adaptive low-overhead channel estimation tracking in RIS-assisted systems. ACCESS, 13, 88589-88599. (www)

  @article{ , abstract = {Efficient channel estimation is paramount for realizing the performance potential of Reconfigurable Intelligent Surface (RIS)-assisted wireless networks. This study introduces Adaptive Low-Overhead Channel Estimation Tracking (ALOCET), a novel solution to balance estimation accuracy with pilot resource efficiency, which is a long-standing challenge. Achieving precise channel knowledge in RIS systems is crucial; however, traditional methods demand excessive pilot overhead. ALOCET addresses this through a resource-conscious two-phase approach. An initial phase acquires a high-fidelity channel snapshot using dense pilots and comprehensive RIS configuration exploration. Subsequently, the low-overhead tracking phase employs a Cramér-Rao Lower Bound (CRLB)-driven dynamic RIS configuration selection. This mechanism selects optimal RIS configurations carrying more channel information to maintain accuracy, significantly reducing pilot demands. Simulation results, focusing on the tracking phase, demonstrate that ALOCET outperforms a representative two-time scale method, while substantially reducing pilot overhead. This adaptive approach provides a scalable and readily implementable framework for real-time channel estimation in future wireless networks.}, archive = {J_ACCESS}, author = {Hamidreza Khaleghi and Stéphane Paquelet}, doi = {10.1109/ACCESS.2025.3570772}, journal = {IEEE Access}, pages = {88589-88599}, shortjournal = {IEEE Access}, title = {Adaptive low-overhead channel estimation tracking in RIS-assisted systems}, volume = {13}, year = {2025}, }
• (2025). Enhancing fault detection and localization in MT-MVDC networks using advanced singular spectrum analysis. ACCESS, 13, 88573-88588. (www)

  @article{ , abstract = {This paper presents a novel methodology for fault detection, classification, and localization in Multi-Terminal Medium Voltage Direct Current (MT-MVDC) networks. The proposed approach utilizes Singular Spectrum Analysis (SSA) to decompose measured positive and negative pole voltages, isolating the seasonal component that represents the traveling wave. Fault detection is based on comparing this component against a predefined threshold, where minimal fluctuations occur under normal conditions, but significant variations emerge after a fault. Fault classification is achieved by analyzing the rate of change of the line-mode current to distinguish between forward and backward faults. For fault localization, the method leverages traveling wave attenuation and dispersion. The first traveling wave is extracted from the voltage seasonal component, and its spreading behavior over distance is analyzed to compute the curvature rate, enabling precise fault location estimation. The methodology is validated through extensive simulations on an MT-MVDC distribution system using PSCAD/EMTDC. MATLAB is employed for signal processing, and the approach is tested under various fault scenarios, including high fault impedance and extreme external faults. Comparative analysis with existing methods highlights the advantages of the proposed technique in terms of accuracy and robustness.}, archive = {J_ACCESS}, author = {Hossam Sabra and Amr Kassem and Ahmed A. A. Ali and Karam M. Abdel-Latif and Ahmed F. Zobaa}, doi = {10.1109/ACCESS.2025.3571376}, journal = {IEEE Access}, pages = {88573-88588}, shortjournal = {IEEE Access}, title = {Enhancing fault detection and localization in MT-MVDC networks using advanced singular spectrum analysis}, volume = {13}, year = {2025}, }
• (2025). Dual-stage detail-preserving virtual try-on network with geometric refinement and multi-scale feature integration. ACCESS, 13, 88557-88572. (www)

  @article{ , abstract = {In this paper, we propose a novel dual-stage virtual try-on framework to address key limitations of existing image-based virtual try-on systems, including inaccurate geometric alignment, loss of garment details, and unnatural garment-body blending. These limitations arise because existing methods demonstrate considerable constraints in precisely handling complex geometric deformations and achieving proper semantic alignment between clothing and body across diverse poses and garment styles. To address these limitations, we design two dedicated modules that respectively focus on accurate geometric alignment and realistic garment-body integration: the Garment Geometric Alignment Module (GGAM) and the Garment-Body Fitting Module (GBFM). GGAM adopts a progressive warping architecture that cascades a Thin-Plate Spline (TPS)-based coarse warping stage with an attention U-Net based refinement network, enabling high-quality garment deformation with fine-grained texture preservation and accurate alignment to the target body structure. GBFM is built upon a newly proposed Trans-Fusion U-Net architecture, which incorporates 1) a group-wise feature learning encoder with self-attention mechanisms, 2) a multi-scale feature fusion strategy, and 3) a cross-attention-based refinement decoder. These architectural components effectively capture long-range dependencies and enable realistic garment-body fitting. Experimental results demonstrate that our method consistently outperforms existing virtual try-on approaches in preserving garment fidelity and achieving more realistic and structurally coherent try-on results.}, archive = {J_ACCESS}, author = {Hyebean Lee and Dasol Jeong and Jiwon Park and Seunghee Han and Jinbeum Jang and Joonki Paik}, doi = {10.1109/ACCESS.2025.3570525}, journal = {IEEE Access}, pages = {88557-88572}, shortjournal = {IEEE Access}, title = {Dual-stage detail-preserving virtual try-on network with geometric refinement and multi-scale feature integration}, volume = {13}, year = {2025}, }
• (2025). Leveraging dual timestamps to enhance process model construction using time-infused inductive miner. ACCESS, 13, 88535-88556. (www)

  @article{ , abstract = {In business process management, the increasing complexity of modern business processes underscores the need for accurately understanding parallel activities to efficiently allocate and utilize limited corporate resources. However, many existing event log analysis algorithms focus solely on the sequence of executed activities, limiting their ability to model processes involving simultaneous activities effectively. This research seeks to enhance the inductive miner, which is known for its flexibility, formal guarantees, and scalability, by enabling it to better capture and represent the complexities of real-world business processes. The core modification involves leveraging dual timestamps, which utilize both the start and end times of activities. This approach enhances the algorithm’s ability to understand parallel structures within process models. Through experimentation, limitations of single timestamp were identified, including reduced accuracy in modeling parallel activities. The proposed approach also demonstrates the capability to effectively minimize dummy activities when discovering process models containing both sequential and parallel activities. Experimental results highlight the improved performance of the proposed algorithm, with metrics showing a fitness score of 0.815, precision of 0.827, generalization of 0.846, and simplicity of 0.834. Moreover, the algorithm achieves the highest conformance results with fewer traces required compared to the alpha miner, fuzzy miner, heuristic miner, and the original inductive miner.}, archive = {J_ACCESS}, author = {Yutika Amelia Effendi and Minsoo Kim}, doi = {10.1109/ACCESS.2025.3571441}, journal = {IEEE Access}, pages = {88535-88556}, shortjournal = {IEEE Access}, title = {Leveraging dual timestamps to enhance process model construction using time-infused inductive miner}, volume = {13}, year = {2025}, }
• (2025). TSAS—YOLOv8: An optimization detection model for capturing small target features and processing key information. ACCESS, 13, 88520-88534. (www)

  @article{ , abstract = {In object detection tasks, small targets are prone to losing critical information during feature extraction by traditional convolutional layers due to their tiny size and sparse features. This not only reduces the detection accuracy but also undermines the model’s generalization performance. To address this issue, we propose the TSAS-YOLOv8 method. This method defines the T-CMUNeXt module and introduces a new Backbone structure based on this module, leveraging its multi-scale feature fusion advantage to capture the subtle features of small targets. Additionally, it employs the Shuffle Attention mechanism to focus on key features, enhancing the detection effect of small targets in all aspects. Furthermore, a specialized small target detection head is added to improve the localization capability for small targets. Validated on the VisDrone2019 dataset, TSAS-YOLOv8 achieves a 7.6% increase in mAP50 and a 4.8% increase in mAP50-95 compared to the original YOLOv8 model, demonstrating superiority even when compared to the latest models such as YOLOv10. On our self-made airport runway foreign object debris dataset FOD-x, TSAS-YOLOv8 shows a 1.8% increase in mAP50 and a 2.2% increase in mAP50-95, proving its good generalization in specific scenarios. In the experiment verifying generalization ability, the TSAS - YOLOv8 model performed outstandingly on the COCO128 dataset, demonstrating its good generalization.Ablation experiments verify that both individual and combined use of each module has a positive effect on performance improvement. In summary, TSAS-YOLOv8 exhibits efficiency and feasibility in small target detection tasks.}, archive = {J_ACCESS}, author = {Yongbo Yuan and Linlin Cao}, doi = {10.1109/ACCESS.2025.3564263}, journal = {IEEE Access}, pages = {88520-88534}, shortjournal = {IEEE Access}, title = {TSAS—YOLOv8: An optimization detection model for capturing small target features and processing key information}, volume = {13}, year = {2025}, }
• (2025). Optimization research on magnetic interference parameter identification and compensation for AUV platforms. ACCESS, 13, 88509-88519. (www)

  @article{ , abstract = {Autonomous Underwater Vehicles (AUVs) have become essential tools for underwater magnetic surveys. However, the magnetic interference from the AUV platform severely impacts measurement accuracy. To improve the accuracy of magnetic disturbance parameter identification and compensation for AUV platforms, a theoretical AUV magnetic disturbance model based on the rotating ellipsoidal shell and magnetic dipole model is established. The Success History-based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) algorithm is employed to optimize and identify model parameters for solving the magnetic field, and Back Propagation Neural Network (BPNN) is designed and optimized through training to enable the direct estimation of the true magnetic field from the measured magnetic field. To further improve training performance, a stacking ensemble learning (STACKING) model is introduced, with L-SHADE and BPNN as base learners and Convolutional Neural Network (CNN) as the meta-learner, integrating the advantages of both algorithms for optimization. Numerical simulations demonstrate that, under a 5° attitude error, the L-SHADE algorithm achieves mean decoding accuracies of 86.42%, 81.9%, and 86.15% for the three magnetic field components after training with low-noise data. Meanwhile, the BPNN achieves prediction accuracies of 86.58%, 83.14%, and 83.09%. By applying the stacking ensemble learning method, the maximum prediction accuracy improves by up to 25.77%. Compared to individual algorithms, the proposed fusion method significantly enhances anti-interference capability and decoding accuracy. This study provides a novel and efficient solution for AUV magnetic interference compensation and offers valuable insights for addressing similar problems.}, archive = {J_ACCESS}, author = {Haodong Wen and Guohua Zhou and Kena Wu and Xinkai Hu and Liezheng Tang and Shuai Xia}, doi = {10.1109/ACCESS.2025.3568122}, journal = {IEEE Access}, pages = {88509-88519}, shortjournal = {IEEE Access}, title = {Optimization research on magnetic interference parameter identification and compensation for AUV platforms}, volume = {13}, year = {2025}, }
• (2025). Evaluating the ANN model performance for PID controller tuning in flow process control: A comparative study. ACCESS, 13, 88499-88508. (www)

  @article{ , abstract = {Accurate controller tuning is important for ensuring optimal performance in flow control processes, particularly onboard ships, where precise control of fluid systems such as oil, gas, and water flows determines the operation of the whole ship. Models can be used to avoid experimentation on a real system, which is essential if systems are unavailable for controller tuning experiments. This paper presents the results of controller tuning using the ANN model. To evaluate the effectiveness of the proposed method, it is compared to the transfer function model-based tuning and to the controller tuning performed on a real system. The controller uses an ideal PID structure, and controller parameters are calculated by four well-known tuning methods for each case. The experiments are conducted on a flow control unit in a laboratory, while modelling and simulations are performed in Matlab. Graphical evaluation and numerical RMSE metrics are used to determine the quality of the tuning method. By evaluating controller tuning performance based on the ANN model, this research aims to validate the effectiveness of ANN models for practical control applications.}, archive = {J_ACCESS}, author = {Nur Assani and Petar Matić}, doi = {10.1109/ACCESS.2025.3571222}, journal = {IEEE Access}, pages = {88499-88508}, shortjournal = {IEEE Access}, title = {Evaluating the ANN model performance for PID controller tuning in flow process control: A comparative study}, volume = {13}, year = {2025}, }
• (2025). AUV path planning based on the flow field method and dynamic window approach. ACCESS, 13, 88484-88498. (www)

  @article{ , abstract = {In order to improve the performance of Autonomous Underwater Vehicles (AUVs) in complex marine environments, efficient and reliable navigation strategies are deemed essential. Over the years, a variety of methods have been developed to solve the underwater vehicle path planning problem. A hybrid path planning method for AUVs operating near the water surface is proposed in this paper. The methodology combines the artificial potential field-based Flow Field Method (FFM) and the Dynamic Window Approach (DWA) to achieve efficient, collision-free navigation. Firstly, a global guidance field is generated by the FFM, which directs the AUV to move toward the goal and avoid static obstacles. The DWA is then integrated to handle real-time local dynamic obstacles, leveraging the AUV’s kinematic constraints. Simulation results demonstrate the effectiveness of this method in complex underwater environment. The adaptability and safety of AUV navigation are significantly improved by this hybrid approach, which is a promising solution in applications of marine exploration, environmental monitoring, and offshore operations.}, archive = {J_ACCESS}, author = {Xiang Gao and Duowei Jia and Xinghua Liu and Jiaxuan Du and Haibo Zhang}, doi = {10.1109/ACCESS.2025.3570790}, journal = {IEEE Access}, pages = {88484-88498}, shortjournal = {IEEE Access}, title = {AUV path planning based on the flow field method and dynamic window approach}, volume = {13}, year = {2025}, }
• (2025). FGMFN: Fine-grained multiscale cross-modal sentiment analysis in advertisements. ACCESS, 13, 88472-88483. (www)

  @article{ , abstract = {Cross-modal sentiment analysis in advertising has gained significant attention due to its potential in brand communication and consumer behavior analysis. However, traditional methods struggle to handle the multi-scale features and redundant objects in advertising images effectively, resulting in limited emotion recognition accuracy. To address the challenges of insufficient multi-scale features and target redundancy in multi-modal sentiment analysis of advertisements, we introduce a novel framework, Fine-Grained Multiscale Cross-Modal Feature Network (FGMFN). The model is designed to process multi-scale feature inputs, facilitate efficient sentiment fusion between images and text. FGMFN employs a multi-scale network to extract key features from advertising images, and uses visual features to guide the textual data representation. Additionally, to reduce textual ambiguity caused by strong intra-class similarity in advertising contexts, we introduce a multi-task learning approach combining image-text matching loss with image-text mutual information loss. This strategy narrows the gap between visual features and sentiment semantics, improving the model’s generalization capabilities. Finally, we construct a fine-grained image-text sentiment analysis dataset (YTB-ADS), which, in contrast to traditional coarse-grained datasets with high intra-class similarity, better serves the specific needs of advertising sentiment analysis. Experimental results show that FGMFN outperforms existing methods on the YTB-ADS dataset, as well as on the publicly available Twitter-2015 and Twitter-2017 datasets, confirming the model’s superior performance in advertising sentiment analysis tasks.}, archive = {J_ACCESS}, author = {Han Wang and Peng Chen and Xiangyu Du}, doi = {10.1109/ACCESS.2025.3571624}, journal = {IEEE Access}, pages = {88472-88483}, shortjournal = {IEEE Access}, title = {FGMFN: Fine-grained multiscale cross-modal sentiment analysis in advertisements}, volume = {13}, year = {2025}, }
• (2025). A smart web-based power quality and energy monitoring system with enhanced features. ACCESS, 13, 88458-88471. (www)

  @article{ , abstract = {The growing demand for advanced power monitoring underscores the limitations of traditional smart meters, which often lack detailed insights into power quality. This capability has become increasingly important as modern electrical systems are more vulnerable to disturbances and inefficiencies. This paper presents the design and development of a low-cost, real-time power quality and energy monitoring device (PQEm), specifically engineered for easy installation in any electrical setup, whether for bulk metering or submetering of individual units or appliances. The PQEm system integrates a highly precise multiphase analog front-end (AFE) power quality IC with a system-on-a-chip (SoC) platform, enabling the collection of real-time data on key energy metrics such as active, reactive, and apparent power, while also detecting a wide spectrum of power quality disturbances, including sags, swells, interruptions, and voltage imbalances, with a detection latency of less than 5 ms. The PQEm device includes time-frequency analysis to enhance visualization and characterization of voltage and current data. It also supports long-term raw data recording at the native sampling frequency, a feature typically reserved for high-end power quality analyzers. The device has been calibrated and validated through both laboratory testing and real-world applications, offering a cost-effective and user-friendly solution that addresses critical gaps in existing smart metering systems while empowering consumers to monitor power quality issues with greater precision.}, archive = {J_ACCESS}, author = {Rami Moustafa and Hussain Shareef and Madathodika Asna and Rachid Errouissi and Jeyraj Selvaraj}, doi = {10.1109/ACCESS.2025.3571623}, journal = {IEEE Access}, pages = {88458-88471}, shortjournal = {IEEE Access}, title = {A smart web-based power quality and energy monitoring system with enhanced features}, volume = {13}, year = {2025}, }
• (2025). Anterior cruciate ligament tear detection based on combination of convolutional neural network enhanced by improved human evolutionary algorithm. ACCESS, 13, 88445-88457. (www)

  @article{ , abstract = {Anterior Cruciate Ligament (ACL) tears are prevalent injuries in sports and physical activities that necessitate prompt and precise diagnosis for optimal treatment and re-habilitation. Conventional diagnostic techniques like physical examination and MRI, may be subjective and protracted. This study proposes a new efficient technique for detecting tears of ACL based on the integration of a Convolutional Neural Network (CNN) and an improved version of Human Evolutionary Algorithm (IHEA). The purpose of the suggested IHEA is to enhance the hyperparameters of the CNN to improve its performance in detecting ACL rupture from MRI scans. The suggested technique has been validated by assessing it on a standard case study and comparing its results with some other advanced methods, including the Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), Generative Adversarial Network (GAN2), Gated Recurrent Unit combined with Flexible Fitness Dependent Optimizer (GRU/FFDO), and GRU optimized by Hybrid Tasmanian Devil Optimization (GRU/HTDO). Final results showed the superiority of the proposed model in diagnosing of the ACL tear.}, archive = {J_ACCESS}, author = {Haibo Shen}, doi = {10.1109/ACCESS.2025.3567303}, journal = {IEEE Access}, pages = {88445-88457}, shortjournal = {IEEE Access}, title = {Anterior cruciate ligament tear detection based on combination of convolutional neural network enhanced by improved human evolutionary algorithm}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on privacy preservation in VANETs: Trends, challenges, and future directions. ACCESS, 13, 88421-88444. (www)

  @article{ , abstract = {Privacy preservation is a fundamental requirement in vehicular ad hoc networks (VANETs) because it addresses sensitive vehicle and driver data. The use of pseudonyms instead of actual vehicle identities is one way to protect vehicle owners from the unauthorized tracking or linking of vehicle movements. In this paper, we conduct a systematic literature review to identify the implementation of pseudonyms in existing privacy preservation schemes via the Kitchenham method. To our knowledge, no study has presented an analysis of the implementation of comprehensive pseudonym life cycles in the last five years. We classify VANET system models and their advancements with the support of emerging technologies, mapping the integration of these technologies to the implementation of the pseudonym life cycle. Furthermore, we categorize the privacy analyses employed in existing schemes. Finally, we identify research gaps and future research directions to advance the development of robust privacy preservation schemes in VANETs. Of the 594 reviewed research papers, 113 satisfied our requirements for primary research (PR). Among them, 71% adopted VANET standard system models, 12% implemented a comprehensive pseudonym life cycle, and 73% conducted both formal and informal security and privacy analyses. We highlight research challenges and suggest future research topics for developing robust privacy-preserving schemes in VANETs.}, archive = {J_ACCESS}, author = {Esti Rahmawati Agustina and Kalamullah Ramli and Arif Rahman Hakim and Ruki Harwahyu}, doi = {10.1109/ACCESS.2025.3570491}, journal = {IEEE Access}, pages = {88421-88444}, shortjournal = {IEEE Access}, title = {A systematic literature review on privacy preservation in VANETs: Trends, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Big data analytics in library services with AI: Personalized content recommendations and catalog optimization. ACCESS, 13, 88412-88420. (www)

  @article{ , abstract = {This paper presents an AI-driven framework designed to enhance user engagement and optimize catalog management in digital libraries. The framework integrates Variational Autoencoder (VAE)-based personalized recommendations with Adam optimizer and Lookahead mechanism for catalog optimization. The VAE model effectively learns latent representations of user-item interactions, providing personalized content recommendations. For catalog optimization, the Adam optimizer with Lookahead stabilizes convergence and refines inventory selection, leading to more efficient resource allocation and reduced costs. Experimental results from a large-scale dataset demonstrate that the proposed approach outperforms traditional methods, achieving significant improvements in recommendation accuracy and user engagement. It reduces the number of low-demand items while enhancing overall catalog efficiency. The proposed framework provides a scalable and adaptable solution for digital libraries, ensuring both user satisfaction and effective resource management. Future work will explore hybrid models incorporating Natural Language Processing (NLP) to improve content understanding and further enhance recommendation quality.}, archive = {J_ACCESS}, author = {Pinjia Hu and Yichi Zhang}, doi = {10.1109/ACCESS.2025.3570200}, journal = {IEEE Access}, pages = {88412-88420}, shortjournal = {IEEE Access}, title = {Big data analytics in library services with AI: Personalized content recommendations and catalog optimization}, volume = {13}, year = {2025}, }
• (2025). Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: A survey-based analysis. ACCESS, 13, 88399-88411. (www)

  @article{ , abstract = {The rapid development of digital technology across industries has highlighted the growing need for enhanced competencies in Artificial Intelligence (AI), Cyber security (CS), and Digital Transformation (DT). While there is extensive research on each of these domains in isolation, few studies have investigated their relationship and joint impact on organizational maturity. This study aims to address this gap by analyzing the relationships among the maturity levels of AI, CS, and DT at the organizational level using Structural Equation Modeling (SEM) and descriptive statistical methods. A mixed-methods design combines quantitative survey data with synthetic modeling techniques to assess organizational preparedness. The findings demonstrate significant bidirectional correlations among AI, CS, and DT, with technology and finance being more advanced than government and education. The research highlights the necessity of an integrated AI-CS strategy and provides actionable recommendations to increase investments in these domains. In contrast to the preceding fragmented evaluations, the current research establishes a comprehensive, empirically grounded framework that acts as a strategic reference point for digital resilience. Follow-up studies will involve collecting real-world industry data in support of empirical validation and predictive ability in measuring AI and CS maturity. This research adds to the existing literature by filling the gaps among fragmented digital maturity models and providing a consistent empirical base for organizations to thrive in an evolving technological environment.}, archive = {J_ACCESS}, author = {Burak Kubilay and Baris Celiktas}, doi = {10.1109/ACCESS.2025.3571537}, journal = {IEEE Access}, pages = {88399-88411}, shortjournal = {IEEE Access}, title = {Relationships among organizational-level maturities in artificial intelligence, cybersecurity, and digital transformation: A survey-based analysis}, volume = {13}, year = {2025}, }
• (2025). High-performance thin PTFE-ni metamaterial absorber for ultra-broadband applications. ACCESS, 13, 88390-88398. (www)

  @article{ , abstract = {This study presents a high-performance, ultra-thin metamaterial absorber consisting of nickel resonator disks arranged in a $4\times 4$ matrix on a polytetrafluoroethylene (PTFE) substrate. The novelty of the design lies in the integration of nickel—a cost-effective alternative to noble metals—and PTFE, whose excellent thermal stability enhances the overall performance of the metamaterial. Additionally, the simplified unit cell structure enables scalability and offers a practical alternative to conventional absorbers. The proposed metamaterial absorber achieves broadband absorption across the ultraviolet, visible, and infrared spectra. A systematic parametric analysis was conducted to optimize the structural parameters, maximizing absorption efficiency while maintaining minimal thickness. Numerical simulations performed in CST Studio Suite validated the optimized design, demonstrating an average absorption of 99.00% and a peak absorption of 99.36% within the $375-850$ nm range. Given the elevated absorption rates achieved, the analysis range was extended to $300-3000$ nm, and a genetic algorithm was employed to refine the geometric parameters. This optimization resulted in a stable absorption profile with an average efficiency of 98.98%, and a peak of 99.96%. This structurally simple design offers an advantage by combining cost-effective material selection with high broadband absorption performance.}, archive = {J_ACCESS}, author = {Camilla C. Moro Carmo and Úrsula C. Resende and Sílvia C. Albuquerque and Maurício D. Almeida}, doi = {10.1109/ACCESS.2025.3570793}, journal = {IEEE Access}, pages = {88390-88398}, shortjournal = {IEEE Access}, title = {High-performance thin PTFE-ni metamaterial absorber for ultra-broadband applications}, volume = {13}, year = {2025}, }
• (2025). Systematic analysis of the links between Obsolescence–Shortage and Reliability–Maintainability–Availability. ACCESS, 13, 88371-88389. (www)

  @article{ , abstract = {Component obsolescence and shortages impact virtually all systems, particularly those with extended lifespans, such as aircrafts or trains. Obsolescence (i.e. state of being outdated) and shortage (i.e. lack of components) are distinct yet interconnected challenges and despite their inevitability, systems must maintain acceptable performance levels over extended periods. In this research, the term ‘shortage’ encapsulates the broader concepts of Diminishing Manufacturing Sources and Material Shortages (DMSMS). This study investigates the impacts of obsolescence and shortages on maintainability, reliability, and availability. Existing scientific literature frequently overlooks this influence when calculating associated indicators, often relegating it to a marginal factor despite its potential significant impact. The aim is to underscore the criticality of incorporating obsolescence and shortage considerations into operational availability assessments. This argument stems from two key factors: the accelerating pace of obsolescence and shortages driven by rapid technological advancements, evolving needs, logistical disruptions, or global conflicts, and the inherent risks associated with overestimating operational availability. This study encompasses both the system design-development phase and the operational phase. It examines the relevant concepts and their interrelationships through the lens of international standards and established scientific literature. Furthermore, an industrial case study from a major automotive supplier, focusing on one of its production lines, serves to illustrate partially these relationships. The paper concludes by presenting the study’s findings and outlining potential avenues for future research in this domain.}, archive = {J_ACCESS}, author = {Sahar Karaani and Marc Zolghadri and Mariem Besbes and Claude Baron and Maher Barkallah and Mohamed Haddar}, doi = {10.1109/ACCESS.2025.3570107}, journal = {IEEE Access}, pages = {88371-88389}, shortjournal = {IEEE Access}, title = {Systematic analysis of the links between Obsolescence–Shortage and Reliability–Maintainability–Availability}, volume = {13}, year = {2025}, }
• (2025). ElHBiAt: Electra pre-training network hybrid of BiLSTM, the attention layer to aspect-based sentiment analysis. ACCESS, 13, 88342-88370. (www)

  @article{ , abstract = {Aspect-based sentiment analysis is one of the famous and practical subjects in natural language processing. Traditional sentiment analysis assigns a polarity to the whole text or document and does not consider the aspects of the text, so it cannot correctly show the author’s opinion. Extracting aspects and determining their polarity has many challenges, such as the lack of rich datasets, negative transfer in cross-domain datasets, and fine-tuning of deep models of the ratio of texts with different contexts. In this research, a model has been proposed for aspect-based sentiment analysis with different contexts. This model uses the Electra as the backbone of the architecture, which is trained with different cross-domain datasets to transfer knowledge to the target domain. Also, in the proposed model of ElHBiAt, the resampling method is used to deal with imbalanced datasets, which causes the neural model to find less orientation than the polarities in the majority. In this model, two layers of attention are used to extract the essential aspects of the text, and the deep neural network BiLSMT is used to process the text in a two-way. The results show that the ElHBiAt model has been able to improve the efficiency by an average of 5.78% in the SemEval dataset, by 2% in the SEntFiN dataset, and by an average of 4.3% in the Drug dataset in terms of the F1-score.}, archive = {J_ACCESS}, author = {Amin Ghanee Nezhad and Kia Jahanbin and Abolfazl Razzaghi Yamchi}, doi = {10.1109/ACCESS.2025.3568484}, journal = {IEEE Access}, pages = {88342-88370}, shortjournal = {IEEE Access}, title = {ElHBiAt: Electra pre-training network hybrid of BiLSTM, the attention layer to aspect-based sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Hyperclustering: High-order Deep/Shallow feature clustering for subway shield tunneling water leakage detection. ACCESS, 13, 88328-88341. (www)

  @article{ , abstract = {Existing tunneling water leakage detection techniques primarily focus on traditional machine learning models, such as decision trees, SVMs, and random forests. These models struggle to handle large-scale and complex sensor data. More recent efforts using deep learning models like CNNs and RNNs also face challenges in capturing the diverse relationships among features. This paper introduces HyperClustering, a new framework designed to enhance subway shield tunneling water leakage detection through multimodal deep/shallow feature fusion techniques. The model addresses challenges such as variability in leakage patterns across different environmental conditions and the complexity of sensor data. It combines deep and shallow learning methods to identify high-quality features that capture the unique characteristics of tunneling operations and leakage events. HyperClustering improves upon these methods by constructing a large-scale hypergraph that models complex relationships among leakage features. This hypergraph uncovers high-order interactions that traditional approaches cannot capture. The model also uses a Gaussian Mixture Model (GMM)-based posterior probability to rank candidate patches for water leakage detection. It prioritizes high-risk areas and minimizes false positives. Experiments on datasets from multiple subway tunneling projects show that the model effectively identifies complex feature interactions. It offers adaptive, data-driven optimization for a responsive leakage detection system. This innovative approach sets HyperClustering apart from existing research by leveraging hypergraph modeling and advanced feature fusion. It provides a more robust and accurate solution for real-world tunneling conditions and paves the way for future advancements in multimodal fusion techniques and real-time adaptive learning in leakage detection systems.}, archive = {J_ACCESS}, author = {Jianjun Xu and Xing Yuan and Lixiao Zheng and Da Lin}, doi = {10.1109/ACCESS.2025.3571523}, journal = {IEEE Access}, pages = {88328-88341}, shortjournal = {IEEE Access}, title = {Hyperclustering: High-order Deep/Shallow feature clustering for subway shield tunneling water leakage detection}, volume = {13}, year = {2025}, }
• (2025). Frequency spectrum analysis of partial discharge measurement in 150-kV outdoor cable sealing End—A case study. ACCESS, 13, 88318-88327. (www)

  @article{ , abstract = {Partial Discharge (PD) is part of the initiation of failure in the insulation part of cable sealing end (CSE) equipment. CSE with a complex manufacturing form, the interface between insulating materials is part of the challenge to detect PD accurately. In this paper, we design and deploy a new UHF antenna sensor design to detect PD signals in power equipment including CSE with its challenges. In addition, information is provided on the range of frequency spectrum present in CSE using frequency domain analysis. The proposed UHF sensor is designed with a size of 100mm x 80mm x 1.6mm in the form of a circular patch with an arm. This new UHF sensor is optimized and fabricated to produce a bandwidth of 0 - 3800 MHz and a voltage standing wave ratio (VSWR) of 1.1. To comply with the level of range of frequencies occurring and the level of sensitivity of the proposed sensor, an online measurement application is carried out on 6 units of 150 kV outdoor CSE. Measurement results from the implementation of the new UHF sensor design are able to detect PD signals in CSE. The frequency spectrum range of PD signals detected from distributed between 400 MHz to 1 GHz. The newly designed UHF sensor confirmed that it can be part of a measurement instrument to be applied to PD monitoring systems in high voltage/medium voltage power apparatus.}, archive = {J_ACCESS}, author = {Muhammad Kodrat and Umar Khayam}, doi = {10.1109/ACCESS.2025.3567320}, journal = {IEEE Access}, pages = {88318-88327}, shortjournal = {IEEE Access}, title = {Frequency spectrum analysis of partial discharge measurement in 150-kV outdoor cable sealing End—A case study}, volume = {13}, year = {2025}, }
• (2025). A comprehensive study on lightweight convolution techniques for malicious traffic synthesis in diffusion models. ACCESS, 13, 88306-88317. (www)

  @article{ , abstract = {Network security relies on effective and accurate malicious traffic detection, which is increasingly important for edge devices. As computing resources are distributed across many devices, detecting malicious traffic at the edge becomes crucial. Additionally, the lack of high-quality malicious traffic data hinders the effectiveness of AI models in training. Furthermore, the limited computational power of edge devices requires lightweight generative models, making it challenging to implement diffusion models for data augmentation. In this paper, we conduct a comprehensive study on lightweight convolution techniques for malicious traffic synthesis in diffusion models. We evaluate the performance of three classic lightweight convolutional architectures in generating malicious traffic data. Two popular datasets, USTC-TFC2016 and N-BaIoT, are utilized to train our generative models, with the generated data being employed to train various classifiers for a thorough performance evaluation. The experimental results demonstrate that the investigated lightweight approaches can efficiently generate high-quality and diverse malicious traffic data for enhancing the performance of intrusion detection systems while the computational complexity is reduced to around 19.69% of the baseline.}, archive = {J_ACCESS}, author = {Fuhao Li and Ifiok Udoidiok and Jielun Zhang}, doi = {10.1109/ACCESS.2025.3564985}, journal = {IEEE Access}, pages = {88306-88317}, shortjournal = {IEEE Access}, title = {A comprehensive study on lightweight convolution techniques for malicious traffic synthesis in diffusion models}, volume = {13}, year = {2025}, }
• (2025). Fixed-time cooperative tracking control with novel finite-time parameter estimation algorithm for nonlinear multi-agent systems. ACCESS, 13, 88291-88305. (www)

  @article{ , abstract = {Cooperative control of multi-agent systems (MASs) has gained significant attention due to its applications in autonomous vehicles, robotics, and distributed sensor networks. A key challenge is achieving accurate tracking and consensus under nonlinear dynamics, system uncertainties, and external disturbances. Most existing approaches offer asymptotic or finite-time convergence guarantees, often relying on prior knowledge of system parameters or specific initial conditions. This research proposes a fixed-time cooperative tracking control with finite-time parameter estimation in nonlinear second-order multi-agent systems (MASs) under directed communication topology. An auxiliary filter is designed to reconstruct the unknown parameters based on the parameter estimation error within a finite time. The adaptive law proposed in this framework is formulated by incorporating a filtered regressor driven both by the consensus and parameter estimation error. The fixed-time convergence of the consensus error to zero, which is aided by the salient feature of the parameter estimation algorithm, is analyzed rigorously via Lyapunov analysis. The fixed-time tracking control, a fractional-order-based nonlinear algorithm, is developed to leverage a modified consensus error and employs sliding mode with adaptive coefficients. Additionally, the robustness of the control schemes is also investigated against bounded disturbances. Finally, the effectiveness of the developed methods is validated through extensive simulations.}, archive = {J_ACCESS}, author = {Ibrahim Bako Abdulhamid and Muhammad Nasiruddin Mahyuddin}, doi = {10.1109/ACCESS.2025.3571289}, journal = {IEEE Access}, pages = {88291-88305}, shortjournal = {IEEE Access}, title = {Fixed-time cooperative tracking control with novel finite-time parameter estimation algorithm for nonlinear multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). Spatial-spectral contrastive graph neural network for few-shot hyperspectral image classification. ACCESS, 13, 88278-88290. (www)

  @article{ , abstract = {Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in three key aspects: 1) suboptimal graph topology construction due to fixed or heuristic-based edge definitions, 2) inefficient propagation of discriminative node features across heterogeneous regions, and 3) inadequate fusion of spatially correlated and spectrally discriminative patterns. To overcome these challenges, we propose a Spatial-Spectral Contrastive Graph Neural Network (SSCGNN), which introduces three novel components. First, we propose a directed graph reconstruction module that transforms conventional undirected graphs into diversity-aware topology through multi-head self-attention mechanisms. Second, we design a collaborative learning framework where the spatial graph network and spectral graph network mutually enhance feature extraction. Finally, we construct a graph contrastive learning module that augments edge representation learning under limited supervision. By constructing positive/negative edge pairs through spectral-spatial similarity metrics, it regularizes the graph structure to emphasize class-discriminative connections and suppress noisy inter-node relationships. Extensive experiments on four benchmark HSI datasets—University of Pavia (PU), Salinas Valley (SA), WHU-Hi-HanChuan (WHU-HH), and WHU-Hi-LongKou (WHU-HL)—demonstrate SSCGNN’s superiority over state-of-the-art few-shot learning methods. With only 5 labeled samples per class, our model achieves overall accuracy (OA) values of: 93.36% (PU, +6.22% vs. baseline GCN), 98.37% (SA, +4.91% vs. spectral-spatial CNN), 84.68% (WHU-HH, +8.03% vs. meta-learning approaches), 95.87% (WHU-HL, +7.15% vs. graph attention networks).}, archive = {J_ACCESS}, author = {Zhaoxia Xue and Zhiwei Liu and Zhaohui Xue and Tingqiang Song}, doi = {10.1109/ACCESS.2025.3569874}, journal = {IEEE Access}, pages = {88278-88290}, shortjournal = {IEEE Access}, title = {Spatial-spectral contrastive graph neural network for few-shot hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). Brushless synchronous machine with dual-frequency inverter for high torque and low ripple operation. ACCESS, 13, 88268-88277. (www)

  @article{ , abstract = {In this paper, a novel brushless synchronous machine topology is introduced with the integration of a double-frequency inverter system, which plays a pivotal role in generating a sub-harmonic magnetomotive force (MMF) that significantly enhances torque performance. There are two different windings on the stator, ABC and XYZ winding, by operating the ABC winding at a fundamental frequency with an 8-pole configuration, and the XYZ winding at double the frequency with a 2-pole arrangement, the machine produces a sub-harmonic MMF that is one-fourth of the fundamental MMF. This precise sub-harmonic MMF control improves the interaction between the stator and rotor, inducing an electromotive force (EMF) in the rotor’s harmonic winding. The rotor employed here has two windings, harmonic and field windings. Harmonic winding induces the sub harmonic MMF and a rotating rectifier on the rotor coverts it to DC, feeding the DC current to field windings. The dual frequency inverter topology results in an improved rotor excitation and increased torque with lower ripple. The results from Finite Element Analysis (FEA) show the DF-BSM generates 12.65 Nm torque on average at 13.6% torque ripple and 80.7% operating efficiency making it superior to conventional brushless synchronous machines. Moreover, the Dual Frequency Brushless Synchronous Machine (DF-BSM) provides superior performance in brushless synchronous machines and is well suited for high torque, low noise applications where efficient brushless operation is critical.}, archive = {J_ACCESS}, author = {Hafiz Mudassir Munir and Qasim Ali and Jose Rodriguez and Mohamed Abdelrahem}, doi = {10.1109/ACCESS.2025.3571054}, journal = {IEEE Access}, pages = {88268-88277}, shortjournal = {IEEE Access}, title = {Brushless synchronous machine with dual-frequency inverter for high torque and low ripple operation}, volume = {13}, year = {2025}, }
• (2025). Employing a mixture of rayleigh and inverse gaussian distributions as SAR clutter texture model in MAP estimation for efficient speckle suppression. ACCESS, 13, 88255-88267. (www)

  @article{ , abstract = {This article proposes a speckle suppression technique based on a binary mixture of the Rayleigh and the reciprocal of Gaussian (RIG) distributions. This model suitably characterizes the texture of synthetic aperture radar (SAR) return from regions with varying degrees of roughness and captures the multimodal behavior observed in extremely heterogeneous SAR clutter. We estimate the RIG mixture model parameters by maximum likelihood (ML) with the expectation maximization (EM) algorithm. We also obtain the Crámer-Rao Bounds (CRBs) for these estimators. Finally, we propose a maximum-a-posteriori (MAP) estimator for efficient despeckling by utilizing the RIG model as a prior distribution for the texture component. The accuracy of RIG-MAP estimation for texture is performed on single-look clutter data from actual sensors and multilook simulated clutter data. Qualitative and quantitative results on despeckling illustrate the effectiveness of the proposed MAP estimator in suppressing speckle while preserving mean, textural information, fine details, etc. Furthermore, the RIG-MAP estimator achieves superior performance compared to MMSE (minimum mean square error) - based Lee filter, Kuan filter, and MAP-based (such as $\beta $ -MAP, $\Gamma $ -MAP, CR-MAP, $\mathcal {G}^{0}$ -MAP) estimators.}, archive = {J_ACCESS}, author = {Dheeren Ku Mahapatra and Saurav Gupta and Biswajit Jena and Ravi Prakash Dwivedi}, doi = {10.1109/ACCESS.2025.3569417}, journal = {IEEE Access}, pages = {88255-88267}, shortjournal = {IEEE Access}, title = {Employing a mixture of rayleigh and inverse gaussian distributions as SAR clutter texture model in MAP estimation for efficient speckle suppression}, volume = {13}, year = {2025}, }
• (2025). A novel framework for RDF schema extraction in NoSQL databases using sentence-BERT. ACCESS, 13, 88243-88254. (www)

  @article{ , abstract = {NoSQL databases, known for their flexibility and scalability, have become pivotal in handling diverse and unstructured data. However, their schema-less nature introduces significant challenges in metadata management, query optimization, and data integration. This paper presents a novel schema extraction framework that captures the structural and semantic complexity of NoSQL databases. By integrating constraints, logical rules, and Sentence-BERT embeddings, the proposed approach generates semantically enriched schemas that ensure accuracy, coherence, and usability. Experimental evaluation highlights its adaptability across diverse datasets, demonstrating improvements in semantic precision, data integration, and metadata quality. The framework provides a practical solution for bridging schema-less and structured database workflows, enhancing interoperability and analytics capabilities.}, archive = {J_ACCESS}, author = {Saad Belefqih and Mohammed Barchane and Ahmed Zellou and El Habib Benlahmar}, doi = {10.1109/ACCESS.2025.3569779}, journal = {IEEE Access}, pages = {88243-88254}, shortjournal = {IEEE Access}, title = {A novel framework for RDF schema extraction in NoSQL databases using sentence-BERT}, volume = {13}, year = {2025}, }
• (2025). Impact of AI-powered adaptive learning platforms on english reading proficiency: Evidence from structural equation modeling. ACCESS, 13, 88230-88242. (www)

  @article{ , abstract = {This study examines the impact and mediating mechanisms of AI-powered adaptive learning platforms on enhancing English reading proficiency. Using structural equation modeling (SEM) on data collected from 118 undergraduate students, this study validates a chained mediation model in which learner engagement mediates the relationship between satisfaction with platform functionalities and perceived learning outcomes. The results show that: (1) satisfaction with key functionalities, such as dynamic content adaptation and real-time feedback, significantly enhances learner engagement; (2) learner engagement strongly predicts perceived improvements in reading skills; and (3) the platform facilitates reading comprehension through a dual intervention mechanism integrating bottom-up decoding and top-down schema activation strategies. These findings address an empirical gap in the application of AI for receptive language development and provide theoretical, methodological, and practical implications for the design of intelligent educational systems and foster pedagogical innovation.}, archive = {J_ACCESS}, author = {Jin Wu and Yiyun Wang and Fang Chen and Xueqi Yin and Yun He}, doi = {10.1109/ACCESS.2025.3571055}, journal = {IEEE Access}, pages = {88230-88242}, shortjournal = {IEEE Access}, title = {Impact of AI-powered adaptive learning platforms on english reading proficiency: Evidence from structural equation modeling}, volume = {13}, year = {2025}, }
• (2025). Development of bimodal emotion recognition system based on skin temperature and heart rate variability using hybrid neural networks. ACCESS, 13, 88215-88229. (www)

  @article{ , abstract = {Most studies indicate that bimodal emotion recognition systems are more objective and accurate. However, many of these systems depend on physiological signals that require direct measurement, which introduces certain limitations. This study aims to develop a new bimodal emotion recognition system based on skin temperature (SKT) and heart rate variability (HRV) using hybrid neural networks. Notably, these physiological signals can be measured remotely, addressing the limitations of direct measurement methods. The integration of these modalities enables the model to effectively utilize both spatial and temporal features for robust emotion classification. The hybrid neural network, combining a convolutional neural network and a gated recurrent unit (CNN+GRU), was trained on experimental SKT and HRV data collected from individuals experiencing basic emotions such as anger, disgust, fear, happiness, sadness, and surprise. The trained model achieved an accuracy of 95.58%, outperforming existing approaches that use only a single data modality. Confusion matrix analysis demonstrated high accuracy in recognizing most basic emotions. The results confirm the effectiveness of the proposed approach in combining physiological and visual signals for improved emotion recognition.}, archive = {J_ACCESS}, author = {Sayat Orynbassar and Duygun Erol Barkana and Evan Yershov and Madiyar Nurgaliyev and Ahmet Saymbetov and Batyrbek Zholamanov and Gulbakhar Dosymbetova and Ainur Kapparova and Nursultan Koshkarbay and Nurzhigit Kuttybay and Askhat Bolatbek and Kymbat Kopbay and Dinara Almen}, doi = {10.1109/ACCESS.2025.3570785}, journal = {IEEE Access}, pages = {88215-88229}, shortjournal = {IEEE Access}, title = {Development of bimodal emotion recognition system based on skin temperature and heart rate variability using hybrid neural networks}, volume = {13}, year = {2025}, }
• (2025). Algorithm for solving distributed workshop scheduling model based on order constraints and assembly constraints. ACCESS, 13, 88200-88214. (www)

  @article{ , abstract = {With the transformation of manufacturing towards intelligence and flexibility, the workshop scheduling problem, as a typical combinatorial optimization problem, is facing increasingly complex constraints and requirements. In the actual production process, order constraints and assembly constraints often significantly affect production efficiency and scheduling effectiveness. The traditional centralized scheduling method is no longer able to meet the requirements for flexibility, real-time performance, and efficiency in modern manufacturing environments. To solve this problem, a distributed workshop scheduling model based on order constraints and assembly constraints is proposed, aiming to improve the scheduling efficiency of the workshop under complex constraint conditions. For this model, an improved greedy algorithm was combined to solve it, and particle swarm optimization algorithm was introduced to enhance the local and global search capabilities of the distributed scheduling system, effectively addressing the computational complexity in large-scale production environments. The experimental results show that the research model performs well in optimizing scheduling, with a maximum completion time of 40.49 hours, an average processing waiting time of 1.46 hours, and a resource utilization rate of over 95%. These results indicate that the proposed solution algorithm can significantly shorten the production cycle, improve resource utilization, and have high real-time scheduling capabilities, making it particularly suitable for solving workshop scheduling problems with complex orders and assembly constraints.}, archive = {J_ACCESS}, author = {Tingting Qu}, doi = {10.1109/ACCESS.2025.3570362}, journal = {IEEE Access}, pages = {88200-88214}, shortjournal = {IEEE Access}, title = {Algorithm for solving distributed workshop scheduling model based on order constraints and assembly constraints}, volume = {13}, year = {2025}, }
• (2025). Generation techniques of orbital angular momentum beams for wireless communication applications. ACCESS, 13, 88174-88199. (www)

  @article{ , abstract = {This paper comprehensively overviews the various techniques of generating Orbital Angular Momentum (OAM) beams from microwave to optical frequencies for wireless communications applications. OAM communications has been explored extensively over the past two decades for its potential to improve spectral efficiency and increase capacity. This review paper broadly categorizes the different generation techniques into active and passive methods. Active methods include arrays of actively excited radiating antenna elements that offer dynamic control over the phase profile, enabling real-time manipulation of OAM beams. Additionally, the mutual coupling between antenna elements in uniform circular arrays is discussed, as it can significantly impact the performance and purity of OAM beams, and strategies to mitigate these effects are explored. Passive methods include spiral phase plates, reflect arrays, metasurfaces, optical rectification, difference frequency generation, laser plasma, discrete dielectric lenses, etc., which utilize static elements to manipulate the phase of an incident plane wave and create a helical phase front characteristic of an OAM wave. The paper discusses each method’s principle, advantages, and challenges, highlighting their potential applications primarily in wireless communication and additionally in optical information processing and radar. The paper also discusses methods for achieving high mode purity, which is essential for reliable OAM mode identification and efficient separation of multiplexed OAM beams. Techniques for multiplexing several OAM modes, such as concentric circular arrays, considering the cross-coupling effects and mitigation methods, Butler matrices, MIMO techniques, and enhancing OAM beam propagation using lenses and superposition with non-diffracting beams, are also discussed.}, archive = {J_ACCESS}, author = {Farhana Firdous and Ravi Kadlimatti}, doi = {10.1109/ACCESS.2025.3569702}, journal = {IEEE Access}, pages = {88174-88199}, shortjournal = {IEEE Access}, title = {Generation techniques of orbital angular momentum beams for wireless communication applications}, volume = {13}, year = {2025}, }
• (2025). A blockchain-assisted cross-domain authentication and key negotiation scheme in IIoT. ACCESS, 13, 88156-88173. (www)

  @article{ , abstract = {Traditional cross-domain schemes for industrial Internet of Things (IIoT) are deficient in decentralization and lack a precise mechanism for updating session keys, resulting in high overhead from repeated authentication and key negotiation. This paper proposes a blockchain-assisted cross-domain authentication and key negotiation scheme, aiming at realizing authentication and key negotiation between different domains of IIoT. First, a session token mechanism is introduced to limit the validity period of the negotiated session key and assist in updating the session key. Second, blockchain is utilized to assist in updating the validity period of the session key, thus significantly reducing the overhead of repeated authentication. Finally, we analyze the security and performance of the scheme, and the results show that the scheme can efficiently verify data integrity and add the functions of key update, user dynamic update, and semi-trusted third-party dynamic update. The experimental results show that compared with other schemes, our scheme reduces 69.4%, 78.9%, 88.8%, and 38.3% of the time consumed in terms of computation overhead and reduces 63.5%, 18.8%, 15.1%, and 52.9% of the cost in terms of communication overhead, respectively. The proposed scheme has a low overhead.}, archive = {J_ACCESS}, author = {Zhengnan Xu and Guofang Dong and Ruicheng Yang}, doi = {10.1109/ACCESS.2025.3565393}, journal = {IEEE Access}, pages = {88156-88173}, shortjournal = {IEEE Access}, title = {A blockchain-assisted cross-domain authentication and key negotiation scheme in IIoT}, volume = {13}, year = {2025}, }
• (2025). A novel framework for financial cybersecurity and fraud detection using XAI-RNN-SGRU. ACCESS, 13, 88134-88155. (www)

  @article{ , abstract = {Cyber threats involve unauthorized access, alteration, or deletion of private information, extortion, and disruption of business operations. Traditional network security methods need more scalability, data protection, and difficulty detecting advanced threats. The hybridization of Explainable Artificial Intelligence (XAI) with Ridgelet Neural Network (RNN) and Soft Gated Recurrent Unit (SGRU) (XAI-RNN-SGRU) is introduced to address these challenges. This model enhances financial data prediction and cybersecurity through advanced preprocessing for handling missing values and data imbalance, and it also normalizes numerical features and encodes categorical variables. Enhanced Principal Component Analysis (EPCA) and Improved Fast Random Opposition-based learning Aphid Ant Optimization (AAO) are utilized for feature extraction and desired feature selection. Then, it combines the Ridgelet Neural Network with Soft Gated Recurrent Unit for accurate predictions and uses the Improved Homomorphic Encryption (IHE) process to reinforce data protection during computations. Comprehensive testing on three data sets reveals outstanding predictive performance with accuracies of 99.55%, 99.99%, and 99.95%, respectively. The introduced method improves data security, scalability, and efficiency in real financial applications. It outperforms current techniques with a bit error rate of 0.00004 and a data protection score of 98.03%, providing secure and reliable financial operations. In addition, it enhances efficiency in encryption and decryption, with rates of 9.25 and 8.30 seconds, respectively, and is exceptionally scalable for mass-market cybersecurity use. Such improvements offer a real-world solution for financial institutions, cybersecurity companies, and digital payment systems, guaranteeing strong fraud detection, improved data security, and enhanced operational efficiency compared to the current methodologies.}, archive = {J_ACCESS}, author = {Smarajit Ghosh}, doi = {10.1109/ACCESS.2025.3570216}, journal = {IEEE Access}, pages = {88134-88155}, shortjournal = {IEEE Access}, title = {A novel framework for financial cybersecurity and fraud detection using XAI-RNN-SGRU}, volume = {13}, year = {2025}, }
• (2025). Synthetic sentiment cue enhanced graph relation-attention network for aspect-level sentiment analysis. ACCESS, 13, 88121-88133. (www)

  @article{ , abstract = {Aspect-level Sentiment Analysis (ASA) constitutes a critical task in natural language processing, aimed at detecting and classifying sentiment polarities associated with specific aspects in textual data. While conventional approaches often fall short in capturing fine-grained sentiment distinctions at the aspect level, neural network-based models, despite their advancements, frequently encounter challenges stemming from insufficient explicit emotional indicators such as aspect-sentiment correlations, contextual dependencies, and salient opinion expressions. To address these limitations, this paper presents a novel Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network (SSC-GRAN), a hybrid framework that synergistically integrates large language models (LLMs) with graph neural networks (GNNs). Our method introduces three key innovations: 1) a synthetic data augmentation paradigm leveraging LLMs to generate semantically coherent sentiment cues, thereby enriching aspect-opinion interactions; 2) a hierarchical graph architecture that models syntactic dependency structures and aspect-context relationships through relation-aware attention mechanisms; and 3) a contrastive learning objective that aligns representations from both authentic and synthetic data to enhance model robustness. Comprehensive experiments across benchmark datasets demonstrate statistically significant improvements in performance, with SSC-GRAN achieving state-of-the-art results. Ablation studies further validate the contributions of each component, while qualitative analyses reveal enhanced interpretability in aspect-sentiment reasoning. This work advances ASA research by bridging the gap between data scarcity and structural modeling, offering a robust framework for fine-grained sentiment understanding.}, archive = {J_ACCESS}, author = {Hongwei Tang and Haining Yan and Ran Song}, doi = {10.1109/ACCESS.2025.3569543}, journal = {IEEE Access}, pages = {88121-88133}, shortjournal = {IEEE Access}, title = {Synthetic sentiment cue enhanced graph relation-attention network for aspect-level sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). A coupled multiphysics framework for advanced characterization of PWM-driven induction motors. ACCESS, 13, 88105-88120. (www)

  @article{ , abstract = {The design of high-performance electrical machines necessitates the intricate integration of multiple physical domains, including electromagnetic, mechanical, and thermal aspects. To meet the ever-evolving demands of the field, there is a pressing need for a platform capable of simultaneously analyzing these interconnected phenomena with both precision and efficiency, all within a practical pre-manufacturing timeframe. This paper introduces a semi-analytical multiphysics assessment framework tailored for inverter-fed traction induction machines (IMs). By integrating three core physical domains, the framework leverages an electromagnetic model to evaluate rotor and stator currents, traction characteristics, and radial air-gap flux density. Vibroacoustic models are employed to predict noise and vibration induced by electromagnetic forces, while a three-dimensional (3D) nodal network thermal model captures transient temperature distributions across motor components. These highly efficient models are seamlessly integrated into a unified framework, allowing for a thorough and precise analysis of any IM in an efficient and timely manner. The framework is also designed for easy integration with optimization tools, enhancing its applicability for performance and design optimization. The developed scheme is examined on an enclosed IM prototype and is verified by comprehensive finite element analyses and experimental testing. The findings introduce a novel approach that integrates advanced computational tools with traditional design methods to assess the multiphysics performance of IMs across their entire performance spectrum. The developed method holds applicability across various applications with implications for other machine types.}, archive = {J_ACCESS}, author = {Omolbanin Taqavi and Pengzhao Song and Alexandre J. Bourgault and Ze Li and Glenn Byczynski and Narayan C. Kar}, doi = {10.1109/ACCESS.2025.3570829}, journal = {IEEE Access}, pages = {88105-88120}, shortjournal = {IEEE Access}, title = {A coupled multiphysics framework for advanced characterization of PWM-driven induction motors}, volume = {13}, year = {2025}, }
• (2025). Leveraging edge intelligence for solar energy management in smart grids. ACCESS, 13, 88093-88104. (www)

  @article{ , abstract = {The rapid advancement of renewable energy, particularly solar power, has been significantly supported by the Internet of Things (IoT) infrastructure, enabling optimized energy management in smart grid systems. However, accurate long-term solar energy forecasting remains a critical challenge, especially exploiting specific characteristics of data locality at the edge. This paper introduces an edge intelligence-driven hybrid deep learning model that integrates Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) for precise solar energy prediction. The proposed model leveraged TCN’s capability to capture long-range temporal dependencies and GRU’s efficiency in sequential data processing. To optimize performance for edge computing environments, we employed Neural Architecture Search, model pruning, and quantization techniques, reducing both model size and complexity. The model has been validated using two benchmark datasets (Alibaba Competition and GEFCom), achieving an impressive mean squared error of 0.0051 on long-term forecasts. The results demonstrate that our approach ensures high forecasting accuracy while maintaining computational efficiency, making it well-suited for real-time, IoT-enabled solar energy applications in smart grids.}, archive = {J_ACCESS}, author = {Trong-Minh Hoang and Tuan-Anh Pham and van-Nhan Nguyen and Duc-Thang Doan and Nhu-Ngoc Dao}, doi = {10.1109/ACCESS.2025.3570595}, journal = {IEEE Access}, pages = {88093-88104}, shortjournal = {IEEE Access}, title = {Leveraging edge intelligence for solar energy management in smart grids}, volume = {13}, year = {2025}, }
• (2025). Development of a holographic directional volumetric display. ACCESS, 13, 88078-88092. (www)

  @article{ , abstract = {In previous studies, we introduced a directional volumetric display which can project in multiple directions by superimposing different two-dimensional images in three-dimensional space. However, thread-type displays exhibit focus and pixel size issues due to projector limitations. Therefore, we employ an electronic holography for image projection in this study. An electronic holography can project a focused image at all positions using interference and diffraction of light. This characteristic of electronic holography can solve the problems of previous studies, such as misalignment of focus and pixel size due to differences in depth. We confirmed clear directional images from the front and side views by combining an electronic holography with a directional volumetric display.}, archive = {J_ACCESS}, author = {Renta Noguchi and Tomoya Isayama and Takashi Nishitsuji and Hirotaka Nakayama and Tomoyoshi Shimobaba and Tomoyoshi Ito and Atsushi Shiraki}, doi = {10.1109/ACCESS.2025.3571063}, journal = {IEEE Access}, pages = {88078-88092}, shortjournal = {IEEE Access}, title = {Development of a holographic directional volumetric display}, volume = {13}, year = {2025}, }
• (2025). Research on the impact and path of human-machine collaborative decision-making on dualistic innovations. ACCESS, 13, 88065-88077. (www)

  @article{ , abstract = {Intelligent and virtualized transformation of the enterprise requires employees to undertake both technological breakthroughs and in-depth development, two innovative activities that seem to compete for resources. How employees, guided by algorithmically integrated decision-making, utilize massive information and technology to catalyze creativity in this path is not yet known. Based on research data from 198 corporate innovators, the enabling mechanisms of human-machine collaborative decision-making for dualistic innovations are examined. The study finds that: human-machine collaborative decision-making positively promotes both exploratory innovations and exploitative innovations; human-machine collaborative decision-making stimulates both types of dualistic innovations by enhancing the level of human-machine trust among employees; and corporate innovation culture positively moderates the direct effect of human-machine collaborative decision-making on dualistic innovations. The findings broaden the application of human-machine cooperation theory in the field of management and provide new ideas for enterprises to accurately identify employees’ attitudes and tendencies towards human-machine cooperation and formulate targeted strategies to stimulate dualistic innovations.}, archive = {J_ACCESS}, author = {Bin Miao and Jiage Zhang and Jiaxing Zhang and Lu Li and Yitong Hou}, doi = {10.1109/ACCESS.2025.3567343}, journal = {IEEE Access}, pages = {88065-88077}, shortjournal = {IEEE Access}, title = {Research on the impact and path of human-machine collaborative decision-making on dualistic innovations}, volume = {13}, year = {2025}, }
• (2025). Fingerprint matching for noisy and distorted patterns using a siamese network with ResNet50 and multihead attention. ACCESS, 13, 88047-88064. (www)

  @article{ , abstract = {Dermatoglyphics, the study of unique ridge patterns on fingertips, plays a crucial role in fingerprint-based identification. However, skin conditions such as psoriasis, eczema, and verruca vulgaris can distort these patterns. This distortion creates challenges for fingerprint matching systems in forensic science and biometric authentication. This paper proposes a Siamese network with a modified ResNet50 architecture and multihead attention mechanisms to improve fingerprint matching under such distortions. The Siamese network enables the system to compare fingerprint pairs effectively, even under distortions. The modified ResNet50 architecture captures intricate ridge patterns, while the multihead attention mechanisms focus on critical fingerprint regions. Overall, the proposed approach enhances the system’s ability to learn discriminative features. As a result, it can effectively differentiate between matched and unmatched fingerprint pairs, even in the presence of moderate to extreme noise. The proposed system was trained on the Sokoto coventry fingerprint dataset (SOCOFing) and a custom dataset. Experimental results demonstrate high accuracy, with the system achieving 99.47% accuracy under low noise conditions and 90% under moderate noise. However, performance declined to 55.56% under extreme distortions. A comparative analysis highlighted the superiority of the proposed system over widely used minutiae-based methods. The latter exhibited a significant drop in matching scores, falling below 15% for highly distorted fingerprints. These findings underscore the limitations of traditional fingerprint recognition techniques and highlight the effectiveness of proposed approach in handling dermatoglyphic distortions.}, archive = {J_ACCESS}, author = {Tinny Sawhney and Ashu Sharma and Pawanesh Abrol and Parveen Kumar Lehana and Chaahat and Vaishali Yadav and Rafał scherer and Oleksii Kulakov and Ahmad Ali Alzubi}, doi = {10.1109/ACCESS.2025.3567328}, journal = {IEEE Access}, pages = {88047-88064}, shortjournal = {IEEE Access}, title = {Fingerprint matching for noisy and distorted patterns using a siamese network with ResNet50 and multihead attention}, volume = {13}, year = {2025}, }
• (2025). ARMC-RL: Adaptive caching with reinforcement learning for efficient 360° video streaming in edge networks. ACCESS, 13, 88030-88046. (www)

  @article{ , abstract = {The growing demand for high-capacity content such as 3D and 360° videos highlights the need for efficient data delivery in B5G/6G networks. Multi-access edge computing (MEC) has emerged as a promising solution, but its limited memory capacity makes cache replacement strategies essential. To address this, we propose an adaptive recency masking caching (ARMC) algorithm for 360° video streaming in MEC environments. The proposed caching mechanism efficiently replaces cached data by combining two techniques: recency masking and Frequency-Filtered Least Recently Used. The use of the techniques is determined by the importance of the cache data and the available cache capacity. In addition, we introduce the concept of an observation window to improve cache performance by reflecting the recency of data request patterns. We conducted experiments using a field of view (FoV) dataset recorded from real users watching videos via head-mounted displays. Given the characteristics of 360° videos, we assumed that the MEC cache would require high-quality tiles matching the user’s FoV at each moment. Through experiments, we confirmed that the proposed method achieved a higher cache hit rate compared to existing cache replacement techniques. In particular, ARMC improved the hit rate by up to 29% compared to Least Frequency Used algorithm under constrained cache conditions of 6% or less of the total data. Higher cache hit rates contribute to reducing transmission latency and lowering bandwidth consumption. Meanwhile, the size of the observation window, a key variable in the proposed technique, varied in terms of its optimal size depending on the cache size and user viewing patterns. To address this issue, we proposed ARMC-RL, a variant of ARMC with reinforcement learning (RL) assistance, which is designed to dynamically estimate the optimal observation window size according to the given environment. Based on the experiments, ARMC-RL, depending on the learning model, achieves cache hit rates that are similar to or higher than those of ARMC and converges to the optimal observation window size by the second episode. This result confirms that ARMC-RL enables the stable automation of ARMC. Ultimately, ARMC-RL can be effectively applied to MEC-based caching systems, enabling adaptive cache optimization in response to dynamic user demand and network conditions.}, archive = {J_ACCESS}, author = {Minji Choi and Somin Park and Jin-Hyun Ahn and Dong Ho Kim and Cheolwoo You}, doi = {10.1109/ACCESS.2025.3571104}, journal = {IEEE Access}, pages = {88030-88046}, shortjournal = {IEEE Access}, title = {ARMC-RL: Adaptive caching with reinforcement learning for efficient 360° video streaming in edge networks}, volume = {13}, year = {2025}, }
• (2025). Leveraging a vision transformer and adversarial unsupervised domain adaptation to generate the NIR band on out-of-domain VHR RGB. ACCESS, 13, 88012-88029. (www)

  @article{ , abstract = {The accessibility of very-high-resolution Remote Sensing (RS) near-infrared (NIR) imagery before the 2010s is limited. In addition, public annotated image datasets often used as benchmarks contain only RGB information. Thus, RGB-to-NIR translation could greatly benefit the RS field. Generative adversarial networks (GANs) and, more recently, vision transformers (ViTs) have shown promising results in generating spectrally super-resolved data. However, the RGB-to-hyperspectral studies scatter their attention to multiple bands, whereas the RGB-to-NIR studies have tackled only vegetation. In addition, prior research regarding NIR prediction on out-of-domain data is extremely limited, and attention models have not been previously tested in the RGB-to-NIR translation. Beyond that, unsupervised domain adaptation (UDA) has been overlooked in the enhancement of cross-domain band generation. This study, at first, evaluates a conditional GAN and two attention networks on predicting NIR on out-of-domain RGB data. The properties of the out-of-domain data are those typically required in RS NIR prediction tasks (different regions/sensors/dates). In a following step, the study attempts to increase the source/target RGB radiometric similarity through CycleGAN-based UDA on unpaired bi-temporal data (lack of geographic correspondence in RGB source/target patches), and thus improve the initial NIR prediction. In the experiments, the vegetation, impervious, and ground classes were assessed. It was shown that MST++ (ViT) produced the most satisfactory out-of-domain NIR predictions and that UDA through a CycleGAN version employing batch normalization managed to significantly enhance the NIR prediction when there was a substantial RGB radiometric domain gap.}, archive = {J_ACCESS}, author = {Viktoria Kristollari and Vassilia Karathanassi}, doi = {10.1109/ACCESS.2025.3570629}, journal = {IEEE Access}, pages = {88012-88029}, shortjournal = {IEEE Access}, title = {Leveraging a vision transformer and adversarial unsupervised domain adaptation to generate the NIR band on out-of-domain VHR RGB}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal frequency distribution analysis in systems with grid-forming and grid-following inverters: A new perspective from frequency domain. ACCESS, 13, 87999-88011. (www)

  @article{ , abstract = {The high penetration of inverter-based resources (IBRs) in power systems has brought challenges to system frequency stability due to reduced inertia. The conventional method for frequency stability assessment uses the center-of-inertia (CoI) frequency as a single indicator to represent the frequency throughout the entire system, which overlooks the spatio-temporal distribution of frequencies among multiple nodes during transients. The frequency distribution is further complicated by the differences in frequency regulation and synchronization of synchronous generators (SGs) and IBRs, either grid-forming (GFM) or grid-following (GFL). To address this challenge, this paper develops a whole-system frequency response matrix in the frequency domain which accommodates the frequency regulation and synchronization dynamics of all apparatuses in a unified framework. Based on this matrix, two frequency-domain metrics, namely spatio-temporal inertia and damping, are introduced to illustrate the frequency distribution characteristics. The spatial distribution is evaluated by using the matrix entries and the temporal distribution is evaluated by using the frequency spectra. The proposed model and metrics are validated via time-domain simulations on two test systems: a modified 4-machine 2-area system and a modified 5-area 68-bus system. The contributions of SGs and IBRs (GFM and GFL) to frequency regulation are examined by comparative analysis using the proposed metrics.}, archive = {J_ACCESS}, author = {Zhen Wang and Yu Shan and Yue Zhu and Ruixu Liu and Yunjie Gu}, doi = {10.1109/ACCESS.2025.3571481}, journal = {IEEE Access}, pages = {87999-88011}, shortjournal = {IEEE Access}, title = {Spatio-temporal frequency distribution analysis in systems with grid-forming and grid-following inverters: A new perspective from frequency domain}, volume = {13}, year = {2025}, }
• (2025). ICNN-LSTM+: A batch-based incremental ransomware detection system using sysmon. ACCESS, 13, 87978-87998. (www)

  @article{ , abstract = {Ransomware remains a persistent and evolving cyber threat, requiring adaptive and efficient detection mechanisms. This study presents a novel CNN-LSTM-based detection system that leverages Sysmon logs for real-time analysis on Windows endpoints. Through the integration of batch-based incremental learning, the model achieves continuous adaptation to previously unseen ransomware variants, eliminating the necessity for full retraining and effectively addressing the constraints associated with conventional static detection methodologies. The proposed framework achieves an average F2-score of 99.65%, with false positive and false negative rates of 0.16% and 3.96%, respectively, even in a highly imbalanced dataset. To comprehensively evaluate its performance, we introduce the Weighted Efficiency Score (WES), a novel metric that balances detection accuracy and throughput. The iCNN-LSTM+ model attains the highest WES among CNN-LSTM architectures, demonstrating its efficiency in real-world applications. Furthermore, parallel LSTM processing with attention mechanisms enhances throughput, making the system scalable for large-scale deployments. These findings establish the iCNN-LSTM+ framework as a resilient and adaptable solution for real-time ransomware detection, effectively mitigating the risks posed by emerging threats.}, archive = {J_ACCESS}, author = {Jamil Ispahany and Md Rafiqul Islam and M. Arif Khan and Md Zahidul Islam}, doi = {10.1109/ACCESS.2025.3569635}, journal = {IEEE Access}, pages = {87978-87998}, shortjournal = {IEEE Access}, title = {ICNN-LSTM+: A batch-based incremental ransomware detection system using sysmon}, volume = {13}, year = {2025}, }
• (2025). CAMP-GNN: A constraint-aware message-passing model for optimal resource allocation in software projects. ACCESS, 13, 87965-87977. (www)

  @article{ , abstract = {Resource allocation in software projects is a critical challenge, with inefficiencies in handling dynamic constraints such as skill mismatches, evolving task dependencies, and budget overruns leading to widespread cost and schedule overruns. To address these issues, this paper introduces CAMP-GNN, a novel constraint-aware graph neural network (GNN) framework designed to optimize resource allocation in software projects, addressing challenges like skill mismatches, evolving task dependencies, and budget overruns. These issues often lead to significant cost and schedule overruns, and CAMP-GNN aims to resolve them by dynamically adapting to project constraints. The framework integrates urgency, skill alignment, and budget slack directly into its message-passing layers, allowing for more precise and context-aware task-resource assignments. A key contribution is SoftStat, the first structured dataset that combines resource pools, allocations, and project details, enabling the modeling of complex task-resource dependencies and financial/temporal constraints. CAMP-GNN also introduces domain-specific message aggregation rules to unify urgency, skill alignment, and budget slack, advancing GNNs beyond basic relational modeling. A novel preprocessing pipeline is proposed to convert raw project timelines, skill dependencies, and budget thresholds into machine-learnable features, ensuring effective integration with computational models. Validated on both synthetic and real-world datasets, CAMP-GNN demonstrates superior performance with a 0.12 MAE, RMSE of 0.18 in allocation, F1 of 0.89 in budget violations, and 085 in deadline violations, 18% cost savings, and a 27% reduction in deadline breaches, outperforming existing methods. Future work will focus on scaling the framework for large-scale projects and improving interpretability for broader adoption.}, archive = {J_ACCESS}, author = {Muhammad Asif and Fahd M. Aldosari and Donia Y. Badawood and Abdu Salam}, doi = {10.1109/ACCESS.2025.3571048}, journal = {IEEE Access}, pages = {87965-87977}, shortjournal = {IEEE Access}, title = {CAMP-GNN: A constraint-aware message-passing model for optimal resource allocation in software projects}, volume = {13}, year = {2025}, }
• (2025). A web-based interface that leverages machine learning to assess an individual’s vulnerability to brain stroke. ACCESS, 13, 87950-87964. (www)

  @article{ , abstract = {Cerebral stroke is a major global health issue, contributing to high mortality and long-term disability. Early identification of individuals at high risk of stroke can significantly improve preventive care outcomes. We present a web-based stroke risk assessment tool that uniquely combines an accessible user interface with robust machine learning modeling. The proposed platform leverages a novel combination of SMOTE oversampling and logistic regression to address class imbalance in patient health records, improving the detection of stroke risk factors over existing methods. We compare a range of algorithms-including traditional classifiers and deep learning models-and report comprehensive performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) for each. Our best model (logistic regression with SMOTE and standard scaling) achieves 93.2% accuracy with a substantially higher F1-score for the stroke-positive class than other models, indicating improved sensitivity to stroke cases. To bridge the gap between complex predictive models and end-users, we deploy this model in an intuitive web interface (built with Streamlit) that non-technical individuals and healthcare providers can easily use. This interface requires no specialized knowledge, preserves user privacy by avoiding any data storage, and provides clear explanations of results. By offering a practical and transparent tool for stroke risk screening, our work advances health informatics with an emphasis on accessibility, interpretability, and early intervention. Potential applications range from personal health self-assessment to integration in clinical workflows for preventive care, ultimately aiming to improve public health outcomes through early detection and intervention in stroke.}, archive = {J_ACCESS}, author = {Divyansh Bhandari and Arnav Agarwal and R. Reena Roy and Rajaram Priyatharshini and Rodriguez Rivero Cristian}, doi = {10.1109/ACCESS.2025.3566093}, journal = {IEEE Access}, pages = {87950-87964}, shortjournal = {IEEE Access}, title = {A web-based interface that leverages machine learning to assess an individual’s vulnerability to brain stroke}, volume = {13}, year = {2025}, }
• (2025). Pattern-based assessment of the association of fetal heart variability with fetal development and maternal heart rate variability. ACCESS, 13, 87941-87949. (www)

  @article{ , abstract = {Prenatal assessment of fetal development is primarily carried out by measuring fetal heart rate (HR) and HR variability (HRV). HRV has been used as an index for the autonomic nervous system (ANS) activity. Hence, fetal ANS (fANS) development has been assessed in previous literature by evaluating fetal HRV (fHRV) at different gestational ages (GAs). Previously, it was demonstrated that fHRV was affected by factors such as fetal behavioral states and maternal conditions. Therefore, reliable evaluation of fANS development based on fHRV depends on the appropriate assessment of fHRV. In this study, we develop an HRV parameter based on maternal and fetal HRV to assess fHRV. The parameter was calculated by using 5-minute segments of non-invasive maternal and fetal electrocardiogram (ECG) records (158 subjects (GA: 19 - 40 weeks)). The parameter was developed by first counting the number of R peaks per 2 seconds (nRpp2s) and then calculating the standard deviation (SD) of nRpp2s (snRpp2s). After that, the HRV parameter was obtained by calculating the difference between maternal and fetal snRpp2s (dmf). The dmf was used to divide data into three patterns to evaluate the association of fHRV with GA and maternal HRV (mHRV) per pattern. The results showed discrepancies in the degree of association of fHRV with GA, implying that fHRV is not always an indicator of fANS development. These differences are expected due to maternal-related influences, as maternal HRV (mHRV) shows a pattern-specific association with fetal HRV (fHRV), that is, the two are correlated in some patterns but not in others. In addition, a comparison analysis of fHRV among the three patterns revealed that the discrepancies concerning fHRV could also be related to fetal behavioral states. The findings of this study provide a novel approach to assessing fHRV and fetal development relative to mHRV.}, archive = {J_ACCESS}, author = {Namareq Widatalla and Mohanad Alkhodari and Kunihiro Koide and Chihiro Yoshida and Yoshiyuki Kasahara and Masatoshi Saito and Yoshitaka Kimura and Ahsan Khandoker}, doi = {10.1109/ACCESS.2025.3570326}, journal = {IEEE Access}, pages = {87941-87949}, shortjournal = {IEEE Access}, title = {Pattern-based assessment of the association of fetal heart variability with fetal development and maternal heart rate variability}, volume = {13}, year = {2025}, }
• (2025). From LLMs to randomness: Analyzing program input efficacy with resource and language metrics. ACCESS, 13, 87928-87940. (www)

  @article{ , abstract = {Security-focused program testing typically focuses on crash detection and code coverage while overlooking additional system behaviors that can impact program confidentiality and availability. To address this gap, we propose a statistical framework that combines embedding-based anomaly detection, resource usage metrics, and resource-state distance measures to systematically profile software behaviors beyond traditional coverage-based methods. Leveraging over 5 million labeled samples from 50 Python programs, we evaluate how these independent scoring terms distinguish among different sources of input, including Large Language Model (LLM)-generated inputs, and demonstrate how standard statistical tests (e.g., Kolmogorov—Smirnov and Kendall’s $\tau $ ) confirm their effectiveness. Our findings show that LLM-generated samples can trigger diverse behaviors but are often less effective at exploring resource usage dynamics (CPU, memory) compared with conventional fuzzing. However, combining LLM outputs with existing techniques broadens behavior coverage and reveals commonalities between commercial LLM outputs. We provide open-source tools for this evaluation framework, demonstrating the potential to refine software testing by integrating behavior metrics into security-testing workflows.}, archive = {J_ACCESS}, author = {Gavin Black and Eric Yocam and Varghese Mathew Vaidyan and Gurcan Comert and Yong Wang}, doi = {10.1109/ACCESS.2025.3571205}, journal = {IEEE Access}, pages = {87928-87940}, shortjournal = {IEEE Access}, title = {From LLMs to randomness: Analyzing program input efficacy with resource and language metrics}, volume = {13}, year = {2025}, }
• (2025). Less is more: The influence of pruning on the explainability of CNNs. ACCESS, 13, 87909-87927. (www)

  @article{ , abstract = {Over the last century, deep learning models have become the state-of-the-art for solving complex computer vision problems. These modern computer vision models have millions of parameters, which presents two major challenges: 1) the increased computational requirements hamper the deployment in resource-constrained environments, such as mobile or IoT devices, and 2) explaining the complex decisions of such networks to humans is challenging. Network pruning is a technical approach to reduce the complexity of models, where less important parameters are removed. The work presented in this paper investigates whether this reduction in technical complexity also helps with perceived explainability. To do so, we conducted a pre-study and two human-grounded experiments, assessing the effects of different pruning ratios on explainability. Overall, we evaluate four different compression rates (i.e., 2, 4, 8, and 32) with 37 500 tasks on Mechanical Turk. Results indicate that lower compression rates have a positive influence on explainability, while higher compression rates show negative effects. Furthermore, we were able to identify sweet spots that increase both the perceived explainability and the model’s performance.}, archive = {J_ACCESS}, author = {Florian Merkle and David Weber and Pascal Schöttle and Stephan Schlögl and Martin Nocker}, doi = {10.1109/ACCESS.2025.3569575}, journal = {IEEE Access}, pages = {87909-87927}, shortjournal = {IEEE Access}, title = {Less is more: The influence of pruning on the explainability of CNNs}, volume = {13}, year = {2025}, }
• (2025). Dynamic threshold-based resource management for fog computing environments. ACCESS, 13, 87898-87908. (www)

  @article{ , abstract = {Fog computing extends cloud services to the network edge, thereby reducing latency and bandwidth usage for time-sensitive applications. However, the limited computational capacity, memory, and bandwidth of fog nodes present significant challenges for efficient resource management. This paper proposes a Dynamic Threshold-based Resource Management (DTRM) strategy that dynamically adjusts resource allocation thresholds to prioritize real-time (RT) requests while minimizing the redirection of non-real-time (NRT) requests to cloud servers. To further support balanced workload distribution, a Quota-Based Round-Robin (QRR) scheduler is introduced, ensuring fairness and low computational overhead across fog nodes. Extensive experimental evaluations demonstrate that DTRM significantly reduces the resource redirection count compared to Static Threshold (ST), Deficit Round Robin (DRR), and Best-Effort (BE) schemes. Moreover, DTRM improves load balancing and resource usage, offering a scalable and adaptive solution for dynamic and bursty request patterns in fog computing environments. These results highlight the potential of DTRM to enhance the overall performance, responsiveness, and efficiency of fog computing, particularly in the context of modern IoT applications.}, archive = {J_ACCESS}, author = {Jui-Pin Yang}, doi = {10.1109/ACCESS.2025.3571394}, journal = {IEEE Access}, pages = {87898-87908}, shortjournal = {IEEE Access}, title = {Dynamic threshold-based resource management for fog computing environments}, volume = {13}, year = {2025}, }
• (2025). Artificial noise physical layer security for V2X joint radar communication systems-based OTFS waveform. ACCESS, 13, 87884-87897. (www)

  @article{ , abstract = {Joint radar communication (JRC) effectively integrates radar capabilities and data transmission that are used in vehicle-to-everything (V2X) applications. Within the JRC, the transmission suffers from a highly dispersive, time-varying channel due to the mobility environment, while the transmission requires a secure method with low latency and time complexity. The confidential information signal must be resilient to radar user eavesdropping while enabling the radar user to get the correct channel radar estimation image. The main contribution of this paper is to provide security for the JRC system used in V2X application networks by employing Artificial Noise (AN) Physical Layer Security (PLS) with low time complexity constraints, which integrates with the Orthogonal Time Frequency Space (OTFS) waveform that offers robustness against time-varying channel degradation considering the 3GPP TS 36 Extended Vehicular A channel model (EVA). Moreover, the practical implementation considers imperfect channel state information and involves a powerful eavesdropper who can estimate legitimate user channels with a certain amount of error. The secrecy capacity, average bit-error rate (BER), time complexity, and execution time results present the performance of the proposed AN scheme and its ability to protect legitimate users’ information in a promising manner while providing the radar user with sufficient quality of service (QoS), where the proposed approach provides a secrecy capacity level up to 7 nats/symbol with an execution time that is 42 times faster than the Advanced Encryption Standard (AES) and 6.4 thousand times better than Rivest-Shamir-Adleman (RSA) cryptographic algorithms.}, archive = {J_ACCESS}, author = {Abdelrahman Abushattal and Ayhan Yazgan}, doi = {10.1109/ACCESS.2025.3571491}, journal = {IEEE Access}, pages = {87884-87897}, shortjournal = {IEEE Access}, title = {Artificial noise physical layer security for V2X joint radar communication systems-based OTFS waveform}, volume = {13}, year = {2025}, }
• (2025). High-performance number theoretic transform on GPU through radix2-CT and 4-step algorithms. ACCESS, 13, 87862-87883. (www)

  @article{ , abstract = {The number theoretic transform (NTT) provides a practical and efficient technique to perform multiplication of very large degree polynomials typically found in fully homomorphic encryption (FHE), lattice-based cryptography, and non-interactive succinct zero-knowledge proof systems such as zk-SNARK. In this paper, we focus on this aspect and present two robust algorithms for efficient NTT using readily available GPU cards as hardware accelerators. These algorithms are based on the radix-2 Cooley-Tukey (CT) and 4-Step techniques, which are rooted in classical FFT research. To this end, our algorithms leverage novel strategy to optimize memory access patterns adaptive to input size, which often is very large. Our approach: 1) reduces and optimizes the number of accesses required for global memory for thread synchronization on the GPU device, and 2) systematically improves and enhances the use of spatial locality. We achieve this effect by carefully controlling parameters such as the number of kernels, thread block size and shape, and thread layout, which directly impact overall NTT performance. The proposed optimizations enable our NTT implementation to handle very large polynomial sizes up to $2^{28}$ , which are usually a limiting factor in existing approaches, and achieve remarkable performance. To the best of our knowledge, our proposed technique is unique and provides a recipe for selecting suitable configurable parameter combinations to achieve top performance for a given polynomial degree. Furthermore, we perform thorough experiments and empirically assess the performance of our proposed algorithms on three mainstream commercial GPU cards by NVIDIA. Finally, we demonstrate that our algorithms compare favorably and outperform an existing commercial-grade open-source implementation in this arena.}, archive = {J_ACCESS}, author = {Alisah Ozcan and Arsalan Javeed and Erkay Savas}, doi = {10.1109/ACCESS.2025.3570024}, journal = {IEEE Access}, pages = {87862-87883}, shortjournal = {IEEE Access}, title = {High-performance number theoretic transform on GPU through radix2-CT and 4-step algorithms}, volume = {13}, year = {2025}, }
• (2025). A package level regression test selection approach for java software systems. ACCESS, 13, 87848-87861. (www)

  @article{ , abstract = {Regression testing of software systems is an important and critical activity yet expensive and resource-intensive. An approach to enhance its efficiency is Regression Test Selection (RTS), which selectively re-executes a subset of relevant tests that are impacted by code modifications. Previous studies on static and dynamic RTS for Java software have shown that selecting tests at the class level is more effective than using finer granularities like methods or statements. Nevertheless, RTS at the package level, which is a coarser granularity than class level, has not been thoroughly investigated or evaluated for Java projects. To address this gap, we propose PKRTS, a static package-level RTS approach that utilizes the structural dependencies of the software system under test to construct a package-level dependency graph. PKRTS analyzes dependencies in the graph and identifies relevant tests that can reach modified packages, i.e., packages containing altered classes. In contrast to conventional static RTS techniques, PKRTS implicitly considers dynamic dependencies, such as Java reflection and virtual method calls, among classes belonging to the same package by treating all those classes as a single cohesive node in the dependency graph. We evaluated PKRTS on 885 revisions of 9 open-source Java projects, with its performance compared to Ekstazi, a state-of-the-art dynamic class-level approach, and STARTS, a state-of-the-art static class-level approach. We used Ekstazi as the baseline to measure the safety and precision violations of PKRTS and STARTS. The results indicated that PKRTS outperformed static class-level RTS in terms of safety violation, which measures the extent to which relevant test cases are missed. PKRTS showed an average safety violation of 2.29% compared to 5.94% safety violation of STARTS. Despite this, PKRTS demonstrated lower precision violation and lower reduction in test suite size than class-level RTS, as it selects higher number of irrelevant test cases. Nevertheless, PKRTS achieved a smaller static dependency graph compared to static class-level RTS, leading to faster graph construction and analysis. In terms of overall execution time for the selected tests across all 885 revisions, PKRTS reduced the test execution time by achieving a total time of 8,757 seconds compared to 10,481 seconds for the retest-all strategy, i.e., executing all test cases.}, archive = {J_ACCESS}, author = {Mohammed Al-Refai and Mahmoud M. Hammad}, doi = {10.1109/ACCESS.2025.3570007}, journal = {IEEE Access}, pages = {87848-87861}, shortjournal = {IEEE Access}, title = {A package level regression test selection approach for java software systems}, volume = {13}, year = {2025}, }
• (2025). A novel polynomial activation for audio classification using homomorphic encryption. ACCESS, 13, 87834-87847. (www)

  @article{ , abstract = {With the increasing popularity of audio recording devices and recognition technology, audio data is increasingly used in speech recognition, event detection, and biometric authentication. Audio data often contains sensitive information, raising privacy concerns when using artificial intelligence on cloud platforms. Homomorphic encryption (HE) addresses this problem by allowing direct computation on encrypted data without decryption. However, HE has high computational costs, especially for deep learning models, which require many nonlinear operations, such as activation functions. Traditional HE methods have difficulty handling these nonlinear operations, affecting performance and accuracy. To overcome this limitation, we propose optimized polynomial-degree activation functions that enhance the compatibility of HE with deep learning models while maintaining high performance. Experimental results on musical instrument and audioMNIST datasets confirm the effectiveness of our method, highlighting its promising potential for secure audio data processing in various applications.}, archive = {J_ACCESS}, author = {Linh Nguyen and Quoc Bao Phan and Lan Zhang and Tuy Tan Nguyen}, doi = {10.1109/ACCESS.2025.3571016}, journal = {IEEE Access}, pages = {87834-87847}, shortjournal = {IEEE Access}, title = {A novel polynomial activation for audio classification using homomorphic encryption}, volume = {13}, year = {2025}, }
• (2025). Deploying agrivoltaics in sub-saharan Africa—A sustainable pathway toward energy-food security-challenges and opportunities: A review. ACCESS, 13, 87810-87833. (www)

  @article{ , abstract = {Agrivoltaics (APV) offer an innovative solution to the pressing energy and food security challenges in Sub-Saharan Africa (SSA). Over 600 million people in this region lack access to energy, and food insecurity remains pervasive. By combining photovoltaic (PV) systems with agricultural production, APV optimizes land-use efficiency, enabling concurrent renewable energy generation and enhancing agricultural productivity. This review critically evaluates the potential of APV systems to address these dual challenges, focusing on SSA’s unique socioeconomic and environmental contexts. Key findings highlight the APV’s ability to mitigate agricultural constraints, such as water scarcity and climate variability, while providing decentralized energy solutions to rural communities. The analysis emphasizes strategic APV design considerations, including panel height, spacing, and crop compatibility, which are essential for optimizing energy and crop yields. However, challenges such as high initial costs, limited technical capacity, and sociocultural acceptance pose significant barriers to its widespread adoption. This review discusses policy recommendations for addressing these barriers, including financial incentives, technology transfer frameworks, and stakeholder engagement strategies. The novelty of this work lies in its tailored approach to SSA, integrating evidence from global APV case studies and proposing localized implementation strategies that align with the region’s development priorities. This study advances the understanding of APV as a pathway to achieving Sustainable Development Goals (SDGs) in SSA by offering a dual-purpose model for energy access and food security. Overcoming adoption barriers through innovative governance, research, and community engagement will be pivotal to unlocking the transformative potential of APV in SSA’s energy-food landscape.}, archive = {J_ACCESS}, author = {Suleiman Ibrahim Abubakar and Chan Hwang See and Firdaus Muhammad Sukki and Ramasamy Mahendiran and Senthilarasu Sundaram and Mohamad Izani Zainal Abidin and Mohd Khairul Amri Kamarudin}, doi = {10.1109/ACCESS.2025.3568717}, journal = {IEEE Access}, pages = {87810-87833}, shortjournal = {IEEE Access}, title = {Deploying agrivoltaics in sub-saharan Africa—A sustainable pathway toward energy-food security-challenges and opportunities: A review}, volume = {13}, year = {2025}, }
• (2025). Toward generating quality test questions and answers using quantized low-rank adapters in LLMs. ACCESS, 13, 87793-87809. (www)

  @article{ , abstract = {Traditional approaches to question-and-answer generation are resource-intensive, necessitating innovative automation techniques to address these challenges. To this end, we propose fine-tuning strategies based on Quantized Low-Rank Adaptation (QLoRA), utilizing a meticulously curated domain-specific dataset. As a case study, we focused on the Korea College Scholastic Ability Test (KCSAT), introducing the KCSAT-ENG dataset, which comprises questions and answers from real and mock tests. Our approach involved fine-tuning the LLaMA-3-8B-Instruct model to generate questions and answers for 22 distinct task types. A key innovation of our work is the deployment of separate models for question-and-answer generation, leveraging a cross-verification process to enhance accuracy. To evaluate the QLoRA technique, we conducted extensive experiments by varying quantization, rank, and alpha values. The results highlighted optimal configurations: the question generation model performed best with $rank, \alpha = 32, 8$ respectively without quantization, while the answer generation model achieved optimal results with $rank, \alpha = 64, 16$ respectively. Compared to a non-fine-tuned LLaMA-3-8B-Instruct model, our question generation model demonstrated a 41.5% improvement, and the answer generation model achieved a 16.1% improvement. These findings underscore the potential of QLoRA-based fine-tuning in creating accurate, cost-effective, and scalable automated educational tools.}, archive = {J_ACCESS}, author = {Jebum Choi and Seongjun Hong and Seoyoon Hong and Jiyeon Park and Eun-Sung Jung}, doi = {10.1109/ACCESS.2025.3570567}, journal = {IEEE Access}, pages = {87793-87809}, shortjournal = {IEEE Access}, title = {Toward generating quality test questions and answers using quantized low-rank adapters in LLMs}, volume = {13}, year = {2025}, }
• (2025). Enhanced reward function design for source term estimation based on deep reinforcement learning. ACCESS, 13, 87777-87792. (www)

  @article{ , abstract = {This study investigates the design of reward functions for deep reinforcement learning-based source term estimation (STE). Estimating the properties of unknown hazardous gas leakage using a mobile sensor, known as STE problems, is challenging due to environmental turbulence and sensor noise. To address this issue, the particle filter is employed to estimate the source term under noisy sensor measurements, and the deep Q-network is used to find the optimal source search policy. In deep reinforcement learning, selecting an appropriate reward function is crucial as it directly impacts the learning performance. Specifically, this paper first reviews existing reward functions based on penalty, distance, concentration, and entropy metrics. To overcome the limitations of existing rewards, we combine their strengths and propose new reward functions such as the Gaussian mixture model (GMM) variance-based reward and the GMM information gain-based reward. To validate the robustness of the proposed approach, simulations are conducted in two types of environments: basic and turbulent, by adjusting the parameters of the noise condition. The simulation results demonstrate that the proposed reward functions outperform existing ones and are particularly robust in noisy environments.}, archive = {J_ACCESS}, author = {Junhee Lee and Hongro Jang and Minkyu Park and Hyondong Oh}, doi = {10.1109/ACCESS.2025.3569827}, journal = {IEEE Access}, pages = {87777-87792}, shortjournal = {IEEE Access}, title = {Enhanced reward function design for source term estimation based on deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). FA-FENet: A feature attention front-end network based on a lightweight CNN architecture for recognizing abnormal underwater illegal fishing behavior. ACCESS, 13, 87753-87776. (www)

  @article{ , abstract = {In the past decade, studies on illegal fishing have neglected to consider illegal underwater fishing. Traditionally, supervisor-based methods have been used to manually interpret underwater behavior; however, existing artificial intelligence (AI) and Internet of Things (IoT) methods are not applicable to illegal underwater fishing. Therefore, in this study, we used a computer vision AI method to perform the challenging task of illegal underwater fishing image recognition. Illegal underwater fishing images have unclear features and high feature similarity. In this study, we define normal underwater travel as wearing diving clothing. Abnormal underwater behavior includes underwater personnel with illegal fishing equipment. Herein, we propose a feature attention front-end network (FA-FENet), a novel end-to-end convolutional neural network (CNN) architecture that differs from previous methods in that it allows flexible integration with various backbone networks. The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. The convolutional layers in our proposed FA-FENet include dilated convolutions, group convolutions, padded convolutions, pooling layers, and activation functions. We also comprehensively review the performance of the proposed FA-FENet. A series of ablation studies demonstrate that the proposed FA-FENet can be applied in shallow backbone networks. In addition, FA-FENet has potential in emerging networks with good performance in illegal underwater fishing identification tasks. Finally, we conducted experiments with an underwater fishing behavior dataset. The experimental results demonstrate that our proposed method outperforms recently reported methods with attention mechanisms. Specifically, FA-FENet-RegNet achieves an accuracy of 88.81%, FA-FENet-ResNet18 reaches 78.32%, and FA-FENet-ShuffleNetV2 attains 80.41%.}, archive = {J_ACCESS}, author = {Xiang-Rui Huang and Liang-Bi Chen}, doi = {10.1109/ACCESS.2025.3571493}, journal = {IEEE Access}, pages = {87753-87776}, shortjournal = {IEEE Access}, title = {FA-FENet: A feature attention front-end network based on a lightweight CNN architecture for recognizing abnormal underwater illegal fishing behavior}, volume = {13}, year = {2025}, }
• (2025). Emotion-based music recommendation system integrating facial expression recognition and lyrics sentiment analysis. ACCESS, 13, 87740-87752. (www)

  @article{ , abstract = {Facial Expression Recognition (FER) has created widespread interest due to its potential uses in personalized technology and mental health, notably in systems that recommend music based on emotion. These systems can improve adaptable user interfaces and support music therapy. While prior research has explored algorithms for FER and their effectiveness in identifying emotions, existing solutions often lack optimal accuracy in pairing emotion recognition with music recommendations, particularly in real-world contexts with diverse user preferences. This study introduces a robust Convolutional Neural Network (CNN)-based model specifically designed for accurate FER, complemented by an innovative music recommendation system. The model was trained and evaluated using the publicly available FER-2013 dataset, which comprises 35,887 labelled facial images, along with a curated music dataset of 1,000 songs annotated with emotions. This approach incorporates a distinctive emotion-based song classification pipeline that utilizes a pre-built text emotion classification model to categorize song lyrics into seven emotional categories: anger, fear, disgust, neutral, surprise, sadness, and happiness. Experimental results demonstrate that our model achieved a FER accuracy of 91.78% and various methodologies discussed for emotion-based music categorization, significantly outperforming baseline methods. These findings offer a scalable framework for personalized and emotionally resonant music experiences, representing a viable solution for applications in tailored entertainment and music therapy.}, archive = {J_ACCESS}, author = {V. S. G. S. Phaneendra Bottu and K. Ragavan}, doi = {10.1109/ACCESS.2025.3570011}, journal = {IEEE Access}, pages = {87740-87752}, shortjournal = {IEEE Access}, title = {Emotion-based music recommendation system integrating facial expression recognition and lyrics sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). A decision-making model for autonomous vehicles considering pedestrian’s time pressure based on game theory and reinforcement learning. ACCESS, 13, 87730-87739. (www)

  @article{ , abstract = {Due to the obvious randomness, pedestrian crossing behavior is hard to predict, which challenges the decision-making of autonomous vehicles (AVs). Recent solutions have been able to adapt to structured road scenes with crossing signals or markings. However, there is still a gap in extending the pedestrian-vehicle interaction (PVI) performance in structured road scenes to unstructured road scenes. Therefore, this paper proposed a vehicle decision-making model considering pedestrian intention based on game theory and reinforcement learning (RL). We designed and conducted a simulation experiment based on a virtual reality platform. Then, leveraging game theory, we established a pedestrian crossing decision-making model considering pedestrian heterogeneity evoked by time pressure (TP). A reward function was developed to enhance driving performance by combining safety, efficiency, and comfort. The RL agent of AVs learns to control the vehicle speed in a pattern that maximizes cumulative rewards through trials and errors by interacting with pedestrians in the simulation environment. The results show that AVs can effectively and safely interact with heterogeneous pedestrians on unstructured roads based on the proposed model. This study contributes to developing AVs that interact better with pedestrians and improve traffic safety, efficiency, and user acceptance of autonomous vehicles.}, archive = {J_ACCESS}, author = {Qing-Feng Lin and Heng-Yu Xue and Yang Lyu and Qing-Kun Li and Ju-Shang Ou}, doi = {10.1109/ACCESS.2025.3566736}, journal = {IEEE Access}, pages = {87730-87739}, shortjournal = {IEEE Access}, title = {A decision-making model for autonomous vehicles considering pedestrian’s time pressure based on game theory and reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Alpha-backpropagation algorithm: Probability estimation for one-hot and quasi-one-hot teachers. ACCESS, 13, 87712-87729. (www)

  @article{ , abstract = {This study proposes theories and applications of probabilistic divergences to neural network training. This theory generalizes the cross-entropy method for backpropagation to the alpha-divergence method. This new method includes the cross-entropy method as a limited case study. An advantage is the speedup during the training phase. The target architecture is the one-hot and quasi-one-hot teacher cases. Quasi-one-hotness occurs when a teacher intentionally weakens its one-hotness moderately. In addition, we devise a reverse process for quasi-one-hotness, that is, quenching. For non-one-hot elements, this process is equivalent to annealing. The new methods automatically weigh backpropagation, reflecting the nature of one-hot and quasi-one-hot teacher data. These strategies are applicable to versatile generative artificial intelligence (AI) tools. Using generic neural networks, we investigated the degree of learning speedup. The new methods present remarkable speedups, illustrations of which require logarithmic scales. This property also allows more elaborate conditional probability estimations by partitioning a neural network. Thus, the alpha-divergence method has become meritorious for accelerating versatile edge AI developments, particularly for low-cost and low-power fine-tuning.}, archive = {J_ACCESS}, author = {Yasuo Matsuyama}, doi = {10.1109/ACCESS.2025.3571404}, journal = {IEEE Access}, pages = {87712-87729}, shortjournal = {IEEE Access}, title = {Alpha-backpropagation algorithm: Probability estimation for one-hot and quasi-one-hot teachers}, volume = {13}, year = {2025}, }
• (2025). SMART-FL: Single-shot merged adaptive resource-aware tensor-fusion for efficient federated learning in heterogeneous cross-silo environments. ACCESS, 13, 87693-87711. (www)

  @article{ , abstract = {Federated Learning (FL) is an evolutionary approach for privacy-preserving distributed machine learning and is particularly significant in cross-silo settings where direct data sharing is restricted. Current Federated Learning techniques have challenges managing different data distributions, communication overhead between participating institutions, and computational resources. This study presents a unique system called SMART-FL (Single-shot Merged Adaptive Resource-Aware Tensor Fusion) for Federated Learning. It uses a novel one-shot learning technique to address these issues. Two key innovations were incorporated into SMART-FL 1) a weighted tensor fusion approach that prioritizes contributions from high-performing clients and 2) a heterogeneous-aware parametric pruning (HAPP) mechanism that automatically adjusts model compression based on client capabilities and data characteristics. The experimental findings, which were obtained in a variety of cross-silo settings, show that SMART-FL outperforms conventional FL strategies by achieving significant gains, such as a 32% reduction in model size, a 21% reduction in communication costs, a 64% reduction in computational costs, and maintaining model accuracy above 98%. With its adaptive pruning and fusion techniques, the framework successfully balances computational efficiency and model performance, performing exceptionally well in resource-constrained scenarios. A comprehensive analysis using multiple metrics such as model performance, computational resource utilization, and communication efficiency shows that SMART-FL effectively tackles the main difficulties of heterogeneous federated learning while preserving high accuracy with a single round of communication and lowering resource requirements. This makes it especially appropriate for practical uses in privacy-sensitive fields like healthcare, where handling diverse data distributions and heterogeneous computational resources is essential.}, archive = {J_ACCESS}, author = {Vineetha Pais and Santhosha Rao and Balachandra Muniyal}, doi = {10.1109/ACCESS.2025.3570610}, journal = {IEEE Access}, pages = {87693-87711}, shortjournal = {IEEE Access}, title = {SMART-FL: Single-shot merged adaptive resource-aware tensor-fusion for efficient federated learning in heterogeneous cross-silo environments}, volume = {13}, year = {2025}, }
• (2025). Improving location-based thermal emission side-channel analysis using iterative transfer learning. ACCESS, 13, 87672-87692. (www)

  @article{ , abstract = {This study applies machine learning to side-channel attacks and proposes an iterative transfer learning method for deep learning models. This study leverages the similarity in training patterns across bytes by first training on a single byte and then using the resulting model as a pretrained foundation for the remaining bytes. This approach enables effective model training with smaller amounts of data while reducing the measurement-to-disclosure (MTD, i.e., the minimum number of traces needed for successful key recovery) in the attack phase. With sufficient data, iterative transfer learning reduces MTD from 55 to 54 using MLP and from 125 to 83 using CNN. Even under limited data conditions, it successfully breaks AES-128 while reducing training samples from 13,600 to 2,000, achieving an average MTD of 635, whereas traditional methods fail. Experimental results demonstrate that the iterative transfer learning approach addresses the persistent data scarcity challenge in deep learning, significantly expanding the applicability of deep learning methods in side-channel attack scenarios.}, archive = {J_ACCESS}, author = {Kai Zhang and Tun-Chieh Lou and Chung-Che Wang and Jyh-Shing Roger Jang and Henian Li and Lang Lin and Norman Chang}, doi = {10.1109/ACCESS.2025.3570763}, journal = {IEEE Access}, pages = {87672-87692}, shortjournal = {IEEE Access}, title = {Improving location-based thermal emission side-channel analysis using iterative transfer learning}, volume = {13}, year = {2025}, }
• (2025). Toward drought modeling in south asia: Machine learning approaches, challenges, and opportunities. ACCESS, 13, 87654-87671. (www)

  @article{ , abstract = {Drought is an environmental and economic problem. Sustainable ecosystems, water resources, food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to the increasing frequency and severity of droughts caused by climate change. Effective drought modeling is crucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deep learning (DL) techniques have been developed as potential drought modeling tools, which offer accurate and reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction, Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1) The selection of the region for this study, for this study South Asia(SA) is selected as region of interest (ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightful information. The geographical scope of this study is the region of South Asia. This region is selected because of its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, and advances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions are evaluated by gathering and analyzing the most important drought indicators and metrics specific to South Asia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This review encapsulates the indicator and metrics (Complex Machine learning and deep learning models) for drought modeling which are most relevant to the SA region. This study sum up as most common challenges in drought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack of explainable and interpretable models, and unavailability of data for model uncertainty analysis. This study proposes that these problems can be solved with modern machine learning techniques such as explainable machine learning and federated or Lack of explainability and interpretability in complex ML/DL models, unavailability of benchmarks. Based on these challenges, this review suggests the following techniques to address these challenges: Data integration (Data fusion), distributed machine learning. (Federated Learning) and explainable AI (XAI, SHAP, LIME, etc.).}, archive = {J_ACCESS}, author = {Mana Saleh Al Reshan and Muhammad Owais Raza and Naeem Ahmed Mahoto and Adel Rajab and Asadullah Shaikh and Mohamed A. Elmagzoub and Khairan D. Rajab}, doi = {10.1109/ACCESS.2025.3567257}, journal = {IEEE Access}, pages = {87654-87671}, shortjournal = {IEEE Access}, title = {Toward drought modeling in south asia: Machine learning approaches, challenges, and opportunities}, volume = {13}, year = {2025}, }
• (2025). Fusion of multimodal audio data for enhanced speaker identification using kolmogorov-arnold networks. ACCESS, 13, 87640-87653. (www)

  @article{ , abstract = {Speaker identification using audio data is quite challenging because of inherent differences between people, ambient noise, and variable recording conditions. Although the classical deep learning methods are effective, they have rather high computational cost, which leads to usually cumbersome parameter tuning processes and hence reduce their applicability to real-world deployments. In this paper, we discuss an efficient version of Kolmogorov-Arnold Network for audio-based speaker identification using three different types of audio inputs: bone conduction, air conduction, and throat microphone recordings, alongside evaluation on the publicly available LibriSpeech dataset to enhance reproducibility. To optimize the performance of these modalities, we investigate data-level, feature-level, and decision-level fusion techniques. An exhaustive comparison is performed against some of the prominent deep learning architectures, namely VGG-19, ResNet-152, EfficientNetV2, and DenseNet-121, both as standalone architectures and within multimodal combinations for the purpose of evaluating the proposed framework. We demonstrate that our model achieves a significant increase in accuracy up to 99.375% through the use of MFCC and GFCC feature inputs with data-level fusion through MFCC features and achieves this level of accuracy using substantially fewer parameters than those of conventional deep learning models. This makes the KAN framework a highly efficient choice for audio-based speaker identification. One of the critical innovations within this design is a class of trainable activation functions that eliminate the need for traditional weight parameters, thereby providing scalable and computationally efficient solutions. This work contributes to the field of biometrics by providing a refined, resource-efficient approach to speaker identification based on multimodal audio data.}, archive = {J_ACCESS}, author = {Aryaman Tamotia and Dhruv Shantu Karmokar and Rethi Komal and K. Khadar Nawas and A. Shahina and A. Nayeemulla Khan}, doi = {10.1109/ACCESS.2025.3569606}, journal = {IEEE Access}, pages = {87640-87653}, shortjournal = {IEEE Access}, title = {Fusion of multimodal audio data for enhanced speaker identification using kolmogorov-arnold networks}, volume = {13}, year = {2025}, }
• (2025). AI knows aesthetics: AI-generated interior design identification using deep learning algorithms. ACCESS, 13, 87621-87639. (www)

  @article{ , abstract = {The integration of Artificial Intelligence (AI) in interior design has revolutionized how spaces are conceptualized, visualized, and classified. As digital tools and generative design models become increasingly sophisticated, distinguishing between AI-generated and real-world interior design images has emerged as a critical challenge. This classification is essential for ensuring authenticity in design visualization, improving recommendation systems, and enhancing virtual and augmented reality applications in architectural planning. Convolutional Neural Networks (CNNs) have proven to be highly effective in image classification tasks due to their ability to learn complex spatial hierarchies, making them particularly well-suited for differentiating between AI-rendered and real interior spaces. However, recent advancements in deep learning, including hybrid AI models and transformer-based vision networks, have further enhanced classification accuracy by integrating multimodal learning techniques. This study explores the use of advanced pre-trained CNN models, including DenseNet, RegNet, and SqueezeNet architecture, to classify interior design images. By leveraging a proprietary dataset composed of diverse AI-generated and real-world interior designs, our model achieved a peak classification accuracy of 97%, demonstrating superior performance compared to traditional deep learning techniques. Additionally, we introduce a novel image preprocessing technique that enhances feature extraction by adjusting lighting, texture, and noise levels, thereby reducing discrepancies between AI-rendered and real images. The results indicate that AI approaches, CNNs, hold immense potential for further improving classification robustness. This study contributes to the growing field of AI-driven interior design classification by presenting a high-accuracy model capable of distinguishing between real and AI-generated environments with unprecedented precision.}, archive = {J_ACCESS}, author = {Fei Liu and Kailing Deng}, doi = {10.1109/ACCESS.2025.3570509}, journal = {IEEE Access}, pages = {87621-87639}, shortjournal = {IEEE Access}, title = {AI knows aesthetics: AI-generated interior design identification using deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Revolutionizing imaging design content with AIGC: User-centered challenges, opportunities, and workflow evolution. ACCESS, 13, 87600-87620. (www)

  @article{ , abstract = {This study examines the influence of Artificial Intelligence Generated Content (AIGC) technology on the workflows and career trajectories of designers. Using the Technology-Organization-Environment (TOE) framework, a mixed-methods approach was employed, integrating expert interviews and quantitative surveys. Semi-structured interviews identified ten primary influencing factors, which were further analyzed through a survey of 531 users to quantify the interrelationships among these factors. Results indicate that career paths and industry environment exert the most significant positive impact on designers’ behavioral intentions, highlighting the rising demand for AIGC and its potential to enhance career prospects. Output availability and cross-functional collaboration demonstrate practical benefits in improving content quality and team efficiency. Technological maturity and public acceptance serve as key adoption drivers, while social construction and innovation underscore AI’s capacity to foster creativity and design diversity. Ethics and privacy reflect user concerns over data security, whereas learning costs negatively affect adoption due to perceived complexity. This study confirms the utility of the TOE framework in guiding the deployment of AIGC technology and underscores the necessity of promoting AI’s sustainable development in design. It provides theoretical, practical, and policy recommendations, including improving the usability and innovation of AIGC tools, strengthening support for team collaboration, and offering career development training for designers. Furthermore, policymakers are urged to address privacy and ethical challenges to ensure the long-term sustainable adoption of AI. This research delivers critical theoretical and practical insights into integration of AIGC in the design field.}, archive = {J_ACCESS}, author = {Zijian Zhu and Tao Yu and Yijing Wang and Junping Xu}, doi = {10.1109/ACCESS.2025.3567750}, journal = {IEEE Access}, pages = {87600-87620}, shortjournal = {IEEE Access}, title = {Revolutionizing imaging design content with AIGC: User-centered challenges, opportunities, and workflow evolution}, volume = {13}, year = {2025}, }
• (2025). An innovative adaptive threshold-based BESS controller utilizing deep learning forecast for peak demand reductions. ACCESS, 13, 87582-87599. (www)

  @article{ , abstract = {Battery-based energy storage systems (BESS) can be installed and controlled by a control strategy to reduce daily peak demands. Majority of the existing controllers for BESS is developed and studied on a simulation platform. Hence, an innovative adaptive threshold-based controller is developed and implemented on a real 200 kWh BESS installed on a university campus in Malaysia to reduce daily peak demands. This controller is developed on the free, open-source Node-RED platform using a deep learning-based one-dimensional convolution neural network (1D-CNN) to forecast the load profile for one day ahead. The controller uses the forecasted load profile to define a threshold or specific power demand level above which the BESS will start discharging power to meet the demand. The threshold is actively adjusted according to the actual and forecasted power demand of the customer as well as preceding peak of the grid power. The performance of the controller is initially assessed through six months of comprehensive simulations, where its daily peak reduction factor ( $\mathrm {K}_{\mathrm {pdr}}$ ) and monthly peak reduction failure rate ( $\mathrm {\eta }_{\mathrm {failure}}$ ) are compared with that of two fixed-threshold controllers, namely forecasted threshold and historical threshold-based controllers, and another two advanced controllers, namely active and fuzzy controllers. The adaptive threshold-based controller achieves an average $\mathrm {K}_{\mathrm {pdr}}$ of 41.62% and $\mathrm {\eta }_{\mathrm {failure}}$ of just 16.55%, which are better than that of other four controllers. The controller is implemented to BESS in a university building to evaluate its practical performance over 21 days under a real operating condition. The controller achieves an average $\mathrm {K}_{\mathrm {pdr}}$ of 49.45% with a $\mathrm {\eta }_{\mathrm {failure}}$ of just 4.76%, which are better than the simulation results.}, archive = {J_ACCESS}, author = {MD Mahmudul Hasan and Yun Seng Lim and Lee Cheun Hau and Xie Cherng Miow and Jianhui Wong and Dylon Hao Cheng Lam and Nor Shahida Hasan and Hamzah B. Adlan}, doi = {10.1109/ACCESS.2025.3570745}, journal = {IEEE Access}, pages = {87582-87599}, shortjournal = {IEEE Access}, title = {An innovative adaptive threshold-based BESS controller utilizing deep learning forecast for peak demand reductions}, volume = {13}, year = {2025}, }
• (2025). Time-dependent association between cardiotocographic features and hypoxic-ischemic encephalopathy. ACCESS, 13, 87562-87581. (www)

  @article{ , abstract = {Neonatal hypoxic-ischemic encephalopathy (HIE) is caused by sustained hypoxemia near birth. Clinical assessment using cardiotocography (CTG), which measures the fetal heart rate (FHR) and maternal uterine pressure (UP), aims to identify infants at increased risk of HIE. Although CTG is nonstationary, current automated methods for its analysis use time invariant discrimination rules. Our objective was to examine the association between features of CTG and the development of HIE to determine if accounting for the time to delivery (TTD) would strengthen these associations. We analyzed 88 features extracted from FHR and UP signals from 25,197 vaginally delivered infants for whom blood gas measurements were available. All infants were categorized according to their blood gas exams into three mutually exclusive groups: 167 HIE, 1,912 acidosis - a precursor to HIE, and 22,903 healthy cases. We evaluated CTG features during the last twelve hours of labor to explore the associations between 1) CTG features and TTD, 2) CTG features and the development of HIE, and 3) the conditional association between CTG features and the development of HIE given TTD. These associations were quantified using the normalized mutual information. We found that all CTG features varied with TTD. Furthermore, 48 out of 88 features were not significantly associated with the outcome of labor and might not be useful in classification studies. We also found that 40 out of 88 features had significant associations with the development of HIE; accounting for TTD increased the association for 26 of these features. Therefore, automated methods for prediction of infants at risk of HIE should focus on this set of CTG features and account for their time-varying properties.}, archive = {J_ACCESS}, author = {Johann Vargas-Calixto and Yvonne W. Wu and Michael Kuzniewicz and Marie-Coralie Cornet and Heather Forquer and Lawrence Gerstley and Aaron W. Scheffler and Philip A. Warrick and Robert E. Kearney}, doi = {10.1109/ACCESS.2025.3570371}, journal = {IEEE Access}, pages = {87562-87581}, shortjournal = {IEEE Access}, title = {Time-dependent association between cardiotocographic features and hypoxic-ischemic encephalopathy}, volume = {13}, year = {2025}, }
• (2025). Voltage waveform and voltage boost ratio of quasi z source DC converters. ACCESS, 13, 87556-87561. (www)

  @article{ , abstract = {In order to optimize the voltage waveforms of quasi Z source converters and investigate their effects on voltage boost ratio, the LTspice software is used to conduct a simulation study on quasi Z source converters after theoretical analysis. The results indicate that: 1) Only by using large inductance values and employing a damping filtering method with capacitor and resistor in series can a good square waveform be obtained; 2) With a good voltage waveform obtained, the voltage boost ratio of quasi Z source converters is approximately equal to the theoretical value; 3) When the voltage waveform is distorted or incomplete, a higher voltage boost ratio can be achieved, but this voltage boost ratio is not desirable; 4) The above three conclusions are also applicable to Boost DC converters.}, archive = {J_ACCESS}, author = {Sheng Qu}, doi = {10.1109/ACCESS.2025.3570743}, journal = {IEEE Access}, pages = {87556-87561}, shortjournal = {IEEE Access}, title = {Voltage waveform and voltage boost ratio of quasi z source DC converters}, volume = {13}, year = {2025}, }
• (2025). A control decision model for automatic driving based on monocular depth estimation. ACCESS, 13, 87545-87555. (www)

  @article{ , abstract = {As one of the key tasks in automatic driving, vision-based depth estimation plays an important role in environment perception and decision control. In this study, a novel control decision model based on monocular depth estimation is proposed for automatic driving. The proposed scheme develops an improved deep learning structure to establish the backbone network of monocular depth estimation method. This is expected to improve the accuracy and robustness of depth estimation. To verify the validity of the proposed model, a large number of experiments and analyses were carried out in this study. In the experiment, several common automatic driving control decision models such as Semantic depth, Fast depth, ResNet50 and Bevgpt are selected as comparison objects, and the performance of the proposed models is comprehensively evaluated through the accuracy evaluation under different epochs. The experimental results show that compared with the comparison model, the proposed model exhibits higher accuracy and stability in depth estimation tasks. This research result provides new ideas and methods for the development of autonomous driving technology, and is expected to make important contributions to the realization of intelligent transportation systems.}, archive = {J_ACCESS}, author = {Rong Zhang}, doi = {10.1109/ACCESS.2025.3569869}, journal = {IEEE Access}, pages = {87545-87555}, shortjournal = {IEEE Access}, title = {A control decision model for automatic driving based on monocular depth estimation}, volume = {13}, year = {2025}, }
• (2025). UAV path optimization for WSN in smart agriculture. ACCESS, 13, 87526-87544. (www)

  @article{ , abstract = {Unmanned Aerial Vehicles (UAVs) play a crucial role in Wireless Sensor Network (WSN) applications, particularly in smart agriculture, enabling efficient data collection from large-scale sensor deployments. A key challenge in this field is optimizing the UAV’s flight path while considering real-world constraints such as UAV dynamics, sensor heterogeneity, communication ranges, hovering requirements, overlapping cluster heads, altitude variation, and UAV limitations. Many existing studies oversimplify this problem by considering some of these constraints while neglecting others, leading to suboptimal solutions. In this work, we propose a novel UAV path optimization algorithm, “OptiFly”, designed for WSN-based agricultural systems, incorporating all these constraints in a single framework. The optimization problem is formulated as a Nonlinear Programming (NLP) model and solved using an appropriate solver. Unlike existing approaches, OptiFly integrates UAV kinematics, dynamics, and aerodynamics into the optimization process while accounting for heterogeneous sensors, as commonly observed in real-world agricultural implementations. The algorithm ensures that the UAV hovers at an optimal position within each sensor’s coverage area, minimizing unnecessary movements. Additionally, we consider overlapping cluster heads, allowing the UAV to hover over their intersection regions and collect data from both at the same point, thereby minimizing travel distance. For sensors with small communication ranges, the UAV dynamically adjusts its altitude to maintain connectivity while conserving energy. Furthermore, OptiFly enables the UAV to be self-aware of its endurance, terminating the mission before exceeding its maximum flight time. Power consumption is also considered based on the UAV’s dynamics. Simulation results demonstrate that OptiFly significantly reduces both travel distance and energy consumption compared to unoptimized and optimized approaches. Additionally, the proposed algorithm proves to have low computational complexity in various scenarios. The achieved improvements validate the effectiveness of our method, making it a promising solution for UAV-WSN applications in smart agriculture and beyond.}, archive = {J_ACCESS}, author = {Katia Karam and Ali Mansour and Mohamad Khaldi and Benoit Clement and Mohammad Ammad-Uddin}, doi = {10.1109/ACCESS.2025.3569642}, journal = {IEEE Access}, pages = {87526-87544}, shortjournal = {IEEE Access}, title = {UAV path optimization for WSN in smart agriculture}, volume = {13}, year = {2025}, }
• (2025). Exponential convergence analysis of serial differentiators for robust and accurate estimation of high-order derivatives. ACCESS, 13, 87516-87525. (www)

  @article{ , abstract = {This paper presents a rigorous mathematical analysis of the higher-order serial differentiator (HOSD), originally proposed by the authors for estimating time derivatives of time-varying signals. Unlike previous studies that partially relied on numerical simulations to demonstrate asymptotic convergence of the estimation error, this work provides a complete analytical proof that both first- and higher-order derivative estimates converge exponentially fast to an arbitrarily small neighborhood of zero. Furthermore, it is analytically shown that replacing the discontinuous sign function in the differentiator dynamics with a smooth saturation function effectively mitigates chattering while preserving convergence to a small neighborhood around zero. The proposed analysis significantly strengthens the theoretical foundation of the HOSD and enhances its robustness and practical applicability in real-time control and estimation systems.}, archive = {J_ACCESS}, author = {Jang-Hyun Park and Dong-Ho Lee}, doi = {10.1109/ACCESS.2025.3570524}, journal = {IEEE Access}, pages = {87516-87525}, shortjournal = {IEEE Access}, title = {Exponential convergence analysis of serial differentiators for robust and accurate estimation of high-order derivatives}, volume = {13}, year = {2025}, }
• (2025). Vehicle motion sickness profiling through interpretable gradient boosting: A framework for advanced driver assistance systems. ACCESS, 13, 87499-87515. (www)

  @article{ , abstract = {Motion sickness poses a significant challenge in vehicle dynamics, impacting passenger comfort and usability. This study presents a robust framework for understanding motion sickness and its origins through advanced modeling and interpretability techniques. The proposed approach leverages both classical and modern algorithms, with CatBoost emerging as the top-performing model after optimization. To address the interpretability of these models, SHAP methods are employed to extract actionable insights, revealing key factors contributing to motion sickness. CatBoost also demonstrates superior interpretability by providing balanced and meaningful feature importance distributions. The combination of advanced performance and interpretability allowed the model to accurately detect diverse motion sickness-inducing scenarios and uncover critical risk patterns.}, archive = {J_ACCESS}, author = {Jon Ander Ruiz Colmenares and Estibaliz Asua}, doi = {10.1109/ACCESS.2025.3570074}, journal = {IEEE Access}, pages = {87499-87515}, shortjournal = {IEEE Access}, title = {Vehicle motion sickness profiling through interpretable gradient boosting: A framework for advanced driver assistance systems}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable wireless passive sensor for highly sensitive crack detection. ACCESS, 13, 87485-87498. (www)

  @article{ , abstract = {Cracks in metal structures are common defects that require monitoring over large areas, often necessitating numerous sensors. This wiring and layout increase design and deployment complexity. Traditional electromagnetic resonant sensors, while simple and sensitive, are limited in coverage. In response, this paper proposes a wireless passive sensor based on electrically reconfigurable resonant rings. These rings, integrated directly onto a microstrip line, confine electromagnetic fields within miniaturized resonant units, significantly enhancing sensitivity. Through electrically control the working sequence of the resonant rings, the sensor can eliminate the impact of inter-unit mutual coupling, enabling the detection of crack depth and width in sub-millimeter level. The proposed sensor has been configured to two states: status 1 (resonant frequency is 955 MHz) and status 2 (resonant frequency is 903 MHz). The measured sensitivities for crack width characterization are 163.9 MHz/mm2 and 153.5 MHz/mm2, respectively and the measured sensitivities for crack depth characterization are 125.75 MHz/mm2 and 128.5 MHz/mm2, respectively. This paper demonstrates its suitability for large-scale coverage in metal components.}, archive = {J_ACCESS}, author = {Jiahui Cai and Liu Tao and Huijuan Gu and Jun Zhang and Bei Huang and Xiangyu Xie and Lihong Dong and Haidou Wang}, doi = {10.1109/ACCESS.2025.3570313}, journal = {IEEE Access}, pages = {87485-87498}, shortjournal = {IEEE Access}, title = {Reconfigurable wireless passive sensor for highly sensitive crack detection}, volume = {13}, year = {2025}, }
• (2025). A novel spanish dataset for financial education text simplification targeting visually impaired individuals. ACCESS, 13, 87472-87484. (www)

  @article{ , abstract = {Automatic Text Simplification (ATS) is a crucial task in natural language processing, aimed at making texts more comprehensible, particularly for specific groups such as individuals with visual impairments. One of the primary challenges in developing models for ATS is the scarcity of data, especially in Spanish. This manuscript introduces a novel dataset tailored for Spanish speakers with visual impairments, consisting of 5,314 pairs of original and simplified sentences created using established simplification rules. Additionally, we evaluate the feasibility of augmenting this dataset using large language models such as Generative Pre-training Transformer (GPT)-3, TUNER, and Multilingual T5 (mT5). We compare the simplifications generated by these models with our dataset to assess their effectiveness in data augmentation. The characteristics of our dataset and the findings from these comparisons are discussed in detail. The dataset is publicly available on Hugging Face at https://huggingface.co/datasets/saul1917/FEINA}, archive = {J_ACCESS}, author = {Nelson Pérez-Rojas and Saul Calderon-Ramirez and Martín Solís and Mario Alberto Romero-Sandoval and Monica Arias-Monge and Horacio Saggion}, doi = {10.1109/ACCESS.2025.3568693}, journal = {IEEE Access}, pages = {87472-87484}, shortjournal = {IEEE Access}, title = {A novel spanish dataset for financial education text simplification targeting visually impaired individuals}, volume = {13}, year = {2025}, }
• (2025). Hybrid topology for domestic conductive and wireless EV chargers. ACCESS, 13, 87462-87471. (www)

  @article{ , abstract = {One of the main challenges for the growth of EVs lies in the charging process. Owners demand more user-friendly and autonomous methods. Wireless charging is a solution that satisfies these requirements, but it should be introduced simultaneously with conductive charging. The combination of these technologies in the EV (hybrid chargers) can be accomplished with three main approaches: (i) the connection of the systems individually, (ii) the design of a specific coil to integrate the wireless power coils and the transformer of the conductive charger and (iii) the design of common power converters and control. The first strategy does not lead to any improvement, whereas the second option derives bulky elements and is strongly dependent on the misalignment conditions and the compensations systems. In this paper, a cost-effective hybrid technology that integrates the conductive and wireless charging power converters that are on-board is presented. This combination is achieved by using the secondary side rectifier of typical wireless charger as an active rectifier for the conductive charger. Using a careful design of the secondary power converters and the filter, the proposed control can effectively work for conductive and wireless charging. The proposed system has been experimentally validated through a prototype hybrid charger designed for domestic applications. The prototype operates at a power level of 500 W and integrates both conductive and wireless charging modes using a shared secondary-side rectifier. Efficiency measurements indicate 88.73% for wireless charging and 89.97% for conductive charging, demonstrating the feasibility of the approach. This design minimizes component count, reduces cost, and simplifies control implementation while maintaining high efficiency levels.}, archive = {J_ACCESS}, author = {Jose M. Gonzalez-Gonzalez and Juan Carlos Quirós Gil and Alicia Triviño Cabrera and van-Binh Vu and Mohamed Dahidah and José A. Aguado}, doi = {10.1109/ACCESS.2025.3566994}, journal = {IEEE Access}, pages = {87462-87471}, shortjournal = {IEEE Access}, title = {Hybrid topology for domestic conductive and wireless EV chargers}, volume = {13}, year = {2025}, }
• (2025). Measuring 5G exposure from multiple terminals and base station in standalone FWA networks. ACCESS, 13, 87438-87461. (www)

  @article{ , abstract = {Despite the broad adoption of 5G terminals, public concerns regarding ElectroMagnetic Field (EMF) exposure are primarily focused on the nearby presence of Radio Base Stations (RBSs), which are currently considered the strongest source of EMF radiation. However, is the RBS exposure really higher than that generated by multiple terminals in the same location? To address this question, we design and evaluate an innovative narrow-band measurement methodology, called Electromagnetic field Measurement for Fixed Wireless Access (EM-FWA), tailored to the evaluation of the exposure from multiple terminals vs. the one radiated by the RBS in a operative 5G StandAlone (SA) Fixed Wireless Access (FWA) network. Our approach, which includes a new narrow-band measurement algorithm, involves exposure assessments upon the generation of Downlink (DL), Uplink (UL) and combined DL/UL data transfers from the pool of terminals. Results, obtained from multiple locations subject to variegate propagation conditions, indicate that almost 100% saturation of the radio link resources in the DL direction is only achieved when three distinct terminals generate traffic in proximity to the RBS, thus justifying the adoption of our methodology. Conversely, terminal exposure significantly exceeds the RBS one at the cell edge, even when a single terminal is employed, with more than 1.4 V/m from CPE and less than 0.2 V/m from RBS. Furthermore, the RBS output power is influenced more by the volume of DL traffic than by the terminal propagation condition. Finally, we demonstrate that the overall exposure cost is minimized when the pool of terminals is placed close to the RBS. We believe that our work can pave the way to a new family of EMF compliance assessment procedures for 5G networks, adopting multiple terminals that generate traffic during the exposure measurements.}, archive = {J_ACCESS}, author = {Luca Chiaraviglio and Margherita Montenero and Chiara Lodovisi and Stefano Salsano and Andrea Mayer and Paolo Lungaroni and Nicola Blefari-Melazzi and Luigi Basset and Marco Lo Duca and Timothy Cicciarelli and Luca Lorenzi and Damiano Trucchi and Roberto Giuliani and Giovanni Di Salvatore and Marco Battistini and Angelo Michele Musi and Daniele Franci and Settimio Pavoncello and Stefano Coltellacci and Enrico Brinciotti and Ahmed Elzanaty and Mohamed-Slim Alouini}, doi = {10.1109/ACCESS.2025.3568246}, journal = {IEEE Access}, pages = {87438-87461}, shortjournal = {IEEE Access}, title = {Measuring 5G exposure from multiple terminals and base station in standalone FWA networks}, volume = {13}, year = {2025}, }
• (2025). Extraction and energy management of solar photovoltaic, fuel cell, and battery systems using a novel multi port power converter for DC microgrid applications. ACCESS, 13, 87418-87437. (www)

  @article{ , abstract = {In DC microgrids, efficient power extraction and an effective energy management strategy (EMS) are crucial when integrating multiple power sources. This study presents a novel Four-Port Converter (FPC) configuration designed to extract power from photovoltaic (PV), battery, and fuel cell (FC) sources while employing an advanced EMS to manage energy distribution and meet the demand of DC loads. The proposed FPC features two unidirectional input ports to accommodate renewable energy sources (RESs) and one bidirectional port for integrating an energy storage device (ESD) within a unified configuration. This paper provides a detailed analysis of the converter’s operation, exploring various operating modes for optimal power extraction and energy management from PV, battery, and FC sources. To achieve this, a closed-loop control strategy, along with the EMS, is implemented. Mathematical modeling and simulations demonstrate that this unified configuration ensures efficient, cost-effective, and uninterrupted power transfer among PV, FC, ESD, and the DC link. The design offers advantages such as a simplified structure, a lower component count, higher voltage gain, and reduced voltage stress on power semiconductors while effectively managing power flow among the sources. Both simulation and experimental results validate the feasibility of the proposed converter, highlighting its operational characteristics under different states and energy management modes. A laboratory prototype of the system was developed for a 60 V, 200 W load to assess the power extraction and energy management performance. The results confirm that the converter achieves an average efficiency of 93.86%. Additionally; a comparative analysis with existing four-port topologies and efficiency evaluations across various operating modes is presented. The findings demonstrate the superior performance of the proposed system in terms of higher voltage gain, lower switching losses, and reduced diode stress.}, archive = {J_ACCESS}, author = {Guguloth Nagesh and M. Venkatesh Naik and Dogga Raveendhra and Praveen Kumar Bonthagorla}, doi = {10.1109/ACCESS.2025.3570190}, journal = {IEEE Access}, pages = {87418-87437}, shortjournal = {IEEE Access}, title = {Extraction and energy management of solar photovoltaic, fuel cell, and battery systems using a novel multi port power converter for DC microgrid applications}, volume = {13}, year = {2025}, }
• (2025). Automated classification and identification of non-functional requirements in agile-based requirements using pre-trained language models. ACCESS, 13, 87401-87417. (www)

  @article{ , abstract = {Non-functional requirements (NFRs) are critical factors for software quality and success. A frequently reported challenge in agile requirements engineering is that NFRs are often neglected due to the focus on functional requirements (FRs) and the limited capability of agile requirements documented as user stories to represent NFRs. With the emergence of transfer learning and large pre-trained language models, various applications in requirements engineering have become feasible, alleviating several longstanding challenges. This study evaluates transformer-based models for the automated identification and classification of NFRs. We leveraged transfer learning with pre-trained transformer models to automate the identification and classification of NFRs in agile textual requirements documented as user stories. A dataset of over 10k user stories was collected and labeled, and pre-trained transformer models, including BERT, RoBERTa, XLNet, and DistilBERT, were fine-tuned to automate the identification of NFRs. We incorporated Focal Loss during training to mitigate the dominance of functionally driven requirements and class imbalances. In addition, thorough experiments on hyperparameter optimization were employed using Bayesian hyperparameter optimization to obtain the combination of hyperparameters that best correlated with the aim of enhancing each model’s performance. Our evaluation demonstrated that the finetuned pre-trained models significantly outperformed comparable prior approaches relying on rule-based techniques or traditional machine learning, with a fine-tuned BERT model achieving an F1 Score of 93.4 %. These findings highlight the potential of pre-trained language models in agile requirements engineering, enabling more efficient NFRs identification, reducing manual review burden, and facilitating a viable and efficient approach to address the neglect of NFRs in agile development processes.}, archive = {J_ACCESS}, author = {Abdulrahim Alhaizaey and Majed Al-Mashari}, doi = {10.1109/ACCESS.2025.3570359}, journal = {IEEE Access}, pages = {87401-87417}, shortjournal = {IEEE Access}, title = {Automated classification and identification of non-functional requirements in agile-based requirements using pre-trained language models}, volume = {13}, year = {2025}, }
• (2025). BreastCNet: Breast cancer detection, classification, and localization convolutional neural network with advanced optimization techniques. ACCESS, 13, 87386-87400. (www)

  @article{ , abstract = {Breast cancer remains a significant global health issue, primarily affecting females and requiring advanced detection methods to improve patient outcomes. While computer-aided diagnostic methods have progressed, limitations in accuracy persist. This study introduced BreastCNet, a Convolutional Neural Network (CNN) with hyperparameter optimization and a multi-task learning framework, enhancing classification and lesion localization. The BreastCNet integrated CNNs with the Grey Wolf Optimizer (GWO) and Parrot Optimizer (PO) in a dual-optimization approach. GWO fine-tuned neuron counts in dense layers to enhance feature learning, while PO dynamically adjusted the learning rate to improve convergence. Specifically, the parrot optimizer adjusted the learning rate from 0.001 to 0.00156, resulting in a 1.5% improvement in accuracy, and the grey wolf optimizer fine-tuned neuron numbers (Dense_1: 6, Dense_2: 866), contributing to a 2.3% increase in validation accuracy. These optimizations mitigated overfitting, accelerated convergence, and improved generalization, making the BreastCNet computationally efficient and clinically viable. The multi-task learning framework simultaneously performed breast ultrasound image classification (benign/malignant) and lesion localization via bounding box regression. The model achieved 98.10% validation accuracy, an AUC of 0.995, an F1-score of 0.98, and an IoU score of 0.96 for lesion localization on the Breast Ultrasound Images (BUSI) dataset, with comparable improvements on the BUSI, Digital Database for Screening Mammography (DDSM), and INbreast datasets. The BreastCNet is highly likely to advance breast cancer detection and lesion localization, providing precise localization and improved generalization, making it a promising tool for clinical applications.}, archive = {J_ACCESS}, author = {Hassan Mahichi and Vahid Ghods and Mohammad Karim Sohrabi and Arash Sabbaghi}, doi = {10.1109/ACCESS.2025.3570364}, journal = {IEEE Access}, pages = {87386-87400}, shortjournal = {IEEE Access}, title = {BreastCNet: Breast cancer detection, classification, and localization convolutional neural network with advanced optimization techniques}, volume = {13}, year = {2025}, }
• (2025). Using functional blocks for rolling stock troubleshooting: Sequential augmented reality assistant (SARA). ACCESS, 13, 87369-87385. (www)

  @article{ , abstract = {Maintenance and troubleshooting contribute to the overall economic efficiency of rolling stock throughout its lifecycle, yet these activities are resource-intensive and incur significant costs. In the era of Industry 4.0 implementation, the escalating complexity of rolling stock poses challenges in troubleshooting and maintenance decision-making. Recognizing this, there is a pressing need for a structured solution that empowers maintenance operators with tools for effective organization, analysis, and real-time presentation of rolling stock system failures, facilitating streamlined troubleshooting and maintenance guidance. Functional blocks, providing systematic structuring of processes, and Augmented Reality (AR), offering enhanced visualization and decision support, emerge as solutions to navigate and streamline these challenges in the evolving industrial landscape. The intricacies of existing fault-diagnosing systems, particularly interconnected transmission of functional blocks for troubleshooting and AR integration, lack detailed explanation, hindering effective solutions. To address these challenges, this work leverages AR for troubleshooting rolling stock failures, introducing functional blocks to 1) collect maintenance and fault data, 2) integrate maintenance information based on failure diagnostics and maintenance patterns, and 3) deliver this information to maintenance operators through AR. The developed Sequential Augmented Reality Assist (SARA) method, demonstrates the sequential implementation of AR troubleshooting functional blocks in an industrial context, providing a systematic solution to the complexities involved.}, archive = {J_ACCESS}, author = {Sara Scheffer and Alberto Martinetti and Roy Damgrave and Leo van Dongen}, doi = {10.1109/ACCESS.2025.3567674}, journal = {IEEE Access}, pages = {87369-87385}, shortjournal = {IEEE Access}, title = {Using functional blocks for rolling stock troubleshooting: Sequential augmented reality assistant (SARA)}, volume = {13}, year = {2025}, }
• (2025). Measuring narrative visualizations: A model-based evaluation framework and validation. ACCESS, 13, 87350-87368. (www)

  @article{ , abstract = {Evaluating narrative visualizations is complex due to their dependence on user characteristics, context, and storytelling elements. This study presents NaVi-Q, a quality model designed to structure the assessment of narrative visualizations, drawing on principles from Software Engineering and Information Visualization. The model was tested in an experiment where 40 participants evaluated 30 visualizations. The results suggest that the model’s criteria align with expert assessments in distinguishing different quality levels. Scores from experts and non-experts followed similar patterns, indicating that the model may be applicable across different levels of experience. These findings suggest that NaVi-Q can be used as a reference for structuring the evaluation of narrative visualizations in research and practice.}, archive = {J_ACCESS}, author = {Andrea Lezcano Airaldi and Juan Andrés Carruthers and Emanuel Irrazábal and Andrés Diaz-Pace}, doi = {10.1109/ACCESS.2025.3570485}, journal = {IEEE Access}, pages = {87350-87368}, shortjournal = {IEEE Access}, title = {Measuring narrative visualizations: A model-based evaluation framework and validation}, volume = {13}, year = {2025}, }
• (2025). Comparative study of machine learning and deep learning models for early prediction of ovarian cancer. ACCESS, 13, 87336-87349. (www)

  @article{ , abstract = {Ovarian cancer remains one of the most difficult gynecological cancers to detect early, often resulting in poor survival rates. This study presents a comparative analysis of machine learning (ML) and deep learning (DL) models for the early prediction of ovarian cancer using clinical and biomarker data. The dataset undergoes comprehensive preprocessing, including handling missing values, outlier removal, normalization, and dimensionality reduction via PCA. Feature selection methods such as Feature Importance, Recursive Feature Elimination (RFE), and autoencoder-based techniques are employed to enhance model performance. Various classifiers, including KNN, SVM, Logistic Regression, Random Forest, and deep networks like ANN, FNN, CNN, and RNN, are evaluated. Ensemble models such as Bagging, AdaBoost, Stacking, and XGBoost are also implemented. Our results show that the Feedforward Neural Network (FNN), combined with autoencoder-based feature selection, achieved the highest accuracy (85.71%), indicating its potential as a reliable predictive model for ovarian cancer. This comparative study highlights the significance of integrating optimized preprocessing, feature engineering, and model selection for effective early diagnosis.}, archive = {J_ACCESS}, author = {Hardik Dhingra and Roopashri Shetty}, doi = {10.1109/ACCESS.2025.3567081}, journal = {IEEE Access}, pages = {87336-87349}, shortjournal = {IEEE Access}, title = {Comparative study of machine learning and deep learning models for early prediction of ovarian cancer}, volume = {13}, year = {2025}, }
• (2025). 3D-printed demultiplexer circuits using suspended-in-air grating couplers for terahertz communications. ACCESS, 13, 87327-87335. (www)

  @article{ , abstract = {Integrated photonic circuits are in great demand for the upcoming THz communications. This work explores 3D printing to realize high-quality, high-refractive-index-contrast integrated components and devices for demultiplexing terahertz channels within the Wavelength Division Multiplexing modality. Namely, by printing integrated circuits using Polypropylene filaments suspended in air, we profit from the high-refractive index contrast of such a material combination to realize relatively compact low-loss waveguides, bends, couplers, and fiber Bragg gratings. The two-nozzle FDM printer allows simultaneous printing with filaments of two distinct sizes of 800um and 400um, with the larger filament used to make waveguides and couplers, and the smaller one used to define high-quality fiber Bragg gratings containing as much as 100 periods and featuring stop bands as wide as 10 GHz. Furthermore, by employing judiciously designed mechanical supports we show how to integrate such subcomponents into functional components such as single-channel drop filters. Finally, we developed a low-loss splicing technique for joining several components into functional devices and demonstrated four-channel THz WDM demultiplexers with in-plane (horizontal) and a more compact out-of-plane (vertical) integration. Experimentally, three-channel demultiplexers of THz signals with individual data rates up to 6 Gbps were demonstrated. Using finite element numerical modeling, integrated circuits were optimized for operation in the 120-165 GHz frequency band featuring ~5 GHz individual channel bandwidths and ~3 GHz inter-channel spectral spacing, and good agreement with the experiments was observed. Additionally, the measured spectra closely resemble the simulated ones but exhibit a frequency shift of several GHz towards higher frequencies. Experimental results further reveal strong sidelobe suppression and a broader Drop port bandwidth (~6 GHz vs. ~4 GHz predicted). However, the measured Drop amplitudes (~0.5–0.6) are lower than the theoretical predictions (~0.8) due to ~10% scattering losses per supporting structure. We believe that the suspended-in-air integrated terahertz circuits hold strong potential for developing various linear optic transformers that will play a key role in energy-efficient analog processing of data streams for the upcoming terahertz communications. This is because of the high quality of the resultant circuits, ease of fabrication, and low infrastructure costs necessary for their manufacturing, thus allowing low-cost fast turnaround prototyping and development of terahertz signal processing devices even with the simplest 3D printing systems.}, archive = {J_ACCESS}, author = {Babak Yahyapour and Frédéric Marcotte and Roya Gachiloo and Maksim Skorobogatiy}, doi = {10.1109/ACCESS.2025.3570976}, journal = {IEEE Access}, pages = {87327-87335}, shortjournal = {IEEE Access}, title = {3D-printed demultiplexer circuits using suspended-in-air grating couplers for terahertz communications}, volume = {13}, year = {2025}, }
• (2025). Stable inverse reinforcement learning via leveraged guided motion planner for driving behavior prediction. ACCESS, 13, 87313-87326. (www)

  @article{ , abstract = {Driving behavior prediction has become increasingly important, owing to rapid advancements in autonomous vehicle technologies. Inverse reinforcement learning (IRL) has emerged as a leading approach domain, as it allows the inference of underlying reward functions from human-driving demonstrations, enabling the modeling of complex behaviors. Among various IRL approaches, maximum-entropy IRL (MaxEnt IRL) has gained prominence in driving-behavior modeling owing to its applicability to continuous state-space problems. In continuous state-space MaxEnt IRL, stochastic motion planners are commonly employed during training to approximate the partition function because integrating over the entire state-space is computationally infeasible. However, traditional motion planners often fail to efficiently explore the state-space surrounding human-demonstrated paths, leading to inaccurate approximations of the partition function. This study proposes a novel, stable MaxEnt IRL framework that integrates an IRL-aware motion planner incorporating a guided exploration and exploitation process to efficiently sample high-quality trajectories. The proposed approach leverages two distributions derived from human-demonstrated paths to balance broad state-space exploration and targeted exploitation of relevant regions. Experiments conducted in a driving simulator demonstrate that the proposed method outperforms existing IRL methods in stability and accuracy, enhancing the prediction of driving behaviors and showcasing the potential of IRL for achieving human-like decision-making in autonomous driving.}, archive = {J_ACCESS}, author = {Minglu Zhao and Masamichi Shimosaka}, doi = {10.1109/ACCESS.2025.3570957}, journal = {IEEE Access}, pages = {87313-87326}, shortjournal = {IEEE Access}, title = {Stable inverse reinforcement learning via leveraged guided motion planner for driving behavior prediction}, volume = {13}, year = {2025}, }
• (2025). Transforming education with large language models: Trends, themes, and untapped potential. ACCESS, 13, 87292-87312. (www)

  @article{ , abstract = {Our research focuses on the transformative intersection of Large Language Models (LLMs) and education in the last six years (2019–2024), examining their potential to modernize educational systems and enhance learning outcomes. Leveraging a comprehensive methodological framework, we analyzed 9,598 publications from Web of Science (WoS), extracting 25,381 education-related terms and mapping academic trends across diverse research areas. Educational research involving LLMs focuses heavily on learning, research and training, with terms such as “education” (1546), “learning” (4828), “research” (4438) and “training” (327) frequently appearing in the analyzed dataset. Specific applications such as grading (121) and tutoring (82) are less emphasized, presenting potential areas for further exploration. Key elements include annual publication patterns, institutional collaborations, citation dynamics and keyword co-occurrence maps. Advanced topic modeling techniques, such as LDA, LDA-BERT and BERT-Clustering, reveal a spectrum of themes, from foundational AI concepts in education to domain-specific applications in fields like legal and financial contexts. The findings highlight major educational themes such as “AI in education”, “medical education” and “programming education”, alongside subfields like “computer science education” and “software engineering education” underscoring a strong focus on technology-driven learning.}, archive = {J_ACCESS}, author = {Simona-Vasilica Oprea and Adela Bâra}, doi = {10.1109/ACCESS.2025.3570649}, journal = {IEEE Access}, pages = {87292-87312}, shortjournal = {IEEE Access}, title = {Transforming education with large language models: Trends, themes, and untapped potential}, volume = {13}, year = {2025}, }
• (2025). Operational current amplifier-based quadrature oscillators family. ACCESS, 13, 87281-87291. (www)

  @article{ , abstract = {This paper investigates the application of the operational current amplifier (OCA) in the design of quadrature sinusoidal oscillators. There are numerous quadrature sinusoidal oscillators based on the three fundamental amplifiers namely the operational amplifier (OpAmp), the operational transconductance amplifier (OTA), and the operational transresistance amplifier (OTRA); however, their counterparts based on the OCA have not been reported. In this paper, several possible quadrature oscillator structures based on the OCA are systematically developed from their voltage mode active-RC counterparts using adjoint network theorem. Consequently, it is shown that the resulted oscillators do not necessarily preserve the attractive features of the voltage mode quadrature oscillators; namely providing explicit quadrature current output signals with equal amplitudes. When attempting to retain these features, it is found that the minimum OCAs needed is three. Analyzing this issue has paved the way for developing a novel OCA based integrator that promotes the invention of a new quadrature oscillator using only two OCAs. Simulation results obtained from a standard 150 nm CMOS process are provided. It is demonstrated that the proposed oscillator consumes approximately $350~\mu $ W while working in the 3 MHz range. Measurement results obtained from a prototype implemented using CMOS OCA ICs also confirms the proposed theory.}, archive = {J_ACCESS}, author = {Hussain A. Alzaher and Ibrahim M. Alkhalifa}, doi = {10.1109/ACCESS.2025.3570250}, journal = {IEEE Access}, pages = {87281-87291}, shortjournal = {IEEE Access}, title = {Operational current amplifier-based quadrature oscillators family}, volume = {13}, year = {2025}, }
• (2025). Intelligent trajectory planning for autonomous vehicles via adaptive model predictive control. ACCESS, 13, 87268-87280. (www)

  @article{ , abstract = {This study introduces an Adaptive Model Predictive Control (AMPC) framework for intelligent trajectory planning in autonomous vehicles navigating complex and dynamic highway environments. The proposed system is designed to adapt in real time to environmental changes, vehicle dynamics, and traffic conditions, with a focus on ensuring safety, efficiency, and collision avoidance. A comprehensive simulation test bench was developed using MATLAB and Simulink, including four representative highway driving scenarios that vary in traffic density, lane configurations, and event complexity (e.g., accidents, junctions). Results demonstrate that the AMPC system significantly improves trajectory tracking, reduces abrupt maneuvers, and maintains vehicle stability even at high speeds. Notably, increased lane availability and lower traffic density contribute to smoother lane changes and fewer collisions. Compared to traditional MPC, the adaptive approach shows greater robustness and responsiveness in unpredictable driving contexts. Although exact computational profiling was not performed, the design follows principles compatible with real-time implementation in automotive contexts.}, archive = {J_ACCESS}, author = {Catarina Gonçalves and M. do Rosário Calado and Nuno Pombo}, doi = {10.1109/ACCESS.2025.3570609}, journal = {IEEE Access}, pages = {87268-87280}, shortjournal = {IEEE Access}, title = {Intelligent trajectory planning for autonomous vehicles via adaptive model predictive control}, volume = {13}, year = {2025}, }
• (2025). An end to end solution for object classification using artificial neural networks and ultrasonic-based sensors. ACCESS, 13, 87254-87267. (www)

  @article{ , abstract = {With the development of technology, target and object recognition issues are becoming increasingly important day by day. In this article, studies on ultrasonic signal processing and object recognition techniques in the literature were examined. An integrated system design that includes ultrasonic signal data obtained from different objects has been designed to contribute to the literature. With this system design, a new method is proposed that includes signal pre-processing, feature extraction and classification processes. In addition, different machine learning and artificial neural networks algorithms are used to organize and classify the data obtained from ultrasonic signals, to obtain high-performance classification rates and to achieve comprehensive object recognition. As a result of the study carried out in this context, the data set obtained from different types of objects at different distances and angles was arranged and passed through various pre-processing and statistical feature extraction methods. In conclusion, a high-performance classification rate of 97.8% using the random forest algorithm and 99.10% using the artificial neural networks algorithm was obtained during the classification phase.}, archive = {J_ACCESS}, author = {Ahmet Karagoz and Gokhan Dindis}, doi = {10.1109/ACCESS.2025.3570762}, journal = {IEEE Access}, pages = {87254-87267}, shortjournal = {IEEE Access}, title = {An end to end solution for object classification using artificial neural networks and ultrasonic-based sensors}, volume = {13}, year = {2025}, }
• (2025). Development of a multi-channel wireless deep brain stimulation and signal acquisition system for neurostimulation and biomarker monitoring. ACCESS, 13, 87241-87253. (www)

  @article{ , abstract = {This study presents the design, development, and validation of a multi-channel wireless deep brain stimulation and signal acquisition system designed for neurostimulation and biomarker monitoring in preclinical models. The system integrates real-time Bluetooth-based data transmission, adaptive closed-loop control, and multi-channel recording, providing a versatile platform for studying neuromodulation therapies. The MitoPark transgenic mouse model was used to evaluate the effects of subthalamic nucleus (STN)-DBS on motor and cognitive functions in Parkinson’s disease. Behavioral assessments, including the Narrow Beam Walking Test, Open Field Task, and Novel Object Recognition Test, were conducted. Results demonstrated that STN-DBS significantly improved motor coordination, as evidenced by enhanced beam-walking performance. Spectral analysis of local field potentials demonstrated a reduction in pathological beta-band activity and an increase in gamma-band power post-DBS, suggesting restoration of neural network dynamics. However, no significant cognitive improvements were observed. Histological and MRI validation confirmed accurate electrode placement in the STN. This study highlights the potential of wireless adaptive DBS for improving motor symptoms and provides a foundation for future research on personalized neurostimulation therapies.}, archive = {J_ACCESS}, author = {Chang-Lin Hu and Guan-Yi Lu and Pei-Ling Kang and Chien-Ju Li and Ming-Jhe Lin and Guan-Lun Li and Fang-Yi Su}, doi = {10.1109/ACCESS.2025.3569186}, journal = {IEEE Access}, pages = {87241-87253}, shortjournal = {IEEE Access}, title = {Development of a multi-channel wireless deep brain stimulation and signal acquisition system for neurostimulation and biomarker monitoring}, volume = {13}, year = {2025}, }
• (2025). Uncertainty estimation in cardio landmark detection and heart disease diagnosis on chest X-ray images. ACCESS, 13, 87229-87240. (www)

  @article{ , abstract = {Landmark detection using chest X-ray images is often a time-consuming task performed by radiologists, followed by the calculation of cardiometric indices and the diagnosis of heart diseases. The goal of this research is to estimate uncertainty in data and knowledge, which leads to increased accuracy and interpretability in models that predict landmark positions and classify human body states. To achieve this, data from about 800 X-ray images labeled by four practicing radiologists was utilized. An uncertainty-aware negative log-likelihood loss was proposed to address both regression and classification problems. The proposed models were trained to predict the mean and log variance of a normal distribution for each input image, with the variance value estimating the uncertainty of the prediction. Natural data uncertainty was also measured from labels provided by different radiologists and implemented in the form of a label transform for the coordinates of the landmarks. The classification models demonstrated good correspondence with methods such as Monte Carlo dropout and deep ensembling, which account for knowledge uncertainty. The proposed uncertainty-aware models achieved the best results in both landmark detection and heart disease diagnosis, with accuracy improvements of 3% and 4.61%, respectively. The proposed methods are effective and easy to use, making their application to other medical problems a subject of interest for further research.}, archive = {J_ACCESS}, author = {Dmitry Lvov and Ivan Stebakov and Alexei Kornaev and Ilya Pershin and Tamerlan Mustafaev and Danil Afonchikov and Ramil Kuleev and Iskander Bariev and Bulat Ibragimov}, doi = {10.1109/ACCESS.2025.3566772}, journal = {IEEE Access}, pages = {87229-87240}, shortjournal = {IEEE Access}, title = {Uncertainty estimation in cardio landmark detection and heart disease diagnosis on chest X-ray images}, volume = {13}, year = {2025}, }
• (2025). PIONet: A positional encoding integrated onehot feature-based RNA-binding protein classification using deep neural network. ACCESS, 13, 87220-87228. (www)

  @article{ , abstract = {RNA-binding proteins (RBPs) are pivotal in regulating various biological processes, including RNA splicing and protein translation. Accurate classification of RBPs is crucial for understanding their functional roles in cellular mechanisms. Here we present PIONet, a deep learning method based on a convolutional neural network (CNN) that accurately classifies RBPs. Traditional one-hot encoding methods fail to capture positional relationships within RNA sequences. The PIONet addresses this limitation and enriches sequence representations with spatial information by integrating positional encoding with one-hot encoding. The CNN model processes these combined features to extract local patterns and motifs critical for RNA-protein interactions. The positional encoding enables the model to effectively learn inter-nucleotide dependencies within the sequence, making it highly suitable for capturing the intricate patterns associated with RBPs. Our model has been evaluated on 24 benchmark datasets, collectively referred to as RBP-24, demonstrating significant improvements in classification accuracy and robustness compared to the state-of-the-art methods. Additionally, we applied transfer learning to enhance the model’s performance, which led to improvements on 23 out of 24 datasets. This framework presents a powerful tool for advancing RBP classification tasks and offers potential applications in understanding post-transcriptional gene regulation.}, archive = {J_ACCESS}, author = {Mahmood A. Rashid and Mayank Chaturvedi and Kuldip K. Paliwal}, doi = {10.1109/ACCESS.2025.3570714}, journal = {IEEE Access}, pages = {87220-87228}, shortjournal = {IEEE Access}, title = {PIONet: A positional encoding integrated onehot feature-based RNA-binding protein classification using deep neural network}, volume = {13}, year = {2025}, }
• (2025). Toward high-accuracy 3D creation of aviation tools via analysis-by-synthesis. ACCESS, 13, 87208-87219. (www)

  @article{ , abstract = {The field of education is rapidly shifting toward digital platforms; however, aviation education continues to rely heavily on traditional paper-based manuals. This persistence is largely due to the inherent complexity, scale, and high costs associated with aviation equipment, making digital transformation particularly challenging. In this paper, we utilizing the analysis-by-synthesis method is presented for multi-view 3D reconstruction of solid objects, specifically focusing on aviation tools. To facilitate this research, a custom dataset was generated using Unity, enabling controlled experimentation and performance evaluation. Comparative analysis with existing benchmarks demonstrates that, despite the intricate nature of aviation tools compared to simpler objects. The proposed architecture effectively reconstructs detailed and accurate digital replicas with 97% accuracy. This advancement has the potential to significantly enhance aviation training by providing immersive, interactive 3D content, bridging the gap between traditional training materials and cutting-edge digital education tools.}, archive = {J_ACCESS}, author = {Gundu Mohamed Hassan and Yoo Sung Kim and Geun Sik Jo}, doi = {10.1109/ACCESS.2025.3566992}, journal = {IEEE Access}, pages = {87208-87219}, shortjournal = {IEEE Access}, title = {Toward high-accuracy 3D creation of aviation tools via analysis-by-synthesis}, volume = {13}, year = {2025}, }
• (2025). Optimization of heterogeneous last-mile delivery of fresh products considering traffic congestions and other real-world parameters. ACCESS, 13, 87193-87207. (www)

  @article{ , abstract = {Logistics and transport are the core of many industrial and business processes. One of the most promising segments in the field is the optimization of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environments and covers a fraction of real industrial applications due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To cope with the complexity computationally, we propose a new procedure based on an adaptive memory metaheuristic combined with local search. The initial solution is obtained with the Clarke-Wright savings algorithm extended here by introducing a dropout factor to include a stochastic attribute. The procedure and corresponding algorithms are tested first on the two existing scientific state-of-art benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs.}, archive = {J_ACCESS}, author = {Nikica Perić and Slaven Begović and Vinko Lešić}, doi = {10.1109/ACCESS.2025.3569578}, journal = {IEEE Access}, pages = {87193-87207}, shortjournal = {IEEE Access}, title = {Optimization of heterogeneous last-mile delivery of fresh products considering traffic congestions and other real-world parameters}, volume = {13}, year = {2025}, }
• (2025). Multi-agent communication for dynamic job-shop scheduling: A robust single-machine scheduling model with genetic algorithm optimization. ACCESS, 13, 87183-87192. (www)

  @article{ , abstract = {Job-shop scheduling is crucial in manufacturing systems but struggles with uncertainties like random job arrivals and disruptions. Traditional scheduling methods often rely on centralized control and predefined rules, limiting flexibility in dynamic environments. This study has proposed a multi-agent communication based on the single-machine scheduling model for dynamic job-shop scheduling. In this model, each machine is considered an autonomous agent who can communicate with other agents to produce a robust local schedule against uncertain job arrivals, processing times, and disruptive events. When an agent becomes idle, and its queue length exceeds 1, it needs to decide which waiting job should be processed next. The proposed communication process allows the decision-making agent and other agents to exchange the intention of their local schedules. Accordingly, the decision-making agent can decide on the next processed job. Each agent schedules its local queue using the single-machine scheduling model, which aims to reduce local tardiness costs. Decisional entities accept extra costs if there is an inconsistency between the start and completion times of two consecutive operations. The genetic algorithm is employed to solve the single-machine scheduling models. For experimentation, a multi-agent job shop simulation program will be developed to implement the proposed communication process. The experimental results show that the proposed method outperforms the stand-alone single-machine scheduling model, achieving up to a 34% reduction in total tardiness cost and improving lead times and scheduling stability under uncertainties. Furthermore, the proposed method requires minimal computation time, making it potentially applicable to IoT-enabled manufacturing systems.}, archive = {J_ACCESS}, author = {Hong-Phuc Nguyen and Quang-Hieu Ngo and van-Can Nguyen}, doi = {10.1109/ACCESS.2025.3569568}, journal = {IEEE Access}, pages = {87183-87192}, shortjournal = {IEEE Access}, title = {Multi-agent communication for dynamic job-shop scheduling: A robust single-machine scheduling model with genetic algorithm optimization}, volume = {13}, year = {2025}, }
• (2025). Memory pooling for enhanced data loading in GPU-accelerated environments. ACCESS, 13, 87175-87182. (www)

  @article{ , abstract = {The RAPIDS Memory Manager (RMM) is developed by NVIDIA as a package that would enable developers to customize GPU memory allocation. RMM enables the use of pool allocation which could improve the performance greatly. This paper proposes a systematic profiling and evaluation framework that leverages NVIDIA’s RMM to optimize and understand data loading performance of the cudf.read_csv operation in GPU accelerated environments. It examines RMM’s impact from multiple aspects, by measuring the execution time required to complete the operation, the memory consumption effect, and by profiling the operation with and without utilizing RMM across various dataset sizes. The finding demonstrates that RMM can have significant speedup of up to 24% by improving the memory management strategy of cuDF. As for other time series data preprocessing operations were overall improved by 14% when utilizing RMM. It could also improve the scalability of cuDF by utilizing managed memory to overcome the limited GPU memory constrains, allowing cuDF to handle datasets that exceeds the GPU memory while maintaining ~10x faster execution than the CPU based Pandas DataFrame. The effect of RMM on GPU memory consumption is also highlighted indicating a trade-off between faster execution and increased memory consumption.}, archive = {J_ACCESS}, author = {Ayaz H. Khan and Hamed Al-Mehdhar}, doi = {10.1109/ACCESS.2025.3570500}, journal = {IEEE Access}, pages = {87175-87182}, shortjournal = {IEEE Access}, title = {Memory pooling for enhanced data loading in GPU-accelerated environments}, volume = {13}, year = {2025}, }
• (2025). A review of footstep energy harvesting systems. ACCESS, 13, 87149-87174. (www)

  @article{ , abstract = {Recently, energy harvesting through human locomotion has emerged as a promising source of clean energy for fueling low-power smart electronic devices. As energy plays a pivotal role in all sectors, the prospect of harnessing clean energy from human footsteps has become a focal point in the quest for sustainable energy solutions. This article presents a comprehensive exploration of various kinetic energy harvesting technologies—such as piezoelectric, rack-and-pinion, triboelectric nanogenerators, human mounted, Pavegen, airflow-based, and fluid-flow-based energy harvesting systems— that are being employed to generate biomechanical energy from simple human motion. These systems require only small displacements such as walking and movement of human limbs. The discussion explores the complexity of design, operating principles, and performance evaluations of these cutting-edge human motion energy harvesters. Furthermore, the article delves into a critical assessment of the limitations, benefits, and challenges confronting piezoelectric and electromagnetic energy harvesting technologies in the realm of low-power practical applications. The review highlights that piezoelectric flooring systems, rack-and-pinion harvesters, triboelectric nanogenerators, and wave spring generators hold significant promise for footstep energy harvesting. With ongoing advancements in piezoelectric materials, the efficiency of these harvesters is expected to improve. Combined with the decreasing power requirements of portable devices, footstep energy harvesters may soon serve as a clean and mobile energy source for such technologies.}, archive = {J_ACCESS}, author = {Kritesh A. Chandra and Sumesh Narayan and Kabir A. Mamun and Aneesh A. Chand and Deepak Prasad and Mohammed Rafiuddin Ahmed}, doi = {10.1109/ACCESS.2025.3570963}, journal = {IEEE Access}, pages = {87149-87174}, shortjournal = {IEEE Access}, title = {A review of footstep energy harvesting systems}, volume = {13}, year = {2025}, }
• (2025). Three-layer retrieval and self-evaluation classification method based on FastText algorithm. ACCESS, 13, 87136-87148. (www)

  @article{ , abstract = {Patent big data serves as a valuable scientific research source for technological innovation, enabling breakthroughs beyond existing knowledge and fostering disruptive ideas. One key challenge in this field is how to efficiently obtain patent documents quickly and accurately. This is a critical focus in the exploration of patent search methodologies. Our approach differs from conventional patent search processes. We have developed a three-level patent classification method that utilizes a multi-step search strategy with specific constraints, alongside an innovative classification system based on the FastText algorithm. By combining these techniques with an emphasis on recall ratio, we can test the efficacy of each level of the database boundaries. This enables swift identification of target patents and allows for focused screening in specific fields, providing robust support for technical or product innovation activities. Furthermore, we applied this method to the retrieval of DBDI patent data, which represents one of the three leading commercial direct ionization ion source technologies globally. Our classification results indicate a remarkable accuracy of 96.97%, reflecting a 21.97% improvement over the TextRNN_Att text algorithm. This effectively demonstrates the success of our proposed methodology. Overall, this study offers a theoretical framework for researching multi-level classification methods in logical retrieval and provides a practical foundation for classifying direct ionization and ionization technologies.}, archive = {J_ACCESS}, author = {Yidan Li and Huanhuan Hong and Luhong Wen}, doi = {10.1109/ACCESS.2025.3570100}, journal = {IEEE Access}, pages = {87136-87148}, shortjournal = {IEEE Access}, title = {Three-layer retrieval and self-evaluation classification method based on FastText algorithm}, volume = {13}, year = {2025}, }
• (2025). Freeways’ road safety analysis through accident database and human behavior. ACCESS, 13, 87122-87135. (www)

  @article{ , abstract = {Road traffic accidents represent a significant public health care, having a serious impact on collective well-being especially in extra-urban areas. So, the academic research is increasingly focusing on this question and examining it from different perspectives. In this paper an extra-urban Italian motorway has been considered and its risk associated with traffic safety has been evaluated through an in-depth analysis of its accident database. It has been evaluated in order to assess the influence of certain factors on the accidents’ severity and frequency. The factors included are road geometry, surface conditions, accident nature, and human behaviour. The statistical analysis of these elements has been integrated with a field experiment, with a total of 21 participants involved, to assess the user level of distraction and vehicle kinematic data in the selected area, through innovative instruments as an Eye Tracking and a Vbox Pro Video. The obtained results revealed that drivers’ behaviour had a more significant impact on accident severity than the road geometric characteristics. Congestion is the primary risk factor for accidents, prompting users to adopt erratic driving behaviours, high speeds and hight levels of inattention.}, archive = {J_ACCESS}, author = {Claudia Brasile and Sofia Palese and Margherita Pazzini and Claudio Lantieri and Valeria Vignali}, doi = {10.1109/ACCESS.2025.3570070}, journal = {IEEE Access}, pages = {87122-87135}, shortjournal = {IEEE Access}, title = {Freeways’ road safety analysis through accident database and human behavior}, volume = {13}, year = {2025}, }
• (2025). Lightweight stereo matching for real-time applications with 2D cost volume aggregation. ACCESS, 13, 87112-87121. (www)

  @article{ , abstract = {Despite the significant advancements in learning-based stereo matching algorithms, a significant challenge remains: the high computational cost and memory demands of 3D convolutions, which hinder real-time deployment on resource-constrained platforms like edge devices. This paper presents a novel approach that completely avoids the use of 3D convolutions, with the goal of achieving faster inference speeds while maintaining comparable accuracy to existing state-of-the-art methods. Our proposed solution revolves around a 2D cost aggregation technique, which serves as a viable alternative to traditional 3D convolutions, delivering similar results in terms of accuracy. This innovative method significantly reduces computational overhead, facilitating more efficient resource utilization and paving the way for real-time applications. Complementing our 2D cost aggregation module, this study introduce a multi-stage feature extractor, designed to enhance feature representation while remaining straightforward and lightweight. The integration of the 2D cost aggregation and multi-stage feature extraction results in an efficient architecture for cost aggregation, simplifying the model and ensuring computational efficiency without sacrificing accuracy. This framework delivers high-performance stereo matching suitable for devices with limited computational capabilities. Through evaluation on benchmark datasets, the proposed network achieves 1.37 px EPE on Scene Flow and 4.22 % and 4.09 % D1-all on KITTI 2012 and KITTI 2015, respectively, yet runs in 13 ms with only 27 GFLOPs and 0.41 M parameters.}, archive = {J_ACCESS}, author = {Thai la and Linh Tao and Dai Watanabe}, doi = {10.1109/ACCESS.2025.3570572}, journal = {IEEE Access}, pages = {87112-87121}, shortjournal = {IEEE Access}, title = {Lightweight stereo matching for real-time applications with 2D cost volume aggregation}, volume = {13}, year = {2025}, }
• (2025). OUMVLP-OF: Multi-view large population gait database with dense optical flow and its performance evaluation. ACCESS, 13, 87100-87111. (www)

  @article{ , abstract = {This paper introduces OUMVLP-OF, a large-scale dataset that provides dense optical flow data derived from the world’s largest multi-view gait database. Unlike conventional gait datasets that primarily rely on binary silhouettes, OUMVLP-OF offers a motion-focused representation that captures fine-grained gait dynamics, enabling a richer and more detailed analysis of human movement. This way, OUMVLP-OF bridges the gap between silhouette-based and motion-based approaches, facilitating the development of novel gait recognition models that leverage both spatial and temporal information. The dataset comprises 10,307 subjects (5,114 males and 5,193 females), with gait sequences recorded from 14 distinct view angles, spanning from 0° to 270° in 15° intervals.A key novelty of OUMVLP-OF lies in its dual-version design: OF-V1, which preserves raw background information to encourage models to focus on subtle gait details while benefiting from natural variability as implicit data augmentation, and OF-V2, which features noise-free optical flow maps, isolating gait dynamics for precise motion analysis. This enables researchers to systematically study the impact of background noise on optical flow-based gait recognition and design more robust recognition models. By introducing the first large-scale optical flow dataset for gait analysis with both noisy and clean versions, OUMVLP-OF provides a unique benchmark for motion-based gait recognition. It advances the state of the art by enabling more robust cross-view recognition, better occlusion handling, and improved generalization across varied environments. To promote further research, OUMVLP-OF will be made publicly available at http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitLPOF.html}, archive = {J_ACCESS}, author = {Allam Shehata and Francisco M. Castro and Nicolás Guil and Manuel J. Marín-Jiménez and Yasushi Yagi}, doi = {10.1109/ACCESS.2025.3570280}, journal = {IEEE Access}, pages = {87100-87111}, shortjournal = {IEEE Access}, title = {OUMVLP-OF: Multi-view large population gait database with dense optical flow and its performance evaluation}, volume = {13}, year = {2025}, }
• (2025). A new hybrid PSO-HHO wrapper based optimization for feature selection. ACCESS, 13, 87090-87099. (www)

  @article{ , abstract = {Datasets used in data analysis often contain irrelevant or redundant attributes. These attributes hinder the performance of predictive models. Therefore, an effective preprocessing feature selection procedure is essential to identify the relevant features and eliminate unnecessary ones. Metaheuristic algorithms, mainly inspired by nature, are strong candidates for the feature selection process, as they can efficiently search large solution spaces. Metaheuristic algorithms provide flexible strategies for complex optimization problems such as traveling salesman problem, scheduling problems, nonlinear integer programming, and multi-objective optimization. Reasonably balancing exploration and exploitation will increase the search algorithm’s performance. This research combines Particle Swarm Optimization (PSO) and Harris Hawk Optimization (HHO) to form a Hybrid PSO-HHO approach to enhance the feature selection process. The wrapper-based approach employs the K-nearest neighbors classifier using the Euclidean distance metric to identify the optimal solutions. The performance of the proposed binary algorithm is evaluated using four standard benchmark datasets obtained from the UCI repository. Classification accuracy and the number of selected features are used as performance indicators. Experimental results demonstrated that the hybrid model achieved mean accuracies of 95.23%, 97.61%, 95.20% and 92.24% across the four benchmark datasets, outperforming traditional PSO and HHO approaches.}, archive = {J_ACCESS}, author = {Sumbul Azeem and Shazia Javed and Iftikhar Naseer and Oualid Ali and Taher M. Ghazal}, doi = {10.1109/ACCESS.2025.3570901}, journal = {IEEE Access}, pages = {87090-87099}, shortjournal = {IEEE Access}, title = {A new hybrid PSO-HHO wrapper based optimization for feature selection}, volume = {13}, year = {2025}, }
• (2025). Cache invalidation-based optimization in next generation wireless network: Taxonomy, review, and future directions. ACCESS, 13, 87068-87089. (www)

  @article{ , abstract = {Caching has emerged as a common approach utilized to optimize the performance of applications on wireless networks, with the objective of enhancing Quality-of-Service (QoS) metrics. However, as data undergoes frequent updates across most of the wireless network applications, it becomes challenging to provide updated data to the clients due to frequent disconnections and the inherent mobility of nodes. Cache Invalidation (CI) emerges as a promising mechanism to address these issues. In this paradigm, the servers broadcast, multicast or unicast the changes made in the cached data, prompting the clients to update their cached data and serve the requests efficiently. However, frequent disconnections and power-saving strategies of Mobile Terminals (MTs) can cause data loss and inconsistencies in the network. Recently, various researchers have extensively analyzed Cache Invalidation Techniques (CIT), and several CIT techniques are proposed to maintain the validity of cache contents in the MTs. This paper presents a novel taxonomy to categorize different CIT techniques that systematically optimize client query response times. Most of the reviewed techniques extend the permissible disconnection time for MTs, thus optimizing power consumption and enhance QoS by satisfying client requests with consistent data. The proposed taxonomy categorizes the existing CIT techniques based on several dimensions, notably: i) Traditional approaches, ii) contents of Invalidation Report (IR), iii) the IR-based Broadcast Time (BT), and the Disconnection Times (DT). Furthermore, this paper also presents an extensive comparative analysis of the existing techniques to examine their defining characteristics. The work also discuss the cache invalidation mechanisms for data-centric networks. This comprehensive review will provide future directions toward further exploration of CIT techniques and assist in selecting the appropriate CIT mechanism based on the requirements of the wireless application.}, archive = {J_ACCESS}, author = {Rajeev Tiwari and Sumit Kumar and Shashank Sheshar Singh and Harish Kumar Shakya}, doi = {10.1109/ACCESS.2025.3570736}, journal = {IEEE Access}, pages = {87068-87089}, shortjournal = {IEEE Access}, title = {Cache invalidation-based optimization in next generation wireless network: Taxonomy, review, and future directions}, volume = {13}, year = {2025}, }
• (2025). Traffic state estimation with spatio-temporal autoencoding transformer (STAT model). ACCESS, 13, 87048-87067. (www)

  @article{ , abstract = {Traffic state estimation is an essential component of Intelligent Transportation System (ITS) designed for alleviating traffic congestion. As traffic data is composed of intricate information which can also be impacted by various factors, scholars have been attempting to utilize state-of-the-art deep learning forecasting models in recent years. However, a more complex and robust model is required to extract long-range correlations with large-scale traffic data sequences. In order to overcome the weaknesses of deep learning models, the superior performance of transformers is expected to address this effectively in time-series forecasting with transport data. Employing the capabilities of transformers in extracting long-term trends and dynamic dependencies, the proposed model improves the deep learning prediction performance for real datasets. The findings indicate that the transformer-based model exhibited promising performance in forecasting long-term traffic patterns and characteristics with a large quantity of data. In this paper, a comparison across conventional hybrid deep learning models with the Spatio-Temporal Autoencoder Transformer (STAT) model was conducted using real-world datasets. The multi-head attention-based transformer model outperformed all other comparative approaches for large-scale data demonstrating its importance in measuring the error criteria. Comprehensive evaluations across various traffic prediction datasets have established the validity of the proposed approach. Also, for an efficient model selection, Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC), Hannan-Quinn Information Criterion (HQIC), and corrected AIC (AICc) tools were used to evaluate and compare models based on their ability to balance fit and complexity. The proposed model has shown improvements in RMSE for the STREET I980 and PEMSBAY datasets of 1.03 and 0.27, respectively. In RMSE for 60 minutes, for the same datasets, the improvements were 0.84 for STREET I980 and 0.56 for PEMS-BAY. These findings underscore the potential of transformer-based models in enhancing the performance of traffic prediction systems.}, archive = {J_ACCESS}, author = {Ali Reza Sattarzadeh and Pubudu N. Pathirana and van Thanh Huynh}, doi = {10.1109/ACCESS.2025.3569694}, journal = {IEEE Access}, pages = {87048-87067}, shortjournal = {IEEE Access}, title = {Traffic state estimation with spatio-temporal autoencoding transformer (STAT model)}, volume = {13}, year = {2025}, }
• (2025). Wavelet-enhanced deep learning ensemble for accurate stock market forecasting: A case study of nifty 50 index. ACCESS, 13, 87036-87047. (www)

  @article{ , abstract = {Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared ( $R^{2}$ ). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study.}, archive = {J_ACCESS}, author = {Priya Singh and Manoj Jha and Harshita Patel}, doi = {10.1109/ACCESS.2025.3568634}, journal = {IEEE Access}, pages = {87036-87047}, shortjournal = {IEEE Access}, title = {Wavelet-enhanced deep learning ensemble for accurate stock market forecasting: A case study of nifty 50 index}, volume = {13}, year = {2025}, }
• (2025). Teacher-student inter-brain and behavioral synchronies in remote education. ACCESS, 13, 87026-87035. (www)

  @article{ , abstract = {Interpersonal synchrony plays a crucial role in education. This study investigated inter-brain synchrony (IBS) during remote education and its effects on learning outcomes, rapport, and behavioral synchrony. Twenty-eight participants conducted a vocabulary learning task with a remote teacher under two conditions: voice and webcam conditions. The results showed that participants formed a higher IBS with the remote teacher in the webcam condition. We also observed that the brain regions of frontopolar prefrontal cortex (FP-PFC) and left orbitofrontal cortex (OFC) were positively associated with learning outcomes and rapport, respectively. Furthermore, we observed that the concurrent behavioral synchrony (BS) of eye movements was related to IBS in the left OFC. Our findings provide valuable insights for future research on interpersonal synchrony and their implications for the development of effective teaching methodologies in remote education.}, archive = {J_ACCESS}, author = {Jaehwan You and Myeongul Jung and Yoonhee Shin and Kwanguk Kim}, doi = {10.1109/ACCESS.2025.3569961}, journal = {IEEE Access}, pages = {87026-87035}, shortjournal = {IEEE Access}, title = {Teacher-student inter-brain and behavioral synchronies in remote education}, volume = {13}, year = {2025}, }
• (2025). Document-level neural machine translation with document embeddings. ACCESS, 13, 87015-87025. (www)

  @article{ , abstract = {Standard neural machine translation (NMT) assumes that document-level context information is irrespective. Most existing document-level NMT methods are satisfied with a smattering sense of shallow document-level information, such as using a few context sentences surrounding the source sentence as document-level information. Our work focuses on exploiting detailed document-level context in terms of multiple forms of document embeddings, which can sufficiently model deeper and richer document-level context. The proposed document-level NMT is implemented to enhance the Transformer baseline by introducing both global and local document-level clues on the source end. We compress the entire document text with explicit boundaries into a token-size global static document embedding, and the neighboring sentences as a token-size local dynamic document embedding and concatenate with the source tokens. Experiments reveal that the proposed method significantly improves the translation performance over strong baselines and other related studies.}, archive = {J_ACCESS}, author = {Linxing Zhu and Shu Jiang and Hai Zhao and Zuchao Li and Jiashuang Huang and Weiping Ding and Bao-Liang Lu}, doi = {10.1109/ACCESS.2025.3568089}, journal = {IEEE Access}, pages = {87015-87025}, shortjournal = {IEEE Access}, title = {Document-level neural machine translation with document embeddings}, volume = {13}, year = {2025}, }
• (2025). Pitch-speed feature space data augmentation for automatic speech recognition improvement in low-resource scenario. ACCESS, 13, 86998-87014. (www)

  @article{ , abstract = {Rapid advancements in Artificial Intelligence (AI) and Human Computer Interaction (HCI) have introduced new sensory interaction paradigms that engage diverse age groups and skill levels. However, these advancements are limited by the quality of Automatic Speech Recognition (ASR) tools available for regional languages. Research on ASR for Low-Resource Languages (LRL) addresses these limitations by developing techniques to improve ASR for local languages, thereby extending technical accessibility to native communities. This paper presents a data augmentation framework to address data sparsity for ASR of LRL. The proposed framework establishes a Pitch-Speed Feature Space (PSFS) through augmentation that encompasses all variations of domain specific speech samples spread evenly across the pitch and speed dimensions. Within this PSFS, the base audio data are augmented multifold to fill in the possible gaps within the feature space with a manageable data size for training the ASR system. The results show a positive trend in the improvement of ASR accuracy, even from augmentation of data from a single speaker. Depending on the individual characteristics of the speaker, a maximum ASR accuracy improvement of 10.3% was achieved. The results of this study contribute significantly to the improvement of the accuracy of ASR for LRL. The framework also complements existing efforts to enhance ASR accuracy in general. In addition to the gains in ASR accuracy, the model trained using the proposed framework can be utilised in a semi-supervised scheme to label unseen data, thereby expanding the dataset to enhance ASR system development for LRL.}, archive = {J_ACCESS}, author = {Syed Muhammad Zahid and Saad Ahmed Qazi}, doi = {10.1109/ACCESS.2025.3569579}, journal = {IEEE Access}, pages = {86998-87014}, shortjournal = {IEEE Access}, title = {Pitch-speed feature space data augmentation for automatic speech recognition improvement in low-resource scenario}, volume = {13}, year = {2025}, }
• (2025). 3D anisotropic asymmetric pyramid (AAP-net) network model for brain metastases segmentation on multi-sequence MRI. ACCESS, 13, 86983-86997. (www)

  @article{ , abstract = {Brain metastases (BM) present a formidable challenge in clinical oncology, demanding precise segmentation and prediction to optimize patient care. Definite detection of BM is crucial for effective treatment planning and prognosis, particularly in radiation therapy. Manual detection of BMs is laborious and prone to error due to their small sizes, irregular shapes, and low contrast in the imaging. The emergence of deep learning technologies has shown promise in automating BM detection, thereby addressing the limitations of manual methods. However, most existing approaches utilize 2D images, overlooking spatial information and employing uniform isotropic images, which deviates from real-life conditions where anisotropic images are more common. This paper presents a novel 3D anisotropic aysmmetric pyramid (AAP-Net) model that integrates advanced deep learning techniques to accurately detect and segment BMs in 3D magnetic resonance imaging (MRI) volumes, capturing the intricate details and spatial relationships crucial for precise diagnosis. By preserving the anisotropic nature of medical images, our model better reflects real-life scenarios, enhancing its efficacy in clinical settings. Through extensive evaluation, we demonstrate the superior performance of our 3D model compared to previous state-of-the-art models. This superiority is evidenced by achieving a mean Dice Similarity Coefficient (DSC) score across K-fold of $0.88~\pm ~0.01$ , Intersection Over Union (IOU) score of $0.80~\pm ~0.02$ , recall and precision values of $0.91~\pm ~0.01$ and $0.93~\pm ~0.01$ respectively using T1 post-contrast MRI images, underscoring the model’s enhanced accuracy and efficacy in BM detection and segmentation.}, archive = {J_ACCESS}, author = {Omar Farghaly and Priya Deshpande}, doi = {10.1109/ACCESS.2025.3570238}, journal = {IEEE Access}, pages = {86983-86997}, shortjournal = {IEEE Access}, title = {3D anisotropic asymmetric pyramid (AAP-net) network model for brain metastases segmentation on multi-sequence MRI}, volume = {13}, year = {2025}, }
• (2025). Experimental evaluation of a 2DOF controller for a novel multi-temperature transport system. ACCESS, 13, 86968-86982. (www)

  @article{ , abstract = {Multi-temperature transport systems offer a significant opportunity to improve supply chain efficiency by enabling the simultaneous transport of goods with diverse temperature requirements. However, these systems present critical environmental challenges due to high energy consumption, refrigerant leakage, and the significant amount of goods being damaged or lost due to inadequate temperature control. To address these issues, this study proposes a novel indirect cascade multi-temperature transport system with two temperature-controlled compartments. The design features a compact, well-sealed refrigeration system with reduced refrigerant charge and utilizes the natural refrigerant propane. A nonlinear control strategy based on feedback linearization is introduced to decouple system dynamics and precisely regulate compartment temperatures. Specifically, a two-degree-of-freedom (2DOF) controller, consisting of a feedforward trajectory planning algorithm and a feedback controller, is implemented. Additionally, an optimization-based input transformation allows prioritization of specific compartments when actuator limitations prevent accurate tracking of all compartment temperatures. The control approach was experimentally evaluated on a test bed and compared with a conventional proportional-integral (PI) controller. Across the conducted experiments, the proposed 2DOF control law reduced temperature deviations during normal operation by 40% to 77%, clearly highlighting its quantitative superiority over the conventional approach.}, archive = {J_ACCESS}, author = {Maximilian Lösch and Stefan Jakubek and Martin Kozek}, doi = {10.1109/ACCESS.2025.3569979}, journal = {IEEE Access}, pages = {86968-86982}, shortjournal = {IEEE Access}, title = {Experimental evaluation of a 2DOF controller for a novel multi-temperature transport system}, volume = {13}, year = {2025}, }
• (2025). Real-time FTM-based victim positioning system using heterogeneous robots in remote and outdoor scenarios. ACCESS, 13, 86949-86967. (www)

  @article{ , abstract = {Accurate victim localization in Remote, Outdoor, Unstructured, and Disaster (ROUD) scenarios remains a significant challenge due to limited infrastructure, complex terrains, and time constraints inherent in Search and Rescue (SAR) operations. This article introduces an innovative real-time positioning system that leverages Fine Time Measurement (FTM), a feature of the IEEE 802.11mc amendment, to detect WiFi-enabled devices typically carried by potential victims. The system integrates a Hybrid Wireless Sensor Network (H-WSN) composed of static and mobile anchors mounted on Uncrewed Ground and Aerial Vehicles (UGVs and UAVs), within a Robot Operating System (ROS)-based architecture. A centralized Feedback Information System (FIS) processes real-time RTT data from the field, executes a multilateration algorithm, and provides live geolocation updates to SAR coordinators. The system was validated during a large-scale SAR drill, successfully locating two types of victims—a semi-hidden surface victim and a buried victim—within a 2000 m2 area in under 7 minutes, without any filtering or post-processing. The maximum positioning error was 22.87 m for the buried victim and 17.14 m for the surface one. The complete system, including source code, dataset, and a containerized environment, is openly available to support reproducibility and further research, highlighting the potential of robotic and wireless technologies to enhance disaster response through accurate, real-time localization in complex environments.}, archive = {J_ACCESS}, author = {Juan Bravo-Arrabal and Carlos Simón Álvarez-Merino and Manuel Toscano-Moreno and Javier Serón-Barba and J. J. Fernandez-Lozano and Jose Antonio Gómez-Ruiz and Emil J. Khatib and Raquel Barco and Alfonso Garcia-Cerezo}, doi = {10.1109/ACCESS.2025.3570203}, journal = {IEEE Access}, pages = {86949-86967}, shortjournal = {IEEE Access}, title = {Real-time FTM-based victim positioning system using heterogeneous robots in remote and outdoor scenarios}, volume = {13}, year = {2025}, }
• (2025). KediNet: A hybrid deep learning architecture for thai dessert recognition. ACCESS, 13, 86935-86948. (www)

  @article{ , abstract = {Obesity and diabetes represent escalating health challenges, exacerbated by the widespread availability and convenience of processed foods. Automated dietary monitoring technologies offer valuable support to individuals managing these conditions by helping them track and regulate their consumption, particularly of high-calorie, sugar-rich foods. Toward this goal, this paper presents KediNet, a hybrid deep learning architecture that combines the strengths of convolutional neural networks (CNNs) and vision transformers (ViTs) for Thai dessert recognition. Our main contributions include: 1) the proposal of KediNet, which outperforms nine state-of-the-art deep learning models; 2) a comprehensive comparison of classification performance across different model architectures, image resolutions, and data augmentation techniques; and 3) the creation of THDESS-20, a new publicly available dataset containing 5,242 images across 20 classes of Thai desserts. Experimental results demonstrate that KediNet achieves 98.64% balanced accuracy at $224\times 224$ resolution, surpassing all other tested models even at higher resolutions, while maintaining inference times suitable for mobile applications. Our analysis shows that KediNet effectively extracts meaningful features from Thai dessert images despite their high intra-class variation and visual similarities between classes. This work advances the field of food recognition for health applications while addressing the specific challenges of ethnic cuisine classification.}, archive = {J_ACCESS}, author = {Nawanol Theera-Ampornpunt and Panisa Treepong and Naphadon Phokabut and Sarawut Phlaichana}, doi = {10.1109/ACCESS.2025.3569435}, journal = {IEEE Access}, pages = {86935-86948}, shortjournal = {IEEE Access}, title = {KediNet: A hybrid deep learning architecture for thai dessert recognition}, volume = {13}, year = {2025}, }
• (2025). ELM-DeepONets: Backpropagation-free training of deep operator networks via extreme learning machines. ACCESS, 13, 86927-86934. (www)

  @article{ , abstract = {Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets typically requires significant computational resources. To address this limitation, we propose ELM-DeepONets, an Extreme Learning Machine (ELM) framework for DeepONets that leverages the backpropagation-free nature of ELM. By reformulating DeepONet training as a least-squares problem for newly introduced parameters, the ELM-DeepONet approach significantly reduces training complexity. Validation on benchmark problems, including nonlinear ODEs and PDEs, demonstrates that the proposed method not only achieves superior accuracy but also drastically reduces computational costs. This work offers a scalable and efficient alternative for operator learning in scientific computing.}, archive = {J_ACCESS}, author = {Hwijae Son}, doi = {10.1109/ACCESS.2025.3570502}, journal = {IEEE Access}, pages = {86927-86934}, shortjournal = {IEEE Access}, title = {ELM-DeepONets: Backpropagation-free training of deep operator networks via extreme learning machines}, volume = {13}, year = {2025}, }
• (2025). Robust authentication: Leveraging hardware fingerprints and AI to enhance security against spoofing. ACCESS, 13, 86912-86926. (www)

  @article{ , abstract = {Fingerprint authentication blends biometric precision with user convenience, leveraging unique ridge patterns for fast, reliable, and tamper-resistant identity verification. However, it remains vulnerable to spoofing attacks, where adversaries create synthetic fingerprints using materials like gelatin. To mitigate this, multi-factor authentication (MFA) enhances security by verifying both the fingerprint and the user’s device, restricting access to authorized hardware and complicating attacks. Although Photo Response Non-Uniformity (PRNU) has been explored for device authentication, its reliability diminishes under varying lighting conditions. To address this, we propose an AI-driven approach to generate robust hardware fingerprints from fingerprint scanners, enabling high-accuracy device identification. Our method, trained on 2,000 fingerprint images from 10 scanners, achieves an impressive 98% accuracy in identifying the source scanner. By integrating fingerprint biometrics, hardware fingerprints, and passwords, our novel MFA protocol significantly enhances security against spoofing and forgery attacks. Through security analysis, we demonstrate its effectiveness in preventing unauthorized access. Finally, we validate our authentication framework using an AI-powered binary classifier model, showcasing its practicality for real-world security applications.}, archive = {J_ACCESS}, author = {K. Nimmy and Kurunandan Jain and S. M. Sachin and P. A. Abekaesh and Parv Venkitasubramaniam}, doi = {10.1109/ACCESS.2025.3569881}, journal = {IEEE Access}, pages = {86912-86926}, shortjournal = {IEEE Access}, title = {Robust authentication: Leveraging hardware fingerprints and AI to enhance security against spoofing}, volume = {13}, year = {2025}, }
• (2025). ResilioMate: A resilient multi-agent task executing framework for enhancing small language models. ACCESS, 13, 86892-86911. (www)

  @article{ , abstract = {Recent advances in large language models (LLMs) have been limited by their processing requirements and vulnerability to adversarial assaults, whilst short language models (SLMs) struggle with performance consistency in complex tasks. This research introduces ResilioMate, a resilient multi-agent framework that enhances SLMs by utilizing distributed cognitive burden distribution, dual-scale memory systems, and collaborative bias prevention strategies. The method employs dynamic task decomposition across specialized agents (e.g., Assistant, Checker) to minimize computational costs and combines short-term trajectory tracking with long-term self-reflective optimization for adaptive execution. At its core, the LeptoConnect model series (1.8B/7B parameters) is created using hybrid attention distillation and dynamic curriculum learning to enable cross-domain competence while retaining SLM efficiency. ResilioMate accomplishes three critical improvements: 1) The 1.8B LeptoConnect model attains 81.6% of GPT-4’s performance in knowledge graph construction through parameter-efficient fine-tuning with structured weight matrices; 2) LeptoConnect-7B achieves a score of 41.3 in database operations, compared to GPT-4’s 32.0, through collaborative cognitive load allocation; and 3) A bias-interception network effectively suppresses adversarial propagation while achieving code correction performance of ROUGE-L’s 42.86. The framework’s dual-scale memory architecture reduces computational redundancy by 26.4% through real-time task tracking and multi-agent knowledge refinement. These developments illustrate ResilioMate’s efficacy in bridging the performance gap between SLMs and LLMs, offering a scalable solution for the deployment of efficient language agents in real-time and peripheral computing environments.}, archive = {J_ACCESS}, author = {Yubing Xiong and Mingrui Huang and Xuechen Liang and Meiling Tao}, doi = {10.1109/ACCESS.2025.3567244}, journal = {IEEE Access}, pages = {86892-86911}, shortjournal = {IEEE Access}, title = {ResilioMate: A resilient multi-agent task executing framework for enhancing small language models}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid architecture with fast lightweight encoder and transformer under attention fusion for the enhancement of sand dust and haze image restoration. ACCESS, 13, 86874-86891. (www)

  @article{ , abstract = {Outdoor weather conditions such as haze, fog, sand dust, and low light significantly degrade image quality, causing color distortions, low contrast, and poor visibility. In spite of the significant importance of restoring such degraded images, challenges still exist in haze removal and sand dust image enhancement and other restoration tasks, making this field relatively underexplored. While Encoder-Decoder-based neural networks have shown noticeable improvements in image restoration, their ability to further improve the image quality still remains constrained. Recent advancements in vision transformers and self-attention mechanisms have achieved remarkable success in various computer vision tasks. However, directly applying Vision Transformers for image restoration presents serious challenges, including feature extraction between local and global representations. This research aims to address these limitations by restoring both sand dust and haze degraded images to a more natural and visually realistic appearance, ensuring enhanced visibility, balanced colors, and refined details. We propose a novel hybrid architecture that combines depth-wise local feature extraction using lightweight Encoders with global feature extraction via Vision Transformers. These features are fused through an attention fusion mechanism, ensuring seamless interaction between local and global feature representations. Finally, a single lightweight Decoder reconstructs a high-quality restored image that closely matches the ground truth. The proposed method effectively reduces feature inconsistency between Vision Transformer-based global features and lightweight encoder-based local features, leading to state-of-the-art performance in both synthetic and real-world sand dust and haze-degraded images. Extensive evaluations show that our proposed method outperforms all previously existing image restoration methods, delivering improved visibility, realistic textures, and superior image quality. Degraded images exhibiting varying degrees of color cast from mild to severe are evaluated both qualitatively and quantitatively. In addition, a comparison of training and testing time and the novel Energy Efficiency Index (EEI) analysis is assessed. The results show that the proposed method outperforms all previous conventional and advanced deep learning methods in terms of visual quality, evaluation metrics, training and testing time, and novel EEI.}, archive = {J_ACCESS}, author = {Muhammad Khawaja Kashif Masood and Enrique Nava Baro and Pablo Otero}, doi = {10.1109/ACCESS.2025.3570983}, journal = {IEEE Access}, pages = {86874-86891}, shortjournal = {IEEE Access}, title = {A novel hybrid architecture with fast lightweight encoder and transformer under attention fusion for the enhancement of sand dust and haze image restoration}, volume = {13}, year = {2025}, }
• (2025). Patch-based deep-learning model with limited training dataset for liver tumor segmentation in contrast-enhanced hepatic computed tomography. ACCESS, 13, 86863-86873. (www)

  @article{ , abstract = {The automatic segmentation of liver tumors plays an important role in the diagnosis and treatment of liver cancer. While deep convolutional neural network (DCNN) models are widely used for segmentation tasks in medical imaging, they require 1,000 to 10,000 annotated cases for effective training. However, assembling datasets of this size is a significant challenge due to the labor-intensive nature of tumor annotation, which often requires the expertise of radiologists. We propose a multi-scale Hessian-enhanced patch-based neural network, which we call an MHP-Net, for liver tumor segmentation with a limited dataset. Our approach involves sampling 3D patches from the input images for training a neural network, rather than using all input images, which are commonly used in DCNN training. We applied a multi-scale Hessian ellipsoid enhancer to extract multi-scale features of the liver tumor. We implemented a region-stratified sampling strategy to prevent overfitting in patch-based neural network training. We evaluated the effectiveness of our model using a dataset from the Liver Tumor Segmentation Benchmark (LiTS). To investigate the performance of the model under limited sample-size conditions, we trained it and state-of-the-art (SOTA) deep learning models with 7, 14, and 28 tumors. Our model achieved average Dice scores of 0.691, 0.709, and 0.719 which were higher than those ranging between 0.395 and 0.641 with the SOTA models. Remarkably, our model also achieved a Dice score (0.709) on par with the top model (0.702) in the MICCAI 2017 worldwide competition, despite utilizing only 1.5% (14 out of 908 tumors) of the training data.}, archive = {J_ACCESS}, author = {Yuqiao Yang and Muneyuki Sato and Ze Jin and Kenji Suzuki}, doi = {10.1109/ACCESS.2025.3570728}, journal = {IEEE Access}, pages = {86863-86873}, shortjournal = {IEEE Access}, title = {Patch-based deep-learning model with limited training dataset for liver tumor segmentation in contrast-enhanced hepatic computed tomography}, volume = {13}, year = {2025}, }
• (2025). Transport mode choice for disaggregated mobility demand generation. ACCESS, 13, 86848-86862. (www)

  @article{ , abstract = {The OpenStreetMap Mobility Demand Generator (OMOD) is an open-source tool that creates realistic synthetic mobility demand for any region in Germany. The tool focuses on ease of use and fast prototyping, and requires only OpenStreetMap (OSM) data from the user. In this work, we introduce the aspect of traffic mode choice into OMOD. We utilize a two-stage multinomial logit model that works with precise origin-destination coordinates and exact departure times to determine each trip’s travel time and other level-of-service attributes based on network information acquired from OpenStreetMap and GTFS public transit schedules. We evaluate the performance of our model in two stages: first, directly on a separate test set of the household travel survey, and second, for a microsimulation of three different German cities. On the household travel survey, we reach accuracies between 64.3 % and 77.8 % with well-fitted calibration curves. Achieving high performance in the microsimulation is more difficult due to the existence of downstream errors. Nonetheless, we achieve close fits for all modes with the exception of the car passenger mode.}, archive = {J_ACCESS}, author = {Leo Strobel and Marco Pruckner}, doi = {10.1109/ACCESS.2025.3570126}, journal = {IEEE Access}, pages = {86848-86862}, shortjournal = {IEEE Access}, title = {Transport mode choice for disaggregated mobility demand generation}, volume = {13}, year = {2025}, }
• (2025). An efficient CMOS RF-DC rectifier based on a dual dynamic self-biasing approach. ACCESS, 13, 86841-86847. (www)

  @article{ , abstract = {This paper presents an efficient CMOS rectifier with dual dynamic gate/body self-biasing network design to improve power conversion efficiency (PCE) and sensitivity. The proposed rectifier is designed using a 180 nm CMOS process and occupies a silicon area of $102~\mu $ m x $78~\mu $ m. At 920 MHz, it achieves a high PCE of 78.5% at an input power ( $P_{IN}$ ) of -18.9 dBm. It demonstrates a dynamic range (DR) of 26.1 dB and a sensitivity of −18.4 dBm, with an output of 1 V across a 100 k $\Omega $ load. The proposed rectifier achieves a 29% increase in DR and a 27% improvement in sensitivity compared to the conventional rectifier. Moreover, the proposed rectifier architecture outperforms prior single-stage rectifiers in terms of PCE, sensitivity, and dynamic range.}, archive = {J_ACCESS}, author = {Ahmed Reda Mohamed and Abdulaziz A. Al-Khulaifi and Munir A. Al-Absi}, doi = {10.1109/ACCESS.2025.3570418}, journal = {IEEE Access}, pages = {86841-86847}, shortjournal = {IEEE Access}, title = {An efficient CMOS RF-DC rectifier based on a dual dynamic self-biasing approach}, volume = {13}, year = {2025}, }
• (2025). Fused YOLO and traditional features for emotion recognition from facial images of tamil and russian speaking children: A cross-cultural study. ACCESS, 13, 86828-86840. (www)

  @article{ , abstract = {Cross-cultural study that avoids bias in the formulation of emotion recognition models is indispensable to address the challenges in facial emotion classification for children, for it being a relatively difficult task. With evidences from literature for the hybrid feature extraction approaches to improve the image classification accuracy, this research work focuses on developing a hybrid framework for emotion recognition from facial images of Tamil and Russian children. The dataset is audio video recording of 28 Tamil speaking and 64 Russian speaking children. The data is collected in a controlled environment and labelled by experts. Traditional features like Grey Level Cooccurrence Matrix (GLCM) and facial landmark are extracted and are fused with You Look Only Once (YOLO V5) features. While facial landmarks and GLCM provide useful information about the facial expressions and texture of the image, YOLO V5 being a single-stage object detector makes the hybrid model super-fast and achieve high accuracy in detecting small objects and in low-light settings. The different classifiers that includes KNN, SVM, Random Forest, XGBoost, and Multilayer Perceptron, is employed yielding the accuracy result of 93%, 86%, 89%, 88%,and 90%. The use of majority voting ensemble of heterogeneous classifiers for the final prediction strengthens the model further, yielding an accuracy as high as 96% for the custom cross-cultural dataset, consisting of facial images of Russian and Indian children. This is consistent with the results obtained for Indian and Russian datasets. Further, the ablation study unveils the effect of feature fusion in boosting the performance and the dominance of YOLO V5 features over the other two.}, archive = {J_ACCESS}, author = {A. Mary Mekala and M. Varalakshmi and C. P. Achyutha Gowda and Leti Manish Kumar and Elena E. Lyakso and Olga Frolova and Ruban Nersisson}, doi = {10.1109/ACCESS.2025.3569771}, journal = {IEEE Access}, pages = {86828-86840}, shortjournal = {IEEE Access}, title = {Fused YOLO and traditional features for emotion recognition from facial images of tamil and russian speaking children: A cross-cultural study}, volume = {13}, year = {2025}, }
• (2025). Economic evaluation of the investment in sensor equipment based on data valuation in prediction model. ACCESS, 13, 86812-86827. (www)

  @article{ , abstract = {As sensor investment decisions become increasingly crucial across industries, the challenge of optimal sensor selection has gained prominence. Traditional approaches primarily address this problem from a performance-oriented perspective, focusing on metrics like prediction or estimation accuracy. However, these methods often overlook the economic value that sensors can contribute. This paper addresses this gap by proposing a framework for evaluating sensors based on their economic contributions. Our approach leverages cooperative game theory and integrates explainable AI (XAI) techniques for feature valuation to assess the impact of each sensor within prediction models. We introduce two valuation methods—Marginal Influence Between Models (MIBM) and Marginal Influence Within Models (MIWM)—and provide guidelines on when to apply each method depending on the scenario. The MIWM method is found to be more effective in capturing the interactions and synergies among sensors, although it comes with higher computational costs. On the other hand, MIBM is advantageous in scenarios where computational efficiency and robustness are prioritized. To support method selection, we also analyze the computational complexity of both approaches and derive error bounds. Through numerical experiments using real-world semiconductor manufacturing data, we demonstrate that sensor selection based on economic value can lead to significantly different outcomes compared to conventional methods. Our findings show the importance of considering both economic benefit and cost factors in sensor selection, particularly in applications involving multiple sensor clusters or heterogeneous data sources.}, archive = {J_ACCESS}, author = {Jeong-Gi Lee and Deok-Joo Lee}, doi = {10.1109/ACCESS.2025.3569962}, journal = {IEEE Access}, pages = {86812-86827}, shortjournal = {IEEE Access}, title = {Economic evaluation of the investment in sensor equipment based on data valuation in prediction model}, volume = {13}, year = {2025}, }
• (2025). EPLC-pose: A lightweight student posture recognition network under panoramic classroom. ACCESS, 13, 86799-86811. (www)

  @article{ , abstract = {With the development of intelligence, concepts such as “smart classroom” have emerged, and the intelligent recognition of classroom behavior has become a hot research topic. However, the current intelligent recognition of classroom behavior still faces challenges such as complex single-person poses, severe occlusions, sampling from non-real teaching classrooms, and the lack of lightweight recognition networks. We aim to use deep learning-based human pose estimation methods to accurately estimate and predict student behaviors in real classrooms and design a network that prioritizes lightweight architecture. Therefore, in this paper, we propose a lightweight network called EPLC-Pose (Efficient Panoramic Lightweight Classroom Pose Network), a novel architectural approach based on the YOLOv8-Pose framework. The network’s backbone and neck components have been enhanced by replacing the traditional C2F module with the innovative C-UIB module. Due to the advanced design of the C-UIB module, the number of parameters has been significantly reduced. Additionally, to address the issue of limb occlusion, the SEAM attention mechanism is introduced in the Neck part. To evaluate our method, we have created a comprehensive panoramic classroom behavior pose dataset (CPKD) consisting of 6000 images. The network has shown competitive results on both the CPKD and MS COCO datasets. Compared to the baseline model, our approach achieves a 46.37% reduction in parameters, a 48.57% decrease in GFLOPS, and an improvement in mAP50 by 1% and 0.4% respectively.}, archive = {J_ACCESS}, author = {Yanhong Ji and Yibo Jin and Zuxuan Wang and Suyan Tan}, doi = {10.1109/ACCESS.2025.3570792}, journal = {IEEE Access}, pages = {86799-86811}, shortjournal = {IEEE Access}, title = {EPLC-pose: A lightweight student posture recognition network under panoramic classroom}, volume = {13}, year = {2025}, }
• (2025). Enhancing hybrid classification for plant diseases with deep feature selection based on analytical entropy and statistical method. ACCESS, 13, 86781-86798. (www)

  @article{ , abstract = {A significant portion of research in the hybrid classification area aims to reduce the number of deep features. However, many approaches insufficiently address the relationships between deep features and specific classes or diseases. This study aims to use information entropy to distinguish between valuable and less valuable features in the extracted deep feature set. The architecture of state-of-the-art models influences the characteristics of deep features, which can be categorized into three types: negative, zero, and positive values. Accurate predictions in typical machine learning models entail identifying valuable features, especially those with the most positive values. We perform one-way analysis of variance (ANOVA) and mutual information (MI) analysis to verify the correlation between the remaining deep features and specific classes or diseases. We discard features with weak relations based on predefined thresholds established by the involved f-statistic and MI mean values. This enhanced hybrid classification approach uses the entropy mean threshold, skewness visualization, the standard deviation, and the mean probability of valuable feature threshold to select valuable features and remove less valuable features, as well as one-way ANOVA and MI to evaluate relations and eliminate weakly related deep features. The results of all experiments show that the proposed approach exhibits higher accuracy than traditional hybrid classification and remains effective across all plant species. For tomatoes, ResNet-34 is the best model, achieving an excellent accuracy of 99.94% with 98 features. For sugarcane, ResNet-34 achieves a remarkable accuracy of 99.41% with 121 features. For corn, EfficientNet-B4 attains a significant accuracy of 99.77% with 123 features.}, archive = {J_ACCESS}, author = {Phanthawat Phiphatkamtorn and Sutasinee Jitanan}, doi = {10.1109/ACCESS.2025.3569760}, journal = {IEEE Access}, pages = {86781-86798}, shortjournal = {IEEE Access}, title = {Enhancing hybrid classification for plant diseases with deep feature selection based on analytical entropy and statistical method}, volume = {13}, year = {2025}, }
• (2025). Neural-XGBoost: A hybrid approach for disaster prediction and management using machine learning. ACCESS, 13, 86768-86780. (www)

  @article{ , abstract = {Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction model (N-XGB), a hybrid model that combines neural networks (NN) for feature extraction with XGBoost for classification. The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting techniques for improved accuracy. The N-XGB model achieves an accuracy of 94.8% and an average F1 score of 0.95 on a real-world dataset that includes wildfires, floods and earthquakes, significantly outperforming baseline models such as random forest, Support vector machine and logistic regression 85% accuracy. The balanced F1 scores for wildfires 0.96, floods 0.93, and earthquakes 0.96 demonstrate the model’s robustness in multi-class classification. The Synthetic Minority Oversampling Technique (SMOTE) balances datasets and improves model efficiency and capability. The proposed N-XGB model provides a reliable and accurate solution for predicting disasters and contributes to improving preparedness, resource allocation and risk management strategies.}, archive = {J_ACCESS}, author = {Muhammad Asim Saleem and Ashir Javeed and Watit Benjapolakul and Wattanasak Srisiri and Surachai Chaitusaney and Pasu Kaewplung}, doi = {10.1109/ACCESS.2025.3569499}, journal = {IEEE Access}, pages = {86768-86780}, shortjournal = {IEEE Access}, title = {Neural-XGBoost: A hybrid approach for disaster prediction and management using machine learning}, volume = {13}, year = {2025}, }
• (2025). Deep learning for time-series forecasting with exogenous variables in energy consumption: A performance and interpretability analysis. ACCESS, 13, 86746-86767. (www)

  @article{ , abstract = {The rise of decentralized energy sources and renewables demands advanced grid planning, with short-term load forecasting (STLF) playing a crucial role. Energy demand in smart grids is highly variable and influenced by external factors, making accurate forecasting challenging. While deep learning models excel in time-series forecasting, their ability to integrate exogenous variables remains uncertain. This study evaluates different deep learning architectures for STLF, including recurrent (Long Short-Term Memory, LSTM), probabilistic (Deep Autoregressive, DeepAR), attention-based (Temporal Fusion Transformer, TFT), and foundation models (TimeLLM), each designed to capture temporal dependencies differently. The “Smart Meters in London” dataset, comprising 1.6 million energy consumption records enriched with weather and socio-demographic data, was used for evaluation. Results show that incorporating exogenous variables reduces forecasting error, with TFT achieving a test MAE of 1.643, RMSE of 2.903, and sMAPE of 18.8% on a lower-variability subset, leveraging long-range dependencies for enhanced predictions. DeepAR outperformed other models on the larger, noisier dataset, achieving a test MAE of 1.699, RMSE of 3.310, and sMAPE of 18.8%, demonstrating strong generalization. LSTM performed reasonably well but struggled to utilize exogenous information, leading to higher forecasting errors. TimeLLM, despite tailored prompting for time-series forecasting, failed to outperform task-specific models, with a test MAE of 2.193, RMSE of 3.473, and sMAPE of 22.89%, highlighting the challenges of adapting foundation models trained on different modalities to structured time-series forecasting. Beyond predictive accuracy, interpretability analysis challenges the assumption that attention mechanisms reliably capture feature importance. Performance degradation analysis revealed that perturbing features ranked highly by SHAP values and permutation feature importance led to sharper error increases than attention-based rankings, with permutation demonstrating the strongest correlation with prediction errors. These findings underscore the role of Explainable AI (XAI) in time-series forecasting, emphasizing the need for robust interpretability frameworks to ensure transparency in deep learning-based predictions.}, archive = {J_ACCESS}, author = {Ava Mohammadi and Gonzalo Nápoles and Yamisleydi Salgueiro}, doi = {10.1109/ACCESS.2025.3570618}, journal = {IEEE Access}, pages = {86746-86767}, shortjournal = {IEEE Access}, title = {Deep learning for time-series forecasting with exogenous variables in energy consumption: A performance and interpretability analysis}, volume = {13}, year = {2025}, }
• (2025). Gutek: Intelligent revision algorithms. ACCESS, 13, 86732-86745. (www)

  @article{ , abstract = {This paper introduces Gutek, a novel open-source framework designed to optimize the learning process through intelligent revision algorithms. Gutek is built on a low-code architecture that facilitates rapid customization and extension with minimal alterations to the core codebase. By leveraging the Spring Framework, the system automates essential processes such as dependency injection, component management, and database operations via Spring Data JPA, while employing reflection to dynamically integrate new revision algorithms, charts, and custom card types. The framework supports bidirectional revision interfaces, offering both conventional and reverse revision modes to enhance memory retention and accommodate diverse learning strategies. Comparative evaluations with established open-source systems, including Anki, Mnemosyne, and OpenCards, reveal that Gutek maintains lower code complexity and superior maintainability, as demonstrated by comprehensive software quality metrics and user-based extensibility assessments. Specifically, Gutek exhibits up to 80% fewer lines of code compared to Anki and Mnemosyne and achieves the lowest number of critical issues reported in SonarQube analysis. These results indicate that Gutek provides extensive customization capabilities with minimal coding effort, making it especially suitable for educators, developers, and practitioners seeking a flexible yet robust learning tool.}, archive = {J_ACCESS}, author = {Łukasz Gałka}, doi = {10.1109/ACCESS.2025.3570624}, journal = {IEEE Access}, pages = {86732-86745}, shortjournal = {IEEE Access}, title = {Gutek: Intelligent revision algorithms}, volume = {13}, year = {2025}, }
• (2025). Landslide deformation uncertainty quantification using conformalized graph neural networks: A case study in sichuan province, china. ACCESS, 13, 86719-86731. (www)

  @article{ , abstract = {Landslide deformation prediction plays a crucial role in geohazard risk management, yet existing methods often struggle to quantify prediction uncertainties effectively. This paper presents GNN-CF, a novel framework that integrates Graph Neural Networks (GNN) with Conformal Prediction for reliable interval forecasting of landslide deformation. The proposed approach addresses two significant challenges: ensuring valid marginal coverage despite dependencies between calibration (training) and test data, and reducing prediction interval inefficiency while maintaining coverage guarantees. The framework incorporates a topology-aware correction model and employs graph-based regularization terms to enhance prediction accuracy. The methodology is validated using field monitoring data from the Wutong Village landslide in Sichuan Province, China, where six GNSS monitoring points track surface deformation patterns. Experimental results demonstrate that GNN-CF consistently outperforms state-of-the-art (SOTA) methods, including GRU-LUBE, DNLSSM, and SA-DeepAR, achieving superior Prediction Interval Coverage Probability (PICP) while maintaining smaller Prediction Interval Normalized Average Width (PINAW). The framework’s effectiveness in balancing coverage and computational complexity makes it a feasible solution for landslide monitoring applications requiring reliable uncertainty quantification.}, archive = {J_ACCESS}, author = {Xiaopeng Ren and Wei Liu and Weiqi Yang and Lixuan Mao and Huajin Li}, doi = {10.1109/ACCESS.2025.3568273}, journal = {IEEE Access}, pages = {86719-86731}, shortjournal = {IEEE Access}, title = {Landslide deformation uncertainty quantification using conformalized graph neural networks: A case study in sichuan province, china}, volume = {13}, year = {2025}, }
• (2025). Myoelectric stimulation silent subwoofer which presents the deep bass-induced body-sensory acoustic sensation. ACCESS, 13, 86705-86718. (www)

  @article{ , abstract = {Building on recent advances in multimodal haptic feedback, we propose a portable, low-noise system that integrates electrical muscle stimulation (EMS) with low-frequency vibrotactile cues to deliver a deep bass-induced body-sensory acoustic experience in virtual reality (VR). Prior to the experiment, each of the twenty-four participants underwent a brief calibration to adjust EMS intensity for optimal perceptibility and comfort. During the VR live concert simulation—where participants wore head-mounted displays (HMDs) and headphones—the system demonstrated rhythm and harmony precision comparable to conventional loudspeaker/subwoofer setups, while substantially reducing ambient noise. Moreover, the gradual acclimation of users to EMS indicates that high-fidelity, multimodal stimulation can further enhance immersion and comfort. These results underscore the potential of our approach for both VR concerts and everyday music listening applications.}, archive = {J_ACCESS}, author = {Keiichi Zempo and Ryo Kashiwabara and Naoto Wakatsuki and Koichi Mizutani}, doi = {10.1109/ACCESS.2025.3565283}, journal = {IEEE Access}, pages = {86705-86718}, shortjournal = {IEEE Access}, title = {Myoelectric stimulation silent subwoofer which presents the deep bass-induced body-sensory acoustic sensation}, volume = {13}, year = {2025}, }
• (2025). Hybrid control strategy for VIENNA rectifiers in more electric aircraft electrical systems. ACCESS, 13, 86686-86704. (www)

  @article{ , abstract = {The increasing adoption of power electronics devices in the more electric aircraft (MEA) industry brings the need for advanced power quality solutions to ensure stable operation under challenging conditions such as unbalanced grids, unbalanced DC loading, arc faults, and variable grid frequencies. This study introduces a hybrid controller for Vienna rectifiers to overcome the addressed power quality issues. The proposed controller strategy integrates Hysteresis Current Control (HCC), a Second Order Generalized Integrator-Phase Locked Loop (SOGI-PLL), and a Proportional Integral Resonant (PIR) controller, collectively improving grid synchronization, enhancing power quality by minimizing total harmonic distortion (THD), and ensuring stable operation during grid disturbances. The proposed strategy achieves a $THD_{i}$ of 2.24%, significantly lower than the 4.74% reported in comparable studies, while maintaining compliance with the requirements of MIL-STD-704F. Extensive simulations validate its effectiveness, demonstrating robust performance in scenarios with unbalanced grids, unbalanced loading, and series arc faults, alongside maintaining minimal DC voltage ripple and near-unity power factor. The findings represent a significant advancement in power electronics, offering a reliable and efficient solution for dynamic and fault-prone electrical systems.}, archive = {J_ACCESS}, author = {Mehmet Can Alphan and Gurkan Soykan}, doi = {10.1109/ACCESS.2025.3567113}, journal = {IEEE Access}, pages = {86686-86704}, shortjournal = {IEEE Access}, title = {Hybrid control strategy for VIENNA rectifiers in more electric aircraft electrical systems}, volume = {13}, year = {2025}, }
• (2025). TFDGiniXML: A novel explainable machine learning framework for early detection of cardiac abnormalities based on nonlinear time-frequency distribution gini index features. ACCESS, 13, 86666-86685. (www)

  @article{ , abstract = {Cardiovascular diseases (CVDs) are the leading cause of global death, with approximately 80% of such CVD mortalities occurring in low and middle-income regions. Early detection of cardiac abnormalities is essential for timely intervention and minimizing mortalities. Automated CVD detection methods are vital, particularly in areas with limited healthcare resources. However, most existing AI-based techniques lack three critical aspects: model interpretability, longer-duration analysis, and effective use of nonlinear time-frequency approaches, which are necessary for ECG signals due to their nonlinear, nonstationary, and multi-component nature. This study proposes explainable intelligent classifiers incorporated with a novel sequence of time-frequency energy Gini Index (GI) features from the QRS complexes of ECG signals to address these challenges and enable early-stage CVD detection. These features are extracted using the Choi-Williams Time-Frequency method, reporting the first instance application of GI measures to nonlinear time-frequency distribution (TFD) for ECG analysis. Features are computed from one-minute windows, covering 30 minutes of ECG recordings. These interpretable features provide clear insights into normal and abnormal ECG patterns. The proposed method was trained and validated using the MIT-BIH Arrhythmia and Fantasia-Normal databases. Eight machine learning classifiers, including SVM, Random Forest, XGBoost, Gaussian Naïve Bayes, KNN, LinearBoost, CatBoost, and Logistic Regression were tested. The best model achieved 100% sensitivity, 94.4% accuracy, 95.24% F1-score, 90% precision, and 92.59% AUC on the test dataset. High sensitivity ensures reliability for medical screening by reducing False Negatives, making the approach suitable for integration into any type of smart device for accurate online and offline monitoring of CVD abnormalities.}, archive = {J_ACCESS}, author = {Mohamed Aashiq and Shaiful Jahari Hashim and Fakhrul Zaman Rokhani and Marsyita Hanafi and Ahmed Faeq Hussein}, doi = {10.1109/ACCESS.2025.3566094}, journal = {IEEE Access}, pages = {86666-86685}, shortjournal = {IEEE Access}, title = {TFDGiniXML: A novel explainable machine learning framework for early detection of cardiac abnormalities based on nonlinear time-frequency distribution gini index features}, volume = {13}, year = {2025}, }
• (2025). Multi-vehicle object recognition method based on YOLOv7-W. ACCESS, 13, 86653-86665. (www)

  @article{ , abstract = {To meet the demands of intelligent traffic control systems for high-precision vehicle object recognition, an improved recognition algorithm, YOLOv7-W, is proposed based on the YOLOv7 algorithm. This algorithm effectively addresses three key challenges: high vehicle miss detection rates, inadequate perception of small-angle targets, and insufficient feature extraction capabilities. To enhance YOLOv7’s feature extraction abilities, the Global Attention Mechanism (GAM) is integrated into the feature extraction process. To improve the perception of small targets, the backbone network is reconstructed using Omni-dimensional Dynamic and Efficient Aggregation network module (ODEANet) to capture more comprehensive global information. The Context-aware Transformer (CoT) block is adopted to replace the original E-ELAN module in the neck network, guiding dynamic attention matrix learning and optimizing visual representations. Meanwhile, the computational complexity in terms of GFLOPs (Giga Floating Point Operations) is reduced by 3.1%. Analysis of the XUPEI-CAR experimental dataset reveals significant variations in features across different traffic flow densities. To improve the matching accuracy of prior frames, the k-means++ clustering algorithm is employed to optimize the prior frame parameters. Experimental results demonstrate that YOLOv7-W achieves recognition accuracies of 97.125%, 94.85%, and 94.6% in free-flow, synchronized flow, and congested traffic scenarios, respectively. Compared to YOLOv7, these improvements result in accuracy increases of 3.65%, 3.2%, and 1.4% in the three scenarios. The optimized architecture maintains real-time performance while reducing model parameters by 13.37%, with frame rates of 74.63, 79.37, and 75.76 FPS in different traffic conditions.}, archive = {J_ACCESS}, author = {Xin Liu and Jingbin Zhao and Zhi Zhang and Rongxing Wu and Xun Li}, doi = {10.1109/ACCESS.2025.3569849}, journal = {IEEE Access}, pages = {86653-86665}, shortjournal = {IEEE Access}, title = {Multi-vehicle object recognition method based on YOLOv7-W}, volume = {13}, year = {2025}, }
• (2025). Purchase now or later? attracting strategic consumers with an extended warranty: A study of new and reman products. ACCESS, 13, 86616-86652. (www)

  @article{ , abstract = {Extended warranties have emerged as a strategic imperative for Original Equipment Manufacturers, playing a pivotal role in fostering consumer trust and influencing their preference between remanufactured and new products. It is customary for vendors to integrate extended warranties and lower product prices towards the end of a selling season, thereby prompting consumers to manage their purchasing timing tactically. In light of consumers’ strategic behavior, Original Equipment Manufacturers are compelled to meticulously determine both product pricing and warranty offerings to uphold profitability. The paper delves into the retailing strategies and optimal pricing decisions with extended warranty of Original Equipment Manufacturers within a Closed−Loop Supply Chain. This exploration takes into consideration two distinct consumer types: myopic and strategic. Additionally, the analysis accounts for a limited volume of product returns and the level of consumer acceptance regarding remanufactured products. The study seeks to investigate the impact of extended warranty and the discounting rate of consumers’ perceived value regarding deferred purchases. The findings suggest that the Original Equipment Manufacturers retail strategy and prices is significantly influenced by the unit manufacturing/production and (re)manufacturing costs, as well as the composition of the consumer base when extended warranty is offered. The results of the numerical experiments yield additional insights to implement straightforward countermeasure strategies with extended warranty.}, archive = {J_ACCESS}, author = {P. Jalapathy and M. Mubashir Unnissa}, doi = {10.1109/ACCESS.2025.3570508}, journal = {IEEE Access}, pages = {86616-86652}, shortjournal = {IEEE Access}, title = {Purchase now or later? attracting strategic consumers with an extended warranty: A study of new and reman products}, volume = {13}, year = {2025}, }
• (2025). Privacy and security challenges in federated learning for UAV systems: A systematic review. ACCESS, 13, 86599-86615. (www)

  @article{ , abstract = {Unmanned Aerial Vehicles have become indispensable assets across various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated Learning as a potential solution. Federated learning (FL), being decentralized and collaborative, holds promise in addressing the privacy risks inherent in centralized data processing while enhancing model performance. In this review, we explore the privacy and security implications of FL in UAV ecosystems. We highlight FL’s potential to mitigate privacy risks by aggregating model updates locally, minimizing data transmission needs. In addition, we examine security challenges and evaluate protective mechanisms. Through a systematic review of the literature, we identified gaps and proposed future research directions, with the aim of improving the security and privacy of FL in UAV applications. Our findings highlight advanced strategies, such as secure aggregation, adversarial defenses, and lightweight cryptographic techniques, to mitigate privacy and security threats in Federated Learning-based UAV. They also underscore the need for real-world validations and regulatory frameworks to ensure resilient and ethically governed deployments in UAV networks.}, archive = {J_ACCESS}, author = {Ahmed Saleh Sulaiman Al Farsi and Ajmal Khan and Muhammad Rizwan Mughal and Mohammed M. Bait-Suwailam}, doi = {10.1109/ACCESS.2025.3570538}, journal = {IEEE Access}, pages = {86599-86615}, shortjournal = {IEEE Access}, title = {Privacy and security challenges in federated learning for UAV systems: A systematic review}, volume = {13}, year = {2025}, }
• (2025). FLavourite: Horizontal and vertical federated learning setting comparison. ACCESS, 13, 86588-86598. (www)

  @article{ , abstract = {Federated learning (FL) enables multiple organizations to collaboratively train AI models while keeping data separate. Depending on data distribution, FL can be set up horizontally (HFL) or vertically (VFL). When multi-modal data are already available in the same infrastructure, the choice of FL setting depends on the workload needs, such as level of privacy, evaluation metrics, and time-to-train. In this paper, we perform a qualitative and extensive evaluation of the HFL and VFL settings to provide guidance on how to choose a specific setting based on workload needs. We assess the classification of Alzheimer’s disease using non-independent and identically distributed (non-i.i.d.) 3D images and tabular data. The results demonstrate how, with the same aggregation function, HFL converges faster to higher accuracy and f1-score than VFL and demonstrate how HFL can be further improved due to a more flexible architecture. Finally, we provide practical considerations on when to choose which setting and share the implementations of the pipelines for reproducibility.}, archive = {J_ACCESS}, author = {Walter Riviera and Ilaria Boscolo Galazzo and Gloria Menegaz}, doi = {10.1109/ACCESS.2025.3569158}, journal = {IEEE Access}, pages = {86588-86598}, shortjournal = {IEEE Access}, title = {FLavourite: Horizontal and vertical federated learning setting comparison}, volume = {13}, year = {2025}, }
• (2025). Gradient-informed pareto-based multi-objective binary topology optimization. ACCESS, 13, 86576-86587. (www)

  @article{ , abstract = {Multi-objective topology optimization (TO) problems frequently arise in practical engineering applications, necessitating the identification of Pareto-optimal solutions. This article introduces a gradient-informed Pareto-based multi-objective TO algorithm with binary decision variables, called GPBTO, tailored to constrained bi-objective optimization problems (CBOPs), a common scenario in engineering design. By leveraging a binary decision space and incorporating a linearization step for objectives and constraints, the method enables the use of efficient integer linear programming (ILP) techniques for evolving the decision vector. Unlike traditional weighted sum (WS) approaches, which are widely used in TO despite their known limitations, GPBTO provides an alternative that integrates gradient-based formulations while facilitating the identification of Pareto-optimal solutions. While WS remains a dominant method in TO, GPBTO represents a promising alternative for cases where Pareto-based solutions are desirable. To the best of the authors’ knowledge, this is the first attempt to integrate Pareto-based TO with binary variables and gradient information, highlighting its potential for further exploration and application.}, archive = {J_ACCESS}, author = {Francesco Lucchini and Riccardo Torchio and Piergiorgio Alotto}, doi = {10.1109/ACCESS.2025.3570329}, journal = {IEEE Access}, pages = {86576-86587}, shortjournal = {IEEE Access}, title = {Gradient-informed pareto-based multi-objective binary topology optimization}, volume = {13}, year = {2025}, }
• (2025). CyclicAugment: Optimized medical image analysis via adaptive augmentation intensity. ACCESS, 13, 86562-86575. (www)

  @article{ , abstract = {Computer-aided diagnosis (CADx) systems play a crucial role in accurately diagnosing and monitoring diseases through medical imaging. However, there are many challenges, such as data scarcity and complex structural patterns, limiting the performance of deep-learning models. Although conventional data-augmentation techniques can help mitigate these limitations, excessive augmentation may distort critical diagnostic information, leading to suboptimal performance. To address this issue, this study proposes CyclicAugment, a novel data-augmentation strategy that dynamically adjusts augmentation intensity in a cyclic manner throughout training. The method gradually transitions from using original data as weak augmentation, subsequently applying strong augmentation before reverting to weak augmentation again, reintroducing the original data. This process ensures stable training through an adaptive learning rate. By cyclically adjusting augmentation intensity, our approach enhances data diversity while preserving essential diagnostic information, thereby improving the generalizability of deep learning models. We validated CyclicAugment across six different medical imaging datasets and multiple deep learning architectures, including convolutional neural networks, vision transformers, and multilayer perceptrons. Our framework achieved a maximum accuracy improvement of 8.8%, reaching a peak performance of 0.927 and thereby demonstrating its effectiveness in enhancing medical image analysis.}, archive = {J_ACCESS}, author = {Min-Jun Kim and Jung-Woo Chae and Hyun-Chong Cho}, doi = {10.1109/ACCESS.2025.3570899}, journal = {IEEE Access}, pages = {86562-86575}, shortjournal = {IEEE Access}, title = {CyclicAugment: Optimized medical image analysis via adaptive augmentation intensity}, volume = {13}, year = {2025}, }
• (2025). PAPR in AFDM: Upper bound and reduction with normalized μ-law companding. ACCESS, 13, 86553-86561. (www)

  @article{ , abstract = {Affine Frequency Division Multiplexing (AFDM) is an advanced waveform technique designed for environments with significant Doppler shifts and time-frequency dispersion. It uses the Discrete Affine Fourier Transform (DAFT) to create flexible waveforms that are resilient to channel variations. Compared to Orthogonal Frequency Division Multiplexing (OFDM), AFDM provides better localization in time and frequency, improved spectral efficiency, and greater robustness, but it suffers due to a high Peak-to-Average Power Ratio (PAPR). To lower it, we applied the normalized $\mu $ -law companding transform to the AFDM waveform. Further, we derived an upper bound for PAPR of the AFDM waveform and demonstrated that the maximum PAPR increases with the number of subcarriers $(N)$ . We also provided an analytical expression for the Complementary Cumulative Distribution Function (CCDF) of the PAPR of AFDM for all values of N. To know the effectiveness of the AFDM waveform, simulations are carried out with respect to bit error rate (BER) over different channels and the CCDF of PAPR.}, archive = {J_ACCESS}, author = {Vundela Madhava Reddy and Hanumantharao Bitra}, doi = {10.1109/ACCESS.2025.3569391}, journal = {IEEE Access}, pages = {86553-86561}, shortjournal = {IEEE Access}, title = {PAPR in AFDM: Upper bound and reduction with normalized μ-law companding}, volume = {13}, year = {2025}, }
• (2025). Methods for condition assessment of the high voltage circuit breakers based on vibration Fingerprint—A survey. ACCESS, 13, 86542-86552. (www)

  @article{ , abstract = {The critical role of high-voltage circuit breakers in the power grid underscores the need for reliable and efficient methods to assess their condition and operational parameters. To support the integration of smart grid concepts and enable condition assessment during circuit breaker exploitation, non-invasive approaches are essential. Among these, methods leveraging vibration fingerprints generated during the opening or closing of circuit breakers have shown significant promise. This paper presents a comprehensive survey of state-of-the-art research in this area, systematically analyzing over 100 influential works from the past two decades. The survey categorizes these methods based on their domain-specific approaches and highlights key challenges related to signal analysis, data acquisition, feature extraction, interpretation, and reasoning. By offering a structured analysis, this survey serves as a valuable resource for researchers and practitioners, providing insights and direction for future advancements in this niche field.}, archive = {J_ACCESS}, author = {Kerim Obarcanin and Bakir Lacevic}, doi = {10.1109/ACCESS.2025.3570000}, journal = {IEEE Access}, pages = {86542-86552}, shortjournal = {IEEE Access}, title = {Methods for condition assessment of the high voltage circuit breakers based on vibration Fingerprint—A survey}, volume = {13}, year = {2025}, }
• (2025). Realistically poseable mouse model for assessing exposures to magnetic field in the intermediate frequency band. ACCESS, 13, 86528-86541. (www)

  @article{ , abstract = {Recently, a mouse model was used to quantify electromagnetic (EM) dosimetry in an investigation of the biological effects of an EM field in the intermediate-frequency band (300 Hz to 10 MHz). However, it did not reflect the realistic postures observed during exposure experiments; thus, the data obtained were not accurate. In this study, a poseable mouse mesh model was developed using photogrammetry techniques; its pose was changed to mimic ten realistic postures observed during exposure experiments. The mouse model was composed of ten different tissues and organs: the eyes, brain, skull, bone, liver, lungs, heart, spleen, glands, and skin. The dosimetry evaluation was performed on the mouse model under a uniformly applied magnetic field intensity. The reliability and accuracy of the EM solver used in the evaluation were validated by performing a dosimetry evaluation on a homogeneous sphere and ellipsoid exposed to a uniform magnetic field; the results were compared with those obtained by the analytical method. Dosimetric quantification was performed using several metrics. The metrics included the whole-body average ( $E^{\mathrm {WBA}}$ ), $99^{\mathrm {th}}$ percentile ( $E^{99}$ ), $99.9^{\mathrm {th}}$ percentile ( $E^{99.9}$ ), and maximum ( $E^{\max }$ ) values of the induced electric field intensity inside the mouse body. For an applied magnetic field density of 1 mT at 100 kHz, the $E^{\mathrm {WBA}}$ , $E^{99}$ , $E^{99.9}$ , and $E^{\max }$ values for the poseable mouse in the standing, upright, and curled-up positions were derived and discussed. The developed poseable mouse model potentially provides accurate exposure data for animal experiments under freely roaming conditions.}, archive = {J_ACCESS}, author = {Siriwat Wasoontarajaroen and Hitomi Ishiwata and Jerdvisanop Chakarothai and Shin Ohtani and Akira Ushiyama and Kenji Hattori and Keiji Wada and Masateru Ikehata and Yukihisa Suzuki}, doi = {10.1109/ACCESS.2025.3569330}, journal = {IEEE Access}, pages = {86528-86541}, shortjournal = {IEEE Access}, title = {Realistically poseable mouse model for assessing exposures to magnetic field in the intermediate frequency band}, volume = {13}, year = {2025}, }
• (2025). WhiteDwarf: A holistic co-design approach to ultra-compact neural inference acceleration. ACCESS, 13, 86509-86527. (www)

  @article{ , abstract = {Recent work on the Strong Lottery Ticket Hypothesis has upgraded the role of sparsity, commonly exploited by algorithms and sparse architectures as an advantageous by-product of training overparameterized models, to the main driver of neural training, opening new co-design opportunities for efficient neural execution. WhiteDwarf is a holistic ultra-compact approach that first uses a triple model compression algorithm to reduce CNN and MLP models to under 10% of the original off-chip size and then uses a triple unstructured sparsity exploitation architecture for under 1% on-chip size. Folded, Signed, and Multi-coated supermasks are integrated into a novel scalar supermask that integrates pruning, quantization, and training. These supermasks offer straightforward compression and an opportunity for a sparse, mixed precision, multiplier-less datapath that does not require complex control. Activation precision is set to FP8 with large models in mind, while INT2–4 weights suffice for sparse and accurate supermask models. The 1K-PE array features parallel, hierarchically synchronous, non-zero gathering, and bit-decomposed computation for exploiting unstructured sparsity down to bit-level. For example, a ResNet-50 is compressed to 5.3% of the original model size while maintaining 74.7% accuracy on ImageNet. The fabricated 40-nm CMOS chip, aimed at high inference accuracy and power efficiency, achieves 12.24 TFLOPS/W at 99% weight sparsity.}, archive = {J_ACCESS}, author = {Ángel López García-Arias and Yasuyuki Okoshi and Jaehoon Yu and Junnosuke Suzuki and Hikari Otsuka and Kazushi Kawamura and Thiem Van Chu and Daichi Fujiki and Masato Motomura}, doi = {10.1109/ACCESS.2025.3568916}, journal = {IEEE Access}, pages = {86509-86527}, shortjournal = {IEEE Access}, title = {WhiteDwarf: A holistic co-design approach to ultra-compact neural inference acceleration}, volume = {13}, year = {2025}, }
• (2025). Research on wafer CMP temperature online detection compensation algorithm based on GA-BP improved neural network. ACCESS, 13, 86498-86508. (www)

  @article{ , abstract = {In chemical mechanical polishing (CMP), temperature is a primary factor that determines the rate of chemical reactions, which directly influences the polishing efficiency and quality of wafers. Therefore, constructing an online monitoring system for wafer temperature to enable real-time feedback control of key node temperatures is beneficial for improving the polishing yield of semiconductor wafers. This paper addresses the challenge that non-contact infrared temperature sensors encounter in the CMP process due to their susceptibility to environmental temperature interference. We propose a backpropagation (BP) neural network temperature compensation model optimized by a dynamic adaptive genetic algorithm. The improved genetic algorithm-optimized backpropagation (GA-BP) neural network model incorporates a dynamic nonlinear probability adjustment mechanism and a fitness calibration mechanism. Compared to traditional BP and standard GA-BP models, the compensation accuracy has improved significantly. Experimental results indicate that the mean absolute error (MAE) of the test set has decreased to 0.2381°C, representing reductions of 82% and 68% compared to BP and GA-BP, respectively. Furthermore, the maximum relative measurement error of the temperature sensor within the environmental temperature range of 3.8 to 75.4°C has decreased from 14% to 2.59%. This model effectively mitigates the shortcomings of the traditional BP network, such as slow convergence rate and vulnerability to local optima, thereby providing a novel approach for sensor temperature compensation in ultra-precision wafer polishing applications.}, archive = {J_ACCESS}, author = {Binjie Li and Kuan Shen and Zhilong Song and Binghai Lyu and Wenhong Zhao}, doi = {10.1109/ACCESS.2025.3566650}, journal = {IEEE Access}, pages = {86498-86508}, shortjournal = {IEEE Access}, title = {Research on wafer CMP temperature online detection compensation algorithm based on GA-BP improved neural network}, volume = {13}, year = {2025}, }
• (2025). A novel proactive defense strategy design for cyber-physical systems in the presence of FDI attacks. ACCESS, 13, 86487-86497. (www)

  @article{ , abstract = {This paper proposes a novel proactive defense strategy tailored to address false-data-injection (FDI) attacks in cyber-physical systems (CPSs). Specifically targeting FDI attacks, which compromise system integrity by manipulating sensor and control inputs, watermarking and moving target defense (MTD) mechanisms are integrated. The watermarking signal is designed to encrypt control inputs, enabling the detection of FDI attacks by exploiting inconsistencies between injected false data and the known watermark pattern. Meanwhile, the MTD strategy, characterized by mode-dependent average dwell time (MDADT), dynamically alters system parameters to obscure attack surfaces. Sufficient conditions for robust $H_{\infty } $ disturbance attenuation in switched systems under these defenses are derived. Furthermore, tailored response mechanisms are formulated to mitigate attack impacts on system performance, with quantitative comparisons demonstrating the efficacy of the proposed methods against FDI attacks. Numerical simulations validate the robustness and superiority of the hybrid defense strategy in scenarios involving FDI attacks.}, archive = {J_ACCESS}, author = {Yateng Wang and Ziang Wei and Rui Liu}, doi = {10.1109/ACCESS.2025.3569807}, journal = {IEEE Access}, pages = {86487-86497}, shortjournal = {IEEE Access}, title = {A novel proactive defense strategy design for cyber-physical systems in the presence of FDI attacks}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based ultra-lightweight architecture for atrial fibrillation detection using single-lead ECG recordings. ACCESS, 13, 86474-86486. (www)

  @article{ , abstract = {Atrial fibrillation (A-Fib), a common cardiac arrhythmia, is linked to serious complications like heart failure and stroke, which early diagnosis could prevent. However, its paroxysmal and often asymptomatic nature complicates detection. In this paper, we propose a novel deep-learning model for single-lead ECG signal analysis, combining a convolutional neural network (CNN) to extract localized features and a long short-term memory (LSTM) network to capture temporal dependencies. To address data imbalance in the PhysioNet/CinC Challenge 2017 dataset, we employ a filtering-based approach with Neurokit, BioSPPy, and EngZeeMod for synthetic data generation. Simulation results show the model achieves a 99.56% F1 score, a 102.25 dB gain in efficiency, a 1.06% performance gain over the state-of-the-art method, and real-time diagnosis with a $0.48 \times 10^{-3}$ second inference time. These simulation results confirm the superior performance of the proposed model.}, archive = {J_ACCESS}, author = {Abbas Khan and Muhammad Rizwan Mughal and Syed Ali Irtaza and Mohsin Khan and Madiha Tahir and Ahtisham Ali and Zoraize Saeed}, doi = {10.1109/ACCESS.2025.3567093}, journal = {IEEE Access}, pages = {86474-86486}, shortjournal = {IEEE Access}, title = {A deep learning-based ultra-lightweight architecture for atrial fibrillation detection using single-lead ECG recordings}, volume = {13}, year = {2025}, }
• (2025). Lightning overvoltage response modeling and protection design for solid state transformer. ACCESS, 13, 86458-86473. (www)

  @article{ , abstract = {The rapid development of the AI and ICT industry has driven massive data center construction. Compared to conventional LFT (line-frequency transformer)-isolated conversion system, SST-based uninterruptible power supply solution offers high efficiency, high power density and control flexibility, making it a competitive power supply solution. Eliminating the LFT and adopting the cascaded modular design enables CHB-based SST to better meet the demands of large-scale and rapid deployment. However, without the isolation provided by LFT, power converter in SST is directly exposed to the MV (Medium Voltage) grid side surges, leading to failure with insufficient overvoltage and overcurrent withstand capabilities of the power electronic devices. The lightning overvoltage, one of the most severe grid-side surges, poses a significant threat to the internal power electronic devices and the insulation of the SST. Yet, existing research lacks a quantitative description of lightning overvoltage response of cascaded SST under three-phase steady-state operation, and is insufficient to evaluate the impact of filter design, floating DC bus design, operating states and grounding scheme. Lightning overvoltage response model for three-phase steady-state operating SST based on the configuration of a 2 MVA 10 kV CHB-based SST is established to assess and quantify the effects of the aforementioned parameters design. A comparison with the LFT-isolated conversion system is made to clarify the isolation effect of LFT, providing design guidelines for the SST lightning protection design.}, archive = {J_ACCESS}, author = {Jiatong Zhang and Jianxiong Yu and Jie Ming and Jiajie Duan and Chushan Li}, doi = {10.1109/ACCESS.2025.3569818}, journal = {IEEE Access}, pages = {86458-86473}, shortjournal = {IEEE Access}, title = {Lightning overvoltage response modeling and protection design for solid state transformer}, volume = {13}, year = {2025}, }
• (2025). Digital inclusive finance and regional economic resilience: Evidence from 279 cities in china. ACCESS, 13, 86438-86457. (www)

  @article{ , abstract = {Using the panel data of 279 prefecture level cities in China from 2011 to 2022, this paper analyzes the mechanism and spatial spillover effect between digital Inclusive Finance and economic elasticity. Firstly, the relationship between digital inclusive finance and economic resilience is initially explored through benchmark regression, and regional differences are revealed through heterogeneity analysis. Secondly, the spatial effects of digital inclusive finance are investigated using the spatial Durbin model. Finally, the moderating effect model and threshold effect model is used to analyze the linear and non-linear effects of mechanism variables in the role of digital inclusive finance on economic resilience. The study shows that digital inclusive finance has a significant positive impact on enhancing economic resilience, and the impact is heterogeneous. Moreover, the positive influence of digital inclusive finance on economic resilience has spatial spillover effects. The moderating and threshold effects show that the financial market environment, industrial structure transformation and upgrading, and government intervention are important factors affecting the impact of digital inclusive finance on economic resilience. Finally, based on the research results, we propose policy recommendations aimed at effectively promoting digital inclusive finance and economic resilience in China.}, archive = {J_ACCESS}, author = {Ziyun Wang and Hanbin Chen}, doi = {10.1109/ACCESS.2025.3570430}, journal = {IEEE Access}, pages = {86438-86457}, shortjournal = {IEEE Access}, title = {Digital inclusive finance and regional economic resilience: Evidence from 279 cities in china}, volume = {13}, year = {2025}, }
• (2025). Modeling and measurement of a self-oscillating fluxgate current sensor for AC and DC application. ACCESS, 13, 86430-86437. (www)

  @article{ , abstract = {This paper studies fluxgate current sensors used for DC to high - frequency current measurement in various industrial fields. Traditional fluxgate technologies have drawbacks in circuit complexity and cost, while the self-oscillating fluxgate technology has model - related issues. An analytical model for the open-loop self-oscillating fluxgate circuit is developed. Key parameters affecting circuit performance are identified, and a trade-off between sensitivity and linear range is found. A three-core four-winding self-oscillating fluxgate sensor is proposed. Experimental results show that DC and power - frequency AC accuracy better than ±0.1%, and high -frequency AC accuracy better than ±1% within 20kHz. This research provides useful insights for self-oscillating fluxgate current sensor design and optimization.}, archive = {J_ACCESS}, author = {Kui Xiong and Changxi Yue and Xiaodong Yin and Haibin Chen and Haohan Zhen}, doi = {10.1109/ACCESS.2025.3570234}, journal = {IEEE Access}, pages = {86430-86437}, shortjournal = {IEEE Access}, title = {Modeling and measurement of a self-oscillating fluxgate current sensor for AC and DC application}, volume = {13}, year = {2025}, }
• (2025). Bayesian deep learning meets self-attention: A risk-aware approach to advertisement optimization. ACCESS, 13, 86413-86429. (www)

  @article{ , abstract = {In the highly competitive landscape of e-commerce advertising, maximizing Return on Advertising Spend (ROAS) is critical, yet remains inherently uncertain due to auction-based bidding dynamics and fluctuating market conditions. Traditional deterministic models fail to capture this uncertainty, necessitating a probabilistic approach that balances predictive accuracy with interpretability. To address this challenge, the paper proposes a novel Hierarchical Bayesian Deep Learning framework that integrates a Bayesian Belief Network (BBN) for structured probabilistic reasoning and a Mixture Density Network (MDN) for full distributional modeling of ROAS. The BBN models dependencies among campaign variables, offering interpretable insights, while the hierarchical deep learning architecture overcomes scalability limitations in high-dimensional settings through self-attention mechanisms. Experiments demonstrate up to 22.8% lower RMSE and 27.4% better Negative Log Likelihood (NLL) and up to 31.2% lower Kullback-Leibler divergence (KLD) than state-of-the-art methods (DeepAR, Prophet, NGBoost), achieving an R2 of 98% with an inference speed of 5.2 ms per campaign, making real-time bidding feasible. Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. By providing precise, uncertainty-aware, and explainable predictions, this approach enables adaptive bidding strategies, optimized budget allocation, and risk management, setting a new benchmark for intelligent decision-making in digital advertising.}, archive = {J_ACCESS}, author = {Arti Jha and Ashutosh Bhatia and Kamlesh Tiwari}, doi = {10.1109/ACCESS.2025.3570537}, journal = {IEEE Access}, pages = {86413-86429}, shortjournal = {IEEE Access}, title = {Bayesian deep learning meets self-attention: A risk-aware approach to advertisement optimization}, volume = {13}, year = {2025}, }
• (2025). High-resolution FMCW radar for small UAV detection using GNU software-defined radio. ACCESS, 13, 86396-86412. (www)

  @article{ , abstract = {This article presents a threat detection system for uncrewed aerial vehicles (UAV) designed to enhance operational security in high-risk environments. The system employs Frequency Modulated Continuous Wave (FMCW) radar, optimized to detect UAVs with a radar cross section (RCS) as small as 0.01 m2, exemplified by models such as the DJI Phantom 3 and Phantom 4. Operating at 2.45 GHz, the radar achieves a maximum detection range of 300 meters and is implemented on the USRP B210 Software Defined Radio (SDR) platform, with real-time signal processing enabled through GNU Radio and integrated GPU resources. The transmission module includes a 100-watt Power Amplifier (PA) and dual-parabolic unidirectional antennas, while the receiver employs a Band-Pass Filter (BPF) and a Low Noise Amplifier (LNA) with a gain of 20 dB to improve sensitivity to reflected signals. The USRP B210 handles signal acquisition and processing, applying two-dimensional Fast Fourier Transform (FFT) techniques to extract doppler shift information. The processed data is visualized as a range-doppler map, facilitating effective target tracking. The system’s modular architecture and parallel processing capability enable precise and efficient detection of low-RCS UAVs, even in cluttered environments. This work demonstrates a practical, cost-effective radar solution for security-sensitive applications, offering advantages in performance, adaptability, and real-time capability over traditional radar systems.}, archive = {J_ACCESS}, author = {P. Janpangngern and P. Changnam and W. Charoensiri and S. Samroeng and J. Pakprom and C. Thongsopa and A. Intarapanich and T. Thosdeekoraphat and N. Santalunai and S. Santalunai}, doi = {10.1109/ACCESS.2025.3570635}, journal = {IEEE Access}, pages = {86396-86412}, shortjournal = {IEEE Access}, title = {High-resolution FMCW radar for small UAV detection using GNU software-defined radio}, volume = {13}, year = {2025}, }
• (2025). Shape penalized decision forests for imbalanced data classification. ACCESS, 13, 86380-86395. (www)

  @article{ , abstract = {Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional machine learning models and modern deep learning techniques struggle with such imbalances, decision trees and random forests combined with data sampling strategies have shown effectiveness, especially for tabular datasets. However, undersampling and oversampling approaches often introduce complexity and risk the loss of valuable information. This paper introduces Shape Penalized Decision Forests, a novel classifier tailored for imbalanced binary classification. Our method integrates a penalty on the surface-to-volume ratio of decision sets within decision tree construction, thereby inherently addressing class imbalance without additional sampling. The proposed approach enhances predictive performance and generalization by leveraging ensemble learning strategies such as bagging and adaptive boosting. We evaluate the method on twenty benchmark tabular imbalanced datasets, spanning diverse sample sizes and imbalance ratios, and demonstrate its superiority over several state-of-the-art data-level and algorithmic-level methods. Furthermore, simulated datasets with visually interpretable structures showcase the model’s generalization capacity. Statistical significance tests validate the robustness of our approach. Finally, we provide a Python package, ‘imbalanced-spdf’, offering an accessible implementation for practitioners and researchers.}, archive = {J_ACCESS}, author = {Rahul Goswami and Aindrila Garai and Payel Sadhukhan and Palash Ghosh and Tanujit Chakraborty}, doi = {10.1109/ACCESS.2025.3569523}, journal = {IEEE Access}, pages = {86380-86395}, shortjournal = {IEEE Access}, title = {Shape penalized decision forests for imbalanced data classification}, volume = {13}, year = {2025}, }
• (2025). Prototypical few-shot learning for histopathology classification: Leveraging foundation models with adapter architectures. ACCESS, 13, 86356-86379. (www)

  @article{ , abstract = {Histopathology is a critical tool for disease diagnosis and identifying cancer via microscopic tissue analysis. Traditional deep learning methods for histopathology often require extensive labeled data, which can be scarce and expensive. This study introduces a framework for the few-shot adaptation of self-supervised histopathology pretrained foundation models using multilayer perception adapters and convolutional adapters. An adapter comprising two linear or convolutional layers with nonlinear activation and residual connections transforms embeddings from foundation models for histopathology classification tasks. This study employs prototypical networks, SimpleShot, and bias-diminishing cosine similarity-based prototypical networks as few-shot learning algorithms. Comprehensive experiments are conducted across benchmark histopathology datasets: NCT, LC25000, Kather, and Camelyon17 Wilds. The results demonstrate that both adapter architectures consistently outperform the linear probe method, whereas multilayer perception adapters have an overall higher accuracy, especially when fine-tuned with five or more samples. The iBOT model with multilayer perception adapters fine-tuned using the bias-diminishing cosine similarity-based prototypical network algorithm achieved remarkable accuracy, reaching 95.04% on Camelyon17 Wilds and 96.55% on the NCT dataset with 20 images per class while using less than 0.002% of the dataset. These findings underscore the effectiveness of the proposed approach in addressing challenges posed by low-data regimes in the computer-aided histopathology domain and the potential for optimizing foundation models with minimal labeled data using prototypical few-shot algorithms.}, archive = {J_ACCESS}, author = {Kazi Rakib Hasan and Sijin Kim and Junghwan Cho and Hyung Soo Han}, doi = {10.1109/ACCESS.2025.3570673}, journal = {IEEE Access}, pages = {86356-86379}, shortjournal = {IEEE Access}, title = {Prototypical few-shot learning for histopathology classification: Leveraging foundation models with adapter architectures}, volume = {13}, year = {2025}, }
• (2025). SHIFA: SBERT-based healthcare information focused arabic question answering. ACCESS, 13, 86344-86355. (www)

  @article{ , abstract = {Question Answering (QA) systems have been developed as a promising solution and an efficient approach to retrieving significant information over the Internet. Answer selection is one of the key components of many types of QA applications, which requires addressing a semantic gap between question-answer pairs. The idea behind this research is to use the SBERT method for entities and context patterns to effectively capture the semantics between QA pairs in two different ways: the feature extractor and the transfer learning model. In the transfer learning approach, the Arabic BERT model is fine-tuned using SBERT on the AraMed dataset to adapt its parameters for Arabic answer selection classification. In the feature extraction approach, SBERT is combined with LSTM, GRU, and CNN classifiers to evaluate its performance in generating semantic embeddings. There are three models presented in SHIFA: the HT-SBERT-LSTM, HT-SBERT-GRU and HT-SBERT-CNN. These models deal with the problem of representing contextual data. The results show that the fine-tuned AraBERTv0.2 with the SBERT model accomplishes state-of the-art performance results and achieves up to 87% in terms of F1-score and accuracy compared to other Arabic pre-trained language models such as AraBERTv2, CAMeLBERT, mBERT and LaBSE. Besides, the HT-SBERT-LSTM reaches an accuracy of 94.25%, while the HT-SBERT-GRU reaches an accuracy of 94.13%. The SHIFA models show that mixing sentence-level embeddings with sequence models achieves competitively contextual and semantic representations.}, archive = {J_ACCESS}, author = {Rahaf Alruwaithi and Sarah AlHumoud}, doi = {10.1109/ACCESS.2025.3570637}, journal = {IEEE Access}, pages = {86344-86355}, shortjournal = {IEEE Access}, title = {SHIFA: SBERT-based healthcare information focused arabic question answering}, volume = {13}, year = {2025}, }
• (2025). Siamese denoising autoencoders for enhancing adversarial robustness in medical image analysis. ACCESS, 13, 86333-86343. (www)

  @article{ , abstract = {Deep learning models have achieved groundbreaking results in computer vision; however, their vulnerability to adversarial examples persists. Adversarial examples, generated by adding minute perturbations to images, lead to misclassification and pose serious threats to real-world applications of deep learning models. This paper proposes a simple, powerful, and efficient adversarial defense method: a Siamese network-based Denoising Autoencoder (Siamese-DAE). This method addresses the reduction in classification accuracy caused by the denoising process. Experiments on Chest X-ray, Brain MRI, Retina, and Skin images, using FGSM, PGD, DeepFool, CW, SPSA, and AutoAttack adversarial algorithms, demonstrate that the Siamese-DAE, trained to remove noise, effectively eliminates perturbations, leading to improved classification accuracy compared not only to the standard classification model but also to relevant denoising defense models.}, archive = {J_ACCESS}, author = {Jaesung Shim and Kyuri Jo}, doi = {10.1109/ACCESS.2025.3570898}, journal = {IEEE Access}, pages = {86333-86343}, shortjournal = {IEEE Access}, title = {Siamese denoising autoencoders for enhancing adversarial robustness in medical image analysis}, volume = {13}, year = {2025}, }
• (2025). Ultrawideband non-line-of-sight classification using transformer-convolutional neural networks. ACCESS, 13, 86323-86332. (www)

  @article{ , abstract = {Ultrawideband technology enables high-precision indoor positioning, but its performance degrades significantly in non-line-of-sight environments due to signal obstruction. Accurate identification of non-line-of-sight channels is essential for mitigating localization errors. While recent deep learning methods, such as convolutional neural networks and long short-term memory models, have been applied to classify non-line-of-sight scenarios from raw channel impulse response data, they often struggle with capturing long-range dependencies, efficiently extracting spatial and temporal features, resulting in suboptimal classification performance. This paper proposes a novel hybrid model combining Transformer architecture and convolutional neural networks for non-line-of-sight classification using raw channel impulse response data from received ultrawideband signals. The Transformer captures global temporal dependencies through self-attention, while convolutional neural networks efficiently extract local spatial features. Experimental results on a public dataset show that the proposed model outperforms benchmark methods in widely used evaluation metrics, highlighting its effectiveness in improving non-line-of-sight detection for ultrawideband-based indoor positioning.}, archive = {J_ACCESS}, author = {Hae-Ji Hwang and Seon-Geun Jeong and Won-Joo Hwang}, doi = {10.1109/ACCESS.2025.3568830}, journal = {IEEE Access}, pages = {86323-86332}, shortjournal = {IEEE Access}, title = {Ultrawideband non-line-of-sight classification using transformer-convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Efficient hardware-assisted heap memory safety for embedded RISC-V systems. ACCESS, 13, 86308-86322. (www)

  @article{ , abstract = {In recent years, memory safety issues in embedded environments have garnered significant attention, with spatial and temporal memory violations in heap memory emerging as critical security threats. To address these challenges, this paper proposes an efficient tag-based memory verification system specifically designed for RISC-V in-order processors. The proposed system integrates a Heap Check Cache (HC-Cache), FIFO Buffer, and Store Head Buffer/Clear Head Buffer (SHB/CHB) with a replay mechanism at the hardware level to minimize performance overhead. On the software side, it incorporates extended RISC-V ISA instructions and a modified Newlib C library to automate metadata management and tag generation without requiring additional modifications to user applications. The system was implemented on an FPGA platform using the Xilinx VC707 board. The implementation results indicate a 16.07% increase in LUTs and a 58.25% increase in Flip-Flops, while maintaining minimal overhead for BRAM (0.66%) and LUTRAM (0.99%). Performance evaluations using workloads such as Mibench, Olden, and SPEC2006 demonstrated a significantly lower average performance overhead compared to previous work. Furthermore, verification using the NIST Juliet Test Suite 1.3 revealed that the system successfully detected 100% of heap-related vulnerabilities, including 1,924 heap-based overflows, 803 double-free errors, and 394 use-after-free errors. These results demonstrate that the proposed system provides high security and performance efficiency, even in resource-constrained embedded environments.}, archive = {J_ACCESS}, author = {Hyunjae Park and Yonghae Kim and Dongwook Kang and Hongil Ju and Gaeil An and Yongwoo Kim}, doi = {10.1109/ACCESS.2025.3570777}, journal = {IEEE Access}, pages = {86308-86322}, shortjournal = {IEEE Access}, title = {Efficient hardware-assisted heap memory safety for embedded RISC-V systems}, volume = {13}, year = {2025}, }
• (2025). Integrating blockchain, SSI, and RBAC for the secure management of defense heritage buildings. ACCESS, 13, 86290-86307. (www)

  @article{ , abstract = {This paper presents a Blockchain-SSI based system designed for the management and monitoring of historical heritage, with a focus on Italian defence heritage buildings. The system supports a modular architecture to integrate cutting-edge technologies, including decentralized applications managed through Smart Contracts and SSI, to enhance security, data integrity, and user autonomy in personal data management. Central to our system is the innovative dual-layer access control that combines SSI with Role-Based Access Control (RBAC), ensuring decentralized and secure management of identities and access permissions. A key component of the building system’s architecture is the blockchain module that enables the certification of data, documents or activities while offering query and verification services on this certified data. Through a detailed exploration of use-case scenarios, this paper illustrates the practical application and benefits of the system, demonstrating its potential to revolutionize heritage conservation practices. The use of Blockchain technology in this context represents an exceptional opportunity for data integrity and reliability among stakeholders, securing and tracing maintenance and processes in historic defence buildings. Additionally, the mechanism for access level control by integrating SSI and RBAC provides robust management of the system while simultaneously empowering individuals to retain control over their data.}, archive = {J_ACCESS}, author = {Gavina Baralla and Luisanna Cocco and Marco Di Francesco and Roberto Tonelli}, doi = {10.1109/ACCESS.2025.3570809}, journal = {IEEE Access}, pages = {86290-86307}, shortjournal = {IEEE Access}, title = {Integrating blockchain, SSI, and RBAC for the secure management of defense heritage buildings}, volume = {13}, year = {2025}, }
• (2025). Deep time series intelligent framework for power data asset evaluation. ACCESS, 13, 86277-86289. (www)

  @article{ , abstract = {Power data asset evaluation occupies the core position in the digitization of the power industry. It involves the analysis and utilization of a large amount of power data. The key is to process time series data, such as power consumption and power generation. These data have both long-term and short-term patterns, and traditional evaluation methods such as autoregressive models or Gaussian processes may be difficult to fully capture their characteristics, resulting in evaluation bias. In response to this challenge, this paper proposes a new deep learning framework, namely Time-Series Convolutional Memory Efficient Network (TSENet). TSENet uses complex Sophisticated Convolutional Neural Network (SCNN) and Expressway Network (ENet), combining the advantages of Long-and Short-term Time-series Network (LSTNet). It can simultaneously capture short-term local features and long-term global trends in power data, help to deeply mine spatial correlations and local patterns in data, effectively extract fine relationships between variables and optimize information flow. In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.}, archive = {J_ACCESS}, author = {Lihong Ge and Xin Li and Li Wang and Jian Wei and Bo Huang}, doi = {10.1109/ACCESS.2025.3567492}, journal = {IEEE Access}, pages = {86277-86289}, shortjournal = {IEEE Access}, title = {Deep time series intelligent framework for power data asset evaluation}, volume = {13}, year = {2025}, }
• (2025). Novel aggregation framework for electric bus fleet scheduling. ACCESS, 13, 86256-86276. (www)

  @article{ , abstract = {In recent years, electric buses have become a more environmentally friendly alternative to combustion buses. However, their specific barriers, such as limited battery capacity, long charging times, and a limited number of charging stations, make their deployment in public transportation difficult. In this paper, we propose a new aggregation framework for the electric bus scheduling problem. Specifically, each electric bus is assigned a list of daily tasks representing service trips and charging sessions. Charging stations are distributed in the road network, and the charging can be done in-between service trips. We present a mixed-integer linear problem formulation to assign vehicles to service trips and find a suitable charging schedule. As it can be very time-consuming to solve this problem by an exact or heuristic algorithm, we propose a new aggregation framework. The aggregation is performed by a two-phase heuristic algorithm that combines a group of service trips into one, which is then served by a single vehicle. We designed different prioritization criteria to select which trips to aggregate. The efficiency of our framework was evaluated by comparing it with the average objective function and average computation time achieved by a genetic algorithm without aggregation and an exact solution, if possible. The testing instances were generated from real-life data, representing three types of scenarios that differ in maximum battery capacity and energy consumption. The experiments confirm a significant reduction (up to 30-70%) in computational time without worsening the quality of solutions for almost all prioritization criteria and scenarios tested compared to the genetic algorithm without aggregation. These improvements are especially notable on large-scale benchmarks of the electric bus scheduling problem.}, archive = {J_ACCESS}, author = {Maros Janovec and Milan Straka and Michal Kohani and Lubos Buzna}, doi = {10.1109/ACCESS.2025.3570130}, journal = {IEEE Access}, pages = {86256-86276}, shortjournal = {IEEE Access}, title = {Novel aggregation framework for electric bus fleet scheduling}, volume = {13}, year = {2025}, }
• (2025). PWM techniques for two-level voltage source inverters: A comparative study. ACCESS, 13, 86235-86255. (www)

  @article{ , abstract = {Pulse width modulation (PWM) techniques are widely used to control the switching of semiconductors in power converters. This paper presents a comprehensive overview of PWM techniques for two-level voltage source inverters and provides a comparative analysis of commonly employed PWM techniques, including sinusoidal PWM, zero-sequence injection PWM, third-harmonic injection PWM, space vector modulation, and optimized pulse pattern with selective harmonic mitigation. Besides providing a detailed literature review, this study includes multiple experimental results to evaluate the performance of these PWM techniques across different key metrics, such as output power quality, DC-link voltage utilization, dynamic response, and complexity. Given the growing trend of electrification in the transportation sector, particular emphasis is placed on motor drive applications. Furthermore, this paper discusses the impact of digital controllers, wide-bandgap semiconductors, and model predictive control on PWM techniques while addressing the challenges and future trends within this field.}, archive = {J_ACCESS}, author = {Battur Batkhishig and Pedro F. da Costa Gonçalves and Giorgio Pietrini and Babak Nahid-Mobarakeh and Ali Emadi}, doi = {10.1109/ACCESS.2025.3563115}, journal = {IEEE Access}, pages = {86235-86255}, shortjournal = {IEEE Access}, title = {PWM techniques for two-level voltage source inverters: A comparative study}, volume = {13}, year = {2025}, }
• (2025). Wireless protocol-based control of smart electric drive using sampled time secure communication of chaotic systems. ACCESS, 13, 86218-86234. (www)

  @article{ , abstract = {This paper presents a novel smart electric drive control system that enables fully wireless bidirectional communication between the controller, power supply, and sensors to facilitate real-time data sharing. The primary challenge in such systems arises from cybersecurity threats in wireless communication, requiring secure encryption and synchronization mechanisms to prevent data interception and tampering. To ensure secure transmissions, a chaos-based encryption scheme is implemented using the Liu chaotic system, formulated in the Takagi-Sugeno fuzzy (TS fuzzy) model to simplify computational complexity in synchronization control. A sliding mode control (SMC) strategy is designed to provide robust tracking performance, ensuring system stability under parameter variations and disturbances. To further enhance the control precision, a homogeneous super-twisting algorithm (HSTA)-based disturbance observer (DO) is introduced, effectively compensating for external perturbations and ensuring smooth operation. The DO also allows the use of a smaller SMC gain, which helps reduce chattering while maintaining control robustness. The Lyapunov stability theory is applied to rigorously analyze the system’s stability, confirming the reliability of the proposed approach in wireless motor control applications. The system is experimentally validated on ESP32 microcontrollers, demonstrating fast response times, minimal steady-state error, and real-time secure motor control. Performance evaluation using speed response signals confirms the effectiveness of the proposed control and encryption techniques, making it suitable for industrial automation and IoT-driven motor control systems.}, archive = {J_ACCESS}, author = {Nhat Quang Dang and Huyen Chau Phan Thi and Quang Dich Nguyen and van Nam Giap}, doi = {10.1109/ACCESS.2025.3569571}, journal = {IEEE Access}, pages = {86218-86234}, shortjournal = {IEEE Access}, title = {Wireless protocol-based control of smart electric drive using sampled time secure communication of chaotic systems}, volume = {13}, year = {2025}, }
• (2025). Octagram propagation matching for multi-scale view stereopsis (OPM-MVS). ACCESS, 13, 86203-86217. (www)

  @article{ , abstract = {Multiple view stereopsis (MVS) algorithms have achieved substantial advancements in 3D reconstruction accuracy and completeness. However, significant challenges persist with texture-less regions, occlusions, thin structures, repetitive structures, and non-Lambertian surfaces due to unreliable photometric consistency and insufficient geometric information. To address these challenges, we propose octagram propagation matching for multi-scale view stereopsis (OPM-MVS), a novel framework for efficient and accurate depth map estimation. The framework introduces octagram checkerboard propagation as a structural sampling method that enhances hypotheses propagation by aggregating cost information from overlapping neighbouring regions along structural lines. This approach ensures accurate and consistent hypotheses selection while improving spatial propagation efficiency. To refine depth estimation, we incorporate multi-scale depth confidence-guided geometric consistency, propagating reliable depth estimates from coarse to finer scales and minimizing propagation error. A dual bilateral weighted normalized cross-correlation is incorporated to reduce irrelevant pixel influence and improve correspondence in texture-less and repetitive regions. A multi-hypotheses joint view selection strategy is guided by neighbouring joint view probability to aggregate the subset views for cost matching and integrates effectively with the proposed propagation method. Experimental evaluations on public benchmarks includes ETH3D and Tanks and Temples to demonstrate the effectiveness of OPM-MVS in achieving superior completeness while maintaining comparable accuracy to COLMAP, ACMH, ACMM and other surveyed MVSNet methods. The framework delivers state-of-the-art performance by accurately reconstructing depth in challenging regions resulting in complete and reliable 3D models. Notably, OPM-MVS achieves an F1-score of 83.50% on the overall ETH3D test dataset under a 2 cm threshold which outperforms COLMAP (73.01%), ACMH (75.89%), ACMM (80.78%), DP-MVS (83.11%), MVP-Stereo (76.12%), TransMVSFormer (75.39%), TAPA-MVS (79.15%), and UGNet (80.83%). These results establish OPM-MVS as a promising solution for high-resolution and large-scale multi-view stereo applications.}, archive = {J_ACCESS}, author = {Ray L. Khuboni and Hongjun Xu}, doi = {10.1109/ACCESS.2025.3569913}, journal = {IEEE Access}, pages = {86203-86217}, shortjournal = {IEEE Access}, title = {Octagram propagation matching for multi-scale view stereopsis (OPM-MVS)}, volume = {13}, year = {2025}, }
• (2025). Double filter and double wrapper feature selection algorithm for high-dimensional data analysis. ACCESS, 13, 86185-86202. (www)

  @article{ , abstract = {With the advent of the big data era, we often deal with datasets containing a large number of redundant features, and in this context, dimensionality reduction of data becomes crucial. To address this issue, this study proposes a double filter and double wrapper (DFDW) feature selection algorithm for high-dimensional data. In the double filter stage, the algorithm first evaluates all features from two perspectives using two filter algorithms: ReliefF and the Pearson correlation coefficient. It then selects the top k features and obtains a candidate feature subset F by taking the intersection. Next, the standard Cauchy distribution was used for population initialization. Subsequently, the algorithm enters the double wrapper stage, where it uses the Random Walk Whale Optimization Algorithm (RWWOA) and the improved Adaptive Differential Evolution (ADE) to jointly optimize and obtain the optimal feature subset. Among them, in order to overcome the problem of single algorithm falling into the local optimum, the Algorithm Iteration Mechanism is proposed, which selectively runs two wrapper algorithms to make the algorithm jump out of local optimum and explore a broader optimization space. Finally, we verified the effectiveness of the algorithm through three sets of comparative experiments. The experimental results show that the DFDW algorithm performed well in obtaining the optimal feature subsets on 10 high-dimensional datasets, with an average classification accuracy of more than 95.1% on 8 datasets, a dimensionality reduction rate of less than 0.64% on all datasets, and the lowest dimensionality reduction rate of 0.19%.}, archive = {J_ACCESS}, author = {Hong Chen and Yuefeng Zheng}, doi = {10.1109/ACCESS.2025.3569271}, journal = {IEEE Access}, pages = {86185-86202}, shortjournal = {IEEE Access}, title = {Double filter and double wrapper feature selection algorithm for high-dimensional data analysis}, volume = {13}, year = {2025}, }
• (2025). Optimal controller design for a mobile robot using genetic algorithm and adaptive PID controller. ACCESS, 13, 86167-86184. (www)

  @article{ , abstract = {This paper addresses the challenge of enabling a mobile robot to reach target coordinates and posture in minimal time by tackling the complexities of kinematic optimal control problems. Determining optimal control gains is analytically challenging, especially when incorporating dynamic models. The mobile robot’s control system comprises two controllers: a kinematic controller, optimized using genetic algorithms and cubic polynomial interpolation, to generate the optimal trajectory, and a dynamic controller using an adaptive PID approach to ensure stable and robust performance despite variations in system parameters like the robot’s weight. The proposed method demonstrates superior performance over existing methods, particularly under dynamic changes in robot characteristics. Simulation results show that the kinematic controller generates optimal trajectories more effectively than traditional techniques, while the adaptive PID controller maintains optimal performance despite disturbances and mass variations. In experiments with various disturbances, the proposed method consistently reached target coordinates with minimal timing discrepancies, outperforming comparative methods. Additionally, it significantly reduces spatial constraints and learning time. These findings contribute to the practical design of mobile robot controllers, ensuring efficient and stable operation, especially in scenarios requiring rapid derivation of optimal gains. Future work should focus on integrating kinematic and dynamic controllers into intelligent systems for mobile robots with arbitrary positions and postures, further enhancing autonomy and adaptability.}, archive = {J_ACCESS}, author = {Jin-Hyun Park}, doi = {10.1109/ACCESS.2025.3570472}, journal = {IEEE Access}, pages = {86167-86184}, shortjournal = {IEEE Access}, title = {Optimal controller design for a mobile robot using genetic algorithm and adaptive PID controller}, volume = {13}, year = {2025}, }
• (2025). Penetration testing and machine learning-driven cybersecurity framework for IoT and smart city wireless networks. ACCESS, 13, 86144-86166. (www)

  @article{ , abstract = {The Internet of Things (IoT) technology development speed, along with its integration into smart city infrastructure, requires strong cybersecurity solutions to protect important systems. The research introduces an extensive framework to protect IoT systems and wireless networks through the integration of penetration testing method with anomaly detection and predictive modeling techniques. In this study, we collected real-time network traffic data as part of our methodology before performing penetration tests with Airmon-ng and Wireshark to create enriched attack scenario datasets. Anomalies were identified using an optimized Isolation Forest model, revealing patterns such as unusual activity involving the Tenda_476300 WiFi network. The Tenda_476300 network exhibited frequent authentication packet anomalies, along with other potential misconfiguration or vulnerability indicators. Predictive modeling utilized both logistic regression (LR) and support vector machines (SVM) for binary classification to identify benign from malicious traffic, resulting in high accuracy rates and precise results. XGBoost achieved better performance than Random Forest (RF) across all metrics when performing as a multiclass classifier to identify Denial of Service (DoS), Distributed Denial of Service (DDoS), and brute force attacks. The reliability and robustness of the constructed models were tested using precision, recall, F1 scores, ROC curves, and precision-recall curves during performance evaluation. The anomaly detection and predictive modeling analysis proves that real-time surveillance systems should incorporate these techniques for proactive security threat discovery and defense. The proposed framework delivers a flexible solution for protecting IoT and smart city wireless networks, which helps create safer, resilient urban environments.}, archive = {J_ACCESS}, author = {Tamara Zhukabayeva and Zulfiqar Ahmad and Aigul Adamova and Nurdaulet Karabayev and Yerik Mardenov and Dina Satybaldina}, doi = {10.1109/ACCESS.2025.3569965}, journal = {IEEE Access}, pages = {86144-86166}, shortjournal = {IEEE Access}, title = {Penetration testing and machine learning-driven cybersecurity framework for IoT and smart city wireless networks}, volume = {13}, year = {2025}, }
• (2025). A theoretical bound which improves the performance of compilation-based multi-agent path finding. ACCESS, 13, 86133-86143. (www)

  @article{ , abstract = {A well-known approach to optimally solving Multi-Agent Path Finding (MAPF) is by compilation to Boolean Satisfiability or Answer Set Programming. Such compilation-based approaches to MAPF are superior to others on dense, relatively small instances. During solving, the underlying solver is invoked multiple times, each with an encoding of the same instance for a different makespan. The runtime of the last solver invocation, whose input is the instance encoded with a theoretical upper bound of the makespan of the optimal solution, is critical to performance. This paper proposes a new theoretical upper bound for such a last invocation, which we prove is correct. Unlike the previously known bound, given a MAPF instance, our bound requires computing a semi-relaxed solution, which is the union of cost-optimal solutions for partitions of such an instance. The computation of our new bound requires optimally solving partitions, which requires more computational resources than those needed for computing the bound currently used by state-of-the-art solvers. We propose a recursive parallel approach that, we call ReBo, which despite additional overhead in upper bound computation, obtains substantially better overall results by exploiting the new bound. ReBo uses a heuristic to select an appropriate partition for bound computation, which does not guarantee that the solution returned is optimal. However, in our benchmarks, composed of 2,890 problems over warehouses and random instances, ReBo never finds a suboptimal solution. In addition, we found that the new bound is significantly tighter than the previously known, on average, 21.2% smaller than the previous bound. This allows us to generate encodings that are around 33.45% smaller, allowing ReBo to solve 4.9% more instances than a state-of-the-art solver in our benchmark set.}, archive = {J_ACCESS}, author = {Rodrigo López and Roberto Asín-Achá and Jorge A. Baier}, doi = {10.1109/ACCESS.2025.3569496}, journal = {IEEE Access}, pages = {86133-86143}, shortjournal = {IEEE Access}, title = {A theoretical bound which improves the performance of compilation-based multi-agent path finding}, volume = {13}, year = {2025}, }
• (2025). Time-dependent analysis of the interaction between energy harvesting, leakage, and consumption processes in a green IoT device. ACCESS, 13, 86120-86132. (www)

  @article{ , abstract = {The Internet of Things (IoT) is transforming industries by enhancing productivity and efficiency; however, energy availability remains a significant challenge due to the limited capacity of batteries and supercapacitors powering IoT devices. The emergence of Green IoT (G-IoT) frameworks, which prioritize energy efficiency and renewable energy integration, offers a promising solution to address this challenge. Despite these advancements, energy storage systems (ESSs) face issues such as capacity degradation, leakage, and charge redistribution, which can lead to energy depletion and service disruptions. Traditional models often assume constant energy harvesting rates, overlooking the time-varying nature of environmental conditions that influence energy availability. In this paper, we propose a novel mathematical framework that incorporates time-dependent fluctuations in the energy harvesting rate to analyse the dynamic interactions between energy harvesting, leakage, and consumption in green IoT systems. Our approach includes an Energy Packet Network (EPN) model to represent transient energy dynamics and a Markov model to capture fluctuations in the energy harvesting rate. Through numerical simulations, we evaluate the impact of key design parameters, such as ESS capacity, mean energy consumption rate, energy leakage, and energy harvesting rate variations, on critical performance metrics including the probability of energy depletion and the transient mean number of stored energy packets. The results highlight the importance of considering time-varying energy harvesting in the design and optimization of IoT systems for long-term operation and sustainability.}, archive = {J_ACCESS}, author = {Godlove Suila Kuaban and Tadeusz Czachórski and Erol Gelenbe and Piotr Czekalski}, doi = {10.1109/ACCESS.2025.3569659}, journal = {IEEE Access}, pages = {86120-86132}, shortjournal = {IEEE Access}, title = {Time-dependent analysis of the interaction between energy harvesting, leakage, and consumption processes in a green IoT device}, volume = {13}, year = {2025}, }
• (2025). A compact five-band omnidirectional RF ambient energy harvesting system for IoT applications. ACCESS, 13, 86110-86119. (www)

  @article{ , abstract = {A compact multiband RF energy harvesting system designed for IoT applications is proposed, operating at 0.94 GHz, 1.8 GHz, 2.1 GHz, 2.6 GHz, and 3.5 GHz. The RF system consists of an omnidirectional modified monopole antenna, a compact multiband rectifier, and an energy management module. In order to achieve omnidirectional radiation patterns and compact size, the antenna current distribution is carefully controlled to avoid high-order modes of traditional monopoles. The overall size of the proposed rectenna is $0.24\lambda _{L}\times 0.22\lambda _{L}$ (the wavelength at the lowest frequency), and the rectenna achieves the power conversion efficiency (PCE) of 19.2% at 0.94 GHz, 24% at 1.8 GHz, 24.5% at 2.1 GHz, 17.7% at 2.6 GHz, and 16.4% at 3.5 GHz with an incident power level of -20 dBm. In addition, the proposed RF system provides an $18.7~\mu $ W peaking output DC power in outdoor ambient environments and powers a commercial temperature sensor as a demonstration. The experimental results highlight that the proposed RF system achieves multiband operation and a stable omnidirectional radiation pattern, offering enhanced frequency coverage in a compact size and competitive efficiency. It has the potential for efficient ambient energy harvesting and powering IoT devices.}, archive = {J_ACCESS}, author = {Wenjun Wang and Jingwei Zhang and Xin Sun and Haoran Zu and Daping He}, doi = {10.1109/ACCESS.2025.3569843}, journal = {IEEE Access}, pages = {86110-86119}, shortjournal = {IEEE Access}, title = {A compact five-band omnidirectional RF ambient energy harvesting system for IoT applications}, volume = {13}, year = {2025}, }
• (2025). Advancing smart sensor networks and carbon-based biosensors through artificial intelligence: A deep learning approach to optoelectronic device innovation. ACCESS, 13, 86083-86109. (www)

  @article{ , abstract = {This research proposes a novel artificial decision-marking framework suitable for modern smart sensor networks and carbon-based biosensor systems which deals with uncertainty and the peculiarity of the data. To achieve the goals, the approach relies on the optoelectronic properties of carbon nanomaterials and already combines AI and deep learning. This raises the level of sensing, real-time data fusion, and adaptive decision-making within the environment to unprecedented levels. To enhance sensor evaluation in sensor manufacturing, we take a step further and implement Dombi Interval Valued Intuitionistic Fuzzy Sets (D-IVIFs) and work with Dombi Interval Valued Intuitionistic Fuzzy Dombi Bonferroni Mean (D-IVIFDBM) for hierarchical decision-making. Moreover, two Multi-Attribute Group Decision Making (MAGDM) methods IVIFWDBM and IVIFWDGBM are developed for expert evaluation aggregation in selection tasks of different criteria to enhance selection accuracy. These experiments did demonstrate improvements in decision accuracy as well as better overall performance than conventional models of comparison. The numerical experiments showed that these methods are more effective than traditional MAGDM models. Introducing such an advanced decision framework in deep learning systems enables improved adaptability, security, and resilience in next-generation sensor networks and biosensor devices. The new paradigm enables real-time signal interpretation and adaptive learning and provides effective solutions in harsh environments where severe fluctuations are common. This study assists in addressing the discrepancy between conceptual decision models and actual physical achievements in smart sensing, which will foster the development of more sophisticated and efficient optoelectronic devices.}, archive = {J_ACCESS}, author = {Keliang Luo}, doi = {10.1109/ACCESS.2025.3567561}, journal = {IEEE Access}, pages = {86083-86109}, shortjournal = {IEEE Access}, title = {Advancing smart sensor networks and carbon-based biosensors through artificial intelligence: A deep learning approach to optoelectronic device innovation}, volume = {13}, year = {2025}, }
• (2025). Prexion: Probabilistic matching-based compression to mitigate compression side channel attacks against HTTPS. ACCESS, 13, 86069-86082. (www)

  @article{ , abstract = {Web servers typically use data compression and encryption to optimize data transmission and protect user privacy. However, compression can inadvertently introduce vulnerabilities to compression side channel attacks (CSCA), which exploit the relationship between data redundancy and compression size to infer sensitive information. In this paper, we introduce Prexion, a novel compression system designed to counter CSCA while maintaining high compression efficiency. Prexion leverages a probabilistic mechanism to selectively include or exclude overlapping sequences in web page data during compression, making it difficult for attackers to exploit patterns in compressed sizes. This approach preserves the benefits of compression without compromising security. Through extensive experiments with real-world web pages, Prexion demonstrates superior performance, achieving up to 4.9 times better compression ratios than the current state-of-the-art solution, Debreach, for web pages with significant amounts of sensitive data. Furthermore, Prexion ensures robust security, with attack success rates consistently below 5% under recommended configurations.}, archive = {J_ACCESS}, author = {Hyoungshick Kim and Sangwon Hyun}, doi = {10.1109/ACCESS.2025.3569988}, journal = {IEEE Access}, pages = {86069-86082}, shortjournal = {IEEE Access}, title = {Prexion: Probabilistic matching-based compression to mitigate compression side channel attacks against HTTPS}, volume = {13}, year = {2025}, }
• (2025). A high-performance SPR biosensor for female reproductive hormone detection. ACCESS, 13, 86057-86068. (www)

  @article{ , abstract = {In this work, a plasmonic sensor using copper (Cu) with palladium (Pd) and hexagonal boron nitride (h-BN) (structure: SF11/Cu/Pd/h-BN/SM) based on surface plasmon resonance (SPR) for the detection of female reproductive hormones (progesterone and estradiol). A comparative study is performed between various layers of Cu and Cu/h-BN, which obtained performance parameters for the proposed structure. The reproductive hormones in females have a long refractive index (RI) between 1.3333 and 1.59606. The proposed sensor for progesterone and estradiol of reproductive hormones in females attains the maximal sensitivity of 97.24°/RIU and 79.4°/RIU. After an analysis, the proposed sensor is found to have a figure of merit (FoM) of 192.9/RIU and 189.29/RIU for the same analyte. Moreover, the penetration depth (PD) of 142.55nm for progesterone was measured. Using Cu/Pd/h-BN/SM as a new type of supporting material with enhanced biosensing activity for the detection of long-range RI of sensing medium (SM), we propose a plasmonic sensor with potential applications.}, archive = {J_ACCESS}, author = {Rajeev Kumar and Suman Garia and Saurav Joshi and Amrindra Pal and Indrasen Singh and Umesh Kumar Sahu}, doi = {10.1109/ACCESS.2025.3569574}, journal = {IEEE Access}, pages = {86057-86068}, shortjournal = {IEEE Access}, title = {A high-performance SPR biosensor for female reproductive hormone detection}, volume = {13}, year = {2025}, }
• (2025). A hybrid P&O-fuzzy-based maximum power point tracking (MPPT) algorithm for photovoltaic systems under partial shading conditions. ACCESS, 13, 86046-86056. (www)

  @article{ , abstract = {Photovoltaic arrays receive varying levels of solar radiation due to factors such as shadows created by clouds, surrounding buildings, and other obstructions. Therefore, an effective Maximum Power Point Tracking (MPPT) algorithm should be used to maximize power generation from non-uniform arrays. Most MPPT algorithms do not perform well during partial shadow conditions, as they can become trapped in local maxima and fail to identify the absolute Global Maximum Power Point (GMP). This paper proposes a new MPPT approach that combines Perturb & Observe (P&O) and Fuzzy Logic Controller (FLC) with the Particle Swarm Optimization (PSO) algorithm. The FLC algorithm is then used to maximize search accuracy. The configurations of this system and the study results demonstrate the dynamic and promising performance of the proposed method under various environmental and shading conditions. In addition to its simplicity and ease of implementation, the proposed algorithm exhibits high accuracy in tracking the maximum power point. This has been evidenced through both simulation and laboratory results of the hardware.}, archive = {J_ACCESS}, author = {Hamed Karimi and Alireza Siadatan and Afshin Rezaei-Zare}, doi = {10.1109/ACCESS.2025.3533314}, journal = {IEEE Access}, pages = {86046-86056}, shortjournal = {IEEE Access}, title = {A hybrid P&O-fuzzy-based maximum power point tracking (MPPT) algorithm for photovoltaic systems under partial shading conditions}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of a novel two-wheeled composite self-balancing robot for stationary operations in unknown terrain. ACCESS, 13, 86032-86045. (www)

  @article{ , abstract = {A two-wheeled self-balancing robot (TWSBR), also widely known as a segway, achieves stability and mobility by dynamically controlling its two base-mounted wheels. While this design provides exceptional flexibility and maneuverability, it presents challenges in maintaining static equilibrium and effective braking, particularly on inclined surfaces. This paper proposes a two-wheeled composite self-balancing robot (TWCSBR) incorporating an additional reaction force mechanism to enhance the static stability of TWSBRs, as well as their walking stability and braking performance on unknow inclined surfaces. For robotic control, an adaptive control framework is developed in this paper to achieve real-time balance and motion control by dynamically adjusting the weight distribution between two balancing strategies. This paper completes the design and construction of the robotic system, followed by six experiments to evaluate the performance of TWCSBR. These experiments assess its static balance capability, robustness against impact loads, braking performance, stable climbing on slopes, adaptability to varying inclines, and ability to traverse complex terrains. The research findings demonstrate that the TWCSBR retains the advantages of high maneuverability and a small turning radius inherent to classic TWSBRs. Additionally, it exhibits enhanced static balance and emergency braking capabilities. Furthermore, the robot can stably traverse slopes up to 29° and adapt effectively to unknown inclines. These results lead to the conclusion that the proposed TWCSBR significantly enhances the robot’s maneuverability and safety, thereby broadening the potential applications of two-wheeled robots in novel environments. In addition, this study can also be applied to bipedal wheel-legged robots.}, archive = {J_ACCESS}, author = {Jiaxuan Chen and Ning He and Zhongxian Xu and Mingyuan Dou and Lile He}, doi = {10.1109/ACCESS.2025.3569325}, journal = {IEEE Access}, pages = {86032-86045}, shortjournal = {IEEE Access}, title = {Design and implementation of a novel two-wheeled composite self-balancing robot for stationary operations in unknown terrain}, volume = {13}, year = {2025}, }
• (2025). A sparse variable step-size least-mean-square algorithm for impulsive noise in a code-division multiple access system. ACCESS, 13, 86026-86031. (www)

  @article{ , abstract = {The conventional least-mean-square (LMS) algorithm has a poor performance when the input autocorrelation’s eigenvalue spread is quite large. For instance, the cost function is inadequately described when impulsive noise is present, making it impossible for the LMS approach to correctly identify the system. The cost function of the conventional LMS method is enhanced by the addition of a $l_{0}$ or $l_{1}$ -norm penalty component. This extra term exploits system sparsity and improves the performance of the LMS algorithm. In this work, we propose a sparse variable step-size LMS approach in a code-division multiple access (CDMA) system. The proposed algorithm makes use of an arctan constraint in the cost function of the algorithm. This constraint enforces a zero attraction of the filter coefficients based on the relative value of each filter coefficient among all entries. Convergence analysis of the proposed algorithm in the mean sense is derived. The CDMA is employed by spreading the transmitted symbols using an m-sequence during the transmission process where the channel state is considered constant over a symbol period. At the receiver, decorrelation stage is considered prior to the channel estimation. This step improves the input signal-to-noise ratio (SNR) at the filter’s input, thereby accelerating the adaptive filter’s convergence. However, this gain, in the rate of convergence, comes at the expense of the system’s spectral efficiency. The proposed method shows high performance, in terms of steady-state mean-square deviation (MSD), compared to those of the zero attracting LMS (ZA-LMS) and the reweighted ZA-LMS (RZA-LMS) algorithms in a system identification setting with an impulsive noise environment. In addition, the proposed algorithm has shown significant performance in estimating the channel for CDMA communication systems in terms of bit-error rate (BER).}, archive = {J_ACCESS}, author = {Mohammad Salman and Ahmed A. F. Youssef and Fahmi Elsayed and Mostafa Rashdan}, doi = {10.1109/ACCESS.2025.3570122}, journal = {IEEE Access}, pages = {86026-86031}, shortjournal = {IEEE Access}, title = {A sparse variable step-size least-mean-square algorithm for impulsive noise in a code-division multiple access system}, volume = {13}, year = {2025}, }
• (2025). External PIM modeling in FDD MIMO. ACCESS, 13, 86012-86025. (www)

  @article{ , abstract = {In this paper, a physical approach is proposed to describe external (air-induced) passive intermodulation (PIM) for a frequency-division duplexing (FDD) multiple-input multiple-output (MIMO) system with an arbitrary number of transceiver chains. It is caused by metallic objects with nonlinear characteristics (“rusty bolt” effect) located near the base station (fence, billboard). PIM penetrates the antenna receiving chain and degrades the uplink signal. Generally, PIM modeling in MIMO systems represents a complicated task that traditionally requires numerous hardware experiments in numerous environment setups. We present a novel approach for replacing experiments with full-cycle physical modeling of external PIM in the MIMO system. The proposed model includes all steps starting from the generation of transmitting signals through the electromagnetic modeling of the antenna array and open space to a set of variable nonlinear sources and back to the receivers. Its performance was validated with synthetic signals equivalent to the measured data in the literature. Thus, this work contributes to more efficient PIM generation by decreasing the number of hardware experiments while increasing the diversity of PIM scenarios, especially for large MIMO systems and multiple PIM sources of any nature.}, archive = {J_ACCESS}, author = {Stanislav Krikunov and Viacheslav Zemlyakov and Andrey Ivanov}, doi = {10.1109/ACCESS.2025.3570224}, journal = {IEEE Access}, pages = {86012-86025}, shortjournal = {IEEE Access}, title = {External PIM modeling in FDD MIMO}, volume = {13}, year = {2025}, }
• (2025). The second phase extended mission orbit design for the korea pathfinder lunar orbiter. ACCESS, 13, 85997-86011. (www)

  @article{ , abstract = {The Korea Pathfinder Lunar Orbiter (KPLO), also known as Danuri, has completed its primary mission and is now in the first phase of its extended mission. Given the current overall programmatic status, particularly the spacecraft’s health and remaining fuel, executing the second phase of the extended mission appears viable. This study presents the design and evaluation of the second phase of the Extended Mission Orbit (EMO) for KPLO, focusing on the practical considerations and rationale behind designing the additional extended mission. The process began with a preference survey conducted among the science Principal Investigators (PIs) of the onboard payloads to maximize scientific output and solidify the extended mission. Following this, real-world operational constraints were considered to refine and finalize the candidate EMOs. The study introduces two viable candidates for the second phase of EMO scenarios for KPLO, along with details of associated operational timelines, maneuvers, and orbital characteristics. The feasibility of both candidate orbits has been confirmed from the perspective of flight dynamics. However, the final decision on which EMO scenario to implement remains undecided. The ultimate selection will greatly depend on the operational results up to 2025. Regardless of which EMO option is ultimately chosen, ongoing analyses based on real-time flight data will allow for adjustments and refinements to the EMO plans, ensuring compliance with planetary protection requirements and optimizing the mission’s success. This study comprehensively addresses all critical aspects of EMO design by combining real-world operational constraints with scientific objectives, providing practical insights for future lunar exploration missions.}, archive = {J_ACCESS}, author = {Young-Joo Song and Seungbum Hong and Jun Bang and Jonghee Bae and Moon-Jin Jeon}, doi = {10.1109/ACCESS.2025.3570068}, journal = {IEEE Access}, pages = {85997-86011}, shortjournal = {IEEE Access}, title = {The second phase extended mission orbit design for the korea pathfinder lunar orbiter}, volume = {13}, year = {2025}, }
• (2025). Time-triggered task offloading scheduling in TSN-based edge computing power networks. ACCESS, 13, 85979-85996. (www)

  @article{ , abstract = {Computing power networks (CPN), connecting distributed computing resources over networks, have been proposed as potential next-generation novel network systems to enable optimal resource allocation among multiple computing nodes. Computing power scheduling is crucial in addressing the conflict between computing power supply and demand, where the deterministic scheduling problem within the CPN needs to be urgently addressed. To break through the performance limitations of standalone computing power and enable time-sensitive services, the integration of edge computing with time-sensitive networking (TSN) emerges as a promising technology, thereby we propose a joint communication-offloading and scheduling (JCOS) scheme that enables computing power sharing to guarantee task determinacy. The abstract modeling of computing power node measure and computing power demand is established to enable the unified quantification of resources and services. Subsequently, we establish the correlation between tasks using Directed Acyclic Graph (DAG) and design a matching and load balancing-based task offloading algorithm (MLB-TOA). Moreover, we propose an efficient routing algorithm for task generation streams (TGS-RA) and an incremental task generation stream injection time planning algorithm (TGS-ITP) in time-sensitive networking. Experiments demonstrate that the proposed JCOS scheme significantly improves scheduling success rate and achieves low end-to-end latency compared to existing offloading approaches. Additionally, the proposed JCOS scheme reduces time overhead and achieves high scheduling success rate compared to existing scheduling approaches. The proposed JCOS scheme ensures the deterministic completion of diverse tasks and provides precise timing control.}, archive = {J_ACCESS}, author = {Hongrui Nie and Yang Wu and Weifeng Zhu and Jinzuo Zhong and Hang Yang and Yu Zhou}, doi = {10.1109/ACCESS.2025.3568848}, journal = {IEEE Access}, pages = {85979-85996}, shortjournal = {IEEE Access}, title = {Time-triggered task offloading scheduling in TSN-based edge computing power networks}, volume = {13}, year = {2025}, }
• (2025). Real-time digital-twin-based cobot-worker collision risk prediction using unity, ROS, and UWB. ACCESS, 13, 85967-85978. (www)

  @article{ , abstract = {The growing use of collaborative robots (cobots) in industrial environments raises concerns about worker safety; flexible and adaptive systems are required to predict and prevent real-time cobot-worker collisions. This study proposes a digital-twin-based, cobot-worker collision risk-prediction system that utilizes the Unity game-development engine, robot operating system (ROS), and ultra-wideband (UWB) communication. The proposed system incorporates three modules: 1) real-time cobot-worker data capture, 2) digital-twin-based dynamic collision-zone management, and 3) collision-risk prediction. The core of the system is a data-driven cobot-worker digital twin that reflects the movements of the cobot and worker using ROS and UWB communication. This dynamic approach addresses the limitations of fixed safety-zone configurations by considering the cobot trajectory and the worker path projection in real time within the digital-twin modules. The system demonstrated low latency in data transmission from ROS to Unity (average 13 ms) and from UWB to Unity (average 140 ms), through implementation and evaluation. It also accurately predicted the minimum distance between the worker and cobot, accounting for varying worker speeds and future prediction times. This research offers a promising solution for enhancing worker safety in collaborative robotic environments by providing a real-time, digital-twin-based collision risk prediction system with dynamic collision zone management.}, archive = {J_ACCESS}, author = {Woojin Kwon and Jieun Yang and Seunghwa Song and Jun Lee and Hyungjung Kim}, doi = {10.1109/ACCESS.2025.3569332}, journal = {IEEE Access}, pages = {85967-85978}, shortjournal = {IEEE Access}, title = {Real-time digital-twin-based cobot-worker collision risk prediction using unity, ROS, and UWB}, volume = {13}, year = {2025}, }
• (2025). Quantum algorithms’ approach: A case study on bladeless wind turbine and solar panel. ACCESS, 13, 85951-85966. (www)

  @article{ , abstract = {The researched data shows that quantum algorithms have been mostly used for battery’s chemistry and conventional grid systems. These algorithms have proved to be computationally cheaper as a common meritorious aspect in almost all implementations. However, usage of quantum algorithms to integrate two renewable energy systems is yet to be explored. This study investigates the application of quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Amplitude Estimation (QAE)—to optimize energy extraction from solar panels and bladeless wind turbines. Nine different optimizers are used on 3066 and 11547 datapoints of wind and solar respectively, to comment about optimality between QAOA, VQD and VQE. Results demonstrate that optimizers like NELDER_MEAD and COBYLA excel in convergence speed, whereas in case of energy minimization SPSA and SLSQP for VQE, COBYLA and P_BFGS for VQD have outperformed other optimizers. QAOA effectively tackles combinatorial optimization tasks as convergence speed for all optimizers has been relatively rapid. Since VQE, VQD and QAOA have not been accurate in finding optimal data point, hence QAE is utilized. Additionally, QAE’s precision in amplifying high-probability quantum states aligns well with the periodic and structured nature of wind and solar energy datasets.}, archive = {J_ACCESS}, author = {Piyush Kumar Sinha and R. Marimuthu}, doi = {10.1109/ACCESS.2025.3570393}, journal = {IEEE Access}, pages = {85951-85966}, shortjournal = {IEEE Access}, title = {Quantum algorithms’ approach: A case study on bladeless wind turbine and solar panel}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review on the control of heat pumps for energy flexibility in distribution networks. ACCESS, 13, 85927-85950. (www)

  @article{ , abstract = {Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration, and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with a focus on energy flexibility in distribution networks. Scheduling-based methods fall into two categories: rule-based controllers (RBCs), which rely on predefined control rules without explicitly seeking optimal solutions, and optimization models, which are designed to determine the optimal scheduling of operations. Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. The paper also examines studies on the impact of HPs on distribution networks, particularly those leveraging energy flexibility strategies. Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. From a grid impact assessment perspective, studies have focused primarily on RBCs for providing energy flexibility through HP operation, without addressing more advanced methods such as real-time optimization using MPC or RL-based algorithms. Incorporating these advanced control strategies could help identify key limitations, including the impact of varying user participation levels and the cost-benefit trade-offs associated with their implementation.}, archive = {J_ACCESS}, author = {Gustavo L. Aschidamini and Mina Pavlovic and Bradley A. Reinholz and Malcolm S. Metcalfe and Taco Niet and Mariana Resener}, doi = {10.1109/ACCESS.2025.3569761}, journal = {IEEE Access}, pages = {85927-85950}, shortjournal = {IEEE Access}, title = {Comprehensive review on the control of heat pumps for energy flexibility in distribution networks}, volume = {13}, year = {2025}, }
• (2025). Memory prefetching evaluation of scientific applications on a modern HPC arm-based processor. ACCESS, 13, 85898-85926. (www)

  @article{ , abstract = {Memory prefetching is a well-known technique for mitigating the negative impact of memory access latencies on memory bandwidth. This problem has become more pressing as improvements in memory bandwidth have not kept pace with increases in computational power. While much existing work has been devoted to finding appropriate prefetching techniques for specific workloads, few provide insight into the behavior of scientific applications to better understand the impact of prefetchers. This paper investigates the impact of hardware prefetchers on the latest Arm-based high-end processor architectures. In this work, we investigate memory access patterns by analyzing locality properties and visualizing delta and repetitive address patterns. A deeper understanding of memory access patterns allows the use of the appropriate prefetcher and reaching a better correlation between access pattern properties and prefetcher performance. This can guide future co-design efforts. We evaluated traditional and innovative prefetchers using a gem5-based model of Arm Neoverse V1 cores. The model features a 16-core architecture, using Amazon’s Graviton 3 processor as a hardware reference, but substituting DDR5 by high bandwidth memory (HBM2). We performed a detailed prefetching evaluation focusing on stencil, sparse matrix-vector multiplication, and Breadth-First Search kernels. These kernels represent a broad range of the applications running on today’s High-Performance Computing (HPC) systems, which are sensitive to memory performance.}, archive = {J_ACCESS}, author = {Nam Ho and Carlos Falquez and Antoni Portero and Estela Suarez and Dirk Pleiter}, doi = {10.1109/ACCESS.2025.3569533}, journal = {IEEE Access}, pages = {85898-85926}, shortjournal = {IEEE Access}, title = {Memory prefetching evaluation of scientific applications on a modern HPC arm-based processor}, volume = {13}, year = {2025}, }
• (2025). SentiTSMixer: A specific model for sales forecasting using sentiment analysis of customer. ACCESS, 13, 85882-85897. (www)

  @article{ , abstract = {Appropriate forecasting of sales can lead to significant revenue gains for any organization, as it allows them to plan their funding, arrange infrastructure, manage the supply chain, and anticipate profits accordingly. However, sales forecasting depends on various factors, such as product quality, market trends, economic conditions, competition, and customer behavior, and it has become even more challenging with the rise of online retailing. In today’s era, especially for online retailing, customer feedback plays a vital role in assessing a product’s quality, as users can express their level of satisfaction through it. Customers can share their opinions using numeric values, such as ratings, and/or through text, such as reviews. Additionally, they can express their views by voting on other reviews they find most helpful, based on their own level of satisfaction. In this research, we have modified the TSMixer model for sales forecasting by amalgamating customer satisfaction levels regarding a specific product. This enhancement allows the model to account for how customer sentiment directly influences sales performance, thereby improving the accuracy of sales forecasting. Experimental results on various types of Amazon data show that, depending on the dataset and the specific error detection techniques used, the proposed model delivers a reduction in error ranging from 65% to 99% compared to established models.}, archive = {J_ACCESS}, author = {Partha Ghosh and Subhashis Das and Subhankar Roy and Ankur Bhattacharjee and Agostino Cortesi and Soumya Sen}, doi = {10.1109/ACCESS.2025.3570080}, journal = {IEEE Access}, pages = {85882-85897}, shortjournal = {IEEE Access}, title = {SentiTSMixer: A specific model for sales forecasting using sentiment analysis of customer}, volume = {13}, year = {2025}, }
• (2025). An ensemble-based classifier to determine the harmonic contribution responsibility between utility’s grid and microgrids. ACCESS, 13, 85873-85881. (www)

  @article{ , abstract = {The integration of nonlinear loads and distributed energy resources in microgrids (MG) has increased the levels of harmonic distortions in distribution feeders. Identifying the responsibility for the harmonic distortions measured at the MG’s point of common coupling (PCC) is essential for managing the power quality. For this purpose, the present paper aims to determine whether the responsibility predominates from the utility’s grid or the MG itself. Thus, an ensemble-based approach was proposed, comparing the behaviour of Random Forest and XGBoost classifiers to identify the side with the predominant harmonic contribution. In order to validate the proposed approach, scenarios were simulated in the IEEE 34-node test feeder using the ATP/ATPDraw software, considering the harmonic sources located at different nodes of the feeder. Three-phase voltage and current signals were acquired at MG’s PCC to train and validate the ensembles. However, a feature extraction stage was performed to obtain time and frequency features used as inputs to the predictive models. The results demonstrated that the proposed approach effectively identified the contribution side, achieving an average $F_{1}$ -score above 99% for both ensembles, representing a feasible solution for determining the harmonic distortion responsibilities at the MG’s PCC.}, archive = {J_ACCESS}, author = {Matheus C. Mella and Ricardo A. S. Fernandes and Daniel Barbosa}, doi = {10.1109/ACCESS.2025.3570682}, journal = {IEEE Access}, pages = {85873-85881}, shortjournal = {IEEE Access}, title = {An ensemble-based classifier to determine the harmonic contribution responsibility between utility’s grid and microgrids}, volume = {13}, year = {2025}, }
• (2025). A new lightweight hybrid model for pistachio classification using transformers and EfficientNet. ACCESS, 13, 85857-85872. (www)

  @article{ , abstract = {In recent years, Vision Transformers (ViTs) have gained prominence as a highly effective method for image classification, often outperforming traditional Convolutional Neural Networks (CNNs). However, their relatively slow processing speed limits their practical use, particularly in real-time applications. Conversely, CNN-based transfer learning models provide faster inference but may struggle with classification accuracy on complex datasets. To address these challenges, Temporal Coordinate Attention (TCA) modules have been introduced to optimize efficiency and performance. This study proposes a hybrid architecture combining EfficientNet, Vision Transformer, and Temporal Channel Attention modules that integrates the accuracy of ViTs, the computational efficiency of CNNs, and the enhancement capabilities of TCA modules. The model is designed to classify Siirt and Kirmizi pistachio varieties with high precision. It achieves outstanding results, including 99.07% accuracy, 99.12% recall, and a Cohen’s Kappa score of 98.10%. These findings highlight the model’s robustness, demonstrating its ability to perform reliable classifications with minimal bias, making it well-suited for real-world applications.}, archive = {J_ACCESS}, author = {Muhammet Çakmak}, doi = {10.1109/ACCESS.2025.3567774}, journal = {IEEE Access}, pages = {85857-85872}, shortjournal = {IEEE Access}, title = {A new lightweight hybrid model for pistachio classification using transformers and EfficientNet}, volume = {13}, year = {2025}, }
• (2025). Microstrip configurations of reflective-mode phase-variation permittivity sensors based on weakly coupled step impedance resonators (SIR): An analysis to the light of circuit duality. ACCESS, 13, 85838-85856. (www)

  @article{ , abstract = {This paper presents and experimentally validates various topologies of one-port reflective-mode phase-variation permittivity sensors based on weakly coupled step impedance resonators (SIRs), the sensing elements, implemented in microstrip technology. The differences between such topologies concern the specific configuration, either series or parallel, of the SIR driven by the feeding (harmonic) signal, and the type of coupling, either electric or magnetic, between the SIRs. Thus, four different sensor topologies result. These sensors are modeled by lumped element circuits that describe their behavior to a very good approximation and are useful to predict the sensitivity in the limit of small perturbations. It is shown that the models of one of the pairs of these four sensors are circuit duals, and such circuit duality applies also to the models of the other pair. In all cases, a high (maximum) sensitivity is achievable provided the coupling (either electric or magnetic) between the SIRs pair is weak. Since the sensing regions in the reported sensors are also small, the resulting figures of merit (FoM), or ratio between the maximum sensitivity and the area of the sensing region expressed in squared guided wavelengths, are very competitive.}, archive = {J_ACCESS}, author = {Joaquím Borràs-Sarsa and Pau Casacuberta and Paris Vélez and Lijuan Su and Xavier Canalias and Ferran Martín}, doi = {10.1109/ACCESS.2025.3569452}, journal = {IEEE Access}, pages = {85838-85856}, shortjournal = {IEEE Access}, title = {Microstrip configurations of reflective-mode phase-variation permittivity sensors based on weakly coupled step impedance resonators (SIR): An analysis to the light of circuit duality}, volume = {13}, year = {2025}, }
• (2025). Power consumption optimization of GPU server with offline reinforcement learning. ACCESS, 13, 85826-85837. (www)

  @article{ , abstract = {The rapid growth of artificial intelligence (AI) has led to increased reliance on power-intensive Graphics Processing Units (GPUs), which are essential for training and deploying large-scale models. However, the escalating energy demands of AI workloads pose sustainability challenges, necessitating efficient power management strategies to reduce carbon footprints. Optimizing GPU server power consumption is complex due to the interdependence of various components. Conventional methods often involve trade-offs: increasing fan speed enhances cooling but raises overall power usage, whereas lowering GPU clock frequencies conserves energy at the cost of longer computation times. To address these challenges, we propose a data-driven optimization framework based on offline reinforcement learning (RL). Our approach collects operational data from a custom-designed workload that simulates varying server loads, capturing key metrics such as power consumption, temperature, and core frequency. The reward function balances power efficiency with performance. The reinforcement learning agent learns from pre-collected server logs, enabling intelligent real-time GPU clock control decisions without costly live experiments. Additionally, periodic fan speed adjustments and pre-training of the Q-network further enhance overall efficiency. Experimental results demonstrate that our method reduces power consumption by 3.62% while improving computation time by 1.51% for synthetic workloads. For LLaMA-2 fine-tuning, power consumption decreases by 6.40% with only a minor 1.27% increase in computation time, demonstrating its practical effectiveness. Our framework was validated on the latest NVIDIA L40S GPU, demonstrating its compatibility with cutting-edge hardware.}, archive = {J_ACCESS}, author = {Heechan Chung and Yeeun Im and Jongchan Park and Taeho Lee and Tae-Young Kim and Hyungjun Kim and Donghwan Lee}, doi = {10.1109/ACCESS.2025.3569803}, journal = {IEEE Access}, pages = {85826-85837}, shortjournal = {IEEE Access}, title = {Power consumption optimization of GPU server with offline reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). A complex wavelet-based OTFS scheme for LEO satellite communication. ACCESS, 13, 85807-85825. (www)

  @article{ , abstract = {This study investigates a wavelet-aided orthogonal time-frequency space (W-OTFS) modulation system operating in low earth orbit (LEO) satellite systems. The discrete wavelet transform (DWT) underlying the complex input signals (high-order modulation) is not applicable in the W-OTFS system because of increased performance errors and peak power. To overcome the problem of high-order modulation in W-OTFS systems, we propose combining the complex DWT paradigm with a newly developed W-OTFS modulation scheme, known as the complex W-OTFS (CW-OTFS) scheme. This paper presents a mathematical model for the proposed CW-OTFS system constructed using a straightforward matrix multiplication approach. The investigation utilized the complex form of the Haar and Daubechies wavelet functions. Additionally, the performance evaluation of the proposed scheme focused on analyzing the bit error rate (BER), peak-to-average power ratio (PAPR), and Cramér-Rao bound (CRB) characteristics for joint sensing and communications. The simulation findings demonstrate the superiority of the proposed CW-OTFS approach over existing state-of-the-art solutions in terms of the BER, PAPR, and CRB performance compared to the W-OTFS scheme in LEO satellite systems.}, archive = {J_ACCESS}, author = {Saifur Rahman Sabuj and Han-Shin Jo}, doi = {10.1109/ACCESS.2025.3569789}, journal = {IEEE Access}, pages = {85807-85825}, shortjournal = {IEEE Access}, title = {A complex wavelet-based OTFS scheme for LEO satellite communication}, volume = {13}, year = {2025}, }
• (2025). How technical variations in 5G network planning for urban areas impact carbon footprint: A life cycle assessment (LCA) approach. ACCESS, 13, 85789-85806. (www)

  @article{ , abstract = {This study aims to address the question of how technical variations in 5G network planning in urban areas impact carbon footprint emissions. In order to answer the question, this study simulated the carbon footprint impact of 5G network design in the city of Depok, Indonesia, through scenarios of variation in three technical parameters: bandwidth, modulation, and MIMO antenna configuration (V-layer). The analysis focuses on the specific global warming potential (GWP) over a 20-year horizon, serving as the key parameter in assessing environmental impact. The findings reveal that increasing throughput per site significantly reduces the carbon footprint of passive tower components. In Scenario #1, wider bandwidths yield substantial reductions in carbon emissions, as increasing bandwidth from 20 MHz to 100 MHz reduces the carbon footprint by 81.40%. Scenario #2 demonstrates the effectiveness of higher-order modulations, such as 256QAM, in reducing carbon emissions. Transitioning from QPSK to 256QAM achieves a 75.00% reduction in carbon footprint. The results reveal a clear trend: as throughput per site increases, the carbon footprint of the passive 5G tower component decreases. However, Scenario #3 highlights a trade-off between energy consumption and antenna configuration, as higher MIMO configurations require more energy. These results signified the importance of optimizing bandwidth and modulation in 5G network design to minimize environmental impact, providing actionable insights for network operators aiming to integrate sustainability into their strategies.}, archive = {J_ACCESS}, author = {Muhammad Irfan Maulana and Muhammad Suryanegara and Ruki Harwahyu and Alfin Hikmaturokhman and Alhaura Edsa Batrisya}, doi = {10.1109/ACCESS.2025.3569754}, journal = {IEEE Access}, pages = {85789-85806}, shortjournal = {IEEE Access}, title = {How technical variations in 5G network planning for urban areas impact carbon footprint: A life cycle assessment (LCA) approach}, volume = {13}, year = {2025}, }
• (2025). Prediction of power system ramping demand using meteorological features. ACCESS, 13, 85775-85788. (www)

  @article{ , abstract = {Power system ramping demand consists of deterministic and uncertain components. This study focuses on predicting uncertain ramping demand influenced by meteorological factors. First, an adaptive trend identification algorithm is proposed to extract key features of upward and downward ramps, including magnitude and duration. Multidimensional correlation analysis quantifies the relationships among net load characteristics, historical load forecasts, weather predictions, historical prediction errors, and uncertain ramping demands. The minimum redundancy maximum relevance (mRMR) method is applied to compress input data features, and a TimeXer-modified prediction framework is developed to capture long-term dependencies in time-series data while integrating meteorological features through attention mechanisms, outputting confidence intervals for uncertain ramping demand. To enhance performance under extreme weather conditions, a method for defining extreme weather indicators is proposed, establishing thresholds based on historical meteorological data distributions and creating binary features for each time step to denote extreme weather. The original TimeXer model’s output layer is modified to predict both the mean and variance of ramping demand, addressing uncertainty in extreme weather scenarios. A Gaussian negative log-likelihood loss function is employed for training to optimize uncertainty prediction performance. Validation using 2024 and 2025 operational data from Shandong, China, indicates that the proposed model improves prediction accuracy by 18.4%, achieves a 92.5% interval coverage rate, and produces narrower prediction interval bandwidths under equivalent coverage rates.}, archive = {J_ACCESS}, author = {Kuan Lu and Song Gao and Jun Li and Kang Chen and Chunhao Yu}, doi = {10.1109/ACCESS.2025.3567145}, journal = {IEEE Access}, pages = {85775-85788}, shortjournal = {IEEE Access}, title = {Prediction of power system ramping demand using meteorological features}, volume = {13}, year = {2025}, }
• (2025). Thermal and structural optimization of parabolic trough systems for enhanced energy conversion efficiency. ACCESS, 13, 85759-85774. (www)

  @article{ , abstract = {This study investigates the optimization of solar thermal energy systems through MATLAB simulations, focusing on critical parameters such as concentration ratio, optical efficiency, heat transfer, energy storage, and receiver design. The aim is to enhance the overall performance of these systems by analyzing key factors such as source temperature effects on absorbance, angular errors in optical efficiency, and the impact of heat transfer fluids and material properties on system efficiency. The methodology includes modeling energy storage with phase change effects, assessing focal efficiency in parabolic concentrators, and comparing various absorber and receiver designs. Additionally, the study evaluates system performance for different concentration ratios and thermal efficiencies. Key results highlight a significant increase in absorbance (57.7%) with rising temperatures and a notable decrease in optical efficiency (65%) with increasing angular errors. Furthermore, energy storage demonstrates a 17,000 kJ increase between 500K and 600K, showing potential for enhanced energy retention at higher temperatures. The findings provide valuable insights into optimizing system components, contributing to the design of more efficient solar thermal systems with improved energy collection and storage capabilities. These results underscore the importance of precise design adjustments for maximizing system efficiency in practical applications.}, archive = {J_ACCESS}, author = {Muhammad Sajjad and Shahryar Shafique Qureshi and Samrat Ray and Ambe Harrison and Saad F. Al-Gahtani and Zakaria M. S. Elbarbary}, doi = {10.1109/ACCESS.2025.3568525}, journal = {IEEE Access}, pages = {85759-85774}, shortjournal = {IEEE Access}, title = {Thermal and structural optimization of parabolic trough systems for enhanced energy conversion efficiency}, volume = {13}, year = {2025}, }
• (2025). Optimized LDPC matrices: Reducing and redistributing 1 bits for enhanced MAP detection in bit-patterned media recording systems. ACCESS, 13, 85745-85758. (www)

  @article{ , abstract = {Bit-patterned media recording (BPMR) is increasingly recognized as a leading technology for next-generation storage systems due to its potential for achieving extremely high area density. In BPMR, data is stored on discrete magnetic islands arranged in a patterned layout, enabling greater storage capacity than traditional magnetic recording techniques. However, as area density increases to meet growing data demands, the magnetic islands are positioned closer together, leading to significant two-dimensional (2D) interference. This interference complicates signal interpretation, making accurate data recovery more challenging and necessitating advanced detection methods. To address these challenges, sophisticated algorithms such as maximum a posteriori (MAP) detection have shown promise in improving data recovery by accounting for the probabilistic nature of signals and noise. MAP detection can effectively mitigate 2D interference when it incorporates extrinsic information (additional data that enhances the detection process). One way to obtain this extrinsic information is through iterative algorithms that utilize the output of self-MAP detection or decoding. Low-density parity-check (LDPC) codes are commonly employed alongside MAP detection to exploit extrinsic information. However, previous studies have shown that suboptimal parity matrix structures in LDPC decoders degrade performance, particularly when using the sum-product algorithm. In this paper, we propose an optimized approach that integrates MAP detection with LDPC decoding for BPMR systems while refining the parity check matrix structure to enhance performance. Our modifications focus on reducing the number of 1-bits to maintain low density (consistent with LDPC standards) while strategically redistributing them throughout the matrix. This optimized parity matrix structure leads to improved performance compared to previous studies.}, archive = {J_ACCESS}, author = {Thien An Nguyen and Jaejin Lee and Jong-Ho Lee}, doi = {10.1109/ACCESS.2025.3568380}, journal = {IEEE Access}, pages = {85745-85758}, shortjournal = {IEEE Access}, title = {Optimized LDPC matrices: Reducing and redistributing 1 bits for enhanced MAP detection in bit-patterned media recording systems}, volume = {13}, year = {2025}, }
• (2025). SMaRT: Stick via motion and recognition tracker. ACCESS, 13, 85728-85744. (www)

  @article{ , abstract = {This paper presents SMaRT (Stick via Motion and Recognition Tracker), a novel multi-object tracking (MOT) approach that integrates motion estimation and re-identification within a unified, efficient framework. Inspired by leading MOT methods like CenterTrack and FairMOT, SMaRT enhances tracking robustness by fusing re-identification features from an advanced teacher-student model. This integration enables the simultaneous regression of object locations and extraction of re-identification vectors within a single neural network. Evaluations on the DIVOTrack, MOT17 and SOMPT22 datasets demonstrate significant improvements over previous state-of-the-art methods in terms of Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and Association Accuracy (AssA). Additionally, SMaRT’s efficiency and accuracy are validated through comprehensive synthetic video experiments, highlighting its adaptability to varied motion patterns and occlusions. The proposed approach offers a robust, accurate, and efficient solution for real-world applications such as surveillance, autonomous driving, and robotics. The tracker is available at: github.com/sompt22/SMaRT.}, archive = {J_ACCESS}, author = {Fatih Emre Simsek and Cevahir Cigla and Koray Kayabol}, doi = {10.1109/ACCESS.2025.3569732}, journal = {IEEE Access}, pages = {85728-85744}, shortjournal = {IEEE Access}, title = {SMaRT: Stick via motion and recognition tracker}, volume = {13}, year = {2025}, }
• (2025). The impact of prediction models on energy-aware resource management in FaaS platforms. ACCESS, 13, 85711-85727. (www)

  @article{ , abstract = {Edge Function-as-a-Service is an emerging computing model that dynamically schedules function executions across distributed edge (close to users) locations to reduce latency and improve user experience. Accurate time-series prediction models, which forecast the future number of function invocations, are crucial for energy-efficient function scheduling, enabling proactive resource allocation. In this work, we evaluate the impact of different neural time-series predictors based on Gaussian processes, recurrent neural networks, and transformer architectures in forecasting the number of function invocations. Furthermore, we propose the Energy-Aware Resource Management (EA-RM) scheduling algorithm, based on a mixed-integer problem, designed to minimize overall energy consumption by reducing the number of edge nodes used. We analyze how prediction accuracy influences function scheduling with respect to energy consumption, using real-world data that include different functions and resources. Experimental results show that the transformer-based predictor outperforms the other considered predictors, leading to more precise function scheduling. Moreover, resource allocation performed through the EA-RM algorithm reduces the energy consumption by ~12-45% on average w.r.t. competitors, and is proven to be more robust w.r.t. the accuracy of the prediction model used.}, archive = {J_ACCESS}, author = {Shahrokh Vahabi and Francesca Righetti and Carlo Vallati and Nicola Tonellotto}, doi = {10.1109/ACCESS.2025.3569068}, journal = {IEEE Access}, pages = {85711-85727}, shortjournal = {IEEE Access}, title = {The impact of prediction models on energy-aware resource management in FaaS platforms}, volume = {13}, year = {2025}, }
• (2025). A new multicriteria approach for the efficiency measures on the forex market. ACCESS, 13, 85698-85710. (www)

  @article{ , abstract = {This paper presents a multi-criteria optimization approach for decision support in the forex market. Decision-makers using technical analysis encounter numerous signals generated by market indicators across various instruments—currency pairs. However, typical online trading systems often lack the capability to validate these signals within a multi-criteria space. This paper introduces a system concept that evaluates signals according to different efficiency measures. A corresponding multi-criteria optimization problem is formulated and solved to select only Pareto-optimal decision variants concerning these efficiency measures. The proposed concept has been implemented using different efficiency measures in an experimental computer-based system and tested through numerical experiments, yielding positive results.}, archive = {J_ACCESS}, author = {Przemysław Juszczuk and Lech Kruś}, doi = {10.1109/ACCESS.2025.3568300}, journal = {IEEE Access}, pages = {85698-85710}, shortjournal = {IEEE Access}, title = {A new multicriteria approach for the efficiency measures on the forex market}, volume = {13}, year = {2025}, }
• (2025). Deep-EFNet: An optimized EfficientNetB0 architecture with dual regularization for scalable multi-class brain tumor classification in MRI. ACCESS, 13, 85682-85697. (www)

  @article{ , abstract = {Brain tumor classification from MRI images plays a crucial role in early diagnosis and treatment planning. While deep learning approaches have shown promise in this domain, achieving high accuracy (ACC) by maintaining model generalization across different datasets remains challenging. Existing approaches in automated brain tumor classification often struggle with feature extraction complexity and model generalization, particularly when dealing with datasets of varying sizes. Additionally, many current methods lack robust performance validation across multiple evaluation metrics. To develop and validate a transfer learning-based deep learning model for accurate multi-class brain tumor classification that demonstrates consistent performance across different dataset sizes while maintaining high classification ACC and reliability. We presented Deep-EFNet, a novel architecture based on EfficientNetB0 with transfer learning. The proposed model leverages the powerful feature extraction capabilities of EfficientNetB0 while incorporating strategic enhancements for improved performance. These enhancements include batch normalization layers for stable training, dropout and L2 regularization mechanisms for preventing overfitting, and a hierarchical dense layer structure for improved feature representation. To validate Deep-EFNet approach comprehensively, we evaluated the model on two distinct datasets containing 3,064 and 7,023 MRI images respectively, each encompassing four classes of brain tumors. The experimental evaluation of Deep-EFNet demonstrated exceptional performance across multiple metrics and datasets. The model achieved an ACC of 95% on 3K-DS (3,064 images) and notably improved to 98% ACC on the larger Dataset 2 (7,023 images). The reliability of these results is further validated by impressive ROC curve scores, achieving perfect 1.00 values across all classes in Dataset 2, while 3K-DS showed equally strong performance with ROC scores of 0.99 for class 0 and 1.00 for the remaining classes. Comprehensive validation through ACC, precision, recall, F1-scores, confusion matrices, and calibration curves further demonstrated the model’s robust and consistent performance across all evaluation metrics. Deep-EFNet demonstrates state-of-the-art performance in brain tumor classification, surpassing existing approaches in both ACC and reliability. The model’s consistent performance across datasets of varying sizes suggests strong generalization capabilities, making it a promising tool for clinical applications in brain tumor diagnosis.}, archive = {J_ACCESS}, author = {Hussein Alshaari and Saeed Alqahtani}, doi = {10.1109/ACCESS.2025.3567919}, journal = {IEEE Access}, pages = {85682-85697}, shortjournal = {IEEE Access}, title = {Deep-EFNet: An optimized EfficientNetB0 architecture with dual regularization for scalable multi-class brain tumor classification in MRI}, volume = {13}, year = {2025}, }
• (2025). Unsupervised classification for circulating tumor cells. ACCESS, 13, 85669-85681. (www)

  @article{ , abstract = {Circulating tumor cell (CTC) detection is crucial for reducing cancer mortality and disease burden. Traditional methods rely on the physical or biological properties of CTCs and involve complex enrichment and separation processes. These methods are often inefficient and limited by cellular heterogeneity and detection accuracy, making them unsuitable for rapid screening. In contrast, intelligent cell recognition and classification using low-resolution microscopy images allows faster detection. Supervised learning has shown excellent performance in CTC detection. However, it heavily depends on large quantities of labeled immunofluorescence image data, which not only consumes significant expert time and resources but also increases costs, limiting its widespread applications. Furthermore, the scarcity of public datasets and the high costs of proprietary datasets further hinder the effectiveness of the detection. Therefore, developing personalized and cost-effective datasets is essential for practical application. Unsupervised methods, which perform classification without the need for manual labeling, offer promising solutions to overcome these challenges. This study adopts an unsupervised classification method that performs excellently on fungal datasets, integrating two-step dimensionality reduction and hybrid clustering voting for automated CTC classification. The main advantage of this method is its domain independence: it does not rely on domain-specific features and is robust to imaging differences, making it applicable to various image classification tasks in specialized domains, which enables the method to be successfully transferred to the field of CTC detection. This method does not require manual labeling, achieves an accuracy of 98.2%, and has a discard rate of 25.23%, effectively distinguishing CTCs from white blood cells (WBCs). This method reduces the reliance on manually labeled data, lowering the cost and complexity of creating personalized CTC datasets while improving detection accuracy. It also enhances the efficiency of deep learning in medical diagnostics, particularly in CTC detection.}, archive = {J_ACCESS}, author = {Ling An and Haibo Hu and Mengke Song and Lin Cheng and Shuo Ba and Zhaocong Liu and Zhuohang Yu and Zhenyu Zhang and Yi Liu and Chichun Zhou}, doi = {10.1109/ACCESS.2025.3564012}, journal = {IEEE Access}, pages = {85669-85681}, shortjournal = {IEEE Access}, title = {Unsupervised classification for circulating tumor cells}, volume = {13}, year = {2025}, }
• (2025). Collaborative coverage strategy using multiple UAVs-UGVs in CRN mapping. ACCESS, 13, 85652-85668. (www)

  @article{ , abstract = {Chemical, Radiological, and Nuclear (CRN) contamination poses a significant threat, potentially leading to mass casualties and long-term environmental repercussions. This paper presents a collaborative framework utilizing a heterogeneous coverage control approach to measure and generate an estimated density distribution map of a designated area. Multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are deployed strategically within partitioned regions, determined through weighted Voronoi tessellation. This method integrates both the robots’ internal parameters and environmental factors. The distinct operational domains of UAVs and UGVs facilitate region decomposition by accounting for variations in CRN dispersion, obstacle representation, and environmental conditions. The resulting cross-partitioned regions are systematically merged to enhance robot distribution efficiency. Each robot autonomously measures within its allocated region, updates contamination data, and generates a dispersion map. The proposed strategy enables an adaptive robot distribution, eliminating uncontaminated grids and improving mapping accuracy. Compared to existing methods, including homogeneous schemes, our approach reduces data variance in CRN-contaminated regions while maintaining mapping efficiency.}, archive = {J_ACCESS}, author = {Agung Nugroho Jati and Bambang Riyanto Trilaksono and Egi Muhammad Idris Hidayat and Widyawardana Adiprawita}, doi = {10.1109/ACCESS.2025.3565779}, journal = {IEEE Access}, pages = {85652-85668}, shortjournal = {IEEE Access}, title = {Collaborative coverage strategy using multiple UAVs-UGVs in CRN mapping}, volume = {13}, year = {2025}, }
• (2025). Migrate or not: Medical digital twins in the era of 6G edge-based networks. ACCESS, 13, 85641-85651. (www)

  @article{ , abstract = {As the deployment of fifth-generation (5G) cellular networks progresses worldwide, researchers are already delving into the potential of sixth-generation (6G) systems. One promising 6G-focused solution that has garnered significant attention of both researchers and healthcare providers is the integration of the digital twin (DT) paradigm with the digital health (eHealth) sector. This paper examines this solution, highlighting the challenges that must be overcome to fully unlock its potential, particularly in managing patient mobility. Specifically, the following contributions are provided. First, we explore the value of the deployment of DTs in eHealth, reviewing global initiatives, standards, potential applications, challenges, and 6G-enabled solutions. Next, we focus on the placement of medical DTs at the network’s edge and their migration in response to patient mobility. Finally, we present findings from a simulation campaign, showing key insights into the trade-offs involved in medical data transfer strategies. These results deepen our understanding of DT migration complexities and highlight the transformative impacts that mobility-driven medical DTs could have on healthcare services and patient outcomes.}, archive = {J_ACCESS}, author = {Chiara Suraci and Olga Chukhno and Gabriel-Miro Muntean and Antonella Molinaro and Giuseppe Araniti}, doi = {10.1109/ACCESS.2025.3569314}, journal = {IEEE Access}, pages = {85641-85651}, shortjournal = {IEEE Access}, title = {Migrate or not: Medical digital twins in the era of 6G edge-based networks}, volume = {13}, year = {2025}, }
• (2025). Survey of asynchronous radiolocation methods. ACCESS, 13, 85613-85640. (www)

  @article{ , abstract = {This publication reviews asynchronous radiolocation methods, emphasizing their analytical descriptions. Radiolocation systems evolve through the integration of advancements from multiple scientific disciplines, including telecommunications, computer science, electronics, and digital signal processing, making them inherently interdisciplinary. Most existing literature focuses on radiolocation systems with synchronized reference stations, supported by theoretical analyses and experimental studies. In contrast, asynchronous systems are less explored. Synchronous systems are preferred for their high measurement accuracy, achieved by providing a common time reference for the main components involved in measuring the radio signal parameters that are related to the position of mobile devices. However, a loss of synchronization or a failure of primary reference stations can prevent object localization, underscoring the need for asynchronous methods as standalone or backup solutions. Additionally, asynchronous approaches allow the integration of multiple independent radiolocation networks, enhancing the efficiency of the localization process. To address this gap, the authors systematized the current knowledge on asynchronous radiolocation methods, which can help researchers to understand this topic and choose appropriate position estimation methods for various applications, taking into account the limitations caused by network or mobile device design. The paper also highlights key asynchronous radiolocation solutions that have been practically implemented, demonstrating their potential and effectiveness in real-world scenarios.}, archive = {J_ACCESS}, author = {Jacek Stefanski and Jaroslaw Sadowski}, doi = {10.1109/ACCESS.2025.3567677}, journal = {IEEE Access}, pages = {85613-85640}, shortjournal = {IEEE Access}, title = {Survey of asynchronous radiolocation methods}, volume = {13}, year = {2025}, }
• (2025). Immersive multiplayer VR: Unreal engine’s strengths, limitations, and future prospects. ACCESS, 13, 85597-85612. (www)

  @article{ , abstract = {Virtual Reality (VR) has revolutionized digital interactions, particularly in multiplayer games, where players engage in immersive experiences that foster social connectivity and collaboration. Unreal Engine is at the core of this evolution, a powerful development platform known for its advanced networking tools, VR-specific capabilities, and seamless cross-platform support. Unreal Engine’s cross-platform support also ensures accessibility and scalability across diverse devices. This paper analyzes Unreal Engine’s role in advancing multiplayer VR development, focusing on its ability to create immersive environments that enhance user engagement and social interaction. We examined key features such as real-time networking for seamless communication, detailed avatar customization, and realistic environmental rendering, all contributing to an enhanced sense of presence. Beyond its technical features, this research identifies key challenges in multiplayer VR gaming, including latency, scalability, and ethical concerns related to inclusivity and user privacy. We propose future directions to address these issues, such as integrating artificial intelligence (AI), enhancing haptic feedback, and optimizing large-scale VR projects. By bridging these gaps, Unreal Engine can strengthen its role as a pioneering platform for social interaction, education, and collaboration in virtual spaces. This study also evaluates Unreal Engine’s effectiveness in facilitating social interaction and compares its capabilities with alternative platforms. The findings highlight best practices for VR game developers and provide recommendations to enhance user engagement and accessibility, ensuring that multiplayer VR continues to evolve as a transformative medium.}, archive = {J_ACCESS}, author = {Santiago Berrezueta-Guzman and Stefan Wagner}, doi = {10.1109/ACCESS.2025.3570166}, journal = {IEEE Access}, pages = {85597-85612}, shortjournal = {IEEE Access}, title = {Immersive multiplayer VR: Unreal engine’s strengths, limitations, and future prospects}, volume = {13}, year = {2025}, }
• (2025). Extended sequential function chart. ACCESS, 13, 85585-85596. (www)

  @article{ , abstract = {Sequential Function Chart (SFC) is one of the PLC programming languages defined in the IEC-61131 standard. SFC has been increasingly used in practical applications because even small controllers can now be programmed in this graphical language. Representing the sequential-parallel control algorithm in the form of a diagram makes the program transparent, and the possibility of using Petri net analysis tools allows for formal verification of the program’s correctness. Unfortunately, in SFC, the graph nodes take only binary values, which limits, for example, the possibility of modeling resources that are produced or consumed during the production process. It also makes the use of signals from analog sensors challenging, thanks to which it is possible to observe the progress of the supervised tasks. Hence, this paper proposes an extension of SFC (eSFC), which introduces nodes that model quantitative resources and variables storing analog signals. In addition, a formal model of eSFC and its algebraic interpretation are proposed, ensuring the unambiguity of the eSFC operation.}, archive = {J_ACCESS}, author = {Lesław Gniewek and Michał Markiewicz}, doi = {10.1109/ACCESS.2025.3567029}, journal = {IEEE Access}, pages = {85585-85596}, shortjournal = {IEEE Access}, title = {Extended sequential function chart}, volume = {13}, year = {2025}, }
• (2025). Multiple fiber-reinforced internal chambers for thumb-assist soft pneumatic actuators. ACCESS, 13, 85563-85584. (www)

  @article{ , abstract = {Soft actuators designed for finger assistance typically provide adequate support for flexion, but often cannot provide effective support for abduction and thumb opposition. In a previous study, we found that a continuum thumb-assistance actuator with multiple chambers (a middle chamber for flexion and two side chambers for abduction-adduction support) achieved a superior pinch performance than a multiactuator approach owing to the spatial constraints around the thumb joints. However, when pressurized, the chambers of our multichamber actuator interacted with each other, causing interference and deviant deformation, the mechanisms of which remain unknown. This study proposed the use of fiber-reinforced interior chambers to reduce the interchamber interference of semicylindrical actuators with multiple degrees-of-freedom (DoF) and proved their effectiveness. Three thumb actuators with exterior, interior, and interior-exterior hybrid fiber reinforcement windings were designed, prototyped, and compared in terms of range of motion, force output, and an enhanced Kapandji test. The causes and effects of interchamber interference were investigated using Finite Element Analysis (FEA). The FEA results suggest that the interchamber interference that occurs when simultaneously actuating the middle and side chamber can improve thumb opposition. Additionally, independent actuation of the middle and side chambers revealed that middle-to-side interference had a positive effect on thumb flexion when the middle chamber was actuated, whereas side-to-middle interference negatively affected thumb abduction when the side chamber was actuated. Our results indicate that hybrid fiber reinforcement mitigates negative interchamber interference while maintaining good multi-DoF support.}, archive = {J_ACCESS}, author = {Pablo E. Tortós-Vinocour and Yuanyuan Wang and Shao Ying Huang and Jose Gomez-Tames and Wenwei Yu}, doi = {10.1109/ACCESS.2025.3569768}, journal = {IEEE Access}, pages = {85563-85584}, shortjournal = {IEEE Access}, title = {Multiple fiber-reinforced internal chambers for thumb-assist soft pneumatic actuators}, volume = {13}, year = {2025}, }
• (2025). Non-profiled partial nibble recovery on power attack resilient adiabatic PRESENT block cipher through AI vulnerability assessment. ACCESS, 13, 85543-85562. (www)

  @article{ , abstract = {Cipher implementations in embedded devices are often vulnerable to Side-Channel Attacks (SCA), particularly power analysis attacks. Circuit-level countermeasures aim to enhance security against SCA at the most fundamental abstraction of VLSI design. Artificial Intelligence (AI) techniques, by eliminating the need for prior knowledge of cryptographic algorithms, have emerged as powerful tools for executing effective SCAs on secure implementations. This study investigates the resilience of encryption keys in a circuit-level implementation by analyzing the complexity of nibble-based attacks. Specifically, Multi-Layer Perceptron (MLP) models are employed to target the round structure of the PRESENT lightweight block cipher. The cipher is implemented using Charge Balancing Symmetric Pre-resolve Adiabatic Logic (CBSPAL), a secure adiabatic logic style, and its susceptibility to power analysis is rigorously evaluated. This research represents a pioneering effort in circuit-level analysis focusing on partial nibble recovery. The CBSPAL-SCA dataset captured at the circuit-level with the 4-bit substitution box (sBox) as the Point of Interest (PoI) is used to conduct non-profiled power analysis attacks across two distinct encryption keys, leveraging various activation functions. The results show that the AI-based nibble attack fails in fully recovering the secret key, thereby demonstrating the effectiveness and power analysis attack resistance of the secure CBSPAL based VLSI IP.}, archive = {J_ACCESS}, author = {Anjana Jyothi Banu and A. Prathiba}, doi = {10.1109/ACCESS.2025.3569570}, journal = {IEEE Access}, pages = {85543-85562}, shortjournal = {IEEE Access}, title = {Non-profiled partial nibble recovery on power attack resilient adiabatic PRESENT block cipher through AI vulnerability assessment}, volume = {13}, year = {2025}, }
• (2025). Automated insights into GitHub collaboration dynamics. ACCESS, 13, 85526-85542. (www)

  @article{ , abstract = {Today, GitHub (Trademark) is the most widely used platform for open-source software development. Large projects may comprise hundreds of distributed collaborators and thousands of GitHub “issues” (structured discussions). It includes basic support for dealing with issues, via “Pull Requests” (PRs)—document changes that can manually be defined to “close” them (i.e., they address and thereby conclude the issue discussion). Unresolved issues can pile up. For example, at the time of writing, the Kubernetes repository has almost 2000 open issues; finding which ones a PR might close is a hard task by itself. We address this by automatically identifying issue-PR relationships using language models (LMs) and leveraging Information Retrieval (IR) techniques. To foster further research, we contribute a carefully curated novel dataset called CodeConvo, reflecting the most influential open-source repositories for code development as well as some technical document repositories. We use this dataset to benchmark several state-of-the-art (SOTA) non-proprietary models that show exceptional performance on the MTEB benchmark, as well as to train and evaluate the performance of Smart Insights into GitHub Issue-PR Relations (SIGIR), our tailored model. The best SIGIR model/data combination yields an average Mean Reciprocal Rank (MRR) above 0.7, around 20% higher than the best baseline performance. Notably, ablation studies revealed that knowledge transfer occurs not only between different programming languages but also between code and technical documents, albeit to a lesser extent. We believe these results are encouraging and can stimulate the practical application of LMs for taming the complexity of very large projects, in GitHub and beyond.}, archive = {J_ACCESS}, author = {Jie Bian and Nikolay Arefev and Max Mühlhäuser and Michael Welzl}, doi = {10.1109/ACCESS.2025.3566309}, journal = {IEEE Access}, pages = {85526-85542}, shortjournal = {IEEE Access}, title = {Automated insights into GitHub collaboration dynamics}, volume = {13}, year = {2025}, }
• (2025). LLM in the loop: A framework for contextualizing counterfactual segment perturbations in point clouds. ACCESS, 13, 85507-85525. (www)

  @article{ , abstract = {Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount.}, archive = {J_ACCESS}, author = {Veljka Kočić and Niko Lukač and Džemail Rožajac and Stefan Schweng and Christoph Gollob and Arne Nothdurft and Karl Stampfer and Javier del Ser and Andreas Holzinger}, doi = {10.1109/ACCESS.2025.3568052}, journal = {IEEE Access}, pages = {85507-85525}, shortjournal = {IEEE Access}, title = {LLM in the loop: A framework for contextualizing counterfactual segment perturbations in point clouds}, volume = {13}, year = {2025}, }
• (2025). Enhancing ECG report generation with domain-specific tokenization for improved medical NLP accuracy. ACCESS, 13, 85493-85506. (www)

  @article{ , abstract = {The automation of medical report generation has been an area of interest for researchers over the years, with significant advancements in computational techniques and natural language processing. Traditionally, the focus has remained on rule-based or template-driven approaches to streamline the documentation process. However, recent breakthroughs in generative AI models have substantially enhanced the capabilities of automated medical report generation. Hence, the field has gained significant attention in recent years due to its potential to support healthcare professionals and improve diagnostic workflows. One key challenge in these models is the reliance on pre-trained, general-purpose tokenizers, which often fail to capture the domain-specific vocabulary essential for accurate medical reporting. When using a general tokenizer, the generated reports may lack coherence, relevance, or even correct medical terminology, leading to poor quality outputs. This problem is particularly acute in specialized fields like ECG, where precise terminology is critical. To address this gap, we train a Byte Pair Encoding (BPE) tokenizer to incorporate ECG-specific vocabulary, resulting in improved coherence and relevance in text generation. The custom tokenizer was integrated into a GPT-2 model for image-to-text generation tasks, where ECG reports were generated from ECG waveforms. Our experiments show that using the custom tokenizer leads to a 85% improvement in coherence and a 45% increase in relevance of generated reports compared to a general-purpose tokenizer.}, archive = {J_ACCESS}, author = {Farzeen Ashfaq and NZ Jhanjhi and Navid Ali Khan and Danish Javed and Mehedi Masud and Mohammad Shorfuzzaman}, doi = {10.1109/ACCESS.2025.3567566}, journal = {IEEE Access}, pages = {85493-85506}, shortjournal = {IEEE Access}, title = {Enhancing ECG report generation with domain-specific tokenization for improved medical NLP accuracy}, volume = {13}, year = {2025}, }
• (2025). A multi-scale deep learning framework combining MobileViT-ECA and LSTM for accurate ECG analysis. ACCESS, 13, 85473-85492. (www)

  @article{ , abstract = {Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. This paper introduces a novel deep learning (DL) architecture designed to enhance the processing, feature extraction, and analysis of ECG signals. The proposed model utilizes a hybrid framework that combines standard and dilated convolutional networks, advanced attention mechanisms, and temporal sequence learning to address the complexities of ECG data. A parallel architecture integrates a MobileViT block for efficient spatial feature extraction and an Efficient Channel Attention (ECA) module to refine feature representations. Simultaneously, a Long Short-Term Memory (LSTM) network captures temporal dependencies in the data. The concatenation of the outputs from both the MobileViT-ECA block and the LSTM network allows for the extraction of both local and global features. Fully connected layers, along with dropout regularization, ensure robust performance and effective classification. We analyzed ECG signals of three different lengths (1, 2, and 3 seconds) to investigate how segment length affects model performance. Additionally, we conducted various classification scenarios and an ablation study to simulate real-world conditions and identify key components of the architecture. Our experimental findings show that the model attains high accuracy in ECG classification and AF identification, with average metrics including accuracy of 87.80%, recall of 87.80%, F1-score of 87.45%, and specificity of 95.66%. The proposed model outperforms existing methods on the same AF dataset, demonstrating its superior performance. This method accurately classifies ECG signals and detects AF, highlighting its potential for clinical application. The results help move forward the development of automated tools for diagnosing heart diseases and create new opportunities for flexible solutions in this field.}, archive = {J_ACCESS}, author = {Abduljabbar S. Ba Mahel and Mehdhar S. A. M. Al-Gaashani and Reem Ibrahim Alkanhel and Dina S. M. Hassan and Mohammed Saleh Ali Muthanna and Ammar Muthanna and Ahmed Aziz}, doi = {10.1109/ACCESS.2025.3568026}, journal = {IEEE Access}, pages = {85473-85492}, shortjournal = {IEEE Access}, title = {A multi-scale deep learning framework combining MobileViT-ECA and LSTM for accurate ECG analysis}, volume = {13}, year = {2025}, }
• (2025). Comparative studies of descriptor-based image matching techniques for AAV applications. ACCESS, 13, 85461-85472. (www)

  @article{ , abstract = {Autonomous aerial vehicles (AAVs) require high operational reliability. One of the key issues is ensuring uninterrupted localization. Unfortunately, the Global Navigation Satellite System (GNSS) is susceptible to interference. This problem can be overcome by redundant localization using vision, where the AAV position is determined by comparing satellite images assigned to a given area with images recorded by an onboard camera. It is a real-time process, so a reliable image-matching technique is crucial. This article presents the results of comparative studies of typical image feature-matching algorithms. Pre-processed images recorded during flights and satellite orthophotomaps are used for these studies. The comparison results are presented graphically. The work consists of three stages: – image labelling, – automatic image-matching, and – analysis of the results. The research goal is to identify the image-matching method that gives the most precise result and is not computationally demanding. The data were collected during flights at various altitudes, with various tilt angles and route lengths. The data diversity allows us to assess the quality of map-matching methods, considering various flight parameters. The flights were carried out in good weather conditions. The most effective image-matching methods, such as DKMv3 and ROMA, were indicated, and their ability to perform efficient matching was highlighted. Additionally, the limitations of vision-based localization, including challenges related to illumination changes, occlusions, and viewpoint variations, are discussed. The presented research is relevant for designing resilient localization systems.}, archive = {J_ACCESS}, author = {Tomasz Pogorzelski and Teresa Zielinska}, doi = {10.1109/ACCESS.2025.3566953}, journal = {IEEE Access}, pages = {85461-85472}, shortjournal = {IEEE Access}, title = {Comparative studies of descriptor-based image matching techniques for AAV applications}, volume = {13}, year = {2025}, }
• (2025). New approaches for network topology optimization using deep reinforcement learning and graph neural network. ACCESS, 13, 85447-85460. (www)

  @article{ , abstract = {The exponential growth in Internet-connected devices has escalated the demand for optimized network topologies to ensure high performance. Traditional optimization methods often fall short in scalability and adaptability when it comes to network topology planning. In this paper, we address the challenge of transforming mesh topologies into tree topologies for wireless networks, with the objective of maximizing throughput. We propose two new methods: Path Selection with Rejection Strategy (PSRS), which leverages Message-Passing Neural Networks (MPNN), and Dual-Agent Tree Topology Exploration (DATTE), which employs Graph Attention Networks (GAT). These schemes integrate Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNNs) to construct efficient tree topologies with the goal of maximizing the minimum throughput of the wireless network. Experimental results validate the scalability and performance gains of the proposed approaches, highlighting their potential for real-world applications.}, archive = {J_ACCESS}, author = {Mohammed Ali and Florent Duchesne and Ghassan Dahman and François Gagnon and Diala Naboulsi}, doi = {10.1109/ACCESS.2025.3569236}, journal = {IEEE Access}, pages = {85447-85460}, shortjournal = {IEEE Access}, title = {New approaches for network topology optimization using deep reinforcement learning and graph neural network}, volume = {13}, year = {2025}, }
• (2025). Revolutionizing mental health sentiment analysis with BERT-fuse: A hybrid deep learning model. ACCESS, 13, 85428-85446. (www)

  @article{ , abstract = {Mental health disorders profoundly affect intellectual ability, emotional resilience, and social stability, leading to lasting effects on both individuals and society. Increasing global concern over mental health calls for innovative solutions, with sentiment analysis emerging as a promising approach for early diagnosis and intervention. Traditional methods are limited by the complexity of mental health-related texts, which contain specialized terminology and domain-specific nuances. To address these challenges, we propose BERT-Fuse, a hybrid deep learning model combining traditional feature extraction techniques (TF-IDF and Bag-of-Words) with advanced neural architectures, including Bidirectional Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer blocks, and Convolutional Neural Networks (CNN). We evaluated three word embedding techniques-BERT, GloVe, and FastText within BERT-Fuse and found that BERT embeddings achieve the highest performance in terms of accuracy, precision, recall, and F1-score. The model was trained on a balanced dataset of text samples representing seven mental health conditions. An ablation study highlights the contributions of key model components to its performance. BERT-Fuse shows promise as a scalable, high-precision system for mental health detection with an impressive average test accuracy of 97.05%. Our model bridges the gap between deep learning and psychological text analysis, paving the way for future mental health assessment tools that are more intelligent and easily accessible.}, archive = {J_ACCESS}, author = {Md. Mithun Hossain and Sanjara and Md. Shakil Hossain and Sudipto Chaki and Md. Saifur Rahman and A. B. M. Shawkat Ali}, doi = {10.1109/ACCESS.2025.3568340}, journal = {IEEE Access}, pages = {85428-85446}, shortjournal = {IEEE Access}, title = {Revolutionizing mental health sentiment analysis with BERT-fuse: A hybrid deep learning model}, volume = {13}, year = {2025}, }
• (2025). Osteosarcoma CT image segmentation based on OSCA-TransUnet model. ACCESS, 13, 85415-85427. (www)

  @article{ , abstract = {This paper mainly conducts research on the segmentation of osteosarcoma CT images by using deep learning methods. Due to the lack of a public dataset for osteosarcoma CT images, we collaborated with Peking University People’s Hospital to construct the osteosarcoma CT dataset CT-OSD. Meanwhile, considering the limited accuracy of existing medical image segmentation models for osteosarcoma CT segmentation, we propose the OSCA-TransUnet model based on the fusion of spatial and channel attention. On the osteosarcoma CT dataset CT-OSD, the DSC index of the OSCA-TransUnet model is 2.44% higher than that of the TransUnet model, and the HD95 index is 2.05 mm lower than that of the TransUnet model. This effectively improves the segmentation accuracy of osteosarcoma CT images. Additionally, we also conducted generalization experiments of the OSCA-TransUnet model on the public medical dataset Synapse. The results show that the DSC index reaches 80.42% and the HD95 index reaches 18.04 mm. The performance is better than that of existing models, demonstrating the good generalization performance of the OSCA-TransUnet model.}, archive = {J_ACCESS}, author = {Zhuoming Fan and Qian Wang and Jun Wang and Yu Liu and Yong Zeng and Jimin Chen}, doi = {10.1109/ACCESS.2025.3569586}, journal = {IEEE Access}, pages = {85415-85427}, shortjournal = {IEEE Access}, title = {Osteosarcoma CT image segmentation based on OSCA-TransUnet model}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimal stealthy attack on cyber-physical systems. ACCESS, 13, 85407-85414. (www)

  @article{ , abstract = {This paper addresses security problems in cyber-physical systems (CPSs). A multi-objective optimal stealthy attack strategy is designed to disrupt the estimation process and degrade the stabilizing performance of the systems simultaneously. First, a feasible region in which the attack sequences remain stealthy is specified. Then, the attack strategy is derived from solving an optimization problem aimed at maximizing the perturbation of CPSs. The paper also outlines an algorithm that demonstrates the implementation of the attack. Finally, a simulation example is presented to illustrate both the stealthiness of the attack and the resulting degradation in system performance.}, archive = {J_ACCESS}, author = {Yibo Huang and Zhihuan Song and Wei Yu}, doi = {10.1109/ACCESS.2025.3561891}, journal = {IEEE Access}, pages = {85407-85414}, shortjournal = {IEEE Access}, title = {Multi-objective optimal stealthy attack on cyber-physical systems}, volume = {13}, year = {2025}, }
• (2025). PerfCam: Digital twinning for production lines using 3D gaussian splatting and vision models. ACCESS, 13, 85390-85406. (www)

  @article{ , abstract = {We introduce PerfCam, an open source Proof-of-Concept (PoC) digital twinning framework that combines camera and sensory data with 3D Gaussian Splatting and computer vision models for digital twinning, object tracking, and Key Performance Indicators (KPIs) extraction in industrial production lines. By utilizing 3D reconstruction and Convolutional Neural Networks (CNNs), PerfCam offers a semi-automated approach to object tracking and spatial mapping, enabling digital twins that capture real-time KPIs such as availability, performance, Overall Equipment Effectiveness (OEE), and rate of conveyor belts in the production line. We validate the effectiveness of PerfCam through a practical deployment within realistic test production lines in the pharmaceutical industry and contribute an openly published dataset to support further research and development in the field. The results demonstrate PerfCam’s ability to deliver actionable insights through its precise digital twin capabilities, underscoring its value as an effective tool for developing usable digital twins in smart manufacturing environments and extracting operational analytics.}, archive = {J_ACCESS}, author = {Michel Gokan Khan and Renan Guarese and Fabian Johnson and Xi Vincent Wang and Anders Bergman and Benjamin Edvinsson and Mario Romero and Jérémy Vachier and Jan Kronqvist}, doi = {10.1109/ACCESS.2025.3567702}, journal = {IEEE Access}, pages = {85390-85406}, shortjournal = {IEEE Access}, title = {PerfCam: Digital twinning for production lines using 3D gaussian splatting and vision models}, volume = {13}, year = {2025}, }
• (2025). Paths in folded hypercubes with any pair of faulty vertices. ACCESS, 13, 85380-85389. (www)

  @article{ , abstract = {When a vertex in the network is subjected to an external attack, such as a hacker attack, it is highly likely to infect one of its neighbors, resulting in both adjacent vertices being unable to operate normally in the network. Hence, it is crucial to ensure fault-tolerance capability when dealing with a pair of adjacent faulty vertices in the network, as it is essential for maintaining system reliability. The folded hypercube, denoted as $FQ_{n}$ , is a recognized variant of the hypercube architecture. It is formed by connecting every pair of vertices with complementary addresses. Notably, for any odd integer n, the $FQ_{n}$ structure exhibits bipartite characteristics. Consider two adjacent faulty vertices, $f_{1 }$ and $f_{2 }$ , within $FQ_{n}$ . Let u and v be any two fault-free vertices in ${FQ_{n}}-\{{f_{1 }},~{f_{2 }}\}{}$ . In previous studies, we investigated the embedding of cycles of various lengths in ${FQ_{n}}-\left \{{{{f_{1 }},~{f_{2 }}}}\right \}{}$ . Furthermore, we explored any fault-free edge in ${FQ_{n}}-\left \{{{{f_{1 }},~{f_{2 }}}}\right \}{}$ could be part of cycles of different lengths. Based on these findings on cycle embedding properties, we were motivated to consider whether, under the same fault-tolerant conditions, paths of various lengths could also be embedded in ${FQ_{n}}-\left \{{{{f_{1 }},~{f_{2 }}}}\right \}{}$ . Therefore, the main results examined in this paper include: 1) for every odd $n{\geq }3$ , ${FQ_{n}}-\{{f_{1 }},~{f_{2 }}\}{}$ contains a fault-free path $P[u,v]$ of every length l in the range ${d_{FQ_{n}}}\left ({{u,v}}\right){\leq }l{\leq }{2^{n}}-3$ , where $l-{d_{FQ_{n}}}\left ({{u,v}}\right)$ is even; and 2) for every even $n{\geq }4$ , ${FQ_{n}}-\{{f_{1 }},~{f_{2 }}\}{}$ contains a fault-free path $P[u,v]$ of every length l in the range $n-1{\leq }l\leq {2^{n}}-3$ . The variety of path lengths embedded in this paper represents the optimal scenario under the fault-tolerant condition.}, archive = {J_ACCESS}, author = {Che-Nan Kuo and Yu-Huei Cheng}, doi = {10.1109/ACCESS.2025.3567161}, journal = {IEEE Access}, pages = {85380-85389}, shortjournal = {IEEE Access}, title = {Paths in folded hypercubes with any pair of faulty vertices}, volume = {13}, year = {2025}, }
• (2025). Green supply chains: Feature analysis and key node identification in multi-layer supply chain networks based on fused multi-scale metric. ACCESS, 13, 85363-85379. (www)

  @article{ , abstract = {Nowadays, the stability, efficiency, and costs of supply chains and their adaptability to market fluctuations have become challenging for global enterprises. Still, supply chain experts have paid little attention to how inter-layer information, intrinsic features of real-world networks, and ESG performance affect the significance of node identification. The paper responds to this above gap by proposing [and testing] a novel modified fused multi-scale metric (FMSM) with environmental, social, and governance performance (ESG) indicators to identify important nodes within multi-layer supply chain networks (MLSCNs). Using real-time data (2012-2022) of 4,562 observations (including 2,276 suppliers and 2,109 customers) from Chinese A-shared list firms, the model accurately identified 35 enterprises in the middle layer as important nodes, further validated by a cascading failure test. A comparative analysis demonstrated superior network, nodes, and predicting efficiency of the FMSM against other centrality and structural hole metrics, however, it is constrained by its dependency on both data quality and quantifiable metrics. The model efficiently highlighted the unexplored complexities of multi-layer supply chain networks characterized by sparsity, hierarchy, and community structures. Additionally, the model indicated that identified important nodes exhibited strong and stable ESG performance, particularly in governance and social responsibility, concentrated in manufacturing, technology, energy, chemicals, and pharmaceutical sectors.}, archive = {J_ACCESS}, author = {Shanshan Wang and Ruoxuan AI and Shoukat Iqbal Khattak and Sadia Tariq}, doi = {10.1109/ACCESS.2025.3562315}, journal = {IEEE Access}, pages = {85363-85379}, shortjournal = {IEEE Access}, title = {Green supply chains: Feature analysis and key node identification in multi-layer supply chain networks based on fused multi-scale metric}, volume = {13}, year = {2025}, }
• (2025). Monte carlo-based strategy for assessing the impact of EEG data uncertainty on confidence in convolutional neural network classification. ACCESS, 13, 85342-85362. (www)

  @article{ , abstract = {Electroencephalography (EEG) data acquisition process in Brain-Computer Interfaces (BCIs) is inevitably affected by uncertainty which introduces variability in the data. This variability, often overlooked, affects the training and testing of neural network (NN) models. This study evaluates the impact of systematic bias (±2%) plus aleatoric uncertainty (2-5% random Gaussian perturbations) on the classification confidence of the EEGNet model for four class-Motor Imagery (MI) tasks using the BCI Competition IV 2a dataset. Through two Monte Carlo simulations with 100 iterations each, perturbed datasets were generated to mimic real-world EEG acquisition uncertainties. Softmax outputs served to analyze overlap in predicted probabilities and quantify model confidence in classification decisions. We introduce robust evaluation metrics, including the proportion of area under the curve (AUC) of probability density functions (PDFs) $\geq 70$ % accuracy, overlap coefficients, and percentile-based thresholding, which provide a more comprehensive assessment of model performance, capturing not only accuracy but also confidence and ambiguity in predictions. Results show that the robustness of EEGNet in the face of realistic measurement uncertainties is prone to inter-subject variability, with the model achieving higher confidence for Subject 1 (average 90.52%) compared to Subject 2 (62.96%). EEGNet demonstrates resilience to directional calibration shifts in the data, with model confidence varying by 0.22% for Subject 1 and by 6.24% for Subject 2, showing that aleatoric precision errors dominate over small systematic shifts. Our approach provides a rigorous framework for quantifying the impact of measurement variability on EEG-based BCI classification, thereby enhancing reliability and generalizability in practical BCI deployments.}, archive = {J_ACCESS}, author = {Pierre Sedi Nzakuna and Vincenzo Gallo and Vincenzo Paciello and Aimé Lay-Ekuakille and Angelo Kuti Lusala}, doi = {10.1109/ACCESS.2025.3570134}, journal = {IEEE Access}, pages = {85342-85362}, shortjournal = {IEEE Access}, title = {Monte carlo-based strategy for assessing the impact of EEG data uncertainty on confidence in convolutional neural network classification}, volume = {13}, year = {2025}, }
• (2025). GMFLDA: Improved prediction of lncRNA-disease association via graph convolutional network. ACCESS, 13, 85330-85341. (www)

  @article{ , abstract = {The rapid expansion of diverse networks has created a growing need to integrate multiple heterogeneous structures to effectively capture both inter- and intra-entity relationships. This integration helps preserve the intrinsic meaning of complex biological interactions. Network-based approaches have been highly effective in predicting node labels and uncovering hidden associations between entities. These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. Specifically, GCNs are leveraged to extract and encode high-fidelity feature representations of lncRNAs and diseases, while deep matrix factorization, implemented via a multi-layer perceptron, facilitates the discovery of potential disease-associated lncRNAs. Our model exhibits outstanding predictive performance, achieving AUCs of 0.9183 and 0.9057 in leave-one-out and five-fold cross-validation experiments, respectively. Extensive comparative evaluations demonstrate that GMFLDA surpasses five state-of-the-art methods, underscoring its superior predictive capability. We anticipate that GMFLDA will serve as a powerful computational tool for biomarker discovery, significantly accelerating the identification of disease-associated lncRNAs while mitigating the time and cost constraints of traditional wet-lab experiments.}, archive = {J_ACCESS}, author = {Kwangsu Kim and Jihwan Ha}, doi = {10.1109/ACCESS.2025.3568461}, journal = {IEEE Access}, pages = {85330-85341}, shortjournal = {IEEE Access}, title = {GMFLDA: Improved prediction of lncRNA-disease association via graph convolutional network}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review of cyber security monitoring in maritime. ACCESS, 13, 85307-85329. (www)

  @article{ , abstract = {In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. One of the key approaches for handling cyber incidents is cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. Although several literature review papers have been published in the field of maritime cyber security, none of the previous studies has focused on cyber security monitoring. The current paper addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. The first contribution of this paper is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, the paper presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this paper is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. The gaps and limitations include several dataset and evaluation issues and a number of understudied research topics. Based on these findings, the paper outlines future research directions for cyber security monitoring in the maritime field.}, archive = {J_ACCESS}, author = {Risto Vaarandi and Leonidas Tsiopoulos and Gábor Visky and Muaan Ur Rehman and Hayretdin Bahşi}, doi = {10.1109/ACCESS.2025.3567385}, journal = {IEEE Access}, pages = {85307-85329}, shortjournal = {IEEE Access}, title = {A systematic literature review of cyber security monitoring in maritime}, volume = {13}, year = {2025}, }
• (2025). Secure THz communication in 6G: A two-stage DRL approach for IRS-assisted NOMA. ACCESS, 13, 85294-85306. (www)

  @article{ , abstract = {The rapid evolution of 6G networks demands innovative solutions to address the dual challenges of ensuring robust physical layer security (PLS) and optimizing energy efficiency. This paper introduces an innovative framework based on deep reinforcement learning (DRL) for enhancing security and energy efficiency in terahertz (THz) communication within intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) systems. Utilizing the deep deterministic policy gradient (DDPG) algorithm, we introduce a two-stage policy learning approach designed to optimize secrecy energy efficiency (SEE) while ensuring secure communication, even in the presence of multiple eavesdroppers (Eves). The first stage focuses on optimizing the secrecy rate (SR) by dynamically adjusting IRS phase shifts and transmit power allocation, ensuring secure communication links. The second stage refines the policy to minimize energy consumption without compromising the achieved security. Our framework addresses the challenges of imperfect channel state information (CSI) and inherent uncertainties in THz communication, making it highly adaptable to real-world scenarios. Through extensive simulations, we demonstrate that the proposed DDPG-based approach outperforms traditional methods such as advantage actor-critic (A2C) and alternating optimization (AO) in terms of convergence speed, stability, and final SEE performance. The results highlight the effectiveness of IRS in enhancing signal reflection and coverage, significantly improving both security and energy efficiency in THz-based 6G networks. This work provides a scalable and sustainable solution for next-generation wireless communication systems, paving the way for secure, energy-efficient, and high-capacity 6G networks.}, archive = {J_ACCESS}, author = {Muhammad Shahwar and Manzoor Ahmed and Touseef Hussain and Muhammad Sheraz and Wali Ullah Khan and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3569708}, journal = {IEEE Access}, pages = {85294-85306}, shortjournal = {IEEE Access}, title = {Secure THz communication in 6G: A two-stage DRL approach for IRS-assisted NOMA}, volume = {13}, year = {2025}, }
• (2025). Feature selection techniques for enhancing app user review analysis. ACCESS, 13, 85279-85293. (www)

  @article{ , abstract = {The rapid growth of user-generated content, particularly app user reviews, presents a significant challenge in analyzing and extracting useful insights. The unstructured nature, inconsistent quality, and large volume of these reviews make it difficult to identify relevant information for app maintenance and updates. This study addresses this challenge by evaluating the impact of different feature selection techniques on the performance of machine learning models in multi-label classification tasks for app user review analysis. Our findings indicate that the subset attributes derived from a combination of Information Gain, GINI Index, and Correlation Matrix can improve the performance of machine learning models. Using a Support Vector Machine with proposed innovative score-based zero shot technique, promising results were achieved on average with 96.75% precision, 69.39% recall, and 80.81% F1-Score. Additionally, high-quality features, such as the percentage Difference can enhance performance in multi-label classification tasks, providing valuable insights for practitioners and researchers. The research implications and significance highlight the practical applications and strategic value of these findings, contributing to the advancement of knowledge and practice in the field of software engineering, particularly for multi-label classification tasks.}, archive = {J_ACCESS}, author = {Amran Salleh and Mohd Hafeez Osman and Sa’Adah Hassan and Mar Yah Said}, doi = {10.1109/ACCESS.2025.3558795}, journal = {IEEE Access}, pages = {85279-85293}, shortjournal = {IEEE Access}, title = {Feature selection techniques for enhancing app user review analysis}, volume = {13}, year = {2025}, }
• (2025). Improving news recommendation accuracy through multimodal variational autoencoder and adversarial training. ACCESS, 13, 85269-85278. (www)

  @article{ , abstract = {Traditional recommender systems, such as collaborative filtering and content filtering, have inherent limitations, including cold start issues and challenges in filtering information. Additionally, the sparsity and noise in news data significantly affect recommendation accuracy. To address these issues, this paper introduces a multi-head self-attention mechanism to capture both long-term and local dependencies within user sequences. A Multimodal Variational Autoencoder (MVAVE) is constructed based on the multi-head self-attention mechanism, incorporating noise injection for denoising to mitigate the impact of data noise on recommendation performance. Building upon MVAVE, an adversarial multimodal data sequence recommender system is further developed by integrating MVAVE with a Generative Adversarial Network (GAN). Through adversarial training, the system reduces the reconstruction loss of user-item interactions and enhances recommendation accuracy. Moreover, multimodal data fusion is utilized to provide richer and more comprehensive information. Experimental results demonstrate superior recommendation performance on the NewsStories and Amazon datasets, achieving optimal results when the number of multi-head attention heads (HEAD) is set to 6 and the hyperparameter is 0.2. Specifically, on the NewsStories dataset, the system achieves a Recall of 21.74% and a Precision of 22.06%; on the Amazon dataset, it achieves a Recall of 18.67% and a Precision of 18.36%. This system effectively captures user preferences, avoids recommending irrelevant content, resolves the limitations of traditional recommender systems, and significantly improves recommendation accuracy.}, archive = {J_ACCESS}, author = {Pei Tang and Shuo Zhu and Bilal Alatas}, doi = {10.1109/ACCESS.2025.3568514}, journal = {IEEE Access}, pages = {85269-85278}, shortjournal = {IEEE Access}, title = {Improving news recommendation accuracy through multimodal variational autoencoder and adversarial training}, volume = {13}, year = {2025}, }
• (2025). An anodic target strategy for microfocused X-ray device: Enhancing and tuning effects of molybdenum nanostructures on radiation properties. ACCESS, 13, 85260-85268. (www)

  @article{ , abstract = {Microfocused X-rays with high brightness and high spatial resolution performance are increasingly important for advanced analytical and imaging applications. Developing high-performance, miniaturized, and tunable microfocused X-ray source devices requires innovative approaches to optimize radiation properties. This study establishes an anodic target strategy based on molybdenum nanostructures to investigate their effects on X-ray radiation properties through Monte Carlo simulations: The molybdenum nano-cylinder with $1\mu $ m diameter and $0.9\mu $ m height under 40 keV electron beam excitation provides optimal balance between radiation efficiency and brightness, while a maximum output flux can be achieved with thicker targets approaching the electron transmission range limit. The effective focal spot size decreases when transforming the cylinder structure into a cone, achieving better balance between focal spot and radiation flux at conification degrees $\alpha _{\mathrm {c}} \lt 0.6$ . Nanostructures orienting with their base facing the incident electron beam maximizes radiation flux and brightness by reducing electron backscattering. Thermal management considerations further reveal that rotating anode designs with pulsed excitation could significantly increase sustainable electron current density. These insights provide theoretical guidance for optimizing microfocused X-ray source devices using tailored nanostructured anodes for applications requiring high spatial resolution such as micro X-ray fluorescence analysis.}, archive = {J_ACCESS}, author = {Fengyuan Liu and Shaozhi Deng and Jia Shi and Yanlin Ke and Fangfei Ming and Shuai Tang and Huanjun Chen and Yan Shen}, doi = {10.1109/ACCESS.2025.3569721}, journal = {IEEE Access}, pages = {85260-85268}, shortjournal = {IEEE Access}, title = {An anodic target strategy for microfocused X-ray device: Enhancing and tuning effects of molybdenum nanostructures on radiation properties}, volume = {13}, year = {2025}, }
• (2025). Advancements in hybrid energy storage systems for rural electrification: A comprehensive case study on siwa oasis in egypt on increasing battery longevity in standalone PV systems. ACCESS, 13, 85239-85259. (www)

  @article{ , abstract = {The use of standalone photovoltaic power systems (PVPS) as a viable solution for rural electrification has gained significant momentum. Electricity for essential home requirements is now commonly available thanks to these systems. Typically, PVPS incorporate devices for storing energy, predominantly Lead-acid (LA) batteries, to address the supply-demand disparity inherent in solar energy. However, the limited lifespan of LA batteries results in increased costs for PVPS. To address this issue, researchers have introduced a solution called the supercapacitor-battery hybrid energy storage scheme (HESS). The objective of this approach is to foster the longevity of batteries by minimizing the stress incurred by fluctuations in charging and discharging. Currently, there are a variety of HESS and associated energy management tactics to choose from, each tailored to specific uses. These systems differ in topology, intricacy, and control algorithms. This research article offers a thorough assessment of the latest advancements in the field of HESS and examines various topologies that could potentially increase the longevity of Lead-acid batteries in PVPS. The study includes theoretical analyses and numerical simulations using MATLAB Simulink to evaluate different HESS topologies in rural residential energy systems. The investigation and comparison of the efficacy of these topologies in lowering the impact on the battery is conducted. Additionally, the proposed strategy’s effectiveness is confirmed by assessing the maximum estimated battery lifespan in all potential operation scenarios. Extensive simulation studies were conducted using MATLAB/Simulink to analyze the microgrid’s operation in various scenarios throughout the year. Ultimately, a HESS has been experimentally proven and its effectiveness in a PVPS standalone setting has been simulated in order to confirm the accuracy of the initial analysis.}, archive = {J_ACCESS}, author = {Khalid Alqunun and Nehad M. Khattab and Ismail Marouani and Kamel Tlijani and Mohamed Abdelhameed Zaky and Loay Mohamed Ali El-Sayed and Ahmed Ibrahim Omar and Badr M. Alshammari and Tawfik Guesmi and Ahmed Hussain Elmetwaly and Ramy Adel Younis}, doi = {10.1109/ACCESS.2025.3569644}, journal = {IEEE Access}, pages = {85239-85259}, shortjournal = {IEEE Access}, title = {Advancements in hybrid energy storage systems for rural electrification: A comprehensive case study on siwa oasis in egypt on increasing battery longevity in standalone PV systems}, volume = {13}, year = {2025}, }
• (2025). Influence of manual cleaning on the thermal behavior of photovoltaic modules. ACCESS, 13, 85231-85238. (www)

  @article{ , abstract = {Our study investigates soiling effects on photovoltaic (PV) modules operating temperature (Tm) in an urban PV plant in Brazil. Our experiments were carried out over an 18-month period under different environmental conditions and manual cleaning intervals varying between 7 and 35 days. Tm measurements were recorded at the center and edges of both clean and dirty modules. The results indicate that clean modules present slightly higher Tm values than dirty ones, with a statistically significant difference confirmed by the application of the t-test, but without substantial impact on the plant operation. Furthermore, as the no-cleaning interval increases, the thermal gradient between the center and the inferior edge tends to reduce, while the gradient between the superior and inferior edges exhibits nonlinear behavior, suggesting distinct phases of soiling deposition. This research contributes to understanding soiling influence on PV module thermal behavior, with implications for optimizing PV efficiency in urban environments.}, archive = {J_ACCESS}, author = {José Janiere S. de Souza and Leticia O. Santos and Paulo C. M. Carvalho}, doi = {10.1109/ACCESS.2025.3570217}, journal = {IEEE Access}, pages = {85231-85238}, shortjournal = {IEEE Access}, title = {Influence of manual cleaning on the thermal behavior of photovoltaic modules}, volume = {13}, year = {2025}, }
• (2025). Design principles for reinforcement learning in congestion control environments. ACCESS, 13, 85217-85230. (www)

  @article{ , abstract = {Reinforcement Learning (RL) has emerged as a powerful tool for optimising sequential decision-making processes. Recent years have seen growth in interest in applying RL to managing Congestion Control (CC) in computer networks, with the goal of handling diverse kinds of traffic that may be challenging for human-designed algorithms. However, implementing RL in CC is a complex process consisting of numerous design choices in developing the state space, action space, and reward function design. This paper evaluates different design strategies for developing the state space and how they contribute to learning. We assess the impact of various action spaces on learning and investigate how the reward function influences network performance in different scenarios. Our findings reveal that metrics used in state space development significantly affect RL agent learning. We demonstrate that a discrete action space leads to an improvement in learning compared to a continuous action space. Through a comparative analysis of 14 reward functions, we show that different reward designs result in varying trade-offs between throughput, delay, and loss. These insights enable the customisation of RL agents to scenarios with low or high bandwidth.}, archive = {J_ACCESS}, author = {Lincoln Kamau Kiarie and Mahsa Derakhshani and Konstantinos G. Kyriakopoulos}, doi = {10.1109/ACCESS.2025.3569093}, journal = {IEEE Access}, pages = {85217-85230}, shortjournal = {IEEE Access}, title = {Design principles for reinforcement learning in congestion control environments}, volume = {13}, year = {2025}, }
• (2025). Hybrid naïve bayes models for scam detection: Comparative insights from email and financial fraud. ACCESS, 13, 85207-85216. (www)

  @article{ , abstract = {Online scams continue to escalate in scale and sophistication, ranging from deceptive phishing emails to complex financial fraud schemes. These evolving threats have surpassed the capabilities of traditional detection systems, creating an urgent demand for scalable, real-time, and interpretable machine learning solutions. This study revisits the Naïve Bayes algorithm—often underestimated in modern cybersecurity—as a core building block for effective scam detection. By looking at two specific case studies on phishing emails and financial fraud detection, we look into the math behind Naïve Bayes and talk about its flaws, such as the assumption that features are independent, problems with high-dimensional data, and extreme class imbalance. We apply advanced feature engineering techniques and introduce hybrid model architectures that integrate Naïve Bayes with deep learning and ensemble methods. Our empirical results reveal that a strategically optimized Naïve Bayes model can deliver competitive accuracy, while maintaining transparency and computational efficiency—key attributes for real-world fraud prevention systems. By bridging theory and application, this research challenges the notion that simplicity implies ineffectiveness. It demonstrates that Naïve Bayes, particularly when hybridized, remains a powerful and adaptable tool in the fight against evolving cyber threats, contributing to the design of robust and responsive cybersecurity frameworks.}, archive = {J_ACCESS}, author = {Lebede Ngartera and Mahamat Ali Issaka and Saralees Nadarajah}, doi = {10.1109/ACCESS.2025.3569216}, journal = {IEEE Access}, pages = {85207-85216}, shortjournal = {IEEE Access}, title = {Hybrid naïve bayes models for scam detection: Comparative insights from email and financial fraud}, volume = {13}, year = {2025}, }
• (2025). Enhanced edge feature fusion and visual state space for precise detection of switch tip close fitting. ACCESS, 13, 85196-85206. (www)

  @article{ , abstract = {Switches are critical components in railway transportation, and the detection of close fitting between switch tips is essential for ensuring operational safety. Current methods primarily rely on manual on-site measurements, which are time-consuming and prone to errors. To address this, we propose DU-Net, an improved image segmentation and detection algorithm based on the U-Net model. DU-Net integrates edge feature enhancement and visual state space to accurately segment tracks in rail transit switch images and detect the close-fitting degree between the basic track and switch tip. An Edge Feature Enhancement Module uses dilated convolutions to enhance local feature extraction, while a Visual State Space Module in the skip connections captures contextual spatial information. A Dynamic Coefficient Fusion Edge Line Detection Module is also incorporated to improve the precision of switch point close contact measurement. Experimental results demonstrate that DU-Net achieves high segmentation and measurement accuracy, with an average Intersection over Union of 97.58% for rail recognition, a Dice similarity coefficient of 98.80%, and an average Hausdorff Distance of 0.426 mm. Furthermore, comparison with manual measurements reveals that measurement errors are within 1 mm, meeting practical application requirements. The proposed method represents a significant step forward in automating and improving the accuracy of switch tip close-fitting detection.}, archive = {J_ACCESS}, author = {Chang Zhengtang and Lan Xiangui and Lai Yihui}, doi = {10.1109/ACCESS.2025.3566393}, journal = {IEEE Access}, pages = {85196-85206}, shortjournal = {IEEE Access}, title = {Enhanced edge feature fusion and visual state space for precise detection of switch tip close fitting}, volume = {13}, year = {2025}, }
• (2025). IMA-FLADRC-based electric-driven high-speed maize precision seeding control strategy and system. ACCESS, 13, 85177-85195. (www)

  @article{ , abstract = {Under high-speed seeding conditions, the accuracy and synchronization of the guiding and seeding motors are critical to seed spacing uniformity. This study develops an electric-driven high-speed maize precision seeding control system, establishing kinematic models for the seeding and guiding motors relative to operating speed. An Improve Mayfly Algorithm-Fuzzy Linear Active Disturbance Rejection Control (IMA-FLADRC)-based control strategy is proposed, integrating a fuzzy algorithm to adaptively adjust the proportional coefficient of the Linear State Error Feedback (LSEF) and optimizing key parameters using an IMA, enhancing system adaptability and robustness. Simulation results show that the IMA-FLADRC-based guiding motor achieves no overshoot or static error, with an adjustment time of 1.121s, a maximum disturbance error of 12.016 r/min, and a disturbance recovery time of 0.008s, while the seeding motor exhibits no static error, minimal overshoot of 0.059%, an adjustment time of 0.374s, a maximum disturbance error of 2.563 r/min, and a disturbance recovery time of 0.003s, outperforming four other strategies. Speed accuracy tests confirm that at 12–16 km/h, the dual-motor speed deviation ratio remains below 1.54%. Bench tests under varying speeds and theoretical seed spacing conditions indicate that the IMA-FLADRC achieves an average seed spacing variation coefficient of 9.10%, with reductions of 2.35, 1.60, and 0.85 percentage points compared to LADRC, FLADRC, and MA-FLADRC, respectively. The average seed spacing deviation is 2.56 mm, the lowest among all strategies. These findings demonstrate that the IMA-FLADRC-based control system ensures highly synchronized dual-motor operation, significantly enhancing seeding uniformity.}, archive = {J_ACCESS}, author = {Song Wang and Shujuan Yi and Bin Zhao and Yifei Li and Dongming Zhang and Tao Chen and Wensheng Sun}, doi = {10.1109/ACCESS.2025.3566590}, journal = {IEEE Access}, pages = {85177-85195}, shortjournal = {IEEE Access}, title = {IMA-FLADRC-based electric-driven high-speed maize precision seeding control strategy and system}, volume = {13}, year = {2025}, }
• (2025). Maximum delay analysis of fixed backoff in IEEE 802.11 considering significant interference for in-vehicle wireless communication. ACCESS, 13, 85170-85176. (www)

  @article{ , abstract = {In in-vehicle wireless communication, each in-vehicle sensor terminal transmits small data in a cycle within which all terminals must transmit data successfully. Clarifying the delay characteristics in such networks is useful for the system design. This paper proposes a maximum delay distribution analysis method for the fixed CW method in CSMA/CA mission-critical periodic communications. The proposed method focuses on the fact that the upper bound of the distribution of the number of collisions and the transition of the number of terminals can be approximated by a binomial distribution. In the first step, the distribution of the total number of slots required for all terminals departure is derived from a two-dimensional Markov-chain model. In the second step, the total slot number distribution is converted into a real-time scale according to the events occurring in a slot, and the upper bound of the maximum delay distribution is derived. This analytical method analytically derives the upper bound of the maximum delay distribution for an arbitrary number of terminals in the network with a fixed CW value while taking interference induced by competition terminals into account. The validity of the proposed method is demonstrated through comparison with computer simulation results.}, archive = {J_ACCESS}, author = {Kosuke Sanada and Takumi Shiohara and Atsuhiro Iwaki and Takeshi Hirai and Tutomu Murase}, doi = {10.1109/ACCESS.2025.3569380}, journal = {IEEE Access}, pages = {85170-85176}, shortjournal = {IEEE Access}, title = {Maximum delay analysis of fixed backoff in IEEE 802.11 considering significant interference for in-vehicle wireless communication}, volume = {13}, year = {2025}, }
• (2025). AdaSpline-net for improved short-term solar irradiance forecasting. ACCESS, 13, 85156-85169. (www)

  @article{ , abstract = {Neural networks utilizing backpropagation are a powerful tool for solar irradiance forecasting, which is vital for climate research and energy market operations. This study addresses the challenge of modeling complex, nonlinear relationships between weather parameters by introducing an innovative adaptive B-spline activation function. The piecewise polynomial approach proposed in this work, integrated into the neural network framework and optimized using the Adaptive Moment Estimation (Adam) algorithm, allows for effective parameter tuning after each epoch, resulting in enhanced forecasting accuracy. Unlike traditional activation functions, which suffer from issues like the “dying ReLU” problem, the adaptive B-spline function provides smooth, flexible mappings with continuous gradients, allowing it to capture intricate data patterns effectively. This adaptability makes it particularly suitable for high-precision environmental applications. Validation using real-world datasets from Singapore and Hawaii shows that the adaptive B-spline significantly outperforms conventional activation functions, delivering up to a 10% improvement in forecasting accuracy for both training and testing datasets. Furthermore, its robustness across various neural network architectures demonstrates its adaptability and compatibility with backpropagation. This research highlights the potential of optimized B-spline activation functions to improve the accuracy and dependability of neural network-based forecasting models.}, archive = {J_ACCESS}, author = {Ngiap Tiam Koh and Anurag Sharma and Jianfang Xiao and Cheng Siong Chin and Chin Jun Xing and Wai Lok Woo}, doi = {10.1109/ACCESS.2025.3569525}, journal = {IEEE Access}, pages = {85156-85169}, shortjournal = {IEEE Access}, title = {AdaSpline-net for improved short-term solar irradiance forecasting}, volume = {13}, year = {2025}, }
• (2025). Research on speech enhancement translation and mel-spectrogram mapping method for the deaf based on Pix2PixGANs. ACCESS, 13, 85139-85155. (www)

  @article{ , abstract = {This study proposes an innovative speech translation method based on Pix2PixGAN, which maps the Mel spectrograms of speech produced by deaf individuals to those of normal-hearing individuals and generates semantically coherent speech output. The objective is to translate speech produced by the deaf into intelligible speech as it would be spoken by hearing individuals, thereby enhancing understandability and supporting assisted communication. A paired Mel spectrogram dataset was constructed using speech from both deaf and normal-hearing individuals. Deaf speech data were manually extracted from video segments, while the corresponding normal-hearing speech was synthesized using a text-to-speech (TTS) system. Mel spectrograms were then extracted as training data. The model is built upon the Pix2PixGAN framework, with deaf speech spectrograms as input and target hearing spectrograms as output. Model performance was evaluated using SSIM, PSNR, and MSE metrics. The results demonstrate excellent fidelity in structure and clarity in signal restoration, particularly in the low-frequency regions associated with semantic content. Unlike traditional deaf speech translation methods, this study innovatively combines Pix2PixGAN with Mel spectrogram representations, reframing the speech translation task as an image-to-image translation problem. By matching and concatenating speech segments from a reference database, the system generates natural and intelligible speech output. User survey results showed high ratings in both semantic consistency and naturalness of the generated speech. This method offers a viable technical pathway for facilitating communication between deaf and hearing individuals and provides a valuable reference for personalized speech enhancement in complex auditory environments.}, archive = {J_ACCESS}, author = {Shaoting Zeng and Xinran Xu and Xinyu Chi and Yuqing Liu and Huiting Yu and Feng Zou}, doi = {10.1109/ACCESS.2025.3569321}, journal = {IEEE Access}, pages = {85139-85155}, shortjournal = {IEEE Access}, title = {Research on speech enhancement translation and mel-spectrogram mapping method for the deaf based on Pix2PixGANs}, volume = {13}, year = {2025}, }
• (2025). Investigation into the identification of AI-generated short dialectal arabic texts. ACCESS, 13, 85131-85138. (www)

  @article{ , abstract = {Identifying AI-generated text (AIGT) has become crucial in addressing the potential misuse of generative AI tools and their impact on trust, fairness, and authenticity of content. This study investigates the detection of AIGT in short dialectal Arabic texts, an area that, to our knowledge, has not been previously explored. Two datasets were constructed featuring texts that were rephrased or generated to match the topics and length of MADAR CORPUS, encompassing dialectical human-written texts (HWTs) originating from five major Arabic cities. Among several AIGT detection methods, the pre-trained language models AraELECTRA and AraBERT outperformed both machine learning and feature-based approaches. Moreover, we analyzed the key features that significantly influenced the accuracy of the model.}, archive = {J_ACCESS}, author = {Haifa Alharthi}, doi = {10.1109/ACCESS.2025.3568696}, journal = {IEEE Access}, pages = {85131-85138}, shortjournal = {IEEE Access}, title = {Investigation into the identification of AI-generated short dialectal arabic texts}, volume = {13}, year = {2025}, }
• (2025). Enhanced software testing model under software warranty policy considering debugger learning and time postponement factors. ACCESS, 13, 85116-85130. (www)

  @article{ , abstract = {In the software development life cycle, ensuring high-quality and reliable software is crucial for developers. Unreliable software can result in customer loss, decreased revenue, and heightened operational risks stemming from defective codes. It is crucial for software developers to engage in rigorous software testing to minimize potential defects. However, striving for perfect and error-free software is often impractical due to constraints in budget, time, and testing resources. This necessitates a balanced approach, where developers must find a middle ground between ensuring software reliability and managing the costs of testing. The proposed model enables software developers to explore various options for conducting a software testing project, each with its own unique distribution of human resources. This allows for the selection of the most effective option. The model takes into account the learning curve, which can significantly impact the efficiency of the software testing process. This study considers not only the human learning factor involved but also the time delay factor in the debugging phase. It proposes a software reliability growth model that calculates both the associated costs and a reliability indicator. Furthermore, this study broadens its scope by examining the categorization of errors, assessing their impact on the system, and estimating the time needed to rectify simple versus complex errors using different truncated exponential distributions. To demonstrate the practical application of this model, numerical examples are provided, along with sensitivity analyses. These analyses offer valuable managerial insights and guidance, aiding in the formulation of informed strategies for software releases.}, archive = {J_ACCESS}, author = {Wang Li and Chih-Chiang Fang}, doi = {10.1109/ACCESS.2025.3569201}, journal = {IEEE Access}, pages = {85116-85130}, shortjournal = {IEEE Access}, title = {Enhanced software testing model under software warranty policy considering debugger learning and time postponement factors}, volume = {13}, year = {2025}, }
• (2025). An efficient encoding spectral information in hyperspectral images for transfer learning of mask R-CNN for instance segmentation of tomato sepals. ACCESS, 13, 85098-85115. (www)

  @article{ , abstract = {The most vulnerable parts of tomatoes are the tips of the sepals, which are the primary entry points for fungal spores. Their precise segmentation within hyperspectral images (HSIs) plays a pivotal role in the development of automated and non-destructive systems for assessing tomatoes’ sensitivity to fungal infections. This research addresses the critical need for encoding spectral information in hyperspectral imaging to enhance the efficiency of such automated systems. We investigate four different techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Probabilistic Principal Component Analysis (PPCA), and Non-Negative Matrix Factorization (NMF), to perform transfer learning for tomato sepal instance segmentation using models previously trained on RGB images. A comparative analysis of three Mask Region-based Convolutional Neural Network (Mask R-CNN) backbone models is conducted: the Faster R-CNN, Deformable ConvNet, and Feature Pyramid Network (FPN) on spectral-encoded HSIs of the Brioso tomato variety. The Mask R-CNN with FPN, integrated with the NMF technique achieved the highest level of accuracy, yielding a Mean Average Precision (mAP) of 94.05%. Furthermore, on the second dataset, which included an additional three tomato varieties: Capricia, Provine, and Sao Paolo, the same model achieved mAP score of 86.42% across all tomato varieties with only a single false positive detection. Additionally, we incorporated a custom convolutional layer initialized it with estimated NMF coefficients, and achieved a mAP score of 87.40%. This demonstrates the potential of integrating spectral information encoding with trained deep learning-based instance segmentation models to enable robust and accurate transfer learning for automated agricultural food quality assessments.}, archive = {J_ACCESS}, author = {Željana Grbović and Marko Panić and Vladan Filipović and Sanja Brdar and Hendrik de Villiers and Manon Mensink and Aneesh Chauhan}, doi = {10.1109/ACCESS.2025.3569213}, journal = {IEEE Access}, pages = {85098-85115}, shortjournal = {IEEE Access}, title = {An efficient encoding spectral information in hyperspectral images for transfer learning of mask R-CNN for instance segmentation of tomato sepals}, volume = {13}, year = {2025}, }
• (2025). Visual commonsense causal reasoning from a still image. ACCESS, 13, 85084-85097. (www)

  @article{ , abstract = {Even from a still image, humans exhibit the ability to ratiocinate diverse visual cause-and-effect relationships of events preceding, succeeding, and extending beyond the given image scope. Previous work on commonsense causal reasoning (CCR) aimed at understanding general causal dependencies among common events in natural language descriptions. However, in real-world scenarios, CCR is fundamentally a multisensory task and is more susceptible to spurious correlations, given that commonsense causal relationships manifest in various modalities and involve multiple sources of confounders. In this work, to the best of our knowledge, we present the first comprehensive study focusing on visual commonsense causal reasoning (VCCR) within the potential outcomes framework. By drawing parallels between vision-language data and human subjects in the observational study, we tailor a foundational framework, VCC-Reasoner, for detecting implicit visual commonsense causation. It combines inverse propensity score weighting and outcome regression, offering dual robust estimates of the average treatment effect. Empirical evidence underscores the efficacy and superiority of VCC-Reasoner, showcasing its outstanding VCCR capabilities.}, archive = {J_ACCESS}, author = {Xiaojing Wu and Rui Guo and Qin Li and Ning Zhu}, doi = {10.1109/ACCESS.2025.3558429}, journal = {IEEE Access}, pages = {85084-85097}, shortjournal = {IEEE Access}, title = {Visual commonsense causal reasoning from a still image}, volume = {13}, year = {2025}, }
• (2025). Enhancing RAG performance by representing hierarchical nodes in headers for tabular data. ACCESS, 13, 85072-85083. (www)

  @article{ , abstract = {Recent developments in retrieval-augmented generation (RAG) techniques have aimed at integrating structured tabular data with external data sources. Nevertheless, because existing approaches have difficulty effectively retrieving and reasoning from diverse and complex table structures, there is a growing demand for innovative methods that surpass traditional retrieval paradigms. This study introduced hierarchical node-based header representation models designed to enhance the processing and integration of external table data, addressing the key limitations of existing RAG techniques when working with structured table sources. By classifying table types designed for the structural complexity and the level of domain knowledge required for their interpretation, this study explores the impact of using embedded information to improve retrieval accuracy. The findings of this study demonstrate that presenting richer and more meaningful information within table headers significantly enhances RAG performance. This improvement is attributed to the utilization of table header representations, which allow for a more precise identification of hierarchical node-based headers during the table retrieval process. Furthermore, this study confirms that vector database achieves greater accuracy when employing a distributed table collection (DTC) structure than a unified context collection (UCC). These results indicate that the structure of the information extracted from text and tables varies considerably. Our experiment results highlight that separating and organizing such information using the DTC structure proved to be an effective strategy for improving the accuracy of table-specific responses.}, archive = {J_ACCESS}, author = {Minchae Song}, doi = {10.1109/ACCESS.2025.3569872}, journal = {IEEE Access}, pages = {85072-85083}, shortjournal = {IEEE Access}, title = {Enhancing RAG performance by representing hierarchical nodes in headers for tabular data}, volume = {13}, year = {2025}, }
• (2025). PAROS: Multi-component robotic system and an image-guided patient alignment for robot-assisted ophthalmic surgery. ACCESS, 13, 85056-85071. (www)

  @article{ , abstract = {Advancements in robot-assisted eye surgery have significantly enhanced precision in delicate procedures by enabling controlled movement of surgical instruments through a small entry point (trocar) placed on the outer layer of the eye (sclera). However, before these procedures can begin, the robot must first be prepared, draped, and calibrated. Then, it is positioned near the patient, where precise patient alignment is performed. Finally, the system ensures accurate alignment between the surgical instrument and the trocar, a critical step for safe and effective instrument insertion and surgical execution. Despite these advancements, integrating robotic systems into clinical practice remains challenging. The current manual preparation and alignment process is time-consuming and error-prone, emphasizing the need for a more automated and adaptive solution. Patient alignment time has been identified as a key factor differentiating robot-assisted and manual surgeries in overall procedure duration, with robot preparation and patient alignment extending surgery time by an average of $51\pm 6$ minutes compared to manual methods. To address these challenges, this paper introduces a multi-component robotic system combined with an image-guided patient alignment methodology designed to reduce preparation and alignment time in robotic eye surgery while maintaining surgical efficiency. The robotic system consists of a 2D-Cart carrying a 4-degree-of-freedom (DOF) frame robot holding a 5-DOF micro-manipulator. It utilizes an image-guided, multi-stage alignment approach, enabling the system to efficiently be localized and stabilized in an optimal position beside the surgical bed and ensuring precise and adaptive positioning relative to the patient in the surgical draped condition. The proposed end-to-end robotic system preparation and patient alignment are validated through a user study involving eight medical experts in a simulated surgical environment using a draped phantom eye. The results demonstrate successful robot-patient alignment in all cases, with the additional setup time significantly reduced to $272\pm 84$ seconds, highlighting the high precision of the system and clinical feasibility. To further assess the effectiveness of the image-guided alignment in real surgical settings, eye detection experiments are conducted on five patients under surgical draped conditions, achieving 90% eye detection accuracy and 75% iris detection accuracy. This study introduces a robust, semi-automated approach to robot-patient alignment, offering a more efficient and adaptable alternative to current manual methods by reducing setup time and enhancing surgical accuracy.INDEX TERMS Medical robots and systems, surgical robotics, planning, clinical integration in medical robotics.}, archive = {J_ACCESS}, author = {Alireza Alikhani and Van Dai Nguyen and Satoshi Inagaki and Benjamin Busam and Koorosh Faridpooya and Mathias Maier and Peter Gehlbach and Iulian Iordachita and Nassir Navab and M. Ali Nasseri and Daniel Zapp}, doi = {10.1109/ACCESS.2025.3564944}, journal = {IEEE Access}, pages = {85056-85071}, shortjournal = {IEEE Access}, title = {PAROS: Multi-component robotic system and an image-guided patient alignment for robot-assisted ophthalmic surgery}, volume = {13}, year = {2025}, }
• (2025). X-SPIDE: An eXplainable machine learning pipeline for detecting smart ponzi contracts in ethereum. ACCESS, 13, 85037-85055. (www)

  @article{ , abstract = {Blockchain technology is revolutionizing digital asset exchange by eliminating the need for central authority control. However, the decentralized nature of blockchain attracts malicious actors, leading to the proliferation of financial scams, with Ponzi schemes being particularly prevalent. Consequently, there is a growing need to develop automatic detection mechanisms for such scams. So far, the problem has been tackled by considering only classifier performances and with limited focus on the explanation and interpretation of the results. However, interpretability and explainability are crucial when classifier decisions may have economic consequences. This paper introduces X-SPIDE (XAI Smart Ponzi Identification and Detection), an explainable machine learning pipeline for Ponzi scheme detection within the Ethereum blockchain that aims to find the trade-off between performance and explainability. X-SPIDE allows for comparing the results of different classifiers, identifying a small set of features that offer strong performance, and understanding how these features contribute to classification — highlighting specific characteristics of malicious contracts. Moreover, we introduce and make publicly available a new comprehensive dataset comprising 7446 smart contracts, incorporating features derived from transaction history, creation, and deployment bytecodes to train and test our pipeline.}, archive = {J_ACCESS}, author = {Luca Pennella and Fabio Pinelli and Letterio Galletta}, doi = {10.1109/ACCESS.2025.3569565}, journal = {IEEE Access}, pages = {85037-85055}, shortjournal = {IEEE Access}, title = {X-SPIDE: An eXplainable machine learning pipeline for detecting smart ponzi contracts in ethereum}, volume = {13}, year = {2025}, }
• (2025). Global fixed-time horizontal tracking control of AUV under parameter perturbation and input quantization. ACCESS, 13, 85026-85036. (www)

  @article{ , abstract = {This paper investigates the horizontal trajectory tracking problem of an autonomous underwater vehicle (AUV) subject to parameter perturbations and ocean current disturbances. A fixed-time integral sliding mode control method is proposed, incorporating a disturbance observer. By integrating the backstepping technique with non-singular terminal sliding mode control, composite disturbances are estimated using a fixed-time neural network disturbance observer. A fixed-time sliding mode controller is designed along with the sliding mode surface. To enhance tracking performance, fixed-time reaching laws are developed, ensuring that tracking errors converge to a smaller range near the origin within a fixed time. To mitigate chattering during signal quantization, a hysteretic quantizer is employed to process input variables. Rigorous theoretical analysis is conducted using Lyapunov stability theory. Simulation results demonstrate the effectiveness of the proposed controller, exhibiting improved robustness and faster convergence under parameter perturbations and external disturbances.}, archive = {J_ACCESS}, author = {Sunwei Jiang and Shuxuan Liu and Zhicheng Liu and Quanming Miao}, doi = {10.1109/ACCESS.2025.3566705}, journal = {IEEE Access}, pages = {85026-85036}, shortjournal = {IEEE Access}, title = {Global fixed-time horizontal tracking control of AUV under parameter perturbation and input quantization}, volume = {13}, year = {2025}, }
• (2025). Context aware task orchestration with deep reinforcement learning in real time fog computing simulation environment. ACCESS, 13, 85004-85025. (www)

  @article{ , abstract = {In the ever-evolving landscape of cloud computing, fog and edge computing have become more prominent because of their natural property of proximity to demanding parts. With all the heterogeneity on the task processor side, generators have a variety of requirements in terms of complexity and latency. This study gathered all these different dimensions together in a robust ecosystem that can simulate a large number of various scenarios that meet changing requirements for task coordination and serves as a versatile platform for exploring strategies. A comprehensive multi-layered cloud simulation model that intricately considers both low-level edge/fog and cloud environment constraints is proposed. At the core of this contribution lies a novel task orchestration model that transforms the orchestration process into reinforcement learning training steps. The proposed approach offers substantial advantages in terms of task succession, energy efficiency, and resource utilization. The system was evaluated in a simulation developed under different fog computing environmental conditions in terms of edge device and task density. The results of the experiment proved the superiority of the proposed system over the existing round-robin heuristic-based algorithms and the offloading of policy baselines with an overall increase in precision 28%. In addition, a smart action space reduction technique is introduced to reduce the complexity of the action space, the proposed technique simplifies the decision-making process, leading to faster convergence and improved training efficiency.}, archive = {J_ACCESS}, author = {Alp Gokhan Hossucu and Suat Ozdemir}, doi = {10.1109/ACCESS.2025.3569781}, journal = {IEEE Access}, pages = {85004-85025}, shortjournal = {IEEE Access}, title = {Context aware task orchestration with deep reinforcement learning in real time fog computing simulation environment}, volume = {13}, year = {2025}, }
• (2025). Adversarial threats to cloud IDS: Robust defense with adversarial training and feature selection. ACCESS, 13, 84992-85003. (www)

  @article{ , abstract = {The increasing adoption of cloud-based infrastructures necessitates robust cybersecurity measures, particularly in Intrusion Detection Systems (IDS). While Machine Learning (ML)-based IDS solutions improve attack detection, they remain highly vulnerable to adversarial attacks, where subtle perturbations deceive the model and evade detection. This paper explicitly evaluates the impact of Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and DeepFool attacks on ML-based IDS models trained on the CSE-CIC-IDS2018 dataset. Our findings clearly indicate that these attacks significantly degrade detection accuracy, with FGSM explicitly reducing accuracy from 85% to 60%, PGD explicitly to 55%, and DeepFool explicitly to 50%. Additionally, our explicit evaluation using the black-box Square Attack demonstrates a significant accuracy reduction to 63%, clearly underscoring IDS susceptibility to practical adversarial threats. To mitigate these vulnerabilities, we explicitly propose a dual-layered defense strategy: (i) adversarial training, explicitly incorporating adversarial examples into model training to improve robustness, and (ii) SHAP-based robust feature selection, explicitly enhancing interpretability and resilience by identifying stable, attack-resistant features. SHAP analysis explicitly reveals that protocol-based and statistical traffic attributes are particularly susceptible to adversarial manipulations. Explicit computational overhead analysis demonstrates that our proposed defense approach introduces modest additional training complexity (approximately 25% overhead) without significantly impacting inference speed, explicitly confirming practical feasibility for real-time deployment. Our defense mechanisms explicitly restore detection accuracy to 88% against gradient-based attacks and 84% against black-box attacks, explicitly reducing adversarial success rates by up to 40%. By explicitly integrating adversarial defenses with SHAP-based interpretability, this research explicitly strengthens IDS frameworks against diverse evasion attacks while explicitly enhancing model transparency and practical applicability.}, archive = {J_ACCESS}, author = {Hariprasad Holla and Shashidhar Reddy Polepalli and Arun Ambika Sasikumar}, doi = {10.1109/ACCESS.2025.3567038}, journal = {IEEE Access}, pages = {84992-85003}, shortjournal = {IEEE Access}, title = {Adversarial threats to cloud IDS: Robust defense with adversarial training and feature selection}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of automated machine learning for hyperparameter optimization and explainable artificial intelligence models. ACCESS, 13, 84966-84991. (www)

  @article{ , abstract = {Artificial intelligence (AI) has been increasingly applied to solve complex real-world problems. One of the most significant challenges in AI lies in selecting and fine-tuning the optimal algorithm for a given task. Automated Machine Learning (AutoML) models have emerged as a promising solution to address this challenge by systematically exploring hyperparameter spaces to identify optimal configurations efficiently. This study addresses critical gaps in the current literature by conducting a comprehensive comparative analysis of AutoML frameworks for hyperparameter optimization and evaluating the effectiveness of various explainability techniques for enhancing model interpretability. For this purpose, Random forest (RF) is selected as the base model and integrated with nine different AutoML frameworks, namely Random search (RS), Grid search (GS), Hyperopt, TPOT, Optuna, GP Minimize, Forest Minimize, GBRT Minimize, and Dummy Minimize. The study focuses on predicting the ultimate moment capacity of Ultra-High-Performance Concrete (UHPC) beams and U-shaped girders. Furthermore, the insights from SHapley Additive exPlanations (SHAP) are also compared with those derived from alternative explainability methods, including Local interpretable model-agnostic explanations (LIME), partial dependence plots (PDP), and sklearn permutation importance rankings to examine the contributions of individual parameters to the ultimate moment capacity predictions of UHPC beams using the best-performing AutoML model. The findings demonstrate that Optuna consistently outperforms its counterparts, achieving the highest predictive accuracy and the lowest computational training time. The findings also highlight SHAP’s superiority in offering detailed, consistent, and actionable insights, making it the preferred method for both global feature importance and individual feature analysis in high-stakes engineering applications.}, archive = {J_ACCESS}, author = {Muhammad Salman Khan and Tianbo Peng and Hanzlah Akhlaq and Muhammad Adeel Khan}, doi = {10.1109/ACCESS.2025.3566427}, journal = {IEEE Access}, pages = {84966-84991}, shortjournal = {IEEE Access}, title = {Comparative analysis of automated machine learning for hyperparameter optimization and explainable artificial intelligence models}, volume = {13}, year = {2025}, }
• (2025). High-performance lung disease identification and explanation using a ReciproCAM-enhanced lightweight convolutional neural network. ACCESS, 13, 84954-84965. (www)

  @article{ , abstract = {Lung diseases significantly contribute to global morbidity and mortality, underscoring the importance of accurate and efficient diagnostic tools. This study introduces a lightweight two-dimensional convolutional neural network (LW-2DCNN) integrated with ReciproCAM, an explainable artificial intelligence (XAI) technique, specifically designed to classify multiple pulmonary conditions from chest computed tomography (CT) scans. Our model effectively addresses limitations of existing diagnostic methods by enhancing computational efficiency, diagnostic accuracy, and interpretability. Utilizing a curated dataset comprising 10,000 CT images from a Korean population, the LW-2DCNN achieved exceptional accuracy of 99.80% across five diagnostic categories: normal, lung cancer, pneumothorax, pneumonia, and interstitial lung disease (ILD). Comparative analyses demonstrated superior performance relative to established benchmark models, including EfficientNetB0, ResNet50, InceptionV3, DenseNet121, NASNetMobile, MobileNetV2, and Xception. The integrated ReciproCAM technique significantly enhanced clinical interpretability by highlighting the precise anatomical regions influencing the model’s diagnostic decisions. Ablation studies validated the optimal choice of Adam optimizer and demonstrated the model’s computational feasibility, predicting and explaining 1000 images in approximately 150 seconds on a CPU-only device. This indicates strong potential for deployment in resource-constrained clinical settings.}, archive = {J_ACCESS}, author = {Hoan-Suk Choi and Jinhong Yang}, doi = {10.1109/ACCESS.2025.3568463}, journal = {IEEE Access}, pages = {84954-84965}, shortjournal = {IEEE Access}, title = {High-performance lung disease identification and explanation using a ReciproCAM-enhanced lightweight convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). High-efficiency switched-capacitor multilevel inverter topology with lower number of switching components. ACCESS, 13, 84941-84953. (www)

  @article{ , abstract = {To increase the voltage from sources such as photovoltaic (PV) systems, fuel cells, and battery storage devices, a boost conversion is required. The proposed switched capacitor multilevel inverter (SCMLI) topology incorporates nine power semiconductor switches, a dual DC voltage source, and a single capacitor, capable of generating a nine-level output voltage with dual voltage gain. Additional features of the proposed SCMLI topology include self-voltage balancing of the capacitor without the need for any additional circuitry, reduced voltage stress across the switches, and an inherent capability for polarity reversal. Furthermore, the topology eliminates the need for bulky components like inductors, leading to a compact and cost-effective design. The performance of the proposed SCMLI is investigated through numerous simulation results under various operating conditions using PLECS software and validated using a laboratory prototype setup. The proposed SCMLI topology is further analyzed by integrating a solar PV system with a maximum power point tracking (MPPT) algorithm to ensure optimal power extraction. The system demonstrates excellent output voltage quality with low total harmonic distortion (THD), meeting the stringent requirements for power conversion in renewable energy applications. An efficiency of 96.3% is achieved at the rated power of 0.9 kW, confirming its potential for high-performance and sustainable energy conversion.}, archive = {J_ACCESS}, author = {Atif Iqbal and Ahmed Awadelseed and Jaroslaw Guzinski}, doi = {10.1109/ACCESS.2025.3569148}, journal = {IEEE Access}, pages = {84941-84953}, shortjournal = {IEEE Access}, title = {High-efficiency switched-capacitor multilevel inverter topology with lower number of switching components}, volume = {13}, year = {2025}, }
• (2025). Fault location method for medium voltage cables using sheath currents in inverter-based resource networks. ACCESS, 13, 84925-84940. (www)

  @article{ , abstract = {Traditional fault location methods face challenges in grids with high penetration of inverter-based resources (IBRs) due to the non-sinusoidal and distorted nature of the fault currents. This paper presents a fault location method for such grids. It is designed for medium voltage cables, employing sheath current measurements and combining time-domain and phasor-domain analysis. The proposed method addresses the limitations of existing techniques by utilizing low-frequency local measurements. Simulation results on a 10 kV test system demonstrate the method’s accuracy, achieving fault location errors below 1.81%. Sensitivity analysis further confirms the method’s robustness against measurement uncertainties. The proposed algorithm’s implementation on an embedded device using IEC 61850-9-2 protocol showcases its practical feasibility for real-time fault location.}, archive = {J_ACCESS}, author = {Arman Ghaderi Baayeh and Jona Cappelle and Michael Kleemann}, doi = {10.1109/ACCESS.2025.3568645}, journal = {IEEE Access}, pages = {84925-84940}, shortjournal = {IEEE Access}, title = {Fault location method for medium voltage cables using sheath currents in inverter-based resource networks}, volume = {13}, year = {2025}, }
• (2025). Location-aware ambient sound model adaptation for on-device human activity recognition in living spaces. ACCESS, 13, 84911-84924. (www)

  @article{ , abstract = {Human activity recognition (HAR) using sound-based approaches provides a non-intrusive and practical solution for detecting and classifying human activities in real-world environments. However, deploying HAR systems across diverse environments presents challenges due to domain shift and variations in environmental acoustics. Traditional HAR models trained on pre-collected datasets often fail to generalize to new settings, leading to performance degradation and misclassification errors. This paper proposes an adaptive ambient sound model optimization system to enhance the robustness and adaptability of HAR in edge computing environments. The system can dynamically customize classification labels based on the installation environment, ensuring flexibility across different locations. To mitigate domain shift, edge-based transfer learning fine-tunes a pre-trained model using locally collected data, improving classification accuracy across varying acoustic conditions. Additionally, a pseudo-labeling mechanism continuously optimizes the model by iteratively refining predictions from high-confidence unlabeled data, enabling long term adaptability without extensive manual annotations. To validate the proposed approach, we implemented the system and conducted experiments using real-world sound data from multiple residential environments. Experimental results demonstrate that the system effectively adapts to different locations while maintaining high classification accuracy, real-time inference, and a short training time, making it suitable for practical edge deployment.}, archive = {J_ACCESS}, author = {Cheolhwan Lee and Homin Kang and Soon Ju Kang}, doi = {10.1109/ACCESS.2025.3568828}, journal = {IEEE Access}, pages = {84911-84924}, shortjournal = {IEEE Access}, title = {Location-aware ambient sound model adaptation for on-device human activity recognition in living spaces}, volume = {13}, year = {2025}, }
• (2025). Adaptive elastic GAN for high-fidelity blood cell image hallucination and classification. ACCESS, 13, 84897-84910. (www)

  @article{ , abstract = {Automated blood cell classification is crucial for hematological analysis, yet the scarcity of annotated medical datasets challenges deep learning models. This study presents a novel semi-supervised Elastic Generative Adversarial Network that enhances classification accuracy, improves synthetic image generation quality, and reduces computational complexity. The model utilizes image hallucination, which generates artificial but realistic images to supplement datasets, addressing data imbalance and improving model generalization. The generated images exhibit high fidelity, meaning they closely resemble real blood cell images in texture, morphology, and structural details, ensuring their effectiveness in training deep learning models. Compared to the conventional semi-supervised Wasserstein Generative Adversarial Network, the proposed model achieves a 5.5% increase in classification accuracy, a 34.5% reduction in computational complexity, and a 21.0% decrease in training time, demonstrating superior efficiency and scalability. In addition, it greatly enhances Fréchet Inception Distance and Wasserstein distance, which verifies improved realism and diversity of synthetic samples. Complementing pruning-based optimization ensures reduced model deployment size, rendering it appropriate for real-time medical diagnosis. Through image hallucination, the proposed method sufficiently alleviates data deficiency and improves the reliability of automated blood cell classification, qualifying it as a prospective tool for advanced hematological studies and diagnosis purposes.}, archive = {J_ACCESS}, author = {Issac Neha Margret and K. Rajakumar}, doi = {10.1109/ACCESS.2025.3568539}, journal = {IEEE Access}, pages = {84897-84910}, shortjournal = {IEEE Access}, title = {Adaptive elastic GAN for high-fidelity blood cell image hallucination and classification}, volume = {13}, year = {2025}, }
• (2025). Transfer learning-based EdgeAI resource allocation in NGEPON and B5G X-haul network for immersive media. ACCESS, 13, 84881-84896. (www)

  @article{ , abstract = {This study introduces an EdgeAI-based framework for the dynamic bandwidth allocation and traffic priority prediction in 25G-Next-Generation Ethernet Passive Optical Network (NGEPON) and Beyond 5G (B5G) X-haul networks, targeting the requirements of immersive media services such as Virtual Reality (VR) and Augmented Reality (AR). The proposed framework employs a Software-Defined Networking (SDN) Cloud-Edge Service Orchestrator with an LSTM-transfer learning-based mechanism (EdgeAI_DBA) to estimate network traffic and optimize content caching at the network edge while dynamically prioritizing bandwidth allocation based on real-time traffic demands for the Fixed wireless NGEPON & B5G X-haul access network. Simulation results under realistic network conditions indicate that EdgeAI_DBA significantly improves packet delay, drop probability, and system throughput. These results emphasize that our EdgeAI_DBA mechanism can deliver high-capacity, low-latency services critical for future immersive media delivery.}, archive = {J_ACCESS}, author = {Razat Kharga and Aliakbar Nikoukar and I-Shyan Hwang and Andrew Tanny Liem and Hamidreza Goudarzi}, doi = {10.1109/ACCESS.2025.3566786}, journal = {IEEE Access}, pages = {84881-84896}, shortjournal = {IEEE Access}, title = {Transfer learning-based EdgeAI resource allocation in NGEPON and B5G X-haul network for immersive media}, volume = {13}, year = {2025}, }
• (2025). Hybrid switched capacitor three winding coupled inductor high step-up DC-DC converter. ACCESS, 13, 84870-84880. (www)

  @article{ , abstract = {Switched capacitor cells have been studied in recent years. These approaches allow different new topologies to be generated. One such technique is the hybrid converter, where a coupled inductor and switched capacitor can be combined. Thus, in this paper, a hybrid topology is proposed that combines a three-winding coupled inductor with switched capacitor cells, with advantages such as simplicity of operation, high efficiency, low current stress, soft switching (ZCS), the possibility of reducing the capacitor value, low voltage stress on the components. To demonstrate that the converter has good performance, two prototypes were built considering different values of the duty cycle and turns ratio of coupled inductor. From there, the maximum efficiency achieves is 97.54 % at 125 W.}, archive = {J_ACCESS}, author = {António Manuel Santos Spencer Andrade and Pablo Henrique Costa da Silva Bernardo Loureiro and Ademir Toebe}, doi = {10.1109/ACCESS.2025.3568608}, journal = {IEEE Access}, pages = {84870-84880}, shortjournal = {IEEE Access}, title = {Hybrid switched capacitor three winding coupled inductor high step-up DC-DC converter}, volume = {13}, year = {2025}, }
• (2025). Wi-fi-enabled vision via spatially-variant pose estimation based on convolutional transformer network. ACCESS, 13, 84855-84869. (www)

  @article{ , abstract = {Wi-Fi-enabled vision offers a transformative paradigm for non-optical pose estimation, particularly in occluded or privacy-sensitive environments where traditional visual systems falter. Despite its promise, extracting reliable pose information from Wi-Fi Channel State Information (CSI) remains a formidable challenge due to spatial variability in torso localization, cross-view discrepancies, and inherent signal perturbations caused by multipath propagation and environmental noise. To address these challenges, we propose a Convolutional Transformer Network. This architecture integrates convolutional layers for localized spatial feature extraction and transformer layers for global temporal dependency modeling. This integrative design effectively captures the spatiotemporal dynamics of CSI signals, enabling robust pose estimation under cross-view and spatially-variant conditions. When evaluated on the benchmark WIDAR 3.0 datasets, the proposed model outperforms the structural and sequential learning baseline CNN-GRU by 1.72% in accuracy. It outperforms sequential models (RNN, GRU, LSTM) and image models (CNN, ViT) across all key metrics, demonstrating robust spatial-temporal modeling capabilities. These results highlight its advancement in non-optical pose estimation and practical applicability in real-world scenarios.}, archive = {J_ACCESS}, author = {Hyeon-Ju Lee and Seok-Jun Buu}, doi = {10.1109/ACCESS.2025.3568505}, journal = {IEEE Access}, pages = {84855-84869}, shortjournal = {IEEE Access}, title = {Wi-fi-enabled vision via spatially-variant pose estimation based on convolutional transformer network}, volume = {13}, year = {2025}, }
• (2025). LogProb: Online parsing evolving logs with complex parameters. ACCESS, 13, 84844-84854. (www)

  @article{ , abstract = {To cope with the massive volume of log messages in complex systems, effective and accurate log parsers are crucial for system maintenance. However, existing log parsers have accuracy issues, particularly when handling evolving logs with complex structures. To address this, this paper proposes LogProb, an online log parsing method that incorporates a token state prediction component and a search tree-based template extraction component. The token state prediction component determines whether each token in a log message is static (template word) or dynamic (parameter value). The token sequence and predicted token states are parsed by the template extraction component to generate event templates. The extraction process includes two stages: identifying candidate templates with predicted static tokens, and using a fast matching algorithm to determine the final template. Experiments on 16 benchmark datasets show that LogProb outperforms state-of-the-art methods in accuracy while maintaining comparable efficiency.}, archive = {J_ACCESS}, author = {Yan Li and Ziying Wang and Jiaming Mao and Zhimin Gu and Haitao Jiang}, doi = {10.1109/ACCESS.2025.3569548}, journal = {IEEE Access}, pages = {84844-84854}, shortjournal = {IEEE Access}, title = {LogProb: Online parsing evolving logs with complex parameters}, volume = {13}, year = {2025}, }
• (2025). Fast and accurate identification of kiwifruit diseases using a lightweight convolutional neural network architecture. ACCESS, 13, 84826-84843. (www)

  @article{ , abstract = {Kiwifruit (Actinidia chinensis Planch.) is highly valued for its nutritional benefits and unique flavor. However, diseases like bacterial canker and soft rot threaten its production, causing significant economic losses. Traditional disease identification methods, which rely on human expertise, are time-consuming and lack scalability. This study utilizes deep learning to enhance kiwifruit disease identification by evaluating eight advanced convolutional neural network (CNN) architectures on real-world field data. Among these, ShuffleNet_V2_x0_5 proved to be the most effective model. By incorporating advanced optimization strategies, including the AdamW optimizer and OneCycleLR scheduler, the model demonstrated rapid convergence and robust performance, achieving over 99% accuracy within five epochs, with only 1.37M parameters and 0.04G FLOPs. The lightweight architecture and computational efficiency make it particularly suitable for resource-limited settings, including mobile and embedded platforms. These findings underscore the utility of ShuffleNet_V2_x0_5 in supporting scalable and efficient disease management within precision kiwifruit agriculture. Our code and models are available at https://github.com/zhanglab-wbgcas/kiwifruit-diseases-classifier.}, archive = {J_ACCESS}, author = {Kangchen Liu and Li Li and Xiujun Zhang}, doi = {10.1109/ACCESS.2025.3564355}, journal = {IEEE Access}, pages = {84826-84843}, shortjournal = {IEEE Access}, title = {Fast and accurate identification of kiwifruit diseases using a lightweight convolutional neural network architecture}, volume = {13}, year = {2025}, }
• (2025). A framework for RF-EMF time series analysis through multi-scale time averaging. ACCESS, 13, 84811-84825. (www)

  @article{ , abstract = {Five European systems for continuous monitoring of outside radio-frequency (RF) electromagnetic field (EMF) provide important insights into the long-term in situ variability of RF-EMF, as a valuable feature for accessing RF-EMF in areas with increased sensitivity to EMF exposure. Unfortunately, their current presentation of field variability through a simple timeline is not suitable for a comprehensive insight into RF-EMF behavior, especially not for applications where annual comparison of field levels over a certain period of time is required. Driven by the need to overview monitored data in some distinctive way and further examine hidden phenomena embedded in system’s EMF data series, this paper presents an additional method for analyzing RF-EMF time series through multi-scale time averaging. A case study was selected to demonstrate the time-averaging framework, using a five-year RF-EMF dataset obtained from a sensor, installed on the campus of the University of Novi Sad, as part of the Serbian EMF RATEL monitoring network. Although this case study reveals some site-specific details, such as a daily ratio between maximum and minimum field levels of 3.6 for weekdays and 2.1 for weekends, the time-averaging framework is applicable to any monitoring network. Furthermore, it is designed to present its findings in a simple manner and to be affirmative of the general population’s perception on unavoidable presence of RF-EMF in the environment, while ultimately contributing to a more rational understanding of the potential impact of everyday RF-EMF on their health.}, archive = {J_ACCESS}, author = {Nikola Djuric and Dragan Kljajic and Nicola Pasquino and Vidak Otasevic and Snezana Djuric}, doi = {10.1109/ACCESS.2025.3569304}, journal = {IEEE Access}, pages = {84811-84825}, shortjournal = {IEEE Access}, title = {A framework for RF-EMF time series analysis through multi-scale time averaging}, volume = {13}, year = {2025}, }
• (2025). Physics-informed deep neural network for low sidelobe time-modulated antenna array synthesis with harmonic suppression. ACCESS, 13, 84803-84810. (www)

  @article{ , abstract = {This paper introduces an innovative approach utilizing a deep neural network (DNN) to optimize the modulation scheme for time-modulated antenna array (TMAA) to verify specific side lobe and maximum harmonics levels. The proposed method involves training a DNN with a physics-informed loss function designed to reduce the discrepancy between the desired and actual beam patterns. This is accomplished by exclusively adjusting the periodic switching time sequence of each element within the TMAA. Specifically, the physics-informed deep neural network (PIDNN) is trained to optimize the switching-on times of for each antenna element. Simulation results demonstrate that the proposed technique achieves the desired beam patterns with significantly lower side lobe level and maximum harmonic levels compared to previously published methods. Additionally, the approach is compared to genetic algorithm (GA) which corresponds to a representative evolutionary optimization algorithm. Numerical results indicate that the PIDNN surpasses the GA in both computational efficiency and loss function evaluation.}, archive = {J_ACCESS}, author = {Tarek Sallam and Qun Wang and Ahmed M. Attiya}, doi = {10.1109/ACCESS.2025.3569091}, journal = {IEEE Access}, pages = {84803-84810}, shortjournal = {IEEE Access}, title = {Physics-informed deep neural network for low sidelobe time-modulated antenna array synthesis with harmonic suppression}, volume = {13}, year = {2025}, }
• (2025). Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methods. ACCESS, 13, 84791-84802. (www)

  @article{ , abstract = {In the field of image processing, improving low-contrast images is essential for enhancing the visibility of features that may be hard to see due to slight intensity variations. This issue often arises with images captured in poor lighting or unfavorable conditions, which can impede tasks such as feature extraction, object recognition, and edge detection. This paper presents an algorithm that employs fuzzy techniques to enhance low-contrast images. The approach utilizes a Pythagorean fuzzy set, which is an advanced version of a fuzzy set. The Pythagorean fuzzy set, by integrating membership and non−membership degrees, offers a more precise representation of image uncertainty. The proposed method constructs an increasing function to calculate the degree of non−membership, thereby amplifying the effectiveness of the enhanced image. By integrating this method with contrast limited adaptive histogram equalization, alongside a color restoration technique and brightness-preserving dynamic histogram equalization, the brightness and natural appearance of the original low-contrast image are maintained. We compare our approach to other methods, including conventional histogram equalization, intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy generators, interval−valued intuitionistic fuzzy with contrast limited adaptive histogram equalization, LightNet, and FlightNet. Experimental results indicate that our method surpasses the techniques currently utilized in terms of visibility and color accuracy. Performance evaluation is performed using metrics such as the absolute mean brightness error, contrast improvement index, and structural similarity index.}, archive = {J_ACCESS}, author = {M. Manivasagan and S. Jagatheswari}, doi = {10.1109/ACCESS.2025.3565121}, journal = {IEEE Access}, pages = {84791-84802}, shortjournal = {IEEE Access}, title = {Low contrast enhancement algorithm for color image using pythagorean fuzzy sets with a fusion of CLAHE and BPDHE methods}, volume = {13}, year = {2025}, }
• (2025). A geometric approach to nonlinear control of a research aircraft. ACCESS, 13, 84779-84790. (www)

  @article{ , abstract = {The paper proposes a new method of flight control design using a geometric approach. The flight control design has been done on the nonlinear manifold Special Euclidean Group or $SE(3)$ , thereby avoiding the singularity inherent in local representations. Hence, the region of operation is enhanced. The complete nonlinear 6-Degree of Freedom (DOF) model has been considered in controller design. The control approach has been developed for the newly designed scaled-down multi-role research aircraft. The control approach has also been extended to the case of stuck faults in multiple control surfaces. A novel approach has also been proposed to handle stuck faults of control surfaces. Mathematical proof has been given to demonstrate the stability of the dynamics with the proposed controller. Simulation results have been given to demonstrate trimmed flight without faults as well as in the presence of multiple faults. Comparison results with the method existing in the literature is also presented. First, the simulation results have been given when there is no fault in the control surfaces. Then, another simulation result have been presented when two of the control surfaces get stuck at its previous condition. A comparison result with incremental nonlinear dynamic inversion sliding mode control (INDI-SMC) has also been given, and it is shown that the proposed method outperforms INDI-SMC. In the proposed method, the average thrust variation around the mean value is very small i.e. only 0.72 N as compared to INDI-SMC which is 10.8 N. Again, it is also observed that the average variation around mean in linear velocity along the body X-axis is very small i.e only 0.68 m/s in the proposed method as compared to INDI-SMC where it is 1.9 m/s.}, archive = {J_ACCESS}, author = {Manmohan Sharma and Soumya Ranjan Mahapatro}, doi = {10.1109/ACCESS.2025.3569653}, journal = {IEEE Access}, pages = {84779-84790}, shortjournal = {IEEE Access}, title = {A geometric approach to nonlinear control of a research aircraft}, volume = {13}, year = {2025}, }
• (2025). Enhancing LoRaWAN localization in industrial environments: Merging path-loss modeling, extended kalman filtering and map-matching. ACCESS, 13, 84765-84778. (www)

  @article{ , abstract = {In industrial environments, accurate location information is crucial for enabling the seamless operation of technologies. Localization based on the signal features of the implemented network (such as RSSI) is becoming an appropriate substitute to solve its problems, such as low power consumption and cost. LoRaWAN, a contemporary LPWAN technology, can provide long-range coverage, an important requirement for several industrial application domains. RSSI-based localization in LoRaWAN is a widely used low-cost method, but it is susceptible to environmental changes and noise, leading to low performance and accuracy, especially in industrial environments. So, applying an adoptable noise-filtering method based on the environment to make measured RSSI usable in industrial applications, such as asset management localization and tracking, is essential. This paper proposes a novel method that merges an Extended Kalman Filter (EKF) with Path-Loss Modeling (PLM) for noise filtering in LoRaWAN systems. Unlike the previous approaches that just use EKF as a noise filtering method on RSSI and use achieved RSSI to provide distance estimation using PLM, our method uses EKF as a location estimation method that gets RSSI as an input. Additionally, we incorporate map-matching to further improve the accuracy of location estimation by leveraging geographical constraints. For the evaluation step, the proposed method is implemented and tested in a harbor in a highly dynamic and harsh industrial environment. The detailed evaluation demonstrates that the proposed approach leads to an improvement between 15% and 46% compared to normal PLM. In addition, adding map-matching leads to a 36% improvement in location estimation.}, archive = {J_ACCESS}, author = {Azin Moradbeikie and Ahmad Keshavarz and Sérgio Ivan Lopes}, doi = {10.1109/ACCESS.2025.3568750}, journal = {IEEE Access}, pages = {84765-84778}, shortjournal = {IEEE Access}, title = {Enhancing LoRaWAN localization in industrial environments: Merging path-loss modeling, extended kalman filtering and map-matching}, volume = {13}, year = {2025}, }
• (2025). Redundant node scheduling algorithm for wireless sensor networks based on topology structure. ACCESS, 13, 84752-84764. (www)

  @article{ , abstract = {Wireless sensor networks face issues such as high energy consumption and the impact of individual node failures on the entire network. In order to effectively regulate redundant nodes to balance node coverage and energy consumption in the network, the Ditian algorithm is introduced and initialized. The computation structure is reconstructed through adaptive adjustment, and the discrimination of redundant nodes is optimized. Meanwhile, the cluster topology is added to wireless sensor networks to control network nodes and complete the information transmission process. The experimental results showed that the improved Ditian had an average remaining energy of 3.6J after 600 runs. In the validation set, the energy efficiency was further improved, with an average remaining energy of 1.92J and a standard deviation of 0.51J. When the RMSE threshold was 0.8, the network lifetime reached 11 minutes, and the running time was significantly better than comparison algorithms. Furthermore, in the simulation experiment verification, the working state nodes of the proposed hybrid algorithm accounted for 1/5 of the total nodes, and the lifespan of the regional network could reach 1,098 seconds, with a duration better than common algorithms. The above results indicate that the algorithm can effectively improve energy utilization, expand its application in multiple fields, and provide new solutions for the efficient operation of wireless sensor networks.}, archive = {J_ACCESS}, author = {Lijuan Shi and jun zheng}, doi = {10.1109/ACCESS.2025.3568464}, journal = {IEEE Access}, pages = {84752-84764}, shortjournal = {IEEE Access}, title = {Redundant node scheduling algorithm for wireless sensor networks based on topology structure}, volume = {13}, year = {2025}, }
• (2025). Syntactic-guided chain of thought for iterative implicit and explicit target detection in aspect-based sentiment analysis. ACCESS, 13, 84738-84751. (www)

  @article{ , abstract = {Prompt engineering is essential for optimizing the performance of large-language models (LLMs), particularly in tasks requiring complex interpretations such as aspect-based sentiment analysis (ABSA). However, existing methodologies often struggle to detect implicit targets, especially in multi-opinion sentences where sentiments are directed toward aspects that are not explicitly mentioned. This study addresses this gap by proposing the Iterative Syntactic-Guided Chain of Thought (IS-COT) framework, which integrates dependency parsing with modular prompt engineering to enhance LLMs’ reasoning capabilities. IS-COT leverages syntactic structures and iterative refinement to detect both explicit and implicit targets while resolving ambiguities in multi-opinion contexts. Experimental evaluations on benchmark datasets, Sem-Eval 2015 (Res15) and Sem-Eval 2016 (Res16), demonstrated the effectiveness of the framework, achieving superior performance with 80.43 F1 scores on Res15 and 84.47 F1 scores on Res16, significantly outperforming state-of-the-art models. These results highlight IS-COT’s potential of IS-COT as a comprehensive and interpretable solution for ABSA, addressing the critical limitations of existing approaches and advancing the field through innovative syntactic and semantic integration.}, archive = {J_ACCESS}, author = {Mohammad Radi and Nazlia Omar and Wandeep Kaur}, doi = {10.1109/ACCESS.2025.3568695}, journal = {IEEE Access}, pages = {84738-84751}, shortjournal = {IEEE Access}, title = {Syntactic-guided chain of thought for iterative implicit and explicit target detection in aspect-based sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Analysis of machine learning performance in spatial interpolation of rainfall data. ACCESS, 13, 84727-84737. (www)

  @article{ , abstract = {The spatialization of precipitation data is crucial for studies on climatology, agriculture, and climate change, as well as for urban and environmental planning. Established spatial interpolation methods such as Inverse Distance Weighting are widely used for this purpose. With technological advancements, machine learning techniques have emerged as more efficient and computationally less costly alternatives for performing spatial interpolation calculations. Therefore, this study aims to analyze the performance of machine learning algorithms, including artificial neural networks, comparing them to the Inverse Distance Weighting method. The accuracy of the models was evaluated using the coefficient of determination, root mean square error, and concordance index, to confirm the effectiveness of these methodologies for maximum daily annual precipitation data. After testing different models for data spatialization and conducting a thorough statistical analysis, we conclude that machine learning models outperformed the Inverse Distance Weighting method, yielding greater variability in accumulated Annual Maximum Daily Precipitation values and improved overall results.}, archive = {J_ACCESS}, author = {Alexandre E. L. Nobrega and Itamir M. Barroca Filho}, doi = {10.1109/ACCESS.2025.3569261}, journal = {IEEE Access}, pages = {84727-84737}, shortjournal = {IEEE Access}, title = {Analysis of machine learning performance in spatial interpolation of rainfall data}, volume = {13}, year = {2025}, }
• (2025). Enhanced carbon flux forecasting via STL decomposition and hybrid ARIMA-ES-LSTM model in amazon forest. ACCESS, 13, 84713-84726. (www)

  @article{ , abstract = {This study presents a hybrid model, STL-ARIMA-ES-LSTM, developed to improve the accuracy of Gross Primary Productivity (GPP) forecasts in the Amazon region. The model integrates seasonal and trend decomposition using Loess (STL) with statistical methods (ARIMA and Exponential Smoothing-ES) and a machine learning technique (Long Short-Term Memory - LSTM). Applied to GPP data from the PE-QFR site, the hybrid model achieved significantly better error metrics, with RMSE of 1.69 gC/m2/day, MAE of 1.35 gC/m2/day, and MAPE of 0.20%, compared to the standalone LSTM (RMSE of 2.16 gC/m2/day, MAE of 1.78 gC/m2/day, and MAPE of 0.27%). Furthermore, the hybrid model showed stronger agreement with the observed data, with correlation coefficient r =0.62 and ${R}^{2} =0.39$ , whereas the LSTM alone yielded r =0.26 and ${R}^{2} =$ –0.002. The STL decomposition allowed effective separation of trend, seasonality, and residual components, enabling tailored modeling of each, which contributed to the improved predictive performance. These results demonstrate the advantage of hybrid approaches in capturing the nonlinear and seasonal patterns of GPP, supporting enhanced environmental monitoring and more informed climate change mitigation strategies in the Amazon.}, archive = {J_ACCESS}, author = {Jean A. C. Dias and Pedro H. Do V. Guimarães and Williane G. S. Pereira and Leonardo De O. Tamasauskas and Marivan S. Gomes and Alan B. S. Corrêa and Karla Figueiredo and Gilson Costa and Gabriel Brito Costa and Fernando A. R. Costa and Marcos C. Da R. Seruffo}, doi = {10.1109/ACCESS.2025.3561166}, journal = {IEEE Access}, pages = {84713-84726}, shortjournal = {IEEE Access}, title = {Enhanced carbon flux forecasting via STL decomposition and hybrid ARIMA-ES-LSTM model in amazon forest}, volume = {13}, year = {2025}, }
• (2025). RelaxVR: Cybersickness reduction in immersive virtual reality through explainable AI and large language models. ACCESS, 13, 84689-84712. (www)

  @article{ , abstract = {Virtual reality (VR) systems are susceptible to cybersickness, significantly hindering user immersion. Very recently, researchers introduced explainable artificial intelligence (XAI) enabled methods for detecting and explaining cybersickness features. Since XAI methods can identify the dominant features causing cybersickness, we argue that this knowledge can also guide the dynamic adoption of effective cybersickness reduction strategies, unlike state-of-the-art static approaches that rely on fixed methods. This paper introduces a new dataset MazeSick and RelaxVR, an interactive XAI-guided VR cybersickness reduction framework to predict, explain, and reduce cybersickness. Specifically, we propose an innovative XAI-guided cybersickness reduction engine, which selects the most appropriate reduction technique based on XAI-provided feature importance scores from a cybersickness reduction library of different reduction techniques. We also design an interactive dialogue system powered by large language models, enabling users to engage in the VR simulation via voice commands to understand the reasoning of cybersickness and select effective reduction strategies through natural language interaction. We deployed RelaxVR on a consumer-grade VR headset (e.g., HTC VIVE Pro) and validated it through a user study on cybersickness evaluation, where participants were immersed in a custom-built VR Maze simulation. Our results demonstrate that RelaxVR effectively reduces cybersickness with minimal impact on immersion, and 94% of the users agreed that RelaxVR is highly effective for reducing cybersickness and easier to use through the dialogue engine for selecting effective reduction strategies.}, archive = {J_ACCESS}, author = {Ripan Kumar Kundu and Khaza Anuarul Hoque}, doi = {10.1109/ACCESS.2025.3566958}, journal = {IEEE Access}, pages = {84689-84712}, shortjournal = {IEEE Access}, title = {RelaxVR: Cybersickness reduction in immersive virtual reality through explainable AI and large language models}, volume = {13}, year = {2025}, }
• (2025). The NavINST dataset for multi-sensor autonomous navigation. ACCESS, 13, 84659-84674. (www)

  @article{ , abstract = {The Navigation and Instrumentation (NavINST) laboratory has developed a comprehensive multisensory dataset from various road-test trajectories in urban environments, featuring diverse lighting conditions, including indoor garage scenarios with dense 3D maps. This dataset includes multiple commercial-grade IMUs and a high-end tactical-grade IMU. Additionally, it contains a wide array of perception-based sensors, such as a solid-state LiDAR—making it one of the first datasets to do so—a mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and two stereo cameras. The dataset also includes forward speed measurements derived from the vehicle’s odometer, along with accurately post-processed high-end GNSS/IMU data, providing precise ground truth positioning and navigation information. The NavINST dataset is designed to support advanced research in high-precision positioning, navigation, mapping, computer vision, and multisensory fusion. It offers rich, multi-sensor data ideal for developing and validating robust algorithms for autonomous vehicles. Finally, it is fully integrated with the robot operating system (ROS), ensuring ease of use and accessibility for the research community. The complete dataset and development tools are available at (navinst.github.io).}, archive = {J_ACCESS}, author = {Paulo Ricardo Marques De Araujo and Eslam Mounier and Qamar Bader and Emma Dawson and Shaza I. Kaoud Abdelaziz and Ahmed Zekry and Mohamed Elhabiby and Aboelmagd Noureldin}, doi = {10.1109/ACCESS.2025.3565878}, journal = {IEEE Access}, pages = {84659-84674}, shortjournal = {IEEE Access}, title = {The NavINST dataset for multi-sensor autonomous navigation}, volume = {13}, year = {2025}, }
• (2025). Lorenz-PSO optimized deep neural network for enhanced phonocardiogram classification. ACCESS, 13, 84645-84658. (www)

  @article{ , abstract = {Phonocardiogram is a crucial functional diagnostic tool in cardiology since cardiovascular disorders kill most people worldwide. Despite deep learning’s popularity, dataset imbalance, signal feature repetition, and noise volatility hurt classification models. Two modifications of Lorenz chaotic system hybrid with PSO are given to improve optimal hyperparameter selection of deep learning architecture EfficientNet-To. A public dataset validates the Lorenz-PSO Optimized Deep Neural Network (LPODNN) framework for multiple disease classes of PCG signals. Compared to Mel Frequency Cepstral Coefficient and Discrete wavelet transforms feature extraction techniques hybrid with support vector machine, deep neural network, and centroid-based kth nearest neighbor classifiers, modification-I and modification-II achieve 99.25% and 99.5% accuracy, respectively. The results are also compared to a fusion of instantaneous frequency-based features with random forest & KNN classifiers and other state-of-the-art methods on accuracy, precision, recall, F1-score, and fitness value. The suggested approach is reliable since fitness value, loss function, and statistical measures are profiled on 100 independent runs. Different variants of Lorenz-PSO combined with deep learning can solve telemedicine and remote diagnosis problems.}, archive = {J_ACCESS}, author = {Awais Mahmood and Mousa Alhajlah and Habib Dhahri and Abdulaziz Almaslukh}, doi = {10.1109/ACCESS.2025.3569327}, journal = {IEEE Access}, pages = {84645-84658}, shortjournal = {IEEE Access}, title = {Lorenz-PSO optimized deep neural network for enhanced phonocardiogram classification}, volume = {13}, year = {2025}, }
• (2025). Efficient dynamic window approach with neural multi-trajectory proposer. ACCESS, 13, 84635-84644. (www)

  @article{ , abstract = {We propose a control method for mobile robots that improves navigation in complex environments by integrating the dynamic window approach (DWA) and reinforcement learning technique. The growing presence of mobile robots in many fields highlights the increasing need for effective navigation that can cope with narrow paths and irregularly positioned obstacles. Traditional DWA and its improved methods are sensitive to the parameter values and the length of the prediction horizon. In this study, we propose the Neural Multi-trajectory Proposer (NMP), which generates prediction trajectories on the basis of reinforcement learning. Instead of prediction assuming the control input is constant, which is done in DWA, the proposed method with NMP improves the control efficiency while maintaining safety. The efficiency of the proposed method was validated in various scenarios, demonstrating superior performance compared with conventional DWA by a factor of at most 2.3 in average moving speed. Moreover, it is shown that the degradation in efficiency for longer prediction horizons is suppressed to 3.9% compared with 32% for the conventional DWA. As a feasibility study, the proposed method was evaluated in a real-world experiment, achieving over twice the average moving speed of conventional DWA.}, archive = {J_ACCESS}, author = {Shinya Yasuda and Hiroshi Yoshida}, doi = {10.1109/ACCESS.2025.3569193}, journal = {IEEE Access}, pages = {84635-84644}, shortjournal = {IEEE Access}, title = {Efficient dynamic window approach with neural multi-trajectory proposer}, volume = {13}, year = {2025}, }
• (2025). Enhancing change point detection through key actor identification: A study in cryptocurrency transactions networks. ACCESS, 13, 84620-84634. (www)

  @article{ , abstract = {This paper proposes a unified framework for analyzing cryptocurrency transaction networks, employing a composite approach that combines key actor identification and change point detection. At first, the framework identifies a representative key actor in a network of cryptocurrency transactions and retrieves its relevant transactions. Then, multiple features are extracted in the form of time series from its transaction history, providing an overview of its activity over time. In addition, feature selection is applied to reduce overlapping information among the extracted features and, finally, change point detection is utilized to identify time locations of significant changes in the selected features. To showcase the applicability of the proposed framework, four cryptocurrency transaction networks are used related to the tokens DAI, WLUNA, USTC and PAX. The analysis results indicate that the estimated change points often coincide with market events that may have influenced the network’s transaction behavior. Overall, this paper presents a new approach for understanding the dynamics of cryptocurrency transaction networks, where, by identifying and examining their representative key actors, we aim to obtain a comprehensive characterization of its overall structure.}, archive = {J_ACCESS}, author = {Ourania Theodosiadou and Andreas Kosmatopoulos and Christos Gkiourtzidis and Taxiarchis Skouras and Theodora Tsikrika and Stefanos Vrochidis and Ioannis Kompatsiaris}, doi = {10.1109/ACCESS.2025.3566624}, journal = {IEEE Access}, pages = {84620-84634}, shortjournal = {IEEE Access}, title = {Enhancing change point detection through key actor identification: A study in cryptocurrency transactions networks}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of an adjustable linkage device for body weight supported gait rehabilitation training. ACCESS, 13, 84607-84619. (www)

  @article{ , abstract = {In response to the increasing number of patients with lower limb dysfunction caused by stroke and other diseases, and the low penetration rate of gait rehabilitation robots caused by the expensive cost and difficult operation of most current device, this study proposes a novel gait rehabilitation device with a single actuator. A gait simulation mechanism is designed based on the single-degree-of-freedom six-bar Klann’s mechanism. This mechanism can generate the ankle trajectory of human gait. As this is a nonanthropomorphic design, it does not need to be aligned with patient’s joints, which facilitates the donning and doffing of the device and avoids discomfort to the patient caused by misalignment. The mechanism is adjustable so that it can provide a fit for different patients. In addition, a novel pneumatic body weight support apparatus is also designed which supports partial body weight of patient. This apparatus can help patients with lower-limb muscle strength deficit carry out the gait rehabilitation training consciously. The proposed gait rehabilitation device can move according to the walking pace of the training, which makes the rehabilitation training more interesting and assists the patients to complete some daily life activities. A prototype is manufactured and conducted a study with a subject. Due to its low cost, small size, ease of operation, the proposed system potentially offers a novel device for body weight supported gait rehabilitation training.}, archive = {J_ACCESS}, author = {Xiaoyu Zhou and Haifan Wu and Zheng Chen}, doi = {10.1109/ACCESS.2025.3567651}, journal = {IEEE Access}, pages = {84607-84619}, shortjournal = {IEEE Access}, title = {Design and analysis of an adjustable linkage device for body weight supported gait rehabilitation training}, volume = {13}, year = {2025}, }
• (2025). An adaptive multi-bernoulli filter for coexisting point target and extended target tracking with unknown detection probability. ACCESS, 13, 84593-84606. (www)

  @article{ , abstract = {This paper proposes an adaptive multi-Bernoulli (MB) filter for coexisting point target and extended target tracking, where the detection probability of each target is unknown and time-varying. The detection probability of the target directly affects the tracking accuracy in multi-sensor multi-target tracking. In realistic tracking circumstances, the detection probability of the target is unknown. Therefore, it is crucial to adaptively estimate the detection probability of the target in real-time. In addition, as the resolution of sensor increases, the point target and extended target often coexist in the field of view. Therefore, it is necessary to construct a generalized measurement model for tracking coexisting point target and extended target. For the coexistence of point and extended target, we model the spatial probability density function which can accommodate both point target and extended target based on a MB filter. For the unknown detection probability, we integrate the detection probability into the target state and the detection probability is characterized as a Beta distribution. To obtain a closed-form solution, the spatial probability density function of each MB component is modeled as a mixture of the Beta-Gaussian distribution and the Beta Gamma Gaussian Inverse Wishart (BGGIW) distribution. Based on the estimated probability of the point target, the target is determined as either a point target or an extended target. Finally, the detection probability, kinematic state, Poisson measurement rate (MR) and extended state of each target are estimated synchronously. Simulation results verify the effectiveness and robustness of the proposed algorithm.}, archive = {J_ACCESS}, author = {Yuansheng Li and Ling Cui and Junhu Ma and Zhaohui Luo}, doi = {10.1109/ACCESS.2025.3567990}, journal = {IEEE Access}, pages = {84593-84606}, shortjournal = {IEEE Access}, title = {An adaptive multi-bernoulli filter for coexisting point target and extended target tracking with unknown detection probability}, volume = {13}, year = {2025}, }
• (2025). Signal-to-noise ratio enhancements enabling 1.6-km outdoor visible light communication using vehicle lights and cameras. ACCESS, 13, 84580-84592. (www)

  @article{ , abstract = {Light-based communications offer a promising alternative to alleviate congestion in crowded radio frequency bands and extend communication frequencies towards the THz range. This paper addresses the challenge of strong background interference in outdoor visible light communication (VLC) and presents novel techniques to enable VLC using existing vehicle lights and cameras, focusing on physical layer enhancements for signal-to-noise ratio (SNR) improvement. These enhancements to commodity hardware-based VLC systems apply to vehicular communication, outdoor Internet-of-Things (IoT) devices, and mobile personal communications devices. New techniques, including preamble-based digital area filtering, optical magnification, and polarization filtering, are experimentally validated at distances up to 1650 m, demonstrating SNR improvements at unprecedented distances over four times farther than previously reported for outdoor VLC. Preamble-based digital area filtering across the camera sensor significantly improves SNR. Compact optical magnification increases both selectivity and array gain, providing over 10 dB of SNR gain at 1.6 km. Polarization filtering provides up to 4 dB of SNR gain even with an unpolarized signal source, and attenuating filters improve SNR at shorter ranges. These results demonstrate the viability of low-cost, long-distance VLC using commodity hardware and provide a foundation for future research on robust VLC for intelligent transportation systems and connected networks.}, archive = {J_ACCESS}, author = {Don E. Barber and Sean M. Kennedy and John C. McEachen and Murali Tummala}, doi = {10.1109/ACCESS.2025.3569153}, journal = {IEEE Access}, pages = {84580-84592}, shortjournal = {IEEE Access}, title = {Signal-to-noise ratio enhancements enabling 1.6-km outdoor visible light communication using vehicle lights and cameras}, volume = {13}, year = {2025}, }
• (2025). LMGDoc: Light multigranular GNN for efficient document understanding. ACCESS, 13, 84569-84579. (www)

  @article{ , abstract = {Document understanding is a critical task in extracting structured information from documents such as forms, receipts, and reports. Visually Rich Documents (VRDs) present unique challenges due to their complex layouts, heterogeneous content (text, tables, images), and diverse structural patterns. While recent transformer-based models have achieved strong performance, they typically rely on large-scale pretraining and have high computational demands, limiting their usability in real-time or resource-constrained settings. In this paper, we propose LMGDoc, a lightweight model capable of understanding and processing VRDs composed of three main components: a Feature Extractor using GloVe for text embedding and a novel layout encoding method, a Graph Constructor for representing document structures at multiple granular levels (word, region, and page), and a Graph Convolution Network (GNN). LMGDoc achieves competitive results on classification and Key Information Extraction (KIE) benchmarks with only 0.4M parameter size compared to state-of-the-art models.}, archive = {J_ACCESS}, author = {Abdellatif Sassioui and Yasser Elouargui and Mohamed El Kamili and Rachid Benouini and El Mehdi Benyoussef and Meriyem Chergui and Mohammed Ouzzif}, doi = {10.1109/ACCESS.2025.3568628}, journal = {IEEE Access}, pages = {84569-84579}, shortjournal = {IEEE Access}, title = {LMGDoc: Light multigranular GNN for efficient document understanding}, volume = {13}, year = {2025}, }
• (2025). Extensive numerical analysis of PDF parameters for wind energy in brazil: A study across 27 cities for 60 years wind speed. ACCESS, 13, 84555-84568. (www)

  @article{ , abstract = {This research conducts a comprehensive numerical assessment of wind energy potential across Brazil, utilizing six decades of wind speed data collected from 27 cities to analyze Probability Density Function (PDF) parameters and enhance wind resource mapping. The study employs the Weibull distribution, characterized by shape factor k and scale factor c, to represent wind speed variations. Nine numerical methods, including Maximum Likelihood Method (MLM), Method of Moments (MM), Empirical Method (EM), Standard Deviation Method (STDM), Modified Maximum Likelihood Method (MMLM), Second Modified Maximum Likelihood Method (SMMLM), Graphical Method (GM), Least Mean Square Method (LSM), and Energy Pattern Factor Method (EPF), were used for parameter estimation. Statistical analysis was conducted using metrics such as Index of Agreement (IA), Root Mean Square Error (RMSE), Chi-square test ( $\chi 2$ ), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE. The results showed that MLM was the most accurate method, followed by STDM, EM, MM, and EPF, while GM and LSM were less effective. The analysis revealed a scale factor range of 2.17-2.36 m/s and a shape factor of 2.5-2.78, indicating a bell-shaped Gaussian PDF. The Mean Wind Speed (MWS) was 2.06 m/s, suggesting potential for small-scale wind turbine deployment for energy production. This study provides valuable insights for wind energy development in Brazil, offering a methodological approach to optimize wind turbine placement and reduce electricity generation costs.}, archive = {J_ACCESS}, author = {Ahmed Badawi and Amaury Souza and I. M. Elzein and Hassan Ali and Alhareth Zyoud}, doi = {10.1109/ACCESS.2025.3568085}, journal = {IEEE Access}, pages = {84555-84568}, shortjournal = {IEEE Access}, title = {Extensive numerical analysis of PDF parameters for wind energy in brazil: A study across 27 cities for 60 years wind speed}, volume = {13}, year = {2025}, }
• (2025). Reliability issues and degradation mechanisms of p-GaN gated E-mode AlGaN/GaN power HEMTs: A critical review. ACCESS, 13, 84541-84554. (www)

  @article{ , abstract = {GaN-HEMTs on silicon (Si) or SiC or sapphire substrates are growing in popularity and is expected to completely transform the power electronics industry. Although GaN HEMTs operate in D-Mode by default, E-Mode operation is necessary for ease of IC design, low power consumption and safe operation. p-GaN gated HEMTs offer a breakdown voltage (V ${}_{\mathrm {BR}}$ ), transconductance (g ${}_{\mathrm {m}}$ ), power added efficiency (PAE) and ON-current (I ${}_{\mathrm {ON}}$ ) of over 2230 V, 205 mS/mm, 55.4 % and 1 A/mm, respectively, which makes them highly suitable for future RF-power switching applications. The time-dependent breakdown of the AlGaN-p-GaN/metal stack (due to avalanche multiplication) is a serious reliability concern in p-GaN HEMTs. Material defects, back gate effects, gate leakage, bias stress effects, ESD & short circuit failures, radiation effects and thermal effects are also important reliability concerns that can result in performance degradation, including current collapse, reduced breakdown voltage, increased on-resistance and device failure. Mechanisms like interface states, ion migration, and electron trapping are also crucial to the aging of p-GaN HEMTs. Understanding these reliability issues and degradation mechanisms is critical for enhancing the robustness of p-GaN HEMTs in power electronics and RF applications. Therefore, this article reviews the reliability issues and various degradation mechanisms of p-GaN gated E-Mode HEMTs such as forward/reverse bias stress effects, back gate effects, current collapse, charge trapping effects, radiation effects, short circuit (SC) & electrostatic discharge (ESD) failures and high temperature reliability issues. RON degradation, gate breakdown, PBTI and NBTI remains serious concerns in the development of p-GaN gated E-Mode HEMTs for future consumer electronics, wireless networks, industrial motors, electric vehicles and space/aeronautic applications.}, archive = {J_ACCESS}, author = {J. Ajayan and Asisa Kumar Panigrahy and Sachidananda Sen and Maneesh Kumar and Shubham Tayal}, doi = {10.1109/ACCESS.2025.3569288}, journal = {IEEE Access}, pages = {84541-84554}, shortjournal = {IEEE Access}, title = {Reliability issues and degradation mechanisms of p-GaN gated E-mode AlGaN/GaN power HEMTs: A critical review}, volume = {13}, year = {2025}, }
• (2025). Porous models for enhanced representation of saturated curly hairs: Simulation and learning. ACCESS, 13, 84517-84540. (www)

  @article{ , abstract = {Simulating the cohesion, adhesion, stiffness, and exaggeration of curls of wet curly hair or fur, expressed through the saturation-hair interaction in physics-based simulations, remains a challenging problem. Wet hair or fur tends to clump and stiffen at the ends, a common phenomenon observed in wet hair or animal fur. Additionally, while wet hair should exhibit adhesion when in contact with solids, the uneven distribution of forces in wet curly hair, manifested as noise, complicates an accurate representation of adhesion. Research into detailed porous models for wet curly hair, driven by saturation-hair interaction, is not yet extensively explored. Previous methods have manually represented wet hair or used static hairstyles for wet curly hair and fur, maintaining shape but resulting in unnatural movement due to the lack of simulation. This paper proposes methods for representing wet curly hair features: 1) curl exaggeration using locally transformed helices, 2) deformation-based cohesion that remains stable in wet curly hair, 3) level-set-based adhesion for efficiently depicting the sticky and elongated forms of wet hair, 4) dynamic stiffness for improved simulation stability, and 5) collecting a detailed synthetic dataset of curly hairs and extending the solver to represent particle movements in wet strands through learning. Experiments in various scenes demonstrate that our proposed methods more realistically represent the saturation-hair interaction compared to previous wet curly hair simulations. Unlike previous methods in which saturation caused cohesion or curls to tangle, our method stably represents porous flow at the strand level. Additionally, we propose to extend the learning representation solver through both numerical simulation algorithms and AI-based approaches.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim and Jung Lee}, doi = {10.1109/ACCESS.2025.3569324}, journal = {IEEE Access}, pages = {84517-84540}, shortjournal = {IEEE Access}, title = {Porous models for enhanced representation of saturated curly hairs: Simulation and learning}, volume = {13}, year = {2025}, }
• (2025). A novel combine between the opinion weight criteria method (OWCM) and FDOSM. ACCESS, 13, 84499-84516. (www)

  @article{ , abstract = {Fuzzy decision by opinion score method (FDOSM) is one of the latest methods in multi criteria decision making area using to select the best alternative from a set of alternatives. FDOSM addressed many problems in other MCDM methods. However, FDOSM suffered from the implicit weight and needs to extract wright to explain the expert weight. Opinion weight criteria method (OWCM) is the latest method was developed to extracting weight for the evaluation criteria. In this article present a novel combine between the OWCM and FDOSM. The methodology of the article contains the four phases, phase one necessary to extract the research gap. Phase two is present the novel combine between OWCM and FDOSM. The new combination applied in network case study was presented in phase three. Finally, in phase four present a comparative analysis for the proposed combine and the basic FDOSM. The final result and the comparative analysis present the effect of the extract weight, and prove that FDOSM needs extracting weight to make more accurate decision.}, archive = {J_ACCESS}, author = {Hind Alaa Abdul-Kareem and Mahmood Maher Salih}, doi = {10.1109/ACCESS.2025.3569405}, journal = {IEEE Access}, pages = {84499-84516}, shortjournal = {IEEE Access}, title = {A novel combine between the opinion weight criteria method (OWCM) and FDOSM}, volume = {13}, year = {2025}, }
• (2025). ML: Generalized additive model for spectrum sensing in nakagami-m fading channel with complex generalized gaussian distribution noise. ACCESS, 13, 84488-84498. (www)

  @article{ , abstract = {Spectrum sensing in cognitive radio presents a challenge in overcoming the spectrum scarcity caused by the rapid growth of wireless devices. Deep learning (DL) and machine learning (ML) are trending approaches to enhance the spectrum sensing performance. This paper introduces the generalized additive model (GAM) as an ML-based spectrum sensing method for cognitive radio networks. In our proposed method, the received signals are transformed using fast Fourier transform (FFT) and the powers of both the real and imaginary components of the FFT signals are calculated separately to construct a dataset. In practical communication channels, spectrum sensing is a critical task for cognitive radio in the presence of non-Gaussian noise, such as noise with a heavy-tailed distribution. We consider the complex generalized Gaussian distribution (CGGD) as a noise model to evaluate the detection performance of the ML-based method in both Gaussian and non-Gaussian noise environments. Furthermore, generalized Gaussian distribution (GGD) noise is used to compare the results of the GAM algorithm with existing methods. As a channel model, the Nakagami-m fading distribution is used for its versatile characteristics, which enable it to model a wide range of fading channels by varying the value of a parameter m in its equation. The effectiveness and practical usefulness of the proposed method are validated through simulations. The simulation results exhibit higher performance compared to the state-of-the-art DL and existing conventional methods.}, archive = {J_ACCESS}, author = {Md. Tofail Ahmed and Mousumi Haque and Yosuke Sugiura and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3568831}, journal = {IEEE Access}, pages = {84488-84498}, shortjournal = {IEEE Access}, title = {ML: Generalized additive model for spectrum sensing in nakagami-m fading channel with complex generalized gaussian distribution noise}, volume = {13}, year = {2025}, }
• (2025). Refining pseudo labels with a teacher-student paradigm for accurate brain lesion segmentation. ACCESS, 13, 84474-84487. (www)

  @article{ , abstract = {In medical image segmentation, obtaining large volume of high quality labeled data is a persistent challenge, especially for intricate tasks like brain lesion segmentation, where annotations are time-consuming, costly, and require expert knowledge. This work introduces a novel learning framework where the abundance of labeled data is scarce, and the model learns from both limited number of annotated data as well as from unlabeled data. The framework initially leverages superpixel segmentation to group pixels with similar properties into compact, perceptually coherent regions while preserving critical structural details, and produce coarse segmentation mask on unlabeled data. In the next phase, a teacher-student model is employed, where the teacher module generates pseudo-labels for unlabeled data, and the student model learns from both of these pseudo-labels and minimal amount of annotated data. In addition, a feature refinement module is introduced to further strengthen the teacher-student framework, where the student module also learns from the mutual agreement between superpixel prediction and the pseudo label generated by the teacher module. The overall framework helps in efficiently using the unlabeled data with the help of handful amount of labeled data. Experimental results across multiple datasets demonstrate the effectiveness of the proposed framework in achieving precise, computationally efficient brain lesion segmentation. The integration of superpixel-based grouping and the student-teacher architecture not only improves segmentation accuracy but also offers a scalable solution for scenarios with limited annotated data. This method provides a promising pathway for advancing medical imaging applications, enabling accurate and efficient segmentation while addressing the challenges of data scarcity and annotation constraints.}, archive = {J_ACCESS}, author = {Pubali Chatterjee and Kaushik Das Sharma and Amlan Chakrabarti}, doi = {10.1109/ACCESS.2025.3566506}, journal = {IEEE Access}, pages = {84474-84487}, shortjournal = {IEEE Access}, title = {Refining pseudo labels with a teacher-student paradigm for accurate brain lesion segmentation}, volume = {13}, year = {2025}, }
• (2025). Simultaneous sensing and stimulation in neuromodulation systems: Quantifying and mitigating charge accumulation effects. ACCESS, 13, 84464-84473. (www)

  @article{ , abstract = {This paper describes the operation of a low-noise amplifier and neurostimulation circuit when both functions are used simultaneously. The design of this circuitry along with the circuit model used to investigate system interactions is described. Expected circuit operation is explored in conjunction with the impact of charge storage at the electrode tissue interface. The test results for the system during independent use of the amplifier and during simultaneous use of the amplifier and stimulator are presented. As seen in the circuit analysis, test results confirm that the charge storage that occurs in the tissue/electrode interface used for stimulation interferes with the measurement of the neural signals of interest when the two circuits share electrodes. By optimizing the configuration used for the amplifier during high frequency stimulation, biological signals can be measured; however, the effective number of bits (ENOB) will be degraded as a function of the stimulation parameters, limiting the applications in which signal amplification can be meaningfully used when sharing an electrode with stimulation circuitry.}, archive = {J_ACCESS}, author = {Heather Orser and Preston Doan}, doi = {10.1109/ACCESS.2025.3569414}, journal = {IEEE Access}, pages = {84464-84473}, shortjournal = {IEEE Access}, title = {Simultaneous sensing and stimulation in neuromodulation systems: Quantifying and mitigating charge accumulation effects}, volume = {13}, year = {2025}, }
• (2025). A bi-objective green location-routing problem under carbon tax policies. ACCESS, 13, 84451-84463. (www)

  @article{ , abstract = {In response to the highly competitive market and the demand for a low-carbon economy, logistics enterprises have paid special attention not only to cost management but also to environmental sustainability for better development. In such context, this study seeks to investigate a Green Location Routing Problem (GLRP) and develops a bi-objective model with the aim of minimizing total costs and carbon emissions. For the solution approach, various multi-objective optimization methods were compared initially, and Non-dominated sorting genetic algorithm II (NSGA-II) was finally selected for use in this study. The model’s applicability is assessed through adapted benchmark instances. A real-world case study further evaluates the impact of carbon tax, highlighting the model’s practical value and effectiveness. Experimental results reveal that the carbon tax policy is effective in reducing carbon emissions in scenarios that focus on minimizing total costs, yet it has no effect in CO2 minimization scenarios; incremental increases in carbon tax levels make the two objectives converge, ultimately yielding a singular, optimized solution. This study offers practical and managerial insights for logistics enterprises and contributes to a better understanding of the interplay between carbon tax policies and logistics decision-making.}, archive = {J_ACCESS}, author = {Dandan Su and Vatcharapol Sukhotu}, doi = {10.1109/ACCESS.2025.3568873}, journal = {IEEE Access}, pages = {84451-84463}, shortjournal = {IEEE Access}, title = {A bi-objective green location-routing problem under carbon tax policies}, volume = {13}, year = {2025}, }
• (2025). YOLOv8-OCHD: A lightweight wood surface defect detection method based on improved YOLOv8. ACCESS, 13, 84435-84450. (www)

  @article{ , abstract = {To overcome the drawbacks of manual defect detection and the challenges of traditional visual inspection algorithms, such as high missed detection rates, slow detection speeds, and difficulties in deployment on embedded devices for detecting subtle wood surface defects, this study aims to improve the utilization efficiency of wood in enterprises. A lightweight wood surface defect detection method, YOLOv8-OCHD, based on deep learning models, is proposed. This method demonstrates significant performance improvement in handling common multi-scale and small target defects on wood surfaces. Firstly, to enhance the ability to capture multi-dimensional features of wood surface defects and reduce information loss, a fully dynamic convolution (ODConv) is introduced. Secondly, a C2f_RVB module is designed, which uses the RepViTBlock technique to optimize feature representation and effectively reduce the number of model parameters. By enhancing the expression of deep features, the C2f_RVB module significantly improves the accuracy of detecting small target defects on wood surfaces. Next, the Haar Wavelet Downsampling module (HWD) is employed to expand the receptive field, reduce model complexity, and improve both training and inference speeds. Finally, at the output stage, a DyHead (Dynamic Head) attention mechanism detection head is introduced to enhance the detection capability of the algorithm by integrating scale perception, spatial perception, and task perception, significantly improving the feature expression ability at the output. Experimental results show that compared to the YOLOv8n baseline model, the proposed method improves detection accuracy for eight defect types in different tree species, with the mean average precision (mAP) increased by 5.9%. Compared with the YOLOv8n algorithm, the model size is significantly reduced, with the parameter count at only 2.9M and the computational load at 7.0G, representing reductions of 21.6% and 15.6%, respectively. This effectively reduces the difficulty and cost of deployment on mobile terminals while significantly improving algorithm accuracy, meeting real-time requirements, and providing a more efficient and feasible technical solution for relevant applications. These findings suggest that the proposed model shows superior performance in accurately detecting wood surface defects on mobile terminals.}, archive = {J_ACCESS}, author = {Zuxing Chen and Junjie Feng and Xueyan Zhu and Bin Wang}, doi = {10.1109/ACCESS.2025.3569175}, journal = {IEEE Access}, pages = {84435-84450}, shortjournal = {IEEE Access}, title = {YOLOv8-OCHD: A lightweight wood surface defect detection method based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Modeling parking occupancy using algorithm of 3D visibility network. ACCESS, 13, 84424-84434. (www)

  @article{ , abstract = {The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones.}, archive = {J_ACCESS}, author = {Matej Babič and Branko Šter and Ladislav Hluchý and Giuliana Baiamonte and Michele Calì}, doi = {10.1109/ACCESS.2025.3562796}, journal = {IEEE Access}, pages = {84424-84434}, shortjournal = {IEEE Access}, title = {Modeling parking occupancy using algorithm of 3D visibility network}, volume = {13}, year = {2025}, }
• (2025). Novel multi-scale attention generative adversarial network for photovoltaic solar cell defect inspection using electroluminescence images. ACCESS, 13, 84409-84423. (www)

  @article{ , abstract = {In the pursuit of promoting green energy, efficient defect inspection in solar cell manufacturing is crucial in enhancing the reliability of solar energy systems. However, traditional deep learning models for automatic defect inspection in photovoltaic (PV) cell electroluminescence (EL) images encounter challenges in industrial settings due to difficulties associated with data acquisition, imbalance, and variability of defects. This paper presents a novel Multi-Scale Attention Generative Adversarial Network (MAGAN), an innovative GAN-based framework specifically designed for data augmentation in the context of solar cell defect detection. When integrated with automated detection techniques, MAGAN markedly improves the accuracy and efficiency of current models. A method for augmenting image datasets of EL was developed to generate a sufficient quantity of images for training machine learning models, addressing sample scarcity and bolstering CNN-based defect classification accuracy. The core of this approach lies in the application of the MCA (Multi-channel Spatial Attention Mechanism) and GLSA (Gate-like Spatial Attention Mechanism) modules, which enhance feature extraction by leveraging channel attention and spatial attention, respectively, thereby reflecting the most recent advancements in attention mechanism technology. The MCA dissects channels into sub-features across various scales, ensuring detailed attention mapping, whereas the GLSA refines spatial cues with a gating mechanism, shedding computational inefficiencies. The effectiveness of this approach is validated by comprehensive experiments against state-of-the-art deep learning models. The experiments demonstrate the exceptional performance of MAGAN, achieving a low FID score of 141.98 and KID score of 0.106 on complex EL images, surpassing previous models and emphasizing data augmentation’s importance in defect detection. With an industry-leading detection accuracy of 87.3%, this study makes a substantial contribution to mitigating data imbalance. This method enhances quality control in solar cell manufacturing. Additionally, it advances defect inspection in the industrial semiconductor sector.}, archive = {J_ACCESS}, author = {Yuanjun Guan and Yang Liu and Jiayi Wang and Tao Wang and Qianchuan Yi and Wenxin Jiang and Xiaopu Gu and Yichen Zhang and Li Zhang and Tianyan Han and Binbing Huang and Lilei Hu}, doi = {10.1109/ACCESS.2025.3565002}, journal = {IEEE Access}, pages = {84409-84423}, shortjournal = {IEEE Access}, title = {Novel multi-scale attention generative adversarial network for photovoltaic solar cell defect inspection using electroluminescence images}, volume = {13}, year = {2025}, }
• (2025). Demonstration of a novel SLM-based PAPR reduction method for GFDM using USRP testbed. ACCESS, 13, 84399-84408. (www)

  @article{ , abstract = {The remarkable attributes like elevated transmission rates, low latency, minimal out band emission and enhanced spectral efficiency of the generalized frequency division multiplexing (GFDM) system have lured the significant attention from research community. However, the inherent superposition of multiple symbols within the time domain results in a notable amount of peak-to-average power ratio (PAPR) that affects the performance due to non-linear front end circuitry. The selected mapping (SLM) technique resolves this problem by employing phase rotation vectors to generate alternative signal representations. Conventionally, phase sequence vectors possess uniform magnitudes, thereby constraining the achievable PAPR reduction. To address this limitation, we propose the use of normalized rows from the Riemann matrix as phase sequence vectors, given their inclusion of components with substantial magnitudes. This approach mitigates excessive peaks and consequently yields superior PAPR reduction gains. To optimize real-time system performance, side information (SI) is also integrated into the transmitted signals alongside the normalized Riemann matrix sequences. Indeed, for validation purpose, an experimental setup utilizing the universal software radio peripheral (USRP) 2953R in conjunction with LabVIEW software is employed for real-time transmission. The observed spectral response from the USRP closely aligned with simulation results, affirming the efficacy of the proposed method.}, archive = {J_ACCESS}, author = {Kishore Vejandla and Prashanth Ragam and Sivaprasad Valluri and Chakravarthy Gunturu and K. N. G. B. Yaswanth and Shravan Kumar Bandari and Sankati Jyothi}, doi = {10.1109/ACCESS.2025.3568497}, journal = {IEEE Access}, pages = {84399-84408}, shortjournal = {IEEE Access}, title = {Demonstration of a novel SLM-based PAPR reduction method for GFDM using USRP testbed}, volume = {13}, year = {2025}, }
• (2025). A digital twin framework with meta- and transfer learning for scalable multi-machine modeling and optimization in semiconductor manufacturing. ACCESS, 13, 84380-84398. (www)

  @article{ , abstract = {Despite recent advances in Digital Twin (DT) technologies for semiconductor manufacturing, no existing research convincingly demonstrates a unified, rapidly scalable, and data-efficient DT framework that can effectively handle stringent multi-objective optimization under severe data scarcity. This absence of a comprehensive approach impedes widespread industry adoption, given the pressing need for flexible, universal solutions that rapidly adapt to diverse machines and processes. This study introduces MOODFG-MLTL, an innovative algorithm that integrates Meta-Learning and Transfer Learning within a Multi-Objective Optimization using Deep-Feature Gaussian Processes (MOODFG) architecture. By harnessing a centralized meta-model repository and dynamically refining surrogate models, the proposed framework efficiently leverages limited data and transfers knowledge across heterogeneous manufacturing configurations. In our experimental validation with Epi SiC processes, MOODFG-MLTL achieves near-optimal wafer thickness and doping control using only 15 samples—reducing data requirements by approximately 50% while converging up to 40% faster compared to conventional approaches. Experimental validation in epitaxial silicon carbide (Epi SiC) scenarios demonstrates that the MOODFG-MLTL not only maintains robust performance under constrained data conditions but also accelerates the deployment of data-efficient, multi-objective DT solutions. This advancement provides a critical foundation for achieving universal, intelligent DT implementations in the semiconductor industry’s evolving landscape.}, archive = {J_ACCESS}, author = {Chin-Yi Lin and Tzu-Liang Tseng and Tsung-Han Tsai}, doi = {10.1109/ACCESS.2025.3568002}, journal = {IEEE Access}, pages = {84380-84398}, shortjournal = {IEEE Access}, title = {A digital twin framework with meta- and transfer learning for scalable multi-machine modeling and optimization in semiconductor manufacturing}, volume = {13}, year = {2025}, }
• (2025). Toward AI-augmented formal verification: A preliminary investigation of ENGRU and its challenges. ACCESS, 13, 84357-84379. (www)

  @article{ , abstract = {State-space graphs and automata serve as fundamental tools for modeling and analyzing the behavior of computational systems. Recurrent neural networks (RNNs) and language models are deeply intertwined, as RNNS provide the foundational architecture that enables language models to process sequential data, capture contextual dependencies, and improve natural language processing tasks. Both RNNs and state-space graphs help evaluate discrete-time systems within this formal framework. However, the basic question of their equivalence remains an open challenge regarding the models governing sentence structure in natural language and the formal model in automata theory. In this paper, we present ENGRU (Enhanced Gated Recurrent Units), a novel deep learning-based approach for formal verification. ENGRU integrates concepts of model checking techniques, Colored Petri Nets (CPNs), and sequential learning to analyze systems at a high level of abstraction. CPN models undergo state-space enumeration via a model-checking tool, generating a state-space graph and an automaton from inherent state transition patterns. These graphs are transformed into sequential representations as sub-paths, enabling ENGRU to learn the execution paths and predict system behaviors. ENGRU effectively predicts goal states within discrete-time models by the competency of gated recurrent mechanisms, encouraging early bug detection and allowing predictive state-space exploration. Experimental results demonstrate that ENGRU’s ability achieves high accuracy and efficiency in goal state predictions. The source code for ENGRU is available at (https://github.com/kaopanboonyuen/ENGRU).}, archive = {J_ACCESS}, author = {Chanon Dechsupa and Teerapong Panboonyuen and Wiwat Vatanawood and Praisan Padungweang and Chakchai So-In}, doi = {10.1109/ACCESS.2025.3568194}, journal = {IEEE Access}, pages = {84357-84379}, shortjournal = {IEEE Access}, title = {Toward AI-augmented formal verification: A preliminary investigation of ENGRU and its challenges}, volume = {13}, year = {2025}, }
• (2025). RGB pixel combination method for underwater camouflaged image categorization using graph neural networks. ACCESS, 13, 84341-84356. (www)

  @article{ , abstract = {Underwater imaging applications require sophisticated image processing techniques aided by Computer Vision (CV). The intensity and light variations under the water demand camouflaged object, plant, and living organism detection in deep seas. This article introduces the Edge-Recognition Method using Graph Neural Networks (ERM-GNN) to address visual differentiations of such particles with precise categorization. This method is reliable in detecting camouflaged object edges and classifying their foregrounds and backgrounds. In the edge detection and object classification process, color features are utilized, relying on RGB variants observed near the edges of the foreground and background images. The edge depth intensity feature is trained repeatedly using the GNN, forming layers between the initial and final pixels of the regions. This approach imposes an $xC_{y}$ pixel combination to analyze depth intensity until the foreground and backgrounds are categorized. The neural network trains the hidden layers by differentiating categorized and non-categorized RGB variants for in-depth detection. Consequently, this combination increases the chances of camouflaged underwater object categorization with better precision. This method outperforms state-of-the-art approaches such as G+L-CET, FEGM, and MFEM by a wide margin, improving recognition accuracy by 14.48%, precision by 12.81%, and object classification by 9.82%. These noteworthy improvements highlight the method’s efficacy and potential use in marine biology and environmental surveillance, necessitating accurate underwater object detection.}, archive = {J_ACCESS}, author = {K Dhamodharan and Thanapal Pandi}, doi = {10.1109/ACCESS.2025.3569449}, journal = {IEEE Access}, pages = {84341-84356}, shortjournal = {IEEE Access}, title = {RGB pixel combination method for underwater camouflaged image categorization using graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Energy- and area-efficient CMOS neuron and max pooling circuit for RRAM-based CNN accelerators. ACCESS, 13, 84329-84340. (www)

  @article{ , abstract = {This study proposes an energy- and area-efficient CMOS neuron and analog max pooling circuit for RRAM-based convolutional neural network (CNN) accelerators. The proposed max pooling circuit implements a $2\times 2$ max pooling operation with a simple analog circuit in the current domain. The current-mode max pooling is performed before the current-to-voltage and voltage-to-digital conversions, thus reducing the number of circuits required for the conversions in the conventional CNN accelerators. Consequently, the $2\times 2$ pooling circuit reduces the energy and area of the accelerator to a quarter. In addition to the max pooling circuit, the neuron circuit improves energy efficiency. The proposed neuron circuit implements the ReLU activation function along with digital conversion by modifying the operating principle of a conventional single-slope analog-to-digital converter. By implementing the ReLU function and digital conversion, the neuron achieves high energy efficiency by applying a high-speed asynchronous operation. A prototype chip, which included the neuron and current-mode max pooling circuit, was fabricated using a 130 nm standard CMOS process. The measurement results of the chip demonstrate its energy efficiency of 5.5 TOPS/W. Additionally, a simulation for image recognition demonstrates that a CNN accelerator employing the proposed neuron and max pooling circuit achieves 96.2 and 82.7% classification accuracies for the MNIST and Fashion-MNIST datasets, respectively.}, archive = {J_ACCESS}, author = {Dowon Kim and Byung-Geun Lee}, doi = {10.1109/ACCESS.2025.3568466}, journal = {IEEE Access}, pages = {84329-84340}, shortjournal = {IEEE Access}, title = {Energy- and area-efficient CMOS neuron and max pooling circuit for RRAM-based CNN accelerators}, volume = {13}, year = {2025}, }
• (2025). Detection of driver drowsiness using adaptive eye characteristic ratio for enhanced road safety. ACCESS, 13, 84313-84328. (www)

  @article{ , abstract = {Every year, countless people lose their lives in serious car accidents, and drowsy driving is a major cause. However, because the earliest indications of exhaustion can be identified before a dangerous scenario develops, the detection and signaling of driver drowsiness are ongoing research topics. This study presents a real-time detection system designed to detect driver’s sleep and deliver timely warnings. The mechanism employs an eye monitoring system that uses eye feature points to detect whether a driver’s eye is tired and delivers an alert if the driver is drowsy. The proposed work recognizes the driver’s face by collecting facial landmarks and gives warning to the driver to avoid crashes by performing adaptive thresholding and drowsiness diagnoses based on Eye Characteristic Ratio. The empirical results show that using random forest classifier, decision tree classifier, KNN classifier, and multinomial classifier, the proposed model achieved 90.48%, 89.44%, 88.63%, and 84.32% accuracy, respectively.}, archive = {J_ACCESS}, author = {Puneet Singh Lamba and Rimjhim Jain and Shakir Khan and Sultan M. Alanazi and Achin Jain and Abu Taha Zamani and Arvind Panwar and Alsadig Mohammed Adam Abdallah}, doi = {10.1109/ACCESS.2025.3566981}, journal = {IEEE Access}, pages = {84313-84328}, shortjournal = {IEEE Access}, title = {Detection of driver drowsiness using adaptive eye characteristic ratio for enhanced road safety}, volume = {13}, year = {2025}, }
• (2025). Optimizing scheduling for enhanced efficiency in cube storage warehouses: A genetic algorithm approach across different schemes. ACCESS, 13, 84303-84312. (www)

  @article{ , abstract = {This study investigates the application of a Genetic Algorithm (GA) for optimizing scheduling in automated cube storage warehouses, focusing on enhancing logistics processing speed. The findings underscore the GA’s capability to optimize scheduling processes across various warehouse configurations, including edge and hall depot station (DS) layouts. We demonstrate that the GA consistently outperforms alternative algorithms regarding total and maximum operation times through comprehensive simulations and detailed graph analyses. Specifically, our results show that the GA reduces total operation time by up to 32% and 31% and maximum operation time by up to 29% and 37%, for edge and hall DS, respectively, effectively balancing the workload among Cube Storage Automated Guided Vehicles.}, archive = {J_ACCESS}, author = {Won Yong Ha}, doi = {10.1109/ACCESS.2025.3566989}, journal = {IEEE Access}, pages = {84303-84312}, shortjournal = {IEEE Access}, title = {Optimizing scheduling for enhanced efficiency in cube storage warehouses: A genetic algorithm approach across different schemes}, volume = {13}, year = {2025}, }
• (2025). Accelerated prediction of terahertz performance metrics in GaN IMPATT sources via artificial neural networks. ACCESS, 13, 84284-84302. (www)

  @article{ , abstract = {This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8–20.1% of the computational time. Mean square errors are observed to be on the order of $10^{-4}$ – $10^{-6}$ , demonstrating the models’ high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.}, archive = {J_ACCESS}, author = {Santu Mondal and Sneha Ray and Aritra Acharyya and Rudra Sankar Dhar and Arindam Biswas and Hiroaki Satoh and Gurudas Mandal and Vitaliy Maksimenko and Victor Krishtop}, doi = {10.1109/ACCESS.2025.3567410}, journal = {IEEE Access}, pages = {84284-84302}, shortjournal = {IEEE Access}, title = {Accelerated prediction of terahertz performance metrics in GaN IMPATT sources via artificial neural networks}, volume = {13}, year = {2025}, }
• (2025). Dam crack instance segmentation algorithm based on improved YOLOv8. ACCESS, 13, 84271-84283. (www)

  @article{ , abstract = {Cracks are primary threats to dam stability and require timely and precise identification and assessment. Dam cracks are typically morphologically complex and suffer from severe background interference. Current algorithms often struggle to strike a balance between detection accuracy and segmentation precision. To address this challenge, this paper proposes an improved YOLOv8m-based instance segmentation method for dam crack. The method integrates Cascaded Group Attention (CGA) to enhance the perception of global information and features of slender crack. It also employs Diverse Branch Block (DBB) technology to strengthen the convolutional network’s ability to extract subtle crack features without increasing inference costs. Additionally, the Minimum Point Distance IoU (MPDIoU) loss is introduced as an improved bounding box regression loss to enhance crack localization accuracy. Experiments are conducted on a dataset from the Xinfengjiang Dam in Heyuan City, Guangdong Province, China. The results show that the proposed method achieves 90.2% box mAP50, which is a 6.0% improvement over the original YOLOv8m. It also achieves 90.4% mask mAP50, which is a 7.5% improvement. Compared to other commonly used instance segmentation models, the proposed method demonstrates significant advantages in both detection and segmentation accuracy, offering a more advanced solution for dam safety monitoring.}, archive = {J_ACCESS}, author = {Shuaisen Ma and Mingyue Xu and Weiwu Feng}, doi = {10.1109/ACCESS.2025.3569408}, journal = {IEEE Access}, pages = {84271-84283}, shortjournal = {IEEE Access}, title = {Dam crack instance segmentation algorithm based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Research on cross-device bearing fault diagnosis transfer method based on NICE-GAN. ACCESS, 13, 84259-84270. (www)

  @article{ , abstract = {In industrial applications, acquiring fault signals under diverse operating conditions remains a significant challenge, particularly for cross-device and cross-condition fault diagnosis. This paper proposes a novel transfer learning framework based on the No-Independent-Component-of-Encoder Generative Adversarial Networks (NICE-GAN) model without independent encoding components to address this challenge. The framework leverages both simulation and experimental data to generate high-fidelity fault signals for target domain diagnosis. Our approach comprises three key components: First, we establish a comprehensive rolling bearing dynamics model based on Hertzian contact theory to generate abundant simulation signals under various working conditions, which are then transformed into time-frequency representations. Second, we employ the NICE-GAN architecture to learn shared features between simulation and experimental data under identical working condition constraints. Third, we utilize the trained generator to synthesize realistic fault data that closely matches experimental characteristics while preserving the underlying fault mechanisms from simulation data. Extensive experimental validation demonstrates that our method achieves diagnostic accuracy approaching that of real experimental data in cross-device fault diagnosis tasks, outperforming existing approaches by a significant margin. This framework provides a promising solution for practical fault diagnosis applications where fault data acquisition is limited.}, archive = {J_ACCESS}, author = {Jiada Wei and Sijia Wang and Weiyu Wang and Xianming Li and Xiaojun Wang}, doi = {10.1109/ACCESS.2025.3567829}, journal = {IEEE Access}, pages = {84259-84270}, shortjournal = {IEEE Access}, title = {Research on cross-device bearing fault diagnosis transfer method based on NICE-GAN}, volume = {13}, year = {2025}, }
• (2025). Efficacy of wearable devices in monitoring and treating parkinson’s disease symptoms: A systematic review and meta analyses. ACCESS, 13, 84240-84258. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms that significantly impact quality of life. Effective symptom management requires continuous monitoring and timely interventions, which traditional clinical assessments often fail to provide. Wearable devices have emerged as promising solutions, offering real-time, objective data collection and personalized interventions. This systematic review and meta-analysis evaluate the efficacy of wearable devices in monitoring and treating PD symptoms. A comprehensive search of PubMed, ScienceDirect, IEEE, Scopus, and Web of Science was conducted following PRISMA guidelines. Studies assessing wearable devices for PD symptom tracking or therapeutic intervention were included. The meta-analysis quantified the accuracy of symptom detection and the effectiveness of wearable-assisted interventions in improving clinical outcomes. Results indicate that wearable devices demonstrate high sensitivity and specificity in tracking motor fluctuations, gait abnormalities, and tremors. Wearable-based interventions, such as cueing and feedback mechanisms, have shown promise in enhancing motor control, reducing bradykinesia, and improving medication adherence. However, challenges such as device adherence, user-friendliness, and data integration into clinical workflows remain barriers to widespread adoption. Wearable technology represents a valuable tool for enhancing PD management through continuous monitoring and personalized interventions. While evidence supports wearables’ potential to improve outcomes, further research is needed to validate long-term efficacy, optimize usability, and assess cost-effectiveness to ensure broader clinical adoption.}, archive = {J_ACCESS}, author = {Mercedes A. Terry and Enrique Alvarez Vazquez}, doi = {10.1109/ACCESS.2025.3569056}, journal = {IEEE Access}, pages = {84240-84258}, shortjournal = {IEEE Access}, title = {Efficacy of wearable devices in monitoring and treating parkinson’s disease symptoms: A systematic review and meta analyses}, volume = {13}, year = {2025}, }
• (2025). Integration of deep learning architectures with GRU for automated leukemia detection in peripheral blood smear images. ACCESS, 13, 84217-84239. (www)

  @article{ , abstract = {This paper investigates the integration of deep learning (DL) architectures with Recurrent Neural Networks (RNNs) for automated leukemia detection in Peripheral Blood Smear (PBS) images. Models such as DenseNet201, EfficientNetB3, Inception v3, InceptionResNetV2, MobileNetV2, ResNet50, VGG16, and Xception, each enhanced with a Gated Recurrent Unit (GRU) layer, are employed to capture spatial and temporal dependencies in image data. Through extensive experimentation on a standard leukemia dataset, the performance of each model is evaluated based on accuracy and computational efficiency. Among these, the Xception + GRU model achieves the highest classification accuracy, attaining an impressive 99.69%. This exceptional result underscores the efficacy of combining Convolutional Neural Networks (CNNs) with RNNs, particularly GRUs, in accurately detecting Leukemia from PBS images. The findings offer valuable contributions to medical image analysis, demonstrating the potential of DL techniques to enhance automated disease diagnosis. By advancing the precision of leukemia detection, this study provides promising implications for improving patient care and treatment outcomes.}, archive = {J_ACCESS}, author = {Amit Kumar Bairwa and Anita Shrotriya and Priyanka Mathur and Sandeep Joshi and Sakshi Shringi and Ambika Kumari}, doi = {10.1109/ACCESS.2025.3565546}, journal = {IEEE Access}, pages = {84217-84239}, shortjournal = {IEEE Access}, title = {Integration of deep learning architectures with GRU for automated leukemia detection in peripheral blood smear images}, volume = {13}, year = {2025}, }
• (2025). Heterogeneous multi-agent task planning method in complex marine environment. ACCESS, 13, 84202-84216. (www)

  @article{ , abstract = {To enable collaborative scouting / strike / assessment of underwater time-sensitive targets by heterogeneous multi-agent systems, in this study a heterogeneous multi-agent collaborative decision-making method is proposed based on deep reinforcement learning. The method integrates two core learning frameworks one for heterogeneous multi-agent task allocation and one for single-agent multi-task learning. The task allocation framework combines the proximal policy optimization algorithm with experience replay to train an Actor network, ensuring stable iterative updates of task allocation policies toward high-reward directions. Concurrently, a Critic network is trained using successor features and temporal difference methods, establishing a foundation for transfer learning. A region partitioning mechanism is introduced to construct a foundational knowledge base, enabling the transfer of sub-region knowledge acquired by multi-agents to target regions, thereby addressing complex underwater task allocation scenarios. The single-agent multi-task learning framework employs an experience replay pool with knowledge transfer attributes and policy distillation technology, allowing each agent to assimilate task-specific expertise from heterogeneous peers across diverse mission scenarios. This capability supports multi-task execution, including path planning, emergency obstacle avoidance, and trajectory tracking.}, archive = {J_ACCESS}, author = {Shoumin Wang and Ning Niu and Zhichao Wang and Yaxuan Lv and Jing Zhang}, doi = {10.1109/ACCESS.2025.3567503}, journal = {IEEE Access}, pages = {84202-84216}, shortjournal = {IEEE Access}, title = {Heterogeneous multi-agent task planning method in complex marine environment}, volume = {13}, year = {2025}, }
• (2025). AdaPDTW: An efficient abstract-adaptive piecewise dynamic time warping for time series classification. ACCESS, 13, 84188-84201. (www)

  @article{ , abstract = {Dynamic Time Warping (DTW) offers precise similarity measure but suffers from high computational cost. To address this issue, we propose an abstract-adaptive PAR-DTW, which computes DTW in a low-dimensional piecewise abstract representation (PAR) space. Unlike existing methods that use globally uniform pointwise measures, our approach clusters abstracts and learns adaptive pointwise measures for each cluster pair. We formulate a joint objective function that combines global classification loss with local clustering loss, and introduce an efficient optimization method based on closed-form solutions. This enhances local adaptability while significantly reducing computational complexity. Experimental results demonstrate that our method achieves precision comparable to DTW and significantly outperforms existing PAR-DTW methods, offering up to four orders of magnitude in speedup.}, archive = {J_ACCESS}, author = {Qinglin Cai and Leiying Chen and Jian Shao and Ling Chen}, doi = {10.1109/ACCESS.2025.3568453}, journal = {IEEE Access}, pages = {84188-84201}, shortjournal = {IEEE Access}, title = {AdaPDTW: An efficient abstract-adaptive piecewise dynamic time warping for time series classification}, volume = {13}, year = {2025}, }
• (2025). Optimization and integration strategies for hybrid renewable energy systems in the brazilian power grid: A systematic review. ACCESS, 13, 84170-84187. (www)

  @article{ , abstract = {The global transition to renewable energy requires efficient Hybrid Renewable Energy Systems (HRES) to mitigate the intermittency of solar and wind power. This paper presents a review of HRES design and optimization methodologies, emphasizing their integration into the national power grid. We analyze optimization techniques, including metaheuristic algorithms and simulation tools such as HOMER, in addition to regulatory and operational challenges. A systematic literature review was conducted using Scopus, applying bibliometric analysis to identify trends in HRES research. Our findings highlight critical gaps in optimization strategies, energy storage integration, and economic feasibility analysis. This study provides insights for researchers and policymakers to enhance the reliability and efficiency of HRES in large-scale grid applications.}, archive = {J_ACCESS}, author = {Dalton F. Guedes Filho and Rodrigo L. Miranda and Lucas A. Correia and Lucas do E. S. Fernandes and Ricardo C. Medrado and Marcio De C. Filho and José B. M. Filho and Manoel H. N. Marinho and Erick G. Sperandio Nascimento}, doi = {10.1109/ACCESS.2025.3569199}, journal = {IEEE Access}, pages = {84170-84187}, shortjournal = {IEEE Access}, title = {Optimization and integration strategies for hybrid renewable energy systems in the brazilian power grid: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Efficient ransomware detection in resource-constrained environments using optimized multi-layer perceptron networks. ACCESS, 13, 84153-84169. (www)

  @article{ , abstract = {Ransomware attacks represent a significant cybersecurity threat, particularly in resource-constrained environments such as the Internet of Things (IoT) and edge computing systems. Traditional detection methods face considerable challenges in these environments due to limited computational resources, which can hinder effective detection and response. To address these challenges, an optimized Multi-Layer Perceptron (MLP) model is proposed for real-time ransomware detection. The model is evaluated on two datasets: the RansomwareData dataset and the Ransomware_headers dataset. It achieves 97% accuracy and a 97% F1-score on the RansomwareData dataset. On the Ransomware_headers dataset, it reaches 96% accuracy and a 96% F1-score. Furthermore, the model demonstrates minimal resource consumption, using only 5.37% of CPU and 7.2 MB of memory, making it suitable for deployment in systems with limited resources. The proposed solution offers a reliable and efficient approach to ransomware detection. Future research will focus on enhancing the model’s robustness against adversarial attacks, exploring ensemble learning techniques, and integrating lightweight, graph-based models.}, archive = {J_ACCESS}, author = {Tuxiang Lin and Rongliang Lin}, doi = {10.1109/ACCESS.2025.3569184}, journal = {IEEE Access}, pages = {84153-84169}, shortjournal = {IEEE Access}, title = {Efficient ransomware detection in resource-constrained environments using optimized multi-layer perceptron networks}, volume = {13}, year = {2025}, }
• (2025). A multi-objective optimization approach for integrated allocation of distributed generation and protection devices in distribution systems. ACCESS, 13, 84139-84152. (www)

  @article{ , abstract = {This paper presents a methodology for the optimal and simultaneous allocation of Distributed Generation (DG), Control and Protection Devices (CPDs) in unbalanced electric power distribution systems. In the proposed approach, the allocation of DGs reduces system losses during operation and enables islanded operation under fault conditions. The network protection system is updated by installing new CPDs and relocating the existing ones. The problem formulation considers overcurrent relays, reclosers, fuses, automatic sectionalizing switches, and the islanding interconnection device. The mathematical model presents three objective functions: investment costs for the acquisition, installation, and maintenance of CPDs and DGs, costs of energy not supplied, and active power losses, considering permanent and temporary faults, as well as load growth over the planning horizon. The model is reformulated as a single-objective optimization problem using weighted sums and solved through genetic algorithm to present a set of solutions from which the grid planner can choose the one that best fits the company’s realities. The proposed methodology was validated using a real 135-bus system, and the results show that for a network in the planning phase, the integrated allocation reduced interruption costs by 55% and total costs by 8%, compared to projects that allocate DGs first and CPDs subsequently. Additionally, it is statistically demonstrated using the Pearson correlation coefficient that the three objective functions considered in this work are statistically related, and therefore, minimizing all three together has strong statistical relevance.}, archive = {J_ACCESS}, author = {Katiani Pereira and Juan M. Home-Ortiz and Edgar M. C. Franco}, doi = {10.1109/ACCESS.2025.3569431}, journal = {IEEE Access}, pages = {84139-84152}, shortjournal = {IEEE Access}, title = {A multi-objective optimization approach for integrated allocation of distributed generation and protection devices in distribution systems}, volume = {13}, year = {2025}, }
• (2025). Mitigating class confusion in class-incremental semantic segmentation. ACCESS, 13, 84130-84138. (www)

  @article{ , abstract = {Class-incremental semantic segmentation (CSS) aims to continuously classify every pixel in an image to its corresponding semantic class. Existing CSS models suffer from two challenges: catastrophic forgetting and background shift. Moreover, when new classes are introduced over time, they struggle to differentiate between new and old classes that are visually similar. We refer to this problem as class confusion. To address the above issues, we propose a novel CSS approach to mitigate class confusion, called MC-Seg. First, we introduce a graph attention decoder (GAD) to capture semantic relationships between embeddings, enabling clearer segmentation across classes. GAD uses joint embeddings as nodes in a graph, enabling visual embeddings to incorporate class information. Furthermore, we propose similarity-aware class discrimination (SCD) to prevent old knowledge from being overwritten by new knowledge, particularly when the two are visually similar. Since visually similar classes are more likely to be confused with each other, SCD increases the distances between their embeddings to preserve essential distinctions. Moreover, to retain the knowledge of other, non-similar classes, we maintain the distances between their embeddings. Extensive experiments on PASCAL VOC 2012, ADE20K and PASCAL Context show that MC-Seg outperforms state-of-the-art CSS methods, especially in alleviating class confusion in incremental learning.}, archive = {J_ACCESS}, author = {Nayoung Ko and Gyeong-Hyeon Kim and Eunwoo Kim}, doi = {10.1109/ACCESS.2025.3569263}, journal = {IEEE Access}, pages = {84130-84138}, shortjournal = {IEEE Access}, title = {Mitigating class confusion in class-incremental semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Agora: A distributed language model framework with API-call support for integrated climate forecasting. ACCESS, 13, 84112-84129. (www)

  @article{ , abstract = {We introduce Agora, a Generative AI-driven system that delivers expert answers and recommendations on climate and agriculture, transforming complex data into clear, natural language explanations. While built for the rural domain, Agora is highly adaptable and can be deployed across various domain applications. It operates as a “mixture-of-experts” language model system, selectively utilizing multiple fine-tuned large language models for inference. By dynamically integrating external data through API calls, Agora ensures real-time, contextually relevant responses. Agora is built for extensibility—it seamlessly integrates new APIs and domains without requiring a full system retrain. Developed entirely with open-source large language models from the LLaMA family, Agora remains open and adaptable, allowing anyone to extend and enhance its capabilities. Optimized for accessibility, Agora runs efficiently on commodity GPUs without compromising performance. By eliminating the need for expensive hardware like NVIDIA’s A100, it makes text generation more affordable and widely accessible. Agora outperforms closed-source models, achieving 78% accuracy on our question-answering benchmark. This result is achieved via dynamic API integration, which pulls in real-time external data, making responses more adaptive, precise, and context-aware.}, archive = {J_ACCESS}, author = {Alexandra Udrescu and Dan-Matei Popovici}, doi = {10.1109/ACCESS.2025.3568028}, journal = {IEEE Access}, pages = {84112-84129}, shortjournal = {IEEE Access}, title = {Agora: A distributed language model framework with API-call support for integrated climate forecasting}, volume = {13}, year = {2025}, }
• (2025). A PSO-CNN-LSTM model for seismic facies analysis: Methodology and applications. ACCESS, 13, 84094-84111. (www)

  @article{ , abstract = {Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. To address the limitations of conventional manual interpretation methods, particularly their low efficiency and strong subjectivity, this study proposes a hybrid CNN-LSTM model integrated with Particle Swarm Optimization (PSO-CNN-LSTM). The model systematically extracts spatial features of seismic reflections through CNN architecture while capturing temporal waveform dependencies via LSTM networks, with PSO automatically optimizing critical parameters including initial learning rate and LSTM neuron count. Experimental results demonstrate that PSO-CNN-LSTM achieves a classification accuracy of 89.74%, surpassing CNN (81.48%), LSTM (81.63%), and basic CNN-LSTM (84.61%) models by 8.26%, 8.11%, and 5.13% respectively. The model exhibits superior performance on the SEG 2020 benchmark dataset, confirming that automated parameter optimization effectively reduces manual intervention while enhancing convergence stability. Practical applications reveal consistent interpretation outcomes between the model’s predictions (using limited training samples) and expert analyses, providing reliable evidence for identifying favorable zones in heterogeneous carbonate reservoirs. The established intelligent waveform classification workflow validates PSO-CNN-LSTM model’s robustness and offers an efficient solution for seismic facies analysis, particularly in complex geological settings.}, archive = {J_ACCESS}, author = {Luyao Liao and Huailai Zhou and Junping Liu and Jie Zhou and Donghang Zhang and Jian Wang}, doi = {10.1109/ACCESS.2025.3566005}, journal = {IEEE Access}, pages = {84094-84111}, shortjournal = {IEEE Access}, title = {A PSO-CNN-LSTM model for seismic facies analysis: Methodology and applications}, volume = {13}, year = {2025}, }
• (2025). Crust: A modular framework for conflict-free replicated data types (CRDTs) development, validation, and benchmarking. ACCESS, 13, 84081-84093. (www)

  @article{ , abstract = {Conflict-free Replicated Data Types (CRDTs) are vital for achieving strong eventual consistency in distributed systems, but their development and evaluation face significant challenges by inadequate tooling. While CRDT research focus on algorithms and applications, critical gaps persist in validation and performance benchmarking. To address this, a novel Rust-based framework “Crust” designed to offer a modular, configurable, and extensible platform for developing, validating, and benchmarking CRDT implementations. Crust includes Core, Config, Network, Validation, and Benchmark moduels, supporting various synchronization methods and emphasizing correctness and performance analysis. This paper details Crust’s design and theoretical basis, showing how it bridges the gap between CRDT theory and practice, addressing tooling gaps to enhance adoption and real-world use. By enabling rigorous testing and performance evaluation, Crust has the potential to accelerate the development and adoption of CRDTs in real-world distributed systems.}, archive = {J_ACCESS}, author = {Yunrui Zhu and Jing Ma}, doi = {10.1109/ACCESS.2025.3568925}, journal = {IEEE Access}, pages = {84081-84093}, shortjournal = {IEEE Access}, title = {Crust: A modular framework for conflict-free replicated data types (CRDTs) development, validation, and benchmarking}, volume = {13}, year = {2025}, }
• (2025). Exploring research and tools in AI security: A systematic mapping study. ACCESS, 13, 84057-84080. (www)

  @article{ , abstract = {With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the pressing need for a structured and comprehensive overview of the current research landscape, enabling researchers to address emerging threats and vulnerabilities effectively. This paper presents a systematic mapping study (SMS), aimed at identifying and classifying the prevailing research topics, tools, and frameworks in the field of AI security. A total of 123 studies were meticulously selected and analyzed, leading to the identification of key metrics, tools, standards, and research themes that are currently shaping the landscape of AI security research. This effort not only aids in distilling the collective wisdom of the research community but also sets a firm foundation for future work in this critical area. The findings from this SMS will serve as an invaluable guide for researchers and practitioners alike, enabling them to navigate the complexities of AI security and fostering the development of innovative, robust security solutions. This study also highlights significant gaps in the current literature, thereby outlining potential directions for new research initiatives.}, archive = {J_ACCESS}, author = {Sidhant Narula and Mohammad Ghasemigol and Javier Carnerero-Cano and Amanda Minnich and Emil Lupu and Daniel Takabi}, doi = {10.1109/ACCESS.2025.3567195}, journal = {IEEE Access}, pages = {84057-84080}, shortjournal = {IEEE Access}, title = {Exploring research and tools in AI security: A systematic mapping study}, volume = {13}, year = {2025}, }
• (2025). MapIO: A gestural and conversational interface for tactile maps. ACCESS, 13, 84038-84056. (www)

  @article{ , abstract = {For individuals who are blind or have low vision, tactile maps provide essential spatial information but are limited in the amount of data they can convey. Digitally augmented tactile maps enhance these capabilities with audio feedback, thereby combining the tactile feedback provided by the map with an audio description of the touched elements. In this context, we explore an embodied interaction paradigm to augment tactile maps with conversational interaction based on Large Language Models, thus enabling users to obtain answers to arbitrary questions regarding the map. We analyze the types of questions the users are interested in asking, engineer the Large Language Model’s prompt to provide reliable answers, and study the resulting system with a set of 10 participants, evaluating how the users interact with the system, its usability, and user experience.}, archive = {J_ACCESS}, author = {Matteo Manzoni and Sergio Mascetti and Dragan Ahmetovic and Ryan Crabb and James M. Coughlan}, doi = {10.1109/ACCESS.2025.3566286}, journal = {IEEE Access}, pages = {84038-84056}, shortjournal = {IEEE Access}, title = {MapIO: A gestural and conversational interface for tactile maps}, volume = {13}, year = {2025}, }
• (2025). PneuX-net: An enhanced feature extraction and transformation approach for pneumonia detection in X-ray images. ACCESS, 13, 84024-84037. (www)

  @article{ , abstract = {Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework.}, archive = {J_ACCESS}, author = {Kashif Munir and Muhammad Usama Tanveer and Hasan J. Alyamani and Amine Bermak and Atiq Ur Rehman}, doi = {10.1109/ACCESS.2025.3568885}, journal = {IEEE Access}, pages = {84024-84037}, shortjournal = {IEEE Access}, title = {PneuX-net: An enhanced feature extraction and transformation approach for pneumonia detection in X-ray images}, volume = {13}, year = {2025}, }
• (2025). Magnetization losses of transposed stacked tape conductors under AC transverse magnetic fields. ACCESS, 13, 84013-84023. (www)

  @article{ , abstract = {Twisted stacked-tape conductors (TSTC) have emerged as promising candidates for next-generation high-temperature superconducting (HTS) cables, addressing the challenges of high-current and low-loss operation in high-field applications such as fusion magnets and power systems. This paper presents a rigorous electromagnetic analysis of TSTC and STC cables, integrating advanced numerical modeling with experimental validation to address critical challenges in the physical understanding of their superconducting current distribution and AC loss prediction. For this, a magneto-angular anisotropy model tailored to GdBCO tapes is reported, which accurately captures the $J_{c}$ dependencies on field magnitude and orientation when adequate ansatz for the 3D modelling of the twisted tapes geometry, and no constant in-field direction conditions, are both considered. We validate the model against recent experimental measurements on eight TSTC prototypes, comprising straight and twisted stacks of up to six tapes under sinusoidal magnetic fields (15–100 mT). The simulations achieve deviations of less than 5%, demonstrating exceptional fidelity in predicting magnetization losses and resolving current density distributions across tape thickness. The results highlight the critical role of twisting geometries in reducing magnetization losses while maintaining current-carrying performance, demonstrating the potential of TSTC designs for scalable, low-loss superconducting cables. Furthermore, the developed model provides a robust tool for optimizing conductor architecture, extending from single tapes to multi-tape architectures, and provides critical physical and computational insights into the interplay between geometric design and energy loss mechanisms. These findings establish a robust foundation for optimizing TSTC cables and similar HTS architectures, like the VIPER conductor, advancing their readiness for deployment in fusion energy, power transmission, and large-scale scientific instruments.}, archive = {J_ACCESS}, author = {Matthew Clegg and Hasan N. H. Al-Ssalih and Harold S. Ruiz}, doi = {10.1109/ACCESS.2025.3566645}, journal = {IEEE Access}, pages = {84013-84023}, shortjournal = {IEEE Access}, title = {Magnetization losses of transposed stacked tape conductors under AC transverse magnetic fields}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of single and multi-view deep learning for cybersecurity anomaly detection. ACCESS, 13, 83996-84012. (www)

  @article{ , abstract = {This paper investigates the application of Deep Multi-View Learning (DMVL) to enhance the responsiveness of intrusion detection systems (IDS) in modern network environments, addressing the limitations of traditional IDS. The study used diverse datasets, including TON_IoT and UNSW-NB15, to evaluate anomaly detection capabilities across various host and network scenarios, with a particular emphasis on dataset diversity, single model diversity, and multi-view models diversity. The experiment found that multi-view models based on Autoencoder (AE) and Convolutional Neural Network (CNN) performed better in general than the corresponding single view model in detection anomaly. However, certain single view models outperformed multi-view models like Deep Generalized Canonical Correlation Analysis (DGCCA) in specific cases, This result showed that although multi-view methods generally had stronger comprehensive analysis capabilities, they were not the best choice in all scenarios. The effectiveness of multi-view methods depended largely on the specific model design and data characteristics. Further investigations comparing multi-view models, such as multi-view AE, CNN, and DGCCA, showed that the multi-view CNN model not only achieved the highest F1 score on the TON_IoT dataset, but also demonstrated strong robustness on the UNSW-NB15 dataset. These findings confirmed the effectiveness of multi-view methods in integrating complementary data in host and enterprise network scenarios based on network environments.}, archive = {J_ACCESS}, author = {Min Li and Yuansong Qiao and Brian Lee}, doi = {10.1109/ACCESS.2025.3564066}, journal = {IEEE Access}, pages = {83996-84012}, shortjournal = {IEEE Access}, title = {A comparative analysis of single and multi-view deep learning for cybersecurity anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Development and validation of an affordable wearable multichannel EEG system with active electrodes and innovative flexible headbands. ACCESS, 13, 83983-83995. (www)

  @article{ , abstract = {The growing interest in electroencephalography (EEG) research has highlighted the need for low-cost, wearable EEG acquisition systems. The high cost of commercially available EEG equipment poses a significant challenge to the widespread adoption of these systems and, consequently, to the advancement of potential real-world applications. This paper presents the design of a low-cost, wearable, 4-channel EEG acquisition system. The solution is based on the ADS1299, a 24-bit analog front-end optimized for biomedical signal acquisition. Control and Bluetooth Low Energy (BLE) communication are handled by a CC2650 BLE-enabled microcontroller. Active wet electrodes are employed, each fitted with a preamplification board. The electrodes are affixed to elastic bands using 3D-printed parts designed to facilitate rapid and straightforward electrode placement. An additional reference electrode is placed in an ear-clip. The headbands allow for the precise positioning of the electrodes at various strategic points on the scalp, adhering to the 10-10 system, thus making it adaptable to a wide range of applications. To evaluate the viability of our design, EEG signals from ten healthy subjects were registered and compared to commercial equipment. Our design demonstrated signal quality comparable to reference EEG equipment, while drastically reducing the cost and enhancing usability for practical applications.}, archive = {J_ACCESS}, author = {Daniel Antolínez and Ana Cisnal and Víctor Martínez-Cagigal and Eduardo Santamaría-Vázquez and Diego Benavides and Juan Granja and Juan Carlos Fraile and Roberto Hornero}, doi = {10.1109/ACCESS.2025.3568302}, journal = {IEEE Access}, pages = {83983-83995}, shortjournal = {IEEE Access}, title = {Development and validation of an affordable wearable multichannel EEG system with active electrodes and innovative flexible headbands}, volume = {13}, year = {2025}, }
• (2025). Modified particle swarm optimization for engineering optimization problems and UAV path planning. ACCESS, 13, 83956-83982. (www)

  @article{ , abstract = {Optimization problems aim to identify the best solution from a wide range of possibilities. The Particle Swarm Optimization (PSO) algorithm is widely recognized for its simplicity and efficiency, but it suffers from issues such as local optima trapping and degraded performance in high-dimensional problems. To address these limitations, this paper proposes a modified PSO algorithm (MPSO). The MPSO incorporates several novel strategies: a Sigmoid-based nonlinear inertia weight decay function, which supports global exploration in the early stages and local refinement in later stages; a non-uniform mutation operator, which amplifies perturbation to enhance global search in the early phases and reduces perturbation to guide convergence in the later phases; and a sine-cosine disturbance strategy, which boosts solution diversity and accelerates global optimization and convergence. Compared to eight PSO variants, MPSO demonstrates superior performance across various benchmark functions in the CEC2017 and CEC2022 suites, particularly excelling in high-dimensional problems. Most of the test functions show better results than the competing algorithms. Finally, MPSO is applied to five engineering optimization problems and UAV path planning tasks, where the experimental results confirm its effectiveness in real-world applications. The algorithm consistently finds high-quality solutions, highlighting its exceptional performance and practical value in solving complex optimization challenges.}, archive = {J_ACCESS}, author = {Xing Zhang and Gaoquan Gu and Cunsheng Zhao}, doi = {10.1109/ACCESS.2025.3560624}, journal = {IEEE Access}, pages = {83956-83982}, shortjournal = {IEEE Access}, title = {Modified particle swarm optimization for engineering optimization problems and UAV path planning}, volume = {13}, year = {2025}, }
• (2025). Improved YOLOv8 algorithm was used to segment cucumber seedlings under complex artificial light conditions. ACCESS, 13, 83944-83955. (www)

  @article{ , abstract = {With the widespread application and rapid proliferation of agricultural solutions in plant factories, “intelligent environmental control technology” has become an important research topic as one of the core functions of plant factories. Accurately obtaining the growth status of plants is the core of intelligent control technology and has been capturing more and more researchers’ attention. In the study of detecting the growth status of cucumber plants, the accurate segmentation of cucumber leaves is crucial. However, under fully artificial lighting conditions in plant factories, the complexity of light is the main challenge for leaf segmentation. Aiming at the challenging problem of cucumber seedling leaf segmentation under a complex artificial lighting environment, this study proposes an improved complex lighting YOLOv8 (CL-YOLOv8) model based on YOLOv8. First, the C2f module in the YOLOv8 backbone network is optimized by replacing the bottleneck with the multi-scale feature module (MSFM) we designed. The convolution kernels of different sizes are employed to capture features at multiple scales in the image, thereby processing target and background information more effectively. Simultaneously, the Efficient Multi-Scale Attention (EMA) mechanism is introduced after MSFM, enabling the model to flexibly adjust the attention allocated to different regions within the image, thus enhancing its robustness to complex lighting variations. Finally, the efficient sub-pixel upsampling block (ESPUB) is designed to improve image resolution without compromising computational efficiency, thereby enhancing the model’s adaptability. The verification results demonstrate that the improved CL-YOLOv8 model, based on the YOLOv8 network, enhances the mAP@50 and mAP@50-95 metrics by 2.4% and 4.1%, respectively, achieving real-time and accurate segmentation of cucumber leaves. The effectiveness and superiority of this approach are validated through comparisons with other widely used segmentation detection algorithms.}, archive = {J_ACCESS}, author = {Duokuo Zhang and Na Li and Mingfu Zhao and Kun Xu}, doi = {10.1109/ACCESS.2025.3568847}, journal = {IEEE Access}, pages = {83944-83955}, shortjournal = {IEEE Access}, title = {Improved YOLOv8 algorithm was used to segment cucumber seedlings under complex artificial light conditions}, volume = {13}, year = {2025}, }
• (2025). Multi-sensor model for earth observation robust to missing data via sensor dropout and mutual distillation. ACCESS, 13, 83930-83943. (www)

  @article{ , abstract = {Multi-sensor data has become a foundation of Earth Observation (EO) research, offering models with enhanced accuracy via optimal fusion strategies. However, the unavailability of sensor data at the regional or country scale during inference can significantly undermine model performance. The literature explores diverse approaches to increasing model robustness to missing sensor scenarios, i.e., to reducing the decline in accuracy caused by missing data at inference time. Nevertheless, most of them have suboptimal behavior when a single-sensor is available for prediction. To address this challenge, we propose a novel method for multi-sensor modeling, Decision-level Sensor Dropout with mutual distillation (DSensD+). This employs a decision-level fusion, ignoring predictions from missing sensors and incorporating the Sensor Dropout (SensD) technique. Unlike works that use the SensD at the input or feature level, we use it at the decision level. Moreover, we include a mutual distillation strategy to improve the robustness. From a practical viewpoint, the additional components in the DSensD+ method are incorporated only for the training phase. During inference, it operates as a standard decision-level fusion model that ignores missing sensors. We validate our method on three EO datasets, spanning binary, multi-class, and multi-label classification tasks for crop- and tree-mapping related applications. Notably, DSensD+ outperforms several state-of-the-art methods, achieving consistent improvements across moderate (single-sensor missing) and extreme (single-sensor available) conditions, as well as with full-sensor data. These results demonstrate the robustness of DSensD+ and highlight the effectiveness of our method for the missing sensor problem, advancing the field of multi-sensor modeling in EO.}, archive = {J_ACCESS}, author = {Francisco Mena and Dino Ienco and Cássio F. Dantas and Roberto Interdonato and Andreas Dengel}, doi = {10.1109/ACCESS.2025.3568706}, journal = {IEEE Access}, pages = {83930-83943}, shortjournal = {IEEE Access}, title = {Multi-sensor model for earth observation robust to missing data via sensor dropout and mutual distillation}, volume = {13}, year = {2025}, }
• (2025). Economic analysis of off-grid energy projects: A FINPLAN model approach. ACCESS, 13, 83916-83929. (www)

  @article{ , abstract = {Off-grid energy projects particularly solar mini-grids, play a crucial role in electrifying remote areas with limited access to centralized grids. This paper presents an economic assessment of a 20.46kWp solar mini-grid project using the model for financial analysis of electric sector expansion plans (FINPLAN) model, a financial planning tool used in energy project financial evaluation. The study aims to analyse the financial viability, risks, and economic benefits of off-grid solar solutions. Key indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), Break-even Point (BEP), and Exchange Risk and Debt Service Coverage Ratio (DSCR) are evaluated to determine the project viability and sustainability. The FINPLAN model approach allows detailed cash flow analysis and risk assessments under various scenarios of energy demand, capital expenditure, operational costs, and subsidy frameworks. The case study of a 20.46kWp Solar PV-Battery Energy Storage System (BESS) project highlights the impact of key financial parameters, such as interest rates and inflation, on project returns. The study reveals that an additional equity investment of $=\!\!\!\!\!\!{\text{N}}$ 189.06 million is needed for procurement in 2025 and early operational costs in 2026, with a positive Net Present Value of $=\!\!\!\!\!\!{\text{N}}$ 15497.33 and an equity Internal Rate of Return (IRR) of 27.66%. The financial ratios show a Working Capital Ratio (WCR) of 1.35-4.0, exchange risk of 0.1-15, break-even point value >0.8 between 2026 and 2036, and a Debt Service Coverage Ratio of 0.3-1.2 between 2026 and 2036. The Best-Case scenario results showed a more balanced case with no flow from the stand-by facility, a positive NPV of $=\!\!\!\!\!\!{\text{N}}$ 17,805.71, and an Equity IRR of 26.96%. The paper recommends the adoption of the FINPLAN tool for appraising off-grid energy projects and power infrastructure expansions.}, archive = {J_ACCESS}, author = {Benneth C. Oyinna and Kenneth E. Okedu and Gauri Kalnoor and Leo Raju and V. B. Murali Krishna and Ilhami Colak}, doi = {10.1109/ACCESS.2025.3561631}, journal = {IEEE Access}, pages = {83916-83929}, shortjournal = {IEEE Access}, title = {Economic analysis of off-grid energy projects: A FINPLAN model approach}, volume = {13}, year = {2025}, }
• (2025). McGANs: Topology optimization using multi-stage CGAN. ACCESS, 13, 83905-83915. (www)

  @article{ , abstract = {The traditional methods of solving topology optimization depends on finite element method (FEM). However, the iterative calculation of FEM increases the time cost of topology optimization significantly. In this paper, a novel strategy based on deep learning is proposed to speed up the topology optimization process. Conditional Generative Adversarial Network (CGAN) is used as the basic network in which the constraints of optimization are taken into consideration as the conditions of generation. Additionally, the stress and strain are generated from initial physical information by networks, and used as the inputs of optimization network. The cascaded network can realize structural topology optimization without iterative calculations in the whole process. In the experiments, the traditional Solid Isotropic Material with Penalization (SIMP) method was replicated and implemented multiple times. It was found that the SIMP method takes 30 - 40 seconds to conduct topology optimization, while the proposed mcGANs only need 1.7682 seconds. Moreover, performance indicators such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) are used to evaluate the performance of mcGANs, which demonstrate the effectiveness and superiority of this strategy.}, archive = {J_ACCESS}, author = {Jiandong Zhu and Ting Ding and Fuhao Cui and Zhi Wang}, doi = {10.1109/ACCESS.2025.3569404}, journal = {IEEE Access}, pages = {83905-83915}, shortjournal = {IEEE Access}, title = {McGANs: Topology optimization using multi-stage CGAN}, volume = {13}, year = {2025}, }
• (2025). A novel compensated PID controller ensuring lyapunov stability for highly uncertain, nonaffine, and time-varying nonlinear systems. ACCESS, 13, 83897-83904. (www)

  @article{ , abstract = {This paper proposes a novel compensated PID controller for a class of strongly uncertain, time-varying nonlinear systems with nonaffine control input structures. The main objective is to ensure Lyapunov stability of the closed-loop system, which consists of a classical PID controller augmented with a compensating term and an unknown nonlinear plant subject to significant unstructured uncertainties. The proposed method integrates a conventional PID control structure with an additional compensating control signal designed to mitigate the effects of unknown nonlinearities. To address the challenge of unmeasurable derivative signals, a higher-order serial differentiator with asymptotic convergence properties is utilized to estimate the time derivatives of the output tracking error. A systematic approach for selecting PID gain parameters is presented to ensure system stability. Numerical simulations are conducted to verify the effectiveness and robustness of the proposed controller, demonstrating its practical applicability.}, archive = {J_ACCESS}, author = {Jang-Hyun Park and Dong-Ho Lee}, doi = {10.1109/ACCESS.2025.3568438}, journal = {IEEE Access}, pages = {83897-83904}, shortjournal = {IEEE Access}, title = {A novel compensated PID controller ensuring lyapunov stability for highly uncertain, nonaffine, and time-varying nonlinear systems}, volume = {13}, year = {2025}, }
• (2025). Assessing dynamic behaviors in converter- dominated power systems via RMS and EMT simulations: A study of hawaii’s NELHA microgrid. ACCESS, 13, 83881-83896. (www)

  @article{ , abstract = {The transition from conventional power systems to converter-based microgrids has significantly advanced sustainability, clean energy integration, and operational reliability. However, this paradigm shift introduces operational challenges due to the intermittent nature of renewable energy sources and the non-linear characteristics of power electronic loads, inducing voltage fluctuations and harmonic distortions that complicate voltage and frequency regulation. Accurate dynamic modeling is hypothesized to be critical for capturing such effects, enabling reliable simulation and control strategy development. This study introduces an innovative dynamic modeling framework for a real-world converter-based microgrid, utilizing both root mean square (RMS) and electromagnetic transient (EMT) simulation methods. The microgrid was modeled in DIgSILENT PowerFactory, with simulations calibrated against high-resolution field measurements from SEL-735 power quality meters. Results show that RMS simulations effectively characterize steady-state dynamics, while EMT simulations are essential for capturing high-frequency transients and non-linear effects from photovoltaic inverters and variable frequency drives (VFDs). This complementary approach provides a comprehensive understanding of microgrid behavior, providing critical insights for improving simulation accuracy, advancing protection schemes, and improving resilience in future low-inertia power networks.}, archive = {J_ACCESS}, author = {Abrar Shahriar Pramanik and Saeed Sepasi and Brian Griswold and Adewale Adegboyega}, doi = {10.1109/ACCESS.2025.3568782}, journal = {IEEE Access}, pages = {83881-83896}, shortjournal = {IEEE Access}, title = {Assessing dynamic behaviors in converter- dominated power systems via RMS and EMT simulations: A study of hawaii’s NELHA microgrid}, volume = {13}, year = {2025}, }
• (2025). Stability analysis of distribution networks with high penetration of distributed energy resources. ACCESS, 13, 83867-83880. (www)

  @article{ , abstract = {Increasing penetration of distributed energy resources (DER) has already resulted in several operational challenges in the power system. While literature focuses on stability challenges to the bulk power system due to high penetration of inverter-based resources (IBR), the challenges from a distribution system becoming unstable due to high DER penetration are not sufficiently addressed. Increasing DER penetration also creates opportunities for a local resource to improve stability in the distribution network without fully relying on support from the transmission system. This work utilizes real-world distribution feeders to first analyze stability challenges due to high-DER penetration and evaluates solutions from GFM-based local resources in a distribution network. This paper then proposes a new screening or system strength index that is based on the equal area method and utilizes only steady-state information in evaluating relative stability of DER interconnections in a large distribution area. The proposed index is further utilized in identifying distribution system strength improvement with advanced inverter controls.}, archive = {J_ACCESS}, author = {Ashwin Venkataramanan and Wenzong Wang and Deepak Ramasubramanian and Aminul Huque and Ali Mehrizi-Sani}, doi = {10.1109/ACCESS.2025.3566476}, journal = {IEEE Access}, pages = {83867-83880}, shortjournal = {IEEE Access}, title = {Stability analysis of distribution networks with high penetration of distributed energy resources}, volume = {13}, year = {2025}, }
• (2025). Data-efficient approach to humanoid control by fine-tuning a pre-trained GPT on action data. ACCESS, 13, 83857-83866. (www)

  @article{ , abstract = {Recent advances in imitation learning have enabled robots to learn multiple tasks from large-scale datasets. However, developing a model for multi-tasking humanoid control faces significant challenges. Human kinematic data is available in open-source datasets for humanoid motion learning, but learning policies from this data requires simulation due to the lack of actions. While dynamic data can accelerate learning via supervision, datasets typically lack substantial amounts of such action labels, that are also difficult to be directly used for training due to the unique structure of each human/humanoid systems. In this study, we pre-trained a Generative Pre-trained Transformer (GPT) based model on expert policy-rollout observations only (without actions) from a humanoid motion dataset. Upon fine-tuning on a smaller dataset with both observations and action labels, we demonstrate that our GPT-based model can predict actions to achieve human-like movements faster in training than training a GPT on the entire dataset from scratch directly. Furthermore, performance evaluation based on motion generation across various behaviors showed that our approach achieves efficient learning comparable to baselines.}, archive = {J_ACCESS}, author = {Siddharth Padmanabhan and Kazuki Miyazawa and Takato Horii and Takayuki Nagai}, doi = {10.1109/ACCESS.2025.3568784}, journal = {IEEE Access}, pages = {83857-83866}, shortjournal = {IEEE Access}, title = {Data-efficient approach to humanoid control by fine-tuning a pre-trained GPT on action data}, volume = {13}, year = {2025}, }
• (2025). Advancing ECG signal classification with a fuzzy classifier approach. ACCESS, 13, 83840-83856. (www)

  @article{ , abstract = {The analysis of ECG signals plays an important role in healthcare, particularly for the detection of heart conditions such as arrhythmia, coronary artery disease, or heart attack. Accurate diagnosis often depends on the effective classification of ECG signals. Over the years, numerous methods and algorithms have been developed to classify ECG signals with reliable accuracy. These methods generally follow a two-stage process: signal preprocessing and classification. Preprocessing is focused on removing noise and extracting relevant features that are used in the classification stage. However, preprocessing can also lead to the loss of small but potentially important information, which may affect the performance of classification algorithms. In this paper, we present an approach for ECG signal classification that addresses the issue of information loss during preprocessing. The proposed method incorporates a fuzzy classifier in the classification stage, which is designed to handle the uncertainty introduced by the loss of information. The proposed approach is evaluated through experimental studies, which include comparisons with other classification approaches. The results show that the use of a fuzzy classifier can improve the accuracy of ECG signal classification, especially in cases where preprocessing leads to information loss. The findings suggest that fuzzy classifiers are suitable for ECG signal analysis.}, archive = {J_ACCESS}, author = {Jan Rabcan and Vitaly Levashenko and Elena Zaitseva and Miroslav Kvassay}, doi = {10.1109/ACCESS.2025.3568086}, journal = {IEEE Access}, pages = {83840-83856}, shortjournal = {IEEE Access}, title = {Advancing ECG signal classification with a fuzzy classifier approach}, volume = {13}, year = {2025}, }
• (2025). An efficient framework for multi-DER integration in distribution networks with time-series demand flexibility. ACCESS, 13, 83828-83839. (www)

  @article{ , abstract = {The increasing penetration of renewable energy sources (RESs) has transformed power system operations. However, balancing supply and demand is more challenging due to the inherent variability of RESs. This paper presents an efficient framework that integrates demand flexibility, RESs, and energy storage in distribution systems to enhance distribution system performance. The study implements a detailed time-series power flow analysis to investigate the impact of distributed energy resources (DERs) on system performance over a 24-hour period. The simulations incorporate a modified IEEE 123-bus network with two PV systems, flexible loads, and a 300 kW/1200 kWh battery. Additionally, the IEEE 8500-node distribution feeder integrates higher-rated PV, wind generators, and a 500 kW/2000 kWh battery to evaluate grid performance under diverse operational conditions. The battery storage system provides essential grid support through strategic charging during high PV generation and discharging during peak demand periods. The simulation results demonstrate robust voltage regulation and effective demand response throughout the feeder despite varying generation and load conditions. The flexible loads effectively respond to system conditions, varying between 23 to 82 kVA. This study demonstrates the viability of coordinated DER operations and their impact on modern distribution networks.}, archive = {J_ACCESS}, author = {Mohamed Massaoudi and Katherine R. Davis and Khandaker Akramul Haque}, doi = {10.1109/ACCESS.2025.3569152}, journal = {IEEE Access}, pages = {83828-83839}, shortjournal = {IEEE Access}, title = {An efficient framework for multi-DER integration in distribution networks with time-series demand flexibility}, volume = {13}, year = {2025}, }
• (2025). Fatigue and distracted driving recognition method based on multimodal information fusion. ACCESS, 13, 83815-83827. (www)

  @article{ , abstract = {Fatigue and distracted driving are two of the leading causes of major accidents. Drivers play a crucial role in automobile safety, and accurately detecting their driving states can significantly enhance the safety of urban road traffic and improve road operation efficiency. Currently, mainstream research relies on visual features, EEG (Electroencephalography), EOG(Electrooculography), and EMG (Electromyography) to identify driving status. However, these methods are not easily scalable due to the high cost of the data acquisition devices and the fact that physical contact with the body can interfere with normal driving functions. This study conducted a simulation experiment using a driving simulator and physiological wristbands, with a highway as the experimental scenario. Data on the physiological and psychological parameters, driving performance, and wrist movement information of 33 drivers were collected. Firstly, features were extracted through time, frequency domain, and nonlinear analyses, and non-parametric tests were performed to analyze the differences in these features under different driving states, thus selecting an effective feature subset. Secondly, based on this, an impaired driving state recognition model was established by combining feature recursive elimination and machine learning, and the impact of different modal inputs on the model performance was analyzed. Finally, SHAP (SHapley Additive exPlanations) interpretable machine learning was employed to analyze the model results in depth. The results indicate that the XGBoost model based on multimodal information input performs the best, with accuracy, precision, F1 score, and recall reaching 94.28%, 94.28%, 94%, and 94.27%, respectively, demonstrating its effectiveness in recognizing driving status. The mean X-axis angular velocity of wrist motion is a key feature for identifying a driver’s driving status. Additionally, the mean X-axis angular velocity, mean lane offset, and mean X-axis acceleration are positively correlated with the probability of fatigued driving, while the standard deviation of yaw rate is positively correlated with the likelihood of a distracted driving state. This study is the first to use a fusion of physiological data, driving performance, and wrist movement information to identify impaired driving states. This method takes into account various impaired driving states and provides a non-invasive, robust, and real-time approach, which has practical significance.}, archive = {J_ACCESS}, author = {Deyong Guan and Qi Wang and Ke Wang and Xinyu Song}, doi = {10.1109/ACCESS.2025.3568625}, journal = {IEEE Access}, pages = {83815-83827}, shortjournal = {IEEE Access}, title = {Fatigue and distracted driving recognition method based on multimodal information fusion}, volume = {13}, year = {2025}, }
• (2025). A novel differential exchange parallel great wall construction algorithm for efficient multilevel image segmentation. ACCESS, 13, 83794-83814. (www)

  @article{ , abstract = {The Great Wall Construction Algorithm (GWCA) is an innovative meta-heuristic algorithm for solving engineering challenges. Parallel strategies are an effective approach to enhancing the performance of algorithms. In this paper, parallel technology is introduced into GWCA by proposing a new communication strategy named Differential Exchange, which is combined with GWCA to design the Parallel Great Wall Construction Algorithm (PGWCA). The goal is to avoid local optima and improve the algorithm’s performance. The performance of the proposed PGWCA has been critically compared with seven other meta-heuristic algorithms on 10, 30, and 50 dimensions, respectively, using the CEC 2017 benchmark function suite. In addition, the PGWCA algorithm has been used to solve the multilevel image threshold segmentation problem by segmenting eight images and evaluating the segmentation results using three metrics: PSNR, SSIM, and FSIM. The results show that out of 29 test functions, PGWCA achieved the best (minimum) value 17, 20, and 16 times, respectively. In the image threshold segmentation experiments, the best performance was achieved 60 times out of all 72 evaluated comparisons. The experimental results show that PGWCA achieves better performance in identifying the optimal solution and improving the convergence performance, and has good performance in image segmentation. The code is available for download at the following link: https://drive.google.com/drive/folders/1I-5k-IzTP7cUvZqdo7YV-jOz1Zfka42j?usp=sharing}, archive = {J_ACCESS}, author = {Jeng-Shyang Pan and Rongli Zhang and Shu-Chuan Chu and Xiao Sui and Junzo Watada}, doi = {10.1109/ACCESS.2025.3567556}, journal = {IEEE Access}, pages = {83794-83814}, shortjournal = {IEEE Access}, title = {A novel differential exchange parallel great wall construction algorithm for efficient multilevel image segmentation}, volume = {13}, year = {2025}, }
• (2025). On the choice of system strength metrics for the allocation and sizing of synchronous condensers in power grids. ACCESS, 13, 83781-83793. (www)

  @article{ , abstract = {The integration of high levels of inverter-based generation (IBG) into power grids requires meticulous planning, especially as IBGs lack the inherent short-circuit capacity of conventional generators, potentially weakening system strength. To mitigate this, synchronous condensers (SCs) are deployed in weak areas to enhance fault current and voltage support. Traditional SC allocation methods use the short-circuit ratio (SCR) as a key metric, but this may not account for control interactions in grids with significant IBG penetration. This study compares two approaches for optimizing SC placement and sizing: SCR-based and network-response short-circuit ratio (NRSCR)-based, a metric that captures dynamic IBG interactions. Applying a genetic algorithm (GA) to the Nigerian power grid as a case study, the SCR-based approach identified two critical points of interconnection (PoIs) requiring 68 MVA of SC capacity, while the NRSCR-based method identified four PoIs, requiring a total of 674 MVA. Although the NRSCR-based method significantly improved system performance during faults—offering faster voltage recovery post-fault and higher fault current contributions—it resulted in a cost increase of approximately 7 times. This highlights the trade-off between cost and performance, underscoring the need for utilities to balance economic and technical considerations when deploying SCs in IBG-dominated grids.}, archive = {J_ACCESS}, author = {Shereefdeen Oladapo Sanni and Olatunji Obalowu Mohammed and Suprava Chakraborty and Ayodele Isqeel Abdullateef and Abdulrahaman Okino Otuoze and Olumoroti Ikotun and Ephraim Bonah Iagyekum}, doi = {10.1109/ACCESS.2025.3568228}, journal = {IEEE Access}, pages = {83781-83793}, shortjournal = {IEEE Access}, title = {On the choice of system strength metrics for the allocation and sizing of synchronous condensers in power grids}, volume = {13}, year = {2025}, }
• (2025). Elevating E-service standards in bangladesh: Merging FAHP and TOPSIS for strategic online business solutions. ACCESS, 13, 83765-83780. (www)

  @article{ , abstract = {The rapid expansion of Bangladesh’s digital economy has unlocked new opportunities for online businesses, but it has also heightened the challenges of delivering high-quality e-services in an increasingly competitive environment. While previous studies have largely focused on customer satisfaction and consumer experiences, the perspective of business owners—those managing daily operations and strategic decisions—has been notably underexplored. Addressing this gap, the present study aims to identify and prioritize the critical success factors (CSFs) influencing the performance of online businesses in Bangladesh from the owners’ viewpoint.A structured survey has been conducted among 57 online business owners, collecting data across ten essential dimensions, including website infrastructure, information sharing, delivery practices, responsiveness, customization, customer service, reliability, payment security, return policies, and service quality challenges. To ensure a data-driven analysis, the Random Forest algorithm is utilized for feature importance extraction, followed by pairwise comparisons through fuzzy triangular matrices. Subsequently, FAHP and TOPSIS techniques are applied to systematically rank the factors and alternatives. Model validation using the XGBoost classifier results in 93% accuracy and a 93% F1-score, demonstrating the robustness of the proposed approach. The results reveal that service quality concerns, payment security, and effective customer support are the most influential determinants of business success. Beyond offering a methodological advancement that integrates machine learning with MCDM, this study delivers actionable insights for business owners, digital strategists, and policymakers aiming to enhance the competitiveness and resilience of Bangladesh’s e-commerce landscape.}, archive = {J_ACCESS}, author = {Farzana Tasnim and Tanjim Mahmud and Shefayatuj Johara Chowdhury and Tasnia Fahrin Choity and Aisha Ahmed and Mohammad Saeed Hasan Chowdhury and Mohammad Shahadat Hossain and Karl Andersson}, doi = {10.1109/ACCESS.2025.3566964}, journal = {IEEE Access}, pages = {83765-83780}, shortjournal = {IEEE Access}, title = {Elevating E-service standards in bangladesh: Merging FAHP and TOPSIS for strategic online business solutions}, volume = {13}, year = {2025}, }
• (2025). Data augmentation with flickering backgrounds and instances for instance segmentation. ACCESS, 13, 83752-83764. (www)

  @article{ , abstract = {Data augmentation (DA) tailored to instances is vital for instance segmentation to improve model robustness and accuracy without high manual annotation costs. Existing erasing methods risk losing information about instances, whereas instance-level methods necessitate additional overhead, such as an object bank and context calculations to decide locations to attach objects to an image. Thus, we propose the instance-centric erasing-based DA method, FlickBI, which enhances focus on the target object and diversity by randomly eliminating confusing information. FlickBI consists of two separate methods: FlickBack and FlickIns. FlickBack removes the unrelated background information based on the given annotations. FlickIns stochastically deletes instances, assuming that instances within one image are independent of each other. The experiments reveal that the proposed simple yet effective method consistently enhances performance across detectors and backbones on three benchmark datasets with just a few lines of code, even if the diversity of transformed images and the number of used instances are lower. On the COCO dataset, FlickBI achieves mask mAP improvements ranging from 0.6 to 5.5. Moreover, on the Cityscapes and LVIS datasets, there is an average improvement in mask AP of +3.1 and +4.8, respectively. Furthermore, FlickBI demonstrates synergistic improvements with other instance-level DA methods.}, archive = {J_ACCESS}, author = {Kisu Lee and Jungeun Kim and Ha Young Kim}, doi = {10.1109/ACCESS.2025.3568400}, journal = {IEEE Access}, pages = {83752-83764}, shortjournal = {IEEE Access}, title = {Data augmentation with flickering backgrounds and instances for instance segmentation}, volume = {13}, year = {2025}, }
• (2025). Decentralized battery-aware connectivity maintenance for multi-UAV missions. ACCESS, 13, 83738-83751. (www)

  @article{ , abstract = {This study presents a novel battery-aware approach for connectivity maintenance for multi-agent systems. It extends a classical connectivity controller by incorporating battery states into the computation of control inputs, enhancing the network’s transition response in the event of agent removals by anticipating battery depletion. Additionally, strategies for agent addition are introduced to maintain the desired level of connectivity within the multi-agent network. The proposed approach is thoroughly tested in the Gymnasium (previously OpenAI Gym) environment using a specific mission scenario. The results demonstrate the controller’s ability to anticipate agent removals and maintain robust performance during transitional stages. The approach is further validated with Crazyflie UAVs in both simulation and real-world environments, with a fleet management system. The system is designed to be modular and easily adaptable to different flight controllers, demonstrating the applicability of the proposed algorithms to real-world multi-robot missions.}, archive = {J_ACCESS}, author = {Khawaja G. Alamdar and Tamara Petrović}, doi = {10.1109/ACCESS.2025.3569206}, journal = {IEEE Access}, pages = {83738-83751}, shortjournal = {IEEE Access}, title = {Decentralized battery-aware connectivity maintenance for multi-UAV missions}, volume = {13}, year = {2025}, }
• (2025). House swallow optimizer: A new meta-heuristic algorithm and its application for DOA estimation of broadband signal in vector hydrophone array. ACCESS, 13, 83717-83737. (www)

  @article{ , abstract = {A new meta-heuristic algorithm, termed the House Swallow Optimizer (HSO), is proposed. Inspired by the sophisticated behavioral repertoire of house swallows, this algorithm addresses the limitations of conventional optimization methods in solving complex, high-dimensional problems. The HSO systematically simulates 6 key survival strategies observed in house swallows—roosting, hovering, preying, nesting, breeding and seasonal migration—through a 5-phase mathematical model to achieve effective exploration-exploitation balance. To evaluate the algorithm’s performance, we conducted comparative studies using 23 standardized benchmark functions against 5 established meta-heuristics: BKA, SCA, SOA, DE, and PSO. The experimental outcomes prove that HSO has a strong ability to balance exploration and development, with out-performance over comparative algorithms. In order to verify the engineering application potential of the optimization algorithm proposed in this paper, aiming at the problems of large estimation error, high computational complexity and low accuracy in the DOA estimation of broadband signal of vector hydrophone array, the HSO algorithm is applied to the ML algorithm, and the DOA estimation of broadband signal is successfully carried out. Initially, the SST algorithm is employed for signal preprocessing, where broadband signals are transformed into narrowband equivalents through eigenvalue decomposition. This step facilitates the formulation of a ML-based DOA estimation model, which is subsequently optimized using the HSO algorithm. Simulation results demonstrate that the proposed HSO-ML method is superior to the ML implementation of other algorithms. Specifically, our approach achieves faster convergence speed, lower root mean square error, lower computational complexity and more robust performance.}, archive = {J_ACCESS}, author = {Jie Dong and Lifu Wang and Shuhui Qiao and Peng Wang and Xiuhui Tan}, doi = {10.1109/ACCESS.2025.3569264}, journal = {IEEE Access}, pages = {83717-83737}, shortjournal = {IEEE Access}, title = {House swallow optimizer: A new meta-heuristic algorithm and its application for DOA estimation of broadband signal in vector hydrophone array}, volume = {13}, year = {2025}, }
• (2025). Comprehensive framework for tuberculosis detection using deep learning and image processing in whole-slide images. ACCESS, 13, 83694-83716. (www)

  @article{ , abstract = {Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTb), remains one of the leading causes of death globally, primarily affecting the lungs. Recent technological advancements have improved TB detection, particularly through the application of deep learning (DL) techniques. This research involves a pathologist using microscopy to identify acid-fast bacilli (AFB), combined with a proposed method that incorporates DL for TB detection. The DL models’ accuracy and efficiency are assessed and compared. The integration of DL aims to assist pathologists by providing an initial analysis, potentially streamlining their workflow. The proposed method provides the accuracy of 94%, which is equivalent to the traditional method. In contrast, the workload of pathologists, who is the bottleneck of the diagnosis, is considerably reduced. This novel approach not only demonstrates the potential of DL in healthcare but also emphasizes the significance of advanced diagnostic methods in addressing global health challenges like TB, ultimately improving healthcare outcomes and reducing mortality rates.}, archive = {J_ACCESS}, author = {Terisara Micaraseth and Shanop Shuangshoti and Kantawatchr Chaiprabha and Natcha Wanpiyarat and Poonchavist Chantranuwatana and Gridsada Phanomchoeng}, doi = {10.1109/ACCESS.2025.3568073}, journal = {IEEE Access}, pages = {83694-83716}, shortjournal = {IEEE Access}, title = {Comprehensive framework for tuberculosis detection using deep learning and image processing in whole-slide images}, volume = {13}, year = {2025}, }
• (2025). Electrical balance of plant approach for offshore wind power plants: A brazilian case study. ACCESS, 13, 83674-83693. (www)

  @article{ , abstract = {Planning the construction of an offshore wind power plant involves balancing design details that affect both technical and financial viability. The electrical balance of plant is a significant part of installation costs, and modeling it can help developers optimize solutions and reduce expenses. This paper presents an approach to model the electrical balance of plant, including inter-array cable routing and sizing, the number and size of HVAC and HVDC export cables, optimized reactive power compensation, offshore and onshore equipment, and grid connection transmission line. The model also accounts for the physical dimensions and weight of offshore and onshore substation components that are significant information for planning further steps such as the constructibility and foundation. This approach is part of CPE-Offshore, software designed to support decision-making for Brazilian offshore wind farms, considering 60 Hz 138 kV and 230 kV HVAC transmission voltages. A generic 1920 MW Brazilian wind farm located 65 km from shore is used as a case study to demonstrate results for different numbers of offshore substations. Additionally, a sensitivity analysis explores reactor combinations and cost comparisons between HVAC and HVDC systems for various plant capacities and distances to the coast. The results indicate a break-even distance of up to 180 km and the most suitable combinations to be evaluated for a 60 Hz system to avoid waste of computational time. This electrical balance of plant approach can be applied to plan an offshore wind power plant in other regions worldwide.}, archive = {J_ACCESS}, author = {Felipe J. Lachovicz and Kristie K. Küster and Ana P. Oening and Débora C. Marcilio and Guilherme M. F. Souza and Robinson A. Silva}, doi = {10.1109/ACCESS.2025.3567909}, journal = {IEEE Access}, pages = {83674-83693}, shortjournal = {IEEE Access}, title = {Electrical balance of plant approach for offshore wind power plants: A brazilian case study}, volume = {13}, year = {2025}, }
• (2025). TransNN-MHA: A transformer-based model to distinguish real and imaginary motor intent for assistive robotics. ACCESS, 13, 83650-83673. (www)

  @article{ , abstract = {Accurately distinguishing between real and imagined motor intent is a fundamental challenge in assistive robotics, as it directly affects human-machine interfaces’ (HMIs) ability to effectively interpret user intent. This distinction is particularly critical for individuals with disabilities who may rely on imagined motor intent, as they cannot perform actual physical movements. In such cases, the ability to differentiate between real and imaginary motor actions allows HMIs to respond appropriately, which improves the precision and usability of assistive devices. Many modern approaches lack the precision required to differentiate these motor actions from Electroencephalogram (EEG) signals. Real-time applications need a high level of precision to guarantee smooth interaction and control, especially for users who depend on imagined movements. It’s important to understand the difference between real and imagined intent to accurately detect user intentions. This is particularly crucial for individuals with disabilities, as it facilitates more effective control of assistive technologies. In this article, we utilize the EEG Motor Movement/Imagery Dataset, consisting of 4087 training samples and 818 test samples, to develop TransNN-MHA, a new Transformer-based Neural Network that incorporates Multi-Head Attention (MHA) mechanisms for classifying real and imaginary motor actions. The proposed model employs a minimalist architecture that omits decoders and positional encodings to optimize EEG classification. We believe this is the first study focused on classifying real and imaginary motor actions using EEG data. We compare TransNN-MHA with Deep learning (CNN, GRU) and hybrid (CNN-Transformer, GRU-Transformer) models. TransNN-MHA achieves 92% accuracy, outperforming CNN-Transformer (86%) and GRU-Transformer (91%), as well as attention-based models like Self-Attention and Spatial-Temporal Transformers. Our novel use of transformers with MHA enhances the classification of EEG signals by capturing long-range dependencies that make it suitable for real-time intent detection in assistive robotics. TransNN-MHA shows strong results across motor tasks that demonstrate the potential for real-world applications where precision in human-machine interaction is critical. Implementation details and code are available at http://github.com/madibabaiasl/EEGIntentPaper}, archive = {J_ACCESS}, author = {Tipu Sultan and Guangping Liu and Pascal Sikorski and Madi Babaiasl}, doi = {10.1109/ACCESS.2025.3567908}, journal = {IEEE Access}, pages = {83650-83673}, shortjournal = {IEEE Access}, title = {TransNN-MHA: A transformer-based model to distinguish real and imaginary motor intent for assistive robotics}, volume = {13}, year = {2025}, }
• (2025). Quantum computer-generated holography. ACCESS, 13, 83643-83649. (www)

  @article{ , abstract = {Hologram calculations pose a significant challenge for future holographic displays with extensive fields of view and wide viewing angles. Classical computers may encounter difficulties with these owing to immense computational demands. This study introduces quantum computer-generated holography (QGH) as a viable solution. QGH employs a point-cloud algorithm in which point-cloud data are represented by the superposition states of qubits, and lightwave interactions on the hologram are calculated using principles of quantum mechanics. QGH can accelerate hologram computation by a factor of $10^{6}$ in a 1 tera-pixel hologram from 1 tera-object points, compared to classical computer-generated holography. The study discusses advantages, limitations, and future directions.}, archive = {J_ACCESS}, author = {Tomoyoshi Shimobaba and Fan Wang and Tomoyoshi Ito}, doi = {10.1109/ACCESS.2025.3564553}, journal = {IEEE Access}, pages = {83643-83649}, shortjournal = {IEEE Access}, title = {Quantum computer-generated holography}, volume = {13}, year = {2025}, }
• (2025). Federated knowledge distillation with 3D transformer adaptation for weakly labeled multi-organ medical image segmentation. ACCESS, 13, 83619-83642. (www)

  @article{ , abstract = {The increasing reliance on medical image segmentation for disease diagnosis, treatment planning, and therapeutic assessment has highlighted the need for robust and generalized deep learning (DL)-based segmentation frameworks. However, existing models often suffer from task-specific limitations, catastrophic forgetting, and poor scalability due to their dependency on narrowly annotated datasets. This creates a significant gap in developing unified, multi-organ segmentation systems that leverage distributed and partially labeled datasets across diverse clinical institutions. To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. Our model adapts the Segment Anything Model (SAM) for volumetric medical imaging by introducing architectural enhancements, including 3D spatial feature adapters and an Auto Prompt Generator (APG), to optimize spatial representation and reduce reliance on manually crafted prompts. Fed3D-KDNet employs a dual knowledge distillation strategy to mitigate catastrophic forgetting and improve cross-client knowledge transfer, ensuring robust generalization across heterogeneous datasets. The proposed methodology was evaluated on multi-organ CT datasets, including the BTCV benchmark, under centralized and federated settings. Experimental results demonstrate that Fed3D-KDNet achieves state-of-the-art performance with an average Dice score of 80.53% and an average Hausdorff Distance (HD) of 11.43 voxels in federated experiments involving seven clients, showing 5.04% improvement in Dice accuracy and a 4.35 voxel reduction in HD. Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. These results validate the efficacy, scalability, and computational efficiency of Fed3D-KDNet, positioning it as a robust solution for multi-organ medical image segmentation in federated environments.}, archive = {J_ACCESS}, author = {Tareq Mahmod AlZubi and Hamza Mukhtar}, doi = {10.1109/ACCESS.2025.3569073}, journal = {IEEE Access}, pages = {83619-83642}, shortjournal = {IEEE Access}, title = {Federated knowledge distillation with 3D transformer adaptation for weakly labeled multi-organ medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhancing internet traffic forecasting in MEC environments with 5GT-trans: Leveraging synthetic data and transformer-based models. ACCESS, 13, 83607-83618. (www)

  @article{ , abstract = {Recently, transformer-based learning models have shown promising results in network traffic prediction across various contexts. However, their potential in Mobile Edge Computing (MEC) environments remains largely unexplored, despite the unique requirements of MEC such as low latency and high-bandwidth communication between end-users and edge servers. This study evaluates the performance of four transformer-based learning models for Internet traffic prediction in MEC environments, comparing them against state-of-the-art models using a real-world dataset from publicly available mobile operator networks. Performance is assessed using mean squared error (MSE) and mean absolute error (MAE) as evaluation metrics. Our experiments demonstrate that the proposed GAN-based transformer model, referred to as 5GT-GAN-Trans, achieves an MSE of 0.428 and an MAE of 0.441, considerably outperforming baseline models such as FED-Former-F (MSE: 0.791, MAE: 0.606), FED-Former-W (MSE: 0.622, MAE: 0.508), Informer (MSE: 0.814, MAE: 0.649), and a standard Transformer model (MSE: 0.803, MAE: 0.640). These numerical improvements underscore the efficacy of transformer-based models for precise Internet traffic forecasting in MEC environments, offering valuable insights for improved network management and enhanced user experience.}, archive = {J_ACCESS}, author = {Ravi Shankar Shukla and Abdulrahman M. Alatawi}, doi = {10.1109/ACCESS.2025.3569389}, journal = {IEEE Access}, pages = {83607-83618}, shortjournal = {IEEE Access}, title = {Enhancing internet traffic forecasting in MEC environments with 5GT-trans: Leveraging synthetic data and transformer-based models}, volume = {13}, year = {2025}, }
• (2025). An efficient methodology for the categorization of software requirements using natural language processing and similarity analysis. ACCESS, 13, 83591-83606. (www)

  @article{ , abstract = {The classification of software requirements into functional (FRs) and non-functional requirements (NFRs) is indispensable for the efficacious implementation of software systems. Traditionally, this endeavor reliant on manual exertion, this process has proven to be both protracted and inherently susceptible to errors, and inaccuracies. Recent advancements in machine learning (ML) have begun to offer promising avenues for automation, enhancing both the efficiency and accuracy of requirement classification. The following research study proposes “NLPReqClassifier,” a lightweight automated classification model that integrates Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity. Leveraging the PROMISE dataset, which is enriched continually by academic and professional input, this model addresses the existing shortcomings of traditional classification methods. The proposed model utilizes a dual-evaluation approach, ensuring relevance and precision across varied software development contexts. By incorporating iterative feedback into the model’s training process, this research not only aligns with academic standards but also meets the practical demands of the industry. The model’s performance was tested in both academic settings dataset and real-world industry dataset, particularly focusing on its application in Enterprise Resource Planning (ERP) systems. The proposed model demonstrated superior capability to categorize a broad spectrum of software requirements accurately, outperforming existing traditional methodologies in terms of adaptability and efficiency. It showed significant improvements over traditional classification methods, particularly in its ability to dynamically adapt to new and evolving requirements. The dual-evaluation process verified the model’s effectiveness, showcasing high precision and recall rates in both controlled and practical environments.}, archive = {J_ACCESS}, author = {Rahat Izhar and Kenneth Cosh and Lachana Ramingwon and Sakgasit Ramingwong and Shahid N. Bhatti}, doi = {10.1109/ACCESS.2025.3568504}, journal = {IEEE Access}, pages = {83591-83606}, shortjournal = {IEEE Access}, title = {An efficient methodology for the categorization of software requirements using natural language processing and similarity analysis}, volume = {13}, year = {2025}, }
• (2025). VGGBM-net: A novel pixel-based transfer features engineering for automated coffee bean diseases classification. ACCESS, 13, 83580-83590. (www)

  @article{ , abstract = {Although coffee is a crucial commodity for developing nations’ economies, processing defects can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study proposes a novel transfer learning-based deep neural network, VGGBM-Net, for classifying coffee bean diseases. It focuses on the timely and precise identification of diseases to improve export quality without compromising safety during production. In this research, the proposed approach is trained using a dataset USK-Coffee of coffee beans to classify healthy and unhealthy beans. The performance of multiple machine learning classifiers, including Random Forest (RF), Logistic Regression (LoR), LightGBM (LGBM), and K-Nearest Neighbor Classifier (KNC), is evaluated alongside neural network techniques such as Convolutional Neural Networks (CNN) and VGG-19. A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. Unlike traditional models, this feature extraction enhances classification accuracy and robustness. This transformation improves feature extraction by capturing more discriminative patterns, leading to superior performance compared to conventional methods. Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. This research establishes a highly effective framework for automated coffee bean classification, setting a benchmark for future studies in agricultural image analysis.}, archive = {J_ACCESS}, author = {Muhammad Shadab Alam Hashmi and Azam Mehmood Qadri and Ali Raza and Saleem Ullah and Aseel Smerat and Changgyun Kim and Muhammad Syafrudin and Norma Latif Fitriyani}, doi = {10.1109/ACCESS.2025.3569078}, journal = {IEEE Access}, pages = {83580-83590}, shortjournal = {IEEE Access}, title = {VGGBM-net: A novel pixel-based transfer features engineering for automated coffee bean diseases classification}, volume = {13}, year = {2025}, }
• (2025). SEM deep learning multiclass noise level classification with data augmentation. ACCESS, 13, 83561-83579. (www)

  @article{ , abstract = {Scanning electron microscopy (SEM) plays an important role in providing high-resolution imaging in various fields, including industrial chip manufacturing, materials science, and nanoscale biology. However, high-resolution imaging often compromises image quality due to noise, such as Gaussian noise, which is caused by electronic noise and signal amplification. Overcoming noise in SEM images traditionally involves the use of various filters, such as the Wiener filter and Gaussian filter, but these methods are often limited in efficiency when handling complex noise patterns, and they can be time-consuming. In recent years, the rapid evolution of deep learning technology has been applied to various machine vision tasks such as classification, detection, denoising, and super-resolution. In this study, a deep learning network, the Gaussian Noise Level Classification Network (GNLCN), is proposed to Classify noise levels in SEM images from 0.0000 to 0.0100, incremented by 0.001. A novel Hybrid Spatial State-Space Model (SSM) block is introduced which uniquely integrates a state-space model with Residual Squeeze-and-Excitation (SE) and other feature enhancement mechanisms to improve spatial feature extraction. The network uses a dual-head classifier, consisting of both a standard classification head and an ordinal classification head. GNLCN is compared with other state-of-the-art (SOTA) models, including ConvNeXT, ResNeXt, EfficientNetV2, Vision Transformer, and VGG-19. Three different SEM datasets are used, namely the NFFA-EUROPE dataset, EPFL CVLab EM dataset, and Biofilms SEM dataset. The proposed network achieved the highest evaluation in accuracy (92.76%), precision (92.77%), recall (92.76%), and F1 score (92.74%), as well as the shortest training time in the NFFA-EUROPE dataset. It also demonstrated consistently strong results on the EPFL CVLab EM dataset and Biofilms SEM dataset, surpassing most competing methods, and thus sets a new benchmark in SEM image analysis. This model significantly enhances noise level classification in SEM images, can be widely applied in various machine vision tasks, and holds the potential to revolutionise SEM imaging techniques.}, archive = {J_ACCESS}, author = {Kai Liang Lew and Kok Swee Sim and Shing Chiang Tan}, doi = {10.1109/ACCESS.2025.3567974}, journal = {IEEE Access}, pages = {83561-83579}, shortjournal = {IEEE Access}, title = {SEM deep learning multiclass noise level classification with data augmentation}, volume = {13}, year = {2025}, }
• (2025). Vehicle routing for infectious healthcare waste collection with transit points: A problem incorporating multiple types of risks and two solution methods from different perspectives. ACCESS, 13, 83542-83560. (www)

  @article{ , abstract = {Infectious healthcare waste is a kind of high-risk material. With the continuous outbreak of infectious diseases in recent years, infectious healthcare waste collection (IHWC) has garnered significant attention. As infectious healthcare waste volume has increased substantially, many countries have chosen to incorporate transit points into their IHWC process to leverage consolidation effects. Besides, transporting infectious healthcare waste involves transport risks, and the storage of such waste at medical institutes and transit points also poses storage risks. In this paper, a vehicle routing problem for IHWC with multiple types of risks and transit points (VRP for IHWC-MR-TP) is proposed, considering the complexity introduced by the simultaneous existence of transport risks, storage risks, and transit points, and aiming to simultaneously minimize the total cost and the maximum risk. A bi-objective model of VRP for IHWC-MR-TP is developed. Based on the characteristics of the problem, two different solution methods are proposed, including a global solution method from a global perspective and a two-stage solution method that divides the problem into two stages for optimization. The model and solution methods are tested through numerical experiments. The results indicate that compared to the global solution method, the two-stage solution method can reduce the solving time by an average of 26.6%, while decreasing the Hypervolume by only an average of 0.5%. Finally, managerial insights are derived through sensitive analysis. The results demonstrate that an increase in collection vehicle speed results in lower cost and risk, therefore decision-makers should pay attention to vehicle maintenance and driver training in normal operations to ensure that relatively high vehicles’ speeds can be maintained during IHWC. Furthermore, as the capacities of vehicles increase, both cost and risk demonstrate a significant downward trend, suggesting that decision-makers should choose vehicles with larger capacities for IHWC.}, archive = {J_ACCESS}, author = {Meng Zhang and Wei Cui and Qi Jiang and Nengmin Wang and Yuan Shang}, doi = {10.1109/ACCESS.2025.3566096}, journal = {IEEE Access}, pages = {83542-83560}, shortjournal = {IEEE Access}, title = {Vehicle routing for infectious healthcare waste collection with transit points: A problem incorporating multiple types of risks and two solution methods from different perspectives}, volume = {13}, year = {2025}, }
• (2025). Deep learning-enhanced ultrasound analysis: Classifying breast tumors using segmentation and feature extraction. ACCESS, 13, 83528-83541. (www)

  @article{ , abstract = {Breast cancer remains a significant global health challenge, requiring accurate and effective diagnostic methods for timely treatment. Ultrasound imaging is a valuable diagnostic tool for breast cancer because of its affordability, accessibility, and non-ionizing radiation properties. This study proposes a classification method for breast ultrasound images that integrates segmentation and feature extraction. Initially, ultrasound images are pre-processed to enhance quality and reduce noise, followed by segmentation using the U-Net++ architecture. Feature extraction is then performed using MobileNetV2, and these features are used to train and validate classification models to differentiate between malignant and benign breast masses. The model’s performance is assessed using accuracy, precision, recall, Mean IoU, and Dice Score metrics. The U-Net++ model achieved superior segmentation performance with a Dice Score of 0.911 and a Mean IoU of 0.838, outperforming related methods such as U-Net (0.888 Dice, 0.79 IoU) and Efficient U-Net (0.904 Dice, 0.80 IoU). In the classification task, MobileNetV2 when paired with the ANN classifier, produced the highest test accuracy at 96.58%, with a precision of 97% and recall of 96%. Our approach demonstrates superior performance compared to other models, such as RMTL-Net, which achieved 91.02% accuracy, and hybrid CAD models with 94% accuracy. This highlights the benefits of combining advanced segmentation and feature extraction techniques, with MobileNetV2 proving to be the better model, offering superior accuracy and robustness in classification tasks. This approach has the potential to support promise for supporting radiologists, enhance diagnostic accuracy, and ultimately improve outcomes for breast cancer patients. In the future, we will use comprehensive datasets to validate our methodology.}, archive = {J_ACCESS}, author = {Ali Hamza and Martin Mezl}, doi = {10.1109/ACCESS.2025.3568588}, journal = {IEEE Access}, pages = {83528-83541}, shortjournal = {IEEE Access}, title = {Deep learning-enhanced ultrasound analysis: Classifying breast tumors using segmentation and feature extraction}, volume = {13}, year = {2025}, }
• (2025). Psychological impact of internet blackouts: A case study with machine learning-based stress analysis. ACCESS, 13, 83505-83527. (www)

  @article{ , abstract = {Access to the internet has become a vital part of modern life, especially for communication and essential services. However, during politically sensitive times, internet blackouts can disrupt daily routines, leading to significant psychological impacts. This study explores the mental health effects of the internet shutdown imposed during the Bangladesh Quota Movement in July 2024, when the government cut off access to control information flow. The blackout hindered communication, financial transactions, and critical services, amplifying stress, feelings of isolation, and emotional distress. A survey of 2,085 participants was conducted to assess the behavioral, emotional, and psychological consequences, particularly in academic, work, and social settings. Stress levels among respondents varied from minimal to extreme, reflecting widespread mental distress. To classify these stress levels, machine learning models; Decision Tree (DT), Random Forest (RF), Bernoulli Naive Bayes (BNB), Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were applied. The SVM model outperformed others, achieving high precision (99.33%), recall (99.00%), F1-score (99.33%), and accuracy (99.49%). This study underscores the urgent need for mental health support during such crises, particularly in low- and middle-income countries like Bangladesh, where mental health care is often neglected. These findings are aligned with Sustainable Development Goal (SDG 3), “Good Health and Well-Being,” stressing the importance of mental health interventions in fostering resilience, well-being, and social stability during crises.}, archive = {J_ACCESS}, author = {Mohammad Ariful Islam and Tahidul Islam and Gahangir Hossain and M. Mofazzal Hossain}, doi = {10.1109/ACCESS.2025.3568434}, journal = {IEEE Access}, pages = {83505-83527}, shortjournal = {IEEE Access}, title = {Psychological impact of internet blackouts: A case study with machine learning-based stress analysis}, volume = {13}, year = {2025}, }
• (2025). Anodapter: A unified framework for generating aligned anomaly images and masks using diffusion models. ACCESS, 13, 83483-83504. (www)

  @article{ , abstract = {In industrial manufacturing, anomaly inspection performance is frequently hampered by the scarcity of anomaly data. To address this issue, synthetic anomaly masks and corresponding images are generated using various methods. These methods typically employ separate branches within a single backbone or distinct models for generating anomaly masks and images. However, such approaches frequently result in misalignment between the anomaly mask and image, and a reduction in realism, which adversely affects the performance of downstream tasks. To address these challenges, we introduce Anodapter, a unified few-shot anomaly generation model that utilizes a single diffusion model to sequentially generate well-aligned anomaly masks and images. Unlike earlier models such as AnomalyDiffusion, which use separate models for mask and image generation, Anodapter integrates both tasks into a single diffusion model through its proposed Switch Adapter, eliminating misalignment and improving realism. This unified approach not only enables the alternating generation of masks and images within a single model, but also significantly enhances both precision and realism. The model is designed to facilitate the generation of anomaly images either from generated or user-specified masks, ensuring precise alignment and high-quality results. To achieve this, Anodapter efficiently separates anomaly information into appearance and spatial components. For spatial control, we introduce a Switch Adapter that manages the spatial arrangements of anomalies. This adapter provides targeted conditioning to the backbone diffusion model to generate well-aligned anomaly masks and images. For appearance control, the model employs specialized prompts with unique identifiers, enabling selective generation of anomaly images or masks. These identifiers assist the model in learning the features of anomalous regions and the overall structure of the image. Through extensive experiments with various datasets, including MVTec AD, MVTec LOCO, and BTAD, we demonstrate that our model can generate realistic and diverse anomaly datasets. It significantly outperforms existing methods in downstream tasks such as anomaly detection, localization, and classification, both quantitatively and qualitatively. Specifically, our model achieved state-of-the-art performance, with an image-level AUROC of 98.48% in anomaly detection, a pixel-level AUROC of 98.82% in anomaly localization, and an anomaly classification accuracy of 71.11% on the MVTec AD dataset, surpassing previous approaches.}, archive = {J_ACCESS}, author = {Minkyoung Shin and Seonggyun Jeong and Yong Seok Heo}, doi = {10.1109/ACCESS.2025.3568866}, journal = {IEEE Access}, pages = {83483-83504}, shortjournal = {IEEE Access}, title = {Anodapter: A unified framework for generating aligned anomaly images and masks using diffusion models}, volume = {13}, year = {2025}, }
• (2025). Enhanced car-following model incorporating multi-vehicles information in heterogeneous traffic. ACCESS, 13, 83470-83482. (www)

  @article{ , abstract = {This paper presents an enhanced full velocity difference (FVD) model for characterising the car-following behaviour of autonomous vehicles (AVs). The model incorporates data from both front and rear vehicles, considering the subject vehicle’s velocity, the acceleration of many front and rear vehicles, as well as the headway and velocity differential between the surrounding vehicles and the subject vehicle. Furthermore, the model incorporates location-related characteristics to precisely quantify the extent to which each front and rear vehicle’s influence changes based on its position relative to the subject vehicle. We determine the most suitable value for each parameter in the proposed model and analyse the effect of specific time delays on the stability of traffic flow. This analysis is based on data collected from a field test that involved a combination of human-driven vehicles (HDVs), AVs, and car-following technologies. Based on the results of the numerical simulation, the proposed model for AVs demonstrates a more seamless acceleration and deceleration compared to the FVD model. Moreover, the model can improve the stability of the mixed-vehicle platoon by augmenting the AV proportion. The model may also be utilised to replicate the behaviour of HDVs and AVs following cars in traffic that consists of a mix of different vehicle types. This capability is beneficial for managing road traffic and designing infrastructure.}, archive = {J_ACCESS}, author = {Boran Li and Dongjie Xie and Yiran Sun and Jingze Ouyang}, doi = {10.1109/ACCESS.2024.3477529}, journal = {IEEE Access}, pages = {83470-83482}, shortjournal = {IEEE Access}, title = {Enhanced car-following model incorporating multi-vehicles information in heterogeneous traffic}, volume = {13}, year = {2025}, }
• (2025). Fully-standard-cell-based synthesizable charge-redistribution SAR ADCs and P&R-based layout automation framework. ACCESS, 13, 83450-83469. (www)

  @article{ , abstract = {This paper presents two fully standard-cell-based synthesizable Successive Approximation Register Analog-to-Digital Converters (SAR ADCs) and the automated layout generation framework. The 1st design is an 8-bit charge-redistribution SAR ADC. A 4-inverter-cell-based analog switch is proposed, effectively mitigating charge injection/absorption in the capacitive digital-to-analog converter. This prototype is fabricated in a 65nm CMOS process and occupies 0.095mm2. With a Nyquist input, the ADC achieves 55.8dB spurious-free dynamic range (SFDR) and 47.7dB signal-to-noise-and-distortion ratio (SNDR) at a sampling frequency of 33.3MS/s without calibration. The power consumption is 0.91mW, achieving Walden Figure of Merit (FoMW) of 0.13pJ/conversion-step. The 2nd design is a 12-bit 2-stage pipelined SAR ADC, where a standard-cell-based amplifier is proposed to amplify the residue voltage after the 1st-stage conversion. This prototype is fabricated also in a 65nm process and occupies 0.136mm2. With a Nyquist input, the pipelined ADC achieves 72.1 dB SFDR and 63.1 dB SNDR at a sampling frequency of 22.2MS/s with a simple foreground calibration of the inter-stage amplifier gain. The power consumption is 2.7mW, yielding a FoMW of 0.105pJ/conversion-step. A layout automation framework based on placement and routing (P&R) tool for the standard-cell-based ADCs is proposed and demonstrated with the 8-bit SAR ADC design as an example.}, archive = {J_ACCESS}, author = {Shuowei Li and Ckristian Duran and Ritaro Takenaka and Tetsuya Iizuka}, doi = {10.1109/ACCESS.2025.3569273}, journal = {IEEE Access}, pages = {83450-83469}, shortjournal = {IEEE Access}, title = {Fully-standard-cell-based synthesizable charge-redistribution SAR ADCs and P&R-based layout automation framework}, volume = {13}, year = {2025}, }
• (2025). Energy efficient AR/VR edge processing: Architecture and optimization. ACCESS, 13, 83426-83449. (www)

  @article{ , abstract = {This article introduces a server-centric cellular Passive Optical Network (C-PON) architecture to support the deployment of Augmented Reality (AR)/ Virtual Reality (VR) event viewing applications in edge data centers. The proposed architecture is compared with the state-of-the-art Spine-and-Leaf architecture. For fair comparison, we model production style environments based on both C-PON and Spine-and-leaf data center architectures. We developed a Mixed Integer Linear Programming (MILP) model with multi-objective function to optimize routing of AR/VR traffic on both C-PON and Spine-and-Leaf architectures. The multi-objective function considers minimizing power consumption and minimizing end-to-end delay within the network architectures. We compare hosting the AR/VR applications in C-PON and in Spine-and-Leaf in terms of the power consumption, the average delay in links, and the end-to-end delay per user. We also developed a heuristic algorithm to enhance the scalability enabling the optimization of complex and larger systems. The results show that C-PON can enable substantial savings in terms of power consumption compared to the state-of-the-art Spine-and-Leaf architecture.}, archive = {J_ACCESS}, author = {Azza E. A. Eltraify and Randa A. Thabit and Opeyemi O. Ajibola and Wafaa B. M. Fadlelmula and Ahmed A. M. Hassan and Ahrar N. S. Hamad and Abdelrahman S. Elgamal and Walter Z. Ncube and Harith S. Ibrahim and Mariam Elmirghani and Sanaa Hamid Mohamed and Louise Krug and Greg Mcsorley and Jaafar M. H. Elmirghani}, doi = {10.1109/ACCESS.2025.3564009}, journal = {IEEE Access}, pages = {83426-83449}, shortjournal = {IEEE Access}, title = {Energy efficient AR/VR edge processing: Architecture and optimization}, volume = {13}, year = {2025}, }
• (2025). Semantic segmentation-driven knowledge distillation-based infrared visible image fusion framework. ACCESS, 13, 83408-83425. (www)

  @article{ , abstract = {The goal of infrared and visible image fusion is to generate a fused image that integrates both prominent targets and fine textures. However, many existing fusion algorithms overly emphasize visual quality and traditional statistical evaluation metrics while neglecting the requirements of real-world applications, especially in high-level vision tasks. To address this issue, this paper proposes a semantic segmentation-driven image fusion framework based on knowledge distillation. By incorporating a distributed structure of teacher and student networks, the framework leverages knowledge distillation to reduce network complexity, ensuring that the fused images are not only visually enhanced but also well-suited for downstream high-level vision tasks. Additionally, the introduction of two discriminators further optimizes the overall quality of the fused images, while the integration of a semantic segmentation module ensures that the fused images provide valuable support for advanced vision tasks. To enhance both fusion performance and segmentation capability, this paper proposes a joint training strategy that enables the fusion and segmentation networks to mutually improve during training. Experimental results on three public datasets demonstrate that the proposed method outperforms nine state-of-the-art fusion approaches in terms of visual quality, evaluation metrics, and semantic segmentation performance. Finally, ablation studies on the segmentation network further validate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Xingshuo Wang}, doi = {10.1109/ACCESS.2025.3566436}, journal = {IEEE Access}, pages = {83408-83425}, shortjournal = {IEEE Access}, title = {Semantic segmentation-driven knowledge distillation-based infrared visible image fusion framework}, volume = {13}, year = {2025}, }
• (2025). RL-MAB-based resource allocation for efficient bandwidth utilization in industrial IoT networks. ACCESS, 13, 83394-83407. (www)

  @article{ , abstract = {Industrial IoT (IIoT) is key to achieving automation in manufacturing and logistics industries. Recently, there have been ongoing efforts to realize automation by deploying mobile devices such as Automated Guided Vehicles (AGVs) in industrial processes, along with sensors and actuators. Industrial Wireless Sensor Networks (IWSN) are essential foundational technologies for implementing IIoT, providing communication methods based on redundancy policies, such as scheduling with pre-designed multiple routing paths and fixed redundant resource allocation, to meet strict industrial requirements like real-time, reliability, and energy efficiency. However, existing IWSNs are vulnerable to frequent location changes of equipment such as AGVs and variations in network environments. Especially, the waste of redundant resources that are not utilized for communication leads to issues of resource efficiency and scalability limitations due to limited wireless resources. To address these challenges, this paper proposes a resource reallocation and scheduling update scheme that identifies and reallocates unutilized redundant resources by employing the Multi-Armed Bandit (MAB) algorithm-one of the representative reinforcement learning techniques focused on reward optimization-in IWSN environment where mobile and fixed devices are deployed together. This approach improves the performance of industrial networks while maintaining compatibility with existing industrial network systems, supporting reliability and real-time processing. The experimental performance evaluation shows that the proposed scheme improves network performance in terms of resource efficiency, scalability, energy efficiency, and mobility support compared to the typical resource allocation and scheduling schemes.}, archive = {J_ACCESS}, author = {Dongyeong Seo and Kidong Nam and Kwansoo Jung}, doi = {10.1109/ACCESS.2025.3560855}, journal = {IEEE Access}, pages = {83394-83407}, shortjournal = {IEEE Access}, title = {RL-MAB-based resource allocation for efficient bandwidth utilization in industrial IoT networks}, volume = {13}, year = {2025}, }
• (2025). Location-allocation of a biorefinery based on linguistic mixed integer nonlinear mathematical modeling. ACCESS, 13, 83373-83393. (www)

  @article{ , abstract = {Significant research is currently being conducted to explore renewable energy sources, such as biomass fuel, in response to recent fluctuations in fossil fuel prices and environmental concerns. However, utilizing biomass energy poses challenges due to the widespread distribution of feedstock and the need to transport multiple low-energy-density materials to conversion facilities. In this investigation, a Linguistics Mixed Integer Nonlinear Programming (LMINLP) approach with Z-number computations was employed to analyze the optimal allocation of locations for a bio-refinery supply chain network. This proposed method significantly impacted the product process, time value for plutocrats, installation investment costs, and transportation network. This research aims to construct an LMINLP model to determine the optimal position of a single installation within a bio-refinery. The primary goal was to make well-informed investment decisions regarding technologies, facilities, capacities, and site selections. A firefly algorithm incorporating a piecewise linearization approach was utilized to solve the model and achieve global optimality. The model’s effectiveness was demonstrated through numerical examples, including those from the ethanol industry.}, archive = {J_ACCESS}, author = {Lei Fan and Negar Karimi and Mohammad Reza Feylizadeh and Mohsen Khorshidnia and Yongzheng Li}, doi = {10.1109/ACCESS.2025.3560778}, journal = {IEEE Access}, pages = {83373-83393}, shortjournal = {IEEE Access}, title = {Location-allocation of a biorefinery based on linguistic mixed integer nonlinear mathematical modeling}, volume = {13}, year = {2025}, }
• (2025). Timing and resource analysis of emergency response process using petri nets. ACCESS, 13, 83361-83372. (www)

  @article{ , abstract = {Emergency response is any systematic response to unexpected events that pose potential risk and/or damage to human life or property. Outbreak of virus, leak of poisonous chemicals, and terrorist attack are just a few examples. The goal of emergency response is to minimize the adverse impact of an incident to society and the environment. To guarantee the implementation of its handling procedure, adequate resources must be available. The success of emergency response also relies on the timeliness of related rescue actions. This paper presents resource-oriented timed workflow nets for the modeling and analysis of the resource requirements in responding to an emergency incident, as well as the time required to complete prescribed response tasks. This new model provides a formal basis for process designers and practitioners to examine the readiness and efficiency of their emergency plans. The presented algorithm can be applied to workflows with sequential, concurrent, decision-making and repetitive structures. A case study on chemical spill response is presented to illustrate the application of the proposed model.}, archive = {J_ACCESS}, author = {Lin Zhao and Hongxia Li and Jiacun Wang and Lei Chen}, doi = {10.1109/ACCESS.2025.3568459}, journal = {IEEE Access}, pages = {83361-83372}, shortjournal = {IEEE Access}, title = {Timing and resource analysis of emergency response process using petri nets}, volume = {13}, year = {2025}, }
• (2025). Multi-feature fusion-based speech disorder classification using MobileNetV3-EfficientNetB7, linformer-performer, and SHAP-aware XGBoost. ACCESS, 13, 83348-83360. (www)

  @article{ , abstract = {Traditional speech disorders (SD) detection relies on subjective analysis, resulting in inconsistent outcome. Direct voice classification lacks effective approaches to capture temporal dependencies. Machine learning (ML) models face challenges in extracting the complex temporal and spectral variations in speech signals. Advanced deep learning (DL) and transfer learning techniques offer a foundation for early screening of SD. However, the lack of interpretability reduces the generalization capabilities of these models. Addressing these shortcomings is essential in order to improve the accuracy of SD detection and clinical trustworthiness. Thus, the proposed study introduces a novel image-based SD classification model to classify healthy and pathological speech with high accuracy and robustness. The raw speech signals are transformed into Mel-Spectrograms to overcome the limitations of direct voice classification. To facilitate the model’s interpretability, the statistical and handcrafted acoustic features are extracted from the raw speech signals. Hybrid MobileNet V3-EfficientNet B7and Linformer-Performer are employed to extract diverse features from the Mel-Spectrograms. An attention-based feature fusion is used to identify critical features indicating the SD patterns from the extracted features. The XGBoost classifier is optimized using Bayesian Optimization and HyperBand (BOHB) to classify the healthy and pathological speech. SHapley Additive exPlanations (SHAP) values is employed to offer valuable insights into the model’s decisions. The proposed model obtains an exceptional performance on two benchmark datasets. On the Saarbruecken Voice Database (SVD), it achieves an accuracy of 98.1% with loss of 0.13. It yields a remarkable generalization accuracy of 98.2% on the VOICE dataset, outperforming the state-of-the-art models. In addition, it contributes a significant advancement in SD detection, setting the stage for future research endeavors.}, archive = {J_ACCESS}, author = {Abdul Rahaman Wahab Sait and Suresh Sankaranarayanan and P. Gouthaman}, doi = {10.1109/ACCESS.2025.3562232}, journal = {IEEE Access}, pages = {83348-83360}, shortjournal = {IEEE Access}, title = {Multi-feature fusion-based speech disorder classification using MobileNetV3-EfficientNetB7, linformer-performer, and SHAP-aware XGBoost}, volume = {13}, year = {2025}, }
• (2025). Color consistency anime style transfer based on hybrid structural decomposition network. ACCESS, 13, 83335-83347. (www)

  @article{ , abstract = {In the anime industry, character design frequently encounters challenges such as oversimplification and homogenization, which severely restrict the diversity and creativity of character appearances. To tackle these issues, we introduce a Hybrid Structural Decomposition Network (HSDN) that aims to generate anime characters with a realistic style by learning the expressive techniques of traditional anime, thereby providing valuable inspiration and references for designers. The proposed HSDN utilizes an encoder to separately extract structural and color features from input images. The structural features are then processed by our style transfer sub-network, which is based on diffusion models. Subsequently, the structural and color features are fused at multiple scales, and a specially designed decoder is employed to generate the final style-transferred image. Additionally, we have incorporated specific skip connections to mitigate local detail loss during image generation and to enhance the stability of the diffusion process. Experimental results demonstrate that our proposed model not only visually generates anime characters with realistic styles but also achieves superior performance in terms of Style FID and Semantic FID compared with state-of-the-art methods.}, archive = {J_ACCESS}, author = {Jun Zhang and Yunhua Zhang}, doi = {10.1109/ACCESS.2025.3569326}, journal = {IEEE Access}, pages = {83335-83347}, shortjournal = {IEEE Access}, title = {Color consistency anime style transfer based on hybrid structural decomposition network}, volume = {13}, year = {2025}, }
• (2025). Curvature scale space corner detection-based registration algorithm for infrared and visible light images of power equipment. ACCESS, 13, 83320-83334. (www)

  @article{ , abstract = {To address the challenges of incomplete feature representation and insufficient localization accuracy in multimodal fault diagnosis of electrical equipment, this paper proposes a dual-modal feature co-registration algorithm termed Contour Angle Orientation-Speeded Up Robust Features (CAO-SURF). The methodology establishes a three-stage progressive feature enhancement framework through feature space fusion and adaptive registration mechanisms for multimodal sensing data. First, a Modal Perception-Based Canny Edge Detection Strategy (MP-Canny) is developed to resolve feature sparsity in low-contrast infrared images and noise interference in visible-light images. Subsequently, a contour angle orientation (CAO) and a Null-Value Area Compensation Strategy (NVACS)-based gradient field reconstruction model are developed to achieve rotation-invariant feature representation. Finally, an improved SURF descriptor integrated with bidirectional geometric constraint matching significantly enhances feature robustness. Systematic ablation experiments demonstrate that the proposed method outperforms state-of-the-art approaches in core metrics, including registration accuracy and recall rate. The innovatively developed mechanism establishes a novel multimodal information fusion paradigm for electrical equipment condition monitoring.}, archive = {J_ACCESS}, author = {HongYan Han and ZhengGuang Wang and Peng Ding and Nan Liu and ZhenYao Li and ZhanQiang Ru and WenQing Zhu and HeLun Song}, doi = {10.1109/ACCESS.2025.3568378}, journal = {IEEE Access}, pages = {83320-83334}, shortjournal = {IEEE Access}, title = {Curvature scale space corner detection-based registration algorithm for infrared and visible light images of power equipment}, volume = {13}, year = {2025}, }
• (2025). A survey on the functionalities of data catalog tools. ACCESS, 13, 83297-83319. (www)

  @article{ , abstract = {Finding all data distributed across numerous systems, understanding its meaning, and assessing its quality are major challenges for many companies and organizations. As a result, both researchers and practitioners have become increasingly interested in data catalogs, as such tools maintain a repository of technical metadata annotated with domain knowledge. Data catalog tools thus significantly improve the findability, accessibility, interoperability, and reusability (FAIR principles) of datasets. Currently, there is no generally accepted definition or interpretation regarding the required functionality of data catalog tools. This has not only led to a wide range of so-called data catalog tools but has also made it difficult for practitioners to gain an overview in order to make a targeted selection of a tool. Therefore, the main contributions of this paper are 1) an analysis and discussion of the most important data cataloging functionalities and 2) a systematic survey that investigates the extent to which existing data catalog tools implement these functionalities. The detailed results of this survey (i.e., the identified features, data source connectors, support of artificial intelligence for each data catalog tool) are additionally provided in a table that can be customized, sorted, and filtered. While the evaluation table is intended primarily to support practitioners, and in particular data stewards and data engineers, we want to promote a common interpretation of data catalogs in the scientific community with the results compiled in this paper.}, archive = {J_ACCESS}, author = {Jasmin Kropshofer and Johannes Schrott and Wolfram Wöß and Lisa Ehrlinger}, doi = {10.1109/ACCESS.2025.3568542}, journal = {IEEE Access}, pages = {83297-83319}, shortjournal = {IEEE Access}, title = {A survey on the functionalities of data catalog tools}, volume = {13}, year = {2025}, }
• (2025). Performance analysis and computational interface for x – R intuitionistic fuzzy control chart. ACCESS, 13, 83285-83296. (www)

  @article{ , abstract = {Quality control, particularly through the use of control charts, has become essential in industry to ensure that processes free from special causes of variability. Many processes are subject to instrument uncertainty, human subjectivity, and operator hesitation at the time of measurement. In such cases, traditional control charts may not be as viable, so intuitionistic fuzzy control charts should be used, as they are able to represent the uncertainty and hesitation of the process. This study evaluates the performance of the $\bar {X}$ -R intuitionistic fuzzy control charts was measured by the Average Run Length (ARL), Standard Deviation Run Length (SDRL) and Run Length Percentiles. Additionally, computational interface was developed in the R programming language, using the Shiny package, capable of facilitating the user’s experience in combining the concepts presented. Different combinations of the $c_{L}$ and $c_{R}$ coefficients in the IF-WABL (Intuitionistic Fuzzy - Weigthed Average Based on Level) defuzzification method were considered, resulting in 17 scenarios. As these coefficients can be adjusted by the user, it is recommended that scenarios 4 to 7 be used for the general performance of the intuitionistic fuzzy $\bar {X}$ -R control chart, as they lead to reductions in ARL and SDRL values and percentiles close to those of the traditional control chart. A maximum reduction of 5.88% in ARL and 5.45% in SDRL was observed, both in scenario 4. This work showed that intuitionistic fuzzy control charts are efficient at detecting special causes, and that the computer interface developed is capable of performing the proposed functions.}, archive = {J_ACCESS}, author = {Amanda Dos Santos Mendes and Túlio S. Almeida and Marcela A. G. Machado and Paloma Maria Silva Rocha Rizol}, doi = {10.1109/ACCESS.2025.3568774}, journal = {IEEE Access}, pages = {83285-83296}, shortjournal = {IEEE Access}, title = {Performance analysis and computational interface for x – R intuitionistic fuzzy control chart}, volume = {13}, year = {2025}, }
• (2025). Optimizing variable admittance control for remote ultrasound scanning under uncertain environment. ACCESS, 13, 83274-83284. (www)

  @article{ , abstract = {Precise force control in remote robotic ultrasound systems is critical for optimizing image quality and ensuring patient safety. However, conventional admittance control strategies face limitations in achieving high-precision force tracking during interaction while maintaining accurate position tracking in free motion. To address this challenge, we propose an adaptive variable admittance approach integrated with a novel coarse-to-fine force control strategy, which requires online estimation of environmental properties. The environmental information is estimated by a fusion algorithm that combines force and position data from sensors with confidence scores derived from ultrasound images. Furthermore, a compensation term is introduced to the variable stiffness control law to mitigate estimation uncertainties, thereby enhancing force tracking accuracy. Additionally, an energy tank mechanism is implemented to guarantee system passivity under varying damping and stiffness conditions. The effectiveness of the proposed method was experimentally validated using a teleoperated ultrasound system, tested on both a vascular phantom model and human upper limb. The proposed controller demonstrated stable force tracking performance while maintaining compliance throughout the interaction process. The force tracking errors were maintained within ranges of 0.2 N and 0.4 N with standard deviation of 0.02 N and 0.2 N for the phantom and human experiments, respectively.}, archive = {J_ACCESS}, author = {Songjie Xiao and Tengyue Wang and Yun Long and Liangjing Yang}, doi = {10.1109/ACCESS.2025.3568150}, journal = {IEEE Access}, pages = {83274-83284}, shortjournal = {IEEE Access}, title = {Optimizing variable admittance control for remote ultrasound scanning under uncertain environment}, volume = {13}, year = {2025}, }
• (2025). Detection of economic crises with language models and comparative analysis of simple time series analysis models and machine learning algorithms on the stock market. ACCESS, 13, 83254-83273. (www)

  @article{ , abstract = {This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective “smart, automatic crises detection and forecasting model selection application”. The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made.}, archive = {J_ACCESS}, author = {Kurban Kotan and Bayram Kotan and Serdar Kirişoğlu}, doi = {10.1109/ACCESS.2025.3567743}, journal = {IEEE Access}, pages = {83254-83273}, shortjournal = {IEEE Access}, title = {Detection of economic crises with language models and comparative analysis of simple time series analysis models and machine learning algorithms on the stock market}, volume = {13}, year = {2025}, }
• (2025). Securing IoT against slow-rate DDoS attacks: Implementation with tofino p4 ASIC hardware. ACCESS, 13, 83240-83253. (www)

  @article{ , abstract = {DDoS attacks are relevant threats that continue to grow in number, power, and complexity. These attacks are not only materialized through stealthy and fast variants in different network layers but also adapt their behavior and objectives, increasingly targeting less protected devices, particularly those prevalent in Internet of Things (IoT) environments. Recent studies have demonstrated the effectiveness of employing paradigms such as Software-defined Networking (SDN) and its complete realization through programmable data planes for the detection and mitigation of DDoS attacks. However, the complexity of real-world implementation, especially leveraging the Programming Protocol-Independent Packet Processors (P4) programming language (de facto standard in programmable data planes), has posed significant challenges, prompting researchers to concentrate primarily on simulation-based approaches. In this work, we propose an SDN-based Intrusion Prevention System (IPS) that leverages the coordination between the programmable data plane and the control plane to detect and mitigate slow-rate DDoS attacks in IoT environments. A decision tree model is deployed within a programmable switch (P4) to detect the attacks. Furthermore, an SDN controller is responsible for generating mitigation policies and deploying them to the programmable switches, effectively blocking malicious flows. Notably, we implement our solution on a high-performance Tofino P4 ASIC switch, achieving an accuracy of up to 88.74% in detecting attacks, with less than 3% of false positives in mitigation. Our implementation proposes alternatives for IoT environment protection, filling gaps by addressing understudied slow-rate attacks and bridges theoretical and physical implementations of programmable data planes.}, archive = {J_ACCESS}, author = {Erick D. Ramirez-Martinez and Jesus A. Perez-Diaz and Noe M. Yungaicela-Naula and Sergio Armando Gutiérrez and Christian Garzón}, doi = {10.1109/ACCESS.2025.3568753}, journal = {IEEE Access}, pages = {83240-83253}, shortjournal = {IEEE Access}, title = {Securing IoT against slow-rate DDoS attacks: Implementation with tofino p4 ASIC hardware}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of 3D shank kinematics during walking: Stroke patients versus healthy individuals. ACCESS, 13, 83229-83239. (www)

  @article{ , abstract = {This study investigated the 3-axis gait kinematics of people with stroke to healthy individuals. The specific focus was stroke effects on shank movements in the sagittal, frontal and transverse planes. Sixteen stroke patients and sixteen healthy participants walked along a 10-meter walkway at self-comfortable walking speed, with shank angular velocity measured using two gyroscopes integrated into an Inertial Measurement Unit (IMU). Gait events in all motion planes, temporal gait parameters, kinematic data, asymmetry indexes (ASI), and Bland-Altman-liked correlations were also computed. Greater diversity gait patterns were observed in stroke patients. Compared with healthy controls, stroke patients showed reduced stance time on their affected side and lower non-affected swing time, causing gait asymmetry, as reflected in higher ASIs. The stroke patients’ shank exhibited limited motion in all motion planes resulting in lower angular velocity and displacement. Sagittal plane angular velocity results were validated using intra-subject comparisons that showed good agreement between limbs in healthy subjects. These empirical findings in this study provide evidence that shank-based IMUs are effective in revealing temporal-spatial gait alterations in people with stroke compared to healthy individuals which can be exploited to develop a targeted rehabilitation plan to reduce abnormalities in the stroke patient group.}, archive = {J_ACCESS}, author = {Thanita Sanghan and Nusreena Hohsoh and Tulaya Dissaneewate and Desmond Y. R. Chong and Goran Stojanović and Rezaul Begg and Surapong Chatpun}, doi = {10.1109/ACCESS.2025.3568523}, journal = {IEEE Access}, pages = {83229-83239}, shortjournal = {IEEE Access}, title = {Comparative analysis of 3D shank kinematics during walking: Stroke patients versus healthy individuals}, volume = {13}, year = {2025}, }
• (2025). Highly-efficient, dual-polarized huygens’ metasurfaces for scan-angle enhancement without directivity degradation. ACCESS, 13, 83219-83228. (www)

  @article{ , abstract = {We hereby propose a dual-polarized beam-deflecting metasurface that can double the scan-angle range of a phased-array antenna without compromising the directivity across all scan angles, including broadside. In particular, our research focuses on expanding the scanning range of a dual-polarized phased-array antenna capable of independently steering TE and TM waves. This is achieved by placing a dual-polarized phase-gradient Huygens’ metasurface in front of the antenna. The Huygens’ metasurface employs crossed meander lines in four impedance layers that are suitably optimized to independently control the local electric and magnetic responses for maximizing the transmission for all incident beams. We validate our approach through theoretical analysis, full-wave simulations, and experimental verification. It is demonstrated that the beam-deflecting HMS achieves effective scan range expansion to $-30^{\circ } \sim 0^{\circ } $ and $0^{\circ } \sim 30^{\circ } $ for TE and TM beams, respectively, using a dual-polarized phased array antenna source that scans from −15° and 15°.}, archive = {J_ACCESS}, author = {Jaemin Kim and Mohammad Soltani and Minseok Kim and George V. Eleftheriades}, doi = {10.1109/ACCESS.2025.3565892}, journal = {IEEE Access}, pages = {83219-83228}, shortjournal = {IEEE Access}, title = {Highly-efficient, dual-polarized huygens’ metasurfaces for scan-angle enhancement without directivity degradation}, volume = {13}, year = {2025}, }
• (2025). A concept for network slicing in wireless mesh networks. ACCESS, 13, 83195-83218. (www)

  @article{ , abstract = {Network slicing is a key technology for 5G and future mobile networks. It enables the creation of different virtual networks on the same physical infrastructure. By applying network slicing to Wireless Mesh Networks (WMNs), it’s possible to provide communication infrastructure in a quick and less complicated way. This article shows an approach based on an evolutionary algorithm connecting multiple WMN nodes to a slice. The WMN is modeled as an undirected graph to get a data structure for the algorithm. A set of simulations with the NS3 Simulator was done to verify the model’s assumptions. The paper then introduces our concept for network slicing in wireless mesh networks. It then explains how evolutionary algorithms are used in this approach. The proposed algorithm was tested based on a modeled network. It shows which number of nodes to connect the algorithm outperforms a brute force approach and how the algorithm works with more significant network instances. The evolutionary algorithm is then compared to two other optimization approaches for this problem. These are the Simulated Annealing and Ant Colonization Optimization algorithms.}, archive = {J_ACCESS}, author = {Alexander Seng and Ulrich Trick and Armin Lehmann and Bogdan Ghita}, doi = {10.1109/ACCESS.2025.3569077}, journal = {IEEE Access}, pages = {83195-83218}, shortjournal = {IEEE Access}, title = {A concept for network slicing in wireless mesh networks}, volume = {13}, year = {2025}, }
• (2025). Privacy for process mining: A systematic literature review. ACCESS, 13, 83171-83194. (www)

  @article{ , abstract = {Process mining is an emerging scientific discipline focused on discovering, inspecting, and enhancing process models using event data collected from information systems, automating the detailed modeling work without the need for extensive manual labor. However, privacy preservation issues arise when handling such data. Although various process mining methods, models, and tools exist to support the Business Process Management life cycle, no systematic review has been conducted to evaluate these methods and models under the lens of privacy. This work aims to fill this gap by analyzing the applicability of privacy-enhanced methods for process mining. Through a systematic literature review, we identified 39 relevant papers, examining them to understand demographic trends, challenges, characteristics, and implementations related to privacy in process mining. Our findings indicate that privacy-preserving process mining approaches have gained significant attention, especially post-2018, with a predominant focus on anonymizing event logs rather than developing privacy-compliant methodologies for process mining. Most approaches aim to prevent linkage attacks while adhering to privacy regulations, with many utilizing noise addition techniques for anonymization. Despite several issues in defining and using privacy in process mining, this review provides valuable insights for researchers and practitioners, marking the first comprehensive analysis in this domain and highlighting areas for future research.}, archive = {J_ACCESS}, author = {Ibrahim Ileri and Tugba Gurgen Erdogan and Ayca Kolukisa-Tarhan}, doi = {10.1109/ACCESS.2025.3568889}, journal = {IEEE Access}, pages = {83171-83194}, shortjournal = {IEEE Access}, title = {Privacy for process mining: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A comprehensive data description for LoRaWAN path loss measurements in an indoor office setting: Effects of environmental factors. ACCESS, 13, 83148-83170. (www)

  @article{ , abstract = {This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen’s Hölderlinstraße Campus in the City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO2) concentration, barometric pressure, and particulate matter levels (PM2.5). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication.}, archive = {J_ACCESS}, author = {Nahshon Mokua Obiri and Kristof van Laerhoven}, doi = {10.1109/ACCESS.2025.3569164}, journal = {IEEE Access}, pages = {83148-83170}, shortjournal = {IEEE Access}, title = {A comprehensive data description for LoRaWAN path loss measurements in an indoor office setting: Effects of environmental factors}, volume = {13}, year = {2025}, }
• (2025). Research on interference resource optimization based on improved whale optimization algorithm. ACCESS, 13, 83136-83147. (www)

  @article{ , abstract = {Interference resource optimization is a prerequisite for air defense suppression mission planning, and the degree of optimization of interference resources directly determines the quality of the interference results. In this problem, this paper establishes an interference resource optimization model with radar detection probability, interference effectiveness, and interference bandwidth utilization as the objective functions. Then, in the solution process, to address the issues of the Whale Optimization Algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved Whale Optimization Algorithm, BIO-WOA) is proposed. First, the population initialization is completed using Bernoulli chaotic mapping based on the whale optimization algorithm, increasing the diversity and uniformity of solutions and enhancing the algorithm’s global search capability. Then, a nonlinear convergence factor is proposed to balance the local and global search capabilities of the algorithm. Subsequently, the centroid opposition-based learning is used to generate mutated whales, improving the algorithm’s ability to escape local optima. Finally, the effectiveness of the algorithm is verified through test functions and simulation experiments.}, archive = {J_ACCESS}, author = {Xuyi Chen and Mingxi Ma and Chengkui Liu and Haifeng Xie and Shaoqi Wang}, doi = {10.1109/ACCESS.2025.3569460}, journal = {IEEE Access}, pages = {83136-83147}, shortjournal = {IEEE Access}, title = {Research on interference resource optimization based on improved whale optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). MECFormer: Multiple exposure correction transformer based on autoencoder. ACCESS, 13, 83123-83135. (www)

  @article{ , abstract = {Images captured with wrong exposure conditions inevitably produce unsatisfactory visual effects. Thus, multiple exposure correction has drawn much attention, which should correct for degraded images due to various wrong exposure conditions. However, the problem of handling the different nature of underexposed and overexposed images makes this task challenging. In this work, we introduce the novel multiple exposure correction transformer, named MECFormer, to tackle this problem. MECFormer consists of autoencoder, encoder, and dual-path aggregation decoder. First, the autoencoder extracts multi-scale exposure features representing the level of input exposure. Second, the encoder embeds input images into multi-scale image features. Third, the dual-path aggregation decoder sequentially restores exposures by effectively aggregating multi-scale exposure features and image features. MECFormer achieves the state-of-the art performance on two multi-exposure correction datasets. Also, we provide extensive ablation studies to show the effectiveness of the proposed components.}, archive = {J_ACCESS}, author = {Jong-Hyeon Baek and Hyo-Jun Lee and Hanul Kim and Yeong Jun Koh}, doi = {10.1109/ACCESS.2025.3565727}, journal = {IEEE Access}, pages = {83123-83135}, shortjournal = {IEEE Access}, title = {MECFormer: Multiple exposure correction transformer based on autoencoder}, volume = {13}, year = {2025}, }
• (2025). DeFiSentinel: AI-enhanced decentralized finance architecture with advanced cryptographic smart contracts for data integrity and threat resilience. ACCESS, 13, 83107-83122. (www)

  @article{ , abstract = {The flexibility of Decentralized Finance (DeFi) has made it popular and fueled the rapid adoption. However, it introduces security vulnerabilities, fraud risks, and data integrity challenges that traditional financial frameworks fail to address. This paper presents DeFiSentinel, a novel AI-enhanced DeFi architecture that integrates Federated Learning (FL), Blockchain, and Cryptographic Smart Contracts to improve financial security and risk management. In this approach, the privacy-preserving risk assessment is done through FL, allowing multiple financial institutions to collaboratively train fraud detection models while maintaining the confidentiality of sensitive data. The integration of a deep neural network (DNN)–based fraud detection mechanism enhances real-time threat resilience. The blockchain-based anomaly detection and cryptographic smart contracts ensure data integrity and tamper-proof financial transactions through DeFiSentinel. According to the findings from the experimental analysis, the FL model achieves an MSE of 0.021 for risk assessment and an $R^{2}$ score of 0.96. The DNN-based fraud detection framework attains a precision of 92.4%, a recall of 91.1%, and an F1-score of 91.7%. Additionally, blockchain latency remains at an average of 3.70 seconds, while smart contract execution maintains low computational overhead. The results confirm that DeFiSentinel effectively mitigates fraud, enhances transaction security, and ensures regulatory compliance in decentralized financial ecosystems. The findings pave the way for scalable, secure, and efficient financial transactions in DeFi, with future work focusing on optimizing scalability and regulatory adaptation.}, archive = {J_ACCESS}, author = {Seyed Mani Rahnama}, doi = {10.1109/ACCESS.2025.3568842}, journal = {IEEE Access}, pages = {83107-83122}, shortjournal = {IEEE Access}, title = {DeFiSentinel: AI-enhanced decentralized finance architecture with advanced cryptographic smart contracts for data integrity and threat resilience}, volume = {13}, year = {2025}, }
• (2025). Automatic identification of amharic text idiomatic expressions using a deep learning approach. ACCESS, 13, 83095-83106. (www)

  @article{ , abstract = {Natural Language Processing (NLP) is a tract of artificial intelligence and linguistics devoted to making computers understand the statements or words written in human languages. Amharic, the most widely spoken language in Ethiopia, uses a lot of idiomatic expressions and proverbs to emphasize the message of the text. The meaning of an idiomatic phrase cannot be inferred from individual words. Developing a model to identify Amharic idiomatic terms is helpful for different NLP applications like machine translation, sentiment analysis, spam classification, intent recognition, and so on. Few studies have been conducted to identify idiomatic expressions using K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN) algorithms for the Amharic language. The KNN model was designed to identify only dual-word idioms by neglecting idioms with three or more words, and this study didn’t use the n-gram technique to identify idioms found in a sentence or paragraph. Like the former model, the CNN model did not use the n-gram technique, and the testing accuracy was not greater than 80%. Due to this gap, we need to develop a deep learning model that identifies Amharic idioms constructed from two or more words. To identify idioms found in a sentence or paragraph, we used the n-gram method to divide the sentence or the paragraph into phrases for enhancing the previous works. For designing our model, we used 4053 idiomatic phrases and 4051 literal Amharic phrases. After the data were collected, text preprocessing techniques were applied to the collected data, and FastText and Word2Vec models were used for word embedding purposes. We conducted experiments with 70:30 and 80:20 data split ratios with FastText and Word2Vec word embedding models along with long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) with and without attention layer algorithms. Among those experiments, the highest accuracy of 98.95% was attained using an 80:20 train-test split ratio, Adamax optimizer, 64 batch sizes, and a 0.001 learning rate by using Bi-LSTM with an attention layer and FastText word embedding model.}, archive = {J_ACCESS}, author = {Habtamu Hunegnaw Limenih and Abebe Belay Adege and Abrham Yaregal Alene and Habtamu Tariku Demasu and Habtamu Molla Belachew}, doi = {10.1109/ACCESS.2025.3567811}, journal = {IEEE Access}, pages = {83095-83106}, shortjournal = {IEEE Access}, title = {Automatic identification of amharic text idiomatic expressions using a deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Stolt interpolation-free range migration algorithm for dynamic-aperture-based computational MIMO microwave imaging. ACCESS, 13, 83082-83094. (www)

  @article{ , abstract = {Computational imaging (CI) techniques realized by dynamic metasurface antennas (DMAs) offer significant advantages in the hardware design reducing the cost of such systems. In this paper, we present a physical layout based solely on DMA panels that can be used to synthesize electrically large apertures suitable for microwave imaging. Although hardware is significantly simplified, the challenge of using CI DMA-based systems presents itself in the signal processing layer. This is because in microwave CI the transfer function of the antennas is used to encode information from the imaged target scene and compress it into a single (or a few) channel(s). As a consequence, real-time Fourier-based image reconstruction algorithms cannot be directly applied because of the nature of the compressed back-scattered signals. In this work, we propose an algorithm that employs a decompression step such that the signal is converted to a suitable form for spatial frequency domain processing. The proposed technique mitigates the challenges of applying the algorithm in the near-field. Crucially, it also eliminates the need for a computationally expensive Stolt interpolation step, which can be a significant bottleneck for conventional Fourier-based image reconstruction algorithms. Numerical and experimental CI results are presented to validate the ability of the proposed system and algorithm to produce reconstructed images of good quality. The benefits of the proposed algorithm in terms of execution time and storage requirements are also explored in this work.}, archive = {J_ACCESS}, author = {Vasiliki Skouroliakou and Maria García-Fernandez and Guillermo Álvarez-Narciandi and Simon Cotton and Okan Yurduseven}, doi = {10.1109/ACCESS.2025.3566175}, journal = {IEEE Access}, pages = {83082-83094}, shortjournal = {IEEE Access}, title = {Stolt interpolation-free range migration algorithm for dynamic-aperture-based computational MIMO microwave imaging}, volume = {13}, year = {2025}, }
• (2025). Pillar-X: Integrating self-learned image features to improve 3D object detection. ACCESS, 13, 83071-83081. (www)

  @article{ , abstract = {Accurate 3D object detection is essential for robust perception systems in autonomous vehicles. This paper presents Pillar-X, a 3D object recognition framework designed to generate and use self-learned image features. Unlike approaches that rely on pre-trained networks, Pillar-X efficiently learns and fuses features from an image directly within an end-to-end pipeline. Comprehensive evaluations on the KITTI and CARLA-generated dataset demonstrate Pillar-X’s significant reduction in false positive detections, especially for the pedestrian and cyclist classes. The proposed model achieves this while maintaining a speed similar to the baseline algorithm (¿20 Hz). By comparing the network using both PASCAL and a specific criterion, it can be concluded that Pillar-X is able to improve accuracy and reliability in 3D object detection tasks.}, archive = {J_ACCESS}, author = {Mihály Csonthó and András Rövid}, doi = {10.1109/ACCESS.2025.3567658}, journal = {IEEE Access}, pages = {83071-83081}, shortjournal = {IEEE Access}, title = {Pillar-X: Integrating self-learned image features to improve 3D object detection}, volume = {13}, year = {2025}, }
• (2025). YOLO-LSD: A lightweight object detection model for small targets at long distances to secure pedestrian safety. ACCESS, 13, 83061-83070. (www)

  @article{ , abstract = {Ensuring the safety of children, the elderly, and individuals with disabilities remains a significant challenge in modern transportation environments, particularly in urban areas with mixed traffic comprising both vehicles and pedestrians. Real-time detection of long-distance and small-scale objects remains difficult due to resolution and computational constraints. This study proposes an improved lightweight object detection model, You Only Look Once – Long-distance Small-target Detection (YOLO-LSD), designed for the long-range recognition of small objects in intelligent transportation applications. The proposed model integrates the C3C2 and the new Efficient Layer Aggregation Network - Convolutional Block Attention Module(ELAN-CBAM) modules to improve the efficiency of feature extraction while reducing computational overhead. The C3C2 module optimizes the network structure by reducing redundant operations, making it more suitable for real-time deployment on embedded devices. The CBAM module improves feature selection by incorporating channel and spatial attention mechanisms, thereby enhancing the robustness of small-object detection under complex urban conditions. The proposed YOLO-LSD model was tested on a customized wearable backpack system equipped with front and rear cameras for environmental perception. YOLO-LSD attains higher detection accuracy for small and distant objects while maintaining lower computational complexity. This study employs the PASCAL VOC $2007+2012$ (VOC0712) dataset to evaluate the proposed model. Compared with YOLOv7, the model achieves a maximum mean Average Precision (mAP) of 80.1%, a minimum parameter count of 30.539M, and the lowest computational cost of 91.6 GFLOPS. This lightweight architecture makes it particularly suitable for intelligent transportation, pedestrian safety, and autonomous mobility applications. In summary, this lightweight architecture, YOLO-LSD, is particularly suitable for intelligent transportation, pedestrian safety and automated mobility applications.}, archive = {J_ACCESS}, author = {Ming-An Chung and Sung-Yun Chai and Ming-Chun Hsieh and Chia-Wei Lin and Kai-Xiang Chen and Shang-Jui Huang and Jun-Hao Zhang}, doi = {10.1109/ACCESS.2025.3567843}, journal = {IEEE Access}, pages = {83061-83070}, shortjournal = {IEEE Access}, title = {YOLO-LSD: A lightweight object detection model for small targets at long distances to secure pedestrian safety}, volume = {13}, year = {2025}, }
• (2025). Mobility management with AI. ACCESS, 13, 83022-83060. (www)

  @article{ , abstract = {Today, considering the complexity of different types of networks worldwide and the variety of different pathways for mobility, researchers are interested in research and analysis in mobility management, one of the critical aspects of network management. Mobility management is used for many cases, such as urban management, location-based services (LBS), collision avoidance and tourism industry improvement. On the other hand, the dramatic advances in artificial intelligence (AI) have attracted much attention to the use of this tool in network management, using which they can quickly predict the following route or destination of users in the network in different scenarios. However, there are many challenges in this field, and ignoring them reduces the accuracy of the models. In this paper, We introduce the methods that researchers have presented and mention the advantages and disadvantages of each. The articles used include reputable journals from 2020 to 2025. Mobility management is done by focusing on four topics human mobility, vehicle, aircraft and ship. After introducing the approaches, we categorized and compared these models with related articles. Then, we showed the important items used in the articles, such as datasets, in the diagram and introduced the most used and famous ones. Also, commonly used metrics were reviewed. We highlight aspects that can help improve the performance of models. By considering these aspects, researchers can introduce more practical models that achieve lower errors using metrics such as Root-mean-square deviation (RMSE).}, archive = {J_ACCESS}, author = {Pooyan Jahanmanesh and Amirfarhad Farhadi and Azadeh Zamanifar}, doi = {10.1109/ACCESS.2025.3566337}, journal = {IEEE Access}, pages = {83022-83060}, shortjournal = {IEEE Access}, title = {Mobility management with AI}, volume = {13}, year = {2025}, }
• (2025). Design method of ICPS comprehensive security controller with multi-target constraints and DoS intrusion tolerance capability driven by double end ADETCS. ACCESS, 13, 83004-83021. (www)

  @article{ , abstract = {With a focus on industrial cyber-physical systems (ICPSs) that are subject to dual-ended denial-of-service (DoS) attacks and actuator faults, and in the context of a double-ended adaptive discrete event-triggered communication scheme (ADETCS), we study the design method of ICPS comprehensive security controller with multi-target constraints. Firstly, a novel double-ended ADETCS is designed to double-screening the transmitted data on both the sensing and execution sides, and a comprehensive security control architecture based on ICPSs with multi-objective constraints that can simultaneously resist dual-ended DoS attacks and actuator failures has been constructed. Secondly, on the sensing side, a method based on integration of data driven and model mechanisms is used to deal with DoS attacks with different levels of severity. On the execution side, a Principal Component Analysis - Long Short-Term Memory (PCA-LSTM) data-driven method is used to reconstruct and compensate the lost data caused by DoS attacks. Thirdly, by introducing multi-target constraints, such as $\alpha $ -stability and $H_{\infty }$ performance index, and based on the constructed Lyapunov-Krascovskii function, a cooperative design method is derived for the observer and the comprehensive security controller under multi-target constraints. Finally, the effectiveness and practicability of the proposed method are verified by a simulation experiment involving an industrial four-capacity water tank. The results indicate that the integration method of data driven and model mechanisms enhances the ability of ICPSs to resist dual-ended DoS attacks, and the double-ended ADETCS further saves network communication resources.}, archive = {J_ACCESS}, author = {Yajie Li and Feiping Wang}, doi = {10.1109/ACCESS.2025.3567444}, journal = {IEEE Access}, pages = {83004-83021}, shortjournal = {IEEE Access}, title = {Design method of ICPS comprehensive security controller with multi-target constraints and DoS intrusion tolerance capability driven by double end ADETCS}, volume = {13}, year = {2025}, }
• (2025). Transmission of 96 gbps over 50-m GI-POF using 6 CWDM channels in s, c, and l optical bands. ACCESS, 13, 82995-83003. (www)

  @article{ , abstract = {An aggregate transmission of 96 Gbps over 50 m of graded index plastic optical fiber (GI-POF) is demonstrated using six coarse wavelength multiplexing (CWDM) channels in S, C and L optical bands. Each channel transmits an equal rate of 16 Gbps with NRZ formats. The used CWDM channels are 1490 and 1510 in the S band, 1530 and 1550 in the C band, and 1570 and 1590 in the L band. In addition, 16, 14 and 12 Gbps were tested at varying levels of optical power for each CWDM channel, and the BER levels were measured for 50 m GI-POF transmission and compared to that of back-to-back transmission.}, archive = {J_ACCESS}, author = {Hossam A. I. Selmy and Gil M. Fernandes and Rogério Nogueira and Ricardo Oliveira}, doi = {10.1109/ACCESS.2025.3567849}, journal = {IEEE Access}, pages = {82995-83003}, shortjournal = {IEEE Access}, title = {Transmission of 96 gbps over 50-m GI-POF using 6 CWDM channels in s, c, and l optical bands}, volume = {13}, year = {2025}, }
• (2025). Modeling and predicting uncertainty in tidal turbine power output: A data-driven time-series approach. ACCESS, 13, 82986-82994. (www)

  @article{ , abstract = {The integration of sustainable energy sources into modern power grids is crucial for achieving a more resilient, equitable, and environmentally sustainable electricity infrastructure. As the demand for renewable energy increases, tidal turbines have emerged as a promising solution for expanding power generation and contributing to a more diverse mix of sustainable energy sources. However, tidal energy is influenced by variations in environmental and hydrodynamic conditions, which affect tidal turbine power output. As a result, the amount of power a tidal turbine generates at any given moment remains uncertain. This uncertainty poses significant challenges for grid energy management and maintaining a supply-demand balance. To address this issue, this study introduces a systematic approach to quantifying, modeling, and predicting uncertainty in tidal turbine power output. Unlike conventional methods that focus on variability, this framework defines uncertainty as unpredictability and applies time-series modeling to characterize and forecast uncertainty in power generation. The methodology employs an Autoregressive Integrated Moving Average (ARIMA) model to capture predictable patterns, while a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model quantifies and predicts uncertainty in tidal turbine power output. The proposed method was implemented using high-resolution experimental data from controlled tidal turbine tests conducted at the FloWave facility at the University of Edinburgh. The results confirm that the GARCH model effectively predicts future uncertainty levels. The findings establish a foundation for advancing tidal energy deployment and enhancing the integration of renewable resources into the power grid.}, archive = {J_ACCESS}, author = {Niousha Talebpour and Mohammad Ilbeigi}, doi = {10.1109/ACCESS.2025.3567884}, journal = {IEEE Access}, pages = {82986-82994}, shortjournal = {IEEE Access}, title = {Modeling and predicting uncertainty in tidal turbine power output: A data-driven time-series approach}, volume = {13}, year = {2025}, }
• (2025). Do sharpness-based optimizers improve generalization in medical image analysis?. ACCESS, 13, 82972-82985. (www)

  @article{ , abstract = {Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets. This success has led to the development of several advanced sharpness-based algorithms aimed at addressing the limitations of SAM, such as Adaptive SAM, Surrogate-Gap SAM, Weighted SAM, and Curvature Regularized SAM. These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. This work provides a review of recent sharpness-based methods for improving the generalization of deep learning networks and evaluates the methods’ performance on three medical image datasets, including breast ultrasound, chest X-ray, and colon histopathology images. Our findings indicate that the initial SAM method successfully enhances the generalization of various deep learning models. While Adaptive SAM improves generalization of convolutional neural networks, it fails to do so for vision transformers. Other sharpness-based optimizers, however, do not demonstrate consistent results. The results reveal that contrary to findings in the non-medical domain, SAM is the only recommended sharpness-based optimizer that consistently improves generalization in medical image analysis, and further research is necessary to refine the variants of SAM to enhance generalization performance in this field.}, archive = {J_ACCESS}, author = {Mohamed Hassan and Aleksandar Vakanski and Boyu Zhang and Min Xian}, doi = {10.1109/ACCESS.2025.3568641}, journal = {IEEE Access}, pages = {82972-82985}, shortjournal = {IEEE Access}, title = {Do sharpness-based optimizers improve generalization in medical image analysis?}, volume = {13}, year = {2025}, }
• (2025). Compressed speech steganalysis through deep feature extraction using 3D convolution and bi-LSTM. ACCESS, 13, 82960-82971. (www)

  @article{ , abstract = {Voice-over-Internet-Protocol (VoIP) based speech steganography techniques provide convenience for covert communication while posing significant threats to network security. Accurately detecting hidden information in voice signals is of critical significance for cybersecurity. In this paper, we focus on comprehensive feature extraction to enhance detection accuracy across varying embedding rates. We propose a steganalysis approach that combines 3D convolution with Bi-LSTM, incorporating an attention mechanism. Firstly, the encoded speech data is partitioned into three-dimensional data blocks, and 3D convolution is used to capture the correlation between consecutive frames. Subsequently, Bi-LSTM is employed to extract the contextual features. Finally, the extracted features are fused and fed into a classification network to determine whether secret information is embedded within the speech stream. We design corresponding experiments on a publicly available dataset comprising 41 hours of Mandarin speech and 72 hours of English speech encoded with the G.723 standard. The proposed method demonstrates superior performance, accuracy improvements of approximately 2%, 3%, 3%, and 4% over state-of-the-art methods.}, archive = {J_ACCESS}, author = {Kesen Li and Feipeng Gao and Jie Yang}, doi = {10.1109/ACCESS.2025.3567601}, journal = {IEEE Access}, pages = {82960-82971}, shortjournal = {IEEE Access}, title = {Compressed speech steganalysis through deep feature extraction using 3D convolution and bi-LSTM}, volume = {13}, year = {2025}, }
• (2025). An analysis of semi-supervised machine learning in electrical machines. ACCESS, 13, 82927-82959. (www)

  @article{ , abstract = {This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate characteristics of electrical machines. SSML provides a key benefit in enhancing the effectiveness and precision of predictive models for optimizing electrical machine performance, reliability, and maintenance by leveraging labeled and unlabeled data. The research investigates important SSML algorithms such as self-training, co-training, generative models, and graph-based methods, highlighting their particular uses in fault diagnosis, condition monitoring, and predictive maintenance of electrical machines. Moreover, the document discusses the specific difficulties associated with this merger and offers remedies to improve the utilization of SSML in this significant area. A detailed table summarizes various methods and emphasizes their role in furthering machine learning within the realm of electrical machines.}, archive = {J_ACCESS}, author = {V. Raju Arvind and S. Shyamsharan and Poorvajaa Gurunathan and Krishna Kumba and Nawin Ra}, doi = {10.1109/ACCESS.2025.3567047}, journal = {IEEE Access}, pages = {82927-82959}, shortjournal = {IEEE Access}, title = {An analysis of semi-supervised machine learning in electrical machines}, volume = {13}, year = {2025}, }
• (2025). A lightweight Forward–Backward independent temporal-aware causal network for speech emotion recognition. ACCESS, 13, 82914-82926. (www)

  @article{ , abstract = {Speech Emotion Recognition (SER) technology analyzes speech characteristics in human-computer interactions to understand user intent and improve interaction experience. It is widely used in the field of intelligent interaction. The significant challenge is to recognize the speech emotion of the speaker faster and more precisely. To address this issue, we propose a lightweight forward-backward independent temporal-aware causal network termed I-TCN to construct bi-directional efficient representations of causality in speech time domain sequences. Specifically, a forward temporal-aware component constructed with dilated causal convolutions and skip connections is deployed to perform forward sequences feature modeling, which captures forward semantic information and causal relationships in the speech signal. It facilitates the prediction of emotional changes in the future. Furthermore, the backward temporal-aware module uses dilated causal convolutions to learn backward information and weighted fusion of multi-level temporal features to enhance the perception of backward emotion changes. Finally, different levels of forward-backward features are fused to refine historical-future emotion change trends and better perceive the details of emotion changes. Experimental results on six different linguistic datasets (EMODB: 95.52%; EMOVO: 92.00%; RAVDESS: 93.75%; SAVEE: 88.54%; CASIA: 94.50%; IEMOCAP: 71.47%) show that the emotion recognition capability of the proposed method is extremely competitive with state-of-the-art technologies. Meanwhile, the numerical results show that the proposed method has a good application prospect with a small number of parameters (0.21M) and low computational cost (80.72 MFLOPs).}, archive = {J_ACCESS}, author = {Sijia Fei and Qiang Feng and Fei Gao}, doi = {10.1109/ACCESS.2025.3567954}, journal = {IEEE Access}, pages = {82914-82926}, shortjournal = {IEEE Access}, title = {A lightweight Forward–Backward independent temporal-aware causal network for speech emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Training-free affordance labeling and exploration using subspace projection and manifold curvature over pre-trained deep networks. ACCESS, 13, 82897-82913. (www)

  @article{ , abstract = {The advancement in computing power has significantly reduced the training times for deep learning, enabling the rapid development of networks designed for object recognition. However, the exploration of object utility, the object’s affordance, as opposed to object recognition, has received comparatively less attention. Existing object affordance models exhibit shortcomings, including limited robustness across diverse architectures and insufficient performance in complex environments. This work focuses on using pre-trained networks trained on object classification datasets to explore object affordances. While these networks have proven instrumental in transfer learning for classification tasks, the presented approach in this study diverges from conventional object classification methods by labeling affordances without modifying the final layers. Instead, pre-trained networks are employed to learn affordance labels without requiring specialized classification layers. Two approaches are tested: the Subspace Projection Method and the Manifold Curvature Method, which facilitate the determination of affordance labels without such modifications. Both the Subspace Projection Method and the Manifold Curvature Method were evaluated using nine distinct pre-trained networks across two different affordance datasets. The Subspace Projection Method achieved a True Positive Rate of up to 94% and 96% for the best-performing networks on each dataset, while the Manifold Curvature Method attained True Positive Rates exceeding 98% and 99% with its top-performing networks. Furthermore, both methods identify affordance labels that are not marked in the ground truth but are present in various cases. The robustness of the Manifold Curvature Method and the exploration capability of both methods highlight the effectiveness of proposed techniques for affordance labeling.}, archive = {J_ACCESS}, author = {İsmaıl Özçıl and A. Buğra Koku}, doi = {10.1109/ACCESS.2025.3565330}, journal = {IEEE Access}, pages = {82897-82913}, shortjournal = {IEEE Access}, title = {Training-free affordance labeling and exploration using subspace projection and manifold curvature over pre-trained deep networks}, volume = {13}, year = {2025}, }
• (2025). Selection of a coastal protection measure via multi-criteria decision analysis: The case of balneário camboriú, brazil. ACCESS, 13, 82881-82896. (www)

  @article{ , abstract = {The present study applies a hybrid Multi-Criteria Decision Method called Multicriteria Instant Runoff Voting (MIRV) to select the most appropriate coastal protection measure for Balneário Camboriú, Brazil, a city that is highly vulnerable to rising sea levels. MIRV combines the features of Instant Runoff Voting and the Borda method to integrate multi-criteria assessment while addressing the monotonicity shortfall of traditional voting systems. The research engages a multidisciplinary team of seven professionals to evaluate seven coastal protection alternatives against 11 criteria, including implementation cost, environmental impact, social impact, and adaptability. The results identify Submerged Artificial Reefs (SAR) as the most suitable measure, balancing protection with ecological and social benefits. Sensitivity analysis confirms the method’s robustness despite varying decision-maker preferences. MIRV demonstrates a scalable, intuitive framework that accommodates diverse stakeholder inputs, facilitating decision-making in complex scenarios. This approach can be extended to similar challenges globally, offering a practical solution for integrating scientific rigor and stakeholder values in environmental planning.}, archive = {J_ACCESS}, author = {Leandro José Tranzola Santos and Miguel Ângelo Lellis Moreira and Igor Pinheiro de Araújo Costa and Marcos Dos Santos}, doi = {10.1109/ACCESS.2025.3567943}, journal = {IEEE Access}, pages = {82881-82896}, shortjournal = {IEEE Access}, title = {Selection of a coastal protection measure via multi-criteria decision analysis: The case of balneário camboriú, brazil}, volume = {13}, year = {2025}, }
• (2025). A D-band CMOS linearity-enhanced low-noise amplifier using multiple-gated transistors technique. ACCESS, 13, 82874-82880. (www)

  @article{ , abstract = {In this paper, a D-band low-noise amplifier (LNA) using the linearity-enhanced technique in 40-nm CMOS process is presented. In this D-band LNA, multiple-gated transistors are used to improve the third-order intercept point (IIP3) while maintaining minimal small-signal gain reduction and nearly identical DC power consumption. This linearized LNA includes three conventional capacitive neutralization common-source differential stages and one multiple-gated transistor (MGTR) stage as the final stage. From the two-tone large-signal measurement results, the linear mode demonstrates a 5 dB improvement in the third-order intercept point (IIP3). In the one-tone large-signal measurement, a 4 dB improvement of the input 1-dB compression point ( $\mathrm{IP}_{\mathrm {1dB}}$ ) can be observed in the linear mode. For the small-signal measurement, this LNA achieves peak small-signal gains of 16.5 dB in normal mode and 14.2 dB in linear mode. The 3-dB bandwidth is 35 GHz (110-145 GHz) in both operating modes. Using the Y-factor method, the measured noise figure is 8.8 dB in normal mode and 9.5 dB in linear mode at 140 GHz. The DC consumption is 42 mW and 43 mW at 0.9 V for normal mode and linear mode. The whole chip area is ${0.32~mm}^{2}$ . To the author’s knowledge, this is the first implementation of the MGTR technique in CMOS circuits operating beyond 100 GHz.}, archive = {J_ACCESS}, author = {Jingbo Zhang and Munkyo Seo}, doi = {10.1109/ACCESS.2025.3568071}, journal = {IEEE Access}, pages = {82874-82880}, shortjournal = {IEEE Access}, title = {A D-band CMOS linearity-enhanced low-noise amplifier using multiple-gated transistors technique}, volume = {13}, year = {2025}, }
• (2025). Understanding software defect prediction through eXplainable neural additive models. ACCESS, 13, 82860-82873. (www)

  @article{ , abstract = {Software defect prediction, leveraging machine learning techniques to proactively identify potential defects in software systems, plays a crucial role in enhancing software quality and reliability. However, a major challenge in this field lies in the opacity of the prediction process and the lack of interpretability of the results, which significantly limits its practical application. To address this issue, this paper introduces eXplainable Neural Additive Models (XNAMs). The proposed model constructs single-feature inputs for software defect data, enabling transparent visualization of the impact of individual features on prediction outcomes. Additionally, it employs feature gradient analysis to examine the average absolute values of feature gradients during forward propagation, thereby quantifying and comparing the contribution of each feature to the decision-making process. Furthermore, feature interaction analysis is conducted to uncover nonlinear interactions between different features. Experimental evaluations on six software projects demonstrate that XNAMs outperform existing models in prediction performance while offering clear explanations of feature contributions, ensuring high transparency and practical applicability.}, archive = {J_ACCESS}, author = {Ruiqi He and Yong Li and Chi Sun}, doi = {10.1109/ACCESS.2025.3567140}, journal = {IEEE Access}, pages = {82860-82873}, shortjournal = {IEEE Access}, title = {Understanding software defect prediction through eXplainable neural additive models}, volume = {13}, year = {2025}, }
• (2025). Solving the single-source capacitated facility location problem using a crow search-based hybrid method. ACCESS, 13, 82847-82859. (www)

  @article{ , abstract = {The Single Source Capacitated Facility Location Problem (SSCFLP) stands as a pivotal yet highly complex challenge in facility location science, underpinning real-world supply chains where single-sourcing constraints are critical for service quality and operational efficiency. This study introduces a hybrid solution framework that merges a Binary Crow Search Algorithm (BinCSA) with an exact method, leveraging XOR logic, bit-flip mechanisms, and a dynamic awareness probability (AP) scheme to prevent premature convergence. Across medium-, large-, and extreme-scale benchmark instances, the enhanced BinCSA demonstrates accelerated convergence relative to the original BinCSA. By decomposing the SSCFLP into two subproblems, the proposed approach eases the burden on the exact solver and circumvents the need for transfer functions. A Markov chain analysis underpins BinCSA’s theoretical convergence guarantees, supported by computational results that confirm irreducibility and convergence within a finite solution space. Experiments on benchmark datasets show that BinCSA yields high-quality solutions with efficient runtimes, performing competitively against existing heuristics as well as a CPLEX baseline. To the best of our knowledge, although the Crow Search Algorithm (CSA) has been applied to uncapacitated facility location problems (UFLP), the present work is among the first to adapt CSA-based methods to SSCFLP, thereby extending the algorithm’s scope of applicability. Overall, these findings highlight BinCSA’s robustness and efficiency in tackling large-scale, NP-hard optimization problems, paving the way for broader applications in supply chain design, telecommunication networks, and other high-tech domains.}, archive = {J_ACCESS}, author = {Wangyue Xu and Martino Luis and Baris Yuce}, doi = {10.1109/ACCESS.2025.3565153}, journal = {IEEE Access}, pages = {82847-82859}, shortjournal = {IEEE Access}, title = {Solving the single-source capacitated facility location problem using a crow search-based hybrid method}, volume = {13}, year = {2025}, }
• (2025). Efficiency of NOMA-assisted FD-AF vehicle-to-vehicle relaying over nakagami-m fading channels. ACCESS, 13, 82834-82846. (www)

  @article{ , abstract = {Nakagami-m distribution is typically used to model various multipath fading channels in relaying networks. This work analyses a NOMA-based vehicle-to-vehicle (V2V) relaying over Nakagami-m fading channels. Specifically, we investigate the performance of a NOMA over the Nakagami-m distributed channels by leveraging a dual-hop full-duplex amplify-and-forward scheme in V2V communications. To this end, we derive closed-form expressions of rate outage probabilities and average received signal-to-interference plus noise ratios for the two vehicular users at non-identical destinations in terms of Tricomi confluent hypergeometric function. The derived analytical results are valid for arbitrary values of the fading parameter m. Furthermore, we also derive the expressions for the lower and upper bounds on rate outage probabilities. The residual self-interference, inherent in the full-duplex V2V relaying is also considered and its impact on the system’s performance is studied. The accuracy of the analytical expressions is validated by simulation results which are found to be in good agreement.}, archive = {J_ACCESS}, author = {Sajid Hussain Alvi and Mohammad Arif and Keivan Navaie}, doi = {10.1109/ACCESS.2025.3568465}, journal = {IEEE Access}, pages = {82834-82846}, shortjournal = {IEEE Access}, title = {Efficiency of NOMA-assisted FD-AF vehicle-to-vehicle relaying over nakagami-m fading channels}, volume = {13}, year = {2025}, }
• (2025). Exploring multiple functionalities of cyber-physical power system within digital substation-based laboratory environment. ACCESS, 13, 82821-82833. (www)

  @article{ , abstract = {The landscape of control centre technologies is dynamically adapting to the swift transformations in the physical world, specifically targeting wide-area monitoring, control, and protection in energy systems. In response to these changes, Cyber-Physical Power System (CPPS) has emerged as a promising solution to develop control centre technologies and is gaining significant attention from scholars and researchers with similar interests. However, the widespread adoption of CPPS technology faces several challenges, such as the need for methodologies to develop further CPPS or the detailed deployment of multiple functions within the CPPS framework. To address these challenges, a specialised CPPS testbed within a laboratory environment is presented in this study. In addition, this paper delves into various functions integrated into CPPS, involving critical aspects such as real-time monitoring, digital communication protocol, state estimation, outlier detection, optimal power flow and self-healing capabilities. By implementing a co-simulation platform based on two real-time simulators, Typhoon HIL, the physical and cyber layers can exchange data through Ethernet LAN within the laboratory environment through communication protocols, IEEE C37.118.1 and IEC 61850. This research lays the foundation for a deeper understanding of the potential applications of the CPPS testbed for future control centre technology as well as serves as a catalyst for further innovations, driving a paradigm toward smart, flexible energy infrastructure.}, archive = {J_ACCESS}, author = {Le Nam Hai Pham and Jose Miguel Riquelme-Dominguez and Francisco Gonzalez-Longatt}, doi = {10.1109/ACCESS.2025.3563260}, journal = {IEEE Access}, pages = {82821-82833}, shortjournal = {IEEE Access}, title = {Exploring multiple functionalities of cyber-physical power system within digital substation-based laboratory environment}, volume = {13}, year = {2025}, }
• (2025). ICBoost: An XGBoost-based unbiased transformed algorithm for survival regression. ACCESS, 13, 82807-82820. (www)

  @article{ , abstract = {Prediction models for disease onset are critical in biomedical research and survival analysis. With machine learning methods increasingly being used to handle survival data with censoring, unbiased transformation theory offers an alternative method for estimating survival tasks in the presence of such censoring, thereby enhancing model accuracy. This study aims to develop a reliable and efficient prediction algorithm that utilizes unbiased transformation to improve machine learning model performance on interval-censored data. Therefore, we present ICBoost, a novel survival algorithm that integrates regression trees and ensemble methods specifically designed for interval-censored data. Unlike right-censored data, where the exact event time is unknown but occurs after a known time point, interval-censored data only provides intervals within which the event occurred. The inherent complexity of interval-censored data poses challenges for accurate survival prediction. To overcome these challenges, we propose a kernel density estimation-based unbiased transformation approach to estimate failure time. Furthermore, we develop a novel ensemble framework that combines XGBoost with censoring unbiased transformation. This framework allows for investigating the relationships between patient survival and covariates, thereby enhancing prediction accuracy and model interpretability. We evaluated the performance of the ICBoost algorithm against existing methods using various real and simulated datasets. Our results showed that ICBoost exhibited superior performance in identifying hidden patterns for survival prediction tasks, particularly in datasets related to Alzheimer’s disease and the emergence of permanent teeth in the medical field. ICBoost outperformed other methods, as evidenced by lower root-mean-square error, mean absolute error, and Brier score values.}, archive = {J_ACCESS}, author = {Jingyi Zhang and Shishun Zhao and Yang Xu and Tao Hu}, doi = {10.1109/ACCESS.2025.3568196}, journal = {IEEE Access}, pages = {82807-82820}, shortjournal = {IEEE Access}, title = {ICBoost: An XGBoost-based unbiased transformed algorithm for survival regression}, volume = {13}, year = {2025}, }
• (2025). A climate-informed approach to create hourly future weather timeseries for power system planning. ACCESS, 13, 82796-82806. (www)

  @article{ , abstract = {Power system planning tools require hourly weather data to capture the variable conditions that influence electricity supply and demand (e.g., temperature, wind, and solar). It is well-established that using current or historical weather conditions is insufficient for planning a resilient system under climate change and that forward-looking data are needed when conducting energy transition studies through 2050. However, nearly all global climate model (GCM) projections are limited to daily temporal resolution, presenting a data gap that must be addressed to incorporate climate change projections directly into existing commercial tools for power system modeling. This paper presents an innovative approach to create hourly weather timeseries for future climates. A monthly quantile delta mapping technique is used to produce realistic hourly weather data for a future climate by adding the monthly climate change signal projected by climate models to historical weather data. This method preserves important, real-world characteristics from the historical record that are otherwise missing from climate model output, such as locationally specific extremes which can be missing from coarse climate projections, local natural variability which may not be well represented in the climate models, and important joint correlations among physically linked variables such as wind, solar, and temperature. This approach has many potential applications in the power sector, including for capacity expansion and production cost modeling where select hourly timeseries are used for complex optimizations or simulations, as well as for resource adequacy assessments that evaluate large samples of realizations to identify possible extremes for stress-testing a future year of interest.}, archive = {J_ACCESS}, author = {Erik T. Smith and Delavane B. Diaz and Jacob Mardian}, doi = {10.1109/ACCESS.2025.3567864}, journal = {IEEE Access}, pages = {82796-82806}, shortjournal = {IEEE Access}, title = {A climate-informed approach to create hourly future weather timeseries for power system planning}, volume = {13}, year = {2025}, }
• (2025). High perplexity mountain flood level forecasting in small watersheds based on compound long short-term memory model and multimodal short disaster-causing factors. ACCESS, 13, 82783-82795. (www)

  @article{ , abstract = {Mountain flood water levels exhibit high variability and complexity, making them challenging to predict, and gathering long-term data of disaster-causing factors is difficult in small watersheds, the available disaster-causing variables are short-term multimodal data. In order to improve the accuracy and real-time performance of mountain flood forecasting, we import Deep Learning (DL) model for mountain flood level prediction utilizing compound time series Long Short-Term Memory (LSTM) model and multimodal short data. On the basis of LSTM model, Convolutional Neural Networks (CNN) module is added to increase the short-term window prediction ability, and Attention module is further added to improve the prediction ability of complex water level changes, forming the compound LSTM, including LSTM-CNN and LSTM-CNN-Attention model. The data of multimodal short disaster-causing factors includes hydrology, meteorology, geography, etc., by integrating the short duration time series data for compound LSTM’s input data. This study evaluates the performance of advanced models on three test data set from representative small watersheds in Zhejiang Province, China, highlighting the effectiveness of the compound LSTM model in these specific areas. The findings emphasize the benefits of utilizing the compound LSTM model for flood forecasting. In particular, the LSTM-CNN-Attention model demonstrates enhanced accuracy and real-time processing capabilities.}, archive = {J_ACCESS}, author = {Songsong Wang and Ouguan Xu}, doi = {10.1109/ACCESS.2025.3567640}, journal = {IEEE Access}, pages = {82783-82795}, shortjournal = {IEEE Access}, title = {High perplexity mountain flood level forecasting in small watersheds based on compound long short-term memory model and multimodal short disaster-causing factors}, volume = {13}, year = {2025}, }
• (2025). Enhancing short-term wind speed prediction based on deep learning with ensemble learning model for small wind turbine applications. ACCESS, 13, 82759-82782. (www)

  @article{ , abstract = {Wind is unstable and unpredictable, and power generation is not constant. Wind speed prediction reduces these disadvantages, and it is essential to measure accurate wind speed predictions to install and stabilize wind power generation systems. This research focused on predicting wind speed data collected from various locations in India, such as Amaravati, Bangalore, Kanyakumari, and Kochi, at heights of 10 m and 50 m, with a sampling period of 1 h for small wind turbine (SWT) applications. This study discusses various deep learning (DL) algorithms for enhancing wind speed forecasting accuracy. The performance of deep learning algorithms is evaluated using multiple metrics, namely, mean square error, normalized mean square error, root mean square error, normalized root means square error, relative root mean square error, mean absolute percentile error, symmetric mean absolute percentage error, and coefficient of determination. For the short-term wind speed prediction, the ensemble learning model approach gave the best results in all sites and among all models applied. The results of the wind speed prediction algorithms show that the Kanyakumari datasets have improved accuracy compared to other locations. At this location, the R2 value is about 0.9942 at a height of 10 m and 0.9955 at 50 m. Further, this dataset is segregated into seasons and months. During the summer seasons, short-term wind speed predictions are more accurate, with R2 values of 0.9885 at 10 m and 0.9904 at 50 m, and November stands out for its highest efficiency in monthly wind speed forecasts, with R2 values reaching 0.9910 at 10 m and 0.9919 at 50 m.}, archive = {J_ACCESS}, author = {J. Sathyaraj and V. Sankardoss}, doi = {10.1109/ACCESS.2025.3567803}, journal = {IEEE Access}, pages = {82759-82782}, shortjournal = {IEEE Access}, title = {Enhancing short-term wind speed prediction based on deep learning with ensemble learning model for small wind turbine applications}, volume = {13}, year = {2025}, }
• (2025). Deepfake detection using spatio-temporal-structural anomaly learning and fuzzy system-based decision fusion. ACCESS, 13, 82747-82758. (www)

  @article{ , abstract = {Deepfake techniques have been evolving rapidly in recent days and pose a severe security threat. Detecting such generated fake videos is a challenging task. Existing deep learning based deep fake detection model struggle in identifying fake videos when experimented on challenging dataset. In this paper, we propose a Fuzzy logic based Deepfake Detector system (Fuzzy-DFD) using a deep learning model ResNet-50 as encoder. The system workflow includes preprocessing, frame extraction, deep fake detection and decision fusion. In preprocessing, frames are extracted from video, facial region is cropped, and frame resizing is performed. Next, optical flow maps are generated using SEA-RAFT model and edge defined frames generated using Frei-Chen mask technique. These three frame variants present a rich representation of video for the deep learning model. 3 ResNet-50 models are employed as encoders to generate feature maps trained using above frame types, ensuring anomaly identification in spatial, temporal and structural domains. A novel three input and one output Fuzzy Inference System is designed for decision fusion combining the detection results of all three classifier models to make decision on video being a fake. The performance of the proposed Fuzzy-DFD system is evaluated by training the model on well-known deep fake classification datasets namely FakeForensics++ and Celeb-DF(v2). Further results are compiled and evaluated using performance indicators like accuracy, Precision, F1-Score, Recall and AUC. Fuzzy-DFD system attained accuracy of 99% and 93% for FaceForensics++ and Celeb-DF(v2) datasets respectively. Comparison study with other SOTA deep fake detection models is conducted, and Fuzzy-DFD significantly outperformed the other models taken for comparison. The anomaly learning through RGB, optical flow and structural characteristics with fuzzy system-based decision fusion technique contributes to a robust deep fake detection system.}, archive = {J_ACCESS}, author = {Brindha Subburaj and R. Ragavendra}, doi = {10.1109/ACCESS.2025.3567523}, journal = {IEEE Access}, pages = {82747-82758}, shortjournal = {IEEE Access}, title = {Deepfake detection using spatio-temporal-structural anomaly learning and fuzzy system-based decision fusion}, volume = {13}, year = {2025}, }
• (2025). Impact of latency and packet error on communication in centralized microgrid control: Modeling and guidelines. ACCESS, 13, 82732-82746. (www)

  @article{ , abstract = {This paper proposes a novel dynamic model in the Laplace domain for an advanced ac microgrid with centralized secondary control. The model applies a centralized power-based control strategy and incorporates communication non-idealities such as latency and packet error. Packet loss and communication failure are considered herein as latency issues. The central controller incorporates an anti-aliasing filter that constrains the frequency of the transmitted signal and the packet transmission rate in compliance with Nyquist’s sampling theorem. The model is validated on a test bench, with experimental implementation of the communication system and central controller, and a real-time simulation of the electrical power system via hardware-in-the-loop. The impact of latency on the controller’s performance is evaluated through stability, control bandwidth, load rejection bandwidth, settling time, and rejection of packet errors. The results show that communication latency directly impacts the controller’s performance, affecting all analyzed performance metrics. Meanwhile, the cutoff frequency of anti-aliasing filter and packet transmission rate influence control stability, response dynamics, and the system ability to reject load variations and packet errors. Finally, the paper proposes a flowchart-based guideline for configuring the microgrid central controller and selecting communication technologies to implement ancillary services with predefined durations in advanced microgrids.}, archive = {J_ACCESS}, author = {Lucas S. Chaves and João Marcus S. Callegari and Lucas S. Araujo and Danilo I. Brandao}, doi = {10.1109/ACCESS.2025.3567911}, journal = {IEEE Access}, pages = {82732-82746}, shortjournal = {IEEE Access}, title = {Impact of latency and packet error on communication in centralized microgrid control: Modeling and guidelines}, volume = {13}, year = {2025}, }
• (2025). SAR analysis in an anatomical head model using CFL-optimized yee cells and an accurate dipole model at 700-MHz for 5G mobile radiation. ACCESS, 13, 82718-82731. (www)

  @article{ , abstract = {Electromagnetic radiation (EMR) exposure from fifth-generation (5G) mobile technology influences human health. This article proposes a numerical analysis to investigate the specific absorption rate (SAR) using an anatomical human head and accurate dipole model to examine the EMR effect at 700 MHz, a low-band frequency for 5G mobile communication. A three-dimensional (3D) digital model of a realistic human head is reconstructed using a series of magnetic resonance imaging (MRI) scans. This model encompasses various tissue types with their respective bio-material properties. The radiation from a mobile terminal is modeled using a dipole-based structure. In this study, the Unsplit-Field Finite-Difference Time-Domain (UF-FDTD) algorithm is modified for numerical analysis, with a perfectly matched layer (PML) serving as the boundary condition. The electromagnetic (EM) simulation is conducted to study the interaction between the two computational objects in the artificial absorbing environment. The Yee cell is utilized for a structured lattice in the UF-FDTD approach, characterized by its uniformity and consistent application throughout the model. The research problem involves correlating the size of the human head model with the unit cell size, which is associated with the average weight of the human brain for adults. The Yee cell is optimized based on the Courant-Friedrichs-Lewy (CFL) condition. This work enhances simulation accuracy twofold. First, the optimization of the unit cell size relative to the wavelength is refined in comparison to other unit cells without a phase error. This optimization leads to an accurate time-step parameter via the CFL criterion, ensuring the stability of the computer simulation. Second, the optimized grid structure of the UF-FDTD algorithm closely approximates the infinitesimal feeding gap, ensuring precise alignment with the exact dimensions of the feed-gap point in the dipole antenna configuration and accurately reflecting the effects of input impedance characteristics. The SAR 1-g and SAR 10-g are reported in association with the IEEE standard and ICNIRP guidelines. The total average absorbed power at a gap distance of 1.12 cm is correlated with the radiated power, given by $P_{avg}/P_{r}$ , with a resulting value of approximately 189.556. The resulting SAR 1-g value is reported as 0.556 W/kg. This investigation contributes to understanding the dosimetry of EMR exposure from 5G mobile technology in the 700 MHz band.}, archive = {J_ACCESS}, author = {Terapass Jariyanorawiss and Komsan Kanjanasit and Wachira Chongburee and Nattapon Sornsungnoen}, doi = {10.1109/ACCESS.2025.3568603}, journal = {IEEE Access}, pages = {82718-82731}, shortjournal = {IEEE Access}, title = {SAR analysis in an anatomical head model using CFL-optimized yee cells and an accurate dipole model at 700-MHz for 5G mobile radiation}, volume = {13}, year = {2025}, }
• (2025). Enhancing elderly care with socially assistive robots: A holistic framework for mobility, interaction, and well-being. ACCESS, 13, 82698-82717. (www)

  @article{ , abstract = {The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation.}, archive = {J_ACCESS}, author = {Fawad Naseer and Muhammad Nasir Khan and Muhammad Tahir and Abdullah Addas and Hasnain Kashif}, doi = {10.1109/ACCESS.2025.3567331}, journal = {IEEE Access}, pages = {82698-82717}, shortjournal = {IEEE Access}, title = {Enhancing elderly care with socially assistive robots: A holistic framework for mobility, interaction, and well-being}, volume = {13}, year = {2025}, }
• (2025). Optimal dual control strategy for multi-interconnected microgrids under unintentional islanding and fault scenarios. ACCESS, 13, 82680-82697. (www)

  @article{ , abstract = {In recent years, microgrids (MGs) have become increasingly popular as a means of improving the efficiency and reliability of power systems. MGs need efficient controllers to guarantee high power quality and system stability in the face of various disturbances to which they may be subjected. This paper investigates the dynamic performance of three interconnected MGs and presents a dual control strategy to improve the dynamic performance during both the grid-connected mode and unintentional islanding occurrence. Conventional fixed-parameter linear controllers such as PI and droop controllers are widely used in MG applications; however, they may not be efficient at rejecting disturbances to MGs. To address this issue, this paper proposes a self-tuning PI (ST-PI) controller, which is a nonlinear adaptive controller that can adapt to different operating points to regulate the system voltage and frequency. The performance of the ST-PI controller is evaluated and compared with that of the conventional PI controller under various scenarios, including severe conditions and transitions between grid-connected and islanded modes. The simulation results demonstrate that the ST-PI outperforms the PI controller under different disturbances in terms of overshoot, undershoot, settling time, and integral time absolute error (ITAE). Additionally, the paper utilizes an advanced bioinspired metaheuristic algorithm, the Newton-Raphson-based optimizer (NRBO), to optimize the design of the proposed controllers via the ITAE as a performance measure.}, archive = {J_ACCESS}, author = {H. K. Shaker and Ines Mahmoud and Magdi A. Mosa and K. M. Abdel-Latif and A. A. Ali and H. E. Keshta}, doi = {10.1109/ACCESS.2025.3567413}, journal = {IEEE Access}, pages = {82680-82697}, shortjournal = {IEEE Access}, title = {Optimal dual control strategy for multi-interconnected microgrids under unintentional islanding and fault scenarios}, volume = {13}, year = {2025}, }
• (2025). Quantized auto encoder-based anomaly detection for multivariate time series data in 5G networks. ACCESS, 13, 82668-82679. (www)

  @article{ , abstract = {With the arrival of 5G technology, networks face critical challenges in detecting anomalies that can significantly impact performance and reliability. This paper introduces QAED (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. We provide a detailed evaluation of our model across 5G data scenarios, demonstrating its enhanced accuracy and efficiency in anomaly detection compared to existing state-of-the-art methods, with gains of up to 8%.}, archive = {J_ACCESS}, author = {Giovanni Trappolini and Antonio Purificato and Federico Siciliano and Luigi D’Addona and Anna Maria Spagnolo and Domenico Dato and Fabrizio Silvestri}, doi = {10.1109/ACCESS.2025.3568133}, journal = {IEEE Access}, pages = {82668-82679}, shortjournal = {IEEE Access}, title = {Quantized auto encoder-based anomaly detection for multivariate time series data in 5G networks}, volume = {13}, year = {2025}, }
• (2025). DMoC-UNet: A dynamic mixture-of-convolution network for enhanced pathological image segmentation. ACCESS, 13, 82652-82667. (www)

  @article{ , abstract = {Pathological image segmentation is a cornerstone in medical image analysis and is crucial for tumor detection, tissue classification, and pathological diagnosis. Existing methods face challenges in addressing complex and diverse tissue structures, multi-scale features, and blurred boundaries, limiting their segmentation accuracy and generalization across datasets. This paper proposes a novel pathological image segmentation method, DMoC-UNet, which integrates Dynamic Mixture-of-Convolution (DMoC) modules, Haar wavelet downsampling, and Dual Attention Fusion (DAF) modules to enhance multi-scale feature extraction and fine-grained boundary segmentation. The DMoC modules enable dynamic routing of features to specialized expert networks, adapting effectively to the diverse tissue characteristics of pathological images. Haar wavelet downsampling preserves spatial details while improving multi-scale representation, and the DAF modules facilitate efficient fusion between shallow and deep features, ensuring semantic consistency. Extensive experiments on three publicly available datasets—EBHI, CRAG, and GlaS—demonstrate that DMoC-UNet outperforms state-of-the-art models, achieving significant improvements in Accuracy of Classification (ACC), Dice Similarity Coefficient (DSC), and Intersection over Union (IoU) metrics.Specifically, DMoC-UNet achieves ACC, DSC, and IoU values of 92.46%, 94.65%, and 90.20% on the EBHI dataset; 85.71%, 88.26%, and 82.79% on the CRAG dataset; and 85.81%, 87.97%, and 83.56% on the GlaS dataset. These results highlight the robustness and adaptability of DMoC-UNet, making it a promising approach for pathological image segmentation. Our code will be released after acceptance.}, archive = {J_ACCESS}, author = {Jingwei Zhu and Lining Qin and Zixin Teng and Xiaomin Li and Kuiwu Li and Haoran Chu}, doi = {10.1109/ACCESS.2025.3568027}, journal = {IEEE Access}, pages = {82652-82667}, shortjournal = {IEEE Access}, title = {DMoC-UNet: A dynamic mixture-of-convolution network for enhanced pathological image segmentation}, volume = {13}, year = {2025}, }
• (2025). The study on the feasibility to develop rice bran oil mixed with Fe3O4, TiO2, and Al2O3 nanoparticles as liquid dielectric in transformers. ACCESS, 13, 82637-82651. (www)

  @article{ , abstract = {The objective of this research was to study the use of rice bran oil mixed with nanoparticles as liquid insulation in transformers by enhancing the efficiency of properties for rice bran oil. To clarify, 3 types of nanoparticles were mixed, i.e., magnetite (Fe3O4), titanium dioxide (TiO2), and aluminium oxide (Al2O3). All of these were mixed in rice bran oil at a 0.01 wt% concentration of rice bran oil volume to improve its electrical, chemical, and physical properties. According to the electrical test, it was found that rice bran oil mixed with 0.01 wt% Fe3O4 nanoparticles of rice bran oil volume contained the highest AC breakdown voltage, with the best lightning impulse breakdown voltage at anode and cathode. As for power loss in the insulators, it was found that Fe3O4, TiO2, and Al2O3 nanoparticles added into rice bran oil could reduce power loss factors in rice bran oil, indicating better electrical efficiency. Besides, rice bran oil mixed with these nanoparticles also contained close relative permittivity to regular rice bran oil and transformer oil. In the test of electrical resistance, it was discovered that transformer oil and rice bran oil mixed with nanoparticles had lower resistance than regular rice bran oil. This meant that the mixture was better at insulating. The chemical property test showed that rice bran oil mixed with nanoparticles had more water than mineral oil and more acidity than transformer oil. This suggests that rice bran oil might still have some issues with long-term use if the right amount of water and acidity are not controlled. The physical property test showed that the surface tension of rice bran oil mixed with all three types of nanoparticles was higher than that of regular transformer oil. For the dynamic light scattering test (DLS) to observe dispersity of nanoparticles in oil, it was found that rice bran oil mixed with Fe3O4 nanoparticles contained a better polydispersity index (PDI) than rice bran oil mixed with TiO2 and Al2O3 nanoparticles, indicating that Fe3O4 nanoparticles added could encourage efficient dispersity. This research revealed the efficiency of nanoparticles to improve properties of rice bran oil, particularly when mixed with 0.01 wt.% Fe3O4 nanoparticles that brought better electrical properties of rice bran oil in terms of breakdown voltage and lower power loss while enhancing its insulatability.}, archive = {J_ACCESS}, author = {Pichai Muangpratoom and Chinnapat Suriyasakulpong and Choosak Kamonkhantithorn and Tongpian Prombud}, doi = {10.1109/ACCESS.2025.3568092}, journal = {IEEE Access}, pages = {82637-82651}, shortjournal = {IEEE Access}, title = {The study on the feasibility to develop rice bran oil mixed with Fe3O4, TiO2, and Al2O3 nanoparticles as liquid dielectric in transformers}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient cell association and load balancing for low battery users in heterogeneous cellular networks. ACCESS, 13, 82621-82636. (www)

  @article{ , abstract = {The future Heterogeneous Cellular Network (HCN) is expected to fulfill the demand for the Internet of Things, Industry 4.0, extended reality, connected vehicles and mobile gadgets. In HCN, the deployment of low power nodes inside the coverage area of a high power node yields the benefit of providing improved data rates to users by offloading a portion of users from a highly loaded high power node to relatively lightly loaded low power nodes. However, the offloading of users through conventional cell association schemes may not guarantee improved load balancing along with the energy efficiency (EE) of critical users such as user equipment (UE) with low battery power. This paper proposes improved cell association schemes based on the battery levels of UE and Deep Q-learning (DQL) to achieve load balancing and to decrease the power consumption of Low Battery Users (LBUs). The proposed approach combines UE battery levels with three conventional association schemes for improvement: minimum distance based cell association, maximum biased received power (MBRP), and maximum biased SINR (MBSINR). Results on cell association, load balancing, and EE of LBUs have been compared with conventional association schemes. The proposed battery based cell association models perform well in favor of LBUs and clearly show the significance of the proposed approach.}, archive = {J_ACCESS}, author = {Abdul Qadeer Khan and Kamran Javed and Ali Raza and Shahryar Saleem and Zubair Saeed}, doi = {10.1109/ACCESS.2025.3568343}, journal = {IEEE Access}, pages = {82621-82636}, shortjournal = {IEEE Access}, title = {Energy-efficient cell association and load balancing for low battery users in heterogeneous cellular networks}, volume = {13}, year = {2025}, }
• (2025). IoT device identification techniques: A comparative analysis for security practitioners. ACCESS, 13, 82610-82620. (www)

  @article{ , abstract = {As the Internet of Things (IoT) continues to grow, networks are increasingly at risk from vulnerable devices that allow access to attackers. Two particular threats are posed by rogue devices (i.e. devices present on a network that should not be) and unpatched devices (devices with out-of-date software or firmware). A growing body of research attempts to address these risks: automated IoT device identification. By using methods to quickly and easily identify IoT devices on a network, vulnerable devices can be identified, improving network security. Although there have been publications that survey this research, they are typically broad, discussing IoT device identification only in passing, and do not provide a methodology to clearly compare existing (or future) research. Our novel approach in this paper is to provide a simple methodology for assessing and comparing research into IoT device identification, bypassing the need to delve into granular details such as specific algorithmic choices or feature selections, which are attributes not all papers have, and instead to focus on common attributes shared across papers. We provide a comprehensive literature review for the topic of identifying IoT devices in networks using passive network traffic, resulting in 69 publications examined. We systematically analyse the literature for key elements common across the studies that can allow a comparative analysis, and define five we determine to be most important. We state why these five elements in particular are important, and discuss trends in these elements across the studies. We then produce a summary table containing just the information for the five elements for each study, and how they can be used to understand and compare techniques, considering their context. This gives security professionals and researchers the necessary tools to compare studies, both current and future, to understand how to secure their networks and what they must consider when completing further research.}, archive = {J_ACCESS}, author = {Ashley Andrews and George Oikonomou and Simon Armour and Paul Thomas and Thomas Cattermole}, doi = {10.1109/ACCESS.2025.3568673}, journal = {IEEE Access}, pages = {82610-82620}, shortjournal = {IEEE Access}, title = {IoT device identification techniques: A comparative analysis for security practitioners}, volume = {13}, year = {2025}, }
• (2025). A secure COVID affected CT scan image encryption scheme using hybrid MLSCM for IoMT environment. ACCESS, 13, 82586-82609. (www)

  @article{ , abstract = {The 2019 coronavirus disease (COVID-19) spread quickly throughout the world, causing a global pandemic. After proper diagnosis, digital images storing patient personal health information are saved and transferred over a public network. As a result, the security aspects of preserving sensitive patient health information are becoming crucial. In this study, we presented an encryption system for COVID-19 CT images to address this problem. This work describes an effective inter-intra bit-level (BL) pixel processing solution for secret medical images through an encryption technique that does not rely on pixel correlation. The hybrid multi-logistic sine chaotic map (MLSCM) technique combines logistic and sine maps, improving randomness and ergodicity and leading to stronger security. The utilization of hybrid MLSCM technology further enhances time efficiency. The system’s security is strengthened by the simultaneous rearrangement and spreading of pixels, which helps to prevent statistical and frequent types of attacks. The SHA-512 hash algorithm is incorporated to generate highly secure keys, ensuring resilience against brute-force and differential attacks. Row-wise and column-based permutation algorithms further enhance security by disrupting statistical processes. The suggested method yields an optimal entropy of 7.9982, which is more significant than the comparable recorded work. The proposed work beats other existing work on calculating the number of pixel change rates (NPCR) and the unified average change intensity (UACI), with values of 99.6233 and 33.5065, respectively. The impact of avalanches for sensitive key analysis has been confirmed with a key space of $1.5491 \times 2^{526}$ , enough to withstand multiple attacks.}, archive = {J_ACCESS}, author = {Sasmita Padhy and Sachikanta Dash and Naween Kumar and Gyanendra Kumar}, doi = {10.1109/ACCESS.2025.3568448}, journal = {IEEE Access}, pages = {82586-82609}, shortjournal = {IEEE Access}, title = {A secure COVID affected CT scan image encryption scheme using hybrid MLSCM for IoMT environment}, volume = {13}, year = {2025}, }
• (2025). Predicting block delays in bitcoin blockchain via bayesian and non-bayesian logistic regression. ACCESS, 13, 82573-82585. (www)

  @article{ , abstract = {Blockchain has found many uses beyond cryptocurrency trading. However, this technology faces the challenge of low transactions per second (TPS), which hinders it from competing at scale with other established digital payment technologies. This low TPS problem is worsened by occasional block generation delays, which usually hike transaction fees as transactors must compete using transaction fees to have their transactions added to a block in a blockchain. These block delays can also be indicative of other underlying issues, such as dishonest mining. This study focuses on the prediction of such delays in a Bitcoin blockchain. Such predictions can help transactors strategically place transactions at moments when delays are unlikely to occur, and the predictions can also help avoid resulting higher transaction fees and potential dishonest miner behavior. The problem is modeled as a prediction of a binary outcome, that is, block delay or block-on-time. Three approaches for training the logistic regression model were compared: frequentist maximum likelihood estimation (MLE), Bayesian Hamiltonian Monte Carlo (HMC), and Bayesian Gibbs sampler. The precision-recall (PR) area under the curve (AUC) was chosen as the main comparison metric because the dataset exhibited a class imbalance problem. The PR AUC values showed that the fitted models performed better than the random classifier, with the Gibbs sampler model performing the best among the three. The PR AUC values also showed that there was a big room for improvement on all the fitted models.}, archive = {J_ACCESS}, author = {Phumzile T. Mgcima and Wilson Tsakane Mongwe and Peter O. Olukanmi}, doi = {10.1109/ACCESS.2025.3564557}, journal = {IEEE Access}, pages = {82573-82585}, shortjournal = {IEEE Access}, title = {Predicting block delays in bitcoin blockchain via bayesian and non-bayesian logistic regression}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis on complementary FET. ACCESS, 13, 82554-82572. (www)

  @article{ , abstract = {Complementary Field Effect Transistors (CFETs) have surfaced as a hopeful path to the continued logic area scaling in CMOS technology. This comprehensive review paper explores recent advancements and integration strategies in CFET technology, spanning monolithic CFET processes, vertically stacked nanosheets, and dynamic complementary pin allocation (DCPA) schemes for physical synthesis. Additionally, the paper investigates fabrication and characterization methodologies for vertically stacked junctionless poly-Si nanosheet CMOS inverters for enhanced performance and scalability. This paper delves into the analytical modeling and performance enhancement of CFETs for advanced technology nodes, comparing them with other transistor architectures like lateral GAAFETs. Various CFET configurations, including Hybrid Channel CFETs and sheet-based CFETs, are explored in terms of parasitic capacitance, self-heating effects, and power-performance-area-cost (PPAC) trade-offs. Moreover, the paper discusses recent research advancements in CFET technologies, covering applications, challenges, and prospects. Topics include gate stack solutions for BTI reliability, design rule considerations, heterogeneous stacked RRAM integration, device and system co-optimization, and electrothermal characterization. The semiconductor industry’s transition focuses on the utilization of (EUV) Extreme Ultraviolet lithography for three-nanometer logic processes of CFET and proposing novel CDU and process window analysis techniques. Furthermore, the paper presents a novel CFET-based NAND gate design, termed the double-cell-height (DCH) architecture, to mitigate challenges associated with increased parasitic capacitance and routing congestion. Advancements in process technologies towards (SRAM) Static Random Access Memory cells based on CFET, also design optimization for sub-2nm CMOS nodes are discussed. Additionally, a modeling framework using Artificial Neural Networks (ANN) to estimate process variation effects on stacked CFET devices is proposed, offering insights into CFET research challenges and opportunities. Lastly, thermal and reliability challenges associated with CFETs are explored, along with investigations into CFET design, thermal modeling, electromigration reliability, and process optimization. Emerging technologies such as monolithic 3D integration and machine learning predictions for design optimization are also discussed. Overall, this review paper provides a comprehensive analysis of recent advancements in CFET technology, offering insights into ongoing research efforts and future directions in the field.}, archive = {J_ACCESS}, author = {Ayush Bhardwaj and Debashish Dash and Aditi Anant and Ved M. Bhanushali and Adarsh Kushwah and Ipshita Mishra and Shubham and Chandan Kumar Pandey}, doi = {10.1109/ACCESS.2025.3568134}, journal = {IEEE Access}, pages = {82554-82572}, shortjournal = {IEEE Access}, title = {Comprehensive analysis on complementary FET}, volume = {13}, year = {2025}, }
• (2025). Statistical performance evaluation of the deep learning architectures over body fluid cytology images. ACCESS, 13, 82540-82553. (www)

  @article{ , abstract = {The analysis of body fluid cytology images is a faster and easier diagnostic test than traditional methods for detecting cancer cells. Currently, there is limited statistical information on the performance of metric measurements obtained from different deep learning architectures (DL). In this study, the simulation results produced by different deep learning architectures are statistically analyzed in detail and the differences between the models are analyzed. Five different DL architectures (VGG16, VGG19, MobileNet, InceptionV3 and DenseNet121) are evaluated. The mean values of the metric measurement parameters (consistency, specificity, precision, accuracy and F1-score) obtained from the confusion matrix were evaluated using the non-parametric statistical methods Kruskal-Wallis and Mann-Whitney U tests. In addition, pairwise comparisons between AUC (Area Under the Curve) values obtained from ROC analysis were statistically evaluated. When the results were analyzed, the highest success performances were obtained in VGG16 with 98.60% sensitivity, InceptionV3 with 95.69% specificity, DenseNet121 with 95.02% accuracy, DenseNet121 with 89.48% F1-score and DenseNet121 with 91.42% AUC. The results obtained in the study showed that the criteria used in the evaluation metrics have different performances for different CNN architectures. In future studies, it will guide researchers on which CNN architecture gives strong results in which evaluation metric.}, archive = {J_ACCESS}, author = {Ersın Uysal}, doi = {10.1109/ACCESS.2025.3567798}, journal = {IEEE Access}, pages = {82540-82553}, shortjournal = {IEEE Access}, title = {Statistical performance evaluation of the deep learning architectures over body fluid cytology images}, volume = {13}, year = {2025}, }
• (2025). Research on quantitative safety risk assessment methods at vehicle controllability level. ACCESS, 13, 82527-82539. (www)

  @article{ , abstract = {In recent years, advancements in electrification, intelligent connectivity, and autonomous driving have made functional safety, particularly the vehicle-level controllability, a critical research focus. Although the ISO 26262 standard provides a systematic framework for automotive functional safety, challenges remain in accurately defining and quantifying controllability levels. In this paper, we analyze the influence of potential faults—such as unintended torque and loss of regenerative braking force—on vehicle controllability under various operating conditions. A database of vehicle attitude data and corresponding controllability levels is established through real-vehicle tests. Feature screening is then performed to identify the most relevant attributes that significantly affect controllability. Based on this database, a controllability model is developed using the Support Vector Machine (SVM) method, which can effectively predict controllability levels. The model is applied in multiple simulation scenarios to assess vehicle controllability under different conditions. Additionally, the safety boundaries of motor torque are determined at varying speeds, providing insights into critical thresholds beyond which the vehicle’s controllability may be compromised. This work presents a novel approach for the quantitative analysis of vehicle controllability and safety risks, offering a reliable tool for evaluating functional safety in modern vehicles.}, archive = {J_ACCESS}, author = {Lei He and Zichong Li and Xuezhu Yang and Yuanle Zhou and Zikun Qu and Zhiqiang Ren}, doi = {10.1109/ACCESS.2025.3568509}, journal = {IEEE Access}, pages = {82527-82539}, shortjournal = {IEEE Access}, title = {Research on quantitative safety risk assessment methods at vehicle controllability level}, volume = {13}, year = {2025}, }
• (2025). Investigating the InSAR phase bias in the SBAS algorithm and its effect on different landcovers. ACCESS, 13, 82514-82526. (www)

  @article{ , abstract = {The Interferometric Synthetic Aperture Radar (InSAR) technique is capable of evaluating and calculating the rate of earth surface displacement over time and across large spatial dimensions with millimeter accuracy. The most common InSAR algorithms for monitoring displacement are SBAS and PS time series. Recent investigations indicate that Multi-Look (ML) Synthetic Aperture Radar (SAR) interferograms with very short baselines are significantly impacted by unwanted short-lived phase artifacts, ultimately impairing the dependability of the InSAR products. In this study, we first calculated the average displacement velocity using a full connection between the interferograms processed with the SBAS algorithm. The results show that the SBAS (full-connection) algorithm is almost free of phase bias and has an 88% correlation with the results obtained from PS-InSAR. By using networks with fewer connections, the processing speed can be increased and the processing volume can be reduced compared to using a full connection SBAS network. Additionally, various networks were processed to assess the amount of phase bias in the SBAS algorithm and to propose an optimal network for this algorithm. The results indicated that the network with eight interferogram connections has the highest correlation with the full SBAS network and the lowest phase bias. Conversely, networks with one to three interferogram connections exhibited the lowest correlation and the highest phase bias. Incorporating interferograms with long time intervals (2, 3, and 4-month) into short-term interferograms (6, 12, and 18-day) has reduced the amount of phase bias. Furthermore, the phase bias varies in soil moisture and different types of vegetation. The results demonstrate a direct correlation between the amount of soil moisture and vegetation and the amount of phase bias; as their levels increase, so does the phase bias.}, archive = {J_ACCESS}, author = {Ebrahim Karami and Siavash Shami and Yasser Maghsoudi and Babak Ranjgar and Saeed Azadnezhad}, doi = {10.1109/ACCESS.2025.3568144}, journal = {IEEE Access}, pages = {82514-82526}, shortjournal = {IEEE Access}, title = {Investigating the InSAR phase bias in the SBAS algorithm and its effect on different landcovers}, volume = {13}, year = {2025}, }
• (2025). Metropolitan area network model design algorithm using regional railways information and daily movement data for beyond 5G research. ACCESS, 13, 82503-82513. (www)

  @article{ , abstract = {Metropolitan area networks (MANs) are crucial for establishing beyond 5G (B5G) infrastructure. Although regional railways information has been used to design MAN models, regional railways information is inadequate for designing the topology of metropolitan areas with underdeveloped railway lines. A MAN model algorithm using regional railways information and other regional data is necessary for designing the network models of many metropolitan areas. Therefore, we proposed a novel MAN model design algorithm that incorporates railroad route information and daily movement data to enhance B5G research. In the proposed algorithm, first, the topology is designed with the existing algorithm that incorporates only railway line information. Next, links are added in the topology according to the daily movement data such as person trip (PT) data. By using the proposed algorithm with daily movement data, the MAN model can be designed in metropolitan areas that are not completely covered by railways. We designed the Nara NMN39 model, Wakayama WMN30 model, and Fukui FMN17 model, for Nara, Wakayama, Fukui prefectures. There prefectures are not entirely covered by the railways in Japan. The designed MAN models are a simple plane graph and are widely used in numerous research domains. We compared the designed models with those developed using the existing algorithm that uses only railway line information. We confirmed that the proposed models could be used in various research applications. We provided insights into the characteristics of the proposed models to facilitate their use in research.}, archive = {J_ACCESS}, author = {Koga Nakamura and Shotaro Kitano and Yusuke Hirota and Takehiro Tsuritani and Hiroshi Hasegawa and Takuji Tachibana}, doi = {10.1109/ACCESS.2025.3568510}, journal = {IEEE Access}, pages = {82503-82513}, shortjournal = {IEEE Access}, title = {Metropolitan area network model design algorithm using regional railways information and daily movement data for beyond 5G research}, volume = {13}, year = {2025}, }
• (2025). Deep learning for real-time PPE usage monitoring using wearable IMU sensors. ACCESS, 13, 82492-82502. (www)

  @article{ , abstract = {Ensuring the proper use of Personal Protective Equipment (PPE) is critical for safeguarding workers in high-risk environments, such as power distribution systems. This work presents an innovative approach to monitoring PPE usage through Deep Neural Networks (DNNs). Using data from inertial measurement units (IMUs) integrated into PPE items, we propose a solution to classify three usage states—namely carrying, still, and wearing—using raw accelerometer and gyroscope data. More specifically, this work assesses the effectiveness of three distinct network architectures — Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and CNN-BiLSTM—on a publicly released dataset on PPE usage provided by this study, while also comparing them against a traditional baseline Multi-Layer Perceptron (MLP) architecture. Our results demonstrate the superiority of BiLSTM in balancing high accuracy with computational efficiency, achieving above 98% accuracy regardless of the PPE type. This work represents the first application of DNNs for PPE monitoring using IMU data, offering significant implications for enhancing safety compliance and operational monitoring in power distribution.}, archive = {J_ACCESS}, author = {Pedro Carvalho da Fonseca Guimarães and Leonardo Braga de Cristo and Marcos Eduardo Pivaro Monteiro and João Luiz Rebelatto and Guilherme de Santi Peron and Ohara Kerusauskas Rayel and Guilherme Luiz Moritz}, doi = {10.1109/ACCESS.2025.3567871}, journal = {IEEE Access}, pages = {82492-82502}, shortjournal = {IEEE Access}, title = {Deep learning for real-time PPE usage monitoring using wearable IMU sensors}, volume = {13}, year = {2025}, }
• (2025). Empowering dysarthric communication: Hybrid transformer-CTC-based speech recognition system. ACCESS, 13, 82479-82491. (www)

  @article{ , abstract = {Dysarthria, a motor speech disorder, impairs the muscles involved in speech production, leading to challenges in articulation, pronunciation, and overall communication. This results in slow, slurred speech that is difficult to understand. Augmentative and Alternative Communication (AAC) aids integrated with speech recognition technology offer a promising solution for individuals with dysarthria. However, Automatic Speech Recognition (ASR) systems trained on typical speech data often struggle to recognize dysarthric speech due to its unique speech patterns and limited training data. To address these challenges, a hybrid Transformer-CTC model has been proposed for improving ASR performance on dysarthric speech. The Transformer architecture employs a self-attention mechanism that models complex dependencies between speech features, enabling it to identify and emphasize important patterns even when training data is limited. This ability is particularly crucial for dysarthric speech, where speech signals often exhibit high variability. On the other hand, Connectionist Temporal Classification (CTC) acts as an effective transcription layer. It aligns speech features with character sequences without requiring precise input-output alignment, making it well-suited for handling the inconsistencies and distortions present in dysarthric speech. The integration of these components creates a powerful architecture capable of learning nuanced speech patterns and delivering accurate transcriptions for dysarthric speech. The model was trained using the UA speech corpus, containing 13 hours of speech from 15 speakers with varying dysarthria levels. The proposed hybrid system achieves an impressive Word Recognition Accuracy (WRA) of 89%, demonstrating its effectiveness in accurately transcribing dysarthric speech. This innovative approach significantly advances the development of ASR technologies tailored to diverse and variable speech patterns, ultimately enhancing communication for individuals with speech disorders.}, archive = {J_ACCESS}, author = {R. Vinotha and D. Hepsiba and L. D. Vijay Anand and P. Malin Bruntha and Linh Dinh and Hien Dang}, doi = {10.1109/ACCESS.2025.3568342}, journal = {IEEE Access}, pages = {82479-82491}, shortjournal = {IEEE Access}, title = {Empowering dysarthric communication: Hybrid transformer-CTC-based speech recognition system}, volume = {13}, year = {2025}, }
• (2025). CNN-based detection of sheath incorrect connection in the cross-bonded HV cable systems using the sheath current phasor difference. ACCESS, 13, 82467-82478. (www)

  @article{ , abstract = {Incorrect connection of metallic sheath is a typical defect in long HV(High-Voltage) cross-boned cable systems. However, the previous detection methods based on sheath current rely on three criteria: the amplitude of the sheath current, the ratio of the sheath current to the load current, and the relative values of the sheath current across the three sheath loops. These criteria, however, are insufficient for accurately identifying the defect. In this paper, a CNN (Convolutional Neural Network) based methodology using the sheath current phasor difference at both ends of the same sheath loop is proposed to distinguish the 34 types of incorrect connection with 2 types of correct connection. Taking an 110 kV cable as the simulation case, the results show that the sheath current phasor difference is significantly affected by the unequal lengths of the three small segments of the cable. Besides, the sheath current phasor difference at both ends of the same loop can not only distinguish the correct and incorrect connection states of cross-bonded high-voltage cable sheaths but also differentiate different connection error modes. The classification model for high-voltage cable cross-bonding errors based on CNN can effectively recognize 36 different cross-bonding configurations of high-voltage cables, with a model accuracy of over 98%. Through comparison with other algorithms, it is found that the CNN exhibits the best performance in identifying various cross-bonding error scenarios.}, archive = {J_ACCESS}, author = {Jing Tu and Zhanran Xia and Yongheng Ai and Ruoxin Song and Bin Yang and Changqun Sun and Xiaoyue Chi and Hang Wang}, doi = {10.1109/ACCESS.2025.3567315}, journal = {IEEE Access}, pages = {82467-82478}, shortjournal = {IEEE Access}, title = {CNN-based detection of sheath incorrect connection in the cross-bonded HV cable systems using the sheath current phasor difference}, volume = {13}, year = {2025}, }
• (2025). Swin transformer and momentum contrast (MoCo) in leukemia diagnostics: A new paradigm in AI-driven blood cell cancer classification. ACCESS, 13, 82441-82466. (www)

  @article{ , abstract = {Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. Moreover, integrating contrastive self-supervised learning with hierarchical attention-based models remains underexplored in hematologic malignancy classification. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. The Swin Transformer’s patch-based embedding and multi-level attention enhance feature discrimination across ALL subtypes, while MoCo generates distinct embeddings, minimizing overlap between cell types. BiET is employed to classify the refined feature vectors, leveraging self-attention mechanisms to improve classification accuracy. The model achieved an overall classification accuracy of 92.5%, with the precision of 90.3%, a recall of 91.1%, and an F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework reduced contrastive loss from 0.5 to 0.097 for benign cells, enhancing feature discrimination. An ablation study revealed that omitting the dynamic queue decreased accuracy by 5%, underscoring its importance for effective feature learning. This approach can be extended to other hematologic malignancies, with potential for further improvement using larger datasets and real-time diagnostic workflows to support precision medicine.}, archive = {J_ACCESS}, author = {Eshika Jain and Pratham Kaushik and Vinay Kukreja and Modafar Ati and Shanmugasundaram Hariharan and Vandana Ahuja and Abhishek Bhattacherjee and Rajesh Kumar Kaushal}, doi = {10.1109/ACCESS.2025.3563256}, journal = {IEEE Access}, pages = {82441-82466}, shortjournal = {IEEE Access}, title = {Swin transformer and momentum contrast (MoCo) in leukemia diagnostics: A new paradigm in AI-driven blood cell cancer classification}, volume = {13}, year = {2025}, }
• (2025). GTRAVEL+: Enhancing ground segmentation for 3D point cloud with temporal noise removal and adaptive plane fitting for urban and off-road environments. ACCESS, 13, 82431-82440. (www)

  @article{ , abstract = {Ground segmentation plays an increasingly important role as a preprocessing module for numerous applications such as Simultaneous Localization and Mapping (SLAM), place recognition, and point cloud registration. The efficiency and accuracy of ground segmentation can directly influence the performance of these applications. While several methods have been proposed, they continue to struggle to meet the comprehensive requirements of robust ground segmentation. To this end, we propose a ground segmentation method called gTRAVEL+, an improvement of TRAVEL that focuses exclusively on enhancing ground segmentation performance. Our method exploits a temporal and locally-aware noise removal strategy, termed Temporal Node-Wise Noise Removal (TNNR), which effectively eliminates noise points that compromise the ground plane fitting process. Additionally, Merge Node Plane Fitting (MNPF) is proposed to address issues of partial under-segmentation arising from suboptimal region sizing. Finally, Rejected Ground Node Revert (RNGR) ensures thorough geometric relationship verification, significantly improving true positives. Quantitative and qualitative experiments conducted on both urban and off-road environments show that our proposed method demonstrates superior performance, achieving a ${F} {_{{1}}}$ -score of 95.16% and competitive speed compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Truong Giang Dao and Dinh Tuan Tran and Duc Manh Nguyen and Joo-Ho Lee and Anh Quang Nguyen}, doi = {10.1109/ACCESS.2025.3567621}, journal = {IEEE Access}, pages = {82431-82440}, shortjournal = {IEEE Access}, title = {GTRAVEL+: Enhancing ground segmentation for 3D point cloud with temporal noise removal and adaptive plane fitting for urban and off-road environments}, volume = {13}, year = {2025}, }
• (2025). A 9.2–18-GHz sub-3.5-dB NF CMOS low noise amplifier using current-reuse stacked inverter with inductive series peaking. ACCESS, 13, 82421-82430. (www)

  @article{ , abstract = {In this paper, we introduce a novel wideband CMOS low noise amplifier (LNA) that employs a current-reuse stacked inverter (CRSI) in conjunction with advanced inductive peaking to significantly enhance noise performance and gain flatness across an ultrawide frequency range. The proposed CRSI integrates two resistive feedback inverters with source-degeneration inductors in a current-reuse configuration. This unique topology not only boosts the transconductance but also aligns the input impedance with the optimal impedance for the minimum noise figure (NF) through precise tuning of the source-degeneration inductors. By incorporating an input-matching network and an output-matching network, simultaneous input and noise matching achievements—resulting in reduced return loss and minimized NF—and a high-pass filter (HPF) configuration for suppressing low-frequency output are realized, respectively. Our meticulously engineered multiple inductive peaking inductors—comprising inter-stage and inter-cell matching inductors—are strategically optimized to maximize gain and enhance NF over the entire operating frequency range of 9.2 to 18 GHz. Fabricated using 65-nm CMOS technology, the proposed LNA occupies a total chip area of 0.38 mm2, including input and output testing pads. Measured results show that the LNA achieves a peak gain of 26.6 dB and a minimum NF of 2.3 dB within the frequency range of 9.2 to 18 GHz. The power consumption of the LNA is 11.4 mW at 2 V, achieving a competitive figure of merit (FoM) among recently reported wideband CMOS LNAs.}, archive = {J_ACCESS}, author = {Namkyung Lee and Jisu Kim and do Hyeon Kim and Sun K. Hong and Juntaek Oh}, doi = {10.1109/ACCESS.2025.3567623}, journal = {IEEE Access}, pages = {82421-82430}, shortjournal = {IEEE Access}, title = {A 9.2–18-GHz sub-3.5-dB NF CMOS low noise amplifier using current-reuse stacked inverter with inductive series peaking}, volume = {13}, year = {2025}, }
• (2025). Improving student learning outcome tracing at HBCUs using tabular generative AI and deep knowledge tracing. ACCESS, 13, 82407-82420. (www)

  @article{ , abstract = {Historically Black Colleges and Universities in the United States serve a vital role in providing educational opportunities and training, particularly for underrepresented students, facing a challenge of lower retention and graduation rates compared to other institutions. To overcome this challenge, this study explores the application of generative artificial intelligence models to generate synthetic data, augmenting real datasets to improve student learning outcome tracing at these colleges and universities using Deep Knowledge Tracing techniques, which potentially offers actionable insights to identify at-risk students and enables proactive interventions to enhance retention and graduation rates in Science, Technology, Engineering and Math education. Utilizing two years of educational data from Prairie View A&M University, it applied data augmentation with tabular generative artificial intelligence models. The experimental results indicate that augmenting training data with synthetic samples generated by these models improved tracing performance measured by AUC and accuracy by approximately 5% and 3%, respectively, underscoring the potential of synthetic data to enhance the monitoring of student learning outcomes in diverse educational contexts. These findings highlight the critical role of data augmentation through generative artificial intelligence in improving the student learning outcome tracing, offering valuable insights for strategies to enhance retention and graduation rates.}, archive = {J_ACCESS}, author = {Ming-Mu Kuo and Xiangfang Li and Pamela Obiomon and Lijun Qian and Xishuang Dong}, doi = {10.1109/ACCESS.2025.3568171}, journal = {IEEE Access}, pages = {82407-82420}, shortjournal = {IEEE Access}, title = {Improving student learning outcome tracing at HBCUs using tabular generative AI and deep knowledge tracing}, volume = {13}, year = {2025}, }
• (2025). Development of K-PANOSE algorithm for measuring and classifying shape elements of hangul fonts. ACCESS, 13, 82388-82406. (www)

  @article{ , abstract = {In the Korean font market, a wide variety of new fonts with diverse designs are continuously being developed, making the selection of appropriate fonts crucial for effective information delivery. However, there has been no standardized method to systematically define and quantify the structural characteristics of Korean fonts. As a result, analyzing the morphological features of fonts in a quantitative manner and using this information for classification or comparison has remained a challenge. PANOSE is a system that quantifies the visual characteristics of fonts, enabling structured classification and similarity evaluation. While it effectively organizes and compares fonts, it was primarily designed for English scripts, failing to fully capture the unique typographic properties of Korean fonts. To address this limitation, this study defines the key morphological elements of Korean fonts and develops an algorithm, K-PANOSE (Korean font PANOSE), to measure them. Based on prior research, we first established key structural attributes of Korean fonts, such as character composition, stroke thickness, and proportional balance. We then designed an algorithm to quantitatively assess these attributes. Finally, we validated that K-PANOSE performs at a comparable level to the existing PANOSE system, demonstrating its ability to effectively analyze and classify Korean fonts. K-PANOSE is not merely a tool for font selection; rather, it introduces a new standard for quantifying and systematically analyzing the structural characteristics of Korean fonts. This advancement enables broader applications, including font recommendation systems, digital typography, and AI-driven design automation, ultimately contributing to the quality, accessibility, and diversity of Korean typography.}, archive = {J_ACCESS}, author = {Soon-Bum Lim and Hyun-Young Kim and Kanghyeon Seo and Juhan Yoo and Yoojeong Song}, doi = {10.1109/ACCESS.2025.3566598}, journal = {IEEE Access}, pages = {82388-82406}, shortjournal = {IEEE Access}, title = {Development of K-PANOSE algorithm for measuring and classifying shape elements of hangul fonts}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of a quantum SVM with an optimized kernel versus classical SVMs. ACCESS, 13, 82378-82387. (www)

  @article{ , abstract = {Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. Businesses across industries face challenges in improving customer retention and reducing churn, making predictive models essential for identifying at-risk customers and enhancing revenue. This paper investigates an optimized quantum embedding kernel for SVM (Quantum SVM - QSVM) applied to a public bank customer dataset, featuring variables on customer relationships and churn indicators. While focused on the financial sector, the methodology is broadly applicable for reducing churn and boosting revenue across industries. QSVM performance is compared to SVMs with established kernels, including Radial Basis Function (RBF), linear, polynomial, and sigmoid. Experiments varied the number of variables from two to seven to evaluate their impact on model performance and kernel behavior. The experiments were conducted on quantum simulators, which faced scalability challenges addressed using reduced datasets. Even so, this study sheds light on the potential of QSVMs to effectively manage increasing numbers of variables in predictive models, offering valuable insights into their capability to handle complex, high-dimensional data and their applicability in real-world scenarios.}, archive = {J_ACCESS}, author = {Matheus Cammarosano Hidalgo}, doi = {10.1109/ACCESS.2025.3567352}, journal = {IEEE Access}, pages = {82378-82387}, shortjournal = {IEEE Access}, title = {Comparative analysis of a quantum SVM with an optimized kernel versus classical SVMs}, volume = {13}, year = {2025}, }
• (2025). Exploration and exploitation mechanism in pairwise test case generation: A systematic literature review. ACCESS, 13, 82342-82377. (www)

  @article{ , abstract = {Software systems are essential in modern life and require rigorous testing to ensure reliability. Pairwise testing, a combinatorial testing methodology, optimizes test case generation by minimizing redundancy while maintaining defect detection effectiveness. This review systematically examines the role of metaheuristic algorithms in pairwise test case generation, focusing on their exploration and exploitation mechanisms to address local optima challenges. Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. These methods consistently outperform traditional techniques by reducing test suite sizes and enhancing fault detection capabilities. The findings underscore the growing adoption of the one-test-at-a-time (OTAT) approach over the one-parameter-at-a-time (OPAT) approach and identify key challenges in scalability, hyperparameter tuning, and constraint handling, highlighting the need for hybrid strategies and adaptive optimization techniques. This review offers valuable insights into advancing Software testing methodologies and presents recommendations for future research to improve efficiency, scalability, and fault detection in pairwise testing.}, archive = {J_ACCESS}, author = {Muhammad Sabo Yahaya and Ahmad Sobri B. Hashim and Abdullateef Oluwagbemiga Balogun and Aminu Aminu Muazu and Fatima Sabo Usman and Dahiru Adamu Aliyu and Abdullahi Uwaisu Muhammad}, doi = {10.1109/ACCESS.2025.3566163}, journal = {IEEE Access}, pages = {82342-82377}, shortjournal = {IEEE Access}, title = {Exploration and exploitation mechanism in pairwise test case generation: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). The accuracy of 3D mesh generation of neuralangelo versus photogrammetry and LiDAR technology. ACCESS, 13, 82312-82341. (www)

  @article{ , abstract = {Photogrammetry and LiDAR have become an essential tool for preserving evidence by creating a point cloud of objects such as vehicles and environments. While effective, these methods often struggle with capturing fine details during the mesh generation phase. Neural surface reconstruction methods, such as Neuralangelo, offer a solution by using neural networks to extract 3D geometry from images. Neuralangelo enhances this process by combining numerical gradients with coarse-to-fine optimization for better detail control. In this study, we evaluated Neuralangelo’s accuracy against traditional photogrammetry and LiDAR techniques for mesh generation. Our experiments involved scanning 3D-printed objects of varying complexity. Neuralangelo produced more geometrically accurate meshes compared to industry-standard tools like Polycam and Pix4DCatch. For example, at ±3 mm, Neuralangelo showed a 53.8% improvement over Pix4DCatch and 29.5% over Polycam, with similar results at smaller tolerances. Everyday objects, like chairs, were also scanned, reinforcing Neuralangelo’s superior performance. These findings suggest that Neuralangelo is a promising alternative to traditional methods, offering greater accuracy in capturing complex details during mesh generation. This technology could be especially useful in accident reconstruction for evidence preservation, allowing for detailed mesh generation of crushed vehicles using video inputs from common smartphones.}, archive = {J_ACCESS}, author = {Ali Farhat and A. Sattar and Alyssa Visalli and Brian Jones}, doi = {10.1109/ACCESS.2025.3567868}, journal = {IEEE Access}, pages = {82312-82341}, shortjournal = {IEEE Access}, title = {The accuracy of 3D mesh generation of neuralangelo versus photogrammetry and LiDAR technology}, volume = {13}, year = {2025}, }
• (2025). Dialogue-to-video retrieval via multi-grained attention network. ACCESS, 13, 82302-82311. (www)

  @article{ , abstract = {Dialogue-to-video retrieval is an interesting while challenging task, which exploits an AI agent to retrieve the video that matches and aligns with the conversational context between users. In particular, given a history of dialogue exchanges, the agent is expected to identify the most fitting video content that complements the ongoing conversation. The computational complexity posed by the processing of videos within deep neural networks encourages us to adapt CLIP, a cutting-edge large multi-modal models (LLMs), into the dialogue-video domain. On this basis, we propose a multi-grained attention network (MGAT), integrating query-scoring, dual-softmax and query-bank normalization techniques. We design a multi-grained attention module to optimize the inadequate modeling of conversation semantics in existing pre-trained models, dynamically assign weights during conversation feature extraction, and introduce conversation context features in multimodal alignment. Most importantly, fine-grained similarity by per-round query-frame scoring and coarse-grained similarity by high-level semantics of all rounds of dialogues for each video are respectively calculated, which are further learned via multi-grained attention mechanism. This approach effectively transfers CLIP’s text-image multimodal knowledge into the dialogue-video retrieval system, alleviating the need for resource-intensive and costly fine-tuning dialogue-video procedures. Extensive experiments on multiple datasets demonstrate that our method outperforms state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Yi Yu and Jiwei Zhang and Suhua Tang and Wei Li}, doi = {10.1109/ACCESS.2025.3567086}, journal = {IEEE Access}, pages = {82302-82311}, shortjournal = {IEEE Access}, title = {Dialogue-to-video retrieval via multi-grained attention network}, volume = {13}, year = {2025}, }
• (2025). Temperature behaviors of receptor of urban rail vehicle in electrical contact with contact rail. ACCESS, 13, 82291-82301. (www)

  @article{ , abstract = {Currently, many urban rail or subway vehicles obtain electrical energy through their receptors in contact with the contact rail linked to the traction network. As urban rail vehicles arrive at the station frequently or stop temporarily on the rail line, the contact rail still needs to provide the electric power for the on-board equipments. At this point, the receptor is in a static state, where the local overheating happens. This could lead to the damage of the receptor, even causing the vehicle to stop running. Based on the electrical contact theory and Fourier’s law, this paper establishes a three-dimensional thermal field finite element model for the receptor and contact rail (referred to as the boot and rail, BAR). Using the model, the change of the contact resistance under the working conditions is investigated. And the effects of the contact force, transmitted (or power supply) current and receptor wear on the temperature behaviors of the receptor are discussed. The results show that the temperature is negatively related to the contact force but closely related to the contact resistance. When the contact force is less than 100 N, the contact resistance is larger and the temperature is obviously enhanced, with the rise rate of $0.05~^{\circ }$ C/s. The temperature of the receptor is positively related to the transmitted current. The larger the current, the higher the temperature, more intense the temperature rise; the smaller the current, the more obvious the saturation phenomenon of the temperature, i.e. the rapid attainment of the thermal equilibrium. The larger the wear area of the BAR, the larger the convective heat flux of the receptor, leading to a lower temperature in its steady state. The innovation of this paper is to establish a three-dimensional thermal field model that can more accurately characterize the BAR system under the static electrical contact, by applying the electrical contact theory to the finite element method. Its contribution is to provide a methodology for the study of the multi-parameter three-dimensional temperature behaviors of the BAR system. And its research results present an important reference for the optimization of the receptor structure and the safe operation of urban rail vehicles.}, archive = {J_ACCESS}, author = {Tong Xing and Jianhua Chen and Zhiqiang Yu and Like Pan and Jianxiang Sun and An Jin and Yuehua Cui and Tao Feng and Wenxia Yan and Xudong Yi and Zimeng Zhao}, doi = {10.1109/ACCESS.2025.3568250}, journal = {IEEE Access}, pages = {82291-82301}, shortjournal = {IEEE Access}, title = {Temperature behaviors of receptor of urban rail vehicle in electrical contact with contact rail}, volume = {13}, year = {2025}, }
• (2025). ACFM: Adaptive channel feature matching for pedestrian re-identification. ACCESS, 13, 82278-82290. (www)

  @article{ , abstract = {Image misalignment is a significant challenge in the field of pedestrian re-identification. Previous studies typically align pedestrian features using additional models or by leveraging auxiliary information. However, these methods are data-dependent and can fail when dealing with substantial scene variations. To address these challenges, this paper proposes an efficient adaptive channel feature matching (ACFM) strategy. This method adaptively aligns channel feature maps according to image content, enables accurate matching of local features across images, and mitigates the need for supplementary data. Building on the ACFM approach, this paper develops a multi-branch adaptive matching network model. The model incorporates a channel attention mechanism to enhance the representation capacity of individual channels and integrates the ACFM algorithm within local branches to optimize the distance metric computation, effectively capturing and aligning local details. The multi-branch structure is designed to handle both global and local features, enabling the network to comprehensively capture and integrate image information. Evaluation experiments conducted on two widely-used benchmark datasets, Market-1501 and DukeMTMC-ReID, demonstrate that the proposed method offers significant advantages over current state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Zhengcai Lu and Zhengwei Tian}, doi = {10.1109/ACCESS.2025.3568806}, journal = {IEEE Access}, pages = {82278-82290}, shortjournal = {IEEE Access}, title = {ACFM: Adaptive channel feature matching for pedestrian re-identification}, volume = {13}, year = {2025}, }
• (2025). Multi-condition magnetic core loss prediction and magnetic component performance optimization based on improved deep forest. ACCESS, 13, 82261-82277. (www)

  @article{ , abstract = {In recent years, power electronic technology has been widely applied in energy conversion in the fields of renewable energy and information and communication technology. Magnetic components play a crucial role in voltage stabilization, conversion, and filtering, conventional loss models struggle to accurately characterize the nonlinear loss characteristics under multi-physics coupling and complex operating conditions. To achieve high efficiency and high power density designs, it is essential to study core loss characteristics and optimize operating parameters. This paper proposes a core loss prediction model based on an improved deep forest algorithm and information entropy-enhanced genetic algorithm. First, a multi-factor analysis of variance (ANOVA) and Bayesian inference are employed to explore the effects of temperature, material, and excitation waveform on core losses, as well as their optimal combinations. Subsequently, a temperature correction factor is introduced into the conventional Steinmetz equation, and the ridge regression regularization technique is applied to improve the goodness-of-fit from 0.954 to 0.986. Next, the deep forest algorithm is employed to perform multi-granularity scanning for feature extraction from the samples, which are then fed into an enhanced cascade forest (with two random forests replaced by XGBoost and LightGBM), key parameters (maximum tree depth and number of trees) are optimized via grid search, constructing a highly accurate and robust core loss prediction model. The model’s superiority is validated from multiple perspectives, accompanied by feature importance analysis using SHAP values. Finally, an information entropy and game theory-based genetic algorithm is utilized to optimize the operating conditions of magnetic components, yielding an optimal combination of low losses and high magnetic energy transfer efficiency.}, archive = {J_ACCESS}, author = {Haotian Shi and Zipeng Jin}, doi = {10.1109/ACCESS.2025.3562736}, journal = {IEEE Access}, pages = {82261-82277}, shortjournal = {IEEE Access}, title = {Multi-condition magnetic core loss prediction and magnetic component performance optimization based on improved deep forest}, volume = {13}, year = {2025}, }
• (2025). Nearest-better clustering-based memetic algorithm for berth allocation and crane assignment problem. ACCESS, 13, 82247-82260. (www)

  @article{ , abstract = {The growth of international trade has accelerated the development of waterway transportation, thereby increasing the demand for the construction of container terminals. Optimizing the Berth Allocation and Crane Assignment Problem (BACAP) is a critical aspect of this process. In this paper, we investigate the capability of differential evolution (DE) algorithms in solving BACAP by modeling berth allocation as a continuous optimization problem. We first analyze an efficient clustering algorithm, Nearest-Better Clustering (NBC), and its effectiveness in partitioning candidate solutions for BACAP into multiple sub-populations. Subsequently, within each sub-population, we propose a novel memetic algorithm (MA) that utilizes the Adaptive Differential Evolution with Optional External Archive (JADE) as a global optimizer, combined with a Neighborhood Search (NS) to enhance the convergence capability of the sub-populations. Finally, to improve search efficiency, we introduce a berth offset distance as a penalty mechanism to minimize berth space wastage. In the experimental section, we conduct experiments on 15 cases and compare the results with four existing algorithms. The experimental results demonstrate that the proposed MA-NBC exhibits superior competitiveness and performance in solving BACAP.}, archive = {J_ACCESS}, author = {Jiawei Wu}, doi = {10.1109/ACCESS.2025.3567823}, journal = {IEEE Access}, pages = {82247-82260}, shortjournal = {IEEE Access}, title = {Nearest-better clustering-based memetic algorithm for berth allocation and crane assignment problem}, volume = {13}, year = {2025}, }
• (2025). Enhancing fast network recovery in LoRaWAN: Implementing bidirectional forwarding detection in OMNeT++. ACCESS, 13, 82229-82246. (www)

  @article{ , abstract = {The paper analyses new techniques in the field of fast network recovery. It addresses the issues of network convergence, fast network recovery, and mechanisms designed for fast network recovery – Fast Reroute, which minimizes the impacts of outages. The paper describes LoRa technology and its communication protocol, LoRaWAN, which is used mainly in smart cities. The article deals with integrating fast link failure detection in the LoRaWAN network. We address increasing availability in the LoRaWAN network and implement the fast failure detection mechanism, Bidirectional Forwarding Detection (BFD), in the OMNeT++ simulation environment. The implementation is done by modifying and supplementing the source code for the FLoRa simulation framework. BFD accelerates network failure detection in LoRaWAN and reduces packet loss by redirecting to a backup route.}, archive = {J_ACCESS}, author = {Jozef Papan and Slavomir Tatarka and Michal Kvet and Michal Hraska}, doi = {10.1109/ACCESS.2025.3568253}, journal = {IEEE Access}, pages = {82229-82246}, shortjournal = {IEEE Access}, title = {Enhancing fast network recovery in LoRaWAN: Implementing bidirectional forwarding detection in OMNeT++}, volume = {13}, year = {2025}, }
• (2025). CBFs-based model predictive control for obstacle avoidance with tilt angle limitation for ball-balancing robots. ACCESS, 13, 82211-82228. (www)

  @article{ , abstract = {This study investigates an automatic navigation method for one type of underactuated system, ball-balancing robot (ballbot), in complex environments with both dynamic obstacles and complex-shaped obstacles. To ensure safe operations, which means that ballbot has to avoid obstacles and maintain tilt angles in a desired range, Nonlinear Model Predictive Control (NMPC) is formulated to predict the position and behavior of the ballbot, followed by the optimization problem assisted by Control Barrier Function (CBF) constraints to drive the ballbot in the safe trajectory. Instead of directly implementing tilt angle limitations on the main NMPC, another Quadratic Programming Optimizer based on CBF is designed outside the main controller to reduce the constraint complexity of optimization. An elliptic-bounded generation method is used to simplify the object boundary, especially concave obstacles definition in NMPC constraints, while Extended State Observer is used for observing, compensating the uncertainty terms, and estimating the velocities of the ballbot. In general, by combining this CBF-based NMPC and Quadratic Programming, this research addresses simultaneously high-quality observer, tracking control, balancing control, complex motion planning and safe-angle constraints for the 3D-ballbot system. The effectiveness of our proposed method is determined by simulations in complicated tracking scenarios with static, dynamic and complex-shaped objects.}, archive = {J_ACCESS}, author = {Minh Duc Pham and Duc Cuong Vu and Thi Thuy Hang Nguyen and Thi-van-Anh Nguyen and Minh Nhat Vu and Tung Lam Nguyen}, doi = {10.1109/ACCESS.2025.3567474}, journal = {IEEE Access}, pages = {82211-82228}, shortjournal = {IEEE Access}, title = {CBFs-based model predictive control for obstacle avoidance with tilt angle limitation for ball-balancing robots}, volume = {13}, year = {2025}, }
• (2025). Consistent geometric patterns observed in three-dimensional displacement of nationwide GNSS stations in japan. ACCESS, 13, 82193-82210. (www)

  @article{ , abstract = {This study explored the possibility of using currently available data to forecast when and where a large earthquake could occur in the near future. Such forecasts require measurements and understanding of past and current three-dimensional (3D) displacement patterns. We analyzed the geometric patterns of 3D displacement using coordinate data from over 1,300 global navigation satellite system stations in Japan. We found that the monthly displacement velocities of a station were on a single flat plane, although a significantly large earthquake had occurred, and the normal planes of all stations were on a single quadratic curve surface. Moreover, the sum of the absolute differences in 15-d velocities indicated a significant displacement over a wide area after the occurrence of a large earthquake.}, archive = {J_ACCESS}, author = {Junichi Susaki}, doi = {10.1109/ACCESS.2025.3563166}, journal = {IEEE Access}, pages = {82193-82210}, shortjournal = {IEEE Access}, title = {Consistent geometric patterns observed in three-dimensional displacement of nationwide GNSS stations in japan}, volume = {13}, year = {2025}, }
• (2025). Quantum-classical autoencoder architectures for end-to-end radio communication. ACCESS, 13, 82181-82192. (www)

  @article{ , abstract = {End-to-end radio communication needs to be optimized against noisy channel conditions and other distortion effects. This paper presents a novel concept, a set of hybrid quantum-classical autoencoder architectures with a comprehensive feasibility study using standard encoded radio signals, to evaluate quantum neural network design requirements for the radio context. The hybrid scenarios include single-sided, i.e., quantum encoder (transmitter) or quantum decoder (receiver), as well as fully quantum radio channel autoencoder (transmitter-receiver) systems. We provide detailed formulas for each scenario and validate our model through an extensive set of simulations. Our results demonstrate model robustness and adaptability. Supporting experiments are conducted utilizing 4 and 16 Quadrature Amplitude Modulation schemes and we expect that the model is adaptable to more general encoding schemes. We explore model performance against both additive white Gaussian noise and Rayleigh fading models. Our numerical findings highlight the importance of designing efficient quantum neural network architectures for meeting application performance constraints – including data re-uploading methods, encoding schemes, and core layer structures.}, archive = {J_ACCESS}, author = {Zsolt I. Tabi and Bence Bakó and Dániel T. R. Nagy and Péter Vaderna and Zsófia Kallus and Péter Hága and Zoltán Zimborás}, doi = {10.1109/ACCESS.2025.3566207}, journal = {IEEE Access}, pages = {82181-82192}, shortjournal = {IEEE Access}, title = {Quantum-classical autoencoder architectures for end-to-end radio communication}, volume = {13}, year = {2025}, }
• (2025). Applicability and enhancement of matrix-completion-based distribution system monitoring with limited measurements. ACCESS, 13, 82170-82180. (www)

  @article{ , abstract = {The transition to a sustainable energy future will result in greater dynamics of aggregated power generation and demand in distribution networks. A vast network with limited real-time measurements presents challenges in comprehensively monitoring the system status and responding promptly to localized disturbances. Sparse signal reconstruction techniques based on affine-constrained rank minimization have been proposed to estimate distribution feeder states with a few measurements, which are presumably well-suited for contexts with very limited observability. Although previous studies on matrix completion-based distribution system state estimation (MCDSSE) have demonstrated acceptable node voltage estimation performance, their effectiveness in estimating branch flow states, to monitor line and transformer loading, remains unproven in the literature. This study assesses the consistency between estimated system states obtained using a variant of the MCDSSE method and actual branch flows and node injection powers to judge the method’s applicability in distribution system monitoring with limited available measurements. The test results confirm that solving a low-rank minimization problem does not enable MCDSSE to recover a realistic AC solution in areas with insufficient measurements, and show that degenerate solutions and multiplicity appear to occur frequently. To leverage its advantage in providing complete solution of the system states, based on operator-defined visibility requirements, four fidelity-monitoring-driven MCDSSE meter placement methods were tested to enhance its acceptability for system monitoring with affordable additional measurements. Numerical examples highlighting practical implications are provided.}, archive = {J_ACCESS}, author = {Jun-Chuan Hu and Zhao-Cheng Chen and M. A. Mohammed Manaz and Chan-Nan Lu}, doi = {10.1109/ACCESS.2025.3567734}, journal = {IEEE Access}, pages = {82170-82180}, shortjournal = {IEEE Access}, title = {Applicability and enhancement of matrix-completion-based distribution system monitoring with limited measurements}, volume = {13}, year = {2025}, }
• (2025). A crawling and rolling soft robot with an interlocking design. ACCESS, 13, 82160-82169. (www)

  @article{ , abstract = {In recent years, the field of soft robotics has gained considerable attention, due to the high versatility of soft robots in adapting their flexible bodies to unknown environments, resulting in the development of a wide range of robots. Inspired by the morphology and locomotion mechanisms of inchworms and earthworms, various worm-like soft robots have been proposed, typically utilizing a single mode of locomotion such as crawling. In this paper, a worm-like pneumatic soft robot with an interlocking design that realizes bidirectional rolling on flat surfaces and bidirectional crawling on flat platforms and within a pipe, is presented. The crawling locomotion is inspired by the two-anchor crawling observed in biological species, where the animal alternately anchors its rear and front ends as its body elongates or shrinks. The robot consists of two multi-chamber radial actuators encapsulated in plastic cage frames and an axial actuator housed between the interlocking parts. The inflation of one of the radial actuators results in anchorage of the robot on that side, allowing the body to move when the central axial actuator is engaged. Sequential actuation of the individual chambers, near the dead ends on both sides, allows the robot to roll. A prototype of the robot was developed and tested. The results confirmed the robot’s ability to crawl bidirectionally at approximately 16 cm/min on flat surfaces, 15 cm/min inside pipes, and roll up to 40 cm/min on flat surfaces. The transition between these modes was achieved through usage of different patterns, without any hardware modifications.}, archive = {J_ACCESS}, author = {K. Bezha and K. Ito}, doi = {10.1109/ACCESS.2025.3567813}, journal = {IEEE Access}, pages = {82160-82169}, shortjournal = {IEEE Access}, title = {A crawling and rolling soft robot with an interlocking design}, volume = {13}, year = {2025}, }
• (2025). Computationally efficient single layer transformer convolutional encoder for accurate price prediction of agriculture commodities. ACCESS, 13, 82144-82159. (www)

  @article{ , abstract = {Predicting the accurate future price of the agricultural crops is important to avoid overproduction or shortages in the food supply chain. To obtain accurate predictions, the process usually involves large and complex datasets, which would add to computational costs for developing a model with good performance. Therefore, this study introduces the single-layer Transformer Convolutional Encoder algorithm (STCE), an improved version of the traditional transformer encoder. STCE is computationally efficient and does not compromise the accuracy of the prediction. In STCE, the fully connected Convolutional Neural Network (CNN) layer is used in the transformer to get the first temporal features and record long-range dependencies with Multi-Head Attention. To minimize complexity while maintaining performance, a single dense layer is used for the output instead of the Multi-Layer Perceptron (MLP) and omit positional encoding, which leverages the natural sequence order of the time series data. Additionally, since time-series price data normally comes with missing values, this study introduce a sequence nearest neighbor imputation algorithm for anchoring that data to complement the STCE method. This study focuses on various vegetable prices, such as tomatoes, long beans, and cucumbers, with empirical validation across various prediction prices, specifically 30-day, 60-day, and 90-day predictions. Predictions made with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) show that the STCE algorithm is better than other deep learning algorithms, even the traditional transformer encoder. STCE algorithm not only has better performance, but it also reduces the computational time in the training with 12% fewer seconds compared to the transformer encoder and 22% fewer seconds for LSTM. This study not only provides valuable insights for farmers and planners in the agriculture market but also highlights the robust potential of transformer encoder in predicting commodity markets.}, archive = {J_ACCESS}, author = {Caceja Elyca Anak Bundak and Mohd Amiruddin Abd Rahman and Nurin Syazwina Mohd Haniff and Nur Syaiful Afrizal and Khairul Adib Yusof and Muhammad Khalis Abdul Karim and Md Shuhazlly Mamat and Romi Fadillah Rahmat}, doi = {10.1109/ACCESS.2025.3567903}, journal = {IEEE Access}, pages = {82144-82159}, shortjournal = {IEEE Access}, title = {Computationally efficient single layer transformer convolutional encoder for accurate price prediction of agriculture commodities}, volume = {13}, year = {2025}, }
• (2025). Augmented reality for machine monitoring in industrial manufacturing: A media comparison in terms of efficiency, effectiveness, and satisfaction. ACCESS, 13, 82129-82143. (www)

  @article{ , abstract = {Usability is a key factor for successfully integrating new technology to aid an operator in production. It is measured using three metrics: efficiency (productivity), effectiveness (quality), and user satisfaction. One prominent technology for operator support is augmented reality (AR), which is mostly handheld or head-mounted. A human-centered approach is required to align the AR integration with the operator’s capabilities. The underlying use case in this study is an energy dashboard visualized using AR and non-AR media, namely, a monitor, tablet, and HoloLens. The resulting media applications were evaluated for usability in terms of efficiency, effectiveness, and satisfaction in the within-study experiments by 16 participants. Overall, the results showed increased efficiency and satisfaction for traditional-monitor users and increased effectiveness for tablet users. Despite the participants’ lack of experience with AR, the AR applications performed comparably to the monitor and even slightly better in some aspects. With the ongoing development of AR software and hardware, AR can become increasingly useful for machine monitoring in production. However, to use AR for more comprehensive tasks, its strengths and weaknesses must be considered.}, archive = {J_ACCESS}, author = {Thomas Schmitt and Philip Viklund and Martina Sjölander and Lars Hanson and Matías Urenda Moris and Kaveh Amouzgar}, doi = {10.1109/ACCESS.2025.3566442}, journal = {IEEE Access}, pages = {82129-82143}, shortjournal = {IEEE Access}, title = {Augmented reality for machine monitoring in industrial manufacturing: A media comparison in terms of efficiency, effectiveness, and satisfaction}, volume = {13}, year = {2025}, }
• (2025). Toward effective comparative opinion mining: A novel vietnamese product review corpus and benchmark approach. ACCESS, 13, 82111-82128. (www)

  @article{ , abstract = {Comparative opinion mining is an important sub-task of aspect-based sentiment analysis that focuses on identifying and interpreting comparisons between entities. It plays a vital role in understanding how users evaluate competing products or services. However, most existing studies are limited to extracting comparative elements from a single sentence. This narrow scope overlooks the complexity of real-world reviews, where multiple comparisons often appear within the same sentence or extend across sentences. Furthermore, comparative opinion mining remains underdeveloped in low-resource languages such as Vietnamese, due to the scarcity of annotated datasets. To address these challenges, we introduce VCOM, a Vietnamese corpus for comparative opinion mining, constructed from real-world product reviews. VCOM contains 2,468 annotated comparative tuples across 9,174 sentences, capturing both intra- and inter-sentence comparisons. Each tuple is labeled with a structured quintuple: subject, object, aspect, predicate, and comparison type. We also propose a deep learning model that leverages a sliding window mechanism to capture contextual dependencies across multiple sentences. This model improves the extraction of complex comparative structures that are often overlooked by sentence-level approaches. To validate our contributions, we conducted extensive experiments that demonstrate the effectiveness of our model on the VCOM corpus. We also organized a shared task based on the intra-sentence subset of VCOM to encourage further research in this domain. Our work serves as a foundation for future advancements in comparative opinion mining, particularly in low-resource settings.}, archive = {J_ACCESS}, author = {Duy-Cat Can and Khanh-Vinh Nguyen and Hung-Manh Hoang and Duc-Loc Vu and Mai-Vu Tran and Hoang-Quynh Le}, doi = {10.1109/ACCESS.2025.3567845}, journal = {IEEE Access}, pages = {82111-82128}, shortjournal = {IEEE Access}, title = {Toward effective comparative opinion mining: A novel vietnamese product review corpus and benchmark approach}, volume = {13}, year = {2025}, }
• (2025). 11.2 v supply, 0.21% biphasic stimuli charge-balanced neurostimulator with switching spike suppression: An application to intraspinal microstimulation for restoring motor function. ACCESS, 13, 82097-82110. (www)

  @article{ , abstract = {In this paper, a new high-voltage and high-current compliance integrated electrical neurostimulator with charge balancing stimuli is presented. The proposed stimulator uses a stacked structure to provide the 11.2 V power supply delivering a biphasic stimulation current of 3.2 mA. It includes a novel charge balancing method to secure safe symmetric and asymmetric biphasic pulse trains with a low charge mismatch of 0.21%. Also, the proposed device properly cancels the switching current spikes generating biphasic stimuli. In addition, two methods based on a gate driver circuit and zero current switching are proposed securing advanced stimulator feature which are validated through extensive testing phase, including a simulation of 29 million periodic stimulation cycles delivering a current of 3.2 mA. The stimulator demonstrates robust performance with an average residual DC current of 11.4 nA after the charge balancing phase. The stimulator is implemented using the standard TSMC 180 nm 1.8 V/3.3 V LV CMOS technology. It operates at a low power budget of $90~\mu $ W. The main core of the circuit occupies an area of 0.181mm2. The fabricated chip has been packaged and successfully tested in a motor function of intraspinal paralysis application.}, archive = {J_ACCESS}, author = {Mostafa Katebi and Abbas Erfanian and Mohammad Azim Karami and Mohamad Sawan}, doi = {10.1109/ACCESS.2025.3567633}, journal = {IEEE Access}, pages = {82097-82110}, shortjournal = {IEEE Access}, title = {11.2 v supply, 0.21% biphasic stimuli charge-balanced neurostimulator with switching spike suppression: An application to intraspinal microstimulation for restoring motor function}, volume = {13}, year = {2025}, }
• (2025). Transformer-LSTM models for automatic scoring and feedback in english writing assessment. ACCESS, 13, 82084-82096. (www)

  @article{ , abstract = {Writing assessment is one of the most important stages in the educational process, but it is also the most resource-demanding one. To address the challenges of scalability and inconsistency, this study proposes a Transformer-LSTM model for automated scoring and feedback generation, enhancing accuracy and reliability in assessment. Integrating the contextual reading abilities of transformers with the sequential analysis strength of LSTMs, the model analyzes significant metrics of writing quality, including grammar, coherence, and structure, while providing individualized, actionable feedback. Using annotated datasets and evaluation metrics like RMSE and feedback relevance, it was established that the model performs well overall and that improvements in grammar and coherence seemed to be the most significant contributors to writing ability. It was also demonstrated that feedback relevance enhances these outcomes, thus confirming its valuable role in promoting structural and grammatical accuracy. Understanding that most existing systems do not encourage significant human feedback, this work demonstrates a scalable approach with potential alignment with human evaluation standards. Finally, this study shows hybrid models’ promise for automated writing assessment as promising scalable, equitable, impact-based tools for global enhancement of educational outcomes.}, archive = {J_ACCESS}, author = {Yunyun Xuan}, doi = {10.1109/ACCESS.2025.3562493}, journal = {IEEE Access}, pages = {82084-82096}, shortjournal = {IEEE Access}, title = {Transformer-LSTM models for automatic scoring and feedback in english writing assessment}, volume = {13}, year = {2025}, }
• (2025). A real time coordinated hydro governor with dual STATCOM for low frequency oscillation damping using hybrid mean DE & TVAPSO. ACCESS, 13, 82065-82083. (www)

  @article{ , abstract = {Hydro power generation is one of the most viable renewable sources to meet future energy demand and being a synchronous source, it has also the capability to enhance power system stability. Low frequency oscillations in power grid poses several challenges to operators and need to be damped at earlier stage to maintain stability of power system. The hydro turbine governors usually impart negative damping to these oscillations. So, there can be modification in governor control action to impart damping for these oscillations. In present work the hydro governors are phase compensated and coordinated with dual static synchronous compensator (STATCOM) so as to provide sufficient damping torque. A state space model of dual STATCOM with compensated hydro governor has been developed. Sudden change in reference voltage, uncertain solar and wind penetrations with plug-in electric vehicle as load being considered with different experimentations to impart critical oscillations in power system and proposed controller has been employed to damp these oscillations. Multimachine two area four hydro generators, 39 bus systems with 10 generators and modified IEEE 68 bus system with sixteen generators at different operating conditions have been investigated with proposed controller for oscillation damping. A new multi-objective mean Differential Evolution with time varying accelerated Particle Swarm Optimization algorithm (MeDETVAPSO) has been implemented to tune controller parameters. The proposed controller shows less values of settling time, oscillations peak value and imparting high damping ratio. Settling time, oscillation peak value and minimum damping ratio with proposed controller have been observed as 1.638s, 0.000375pu and 0.987 respectively. These experimentations have been conducted in MATLAB Simulink and verified with real time simulation based on OPAL-RT 4510. The damping capability of proposed controller has been found to be better in contrast to power system stabilizer (PSS), compensated hydro governor, dual STATCOM and performance of MeDETVAPSO has been observed to be better than PSO and DE to tune controller parameters.}, archive = {J_ACCESS}, author = {Samarjeet Satapathy and Narayan Nahak and Renu Sharma}, doi = {10.1109/ACCESS.2025.3567088}, journal = {IEEE Access}, pages = {82065-82083}, shortjournal = {IEEE Access}, title = {A real time coordinated hydro governor with dual STATCOM for low frequency oscillation damping using hybrid mean DE & TVAPSO}, volume = {13}, year = {2025}, }
• (2025). Community detection in multi-aspect functional brain networks: Robust tensor decomposition approach. ACCESS, 13, 82043-82064. (www)

  @article{ , abstract = {Functional organization of the brain can be characterized as a network of interconnected regions. Study of brain networks has offered new insights on human behavior and neurodegenerative diseases. Recent studies show that the functional connectivity networks are dynamic with the topology and community structure evolving with time during task performance and rest. Most of the current work on analyzing the community structure of dynamic networks either focus on single subjects or extract the group level community structure. In this paper, we present a framework for community detection in dynamic functional connectivity networks across multiple subjects and at the individual level, simultaneously. The proposed approach is based on a structured robust tensor decomposition with spectral clustering, temporal smoothness and co-clustering regularization terms to extract both the group and individual level community structures. Moreover, the temporal evolution of the community structure within the group is tracked to identify change points that may correspond to events of interest. The proposed framework is applied to dynamic functional connectivity networks constructed from task-based electroencephalogram (EEG) data.}, archive = {J_ACCESS}, author = {Esraa Al-Sharoa and Mohammad Al-Wardat and Selin Aviyente}, doi = {10.1109/ACCESS.2025.3567703}, journal = {IEEE Access}, pages = {82043-82064}, shortjournal = {IEEE Access}, title = {Community detection in multi-aspect functional brain networks: Robust tensor decomposition approach}, volume = {13}, year = {2025}, }
• (2025). Highly compact swing assistive knee prosthesis integrating active and passive hydraulic systems. ACCESS, 13, 82032-82042. (www)

  @article{ , abstract = {This work proposes a novel partially powered knee prosthesis to give full support to swing related activities. It represents a technological step forward when compared to microprocessor-controlled knees. The device is based on the electro-hydrostatic actuation principle, combined with the use of a series rotary valve to aid during dissipative phases. Another key feature is the backdrivability enabled by a directly coupled fluid-based actuation principle. To this end, we present a compact design that is subsequently built and tested experimentally with an able-body adapter to simulate a real-case scenario of level walking and stair tasks. Results are compared with the features of existing commercial and research devices. To the best of the authors’ knowledge, the proposed prosthesis is the most compact electro-hydrostatic swing assistive device with the potential to improve the walking gait, for instance by increasing toe clearance and likely reducing the occurrence of stumbling or falling events. Additionally, the prototype enables stair ascent in a step-over fashion, a capability generally unattainable with commercial microprocessor-controlled knees.}, archive = {J_ACCESS}, author = {Marco Puliti and Federico Tessari and Renato Galluzzi and Nicola Amati and Andrea Tonoli and Matteo Laffranchi}, doi = {10.1109/ACCESS.2025.3567881}, journal = {IEEE Access}, pages = {82032-82042}, shortjournal = {IEEE Access}, title = {Highly compact swing assistive knee prosthesis integrating active and passive hydraulic systems}, volume = {13}, year = {2025}, }
• (2025). Developing in-beam images from gadanki ionospheric radar interferometer data. ACCESS, 13, 82015-82031. (www)

  @article{ , abstract = {This paper aims to produce in-beam images from Gadanki ionospheric radar interferometer (GIRI) data and thereby studying ionospheric irregularities. Here, multiple receiving channels are connected with collinear baselines in spaced antenna (SA) array interferometry mode. In order to obtain high-resolution images using coherent radar imaging (CRI), the backscattered signals received at the multiple receiving channels have been analysed. Generating these images is related to spectrum estimation, which has two challenges: (i) Amount of the data is limited and in some cases very small. (ii) Data is corrupted by noise in the environment. Due to statistical fluctuation and noise in the backscattered signal, visibility spectrum data is noisy and incompletely sampled. To make optimal use of data while generating the images, it is important to remove the background noise from the visibility spectrum in Fourier method. Using the active phased antenna array configuration, images produced with small time resolution allow us to observe the temporal evolution of the ionospheric irregularities inside the radiating beam.}, archive = {J_ACCESS}, author = {Timiti Vamsi Krishna and Ravindra Dhuli}, doi = {10.1109/ACCESS.2025.3563207}, journal = {IEEE Access}, pages = {82015-82031}, shortjournal = {IEEE Access}, title = {Developing in-beam images from gadanki ionospheric radar interferometer data}, volume = {13}, year = {2025}, }
• (2025). A near-field focused lens based on multilayer transmitarray. ACCESS, 13, 82003-82014. (www)

  @article{ , abstract = {Near-field focused antennas are one of the most widely concerned industrialization research studies. Among these application scenarios, the microwave near-field focused lens based on transmitarray is one of the intensively designs, which usually consist of Multilayer Frequency Selective Surface (M-FSS). In this paper, the theoretical relationship between the transmission response of a single-layer FSS and that of an M-FSS is established, taking into account the interlayer interactions. Through the derived results, a transmitted element based on four-layer FSS was swiftly identified, which could achieve a phase shift of approximately 330 degrees with a transmission magnitude of better than -1.5 dB. A 3-bit quantized near-field focused lens was subsequently designed, fabricated, and tested, whose operating frequency band is 20.5-21.7 GHz. The simulated and measured results show that the presented near-field focused lens has good electromagnetic wave convergence. And both the simulation and measurement verify the validity of the derived expression, indicating its availability for M-FSS transmitarray element design, thereby streamlining the design process.}, archive = {J_ACCESS}, author = {Yuying Li and Zhaoran Chen and Yang Li and Xiayuan Yao}, doi = {10.1109/ACCESS.2025.3567426}, journal = {IEEE Access}, pages = {82003-82014}, shortjournal = {IEEE Access}, title = {A near-field focused lens based on multilayer transmitarray}, volume = {13}, year = {2025}, }
• (2025). Integrating machine learning and multi-objective optimization in biofuel systems: A review. ACCESS, 13, 81983-82002. (www)

  @article{ , abstract = {The optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction. Recent studies have explored various multi-objective optimization (MOO) techniques integrated with machine learning (ML) models to enhance process efficiency. This review synthesizes key advancements in biofuel optimization, highlighting the use of techniques such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Response Surface Methodology (RSM). Studies have leveraged hybrid models, including Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) networks for emission control and Neutrosophic Fuzzy Optimization (NFO) for uncertainty handling. While existing models demonstrate improvements in predictive accuracy and optimization effectiveness, challenges remain in model generalization, computational complexity, and real-time adaptability. Future research directions include expanding datasets, incorporating adaptive optimization strategies, integrating uncertainty quantification, and refining hybrid modeling approaches for robust decision-making in biofuel production systems.}, archive = {J_ACCESS}, author = {Ivan P. Malashin and Dmitry A. Martysyuk and Vadim S. Tynchenko and Andrei P. Gantimurov and Vladimir A. Nelyub and Aleksei S. Borodulin}, doi = {10.1109/ACCESS.2025.3568297}, journal = {IEEE Access}, pages = {81983-82002}, shortjournal = {IEEE Access}, title = {Integrating machine learning and multi-objective optimization in biofuel systems: A review}, volume = {13}, year = {2025}, }
• (2025). Joint lagrangian relaxation and dynamic programming approach for a multi-period multi-product location-inventory problem. ACCESS, 13, 81967-81982. (www)

  @article{ , abstract = {We propose a multi-period multi-product location-inventory model considering an (R, s, S) periodic review policy and modular stochastic capacity constraints. The objective is to define which regional warehouses to open, maintain, operate, or close, which products to assign, and which customers should be supplied by the chosen distribution centers. Additionally, it involves setting order sizes and reorder points to minimize system costs while meeting service level conditions. This problem is structured as a non-convex mixed integer nonlinear programming model. We propose a Lagrangian relaxation algorithm and the subgradient approach as a solution method. We relax the customer allocation, distribution center variance, and demand constraints. Then, the relaxed problem is decomposed into location subproblems for each warehouse, in which inventory subproblems are embedded. So, each location subproblem is discomposed for each period and exactly solved through dynamic programming. Computational experiments prove that the presented approach gives solutions close to optimal and low gaps in a low computational time. Additionally, it exhibits meaningful incidences in the decisions on inventory control policy, facility location decisions, total costs, and risk pooling effects for different review intervals.}, archive = {J_ACCESS}, author = {Claudio Araya-Sassi and Germán Paredes-Belmar and Gabriel Gutiérrez-Jarpa and Ricardo Gatica Escobar}, doi = {10.1109/ACCESS.2025.3567260}, journal = {IEEE Access}, pages = {81967-81982}, shortjournal = {IEEE Access}, title = {Joint lagrangian relaxation and dynamic programming approach for a multi-period multi-product location-inventory problem}, volume = {13}, year = {2025}, }
• (2025). Disentangling high-paced alternating I/O in gaze-based interaction. ACCESS, 13, 81948-81966. (www)

  @article{ , abstract = {Gaze-based input to machines utilizes the ability of eye-gaze to serve as a user’s “output.” However, gaze should also support information flow in the opposite direction, namely, “input” to the user’s visual system from a machine’s output. The two functions can be easily separated in some tasks, like eye typing, but more complex scenarios typically require users to perform additional actions to avoid misinterpreting their intent. In this study, we modeled a free-behavior interaction with rapid transitions between visual search, decision-making, and gaze-based input operations through an engaging game called EyeLines. When playing the game, 15 volunteers selected screen objects using a 500 ms dwell time without additional actions for intention confirmation. By applying machine learning algorithms to gaze features and action context information, we achieved a threefold reduction in false positives, improved the quality of in-game decisions, and increased participant satisfaction with system ergonomics. To our knowledge, this is the first study that demonstrates the effectiveness of machine learning applied to gaze features in enhancing gaze-based interaction within visually challenging environments.}, archive = {J_ACCESS}, author = {Yulia G. Shevtsova and Artem S. Yashin and Sergei L. Shishkin and Anatoly N. Vasilyev}, doi = {10.1109/ACCESS.2025.3568650}, journal = {IEEE Access}, pages = {81948-81966}, shortjournal = {IEEE Access}, title = {Disentangling high-paced alternating I/O in gaze-based interaction}, volume = {13}, year = {2025}, }
• (2025). Human scene understanding mechanism-based image captioning for blind assistance. ACCESS, 13, 81933-81947. (www)

  @article{ , abstract = {This paper, a model is proposed that generates descriptive sentences for input images visually impaired individuals. For this purpose, a novel image captioning approach is introduced, integrating the principles of human visual Understanding mechanisms with the Vision Transformer (ViT) architecture, further enhanced by deep reinforcement learning. First, features are extracted from the image based on human visual perception. Second, the image features are encoded through the encoding block of ViT and input into the long short-term memory (LSTM) network to generate annotations for the image. Finally, reinforcement learning is optimized to further enhance the accuracy of the generated captions. Evaluations were performed utilizing the MSR-VTT benchmark dataset, which is widely used for image captioning tasks. Experimental results on the MSR-VTT benchmark dataset demonstrate that the proposed model achieves BLEU-4 of 43.0, METEOR of 29.1, ROUGE-L of 62.7, and CIDEr-D of 54.9, surpassing state-of-the-art baseline models across all evaluation metrics. The proposed model can be applied to video annotation applications for the visually impaired. In contrast to prior works that primarily rely on conventional convolutional architectures, the proposed model uniquely incorporates human-inspired visual perception principles and Vision Transformer-based global encoding, offering a novel and interpretable framework tailored for assistive image captioning.}, archive = {J_ACCESS}, author = {Jong-Hoon Kim and Sung-Wook Park and Jun-Ho Huh and Se-Hoon Jung and Chun-Bo Sim}, doi = {10.1109/ACCESS.2025.3564991}, journal = {IEEE Access}, pages = {81933-81947}, shortjournal = {IEEE Access}, title = {Human scene understanding mechanism-based image captioning for blind assistance}, volume = {13}, year = {2025}, }
• (2025). Addressing activation outliers in LLMs: A systematic review of post-training quantization techniques. ACCESS, 13, 81917-81932. (www)

  @article{ , abstract = {Large Language Models (LLMs) have transformed natural language processing, yet their deployment remains challenging due to substantial computational, memory, and energy demands. Post-training quantization has emerged as a key strategy for enabling efficient inference, particularly in resource-constrained settings. This systematic review focuses on weight-activation quantization, with a unique emphasis on the emergent outlier phenomenon in LLM activations. This work evaluates recent techniques that mitigate activation outliers and improve quantization efficiency, distinguishing itself from prior reviews. Using the PRISMA methodology, we examine 52 recent studies to uncover key trends and evaluate the effectiveness of different approaches. By synthesizing insights from these works, this review presents a diverse set of techniques and their implications for activation quantization, laying the groundwork for future research and practical advancements in LLM deployment.}, archive = {J_ACCESS}, author = {Patrik Czakó and Gábor Kertész and Sándor Szénási}, doi = {10.1109/ACCESS.2025.3568702}, journal = {IEEE Access}, pages = {81917-81932}, shortjournal = {IEEE Access}, title = {Addressing activation outliers in LLMs: A systematic review of post-training quantization techniques}, volume = {13}, year = {2025}, }
• (2025). AI and human resources in a literature-driven investigation into emerging trends. ACCESS, 13, 81897-81916. (www)

  @article{ , abstract = {Throughout the years, technology has faced many advancements, the star being the power of Artificial Intelligence (AI), which continues to strike through. This concept has rapidly gained popularity and has raised points of concern in almost every government from the European Union (EU) because of the challenges it possesses in terms of efficiency, decision-making and transparency. This paper revolves around building up an extensive literature review of the academic landscape surrounding the role of AI in Human Resources (HR) in the public sector, analyzing Web of Science publication trends and thematic patterns, spanning 12,121 publications from 2020 to 2024, where both Python and R scripts are applied to extract insights. The findings highlight the relevance of a human-centric approach to AI adoption by addressing ethical, cultural and compliance concerns, with the aid of advanced Natural Language Processing (NLP) techniques, such as Latent Dirichlet Allocation (LDA) for topic modelling and keyword co-occurrence networks for thematic exploration. Moreover, a Hugging Face Named Entity Recognition (NER) model is employed to systematically identify and categorize AI techniques within the analyzed abstracts, providing a foundation for subsequent frequency and trend analyses. The analysis brings out a steady growth in publication volume, with an average of 2,500 papers annually and a significant concentration of research within domains such as neural networks, algorithm optimization and digital transformation. Apart from the interdisciplinary focus of the subject, we aim to shed light on the importance of AI-driven HR strategies in directing administrative insufficiencies of the public sector.}, archive = {J_ACCESS}, author = {Cristina Iancu and Simona-Vasilica Oprea}, doi = {10.1109/ACCESS.2025.3568338}, journal = {IEEE Access}, pages = {81897-81916}, shortjournal = {IEEE Access}, title = {AI and human resources in a literature-driven investigation into emerging trends}, volume = {13}, year = {2025}, }
• (2025). An error analysis method for correlation-based parameter estimators in discrete-time signals under gaussian noise. ACCESS, 13, 81889-81896. (www)

  @article{ , abstract = {In this study, we expand on a previously introduced method for computing the error statistics associated with estimating the time of arrival of a known signal in sampled data with additive white Gaussian noise (AWGN). When matched filtering techniques are used to localize sampled signals, there is a probability of error, which takes the form of a sample index offset from the true time of arrival. This method allows us to fully calculate the probability mass function for the estimation error, which at lower signal to noise ratios (SNR) does not fit a Gaussian shape and depends heavily on the shape of the signal being detected. We show that the method can be extended to work on cases involving complex signals, interpolated signals, and signals that undergo pre-filtering steps. In addition, we demonstrate that the same method can be used to characterize frequency of arrival error probabilities, and the joint estimation of frequency and time of arrival.}, archive = {J_ACCESS}, author = {Chris Hall and Ivan Djordjevic}, doi = {10.1109/ACCESS.2025.3566385}, journal = {IEEE Access}, pages = {81889-81896}, shortjournal = {IEEE Access}, title = {An error analysis method for correlation-based parameter estimators in discrete-time signals under gaussian noise}, volume = {13}, year = {2025}, }
• (2025). Sign language Recognition—Dataset cleaning for robust word classification in a landmark-based approach. ACCESS, 13, 81877-81888. (www)

  @article{ , abstract = {Communication barriers between hard-of-hearing and hearing individuals can be mitigated through advancements in sign language recognition (SLR) systems. These SLR systems can also improve the user experience of hard-of-hearing people when interacting with conversational systems that could emerge in the near future. This work explores a landmark-based approach for word classification within an SLR system. The study investigates the impact of a novel data-cleaning methodology on model performance during training. Specifically, a data cleaning process focused on video trimming and sign placement correction is shown to significantly improve dataset quality, resulting in more accurate classification. This cleaner data not only facilitated a more stable training process for the RNN model but also effectively delayed the onset of overfitting compared to a model trained on the original data. The findings highlight the critical role of data quality, particularly when dealing with the limitations inherent to small datasets commonly encountered in SLR tasks. The contribution of this study lies in demonstrating how targeted data cleaning enhances model stability and performance in resource-limited SLR systems.}, archive = {J_ACCESS}, author = {Pawel Antonowicz and David Kasperek and Michal Podpora}, doi = {10.1109/ACCESS.2025.3566338}, journal = {IEEE Access}, pages = {81877-81888}, shortjournal = {IEEE Access}, title = {Sign language Recognition—Dataset cleaning for robust word classification in a landmark-based approach}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of low-power ultra-wideband CMOS variable-gain LNAs with sub-2.5 dB NFmin using coupled microstrip-line neutralization and AGF-inductor. ACCESS, 13, 81866-81876. (www)

  @article{ , abstract = {This study reports low power-dissipation (PD) and low noise-figure (NF) ultra-wideband CMOS variable-gain amplifiers (VGAs). VGA1 and VGA2 use coupled microstrip-line (CL) neutralization with an adjustable-gain-flatness inductor and multiple inductive peaking, featuring complementary common-source (CCS) input and common-source (CS) output stages to achieve low PD and NF values and wide bandwidths. The concurrent current steering of the first and second stages yields large gain-tuning ranges with low NFs. VGA1 and VGA2 with PD of 5.85 mW in the low-noise mode achieve excellent S21 bandwidths (BWs) of 32.3 GHz (2.9–35.2 GHz) and 26 GHz (3.8–29.8 GHz), maximum S21 of 11.9 dB and 13.6 dB, average NF ( $\mathrm{NF}_{\mathrm {avg}}$ ) of 4.11 and 3.93 dB, respectively, and minimum NF of 2.43 dB. The input 1-dB compression point ( $\mathrm{IP}_{\mathrm {1dB}}$ ) of VGA1 is −11.5 dBm. The chip area of both VGA1 and VGA2 is 0.356 mm2. Moreover, VGA1 attains a decent gain tunning range of 20 dB (−3.8–16.2 dB) under 1.35 dB/step for 5G NR band N257. Additionally, VGA2 attains an excellent gain tunning range of 32 dB (−14.6–17.4 dB), gain flatness of $\pm ~0.75$ dB, and NFavg of 3.62 dB between 3.8–24.1 GHz. To the authors’ knowledge, the BW and NFmin/ FOM performance are one of the best results ever reported for millimeter-wave CMOS VGAs (or low-noise amplifiers) with a PD of less than 15 mW.}, archive = {J_ACCESS}, author = {Jin-Fa Chang}, doi = {10.1109/ACCESS.2025.3568004}, journal = {IEEE Access}, pages = {81866-81876}, shortjournal = {IEEE Access}, title = {Design and analysis of low-power ultra-wideband CMOS variable-gain LNAs with sub-2.5 dB NFmin using coupled microstrip-line neutralization and AGF-inductor}, volume = {13}, year = {2025}, }
• (2025). Capture and process differences between ontologies. ACCESS, 13, 81848-81865. (www)

  @article{ , abstract = {The Ontology Development Tracker (OnDeT) has been developed to facilitate the computation and interactive exploration of differences between multiple versions of a Git-hosted ontology throughout its temporal evolution. Existing tools focus primarily on pairwise comparisons, which limits the ability to analyze the entire evalutionary history of the ontology. OnDeT addresses this limitation by integrating three existing tools for pairwise comparisons between ontologies. Based on changes have been made to the ontology, the OnDeT tool captures and processes differences between different versions of the same ontology. A comprehensive user interface allows users to examine and explore differences in chronological order, as well as to explore specific differences based on a selected class name and dates. In addition, a semantic representation of these differences has been developed using the Provenance Ontology (PROV-O) specification. The OnDeT Knowledge Graph (KG) leverages this representation to provide a detailed semantic overview of all differences. OnDeT is integrated into the Technische Informationsbibliothek (TIB) Terminology Service (TS). Finally, to validate its correctness, OnDeT was evaluated using reasoners and a series of competency questions.}, archive = {J_ACCESS}, author = {Felix Engel and Mark Vanin and Nenad Krdzavac}, doi = {10.1109/ACCESS.2025.3567753}, journal = {IEEE Access}, pages = {81848-81865}, shortjournal = {IEEE Access}, title = {Capture and process differences between ontologies}, volume = {13}, year = {2025}, }
• (2025). Unsupervised hybrid VAE-based anomaly detection for vehicle onboard LiDAR sensors. ACCESS, 13, 81835-81847. (www)

  @article{ , abstract = {Intelligent transportation systems (ITS) are revolutionizing road safety, particularly in urban areas. Innovative sensors like LiDAR are being deployed to monitor traffic flow in real-time, providing precise data on vehicle movements, road conditions, and congestion patterns. These advancements open the path for safer roads and more efficient transportation, with the potential for autonomous driving in the future. In this context, since various environmental factors, such as weather fluctuations and terrain diversity, can introduce anomalies that pose risks and malfunctions to a variety of sensor driving systems, the detection of anomalies in spatial-temporal (ST) preprocessed LiDAR data becomes crucial to ensuring safety. In this paper, we propose a novel low-complex unsupervised model for anomaly detection (AD) within ST preprocessed LiDAR data named CNN-BiLSTM VAE that combines variational auto-encoder (VAE) reconstruction capabilities, convolutional neural networks (CNN) spatial characteristic learning capabilities, and bidirectional long-short-term memory (BiLSTM) networks time series learning capabilities in a symmetric mirror-to-mirror (M2M) architecture. Our method aims to detect abnormal behavior and unusual ST patterns in preprocessed, unlabeled LiDAR data without any prior knowledge of the presence of anomalies by learning complex patterns and dependencies within the ST data, while considering the effects of multiple environmental factors. Experimental validation against benchmark models demonstrates the effectiveness of our proposed model in detecting anomalies within preprocessed multidimensional ST LiDAR data. The model strikes a good balance between complexity and efficiency, achieving a 10% accuracy improvement compared to existing models, with an accuracy of 95.1% and an F1-score of 82.6%. Additionally, injecting diverse environmental anomalies enables testing the model’s performance and reliability in real-world scenarios influenced by environmental factors, ensuring robustness across various conditions.}, archive = {J_ACCESS}, author = {Nourhen Sboui and Hakim Ghazzai and Mohamed Hadded and Mourad Elhadef and Gianluca Setti}, doi = {10.1109/ACCESS.2025.3568395}, journal = {IEEE Access}, pages = {81835-81847}, shortjournal = {IEEE Access}, title = {Unsupervised hybrid VAE-based anomaly detection for vehicle onboard LiDAR sensors}, volume = {13}, year = {2025}, }
• (2025). Directed fuzzy edge graphs under q-ROF environment: A framework for optimal pathfinding. ACCESS, 13, 81823-81834. (www)

  @article{ , abstract = {This paper introduces a novel framework of Directed Edge q-Rung Orthopair Fuzzy Graphs (DEq-ROFGs), where graph vertices are crisp, and edges are characterized by q-rung orthopair fuzzy numbers (q-ROFNs). This structure captures the uncertainty in edge relationships while retaining deterministic node identities, making it ideal for applications in uncertain environments such as social networks, supply chains, healthcare systems, and recommendation systems. The paper defines foundational properties of DEq-ROFGs including subgraphs, completeness, and various degree-based metrics, and it establishes a proposition regarding the balance between in-degrees and out-degrees. The core contribution is a novel path-finding algorithm based on Hamacher operators and an improved score function, which identifies optimal paths between nodes under uncertainty. Unlike classical algorithms, it considers the suitability of a path, not just its length. Applied to an emergency road network scenario, the algorithm successfully determines the optimal route for service vehicles, and the choice between these routes can be made based on the score of the resulting path length. Comparative simulations show their effectiveness over traditional methods. Further analysis shows that increasing the q-value reduces both path score and length, and that Einstein operators yield higher destination scores than Hamacher and Dombi, confirming the model’s adaptability and robustness.}, archive = {J_ACCESS}, author = {Nazia Nazir and Tanzeela Shaheen and Wajid Ali and Md Rafiul Hassan and Mohammad Mehedi Hassan}, doi = {10.1109/ACCESS.2025.3565633}, journal = {IEEE Access}, pages = {81823-81834}, shortjournal = {IEEE Access}, title = {Directed fuzzy edge graphs under q-ROF environment: A framework for optimal pathfinding}, volume = {13}, year = {2025}, }
• (2025). Detecting advanced persistent threat exfiltration with ensemble deep learning tree models and novel detection metrics. ACCESS, 13, 81803-81822. (www)

  @article{ , abstract = {Advanced Persistent Threats (APTs) involve attackers maintaining a long-term presence on victim systems, leading to the stealthy exfiltration of sensitive data during network transfers. Despite existing methods to detect and halt APT data exfiltration, these attacks continue to pose significant threats to sensitive information and result in substantial commercial losses. Current approaches primarily focus on preemptive measures, which are insufficient once early-stage detection fails due to a lack of continuous monitoring. We propose an effective and efficient network monitoring method to address this gap and detect APT exfiltration during data transfer. Our approach assumes the presence of an undetected APT attacker within the victim system. We examine data exfiltration across three exfiltration traffic environments: exfiltration over command control channels, exfiltration over transfer size limitations, and their combinations. We introduce two detection metrics: Package Transfer Rate and Byte Transfer Rate. Utilizing these metrics, we measure network traffic, categorize APT attack environments, and train deep neural network models, named EDXGB, using ensembled decision trees to predict APT exfiltration. Our method is validated on two public datasets and compared against six baseline methods. Additionally, we simulate real-world exfiltration scenarios by creating three exfiltration traffic environments for each dataset. The results demonstrate that our method effectively detects APT exfiltration across various network environments, enhancing data protection and secure transfer. The code is open source and available at https://github.com/cxjuan/EDXGB-for-APT.}, archive = {J_ACCESS}, author = {Xiaojuan Cai and Haibo Zhang and Chuadhry Mujeeb Ahmed and Hiroshi Koide}, doi = {10.1109/ACCESS.2025.3567772}, journal = {IEEE Access}, pages = {81803-81822}, shortjournal = {IEEE Access}, title = {Detecting advanced persistent threat exfiltration with ensemble deep learning tree models and novel detection metrics}, volume = {13}, year = {2025}, }
• (2025). Design and investigation of permanent magnet traction machine with non-uniform air gap for high-speed trains. ACCESS, 13, 81794-81802. (www)

  @article{ , abstract = {This paper proposes a novel permanent magnet traction machine with a non-uniform air-gap rotor topology specifically designed for high-speed rail applications exceeding 400 km/h. The innovative rotor configuration could effectively suppress air-gap magnetic field distortion during high-speed operation, thereby reducing harmonic components in the magnetic field and minimizing core losses while enhancing overall efficiency. Firstly, the basic topology of the proposed non-uniform air-gap rotor is studied and the main design parameters of the traction machine used for high-speed rail train with speed over than 400km/h are analyzed. Secondly, the influence of main design parameters of the proposed rotor with non-uniform air gap on the key electromagnetic characteristics such as no-load characteristics, rated operating characteristics, losses, and efficiency is analyzed. Then, the no-load back electromotive force (EMF), rated torque, losses, efficiency and inductance of the proposed traction machine with non-uniform air gap are comprehensively analyzed based on the optimized structure. Finally, experimental validation through prototype testing confirms that the proposed non-uniform air-gap permanent magnet traction machine could successfully meet the operational requirements of high-speed rail train exceeding 400 km/h.}, archive = {J_ACCESS}, author = {Weiye Li and Gangyan Li and Yinglu Luo and Yu Wang and Jun Peng and Jun Xu and Ao Hu and Mingjie He}, doi = {10.1109/ACCESS.2025.3564593}, journal = {IEEE Access}, pages = {81794-81802}, shortjournal = {IEEE Access}, title = {Design and investigation of permanent magnet traction machine with non-uniform air gap for high-speed trains}, volume = {13}, year = {2025}, }
• (2025). The transparency challenge in blockchain-enabled sustainable development goals applications: Exploring privacy-preserving techniques and emerging platforms. ACCESS, 13, 81769-81793. (www)

  @article{ , abstract = {Blockchain technology has emerged as a key enabler for achieving the United Nations’ Sustainable Development Goals (SDGs) by offering transparent, decentralized solutions across multiple sectors. However, its inherent transparency introduces significant privacy challenges, particularly in sensitive areas such as healthcare, energy, finance, and supply chains, where confidentiality is critical. To address this tension, this paper explores a range of emerging privacy-preserving techniques, including Ring Signatures, Zero-Knowledge Proofs (ZKPs), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEEs), and mixers, analyzing their implementation, effectiveness, and limitations. Furthermore, we conduct a detailed technical study of next-generation blockchain platforms that integrate these techniques to balance privacy, scalability, and interoperability. Our contributions include a deep dive into privacy challenges in blockchain applications for SDGs, a comparative analysis of privacy-preserving solutions, a comprehensive evaluation of emerging platforms, and a review of existing research that leverages these privacy techniques in SDG-related domains. This work aims to provide critical insights into how blockchain technology can be effectively adapted to support sustainable development while safeguarding sensitive data.}, archive = {J_ACCESS}, author = {Ahcène Bounceur and Ahmed-Sami Berkani and Hamouma Moumen and Saber Benharzallah}, doi = {10.1109/ACCESS.2025.3567341}, journal = {IEEE Access}, pages = {81769-81793}, shortjournal = {IEEE Access}, title = {The transparency challenge in blockchain-enabled sustainable development goals applications: Exploring privacy-preserving techniques and emerging platforms}, volume = {13}, year = {2025}, }
• (2025). Perturb, attend, detect, and localize (PADL): Robust proactive image defense. ACCESS, 13, 81755-81768. (www)

  @article{ , abstract = {Image manipulation detection has gained significant attention due to the rise of Generative Models (GMs). Passive detection methods often overfit to specific GMs, limiting their effectiveness. Recently, proactive approaches have been introduced to overcome this limitation. However, these methods suffer from two vulnerabilities: i) the manipulation detector is not robust to noise and hence can be easily fooled; ii) they rely on fixed perturbations for image protection, which offers an exploit for malicious attackers, enabling them to evade detection. To overcome these issues, we propose PADL, a novel solution that is able to create image-specific perturbations for protecting images. PADL’s key objective is to provide a secure and adaptive protection mechanism that ensures the authenticity of images by detecting and localizing manipulations, drastically reducing the possibility of reverse engineering. The method consists of two key components: an encoder, which conditions a learnable perturbation on the input image to ensure uniqueness and robustness against attacks, and a decoder, which extracts the perturbation and leverages it for manipulation detection and localization. PADL can detect manipulation of a protected image and pinpoint regions that have undergone alterations. Unlike previous proactive defenses that rely on a finite set of perturbations, PADL’s tailored protection significantly reduces the risk of reverse engineering. Although being trained only on images of faces manipulated with STGAN, PADL generalizes to a range of unseen models with diverse architectural designs, such as StarGANv2, CycleGAN, BlendGAN, DiffAE, StableDiffusion, and StableDiffusionXL and also to unseen data domains. Finally, we propose a novel evaluation protocol that fairly assesses localization performance in relation to detection accuracy, providing a better reflection of real-world scenarios. Future research will aim to extend PADL to work on more challenging scenarios, including video content protection and high-resolution images, ensuring its effectiveness across diverse media formats and real-world applications. The source code will be publicly released.}, archive = {J_ACCESS}, author = {Filippo Bartolucci and Iacopo Masi and Giuseppe Lisanti}, doi = {10.1109/ACCESS.2025.3565824}, journal = {IEEE Access}, pages = {81755-81768}, shortjournal = {IEEE Access}, title = {Perturb, attend, detect, and localize (PADL): Robust proactive image defense}, volume = {13}, year = {2025}, }
• (2025). Gender classification from pashto handwritten text images. ACCESS, 13, 81736-81754. (www)

  @article{ , abstract = {Computer vision (CV) is a subfield of computer science that enables machines to perceive, interpret, and understand visual data. It combines image processing, analysis, and machine learning to extract meaningful insights from images and videos. One key application of CV is the classification of gender from handwritten text images-a complex task, especially in languages such as Pashto due to the intricacy of the script and the limited resources. This research presents a novel approach to gender classification using Pashto Handwritten Text Images (PHTI). The study evaluates and compares the performance of traditional machine learning and modern deep learning models: Support Vector Machine (SVM) and You Only Look Once version 8 (YOLO8), a CNN-based object detection framework. The PHTI dataset, introduced in this study, consists of $36,086$ text line images written by 200 male and 200 female native Pashto writers. The goal is to assess how effectively SVM and YOLO8 can classify gender based on visual handwriting patterns in the Pashto language. The experimental results reveal that YOLO8 outperforms SVM in classification accuracy. Although SVM, a widely used machine learning model, achieved an accuracy of 61.29%, YOLO8 reached a significantly higher accuracy of 77% in the same data set. This performance gap highlights the advantages of using advanced deep learning models for complex pattern recognition tasks in CV. This research provides a pioneering contribution by focusing on Pashto, a low-resource language, in the context of gender classification using handwritten text images. It opens new pathways for applying CV in regional languages and supports the development of intelligent systems capable of handling various scripts.}, archive = {J_ACCESS}, author = {Khan Sultan and Riaz Ahmad and Siraj Muhammad and Sulaiman Almutairi and Mohammed Abohashrh and Khalil Ullah and Abdallah Namoun and Ibrar Hussain}, doi = {10.1109/ACCESS.2025.3567555}, journal = {IEEE Access}, pages = {81736-81754}, shortjournal = {IEEE Access}, title = {Gender classification from pashto handwritten text images}, volume = {13}, year = {2025}, }
• (2025). Human presence detection sensor using polarization insensitive metamaterial absorbers. ACCESS, 13, 81725-81735. (www)

  @article{ , abstract = {Non-invasive crowd estimation has remained a challenging issue among researchers. Methods such as image analysis and Wi-Fi/Bluetooth probing can always be used to identify and track people. Lately, the authors have introduce a non-invasive method for crowd estimation based on ambient RF energy measurements. In this paper, a sensitive sensor based on metamaterials is introduced to measure the variations within an environment of the available RF energy levels for crowd estimation purposes. Considering human body resonance at 2 GHz, a metamaterial absorber is designed to operate at 2.4 GHz while remaining insensitive to polarisation and incident angle. This absorber, is equipped with a feed network and rectifier to efficiently absorb and transfer the maximum available Wi-Fi energy into a measurable DC voltage. To evaluate the performance of the sensor, the proposed structure is fabricated as an array, and its performance is tested in both the lab and a controlled real-world environment.}, archive = {J_ACCESS}, author = {Farzad Tofigh and Justin Lipman}, doi = {10.1109/ACCESS.2025.3567637}, journal = {IEEE Access}, pages = {81725-81735}, shortjournal = {IEEE Access}, title = {Human presence detection sensor using polarization insensitive metamaterial absorbers}, volume = {13}, year = {2025}, }
• (2025). Efficient chinese-malay speech-text translation via layer-freezing adaptation of multimodal foundation models. ACCESS, 13, 81714-81724. (www)

  @article{ , abstract = {This paper addresses the challenge of Chinese-Malay speech-to-text translation (S2TT), a crucial yet under-resourced language pair in computational linguistics. We introduce Layer-Freezing Adaptive Fine-Tuning (LFAFT), a parameter-efficient strategy that selectively freezes and unfreezes Transformer layers to optimize model adaptation. LFAFT achieves an 11.8% relative improvement in BLEU-4 scores while reducing trainable parameters by 45% compared to full fine-tuning. Using our newly constructed Chinese-Malay parallel corpus, our approach improves BLEU scores from 1.86 to 9.30 (+7.44 points) compared to existing Chinese-Malay speech translation systems. This work not only establishes the first large-scale Chinese-Malay S2TT dataset but also presents an efficient adaptation method that makes low-resource speech translation more accessible and computationally feasible.}, archive = {J_ACCESS}, author = {Xiao Liang and Yen-Min Jasmina Khaw and Soung-Yue Liew and Tien-Ping Tan and Donghong Qin}, doi = {10.1109/ACCESS.2025.3568474}, journal = {IEEE Access}, pages = {81714-81724}, shortjournal = {IEEE Access}, title = {Efficient chinese-malay speech-text translation via layer-freezing adaptation of multimodal foundation models}, volume = {13}, year = {2025}, }
• (2025). AI adoption for collaboration: Factors influencing inclusive learning adoption in higher education. ACCESS, 13, 81690-81713. (www)

  @article{ , abstract = {The integration of artificial intelligence (AI) technologies into education has gained considerable attention for its potential to revolutionize teaching and learning practices. Despite this, the adoption of AI in higher education, particularly to support inclusive learning environments, remains insufficiently studied. This research examines the factors affecting the adoption of AI for collaboration among university students, with a focus on promoting inclusivity. Data was gathered from 443 students at King Faisal University using a survey designed to measure constructs related to AI adoption in collaborative learning. The questionnaire, based on the Technology Acceptance Model (TAM), was tailored to the educational AI context. “Structural equation modeling (SEM)” and hypothesis testing were applied to evaluate the relationships between constructs. The results indicated that “perceived ease of use”, “perceived usefulness”, and “behavioral intention to use AI” are significant predictors of AI adoption among students. Additionally, trust and familiarity with AI were identified as key drivers of behavioral intention. However, the hypothesis that the perceived quality of AI output influences “perceived usefulness” was not supported, suggesting students may not strongly link the quality of AI outputs to its usefulness in academic contexts. This study adds to the body of literature by providing empirical insights into the factors influencing AI adoption for collaboration, offering practical recommendations for creating more inclusive and effective educational environments. The study highlights the importance of building student trust and familiarity with AI to enhance its adoption for inclusive and collaborative learning in higher education.}, archive = {J_ACCESS}, author = {Ibrahim Youssef Alyoussef and Amr Mohammed Drwish and Fatimah Adel Albakheet and Rafdan Hassan Alhajhoj and Amal Ahmed Al-Mousa}, doi = {10.1109/ACCESS.2025.3567656}, journal = {IEEE Access}, pages = {81690-81713}, shortjournal = {IEEE Access}, title = {AI adoption for collaboration: Factors influencing inclusive learning adoption in higher education}, volume = {13}, year = {2025}, }
• (2025). NEUF: Learning point cloud quality by non-euclidean fast filtering. ACCESS, 13, 81677-81689. (www)

  @article{ , abstract = {This paper addresses the problem of no reference visual quality assessment in point clouds, useful for extended reality communication service such as remote surgery and education. Accurate, computationally efficient metrics for point cloud visual quality are needed, but the literature lacks a unified view of the signal attributes associated with psycho-visual features of point clouds. We propose a method to estimate point cloud visual quality by jointly assessing geometry and color noise in areas critical to visual perception. Our approach introduces a novel descriptor that identifies visual degradation through variations in local color and normal components. This descriptor, along with its saliency-weighted variant, leverages Non-Euclidean Laplacian Filtering (NEUF). The NEUF algorithm extracts descriptors using advanced non-Euclidean filtering techniques and employs regression learning to predict end-users’ perceived subjective quality by optimally selecting the most relevant features. We show by simulations that the NEUF approach mixing non Euclidean filtering with data-driven learning is effective on various point cloud categories, including human, non-human, natural, and synthetic. The NEUF-based method for point cloud quality estimation outperforms existing methods, offering a no-reference quality estimator that can be used for the design of cutting-edge extended reality services.}, archive = {J_ACCESS}, author = {Eleonora Di Salvo and Azeddine Beghdadi and Tiziana Cattai and Chiara Lumare and Gaetano Scarano and Stefania Colonnese}, doi = {10.1109/ACCESS.2025.3567190}, journal = {IEEE Access}, pages = {81677-81689}, shortjournal = {IEEE Access}, title = {NEUF: Learning point cloud quality by non-euclidean fast filtering}, volume = {13}, year = {2025}, }
• (2025). Hybrid-vector deadbeat model predictive torque control for SPMSM with improved switching loss and synthesis accuracy. ACCESS, 13, 81666-81676. (www)

  @article{ , abstract = {To improve the performance of the dual-vector model predictive torque control (MPTC) scheme for SPMSM, for sectors II, IV, and VI, this paper attempts to improve the traditional dual-vector modulation with three-segment symmetrical sequences from two aspects: switching loss and synthesis accuracy. First, the switching state of the zero vector is changed from 000 to 111, thereby reducing the number of switching actions by half within a PWM cycle. However, theoretical analysis and simulation results indicate that this dual-vector modulation will introduce more switching actions and losses between two consecutive PWM cycles. Therefore, the traditional dual-vector modulation has the minimum switching actions and cannot be further improved in switching loss. Then, the commutation instants are modified to increase another applied active vector, and the three-vector modulation with five-segment symmetrical sequences and no vector synthesis error is adopted in sectors II, IV, and VI. For other sectors, the dual-vector modulation with three-segment symmetrical sequences is adopted, which has only one switching action within a PWM cycle. Therefore, a hybrid-vector MPTC is formed. Finally, the feasibility and effectiveness of the proposed hybrid-vector MPTC for PMSM are verified by the experimental results, compared to the dual-vector MPTC with three-segment symmetrical sequences and the three-vector MPTC with five-segment symmetrical sequences, under the same switching frequency, the best control performance can be obtained with the minimum switching losses.}, archive = {J_ACCESS}, author = {Weiyang Hou and Yiru Miao and Ruizhi He and Wu Liao and Qiwei Xu and Shoudao Huang and Yuxiaoying Tu}, doi = {10.1109/ACCESS.2025.3564405}, journal = {IEEE Access}, pages = {81666-81676}, shortjournal = {IEEE Access}, title = {Hybrid-vector deadbeat model predictive torque control for SPMSM with improved switching loss and synthesis accuracy}, volume = {13}, year = {2025}, }
• (2025). An intelligent smart dynamic feature analysis based approach by utilizing deep learning to improve the breast cancer detection. ACCESS, 13, 81643-81665. (www)

  @article{ , abstract = {Breast cancer remains one of the important global health concerns with high rates of mortality, highlighting the significance of more sophisticated diagnostic methods. Conventional methods, generally comprised of costly imaging and invasive biopsies, are of high burdens. Motivated by the limitations, the present study comes up with an innovative automated solution for the identification of breast cancer using deep learning analysis of mammograms. Moving away from the traditional approaches with inherent pre-processing and feature extraction constraints, this research focuses on a two-pronged improvement strategy: improved mammogram quality and highly optimized deep learning architecture. Specifically, we present a new Optimized InceptionResNetV2 model significantly optimized through the thoughtful addition of large data augmentation to increase robustness, LeakyReLU activation to facilitate gradient flow and accelerate learning, and MeanDropout regularization to mitigate overfitting and improve generalization. The model was also trained using Quantization aware training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. The performance on our proposed approach for the massive mammogram dataset reflects an evident improvement in detection performance over traditional techniques. Our InceptionResNetV2 optimized achieved state-of-the-art accuracy with outstanding measures of 98.06% sensitivity, 97.05%, positive predictive value (PPV) and specificity of 99.60%, negative predictive value (NPV) of 86.83%, 97.94% accuracy, F1-score of 96.90%, Matthew’s correlation coefficient (MCC) of 90.67%, and AUC of 0.9939. The benefits of proposed system are that it can deliver a more efficient, precise, and possibly cost-effective diagnostic tool for breast cancer. Through synergistic integration of architectural optimization, sophisticated regularization methods, and deployment-aware training, our proposed system enables earlier and more accurate detection. This innovation has the potential to greatly enhance healthcare delivery by equipping radiologists and clinicians with a strong second opinion and initial read capability, ultimately leading to improved patient outcomes and decreased mortality related to this widespread disease.}, archive = {J_ACCESS}, author = {Seong-O Shim and Lal Hussain and Eesa Alsolami and Monagi H. Alkinani}, doi = {10.1109/ACCESS.2025.3566353}, journal = {IEEE Access}, pages = {81643-81665}, shortjournal = {IEEE Access}, title = {An intelligent smart dynamic feature analysis based approach by utilizing deep learning to improve the breast cancer detection}, volume = {13}, year = {2025}, }
• (2025). Phase difference of arrival aided downlink time difference of arrival ultra-wide band positioning system. ACCESS, 13, 81628-81642. (www)

  @article{ , abstract = {Currently there is a growing demand for accurate and reliable positioning indoors or in other environments, where Global Navigation Satellite Systems (GNSS) are unavailable. The precise positioning is required for example by autonomous vehicles used in the industry, safety anti-collision systems or for tracking of goods. Ultra-Wide Band (UWB) positioning systems provide precise location information and are therefore often utilized in such scenarios. The GNSS-like Downlink Time Difference of Arrival (TDoA) method is well suited for these problems, enabling unlimited number of simultaneous users. The addition of independent measurements is often sought for improved precision and robustness of the position estimates. So far, only time-based measurements were available, however, UWB boards capable of Phase Difference of Arrival (PDoA) and thus Angle of Arrival measurements have been introduced recently. In this article we leverage UWB-PDoA board with two Qorvo DW1000 transceivers that share a common clock. Its position is estimated by fusing the TDoA and PDoA by Extended Kalman Filter (EKF) in downlink-based system. The EKF tracks the clock bias between the two chips; consequently, prior calibration is unnecessary, as it is done online. Moreover, precise localization is enabled through the algorithm that resolves the ambiguity of PDoA measurements. The positioning performance evaluation shows improvement in accuracy with respect to the TDoA-only solution. The root-mean-square error decreased from 16.38 cm to 15.09 cm, i.e., 8 %, when evaluated among all test points. In the best case, reduction from 14.79 cm to 10.14 cm, i.e., 35 % was observed.}, archive = {J_ACCESS}, author = {Josef Krška and Christian Gentner and Václav Navrátil}, doi = {10.1109/ACCESS.2025.3567821}, journal = {IEEE Access}, pages = {81628-81642}, shortjournal = {IEEE Access}, title = {Phase difference of arrival aided downlink time difference of arrival ultra-wide band positioning system}, volume = {13}, year = {2025}, }
• (2025). Sustainability evaluation of sports industry policy using circular picture fuzzy MAGDM with MACROS method. ACCESS, 13, 81618-81627. (www)

  @article{ , abstract = {This study proposes a novel Circular Picture Fuzzy Set (C-PFS) framework integrated with Multi-Attribute Group Decision-Making (MAGDM) using the MACROS method to evaluate the sustainability of sports industry policies. The model addresses limitations of conventional fuzzy and picture fuzzy sets by introducing radius parameters for membership, non-membership, and neutrality, thereby enhancing precision in uncertain decision-making environments. In order to improve on conventional fuzzy and picture fuzzy models, this study makes use of the circular picture fuzzy set (C-PFS) framework, which adds special radius parameters for membership, non-membership, and neutrality. This refined method works especially well for analyzing complicated and unpredictable decision-making situations, like sustainability evaluations of sports industry regulations. In order to thoroughly examine policy sustainability, we suggest a novel analytical approach that combines C-PFS with a multi-attribute group decision-making (MAGDM) technique using the MACROS method. The proposed model effectively lowers uncertainty and subjective biases in policy evaluations by integrating qualitative and quantitative sustainability criteria. To demonstrate its applicability, we assess key sustainability facets of sports industry policies in a case study, including social inclusivity, environmental stewardship, and economic viability. According to the results, the C-PFS-based method offers a more accurate and reliable assessment than traditional techniques. This framework can be applied to other policy evaluation domains and improves decision-making processes in sports policy assessment.}, archive = {J_ACCESS}, author = {Qingyuan Li and Xiaodong Sun}, doi = {10.1109/ACCESS.2025.3567638}, journal = {IEEE Access}, pages = {81618-81627}, shortjournal = {IEEE Access}, title = {Sustainability evaluation of sports industry policy using circular picture fuzzy MAGDM with MACROS method}, volume = {13}, year = {2025}, }
• (2025). A novel intuitionistic fuzzy decision support system based on extended WASPAS method: A case study for the selection of basketball players and basketball fields for maximum performance. ACCESS, 13, 81606-81617. (www)

  @article{ , abstract = {The choice of basketball players and their playing field selection represents a vital decision-making (DM) process that determines team success and game results. The selection of top players requires evaluating skills, physical attributes, and team dynamics, and selecting an appropriate field demands examination of environmental elements and infrastructure. The sports industry needs advanced decision-support methodologies to optimize its selection processes because these decisions prove complex. The multi-criteria group DM (MCDM) technique allows a panel of decision-makers (DMks) to establish a methodical system to identify the best alternative among competing criteria that produce conflicting results. The extended weighted aggregated sum product assessment (WASPAS) method serves as an effective technique because it combines the weighted sum model (WSM) and weighted product model (WPM) to evaluate and rank alternatives. The research investigates how the extended WASPAS method operates within an IFS environment to boost the processes of sports industry DM. The research selects basketball players and suitable basketball fields to maximize performance through an evaluation process that includes various performance and environmental factors. The proposed model utilizes IFS capabilities to assess decision alternatives by evaluating player skills and physical attributes, team compatibility, court surface, and environmental conditions. The extended WASPAS method produces findings comparable to traditional MCGDM approaches in establishing their effectiveness. The extended WASPAS integrated with IFS improves decision reliability and accuracy when supporting basketball player selection and venue assessments.}, archive = {J_ACCESS}, author = {Chenhan Xu and Xiaotong Yuan}, doi = {10.1109/ACCESS.2025.3563998}, journal = {IEEE Access}, pages = {81606-81617}, shortjournal = {IEEE Access}, title = {A novel intuitionistic fuzzy decision support system based on extended WASPAS method: A case study for the selection of basketball players and basketball fields for maximum performance}, volume = {13}, year = {2025}, }
• (2025). Voices of people with disabilities: Integrating topic modeling and sentiment analysis to study disability discourse on social media. ACCESS, 13, 81569-81605. (www)

  @article{ , abstract = {People with Disability (PwD) are some of society’s marginalized and vulnerable groups. They are mostly disadvantaged because accessibility to communal structures and social services remains challenging. Sometimes, PwDs are misunderstood because not all disabilities are visible or outward, which makes it difficult to implement useful interventions for them. Thus, the voices of PwDs, as expressed freely on social media, must be studied to better understand the fundamental challenges they face. In this research, we analyze the comments expressed in Disability communities on Reddit in the last 5 years (from 2019 to 2024) to uncover the concerns and sentiments of PwDs. Comments were collected through the Reddit API from 4 Disability subreddits, namely r/ADHD, r/Blind, r/deaf, and r/disability. Overall, a total of 601,215 comments were extracted for analysis. We applied topic modeling algorithms, namely Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and two variations of BERTopic (BERTopic with K-means clustering and BERTopic with HDBSCAN clustering) on each subreddit’s comments to extract hidden topics. The NMF discovered 15 topics in the r/Blind and 20 topics in the r/deaf. Furthermore, related topics were merged into themes, and we discovered nine themes in both r/ADHD and r/Blind, eight themes in r/deaf, and seven themes in r/disability. Additionally, a pre-trained transformer, SiEBERT, was used to determine the sentiments for the themes in each subreddit. The themes discovered across at least two subreddits are Mobility, Diagnosis, Education, Assistive and Accessible Technology, Support, Disability Accommodations, and Relations. PwD with ADHD struggle with the effects of medications, household chores, sleep, attention span, and oversubscribing to online payment services. The PwD, who are visually impaired, feel alienated by society, struggle with public transit systems, have limited employment, and experience harassment. Those with difficulty hearing express trouble with hearing devices, educational materials, technological challenges, limited workplace accommodations, and bad treatment from people. Our research discussed the themes and provided recommendations where applicable.}, archive = {J_ACCESS}, author = {Richard K. Lomotey and Sandra Kumi and Christian Nyaku and Ralph Deters}, doi = {10.1109/ACCESS.2025.3566014}, journal = {IEEE Access}, pages = {81569-81605}, shortjournal = {IEEE Access}, title = {Voices of people with disabilities: Integrating topic modeling and sentiment analysis to study disability discourse on social media}, volume = {13}, year = {2025}, }
• (2025). How adaptation and user engagement affect trust in audio guide agents. ACCESS, 13, 81553-81568. (www)

  @article{ , abstract = {This paper describes how user engagement affects users’ trust in audio guide agents to adapt to their environment. Audio guide agents are expected to be used more in the future because they can perform without interrupting the user’s actions. Because agents adapt to their environment and work collaboratively with users, trust in the agent is important. Therefore, we examined whether adaptation itself affects trust and whether intentional user operations as a means of user engagement affect trust. A participant experiment was conducted with two independent variables (the degree of adaptation to ambient noise and the availability of user operation), and the subjective evaluation of the impression of the agent was the dependent variable. For the factor of adaptability, agents with low and high noise-canceling performance were provided. For the factor of user engagement, a volume adjustment function for ambient noise was or was not provided. In an experimental laboratory environment where environmental sounds were controlled, it was verified that users perceive the agent as more intelligent and human-like only when the agent’s adaptation to the environment is low. However, agent adaptation increased trust in the agent, and user engagement also increased trust regardless of the agent’s adaptation level. On the other hand, in a real-world environment, adaptation tended to increase trust, but user engagement did not affect trust. This may be attributed to the impact of cognitive load on users in real environments, which could have hindered participants from operating as intended, even under conditions with operations. In systems where users and agents work collaboratively, the results suggest that intentionally increasing user engagement may increase trust in the agent when users are able to operate appropriately.}, archive = {J_ACCESS}, author = {Mari Saito and Seiji Yamada}, doi = {10.1109/ACCESS.2025.3567067}, journal = {IEEE Access}, pages = {81553-81568}, shortjournal = {IEEE Access}, title = {How adaptation and user engagement affect trust in audio guide agents}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of time series forecasting models and the novel ELSTMD approach for renewable energy generation. ACCESS, 13, 81537-81552. (www)

  @article{ , abstract = {The increasing number of people and the huge industrial evolution have led to serious environmental consequences, which have made energy from Renewable Energy Sources (RESs) inevitable. However, these energy sources suffer from intermittency as they depend on natural sources, which change over time. Consequently, this research addresses the issue by proposing an ANN model for reliable renewable energy forecasting. The Encoding Long Short-Time Memory Decoding (ELSTMD) model consists of two encodings, one LSTM, and two decoding layers. Moreover, the introduced model and 21 time series forecasting models are evaluated, including statistical, Artificial Intelligence (AI), and hybrid approaches. While making predictions, the impact of 17 distinct features, categorized into five primary groups—electricity, weather, economic, and seasonality-related attributes, as well as natural disasters— has been analyzed. Thus, the models are trained as univariate time series with exogenous variables. To evaluate the model’s generalization, an additional dataset involving photovoltaic (PV) panels was used. The proposed model outperforms all competitors on both datasets, achieving an (R2, MAE, MSE, RMSE) of (0.56714, 0.06398, 0.00691, 0.08313) on the first dataset and (0.76574, 0.09344, 0.01417, 0.11903) on the second dataset. The most notable advantage of this research can be summarized as providing precise predictions for the electricity generated from RESs, which can help reduce the intermittency, randomness, and stability issues associated with these sources. It also plays a crucial role in optimizing electrical power systems’ planning and storage efficiency.}, archive = {J_ACCESS}, author = {Yousef Alkhanafseh and Tahir Cetin Akinci and Alfredo A. Martinez-Morales}, doi = {10.1109/ACCESS.2025.3558120}, journal = {IEEE Access}, pages = {81537-81552}, shortjournal = {IEEE Access}, title = {A comparative analysis of time series forecasting models and the novel ELSTMD approach for renewable energy generation}, volume = {13}, year = {2025}, }
• (2025). Understanding the impacts of top-tier streamer de-emphasizing in live streaming E-commerce: A social evolution game perspective. ACCESS, 13, 81524-81536. (www)

  @article{ , abstract = {Live-streaming e-commerce has achieved significant growth by combining live interactive broadcasts with online shopping, with platform revenues largely depending on a small number of top-tier streamers. However, this dependence grants these top-tier streamers excessive bargaining power and makes platforms vulnerable to their negative impacts. To address this issue, this study aims to provide strategy recommendations for live-streaming e-commerce platform managers to reduce reliance on top-tier streamers. We achieve this goal by constructing a social evolutionary game model (SEGINE). This model utilizes real-world data from Chinese social media and live-streaming e-commerce platforms to simulate the diffusion process of live-streamed product information within social networks and analyzes the impact of different “de-emphasize top-tier streamers” strategies on information dissemination. By integrating the characteristics of live-streaming platforms and social networks, we consider the effects of indirect network externalities on evolutionary game dynamics, thereby providing a basis for platform managers to formulate relevant decisions. The experimental results show that appropriately limiting the influence of top-tier streamers may help build a healthier and more sustainable live-streaming e-commerce ecosystem. The novelty of this study lies in its first-time application of a social evolutionary game model to analyze the interaction between live-streaming e-commerce platforms and social networks, and to explore the potential impacts of strategies that de-emphasize top-tier streamers.}, archive = {J_ACCESS}, author = {Renrui Duan and Bo Li and Jingyuan Li and Jianye Yu and Qiong Wu and Yuanzhuo Wang and Biao Cao}, doi = {10.1109/ACCESS.2025.3566741}, journal = {IEEE Access}, pages = {81524-81536}, shortjournal = {IEEE Access}, title = {Understanding the impacts of top-tier streamer de-emphasizing in live streaming E-commerce: A social evolution game perspective}, volume = {13}, year = {2025}, }
• (2025). An analytic policy gradient-based deep reinforcement learning motion cueing algorithm for driving simulators. ACCESS, 13, 81507-81523. (www)

  @article{ , abstract = {The proposed motion cueing algorithm (MCA), based on a reinforcement learning algorithm using gradient information to directly update the control policy, introduces three significant enhancements. First, transform the complex simulator environment into a differentiable simulator environment that provides gradient information at each time step and use this gradient information to directly update the control policy. Second, the network architecture is reconfigured into a concurrent controller format, similar to Model Predictive Control (MPC). This controller processes a sequence of vehicle motion reference signals over a future period, utilizing a multi-layer perceptron to generate the simulator’s motion reference control signal sequences for the same duration. Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. As the prediction horizon increases, the algorithm demonstrates superior computational efficiency, which helps reduce the incidence of motion sickness during the use of the driving simulator. Third, a loss function specifically designed for the motion simulator is proposed. This function incorporates constraints derived from the MPC framework to address workspace limitations and applies them to workspace management. These constraints restrict the platform’s acceleration and speed near the workspace boundaries, allowing for better utilization of the available space. The algorithm is validated using Carla’s autonomous driving simulation software as the dataset generator. During the training process, the proposed algorithm in this paper achieves an order-of-magnitude improvement in convergence speed compared to conventional training methods of PPO and DDPG. Simulations with a 10-step prediction horizon indicate that the Root Mean Square Error (RMSE) produced by this algorithm is comparable to that of the MCA based on MPC (MPC-MCA) and significantly lower than that of the MCA based classical washout (CW-MCA). At higher prediction horizons, the algorithm achieves performance on par with state-of-the-art MPC-based motion cueing algorithms while exhibiting reduced algorithmic delay. Additionally, the proposed algorithm delivers quicker results and improved tracking performance across all prediction horizons, ultimately surpassing the current state-of-the-art MPC-MCA.}, archive = {J_ACCESS}, author = {Xiaowei Huang and Xuhua Shi and Peiyao Wang and Hongzan Xu and Xiaojun Tang and Gaoran Zhang}, doi = {10.1109/ACCESS.2025.3564597}, journal = {IEEE Access}, pages = {81507-81523}, shortjournal = {IEEE Access}, title = {An analytic policy gradient-based deep reinforcement learning motion cueing algorithm for driving simulators}, volume = {13}, year = {2025}, }
• (2025). A topical review of quantum and classical machine learning approaches to disaster escape routing problems. ACCESS, 13, 81494-81506. (www)

  @article{ , abstract = {The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra’s and A* algorithms. However, most classical algorithms are most effective on static graphs. They either cannot be adapted to dynamic graphs or become computationally expensive. The challenge of processing dynamic graphs, which demands significant computational resources, can be addressed using Classical and Quantum Machine Learning methods. For this review, we consider a dynamically changing graph representing a disaster-stricken city. Our problem, termed the Disaster Escape Routing problem, aims to find the optimal path within this dynamic graph. We review and analyze an existing hybrid quantum-classical machine learning model alongside classical machine learning models specifically for this problem. We also explore Variational Quantum Circuits and Encoding Methods. Our study suggests that hybrid quantum-classical machine learning, Graph Neural Networks, and Temporal Graph Networks offer high performance in terms of path prediction and accuracy in finding the optimal path. This review also identifies Kolmogorov Arnold Networks as a promising approach to solving escape routing problems. Additionally, an integrated approach combining strengths of all the models has been hypothesized to enhance emergency escape route planning.}, archive = {J_ACCESS}, author = {A. Vinil and Parameswaran Iyer and Jetain Chetan and Aniket Bembale and Nagendra Singh}, doi = {10.1109/ACCESS.2025.3567667}, journal = {IEEE Access}, pages = {81494-81506}, shortjournal = {IEEE Access}, title = {A topical review of quantum and classical machine learning approaches to disaster escape routing problems}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid model for loan default prediction in maritime finance based on topological data analysis and machine learning. ACCESS, 13, 81474-81493. (www)

  @article{ , abstract = {The global shipping industry, pivotal to international trade, faces inherent financial vulnerabilities due to cyclical asset values, volatile freight rates, and high leverage, rendering traditional credit risk models inadequate. This study proposes a novel framework integrating topological data analysis (TDA) and machine learning (ML) to enhance default prediction in maritime finance. By constructing correlation-based networks of shipping firms and extracting topological persistence features, such as cyclical trends and structural interdependencies, via Vietoris-Rips complexes, the model captures nonlinear risk patterns overlooked by conventional metrics. Network properties (e.g., centrality, clustering) and financial indicators (loan terms, vessel attributes, economic indices) are synthesized, enabling Graph Neural Networks (GNNs) to leverage relational and topological insights. Evaluated on a dataset of shipping loans spanning financial, operational, and macroeconomic variables, the TDA-enhanced models demonstrate significant improvements: logistic regression accuracy rose from 62.8% to 85.1%. At the same time, ROC-AUC for SVM and XGBoost reached 0.931 and 0.973, respectively. Key features like loan amount, vessel age, and debt-to-equity ratios exhibited strong correlations with defaults. Results underscore that incorporating TDA-derived persistence features improves early detection of financial distress, particularly in capturing systemic risks from interconnected market shocks. This hybrid approach offers a robust tool for lenders and policymakers to mitigate defaults in a sector critical to global commerce.}, archive = {J_ACCESS}, author = {Mohammad Amin Kheneifar and Babak Amiri}, doi = {10.1109/ACCESS.2025.3566066}, journal = {IEEE Access}, pages = {81474-81493}, shortjournal = {IEEE Access}, title = {A novel hybrid model for loan default prediction in maritime finance based on topological data analysis and machine learning}, volume = {13}, year = {2025}, }
• (2025). Optimizing network expansion and battery storage to mitigate cascading outages via detailed system failures simulations. ACCESS, 13, 81462-81473. (www)

  @article{ , abstract = {Power networks have traditionally been designed to withstand credible outages, such as N-1 or N-2, often overlooking the complex interdependencies that can lead to cascading outages in modern systems. To address these evolving risks, planning models must adapt to better anticipate and mitigate cascading outages, while also guiding network expansion and the integration of advanced technologies. This paper presents a methodological framework for optimizing network expansion and battery storage investments to mitigate cascading outages. The framework uses an Optimization via Simulation (OvS) approach that incorporates detailed system failure simulations to assess the effectiveness of various network enhancements, including transmission lines, power transformers, reactive power compensation devices and battery storage units. In addition, a sampling process is used to select the number of trigger outage scenarios to be considered within the OvS approach. The effectiveness of the proposed framework is demonstrated on two test networks: a modified version of the IEEE 24-bus test network and the German transmission network. The main findings demonstrate: (a) that incorporating cascading outages into investment planning leads to different network enhancement decisions compared to conventional planning models (which exclude cascading outages), (b) that an optimal mix of network enhancements can significantly mitigate cascading outages, and (c) the computational scalability of the proposed framework.}, archive = {J_ACCESS}, author = {Alex Villamarín-Jácome and Rodrigo Moreno and Mathaios Panteli and Matthias Noebels and Eduardo Alejandro Martínez Ceseña and Robin Preece}, doi = {10.1109/ACCESS.2025.3567436}, journal = {IEEE Access}, pages = {81462-81473}, shortjournal = {IEEE Access}, title = {Optimizing network expansion and battery storage to mitigate cascading outages via detailed system failures simulations}, volume = {13}, year = {2025}, }
• (2025). An interactive framework for personalized navigation based on metacosmic cultural tourism and large model fine-tuning. ACCESS, 13, 81450-81461. (www)

  @article{ , abstract = {With the wide application of large language models (LLMs) and the rapid growth of metaverse tourism demand, the digital tour and personalized interaction of historical sites have become the key to improving users’ digital travel experience. Creating an environment where users can access rich cultural information and enjoy personalized, immersive experiences is a crucial issue in the field of digital cultural travel. To this end, we propose a tourism information multimodal generation personalized question-answering interactive framework TIGMI (Tourism Information Generation and Multimodal Interaction) based on LLM fine-tuning, which aims to provide a richer and more in-depth experience for virtual tours of historical monuments. Taking Qutan Temple as an example, the framework combines LLM, retrieval augmented generation (RAG), and auto-prompting engineering techniques to retrieve accurate information related to the historical monument from external knowledge bases and seamlessly integrates it into the generated content. This integration mechanism ensures the accuracy and relevance of the generated answers. Through TIGMI’s LLM-driven command interaction mechanism in the 3D digital scene of Qutan Temple, users are able to interact with the building and scene environment in a personalized and real-time manner, successfully integrating historical and cultural information with modern digital technology. This integration significantly enhances the naturalness of interaction and personalizes the user experience, thereby improving user immersion and information acquisition efficiency. Evaluation results show that TIGMI excels in question-answering and multimodal interactions, significantly enhancing the depth and breadth of services provided by the personalized virtual tour. We conclude by addressing the limitations of TIGMI and briefly discuss how future research will focus on further improving the accuracy and user satisfaction of the generated content to adapt to the dynamically changing tourism environment.}, archive = {J_ACCESS}, author = {Hang Guo and Zhiqiang Liu and Chuanqian Tang and Xiaodan Zhang}, doi = {10.1109/ACCESS.2025.3565359}, journal = {IEEE Access}, pages = {81450-81461}, shortjournal = {IEEE Access}, title = {An interactive framework for personalized navigation based on metacosmic cultural tourism and large model fine-tuning}, volume = {13}, year = {2025}, }
• (2025). POQ: Is there a pareto-optimal quantization strategy for deep neural networks?. ACCESS, 13, 81434-81449. (www)

  @article{ , abstract = {Efficient deployment of deep learning models on resource-constrained devices requires balancing accuracy with energy consumption and/or latency. Quantization is a proven method to achieve this balance by reducing the precision of neural network weights and activations. However, simply changing the precision does not enable direct iso-accuracy and iso-energy comparisons. To address this, we combine a realistic processor energy model with a network filter multiplier that scales the number of channels, thereby enabling such comparisons. This work presents a Pareto-Optimal Quantization (POQ) methodology aimed at mapping a neural network architecture to a specific hardware platform while systematically exploring the design space in between to identify the most effective quantization strategy. Our approach evaluates how different design choices impact the accuracy-energy trade-off. Using detailed energy modeling instead of proxy metrics, our results reveal that 8-bit integer (int8) quantization is Pareto-Optimal for MobileNetV2, providing up to $2.8\times $ energy savings or 10% higher accuracy compared to 16-bit floating-point (fp16). Furthermore, employing high-precision residuals shifts the Pareto frontier, making 4-bit integer (int4) quantization optimal, achieving up to $1.9\times $ additional energy reduction or 2% additional accuracy gains. Moreover, our findings emphasize the role of DRAM energy in certain model configurations and highlight the importance of precise energy modeling. These results reflect the application of our POQ methodology to the practical deployment of energy-efficient deep learning models on constrained hardware.}, archive = {J_ACCESS}, author = {Floran De Putter and Sherif Eissa and Henk Corporaal}, doi = {10.1109/ACCESS.2025.3567046}, journal = {IEEE Access}, pages = {81434-81449}, shortjournal = {IEEE Access}, title = {POQ: Is there a pareto-optimal quantization strategy for deep neural networks?}, volume = {13}, year = {2025}, }
• (2025). LightHand99K: A synthetic dataset for hand pose estimation with wrist-worn cameras. ACCESS, 13, 81423-81433. (www)

  @article{ , abstract = {Although hand-pose estimation using external camera systems has made significant progress driven by large annotated datasets, wrist-worn camera-based hand-pose estimation offers unique advantages owing to its ability to capture nearby images. However, datasets primarily collected from multi-angle external systems are unsuitable for wrist-camera-based estimation. To address this gap, we developed LightHand99K, a synthetic dataset comprising 99,792 synthetic RGB hand images designed explicitly for hand-pose estimation using wrist-worn cameras. The dataset was generated using a Unity 3D-based hand image generator optimized to efficiently enhance the performance of hand-pose estimation models for wrist-worn camera images by incorporating human finger joint movement information while minimizing computational cost. This tool allows users to customize their poses, camera angles, backgrounds, lighting, and hand-orientation settings. Incorporating three data augmentation techniques, LightHand99K demonstrated a 36% increase in area under the curve (AUC) and a 6.2-mm reduction in average endpoint error (EPE) compared to existing datasets. These results underscore the value of LightHand99K in advancing hand-pose estimation, particularly in wrist-worn camera applications. The dataset and generation tool are publicly available at (https://github.com/leejeongho3214/LightHand).}, archive = {J_ACCESS}, author = {Jeongho Lee and Changho Kim and Jaeyun Kim and Seon Ho Kim and Younggeun Choi and Sang-Il Choi}, doi = {10.1109/ACCESS.2025.3567313}, journal = {IEEE Access}, pages = {81423-81433}, shortjournal = {IEEE Access}, title = {LightHand99K: A synthetic dataset for hand pose estimation with wrist-worn cameras}, volume = {13}, year = {2025}, }
• (2025). Improved resistive switching characteristics by o2 plasma treatment in tri-layer HfO2/Al2O3/HfO2 RRAM. ACCESS, 13, 81417-81422. (www)

  @article{ , abstract = {In this study, a tri-layer HfO2/Al2O3/HfO2 (7/6/7 nm) structure was fabricated using a cost-effective RF sputtering process and treated with oxygen plasma to serve as the resistive switching (RS) active layer for RRAM devices. Compared with a single-layer HfO2 and an untreated tri-layer structure, the oxygen plasma-treated device exhibited significantly enhanced performance. These improvements include a higher ON/OFF current ratio, extended endurance exceeding 1600 cycles, and stable retention beyond $10^{4}$ seconds. Furthermore, multilevel resistive switching behavior was reliably demonstrated by modulating the compliance current. The results highlight the effectiveness of oxygen plasma treatment in improving switching uniformity and electrical stability, while maintaining a scalable and industry-friendly fabrication process. This work underscores the potential of plasma-treated tri-layer structures for high-density, multi-level non-volatile memory applications.}, archive = {J_ACCESS}, author = {Shyh-Jer Huang and Wei-Hsuan Hsieh and Rong-Ming Ko and Zi-Hao Wang and Yan-Kuin Su}, doi = {10.1109/ACCESS.2025.3563689}, journal = {IEEE Access}, pages = {81417-81422}, shortjournal = {IEEE Access}, title = {Improved resistive switching characteristics by o2 plasma treatment in tri-layer HfO2/Al2O3/HfO2 RRAM}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based response-to-name detection: Empirical study on early screening of autism spectrum disorder in children. ACCESS, 13, 81406-81416. (www)

  @article{ , abstract = {Early screening and intervention for children with autism spectrum disorder (ASD) is crucial for their long-term outcomes and quality of life, and response-to-name (RTN) tests have shown promise in aiding early detection. Leveraging computer vision (CV) and deep learning techniques, this study explores an automatic recognition algorithm for RTN tests based on Residual Network 18 (ResNet18) and Bidirectional Long Short-Term Memory (Bi-LSTM) for enhanced early screening of ASD in children. Using a dataset of 45 RTN cases, including 22 children with ASD and 23 typically developing (TD) children, various deep learning architectures such as Visual Geometry Group 16 (VGG16) and ResNet18 combined with Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU), and Bi-LSTM were employed to automate RTN test recognition and evaluate algorithm performance. The results revealed that the ResNet18-Bi-LSTM combination achieved the highest efficacy, with a remarkable accuracy of 100%, demonstrating robust stability and accuracy even with small datasets, and holding potential to assist in early ASD screening for children.}, archive = {J_ACCESS}, author = {Ji Wang and Zhaoqing Liu and Yujie Liu and Lin Li and Lingyu Shao and Xiao Zhang and Shuyan Li and Boming Song}, doi = {10.1109/ACCESS.2025.3567367}, journal = {IEEE Access}, pages = {81406-81416}, shortjournal = {IEEE Access}, title = {Deep learning-based response-to-name detection: Empirical study on early screening of autism spectrum disorder in children}, volume = {13}, year = {2025}, }
• (2025). ResTreeNet: A height-aware LiDAR tree classification model with explainable AI for forestry applications. ACCESS, 13, 81392-81405. (www)

  @article{ , abstract = {Tree species classification plays a crucial role in forest management, biodiversity conservation, and ecological monitoring. Light detection and ranging (LiDAR) technology, capturing highly detailed 3D structural information of vegetation, has become a powerful tool for automated tree classification. Among LiDAR techniques, terrestrial LiDAR provides high-resolution point-cloud data by scanning trees from the ground level, enabling precise species identification. However, applying deep learning models to LiDAR-based tree classification remains challenging due to the computational complexity of existing 3D architectures, which often struggle with scalability and practical large-scale implementation. To address these critical limitations, we propose ResTreeNet, an efficient and lightweight deep learning model designed explicitly for tree classification using terrestrial LiDAR point clouds. Our innovative approach combines residual networks for hierarchical feature extraction, a height-based grouping strategy to enhance structural representation, and a parameterized geometric transformation module to improve translation invariance and model adaptability. This work integrates explainable artificial intelligence (XAI) techniques, including gradient-weighted class action mapping (Grad-CAM) visualizations, to provide transparent and interpretable insight into the classification reasoning of the model, addressing the critical need for understanding automated decision-making processes. The comprehensive evaluation on a terrestrial LiDAR dataset demonstrates the superior performance of ResTreeNet, achieving a state-of-the-art accuracy of 94.02% on samples with 1024-points, surpassing the existing models by 2.03%. The robust capabilities of the model are further validated by outstanding classification metrics, including precision (94.24%), recall (93.63%), and the F1-score (93.54%), ensuring a balanced and reliable approach to tree species classification. With its lightweight architecture (requiring only 0.47 million parameters) and computational efficiency, ResTreeNet is a practical solution for large-scale ecological research, offering an innovative approach to automated forest monitoring and sustainable resource management.}, archive = {J_ACCESS}, author = {Asrat Kaleab Taye and Jeong-Mook Park and Hyung-Ju Cho and Jin-Taek Kang and Yeon-Ok Seo}, doi = {10.1109/ACCESS.2025.3567042}, journal = {IEEE Access}, pages = {81392-81405}, shortjournal = {IEEE Access}, title = {ResTreeNet: A height-aware LiDAR tree classification model with explainable AI for forestry applications}, volume = {13}, year = {2025}, }
• (2025). Predicting ship waiting times using machine learning for enhanced port operations. ACCESS, 13, 81377-81391. (www)

  @article{ , abstract = {Port congestion and prolonged ship waiting times pose challenges for global trade and increase operational costs and inefficiencies. In this study, a novel machine learning-based predictive approach was proposed to improve port operations by accurately forecasting vessel waiting times. By using a dataset of 121,401 voyage records, we evaluated nine regression models, including conventional, ensemble-based, and deep learning models. Shapley additive explanation (SHAP)-based feature selection is typically applied to enhance interpretability, and its effect is compared with principal component analysis-based dimensionality reduction and nonselection methods. The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. A stacking ensemble model, integrating random forest regressor, XGBR, LightGBM regressor, and CatBoost regressor, improves performance, achieving an RMSE of 18.9023, MAE of 12.3287, and an R2 of 0.3265. ANOVA tests confirm numerous differences in model performance and computational complexity. The results demonstrated that tree-based ensemble models outperform deep learning models in this setting. The proposed approach enables proactive scheduling, reduces congestion, and cost savings. The scalability of the model renders it suitable for broad maritime logistics and intelligent transportation systems.}, archive = {J_ACCESS}, author = {Min-Hwa Choi and Woongchang Yoon}, doi = {10.1109/ACCESS.2025.3566429}, journal = {IEEE Access}, pages = {81377-81391}, shortjournal = {IEEE Access}, title = {Predicting ship waiting times using machine learning for enhanced port operations}, volume = {13}, year = {2025}, }
• (2025). Site-specific measurement and analysis of building entry loss in high-rise buildings across 1 to 30 GHz. ACCESS, 13, 81365-81376. (www)

  @article{ , abstract = {This study investigated a site-specific approach to building entry loss to enable robust mobile wireless access for non-terrestrial networks using high-altitude platform stations and highly efficient fixed wireless access for terrestrial networks. In this study, outdoor-to-indoor propagation measurements were conducted in two distinct high-rise office buildings. These measurements considered various incident elevation and azimuth angles relative to the building façade and different indoor antenna locations. One set of measurements was performed using a helicopter at 1.47 GHz and 3.35 GHz, and the other set of measurements was conducted along a terrestrial path at 3.35 GHz and 29.3 GHz. Based on the obtained measurement results and geometrical optics theory, we analyzed the site-specific building entry loss, observing the nonmonotonic characteristics with the indoor entry distance. Based on the results, we propose a model that incorporates various parameters, e.g., the locations of the outdoor and indoor stations, as well as the dimensions of the building rooms and windows. Compared with standardized frameworks and existing models, the proposed model effectively characterized the site-specific building entry loss and demonstrated higher predictive accuracy on measured data for frequencies from 1 to 30 GHz.}, archive = {J_ACCESS}, author = {Shoma Tanaka and Sho Kimura and Ho-Yu Lin and Akihiro Sato and Hideki Omote}, doi = {10.1109/ACCESS.2025.3565732}, journal = {IEEE Access}, pages = {81365-81376}, shortjournal = {IEEE Access}, title = {Site-specific measurement and analysis of building entry loss in high-rise buildings across 1 to 30 GHz}, volume = {13}, year = {2025}, }
• (2025). LLM-MalDetect: A large language model-based method for android malware detection. ACCESS, 13, 81347-81364. (www)

  @article{ , abstract = {Android malware poses a significant cybersecurity threat, enabling unauthorized data access, financial fraud, and device compromise. Although deep learning methods are widely used for malware detection, they often struggle with stability and adaptability in the face of evolving threats. While large language models (LLMs) have shown promise in this area, their application to Android malware detection remains underexplored, particularly with regard to optimizing the semantic relationships within Android application packages (APKs). To address this gap, we introduce LLM-MalDetect, a novel framework that improves LLM-based APK analysis by explicitly modeling semantic dependencies and leveraging structured prompt engineering for optimized detection. Our approach formalizes LLM adaptation through a robust string-based feature extraction method and a tailored fine-tuning strategy to enhance precision. Evaluations on benchmark datasets demonstrate that LLM-MalDetect achieves up to 98.97% accuracy, outperforms existing methods in terms of robustness, and enables real-time analysis.}, archive = {J_ACCESS}, author = {Ruirui Feng and Hui Chen and Shuo Wang and Md Monjurul Karim and Qingshan Jiang}, doi = {10.1109/ACCESS.2025.3565526}, journal = {IEEE Access}, pages = {81347-81364}, shortjournal = {IEEE Access}, title = {LLM-MalDetect: A large language model-based method for android malware detection}, volume = {13}, year = {2025}, }
• (2025). GNSTAM: Integrating graph networks with spatial and temporal signature analysis for enhanced android malware detection. ACCESS, 13, 81326-81346. (www)

  @article{ , abstract = {The sophistication of Android malware poses significant threats to user security and privacy. Traditional detection methods struggle with rapid malware evolution and benign application diversity, leading to high false positive rates and limited adaptability. This paper introduces a hybrid methodology leveraging advanced machine learning techniques to enhance accuracy and adaptability in Android malware detection. It begins with collecting and preprocessing a comprehensive dataset of benign and malicious applications. An efficient Generative Adversarial Network (GAN) is employed to generate synthetic malware samples, effectively augmenting the dataset and enhancing the diversity of the malware samples under study process. To model the intricate relationships between applications, an efficient Graph Neural Network (GNN) process is utilized. Incorporating transformers, sequences of system and API calls are analyzed, harnessing this ability to discern patterns indicative of malicious activities. Additionally, a one-shot learning model tailored for the detection of new malware variants with minimal examples is introduced, enabling rapid adaptation to emerging threats. Federated learning preserves user privacy by training the model across a distributed network. A reinforcement learning model initiates proactive defenses, identifying optimal actions against malware threats. This methodology advances Android malware detection, showing over 5.9% improvement in detection accuracy, 4.5% reduction in false positives, and enhanced adaptability to new malware variants. It ensures enhanced security for Android users while preserving privacy. Evaluation results highlight its practical applicability in real-time scenarios.}, archive = {J_ACCESS}, author = {Yogesh Kumar Sharma and Deepak Singh Tomar and R. K. Pateriya and Surendra Solanki}, doi = {10.1109/ACCESS.2025.3567338}, journal = {IEEE Access}, pages = {81326-81346}, shortjournal = {IEEE Access}, title = {GNSTAM: Integrating graph networks with spatial and temporal signature analysis for enhanced android malware detection}, volume = {13}, year = {2025}, }
• (2025). Enhanced multiple sound event detection and classification using physical signal properties in recurrent spiking neural networks. ACCESS, 13, 81312-81325. (www)

  @article{ , abstract = {Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value ¡ 0.05). These findings suggest practical applications in various fields such as surveillance, autonomous vehicles, and smart home systems, where robust sound event detection is critical.}, archive = {J_ACCESS}, author = {Zahra Roozbehi and Ajit Narayanan and Mahsa Mohaghegh and Samaneh-Alsadat Saeedinia}, doi = {10.1109/ACCESS.2025.3563346}, journal = {IEEE Access}, pages = {81312-81325}, shortjournal = {IEEE Access}, title = {Enhanced multiple sound event detection and classification using physical signal properties in recurrent spiking neural networks}, volume = {13}, year = {2025}, }
• (2025). Fusion of visual attention and scene descriptions with deep reinforcement learning for AAV indoor autonomous navigation. ACCESS, 13, 81298-81311. (www)

  @article{ , abstract = {Autonomous indoor AAV navigation in GPS-denied environments is challenging. Issues include low lighting and obstacles. This work proposes a dual guidance framework to address these challenges. A CNN-based model with attention mechanisms and scene aware features serves as input to deep reinforcement learning. The attention module focuses on key visual elements. It highlights important regions and helps the AAV avoid collisions. The scene aware component uses computer vision techniques like pedestrian detection and brightness assessment. It gathers environmental information to assist navigation. These two models enhance the ability of DRL to adjust the AAV trajectory. This ensures safe and efficient navigation in complex indoor spaces. The approach was tested in a simulated indoor environment with pedestrians, created using Unreal Engine. The results show its effectiveness in enabling safe AAV navigation. Both the scene and attention models contribute significantly. Compared to ResNet, EfficientNet, DenseNet, and transformers, the CNN-based attention model performed better in accuracy and F1-score. The proposed navigation method also outperformed PPO and similar approaches in the literature.}, archive = {J_ACCESS}, author = {Hussein Samma and Ahmed Abubaker and Mubarak Badamasi Aremu and Mohammed Abdel-Nasser and Sami El-Ferik}, doi = {10.1109/ACCESS.2025.3566428}, journal = {IEEE Access}, pages = {81298-81311}, shortjournal = {IEEE Access}, title = {Fusion of visual attention and scene descriptions with deep reinforcement learning for AAV indoor autonomous navigation}, volume = {13}, year = {2025}, }
• (2025). Holistic pose estimation and dynamic motion analysis for telerehabilitation of physically disabled individuals. ACCESS, 13, 81279-81297. (www)

  @article{ , abstract = {Telerehabilitation systems leveraging depth video analysis provide an effective solution for remote physiotherapy, particularly for individuals with physical disabilities. This study presents an advanced exercise classification framework that integrates multi-modal feature extraction and attention-based transformation to enhance rehabilitation monitoring. The proposed pipeline begins with depth image preprocessing, followed by human detection using a pre-trained Histogram of Oriented Gradients (HOG)-Support Vector Machine (SVM) model. The human silhouette is segmented using the GrabCut algorithm, enabling robust region-of-interest extraction. We propose a novel Lightweight Two-tier Key Body Point Detection (LT-KBPD) algorithm to efficiently and accurately identify key skeletal points, which are then used to extract both static and dynamic kinematic features. In parallel, silhouette-based analysis is performed, where shape descriptors, dense optical flow, Gaussian Mixture Model (GMM)-based body part segmentation, and contour analysis extract spatial and motion-related features. The extracted feature sets are fused into a comprehensive feature vector and further refined using an attention-based transformation mechanism to highlight salient features relevant to exercise classification. Finally, a Long Short-Term Memory (LSTM) network is employed to model temporal dependencies and classify exercises with high accuracy. The proposed approach is validated on three benchmark depth-video datasets: Kimore, K3DA and MEx - Multi-modal Exercise Dataset, achieving classification accuracies of 92.19%, 91.35%, and 85.51%, respectively. These results demonstrate the system’s effectiveness in accurately recognizing rehabilitation exercises for individuals with physical disabilities. Future work aims to enhance the adaptability of the system through personalized rehabilitation feedback and improved temporal modeling techniques.}, archive = {J_ACCESS}, author = {Aleena Kamal and Shaheryar Najam and Mohammed Alshehri and Yahya AlQahtani and Abdulmonem Alshahrani and Bayan Alabdullah and Jeongmin Park}, doi = {10.1109/ACCESS.2025.3565024}, journal = {IEEE Access}, pages = {81279-81297}, shortjournal = {IEEE Access}, title = {Holistic pose estimation and dynamic motion analysis for telerehabilitation of physically disabled individuals}, volume = {13}, year = {2025}, }
• (2025). HAMF: A novel hierarchical attention-based multi-modal fusion model for parkinson’s disease classification and severity prediction. ACCESS, 13, 81252-81278. (www)

  @article{ , abstract = {Parkinson’s Disease (PD) is a neurodegenerative disorder that requires correct diagnosis and continuous monitoring of the disease severity. The state-of-the-art methods tend to be unimodal or lack robustness in generalizing between modalities, and hence cannot be applied clinically in diverse populations. A comprehensive approach is a multi-modal framework that overcomes these limitations by integration of brain Magnetic Resonance Imaging (MRI) data, gait analysis, and speech signals for enhanced classification and severity estimation of PD. A Hierarchical Attention-based Multi-modal Fusion (HAMF) model is developed in this paper to employ hierarchical attention mechanism at feature and decision levels to help the model learn representations at various levels. This leads to richer feature extraction, besides fusing different data modalities with accurate integration. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods are used in optimizing the model, by which the convergence speed raised by 15–20 %. An accuracy of 94.2 % was achieved, thus improving by 4–5 %, compared to the existing methodologies. Temporal Convolutional Network (TCN) which can capture long-range temporal dependencies, was used in the longitudinal severity estimation task, achieving a Mean Squared Error (MSE) of 0.12 in disease progression forecasting. Beyond this, Domain-Adversarial Neural Network (DANN) enables improved cross-domain generalization and maintains a consistent classification accuracy of 90-93% on diversified datasets. Finally, SHapley Additive exPlanations - Class Activation Maps (SHAP-CAM) further enhanced the model explainability. During the conduct of this work, 85% of all cases provided clinically interpretable insights that allowed clinicians to conduct personalized treatment planning in a more robust and interpretable way. This work substantially extends current multi-modal diagnosis and analysis of PD progression by offering a robust and interpretable tool to clinicians for personalized treatment planning.}, archive = {J_ACCESS}, author = {Anitha Rani Palakayala and P. Kuppusamy and D. Kothandaraman and Gunakala Archana and Jaideep Gera}, doi = {10.1109/ACCESS.2025.3563177}, journal = {IEEE Access}, pages = {81252-81278}, shortjournal = {IEEE Access}, title = {HAMF: A novel hierarchical attention-based multi-modal fusion model for parkinson’s disease classification and severity prediction}, volume = {13}, year = {2025}, }
• (2025). Recent trends in the quality module of sustainable water management models-A systematic review. ACCESS, 13, 81236-81251. (www)

  @article{ , abstract = {Water resources are crucial natural assets that all living organisms rely upon. Water is essential for consumption, industrial processes, and farming activities. In recent years, human activities and natural disasters have contributed to water pollution. It is vital to manage water quality to achieve sustainable development. This article examines the water quality index and the use of machine learning and deep learning algorithms in managing various water resources. Machine learning models play a role in different systems for water treatment and management such as predicting water quality and developing technologies to address water-related challenges. Additionally, it suggests methods to reduce water pollution and enhance the quality of various water resources including lakes, rivers, groundwater. It also evaluates different models that provide insights into natural disasters (for instance, GIS offers data on water levels in different areas, which can help prevent floods and droughts in real-time scenarios). Issues related to water resource management is investigated using machine learning algorithms, deep learning algorithms, and hybrid algorithms, with metrics such as RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percent Error), R2 (Coefficient of Determination), MSE (Mean Square Error), and RAE (Relative Absolute Error). Many studies focus on hybrid algorithms for analyzing water resource management. In this work, we have proved that water quality analysis achieved better results in ML and DL models. Over the next decade, the integration of machine learning tools is anticipated to accelerate the development of sustainable water resource management strategies.}, archive = {J_ACCESS}, author = {C. Subashini and Suseela Sellamuthu}, doi = {10.1109/ACCESS.2025.3560783}, journal = {IEEE Access}, pages = {81236-81251}, shortjournal = {IEEE Access}, title = {Recent trends in the quality module of sustainable water management models-A systematic review}, volume = {13}, year = {2025}, }
• (2025). Unified models and MILP-based scheduling method for home DC microgrids: A novel approach to overloading. ACCESS, 13, 81218-81235. (www)

  @article{ , abstract = {DC microgrids (MGs) have emerged as an alternative interconnection method for DC-type loads and distributed energy resources (DERs). Owing to the vulnerability of grid-connected converters (GCCs) to overloading, it is crucial to effectively manage both the controllable devices within DC MGs and the power exchange through GCCs to ensure the stable and economical operation of DC MGs. Among the various types of DC MGs, this paper proposes a novel scheduling method specifically for home DC MGs, which are small-scale DC MGs, to account for various controllable devices and mitigate overloading at GCCs more effectively. The proposed method primarily addresses three limitations in the previous research: the use of device-specific models, GCC power limits expressed by hard constraints, and the absence of effective strategies for reducing the risk associated with prediction errors. Initially, controllable devices are classified into three types based on their operational characteristics: power-controllable, state-controllable, and time-controllable devices. For each of these types, a unified model is developed, which will then be used in the scheduling problem. For stable and economical operation, three prioritized strategies are proposed: 1) minimizing overloading at the GCC, i.e., GCC limits are represented by soft constraints, 2) minimizing operational costs, and 3) addressing the required power demand as quickly as possible, while delaying the use of stored energy as late as possible, thus reducing load shedding possibility due to prediction errors. The scheduling problem in which to implement these strategies is formulated as a mixed-integer nonlinear programming (MINLP) problem, for which finding optimal solutions can be challenging. Therefore, a method to relax the MINLP problem into a mixed-integer linear programming (MILP) problem is also proposed. Finally, the effectiveness of the proposed method is validated through various case studies using MATLAB.}, archive = {J_ACCESS}, author = {Je-Heon Kang and Hye-Gyu Lee and Pyeong-Ik Hwang}, doi = {10.1109/ACCESS.2025.3566580}, journal = {IEEE Access}, pages = {81218-81235}, shortjournal = {IEEE Access}, title = {Unified models and MILP-based scheduling method for home DC microgrids: A novel approach to overloading}, volume = {13}, year = {2025}, }
• (2025). Autonomous aircraft tactical pop-up attack using imitation and generative learning. ACCESS, 13, 81204-81217. (www)

  @article{ , abstract = {This study presents a methodology for developing models that replicate the complex pop-up attack maneuver in air combat operations, using flight data from a Brazilian Air Force pilot in a 6-degree-of-freedom flight simulator. By applying imitation learning techniques and comparing three algorithms – Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) – the research trains models to predict aircraft control inputs through sequences of state-action pairs. The performances of these models were evaluated in terms of Root Mean Squared Error (RMSE), coefficient of determination (R2), training time, and inference time. To further enhance the training dataset with the aim of improving the robustness of the models, a Variational Autoencoder (VAE) was employed to generate synthetic data. These findings demonstrate the potential for deploying such models in fully autonomous aircraft, enhancing autonomous combat systems’ reliability and operational effectiveness in real-world scenarios.}, archive = {J_ACCESS}, author = {Joao P. A. Dantas and Marcos R. O. A. Maximo and Takashi Yoneyama}, doi = {10.1109/ACCESS.2025.3567186}, journal = {IEEE Access}, pages = {81204-81217}, shortjournal = {IEEE Access}, title = {Autonomous aircraft tactical pop-up attack using imitation and generative learning}, volume = {13}, year = {2025}, }
• (2025). Flexibility service for balance responsible parties’ industrial customers: A day-ahead cost optimization approach using price forecasting. ACCESS, 13, 81188-81203. (www)

  @article{ , abstract = {Electricity-intensive industries are highly impacted by electricity prices and their fluctuations. While these industries can change their consumption profiles to avoid peak price periods, they must also adhere to production schedules dictated by their unique manufacturing processes and related constraints. Balance Responsible Parties (BRPs), seek to optimize the electricity costs of their portfolio of customers and have limited access to real-time industrial flexibility. So, the concept of Daily Industrial Flexibility (DIF) is introduced as a trade-off between real-time flexibility and predetermined industrial schedules. Each factory can propose different fixed day-ahead consumption profiles to the BRP, each with a specific activation cost. In this context, this paper proposes a day-ahead flexibility service to minimize the BRP’s costs of electricity purchase on the day-ahead market, activating DIFs within the portfolio of industrial customers. A forecasting model for the electricity price is implemented and an optimization model is formulated to minimize day-ahead portfolio costs. Finally, the case study of a Catalan BRP is presented, considering DIF offers from its largest customers. Results demonstrate that the proposed approach enables accurate predictions of the BRP’s customers operation before DAM closure, successfully identifying alternative schedules to reduce portfolio costs.}, archive = {J_ACCESS}, author = {Adriano Caprara and Sara Barja-Martinez and Mònica Aragüés-Peñalba and Eduard Bullich-Massagué}, doi = {10.1109/ACCESS.2025.3567879}, journal = {IEEE Access}, pages = {81188-81203}, shortjournal = {IEEE Access}, title = {Flexibility service for balance responsible parties’ industrial customers: A day-ahead cost optimization approach using price forecasting}, volume = {13}, year = {2025}, }
• (2025). Mixed-embeddings and deep learning ensemble for DGA classification with limited training data. ACCESS, 13, 81167-81187. (www)

  @article{ , abstract = {Recent papers in the cybersecurity research field of Domain Generation Algorithms (DGAs) detection show the increase of performances associated with the introduction of unsupervised neural vectorized representation of domain names in the supervised classification process. In this paper we explore the effectiveness of this approach by proposing a novel mixed pre-trained neural embeddings model which integrates different vectorized representations of domain names: n-grams streams and words. We used the embeddings with two different classifiers, both based on ensemble architectures: a stacking model and an end-to-end multi-input neural architecture. We trained and tested the classifiers with two datasets, differing both in the distribution of domain names between real and DGAs and in the number and type of DGAs. The obtained results show that our solution provides considerable advantages with respect to state-of-the-art single classifiers both in classification accuracy and in the detection of challenging DGAs, such as those based on word dictionaries. The improvement of performance is significant in a particularly relevant operating condition, known as few-shot-learning, where only few examples of DGA-generated domain names are available for the classifier training.}, archive = {J_ACCESS}, author = {Christian Morbidoni and Alessandro Cucchiarelli and Luca Spalazzi}, doi = {10.1109/ACCESS.2025.3565022}, journal = {IEEE Access}, pages = {81167-81187}, shortjournal = {IEEE Access}, title = {Mixed-embeddings and deep learning ensemble for DGA classification with limited training data}, volume = {13}, year = {2025}, }
• (2025). Fast and accurate video analysis and visualization of classroom activities using multiobjective optimization of extremely low-parameter models. ACCESS, 13, 81143-81166. (www)

  @article{ , abstract = {The paper considers the problem of video activity recognition in real-life collaborative classroom learning environments. Video analysis of real-life collaborative classroom learning environments faces significant challenges not encountered in current, advanced video recognition datasets. In collaborative learning environments, students are arranged in small groups where they interact within their group. Video analysis needs to deal with long-term activity recognition (of one hour or more session videos), detect multiple simultaneous activities, rapid transitions between activities, occlusions, and numerous individuals performing similar activities in the background that are not part of the group being analyzed. Developing ground truth datasets for analyzing complex video datasets is prohibitively expensive. We dramatically reduce the requirement for large ground truth datasets by creating separate, custom datasets for object detection and video activity recognition. We then introduce a separable, extremely low-parameter system for video activity recognition that can be optimally trained using the derived datasets without the need for transfer learning from larger systems trained on large datasets. We further develop an interactive WebApp for visualizing the results over long video sessions. Overall, the extremely low-parameter activity classification model uses just 18.7K parameters for each activity, requiring 136.32 MB of memory. On a moderate GPU (RTX 5000), the activity classification model runs at an impressive 4,620 ( $154\times 30$ ) frames per second. Our approach uses at least 1,000 fewer parameters than several well-established methods for video recognition. Our extremely low-parameter classifiers can process 90 minutes of video in just 26 seconds. Furthermore, our models are much easier to train, they are much faster, and outperform comparable methods.}, archive = {J_ACCESS}, author = {Venkatesh Jatla and Sravani Teeparthi and Ugesh Egala and Sylvia Celedón-Pattichis and Marios S. Pattichis}, doi = {10.1109/ACCESS.2025.3567437}, journal = {IEEE Access}, pages = {81143-81166}, shortjournal = {IEEE Access}, title = {Fast and accurate video analysis and visualization of classroom activities using multiobjective optimization of extremely low-parameter models}, volume = {13}, year = {2025}, }
• (2025). CPS-IIoT-P2Attention: Explainable privacy-preserving with scaled dot-product attention in cyber-physical system-industrial IoT network. ACCESS, 13, 81118-81142. (www)

  @article{ , abstract = {The field of Cyber-Physical Industrial Internet of Things (CPS-IIoT) is rapidly developing, raising significant concerns about cyber-attacks due to the susceptibility of its devices and networking protocols. A breach in one device can compromise the entire system, necessitating robust security solutions. Existing methods fail to address the diversity and compatibility of CPS-IIoT environments. In this research, we propose a new privacy-preservation via Pearson correlation coefficient and agglomerative clustering with Bidirectional long short-term memory (BiLSTM) integrated with a scaled dot-product attention for cyber-attacks detection in CPS-IIoT. The inclusion of agglomerative clustering and scaled dot product attention mechanism in our proposed system is a unique characteristic, specifically tailored for CPS-IIoT contexts. These mechanisms adaptively modify their emphasis to prioritize crucial features within the CPS-IIoT network traffic data, providing additional computational resources to data segments that are likely to include abnormalities and patterns that indicate security issues. We evaluated the performance of our proposed model by conducting experiments on two relevant datasets: UNSW-NB15, and a novel IIoT dataset named X-IIoTID. The X-IIoTID is a versatile intrusion data designed to accommodate the diversity and compatibility of Industrial IoT systems, regardless of their connectivity and device specifications. The data encompasses the actions of emerging IIoT connectivity protocols, recent device activities, a range of attack types and situations, as well as multiple attack protocols. We used a CPS-IIoT testbed that emulates a real industrial facility to demonstrate our proof of concept. Our system exhibits outstanding performance attaining 99.60% of accuracy, 100% of AUC, 97.98% of recall, 100% of precision, F1 of 98.23%, kappa of 96.07%, and Mathew correlation coefficient of 96.54% on UNSW-NB15. On the representative and realistic X-IIoTID data, our proposed system exhibits outstanding performance attaining 99.99% of accuracy, 100% of AUC, recall of 99.97%, 99.98% of precision, 99.87% of F1-score, 99.97% of kappa, and Mathew correlation coefficient of 99.98%. Additionally, we design SHAPley Additive exPlanations from eXplainable AI to enhance our proposed model interpretability and reliability. The findings revealed that the scaled dot product attention mechanism dramatically boosts model performance, while Pearson correlation and agglomerative clustering safeguard data privacy in CPS-IIoT, surpassing the performance of existing state-of-the-art (SOTA) models. Our model achieves 2.7% higher accuracy than TinyLSTM at only 11% higher energy cost, justifying its use in accuracy-critical CPS-IIoT scenarios.}, archive = {J_ACCESS}, author = {Yakub Kayode Saheed and Joshua Ebere Chukwuere}, doi = {10.1109/ACCESS.2025.3566980}, journal = {IEEE Access}, pages = {81118-81142}, shortjournal = {IEEE Access}, title = {CPS-IIoT-P2Attention: Explainable privacy-preserving with scaled dot-product attention in cyber-physical system-industrial IoT network}, volume = {13}, year = {2025}, }
• (2025). Lightweight blockchain for data integrity and traceability in IoT networks. ACCESS, 13, 81105-81117. (www)

  @article{ , abstract = {Data integrity and traceability are important challenges to provide security in the Internet of Things (IoT) networks, which are often vulnerable to data manipulation attacks due to their use of low-resource devices and wireless communication technologies. In this regard, blockchain is a promising solution to enhance IoT security, but the implementation of a conventional blockchain requires high computational and network connectivity resources that are not compatible with IoT networks. In this paper, we propose a lightweight blockchain for data integrity and traceability in IoT networks that adapts the Distributed Ledger Technology (DLT) feature of blockchain to the LoRaWAN wireless communication protocol. Our proposal offers data integrity without the need for complex consensus algorithms or cryptographic operations. We also have designed and implemented a logical LoRaWAN P2P topology that enables communication between the IoT nodes which comprise LoRaWAN’s characteristic star topology. Finally, we evaluate our proposal and demonstrate its feasibility and performance in terms of data traceability, and network overhead.}, archive = {J_ACCESS}, author = {Laura García and Carlos Cancimance and Rafael Asorey-Cacheda and Claudia-Liliana Zúñiga-Cañón and Antonio-Javier Garcia-Sanchez and Joan Garcia-Haro}, doi = {10.1109/ACCESS.2025.3567773}, journal = {IEEE Access}, pages = {81105-81117}, shortjournal = {IEEE Access}, title = {Lightweight blockchain for data integrity and traceability in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Robust invariant set design for reliable stopping in adaptive cruise control via model predictive control. ACCESS, 13, 81096-81104. (www)

  @article{ , abstract = {Adaptive cruise control (ACC) systems are critical for ensuring safe and efficient operation in autonomous vehicles, especially in scenarios requiring precise stopping, such as approaching traffic lights or handling emergency braking. However, many existing ACC systems rely on controllers that become infeasible or fail to ensure stability in safety-critical situations, particularly under short prediction horizons and high velocity. This paper presents a novel approach that integrates invariant sets and linear quadratic regulator (LQR) into model predictive control (MPC) to address these limitations, enabling reliable stopping performance. This approach maximizes the set of recursively feasible states for the ACC compared to the current approaches. The present MPC-LQR–invariant set framework is robust under sensor noise, parameter uncertainty, and external disturbances, validating the approach for real-world scenarios. Additionally, we analyze and compare the feasibility and energy efficiency of the system for different terminal set strategies as control invariant set ( ${\mathrm {C}}_{\infty }$ ) and positive invariant set ( ${\mathrm {O}}_{\infty }$ ). The results suggest an interplay between energy efficiency and feasibility in short-horizon cases, providing actionable insights into selecting appropriate invariant set based on specific application. While control invariant set offers a larger feasible region, positive invariant set provides a more efficient energy consumption. This approach enhances ACC systems compared to previous controllers, providing safer and more reliable stopping for critical situations.}, archive = {J_ACCESS}, author = {Shila Alizadehghobadi and Reza Ehsani}, doi = {10.1109/ACCESS.2025.3568017}, journal = {IEEE Access}, pages = {81096-81104}, shortjournal = {IEEE Access}, title = {Robust invariant set design for reliable stopping in adaptive cruise control via model predictive control}, volume = {13}, year = {2025}, }
• (2025). ArabSis: Arabic corpus sentiment analysis. ACCESS, 13, 81083-81095. (www)

  @article{ , abstract = {Despite rapid progress in natural language processing (NLP), the development of specialized resources for niche domains—critical for specialized applications like affective computing and emotionally intelligent AI—remains a persistent challenge. While benchmark datasets abound for general tasks, languages like Arabic and fields like multi-dimensional sentiment analysis beyond binary classification as positive or negative suffer from resource scarcity, limiting progress in human-centric applications. To address this gap, we present ArabSis: a novel Arabic corpus for multi-dimensional sentiment analysis across five categorical emotions (Joy, Sadness, Fear, Liking, Hatred). Our work introduces a reproducible framework for creating specialized corpora in low-resource languages, enabling future research in regressive dimensional sentiment analysis and other specialized NLP applications. The ArabSis corpus, developed through systematic data augmentation and human labelling, facilitates advanced analysis using traditional NLP techniques (TF-IDF, Bag of Words) and modern deep learning approaches. It also targets the universal Arabic language whereas previous research focuses on Arabic regardless of the dialect which make small nuances and inconsistencies among dialects unnoticeable and unfixable. We evaluate machine learning (ML) and deep learning (DL) models in one-vs-all classification tasks, demonstrating that ML models (e.g., SVMs, Random Forests) outperform DL counterparts on smaller datasets. An ensemble method combining top-performing models achieves 98.6% accuracy through score averaging and majority voting systems, though revealing inherent biases in ensemble voting mechanisms. The study provides a comprehensive pipeline encompassing data preprocessing, exploratory analysis, and model training, validated through 5-fold cross-validation, establishing a blueprint for developing specialized NLP resources, particularly for under-resourced languages.}, archive = {J_ACCESS}, author = {Ziad Doughan and Sari Itani and Samir Itani}, doi = {10.1109/ACCESS.2025.3567755}, journal = {IEEE Access}, pages = {81083-81095}, shortjournal = {IEEE Access}, title = {ArabSis: Arabic corpus sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). The role of EEG-based emotional feedback in enhancing the effectiveness of tai chi sports programs for oral health promotion. ACCESS, 13, 81063-81082. (www)

  @article{ , abstract = {The integration of advanced emotion-driven feedback mechanisms, such as EEG-based emotional analysis, represents an innovative approach to enhancing the effectiveness of Tai Chi sports programs tailored for oral health promotion. Traditional oral health strategies, which often rely on educational campaigns and routine reinforcement of hygiene practices, have demonstrated short-term improvements in knowledge and awareness but struggle to sustain long-term behavioral changes, particularly among populations with low health literacy or motivational barriers. These conventional methods lack personalization and dynamic interaction, limiting their ability to engage diverse populations effectively. To address these shortcomings, our framework bridges the gap between conventional approaches and modern neurofeedback methodologies by integrating EEG-based emotional feedback into Tai Chi sports programs. Tai Chi, a mind-body exercise rooted in traditional Chinese medicine, is recognized for its ability to promote both physical and mental well-being through controlled movement, breath regulation, and cognitive focus. The incorporation of real-time emotional monitoring within Tai Chi sessions allows for dynamic adaptation of training content based on participants’ emotional states, fostering deeper engagement, improved stress regulation, and sustained commitment to positive oral health practices. Additionally, the inclusion of gamified elements and culturally relevant adaptations within Tai Chi practice further enhances user interaction, creating a behaviorally sensitive framework that maximizes long-term adherence and effectiveness in diverse communities. This novel approach leverages the holistic benefits of Tai Chi alongside cutting-edge neurofeedback techniques, offering a transformative pathway for enhancing oral health promotion through sports-based interventions.mework that maximizes long-term adherence and effectiveness in diverse communities.}, archive = {J_ACCESS}, author = {Si Wei}, doi = {10.1109/ACCESS.2025.3561179}, journal = {IEEE Access}, pages = {81063-81082}, shortjournal = {IEEE Access}, title = {The role of EEG-based emotional feedback in enhancing the effectiveness of tai chi sports programs for oral health promotion}, volume = {13}, year = {2025}, }
• (2025). Driving the development and improvement of occupational health and safety through industry 4.0 technologies. ACCESS, 13, 81048-81062. (www)

  @article{ , abstract = {Industry 4.0 (I4.0) has impacted different areas of organizations in various ways. In Occupational Health and Safety (OHS), I4.0 technologies can benefit organizations and their employees when implemented to improve working conditions and reduce occupational accidents and illnesses. Smart technologies can be used to identify and monitor risks in real time, enabling immediate implementation of corrective actions to prevent accidents. This article aims to propose drivers with recommendations for the development and improvement of OHS in organizations through I4.0 technologies. To this end, we conducted content analysis of scientific articles and reviews selected from the Scopus database. We identified forty-one boosting elements and systematized them into six drivers based on the scientific literature. These drivers contain practical recommendations that help organizations improve OHS, reduce workplace accidents, and improve employee working conditions. This work contributes to enhancing the safety conditions in organizations through I4.0 technologies. It also contributes to advancing knowledge in this area, supporting new research related to OHS and I4.0.}, archive = {J_ACCESS}, author = {Camilli Nobrega Dos Santos and Ana Carolina Ferreira Costa and Fernanda Ellen Francisco and Otávio José De Oliveira}, doi = {10.1109/ACCESS.2025.3567188}, journal = {IEEE Access}, pages = {81048-81062}, shortjournal = {IEEE Access}, title = {Driving the development and improvement of occupational health and safety through industry 4.0 technologies}, volume = {13}, year = {2025}, }
• (2025). Enhancing bounding box regression for object detection: Dimensional angle precision IoU-loss. ACCESS, 13, 81029-81047. (www)

  @article{ , abstract = {Bounding Box Regression (BBR) plays a critical role in object detection by refining the predicted location and size of objects to enhance model accuracy. This process involves adjusting the coordinates of the proposed bounding boxes to enhance their precision. The Intersection over Union (IoU) loss metric was introduced to improve the IoU metric for integration into the model training process, measure discrepancies between the model’s predictions and ground truth, and ensures meaningful gradient updates during training. In practice, IoU loss has demonstrated improvements in object detection performance by enhancing the localization accuracy of bounding boxes. Despite significant technological advancements and the various advantages and disadvantages of IoU loss, improving the accuracy and efficiency of BBR remains an active research area in computer vision. Various IoU loss variations have evolved with new formulations and methods to improve accuracy and convergence speed. A new loss function, Dimensional Angle Precision IoU (DAPIoU) loss, is introduced in this research to enhance BBR and serve as a new object detection loss function to address the limitations in previous loss function research results. This study conducts three types of experiments: single-group BBR simulation experiment on synthetic data, simulation experiment on synthetic data, and experiment on real-world datasets. The datasets used are MS-COCO and PASCAL VOC datasets. The object detection models used are YOLOv7, YOLOv9, and Faster R-CNN. The results from the real-world datasets experiments are evaluated using the mean Average Precision (mAP) method, including object size metrics, comparing several previous loss functions based on IoU.}, archive = {J_ACCESS}, author = {Hilmy Aliy Andra Putra and Aniati Murni Arymurthy and Dina Chahyati}, doi = {10.1109/ACCESS.2025.3567767}, journal = {IEEE Access}, pages = {81029-81047}, shortjournal = {IEEE Access}, title = {Enhancing bounding box regression for object detection: Dimensional angle precision IoU-loss}, volume = {13}, year = {2025}, }
• (2025). Leveraging machine and deep learning algorithms for hERG blocker prediction. ACCESS, 13, 81018-81028. (www)

  @article{ , abstract = {The human ether-a-go-go-related (hERG) gene is crucial in enabling the regulation of repolarisation process in the heart. Some chemicals act as hERG blockers, resulting in prolonged QT intervals. Predicting the binding capability of molecules with hERG channels is expected to reduce the burden of cardiotoxicity testing in drug evaluation. The application of machine learning (ML) and deep learning (DL) models in the field of toxicity has gained burgeoning interest. The current study utilises state-of-the-art ML and DL models for predicting the hERG-blocking ability of chemical compounds using a dataset of 8337 molecules. It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. While the threshold for blockers is defined as $\leq 10~\mu $ M and for non-blockers, it is $\gt 10~\mu $ M, our analysis indicates that a threshold of 60- $80~\mu $ M provides a more accurate cut-off for non-blockers. This adjustment highlights the importance of concentration levels in reflecting the variability specific to individual interaction sites. The algorithm results show that the internal validation performance of RF, XGBoost, and MLP is strong, with AUC scores of 0.90, 0.90, and 0.87, respectively. In summary, the current study provides a machine learning framework for computation cardiotoxicity assessment by analysis of the hERG blocker concentration cut-offs using different fingerprints at multiple thresholds.}, archive = {J_ACCESS}, author = {Syed Mohammad and Vaisali Chandrasekar and Omar Aboumarzouk and Ajay Vikram Singh and Sarada Prasad Dakua}, doi = {10.1109/ACCESS.2025.3566440}, journal = {IEEE Access}, pages = {81018-81028}, shortjournal = {IEEE Access}, title = {Leveraging machine and deep learning algorithms for hERG blocker prediction}, volume = {13}, year = {2025}, }
• (2025). Competing risks failure model under progressive censoring with random removals based on the generalized power half logistic geometric distribution. ACCESS, 13, 81002-81017. (www)

  @article{ , abstract = {In numerous survival analysis experiments, subjects may experience failure or death due to multiple causes. Whether these causes are dependent or independent, this study delves into the competing risk lifetime model under progressively type-II censoring schemes, where removal events follow a binomial distribution. Specifically, we focus on the generalized power half logistic geometric lifetime failure model in the context of independent causes. We consider the removal of subjects at each failure time according to a binomial distribution with known parameters. Both classical and Bayesian approaches facilitate point- and interval-estimation procedures for parameters and parametric functions. The Bayesian estimate is derived using the Markov Chain Monte Carlo (MCMC) method, incorporating symmetric and asymmetric loss functions. The Metropolis-Hasting algorithm is applied to generate MCMC samples from the posterior density function. A simulated data set is utilized to evaluate the performance of the two estimation approaches under the proposed censoring scheme. In addition, a real dataset is employed for illustrative purposes.}, archive = {J_ACCESS}, author = {Ahlam H. Tolba and Ahmed Ramses El-Saeed}, doi = {10.1109/ACCESS.2025.3567310}, journal = {IEEE Access}, pages = {81002-81017}, shortjournal = {IEEE Access}, title = {Competing risks failure model under progressive censoring with random removals based on the generalized power half logistic geometric distribution}, volume = {13}, year = {2025}, }
• (2025). Decoding the mystery: How can LLMs turn text into cypher in complex knowledge graphs?. ACCESS, 13, 80981-81001. (www)

  @article{ , abstract = {The integration of Knowledge Graphs (KGs) with Question Answering (QA) systems is transforming the landscape of Artificial Intelligence (AI). Through the combination of these technologies, novel features can be provided for the translation of questions in natural language into database queries. Even if a lot of work is emerging in this domain, this is not the case when we refer to translation of text to Cypher queries, where Cypher is one of the dominant query languages used for the development of KGs (e.g., based on the Neo4j technology). In this context, this paper provides a robust and efficient framework to systematically assess the efficiency of Large Language Models (LLMs) to support Text-to-Cypher conversion, focusing on the evaluation of open-source LLMs. The framework utilizes metrics and validators offered by an open-source software library that we developed, called CyVer. This study also assesses the impact of different schema representations of the KG on schema-aware query generation and the performance of LLMs on questions of different complexity requiring a depth of reasoning on the KG. A case study is described based on the application of the detailed framework in a KG with a large and complex schema that hosts data to track information related to the Sustainable Development Goals (SDGs). The experimental results demonstrate the effectiveness of the proposed framework, highlight the importance of the size of open-source models in the semantic comprehension of questions and the generation of valid Cypher queries, and stress the challenge for the generation of accurate queries in the case of questions requiring complex Cypher logic.}, archive = {J_ACCESS}, author = {Ioanna Mandilara and Christina Maria Androna and Eleni Fotopoulou and Anastasios Zafeiropoulos and Symeon Papavassiliou}, doi = {10.1109/ACCESS.2025.3567759}, journal = {IEEE Access}, pages = {80981-81001}, shortjournal = {IEEE Access}, title = {Decoding the mystery: How can LLMs turn text into cypher in complex knowledge graphs?}, volume = {13}, year = {2025}, }
• (2025). Hybrid dual-input model for respiratory sound classification with mel spectrogram and waveform. ACCESS, 13, 80971-80980. (www)

  @article{ , abstract = {Respiratory sounds serve as early indicators of lung diseases. The development of computer-aided classification systems has become a key enabler for timely diagnosis and treatment. The technology has improved basic services, particularly in resource-limited urban settings. We proposed an advanced hybrid dual-input model tailored for the intelligent classification of respiratory sounds. In this model, we employed Mel-spectrograms and waveform representations as feature extraction methods, utilizing the strengths of multiple modalities to enhance model performance. The classification framework integrates the Squeeze-and-Excitation (SE) attention mechanism into the ResNet architecture to construct the Bi-SEResNet model and adopts the Data-Efficient Image Transformer (DeiT) as the final classification layer. Model performance is evaluated using the SPR-Sound dataset, which includes two classification tasks: a 2-category classification of respiratory sound events into Normal and Abnormal, and a 7-category classification involving Normal, Rhonchi, Wheeze, Stridor, Coarse Crackle, Fine Crackle, and Wheeze & Crackle. Performance was assessed using sensitivity (SE), specificity (SP), average score (AS), and harmonic score (HS) as a composite score. The proposed framework achieved scores of 89.26 and 83.63 for 2-category classification and 7-category classification tasks, respectively.}, archive = {J_ACCESS}, author = {Fan Wang and Jiacheng Gao and Ying Wang and Guoheng Huang and Xiaochen Yuan}, doi = {10.1109/ACCESS.2025.3566477}, journal = {IEEE Access}, pages = {80971-80980}, shortjournal = {IEEE Access}, title = {Hybrid dual-input model for respiratory sound classification with mel spectrogram and waveform}, volume = {13}, year = {2025}, }
• (2025). Gaussian q function approximation in wireless communication system’s design: A gradient-based optimization approach. ACCESS, 13, 80958-80970. (www)

  @article{ , abstract = {This paper proposes a novel iterative gradient-based optimization approach aimed at achieving more precise and streamlined approximations for the Gaussian Q function—an essential element in communication system’s design. Our optimization strategy involves determining optimal weights for exponential functions within a defined range of the argument (x) of the Gaussian Q function. The primary goal is to iteratively minimize the error or cost function, progressively refining local minima across successive iterations. The results strikingly highlight the exceptional accuracy of our proposed approach in approximating the Gaussian Q function, positioning it as a robust contender for real-world communication system’s design. Importantly, our method showcases a clear superiority in accuracy over prevailing techniques. Moreover, to comprehensively evaluate the efficacy of our approach, we simplify the key metrics like symbol error probability (SEP) of various coherent digital modulation techniques over a versatile multi-cluster fluctuating two-ray fading model, which includes majority of the classical fading models like Rayleigh, Nakagami- $m,q$ , Rician, Rician-shadowed, fluctuating two-wave, two-wave with diffused power, etc. As an application example, the numerical results of the SEP of square quadrature amplitude modulation (SQAM) are validated against the exact results obtained via MATHEMATICA software package. In addition, a comparative analysis on the time required to compute the exact and proposed SEP values is also highlighted in this paper. The asymptotic SEP is also derived which gives an idea on the diversity order of the system. Lastly, extensive Monte-Carlo simulations have also been carried out to validate the proposed work.}, archive = {J_ACCESS}, author = {Leuva Bhumika Ranchhodbhai and Dharmendra Sadhwani}, doi = {10.1109/ACCESS.2025.3566837}, journal = {IEEE Access}, pages = {80958-80970}, shortjournal = {IEEE Access}, title = {Gaussian q function approximation in wireless communication system’s design: A gradient-based optimization approach}, volume = {13}, year = {2025}, }
• (2025). Multi-agent reinforcement learning for distributed flexible job shop scheduling with random job arrival. ACCESS, 13, 80941-80957. (www)

  @article{ , abstract = {Distributed flexible job shops are increasingly becoming the predominant production method in manufacturing due to their advantages in low-cost production and high customization. In practical production environments, jobs arrive randomly but follow a regular pattern. This paper addresses the scheduling problem of the Distributed Flexible Job shop Scheduling Problem (DFJSP) with random job arrivals. The DFJSP consists of three sub-problems: factory selection, job assignment, and operation sequencing. To tackle this issue, the DFJSP is modeled as a Markov Decision Process (MDP), and a multi-agent approach based on deep reinforcement learning (DRL) is proposed. This approach includes a Distribute Agent (DA) and a Sequence Agent (SA). For the MDP of the DA, we designed 12 state features, 5 candidate actions, and a reward based on the current state of production tardiness. The SA is configured with 7 state features, 6 candidate actions, and rewards that reflect delay conditions. A deep Q-network (DQN) framework that incorporates a linearly decreasing threshold probability was designed to effectively balance exploration and exploitation during the training phase. Comparative experiments conducted on randomly generated instances demonstrate the effectiveness of the DA when used both independently and in conjunction with the SA.}, archive = {J_ACCESS}, author = {Yuhang Yan and Wenchao Yi and Zhi Pei and Yong Chen}, doi = {10.1109/ACCESS.2025.3564433}, journal = {IEEE Access}, pages = {80941-80957}, shortjournal = {IEEE Access}, title = {Multi-agent reinforcement learning for distributed flexible job shop scheduling with random job arrival}, volume = {13}, year = {2025}, }
• (2025). Adaptive cooperative quality weight spectrum sensing for mitigating byzantine attacks in cognitive radio. ACCESS, 13, 80930-80940. (www)

  @article{ , abstract = {Spectrum sensing data falsification (SSDF) attack, i.e., Byzantine attack, is one of the critical threats of the cooperative spectrum sensing where the Byzantine attackers (BAs) forward incorrect local sensing results to mislead the fusion center on channel availability decisions. By using traditional voting rule, the cooperative spectrum sensing performance deteriorates significantly due to incorrect local sensing results. Then, reliability weight strategy becomes the popular solution to avoid incorrect sensing results from BAs and unreliable cognitive radio users (CRUs). However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Based on existing techniques, the BAs and CRUs may be assigned with low reliability weights or distinguished from the data fusion account. However, it is very difficult to detect the attackers since they also occasionally provide correct sensing results to the fusion center for concealing the attack objective. Then, existing techniques still suffer from BAs and negative impact of unreliable CRUs. In this paper, we propose the adaptive cooperative quality weight algorithm for mitigating the Byzantine attack issue by distinguishing the BAs and CRUs from the data fusion account while selecting only useful CRUs since the number of members in the account is also the important factor for cooperative spectrum sensing. In our proposed algorithm, we adopt a stable preference ordering towards ideal solution (SPOTIS) for determining the reliability of SUs which shows low computational complexity as compared to other reliability weight-based techniques. To achieve high sensing performance, our global decision threshold is adapted according to the reliability of reliable users. From the simulation results, our proposed algorithm significantly improves global detection probability and total error probability compared to the traditional voting rule, with the highest percentage of improvement reaching 113.9% for detection probability and 93.25% for total error probability under 6 BAs with 8 CRUs.}, archive = {J_ACCESS}, author = {Kanabadee Srisomboon and Luepol Pipanmekaporn and Akara Prayote and Wilaiporn Lee}, doi = {10.1109/ACCESS.2025.3567339}, journal = {IEEE Access}, pages = {80930-80940}, shortjournal = {IEEE Access}, title = {Adaptive cooperative quality weight spectrum sensing for mitigating byzantine attacks in cognitive radio}, volume = {13}, year = {2025}, }
• (2025). Road perception for autonomous driving: Pothole detection in complex environments based on improved YOLOv8. ACCESS, 13, 80914-80929. (www)

  @article{ , abstract = {Road surface pothole detection is crucial for ensuring the driving safety and path planning of autonomous vehicles. However, existing detection methods are often affected by variations in lighting, weather conditions, and complex environments, resulting in lower detection precision and recall rates. To address this, this paper proposes an innovative improved algorithm, which is based on the YOLOv8 model, and introduces the MSF-HFEB module in the innovative design. By cleverly blending the strengths of Convolutional Neural Network (CNN) and the Transformer architecture, the algorithm accomplishes multi-scale feature extraction and fusion for both local and global aspects of road potholes. This design significantly improves the robustness of the algorithm under different lighting and complex environmental conditions. Additionally, the algorithm incorporates the LSKA attention mechanism and an integrated MHSA_CGLU module. By utilizing large-scale convolutional kernels for weighted feature screening, it further enhances the capability of multi-scale feature extraction and the richness of nonlinear feature representation. Experiments on public datasets have validated the effectiveness of our approach: compared to the original YOLOv8 model, the detection precision and recall rate of our algorithm have been improved by 9.8% and 11.6% respectively, and the F1 score has reached 84.9%. The outcomes of this research not only enhance the driving safety and comfort of autonomous vehicles in complex environments but also offer robust technical support for obstacle avoidance and path planning in self-driving vehicles.}, archive = {J_ACCESS}, author = {Siyuan Kong and Qiao Meng and Xin Li and Zhijie Wang and Xin Liu and Bingyu Li}, doi = {10.1109/ACCESS.2025.3566944}, journal = {IEEE Access}, pages = {80914-80929}, shortjournal = {IEEE Access}, title = {Road perception for autonomous driving: Pothole detection in complex environments based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Orbital angular momentum for wireless communications: Key performance indicators and performance comparison. ACCESS, 13, 80889-80913. (www)

  @article{ , abstract = {Orbital angular momentum (OAM) is an intrinsic property of electromagnetic (EM) waves that has opened new possibilities for enhancing the capacity of wireless communications. Consequently, it has garnered significant attention in recent years. For wireless communications, antenna arrays are the most effective and widely studied approaches for OAM-wave generation. Various types of antenna arrays have been explored in research and development; however, a comprehensive comparison of these arrays remains lacking. This paper addresses this gap by first reviewing the various types of phased arrays that have been considered for OAM generation in the literature. Subsequently, it addresses the key performance indicators (KPIs) of the antenna arrays for OAM-wave generation. These KPIs include directivity, the largest producible OAM-mode (LPM), OAM-mode multiplexing capability, divergence angle, and mode purity. Based on the KPIs, a comparative analysis is conducted across several types of antenna arrays, including uniform square arrays (USA), uniform circular arrays (UCA), three-dimensional (3D) helical circular arrays (HCA), 3D helical circular sub-arrays (HCSA), and concentric UCAs (CUCA), under various settings. The study highlights the advantages and limitations of each antenna array type and examines how different parameters influence their performance.}, archive = {J_ACCESS}, author = {Zihao Wang and Mohammed El-Hajjar and Lie-Liang Yang}, doi = {10.1109/ACCESS.2025.3567732}, journal = {IEEE Access}, pages = {80889-80913}, shortjournal = {IEEE Access}, title = {Orbital angular momentum for wireless communications: Key performance indicators and performance comparison}, volume = {13}, year = {2025}, }
• (2025). Advances in composite power system reliability assessment: Trends and machine learning role. ACCESS, 13, 80871-80888. (www)

  @article{ , abstract = {This paper provides a detailed review of reliability assessment methods for composite power systems, focusing on integrating renewable energy and advanced computational approaches. The study classifies existing research into three main areas: investigation studies, planning and optimization studies, and efficient evaluation studies. Findings indicate that machine learning techniques are increasingly important in improving accuracy and computational performance in reliability analysis. A comparative examination of conventional and Machine Learning (ML)-based methods demonstrates that deep learning models, such as Convolutional Neural Networks, offer substantial reductions in computational time while maintaining reliability assessment precision. The review also identifies key research gaps, such as the need for realistic test systems and enhanced hybrid ML strategies. Additionally, recommendations are proposed to address these challenges and strengthen the effectiveness of future reliability evaluation methodologies. The insights presented in this study aim to support researchers and industry professionals in developing more efficient and scalable reliability assessment models for evolving power systems.}, archive = {J_ACCESS}, author = {Chiranjeevi Yarramsetty and Tukaram Moger and Debashisha Jena and Veeranki Srinivasa Rao}, doi = {10.1109/ACCESS.2025.3567464}, journal = {IEEE Access}, pages = {80871-80888}, shortjournal = {IEEE Access}, title = {Advances in composite power system reliability assessment: Trends and machine learning role}, volume = {13}, year = {2025}, }
• (2025). Two-level multi-objective design optimization including torque ripple minimization for stator excited synchronous and flux switching machines. ACCESS, 13, 80857-80870. (www)

  @article{ , abstract = {Electric machine performance can be enhanced through torque ripple mitigation, which reduces mechanical vibrations and improves system stability and operation. This paper proposes a novel two-level optimization method for synchronous flux-switching and hybrid excitation machines, featuring an innovative multi-point spline shaping approach for torque ripple minimization. The study employs experimentally validated models based on a prototype with a similar topology. The optimization process is implemented on two distinct designs: a 20-pole inner rotor PM-excited stator machine and a 28-pole outer rotor DC-excited stator machine. Analysis results demonstrate that the proposed innovative method significantly reduces torque ripple in both configurations. Force calculations and structural stress analysis confirm the machines’ manufacturability and their capability to operate at rated speeds, validating the effectiveness of the multi-point spline pole shaping method.}, archive = {J_ACCESS}, author = {Ali Mohammadi and Oluwaseun A. Badewa and Yaser Chulaee and Dan M. Ionel}, doi = {10.1109/ACCESS.2025.3567325}, journal = {IEEE Access}, pages = {80857-80870}, shortjournal = {IEEE Access}, title = {Two-level multi-objective design optimization including torque ripple minimization for stator excited synchronous and flux switching machines}, volume = {13}, year = {2025}, }
• (2025). Breast tumor-like-masses segmentation from scattering images obtained with an ultrahigh-sensitivity talbot-lau interferometer using convolutional neural networks. ACCESS, 13, 80847-80856. (www)

  @article{ , abstract = {Segmentation of breast tumors using imaging techniques remains a critical challenge in medical diagnostics, with limitations in contrast and resolution affecting early detection. Conventional mammography methods still have some limitations in differentiating between healthy and tumorous tissue, particularly in cases with low-density differences. This study investigates the potential of combining ultrahigh-sensitivity Talbot-Lau interferometry with Convolutional Neural Networks (CNNs) to enhance breast tumor segmentation from scattering images. The research aims to improve tumor segmentation accuracy and efficiency by leveraging phase contrast imaging. The experimental setup utilized an ultrahigh-sensitivity Talbot-Lau interferometer operated with a conventional X-ray tube to generate scattering images, which were processed using a Fourier Transform-based algorithm. Five CNN architectures - U-Net, ResNet50, DeepLabV3, PSPNet, and SegNet -were trained and tested on an augmented dataset of 320 images. Performance was evaluated based on accuracy, precision, specificity, recall, and F1-score. DeepLabV3 achieved the highest accuracy (87.07%) and F1-score (90.92%), followed by PSPNet with 85.94% accuracy. However, significant fluctuations were observed in validation accuracy, indicating sensitivity to dataset variability. U-Net demonstrated the most stable performance with an accuracy of 86.34% and an F1-score of 90.2%, making it the most reliable model for tumor segmentation in scattering images. The combination of Talbot-Lau interferometry with CNNs presents a promising approach for breast tumor segmentation. U-Net emerged as the most stable model, suggesting its potential application in medical diagnostics. Future work will focus on optimizing CNN architecture and expanding the dataset to improve the segmentation of small tumor-like masses.}, archive = {J_ACCESS}, author = {Ionuţ-Cristian Ciobanu and Nicoleta Safca and Elena Anghel and Dan Popescu}, doi = {10.1109/ACCESS.2025.3565370}, journal = {IEEE Access}, pages = {80847-80856}, shortjournal = {IEEE Access}, title = {Breast tumor-like-masses segmentation from scattering images obtained with an ultrahigh-sensitivity talbot-lau interferometer using convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing student management through hybrid machine learning and rough set models: A framework for positive learning environments. ACCESS, 13, 80834-80846. (www)

  @article{ , abstract = {Effective student management is crucial for fostering productive learning environments. This study presents a hybrid framework integrating machine learning (ML) techniques with rough set theory to enhance student management by identifying at-risk students and enabling personalized interventions. The model combines classification algorithms with rough set-based decision rules to analyze complex student data, including academic performance, behavior patterns, and levels of engagement. The ML layered approach detects patterns and outliers, supporting data-driven decisions to improve student well-being and educational outcomes. Evaluation on the Open University Learning Analytics Dataset (OULAD) demonstrated high accuracy (97.85%) in predicting student outcomes and precision (94.62%) in identifying students needing support. The hybrid approach outperformed conventional methods by approximately 15%, showcasing its transformative potential. This framework effectively monitors student performance and enables customized interventions to meet individual learning needs, fostering a more supportive educational environment.}, archive = {J_ACCESS}, author = {Ateeq Ur Rehman Butt and Hamid Ali and Muhammad Asif and Hessa Alfraihi and Mohamad Khairi Ishak and Khalid Ammar}, doi = {10.1109/ACCESS.2025.3565585}, journal = {IEEE Access}, pages = {80834-80846}, shortjournal = {IEEE Access}, title = {Enhancing student management through hybrid machine learning and rough set models: A framework for positive learning environments}, volume = {13}, year = {2025}, }
• (2025). Vibration suppression of AMBs-high speed motor using an enhanced linear extended state observer. ACCESS, 13, 80822-80833. (www)

  @article{ , abstract = {In order to solve the problem of rotor vibration caused by the unbalanced mass of the Active magnetic bearing (AMBs) rotor system, an Enhanced Linear Extended State Observer (ELESO) is designed based on the linear Extended State Observer (LESO). Firstly, the lumped disturbance including the unbalanced force is observed by ELESO, and the observed value is filtered by the band-pass filter (BF), and secondly, the lumped disturbance is compensated in the frequency band where the peak of rotor vibration is generated, so as to reduce the peak vibration of the rotor, and the saturation of the power amplifier caused by compensation in the full frequency band can be avoided. Then, the poles of the closed-loop AMBs rotor system are analyzed, and it is proved that the proposed control method can ensure the stable operation of the system. Finally, the correctness and effectiveness of the proposed method are verified under the working conditions of no disturbance and sensor runout. The results show that the designed control method has a smaller unbalanced vibration peak and a significant reduction in the control current when the rotor is operating in the full speed range compared with the direct use of LESO.}, archive = {J_ACCESS}, author = {Lei Gong and Pai He and Dali Dai and Zhiguang Hua and Jingwen Chen}, doi = {10.1109/ACCESS.2025.3567711}, journal = {IEEE Access}, pages = {80822-80833}, shortjournal = {IEEE Access}, title = {Vibration suppression of AMBs-high speed motor using an enhanced linear extended state observer}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Green traffic-oriented heavy-duty vehicle emission characteristics of china VI based on portable emission measurement systems”. ACCESS, 13, 80821. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Green Traffic-Oriented Heavy-Duty Vehicle Emission Characteristics of China VI Based on Portable Emission Measurement Systems”).}, archive = {J_ACCESS}, author = {Yingshuai Liu and Jianwei Tan}, doi = {10.1109/ACCESS.2025.3566788}, journal = {IEEE Access}, pages = {80821}, shortjournal = {IEEE Access}, title = {Corrections to “Green traffic-oriented heavy-duty vehicle emission characteristics of china VI based on portable emission measurement systems”}, volume = {13}, year = {2025}, }
• (2025). Enhancing smart microgrid resilience and virtual power plant profitability through hybrid IGWO-PSO optimization with a three-phase bidding strategy. ACCESS, 13, 80796-80820. (www)

  @article{ , abstract = {The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy.}, archive = {J_ACCESS}, author = {T. Yuvaraj and T. Sengolrajan and Natarajan Prabaharan and K. R. Devabalaji and Akie Uehara and Tomonobu Senjyu}, doi = {10.1109/ACCESS.2025.3565460}, journal = {IEEE Access}, pages = {80796-80820}, shortjournal = {IEEE Access}, title = {Enhancing smart microgrid resilience and virtual power plant profitability through hybrid IGWO-PSO optimization with a three-phase bidding strategy}, volume = {13}, year = {2025}, }
• (2025). LIPCPM: A novel model for anti-sloshing and stable bipedal robot locomotion. ACCESS, 13, 80786-80795. (www)

  @article{ , abstract = {In this paper, we propose a novel model, the Linear Inverted Pendulum with Cart-Plate Model (LIPCPM), which combines the Linear Inverted Pendulum Model (LIPM) with a mass-spring-damper model to simultaneously control both the legged robot and the sloshing phenomenon. A legged robot transporting liquid-filled container exhibits significant nonlinearities due to the sloshing dynamics of fluid movement within a confined container and the dynamics of legged robot itself. Therefore, controlling the legged robot and sloshing phenomenon together will bring challenges. Since the robot receives the reaction forces by the sloshing dynamics, simply implementing LIPM on the robot carrying a liquid container would not be enough. So, we analyzed how the reaction forces affect the robot with actual experiments and tabulated the system parameters that can be integrated with the LIPM. The experimental results show that the reaction forces by the sloshing dynamics are similar to those of a second-order response. The proposed model could be utilized with the Model Predictive Control (MPC), and predicted future states of the robot and liquid were controlled as we desired. Finally, we demonstrate the effectiveness of the proposed system in simulation with experimental-based system parameters for reliability. According to the results, the Root Mean Square Error (RMSE) of sloshing phenomenon under various conditions was decreased by approximately 70 %.}, archive = {J_ACCESS}, author = {Hosun Kang and Jaehyung Park and Dongyoung Lee and Jungmin Lee and Inho Lee}, doi = {10.1109/ACCESS.2025.3564291}, journal = {IEEE Access}, pages = {80786-80795}, shortjournal = {IEEE Access}, title = {LIPCPM: A novel model for anti-sloshing and stable bipedal robot locomotion}, volume = {13}, year = {2025}, }
• (2025). Relaxation parameter optimization in electrical-to-mechanical co-simulation based on time windowing WR technique. ACCESS, 13, 80775-80785. (www)

  @article{ , abstract = {This paper presents an innovative approach to enhancing the time windowing waveform relaxation (WR) technique in electrical-to-mechanical co-simulation by optimizing relaxation parameters for improved performance. An analytical model is introduced to determine the optimal number of time windows, considering the circuit’s dynamic characteristics. Additionally, a genetic algorithm (GA) is applied to refine relaxation parameters (e.g., impedance values), effectively addressing challenges posed by nonlinear device behaviors and improving the accuracy of co-simulation results. In the case study, it is crucial to emphasize that the full system simulation is used exclusively for retrospective validation of the co-simulation, without incorporating its results as inputs or convergence criteria. The proposed method significantly reduces the average error between co-simulation results and the full system output voltage, decreasing it from -2.8 dB to less than −26.2 dB. This demonstrates improved alignment and faster convergence compared to using the WR method. The validation of GA method is then applied to the co-simulation of an electrical (buck converter) and mechanical (DC motor) system, with results compared against the full system. The WR method exhibited significantly lower power efficiency (57.4%) compared to the full system (81.4%), rendering it insufficient. Time windowing WR enhanced power efficiency through simulation segmentation, reaching 74.6%, though it still fell short of the full system. The most advanced approach, time windowing WR with GA optimization, dynamically adjusted relaxation parameters, resulting in a power efficiency of 80.8%, nearly matching the 81.4% recorded in the single-kernel simulation. These findings underscore the effectiveness of integrating time segmentation with adaptive optimization to enhance simulation performance.}, archive = {J_ACCESS}, author = {Md Moktarul Alam and Richard Perdriau and Mohammed Ramdani and Mohsen Koohestani}, doi = {10.1109/ACCESS.2025.3567752}, journal = {IEEE Access}, pages = {80775-80785}, shortjournal = {IEEE Access}, title = {Relaxation parameter optimization in electrical-to-mechanical co-simulation based on time windowing WR technique}, volume = {13}, year = {2025}, }
• (2025). Classifying software modeling languages: The CLUPIR model approach. ACCESS, 13, 80759-80774. (www)

  @article{ , abstract = {Software Modeling Languages (MLs) are essential tools in the fields of systems analysis and documentation, as well as in facilitating communication among stakeholders involved in software development. However, the sheer variety of available MLs presents a significant challenge for software designers in selecting the most suitable language to model a specific problem or situation. To address this issue, this study introduces CLUPIR, a classification model designed to organize and catalog MLs based on a comprehensive set of aspects. The development of CLUPIR involved conducting a systematic mapping of the existing literature on classification models with a particular focus on the classification aspects used by these models. This thorough analysis informed the selection of the aspects for the CLUPIR model. To demonstrate its practical applications, eight distinct MLs were classified using the CLUPIR framework. Additionally, the model’s effectiveness and relevance were validated through a study involving 30 software industry professionals. These participants provided insight into the utility and applicability of CLUPIR in real-world scenarios. Ultimately, this research contributes to streamlining the process of selecting appropriate MLs for software design by offering a structured aspect-driven classification approach tailored to industry needs.}, archive = {J_ACCESS}, author = {João Seabra and Lyrene Fernandes da Silva}, doi = {10.1109/ACCESS.2025.3567005}, journal = {IEEE Access}, pages = {80759-80774}, shortjournal = {IEEE Access}, title = {Classifying software modeling languages: The CLUPIR model approach}, volume = {13}, year = {2025}, }
• (2025). A novel data-driven method of real-time transient stability assessment for AC/DC hybrid power systems. ACCESS, 13, 80748-80758. (www)

  @article{ , abstract = {To effectively tackle the operational challenges facing today’s bulk power systems with growing uncertainties, this paper presents a novel data-driven method for transient stability assessment method of the AC/DC hybrid power systems using auto-encoder-based algorithms for feature extraction and convolutional deep belief networks and Boltzmann machines for training accurate and robust models. First, a feature set is established from power grid operational states, and a stack-based noise reduction automatic encoder (SDAE) is used to extract key features that significantly affect the transient stability of the system. Next, transient stability assessment models are trained based on attention mechanisms, improved convolutional deep belief networks (CDBN), and multiple convolution-constrained Boltzmann machines to learn effective features from the input data adaptively. With such a design, the attention mechanism improves the model’s attention to instability samples, and the high-precision assessment of the systems’ transient stability is achieved. The experimental results conducted on the New England 39-bus system and the WECC 179-bus system show that the proposed method can accurately assess the transient stability of the system with multi-infeed DC by outperforming traditional supervised learning models considering various noise levels.}, archive = {J_ACCESS}, author = {Haifeng Li and Zhiwei Wang and Tao Jin and Xian Xu and Lin Shi}, doi = {10.1109/ACCESS.2025.3528351}, journal = {IEEE Access}, pages = {80748-80758}, shortjournal = {IEEE Access}, title = {A novel data-driven method of real-time transient stability assessment for AC/DC hybrid power systems}, volume = {13}, year = {2025}, }
• (2025). A fuzzy neural network-based intelligent prediction model for useful lifespan of lithium-ion batteries. ACCESS, 13, 80737-80747. (www)

  @article{ , abstract = {With the rapid development of the new energy industry, accurate prediction of the service life of lithium batteries as core energy storage components is of great significance for optimizing energy configuration and reducing operation and maintenance costs. Traditional prediction methods are difficult to accurately capture their dynamic characteristics. The research aims to utilize the powerful learning ability of hybrid neural networks to achieve accurate prediction of the service life of lithium batteries. The study first analyzed the application of existing neural network models in predicting the service life of lithium batteries. By comparing the predictive performance of these models, a new hybrid neural network model was proposed, which combines the advantages of convolutional neural networks (CNN) in feature extraction and long short-term memory networks (LSTM) in processing time-series data, while introducing the advantages of other neural network structures to improve the predictive ability of the model. The experimental results show that the hybrid neural network model proposed in this study performs well in predicting the inflection point of lithium battery remaining life. Compared with existing models, this model not only improves prediction accuracy, but also significantly enhances prediction stability.}, archive = {J_ACCESS}, author = {Yaowei Xu and Zhenxing Hao and Wei Li and Angang Cao}, doi = {10.1109/ACCESS.2025.3567066}, journal = {IEEE Access}, pages = {80737-80747}, shortjournal = {IEEE Access}, title = {A fuzzy neural network-based intelligent prediction model for useful lifespan of lithium-ion batteries}, volume = {13}, year = {2025}, }
• (2025). NMS-KSD: Efficient knowledge distillation for dense object detection via non-maximum suppression and feature storage. ACCESS, 13, 80723-80736. (www)

  @article{ , abstract = {Recently, many studies have proposed knowledge distillation (KD) frameworks for object detection. However, these frameworks did not take into account the inefficiencies caused by the teacher detector. The inefficiency refers to the computational cost incurred during the process of passing input data to the teacher model to acquire its knowledge. To solve this inefficiency in image classification, Fast Knowledge Distillation (FKD) was proposed, which stores the teacher model’s knowledge in advance and then uses it in the distillation process. However, directly applying FKD’s knowledge storage mechanism to dense object detectors causes a storage space problem. To address this issue, we propose NMS-KSD, a novel knowledge storage distillation method designed for dense object detection tasks. The core of NMS-KSD is the integration of Non-Maximum Suppression (NMS) and channel max pooling to effectively select and store key features from the teacher model’s intermediate feature maps. By storing and reusing these key features, NMS-KSD addresses the inefficiencies of traditional KD frameworks, significantly reducing training time while maintaining high performance. We validate the effectiveness and efficiency of our method across various dense object detectors through extensive experiments on the COCO, PASCAL VOC, and Cityscapes datasets.}, archive = {J_ACCESS}, author = {Suho Son and Byung Cheol Song}, doi = {10.1109/ACCESS.2025.3567103}, journal = {IEEE Access}, pages = {80723-80736}, shortjournal = {IEEE Access}, title = {NMS-KSD: Efficient knowledge distillation for dense object detection via non-maximum suppression and feature storage}, volume = {13}, year = {2025}, }
• (2025). ForBac: A static analysis approach with forward and backward analysis for precision in floating-point computations. ACCESS, 13, 80705-80722. (www)

  @article{ , abstract = {Accuracy in floating-point computations is critical for reliable software, especially in systems where precision impacts safety and performance. Inaccuracies from rounding errors and precision loss can lead to significant issues in applications like scientific computing and engineering simulations. To address these challenges, we introduce ForBac, a static analysis model designed to enhance floating-point precision by combining forward and backward analysis techniques. This dual approach improves the accuracy of floating-point computations compared to traditional methods. We evaluated ForBac on standard benchmarks, including Newton-Raphson and Jacobi Iteration methods, demonstrating its ability to maintain accuracy across complex computations. Results show that ForBac not only improves precision but also offers a scalable solution for analyzing floating-point operations in varied computational contexts.}, archive = {J_ACCESS}, author = {M. G. Thushara and K. Somasundaram}, doi = {10.1109/ACCESS.2025.3567762}, journal = {IEEE Access}, pages = {80705-80722}, shortjournal = {IEEE Access}, title = {ForBac: A static analysis approach with forward and backward analysis for precision in floating-point computations}, volume = {13}, year = {2025}, }
• (2025). Feasibility of grid flexibility provision using heat pump water heaters under midnight power services: A case of south korea. ACCESS, 13, 80695-80704. (www)

  @article{ , abstract = {Sector coupling (SC) has recently emerged as a solution to alleviate the curtailment caused by surplus power from variable renewable energy sources. Power-to-heat (P2H) is considered an economically viable SC approach that utilizes commercially available technologies. Previous studies demonstrated that electric water heaters and heat-pump water heaters (HPWH) effectively manage the demand and enhance flexibility. However, the practical implementation of P2H has been limited owing to policy limitations. This study proposed a P2H implementation model for South Korea that utilizes existing HPWHs deployed under the midnight power service, with a focus on flexibility provision through a reverse demand response (DR) program. The flexibility provision effect, energy efficiency, and economic impact were analyzed to evaluate the feasibility of the model. The case study results indicated that flexibility provision up to 100% of the device capacity was achievable, participation in the reverse DR program reduced power consumption by 4.4%, and operation costs decreased by 6–17% based on the DR scenarios.}, archive = {J_ACCESS}, author = {Du-Na Kim and Jin-Wook Lee and Sung-Yong Son}, doi = {10.1109/ACCESS.2025.3566375}, journal = {IEEE Access}, pages = {80695-80704}, shortjournal = {IEEE Access}, title = {Feasibility of grid flexibility provision using heat pump water heaters under midnight power services: A case of south korea}, volume = {13}, year = {2025}, }
• (2025). Deep learning-driven beam-steering for dual-polarized 28 GHz antenna arrays in 5G wireless networks. ACCESS, 13, 80680-80694. (www)

  @article{ , abstract = {This study explores the development of a 28 GHz array antenna with beam-steering capability, consisting of four elements with dual linear polarization at ±45 degrees. We propose a method for synthesizing the array antenna’s radiation pattern using an active element pattern-deep neural network (AEP-DNN). Beam-steering has become an attractive feature for researchers, as it enables users to move freely without affecting signal strength. An array analysis was conducted using a feedforward deep neural network (DNN) to generate a radiation pattern that achieves the desired steering angles. The proposed method takes radiation patterns as inputs and outputs the corresponding phase values for the antenna elements. The training dataset for the array antenna consisted of 6,859 radiation patterns, generated by adjusting the antenna element phases, which were then used to train the DNN model with minimal complexity. The radiation pattern was computed using AEP method since it is faster and less complex compared to full-wave modelling methods. The DNN model was initially tested using radiation patterns from an ideal square shape. After training, the model was evaluated by inserting desired beam-steering angles of 5 and 10 degrees, and it was found that the radiation pattern produced by the DNN closely matched the intended input pattern. The DNN learning process takes approximately 2 to 3 minutes in terms of processing time. The training and validation Root Mean Square Error (RMSE) and loss values converge to a minimum range of 1.3 to 2.3. Furthermore, the AEP-DNN method was successfully validated using the pattern multiplication method, full-wave modelling, and measurement methods to verify the feasibility and reliability of the training and validation data, as well as the resulting radiation pattern. This antenna, incorporating AEP-DNN technology, holds significant potential for various applications, particularly in mobile communications.}, archive = {J_ACCESS}, author = {Siti Zainab M. Hamzah and Norun Farihah Abdul Malek and Sarah Yasmin Mohamad and Farah Nadia Mohd Isa and Teddy Surya Gunawan and Kuo-Sheng Chin}, doi = {10.1109/ACCESS.2025.3566252}, journal = {IEEE Access}, pages = {80680-80694}, shortjournal = {IEEE Access}, title = {Deep learning-driven beam-steering for dual-polarized 28 GHz antenna arrays in 5G wireless networks}, volume = {13}, year = {2025}, }
• (2025). End-to-end horse gait classification in uncontrolled environments using inertial sensors. ACCESS, 13, 80668-80679. (www)

  @article{ , abstract = {Locomotor injuries in horses are a major cause of underperformance and serious welfare issue. Veterinarians typically investigate horses’ lameness through visual examination at separate gaits (walk, trot, gallop). To evaluate lameness objectively, Inertial Measurement Units (IMU) based systems have been developed. It is necessary to accurately identify the gait of each stride as vertical displacement symmetry is assessed at a defined gait, essentially trot. This study aimed to classify gaits into six classes and to assess the training sample size required to maximize the performance. Unlike previous methods, we used raw IMU data without manually preselecting specific signal segments. Seven sensors were strategically placed on the limbs, head, withers, and pelvis of horses. 1440 horses were used in our unsupervised model and the gait of 110 horses was labelled using IMU data for our supervised models. We divided the six gaits classification task into two subtasks: a four-gaits classification and a gallop-specific classification. In the first subtask, we compared the performance of a machine learning (XGBoost), a deep learning (LSTM) and a transfer learning (ENCOD-CNN) model, depending on the labelled training sample size. Our results show that the transfer learning approach outperformed the other models, achieving test accuracy of 91.9%. Our gallop classification task achieves 97.1% accuracy and the total pipeline reaches 91.2% accuracy. Beyond improving gait classification in a real clinical setting, this research demonstrates the potential of transfer learning for time-series datasets and provides a quantitative assessment of the required labeled sample size for effective implementation.}, archive = {J_ACCESS}, author = {Mahaut Gérard and Sandrine Hanne-Poujade and Guillaume Dubois and Henry Chateau and Neila Mezghani}, doi = {10.1109/ACCESS.2025.3564324}, journal = {IEEE Access}, pages = {80668-80679}, shortjournal = {IEEE Access}, title = {End-to-end horse gait classification in uncontrolled environments using inertial sensors}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Research on underwater small target detection technology based on single-stage USSTD-YOLOv8n”. ACCESS, 13, 80666-80667. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Research on Underwater Small Target Detection Technology Based on Single-Stage USSTD-YOLOv8n”).}, archive = {J_ACCESS}, author = {Weiguo Yi and Jinwei Yang and Lingwei Yan}, doi = {10.1109/ACCESS.2025.3566434}, journal = {IEEE Access}, pages = {80666-80667}, shortjournal = {IEEE Access}, title = {Corrections to “Research on underwater small target detection technology based on single-stage USSTD-YOLOv8n”}, volume = {13}, year = {2025}, }
• (2025). Recent advances in federated learning for connected autonomous vehicles: Addressing privacy, performance, and scalability challenges. ACCESS, 13, 80637-80665. (www)

  @article{ , abstract = {This review paper systematically explores the use of Federated Learning (FL) in Connected Autonomous Vehicles (CAVs) to improve vehicle performance, safety and user experience. FL presents a decentralized infrastructure that allows collaborative learning, while also ensuring data privacy, as CAVs increasingly rely on machine learning to process large amounts of sensor data. FL has been demonstrated to contribute to cooperative perception, vehicle trajectory prediction, traffic flow optimization, predictive maintenance, personalized in vehicle experiences, and advanced driver assistance systems (ADAS) through recent advances. Secondly, FL speeds up anomaly and intrusion detection, critical safety and security matters in CAV ecosystems. However, despite all these advancements, there are still challenges of data heterogeneity, communication overhead, security vulnerabilities, model convergence as well as regulatory compliance that constitute major barriers to FL deployment. This paper synthesizes recent contributions on this topic, identifies some gaps in the current research, and proposes future directions to fill those gaps. Federated Learning is a promising technology path towards driving innovations and improving the power and effectiveness of Autonomous Transportation Systems.}, archive = {J_ACCESS}, author = {Asad Ali and Huang Jianjun and Ayesha Jabbar}, doi = {10.1109/ACCESS.2025.3562128}, journal = {IEEE Access}, pages = {80637-80665}, shortjournal = {IEEE Access}, title = {Recent advances in federated learning for connected autonomous vehicles: Addressing privacy, performance, and scalability challenges}, volume = {13}, year = {2025}, }
• (2025). Design and optimization of IPMSM for enhanced efficiency, cost reduction, and performance in light electric vehicles. ACCESS, 13, 80621-80636. (www)

  @article{ , abstract = {Light electric vehicles (LEVs) offer environmentally friendly solutions to challenges in sustainable transport, zero emissions, noise reduction, and parking. Among the various motor types, interior permanent magnet synchronous motors (IPMSMs) are the most commonly used in LEVs with belt-drive systems due to their high-power density and efficiency. The main disadvantages of IPMSMs are torque ripple, which can cause noise and vibration, and the cost and availability of the permanent magnets (PMs), which are particularly crucial for LEVs. In this study, the requirements and dimensions of the IPMSM have been determined through analytical analyses and experimental studies of the motor used in a commercial two-wheeled LEV. Electromagnetic analyses were performed to evaluate four magnet configurations—V-shape, U-shape, double V-shape, and double I-shape—leading to the selection of the double I-shape arrangement for its superior performance. Optimization studies, guided by defined cost functions, were then carried out using ANSYS OptiSlang to enhance the motor’s design and overall efficiency. The designed IPMSM achieved a 1.4% improvement in efficiency compared to the commercially used motor, with an expanded high-efficiency operating region on the efficiency map and a 2% reduction in torque ripple. Additionally, the motor cost was reduced by 13% through a 22% increase in reluctance torque and a 12% decrease in magnet quantity. Thus, the cost of this cargo-purpose LEV, which is sold in thousands of units per year, has been reduced by 2.7%, while its range has been increased by 2.3% using the WLTC Class-1 drive cycle through the expanded efficiency map. In addition, compared to the delta-type reference PM rotor geometry, the double-I structure enhances manufacturability while maintaining comparable thermal performance.}, archive = {J_ACCESS}, author = {Kevser Ozer and Murat Yilmaz}, doi = {10.1109/ACCESS.2025.3567142}, journal = {IEEE Access}, pages = {80621-80636}, shortjournal = {IEEE Access}, title = {Design and optimization of IPMSM for enhanced efficiency, cost reduction, and performance in light electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Development of mercury-free far-UVC light source using luminous array film technology and its germicidal effects. ACCESS, 13, 80612-80620. (www)

  @article{ , abstract = {Far-UVC has emerged as a promising alternative for ultraviolet germicidal irradiation (UVGI). We developed a novel far-UVC light source using Luminous Array Film (LAFi), which excites phosphors by plasma discharge in a neon-xenon gas mixture within a glass tube. An optimal phosphor composition was determined, comprising aluminum phosphate, lutetium phosphate, and scandium. The plasma light module (PLM228) emits far-UVC radiation with a peak wavelength of 228 nm. Improvements in emission intensity and ozone generation suppression were achieved by modifying the glass materials. Escherichia coli, methicillin-resistant and methicillin-sensitive Staphylococcus aureus, and influenza A virus were inactivated by approximately 99.9% (−3 log10) upon exposure to 3 mJ/cm2 irradiation using PLM228. The bactericidal and virucidal effects of PLM228 surpassed those of other UV and far-UVC devices. PLM228 demonstrates significant potential as a mercury-free alternative UV germicidal light source.}, archive = {J_ACCESS}, author = {Koichi Shinoda and Kazuaki Mawatari and Thi Kim Ngan Bui and Yasuko Kadomura-Ishikawa and Takashi Uebanso and Hitoshi Hirakawa and Kenji Awamoto and Masayuki Wakitani and Tsutae Shinoda and Akira Takahashi}, doi = {10.1109/ACCESS.2025.3561147}, journal = {IEEE Access}, pages = {80612-80620}, shortjournal = {IEEE Access}, title = {Development of mercury-free far-UVC light source using luminous array film technology and its germicidal effects}, volume = {13}, year = {2025}, }
• (2025). Urban parking demand forecasting using xLSTM-informer model. ACCESS, 13, 80601-80611. (www)

  @article{ , abstract = {With the rapid advancement of urbanization, traffic congestion and parking shortages have emerged as critical challenges for modern cities. Accurate parking demand forecasting plays a pivotal role in supporting intelligent traffic management, resource allocation, and the efficient operation of parking facilities. While deep learning techniques have notably enhanced prediction capabilities, current studies often fail to effectively combine the strengths of diverse model architectures. In this study, we propose a hybrid forecasting model—xLSTM-Informer—which integrates the Extended Long Short-Term Memory Network (xLSTM) and the Informer architecture. To validate the model’s generalizability and scalability, we extend our analysis beyond a single commercial parking lot and include four additional sites representing diverse urban land-use types—office, residential, hospital, and transit hub areas—across different spatial scales. Extensive experiments conducted on real-world hourly parking datasets demonstrate that the proposed xLSTM-Informer model consistently outperforms baseline models. Notably, it achieves a mean absolute percentage error (MAPE) of 3.93% for vehicle entry and 8.66% for vehicle exit prediction, along with strong performance in R2, RMSE, and MAE metrics. These results highlight the model’s robustness, high accuracy, and potential for practical deployment in city-scale parking demand forecasting.}, archive = {J_ACCESS}, author = {Jimei Ma and Zhong Wang}, doi = {10.1109/ACCESS.2025.3566458}, journal = {IEEE Access}, pages = {80601-80611}, shortjournal = {IEEE Access}, title = {Urban parking demand forecasting using xLSTM-informer model}, volume = {13}, year = {2025}, }
• (2025). CFTformer: End-to-end cross-frame multi-object tracking with transformer. ACCESS, 13, 80587-80600. (www)

  @article{ , abstract = {Multi-object tracking (MOT) has been at the center of numerous applications from autonomous vehicles (AVs) to surveillance and even retail analytics. Traditional MOT methods typically rely on motion-based and appearance-based similarity information to associate detections across frames. However, the new transformer attention-based approach to MOT has removed the need for complex post-processing steps, such as graph optimization, allowing for end-to-end query tracking across frames. While the new transformer-based approaches offer many advantages, in the majority of these models the temporal dimension of the sequence is only considered in either the iterative processing of the frames or the memory of the queries. The proposed cross-frame multi-object tracking transformer (CFTforrmer) aims to improve one of the challenging areas of tracking, the association across different frames in the temporal dimension. In the proposed approach, the temporal identities of the frames are included in the positional encoding of the patches. This approach allows the encoder-decoder to track the queries more efficiently across the frames. For this model, scalable deformable-attention layers were used to design the encoder and decoder to decrease the computational cost. CFTformer also employs the proposed attention-based trajectory refinement (ATR) scheme to improve the tracking performance in blurred frames. The three-dimensional positional encoding of the patches helps the proposed ATR module to better capture the trajectories of the queries and generate smoother predictions. Overall, the model was able to achieve 1.7% and 0.6% improvement in the identification F1 score (IDF1) metric on MOT17 and MOT20 datasets while having ~0-15% lower number of identity switches, compared to other transformer-based approaches. More accurate tracking and lower identity switches make this algorithm more suitable to be used in the field of autonomous driving. To access model’s performance in AV applications, the BDD100K dataset was utilized for training and evaluation where the proposed approach achieved a 1.9% improvement in the IDF1 compared to other transformer-based models.}, archive = {J_ACCESS}, author = {Abdollah Amirkhani and Seyed Alireza Khoshnevis}, doi = {10.1109/ACCESS.2025.3567349}, journal = {IEEE Access}, pages = {80587-80600}, shortjournal = {IEEE Access}, title = {CFTformer: End-to-end cross-frame multi-object tracking with transformer}, volume = {13}, year = {2025}, }
• (2025). Features of extended reality environments in semiconductor fabrication process education: A scoping review. ACCESS, 13, 80576-80586. (www)

  @article{ , abstract = {The passing of the CHIPS and Science Act in the United States has signaled a renewed interest in expanding the domestic semiconductor industry. To fuel this expansion and the new job opportunities it creates, academic institutions and companies alike will need to educate and train a new and growing workforce in the field of semiconductor fabrication. Unfortunately, the ability to provide hands-on learning experiences currently lacks accessibility due to the costs of starting and maintaining a cleanroom. However, extended reality (XR) offers a lower-cost alternative to address this issue. This scoping review seeks to determine the features present in existing XR environments used for semiconductor fabrication process education. Using the framework outlined by Arksey and O’Malley, only five conference articles from an initial pool of 1,116 articles were included in the review. These papers were all published between 2018 and 2023 and only focused on virtual reality devices. Across the five articles, 12 different features were identified. Notable limitations of this work include restricting the scope to six semiconductor fabrication processes, as well as excluding articles that did not specify the XR device used. Overall, the findings show that there has been very little research on the use of XR in the field of semiconductor fabrication education.}, archive = {J_ACCESS}, author = {Dennis R. Parnell and Atayliya N. Irving and Connor S. Smith and Amy G. Buhler and Jeremy A. Magruder Waisome}, doi = {10.1109/ACCESS.2025.3566609}, journal = {IEEE Access}, pages = {80576-80586}, shortjournal = {IEEE Access}, title = {Features of extended reality environments in semiconductor fabrication process education: A scoping review}, volume = {13}, year = {2025}, }
• (2025). Facial expression recognition and digital images infosecurity for prevention care system application in sudden infant death syndrome monitoring. ACCESS, 13, 80559-80575. (www)

  @article{ , abstract = {This study presents a preventive care system for monitoring sudden infant death syndrome (SIDS), integrating digital image infosecurity and facial expression recognition (FER) technologies. For image infosecurity, a symmetric advanced encryption standard (SAES)-based scheme is introduced to ensure the secure transmission of infant images over public communication channels. In the FER, the YOLOv10 (You Only Look Once, version 10)-based classifier functions as an automatic object detection (OD) framework, enabling infant facial capture and expression recognition. Its classification mechanism categorizes expressions into four classes: normal, smile, sleep, and crying. Thus, to ensure secure SIDS monitoring, the SAES-based scheme implements robust block encryption and decryption processes to protect the privacy and security of infant images, while the YOLOv10 model enhances real-time OD, feature extraction, and pattern recognition capabilities. In experimental evaluations, a dataset of 5000 different facial expression images was self-collected and labeled with four classes of expressions. For training the YOLOv10-based classifier, the dataset was split into 60% for training (3,000 images) and 40% for testing (2000 images). The stochastic gradient descent (SGD) algorithm was employed to optimize the classifier’s model parameters for further enhancing accuracy for the intended purposes. For image infosecurity testing, the number of pixel changing rate (NPCR), unified averaged changed intensity (UACI), and structural similarity index measurement (SSIM) were employed to evaluate the performances for encryption and decryption processes, ensuring confidentiality, recoverability, and availability of transmitted images. For FER testing, the ten-fold cross-validation method was applied to evaluate the classifier’s performances, and the feasibility of the proposed method could be evaluated, achieving average Precision (%) of 91.40%, average Recall (%) of 91.15%, and average Accuracy (%) of 95.10%, respectively. The experimental results validated the feasibility of the YOLOv10-based classifier for real-time facial capture and expression recognition, demonstrating its effectiveness and reliability. Therefore, this integrated preventive care system not only safeguards sensitive message data for enhancing security levels but also facilitates accurate infant expression recognition for real-time SIDS monitoring. Hence, while assisting parents in effectively caring for their infants, it is also expected to contribute to reducing the incidence of SIDS under secure monitoring conditions.}, archive = {J_ACCESS}, author = {Chih-Te Tsai and Chia-Hung Lin and Hsiang-Yueh Lai and Yu-En Cheng and Ping-Tzan Huang and Neng-Sheng Pai and Chien-Ming Li}, doi = {10.1109/ACCESS.2025.3566108}, journal = {IEEE Access}, pages = {80559-80575}, shortjournal = {IEEE Access}, title = {Facial expression recognition and digital images infosecurity for prevention care system application in sudden infant death syndrome monitoring}, volume = {13}, year = {2025}, }
• (2025). AAMS: Application-aware MCS selection with mode switching for QoE-driven large-scale media transmission in 6G mobile networks. ACCESS, 13, 80540-80558. (www)

  @article{ , abstract = {Large-scale media data, such as 3D point cloud videos, have received increasing attention as a key content type for 6G mobile communication services. When delivering large-scale media in real time, it is necessary to transmit high-resolution data to meet the high demands of users. However, frequent retransmissions due to transmission errors may cause various issues across the entire system. Ultra-low latency and ultra-high-speed transmission are critical factors that guarantee high Quality of Experience (QoE) in real-time, large-scale media transmission systems. However, conventional modulation and coding scheme (MCS) selection approaches, which primarily focus on maximizing cell throughput, operate based on a fixed target block error rate without considering user content characteristics or usage environments. Consequently, these approaches face limitations in supporting a wide range of future services with diverse QoE requirements. This study proposes a novel MCS selection scheme that not only considers cell throughput maximization but also ensures retransmission stability for real-time, large-scale media data transmission. The proposed scheme adaptively adjusts the MCS selection thresholds based on user network conditions and feedback information associated with each MCS level. In addition, by enabling the selection of only the essential MCS levels needed for service provision, it maximizes the transmission efficiency while enhancing the reliability of real-time, large-scale media data transmission. Moreover, to address diverse QoE requirements, the proposed scheme is further enhanced through the introduction of a mode-switching mechanism for MCS selection based on service types and user mobility. Finally, the simulation results based on 3D point cloud video transmission demonstrate that the proposed scheme effectively mitigates retransmission issues in large-scale data services with high resolution, thereby enhancing user QoE.}, archive = {J_ACCESS}, author = {Somin Park and Minji Choi and Jin-Hyun Ahn and Dong Ho Kim and Cheolwoo You}, doi = {10.1109/ACCESS.2025.3566780}, journal = {IEEE Access}, pages = {80540-80558}, shortjournal = {IEEE Access}, title = {AAMS: Application-aware MCS selection with mode switching for QoE-driven large-scale media transmission in 6G mobile networks}, volume = {13}, year = {2025}, }
• (2025). Reported challenges in deep learning-based human pose estimation: A systematic review. ACCESS, 13, 80520-80539. (www)

  @article{ , abstract = {Human Pose Estimation (HPE) is the technique of precisely estimating the locations of human joints in images and videos. Deep learning has been frequently applied recently to improve HPE performance. However, a number of challenges, including occlusions, crowded environments, and hidden joints, make it difficult to create an effective HPE model. This systematic review provides a comprehensive analysis of HPE reported challenges, focusing on hidden joint issues due to their significant impact on performance. This study systematically examines a number of HPE models, choosing 74 papers on deep learning-based HPE that have been published since 2018 based on specific criteria. The reported challenges are listed along with their frequency and related references, the review also covers datasets, loss functions, evaluation metrics, and pre-trained models used in HPE. Our analysis demonstrates that occlusion and crowded scenes are still the main issues that affect the model performance, highlighting the importance of addressing hidden joint problem. The review presents a number of findings, such as the most widely utilized architecture in HPE literature is the residual network, the mean square error is the most frequently used loss function. Furthermore, MPII and Human3.6M are the most used datasets for 2D and 3D pose estimation, respectively. Finally, this study highlights the gaps in the field, which could lead to new research opportunities.}, archive = {J_ACCESS}, author = {Nagwa R. Fisal and Ahmed Fathalla and Dina A. Elmanakhly and Ahmad Salah}, doi = {10.1109/ACCESS.2025.3567337}, journal = {IEEE Access}, pages = {80520-80539}, shortjournal = {IEEE Access}, title = {Reported challenges in deep learning-based human pose estimation: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Kans-unet model and its application in image patch-shaped detection. ACCESS, 13, 80508-80519. (www)

  @article{ , abstract = {This study proposes a new detection method Kans-Unet, which combines Kans and U-net architecture. Specifically, the method embeds a convolution module based on Kans (Kan-Conv) in the encoder. The module applies a learnable activation function at the edge of the network, which not only reduces the number of model parameters but also significantly improves the generalization performance of the network. Secondly, in the transition region between encoder and decoder, the Feature Pyramid Attention (FPA) module is introduced to enhance the ability of the model to capture and analyze multiple scale features. In the decoder part, the CBAM attention mechanism module is integrated to effectively enhance the network’s attention to key information, and to improve the expression ability of image features. It is found that a wide range of patch-shaped anomaly regions are distributed in the VLF band power spectrum image of the space ionospheric electric field, and these patch-shaped anomaly regions are effectively detected by using the Kans-Unet model. The experimental results show that the KANs-Unet algorithm exhibits better detection performance compared to current mainstream semantic segmentation algorithms in the task of detecting abnormal speckle areas. It solves the problem of long model training time caused by insufficient computing power and provides a new method for the detection and analysis of abnormal features in power spectrum images.}, archive = {J_ACCESS}, author = {Xingsu Li and Zhong Li and Jianping Huang and Ying Han and Kexin Zhu and Bo Hao and Junjie Song and Yumeng Huo}, doi = {10.1109/ACCESS.2025.3562387}, journal = {IEEE Access}, pages = {80508-80519}, shortjournal = {IEEE Access}, title = {Kans-unet model and its application in image patch-shaped detection}, volume = {13}, year = {2025}, }
• (2025). From centralization to decentralization: Blockchain’s role in transforming social media platforms. ACCESS, 13, 80478-80507. (www)

  @article{ , abstract = {Blockchain is influencing the social media platforms by promising to solve the biggest issues of today, such as privacy concerns and content moderation, and the ability to provide a decentralized system for the management of social media platforms through cryptographic techniques and distributed structures. Conventionally, social media systems are based on central architectures that may indirectly solve the problems of data privacy and platform responsibility since there are increased threats, including identity theft and fake news spread across such systems. Blockchain has stepped up as an anti-centralized platform that allows multiple numbers of users to transact securely and authentically through P2P channels without a middleman. Here in this survey, we introduce a brief overview of the latest trends of the blockchain protocols and the interactions with the social media platforms, as well as present a systematic review of the related area. Specifically, the use case we consider and elaborate on is how blockchain can be applied to various types of social media services: smart contract-based content moderation platforms, user incentives using token economics, social media governance through decentralized autonomous organizations, and cross-chain connections. We then go further and offer a comprehensive literature review of blockchain incorporating main social media applications that cutting across them include identity, tokenization, and self-governance. The key points that can be derived from this review of the blockchain-enabled social media services and applications are also underlined here. We conclude this survey by listing the existing issues in the field, including scalability and compliance with the existing regulations, and suggesting the possible areas of research in this ever-evolving field.}, archive = {J_ACCESS}, author = {Bidita Sarkar Diba and Jayonto Dutta Plabon and Nishat Tasnim and Mehjabin Hossain and Durjoy Mistry and Sourav Sarker and M. F. Mridha and Yuichi Okuyama and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3561663}, journal = {IEEE Access}, pages = {80478-80507}, shortjournal = {IEEE Access}, title = {From centralization to decentralization: Blockchain’s role in transforming social media platforms}, volume = {13}, year = {2025}, }
• (2025). Leveraging priority queueing in IoT-edge-fog-cloud infrastructures for efficient healthcare monitoring. ACCESS, 13, 80461-80477. (www)

  @article{ , abstract = {The rapid growth of the Internet of Healthcare Things (IoHT) has led to challenges in real-time processing, prioritization, and resource allocation of heterogeneous healthcare data. Existing edge-fog-cloud approaches often fail to effectively handle critical medical events and ensure timely interventions. This paper presents a novel IoHT framework that integrates an M/M/C/K priority queue model (M: Markovian arrival/service rates, C: servers, K: capacity) with a three-tier edge-fog-cloud architecture. The proposed approach introduces a dynamic priority assignment mechanism that leverages real-time patient data for swift processing of critical events and an adaptive resource allocation strategy that optimizes performance under varying workloads. Simulations and real-world case studies demonstrate the framework’s superiority, achieving a 30% reduction in average response time for critical events and a 25% improvement in resource utilization compared to state-of-the-art methods. Contributions include: 1) a novel M/M/C/K priority queue model integrated with edge-fog-cloud architecture; 2) dynamic priority assignment and adaptive resource allocation strategies; and 3) comprehensive evaluation through simulations and case studies. By addressing key challenges in IoHT data processing and prioritization, this work enables the development of efficient, responsive, and reliable IoHT systems for timely and personalized healthcare interventions, ultimately improving patient outcomes and quality of care.}, archive = {J_ACCESS}, author = {Dang Van Anh and Van-Hau Nguyen}, doi = {10.1109/ACCESS.2025.3565679}, journal = {IEEE Access}, pages = {80461-80477}, shortjournal = {IEEE Access}, title = {Leveraging priority queueing in IoT-edge-fog-cloud infrastructures for efficient healthcare monitoring}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven QoT prediction for enhanced optical networks in DWDM system. ACCESS, 13, 80445-80460. (www)

  @article{ , abstract = {This research demonstrated the machine learning (ML) classifiers with regression learning to improve an optical system’s quality of transmission (QoT). In Optical Communication, the data can be communicated from source to destination through the established lightpaths. However, as the signal traverses the optical links and devices, its QoT may deteriorate due to various impairments. The QoT is an essential component that determines the connectivity of an optical network. Therefore, ensuring a QoT guarantee is necessary to establish a successful lightpath. Predicting the QoT before establishing lightpaths can guide the routing and allocation of resources required for the lightpaths. In this research, using ML models an appropriate QoT analytical prediction model is developed computationally. Simulations were conducted at a 10 Gbps data rate per channel for 64-channel DWDM systems. The proposed model significantly improves in detecting fiber nonlinearity, and performance was studied using Q-factor, BER, and noise power. The results indicate that the SVM-based classifier with regression learning performs better than any other classifiers discussed in this research. This study assesses the efficiency of the proposed ML models in predicting the QoT for established lightpaths. Results indicate that all the ML classifiers with Regression models can accurately predict the transmission quality for over 90% of lightpaths. However, the proposed SVM-based classifier with a regression model demonstrates superior generalization, with a nearly perfect QoT prediction rate of around 99% for the established lightpaths. In the network planning stage, residual margins are added to compensate for inaccuracies, which ensures accurate signal reception. The proposed ML model achieved a lightpath residual margin with a 0.7dB error.}, archive = {J_ACCESS}, author = {Sudha Sakthivel and Mohammad Riyaz Belgaum and Aznida Abu Bakar Sajak and Muhammad Mansoor Alam and Mazliham Mohd Su'ud}, doi = {10.1109/ACCESS.2025.3565502}, journal = {IEEE Access}, pages = {80445-80460}, shortjournal = {IEEE Access}, title = {Machine learning-driven QoT prediction for enhanced optical networks in DWDM system}, volume = {13}, year = {2025}, }
• (2025). Adaptive polynomial approximation-based data-driven model of parameter design for bidirectional L-LLC converter. ACCESS, 13, 80429-80444. (www)

  @article{ , abstract = {The L-LLC resonant converters have excellent performance to realize bi-directional power flow due to the symmetrical circuit structure of the resonant tank. The traditional frequency-domain based design approach of L-LLC converter suffers inaccuracies, and the traditional time-domain based design approach usually utilizes discrete process to find the optimal resonant parameters, which requires large computation and cannot ensure the actual optimization. Therefore, an optimal design method of the L-LLC converter based on data-driven model is proposed in this paper. Based on the numerical calculation method in bidirectional power flow, a closed resonant parameter space is established by satisfying the constraints including soft switching, operational mode, switching frequency and resonant capacitor voltage stress. The optimization objective is to minimize the overall losses of the converter. To reduce computational complexity, the adaptive polynomial approximation (APA) method is employed to fit the constraints and optimization objectives, thus the surrogate model is constructed. Then, the nondominated sorting genetic algorithm (NSGA-II) is employed to search the pareto-based optimal solution, which guarantees the synthetical minimal power losses in bi-direction power flow. Finally, a 400V/1000W experimental prototype was built to validate the effectiveness of the proposed design method.}, archive = {J_ACCESS}, author = {Xin Zhou and Huan Luo and Buxiang Zhou}, doi = {10.1109/ACCESS.2025.3561932}, journal = {IEEE Access}, pages = {80429-80444}, shortjournal = {IEEE Access}, title = {Adaptive polynomial approximation-based data-driven model of parameter design for bidirectional L-LLC converter}, volume = {13}, year = {2025}, }
• (2025). Leveraging cross-project similarity for data augmentation and security bug report prediction. ACCESS, 13, 80416-80428. (www)

  @article{ , abstract = {Accurately identifying security bug reports remains a key challenge in software development. Due to the varying expertise of bug reporters, many security bug reports are incorrectly labeled as non-security bug reports, this increases the security risk of the software and the workload of developers to identify these incorrectly labeled reports from bug reports. This study aims to improve the prediction of security bug reports by addressing the class imbalance problem and enhancing the generalization ability of the model across projects. To achieve this goal, we propose a deep learning-based prediction method combined with a novel data augmentation method based on cross-project text similarity. The bug report data is collected from four open-source projects: Ambari, Camel, Derby, and Wicket, where the number of security bug reports is 56, 74, 179, and 47, respectively, and the number of non-security bug reports is significantly higher. To alleviate the imbalance phenomenon and leverage cross-project knowledge, we augment the dataset by identifying and merging semantically similar security bug reports from other projects. We evaluate 5 deep learning models, including CNN, LSTM, GRU, Transformer, and BERT. Our approach achieved F1 scores between 0.60 and 0.98, with the best performance using LSTM and GRU models, especially LSTM on Ambari, GRU on Camel and Ambari, they both achieved an F1 score of 0.98. The overall average F1 score is 0.77, a significant improvement over the baseline classification. The results show that data augmentation based on cross-project similarities is an effective strategy to improve security bug report prediction, especially in imbalanced datasets. This approach can help developers detect security-related issues more effectively, reduce the risk of misclassification, and enhance overall software security.}, archive = {J_ACCESS}, author = {Jinfeng Ji and Geunseok Yang}, doi = {10.1109/ACCESS.2025.3564818}, journal = {IEEE Access}, pages = {80416-80428}, shortjournal = {IEEE Access}, title = {Leveraging cross-project similarity for data augmentation and security bug report prediction}, volume = {13}, year = {2025}, }
• (2025). Wideband circularly-polarized hexagonal SIW cavity-backed slot antenna with enhanced bandwidth and compact design. ACCESS, 13, 80407-80415. (www)

  @article{ , abstract = {This paper presents a novel wideband circularly polarized (CP) cavity-backed slot antenna based on Substrate Integrated Waveguide (SIW) technology, designed for compact and high-efficiency performance. The proposed antenna utilizes a hexagonal SIW cavity to simultaneously excite two closely spaced resonant modes (TM ${}_{\mathrm {110}}$ and TM ${}_{\mathrm {210}}$ ), resulting in enhanced bandwidth for linear polarization (LP). To achieve circular polarization, a passive, single-layer linear-to-circular polarization converter is integrated above the cavity, offering a structurally simple and PCB-compatible solution. Unlike conventional CP designs that rely on complex feeding networks or multilayered structures, this configuration maintains a planar profile and efficient performance. A fabricated prototype demonstrates strong agreement between simulation and measurement, achieving a peak gain of 9.2 dBic and a 14% axial ratio (AR) bandwidth. These results highlight the antenna’s suitability for modern wireless systems requiring wideband CP functionality, including satellite communications, 5G modules, and compact embedded devices.}, archive = {J_ACCESS}, author = {Vahid Sarani and Esraa Mousa Ali and Milad Shariatifar and Bal S. Virdee and Dion Mariyanayagam and Nasr Rashid and Mohammad Alibakhshikenari and Mariana Dalarsson}, doi = {10.1109/ACCESS.2025.3566948}, journal = {IEEE Access}, pages = {80407-80415}, shortjournal = {IEEE Access}, title = {Wideband circularly-polarized hexagonal SIW cavity-backed slot antenna with enhanced bandwidth and compact design}, volume = {13}, year = {2025}, }
• (2025). Exploring deep clustering methods in vibro-acoustic sensing for enhancing biological tissue characterization. ACCESS, 13, 80395-80406. (www)

  @article{ , abstract = {Nonlinear dimensionality reduction techniques, often referred to as manifold learning, are increasingly valuable for data visualization and unsupervised clustering. In the context of surgery and medicine, these methods facilitate the analysis of complex datasets, enabling pattern recognition in surgical data. This study explores the characterization of six tissue types through manifold learning and unsupervised clustering, utilizing vibro-acoustic (VA) signals collected from manual palpation experiments. A wireless sensor mounted at the tip of a surgical instrument was used to acquire 1,680 VA signals, which were processed using Fourier transform and cepstral analysis for feature extraction. We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy ( $92~\%$ vs. $87~\%$ ) and better-defined clusters with greater compactness. The observed differences in performance are linked to the intrinsic properties of the tissues, particularly surface characteristics such as friction and moisture, which affect signal consistency. Additionally, we compared our approach with previous works, including a study utilizing the same dataset, where our methodology demonstrated improved accuracy. Future research will focus on refining the VAE model, increasing the diversity of tissue samples, and validating the proposed approach in real surgical settings to enhance its applicability in minimally invasive surgery.}, archive = {J_ACCESS}, author = {Robin Urrutia and Diego Espejo and Montserrat Guerra and Karin Vio and Thomas Sühn and Nazila Esmaeili and Axel Boese and Patricio Fuentealba and Alfredo Illanes and Christian Hansen and Víctor Poblete}, doi = {10.1109/ACCESS.2025.3566280}, journal = {IEEE Access}, pages = {80395-80406}, shortjournal = {IEEE Access}, title = {Exploring deep clustering methods in vibro-acoustic sensing for enhancing biological tissue characterization}, volume = {13}, year = {2025}, }
• (2025). Ambient intelligence to detect misuse of electricity consumption based on IoT using blockchain technology. ACCESS, 13, 80371-80394. (www)

  @article{ , abstract = {Electricity abuse and energy inefficiencies are still open issues in smart grid systems, demanding high-performance anomaly detection mechanisms. In this paper, we propose an IoT-enabled electricity monitoring system that combines machine learning (LightGBM) and blockchain (Polygon network) for real-time anomaly detection, secure data storage, and transparent energy tracking. IoT smart meters are utilized to monitor real-time electricity usage data, whereas LightGBM classifies anomalies efficiently with high precision. The key innovation is the use of blockchain for decentralized anomaly logging with tamper-proof records and enhanced trustworthiness. Unlike centralized approaches, Polygon blockchain immutably stores electricity data, giving verifiable anomaly logs. Using an interactive IoT dashboard and real-time notifications, users can monitor consumption patterns and respond to anomalies efficiently. The proposed system achieves 96.77% accuracy, an AUC-ROC of 0.99, and an F1-score of 96.69%, outperforming CNN-LSTM and CNN-XGBoost in both accuracy and use of computational resources. Despite these advantages, blockchain transaction costs (0.001996 POL per transaction) and IoT-wallet integration complexity pose challenges. Future work will explore cost-reducing blockchain optimizations such as meta-transactions and relayers, along with model enhancements for IoT scalability. This approach provides a low-cost, scalable, and secure solution for smart energy management, enhancing grid security and sustainability.}, archive = {J_ACCESS}, author = {Soiful Hadi and Wahyul Amien Syafei and Adi Wibowo}, doi = {10.1109/ACCESS.2025.3561677}, journal = {IEEE Access}, pages = {80371-80394}, shortjournal = {IEEE Access}, title = {Ambient intelligence to detect misuse of electricity consumption based on IoT using blockchain technology}, volume = {13}, year = {2025}, }
• (2025). Border feature enhancement in ultrasound image segmentation. ACCESS, 13, 80355-80370. (www)

  @article{ , abstract = {Semantic segmentation of ultrasonic images is a crucial and challenging task in computer vision in AI-assisted medicine. In prevailing methods, each pixel in the image is treated equally while ignoring the fact that border pixels contain more critical information. This limitation reduces segmentation performance and may result in irregular and ambiguous segmentation results, even under high-quality manual labels. To address this issue, we propose a novel boundary enhancement scheme that improves segmentation accuracy by incorporating a complementary decoder structure (Bor-decoder). This dual-decoder scheme simultaneously predicts both the target area and its border in ultrasonic images, with feature fusion between these two decoders to boost overall performance. Our approach automatically generates border labels from segmentation mask annotations without requiring additional manual border annotations. Experimental results show that the two baseline models enhanced with our boundary enhancement scheme outperform state-of-the-art methods, achieving the highest Dice scores on three datasets: TN3k 86.41% (85.45% without Bor-decoder), DDTI 82.97% (82.54%), and FLUIS 83.82% (82.43%). Furthermore, our method significantly improves the detection of small and multiple targets in ultrasound images, enhancing clinical diagnostic accuracy and supporting more reliable medical assessment.}, archive = {J_ACCESS}, author = {Xianghao Cui and Qingjing Fei and Ruixuan Li and Wen Xiong and Fang Hou and Zhi Chen and Qi He}, doi = {10.1109/ACCESS.2025.3566204}, journal = {IEEE Access}, pages = {80355-80370}, shortjournal = {IEEE Access}, title = {Border feature enhancement in ultrasound image segmentation}, volume = {13}, year = {2025}, }
• (2025). Integrating a fuzzy fitness function in genetic programming to generate breast tissue segmentation models. ACCESS, 13, 80340-80354. (www)

  @article{ , abstract = {Genetic programming (GP) and fuzzy logic are used to automatically segment mammography images. GP allows the evolution of optimized segmentation models, guided by a fuzzy logic-based fitness function that incorporates medical criteria to improve the consistency and accuracy of the segmentation process. Unlike conventional approaches, this function optimizes the segmentation and provides a descriptive representation of the breast tissue, allowing a closer evaluation to that performed by specialists. The proposed method was evaluated in the INbreast and BCDR databases, obtaining a Jaccard index of 0.82 and 0.78, respectively, and a comparative analysis was performed using ROC curves, reaching an AUC of 0.91 in INbreast and 0.89 in BCDR, demonstrating the model’s ability to discriminate between fibroglandular and fat tissue. Its performance was compared with state-of-the-art methods, such as LIBRA, hybrid segmentation with Fuzzy C-Means, and NASGP-Net, showing that integrating fuzzy logic in genetic programming to lead the search allows competitive results with a lower computational burden. These results demonstrate the impact of fuzzy fitness functions in the evolution of segmentation models, highlighting the effectiveness of this approach in improving the segmentation and classification of medical images, in addition to the descriptive capabilities inherent to the fuzzy fitness function.}, archive = {J_ACCESS}, author = {Ingrid-Aurora Valencia-Hernández and Carlos Alberto Reyes García and Alicia Morales-Reyes and Gabriela del C. Lopez-Armas and José-Antonio Fuentes-Tomás and Efrén Mezura-Montes}, doi = {10.1109/ACCESS.2025.3565462}, journal = {IEEE Access}, pages = {80340-80354}, shortjournal = {IEEE Access}, title = {Integrating a fuzzy fitness function in genetic programming to generate breast tissue segmentation models}, volume = {13}, year = {2025}, }
• (2025). Communication modeling approaches in energy system applications: A systematic overview. ACCESS, 13, 80329-80339. (www)

  @article{ , abstract = {The integration and consideration of communication networks in energy system studies is of critical importance due to the mutual dependencies that exist between the systems. These interdependencies have an impact on the overall performance, stability, and robustness of the energy system. There is no comprehensive guide or overview for integrating communication into power system applications, despite its recognized importance and the wide variety of existing solutions that take into account interdependencies. By providing a detailed overview, characterization, and classification of existing approaches to communication modeling, this paper contributes to filling this gap in existing guidelines or general descriptions for the integration of communication in power system applications. In addition to discussing the multiple objectives associated with different focus for modeling communication, we consider the required data and system perspectives. Therefore, this paper provides guidance on the selection of a suitable and applicable communication modeling approach, given the specific objective and prerequisite.}, archive = {J_ACCESS}, author = {Malin Radtke and Emilie Frost and Astrid Nieße and Sebastian Lehnhoff}, doi = {10.1109/ACCESS.2025.3566594}, journal = {IEEE Access}, pages = {80329-80339}, shortjournal = {IEEE Access}, title = {Communication modeling approaches in energy system applications: A systematic overview}, volume = {13}, year = {2025}, }
• (2025). Microstate co-occurrence matrices-based brain activity pattern changes analysis on chronic tinnitus subjects. ACCESS, 13, 80316-80328. (www)

  @article{ , abstract = {Chronic tinnitus is a subjective condition where individuals experience persistent noise or ringing without external stimuli, impacting daily life, sleep, and mental health. It affects over 10% of the global population, with 2% of adults experience severe distress. To better understand its neural mechanisms, we propose a novel approach combining co-occurrence matrix with EEG microstate analysis. Specifically, we quantified EEG microstate stability and transition frequences using co-occurrence probability (CP) and calculated the relative difference proportion of CP (DPCP) to compare tinnitus and healthy subjects under opening-eye (OE) and closing-eye (CE) conditions. Results show that tinnitus patients exhibit increased stability of microstate D (DPCP =112.2%, CP =74.67%, p < 0.01) and stronger interactions between microstates C and D (DPCP $\ge 91.43$ %, p < 0.05), indicating abnormal salience and executive control network activity. Meanwhile, microstates A and B decrease significantly (DPCP $\lt -34.78$ %, p < 0.01), suggesting impaired auditory/visual processing and default mode network function. In contrast, under CE, tinnitus patients show reduced microstate C stability (DPCP $= -51.72$ %, CP $= -25.97$ %, p < 0.05) and increased occurrence of microstates A and B (DPCP >45%, p < 0.05), reflecting altered network adaptability. Unlike healthy individuals, tinnitus patients exhibit minimal changes between OE and CE, indicating the reduction of brain flexibility. This study advances EEG microstate analysis by integrating co-occurrence matrix algorithms, offering a direct and comprehensive assessment of microstate stability and interactions. The findings provide new insights into tinnitus-related brain network dysfunction and suggest EEG microstates co-occurrence probabilities as potential neurophysiological markers for diagnosis and intervention.}, archive = {J_ACCESS}, author = {Yingying Wang and Gui Yang and Hongyu Liu and Xin Huang and Shuqing Han and Zhixiang Gu and Liyang Shao and Lei Zhang and Yuan Tao}, doi = {10.1109/ACCESS.2025.3562080}, journal = {IEEE Access}, pages = {80316-80328}, shortjournal = {IEEE Access}, title = {Microstate co-occurrence matrices-based brain activity pattern changes analysis on chronic tinnitus subjects}, volume = {13}, year = {2025}, }
• (2025). Low-latency wireless LAN relay method for motor system control in a robot arm frame. ACCESS, 13, 80308-80315. (www)

  @article{ , abstract = {Various robot arms have been introduced in factories, and their control is facilitated by numerous cables, which can have drawbacks such as limiting the arm’s range of motion. Therefore, wireless control of motors is desirable. This study proposed a wireless relay method for controlling all motors with low latency, even in an environment with poor propagation. Packet error rates (PERs) of wireless communication between a central controller outside a robot arm frame and a wireless module inside the robot arm frame were evaluated, revealing that some angles of the robot arm frame resulted in markedly higher PERs. When each motor is controlled via its own wireless module, all the wireless modules must communicate with a central controller having low latency. To resolve this problem, some wireless modules relay control signals to the poorest-performing wireless module while others relay the state signals of the poorest-performing wireless module to the central controller. State signals include the encoder value of the motor. Low-latency relay is achieved by regulating the timing of transmission for each wireless module. This paper assumes that the central controller must control the robot arm in a closed loop with a response time within a control cycle of 10 ms. When using the proposed method for six axes with wireless LAN, the response time was less than 10 ms. Furthermore, the proposed method reduced the maximum value of the overshoot by a factor of around 15 compared with the method without using any relay.}, archive = {J_ACCESS}, author = {Tsuyoshi Kogawa and Toshihisa Nabetani and Tomoya Tandai}, doi = {10.1109/ACCESS.2025.3563694}, journal = {IEEE Access}, pages = {80308-80315}, shortjournal = {IEEE Access}, title = {Low-latency wireless LAN relay method for motor system control in a robot arm frame}, volume = {13}, year = {2025}, }
• (2025). Dynamic real-time anchor selection for accurate UWB indoor positioning-based deep neural networks. ACCESS, 13, 80283-80307. (www)

  @article{ , abstract = {In wireless localization systems, enhancing location estimation performance is critical, particularly in challenging environments, such as military urban operations and emergency response scenarios. Ultra-wideband (UWB) positioning systems using two-way-ranging (TWR) schemes avoid synchronization issues, but face challenges related to scalability, anchor selection, and poor channel characteristics. Existing methods often rely on exhaustive geometric calculations, leading to inefficiencies in dynamic and time-critical scenarios. This paper proposes a lightweight sequential branching deep network for dynamic indoor positioning (LSB-DIP Net) is proposed to address these challenges. By integrating multi-scale feature extraction, sequential learning, and advanced activation functions with the conventional linearized least squares method, the LSB-DIP Net enables robust, accurate, and dynamic UWB positioning in real-time. The model effectively mitigates non-line-of-sight (NLOS) ranging errors, evaluates anchor channel quality online, and selects optimal anchor combinations, ensuring scalability and adaptability for diverse deployment scenarios. The proposed approach demonstrates exceptional performance in dynamic setups, achieving low mean squared error (MSE) of 0.0051m2, high accuracy in identifying anchor channels of 99.44%, with a maximum positional error of less than 0.17 m in harsh environments. Validated across public datasets, the system ensures generalizability and outperforms state-of-the-art counterparts in the market, making it a reliable tool for real-time applications in communication and navigation systems.}, archive = {J_ACCESS}, author = {Ammar Fahem Majeed and Rashidah Arsat and Muhammad Ariff Baharudin and Nurul Mu’Azzah Abdul Latiff and Abbas Albaidhani}, doi = {10.1109/ACCESS.2025.3563815}, journal = {IEEE Access}, pages = {80283-80307}, shortjournal = {IEEE Access}, title = {Dynamic real-time anchor selection for accurate UWB indoor positioning-based deep neural networks}, volume = {13}, year = {2025}, }
• (2025). A TMR sensor array for non-invasive current measurement in three-phase cable and error analysis. ACCESS, 13, 80272-80282. (www)

  @article{ , abstract = {The inevitable trajectory of future distribution network evolution is characterized by automation and digitization. With the development of tunnel magnetoresistance (TMR) sensor technology, the application of non-invasive and non-contact current detection technology is receiving more and more attention. Based on the analysis of the magnetic field around the three-phase cable, this paper presents a non-invasive current measurement system using a magnetic field sensor array. The system employs 8 TMR sensors arranged in a circular array surrounding the measured cable to reconstruct the currents flowing in individual conductors within the cable. Characteristics of magnetic field distribution in the external space of cables and its influence on the measurement error were investigated by finite element simulation. The performance of the TMR sensors was tested using a dedicated platform to ensure stable measurements. The sensor array was calibrated using the method proposed for sensitivity, phase shift, spatial position, and direction deflection angle to minimize measurement errors. The current reconstruction method was introduced by minimizing the difference between the measured and calculated magnetic flux density using a stochastic optimization algorithm. Simulation and experimental results demonstrate that the proposed system can accurately reconstruct current with an error of less than 2% after calibration, highlighting its potential for non-invasive current monitoring in three-phase power cables.}, archive = {J_ACCESS}, author = {Ruixi Luo and Hui He and Qiang Shi and Junyi Cai and Ziyang Ye and Xiang Zhang}, doi = {10.1109/ACCESS.2025.3567698}, journal = {IEEE Access}, pages = {80272-80282}, shortjournal = {IEEE Access}, title = {A TMR sensor array for non-invasive current measurement in three-phase cable and error analysis}, volume = {13}, year = {2025}, }
• (2025). AIoT monitoring for early identification of diseases in grapevines: Complete study. ACCESS, 13, 80258-80271. (www)

  @article{ , abstract = {This study explores the application of the Artificial Intelligence of Things (AIoT) in viticulture for the early detection of grapevine diseases. By integrating Internet of Things (IoT) sensors with machine learning algorithms, the system is designed to detect potential grapevine pathogens in real time. Deployed at the Murfatlar vineyard in Romania, which grows Cabernet Sauvignon and Sauvignon Blanc, the system allows for proactive disease management, thus improving grapevine health and reducing crop losses. IoT sensors are installed in the field to collect real-time data on grapevine health, which is then transmitted to the cloud for storage and analysis. Machine learning (ML) algorithms, running on a server with an NVIDIA R3900 card, process this data to predict potential infections caused by pathogens such as Plasmopara viticola, Uncinula necator, and Botrytis. Cloud computing facilitates data processing and real-time visualization, allowing farmers to make timely, data-driven decisions for disease control. The paper outlines the hardware and software components that constitute the diagnostic system.}, archive = {J_ACCESS}, author = {Mihaela Hnatiuc and Domnica Alpetri and Sorin-Robertino Sintea and Bogdan Hnatiuc and Gabriel Margarit Raicu and Mirel Paun and Ionica Dina}, doi = {10.1109/ACCESS.2025.3567454}, journal = {IEEE Access}, pages = {80258-80271}, shortjournal = {IEEE Access}, title = {AIoT monitoring for early identification of diseases in grapevines: Complete study}, volume = {13}, year = {2025}, }
• (2025). Fully automated bladder volume measurement system using a T-shaped ultrasound probe and free-hand motion-based 3D reconstruction. ACCESS, 13, 80247-80257. (www)

  @article{ , abstract = {Accurate measurement of bladder volume is critical for the effective management of urinary diseases. Among the available modalities, ultrasound imaging is favored due to its cost-effectiveness, non-ionizing nature, and capability to provide real-time bladder volume measurements. To enhance convenience in clinical practice, ultrasound bladder scanners have been developed and adopted in various settings. However, wobbling probe devices present significant limitations, including their inability to provide real-time images, the requirement for pre-scan alignment to ensure precise probe positioning, and the necessity for multiple volume estimations to achieve measurement accuracy. Additionally, conventional image-based estimations utilizing standard ultrasound machines or recently developed bladder scanners face challenges related to the use of shape coefficients. These methods typically rely on two image planes, which fail to fully capture the bladder’s shape, resulting in considerable errors in volume estimation when compensating for shape with coefficients. To overcome these limitations, this study proposes a novel automated bladder volume measurement technique that employs a T-shaped cross-array ultrasound probe in conjunction with free-hand motion-based 3D reconstruction. The T-shaped cross-array enables the simultaneous acquisition of sagittal and transverse view images of the bladder. By moving the probe at the suprapubic region, continuously acquired sagittal images can be combined into a 3D volume, with motion compensation achieved via the transverse images. Consequently, the proposed method can accurately reconstruct the entire bladder and measure its volume without the need for shape correction coefficients, while also reducing errors associated with ultrasound probe positioning. The proposed methodology was validated through phantom experiments simulating both spherical and irregular bladder shapes. The results demonstrated an error rate ranging from -8.8% to +5.6% when compared to the ground truth, indicating the method’s high accuracy. These results indicate that the proposed method, in conjunction with the developed array and system, has the potential to reduce the necessity for catheterization in clinical settings and enhance clinical decision-making.}, archive = {J_ACCESS}, author = {Daehyun Park and Hyunwoo Cho and Ilseob Song and Jihun Jang and Yangmo Yoo}, doi = {10.1109/ACCESS.2025.3566313}, journal = {IEEE Access}, pages = {80247-80257}, shortjournal = {IEEE Access}, title = {Fully automated bladder volume measurement system using a T-shaped ultrasound probe and free-hand motion-based 3D reconstruction}, volume = {13}, year = {2025}, }
• (2025). Local, global, and saga transactions for SOA services and microservices (∗Services). ACCESS, 13, 80206-80246. (www)

  @article{ , abstract = {Transactions are a key issue in enterprise applications. Considering SOA and microservices as some of the most prevalent solutions for enterprise applications, transaction management differs significantly between these two solutions. This paper defines MOF metamodels for characterizing transaction management in SOA services and microservices. To obtain these metamodels, the paper also defines additional MOF metamodels: one for the main components of the multitier architecture, one for the hexagonal architecture, one that relates both architectures, one for SOA services, and one for microservices. The paper also includes the design of a simple transaction manager that illustrates the details of how to implement global transactions for SOA services. This transaction manager, or other simpler ones, can also be used with microservices. In addition, several types of transactions are analyzed and implemented in SOA services and microservices to compare the differences between both approaches. The paper is intended as a guide for those lecturers with subjects focused on enterprise application architectures. It can also be used by developers looking for a detailed comparison between SOA services and microservices to decide which is best for their needs.}, archive = {J_ACCESS}, author = {Antonio Navarro}, doi = {10.1109/ACCESS.2025.3566230}, journal = {IEEE Access}, pages = {80206-80246}, shortjournal = {IEEE Access}, title = {Local, global, and saga transactions for SOA services and microservices (∗Services)}, volume = {13}, year = {2025}, }
• (2025). IOTA-assisted self-sovereign identity framework for decentralized authentication and secure data sharing. ACCESS, 13, 80191-80205. (www)

  @article{ , abstract = {The Internet of Things (IoT) demands robust mechanisms for secure communication and trust establishment among connected devices. Traditional Public Key Infrastructure (PKI) solutions face limitations in scalability, centralization and single points of failure. These limitations hinder their effectiveness in dynamic IoT environments. To address these challenges, this paper introduces a new decentralized authentication protocol for secure identity management and data exchange in IoT, called ISIF (IOTA-Assisted Self-Sovereign Identity Framework). This framework is based on Self-Sovereign Identity (SSI) principles and leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to enable mutual authentication without relying on centralized authorities. DIDs ensure decentralized identity management and VCs provide verifiable context-specific claims. This dual-layer approach enables robust and attribute-based authentication, which reduces the risk of unauthorized access and improving interoperability in decentralized IoT environments. ISIF employs the IOTA Tangle as a distributed ledger to manage and verify DIDs and VCs. This offers a decentralized, immutable record that supports efficient and tamper-resistant identity management. ISIF ensures that all interactions within the IoT network are securely authenticated and resilient to tampering. The experimental results show that the framework maintains efficient DID generation and VC issuance times even as network size scales, overcoming the bottlenecks inherent in PKI-based systems. Experimental results demonstrate that ISIF maintains efficient DID generation and VC issuance, even as network size scales. Experimental results show that DID generation time increases from 1.85 ms (for 50 nodes) to 10.81 ms (for 250 nodes), while VC issuance time ranges from 2.66 ms to 13.21 ms. Similarly, VC verification time increases from 3.54 ms to 22.27 ms as the network scales. Despite these increases, the overall end-to-end (E2E) delay remains low (0.16–0.33 ms), ensuring efficient real-time authentication. These findings confirm ISIF’s feasibility for large-scale IoT authentication without performance degradation. Furthermore, the IOTA Tangle’s performance in handling varied payload sizes affirms its suitability for managing block generation and retrieval in IoT, ensuring practical processing times that uphold security and decentralization.}, archive = {J_ACCESS}, author = {Assiya Akli and Khalid Chougdali}, doi = {10.1109/ACCESS.2025.3567137}, journal = {IEEE Access}, pages = {80191-80205}, shortjournal = {IEEE Access}, title = {IOTA-assisted self-sovereign identity framework for decentralized authentication and secure data sharing}, volume = {13}, year = {2025}, }
• (2025). Multi-attribute graph estimation with sparse-group non-convex penalties. ACCESS, 13, 80174-80190. (www)

  @article{ , abstract = {We consider the problem of inferring the conditional independence graph (CIG) of high-dimensional Gaussian vectors from multi-attribute data. Most existing methods for graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. In this paper we provide a unified theoretical analysis of multi-attribute graph learning using a penalized log-likelihood objective function. We consider both convex (sparse-group lasso) and sparse-group non-convex (log-sum and smoothly clipped absolute deviation (SCAD) penalties) penalty/regularization functions. An alternating direction method of multipliers (ADMM) approach coupled with local linear approximation to non-convex penalties is presented for optimization of the objective function. For non-convex penalties, theoretical analysis establishing local consistency in support recovery, local convexity and precision matrix estimation in high-dimensional settings is provided under two sets of sufficient conditions: with and without some irrepresentability conditions. We illustrate our approaches using both synthetic and real-data numerical examples. In the synthetic data examples the sparse-group log-sum penalized objective function significantly outperformed the lasso penalized as well as SCAD penalized objective functions with $F_{1}$ -score and Hamming distance as performance metrics.}, archive = {J_ACCESS}, author = {Jitendra K. Tugnait}, doi = {10.1109/ACCESS.2025.3566959}, journal = {IEEE Access}, pages = {80174-80190}, shortjournal = {IEEE Access}, title = {Multi-attribute graph estimation with sparse-group non-convex penalties}, volume = {13}, year = {2025}, }
• (2025). Channel coding QoS analysis in 5G V2X dynamic propagation models. ACCESS, 13, 80149-80173. (www)

  @article{ , abstract = {Vehicle-to-everything (V2X) communications is a crucial area of wireless communications and is of primary interest for automakers, smart cities, municipal services, traffic planners, and law enforcement authorities. V2X systems are beneficial for increasing driving safety, driving time, the ability to avoid accidents, traffic congestion and energy saving. However, the V2X communication is not only characterized by high mobility and but also by rapid and unexpected changes in the communication environment, which drastically degrade the fading features and the quality of the communication channel. Maintaining tolerable quality of service (QoS) in a V2X communication environment is therefore essential. Thus, under the premise of ensuring channel quality, channel coding is employed as the predominant process enhancing reliability by introducing high data redundancy. In this study, we investigate the impact of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes, and 5G-NR low-density parity-check (LDPC) codes on the QoS of a V2X communication system. To this end, we propose space and time-varying V2X propagation models, based on real traffic data, that simulate V2X communication in real urban and highway environments and various traffic density conditions, based on the 3rd-Generation Partnership Project (3GPP) specifications. Through these simulations we investigate the frame error rate (FER) performance and QoS efficiency of the V2X dynamic propagation models which are achieved by the 4G-LTE and 5G-NR channel coding schemes. Our analysis introduces novel large-scale V2X fading models and ultimately proves that turbo-based coding schemes have superior performance for small-frame 5G V2X communication.}, archive = {J_ACCESS}, author = {Dimitrios Chatzoulis and Costas Chaikalis and Apostolos Xenakis and Dimitrios Kosmanos and Kostas E. Anagnostou}, doi = {10.1109/ACCESS.2025.3566607}, journal = {IEEE Access}, pages = {80149-80173}, shortjournal = {IEEE Access}, title = {Channel coding QoS analysis in 5G V2X dynamic propagation models}, volume = {13}, year = {2025}, }
• (2025). Pretreatment methods for enhancing machine learning performance on metabolomics data. ACCESS, 13, 80133-80148. (www)

  @article{ , abstract = {Pretreatment methods are critical for metabolomics data analysis, yet their impact on machine learning performance remains insufficiently explored. This paper introduces a novel approach to systematically evaluate eight pretreatment methods—Centering, Autoscaling, Range Scaling, Pareto Scaling, Vast Scaling, Level Scaling, Log Transformation, and Power Transformation—across four diverse metabolomics datasets (MTBLS161, MTBLS547, ST001000, and ST001047). The novelty of this paper lies in its comprehensive assessment of how these methods influence model-specific performance, particularly for Gradient Boosting Classifier, Multi-Layer Perceptron, Support Vector Classifier, and Random Forest. The findings reveal that Vast Scaling and Autoscaling consistently outperform other methods, enhancing classification accuracy and robustness by effectively normalizing metabolite intensities and preserving variance. Simpler methods, like Centering and Log Transformation, offered limited improvements in high-dimensional datasets. This study establishes a novel framework for designing tailored preprocessing pipelines, advancing metabolomics data analysis and enabling the extraction of meaningful biological insights through machine learning.}, archive = {J_ACCESS}, author = {Rustam}, doi = {10.1109/ACCESS.2025.3567153}, journal = {IEEE Access}, pages = {80133-80148}, shortjournal = {IEEE Access}, title = {Pretreatment methods for enhancing machine learning performance on metabolomics data}, volume = {13}, year = {2025}, }
• (2025). A fine-grained image classification method based on ConvNeXt heatmap localization and contrastive learning. ACCESS, 13, 80123-80132. (www)

  @article{ , abstract = {Aiming at the challenges of high intra-class disparity and low inter-class disparity in fine-grained image classification, a multi-branch fine-grained image classification method based on ConvNeXt network as the backbone and using GradCAM heatmap for cropping and attention erasure is proposed. This method uses GradCAM to obtain the attention heatmap of the network through gradient reflow, locates the region with discriminative features, crops and enlarges the region, and makes the network focus on local deeper features. In addition, supervised contrastive learning is introduced to expand between-class differences and reduce intra-class differences. Finally, a heatmap attention erasure operation is performed to enable the network to focus on other regions useful for classification while focusing on the most discriminative features. The proposed method achieved 91.8%, 94.9%, 94.0% and 94.4% classification accuracy on CUB-200-2011, Stanford Cars, FGVC Aircraft, and Stanford Dogs datasets, respectively, which is better than many mainstream fine-grained image classification methods. And our method proposed in this paper achieves stop-3 and top-1 classification accuracy on the CUB-200-2011 and Stanford Dogs datasets, respectively.}, archive = {J_ACCESS}, author = {Qiyu Pan and Kaiyuan Liu and Shijie Zheng and Gaocai Wang}, doi = {10.1109/ACCESS.2025.3567488}, journal = {IEEE Access}, pages = {80123-80132}, shortjournal = {IEEE Access}, title = {A fine-grained image classification method based on ConvNeXt heatmap localization and contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Multi-task reinforcement learning based on parallel recombination networks. ACCESS, 13, 80113-80122. (www)

  @article{ , abstract = {Multi-task Reinforcement learning is a key current trend in the field of reinforcement learning. It can accomplish multiple tasks using a single network, which is superior to single-task learning in integrating information from different tasks. However, uncertainty remains on the issue of how to effectively share parameters across tasks in the network. To address this problem, this paper proposes a ’soft parallel recombination network’ approach, which can share task information across network layers without being limited to between adjacent layers, thus enhancing the information sharing capability of the network. Specifically, the types of multi-task learning in this paper include various manipulator control tasks executed in the Meta-world environment, such as pick-and-place, push, and stacking. For optimal performance, a weight network is introduced which automatically determines the optimal path for each task and outputs the probability of each module being selected. The proposed method efficiently learns the relationships between tasks from parallel recombination networks and determines the optimal path for a task through a weight network. Further, the weight relationship between the current training samples and the current strategy is found, which improves the training efficiency in combination with the parallel recombination network. By combining the proposed ’Soft Parallel Recombination Network’ method with the SAC algorithm (PRSAC) and validating it on the Meta-world multi-task training platform, the experimental results demonstrate that the proposed method significantly outperforms existing baseline algorithms in terms of sample efficiency and performance.}, archive = {J_ACCESS}, author = {Manlu Liu and Qingbo Zhang and Weimin Qian}, doi = {10.1109/ACCESS.2024.3449072}, journal = {IEEE Access}, pages = {80113-80122}, shortjournal = {IEEE Access}, title = {Multi-task reinforcement learning based on parallel recombination networks}, volume = {13}, year = {2025}, }
• (2025). Research on physical reliability rendering algorithm. ACCESS, 13, 80103-80112. (www)

  @article{ , abstract = {Computer graphics real-time rendering technology is developing rapidly. During the development process, the requirements for rendering realism and speed are getting higher. Physics-based rendering (PBR) uses a more physical way to simulate light, which can achieve a more realistic rendering effect and improve the reliability of rendering algorithm. In the core sampling part of PBR algorithm, a large amount of precomputation is required. To improve the computational efficiency, this paper proposes a precomputation-based Sobel algorithm with uniform spherical sampling method from the diffuse light irradiance map of PBR algorithm, which uses the Sobel algorithm with better uniformity to generate low differential sequences and uniform spherical sampling to obtain the sampling vector. In terms of sampling efficiency, it is better than the traditional sampling method. Compared with the original algorithm, the improved algorithm has basically the same rendering effect under the premise of improving reliability, and can be better used for real-time rendering.}, archive = {J_ACCESS}, author = {Xingyu Wang and Lingzeng Meng}, doi = {10.1109/ACCESS.2024.3430535}, journal = {IEEE Access}, pages = {80103-80112}, shortjournal = {IEEE Access}, title = {Research on physical reliability rendering algorithm}, volume = {13}, year = {2025}, }
• (2025). The role of education in building national soft power: An empirical analysis from a global perspective using deep neural networks. ACCESS, 13, 80088-80102. (www)

  @article{ , abstract = {In recent years, deep learning technologies have rapidly transformed various sectors, particularly in fields such as healthcare, finance, and education. These advancements have led to significant achievements in predictive modeling, data analysis, and decision-making processes. However, there remains a need for more sophisticated models that can accurately capture complex relationships in dynamic, real-world datasets. To address this, this paper proposed a novel deep neural network (DNN) model designed to predict soft power based on educational factors. The proposed method involves collecting global educational data, including international student enrollment, research output, and the presence of top-ranked universities, and using this data to train a DNN model for soft power prediction. First, we preprocess the data by standardizing the features and handling missing values. Secondly, we build and train a DNN with multiple hidden layers to capture nonlinear relationships between educational factors and soft power scores. Thirdly, we evaluate the model’s performance using several performance metrics, such as accuracy, RMSE, MAE, and precision. Finally, we compare the performance of our proposed DNN model with other machine learning algorithms, such as Random Forest and Support Vector Machines, demonstrating superior predictive accuracy. The experimental results indicate that the DNN model outperforms others, achieving the highest accuracy of 95.83%, exceptional sensitivity (94.45%), and specificity (96.26%). Its F1 Score of 95.56%, precision (95.87%), and recall (94.43%) reflect balanced and robust classification. The model also shows superior reliability with an MCC of 0.923 and an AUC of 0.978. Low error rates, including MAE (0.11), MSE (0.05), and RMSE (0.22), highlight its accuracy and predictive precision. The experimental results show that the DNN model significantly outperforms traditional machine learning models, providing a more accurate and robust prediction of soft power.}, archive = {J_ACCESS}, author = {Yun Bai}, doi = {10.1109/ACCESS.2025.3562589}, journal = {IEEE Access}, pages = {80088-80102}, shortjournal = {IEEE Access}, title = {The role of education in building national soft power: An empirical analysis from a global perspective using deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Adaptive ruminant optimization with LoRa-based communication for formation control of multiple UAVs. ACCESS, 13, 80076-80087. (www)

  @article{ , abstract = {In a dynamic environment with mountains and hazardous peaks, avoiding collisions and maintaining the desired formation is a crucial problem. This paper addresses this problem by presenting a novel formation control strategy of a cluster of UAVs in three different scenarios. The first scenario is designed to test the designed algorithm and hence contains no obstacles. The second scenario introduces some obstacles in the form of mountains to see whether the proposed algorithm can avoid the obstacles while maintaining the formation. In the last scenario, all the UAVs join together in one big cluster and have to avoid the obstacles while maintaining the formation. To design the environment for the scenarios, this study uses graph theory. To address the aforementioned scenarios, this paper offers a novel strategy by integrating a bio-inspired algorithm called the Adaptive Ruminant Optimization Algorithm (AROA) with the Long Range (LoRa) communication to achieve the formation control of multiple UAVs. Initially, AROA offers the best agents of each of the swarm. Then, the proposed method helps choose the best agent to be the leader for each of the swarm. The leader of each swarm finds the best trajectory for each swarm. LoRa-based networking technique is used for the connectivity between the UAVs. In addition, this study uses basis splines (B-splines) to smooth the planned trajectories of UAVs. Lastly, simulations demonstrate the better convergence and efficiency of the designed strategy by comparing it with classic algorithms. The simulations also show that the proposed method successfully maintains formation control in all three scenarios.}, archive = {J_ACCESS}, author = {Muhammad Aamir Khan and Zain Anwar Ali and Muhammad Haris Muneer and Raza Hasan}, doi = {10.1109/ACCESS.2025.3565815}, journal = {IEEE Access}, pages = {80076-80087}, shortjournal = {IEEE Access}, title = {Adaptive ruminant optimization with LoRa-based communication for formation control of multiple UAVs}, volume = {13}, year = {2025}, }
• (2025). Compressing neural networks on limited computing resources. ACCESS, 13, 80063-80075. (www)

  @article{ , abstract = {Network compression is a crucial technique for applying deep learning models to edge or mobile devices. However, the cost of achieving higher benchmark performance through compression is continuously increasing, making network compression a significant burden—especially for small industries focused on developing compact models. Specifically, existing network compression techniques often require extensive computational resources, rendering them impractical for edge devices and small-scale applications. To democratize network compression, we propose a general-purpose framework that combines novel filter pruning and knowledge distillation techniques. First, unlike conventional filter pruning methods based on static heuristics and costly neural architecture search (NAS)-based approaches, our method leverages meta-learning for rapid and fine examination of the importance of each gate. This enables rapid and stable sub-network discovery, significantly improving the pruning process. Second, to minimize the computational cost of knowledge distillation, we introduce a synthetic teacher assistant that leverages precomputed fixed knowledge—referring to the stored feature maps/logits of the teacher network. By leveraging fixed knowledge, we mitigate the cost incurred by the teacher network and facilitate the transmission of fixed knowledge to the student via synthetic teacher assistants, thereby preventing distribution collapse. Our proposed framework dramatically reduces the compression overhead while maintaining high accuracy, achieving a 55.2% reduction in FLOPs of ResNet-50 trained on ImageNet while preserving 76.2% top-1 accuracy with only 199 GPU hours—significantly lower than previous state-of-the-art methods. Overall, our framework democratizes deep learning compression by offering a cost-effective and computationally feasible solution, enabling broader adoption in low-resource environments.}, archive = {J_ACCESS}, author = {Seunghyun Lee and Dongjun Lee and Minju Hyun and Heeje Kim and Byung Cheol Song}, doi = {10.1109/ACCESS.2025.3567102}, journal = {IEEE Access}, pages = {80063-80075}, shortjournal = {IEEE Access}, title = {Compressing neural networks on limited computing resources}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on the negative impacts of AI-generated virtual digital humans. ACCESS, 13, 80047-80062. (www)

  @article{ , abstract = {AI-generated virtual digital humans are profoundly reshaping digital innovation and consumer ecosystems. However, the dynamic evolution of their application scenarios, inherent conceptual ambiguities, and continuously advancing technological features may collectively engender negative consumer perceptions. It is imperative to systematically examine the comprehensive impact, inherent technological limitations, and emergent challenges associated with virtual digital humans. Employing the Theory, Context, Characteristics, and Metrics (TCCM) analytical framework, this study conducts a systematic review of 51 authoritative journal articles published through January 2025. The findings reveal the negative effects of AI-generated virtual digital humans on consumers and compare the relationship between hallucinations and serendipity. Additionally, this study further explores the dimensions of assessing these negative impacts. Finally, we outline the potential avenues for future research.}, archive = {J_ACCESS}, author = {Yongzhong Yang and Shihui Li and Shuoli Qiu}, doi = {10.1109/ACCESS.2025.3564041}, journal = {IEEE Access}, pages = {80047-80062}, shortjournal = {IEEE Access}, title = {A systematic literature review on the negative impacts of AI-generated virtual digital humans}, volume = {13}, year = {2025}, }
• (2025). Thermally balanced DPWM method with neutral point voltage balancing capability for single-phase five-level T-type NPC inverters. ACCESS, 13, 80033-80046. (www)

  @article{ , abstract = {Discontinuous pulse width modulation (DPWM) is useful to improve the efficiency of a single-phase five-level T-type neutral-point-clamped (5LT-NPC) inverter and the reliability of the power devices. However, depending on how it is applied, the thermal loading imbalance between the two legs of the 5LT-NPC inverter occurs, which results in a reduction in the reliability improvement effect in the power devices by the DPWM. Furthermore, the DPWM strategy should be equipped with the ability to balance the neutral point voltage since the imbalance between the voltages of the two capacitors leads to the distortion of the output current and also the failure of the DC-link capacitor and power devices if its voltage is over the rated value. This paper proposes a Thermally Balanced DPWM (TB-DPWM) method with neutral point voltage balancing capability for the 5LT-NPC. Through the proposed method, the thermal loading balancing between the two legs can be achieved simply without consideration of the power factor. Furthermore, it is also able to balance the neutral point voltage while maintaining the DPWM. The effectiveness and feasibility of the proposed method are verified through the simulations and experiments.}, archive = {J_ACCESS}, author = {Kyu-Jin Min and Ui-Min Choi}, doi = {10.1109/ACCESS.2025.3566732}, journal = {IEEE Access}, pages = {80033-80046}, shortjournal = {IEEE Access}, title = {Thermally balanced DPWM method with neutral point voltage balancing capability for single-phase five-level T-type NPC inverters}, volume = {13}, year = {2025}, }
• (2025). The impact of virtual reality on technical training: A case study in live-line maintenance. ACCESS, 13, 80019-80032. (www)

  @article{ , abstract = {This paper presents an evaluation of the effectiveness of virtual reality (VR) as a training tool for electricians working in high-risk environments within the energy infrastructure sector. A study was conducted involving 124 participants, divided into four groups, to compare the outcomes of VR-based training, traditional training, and a combination of both methods. The Virtual Electrician Training system, a comprehensive VR platform featuring realistic 3D models and interactive scenarios, was used to simulate real-world tasks. Results indicate that participants who received VR training followed by traditional training demonstrated the highest knowledge retention and skill acquisition, suggesting that VR is an effective introductory training tool. The study also identified challenges such as user discomfort and varying ease of use, highlighting the need for further refinement of VR systems. The findings support the integration of VR into technical training programs, with potential benefits including improved safety, enhanced learning outcomes, and cost savings. This research contributes to the growing body of knowledge on the application of VR in industrial training and offers insights into optimizing training sequences for maximum effectiveness.}, archive = {J_ACCESS}, author = {Filip Górski and Jakub Gapsa and Damian Grajewski and Przemysław Zawadzki and Mikołaj Maik and Paweł Sobociński and Przemysław Starzyński and Rafał Wojciechowski and Krzysztof Walczak}, doi = {10.1109/ACCESS.2025.3566937}, journal = {IEEE Access}, pages = {80019-80032}, shortjournal = {IEEE Access}, title = {The impact of virtual reality on technical training: A case study in live-line maintenance}, volume = {13}, year = {2025}, }
• (2025). Navigating ethical dimensions in the metaverse: Challenges, frameworks, and solutions. ACCESS, 13, 79996-80018. (www)

  @article{ , abstract = {The Metaverse is rapidly evolving into a transformative digital ecosystem, bringing with it unprecedented opportunities and a complex array of ethical challenges. This narrative review, based on an in-depth analysis of 105 full publications, explores the key ethical themes associated with the Metaverse, including privacy and data security, identity and behavior, digital inclusivity, mental and physical health, ethical AI, content moderation, intellectual property, governance, environmental sustainability, harassment, cultural representation, and economic implications. Proposed solutions for these challenges encompass privacy-by-design frameworks, robust identity verification systems, equitable access initiatives, explainable AI, and blockchain-based intellectual property protections. Additionally, the review examines governance and legal initiatives, such as frameworks developed by the World Economic Forum, European Commission, United Nations, and IEEE standards. These efforts aim to enhance transparency, ensure universal accessibility, promote ethical AI, and foster safety within the Metaverse. By synthesizing insights from this extensive body of literature, this review offers a thorough exploration of the ethical dimensions of the Metaverse and presents practical recommendations for fostering responsible and inclusive digital ecosystems.}, archive = {J_ACCESS}, author = {Mousa Al-Kfairy and Saed Alrabaee and Omar Alfandi and Amr Taha Mohamed and Souheil Khaddaj}, doi = {10.1109/ACCESS.2025.3564498}, journal = {IEEE Access}, pages = {79996-80018}, shortjournal = {IEEE Access}, title = {Navigating ethical dimensions in the metaverse: Challenges, frameworks, and solutions}, volume = {13}, year = {2025}, }
• (2025). A robust resource-aware framework for YOLO-based object detection in dynamic and multi-platform resource-constrained environments. ACCESS, 13, 79977-79995. (www)

  @article{ , abstract = {Efficient management of resources such as CPU, RAM, and disk storage is a critical challenge for object detection systems that rely on multi-camera video streaming, especially for resource-constrained devices. These constraints frequently result in the risk of overheating and potential system failure during high workloads. This paper presents a novel Resource-Aware Framework (RA-Framework), designed to optimize resource allocation using dynamic adaptation mechanisms. The RA-Framework dynamically manages CPU, RAM, and disk storage resources for YOLO-based object detection, supporting multiple camera streams and scheduling mechanisms. The framework was implemented in resource-constrained environments using various YOLO models with single and dual-camera setups. Experimental results showed that the RA-Framework successfully reduced average CPU usage by up to 68.62%, decreased RAM consumption by approximately 13.47%, and extended the time for disk usage to increase by 1% by more than 500% in specific scenarios. In contrast, systems without the RA-Framework experienced higher resource utilization and greater susceptibility to resource scarcity or even overheating.}, archive = {J_ACCESS}, author = {Ary Mazharuddin Shiddiqi and Dini Adni Navastara and Hudan Studiawan and Yasinta Romadhona and Muhammad Fath Mushaffa Azhar and Fayruz Rahma and Arif Bramantoro and Ahmad A. Alzahrani}, doi = {10.1109/ACCESS.2025.3564000}, journal = {IEEE Access}, pages = {79977-79995}, shortjournal = {IEEE Access}, title = {A robust resource-aware framework for YOLO-based object detection in dynamic and multi-platform resource-constrained environments}, volume = {13}, year = {2025}, }
• (2025). Advances in MEMS hydrophone technology. ACCESS, 13, 79955-79976. (www)

  @article{ , abstract = {MEMS (Micro-Electro-Mechanical System) hydrophone, with the advantages of low cost, low power consumption, and miniaturization, has rapid development and wide application in acoustic detection. The complexity of the marine environment and the development of underwater equipment have proposed higher requirements for the performance of MEMS hydrophones. However, the sensor performance of MEMS hydrophones is affected by multiple factors, such as structure, principle, and MEMS processing technology. Many novel MEMS hydrophones with new principles, new structures, and new materials have been established to improve the sensitivity and bandwidth of MEMS hydrophones. In this study, MEMS hydrophones are divided into optical MEMS hydrophones, electrical MEMS hydrophones, and thermal MEMS hydrophones according to the principle used. Additionally, the basic working principles of different MEMS hydrophones are summarized, and the characteristics of MEMS hydrophones with different principles are analyzed. Furthermore, the advantages and disadvantages of different materials and structures are compared regarding the tested performance parameters. The contribution of this study lies in systematically reviewing the development process of three types of MEMS hydrophones, especially the development and improvement of piezoresistive, hot-wire, and interferometric MEMS hydrophones, involving the research hotspots and latest progress. Finally, the characteristics of MEMS hydrophone development are demonstrated, the existing problems are discussed, and the future development trend is prospected.}, archive = {J_ACCESS}, author = {Ming Liu and Lin Zhang and Weiyu Qu and Lianglong da and Tong Liu}, doi = {10.1109/ACCESS.2025.3566557}, journal = {IEEE Access}, pages = {79955-79976}, shortjournal = {IEEE Access}, title = {Advances in MEMS hydrophone technology}, volume = {13}, year = {2025}, }
• (2025). Enhancing Human–Computer interaction with cultural nuance: A deep reinforcement learning perspective. ACCESS, 13, 79940-79954. (www)

  @article{ , abstract = {Human-Computer Interaction (HCI) has drawn a lot of attention lately as one of the most important and thought-provoking uses of artificial intelligence. The computer must consider a number of factors and circumstances to guarantee successful human-computer interaction. In order to improve the HCI model and create a more enduring relationship, it is essential to carefully assess each person’s temperament. The efficacy of sentiment analysis methods integrated into HCI models is examined in this paper in relation to cultural aspects. To achieve this goal, a deep reinforcement learning architecture is presented to correctly categorize people’s temperaments while taking their cultural background into account. In this paradigm, the task of replicating the affective states of basic cultures falls to each deep neural network. A wide variety of ethnic and cultural origins can be simulated by using weighted mixtures of these basic cultural models. Therefore, to accurately evaluate the temperaments of particular individuals, the proposed method uses an autonomous weighting mechanism based on learning to determine the weighted combinations of basic models. A dataset of audiovisual content related to various civilizations was used to evaluate the effectiveness of the suggested methodology. The results of the studies show that adding cultural backgrounds to temperament recognition can result in significant gains, as demonstrated by a 4.6% reduction in detection error over previous models. Furthermore, mood detection error can be decreased by up to 2.95 percent by using reinforcement learning techniques that make use of weighted combinations of basic models. With the mean absolute error of 0.129, the obtained results demonstrate that the proposed model can identify emotional states across different cultures. This demonstrates how well the model enhances the functionality of HCI models.}, archive = {J_ACCESS}, author = {Xiaohui Wang}, doi = {10.1109/ACCESS.2025.3552038}, journal = {IEEE Access}, pages = {79940-79954}, shortjournal = {IEEE Access}, title = {Enhancing Human–Computer interaction with cultural nuance: A deep reinforcement learning perspective}, volume = {13}, year = {2025}, }
• (2025). Discovery latency analysis of ultra-dense internet-of-things networks. ACCESS, 13, 79929-79939. (www)

  @article{ , abstract = {This paper presents a comprehensive performance analysis and optimization of ultra-dense Internet-of-Things (IoT) networks. While the high density of Bluetooth Low Energy (BLE) devices in IoT networks improves wireless coverage and enhances connectivity, these coexisting devices can also lead to increased interference and collisions, degrading device discovery performance and increasing the network’s overall latency. In this work, we provide a mathematical analysis of the discovery latency in densely deployed BLE networks. Our mathematical model constructs a Markov chain model that implements the impacts of packet collisions and duplication on discovery latency. Based on a thorough performance analysis, we propose a novel duplication-avoidance protocol in which BLE devices cooperatively adjust advertising and minimize the network’s overall discovery latency. Our simulation results shows that the proposed novel duplication-avoidance protocol consistently reduces latency compared to the standard discovery protocol in densely deployed BLE networks, with relative improvements ranging from 7.63% to 8.16%.}, archive = {J_ACCESS}, author = {Siti Nur Fatihah and Gilang Raka Rayuda Dewa and Jindae Kim and Illsoo Sohn}, doi = {10.1109/ACCESS.2025.3566924}, journal = {IEEE Access}, pages = {79929-79939}, shortjournal = {IEEE Access}, title = {Discovery latency analysis of ultra-dense internet-of-things networks}, volume = {13}, year = {2025}, }
• (2025). Study on tourism development using CRITIC method for tourist satisfaction. ACCESS, 13, 79915-79928. (www)

  @article{ , abstract = {This paper presents a novel approach for evaluating tourist satisfaction and developing optimized strategies by integrating the CRITIC method, deep learning with Multilayer Perceptron (MLP), and Genetic Algorithms (GA). The CRITIC method was employed to calculate the weights of various satisfaction indicators, such as accommodation quality and local services, based on their variance and correlation. These weights informed the MLP model, which accurately predicted tourist satisfaction with a mean absolute error (MAE) of 0.12 and a root mean square error (RMSE) of 0.18. Using the GA, the study identified optimal strategy combinations that improved satisfaction scores by up to 15% compared to baseline strategies. The integration of CRITIC, MLP, and GA provided a comprehensive framework that not only enhanced the accuracy of satisfaction evaluations but also optimized development strategies effectively. This approach demonstrated significant improvements in tourist satisfaction across various regions, with notable results including an increase in satisfaction scores from 4.0 to 4.3 in specific areas. The paper highlights the efficacy of combining these advanced methods to address complex evaluation and optimization challenges in tourism. Key contributions include the development of a robust, data-driven methodology for strategy optimization and the provision of actionable insights into satisfaction drivers, offering a valuable tool for tourism managers and policymakers. Future research directions involve expanding the methodology to different tourism sectors and incorporating real-time data for even more dynamic evaluations.}, archive = {J_ACCESS}, author = {Xi Yang and Noor Azman Ali and Huam Hon Tat}, doi = {10.1109/ACCESS.2025.3552279}, journal = {IEEE Access}, pages = {79915-79928}, shortjournal = {IEEE Access}, title = {Study on tourism development using CRITIC method for tourist satisfaction}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on deep learning in abdominal imaging: Datasets, techniques, and performance metrics. ACCESS, 13, 79894-79914. (www)

  @article{ , abstract = {Integrating Deep Learning (DL) into abdominal imaging represents a significant leap forward in diagnosing and managing abdominal conditions, offering the potential to transform conventional medical practices. This comprehensive survey explores the application of DL techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN), across various domains of abdominal imaging, including liver, spleen, kidney, and other structures such as subcutaneous adipose tissue (SAT), muscle, viscera, and bone. It discusses the critical role of performance metrics in evaluating model efficacy and clinical applicability. Furthermore, the paper highlights emerging trends in DL, such as integrating multimodal data and exploring unsupervised and semi-supervised learning techniques, which promise to address current limitations and pave the way for future advancements. Ethical considerations, including algorithmic bias, transparency in model development, and equitable patient care, are thoroughly examined to underscore the importance of ethical practices in deploying Artificial Intelligence (AI) technologies in healthcare.}, archive = {J_ACCESS}, author = {Mariem Bellal and Sanaa El Fkihi and Korhan Cengiz and Nikola Ivković}, doi = {10.1109/ACCESS.2025.3566627}, journal = {IEEE Access}, pages = {79894-79914}, shortjournal = {IEEE Access}, title = {A comprehensive survey on deep learning in abdominal imaging: Datasets, techniques, and performance metrics}, volume = {13}, year = {2025}, }
• (2025). Task offloading and computation in hybrid uRLLC and eMBB cognitive radio enabled 5G based multiuser edge computing. ACCESS, 13, 79871-79893. (www)

  @article{ , abstract = {Multi-user edge computing (MEC) is a network architecture that enables cloud computing capabilities at the edge of a network, reducing latency and user equipment’s energy consumption. An MEC system that can efficiently supports both the ultra-Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB) services is crucial in providing a diverse and efficient communication for various Internet of Things (IoT) applications. The current MEC models in literature are either deterministic or based on average-based metric hence, not suitable in a practical scenario where task offloading and computation activities are stochastic processes and, the wireless channel is often not interference-free. In this paper, we study the joint task offloading and computation in a mixed traffic of two 5G-based MEC. We consider user equipment (UE) that are cognitive radio enabled and, whose performance are energy constrained. In view of this, energy efficient MEC is formulated as a stochastic optimization with long-term objective while, taking into consideration the tail distribution of the eMBB queue length. The target is to minimize energy consumption and, maximize the achievable data rate subject to probabilistic and statistical constraint on the eMBB task length based on Extreme Value Theorem (EVT), uRLLC reliability and, system capacity. The performance of the proposed MEC model is studied in terms of the latency, energy consumption, user density, and reliability. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed model over the existing model.}, archive = {J_ACCESS}, author = {Akinbode A. Olawole and Fambirai Takawira and Chabalala S. Chabalala}, doi = {10.1109/ACCESS.2025.3567139}, journal = {IEEE Access}, pages = {79871-79893}, shortjournal = {IEEE Access}, title = {Task offloading and computation in hybrid uRLLC and eMBB cognitive radio enabled 5G based multiuser edge computing}, volume = {13}, year = {2025}, }
• (2025). Improving image embeddings with colour features in indoor scene geolocation. ACCESS, 13, 79860-79870. (www)

  @article{ , abstract = {Embeddings remain the best way to represent image features, but do not always capture all latent information. This is still a problem in representation learning, and computer vision descriptors struggle with precision and accuracy. Improving image embedding with other features is necessary for tasks like image geolocation, especially for indoor scenes where descriptive cues can have less distinctive characteristics. This work proposes a model architecture that integrates image N-dominant colours and colour histogram vectors in different colour spaces with image embedding from deep metric learning and classification perspectives. The results indicate that the integration of colour features improves image embedding, surpassing the performance of using embedding alone. In addition, the classification approach yields higher accuracy compared to deep metric learning methods. Interestingly, different saturation points were observed for image colour-improved embedding features in models and colour spaces. These findings have implications for the design of more robust image geolocation systems, particularly in indoor environments.}, archive = {J_ACCESS}, author = {Opeyemi Bamigbade and Mark Scanlon and John Sheppard}, doi = {10.1109/ACCESS.2025.3564496}, journal = {IEEE Access}, pages = {79860-79870}, shortjournal = {IEEE Access}, title = {Improving image embeddings with colour features in indoor scene geolocation}, volume = {13}, year = {2025}, }
• (2025). Transformer-based approach for solving mathematical problems using automatic speech recognition. ACCESS, 13, 79845-79859. (www)

  @article{ , abstract = {In this paper, we introduce Vox Calculi, a system designed to solve mathematical problems using voice transcriptions. By leveraging state-of-the-art pretrained Automatic Speech Recognition (ASR) models, we accurately transcribe users’ voice recordings. Additionally, we develop a specialized mathematical parser that converts natural language mathematical expressions into symbolic representations and numerical values while preserving the remainder of the transcription. We utilize Transformer and TP-Transformer models, trained on DeepMind’s Mathematics dataset, to generate an appropriate mathematical answer from the provided input sequence. An in-depth evaluation of both the ASR and Transformer models demonstrates highly satisfactory results. Furthermore, we propose potential future improvements to enhance the system’s performance.}, archive = {J_ACCESS}, author = {Ante Grgurević and Marina Bagić Babac}, doi = {10.1109/ACCESS.2025.3564121}, journal = {IEEE Access}, pages = {79845-79859}, shortjournal = {IEEE Access}, title = {Transformer-based approach for solving mathematical problems using automatic speech recognition}, volume = {13}, year = {2025}, }
• (2025). Predicting electromagnetic field exposure using artificial intelligence methods. ACCESS, 13, 79832-79844. (www)

  @article{ , abstract = {This study explores the application of Artificial Intelligence (AI) in predicting electromagnetic field (EMF) exposure levels in urban environments. Machine Learning (ML) and Deep Learning (DL) models were developed to estimate the electric field (E, in V/m) at specific locations without requiring direct measurements. Measurements were conducted in Thessaloniki, Greece, covering a diverse urban landscape, including commercial and residential areas. The methodology involved collecting EMF data using high-precision equipment, validating and refining publicly available datasets, and extracting key features using Geographic Information System (GIS) tools. Essential input features included distances to the nearest base stations, their installation heights, classification of base stations regarding their emitted power, number of obstructing buildings, built area density, and line-of-sight (LOS) conditions. The AI models demonstrated strong predictive performance, with mean absolute errors (MAE) slightly above 0.3 V/m. The study highlights the importance of proper data preprocessing, feature selection, and integration of real-world measurements into AI-based prediction models. The results suggest that AI can serve as a reliable alternative for EMF exposure assessment, potentially reducing the need for costly and time-consuming field measurements while ensuring compliance with safety regulations.}, archive = {J_ACCESS}, author = {Athanasios Manassas and Spyridon Delidimitriou and Joe Wiart and Theodoros Samaras}, doi = {10.1109/ACCESS.2025.3566641}, journal = {IEEE Access}, pages = {79832-79844}, shortjournal = {IEEE Access}, title = {Predicting electromagnetic field exposure using artificial intelligence methods}, volume = {13}, year = {2025}, }
• (2025). Convolutional neural networks for automatic identification of individuals at terrestrial terminals. ACCESS, 13, 79825-79831. (www)

  @article{ , abstract = {The objective of this study was to develop an automated system for the identification of wanted individuals in terrestrial terminals using Convolutional Neural Networks (CNN). The research was conducted under a quantitative approach and a quasi-experimental design. A private dataset, intended for educational purposes, consisting of images and videos of individuals in dynamic environments, was employed to enable identification testing under real-world conditions. The methodology encompassed the structured loading of biometric data, facial detection using the Multi-Task Cascaded Convolutional Neural Network (MTCNN) model, and the generation of facial embeddings through the InceptionResNetV1 model. Extracted features and data were stored in a MySQL database. To optimize the search process during real-time identification, the embeddings were transferred to FAISS, a library optimized for similarity search in large-scale datasets. In FAISS, the embeddings were stored in vector format to facilitate fast and efficient querying. Subsequently, identification tasks were performed on video sequences captured in real time. The results revealed high performance of the proposed system, achieving an 89% accuracy in facial detection and 98.60% accuracy in real-time person identification when compared against the data stored in the database. Finally, the trained models were integrated into a web application that enables real-time identification using IP cameras, leveraging YOLO version 8 architecture for tracking identified individuals. These results confirm that deep learning models can be effectively incorporated into surveillance and control systems in public spaces, enhancing existing security mechanisms and offering a viable solution for real-time automatic person identification.}, archive = {J_ACCESS}, author = {Darwin Yarango-Farro and Alex Mondragon-Fernandez and Heber I. Mejia-Cabrera and Juan Arcila-Diaz}, doi = {10.1109/ACCESS.2025.3561633}, journal = {IEEE Access}, pages = {79825-79831}, shortjournal = {IEEE Access}, title = {Convolutional neural networks for automatic identification of individuals at terrestrial terminals}, volume = {13}, year = {2025}, }
• (2025). Embodied conversational agents in extended reality: A systematic review. ACCESS, 13, 79805-79824. (www)

  @article{ , abstract = {Embodied conversational agents (ECAs) that can interact with users in a human-like manner have demonstrated promising potential in various endeavors. With the ongoing advancement in extended reality (XR) and artificial intelligence (AI), ECAs are becoming increasingly sophisticated. Although previous reviews have predominantly focused on ECAs for non-XR applications, a growing number of research papers are exploring the capabilities of ECAs that utilize XR technologies. However, no prior systematic review has focused explicitly on XR ECAs, leading to a gap in understanding how ECAs are designed, implemented, and evaluated within immersive environments. Our work identified the gap between the existing reviews and the current trends in XR ECAs. We began with 1,717 related papers from January 2014 to June 2024. We narrowed down the selection to 23 papers using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which employed an iterative screening procedure and criteria defined by our research team. The resulting papers were analyzed and discussed in terms of the features of the ECA application, its design and implementation, and use cases. Our analysis highlights key trends in XR ECA design, including the dominance of VR-based implementations using head-mounted displays, the prevalence of human-like and female-presenting agents, the move from rule-based to neural-based conversational systems, and the primary use cases in training, therapy, and social interaction. We also summarize the evaluation methods employed across studies and discuss future research directions for developing more adaptive and human-like ECAs in XR environments.}, archive = {J_ACCESS}, author = {Fu-Chia Yang and Pedro Acevedo and Siqi Guo and Minsoo Choi and Christos Mousas}, doi = {10.1109/ACCESS.2025.3566698}, journal = {IEEE Access}, pages = {79805-79824}, shortjournal = {IEEE Access}, title = {Embodied conversational agents in extended reality: A systematic review}, volume = {13}, year = {2025}, }
• (2025). EffNet-SVM: A hybrid model for diabetic retinopathy classification using retinal fundus images. ACCESS, 13, 79793-79804. (www)

  @article{ , abstract = {The manual diagnosis of diabetic retinopathy (DR) is often invasive, time-consuming, expensive, and prone to human error. Additionally, it can be subjective, depending on the clinician’s professional experience. Recently, automated computer-aided diagnosis (CAD) systems have significantly reduced the time and effort required for diagnosis while achieving superior performance compared to traditional methods. Researchers have extensively explored deep learning (DL) and convolutional neural networks (CNNs) for diagnosing DR from fundus images, yielding promising results and offering a viable alternative to conventional diagnostic approaches. In this study, a hybrid model named EffNet-SVM is proposed for the classification of DR and no DR cases using retinal fundus images. The model is trained and tested using the Asia Pacific Tele-Ophthalmology Society (APTOS) dataset, which includes both DR and no DR images. The EffNet-SVM utilizes EfficientNetV2-Small for feature extraction from input fundus images, and the extracted features are then classified using a support vector machine (SVM) with a radial basis function (RBF) kernel. The EffNet-SVM model outperformed eight state-of-the-art DL models from the literature, achieving the highest accuracy of 97.26%. Performance metrics validate that the proposed hybrid model can be effectively integrated into CAD systems for the automated analysis of fundus images.}, archive = {J_ACCESS}, author = {K. V. Naveen and B. N. Anoop and K. S. Siju and Mithun Kumar Kar and Vipin Venugopal}, doi = {10.1109/ACCESS.2025.3566073}, journal = {IEEE Access}, pages = {79793-79804}, shortjournal = {IEEE Access}, title = {EffNet-SVM: A hybrid model for diabetic retinopathy classification using retinal fundus images}, volume = {13}, year = {2025}, }
• (2025). An iterative IpDFT method for frequency estimation with elimination of the spectral leakage. ACCESS, 13, 79784-79792. (www)

  @article{ , abstract = {The signal processing technology is developing rapidly, and the estimation of multi-frequency signals is becoming increasingly important in engineering applications, such as power electronics, speech signal processing, wireless communications, and so on. In this paper, we propose to use the two-point IpDFT method with the rectangular window (I2pNDFT) to estimate frequency. In the process of frequency estimation, we eliminate the spectral leakage by fully considering the influence of the positive and the negative spectra. Subsequently, we propose an interpolator for a single-frequency complex exponential signal with the objective of further enhancing the accuracy of estimation. We extend the applicability of this IpDFT method and its improvement to estimate the fundamental frequency of a signal with harmonic components. The results of the simulation demonstrate that our method is more effective than other methods.}, archive = {J_ACCESS}, author = {Jing Lu and Bin Wei and Kai Wang and Lu Tang and Xusheng Tang}, doi = {10.1109/ACCESS.2024.3436863}, journal = {IEEE Access}, pages = {79784-79792}, shortjournal = {IEEE Access}, title = {An iterative IpDFT method for frequency estimation with elimination of the spectral leakage}, volume = {13}, year = {2025}, }
• (2025). K-means++ clustering-based approach for SDN controller placement in LEO satellite networks. ACCESS, 13, 79771-79783. (www)

  @article{ , abstract = {Software-Defined Networking (SDN) offers a solution for the efficient management of Low Earth Orbit (LEO) satellite networks through centralized control and programmability. However, the dynamic nature of LEO satellites has a unique requirement for balanced controller placement, considering low-latency communication and balanced load distribution. Therefore, a strategic, low-latency controller placement is essential to ensure optimal performance and scalability in LEO satellite networks. The proposed study presents a clustering-based technique, K-Mean++, for placing SDN controllers in the satellite network, aiming to minimize communication delay between the controller and the Open Virtual Switch (OVS) satellites. The satellites are divided into clusters, and a candidate satellite (cluster-head) is selected for controller placement. The proposed technique works in collaboration with the K-Means clustering algorithm. The performance is evaluated on switch-to-controller delay, controller-to-controller delay, controller load coefficient of variance, and traffic distribution efficiency. The results show that the proposed technique outperforms its counterparts with a 59% reduction in worst-case switch-to-controller delay, a 5% reduction in control domain delay, a 26% improvement in controller load distribution, and a 5% enhancement in control domain traffic distribution, making it an optimal solution for SDN controller placement in LEO satellite networks.}, archive = {J_ACCESS}, author = {Syed Muhammad Ammar Hassan Bukhari and Wang-Cheol Song}, doi = {10.1109/ACCESS.2025.3565954}, journal = {IEEE Access}, pages = {79771-79783}, shortjournal = {IEEE Access}, title = {K-means++ clustering-based approach for SDN controller placement in LEO satellite networks}, volume = {13}, year = {2025}, }
• (2025). Joint encryption and optimization for 6G MEC-enabled IoT networks. ACCESS, 13, 79757-79770. (www)

  @article{ , abstract = {With the advent of advancements in future sixth-generation (6G) communication systems, Internet of Things (IoT) devices, characterized by their limited computational and communication capacities, have become integral in our lives. These devices are deployed extensively to gather vast amounts of data in real-time applications. However, their restricted battery life and computational resources present significant challenges in meeting the requirements of advanced communication systems. Mobile Edge Computing (MEC) has emerged as a promising solution to these challenges within the IoT realm in recent years. Despite its potential, securing MEC infrastructure in the context of IoT remains an open task. This study explores the operational dynamics of a secured IoT-enabled MEC infrastructure, focusing on providing real-time, on-demand, secure computational resources to low-powered IoT devices. It outlines a joint optimization problem to maximize computational throughput, minimize device energy consumption, reduce computational latency, and mitigate security overhead. An optimization algorithm is introduced to address these challenges by jointly allocating resources, thereby optimizing throughput, conserving energy, and meeting latency benchmarks through dynamic system adaptation. The effectiveness of the proposed model and algorithm is demonstrated through comparisons with relevant benchmark schemes, highlighting its efficiency in various scenarios. This work showcases the potential of advancements in encryption to deliver scalable security solutions with reduced resource consumption as the number of devices increases.}, archive = {J_ACCESS}, author = {Manzoor Ahmed and Wali Ullah Khan and Fatma S. Alrayes and Yahia Said and Ali M. Al-Sharafi and Mi-Hye Kim and Khongorzul Dashdondov and Inam Ullah}, doi = {10.1109/ACCESS.2025.3565415}, journal = {IEEE Access}, pages = {79757-79770}, shortjournal = {IEEE Access}, title = {Joint encryption and optimization for 6G MEC-enabled IoT networks}, volume = {13}, year = {2025}, }
• (2025). Hadamard product arguments and their applications. ACCESS, 13, 79736-79756. (www)

  @article{ , abstract = {The Hadamard product (also known as element-wise multiplication) is a fundamental operation in linear algebra, performed by multiplying corresponding elements of two matrices with the same dimensions. This operation plays a crucial role in various fields, including cryptography, where it enables efficient and parallelizable computations on large datasets—particularly in the design of cryptographic protocols such as zero-knowledge proofs. In this paper, we propose a transparent and efficient method for proving the Hadamard product between vectors that are independently committed in the groups $\mathbb {G}_{1}$ and $\mathbb {G}_{2}$ under a pairing operation $ e: \mathbb {G}_{1} \times \mathbb {G}_{2} \to \mathbb {G}_{T} $ . For a vector of length n, the prover has a complexity of ${\mathcal {O}}_{\lambda }(n)$ , while the proof size is ${\mathcal {O}}_{\lambda }(\log n)$ . The verifier operates with a complexity of ${\mathcal {O}}_{\lambda }(\log n)$ , which includes $\mathcal {O}(\log n)$ operations in $\mathbb {G}_{T}$ and only $\mathcal {O}(1)$ pairing operations, making verification highly efficient. We prove the security of our scheme under the Symmetric External Diffie-Hellman (SXDH) assumption. Furthermore, we propose an aggregator for Groth16 (EUROCRYPT 2016) zk-SNARKs and a proof aggregation technique for the general case of the KZG polynomial commitment scheme (ASIACRYPT 2010), where all $\textsf {crs}$ are distinct. Both applications do not require an additional trusted setup, support logarithmic-sized aggregated proofs, and significantly reduce the verifier’s pairing operations to $\mathcal {O}(1)$ .}, archive = {J_ACCESS}, author = {Kyeongtae Lee and Hankyung Ko and Donghwan Oh and Jihye Kim and Hyunok Oh}, doi = {10.1109/ACCESS.2025.3566104}, journal = {IEEE Access}, pages = {79736-79756}, shortjournal = {IEEE Access}, title = {Hadamard product arguments and their applications}, volume = {13}, year = {2025}, }
• (2025). YOLO-air: An efficient deep learning network for small object detection in drone-based imagery. ACCESS, 13, 79718-79735. (www)

  @article{ , abstract = {UAV imagery is widely used in areas like traffic safety, disaster rescue, and airspace management, due to its small size and low cost. However, it poses unique challenges for object detection due to small objects, complex backgrounds, and noise interference. To tackle these challenges, we propose YOLO-Air, a novel small object detection network designed specifically for UAV imagery. We propose SECAConv (Squeeze-Excitation Convolution with Attention), which enhances the feature representation of small objects through dynamic weight allocation and channel attention mechanisms. Additionally, we design the novel AeroFPN (Aerial Feature Pyramid Network) to optimize feature transmission by alleviating shallow feature loss through the inclusion of the xsmall detection head. Furthermore, we develop ASFM (Adaptive Scale Fusion Module), which suppresses background noise interference through effective multi-scale feature fusion and adaptive channel attention mechanisms, thereby improving the network’s ability to detect small objects. Experimental results demonstrate that YOLO-Air achieves significant accuracy improvements on both the VisDrone-DET2019 and AI-TOD datasets. Compared to the baseline YOLOv8n, YOLO-Air improved $mAP_{50}$ from 41.2% to 44.5% on the VisDrone-DET2019 dataset, and from 44.9% to 47.5% on the AI-TOD dataset, while maintaining computational efficiency. These results validate YOLO-Air as an effective solution for small object detection in UAV aerial imagery.}, archive = {J_ACCESS}, author = {Jigang Qiu and Fangkai Cai and Ning Fu and Yuanfei Yao}, doi = {10.1109/ACCESS.2025.3565560}, journal = {IEEE Access}, pages = {79718-79735}, shortjournal = {IEEE Access}, title = {YOLO-air: An efficient deep learning network for small object detection in drone-based imagery}, volume = {13}, year = {2025}, }
• (2025). A novel high performance object identification approach in care homes using gaussian preprocessing. ACCESS, 13, 79705-79717. (www)

  @article{ , abstract = {As the global population ages, the prevalence of dementia is expected to surge, necessitating innovative solutions to support the growing number of affected individuals. This research shows a innovative approach for the application of Convolutional Neural Networks for object identification in nursing home environments to enhance the independence and quality of life of dementia patients. Therefore we compare the performance of four well-known Convolutional Neural Network architectures — VGG16, VGG19, InceptionV3, and ResNet50 — to identify the most effective model for this application. Building on this, we use Gaussian blurring as an innovative preprocessing technique to improve model accuracy by reducing high-frequency noise and enhancing feature extraction. Our results indicate that ResNet50 outperforms other models, achieving the highest accuracy with 90.53%. Optimization through data augmentation and fine-tuning further enhances ResNet50’s performance to 94.69%. Applying the Gaussian filter significantly improves the model’s accuracy to 96.81%, demonstrating its potential as a crucial preprocessing step and setting a new benchmark for this application. Our findings provide valuable insight into applying the Gaussian filter on Convolutional Neural Networks and pave the way for developing intelligent support systems to assist dementia patients in their daily tasks and improve their overall well-being.}, archive = {J_ACCESS}, author = {Sara Auweiler and Melina Mueller and Diana Puhla and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3566223}, journal = {IEEE Access}, pages = {79705-79717}, shortjournal = {IEEE Access}, title = {A novel high performance object identification approach in care homes using gaussian preprocessing}, volume = {13}, year = {2025}, }
• (2025). Trust-based distributed resource allocation in edge-enabled IIoT networks. ACCESS, 13, 79694-79704. (www)

  @article{ , abstract = {Due to the emergence of computation-intensive and latency-sensitive applications, the Industrial Internet of Things (IIoT) paradigm imposes several new challenges to the Mobile Edge Computing (MEC) platform with minimal compute resources. MEC has emerged as a new paradigm that can enhance the performance of edge services through optimal service execution. As IIoT devices are mostly resource and energy-constrained, therefore it provides several challenges: 1) maximization of resource utilization; 2) minimization of average delay; 3) high throughput required to execute the edge services. To handle such issues, a trust-based distributed resource allocation (TDRA) framework is developed to offer optimal throughput and resource utilization to IIoT-enabled MEC. On the other hand, we set different priorities for edge services based on application types for optimal resource allocation. We formulate the proposed scheme mathematically and analyze the complexity of TDRA. We present extensive experimental results to illustrate the superiority of TDRA compared to existing solutions in the literature with regard to resource utilization, utility, delay, cost, and fairness.}, archive = {J_ACCESS}, author = {Amit Samanta and Tri Gia Nguyen}, doi = {10.1109/ACCESS.2025.3567068}, journal = {IEEE Access}, pages = {79694-79704}, shortjournal = {IEEE Access}, title = {Trust-based distributed resource allocation in edge-enabled IIoT networks}, volume = {13}, year = {2025}, }
• (2025). Experimental 5G network performance analysis in a semi-anechoic and semi-reverberation chamber. ACCESS, 13, 79678-79693. (www)

  @article{ , abstract = {5G New Radio wireless networks based on the International Mobile Telecommunications specifications are fundamental for current mobile communications. This work focuses on the performance analysis of a private 5G network in an experimental and controlled environment. For this purpose, exhaustive experimentation considering 192 combinations of factors has been proposed and carried out in a controlled environment within a semi-anechoic and semi-reverberant chamber. This enables accurate observation of the impact of the studied factors on a series of Key Performance Indicators that are fundamental in 5G networks, such as latency, throughput, and reliability. This dataset has been analyzed using statistical algorithms such as Principal Component Analysis and SHapley Additive ExPlanations. The results show how network performance can be affected not only by the environment, but also by its configuration. Furthermore, the configurations contribute differently depending on the indicator, showing that these can be improved without an excessive impact on other indicators: (i) throughput improves by more than 26% by modifying parameters such as bandwidth, duplexing method, or number of antennas; (ii) latency improves over 7% by modifying the duplexing method or 12% by switching between radio access technologies; (iii) reliability is exceptionally high in controlled environments. Our study shows that reliability can achieve improvements of up to 196.5% in terms of lost and retransmitted packets. The insights found in this work, based on the proposed configurations and their respective improvements, can be extrapolated to proposed use cases for 5G networks.}, archive = {J_ACCESS}, author = {Felix Delgado-Ferro and Alejandro Ramírez-Arroyo and Jorge Navarro-Ortiz and Juan F. Valenzuela-Valdés}, doi = {10.1109/ACCESS.2025.3566446}, journal = {IEEE Access}, pages = {79678-79693}, shortjournal = {IEEE Access}, title = {Experimental 5G network performance analysis in a semi-anechoic and semi-reverberation chamber}, volume = {13}, year = {2025}, }
• (2025). Data compactness versus prediction performance: Achieving both by pruning redundant samples with dominant patterns and hamming distance based sampling scheme. ACCESS, 13, 79655-79677. (www)

  @article{ , abstract = {Machine learning (ML) practitioners are always in pursuit of refined data to develop robust and generalizable ML models to solve real-world problems. However, most real-world datasets are noisy, imbalanced, and contain redundant samples, prompting the need to address these problems before the datasets are processed by ML models. Among other factors, redundant samples are becoming a greater challenge in ML development due to advancements in data-capturing tools (sensors, wearable devices, etc.). Redundant samples can increase computing and storage requirements while minimally contributing to predictive performance, necessitating their removal before the training phase. However, removing redundant samples without degrading predictive performance from ML models is challenging because it requires deep analysis of all the data and the correlations among features. In this paper, we propose a dominant pattern and Hamming distance-based sampling scheme to prune redundant samples from the data without degrading predictive performance. Specifically, we reduce the data size by a reasonable margin while maintaining predictive performance similar to or better than the original data with reduced training time. Our sampling scheme has five key steps: data pre-processing, dominant pattern extraction by exploiting correlations between features, Hamming distance-based data classification into diverse and less diverse parts, data clustering for redundant-sample pruning from less diverse parts, and fine-tuning/synthesizing the final data. The key objective is to curate compact, diverse, and high-fidelity data that accurately preserves the characteristics of original data while solving accuracy-versus-time trade-offs. Experiments are performed on benchmark datasets of binary nature using diverse ML classifiers, and predictive performance, data properties, and computing time are compared with original data and prior data reduction schemes. From the experiments and analysis, our scheme yielded much better results without compromising predictive performance and data fidelity than its counterparts and original data. Our results and analysis provide a new perspective on building ML models with compact data (i.e., a mini-version of the original data) while providing better predictive performance.}, archive = {J_ACCESS}, author = {Abdul Majeed and Seong Oun Hwang}, doi = {10.1109/ACCESS.2025.3566430}, journal = {IEEE Access}, pages = {79655-79677}, shortjournal = {IEEE Access}, title = {Data compactness versus prediction performance: Achieving both by pruning redundant samples with dominant patterns and hamming distance based sampling scheme}, volume = {13}, year = {2025}, }
• (2025). Fast and accurate cross-PVT full-chip leakage power estimation with multi-task learning. ACCESS, 13, 79646-79654. (www)

  @article{ , abstract = {As semiconductor technology continues to scale, power consumption has become a primary focus of researchers and engineers. In particular, cross-PVT (Process, Voltage, and Temperature) full-chip leakage power estimation is urgently necessary, as it provides crucial guidance for power delivery network design and low-power design strategies and ensures efficient chip design. To address this challenge, we propose an Artificial Neural Network (ANN) model that efficiently integrates data from multiple PVT and provides accurate estimation, given the limited data available for training. This approach enables full-chip leakage power estimation using only one critical cell at one operating state. Our method has been validated using data from a 14 nm industrial FinFET technology node. Compared to the traditional simulation-based method, our method can achieve around 1.76% relative error on average and largely reduced turnaround time, considering all cell types and states. In this case, our method outperforms previous leakage power estimation techniques which fail to provide accurate estimation in low temperature range. When focusing solely on one type of critical cell, the method delivers further acceleration, while maintaining 8.5% maximum relative error.}, archive = {J_ACCESS}, author = {Zhuomin Chai and Wei Liu and Yibo Lin and Runsheng Wang}, doi = {10.1109/ACCESS.2025.3566719}, journal = {IEEE Access}, pages = {79646-79654}, shortjournal = {IEEE Access}, title = {Fast and accurate cross-PVT full-chip leakage power estimation with multi-task learning}, volume = {13}, year = {2025}, }
• (2025). Proposal of a non-dominant hand training device utilizing Flexion/Extension vibration sensory feedback based on image sensing. ACCESS, 13, 79634-79645. (www)

  @article{ , abstract = {The utilization of the non-dominant hand contributes to improving the efficiency of hand movements. However, traditional training methods require substantial time to master, making it important to find more efficient ways to improve drawing ability within a limited timeframe. Therefore, this study aimed to improve the efficiency of training by developing a device that improves the accuracy of the non-dominant hand drawing on shape drawing tasks. The device is worn on the user’s finger and provides vibration feedback to guide flexion and extension of the finger, encouraging the user to adopt the optimal fingertip posture for their individual needs. The optimal posture of the fingertip is determined based on the movements of the dominant hand. To evaluate the effectiveness of the device, an experimental shape-tracing task was performed with 44 participants. The participants were tasked with tracing 30 shapes accurately and quickly, and the shifts of the traced lines from the reference lines were quantified. The results of the experiment showed that, while the group without the device experienced a 4.243% increase in Total Shifts, the group that used the device successfully reduced Total Shifts by 4.689%, demonstrating an improvement in drawing accuracy. These results indicate that the proposed device effectively enhances drawing ability within a fixed period and is effective in shortening the training time for the non-dominant hand.}, archive = {J_ACCESS}, author = {Shuji Saburi and Hajime Nobuhara}, doi = {10.1109/ACCESS.2025.3564165}, journal = {IEEE Access}, pages = {79634-79645}, shortjournal = {IEEE Access}, title = {Proposal of a non-dominant hand training device utilizing Flexion/Extension vibration sensory feedback based on image sensing}, volume = {13}, year = {2025}, }
• (2025). Aleatoric and epistemic uncertainty reduction for vision-based landing system for fixed-wing aircraft: VLS-FWA. ACCESS, 13, 79623-79633. (www)

  @article{ , abstract = {Visual information plays a crucial role during the final approach and landing phases of an aircraft, serving as a complementary source for the navigation system. It provides additional guidance when radio navigation such as Instrument Landing System is unserviceable or compromised and enables a fully vision-based landing. The relative position and orientation of an aircraft can be derived from the features of the runway captured in images. However, conventional methods for detecting runways often suffer from high latency and low accuracy, making them inadequate and uncertain for ensuring a safe landing. In response to these limitations, this paper introduces a real-time runway detection model named Vision-based Landing System for Fixed-Winged Aircraft (VLS-FWA), which leverages deep convolutional neural networks in cascade. Moreover, predicted variance is also calculated and displayed in the pilot’s Head-Up Display (HUD) by employing methods to aid in the reduction of aleatoric and epistemic uncertainty in both real-world and synthetic scenes.}, archive = {J_ACCESS}, author = {Dheeraj Bharti and K. S. Venkatesh}, doi = {10.1109/ACCESS.2025.3567010}, journal = {IEEE Access}, pages = {79623-79633}, shortjournal = {IEEE Access}, title = {Aleatoric and epistemic uncertainty reduction for vision-based landing system for fixed-wing aircraft: VLS-FWA}, volume = {13}, year = {2025}, }
• (2025). Enhancing medicare fraud detection with a CNN-transformer-XGBoost framework and explainable AI. ACCESS, 13, 79609-79622. (www)

  @article{ , abstract = {Healthcare fraud is a critical challenge, contributing significantly to rising healthcare costs and financial losses. This article proposes a hybrid architecture for healthcare fraud detection, combining deep learning-based feature representation with gradient boosting classification and explainable AI techniques. The framework integrates convolutional neural networks (CNNs), transformers, and XGBoost to capture intricate patterns in claims data while maintaining interpretability through Shapley additive explanations. The model we proposed was tested on two datasets: the Medicare Provider Fraud dataset and the Healthcare Providers dataset. On the Medicare dataset, the framework achieved an F1-score of 0.95 on the training set and 0.92 on the test set, with an AUC-ROC of 0.98 and 0.97, respectively, outperforming state-of-the-art models such as LightGBM and CatBoost. On the Healthcare Providers dataset, the framework attained a test F1-score of 0.92 and an AUC-ROC of 0.96, consistently surpassing traditional models like Support Vector Machines and Random Forest. Key contributions include integrating domain-specific features, such as provider-patient interaction graphs and temporal patterns, and using explainability techniques to enhance trustworthiness. Furthermore, the framework demonstrated computational efficiency, with a training time of 150 seconds on the primary dataset, making it suitable for real-world deployment.}, archive = {J_ACCESS}, author = {Mohammad Balayet Hossain Sakil and Md Amit Hasan and Md Shahin Alam Mozumder and Md Rokibul Hasan and Shafiul Ajam Opee and M. F. Mridha and Zeyar Aung}, doi = {10.1109/ACCESS.2025.3562577}, journal = {IEEE Access}, pages = {79609-79622}, shortjournal = {IEEE Access}, title = {Enhancing medicare fraud detection with a CNN-transformer-XGBoost framework and explainable AI}, volume = {13}, year = {2025}, }
• (2025). Multi-site wind farms dependence structure using vine copulas: Impacts of dataset sizes and employed copulas. ACCESS, 13, 79596-79608. (www)

  @article{ , abstract = {Using copulas in statistics evaluates the dependence between random variables. Copula modeling has significantly been used in many areas, especially in the search for multivariate distributions. As wind energy rapidly becomes an important renewable energy source, it is very important to deeply evaluate any potential existing dependencies among the data. This study introduces a comprehensive application of vine copulas in modeling multi-site wind speed dependencies for different sizes of datasets, offering a more flexible approach than traditional correlation-based methods. Unlike previous studies, this work systematically evaluates the impact of dataset sizes on the selection of the best-performing vine copula structures, providing valuable insights for improving wind forecasting and grid stability. These evaluations are studied using R-vine, C-and D-vine models by applying pair copulas families and the pairwise empirical Kendall’s $\tau $ values, where the appropriate model is selected based on the Akaike Information Criteria (AIC), the Bayesian Information Criteria (BIC), and the likelihood method (Log-likelihood). This study finds out the best vine copula model for three different wind speed datasets, namely hourly (large), daily (medium), and small (weekly). Also, using all pair copulas families (Clayton, Frank, Gumbel, Student’s t, and Gaussian), vine copula simulations provide guidelines for conceptual understanding of mutual impacts and correlations among multi-site wind farms. Simulations also show that the R-vine copula is the best structure for both large and small datasets, while the C-vine copula is the best structure for medium datasets.}, archive = {J_ACCESS}, author = {Amir Shahirinia and Zeinab Farahmandfar and Mohammad Tavakoli Bina and Hossein Hafezi and Vincent Tanoe}, doi = {10.1109/ACCESS.2025.3565217}, journal = {IEEE Access}, pages = {79596-79608}, shortjournal = {IEEE Access}, title = {Multi-site wind farms dependence structure using vine copulas: Impacts of dataset sizes and employed copulas}, volume = {13}, year = {2025}, }
• (2025). Federated learning framework based on distributed storage and diffusion model for intrusion detection on IoT networks. ACCESS, 13, 79571-79595. (www)

  @article{ , abstract = {The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.}, archive = {J_ACCESS}, author = {Ricardo Manzano and Marzia Zaman and Darshana Upadhyay and Nishith Goel and Srinivas Sampalli}, doi = {10.1109/ACCESS.2025.3565409}, journal = {IEEE Access}, pages = {79571-79595}, shortjournal = {IEEE Access}, title = {Federated learning framework based on distributed storage and diffusion model for intrusion detection on IoT networks}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning framework for deepfake detection using temporal and spatial features. ACCESS, 13, 79560-79570. (www)

  @article{ , abstract = {The rise of deep-fake technology has sparked concerns as it blurs the distinction between fake media by harnessing Generative Adversarial Networks (GANs). This has raised issues surrounding privacy and security in the realm. This has led to a decrease in trust during online interactions; thus, emphasizing the importance of creating reliable methods for detection purposes. Our research introduces a model for detecting deepfakes by utilizing an Enhanced EfficientNet B0 structure in conjunction with Temporal Convolutional Neural Networks (TempCNNs). This approach aims to tackle the challenges presented by the evolving sophistication of deep-fake techniques. The system dissects video inputs into frames to extract features comprehensively by using Multi Test Convolutional Networks (MTCNN). This method ensures face detection and alignment by focusing on facial regions. To enhance the model’s adaptability, to different scenarios and datasets we implement data augmentation techniques such as CutMix, MixUp and Random Erasing. These strategies help the model maintain its strength, against distortions found in deepfake content. The backbone of EfficientNet B0 utilizes Mobile Inverted Bottleneck Convolutions (MBConv) and Squeeze and Excitation (SE) blocks to enhance feature extraction by adjusting channels to highlight details effectively. A Feature Pyramid Network (FPN) facilitates the fusion of scale features capturing intricate details as well, as broader context. When tested on the FFIW 10 K dataset, which comprises 10,000 videos evenly split between manipulated content, the model attained a training accuracy of 91.5 % and a testing accuracy of 92.45 %, after 40 epochs. The findings showcase the model’s proficiency, in identifying videos with precision and tackling the issue of class imbalances found in datasets – a valuable contribution, to advancing dependable deepfake detection solutions. Furthermore, the model achieves an impressive balance between accuracy and computational efficiency, attaining 92.45% testing accuracy with a lightweight computational cost of 0.45 GFLOPs, making it a highly practical choice for real-world deployment.}, archive = {J_ACCESS}, author = {Fazeel Zafar and Talha Ahmed Khan and Salas Akbar and Muhammad Talha Ubaid and Sameena Javaid and Kushsairy Abdul Kadir}, doi = {10.1109/ACCESS.2025.3566008}, journal = {IEEE Access}, pages = {79560-79570}, shortjournal = {IEEE Access}, title = {A hybrid deep learning framework for deepfake detection using temporal and spatial features}, volume = {13}, year = {2025}, }
• (2025). Adaptive node-oriented data placement for heterogeneous hadoop clusters. ACCESS, 13, 79548-79559. (www)

  @article{ , abstract = {Modern society is experiencing a data explosion thanks to rapid IT development and the increasing intelligence of devices. The vast and complex data can be utilized to extract actionable insight using a big data processing framework. Hadoop is a popular big data processing framework on heterogeneous commodity hardware. While Hadoop offers a robust framework for large-scale, data-intensive tasks via its MapReduce paradigm, hardware heterogeneity across nodes often leads to straggler effects that degrade Hadoop cluster performance. This paper introduces the Adaptive Node-Oriented Data placement for Efficient Hadoop Execution (ANODE) method, which leverages historical job execution data to dynamically assess each node’s processing capability. By employing an agent-based mechanism, ANODE optimizes block allocation within the data node, alleviating imbalances caused by Hadoop’s default uniform placement strategy. Experimental results on a heterogeneous eleven-node Hadoop cluster demonstrate that ANODE reduces job completion times by up to 25%, significantly enhancing data locality and resource utilization compared to the default approach.}, archive = {J_ACCESS}, author = {Vishnu Prasad Verma and Santosh Kumar and Nenavath Srinivas Naik}, doi = {10.1109/ACCESS.2025.3565759}, journal = {IEEE Access}, pages = {79548-79559}, shortjournal = {IEEE Access}, title = {Adaptive node-oriented data placement for heterogeneous hadoop clusters}, volume = {13}, year = {2025}, }
• (2025). WAPS-quant: Low-bit post-training quantization using weight-activation product scaling. ACCESS, 13, 79534-79547. (www)

  @article{ , abstract = {Post-Training Quantization (PTQ) has been effectively compressing neural networks into very few bits using a limited calibration dataset. Various quantization methods utilizing second-order error have been proposed and demonstrated good performance. However, at extremely low bits, the increase in quantization error is significant, hindering optimal performance. Previous second-order error-based PTQ methods relied solely on quantization scale values and weight rounding for quantization. We introduce a weight-activation product scaling method that, when used alongside weight rounding and scale value adjustments, effectively reduces quantization error even at very low bits. The proposed method compensates for the errors resulting from quantization, thereby achieving results closer to the original model. Additionally, the method effectively reduces the potential increase in computational and memory complexity through channel-wise grouping, shifting, and channel mixing techniques. Our method is validated on various CNNs, and extended to ViT and object detection models, showing strong generalization across architectures. We conducted tests on various CNN-based models to affirm the superiority of our proposed quantization scheme. Our proposed approach enhances accuracy in 2/4-bit quantization with less than 1.5% computational overhead, and hardware-level simulation confirms its suitability for real-time deplo1yment with negligible latency increase. Furthermore, hardware-level simulation on a silicon-proven ASIC NPU confirms that our method achieves higher accuracy with negligible latency overhead, making it practical for real-time edge deployment.}, archive = {J_ACCESS}, author = {Geunjae Choi and Kamin Lee and Nojun Kwak}, doi = {10.1109/ACCESS.2025.3566307}, journal = {IEEE Access}, pages = {79534-79547}, shortjournal = {IEEE Access}, title = {WAPS-quant: Low-bit post-training quantization using weight-activation product scaling}, volume = {13}, year = {2025}, }
• (2025). The design and realization of a self-hosted and open-source agile internal development platform. ACCESS, 13, 79516-79533. (www)

  @article{ , abstract = {Platform Engineering (PE) is a growing area of Software Engineering, with many facets, including the elusive concept of Internal Development Platforms (IDPs). They integrate various technologies and tools to support the realization of software projects, especially their managing and developing roles, fostering Agile collaboration and enhancing team productivity. In particular for the new trends of Agile remote working and for Agile applied to the Digital Transformation in the Public Sector. While proprietary IDPs abound, academic research that could naturally lead to open-source IDPs remain scarce. In particular for Scrum, a fundamental Agile methodology in the strategic area of Digital Transformation in the Public Sector, where IDP self-hosting is also appropriate. This paper addresses this gap by proposing the design and realization of the first, to the best of our knowledge, IDP supporting Scrum, being open-source and with self-hosting capabilities. The end-product of our research is the Compositional Agile System (CAS), an extendable microservice architecture-based IDP. Its design, as well as CAS, makes full use of hands-on previous successful experiences of IDP development by members of this team, specifically non-trivial tasks in Italian Public Administration and to foster remote working in University Software Engineering classes, a practice started during the pandemic but now a standard. We offer several architectural and example scenarios where CAS can be of use, concentrating on the Public Administration. Particularly relevant is the use of CAS in analyzing a pillar of Italian Digital Administration available to its citizens, i.e., AppIo. The software is available as an open resource to the research community.}, archive = {J_ACCESS}, author = {Paolo Ciancarini and Raffaele Giancarlo and Gennaro Grimaudo and Marcello Missiroli and Tian Cheng Xia}, doi = {10.1109/ACCESS.2025.3564141}, journal = {IEEE Access}, pages = {79516-79533}, shortjournal = {IEEE Access}, title = {The design and realization of a self-hosted and open-source agile internal development platform}, volume = {13}, year = {2025}, }
• (2025). Skull-mimicking phantoms with tunable properties for transcranial ultrasound imaging. ACCESS, 13, 79503-79515. (www)

  @article{ , abstract = {Non-invasive transcranial ultrasound imaging for older children and adults is constrained by acoustic effects caused by cranial bone in humans. There is very significant acoustic degradation of wave propagation through the cranium, particularly the diploe layer which is characterized by high porosity. Due to limited access to human skull samples for transcranial ultrasound imaging studies, the authors propose an epoxy resin-based skull-shaped phantom. This phantom mimic the multiple layers (inner/outer tables and the diploe region), enabling the creation of samples with realistic thickness and porosity variability. Appropriate material, replicating human bone composition, and poppy seeds, simulating diploe porosity, are incorporated. Using the data generated in this study, we developed a formula that allows inputting the desired acoustic properties of the skull to obtain the optimal material ratios for skull construction. The experimental evaluation setup involves acoustic transmission measurements and mechanical characteristic assessments. Results show that the developed phantoms exhibit comparable properties to actual human skulls. This research contributes to understanding and overcoming challenges in transcranial ultrasound imaging, paving the way for advancements in diagnostic tools by proposing accurate alternatives to human skulls for system design and development.}, archive = {J_ACCESS}, author = {Loïc Saint-Martin and Kamran Avanaki}, doi = {10.1109/ACCESS.2025.3566388}, journal = {IEEE Access}, pages = {79503-79515}, shortjournal = {IEEE Access}, title = {Skull-mimicking phantoms with tunable properties for transcranial ultrasound imaging}, volume = {13}, year = {2025}, }
• (2025). Optimizing AI-driven EdTech solutions for enhanced english learning: A TOPSIS approach. ACCESS, 13, 79479-79502. (www)

  @article{ , abstract = {The development of educational technology (EdTech) and artificial intelligence (AI) brings about a revolution in English learning by providing flexible, effective, and customized solutions. The purpose of this study is to assess the impact of AI and EdTec on education. In this article, we defined the multi-criteria decision-making (MCDM) procedure to manage ambiguity and awkward information by integrating the Technique for Order of Preference by Similarity to the Ideal Solution (Topsis) method with Circular q-Rung orthopair fuzzy set (Cq-ROFS), and Bonferroni mean (BM) operators to evaluate and prioritize AI-driven EdTech tools. The methodology incorporates multiple attributes, such as adaptability, learner engagement, cost-effectiveness, and scalability, within an MCDM framework. These results highlight the huge potential of intelligent teaching programs, flexible learning environments, and AI-powered language models to improve English ability. This research demonstrates how AI has advanced in education from simple computer-assisted language learning to complex AI-driven platforms like chatbots and intelligent systems for teaching. These developments, such as automated grading and feedback, have given teachers the ability to improve administrative effectiveness and instructional quality. Furthermore, customized and interactive learning experiences that are adapted to the needs and preferences of each student have been made possible by AI-based EdTech solutions. The study used the TOPSIS technique to rank important criteria for optimizing various solutions, highlighting their contribution to preservation, interaction, and overall efficacy in English language learning.}, archive = {J_ACCESS}, author = {Liu Yang and Lu Li and Li Wang}, doi = {10.1109/ACCESS.2025.3561694}, journal = {IEEE Access}, pages = {79479-79502}, shortjournal = {IEEE Access}, title = {Optimizing AI-driven EdTech solutions for enhanced english learning: A TOPSIS approach}, volume = {13}, year = {2025}, }
• (2025). Segmenting online shoppers: A combined cluster and logistic regression approach for forecasting purchase behavior. ACCESS, 13, 79467-79478. (www)

  @article{ , abstract = {This paper examines customers’ online shopping behavior and its relationship to purchase decisions. There are two research questions for the paper: 1) How can marketers segment prior customers’ online shopping behavior to develop a more accurate consumer segmentation for forecasting purchase behavior? 2) Which factors influence customers’ online shopping behavior? The analysis used a dataset of 12,330 samples with ten continuous and eight categorical variables. Cluster and logistic regression analyses were conducted on the dataset. Cluster analysis grouped customers by operating system, browser, location, and traffic type, while logistic regression estimated online customers’ purchase intentions. The results show that different factors influence various customer segments. In Group 1, administrative factors, product-related duration, exit rate, and page value significantly affect purchase behavior. In Group 2, product-related factors, exit rate, page value, and special days play an important role. In Group 3, product-related factors, bounce rate, page value, and visitor type are key factors. This paper contributes by using cluster analysis and logistic regression to segment online customer usage behavior and forecast purchase behavior. It evaluates key predictors of online shopping behavior that differ across customer groups, supporting targeted marketing strategies and decision-making.}, archive = {J_ACCESS}, author = {Wong Ming Wong and Wunhong Su}, doi = {10.1109/ACCESS.2025.3565897}, journal = {IEEE Access}, pages = {79467-79478}, shortjournal = {IEEE Access}, title = {Segmenting online shoppers: A combined cluster and logistic regression approach for forecasting purchase behavior}, volume = {13}, year = {2025}, }
• (2025). YOLO-HF: Early detection of home fires using YOLO. ACCESS, 13, 79451-79466. (www)

  @article{ , abstract = {Domestic fires (residential and indoor) result in substantially higher deaths compared to other types of conflagrations, making early detection crucial to minimizing the loss of life and property. Existing studies and datasets predominantly focus on wildfire detection, with few datasets that capture the complexity of indoor environments and the subtle characteristics of early-stage fires. This gap causes object detection algorithms to produce high false positive and false negative rates in research on the early detection of such fires. To address this, we developed a domestic-fire dataset and proposed YOLO-HF, an early home-fire detection model based on YOLOv5s. To enhance the detection performance for small flames and smoke in a fire’s early stages, we proposed the parametric boosted channel attention (PBCA) mechanism. PBCA fuses global information and adaptively adjusts weights, improving the model’s ability to select and represent key features with minimal parameter overhead. Additionally, we incorporated the RepNCSPELAN4 feature extraction module, which preserves and leverages multi-scale feature information through multi-stage convolution and multi-layer feature fusion. To mitigate information loss caused by the stride setting in traditional downsampling methods, we employed space-to-depth (SPD) downsampling, which retains more spatial details. The experimental results demonstrate that YOLO-HF achieves a 4.6% improvement in Recall, 2.8% in mAP50, and 4.2% in mAP50-95 compared to existing models, all while maintaining its lightweight design. The model size is 13.8 MB, and its frame rate of 66.9 fps, meets real-time detection requirements. Detection experiments using content captured with domestic cameras validated the model’s generalization capability and robustness.}, archive = {J_ACCESS}, author = {Bo Peng and Tae-Kook Kim}, doi = {10.1109/ACCESS.2025.3566907}, journal = {IEEE Access}, pages = {79451-79466}, shortjournal = {IEEE Access}, title = {YOLO-HF: Early detection of home fires using YOLO}, volume = {13}, year = {2025}, }
• (2025). Compact 5G broadband antenna for sub-6 GHz wireless applications. ACCESS, 13, 79444-79450. (www)

  @article{ , abstract = {The design and analysis of a compact broadband antenna supporting the long-term evolution (LTE) bands 41/42/43/46/48/52 and the sub-6 GHz bands N46/N77/N78/N79, Bluetooth, and Wi-Fi are presented. The proposed antenna consists of a 50-ohm microstrip feedline, a Yagi-shaped patch radiator, and a partial ground plane under the feedline. The slot-loading technique is employed to achieve a wide band of operation and to minimize the overall size of the antenna. Using the low-loss dielectric material substrate, Rogers RO4003C, the form factor of the antenna is appreciably reduced to $26\times 17\times 0.813$ mm3 without compromising the performance result of the antenna. The finite element method-based high-frequency electromagnetic solver from ANSYS Corporation is used to simulate and optimize the antenna. A prototype of the designed antenna is fabricated and measured to verify its performance. It is observed that in the operating impedance bandwidth from 2.44 GHz to 5.74 GHz (voltage standing wave ratio <2), a wide bandwidth of 3.3 GHz with a fractional bandwidth of 80.68% is achieved. In addition, the antenna exhibits an omnidirectional radiation pattern with an acceptable average gain level of 2 dBi over the whole operating bandwidth.}, archive = {J_ACCESS}, author = {Manal K. Fattoum and Samira M. Joujou and Sarah M. Ibrahim and Heba El-Halabi}, doi = {10.1109/ACCESS.2025.3566448}, journal = {IEEE Access}, pages = {79444-79450}, shortjournal = {IEEE Access}, title = {Compact 5G broadband antenna for sub-6 GHz wireless applications}, volume = {13}, year = {2025}, }
• (2025). Enhanced power quality control in grid tied PVG-UAPF systems using a modified second order generalized integrator and notch filter. ACCESS, 13, 79430-79443. (www)

  @article{ , abstract = {Modern electrical distribution networks encounter significant challenges due to DC offsets, harmonics from nonlinear loads, and distorted grid voltages. These issues lead to increased total harmonic distortion (THD), reduced power factor, degraded power quality, and potential instability. Conventional controllers, such as second order generalized integrator (SOGI) and enhanced SOGI (ESOGI), are able to mitigate these issues but involve a trade off between harmonic filtering performance and dynamic response. Moreover, the gain magnitudes achieved for rejecting DC offsets, lower order harmonics, and higher order harmonics are relatively low. To address these limitations, this work introduces a modified second order generalized integrator with a notch filter (MSOGI) in a photovoltaic generator fed unified active power filter (PVG-UAPF) framework. By combining SOGI with a notch filter, the proposed approach leverages their complementary characteristics, achieving both superior harmonic filtering and a fast dynamic response. Furthermore, the proposed control structure effectively suppresses DC offsets and compensates for grid side and load side abnormalities while maintaining a unity power factor in the grid. The effectiveness of the proposed controller in a grid synchronized PVG-UAPF system is evaluated through MATLAB simulations under diverse operating conditions and further validated via real time simulation using the OPAL-RT 5700 platform. Compared to SOGI and ESOGI, the proposed method enhances harmonic filtering accuracy and dynamic response, achieving lower THD values of 1.85% for grid currents and 2.04% for load voltages, ensuring compliance with IEEE-519 standards.}, archive = {J_ACCESS}, author = {K. M. Sanjeeva Kumar and Satyajit Das}, doi = {10.1109/ACCESS.2025.3565152}, journal = {IEEE Access}, pages = {79430-79443}, shortjournal = {IEEE Access}, title = {Enhanced power quality control in grid tied PVG-UAPF systems using a modified second order generalized integrator and notch filter}, volume = {13}, year = {2025}, }
• (2025). SPARCQ: Enhancing scalability and adaptability of proactive edge caching through Q-learning. ACCESS, 13, 79410-79429. (www)

  @article{ , abstract = {The exponential growth of network traffic and data-intensive applications demands innovative solutions to manage data efficiently and ensure high-quality user experiences. Proactive edge caching has become a crucial technique for enhancing network performance by predicting and pre-storing content closer to users before access. Accurate prediction models, such as Long Short-Term Memory (LSTM) networks, are crucial for effective proactive caching. However, these models rely on carefully tuned hyperparameters to maintain predictive accuracy, and manual tuning is impractical in dynamic and diverse network environments, limiting scalability and adaptability. To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. By dynamically adjusting hyperparameters, our approach ensures accurate predictions, improving caching efficiency and adaptability. Using the MovieLens dataset, we achieve an average improvement of 8% in cache hit ratios compared to baseline models, including popularity-based and untuned models. Additionally, our framework demonstrates scalability and robustness across geographically distributed regions, consistently adapting to diverse and evolving data patterns.}, archive = {J_ACCESS}, author = {Shruti Lall and Johan de Clercq and Nelishia Pillay and Bodhaswar T. Maharaj}, doi = {10.1109/ACCESS.2025.3566002}, journal = {IEEE Access}, pages = {79410-79429}, shortjournal = {IEEE Access}, title = {SPARCQ: Enhancing scalability and adaptability of proactive edge caching through Q-learning}, volume = {13}, year = {2025}, }
• (2025). Few-shot learning with prototypical networks for improved memory forensics. ACCESS, 13, 79397-79409. (www)

  @article{ , abstract = {Securing computer systems requires effective methods for malware detection. Memory forensics analyzes memory dumps to identify malicious activity, but faces challenges including large and complex datasets, constantly evolving malware threats, and limited labeled data for training algorithms among others. This research introduces a novel approach for malware detection using memory forensics and prototypical networks. As the first application of prototypical networks to the Dumpware10 dataset (to the best of authors knowledge), our findings highlight the potential of few-shot learning for memory forensics-based malware detection, opening new avenues for research in this domain. Prototypical networks are a type of few-shot learning algorithm that excels at classifying new categories with minimal examples. Utilizing the publicly available Dumpware10 dataset, which includes 10 malware classes and one benign class, we preprocess memory dumps using denoising and A-Hash functions to reduce noise and redundancy. The prototypical network is trained on the first four malware classes and the benign class. It’s then tested on a dataset with one additional class (first five malware classes and the benign class). We progressively increase the number of test classes to eleven. Within each training episode, five training images are used as support samples, with all remaining images designated as query samples. Our goal isn’t to predict exact class labels, but to assess the similarity between query images and prototypes using a distance metric. If the label of a prototype matches the query image and the distance falls below a threshold, it’s considered a true positive. This approach achieves an average accuracy of 92% with eleven classes, the highest across all scenarios and comparable to previous work using machine and deep learning algorithms on this dataset.}, archive = {J_ACCESS}, author = {Muhammad Fahad Malik and Ammara Gul and Ayesha Saadia and Faeiz M. Alserhani}, doi = {10.1109/ACCESS.2025.3565802}, journal = {IEEE Access}, pages = {79397-79409}, shortjournal = {IEEE Access}, title = {Few-shot learning with prototypical networks for improved memory forensics}, volume = {13}, year = {2025}, }
• (2025). Optimized metamaterials for design of enhanced-performance high order mode dipole-driven yagi-uda antenna for millimeter wave applications. ACCESS, 13, 79385-79396. (www)

  @article{ , abstract = {This paper presents the development of a Yagi antenna optimized for third-order mode operation within the millimeter-wave (mmWave) spectrum. A third-order mode driven dipole, along with a reflector, is introduced to enhance the antenna’s gain. The rigorous numerical optimization procedure was employed to precisely adjust the dimensions and positions of the driven dipole, director, and reflectors. The optimized Yagi antenna operates within the fifth generation (5G) band at 28 GHz, with a bandwidth of 2.6 GHz. Radiation pattern analysis indicates that the gain of the antenna in this higher resonant mode exceeds that of a conventional Yagi-Uda antenna, achieving a gain of 9.35 dBi at 28 GHz. To further increase the gain and address the path loss challenges in the mmWave spectrum, a near-zero index metamaterial (NZIM) array was integrated. A $5\times 5$ unit cell array was embedded into the same antenna substrate, positioned in front of the reflector. The metamaterial array was optimized using the trust-region (TR) algorithm, resulting in a significant gain enhancement, reaching 13.8 dBi at 28 GHz while maintaining the operational bandwidth in terms of impedance matching. The antenna was subsequently fabricated and tested, with experimental results demonstrating a strong correlation with the simulated outcomes across all key parameters.}, archive = {J_ACCESS}, author = {Bashar A. F. Esmail and Dustin Isleifson and Slawomir Koziel and Anna Pietrenko-Dabrowska}, doi = {10.1109/ACCESS.2025.3566985}, journal = {IEEE Access}, pages = {79385-79396}, shortjournal = {IEEE Access}, title = {Optimized metamaterials for design of enhanced-performance high order mode dipole-driven yagi-uda antenna for millimeter wave applications}, volume = {13}, year = {2025}, }
• (2025). In-depth review and comparative analysis of DRAM-based PUFs. ACCESS, 13, 79367-79384. (www)

  @article{ , abstract = {Dynamic Random-Access Memory Physical Unclonable Functions (DRAM PUFs) are gaining interest in hardware security, particularly for resource-constrained IoT devices such as smart sensors in the era of rapid digitalization. Since DRAM is present in most of the embedded devices, implementing DRAM PUFs does not require a lot of additional hardware components. In addition, DRAM consumes less power than SRAM and DRAM’s size is generally larger, which leads to larger challenge-response pair (CRP) space, enhancing security and robustness. Despite their potential, DRAM PUFs come with some limitations in device authentication. This paper reviews three types of DRAM PUFs, including start-up DRAM PUF, retention-based DRAM PUF, and latency-based DRAM PUF. It explores their various compositions and examines the individual characteristics of each type. Latency-based DRAM PUFs have been identified as one of the highly promising and reliable PUFs for authentication. We then analyze the research gap in this area to identify opportunities for improvement and provide insights that can help shape future research directions.}, archive = {J_ACCESS}, author = {Yuin Yee Chew and Wei Liang Lim and Jun Liang Tan and Chia Yee Ooi}, doi = {10.1109/ACCESS.2025.3566068}, journal = {IEEE Access}, pages = {79367-79384}, shortjournal = {IEEE Access}, title = {In-depth review and comparative analysis of DRAM-based PUFs}, volume = {13}, year = {2025}, }
• (2025). EEG-based emotion detection using roberts similarity and PSO feature selection. ACCESS, 13, 79353-79366. (www)

  @article{ , abstract = {In this paper, a novel classifier based on Robert’s similarity measure is introduced for emotion detection using electroencephalogram (EEG) signals. Traditional machine learning classifiers machine learning such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF), often struggle to accurately capture both linear and nonlinear patterns in EEG signals and face limitations in handling high-dimensional datasets. The proposed classifier addresses these challenges by segmenting EEG signals into block sizes categorized as small (1 to 10 samples), medium (20 to 100 samples), and large (200 to 1,000 samples), demonstrating particularly strong performance with medium and large block sizes to capture essential features. Integration of Particle Swarm Optimization (PSO) for feature selection, with Robert’s similarity as the fitness function, effectively refines the feature set, boosting classification accuracy and computational efficiency. Evaluation on an EEG brainwave dataset demonstrated that the method achieved an accuracy of 98.75% with feature selection, compared to 94.04% without it in emotional state detection. The results demonstrate that the proposed classifier is a valuable tool for diverse fields, including healthcare by detecting patient stress, education by assessing student engagement, customer service by monitoring satisfaction, and smart environments by enabling adaptive responses. Furthermore, the classifier has potential for broader industrial applications, such as improving workplace productivity by monitoring employee stress and enhancing safety in autonomous vehicle systems, making it a versatile solution for emotionally-aware systems across multiple domains.}, archive = {J_ACCESS}, author = {Mustafa Hussein Mohammed and Mustafa Noaman Kadhim and Dhiah Al-Shammary and Ayman Ibaida}, doi = {10.1109/ACCESS.2025.3555526}, journal = {IEEE Access}, pages = {79353-79366}, shortjournal = {IEEE Access}, title = {EEG-based emotion detection using roberts similarity and PSO feature selection}, volume = {13}, year = {2025}, }
• (2025). An extensive analysis of power quality improvement in a three-stage 15-level inverter using various ANN algorithms. ACCESS, 13, 79331-79352. (www)

  @article{ , abstract = {The performance of multilevel inverters (MLIs) in microgrids plays a vital role in managing daily fluctuations in power demand and supporting the reliable operation of renewable energy sources (RES). To enhance reliability, a 3-stage, 15-level inverter is designed, offering improved performance with a reduced number of switches and lower Total Harmonic Distortion (THD). The number of switches is lowered by using a better Pulse Width Modulation (PWM) method, and Artificial Neural Network (ANN) algorithms help with finding faults. The research integrates both PWM and ANN techniques to enhance the power quality of multilevel inverters (MLIs). A controller using an ANN and a Field-Programmable Gate Array (FPGA) is suggested to correctly identify problems and measure Total Harmonic Distortion (THD) in different fault situations. Datasets for training and testing the ANN models are collected from MATLAB simulations and experimental setups. The performance of different ANN algorithms is analyzed to identify the most effective approach. Fault classification is conducted based on THD measurements for various fault types, including first module failure, second module failure, third module failure, single switch failure, two switches failing in the same leg, and two switches failing in different legs.}, archive = {J_ACCESS}, author = {K. Sakthivel and S. Albert Alexander}, doi = {10.1109/ACCESS.2025.3566266}, journal = {IEEE Access}, pages = {79331-79352}, shortjournal = {IEEE Access}, title = {An extensive analysis of power quality improvement in a three-stage 15-level inverter using various ANN algorithms}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven task offloading and resource allocation in wireless IoT networks. ACCESS, 13, 79314-79330. (www)

  @article{ , abstract = {Mobile IoT networks face major problems in completing efficient task offloading and allocating resources effectively, as all major actions require ample energy consumption and service delays. This paper proposes a solution in the form of a reinforcement learning-based framework that is capable of dynamically optimizing these activities with the help of Group Relative Policy Optimization (GRPO). The new method outcompetes traditional heuristic-nometric methods by being able to adjust device inactivity, network activity, and available resources. Devices are structured into mobility groups and task offloading is defined as Markov Decision Process (MDP). An ample amount of offloading strategies can be obtained by introducing declaration of tasks and corresponding computers, network throughput, and queueing delays. Additionally, GRPO aids in the allocation of computing power, network bandwidth, or storage to maximize resource allocation without breaking task deadlines and other resource limitations. Comparing the GRPO-based method to traditional methods via simulation showed increased performance and reduced service delay. The results clearly demonstrate that GRPO helps reduce service delay and consumption of energy significantly in high load with high dynamism conditions. With the inclusion of decision-making processes along with constraint-tolerance and reinforcement learning, this research aids in IoT frameworks that require low energy output with less delay. The recommended method helps build a strong base for efficient task delegation alongside increased performance and scalability, which subsequently enhances AI edge computing and smart management of IoT systems.}, archive = {J_ACCESS}, author = {Zahraa Hashim Kareem and Rami Qais Malik and Sarmad Jawad and Firas Abedi}, doi = {10.1109/ACCESS.2025.3563059}, journal = {IEEE Access}, pages = {79314-79330}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven task offloading and resource allocation in wireless IoT networks}, volume = {13}, year = {2025}, }
• (2025). Optimal tooth flank modifications for compound planetary robot reducer considering edge contact. ACCESS, 13, 79304-79313. (www)

  @article{ , abstract = {The aim of this study was to optimize gear tooth flank modification in a compound planetary reducer with a reduction ratio of 100:1 to achieve uniform load distribution and minimize the face load factor ( $K_{H\beta }$ ). To achieve this, a prototype of the compound planetary reducer was designed and manufactured, and tests were conducted under temperature-controlled, unlubricated conditions to induce wear on gear tooth surfaces. Microscopic image observations confirmed edge contact on the gear tooth surfaces. Based on these observations, a digital mock-up (DMU) simulation model was developed and validated to align with the experimental results. A parametric analysis of lead slope change was performed using the validated model to determine the optimal value. After optimizing the lead slope, crowning modification was applied to shift the load distribution toward the center of the tooth flank. Comparison of the gear face load factor ( $K_{H\beta }$ ) showed that applying the modifications improved load imbalance by up to 83.45%, effectively solving the edge contact problem compared to the results without modification. The combined approach of testing under unlubricated conditions and validating the DMU model through both experimental and analytical methods resulted in highly reliable findings, offering valuable insights into improving the performance and durability of compound planetary reducers.}, archive = {J_ACCESS}, author = {Jung-Ho Park and Dongu Im and Dong-Joo Moon and Beom-Soo Kim and Young-Jun Park and Jeong-Gil Kim and Seung-Je Cho and Heung-Sub Kim}, doi = {10.1109/ACCESS.2025.3566048}, journal = {IEEE Access}, pages = {79304-79313}, shortjournal = {IEEE Access}, title = {Optimal tooth flank modifications for compound planetary robot reducer considering edge contact}, volume = {13}, year = {2025}, }
• (2025). Detection of spark erosion on insulated rail joints by deep learning. ACCESS, 13, 79291-79303. (www)

  @article{ , abstract = {Insulated Rail Joints (IRJs) are among the critical elements of railway infrastructure, providing structural continuity and electrical isolation within track systems. These joints play a very important role in the detection of trains and in operational safety by segmenting rail networks into electrically isolated sections. Although being a critical part, IRJs suffer from degradation due to high mechanical loads, electrical discharges, and repeated stress. One significant form of localized damage is spark erosion. If left unaddressed, such defects may lead to severe operational failures and safety risks, making effective monitoring systems essential. This paper presents a deep learning-based approach for automatic detection of spark erosion in IRJs. The proposed methodology is based on a two-stage framework: in the first stage, the SqueezeNet convolutional neural network is used for the classification of rail images and the detection of IRJs. In the second stage, a semantic segmentation network is applied to accurately localize and detect spark erosion regions in the detected IRJ images. The study uses two different data sets: one is a high-resolution rail image data set for IRJ classification, and the other labeled images of spark erosion. In addition, advanced image preprocessing and augmentation techniques are adopted to deal with class imbalance and enhance model robustness. Experimental results prove the effectiveness of the proposed method, which achieves 98.1% accuracy in IRJ classification and accurate segmentation of spark erosion areas. These findings underline the potential of two-stage deep learning frameworks to provide real-time, automated, and highly accurate monitoring solutions for railway maintenance. The proposed methodology contributes to enhancing the safety, efficiency, and reliability of railway operations by allowing the early detection of critical defects.}, archive = {J_ACCESS}, author = {Utku Kaya}, doi = {10.1109/ACCESS.2025.3567193}, journal = {IEEE Access}, pages = {79291-79303}, shortjournal = {IEEE Access}, title = {Detection of spark erosion on insulated rail joints by deep learning}, volume = {13}, year = {2025}, }
• (2025). Multi-factor optimization and failure-tolerant design of cable-driven parallel manipulators in deep-sea robotics. ACCESS, 13, 79280-79290. (www)

  @article{ , abstract = {This article presents a framework for optimizing cable-driven parallel manipulators (CPMs) in deep-sea environments, addressing factors as failure tolerance, stiffness, and workspace within a unified framework. While previous studies have examined these factors individually, few have integrated them into a unified framework. The presented framework evaluates CPMs with six, eight, and ten cables, using inverse kinematics, optimization, and stiffness analysis. The six-cable framework is fully constrained, whereas the eight-cable and ten-cable systems are over-constrained, providing additional redundancy in failure scenarios. Post-failure tensions are maintained within safe bounds, defined by mechanical load ratings and operational safety margins. Results indicate that increasing the number of cables improves workspace coverage, enhances stiffness, and reduces post-failure tensions. The ten-cable configuration, in particular, increases operational volume by approximately 20% and reduces peak post-failure tensions by around 15% compared to the six-cable baseline. Our framework also considers adaptive positioning strategies to improve system performance under varying ocean conditions. Future work will analyze the effects of wave-induced motions on system stability and develop sensor-based failure-detection techniques to enhance real-time failure mitigation and system robustness in dynamic environments.}, archive = {J_ACCESS}, author = {Asim Ghaffar and Muhammad Zia Ur Rahman and Víctor Leiva and Cecilia Castro and Carlos Martin-Barreiro}, doi = {10.1109/ACCESS.2025.3561041}, journal = {IEEE Access}, pages = {79280-79290}, shortjournal = {IEEE Access}, title = {Multi-factor optimization and failure-tolerant design of cable-driven parallel manipulators in deep-sea robotics}, volume = {13}, year = {2025}, }
• (2025). Out-of-step protection for a synchronous generator via energy function-based stability assessment using a single local speed measurement. ACCESS, 13, 79264-79279. (www)

  @article{ , abstract = {This study proposes a scheme for determining the loss of synchronism in synchronous generators based on the direct method, utilizing a single local speed measurement within a generator system. By employing individual machine energy functions, this approach establishes a theoretical foundation for determining synchronization thresholds from the perspective of a single generator. Building on this foundation, clear criteria are suggested for determining whether a generator remains stable. The feasibility of the proposed method is verified through its application to synchronous generators, considering a comprehensive setup environment for implementing the proposed algorithm. Case studies are conducted using time-domain simulations in MATLAB/Simulink on the IEEE 39-bus system with varying system inertia and the IEEE 118-bus system. These case studies evaluate the robustness of the proposed criteria and algorithm in determining out-of-step conditions from an energy conversion perspective, analyzing variations in system inertia, control system parameters, power system scale, and fault scenarios across benchmark systems. Furthermore, the performance of the proposed protection scheme is compared with previous studies, highlighting its advantages. By interpreting the loss of synchronism phenomenon using only local measurements from existing systems, the proposed scheme demonstrates robustness against changes in the characteristics of the connected power system. It also simplifies the detailed settings required for effective operation of the protection scheme.}, archive = {J_ACCESS}, author = {Jongju Kim}, doi = {10.1109/ACCESS.2025.3566237}, journal = {IEEE Access}, pages = {79264-79279}, shortjournal = {IEEE Access}, title = {Out-of-step protection for a synchronous generator via energy function-based stability assessment using a single local speed measurement}, volume = {13}, year = {2025}, }
• (2025). IYOLOV7-TPE-SS: Leveraging improved YOLO model with multilevel hyperparameter optimization for road damage detection on edge devices. ACCESS, 13, 79247-79263. (www)

  @article{ , abstract = {Digital twin cities integrated road damage inspection to support smart road infrastructure. The road’s condition was assessed using road damage survey metrics, and the issue was resolved manually. Real-time, automatically, and precisely employing artificial intelligence (AI) is needed to improve road safety and reduce maintenance costs. This research on road defect detection utilizes improved YOLOV7 (iYOLOV7) while multilevel hyperparameter optimization using combination of the Tree-Structured Parzen Estimator (TPE) and Search Space (SS). This model we call TPE-SS. The iYOLOV7 provides real-time processing on edge devices that have limited storage, low-speed processors, and memory. The research investigates how leveraging multilevel hyperparameter optimization using the TPE-SS model improves system performance where road damage is detected. The results of iYOLOV7 model demonstrate the smallest total loss of 0.1, indicating a significant performance improvement where a precision value of 0.986, an average recall (AR) of 0.970, a mean average precision mAP@0.5 of 0.988, a mean average precision mAP@0.5-0.9 of 0.806, and an F1-score of 0.978. The measurement results and analysis findings reveal that our proposed method is accurate enough. The embedded system employs NVIDIA Jetson Nano for inference, which takes only 0.135 seconds and has a scalability performance of 7.470 FPS to recognize things associated with road damage. Although the employed NVIDIA AGX Orin edge device may produce a higher FPS of 67.034 and a faster inference time of 0.014 seconds. This means that the iYOLOV7 model is extremely feasible and practical for usage on edge devices to identify road damage.}, archive = {J_ACCESS}, author = {Haniah Mahmudah and Aulia Siti Aisjah and Syamsul Arifin and Catur Arif Prastyantyo}, doi = {10.1109/ACCESS.2025.3562612}, journal = {IEEE Access}, pages = {79247-79263}, shortjournal = {IEEE Access}, title = {IYOLOV7-TPE-SS: Leveraging improved YOLO model with multilevel hyperparameter optimization for road damage detection on edge devices}, volume = {13}, year = {2025}, }
• (2025). Experimental validation of induction heating as an energy-efficient solution for concrete curing. ACCESS, 13, 79237-79246. (www)

  @article{ , abstract = {In this paper, we propose an innovative, environmentally friendly, and efficient technology for concrete curing through induction heating. This technology is complemented by a comprehensive design methodology aimed at achieving uniform heating across the formwork surface. Induction heating concrete curing is a new approach based on the principle of electromagnetic induction for the purpose of concrete curing, and it offers improved heating efficiency and safety in formwork heating. However, the heating range of the formwork surface is limited depending on the induction heating coil. Thus, we introduce a coil structure derived from evaluations of formwork’s surface temperature distribution through finite element analysis simulations. Heating experiments were conducted on formwork without concrete and formwork with concrete, using a coil designed through simulation. In the experiment without concrete, a uniform temperature distribution was confirmed with a small temperature deviation of 4.28°C. For the experiment with concrete, induction heating experiments and steam curing experiments were conducted at low and room temperatures to compare surface temperature distribution, power consumption, and concrete strength. The experimental results show that room temperature induction heating has 11.39% higher strength and 41.86% less power consumption than low temperature induction heating curing. It also shows 3.51% higher strength and 30.56% less power consumption than steam curing. Room temperature induction heating curing is superior to steam curing in terms of strength and power consumption. Also, although low temperature induction heating curing has lower performance than steam curing, the difference is not significant, so it can be a feasible alternative.}, archive = {J_ACCESS}, author = {Dong-Jin Kim and Chi-Hyung Ahn}, doi = {10.1109/ACCESS.2025.3563524}, journal = {IEEE Access}, pages = {79237-79246}, shortjournal = {IEEE Access}, title = {Experimental validation of induction heating as an energy-efficient solution for concrete curing}, volume = {13}, year = {2025}, }
• (2025). DExIMA: Design explorer for in-memory architectures. ACCESS, 13, 79217-79236. (www)

  @article{ , abstract = {In recent years, Computer-Aided Design (CAD) software have become indispensable tools for designing, testing, and synthesizing logic circuits. Commercial software provided by companies like Synopsys, Cadence, or Mentor dominate the Electronic Design Automation (EDA) field. Nevertheless, several Open Source tools are also available, and some of them aim at assisting the designer in emerging Beyond-von-Neumann computing paradigms, such as Logic-in-Memory (LiM). LiM is a promising architectural and technological solution to the von Neumann Bottleneck, i.e. the performance gap between the CPU and the memory in classical CPU-Memory structures. In literature, various approaches to the LiM paradigm have been proposed. This paper introduces the Design Explorer for In-Memory Architectures (DExIMA) tool, which has the ambitious aim of providing a well-defined design flow strategy for the development, validation and performance estimation of a wide range of LiM architectures. Currently, DExIMA focuses on Coarse-grain Logic-in-Memory (CGLiM) architectures, which integrate memory and computation elements at a coarse-grain level. Nevertheless, DExIMA encompasses a flexible architectural model and a modular performance estimation engine that can be adapted to LiM implementations where memory and logic elements are more finely integrated. Hence, DExIMA is a versatile tool offering an environment for testing and comparing different LiM solutions, empowering designers to explore novel approaches in LiM architecture design.}, archive = {J_ACCESS}, author = {Andrea Coluccio and Alessio Naclerio and Marco Vacca and Giovanna Turvani and Mariagrazia Graziano and Maurizio Zamboni}, doi = {10.1109/ACCESS.2025.3566614}, journal = {IEEE Access}, pages = {79217-79236}, shortjournal = {IEEE Access}, title = {DExIMA: Design explorer for in-memory architectures}, volume = {13}, year = {2025}, }
• (2025). Context-aware machine learning-based beam selection with multi-panel devices in the presence of self-blockage. ACCESS, 13, 79203-79216. (www)

  @article{ , abstract = {Context-aware beam management in millimeter-wave (mmWave) wireless communication systems has received increasing attention over the past few years. Machine learning (ML) has played a key role in leveraging different types of context information from the device position and orientation to more ambitious scenarios using RADAR, LIDAR, or camera images. However, most studies in this area consider simplified configurations for user terminals without considering the self-blockage effects owing to the user’s hand and body. This study is a step towards more realistic configurations and scenarios, where methods for location- and orientation-aware beam alignment are evaluated for multi-panel hand-held devices under mild and severe self-blockage conditions. We propose deterministic and probabilistic hand grip schemes that determine the blockage status of device panels. The probabilistic schemes are introduced to account for the inherent randomness of self-blockage owing to variations in the user’s hand grip. Contrary to the blockage models that introduce attenuation in multipath components depending on their angles-of-arrival, we propose two blockage models that introduce blockage losses over all the received paths, which more realistically emulate panels blocked by “hard” hand gripping. Our numerical simulations show that the multi-panel ML-based beam alignment method is capable of leveraging the terminal’s location and orientation information even under severe self-blockage conditions, achieving performance close to genie-aided alignment with just a few beam-pair measurements.}, archive = {J_ACCESS}, author = {Sajad Rezaie and João Morais and Ahmed Alkhateeb and Preben Mogensen and Carles Navarro Manchón}, doi = {10.1109/ACCESS.2025.3566004}, journal = {IEEE Access}, pages = {79203-79216}, shortjournal = {IEEE Access}, title = {Context-aware machine learning-based beam selection with multi-panel devices in the presence of self-blockage}, volume = {13}, year = {2025}, }
• (2025). Based on the improved EDCSTFN model, modis, landsat 8, and sentinel-2 data were fused to obtain 10 m dense time series images. ACCESS, 13, 79189-79202. (www)

  @article{ , abstract = {High temporal and spatial resolution Earth observation data are crucial in remote sensing, but it is difficult to acquire images that guarantee high temporal and spatial resolution simultaneously due to satellite, technology and budget constraints. In this paper, time series image data with 10 m resolution are generated by spatio-temporal fusion of Modis, Landsat and Sentinel data, which reduces the temporal resolution to 1-2 days, while the existing EDCSTFN model is improved in order to overcome the problem of difficulty in global information extraction due to convolution limitation. The encoder and residual encoder use multi-scale convolution to capture more information from raw Landsat data and enhance feature extraction. In addition, a channel attention module (SE) is introduced to model the nonlinear relationship across channels, which improves the nonlinear capability of the model and reduces the sensitivity to the quality of input data. This approach not only improves the fusion accuracy, but also increases the computational efficiency, leading to the proposal of a new architecture, MIEDCSTFN. 10m-resolution data for the corresponding dates are generated using the output Landsat data from the improved EDCSTFN model as input to the DSTFN model. Comparative validation with several models shows that the improved model has higher accuracy and robustness, and the obtained 10m data are very close to the real data. Compared with the original model, SSIM improves 12.54%, RMSE improves 46.38%, SAM improves 15.46%, ERGAS improves 15.74%, and the experimental results show that the improved model has excellent performance and significant advantages in improving image fusion effect.}, archive = {J_ACCESS}, author = {Jie Chang and Wen Du and Bo Zhang and Sien Guo and Ying Yin and Zhuo Wang and Tongyu Xu and Ziyi Feng}, doi = {10.1109/ACCESS.2025.3564968}, journal = {IEEE Access}, pages = {79189-79202}, shortjournal = {IEEE Access}, title = {Based on the improved EDCSTFN model, modis, landsat 8, and sentinel-2 data were fused to obtain 10 m dense time series images}, volume = {13}, year = {2025}, }
• (2025). Real-time object detection using low-resolution thermal camera for smart ventilation systems. ACCESS, 13, 79180-79188. (www)

  @article{ , abstract = {Deep learning-based object detection research has primarily evolved around RGB camera imagery. While state-of-the-art models like YOLO, SSD, and RetinaNet demonstrate high accuracy in RGB images, their direct application to thermal imagery faces performance degradation due to the inherent differences between RGB and thermal characteristics, coupled with computational demands unsuitable for embedded environments. These challenges are particularly pronounced when using low-resolution thermal sensors, where object boundary ambiguity and thermal pattern uncertainty become significant obstacles. We propose FLARE (Fast and Lightweight Architecture for Real-time Estimation), an ultra-lightweight deep learning model for real-time object detection in low-resolution ( $32\times 24$ ) thermal images. FLARE implements a Feature Compression Block for efficient feature map compression and integration, along with a Spatial Denoise Block that enables adaptive processing across temperature regions to handle thermal sensor noise characteristics. To address the scarcity of thermal data, we developed the TOI (Thermal Object Insertion) data augmentation technique, which generates new training data while preserving thermal pattern characteristics. A composite loss function combining box, classification, and confidence losses was implemented to enhance object detection performance. Implementation on an STM32 MCU demonstrated that our proposed model achieved a higher mAP compared to existing lightweight models, while reducing Flash usage by 40.39 %, RAM by 49.02 %, inference time by 35.18 %, and MAC operations by 38.00 %. Comparative experiments with YOLOv8n, a state-of-the-art lightweight object detection model, on Raspberry Pi 4B showed that FLARE achieved higher mAP (0.72% improvement) while utilizing only 4.72% of YOLOv8n’s memory consumption and achieving 11.34 times faster execution speed.}, archive = {J_ACCESS}, author = {Jun-Hee Lee and Eung-Tea Kim}, doi = {10.1109/ACCESS.2025.3566635}, journal = {IEEE Access}, pages = {79180-79188}, shortjournal = {IEEE Access}, title = {Real-time object detection using low-resolution thermal camera for smart ventilation systems}, volume = {13}, year = {2025}, }
• (2025). Optimizing heart-kidney interaction for cancer detection through physiological process simulation as a decision support system. ACCESS, 13, 79169-79179. (www)

  @article{ , abstract = {A decision support system (DSS) is a computer-based tool used to improve decision-making capabilities for any organization by analyzing the available data. The heart-kidney (HK) model proposed in this paper, as a DSS, simulates the physiological processes of the heart and kidneys in the human body to support cancer detection. The first part of the HK algorithm is inspired by the functions of the heart, where blood molecules (solutions) are sent to the heart, simulating the processes of oxygenation and the pumping of oxygenated blood to the kidneys as a local search. The solutions generated by the heart are then fed into the kidney-inspired algorithm (KA), which performs filtration, reabsorption, secretion, and excretion. The heart component enhances the KA’s local search capability, guiding the algorithm toward optimal solutions. The HK model is evaluated on eight test functions, with results compared to those of the heart algorithm and KA alone. Additionally, the HK algorithm’s effectiveness as a decision support system is tested on benchmark classification and time series prediction problems, with statistical analysis against existing methods in the literature. Finally, the HK algorithm is applied to cancer detection from microRNA data, demonstrating its potential in optimizing decision-making processes in healthcare.}, archive = {J_ACCESS}, author = {Najmeh Sadat Jaddi and Salwani Abdullah and Zalinda Othman and Yazrina Yahya and Mohd Zakree Ahmad Nazri and Say Leng Goh and Fatemeh Alvankarian and Jafar Alvankarian}, doi = {10.1109/ACCESS.2025.3562341}, journal = {IEEE Access}, pages = {79169-79179}, shortjournal = {IEEE Access}, title = {Optimizing heart-kidney interaction for cancer detection through physiological process simulation as a decision support system}, volume = {13}, year = {2025}, }
• (2025). Moiré pattern-based stripe density measurement using low-frequency component detection. ACCESS, 13, 79150-79168. (www)

  @article{ , abstract = {This article presents the measurement of stripe density using image processing. The two essential techniques are a digital grid overlay on the target image to create a moiré pattern and a moving average filter to remove the high-frequency components of the moiré pattern image. The moving average filter produces a low-frequency signal whose frequency is equal to the difference between the frequency (density) of the target stripes image and the frequency of the overlaid grid stripes. We use a method where grid lines are drawn with a lower density on the left side and progressively increase in density towards the right, surpassing the target stripe density. When superimposed on the target image, if the frequency of the grid stripes matches the frequency of the target stripes, the moving average filter will produce an output signal with a frequency equal to zero. An automatic stripe density measurement system is achieved by detecting situations where the frequency value of the output signal from the moving average filter equals zero. The results of measuring the density of simulated stripes created with the program for densities ranging from 30-180 per unit length (L−1) and the number of threads of the fabric on six images showed that the system could measure the actual values consistently, with an error of less than ±1 L−1.}, archive = {J_ACCESS}, author = {Pongsak Khokhuntod and Anucha Kaewpoonsuk}, doi = {10.1109/ACCESS.2025.3566459}, journal = {IEEE Access}, pages = {79150-79168}, shortjournal = {IEEE Access}, title = {Moiré pattern-based stripe density measurement using low-frequency component detection}, volume = {13}, year = {2025}, }
• (2025). Robust control frameworks for heave disturbance suppression in offshore managed pressure drilling environments. ACCESS, 13, 79137-79149. (www)

  @article{ , abstract = {Managed Pressure Drilling (MPD) systems have transformed drilling operations by enabling precise wellbore pressure control, particularly in challenging conditions. Automatic MPD, leveraging advanced choke valve mechanisms, effectively mitigates pressure fluctuations caused by drill string oscillations induced by Heave Motion. This study introduces advanced nonlinear control strategies, including Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC), to enhance pressure regulation. Controller gains are optimized using an improved Grey Wolf Optimization algorithm, targeting the minimization of the Integral Time Absolute Error. Stability of the controllers is rigorously analyzed through Lyapunov criteria. Comprehensive performance evaluations via MATLAB/Simulink simulations, complemented by real-time validation using a C2000 Delfino MCU F28379D Launchpad, demonstrate the robustness and efficacy of the proposed control methodologies.}, archive = {J_ACCESS}, author = {Atif Rehman and Rimsha Ghias and Iftikhar Ahmad and Hammad Iqbal Sherazi}, doi = {10.1109/ACCESS.2025.3560095}, journal = {IEEE Access}, pages = {79137-79149}, shortjournal = {IEEE Access}, title = {Robust control frameworks for heave disturbance suppression in offshore managed pressure drilling environments}, volume = {13}, year = {2025}, }
• (2025). Toward efficient generalization in 3D human pose estimation via a canonical domain approach. ACCESS, 13, 79118-79136. (www)

  @article{ , abstract = {Recent advancements in deep learning methods have significantly improved the performance of 3D Human Pose Estimation (HPE). However, performance degradation caused by domain gaps between source and target domains remains a major challenge to generalization, necessitating extensive data augmentation and/or fine-tuning for each specific target domain. To address this issue more efficiently, we propose a novel canonical domain approach that maps both the source and target domains into a unified canonical domain, alleviating the need for additional fine-tuning in the target domain. To construct the canonical domain, we introduce a canonicalization process to generate a novel canonical 2D-3D pose mapping that ensures 2D-3D pose consistency and simplifies 2D-3D pose patterns, enabling more efficient training of lifting networks. The canonicalization of both domains is achieved through the following steps: 1) in the source domain, the lifting network is trained within the canonical domain; 2) in the target domain, input 2D poses are canonicalized prior to inference by leveraging the properties of perspective projection and known camera intrinsics. Consequently, the trained network can be directly applied to the target domain without requiring additional fine-tuning. Experiments conducted with various lifting networks and publicly available datasets (e.g., Human3.6M, Fit3D, MPI-INF-3DHP) demonstrate that the proposed method substantially improves generalization capability across datasets while using the same data volume.}, archive = {J_ACCESS}, author = {Hoosang Lee and Jeha Ryu}, doi = {10.1109/ACCESS.2025.3566109}, journal = {IEEE Access}, pages = {79118-79136}, shortjournal = {IEEE Access}, title = {Toward efficient generalization in 3D human pose estimation via a canonical domain approach}, volume = {13}, year = {2025}, }
• (2025). Top emission organic light emitting diodes fabricated on stainless steel foil. ACCESS, 13, 79107-79117. (www)

  @article{ , abstract = {This study presents the fabrication and optimization of top-emitting organic light-emitting diodes (TEOLEDs) on stainless steel (SS) substrates, aiming to address challenges in surface roughness, hole injection efficiency, and light extraction. Due to the dual-sided conductivity of SS foil, a solution-type organic material (TOC 100) was spin-coated as an insulating and planarization layer, reducing the surface roughness from 8.61 nm to 0.46 nm, as measured by atomic force microscopy (AFM). The device structure was SS/Al/HATCN/TAPC/BH-08(host):OD-01(dopant)/TPBi/LiF/Al/Ag, with electro-optical characteristics comparable to TEOLEDs on glass substrates. However, the current density was slightly lower due to the roughness of the aluminum anode. The device optimizations included adding a gold layer (3 nm) to the aluminum anode, which increased hole injection efficiency and improved luminance from 4.99 cd/m2 to 24.65 cd/m2 at 14 V when the hole transport layer (TAPC) thickness was reduced from 20 nm to 10 nm. Adjusting cathode thicknesses (Al/Ag at 6/8 nm versus 12/10 nm) enhanced light output significantly, with luminance increasing from 9.5 cd/m2 to 24.7 cd/m2 at 15 V. Furthermore, using Ytterbium/Silver (Yb/Ag) cathodes improved current efficiency to 6.3 cd/A at 8 V compared to 4.5 cd/A for Liq/Al/Ag cathodes. Encapsulation extended TEOLED lifetime from 365 minutes to 490 minutes for luminance decay from 800 to 400 cd/m2. Flexibility tests demonstrated that the TEOLEDs maintained functionality under bending radii of 0.5 cm with brightness of 142 cd/m2 at 5 V. Additionally, thermal imaging revealed superior heat dissipation on SS substrates compared to glass substrates, with backside temperatures of $24.6~^{\circ }$ C versus $33.9~^{\circ }$ C when biased at 10 V.}, archive = {J_ACCESS}, author = {Fuh-Shyang Juang and Yu-Hsin Tuan and Hong-Kai Chen and Min-Chieh Lin and Hung-Lun Lin and Jun-Lin Huang}, doi = {10.1109/ACCESS.2025.3564910}, journal = {IEEE Access}, pages = {79107-79117}, shortjournal = {IEEE Access}, title = {Top emission organic light emitting diodes fabricated on stainless steel foil}, volume = {13}, year = {2025}, }
• (2025). Combined multi-agent and centralized resource allocation in cloud radio access networks. ACCESS, 13, 79098-79106. (www)

  @article{ , abstract = {The fifth generation (5G) mobile network is designed to facilitate high data rates with massive connectivity with the benefit of small cell technology. The cloud radio access network (C-RAN) is a promising mobile network architecture that can meet the ever-increasing resource demand of a growing number of users. In C-RAN, base station functionalities are separated into baseband units (BBUs) and remote radio heads (RRHs), with BBUs centralized and virtualized via cloud computing. However, this architecture introduces new challenges in efficiently allocating resources to dynamic users. This paper aims to design a resource allocation scheme that improves system efficiency and satisfies dynamic user demands in C-RAN. We propose a hybrid resource allocation approach that combines centralized control and multi-agent-based decision-making. The centralized controller, located within the BBU pool, collaborates with virtual base stations (VBSs) acting as multi-agent system (MAS) agents. The resource allocation solution is derived by jointly considering the real-time resource requests from agents and the historical demand estimates generated by the centralized controller. Through simulation-based evaluation, we compare our proposed scheme with conventional random and fixed resource allocation methods. The results demonstrate improved performance in terms of resource utilization, reduced unfulfilled demand, and fairness among VBS agents. The proposed combined resource allocation strategy effectively meets dynamic user requirements while maintaining system efficiency in C-RAN. Our work highlights the importance of integrating historical demand trends with real-time agent requests for improved long-term resource planning.}, archive = {J_ACCESS}, author = {Rehenuma Tasnim Rodoshi and Rezoan Ahmed Nazib and Chanjun Chun and Wooyeol Choi}, doi = {10.1109/ACCESS.2025.3566389}, journal = {IEEE Access}, pages = {79098-79106}, shortjournal = {IEEE Access}, title = {Combined multi-agent and centralized resource allocation in cloud radio access networks}, volume = {13}, year = {2025}, }
• (2025). Dataset construction and effectiveness evaluation of spoken-emotion recognition for human machine interaction. ACCESS, 13, 79084-79097. (www)

  @article{ , abstract = {The widespread use of large language models (LLMs) and voice-based agents has rapidly expanded Human-Computer Interaction (HCI) through spoken dialogue. To achieve more natural communication, nonverbal cues—especially those tied to emotional states—are critical and have been studied via deep learning. However, three key challenges persist in existing emotion recognition datasets: 1) most assume human-to-human interaction, neglecting shifts in speech patterns when users address a machine, 2) many include acted emotional expressions that differ from genuine internal states, and 3) even non-acted datasets often rely on third-party labels, creating potential mismatches with speakers’ actual emotions. Prior studies report that agreement between external labels and speakers’ internal states can be as low as 60–70%. To address these gaps, we present the VR-Self-Annotation Emotion Dataset (VSAED), consisting of 1,352 naturally induced and non-acted Japanese utterances (1.5 hours). Each utterance is labeled with self-reported internal emotional states spanning six categories. We investigated: 1) how effectively non-acted, machine-oriented speech conveys internal emotions, 2) whether speakers alter expressions when aware of an emotion recognition system, and 3) whether specific conditions yield notably high accuracy. In experiments using a HuBERT-based classifier, we achieve around 40% recognition accuracy, underscoring the complexity of capturing subtle internal emotions. These findings highlight the importance of domain-specific datasets for human-machine interactions.}, archive = {J_ACCESS}, author = {Mitsuki Okayama and Tatsuhito Hasegawa}, doi = {10.1109/ACCESS.2025.3565537}, journal = {IEEE Access}, pages = {79084-79097}, shortjournal = {IEEE Access}, title = {Dataset construction and effectiveness evaluation of spoken-emotion recognition for human machine interaction}, volume = {13}, year = {2025}, }
• (2025). Negative-sequence reactive power control and active power control for DC/3ϕ power conversion using single-delta bridge-cell (SDBC) converter. ACCESS, 13, 79070-79083. (www)

  @article{ , abstract = {This paper focuses on a DC/ $3\phi $ power converter combining the single-delta bridge-cell (SDBC) converter and a single-phase PWM converter via a medium-frequency transformer for voltage flicker mitigation. In this application, the converter is required to produce negative-sequence reactive power and active power in addition to positive-sequence reactive power and harmonic power. However, in conventional SDBC-based STATCOM, steady-state active power control is not possible, and furthermore, it suffers from large voltage fluctuation in the DC-capacitor voltages when low-frequency active power is controlled. On the other hand, the proposed converter is capable of controlling active power and has been confirmed to operate at 37% of the rated reactive power, demonstrating its adaptability for flicker compensation. Nevertheless, the proposed converter still faces two challenges when intended for flicker compensation operation. The relationship between the deliverable negative-sequence reactive power and active power has not been clarified in previous research. Additionally, the operation of the system during low-frequency active power control has not been analyzed. This paper attempts to solve the first problem by conducting theoretical power analysis when negative-sequence reactive power is controlled by supplying circulating current from the single-phase converter. The second problem is also addressed by presenting current reference values for low-frequency active power control and conducting circuit analysis. The validity of the control and theoretical analysis developed in this paper is experimentally verified using a 100-V, 5-kVA downscaled model.}, archive = {J_ACCESS}, author = {Atsuya Suzuki and Makoto Hagiwara and Yu-Chen Su}, doi = {10.1109/ACCESS.2025.3566447}, journal = {IEEE Access}, pages = {79070-79083}, shortjournal = {IEEE Access}, title = {Negative-sequence reactive power control and active power control for DC/3ϕ power conversion using single-delta bridge-cell (SDBC) converter}, volume = {13}, year = {2025}, }
• (2025). Cost-effective optimization of sizing and charging profiles for PHEV parking lots in smart microgrids using harmony search algorithm. ACCESS, 13, 79053-79069. (www)

  @article{ , abstract = {This paper presents an optimization approach for managing the charging and discharging of electric vehicles (EVs) in parking lots using the Harmony Search (HS) and Differential Evolution (DE) algorithms. The study is conducted on a standard IEEE 33-bus grid considering three EV penetration levels: 11.3%, 35%, and 45%. The objective is to minimize operational costs while improving grid performance. Simulation results indicate that increasing EV penetration slightly raises overall expenses due to the higher cost of vehicle charging compared to the revenue from discharging. However, EV participation significantly reduces ohmic losses and improves the grid load profile. The proposed HS algorithm outperforms the DE algorithm by achieving lower microgrid costs and better convergence efficiency. Specifically, HS reduces energy losses by up to 40%, demonstrating its effectiveness in optimizing energy management for microgrids with high EV integration.}, archive = {J_ACCESS}, author = {Mehrdad Ahmadi Kamarposhti and Hassan Shokouhandeh and Emad M. Ahmed and Zaki A. Zaki and Ilhami Colak and El Manaa Barhoumi and Kei Eguchi}, doi = {10.1109/ACCESS.2025.3564841}, journal = {IEEE Access}, pages = {79053-79069}, shortjournal = {IEEE Access}, title = {Cost-effective optimization of sizing and charging profiles for PHEV parking lots in smart microgrids using harmony search algorithm}, volume = {13}, year = {2025}, }
• (2025). Evolutionary algorithms for edge server placement in vehicular edge computing. ACCESS, 13, 79030-79052. (www)

  @article{ , abstract = {Vehicular Edge Computing (VEC) is a critical enabler for intelligent transportation systems (ITS). It provides low-latency and energy-efficient services by offloading computation to the network edge. Effective edge server placement is essential for optimizing system performance, particularly in dynamic vehicular environments characterized by mobility and variability. The Edge Server Placement Problem (ESPP) addresses the challenge of minimizing latency and energy consumption while ensuring scalability and adaptability in real-world scenarios. This paper proposes a framework to solve the ESPP using real-world vehicular mobility traces to simulate realistic conditions. To achieve optimal server placement, we evaluate the effectiveness of several advanced evolutionary algorithms. These include the Genetic Algorithm (GA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Teaching-Learning-Based Optimization (TLBO). Each algorithm is analyzed for its ability to optimize multiple objectives under varying network conditions. Our results show that ACO performs the best, producing well-distributed pareto-optimal solutions and balancing trade-offs effectively. GA and PSO exhibit faster convergence and better energy efficiency, making them suitable for scenarios requiring rapid decisions. The proposed framework is validated through extensive simulations and compared with state-of-the-art methods. It consistently outperforms them in reducing latency and energy consumption. This study provides actionable insights into algorithm selection and deployment strategies for VEC, addressing mobility, scalability, and resource optimization challenges. The findings contribute to the development of robust, scalable VEC infrastructures, enabling the efficient implementation of next-generation ITS applications.}, archive = {J_ACCESS}, author = {A. Surayya and Muzakkir Muzakkir Hussain and V. Dinesh Reddy and Ashu Abdul and Firoj Gazi}, doi = {10.1109/ACCESS.2025.3566172}, journal = {IEEE Access}, pages = {79030-79052}, shortjournal = {IEEE Access}, title = {Evolutionary algorithms for edge server placement in vehicular edge computing}, volume = {13}, year = {2025}, }
• (2025). Research on vehicle route optimization for half-open multi-energy urban distribution considering order priority. ACCESS, 13, 79007-79029. (www)

  @article{ , abstract = {Aiming at the current problems of increasingly serious tailpipe pollution of urban distribution vehicles and irrational distribution route planning, we construct a fuel-electric hybrid multi-trip multi-center half-open joint distribution vehicle routing optimization model (F-EHOMTMDVRPOPTW) considering order priority and fuzzy time window, and introduce Tent chaotic mapping combined with an improved discrete sparrow search algorithm (DSSA) with stochastic key encoding strategy for solving. Based on the traditional fuel VRP, new energy vehicles are added to form a multi-energy vehicle formation. Considering the order priority and customer time window in the context of urban distribution with non-new energy vehicle restriction, and constructing a distribution model for minimizing the total cost with the transportation cost, charging cost of new energy vehicles, carbon emission and fuel consumption cost, and penalty cost for violating the time window. On the basis of the sparrow search algorithm, Tent chaotic mapping is added and random key coding strategy is inserted for discretization, which increases the diversity of the initial population of the sparrow search algorithm and improves the algorithm’s global optimization seeking ability. Finally, the algorithms and models are analyzed in simulation experiments using benchmark test functions and arithmetic examples to verify the reasonableness of the models and the effectiveness of the algorithms, which provides theoretical basis for urban distribution and utilization of energy.}, archive = {J_ACCESS}, author = {Mingxuan Zhang}, doi = {10.1109/ACCESS.2025.3565924}, journal = {IEEE Access}, pages = {79007-79029}, shortjournal = {IEEE Access}, title = {Research on vehicle route optimization for half-open multi-energy urban distribution considering order priority}, volume = {13}, year = {2025}, }
• (2025). The impact of digital climate on employee wellbeing in the digital transformation era. ACCESS, 13, 78992-79006. (www)

  @article{ , abstract = {Organizations pursuing enduring success in the digital transformation era need to understand employee interactions with rapidly changing technologies. Our research introduces digital climate as a vital organizational climate subdimension that represents employee views about technology readiness, digital resources, and digital leadership. A Grounded Theory research method was used in a public government organization to conduct 25 semi-structured interviews, which uncovered three main digital climate dimensions, including User Digital Experience, Digital Process Efficiency, and Organization’s Digital Effectiveness. Employee involvement in digital tool selection and customization combined with workflow optimization and rapid technical support delivery leads to improved employee well-being through technostress reduction, burnout prevention, and job satisfaction enhancement. The research demonstrates how digital climate affects employee well-being, providing scholars and practitioners with tools to measure and control digital transformation’s impact on employee well-being for sustainable competitive advantage in contemporary workplaces and future development.}, archive = {J_ACCESS}, author = {Ziv Avtalion and Itzhak Aviv and Irit Hadar and Gil Luria and Oshri Bar-Gil}, doi = {10.1109/ACCESS.2025.3566211}, journal = {IEEE Access}, pages = {78992-79006}, shortjournal = {IEEE Access}, title = {The impact of digital climate on employee wellbeing in the digital transformation era}, volume = {13}, year = {2025}, }
• (2025). Dynamic use of RSSI in LoRa networks for decision making in outdoor applications. ACCESS, 13, 78982-78991. (www)

  @article{ , abstract = {Livestock monitoring is an important task since it enables real-time tracking of animal health and behaviour, improving productivity, reducing losses, and ensuring better resource management in farming. This paper proposes a strategy to reduce the energy consumption of a microcontroller designed for livestock monitoring in a controlled environment using LoRaWAN and GPS technologies. The aim is to extend the monitoring period implementing a novel method based on the RSSI parameter. The strategy estimates the distance of the livestock to a gateway avoiding the need of continuously using a GPS. Experimental test for different periods of time were carried out, considering variable duty cycles depending on the position of the livestock with respect to the RSSI zone. An important reduction on the energy consumed by the microcontroller was obtained, validating the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Carlos Muñoz and Cristián Pesce and Ignacio Huircán and Oscar Villagra and Rodrigo Villalobos and Javier Riedemann}, doi = {10.1109/ACCESS.2025.3566020}, journal = {IEEE Access}, pages = {78982-78991}, shortjournal = {IEEE Access}, title = {Dynamic use of RSSI in LoRa networks for decision making in outdoor applications}, volume = {13}, year = {2025}, }
• (2025). GVDF-RRT*: An improved F-RRT* path planning algorithm based on generalized voronoi diagram. ACCESS, 13, 78968-78981. (www)

  @article{ , abstract = {The sampling-based RRT* algorithm has been extensively employed for path planning. The improved variant, F-RRT*, significantly reduces the initial cost by generating new nodes; however, it also leads to increased computational time and poses challenges in narrow passage environments. To address these issues, this paper proposes an improved F-RRT* algorithm based on the Generalized Voronoi Diagram (GVD), called GVDF-RRT*. First, a sparse sampling strategy based on GVD is proposed, which improves the sampling efficiency and reduces the initial sampling nodes and initial time by guiding the random tree to explore the space quickly through GVD nodes. At the same time, the success rate of planning in narrow passage environments is improved by equalizing the sampling probability of each channel in the map. Secondly, a node direct connection mechanism is established, which reduces the redundant computation and further reduces the initial time by hierarchical processing of creation nodes, while combined with the forward optimization mechanism to optimize the paths in iteration, effectively reducing the path cost. Finally, simulations in three test environments show that, compared to RRT*, Q-RRT*, and F-RRT*, the GVDF-RRT* algorithm reduces the initial number of sampled nodes by 47.42%-76.57%, the initial time by 37.07%-86.00%, and the initial path cost by 0.47%-0.91%, while significantly improving the success rate in narrow passage environments. This further demonstrates that the proposed GVDF-RRT* significantly outperforms the baseline method in terms of initial performance and adaptability to narrow passage environments.}, archive = {J_ACCESS}, author = {Yitao Zhong and Xiaojing Zhu and Jianfang Huang and Yaokun Ye and Zhaoming Wang and Yuhuai Wang}, doi = {10.1109/ACCESS.2025.3566306}, journal = {IEEE Access}, pages = {78968-78981}, shortjournal = {IEEE Access}, title = {GVDF-RRT*: An improved F-RRT* path planning algorithm based on generalized voronoi diagram}, volume = {13}, year = {2025}, }
• (2025). Study of the effect of 3D printing parameters on the dielectric constant in piezoelectric polylactic Acid/Barium titanate composites via fused granular fabrication. ACCESS, 13, 78959-78967. (www)

  @article{ , abstract = {This research aims to study the fabrication of electronic devices using pellet extrusion 3D printing with composite material. The process employed a polymer-based composite material that incorporated a substantial amount of barium titanate powder, reaching 80 wt.%. A thorough examination was conducted to understand the various factors influencing the printed components, using a design of experiments (DOE) approach. This method identifies the optimal printing parameters for a Custom Fused Granular Fabrication printer, such as layer height (0.32), barrel temperature (240°C), flow rate (500), and printing speed (2 mm/s). The dielectric constant of the polylactic acid - barium titanate composite reached 8.47 and a loss tangent of 0.099 in the X-band, making it suitable for applications involving high frequencies with a lower cost process and using a more sustainable polymer.}, archive = {J_ACCESS}, author = {Rodrigo Ruz and Dreidy Vásquez and Rodrigo Ortiz and Francisco Pizarro}, doi = {10.1109/ACCESS.2025.3565941}, journal = {IEEE Access}, pages = {78959-78967}, shortjournal = {IEEE Access}, title = {Study of the effect of 3D printing parameters on the dielectric constant in piezoelectric polylactic Acid/Barium titanate composites via fused granular fabrication}, volume = {13}, year = {2025}, }
• (2025). Toward an ontology of wargame design. ACCESS, 13, 78928-78958. (www)

  @article{ , abstract = {Governments rely on military power to address conflicts, manage crises, and adapt to the evolving nature of modern warfare, which is often characterized by uncertainty and disorder. Wargames, traditionally military tools for simulating conflicts and decision-making, have gained prominence in civilian applications, including business, cybersecurity, disaster management, and critical infrastructure protection. Despite their utility, designing wargames is a time-intensive process with significant challenges, such as scenario creation and decision modeling, necessitating structured and systematic approaches. This research formalizes wargame design through ontology-driven conceptual modeling, structuring its key concepts, characteristics, and elements. Ontologies provide a structured representation of knowledge, facilitating communication, knowledge management, and collaborative design. As a result, we developed core ontologies for wargame design based on the Unified Foundational Ontology (UFO) and implemented them using OntoUML. Our innovation lies in analyzing wargame design processes across various countries and military organizations to develop a comprehensive reference model for wargame design. Additionally, we are the first to apply UFO for conceptual modeling of the wargame domain. These ontologies enhance wargame design by fostering standardization, adaptability, and support for intelligent systems, enabling dynamic and responsive scenarios. These contributions enhance wargame design efficiency and effectiveness, applicable to military and civilian contexts.}, archive = {J_ACCESS}, author = {Leandro Ouriques and Carlos Eduardo Barbosa and Joshua Kritz and Geraldo Xexéo}, doi = {10.1109/ACCESS.2025.3566249}, journal = {IEEE Access}, pages = {78928-78958}, shortjournal = {IEEE Access}, title = {Toward an ontology of wargame design}, volume = {13}, year = {2025}, }
• (2025). Yield diagnosis and tuning for emerging semiconductors during research stage. ACCESS, 13, 78915-78927. (www)

  @article{ , abstract = {The process of taking a new semiconductor device from the lab to the factory involves a lot of time, funds and manpower, a large portion of which is spent on device yield improvement. In recent years new methods have been tried to rapidly improve yields and using machine learning (ML) algorithms is one option. However, they usually require a large dataset, which is often unavailable at the device research stage, emerging semiconductors (e.g., 2D materials) are extremely costly to pilot. In this paper, we propose a yield diagnosis and tuning scheme based on ensemble learning and Bayesian optimization, which demonstrate outstanding performance even with a limited data volume. We use real 2-D semiconductor device fabrication process data for scheme evaluation. Experimental results show that the algorithm for yield prediction has achieved regression fitting results whose mean absolute error (MAE) is no more than 8 points and explained variance (EVAR) is no less than 0.62, this indicates that the model fits well on this dataset. We also remanufactured a batch of devices based on the yield tuning recommendations to validate the effectiveness of our approach. The test results indicated a final yield score of 86 points, after evaluating several key indicators such as mobility and hysteresis, resulting in a 62% improvement.}, archive = {J_ACCESS}, author = {Chunshan Wang and Zizhao Ma and Yuxuan Zhu and Chensheng Jin and Dongyu Chen and Chuxin Zhang and Yining Chen and Wenzhong Bao and Yufeng Xie}, doi = {10.1109/ACCESS.2025.3563761}, journal = {IEEE Access}, pages = {78915-78927}, shortjournal = {IEEE Access}, title = {Yield diagnosis and tuning for emerging semiconductors during research stage}, volume = {13}, year = {2025}, }
• (2025). Age of information-optimized differential protection strategy for smart grids. ACCESS, 13, 78905-78914. (www)

  @article{ , abstract = {Differential protection is a fundamental mechanism in power systems for detecting and isolating faults. However, traditional protection schemes face significant challenges in modern smart grids due to communication delays and the inability to dynamically adapt to real-time information. These limitations often result in reduced fault detection accuracy and delayed system response, threatening the reliability and stability of the grid. To address these shortcomings, we propose an Age of Information (AoI)-optimized differential protection strategy tailored for smart grids. By modeling the differential protection problem as a remote Markov Decision Process (MDP), we incorporate AoI as a critical factor to capture the freshness of information in decision-making under stochastic delays. Our analysis reveals that treating AoI as auxiliary side information, rather than a standalone optimization goal, significantly enhances the timeliness of information and indirectly improves fault detection accuracy. By leveraging AoI, we show that it is possible to enhance the timeliness and accuracy of fault detection, even under adverse network conditions, by ensuring that the system relies on the freshest information available. In particular, our results demonstrate a 6.5% improvement in information freshness compared to traditional methods, leading to enhanced system performance. This study provides new insights into leveraging information freshness to overcome the inherent limitations of traditional differential protection schemes, thereby improving the efficiency and resilience of modern power systems.}, archive = {J_ACCESS}, author = {Jiajia Fu and Zhongmiao Kang and Ying Zeng and Zanhong Wu}, doi = {10.1109/ACCESS.2025.3566264}, journal = {IEEE Access}, pages = {78905-78914}, shortjournal = {IEEE Access}, title = {Age of information-optimized differential protection strategy for smart grids}, volume = {13}, year = {2025}, }
• (2025). Capturing semantic relationships using full dependency forests to improve consistency in long document summarization. ACCESS, 13, 78895-78904. (www)

  @article{ , abstract = {There are complex discourse relationships between sentences, which can be viewed as a tree structure. This semantic structure provides important information for summarization and helps to generate concise and coherent summaries. However, current neural network-based models usually treat articles as simple sentence sequences, ignoring the intrinsic structure. To integrate discourse tree information, we propose a generative summarization model that incorporates tree structure. The article’s structure can be more accurately captured by this model, which can also produce succinct summaries by leveraging the semantic dependencies of the source material. Also, since large models are difficult to apply in downstream tasks, we try to add noise to the pre-training parameters to improve the performance of the model on the long document summarization task. Experimental results show that our model ROUGE scores outperform the state-of-the-art best models in both pubMed and arXiv datasets. We further performed human evaluation, and N-gram evaluation. The results show that our method also improves the cohesiveness and semantic coherence of abstracts.}, archive = {J_ACCESS}, author = {Yanjun Wu and Luong Vuong Nguyen and O-Joun Lee}, doi = {10.1109/ACCESS.2025.3565823}, journal = {IEEE Access}, pages = {78895-78904}, shortjournal = {IEEE Access}, title = {Capturing semantic relationships using full dependency forests to improve consistency in long document summarization}, volume = {13}, year = {2025}, }
• (2025). Enhancing property-based token attestation with homomorphic encryption (PTA-HE) for secure mobile computing. ACCESS, 13, 78875-78894. (www)

  @article{ , abstract = {This paper proposes PTA-HE, an enhanced Property-based Token Attestation scheme integrated with Homomorphic Encryption (HE), specifically designed to address critical security challenges in mobile cloud computing environments. Traditional Property-based Token Attestation (PTA) protocols, although foundational, inherently lack robust mechanisms to secure sensitive data during active processing stages, exposing data to potential confidentiality and integrity breaches. Our main contributions are: the introduction of PTA-HE, which resolves these vulnerabilities by enabling computations directly on encrypted data, ensuring continuous protection and resilience against unauthorized access and manipulation; a strategic employment of Trusted Third Parties (TTPs) for secure attestation management, leveraging HE to maintain data confidentiality throughout the entire attestation workflow; rigorous experimental evaluations quantifying computational overhead, communication costs, latency, and scalability implications, transparently illustrating the performance trade-offs associated with enhanced security; and formal verification using the Scyther tool demonstrating PTA-HE’s superior correctness and robustness against multiple security threats, such as replay and man-in-the-middle attacks. Consequently, PTA-HE provides a highly effective and practical solution for secure mobile computing applications requiring stringent assurances of data privacy and integrity.}, archive = {J_ACCESS}, author = {Thinh Le Vinh and Huan Thien Tran and Samia Bouzefrane}, doi = {10.1109/ACCESS.2025.3565945}, journal = {IEEE Access}, pages = {78875-78894}, shortjournal = {IEEE Access}, title = {Enhancing property-based token attestation with homomorphic encryption (PTA-HE) for secure mobile computing}, volume = {13}, year = {2025}, }
• (2025). Wind turbine fault identification in sample imbalance scenarios using FRBCS with GAN oversampling and metric learning. ACCESS, 13, 78864-78874. (www)

  @article{ , abstract = {The operation data from different wind turbines (WTs) is not universal, due to the influence of installation, location, and environmental conditions, which caused incomplete and imbalanced fault sample data, and influenced the fault identification accuracy. Therefore, this paper proposes a fuzzy rule-based classification system (FRBCS) based on generative adversarial network (GAN) oversampling and metric learning. The GAN oversampling algorithm is introduced to eliminate the imbalance of the actual samples, and the metric learning algorithm (MLA) is adopted to improve the separability of different fault samples of the FRBCS. 10 common faults of megawatt-level WTs are used to validate the effectiveness of the proposed method, the results show that the proposed method effectively improves fault identification accuracy in the presence of imbalanced fault samples.}, archive = {J_ACCESS}, author = {Changsheng Kang and Xiaoyi Qian and Lixin Wang and Ziheng Dai and Shuai Guan and Yi Zhao and Wenyao Sun}, doi = {10.1109/ACCESS.2024.3522587}, journal = {IEEE Access}, pages = {78864-78874}, shortjournal = {IEEE Access}, title = {Wind turbine fault identification in sample imbalance scenarios using FRBCS with GAN oversampling and metric learning}, volume = {13}, year = {2025}, }
• (2025). Leveraging text signed distance function map for boundary-aware guidance in scene text segmentation. ACCESS, 13, 78850-78863. (www)

  @article{ , abstract = {Scene text segmentation is to predict pixel-wise text regions from an image, enabling in-image text editing or removal. One of the primary challenges is to remove noises including non-text regions and predict intricate text boundaries. To deal with that, traditional approaches utilize a text detection or recognition module explicitly. However, they are likely to highlight noise around the text. Because they did not sufficiently consider the boundaries of text, they fail to accurately predict the fine details of text. In this paper, we introduce leveraging text signed distance function (SDF) map, which encodes distance information from text boundaries, in scene text segmentation to explicitly provide text boundary information. By spatial cross attention mechanism, we encode the text-attended feature from the text SDF map. Then, both visual and text-attended features are utilized to decode the text segmentation map. Our approach not only mitigates confusion between text and complex backgrounds by eliminating false positives such as logos and texture blobs located far from the text, but also effectively captures fine details of complex text patterns by leveraging text boundary information. Extensive experiments demonstrate that leveraging text SDF map in scene text segmentation provides superior performances on various scene text segmentation datasets.}, archive = {J_ACCESS}, author = {Ho Jun Kim and Hak Gu Kim}, doi = {10.1109/ACCESS.2025.3566045}, journal = {IEEE Access}, pages = {78850-78863}, shortjournal = {IEEE Access}, title = {Leveraging text signed distance function map for boundary-aware guidance in scene text segmentation}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of high-K wrapped GaN gate all around FET as high-frequency device in IOT systems. ACCESS, 13, 78833-78849. (www)

  @article{ , abstract = {Gallium Nitride (GaN)-based Gate Stack (GS) Gate-All-Around Field Effect Transistors (GAA FETs) are promising candidates for next-generation energy-efficient electronics due to their exceptional material properties, such as high electron mobility, wide bandgap, and superior thermal stability. This study focuses on the performance evaluation of GaN-based GAA FETs for both DC and AC explorations incorporating high-k dielectric spacers and source/drain underlap engineering. The DC analyses parameters such as subthreshold slope, threshold voltage, drain current, leakage current and current ratio. ~95% reduction in off-state leakage current and ~606% increase in switching ratio is acquired in comparison to proposed 2nm technology node IRDS2025. Additionally, the subthreshold swing is optimized around ~65mV/decade indicating superior leakage control and switching performance. AC analysis evaluates key figures of merits, including transconductance, cut-off frequency, and parasitic capacitances. It is observed that the high-k spacer significantly enhances electrostatic control reducing short-channel effects (SCEs) and improving device stability. The optimized underlap minimizes parasitic capacitance, leading to ~103% increase in cut-off frequency and ~163% improvement in transconductance, resulting in enriched high-frequency performance. These findings underscore the potential of GaN-based GAA FETs with high-k spacer and underlap designs for low-power, high-speed applications in green and sustainable electronics, particularly for IoT systems and 5G technologies. The integration of gate underlap with high-k dielectric spacer region effectively reduces SCEs and parasitic resistances. By controlling the electric field distribution and improving electrostatics integrity this design achieves augmented switching speed while suppresses leakage significantly, making the device a strong candidate for next-generation digital and RF applications.}, archive = {J_ACCESS}, author = {Sneha Singh and Rudra Sankar Dhar and Amit Banerjee and Vinay Gupta}, doi = {10.1109/ACCESS.2025.3564606}, journal = {IEEE Access}, pages = {78833-78849}, shortjournal = {IEEE Access}, title = {Design and analysis of high-K wrapped GaN gate all around FET as high-frequency device in IOT systems}, volume = {13}, year = {2025}, }
• (2025). Efficient approach for brain tumor detection and classification using fuzzy thresholding and deep learning algorithms. ACCESS, 13, 78808-78832. (www)

  @article{ , abstract = {Accurate and efficient brain tumor diagnosis remains a critical challenge in medical imaging. This study proposes a novel framework that integrates fuzzy logic-based segmentation with deep learning (DL) techniques to enhance brain tumor detection and classification in magnetic resonance imaging (MRI) scans. In the first stage, a fuzzy thresholding approach is applied to segment MRI images into healthy and abnormal regions, enabling the precise extraction of tumor areas. In the second stage, an optimized convolutional neural network (CNN) model classifies tumors into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed method is evaluated across three large public datasets comprising more than 23,000 MRI images. Experimental results demonstrate that the model achieved an accuracy of 98% and a Dice similarity coefficient of 97.97%, confirming its high effectiveness in accurately extracting tumor regions and classifying them correctly. Furthermore, the proposed system outperforms conventional machine learning, deep learning, and transfer learning techniques. In addition to classification, the system accurately estimates tumor size, providing valuable clinical insights. These findings highlight the potential of combining fuzzy logic with DL to improve automated brain tumor diagnostics, enhance diagnostic reliability, and support clinical decision-making.}, archive = {J_ACCESS}, author = {Nashaat M. Hussain Hassan and Wadii Boulila}, doi = {10.1109/ACCESS.2025.3566332}, journal = {IEEE Access}, pages = {78808-78832}, shortjournal = {IEEE Access}, title = {Efficient approach for brain tumor detection and classification using fuzzy thresholding and deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Z packed U-cell modular multilevel converter for STATCOM applications. ACCESS, 13, 78795-78807. (www)

  @article{ , abstract = {The Modular Multilevel Converter (MMC) is a promising topology for STATCOM applications due to its key features, such as modularity, scalability, and reduced harmonic content. Increasing the number of voltage levels in MMC reduces harmonics but simultaneously increases the number of submodules (SMs) per arm, leading to larger sizes and higher costs, which presents a challenge. To address this, this article introduces a novel 17-level MMC-STATCOM based on the Z Packed U-Cell (ZPUC) converter as its SM, which enables the generation of more voltage levels with fewer components and reduced harmonic content, offering significant advantages in terms of size and cost. Given the complex structure of the proposed converter and the associated challenges in building a physical prototype, real-time (RT) simulation using FPGA technology is employed for validation. The key contributions include integrating the ZPUC-SM into a three-phase STATCOM for the first time and adapting the converter model and its control system to RT tools, including RT-LAB with an electric hardware solver for FPGA execution. In addition, capacitor voltage balancing and energy sorting algorithms are integrated within Phase-Shift Pulse Width Modulation, eliminating the need for an additional controller while maintaining the floating capacitors of ZPUC-SMs balanced and regulated. The specifications of the proposed converter are defined, the mathematical model and control system are derived, and a real-time implementation based on CPU and FPGA execution is built to verify the scheme. The obtained RT simulation results provide practical evidence confirming the effective operation of the proposed scheme in VAR compensation mode.}, archive = {J_ACCESS}, author = {Sandy Atanalian and Fadia Sebaaly and Rawad Zgheib and Kamal Al-Haddad}, doi = {10.1109/ACCESS.2025.3566015}, journal = {IEEE Access}, pages = {78795-78807}, shortjournal = {IEEE Access}, title = {Z packed U-cell modular multilevel converter for STATCOM applications}, volume = {13}, year = {2025}, }
• (2025). ABL-SMOTE: A novel resampling method by handling noisy and borderline challenge for imbalanced dataset for software defect prediction. ACCESS, 13, 78781-78794. (www)

  @article{ , abstract = {Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems.}, archive = {J_ACCESS}, author = {Kamal Bashir and Sara Abdelwahab Ghorashi and Ali Ahmed and Abdolraheem Khader}, doi = {10.1109/ACCESS.2025.3564183}, journal = {IEEE Access}, pages = {78781-78794}, shortjournal = {IEEE Access}, title = {ABL-SMOTE: A novel resampling method by handling noisy and borderline challenge for imbalanced dataset for software defect prediction}, volume = {13}, year = {2025}, }
• (2025). Design and control of multi-rigid link pectoral fins using virtual spring-damper system for a manta ray robot. ACCESS, 13, 78769-78780. (www)

  @article{ , abstract = {Biomimetic robots have the advantage of propulsion efficiency and maneuverability. Manta ray robots with soft, flexible, and distributed multi-stage pectoral fins have been proposed. Robots with flexible fins can be propelled by varying the phase difference and amplitude of each pectoral fin. However, it is still difficult to uniquely define the state of soft, flexible fins, which are passively controlled. Robots with soft and flexible fins are susceptible to various water conditions. In this study, we designed a manta ray robot with multi-rigid link pectoral fins that can actively reproduce uniquely define bending. To actively reproduce the bending and propulsion of the manta ray, we propose a virtual spring-damper system. The validity of this design and control method was verified through experiments on the movement trajectories and swimming. It was found that the manta ray robot using the proposed design and control reproduced bending and achieved high speed and turning performance. This shows that the proposed design and control methods exhibit the same or better performance than the soft fins. These methods expected to be further applications for biomimetics robots, enabling active control, with uniquely defined bending and fin performance.}, archive = {J_ACCESS}, author = {Takumi Asada and Tatsunari Tsujimoto and Takao Oki and Hideo Furuhashi}, doi = {10.1109/ACCESS.2025.3562081}, journal = {IEEE Access}, pages = {78769-78780}, shortjournal = {IEEE Access}, title = {Design and control of multi-rigid link pectoral fins using virtual spring-damper system for a manta ray robot}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of hybrid feature selection methods for microarray-based cancer detection. ACCESS, 13, 78739-78768. (www)

  @article{ , abstract = {Microarray data-based cancer detection advances early diagnosis and personalized medicine by utilizing gene expression data to develop comprehensive cancer profiles, measuring thousands of genes simultaneously. However, the inherent high-dimensional nature of microarray data introduces substantial challenges in data analysis and interpretation. To resolve these issues, gene selection techniques such as filter, wrapper, and embedded methods have been implemented to remove irrelevant genes and reduce the dimensionality of the data. Even with such usefulness, these methods are bound to restrictive elements individually that could compromise the precision of cancer detection systems. More recently, the focus of research has shifted to hybrid approaches that merge several feature selection techniques to mitigate the weaknesses of one method while maximizing the strengths of others. This paper offers an extensive review on feature selection techniques for microarray data and focuses on evaluating the performance of different hybrid methods as an important research gap. The research assesses various combinations of Filter, Wrapper, and Embedded techniques to determine how such hybrid approaches enhance classification accuracy. Hybrid approaches, those that integrate several techniques, have the ability to enhance diagnostic accuracy as well as improve understanding at the biological level. This paper provides a comparative evaluation of hybrid feature selection methods to enhance microarray-based cancer classification. It aims to guide researchers in choosing appropriate strategies that optimize the dataset analysis.}, archive = {J_ACCESS}, author = {Azka Khoirunnisa and Didit Adytia and Mustafa Mat Deris and Adiwijaya}, doi = {10.1109/ACCESS.2025.3564706}, journal = {IEEE Access}, pages = {78739-78768}, shortjournal = {IEEE Access}, title = {Comprehensive review of hybrid feature selection methods for microarray-based cancer detection}, volume = {13}, year = {2025}, }
• (2025). A bidirectional gated recurrent unit and temporal convolutional network with a self-attention mechanism to improve traffic flow prediction performance. ACCESS, 13, 78727-78738. (www)

  @article{ , abstract = {An effective temporal modeling approach is crucial for improving traffic flow prediction accuracy. Traditional traffic flow prediction methods have certain limitations in capturing long-term dependencies and enhancing computational efficiency. This is especially true when dealing with long-sequence data, where the prediction accuracy of these methods often falls short of expectations. This paper proposes a hybrid model that combines the Bidirectional Gated Recurrent Unit (BiGRU) and Temporal Convolutional Network (TCN) with a Self-Attention (SA) mechanism to enhance prediction performance. The BiGRU captures bidirectional temporal dependencies, while the TCN enhances training efficiency and models long-sequence dependencies through parallel computation. The self-attention mechanism further improves the model’s ability to capture long-term dependencies, enhancing overall prediction performance. The model is validated on several real-world traffic datasets and its performance is compared with traditional methods. Results show that the BiGRU-TCN-SA model reduces Mean Absolute Error (MAE) by 30.04%, 23.09%, 20.84%, 14.94%, and 11.57%, compared to LSTM, TCN, LSTM-TCN, BiLSTM-TCN, and BiGRU-TCN models, respectively. Root Mean Square Error (RMSE) is reduced by 20.91%, 15.39%, 11.99%, 9.27%, and 6.24%, respectively. We further validate the BiGRU-TCN-SA model using Akaike Information Criterion (AIC) and Schwarz’s Bayesian Information Criterion (SBIC) on ablation experiments, the proposed model achieves the lowest AIC (34.35) and SBIC (121.63) values compared to baseline models, demonstrating the superior performance of the BiGRU-TCN-SA model in traffic flow prediction tasks.}, archive = {J_ACCESS}, author = {Yingying Liu and Jing Gu and Xiaoxuan Qi}, doi = {10.1109/ACCESS.2025.3564667}, journal = {IEEE Access}, pages = {78727-78738}, shortjournal = {IEEE Access}, title = {A bidirectional gated recurrent unit and temporal convolutional network with a self-attention mechanism to improve traffic flow prediction performance}, volume = {13}, year = {2025}, }
• (2025). Reducing unnecessary handovers using logistic regression in 5G networks. ACCESS, 13, 78707-78726. (www)

  @article{ , abstract = {The evolution of wireless communication has brought great benefits to society, such as multi-connectivity, increased connection speed, low latency, and elevated throughput. However, it has also raised concerns regarding security and the procedures of network swaps, more known as handovers. This paper proposes using machine learning in 5G mobile networks, employing a Logistic Regression algorithm to predict handovers. Considering selection criteria like Reference Signal Received Power, Signal-to-Interference-Plus-Noise Ratio, Received Signal Strength Indicator, and distance, our proposal shows that it can significantly reduce the consequences of frequent handovers and provide a better quality of service to the user, together with low computational complexity. The presented results prove the viability of this proposal.}, archive = {J_ACCESS}, author = {Alison M. Fernandes and Hermes I. Del Monego and Bruno S. Chang and Anelise Munaretto}, doi = {10.1109/ACCESS.2025.3564532}, journal = {IEEE Access}, pages = {78707-78726}, shortjournal = {IEEE Access}, title = {Reducing unnecessary handovers using logistic regression in 5G networks}, volume = {13}, year = {2025}, }
• (2025). Stochastic explicit calibration algorithm for survival models. ACCESS, 13, 78679-78706. (www)

  @article{ , abstract = {Calibration is essential for risk evaluation in various fields; including medicine, finance, and reliability analysis. Although extensive research has focused on calibration in classification and regression tasks using deep neural networks, survival analysis remains relatively underexplored, resulting in the lack of improved calibration methods. In particular, while previous work has proposed a calibration method for survival analysis, it relies on fixed bins, which can lead to biased calibration assessments and substantial loss of predictive accuracy in pursuit of calibration. This gap can hinder an accurate assessment of survival functions, leading to increased risk management costs. In this study, we introduce Stochastic Explicit Calibration (S-cal), an algorithm that employs random intervals instead of fixed bins, thereby advancing the calibration methods used in deep networks. The calibration performance of S-cal is evaluated using metrics specifically designed for handling censored data, such as D-calibration and the Kolmogorov-Smirnov metric. Extensive experiments on synthetic and real-world datasets demonstrate that S-cal consistently outperforms existing methods in terms of calibration accuracy. In addition, we highlight how improved calibration can improve downstream tasks, including optimizing resource allocation and improving patient care decisions. This work presents a significant advancement in the study of calibration for survival analysis, offering valuable information for more reliable risk assessment models.}, archive = {J_ACCESS}, author = {Jeongho Park and Sangwook Kang and Gwangsu Kim}, doi = {10.1109/ACCESS.2025.3566156}, journal = {IEEE Access}, pages = {78679-78706}, shortjournal = {IEEE Access}, title = {Stochastic explicit calibration algorithm for survival models}, volume = {13}, year = {2025}, }
• (2025). A method for service function chain migration based on server failure prediction in mobile edge computing environment. ACCESS, 13, 78664-78678. (www)

  @article{ , abstract = {Mobile Edge Computing (MEC) is a key technology for delivering low-latency services to mobile and edge devices, supporting applications like autonomous vehicles and smart cities. However, traditional hardware-based middleboxes limit flexibility and scalability, leading to the adoption of Network Function Virtualization (NFV). NFV enables the deployment of network functions as software, optimizing resource allocation and reducing costs. This study proposes a proactive failure prediction and migration strategy for MEC environments. Using a Long Short-Term Memory (LSTM) algorithm optimized by Super SAPSO (Simulated Annealing Particle Swarm Optimization), the model forecasts server failures with improved accuracy, reducing False Alarm Rates and improving Failure Detection Rates. For migration, the Improved Sparrow Search Algorithm (ISSA) is applied, factoring in CPU, memory, and server security thresholds to identify suitable migration servers for Virtual Network Functions (VNFs). ISSA’s fitness function balances migration cost and time, minimizing the impact of security constraints on migration efficiency. Results show that ISSA with a security check improves migration success ratios, especially at lower Service Function Chaining (SFC) arrival rates, while keeping migration times consistent across arrival rates of SFCs. This approach ensures high success rates, and reduced costs offering a resilient and efficient solution for dynamic MEC environments and laying the groundwork for adaptive, secure, and resource-efficient edge computing systems.}, archive = {J_ACCESS}, author = {Joelle Kabdjou and Norihiko Shinomiya}, doi = {10.1109/ACCESS.2025.3564967}, journal = {IEEE Access}, pages = {78664-78678}, shortjournal = {IEEE Access}, title = {A method for service function chain migration based on server failure prediction in mobile edge computing environment}, volume = {13}, year = {2025}, }
• (2025). A novel Encoder–Decoder model for short-term multistep wind power prediction. ACCESS, 13, 78647-78663. (www)

  @article{ , abstract = {Wind power has emerged as a vital renewable energy source. However, its inherent temporal variability and non-stationarity pose significant challenges for accurate forecasting. To solve this problem, this study proposes a novel multi-step wind power forecasting model based on an encoder-decoder architecture. It incorporates a multi-frequency attention mechanism into a multi-layer long short-term memory (LSTM) network. The former increases the ability of the model to capture long-term dependencies and global features, while the later focuses on short-term internal dynamics. Together, they allow the model to accurately extract critical frequency information across various time scales, and adaptively emphasize key features within the temporal domain. The experimental results show that the proposed model outperforms different benchmark methods in terms of many performance indicators, including the mean absolute error, mean squared error, and root mean squared error. This demonstrates its high potential to provide technical support for the precise wind power forecasting, and to contribute to the efficient integration of wind energy into power grids.}, archive = {J_ACCESS}, author = {Qiushi Wang and Dekuan Wang and Yifan Li}, doi = {10.1109/ACCESS.2025.3565053}, journal = {IEEE Access}, pages = {78647-78663}, shortjournal = {IEEE Access}, title = {A novel Encoder–Decoder model for short-term multistep wind power prediction}, volume = {13}, year = {2025}, }
• (2025). ISAC-driven software development with large language models: A foundation for enhanced public life support. ACCESS, 13, 78641-78646. (www)

  @article{ , abstract = {The rapid evolution of integrated sensing and communication (ISAC) technologies has created an unprecedented data overflow, making traditional analysis methods obsolete. The emergence of large language models (LLMs) such as ChatGPT has revolutionized various fields, including software engineering, by enabling the development of novel communication-aware software applications. This study explores the synergy between ISAC and LLMs in software development, using the Internet of Vehicle-Monitoring Software as a case study. The experimental data shows that without LLM, the average packet loss rate increases from 0.2% to 1.2%. It can be seen that the potential of ISAC-LLM integration is to release new opportunities for data-driven innovation by combining communication awareness technology with large-scale software development. By integrating communication awareness technology with large-scale software development, the potential of ISAC-LLM convergence is demonstrated in unlocking new opportunities for data-driven innovation. The research lays the foundation for further exploring the capabilities of LLMs in software development and harnessing their potential for transformative impact.}, archive = {J_ACCESS}, author = {Yaqiong Huang and Xin Jin and Li Zhang}, doi = {10.1109/ACCESS.2025.3550156}, journal = {IEEE Access}, pages = {78641-78646}, shortjournal = {IEEE Access}, title = {ISAC-driven software development with large language models: A foundation for enhanced public life support}, volume = {13}, year = {2025}, }
• (2025). Experimental investigation of ultra-fast CDR scheme for high-speed free-space optical data transmission with multiple access. ACCESS, 13, 78634-78640. (www)

  @article{ , abstract = {Time-division multiple access schemes such as time-slot coding (TSC) provide an effective and simple multi-user access framework for indoor free-space optical communications (FSO) with limited inter-user interference. For realistic multi-user scenarios employing TSC, clock data recovery is indispensable to achieve timing synchronization for different users, which lacks investigation in the previous study on TSC. In this paper, for the first time, we experimentally demonstrate a time-slot coded multi-user FSO system employing clock data recovery, in which a sum data rate of 40-Gb/s with bit-error-rate (BER) of each user around the 7% hard-decision forward error correction (HD-FEC) limit can be achieved. Moreover, to further improve the system performance, we propose a novel clock data recovery scheme for multi-user FSO systems employing TSC. Experimental results show that more than 3dB received optical power gain can be obtained to achieve the reference BER level of the 7% HD-FEC limit as compared to the system employing the conventional clock data recovery scheme. Higher-precision timing synchronization and ultra-fast convergence speed can also be achieved via our proposed clock data recovery scheme.}, archive = {J_ACCESS}, author = {Xinyu Wang and Meng Liu and Yuan Wan and Ampalavanapillai Nirmalathas and Christina Lim and Jianghao Li}, doi = {10.1109/ACCESS.2025.3565527}, journal = {IEEE Access}, pages = {78634-78640}, shortjournal = {IEEE Access}, title = {Experimental investigation of ultra-fast CDR scheme for high-speed free-space optical data transmission with multiple access}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of activation functions in deep residual networks for short-term load forecasting. ACCESS, 13, 78618-78633. (www)

  @article{ , abstract = {Short-Term Load Forecasting (STLF) is essential for ensuring efficient and reliable power system operations, requiring accurate predictions of electricity demand. Deep Residual Networks (DRNs), with their ability to mitigate gradient vanishing and model complex nonlinear relationships in load data, have emerged as a powerful tool for STLF. This study evaluates the performance of various activation functions within DRN models, focusing on their impact on predictive precision and generalization. Experiments were conducted using the DRN architecture for STLF on two distinct datasets: ISO-NE and Malaysia. The findings demonstrate that activation functions significantly influence the predictive performance of DRN-based STLF models. Specifically, the DRN model using Swish achieved the best results on the ISO-NE dataset (Mean Absolute Percentage Error, MAPE = 1.3806%), while the DRN model with Hyperbolic Tangent (Tanh) excelled on the Malaysia dataset (MAPE = 4.9809%). These results underscore the importance of aligning activation function selection with dataset characteristics to optimize the performance of DRN models in STLF. This study provides valuable insights for advancing STLF research and guiding practical applications in load forecasting.}, archive = {J_ACCESS}, author = {Junchen Liu and Faisul Arif Ahmad and Khairulmizam Samsudin and Fazirulhisyam Hashim and Mohd Zainal Abidin Ab Kadir}, doi = {10.1109/ACCESS.2025.3565798}, journal = {IEEE Access}, pages = {78618-78633}, shortjournal = {IEEE Access}, title = {Performance evaluation of activation functions in deep residual networks for short-term load forecasting}, volume = {13}, year = {2025}, }
• (2025). A miniaturized and low-loss phase shifter based on slow-waves and liquid crystal. ACCESS, 13, 78607-78617. (www)

  @article{ , abstract = {This article proposes a miniaturized, low-loss, and continuously tunable slow-wave (SW) liquid crystal (LC) phase shifter, designed specifically for the 60 GHz millimeter-wave band. The 60 GHz frequency range, with its wide bandwidth and short wavelength, enables compact and high-performance designs, making it ideal for next-generation communication systems. A three-parallel-stub slow-wave unit, incorporating fine rectangular branches and gaps, is proposed to enhance the slow-wave effect. A rectangular defected ground structure (DGS) is introduced to increase the bandwidth, while gradient stubs in the coplanar waveguide (CPW) are employed to improve impedance matching between the CPW port and the inverted microstrip line (IMSL) port. The equivalent circuit model of the proposed slow-wave unit is analyzed, and the evolutionary process of the structural design, along with its impact on phase velocity, is examined through comparison. Measurement results indicate that the structure achieves a phase shift of up to $250~^{\circ }$ / $\lambda $ and a figure of merit (FoM) of $41.7~^{\circ }$ /dB, with an insertion loss of more than -6 dB. The simulation and measured results are in good agreement, demonstrating the feasibility of the proposed design in the 60 GHz band.}, archive = {J_ACCESS}, author = {Daming Du and Hua Zhu and Hao Liang and Jiejun Peng and Zhengfang Qian}, doi = {10.1109/ACCESS.2025.3562301}, journal = {IEEE Access}, pages = {78607-78617}, shortjournal = {IEEE Access}, title = {A miniaturized and low-loss phase shifter based on slow-waves and liquid crystal}, volume = {13}, year = {2025}, }
• (2025). Target inference three-dimensional intercept-angle guidance with deep latent reinforcement meta-learning. ACCESS, 13, 78592-78606. (www)

  @article{ , abstract = {In this paper, a novel guidance law is proposed for interception of various types of targets at desired impact angle in three-dimensional coupled dynamics. Impact angles are defined using velocity vectors of missile and target, rather than line-of-sight (LOS) angles in azimuth and elevation planes. The proposed guidance law is derived using deep latent reinforcement meta-learning. Different from existing online gradient descent-based reinforcement meta-learning method, the proposed approach is aware of environment variations through latent context vector inferred with amortized variational inference. Target acceleration is also estimated with the latent context vector to facilitate guidance law design. In addition, the proposed approach is built as an adaptive guidance law with range-adaptive hyperbolic tangent function to attenuate control effort. Numerical simulation results with different initial condition and measurement noise validate the effectiveness and robustness of proposed guidance law.}, archive = {J_ACCESS}, author = {Weihong Wang and Chen Liang and Chaoran Yan and Zhenghua Liu and Na Wang and Zhengjie Sun and Yongming Wen and Xuemei Huang and Yue Yu}, doi = {10.1109/ACCESS.2025.3562123}, journal = {IEEE Access}, pages = {78592-78606}, shortjournal = {IEEE Access}, title = {Target inference three-dimensional intercept-angle guidance with deep latent reinforcement meta-learning}, volume = {13}, year = {2025}, }
• (2025). Resource-efficient compressive PPG sensing encoders with best sensing matrix and sparse recovery algorithm for IoT wearable and edge devices. ACCESS, 13, 78571-78591. (www)

  @article{ , abstract = {Resource-constrained wearable photoplethysmography (PPG) monitoring devices highly demand energy consumption reduction strategies to maximize the battery lifetime under continuous health monitoring applications. In this paper, we present a digital compressed PPG sensing (DCS-PPG) framework with a resource-efficient sensing matrix, sparse recovery algorithm, and best sparsifying matrix for wearable and edge PPG analysis devices in achieving a higher compression ratio (CR) with low waveform distortion and enabling data encryption or parameter extraction without reconstruction. We consider four sensing matrices (deterministic block binary diagonal (DBBD), random sparse fixed binary (RSFB), random Bernoulli (RBe), and random Gaussian (RG)), four sparsifying matrices (discrete cosine $\Psi =[C]$ , a combination of discrete cosine and discrete sine $\Psi =[C~S]$ , a combination of impulse, discrete cosine and discrete sine $\Psi =[I~C~S]$ and Haar wavelet), and six sparse recovery algorithms (orthogonal matching pursuit (OMP), approximate message passing (AMP), L1-minimization (L1-min), compressive sampling matching pursuit (CoSaMP), iterative hard thresholding (IHT) and iterative soft thresholding (IST)). On a wide variety of normal and abnormal PPG signals and five distortion measures, results show that the DCS-PPG framework based on the DBBD sensing matrix and OMP-based recovery algorithm with the sparsifying matrix $\Psi =[I~C~S]$ achieved a lower percentage root mean square difference (PRD) of $1.86~\pm ~1.65$ % at a higher CR of 3 with a lesser recovery time as compared to the results of other algorithms. Results show that the DBBD-based DCS-PPG sensing encoder can enable simultaneous data reduction and parameter extraction in the CS domain. The RSFB-based DCS-PPG sensing encoder can enable simultaneous data reduction and encryption with a lower recovery time and PRD of $2.55~\pm ~1.19$ % at a CR of 2.4 using AMP-based reconstruction with the sparsifying matrix $\Psi =[I~C~S]$ . We demonstrate the real-time feasibility of the DBBD and RSFB-based DCS-PPG encoder on an Arduino Due platform, while the Raspberry Pi platform is used to implement sparse recovery algorithms for reconstructing the compressed and encrypted data. The real-time implementation of the RSFB and AMP-based DCS-PPG encoding and decoding framework achieves a lower PRD of $2.93 \pm 1.10\%$ and energy consumption (EC) of $430.26 \pm 30.3$ mJ at a CR of 2.4 over a duration of two seconds, compared to other reconstruction algorithms such as OMP (PRD=5.84%) and L1-min (PRD=7.05%), while also achieving energy savings of $\approx 96.3\%$ with respect to L1-min. The real-time evaluation results on the Arduino platform show that the digital compressed sensing (DCS) using sensing matrix DBBD achieved energy savings of $\approx 86.8\%$ and a processing time of $295 \pm 30.5~\mu $ s compared to the DCS using RSFB. The results show that selecting the optimal sensing matrix, recovery algorithm, and sparsifying matrix plays a vital role in improving energy efficiency with reduced latency.INDEX TERMS Compressed sensing, data compression and encryption, energy efficiency, Internet of Things, PPG signal analysis, sparse recovery algorithms, wearable health monitoring.}, archive = {J_ACCESS}, author = {Jayarani Medakal Anirudhan and M. Sabarimalai Manikandan and Subrahmanyam Mula and Linga Reddy Cenkeramaddi}, doi = {10.1109/ACCESS.2025.3562291}, journal = {IEEE Access}, pages = {78571-78591}, shortjournal = {IEEE Access}, title = {Resource-efficient compressive PPG sensing encoders with best sensing matrix and sparse recovery algorithm for IoT wearable and edge devices}, volume = {13}, year = {2025}, }
• (2025). Fault diagnosis and prognosis with inferential sensors: A hybrid approach integrating symbolic regression and information theory. ACCESS, 13, 78555-78570. (www)

  @article{ , abstract = {We propose a fault diagnosis approach that integrates symbolic regression and information theory to optimize inferential sensors, which, along with traditional detection techniques and hard sensors, contribute to a comprehensive fault detection and diagnosis algorithm. Additionally, we introduce an advanced predictive scheme to estimate time-dependent fault evolution functions that enable us to compute the remaining useful life (RUL) of the system. Our methodology employs symbolic regression for parameter trending via a genetic programming algorithm, integrating the system model to discover the fault’s dependence on time. This relation analytically describes the fault progression, providing valuable insights into predicting system health. For comparison, we apply a dynamic degradation-based regression model using the inferential sensors as health indicators. The effectiveness, robustness and superiority of the proposed diagnosis and prognosis methods over traditional power function-based degradation models are demonstrated through experiments on a dynamic cross-flow plate-fin heat exchanger system with varying levels of fault progression, measurement noise, and uncertainty.}, archive = {J_ACCESS}, author = {Efi Safikou and Krishna R. Pattipati and George M. Bollas}, doi = {10.1109/ACCESS.2025.3564467}, journal = {IEEE Access}, pages = {78555-78570}, shortjournal = {IEEE Access}, title = {Fault diagnosis and prognosis with inferential sensors: A hybrid approach integrating symbolic regression and information theory}, volume = {13}, year = {2025}, }
• (2025). OTFS-based physical layer authentication for UAV-assisted data collection in wireless sensor network. ACCESS, 13, 78541-78554. (www)

  @article{ , abstract = {Autonomous aerial vehicle (commonly known as UAV) wireless communications have become widespread in military and civilian applications, partly due to its inherent line-of-sight (LoS) air-to-ground channels, easy deployment and high mobility. They have been adopted for data collection in the Internet of Things (IoT). For a communication scenario with a single UAV data collector in a delay-sensitive application, authentication is an important procedure which prevents illegal or malicious sensors from sending fake data to the data collector. However, traditional authentication schemes depend on cryptography, which is energy and time consuming. Thus, they are unsuitable for latency-intolerable applications and energy-constrained UAVs. This work aims to design a keyless, energy-efficient authentication scheme suitable for high-speed mobility scenarios. To this end, we propose an Orthogonal Time Frequency Space (OTFS)-based lightweight physical layer authentication (PLA) scheme. We begin by deriving the relationship between the location of the transmitter and normalized OTFS Doppler shift to develop the hypothesis test. Henceforth, we derive the probability density function (PDF) expressions of false alarm and missed detection. The PDF of the false alarm and missed detection are the basis for setting an optimal detection threshold for the authentication hypothesis test. Numerical results demonstrate that the proposed scheme holds well to fading effects for a robust and secure authentication scheme, and outperforms Orthogonal Frequency Division Multiplexing (OFDM) for PLA. The advantages of OTFS over OFDM for PLA are well elaborated.}, archive = {J_ACCESS}, author = {Agnes Francis Kawoya and Hua Wang}, doi = {10.1109/ACCESS.2025.3565375}, journal = {IEEE Access}, pages = {78541-78554}, shortjournal = {IEEE Access}, title = {OTFS-based physical layer authentication for UAV-assisted data collection in wireless sensor network}, volume = {13}, year = {2025}, }
• (2025). TMFPMap-NVIG: Temporal multi-scale fusion perception online HD map construction for navigation of visually impaired groups. ACCESS, 13, 78527-78540. (www)

  @article{ , abstract = {Accurate online high-definition (HD) map construction is crucial for visually impaired individuals navigating complex outdoor environments. Existing methods relying on single-frame inputs suffer from occlusions and distortions in dynamic scenes. To address these challenges, this paper proposes TMFPMap-NVIG, a novel online HD map generation framework designed specifically for assistive navigation. TMFPMap-NVIG introduces an Adaptive Feature Extractor using multi-scale strategies for capturing global and local information, a Vectorized Element Decoder that integrates hierarchical decoding and critical region prioritization to generate detailed map elements, Temporal Query Mapping for cross-frame consistency of static elements, and a Multi-Frame Feature Aggregator enhancing spatiotemporal coherence via recursive feature updates. Evaluations conducted on the nuScenes and Argoverse2 datasets show TMFPMap-NVIG surpasses state-of-the-art methods by 4.8%-15.2% in mean Average Precision (mAP), with accuracy improvements of 7.3%, 4.9%, and 2.2% for detecting crosswalks, lane lines, and road boundaries, respectively, demonstrating robust performance under environmental disturbances.}, archive = {J_ACCESS}, author = {Jingtao Zhang and Manping Fan and Yupeng Chen and Ruoyu Wang and Zhiwen Zheng and Jialing Xiao and Xiangxiang Wang and Yongbing Yu and Qi He and BO Gong and Lirui Xue and Liyong Ren}, doi = {10.1109/ACCESS.2025.3565770}, journal = {IEEE Access}, pages = {78527-78540}, shortjournal = {IEEE Access}, title = {TMFPMap-NVIG: Temporal multi-scale fusion perception online HD map construction for navigation of visually impaired groups}, volume = {13}, year = {2025}, }
• (2025). Decoding phishing evasion: Analyzing attacker strategies to circumvent detection systems. ACCESS, 13, 78513-78526. (www)

  @article{ , abstract = {Phishing remains a critical security threat, involving the creation of fraudulent websites to capture sensitive information. Despite existing detection systems, sophisticated attackers have developed advanced evasion techniques that undermine these defenses. This paper highlights the significant challenge of these novel methods, focusing on how attackers manage to prolong the operational lifespan of phishing sites. Our research investigates how attackers circumvent traditional security layers by employing a combination of target filtering mechanisms, bot detection evasion, blacklisting avoidance, and honeypots. Our experimental findings indicate that these evasion strategies can achieve an effectiveness rate of 80% to 85% in extending the viability of phishing sites. We have empirically demonstrated the exposure of current systems to these attacks, revealing specific vulnerabilities and exploitation points. These results underscore the urgent need for enhanced detection frameworks that address the layered and adaptive nature of modern phishing tactics. Our work highlights a critical gap in current security measures and poses a challenge to solution providers: there is a pressing need for novel mitigations to safeguard users against these sophisticated phishing threats.}, archive = {J_ACCESS}, author = {A. Ghafoor and M. A. Shah and M. A. Al-Naeem and C. Maple}, doi = {10.1109/ACCESS.2025.3565619}, journal = {IEEE Access}, pages = {78513-78526}, shortjournal = {IEEE Access}, title = {Decoding phishing evasion: Analyzing attacker strategies to circumvent detection systems}, volume = {13}, year = {2025}, }
• (2025). A two-stream deep learning framework for robust coral reef health classification: Insights and interpretability. ACCESS, 13, 78490-78512. (www)

  @article{ , abstract = {Coral reefs, vital ecosystems for marine life and coastal communities, are facing grave threats from climate change and human activities, including pollution. To effectively conserve these ecosystems, monitoring and categorizing coral health into three states: Need to see healthy, Dead Coral and Bleached Coral. In this study, we introduce a two-stream deep learning framework designed to robustly classify coral health, emphasizing interpretability as integral to its applicability. The framework uniquely incorporates two parallel processing streams: one dedicated to analyzing the structural aspects of coral imagery and another focused on capturing color variations, both critical for accurate health assessment. We created a specialized dataset of 20,000 images by combining five public datasets, applying a class fusion process to ensure consistent class definitions across diverse data sources. Our methodology addresses challenges such as underwater variation and class imbalance through tailored data augmentation, multi-scale feature analysis, temporal modeling, and weighted loss functions. These innovations allow the model not only to achieve high accuracy but also to provide insights into the decision-making process, enhancing the model’s utility for ecological researchers and conservation efforts. The experiment successfully identifies the corals’ health states with an accuracy of 98.56% and mAP of 0.98, demonstrating the framework’s reliability for coral health state identification. To make ecological researchers and conservationists understand the model’s decision-making process, we use explainable AI (XAI) tools such as the Grad-CAM and LIME. More evaluation metrics, including precision, recall, F1-score, confusion matrices, and precision-recall curves, are also conducted to validate the performance of the model. An innovative approach combining technology and conservation to monitor coral reefs using AI-driven, this scalable and accurate AI-driven approach provides a potential solution for monitoring coral reefs. The results highlight the promising role of advanced AI in global conservation by lessening the ecological decline of coral reef systems.}, archive = {J_ACCESS}, author = {Arslan Abdul Ghaffar and Gyu Sang Choi}, doi = {10.1109/ACCESS.2025.3561226}, journal = {IEEE Access}, pages = {78490-78512}, shortjournal = {IEEE Access}, title = {A two-stream deep learning framework for robust coral reef health classification: Insights and interpretability}, volume = {13}, year = {2025}, }
• (2025). Optimized captain selection using diffusion-based player behavior synthesis and multicriteria decision making with intuitionistic rough fuzzy under dombi-AHP and PROMETHEE methods. ACCESS, 13, 78471-78489. (www)

  @article{ , abstract = {The selection of the team captain is an essential decision in team-based sports and professional situations, and it requires an optimized option across multiple criteria. Many existing approaches fail to summarize the difficulties of player behaviors and leadership qualities. In this article, we established a novel model by combining diffusion-based player behavior synthesis with multi-criteria decision-making (MCDM) approach; purposely, we use Dombi aggregation operators (AOs), analytical hierarchy process (AHP), and preference ranking organization method for enrichment evaluation (PROMETHEE) based on an intuitionistic rough fuzzy (IRF) framework. We presented a detailed case study for suitable caption selection based on defined alternatives such as leadership skills, game performance metrics, psychological stability, and tactical awareness. To show the applicability of our methodologies, we discuss numerical examples based on caption selection for football teams. To use the AHP approach, compute the weight vector for criteria. Then, we use our proposed approach, such as intuitionistic rough fuzzy Dombi weighted averaging (IRFDWA) operators and PROMETHEE, to rank candidates. The analysis of Dombi-AHP is an advanced modification of the AHP. It handles the complex MCDM elaborate in captain selection. AHP is selected due to its structured pairwise comparison method, which improves decision accuracy by systematically quantifying subjective decisions. We comprehensively compare our approach with other existing aggregation methods and discuss the sensitive analysis of the proposed theory. Then, provide a solid conclusion.}, archive = {J_ACCESS}, author = {Ke Wang}, doi = {10.1109/ACCESS.2025.3564619}, journal = {IEEE Access}, pages = {78471-78489}, shortjournal = {IEEE Access}, title = {Optimized captain selection using diffusion-based player behavior synthesis and multicriteria decision making with intuitionistic rough fuzzy under dombi-AHP and PROMETHEE methods}, volume = {13}, year = {2025}, }
• (2025). Optimizing personalized recommender systems for teachers’ digital learning models using deep learning algorithms. ACCESS, 13, 78461-78470. (www)

  @article{ , abstract = {Digital Learning Models for Teachers represent a significant advancement in the education sector, leveraging digital technology to enhance teaching effectiveness by creating personalized and customized learning experiences. These models enable teachers to receive more accurate feedback on their teaching. However, a current challenge is the need to recommend appropriate learning question types tailored to individual student needs, which can lead to suboptimal learning outcomes. To address this issue, this paper proposes a personalized recommendation algorithm based on Graph Neural Networks (PRAGNN) for teachers’ digital learning models. Specifically, the approach integrates DINA cognitive diagnosis and gray partial correlation evaluation to construct a student model that captures students’ mastery of knowledge points and cognitive ability levels. Additionally, a graph convolutional neural network (GCN) is employed, leveraging the sequential relationships between subject knowledge points to automatically capture semantic information from the higher-order structure of knowledge points, thereby enabling personalized recommendations. The algorithm achieved an accuracy of 85.7% through comparative experiments. This research presents a novel approach for developing personalized recommendation systems in teachers’ digital learning models, with the potential to significantly improve learning outcomes in the educational field.}, archive = {J_ACCESS}, author = {Jun Zhong and Wenjuan Zhang}, doi = {10.1109/ACCESS.2025.3561294}, journal = {IEEE Access}, pages = {78461-78470}, shortjournal = {IEEE Access}, title = {Optimizing personalized recommender systems for teachers’ digital learning models using deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Beyond social norms: Psychological and hedonic drivers of metaverse adoption among generation y. ACCESS, 13, 78441-78460. (www)

  @article{ , abstract = {The Metaverse has rapidly emerged as a transformative technological frontier, accelerated by key events such as the COVID-19 pandemic and Facebook’s rebranding as Meta. However, the factors influencing Generation Y’s adoption of Metaverse services remain underexplored. This study investigates the psychological, motivational, and social drivers shaping Generation Y’s adoption intentions, positioning the Metaverse as a bridge between the pre-digital and post-digital eras. Applying the Decomposed Theory of Planned Behavior, the findings reveal that perceived behavioral control and attitude significantly influence adoption, highlighting the roles of self-efficacy, user confidence, and positive perceptions of the technology. In contrast, subjective norms, central to traditional adoption models, exert no significant effect, suggesting a generational shift toward digital autonomy. Based on a survey of 341 Generation Y respondents in Taiwan, the findings provide practical insights for developers to prioritize user-friendly interfaces, immersive features, and customization options. Marketers are advised to leverage influencer-driven strategies and personalized digital interactions. The research contributes to theoretical advancements by challenging established models, such as the Unified Theory of Acceptance and Use of Technology, and calls for further research on cross-cultural differences and digital communities.}, archive = {J_ACCESS}, author = {Agnieszka Maria Koziel and Chien-Wen Shen and Irfandi Djailani and Yen-Ling Chang}, doi = {10.1109/ACCESS.2025.3563758}, journal = {IEEE Access}, pages = {78441-78460}, shortjournal = {IEEE Access}, title = {Beyond social norms: Psychological and hedonic drivers of metaverse adoption among generation y}, volume = {13}, year = {2025}, }
• (2025). MP-NER: Morpho-phonological integration embedding for chinese named entity recognition. ACCESS, 13, 78427-78440. (www)

  @article{ , abstract = {Named Entity Recognition (NER) aims to automatically extract specific entities from unstructured text. Compared with English NER, Chinese NER faces challenges due to heterophony, where the same Chinese character may have different pronunciations and meanings. Additionally, the lack of clear separators between Chinese characters exacerbates these challenges, leading to difficulties in boundary detection and entity category determination. Inspired by the hieroglyphic and phonetic features of Chinese characters, this study proposes a multi-feature fusion embedding model (MP-NER). The model employs CNN for extracting radicals and phonetic features of Chinese characters, combines the encoded information from these features with pre-trained word vectors to generate fusion embedding vectors, and uses a fully-connected layer for feature transformation. Experiments were conducted on the Chinese benchmark datasets Resume, Weibo and MSRA. Compared to current mainstream models, the proposed model demonstrates superior performance in terms of F1 score, F1 score stability, and individual entity recognition accuracy. Ablation experiments further validate the effectiveness of the introduced radicals and phonetic features. The experimental results demonstrate that this model effectively captures the semantic information of Chinese characters, addresses the problem of Chinese character heterophony, and improves entity recognition performance. The code and datasets available at: https://github.com/FAKLITS/MP-NER}, archive = {J_ACCESS}, author = {Pu Li and Guopeng Cheng and Guojun Deng and Shuanghong Qu and Min Huang and Guoxiang Li}, doi = {10.1109/ACCESS.2025.3565908}, journal = {IEEE Access}, pages = {78427-78440}, shortjournal = {IEEE Access}, title = {MP-NER: Morpho-phonological integration embedding for chinese named entity recognition}, volume = {13}, year = {2025}, }
• (2025). A novel method for traceability of crude oil leakage on offshore platforms based on improved YOLOv5 model multi-target identification and correlation analysis. ACCESS, 13, 78416-78426. (www)

  @article{ , abstract = {In order to meet the requirement of oil leakage monitoring on offshore platforms, The paper investigates a method for identifying oil spill targets and potential associated targets that may lead to oil leakage based on the YOLOv5 model. In response to practical issues such as the complex structure of offshore platform scenes and the overlap of associated oil spill targets, a deformable convolution DCNv2 module is introduced to improve the model’s recognition accuracy for multi-shaped targets. Additionally, the CBAM attention module is incorporated to enhance the model’s ability to learn target features. The EIOU loss function is used to improve the model’s convergence speed and its ability to identify small or partially occluded targets. Based on the positional relationship between the spilled crude oil target and the source target detection anchor frame, the possible source targets are classified into three association levels and assigned different association weights. By comprehensively applying parameters such as source-target association weight, leakage risk probability, and target overlap area, a calculation method of the association degree between the source target and the leaked crude oil target is proposed. Using the method mentioned above, the traceability analysis of crude oil leakage can be carried out based on the association degree size. The efficacy of the optimized model for multi-target detection in offshore platforms is substantiated through ablation studies and comparative analyses with analogous models. The effectiveness of the proposed crude oil spill traceability method for offshore platforms is verified by detecting and analyzing the experimental scenarios of offshore platforms.}, archive = {J_ACCESS}, author = {Zhenghua Wang and Shihai Zhang and Chongnian Qu and Zongyi Zhang and Feng Sun}, doi = {10.1109/ACCESS.2025.3561278}, journal = {IEEE Access}, pages = {78416-78426}, shortjournal = {IEEE Access}, title = {A novel method for traceability of crude oil leakage on offshore platforms based on improved YOLOv5 model multi-target identification and correlation analysis}, volume = {13}, year = {2025}, }
• (2025). Design optimization and performance analysis of high torque density and efficiency motors for eVTOL applications. ACCESS, 13, 78403-78415. (www)

  @article{ , abstract = {As the development of electric vertical takeoff and landing (eVTOL) aircraft rapidly progresses, efficient, lightweight, and high-torque-density drive motors have emerged as a critical technology. This study focuses on designing a drive motor suitable for eVTOLs, emphasizing improvements in torque density and efficiency to meet the needs of urban low-altitude operations. In response to the impact of winding cross-sectional area changes during motor optimization design, we have developed a design method for permanent magnet synchronous motors based on fixed current density, and analyzed the optimal main dimensions, air gap length, and magnet thickness of the motor. Additionally, the enhancement effects of Halbach magnets and a 30-degree phase shift arrangement on motor performance were examined. To address the strong coupling issue of stator structural parameters, a multi-objective optimization algorithm was introduced to achieve a balance between high torque density and high efficiency. The optimization results were validated through finite element simulation, with the motor’s torque density and efficiency reaching 56.5 N $\cdot $ m/kg and 93.5%, respectively. Further experimental results indicate that the prototype’s torque density is 55 N $\cdot $ m/kg with a peak efficiency of 92.5%, confirming the effectiveness and reliability of the optimized design. Moreover, simulations of a single set of three-phase winding fault conditions demonstrate the motor’s excellent fault tolerance. This research provides innovative design approaches for the efficiency and lightweighting of eVTOL motors, offering broad application prospects.}, archive = {J_ACCESS}, author = {Qiang He and Haoyu Wang and Qing Li}, doi = {10.1109/ACCESS.2025.3563860}, journal = {IEEE Access}, pages = {78403-78415}, shortjournal = {IEEE Access}, title = {Design optimization and performance analysis of high torque density and efficiency motors for eVTOL applications}, volume = {13}, year = {2025}, }
• (2025). GeoRF: Geometric constrained RaDAR fields. ACCESS, 13, 78391-78402. (www)

  @article{ , abstract = {4D millimeter-wave radar demonstrates considerable potential in the field of autonomous driving. It facilitates stable perception in adverse weather conditions and complex lighting environments, in addition to featuring low cost and high data-processing efficiency. The realization of Novel view synthesis (NVS) for 4D radar is of significant practical importance. However, due to the substantial disparities in the principles of point cloud generation between LiDAR and 4D radar, existing methods for NVS of LiDAR point clouds are not applicable to 4D radar. Specifically, during the process of projecting radar point clouds into cylindrical coordinates, radar produces irregular point distributions, in contrast to LiDAR’s regular angular resolution. This phenomenon results in range map that are filled with numerous empty pixels. Furthermore, the intrinsic irregularity in imaging and the prevalence of empty pixels compromise inter-frame geometric consistency, presenting a challenge that is distinct from LiDAR-based systems. To address these challenges, we associate ray-drop probability with viewpoints and utilize a U-Net architecture to learn the distribution of radar point clouds, effectively addressing data sparsity in a viewpoint-dependent manner. Extensive experiments demonstrate that our method achieves superior reconstruction results on both the publicly available VOD dataset, K-RaDAR dataset and Dual-Radar dataset.}, archive = {J_ACCESS}, author = {Wenbo Sheng and Haoran Liu and Kenan Fan and Pengxiang Su}, doi = {10.1109/ACCESS.2025.3563135}, journal = {IEEE Access}, pages = {78391-78402}, shortjournal = {IEEE Access}, title = {GeoRF: Geometric constrained RaDAR fields}, volume = {13}, year = {2025}, }
• (2025). Path planning of library management robot based on PDO-ACO algorithm. ACCESS, 13, 78376-78390. (www)

  @article{ , abstract = {A path planning method based on Particle Differential Optimization-Ant Colony Optimization (PDO-ACO) algorithm for library management robots is proposed in the study, aiming to solve the problem of low efficiency of current path planning methods in complex environments. The method combines the negative feedback improved ant colony algorithm and particle difference optimization algorithm to enhance the accuracy, stability and environmental adaptability of path planning. The study innovatively introduces the negative feedback mechanism to improve the ant colony algorithm, which effectively avoids the problem that the traditional algorithm is prone to fall into the local optimum, and combines with the particle difference optimization algorithm to be applied to the path planning of the library management robot, which demonstrates high adaptability and robustness. The experimental results show that the method outperforms other common path planning methods in terms of error rate and redundant path rate, and the average path lengths are 54.507m and 57.456m, respectively, and the convergence speed and accuracy are improved by 57% and 45.5%, respectively. The method shows high accuracy, stability and robustness in the actual library management environment, which can effectively avoid the risks in path planning, reduce the management cost, and provide a new technical solution for the construction of intelligent library system.}, archive = {J_ACCESS}, author = {Na Lin}, doi = {10.1109/ACCESS.2025.3565519}, journal = {IEEE Access}, pages = {78376-78390}, shortjournal = {IEEE Access}, title = {Path planning of library management robot based on PDO-ACO algorithm}, volume = {13}, year = {2025}, }
• (2025). WiPy-RT: A fast ray tracing modeling platform for RIS-assisted wireless channels. ACCESS, 13, 78360-78375. (www)

  @article{ , abstract = {Recent advancements in wireless technologies, particularly in the context of the sixth generation (6G) mobile communications and Internet of Things (IoT) systems, have introduced a wide range of requirements and challenges in wireless communication. These developments necessitate comprehensive channel information, encompassing the three-dimensional (3D) features of electromagnetic (EM) signals. Such signals are now typically transmitted by antenna arrays, manipulated by Reconfigurable Intelligent Surface (RIS) structures, and received by another set of antenna arrays. This complex propagation environment demands sophisticated modeling techniques to accurately capture and predict channel behavior, essential for the design and optimization of next-generation wireless systems. Ray tracing (RT) based simulators have gained significant traction in recent years, proving their worth in accurately and efficiently simulating EM propagation environments. These simulators have demonstrated remarkable accuracy in modeling complex wireless scenarios, making them invaluable tools for researchers and engineers. However, the intricate interactions between EM waves and the physical environment in three-dimensional space can still render the simulation process time-consuming, especially for scenarios with high complexity. Fortunately, recent state-of-the-art developments in Graphics Processing Unit (GPU) and Central Processing Unit (CPU) technologies have substantially mitigated this challenge. These hardware advancements have dramatically reduced the computational overhead associated with complex environmental simulations, making it feasible to conduct comprehensive and realistic RT-based analyses of sophisticated wireless environments. This paper introduces WiPy-RT, a high-performance ray tracing simulator designed for modeling RIS-enabled wireless environments. WiPy-RT features an interactive interface that leverages highly configurable and open-source Python libraries for 3D structures, including PyVista, Trimesh, and PyTorch3D. This GPU-accelerated simulator excels in simulating first-order diffraction and multi-reflection (up to 6 orders of reflection) ray tracing scenarios, while being capable of modeling RIS behavior with large-scale unit cell arrays. Benchmarking against the state-of-the-art WiThRay simulator, WiPy-RT demonstrated an average simulation time of 0.8 seconds, compared to WiThRay’s 16 minutes, achieving over 98% reduction in simulation time. Additionally, WiPy-RT provided higher resolution and accuracy in power distribution modeling for complex 3D urban environments. WiPy-RT introduces several novel features, including an intelligent ray launching method using Gaussian Kernel Density Estimation (KDE) and an efficient RIS modeling approach employing Random Forest Regression. Comparative analysis shows that WiPy-RT achieves up to 1000x speedup over existing solutions while maintaining high accuracy. WiPy-RT’s fast execution and ability to handle complex 3D environments make it particularly suitable for advanced wireless communication research, including the simulation of massive MIMO systems and intelligent reflecting surfaces. The interdisciplinary nature of WiPy-RT, combining techniques from machine learning and computational electromagnetics, represents a significant step forward in addressing the challenges of modeling next-generation wireless systems.}, archive = {J_ACCESS}, author = {Mohammadreza Farashahi and Boon-Chong Seet and Xue Jun Li}, doi = {10.1109/ACCESS.2025.3566343}, journal = {IEEE Access}, pages = {78360-78375}, shortjournal = {IEEE Access}, title = {WiPy-RT: A fast ray tracing modeling platform for RIS-assisted wireless channels}, volume = {13}, year = {2025}, }
• (2025). Integrating sentiment analysis with machine learning for cyberbullying detection on social media. ACCESS, 13, 78348-78359. (www)

  @article{ , abstract = {The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape which poses detrimental effects on individuals and broader societal frameworks. A viable approach to addressing this pervasive issue lies in the accurate identification of cyberbullying within social media as it constitutes a substantial portion of digital interactions. State-of-the-art solutions predominantly rely on pre-trained language models and machine learning algorithms; however, these methods are often associated with substantial computational overheads and the development of advanced cyberbullying detection algorithms remains limited. This paper presents a unique framework that integrates sentiment analysis with machine learning algorithms to enhance the detection of cyberbullying on social media. Sentiment analysis explores the linguistic and emotional characteristics of cyberbullying messages which helps the framework to identify key sentiment indicators that differentiate harmful interactions from benign ones. Furthermore, we provide the most optimal text preprocessing steps which are ordered in a way that improves text quality for cyberbullying detection. These steps ensure high-quality input data for machine learning models which significantly enhances their performance. Additionally, we tackle the challenge of penta-class imbalanced data by incorporating resampling within the framework without causing bias. We employ several machine learning algorithms within the framework, the Extra Tree classifier achieved the best results with an accuracy of 95.38% and F1 score of 0.95. The results demonstrate that the integration of sentiment analysis significantly improves classification accuracy compared to conventional methods for the task of cyberbullying detection.}, archive = {J_ACCESS}, author = {Maram Fahaad Almufareh and Noor Zaman Jhanjhi and Mamoona Humayun and Ghadah Naif Alwakid and Danish Javed and Saleh Naif Almuayqil}, doi = {10.1109/ACCESS.2025.3558843}, journal = {IEEE Access}, pages = {78348-78359}, shortjournal = {IEEE Access}, title = {Integrating sentiment analysis with machine learning for cyberbullying detection on social media}, volume = {13}, year = {2025}, }
• (2025). An EPS-kNN fault propagation analysis method for industrial processes based on the fusion of knowledge and data. ACCESS, 13, 78335-78347. (www)

  @article{ , abstract = {To reduce the false connections resulting from numerous redundant paths in the fault propagation analysis for industrial processes, an equal probability symbolized k-nearest neighbors (EPS-kNN) fault propagation analysis method based on the fusion of fault data and process knowledge is proposed. Subblock interaction monitoring is introduced to identify potential fault areas and eliminate redundant variables. The complex network model is incorporated into the EPS-kNN-based fault propagation path identification to improve accuracy and interpretability. Different types of faults in the Tennessee Eastman process and Ammonia synthesis process are applied to verify the effectiveness, and the result shows that the proposed method can identify the fault propagation path more effectively and reduce the occurrence of false connections compared with the traditional methods.}, archive = {J_ACCESS}, author = {Wang Lixin and Qian Xiaoyi and Feng Yanliang and Dai Ziheng and Kang Changsheng and Guan Shuai and Zhao Yi}, doi = {10.1109/ACCESS.2025.3558631}, journal = {IEEE Access}, pages = {78335-78347}, shortjournal = {IEEE Access}, title = {An EPS-kNN fault propagation analysis method for industrial processes based on the fusion of knowledge and data}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection using hybrid pearson correlation and GS-PSO optimized random forest technique for RPL-based IoT. ACCESS, 13, 78320-78334. (www)

  @article{ , abstract = {RPL (Routing Protocol for Low-Power and Lossy Networks) is a standardized routing protocol of IoT proposed by IETF (Internet Engineering Task Force) Working Group. The escalating surge of cyberattacks targeting IoT systems has exposed critical security vulnerabilities in RPL-based networks. Attackers exploit routing spoofing, resource depletion tactics, and topology manipulation strategies, while inherent constraints of resource-limited devices and scalability challenges in mass deployments amplify these risks. This critical security gap therefore necessitates the deployment of lightweight IDS (Intrusion Detection Systems) to achieve real-time anomaly detection and monitoring However, the current IDS for RPL-based IoT has certain limitations such as routing attacks dataset lack coverage of a sufficient variety of routing attacks and detection methods involve significant computational overhead. To address these problems, we have conducted the following research work. First, a novel routing attacks dataset that covers four RPL routing attack types is constructed through simulation on the Cooja IoT platform. And we develop feature extraction algorithms for dataset to extract 24 features implementing in IDS model training. Second, we propose an efficient routing detection method that accelerates model training speed by using Hybrid Pearson Correlation and GS-PSO(Grid Search-Particle Swarm Optimization) Optimized Random Forest Technique. The Pearson correlation can effectively extract key data features for different routing attacks. And the hybrid GS-PSO algorithm can optimize the hyperparameters of IDS model and enhance the accuracy of the detection mechanism while significantly reducing computational overhead. Finally, we demonstrate our IDS model has higher detection accuracy and lower computation time compared to other existing models through simulation experiments.}, archive = {J_ACCESS}, author = {Wei Yang and Xinlong Wang and Zhiming Zhang and Shaolong Chen and Chengqi Hou and Siwei Luo}, doi = {10.1109/ACCESS.2025.3566368}, journal = {IEEE Access}, pages = {78320-78334}, shortjournal = {IEEE Access}, title = {Intrusion detection using hybrid pearson correlation and GS-PSO optimized random forest technique for RPL-based IoT}, volume = {13}, year = {2025}, }
• (2025). DASL: An index for enhancing tail latency, microarchitecture friendliness, and restructuring overhead. ACCESS, 13, 78303-78319. (www)

  @article{ , abstract = {The skip list is a popular in-memory index in modern database systems. It maintains multiple levels of lists, which makes it efficient in traversing sorted data. In addition, it is flexible in inserting and deleting data, while avoiding the restructuring overhead of tree-based structures. However, there are considerable challenges in the conventional skip list design. First, the linked list structure has a drawback in utilizing microarchitecture features such as cache, pipeline, and SIMD (Single Instruction Multiple Data) capability. Second, the skip list randomly selects the level of a new node. That is, the skip list runs based on probability rather than data distribution, which can lead to suboptimal lookup performance. Unlike balanced tree structures, the worst-case lookup performance of a skip list remains O(n). This paper proposes a new data structure called DASL (Deterministic Arrayed Skip List). It follows the algorithm of the skip list, but seamlessly integrates the array and devises a new deterministic raise operation in order to obtain flexibility, microarchitecture-friendliness, and reduced tail latency. In specific, a node in DASL consists of an array structure with multiple elements instead of a single element, taking advantage of the array within a list structure. Additionally, the raise operation is conducted deterministically instead of probabilistically, allowing data to be more balanced in multiple lists. Furthermore, we devise two optimization techniques, utilization-based adaptive intra-node search and uneven split operation. Experimental results with various synthetic and real-world workloads demonstrate that DASL outperforms other state-of-the-art in-memory indexes, including skip list, B+tree, and ART.}, archive = {J_ACCESS}, author = {Hojin Shin and Gunhee Choi and Bryan S. Kim and Seehwan Yoo and Jongmoo Choi}, doi = {10.1109/ACCESS.2025.3565528}, journal = {IEEE Access}, pages = {78303-78319}, shortjournal = {IEEE Access}, title = {DASL: An index for enhancing tail latency, microarchitecture friendliness, and restructuring overhead}, volume = {13}, year = {2025}, }
• (2025). An adaptive vehicle location prediction using machine learning: A case study of campus shuttle bus. ACCESS, 13, 78290-78302. (www)

  @article{ , abstract = {Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service; however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to lower accuracy and mismatched pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of its use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions.}, archive = {J_ACCESS}, author = {Tarak Nandy and Raenu Kolandaisamy and Rafidah Md Noor and Sananda Bhattacharyya}, doi = {10.1109/ACCESS.2025.3562785}, journal = {IEEE Access}, pages = {78290-78302}, shortjournal = {IEEE Access}, title = {An adaptive vehicle location prediction using machine learning: A case study of campus shuttle bus}, volume = {13}, year = {2025}, }
• (2025). An empirical study of user playback interactions and engagement in mobile video viewing. ACCESS, 13, 78272-78289. (www)

  @article{ , abstract = {Mobile video viewing on popular platforms such as YouTube and Netflix is widespread, yet the role of specific viewing interactions in shaping user engagement remains underexamined. This study investigates how skipping behaviors (including their types and directions) and playback speed adjustments relate to user engagement, with a focus on video abandonment and user satisfaction. We developed a custom mobile web application for video viewing and collected viewing logs and self-reports from 25 participants during two 10-day field studies. Our findings reveal that different skip types and directions are associated with distinct engagement outcomes. For example, scrubbing often correlates with higher abandonment, whereas backward skips may indicate greater engagement. Playback speed adjustments can signify deeper involvement, allowing users to tailor their viewing speeds without missing key content. Notably, video abandonment did not always equate with dissatisfaction; some users left after meeting their immediate viewing goals. These insights suggest that users’ playback interactions may serve as indicators of user engagement and can be incorporated into video recommendation systems to enhance user satisfaction. We conclude by discussing the design implications of enhancing user satisfaction.}, archive = {J_ACCESS}, author = {Eunnho Kim and Seungjae Oh and Sangkeun Park}, doi = {10.1109/ACCESS.2025.3566402}, journal = {IEEE Access}, pages = {78272-78289}, shortjournal = {IEEE Access}, title = {An empirical study of user playback interactions and engagement in mobile video viewing}, volume = {13}, year = {2025}, }
• (2025). On the upper mid-band 8 × 8 and 8 × 4 device MIMO performance based on applying compact backcover four-antenna modules for 6G smartphones with increased data rates. ACCESS, 13, 78257-78271. (www)

  @article{ , abstract = {For future six-generation (6G) upper mid-band mobile communications, the higher-order device multi-input-multi-output (MIMO) such as the $8\times 8$ or $8\times 4$ MIMO is envisioned for the smartphone to achieve increased data rates for the user. However, it is worth noting that the $8\times 8$ MIMO requires twice the transmitted power of current fifth-generation (5G) $4\times 4$ MIMO and has a much more complex system architecture. Thus, whether it is cost effective to apply the $8\times 8$ MIMO for increased data rates is not clear. In this study, we demonstrate that the $8\times 4$ MIMO (8 receive antennas for 4 spatial streams) with the same transmitted power as the 5G $4\times 4$ MIMO can outperform the $8\times 8$ MIMO to achieve increased data rates and lower energy per bit as well. In the outdoor scenario testing, the $8\times 4$ MIMO shows a spectral efficiency of about 31 or 26 bps/Hz, while that of the $8\times 8$ MIMO is only about 22 bps/Hz. Additionally, the required energy per bit transmitted in the $8\times 4$ MIMO is less than one-half that of the $8\times 8$ MIMO. The is mainly because the $8\times 4$ MIMO uses only one-half transmitted power and has a much larger MIMO efficiency (measured system capacity/ideal system capacity) than the $8\times 8$ MIMO. Thus, the $8\times 4$ MIMO can support a higher signal modulation of 256 or 1024 Quadrature Amplitude Modulation (QAM), while the $8\times 8$ MIMO supports only 16 QAM. Hence, although the $8\times 8$ MIMO has a potentially much larger system channel capacity, its actual data throughput cannot be proportionally larger. We experimentally evaluate the MIMO performance in the 7.1 GHz band by applying compact backcover four-antenna (B4A) modules as receive antennas in the smartphone-like device covering 6.425-8.4 GHz in the upper mid-band, which is promising to be a prime 6G mobile spectrum. The applied compact B4A module provides a promising solution for the challenge in embedding at least 8 MIMO antennas in 6G smartphones for upper mid-band MIMO communications. We present details of the $8\times 8$ and $8\times 4$ MIMO performance testing and the applied B4A modules for promising 6G smartphone applications.}, archive = {J_ACCESS}, author = {Kin-Lu Wong and Wan-Ting Li and Chin-Lung Wang and Chun-Chi Wen and Wei-Yu Li}, doi = {10.1109/ACCESS.2025.3565719}, journal = {IEEE Access}, pages = {78257-78271}, shortjournal = {IEEE Access}, title = {On the upper mid-band 8 × 8 and 8 × 4 device MIMO performance based on applying compact backcover four-antenna modules for 6G smartphones with increased data rates}, volume = {13}, year = {2025}, }
• (2025). Short-term power load prediction based on level processing method and improved GWO algorithm. ACCESS, 13, 78243-78256. (www)

  @article{ , abstract = {In the field of short-term power load prediction, the current prediction methods have low prediction accuracy. To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accuracy. The genetic algorithm is applied to optimize the traditional grey wolf algorithm. Then, combined with the level set algorithm in the level processing algorithm, a genetic grey wolf hybrid model that integrates level processing is constructed. The variables in the load data are processed and analyzed through the level set algorithm. The final position of the population is determined based on the improved grey wolf genetic algorithm. Comparative experiments are conducted among the proposed model, the long short-term memory model, as well as the variational mode decomposition model. The average prediction accuracy remained within 0.652-0.859, significantly higher than the other two comparative models. The mean absolute error was 1.869, significantly lower than the other two models. The F1 score and accuracy were 0.891 and 90.32%, demonstrating that its predictive performance was significantly better than the other two models. Precision-recall curve, accuracy, mean absolute error, F1 score and other indicators are applied to evaluate the performance of the three models. The proposed model can accurately perform load prediction analysis in short-term power load prediction, and its prediction performance exceeds the other two prediction models. The prediction method can accurately predict short-term power load, providing useful references and inspirations for future researchers in power load prediction, and promoting the continuous development and progress of short-term power load prediction technology.}, archive = {J_ACCESS}, author = {Yuntong Li}, doi = {10.1109/ACCESS.2025.3565625}, journal = {IEEE Access}, pages = {78243-78256}, shortjournal = {IEEE Access}, title = {Short-term power load prediction based on level processing method and improved GWO algorithm}, volume = {13}, year = {2025}, }
• (2025). Hardware-based calculation of mean square error for automatic target recognition in SAR images. ACCESS, 13, 78231-78242. (www)

  @article{ , abstract = {This paper presents a circuit architecture aiming for FPGA synthesis of a processing stage of an Automatic Target Recognition (ATR) algorithm to classify non-cooperative targets in Synthetic Aperture Radar (SAR) images using the SAMPLE database. A reference algorithm in Python was used to determine whether the mathematical operations performed by the architecture are correct, comparing the results between different processing phases, such as the determination of the equivalence of coordinates between the target and the models (i.e., coordinates “matching”), partial calculations of the mean squared error, and the final result of the operations. The architecture was described in VHDL and synthesized for the Bajie Board development system with an FPGA XC7Z010-1CL400C. For a dataset of 50 coordinate points, the algorithm’s execution time in Python decreased from 4 milliseconds to approximately $30~\mu $ s in the synthesized system. Similarly, for a larger dataset of 400 coordinate points, the reduction was from 184 ms to 1.9 ms.}, archive = {J_ACCESS}, author = {Lucas Urbanski and Gustavo Farhat Araujo and Renato Machado and Roberto D'Amore}, doi = {10.1109/ACCESS.2025.3565674}, journal = {IEEE Access}, pages = {78231-78242}, shortjournal = {IEEE Access}, title = {Hardware-based calculation of mean square error for automatic target recognition in SAR images}, volume = {13}, year = {2025}, }
• (2025). SymbolNet: Bridging latent neural representations and symbolic reasoning via intermediate feature interpretation. ACCESS, 13, 78221-78230. (www)

  @article{ , abstract = {The interpretation of intermediate representations in deep neural networks is critical for enhancing the transparency, trustworthiness, and applicability of artificial intelligence (AI) systems. In this paper, we propose SymbolNet, a framework that extracts mid-level features from trained models and transforms them into human-interpretable symbolic representations. SymbolNet constructs a symbolic graph composed of nodes and edges that capture both the semantic meaning and relational structure within the model’s internal reasoning process. This symbolic decoding bridges the model’s internal computations with human cognitive understanding, enabling structured and meaningful interpretation of AI behavior. Experimental results on the GTSRB dataset demonstrate that SymbolNet improves classification accuracy by 4% over the baseline and significantly enhances robustness against various noise conditions and adversarial attacks. Our work contributes to the field of explainable AI by introducing a novel approach that reveals the internal learning dynamics of non-interpretable models through symbolic reasoning.}, archive = {J_ACCESS}, author = {Sungheon Jeong and Hyungjoon Kim and Yoojeong Song}, doi = {10.1109/ACCESS.2025.3565638}, journal = {IEEE Access}, pages = {78221-78230}, shortjournal = {IEEE Access}, title = {SymbolNet: Bridging latent neural representations and symbolic reasoning via intermediate feature interpretation}, volume = {13}, year = {2025}, }
• (2025). Azimuthal equidistant mapping and projection of head CT scans for fracture visualization. ACCESS, 13, 78214-78220. (www)

  @article{ , abstract = {Skull fractures of the base and facial regions of the skull are challenging to identify, even by expert radiologists. The intricate nature and subtle features of such fractures, demand meticulous attention and ample time allocation to assure effective diagnosis and positive patient outcomes. This paper introduces a novel approach, utilizing azimuthal equidistant mapping, to better visualize the skull and, thus, enhance the diagnostic speed and accuracy of skull fracture evaluation. The proposed method maps the inherently spherical shape of the skull into a planar representation, providing radiologists with an alternative view of each complete hemisphere. Applying azimuthal equidistant mapping to CT volumetric data preserves the skull’s anatomy, while fractures, which are usually perpendicular to the bone’s surface, become more visible in projection views. The enhanced visibility is particularly useful for subtle basilar or facial fractures and increases the likelihood of detection. The effectiveness of the technique was evaluated using the CQ500 dataset. Azimuthal equidistant mapping and projection can become a valuable addition to conventional radiologic evaluation, offering an alternative view for radiologists and emergency department staff, which can increase the effectiveness and time efficiency of skull fracture detection.}, archive = {J_ACCESS}, author = {Nicolas Hadjittoouli and Christos Nikolaou and Costas Pitris}, doi = {10.1109/ACCESS.2025.3565672}, journal = {IEEE Access}, pages = {78214-78220}, shortjournal = {IEEE Access}, title = {Azimuthal equidistant mapping and projection of head CT scans for fracture visualization}, volume = {13}, year = {2025}, }
• (2025). A non-invertible secure template generation using AES encrypted MCC and random triangle projection. ACCESS, 13, 78194-78213. (www)

  @article{ , abstract = {In recent years, due to the exponential growth in the applicability of biometric authentication systems, it has become essential to address the privacy and security concerns of user biometric information. The cancellable biometric template generation is one of the promising solutions in this situation which protects both the system and the user’s biometric data from unauthorized access. The complexity of such a system which sustains improved performance while maintaining the anonymity of users by introducing non-invertibility, unlinkability, and encapsulation of encrypted templates is a difficult task. The random projection-based cancellable biometric template generation is one of the efficient techniques to secure the information of users. But still, this approach suffers from the adversary attack where an attacker can obtain the original template by performing repeated random projections on the stored template. In this paper, we proposed a technique: MCC Encoded Random Triangle Hashing which protects the biometric template by encoding the projection matrix with the minutiae cylindrical codes obtained from the minutiae information. This technique addresses the problem where the projection matrix is leaked to an attacker which can lead to the reconstruction of the original biometric template. The performance of the proposed technique is evaluated using FMR, FAR, GAR, ROC Curve, and EER parameters on six FVC fingerprint databases FVC2000 DB1, FVC2000 DB2, FVC2002 DB1, FVC2002 DB3, FVC2004 DB1, and FVC2004 DB3 which is publicly available.}, archive = {J_ACCESS}, author = {Mohd Imran and Mohammad Sarosh Umar and Furkan Ahmad}, doi = {10.1109/ACCESS.2025.3562758}, journal = {IEEE Access}, pages = {78194-78213}, shortjournal = {IEEE Access}, title = {A non-invertible secure template generation using AES encrypted MCC and random triangle projection}, volume = {13}, year = {2025}, }
• (2025). 1D convolutional neural network-based hierarchical classification of eye movements using noncontact electrooculography. ACCESS, 13, 78182-78193. (www)

  @article{ , abstract = {This study addresses the discomfort and challenges posed by traditional electrooculography (EOG) measurement methods that require skin-contact electrodes by developing a non-contact EOG signal measurement device. The primary objective of this research is to implement a hierarchical deep learning model for classifying data collected from a non-contact EOG device into five types of eye movements. In this study, indium tin oxide (ITO) film was employed as a capacitive coupled electrode to measure EOG signals without skin contact. A hierarchical classification model based on a 1D convolutional neural network (CNN) was proposed for signal analysis, and K-fold cross-validation was used to train and validate the model. Unlike conventional Ag/AgCl electrodes, the proposed device enables EOG signal measurement without direct skin contact. ITO film was integrated into glasses to create non-contact electrodes while minimizing signal noise. Additionally, various signal feature extraction methods, including Fast Fourier Transform (FFT), Band Power, and Hilbert-Huang Transform, were applied to enhance classification accuracy. The model using the FFT method achieved 73% accuracy in Step 1 classification, with 84% accuracy for vertical channels and 81% for horizontal channels in Step 2. The Band Power method yielded 59% accuracy in Step 1, with 62% accuracy for vertical channels and 90% for horizontal channels in Step 2. The Hilbert-Huang Transform method produced 68% accuracy in Step 1, with 63% for vertical channels and 66% for horizontal channels in Step 2. The proposed non-contact EOG measurement system demonstrated improved usability and performance over traditional methods. It is expected to achieve practical applications in human-computer interaction (HCI) systems for patients with neurological disorders.}, archive = {J_ACCESS}, author = {Hyo Won Son and Tae Mu Lee and Sang Hyuk Kim and Hyun Jae Baek}, doi = {10.1109/ACCESS.2025.3566142}, journal = {IEEE Access}, pages = {78182-78193}, shortjournal = {IEEE Access}, title = {1D convolutional neural network-based hierarchical classification of eye movements using noncontact electrooculography}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-driven hunter-prey algorithm applied to 3D underwater sensor network coverage optimization. ACCESS, 13, 78161-78181. (www)

  @article{ , abstract = {As one of the key application scenarios of wireless sensor networks, the coverage optimization of underwater wireless sensor networks (UWSNs) requires special consideration of three-dimensional spatial characteristics, which distinctly differs from traditional terrestrial environment coverage issues. To address the problems of low coverage and uneven distribution in UWSNs within a three-dimensional space, we propose a Reinforcement Learning-driven Hunter-Prey Optimization (RL-HPO) algorithm. Firstly, a nonlinear convergence factor is designed to regulate the exploration and exploitation phases, achieving an effective balance between these two stages. Secondly, by incorporating the concept of Q-learning, the algorithm can adaptively select the optimal action strategy at different stages, thereby enhancing the effectiveness of actions executed in each phase. Lastly, the Nelder-Mead simplex strategy is introduced to perturb poorly performing individuals within the population, fully exploiting their search potential and preventing the algorithm from getting trapped in local optima. The performance of the RL-HPO algorithm in three-dimensional WSN environments, both with and without obstacles, was evaluated through simulation experiments. Comparisons were made with PSO, HPO, SSA, ALGWO, and SWOA. The results demonstrate that RL-HPO significantly outperforms other algorithms in key metrics such as coverage rate, moving distance, and network connectivity. In obstacle-free scenarios, RL-HPO achieved the highest coverage rate of 96.5%, while in scenarios with obstacles, the coverage rate reached 93.3%, representing improvements of 12.58%, 13.41%, 4.15%, 8%, and 13.95% over the other algorithms, respectively.}, archive = {J_ACCESS}, author = {Zibo Huang and Fangxiu Wang and Chen Su and Yi Wang and Hui Liu}, doi = {10.1109/ACCESS.2025.3565953}, journal = {IEEE Access}, pages = {78161-78181}, shortjournal = {IEEE Access}, title = {Reinforcement learning-driven hunter-prey algorithm applied to 3D underwater sensor network coverage optimization}, volume = {13}, year = {2025}, }
• (2025). Recommendations for target muscle activation protocol during trunk rehabilitation robot-based seated perturbation training. ACCESS, 13, 78150-78160. (www)

  @article{ , abstract = {Rehabilitation interventions involving forced perturbations of seated balance can improve a patient’s balancing ability with increased muscle activations. Investigating training parameters that affect the specific muscle activation is essential to providing patient-tailored training. However, there is limited information on the combined effects of different seated perturbation training parameters to provide target activation of lower limb muscles although various parameters can affect activating lower limb muscles. Accordingly, by using the trunk rehabilitation robot that can provide quantified perturbation, we investigated the effects of variations in perturbation speed (slow (SS) and fast (FS) speed), direction (mediolateral (ML) & anteroposterior (AP)), and leg support condition (seat-connected (SC) and ground-connected (GC) footrest) on the lower limb muscle activity as well as postural stability and trunk movement of 18 healthy young participants. The experimental results show that variation in training parameters affects balance, trunk movement, and muscle activity outcomes. From the experimental results, FS & GC with AP-directional perturbation can be the effective training protocol for Gastrocnemius (0.16 %PA, percentage of Peak Amplitude). In addition, considering balance instability results, FS & SC with AP-directional perturbation can be adopted as the effective training protocol to target the Vastus Lateralis (0.15 %PA), Semitendinosus (0.12 %PA), and Tibialis Anterior (0.16 %PA). Thus, modulation of footrest condition, perturbation speed and direction may be feasible for tuning perturbation exercises to achieve desired rehabilitation outcomes.}, archive = {J_ACCESS}, author = {Amre Eizad and Hosu Lee and Junyeong Lee and Won-Kyung Song and Jungwon Yoon}, doi = {10.1109/ACCESS.2025.3559920}, journal = {IEEE Access}, pages = {78150-78160}, shortjournal = {IEEE Access}, title = {Recommendations for target muscle activation protocol during trunk rehabilitation robot-based seated perturbation training}, volume = {13}, year = {2025}, }
• (2025). Real-time acoustic holography for target pattern acoustic field reconstruction with iterative unsupervised learning. ACCESS, 13, 78138-78149. (www)

  @article{ , abstract = {The ability to reconstruct acoustic field is essential for applications such as particle manipulation, medical therapy, and ultrasonic imaging. Conventional acoustic field reconstruction algorithms are limited in both speed and accuracy when generating acoustic holograms, which further constrains the practical applications of acoustic holography. In this study, we propose an acoustic field reconstruction method that achieves both speed and accuracy by leveraging an acoustic model tailored for diffraction. A unit transducer is integrated with an acoustic lens, utilizing lens thickness and acoustic velocity variations to produce accurate phase maps at specific frequencies. To reduce the dependence on labeled datasets, we propose a deep learning framework with an autoencoder structure for unsupervised training without relying on any a priori information. Additionally, a novel loss function is designed for the framework to realize the energy efficiency constraint. The proposed method is compared with traditional Angular Spectrum Method (ASM) and Automatic Differentiation (Diff-PAT). Experimental results show that the Peak Signal-to-Noise Ratio (PSNR) of this method reaches 21.59, which is 24.1% higher than that of ASM and 19.2% higher than that of Diff-PAT. After network training, the proposed method generates phase holograms hundreds of times faster than ASM and Diff-PAT. Under the same network architecture, our newly designed loss function improves energy efficiency to 2.1 times that of the original method.}, archive = {J_ACCESS}, author = {Minjie Ma and Xuewei Wang and Yang Li and Jia Wang}, doi = {10.1109/ACCESS.2025.3563250}, journal = {IEEE Access}, pages = {78138-78149}, shortjournal = {IEEE Access}, title = {Real-time acoustic holography for target pattern acoustic field reconstruction with iterative unsupervised learning}, volume = {13}, year = {2025}, }
• (2025). Mamba-based VoxelMorph framework for cardiovascular disease imaging and risk assessment. ACCESS, 13, 78120-78137. (www)

  @article{ , abstract = {Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD), remain the leading cause of global mortality, necessitating advanced diagnostic solutions. Accurate deformable image registration plays a crucial role in enhancing segmentation precision and classification performance in cardiovascular imaging. However, existing registration methods, including VoxelMorph, face limitations in computational efficiency and memory usage, restricting their real-time applicability for high-resolution cardiac imaging. This study proposes the Mamba-Optimized VoxelMorph framework, which leverages GPU-based parallelization and memory optimization to address these challenges. The framework achieves superior registration accuracy, yielding a Dice Similarity Coefficient (DSC) of 0.95 and Normalized Cross-Correlation (NCC) of 0.90, while reducing computational time by 40% and memory usage to 800 MB. These advancements ensure efficient alignment of complex cardiac structures, thereby improving segmentation accuracy and classification reliability. By addressing these critical limitations, the Mamba-Optimized VoxelMorph framework significantly enhances cardiovascular imaging, enabling precise, scalable, and real-time deformable image registration for improved CAD diagnosis and treatment planning.}, archive = {J_ACCESS}, author = {Muhammad Kashif Jabbar and Huang Jianjun and Ayesha Jabbar and Zaka Ur Rehman}, doi = {10.1109/ACCESS.2025.3564962}, journal = {IEEE Access}, pages = {78120-78137}, shortjournal = {IEEE Access}, title = {Mamba-based VoxelMorph framework for cardiovascular disease imaging and risk assessment}, volume = {13}, year = {2025}, }
• (2025). Corporate education for digital transformation in the semiconductor industry: Proactive consideration in early implementation phase. ACCESS, 13, 78107-78119. (www)

  @article{ , abstract = {Contribution: This study addresses the gap in understanding the practical context of corporate education for Digital Transformation (DT) within the semiconductor industry. Background: The COVID-19 pandemic accelerated global digital transformation initiatives, with South Korea’s semiconductor industry implementing comprehensive DT education programs since 2020. However, its practical and empirical influence and limitations have never been rigorously examined. Research Question: What impact, barriers and improvement strategies do DT education programs have on individual and organizational DT competencies in the semiconductor industry? Methodology: Employing a qualitative research design, in-depth interviews were conducted with 20 employees from diverse job roles and educational backgrounds within the semiconductor industry. Findings: The research reveals that while general DT education improves digital literacy and fosters individual and team-level innovation, this is often realized through small-scale digital transformation initiatives. However, significant challenges hinder further potential, including short-term performance evaluation frameworks, restrictive IT policies, a lack of advanced, role-specific training, and insufficient understanding of DT concepts and strategies among organizational leaders. To address these challenges, the study recommends refining evaluation systems, easing policy constraints, and tailoring training programs to specific roles. Additionally, it emphasizes the importance of leadership-focused DT education to enhance leaders’ understanding of digital transformation, enabling them to better guide and support organizational initiatives. These measures aim to amplify the practical applications of DT efforts, fostering sustained organizational growth and innovation. Limitation and Future Work: This study faces several limitations, including an industry bias toward semiconductors, a short observation period, subjective self-reported data, inconsistent program implementation, and limited measures of organizational performance. Future research could address these gaps by conducting cross-industry comparisons, long-term assessments, quantitative ROI analyses, and investigations of leadership training and organizational culture.}, archive = {J_ACCESS}, author = {Hochan Lee and Sangyoon Yi}, doi = {10.1109/ACCESS.2025.3565486}, journal = {IEEE Access}, pages = {78107-78119}, shortjournal = {IEEE Access}, title = {Corporate education for digital transformation in the semiconductor industry: Proactive consideration in early implementation phase}, volume = {13}, year = {2025}, }
• (2025). Research on electromagnetic safety of UAV inspection in substation. ACCESS, 13, 78097-78106. (www)

  @article{ , abstract = {As power grids modernize, ensuring reliable substation operation grows increasingly critical. Uncrewed aerial vehicle (UAV) inspections provide significant advantages over traditional manual and robotic methods, such as cost-effectiveness, greater maneuverability, improved efficiency, and the ability to operate in closer proximity to equipment. However, the complex electromagnetic-field environment within substations poses challenges for UAV operations. Determining a safe distance between UAVs and high-voltage equipment is essential for deploying UAVs effectively in substation inspections. This study investigates electromagnetic field exposure during UAV-based substation inspections. A computational model was developed to evaluate the electromagnetic field and simulate the surface field distribution during UAV operation. The results indicate that the UAV is affected by strong electromagnetic fields near the four rotor blades and the central wing section. Laboratory high-voltage tests, combined with simulations, confirmed that the UAV can perform inspections while maintaining a 50 cm distance from high-voltage equipment. Furthermore, when the UAV approaches the equipment to the point of gap discharge, it can continue operating normally for a limited time, demonstrating its certain ability to withstand high-voltage electric fields. These findings provide critical insights for establishing safe operating distances for UAVs in substations.}, archive = {J_ACCESS}, author = {Wensheng Li and Mengyuan Chen and Haohao Jin and Ying Zhang and Hao Wu and Lin Yi and Li Cai}, doi = {10.1109/ACCESS.2025.3565919}, journal = {IEEE Access}, pages = {78097-78106}, shortjournal = {IEEE Access}, title = {Research on electromagnetic safety of UAV inspection in substation}, volume = {13}, year = {2025}, }
• (2025). Fault-tolerant methods for three-level T-type inverter to balance neutral-point voltage. ACCESS, 13, 78084-78096. (www)

  @article{ , abstract = {This paper proposes new fault-tolerant (FT) space-vector modulation (SVM) techniques for three-level T-type inverter (3L-T2I) to balance neutral-point voltage (NPV) under faulty conditions. Unlike conventional FT-SVM methods, which use three-nearest vectors to synthesize output voltages, the proposed SVM methods add one small vector to conventional switching sequences to obtain NPV balance. Dwell-time of extra vectors are maximized to decrease NPV balanced time. Comparing to conventional FT-SVM methods, the proposed approaches can significantly reduce the difference in capacitor voltages. As a result, the amplitudes of DC component and high-order harmonics of output currents are also reduced. Consequently, the quality of output current is improved. Experimental results, which are conducted by a 1.1-kVA laboratory prototype, are presented to verify the effectiveness of the proposed FT methods. Comparison studies based on experimental results are also presented to demonstrate the advantages of NPV balance of the proposed method compared to conventional FT methods.}, archive = {J_ACCESS}, author = {Duc-Tri do and Anh-Tuan Nguyen-Phan and Khai M. Nguyen and Vinh-Thanh Tran}, doi = {10.1109/ACCESS.2025.3565856}, journal = {IEEE Access}, pages = {78084-78096}, shortjournal = {IEEE Access}, title = {Fault-tolerant methods for three-level T-type inverter to balance neutral-point voltage}, volume = {13}, year = {2025}, }
• (2025). Advances in real-time reconstruction of deformable tissues for endoscopic surgery: A comprehensive review of gaussian splatting techniques and applications. ACCESS, 13, 78073-78083. (www)

  @article{ , abstract = {Endoscopic imaging has become an indispensable tool in modern medical practice, enabling minimally invasive diagnosis and treatment. A crucial aspect of endoscopic procedures is the accurate reconstruction of the observed scene, which facilitates spatial understanding, surgical planning, and guidance. This comprehensive review delves into the latest advancements and applications of Gaussian splatting in endoscopic scene reconstruction. We explore the fundamental principles of this technique, examining how Gaussian kernels are utilized to fuse depth cues and generate detailed 3D models. Furthermore, we analyze the various refinements and extensions that have been proposed to enhance the performance and robustness of Gaussian splatting, addressing challenges such as occlusions, sparse data, and real-time processing. Through this comprehensive survey, we aim to provide researchers and medical professionals with a deep understanding of the state-of-the-art in Gaussian splatting for endoscopic scene reconstruction, highlighting its pivotal role in advancing endoscopic imaging and interventions.}, archive = {J_ACCESS}, author = {Di Ding and Haoyu Wang and Tianliang Yao and Rong Luo and Xusen Sun}, doi = {10.1109/ACCESS.2025.3565920}, journal = {IEEE Access}, pages = {78073-78083}, shortjournal = {IEEE Access}, title = {Advances in real-time reconstruction of deformable tissues for endoscopic surgery: A comprehensive review of gaussian splatting techniques and applications}, volume = {13}, year = {2025}, }
• (2025). Machine learning framework for early detection of chronic kidney disease stages using optimized estimated glomerular filtration rate. ACCESS, 13, 78057-78072. (www)

  @article{ , abstract = {Chronic Kidney Disease (CKD) is a progressive condition that requires accurate diagnosis and staging for effective clinical management. Conventional CKD diagnosis relies on estimated Glomerular Filtration Rate (eGFR), a measure of kidney function derived from serum biomarkers such as serum creatinine (SCr) and cystatin C (SCysC). However, eGFR calculations may be inaccurate when applied to diverse patient populations. This study proposes a machine learning (ML) system that integrates regression-based eGFR estimation, metaheuristic optimization using the Grey Wolf Optimizer (GWO), and multi-class classification with various ML models to enhance CKD staging and classification. The model estimates eGFR using three established CKD Epidemiology Collaboration (CKD-EPI) equations incorporating SCr, SCysC, and their combined values. Regression models assess predictive performance, specifically Linear Regression (LR) and Support Vector Regression (SVR). SVR demonstrates superior performance compared to LR for $\text {CKD-EPI}_{\text {SCr-SCysC}}$ achieved a root mean squared error (RMSE) of 3.03, a mean absolute percentage error (MAPE) of 2.97%, and a coefficient of determination ( $\text {R}^{2}$ ) score of 0.97. The application of GWO for hyperparameter tuning has resulted in a 37.3% reduction in root mean square error (RMSE), a 37.4% drop in mean absolute percentage error (MAPE), and a 2.06% improvement in $\text {R}^{2}$ to improve the precision of prediction. Once the model fine-tunes the eGFR estimations, it feeds them into various algorithms for CKD stage classification, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Among these, XGBoost achieves the highest classification accuracy of 97.76%, along with an F1-score of 97.45%, demonstrating its effectiveness in CKD staging. Shapley Additive Explanations (SHAP) provide global and local feature importance insights, enhancing clinical decision-making and model transparency. Future research will validate the model using more extensive and more diverse datasets. Additionally, it will incorporate extra clinical parameters, including biomarkers and genetic data, to enhance the precision of CKD risk prediction. This research enhances AI-driven nephrology by providing a scalable, interpretable, and highly accurate solution for diagnosing and managing CKD.}, archive = {J_ACCESS}, author = {Samit Kumar Ghosh and Namareq Widatalla and Ahsan H. Khandoker}, doi = {10.1109/ACCESS.2025.3565549}, journal = {IEEE Access}, pages = {78057-78072}, shortjournal = {IEEE Access}, title = {Machine learning framework for early detection of chronic kidney disease stages using optimized estimated glomerular filtration rate}, volume = {13}, year = {2025}, }
• (2025). DC-SSCB snubber for electric propulsion ships with effective surge voltage reduction and fast dissipation of fault current energy. ACCESS, 13, 78044-78056. (www)

  @article{ , abstract = {This paper proposes a snubber circuit for SSCB that performs voltage clamping operation in two stages to reduce surge voltage. The surge voltage is suppressed in the first stage using a MOV with low operating initiation and clamping voltages. Then, the fault current gradually rises with a gentle slope and reaches the clamping voltage of the second MOV, which shows the effect of reducing the surge voltage. By adding RC circuits connected in series and parallel to two MOVs, fast dissipation of energy generated by fault current, high interrupting capacity, and low energy loss are ensured. Theoretical analysis is performed on the operational mode of the proposed snubber circuit, and its validity is verified through simulation and experiment. The effects of dissipated energy due to fault current, energy dissipation time, surge voltage reduction rate, voltage ringing, and stray inductance are analyzed, and compared with the prior snubber circuit, the proposed snubber circuit is evaluated to be suitable for SSCB of electric propulsion ships.}, archive = {J_ACCESS}, author = {Jae-Hun Cha and Guangxu Zhou and Sung-Geun Song and Feel-Soon Kang}, doi = {10.1109/ACCESS.2025.3564989}, journal = {IEEE Access}, pages = {78044-78056}, shortjournal = {IEEE Access}, title = {DC-SSCB snubber for electric propulsion ships with effective surge voltage reduction and fast dissipation of fault current energy}, volume = {13}, year = {2025}, }
• (2025). Self-supervised drift-resilient classification for time series industrial anomaly detection. ACCESS, 13, 78021-78043. (www)

  @article{ , abstract = {In modern industrial environments, early detection of anomalies is essential to prevent unplanned downtime and maintain operational efficiency. Traditional rule-based and supervised methods often struggle with data drift, limited labeled data, and the inability to capture the complex interdependencies inherent in interconnected industrial systems. To address these challenges, we propose a drift-resistant self-supervised anomaly detection framework specifically designed for industrial applications. Unlike conventional fault classification approaches that identify predefined defect types, our model focuses on detecting subtle, evolving anomalies in time-series vibration data. The framework integrates advanced statistical and spectral feature extraction with a dynamic, rolling-window-based data grouping strategy, enabling the model to adapt robustly to temporal variations. Evaluation is based solely on accuracy and the experimental results demonstrate that our approach achieves up to 75% faster anomaly alerts compared to conventional ISO 10816 standards. Validation in NASA bearing datasets, as well as real-world vibration data from industrial fans, motors, and gearboxes confirms the model scalability and practical applicability. This work provides a cost-effective and reliable solution for continuous condition monitoring, thereby laying a strong foundation for enhanced predictive maintenance in complex industrial settings.}, archive = {J_ACCESS}, author = {Myung-Kyo Seo and Byeong Hoon Yoon and Junseung Ryu and Hyung Ju Hwang}, doi = {10.1109/ACCESS.2025.3565886}, journal = {IEEE Access}, pages = {78021-78043}, shortjournal = {IEEE Access}, title = {Self-supervised drift-resilient classification for time series industrial anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Rice variety identification based on transfer learning architecture using DENS-INCEP. ACCESS, 13, 78007-78020. (www)

  @article{ , abstract = {Rice is a vital staple food for billions of people worldwide, especially in Asia, Africa, and Latin America, where it plays a key role in daily caloric intake and nutrition. With numerous varieties differing in size, shape, colour, texture, and nutritional content, accurate rice variety identification is critical for optimizing production and ensuring food quality. Environmental factors such as soil type and climate further influence these variations, making precise identification essential for improving productivity and reducing waste. However, traditional manual methods of identification, relying on visual characteristics, are prone to human error, resulting in variety mixing, reduced quality, and higher costs. This study addresses these challenges by employing the DENS-INCEP model, a transfer learning approach that integrates DenseNet-201 with the Inception module. DenseNet-201 serves as the backbone for feature extraction, while the Inception module enhances the model’s ability to capture multi-scale shape-related features, significantly improving classification accuracy. The model achieved remarkable performance, with an average accuracy of 99.94% across multiple rice varieties. By implementing the DENS-INCEP model, this study contributes to Sustainable Development Goal (SDG) 2 by improving food security through enhanced rice production and supply chain stability. Additionally, it supports SDG 9 by fostering innovation and advancing sustainable agricultural technologies. Furthermore, by reducing errors, waste, and inefficiencies in production and distribution, the model aligns with SDG 12, which emphasizes sustainable consumption and production. Overall, the DENS-INCEP model offers a robust and efficient solution to rice variety identification, addressing global food security challenges while promoting sustainability.}, archive = {J_ACCESS}, author = {Wahyudi Agustiono and Kurniawan Eka Permana and Caroline Chan and Deshinta Arrova Dewi and Moch. Miftachur Rifqi Al Husain}, doi = {10.1109/ACCESS.2025.3562585}, journal = {IEEE Access}, pages = {78007-78020}, shortjournal = {IEEE Access}, title = {Rice variety identification based on transfer learning architecture using DENS-INCEP}, volume = {13}, year = {2025}, }
• (2025). Monitoring 5G backhaul: An in-band telemetry approach for quality of service. ACCESS, 13, 77990-78006. (www)

  @article{ , abstract = {The use of Ultra-Dense Networks within 5G communications can be leveraged by the adoption of Millimeter-Wave (mmWave) technology for the backhaul, resulting in cost reductions and faster deployment of the infrastructure. However, this shift also introduces new concerns and restrictions. The wireless link susceptibility to signal strength and link loss degradation, due to loss of line-of-sight, makes the link quality inconsistent. The heterogeneity of network nodes poses an additional challenge for tracking link quality changes reliably. An effective network monitoring system using 5G Quality of Service (QoS) Indicators is necessary to correctly characterize and track channel and flow quality conditions in a 5G wireless backhaul. To tackle these challenges, we introduce an In-Band Telemetry (INT) approach, consisting of a P4-compatible dataplane model and an aggregation agent capable of gathering and processing per-packet measurements, exposing them as link and QoS flow quality metrics, suitable for integration with Software Defined Network (SDN) environments and 5G networks. Our study compares the accuracy achieved by the proposed in-band solution to a commercial network management system, in an outdoor test-bed with an obstructed mmWave backhaul link. The results demonstrate that this approach exhibits minimal measurement errors when assessing the throughput, latency, and Packet Error Rate (PER) of mmWave links. The solution attains an average forwarding overhead of approximately 17%, while maintaining a per-node aggregation processing total time upper-bound of 45 ms at 2.5 Gbps line rate.}, archive = {J_ACCESS}, author = {Eurico Dias and Duarte Raposo and Miguel Luís and Pedro Rito and Susana Sargento}, doi = {10.1109/ACCESS.2025.3565274}, journal = {IEEE Access}, pages = {77990-78006}, shortjournal = {IEEE Access}, title = {Monitoring 5G backhaul: An in-band telemetry approach for quality of service}, volume = {13}, year = {2025}, }
• (2025). Win CoAP: A window-based congestion control approach for burst traffic in IoT networks. ACCESS, 13, 77973-77989. (www)

  @article{ , abstract = {Constrained Application Protocol (CoAP) is a widely used communication protocol in Internet of Things (IoT) networks. Congestion is a major concern in IoT networks because it affects the performance of applications and network reliability. Congestion control (CC) must be employed by the application since CoAP is based on User Datagram Protocol (UDP). IoT devices transfer a burst of data upon event detection. There are suitable features in Non-confirmable (NON) message type of CoAP for burst data transfer. The absence of a CC method for NON message type in CoAP hinders its applicability. OBSERVE defines static rate control for NON messages which is inappropriate for dynamic needs. The Block-wise Transfer also employs static rate control for NON message blocks, resulting in the same limitation. CoAP simple congestion control/advanced (COCOA) provides a dynamic CC method for the NON message type. COCOA employs reliable packets inside the NON message burst at regular intervals to capture network dynamics. Infringement of reliable packets adds up overhead and periodic monitoring caters to insufficient vigilance. In this paper, we propose a window-based dynamic congestion control method called Win CoAP to regulate the burst traffic based on the window of packets. The window size is additively increased on successful delivery and adaptively decreased otherwise. Win CoAP is implemented and validated against COCOA using the Cooja simulator in Contiki OS. We performed extensive simulations using various topologies and burst rates. Win CoAP outperforms COCOA in all analyzed scenarios and can improve the efficiency of event-based applications.}, archive = {J_ACCESS}, author = {Archana K. Rajan and Masaki Bandai}, doi = {10.1109/ACCESS.2025.3564901}, journal = {IEEE Access}, pages = {77973-77989}, shortjournal = {IEEE Access}, title = {Win CoAP: A window-based congestion control approach for burst traffic in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Detection of malicious clients in federated learning using graph neural network. ACCESS, 13, 77952-77972. (www)

  @article{ , abstract = {Federated Learning (FL) facilitates decentralized model training without the exchange of raw data, thereby guaranteeing privacy. However, due to its distributed nature, this paradigm is susceptible to adversarial threats such as sign-flipping attacks, in which malicious clients reverse model parameter signs in order to poison the global aggregation process. This study introduces a detection framework that is graph-based and leverages Graph Attention Networks (GATs) to overcome these challenges. The framework detects malicious clients with high accuracy by representing FL local models as directed graphs and capturing layer-wise statistical features. The efficacy of the approach is demonstrated by extensive experiments on the FEMNIST dataset, which simulate varying attacker percentages (15%, 35%) and attack probabilities (0.5, 0.7, 1.0). The GAT model obtains a 100% detection rate with zero false positives within an optimal threshold range of 0.5–0.9, as demonstrated by the results. Furthermore, isolating detected attackers during targeted rounds (20-60) substantially maintains FL global model performance, thereby mitigating the cascading effects of poisoned updates and ensuring system stability. This work offers a practicable, scalable, and robust solution to improve the security of FL systems against adversarial behaviors.}, archive = {J_ACCESS}, author = {Anee Sharma and Ningrinla Marchang}, doi = {10.1109/ACCESS.2025.3565712}, journal = {IEEE Access}, pages = {77952-77972}, shortjournal = {IEEE Access}, title = {Detection of malicious clients in federated learning using graph neural network}, volume = {13}, year = {2025}, }
• (2025). Automatic endoscopic navigation for monocular depth and ego-motion estimation in wireless capsule endoscopy through transformer network. ACCESS, 13, 77931-77951. (www)

  @article{ , abstract = {Gastrointestinal (GI) cancers are among the most prevalent globally. Wireless capsule endoscopy (WCE), a minimally invasive technology, offers a promising alternative for diagnosing and treating GI diseases. Accurate depth estimation from WCE but remains challenging due to the complexity of the GI environment and limited datasets. In this paper, we propose an automatic endoscopic navigation system for monocular depth and ego-motion estimation in wireless capsule endoscopy (WCE) through a Transformer-based encoder-decoder network. Minimally invasive surgeries, including gastrointestinal (GI) procedures, face unique challenges such as restricted field of view, illumination variation, and texture sparsity, which complicate depth estimation and pose estimation tasks. Traditional Structure from Motion (SfM) and SLAM methods are often inadequate for GI scenes due to these inherent complexities. To address these issues, we introduce a novel self-supervised neural network framework that integrates a dual-attention mechanism within a modified ResNet. This model simultaneously predicts depth maps and ego-motion from monocular GI images, without requiring ground truth depth data. Our approach enhances feature extraction through spatial and channel-wise attention, allowing the network to capture both local and global contextual information. Furthermore, a multi-scale structural similarity index combined with L1 loss function is employed to improve the accuracy of depth estimation in challenging endoscopic environments. The model leverages a multi-interval frame sampling strategy to simulate diverse ego-motion scenarios, making it robust to low frame rate inputs typically seen in WCE. For ego-motion estimation on the ColonSim dataset, our model achieves an Absolute Trajectory Error (ATE) of 0.09 m at 30 FPS, outperforming the next-best model, SC-SfMLearner, by 44.4%. Additionally, for depth estimation, our model records an Absolute Relative Error (Abs Rel) of 0.33, a Squared Relative Error (Sq Rel) of 0.27, and a Root Mean Square Error (RMSE) of 0.94 on the EndoSLAM dataset.}, archive = {J_ACCESS}, author = {Junjun Huang and Tianran He and Juan Xu and Weiting Wu and Wei Wu}, doi = {10.1109/ACCESS.2025.3565749}, journal = {IEEE Access}, pages = {77931-77951}, shortjournal = {IEEE Access}, title = {Automatic endoscopic navigation for monocular depth and ego-motion estimation in wireless capsule endoscopy through transformer network}, volume = {13}, year = {2025}, }
• (2025). Topic-weighted kernels: Text kernels integrating topic weights and deep word embeddings for semantic text analytics. ACCESS, 13, 77918-77930. (www)

  @article{ , abstract = {Traditional text classification models, such as text kernels, primarily consider the syntactic aspects of text data. This paper introduces Topic-Weighted Kernels, a new text analytics framework that combines global topical themes with word-level semantics in a text kernel architecture. Three new text kernels are proposed to improve text analysis - (a) the Topic-Weighted Base Kernel, (b) the Topic-Weighted Word2Vec kernel, and (c) the Topic-Weighted BERT (Bidirectional Encoder Representations from Transformers) kernel. These kernels leverage topic modeling and deep word embeddings to capture thematic and semantic information within textual data. Text kernels consider global and local semantics for text analysis tasks and improve model performance. Experiments on diverse datasets demonstrate that Topic-Weighted Kernels outperforms existing methods for text analysis tasks. The Topic-Weighted BERT Kernel achieves top-tier performance, with F1 scores reaching 99% on lighter datasets and significantly boosting performance on more complex datasets. For the tasks of multi-label text classification on the Reuters-90 dataset and sentiment analysis on the IMDB dataset, the model achieves F1 scores of 90.76% and 96.66%, respectively, demonstrating state-of-the-art performance. The Topic-Weighted Kernel approach improves the performance while enabling a better contextual representation for various text analysis tasks such as single and multi-label classification and sentiment analysis. The proposed framework integrates semantics from word embeddings and topic models to text kernels for capturing intricate patterns in textual data that aid in more contextual text analytics.}, archive = {J_ACCESS}, author = {Nikhil V. Chandran and V. S. Anoop and S. Asharaf}, doi = {10.1109/ACCESS.2025.3565816}, journal = {IEEE Access}, pages = {77918-77930}, shortjournal = {IEEE Access}, title = {Topic-weighted kernels: Text kernels integrating topic weights and deep word embeddings for semantic text analytics}, volume = {13}, year = {2025}, }
• (2025). Controlled shared memory (COSM) isolation: Design and testbed evaluation. ACCESS, 13, 77893-77917. (www)

  @article{ , abstract = {Recent memory-sharing approaches, e.g., based on the Compute Express Link (CXL) standard, allow the flexible high-speed sharing of data (i.e., data communication) among multiple hosts. In information systems for sensitive data, the data sharing between hosts, must be closely controlled. Security policies may require strict isolation, so-called air-gapping. However, strict isolation mechanisms are currently lacking in data communications based on shared memory. We propose the novel COntrolled Shared Memory (COSM) framework for strictly and dynamically controlling the data communication via shared memory approaches. We introduce the novel concept of COSM isolation, which restricts data communication via shared memory regions with first-level isolation based on a write-and-read permission matrix and second-level isolation based on data inspection. These isolation levels are enforced by the memory controller on an externally-attached shared memory device (ESMD). COSM isolation is thus generally more secure than the existing software-based isolation (e.g., virtual machine isolation of a hypervisor) and existing hardware-assisted isolation (e.g., single-root input/output virtualization). We implement COSM host-to-host isolation in a testbed with an ESMD built on a Field Programmable Gate Array (FPGA). We evaluate the host data write and read rates [bit/s] and latencies under various ESMD loads as well as write-and-read permission configurations. The introduced COSM isolation can serve as the foundation for a new sub-field of research within the information technology (IT) security research field.}, archive = {J_ACCESS}, author = {Vignesh Sundaravarathan and Martin Reisslein and Akhilesh S. Thyagaturu and Nick Ross and Gurpreet Singh Kalsi and Jason Howard and Jan Kaisrlik and Bartosz Matwiejczyk and Marek M. Landowski and Piotr Dorozynski and Harvey Vrsalovic and Sanjaya Tayal}, doi = {10.1109/ACCESS.2025.3564391}, journal = {IEEE Access}, pages = {77893-77917}, shortjournal = {IEEE Access}, title = {Controlled shared memory (COSM) isolation: Design and testbed evaluation}, volume = {13}, year = {2025}, }
• (2025). Intelligent air gap segmentation in electric motor color images using a self-organizing map. ACCESS, 13, 77879-77892. (www)

  @article{ , abstract = {Image segmentation plays an important role in automating various inspection procedures that are still performed manually. In the production of electric motors, a relevant inspection task involves examining the air gap between the rotor and the stator to identify common defects. This paper introduces an intelligent approach based on a self-organizing map (SOM) that is trained individually for each image to automatically segment the air gap region in electric motor images. This procedure eliminates the need for a large dataset of manually segmented images, which is typically required by most of the intelligent segmentation methods in the literature. The procedure begins with a polar transformation of the motor image, which helps to identify the region of interest for segmentation. The selected region is then used to build a training dataset for the SOM. Each training example consists of a frame extracted around every pixel in the region. Once map training is complete, an automated procedure extracts the air gap segment from the map output by selecting the pixel cluster that corresponds to the air gap. Three configurations of this approach were evaluated, exploring the impact of using different color spaces (RGB, LAB, and HSV). To evaluate the proposed approach, six motor samples were tested, using different illumination conditions. The results were compared with two unsupervised segmentation methods: Otsu’s method and K-Means. The proposed approach outperformed both baseline methods in terms of evaluated metrics, achieving a 65% higher intersection-over-union score than the best baseline method (K-Means). This study demonstrates the efficacy of the proposed image segmentation method, particularly in scenarios where only a limited number of images are available, making it impractical to train a supervised model.}, archive = {J_ACCESS}, author = {Bernardo Barancelli Schwedersky and João Paulo Zomer Machado and Rodolfo César Costa Flesch and Cassiano Montibeller}, doi = {10.1109/ACCESS.2025.3564562}, journal = {IEEE Access}, pages = {77879-77892}, shortjournal = {IEEE Access}, title = {Intelligent air gap segmentation in electric motor color images using a self-organizing map}, volume = {13}, year = {2025}, }
• (2025). Analysis of real-time frequency event in indian power system: Quantifying frequency response reserves and effective-droop performance. ACCESS, 13, 77862-77878. (www)

  @article{ , abstract = {In modern power systems, characterized by converter-interfaced generation and electronic interface loads, system inertia ( $M_{\text {sys}}$ ) is declining. While existing methods primarily focus on inertia estimation, a broader understanding of its impact on frequency response is imperative. This study proposes a comprehensive approach to quantify reserve supports beyond $M_{\text {sys}}$ , i.e., self-regulation ( $D_{\text {self}}$ ) from frequency-dependent loads, governor response ( $FR_{\text {Gov}}$ ), and effective governor-droop ( $R_{\text {eff.droop}}$ ) performance. Using high-resolution frequency dynamics and rate of change of frequency (RoCoF) data, a Gaussian filter (GF) is applied to remove transients and detect event onset based on RoCoF triggers. The response time frames of different ancillary support mechanisms are distinguished through robust curve fitting and RoCoF analysis over the average frequency trajectory. This method provides precise time-frequency coordinates corresponding to inertia, load, and governor response time frames. The proposed method is first validated through real-time simulation on the IEEE 24-bus system using a high-fidelity real-time digital simulator (RTDS). Additionally, it is tested on nine real-frequency excursion events following renewable generation trips in the Indian power system between May 2020 and 2023. The calculated results closely align with actual system event estimates, demonstrating the effectiveness of the approach with minimal deviations. Furthermore, a case study on operational challenges arising from high renewable energy penetration in the Indian power system on August 11, 2022, is discussed.}, archive = {J_ACCESS}, author = {Khwrwmdao Basumatary and Le Nam Hai Pham and Anup Shukla and Francisco M. Gonzalez-Longatt}, doi = {10.1109/ACCESS.2025.3566328}, journal = {IEEE Access}, pages = {77862-77878}, shortjournal = {IEEE Access}, title = {Analysis of real-time frequency event in indian power system: Quantifying frequency response reserves and effective-droop performance}, volume = {13}, year = {2025}, }
• (2025). Estimation of required characteristic modes for stirrers in a reverberation chamber. ACCESS, 13, 77847-77861. (www)

  @article{ , abstract = {This paper investigates an approach for assessing stirrer performance in a reverberation chamber (RC) using characteristic mode analysis (CMA). In the design of a reverberation chamber, evaluating stirrer performance is crucial; however, it is considered challenging to assess it independently. In this study, we focus on evaluation based on the number of characteristic modes and clarify the quantitative relationship between electric field uniformity and the number of characteristic modes through RC analysis of 117 configurations. For the 117 cases obtained, non-parametric estimation was performed to model the data without assuming any physical relationships. The findings suggest that the stirrer’s influence on field uniformity stabilizes when its characteristic mode count reaches 40 to 60, aligning with the cavity mode density at the lowest usable frequency (LUF). The derived criterion also meets the IEC’s “medium” field anisotropy requirement. Additionally, comparison with geometric parameters in the IEC standard indicates that conventional metrics, such as stirrer length, can distinguish performance variations by up to 33.3%, whereas the characteristic mode-based evaluation improves this distinction to 66.7%. In addition, CMA-based evaluation can reduce design time by 27%.}, archive = {J_ACCESS}, author = {Kodai Yaguchi and Takahiro Aoyagi}, doi = {10.1109/ACCESS.2025.3565699}, journal = {IEEE Access}, pages = {77847-77861}, shortjournal = {IEEE Access}, title = {Estimation of required characteristic modes for stirrers in a reverberation chamber}, volume = {13}, year = {2025}, }
• (2025). Balancing diversity in session-based recommendation between relevance and unexpectedness. ACCESS, 13, 77833-77846. (www)

  @article{ , abstract = {Recommender systems encounter the potential problem of filter bubble, neglecting the diversity of recommendations. These systems are inevitable to lower user experience because they cannot but provide tedious recommendations. Although several solutions have been introduced to increase diversity, it is still challenging to prevent accuracy loss with diversity enhancement. This study presents a new user-oriented algorithm for session-based recommendations that aims to improve diversity in consideration of two serendipity components—relevance and unexpectedness. Specifically, our approach first adopts serendipitous preference embedding into the recommender system based on session and graph neural networks. Next, we leverage a greedy algorithm of the maximum a posteriori (MAP) inference for the determinantal point process to re-rank items. Lastly, it additionally incorporates personalized trade-off balancing through a parameter that can be controlled by the user. To validate our approach, we conducted an experiment with two real-world datasets to demonstrate its ability to balance accuracy and diversity. The results showed that our approach generated not only relevant but unexpected recommendations, successfully improving diversity without accuracy loss. This study contributes to recommendation diversification methods, especially for session-based recommender systems under the user-centric perspective.}, archive = {J_ACCESS}, author = {Sangyeon Kim and Sanghyeok Boo and Gyewon Jeon and Dongmin Shin and Sangwon Lee}, doi = {10.1109/ACCESS.2025.3565767}, journal = {IEEE Access}, pages = {77833-77846}, shortjournal = {IEEE Access}, title = {Balancing diversity in session-based recommendation between relevance and unexpectedness}, volume = {13}, year = {2025}, }
• (2025). Management of the chain of custody of digital evidence using blockchain and self-sovereign identities: A systematic literature review. ACCESS, 13, 77804-77832. (www)

  @article{ , abstract = {Digital evidence plays an increasingly crucial role in judicial proceedings due to the exponential growth in the creation, storage, and transmission of digital data. However, its inherent volatility and susceptibility to tampering necessitate robust mechanisms to ensure integrity and authenticity, making an effective chain of custody (CoC) a fundamental requirement. While state-of-the-art reviews identify various aspects, it is necessary to include the use of Self-Sovereign Identity (SSI) systems within the scope of research. To address this challenge, this article conducts a systematic review of the literature on the use of blockchain and SSI in managing the chain of custody of digital evidence. The review began with 9,178 studies, which, after a rigorous process applying inclusion and exclusion criteria, resulted in 39 studies directly related to the research topic. The study maps and reviews techniques, tools, methods, approaches, and security components for managing the chain of custody of digital evidence. The findings confirm the widespread adoption of blockchain for preserving digital evidence while indicating that SSI remains an emerging and underexplored concept in forensic applications. The results highlight the need for further research on off-chain storage mechanisms, privacy-preserving techniques such as Zero-Knowledge Proofs (ZKPs) to enhance security, auditability, and interoperability when combined with Verifiable Credentials (VCs). By mapping the current state of research, this study provides valuable insights into CoC, Blockchain, and SSI in forensic-based proposals, identifying research gaps, limitations, and opportunities for developing more robust and scalable evidence management systems.}, archive = {J_ACCESS}, author = {Leandro Loffi and Gerson Luiz Camillo and Cristiano Antonio De Souza and Carla Merkle Westphall and Carlos Becker Westphall}, doi = {10.1109/ACCESS.2025.3560191}, journal = {IEEE Access}, pages = {77804-77832}, shortjournal = {IEEE Access}, title = {Management of the chain of custody of digital evidence using blockchain and self-sovereign identities: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Trending machine learning methods for vehicle, pedestrian, and traffic for detection and tracking task in the post-covid era: A literature review. ACCESS, 13, 77790-77803. (www)

  @article{ , abstract = {This study, aimed at professionals in research and development in the fields of computer vision, artificial intelligence, and intelligent transportation, presents a systematic literature review on recent machine learning methodologies applied to the detection and tracking of vehicles, pedestrians, and traffic flow. The analysis of articles published between 2022 and 2025 (early access) in the post-COVID era explored the integration of machine learning and deep learning to address traffic challenges, allowing for the comparison of different approaches and the formulation of hypotheses based on the 46 articles that comprised the review corpus. Furthermore, the evaluation of the reported metrics revealed inconsistencies in the methodologies employed, attributed to the lack of standardization across the studies. In light of this, this work proposes alternatives for future experiments, emphasizing the emerging potential of the field through the adoption of new standardization systems and the exploration of experimental combinations.}, archive = {J_ACCESS}, author = {João Vitor de Andrade Porto and Péter Tamás Szemes and Hemerson Pistori and József Menyhárt}, doi = {10.1109/ACCESS.2025.3565901}, journal = {IEEE Access}, pages = {77790-77803}, shortjournal = {IEEE Access}, title = {Trending machine learning methods for vehicle, pedestrian, and traffic for detection and tracking task in the post-covid era: A literature review}, volume = {13}, year = {2025}, }
• (2025). SVF: Support vector federation. ACCESS, 13, 77778-77789. (www)

  @article{ , abstract = {In recent years, Federated Learning applied to neural networks has garnered significant attention, yet applying this approach to other machine learning algorithms remains underexplored. Support Vector Machines (SVMs), in particular, have seen limited exploration within the federated context, with existing techniques often constrained by the necessity to share the weight vector of the linear classifier. Unfortunately, this constraint severely limits the method’s utility, restricting its application to linear feature spaces. This study addresses and overcomes this limitation by proposing an innovative approach: instead of sharing weight vectors, we advocate sharing support vectors while safeguarding client data privacy through vector perturbation. Simple random perturbation works remarkably well in practice, and indeed we provide a bound on the approximation error of the learnt model which goes to zero as the number of input features grows. We also introduce a refined technique that involves strategically moving the support vectors along the margin of the decision function, which we empirically show to slightly improve the performances. Through extensive experimentation, we demonstrate that our proposed approach achieves state-of-the-art performance and consistently enables the federated classifier to match the performance of classifiers trained on the entire dataset.}, archive = {J_ACCESS}, author = {Mirko Polato and Roberto Esposito and Lorenzo Sciandra}, doi = {10.1109/ACCESS.2025.3562053}, journal = {IEEE Access}, pages = {77778-77789}, shortjournal = {IEEE Access}, title = {SVF: Support vector federation}, volume = {13}, year = {2025}, }
• (2025). GenAINet: Enabling wireless collective intelligence via knowledge transfer and reasoning. ACCESS, 13, 77764-77777. (www)

  @article{ , abstract = {genai and communication networks are expected to have groundbreaking synergies for 6G. Connecting Generative Artificial Intelligence (GenAI) agents via a wireless network can potentially unleash the power of Collective Intelligence (CI) and pave the way for Artificial General Intelligence (AGI). However, current wireless networks are designed as a “data pipe” and are not suited to accommodate and leverage the power of GenAI. In this paper, we propose the GenAINet framework in which distributed GenAI agents communicate knowledge (facts, experiences, and methods) to accomplish arbitrary tasks. We first propose an architecture for a single GenAI agent and then provide a network architecture integrating GenAI capabilities to manage both network protocols and applications. Building on this, we investigate effective communication and reasoning problems by proposing a semantic-native GenAINet. Specifically, GenAI agents extract semantics from heterogeneous raw data, build and maintain a knowledge model representing the semantic relationships among pieces of knowledge, which is retrieved by GenAI models for planning and reasoning. Under this paradigm, different levels of collaboration can be achieved flexibly depending on the complexity of targeted tasks. Furthermore, we conduct two case studies in which, through wireless device queries, we demonstrate that extracting, compressing and transferring common knowledge can improve query accuracy while reducing communication costs; and in the wireless power control problem, we show that distributed agents can complete general tasks independently through collaborative reasoning without predefined communication protocols. Finally, we discuss challenges and future research directions in applying Large Language Models (LLMs) in 6G networks.}, archive = {J_ACCESS}, author = {Hang Zou and Qiyang Zhao and Samson Lasaulce and Lina Bariah and Mehdi Bennis and Mérouane Debbah}, doi = {10.1109/ACCESS.2025.3565859}, journal = {IEEE Access}, pages = {77764-77777}, shortjournal = {IEEE Access}, title = {GenAINet: Enabling wireless collective intelligence via knowledge transfer and reasoning}, volume = {13}, year = {2025}, }
• (2025). Field evaluation of 5G mmWave relays in various topologies: NLOS coverage enhancement and tolerance against blockage. ACCESS, 13, 77749-77763. (www)

  @article{ , abstract = {As time progresses, the use of higher frequencies becomes increasingly necessary due to the growing issue of frequency congestion. While discussions are ongoing regarding the potential use of millimeter-wave (mmWave) frequencies introduced in 5G for the Beyond 5G (B5G) / 6G era, the deployment of 5G mmWave networks has remained limited and less widespread. In addition, some countries and regions have already halted mmWave frequency allocations or paused base station installations. The primary reasons for this are the limited coverage of mmWave, which makes widespread deployment cost-ineffective, and the limited availability of mmWave-compatible devices. Therefore, this paper addresses the coverage challenges associated with mmWave to encourage the future adoption of higher frequencies. One promising technology for expanding coverage is analog relay stations (RSs), which offer a relatively low-cost and efficient approach to network area design. This technology has been extensively studied. However, in the mmWave band, the propagation environment is particularly challenging due to factors such as beamforming, blockage, and scattering caused by obstacles like trees and foliage. In this study, we conduct experimental evaluations of coverage enhancement and communication quality improvement by installing analog RSs within the B5G demonstration field at the Ookayama Campus of the Institute of Science Tokyo (formerly Tokyo Institute of Technology). The evaluations include a comprehensive assessment of the impacts of NLOS coverage enhancement using various relay topologies and improved tolerance against blockage, providing practical guidelines for deploying RSs in real-world environments.}, archive = {J_ACCESS}, author = {Naoya Okubo and Keishi Tokugawa and Jin Nakazato and Suwen Ke and Yiming Chen and Gia Khanh Tran and Kei Sakaguchi and Mitsuhiro Kuchitsu and Tsuyoshi Itagaki}, doi = {10.1109/ACCESS.2025.3562737}, journal = {IEEE Access}, pages = {77749-77763}, shortjournal = {IEEE Access}, title = {Field evaluation of 5G mmWave relays in various topologies: NLOS coverage enhancement and tolerance against blockage}, volume = {13}, year = {2025}, }
• (2025). Performance and cost of dynamic reliability: A 6G industrial environment perspective. ACCESS, 13, 77734-77748. (www)

  @article{ , abstract = {The use of robots in modern manufacturing facilities, where also human workers operate, offers substantial advantages, but also introduces safety hazards that need to be mitigated with all possible means. Ultra-reliable communication services offered by 5G mobile networks allow for automated remote control of the machines, thus enabling a new generation of network applications that can enforce safety procedures in a hyper-connected industrial environment. However, in the context of Human-Robot Collaboration scenarios very strict safety requirements must be satisfied, determining communication reliability levels to be guaranteed that go beyond the up to $10^{-5}$ capabilities of 5G ultra-reliable deployments. Moreover, in 5G the reliability is imposed as fixed requirement to be achieved at all times, which may lead to resource overprovisioning. Resource efficiency can be improved by dynamically adapting the target network reliability level based on the actual needs of the connected devices, still guaranteeing safe human-robot interactions. The idea of dynamic reliability applied to 6G industrial environments, introduced in our previous work, is further investigated in this paper by evaluating performance and costs of the approach, and by exploring how to leverage the knowledge of the specific nature of the task performed by robots cooperating with humans. The results of our study allow to quantify the resource gain that can be achieved with dynamic reliability compared to the solution currently offered by 5G systems, as well as the cost that must be factored in in terms of network signalling overhead.}, archive = {J_ACCESS}, author = {Gianluca Davoli and Federico Tonini and Davide Borsatti and Wint Yi Poe and Riccardo Trivisonno and Walter Cerroni}, doi = {10.1109/ACCESS.2025.3558750}, journal = {IEEE Access}, pages = {77734-77748}, shortjournal = {IEEE Access}, title = {Performance and cost of dynamic reliability: A 6G industrial environment perspective}, volume = {13}, year = {2025}, }
• (2025). Dynamic AI-enhanced therapeutic framework for precision medicine using multi-modal data and patient-centric reinforcement learning. ACCESS, 13, 77709-77733. (www)

  @article{ , abstract = {The integration of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing precision medicine by enabling the customization of therapeutic strategies to individual patient profiles. The study proposed Personalized AI-Driven Therapeutic Framework (PAI-DTF) combines real-time multi-modal data fusion, deep architectures, and Patient-In-the-Loop Reinforcement Learning (PIL-RL) to enforce adaptive personalized therapeutic strategies. Here, genomic data integrated with EHR coupled with the metering from wearable devices are combined using an adaptive temporal-fusion mechanism to ensure it is context-aware and timely. The Hierarchical Deep Neural Network (HDNN) structure is the combination of autoencoders and task-specific sub-networks. A new module called Dynamic Biomarker Importance (DBI) that adjusts biomarker priorities based on evolving patient data to permit adaptive precision. PIL-RL adds feedback and adherence behavior into treatment optimization, achieving higher precision, recall, F1 score, and more advanced measures MCC and Cohen’s Kappa. The further robustness also enhances consistency of predictions across 94.8% cases with different hybrid models aggregated by a combination of ensemble learning and meta-reinforcement strategies.}, archive = {J_ACCESS}, author = {R. Gayathri and S. K. B. Sangeetha and R. Sangeetha and G. Leena Rosalind Mary and Sandeep Kumar Mathivanan and Usha Moorthy}, doi = {10.1109/ACCESS.2025.3564971}, journal = {IEEE Access}, pages = {77709-77733}, shortjournal = {IEEE Access}, title = {Dynamic AI-enhanced therapeutic framework for precision medicine using multi-modal data and patient-centric reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Recognizing special art pieces through EEG: A journey in neuroaesthetics classification. ACCESS, 13, 77696-77708. (www)

  @article{ , abstract = {This paper describes three methods for analyzing electroencephalography (EEG) signals to classify users’ brain responses to exposure to art. The first two methods exploit classical machine learning approaches based on various sets of features extracted using different techniques for EEG analysis. In particular, the first method analyzes features extracted from time and frequency domains using an ensemble classifier, while the second one analyzes the Phase Locking Values of different channels using a classifier based on K-Nearest Neighbors. The third method retrains a well-known Convolutional Neural Network, namely VGG16, for image classification to analyze the scalograms obtained by applying the continuous wavelet transform to the EEG. These methods are evaluated employing a public dataset collected using mobile tools from museum visitors at an art exhibit. The dataset suffers from the problem of unbalanced classes, and this work also evaluates the impact of mitigation actions. The results reveal a significant difference between models tailored to the subject being tested, that achieve up to 95.43% of accuracy, and those trained without data from that subject (leave-one-subject-out strategy), that achieve up to 65.35% of accuracy. This suggests that, at this stage, customized approaches are more appropriate for the neuroaesthetics field, whereas models with general applicability need further development. We also conducted an analysis to assess whether age influenced the performance of the models. We split the visitors into three groups based on their age: adolescents, young adults, and adults. The analysis performed with a leave-one-subject-out strategy revealed higher accuracy for adolescents (73.21%) and adults (66.98%) than young adults (60%).}, archive = {J_ACCESS}, author = {Maurizio Palmieri and Marco Avvenuti and Francesco Marcelloni and Alessio Vecchio}, doi = {10.1109/ACCESS.2025.3562908}, journal = {IEEE Access}, pages = {77696-77708}, shortjournal = {IEEE Access}, title = {Recognizing special art pieces through EEG: A journey in neuroaesthetics classification}, volume = {13}, year = {2025}, }
• (2025). NSEA: A resilient ERP framework integrating quantum-safe cryptography and neuro-symbolic reasoning for industrial adaptability. ACCESS, 13, 77686-77695. (www)

  @article{ , abstract = {This study proposes a neurosymbolic ERP architecture (NSEA) incorporating post-quantum cryptographic protocols to enable real-time cybersecurity and adaptive operational optimization. NSEA integrates CRYSTALS-Kyber lattice-based post-quantum cryptography with temporal graph neural networks and symbolic rule engines to enhance cybersecurity, decision automation, and legacy system compatibility. On industrial IoT (IIoT) edge devices, Kyber-512 achieves sub- $5~\mu $ s encryption latency and mitigates 72% of phishing attacks through dynamic response mechanisms. In demand forecasting tasks, the hybrid reasoning model reduced mean absolute percentage error (MAPE) to 6.8% and resolved 89% of inventory stockout alerts within 15 minutes. Field validation in air conditioning and automotive supply chains demonstrated a 63.4% improvement in disruption recovery time and a 62.9% reduction in logistics cost overruns, compared to baseline systems. A resilience metric based on Kolmogorov-Sinai entropy (DRE $= 0.81\pm 0.05$ ) quantifies the system’s robustness under simultaneous cyber and operational stress. Backward compatibility testing showed 92% schema alignment with legacy ERP modules through API translation, enabling a low-risk migration path. NSEA offers a unified and future-proof ERP solution that integrates security, intelligence, and interoperability in complex industrial environments.}, archive = {J_ACCESS}, author = {Yong Lin and Yadi Lin}, doi = {10.1109/ACCESS.2025.3562739}, journal = {IEEE Access}, pages = {77686-77695}, shortjournal = {IEEE Access}, title = {NSEA: A resilient ERP framework integrating quantum-safe cryptography and neuro-symbolic reasoning for industrial adaptability}, volume = {13}, year = {2025}, }
• (2025). Internet of things-based smart infant-incubators using machine learning analysis. ACCESS, 13, 77673-77685. (www)

  @article{ , abstract = {Many international manufacturers of infant incubators use IoT technology, presenting tough competition for local manufacturers. This research examines methods to fulfill parents’ need to monitor their babies directly when they are lying inside an incubator. This bridges the gap present in neonatal care units. As such, this study proposes a prototype for an incubator that employs IoT along with different sensors to monitor babies occupying these incubators in real time and send the data to a remote server. With the different technologies used in this monitoring system, parents have the ability to listen to their babies remotely through a mobile application. A convolutional neural network (CNN) algorithm is used to take neonatal care a step further. It then goes beyond monitoring by interpreting the nature of a baby’s cries inside the specified incubator. The proposed prototype represents a step forward in the applications of Industry 4.0 in healthcare, especially those related to infant care and, more specifically, incubators for babies. This research is part of advancements in the use of IoT and handheld applications for the future development of baby incubators, fulfilling the needs of parents to constantly monitor their infants and neonatal care, specifically within modern healthcare.}, archive = {J_ACCESS}, author = {Mahmoud Gamal and Ibrahim Radi and Amr Yousef and and Ali Gaber Mohamed Ali}, doi = {10.1109/ACCESS.2025.3564872}, journal = {IEEE Access}, pages = {77673-77685}, shortjournal = {IEEE Access}, title = {Internet of things-based smart infant-incubators using machine learning analysis}, volume = {13}, year = {2025}, }
• (2025). Denoising of heart sounds using lightweight FCNs and spectrograms with and without context. ACCESS, 13, 77656-77672. (www)

  @article{ , abstract = {Cardiac auscultation using a digital stethoscope is an important method for diagnosis of cardiovascular diseases (CVDs). However, heart sound recordings are often contaminated with adventitious noise, especially in crowded, noisy settings such as resource-constrained hospitals. This noise can confound accurate diagnosis of heart pathologies. We propose a method for denoising heart sounds using fully convolutional networks (FCNs) based on the Spleeter U-Net architecture. We first generate a spectrogram of the heart sound recording and then use FCNs to semantically segment this into noise and signal components. We present an adaptation of the full Spleeter design, and also a lighter version operating on smaller spectrograms. This is aimed at reducing latency in a future real-time implementation of this scheme. We investigate whether providing this latter network with context improves the performance. We evaluate the denoising performance by artificially contaminating clean heart sounds with real-world noise (additive white Gaussian noise (AWGN), ambient hospital noise, lung sounds, and speech). Our best model was the lighter model with context, which we call the denoiser with context (DWC). We tested all models with different contamination types at different signal-to-noise ratios (SNRs), and found that the DWC gave an overall average improvement of 10.322 dB, with average increases ranging from 6.151 dB to 14.479 dB. We also implement the denoising inference on an edge device to show the feasibility of running this scheme on an embedded system. This work is a step towards a real-time deep learning-based denoiser for use with a digital stethoscope.}, archive = {J_ACCESS}, author = {Declan Duggan and Andriy Temko and Volodymyr Sarana and Andreea Factor and Emanuel Popovici}, doi = {10.1109/ACCESS.2025.3566288}, journal = {IEEE Access}, pages = {77656-77672}, shortjournal = {IEEE Access}, title = {Denoising of heart sounds using lightweight FCNs and spectrograms with and without context}, volume = {13}, year = {2025}, }
• (2025). Development and evaluation of a calibration methodology for pressure-resistive sensors in a specialized diagnostic laboratory. ACCESS, 13, 77642-77655. (www)

  @article{ , abstract = {This paper presents the development and evaluation of a calibration methodology for pressure-resistive sensors, implemented within a specialised diagnostic laboratory. The approach utilises algorithmic correction, structured measurement routines, and real-time signal processing to perform sensor calibration exclusively at the software level, without requiring hardware intervention. A custom sensor profiling framework has been designed to enable precise analysis of sensor characteristics, degradation, and long-term behaviour. The calibration algorithm employs mathematical interpolation and dataset synchronisation techniques to enhance accuracy and reduce measurement uncertainty. Experimental validation demonstrated a reduction in average deviation from 18.1% to 1.1%, with an overall tolerance range of just 2.6%. These results confirm the effectiveness of the proposed methodology in improving sensor precision and stability. The system is particularly suited for industrial environments where reliable pressure sensing is essential for safety, efficiency, and predictive maintenance.}, archive = {J_ACCESS}, author = {Roman Zelník and Roman Ruzarovsky and Tibor Horak and Martin Csekei and Dávid Michal and Ján Šido}, doi = {10.1109/ACCESS.2025.3563429}, journal = {IEEE Access}, pages = {77642-77655}, shortjournal = {IEEE Access}, title = {Development and evaluation of a calibration methodology for pressure-resistive sensors in a specialized diagnostic laboratory}, volume = {13}, year = {2025}, }
• (2025). Monkeypox transmission dynamics using fractional disease informed neural network: A global and continental analysis. ACCESS, 13, 77611-77641. (www)

  @article{ , abstract = {The global outbreak of Monkeypox has emerged as a significant concern in recent years, as per the World Health Organization (WHO). It is essential to study the transmission dynamics of the disease to project its progression and design future mitigation strategies. Models and projections provide evidence for rapid policy decisions and timely interventions. The proposed study attempts to address two significant existing limitations. First, classical models of monkeypox exhibit an inability to capture epidemiological patterns associated with global and regional data. Second, research on the transmission dynamics of Monkeypox in areas such as Asia, North America, South America, Antarctica, and globally is needed. This study aims to fill these gaps by hybridizing the power of fractional calculus and Neural Networks (NN) to develop a more unified understanding of disease dynamics, enabling effective strategies for controlling and mitigating Monkeypox transmission. The study also investigates and compares monkeypox transmission dynamics across all six continents and globally, using a comprehensive WHO dataset. The model was trained using 1053 days of data points from January 5, 2022, to March 18, 2025, and was subsequently used to obtain future predictions of Monkeypox transmission dynamics across all studied regions. The results show that in fractional Disease-Informed Neural Networks (DINN), the memory effect and nonlocality associated with the disease transmission were captured using the Caputo Fractional derivative to understand and mitigate Monkeypox transmission. The study utilised data from the world, as well as from Africa, Asia, Oceania, Europe, North America, and South America, to estimate disease and fractional-order parameters for various regions using the trained DINN. Notable findings include that the progression rate of the disease from exposure to infectious humans $({\beta })$ is relatively high in Oceania (0.1558) and Asia (0.1497), whereas Africa (0.0703) has the lowest value. The clinically ill rate $({\gamma })$ is highest in Asia (0.2694), followed by South America (0.2503) and Europe (0.2438), with the lowest value observed in Africa (0.1225). The contact rate between infected rodents and susceptible humans $({{\eta }_{2}})$ is highest in Asia. For fractional-order parameters, $q_{6}$ consistently has very high values across all regions, indicating low memory effect and nonlocality. These findings underscore the critical role of DINN, using fractional-order compartmental models, in studying and controlling Monkeypox disease globally and in various regions. Notably, this hybrid model is computationally expensive; thus, further research on addressing this limitation could be a future scope.}, archive = {J_ACCESS}, author = {Chinwe Peace Igiri and Samuel Shikaa}, doi = {10.1109/ACCESS.2025.3559005}, journal = {IEEE Access}, pages = {77611-77641}, shortjournal = {IEEE Access}, title = {Monkeypox transmission dynamics using fractional disease informed neural network: A global and continental analysis}, volume = {13}, year = {2025}, }
• (2025). A two-stage U-net framework for interactive segmentation of lung nodules in CT scans. ACCESS, 13, 77599-77610. (www)

  @article{ , abstract = {Segmentation of lung nodules in CT images is an important step during the clinical evaluation of patients with lung cancer. Furthermore, early assessment of the cancer is crucial to increase the overall survival chances of patients with such disease, and the segmentation of lung nodules can help detect the cancer in its early stages. Consequently, there are many works in the literature that explore the use of neural networks for the segmentation of lung nodules. However, these frameworks tend to rely on accurate labelling of the nodule centre to then crop the input image. Although such works are able to achieve remarkable results, they do not take into account that the healthcare professional may fail to correctly label the centre of the nodule. Therefore, in this work, we propose a new framework based on the U-Net model that allows to correct such inaccuracies in an interactive fashion. It is composed of two U-Net models in cascade, where the first model is used to predict a rough estimation of the lung nodule location and the second model refines the generated segmentation mask. Our results show that the proposed framework is able to be more robust than the studied baselines. Furthermore, it is able to achieve state-of-the-art performance, reaching a Dice of 91.12% when trained and tested on the LIDC-IDRI public dataset.}, archive = {J_ACCESS}, author = {Luís Fernandes and Tania Pereira and Hélder P. Oliveira}, doi = {10.1109/ACCESS.2025.3566203}, journal = {IEEE Access}, pages = {77599-77610}, shortjournal = {IEEE Access}, title = {A two-stage U-net framework for interactive segmentation of lung nodules in CT scans}, volume = {13}, year = {2025}, }
• (2025). An artificial neural network for short time air temperature prediction. ACCESS, 13, 77593-77598. (www)

  @article{ , abstract = {Air temperature is an extremely important factor in agriculture, from planting to post-harvest processes, and having the ability to predict air temperature can be a valuable tool for avoiding damage, maximizing production quality, and optimizing resources. In this work, we propose a simple air temperature prediction model based on a small neural network with a relatively small volume of training data. This work uses data from the Climatology and Biogeography Laboratory of the University of São Paulo (USP), from the Experimental Meteorological Station in São Paulo City, Brazil. The dataset corresponds to air temperature data collected during the years 2018 and 2020. For machine learning, two types of artificial neural networks were adopted: one of the long short-term memory recurrent network and one feed-forward network. Three past air temperatures were used to predict the next hour’s air temperature, and chain predictions were used to predict up to 24 hours. The feed-forward neural network presented the best results, with most errors below 2°C. The results show that it is possible to use a simple neural network, using only air temperature as the meteorological variable, to predict air temperature for the next hours. The simplicity of the model makes its application more feasible for various problems in agriculture.}, archive = {J_ACCESS}, author = {Olívia S. Gomes and Manuel O. Binelo and Marcia de F. B. Binelo and João Paulo C. Oliveira and Emerson Galvani and Rogério Rozolen Alves}, doi = {10.1109/ACCESS.2025.3565731}, journal = {IEEE Access}, pages = {77593-77598}, shortjournal = {IEEE Access}, title = {An artificial neural network for short time air temperature prediction}, volume = {13}, year = {2025}, }
• (2025). Folded context condensation in path integral formalism for infinite context transformers. ACCESS, 13, 77582-77592. (www)

  @article{ , abstract = {In this work, we present a generalized formulation of the Transformer algorithm by reinterpreting its core mechanisms within the framework of Path Integral formalism. In this perspective, the attention mechanism is recast as a process that integrates all possible transition paths leading to future token states, with temporal evolution governed by the feed-forward network (FFN). By systematically mapping each component of the Transformer to its counterpart in the Path Integral formulation, we obtain a more compact and efficient representation, in which the contextual information of a sequence is condensed into memory-like segments. These segments are recurrently processed across Transformer layers, enabling more effective long-term information retention. We validate the effectiveness of this approach through the Passkey retrieval task and a summarization task, demonstrating that the proposed method preserves historical information while exhibiting memory usage that scales linearly with sequence length. This contrasts with the non-linear memory growth typically observed in standard attention mechanisms. We expect that this quantum-inspired generalization of the Transformer architecture will open new avenues for enhancing both the efficiency and expressiveness of future Transformer models.}, archive = {J_ACCESS}, author = {Won-Gi Paeng and Daesuk Kwon and Kyungwon Jeong and Honggyo Suh}, doi = {10.1109/ACCESS.2025.3565773}, journal = {IEEE Access}, pages = {77582-77592}, shortjournal = {IEEE Access}, title = {Folded context condensation in path integral formalism for infinite context transformers}, volume = {13}, year = {2025}, }
• (2025). An internet of things ecosystem for atmospheric water generators. ACCESS, 13, 77565-77581. (www)

  @article{ , abstract = {In an era of diminishing water resources, Atmospheric Water Generators (AWG) offer an innovative solution to address the global water crisis by harnessing alternative local water sources. Recent advances in AWG technology have demonstrated the feasibility of converting atmospheric moisture into potable water, making AWGs suitable for use as domestic appliances and positioning them as key players in tackling the water-energy nexus. Inethis context, efficient data collection and management from AWG systems are critical for improving technological capabilities and performance optimization. This work presents an Internet of Things (IoT) ecosystem that integrates IoT-enabled AWG machines, utilizing a biopolymer for water generation, and an IoT platform to collect, store, and manage data from these devices. Weeenhance existing AWG machines with IoT functionalities, allowing for remote monitoring and control of a fleet of units. Additionally, weedemonstrate the implementation of this ecosystem in a real-world scenario, enabling the management and oversight of operational AWG machines. Communication tests were conducted to evaluate transmission performance. Since AWGs may be located in remote regions, weetested the scenario in which AWGs are connected to the Internet via Low Earth Orbit Satellites Internet Service Providers (e.g. Starlink). The results showed that latency reached values around 100ems, and less than 1es even for the extreme scenario of total network congestion, guaranteeing that the data transmission latency is within acceptable limits for this application, ensuring effective monitoring and control of the AWG systems.}, archive = {J_ACCESS}, author = {Francesco Gaggini and Rafael Natalio Fontana Crespo and Matteo Calò and Vincenzo Maria Gentile and Alberto Macii and Edoardo Patti}, doi = {10.1109/ACCESS.2025.3562742}, journal = {IEEE Access}, pages = {77565-77581}, shortjournal = {IEEE Access}, title = {An internet of things ecosystem for atmospheric water generators}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection and root cause analysis in cloud-native environments using large language models and bayesian networks. ACCESS, 13, 77550-77564. (www)

  @article{ , abstract = {Cloud computing technologies offer significant advantages in scalability and performance, enabling rapid deployment of applications. The adoption of microservices-oriented architectures has introduced an ecosystem characterized by an increased number of applications, frameworks, abstraction layers, orchestrators, and hypervisors, all operating within distributed systems. This complexity results in the generation of vast quantities of logs from diverse sources, making the analysis of these events an inherently challenging task, particularly in the absence of automation. To address this issue, Machine Learning techniques leveraging Large Language Models (LLMs) offer a promising approach for dynamically identifying patterns within these events. In this study, we propose a novel anomaly detection framework utilizing a microservices architecture deployed on Kubernetes and Istio, enhanced by an LLM model. The model was trained on various error scenarios, with Chaos Mesh employed as an error injection tool to simulate faults of different natures, and Locust used as a load generator to create workload stress conditions. After an anomaly is detected by the LLM model, we employ a dynamic Bayesian network to provide probabilistic inferences about the incident, proving the relationships between components and assessing the degree of impact among them. Additionally, a ChatBot powered by the same LLM model allows users to interact with the AI, ask questions about the detected incident, and gain deeper insights. The experimental results demonstrated the model’s effectiveness, reliably identifying all error events across various test scenarios. While it successfully avoided missing any anomalies, it did produce some false positives, which remain within acceptable limits.}, archive = {J_ACCESS}, author = {Diego Frazatto Pedroso and Luís Almeida and Lucas Eduardo Gulka Pulcinelli and William Akihiro Alves Aisawa and Inês Dutra and Sarita Mazzini Bruschi}, doi = {10.1109/ACCESS.2025.3565220}, journal = {IEEE Access}, pages = {77550-77564}, shortjournal = {IEEE Access}, title = {Anomaly detection and root cause analysis in cloud-native environments using large language models and bayesian networks}, volume = {13}, year = {2025}, }
• (2025). High-gain wide-band yagi-uda antenna for millimeter-wave applications. ACCESS, 13, 77539-77549. (www)

  @article{ , abstract = {As the need for high-speed wireless communication continues to grow, millimeter-wave (mm-wave) technologies have become essential for next-generation wireless systems, such as 5G and beyond. High-gain, wide-band antennas are crucial in these applications to ensure efficient signal transmission and reception. In this work, a wide-band, high-gain mm-wave printed Yagi-Uda antenna has been designed for operation in the 58–68 GHz frequency band. The proposed antenna features a bowtie-driven element, three two-arm reflectors, and four rhombus-shaped directors, designed to enhance gain and bandwidth performance. At 63 GHz, the antenna achieves a maximum gain of 12 dB and a best side lobe level (SLL) of −15 dB. Across the entire operational band, the gain remains above 10 dB, while the SLL is maintained below −12 dB, ensuring stable and efficient performance. The compact design, with total dimensions of 15.6 mm $\times 8.5$ mm, makes it suitable for integration into modern mm-wave communication systems. To validate the proposed design, a prototype is fabricated, and experimental assessments of return loss, radiation patterns, gain, and SLL are conducted. Numerical simulations and experimental measurements exhibit strong agreement, confirming the antenna’s effectiveness in high-frequency applications.}, archive = {J_ACCESS}, author = {Asmaa E. Farahat and Khalid F. A. Hussein}, doi = {10.1109/ACCESS.2025.3565842}, journal = {IEEE Access}, pages = {77539-77549}, shortjournal = {IEEE Access}, title = {High-gain wide-band yagi-uda antenna for millimeter-wave applications}, volume = {13}, year = {2025}, }
• (2025). Efficient sedimentary facies recognition using vision transformer and weakly supervised deep multi-view clustering. ACCESS, 13, 77522-77538. (www)

  @article{ , abstract = {Sedimentary facies recognition plays a crucial role in geological exploration and oil-gas resource evaluation. However, traditional recognition methods are limited by their ability to extract local features and efficiently utilize unlabeled data, making it difficult to effectively handle complex sedimentary facies characteristics. To address this, this paper proposes an efficient sedimentary facies recognition method based on Vision Transformer (ViT) and weakly supervised deep multi-view clustering. First, we use ViT for feature extraction from sedimentary facies images, capturing global information through the self-attention mechanism, thereby enhancing the ability to recognize complex sedimentary facies patterns. Secondly, we introduce deep multi-view clustering, which integrates multi-angle features such as color, texture, and shape, improving the model’s robustness and classification accuracy. Additionally, we apply weak supervision, combining a small amount of labeled data with a large amount of unlabeled data, and use strategies like pseudo-labeling to enhance the model’s generalization ability and reduce the cost of data labeling. Experimental results show that this method achieves higher accuracy and robustness across multiple sedimentary facies datasets, significantly outperforming traditional Convolutional Neural Networks methods in recognition performance. This research provides a novel solution for sedimentary facies recognition, especially suited for scenarios with limited labeled data and complex, diverse features, and offers new research directions for future deep learning-based geological data analysis.}, archive = {J_ACCESS}, author = {Hao Wu and Yu-Jie Dai and Xin-Yu Liu}, doi = {10.1109/ACCESS.2025.3562544}, journal = {IEEE Access}, pages = {77522-77538}, shortjournal = {IEEE Access}, title = {Efficient sedimentary facies recognition using vision transformer and weakly supervised deep multi-view clustering}, volume = {13}, year = {2025}, }
• (2025). Acute respiratory infections identification with cough sounds and overlapping patch modulated vision transformers. ACCESS, 13, 77507-77521. (www)

  @article{ , abstract = {Rapid detection of Acute Respiratory Infections (ARI) is crucial to reduce breathing difficulties and severe life-threatening conditions. Automatic cough identification is being conducted using speech frequency analysis and machine learning models. Learning models trained on Mel frequency spectrum(MFCC) features of cough sounds represented as images have recorded an average binary classification accuracy of 68%. Variable cough sound vs silent intervals between samples of a class in MFCC spectral images has shown to influence training algorithms to learn meaningful patterns for classification. To learn all possible local patterns in the MFCC cough images using a vision transformer model (ViT), we propose an image patch overlapping vision transformer $IPO-ViT$ . The patch overlapping factor $k\ $ controls the quantity of common pixels between them. The $IPO-ViT$ patch encoder computes all possible local pixel pattern relationships by breaking the image into overlapping patches and equating them across all classes making a balanced augmented dataset. The $IPO-ViT$ is evaluated on our own 511 – sound cough dataset (IndiCough_2024) with 5 classes captured at AJ Institute of Medical Sciences, paediatric division along with benchmarks EPFL COUGH VID, Coswara for COVID-19 Diagnosis and Covid19-Cough. The $IPO-ViT$ achieved higher accuracies of around 92.33% over the state-of-the-art cough sound-based disease identification networks.}, archive = {J_ACCESS}, author = {P. V. V. Kishore and D. Anil Kumar and Pasupuleti Sasikiran and Kaja Krishna Mohan and P. Praveen Kumar and Mogadala Vinod Kumar}, doi = {10.1109/ACCESS.2025.3565969}, journal = {IEEE Access}, pages = {77507-77521}, shortjournal = {IEEE Access}, title = {Acute respiratory infections identification with cough sounds and overlapping patch modulated vision transformers}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on measuring brain activity while reviewing code and paintings. ACCESS, 13, 77493-77506. (www)

  @article{ , abstract = {In recent years, the intersection of cognitive neuroscience and software engineering has sparked significant interest, particularly in understanding developers’ mental activities. This systematic literature review (SLR) investigates the current methodologies for measuring and analyzing mental activity, particularly using EEG when reviewing code and paintings. The research addresses four key questions: (1) What methods are available to measure mental activity when reviewing code and pictures? (2) What are the methods for analyzing brain activity data especially extracted using EEG? (3) How do brain activity patterns differ when studying code and pictures obtained with EEG? and (4) Are there any differences in stress levels when reviewing code and drawing obtained with EEG? Our findings reveal various techniques for capturing and analyzing EEG data, yet highlight a significant gap in the empirical research addressing the last two questions. This underscores the need for targeted experiments to explore these specific brain activity patterns and stress level differences. These are crucial for advancing our understanding of cognitive processes in software engineering and related fields.}, archive = {J_ACCESS}, author = {Gcinizwe Dlamini and Abu Huraira and Zamira Kholmatova and Andrei Mikriukov and Guzel Safiullina and Giancarlo Succi and Alexander Tormasov}, doi = {10.1109/ACCESS.2025.3562001}, journal = {IEEE Access}, pages = {77493-77506}, shortjournal = {IEEE Access}, title = {A systematic literature review on measuring brain activity while reviewing code and paintings}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of SWIPT-assisted cooperative NOMA network with non-linear EH, interference, and imperfect SIC. ACCESS, 13, 77481-77492. (www)

  @article{ , abstract = {Simultaneous wireless information and power transfer (SWIPT) provides an efficient approach towards prolonging the lifespan of wireless systems in 5G and beyond 5G communications by minimizing the reliance of devices on batteries and power sources. This paper suggests a SWIPT-assisted cooperative nonorthogonal multiple access (C-NOMA) network where a SWIPT enabled energy harvesting (EH) relay harvests energy from the source and utilizes it to forward the information to the end-user devices in the downlink. In order to obtain a realistic analysis, we aim to utilize a non-linear energy harvesting model and imperfect successive interference cancellation (imp-SIC) at the end user. The closed form expressions of outage probability (OP) and throughput are derived by considering multiple interfering signals at relay. Subsequently, the impact of the number of interferers on harvested energy and the OP, the power splitting (p.s.) ratio, distance between the nodes, and power allocation (p.a) factors are discussed in detail, and a comparison is presented against the traditional orthogonal multiple access (OMA) under the same conditions. The analysis clearly depicts that the proposed SWIPT-based C-NOMA system outperforms OMA in all aspects. Monte Carlo simulations are performed to corroborate the accuracy of the proposed analytical framework.}, archive = {J_ACCESS}, author = {Nausheen Ashraf and Shahzad Amin Sheikh and Mahrukh Liaqat and Sajjad Ahmad Khan}, doi = {10.1109/ACCESS.2025.3563111}, journal = {IEEE Access}, pages = {77481-77492}, shortjournal = {IEEE Access}, title = {Performance analysis of SWIPT-assisted cooperative NOMA network with non-linear EH, interference, and imperfect SIC}, volume = {13}, year = {2025}, }
• (2025). Localization of AUVs for ship hull inspection: A review. ACCESS, 13, 77468-77480. (www)

  @article{ , abstract = {Ship hull inspection is crucial for assessment of hull damage, biological fouling, and for detection of foreign objects (mines, illegal drug packages, etc.). These inspections require advanced planning, long periods of time, resources (people, facilities, equipment), and cost (time and money) to conduct. With this in mind, the purpose of this review is to provide an assessment of the state of the art technologies and techniques for ship hull inspection; identifying the key challenges and the directions for further research and development. This review first discusses platform types for inspection and imaging types, this discussion proposes that ship hull inspection could be completed quicker by using multiple imaging nodes, it also proposes that inspection complexity and resource could be reduced through use of autonomous vehicles. These proposals are the basis for the next sections of review where localization techniques and methods for node organization to achieve full coverage of the vessel being imaged become the focus. The review identifies that current techniques could be utilized, however require adaption and combination together for this specific use case. The conclusions from this review lead into what future research directions are required, focusing around the localization and organization issues.}, archive = {J_ACCESS}, author = {Gary A. Benson and Paul D. Mitchell and Benjamin Henson}, doi = {10.1109/ACCESS.2025.3563695}, journal = {IEEE Access}, pages = {77468-77480}, shortjournal = {IEEE Access}, title = {Localization of AUVs for ship hull inspection: A review}, volume = {13}, year = {2025}, }
• (2025). Large language models are zero-shot next location predictors. ACCESS, 13, 77456-77467. (www)

  @article{ , abstract = {Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and pollution reduction. However, next-location predictors often require a significant amount of individual-level information that may be scarce or unavailable (e.g., in cold-start scenarios). Large Language Models (LLMs) have demonstrated strong generalization and reasoning capabilities while being rich in geographical knowledge, suggesting that they can operate as zero-shot next-location predictors. In our study, we evaluate over 15 LLMs on three real-world mobility datasets and find that they achieve accuracies up to 36.2%, representing a relative improvement of almost 640% compared to traditional models designed for human mobility. We further assess data contamination risks and explore the potential for using LLMs as text-based explainers for next-location predictions. Our results indicate that, irrespective of model size, LLMs can both predict and justify their decisions effectively.}, archive = {J_ACCESS}, author = {Ciro Beneduce and Bruno Lepri and Massimiliano Luca}, doi = {10.1109/ACCESS.2025.3565297}, journal = {IEEE Access}, pages = {77456-77467}, shortjournal = {IEEE Access}, title = {Large language models are zero-shot next location predictors}, volume = {13}, year = {2025}, }
• (2025). Snapshot cherry-picking attack in CEX proof of reserves and its mitigation. ACCESS, 13, 77445-77455. (www)

  @article{ , abstract = {The rapid growth of the crypto asset industry has led to the adoption of proof of reserves (PoR) protocols for transparency in centralized exchanges (CEXs). By providing proofs to users that the exchange’s total reserves equal or exceed its total liabilities, PoR allows these exchanges to demonstrate that they have enough funds. This paper identifies a vulnerability in current PoR methods, where malicious CEXs can manipulate snapshots to F understate liabilities, making reserves appear larger. To address this, we propose a framework where users take their own snapshots during a strategic trading pause, allowing the validation of the PoR result. The framework is compatible with existing PoR methods. We also propose a user-driven handshake (UDH) pause model to minimize disruptions. We evaluate the effectiveness of the framework in preventing snapshot cherry-picking as well as its practicality in minimizing trade pauses.}, archive = {J_ACCESS}, author = {Beomjoong Kim and Dongjun Lee and Junghee Lee and Wonjun Lee}, doi = {10.1109/ACCESS.2025.3564999}, journal = {IEEE Access}, pages = {77445-77455}, shortjournal = {IEEE Access}, title = {Snapshot cherry-picking attack in CEX proof of reserves and its mitigation}, volume = {13}, year = {2025}, }
• (2025). Design of cloud-based complex VR 3D scene interaction based on attribute preferences. ACCESS, 13, 77431-77444. (www)

  @article{ , abstract = {This paper presents a novel approach to enhancing real-time 3D design collaboration in cloud-rendered virtual reality (VR) environments through adaptive interface personalization and efficient resource management. The study leverages Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to address critical challenges such as user interface adaptability and cloud resource allocation. Our methodology incorporates an RL framework for dynamic interface personalization, which adjusts in real-time based on user interactions and feedback. The RL agent continuously learns from user behavior to optimize the interface, leading to improved navigation, reduced latency, and higher user satisfaction. Concurrently, PSO is employed to optimize cloud resource allocation, managing CPU, GPU, and bandwidth to minimize latency and enhance performance efficiency. Key findings indicate that the RL-based adaptive interface significantly improved user interaction, with reductions in latency up to 30% and increased task completion rates by 50%. PSO optimization led to a 17% to 23% improvement in resource utilization, ensuring a responsive and efficient VR environment even under high load conditions. The implications of these findings are substantial for real-time 3D design collaboration. By integrating RL for personalization and PSO for resource management, our approach facilitates a more intuitive and seamless collaborative experience, enhancing productivity and satisfaction. This research sets a precedent for future advancements in VR environments, emphasizing the value of combining advanced algorithms to address complex challenges in immersive technology.}, archive = {J_ACCESS}, author = {Ze Yu Yang and Yeom Jun Young}, doi = {10.1109/ACCESS.2025.3552283}, journal = {IEEE Access}, pages = {77431-77444}, shortjournal = {IEEE Access}, title = {Design of cloud-based complex VR 3D scene interaction based on attribute preferences}, volume = {13}, year = {2025}, }
• (2025). Large language models in human-robot collaboration with cognitive validation against context-induced hallucinations. ACCESS, 13, 77418-77430. (www)

  @article{ , abstract = {The recent leap in Large Language Models (LLMs) has paved the way for several research ideas. LLMs are employed not only for personal use but also in professional contexts to enhance human productivity at work. A significant area of research is human-robot collaboration (HRC), which focuses on developing methodologies for effective interaction between humans and AI-enabled machines. In this regard, exploitation of LLMs appears to be a practical approach. However, these models are susceptible to several limitations, including context-induced errors, the propagation of misleading information, and hallucinations. Such deficiencies impede the seamless application of LLMs in scenarios where a high degree of accuracy is essential. To address this issue, this study introduces a dual-agent system designed to validate the responses generated by LLMs. This novel system is integrated into a framework called “CogniVera”, which facilitates collaborative tasks involving a collaborative robot (cobot) through vocal interactions. This initiative represents a significant advancement in HRC, enabling robots to communicate vocally with human operators during assembly tasks. To evaluate the feasibility of this approach, a focused case study will be conducted, concentrating on the human-robot collaborative task of box assembly utilizing vocal communication. The outcomes of this study are anticipated to yield valuable insights into the efficacy of the proposed dual-agent system in enhancing the reliability and performance of LLMs in practical applications.}, archive = {J_ACCESS}, author = {Nadun Ranasinghe and Wael M. Mohammed and Kostas Stefanidis and Jose L. Martinez Lastra}, doi = {10.1109/ACCESS.2025.3565918}, journal = {IEEE Access}, pages = {77418-77430}, shortjournal = {IEEE Access}, title = {Large language models in human-robot collaboration with cognitive validation against context-induced hallucinations}, volume = {13}, year = {2025}, }
• (2025). Social commerce platforms role in FoMO driven consumption: The hidden psychological toll of continuous use. ACCESS, 13, 77395-77417. (www)

  @article{ , abstract = {This study examines the influence of Fear of Missing Out (FoMO) on continuous usage of social commerce platforms (SCPs) and explores its psychological and behavioral ramifications within the context of attention economy. Drawing on Self-Determination Theory (SDT) and Information Overload Theory (IOT), a survey of 872 active social commerce platform (SCP) users was conducted. Structural Equation Modeling (SEM) in SmartPLS 4 was utilized to analyze direct, indirect, and moderating relationships among key constructs, including Psychological Anxiety (PA), Nomophobia (NOMO), and Problematic Social Media Use (PSMU). The results indicate that FoMO substantially drives compulsive platform usage ( $\beta =0.453$ , p < 0.001), strongly predicts PSMU ( $\beta =0.826$ , p < 0.001), and indirectly increases psychological anxiety, nomophobia, phubbing behavior, and social network fatigue. Furthermore, informational incentives (e.g., personalized content, real-time updates) significantly amplify users’ anxiety, fatigue, and compulsive digital behaviors by exacerbating cognitive overload (interaction effects ranging from $\beta =0.059$ to 0.178, p < 0.001). Overall, the findings underscore the dual role of SCPs as both facilitators of engagement and sources of psychological strain, emphasizing the necessity of sustainable digital marketing strategies that prioritize user well-being. These insights offer valuable implications for academic researchers and industry practitioners, suggesting that integrating SDT and IOT can advance our understanding of the multifaceted relationships among FoMO, platform usage, and psychological health in the digital era.}, archive = {J_ACCESS}, author = {Ebru Enginkaya and Munise Hayrun Sağlam}, doi = {10.1109/ACCESS.2025.3565127}, journal = {IEEE Access}, pages = {77395-77417}, shortjournal = {IEEE Access}, title = {Social commerce platforms role in FoMO driven consumption: The hidden psychological toll of continuous use}, volume = {13}, year = {2025}, }
• (2025). Advancing sentiment analysis for low-resource languages using fine-tuned LLMs: A case study of customer reviews in turkish language. ACCESS, 13, 77382-77394. (www)

  @article{ , abstract = {This study investigates the application of advanced fine-tuned Large Language Models (LLMs) for Turkish Sentiment Analysis (SA), focusing on e-commerce product reviews. Our research utilizes four open-source Turkish SA datasets: Turkish Sentiment Analysis version 1 (TRSAv1), Vitamins and Supplements Customer Review (VSCR), Turkish Sentiment Analysis Dataset (TSAD), and TR Customer Review (TRCR). While these datasets were initially labeled based on star ratings, we implemented a comprehensive relabeling process using state-of-the-art LLMs to enhance data quality. To ensure reliable annotations, we first conducted a comparative analysis of different LLMs using the Cohen’s Kappa agreement metric, which led to the selection of ChatGPT-4o-mini as the best-performing model for dataset annotation. Our methodology then focuses on evaluating the SA capabilities of leading instruction-tuned LLMs through a comparative analysis of zero-shot models and Low-Rank Adaptation (LoRA) fine-tuned LlaMA-3.2-1B-IT and Gemma-2-2B-IT models. Evaluations were conducted on both in-domain and out-domain test sets derived from the original star-ratings-based labels and the newly generated GPT labels. The results demonstrate that our fine-tuned models outperformed leading commercial LLMs by 6% in both in-domain and out-domain evaluations. Notably, models fine-tuned on GPT-generated labels achieved superior performance, with in-domain and out-domain F1-scores reaching 0.912 and 0.9184, respectively. These findings underscore the transformative potential of combining LLM relabeling with LoRA fine-tuning for optimizing SA, demonstrating robust performance across diverse datasets and domains.}, archive = {J_ACCESS}, author = {Rukiye Savran Kiziltepe and Ercan Ezin and Ömer Yentür and Arwa M. Basbrain and Murat Karakus}, doi = {10.1109/ACCESS.2025.3566000}, journal = {IEEE Access}, pages = {77382-77394}, shortjournal = {IEEE Access}, title = {Advancing sentiment analysis for low-resource languages using fine-tuned LLMs: A case study of customer reviews in turkish language}, volume = {13}, year = {2025}, }
• (2025). Enhanced pollen classification via hybrid neural network with attention mechanism and view separation: An equatorial and polar approach. ACCESS, 13, 77365-77381. (www)

  @article{ , abstract = {Accurate classification of pollen grains is crucial for various fields, including ecology and food engineering. However, current approaches often face challenges due to limited data and the morphological variability of pollen grains. This study proposes a novel methodology emphasizing the separation of pollen images into equatorial and polar views, resulting in a significant improvement in classification accuracy. Using pseudo-labeling techniques, the dataset was segmented into these views, enabling the creation of customized datasets for classification. Experiments conducted with the Cretan Pollen Dataset, using a hybrid neural network combining of VGG19 and the CBAM attention mechanism, trained from scratch with data augmentation, demonstrated remarkable performance compared to previous studies. The VGG19+CBAM network achieved an accuracy of 98.78% and a precision of 98.72% with the dataset in its original format, and a precision of 98.06% with the view-separated dataset. This work highlights the fundamental role of view separation in enhancing classification results and advancing automated pollen analysis.}, archive = {J_ACCESS}, author = {Júlio César da Silva Soares and Kelson Rômulo Teixeira Aires and Juliana do Nascimento Bendini and Wesley Vitor Barbosa Brandão and Rodrigo de Melo Souza Veras}, doi = {10.1109/ACCESS.2025.3562316}, journal = {IEEE Access}, pages = {77365-77381}, shortjournal = {IEEE Access}, title = {Enhanced pollen classification via hybrid neural network with attention mechanism and view separation: An equatorial and polar approach}, volume = {13}, year = {2025}, }
• (2025). State-of-charge-based energy management strategy for hybrid energy storage system in DC microgrid. ACCESS, 13, 77353-77364. (www)

  @article{ , abstract = {Integration of photovoltaic (PV) systems increases demand for DC microgrids (DCMGs). Stability of PV-based DCMG is enhanced by integrating battery storage into it. Addition of another energy storage element, i.e., supercapacitor (SC) further ameliorates battery’s longevity and dynamic stability of DC microgrid. Combination of battery and SC is known hybrid energy storage system (HESS). Control and power sharing of battery and SC play a significant role in HESS operation. Conventional constant frequency low pass filter-based HESS control can share dynamic power between battery and SC. However, the constant and slow dynamics of HESS control affect DC bus regulation and battery’s state-of-charge (SoC). To improve dynamic performance of HESS, this paper proposes, a SoC-based variable frequency low pass filter-based energy management strategy (EMS). Proposed EMS controls power splitting between battery and SC and reduces battery’s sluggishness under necessary conditions. Proposed strategy enhances dynamic DC bus stability, battery life and utilizes SC effectively. Proposed EMS is validated with Matlab simulation and real-time digital simulation.}, archive = {J_ACCESS}, author = {Mrutunjaya Panda and Mukul Chankaya and Satyajit Mohanty and S. D. Sandeep}, doi = {10.1109/ACCESS.2025.3564786}, journal = {IEEE Access}, pages = {77353-77364}, shortjournal = {IEEE Access}, title = {State-of-charge-based energy management strategy for hybrid energy storage system in DC microgrid}, volume = {13}, year = {2025}, }
• (2025). On-chip age estimation using machine learning. ACCESS, 13, 77334-77352. (www)

  @article{ , abstract = {The semiconductor supply chain industry is spread worldwide to reduce costs and meet the high demand for integrated circuits (ICs) in electronic systems. The high utilisation of electronic devices in the next decade is forecasted to reach trillions, increasing the already high volume of e-waste. It will lead to concerns about the security and reliability of ICs, particularly those exposed to counterfeiting, i.e., recycled and remarked ICs. This paper harvests aging degradation induced by bias temperature instability (BTI) and hot carrier injection (HCI), observing frequency (f) and discharge time ( $\tau _{dv}$ ) affected by changes in drain current and sub-threshold leakage current over the lifetime of an IC to estimate the IC age. This is carried out using Cadence simulations, implementing 13- and 51-stage ring oscillators (ROs) using a 22-nm CMOS technology and aging model provided by GlobalFoundries (GF). The machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, $\tau _{dv}$ , f, aging time and inter-die and intra-die process variation (PV). The data sampling is performed over a simulated 12-year period with representative temperatures between 20°C up to 100°C and with additional testing data from 25°C up to 75°C. Incorporating the PV effect with the SVR model allows the proposed SVR model to be adopted in practical IC implementation. The results demonstrate high accuracy in aging estimation by SVR with/without PV effects. The proposed SVR model detects the age of an IC with an error accuracy between 0.206 and 0.667 (deviation of 74.16 and 240.12 days), and 0.091 and 0.237 (deviation of 32.76 and 85.32 days) based on the Root Mean Square Error (RMSE) for 13- and 51-satge RO, respectively. It outperforms the state-of-the-art IC age prediction models even when learning and validating the model with aging and PV.}, archive = {J_ACCESS}, author = {Turki Alnuayri and Saqib Khursheed and Daniele Rossi}, doi = {10.1109/ACCESS.2025.3565913}, journal = {IEEE Access}, pages = {77334-77352}, shortjournal = {IEEE Access}, title = {On-chip age estimation using machine learning}, volume = {13}, year = {2025}, }
• (2025). A channel attention-based denoising autoencoder U-net and classifier (UNA-DAEC) for signal identification under challenging SNR conditions. ACCESS, 13, 77317-77333. (www)

  @article{ , abstract = {The use of Low-Power Wide Area Network (LPWAN) technologies, such as Long Range (LoRa), Sigfox, and IEEE 802.15.4g (ZigBee), has grown significantly, addressing a wide range of applications including smart metering, agriculture, smart homes, and healthcare. These technologies are valued for their simplicity, flexible connectivity, low power consumption, efficient modulation techniques, and moderate data rates. As a result, they can coexist within the same environment, serving either similar or distinct applications. However, the increasing deployment of devices and technologies has amplified the likelihood of interference between them, leading to performance degradation, particularly in real-world scenarios under challenging conditions where noise power surpasses signal power. The rapid proliferation of these technologies, especially within unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, underscores the need for effective techniques to ensure seamless coexistence without disrupting communication. To address this challenge, we investigate the role of data representation and propose a Channel Attention-based Denoising Autoencoder U-Net and Classifier (UNA-DAEC). This model is designed to denoise multi-label LPWAN signals affected by white Gaussian noise and accurately classify overlapping transmissions, specifically IEEE 802.15.4g, Sigfox, and LoRa signals, within the same environment. The primary objective of UNA-DAEC is to achieve reliable signal classification in low Signal-to-Noise Ratio (SNR) conditions. This is achieved by first denoising the noisy signals to obtain optimal representations, enabling high classification accuracy with a single forward and backward propagation. Our results further demonstrate that data representation plays a critical role in identifying and classifying LPWAN signals, particularly in challenging low-SNR environments, with a significant performance of 44%, 7%, and 26% over CNN-based IQ, CNN-based FFT and DAE+Classifier methods, respectively, at -10 dB SNR.}, archive = {J_ACCESS}, author = {Ife Olalekan Ebo and Idowu Ajayi and Lina Mroueh and Youmni Ziade and Sylvain Azarian}, doi = {10.1109/ACCESS.2025.3565853}, journal = {IEEE Access}, pages = {77317-77333}, shortjournal = {IEEE Access}, title = {A channel attention-based denoising autoencoder U-net and classifier (UNA-DAEC) for signal identification under challenging SNR conditions}, volume = {13}, year = {2025}, }
• (2025). Adverse to normal image reconstruction using inverse of StarGAN for autonomous vehicle control. ACCESS, 13, 77305-77316. (www)

  @article{ , abstract = {Controlling autonomous vehicles in adverse weather conditions poses unique challenges, particularly in reconstructing images from diverse adverse environments—such as rain, snow, fog and night light—into clear, normal-appearing visuals. Although StarGAN has been successfully applied to multi-domain conversion, its design typically focuses on transforming a single image into multiple styles rather than converting images from multi-domains into a unique representation. To address this limitation, we propose an inverse version of StarGAN that processes images captured by the vehicle’s camera and transforms them into clear visuals simulating normal conditions. Initially, the model is trained with explicit weather domain labels and later with shuffled labels, a strategy that reduces dependence on specific label severities and enhances the model’s adaptability in dynamic environments. Since CNNs often overfit when dealing with multi-domain tasks, we incorporate Capsule Neural Networks to mitigate overfitting, capture spatial hierarchies and improve sensitivity to object orientation. Furthermore, a perceptual loss function is employed to preserve fine image details. Our extensive dataset comprises over 100,000 samples collected from the AVIS ENGINE simulator along with real-world validation from the BDD100k dataset under rain, snow, fog, and nightlight conditions. Results indicate an average PSNR of 22.21 and an SSIM of 0.92 in closed-loop simulations, demonstrating robust and real-time performance. This work represents a significant step forward in enhancing the safety, reliability, and overall robustness of autonomous systems operating in challenging and unpredictable weather conditions.}, archive = {J_ACCESS}, author = {Maryam Asad Samani and Mohammad Farrokhi}, doi = {10.1109/ACCESS.2025.3564848}, journal = {IEEE Access}, pages = {77305-77316}, shortjournal = {IEEE Access}, title = {Adverse to normal image reconstruction using inverse of StarGAN for autonomous vehicle control}, volume = {13}, year = {2025}, }
• (2025). Steel defect detection based on YOLO-SAFD. ACCESS, 13, 77291-77304. (www)

  @article{ , abstract = {In industrial production, defect detection of steel materials is critical for maintaining quality. However, traditional inspection methods are labor-intensive and error-prone, while existing deep learning-based detection approaches generally suffer from poor performance in industrial defect detection and insensitivity to small defects. This paper presents YOLO-SAFD, an advanced framework based on YOLOv5, designed to address these challenges. The proposed model incorporates two key innovations: 1) the Squeezed and Excited Asymptotic Feature Pyramid Network (SAFPN), which enhances multi-scale feature fusion and improves the detection of small defects, increasing the mean Average Precision (mAP) from 0.78 (YOLOv5 baseline) to 0.84; 2) the Diverse Branch Block (DBB), which replaces conventional convolutions to enrich feature diversity while reducing computational complexity, cutting the model parameters from 13.8M to 4.82M. Experimental results on the NEU-DET dataset demonstrate that YOLO-SAFD achieves a detection precision of 0.83, a recall of 0.75, and an mAP50:95 of 0.43, outperforming the baseline YOLOv5 and highlighting its superior detection accuracy and efficiency for real-time industrial applications.}, archive = {J_ACCESS}, author = {Feihong Yu and Jinshan Zhang and Dingdiao Mu}, doi = {10.1109/ACCESS.2025.3565587}, journal = {IEEE Access}, pages = {77291-77304}, shortjournal = {IEEE Access}, title = {Steel defect detection based on YOLO-SAFD}, volume = {13}, year = {2025}, }
• (2025). SDN controller selection and secure resource allocation. ACCESS, 13, 77278-77290. (www)

  @article{ , abstract = {Software Defined Networking (SDN) has emerged as a promising paradigm for network management. However, in energy-effective task scheduling and security, the centralized control architecture of SDN brings challenges. This research proposes a new approach for blockchain-based secure resource allocation with controller selection in SDN, utilizing the Entropy Oppositional Based Learning-Interpolation Blue Monkey Optimization Algorithm (EOBL-IBMOA). By establishing a blockchain-centric secure resource allocation with controller selection, the proposed technique addresses challenges in SDN. Here, user registration, load balancing, attack detection, controller selection, and resource allocation phases are included. Utilizing XOR Left Shift (XORLS), the user details are secured by IP traceback and hash codes are generated using the Mid Square-based KECCAK 512 (MS-KECCAK 512) algorithm. For effective traffic balancing, the load balancer uses the Minshev-KMeans algorithm. Attack classification is attained through the Quantile Transformer Scaling based SoftmaxGELU Gated Recurrent Units (QTS-SGGRU) approach. EOBL-IBMOA is used by controller selection and resource allocation for optimal Virtual Machine (VM) selection. The proposed technique’s superiority is described by experimental comparisons. The proposed approach attains effective resource allocation with reduced response time and high throughput, outperforming the prevailing works.}, archive = {J_ACCESS}, author = {Sasibhushana Rao Pappu and Kalyana Chakravarthy Chilukuri}, doi = {10.1109/ACCESS.2025.3565117}, journal = {IEEE Access}, pages = {77278-77290}, shortjournal = {IEEE Access}, title = {SDN controller selection and secure resource allocation}, volume = {13}, year = {2025}, }
• (2025). A survey for blockchain-enabled resource management in edge networks. ACCESS, 13, 77252-77277. (www)

  @article{ , abstract = {As more people and devices come online with the rapid growth of the internet, the need for secure and efficient network resource management becomes increasingly important. The boom of these connections on Next Generation Wireless Networks through the advent of technologies like the Internet of Things has changed the landscape of traditional edge networks. This necessitates a drastic change in the existing edge network infrastructure. As these devices come online, make requests, and go offline, the allocation and management of edge network resources become paramount. Blockchain, with its growing popularity, presents itself as a potential solution. The inherent features of blockchain, such as decentralization, transparency, security, and immutability, make it possible for network providers to manage network resources efficiently. This survey paper delves into the diverse applications of blockchain in edge network resource management, offering a comprehensive discussion of its potential to revolutionize network infrastructure. Unlike other surveys, we focus this work on three specific contexts of blockchain integration for network resource management: Cloud and Edge Computing, Radio Access Networks in Next Generation Wireless Networks, Reinforcement Learning, and Federated Learning. We provide an extensive analysis of recent research advances in these areas, and the main findings are highlighted. Understanding the current use of blockchain and network resource management opens the door to new research and more efficient and secure networks. The open research issues that arise from the combination of blockchain with the management of edge network resources are identified. Finally, the future directions to fuel innovative research are presented in this survey.}, archive = {J_ACCESS}, author = {Sharon A. Boamah and Jean D. Louis and Janise McNair and Juan E. Gilbert}, doi = {10.1109/ACCESS.2025.3562126}, journal = {IEEE Access}, pages = {77252-77277}, shortjournal = {IEEE Access}, title = {A survey for blockchain-enabled resource management in edge networks}, volume = {13}, year = {2025}, }
• (2025). Seven-level soft charging switched-capacitor multilevel inverter. ACCESS, 13, 77239-77251. (www)

  @article{ , abstract = {This paper proposes a novel 7-level switched capacitor multilevel inverter (SC-MLI) topology with soft charging capability and analyzes the performance in terms of efficiency and component optimization. The proposed structure achieves high efficiency up to 97.32% while producing the same output level with fewer components compared to existing SC-MLI designs in the literature. Moreover, the total cost function (CF) value of 3.26 is among the lowest values, indicating that the system offers an economically advantageous solution. Simulation and experimental studies confirm that the system generates stable output waveforms under resistive and inductive loads and operates stably over a wide frequency and modulation index (MI) range. In particular, the effect of the soft charging technique on capacitor currents has been investigated and it has been found that this technique minimizes switching losses by reducing inrush current transients. The Sinusoidal Puls Width Modulation (SPWM) method using high switching frequency produces a natural soft charging effect on the capacitor currents, producing lower peak currents compared to Nearest Level Control (NLC) method and improving the overall efficiency of the system. The study shows that SC-MLI is optimized as a function of low component count, high efficiency and low cost. The results reveal that this structure is suitable for high-performance applications such as renewable energy systems, electric vehicle power conversion systems, uninterruptible power supplies (UPS), microgrids, and motor drives. These findings provide an important reference for the future development of SC-MLI designs.}, archive = {J_ACCESS}, author = {Mehmet Memiş and Murat Karakiliç}, doi = {10.1109/ACCESS.2025.3560576}, journal = {IEEE Access}, pages = {77239-77251}, shortjournal = {IEEE Access}, title = {Seven-level soft charging switched-capacitor multilevel inverter}, volume = {13}, year = {2025}, }
• (2025). Virtual load theory: A general approach for mitigating mutual coupling in antenna arrays. ACCESS, 13, 77228-77238. (www)

  @article{ , abstract = {This paper introduces a generalized Virtual Load Theory (VLT) for mitigating mutual coupling in antenna arrays through digital signal preprocessing. The method employs Coupling Compensation Matrices (CCMs) derived from generalized Thévenin-Helmholtz equivalent circuits, formulated using impedance and scattering matrices. A key finding reveals that the scattering matrix-based CCM does not always align with the impedance matrix-based solution; this discrepancy is analyzed and resolved to ensure theoretical consistency. Unlike traditional compensation techniques, VLT enables effective coupling mitigation without requiring physical modifications to the array or continuous recalculations. We define two major antenna elements as current-driven (CD), such as a dipole antenna, and voltage-driven (VD), such as a patch antenna. In receiving arrays, we demonstrate that the open-circuit voltage, $V_{\text {oc}}$ , for CD elements and short-circuit current, $I_{\text {sc}}$ , for VD elements are inherently immune to mutual coupling. Simulation results show that the method successfully recovers embedded element patterns, closely matching isolated patterns even under severe coupling conditions. Experimental validation using a two-monopole prototype array with $0.13\lambda $ element spacing further confirms the effectiveness of VLT in eliminating mutual coupling effects in far-field radiation patterns.}, archive = {J_ACCESS}, author = {Majid Manteghi}, doi = {10.1109/ACCESS.2025.3565186}, journal = {IEEE Access}, pages = {77228-77238}, shortjournal = {IEEE Access}, title = {Virtual load theory: A general approach for mitigating mutual coupling in antenna arrays}, volume = {13}, year = {2025}, }
• (2025). The weighted iterative algorithms for state of health estimation using volterra series model: Least squares algorithm and gradient descent algorithm. ACCESS, 13, 77221-77227. (www)

  @article{ , abstract = {The state of health (SOH) can usually be described by an exponential model. Traditional least squares and gradient descent algorithms are inefficient for such a special model. In this paper, a Volterra series model is used to approximate the exponential SOH model, where some of the collected data are contaminated by outliers. To alleviate the harmful effects caused by the outliers, a weighted least squares algorithm and a weighted gradient descent algorithm are proposed. The algorithms can adaptively ignore those data which are contaminated by outliers, and only utilize the normal data to identify the Volterra model. Compared with the traditional algorithms, the proposed methods have the following advantages: 1) approximated model has a more simple structure; 2) proposed algorithms can adaptively ignore those data which are contaminated by outliers. A simulation example demonstrates the effectiveness of the proposed methods.}, archive = {J_ACCESS}, author = {Yongqiang Zhu and Jing Chen}, doi = {10.1109/ACCESS.2025.3565666}, journal = {IEEE Access}, pages = {77221-77227}, shortjournal = {IEEE Access}, title = {The weighted iterative algorithms for state of health estimation using volterra series model: Least squares algorithm and gradient descent algorithm}, volume = {13}, year = {2025}, }
• (2025). Prediction of electrotactile stimulus threshold in real time using voltage waveforms between electrodes. ACCESS, 13, 77210-77220. (www)

  @article{ , abstract = {Electrotactile stimulation provides tactile feedback by directly stimulating tactile nerves with electrical currents. However, variations in individual skin impedance hinder the practical application of this technology, and developing an algorithm to accurately estimate the sensation threshold in real-time remains challenging. In this study, we explored four methods to predict the electrotactile sensation threshold across all five fingers. First, we measured the correlation between skin impedance (resistance R and capacitance C) and the sensation threshold. Second, we applied Random Forest regression using R and C related data as inputs. Third, we employed an LSTM (Long Short-Term Memory) neural network with an attention mechanism, trained on raw voltage waveforms during charging and discharging phases at the detectable sensation threshold (LSTM-1). Fourth, similar to the third method, we trained the model on waveforms recorded at both detectable and undetectable sensation thresholds (LSTM-2). The results indicate that the LSTM-2 model, enhanced with attention, outperformed the other methods, achieving an $R ^{2}$ of 0.910 and a mean squared error (MSE) of 0.661 mA. By utilizing voltage data from both charging and discharging phases, the LSTM-2 model captured the dynamic properties of skin impedance more effectively than methods relying solely on resistance and capacitance measurements. Furthermore, the LSTM-2 model provided stable tactile feedback over a 20-minute continuous stimulation period, maintaining consistent perception intensity and adapting to changes in skin impedance. Our results demonstrate that LSTM-based models can enhance the accuracy of current threshold estimation and provide stability during long-term stimulation, reducing the need for manual recalibration. The results also suggest that voltage waveforms during the discharging phase and at undetectable thresholds play a critical role in improving stimulation accuracy and should not be overlooked.}, archive = {J_ACCESS}, author = {Vibol Yem and Yasushi Ikei and Hiroyuki Kajimoto}, doi = {10.1109/ACCESS.2025.3560777}, journal = {IEEE Access}, pages = {77210-77220}, shortjournal = {IEEE Access}, title = {Prediction of electrotactile stimulus threshold in real time using voltage waveforms between electrodes}, volume = {13}, year = {2025}, }
• (2025). Cooling system design and temperature field analysis for high-speed switched reluctance motor considering multi-field coupling of electromagnetic-thermal-flow. ACCESS, 13, 77196-77209. (www)

  @article{ , abstract = {Given the challenges of high loss density and heat dissipation in high-speed switched reluctance motors (HSRMs), this paper proposes a thermal analysis and cooling design method based on electromagnetic-thermal-flow (EM-TF) coupling. A 3 kW, 20,000 r/min HSRM is investigated. Accurate models of AC copper and windage losses are developed using fine 3-D winding modeling and full-domain CFD, enabling realistic heat source distribution. The total losses, including copper, core, and windage losses, are used as inputs for thermal simulation. Three cooling structures are compared in terms of thermal and flow field performance, and the spiral waterway is identified as optimal. To improve prediction accuracy, both unidirectional and bidirectional EM-TF coupling methods (UEMTFC and BEMTFC) are employed. The latter considers temperature-dependent properties of materials and internal air. Results show that BEMTFC significantly improves thermal prediction, with winding temperature deviations under $4~^{\circ }$ C compared to experimental results. These results confirm the effectiveness of the proposed method in improving the accuracy of thermal analysis for high-speed electrical machines.}, archive = {J_ACCESS}, author = {Lukuan Qiao and Aimin Liu and Da Ren and Fangui Meng and Yuchen Wang and Yao Liu}, doi = {10.1109/ACCESS.2025.3562070}, journal = {IEEE Access}, pages = {77196-77209}, shortjournal = {IEEE Access}, title = {Cooling system design and temperature field analysis for high-speed switched reluctance motor considering multi-field coupling of electromagnetic-thermal-flow}, volume = {13}, year = {2025}, }
• (2025). A robust hybrid CNN+ViT framework for breast cancer classification using mammogram images. ACCESS, 13, 77187-77195. (www)

  @article{ , abstract = {Breast cancer is the most frequent type of cancer largely experienced by women currently, although it could happen to men also. It appears when abnormal breast tissue cells grow rapidly and form tumors. Mammogram is a technique that is employed by doctors to analyse the breast in the diagnosis of early cancer. These mammograms are classified into Benign and Malignant. This research addresses the variability and potential oversight in radiologists’ manual mammogram interpretations, aiming to enhance classification accuracy by combining Convolution Neural Networks (CNNs) and Vision Transformers (ViTs). CNN is a successful image classification that uses hierarchical feature extraction, ViTs capture the global context but require substantial data and computation. In this research, we have used CLAHE-enhanced mammogram images from Kaggle for training and applied a CNN+ViT model. We have also used a few pre-trained models such as DenseNet, Inception, SE Resnet, and XceptionNet for comparative analysis. The CNN+ViT model gave us an accuracy of 90.1% showing robust performance. Although XceptionNet achieved perfect accuracy, it may indicate overfitting.}, archive = {J_ACCESS}, author = {Vasudha Rani Patheda and Gunda Laxmisai and B. V. Gokulnath and S. P. Siddique Ibrahim and S. Selva Kumar}, doi = {10.1109/ACCESS.2025.3563218}, journal = {IEEE Access}, pages = {77187-77195}, shortjournal = {IEEE Access}, title = {A robust hybrid CNN+ViT framework for breast cancer classification using mammogram images}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection in industrial machine sounds using high-frequency features and gate recurrent unit networks. ACCESS, 13, 77165-77186. (www)

  @article{ , abstract = {Detecting anomalies in industrial sound is critical for maintaining operational efficiency, preventing costly equipment failures, and ensuring workplace safety. However, it presents significant challenges due to the complexity and variability of industrial environments, including background noise and fluctuating operating conditions. This paper proposes a comprehensive approach that leverages machine learning (ML) and deep learning (DL) techniques to address these challenges. Using three datasets, the Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization 2022 (MIMII DG 2022), the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022, and DCASE 2024, we evaluate the performance of various ML and DL models under different experimental conditions. Our study focuses on feature extraction methods such as Mel-spectrograms and Mel-frequency Cepstral Coefficients (MFCCs) to capture critical acoustic characteristics of industrial machinery. Both supervised ML and DL techniques are employed to explore effective anomaly detection strategies. Extensive experimentation and evaluation using metrics such as confusion matrix, accuracy, precision, recall, and F1 score highlight the effectiveness of our approach in real-world industrial scenarios. Experimental results demonstrate that eXtreme Gradient Boosting (XGBoost) outperforms Support Vector Machine (SVM) and Decision Tree (DT) models in the ML approach across both feature sets. In the DL approach, Gated Recurrent Units (GRU) perform better on MFCC features than Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. GRU emerges as the best-performing model on testing datasets, achieving a precision of 99.56% and an F1 score of 99.55% on the DCASE 2024 dataset, a precision of 94% and an F1 score of 93.95% on the DCASE 2022 dataset, and a precision of 92.2% and an F1 score of 92.06% on the MIMII DG 2022 dataset. These results underscore the potential of DL for real-time industrial sound analysis in predictive maintenance, offering significant improvements over traditional methods.}, archive = {J_ACCESS}, author = {Thi-Thu-Huong Le and Andro Aprila Adiputra and Jiwon Yun and Howon Kim}, doi = {10.1109/ACCESS.2025.3565812}, journal = {IEEE Access}, pages = {77165-77186}, shortjournal = {IEEE Access}, title = {Anomaly detection in industrial machine sounds using high-frequency features and gate recurrent unit networks}, volume = {13}, year = {2025}, }
• (2025). An effective ANFIS approach for interconnected DFIG-based wind energy system with model-in-loop validation. ACCESS, 13, 77147-77164. (www)

  @article{ , abstract = {This study introduces an effective control strategy for interconnected doubly-fed induction generator-based wind energy systems with model-in-loop validation. Using an adaptive neuro-fuzzy inference system, the proposed controller employs an improved fuzzy rule set and membership function configuration. While existing ANFIS controllers typically use between 9 and 25 fuzzy rules for the grid side and rotor side converter in a doubly-fed induction generator-based wind energy system, this research extends to 49 rules with 7 associated membership functions and considering two inputs to the ANFIS controller for the grid as well as rotor side, resulting in higher precision and control performance Real-time simulations conducted on the OPAL RT OP5700 platform validate the controller’s effectiveness under varying wind conditions, specifically at 15 m/s and 10 m/s. Furthermore, dynamic and transient performances are thoroughly assessed in the presence of grid-side faults such as single-line-to-ground and three-phase-to-ground faults. A comparative analysis with traditional PI and fuzzy logic controllers reveals that the proposed ANFIS controller attains a 14% acceleration in fault recovery under LG and LLLG fault conditions. This result suggests that the proposed ANFIS controller performs better than its traditional counterparts. The enhanced ANFIS demonstrated robustness and adaptability, significantly improving resilience during variations in wind speed and fault tolerance in real-time wind energy systems.}, archive = {J_ACCESS}, author = {J. Sarathi and D. Rama Prabha}, doi = {10.1109/ACCESS.2025.3565073}, journal = {IEEE Access}, pages = {77147-77164}, shortjournal = {IEEE Access}, title = {An effective ANFIS approach for interconnected DFIG-based wind energy system with model-in-loop validation}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable three-switch leg multi-port boost inverters with novel modulation schemes for hybrid DC/AC microgrid systems. ACCESS, 13, 77124-77146. (www)

  @article{ , abstract = {This paper proposes reconfigurable single-stage three-switch leg multi-port boost inverters (TSLMPBIs) that feature novel hybrid modulation schemes for hybrid DC/AC microgrid system applications. The TSLMPBI topologies are designed to accommodate various configurations, including two DC ports with two AC ports, three DC ports with one AC port, and three DC ports with two AC ports, tailored for different applications. A hybrid modulation scheme was proposed, dynamically adapted to the configuration of DC and AC ports, facilitating seamless boost and dual inversion operations or combined boost, buck, and inversion functionalities within a single converter. The AC ports operate at various voltages, frequencies, and phases, while the DC ports support multiple voltage levels. The proposed control modulation scheme effectively addresses crossover issues in modulation signals, resulting in enhanced AC voltage gain, improved DC bus utilization, and optimized power transfer among DC and AC ports. Additionally, these topologies reduce active switch count compared to state-of-the-art designs, even with increased ports. To validate the performance of the proposed TSLMPBI and its control modulation scheme, a 500 W laboratory prototype was developed, and experimental results under various operating scenarios were presented and analyzed.}, archive = {J_ACCESS}, author = {M. Rama Narayana Reddy and B. Dastagiri Reddy and Prajof Prabhakaran and Andrii Chub and Samir Kouro}, doi = {10.1109/ACCESS.2025.3565791}, journal = {IEEE Access}, pages = {77124-77146}, shortjournal = {IEEE Access}, title = {Reconfigurable three-switch leg multi-port boost inverters with novel modulation schemes for hybrid DC/AC microgrid systems}, volume = {13}, year = {2025}, }
• (2025). ALCS-PP: Adaptive latency control scheme for packet pacing in self-clocked congestion control. ACCESS, 13, 77080-77123. (www)

  @article{ , abstract = {Effective congestion control is crucial for maintaining network stability, especially in dynamic environments. Self-Clocked Rate Adaptation for Multimedia (SCReAM) is a hybrid loss and delay-based algorithm for real-time multimedia transmission. It encounters challenges with static pacing and low latency under fluctuating round-trip time (RTT) and jitter, resulting in ineffective bandwidth utilization. To address these issues, this paper proposes an Adaptive Latency Control Scheme for Packet Pacing (ALCS-PP), a novel congestion control scheme featuring dynamic rate adjustment and adaptive packet pacing. The evaluation process is divided into three phases. The first phase involves initial performance evaluation using default parameters to establish a baseline comparison. The next phase explores parameter-specific experiments, where individual parameters such as target latency, jitter thresholds, and rate scaling factors are tuned to assess their impact on performance. The last phase investigates multi-parameter combinations to optimize the overall system configuration under various network conditions. The proposed scheme effectively reduced network queue delay (NQD) by up to 21.91% and smoothed RTT (sRTT) by 13.44%, ensuring better responsiveness in real-time multimedia transmission. These improvements are achieved through adaptive tuning using dynamic soft clamping and gradual corrections, minimizing performance oscillations. Additionally, the scheme maintains metrics such as network throughput (NThput), congestion window (CWND), and bytes in flight (BiF) close to SCReAM’s baseline values, ensuring stable performance. The experimental evaluation highlights the algorithm’s capability to balance delay reduction and network stability, making it suitable for delay-sensitive applications such as 5G, Internet of Things (IoT), and real-time multimedia streaming.}, archive = {J_ACCESS}, author = {Haider Dhia Zubaydi and Ahmed Samir Jagmagji and Sándor Molnár}, doi = {10.1109/ACCESS.2025.3561318}, journal = {IEEE Access}, pages = {77080-77123}, shortjournal = {IEEE Access}, title = {ALCS-PP: Adaptive latency control scheme for packet pacing in self-clocked congestion control}, volume = {13}, year = {2025}, }
• (2025). ROPGCViT: A novel explainable vision transformer for retinopathy of prematurity diagnosis. ACCESS, 13, 77064-77079. (www)

  @article{ , abstract = {Retinopathy of Prematurity (ROP) is a severe disease that occurs in premature babies due to abnormal development of retinal vessels and can lead to permanent vision loss. Fundus images are critical in the diagnosis of ROP; however, the examination of fundus images is a subjective, time-consuming, and error-prone process that requires experience. This situation can lead to delayed diagnosis and inaccurate evaluations. Therefore, the need for computer-aided diagnosis (CAD) systems is increasing day by day. Deep learning (DL) methods have a high potential in analyzing such complex images. In this study, a total of 50 DL models, 25 Convolutional Neural Network (CNN), and 25 Vision Transformer (ViT) models were tested to diagnose ROP from fundus images. Furthermore, the ROPGCViT model based on the Global Context Vision Transformer (GCViT) was proposed. GCViT was enhanced with Squeeze-and-Excitation (SE) block and Residual Multilayer Perceptron (RMLP) structures to effectively learn local and global context information. With a dataset of 1099 fundus images, the performance of the model was evaluated in terms of accuracy, precision, recall, f1-score, and Cohen’s kappa score. To enhance explainability, the Gradient-Weighted Class Activation Mapping (Grad-CAM) method was utilized to visualize the regions of fundus images the model focused on during classification, providing insights into its decision-making process. ROPGCViT outperformed both 50 DL models and methods in the literature with 94.69% accuracy, 94.84% precision, 94.69% recall, 94.60% f1-score, and Cohen’s kappa score of 93.10%. Additionally, the Grad-CAM visualizations demonstrated the ability of the model to focus on clinically relevant regions, enhancing trust and interpretability for experts. The proposed ROPGCViT model provides a robust solution for ROP diagnosis with high accuracy, flexibility, and generalization capacity.}, archive = {J_ACCESS}, author = {Mustafa Yurdakul and Kübra Uyar and Şakír Taşdemír and Írfan Atabaş}, doi = {10.1109/ACCESS.2025.3564213}, journal = {IEEE Access}, pages = {77064-77079}, shortjournal = {IEEE Access}, title = {ROPGCViT: A novel explainable vision transformer for retinopathy of prematurity diagnosis}, volume = {13}, year = {2025}, }
• (2025). FedHSP: A robust federated learning framework coherently addressing heterogeneity, security, and performance challenges. ACCESS, 13, 77049-77063. (www)

  @article{ , abstract = {Federated Learning (FL) is a machine learning training method that leverages local model gradients instead of accessing private data from individual clients, ensuring privacy. However, the practical implementation of FL faces significant challenges. Heterogeneous clients and edge devices with varying computational abilities and unreliable communication channels introduce latency issues to the algorithm. Furthermore, the algorithm is susceptible to attacks from malicious clients, allowing them to insert unwanted updates while benefiting from the global model. These challenges severely impact the algorithm’s performance, rendering it unsuitable for real-time applications. To address these issues, we propose FedHSP, a comprehensive system that tackles device heterogeneity and protects against various forms of attacks. FedHSP incorporates multiple model complexities to accommodate heterogeneous clients. Additionally, it employs a Variational Auto Encoder with dynamic thresholding to detect and eliminate malicious clients. In this paper, we demonstrate FedHSP’s effectiveness in detecting model poisoning attacks. We also show the mitigation of malicious model updates sent to the server. The evaluation is done using the MNIST dataset under various settings. Our experiment results show the drastic performance deviation due to attacks and the successful detection and mitigation with the proposed system.}, archive = {J_ACCESS}, author = {Divya G. Nair and C. V. Aswartha Narayana and K. Jaideep Reddy and Jyothisha J. Nair}, doi = {10.1109/ACCESS.2025.3563544}, journal = {IEEE Access}, pages = {77049-77063}, shortjournal = {IEEE Access}, title = {FedHSP: A robust federated learning framework coherently addressing heterogeneity, security, and performance challenges}, volume = {13}, year = {2025}, }
• (2025). Hybrid machine learning-based multi-stage framework for detection of credit card anomalies and fraud. ACCESS, 13, 77039-77048. (www)

  @article{ , abstract = {Recently, tremendous growth in e-business has arisen in an increasing number of online transactions. Such widespread adaptation of e-payments has been going along with the increase in deceitful activities, which results in tremendous losses in the financial sector. This led to a novel research paradigm using statistical and auto-data-driven techniques to detect anomalies and fraud. Thus, traditional techniques fail to provide a secure medium for online transactions. Consequently, building a credit card fraud (CCF) detector is essential for secure online operations. Therefore, based on the abovementioned constraints, this paper presents a comprehensive study incorporating heterogeneous machine learning (ML) techniques for CCF detection. The proposed framework utilizes a multi-stage classification system that employs multiple classifiers, i.e., logistic regression, support vector machine (SVM) XGBoost, Random Forest, K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). Furthermore, to accomplish the intensive class imbalance, the proposed technique uses a sampling technique with an internal features selection technique implemented based on voting among different methods. The key finding indicates that the proposed model surpasses the existing DNN simple voting, traditional stacking framework with a fraud recall value of 0.901, a legitimate recall value of 0.995, and a model cost value of 0.421.}, archive = {J_ACCESS}, author = {Hatoon S. Alsagri}, doi = {10.1109/ACCESS.2025.3565612}, journal = {IEEE Access}, pages = {77039-77048}, shortjournal = {IEEE Access}, title = {Hybrid machine learning-based multi-stage framework for detection of credit card anomalies and fraud}, volume = {13}, year = {2025}, }
• (2025). On-device automatic speech recognition for low-resource languages in mixed reality industrial metaverse applications: Practical guidelines and evaluation of a shipbuilding application in galician. ACCESS, 13, 77017-77038. (www)

  @article{ , abstract = {As the Metaverse and Mixed Reality (MR) technologies continue to evolve, enabling natural and intuitive user interfaces is crucial. However, supporting low-resource languages in these advanced systems presents unique challenges. This article explores the development and deployment of an on-device Automatic Speech Recognition (ASR) system for Galician, a low-resource language spoken by less than 3 million people, implemented on the Microsoft HoloLens 2 MR glasses. The system prioritizes data privacy and security by eliminating the need for Internet connectivity or external processing. Key implementation choices, including software and libraries, are detailed, along with optimization strategies for minimizing latency. Performance evaluations, taking into account noise-simulated environments, demonstrate the high accuracy and low latency of the system, proving its effectiveness as an on-device ASR system for current and future Metaverse applications. In order to demonstrate the effectiveness of the developed system, it has been incorporated in an electrical outfitting application for Navantia, one of the largest shipbuilding companies in the world, illustrating its practical utility in an industrial scenario like a shipyard. The results obtained show a Character Error Rate (CER) below 6% and a latency of under 3 seconds using an ARM64 quantized model, which validates the effectiveness of the system for real-time voice control in industrial MR environments.}, archive = {J_ACCESS}, author = {Antón Valladares-Poncela and Paula Fraga-Lamas and Tiago M. Fernández-Caramés}, doi = {10.1109/ACCESS.2025.3564137}, journal = {IEEE Access}, pages = {77017-77038}, shortjournal = {IEEE Access}, title = {On-device automatic speech recognition for low-resource languages in mixed reality industrial metaverse applications: Practical guidelines and evaluation of a shipbuilding application in galician}, volume = {13}, year = {2025}, }
• (2025). CIRA-cyber intelligent risk assessment methodology for industrial internet of things based on machine learning. ACCESS, 13, 77001-77016. (www)

  @article{ , abstract = {The Industrial Internet of Things (IIoT) has transformed the industrial control system to Industry 4.0 and plays an indispensable role in improving the efficiency and productivity of industries. The unique features of the IIoT system have potential innovative models in various industrial fields by providing pervasive communication, fast production, efficient data analytics, and well decision-making systems. As IIoT technologies continue to grow, evolve, and mature, cybersecurity threats and adversarial attacks have also increased, compromising the information security triads, i.e., confidentiality, integrity, and availability within IIoT systems. There is a growing need for cybersecurity awareness and a deeper understanding of cyber threats and vulnerabilities in IoT devices to protect valuable data and information. Risk assessment is a proactive approach to identifying, analyzing, and mitigating potential cybersecurity threats. Implementing intelligent and resilient cyber risk assessment is crucial to taking appropriate security measures to avoid cyber threats before exploiting vulnerabilities and turning them into liabilities for IIoT systems. This research proposes CIRA, an intelligent and resilient cyber risk assessment methodology to identify, assess, and mitigate potential cybersecurity risks using leading machine learning models. To select a suitable machine learning model, centralized and decentralized ML models are applied and compared using a synthetic dataset. The results of extensive trials revealed that the decentralized (federated) machine learning models like FXboost and FSVM outperformed in terms of prediction accuracy by 98%, with a low communication cost of 0.29 MB, a low False Alarm Rate, and excellent performance in the ROC curve. Centralized machine models such as XGBoost and LGBM have a prediction accuracy of 99%, less training time, low FAR, and the best ROC value. The performance of the models is evaluated with the state-of-the-art matrices such as precision, recall, and F.1 score has shown that the centralized ML models are feasible for small to medium datasets, however, Federated Learning techniques are outperformed for medium to large datasets to IIoT systems with safety, security, accuracy, and scalability of IIoT nodes.}, archive = {J_ACCESS}, author = {Ghulam Abbas and Mushtaq Ali and Muneer Ahmad and Ajmal Khan}, doi = {10.1109/ACCESS.2025.3559617}, journal = {IEEE Access}, pages = {77001-77016}, shortjournal = {IEEE Access}, title = {CIRA-cyber intelligent risk assessment methodology for industrial internet of things based on machine learning}, volume = {13}, year = {2025}, }
• (2025). Measuring public opinions on renewable energy and climate change using structural equations modeling. ACCESS, 13, 76981-77000. (www)

  @article{ , abstract = {Public attitudes towards energy sources and climate change are increasingly complex. This paper explores Americans’ perceptions of energy sources including renewables, Electric Vehicles (EVs), government policies and climate change. A survey conducted by the Pew Research Center (PRC), comprising over 10,000 responses to 52 questions on energy sources and climate change, is analyzed. In this paper, we propose a data analysis framework that consists of Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), cluster analysis and Principal Component Analysis (PCA) to segment respondents and identify key variables. Furthermore, the Structural Equation Model (SEM) is created to examine relationships between latent and observed variables, using maximum likelihood estimation. The results validated the identified factors, with high loading on key variables indicating strong contributions to attitudes towards energy and climate policies.}, archive = {J_ACCESS}, author = {Ionuţ Nica and Simona-Vasilica Oprea and Adela Bâra}, doi = {10.1109/ACCESS.2025.3565676}, journal = {IEEE Access}, pages = {76981-77000}, shortjournal = {IEEE Access}, title = {Measuring public opinions on renewable energy and climate change using structural equations modeling}, volume = {13}, year = {2025}, }
• (2025). Energy management of cooperative combined cooling, heating, and power microgrids considering user comfort. ACCESS, 13, 76963-76980. (www)

  @article{ , abstract = {The imbalance due to increasing energy demand and the depletion of energy resources leads to an unstable power system. In conventional microgrids, the surplus heat of micro-turbines is wasted as electrical energy is used for various loads. Demand-side management scheduling the shiftable appliances based on cost-per-unit, overlooking consumer preferences and comfort. To address these challenges, a unified framework for managing and trading energy within renewable integrated combined cooling heating and power microgrids is proposed. Within this framework, each combined cooling heating and power microgrid initially employs demand-side management to fulfill its energy requirements while adhering to different constraints. If the load demands of any combined cooling heating and power microgrid are not fulfilled by its local renewable energy generation, then energy can be procured from nearby connected combined cooling heating and power microgrids or from the utility. Conversely, excess energy can be sold out to nearby combined cooling heating and power microgrids or to the utility. Additionally, in emergency situations, diesel generators can supplement the energy needs of any microgrids. Simulation results demonstrate 34.01% and 32.98% reductions in system costs and peak power demand while considering user preferences and comfort. Reducing the peak power demand will improve the system’s performance and efficiency.}, archive = {J_ACCESS}, author = {Muhammad Riaz and Sadiq Ahmad and Muhammad Naeem and Amad Zafar and Kwang Su Kim}, doi = {10.1109/ACCESS.2025.3562311}, journal = {IEEE Access}, pages = {76963-76980}, shortjournal = {IEEE Access}, title = {Energy management of cooperative combined cooling, heating, and power microgrids considering user comfort}, volume = {13}, year = {2025}, }
• (2025). A global approach to artificial intelligence. ACCESS, 13, 76946-76962. (www)

  @article{ , abstract = {This paper presents the initial foundations of a new Global approach to Artificial Intelligence based on the modeling of global intelligence and the development of artificial cooperative systems to support this. The research work brings numerous investigations and some recent creations to develop intelligent systems in urgent global, cooperative domains. The investigations present an overall view of current issues like true natural i.e. global intelligence. The creations include several results in global solutions explorations as well as new models, services, architectures and processes which could be of special interest for global organizations that urgently need to achieve intelligent cooperation. The major aim of the approach is to help these organizations, and other related institutions, to succeed in their complex daily duties where true thinking, more natural intelligence and new global artificial systems are required. Further, the paper provides some guidelines for the next evolutions of the work in the form of a brief, open research agenda and, some general actions for deploying Global Artificial Intelligence into our organizations, suggesting how to build new global artificial intelligent systems in real and virtual ecosystems.}, archive = {J_ACCESS}, author = {Ana M. González de Miguel and Antonio Sarasa-Cabezuelo}, doi = {10.1109/ACCESS.2025.3565156}, journal = {IEEE Access}, pages = {76946-76962}, shortjournal = {IEEE Access}, title = {A global approach to artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Pseudo-target optimization strategy based on policy iteration algorithm. ACCESS, 13, 76934-76945. (www)

  @article{ , abstract = {With autonomous robots becoming increasingly integrated into human society, the efficiency of their path optimization is of paramount importance. To address the issue of redundant states in the application of the policy iteration algorithm in the environmental model optimization process and to accelerate the convergence speed of the algorithm, this paper proposes a pseudo-target optimization algorithm based on the policy iteration algorithm in combination with the Manhattan distance. The proposed algorithm prioritizes updating the surrounding state policy centered on the pseudo-target state, the set composed of these states is called a local state set. Once the state policies within the aforementioned set have converged, the next pseudo-target state is identified through the use of the Manhattan distance, and the reward distribution is subsequently changed. Furthermore, the objective of quantifying the inflection point can be accomplished by regulating the length parameter n of the local state set, which corresponds to the different steering characteristics of robots in practical applications. Finally, the superiority of the algorithm is validated through experimental simulation. The optimized algorithm increases the convergence speed by more than 50 % when the parameter n is small, and gradually approaches the convergence speed before optimization when the parameter n is large. The maximum difference in the number of inflection points corresponding to different n values is nine. With the efficiency inversely proportional to the inflection point, this enables a balance between the steering characteristics of autonomous robots and the convergence speed of the algorithm in practical applications.}, archive = {J_ACCESS}, author = {Yiming Meng and Daichen Liu and Ziyuan Wang}, doi = {10.1109/ACCESS.2025.3564646}, journal = {IEEE Access}, pages = {76934-76945}, shortjournal = {IEEE Access}, title = {Pseudo-target optimization strategy based on policy iteration algorithm}, volume = {13}, year = {2025}, }
• (2025). Comments on “Exploring secure V2X communication networks for human-centric security and privacy in smart Cities”—Privacy-preserving certificate in V2X communications. ACCESS, 13, 76923-76933. (www)

  @article{ , abstract = {The state-of-the-art study, Rishiwal et al. (2024) proposed a blockchain-based vehicular network framework designed to enhance communication security and protect vehicle privacy in vehicle-to-everything (V2X) communications. The proposed framework validates vehicle legitimacy using individual certificates and subsequently transmits data collected and integrated by intermediate entities to the blockchain, thereby avoiding direct exposure of vehicle privacy. However, the framework does not prevent intermediate entities from accessing vehicle privacy, leaving vehicles vulnerable to tracking and monitoring by these entities. In response to this limitation, this study proposes a key expansion method based on elliptic curve cryptography (ECC). This approach employs a two-stage key expansion process, referred to as “butterfly key expansion,” to ensure that vehicle privacy is not exposed to any device within the V2X communications. Experimental results demonstrate that the key expansion time does not significantly differ from the key generation time. Furthermore, the signature generation and verification times using the expanded keys are comparable to those of the original keys. These findings indicate that the proposed method achieves enhanced privacy protection without imposing additional computational overhead.}, archive = {J_ACCESS}, author = {Abel C. H. Chen}, doi = {10.1109/ACCESS.2025.3563461}, journal = {IEEE Access}, pages = {76923-76933}, shortjournal = {IEEE Access}, title = {Comments on “Exploring secure V2X communication networks for human-centric security and privacy in smart Cities”—Privacy-preserving certificate in V2X communications}, volume = {13}, year = {2025}, }
• (2025). Wideband beam-steering flat dielectric lens antenna for 5G communications. ACCESS, 13, 76914-76922. (www)

  @article{ , abstract = {A compact cylindrical-shaped flat dielectric lens (FDL) antenna, partially sandwiched within a parallel-plate waveguide (PPW), is presented. The design elegantly combines uniform FDL functionality within a PPW with a wideband rectangular waveguide-based feed network, resulting in a streamlined, highly manufacturable design suited for commercial beam-steering applications. In the feed network, an array of probe-fed rectangular waveguides, each accompanied by a symmetric E-plane step discontinuity and a partial-height conducting post, is employed to enable the launch of multiple beams over a wide operating frequency band. The lens design follows the procedure of partially sandwiching the cylindrical uniform dielectric between parallel conducting plates, allowing the dielectric extension beyond the plates to improve free-space matching and acting as a dielectric rod antenna to enhance directivity. The overall radiation performance of the antenna versus the feed network positions along the focal length of the FDL is precisely optimized through the use of the ray-tracing technique and genetic algorithm. This synergy yields a streamlined and highly manufacturable architecture specifically tailored to meet the demanding requirements of commercial beam-steering applications. The proposed FDL antenna with a nine-element feed network is designed to achieve azimuthal beam steering between ±33°. The results show that the gain remains almost constant within this range, with nearly 0.1 dB measured fluctuation. To validate the performance, the proposed FDL antenna is fabricated and measured. The measurements agree with the simulation results, demonstrating a wide operational bandwidth of 50% spanning from 24 to 40 GHz with a stable beam-steering capability. The antenna maintains consistent radiation characteristics throughout the operating range, with sidelobe levels below −15 dB and a peak gain of 16.7 dBi at the central frequency of 32 GHz. These features make the antenna suitable for various applications, ranging from 5G communications to radar systems.}, archive = {J_ACCESS}, author = {Zahra Rahimian Omam and Saeideh Shad and Bahram Khalichi and Hani Mehrpouyan and Mohsen Khalily and Ahmed A. Kishk}, doi = {10.1109/ACCESS.2025.3565474}, journal = {IEEE Access}, pages = {76914-76922}, shortjournal = {IEEE Access}, title = {Wideband beam-steering flat dielectric lens antenna for 5G communications}, volume = {13}, year = {2025}, }
• (2025). TEDVIL: Leveraging transformer-based embeddings for vulnerability detection in lifted code. ACCESS, 13, 76894-76913. (www)

  @article{ , abstract = {Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows in Low-Level Virtual Machine (LLVM) intermediate representation code. For comparison, simpler word2vec models (Skip-Gram and Continuous Bag of Words) were also trained, and their embeddings were used to train LSTMs. The results show that the LSTMs using GPT-2 embeddings outperformed those using GPT-1, BERT, RoBERTa, and word2vec embeddings, achieving a top accuracy of 92.5% and an F1-score of 89.7%. Notably, these results are achieved when the embedding model is trained with a dataset of just 48,000 functions, demonstrating effectiveness in resource-constrained settings. The findings underscore the effectiveness of TEDVIL in identifying hard-to-detect vulnerabilities in compiled code, and lay the groundwork for future research in leveraging transformer-based models for vulnerability detection.}, archive = {J_ACCESS}, author = {Gary A. McCully and John D. Hastings and Shengjie Xu}, doi = {10.1109/ACCESS.2025.3565980}, journal = {IEEE Access}, pages = {76894-76913}, shortjournal = {IEEE Access}, title = {TEDVIL: Leveraging transformer-based embeddings for vulnerability detection in lifted code}, volume = {13}, year = {2025}, }
• (2025). Building fast dynamic 3D maps for trajectory planning of autonomous ground vehicles using non-repetitive scanning LiDAR sensor. ACCESS, 13, 76879-76893. (www)

  @article{ , abstract = {Building fast and reliable maps of the environment is a fundamental task for autonomous navigation. However, this process offers several challenges such as accurate registration of 3D point clouds. Recently, non-repetitive scanning LiDAR sensors have emerged as a promising alternative for 3D data acquisition, leveraging some of these challenges. In this paper, we present a 3D point cloud registration method that exploits the unique scanning pattern of such a sensor to register successive 3D scans. Errors accumulated over larger distances due to drift as well as registration errors due to the presence of dynamic objects in the scenes were improved by re-enforcing the registration method by first segmenting and classifying static and dynamic objects by analyzing the deformation in the unique, non-repetitive, Spirograph-type, scanning pattern of the sensor and then incorporating a fast NDT (Normal Distribution Transform) based registration method as well as loop closure detection for fine alignment. The novel method is then extended to build and update fast dynamic maps of the environment for trajectory planning for autonomous navigation. The proposed method is evaluated on three real and different datasets and compared with other state-of-the-art methods. The results not only demonstrates the suitability of these types of sensors for such applications but also show that the proposed method is comparable with other methods in terms of accuracy and surpasses them in performance in terms of processing time, making it suitable for real time applications.}, archive = {J_ACCESS}, author = {Ahmad K. Aijazi and Paul Checchin}, doi = {10.1109/ACCESS.2025.3564764}, journal = {IEEE Access}, pages = {76879-76893}, shortjournal = {IEEE Access}, title = {Building fast dynamic 3D maps for trajectory planning of autonomous ground vehicles using non-repetitive scanning LiDAR sensor}, volume = {13}, year = {2025}, }
• (2025). Image segmentation method combining nonlocal criteria with graph cut theory. ACCESS, 13, 76868-76878. (www)

  @article{ , abstract = {Image segmentation, a fundamental problem in image processing, involves distinguishing the foreground from the background. Traditional image segmentation methods are typically divided into local and global approaches. Local methods often result in blurred segmentation due to their reliance on overly localized color sampling models. Conversely, global methods, which depend on the overall color distribution, struggle with images where the foreground and background share similar global characteristics. To overcome these limitations, a novel image segmentation method that integrates nonlocal criteria with graph cut theory is proposed. Initially, the method transforms the image’s color space to the HSV color space, enhancing color contrast. Following this, nonlocal criteria are utilized to compute the data term within the graph cut model, which is then incorporated into the traditional graph cut framework for final segmentation. This approach broadens the neighborhood sampling range in local models, allowing the model to identify more valuable nonlocal samples while mitigating the impact of color similarity in global models. Experimental results across various datasets indicate a marked improvement in segmentation quality achieved by the proposed method.}, archive = {J_ACCESS}, author = {Guilin Yao and Heyuan Liu and Dongliang Zhang}, doi = {10.1109/ACCESS.2025.3564942}, journal = {IEEE Access}, pages = {76868-76878}, shortjournal = {IEEE Access}, title = {Image segmentation method combining nonlocal criteria with graph cut theory}, volume = {13}, year = {2025}, }
• (2025). Automated detection of road defects using LSTM and random forest. ACCESS, 13, 76857-76867. (www)

  @article{ , abstract = {Road networks represent an essential factor in economic prosperity and well-being. Assorted damages and defects on the road surface mainly impact the transportation efficiency of road networks and driving safety. In current traditions, it may take weeks or even months before competent government departments repair such road conditions due to their lack of timely awareness. This research proposes a framework for collecting data and analysis using supervised machine learning (ML) techniques, more specifically Long/Short-Term Memory (LSTM), Random Forest (RF), and networked sensor-enhanced vehicles for effective and efficient monitoring and assessment of road surface conditions. Data collected in four different scenarios and multiple vehicle data help to discover abnormal defects and verify automated detection. ML techniques were developed to identify bumps and potholes automatically, which are the two significant types of road defects. The experiment results of both LSTM and RF models can automatically identify bumps and pothole defects with an accuracy rate of up to 99.8% and a good detection effect. Cross-validation was set to validate the accuracy of the two decision-making strategies, LSTM and RF. Automation technology is expected to provide qualitative and quantitative information about road defect conditions, enabling timely maintenance to improve transportation efficiency and driving safety.}, archive = {J_ACCESS}, author = {Abdulrahman Alrajhi and Kaushik Roy and James Kribs and Sultan S. Almalki}, doi = {10.1109/ACCESS.2025.3564172}, journal = {IEEE Access}, pages = {76857-76867}, shortjournal = {IEEE Access}, title = {Automated detection of road defects using LSTM and random forest}, volume = {13}, year = {2025}, }
• (2025). Joint optimization of EV charging and renewable distributed energy with storage systems under uncertainty. ACCESS, 13, 76838-76856. (www)

  @article{ , abstract = {Electric Vehicles (EVs) are essential to achieving the 2030 United Nations Sustainable Development Goals by reducing emissions and improving air quality. The strategic placement of Electric Vehicle Charging Stations (EVCSs) in urban areas is critical to supporting the transition to clean transportation. However, as EV adoption increases, challenges such as rising power losses, voltage profile degradation, and voltage instability emerge within microgrids. These issues can be mitigated by integrating Energy Storage Systems (ESSs) to enhance efficiency. This study presents an integrated planning approach to optimize the allocation of EVCSs based on the spatial-temporal distribution of traffic flows. A stochastic model is also introduced to determine the optimal placement of the energy storage system, accounting for uncertainty factors such as fluctuating electrical loads and the intermittency of renewable energy sources. The energy storage system allocation model is formulated as a multi-objective optimization problem aimed at improving voltage profiles, minimizing power losses, and maximizing voltage stability. The mathematical models of EVCSs and ESSs, and an economic analysis of the microgrid is included, considering the costs associated with energy storage system integration. The proposed model’s effectiveness is validated through a case study on a benchmark transportation network, with results indicating its ability to mitigate the negative effects of EV integration on microgrids. Additionally, the study introduces a stochastic framework to simulate the inherent uncertainties in electrical loads and renewable energy sources.}, archive = {J_ACCESS}, author = {Talal Alharbi and Ahmed Abdalrahman and Mostafa H. Mostafa and Loay Alkhalifa}, doi = {10.1109/ACCESS.2025.3562531}, journal = {IEEE Access}, pages = {76838-76856}, shortjournal = {IEEE Access}, title = {Joint optimization of EV charging and renewable distributed energy with storage systems under uncertainty}, volume = {13}, year = {2025}, }
• (2025). A novel spectrum sensing method for multiple unknown signal sources using frequency domain energy detection and DBSCAN. ACCESS, 13, 76811-76837. (www)

  @article{ , abstract = {Spectrum sensing is a fundamental aspect of cognitive radio technology, tasked with detecting spectrum holes and unknown signal sources to improve spectrum management and resource utilization. While the energy detection (ED) method is widely used for spectrum sensing, it struggles with noise uncertainty (NU) and in low signal-to-noise ratio (SNR) environments. To address these challenges, we present a novel spectrum sensing approach called FDED-DBSCAN, which combines frequency domain energy detection (FDED), frequency domain noise estimation (FDNE), and the density-based spatial clustering of applications with noise (DBSCAN) algorithm; The DBSCAN algorithm clusters signals based on their time-frequency two-dimensional feature space. The proposed method was implemented using the GNU Radio toolkit for efficient acquisition and processing of in-phase and quadrature (I/Q) data from software-defined radio (SDR) hardware, and transformed this data into power spectral density (PSD) for analysis. The FDED algorithm detects signal occupancy in sub-channels, while FDNE calculates an adaptive threshold based on noise variance estimates. Multiple rounds of FDED detection are compiled into a two-dimensional signal detection matrix, which is subsequently analyzed by the DBSCAN algorithm to identify signal clusters. This integrated approach facilitates accurate detection of multiple unknown signals and their bandwidths over a given frequency range, even under low SNR conditions. Experimental validation across three scenarios—single-signal comparison with ED (Scenario I), Orthogonal Frequency Division Multiplexing (OFDM)/Narrowband signal detection (Scenario II), and real-world multi-signal acquisition including FM bands (Scenario III)—confirm FDED-DBSCAN’s superiority over conventional ED, particularly in unsupervised detection of unknown signals. This method demonstrates robust potential for practical applications in complex radio environments.}, archive = {J_ACCESS}, author = {Rui Gao and Guanghui Yan and Ruiting Niu and Wenwen Chang and Tianfeng Yan and Chunyang Tang}, doi = {10.1109/ACCESS.2025.3564955}, journal = {IEEE Access}, pages = {76811-76837}, shortjournal = {IEEE Access}, title = {A novel spectrum sensing method for multiple unknown signal sources using frequency domain energy detection and DBSCAN}, volume = {13}, year = {2025}, }
• (2025). HuberAIME: A robust approach to explainable AI in the presence of outliers. ACCESS, 13, 76796-76810. (www)

  @article{ , abstract = {With the increasing accuracy of machine-learning models in recent years, explainable artificial intelligence (XAI), which allows for an understanding of the internal decisions made by these models, has become essential. However, many explanation methods are vulnerable to outliers and noise, and the results may be distorted by extreme values. This study devised a new method named HuberAIME, which is a variant of approximate inverse model explanations (AIME) and is robust to the Huber loss. HuberAIME limits the impact of outliers by weighting with iterative reweighted least squares and prevents the feature importance estimation of AIME from being degraded by extreme data points. Comparative experiments were conducted using the Wine dataset, which has almost no outliers, the Adult dataset, which contains extreme values, and the Statlog (German Credit) dataset, which has moderate outliers, to demonstrate the effectiveness of the proposed method. SHapley Additive exPlanations, AIME, and HuberAIME were evaluated using six metrics (explanatory accuracy, sparsity, stability, computational efficiency, robustness, and completeness). HuberAIME was equivalent to AIME on the Wine dataset. However, it outperformed AIME on the Adult dataset, exhibiting high fidelity and stability. On the Germain Credit dataset, AIME itself showed a certain degree of robustness, and there was no significant difference between AIME and HuberAIME. Overall, HuberAIME is useful for data that include serious outliers and maintains the same explanatory performance as AIME in cases of few outliers. Thus, HuberAIME is expected to improve the reliability of actual operations as a robust XAI method.}, archive = {J_ACCESS}, author = {Takafumi Nakanishi}, doi = {10.1109/ACCESS.2025.3565279}, journal = {IEEE Access}, pages = {76796-76810}, shortjournal = {IEEE Access}, title = {HuberAIME: A robust approach to explainable AI in the presence of outliers}, volume = {13}, year = {2025}, }
• (2025). A novel approach to portfolio construction: An application of FinBERT sentiment analysis and credibilistic CVaR criterion. ACCESS, 13, 76775-76795. (www)

  @article{ , abstract = {Portfolio optimization continues to be a complex and challenging task within the fields of finance and management. Two critical factors that can improve the performance of traditional models are incorporating the effects of both financial and operational performance of companies and addressing the inherent uncertainty surrounding expected returns. This article addresses these two challenges. To evaluate the financial and operational efficiency of firms, we analyze their quarterly reports using the FinBERT model, incorporating their influence into the optimization framework through adjusted returns. To address the unpredictability linked to anticipated returns, we utilize fuzzy trapezoidal numbers in our methodology. Furthermore, conventional risk measurement systems, which rely based on probability-based assumptions and past data, often find it challenging to address the unique dynamics and inherent uncertainties of the market. In contrast, our suggested approach utilizes a credibilistic conditional value at risk (CCVaR) framework to evaluate portfolio risk. The approach additionally factors in transaction costs and incorporates practical constraints like cardinality and upper and lower bounds, maintaining the portfolio’s diversification, well-balanced, and reflective of practical scenarios. We apply the proposed approach to real-world data from DJIA stocks. Experimental findings highlight the approach’s efficacy in creating mixed portfolios that effectively create an equilibrium between risk and return. This research enhances the domain of investiture management by developing advanced portfolio optimization methods for stock market assets and offering a reliable approach for handling risk in today’s increasingly complex financial landscape.}, archive = {J_ACCESS}, author = {Esmaeil Taheripour and Seyed Jafar Sadjadi and Babak Amiri}, doi = {10.1109/ACCESS.2025.3564615}, journal = {IEEE Access}, pages = {76775-76795}, shortjournal = {IEEE Access}, title = {A novel approach to portfolio construction: An application of FinBERT sentiment analysis and credibilistic CVaR criterion}, volume = {13}, year = {2025}, }
• (2025). Simulation and machine learning in beyond visual range air combat: A survey. ACCESS, 13, 76755-76774. (www)

  @article{ , abstract = {Beyond Visual Range (BVR) air combat is an essential part of modern aerial warfare, relying on advanced radar, missile systems, and decision-support technologies. This survey provides a comprehensive overview of how simulation and Machine Learning (ML) tools have been used to analyze BVR combat, covering key methodologies, applications, and challenges. We examine how ML enables adaptive tactics to improve behavior recognition and threat assessment to enhance situational awareness. The paper also traces the historical evolution of BVR combat, outlining key engagement phases such as detection, missile launch, and post-engagement assessment. A key focus is on the role of simulation environments in modeling realistic combat scenarios, supporting pilot training, and validating AI-driven decision-making strategies. We analyze state-of-the-art simulation tools, comparing their capabilities and limitations for studying multi-agent coordination and real-time adaptability. This survey’s main contributions include descriptions of ML applications in BVR air combat, evaluations of simulation tools, identifications of research gaps, and insights into future research directions. This work provides an overview of how traditional simulation approaches merge with artificial intelligence to enable advanced, effective human and autonomous decision-making systems in dynamic and contested environments.}, archive = {J_ACCESS}, author = {Andre N. Costa and Joao P. A. Dantas and Edvards Scukins and Felipe L. L. Medeiros and Petter Ögren}, doi = {10.1109/ACCESS.2025.3563811}, journal = {IEEE Access}, pages = {76755-76774}, shortjournal = {IEEE Access}, title = {Simulation and machine learning in beyond visual range air combat: A survey}, volume = {13}, year = {2025}, }
• (2025). Class e inverter single capacitor wireless power transmission system based on stable ground. ACCESS, 13, 76747-76754. (www)

  @article{ , abstract = {Single-capacitor coupled wireless energy transmission has been widely noticed due to its advantages of simple coupling mechanism and absence of cross-coupling capacitance. However, because of its lack of traditional electrical circuit, the coupling mechanism electrical energy transmission mechanism and the establishment of equivalent model are not completely unified by theory and experiment at present. And the efficiency of the existing single capacitor coupled wireless energy transmission systems are relatively low. In this paper, for the single-capacitor coupling mechanism, based on the self-capacitance theory, an equivalent model of the coupling mechanism is proposed for the case where both the transmitter and receiver have a stable ground, and a single-capacitor wireless energy transmission system based on a class E inverter is designed. The resonant circuit is selected as a CLC-LC-LC high-order resonant network. The paper firstly describes the system topology design and the equivalent capacitance of the coupling mechanism, and then the whole circuit topology is equivalent to a classical Class E inverter for parameter design. The feasibility of the selected parameters is verified by simulation. Finally, an experimental prototype is built based on the parameter design, and the experiment shows that the system has high voltage gain, with an efficiency up to 77%; it can still provide high output power with half of the lateral offset of the pole plate; and it possesses high load range, with an efficiency of more than 70% in the load range of $160\Omega $ - $220\Omega $ .}, archive = {J_ACCESS}, author = {Xin Geng and Jiapeng Wang and Jixing Wang and Junjie Ma and Chongqin Wang}, doi = {10.1109/ACCESS.2025.3564844}, journal = {IEEE Access}, pages = {76747-76754}, shortjournal = {IEEE Access}, title = {Class e inverter single capacitor wireless power transmission system based on stable ground}, volume = {13}, year = {2025}, }
• (2025). Research on the application of deep learning algorithm in the damage detection of steel structures. ACCESS, 13, 76732-76746. (www)

  @article{ , abstract = {This study introduces a novel Convolutional Long Short-Term Memory (ConvLSTM) network for steel structure damage detection, aimed at enhancing the accuracy and reliability of structural health monitoring systems. The model combines three key innovations: multi-scale convolutional layers for spatial feature extraction, bidirectional LSTM units for temporal dependency modeling, and an attention mechanism for adaptive sensor data fusion. Evaluated on a dataset from a 21-story reinforced concrete frame under seismic scenarios, the model achieves 97.8% damage detection accuracy, outperforming traditional methods and other deep learning architectures. The model’s efficacy was further validated through real-world applications on diverse structures, including a 30-story office building, a cable-stayed bridge, and an offshore oil platform. Transfer learning strategies were successfully implemented to adapt the model to different structural contexts, addressing the challenge of limited labeled data. The proposed approach exhibited remarkable robustness and long-term stability across various environmental conditions and loading scenarios, advancing structural health monitoring for proactive maintenance and safety assessments. The findings pave the way for the integration of deep learning techniques in next-generation structural integrity management systems.}, archive = {J_ACCESS}, author = {Qingyun Ge and Caimei Li and Fulian Yang}, doi = {10.1109/ACCESS.2025.3560640}, journal = {IEEE Access}, pages = {76732-76746}, shortjournal = {IEEE Access}, title = {Research on the application of deep learning algorithm in the damage detection of steel structures}, volume = {13}, year = {2025}, }
• (2025). Low SAR multi-coupling feed antenna and multi-device MIMO system design: Suitable for multi-band wireless communications. ACCESS, 13, 76715-76731. (www)

  @article{ , abstract = {This article introduces the design of a multi-coupling feed antenna (MCFA) appropriate for 4G LTE, 5G NR (sub-6 GHz), and WLAN frequency bands. Most antenna designs used in different consumer electronic products only include specific 5G NR frequency bands, while the MCFA antenna proposed in this article covers applications in multiple frequency bands and will meet the needs of more communication devices. MCFA is designed on an FR-4 substrate and uses the coupled-feed structure to achieve diverse operating frequencies. This article also uses the antenna to construct a multi-coupling feed antenna MIMO system (MCFA-MIMO system), which is suitable for electronic devices such as laptops, smartphones, and Wi-Fi base stations. The antenna uses a simple structural design and does not require any RLC components to achieve multiple operating frequency bands, isolation below -11dB, a maximum gain of 4.7, and a maximum efficiency of 78%. The measurement and simulation results of the proposed antennas prove that each multi-band antenna performs consistently in MIMO systems and has high gain and high isolation performance. The simulation values of the specific absorption rate (SAR) of MCFA-MIMO system comply with international specifications and will not harm human health. This makes this antenna design a candidate for modern portable electronic devices.}, archive = {J_ACCESS}, author = {Ming-An Chung and Zhi-Xuan Zhang and Chia-Chun Hsu and Chia-Wei Lin}, doi = {10.1109/ACCESS.2025.3565750}, journal = {IEEE Access}, pages = {76715-76731}, shortjournal = {IEEE Access}, title = {Low SAR multi-coupling feed antenna and multi-device MIMO system design: Suitable for multi-band wireless communications}, volume = {13}, year = {2025}, }
• (2025). Graph-based immune checkpoint inhibitor response prediction model for metastatic renal cell carcinoma patients. ACCESS, 13, 76703-76714. (www)

  @article{ , abstract = {Immune checkpoint inhibitors (ICIs) have become essential in managing metastatic Renal Cell Carcinoma (mRCC), although selecting the most suitable patients for each specific treatment remains an unmet medical need. In this work, we aimed to create and evaluate a treatment response prediction machine learning model based on tumor gene expression data of patients with mRCC. More specifically, we designed and developed a graph-based prediction model, including information about potential protein-protein interactions (PPI) between the considered expressed genes. For this work, we extracted the expression data collected in two different ICIs treatments (nivolumab and avelumab+axitinib) and organized them into two different graph datasets (considering two initial PPI networks: BioGrid and kidney-specific PPT-Ohmnet). Then, we compressed these four different graph datasets with a graph embedding technique (Graph2Vec) and evaluated the embeddings as input for a supervised classification model (Random Forest) to predict the binarized progression-free survival (PFS). Results showed that the Nivolumab and PPT-Ohmnet graph dataset obtained the highest classification performance, with an AUC of 74%. These results suggested that our pipeline could accurately model the response to the drugs, especially those modeling a short-term response (in this case, nivolumab), and that this graph-based approach benefits from tissue-specific networks (kidney-specific PPT-Ohmnet PPI). This work is presented as a data-driven solution towards improving drug selection and identifying different responses to immunotherapy treatments, including poor responses that should be offered alternative therapeutic options.}, archive = {J_ACCESS}, author = {Sandra Alonso and Laura Hernández-Lorenzo and Ignacio Durán and Cristina Rodriguez-Antona and Jesús García-Donás and Jose L. Ayala}, doi = {10.1109/ACCESS.2025.3565132}, journal = {IEEE Access}, pages = {76703-76714}, shortjournal = {IEEE Access}, title = {Graph-based immune checkpoint inhibitor response prediction model for metastatic renal cell carcinoma patients}, volume = {13}, year = {2025}, }
• (2025). A review on research and application of AI-based image analysis in the field of computer vision. ACCESS, 13, 76684-76702. (www)

  @article{ , abstract = {The rapid development of artificial intelligence has significantly advanced the field of computer vision, particularly in image analysis and understanding. This paper provides a comprehensive review of the current state of the field, key technologies, and their applications across various real-world scenarios. It delves into the value of image analysis in critical areas such as personalized art, healthcare and medical image analysis, security monitoring and recognition technology, autonomous driving and traffic management, as well as industrial automation and quality control. This paper not only highlights the challenges and limitations, including dataset constraints, algorithm generalization, real-time computational costs, and privacy and ethical concerns, but also offers a forward-looking analysis of development trends such as interdisciplinary integration, weakly supervised and unsupervised learning, algorithm optimization and hardware advancements, and the protection of personal privacy and information. These insights provide a profound perspective on the future trajectory of computer vision and AI-driven image analysis.}, archive = {J_ACCESS}, author = {Pan Wu and Xiaoqiang He and Wenhao Dai and Jingwei Zhou and Yutong Shang and Yourong Fan and Tao Hu}, doi = {10.1109/ACCESS.2025.3565300}, journal = {IEEE Access}, pages = {76684-76702}, shortjournal = {IEEE Access}, title = {A review on research and application of AI-based image analysis in the field of computer vision}, volume = {13}, year = {2025}, }
• (2025). Research on target detection algorithm for solder joint defects based on the improved YOLOv8. ACCESS, 13, 76676-76683. (www)

  @article{ , abstract = {Aiming at the problem of low accuracy in solder joint defect detection caused by the complex background and difficult to extract defect features of circuit boards using through-hole technology (THT), an improved YOLOv8 solder joint defect target detection algorithm was proposed. Firstly, the FasterNet module was used to improve the C2f module of the baseline model, and the Efficient Multi-Scale Attention (EMA) attention mechanism was integrated to reduce model parameters and make the algorithm pay more attention to the solder joint targets. Secondly, the neck network of the baseline model was reconstructed and the Slide Loss function was introduced. The fusion of bidirectional cross scale connections and weighted features improved the feature extraction ability of the model and the detection accuracy of solder joint defects. Finally, the improved algorithm was experimentally validated on the target detection dataset of solder joint defect, and its generalization ability was also verified. Results indicate that the algorithm not only has a good detection performance on solder joint defects, but also has strong generalization ability and robustness.}, archive = {J_ACCESS}, author = {Chengkai Zhang and Xiaocui Feng and Pujun Mao and Fuyan Lv}, doi = {10.1109/ACCESS.2025.3564906}, journal = {IEEE Access}, pages = {76676-76683}, shortjournal = {IEEE Access}, title = {Research on target detection algorithm for solder joint defects based on the improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Software reliability estimation of component-based systems. ACCESS, 13, 76665-76675. (www)

  @article{ , abstract = {Component-based software systems (CBSS) have gained significant traction among researchers and practitioners because of their numerous advantages. By assembling software from reusable components, CBSS can reduce time-to-market and maintenance costs while enhancing productivity, quality, and reusability. This modular approach allows developers to focus on specific functionalities, thereby leading to more efficient and manageable codebases. However, estimating the reliability of a CBSS poses challenges because the factors influencing reliability are complex, and often only their effects are observable. The interactions between the individual components, their integration into the overall architecture, and varying operational profiles contribute to this complexity. Traditional reliability estimation methods may not adequately address these intricacies, thus necessitating more sophisticated approaches. To address this, a new approach has been developed that considers individual component reliabilities, overall software architecture, and real-world usage patterns to estimate the total reliability of CBSS. This approach integrates component-level data with system-level interactions to provide a more accurate reliability assessment. By accounting for the unique behaviors and dependencies of each component within the system architecture, this approach offers a holistic view of software reliability. This approach was validated through a large-scale case study that demonstrated its high accuracy, applicability, and practical utility. The case study involved analyzing a complex CBSS in a real-world environment, where the proposed reliability estimation approach was applied and its predictions were compared with the actual system performance. The results confirmed the effectiveness of the approach in accurately estimating the system reliability, highlighting its potential for broader application in CBSS development and maintenance.}, archive = {J_ACCESS}, author = {Ibrahim Ayman Tagen and Khaled T. Wassef and Ramadam Moawad and Sabah Sayed Mohammad}, doi = {10.1109/ACCESS.2025.3564577}, journal = {IEEE Access}, pages = {76665-76675}, shortjournal = {IEEE Access}, title = {Software reliability estimation of component-based systems}, volume = {13}, year = {2025}, }
• (2025). Phase-shift control for enhancing misalignment tolerance in UCA-based OAM-MIMO systems. ACCESS, 13, 76654-76664. (www)

  @article{ , abstract = {This paper presents a novel approach for achieving high-capacity transmission utilizing sub-terahertz (THz) radio orbital angular momentum (OAM) multiplexing, specifically in scenarios where the radio link is susceptible to antenna misalignment. We focus on a radio OAM multiplexing system incorporating uniform circular antenna arrays (UCAs) at both the transmitter and receiver. While it is established that UCA-based OAM multiplexing can significantly enhance transmission capacity, its performance is notably sensitive to antenna misalignment, leading to capacity degradation. To mitigate this issue, we propose a novel closed-loop OAM system featuring adaptive phase-shift control for transmission signals. We also introduce efficient methods for optimizing the phase shift. An experimental demonstration of the radio OAM system employing the proposed phase-shift control method is provided, showcasing its efficacy in compensating for the effects of misalignment.}, archive = {J_ACCESS}, author = {Norifumi Kamiya and Masahiro Kawai and Eisaku Sasaki}, doi = {10.1109/ACCESS.2025.3565738}, journal = {IEEE Access}, pages = {76654-76664}, shortjournal = {IEEE Access}, title = {Phase-shift control for enhancing misalignment tolerance in UCA-based OAM-MIMO systems}, volume = {13}, year = {2025}, }
• (2025). Safe model predictive control of a non-holonomic mobile manipulator under multiple constraints using control barrier functions. ACCESS, 13, 76638-76653. (www)

  @article{ , abstract = {This paper introduces a novel control strategy to ensure safety in the navigation of a mobile manipulator comprising a fixed-base manipulator mounted on a mobile platform. The approach initially addresses the trajectory tracking problem of a non-holonomic mobile manipulator (NH-MM) by employing decoupling dynamic control through model predictive control-optimizable control barrier function (MPC-OCBF). This allows for independent control of the end-effector and mobile platform trajectories, obstacle avoidance, and simultaneously adjusting the joint and control input limitations. The objective is to leverage the system’s redundancy to enable the mobile platform to effectively navigate feasible obstacle avoidance scenarios without affecting the primary task performance of the end-effector. Additionally, the method aims to perform obstacle avoidance when the redundancy of the manipulator is insufficient, addressing non-feasible obstacle avoidance scenarios. Through this approach, the mobile manipulator effectively avoids obstacles, allowing the end-effector to autonomously carry out its intended task. The effectiveness of the proposed method is validated through simulations and comparison with existing approaches. Additionally, a quantitative analysis is provided to evaluate and compare the performance of the controllers.}, archive = {J_ACCESS}, author = {Zahra Kashi and Meysam Yadegar and Nader Meskin and Christos G. Cassandras}, doi = {10.1109/ACCESS.2025.3565550}, journal = {IEEE Access}, pages = {76638-76653}, shortjournal = {IEEE Access}, title = {Safe model predictive control of a non-holonomic mobile manipulator under multiple constraints using control barrier functions}, volume = {13}, year = {2025}, }
• (2025). A head-driven algorithm for estimating upper and lower body motion in virtual reality environments. ACCESS, 13, 76627-76637. (www)

  @article{ , abstract = {This study proposes a novel algorithm for effectively estimating partial upper and lower body movements in a Virtual Reality (VR) environment using only head movements and synchronized head rotation axes, without the need for additional hardware. The proposed algorithm calculates the angle between the avatar’s pelvis and the head rotation axis to naturally reproduce the user’s upper body inclination and lower body bending. Notably, it offers the advantage of efficiently utilizing limited computational resources in multiplayer environments. The experiment was conducted in two stages. In the first stage, the objective performance of the algorithm was evaluated by comparing it with ground truth inclination data. In the second stage, participants performed two types of games (e.g., a dodgeball game and a limbo game) to assess their sense of immersion and embodiment. The objective results demonstrated that the proposed algorithm accurately and naturally expressed upper and lower body movements. Additionally, post-experiment surveys indicated that participants reported a high level of immersion and a natural interaction experience. This study presents a cost-effective solution for tracking upper and lower body movements in VR environments without requiring additional hardware, significantly enhancing the immersion of the VR experience. Future research will explore the expansion of the method to include upper body rotation estimation and full-body motion tracking, incorporating user locomotion.}, archive = {J_ACCESS}, author = {Jemin Lee and Jeonghyeon Kim and Youngwon Kim}, doi = {10.1109/ACCESS.2025.3565232}, journal = {IEEE Access}, pages = {76627-76637}, shortjournal = {IEEE Access}, title = {A head-driven algorithm for estimating upper and lower body motion in virtual reality environments}, volume = {13}, year = {2025}, }
• (2025). Design and optimization of field mill sensors for accurate measurement of HVdc ground electric fields based on amplifier parallel optimization and wavelet decomposition techniques. ACCESS, 13, 76620-76626. (www)

  @article{ , abstract = {With the rapid development of high-voltage direct current (HVdc) transmission technology, the demand for monitoring the DC total electric field at ground level has increased significantly. The methods of the traditional electric field measurement are susceptible to noise interference, which affect the accuracy of measurement. This paper investigates the sources of noise interference in electric field measurements and proposes a signal processing method based on amplifier parallel optimisation of the signal conditioning circuit and Morlet wavelet decomposition. By improving the signal-to-noise ratio during the measurement of the device, the anti-interference capability and measurement accuracy of the field mill sensor are significantly enhanced. The experimental results show that the proposed signal processing methods of amplifier parallel optimisation signal conditioning circuit and Morlet wavelet decomposition can effectively eliminate the interfering signals during the measurement process, improve the signal-to-noise ratio of the measurement circuit, and retain the details and response speed of the signals, which improves the accuracy of the measurement of the ground electric field of the ultra-high-voltage direct current transmission.}, archive = {J_ACCESS}, author = {Yuehua Huang and Yun Lu and Zhuo Chen and Ziyang Yu and Zhengyu Xiang}, doi = {10.1109/ACCESS.2025.3565001}, journal = {IEEE Access}, pages = {76620-76626}, shortjournal = {IEEE Access}, title = {Design and optimization of field mill sensors for accurate measurement of HVdc ground electric fields based on amplifier parallel optimization and wavelet decomposition techniques}, volume = {13}, year = {2025}, }
• (2025). YOLOv9-AAG: Distinguishing birds and drones in infrared and visible light scenarios. ACCESS, 13, 76609-76619. (www)

  @article{ , abstract = {The ability to distinguish between birds and drones is essential for applications in wildlife preservation, aviation security, and defense operations. Reliable identification not only reduces the risk of bird strikes and monitors potential drone threats but also fosters the development of a secure and intelligent ecological environment. However, achieving high precision in recognition under complex and dynamic conditions remains a significant hurdle. To overcome this, we present an advanced detection framework, YOLOv9-AKConv-AFF-GSConv(YOLOv9-AAG), which substantially improves the accuracy of distinguishing birds from drones, even in varied poses, and enhances generalization capabilities in both infrared and visible light scenarios. The framework incorporates several key advancements. First, the GSConv is embedded into the head of the RepNSCPELAN4-AKConv module, enabling efficient extraction of edge and contour information without introducing additional computational cost. Second, the inclusion of the Attentional Feature Fusion (AFF) mechanism within AKConv amplifies the representation of shape features, particularly under challenging environments and multimodal scenarios. Lastly, the optimized AKConv module is effectively integrated with the original RepNSCPELAN4 model, facilitating more accurate and robust feature extraction for small targets, including birds and drones. Experimental evaluations underscore the effectiveness of the proposed framework. On an optical dataset containing multiple bird and drone targets, YOLOv9-AAG achieved recognition accuracy rates of 78.2%, while on an infrared dataset, it reached 86.1%. These results reflect improvements of 7.4% and 6.0%, respectively, over the baseline YOLOv9 model. Additionally, the F1 scores increased by 5.9% and 2.8%. On the publicly available CUB-200-2011 dataset and a combined dataset of visible and infrared images, the model attained mean Average Precision (mAP) scores of 81.9% and 81.6%, respectively, outperforming cutting-edge approaches, including YOLOv9, YOLOv10, and YOLOv11. Furthermore, It was also tested on two videos of drones and birds, the experimental resultsdemonstrated an average detection accuracy of approximately 72% for small bird targets and 80% for drones, highlighting the model’s practical capability to effectively distinguish between these two classes.}, archive = {J_ACCESS}, author = {Peichun Suo and Jincan Zhu and Qingyang Zhou and Weili Kou and Xiuyan Wang and Wen Suo}, doi = {10.1109/ACCESS.2025.3560730}, journal = {IEEE Access}, pages = {76609-76619}, shortjournal = {IEEE Access}, title = {YOLOv9-AAG: Distinguishing birds and drones in infrared and visible light scenarios}, volume = {13}, year = {2025}, }
• (2025). Issues, challenges, and solution options for on-grid multi-microgrid game theory: A systematic review. ACCESS, 13, 76574-76608. (www)

  @article{ , abstract = {Many studies have been conducted on the implementation of game theory approaches in optimizing energy trading and scheduling in multi-microgrid (MMG) systems, both in cooperative and non-cooperative games. This is because game theory is a rich field of study that can illustrate the self-interest nature of each player, in this case, each agent in the MMG system. However, there are still various issues in its implementation, both technical and non-technical. This article will focus on a systematic review of various relevant and credible literature papers on the game theory-based on-grid MMG topic using the Kitchenham method. The on-grid MMG system differs from the off-grid MMG system in that each MMG is connected with the conventional grid. This connection provides more flexibility in operation and various features, including multi-level interactions beyond peer-to-peer, asymmetric agents, the presence of distribution network operator, support and backup power from the grid, and an energy market pool. This literature review is done by collecting, analyzing, and understanding the issues and challenges in the implementation, the types of games and algorithms, techniques, and solution approaches used, as well as determining how far each game and existing solution can solve these problems. From the systematic review result, in general there are two types of games that can be played between agents: cooperative and non-cooperative, each with its own pros and cons. The technical issues and solutions in these games have been summarized with particular concern related to power systems, renewable energy, energy storage, agent interests, uncertainties, ICT, privacy and security, computation solutions, mechanisms, and cost and revenue. Research and future works recommendation related with on-grid MMG game theory subject is also described.}, archive = {J_ACCESS}, author = {Dimas Jalaluddin Ahmad and Nanang Hariyanto and Umar Khayam}, doi = {10.1109/ACCESS.2025.3562547}, journal = {IEEE Access}, pages = {76574-76608}, shortjournal = {IEEE Access}, title = {Issues, challenges, and solution options for on-grid multi-microgrid game theory: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Modal analysis of stator core assembly of electric traction motors with self-bonding core and bar-type windings. ACCESS, 13, 76561-76573. (www)

  @article{ , abstract = {In electric vehicles (EVs), the noise and vibration of the traction motors are key indicators of vehicle quality. The dynamic characteristics of the stator core assembly of the traction motor are crucial in this regard. This study introduces a methodology to predict the dynamic characteristics of self-bonding stator core assemblies with bar-type windings at the design stage, without experimental tuning. The dynamic characteristics of the stator core assemblies were analyzed after using the homogenization method, which considers lateral contractions, to derive the equivalent material properties of the individual components. The stator core assemblies were modeled following the sequence of steps in the actual manufacturing process. Additionally, the study considers the asymmetric mass and stiffness distribution caused by bar-type windings and their boundary interactions with the stator core, and proposes a modeling approach that enables these effects to be reflected in dynamic predictions. Comparisons with experimental modal analysis showed that the proposed method predicted the natural frequencies with deviations of less than 2.8% for the stator core and 3.5% for the stator core assembly. The average MAC values were 0.965 and 0.910, respectively, confirming the accuracy of mode shape prediction. This demonstrates the proposed method can be effective in improving the dynamic performance of the traction motors used in EVs.}, archive = {J_ACCESS}, author = {Seonbin Lim and Onemook Kim and Yeonjong Kim and No-Cheol Park}, doi = {10.1109/ACCESS.2025.3564571}, journal = {IEEE Access}, pages = {76561-76573}, shortjournal = {IEEE Access}, title = {Modal analysis of stator core assembly of electric traction motors with self-bonding core and bar-type windings}, volume = {13}, year = {2025}, }
• (2025). Time delay identification method of wind turbine drivetrain test bench based on instability characteristics observation. ACCESS, 13, 76548-76560. (www)

  @article{ , abstract = {There is a huge difference in rotational inertia between the wind turbine drivetrain test bench (WTDTB) and the actual wind turbine, which needs to be accurately compensated. As a closed-loop control system with time delay, WTDTB working in the inertia simulation mode is prone to instability. The existing researches solve this problem by the time delay stability control algorithm based on high-order filter, which is on the premise that the filter order and the time delay order of the inertia compensation loop accurately match. However, the torque command response method currently used to measure time delay is not effective in practical applications. In order to accurately obtain the time delay, this paper draws on the idea of soft measurement based on operation parameters, finds and proves that there is a definite quantitative relationship between the compensation torque oscillation period and the time delay. On this basis, a time delay identification method based on instability characteristics observation is proposed. This method causes the controlled oscillation of the test bench by reasonably designing the oscillation experiment, extracts the oscillation period, and determines the time delay accordingly. The experiment results based on 15kW WTDTB verify the feasibility and effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Lianjun Zhou and Zaiyu Chen and Minghui Yin and Senhua Luo}, doi = {10.1109/ACCESS.2025.3565968}, journal = {IEEE Access}, pages = {76548-76560}, shortjournal = {IEEE Access}, title = {Time delay identification method of wind turbine drivetrain test bench based on instability characteristics observation}, volume = {13}, year = {2025}, }
• (2025). Exploring the pitfalls of black boxes in media forensics: A case study in source camera identification. ACCESS, 13, 76528-76547. (www)

  @article{ , abstract = {The increasing reliance on deep models as black-box solutions raises critical concerns, particularly in media forensics, where explainability and robustness are essential. These models often fail to address biases in experimental design, potentially yielding misleading conclusions. To combat these risks, we advocate for rigorous hypothesis-driven methodologies that define and test competing explanatory hypotheses to reduce biases and improve reliability. We present a case study on device identification, focusing on a method proposed by Manisha et al., which claims exceptional performance using a hybrid ResNet101-SVM classifier. Our investigation reveals that this performance is likely attributable to dataset biases rather than true device-specific fingerprints. To systematically decouple content- and device-specific features, we introduce a novel “Sybil” approach, which partitions datasets based on image content. Our Sybil experiments strongly suggest that the classifier exploits content-specific biases, rather than intrinsic device fingerprints, to achieve high accuracy. We demonstrate that even datasets like FloreView, which are carefully crafted to limit biases due to the acquisition, are not immune to these biases. This analysis highlights the dangers of black-box methodologies and highlights the necessity of hypothesis-driven experimental designs in media forensics.}, archive = {J_ACCESS}, author = {Aya A. Mostafa and Fernando Pérez-González and Miguel Masciopinto}, doi = {10.1109/ACCESS.2025.3563784}, journal = {IEEE Access}, pages = {76528-76547}, shortjournal = {IEEE Access}, title = {Exploring the pitfalls of black boxes in media forensics: A case study in source camera identification}, volume = {13}, year = {2025}, }
• (2025). An adaptive, model-robust, PV maximum power point tracking algorithm. ACCESS, 13, 76516-76527. (www)

  @article{ , abstract = {Maximizing the output power of photovoltaic (PV) systems is crucial in all PV applications to improve energy efficiency and system performance. Maximum power point tracking (MPPT) is utilized in PV systems to track the voltage that maximizes their output power under varying conditions. In this paper, an enhanced MPPT estimation algorithm is proposed based on the H-adaptive extended Kalman filter (EKF). The proposed method adapts to possible changes in the system’s noise statistics due to variations in irradiance, operating temperature, and system’s aging. The approach is compared to common MPPT estimation techniques; namely the Perturb and Observe (P&O) method and the EKF. The method was validated using experimental data collected from a PV array, to demonstrate practical applicability. Results showcase the adaptability of the proposed approach to variations in the process noise covariance, measurement noise covariance, and the dynamic system scaling factor parameter. These attributes are essential for a sustained extraction of the maximum power from the PV system.}, archive = {J_ACCESS}, author = {Mohammad Rashwan and Karim Diab and Ahmed Elsergany and Mamoun F. Abdel-Hafez and Ala A. Hussein}, doi = {10.1109/ACCESS.2025.3564657}, journal = {IEEE Access}, pages = {76516-76527}, shortjournal = {IEEE Access}, title = {An adaptive, model-robust, PV maximum power point tracking algorithm}, volume = {13}, year = {2025}, }
• (2025). Robust road surface classification using time series augmented intelligent tire sensor data and 1-D CNN. ACCESS, 13, 76508-76515. (www)

  @article{ , abstract = {Tire-road friction coefficient information is an essential factor in the driving stability and safety of a vehicle. In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. However, since tire vibration characteristics vary depending on conditions such as tire pressure, load, and driving status, it is still difficult to develop a road surface classification algorithm that is robust to various situations. To overcome this limitation, this paper proposes a road surface classification algorithm using a one-dimensional convolutional neural network (CNN) based on acceleration signals obtained through an intelligent tire sensor attached inside the tire. Moreover, a time series data augmentation method is applied to ensure that the learning network has the robustness to perform well under different tires and driving conditions than that in the training dataset. A road surface classification algorithm is trained using a dataset of accelerations measured on dry asphalt, wet asphalt, and basalt tile roads, and the performance of the trained algorithm is validated through test scenarios considering different tire conditions and vehicle types. Furthermore, the performance of different CNN architectures is compared and the algorithm with the best performance is suggested. The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.}, archive = {J_ACCESS}, author = {Seokchan Kim and Yeong-Jae Kim and Dongwook Lee and Hanmin Lee}, doi = {10.1109/ACCESS.2025.3565656}, journal = {IEEE Access}, pages = {76508-76515}, shortjournal = {IEEE Access}, title = {Robust road surface classification using time series augmented intelligent tire sensor data and 1-D CNN}, volume = {13}, year = {2025}, }
• (2025). Enhancing genomic data representation through BERT-LSTM hybrid architecture. ACCESS, 13, 76497-76507. (www)

  @article{ , abstract = {This study proposes a novel approach for effective genetic sequence representation, focusing on the challenges of compressing and analyzing complex genomic data. We introduce a hybrid architecture that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to generate comprehensive and compact gene embeddings. Our method processes genetic sequence data through k-mer tokenization and employs BERT to capture complex patterns, followed by LSTM to preserve essential sequential information while creating fixed-size representations. Using data from 623 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we analyzed genetic sequences across 10 genes to evaluate our approach. The effectiveness of our method is demonstrated through both visualization and quantitative evaluation. The t-distributed stochastic neighbor embedding (t-SNE) visualization revealed improved clustering of gene embeddings compared to traditional approaches, while our model achieved 82% accuracy in gene classification tasks. Our findings indicate that the combination of BERT and LSTM effectively captures both local and global genetic patterns while creating meaningful compressed representations, providing a promising framework for genetic sequence analysis.}, archive = {J_ACCESS}, author = {Kyeong Ho Kim and Minji Kim and Sohui Kim and Jong-Min Lee}, doi = {10.1109/ACCESS.2025.3560282}, journal = {IEEE Access}, pages = {76497-76507}, shortjournal = {IEEE Access}, title = {Enhancing genomic data representation through BERT-LSTM hybrid architecture}, volume = {13}, year = {2025}, }
• (2025). An AI-ISM methodology for structural modeling of sustainable supply chain management drivers. ACCESS, 13, 76481-76496. (www)

  @article{ , abstract = {Achieving Sustainable Supply Chain Management (SSCM) has become a strategic priority for organizations integrating environmental, social, and economic objectives. Interpretive Structural Modeling (ISM) is a powerful methodology for analyzing interdependencies among SSCM drivers by structuring them into a multilevel hierarchical framework. However, traditional ISM relies on subjective expert judgments, making it prone to inconsistencies and biases. To address these challenges, this study proposes a novel Hybrid AI-ISM methodology, integrating Genetic Algorithms (GA) to optimize the reachability matrix by minimizing transitivity violations and reciprocal inconsistencies. The research adopts a structured, expert-driven approach, engaging 25 domain specialists from executive, production, and supply chain management sectors within the steel industry. By leveraging AI-driven optimization, the proposed framework enhances accuracy, objectivity, and scalability, refining hierarchical structuring for effective SSCM decision-making. This study contributes to the SSCM literature by introducing a bias-resistant, computationally enhanced ISM framework, facilitating granular decision-making across organizational levels. Findings reveal notable differences in expert perspectives across professional roles. Executives emphasize strategic sustainability initiatives, particularly the Adoption of Renewable Energy, while production managers prioritize operational aspects, including Waste Minimization and Supply Chain Flexibility. Conversely, supply chain managers focus on stakeholder engagement and risk mitigation, highlighting Community Engagement and Supply Chain Disruption Management as critical drivers. The study provides actionable insights for policymakers, industry leaders, and supply chain professionals seeking to drive sustainable transformations in global supply chains.}, archive = {J_ACCESS}, author = {Reza Roshanpour and Mohammadreza Parsanejad and Mir Saman Pishvaee}, doi = {10.1109/ACCESS.2025.3559838}, journal = {IEEE Access}, pages = {76481-76496}, shortjournal = {IEEE Access}, title = {An AI-ISM methodology for structural modeling of sustainable supply chain management drivers}, volume = {13}, year = {2025}, }
• (2025). Music emotion classification based on heterogeneous graph neural networks. ACCESS, 13, 76473-76480. (www)

  @article{ , abstract = {The classification of musical emotions is crucial for the indexing, structuring, searching, and recommending of tracks and albums across various music platforms. Consequently, the automated categorization of musical emotions has become a vital element in nearly all music applications. Recent studies have mainly concentrated on utilizing textual, audio, or multimodal data for genre classification, frequently neglecting the impact of singers, composers, and listener preferences. In practice, composers possess unique compositional styles, listeners have varied musical preferences, and singers focus on particular music genres. These different viewpoints offer significant insights into the classification of musical emotions, greatly enhancing the effectiveness of classification performance. In this paper, we introduce a novel heterogeneous graph neural network (HGN) that models the relationships of music emotion preferences among singers, composers, and listeners, in order to generate accurate node feature representations for downstream tasks. The experimental results show that our model significantly outperforms current state-of-the-art (SOTA) methods on two datasets for music emotion classification.}, archive = {J_ACCESS}, author = {Jing-Ying Guo and Peng Wang}, doi = {10.1109/ACCESS.2025.3562532}, journal = {IEEE Access}, pages = {76473-76480}, shortjournal = {IEEE Access}, title = {Music emotion classification based on heterogeneous graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Achieving business scalability with composable enterprise architecture. ACCESS, 13, 76464-76472. (www)

  @article{ , abstract = {This study introduces Composable Enterprise Architecture (CEA) as a strategic approach to address the inherent rigidity and operational constraints of traditional enterprise systems, which often impede adaptability to fast-changing market conditions. By implementing a modular, cloud-native framework centered on Packaged Business Capabilities (PBCs), CEA enhances system reusability, autonomy, and scalability. Based on semi-structured interviews with industry stakeholders and extensive operational data analysis, the study reports significant benefits achieved through CEA adoption, including a 55% reduction in system downtime, an increase in deployment frequency from quarterly to monthly, and a 60% decrease in time-to-market for new features. These structural improvements streamline updates, reduce operational risks, and empower organizations to respond dynamically to market changes. As such, CEA offers a valuable architectural strategy to drive digital transformation and enhance agility across varied industries.}, archive = {J_ACCESS}, author = {Wei-Ji Wang}, doi = {10.1109/ACCESS.2025.3564710}, journal = {IEEE Access}, pages = {76464-76472}, shortjournal = {IEEE Access}, title = {Achieving business scalability with composable enterprise architecture}, volume = {13}, year = {2025}, }
• (2025). Compact tunable filter based on dual-mode resonators with 1.5-octave tuning range wide stopband and single bias control. ACCESS, 13, 76454-76463. (www)

  @article{ , abstract = {This paper presents a compact four-pole tunable bandpass filter (BPF) with a wide center-frequency tuning range and a wide stopband using a single control voltage. The designed filter is based on an asymmetric T-shaped dual-mode resonator (DMR) comprising three varactor-loaded stubs (VLSs) that function as series resonators. Each VLS has its own distinct resonant frequency, and the two pole frequencies of the T-shaped resonator are located between these three resonant frequencies. The proposed filter exhibits a wide tuning range from 300 to 960 MHz (over 3:1 tuning range), with an upper stopband extending to $24.3f_{m}$ , where $f_{m}$ is the lowest center frequency within the tuning range. The core size of the filter is only $0.067\lambda _{g} \times 0.067\lambda _{g}$ , where $\lambda _{g}$ is the guided wavelength at the lowest center frequency. To the best of the authors’ knowledge, this is the first four-pole tunable BPF with center-frequency control that accommodates an asymmetric DMR.}, archive = {J_ACCESS}, author = {Dojeon Jung and Minjae Jung and Byung-Wook Min}, doi = {10.1109/ACCESS.2025.3564178}, journal = {IEEE Access}, pages = {76454-76463}, shortjournal = {IEEE Access}, title = {Compact tunable filter based on dual-mode resonators with 1.5-octave tuning range wide stopband and single bias control}, volume = {13}, year = {2025}, }
• (2025). Load modeling and CVR analysis for residential appliances and distribution Network—A review. ACCESS, 13, 76437-76453. (www)

  @article{ , abstract = {This paper presents an extensive review of load modeling techniques and Conservation Voltage Reduction (CVR), focusing on their impact on appliances and the distribution network. It explores both component-based and measurement-based load modeling methods, including static and dynamic models, with a special emphasis on the ZIP load model. By analyzing how different load types respond to voltage variations, the paper provides insights into optimizing energy efficiency and system stability. Additionally, it examines the Conservation Voltage Reduction Factor (CVR ${}_{\mathrm {f}}$ ) for various residential loads and low voltage network, evaluating their voltage sensitivity that enhances energy savings. The review synthesizes findings from existing studies, bridging the gap between theoretical advancements and practical implementation while outlining future research directions for improved power system performance. A significant contribution of this paper is its extensive review of load modeling methodologies and CVRf analysis, along with a detailed ZIP based load categorization that enhances the understanding of dominant load behaviors, ultimately guiding more effective CVR implementation and energy efficient improvements.}, archive = {J_ACCESS}, author = {Mithila Seva Bala Sundaram and Tan Chia Kwang and Jeyraj A L Selvaraj Selvaraj and Ab Halim Abu Bakar}, doi = {10.1109/ACCESS.2025.3560915}, journal = {IEEE Access}, pages = {76437-76453}, shortjournal = {IEEE Access}, title = {Load modeling and CVR analysis for residential appliances and distribution Network—A review}, volume = {13}, year = {2025}, }
• (2025). Study on the optimized production scheduling of polymetallic mines considering the vehicle routing problem. ACCESS, 13, 76412-76436. (www)

  @article{ , abstract = {The reasonable scheduling of production processes in polymetallic mines is crucial for comprehensively utilizing mineral resources and ensuring economic benefits. The simultaneous mining of high- and low-grade ores, scientific ore transportation, and reasonable ore blending are prerequisites for stable polymetallic beneficiation and guaranteed concentrate quality. Therefore, establishing an optimization model for this problem and designing algorithms to solve it have great significance in the theory of optimization algorithms and in production practices for polymetallic mining enterprises. Based on the production processes of polymetallic mines, this paper establishes a two-stage optimization model for mining, ore transportation, and ore blending production scheduling considering the vehicle routing problem in a truck transportation process. An improved imperialist competition algorithm is designed to solve the vehicle routing problem for the second stage model. The computational results show that the proposed algorithm reduces ore transportation costs by no less than 0.83% compared to other algorithms, which translates to an annual cost saving of at least 100,000 yuan for the mining enterprise of this case study. By solving the model, the Pareto front between unit cost and total grade fluctuation in the entire production process, as well as the rules for making decisions on production scheduling schemes, are obtained. The relevant numerical results of the production scheduling scheme corresponding to the “inflection point” on the final Pareto front are also provided.}, archive = {J_ACCESS}, author = {Jianfu Luo and Taosuo Wu and Hongmei Bai and Hui Jie}, doi = {10.1109/ACCESS.2025.3563198}, journal = {IEEE Access}, pages = {76412-76436}, shortjournal = {IEEE Access}, title = {Study on the optimized production scheduling of polymetallic mines considering the vehicle routing problem}, volume = {13}, year = {2025}, }
• (2025). ZAS-F: A zero-shot abstract sub-goal framework empowers robots for long horizontal inventory tasks. ACCESS, 13, 76400-76411. (www)

  @article{ , abstract = {Long-horizon tasks in unstructured environments are notoriously challenging for robots because they require the prediction of extensive action plans with thousands of steps while adapting to ever-changing conditions by reasoning among multimodal sensing spaces. Humans can efficiently tackle such compound problems by breaking them down into easily reachable abstract sub-goals, significantly reducing complexity. Inspired by this ability, we explore how we can enable robots to acquire sub-goal formulation skills for long-horizon tasks and generalize them to novel situations and environments. To address these challenges, we propose the Zero-shot Abstract Sub-goal Framework (ZAS-F), which empowers robots to decompose overarching action plans into transferable abstract sub-goals, thereby providing zero-shot capability in new task conditions. ZAS-F is an imitation-learning-based method that efficiently learns a task policy from a few demonstrations. The learned policy extracts abstract features from multimodal and extensive temporal observations and subsequently uses these features to predict task-agnostic sub-goals by reasoning about their latent relations. We evaluated ZAS-F in radio frequency identification (RFID) inventory tasks across various dynamic environments, a typical long-horizon task requiring robots to handle unpredictable conditions, including unseen objects and structural layouts. Our experiments demonstrated that ZAS-F achieves a learning efficiency 30 times higher than previous methods, requiring only $8{k}$ demonstrations. Compared to prior approaches, ZAS-F achieves a 98.3% scanning accuracy while significantly reducing the training data requirement. Further, ZAS-F demonstrated strong generalization, maintaining a scan success rate of 99.4% in real-world deployment without additional fine-tuning. In long-term operations spanning 100 rooms, ZAS-F maintained consistent performance compared to short-term tasks, highlighting its robustness against compounding errors. These results establish ZAS-F as an efficient and adaptable solution for long-horizon robotic tasks in unstructured environments.}, archive = {J_ACCESS}, author = {Yongshuai Wu and Jian Zhang and Shaoen Wu and Shiwen Mao and Ying Wang}, doi = {10.1109/ACCESS.2025.3564871}, journal = {IEEE Access}, pages = {76400-76411}, shortjournal = {IEEE Access}, title = {ZAS-F: A zero-shot abstract sub-goal framework empowers robots for long horizontal inventory tasks}, volume = {13}, year = {2025}, }
• (2025). Retinal image analysis for heart disease risk prediction: A deep learning approach. ACCESS, 13, 76388-76399. (www)

  @article{ , abstract = {Heart disease is considered as one of the leading causes of death worldwide. Predicting heart diseases from retinal fundus images is a promising approach in the early detection and monitoring of cardiovascular health conditions. The change in the retinal microvasculature is an indication towards systemic diseases such as cardiovascular diseases and hypertension. This study aims to explore the potential use of deep learning for early detection and prediction of cardiovascular health. through retinal images. The connection between the heart and the small blood vessels are called microvasculature. Imaging the retinal vessels provides a noninvasive way to study the cardiovascular system. By leveraging the potential of Convolutional Neural Network, retinal images are analyzed to identify patterns and anomalies which strongly correlates with cardiovascular conditions. Experimental results show that our method improves the accuracy in prediction of heart diseases, hence opens a novel and improved non-invasive approach to predict Cardiovascular diseases.}, archive = {J_ACCESS}, author = {N. D. Bisna and P. Sona and Ajay James}, doi = {10.1109/ACCESS.2025.3562433}, journal = {IEEE Access}, pages = {76388-76399}, shortjournal = {IEEE Access}, title = {Retinal image analysis for heart disease risk prediction: A deep learning approach}, volume = {13}, year = {2025}, }
• (2025). An automated framework of superpixels-saliency map and gated recurrent unit deep convolutional neural network for land cover and crops disease classification. ACCESS, 13, 76370-76387. (www)

  @article{ , abstract = {In this work, we proposed an automated deep learning and saliency map architecture for the segmentation of crops, leaf disease segmentation, and land cover classification. The proposed framework is based on two embedded steps. In the first step, crop leaf disease segmentation was performed using superpixel clustering-based saliency maps and Bayesian optimization. The contrast enhancement technique is designed in the first segmentation phase and is passed to the saliency technique for the disease segmentation. In the second phase, EfficientNet-b0 architecture is fine-tuned with hyperparameters optimized via Bayesian Optimization. Also, the fine-tuned model is embedded with a single self-attention residual block fused with an efficient average pool layer. Training has been performed on segmented and contrast-enhanced images that were later fused using a serial-embedded approach. The extracted features in the testing phase are further optimized using the modified moth flame-controlled bisection (MFcB) technique. Finally, the extracted features are classified using machine learning classifiers for the final classification. Experiments are performed on the publically available cucumber leaf dataset and Remote sensing dataset with an improved accuracy of 97.6% and 92.90%, respectively. A comparison with state-of-the-art techniques shows that the proposed architecture has improved performance.}, archive = {J_ACCESS}, author = {Irfan Haider and Muhammad Attique Khan and Muhammad Nazir and Saleha Masood and Naoufel Kraiem and Dina Abdulaziz Alhammadi}, doi = {10.1109/ACCESS.2025.3561151}, journal = {IEEE Access}, pages = {76370-76387}, shortjournal = {IEEE Access}, title = {An automated framework of superpixels-saliency map and gated recurrent unit deep convolutional neural network for land cover and crops disease classification}, volume = {13}, year = {2025}, }
• (2025). Explainable climate-based time series modeling for predicting chemical compositions in tobacco leaves. ACCESS, 13, 76352-76369. (www)

  @article{ , abstract = {The quality of tobacco leaves significantly influences cigarette flavor and market value, with chemical composition serving as a critical quality indicator. However, existing tobacco quality prediction studies mainly rely on physical leaf samples, resulting in significant time lags and limited applicability for early decision-making. To address this, we construct a $153\times 6$ climate factor matrix covering the tobacco growth period (May 1 to September 30), incorporating daily maximum temperature, minimum temperature, mean temperature, precipitation, mean sunlight intensity, and atmospheric pressure. Notably, atmospheric pressure is introduced for the first time to enhance model generalizability. We develop and compare five predictive models: multiple linear regression (MLR), eXtreme Gradient Boosting (XGBoost), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network combined with LSTM (CNN+LSTM). The models are trained and validated using climate and chemical composition data from 98 tobacco-growing counties in Yunnan Province (2013-2021). Experimental results demonstrate that the CNN+LSTM model achieves superior predictive accuracy, effectively capturing complex spatiotemporal interactions in climate factors. The mean absolute percentage errors (MAPE) for total sugar, reducing sugar, and total nitrogen remain within 10%-20%, while nicotine and potassium exhibit errors in the range of 20%-30%. Furthermore, Integrated Gradients (IG) analysis is employed to interpret the CNN+LSTM model, revealing the contribution of individual climate factors to chemical accumulation patterns. Our approach improves on the time lag issue of existing studies, helps producers plan resources in advance, and provides a data-driven approach for optimizing tobacco cultivation and quality management.}, archive = {J_ACCESS}, author = {Hong He and Yunwei Zhang and Bin Li and Chengjin Tao}, doi = {10.1109/ACCESS.2025.3564289}, journal = {IEEE Access}, pages = {76352-76369}, shortjournal = {IEEE Access}, title = {Explainable climate-based time series modeling for predicting chemical compositions in tobacco leaves}, volume = {13}, year = {2025}, }
• (2025). Deep learning and federated learning in breast cancer screening and diagnosis: A systematic review. ACCESS, 13, 76322-76351. (www)

  @article{ , abstract = {Breast cancer remains one of the leading causes of death among women worldwide. Early detection and diagnosis are crucial for effective treatment and improved patient outcome. In recent years, both deep learning and federated learning have demonstrated significant advancements in terms of their ability to reliably and securely diagnose breast cancer using various imaging techniques. Consequently, a thorough summary of recent advancements in the field of breast cancer screening and diagnosis using deep learning and federated learning, along with future research directions, is imperative. This study presents a comprehensive review of recent deep learning and federated learning-based methods for breast cancer screening and diagnosis from the classification, segmentation, and detection viewpoints using ultrasonography, mammography, magnetic resonance imaging, and histopathology images. In addition, novel deep learning and federated learning architectures that are used to reliably and securely diagnose breast cancer using various imaging techniques are highlighted. Finally, a discussion of the associated challenges and future research directions are presented.}, archive = {J_ACCESS}, author = {Alexandru Ciobotaru and Cosmina Corches and Dan Gota and Liviu Miclea}, doi = {10.1109/ACCESS.2025.3560211}, journal = {IEEE Access}, pages = {76322-76351}, shortjournal = {IEEE Access}, title = {Deep learning and federated learning in breast cancer screening and diagnosis: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Exploring immersive virtual reality in higher education: Research gap and future Direction—A scoping review. ACCESS, 13, 76308-76321. (www)

  @article{ , abstract = {The rapid advancement of technology in the post-COVID-19 era has positioned immersive learning as a transformative approach to enhance educational experiences. Despite its vast potential, recent research developments reveal persistent challenges and gaps that impede widespread adoption. This study conducts a scoping review using the PRISMA methodology to systematically analyze current literature, identify research gaps, seek the challenge, and propose future research directions. From an initial pool of 414 papers, 75 were selected, comprising 60 research studies and 15 review papers. Notably, 55 studies focus on immersive virtual reality (IVR) purely for educational enhancement in traditional academic settings, while 20 explore the implementation of IVR in education with gaming activities. The analysis indicates a predominance of mixed-methods research within education, computer engineering, and computer science. Most studies are limited by short durations (typically 30 minutes) and small participant groups (under 50), raising concerns about the generalizability of findings. Key themes identified include learning context (21 papers), learning design strategies (ten papers), and immersion elements such as avatars and haptic feedback (six papers). While positive impacts like increased satisfaction, motivation, engagement, knowledge enhancement, and usability are reported, negative effects such as motion sickness (13 papers) and dizziness (11 papers) persist. Crucially, only 11 studies exhibit high statistical power, underscoring the need for more robust research designs. Challenges identified encompass participant limitations, homogeneity, user discomfort, hardware unfamiliarity, and cognitive load—all intricately linked to design strategies. The implications of this review highlight the necessity for future research to focus on long-term studies, optimize user experience, develop cost-effective content creation methods, and integrate gamification into learning design. Addressing these areas is essential for overcoming current barriers and fully realizing the potential of immersive learning in education.}, archive = {J_ACCESS}, author = {Sunardi and Meyliana and Spits Warnars Harco Leslie Hendric and Yusep Rosmansyah}, doi = {10.1109/ACCESS.2025.3565385}, journal = {IEEE Access}, pages = {76308-76321}, shortjournal = {IEEE Access}, title = {Exploring immersive virtual reality in higher education: Research gap and future Direction—A scoping review}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on risk assessment in cloud computing: Recent research advancements. ACCESS, 13, 76289-76307. (www)

  @article{ , abstract = {The era of Industry 4.0 is characterized by a significant shift in information and communication technology, which has facilitated the emergence of new technology-driven investments. Key components such as Cloud Computing, Artificial Intelligence (AI), Big Data, and the Internet of Things (IoT) form the foundation of this industrial revolution. Cloud Computing serves as a service offering organizations a range of opportunities such as flexibility, scalability, and cost-effectiveness. However, it is essential for organizations to guarantee the security of their resources, especially applications, data, and cloud-based networks to use all cloud offered services effectively for both individuals and organizations. Risk assessment (RA) is essential for identifying, analyzing, and evaluating potential risks that could negatively impact cloud resources, yet developing a tailored approach continues to be challenging, because of the complex nature of cloud. The objective of this systematic literature review is to thoroughly examine the latest advancements in cloud computing security, with a primary focus on RA. Our study explores also various cloud RA scenarios where Machine Learning (ML) and Deep Learning (DL) techniques are applied to identify and mitigate potential risks in cloud environments. This study highlights the latest proposed solutions for assessing risks on cloud, thereby contributing to the development of a comprehensive RA while addressing the specific characteristics of the cloud and its complex nature. Additionally, it addresses process for future research work to enhance RA according many criteria.}, archive = {J_ACCESS}, author = {Saadia Drissi and Meriyem Chergui and Zakaria Khatar}, doi = {10.1109/ACCESS.2025.3561123}, journal = {IEEE Access}, pages = {76289-76307}, shortjournal = {IEEE Access}, title = {A systematic literature review on risk assessment in cloud computing: Recent research advancements}, volume = {13}, year = {2025}, }
• (2025). Adaptive hybrid tripping microgrid protection strategy with embedded hardware validation. ACCESS, 13, 76271-76288. (www)

  @article{ , abstract = {This paper proposes an adaptive hybrid-tripping-based protection strategy for microgrids (MGs) that enables a fast and reliable response to faults by leveraging phase voltage and current measurements from relay locations. The protection coordination problem was addressed by optimizing the relay settings for different MG operating scenarios, ensuring proper coordination between the primary and backup relays. Comprehensive performance evaluation using PSCAD simulations demonstrated that the proposed protection scheme operates with 50% of faults cleared within 41.5 ms, while 90% of cases are cleared within 530.8 ms across various fault conditions in both grid-connected and islanded operating conditions. The backup relays exhibited a minimum trip time of 230 ms and a median trip time of 299.6 ms, while the coordination time intervals remained within safe margins (50% of cases maintaining a margin of 246.7 ms), ensuring selectivity. Moreover, real-time hardware-in-the-loop (HIL) tests using TMSF28335 microcontrollers validated the scheme’s practical applicability, showing a strong correlation between simulated and experimental results. The mean difference between the simulated and experimental trip times was 29 ms, with maximum deviations below 7.2% (64 ms) and a minimum deviation of 5 ms. The results confirm the effectiveness of the proposed strategy in reducing tripping times while maintaining coordination, making it a promising solution for both islanded and grid-connected MG operating modes.}, archive = {J_ACCESS}, author = {Pedro Henrique Aquino Barra and Ricardo Augusto Souza Fernandes and Denis Vinicius Coury}, doi = {10.1109/ACCESS.2025.3565226}, journal = {IEEE Access}, pages = {76271-76288}, shortjournal = {IEEE Access}, title = {Adaptive hybrid tripping microgrid protection strategy with embedded hardware validation}, volume = {13}, year = {2025}, }
• (2025). MMEFU-net: A mamba-guided multi-encoder fusion U-net for tumor segmentation in CT images. ACCESS, 13, 76257-76270. (www)

  @article{ , abstract = {Tumor segmentation is crucial for cancer diagnosis, treatment planning, and surgical interventions. However, traditional manual delineation methods are time-consuming, labor-intensive, and prone to subjective variability, highlighting the need for automated approaches. The significant variability in lesion shape and size within medical images further complicates segmentation tasks, necessitating models that can effectively capture both local and global features to achieve accurate results. To address these challenges, we propose MMEFU-Net, a novel deep learning framework designed for efficient and accurate tumor segmentation in medical CT images. The architecture integrates a multi-encoder structure comprising a detail branch, a context branch, and a Mamba-guided branch to extract complementary features, addressing the limitations of single-encoder models. Key innovations include the Mamba-Guided Fusion Attention (MGFA) module for balancing local details and global context, the Pixel Attention Feature Fusion (PAFF) module for enhancing detail branch features with complementary information from the context branch, the Dilated Multi-Scale Fusion (DMSF) module for robust multi-scale feature modeling, and the introduction of the lightweight DySample operator for precise and efficient dynamic upsampling. Comprehensive experiments on five public and private benchmark datasets, encompassing both liver tumors and lung cancers, demonstrate that MMEFU-Net outperforms state-of-the-art U-Net, Transformer-based, and Mamba-based architectures. The model achieves superior Dice Similarity Coefficients (DSC) and Intersection over Union (IoU) scores while significantly reducing computational costs. Notably, MMEFU-Net improves the DSC by 2.16% compared to nnU-Net on the LiTS2017 dataset, with a $35\times $ reduction in parameters and a $25\times $ reduction in computational complexity. These findings suggest that MMEFU-Net has the potential to become a scalable and efficient solution for tumor segmentation in clinical practice.}, archive = {J_ACCESS}, author = {Renzheng Xue and Zifeng Zhang and Yaxin Zhao and Qing Zhang and Minghui Liang}, doi = {10.1109/ACCESS.2025.3564679}, journal = {IEEE Access}, pages = {76257-76270}, shortjournal = {IEEE Access}, title = {MMEFU-net: A mamba-guided multi-encoder fusion U-net for tumor segmentation in CT images}, volume = {13}, year = {2025}, }
• (2025). High-power microwave antennas: A comprehensive review. ACCESS, 13, 76217-76256. (www)

  @article{ , abstract = {High-power antennas have emerged as a critical area of focus for researchers in recent design efforts, particularly for high-power microwave (HPM) technologies. The increasing demand for gigawatt power handling microwave systems in directed energy and electronic warfare applications necessitates the integration of innovative high-power design strategies into traditional antenna design procedures, where the emphasis is on developing wideband and high-gain antennas. Owing to the lack of a comprehensive study of recently proposed antennas for high-power applications, this review paper provides an in-depth discussion, comparison, and examination of the latest advancements in HPM antenna technology. In addition, this review provides a qualitative and quantitative assessment of HPM antennas using widely used antenna metrics such as the center frequency, electrical size, bandwidth, peak gain, and power handling capability. Moreover, this review provides a comprehensive overview of the methodology employed in designing high-power microwave antennas and discusses the outcomes obtained. In addition, the employment of special types of gases, low-loss dielectric materials, and smoothly varying structures to minimize charge buildups, air breakdown, and arching has proven to help improve power handling capability. Using the quantitative antenna metric information published in the open literature, we compiled a table showing performance comparisons between various antenna types and created figures depicting inter- and intra-antenna relationships and tradeoffs. Among the various HPM antennas, the horn antenna can be determined as a versatile and state-of-the-art HPM antenna element demonstrating 10s – 100s of megawatts (MWs) of peak power per element, narrow-to-ultrawideband operating bandwidth, small-to-large electrical size, and medium-to-high gain values. Antennas such as the cavity, impulse radiating, leaky-wave, reflectarray, and other array antennas have achieved state-of-the-art high peak power performance with a tradeoff between electrical size, gain, and bandwidth. In conclusion, this paper revisits HPM antenna designs, highlights noticeable challenges for HPM antenna researchers and engineers, suggests future research directions, and summarizes the key outcomes.}, archive = {J_ACCESS}, author = {Mashrur Zawad and Sohel Rana and Travis D. Fields and Anthony N. Caruso and Kalyan C. Durbhakula and Mohamed Z. M. Hamdalla}, doi = {10.1109/ACCESS.2025.3564973}, journal = {IEEE Access}, pages = {76217-76256}, shortjournal = {IEEE Access}, title = {High-power microwave antennas: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Toward robust AR surgical navigation: Eliminating external trackers and ensuring OST-HMD alignment under various operating room lighting conditions. ACCESS, 13, 76198-76216. (www)

  @article{ , abstract = {Existing integrations of augmented reality (AR) into surgical navigation systems heavily depend on external tracking systems prone to occlusion and increased complexity of setting up the devices. These issues can compromise the accuracy and usability of AR navigation systems in a clinical operating room. Additionally, the impact of illumination exposure on AR system performance in a surgical room remains underexplored. Current methods for addressing the misalignment issue of AR contents in optical-see-through head-mounted displays (OST-HMDs) also lack sufficient precision, particularly for depth orientation. This study evaluated two inside-out tracking systems, the monocular tracker and stereo trackers, using the RGB and grayscale cameras of Microsoft HoloLens 2. Experimental assessments showed that the proposed stereo tracker achieved superior accuracy, with translational and rotational error of 0.73 mm and 0.15°, respectively, outperforming the proposed monocular tracker and existing reported literature. The proposed automated method to mitigate the impact of excessive light exposure can maintain a 98% marker detection rate across various lighting conditions and marker configurations. These results showed that the proposed techniques can eliminate the dependence on external trackers by providing robust performance in any surgical setting. A phantom study on AR-guided needle insertion further validated the efficacy of the proposed system, achieving an accuracy of $1.65~\pm ~0.16$ mm and $2.69~\pm ~0.19^{\circ }$ . These results meet the precision requirements for clinical surgeries, highlighting the potential of the proposed AR system to improve surgical outcomes.}, archive = {J_ACCESS}, author = {Nova Eka Diana and Deukhee Lee}, doi = {10.1109/ACCESS.2025.3565198}, journal = {IEEE Access}, pages = {76198-76216}, shortjournal = {IEEE Access}, title = {Toward robust AR surgical navigation: Eliminating external trackers and ensuring OST-HMD alignment under various operating room lighting conditions}, volume = {13}, year = {2025}, }
• (2025). PPLIO: Plane-to-plane LiDAR-inertial odometry with multi-view constraint in real-time. ACCESS, 13, 76185-76197. (www)

  @article{ , abstract = {Recent feature-based LiDAR-inertial odometry (LIO) algorithms mainly adopt a point-to-plane model, which requires complex association between numerous feature points and planes. Since these methods only use distance as a parameter to associate feature points, they are prone to a mismatch of features in the presence of initial errors. In this study, we propose a LIO system based on a plane-to-plane model with a sliding window, named PPLIO. To extract high-quality plane features, the geometric and statistical properties are utilized and robust data association is performed with two parameters from the plane basis: distance and normal vector. In the state estimator part, we design two plane-to-plane measurement models using the plane features including the proposed plane RANSAC for outlier rejection. Each measurement model is formulated in the multi-state constraint Kalman filter framework for accurate estimation. For detailed verification, we conduct the Monte-Carlo simulation to evaluate the feature accuracy and the data association. In addition, we perform comparative evaluation against state-of-the-art LIO/LIO-mapping algorithms on both indoor and outdoor experiments, and present results that PPLIO outperforms other real-time algorithms in localization performance with detailed analysis.}, archive = {J_ACCESS}, author = {Hanyeol Lee and Jae Hyung Jung and Won Young Chung and Suyong Lee and Chan Gook Park}, doi = {10.1109/ACCESS.2025.3559081}, journal = {IEEE Access}, pages = {76185-76197}, shortjournal = {IEEE Access}, title = {PPLIO: Plane-to-plane LiDAR-inertial odometry with multi-view constraint in real-time}, volume = {13}, year = {2025}, }
• (2025). A review on lung cancer classification using deep learning techniques. ACCESS, 13, 76161-76184. (www)

  @article{ , abstract = {Cancer of the lung system is arguably the second largest factor in the worldwide fatality explosion. It originates from smoking too much and tobacco use. Uncontrolled cell growth in the lung region will hurt human survival. The exponential increase in medical reports might challenge the manual interpretation of disease prediction. Therefore, using Computer-Aided Diagnosis (CAD) approaches, a tumor might be detected early from an appropriate appearance. This study thoroughly evaluates various lung cancer detection methods for classifying a nodule. The area of lung cancer prediction is becoming increasingly popular due to the many benefits of automated medical diagnosis using Deep Learning techniques. This study discusses potential deep learning (DL) strategies for lung disease applications, an overview of DL approaches, and novel elements of the methods under investigation. Classification and segmentation are the two main deep learning techniques for lung cancer detection and screening, which are the focus of this review. We will also talk about the benefits and drawbacks of contemporary deep learning models. The resulting study demonstrates the significant potential of deep learning approaches for exact and efficient computerized lung cancer monitoring and detection employing CT scans. Following this study, an overview of prospective further studies is given, focused on enhancing deep learning to advance automated techniques for diagnosing lung cancer.}, archive = {J_ACCESS}, author = {S. Malarvannan and M. Angulakshmi}, doi = {10.1109/ACCESS.2025.3564633}, journal = {IEEE Access}, pages = {76161-76184}, shortjournal = {IEEE Access}, title = {A review on lung cancer classification using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Methods for standardizing metadata at pusdatinrenbang bappenas. ACCESS, 13, 76145-76160. (www)

  @article{ , abstract = {Metadata interoperability remains a significant challenge for Indonesian government institutions, particularly at Pusdatinrenbang Bappenas, due to the lack of a unified metadata standard. Existing standards, such as KUGI, cannot be implemented effectively across institutions because of differences in data structure and spatial context. This study proposes a structured metadata standardization method based on the MAFRA framework to address this issue. The method includes three main stages: Lifting and Normalization to unify metadata formats, Metadata Mapping to identify relationships between metadata elements, and Metadata Integration to create a consistent and interoperable metadata structure. The method was tested on two datasets (public housing and fisheries production) from Bappenas, PUPR, and BPS. The results show that the proposed method effectively improves data interoperability and consistency across institutions. Validation through questionnaires confirmed that the method meets user expectations and can be applied to other metadata domains with similar structural issues. The key contributions of this study include the adaptation of the MAFRA framework for government metadata, the development of a structured metadata standardization process, and the establishment of a scalable model for future metadata integration between institutions with overlapping functions.}, archive = {J_ACCESS}, author = {P. H. Irham Febrieka and Panca Oktavia Hadi Putra}, doi = {10.1109/ACCESS.2025.3559936}, journal = {IEEE Access}, pages = {76145-76160}, shortjournal = {IEEE Access}, title = {Methods for standardizing metadata at pusdatinrenbang bappenas}, volume = {13}, year = {2025}, }
• (2025). GaLAM: Two-stage outlier detection algorithm. ACCESS, 13, 76135-76144. (www)

  @article{ , abstract = {Image matching aims to establish correspondences between images and estimate relative transformations, serving as a critical component in many geometric computer vision tasks. However, due to the limitations of descriptors and the presence of moving objects in the target region, results from nearest-neighbor matching often include numerous outliers. Recently, significant efforts have focused on effectively filtering these outliers to improve image matching performance. In this paper, we propose Globally-and-Locally-Affine Matching (GaLAM), a two-stage outlier detection method. In the first stage, GaLAM optimizes the edge features of Adaptive Locally Affine Matching (AdaLAM) and replaces the statistical neighborhood-based approach with classical geometric deviation metrics. In the second stage, Progressive Sample Consensus (PROSAC) is used to sample seed points that align with the fundamental matrix, followed by filtering outliers based on projection deviation. This approach enhances the model’s robustness in complex scenes, particularly in scenarios with moving objects in the target region. We evaluated GaLAM on diverse datasets, and the results demonstrate that our method achieves significant improvements over AdaLAM in most scenarios.}, archive = {J_ACCESS}, author = {Xiaojun Lu and Zhe Yan and Ziyun Fan}, doi = {10.1109/ACCESS.2025.3561823}, journal = {IEEE Access}, pages = {76135-76144}, shortjournal = {IEEE Access}, title = {GaLAM: Two-stage outlier detection algorithm}, volume = {13}, year = {2025}, }
• (2025). An efficient DoA estimation approach for 2-D planar array antennas via axial decomposition of antenna current green’s function. ACCESS, 13, 76121-76134. (www)

  @article{ , abstract = {Array manifold, constructed from the received signals by incoming waves in antenna arrays, serves as a fundamental framework for characterizing electromagnetic behavior in direction finding systems. For densely arrayed 2-D planar antenna systems, strong inter-element mutual coupling distorts the array manifold, which directly degrading direction finding performance. While electromagnetic numerical techniques can be used to analyze mutual coupling effects in receiving antenna systems, large computational costs are required for 2-D array structures. This paper presents a fast reciprocal analysis method, based on a directional decomposition approach, for mutual coupling characterization of 2-D receiving antenna systems. The proposed method incorporates antenna current Green’s function theory to analyze the reciprocal property between transmit and receive modes of 2-D array antennas. Through directional decomposition, 2-D array problem is transformed into two separate 1-D array analyses, reducing computational complexity from $\mathcal {O}(M_{B}^{2}(N_{x}^{3} N_{y}^{3}))$ to $\mathcal {O}(M_{B}^{2}(N_{x}^{3} + N_{y}^{3}))$ . Building upon previous work that effectively characterized transmit-mode behavior, this study validates the directional decomposition approach in receiving modes based on reciprocity. The approach accurately predicts both receive-mode antenna current Green’s function and array manifolds while preserving mutual coupling and truncation effects. Validation is performed through direction finding scenarios. Validation through comparison with full-wave analysis demonstrates high correlation in array manifold components across all observation angles ( $\theta : -63^{\circ }$ to 63°, $\phi : -126^{\circ }$ to 126°). In direction of arrival estimation applications with various multiple-source scenarios, the method achieves angular resolution comparable to conventional full-wave analysis while reducing computation time to 0.18% of the original requirement.}, archive = {J_ACCESS}, author = {Jeong-Wan Lee and Sung-Jun Yang}, doi = {10.1109/ACCESS.2025.3565641}, journal = {IEEE Access}, pages = {76121-76134}, shortjournal = {IEEE Access}, title = {An efficient DoA estimation approach for 2-D planar array antennas via axial decomposition of antenna current green’s function}, volume = {13}, year = {2025}, }
• (2025). Implementation of a real-time analysis simulation system for traffic signal control algorithms to reduce vehicle exhaust emissions and improve traffic flow. ACCESS, 13, 76109-76120. (www)

  @article{ , abstract = {Traffic signal control (TSC) is a part of intelligent transportation systems to reduce traffic congestion and emissions. Recently, dynamic traffic signal control systems using artificial intelligence and reinforcement learning have been studied to achieve these goals. Although verification of TSC algorithms is ideally performed in a real environment, traffic simulation tools are widely used due to safety issues. However, existing simulation tools have limitations in real-time result analysis, making it difficult to analyze the strengths and weaknesses of each algorithm according to each traffic situation. In this paper, we propose RTASS, a real-time analysis simulation system for TSC algorithms that enables real-time comparison and result analysis, to improve the problems of such a simulation environments. To validate RTASS, we experiment using actual intersection data. As a result of comparing the traffic volume of the actual intersection data and the simulation, we confirmed that the error rate was maintained within 2% on average. In addition, we evaluate the performance of each TSC algorithm using RTASS and verify the results by comparing two existing methods and two proposed TSC methods. We confirmed that real-time comparison of traffic analysis and algorithms is possible using the proposed simulation system.}, archive = {J_ACCESS}, author = {Hyeokgyu Kwon and Jinhwan Jang and Jongsik Kim and Dongmahn Seo and Soobin Jeon}, doi = {10.1109/ACCESS.2025.3564623}, journal = {IEEE Access}, pages = {76109-76120}, shortjournal = {IEEE Access}, title = {Implementation of a real-time analysis simulation system for traffic signal control algorithms to reduce vehicle exhaust emissions and improve traffic flow}, volume = {13}, year = {2025}, }
• (2025). MetaZero: A novel meta-learning method suitable for generalized zero-shot learning. ACCESS, 13, 76100-76108. (www)

  @article{ , abstract = {Generalized zero-shot learning (GZSL) aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided. Most GZSL methods only optimize models based on seen classes but fail to explicitly mimic zero-shot learning settings that transfer knowledge from seen classes to unseen classes at the training time. Meta-learning has been introduced to mitigate this problem, but the models of current meta-learning approaches only learn to face the novel classes rather than learn to face the strong biased problem during the training, which is the main reason for the poor performance of most methods in GZSL. Humans can improve their knowledge with well-designed tests. According to this phenomenon, We propose a novel meta-learning method that is more suitable for GZSL termed MetaZero, aiming to alleviate the strong bias problem in GZSL. Specifically, MetaZero modifies the division of meta train set and meta test set, so that the model can directly face the strong bias problem in the meta test stage. In this way, with the iteration of training, the model can become an expert to solve the strong bias problem. Our extensive experiments manifest that our method achieves the significant gains than the SOTA GZSL methods.}, archive = {J_ACCESS}, author = {Zeqing Zhang and Zefei Zhang and Na Jin and Fanchang Yang and Wei Zhao}, doi = {10.1109/ACCESS.2025.3558721}, journal = {IEEE Access}, pages = {76100-76108}, shortjournal = {IEEE Access}, title = {MetaZero: A novel meta-learning method suitable for generalized zero-shot learning}, volume = {13}, year = {2025}, }
• (2025). Meta learner-based transfer learning: Bridging simulation and actual router metrics. ACCESS, 13, 76085-76099. (www)

  @article{ , abstract = {Future carrier networks for the sixth generation of mobile communications are expected to guarantee performance across heterogeneous networks that cover multiple technologies. This integration requires the effective verification of network performance under unpredictable traffic conditions. To address this challenge, a router modeling approach defined as the data-driven estimation of actual router performance has been proposed to facilitate router performance verification. However, the creation of router models has been limited by the high cost and difficulty of acquiring the extensive real-world datasets for various traffic patterns required to train actual router models. Our motivation is to enable the digital verification of router performance with limited real-world datasets. This paper proposes a meta learner-based transfer learning method to estimate actual router metrics using models trained on network simulation data. The proposed method aims to build a model of actual router metrics based on limited real-world datasets by supplementing them with different ranges and types of simulation data. The proposed method addresses the differences in these datasets by utilizing Neural Processes as a meta-learner combined with Partial Least Squares analysis to capture and bridge representations between simulation and real-world datasets for packet multiplexing tasks for transfer learning. The results show that the proposed method improves the estimated accuracy of actual router metrics: throughput, packet loss rate, and packet delay. Additionally, the proposed method demonstrates robustness and effectiveness in scenarios where real-world data is limited while maintaining lower computational complexity compared to conventional methods. Our results suggest that this approach may assist network operators in estimating network performance, even with limited real-world data.}, archive = {J_ACCESS}, author = {Kyota Hattori and Tomohiro Korikawa and Chikako Takasaki}, doi = {10.1109/ACCESS.2025.3564954}, journal = {IEEE Access}, pages = {76085-76099}, shortjournal = {IEEE Access}, title = {Meta learner-based transfer learning: Bridging simulation and actual router metrics}, volume = {13}, year = {2025}, }
• (2025). Voltage control for distribution networks based on large language model-assisted deep reinforcement learning. ACCESS, 13, 76072-76084. (www)

  @article{ , abstract = {With the continuous integration of large-scale distributed energy resources into distribution networks, numerous challenges arise regarding security, stability, and economic performance, particularly voltage violations and increased network losses. Furthermore, existing deep reinforcement learning (DRL) methods often rely on extensive real-world operational data for agent training. Yet, the lack of diversity in the collected data can significantly limit the generalization ability of agents under varying operating conditions. This paper proposes a regional voltage optimization control strategy for distribution networks to address these issues based on DRL supported by large language model (LLM). By integrating LLM technologies with DRL, the approach leverages prompt engineering to guide large-language models in generating customized datasets for DRL agent training, enabling data augmentation. This reduces the dependence on real-world data while improving the generalizability of agents. The proposed control strategy was validated on modified IEEE 33-bus and 123-bus distribution systems. The experimental results effectively mitigate voltage violations and reduce network losses while exhibiting strong robustness and generalization under various operating conditions.}, archive = {J_ACCESS}, author = {Limei Yan and Chongyang Cheng}, doi = {10.1109/ACCESS.2025.3565280}, journal = {IEEE Access}, pages = {76072-76084}, shortjournal = {IEEE Access}, title = {Voltage control for distribution networks based on large language model-assisted deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Optimization of MLCC bar design to minimize failure rates under isostatic pressing using numerical analysis and homogenization technique. ACCESS, 13, 76058-76071. (www)

  @article{ , abstract = {Multilayer ceramic capacitors (MLCCs) are electronic components constructed with alternating layers of electrode and dielectric materials. Nevertheless, few studies have examined reliability issues during the manufacturing process, leaving a gap in understanding how production conditions impact MLCC performance. The manufacturing process of MLCCs utilizes isostatic pressing to laminate and compress these layers to form an MLCC bar. However, due to the heterogeneous multilayered structure of the bar, non-uniform deformation occurs after the procedure. To address this issue, this study aims to optimize the design of MLCC bar to minimize non-uniform deformation and reduce subsequent failure rates of MLCC production under isostatic pressing condition. Finite Element Analysis (FEA) is employed as the primary research tool, incorporating a homogenization method to simplify the geometry while maintaining analytical accuracy. The findings indicate that the design of the index regions is a critical design parameter in determining the deformation patterns and failure rates. Particularly, the stiffness of the side and corner index regions influences the curvature observed along the edges. Through a design sensitivity analysis on these regions, the study successfully optimized the MLCC bar design, reducing the failure rate to 0% from a previous high of 30%.}, archive = {J_ACCESS}, author = {Kent Justine B. Legada and Changwoon Han}, doi = {10.1109/ACCESS.2025.3564892}, journal = {IEEE Access}, pages = {76058-76071}, shortjournal = {IEEE Access}, title = {Optimization of MLCC bar design to minimize failure rates under isostatic pressing using numerical analysis and homogenization technique}, volume = {13}, year = {2025}, }
• (2025). Quantitative method for ultrasonic testing of lead seal defects in high voltage cable accessories. ACCESS, 13, 76047-76057. (www)

  @article{ , abstract = {Phased array ultrasound technology has demonstrated its capability in detecting lead seal defects within high-voltage cable terminals. However, conventional ultrasound quantitative methods often fall short in accurately measuring the dimensions of these defects. This paper introduces a novel method for the detection and quantification of lead seal defects in high-voltage cable terminals. By focusing on the longitudinal wave fan scan images of these defects and integrating threshold segmentation with corrosion algorithms, the method provides real-time information on defect characteristics, including cross-sectional area and height. The findings reveal significant improvements over the traditional −6dB method: a 5% reduction in distance error, a 10% enhancement in defect size accuracy, and an overall accuracy rate exceeding 85%. This research holds substantial reference value for the engineering application of lead sealing defect detection in high-voltage cables, contributing to the advancement of lead sealing technology and ensuring the reliability and safety of power grid operations.}, archive = {J_ACCESS}, author = {Zheng Hai and Cai Qiushen and Zheng Jishi and Zhen Zhiming and Zou Wei and Chen Jianping}, doi = {10.1109/ACCESS.2025.3565290}, journal = {IEEE Access}, pages = {76047-76057}, shortjournal = {IEEE Access}, title = {Quantitative method for ultrasonic testing of lead seal defects in high voltage cable accessories}, volume = {13}, year = {2025}, }
• (2025). Efficient deep learning models revolutionize doctor’s training for point-of-care ultrasound. ACCESS, 13, 76038-76046. (www)

  @article{ , abstract = {Point-of-care ultrasound (PoCUS) is a valuable diagnostic tool for pericardial effusion (PCE). However, the time constraints for trainees and experts pose significant barriers to PoCUS learning. This study aims to develop a deep learning (DL) model for detecting and localizing PCE and to investigate the learning efficacy of using this model as an adjunct in PoCUS training. A total of 101 patients with moderate/large PCE and 104 controls without PCE were included, and the images were extracted from the ultrasound (US) clips. We applied preprocessing techniques, including standardized image sizes and background removal, to reduce interference, and post-processing techniques, including adding filters to refine small effusion regions. We developed three DL models based on U-Net, Res-UNet, and UNet++ and compared their performance. Additionally, 14 emergency medicine residents were recruited to complete classification and segmentation tasks on 10% of randomly selected US images. Personalized feedback from the best-performing DL model was provided. Three months later, the residents annotated another set of images. Their learning performance was also evaluated. The UNet++ algorithm surpassed the other two, attaining an impressive sensitivity of 96%, specificity of 97%, area under the curve (AUC) of 98%, intersection over union (IOU) of 81%, and minimal latency. The overall sensitivity increased by 4% in the classification task after training with the UNet++ model, although there were no statistically significant differences in all evaluation metrics. The UNet++ model achieved a balance between high accuracy, IOU, and latency. Although there were no significant differences in the evaluation metrics after UNet++-assisted learning, the overall sensitivity increased by 4%, indicating an improved ability to recognize true positives and reduce false negatives. Our results demonstrated that AI could enhance the interpretation of rare PoCUS conditions and reduce the time demands on both trainees and teachers.}, archive = {J_ACCESS}, author = {Hsin-Hung Chou and Yi-Chung Chang and Wan-Ching Lien and Lung-Chun Lin and Xing-Zheng Lin and Ting-En Hsu and Yueh-Ping Liu and Li Liu and Yen-Ting Chan and Feng-Sen Kuan}, doi = {10.1109/ACCESS.2025.3562674}, journal = {IEEE Access}, pages = {76038-76046}, shortjournal = {IEEE Access}, title = {Efficient deep learning models revolutionize doctor’s training for point-of-care ultrasound}, volume = {13}, year = {2025}, }
• (2025). A review of advanced deep learning methods of multi-target segmentation for breast cancer WSIs. ACCESS, 13, 76016-76037. (www)

  @article{ , abstract = {Breast cancer is one of the most common cancers among women, with its heterogeneity posing significant challenges for diagnosis and treatment, profoundly impacting patient prognosis and quality of life. Whole Slide Imaging (WSI) in digital pathology provides high-resolution images that enable a comprehensive examination of the tumor microenvironment, offering advanced tools for breast cancer diagnosis and prognostic evaluation. However, manually reviewing whole slide images (WSIs) for tissue segmentation is time-consuming and prone to errors, highlighting the need for multi-target deep learning models to automate the segmentation of these complex structures. Multi-target segmentation offers distinct advantages by simultaneously processing multiple interrelated tissue regions within a single image, thereby enhancing accuracy and efficiency. Despite the potential of deep learning techniques in automating pathological analysis, their clinical adoption faces significant challenges. To address these, this paper proposes six criteria focused on clinical acceptability of deep learning methods: inherent limitations of WSIs, feature extraction, annotation requirements, efficiency, automated quantification, and interpretability. A rigorous review of publicly available datasets and deep learning methods identifies key challenges for clinical adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzes 29 core articles, highlighting the critical role of multi-target segmentation in breast cancer digital pathology while assessing the limitations of these techniques in clinical applications. Based on this analysis, this paper proposes six criteria to enhance the diagnostic performance of deep learning methods in multi-target segmentation for breast cancer digital pathology and to improve the clinical acceptability of deep learning methods.}, archive = {J_ACCESS}, author = {Qiaoyi Xu and Afzan Adam and Azizi Abdullah and Nurkhairul Bariyah}, doi = {10.1109/ACCESS.2025.3565648}, journal = {IEEE Access}, pages = {76016-76037}, shortjournal = {IEEE Access}, title = {A review of advanced deep learning methods of multi-target segmentation for breast cancer WSIs}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of deep learning-based lossy point cloud geometry compression coding solutions. ACCESS, 13, 76000-76015. (www)

  @article{ , abstract = {The quality evaluation of three deep learning-based coding solutions for point cloud geometry, notably ADLPCC, PCC GEO CNNv2, and PCGCv2, is presented. The MPEG G-PCC was used as an anchor. Furthermore, LUT SR, which uses multi-resolution Look-Up tables, was also considered. A set of six point clouds representing landscapes and objects was used. As point cloud texture has a great influence on the perceived quality, two different subjective studies that differ in the texture addition model are reported and statistically compared. In the first experiment, the dataset was first encoded with the identified codecs. Then, the texture of the original point cloud was mapped to the decoded point cloud using the Meshlab software, resulting in a point cloud with both geometry and texture information. Finally, the resulting point cloud was encoded with G-PCC using the lossless-geometry-lossy-atts mode, while in the second experiment the texture was mapped directly onto the distorted geometry. Moreover, both subjective evaluations were used to benchmark a set of objective point cloud quality metrics. The two experiments were shown to be statistically different, and the tested metrics revealed quite different behaviors for the two sets of data. The results reveal that the preferred method of evaluation is the encoding of texture information with G-PCC after mapping the texture of the original point cloud to the distorted point cloud. The results suggest that current objective metrics are not suitable to evaluate distortions created by machine learning-based codecs. Finally, this paper presents a study on the compression performance stability of the tested machine learning-based codecs using different training sessions. The obtained results show that the tested codecs revealed a high level of stability across all training sessions for most of the content, although some undesirable exceptions may be found.}, archive = {J_ACCESS}, author = {Joao Prazeres and Rafael Rodrigues and Manuela Pereira and Antonio M. G. Pinheiro}, doi = {10.1109/ACCESS.2025.3561895}, journal = {IEEE Access}, pages = {76000-76015}, shortjournal = {IEEE Access}, title = {Performance analysis of deep learning-based lossy point cloud geometry compression coding solutions}, volume = {13}, year = {2025}, }
• (2025). Secure lattice-based signature scheme for internet of things applications. ACCESS, 13, 75985-75999. (www)

  @article{ , abstract = {The enormous benefits of Internet of Things (IoT) technology have driven its deployment in various applications. Additionally, the development of quantum computers has directed attention towards lattice-based cryptography. Consequently, the computational capabilities of quantum computers pose a threat to the security of the existing IoT signature mechanisms. Quantum computers are proficient at unraveling the complexity bound of computationally hard problems like the integer factorization problem (IFP) and the discrete logarithm problem (DLP). As a result, security is an essential requirement for the IoT communication network against quantum attacks. The amalgamation of certificateless public key cryptosystems (CL-PKC) and lattice-based cryptography (LBC) is one of the solution for alleviating these security menaces. Lucidly, CL-PKC prevents key escrow issues and key management problems; LBC prevents quantum attacks. The Shortest Integer Solution (SIS) problem, which the NTRU lattices offer, serves as the basis for this paper’s introduction of a certificateless signature mechanism for IoT environments. By adopting the Random Oracle Model, we demonstrated the security of the suggested mechanism against Type 1 and Type 2 attackers. Furthermore, security analysis and performance evaluation demonstrate robust communication, as evidenced by metrics such as the computational cost of CL-Sign and CL-Verify phases at $536~\mu s$ , $376.81~\mu s$ and communication cost of KGC at 418 bits, CL-Sign at 532 bits and CL-Verify at 446 bits. Also, we calculate the cost of single-message signature generation and verification on an IoT device. These results show that the suggested mechanism’s security and computational efficiency are more reliable, and efficient than other relevant competing frameworks.}, archive = {J_ACCESS}, author = {Sunil Prajapat and Deepika Gautam and Pankaj Kumar and Srinivas Jangirala and Ashok Kumar Das and Biplab Sikdar}, doi = {10.1109/ACCESS.2025.3565200}, journal = {IEEE Access}, pages = {75985-75999}, shortjournal = {IEEE Access}, title = {Secure lattice-based signature scheme for internet of things applications}, volume = {13}, year = {2025}, }
• (2025). Fabrication, modeling, and design of position and pressure control system of a soft pneumatic linear actuator. ACCESS, 13, 75973-75984. (www)

  @article{ , abstract = {The field of Soft Robotics proposes the use of flexible and compliant materials in the fabrication of soft actuators that can interact with humans in a safer way than conventional robots. While soft pneumatic actuators operate with positive pressure inflating their chambers and creating movement, vacuum actuators operate with negative pressure collapsing their chambers to generate movement. The latter have the benefit that they do not burst due to excessive pressure during contraction and since they shrink in size when activated, they can fit into tight spaces and are therefore more compact, aside from possibly being able to expand with positive pressure. This work presents a soft pneumatic linear actuator designed as a bellow-shaped structure made of silicone rubber that operates with negative pressure to contract and with positive pressure to extend. A Finite Element Method dynamic analysis was performed, using the SIMULIA Abaqus software, to predict the actuator’s behavior. From the definition of the geometry, molds composed of modular pieces were designed and 3D printed to fabricate the actuator prototype. A test platform was designed to perform tests to characterize the actuator and then implement a position and pressure cascade control system, designed with a fuzzy pressure controller in the inner loop and a PI position controller in the outer loop, to regulate both the contraction and the extension of the actuator.}, archive = {J_ACCESS}, author = {Leandro Seiti Goia and Diego Colón and Alexandre Brincalepe Campo}, doi = {10.1109/ACCESS.2025.3564237}, journal = {IEEE Access}, pages = {75973-75984}, shortjournal = {IEEE Access}, title = {Fabrication, modeling, and design of position and pressure control system of a soft pneumatic linear actuator}, volume = {13}, year = {2025}, }
• (2025). A comprehensive comparative analysis of deep learning models for student performance prediction in virtual learning environments: Leveraging the OULA dataset and advanced resampling techniques. ACCESS, 13, 75953-75972. (www)

  @article{ , abstract = {Predicting student performance in Virtual Learning Environments (VLEs) has become increasingly important with the growth of online education. Early identification of at-risk students allows timely interventions to improve academic outcomes. This study evaluates the performance of several Deep Learning (DL) models for tabular data, including ResNet, NODE, AutoInt, TabNet, TabTransformer (TT), SAINT, and GatedTabTransformer (GTT). Moreover, it examines the role of resampling techniques, including SMOTE, ROS, ADASYN, RUS, and Tomek Links, in addressing class imbalance. Using the OULA dataset, eight experiments were conducted for binary and multi-class classification tasks, testing different feature combinations: 1) behavioral, 2) demographic and behavioral, 3) academic and behavioral, and 4) demographic, academic, and behavioral. The results indicate that incorporating a comprehensive set of characteristics can significantly enhance the model’s performance, with academic characteristics proving more predictive than demographic characteristics. The SAINT model achieved the highest performance in binary classification (94.33% accuracy), leveraging its ability to capture meaningful yet straightforward feature interactions. For multi-class classification, SAINT again outperformed other models, achieving an accuracy of 73.22% when using the Tomek Links method, excelling in managing complex feature interactions and underrepresented classes such as “Distinction.” Statistical analysis was done using the Friedman aligned ranks test and the Nemenyi post-test to compare how well the models performed based on F1-scores from several experiments. The non-parametric Friedman test revealed significant differences among the models ( $p = 0.00013$ ). SAINT and AutoInt consistently outperformed the other approaches, while ResNet and TT demonstrated the weakest performance. Post-hoc analysis using the Nemenyi test did not show statistically significant differences among mid-tier models (TabNet, GTT, NODE). A critical difference (CD) further confirmed that SAINT and AutoInt are the most effective architectures for addressing complex, imbalanced educational data. These findings highlight the importance of aligning model selection and resampling techniques with the complexity of the task and the characteristics of the data.}, archive = {J_ACCESS}, author = {Bayan A. Alnasyan and Mohammed Basheri and Madini O. Alassafi}, doi = {10.1109/ACCESS.2025.3564719}, journal = {IEEE Access}, pages = {75953-75972}, shortjournal = {IEEE Access}, title = {A comprehensive comparative analysis of deep learning models for student performance prediction in virtual learning environments: Leveraging the OULA dataset and advanced resampling techniques}, volume = {13}, year = {2025}, }
• (2025). Chinese image captioning based on deep fusion feature and multi-layer feature filtering block. ACCESS, 13, 75935-75952. (www)

  @article{ , abstract = {Cross-modal research has long been a critical pillar for the future development of human-computer interaction. With deep learning achieving remarkable results in computer vision and natural language processing, image captioning has emerged as a key focus area in artificial intelligence research. Traditionally, most image captioning studies have focused on the English context; however, interdisciplinary efforts should not be confined to monolingual environments. Instead, it is essential to expand into multiple languages, given that Chinese is one of the world’s most widely used logographic languages. The study of Chinese image captioning holds immense value but presents significant challenges due to the complexity of Chinese semantic features. To address these difficulties, we propose a Deep Fusion Feature Encoder, which enables the model to extract more detailed visual features from images. Additionally, we introduce Swi-Gumbel Attention and develop a Feature Filtering Block based on it, aiding the model in accurately capturing core semantic elements during caption generation. Experimental results demonstrate that our method achieves superior performance across multiple Chinese datasets. Specifically, in the experimental section of this paper, we compared our proposed model with those based on recurrent neural networks and transformer, demonstrating both its advantages and limitations. Additionally, we provided insights into future research directions for Chinese image captioning. Through ablation experiments, we validated the effectiveness of the Deep Fusion Feature Encoder, Swi-Gumbel Attention, and Triple-Layer Feature Filtering Block. We also explored the impact of different architectural configurations within the Multi-Layer Feature Filtering Block on caption accuracy.}, archive = {J_ACCESS}, author = {Xi Yang and Xingguo Jiang and Jinfeng Liu}, doi = {10.1109/ACCESS.2025.3564873}, journal = {IEEE Access}, pages = {75935-75952}, shortjournal = {IEEE Access}, title = {Chinese image captioning based on deep fusion feature and multi-layer feature filtering block}, volume = {13}, year = {2025}, }
• (2025). Energy consumption minimization algorithm in IoT network integrating data and energy transfer. ACCESS, 13, 75924-75934. (www)

  @article{ , abstract = {Nowadays, Internet of Things (IoT) devices are widely deployed and how to power such massive IoT devices has become a significant challenge. Especially for the battery-powered IoT devices, limited energy storage and continuous energy consumption result in frequent battery replacement and potential environmental pollution. Because radio frequency (RF) signals carry not only information but also energy, Wireless Power Transfer (WPT) in RF domain has been proved to be a reliable and controllable technology to deal with this problem. A typical Data and Energy Integrated Network (DEIN) commonly conceives one DEIN base station and numerable DEIN devices. The DEIN base station is capable of transmitting data and energy integrated signals to the DEIN devices with Simultaneous Wireless Information and Power Transfer (SWIPT) technology, and the DEIN devices are capable of harvesting energy from the downlink RF signals for uplink data transmission. In order to coordinate the wireless data and energy transmission and avoid conflicts, a transmission protocol based on the Time-Division Multiple Access (TDMA) scheme is designed. Furthermore, an optimization problem of minimizing the energy consumption of the DEIN base station is formulated. In order to address this problem, this paper proposes an energy-efficient algorithm by jointly optimizing of the transmission power of the DEIN base station, the uplink and downlink time slots and power splitting factors of the IoT devices. Both the effectiveness and efficiency of the proposed joint optimization algorithm have been validated with the simulation.}, archive = {J_ACCESS}, author = {Yang Wang and Qin Yu and Jie Hu and Kun Yang}, doi = {10.1109/ACCESS.2025.3565110}, journal = {IEEE Access}, pages = {75924-75934}, shortjournal = {IEEE Access}, title = {Energy consumption minimization algorithm in IoT network integrating data and energy transfer}, volume = {13}, year = {2025}, }
• (2025). Neuroevolutionary convolutional neural network design for low-resolution face recognition. ACCESS, 13, 75911-75923. (www)

  @article{ , abstract = {Face recognition (FR) is one of the most widely used biometric methods for identity authentication. Although most of the recently proposed methods demonstrate remarkable performance on high-quality datasets, such as LFW, their effectiveness is limited when assessed on low-resolution images. To address this challenge, knowledge distillation and super-resolution techniques have been applied, primarily using the ResNet architecture. However, the performance of deep learning approaches depends in part on the architecture used. In this study, we use neuroevolution with a genetic algorithm (GA) to design Convolutional Neural Networks (CNNs) automatically for low-resolution (LR) FR. To reduce the search time, a binary classifier is used to identify which generated architectures should be trained and which should not. The selected architectures are then trained and evaluated using QUMUL-TinyFace (training partition), a native LR dataset, to obtain their fitness, while the remaining architectures are assessed using a performance predictor model to estimate their fitness, bypassing the training stage. The classifier and performance predictor are trained using the CNN architectures evaluated from previous generations, with the architecture encoding used as a feature vector. The proposed method was assessed on both the QMUL-TinyFace (for face identification) and QMUL-SurvFace (for face verification) datasets, achieving a rank-1 recognition rate of 74.7% and a mean verification accuracy rate of 85.1%, respectively, outperforming results from previously published methods.}, archive = {J_ACCESS}, author = {Jhon I. Pilataxi and Juan P. Perez and Claudio A. Perez and Kevin W. Bowyer}, doi = {10.1109/ACCESS.2025.3565304}, journal = {IEEE Access}, pages = {75911-75923}, shortjournal = {IEEE Access}, title = {Neuroevolutionary convolutional neural network design for low-resolution face recognition}, volume = {13}, year = {2025}, }
• (2025). Research on digital media art for image caption generation based on integrated transformer models in CLIP. ACCESS, 13, 75894-75910. (www)

  @article{ , abstract = {Digital media art has a wide application in the field of image caption generation. In digital media art exhibitions or online works displays, some complex image works may have multiple layers of meanings or abstract expressions, which can help viewers better understand the works. It can also serve as another auxiliary element besides sound, collaborating with visual elements to provide a richer experience for the audience. The purpose of picture captioning is to provide textual descriptions that correlate to input images. The CLIP paradigm is highly versatile to resolve vision-text difficulties. In the field of picture description, the standard Transformer architecture has also exhibited good effects, which uses an image encoder and a text decoder. Large parameter numbers and the demand for further data preprocessing are still significant difficulties. In order to replace the fundamental features of conventional multi-modal fusion models, we propose a New Multi-modal Fusion Attention module (NMFA), which efficiently decreases parameter sizes and computational complexity in half. Expanding upon this, we propose the Transformer Fusion CLIP (TFC) model, which minimizes parameter sizes and processing demands while getting remarkable assessment scores. Additionally, we strengthen the mechanism for cumulative points and reward sequence length to encourage the construction of larger sequences. Finally, we combine the enhanced beam search technique to further train the TFC model. Results from our testing on the MSCOCO dataset reveal that we have not only greatly improved the efficiency of the TFC model but also speeded up its runtime by eight times and reduced model parameters by over 50%.}, archive = {J_ACCESS}, author = {Lu Gao and Xiaofei Pang}, doi = {10.1109/ACCESS.2025.3565682}, journal = {IEEE Access}, pages = {75894-75910}, shortjournal = {IEEE Access}, title = {Research on digital media art for image caption generation based on integrated transformer models in CLIP}, volume = {13}, year = {2025}, }
• (2025). Assessing the relevance of scores in table tennis: Theoretical framework and empirical evidence. ACCESS, 13, 75885-75893. (www)

  @article{ , abstract = {The score is an indispensable element in competitive sports, playing a crucial role in determining the outcome of a match. However, the importance of scores in table tennis remains unexplored, as the hundreds of possible score scenarios in a single set make it especially challenging to assess their significance scientifically. This study is the first attempt to quantify the relevance of all possible scores in table tennis matches through probabilistic computing and empirical verification. Depending on the overall winning probability, relevance values were determined for the 121 possible scores. An extensive dataset consisting of 1,364 matches and 103,772 individual scores from top-level table tennis was examined to test the empirical winning probabilities of each score. The results highlight a significant correlation between the theoretical score relevance and players’ empirical winning probabilities. Winners perform better at more relevant scores, such as close scores or crunch time, with this trend being more pronounced among male players. Z-tests were used to identify scores with positive or negative deviations from the average scoring rate. Individual players show considerable variation in scoring performance across different score situations. These findings provide significant implications for players’ training and competition. The approach presented in this study can also be applied to other scoring games and sports.}, archive = {J_ACCESS}, author = {Ruizhi Liu and Miran Kondrič and Martin Lames}, doi = {10.1109/ACCESS.2025.3563768}, journal = {IEEE Access}, pages = {75885-75893}, shortjournal = {IEEE Access}, title = {Assessing the relevance of scores in table tennis: Theoretical framework and empirical evidence}, volume = {13}, year = {2025}, }
• (2025). Enabling quantum-resistant EDHOC: Design and performance evaluation. ACCESS, 13, 75861-75884. (www)

  @article{ , abstract = {The Ephemeral Diffie-Hellman over COSE (EDHOC) is a compact and lightweight key establishment protocol for constrained scenarios that provides end-to-end application layer security context. However, because of the vulnerability of the discrete logarithm problem to Shor’s algorithm, a quantum resistant replacement for Diffie-Hellman is required. This paper proposes an architecture to transform EDHOC into a quantum-resistant protocol, with an open-source implementation supporting various Post Quantum Cryptography (PQC) schemes from the National Institute of Standards and Technology (NIST) PQC standardization process. Necessary modifications to EDHOC are analyzed, integrating PQC Key Encapsulation Mechanisms (KEMs) and PQC digital signatures in a complete Post-Quantum (PQ) version of the EDHOC protocol (PQ-EDHOC). PQC operations often involve complex computations and require larger byte sizes, which can challenge resource-constrained devices designed for lightweight operation in constrained network environments. To evaluate the applicability of various PQC schemes in a realistic PQ EDHOC environment, various PQC KEM and Digital Signature combinations are assessed for execution time, energy consumption, memory usage, and network performance on an nRF52840 ARM Cortex-M4 platform with a 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) over BLE (Bluetooth LowEnergy) network. Results indicate that NIST standardized ML-KEM emerges as the only suitable KEM choice for resource-constrained environments, while promising new signature schemes, like HAWK, show significant performance improvement over the NIST standardized ML-DSA and selected for standardization FALCON.}, archive = {J_ACCESS}, author = {Lidia Pocero Fraile and Georgios Tasopoulos and Christos Koulamas and Raymond K. Zhao and Nazatul Haque Sultan and Francesco Regazzoni and Apostolos P. Fournaris}, doi = {10.1109/ACCESS.2025.3554010}, journal = {IEEE Access}, pages = {75861-75884}, shortjournal = {IEEE Access}, title = {Enabling quantum-resistant EDHOC: Design and performance evaluation}, volume = {13}, year = {2025}, }
• (2025). Magnetic resonance imaging brain segmentation using bi-directional convolutional long short-term memory U-net with densely connected convolutions. ACCESS, 13, 75847-75860. (www)

  @article{ , abstract = {Despite advancements in imaging technologies, traditional diagnostic methods relying on manual interpretation by clinicians remain prone to errors. This underscores the need for robust automated segmentation techniques. Accurate three-dimensional (3D) Magnetic Resonance Imaging (MRI) brain tumor segmentation is critical for effective diagnosis in neuroimaging. Given the complexity of brain tumors, deep learning approaches have emerged as more suitable than traditional methods for efficient and precise segmentation. The dual difficulties of high computing needs in 3D convolutional networks and the necessity of more precise brain tumor diagnosis are addressed by this work. We provide an artificial intelligence (AI) framework combining bi-directional convolutional long short-term memory (ConvLSTM) layers with densely connected convolutions (DCC). Our method enhances spatial feature learning employing dense connections, and catches complex temporal links across MRI slices. Particularly for the identification of the Enhancing Tumor (ET) region, this novel combination solves the complexity limitation of convolutional techniques, hence improving segmentation robustness. Our approach is validated by extensive testing on the Brain Tumor Segmentation (BraTS) 2020 and 2021 datasets. On the BraTS 2021 dataset, the proposed M-BDCU-Net model beats many state-of-the-art approaches for particular tumor sub-regions with Dice Similarity Coefficient (DSC) scores of 0.81 for Tumor Core (TC), 0.85 for Enhancing Tumor (ET), and 0.82 for Whole Tumor (WT). With 8.81 million parameters and 60.34 GFLOPs, our model also greatly lowers complexity by up to 15% and balances accuracy with efficiency. It is thus much lighter than current models. These results highlight how rapid, more dependable, and exact brain tumor detection our system could allow. This work fills important voids in present research by combining methodological innovation with exhaustive evaluation and provides a useful answer for therapeutic uses.}, archive = {J_ACCESS}, author = {Meshari D. Alanazi and Amna Maraoui and Imen Werda and Ahmed Ben Atitallah and Turki M. Alanazi and Mohammed Albekairi and Anis Sahbani and Amr Yousef}, doi = {10.1109/ACCESS.2025.3562132}, journal = {IEEE Access}, pages = {75847-75860}, shortjournal = {IEEE Access}, title = {Magnetic resonance imaging brain segmentation using bi-directional convolutional long short-term memory U-net with densely connected convolutions}, volume = {13}, year = {2025}, }
• (2025). A survey on video compression optimization techniques for accuracy enhancement in video analytics applications (VAPs). ACCESS, 13, 75822-75846. (www)

  @article{ , abstract = {Video analytics is essential in smart city management, traffic monitoring, and security surveillance, where real-time decision-making is critical. However, the efficiency of these applications depends on optimizing video compression parameters to maintain high detection accuracy while minimizing bandwidth usage and computational costs. This paper presents a comprehensive survey of video compression optimization techniques, focusing on Quantization Parameter (QP), Frames per Second (FPS), Entropy Coding, and Motion Estimation for video analytics tasks. We examine traditional compression algorithms, machine learning-based approaches, dynamic parameter adjustment strategies, and hybrid models, each offering unique strengths and limitations. Our findings highlight that adaptive compression techniques improve the trade-off between detection accuracy and efficiency. However, challenges remain, particularly in dynamic and bandwidth-constrained environments. To evaluate these techniques, we employ AccMPEG. This video compression framework dynamically adjusts compression settings based on real-time inference feedback, ensuring an optimal balance between detection accuracy and bitrate efficiency. In addition, we assess perceptual quality using Just Noticeable Distortion (JND) and Mean Opinion Score (MOS). The results indicate higher QP values lead to noticeable quality degradation, particularly at lower bitrates. Motion estimation techniques influence perceived quality, and TESA achieves MOS ratings higher than alternative methods. Furthermore, H.265 demonstrates superior MOS scores compared to H.264 at lower bitrates, reinforcing its higher compression efficiency while preserving visual clarity. Despite these advancements, the generalization of findings is limited, as the dataset consists mainly of traffic videos, which may not fully represent other video analytics applications. Future research should explore a broader range of datasets and develop adaptive compression frameworks that integrate real-time inference feedback and advanced machine learning models.}, archive = {J_ACCESS}, author = {Kholidiyah Masykuroh and Hendrawan and Eueung Mulyana and Farhan Krishna}, doi = {10.1109/ACCESS.2025.3561063}, journal = {IEEE Access}, pages = {75822-75846}, shortjournal = {IEEE Access}, title = {A survey on video compression optimization techniques for accuracy enhancement in video analytics applications (VAPs)}, volume = {13}, year = {2025}, }
• (2025). Digital twins in fusion energy research: Current state and future directions. ACCESS, 13, 75787-75821. (www)

  @article{ , abstract = {This review paper explores the pivotal role of digital twin (DT) technology in advancing nuclear fusion research and its potential to address key challenges in achieving fusion energy as a sustainable power source. Whilst DTs have been developed to tackle complex issues in data analysis, real-time control, optimisation, and simulation, existing efforts are largely fragmented and constrained to isolated applications due to the experimental and evolving nature of fusion reactors. To bridge this gap, there is a pressing need for an integrated, end-to-end DT framework that can unify these capabilities, enabling a more holistic approach to reactor design and operation. This paper systematically reviews the current state of DT development, focusing on its application to critical components such as the divertor, breeder blanket, and magnets, which serve as ideal candidates for modular DT instances. By leveraging high-performance computing and vast datasets generated by fusion experiments worldwide, we identify opportunities to create scalable, data-driven surrogate models that can continuously refine live DT systems. The findings highlight the potential for centralised storage of the data and models to accelerate simulation-based testing (in silico) and optimisation of reactor components, ultimately contributing to the faster realisation of commercially viable fusion reactors. This integration of DT technology promises to significantly reduce the need for costly, iterative physical testing, paving an efficient pathway to achieving fusion as a reliable energy source.}, archive = {J_ACCESS}, author = {Michael I. Battye and Suresh Perinpanayagam}, doi = {10.1109/ACCESS.2025.3561920}, journal = {IEEE Access}, pages = {75787-75821}, shortjournal = {IEEE Access}, title = {Digital twins in fusion energy research: Current state and future directions}, volume = {13}, year = {2025}, }
• (2025). A review on disability-inclusive public transportation: Current barriers and prospects. ACCESS, 13, 75769-75786. (www)

  @article{ , abstract = {People with disabilities face significant barriers in using public transport, limiting their access to healthcare, employment, education, and daily life activities. A comprehensive review of existing research critically identifies these barriers, analyzes efficient interventions, and uses modern technology to create inclusive public transport systems that empower people with disabilities and foster a more equitable society. The current review systematically identifies the key barriers and prospects of disability-inclusive public transportation. It also highlights the policy shortcomings, accessibility gaps, and current technological advancements to foster inclusivity and improve mobility needs for disabled individuals. Searching scientific databases Scopus and Web of Science yielded 1100 articles; 35 met the inclusion criteria. The selected studies were conducted in various countries, emphasizing the topic’s geographical importance. Several studies in the last five years indicated the topic’s growing interest and potential impact. The present study categorized the key barriers into five types: inaccessible infrastructure, information and communication, attitudinal, economic, and safety and security. Case studies from diverse geographical and social settings correlated the barriers with the satisfaction levels of people with disabilities. The case studies highlighted the critical problems disabled people face that should be considered while designing successful transportation systems and travel chains. The study also illustrates the key features of a successful transportation system for disabled people based on best practices adopted worldwide. The prospects of using advanced technology, such as machine learning and modern imagery techniques, are also discussed.}, archive = {J_ACCESS}, author = {Meshal Almoshaogeh and Arshad Jamal and Tufail Ahmed and Fawaz Alharbi and Mudassir Iqbal and Husnain Haider and Majed Alinizzi}, doi = {10.1109/ACCESS.2025.3564225}, journal = {IEEE Access}, pages = {75769-75786}, shortjournal = {IEEE Access}, title = {A review on disability-inclusive public transportation: Current barriers and prospects}, volume = {13}, year = {2025}, }
• (2025). Lightweight detection algorithm for breast-mass features in ultrasound images. ACCESS, 13, 75754-75768. (www)

  @article{ , abstract = {Real-time analysis of ultrasound videos using embedded terminals enables the rapid detection of breast masses and plays a crucial role in early breast cancer screening and diagnosis. However, as a paired organ with a large area, the breast consists of interwoven fatty layers, mammary ducts, glandular tissues, and masses, leading to a high false-positive rate in target detection. Unlike standard imaging modalities such as computed tomography, X-ray, and magnetic resonance imaging, ultrasound detection heavily depends on the sonographer’s techniques and expertise, which can lower detection accuracy, particularly in identifying small masses. This paper proposes a novel architecture, LEW-YOLO, to address the real-time detection demands of embedded terminal devices for breast ultrasound. First, we introduce an efficient multiscale convolutional (EMSC) module to improve feature extraction from complex backgrounds and enhance multiscale representation. Unlike traditional methods, EMSC employs multiple convolution branches with varying kernel sizes, enabling more effective multiscale feature capture. Second, the detection head of YOLOv8 is replaced with a lightweight shared detail-enhanced convolutional detection head (LSDECD) to improve the model’s ability to detect small masses. Finally, a weighted intersection-over-union (WIoU) loss function is integrated to better capture the complex boundaries of malignant masses. Experiments on the public BUSI dataset demonstrate that LEW-YOLO achieves an mAP50 of 91.6%, surpassing the YOLOv8n baseline (89.0%) by 2.6%. On the BUET dataset, LEW-YOLO attains an mAP50 of 83.3%, outperforming YOLOv8 (72.3%) by 11.0%. Moreover, the parameter count and GFLOPs are reduced by 26.6% and 22.2%, respectively. Thus, the proposed model effectively balances detection performance and lightweight design, making it well-suited for real-time applications on resource-constrained computing devices.}, archive = {J_ACCESS}, author = {Taojuan Li and Wen Liu and Mingxian Song and Zheng Gu and Ling Hai}, doi = {10.1109/ACCESS.2025.3564940}, journal = {IEEE Access}, pages = {75754-75768}, shortjournal = {IEEE Access}, title = {Lightweight detection algorithm for breast-mass features in ultrasound images}, volume = {13}, year = {2025}, }
• (2025). Robust sliding mode control based on fractional order reaching law for rehabilitation robots. ACCESS, 13, 75744-75753. (www)

  @article{ , abstract = {Rehabilitation robots, particularly lower-limb exoskeletons, are transforming healthcare by assisting individuals with mobility impairments. This study introduces a novel Sliding Mode Control (SMC) system based on a fractional-order reaching law, designed to enhance control performance and robustness. The proposed approach effectively manages the exoskeleton’s dynamic behavior, particularly during the transient regime, by reducing initial torque energy demand during start-up, ensuring precise trajectory tracking, and prioritizing patient safety and comfort. The method’s effectiveness is validated through MATLAB simulations and supported by a rigorous dual stability analysis, demonstrating asymptotic and finite-time convergence of the system in the reaching and sliding phases. A Comparison study with traditional SMC techniques proves that the FO-RL-SMC significantly improves energy efficiency during the transient phase and the overall dynamical behavior of the system. These results highlight the potential of the proposed FO-RL-SMC system to advance the performance of rehabilitation robots, emphasizing its value in addressing complex control challenges and improving patient outcomes.}, archive = {J_ACCESS}, author = {Rim Jallouli-Khlif and Fatma Abdelhedi and Chadia Zayane and Ahmed Said Nouri and Nabil Derbel}, doi = {10.1109/ACCESS.2025.3565291}, journal = {IEEE Access}, pages = {75744-75753}, shortjournal = {IEEE Access}, title = {Robust sliding mode control based on fractional order reaching law for rehabilitation robots}, volume = {13}, year = {2025}, }
• (2025). Scoring system for quantifying the privacy in re-identification of tabular datasets. ACCESS, 13, 75727-75743. (www)

  @article{ , abstract = {This study introduces a System for Calculating Open Data Re-identification Risk (SCORR), a framework for quantifying privacy risks in tabular datasets. SCORR extends conventional metrics such as k-anonymity, l-diversity, and t-closeness with novel extended metrics, including uniqueness-only risk, uniformity-only risk, correlation-only risk, and Markov Model risk, to identify a broader range of re-identification threats. It efficiently analyses event-level and person-level datasets with categorical and numerical attributes. Experimental evaluations were conducted on three publicly available datasets: OULAD, HID, and Adult, across multiple anonymisation levels. The results indicate that higher anonymisation levels do not always proportionally enhance privacy. While stronger generalisation improves k-anonymity, l-diversity and t-closeness vary significantly across datasets. Uniqueness-only and uniformity-only risk decreased with anonymisation, whereas correlation-only risk remained high. Meanwhile, Markov Model risk consistently remained high, indicating little to no improvement regardless of the anonymisation level. Scalability analysis revealed that conventional metrics and Uniqueness-only risk incurred minimal computational overhead, remaining independent of dataset size. However, correlation-only and uniformity-only risk required significantly more processing time, while Markov Model risk incurred the highest computational cost. Despite this, all metrics remained unaffected by the number of quasi-identifiers, except t-closeness, which scaled linearly beyond a certain threshold. A usability evaluation comparing SCORR with the freely available ARX Tool showed that SCORR reduced the number of user interactions required for risk analysis by 59.38%, offering a more streamlined and efficient process. These results confirm SCORR’s effectiveness in helping data custodians balance privacy protection and data utility, advancing privacy risk assessment beyond existing tools.}, archive = {J_ACCESS}, author = {Jakob Folz and Manjitha D. Vidanalage and Robert Aufschläger and Amar Almaini and Michael Heigl and Dalibor Fiala and Martin Schramm}, doi = {10.1109/ACCESS.2025.3563309}, journal = {IEEE Access}, pages = {75727-75743}, shortjournal = {IEEE Access}, title = {Scoring system for quantifying the privacy in re-identification of tabular datasets}, volume = {13}, year = {2025}, }
• (2025). Application of artificial intelligence in tinnitus diagnosis and treatment: A pilot study. ACCESS, 13, 75718-75726. (www)

  @article{ , abstract = {Tinnitus is the perception of phantom sound when there is no sound source and is often described as ringing in the ear. Symptoms of tinnitus and severity vary significantly from person to person, largely be attributed to an individual’s perception to the condition and increasing amount of people seek medical intervention. Unlike other diseases, tinnitus pathophysiology is complex and sometimes inexplicable. Currently, there are no universally accepted treatment options for this condition. Moreover, there is no well-established correlation between tinnitus features and projection of treatment. In practice, the treatments provided by practitioners are not based on defined and regulated rules or expected patient outcomes. Instead, they are at a certain level and differ significantly among clinicians and across regions. The complexity of tinnitus features and lack of well-adapted prognostic treatments present an excellent opportunity for Artificial Intelligence (AI) and Machine Learning (ML). AI models can learn intricate patterns between tinnitus features and treatments, as suggested by experts. In this study, we trained an AI model with an expert system to predict tinnitus treatment based on tinnitus symptoms. We describe the curation of the input data, the algorithm (CTGan) used to extend the dataset, and the ML model (Random Forest) to predict each of the suggested treatments. The average accuracy score of the trained model is currently between 0.81 and 0.96 for most treatment predictions, based on a small input dataset size of 300 samples. The generalizability of these results to broader clinical settings requires further validation using larger real-world datasets.}, archive = {J_ACCESS}, author = {Yu Wang and Kaixiang Pan and Richard Tyler and Zhaoyi Lu and Shan Xiong and Yufei Xie and Tao Pan}, doi = {10.1109/ACCESS.2025.3561315}, journal = {IEEE Access}, pages = {75718-75726}, shortjournal = {IEEE Access}, title = {Application of artificial intelligence in tinnitus diagnosis and treatment: A pilot study}, volume = {13}, year = {2025}, }
• (2025). MSFA-BEVNet: Optimization of BEV scene recognition driven by multiscale feature fusion and alignment. ACCESS, 13, 75707-75717. (www)

  @article{ , abstract = {Scene understanding and multisource data fusion are critical challenges in autonomous self-driving systems.In particular, optimizing information fusion strategies for three-dimensional Bird’s Eye View (BEV) scene recognition tasks is crucial for accurate perception and decision-making in dynamic environments. This study proposes a novel architecture that integrates multiscale feature extraction and crossmodal structural alignment to enhance the representation and detection capabilities of BEV features. Specifically, we employ a DCN-based block for visual feature extraction, comprising layer normalization (LN), feedforward networks (FFNs), and the Gaussian Error Linear Unit (GELU) activation function, aligned with the Vision Transformer (ViT) paradigm to improve feature modeling. To fully utilize multiscale information, a dedicated multiscale feature fusion block is introduced to extract expressive scene features within the feature space. Furthermore, we leverage LiDAR to generate LIDAR BEV features and propose a feature alignment block to enhance the complementarity between camera and LiDAR BEV features. The proposed architecture effectively supports precise scene recognition and adaptive decision-making in multi-sensor fusion environments, providing robust perception capabilities for autonomous driving in complex scenarios.}, archive = {J_ACCESS}, author = {Xiubin Cao and Yifan Li and Hongwei Li}, doi = {10.1109/ACCESS.2025.3565328}, journal = {IEEE Access}, pages = {75707-75717}, shortjournal = {IEEE Access}, title = {MSFA-BEVNet: Optimization of BEV scene recognition driven by multiscale feature fusion and alignment}, volume = {13}, year = {2025}, }
• (2025). GateIDS: A single network architecture for attack detection and multi-class classification. ACCESS, 13, 75697-75706. (www)

  @article{ , abstract = {Network security faces escalating challenges from sophisticated cyber threats, necessitating advanced Intrusion Detection Systems (IDS) capable of both detecting attacks and classifying their patterns. This paper introduces GateIDS, a pioneering single-network architecture that integrates binary attack detection and multi-class attack pattern classification within a unified framework. Leveraging Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Transformer backbones, GateIDS employs a bifurcated design trained end-to-end to address these dual tasks efficiently. To mitigate class imbalance in the UNSW-NB15 dataset, a class-specific oversampling strategy is applied, enhancing the detection of minority attack types. Experimental results demonstrate that the DNN variant achieves superior performance, with a binary classification accuracy of 98.30% and a pattern classification accuracy of 86.68%, alongside an inference time of 0.1752 seconds, outperforming LSTM (89.17%, 46.71%) and Transformer (90.63%, 45.79%) variants. This work marks the first unified dual-task IDS, offering computational efficiency and scalability over traditional separate-model approaches. GateIDS establishes a robust baseline for future IDS research, with potential enhancements through clustering and real-world deployment.}, archive = {J_ACCESS}, author = {Sungkwan Youm and Kwang-Seong Shin}, doi = {10.1109/ACCESS.2025.3564068}, journal = {IEEE Access}, pages = {75697-75706}, shortjournal = {IEEE Access}, title = {GateIDS: A single network architecture for attack detection and multi-class classification}, volume = {13}, year = {2025}, }
• (2025). Optimality versus generality: Performance assessment of meta-heuristics in educational timetabling. ACCESS, 13, 75679-75696. (www)

  @article{ , abstract = {Educational timetabling, a principal branch of operations research, presents challenging combinatorial optimization problems widely encountered in educational institutions. Meta-heuristics have commonly been applied to these problems and managed to attain promising performance in terms of optimality. However, their general applicability has been overlooked, hindering their effectiveness as versatile solvers. The limited generalizability of current approaches is the primary hurdle between the literature and real-world applications. This paper addresses this gap by introducing a generality taxonomy and conducting comprehensive theoretical and empirical analyses. This study highlights the adverse impact of extreme parameter tuning on generality, emphasizing the need for more generalized approaches. Furthermore, it introduces a performance assessment framework, penalizing problem-tailored solutions. It also examines the optimality vs. generality performance of the state-of-the-art approaches of the latest university course timetabling benchmark to further reinforce our claim and validate the efficacy of our framework. Our findings indicate that the current literature prioritizes optimality over generality. We believe adopting the proposed assessment framework is crucial for bridging the gap between research and practical applications, enabling fairer comparisons, and encouraging more adaptable approaches.}, archive = {J_ACCESS}, author = {Sina Abdipoor and Razali Yaakob and Say Leng Goh and Salwani Abdullah and Hazlina Hamdan and Khairul Azhar Kasmiran}, doi = {10.1109/ACCESS.2025.3565522}, journal = {IEEE Access}, pages = {75679-75696}, shortjournal = {IEEE Access}, title = {Optimality versus generality: Performance assessment of meta-heuristics in educational timetabling}, volume = {13}, year = {2025}, }
• (2025). SV-SAE: Layer-wise pruning for autoencoder based on link contributions. ACCESS, 13, 75666-75678. (www)

  @article{ , abstract = {Autoencoders are a type of deep neural network and are widely used for unsupervised learning, particularly in tasks that require feature extraction and dimensionality reduction. While most research focuses on compressing input data, less attention has been given to reducing the size and complexity of the autoencoder model itself, which is crucial for deployment on resource-constrained edge devices. This paper introduces a layer-wise pruning algorithm specifically for multilayer perceptron-based autoencoders. The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder as a coalition of interconnected units and links, where the Shapley value quantifies their individual contributions to overall performance. This enables the selective removal of less important components, achieving an optimal balance between sparsity and accuracy. Experimental results confirm that the SV-SAE reaches an accuracy of 99.25%, utilizing only 10% of the original links. Notably, the SV-SAE remains robust under high sparsity levels with minimal performance degradation, whereas other algorithms experience sharp declines as the pruning ratio increases. Designed for edge environments, the SV-SAE offers an interpretable framework for controlling layer-wise sparsity while preserving essential features in latent representations. The results highlight its potential for efficient deployment in resource-constrained scenarios, where model size and inference speed are critical factors.}, archive = {J_ACCESS}, author = {Joohong Rheey and Hyunggon Park}, doi = {10.1109/ACCESS.2025.3565296}, journal = {IEEE Access}, pages = {75666-75678}, shortjournal = {IEEE Access}, title = {SV-SAE: Layer-wise pruning for autoencoder based on link contributions}, volume = {13}, year = {2025}, }
• (2025). Multi-objective-based multi-heterogeneous- agent deep reinforcement learning for minimization of voltage deviation and operation cost in active distribution system. ACCESS, 13, 75648-75665. (www)

  @article{ , abstract = {The increasing penetration of renewable energy sources (RESs) has led to the proliferation of small-scale distributed energy resources (DERs) in modern power systems. Effective coordination of these DERs in active distribution systems benefits both utilities and consumers. This paper introduces a novel distributed active voltage and operation cost control (DAVOCC) framework designed to minimize node voltage deviations and operation costs. The proposed framework employs a multi-objective optimization approach, integrating three advanced algorithms: multi-agent proximal policy optimization (MAPPO), multi-agent asynchronous actor-critic (MAA2C), and multi-agent twin delayed deep deterministic policy gradient (MATD3). Battery energy storage systems (BESSs) and diesel generators (DGs) are used as heterogeneous agents, with their actions being constrained within predefined limits to ensure safe operation. The proposed framework is trained and tested on real-world data in a modified IEEE 33-node distribution system by featuring centralized training and decentralized execution (CTDE) framework. The obtained results demonstrate that three algorithms effectively maintain node voltage deviations within acceptable limits, with the MATD3-based algorithm achieving superior performance. Specifically, it delivers node voltage deviations close to nominal values, with an average deviation of 0.0042 p.u. and a standard deviation of 0.0065 p.u. Furthermore, the MATD3 algorithm reduces operational costs to 56,837.85 THB/day while generating the highest net profit of 725,943.71 THB/day from energy trading. These findings underscore the potential of the developed DAVOCC framework in optimizing the power management of BESSs and DGs, reducing the dependence on external grid energy and ensuring effective voltage regulation in active distribution systems.}, archive = {J_ACCESS}, author = {Anurak Deanseekeaw and Watcharakorn Pinthurat and Boonruang Marungsri}, doi = {10.1109/ACCESS.2025.3565123}, journal = {IEEE Access}, pages = {75648-75665}, shortjournal = {IEEE Access}, title = {Multi-objective-based multi-heterogeneous- agent deep reinforcement learning for minimization of voltage deviation and operation cost in active distribution system}, volume = {13}, year = {2025}, }
• (2025). Unverified rumor detection. ACCESS, 13, 75635-75647. (www)

  @article{ , abstract = {Currently, early rumor detection has garnered significant attention due to the rapid dissemination of information on social media. Many existing studies approach rumor detection as a traditional classification task, disregarding the existence of unverified rumors that require future verification. To address this limitation, we present T3-ERD, a novel framework that employs an improved BigBird transformer-based model for detecting rumors of specific types while concurrently leveraging reinforcement learning to determine early time checkpoints and categorize unverified rumors. And we introduce a novel bucket strategy, Online Dynamic, which dynamically clusters comments and processes them in batches, demonstrating remarkable effectiveness. The T3-ERD achieves F1 score, accuracy, and early rate of 0.830, 0.837, and 0.220 on the Twitter15 dataset, and 0.837, 0.844, and 0.205 on the Twitter16 dataset, respectively.}, archive = {J_ACCESS}, author = {Chang Shu and Xiaochu Tang and Jing Xiao and Yuanxu Chen}, doi = {10.1109/ACCESS.2025.3564638}, journal = {IEEE Access}, pages = {75635-75647}, shortjournal = {IEEE Access}, title = {Unverified rumor detection}, volume = {13}, year = {2025}, }
• (2025). Hybrid vision system for enhanced situational awareness in unmanned surface vehicles: Decision-level camera-LiDAR fusion with supervised and unsupervised approaches. ACCESS, 13, 75618-75634. (www)

  @article{ , abstract = {The integration of LiDAR and cameras with an efficient data fusion approach significantly improves Enhanced Situational Awareness (ESA) for small-sized Unmanned Surface Vehicles (USVs). This is critical for early obstacle detection, particularly when maritime obstacles are only 5 to 10 seconds away. This is important to ensure effective collision avoidance in regions without reliable Global Positioning System (GPS) and Automatic Identification System (AIS), while maintaining low computational requirements and real-time performance. This paper presents a system aimed at ESA using an unsupervised approach and a trained deep learning model to detect multiple maritime obstacles in denied GPS zones. The proposed system incorporates a technical mechanism that uses a compact hardware system with an integrated advanced computing module and sensors for small-sized USVs. It addresses the challenges of achieving peak performance on an edge machine learning computer integration to reduce computational overhead. Further, it minimizes temporal detection variations using sophisticated filters and clustering in dynamic maritime environments with synchronized LiDAR and camera data fusion. The detection model, trained using Maritime Federated Large Dataset (MFLD2), achieved over 99% operational accuracy with the proposed data fusion approach. The system’s capacity to precisely identify obstacle location and distance is validated by experimental findings, enabling real-time situational awareness.}, archive = {J_ACCESS}, author = {Yousef Abd Alhattab and Hasan F. M. Zaki and Abd Halim Bin Embong and Zulkifli Bin Zainal Abidin and Ahmed Rimaz Faizabadi and Ahmad Imran Ibrahim}, doi = {10.1109/ACCESS.2025.3562299}, journal = {IEEE Access}, pages = {75618-75634}, shortjournal = {IEEE Access}, title = {Hybrid vision system for enhanced situational awareness in unmanned surface vehicles: Decision-level camera-LiDAR fusion with supervised and unsupervised approaches}, volume = {13}, year = {2025}, }
• (2025). SparkNet–A solar panel fault detection deep learning model. ACCESS, 13, 75599-75617. (www)

  @article{ , abstract = {Solar power is a clean, renewable energy source with minimal greenhouse gas emissions, combating climate change and increasing energy self-sufficiency. Early fault detection like Shading, cracking, or electrical malfunctions is crucial for maximum efficiency and system failure prevention. This work presents SparkNet, a deep learning model developed for the identification of faults due to contaminants on the surface of solar panels. SparkNet uses Fire Modules architecture, which comprises Squeeze and Expand layers. It is used to extract features and detect panel surface abnormalities, trained on a comprehensive dataset of clean and faulty solar panels under various environmental conditions, and improve computational efficiency by minimizing channel sizes while preserving rich feature representations. SparkNet, when implemented with a dataset of images of solar panels and different weather conditions, achieves an average of 95% in the quantitative performance metrics, including accuracy, precision, recall, and F1 score, better than the other state-of-the-art models. Through comprehensive ablation experiments, SparkNet features are added with best-in-class machine learning (ML) classifiers to test robustness in detecting faults, and results confirm the effectiveness of SparkNet features in detecting accurate fault detection without the use of ML classifiers.}, archive = {J_ACCESS}, author = {Rohith G and Dikshithula Sai Manish and Rahul Narasimhan A and Anurag Uttam Dhavale and Rohan Reji John}, doi = {10.1109/ACCESS.2025.3564714}, journal = {IEEE Access}, pages = {75599-75617}, shortjournal = {IEEE Access}, title = {SparkNet–A solar panel fault detection deep learning model}, volume = {13}, year = {2025}, }
• (2025). HERCULES: Haptically-enabled remotely controlled ultrasound examination system. ACCESS, 13, 75585-75598. (www)

  @article{ , abstract = {The main purpose of the study is to investigate and demonstrate the feasibility and practicality of using a haptically-enabled remotely controlled ultrasound examination system (HERCULES) to perform point-of-care ultrasound. Robotic ultrasound is an emerging and important technology. This technology can help in performing ultrasound imaging in potentially contagious patients while minimizing risks of infections for sonographers (persons who perform ultrasound). This study assesses whether the robotic ultrasound system can reduce the musculoskeletal injuries sonographers endure. We developed a haptically-enabled robotic ultrasound system, which provides sonographers with a sense of touch throughout the scan. The system has haptic capabilities in which the sonographer can feel the contact force remotely and would be able to apply pressure appropriately to safeguard the patient. The system is equipped with various force thresholds. The sonographer can view the patient as well as the transducer’s position and orientation. More than 500 robotic images were captured, and a supplementary evaluation by expert radiologists was conducted to provide initial insights into image quality. In total, 56 subjects, 31 female and 25 male, aged from 21 to 55 years, participated in the clinical trials. An assessment is also carried out on the stimulation of the sonographer’s muscles during conventional vs. robotic scanning. As a result, the sonographer experienced substantial relief in back and neck muscles, right abductor pollicis brevis and right C4 paraspinal, by 88.12%, 89.19%, 93.57%, 82.0%, 72.83%, and 75.1% reduction from manual to teleoperated scenario, respectively. Subjects also reported a much more comfortable experience during robotic ultrasound scans.}, archive = {J_ACCESS}, author = {Parham M. Kebria and Saeid Nahavandi and Numan Kutaiba and Niki Koutrouza and Natalie Yang and Hamed Asadi and Glenn Guest}, doi = {10.1109/ACCESS.2025.3565231}, journal = {IEEE Access}, pages = {75585-75598}, shortjournal = {IEEE Access}, title = {HERCULES: Haptically-enabled remotely controlled ultrasound examination system}, volume = {13}, year = {2025}, }
• (2025). Bridge crack segmentation algorithm based on improved U-net. ACCESS, 13, 75576-75584. (www)

  @article{ , abstract = {This paper introduces an enhanced U-Net network designed to extract fine crack features against complex backgrounds and balance crack pixel categories for bridge crack segmentation. Initially, the model integrates an ECA (Efficient Channel Attention) module with the original U-Net to dynamically calibrate channel features. This enhances the detection of crack-related features and suppresses irrelevant textures. The architecture is further enhanced by a DSConv (Dynamic Snake Convolution) layer, which the employs learnable offset vectors to adaptively deform convolutional kernels. This allows for precise mapping along the contours of curvilinear cracks. Finally, the model utilizes a weighted combination loss function to achieve a balance between pixel-level classification and global shape consistency, effectively addressing gradient conflicts in scenarios with class imbalance. This study’s algorithm was validated for crack segmentation using a custom dataset. Experimental results show that it outperforms other algorithms in accuracy and improves upon the original U-Net across all metrics, with mIoU (Mean Intersection over Union) and F1-Score increasing by 1.23% and 1.89%, respectively.}, archive = {J_ACCESS}, author = {Yanhao Fang and Jie LYu and Yonghua Xia and Yingke Wang}, doi = {10.1109/ACCESS.2025.3559388}, journal = {IEEE Access}, pages = {75576-75584}, shortjournal = {IEEE Access}, title = {Bridge crack segmentation algorithm based on improved U-net}, volume = {13}, year = {2025}, }
• (2025). Evaluation of the performance of new type of segmented stator permanent magnet synchronous motor. ACCESS, 13, 75563-75575. (www)

  @article{ , abstract = {The paper presents the evaluation study to enhance the design of the motor’s permanent magnet (PM), with the goal of increasing the performance of the segmented stator Permanent Magnet Synchronous Motor (PMSM). The rotor is externally mounted and embedded permanent magnet placed into the groove. By adjusting the width, tip angle, ratio dimension and skew angle of the PMs optimize the magnetic flux flow inside the motor thereby improving performance of the motor under considerations. Finite Element Method (FEM) is used to evaluate for optimizing the parameters to reach the maximum torque through parametric optimization. The numerical results are compared in terms of static torque, cogging torque, iron losses, torque-to-detent torque ratio, and torque constant parameter variations of the models. The findings with optimized flux flow evolve a new type of outer rotor embedded PMSM structure that is fabricated and tested for practical adoption for static and dynamic characteristics. The proposed new type of segmented rotor exhibits low cogging torque with torque ratio of 62 against the conventional structure with value of 14. The proposed model is fabricated and evaluated for its performance against the numerical results and are in close agreement.}, archive = {J_ACCESS}, author = {Hairul Faizi Hairulnizam and Norhisam Misron and Nur Amira Ibrahim and Hafiz Rashidi Ramli and Chockalingam Aravind Vaithilingam}, doi = {10.1109/ACCESS.2025.3564321}, journal = {IEEE Access}, pages = {75563-75575}, shortjournal = {IEEE Access}, title = {Evaluation of the performance of new type of segmented stator permanent magnet synchronous motor}, volume = {13}, year = {2025}, }
• (2025). Self-supervised pretraining and quantization for fault tolerant neural networks: Friend or foe?. ACCESS, 13, 75546-75562. (www)

  @article{ , abstract = {Deep neural networks (DNNs) are increasingly being applied in critical domains such as healthcare and autonomous driving. However, their predictive capabilities can degrade in the presence of transient hardware faults, which can lead to potentially catastrophic and unpredictable errors. Consequently, various techniques have been proposed to enhance DNN fault tolerance by modifying the network structure or training procedure, thereby reducing the need for costly hardware redundancy. However, there are design or training choices whose impact on fault propagation has been overlooked in the literature. Specifically, self-supervised learning (SSL) as a pre-training technique has been shown to enhance the robustness of learned features, resulting in improved performance on downstream tasks. This study investigates the error tolerance of different DNN and SSL techniques in image classification and segmentation tasks, including those relevant to Earth Observation. Experimental results suggest that SSL pretraining, whether used alone or in combination with error mitigation techniques, generally enhances DNN fault tolerance. We complement these findings with an in-depth analysis of the fault tolerance of quantized networks. In this context, the use of standard SSL techniques leads to a decrease in accuracy. However, this issue can be partially addressed by employing methods that, during the pretraining phase, incorporate the quantization error into the loss function.}, archive = {J_ACCESS}, author = {Rosario Milazzo and Sophie M. Fosson and Lia Morra and Luca Sterpone}, doi = {10.1109/ACCESS.2025.3564834}, journal = {IEEE Access}, pages = {75546-75562}, shortjournal = {IEEE Access}, title = {Self-supervised pretraining and quantization for fault tolerant neural networks: Friend or foe?}, volume = {13}, year = {2025}, }
• (2025). Action-state Testing—A model for test design automation. ACCESS, 13, 75535-75545. (www)

  @article{ , abstract = {Model-based testing (MBT) is essential in software testing, offering automation, comprehensive coverage, and defect prevention. It uses abstract models to automatically design and generate test cases, representing the expected system behaviour, including states, transitions, inputs, and outputs. This paper explores the action-state testing modelling technique, originally introduced by the authors in Forgács and Kovács (2000). In this approach, a model step comprises an action (input), one or more responses (outputs), and an optional state. The steps can be arranged sequentially, or they may be forked and joined. Sequential steps appear within the same test case. Forked steps are distributed across different test cases. The joined steps also belong to separate test cases. In addition, the graphical model can be constructed using a text editor. This paper builds upon the concept by establishing its theoretical foundation. We demonstrate how the action-state model eliminates the need for guard conditions and coding, maintains a concise and manageable structure, and seamlessly incorporates outputs, ultimately enhancing testing efficiency. Additionally, we provide guidelines for adding new states and empirically validate the benefits of action-state testing over alternative techniques, achieving a 100% defect detection percentage (DDP). This paper marks the first instalment of the author’s Test Design Trilogy, dedicated to refining and unifying various test design techniques.}, archive = {J_ACCESS}, author = {István Forgács and Attila Kovács}, doi = {10.1109/ACCESS.2025.3563337}, journal = {IEEE Access}, pages = {75535-75545}, shortjournal = {IEEE Access}, title = {Action-state Testing—A model for test design automation}, volume = {13}, year = {2025}, }
• (2025). Data-adaptive dynamic time warping-based multivariate time series fuzzy clustering. ACCESS, 13, 75525-75534. (www)

  @article{ , abstract = {Multivariate time series (MTS) clustering has become a critical research area. Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and contribution of MTS dimensions, leading to a failure in accurately modeling the intricate correlations and dependencies among dimensions. Meanwhile, the lack of adaptive regulation for MTS dimensions in distance measures significantly impacts clustering accuracy. In view of these issues, we propose a data-adaptive dynamic time warping (DTW) based fuzzy clustering method for MTS. This method utilizes locally weighted DTW as the kernel distance measure, enabling the adaptive regulation of MTS dimensions. To address the non-convex optimization problem associated with DTW-based clustering, we formulate a comprehensive objective function and present an efficient optimization method based on closed-form solutions. This unsupervised learning method significantly improves the precision of DTW, leading to more accurate and interpretable clustering outcomes. Extensive experiments conducted on eight public datasets, along with comparisons to 10 benchmark methods, demonstrate the competitive performance of our method in terms of both accuracy and efficiency.}, archive = {J_ACCESS}, author = {Qinglin Cai and Leiying Chen and Jian Shao and Ling Chen}, doi = {10.1109/ACCESS.2025.3564539}, journal = {IEEE Access}, pages = {75525-75534}, shortjournal = {IEEE Access}, title = {Data-adaptive dynamic time warping-based multivariate time series fuzzy clustering}, volume = {13}, year = {2025}, }
• (2025). Exploring consistent feature selection for software fault prediction: An XAI-based model-agnostic approach. ACCESS, 13, 75493-75524. (www)

  @article{ , abstract = {Numerous feature selection (FS) techniques have been widely applied in Software Engineering (SE) to improve the predictive performance of machine learning (ML) models. However, the consistency of these FS techniques, i.e., their ability to select stable features under various data changes, remains underexplored. While previous studies have examined the stability of traditional FS methods (e.g., Information Gain, Genetic Search), their findings are limited in scope. With the increasing use of eXplainable Artificial Intelligence (XAI) in SE, it is essential to assess the consistency of model-agnostic FS techniques to ensure their reliability in dynamic learning environments. In this study we evaluated the consistency of two prominent XAI-based techniques, Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP), across five ML models: Linear Regression (LR), Multi-layer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM). Experiments are conducted on six Software Fault Prediction (SFP) datasets using various validation strategies (e.g., 3-fold cross-validation, bootstrap), normalization and dataset modifications. The findings of the study reveal that model-agnostic FS techniques exhibit higher consistency than traditional techniques across all scenarios. Under validation-based changes, SHAP with SVM and DT achieves better average consistency (100%), while MLP records the lowest (74.27%). For PFI, LR, DT, and SVM also reach 100% consistency, with MLP again being the lowest (44.03%). In data modification scenarios, SHAP with MLP shows the highest consistency (76.20%), whereas SVM performs the lowest (70.98%). Using PFI, RF achieves the highest (77.24%), and SVM the lowest (62.84%). Overall, SHAP outperforms PFI across most conditions, particularly under 5-fold CV, bootstrap, and LOO CV, while PFI is more stable when new instances are added to the training set. These findings confirm that both SHAP and PFI offer better consistency than traditional FS techniques, underscoring their reliability for real-world SFP tasks.}, archive = {J_ACCESS}, author = {Adam Khan and Asad Ali and Jahangir Khan and Fasee Ullah and Muhammad Faheem}, doi = {10.1109/ACCESS.2025.3558913}, journal = {IEEE Access}, pages = {75493-75524}, shortjournal = {IEEE Access}, title = {Exploring consistent feature selection for software fault prediction: An XAI-based model-agnostic approach}, volume = {13}, year = {2025}, }
• (2025). Adaptive task scheduling in fog computing using federated DQN and K-means clustering. ACCESS, 13, 75466-75492. (www)

  @article{ , abstract = {Task scheduling in distributed cloud and fog computing applications must be efficient to optimize resource utilization, minimize latency, and comply with strict service level agreements. The dynamic and heterogeneous of fog computing challenges such as balancing performance with variable task loads, resource constraints, and energy efficiency. This paper introduces a new Federated Learning Deep Queue-Learning (FLDQN) framework that integrates reinforcement learning with federated learning to overcome these challenges. The FLDQN approach enables decentralized task scheduling because fog nodes can train their local DQN models on localized data, merging their experiences to form a global model without sensitive data sharing. It further improves task prioritization by classifying tasks based on their execution time and deadlines, ensuring that high-priority tasks are scheduled first, which reduces SLA violations and task rejection rates. The proposed model dynamically adapts to the changing conditions of the fog environment through iterative learning that continuously improves work allocation. Substantial experiments conducted on large and small sizes of dataset show that the proposed FLDQN outperforms others, including standalone DQN and graph-based GGCN. For all dataset sizes small, medium, and large datasets, it reduces makespan by up to 30%, improves throughput, and reduces energy by distributing tasks on the most efficient node based on current system states. The results demonstrate the ability of FLDQN to optimize task execution in real time, while addressing the scalability and privacy issues inherent in fog computing. Combining federated and reinforcement learning proposed framework provides a flexible solution for distributed task scheduling, which is well suited for latency-sensitive applications in industrial automation, healthcare monitoring, and smart city deployments.}, archive = {J_ACCESS}, author = {Prashanth Choppara and S. Sudheer Mangalampalli}, doi = {10.1109/ACCESS.2025.3563487}, journal = {IEEE Access}, pages = {75466-75492}, shortjournal = {IEEE Access}, title = {Adaptive task scheduling in fog computing using federated DQN and K-means clustering}, volume = {13}, year = {2025}, }
• (2025). Sri lankan SMEs’ performance through cloud computing adoption: An SEM-ANN analysis. ACCESS, 13, 75448-75465. (www)

  @article{ , abstract = {This study identifies the determinants of cloud computing adoption and its effect on the performance of Sri Lankan small and medium-sized enterprises (SMEs). The Technology-Organization-Environment (TOE) framework, Technology Acceptance Model (TAM), and individual context were used to derive the study variables. This quantitative cross-sectional study adopted items from previous validated studies. Google Form was employed to collect data, and 418 responses were received from Sri Lankan SMEs. Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS 4 and Artificial Neural Network (ANN) analysis via IBM SPSS 29 were used for data analysis. Based on the results, all hypotheses are confirmed except for one, and SME performance is significantly affected by cloud computing adoption. This study adds to the existing empirical evidence on cloud computing adoption by introducing an all-inclusive model that integrates the TOE, TAM, and individual factors. This demonstrates the effectiveness of the PLS-SEM/ANN hybrid methodology in analysing the determinants of cloud computing adoption. The significance of top management as a factor is highlighted by providing training and education to employees. Managers can benefit from this result by improving cloud computing adoption among SMEs in Sri Lanka. This is the first study of its kind in Sri Lanka, integrating the TOE, TAM, and individual variables and using a hybrid methodology combining PLS-SEM and ANN.}, archive = {J_ACCESS}, author = {Samsudeen Sabraz Nawaz and Samantha Thelijjagoda}, doi = {10.1109/ACCESS.2025.3564339}, journal = {IEEE Access}, pages = {75448-75465}, shortjournal = {IEEE Access}, title = {Sri lankan SMEs’ performance through cloud computing adoption: An SEM-ANN analysis}, volume = {13}, year = {2025}, }
• (2025). Hybrid signaling-droop DC-link voltage control for trains with multiple onboard power sources. ACCESS, 13, 75434-75447. (www)

  @article{ , abstract = {The increasing trend in efficiency and performance requirements of electric trains is leading to the deployment of more onboard energy storage elements. Hydrogen fuel cells along with battery systems are being considered by several train manufacturers. Control of the dc-link connecting all energy sources and loads can be challenging due to the need to guarantee reliable operation in all possible scenarios. This paper presents a dc-link control strategy for trains with multiple energy sources. The proposed method combines the advantages of dc bus signalling and droop methods, and allows smooth enabling and disabling of power sources by voltage command modification without any change in the overall control structure. Further, it is fault tolerant to communication loss between the battery and the central control. The proposed concepts are validated in multiple scenarios by means of simulation, as well as experimentally using a down-scaled test bench.}, archive = {J_ACCESS}, author = {Saad Ahmad and Mariam Saeed and Juan Manuel Guerrero and Iker Muniategui-Aspiazu and Guillermo Nuñez and Igor Larrazabal and Fernando Briz}, doi = {10.1109/ACCESS.2025.3564406}, journal = {IEEE Access}, pages = {75434-75447}, shortjournal = {IEEE Access}, title = {Hybrid signaling-droop DC-link voltage control for trains with multiple onboard power sources}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization of off-grid E-bikes charging stations powered by PhotoVoltaics. ACCESS, 13, 75416-75433. (www)

  @article{ , abstract = {The integration of renewable energy in the power supply chain of Electric Vehicles (EVs) is fundamental in order to decarbonize the transportation sector. Yet, this poses additional threats to the smooth functioning of power systems. In the case of e-bikes, the load is modest and it becomes conceivable to exploit as much as possible distributed renewable power generation coupled with storage. For this reason, attention has recently been growing towards the development of off-grid charging stations for Light EVs (LEVs) powered by renewables. For this kind of charging stations, the power supply for the e-bikes can arrive solely from renewable power production or storage and it is not guaranteed that there is power available for the recharge whenever the demand occurs. Hence, the design of such systems needs to consider two conflicting objectives, which are the minimization of the costs and of the number of not served e-bikes. Based on such premise, this work contributes to the multi-objective optimization of off-grid charging stations for e-bikes. A Genetic Algorithm is employed to determine the most appropriate rated power of the installed PhotoVoltaic (PV) systems and of the energy storage, by incorporating statistical methods to estimate the daily number of e-bikes requiring charging, hence making the optimization process more reflective of actual usage patterns. Under the assumed conditions, the optimized solution guarantees a high quality of service, as the number of uncharged e-bikes is less than the 5%. The Capital Expenditure (CapEx) and Operational Expenditure (OpEx) are estimated for the identified optimized charging station and compared against the grid-connected case and it arises that the off-grid system is slightly more profitable after 3 years, due to the savings in the energy costs.}, archive = {J_ACCESS}, author = {Fabio Corti and Gabriele Maria Lozito and Davide Astolfi and Salvatore Dello Iacono and Antony Vasile and Marco Pasetti and Alberto Reatti and Alessandra Flammini}, doi = {10.1109/ACCESS.2025.3564117}, journal = {IEEE Access}, pages = {75416-75433}, shortjournal = {IEEE Access}, title = {Multi-objective optimization of off-grid E-bikes charging stations powered by PhotoVoltaics}, volume = {13}, year = {2025}, }
• (2025). 5G new radio for non-terrestrial networks: Analysis and comparison of HARQ and RLC ARQ performance over satellite links. ACCESS, 13, 75400-75415. (www)

  @article{ , abstract = {In this paper, we study the extension of 5G New Radio (NR) to Non-Terrestrial Networks (NTN). For terrestrial ones, Hybrid Automatic Repeat reQuest (HARQ) is the main retransmission solution used by 5G NR at the physical and MAC layers, enhancing decoding performance through diversity and coding gain. However, for NTN, its implementation faces challenges due to the significant delays caused by the long distances of satellites. In the first part, we begin by investigating the minimum number of HARQ processes required for various LEO scenarios, as well as the relationship between the number of processes and the coherence time of the satellite link. Next, while the performance of 5G retransmission schemes over AWGN and terrestrial channels is well explored, this is not the case for realistic satellite channel models. To address this, we have developed an open-source simulator that accurately implements all the blocks of the data channel transmission and reception chain, including the retransmission schemes and the Land Mobile Satellite (LMS) channel. We consider the 5G NR Physical Downlink Shared Channel (PDSCH) and we present and discuss a number of results over the LMS channel, which are important to understand the HARQ performance for the NTN satellite scenario. In the second part, we consider the 5G NR alternative retransmission solution, RLC ARQ, which is available at the Radio Link Control (RLC) layer. This method might be interesting for satellite links, because it adds minimal complexity to the receiver side, but it provides less enhancement to signal reception capabilities and more latency. We first present an analytic model to compute its performance over the LMS channel, then we analyze its behavior. Finally, we provide a detailed comparison and discussion of HARQ and RLC ARQ performance in terms of block error rate, spectral efficiency, and latency. This extensive analysis provides valuable insights for researchers and space agencies interested in applying 5G NR to satellite-based Non-Terrestrial Networks.}, archive = {J_ACCESS}, author = {Riccardo Tuninato and Gabriel Maiolini Capez and Nicolò Mazzali and Roberto Garello}, doi = {10.1109/ACCESS.2025.3563983}, journal = {IEEE Access}, pages = {75400-75415}, shortjournal = {IEEE Access}, title = {5G new radio for non-terrestrial networks: Analysis and comparison of HARQ and RLC ARQ performance over satellite links}, volume = {13}, year = {2025}, }
• (2025). Data-driven customer retention strategies in E-commerce: A fuzzy Z-number approach. ACCESS, 13, 75388-75399. (www)

  @article{ , abstract = {Customer attrition continues to be an issue in the e-business environment, which may result in considerable losses in terms of sales. This research proposes a new hybrid model for predicting and controlling customer churn in a more efficient way. Our model, which uses both machine learning and fuzzy logic techniques, provides a stable solution to this important issue. For the purpose of evaluating customer churn, we use the Support Vector Machine (SVM) model. In order to improve the performance of the SVM, we tune its parameters using the Coyote Optimization Algorithm (COA). After customer churn has been predicted, Fuzzy Z-numbers are used to help make the right decisions on which customers should be retained. This approach enables us to bring in quantitative and qualitative aspects of the decision-making process like the customers’ behavior, their preferences, and demographic data. The performance of the proposed model is thoroughly tested on a dataset of 8,913 transactions from the WooCommerce platform. The outcomes show better efficiency in terms of precision, which is equal to 0.98, and accuracy of 97.2%, showing improvements compared to the previous methods. This research offers a useful instrument that will help e-commerce companies improve their customer retention strategies and achieve sustainable growth.}, archive = {J_ACCESS}, author = {Siyin Hu and An Chen}, doi = {10.1109/ACCESS.2025.3550190}, journal = {IEEE Access}, pages = {75388-75399}, shortjournal = {IEEE Access}, title = {Data-driven customer retention strategies in E-commerce: A fuzzy Z-number approach}, volume = {13}, year = {2025}, }
• (2025). An optimization model for harmonic parameters estimation of transmission lines using phasor measurements. ACCESS, 13, 75377-75387. (www)

  @article{ , abstract = {Transmission line parameter estimation is a critical issue for power system operation and planning studies. With the advent of synchrophasor technology, numerous new methods have been developed using PMUs (Phasor Measurement Units) based on real-time measurements from the sending and receiving ends of a transmission line. Typically, the equivalent PI section of the line is determined for the fundamental frequency only. For other harmonic orders, impedances can be measured at specific points in the electric network using a frequency scan approach. However, few studies have focused on the separate estimation of all longitudinal resistances, reactances and shunt admittances for higher harmonic orders. This paper presents a novel approach to this long-standing issue by formulating an optimization problem, where the least squares equation is minimized, subject to inequality constraints associated with the harmonic impedances of a transmission line, modeled with a quadrupole representation. The proposed optimization method allows for the estimation of line impedance parameters with reduced errors for all harmonic orders considered in the analysis. Computational simulations are carried out to demonstrate the applicability of the method, using different line models including a real Brazilian transmission line, and the impact of instrument transformers on the estimated results is also assessed.}, archive = {J_ACCESS}, author = {Leticia L. S. de Sousa and Igor D. Melo and Carlos A. Duque and Candida A. Meneghin and Paulo F. Ribeiro}, doi = {10.1109/ACCESS.2025.3563771}, journal = {IEEE Access}, pages = {75377-75387}, shortjournal = {IEEE Access}, title = {An optimization model for harmonic parameters estimation of transmission lines using phasor measurements}, volume = {13}, year = {2025}, }
• (2025). A new keyed hash function based on compounded chaotic maps. ACCESS, 13, 75363-75376. (www)

  @article{ , abstract = {In this paper, we firstly construct a new chaotic map by compounding a couple of chaotic maps and, using specific mathematical tools (i.e., Lyapunov exponent), we prove its sensitivity to the initial conditions and the underlying chaotic behavior. Secondly, using this chaotic map, we propose a new chaotic keyed hash function which involves each byte of the message in calculation of the chaotic map initial point and the compounded maps control parameters. Corroborating this method with the proven sensitivity of the used chaotic map, we obtain an avalanche effect in the hashing process and the guarantee that all the bits of the message will be related to the hash value. Moreover, the exhaustive numerical simulations show that the proposed hash function has very good confusion and diffusion capabilities, strong collision resistance, high level of security and good speed, being suitable for applications regarding data integrity or authentication, such as ciphers or blockchain applications.}, archive = {J_ACCESS}, author = {Ana-Cristina Dăscălescu and Radu-Eugen Boriga and Iustin Priescu}, doi = {10.1109/ACCESS.2025.3563108}, journal = {IEEE Access}, pages = {75363-75376}, shortjournal = {IEEE Access}, title = {A new keyed hash function based on compounded chaotic maps}, volume = {13}, year = {2025}, }
• (2025). Research on sleep stage classification of electroencephalogram signals based on CNN and LSTM. ACCESS, 13, 75351-75362. (www)

  @article{ , abstract = {Chronic sleep deprivation can seriously affect physical and mental health and increase the risk of many chronic diseases. Sleep staging methods can assess sleep quality, but the manual interpretation is inconvenient and subjective. Establishing efficient automated sleep staging models is crucial to saving time and cost, improving efficiency, and helping patients with sleep disorders receive timely treatment. In recent years, artificial intelligence has been rapidly developing in the medical field, diagnostic efficiency has become an auxiliary tool, and even the accuracy rate exceeds that of doctors. In this paper, deep learning technology is applied to sleep EEG signal staging, exploring the basic theory and preprocessing method of EEG signal, and comparing the performance of the automatic sleep staging model through experiments. The process mainly involves processing the original dataset, establishing a standard dataset, and extracting features using a band-pass filter combined with VMD and FFT methods. Two automatic sleep staging models based on LSTM and LSTM+CNN were proposed. The models, which integrate VMD and FFT methods, achieved accuracies of 87% and 92%, respectively. Experimental results showed that the LSTM+CNN model, which combines VMD and FFT methods, performed better in terms of classification accuracy and loss values. Compared to using LSTM alone for sleep staging, it demonstrated outstanding classification performance. This technology is expected to provide healthcare institutions and doctors with faster and more accurate sleep monitoring and diagnostic tools to improve patients’ sleep health and quality of life.}, archive = {J_ACCESS}, author = {Liying Zhu and Qingxin Guan and Yunqing Liu and Jiayue Xu and Sha Ma}, doi = {10.1109/ACCESS.2025.3559350}, journal = {IEEE Access}, pages = {75351-75362}, shortjournal = {IEEE Access}, title = {Research on sleep stage classification of electroencephalogram signals based on CNN and LSTM}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based control of active power filters using LSTM and GRU networks for harmonic and frequency estimation. ACCESS, 13, 75332-75350. (www)

  @article{ , abstract = {Deep Learning (DL) techniques provide a powerful tool enhancing the learning capabilities of the neural networks (NN), and are increasingly applied in the field of electric power systems. In particular, the long short-term memory (LSTM) and the gated recurrent unit (GRU) networks allow improvements on signal processing. The relevance of suppressing electrical disturbances justifies the efforts to apply new control algorithms to the active power filters (APF). Despite the existence of many control techniques, the NN-based proposals generally present significant shortcomings. Therefore, in this work, a new neural controller is presented for further improvement, using previously trained NNs, without need of adaptive algorithms. The generation of the three-phase APF reference currents is based on LSTM and GRU networks, that extract the full necessary information from currents and voltages, thus avoiding the need of an additional phase synchronization control. It is a novel proposal comprising FCE (fundamental Fourier coefficients estimation) and FE (frequency estimation) along with a simple computation process, for harmonic distortion and reactive power compensation. It has been tested with many practical loads and conditions through simulation and experimental platforms. Its general high performance confirms a substantial progress compared to other NN controllers, and it could be an alternative to other techniques.}, archive = {J_ACCESS}, author = {Juan L. Flores-Garrido and Patricio Salmerón and Juan A. Gómez-Galán and Alejandro Pérez-Vallés}, doi = {10.1109/ACCESS.2025.3564636}, journal = {IEEE Access}, pages = {75332-75350}, shortjournal = {IEEE Access}, title = {Deep learning-based control of active power filters using LSTM and GRU networks for harmonic and frequency estimation}, volume = {13}, year = {2025}, }
• (2025). Synthesis of variable gain robust state observer for linear systems with structured uncertainties. ACCESS, 13, 75323-75331. (www)

  @article{ , abstract = {This paper proposes a new variable gain robust state observer for a class of linear uncertain systems. The variable gain robust state observer proposed in this paper consists of fixed observer gain matrices and nonlinear modification functions which are determined by appropriate updating rules. It is shown that sufficient conditions for the existence of the proposed variable gain robust state observer can be reduced to solvability of Linear Matrix Inequalities (LMIs). Finally, we give simple numerical examples so as to illustrate the effectiveness of the proposed variable gain robust state observer.}, archive = {J_ACCESS}, author = {Kazuhiro Hirasawa and Hayate Sawato and Daiki Asada and Hidetoshi Oya}, doi = {10.1109/ACCESS.2025.3563214}, journal = {IEEE Access}, pages = {75323-75331}, shortjournal = {IEEE Access}, title = {Synthesis of variable gain robust state observer for linear systems with structured uncertainties}, volume = {13}, year = {2025}, }
• (2025). Investigating the behavioral adoption intention of mobile government services from the value-based adoption model perspective. ACCESS, 13, 75305-75322. (www)

  @article{ , abstract = {This research investigated the determinants affecting the adoption of m-government services through the framework of the Value-Based Adoption Model. This study bridges the gap in the literature in two aspects: first relating to the moderating influence of mobility and security respectively on the relationship between benefits (usefulness and enjoyment) and perceived value of m-government services. Second the moderating effect of mobility and security respectively on the interaction between perceived value and the adoption intention of m-government services. The analysis utilizing a dataset comprising 467 Chinese citizens was processed with Smart Partial Least Squares software, employing structural equation modeling techniques. The findings indicate that perceived usefulness, enjoyment, technical aspects, and perceived costs directly influence the perceived value of m-government services. Furthermore, the perceived value associated with m-government was identified as influencing the adoption behavior of m-government services. The moderation analysis showed that mobility played a significant role in influencing the relationship between the benefits of usefulness and enjoyment and the perceived value of m-government services. Moreover, mobility was a key factor in moderating the influence of perceived value on the adoption of m-government services. In addition, security played a crucial role in influencing the relationship between enjoyment and the perceived value of m-government. It also had a significant moderating effect on the relationship between perceived value and the intention to adopt m-government services. Also, the findings of the study revealed that security does not play a significant moderating role in the effect of usefulness on the perceived value of m-government services. These results provide insights into the implications of these findings for the development of value-oriented m-government services.}, archive = {J_ACCESS}, author = {Isaac Kofi Mensah and Deborah Simon Mwakapesa}, doi = {10.1109/ACCESS.2025.3564732}, journal = {IEEE Access}, pages = {75305-75322}, shortjournal = {IEEE Access}, title = {Investigating the behavioral adoption intention of mobile government services from the value-based adoption model perspective}, volume = {13}, year = {2025}, }
• (2025). A generalizable telerobotic solution for undisrupted man-robot workflow coupling via shape-variable virtual fixture. ACCESS, 13, 75294-75304. (www)

  @article{ , abstract = {This paper introduces a generalizable telerobotic solution for the coupling between manual and machine workflow tracks to facilitate the incorporation of personnel-required procedures in lab automation processes. The proposed shape-variable virtual fixture can be generalized for providing synchronized dexterous guidance during human interaction with a robotized automation process without disrupting the automated workflow. Accuracy performance evaluation showed that the use of our proposed shape-variable virtual fixture reduced positioning and orientation errors by 50%. Stability and transparency are validated by evaluation of the force profiles during the teleoperation process while interacting with the environment. User experience test based on the NASA-TLX evaluation also indicates reduced workload and task complexity experienced by the subjects of informed consent, suggesting better usability with our proposed method. This study demonstrates the feasibility and usability of the proposed telerobotic approach using shape-variable virtual fixtures for generalized man-robot collaborated workflows. It is envisioned that the work would pave a path for the realization of safer and more intuitive human-robot collaboration while maintaining the efficiency of the ever-increasing task complexity in modern smart laboratories.}, archive = {J_ACCESS}, author = {Yunze Shi and Tengyue Wang and Chen Lei and Liangjing Yang}, doi = {10.1109/ACCESS.2025.3558881}, journal = {IEEE Access}, pages = {75294-75304}, shortjournal = {IEEE Access}, title = {A generalizable telerobotic solution for undisrupted man-robot workflow coupling via shape-variable virtual fixture}, volume = {13}, year = {2025}, }
• (2025). Online federated deep probabilistic learning-based smart healthcare on multi-cloud systems. ACCESS, 13, 75265-75293. (www)

  @article{ , abstract = {Healthcare management is one of the research topics that is being addressed intensively during the last decade. It is one of the corner stones that smart cities are built upon. This article addresses the problem of patient diagnoses. The article proposes an unsupervised deep Bayesian neural network architecture that learns the probabilities of different diagnoses based on factors like symptoms, patient history, and patient personal information. The outcome of the work is to optimize the healthcare process in a way to find the balance between minimizing test costs and maximizing the Precision of the diagnosis process. Every medical institution like hospitals and clinics provide data of symptoms, tests orders and the effectiveness of those tests in finding the actual diagnosis. An overall model is built based on federated learning to optimize the selection process of tests based on observed symptoms. Experiments show that the proposed model succeeds in improving the process with respect to time, cost, and Precision. The obtained results show that the more clouds involved in the experiment the less the error is and the faster the learning curve is.}, archive = {J_ACCESS}, author = {Yehia Kotb and Soraia Oueida and Nour Mostafa and Nawaf Ali}, doi = {10.1109/ACCESS.2025.3557877}, journal = {IEEE Access}, pages = {75265-75293}, shortjournal = {IEEE Access}, title = {Online federated deep probabilistic learning-based smart healthcare on multi-cloud systems}, volume = {13}, year = {2025}, }
• (2025). Bidirectional wireless charging system for electric vehicles: A review of power converters and control techniques in V2G application. ACCESS, 13, 75246-75264. (www)

  @article{ , abstract = {Electric Vehicles (EVs) play a crucial role in integrating renewable energy into the Smart Grid by functioning as both energy consumers and mobile energy storage systems. This dual role enhances grid flexibility, allowing EVs to support power balance during peak demand and store excess renewable energy during off-peak periods. To fully utilize this potential, EV chargers must support bidirectional power flow, enabling seamless energy exchange between the grid and vehicles. This capability extends to wireless charging systems, which are gaining popularity due to their convenience, safety, and efficiency. This paper comprehensively reviews the control strategies and power converter topologies employed in bidirectional wireless charging systems for Vehicle-to-Grid (V2G) applications. The study highlights key considerations: compensation network design, power factor correction, and system efficiency optimization. A detailed analysis of control algorithms managing active and reactive power is conducted using simulation models and experimental setups. The results demonstrate that advanced control strategies optimize power flow and enhance grid stability and reliability. Moreover, the paper discusses the practical challenges of wireless V2G systems, such as grid synchronization, coil misalignment, and communication delays between primary and secondary controllers. The findings underscore the importance of innovative control algorithms and compensation techniques in overcoming these challenges and ensuring efficient energy transfer. The study concludes that the successful implementation of advanced bidirectional wireless charging systems can significantly contribute to a more resilient and sustainable energy future, facilitating the seamless integration of renewable energy into the grid.}, archive = {J_ACCESS}, author = {Subia Meraj and Saad Mekhilef and Marizan Binti Mubin and Harikrishnan Ramiah and Mehdi Seyedmahmoudian and Alex Stojcevski}, doi = {10.1109/ACCESS.2025.3561396}, journal = {IEEE Access}, pages = {75246-75264}, shortjournal = {IEEE Access}, title = {Bidirectional wireless charging system for electric vehicles: A review of power converters and control techniques in V2G application}, volume = {13}, year = {2025}, }
• (2025). Zero-outage secrecy in short packet IoT-aided cellular cooperative network with EH-NOMA. ACCESS, 13, 75235-75245. (www)

  @article{ , abstract = {The zero-outage secrecy with energy harvesting is considered in short packet IoT-aided cellular cooperative networks using the inspired non-orthogonal multiple access (NOMA) strategy. The scheme ensures zero-outage secrecy through two cooperative transmissions: one for only artificial noise (AN) and another for both the artificial noise and the secure message. To ensure that only legitimate users can decode the message while the eavesdropper’s probability of success remains zero, which is so-called zero-outage secrecy, the scheme additionally employs the reverse training strategy to prevent the channel state information leakage to the eavesdroppers. The results show that the proposed scheme can achieve zero-outage secrecy for both IoT and cellular messages even in the case the eavesdroppers take advantage in their location or large number of antennas so that their channels become powerful. When forwarding the secure cellular message, the controller IoT node imposes the IoT message using such a large amount of harvested power that the IoT user can achieve the highest zero-outage secrecy throughput and energy efficiency, there is not much loss in the zero-outage secrecy throughput obtained at the cellular user. It also highlights that when the transmit power increases, the zero-outage secrecy throughput also increases, indicating the practical implementation of the proposed scheme.}, archive = {J_ACCESS}, author = {Hien Quang Ta and Nga B. T. Nguyen and Tien-Tung Nguyen and Khuong Ho-Van}, doi = {10.1109/ACCESS.2025.3562392}, journal = {IEEE Access}, pages = {75235-75245}, shortjournal = {IEEE Access}, title = {Zero-outage secrecy in short packet IoT-aided cellular cooperative network with EH-NOMA}, volume = {13}, year = {2025}, }
• (2025). On the challenges of quantum circuit encoding using deep and reinforcement learning. ACCESS, 13, 75220-75234. (www)

  @article{ , abstract = {Quantum computers may solve some computing tasks more efficiently than classical com- puters, but to do so requires the design of appropriate quantum circuits. However, it is hard to design even simple quantum programs. Recent results have shown that deep learning augmented search has the potential to discover good circuits for a problem, but it is not yet sufficiently understood how well deep learning is able to model such a complex domain: the search space and output space grow exponentially. This work explores the ability of neural networks to encode quantum circuits for tasks that require knowledge about the unitary representation of the circuit. To this end, we trained neural networks to directly learn to predict the unitary of a circuit and applied reinforcement learning to train neural networks to solve circuit based quantum state preparation. This work finds that encoding quantum circuits is quite difficult especially with regards to the amount of entanglement applied in both application domains. The use of intermediate states and structure in quantum circuits combined with a reasonable inductive bias, where applicable, can alleviate these problems to some extent. The use of intermediate states also greatly improves the results of Deep Reinforcement Learning for quantum state preparation. Based on these results, we discuss the next set of challenges to address if we are to design neural network-based approaches for the automatic generation of quantum circuits.}, archive = {J_ACCESS}, author = {Patrick Selig and Niall Murphy and David Redmond and Simon Caton}, doi = {10.1109/ACCESS.2025.3564392}, journal = {IEEE Access}, pages = {75220-75234}, shortjournal = {IEEE Access}, title = {On the challenges of quantum circuit encoding using deep and reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Feature reweighting-based factorization machine for effective learning latent representation. ACCESS, 13, 75204-75219. (www)

  @article{ , abstract = {Factorization machines (FMs) are widely employed as supervised predictors in collaborative recommendation. FMs can efficiently model second-order feature interactions through inner products, which is beneficial for mitigating the negative effects of data sparsity. However, existing research has largely overlooked the potential correlations and attributes among features in FMs. These inherent relationships between features can enhance our understanding and facilitate the modeling of meaningful feature representations. To address this gap and capture intrinsic correlation information in the data, we propose a novel model named Feature Reweighting-based Factorization Machine (FRFM) in this paper. Specifically, we incorporate similarity into FM and quantify the strength of interactions between features using a similarity calculation method based on mutual information. We then introduce a feature reweighting strategy to effectively learn latent representations, ensuring that similar features exhibit comparable first-order weights and second-order embedding vectors based on their similarity. By assigning different weights to different feature pairs, FRFM adeptly captures the potential correlations and attributes among features within the model. Furthermore, FRFM can be seamlessly integrated into other models to enhance their performance. Extensive experiments conducted on six real-world datasets demonstrate the advantages of our proposed FRFM compared to the state-of-the-art methods.}, archive = {J_ACCESS}, author = {Xiebing Chen and Bilian Chen and Yue Wang and Langcai Cao}, doi = {10.1109/ACCESS.2025.3564895}, journal = {IEEE Access}, pages = {75204-75219}, shortjournal = {IEEE Access}, title = {Feature reweighting-based factorization machine for effective learning latent representation}, volume = {13}, year = {2025}, }
• (2025). Privacy-diffusion: Privacy-preserving stable diffusion without FHE and differential privacy. ACCESS, 13, 75194-75203. (www)

  @article{ , abstract = {Text-to-image generation is trending in the generative artificial intelligence (GenAI) field. Among open-sourced image generation projects, Stable Diffusion is the state-of-the-art. Many artists and service providers customize the diffusion model to generate featured high-quality images. However, there is no protection to the privacy of the input text prompt, output image, and customized model. Privacy is very important since it can increase users’ willingness to use the service and protect the service provider’s intellectual property. Existing privacy-preserving diffusion model require fully homomorphic encryption (FHE) to ensure its privacy and security. Nonetheless, FHE is very time-consuming and may reduce accuracy due to approximations and deteriorate image quality. In this research, we propose Privacy-Diffusion, a privacy-preserving diffusion framework without FHE. By utilizing the irreversible property of neural network layers and the property that the predicted noise in the diffusion process is a normalized Gaussian distribution. Our framework can be applied to all kinds of diffusion models to protect clients’ input text prompt and the generated image from being learned by the server, as well as customized models from being learned by the clients. Our protocol is secure and efficient. Compared with existing research, HE-diffusion, which spent 200% extra time and visible quality loss, our protocol can reach the same security level with only 19% extra time and has no quality loss. To the best of our knowledge, our Privacy-Diffusion is the first protocol that achieves this goal without using FHE and maintain the same high-quality image output as the original model.}, archive = {J_ACCESS}, author = {Po-Chu Hsu and Ziying Yu and Shuhei Mise and Hideaki Miyaji}, doi = {10.1109/ACCESS.2025.3562563}, journal = {IEEE Access}, pages = {75194-75203}, shortjournal = {IEEE Access}, title = {Privacy-diffusion: Privacy-preserving stable diffusion without FHE and differential privacy}, volume = {13}, year = {2025}, }
• (2025). Audio-in-image steganography using analysis and resynthesis sound spectrograph. ACCESS, 13, 75184-75193. (www)

  @article{ , abstract = {Steganography is the practice of concealing secret information within a non-secret medium, such as an image or audio file, to prevent detection by unauthorised observers. Currently, there is a shortage of solutions for audio-in-image storage, with a complete absence of methods that do not rely on deep learning approaches. This is problematic because learning models often plateau at a certain level of accuracy, making further improvements difficult. Audio steganography, like other forms of steganography, is vulnerable to data malleability and requires solutions that offer customisable security features while maintaining robustness. This paper presents a novel approach for audio-in-image steganography involving the analysis and resynthesis of sound spectrographs (ARSS). This framework ensures efficient and secure data storage using a simplistic, multi-channel, and non-lossy approach to embed an audio spectrograph in a high-resolution image file. It allows for minimal pre-processing and post-processing to conceal one or more sound spectrographs within a single cover image while focusing on maintaining high fidelity in sound resynthesis during retrieval. Referred to as ASA, this proposed approach achieves a mean PSNR value of 42.54 dB for the stego-image and 45.02 dB for the reconstructed image, outperforming most existing methods while also offering high storage efficiency and infrastructure for additional security measures.}, archive = {J_ACCESS}, author = {A. Aravind Krishnan and Yukta Ramesh and Udbhav Urs and Megha Arakeri}, doi = {10.1109/ACCESS.2025.3563781}, journal = {IEEE Access}, pages = {75184-75193}, shortjournal = {IEEE Access}, title = {Audio-in-image steganography using analysis and resynthesis sound spectrograph}, volume = {13}, year = {2025}, }
• (2025). Shaped lens antenna for 5G mobile with wide angle multi-beam and optimum feed position. ACCESS, 13, 75171-75183. (www)

  @article{ , abstract = {Millimeter wave operation in 5G base stations necessitates small cell size and advanced multi-beam capabilities. At millimeter waves, base station antennas must be highly compact, making dielectric lens antennas with excellent multi-beam radiation patterns an ideal choice. To ensure lightweight and easy installation, it is crucial to design lenses with minimal thickness and curvature. This study applies a lens shaping method for Abbe’s sine condition to the newly developed straight line condition (SLC) to validate the method’s ability to produce a thin lens with good performance. The designed lens, operating at 28GHz, has a diameter of 202 mm and a thickness of 63 mm. The optimum feed positions for multi-beam operations are determined by focal region ray tracing. Additionally, the impact of feed position errors on radiation pattern degradation is analyzed to ensure accurate measurements. Electromagnetic simulator calculations demonstrate wide-angle multi-beam performance from 0° to 30°. The calculated results are compared with measured results from a fabricated lens antenna, showing good agreement in multi-beam radiation patterns and antenna gain variations. Consequently, the study confirms the achievement of a thin lens with small curvature and excellent multi-beam radiation patterns.}, archive = {J_ACCESS}, author = {Farizah Ansarudin and Nurul Huda Abd Rahman and Yoshihide Yamada and Norsiha Zainudin and M. Muhsin and Kamilia Kamardin and Yoshiki Sugimoto and Kunio Sakakibara and Norbahiah Misran}, doi = {10.1109/ACCESS.2025.3561340}, journal = {IEEE Access}, pages = {75171-75183}, shortjournal = {IEEE Access}, title = {Shaped lens antenna for 5G mobile with wide angle multi-beam and optimum feed position}, volume = {13}, year = {2025}, }
• (2025). F-OSFA: A fog level generalizable solution for zero-day DDOS attacks detection. ACCESS, 13, 75157-75170. (www)

  @article{ , abstract = {The globalization and digitization of society have caused a surge in network traffic, making reliable online services essential for user trust and system functionality. However, these services face ever-increasing threats, particularly complex and well-developed Distributed Denial of Service (DDoS) attacks. Zero-day DDoS attacks, a type of DDoS attack, are especially challenging because their new and unseen nature and lack of training data render traditional Intrusion Detection and Prevention Systems (IDPS) ineffective. To tackle this, we propose the Fog-based One Solution For All (F-OSFA) system - a model with three specialized components. The first component uses a hybrid machine learning and deep learning framework that combines convolutional neural networks (CNNs) and decision trees to detect traditional DDoS attacks. The second component employs a few-shot learning module with a contractive autoencoder for zero-day attack detection. The third component is a signature-based resource usage analyzer to counter attacks mimicking normal traffic. We demonstrate the efficacy of F-OSFA on publicly available datasets and prove the scheme is generalizable and effective. F-OSFA achieves an accuracy of 99.72% on CICDDoS2019 and 99.96% on CICIDS2017. In addition, it demonstrates its efficacy in the zero-day scenario as well by achieving a 96.77% on CICDDoS2019 and 95.98% on CICIDS2017. These evaluations testify to F-OSFA as a reliable and versatile solution against ever-evolving DDoS threats.}, archive = {J_ACCESS}, author = {Muhammad Rashid Minhas and Qaisar M. Shafi and Shoab Ahmed Khan and Tahir Ahmad and Subhan Ullah and Attaullah Buriro and Muhammad Azfar Yaqub}, doi = {10.1109/ACCESS.2025.3557822}, journal = {IEEE Access}, pages = {75157-75170}, shortjournal = {IEEE Access}, title = {F-OSFA: A fog level generalizable solution for zero-day DDOS attacks detection}, volume = {13}, year = {2025}, }
• (2025). Hottel zone physics-constrained networks for industrial furnaces. ACCESS, 13, 75134-75156. (www)

  @article{ , abstract = {This paper investigates a novel approach to improve the temperature profile prediction of furnaces in foundation industries, crucial for sustainable manufacturing. While existing methods like the Hottel Zone model are accurate, they lack real-time inference capabilities. Deep learning methods excel in speed and prediction but require careful generalization for real-world applications. We propose a regularization technique that leverages the Hottel Zone method to make deep neural networks physics-aware, improving prediction accuracy for furnace temperature profiles. Our approach demonstrates effectiveness on various neural network architectures, including Multi-Layer Perceptrons (MLP), Long Short-Term Memory (LSTM), Extended LSTM (xLSTM) and Kolmogorov-Arnold Networks (KANs). We also discussion the data generation involved.}, archive = {J_ACCESS}, author = {Ujjal Kr Dutta and Aldo Lipani and Chuan Wang and Yukun Hu}, doi = {10.1109/ACCESS.2025.3563413}, journal = {IEEE Access}, pages = {75134-75156}, shortjournal = {IEEE Access}, title = {Hottel zone physics-constrained networks for industrial furnaces}, volume = {13}, year = {2025}, }
• (2025). Experimental investigation of air humidity impact on natural convection cooling of electronic circuits. ACCESS, 13, 75123-75133. (www)

  @article{ , abstract = {Elevated temperature is the main cause of electronic circuits failures, thus a proper thermal management is indispensable. The heat from operating circuits is dissipated by conduction, radiation and convection. Several papers related to the impact of air relative humidity on natural convection cooling of electronic circuits, based on theoretical analysis, suggesting such impact, were published so far. The goal of the presented research was to investigate the problem experimentally. A dedicated measurement setup composed of a cubic enclosure placed inside a climatic chamber was built. A bipolar transistor placed inside the enclosure was used as a controlled heat source. To assess the cooling efficiency, the transistor junction temperature was maintained constant using a custom build controller circuit and the power dissipated in the device was measured. Additionally, the surface temperature of the transistor was measured using a thermographic camera and a MWIR optical fiber. The measurements were repeated for different temperature and relative humidity combinations. Based on obtained measurement results and their analysis, backed-up by simulations, it was found that air relative humidity has no impact on electronic circuits cooling efficiency in the natural convection scenario. The theoretical background of the results was also explained.}, archive = {J_ACCESS}, author = {Marcin Kałuża and Marcin Lebioda}, doi = {10.1109/ACCESS.2025.3562676}, journal = {IEEE Access}, pages = {75123-75133}, shortjournal = {IEEE Access}, title = {Experimental investigation of air humidity impact on natural convection cooling of electronic circuits}, volume = {13}, year = {2025}, }
• (2025). Explainable machine learning for radio environment mapping: An intelligent system for electric field strength monitoring. ACCESS, 13, 75104-75122. (www)

  @article{ , abstract = {The accurate characterization of signal propagation is critical for optimizing wireless network performance and supporting applications such as electromagnetic field (EMF) exposure assessment and the development of Radio Environmental Maps (REMs). This study proposes a novel, explainable machine learning system to predict electric field strength across diverse urban, semi-urban, and rural environments in Cyprus. The system is trained on a rich dataset comprising 6,543 EMF measurements collected in 2023 at mobile phone and digital TV stations, following CEPT/ECC/REC/(02)04 recommendations. The dataset includes geospatial and environmental features such as antenna distance, population density, urbanization level, and detailed built environment characteristics (e.g., volume, surface, and height). We evaluate multiple machine learning models—kNN, neural networks, decision trees, random forests, XGBoost, and LightGBM—using a two-semester split for training and assessment. Best performance was achieved with the Random Forest model, which yielded the lowest RMSE among all models. Gradient boosting models (XGBoost and LightGBM) also performed well, with RMSE values slightly higher than RF while offering flexible and scalable configurations. In contrast, k-NN and neural networks showed higher RMSE values, indicating they were less effective for this specific task. Across all models, confidence intervals were narrow, demonstrating stable and reliable predictions. Explainable AI techniques revealed that antenna distance, building volume, and population density are the most influential predictors of EMF intensity. Our approach outperforms traditional signal models by incorporating urban morphology and demographic context. As part of this system, we also create a Geographic Information System (GIS) that displays electromagnetic field strength maps derived from our explainable machine learning models. This contributes a scalable, interpretable framework for EMF exposure mapping to support regulatory monitoring, urban planning, and smart city initiatives.}, archive = {J_ACCESS}, author = {Yiannis Kiouvrekis and Theodor Panagiotakopoulos and Efthymia Nousi and Ioannis Filippopoulos and Agapi Ploussi and Ellas Spyratou and Efstathios P. Efstathopoulos}, doi = {10.1109/ACCESS.2025.3564650}, journal = {IEEE Access}, pages = {75104-75122}, shortjournal = {IEEE Access}, title = {Explainable machine learning for radio environment mapping: An intelligent system for electric field strength monitoring}, volume = {13}, year = {2025}, }
• (2025). A novel partitioned random forest method-based facial emotion recognition. ACCESS, 13, 75089-75103. (www)

  @article{ , abstract = {Facial emotion recognition (FER) has been applied to various sectors, including e-learning, marketing, humanoid robot design, HMI/HCI applications, and medicine. The rapid development of intelligent technologies has led researchers to strive to improve facial emotion recognition techniques. A range of machine learning (ML) methods can be used to recognize facial expressions based on data from small to large datasets. Random Forest (RF) is simpler and more efficient than other ML algorithms. Some modified versions of RF have been developed to improve classification accuracy in the literature. Most improved RF versions modify attribute selection processes or combine them with other machine learning algorithms, increasing their complexity. Identifying an appropriate training dataset and determining its size remain open questions. The partitioned random forests (PRFs) approach is proposed as a modified strategy for improving FER. The proposed method divides multiple regions (different data lengths) into the feature space, allowing the algorithm to learn more complex decision boundaries. Using three statistical measures Lyapunov exponents (LE), Correlation Dimension (CD), and approximate entropy (AE), we evaluated the performance of machine learning algorithms over different data lengths. A crucial role for classification accuracy is played by the Lyapunov exponent or LE and the size of the dataset. A PRF is more effective on smaller datasets and with higher LE values. The proposed method for partitioning the datasets has been successfully tested on the FER dataset to classify six basic emotions (sadness, anger, fear, surprise, disgust, and happiness). Based on our numerical results, PRF performed better than traditional RF and other ML methods for FER, providing 98.37% mean absolute accuracy. Thus, a robust and useful method for improving classification rates is proposed for both small and large datasets.}, archive = {J_ACCESS}, author = {Hanif Heidari and M. Murugappan and Javeed Shaikh-Mohammed and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3560362}, journal = {IEEE Access}, pages = {75089-75103}, shortjournal = {IEEE Access}, title = {A novel partitioned random forest method-based facial emotion recognition}, volume = {13}, year = {2025}, }
• (2025). A symbiosis of multi-criteria decision making and electroencephalography: A review of techniques, applications, and future directions. ACCESS, 13, 75071-75088. (www)

  @article{ , abstract = {Multi-criteria decision-making (MCDM) problems are generally oriented toward strategic decisions and have a significant economic aspect, holding a special place in decision-making processes. To address these problems, specialized techniques have been developed and utilized. Electroencephalography (EEG) is a technique that measures brain activity, and the results can be used both to diagnose neurological disorders and to create more productive and healthier working conditions in a wide variety of areas. This paper discusses the collaborative nature between MCDM and EEG, focusing on how EEG and MCDM processes can enhance each other. Based on the database search results, thirty-five out of 149 papers were selected for the review process. By examining the literature on both macro and micro scales, the following were mainly retrieved: a) the MCDM techniques applied and their use in analyzing EEG data, b) EEG devices and wave types used in the MCDM process, c) the attributes of MCDM or EEG that were focused on, and d) current trends in knowledge and research opportunities. The results of this study will help identify potential future research areas, as well as provide a comprehensive overview of the existing literature. To summarize the findings, it can be concluded that EEG measurements of decision-makers’ cognitive states during the application of MCDM techniques improve the MCDM process and the presentation of these techniques, or they enhance the results obtained by MCDM based on the cognitive states of decision-makers when evaluating alternatives. Additionally, MCDM techniques contribute to improvements in the classification or feature extraction stages of data obtained through EEG.}, archive = {J_ACCESS}, author = {Aylin Adem and Erman Çakıt and Metin Dağdeviren and Anna Szopa and Waldemar Karwowski}, doi = {10.1109/ACCESS.2025.3562099}, journal = {IEEE Access}, pages = {75071-75088}, shortjournal = {IEEE Access}, title = {A symbiosis of multi-criteria decision making and electroencephalography: A review of techniques, applications, and future directions}, volume = {13}, year = {2025}, }
• (2025). Vision-based measurement for quality control inspection integrated into a die-casting process in industry 4.0 era. ACCESS, 13, 75047-75070. (www)

  @article{ , abstract = {High-pressure die casting (HPDC) is a widely adopted manufacturing process in the automotive industry, renowned for producing complex metal components with high precision and surface quality. However, HPDC is inherently susceptible to defects such as shrinkage, high porosity, and filling irregularities, which can compromise product integrity and escalate production costs through increased rework and material waste. Although effective, traditional Statistical Process Control (SPC) methods require significant statistical expertise and are often cost-prohibitive for smaller manufacturers, limiting their widespread adoption. This study introduces and validates a tailored Vision-Based Measurement (VBM) system designed to automate real-time quality control within HPDC processes, enabling 100% inspection coverage without human intervention. Utilizing a COGNEX IS7600M camera and advanced image processing techniques, including Hough Transform and Sobel edge detection, the VBM system accurately measures the critical dimensions of a metallic clamping fork, a key automotive component. The system is integrated into the manufacturing workflow using the RAMI 4.0 architectural model, ensuring seamless communication with existing software applications used in the production process. Experimental validation involved assessing measurement uncertainty and implementing SPC charts, demonstrating enhanced process stability and a significant reduction in Non-Pass Rates (NPR) from 147 to 63, representing an approximately 57% decrease. A Failure Mode and Effects Analysis (FMEA) highlighted substantial reductions in direct and indirect defect detection and handling costs, resulting in financial savings of R ${\$}$ 2,179.50 per batch by minimizing rework and material waste. Additionally, the VBM system reduced inspection time from several minutes per component to approximately 7.7 seconds, lowering labor costs. Financial analysis revealed considerable cost savings, underscoring the system’s economic viability. This research demonstrates that integrating a VBM system into HPDC improves measurement accuracy and process control while offering significant financial benefits, making it a robust solution for enhancing quality control in the context of Industry 4.0.}, archive = {J_ACCESS}, author = {Romulo Gonçalves Lins and Reinaldo Eduardo Dos Santos and Ricardo Gaspar}, doi = {10.1109/ACCESS.2025.3563800}, journal = {IEEE Access}, pages = {75047-75070}, shortjournal = {IEEE Access}, title = {Vision-based measurement for quality control inspection integrated into a die-casting process in industry 4.0 era}, volume = {13}, year = {2025}, }
• (2025). Self SOC estimation for second-life lithium-ion batteries. ACCESS, 13, 75037-75046. (www)

  @article{ , abstract = {Lithium-ion batteries (LIB) are the mainstream technology for energy storage in several industrial segments, such as mobility and stationary systems for solar, wind, or other alternative energy source. This technology has a long lifetime, low self-discharge, high capacity and density, and can store energy longer. Despite that, LIB is suitable to supply power for electric mobility if its state of health (SOH) is higher than 80%. Then, an alternative for batteries with SOH below that is recycling or second-life batteries (SLB). The first is expensive and complex; only some companies retain the technology. Still, the SLB can be an excellent solution to maintaining LIBs in operation for slow vehicles or stationary systems. However, SLB requires intelligent battery management systems (BMS) because the packs are composed of cells with different characteristics, which makes the operation more difficult. This work presents a system consisting of two Machine Learning (ML) layers to automatically estimate the state of charge (SOC) of SLB independent of the battery’s capacity or age. In the first phase, a Random Forest (RF) model was built and trained to discover the curve capacity of different SLB characteristics and capacities. After the capacity curve selection, in the second phase, a new RF model was built and trained for each capacity curve to make SOC inferences of the batteries. The discharge data curve of one hundred batteries was used for the development, whereas eighteen were used for the training and eighty-two for tests. The results indicated a root square mean error (RSME) below 45 mAh for the capacity estimation (phase 1), and an RSME below 0.87% was found in the second phase for the SOC estimation. Finally, the capacity and SOC models have been inserted in a Raspberry system to measure the main parameters of SLB (voltage and current) and make inferences in real time independent of the cell’s age. The results showed an RSME below 100 mAh for the first layer and below 1% for the second layer of the system. The excellent results indicated that the proposed idea can be applied to SLB in a general way.}, archive = {J_ACCESS}, author = {Joelton Deonei Gotz and Emilson Ribeiro Viana and José Rodolfo Galvão and Fernanda Cristina Corrêa and Milton Borsato and Alceu André Badin}, doi = {10.1109/ACCESS.2025.3564072}, journal = {IEEE Access}, pages = {75037-75046}, shortjournal = {IEEE Access}, title = {Self SOC estimation for second-life lithium-ion batteries}, volume = {13}, year = {2025}, }
• (2025). Evolving many-objective job shop scheduling dispatching rules via genetic programming with adaptive search based on the frequency of features. ACCESS, 13, 75020-75036. (www)

  @article{ , abstract = {Job Shop Scheduling (JSS) is a critical application in diverse fields, such as cloud computing and manufacturing. Genetic Programming (GP) is acknowledged for its wide use in evolving dispatching rules for JSS, offering an automated approach to heuristic generation. A dispatching rule can use many machine-related, job-related, and system-related features to create scheduling heuristics in JSS. Proper feature selection is a critical factor for the success of heuristics. Moreover, there can be many features in JSS whose importance varies from one scenario to another. It has been shown that irrelevant and redundant features can adversely affect performance. Feature selection is a promising task to select relevant features and reduce genetic programming hyper-heuristics (GPHH) search space. However, more research is needed to quantify the contribution of features in the GPHH to many-objective JSS. The proposed algorithm introduces an adaptive search strategy that is implemented through re-initialization during the evolutionary process. In addition, relevant features are selected based on their frequency of occurrence in diverse sets of best individuals. The proposed algorithm, Adaptive Feature Selection-GP-NSGA-III (AFS-GP-NSGA-III), is compared with the FS-GP-NSGA-III and standard GP-NSGA-III on a four-objective JSS problem. The experimental results indicate that feature selection using GP and adaptive search can improve the performance of the algorithm. Furthermore, the findings suggest the practical applicability of the proposed algorithm for generating improved dispatching rules for training and unseen test instances using only the selected relevant feature subset.}, archive = {J_ACCESS}, author = {Atiya Masood and Mansoor Ebrahim and Fahad Najeeb and Syed Muhammad Daniyal}, doi = {10.1109/ACCESS.2025.3558521}, journal = {IEEE Access}, pages = {75020-75036}, shortjournal = {IEEE Access}, title = {Evolving many-objective job shop scheduling dispatching rules via genetic programming with adaptive search based on the frequency of features}, volume = {13}, year = {2025}, }
• (2025). ABDviaMSIFAT: Abnormal crowd behavior detection utilizing a multi-source information fusion technique. ACCESS, 13, 74996-75015. (www)

  @article{ , abstract = {Detecting abnormal crowd behavior in surveillance videos is a significant challenge due to the intricate and constantly evolving crowd dynamics. To solve this issue, we suggest a new method that combines data from various sources with different characteristics to enhance the precision of detecting human behavior in crowds. Our approach involves two separate pipelines that work simultaneously to produce scores for frames in a video segment. These scores are later modified for the individual level of the group, allowing for behavior recognition through the assessment of fuzzy logic functions. In the first pipeline, we utilize a depth-wise Separable Convolutional Neural Network (DWS-CNN) that provides reduced filtering compared to standard CNNs. The second pipeline combines the LiteFlowNet detector with the MOSSE tracker and a DSC-GRU network to generate high-level captions for objects in video frames. We implement the weighted average (WA) method to improve anomaly detection accuracy. Methods like weighted averages can mitigate the influence of outliers and noise in the outcomes or evaluations. Utilizing linguistic variables to represent scores and computing weighted averages of scores from two pipelines enhances the quality and reliability of these variables, creating fuzzy predicates that characterize people’s movements, presence, and responses at a microscopic scale. Our approach exceeds conventional visual and motion-centric methods, enabling a more comprehensive grasp of abnormal behaviors. Our suggested approach outperforms state-of-the-art methods in terms of effectiveness and performance based on tests done on well-known datasets.}, archive = {J_ACCESS}, author = {Ali Ahmad Hamid and S. Amirhassan Monadjemi and Bijan Shoushtarian}, doi = {10.1109/ACCESS.2024.3436007}, journal = {IEEE Access}, pages = {74996-75015}, shortjournal = {IEEE Access}, title = {ABDviaMSIFAT: Abnormal crowd behavior detection utilizing a multi-source information fusion technique}, volume = {13}, year = {2025}, }
• (2025). HMSTNet: A deep learning multimodal approach for personalized english literature recommendations. ACCESS, 13, 74984-74999. (www)

  @article{ , abstract = {Recommendation systems serve as fundamental elements of contemporary technology by reshaping how users find content in all fields including educational applications. Academic English literature benefits strongly from recommendation systems because these systems enable customized learning opportunities to help students discover relevant content according to personal learning needs. English literature represents features challenging recommendation systems because it consists of extensive text content combined with multiple stylistic attributes and extensive metadata structures. The study presents a deep learning framework called HMSTNet to manage complex recommendation scenarios. Novel architecture integrates metadata datasets alongside stylistic information and BERT-powered text-based embeddings for understanding of content and user systemic preferences. Through its multi-branch structure Hybrid Multimodal Semantic Text Neural Network (HMSTNet) based on LSTM model to recognize difficult meta-relations between text components while utilizing user embeddings to maintain context awareness. This research serves to unite conventional recommendation algorithms with English literature reading requirements. The complex nature of literary data poses challenges to k-Nearest Neighbors (kNN) and Singular Value Decomposition (SVD) whereas conventional methods show limited success in both context understanding and accuracy. The model HMSTNet outperforms others with outstanding results reaching 95.23% accuracy. Multiple metrics demonstrate the efficiency of this model because they measure highest MBD with KNN model of 6.8m along with Theil’s U-statistic of 0.04 and 90th percentile error is 8.41 to confirm minimized prediction errors and enhanced accuracy and fail-safe capability. Through its reliable framework HMSTNet both delivers personalized content recommendations and supports educational technology development which deepens student engagement with literature and promotes lifelong learning.}, archive = {J_ACCESS}, author = {Xiaopeng Wang and Xue Liang}, doi = {10.1109/ACCESS.2025.3564677}, journal = {IEEE Access}, pages = {74984-74999}, shortjournal = {IEEE Access}, title = {HMSTNet: A deep learning multimodal approach for personalized english literature recommendations}, volume = {13}, year = {2025}, }
• (2025). A market surveillance and an evaluation methodology based on power quality, electromagnetic compatibility, and photometric data for LED light sources. ACCESS, 13, 74967-74983. (www)

  @article{ , abstract = {In lighting projects, cost and energy consumption are often the primary selection criteria for LED lamps. However, additional technical factors such as electromagnetic emissions, harmonic current emissions, overvoltage susceptibility, visual flicker and photometric performance are crucial for ensuring system reliability and long-term regulatory compliance. Ignoring these factors results into power quality issues, electromagnetic interference, flicker, biological hazards and reduced lamp lifespan- particularly in commercial and hospitality environments where lighting operates extensively. This study introduces a comprehensive evaluation framework that extends beyond cost and energy efficiency, incorporating in a versatile and scalable manner technical performance criteria into the selection process. Within a market surveillance study, ten commercially available LED lamps were assessed through measurements and tests in an accredited laboratory. While all tested lamps currently meet harmonized Standards, a large number would exceed the stricter harmonic current limits of EN IEC 61000-3-2 (2019), once harmonized. A weight-based ranking methodology addressing installation-specific requirements is proposed as a tool for identifying the optimum lighting solution. The results showcase that the best performing lamps are not necessarily the most expensive. Additionally, the study explores various electrical environments and lighting scenarios in relation to key UN Sustainable Development Goals 1, 3, 7, 9, 11, 13, 15 and 17.}, archive = {J_ACCESS}, author = {Themistoklis L. Athanasiadis and Eleni P. Nicolopoulou and Evangelos-Nikolaos D. Madias and Aikaterini D. Polykrati and Christos A. Christodoulou and Lambros T. Doulos and Ioannis F. Gonos}, doi = {10.1109/ACCESS.2025.3563440}, journal = {IEEE Access}, pages = {74967-74983}, shortjournal = {IEEE Access}, title = {A market surveillance and an evaluation methodology based on power quality, electromagnetic compatibility, and photometric data for LED light sources}, volume = {13}, year = {2025}, }
• (2025). Neurotechnology in gaming: A systematic review of visual evoked potential-based brain-computer interfaces. ACCESS, 13, 74944-74966. (www)

  @article{ , abstract = {Brain-computer interfaces (BCIs) have received considerable attention in gaming, enabling innovative interactions with digital environments. Visual Evoked Potentials (VEPs)—robust, noninvasive neural responses to visual stimuli—offer high information transfer rates, making them particularly promising. This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, examines VEP-based BCIs in gaming. We searched the Web of Science and Google Scholar, identifying 16 347 studies from the past decade, with 46 selected for in-depth analysis after rigorous screening. The review explores VEP response modeling, electroencephalography (EEG) signal acquisition and processing, stimulation paradigms, and their gaming applications. These systems enhance accessibility for players with physical or cognitive impairments, support adaptive difficulty scaling, personalize gameplay, aid neurorehabilitation, and enable multiplayer interactions. However, challenges remain, including technical limitations, complex data interpretation, user adaptability, and ergonomic issues. Advances in signal processing, personalized calibration, and hybrid multimodal approaches could improve usability. Future research should focus on integrating VEP-based BCIs with emerging technologies, optimizing user comfort, and developing adaptive interaction models to enhance immersion and accessibility. By addressing these challenges and utilizing neuroscience and computational advancements, VEP-based BCIs promise to transform gaming into a more inclusive and immersive experience for diverse users.}, archive = {J_ACCESS}, author = {Aigerim Keutayeva and China Jesse Nwachukwu and Muslim Alaran and Zhenis Otarbay and Berdakh Abibullaev}, doi = {10.1109/ACCESS.2025.3564328}, journal = {IEEE Access}, pages = {74944-74966}, shortjournal = {IEEE Access}, title = {Neurotechnology in gaming: A systematic review of visual evoked potential-based brain-computer interfaces}, volume = {13}, year = {2025}, }
• (2025). Hybrid impedance-based controller design of grid-connected converter for whole-system stabilization of grid-connected DC microgrids. ACCESS, 13, 74930-74943. (www)

  @article{ , abstract = {The grid-connected converter (GCC) in a grid-connected DC microgrid performs an important role in stabilising the interaction of the AC and DC sections. With the development of DC microgrids, the DC section will gradually integrate more distributed energy resources and be compatible with various system operations, so it will be increasingly challenging to design the controller of GCCs to ensure the whole-system stability. To address this problem, this paper proposes a hybrid impedance-based controller design from the whole-system stabilisation perspective. Based on the concept of hybrid impedance, a stability criterion considering the AC and DC coupling dynamics is proposed as a benchmark for the controller design of GCCs. In addition, with the help of linear matrix inequalities, a step-by-step algorithm is developed to automatically and efficiently tune the control parameter to achieve the desired design criterion. The effectiveness of the proposed method is verified through case studies including simulation and hardware-in-loop experiment. The further comparison study also shows that the proposed method has a stronger whole-system stabilisation ability compared to some reviewed methods. In some specific tests, the proposed method can limit the current THD to about 2% compared to other methods that cause the THD to exceed 20%.}, archive = {J_ACCESS}, author = {Yuxin Zhu and Fei Wang and Zhengyu Lin and James Fleming}, doi = {10.1109/ACCESS.2025.3562856}, journal = {IEEE Access}, pages = {74930-74943}, shortjournal = {IEEE Access}, title = {Hybrid impedance-based controller design of grid-connected converter for whole-system stabilization of grid-connected DC microgrids}, volume = {13}, year = {2025}, }
• (2025). Enhanced position-aided beam prediction using real-world data and enhanced-convolutional neural networks. ACCESS, 13, 74917-74929. (www)

  @article{ , abstract = {Millimeter-wave (mmWave) communication systems utilize narrow beamforming to ensure adequate signal power. However, beam alignment requires significant training overhead, especially in high-mobility scenarios. Previous research has utilized synthetic data for position-aided beam prediction, which does not fully capture real-world complexities. In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. For 64-beams Scenario 1, the E-CNN model showed an increase in average top-1 accuracy from 55.57% to 63.92%, and in case of 32-beams, the accuracy increased from 71.34 % to 82.06%. For 16-beams, the accuracy increased from 86.17% to 94.64 %, while for 8-beams, the accuracy increased from 90.24% to 97.11%. In addition, besides showing significant power loss reduction in various scenarios, the proposed E-CNN model has demonstrated robustness regarding real-word conditions and adaptability for various beam setups. The model realized as high as a 50% power loss reduction in arguably the most challenging graphs, which is an exercise in reliability. This research fills the existing gap between the simulated aid beam alignment and real-world position beam aided alignment, which can be useful in improving beamforming in the upcoming wireless networks.}, archive = {J_ACCESS}, author = {Ahmed Abd El Moaty Mohamed Gouda and Ehab K. I. Hamad and Aziza I. Hussein and M. Mourad Mabrook and A. A. Donkol}, doi = {10.1109/ACCESS.2025.3563797}, journal = {IEEE Access}, pages = {74917-74929}, shortjournal = {IEEE Access}, title = {Enhanced position-aided beam prediction using real-world data and enhanced-convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Toward fully-automated hardware-in-the-loop grid code compliance certification of inverter-based distributed energy resources. ACCESS, 13, 74900-74916. (www)

  @article{ , abstract = {Inverter-based Distributed Energy Resources (DERs) third-party compliance testing has been widely required by most grid codes to guarantee interoperability with the electrical power system. However, most grid code certification procedures only accept the evaluation of the whole product under full-power environments. For high-power equipment, this procedure is costly, time-intensive, and complex to be performed. Due to this, many efforts have been made to make possible using Controller Hardware-in-the-Loop (CHIL) testing for validation of the inverter firmware from early product development through final certification, facilitating the identification of early issues and reducing debugging time and costs. On the other hand, since most CHIL tests are performed in environments distinct from full power laboratory testing, both approaches often follow different procedures and do not take into account the same metrics, which can result in low fidelity between CHIL and power laboratory results. In order to make possible to use CHIL for compliance testing, model validation has to be performed, guaranteeing that model fidelity is satisfactory. In this context, this paper presents a comprehensive methodology to validate CHIL models and to clearly identify, measure, and reduce differences between the results obtained with CHIL and with power laboratory testing environments. The methodology is based on standardized quantitative test procedures applied to both CHIL and power laboratory testing. The proposed methodology is extensively validated through experimental results carried out on a commercial single- phase photovoltaic inverter. Results demonstrate the validity of CHIL testing methodology and propose an alternative method for grid code compliance testing and certification.}, archive = {J_ACCESS}, author = {Luiz Fernando Rissotto Menegazzo and Anderson Luis do Nascimento Severo and Ricardo Jochann Franceschi Bortolini and Lucas Bisêlo Piton and Henrique Magnago and Fernanda de Morais Carnielutti and Lucas Vizzotto Bellinaso and Leandro Michels and Humberto Pinheiro}, doi = {10.1109/ACCESS.2025.3564692}, journal = {IEEE Access}, pages = {74900-74916}, shortjournal = {IEEE Access}, title = {Toward fully-automated hardware-in-the-loop grid code compliance certification of inverter-based distributed energy resources}, volume = {13}, year = {2025}, }
• (2025). A body design leveraging passive dynamics for mobile robots coexisting with humans. ACCESS, 13, 74887-74899. (www)

  @article{ , abstract = {Recent advancements in deep learning and large-scale language models have driven interest in creating conversational robots capable of heartful interactions with humans. However, most existing systems are tailored for specific contexts and lack a comprehensive framework to assist humans in daily activities. Developing a fully autonomous robot that can operate alongside humans and interact with them in dynamic environments is a complex task that requires addressing particular software and hardware demands. These demands often conflict, resulting in trade-offs based on the intended application. To overcome this challenge, we propose a body design that leverages passive dynamics. We illustrate how the essential devices for computation, sensing, and actuation can be integrated into the robot without compromising its embodiment requirements. Conversely, we utilize the weight and placement of these devices to achieve the physical characteristics needed for a robotic body that is well-suited for coexistence with humans. We validate this approach through the development of Indy, a mobile communication robot platform designed to coexist with humans. The employed mechanisms enable Indy to maintain an upright posture without active control and ensure compliance during physical interactions. Experimental results indicate that the generated body movement positively impacts users’ perceptions, promoting a sense of anthropomorphism and enhancing the interaction experience. This confirms that the selected design approach not only provides mechanical advantages but also improves the natural appearance of the robot’s movements.}, archive = {J_ACCESS}, author = {Huthaifa Ahmad and Yuya Okadome and Yasutomo Kawanishi and Koichiro Yoshino and Takashi Minato and Michihiko Minoh and Yutaka Nakamura}, doi = {10.1109/ACCESS.2025.3563982}, journal = {IEEE Access}, pages = {74887-74899}, shortjournal = {IEEE Access}, title = {A body design leveraging passive dynamics for mobile robots coexisting with humans}, volume = {13}, year = {2025}, }
• (2025). Satellite massive array with reconfigurable antenna units for long-life and energy saving satellites. ACCESS, 13, 74876-74886. (www)

  @article{ , abstract = {A synergic cooperation between terrestrial and non-terrestrial networks is a pivotal feature for achieving global wireless coverage. In this framework, the satellite swarm-based massive array represents one of the most promising key enabling technologies in coping with the ever-increasing system requirements for beyond 5G (B5G) and 6G systems. In this paper, a novel strategy for the synthesis of satellite massive arrays with reconfigurable antenna units (SMART-U) is presented. The radiative performance of the proposed configuration are analysed for low-earth and geostationary orbiting satellites and assess a remarkable robustness and flexibility of the new synthesis architecture. A remarkable scanning range improvement and peak side lobe level (PSLL) lowering of the addressed concentric ring array (CRA) is achieved by exploiting a multiobjective optimization based on the Pareto front. The proposed SMART-U phased array can efficiently cope with adverse satellite attitudes by tailoring the circularly polarized (CP) field radiated by each unit offering a clear advantage with respect to conventional swarm-based arrays with a static antenna element factor.}, archive = {J_ACCESS}, author = {Francesco Alessio Dicandia and Simone Genovesi}, doi = {10.1109/ACCESS.2025.3563970}, journal = {IEEE Access}, pages = {74876-74886}, shortjournal = {IEEE Access}, title = {Satellite massive array with reconfigurable antenna units for long-life and energy saving satellites}, volume = {13}, year = {2025}, }
• (2025). Steganography in IoT: A comprehensive survey on approaches, challenges, and future directions. ACCESS, 13, 74844-74875. (www)

  @article{ , abstract = {The Internet of Things (IoT) has raised significant security concerns, especially with regard to secure data transfer among resource-constrained devices. While effective, traditional encryption techniques are often computationally expensive and easily identifiable, making them unsuitable for many IoT applications. Steganography is an intriguing approach that allows hiding sensitive information within seemingly ordinary data, preventing unauthorized parties from detecting and accessing it. Existing studies on IoT security lack a comprehensive analysis of steganographic techniques tailored for IoT-specific constraints. This study bridges this gap by providing a comprehensive review of steganographic methods for IoT, exploring approaches across many domains such as spatial, frequency, and hybrid methods used in images, videos, audio, text, network traffic, hybrid approaches, and quantum-based methods. We evaluated and validated the techniques employed using key metrics such as imperceptibility, robustness, and embedding capacity while emphasizing the computational restrictions, real-time processing requirements of IoT devices, and the vital requirement for energy-efficient algorithms. This survey also investigates the integration of steganography and cryptographic approaches, as well as advances in Machine Learning (ML), Deep Learning (DL), and quantum techniques that could revolutionize the field. The study ends with future research directions that underscore the importance of innovative steganographic techniques that strike a compromise between security, efficiency, and scalability in IoT applications.}, archive = {J_ACCESS}, author = {Maha Driss and Lamia Berriche and Safa Ben Atitallah and Siwar Rekik}, doi = {10.1109/ACCESS.2025.3564120}, journal = {IEEE Access}, pages = {74844-74875}, shortjournal = {IEEE Access}, title = {Steganography in IoT: A comprehensive survey on approaches, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Defects localization and classification method of power transmission line insulators aerial images based on YOLOv5 EfficientNet and SVM. ACCESS, 13, 74833-74843. (www)

  @article{ , abstract = {Accurate localization and classification of defects in insulator images captured by UAVs (Unmanned Aerial Vehicles) are critical for the maintenance of high-voltage power transmission grids. However, existing methods face challenges in achieving satisfactory accuracy, particularly in complex environments. Moreover, previous approaches primarily focus on detecting a single type of insulator defect without further categorizing them. To address these limitations, we propose a novel deep-learning framework that integrates YOLOv5, EfficientNet and Support Vector Machines (SVM) to improve both the localization and classification of insulator defects in aerial images. The framework specifically targets overcoming the challenges associated with low signal-to-noise ratios in defect detection. The proposed approach divides the task into two primary modules: 1) YOLOv5-based object detection for accurate defect localization, and 2) defect classification using EfficientNet and SVM. Specifically, YOLOv5 enhances feature extraction to improve the signal-to-noise ratio, while EfficientNet and SVM enable precise defect classification. The effectiveness of the proposed method is validated through experiments conducted on a range of insulator image datasets collected from the Guangdong power grid. Comparative analysis of experimental results shows that the proposed method outperforms existing approaches in terms of both accuracy and practical applicability, achieving a top-1 accuracy of 98.22%, an F1-score of 98.53%, and an accuracy improvement of 3.89% over conventional backbone-based networks.}, archive = {J_ACCESS}, author = {Lin Li and Qiaoling Yin and Xiaofeng Wang and Hang Wang}, doi = {10.1109/ACCESS.2025.3559657}, journal = {IEEE Access}, pages = {74833-74843}, shortjournal = {IEEE Access}, title = {Defects localization and classification method of power transmission line insulators aerial images based on YOLOv5 EfficientNet and SVM}, volume = {13}, year = {2025}, }
• (2025). TF-CMFA: Robust multimodal 3D object detection for dynamic environments using temporal fusion and cross-modal alignment. ACCESS, 13, 74821-74832. (www)

  @article{ , abstract = {In recent years, multimodal 3D object detection methods have garnered significant attention in autonomous driving systems due to their impressive detection performance. However, most existing research seldom addresses the issues of robustness and performance degradation in dynamic environments due to the difficulty of aligning modal features. In this paper, we introduce an innovative efficient fusion method that integrates time series features to improve the accuracy of 3D object detection through multi-sensor fusion, making it more suitable for dynamic and realistic scenarios such as automated driving, and verifying its robustness. The proposed framework incorporates a Temporal Local Self-Fusion Module (TLSFM) in the LiDAR stream to enrich the representation of LiDAR BEV features. To better align BEV features in image streams and point cloud streams, a Cross-Modal Fusion Alignment (CMFA), is introduced. The Temporal Fusion-CMFA (TF-CMFA) framework which contains TLSFM and CMFA module, demonstrates state-of-the-art performance, achieving a mean average precision (mAP) score of 74.4% and a NuScenes detection score (NDS) of 75.7% on the NuScenes benchmark dataset. Performance improvements recorded on the Waymo dataset, with improvements of +2.1 and +2.3 in the ALL-L1 and ALL-L2 metrics compared to VoxelMamba. Finally, robustness experiments demonstrate the performance of proposed approach under sensor failure conditions, demonstrating its exceptional robustness under such conditions.}, archive = {J_ACCESS}, author = {Yujing Wang and Abdul Hadi Abd Rahman and Fadilla ’Atyka Nor Rashid}, doi = {10.1109/ACCESS.2025.3563483}, journal = {IEEE Access}, pages = {74821-74832}, shortjournal = {IEEE Access}, title = {TF-CMFA: Robust multimodal 3D object detection for dynamic environments using temporal fusion and cross-modal alignment}, volume = {13}, year = {2025}, }
• (2025). A study on the STGCN-LSTM sign language recognition model based on phonological features of sign language. ACCESS, 13, 74811-74820. (www)

  @article{ , abstract = {Many isolated words in Chinese Sign Language (CSL) exhibit significant feature similarities, which are primarily conveyed through hand, face, and body movements. Among these, hand features are particularly crucial, as they carry substantial information about the sign language words. However, the spatial and temporal dimensions involved in sign language expressions are often relatively small, leading to a similarity problem that increases the difficulty of sign language recognition. The general extraction and recognition of sign language features can result in confusion when distinguishing similar words, and fine-grained feature extraction of sign language actions may incur high computational costs. To deal with these challenges, this paper proposes a dual-stream deep learning model built on Spatio-Temporal Graph Convolutional Network-Long Short-Term Memory(STGCN-LSTM), which aims to capture both the local features of sign language and the global spatio-temporal characteristics of sign words. The model focuses on capturing both local features in sign language and the overall spatio-temporal characteristics of sign language words. By learning four key sign language phonetic features—hand shape, hand position, hand orientation, and hand motion trajectory—as well as spatio-temporal features derived from whole-body skeletal data, the model aims to improve the recognition of Chinese Sign Language. In this paper, the effectiveness of the proposed model is validated on the SRL500 dataset and the sub-dataset of similar SRL500 consisting of similar words selected from it, and the recognition accuracies obtained are 95.2% and 93.0%, respectively.}, archive = {J_ACCESS}, author = {Yuxin Han and Yong Han and Qi Jiang}, doi = {10.1109/ACCESS.2025.3560779}, journal = {IEEE Access}, pages = {74811-74820}, shortjournal = {IEEE Access}, title = {A study on the STGCN-LSTM sign language recognition model based on phonological features of sign language}, volume = {13}, year = {2025}, }
• (2025). Graph-aware multimodal deep learning for classification of diabetic retinopathy images. ACCESS, 13, 74799-74810. (www)

  @article{ , abstract = {Diabetic Retinopathy (DR) is one of the leading causes of blindness worldwide, where early detection is critical to preventing irreversible vision loss. Traditional diagnostic methods primarily rely on single-modality data, such as retinal images, or the analysis of image features, which may limit diagnostic accuracy. To overcome these limitations, we propose Diabetic Retinopathy Diagnosis, namely “DRdiag”, a novel model proposed for the early diagnosis of DR. DRdiag integrates multiple modalities: the Fundus images and the duration of disease evolution which is an important factor in the diagnosis of DR as the duration of the disease directly influences the onset and progression of retinal lesions, leveraging two distinct models: a Convolutional Neural Network (CNN) based on DenseNet121 for image feature extraction and a Graph Neural Network (GNN) for capturing complex relationships between patient features. By combining the strengths of these advanced models, DRDiag aims to provide a more comprehensive and accurate diagnostic system, ultimately supporting timely intervention and improved patient outcomes. The experiments are conducted using two datasets, APTOS2019 and Messidor-2, which provide diverse retinal images for robust evaluation. The proposed model achieves high performance on diabetic retinopathy classification. On Messidor-2, it attains 0.976 accuracy, 0.957 kappa, 0.961 F1-score, 0.958 recall, 0.965 precision, and 0.990 specificity. On APTOS2019, it achieves 0.980 accuracy, 0.960 kappa, 0.963 F1-score, 0.959 recall, 0.968 precision, and 0.995 specificity. These results confirm its robustness and reliability.}, archive = {J_ACCESS}, author = {Amina Zedadra and Ouarda Zedadra and Mahmoud Yassine Salah-Salah and Antonio Guerrieri}, doi = {10.1109/ACCESS.2025.3564529}, journal = {IEEE Access}, pages = {74799-74810}, shortjournal = {IEEE Access}, title = {Graph-aware multimodal deep learning for classification of diabetic retinopathy images}, volume = {13}, year = {2025}, }
• (2025). RBEDH: A decentralized role-based event driven hybrid framework for smart contracts. ACCESS, 13, 74781-74798. (www)

  @article{ , abstract = {The complex nature of smart contracts necessities the development of a novel adaptable framework. As blockchain technology continues to expand into diverse fields, the demand for secure, efficient, and transparent systems becomes increasingly critical. RBEDH is the integration of Role Based Access Control (RBAC), Event Driven Architecture (EDA) and Hybrid Functionalities. The framework is applied to the scholarly publishing sector, where academic integrity, transparency, and reliability are crucial for ensuring the credibility and trustworthiness of published research. The smart contracts are executed between authors, reviewers and publishers. Functionality test was conducted using Ganache, Ropsten, and Ethereum Mainnet. These evaluations confirmed the consistency and effectiveness of contract deployment. Vulnerabilities such as re-entrancy, integer overflow/underflow, unauthorized access were tested using Securify, Mythril, Smartcheck and Oynete. It is found that the system is secure and it not susceptible to any of the above vulnerability. Further timestamp dependency was tested through Manticore, Slither and Echidna and the test results indicate the absence of vulnerability. Scalability and performance analysis results proves that the proposed framework is better on the basis of average energy consumption, latency and memory requirement when compared with the existing literature.}, archive = {J_ACCESS}, author = {Eashwar Sivakumar and Kiran Jot Singh and Paras Chawla and Geetha Ganesan}, doi = {10.1109/ACCESS.2025.3554630}, journal = {IEEE Access}, pages = {74781-74798}, shortjournal = {IEEE Access}, title = {RBEDH: A decentralized role-based event driven hybrid framework for smart contracts}, volume = {13}, year = {2025}, }
• (2025). Transforming classical hydropower generation for flexibility in the global energy transition: Challenges, requirements, and prospects. ACCESS, 13, 74762-74780. (www)

  @article{ , abstract = {This review article covers the multidisciplinary challenges shaping hydropower development within a broader energy transition. Classical hydropower generation facilities are moving toward flexible operations to adapt to and facilitate the integration of variable renewable energy sources (VRES). This review article explores the paradigm shift in terms of machine design considerations, power plant operation, and grid regulator implications. Machine designers, consider magnetic saturation, energy efficiency estimation, and mechanical wear and tear. This could involve, high flux densities in certain core regions, lower weighted average efficiencies, and higher machine failures owing to insulation degradation. Moreover, power plant operators must consider the effects of the flexible power production caused by VRES. In some cases, hydropower ramping ranges from 1 to 5 megawatts (MW) per second for large hydropower plants, which pose technical challenges for operators. Moreover, transmission system operators must strengthen reactive power mandates and system-bearing ancillary service markets to ensure grid stability. In certain regions, there could be longer periods with a significant reduction in physical inertia and short-circuit grid strength owing to the lower share of classical non-inverter-based generation facilities in VRES-rich power systems.}, archive = {J_ACCESS}, author = {Yannick Cyiza Karekezi and Frederic Maurer and Dany Josue Tome Robles and Thomas Øyvang and Jonas Kristiansen Nøland}, doi = {10.1109/ACCESS.2025.3564123}, journal = {IEEE Access}, pages = {74762-74780}, shortjournal = {IEEE Access}, title = {Transforming classical hydropower generation for flexibility in the global energy transition: Challenges, requirements, and prospects}, volume = {13}, year = {2025}, }
• (2025). Cross-dataset representation learning for unsupervised deep clustering in human activity recognition. ACCESS, 13, 74750-74761. (www)

  @article{ , abstract = {This study introduces a novel representation learning method to enhance unsupervised deep clustering in Human Activity Recognition (HAR). Traditional unsupervised deep clustering methods often struggle to extract effective feature representations from unlabeled data, failing to fully capture the true underlying structure of the data. As a result, classification performance is frequently suboptimal. To address this limitation, we propose leveraging an autoencoder integrated with models pre-trained on diverse HAR datasets to extract robust and transferable feature representations from target data. These representations are subsequently utilized within an unsupervised deep clustering framework, enabling effective discovery of the data’s latent structure and significantly improving clustering performance. The proposed method was evaluated on three HAR datasets and compared against conventional approaches, including autoencoder-based deep clustering and traditional classification methods such as k-means and HMM. As a result, the proposed method achieved F1 scores ranging from 0.441 to 0.781, significantly outperforming the baseline scores of 0.215 to 0.459. Furthermore, with fine-tuning using only 50 samples, the proposed method achieved even higher accuracy, with F1 scores ranging from 0.66 to 0.88. Additionally, it exhibited higher accuracy and robustness compared to traditional classification methods, highlighting its effectiveness in unsupervised learning scenarios. This study not only advances recognition accuracy in HAR but also demonstrates the potential of cross-dataset representation learning to effectively utilize unlabeled data. The proposed method offers a scalable and practical solution with broad applicability beyond HAR to other domains.}, archive = {J_ACCESS}, author = {Tomoya Takatsu and Tessai Hayama and Hu Cui}, doi = {10.1109/ACCESS.2025.3562897}, journal = {IEEE Access}, pages = {74750-74761}, shortjournal = {IEEE Access}, title = {Cross-dataset representation learning for unsupervised deep clustering in human activity recognition}, volume = {13}, year = {2025}, }
• (2025). Optimizing large railway vision models for efficient in-context learning. ACCESS, 13, 74739-74749. (www)

  @article{ , abstract = {Large railway vision models (LRVMs) have exhibited remarkable performance in tackling diverse railway-related vision-based tasks, attributed to their capacity for in-context learning (ICL). However, in terms of speed and memory usage, these models suffer from inefficient hardware utilization, particularly when training from scratch with suboptimal batch sizes. Consequently, LRVMs face challenges in achieving high throughput compared to state-of-the-art, task-specific railway models. To address this problem, we propose a novel framework with cross-block feature union (CBFU) designed to optimize the computation time and GPU memory allocations for LRVMs, in both the training and inference phases while preserving accuracy. Our key innovation lies in the synergistic integration of CBFU with cascaded group attention (CGA) and multi-level feature concatenation, which collectively address the inefficiencies of prior LRVMs—a challenge unresolved in existing work. Drawing inspiration from Efficient-ViT, we incorporate the CGA layer as part of the LRVM’s building blocks to alleviate computational bottlenecks of the multi-head self-attention (MHSA). Additionally, we implement a multi-level feature concatenation technique to enhance transferability across different railway-related tasks and reduce the computation cost. Compared to existing LRVMs, our model demonstrates significant improvement in computational efficiency, achieving a 19-fold reduction in FLOPs. Furthermore, the size of our model is about $9\times $ smaller and runs 4.1 and $27\times $ faster during training and inference, respectively. These advancements uniquely position our framework as the first to balance accuracy, speed, and memory efficiency for railway vision systems, enabling real-world deployment where prior LRVMs failed. Overall, the proposed advancements enable the usage of LRVMs in resource-limited railway scenarios.}, archive = {J_ACCESS}, author = {Xuemei Zhan and Xubo Wu and Hua Ma}, doi = {10.1109/ACCESS.2025.3563890}, journal = {IEEE Access}, pages = {74739-74749}, shortjournal = {IEEE Access}, title = {Optimizing large railway vision models for efficient in-context learning}, volume = {13}, year = {2025}, }
• (2025). Inpatient length of stay and mortality prediction utilizing clinical time series data. ACCESS, 13, 74724-74738. (www)

  @article{ , abstract = {Electronic Health Records (EHRs), which include demographic information, clinical notes, vital signs, laboratory test results, and others, provide rich information for clinical outcome prediction. In this work, we propose a novel attention embedded residual long short-term memory (LSTM) fully Convolutional Network (FCN) to perform the clinical predictions of inpatients’ length of stay (LoS) and mortality. The proposed model is uniquely composed of a convolutional neural network (CNN) layer, three residual blocks, an LSTM unit, an FCN module, and a self-attention module. This innovative architecture allows for comprehensive feature extraction, where the CNN and residual blocks enhance clinical data features, the FCN and LSTM separately extract spatial and temporal features, and the self-attention mechanism focuses on pertinent information while filtering out noise. By optimizing the loss function to address class imbalance and overfitting, our model ensures robust and accurate predictions. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, validating its effectiveness and feasibility in inpatient length of stay and mortality prediction.}, archive = {J_ACCESS}, author = {Junde Chen and Mason Li and Miles Milosevich and Tiffany Le and Andrew Bahsoun and Yuxin Wen}, doi = {10.1109/ACCESS.2025.3563199}, journal = {IEEE Access}, pages = {74724-74738}, shortjournal = {IEEE Access}, title = {Inpatient length of stay and mortality prediction utilizing clinical time series data}, volume = {13}, year = {2025}, }
• (2025). SimE: A knowledge graph embedding model to encode self-similar structures through algebraic and geometric transformations. ACCESS, 13, 74705-74723. (www)

  @article{ , abstract = {Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to effectively capture and encode their complex structures into useful numerical representations. The fractal-like nature of these graphs, where patterns repeat at various scales and complexities, requires specialized algorithms that can adapt and learn from the multi-level structures inherent in the data. This similarity to fractals requires methods that preserve the recursive detail of knowledge graphs while facilitating efficient learning and extraction of relational patterns. In this study, we explore the integration of similarity group with attention mechanisms to represent knowledge graphs in complex spaces. In our approach, SimE, we make use of the algebraic (bijection) and geometric (similarity) properties of the similarity transformations to enhance the representation of self-similar fractals in KGs. We empirically validate the capability of providing representations of bijections and similarities in benchmark KGs. We also conducted controlled experiments that captured one-to-one, one-to-many, and many-to-many relational patterns and studied the behavior of state-of-the-art models including the proposed SimE model. Because of the lack of benchmark fractal-like KG datasets, we created a set of fractal-like testbeds to assess the subgraph similarity learning ability of models. The observed results suggest that SimE captures the complex geometric structures of KGs whose statements satisfy these algebraic and geometric properties. In particular, SimE is competitive with state-of-the-art KG embedding models and is able to achieve high values of Hits@1. As a result, SimE is capable of effectively predicting correct links and ranking them with the highest ranks. SimE is publicly available on GitHub https://github.com/NIMI-research/SimE.}, archive = {J_ACCESS}, author = {Kossi Amouzouvi and Yashrajsinh Chudasama and Disha Purohit and Ariam Rivas and Bowen Song and Jens Lehmann and Sahar Vahdati and Maria-Esther Vidal}, doi = {10.1109/ACCESS.2025.3564130}, journal = {IEEE Access}, pages = {74705-74723}, shortjournal = {IEEE Access}, title = {SimE: A knowledge graph embedding model to encode self-similar structures through algebraic and geometric transformations}, volume = {13}, year = {2025}, }
• (2025). An end-to-end framework for neonatal asphyxia detection through video analysis. ACCESS, 13, 74689-74704. (www)

  @article{ , abstract = {Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn’s skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention.}, archive = {J_ACCESS}, author = {Kurnianingsih and Sou Nobukawa and Melyana Nurul Widyawati and Cipta Pramana and Nurseno Bayu Aji and Afandi Nur Aziz Thohari and Dwiana Hendrawati and Eri Sato-Shimokawara and Naoyuki Kubota}, doi = {10.1109/ACCESS.2025.3563702}, journal = {IEEE Access}, pages = {74689-74704}, shortjournal = {IEEE Access}, title = {An end-to-end framework for neonatal asphyxia detection through video analysis}, volume = {13}, year = {2025}, }
• (2025). Segmentation techniques applied to CNNs for cervical cancer classification. ACCESS, 13, 74678-74688. (www)

  @article{ , abstract = {Cervical cancer continues to be a significant global health issue, ranking as the fourth most prevalent cancer affecting women. Enhancing population screening programs by refining the examination of cervical samples conducted by skilled pathologists offers a compelling alternative for early detection of this disease. Deep Learning facilitates the development of automatic classification models to aid experts in this task. However, it is increasingly important to bring explainability to the model both to understand how the network learns to identify pathology and to bring confidence to the diagnosis. In this paper, we design an automatic segmentation masks for the classification of cervicovaginal cell images. This automatic segmentation is combined in a classification model that allows the models to improve their performance thanks to the morphological information provided by the combined segmentation in a Global Average Pooling layer with the convolutional network analysis of the original image. The models will be trained with real data so that learning can recognize the diversity of colors, shapes and sizes of human cell nuclei. The results show a robust and explainable model with satisfactory results, obtaining an F1 Score value of 0.935 in binary classification of revisable and non-revisable cell.}, archive = {J_ACCESS}, author = {Ana Ortiz-González and Raquel Martínez-España and Juan Morales-García and Baldomero Imbernón and José Martínez-Más and Mauricio A. Álvarez and Oscar David Romero and Juan Pedro Martínez-Cendán and Andrés Bueno-Crespo}, doi = {10.1109/ACCESS.2025.3562762}, journal = {IEEE Access}, pages = {74678-74688}, shortjournal = {IEEE Access}, title = {Segmentation techniques applied to CNNs for cervical cancer classification}, volume = {13}, year = {2025}, }
• (2025). Improvement and characteristic analysis of car-following models in mixed traffic flow based on empirical datasets. ACCESS, 13, 74667-74677. (www)

  @article{ , abstract = {To fully utilize the advantages of Connected and Automated Vehicles (CAV) in mixed traffic flow, this study focuses on investigating the car-following characteristics of mixed traffic flow. First, the potential car-following patterns in mixed traffic are analyzed, and an improvement to the Intelligent Driver Model (IDM) is made by incorporating dynamic response times. Next, based on the NGSIM and Gunter datasets, Genetic Algorithm (GA) are employed to calibrate and evaluate IDM parameters. Subsequently, an analysis of mixed traffic flow characteristics is conducted, including a sensitivity analysis of key parameters. Finally, the stability and safety of mixed traffic flow are analyzed. The results demonstrate that the improved numerical simulations closely match real-world data. CAV can enhance the stability of mixed traffic flow under certain conditions. The improved model provides a theoretical basis for analyzing future traffic flow characteristics.}, archive = {J_ACCESS}, author = {Huanfeng Liu and Hanfei Wang and Ziyan Wu and Anning Song and Zishuo Zhang}, doi = {10.1109/ACCESS.2025.3563117}, journal = {IEEE Access}, pages = {74667-74677}, shortjournal = {IEEE Access}, title = {Improvement and characteristic analysis of car-following models in mixed traffic flow based on empirical datasets}, volume = {13}, year = {2025}, }
• (2025). Lightweight image encryption algorithm using 4D-NDS: Compound dynamic diffusion and single-round efficiency. ACCESS, 13, 74656-74666. (www)

  @article{ , abstract = {With the development of the Internet of Things (IoT) and communication technologies, multimedia information has become an essential part of communication. As a carrier with high information density and large transmission volume, image data is increasingly at risk of information leakage. To address the issue of digital private image leakage, this paper proposes a reliable image security protection scheme based on a non-degenerate hyperchaotic system for dynamic permutation and single-round composite diffusion. By leveraging the high dynamic properties of non-degenerate chaos, the scheme effectively addresses the insufficient sensitivity of pseudo-random sequences to initial values and enhances the sensitivity of the key to initial conditions. Additionally, the single-round composite double-diffusion system significantly improves the security and reliability of the algorithm, with the information entropy of the encrypted image reaching 7.9994. Experimental and theoretical verification demonstrate that the algorithm exhibits high sensitivity and effective robustness, capable of resisting common attacks and providing a reliable security solution for communication and the IoT.}, archive = {J_ACCESS}, author = {Yunlong Liao and Yiting Lin and Qiutong Li and Zheng Xing and Xiaochen Yuan}, doi = {10.1109/ACCESS.2025.3560686}, journal = {IEEE Access}, pages = {74656-74666}, shortjournal = {IEEE Access}, title = {Lightweight image encryption algorithm using 4D-NDS: Compound dynamic diffusion and single-round efficiency}, volume = {13}, year = {2025}, }
• (2025). Study on PV defect detection based on CWE YOLOv8. ACCESS, 13, 74647-74655. (www)

  @article{ , abstract = {This study proposes an enhanced photovoltaic panel defect detection method based on an optimized YOLOv8 model, addressing common defects like scratches, broken grids, and dirt. To improve feature extraction for small or blurry defects, the C2f-DySnakeConv module is added to the backbone network. The traditional upsampling process is replaced by the WaveletPool module, which uses wavelet transforms to enhance multi-scale features and improve feature resolution. A lightweight EfficientHead detection head is also introduced to boost the model’s accuracy and robustness in complex detection scenarios. Additionally, to combat sample imbalance and improve the classification of challenging defects, a dynamic weight-adjusted Focal Loss function is employed. Experimental results show that the proposed CWE-YOLOv8 model achieves an mAP of 87.9%, a 5.1% improvement over the YOLOv8n baseline. Despite these advancements, the model retains a compact size of 7.26MB, only slightly larger than the original model’s 5.94MB. This approach offers a high-precision, real-time, and lightweight solution for PV defect detection, with notable potential for engineering applications.}, archive = {J_ACCESS}, author = {Xiaojuan Zhang and Ruixu Yao and Bo Jing and Xiaoxuan Jiao and Mengdi Ren}, doi = {10.1109/ACCESS.2025.3561044}, journal = {IEEE Access}, pages = {74647-74655}, shortjournal = {IEEE Access}, title = {Study on PV defect detection based on CWE YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Integration of convolutional neural network and image processing for pulp fibril detection and measurement. ACCESS, 13, 74634-74646. (www)

  @article{ , abstract = {The fibrillation index is a critical metric in paper manufacturing, quantifying the degree of fibrillation achieved during the pulp refining process. Optimizing this metric enhances both paper quality and production efficiency. However, traditional measurement methods—such as manual visual examination of pulp samples under microscopy—are slow, error-prone, and labor-intensive, limiting their scalability in industrial applications. This study proposes a novel method that integrates deep learning with image processing techniques to automate fibril detection and fibrillation index computation. The proposed method leverages the discriminative capabilities of convolutional neural networks (CNNs) with adaptive image processing techniques to overcome key challenges such as low contrast, image noise, and variability in fibril morphology. The patch-based classification approach effectively filters out irrelevant objects, especially those whose features visually resemble fibrils, thus improving fibril segmentation accuracy. The method was comprehensively validated against expert-labeled ground truth images and achieved a promising average error rate of $0.4494~{\pm }~0.4187$ . Experimental results also demonstrate the strong robustness of the proposed method, with consistent performance across diverse refining conditions and image qualities, making it suitable for real-world application in the pulp and paper industry. Furthermore, this study paves the way for broader applications in materials science and biomedical imaging, where precise feature detection in microscopic images is essential.}, archive = {J_ACCESS}, author = {Tanachot Chirakitsakul and Pakaket Wattuya and Phichit Somboon and Panthira Jansakra and Chakrit Watcharopas}, doi = {10.1109/ACCESS.2025.3562873}, journal = {IEEE Access}, pages = {74634-74646}, shortjournal = {IEEE Access}, title = {Integration of convolutional neural network and image processing for pulp fibril detection and measurement}, volume = {13}, year = {2025}, }
• (2025). DELTa: Dynamic energy-and-latency-aware task scheduling for fog-cloud paradigm. ACCESS, 13, 74617-74633. (www)

  @article{ , abstract = {The rapid proliferation of IoT devices like smartphones, smartwatches, etc. has significantly elevated the quantity of data requiring execution. It poses challenges for centralized Cloud computing servers, such as latency overhead and increased consumption of energy. To address this, Fog computing has been incorporated in this research as a complementary paradigm to the Cloud-based computing model. It thereby reduces the huge distance between end-user IoT gadgets and the Cloud computing servers. In order to improve the latency and waiting time this research employs a Multi-Level Queue (MLQ) for classifying tasks based on their priority. For parallel processing, a Self-adaptive Fuzzy C-means++ (SaFCm++) approach has been applied to cluster the Fog computing nodes depending on the heterogeneity of the computational nodes. For efficient scheduling, a Dynamic Energy-and-Latency-aware Task scheduling (DELTa) approach has been proposed to optimize latency, makespan, and utilization of resources. Extensive simulations are carried out for different test cases considering task and machine heterogeneity, followed by a statistical analysis. The overall improvements of the proposed method over other baselines are 9.75% for makespan, 64.59% for service latency, 13.90% for resource utilization, and 12.75% for energy consumption. The proposed energy-efficient scheduling method effectively manages diverse tasks and resources, enhancing task processing efficiency while minimizing energy consumption and maximizing resource utilization. Furthermore, the value of the Friedman statistics $F_{F}$ for the proposed model is estimated at 5.44, which shows superiority over the crucial value for four methodologies and four evaluation matrices.}, archive = {J_ACCESS}, author = {Abhijeet Mahapatra and Rosy Pradhan and Santosh K. Majhi and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3563103}, journal = {IEEE Access}, pages = {74617-74633}, shortjournal = {IEEE Access}, title = {DELTa: Dynamic energy-and-latency-aware task scheduling for fog-cloud paradigm}, volume = {13}, year = {2025}, }
• (2025). Vision transformer and brain connectivity patterns for estimating cognitive states. ACCESS, 13, 74606-74616. (www)

  @article{ , abstract = {The transformer model is excellent at handling time series signals (such as electroencephalography: EEG) because it can extract information from long-term dependencies effectively. This work combines binarization of EEG connectivity features, cognitive state classification using the vision transformer (ViT), and identifying graphical connectivity patterns for each cognitive state of the mental arithmetic task. The common spatial pattern (CSP) filter coefficient-based channel selection method selects the optimum EEG channels from the input channel set. Then, the Singular Value Decomposition (SVD) method is applied to prepare the binarized connectivity feature matrices, eliminating noisy connections between the optimum channels. The binarized functional-effective connectivity features are passed to the ViT model for cognitive state classification. The ViT model achieves the maximum classification accuracy of 94.86% with the phase-based connectivity feature. The proposed model improves classification accuracy by 6.15% compared to the state-of-the-art studies. This study also suggests a robust brain connectivity network to build a graphical connectivity pattern for each cognitive state. My findings of the EEG-based graphical patterns will bring further understanding of the scalp-level EEG channel patterns among different brain regions for other cognitive tasks.}, archive = {J_ACCESS}, author = {Debashis Das Chakladar}, doi = {10.1109/ACCESS.2025.3564156}, journal = {IEEE Access}, pages = {74606-74616}, shortjournal = {IEEE Access}, title = {Vision transformer and brain connectivity patterns for estimating cognitive states}, volume = {13}, year = {2025}, }
• (2025). Subsonic and transonic mach number tracking control strategy design and application in an intermittent wind tunnel. ACCESS, 13, 74596-74605. (www)

  @article{ , abstract = {In order to ensure the quality of test data, the Mach number control deviation should be kept within a given range during the continuous-traverse mode force measurement test in a wind tunnel. Currently, when establishing the continuous-traverse mode force measurement test technique in a one-meter-scale sub-transonic and supersonic wind tunnel, the maximum Mach number control deviation exceeds the given range (0.003) under the conditions of sub-transonic speed, large model blockage ratio and wide angle of attack range. This deviation fails to meet the test requirements. By analyzing the process of sub-transonic Mach number adjustment, it is found that the significant Mach number control deviation arises due to two main factors: first, the static pressure fluctuates substantially as a result of changes in the angle of attack; second, the choke finger fails to adjust accurately and eliminate these static pressure fluctuations in real time. To achieve Mach number control deviation less than 0.003, a Mach number tracking control strategy has been proposed, which is based on the choke finger model predictive control algorithm with feedforward-feedback structure and choke finger compensation algorithm. The proposed strategy has been successfully implemented in continuous-traverse mode force measurement test. The test results show that the Mach number control deviation remains within the given range of 0.003 by using Mach number tracking control strategy, and the strategy has good robustness under the condition that the model blockage ratio is less than 0.69%.}, archive = {J_ACCESS}, author = {Liebo Ma and Cheng Yan and Bo Xiong and Liang Wang}, doi = {10.1109/ACCESS.2025.3563672}, journal = {IEEE Access}, pages = {74596-74605}, shortjournal = {IEEE Access}, title = {Subsonic and transonic mach number tracking control strategy design and application in an intermittent wind tunnel}, volume = {13}, year = {2025}, }
• (2025). Enriching emotional expressiveness in embodied conversational agents using anime effects. ACCESS, 13, 74581-74595. (www)

  @article{ , abstract = {Embodied Conversational Agents (ECAs) can simulate human behaviors and express natural emotions. To further enhance these emotional expressions, we focused on applying attractive anime effects to the ECAs. Anime effects are visual techniques that are mainly used in anime production, encompassing a wide range of unique types and combinations. Among these, we focused on anime effects that indirectly support and enhance the emotions expressed by anime characters. If properly applied to ECAs, it is expected to dramatically enhance their ability to express emotion. However, the absence of established methods to apply diverse anime effects to ECAs has constrained their emotional expressiveness. Therefore, we proposed a system that automatically applies these diverse anime effects to the ECAs. First, anime effects used in several anime works were surveyed and organized according to visual, location, and emotion types for application to ECAs. Utilizing the organized dataset, a system was developed that automatically applies diverse anime effects to an ECA according to its profile (gender and personality) and emotions. The proposed system was evaluated with 21 participants using two static use cases based on watching videos and with eight participants using two interactive use cases using digital signage. The results showed that the proposed system enhanced the intensity of emotional expression of ECAs, suggesting an improvement in their interactions with users. Additionally, the applied anime effects allowed users to perceive differences in personality between the two ECAs.}, archive = {J_ACCESS}, author = {Keishi Tainaka and Toshiharu Horiuchi and Hanif Fermanda Putra and Tatsuya Kobayashi}, doi = {10.1109/ACCESS.2025.3559082}, journal = {IEEE Access}, pages = {74581-74595}, shortjournal = {IEEE Access}, title = {Enriching emotional expressiveness in embodied conversational agents using anime effects}, volume = {13}, year = {2025}, }
• (2025). Extracting learning data from handwritten notes: A new approach to educational data analysis based on image segmentation and generative AI. ACCESS, 13, 74567-74580. (www)

  @article{ , abstract = {While handwritten notes offer valuable insights into students’ knowledge retention, traditional analysis methods are often time-consuming and limited in scope. This study introduces an efficient approach for educational data analysis by combining image segmentation with generative AI to extract learning insights from students’ handwritten notes. Leveraging the Attention Multi-task U-Net for accurate segmentation and GPT-4o for content analysis, our method precisely identifies and categorizes text, charts, and formulas within notes. The extracted data provides educators with a detailed view of students’ knowledge retention, highlights the areas students focus on, and identifies critical knowledge points that may be missing from notes. Our experiments on student notes from a Digital Signal Processing course demonstrate the method’s high accuracy and significant efficiency improvements in teachers’ review of student notes. This research contributes to educational technology and data mining by introducing an automated, scalable method that supports more personalized and effective educational strategies.}, archive = {J_ACCESS}, author = {Yunyu Zhou and Cheng Tang and Atsushi Shimada}, doi = {10.1109/ACCESS.2025.3561916}, journal = {IEEE Access}, pages = {74567-74580}, shortjournal = {IEEE Access}, title = {Extracting learning data from handwritten notes: A new approach to educational data analysis based on image segmentation and generative AI}, volume = {13}, year = {2025}, }
• (2025). Permanent magnet synchronous motor stator and rotor fault detection using transfer learning and field-circuit model. ACCESS, 13, 74555-74566. (www)

  @article{ , abstract = {Currently, modern permanent magnet synchronous motor diagnostic systems based on artificial neural networks are designed to detect selected types of damage. Changing the type of failure requires the repeated training and implementation of a new neural structure. The main point in the development of new diagnostic applications is the extraction of damage symptoms, which significantly extends system implementation. Therefore, it is important to find a solution to implement universal diagnostic patterns that ensure the detection of various types of damage. Furthermore, the diagnostic application should be precise and characterized by a short reaction time to problems that occur. Such possibilities are now offered by transfer learning techniques used for deep neural networks. This article presents the concept of applying the transfer learning of a deep convolutional neural network to the diagnosis of damage to the electrical and magnetic circuits of a permanent magnet synchronous motor using the information obtained from the mathematical model. This research utilizes information derived from a mathematical model, particularly the field-circuit model of the PMSM, focusing on stator and permanent magnet failures.}, archive = {J_ACCESS}, author = {Maciej Skowron and Mateusz Krzysztofiak}, doi = {10.1109/ACCESS.2025.3564631}, journal = {IEEE Access}, pages = {74555-74566}, shortjournal = {IEEE Access}, title = {Permanent magnet synchronous motor stator and rotor fault detection using transfer learning and field-circuit model}, volume = {13}, year = {2025}, }
• (2025). Beyond the determiner “Al-”: Expanding the determiner class in arabic, and elimination of lexical ambiguities by grammars. ACCESS, 13, 74531-74554. (www)

  @article{ , abstract = {Arabic nouns can be marked for definiteness or indefiniteness. The definite article is the prefix “Al-,” which confines the determiner class to a single element “Al-.” This topic is generally discussed under noun inflections, such as Gender, Number, Definiteness, and Case (GNDC), in grammar textbooks. The primary aim of this paper is to expand the Arabic determiner class (DET) by incorporating additional lexical items and providing a detailed description of their syntactic context within noun phrases (NPs). In traditional grammar, these lexical items are typically classified as noun adjectives and, at best, are referred to as noun specifiers since they modify the head noun. However, these “nouns” exhibit limited inflection compared to regular adjectives and occupy a specific position within the NP sequence. Additionally, the modified head nouns are constrained in their inflectional attributes, Number, and Definiteness. Our approach is qualitative and guided by morpho-syntactic considerations. We conduct an in-depth analysis of the grammatical features of ten lexical items, focusing particularly on the dependencies between the determiner and the following noun. This analysis also addresses some semantic properties. By focusing on context-sensitive grammatical rules, the study shows how these methods can enhance precision in parsing and reduce ambiguity in NLP tasks, highlighting the potential for developing more refined grammar for Arabic. This work is a prototype for comprehensive studies in linguistics and NLP.}, archive = {J_ACCESS}, author = {Albatool Abalkheel and Alexis Amid Neme}, doi = {10.1109/ACCESS.2025.3563414}, journal = {IEEE Access}, pages = {74531-74554}, shortjournal = {IEEE Access}, title = {Beyond the determiner “Al-”: Expanding the determiner class in arabic, and elimination of lexical ambiguities by grammars}, volume = {13}, year = {2025}, }
• (2025). Blockchain knowledge and intention to use blockchain-based services. ACCESS, 13, 74521-74530. (www)

  @article{ , abstract = {This study explored the public’s understanding of blockchain technology and its impact on the intention to use blockchain-based services for wider acceptance of blockchain technology. 400 South Koreans in their 20s and 30s were surveyed to assess their subjective and objective knowledge of blockchain technology and their intention to use blockchain-based services. Subjective knowledge and intention to use were evaluated using a 7-point Likert scale, while objective knowledge was assessed through a multiple-choice questionnaire, encompassing four subdomains: basic concepts, building blocks, features, and practical applications. The study revealed that 1) over half of the respondents rated their knowledge level as below ‘neutral’ (1 to 3 points) (253 out of 400); 2) the majority of respondents showed a low level of objective knowledge (380 out of 400 scored under 50%), especially regarding the technology’s building blocks; 3) a moderate-to-strong monotonic relationship was observed between subjective knowledge and the intention to use blockchain-based services (r ${}_{\mathrm {s}} =0.580$ ); and 4) a weak-to-moderate monotonic relationship was found between objective knowledge and the intention to use blockchain-based services (r ${}_{\mathrm {s}} =0.334$ ). The study underscores the importance of educating individuals to enhance their subjective and objective knowledge of blockchain technology to promote its broader acceptance.}, archive = {J_ACCESS}, author = {Philjun Moon and Hyesun Chung and Jungmin Ryu and Kitae Hwang and Hayoun Moon and Woojin Park}, doi = {10.1109/ACCESS.2025.3562987}, journal = {IEEE Access}, pages = {74521-74530}, shortjournal = {IEEE Access}, title = {Blockchain knowledge and intention to use blockchain-based services}, volume = {13}, year = {2025}, }
• (2025). A mixed integer formulation and an NSGA-II for the cumulative capacitated vehicle routing problem with priority indexes. ACCESS, 13, 74499-74520. (www)

  @article{ , abstract = {The aim of the paper presented herein is to propose a new mixed integer formulation and an efficient metaheuristic for the Biobjective Cumulative Capacitated Vehicle Routing Problem that incorporates Priority Indexes, an extension of the classical capacitated vehicle routing problem (CCVRP) in which customers are served according to a certain level of preferences by a fixed fleet of heterogeneous vehicles. In this problem, the objectives to minimize consist of (i) the total latency and (ii) the total tardiness of the system. The proposed formulation is based on the multilevel network model for the single objective CCVRP and is implemented via AUGMECON -II. The exact approach proved effective by solving instances of up to 15 nodes in reasonable computational times. In addition, an NSGA-II scheme was designed to deal with larger instances at competitive computational times. This algorithm displayed efficient performance by providing high-quality solutions for instances of up to 100 nodes. Likewise, a comparative analysis was conducted between the proposed approaches and a Particle Swarm Optimization algorithm already presented in the literature. The results indicate the superiority of both of our proposed approaches in providing high-quality Pareto fronts in competitive computational times.}, archive = {J_ACCESS}, author = {Karina Corona-Gutiérrez and Samuel Nucamendi-Guillén and Iris Martínez-Salazar and J. Marcos Moreno-Vega}, doi = {10.1109/ACCESS.2025.3560896}, journal = {IEEE Access}, pages = {74499-74520}, shortjournal = {IEEE Access}, title = {A mixed integer formulation and an NSGA-II for the cumulative capacitated vehicle routing problem with priority indexes}, volume = {13}, year = {2025}, }
• (2025). STT-HDC: An efficient time-domain in-memory hyper-dimensional computing design based on STT-MRAM. ACCESS, 13, 74485-74498. (www)

  @article{ , abstract = {This paper presents an efficient in-memory hyperdimensional computing (HDC) design based on spin transfer-torque magnetoresistive RAM (STT-MRAM), named STT-HDC. A novel time-domain sense amplifier circuit is proposed that significantly simplifies Hamming distance computation of HDC models while dramatically improving energy efficiency. Our design is evaluated using HSPICE simulation under the 28nm FD-SOI technology PDK (Process Design Kit). Simulation results indicate that our approach delivers an energy efficiency of 3.12(fJ) per bit, achieving a significant reduction in energy consumption relative to previous implementations. This substantial enhancement in energy performance, coupled with the simplified computation model, paves the way for more practical and scalable HDC systems in resource-constrained environments. The influence of variations in different process corners, and temperature is also thoroughly covered in the analysis.}, archive = {J_ACCESS}, author = {Thi-Nhan Pham and Quang-Kien Trinh and Thanh-Dat Nguyen and Ik-Joon Chang}, doi = {10.1109/ACCESS.2025.3564440}, journal = {IEEE Access}, pages = {74485-74498}, shortjournal = {IEEE Access}, title = {STT-HDC: An efficient time-domain in-memory hyper-dimensional computing design based on STT-MRAM}, volume = {13}, year = {2025}, }
• (2025). Embedded system for interactive pneumatic hand rehabilitation: Real-time gaming interface with cognitive stimulation for motor recovery. ACCESS, 13, 74470-74484. (www)

  @article{ , abstract = {Restoring fine motor skills in individuals with upper extremity sensory-motor post-stroke impairments necessitates repetitive, task-specific exercises to promote functional recovery. Given the substantial time commitment required for therapy, rehabilitation tools must be not only effective but also engaging by adopting a game-based approach to mitigate the monotony of prolonged repetitive exercises. This paper presents a user-friendly finger-thumb mechanism designed to support the index and middle fingers as well as the thumb, specifically for patients with hand injuries. The device establishes a wireless connection to a gaming platform enabling patients to engage with computer games in real-time through purposeful thumb and finger movements. This connectivity is established through two Raspberry Pi boards utilizing a server-client network. Additionally, the device incorporates assistive or resistive forces during gameplay to adjust the level of assistance or challenge based on the individual’s motor control proficiency. With a minimal data transfer delay of 10 ms, and a 50 ms delay for game event updates, patients can seamlessly participate in the gaming experience and modify events in real-time through the newly developed wearable device. In the experiments, assistive mode achieved a 100% success rate, while resistive mode dropped to 37-45%. Movement errors varied across modes, with assistive mode showing the lowest errors, indicating more accurate and consistent performance.}, archive = {J_ACCESS}, author = {Narges Ghobadi and Witold Kinsner and Tony Szturm and Nariman Sepehri}, doi = {10.1109/ACCESS.2025.3563824}, journal = {IEEE Access}, pages = {74470-74484}, shortjournal = {IEEE Access}, title = {Embedded system for interactive pneumatic hand rehabilitation: Real-time gaming interface with cognitive stimulation for motor recovery}, volume = {13}, year = {2025}, }
• (2025). Grip margin-based control allocation for enhanced vehicle stability in four-wheel independent steering vehicle. ACCESS, 13, 74457-74469. (www)

  @article{ , abstract = {This study aims to enhance the control performance of four-wheel independent steering vehicles (4WIS), which are increasingly recognized as a key innovation in electric vehicle technology. 4WIS provides a high degree of freedom and dynamic performance by enabling the independent steering of each wheel. However, as an over-actuated system, it requires the allocation of four steering angles from a single driver input, making control allocation critical. Existing methods often fail to fully consider the friction limits of individual tires, potentially compromising vehicle stability, especially under severe driving conditions. To address this, a grip margin-based control allocation (GMCA) technique is proposed, which distributes control forces according to each tire’s available friction, minimizing yaw rate error and side-slip angle while maintaining stability. GMCA is formulated using computationally efficient algebraic operations, enabling real-time implementation. Validation through Simulink/CarMaker co-simulation confirms that GMCA improves maneuverability and lateral stability over conventional methods, offering practical benefits for precise control of 4WIS vehicles under severe road conditions.}, archive = {J_ACCESS}, author = {Gyu-Yong Hwang and Chulhee Han and Min-Sang Seong and Dongwook Lee and Jong-Seok Oh}, doi = {10.1109/ACCESS.2025.3564439}, journal = {IEEE Access}, pages = {74457-74469}, shortjournal = {IEEE Access}, title = {Grip margin-based control allocation for enhanced vehicle stability in four-wheel independent steering vehicle}, volume = {13}, year = {2025}, }
• (2025). Complete coverage path planning strategy for reconfigurable robot with variable footprint. ACCESS, 13, 74446-74456. (www)

  @article{ , abstract = {Autonomous mobile robots (AMRs) face challenges in navigating complex environments efficiently. To manoeuvre through both narrow and wide spaces, AMRs require two essential design features: a compact form for tight areas as well as a large configuration with omnidirectional movement for wide spaces. This study utilizes inhouse designed Expand and Collapse Variable Width Robot (ECVWR) to demonstrate effective area coverage. These ECVWR can contract to navigate constrained spaces and expand to optimize coverage in open areas. Existing methods for achieving complete area coverage do not account for the reconfiguration or the change in footprint. To address this issue, we propose the Depth-First Search (DFS) for Complete Coverage Path Planning Strategy (CCPPS) for ECVWRs. This method allows adjustments to the generation of waypoints in CCPPS, minimizing the path length. The simulation study shows that our proposed CCPPS outperforms contemporary state-of-the-art CCPPS, namely, GBNN, exhibiting superior expanded area coverage, reduced travel distance, and enhanced computational efficiency. Moreover real-world experiment to further benchmark the efficacy of our proposed algorithm. The proposed CCPPS is generic and can be extended to other variable footprint robots.}, archive = {J_ACCESS}, author = {Qinrui Tang and Lim Yi and Ash Yaw Sang Wan and Abdullah Aamir Hayat and Anh Vu Le and Mohan Rajesh Elara}, doi = {10.1109/ACCESS.2025.3560152}, journal = {IEEE Access}, pages = {74446-74456}, shortjournal = {IEEE Access}, title = {Complete coverage path planning strategy for reconfigurable robot with variable footprint}, volume = {13}, year = {2025}, }
• (2025). HGTFM: Hierarchical gating-driven transformer fusion model for robust multimodal sentiment analysis. ACCESS, 13, 74430-74445. (www)

  @article{ , abstract = {In multimodal sentiment analysis, a significant challenge lies in quantifying the contribution of each modality and achieving effective modality fusion. This paper presents a Hierarchical Gating-Driven Transformer Fusion Model (HGTFM), which effectively achieves multimodal data fusion through an advanced transformer architecture and a Hierarchical Gated Fusion Module (HGFM). The model first encodes audio and video features using LSTM and CNN, respectively, while employing BERT to encode text features. This approach effectively enhances the model ability to manage long-range temporal dependencies. Furthermore, the model introduces HGFM, which dynamically integrates multimodal features across different levels after processing by the modality interaction network (MIN), thus intelligently adjusting the contribution weights of each modality. In particular, the model incorporates a Unimodal Label Generation Module (ULGM), which considers the differences between the modalities. This enables HGFM to more accurately identify the contributions of samples with significant modal variations, thus optimizing the module potential. The experimental results on several established benchmark datasets demonstrate that HGTFM achieves comparable performance improvements in multimodal sentiment analysis tasks.}, archive = {J_ACCESS}, author = {Chengcheng Yang and Zhiyao Liang and Dashun Yan and Zeng Hu and Ting Wu}, doi = {10.1109/ACCESS.2025.3560641}, journal = {IEEE Access}, pages = {74430-74445}, shortjournal = {IEEE Access}, title = {HGTFM: Hierarchical gating-driven transformer fusion model for robust multimodal sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Comparative study of signal control models for roundabouts. ACCESS, 13, 74417-74429. (www)

  @article{ , abstract = {Traffic signal is one of the control approaches used for roundabouts. Although many models are used for signalized roundabouts, studies involving their comparative analysis are insufficient. This study focuses on evaluating traffic signal timing approaches used for roundabouts. Different control models of roundabouts were carried out by using VISSIM software. We compared three different types of signal control methods, including priority control, entrance signalization, and full signalization control approaches. A number of simulation experiments were designed. Signal timing values were calculated by using the data obtained from field observations. The new signal timing process and calculating method were proposed for the two-stage left-turn signal control. Some detailed experiments were also conducted on the Yang model and the model developed by Cakici and Murat. Some threshold values were determined in the experiments. Different intersection geometries and traffic conditions were taken into account. Very interesting and encouraging results were obtained.}, archive = {J_ACCESS}, author = {Ruijun Guo and Yuan Xu and Yue Yu and Wanxiang Wang and Zhongchuan Gao}, doi = {10.1109/ACCESS.2025.3560646}, journal = {IEEE Access}, pages = {74417-74429}, shortjournal = {IEEE Access}, title = {Comparative study of signal control models for roundabouts}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based image pattern recognition using histogram of oriented gradient for islanding detection. ACCESS, 13, 74396-74416. (www)

  @article{ , abstract = {A vital issue faced by the distribution network is the occurrence of unintentional islanding. The failure to identify unintentional islanding results in significant implications for both the power system and human lives. In this paper, a novel machine learning islanding detection method (IDM) based on image classification utilizing the histogram of oriented gradient (HOG) feature is proposed. In particular, the set of parameters are utilized, namely total harmonic distortion (THD) of both three phase currents and voltages, and rate of change of negative sequence voltage, are first transformed into time-frequency representations (i.e., spectrograms via the short time Fourier transform, and scalograms through continuous wavelet transform). Then, the HOG features are extracted from these images and used to train the machine learning (ML) algorithms to distinguish between occurrences of islanding and non-islanding events. Performance metrics including F1 score, recall, accuracy, precision and misclassification error are employed in the assessment process. Numerical results show that our image-based detector achieves faster detection times and higher detection accuracy versus state-of-art methods, thus confirming the validity of such approach for identifying islanding events.}, archive = {J_ACCESS}, author = {Kumaresh Pal and Kumari Namrata and Ashok Kumar Akella and Manoj Gupta and Pannee Suanpang and Aziz Nanthaamornphong}, doi = {10.1109/ACCESS.2025.3564145}, journal = {IEEE Access}, pages = {74396-74416}, shortjournal = {IEEE Access}, title = {Machine learning-based image pattern recognition using histogram of oriented gradient for islanding detection}, volume = {13}, year = {2025}, }
• (2025). Centralised decision support in maritime vessel traffic services: A polymatrix game solution. ACCESS, 13, 74375-74395. (www)

  @article{ , abstract = {Given the increasing complexity of maritime traffic, particularly with the growing integration of autonomous vessels alongside conventional vessels, the need for enhanced coordination and decision support systems has become critical to ensure safety and efficiency. This article presents a concept for a centralised decision support system for local maritime traffic and can be used in centres for remote operations or vessel traffic services. By formalising a non-zero-sum asymmetric polymatrix game, we achieve collision and grounding avoidance of non-cooperative agents. Time-varying and state-dependent payoff matrices are constructed using a reward framework that combines metrics for compliance with maritime traffic rules, global collision risk, grounding risk, and an operational efficiency metric for speed and route optimisation. A pure Nash equilibrium is reached in a polymatrix game, a fully connected graph of traffic participants on vertices with bimatrix games on the edges. Strategy probability distributions in infinite populations located on the graph’s vertices are updated with the replicator revision protocol. The equilibrium constitutes a control message consisting of advised speed and heading commands broadcast to all traffic participants. To validate the proposed idea, we have developed a scalable multi-agent system simulator and present results from a collision avoidance scenario with three vessels and 49 strategies available to each. Engaged vessels achieved safe and efficient passage, guided by the advised speed and heading changes.}, archive = {J_ACCESS}, author = {Luka Grgičević and Erlend M. Coates and Thor I. Fossen and Robin T. Bye and Ottar L. Osen}, doi = {10.1109/ACCESS.2025.3564718}, journal = {IEEE Access}, pages = {74375-74395}, shortjournal = {IEEE Access}, title = {Centralised decision support in maritime vessel traffic services: A polymatrix game solution}, volume = {13}, year = {2025}, }
• (2025). Cluster sort: A novel hybrid approach to efficient in-place sorting using data clustering. ACCESS, 13, 74359-74374. (www)

  @article{ , abstract = {This research work introduces a clustering-based in-place sorting algorithm, cluster sort. It is designed in such a way that it improves sorting efficiency by using data locality. It works in two phases: first, data elements are clustered based on the similarity in the values and then each of these clusters is sorted independently using comb or shell sort. This is a hybrid approach, and it helps minimize multiple comparisons within the clusters, which in turn improves performance, especially in large datasets with specific distributions. The traditional algorithms were selected due to their well-known efficiency and widespread use in sorting large datasets. This experiment of ours includes ordered, reverse-ordered, Gaussian, repeated values, same values, and uniform distributions. The results acquired from this experiment show that Cluster Sort (comb) outperforms Cluster Sort (shell) by 88% in ordered datasets and is 92.32% faster than Merge Sort. For reverse-ordered datasets, Cluster Sort (Comb) is 70.79% faster than Bucket Sort and 90.69% faster than Cluster Sort (Shell). In Gaussian distributions, Cluster Sort (Shell) improves by 25.88% over Bucket Sort and 74.41% over Merge Sort. In repeated-value datasets, although Quick Sort is faster, Cluster Sort (Shell) surpasses Bucket Sort by 13.64%. For uniform distributions, Cluster Sort (Shell) is 30.28% faster than Bucket Sort and 75% faster than Merge Sort. The reduction in unwanted comparisons through clustering is essentially what made Cluster Sort significantly outperform traditional algorithms in these datasets with inherent patterns, making Cluster Sort a coherent choice for practical applications.}, archive = {J_ACCESS}, author = {Mohit Subramaniam and Tanya Tripathi and Omkumar Chandraumakantham}, doi = {10.1109/ACCESS.2025.3564380}, journal = {IEEE Access}, pages = {74359-74374}, shortjournal = {IEEE Access}, title = {Cluster sort: A novel hybrid approach to efficient in-place sorting using data clustering}, volume = {13}, year = {2025}, }
• (2025). From static to AI-driven detection: A comprehensive review of obfuscated malware techniques. ACCESS, 13, 74335-74358. (www)

  @article{ , abstract = {The frequency of cyber attacks targeting individuals, businesses, and organizations globally has escalated in recent years. The evolution of obfuscated malware, designed to evade detection, has been unprecedented, employing new and sophisticated mechanisms to breach systems, steal sensitive data, and disrupt operations. This work advances research on obfuscated malware detection by offering a comprehensive review of studies conducted over the past decade on multiple platforms. In addition, the diversity of obfuscation techniques and the effectiveness of detection methods, such as static, dynamic, hybrid, and AI, are presented in a comparative manner. Furthermore, memory forensics, an often underexplored area, is of paramount importance for real-time analysis and the detection of advanced obfuscated malware. Hybrid analysis, which amalgamates the strengths of various approaches, emerges as a robust solution against obfuscated malware detection. The role of AI in detecting advanced ransomware, spyware, and fileless malware by enabling real-time detection and adaptive defenses against these increasingly prevalent threats is presented. In addition, a novel framework is proposed, combining Generative AI and digital twins to simulate and predict malware behavior, offering enhanced detection capabilities. This study synthesizes the findings of 76 approaches for the detection of obfuscated malware, incorporates cutting-edge technologies, and identifies open research challenges, such as ensuring scalability, enhancing generalization across platforms, and reducing resource requirements for constrained environments to guide future advancements in obfuscated malware detection.}, archive = {J_ACCESS}, author = {Saranya Chandran and Sreelakshmi R. Syam and Sriram Sankaran and Tulika Pandey and Krishnashree Achuthan}, doi = {10.1109/ACCESS.2025.3550781}, journal = {IEEE Access}, pages = {74335-74358}, shortjournal = {IEEE Access}, title = {From static to AI-driven detection: A comprehensive review of obfuscated malware techniques}, volume = {13}, year = {2025}, }
• (2025). Exploring text similarity in human and AI-generated scientific abstracts: A comprehensive analysis. ACCESS, 13, 74313-74334. (www)

  @article{ , abstract = {Text similarity is a crucial area of study that evaluates how similar texts are both semantically and syntactically. As data volumes increase, understanding the similarities and relationships between texts becomes essential, particularly in natural language processing (NLP) tasks such as text generation, summarization, and classification. This study examines the similarities between human-written scientific abstracts, AI-paraphrased abstracts, and AI-generated abstracts. Various methods, including cosine similarity, Word2Vec, and BERT, were evaluated based on mean, median, and standard deviation metrics. Among these, Word2Vec and FastText achieved the highest mean similarity scores (0.930), while BERT demonstrated superior performance with the highest median (0.841) and the lowest standard deviation (0.019) in the ‘Human-Paraphrased’ category, showing consistent results across datasets. Additionally, the research investigates the implications of these similarities for text analysis and ethical standards, comparing various techniques for measuring text similarity and analysing their effectiveness. The findings offer valuable insights into the application areas of text similarity analysis.}, archive = {J_ACCESS}, author = {Tugba Celikten and Aytug Onan}, doi = {10.1109/ACCESS.2025.3564867}, journal = {IEEE Access}, pages = {74313-74334}, shortjournal = {IEEE Access}, title = {Exploring text similarity in human and AI-generated scientific abstracts: A comprehensive analysis}, volume = {13}, year = {2025}, }
• (2025). Visual-tactile experiment for the elderly with perceptual limitations: A case study on neck massager design for the geriatric population. ACCESS, 13, 74298-74312. (www)

  @article{ , abstract = {This study established a subjective-objective combination of emotional demands and an experimental research approach to address the hurdles to smart device use in the elderly caused by deterioration in their perceptual abilities. We used a neck massager as the research object. This product is a regularly used item for the elderly to alleviate neck soreness, but the experience is unsatisfactory in terms of human-computer interfaces, such as button usage. 1) Proposing to analyze product user needs from an emotional perspective, this study designed and implemented visual-tactile perception experiments involving 12 elderly users, including experiments on button shape, button size, button spacing, button material, button texture, and product material. The impact of button specifications and product materials on elderly users’ emotional preferences was objectively investigated by evaluating the $\alpha $ - and $\beta $ -band power characteristics of the participants’ electroencephalography frontal lobe electrodes (FP1, FP2, F3, F4) and central zone electrodes (FPZ, F ${}_{\mathrm {Z}}$ ). 2) Aging-friendly design suggestions for smart products were proposed, and neck massage for the elderly was designed. The findings indicate that the button parameters of round shape, 15 mm diameter, 4.6 mm pitch, and 1 mm depth of concave pattern, as well as the product contact surface material of velvet, are ideal. This study offers a scientific approach to product design and a design reference for aging comparable products in terms of button specifications and material selection.}, archive = {J_ACCESS}, author = {Yanmin Xue and Shuang Wang and Mengting Xu and Mengcheng Wang and Yang Liu and Kai Qi and Zhi Qiao and Suihuai Yu}, doi = {10.1109/ACCESS.2025.3564061}, journal = {IEEE Access}, pages = {74298-74312}, shortjournal = {IEEE Access}, title = {Visual-tactile experiment for the elderly with perceptual limitations: A case study on neck massager design for the geriatric population}, volume = {13}, year = {2025}, }
• (2025). YOLO-WWBi: An optimized YOLO11 algorithm for PCB defect detection. ACCESS, 13, 74288-74297. (www)

  @article{ , abstract = {The manufacturing quality of printed circuit boards (PCBs) significantly influences the functionality and life expectancy of electronic devices. This paper introduces a YOLO-WWBi based on improved YOLO11 framework method for the detection of surface defects. First, an improved weighted and re-parameterized ghost multi-scale feature aggregation module (WRGMSFA) is designed. This module focuses more on defect information channels, enhancing multi-scale feature extraction capabilities while suppressing redundant information. Then, BiFPN is integrated into the neck to enhance the quality of fused features and deepen the interaction of feature information. Finally, the WIoU loss function was employed to optimize the localization performance of defect positions, thereby enhancing robustness in highly similar PCB background interference. The experimental results indicate that YOLO-WWBi has an mAP of 96.6%, surpassing YOLO11 by 5.4 points. Its performance metrics indicate that the requirements for the high-precision, real-time detection of PCB defects are satisfactorily met.}, archive = {J_ACCESS}, author = {Yi Zhao and Zhidi Jiang}, doi = {10.1109/ACCESS.2025.3564734}, journal = {IEEE Access}, pages = {74288-74297}, shortjournal = {IEEE Access}, title = {YOLO-WWBi: An optimized YOLO11 algorithm for PCB defect detection}, volume = {13}, year = {2025}, }
• (2025). SDN-based edge computing in vehicular communication networks: A survey of existing approaches. ACCESS, 13, 74252-74287. (www)

  @article{ , abstract = {Improved communication performance in vehicular communication networks (VCN) is achieved by enabling additional computation and storage support at the levels of edge, fog, and cloud. Specifically, the edge/fog servers located near vehicles enable improved networking performance, especially in terms of load balancing and end-to-end delay. Recently, Software Defined Networking (SDN) in general and SDN-based edge/fog computing in particular have been employed in the context of VCN, enabling further communications performance improvements to be achieved with the help of SDN-based fog/edge computing in the VCN (SDV-F). This paper surveys a large number of innovative approaches proposed for communications performance enhancement in the context of SDV-F and categorizes them based on different aspects, including Artificial Intelligence (AI) and security support. The advantages and limitations of the proposed approaches in each category are highlighted. Finally, various directions that can be considered as promising research avenues by researchers in the future are presented and discussed.}, archive = {J_ACCESS}, author = {Syed Aizaz Ul Haq and Muqaddas Imran and Nadir Shah and Gabriel-Miro Muntean}, doi = {10.1109/ACCESS.2025.3561083}, journal = {IEEE Access}, pages = {74252-74287}, shortjournal = {IEEE Access}, title = {SDN-based edge computing in vehicular communication networks: A survey of existing approaches}, volume = {13}, year = {2025}, }
• (2025). NetraAadhaar: A deep learning-driven aadhaar verification platform for the aid of visually impaired. ACCESS, 13, 74229-74251. (www)

  @article{ , abstract = {Visually impaired individuals face challenges in verifying Aadhaar cards due to the absence of braille, often relying on others for assistance. Despite the widespread use of Aadhaar cards across India, research on assistive technologies for such users remains limited, making this work uniquely significant. To address this, we introduce NetraAadhaar, a deep learning-based mobile application designed to assist visually impaired individuals in Aadhaar card verification. The framework consists of: (i) text region extraction from Aadhaar cards using YOLOv8, (ii) recognition of extracted text via Tesseract OCR engine, (iii) text-to-speech conversion for auditory verification, and (iv) the developed framework is fine-tuned and seamlessly integrated into an end-to-end mobile application for real-time use. NetraAadhaar achieves an mAP-50 score of 92.5% for text detection and an overall text recognition accuracy of 87. 79%, with a precision greater than 90% in key classes, demonstrating its robustness. Compared to traditional OCR-based assistive tools, NetraAadhaar offers higher accuracy, real-time performance, and an end-to-end automated pipeline making Aadhaar verification significantly more accessible and reliable for visually impaired users. NetraAadhaar empowers visually impaired users to verify their Aadhaar cards independently, reducing reliance on others and mitigating risks of identity fraud. The source code, dataset and developed application are publicly available for academic and research purposes at https://github.com/Adinp1213/NetraAadhar.}, archive = {J_ACCESS}, author = {Aditya Patil and Tauseef Khan and Ayatullah Faruk Mollah}, doi = {10.1109/ACCESS.2025.3563786}, journal = {IEEE Access}, pages = {74229-74251}, shortjournal = {IEEE Access}, title = {NetraAadhaar: A deep learning-driven aadhaar verification platform for the aid of visually impaired}, volume = {13}, year = {2025}, }
• (2025). Impact of emotional intelligence on leadership and team dynamics in agile software engineering projects. ACCESS, 13, 74217-74228. (www)

  @article{ , abstract = {Agile software engineering emphasizes collaboration, adaptability, and team cohesion, where Leadership and interpersonal dynamics are critical for project success. Emotional Intelligence (EI), the ability to recognize, understand, and manage emotions, has been theorized to enhance communication, conflict resolution, and team performance in Agile environments. However, empirical evidence on its direct and mediated impacts remains underexplored. This study examines how EI influences leadership effectiveness and team dynamics in Agile software projects, focusing on quantifying these relationships to inform actionable strategies. A quantitative approach was employed, utilizing structured surveys from 51 Agile professionals (Scrum Masters, developers, and product owners) actively engaged in frameworks such as Scrum, Kanban, and Extreme Programming (XP). Validated instruments—the Emotional Intelligence Scale (EIS), Multifactor Leadership Questionnaire (MLQ), and Team Climate Inventory (TCI) measured EI competencies, leadership behaviors, and team dynamics. Data were analyzed using correlation, regression, and mediation analyses. Results revealed significant positive correlations between EI and leadership effectiveness (r=0.504, p<0.01) and EI and team dynamics (r=0.434, p<0.01). Mediation analysis demonstrated that leadership effectiveness fully mediates the relationship between EI and team outcomes ( $\beta =0.290$ , p<0.01), highlighting the pivotal role of emotionally intelligent leaders in fostering trust and adaptability. Teams led by individuals with high EI reported a 20% increase in trust and marked improvements in collaboration and decision-making. These findings underscore EI as a critical catalyst for Agile success. Organizations are urged to prioritize EI development through targeted leadership training programs and integrate Digital Emotional Intelligence (DEQ) tools (Zoom and Microsoft Teams) to bridge emotional gaps in distributed teams. This study provides actionable insights for optimizing team performance, conflict resolution, and project outcomes in fast-paced software engineering environments, advocating for a human-centric approach to Agile methodologies.}, archive = {J_ACCESS}, author = {Firdews A. Alsalman and Shaymaa A. Chyad}, doi = {10.1109/ACCESS.2025.3563424}, journal = {IEEE Access}, pages = {74217-74228}, shortjournal = {IEEE Access}, title = {Impact of emotional intelligence on leadership and team dynamics in agile software engineering projects}, volume = {13}, year = {2025}, }
• (2025). Advanced detection of violence from video: Performance evaluation of transformer and state of the art of convolution of neural network transformer. ACCESS, 13, 74200-74216. (www)

  @article{ , abstract = {Safety is paramount in every aspect of human concern. In situations where violence occurs beyond the control of observers, it is essential for trained professionals to intervene. These scenarios can be detected by advanced surveillance systems equipped with cameras or sensors. The performance of these systems is highly dependent on the models they incorporate. Although various machine learning and deep learning models have been explored to address these challenges, significant work remains in handling a diverse range of images. This study aims to bridge this gap by training four models—VGG16, MobileNetV2, Yolov6 and Ensemble Model, and a vision Transformer model (‘google/vit-base-patch16-224-in21k’) —with tuned parameters on publicly available video frames. Our analysis established that the Transformer model achieved the highest accuracy, making it the most suitable for applications involving the detection of violent incidents. This research introduces a novel application of AI for semi-automated violence recognition, demonstrating AI’s significant potential in improving public safety. It also emphasizes the superior performance of the Transformer model in accurately detecting violent frames. Semi-automated violence recognition refers to a system where AI models assist in identifying violent incidents within video footage, but human intervention is still required for certain tasks, such as verification or decision-making. The AI system automatically detects potential violent events, flagging them for further review by human operators, who then confirm or act upon these alerts. The Transformer model achieved outstanding results: for the Violence Dataset, it attained a precision of 99%, recall of 99%, F1 score of 99%, and accuracy of 99%. To further verify the performance of the Transformer model, we tested it on the Road-Anomaly Dataset, which presents a challenging scenario with imbalanced classes. For this dataset, the Transformer model achieved a precision of 98%, recall of 97%, F1 score of 98%, and accuracy of 98%. These results affirm the model’s efficacy, positioning it as the top choice for real-time surveillance and public safety applications.}, archive = {J_ACCESS}, author = {Abdulrahman Alshalawi and Wadood Abdul and Ghulam Muhammad}, doi = {10.1109/ACCESS.2025.3564435}, journal = {IEEE Access}, pages = {74200-74216}, shortjournal = {IEEE Access}, title = {Advanced detection of violence from video: Performance evaluation of transformer and state of the art of convolution of neural network transformer}, volume = {13}, year = {2025}, }
• (2025). High-accuracy iris center localization using mediapipe keypoints and xception-based deep regression. ACCESS, 13, 74185-74199. (www)

  @article{ , abstract = {The precise identification of the iris centre is essential for numerous applications, such as biometrics, telemedicine, and ocular health diagnostics. This paper presents a novel approach that combines Mediapipe’s keypoint detection for accurate eye area identification with a deep regression framework based on the Xception architecture, specifically aimed at predicting the coordinates and radius of the iris centre. The model utilizes a manually annotated dataset from the Columbia Gaze dataset, incorporating an Xception backbone with three distinct regression outputs to estimate the x and y coordinates of the iris centre and its radius. Comprehensive testing was undertaken to refine critical parameters, such as the depth of the backbone layers, selection of optimizer, and resolution of input images, with training conducted over 200 epochs via the Huber loss function. The optimal configuration—comprising a 130-layer backbone, Adam optimizer, and an input resolution of $186\times 186$ pixels—produced a mean Euclidean distance ( $\mu _{ED}$ ) of 0.736 and a standardized Euclidean distance ( $S_{ED}$ ) of 2.208 on the GI4E dataset. Upon evaluation using the BioID dataset, it attained $\mu _{ED}$ and $S_{ED}$ scores of 1.560 and 3.045, respectively. The model exhibited near real-time performance, achieving an average frame processing time of 0.056 seconds (about 17.7 frames per second) on a MacBook Air M3. These findings highlight the method’s enhanced efficacy relative to current methodologies, offering an effective and reliable alternative for real-time iris localization and analysis.}, archive = {J_ACCESS}, author = {Wattanapong Kurdthongmee and Arsanchai Sukkuea and Korakot Suwannarat and Piyadhida Kurdthongmee}, doi = {10.1109/ACCESS.2025.3564365}, journal = {IEEE Access}, pages = {74185-74199}, shortjournal = {IEEE Access}, title = {High-accuracy iris center localization using mediapipe keypoints and xception-based deep regression}, volume = {13}, year = {2025}, }
• (2025). A dielectric-loaded FSS-based radome design with an adjustable RCS reduction band. ACCESS, 13, 74175-74184. (www)

  @article{ , abstract = {This paper presents a hybrid radome panel that features a controllable radar cross section (RCS) reduction band. To achieve RCS suppression, the structure combines periodic frequency selective surfaces (FSSs) and dielectric loadings of different compositions, which leads to wave cancellation due to propagation delays of opposite phases. By modifying the thickness of the radome and the relative permittivity ratio, the RCS reduction band can be adjusted and optimized to obtain a wider reduction bandwidth. Furthermore, the evaluation method implemented in this work, that is, summing up the reflectivity of each radome segment, can rapidly assess the radome’s RCS reduction performance. Simulation and measurement results of the 75 mm $\times 150$ mm radome panel are in good agreement. They validate that the panel provides substantial RCS reduction in X- and Ku-bands, while the C-band antenna operation is unperturbed.}, archive = {J_ACCESS}, author = {Yuan-Chang Hou and Sang-Fei Wang and Wen-Jiao Liao}, doi = {10.1109/ACCESS.2025.3562943}, journal = {IEEE Access}, pages = {74175-74184}, shortjournal = {IEEE Access}, title = {A dielectric-loaded FSS-based radome design with an adjustable RCS reduction band}, volume = {13}, year = {2025}, }
• (2025). Ball bearing fault diagnosis based on hybrid adversarial learning. ACCESS, 13, 74163-74174. (www)

  @article{ , abstract = {Ball bearings are prone to faults in their inner and outer rings and rolling elements. Timely detection of these faults is crucial, especially when adversarial perturbations are present, as deep learning-based fault diagnosis models may misclassify these faults. To address this issue, this study proposes a hybrid adversarial learning method that combines convolutional neural networks with a generative adversarial network framework. In this method, the generator introduces perturbations and adaptively adjusts them based on their magnitude and gradient information. The discriminator was used to verify the effectiveness of adversarial perturbations. The goal of this hybrid adversarial learning method is to improve the fault recognition accuracy of a model when subjected to perturbation attacks. The experimental results show that under adversarial perturbation attacks, the proposed method outperforms other deep learning models and defence methods, demonstrating the effectiveness of this approach.}, archive = {J_ACCESS}, author = {Xiaofeng Bai and Qazi Mazhar Ul Haq and Aftab Alam Khan and Chaohui Li and Hind Alsharif and Muhammad Shahid Anwar and Chengwei Li}, doi = {10.1109/ACCESS.2025.3564484}, journal = {IEEE Access}, pages = {74163-74174}, shortjournal = {IEEE Access}, title = {Ball bearing fault diagnosis based on hybrid adversarial learning}, volume = {13}, year = {2025}, }
• (2025). Optimized scheduling of integrated energy systems considering electric vehicles and carbon emission trading. ACCESS, 13, 74147-74162. (www)

  @article{ , abstract = {This study proposes an optimized dispatch model for integrated energy systems (IES) incorporating electric vehicles (EVs) and carbon emission trading (CET) to analyze their dynamic interactions. The model aims to minimize IES operating costs by systematically evaluating the impacts of EV penetration levels and CET parameters on economic and environmental performance. Methodologically, Monte Carlo simulations characterize stochastic EV behaviors, a stepwise CET mechanism is developed with emission thresholds, and piecewise linearization combined with convex relaxation transforms nonlinear constraints into tractable formulations. The YALMIP/GUROBI solver is employed to compare eight dispatch scenarios. Key results demonstrate that integrating EVs with carbon capture under CET reduces operating costs by 7.64% and emissions by 17.68%. System emissions exhibit a U-shaped relationship with EV penetration, reaching a minimum at 9.20%. Maximum emission reductions from EV charging optimization occur when CET prices range 250-300 CNY/ton and EV penetration spans 12.31%-19.43%, providing critical thresholds for policy design.}, archive = {J_ACCESS}, author = {Yanhong Xiao and Bin Qian and Jiaxiang Ou and Weibin Lin and Houpeng Hu and Xiaoming Lin and Yuedan Deng and Jianlin Tang}, doi = {10.1109/ACCESS.2025.3563994}, journal = {IEEE Access}, pages = {74147-74162}, shortjournal = {IEEE Access}, title = {Optimized scheduling of integrated energy systems considering electric vehicles and carbon emission trading}, volume = {13}, year = {2025}, }
• (2025). Multimodal and temporal graph fusion framework for advanced phishing website detection. ACCESS, 13, 74128-74146. (www)

  @article{ , abstract = {Phishing attacks are among the persistent threats that are dynamically evolving and demand advanced detection mechanisms to counter more sophisticated techniques. Traditional detection approaches are usually based on single-modal features or static analysis, failing to capture the complex, multi-faceted nature of phishing websites and their dynamic behaviors. Thus, we present a robust Multi-Modal and Temporal Graph Fusion Framework integrating advanced learning paradigms that enhance accuracy and adaptability in phishing detection. Our work proposes four brand-new methods: Multi-Modal Hypergraph Fusion Network (MM-HFN), Temporal Graph Neural Network with Attention (TGNN-Att), Federated Graph Contrastive Learning Network (FGCL-Net), and Multi-Modal Temporal Hypergraph Fusion Network (MMTHF-Net). MM-HFN leverages hypergraphs to capture complex, high-order relationships at textual levels (BERT) and graph-based features versus visual ones (CNNs) for an accuracy in the 95-97% range. TGNN-Att addresses temporal variations in phishing behavior by using attention-enhanced temporal graph networks and LSTMs, providing dynamic detection with 94-96% accuracy. FGCL-Net ensures privacy-preserving learning across decentralized datasets through federated contrastive learning, achieving 93-95% accuracy while safeguarding data privacy. Finally, MMTHF-Net fuses multi-modal and temporal features into a dynamic hypergraph framework, achieving state-of-the-art accuracy of 96-98% with an F1-score of 0.97. These approaches together allow for exact, real-time phishing detection by capturing static and temporal behaviors, high-order relationships, and cross-modal features. The framework proposed demonstrates significant improvements compared to the state of the art, eliminating the shortcomings of single-modality and static analysis while offering scalability, privacy, and adaptability levels.}, archive = {J_ACCESS}, author = {S. Kavya and D. Sumathi}, doi = {10.1109/ACCESS.2025.3564530}, journal = {IEEE Access}, pages = {74128-74146}, shortjournal = {IEEE Access}, title = {Multimodal and temporal graph fusion framework for advanced phishing website detection}, volume = {13}, year = {2025}, }
• (2025). Open datasets for upper limb Motion—A systematic review. ACCESS, 13, 74107-74127. (www)

  @article{ , abstract = {Open biomechanical datasets play a critical role in advancing scientific research, particularly with the growing use of machine learning, which requires a large amount of data for training and validation. However, the availability and characteristics of such datasets, particularly in the context of upper limb motion, are still underexplored. This systematic review aims to identify, assess, and categorize existing open datasets related to upper limb motion, focusing on the tasks analyzed, subject demographics, and data collection methods employed. We conducted a comprehensive search across multiple databases, including Scopus, Zenodo, PubMed, Xplore, and Google Dataset Search, selecting publicly accessible datasets that focus on upper limb motion based on predefined inclusion and exclusion criteria. Additional datasets were identified through manual searches. A total of 63 datasets met the criteria for further analysis, and their quality was assessed based on subject types, data collection methods, and sample sizes. The majority of the datasets centered on activities of daily living, with 69.8% of the studies involving healthy subjects. Marker-based motion capture was the most common data collection method, though the use of markerless systems (e.g., inertial sensors, video cameras) is on the rise. Key limitations included the absence of standardized movement protocols and small subject sample sizes across studies. In contrast to lower limb gait analysis, there is no single dominant task for studying upper limb motion. Standardizing movement protocols and incorporating a broader range of daily activities into datasets could enhance the development of assistive technologies and rehabilitation programs. Future research should focus on creating more standardized, diverse datasets to improve the accuracy and generalizability of biomechanical analyses.}, archive = {J_ACCESS}, author = {Ranieri Santanché and Antonia Centrone and Lorenza Mattei and Francesca Di Puccio}, doi = {10.1109/ACCESS.2025.3564350}, journal = {IEEE Access}, pages = {74107-74127}, shortjournal = {IEEE Access}, title = {Open datasets for upper limb Motion—A systematic review}, volume = {13}, year = {2025}, }
• (2025). SP-pillars: An efficient LiDAR 3D objects detection framework with multi-scale feature perception and optimization. ACCESS, 13, 74092-74106. (www)

  @article{ , abstract = {In autonomous driving, achieving rapid detection of target categories and locations is a key technology. However, the data volume of radar point clouds is enormous, and processing efficiency becomes a limiting factor, so the balance between speed and accuracy is crucial. To address this challenge, this paper proposes a 3D object detection algorithm SP-Pillars that can effectively learn point cloud features. Firstly, a Pillar Feature Weighted Network (PFWNet) is proposed for processing point cloud information, which divides the point cloud into pillar structures and uses SPCV feature attention network to focus on its multi-level feature information. After feature extraction and dimensionality reduction, pseudo images are generated. Subsequently, before extracting pseudo image features, a multi-core perception network (PKINet) is introduced to further mine local contextual information and reduce computational complexity, enabling the backbone network to effectively learn features. The experimental evaluation results on the KITTI dataset indicate that the proposed algorithm is reliable and effective. Compared with other related algorithms, this algorithm exhibits excellent detection performance, slightly improving detection speed while maintaining high accuracy, meeting the requirements of real-time processing, and has important application value in optimizing autonomous driving technology.}, archive = {J_ACCESS}, author = {Tingshuai Chen and Ye Yuan and Bingyang Yin and Yuanhong Liao}, doi = {10.1109/ACCESS.2025.3564665}, journal = {IEEE Access}, pages = {74092-74106}, shortjournal = {IEEE Access}, title = {SP-pillars: An efficient LiDAR 3D objects detection framework with multi-scale feature perception and optimization}, volume = {13}, year = {2025}, }
• (2025). Automated parkinson’s disease diagnosis using decomposition techniques and deep learning for accurate gait analysis. ACCESS, 13, 74078-74091. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders.}, archive = {J_ACCESS}, author = {S. Jeba Priya and C. Anand Deva Durai and M. S. P. Subathra and S. Thomas George and Andrew Jeyabose}, doi = {10.1109/ACCESS.2025.3562566}, journal = {IEEE Access}, pages = {74078-74091}, shortjournal = {IEEE Access}, title = {Automated parkinson’s disease diagnosis using decomposition techniques and deep learning for accurate gait analysis}, volume = {13}, year = {2025}, }
• (2025). A comprehensive framework for intelligent, scalable, and performance-optimized software development. ACCESS, 13, 74062-74077. (www)

  @article{ , abstract = {Integrating Artificial Intelligence (AI) into the Software Development Life Cycle (SDLC) has become necessary to enhance efficiency, scalability, and performance in modern software systems. Instead of incorporating the AI functionality into their SDLC, traditional SDLC models typically add-on the AI software functionality after they have integrated AI functionality into their application or software process. Because of this, developers undergo inefficiencies in their development workflows, experience performance bottlenecks during testing, and experience challenges of incorporating AI to improve an application’s performance through optimization. This paper proposes a new AI-Optimized Software Development Life Cycle (AI-SDLC), which is a holistic and comprehensive framework that encases the embedded AI capabilities and optimization strategies throughout the SDLC process during every stage of the system development, so that requirements-gathering, development, testing, and maintenance are hybrid software processes and not dictated by AI vs. traditional software development processes. AI-SDLC presents new development roles, such as AI Integration Specialist, Code Optimizer, and UX Optimization Specialist, which helps developers work across disciplines and increases collaborative interaction between traditional developers and AI engineers. AI-SDLC also utilizes an AI-driven automated hybrid software process in areas such as requirement elicitation, design/architecture validation, testing, deployment monitoring, and scalability to produce robust high-performance systems in all areas of practicing software development life cycle work. The discourse includes a rich case study based on a Smart Logistics Management System to demonstrate practical implementation of the AI-SDLC and how it facilitates improvement in system efficiency and improved user experience. Additionally, the discussion also highlights the possibilities of AI-SDLC practical implementation in other industrial domain areas such as e-Commerce, finance, aviation and enterprise solution based projects with practical considerations for implementation. In conclusion, the discussion provides findings that support AI-SDLC as a structured and intelligence-driven approach to Software Development Life Cycle implementation that addresses the weaknesses of traditional software design and development frameworks.}, archive = {J_ACCESS}, author = {Noor Arshad and Talal Ashraf Butt and Muhammad Iqbal}, doi = {10.1109/ACCESS.2025.3564139}, journal = {IEEE Access}, pages = {74062-74077}, shortjournal = {IEEE Access}, title = {A comprehensive framework for intelligent, scalable, and performance-optimized software development}, volume = {13}, year = {2025}, }
• (2025). RF-based sensing and AI decision support for stroke patient monitoring: A digital twin approach. ACCESS, 13, 74047-74061. (www)

  @article{ , abstract = {Stroke occurs when the blood flow to a certain region of the brain is disrupted. It is a leading cause of long-term disability and can result in cognitive impairments, speech difficulties, and motor dysfunction. Regular monitoring and timely intervention are critical to minimizing the damage and improving outcomes. This article presents a novel Radio Frequency (RF) sensing and Artificial Intelligence (AI) based Digital Twin (DT) model for effective detection of stroke. Through backscattering RF signals, the proposed Ultra Wide Band (UWB) antenna provides stroke detection. The implementation of Machine Learning (ML) and Deep Learning (DL) technologies for stroke classification provides the necessary decision support to healthcare professionals in DT stroke patient monitoring. The statistical and autonomous (AutoEncoders (AE) and Stacked AutoEncoders (SAE) with structure 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128) feature data is enlarged through Gaussian noise feature data augmentation. The Fine KNN algorithm provides the 93.4% and 92.3% classification accuracies of binary and multi-class classification respectively. Out of the 4 autonomous feature extraction methods, the Fine KNN algorithm with SAE structure 64-32-16-32-64 provided the highest accuracies of 88.2% and 74.8% for binary and multi-class classification.}, archive = {J_ACCESS}, author = {Sagheer Khan and Usman Anwar and Aftab Khan and Tughrul Arslan}, doi = {10.1109/ACCESS.2025.3564887}, journal = {IEEE Access}, pages = {74047-74061}, shortjournal = {IEEE Access}, title = {RF-based sensing and AI decision support for stroke patient monitoring: A digital twin approach}, volume = {13}, year = {2025}, }
• (2025). A novel transformer-based approach for reliability evaluation of composite systems with renewables and plug-in hybrid electric vehicles. ACCESS, 13, 74032-74046. (www)

  @article{ , abstract = {This paper proposes a novel hybrid framework that integrates machine learning (ML) techniques with Sequential Monte Carlo Simulation (SMCS) to enhance the reliability assessment of modern power systems incorporating renewable energy resources (RER) and plug-in hybrid electric vehicle (PHEVs) integration. While PHEVs can leverage RER to significantly reduce greenhouse gas emissions, the increased energy demand from large PHEVs fleets poses potential challenges to power system reliability. To address these issues, this research presents an advanced mixed-integer linear programming (MILP)-based algorithm for optimizing EV charging. The algorithm prioritizes clean energy utilization through intelligent power allocation strategies while considering cost-revenue trade-offs. A probabilistic model is developed to account for factors such as driving distance, charging times, locations, battery state of charge, and charging needs of PHEVs. The proposed approach is tested on the IEEE RTS-79 test system and evaluates multiple ML architectures, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Transformer models, often combined with boosting algorithms, across three scenarios: base case, uncontrolled charging, and intelligent charging. Results highlight that ML-based approaches, particularly the Transformer model, achieve computational time reductions of up to 49% compared to traditional SMCS methods while maintaining comparable accuracy. The Transformer model identified 1,788 loss-of-load states compared to 1,510 actual instances, requiring only 176 minutes of computation. Among all models, the BiLSTM with Adaptive Boosting (BiLSTM+AB) achieved the lowest overestimation, exceeding actual instances by just 256 states. Performance metrics such as Loss of Load Probability (LOLP) and Expected Demand Not Supplied (EDNS) validate the effectiveness of the proposed ML approaches in balancing accuracy and computational efficiency.}, archive = {J_ACCESS}, author = {Chiranjeevi Yarramsetty and Tukaram Moger and Debashisha Jena and Veeranki Srinivasa Rao}, doi = {10.1109/ACCESS.2025.3564407}, journal = {IEEE Access}, pages = {74032-74046}, shortjournal = {IEEE Access}, title = {A novel transformer-based approach for reliability evaluation of composite systems with renewables and plug-in hybrid electric vehicles}, volume = {13}, year = {2025}, }
• (2025). MonoDFM: Density field modeling-based end-to-end monocular 3D object detection. ACCESS, 13, 74015-74031. (www)

  @article{ , abstract = {Monocular 3D object detection aims to infer the 3D properties of objects from a single RGB image. Existing methods primarily rely on planar features to estimate 3D information directly. However, the limited 3D information available in 2D images often results in suboptimal detection accuracy. To address this challenge, we propose MonoDFM, an end-to-end monocular 3D object detection method based on density field modeling. By modeling the density field from the features of a single image, MonoDFM enables a more accurate transition from 2D to 3D representations, improving 3D attribute prediction accuracy. Unlike traditional depth map methods, which are limited to visible regions, MonoDFM infers geometric information from occluded regions by predicting the scene’s density field. Moreover, compared with more complex approaches like Neural Radiance Fields (NeRF), MonoDFM provides a streamlined and efficient prediction process. Experiments conducted on the KITTI dataset show that MonoDFM achieves $\mathrm {AP_{3D}}$ of (25.13, 16.61, 13.82) and $\mathrm {AP_{BEV}}$ of (32.61, 22.14, 18.71) on the KITTI benchmark for the Car category under three difficulty settings (easy, moderate, and hard), achieving competitive performance. Ablation studies further validate the effectiveness of each component. As a result, MonoDFM offers an effective approach to monocular 3D object detection, demonstrating strong performance.}, archive = {J_ACCESS}, author = {Gang Liu and Xinrui Huang and Xiaoxiao Xie}, doi = {10.1109/ACCESS.2025.3563248}, journal = {IEEE Access}, pages = {74015-74031}, shortjournal = {IEEE Access}, title = {MonoDFM: Density field modeling-based end-to-end monocular 3D object detection}, volume = {13}, year = {2025}, }
• (2025). SE-enhancer: Low-resource machine translation based on enhanced SimCSE and layer fusion. ACCESS, 13, 73999-74014. (www)

  @article{ , abstract = {Low-resource machine translation holds significant practical importance for the translation of small languages. Currently, the primary challenge in low-resource machine translation is the scarcity of bilingual parallel corpora. To address this issue, this paper proposes a SE-Enhancer model based on enhanced SimCSE. We first introduced an embed-fusion module to integrate word embeddings and sentence embeddings, thereby enriching the feature representation of source sentences. Then, a layer fusion module based on feedforward neural networks and self-attention is integrated into the model to reduce information loss by integrating multi-layer features, enhancing the decoder’s translation capabilities and overall model performance. The experimental results demonstrate that SE-Enhancer achieves BLEU score improvements of 1.24, 1.37, and 1.11 over the Transformer baseline model on three common low-resource machine translation tasks from the IWSLT dataset. Length analysis further demonstrates that the model excels at capturing long-distance dependencies and exhibits strong generalization capabilities. In practical applications, this model can be utilized in low-resource fields such as legal, healthcare, and education translation.}, archive = {J_ACCESS}, author = {Dongsheng Wang and Qile Bo and Fei Wang and Liming Wang and Kun Tang}, doi = {10.1109/ACCESS.2025.3563342}, journal = {IEEE Access}, pages = {73999-74014}, shortjournal = {IEEE Access}, title = {SE-enhancer: Low-resource machine translation based on enhanced SimCSE and layer fusion}, volume = {13}, year = {2025}, }
• (2025). IG-FIQA: Improving classifiability-based face image quality assessment through intra-class variance guidance. ACCESS, 13, 73987-73998. (www)

  @article{ , abstract = {In the realm of face image quality assessment (FIQA), methods based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in such methods. To address this issue, we present intra-class variance guidance for FIQA (IG-FIQA), a novel approach to guide FIQA training, introducing a weight parameter to alleviate the adverse impact of these classes. This method involves estimating sample intra-class variance at each iteration during training, ensuring minimal computational overhead and straightforward implementation. Furthermore, this paper proposes an on-the-fly data augmentation methodology for improved generalization performance in FIQA. Across various benchmark datasets, our proposed method, IG-FIQA, achieved notable accuracy improvements compared to conventional state-of-the-art (SOTA) FIQA methods and ensures stable performance in face recognition systems.}, archive = {J_ACCESS}, author = {Minsoo Kim and Gi Pyo Nam and Haksub Kim and Haesol Park and Ig-Jae Kim}, doi = {10.1109/ACCESS.2025.3562654}, journal = {IEEE Access}, pages = {73987-73998}, shortjournal = {IEEE Access}, title = {IG-FIQA: Improving classifiability-based face image quality assessment through intra-class variance guidance}, volume = {13}, year = {2025}, }
• (2025). From reports to actions: Bridging the customer usability gap in penetration testing. ACCESS, 13, 73975-73986. (www)

  @article{ , abstract = {Penetration testing reports play a significant role in helping organizations identify and mitigate security vulnerabilities. The report effectiveness relies on the extent to which customers can translate the findings into actionable decisions. Our study investigates usability gaps in penetration testing reports from a customer-centric perspective, focusing on the challenges organizations face in understanding, prioritizing, and acting on the provided insights. Within the study, we demonstrated a penetration testing scenario together to IT professionals scaled from technical to managerial. We provided them with a selected reported vulnerability finding from the scenario. By conducting surveys and focus groups, we aimed to identify common gaps. Based on data from 25 focus group participants, we conducted a thematic analysis, identifying 8 themes with 29 findings categorized as possible improvements, gaps, and general perceptions, highlighting weaknesses in PT reports. The results highlight the necessity of aligning report content with the needs of specific target audiences. Key findings reveal gaps in defining the scope, rules of engagement, and methodology before conducting PT, as well as inadequacies in describing findings within reports. Additionally, our research underscores the importance of incorporating positive findings and enhancing security recommendations by avoiding generic mitigations, providing multiple mitigation options, assessing their impact, and specifying preferred solutions when applicable. The study explores how these gaps can affect decision-making processes, risk mitigation efforts, and overall cybersecurity outcomes. By highlighting these issues, our research sheds light on the need for improved usability in penetration testing deliverables to better serve customer needs and enhance cybersecurity outcomes.}, archive = {J_ACCESS}, author = {Katarina Galanska and Agata Kruzikova and Maria Pibilota Murumaa and Václav Matyáš and Mike Just}, doi = {10.1109/ACCESS.2025.3561220}, journal = {IEEE Access}, pages = {73975-73986}, shortjournal = {IEEE Access}, title = {From reports to actions: Bridging the customer usability gap in penetration testing}, volume = {13}, year = {2025}, }
• (2025). Game theoretic approach to QoS oriented machine learning model development toward 5G network migration planning. ACCESS, 13, 73949-73974. (www)

  @article{ , abstract = {With the increasing number of connected devices, the demand for mobile data has grown exponentially. The existing legacy 4G/LTE network has been unable to meet the demands and keep up with the expectations in terms of speed, latency, number of connected devices and quality of service. The situation demands a transition to 5G network; however, the transition comes with significant challenges for telecom operators, requiring substantial initial investments in infrastructure, technology, and human resources. This paper presents a novel two-phased approach to facilitate efficient 5G migration using machine learning and evolutionary game theory. This study primarily focuses on the development of a simulation-based analysis and a mathematical framework. In the first phase, a machine learning model is trained on Long Term Evolution (LTE) network simulation data to predict upgradability scores for existing 4G base stations, enabling a data-driven approach based on quality of service to prioritize Radio Access Network (RAN) migration. The second phase employs evolutionary game theory to observe the migration patterns for both core and RAN components of interconnected telecom operators in three distinct scenarios over a span of five years. The simulation considers critical factors such as revenue, customer retention/acquisition, traffic volume, coverage area, 5G based service demand, and human resources requirements, providing a framework for future network migration. Our research addresses the growing strain on legacy 4G networks caused by exponential growth in mobile data traffic and connected devices. By combining machine learning and game theoretic modeling, we offer telecom operators an approach to make informed decision regarding optimal migration strategies. This study contributes to the body of knowledge on network migration strategies and provides practical insights for telecom operators navigating the complex landscape of 5G deployment and migration.}, archive = {J_ACCESS}, author = {Arjun Ray and Manish Kr. Yadav and Babu R. Dawadi and Krishna R. Bhandari}, doi = {10.1109/ACCESS.2025.3563096}, journal = {IEEE Access}, pages = {73949-73974}, shortjournal = {IEEE Access}, title = {Game theoretic approach to QoS oriented machine learning model development toward 5G network migration planning}, volume = {13}, year = {2025}, }
• (2025). Recursive shrinking toward effective cluster isolation for robust electronic noses. ACCESS, 13, 73939-73948. (www)

  @article{ , abstract = {In electronic noses (e-Noses), the employed sensors’ responses consist of overlapping clusters leading to inaccurate analysis. Larger intra-cluster distances and smaller inter-cluster distances within the dataset cause overlapping clusters. The lack of well-separated clusters hinders pattern recognition techniques from excelling and requires effective isolation for optimal performance. This work proposes recursive shrinking towards effective cluster isolation utilizing the synergy of principal component analysis and the bisection method. The clusters shrink towards their centers on each recursion by optimizing an objective function, effective inter-cluster distance (EICD). Overlapping characterizes negative EICD. The experimental findings demonstrate the effectiveness of the suggested approach on a dataset that includes responses from five different alcohol categories: 1-octanol, 1-propanol, 2-butanol, 2-propanol, and 1-isobutanol. The used dataset exhibits highly overlapped clusters with negative-valued EICD. Clusters of 1st, 2nd, 3rd, and 4th alcohol overlap with subsequent peers (i.e., 1-2, 3, 4, 5; 2-3, 4, 5; 3-4, 5; 4-5) and achieve negative EICD. Recursive shrinking produces completely isolated clusters with positive EICD values. The results depict the effectiveness of isolation numerically and graphically.}, archive = {J_ACCESS}, author = {Shiv Nath Chaudhri}, doi = {10.1109/ACCESS.2025.3564211}, journal = {IEEE Access}, pages = {73939-73948}, shortjournal = {IEEE Access}, title = {Recursive shrinking toward effective cluster isolation for robust electronic noses}, volume = {13}, year = {2025}, }
• (2025). A green cloud-based framework for energy-efficient task scheduling using carbon intensity data for heterogeneous cloud servers. ACCESS, 13, 73916-73938. (www)

  @article{ , abstract = {Managing modern data centre operations is increasingly complex due to rising workloads and numerous interdependent components. Organizations that still rely on outdated, manual data management methods face a heightened risk of human error and struggle to adapt quickly to shifting demands. This inefficiency leads to excessive energy consumption and higher CO2 emissions in cloud data centres. To address these challenges, integrating advanced automation within Infrastructure as a Service (IaaS) has become essential for IT industries, representing a significant step in the ongoing transformation of cloud computing. For data centres aiming to enhance efficiency and reduce their carbon footprint, intelligent automation provides tangible benefits, including optimized resource allocation, dynamic workload balancing, and lower operational costs. As computing resources remain energy-intensive, the growing demand for AI and ML workloads is expected to surge by 160% by 2030 (Goldman Sachs). This heightened focus on energy efficiency has driven the need for advanced scheduling systems that reduce carbon emissions and operational expenses. This study introduces a deployable cloud-based framework that incorporates real-time carbon intensity data into energy-intensive task scheduling. By utilizing AWS services, the proposed algorithm dynamically adjusts high-energy workloads based on regional carbon intensity fluctuations, using both historical and real-time analytics. This approach enables cloud service providers and enterprises to minimize environmental impact without sacrificing performance. Designed for seamless integration with existing cloud infrastructures—including AWS, Google Cloud, and Azure—this scalable solution utilizes Kubernetes-based scheduling and containerized workloads for intelligent resource management. By combining automation, real-time analytics, and cloud-native technologies, the framework significantly enhances energy efficiency compared to traditional scheduling methods. Moreover, the proposed system aligns with key United Nations Sustainable Development Goals (SDGs), including climate action (SDG 13), clean energy (SDG 7), sustainable urban development (SDG 11), and infrastructure innovation (SDG 9). By promoting energy-efficient cloud computing, this research supports a more sustainable, cost-effective digital ecosystem that meets the growing demands of high-performance computing and AI-driven workloads.}, archive = {J_ACCESS}, author = {B. M. Beena and Prashanth Cheluvasai Ranga and Thotapalli Sri Surya Manideep and Sneha Saragadam and Garikipati Karthik}, doi = {10.1109/ACCESS.2025.3562882}, journal = {IEEE Access}, pages = {73916-73938}, shortjournal = {IEEE Access}, title = {A green cloud-based framework for energy-efficient task scheduling using carbon intensity data for heterogeneous cloud servers}, volume = {13}, year = {2025}, }
• (2025). Identification of modal parameters using an improved sparse blind source separation method. ACCESS, 13, 73903-73915. (www)

  @article{ , abstract = {During the past decade, blind source separation (BSS) method has become an effective tool to characterize and identify modal parameters of linear systems. However, in practical engineering, the assumptions of guaranteeing conventional BSS method successful application cannot be frequently satisfied, which often lead to some challenging issues. One of these challenges is how to tackle the challenge of modal identification in situations of under-determination, which means that the total sensors should be not more than that of the active modals. In this paper, we explore an efficient under-constrained BSS approach called sparse BSS (SBSS) for addressing this problem. The drawbacks of conventional BSS are first listed and an improved SBSS method is proposed to deal with the above mentioned problem, which is shown to be more suitable for engineering applications. A 5-degrees-of-freedom numerical system and two analyses are employed to confirm the effectiveness of the suggested approach. The identified outcomes of modal parameters show highly satisfied accuracy via comparative analysis, which illustrates that the proposed technique has a potential application in structural engineering.}, archive = {J_ACCESS}, author = {Gang Yu and Aoran Wang}, doi = {10.1109/ACCESS.2025.3562428}, journal = {IEEE Access}, pages = {73903-73915}, shortjournal = {IEEE Access}, title = {Identification of modal parameters using an improved sparse blind source separation method}, volume = {13}, year = {2025}, }
• (2025). Peduncle detection of ripe strawberry to localize picking point using DF-mask R-CNN and monocular depth. ACCESS, 13, 73889-73902. (www)

  @article{ , abstract = {Accurate localization of picking points and depth estimation is critical for implementing a robotic strawberry harvesting system. Due to the delicate nature of strawberries, harvesting must be performed without bruising or damage, typically by grasping and cutting the peduncle of the ripe strawberry. However, accurately detecting and localizing the thin peduncle in a cluttered environment is a significant challenge. This study proposed depth fused Mask R-CNN (DF-Mask R-CNN), which integrates depth information of the scene with the RGB image to enhance the detection, localization, and segmentation of strawberries and their peduncles in a greenhouse environment. To generate a dense depth map, a cutting-edge monocular depth estimator, ZoeDepth was used. The proposed DF-Mask R-CNN with ResNet101-FPN exhibited superior instance segmentation performance, with an overall mAP of 81.9%, with mAPsmall at 33.3%, mAPmedium at 78.79%, mAPlarge at 88.8 and AP ${}^{\mathrm {IOU=0.5}}$ at 98.1%. In tests with 300 ripe strawberry samples, the method demonstrated a robust picking point detection, with a mean absolute error and root mean square error of 1.98 cm and 2.12 cm, respectively. These results highlight the effectiveness of the DF-Mask R-CNN model combined with the ZoeDepth estimator in enhancing the detection, localization, and segmentation of strawberries and their peduncles. This approach enables precise picking point localization and depth estimation for efficient vision systems for robotic strawberry harvesting.}, archive = {J_ACCESS}, author = {Niraj Tamrakar and Bhola Paudel and Sijan Karki and Nibas Chandra Deb and Elanchezhian Arulmozhi and Jung Hoo Kook and Myeong Yong Kang and Dae Yeong Kang and Oluwasegun Moses Ogundele and Bikash Nakarmi and Moon Byung-Eun and Hyeon Tae Kim}, doi = {10.1109/ACCESS.2025.3564288}, journal = {IEEE Access}, pages = {73889-73902}, shortjournal = {IEEE Access}, title = {Peduncle detection of ripe strawberry to localize picking point using DF-mask R-CNN and monocular depth}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral image classification based on two-branch feature fusion network. ACCESS, 13, 73870-73888. (www)

  @article{ , abstract = {Effective discriminative spectral-spatial feature representation is crucial for hyperspectral image classification (HSIC). Some current methods typically extract spectral and spatial information directly from spectral-spatial 3D patches, without considering the correlation between features, resulting in a high number of misclassifications at the boundaries of land cover classes. This article proposed a spectral-spatial two-branch feature fusion network (TFFN). The spatial branch utilizes distance similarity metrics to capture the spatial relationships between central and neighboring pixels, and utilizes multiscale convolutional modules to expand the receptive field, capturing different levels of features and contextual information, resulting in more robust spatial information. The spectral branch utilizes a bidirectional long short-term memory (Bi-LSTM) network and linear attention mechanism to capture spectral features. In the end, the fused feature information from both branches serves as the basis for classification, enabling high-precision categorization. Experimental results on the datasets of four public demonstrate that the overall classification accuracy of the TFFN model exceeds 97%, especially on the Indian Pines dataset with an imbalanced distribution of ground objects.}, archive = {J_ACCESS}, author = {Qiongdan Huang and Liang Li and Mengyang Zhao and Jiapeng Wang and Shilin Kang}, doi = {10.1109/ACCESS.2025.3563093}, journal = {IEEE Access}, pages = {73870-73888}, shortjournal = {IEEE Access}, title = {Hyperspectral image classification based on two-branch feature fusion network}, volume = {13}, year = {2025}, }
• (2025). Gradient-enhanced kriging-based parallel efficient global optimization algorithm and its application in aerodynamic shape optimization. ACCESS, 13, 73848-73869. (www)

  @article{ , abstract = {The parallel efficient global optimization (EGO) algorithm was developed to leverage the rapid advancements in high-performance computing. However, conventional parallel EGO algorithm based on ordinary kriging (OK) model faces limitations when applied to complex, nonlinear, and high-dimensional engineering optimization problems, as OK model relies only on sample point locations, restricting the available training information. To further enhance the optimization potential of the parallel EGO algorithm, an improved system that integrates the parallel EGO algorithm with gradient-enhanced kriging (GEK) is proposed. The integration serves two main purposes: on the one hand, GEK provides a more comprehensive representation of the design space, improving the surrogate model’s predictive accuracy; on the other hand, as the number of infill points increases, GEK mitigates the unreliability of the pseudo expected improvement (PEI) function, facilitating the efficient search for high-quality global optima. To ensure high performance, various benchmark functions were used to evaluate the impact of different optimizers on the proposed system, ultimately identifying the most effective optimizer. Additionally, optimization tests were conducted on benchmark functions, airfoil aerodynamic optimization, and automotive aerodynamic optimization to compare the proposed system with traditional parallel EGO systems in optimization performance. The results demonstrate that the modified Gaussian bare-bones differential evolution (MGBDE) optimizer consistently accelerated convergence to high-quality solutions in most benchmark functions, achieving the highest average ranking across all tests. Compared to traditional parallel EGO system, the proposed method showed superior convergence speed, solution quality, and computational efficiency. Specifically, for airfoil and automotive aerodynamic optimization, when achieving the target performance improvements of 2.4% and 7.0%, the proposed system reduced the wall-clock time cost from 1.65h to 0.33h and from 203h to 14.5h, respectively, significantly improving optimization efficiency.}, archive = {J_ACCESS}, author = {Hang Fu and Qingyu Wang and Takuji Nakashima and Asahi Kawasaki and Chenguang Lai and Keigo Shimizu and Rahul Bale and Makoto Tsubokura}, doi = {10.1109/ACCESS.2025.3560292}, journal = {IEEE Access}, pages = {73848-73869}, shortjournal = {IEEE Access}, title = {Gradient-enhanced kriging-based parallel efficient global optimization algorithm and its application in aerodynamic shape optimization}, volume = {13}, year = {2025}, }
• (2025). Predicting fundamental frequency patterns in electrolaryngeal speech using automated phoneme extraction. ACCESS, 13, 73831-73847. (www)

  @article{ , abstract = {We propose a system to enhance electrolaryngeal speech naturalness using automatically extracted phoneme representations. Phonemes provide sufficient information for predicting reasonably natural fundamental frequency patterns. Previous studies using forced-aligned phoneme labels to create shared features between electrolaryngeal and normal speech for fundamental frequency prediction are limited by their reliance on transcriptions, thereby restricting real-time use. To overcome this, our system leverages phonetic posteriorgrams from an automatic speech recognition system. By transforming these phonetic posteriorgrams into clustered phoneme embeddings, we predict natural fundamental frequency patterns without requiring transcriptions. Our experiments demonstrate that this approach not only provides a robust, real-time solution for electrolaryngeal speech enhancement but also enables effective training with limited electrolaryngeal speech data and large, publicly available normal speech datasets.}, archive = {J_ACCESS}, author = {Mohammad Eshghi and Tomoki Toda}, doi = {10.1109/ACCESS.2025.3564648}, journal = {IEEE Access}, pages = {73831-73847}, shortjournal = {IEEE Access}, title = {Predicting fundamental frequency patterns in electrolaryngeal speech using automated phoneme extraction}, volume = {13}, year = {2025}, }
• (2025). Text-guided diverse scene interaction synthesis by disentangling actions from scenes. ACCESS, 13, 73818-73830. (www)

  @article{ , abstract = {Generating human motion within 3D scenes from textual descriptions remains a challenging task because of the scarcity of hybrid datasets encompassing text, 3D scenes, and motion. Existing approaches suffer from fundamental limitations: a lack of datasets that integrate text, 3D scenes, and motion, and a reliance on end-to-end methods, which constrain the diversity and realism of generated human-scene interactions. In this paper, we propose a novel method to generate motions of humans interacting with objects in a 3D scene given a textual prompt. Our key innovation focuses on decomposing the motion generation task into distinct steps: 1) generating key poses from textual and scene contexts and 2) synthesizing full motion trajectories guided by these key poses and path planning. This approach eliminates the need for hybrid datasets by leveraging independent text-motion and pose datasets, significantly expanding action diversity and overcoming the constraints of prior works. Unlike previous methods, which focus on limited action types or rely on scarce datasets, our approach enables scalable and adaptable motion generation. Through extensive experiments, we demonstrate that our framework achieves unparalleled diversity and contextually accurate motions, advancing the state-of-the-art in human-scene interaction synthesis.}, archive = {J_ACCESS}, author = {Hitoshi Teshima and Naoki Wake and Diego Thomas and Yuta Nakashima and Hiroshi Kawasaki and Katsushi Ikeuchi}, doi = {10.1109/ACCESS.2025.3562086}, journal = {IEEE Access}, pages = {73818-73830}, shortjournal = {IEEE Access}, title = {Text-guided diverse scene interaction synthesis by disentangling actions from scenes}, volume = {13}, year = {2025}, }
• (2025). Neural network-based optimization for bandwidth enhancement of millimeter-wave franklin antenna with proximity-coupled feed. ACCESS, 13, 73803-73817. (www)

  @article{ , abstract = {This study aims to present the optimization of the Franklin microstrip antenna using a proximity-coupled feed. The initial model is established through mathematical calculations, followed by refinement using various machine learning methods, including Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient, and Adaptive Neuro Fuzzy Inference System (ANFIS). The antenna performance evaluation comprises of a comprehensive technique that combines simulation, measurement, and development of R-L-C equivalent circuit models, as well as the application of machine learning methods. The HFSS-based simulation was conducted in the frequency range of 20-36 GHz, which was validated through measurement. The measured antenna was constructed on a substrate with relative permittivity $\varepsilon _{r}$ of 2.2, thickness of 1.57 mm, and tan $\delta $ of 0.0013. Among the applied methods, ANFIS outperformed the others, yielding a simulation and measurement bandwidth of 16 GHz, a simulation gain of 10.45 dBi, and a radiation efficiency of 97.7%. The proposed dimensions of the machine-learning-optimized antenna put it as a robust choice for the 5G technology, simultaneously providing high bandwidth and gain, as supported by both simulation and measurement results.}, archive = {J_ACCESS}, author = {Ahmad Firdausi and Eko Setijadi and Gamantyo Hendrantoro and Mudrik Alaydrus}, doi = {10.1109/ACCESS.2025.3561268}, journal = {IEEE Access}, pages = {73803-73817}, shortjournal = {IEEE Access}, title = {Neural network-based optimization for bandwidth enhancement of millimeter-wave franklin antenna with proximity-coupled feed}, volume = {13}, year = {2025}, }
• (2025). Single image super-resolution reconstruction based on multilayer residual networks with attention mechanisms. ACCESS, 13, 73794-73802. (www)

  @article{ , abstract = {Small and medium-sized unmanned aerial vehicles (UAVs) often experience loss of texture detail and edge blurring in aerial images due to limitations in flight altitude and payload capacity. To enhance image quality, this paper proposes a multilayer residual network super-resolution reconstruction method that utilizes an attention mechanism. This method effectively extracts high-frequency feature information from images and improves detail recovery through a nested residual network structure and a channel attention mechanism. The network employs a deeply parallel architecture to fuse multilayer features, further enhancing reconstruction accuracy. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics on the Set5, Set14, and various open-source aerial imagery datasets. The reconstructed images exhibit significantly improved texture details and edge clarity. This method offers an effective solution for the high-quality reconstruction of UAV aerial images. We have named this method MARNet (Multilayer Attention Residual Network), which utilizes nested residual blocks and channel attention to achieve high-fidelity reconstruction.}, archive = {J_ACCESS}, author = {Zhao Lu and Zhenbiao Tu and Zongxin Zhang and Chengze Jiang and Pengyun Chen}, doi = {10.1109/ACCESS.2025.3562843}, journal = {IEEE Access}, pages = {73794-73802}, shortjournal = {IEEE Access}, title = {Single image super-resolution reconstruction based on multilayer residual networks with attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Data-driven posture control of tensegrity manipulator based on strut’s inclination angle. ACCESS, 13, 73785-73793. (www)

  @article{ , abstract = {Tensegrity structures have been utilised in soft robotics due to their flexible and lightweight nature. However, unlike traditional robots, these structures lack joint angles, which makes it challenging to use conventional angle sensors, and thus estimating the posture of the robot remains a challenge. To overcome this, we propose a data-driven approach for posture control of a redundant tensegrity manipulator using inclination angles of all struts, these angles are calculated relative to the gravitational direction. Specifically, we train a simple feedforward neural network (NN) to approximate a mapping from the inclination angles to the control inputs with a conditioning layer-wise averaged pressures. This network acts as a posture controller, mapping the desired inclination angles to the corresponding control inputs with conditioning by average pressure in the layer. The desired inclination angles corresponding to the desired posture can be obtained by demonstrating the robot in a direct teaching manner. We used the tensegrity manipulator consisting of 20 struts and 40 actuators to validate our approach. The experimental results showed that the tensegrity manipulator can reproduce the desired demonstrated postures.}, archive = {J_ACCESS}, author = {Kazuki Wada and Yuhei Yoshimitsu and Tufail Ahmad Bhat and Shuhei Ikemoto}, doi = {10.1109/ACCESS.2025.3563756}, journal = {IEEE Access}, pages = {73785-73793}, shortjournal = {IEEE Access}, title = {Data-driven posture control of tensegrity manipulator based on strut’s inclination angle}, volume = {13}, year = {2025}, }
• (2025). Efficient packaging line object counting by cross-frame association with wavelet convolutions and trajectory compensation. ACCESS, 13, 73773-73784. (www)

  @article{ , abstract = {Real-time object counting in the industry pipeline is critical for improving efficiency and accuracy in industries like manufacturing and logistics. This paper introduces a novel multi-object association method, namely tracking method, which is specifically designed for real-time object counting in industrial environments. We present a specialized object counting dataset and a joint method that enhances multi-object tracking performance by focusing on both detection and tracking. Our approach features WT-YOLO for object detection, a Wavelet Transform-based convolutional neural network that leverages the wavelet transform to capture both spatial and frequency domain information. WT-YOLO significantly improves detection accuracy in complex scenes with occlusions and irregular shapes by expanding the receptive field of the network and capturing low-level shape signals, thereby enhancing the network’s shape bias. Experimental results demonstrate that WT-YOLO significantly outperforms YOLO-v8 and YOLO-MS on both the object dataset and a damaged COCO dataset, even with similar parameter settings. Additionally, we introduce the Virtual Trajectory Compensation (VTC) module, which adapts to the non-linear motion of objects on packaging lines by enhancing the parameter updating of motion models during re-correlation. The VTC module is a plug-and-play addition that can be seamlessly integrated into Kalman filter-based trackers to mitigate tracking loss issues. Our method achieves high accuracy and speed, making it suitable for real-time applications. Our work offers new insights and advancements in multi-object tracking for industrial settings by addressing the challenges of occlusions, irregular shapes, and non-linear motion in object counting tasks.}, archive = {J_ACCESS}, author = {Longxuan Wei and Yutao Zhu and Yufeng Li and Ming Qian and Xiang Zuo and Boan Chen and Shiyu Liang and Zhouhan Lin and Junchi Yan}, doi = {10.1109/ACCESS.2025.3557947}, journal = {IEEE Access}, pages = {73773-73784}, shortjournal = {IEEE Access}, title = {Efficient packaging line object counting by cross-frame association with wavelet convolutions and trajectory compensation}, volume = {13}, year = {2025}, }
• (2025). Stability analysis of fault and free-surface cut rock blocks at the vault of large underground powerhouses. ACCESS, 13, 73755-73772. (www)

  @article{ , abstract = {To address the safety concerns of vault surrounding rock damage induced by rock blocks under high-stress conditions of underground powerhouses, a microseismic (MS) monitoring system was established. This study investigates the spatiotemporal evolution of MS events in the vault, the associated fracture patterns, and the correlation between MS events and surrounding rock damage during the excavation of the underground powerhouse. The spatial evolution patterns of the stress field, displacement field, and fracture field during the process of vault surrounding rock damage were revealed. The research results indicate that: 1) The presence of fault and free-surface cut rock blocks at the vault of the main powerhouse enhances the disturbance effect of excavation on the surrounding rock; 2) MS events exhibit localized clustering characteristics in space, with certain clusters highly coinciding with the location of the vault rock blocks. The MS activity characteristics in the regional rock mass, can serve as a qualitative indicator for early warning of surrounding rock deformation; 3) The combination of MS monitoring and numerical simulation effectively identifies surrounding rock damage information in the rock block areas of underground powerhouses. The research findings can serve as a reference for analysing surrounding rock damage and stability in similar engineering projects.}, archive = {J_ACCESS}, author = {Lingfeng Sun and Nuwen Xu and Peiwei Xiao and Yuepeng Sun and Hongjian Qian and Tao Li and Biao Li}, doi = {10.1109/ACCESS.2025.3560981}, journal = {IEEE Access}, pages = {73755-73772}, shortjournal = {IEEE Access}, title = {Stability analysis of fault and free-surface cut rock blocks at the vault of large underground powerhouses}, volume = {13}, year = {2025}, }
• (2025). Toward a neural network-based approach for improved atmospheric infrasound localization. ACCESS, 13, 73745-73754. (www)

  @article{ , abstract = {Accurate infrasound localization represents a challenging problem in acoustics that yields many important applications. Current methods for distance and altitudinal localization have only been evaluated in limited short-range scenarios and fail to take full advantage of real-time atmospheric surroundings to maximize accuracy. We develop a neural network architecture that uses both the local acoustic pressure and atmospheric data to produce accurate localization predictions including the lateral distance and altitude of an infrasonic source, using only a singular-sensor arrangement for a more flexible and practical implementation that can be transferred to real-time aerial detection. To improve generalizability, we train our neural network in a variety of atmospheric conditions and conduct validation testing on samples generated from designated hold-out atmospheric profiles. We generated a comprehensive infrasonic raypath dataset using an experimentally validated infrasound raytracing tool, a technique inspired by other fields where controlled data is difficult to obtain in real life. A preliminary comparison with existing infrasound localization methods in similar conditions demonstrated that our neural network-based approach approximately halved both median distance and altitudal localization errors. These promising results highlight the potential of our neural network-based approach to significantly enhance infrasound localization accuracy across diverse atmospheric conditions. Future steps would involve more thorough validation and benchmarking of the model in real-world scenarios to detect any trends in localization error that were not captured in this work.}, archive = {J_ACCESS}, author = {Addison J. Wu and Arnav Joshi and Jean-Pierre Hickey}, doi = {10.1109/ACCESS.2025.3561265}, journal = {IEEE Access}, pages = {73745-73754}, shortjournal = {IEEE Access}, title = {Toward a neural network-based approach for improved atmospheric infrasound localization}, volume = {13}, year = {2025}, }
• (2025). Toward fair spectrum sharing: An enhanced fixed waiting time model for LAA and wi-fi networks coexistence. ACCESS, 13, 73735-73744. (www)

  @article{ , abstract = {This study addresses the challenge of fair spectrum sharing in unlicensed bands for Licensed Assisted Access (LAA) and Wi-Fi coexistence. Existing methods, particularly the 3GPP Category 4 Listen Before Talk (Cat 4 LBT) algorithm, fail to fully meet the fairness criteria due to limitations in dynamic Contention Window (CW) adjustments. To improve spectrum efficiency, we propose an Enhanced Fixed Waiting Time (Enhanced FWT) approach, which leverages a theoretical model of Wi-Fi ON periods to determine fixed waiting times for LAA networks. By employing the $\beta $ distribution to represent Wi-Fi activity more accurately, this model avoids the need for dynamic CW adjustments. Simulation results demonstrate that Enhanced FWT method significantly enhances throughput compared to both the Cat 4 LBT and traditional empirical FWT methods, especially in dense network conditions. This approach, compliant with 3GPP fairness standards, shows promise for robust spectrum sharing, promoting fair LAA/Wi-Fi coexistence in unlicensed bands.}, archive = {J_ACCESS}, author = {Moawiah Alhulayil and Miguel López-Benítez and Mohammed Alammar and Abdulrahman Al Ayidh}, doi = {10.1109/ACCESS.2025.3564136}, journal = {IEEE Access}, pages = {73735-73744}, shortjournal = {IEEE Access}, title = {Toward fair spectrum sharing: An enhanced fixed waiting time model for LAA and wi-fi networks coexistence}, volume = {13}, year = {2025}, }
• (2025). An interpretable combined forecasting method for stock market based on fuzzy time series model and linear-trend fuzzy information granulation. ACCESS, 13, 73722-73734. (www)

  @article{ , abstract = {Stock market forecasting demands models that balance high accuracy with interpretability, particularly when handling highly volatile and uncertain data. This study introduces a novel interpretable forecasting framework that integrates the Fuzzy Time Series (FTS) model with the Linear Fuzzy Information Granule (LFIG) method. The proposed model addresses two major limitations: the inability of conventional FTS models to effectively capture trend dynamics, and the limited capacity of the LFIG method to account for the influence of recent data. The core contributions of this work are threefold: 1) a variable-sized interval partitioning technique optimized via fuzzy C-means clustering and the principle of justifiable granularity, achieving adaptive data segmentation that balances coverage and specificity; 2) a trend extraction mechanism based on LFIG approach, which applies time-dependent linear functions within sliding windows to quantify short-term trends and associated uncertainties; and 3) a fusion of FTS and LFIG outputs via the ordered weighted averaging operator, which emphasizes trend-consistent predictions to enhance forecasting accuracy. Experimental evaluation on five benchmark datasets from Yahoo Finance demonstrates that the proposed model outperforms eight state-of-the-art forecasting methods in terms of predictive performance. Furthermore, it maintains interpretability through transparent fuzzy rules and explicit trend representations, providing a robust and explainable framework for stock market forecasting.}, archive = {J_ACCESS}, author = {Jindong Feng and Zengtai Gong}, doi = {10.1109/ACCESS.2025.3564135}, journal = {IEEE Access}, pages = {73722-73734}, shortjournal = {IEEE Access}, title = {An interpretable combined forecasting method for stock market based on fuzzy time series model and linear-trend fuzzy information granulation}, volume = {13}, year = {2025}, }
• (2025). Lane-keeping assistance system based on model predictive control with smooth transitions between operational modes. ACCESS, 13, 73709-73721. (www)

  @article{ , abstract = {The lane-keeping assistance system (LKAS) is one of the core functions of advanced driver assistance systems (ADAS) that prevents unintended lane departures. LKAS widely utilizes shared steering control, in which both the driver and the vehicle controller share lane-keeping control by integrating the driver into the control loop. The shared control approach can be formulated as a multi-objective optimization problem that optimizes between maintaining driver control and reducing driving burden, while preventing unintended lane departures. A model predictive control (MPC)-based method effectively can address multi-objective optimization problems in shared control. In addition, it provides the advantage of switching the operational mode by adjusting the weights in the cost function according to assessed risk. However, an abrupt transition between operational modes can cause unstable motion such as severe lateral jerk or hysteresis, resulting in driver discomfort. To address this issue, we propose a shared control framework that ensures smooth transitions between operational modes by applying a softly switched MPC method, in which the weights are modulated over the prediction horizon. Unlike existing approaches, the proposed method with the soft-switching scheme could enhance path-tracking accuracy, maintain steering stability, and suppress unstable lateral motion while improving driver comfort during switching between operational modes. Simulation experiments with various maneuvers and road curvatures demonstrated that the proposed framework could substantially suppress unstable lateral motion during mode transitions, even in severe cases, while complying with safety regulations.}, archive = {J_ACCESS}, author = {Younsung Hong and Jae-Sung Moon and Yunhyoung Hwang}, doi = {10.1109/ACCESS.2025.3563626}, journal = {IEEE Access}, pages = {73709-73721}, shortjournal = {IEEE Access}, title = {Lane-keeping assistance system based on model predictive control with smooth transitions between operational modes}, volume = {13}, year = {2025}, }
• (2025). Design-LIME: An interpretable visualization method for electric motor design based on deep learning. ACCESS, 13, 73697-73708. (www)

  @article{ , abstract = {A novel visualization method for interpreting the resultant design from topology optimization (TO) is proposed. We employ a pre-trained deep learning (DL) model to predict the degree of influence of transitions from air to magnetic materials, and build an interpretable linear model to display the visualization result. The proposed method, Design-LIME, is applied for visualizing the impact of effective regions on the torque characteristics of interior permanent magnet synchronous motors (IPMSMs). Compared to conventional visualization methods based on explainable artificial intelligence (XAI), Design-LIME presents accurate and simple visualization results. Furthermore, a novel multistep TO method is proposed. The proposed TO utilizes Design-LIME to efficiently address the electromagnetic and mechanical characteristics of IPMSMs by extracting the effective region of the IPMSM characteristics. The proposed TO method improves search performance by 18.7% when compared with the conventional single-step optimization method. The proposed method enables more efficient motor designs with improved electromagnetic and mechanical performance. The proposed method contributes to the streamlining of the design process not only for motors but also for various electrical devices.}, archive = {J_ACCESS}, author = {Kazuhisa Iwata and Hidenori Sasaki}, doi = {10.1109/ACCESS.2025.3563351}, journal = {IEEE Access}, pages = {73697-73708}, shortjournal = {IEEE Access}, title = {Design-LIME: An interpretable visualization method for electric motor design based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Enhancing situational awareness: Anomaly detection using real-time video across multiple domains. ACCESS, 13, 73680-73696. (www)

  @article{ , abstract = {Though it is challenging, anomaly detection in real-time video data has significant implications in many disciplines like public safety, education, and agriculture. This paper proposes a novel approach to handle the complexity of dynamic real-world anomaly detection scenarios using three state of the art machine learning models: Convolutional Neural Networks (CNN), Region-based Convolutional Neural Network (R-CNN), and You Only Look Once (YOLO). The proposed system is precisely crafted to fast inform authorities of irregularities, so improving situational awareness and security procedures. Using trained models, applying the most effective one for real-time detection, and extensively investigating video data are part of the study method suggested here. The key objectives are to guarantee flexibility to match numerous circumstances and reduce false positives. First findings show the system’s good performance in numerous settings, thereby offering the basis for additional development and study. Especially, following rigorous training and testing, every model displayed varying degrees of accuracy. Emphasizing its effectiveness in focused detection tasks, the CNN model shown a remarkable accuracy of 73% in spotting anomalies inside specific areas of interest. R-CNN, on the other hand, shown greater performance in demanding environments and an accuracy of 80%. Especially appropriate for quick anomaly identification, YOLO, renowned for its speed and accuracy in real-time object detection, shone out with an amazing 91%. This work greatly helps the field of surveillance technologies by providing specific techniques to increase security and safety in useful surroundings. Emphasizing the transformational possibilities of novel anomaly detection systems, this work underlines the importance of constant research and deployment of innovative concepts to boost security and safety measures.}, archive = {J_ACCESS}, author = {Rajan Singh and Amrit Pal and Shruti Mishra and Abishi Chowdhury}, doi = {10.1109/ACCESS.2025.3560874}, journal = {IEEE Access}, pages = {73680-73696}, shortjournal = {IEEE Access}, title = {Enhancing situational awareness: Anomaly detection using real-time video across multiple domains}, volume = {13}, year = {2025}, }
• (2025). DiffTST: Diff transformer for multivariate time series forecast. ACCESS, 13, 73671-73679. (www)

  @article{ , abstract = {Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research. However, these models often incorporate irrelevant or weakly relevant information during the processing of time series, leading to noise. This phenomenon diverts the attention mechanism from crucial features within the time series, thereby impacting the overall forecasting performance. To mitigate this issue, our study introduces DiffTST, which employs a Differential Transformer to enhance the model’s focus on relevant context within the time series, thereby mitigating the influence of noise on forecasting accuracy. The model utilizes independent channels to process time series data, ensuring that each input token contains information from a single channel exclusively. Furthermore, each channel is segmented into multiple patches to facilitate the extraction of local information. Subsequently, the Differential Transformer module is employed to process the sequence features of these patches, alleviating the tendency of Transformer-based models to allocate excessive attention to irrelevant sequence information. Ultimately, the forecast outcomes are derived through a Multi-Layer Perceptron. Our findings indicate that DiffTST achieves higher or comparable long-term forecasting accuracy compared to the current state-of-the-art Transformer-based models. On the main datasets (Weather, Traffic, Electricity), our method reduces MSE by 0.008, 0.087, and 0.023 and MAE by 0.004, 0.069, and 0.025 compared to PatchTST.}, archive = {J_ACCESS}, author = {Song Yang and Wenyong Han and Yaping Wan and Tao Zhu and Zhiming Liu and Shuangjian Li}, doi = {10.1109/ACCESS.2025.3563070}, journal = {IEEE Access}, pages = {73671-73679}, shortjournal = {IEEE Access}, title = {DiffTST: Diff transformer for multivariate time series forecast}, volume = {13}, year = {2025}, }
• (2025). Monte carlo integration with efficient importance sampling for underwater wireless optical communication simulation. ACCESS, 13, 73652-73670. (www)

  @article{ , abstract = {Underwater optical communications have been proposed for various applications, ranging from coastal protection to short-range submarine communications. The development of dedicated communication systems requires intensive simulation of use cases with efficient methods, both in terms of accuracy and computational time. However, these simulations are challenging due to the complexity of the physical mechanisms of light propagation in water, which involves numerous scattering events on the various particles constituting the propagation medium. Previous tools have primarily relied on the Prahl algorithm, based on Monte Carlo simulation, and are therefore difficult to improve. Recently, a new framework, hereafter referred to as Xiao1, has been developed using an integral formalization of the propagation and Monte Carlo integration for its computation, achieving improved computational times compared to older Prahl techniques for the same level of accuracy. This paper builds upon this framework and proposes to incorporate further importance sampling into the Monte Carlo integration algorithm. It calculates a sub-domain around the receiver for each scattering point and selects a connecting sample with importance within this sub-domain. This paper presents the complete derivation of this new method. It then presents several case studies in which the simulations demonstrate that this new method performs significantly better. Depending on the configuration, these simulations exhibit a reduction in computational times by a factor ranging from 1.09 to 4048 compared with Prahl and from 1.07 to 2134 compared with Xiao1.}, archive = {J_ACCESS}, author = {Ruqin Xiao and Pierre Combeau and Lilian Aveneau}, doi = {10.1109/ACCESS.2025.3564070}, journal = {IEEE Access}, pages = {73652-73670}, shortjournal = {IEEE Access}, title = {Monte carlo integration with efficient importance sampling for underwater wireless optical communication simulation}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving keyword search with access control for secret sharing-based data outsourcing. ACCESS, 13, 73625-73651. (www)

  @article{ , abstract = {Searchable encryption, also known as secure search, is a technology that enables search operations on encrypted data while maintaining confidentiality. Extensive research has been conducted on searchable encryption utilizing public key and symmetric encryptions. However, public key encryption incurs significant computational expenses and is inefficient for querying large databases, particularly in cloud settings. Hence, we focus on searchable encryption using a secret sharing scheme (also known as searchable secret sharing), which is recognized for its minimal computational complexity. A secret sharing scheme is a method for transforming secret inputs into several distinct values known as shares. Kamal et al. (2021) introduced a simple searchable encryption method utilizing secret sharing schemes, yet did not include user access control capabilities. In this study, we introduce a secure search method with user access control that employs secure computation based on a $(k,n)$ threshold secret sharing scheme, where every piece of data stored in the cloud has an owner, and owners grant access to users at their discretion. We assume a client-server model to perform secure computation between the owner, player, and $n\geq k$ cloud servers. Furthermore, we provide an in-depth analysis of the security aspects of our proposed distribution, query generation, and search processes, demonstrating that our approach is resilient to honest-but-curious adversaries with information from up to $k-1$ computing servers. Moreover, we include an improved and efficient method using an $(n,n)$ additive secret sharing scheme when assuming $n=k$ computing servers. We compare the proposed methods in terms of their computational and communication costs. Finally, we present a detailed performance analysis using Python, including a comparison with conventional secret sharing-based methods.}, archive = {J_ACCESS}, author = {Ahmad Akmal Aminuddin Mohd Kamal and Masamune Okada and Masaya Fujisawa}, doi = {10.1109/ACCESS.2025.3562667}, journal = {IEEE Access}, pages = {73625-73651}, shortjournal = {IEEE Access}, title = {Privacy-preserving keyword search with access control for secret sharing-based data outsourcing}, volume = {13}, year = {2025}, }
• (2025). Additive manufactured compact microwave absorbers. ACCESS, 13, 73619-73624. (www)

  @article{ , abstract = {A high-performance, compact microwave absorber was created using Fused Deposition Modeling (FDM) 3D printing. Both a narrowband and a broadband absorber were created. The narrowband absorber was designed at 4.9 GHz, mid-band in WR-187 waveguide. The broadband absorber tried to achieve the best attenuation across the entire 3.95 to 5.85 GHz band. Two types of carbon loaded polylactic acid (PLA) plastic and one type of unloaded PLA were 3D printed with variable percentages of air to achieve different values of effective dielectric constant and loss tangent. The absorber comprised five or six rectangular pieces of these plastic materials. The thickness and fill factor values for each piece were optimized to minimize reflection through fast analytic modeling in MATLAB. The results were then verified by HFSS simulation as well. The stack progressed from the lowest loss and lowest dielectric constant to the highest at the shorting end. The final narrowband load had simulated return loss of 87 dB at 4.9 GHz with an analytic solution in MATLAB. The measured return loss of the 3D printed attenuator was 73 dB at 4.929 GHz. The total length of the absorber was 2.44 inches. A commercial absorber for WR-187 with return loss of 40 dB has length of 13 inches. The experiment proves that an effective and compact microwave absorber can be created using 3D printing.}, archive = {J_ACCESS}, author = {Gregory Peter Le Sage and Muhammad Shumail}, doi = {10.1109/ACCESS.2025.3563137}, journal = {IEEE Access}, pages = {73619-73624}, shortjournal = {IEEE Access}, title = {Additive manufactured compact microwave absorbers}, volume = {13}, year = {2025}, }
• (2025). LLM-assisted ontology restriction verification with clustering-based description generation. ACCESS, 13, 73603-73618. (www)

  @article{ , abstract = {An ontology is a scheme for structuring relationships between concepts in a domain, promoting data interoperability and system integration. However, poorly designed ontologies can lead to errors and performance issues. While systems engineering has standardized evaluation guidelines (e.g., ISO/IEC), ontology engineering lacks such standards, leading to various independent evaluation methods. One frequent issue among novice developers is the misuse of ontology restrictions, particularly ‘allValuesFrom’ and ‘someValuesFrom’, which can significantly impact the correctness and reliability of ontologies. However, existing studies have not adequately addressed effective methods for detecting such errors. To address this gap, we propose a context-aware verification framework utilizing large language models to detect and correct misuse in ontology restrictions. Unlike conventional methods, our framework integrates contextual descriptions derived from ontological axioms, enabling more accurate verification. Additionally, we introduce a clustering-based description generation method that systematically organizes contextual information, further enhancing verification accuracy. Experimental evaluation conducted on diverse ontology datasets suggests that contextual integration improves verification performance. Moreover, the clustering-based description generation improves restriction misuse detection and correction compared to traditional approaches. By automating ontology restriction verification, this study contributes significantly to enhancing the reliability of ontology evaluation and provides a foundation for developing more scalable and standardized verification techniques.}, archive = {J_ACCESS}, author = {Seungyeon Kim and Donghyun Kim and Seokju Hwang and Kyong-Ho Lee and Kyunghwa Lee}, doi = {10.1109/ACCESS.2025.3562560}, journal = {IEEE Access}, pages = {73603-73618}, shortjournal = {IEEE Access}, title = {LLM-assisted ontology restriction verification with clustering-based description generation}, volume = {13}, year = {2025}, }
• (2025). Adaptive PPO with multi-armed bandit clipping and meta-control for robust power grid operation under adversarial attacks. ACCESS, 13, 73586-73602. (www)

  @article{ , abstract = {The seamless and resilient operation of power grids is crucial for ensuring a reliable electricity supply. However, maintaining high operational stability is increasingly challenging due to evolving grid complexities and potential adversarial threats. This paper proposes a novel composite enhanced proximal policy optimization (CePPO) algorithm to improve power grid operation under adversarial conditions. Specifically, our approach introduces three key innovations: 1) multi-armed bandit (MAB) mechanism for dynamic epsilon-clipping that adaptively adjusts exploration-exploitation trade-offs; 2) meta-controller framework that automatically tunes hyperparameters including the activation learning rate (ALR) penalties and exploration factors; and 3) integrated gradient-based optimization approach that combines policy gradients with environmental feedback. The effectiveness of the proposed model on the IEEE 14-bus system demonstrates that the CePPO achieves approximately 50% higher average rewards and 51% longer stability periods compared to standard PPO while reducing computational overhead by 35%. CePPO demonstrates superior performance under adversarial attacks compared to baseline approaches. The simulation results validate that CePPO’s adaptive parameter tuning and enhanced exploration strategies make it particularly well-suited for the dynamic nature of power grid control. To foster further research and reproducibility, the code is available upon request at https://github.com/Dr-Kate-Davis-s-Research-Team/DRL-CP.S}, archive = {J_ACCESS}, author = {Mohamed Massaoudi and Katherine R. Davis}, doi = {10.1109/ACCESS.2025.3563419}, journal = {IEEE Access}, pages = {73586-73602}, shortjournal = {IEEE Access}, title = {Adaptive PPO with multi-armed bandit clipping and meta-control for robust power grid operation under adversarial attacks}, volume = {13}, year = {2025}, }
• (2025). Online parameter identification for equivalent circuit model of li-ion batteries using the eigenvalue Addition/Deletion least-squares method with a forgetting factor. ACCESS, 13, 73569-73585. (www)

  @article{ , abstract = {It is necessary to identify the parameters for an equivalent circuit model of Li-ion batteries and understand their degradation conditions in electric vehicles and consumer electronic devices. As a parameter identification method, the recursive least-squares method with a forgetting factor (FRLS) has been used in several applications of battery management systems (BMS). However, the inverse covariance matrix of FRLS is singular and a covariance matrix does not exist depending on the current pattern (in one example, the current is maintained at zero). In this case, the parameter identification using FRLS is divergent. One approach to solving this challenge is to realize FRLS for the Moore-Penrose inverse (MIFRLS). However, when tested with MIFRLS, certain parameters became almost zero when the current was maintained at zero, and the challenge still remained. In this study, we proposed a new method to solve this challenge and demonstrated improvements in identification. First, we verified that a certain parameter becomes almost zero when the current was maintained at zero. We found that this is caused by noise and the divergence term in the covariance matrix owing to the rank reduction. To solve this challenge, we propose a new method called “an eigenvalue addition/deletion MIFRLS” for BMS which removes eigenvectors with large eigenvalues from the covariance matrix to avoid the divergence term. Lastly, we describe the proposed method to consistently identify parameters, as confirmed by fake numerical tests with known true values, and the average computation time was only 1.47 times longer than that of FRLS.}, archive = {J_ACCESS}, author = {Takeshi Inoue and Daiki Komatsu}, doi = {10.1109/ACCESS.2025.3558602}, journal = {IEEE Access}, pages = {73569-73585}, shortjournal = {IEEE Access}, title = {Online parameter identification for equivalent circuit model of li-ion batteries using the eigenvalue Addition/Deletion least-squares method with a forgetting factor}, volume = {13}, year = {2025}, }
• (2025). UETT4K anti-UAV: A large scale 4K benchmark dataset for vision-based drone detection in high-resolution imagery. ACCESS, 13, 73553-73568. (www)

  @article{ , abstract = {In recent years, incidents involving drones have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective drone detection system. However, DCNNs rely heavily on extensive and well-labeled datasets, which are essential for achieving high accuracy and effectiveness. For drone detection tasks, a dataset of high-resolution images is especially valuable, as it provides more contextual information for DCNNs, enabling more accurate drone detection. This work presents a new drone detection dataset composed of 4K resolution images named University of Engineering and Technology Taxila 4K Anti-UAV (UETT4K Anti-UAV). The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. A custom dataset of 33601 images, manually annotated with hand-labeled bounding boxes, is created from these videos. We used our dataset to train, validate, and test eight state-of-the-art YOLOv6v3 algorithm models. The test results and qualitative analysis guide selecting the most suitable model for drone detection applications. The proposed extensive 4K dataset offers a valuable resource for effectively training deep learning models to detect drones across diverse conditions accurately.}, archive = {J_ACCESS}, author = {Mughees Sarwar Awan and Syed Azhar Ali Zaidi and Junaid Mir}, doi = {10.1109/ACCESS.2025.3562890}, journal = {IEEE Access}, pages = {73553-73568}, shortjournal = {IEEE Access}, title = {UETT4K anti-UAV: A large scale 4K benchmark dataset for vision-based drone detection in high-resolution imagery}, volume = {13}, year = {2025}, }
• (2025). Load balancing and alternative path selection in self-organized networks: A data plane approach. ACCESS, 13, 73540-73552. (www)

  @article{ , abstract = {This paper presents a novel load balancing algorithm tailored for deeply programmable networks, offering a decentralized approach to optimizing packet forwarding and load distribution. Unlike conventional systems reliant on centralized controllers or manual configurations, this algorithm operates entirely within the data plane, leveraging controlled flooding to dynamically discover and reroute traffic based on real-time congestion data. By detecting latency spikes indicative of congestion, switches autonomously select alternative paths to maintain optimal traffic flow. Implemented in the P4 data plane programming language, the algorithm is rigorously evaluated against existing load balancing methods for self-organized networks. The results demonstrate significant reductions in data transmission times, improved path symmetry, and enhanced scalability under various network conditions, making it a robust solution for modern, self-organized, and high-performance networks.}, archive = {J_ACCESS}, author = {Gergely Sárközi and Péter Vörös}, doi = {10.1109/ACCESS.2025.3563806}, journal = {IEEE Access}, pages = {73540-73552}, shortjournal = {IEEE Access}, title = {Load balancing and alternative path selection in self-organized networks: A data plane approach}, volume = {13}, year = {2025}, }
• (2025). Reducing head pose estimation data set bias with synthetic data. ACCESS, 13, 73530-73539. (www)

  @article{ , abstract = {Data set bias not only compromises the fairness, accuracy and effectiveness of trained models, but also leads to a lower performance in real-world scenarios compared to the evaluation results obtained with a specific data set. This issue is especially evident in the estimation of head pose, as current data sets suffer from a limited number of images, imbalanced data distributions, the high cost of annotation, and ethical concerns. Synthetic data offers a promising solution to address these challenges, but current semi-synthetic data sets fail to deliver satisfactory results, likely due to the limited realism of the generated faces and the heavily skewed pose distribution. In this paper, we report the existence of data set biases in the most widely used head pose estimation benchmarks, which lead to an optimistic estimation of model performance in real-world scenarios. To mitigate this issue, we create a synthetic image data set using a generative model with explicit control over the head pose. Our experiments demonstrate that incorporating our synthetic images leads to improved generalization and accuracy.}, archive = {J_ACCESS}, author = {Roberto Valle and José M. Buenaposada and Luis Baumela}, doi = {10.1109/ACCESS.2025.3561506}, journal = {IEEE Access}, pages = {73530-73539}, shortjournal = {IEEE Access}, title = {Reducing head pose estimation data set bias with synthetic data}, volume = {13}, year = {2025}, }
• (2025). Where the women are: Gender imbalance in computing and faculty perceptions of theoretical and applied research. ACCESS, 13, 73520-73529. (www)

  @article{ , abstract = {Women remain under-represented in academic science, and this is especially true in computing. While there is limited research on gender differences in research focus, there is evidence that women may be more likely to conduct applied research. We surveyed tenured and tenure-track faculty in the United States to understand perceptions of hypothetical researchers who engage in applied or theoretical work. Faculty rated researchers engaged in applied research as less likely to publish their work, receive tenure/be promoted, obtain awards, and get funding for their work. Faculty further rated these researchers as less brilliant, creative, and technically skilled than they rated their theory-focused counterparts. Data from publications, hiring, funding, and awards suggests that applied research may indeed lead to worse career outcomes. We further show that women are more highly represented in applied research areas than theoretical ones. Negative perceptions of applied researchers must now be addressed to avoid exacerbating the gender gap.}, archive = {J_ACCESS}, author = {Samantha Kleinberg and Jessecae K. Marsh}, doi = {10.1109/ACCESS.2025.3564170}, journal = {IEEE Access}, pages = {73520-73529}, shortjournal = {IEEE Access}, title = {Where the women are: Gender imbalance in computing and faculty perceptions of theoretical and applied research}, volume = {13}, year = {2025}, }
• (2025). PRIDES: A probabilistic model for recurrent user interest drift identification in session-based recommendation. ACCESS, 13, 73498-73519. (www)

  @article{ , abstract = {A Session-Based Recommendation (SBR) identifies correlations among session interactions to understand user preferences and generate appropriate recommendations. A key challenge in this context is the dynamic change in user preferences, particularly when preferences disappear and reappear within a session, a phenomenon referred to as Recurrent User Interest Drift (RUID). Effectively capturing RUID is significant for aligning recommendations with ongoing user preferences. Existing SBR approaches often misclassify user preferences that differ from other session interactions as noise (unintentional interactions), relying on dwell time (the amount of time a user spends viewing an item) or neighboring sessions, thereby overlooking their potential reappearance as RUID later in the session. To the best of our knowledge, this work is the first to address the challenge of identifying RUID in SBR. The proposed approach assigns probabilistic scores to each interaction by considering its similarity to the immediate previous interaction, its inclusion among popular items (items with a higher number of interactions), its similarity to previous interactions, and the dwell time. As user preference may reappear anytime during the session, and RUID identification requires analyzing subsequent interactions, a list-based approach is used to retain these interactions until the session ends, enabling effective RUID identification. The matrix factorization-based attentive session encoder incorporates both short-term (ongoing) preferences and long-term (historical) preferences to generate personalized recommendations. Experimental results on three benchmark datasets, Yoochoose, Last.fm, and Gowalla, show that our method outperforms 14 state-of-the-art baselines, achieving an improvement of 2.28% in recall@20 and 1.39% in Mean Reciprocal Rank (MRR@20) on Yoochoose, 3.58% in recall@20 and 2.70% in MRR@20 on Last.fm, and 5.35% in recall@20 and 4.17% in MRR@20 on Gowalla datasets.}, archive = {J_ACCESS}, author = {Vinnakota Saran Chaitanya and P. Santhi Thilagam}, doi = {10.1109/ACCESS.2025.3564460}, journal = {IEEE Access}, pages = {73498-73519}, shortjournal = {IEEE Access}, title = {PRIDES: A probabilistic model for recurrent user interest drift identification in session-based recommendation}, volume = {13}, year = {2025}, }
• (2025). Design and comprehensive analysis of magnetic-geared permanent magnet synchronous motor for single-propeller underwater propulsion. ACCESS, 13, 73486-73497. (www)

  @article{ , abstract = {This paper presents a magnetic-geared permanent magnet synchronous motor (MG-PMSM), which is an integrated structure composed of a permanent magnet synchronous motor (PMSM) and a magnetic gear (MG) for autonomous underwater vehicles (AUVs). The MG-PMSM features a dual rotor structure: a high-speed permanent magnet rotor (PMR) and a low-speed pole piece rotor (PPR). To meet the required load conditions of the single propeller of the AUV, the PPR, directly connected to the output shaft, operates under load conditions, while the PMR operates under no-load conditions. The aim of this study is to use finite element analysis (FEA) to investigate the overall electromagnetic performance of the proposed MG-PMSM with different gear ratios and slot/pole combinations. Subsequently, a structural analysis of the PPR and its support structure is conducted using FEA to validate the strength of the PPR structure. Furthermore, thermal analysis is performed using computational fluid dynamics. The results of the structural and thermal analyses demonstrate sufficient structural strength and thermal stability, respectively. Finally, the electromagnetic performances of the proposed MG-PMSM are compared with the PMSM and MG configuration. The results show that, although the MG-PMSM exhibits nearly identical efficiency, it achieves an 11.4% increase in volumetric torque density per unit active volume compared to the PMSM and MG configuration. Moreover, when accounting for the inactive volume of the integrated underwater propulsion system, the volumetric torque density is expected to increase further.}, archive = {J_ACCESS}, author = {Jang-Hyun Park and Do-Kwan Hong}, doi = {10.1109/ACCESS.2025.3564241}, journal = {IEEE Access}, pages = {73486-73497}, shortjournal = {IEEE Access}, title = {Design and comprehensive analysis of magnetic-geared permanent magnet synchronous motor for single-propeller underwater propulsion}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection in IoT and IIoT: Comparing lightweight machine learning techniques using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset datasets. ACCESS, 13, 73468-73485. (www)

  @article{ , abstract = {The security of Internet of Things (IoT) and Industrial Internet of Things (IIoT) systems has been significantly enhanced through the integration of effective intrusion detection systems (IDSs). Machine learning (ML) has emerged as a highly efficient approach for designing cyber-attack detection systems to improve the security. This study reviewed recent advancements in the literature utilizing the TON_IoT, WUSTL-IIoT-2021, and Edge-IIoTset datasets. A comprehensive performance analysis of various supervised ML classification techniques was conducted to identify lightweight models suitable for deployment in resource-constrained IoT and IIoT environments. The performance of Decision Tree (DT), Random Forest (RF), and three ensemble techniques: Bagging, Stacking, and LightGBM (LGBM), was evaluated. The TON_IoT, WUSTL-IIOT-2021, and Edge-IIoTset imbalanced datasets, representing three distinct IIoT environments and encompassing numerous samples of different attack types, were used. The impact of imbalanced class distributions on model performance was analyzed. The imbalanced datasets were customized for training and testing ML models, with feature selection performed using Mutual Information (MI). Model performance was assessed using several metrics: Precision, Recall, Micro-F1, Model Size, and Training Time. Furthermore, a cross-dataset transfer learning approach was applied to evaluate how models trained on the TON_IoT dataset generalize when tested on the WUSTL-IIoT-2021 dataset, demonstrating the ability of the models to generalize across datasets with common features and attack labels. For real-time intrusion detection and network traffic analysis, we set up an experiment to deploy the trained ML models in a live network environment. The experiment provided real-time insights into CPU usage, memory consumption, and network activity, with predictions continuously logged for monitoring and further analysis.}, archive = {J_ACCESS}, author = {Shereen Ismail and Salah Dandan and Ala'a Qushou}, doi = {10.1109/ACCESS.2025.3554083}, journal = {IEEE Access}, pages = {73468-73485}, shortjournal = {IEEE Access}, title = {Intrusion detection in IoT and IIoT: Comparing lightweight machine learning techniques using TON_IoT, WUSTL-IIOT-2021, and EdgeIIoTset datasets}, volume = {13}, year = {2025}, }
• (2025). RPEE-heads benchmark: A dataset and empirical comparison of deep learning algorithms for pedestrian head detection in crowds. ACCESS, 13, 73451-73467. (www)

  @article{ , abstract = {The automatic detection of pedestrian heads in crowded environments is essential for crowd analysis and management tasks, particularly in high-risk settings such as high dense railway platforms and event entrances. These environments, characterized by dense crowds and dynamic movements, are underrepresented in public datasets, posing challenges for existing deep learning models. To address this gap, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a novel, diverse, high-resolution, and accurately annotated resource. It includes 109,913 annotated pedestrian heads across 1,886 images from 66 video recordings, with an average of 56.2 heads per image. Annotations include bounding boxes for visible head regions. In addition to introducing the RPEE-Heads dataset, this paper evaluates eight state-of-the-art object detection algorithms using the dataset and analyzes the impact of head size on detection accuracy. The experimental results show that You Only Look Once v9 and Real-Time Detection Transformer outperform the other algorithms, achieving mean average precisions of 90.7% and 90.8%, with inference times of 11 and 14 milliseconds, respectively. Moreover, the findings underscore the need for specialized datasets like RPEE-Heads for training and evaluating accurate models for head detection in railway platforms and event entrances. The dataset and pretrained models are available at https://doi.org/10.34735/ped.2024.2.}, archive = {J_ACCESS}, author = {Mohamad Abubaker and Zubayda Alsadder and Hamed Abdelhaq and Maik Boltes and Ahmed Alia}, doi = {10.1109/ACCESS.2025.3563311}, journal = {IEEE Access}, pages = {73451-73467}, shortjournal = {IEEE Access}, title = {RPEE-heads benchmark: A dataset and empirical comparison of deep learning algorithms for pedestrian head detection in crowds}, volume = {13}, year = {2025}, }
• (2025). Binocular vision-based target detection algorithm. ACCESS, 13, 73440-73450. (www)

  @article{ , abstract = {In the field of target detection, algorithms are challenged with multi-objective optimization problems in identifying detection targets, and it is also crucial to improve the recognition of small and insignificant targets. In this study, the Feffol network model is proposed to achieve excellent performance in complex detection tasks. In the feature extraction stage, the Feffol network model carefully chooses Efficient-v2 as the backbone network structure, which can provide a solid foundation for subsequent detection by taking advantage of its efficient feature extraction capability. In addition, an Ebifpn feature pyramid module with an spatial pyramid pooling-cross-stage partial connections(SppCSP) structure was innovatively introduced. This design not only effectively expands the feature sensing field, but also greatly enhances the fusion and expression of feature information of different sizes, so that the model can capture the target features more comprehensively. In addition, to solve the key problems in the detection process, the Feffol model adopts the Focal Loss and CIoU Loss functions. The former can effectively balance the positive and negative samples to avoid over-learning of easy-to-classify samples during the training process; the latter successfully solves the problem of the failure of the detection method when there is no intersection between the prediction frame and the real detection frame, which significantly improves the robustness of the model. The experimental results show that the Feffol network model has excellent performance, with a detection accuracy as high as 90.09% and a detection speed of 9.75it/s. Compared with other mainstream networks, the detection accuracy of Feffol network has been significantly improved, which fully proves its advancement and effectiveness in the task of target detection, and provides new ideas and methods for the further development of this field.}, archive = {J_ACCESS}, author = {Huiguo Zhang}, doi = {10.1109/ACCESS.2025.3563965}, journal = {IEEE Access}, pages = {73440-73450}, shortjournal = {IEEE Access}, title = {Binocular vision-based target detection algorithm}, volume = {13}, year = {2025}, }
• (2025). A case study on SAL-based teaching and learning in a functional clothing and materials course using generative AI. ACCESS, 13, 73430-73439. (www)

  @article{ , abstract = {With the rapid development of big data and generative artificial intelligence (AI), education in the apparel industry is evolving beyond traditional theoretical instruction. This study explores the application of Smart Action Learning (SAL) and generative AI tools in the “Functional Clothing and Materials” course to enhance student engagement, problem-solving abilities, and practical skill acquisition. SAL, a student-centered learning approach, incorporates blended learning, flipped learning, and problem-based learning (PBL) methodologies to foster self-directed and cooperative learning. The study was conducted over an eight-week period with second-year students in an apparel major, integrating AI-based problem-solving techniques into coursework. The course design included a five-stage instructional framework: instructor-led lectures, problem identification, AI-assisted research, collaborative solution development, and final presentations. The effectiveness of this approach was evaluated through surveys measuring self-directed learning ability, cooperative learning ability, problem-solving ability, and academic achievement. Results indicate that students demonstrated high levels of engagement, improved problem-solving abilities, and increased learning satisfaction through AI-assisted collaborative projects. The findings suggest that incorporating generative AI into fashion education enhances creative thinking, industry readiness, and adaptability to emerging trends.}, archive = {J_ACCESS}, author = {Hyun Ah Kim}, doi = {10.1109/ACCESS.2025.3561838}, journal = {IEEE Access}, pages = {73430-73439}, shortjournal = {IEEE Access}, title = {A case study on SAL-based teaching and learning in a functional clothing and materials course using generative AI}, volume = {13}, year = {2025}, }
• (2025). Comprehensive evaluation of the impacts of mixed traffic conditions on urban networks. ACCESS, 13, 73408-73429. (www)

  @article{ , abstract = {The emergence of connected and autonomous vehicles (CAVs) has become a focal point in the literature. This study proposes a comprehensive evaluation framework integrating multi-criteria analysis (MCA) methods with traffic microsimulation modeling to assess the impacts of mixed traffic conditions, comprising CAVs and human-driven vehicles (HDVs), on urban networks from various perspectives: traffic efficiency, environmental performance, and traffic safety. To this end, findings obtained from simulations of two real urban networks are employed to evaluate the impacts of penetration rates and different driving behaviors of CAVs. Six penetration rates (15%, 30%, 45%, 60%, 75%, and 90%), and three different driving behaviors of CAVs, namely defensive, normal, and aggressive, are considered in the scope of this study. While the Criteria Importance Through Intercriteria Correlation (CRITIC) method is utilized for determining objective criteria weights, the designed scenarios are scored by means of the Combined Compromise Solution (CoCoSo) method. The findings of the study indicate that defensive driving behavior enhances traffic safety, albeit with trade-offs in reduced traffic efficiency and increased traffic emissions. On the other hand, while aggressive driving behavior improves traffic efficiency and reduces traffic emissions, it also introduces safety risks, particularly at low penetration rates. According to the outcomes of the comprehensive evaluation, scenarios comprising only HDVs lose their dominance beyond 60% and 75% penetration rates, depending on the network. The proposed approach is expected to be effective in assessing the impacts of mixed traffic conditions on urban networks and can provide valuable insights to transportation policymakers and practitioners.}, archive = {J_ACCESS}, author = {Mehmet Nedim Yavuz and Halit Ozen}, doi = {10.1109/ACCESS.2025.3563785}, journal = {IEEE Access}, pages = {73408-73429}, shortjournal = {IEEE Access}, title = {Comprehensive evaluation of the impacts of mixed traffic conditions on urban networks}, volume = {13}, year = {2025}, }
• (2025). Advancing grid-forming inverter technology: Comprehensive PQ capability and performance analysis. ACCESS, 13, 73391-73407. (www)

  @article{ , abstract = {This paper presents a performance analysis of grid-forming (GFM) inverter technology, which is essential to ensure stable and reliable operation of power systems with high penetration of inverter-based resources (IBRs). Recognizing that IBR operational constraints are distinct from those of synchronous generators, this study develops advanced PQ capability models and algorithmic frameworks that accurately characterize GFM inverter operational constraints across various coupling filter configurations (L, LC, and LCL). Electromagnetic transient (EMT) simulations show that the Enhanced Voltage Regulation (EVR) and Controlled Proportional-Integral Droop (CPID) strategies proposed in this paper improve voltage and frequency stability under dynamic loading and fault conditions, outperforming conventional droop methods. Real-time hardware validation confirms the robustness of the proposed approaches and their effectiveness in suppressing harmonics and recovering from faults. Key contributions include the derivation of practical PQ capability boundaries under realistic constraints, identification of superior control strategies for robust power quality (PQ), and successful GFM and grid-following (GFL) integration in parallel operations. These findings provide actionable insights for optimizing GFM inverter design and ensuring IEEE 1547 compliance in low-inertia grids.}, archive = {J_ACCESS}, author = {Md Nurunnabi and Shuhui Li and Himadry Shekhar Das}, doi = {10.1109/ACCESS.2025.3561788}, journal = {IEEE Access}, pages = {73391-73407}, shortjournal = {IEEE Access}, title = {Advancing grid-forming inverter technology: Comprehensive PQ capability and performance analysis}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on digital twin: Focusing on security threats and requirements. ACCESS, 13, 73362-73390. (www)

  @article{ , abstract = {Digital Twin (DT) aims to seamlessly replicate physical objects or processes in virtual environments, garnering attention for supporting diverse intelligent management services such as monitoring, analysis, and control. Integration with technologies like artificial intelligence (AI), big data, edge computing, and the Internet of Things (IoT) has propelled the development of DTs. However, the DT may not only introduce new potential security flaws but also inherit existing vulnerabilities from the technologies they incorporate and interact with due to their integration with diverse technologies. Despite its growing impact, research addressing the security concerns of DTs remains insufficient and still in its early stages. In this article, we provide a comprehensive and in-depth review of the current state of DTs, focusing on their security aspects. We first depict an overview of DTs, including the definition of the DT and various DT applications, and then present the architecture for DTs. Subsequently, we conduct a detailed examination of security attacks and threats across the functional layers of the DT architecture. In contrast to the previous surveys on DTs, we derive security properties and security functional requirements (SFRs) based on the Common Criteria (CC) standard for mitigating these attacks and threats. We also introduce technologies that enable the achievement of SFRs with a future research perspective. By addressing these critical security aspects, our article enhances the security and trustworthiness of DTs, contributing to their safe and reliable deployment in various domains.}, archive = {J_ACCESS}, author = {Hyeran Mun and Kyusuk Han and Ernesto Damiani and Hyun Ku Yeun and Tae-Yeon Kim and Luigi Martino and Chan Yeob Yeun}, doi = {10.1109/ACCESS.2025.3563621}, journal = {IEEE Access}, pages = {73362-73390}, shortjournal = {IEEE Access}, title = {A comprehensive survey on digital twin: Focusing on security threats and requirements}, volume = {13}, year = {2025}, }
• (2025). End-to-end learning framework incorporating image reconstruction and recognition models. ACCESS, 13, 73355-73361. (www)

  @article{ , abstract = {Edge computing-based image recognition applications face significant challenges, including increased latency and network load when transmitting large volumes of images to edge servers. To address these issues, this study proposes a novel solution that involves sending compressed data from a front-end device over a network and subsequently reconstructing the images on the server side for recognition purposes. The proposed framework places the image recognition model directly after the image reconstruction model. The reconstruction model is based on a recurrent neural network, and ResNet-18 is used for recognition. This reduction in image quality results in lower recognition performance when using reconstructed images compared with original images. To mitigate this issue, we propose an end-to-end learning framework that jointly optimizes image reconstruction and recognition, specifically optimizing the reconstruction model for the recognition task. The proposed method achieves approximately 99% data compression without degrading classification performance. It improves image quality by 2.1 dB and classification accuracy by 11.2% over the baseline. The proposed method not only significantly improves the quality of reconstructed images without any loss in the image compression rate but also enhances the classification accuracy of these images.}, archive = {J_ACCESS}, author = {Ren Shibata and Yuji Yamauchi}, doi = {10.1109/ACCESS.2025.3563476}, journal = {IEEE Access}, pages = {73355-73361}, shortjournal = {IEEE Access}, title = {End-to-end learning framework incorporating image reconstruction and recognition models}, volume = {13}, year = {2025}, }
• (2025). Implementing generative AI into ERP software. ACCESS, 13, 73342-73354. (www)

  @article{ , abstract = {Enterprise Resource Planning (ERP) systems digitize all business processes within companies in order to enhance automation and optimize efficiency. These solutions integrate data and processes across multiple functions such as sales, marketing, finance, supply chain, manufacturing, services, procurement, and human resources, serving as a central repository of information for numerous organizations. ERP systems typically encompass tens of thousands of business processes and manage data across thousands of tables, creating significant opportunities for the integration of Generative Artificial Intelligence (AI) for increasing process automatization and optimization. Nonetheless, embedding Generative AI into ERP solutions is a complex task due to the intricate nature of these systems, which consist of hundreds of millions of lines of code and cater to a wide array of industry-specific and regional requirements. Consequently, the key research question addressed in this paper is: How to systematically develop and operate Generative AI business applications in ERP systems? This article aims to answer this question by conducting a use case analysis, deriving business requirements, designing and implementing a solution framework, and evaluating its effectiveness through real-world ERP use cases.}, archive = {J_ACCESS}, author = {Siar Sarferaz}, doi = {10.1109/ACCESS.2025.3564133}, journal = {IEEE Access}, pages = {73342-73354}, shortjournal = {IEEE Access}, title = {Implementing generative AI into ERP software}, volume = {13}, year = {2025}, }
• (2025). Harnessing syntax GCN and multi-view interaction for conversational aspect-based quadruple sentiment analysis. ACCESS, 13, 73332-73341. (www)

  @article{ , abstract = {Conversational Aspect-Based Sentiment Analysis (DiaASQ) aims to extract fine-grained sentiment quadruples {target, aspect, opinion, polarity} from multiple segments of dialogue. The composition of these quadruples is often not limited to a single utterance but may span across the entire conversation. Therefore, the analysis needs to consider both the syntactic structure of individual utterances and the interactions between different views. However, previous studies often lack modeling of dialogue features and overlook the interdependence between utterances. To address this, this paper introduces the Syntax and Consecutive Multi-View Network. This model captures the syntactic dependencies among utterances using Graph Convolutional Networks (GCN) and employs multi-view interactions along with three consecutive multi-head attention modules to construct contextual connections within the dialogue. Finally, a triple scorer is used to decode the quadruples, thereby enhancing the weakly labeled relationships within the quadruples. Extensive experiments on standard datasets demonstrate that the proposed model significantly outperforms baseline methods across multiple dimensions.}, archive = {J_ACCESS}, author = {Houwei Kang and Chunling Wu}, doi = {10.1109/ACCESS.2025.3562967}, journal = {IEEE Access}, pages = {73332-73341}, shortjournal = {IEEE Access}, title = {Harnessing syntax GCN and multi-view interaction for conversational aspect-based quadruple sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). DCT-DiffPose: A lightweight diffusion model with multi-hypothesis for 3D human pose estimation. ACCESS, 13, 73319-73331. (www)

  @article{ , abstract = {3D human pose estimation is a crucial task in computer vision with extensive applications, yet it remains challenging due to depth ambiguity and constraints on computational efficiency. In this paper, we propose DCT-DiffPose, a novel framework that integrates a diffusion model with Confidence and Consistency-based Multi-Hypothesis Aggregation (CCMA). Moreover, it incorporate the Discrete Cosine Transform (DCT) for frequency-domain feature extraction. Specifically, the diffusion model generates diverse and plausible hypotheses, and CCMA aggregates them based on confidence and consistency, effectively addressing depth ambiguity. Additionally, we incorporate DCT into the self-attention mechanism to transform input data into the frequency domain, thereby enhancing feature extraction while significantly reducing computational complexity. To validate DCT-DiffPose, we conducted extensive experiments on the Human3.6M and MPI-INF-3DHP datasets. Our method achieves a 19% lower Mean Per Joint Position Error (MPJPE) and a 55% reduction in FLOPs compared to D3DP. The results demonstrate its excellent trade-off between accuracy and complexity.}, archive = {J_ACCESS}, author = {Linzhan Zhong and Fangni Chen and Bolun Zheng and Rui Feng and Lei Zhang and Jian Wan}, doi = {10.1109/ACCESS.2025.3563898}, journal = {IEEE Access}, pages = {73319-73331}, shortjournal = {IEEE Access}, title = {DCT-DiffPose: A lightweight diffusion model with multi-hypothesis for 3D human pose estimation}, volume = {13}, year = {2025}, }
• (2025). ConvGRU: A lightweight intrusion detection system for vehicle networks based on shallow CNN and GRU. ACCESS, 13, 73297-73318. (www)

  @article{ , abstract = {The rapid proliferation of connected vehicles has significantly expanded the attack surface of the Internet of Vehicles (IoV), introducing severe security risks. In such resource-constrained environments, developing lightweight solutions is crucial to ensuring real-time detection and efficient deployment. To address these challenges, this study proposes ConvGRU, a lightweight vehicular network intrusion detection model that integrates a shallow Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU). By employing optimizations such as small convolutional kernels and depthwise separable convolutions, the model significantly reduces the number of parameters and computational overhead, making it well-suited for resource-limited IoV environments. The shallow CNN effectively captures spatial features, while the GRU extracts temporal dependencies, enhancing the model’s generalization ability. ConvGRU achieves an accuracy, precision, recall, and F1-score exceeding 0.99 on the HCRL-Car-hacking, OTIDS, and CICIDS-2018 datasets, with only 112.55K parameters and a memory footprint of merely 0.43 MB. Experimental results demonstrate that this intrusion detection solution substantially improves malicious traffic detection accuracy while ensuring efficient operation in resource-constrained vehicular environments.}, archive = {J_ACCESS}, author = {Shaoqiang Wang and Jiahui Cheng and Yizhe Wang and Shutong Li and Lei Kang and Yinfei Dai}, doi = {10.1109/ACCESS.2025.3563908}, journal = {IEEE Access}, pages = {73297-73318}, shortjournal = {IEEE Access}, title = {ConvGRU: A lightweight intrusion detection system for vehicle networks based on shallow CNN and GRU}, volume = {13}, year = {2025}, }
• (2025). Micro mechanism research of photoacoustic effect based on gold nanocomposites in multiphysics field. ACCESS, 13, 73285-73296. (www)

  @article{ , abstract = {It is significant to precisely control the size and spacing of gold nanoparticles for enhancing the effect of localized surface plasmon resonance (LSPR) in photothermal (PT) therapy and photoacoustic (PA) imaging. A multi-layer array of gold nanoparticles in the optic-thermal-acoustic field was modeled in this study using numerical simulation methods as a means of quantitatively analyzing the microscale heat transfer parameters and nonlinear enhanced PA/PT effects. High special resolution was achieved by taking isolated gold nanoparticles with radii of 20 nm, 40 nm, and 50 nm as examples by finite element analysis (FEA). The peak value of acoustic pressure was 74 Pa under the excitation of 5 ns pulse laser with a wavelength of 532 nm. Gold nanoarray was packed by nanoparticle with separation spacings of 6 nm, 7 nm, 8 nm, 15 nm, 20 nm, and 40 nm. The optical and thermoacoustic properties of the nanoarray varied as the assembled density of the gold nanoarray increased. The model also verified that mixing the polydimethylsiloxane (PDMS) with gold nanoparticles improved the efficiency of the thermal transformation of nanoarrays by at least 43.52% and amplified PA pressure by 3 grades. The results showed that high PA generation efficiency could be achieved by rationally constructing the composites of PDMS and gold nanoarray.}, archive = {J_ACCESS}, author = {Cong Du and Zihui Sun and Yanxue Wang and Jinduo Xing and Jianbo Feng and Aihua Zhu}, doi = {10.1109/ACCESS.2024.3438366}, journal = {IEEE Access}, pages = {73285-73296}, shortjournal = {IEEE Access}, title = {Micro mechanism research of photoacoustic effect based on gold nanocomposites in multiphysics field}, volume = {13}, year = {2025}, }
• (2025). V-model based integration of mechatronics and power electronics for a solar-powered wire-traversing robot. ACCESS, 13, 73261-73284. (www)

  @article{ , abstract = {This work focuses on the design of a solar-powered wire-traversing robot for environmental monitoring in remote areas, where solar power harvesting ensures continuous operation without frequent external charging. This paper presents an integrated power electronics design, emphasizing system-level considerations for efficient energy management and enhanced battery life, extending operational time and reliability. Experimental results showed the RaccoonBot could dynamically locate maximum solar exposure with 0.134[mm] resolution, maintain up to 7.5[W] of charging power, and consume just 0.025[A] in standby. These features, combined with the bio-inspired solar-tracking features and fail-safe design, enable over a week of autonomous operation, extending runtime from 5 hours to weeks. The findings validate the V-model’s effectiveness in creating efficient, reliable systems, demonstrated by the RaccoonBot for environmental monitoring.}, archive = {J_ACCESS}, author = {Efrain Mendez-Flores and Diana Morales and Magnus Egerstedt}, doi = {10.1109/ACCESS.2025.3563235}, journal = {IEEE Access}, pages = {73261-73284}, shortjournal = {IEEE Access}, title = {V-model based integration of mechatronics and power electronics for a solar-powered wire-traversing robot}, volume = {13}, year = {2025}, }
• (2025). CANini: In-depth traffic analysis for design and robustness evaluation of DTree-based IDS in automotive networking systems. ACCESS, 13, 73236-73260. (www)

  @article{ , abstract = {In-Vehicle Networks (IVNs) have become increasingly complex due to the need to enhance driving experience and provide passengers with a broader range of services. The Controller Area Network (CAN) bus is widely used in modern vehicles, enabling communication between various electronic control units. The CAN bus exhibits cybersecurity vulnerabilities that require the use of protection mechanisms. An Intrusion Detection System (IDS) can efficiently identify and mitigate threats in CAN networks. In this article, we perform an extensive and thorough analysis of a CAN dataset, namely CANini. The CANini dataset comes from the augmentation of the CAN-MIRGU dataset, which has been collected from a real vehicle under various driving scenarios. Our analysis offers a comprehensive and extensive characterization of CAN network traffic, examining specifically inter-frame arrival times and patterns in data bytes. We proceeded to test these features by designing a simple anomaly-based IDS that employs a multiclass Decision Tree classifier. To find and assess the weaknesses of our selected architecture, a robustness analysis was performed on the IDS by manipulating the properties of the CAN traffic. Our novel analysis provides critical insights into the behavior and structure of CAN in a typical IVN, which is crucial for the development and validation of a reliable IDS.}, archive = {J_ACCESS}, author = {Nicasio Canino and Pierpaolo Dini and Stefano Mazzetti and Daniele Rossi and Sergio Saponara}, doi = {10.1109/ACCESS.2025.3561591}, journal = {IEEE Access}, pages = {73236-73260}, shortjournal = {IEEE Access}, title = {CANini: In-depth traffic analysis for design and robustness evaluation of DTree-based IDS in automotive networking systems}, volume = {13}, year = {2025}, }
• (2025). A novel semantic driven meta-learning model for rare attack detection. ACCESS, 13, 73219-73235. (www)

  @article{ , abstract = {Accurate network intrusion detection requires extracting relevant semantic features to minimize mis-classification and identify various kinds of attacks. Traditional models often struggle with uncertain traffic patterns, reducing their reliability. This work presents a Novel Semantic-Driven Meta-Learning Model - a hybrid framework that systematically refines categorization decisions. It uses advanced learning techniques to improve detection through a two-stage verification process. Our approach enhances intrusion detection by integrating an attention-based model for semantic feature extraction and the Simple Neural Attentive Meta-Learner (SNAIL) for rare attack class detection. In the first phase, machine learning classifiers perform macro-classification to distinguish between normal and attack traffic. To reduce mis-classification, an additional classifier verifies the predicted class label using a refined subset of features. In the second phase, the SNAIL model conducts micro-classification, further differentiating attack classes with high precision. This dual-phase strategy is particularly effective in addressing the challenge of detecting rare classes in highly imbalanced network intrusion datasets. Using the NSL-KDD, CSE-CIC-IDS 2018, and CIC-ToN-IoT datasets, we assess our model, showing its better performance in precisely detecting unusual attack types while preserving a low false alarm rate with semantic characteristics. The proposed model demonstrates a higher detection rate for most of the rare classes considered in our study compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Y. Annie Jerusha and S. P. Syed Ibrahim and Vijay Varadharajan}, doi = {10.1109/ACCESS.2025.3564031}, journal = {IEEE Access}, pages = {73219-73235}, shortjournal = {IEEE Access}, title = {A novel semantic driven meta-learning model for rare attack detection}, volume = {13}, year = {2025}, }
• (2025). Unveiling samsung quantum galaxy: Securing smartphones with quantum and post-quantum cryptography. ACCESS, 13, 73202-73218. (www)

  @article{ , abstract = {Quantum technologies have emerged as vital tools for enhancing mobile communication security. The Samsung Galaxy series now features a quantum random number generator (QRNG) that offers true randomness, strengthening cryptographic protection. Despite its potential, QRNG integration remains largely unexplored in practical applications. This paper investigates the QRNG chip’s integration and functionality in the Samsung Quantum Galaxy smartphone through digital forensics and reverse engineering. We identified a lack of effective QRNG utilization in existing applications and addressed this gap by developing a secure instant messaging and VoIP application that combines QRNG with post-quantum cryptography (PQC). Our solution demonstrates that leveraging QRNG-generated randomness alongside PQC significantly improves security against emerging quantum threats, establishing a foundation for enhanced mobile data protection.}, archive = {J_ACCESS}, author = {Omar Alibrahim}, doi = {10.1109/ACCESS.2025.3563826}, journal = {IEEE Access}, pages = {73202-73218}, shortjournal = {IEEE Access}, title = {Unveiling samsung quantum galaxy: Securing smartphones with quantum and post-quantum cryptography}, volume = {13}, year = {2025}, }
• (2025). Design and test of an embedded real-time compact voltage fingerprinting algorithm for enhanced automotive cybersecurity. ACCESS, 13, 73183-73201. (www)

  @article{ , abstract = {The article proposes an algorithm for monitoring data traffic in CAN networks on board vehicles. This algorithm detects cyber-attacks through statistical analysis of voltage samples from the protocol’s physical layer. The method aims to be compact and achieve real-time throughput for implementation on an embedded platform. In the article are shown tests of the proposed method on the Automotive MCU AURIX TC375. The Electronic Control Unit (ECU) classification algorithm results from the re-elaboration of the K-Nearest Neighbor (KNN) method. Using.dbc and recorded.asc traces acquired from a real vehicle (Giulietta Alfa Romeo model), the message traffic is reconstructed and replicated via experimental prototype, providing different voltage levels for each device used to emulate the ECU. The article evaluates the algorithm performance through extensive experiments, assessing its ability to detect traffic anomalies in various attack scenarios.}, archive = {J_ACCESS}, author = {Pierpaolo Dini and Ettore Soldaini and Sergio Saponara}, doi = {10.1109/ACCESS.2025.3561628}, journal = {IEEE Access}, pages = {73183-73201}, shortjournal = {IEEE Access}, title = {Design and test of an embedded real-time compact voltage fingerprinting algorithm for enhanced automotive cybersecurity}, volume = {13}, year = {2025}, }
• (2025). FProcessor: NonIntrusive and on-the-fly file data preprocessing using eBPF. ACCESS, 13, 73173-73182. (www)

  @article{ , abstract = {This paper introduces fProcessor, a tool designed for nonintrusive, on-the-fly preprocessing of data being written to files. “Nonintrusive” means that fProcessor requires no modifications to existing code, allowing applications to remain unaware of the monitoring activities. “On-the-fly” indicates that fProcessor dynamically executes preprocessing operations on file contents during the write process. It supports three common preprocessing operations: tracking, detection, and reduction. These operations are applicable in scenarios such as capturing log file content, filtering data being written, and reducing file content to conserve storage space. Traditionally, these tasks necessitate specific application-layer code. In contrast, fProcessor intercepts file content at the Linux kernel level, simplifying the development of application-layer code. The advantage of fProcessor lies in its use of eBPF technology to implement file preprocessing, demonstrating low runtime overhead and enabling efficient data processing. fProcessor is free and open source software (https://github.com/Prometheus-first/fProcessor)}, archive = {J_ACCESS}, author = {Peng Wang and Aonan Zhang and Haozhe Lang and Xiaofan Xun and Shichen Zhang and Lijuan Diao}, doi = {10.1109/ACCESS.2025.3563259}, journal = {IEEE Access}, pages = {73173-73182}, shortjournal = {IEEE Access}, title = {FProcessor: NonIntrusive and on-the-fly file data preprocessing using eBPF}, volume = {13}, year = {2025}, }
• (2025). Hybrid islanding detection of an inverter-based high-noise microgrid. ACCESS, 13, 73161-73172. (www)

  @article{ , abstract = {Unintentional islanding occurs when a microgrid continues operating independently after disconnection from the main grid, which can lead to voltage and frequency instability, power quality degradation, and safety risks. Few local and remote methods consider islanding detection in noisy environments. Noise can interfere with measurements and cause failed or delayed islanding detection. This paper proposes a hybrid islanding detection method that utilizes a Van der Pol Duffing oscillator (VDPDO), focusing on decreasing the detection period, zero power mismatch nondetection zone, and power quality degradation. The proposed method uses the fast Fourier transform (FFT) symmetry characteristic of a hybrid VDPDO (HVDPDO) output signal and addresses the set point threshold using the Melnikov function. This method detects the changes in frequency, phase, and voltage amplitude of the point of common coupling (PCC) simultaneously to distinguish islanded operation from other faults. For the asymmetric FFT waveform of the passive HVDPDO, the islanding system sends a signal to the inverter to change the mode from grid-following to grid-forming and injects perturbations into the frequency, phase, and voltage amplitude, simultaneously. The active HVDPDO detects islanded operations with a small non-detection zone. The inverter reverts to grid-following mode for other faults, and the main grid damps the intentionally small perturbations. Simulation and experimental results validate that the proposed method meets the IEEE standard 1547 criteria.}, archive = {J_ACCESS}, author = {Hossein Amini and Ali Mehrizi-Sani and Reza Noroozian}, doi = {10.1109/ACCESS.2025.3563679}, journal = {IEEE Access}, pages = {73161-73172}, shortjournal = {IEEE Access}, title = {Hybrid islanding detection of an inverter-based high-noise microgrid}, volume = {13}, year = {2025}, }
• (2025). Control optimization of steam boilers via reinforcement learning. ACCESS, 13, 73146-73160. (www)

  @article{ , abstract = {Steam boilers present complex control challenges due to their nonlinear dynamics and fluctuating load conditions. This study proposes a novel hybrid adaptive control framework that significantly enhances system performance by synergizing off-policy deep reinforcement learning, model reference adaptive control (MRAC), and optimized PID control through a weighted fusion strategy. Three innovative hybrid controllers, Weighted-Fusion-DDPG-MRAC-PID, Weighted-Fusion-TD3-MRAC-PID, and Weighted-Fusion-SAC-MRAC-PID, are developed and rigorously benchmarked against standalone MRAC and optimized PID for precise steam flow and drum pressure regulation in a two-state boiler system under four diverse operational conditions. The adaptive fusion mechanism dynamically modulates control actions (feedwater flow and applied heat) in response to system variations, ensuring superior stability. Furthermore, a novel nonlinear settling time algorithm is introduced to accurately assess transient responses in complex signals, addressing the limitations of conventional methods. Extensive experimental validation confirms the superiority of hybrid controllers, achieving significantly faster settling times, peak overshoot reductions to $(\leq 2\%)$ , and lower error metrics than MRAC alone. While optimized PID serves as a baseline, hybrid controllers consistently achieve faster settling times and improved robustness under most nonlinear operating conditions. Weighted Fusion-DDPG-MRAC-PID offers the best trade-off between performance and computational efficiency. These findings underscore the practical flexibility of the proposed framework for advanced steam boiler control.}, archive = {J_ACCESS}, author = {Emmanuel Okafor and Maad Alowaifeer}, doi = {10.1109/ACCESS.2025.3563590}, journal = {IEEE Access}, pages = {73146-73160}, shortjournal = {IEEE Access}, title = {Control optimization of steam boilers via reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Molten pool image segmentation based on adaptive multi-scale attention mechanism. ACCESS, 13, 73136-73145. (www)

  @article{ , abstract = {This study presents an innovative segmentation algorithm that seamlessly integrates an adaptive multi-scale attention mechanism with the Lossab loss function to tackle challenges like overlapping molten pool contours and arc interference during image segmentation. Initially, an adaptive multi-scale channel attention mechanism is introduced and fused with a feature pyramid, forming a multi-scale attention pyramid structure that significantly enhances the network’s capability to extract intricate molten pool features. Subsequently, a tailored Lossab loss function is devised to boost the network’s precision in detecting the edges of the molten pool. Experimental outcomes reveal that the proposed method attains a remarkable segmentation accuracy of 96.77% within the molten pool region on the TIG molten pool image dataset. The AMSNet algorithm outperforms PIDNet by 2.51% and SegNet by 4.66% in terms of molten pool segmentation accuracy, providing solid evidence of its effectiveness in tackling the task of molten pool image segmentation. The model performs well in computational efficiency, data processing and complexity balance, making it suitable for industrial field deployment.}, archive = {J_ACCESS}, author = {Yuefeng Chen and Weihua Liu and Qing Yang and Huabin Chen and Qi Jiang}, doi = {10.1109/ACCESS.2025.3560702}, journal = {IEEE Access}, pages = {73136-73145}, shortjournal = {IEEE Access}, title = {Molten pool image segmentation based on adaptive multi-scale attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Day-ahead coordination for flexibility enhancement in hydrogen-based energy hubs in presence of EVs, storage, and integrated demand response”. ACCESS, 13, 73135. (www)

  @article{ , abstract = {In the above article [1], reference 24 was retracted. As the work in this reference is no longer reliable, we are removing it from the reference list and replacing it with [2].}, archive = {J_ACCESS}, author = {Mohammad Mehdi Amiri and Mohammad Taghi Ameli and Mohammad Reza Aghamohammadi and Erfan Bashooki and Hossein Ameli and Goran Strbac}, doi = {10.1109/ACCESS.2025.3563911}, journal = {IEEE Access}, pages = {73135}, shortjournal = {IEEE Access}, title = {Corrections to “Day-ahead coordination for flexibility enhancement in hydrogen-based energy hubs in presence of EVs, storage, and integrated demand response”}, volume = {13}, year = {2025}, }
• (2025). Handwritten W-net for high-frequency guided single-image super-resolution. ACCESS, 13, 73124-73134. (www)

  @article{ , abstract = {Super-resolution (SR) imaging involves converting low-resolution images into corresponding high-resolution data in specific scenarios. Recent advances in intelligent image reconstruction algorithms have been accompanied by impressive visual outcomes in SR generative adversarial networks (GANs). However, existing research has methodological limitations owing to the constraints posed by the model architecture and loss guidance used for optimization. Consequently, unnatural artifacts often appear in the complex backgrounds of images, leading to unappealing visual results. To address these problems, we propose an enhanced U-Net, called the handwritten W-Net (HW-Net) architecture, as well as a loss function that uses high-frequency components for guidance. The former improves the best approximate representation of image regions, whereas the latter guides the generation of SR images with more detailed features. We experimentally implemented a biased strategy called parameter transfer, which is suitable for hybrid parameter models. This approach facilitates the blending of weight parameters between similar but different models, significantly improving the peak signal-to-noise ratio and structural similarity index measure metrics without sacrificing the learned perceptual image patch similarity. Across nine SR benchmark datasets, the proposed combinations, with and without parameter transfer, demonstrated excellent performance across multiple evaluation metrics, surpassing the state-of-the-art SRGAN. The code is available at https://github.com/DaHuaSen/H2SR.}, archive = {J_ACCESS}, author = {Xiang Yang and Chen Fang and Xiaolan Xie and Minggang Dong}, doi = {10.1109/ACCESS.2025.3564515}, journal = {IEEE Access}, pages = {73124-73134}, shortjournal = {IEEE Access}, title = {Handwritten W-net for high-frequency guided single-image super-resolution}, volume = {13}, year = {2025}, }
• (2025). Nonlinear programming-based robot motion planning for automatic gait measurement of a passing pedestrian in corridors. ACCESS, 13, 73109-73123. (www)

  @article{ , abstract = {Patrol robots in hospital wards are expected not only to ensure the safety of their movements, but also to perform gait measurement as part of their monitoring functions. In this study, we present a system that enables a patrol robot to perform a 5-meter walk test (5mWT) on passing pedestrians in a corridor. The pedestrian’s position and velocity are measured sequentially, and the robot’s trajectory is generated based on the results using a nonlinear programming method, followed by Model Predictive Control tracking. This allows for adaptive gait measurement during passing interactions, taking into account arbitrary pedestrian trajectories. Furthermore, a parameter estimation method tailored to the characteristics of the acquired data, namely whole-body, continuous and multi-view measurements, is proposed. Through simulation and experiments, we confirmed the feasibility of the measurement operation and accuracy of the derived gait parameters. The proposed method can not only be used anywhere, but also measure gait over a long distance with high accuracy using a single sensor. Moreover, it could contribute to the realization of gait measurement without pedestrian’s tension.}, archive = {J_ACCESS}, author = {Kazuyuki Hayashide and Mitsuhiro Takahashi and Masaki Takahashi}, doi = {10.1109/ACCESS.2025.3563294}, journal = {IEEE Access}, pages = {73109-73123}, shortjournal = {IEEE Access}, title = {Nonlinear programming-based robot motion planning for automatic gait measurement of a passing pedestrian in corridors}, volume = {13}, year = {2025}, }
• (2025). Inductive construction of variational quantum circuit for constrained combinatorial optimization. ACCESS, 13, 73096-73108. (www)

  @article{ , abstract = {In this study, we propose a new method for constrained combinatorial optimization using variational quantum circuits. Quantum computers are considered to have the potential to solve large combinatorial optimization problems faster than classical computers. Variational quantum algorithms, such as Variational Quantum Eigensolver (VQE), have been studied extensively because they are expected to work on noisy intermediate scale devices. Unfortunately, many optimization problems have constraints, which induces infeasible solutions during VQE process. Recently, several methods for efficiently solving constrained combinatorial optimization problems have been proposed by designing a quantum circuit so as to output only the states that satisfy the constraints. However, the types of available constraints are still limited. Therefore, we have started to develop variational quantum circuits that can handle a wider range of constraints. The proposed method utilizes a forwarding operation that maps from feasible states for subproblems to those for larger subproblems. As long as appropriate forwarding operations can be defined, iteration of this process can inductively construct variational circuits outputting feasible states even in the case of multiple and complex constraints. In this paper, the proposed method was applied to facility location problem. As a result, feasible solutions were obtained with 100%, and the probability of obtaining optimal solutions was over 22 times higher than that of conventional VQEs. Nevertheless, the cost of the obtained circuit was comparable to that of conventional circuits.}, archive = {J_ACCESS}, author = {Hyakka Nakada and Kotaro Tanahashi and Shu Tanaka}, doi = {10.1109/ACCESS.2025.3563960}, journal = {IEEE Access}, pages = {73096-73108}, shortjournal = {IEEE Access}, title = {Inductive construction of variational quantum circuit for constrained combinatorial optimization}, volume = {13}, year = {2025}, }
• (2025). Real estate attribute value extraction using large language models. ACCESS, 13, 73076-73095. (www)

  @article{ , abstract = {Attribute value extraction (AVE) is critical in transforming unstructured text into structured data for various applications. While existing datasets for AVE predominantly focus on e-commerce and English language data, there is a lack of publicly available datasets tailored to other domains. This paper introduces the Real Estate Attribute Value Extraction (RAVE) dataset, specifically designed for extracting structured attributes from unstructured real estate advertisements. The RAVE dataset consists of manually annotated Slovak real estate listings, which have been translated into English for broader applicability. The paper evaluates the performance of multiple publicly available large language models in solving the AVE task on RAVE. Through extensive experimentation, we analyse the impact of additional attribute descriptions, selecting relevant sentences, and using ground-truth-based attribute definition in structured output generation. The findings indicate that providing a schema with only relevant attributes (Oracle Attributes) significantly enhances performance and reduces computational overhead while improving the F1 score. Under basic conditions without modifications at the input, the largest model tested, Qwen2.5:32b, achieved a micro F1 score of 10.04%. Applying all tested input modifications (Oracle Attributes, Oracle Sentences, and Additional Descriptions) allowed the largest model tested to achieve a micro F1 score of 97.92%, demonstrating the effectiveness of these techniques in improving extraction accuracy and efficiency. The RAVE dataset is publicly available, facilitating further research in AVE and real estate information extraction.}, archive = {J_ACCESS}, author = {Michal Kvet and Miroslav Potočár and Slavomír Tatarka}, doi = {10.1109/ACCESS.2025.3564511}, journal = {IEEE Access}, pages = {73076-73095}, shortjournal = {IEEE Access}, title = {Real estate attribute value extraction using large language models}, volume = {13}, year = {2025}, }
• (2025). Chip implementation of two new VCII-based Voltage/Transimpedance-mode KHN-equivalent biquads. ACCESS, 13, 73056-73075. (www)

  @article{ , abstract = {This paper presents two new VCII-based Kerwin-Huelsman-Newcomb (KHN) equivalent biquad circuits, each comprising three second-generation voltage conveyors (VCIIs), two grounded capacitors, and five resistors. Either voltage mode (VM) or trans-impedance mode (TIM) can operate in each proposed circuit configuration. Transfer function analysis using a VM inverting bandpass (IBP) filter yields two additional non-inverting/inverting KHN biquad transfer functions for the two proposed VM/TIM KHN-equivalent biquads. The first proposed VM/TIM KHN-equivalent biquad can simultaneously implement an IBP filter, a non-inverting low-pass (NLP) filter, and an inverting high-pass (IHP) filter. In contrast, the second proposed VM/TIM KHN-equivalent biquad can simultaneously implement an IBP filter, an inverting low-pass (ILP) filter, and a non-inverting high-pass (NHP) filter. Each proposed VM/TIM KHN-equivalent biquad features three low-impedance voltage outputs in the designed circuit, eliminating the need for additional voltage buffers (VBs) in the circuit measurements. The two proposed KHN-equivalent biquads are integrated into a single chip, occupying a total area of 1.44 mm2. This technology uses the TSMC $0.18~\mu $ m 1P6M CMOS process, with the chip operating at a supply voltage of ±0.9 V. The measured power dissipation of the first KHN-equivalent biquad is 2.7 mW, while the measured power dissipation of the second one is 3.24 mW. The measured spurious-free dynamic range (SFDR) of the first KHN-equivalent biquad is 41.18 dBc, while the measured SFDR of the second one is 40.94 dBc. With an input voltage of 1.2 Vpp, the measured total harmonic distortion (THD) values for both KHN-equivalent biquads are below 1 %. The proposed two KHN-equivalent biquads have the advantages of high density, system integration, efficiency, low cost, low power consumption, and effective utilization of chip layout area. Simulations and on-chip measurements are carried out for both KHN-equivalent biquads to validate the theoretical design and demonstrate their on-chip feasibility.}, archive = {J_ACCESS}, author = {Hua-Pin Chen and San-Fu Wang and Ming-Jin-Yu and Liang-Yen Chen and Yu-Hsi Chen}, doi = {10.1109/ACCESS.2025.3564555}, journal = {IEEE Access}, pages = {73056-73075}, shortjournal = {IEEE Access}, title = {Chip implementation of two new VCII-based Voltage/Transimpedance-mode KHN-equivalent biquads}, volume = {13}, year = {2025}, }
• (2025). Blockchain-based crowdsourcing framework for machine learning ground truth. ACCESS, 13, 73041-73055. (www)

  @article{ , abstract = {Machine learning has evolved from a lab curiosity to a widely used technology that is fundamentally reliant on ground truth data for model training and evaluation. This research addresses the challenges in obtaining accurate ground truth data due to limited domain expertise, sparse and unrepresentative datasets, and the high costs associated with data acquisition. The quality of this data significantly influences the reliability of machine learning models, prompting research into methods to improve ground-truth reliability. This research proposes a framework that utilize blockchain-based crowdsourcing for ground-truth data annotation. Blockchain technology, with its decentralized immutable ledger system, offers a secure method for data verification and collection from decentralized entities. The proposed framework was implemented in an Ethereum network environment using blockchain technology and smart contracts. Next, we evaluated the collected ground truth by measuring the inter-rater agreement among the participants. The experimental results indicate that blockchain can enhance annotation consistency, showing a higher reliability of crowd-sourced data compared to expert opinions. Most annotator pairs demonstrated moderate to strong agreement, confirming the potential of blockchain technology in improving ground truth data annotation.}, archive = {J_ACCESS}, author = {Asma S. Alzahrani and Dimah H. Alahmadi and Nesreen M. Alharbi and Hana A. Almagrabi}, doi = {10.1109/ACCESS.2025.3564534}, journal = {IEEE Access}, pages = {73041-73055}, shortjournal = {IEEE Access}, title = {Blockchain-based crowdsourcing framework for machine learning ground truth}, volume = {13}, year = {2025}, }
• (2025). Fire and smoke detection based on improved YOLOV11. ACCESS, 13, 73022-73040. (www)

  @article{ , abstract = {Fire and smoke detection is an important measure to ensure the safety of people’s lives and property, as well as a crucial link in maintaining ecological balance and supporting scientific research. Traditional object detection methods rely more on manually designed features and rules. Although they are relatively simple to implement, their performance is limited in complex and variable practical applications. In contrast, deep learning-based methods can automatically learn deep features in data and have higher accuracy and stronger generalization ability. However, complex backgrounds, large environmental changes, and data requirements pose great challenges to high-precision outdoor smoke detection. To address these issues, this paper proposes an improved model, YOLOV11-DH3, based on YOLOV11. In this paper, the core DCN2 (Deformable Convolutional Networks2) of the YOLOV11 Head is replaced with the DCN3 module to form a new detection head. In addition, the loss function CIOU in YOLOV11 is replaced with IOU to consider the irregular shape of fire and smoke and the problem of multi-scale targets. To evaluate the performance of the algorithm, comprehensive experiments were conducted on two distinct datasets: a public fire and smoke dataset provided by Baidu Paddle featuring close-range views and a public wildfire smoke dataset from the YOLO official website with distant outdoor perspectives. The experimental results show that on the Baidu Paddle dataset, the average accuracy of the model is improved by 1.4% compared to the original model, reaching 58%, the F1 score is improved by 2%, reaching 58%, with a precision of 91.6% and recall of 90%. Our cross-dataset analysis provides valuable insights into model performance across different detection scenarios. The proposed model demonstrates the ability to accurately detect fire and smoke in complex backgrounds, and this progress is of great significance for protecting people’s lives and maintaining ecological balance.}, archive = {J_ACCESS}, author = {Zhipeng Xue and Lingyun Kong and Haiyang Wu and Jiale Chen}, doi = {10.1109/ACCESS.2025.3564434}, journal = {IEEE Access}, pages = {73022-73040}, shortjournal = {IEEE Access}, title = {Fire and smoke detection based on improved YOLOV11}, volume = {13}, year = {2025}, }
• (2025). Adaptive input sampling: A novel approach for efficient object detection in high resolution traffic monitoring images. ACCESS, 13, 73003-73021. (www)

  @article{ , abstract = {Real-time traffic monitoring based on artificial intelligence (AI) models is essential for efficient transportation management, but it faces significant computational challenges, particularly when processing high-resolution video footage. Modern traffic monitoring systems typically rely on AI-based object detection and tracking models, which require processing every frame of the video to detect and track vehicles. However, even the most efficient object detection models are computationally expensive, making real-time processing of high-resolution videos impractical. To address this challenge, we introduce the Adaptive Input Sampler (AIS), a preprocessing module designed to selectively activate the object detector only when vehicles are present in the scene. The AIS dynamically targets specific areas of the frame where vehicles are detected, sampling these regions for downstream processing. This approach significantly reduces the computational burden on the object detector while preserving the original pixel details, enabling substantial computational savings—especially in scenarios with few or no vehicles in the monitored scene. Our experimental results demonstrate that the AIS achieves 96.40% traffic counting accuracy and 95.60% vehicle classification accuracy, while reducing the original input data size by up to 79.00%. By optimizing the volume of input data, the AIS improves computational efficiency and accelerates inference results, making it a promising solution for real-time traffic monitoring applications. The AIS represents a significant step forward in addressing the computational challenges of high-resolution video processing, offering a balance between accuracy, efficiency, and scalability for modern transportation systems.}, archive = {J_ACCESS}, author = {Tewodros Syum Gebre and Eden Tsehaye Wasehun and Freda Elikem Dorbu and Gazali Oluwasegun Agboola and Ali Karimoddini and Leila Hashemi-Beni}, doi = {10.1109/ACCESS.2025.3560880}, journal = {IEEE Access}, pages = {73003-73021}, shortjournal = {IEEE Access}, title = {Adaptive input sampling: A novel approach for efficient object detection in high resolution traffic monitoring images}, volume = {13}, year = {2025}, }
• (2025). Dynamic pricing models in E-commerce: Exploring machine learning techniques to balance profitability and customer satisfaction. ACCESS, 13, 72994-73002. (www)

  @article{ , abstract = {This research investigates the application of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, paired with gradient-based optimization techniques for dynamic pricing in e-commerce. The primary objective is to develop a pricing model that effectively balances profitability with customer satisfaction by leveraging sequential data, such as time-series and customer behavior patterns. The approach utilizes LSTM’s ability to capture long-term dependencies in sequential data, while optimization methods like Stochastic Gradient Descent (SGD) enhance model convergence and performance. Key findings include the superior predictive accuracy of LSTM-based models over traditional approaches like Linear Regression and Decision Trees, particularly in real-time data updates and price elasticity scenarios. Additionally, the analysis revealed that LSTM models could efficiently adapt pricing strategies in response to market dynamics, significantly improving profitability while maintaining customer satisfaction. This study provides valuable insights into the application of advanced machine learning techniques in e-commerce pricing. The results suggest that LSTM-based dynamic pricing models could optimize revenue generation, offering substantial implications for pricing strategy development in modern retail environments. Future work may explore hybrid models and multi-objective optimization techniques to further refine these models.}, archive = {J_ACCESS}, author = {Xiaochen Guo and Lei Zhang}, doi = {10.1109/ACCESS.2025.3563371}, journal = {IEEE Access}, pages = {72994-73002}, shortjournal = {IEEE Access}, title = {Dynamic pricing models in E-commerce: Exploring machine learning techniques to balance profitability and customer satisfaction}, volume = {13}, year = {2025}, }
• (2025). Reconfiguring role-based access control via role clustering. ACCESS, 13, 72984-72993. (www)

  @article{ , abstract = {Role-based access control (RBAC) systems have become a widely used and accepted method by many organizations today due to their efficiency and ease of management. However, to truly benefit from RBAC systems, the roles in the relevant system must be well defined. Bottom-up, top-down, and hybrid approaches exist to accurately define the roles to be included in access control systems. Even if RBAC systems are designed carefully, the role privilege assignments tend to wear out and become chaotic after some time. This study demonstrates a role mining approach to reconfiguring such existing client specific RBAC systems by identifying clusters of similar roles using the assigned privileges. In this study, masked RBAC data of 10 clients of a software company are used. The approach creates role clusters via Agglomerative Hierarchical Clustering, an unsupervised clustering algorithm that works in a bottom-up technique. The experiment results show that it is better using the resulting candidate clusters as a reference in reconfiguring the RBAC systems, instead of using them to replace the existing roles. Additionally, it has been observed that in some client tables with a limited number of unique roles, the pairwise F1 score was remarkably high, indicating a strong correspondence between the candidate and expected roles.}, archive = {J_ACCESS}, author = {Osman Durdag and Ahmet Coskuncay}, doi = {10.1109/ACCESS.2025.3563057}, journal = {IEEE Access}, pages = {72984-72993}, shortjournal = {IEEE Access}, title = {Reconfiguring role-based access control via role clustering}, volume = {13}, year = {2025}, }
• (2025). A survey of data stream-based intrusion detection systems. ACCESS, 13, 72953-72983. (www)

  @article{ , abstract = {Detecting malicious activities in network environments poses a challenge that attracts significant attention due to its complexity and importance. Advances in the field have led to the development of several algorithms that approach the problem under the view of a data stream machine learning task. This task involves a set of steps: data collection or choice of public datasets, data pre-processing, data reduction, development or application of data mining techniques, and evaluation methodology. However, these steps must address the inherent issues of dynamic environments such as data streams and intrusion detection systems. These issues include, but are not limited to, the continuous influx of data, changes in both normal and attack class distributions, the emergence of new attack types, and the scarcity of labeled data examples to update the decision models. This survey provides an overview of intrusion detection systems (IDS) using data stream machine learning techniques, characterizing the literature approaches according to the classic steps of the data mining task. In addition, we discuss recommendations for practical IDS development and highlight datasets and tools that can aid in detecting malicious behavior. Finally, we outline potential avenues for future research and open questions in the field.}, archive = {J_ACCESS}, author = {Rodrigo Sanches Miani and Gustavo Di Giovanni Bernardo and Guilherme Weigert Cassales and Hermes Senger and Elaine Ribeiro de Faria}, doi = {10.1109/ACCESS.2025.3561105}, journal = {IEEE Access}, pages = {72953-72983}, shortjournal = {IEEE Access}, title = {A survey of data stream-based intrusion detection systems}, volume = {13}, year = {2025}, }
• (2025). Wake homing torpedo guidance using a hierarchical deep reinforcement learning framework. ACCESS, 13, 72938-72952. (www)

  @article{ , abstract = {This paper proposes a novel Hierarchical Deep Reinforcement Learning (HRL) framework for wake homing torpedo guidance, applying the Discrete Event System Specification (DEVS) formalism to design high-level policies and reward shaping functions. Wake homing torpedo guidance generates course commands to enable the torpedo to follow the wake trajectory of a target ship. When the target ship evades the incoming torpedo, the wake trajectory becomes curved, often causing the torpedo to lose track due to the narrow detection range of the wake detection sensor. This necessitates a sophisticated algorithm to consistently track the target ship, particularly in scenarios where the torpedo exits and re-enters the wake trajectory in noisy environments. While heuristic algorithms can handle typical wake trajectories, developing a robust solution for unknown trajectories remains a significant challenge. To address this, we apply a novel reinforcement learning approach to develop the guidance logic and compare its performance with a conventional algorithm-based method. The performance and effectiveness of the proposed approach are demonstrated through numerical simulations.}, archive = {J_ACCESS}, author = {Kunchul Hwang and Jinwhan Kim}, doi = {10.1109/ACCESS.2025.3563906}, journal = {IEEE Access}, pages = {72938-72952}, shortjournal = {IEEE Access}, title = {Wake homing torpedo guidance using a hierarchical deep reinforcement learning framework}, volume = {13}, year = {2025}, }
• (2025). A DFT-based scheme for PAPR reduction in SR-OFDM systems. ACCESS, 13, 72930-72937. (www)

  @article{ , abstract = {This paper focuses on the peak-to-average power ratio (PAPR) reduction in symbol repetition-based orthogonal frequency division multiplexing (SR-OFDM) systems, and a discrete Fourier transform (DFT)-based scheme is proposed to reduce the PAPR of SR-OFDM signals. It is demonstrated that an SR-OFDM system exhibits a larger PAPR than a classical orthogonal frequency division multiplexing (OFDM) system because of the signal superposition of multiple branches, although the cyclic prefix (CP) overhead can be reduced in SR-OFDM systems. Besides, it is shown that the high PAPR is mainly because of the inverse DFT operation at the SR-OFDM transmitter. Therefore, a DFT-based scheme is proposed in this paper to compensate for the inverse DFT operation, without requiring side information or causing signal distortion. To verify the proposed scheme, a theoretical analysis is provided for the signal characteristics, and simulation results of the complementary cumulative distribution function are also given, together with the bit error ratio.}, archive = {J_ACCESS}, author = {Haojia Lv}, doi = {10.1109/ACCESS.2025.3564160}, journal = {IEEE Access}, pages = {72930-72937}, shortjournal = {IEEE Access}, title = {A DFT-based scheme for PAPR reduction in SR-OFDM systems}, volume = {13}, year = {2025}, }
• (2025). Leveraging BERT, DistilBERT, and TinyBERT for rumor detection. ACCESS, 13, 72918-72929. (www)

  @article{ , abstract = {The rapid spread of false information on social media has become a major challenge in today’s digital world. This has created a need for an effective rumor detection system that can identify and control the spread of false information in real-time. The proposed work introduces a rumor detection system by integrating transformer-based models such as BERT, DistilBERT, and TinyBERT with traditional Machine Learning (ML) techniques. The classifiers include Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB) help in categorizing content as either rumor or non-rumor based on the patterns. The proposed work evaluated BERT, DistilBERT, TinyBERT combined with ML models (SVM, DT, RF, NB) across PHEME dataset using 70:30, 60:40, and 80:20 splits. Overall, BERT + DT and TinyBERT + SVM provided significant results, with BERT + RF and DistilBERT + NB demonstrating better classification capabilities across various events and split ratios on the dataset.}, archive = {J_ACCESS}, author = {Aijazahamed Qazi and R. H. Goudar and Rudragoud Patil and Geetabai S. Hukkeri and Dhanashree Kulkarni}, doi = {10.1109/ACCESS.2025.3563301}, journal = {IEEE Access}, pages = {72918-72929}, shortjournal = {IEEE Access}, title = {Leveraging BERT, DistilBERT, and TinyBERT for rumor detection}, volume = {13}, year = {2025}, }
• (2025). Wav2Lip bridges communication gap: Automating lip sync and language translation for indian languages. ACCESS, 13, 72906-72917. (www)

  @article{ , abstract = {Translating spoken speech in videos from one language to another is known as audio-visual translation (AVT). This paper describes the implementation of an automated AVT and lip-synced dubbing application. It addresses the difficulty of synchronizing mouth movements with translated speech by building a web application that synthesizes the speaker’s lip movements to match translated audio. Using ASR models, the speech from the source video is converted to text, translated into several languages, and then automatically synthesized into speech in the target language. A lip synchronization model, Wav2Lip, is used to alter the mouth movements in the video to correspond to the target language. We compare our work with two well-known ASR systems: Wav2vec 2.0 and Google Speech Recognition. Wav2vec 2.0 performs better with the lesser average WER% of 15.38 and is used in our final web application. The performance of the video dubbing component is discussed with the generated speech in Tamil, Telugu, Hindi, and English, and we determine that our generated videos outperform the existing ones. Our proposed AVT application is user-friendly for a wide variety of speakers, utilizing readily available TTS systems instead of training on an individual speaker’s voice.}, archive = {J_ACCESS}, author = {Vaishnavi Venkataraghavan and Shoba Sivapatham and Asutosh Kar}, doi = {10.1109/ACCESS.2025.3562883}, journal = {IEEE Access}, pages = {72906-72917}, shortjournal = {IEEE Access}, title = {Wav2Lip bridges communication gap: Automating lip sync and language translation for indian languages}, volume = {13}, year = {2025}, }
• (2025). Ontologies for smart agriculture: A path toward explainable AI—A systematic literature review. ACCESS, 13, 72883-72905. (www)

  @article{ , abstract = {Smart agriculture has grown significantly over the last few years, particularly with the integration of advanced technologies (e.g., the Internet of Things, robots, artificial intelligence, etc.), leading to the development of intelligent agricultural systems. However, these systems often lack data integration, interoperability, and semantic explainability. Various approaches have been proposed to address these challenges. This study provides a comprehensive literature review that addresses, on the one hand, the use of semantic resources (e.g., semantic web technologies and ontologies) to tackle data integration and interoperability in smart agriculture systems and, on the other hand, the integration of explainable artificial intelligence into smart agricultural systems. Furthermore, it aims to identify limitations in existing studies and explore potential avenues for future research. This research introduces key concepts related to smart agriculture, semantic resources, and explainable artificial intelligence. Subsequently, three clusters of studies are presented, including semantic resources for smart agriculture, leveraging the explainable artificial intelligence for smart agriculture, and the role of semantic resources in the explainable artificial intelligence-based agriculture systems. Lastly, the limitations of the semantic-based smart agriculture system are examined, along with potential future research areas.}, archive = {J_ACCESS}, author = {Rima Grati and Najla Fattouch and Khouloud Boukadi}, doi = {10.1109/ACCESS.2025.3563202}, journal = {IEEE Access}, pages = {72883-72905}, shortjournal = {IEEE Access}, title = {Ontologies for smart agriculture: A path toward explainable AI—A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). NSGT: A plasma position prediction method for the HL-2A tokamak device based on transformer. ACCESS, 13, 72875-72882. (www)

  @article{ , abstract = {In the discharge experiments of the HL-2A Tokamak device, plasma position prediction plays a crucial role in aspects such as the closed - loop control of HL-2A and verifying the correctness of the plasma controller design. In previous studies, methods based on traditional equations and LSTM have made significant progress in plasma position prediction. However, plasma position prediction is of extremely high complexity, and these methods have difficulty in achieving further improvement on this issue. To address the problem that the performance of traditional methods is hard to enhance further, we propose a Non - Stationary - Gated Linear Unit Transformer (NSGT) model integrating multi - source data processing. In this model, we select data from historical discharge experiments as the input of the NSGT model, and use the improved Transformer structure to model the global features of the multi - source fused data. The experimental results show that the NSGT model has a significant reduction in the root - mean - square error of prediction compared with the LSTM method, effectively supporting the real - time adjustment of the closed - loop control system. This method not only improves the prediction accuracy but also provides important technical support for achieving more stable and safe operation of the Tokamak device.}, archive = {J_ACCESS}, author = {Ling Zhou and Bin Yang and Xiangcheng Lin and Kaihao Zhu and Ziran Lan and Xiangwen Li}, doi = {10.1109/ACCESS.2025.3557039}, journal = {IEEE Access}, pages = {72875-72882}, shortjournal = {IEEE Access}, title = {NSGT: A plasma position prediction method for the HL-2A tokamak device based on transformer}, volume = {13}, year = {2025}, }
• (2025). EG-VAN: A global and local attention-based dual-branch ensemble network with advanced color balancing for multi-class skin cancer recognition. ACCESS, 13, 72852-72874. (www)

  @article{ , abstract = {The accurate diagnosis of skin cancer remains challenging due to factors like intra-class variation and inter-class similarity in dermoscopic images. Despite their sophistication, existing methods struggle to generalize well across diverse cases, and their computational complexity often limits real-world applicability. To address these challenges, we introduce a novel dual-path architecture in the Efficient Global-Vision Attention-Net (EG-VAN) for early detection of skin cancer. The dual-path design uniquely combines (1) EfficientNetV2S, which processes multi-scale features with high efficiency, and (2) a modified ResNet50 enhanced with a Spatial-Context Group Attention (SCGA) module and Non-Local Block. The SCGA module captures local-contextual relationships in horizontal and vertical directions to focus on subtle lesion details, directly addressing intra-class variation, while the Non-Local Block captures long-range dependencies to resolve inter-class similarity. This synergistic dual-path fusion ensures comprehensive feature extraction, enabling EG-VAN to outperform single-path architectures. Further, we introduce a Multi-Scale Feature Fusion (MFF) module to aggregate hidden features across scales, improving generalization across skin cancer types. Additionally, our pioneering color balancing method, inspired by Gray World and Retinex theory, recalibrates color distribution to highlight lesion regions, enhancing the dual-path network’s input quality. We validate our EG-VAN through extensive experiments on the 9-class skin cancer dataset. Our experiments show improvement in performance by achieving an accuracy of 98.20%, a recall of 95.74%, and an F1-Score of 96.68%.}, archive = {J_ACCESS}, author = {Adnan Saeed and Khurram Shehzad and Saim Ahmed and Ahmad Taher Azar}, doi = {10.1109/ACCESS.2025.3561240}, journal = {IEEE Access}, pages = {72852-72874}, shortjournal = {IEEE Access}, title = {EG-VAN: A global and local attention-based dual-branch ensemble network with advanced color balancing for multi-class skin cancer recognition}, volume = {13}, year = {2025}, }
• (2025). H∞ time-varying group formation-containment tracking control of multi-agent system with multiple leaders. ACCESS, 13, 72841-72851. (www)

  @article{ , abstract = {The $H_{\infty } $ time-varying group formation-containment tracking (TVGFCT) problem of multi-agent system (MAS) with multiple leaders is investigated in this paper. The MAS is assumed to be composed of multiple tracking leaders, formation leaders and followers and all the agents are divided into different groups. Firstly, based on the relative information, the distributed observers for the formation leaders are designed to estimate the convex combination of tracking leaders in the same group. Then the observer-based $H_{\infty } $ time-varying formation tracking protocols are proposed to make the formation leaders realize the desired time-varying formation while tracking the convex combination of tracking leaders, and an algorithm to construct the protocols is given. Then the containment tracking protocols are designed and the sufficient conditions to ensure the effectiveness of the proposed protocols are presented, and the algorithm to construct the containment tracking protocols is also given. Finally, the simulation results illustrate that when there exist external disturbances, the designed formation-containment protocols can make the MAS realize $H_{\infty } $ TVGFCT.}, archive = {J_ACCESS}, author = {Nani Han and Jianji Wang}, doi = {10.1109/ACCESS.2025.3561144}, journal = {IEEE Access}, pages = {72841-72851}, shortjournal = {IEEE Access}, title = {H∞ time-varying group formation-containment tracking control of multi-agent system with multiple leaders}, volume = {13}, year = {2025}, }
• (2025). Elliptical model-based robust pedestrian pose tracking by LiDAR via bi-level moving horizon random sample consensus. ACCESS, 13, 72829-72840. (www)

  @article{ , abstract = {Recent advancements in autonomous vehicle research have heightened the need for precise pedestrian pose tracking to ensure safe navigation. Utilizing LiDAR sensors, conventional bounding box (BB) methods are generally used but are sensitive to limb motion. The previous moving horizon-based maximum likelihood estimation sample consensus (MHESAC) study addressed this robustness limitation but suffered from extensive point cloud (PC) sampling, limiting real-time applications. This paper proposes an elliptical pedestrian model tracking within a sequential bi-level MHESAC (B-MHESAC) framework that combines random sampling consensus (RANSAC) for local model fitting per time step and maximum likelihood estimation sample consensus (MLESAC) for optimal model sequence selection throughout the horizon, significantly reducing PC sampling while preserving accuracy. Under significant outlier contamination, B-MHESAC requires less trials than MHESAC for multiple horizons and samples. Moreover, at long horizon lengths, B-MHESAC required trial’s exponential growth is at least reduced to the fifth root of MHESAC, allowing real-time estimations. The proposed method was evaluated in an indoor single pedestrian tracking experiment. Experimental results demonstrate that B-MHESAC is more robust than conventional RANSAC in model fitting. Additionally, the elliptical model-based B-MHESAC’s tracking performance is competitive with the MHESAC method’s and superior to other Kalman filter-based BB and circle model methods.}, archive = {J_ACCESS}, author = {Haziq Muhammad and Zool H. Ismail and Kazuma Sekiguchi and Kenichiro Nonaka}, doi = {10.1109/ACCESS.2025.3563030}, journal = {IEEE Access}, pages = {72829-72840}, shortjournal = {IEEE Access}, title = {Elliptical model-based robust pedestrian pose tracking by LiDAR via bi-level moving horizon random sample consensus}, volume = {13}, year = {2025}, }
• (2025). PTFNet: Robotic-relevant, single-view obstacle footprint estimation from sparse and incomplete point clouds. ACCESS, 13, 72812-72828. (www)

  @article{ , abstract = {In order for robots to navigate successfully, they need to correctly estimate the traversability of their surroundings. To do so, an orthogonal projection of the spatial obstacles perceived by the robot’s sensors is usually utilized. As the sensors can only see the surfaces closest to them, it is difficult to judge the traversability without guessing the shape of the obstacle. In this work, we introduce a novel approach that can estimate the obstacle’s 2D footprint straight from incomplete 3D data in the form of a point cloud. Unlike existing point cloud completion methods, we formulate the problem such that our approach does not require the reconstruction of the entire 3D object and subsequent projection. Instead, it focuses on rendering a 2D representation directly from segmented sensor scans, even if the available points are very sparse. At its core, we propose a lightweight, multi-modal autoencoder that takes an input of a voxelized incomplete point cloud and outputs an estimated footprint that is directly applicable to the occupancy grid. In the absence of similar methods, we validate the proposal on a real dataset coined UR, which was collected specifically for this publication, and prove the method’s applicability in real-life scenarios. The system achieves good performance even on point clouds with as few as 30 points. Additionally, we utilize an open-source synthetic dataset to compare our method, in an indirect fashion, with available point cloud completion algorithms by projecting their outputs on a 2D plane. Compared to other pipelines, our method proves superior in terms of computational resource consumption in complete robotic pipeline tests and achieves satisfactory accuracy on various test objects. We provide collected UR data to the community under the Repository.}, archive = {J_ACCESS}, author = {Konrad P. Cop and Tomasz P. Trzciński}, doi = {10.1109/ACCESS.2025.3562671}, journal = {IEEE Access}, pages = {72812-72828}, shortjournal = {IEEE Access}, title = {PTFNet: Robotic-relevant, single-view obstacle footprint estimation from sparse and incomplete point clouds}, volume = {13}, year = {2025}, }
• (2025). Holistic digital doctor for X-ray reading using VR-enhanced 3D touch bone model user interface. ACCESS, 13, 72790-72811. (www)

  @article{ , abstract = {The Digital Medical Doctor (DD) platform takes a unique approach by allowing X-ray interpretation through touch devices, eliminating the requirement for medical visits simply to read X-rays. In the context of joint analysis, our suggested joint track methodology departs from the traditional keyboard and mouse matching technique, proposing an image-matching approach employing a joystick instead. However, we innovate this using a touch interface, which is more accurate and effective than joystick-based X-ray picture matching. Given the growing incidence of knee problems among women, a dataset concentrating only on knee examinations is chosen. Our study employs a hybrid image-matching technique, incorporating the Touch Bone Model (TBM) designed on a 3D modeler for knee matching on touch screens. Utilizing the Joint Track dataset, the results of DD are verified by the doctor. The results reveal whether the report is positive or negative. We saw increased efficiency and lower resource usage by incorporating TBM into the mobile version of DD, resulting in time and cost savings. This novel method is in line with the expanding environment of healthcare technology, intending to improve diagnostic processes while encouraging ease and resource efficiency.}, archive = {J_ACCESS}, author = {Asma Bibi and Muhammad Faran Majeed and Jaroslav Frnda and Almas Abbasi and Vladimir Bures and Muhammad Shahid Anwar and Bekbaev Gamzatdin}, doi = {10.1109/ACCESS.2025.3560565}, journal = {IEEE Access}, pages = {72790-72811}, shortjournal = {IEEE Access}, title = {Holistic digital doctor for X-ray reading using VR-enhanced 3D touch bone model user interface}, volume = {13}, year = {2025}, }
• (2025). Efficient computation of the k nearest neighbors query using incremental radius on a k²-tree. ACCESS, 13, 72778-72789. (www)

  @article{ , abstract = {Proximity searches within metric spaces are critical for numerous real world applications, including pattern recognition, multimedia information retrieval, and spatial data analysis, among others. With the exponential increase in data volume, the demand for memory efficient structures to store and process information has become increasingly important. In this paper, we present an alternative algorithm for efficient computation of the K-nearest neighbors (KNN) query using the $k^{2}$ -tree compact data structure, using the incremental radius technique. This approach offers an alternative to the existing algorithm that utilizes a priority queue over $k^{2}$ -trees. Through both theoretical and experimental analysis, we demonstrate that our proposed algorithm is up to 2 times faster compared to the priority queue based solution, while also providing substantial improvements in memory efficiency.}, archive = {J_ACCESS}, author = {Rodrigo Torres-Avilés and Mónica Caniupán}, doi = {10.1109/ACCESS.2025.3564185}, journal = {IEEE Access}, pages = {72778-72789}, shortjournal = {IEEE Access}, title = {Efficient computation of the k nearest neighbors query using incremental radius on a k²-tree}, volume = {13}, year = {2025}, }
• (2025). Scattering medium removal using adaptive masks for scatter in the spatial frequency domain. ACCESS, 13, 72769-72777. (www)

  @article{ , abstract = {To address this issue, this paper presents an adaptive method for removing scattering media using a mask based on wireless communication fading models. We hypothesize a similarity between light propagation and wireless communication systems, which incorporates scattering estimates through models such as the Rayleigh and Rician fading models, which are applied to process the captured images and mitigate scattering effects. Our proposed method incorporates two systems: the Scattered Image Model and the Scattering Media Model. The conventional dehazing method requires processing sequences’ approximated depth map or specific background. However, the proposed method functions regardless of the image’s depth and specific background colors. To validate the proposed method, we conducted optical experiments and tested outdoor images. The results were compared with conventional haze-removal methods, such as dark channel prior and Peplography, using various image quality metrics, e.g., the Peak Signal-to-Noise ratio, Structural Similarity Index Measurement, Tone Mapped Image Quality, and Feature Similarity Index Measurement extended to color imagery. The experimental results demonstrated significant improvements over the conventional methods across all metrics.}, archive = {J_ACCESS}, author = {Jongpil Jeong and Min-Chul Lee}, doi = {10.1109/ACCESS.2025.3563369}, journal = {IEEE Access}, pages = {72769-72777}, shortjournal = {IEEE Access}, title = {Scattering medium removal using adaptive masks for scatter in the spatial frequency domain}, volume = {13}, year = {2025}, }
• (2025). Community-based memetic algorithm for influence maximization in large-scale networks. ACCESS, 13, 72754-72768. (www)

  @article{ , abstract = {Effective information diffusion across large-scale network is key for influence maximization. Recent research has shown a significant surge in interest in modeling, performance estimation, and seed identification across various networked systems. Moreover, a simulation of useful interactions among many significant groups within networks was developed to simulate real-world marketing and spreading information more accurately. A good diffusion model identifies the minimum number of effective seeds capable of achieving maximum diffusion effects across the network. Limited focus has been placed on measuring the strength of seeds in competitive spreading situations. There is a research gap in determining effective strategy for this purpose. This study proposes a memetic algorithm based on a community for large-scale social networks. The proposed algorithm optimizes the influence spread by identifying the most influential nodes among the communities, depending on their inter- or intra-community propagation dynamics. This algorithm combines the concept of genetic algorithm with a reachability-based local search method to accelerate the convergence process. This approach offers a robust method for maximizing the influence of network structure and interactions. An experimental evaluation on real-world social network datasets shows the performance superiority of this community-based memetic algorithm (CBMA-IM) over existing algorithms.}, archive = {J_ACCESS}, author = {Mithun Roy and Indrajit Pan}, doi = {10.1109/ACCESS.2025.3563308}, journal = {IEEE Access}, pages = {72754-72768}, shortjournal = {IEEE Access}, title = {Community-based memetic algorithm for influence maximization in large-scale networks}, volume = {13}, year = {2025}, }
• (2025). Intent-based multi-cloud storage management powered by a fine-tuned large language model. ACCESS, 13, 72736-72753. (www)

  @article{ , abstract = {Storage resources are essential in heterogeneous multi-cloud environments. In response to the growing demand for efficient storage resource management (SRM) in these environments, this paper proposes an intent-based storage management (IBSM) system powered by a fine-tuned large language model (LLM). To overcome the limitations of existing methods, the IBSM system focuses on enhancing the controllability, completeness, and reliability of SRM in multi-cloud environments. Specifically, the IBSM system employs a dual-phase joint intent classification algorithm, which leverages a fine-tuned LLM to accurately identify user intents across diverse knowledge backgrounds. Additionally, the system constructs a collaborative intent decomposition method, which guarantees the integrity of intents. Furthermore, the system integrates an automated intent deployment mechanism that supports error recovery through checkpoints. Experimental results show that the system achieves a whole end-to-end (E2E) lifecycle for managing user intents. The E2E time is reduced by at least half compared to the manual approach, with an average of 50.14% dedicated to interactive tasks. Performance metrics for intent classification, including accuracy, precision, and recall, all exceed 90%. Moreover, the recovery time is reduced by an average of 30.6%. Therefore, the system provides a valuable solution for the autonomous management of multi-cloud resources.}, archive = {J_ACCESS}, author = {Jingya Zheng and Gaofeng Tao and Shuxin Qin and Dan Wang and Zhongjun Ma}, doi = {10.1109/ACCESS.2025.3563200}, journal = {IEEE Access}, pages = {72736-72753}, shortjournal = {IEEE Access}, title = {Intent-based multi-cloud storage management powered by a fine-tuned large language model}, volume = {13}, year = {2025}, }
• (2025). A comparative study of evolutionary strategies for aggregation tasks in robot swarms: Macro- and micro-level behavioral analysis. ACCESS, 13, 72721-72735. (www)

  @article{ , abstract = {Collective decision-making is widely observed in natural organisms, especially insects and animals. In this regard, aggregation represents one of the paramount behaviors, as it can be useful for protecting groups against predators or speeding up the foraging process. In the field of autonomous robotics, aggregation is often studied through various paradigms, with evolutionary algorithms being one of the most widely used tools for evolving this collective behavior. In this work, we compared three modern evolutionary strategies — Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Exponential Natural Evolution Strategies (xNES) and OpenAI Evolutionary Strategy (OpenAI-ES) — for their ability to evolve an aggregation behavior in a swarm of robots. Specifically, we systematically varied the number of agents in the group, the environmental conditions (i.e., the number of target nests) and the parameters tuning the fitness function. Our aim is to verify whether and how the selected methods are effective at addressing the problem. The results we obtained indicate how OpenAI-ES achieves better performance in all the considered scenarios. Furthermore, it displays qualitatively more diverse strategies than the other two methods.}, archive = {J_ACCESS}, author = {Paolo Pagliuca and Alessandra Vitanza}, doi = {10.1109/ACCESS.2025.3554344}, journal = {IEEE Access}, pages = {72721-72735}, shortjournal = {IEEE Access}, title = {A comparative study of evolutionary strategies for aggregation tasks in robot swarms: Macro- and micro-level behavioral analysis}, volume = {13}, year = {2025}, }
• (2025). Decoding customer experience: A comparative analysis of electric and internal combustion vehicles in the U.S. market through structured topic modeling. ACCESS, 13, 72674-72720. (www)

  @article{ , abstract = {Amid global environmental challenges, the transition from internal combustion vehicles (ICVs) to electric vehicles (EVs) is a priority for governments and automobile manufacturers. This shift requires a deep understanding of consumer preferences and evolving adoption trends. Existing research highlights critical gaps, such as the lack of comparative studies analyzing EVs and ICVs’ consumer-perceived value and their evolution over time, and the limitations of static survey methods – currently predominant but constrained in capturing comprehensive consumer insights. To address these gaps, our study utilizes computational text analytics to analyze 13 years of online customer reviews from two major U.S. automotive websites. Using Structured Topic Modeling (STM), we identified 30 factors (in 14 subcategories) influencing EV customer experiences and 40 factors (in 12 subcategories) for ICV customers. By integrating metadata contexts such as satisfaction levels (rating), review timelines, and predicted author gender, we uncovered patterns in functional and non-functional values driving consumer perceptions. This research advances computational text analytics by 1) introducing enhanced methods for STM quality control, 2) developing a comprehensive framework of factors driving EV and ICV consumer perceptions, and 3) presenting longitudinal insights into these evolving preferences. The findings provide actionable insights for policymakers and industry stakeholders. For the EV market, prioritizing affordability, charging infrastructure, and environmental benefits can accelerate adoption. For ICVs, enhancing highway fuel efficiency, reliability, and advanced safety features can enhance customer loyalty. This study lays the groundwork for customer-focused automotive solutions, bridging theoretical understanding with practical application.}, archive = {J_ACCESS}, author = {Nina Rizun and Bartosz Duzinkewicz}, doi = {10.1109/ACCESS.2025.3561219}, journal = {IEEE Access}, pages = {72674-72720}, shortjournal = {IEEE Access}, title = {Decoding customer experience: A comparative analysis of electric and internal combustion vehicles in the U.S. market through structured topic modeling}, volume = {13}, year = {2025}, }
• (2025). Multi-stage adversarial defense for online DDoS attack detection system in IoT. ACCESS, 13, 72657-72673. (www)

  @article{ , abstract = {Machine learning-based Distributed Denial of Service (DDoS) attack detection systems have proven effective in detecting and preventing DDoD attacks in Internet of Things (IoT) systems. However, these DDoS attack-detection models are vulnerable to adversarial attacks. Adversarial attacks are small, crafted adversarial perturbations that are designed to fool machine learning model, leading misclassification of input. The existing defence technique primarily focuses on batch learning- based DDoS attack detection systems, that are not equipped to handle multiple and unknown adversarial attacks in real time. To address this challenge, a novel Multistage Adversarial Attack Defense (MSAAD) framework has been developed to protect online DDoS attack detection systems from adversarial attacks. The framework consists three defense layers: 1) the Resilient Adversarial Detector and Purification (RADP), which detects and purifies adversarial attacks targeting online DDoS attack detection systems against multiple and unknown adversarial attacks. 2) a multiple classifier, that increase complexity for an attacker to replicate the DDoS attack detection model. 3) the Multi-Armed Bandit (MAB) with Thompson Sampling, which dynamically selects the optimal classifier or ensemble of classifiers for each incoming traffic request. The experiment result shows the effectiveness of the proposed MSAAD framework using the IOTID20 and CICIoT2023 datasets. The accuracy of the MLBTSE based DDoS attack detection model improved from a range of 32.38%-60.58% to 99.39%-99.48% for the IOTID20 dataset and from range of 66.60%-86.20% to 99.01%-99.14% for the CICIoT2023 datasets, respectively, in the adversarial attack scenario.}, archive = {J_ACCESS}, author = {Yonas Kibret Beshah and Surafel Lemma Abebe and Henock Mulugeta Melaku}, doi = {10.1109/ACCESS.2025.3560186}, journal = {IEEE Access}, pages = {72657-72673}, shortjournal = {IEEE Access}, title = {Multi-stage adversarial defense for online DDoS attack detection system in IoT}, volume = {13}, year = {2025}, }
• (2025). Leveraging blockchain technology with enhanced MDSVA for robust meteorological sensor data validation. ACCESS, 13, 72633-72656. (www)

  @article{ , abstract = {With the proliferation of meteorological sensor networks, ensuring data quality and reliability has become increasingly challenging. Traditional validation methods often fail to handle complex sensor behaviors, environmental variations, and real-time data verification requirements. This paper presents an innovative approach that leverages blockchain technology with a custom Multidimensional Sensor Validation Algorithm (MDSVA) to comprehensively address these challenges. Our enhanced MDSVA introduces a sophisticated five-component validation function that simultaneously addresses drift and noise compensation, environmental adaptation, periodic variations, dynamic thresholds, and transient adjustments. These components work multiplicatively, ensuring that significant issues in any single aspect substantially affect the overall validation result, while minor variations across multiple components have a more moderate combined effect. The mathematical model employs weighted exponential decay for noise reduction, temperature-based environmental scaling, sinusoidal periodic compensation, logistic threshold transitions, and temporal adaptation functions. This holistic approach significantly improves traditional validation methods by providing more nuanced and context-sensitive data validation. Integration with Hyperledger Fabric blockchain technology is achieved through smart contracts that implement this advanced validation algorithm, ensuring immutable record keeping and distributed consensus on data quality. The experimental results show that our implementation achieved a precision of 94.7% in sensor drift detection, with consistent performance (±1.2% variation) in diverse environments over 24 weeks. The system maintained 99.99% uptime with 4.2 second recovery time and demonstrated robust resilience by handling up to 30% simultaneous node failures while keeping resource utilization below 85%. This research contributes to the field of meteorological data quality assurance by providing a robust, mathematically sound, and technologically advanced solution that meets the growing demands of modern weather monitoring systems.}, archive = {J_ACCESS}, author = {Md Abdullah Al Mamun and Mei Li and Bijon Kumar Pramanik and Faisal Hussain and A. Z. M. Shakilur Rahman}, doi = {10.1109/ACCESS.2025.3563099}, journal = {IEEE Access}, pages = {72633-72656}, shortjournal = {IEEE Access}, title = {Leveraging blockchain technology with enhanced MDSVA for robust meteorological sensor data validation}, volume = {13}, year = {2025}, }
• (2025). UAV high-speed target reconnaissance and deblurring. ACCESS, 13, 72620-72632. (www)

  @article{ , abstract = {In military operations, the detection of enemy targets by reconnaissance aircraft usually results in obvious motion blur in imaging due to the rapid movement or position change of the target, which poses a challenge for accurate target identification. To address the imaging blur issue in high-speed dynamic scenarios and enhance the visual quality of enemy target imagery, this study proposes a blurred image restoration method based on reconnaissance imaging models. First, there is a lack of reliable simulation models and quantitative frameworks to assess the extent of such blurring. To this end, we developed an optical imaging simulation model for imaging systems that incorporates motion blur effects to more accurately reflect real-world scenarios. To fulfill the need for quantitative metrics, we introduce a velocity estimation method that calculates the blur length and accurately measures the degree of blur removal to estimate the target motion velocity to validate the real-world imaging of a reconnaissance aircraft imaging system. Additionally, to address the problem of imaging blurring, we propose a motion deblurring optimization strategy based on the pre-trained DeblurGAN-v2 model, which is specifically designed to restore the clarity of target imaging under high-speed dynamic conditions. The effectiveness of the simulation model and the deblurring strategy is rigorously verified through a series of low-speed UAV tests simulating battlefield conditions. The results show that our equivalent imaging model is able to achieve minimal relative error and accurately reproduce motion-induced blur when detecting a variety of ground targets.}, archive = {J_ACCESS}, author = {Zhaoqi Hou and Zhen Zheng and Yiqing Gu and Chuanhao Zhang and Haojie Li}, doi = {10.1109/ACCESS.2025.3560086}, journal = {IEEE Access}, pages = {72620-72632}, shortjournal = {IEEE Access}, title = {UAV high-speed target reconnaissance and deblurring}, volume = {13}, year = {2025}, }
• (2025). Innovative filter for nonlinear multitarget tracking: Improved SCKF-GM-DLPMBM filter and its implementation. ACCESS, 13, 72603-72619. (www)

  @article{ , abstract = {The Poisson multi-Bernoulli mixture (PMBM) filter is capable of estimating the states of multiple targets based on available measurements. To address the limitations of the traditional PMBM filter, which involves the enumeration of assumptions that increases computational time and leads to inaccurate state estimates under noisy conditions, we propose the dual-label PMBM (DLPMBM) filter. This paper enhances the PMBM filter by incorporating labels for both measurements and targets. In the prediction and update phases, the filter is divided into a labeled Poisson point process (LPPP) and a labeled multi-Bernoulli mixture (LMBM) process, which predict and update undetected targets, potential targets, and surviving targets. During the measurement generation phase, each measurement is assigned a unique label, and an improved elliptical gate is used to filter the measurements, embedding them into the LPPP and LMBM measurement update processes. This approach reduces the enumeration of global hypotheses. Furthermore, to address the imprecise estimates of the conventional PMBM filter, an optimization method and its implementation are proposed in this study. To mitigate the uncertainties of conventional filters under nonlinear conditions, we develop an implementation of the Gaussian mixture DLPMBM filter using the square-root cubature Kalman filter (SCKF). The covariance matrix of unknown process noise is improved by integrating the Sage-Husa filter. To ensure the positive definiteness of the estimated covariance, Cholesky decomposition is employed in both the prediction and update phases of the DLPMBM filter. Finally, multitarget tracking experiments are conducted to demonstrate the performance of the proposed DLPMBM filter.}, archive = {J_ACCESS}, author = {Yubin Zhou and Bo Li and Jinyu Zhang and Zhikang Li}, doi = {10.1109/ACCESS.2025.3562215}, journal = {IEEE Access}, pages = {72603-72619}, shortjournal = {IEEE Access}, title = {Innovative filter for nonlinear multitarget tracking: Improved SCKF-GM-DLPMBM filter and its implementation}, volume = {13}, year = {2025}, }
• (2025). Wireless communication in underground mining teleoperation: A systematic review. ACCESS, 13, 72577-72602. (www)

  @article{ , abstract = {The mining industry is undergoing a transformational shift driven by automation and digitalization, enabling the teleoperation of machinery, improving safety, and enhancing operational efficiency in challenging underground environments. However, achieving safe and efficient teleoperation requires meeting stringent wireless communication requirements for low latency and high throughput. There is no unified consensus on the communication requirements or the most suitable technologies for different teleoperation modes, such as remote driving, assistance, and monitoring. This paper systematically reviews the literature on wireless communication challenges for underground mining teleoperation, identifying open research problems, proposed solutions, and key findings. The included studies are categorized into six thematic research areas, with teleoperation modes mapped to different levels of automation. Additionally, studies characterizing radio wave propagation in underground mine environments and evaluating various communication technologies are analyzed to define communication requirements in terms of deadlines, reliability, latency, and data rate for different teleoperation modes. The performance metrics used in the reviewed studies are also defined and attributed to the six thematic research areas. The findings of this systematic review highlight Wi-Fi, LTE, and 5G as the prevailing technologies for underground mine teleoperation. Moreover, the identified research gaps underscore the need for further research on handling communication outages, achieving low latency and high throughput for improved QoS, hybrid communication technologies, enabling a higher level of autonomy, and designing communication solutions tailored to specific teleoperation scenarios.}, archive = {J_ACCESS}, author = {Shahriar Hasan and Katrin Sjöberg and Peter Wallin}, doi = {10.1109/ACCESS.2025.3563138}, journal = {IEEE Access}, pages = {72577-72602}, shortjournal = {IEEE Access}, title = {Wireless communication in underground mining teleoperation: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Utilize data augmentation and flexi corner block for road damage detection. ACCESS, 13, 72564-72576. (www)

  @article{ , abstract = {Road damage detection is crucial for ensuring road safety and maintaining infrastructure durability, especially as increasing traffic and aging road networks create growing challenges for transportation systems worldwide. This process typically involves detecting irregularities on the road surface, such as cracks and potholes, from images. Current methods, integrating Single Shot Detector with MobileNet and Faster R-CNN, have successfully detected large potholes and long, deep cracks. However, detecting smaller, shallower cracks and minor potholes remains a challenge. Additionally, the appearance of road damage can vary under different weather conditions, making consistent detection difficult. To address these challenges, three modules are proposed. The Weather Trim Augment module leverages stable diffusion to generate weather-specific road damage datasets by adjusting prompt words and parameters, ensuring crack location and type remain unchanged, thus improving detection under various weather conditions. The Flexi Corner Block module enhances road damage detection by combining deformable convolutions with lightweight MLP and Learnable Local Attention, improving feature learning in corner areas. Moreover, the HXIOU loss function uses weighted calculations to effectively mine difficult samples, enhancing detection capabilities for challenging damages such as blurry potholes and fine cracks. Experimental results on the RDD2020 and CNRDD datasets demonstrate improved performance. On one hand, the proposed method achieves 64.9 on Test1 and a 40.6 F1-Score, demonstrating strong detection capabilities. On the other hand, the model exhibits robust generalization under adverse weather conditions, such as rain and snow, further validating its effectiveness in diverse environments.}, archive = {J_ACCESS}, author = {Zhaohui Wu and Runjing Zhao and Xingliang Sun and Zhaojia Li}, doi = {10.1109/ACCESS.2025.3561831}, journal = {IEEE Access}, pages = {72564-72576}, shortjournal = {IEEE Access}, title = {Utilize data augmentation and flexi corner block for road damage detection}, volume = {13}, year = {2025}, }
• (2025). ARUC-GAN: A CycleGAN-based attention residual U-net for low-dose CT denoising in smart healthcare. ACCESS, 13, 72544-72557. (www)

  @article{ , abstract = {Low-dose computed tomography (LDCT) is increasingly adopted in medical imaging to minimize radiation exposure. However, the diagnostic accuracy is primarily affected by different noise sources, e.g., quantum, electronic, and reconstruction. Moreover, conventional denoising methods struggle with the non-uniform noise distributions in LDCT images and often require complex projection data, which limits their effectiveness and generalizability. Herein, we propose an AI-based denoising approach using an Attention Residual U-Net (ARU-Net) architecture integrated into the CycleGAN framework named Attention Residual U-Net CycleGAN (ARUC-GAN). The proposed framework outperforms state-of-the-art denoising models, such as RED-CNN, EDCNN, and CTformer, according to experimental evaluation on the Mayo Clinic abdominal CT dataset. The model exhibits a peak signal-to-noise ratio (PSNR) of 34.82 dB, a structural similarity index (SSIM) of 0.85, and an RMSE of 0.018. Furthermore, the model successfully maintains edge structures with an Edge Keeping Index (EKI) of 0.835. The visual examination validates ARUC-GAN’s superior texture and detail preservation. The results indicate that the suggested method has great promise as a smart health tool for improving diagnosis accuracy in LDCT scans.}, archive = {J_ACCESS}, author = {Mohammad Mansour and Djamel Djenouri and Lobna A. Said and Shahid Latif and Ahmed Maalel and Ameer M. S. Abdelhadi}, doi = {10.1109/ACCESS.2025.3560945}, journal = {IEEE Access}, pages = {72544-72557}, shortjournal = {IEEE Access}, title = {ARUC-GAN: A CycleGAN-based attention residual U-net for low-dose CT denoising in smart healthcare}, volume = {13}, year = {2025}, }
• (2025). A novel LSTM architecture for automatic modulation recognition: Comparative analysis with conventional machine learning and RNN-based approaches. ACCESS, 13, 72526-72543. (www)

  @article{ , abstract = {The recognition of modulation types in received signals is essential for signal detection and demodulation, with broad applications in telecommunications, defense, and wireless communications. This paper introduces a pioneering approach to automatic modulation recognition (AMR) through the development of a highly optimized long short-term memory (LSTM) network. The proposed framework is engineered to capture intricate temporal dependencies within modulated signals, leveraging a gated architecture that effectively mitigates the vanishing gradient problem. This innovation markedly improves recognition accuracy, particularly in low-SNR conditions where traditional methods are often limited. A defining contribution of this work is the introduction of a novel, adaptive temporal-spectral feature learning mechanism, which seamlessly integrates both temporal and spectral characteristics of the signal. This paradigm eliminates the need for manual feature extraction, enhances interpretability, and significantly boosts classification efficiency. Furthermore, the proposed framework is designed for low-complexity deployment, ensuring its scalability and suitability for next-generation wireless networks and real-time communication systems. The proposed architecture is capable of distinguishing between seven modulation classes: BASK, 4-ASK, BFSK, 4-FSK, BPSK, 4-PSK, and 16-QAM. Performance is evaluated across a broad range of signal-to-noise ratios (SNR), from −10 dB to +30 dB, through extensive simulations. Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). Comparative analysis reveals that the proposed framework outperforms conventional machine learning techniques, with accuracy improvements ranging from 1.77% to 34.03% over the best- and worst-performing methods. Additionally, the proposed model achieves a performance gain of 2.02% over the deep learning (DL)-based RNN, further highlighting its superior capability in AMR.}, archive = {J_ACCESS}, author = {Sam Ansari and Soliman Mahmoud and Sohaib Majzoub and Eqab Almajali and Anwar Jarndal and Talal Bonny}, doi = {10.1109/ACCESS.2025.3564032}, journal = {IEEE Access}, pages = {72526-72543}, shortjournal = {IEEE Access}, title = {A novel LSTM architecture for automatic modulation recognition: Comparative analysis with conventional machine learning and RNN-based approaches}, volume = {13}, year = {2025}, }
• (2025). Boundary control behaviors of multiple low-cost AUVs using acoustic communication. ACCESS, 13, 72506-72525. (www)

  @article{ , abstract = {This study presents acoustic-based methods for the formation control of multiple autonomous underwater vehicles (AUVs). This study proposes two different models for implementing boundary and path control on low-cost AUVs using acoustic communication and a single central acoustic beacon. Both models are based on the history of relative range and do not rely on the full knowledge of the AUVs states based on a centralized beacon system. Two methods are presented: the Range Variation-Based (RVB) model completely relies on range data obtained by acoustic modems, whereas the Heading Estimation-Based (HEB) model uses ranges and range rates to estimate the position of the central boundary beacon and perform assigned behaviors. The models are tested on two formation control behaviors: Fencing and Milling. Fencing behavior ensures AUVs return within predefined boundaries, while Milling enables the AUVs to move cyclically on a predefined path around the beacon. Models are validated by successfully performing the boundary control behaviors in simulations, pool tests, including artificial underwater currents, and field tests conducted in the ocean. All tests were performed with fully autonomous platforms, and no external input or sensor was provided to the AUVs during validation. Quantitative and qualitative analyses are presented in the study, focusing on the effect and application of a multi-robot system.}, archive = {J_ACCESS}, author = {Mohammed Tarnini and Saverio Iacoponi and Andrea Infanti and Cesare Stefanini and Giulia de Masi and Federico Renda}, doi = {10.1109/ACCESS.2025.3564077}, journal = {IEEE Access}, pages = {72506-72525}, shortjournal = {IEEE Access}, title = {Boundary control behaviors of multiple low-cost AUVs using acoustic communication}, volume = {13}, year = {2025}, }
• (2025). Machine translation performance for low-resource languages: A systematic literature review. ACCESS, 13, 72486-72505. (www)

  @article{ , abstract = {Machine translation (MT) for low-resource languages continues to face significant challenges because of limited digital resources and parallel corpora, despite remarkable developments in neural machine translation (NMT). Addressing these challenges requires a thorough review of existing research to identify effective strategies and methods. To achieve this, a systematic literature review (SLR) is conducted following PRISMA guidelines and systematically analysing studies published in various academic databases in the last five years (between 2020 and 2024). A total of 69 relevant articles were examined to evaluate the performance of MT, explore persistent challenges and assess the effectiveness of proposed or used solutions. The analysis shows that while NMT has emerged as the predominant approach, its effectiveness is often reduced by the scarcity of training data and the structural complexity of low-resource languages. Strategies such as active learning, data augmentation, multilingual models and transfer learning are identified as critical for improving translation performance. Additionally, emerging research trends, including data pre-processing, optimization of decoder and rule-based approach demonstrate promising directions for addressing existing limitations. In terms of evaluation, most of the studies used Character n-gram F-score (ChrF), Translation Edit Rate (TER), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Word Error Rate (WER) and Bilingual Evaluation Underscore (BLEU) as techniques’ validation metrics. This review provides a detailed evaluation of the current state of MT for low-resource languages and emphasizes the need for further research into underrepresented languages and the development of comprehensive datasets.}, archive = {J_ACCESS}, author = {Taofik O. Tafa and Siti Zaiton Mohd Hashim and Mohd Shahizan Othman and Hitham Alhussian and Maged Nasser and Said Jadid Abdulkadir and Sharin Hazlin Huspi and Sarafa O. Adeyemo and Yunusa Adamu Bena}, doi = {10.1109/ACCESS.2025.3562918}, journal = {IEEE Access}, pages = {72486-72505}, shortjournal = {IEEE Access}, title = {Machine translation performance for low-resource languages: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Broadening misalignment tolerance using broadside coupled wedge-waveguide to microstrip inline transition for MMIC packaging. ACCESS, 13, 72477-72485. (www)

  @article{ , abstract = {A broadside aperture coupling was utilized instead of end-wall aperture coupling for millimeter wave inline waveguide-to-microstrip transition (WMT), allowing a larger misalignment tolerance of ±250 um in the vertical direction and ±300 um in the horizontal direction within the insertion loss degradation of 0.1 dB. Additionally, a simple wedge waveguide configuration eliminated the necessity for complex taper or ridge waveguide sections. It simplifies manufacturing and testing procedures by eliminating the need for E-plane or H-plane splits. Furthermore, the transition exhibits broadband and low-loss performance. The fractional bandwidth reached as high as 44%, while the insertion loss of the back-to-back transition was measured at 8.5 dB, which includes the loss from the 1-meter-long waveguide (5.5 dB). The transition is well-suited for microwave and sub-millimeter frequencies due to its low loss, broad bandwidth, compact design, and ease of integration with MMICs.}, archive = {J_ACCESS}, author = {Hafeez-Ur-Rehman and Ha Il Song and Sean Park and Jae-Hyung Jang}, doi = {10.1109/ACCESS.2025.3563629}, journal = {IEEE Access}, pages = {72477-72485}, shortjournal = {IEEE Access}, title = {Broadening misalignment tolerance using broadside coupled wedge-waveguide to microstrip inline transition for MMIC packaging}, volume = {13}, year = {2025}, }
• (2025). PanoSCU: A simulation-based dataset for panoramic indoor scene understanding. ACCESS, 13, 72456-72476. (www)

  @article{ , abstract = {Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions—making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene Change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU supports eight research tasks: single-view and panoramic detection, single-view and panoramic segmentation, single-view and panoramic change understanding, embodied object tracking, and change reversal. We also present PanoStitch, a training-free method for automatic panoramic data collection within embodied environments. We evaluate state-of-the-art methods on panoramic segmentation and change understanding tasks. There is a gap in existing methods, as they are not designed for panoramic inputs and struggle with varying ratios and sizes, resulting from the unique challenges of visual object rearrangement. Our findings reveal these limitations and underscore PanoSCU’s potential to drive progress in developing models capable of robust panoramic reasoning and fine-grained scene change understanding.}, archive = {J_ACCESS}, author = {Mariia Khan and Yue Qiu and Yuren Cong and Jumana Abu-Khalaf and David Suter and Bodo Rosenhahn}, doi = {10.1109/ACCESS.2025.3561055}, journal = {IEEE Access}, pages = {72456-72476}, shortjournal = {IEEE Access}, title = {PanoSCU: A simulation-based dataset for panoramic indoor scene understanding}, volume = {13}, year = {2025}, }
• (2025). Implementation for lightweight deep learning for anomaly detection and denoising on gravitational waves. ACCESS, 13, 72442-72455. (www)

  @article{ , abstract = {As gravitational wave astronomy has advanced, the need for effective and quick signal processing has never been more critical. New detectors such as Laser Interferometer Gravitational-Wave Observatory (LIGO) produces huge volumes of data, which poses a significant challenge to identify genuine astrophysical events amidst transient noise. With this in view, this study proposes a new, efficient deep-learning architecture designed to process gravitational wave signals. Motivated by the WaveNet model, our method uses dilated convolutions to precisely model long-term dependencies in the data ensuring that subtle characteristics are captured. In addition, higher-order recurrent layers like Long Short-Term Memory(LSTM) networks are also used to precisely model temporal characteristics so that accuracy is preserved with enhanced anomaly detection and noise removal. Our experimental validation shows that this method has achieved a Root Mean Square Error (RMSE) of 0.00228, a Residual Signal Ratio (RSR) of 0.08798, a Peak Signal-to-Noise Ratio (PSNR) of 33.34 dB, and an average volume error of 0.03965. These performance metrics show the framework’s ability to operate in real time with minimal computational overhead is a key requirement given the massive datasets involved in gravitational wave science. Also, by an explicit consideration of initial and boundary conditions, our scheme exhibits not only stability for the current missions but also scalability for future detection missions, like Laser Interferometer Space Antenna (LISA), allowing these missions to enhance their sensitivity and reliability. Our study demonstrates the development of state-of-the-art deep learning methods to surmount the specific obstacles concerning gravitational wave detection, paving the way for real-time processing of astrophysical data and an improved understanding of the Universe.}, archive = {J_ACCESS}, author = {R. K. Mohith Niranjen and C. Yogesh and Anirudh Vinodh and Tharun Sureshkumar and S. Vatchala}, doi = {10.1109/ACCESS.2025.3562595}, journal = {IEEE Access}, pages = {72442-72455}, shortjournal = {IEEE Access}, title = {Implementation for lightweight deep learning for anomaly detection and denoising on gravitational waves}, volume = {13}, year = {2025}, }
• (2025). Real-time detection and identification of ARP spoofing attacks in microgrids. ACCESS, 13, 72427-72441. (www)

  @article{ , abstract = {The integration of smart devices and advanced communication infrastructure has turned power systems into cyber-physical systems (CPS), introducing cyber vulnerabilities. One such vulnerability arises from the use of the address resolution protocol (ARP), which is commonly employed in power systems’ information technology (IT) infrastructure to assign internet protocol (IP) addresses to devices such as relays, controllers, and meters. Due to the lack of authentication in ARP, attackers can exploit it to infiltrate substation automation systems (SAS). To detect and locate ARP spoofing attacks, a novel network intrusion detection system (NIDS) was developed using Snort3, TShark, and Python scripts to monitor ARP broadcast messages. This detection method was tested on a dedicated, real-time multi-agent CPS testbed, where a microgrid is simulated as the physical layer using a real-time digital simulator (RTDS), while the cyber layer consists of a multi-agent control implemented in a graphical network simulator (GNS3) together with Raspberry Pi devices. The real-time operator’s view is developed in Grafana visualization, mimicking the real-world microgrid operation. Two common practical ARP attacks, known as man-in-the-middle (MITM) and false data injection (FDI) attacks, were conducted to evaluate the performance of the proposed NIDS method. Both MITM and FDI attacks were implemented using IT network testing tools, such as Ettercap and the Scapy library in Python. The results have shown that the proposed NIDS system can detect, localize, and publish the IP address of the attacker in both MITM and FDI attack scenarios. In addition, the impact analysis results indicated that for an identical malicious payload, the FDI attack is more severe when compared to MITM due to the intermittent nature of false data injection.}, archive = {J_ACCESS}, author = {Kalinath Katuri and Ha Thi Nguyen and Emmanouil Anagnostou}, doi = {10.1109/ACCESS.2025.3563721}, journal = {IEEE Access}, pages = {72427-72441}, shortjournal = {IEEE Access}, title = {Real-time detection and identification of ARP spoofing attacks in microgrids}, volume = {13}, year = {2025}, }
• (2025). A semantic model to describe RESTful services. ACCESS, 13, 72402-72426. (www)

  @article{ , abstract = {With the spread of Web service technology, developers in heterogeneous communities have aimed to address the sharing and use of services on the Web in a growing and disorganized service description infrastructure. Service-oriented computing has proven to be essential, although it requires a set of appropriate supporting technologies. The RESTful service approach has replaced the typical Web service protocol stack introduced by W3C. RESTful style can be used by companies to expose data on the Internet, but to adopt this paradigm, developers must change how they implement and share services on the Web. RETSTful style adopts a uniform interface and the correct use of HTTP verbs and their semantics; these choices require replacing the traditional stack of constructors and parameters used by W3C’s Web services. The problem with RESTful design is that it uses human-readable formats to publish descriptions. Current methods of addressing this issue lack an agreed machine-readable semantic model to define service descriptions that support complex automatic operations via the services. This paper presents a semantic model for describing RESTful services based on the OWL-S ontology, which can store service descriptions in a repository that represents all RESTful objects with a semantic machine-readable language, OWL-DL. The authors considered the context of worldwide civil aviation, through the ICAO SWIM program, to build an application context for the proposed method. The results obtained indicate the usefulness of the proposed model.}, archive = {J_ACCESS}, author = {Luís Antonio de Almeida Rodriguez and José Maria Parente de Oliveira}, doi = {10.1109/ACCESS.2025.3562503}, journal = {IEEE Access}, pages = {72402-72426}, shortjournal = {IEEE Access}, title = {A semantic model to describe RESTful services}, volume = {13}, year = {2025}, }
• (2025). Modulation assessment with IQD correction for two-way sequential AAF relaying transmissions. ACCESS, 13, 72389-72401. (www)

  @article{ , abstract = {Modulation assessment (MA) is a fundamental element in the development of adaptive radios that can interact effectively with their context to optimize resource usage. This assessment has been extensively studied in the literature over the past several decades. Nevertheless, there have been a few experiments conducted on relaying systems. Previous explorations of MA for relaying broadcasts have neglected the presence of in-phase and quadrature-phase discrepancies (IQD). However, these discrepancies exist in practical wireless transmissions due to manufacturing faults in the transceiver components, which can lead to substantial performance deterioration. In this paper, we create an original maximum-likelihood (ML) solution to the MA challenge in the circumstance of these discrepancies for amplify-and-forward (AAF) two-way sequential relaying transmissions (TWRT). By assuming that each terminal autonomously creates its own IQD, we depart a stage closer to effectively implementing transmissions in actual scenarios. We deploy an expectation-maximization (EM) tool to execute the indicated solution using an iterative approach. We take use of the data duplication inherent in AAF-TWRT signals to support the offered solution. During each repetition, the soft content provided by the detection procedure is utilized to develop the a posteriori expectations of the broadcast information. These expectations are then included in the assessment process as if they were known symbols. Additionally, the proposed solution involves a supplementary duty, which involves calculating the IQD parameters that are initially triggered at each terminal. The simulation results have validated the viability of the proposed architecture by demonstrating its superior MA performance compared to the standard ones. Based on the results, the offered solution reaches convergence within five cycles and achieves exceptional assessment accuracy across a broad spectrum of signal-to-noise ratios. Moreover, the minimum ratio of known to unknown data symbols for the repetitive operation to be executed effectively is 0.025. Beyond that, the proposed approach exhibits resilience to transmission impairments, including timing and frequency mismatches, colored noise, Doppler shifts, and multipath fading.}, archive = {J_ACCESS}, author = {Hala Mostafa and Mohamed Marey and Khaled Mohamad Almustafa and Juliano Katrib}, doi = {10.1109/ACCESS.2025.3560859}, journal = {IEEE Access}, pages = {72389-72401}, shortjournal = {IEEE Access}, title = {Modulation assessment with IQD correction for two-way sequential AAF relaying transmissions}, volume = {13}, year = {2025}, }
• (2025). Smart farming: Enhancing urban agriculture through predictive analytics and resource optimization. ACCESS, 13, 72375-72388. (www)

  @article{ , abstract = {Optimal agricultural methods need precise crop health and ecological strain monitoring. This study proposes a novel data science strategy to improve crop health prediction and stress assessment. ResXceNet-HBA is a cutting-edge classification model that uses ResNet blocks, Xception modules with Adaptive Depthwise Separable Convolutions, and HBA-optimized parameters. This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. Our system also employs Adaptive Synthetic Resampling with Environmental Context. Using novel measures including the Crop Type Generalisation Score (CTGS) and Environmental Sensitivity Index (ESI), the ResXceNet-HBA model achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score. These results beat ResNet, CNN, and Inception V2. The model executed in 50.9 seconds, faster than the alternatives. The confusion matrix exhibits minimal false positives and negatives, suggesting good prediction accuracy. ResXceNet-HBA’s statistics and resource optimization value increases. Precision farming and sustainable agriculture benefit from our strategy’s significant environmental stress and crop health assessments.}, archive = {J_ACCESS}, author = {Eman Ali Aldhahri and Abdulwahab Ali Almazroi and Monagi Hassan Alkinani and Nasir Ayub and Elham Abdullah Alghamdi and Nourah Fahad Janbi}, doi = {10.1109/ACCESS.2025.3530006}, journal = {IEEE Access}, pages = {72375-72388}, shortjournal = {IEEE Access}, title = {Smart farming: Enhancing urban agriculture through predictive analytics and resource optimization}, volume = {13}, year = {2025}, }
• (2025). BT-YOLO11: Automatic driving road target detection in complex scenarios. ACCESS, 13, 72364-72374. (www)

  @article{ , abstract = {Due to the problems faced by autonomous driving scenarios in complex scenarios, generalized target detectors are challenged, especially the detection accuracy of occluded and small targets. In order to solve this problem, a target detection algorithm for complex scenes BT-YOLO11 is proposed based on the latest YOLO11. In the feature extraction stage, Bi-level Routing Attention is introduced to enhance the model’s ability to capture feature information. The Tri-directional Feature Pyramid Net is used in the feature fusion stage, which adequately fuses different levels of feature information and improves the accuracy and robustness of the algorithm. By introducing an Adaptive Threshold Focus Loss function, the model focuses more on detection targets that are difficult to classify, the model generalization ability is improved. The experimental results show that the improved algorithm exhibits very competitive performance on the KITTI dataset. Specifically, it achieves a mAP50 metric of 95% and 77% under the more challenging mAP50:95 evaluation criterion. Compared to the YOLOv11s model, the algorithm proposed in this study improves the mAP50 by 2.6% and the mAP50:95 by 4.4%.}, archive = {J_ACCESS}, author = {Qi Wang and Qi Long Wang}, doi = {10.1109/ACCESS.2025.3562747}, journal = {IEEE Access}, pages = {72364-72374}, shortjournal = {IEEE Access}, title = {BT-YOLO11: Automatic driving road target detection in complex scenarios}, volume = {13}, year = {2025}, }
• (2025). Dual-resource optimization configuration and collaborative scheduling for flexible job shop under worker flexibility constraints. ACCESS, 13, 72347-72363. (www)

  @article{ , abstract = {Worker resources exhibit high flexibility, adaptability, and individual differences. It is critical to complete manufacturing tasks with a suitable worker resource allocation plan, given the increasing labor costs and the dynamic changes in production demands. It is often assumed in studies on traditional flexible job shop scheduling problems that the attributes of manufacturing resources, such as the number of workers and their skill levels, are known and fixed. This paper focuses on achieving resource allocation via personnel flexibility quantification and dual-resource collaborative scheduling optimization. A method based on the worker ability matrix, proficiency, and skill level coefficients is introduced to quantify and evaluate worker flexibility and individual differences. On this basis, an integrated optimization model for worker allocation and production scheduling is established considering the maximum completion time, total manufacturing cost, and worker load balancing. An algorithm based on a two-level decision model is proposed to solve this problem. The upper-level model for worker skill optimization configuration is based on a rule-based greedy algorithm, and the lower-level collaborative model for production scheduling is based on a hybrid genetic-simulated annealing algorithm. In the proposed algorithm, population initialization and neighborhood search strategies based on mixed multi-rules and critical paths are designed. A feasible production guidance scheme for the job shop is obtained by iteratively solving the two-level decision model, which provides the foundation for subsequent production. Test case results validate that the proposed algorithm can determine multi-skilled worker configuration strategies and achieve excellent scheduling solutions, completing the production tasks quicker, with lower costs and a more balanced task distribution.}, archive = {J_ACCESS}, author = {Zijie Ren and Hongyi Qu and Mejdl Safran and Jiafu Wan}, doi = {10.1109/ACCESS.2025.3562754}, journal = {IEEE Access}, pages = {72347-72363}, shortjournal = {IEEE Access}, title = {Dual-resource optimization configuration and collaborative scheduling for flexible job shop under worker flexibility constraints}, volume = {13}, year = {2025}, }
• (2025). Density peaks clustering algorithm based on neighborhood radius and membership degree. ACCESS, 13, 72329-72346. (www)

  @article{ , abstract = {The density peaks clustering (DPC) algorithm is a density-based clustering method that effectively identifies clusters with uniform densities. However, if the datasets have uneven density, clusters with lower densities tend to have lower decision values, which often leads to the cluster centers being overlooked. To address this limitation, a novel density peaks clustering algorithm incorporating neighborhood radius and membership degree is proposed. The method begins by introducing k-nearest neighbor density estimation to establish a density threshold, segmenting datasets into high-density and low-density regions. In the high-density region, the DPC algorithm is applied to perform initial clustering, identifying prominent cluster structures. For low-density points, neighborhood radius and density criteria are employed to assign these points to appropriate high-density clusters, thereby reducing misclassification. In addition, the membership degree concept is incorporated to improve the accuracy of low-density point assignments. Low-density points that remain unassigned undergo secondary clustering using the DPC algorithm. The proposed approach is evaluated on eight synthetic datasets and eleven real-world datasets, with comparisons to DPC-KNN, DPC, K-means, and DBSCAN. The experimental results demonstrate that the proposed algorithm consistently outperforms these methods in clustering performance, highlighting its effectiveness and robustness.}, archive = {J_ACCESS}, author = {Fuxiang Li and Tingting Jiang and Jia Wei and Shu Li and Yunxiao Shan}, doi = {10.1109/ACCESS.2025.3563990}, journal = {IEEE Access}, pages = {72329-72346}, shortjournal = {IEEE Access}, title = {Density peaks clustering algorithm based on neighborhood radius and membership degree}, volume = {13}, year = {2025}, }
• (2025). HGLFNet: Hybrid global semantic and local detail feature network for lane detection. ACCESS, 13, 72314-72328. (www)

  @article{ , abstract = {Lane detection is a crucial component of autonomous driving perception systems. Existing methods still encounter performance limitations in complex situations like occlusions and extreme illumination. To address these challenges, this paper introduces a novel Hybrid Global Semantic and Local Detail Feature Network (HGLFNet), designed to enhance lane detection accuracy and robustness in complex scenarios. Technically, we propose a Global Semantic Feature Extraction Module (GSFEM), integrating a Long-Range Information Capture Unit (LRICU) and a Multi-Scale Aggregation Unit (MSAU). GSFEM innovatively utilizes large-kernel convolutions to efficiently capture global contextual information, significantly improving the model’s perception of complete lane line shapes and overcoming the limited receptive field of conventional methods. Furthermore, we develop a Semantic Feature Fusion Module (SFFM) to effectively bridge the semantic gap between global semantic features and local detailed features. SFFM incorporates a Deformable Feature-based Attention Mechanism (DFAM) to adaptively enhance the representational power of fused features and highlight crucial lane line characteristics. HGLFNet effectively integrates global semantic context with local detailed information, enhancing the network’s ability to detect thin and occluded lane line structures. In quantitative evaluation, HGLFNet is compared with state-of-the-art methods. On the VIL-100 dataset, the F1 score and accuracy are improved by 0.75% and 0.38%, respectively, compared to ADNet. On the Tusimple dataset, the F1 score and accuracy are improved by 0.49% and 0.11%, respectively. On the more challenging CULane dataset, the F1 score is improved by 0.03%. The experimental results comprehensively demonstrate that HGLFNet surpasses the existing state-of-the-art techniques in both accuracy and efficiency, providing a novel and effective solution for lane detection in complex scenarios and showing significant potential in practical applications.}, archive = {J_ACCESS}, author = {Lei Ding and Chunhui Tang and Yi Fang}, doi = {10.1109/ACCESS.2025.3558597}, journal = {IEEE Access}, pages = {72314-72328}, shortjournal = {IEEE Access}, title = {HGLFNet: Hybrid global semantic and local detail feature network for lane detection}, volume = {13}, year = {2025}, }
• (2025). Cyber-resilient detection of power quality events with NSCT and PCA-SVM. ACCESS, 13, 72292-72313. (www)

  @article{ , abstract = {The increasing reliance on smart grids, coupled with the integration of renewable energy and growing cyber-physical interactions, has heightened the vulnerability of power systems to both power quality (PQ) disturbances and cyber-attacks. This paper presents an innovative detection framework that combines Nonsubsampled Contourlet Transform (NSCT) with Principal Component Analysis (PCA) and Support Vector Machine (SVM) classification to accurately detect and classify PQ disturbances under the influence of cyber threats, such as False Data Injection (FDI) and Denial of Service (DoS) attacks. The proposed methodology leverages NSCT’s multiscale decomposition capabilities to extract fine-grained signal features, while PCA optimizes feature selection for enhanced computational efficiency. Comprehensive experiments conducted on synthetic and real-world datasets validate the framework’s effectiveness, demonstrating superior detection accuracy, robustness to noise, and resilience against cyber-attacks. The proposed NSCT-PCA- SVM approach represents a significant step forward in ensuring secure and reliable smart grid operations.}, archive = {J_ACCESS}, author = {Sayanti Chatterjee and Pampa Sinha and Ranjith Kumar Gatla and Devineni Gireesh Kumar and D. S. Naga Malleswara Rao and Kaushik Paul and Taha Selim Ustun and Ahmet Onen}, doi = {10.1109/ACCESS.2025.3562606}, journal = {IEEE Access}, pages = {72292-72313}, shortjournal = {IEEE Access}, title = {Cyber-resilient detection of power quality events with NSCT and PCA-SVM}, volume = {13}, year = {2025}, }
• (2025). Computer-aided quantum algorithms for real-time energy market trading. ACCESS, 13, 72279-72291. (www)

  @article{ , abstract = {This study presents an innovative approach for optimizing real-time energy market trading strategies by integrating Proximal Policy Optimization (PPO) with Quantum Annealing (QA). The primary objective is to enhance trading performance by leveraging the adaptive capabilities of PPO alongside the optimization power of QA. The methodology involves using PPO to develop a dynamic trading strategy that adapts to real-time market conditions. PPO’s reinforcement learning framework is employed to manage state representations, action spaces, and reward functions, thereby continuously refining the trading policy. The optimization of trading parameters is achieved through Quantum Annealing, which solves Quadratic Unconstrained Binary Optimization (QUBO) problems derived from the trading strategy formulation. Key findings from the study demonstrate significant improvements in trading outcomes. The integration of QA with PPO led to a 25% increase in total return, a 15% enhancement in Sharpe ratio, a 10% reduction in maximum drawdown, and a 20% decrease in transaction costs. These results highlight the effectiveness of combining quantum optimization techniques with reinforcement learning for superior trading strategy development. The significance of this study lies in its contribution to advancing real-time trading strategy optimization through the synergistic use of machine learning and quantum computing. This approach offers potential applications across various financial sectors and domains requiring sophisticated real-time decision-making and optimization. Future research is suggested to further explore the scalability and practical implementation of this integrated methodology in more complex and larger-scale optimization problems.}, archive = {J_ACCESS}, author = {Jinghui Wu and Wenjun Zhu and Yun Xu and Kangan Shu and Wei Yang}, doi = {10.1109/ACCESS.2025.3553369}, journal = {IEEE Access}, pages = {72279-72291}, shortjournal = {IEEE Access}, title = {Computer-aided quantum algorithms for real-time energy market trading}, volume = {13}, year = {2025}, }
• (2025). Common-ground bridgeless unity power factor single-phase rectifier. ACCESS, 13, 72269-72278. (www)

  @article{ , abstract = {A common AC and DC ground bridgeless power converter topology is presented that performs the near-unity power factor function with low harmonic distortion of a single-phase rectifier with sinusoidal input line voltage. The operation principle and design-oriented analysis are presented, along with the transfer functions and closed-loop input current control strategy. A non-optimized experimental prototype was developed and tested in the laboratory to validate the operating principle and the results of theoretical analysis. The distinctive feature of the proposed rectifier is that it eliminates the leakage current that circulates through the parasitic capacitances existing between the power semiconductor and heatsink, which causes common-mode electromagnetic interference in conventional boost-based PFC rectifiers.}, archive = {J_ACCESS}, author = {Ivo Barbi and Hugo Rolando Estofanero Larico}, doi = {10.1109/ACCESS.2025.3563831}, journal = {IEEE Access}, pages = {72269-72278}, shortjournal = {IEEE Access}, title = {Common-ground bridgeless unity power factor single-phase rectifier}, volume = {13}, year = {2025}, }
• (2025). An early warning method based on blending of deep generative model and oversampling model for online learning. ACCESS, 13, 72248-72268. (www)

  @article{ , abstract = {Early warning for learning performance requires to identify the maximum number of at-risk students as early as possible within a semester. However, educational data often suffer from the issue of data imbalance, making it challenging to simultaneously achieve both high precision (accurate identification) and high recall (comprehensive coverage) in at-risk student detection. Deep generative models and oversampling models are effective methods to solve data imbalance issues, which can improve classification performance. This paper proposes a method that combines the advantages of deep generative models and oversampling models to build a blending model for dealing with imbalanced educational data, which can effectively improve the precision, recall, F1-score and AUC for online learning early warning. First, we compare baseline models to select the best classifier, then choose the highest-precision deep generative model and the highest-recall oversampling model to construct blending models, which are shown to improve early warning prediction metrics. Finally, interpretable models are used to analyze differences in at-risk student prediction between the blending model, deep generative model, and oversampling model. The proposed models are validated on both extremely imbalanced datasets and new semester datasets. Results show that: (1) Compared to the baseline model, both the base learners built by the deep generative model and the oversampling model can improve the evaluation metrics of the model, the deep generative base learners achieve higher precision than the oversampling model, while the oversampling base learners achieve higher recall than the deep generative base learners. (2) The blending model composed of deep generative base learner and oversampling base learner can further improve the F1-score and AUC based on their individual strengths, the proposed blending model can also conduct effective early warning three units earlier than baseline models. (3) Compared to its base learners, blending model G-B-Blending changes the key variables for prediction, and the at-risk students identified by the blending model come from the union set of at-risk students identified by GAN+GB and B-SMOTE+GB individually. (4) The blending model proposed in this paper achieves better prediction results than the baseline on both extremely imbalanced datasets and new semesters datasets, it can identify more at-risk students more accurately at earlier units, allowing teachers to save more energy and time for teaching interventions. This research provides significant insights for dealing with imbalanced datasets by blending with deep generative model and oversampling model in education.}, archive = {J_ACCESS}, author = {Mingyan Zhang and Yiqing Wang and Jui-Long Hung and Jie Wang and Chao Duan}, doi = {10.1109/ACCESS.2025.3563642}, journal = {IEEE Access}, pages = {72248-72268}, shortjournal = {IEEE Access}, title = {An early warning method based on blending of deep generative model and oversampling model for online learning}, volume = {13}, year = {2025}, }
• (2025). A scalable, lightweight AI-driven security framework for IoT ecosystems: Optimization and game theory approaches. ACCESS, 13, 72235-72247. (www)

  @article{ , abstract = {The rapid growth of IoT has introduced significant security challenges, particularly in scalability, real-time threat detection, and resource management. Traditional security models struggle with an increasing number of interconnected devices, often reacting to threats rather than proactively mitigating them. This study proposes a three-layer security framework that combines artificial intelligence-based intrusion detection, blockchain for decentralized trust management, and edge computing for efficient resource utilization. Machine learning enhances anomaly detection, blockchain ensures secure data integrity, and edge computing reduces latency. Optimization techniques improve the detection accuracy from 94.2% to 94.78%, reduce the response time by 14.98%, and optimize the energy consumption by 12.01%. Game theory models the interactions between attackers and defenders, whereas differential equations simulate system behavior under cyber threats. The performance evaluation demonstrates that the proposed framework provides a scalable, adaptive, and efficient IoT security solution, making it suitable for resource-constrained environments and real-time applications.}, archive = {J_ACCESS}, author = {Krishna Chaitanya Chaganti}, doi = {10.1109/ACCESS.2025.3558623}, journal = {IEEE Access}, pages = {72235-72247}, shortjournal = {IEEE Access}, title = {A scalable, lightweight AI-driven security framework for IoT ecosystems: Optimization and game theory approaches}, volume = {13}, year = {2025}, }
• (2025). SERLogic: A logic-integrated framework for enhancing sequential recommendations. ACCESS, 13, 72221-72234. (www)

  @article{ , abstract = {Sequential recommendation models are used to predict users’ next top-K preferred items based on their historical interactions. However, these models often struggle in “fuzzy areas” where recommendation scores are near decision thresholds, leading to false positives and false negatives. To overcome this limitation, we propose $\textsf {SERLogic}$ , an innovative framework that incorporates logic rules, termed $\mathsf {TIE^{+}\!s}$ , into existing sequential recommendation models to enhance their accuracy without the need for training a new machine learning model. $\mathsf {TIE^{+}\!s}$ represent a novel class of graph prediction rules characterized by a dual graph pattern $\mathcal {Q}$ and a dependency $X \rightarrow (x, likes, y)$ , where $\mathcal {Q}$ exhibits a dual star structure, and X extends ML sequential recommendation models and 1-WL test as predicates. With $\textsf {SERLogic}$ , we show 1) validation problem for $\mathsf {TIE^{+}\!s}$ is in polynomial time ( $\textsf {PTIME}$ ), enabling efficient verification of whether a graph satisfies a set of $\mathsf {TIE^{+}\!s}$ ; 2) creator-critic algorithm that iteratively learns high-quality $\mathsf {TIE^{+}\!s}$ ; 3) parallel algorithm that applies the discovered $\mathsf {TIE^{+}\!s}$ to generate recommendations efficiently. Empirical evaluation on real-world datasets reveals that $\textsf {SERLogic}$ significantly enhances the performance of sequential recommendation models in terms of Recall@K and NDCG@K, while also achieving superior computational efficiency.}, archive = {J_ACCESS}, author = {Lihang Fan}, doi = {10.1109/ACCESS.2025.3563977}, journal = {IEEE Access}, pages = {72221-72234}, shortjournal = {IEEE Access}, title = {SERLogic: A logic-integrated framework for enhancing sequential recommendations}, volume = {13}, year = {2025}, }
• (2025). A GAN guided NCCT to CECT synthesis with an advanced CNN-transformer aggregated generator. ACCESS, 13, 72202-72220. (www)

  @article{ , abstract = {Computed tomography (CT) is essential for diagnosing and managing various diseases, with contrast-enhanced CT (CECT) offering higher contrast images following contrast agent injection. Nevertheless, the usage of contrast agents may cause side effects. Therefore, achieving high-contrast CT images without the need for contrast agent injection is highly desirable. The main contributions of this paper are as follows: 1) We designed a GAN-guided CNN-Transformer aggregation network called GCTANet for the CECT image synthesis task. We propose a CNN-Transformer Selective Fusion Module (CTSFM) to fully exploit the interaction between local and global information for CECT image synthesis. 2) We propose a two-stage training strategy. We first train a non-contrast CT (NCCT) image synthesis model to deal with the misalignment between NCCT and CECT images. Then we trained GCTANet to predict real CECT images using synthetic NCCT images. 3) A multi-scale Patch hybrid attention block (MSPHAB) was proposed to obtain enhanced feature representations. MSPHAB consists of spatial self-attention and channel self-attention in parallel. We also propose a spatial channel information interaction module (SCIM) to fully fuse the two kinds of self-attention information to obtain a strong representation ability. We evaluated GCTANet on two private datasets and one public dataset. On the neck dataset, the PSNR and SSIM achieved were $35.46\pm 2.783$ dB and $0.970\pm 0.020$ , respectively; on the abdominal dataset, $25.75\pm 5.153$ dB and $0.827\pm 0.073$ , respectively; and on the MRI-CT dataset, $29.61\pm 1.789$ dB and $0.917\pm 0.032$ , respectively. In particular, in the area around the heart, where obvious movements and disturbances were unavoidable due to the heartbeat and breathing, GCTANet still successfully synthesized high-contrast coronary arteries, demonstrating its potential for assisting in coronary artery disease diagnosis. The results demonstrate that GCTANet outperforms existing methods.}, archive = {J_ACCESS}, author = {Haozhe Wang and Dawei Gong and Rongzhen Zhou and Junbo Liang and Ruili Zhang and Wenbin Ji and Sailing He}, doi = {10.1109/ACCESS.2025.3563375}, journal = {IEEE Access}, pages = {72202-72220}, shortjournal = {IEEE Access}, title = {A GAN guided NCCT to CECT synthesis with an advanced CNN-transformer aggregated generator}, volume = {13}, year = {2025}, }
• (2025). Toward precise quantification of energy expenditure in wheelchair users. ACCESS, 13, 72189-72201. (www)

  @article{ , abstract = {Modern electronic devices like smart bands, smartwatches, smartphones, and treadmills are widely used to track exertion metrics, also called energy expenditure, such as step counts, running, time, and distance. However, these devices often fail to meet the needs of individuals with mobility impairments, such as wheelchair users, for whom such metrics are hard to evaluate. This research introduces a tailored model to track and quantify exertion data for manual wheelchair users. The existing Heart Intensity Metric (HIM), which relies on parameters such as heart rate, weight, age, and time (exercise duration), is adapted with a revised Activity Intensity Assessor (AIA). The model incorporates critical factors for wheelchair users, including heart rate, adjusted movement status (1 for movement and zero for no movement), and inclination status, with new parameters, such as Metabolic Equivalent of Task (MET), and wheelchair speed. The revised AIA is then adapted for the energy expenditure formula to calculate calorie-burning estimation specifically for manual wheelchair users. The revised approach minimizes false positives commonly produced by existing approaches for manual wheelchair users, especially in scenarios involving non-movement exercises like upper limb activities. Unlike prior models, the proposed AIA ensures precise energy expenditure calculations, even during stationary activities, and reflects a zero-calorie expenditure when no exercise occurs. Results are statistically verified and demonstrate that traditional formulas yield inaccurate calorie estimations for wheelchair users, while the revised model aligns better with physiological realities. This work provides a practical framework for designing electronic tools that effectively track energy expenditure/total energy (ET), also known as exertion efforts, and estimate calories burnt by manual wheelchair users. The scope of this study is limited to examining energy expenditure exclusively for manual wheelchairs. The electric wheelchairs are beyond the scope of this study.}, archive = {J_ACCESS}, author = {Iftikhar Alam and Zulfiqar Ali and Wassauf Khalid and Gauhar Ali and Mohammed ELAffendi}, doi = {10.1109/ACCESS.2025.3563385}, journal = {IEEE Access}, pages = {72189-72201}, shortjournal = {IEEE Access}, title = {Toward precise quantification of energy expenditure in wheelchair users}, volume = {13}, year = {2025}, }
• (2025). Bayesian analysis enhances sales and warranty strategies for repairable industrial products by considering hybrid deterioration modes. ACCESS, 13, 72169-72188. (www)

  @article{ , abstract = {An effective warranty policy not only fulfills a manufacturer’s or vendor’s obligations but also plays a crucial role in enhancing customer confidence and encouraging future purchases. To attract more customers and drive sales, companies may extend the service life of their products. However, they cannot offer unlimited warranties to dominate the market, as the associated warranty costs will ultimately outweigh the profits. Therefore, manufacturers must strike a balance between the advantages of providing longer warranties to foster customer trust and the potential financial implications involved. Despite the significance of this issue, there has been limited research on hybrid deteriorating systems that encompass both maintainable and non-maintainable failure modes. Furthermore, conducting preventive maintenance analyses is challenging when historical failure data is insufficient. To address these gaps, this study introduces a Bayesian statistical approach to handle preventive maintenance challenges. The system’s deterioration is modeled using Non-Homogeneous Poisson processes (NHPP) with power-law failure intensity functions. A mathematical model, along with a solution algorithm, has been developed to assist manufacturers in making informed decisions regarding pricing, production, and warranty strategies. Furthermore, to facilitate the practical application of these models, the study offers solution algorithms and a computerized framework that enables decision-makers to implement automated decision-making processes.}, archive = {J_ACCESS}, author = {Chih-Chiang Fang and Liping Ma and Wenfeng Kuo}, doi = {10.1109/ACCESS.2025.3563938}, journal = {IEEE Access}, pages = {72169-72188}, shortjournal = {IEEE Access}, title = {Bayesian analysis enhances sales and warranty strategies for repairable industrial products by considering hybrid deterioration modes}, volume = {13}, year = {2025}, }
• (2025). HERCULE: High-efficiency resource coordination using kubernetes and machine learning in edge computing for improved QoS and QoE. ACCESS, 13, 72153-72168. (www)

  @article{ , abstract = {Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent request retransmissions, which are not well addressed by current state-of-the-art solutions. In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. By analyzing application keywords and edge server metrics, such as geographic location, workload, and keyword-based request frequency, the proposed solution dynamically selects the optimal edge server for application deployment. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and minimized retransmissions between servers. Compared to existing approaches, the proposed model provides a more robust solution to the complex and dynamic requirements of edge computing environments.}, archive = {J_ACCESS}, author = {Garrik Brel Jagho Mdemaya and Miguel Landry Foko Sindjoung and Milliam Maxime Zekeng Ndadji and Mthulisi Velempini}, doi = {10.1109/ACCESS.2025.3563161}, journal = {IEEE Access}, pages = {72153-72168}, shortjournal = {IEEE Access}, title = {HERCULE: High-efficiency resource coordination using kubernetes and machine learning in edge computing for improved QoS and QoE}, volume = {13}, year = {2025}, }
• (2025). Novel digital twin deployment approaches: Local and distributed digital twin. ACCESS, 13, 72142-72152. (www)

  @article{ , abstract = {The Digital Twin (DT) technology is considered as a backbone in the Industrial 4.0 revolution as it is playing a vital role in the digitization of various industries. A DT is a virtual representation of a physical entity, thus having the ability to simulate real data generated at physical space to optimize, estimate, control, monitor and forecast states/configurations. Despite enormous benefits, DT technology has several implementation challenges. Although deploying DT on edge or cloud platforms yields a plethora of services, its implementation in both spaces faces certain limitations. These limitations include latency, data communication overload, transmission energy consumption, privacy concerns, and communication inefficiencies. It is evident that these shortcomings could significantly impact real-time monitoring and control. Therefore, when considering whether to deploy DT on the edge or on the cloud, it is necessary to make a trade-off, or alternatively, adopt a hybrid approach. However, it is important to acknowledge that even with a hybrid approach, the aforementioned issues will persist to some extent. To address these challenges, this article introduces two innovative approaches. Local DT (LDT) and Distributed DT (DDT). These deployment strategies are designed to mitigate latency, minimize data communication overload, reduce energy consumption, improve communication efficiency, and strengthen privacy measures. Thus, resulting in environmental and economic sustainability. Consequently, these advancements facilitate superior real-time monitoring and control capabilities. Through the utilization of LDT and DDT methodologies, organizations can harness the full potential of DT technology, thereby maximizing its benefits.}, archive = {J_ACCESS}, author = {Shahid Rauf and Fazal Muhammad and Akhtar Badshah and Hisham Alasmary and Muhammad Waqas and Sheng Chen}, doi = {10.1109/ACCESS.2025.3561354}, journal = {IEEE Access}, pages = {72142-72152}, shortjournal = {IEEE Access}, title = {Novel digital twin deployment approaches: Local and distributed digital twin}, volume = {13}, year = {2025}, }
• (2025). Federated learning-based state of charge estimation in electric vehicles using federated adaptive client momentum. ACCESS, 13, 72128-72141. (www)

  @article{ , abstract = {Batteries are essential for Electric Vehicles (EVs). Traditional Battery Management System (BMS) algorithms can be inadequate for State of Charge (SoC) estimation due to incorrect measurements and unobservable battery characteristics. Centralized machine learning methods are used to improve SoC estimation. Both privacy and high bandwidth requirements are the main disadvantages during the implementation. Federated Learning (FL) solves these issues by performing local learning on devices, protecting data privacy, and only aggregating model updates at the central server. While FL approaches can help preserve data privacy during model training, collaborative learning can facilitate the integration of priori data learned by the agents in the fleet with the rest of the fleet members to improve charge prediction. This study proposes a new aggregation rule named Federated Adaptive Client Momentum (FedACM) to handle data imbalance and heterogeneity in SoC estimation. The proposed method is initially validated via experimental results using the collected data from Musoshi L5 type EV. It is also tested using publicly known datasets such as NASA, BMW i3, and Standford University Battery Datasets. These experiments show that the proposed aggregation rule performs better than current state-of-the-art rules for FL.}, archive = {J_ACCESS}, author = {Metin Yılmaz and Eyüp Çinar and Ahmet Yazıcı}, doi = {10.1109/ACCESS.2025.3563188}, journal = {IEEE Access}, pages = {72128-72141}, shortjournal = {IEEE Access}, title = {Federated learning-based state of charge estimation in electric vehicles using federated adaptive client momentum}, volume = {13}, year = {2025}, }
• (2025). Influencing factors on the increase in the number of traffic accidents between pedestrians and vehicles during twilight: A perspective of pedestrians. ACCESS, 13, 72116-72127. (www)

  @article{ , abstract = {Traffic accidents occur more frequently during twilight hours, particularly collisions between pedestrians and vehicles. This study aimed to identify factors contributing to this increase by examining pedestrian behavior. To investigate changes in pedestrians’ visual behavior, an experiment was conducted in which participants, wearing an eye tracker, walked along a sidewalk while encountering vehicles at different times: during daytime, twilight, and nighttime. From the obtained experimental data, visual field images were captured at the moment participants (pedestrians) first observed each approaching vehicle. An index called the extended time to collision (ETTC) was introduced to measure the time available for pedestrians to avoid potential danger. This index was used to evaluate the critical awareness timing. The analysis revealed that the ETTC at the moment when pedestrians first saw approaching vehicles was significantly shorter during twilight than during daytime or nighttime. This was because pedestrians observed fewer approaching vehicles in the distance during twilight. This finding highlights the importance of pedestrian awareness of delayed noticing of approaching vehicles during twilight to reduce the number of accidents.}, archive = {J_ACCESS}, author = {Kohei Nomoto and Yifan Chen}, doi = {10.1109/ACCESS.2025.3557936}, journal = {IEEE Access}, pages = {72116-72127}, shortjournal = {IEEE Access}, title = {Influencing factors on the increase in the number of traffic accidents between pedestrians and vehicles during twilight: A perspective of pedestrians}, volume = {13}, year = {2025}, }
• (2025). Advanced 5G channel estimation in mmWave MIMO systems: Leveraging compressive sensing for enhanced performance. ACCESS, 13, 72104-72115. (www)

  @article{ , abstract = {Pilot overhead poses a significant challenge in mmWave massive multiple-input multiple-output (MIMO) systems, as it fundamentally limits the accurate acquisition of channel state information (CSI). In this paper, we propose an enhanced adaptive channel estimation technique that leverages compressive sensing (CS) principles to effectively mitigate pilot overhead while maintaining high estimation accuracy. The proposed approach combines compressive sampling matching pursuit (CoSaMP) and sparsity adaptive matching pursuit (SAMP) algorithms, augmented by a novel iterative reweighting strategy and adaptive thresholding mechanism. The simulation results demonstrate that the proposed method achieves superior normalized mean square error (NMSE) performance compared to traditional CS-based techniques. Furthermore, the proposed technique achieves a substantial reduction in computational complexity and pilot overhead compared to traditional channel estimation methods, offering significant improvements in the performance of mmWave MIMO systems.}, archive = {J_ACCESS}, author = {Zaid Albataineh and Mohammad Al Bataineh and Khaled Farouq Hayajneh and Raed Al Athamneh}, doi = {10.1109/ACCESS.2025.3560829}, journal = {IEEE Access}, pages = {72104-72115}, shortjournal = {IEEE Access}, title = {Advanced 5G channel estimation in mmWave MIMO systems: Leveraging compressive sensing for enhanced performance}, volume = {13}, year = {2025}, }
• (2025). Sb-PiPLU: A novel parametric activation function for deep learning. ACCESS, 13, 72087-72103. (www)

  @article{ , abstract = {The choice of activation function—particularly non-linear ones—plays a vital role in enhancing the classification performance of deep neural networks. In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. Common issues include the dying neuron problem, bias shift, gradient explosion, and vanishing gradients. To address these challenges, we introduce a new activation function: Softsign-based Piecewise Parametric Linear Unit (Sb-PiPLU). This function offers improved non-linear approximation capabilities for neural networks. Its piecewise, parametric design allows for greater adaptability and flexibility, which in turn enhances overall model performance. We evaluated Sb-PiPLU through a series of image classification experiments across various Convolutional Neural Network (CNN) architectures. Additionally, we assessed its memory usage and computational cost, demonstrating that Sb-PiPLU is both stable and efficient in practical applications. Our experimental results show that Sb-PiPLU consistently outperforms conventional activation functions in both classification accuracy and computational efficiency. It achieved higher accuracy on multiple benchmark datasets, including CIFAR-10, CINIC-10, MWD, Brain Tumor, and SVHN, surpassing widely-used functions such as ReLU and Tanh. Due to its flexibility and robustness, Sb-PiPLU is particularly well-suited for complex image classification tasks.}, archive = {J_ACCESS}, author = {Ayan Mondal and Vimal K. Shrivastava and Ayan Chatterjee and Raghavendra Ramachandra}, doi = {10.1109/ACCESS.2025.3561464}, journal = {IEEE Access}, pages = {72087-72103}, shortjournal = {IEEE Access}, title = {Sb-PiPLU: A novel parametric activation function for deep learning}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-based real-time contrasts control chart using an adaptive window size. ACCESS, 13, 72077-72086. (www)

  @article{ , abstract = {In this study, we propose a real-time contrasts control chart based on reinforcement learning (RL-RTC). Effective process monitoring, which directly influences productivity and yield, has become increasingly important. The traditional RTC control chart offers a promising approach to process monitoring by transforming the monitoring problem into a real-time classification task, enabling more timely and accurate detection of issues. However, the parameters of the traditional RTC control chart are typically set based on empirical methods, which limits the ability to fine-tune them effectively. To address this limitation and improve the performance of the RTC method, we propose the RL-RTC control chart, which leverages reinforcement learning for more adaptive parameter control. The proposed method takes the data itself as a state of reinforcement learning (RL) and adaptively decides the parameter of the RTC control chart in real-time. In this paper, the control of the moving window size is defined as an action taken by the RL agent. By automatically learning an optimal policy, the RL-RTC method eliminates the need for manual parameter tuning and enhances adaptability in dynamic process environments. The RL-RTC approach can detect shifts more quickly while maintaining the ability to identify the causes of faults. Compared to a conventional RTC control chart, experimental results demonstrate that the RL-RTC method offers improved performance by fine-tuning the window size in response to changing process conditions. Therefore, a wide scope of applications is expected for adaptively controlling the other RTC control chart parameters.}, archive = {J_ACCESS}, author = {Kyuchang Chang and Seung Hwan Park}, doi = {10.1109/ACCESS.2025.3561118}, journal = {IEEE Access}, pages = {72077-72086}, shortjournal = {IEEE Access}, title = {Reinforcement learning-based real-time contrasts control chart using an adaptive window size}, volume = {13}, year = {2025}, }
• (2025). Hybrid raft-PoW blockchain consensus algorithm. ACCESS, 13, 72067-72076. (www)

  @article{ , abstract = {The Raft consensus algorithm is widely used in private networks as an alternative to the energy-intensive PoW consensus algorithm in blockchains. The Raft consensus algorithm’s voting mechanism performs well in reliable and well-planned networks with optimized timeouts. However, in unreliable or poorly configured networks, it encounters several challenges. These include multiple candidacies, repeated election cycles, insufficient or failed leader elections during network splits, prolonged leader election times and vulnerability to Sybil attacks. In this study, a novel Hybrid Raft-PoW consensus algorithm is introduced. It integrates the hash puzzle-based competition of the PoW consensus algorithm with the fast leader election mechanism of the Raft consensus algorithm. This combination ensures both speed and certainty in leader election, ensuring that leadership is delegated to the most capable nodes. At the same time, it promotes decentralization by ensuring a fair distribution across nodes, achieving at least 80% leadership distribution. Therefore, the proposed Hybrid Raft-PoW consensus algorithm improves or eliminates problems caused by Raft consensus algorithm’s leader election mechanism.}, archive = {J_ACCESS}, author = {Mehtap Köse Ulukök and Irfan Sariyildiz and Vesile Evrim}, doi = {10.1109/ACCESS.2025.3562725}, journal = {IEEE Access}, pages = {72067-72076}, shortjournal = {IEEE Access}, title = {Hybrid raft-PoW blockchain consensus algorithm}, volume = {13}, year = {2025}, }
• (2025). Fastest prostate cancer prediction on whole-mount pathological slides using self-reform multilayer transformer. ACCESS, 13, 72054-72066. (www)

  @article{ , abstract = {In the clinical diagnosis of prostate cancer (PCa), the pathological image is regarded as the “golden standard”. Pathological images are characterized by their immense size with giga-pixel. Moreover, pixels in each whole-mount slide of PCa are increased by approximately $4\sim 9$ times, therefore containing more information for diagnosis. Therefore, rapid feature extraction of large prostate slices can effectively improve the diagnostic efficiency of prostate pathological images. This article introduces the self-reform multilayer transformer (SMT) to enhance acceleration of model training and inference in prostate pathological images. The SMT workflow involves a stepwise focus among three layers, progressing from macro-scale to micro-scale. A gradient-based strategy is proposed to efficiently focus on crucial regions, and a backward rethinking strategy is also proposed to enhance the precision of former layers. This iterative process of forward and backward training improves classification and focusing performance. Extensive experiments demonstrate that SMT achieves up to $2.71\times $ faster training and $775\times $ faster inference compared to traditional patch-cropping methods, while maintaining comparable accuracy and requiring less storage compared to existing SOTA methods. The current approach of cropping each slide into patches results in extensive time and storage consumption. The SMT of this paper applies forward focusing and backward rethinking to achieve fast and accurate diagnosis of PCa. Even for samples with microcarcinoma, SMT can still focus on cancer region precisely, which has great significance for avoiding missed diagnosis in clinic. This paper indicates that most regions of pathological slides are inessential or repetitive for diagnosis. The strategy of SMT can provide a new perspective for future task on pathological images.}, archive = {J_ACCESS}, author = {Linjie Xu and Xiaotian Yu and Haoming Luo and Jili Wang and Wenjie Liang and Jing Zhang and Zunlei Feng and Cheng Lu and Xiuming Zhang}, doi = {10.1109/ACCESS.2025.3557946}, journal = {IEEE Access}, pages = {72054-72066}, shortjournal = {IEEE Access}, title = {Fastest prostate cancer prediction on whole-mount pathological slides using self-reform multilayer transformer}, volume = {13}, year = {2025}, }
• (2025). RSSI-based autonomous tracking system for radio-tagged flying insects using UAV with rotational antenna. ACCESS, 13, 72040-72053. (www)

  @article{ , abstract = {The escalating effects of climate change have heightened the urgency of ecosystem conservation efforts worldwide. In this context, pollinators such as honeybees play a critical role in maintaining ecosystem health. However, they face appreciable threats, including the proliferation of invasive alien species (IAS) and colony collapse disorder. In this study, we propose a novel tracking system that uses unmanned aerial vehicles (UAVs) equipped with rotational directional antennas and radio telemetry systems. This approach, which represents an improvement over our previous works, aims to protect biodiversity and ecosystem resilience by mitigating the adverse impacts of IAS on pollinator populations. To implement the proposed tracking strategy, a motor-based directional antenna that can rotate 360° is attached to a UAV, and the received signal strength indicator (RSSI) value is used to localize the hornets to which sensors are attached. The proposed tracking system was verified in a simulated field environment that was constructed by considering the RSSI values obtained in a forest. In addition, the robustness and field applicability of the tracking system were improved by applying it to scenarios consisting of different paths that were constructed considering the dynamic nature of hornets.}, archive = {J_ACCESS}, author = {Jeonghyeon Pak and Bosung Kim and Hyoung Il Son}, doi = {10.1109/ACCESS.2025.3562007}, journal = {IEEE Access}, pages = {72040-72053}, shortjournal = {IEEE Access}, title = {RSSI-based autonomous tracking system for radio-tagged flying insects using UAV with rotational antenna}, volume = {13}, year = {2025}, }
• (2025). Observability in microservices: An in-depth exploration of frameworks, challenges, and deployment paradigms. ACCESS, 13, 72011-72039. (www)

  @article{ , abstract = {The widespread adoption of microservices-based architectures in native cloud systems has amplified the need for robust observability strategies to ensure system reliability and performance. Microservices, while enabling scalability and flexibility, introduce complexities such as distributed failure points, performance bottlenecks, and resource mismanagement. This paper provides a comprehensive review of state-of-the-art observability frameworks and tools designed for microservices architecture. It focuses on key aspects of observability at the system, service, and network levels, utilizing logs, traces, and metrics as core data sources. A thematic taxonomy is proposed, classifying the diverse approaches to monitoring microservices based on implementation paradigms, deployment platforms, and architecture patterns. The study compares existing solutions in terms of their capabilities, limitations, and effectiveness in addressing observability challenges in cloud, edge, and fog environments. Furthermore, open research issues are identified, and practical recommendations are provided to optimize observability in complex, distributed microservices ecosystems. This work aims to serve as a valuable resource for system architects, DevOps engineers, and researchers striving to enhance the reliability and performance of modern cloud-native systems.}, archive = {J_ACCESS}, author = {Ummay Faseeha and Hassan Jamil Syed and Fahad Samad and Sehar Zehra and Hamza Ahmed}, doi = {10.1109/ACCESS.2025.3562125}, journal = {IEEE Access}, pages = {72011-72039}, shortjournal = {IEEE Access}, title = {Observability in microservices: An in-depth exploration of frameworks, challenges, and deployment paradigms}, volume = {13}, year = {2025}, }
• (2025). A comparison of the meta quest pro and HTC vive focus 3 eye-tracking systems: Analysis of data accuracy and spatial precision. ACCESS, 13, 71995-72010. (www)

  @article{ , abstract = {The rise of Virtual Reality has highlighted eye gaze as a key interaction method. Data reliability becomes critical in this context, with gaze accuracy and precision serving as leading indicators of data quality. This study compared the spatial accuracy and precision of Meta Quest Pro and HTC Vive Focus 3 headsets using eye movement data collected during a subjective experiment involving 30 users. Participants were asked to look at visual stimuli placed in a virtual environment under both head-free and head-constrained conditions. The stimuli were positioned at various locations and distances from the users. The analysis revealed inconsistencies between manufacturer-provided data, obtained under ideal conditions, and data collected in different settings. Moreover, the results showed greater spatial accuracy for Meta Quest Pro and higher spatial precision for HTC Vive Focus 3. This study aims to offer an extensive examination of the performance of these systems, thus assisting researchers in choosing suitable eye-tracking technology for diverse applications.}, archive = {J_ACCESS}, author = {Erica Rocchi and Anna Ferrarotti and Marco Carli}, doi = {10.1109/ACCESS.2025.3562672}, journal = {IEEE Access}, pages = {71995-72010}, shortjournal = {IEEE Access}, title = {A comparison of the meta quest pro and HTC vive focus 3 eye-tracking systems: Analysis of data accuracy and spatial precision}, volume = {13}, year = {2025}, }
• (2025). Inversion of magnetic anomaly based on cross attention transformer. ACCESS, 13, 71984-71994. (www)

  @article{ , abstract = {Three-dimensional magnetic anomaly inversion is regarded as one of the most effective methods for accurately retrieving subsurface magnetization distributions. However, existing deep learning methods for magnetic anomaly inversion suffer from issues such as the lack of accuracy in some model structures, poor boundary details, and the skin effect. To address this technical challenge, we propose a magnetic anomaly inversion method based on Transformer architectures, with constraints from magnetic anomaly measurement data. Our method employs a hierarchical encoder-decoder network constructed with Transformer Blocks and introduces three key innovations: 1) We propose a Transformer Block based on cross-attention mechanism. Leveraging this mechanism, the Transformer Block can extract features from both magnetic anomaly and magnetic gradient anomaly data, thereby significantly enhancing the accuracy of boundary detection. 2) We propose a learnable Multi-Scale Feature Fusion Module. This module is devised to integrate the multi-scale features from each stage of the encoder, facilitating the decoder to achieve high-precision inversion. 3) We propose a forward constraint loss function. During network training, this loss function ensures that the inversion results adhere to geophysical principles. This methodology not only elevates the inversion accuracy but also effectively alleviates the skin effect. Experimental results show that, compared to other methods, our approach can accurately reconstruct the shape and location of the magnetization model, improve structural accuracy, enhance boundary details, and reduce the skin effect. Furthermore, the method was applied to magnetic anomaly data from a region in Tianjin, China, successfully predicting the distribution of magnetically related pipeline. This demonstrates its potential as a valuable tool for magnetic anomaly inversion.}, archive = {J_ACCESS}, author = {Juntao Lei and Jieru Chi and Shandong Li}, doi = {10.1109/ACCESS.2025.3563187}, journal = {IEEE Access}, pages = {71984-71994}, shortjournal = {IEEE Access}, title = {Inversion of magnetic anomaly based on cross attention transformer}, volume = {13}, year = {2025}, }
• (2025). Challenges and solutions in implementing informed consent in digital environments: A scoping review. ACCESS, 13, 71965-71983. (www)

  @article{ , abstract = {In times of ubiquitous data collection and processing, the need for privacy and control is stronger than ever. The implementation of informed consent is becoming increasingly important. The obligation to obtain informed consent and the user’s right to information and to refuse or withdraw consent is already defined in the GDPR. Particularly within the mHealth sector, where the collection of particularly sensitive health data occurs, the realisation of informed consent presents an important challenge. However, many applications are still not compliant, and companies seem to struggle with the implementation of effective informed consent. This scoping review analyses how the technical implementation of informed consent has been addressed in the literature to date, what challenges need to be overcome when implementing informed consent, and what solutions are proposed and discussed in the current literature on the implementation of informed consent.}, archive = {J_ACCESS}, author = {Ramona Schmidt and Ina Schiering and Harald Zwingelberg and Michael Friedewald}, doi = {10.1109/ACCESS.2025.3562773}, journal = {IEEE Access}, pages = {71965-71983}, shortjournal = {IEEE Access}, title = {Challenges and solutions in implementing informed consent in digital environments: A scoping review}, volume = {13}, year = {2025}, }
• (2025). A low computational cost deep learning approach for localization and classification of diseases and pests in coffee leaves. ACCESS, 13, 71943-71964. (www)

  @article{ , abstract = {Coffee cultivation is of extreme economic importance in many regions of the world, but productivity is hampered by the various diseases and pests that affect the leaves of the plants, damaging both the quality and yield of the harvest. In this context, deep learning presents itself as a promising solution for the automatic identification of plant diseases, reducing dependence on human inspection and increasing efficiency in crop management. In this sense, this study proposes a novel two-stage approach, detecting the diseased region of the coffee leaf and classifying the diseases into Miner, Rust, Cercospora and Phoma on coffee leaves. A new dataset, derived from the BRACOL and Diseases and Pests in Coffee Leaves datasets, was created and used to improve class balance and robustness. In the first stage, the YOLOv8 model is being used to detect the diseased regions. For the second stage, the InceptionResNetv2, DenseNet169, Resnet50 and ShuffleNet models are being trained and used to classify the detected region, and a modification to a low computational cost classification architecture called SmallPavicNet-MC is being proposed. The results obtained are compared and the performance analysis of the detection models shows that YOLOv8 obtained the best performance with a mAP (Mean Average Precision) of 85.1% and for classification the DenseNet169 model obtained the highest average accuracy with 97.93%. The SmallPavicNet-MC model presents itself as the best alternative with reduced complexity and an accuracy of 97.77%. The combination of promising performance and reduced computational cost suggests that SmallPavicNet-MC can be integrated into plantation monitoring systems, contributing to more effective management of diseases and pests affecting coffee production.}, archive = {J_ACCESS}, author = {Clécio Elias Silva E. Silva and Jonatan Borges Fragoso and Thuanne Paixão and Ana Beatriz Alvarez and Facundo Palomino-Quispe}, doi = {10.1109/ACCESS.2025.3562832}, journal = {IEEE Access}, pages = {71943-71964}, shortjournal = {IEEE Access}, title = {A low computational cost deep learning approach for localization and classification of diseases and pests in coffee leaves}, volume = {13}, year = {2025}, }
• (2025). Adaptive recursive terminal sliding mode control for MEMS gyros using improved neural network with constrained input mapping. ACCESS, 13, 71930-71942. (www)

  @article{ , abstract = {To drive the gyroscope into a command vibration mode, and to simultaneously estimate the angular velocity, this paper proposes an improved fully adjusted neural network-based adaptive recursive terminal sliding mode control for Z-axis MEMS gyroscopes. Firstly, a recursive terminal sliding mode control method is proposed to drive the gyroscope into a desired vibration mode, and the gyro tracking error can converge to 0 in a finite time. Then, the time-varying angular velocity in the gyro system is estimated using a modified neural network, in which a constrained input mapping mechanism is introduced to map the neural network input signal into a predefined range. The constrained input mapping can help to guide the neural network parameter initialization to improve the neural network parameter adjustment efficiency. Meanwhile, the Taylor expansion technique is integrated into the design process to generate adaptive algorithms to tune the center and width in the neural network to enhance the adaptation capacity of the neural network. The performance of the proposed method is evaluated on a Z-axis MEMS gyroscope system through numerical simulations, and results validate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Luo Laiwu and Zhang Xinsong and Li Zimeng and Zhang Shengcheng and Guo Yunxiang and Ren Lei and Lu Cheng}, doi = {10.1109/ACCESS.2024.3508833}, journal = {IEEE Access}, pages = {71930-71942}, shortjournal = {IEEE Access}, title = {Adaptive recursive terminal sliding mode control for MEMS gyros using improved neural network with constrained input mapping}, volume = {13}, year = {2025}, }
• (2025). Model-based systems engineering for digital twin system development applied to an aircraft seat test bench. ACCESS, 13, 71908-71929. (www)

  @article{ , abstract = {In recent years, the Digital Twin has attracted significant attention in academia and industry as a powerful technology for creating virtual replicas of physical systems tailored to specific purposes. Digital Twins can be developed at various levels of maturity, accommodating different system types and scopes. Consequently, diverse domain-specific approaches to Digital Twins development exist, yet no standardized method defines, verifies, and validates their requirements. This is essential for a consistent and reliable implementation of Digital Twins. This paper explores the application of Model-Based Systems Engineering in the design, development, verification, and validation of a Digital Twin System. It presents a comprehensive methodology that encompasses all phases of a Digital Twin System’s life cycle. The methodology was applied to model the requirements and the functional, behavioral, and structural aspects of a Digital Twin System for an aircraft seat testbench, supporting its design, implementation, and operation at the prototype level. The Digital Twin replicates the seat testbench, enabling simulations for early system validation under various conditions. Integrating Model-Based Systems Engineering with Digital Twin technologies enhances the clear definition of scope and technical requirements, contributing to more effective testing and development processes in the aeronautical industry. This work underscores the benefits of a model-driven approach for Digital Twin design, laying the foundation for future applications in complex engineering systems due to its repeatability.}, archive = {J_ACCESS}, author = {Agnese Pasquariello and Imane Bouhali and Dominik Leherbauer and Nourhene Abdeljabbar and Faida Mhenni and Stanislao Patalano and Peter Hehenberger and Andrea Rega}, doi = {10.1109/ACCESS.2025.3562932}, journal = {IEEE Access}, pages = {71908-71929}, shortjournal = {IEEE Access}, title = {Model-based systems engineering for digital twin system development applied to an aircraft seat test bench}, volume = {13}, year = {2025}, }
• (2025). Driver drowsiness detection using swin transformer and diffusion models for robust image denoising. ACCESS, 13, 71880-71907. (www)

  @article{ , abstract = {With the rapid development of intelligent transportation systems and growing emphasis on driver safety, real-time detection of driver drowsiness has become a critical area of research. This study presents a robust and scalable driver drowsiness detection framework that integrates a Swin Transformer-based deep learning model with a diffusion model for image denoising. While conventional convolutional neural networks (CNNs) are effective in standard vision tasks, they often suffer performance degradation in real-world driving scenarios due to noise, poor lighting, motion blur, and adversarial attacks. To address these challenges, the proposed model focuses on eye-state detection, specifically, prolonged eye closure, as a primary indicator of driver disengagement and fatigue. Our system introduces a novel preprocessing stage using a denoising diffusion model built on a U-Net encoder-decoder architecture, effectively mitigating the impact of Gaussian noise and adversarial perturbations. Additionally, we incorporate adversarial training with Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks, demonstrating significant improvements in classification accuracy and resilience. Evaluations are conducted on two benchmark datasets, Eye-Blink and Closed Eyes in the Wild (CEW), under both clean and noisy conditions. Comparative experiments show that the proposed system outperforms several state-of-the-art models, including ViT, ResNet50V2, InceptionV3, MobileNet, DenseNet169, and VGG19, in terms of accuracy (up to 99.82%), PSNR (up to 41.61 dB), and SSIM (up to 0.984), while maintaining competitive inference times suitable for practical deployment. Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adversarial conditions. While this study primarily targets static image datasets, preliminary evaluations on dynamic video frames suggest potential for real-time monitoring applications. Overall, this research delivers a high-performing driver monitoring system capable of real-time drowsiness detection, even under adverse visual conditions. It lays a strong foundation for future extensions, including temporal modeling, real-time deployment, and multimodal integration (e.g., combining visual input with physiological signals such as EEG and heart rate) to further enhance driver safety and awareness in smart vehicles.}, archive = {J_ACCESS}, author = {Samy Abd El-Nabi and Ahmed F. Ibrahim and El-Sayed M. El-Rabaie and Osama F. Hassan and Naglaa F. Soliman and Khalil F. Ramadan and Walid El-Shafai}, doi = {10.1109/ACCESS.2025.3561717}, journal = {IEEE Access}, pages = {71880-71907}, shortjournal = {IEEE Access}, title = {Driver drowsiness detection using swin transformer and diffusion models for robust image denoising}, volume = {13}, year = {2025}, }
• (2025). Technology anxiety in virtual reality adoption: Examining the impact of age, past experience, and cybersickness. ACCESS, 13, 71858-71879. (www)

  @article{ , abstract = {This study examines the role of Technology Anxiety (TA), age, past use, and cybersickness in the adoption of Virtual Reality (VR) technology. Using an extended Technology Acceptance Model (TAM), the research integrates age and past use as antecedents of TA and evaluates their influence on perceived ease of use (PEoU), perceived enjoyment (PENJ), and user attitudes. Data from 206 participants were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) following a VR pilgrimage experience. The findings challenge conventional assumptions, revealing that past VR use increased TA, contradicting prior studies that associate familiarity with reduced anxiety. Additionally, older users exhibited lower TA levels than younger participants, highlighting a potential shift in how age influences technology adoption. TA significantly enhanced PENJ, indicating that anxiety may amplify emotional engagement in immersive settings, rather than solely acting as a barrier. While TA enhanced PEoU, it had a negative correlation with cybersickness, suggesting that anxious users might interact with VR more cautiously, thereby limiting sensory mismatches. Moreover, cybersickness did not significantly influence attitudes toward the system, emphasizing the dominance of engagement over physical discomfort in emotionally significant experiences. Attitude toward the system strongly predicted use intention, highlighting the necessity of designing VR experiences that balance usability with emotional engagement. This study provides new insights into the psychological and demographic factors influencing VR adoption and offers practical strategies for optimizing user experience, particularly in religious and cultural applications.}, archive = {J_ACCESS}, author = {Eman Al Khalifah and Ramy Hammady and Mahmoud Abdelrahman and Ons Al-Shamaileh and Mostafa Marghany and Hatana El-Jarn and Alyaa Darwish and Yusuf Kurt}, doi = {10.1109/ACCESS.2025.3562383}, journal = {IEEE Access}, pages = {71858-71879}, shortjournal = {IEEE Access}, title = {Technology anxiety in virtual reality adoption: Examining the impact of age, past experience, and cybersickness}, volume = {13}, year = {2025}, }
• (2025). An explainable AI and optimized multi-branch convolutional neural network model for eye anemia diagnosis. ACCESS, 13, 71840-71857. (www)

  @article{ , abstract = {This paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area Under the Curve (AUC) of 0.973, indicating strong discriminative power. The parallel branch CNN architecture significantly improves training speed and reduces inference time. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization effectively clusters data points, showcasing the model’s ability to distinguish between anemic and non-anemic cases. To ensure model transparency and reliability, we utilize the SHapley Additive exPlanations (SHAP) method to understand feature importance. This non-invasive approach holds significant promise for early and efficient anemia detection, particularly in resource-constrained settings.}, archive = {J_ACCESS}, author = {Kamel K. Mohammed and Nadia Dahmani and Rania Ahmed and Ashraf Darwish and Aboul Ella Hassanien}, doi = {10.1109/ACCESS.2025.3560689}, journal = {IEEE Access}, pages = {71840-71857}, shortjournal = {IEEE Access}, title = {An explainable AI and optimized multi-branch convolutional neural network model for eye anemia diagnosis}, volume = {13}, year = {2025}, }
• (2025). BorB: A novel image segmentation technique for improving plant disease classification with deep learning models. ACCESS, 13, 71822-71839. (www)

  @article{ , abstract = {Disease detection from leaf images has been among the popular studies in recent years. Classifying leaf diseases using computational methods provides great convenience for farming. In the studies carried out in this field, systems that work with high accuracy and are least affected by environmental factors that can be used in agricultural lands come to the fore. This study investigates the application of deep learning architectures for accurate and efficient plant disease detection within the context of the ongoing digital transformation of the agricultural sector. Recognizing the critical role of AI in modernizing agriculture, this research focuses on enhancing the accuracy of the classification of plant diseases. To facilitate this research, a novel dataset, “EruCauliflowerDB”, was meticulously curated, comprising high-resolution images of cauliflower plants infected with Alternaria Leaf Spot and Black Rot. The obtained EruCauliflower dataset contains 114 images from the Alternaria Leaf Spot disease class and 99 images from the Black Rot disease class. A novel integrated classification system was developed, encompassing three key stages. First, a novel segmentation method, “BorB,” was introduced to effectively isolate diseased leaf regions. This segmentation method enables us to extract features of leaf images in Lab and RGB formats. Combining the features obtained from the two image formats with the OR logical operation separates the leaf region from the background. Second, data augmentation techniques, including geometric transformations, were applied to the segmented images to enhance data diversity and improve model robustness. Finally, four state-of-the-art deep learning models—VGG16, ResNet50, EfficientNetB3, and MobileNetV3 Large—were employed for disease classification. The proposed integrated system demonstrated exceptional performance, achieving 100% classification accuracy on the EruCauliflowerDB dataset across all four models. To assess the system’s robustness, further evaluations were conducted on the independent MangoLeafBD dataset, yielding consistent results with 100% classification accuracy. The proposed Integrated Classifier method was applied by selecting 15 classes from the PlantVillage, another multi-class dataset. As a result of the experiments, PlantVillage plant leaf images were classified with 99.78% accuracy. Experimental results show that the proposed method can be effectively utilized in real-world agricultural settings to assist farmers in early disease detection, thereby reducing crop losses and improving yield quality.}, archive = {J_ACCESS}, author = {T. Ozcan and E. Polat}, doi = {10.1109/ACCESS.2025.3563160}, journal = {IEEE Access}, pages = {71822-71839}, shortjournal = {IEEE Access}, title = {BorB: A novel image segmentation technique for improving plant disease classification with deep learning models}, volume = {13}, year = {2025}, }
• (2025). New monolithic modular DC-DC converter for constant current offshore interconnection with VSC HVdc network. ACCESS, 13, 71806-71821. (www)

  @article{ , abstract = {The rapid growth in HVDC (High Voltage Direct Current) grids has shown the falls of point-to-point connections. Several challenges such as the requirement of DC (Direct Current) fault blocking capability, interfacing of different grounding schemes, offering multi-vendor interoperability, and difficult to achieve high DC voltage stepping, represent serious issues to deployment of HVDC grids. DC-DC converters are considered the optimum candidate to overcome these challenges in HVDC grids interconnection. In this paper, a novel isolated hybrid monolithic modular DC-DC converter is proposed that interconnects LCC/VSC (Line Commutated Converter/Voltage Source Converter) based HVDC networks. It achieves smaller count of semiconductors, lower conduction losses and DC fault blocking. Detailed mathematical analysis, design, and control of the proposed DC-DC converter are illustrated. Also, both simulation model and experimental test rig are built to validate the proposed DC-DC converter under different normal operational and fault scenarios.}, archive = {J_ACCESS}, author = {Mohamed Mansour and Mohamed G. Abdel-Moneim and Ayman S. Abdel-Khalik and Shehab Ahmed and Khaled H. Ahmed}, doi = {10.1109/ACCESS.2025.3562802}, journal = {IEEE Access}, pages = {71806-71821}, shortjournal = {IEEE Access}, title = {New monolithic modular DC-DC converter for constant current offshore interconnection with VSC HVdc network}, volume = {13}, year = {2025}, }
• (2025). Eco-driving level evaluation model for electric buses entering and leaving stops. ACCESS, 13, 71792-71805. (www)

  @article{ , abstract = {This paper evaluates the ecological level of driving behavior of electric buses when entering and leaving stops. A dataset of entering and leaving stops is first created based on the natural driving data of electric buses. The representative parameters of driving behaviors for entering and leaving stops are then selected through correlation analysis and multiple stepwise linear regression analysis. Afterwards, the threshold value for defining the eco-driving behavior is determined by analyzing the energy consumption characteristics of entering and leaving stops. Finally, the Random Forest (RF), Gradient-Boosted Decision Trees (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms are applied to develop the evaluation models of eco-driving level for entering and leaving stops. The obtained results show that the accuracies of the LightGBM model for the evaluation of the eco-driving level in entering and leaving stops are 89% and 86.7%, respectively. These values are better than those of the RF and GBDT algorithms, and thus they demonstrate that the LightGBM model can more accurately evaluate the eco-driving in entering and leaving stops.}, archive = {J_ACCESS}, author = {Aihong Lyu and Huiming Zhang and Yubo Shen and Yali Zhang}, doi = {10.1109/ACCESS.2025.3561469}, journal = {IEEE Access}, pages = {71792-71805}, shortjournal = {IEEE Access}, title = {Eco-driving level evaluation model for electric buses entering and leaving stops}, volume = {13}, year = {2025}, }
• (2025). Relationships among e-voting, e-auction, e-cheque, and e-cash. ACCESS, 13, 71773-71791. (www)

  @article{ , abstract = {This paper illustrates how a transformation technique can be used to demonstrate the relationship among e-voting, e-auction, e-cheque, and e-cash, even though they serve distinct purposes. Initially, we examine the definitions of e-cheque and e-cash schemes, along with their respective security models. Subsequently, we develop a systematic framework to transform e-cheque into e-cash, supported by formal proof demonstrating the security of the conversion. As a corollary, we further exemplify this integration with a generic transformation framework to obtain e-cash from e-voting directly by combining an existing transformation of e-voting into e-cheque and the transformation of e-cheque into e-cash which we established earlier. We apply our transformation framework to Li et al.’s transformed e-cheque scheme and Li et al.’s e-voting scheme to obtain a concrete, secure e-cash scheme that is IND-CCEA, IND-CIA, and EUF-CIA as an instance. Finally, we examine how our result contributes to the integration and relationship between e-voting, e-auction, e-cheque, and e-cash systems.}, archive = {J_ACCESS}, author = {Yun-Xing Kho and Swee-Huay Heng and Syh-Yuan Tan and Ji-Jian Chin}, doi = {10.1109/ACCESS.2025.3560552}, journal = {IEEE Access}, pages = {71773-71791}, shortjournal = {IEEE Access}, title = {Relationships among e-voting, e-auction, e-cheque, and e-cash}, volume = {13}, year = {2025}, }
• (2025). Critical review on powertrain technologies for electric vehicles: Classification, broadly adopted topologies, and future challenges. ACCESS, 13, 71755-71772. (www)

  @article{ , abstract = {The shift toward electric vehicles (EVs) is inspired by the commitment to mitigate emissions and reliance on fossil fuels, with drive systems at the core of the transformation. EVs replace the traditionally internal combustion engines (ICE) by adopting electric motors powered by batteries. However, there are major issues that may complicate the adoption of EVs including battery limitations, energy density, charging speed, insufficient charging stations, cost, and reliance on critical materials for instance, lithium and cobalt. This paper provides a holistic review about powertrain technologies for EVs discussing the key elements to be considered to ensure efficient charging system, high output power to increase the charging speed, etc. These can be achieved by incorporating cutting-edge technologies in switching power electronic devices (i.e., utilizing silicon-carbide and gallium-nitride), broadly efficient power topologies, leveraging solid-state and silicon anode batteries, thermal management, etc. It also explores several critical technological challenges that impede the performance of EVs, including battery degradation, charging infrastructure, and other related issues along with solutions to address these challenges, such as advancements in battery chemistry, the development of higher energy-density materials, and the implementation of advanced thermal management systems to enhance overall efficiency and longevity. In top of that, this review clearly highlights the classification of the recently adopted power train technologies, broadly adopted converters, as well as future trends on breakthroughs in next-generation solid-state batteries with higher energy density and faster charging, and charging station accessibility improvement. It also offers a constructive insight on integrating some renewable energy sources to enhance EV accessibility and sustainability. Overall, while challenges remain, innovations in batteries, electronics, and infrastructure are paving the way for broader EV adoption.}, archive = {J_ACCESS}, author = {Sadeq Ali Qasem Mohammed and Samer Saleh Hakami and Mahmoud Kassas and Mohammad M. Almuhaini}, doi = {10.1109/ACCESS.2025.3561577}, journal = {IEEE Access}, pages = {71755-71772}, shortjournal = {IEEE Access}, title = {Critical review on powertrain technologies for electric vehicles: Classification, broadly adopted topologies, and future challenges}, volume = {13}, year = {2025}, }
• (2025). A comparative study between soft actor-critic (SAC) and deep deterministic policy gradient (DDPG) algorithms for solar PV MPPT control under partial shading conditions. ACCESS, 13, 71738-71754. (www)

  @article{ , abstract = {The use of photovoltaic (PV) arrays in smart grid systems is growing due to the increasing energy demand and greenhouse gas emissions. However, due to the intermittent nature of PV arrays, the Maximum Power Point Tracking (MPPT) algorithm is typically employed to optimize the system’s energy production. In the past, the conventional perturb and observe (P&O) method was proposed for solar PV MPPT control. While the P&O method can estimate the PV maximum power under uniform irradiation, it often exhibits sluggish tracking and unstable steady-state oscillations and fails to track the global maximum power point (GMPP) under partial shading conditions (PSCs). These problems have been addressed using deep reinforcement learning algorithms, such as the deep deterministic policy gradient (DDPG) algorithm. However, due to the DDPG’s intrinsic drawbacks, such as unstable training, Q-value overestimation, brittle convergence, and hyperparameter sensitivity, it often produces steady-state power oscillations near the GMPP under PSCs, resulting in power loss. This paper presents a soft actor-critic (SAC) algorithm for solving PV MPPT control problems under PSCs. Unlike DDPG, which utilizes only one Q-network in the critic, SAC utilizes two Q-networks in the critic and maximum entropy policy in the reward function, which guarantees its training stability and improves its exploration and robustness in the presence of “estimation and model errors”. Despite its potential, the SAC-based MPPT approach has not been extensively explored or compared with DDPG to determine the superior method for PV MPPT control. This paper provides a comprehensible comparative analysis of DDPG and SAC, including their optimal hyperparameter configurations for PV MPPT control. To solve the MPPT control problem, the mathematical model of the boost converter and the solar PV system were developed. Then, a Markov Decision Process model was formulated, which represents the PV system’s behavior. For completeness in the comparison, the conventional P&O algorithm was also included. Simulation results show that SAC and DDPG algorithms outperform the P&O method under PSCs and varying irradiance levels. It is shown that the SAC algorithm exhibits superior performance, achieving high tracking efficiency and eliminating power oscillations near the PV MPP and GMPP compared to the DDPG method.}, archive = {J_ACCESS}, author = {Sampson E. Nwachukwu and Komla A. Folly and Kehinde O. Awodele}, doi = {10.1109/ACCESS.2025.3561807}, journal = {IEEE Access}, pages = {71738-71754}, shortjournal = {IEEE Access}, title = {A comparative study between soft actor-critic (SAC) and deep deterministic policy gradient (DDPG) algorithms for solar PV MPPT control under partial shading conditions}, volume = {13}, year = {2025}, }
• (2025). Impact of elevation angle on multi-beam LEO satellite communication systems. ACCESS, 13, 71723-71737. (www)

  @article{ , abstract = {Compared to well-established geosynchronous equatorial orbit (GEO) satellite networks, low Earth orbit (LEO) satellites bring new challenges to overcome, such as the distortion of the satellite footprint with varying elevation angle. The impact of the elevation angle on system behavior is not sufficiently studied in literature, and guidelines to parameterize LEO systems are lacking. This paper addresses these gaps by providing a framework to analyze the satellite footprint behavior of arbitrary multi-beam satellite systems with large antenna arrays and analyzing the system behavior of a LEO satellite operating in the Ka-band (30GHz) for varying elevation angle and serving area size. The analysis considers the directivity and antenna array steering of the antenna array and the curvature of the Earth. The provided framework allows repeatable analysis and offers a means to parameterize systems in terms of serving area size, beam design, and operating elevation angles. Analysis over elevation angles confirms the strong influence of the satellite elevation angle on the system performance and indicates that elevation angle dependence of LEO systems needs to be considered in the evaluation of future technologies. It is shown that the system drifts from a noise-limited regime at high elevation angles to an interference-limited regime with decreasing elevation angle. The findings suggest a minimum elevation angle of 30° for practical systems, as lower elevation angles show excessive propagation loss and severe interference due to beam distortion. Link budget analysis further indicates that systems require highly directional antennas with large gain to serve handheld user devices.}, archive = {J_ACCESS}, author = {Agnes Fastenbauer and Megumi Kaneko and Philipp Svoboda and Markus Rupp}, doi = {10.1109/ACCESS.2025.3563252}, journal = {IEEE Access}, pages = {71723-71737}, shortjournal = {IEEE Access}, title = {Impact of elevation angle on multi-beam LEO satellite communication systems}, volume = {13}, year = {2025}, }
• (2025). Low complexity and scalable architecture of dual-mode training and inference hardware accelerator for deep Q-network (DQN) based edge computing. ACCESS, 13, 71705-71722. (www)

  @article{ , abstract = {Reinforcement Learning (RL) has shown great potential in a wide range of applications, including robotics, autonomous vehicles, and communication systems. However, deploying RL on edge devices poses significant challenges due to limited computational resources and the complexity of real-time decision-making. This paper proposes a novel hardware accelerator architecture for Deep Q-Networks (DQN) tailored to edge computing environments. The design supports both inference and training modes and utilizes shared hardware modules, approximate activation functions, and lightweight multipliers to reduce area and power consumption. The system also enables flexible configuration of neural network parameters and the execution of key RL components—such as policy generation, environment modeling, and reward calculation—via software on the processing system (PS). Quantization techniques, including Piecewise Linear (PWL) approximations and fixed-point representation, are employed to maintain computational accuracy and model convergence. The proposed accelerator, implemented on an Ultra96 FPGA at 70 MHz, achieves $671\times $ and $287\times $ speedups for inference and training, respectively, with only 13 mW power consumption. This architecture provides an efficient and scalable solution for deploying RL-based autonomous systems in resource-constrained edge environments.}, archive = {J_ACCESS}, author = {A. Sumarudin and Nana Sutisna and Infall Syafalni and Bambang Riyanto Trilaksono and Trio Adiono}, doi = {10.1109/ACCESS.2025.3562835}, journal = {IEEE Access}, pages = {71705-71722}, shortjournal = {IEEE Access}, title = {Low complexity and scalable architecture of dual-mode training and inference hardware accelerator for deep Q-network (DQN) based edge computing}, volume = {13}, year = {2025}, }
• (2025). Verifying robustness of neural networks in vision-based end-to-end autonomous driving. ACCESS, 13, 71688-71704. (www)

  @article{ , abstract = {This paper addresses the verification of neural network robustness against perturbations of input data in vision-based end-to-end autonomous driving systems. The main contributions of this work are: i) We provide a comprehensive analysis of current neural-network-based perception and decision-making components in autonomous vehicles, highlighting their susceptibility to attacks or perturbation. ii) We develop and implement a novel framework for systematically evaluating and verifying the robustness of neural networks against such perturbations, focusing on imperceptible image modifications. iii) We present empirical results demonstrating our verification framework’s effectiveness in identifying weaknesses and providing probabilistic guarantees on network behavior. Our work underscores the critical need for robust verification methods to ensure the reliability and safety of autonomous driving systems, paving the way for safer integration of neural networks in autonomous vehicles.}, archive = {J_ACCESS}, author = {Cinzia Bernardeschi and Giuseppe Lami and Francesco Merola and Federico Rossi}, doi = {10.1109/ACCESS.2025.3563120}, journal = {IEEE Access}, pages = {71688-71704}, shortjournal = {IEEE Access}, title = {Verifying robustness of neural networks in vision-based end-to-end autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Neural network pruning for lightweight metal corrosion image segmentation models. ACCESS, 13, 71673-71687. (www)

  @article{ , abstract = {Metal corrosion detection is essential for ensuring structural safety and minimizing economic losses. While deep learning (DL)-based image segmentation has improved corrosion detection accuracy and efficiency, its high computational demands hinder deployment on resource-constrained edge devices. This study investigates lightweight DL models for corrosion segmentation by applying structured pruning to reduce computational costs while maintaining accuracy. We evaluate five segmentation architectures (U-Net, U-Net++, FPN, LinkNet, and MA-Net) and three pruning strategies (linear, automated gradual pruning, and movement pruning) on two corrosion image datasets (NEA and SSCS). Detailed trade-off analysis between model size, computational cost (MAC), and segmentation performance (IoU) reveals that pruning up to 90% sparsity leads to a $\leq 10\%$ IoU drop on SSCS and $\leq 5\%$ on NEA, demonstrating that significant compression is possible with minimal accuracy loss. However, some architectures (e.g., LinkNet) and pruning strategies (e.g., movement pruning) show significant performance deterioration, suggesting that pruning effectiveness varies across models. These findings provide insights into optimizing corrosion segmentation models for efficient deployment on edge devices, balancing accuracy and resource constraints.}, archive = {J_ACCESS}, author = {Vincent F. Yu and Gemilang Santiyuda and Shih-Wei Lin and Udjianna S. Pasaribu and Yuli Sri Afrianti}, doi = {10.1109/ACCESS.2025.3562435}, journal = {IEEE Access}, pages = {71673-71687}, shortjournal = {IEEE Access}, title = {Neural network pruning for lightweight metal corrosion image segmentation models}, volume = {13}, year = {2025}, }
• (2025). Leveraging the potential of novel data in power line communication of electricity grids. ACCESS, 13, 71662-71672. (www)

  @article{ , abstract = {Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We usually only become particularly aware of this dependence by the time the electricity grid is no longer available. However, significant changes, such as the transition to renewable energy (photovoltaic, wind turbines, etc.) and an increasing number of energy consumers with complex load profiles (electric vehicles, home battery systems, etc.), pose new challenges for the electricity grid. At the same time, these challenges are usually too complex to be solved with traditional approaches. In this gap, where traditional approaches are reaching their limits, Machine Learning has become a popular tool to bridge this shortcoming through data-driven approaches. To enable novel ML implementations is we propose FiN-2 dataset, the first large-scale real-world broadband powerline communications (PLC) dataset. FiN-2 was collected during real practical use in a part of the German low-voltage grid that supplies energy to over 4.4 million people and shows well over two billion data points collected by more than 5100 sensors. In addition, we present different use cases in asset management, grid state visualization, forecasting, predictive maintenance, and novelty detection to highlight the benefits of these types of data. For these applications, we particularly highlight the use of novel machine learning architectures to extract rich information from real-world data that cannot be captured using traditional approaches. By publishing the first large-scale real-world dataset, we also aim to shed light on the previously largely unrecognized potential of PLC data and emphasize machine-learning-based research in low-voltage distribution networks by presenting a variety of different use cases.}, archive = {J_ACCESS}, author = {Christoph Balada and Max Bondorf and Sheraz Ahmed and Markus Zdrallek and Andreas Dengel}, doi = {10.1109/ACCESS.2025.3560811}, journal = {IEEE Access}, pages = {71662-71672}, shortjournal = {IEEE Access}, title = {Leveraging the potential of novel data in power line communication of electricity grids}, volume = {13}, year = {2025}, }
• (2025). Enhancing object detection in assistive technology for the visually impaired: A DETR-based approach. ACCESS, 13, 71647-71661. (www)

  @article{ , abstract = {This paper presents a real-time obstacle detection and recognition system designed to enhance navigation for visually impaired individuals through assistive technology. The system integrates a mobile application equipped with a mini camera for real-time image capture and employs advanced deep learning techniques for object detection and classification. A comparative evaluation of YOLOv8, Faster R-CNN and DETR (Detection Transformer) is conducted based on precision, Recall, F1-score, confidence score and processing efficiency. DETR demonstrates superior performance, achieving a 99% confidence score, 98% precision and a processing speed of 40ms per frame. While faster R-CNN and YOLOv8 provide competitive results, they offer a trade-off between accuracy and computational efficiency. The system follows a structured a structured workflow, including real-time acquisition, preprocessing, innovative data augmentation and optimization for edge devices using TensorFlow Lite for efficiency deployment. It classifies 80 types of obstacles, such as pedestrians, vehicles and traffic signal and provides immediate audio feedback to ensure safe navigation. The model trained over 20 epochs achieves an accuracy of 98% in the final epoch. This study introduces a scalable and practical solution integrating IoT and real-time image processing, empowering visually impaired users with enhanced mobility and safety.}, archive = {J_ACCESS}, author = {Sunnia Ikram and Imran Sarwar Bajwa and Sujan Gyawali and Amna Ikram and Najah Alsubaie}, doi = {10.1109/ACCESS.2025.3558370}, journal = {IEEE Access}, pages = {71647-71661}, shortjournal = {IEEE Access}, title = {Enhancing object detection in assistive technology for the visually impaired: A DETR-based approach}, volume = {13}, year = {2025}, }
• (2025). Enhancing privacy in IoT networks: A comparative analysis of classification and defense methods. ACCESS, 13, 71611-71646. (www)

  @article{ , abstract = {The rapid proliferation of Internet of Things (IoT) devices has led to a substantial increase in network packet traffic, raising significant privacy concerns. Although traffic encryption is employed to protect the privacy of IoT devices, attackers can still leverage Machine Learning (ML) and Deep Learning (DL) techniques to classify device types by analyzing packet characteristics, such as size and timing. The main challenges in the state of the art are the lack of effective methods for exposing privacy violations in encrypted IoT traffic, and the absence of robust defense mechanisms to mitigate privacy breaches caused by network traffic analysis. Considering these challenges, this study presents two key contributions: (i) a novel vector-based classification method that enhances device-type identification from encrypted IoT traffic using advanced ML and DL techniques, and (ii) a robust defense mechanism based on Differential Privacy (DP) and advanced padding techniques against traffic analysis attacks. Therefore, the study examines privacy risks associated with sequential IoT device data and evaluates the effectiveness of ML algorithms using two datasets. The results demonstrate that the proposed vector-based classification method significantly improves the attacker’s classification accuracy, even when privacy-preserving techniques, such as padding, are used to obscure device-type classification. For this purpose, the study evaluates eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for IoT traffic classification, achieving an accuracy rate of 99.61% with XGBoost, 96.74% with LSTM, and 96.94% with GRU. Additionally, the Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (kNN), and GRU classification algorithms are also evaluated and compared with the XGBoost and LSTM classifiers for the proposed attack model. As a defense mechanism, DP is applied using the Fourier Perturbation Algorithm (FPA) to optimize padding strategies while maintaining network efficiency. A comparative analysis with state of the art padding techniques, including Adaptive Packet Padding (APP), and the proposed DP-based defense mechanism demonstrates that the proposed defense approach achieves a superior privacy-utility balance. The findings reveal that while padding techniques reduce classification accuracy, the novel vector method significantly enhances attack performance, underscoring the need for stronger defense strategies. Consequently, this study addresses a critical gap in the literature by providing a comprehensive evaluation of privacy risks, classification robustness, and the effectiveness of DP-based defense in IoT network traffic. Thus, the proposed research provides practical insights for enhancing privacy preservation while maintaining network performance, thereby contributing to the development of more secure IoT communication frameworks.}, archive = {J_ACCESS}, author = {Ahmet Emre Ergun and Ozgu Can and Murat Kantarcioglu}, doi = {10.1109/ACCESS.2025.3563799}, journal = {IEEE Access}, pages = {71611-71646}, shortjournal = {IEEE Access}, title = {Enhancing privacy in IoT networks: A comparative analysis of classification and defense methods}, volume = {13}, year = {2025}, }
• (2025). BlindFG: Learning contextual fishing trajectories for unreported fishing gear classification. ACCESS, 13, 71598-71610. (www)

  @article{ , abstract = {Unreported position data of fishing vessels significantly hinder the accurate identification of their behaviors. AIS (Automatic Identification System) data—a self-reporting tool used for vessel tracking—is often incomplete because IUU fishing vessels deliberately turn it off to evade detection. In this paper, we propose BlindFG, a novel framework for unreported fishing gear classification that leverages a fishing-conditioned latent space to overcome the limitations imposed by AIS-off periods. BlindFG integrates a Fishing-Conditioned Generative Model (FCGen) with a Fishing Context Sommelier (FCSom) to both reconstruct missing trajectory data and classify vessel fishing types under partial AIS availability. FCGen employs a conditional variational architecture, incorporating fishing context embeddings derived from one-hot encoded fishing gear labels to guide the generation of AIS-Off trajectories. FCSom utilizes the completed trajectories within a 1D convolutional classification framework. By aggregating candidate predictions across multiple fishing type hypotheses, the system effectively discriminates among various fishing behaviors, even in the presence of partial AIS data. Experimental results on global datasets demonstrate that BlindFG significantly improves the accuracy of fishing type classification and offers a robust solution for enhancing maritime situational awareness and combating IUU fishing activities. Experimental evaluations demonstrate that our method significantly enhances the prediction of vessel trajectories and the accurate classification of AIS-off fishing practices worldwide.}, archive = {J_ACCESS}, author = {Changha Lee and Chan-Hyun Youn}, doi = {10.1109/ACCESS.2025.3562624}, journal = {IEEE Access}, pages = {71598-71610}, shortjournal = {IEEE Access}, title = {BlindFG: Learning contextual fishing trajectories for unreported fishing gear classification}, volume = {13}, year = {2025}, }
• (2025). GD-MVO: Monocular geometric visual odometry using image depth prediction. ACCESS, 13, 71586-71597. (www)

  @article{ , abstract = {Traditional geometric methods estimate camera motion trajectories by analyzing image feature points or pixel information, demonstrating robust performance in certain scenarios. However, these approaches struggle in low-texture and highly dynamic environments. In contrast, end-to-end neural networks can directly predict camera motion trajectories from consecutive image frames, eliminating the need for complex feature extraction and matching processes. Nonetheless, the accuracy of this end-to-end approach still has a significant gap compared to traditional methods. We designed GD-MVO, a monocular visual odometry system that combines the strengths of two approaches. Our system uses both monocular and stereo image pairs to train a deep neural network for depth prediction, then integrates this depth information with geometric features extracted from sequential images to achieve more accurate visual positioning. In this work, we propose 1) a novel integration of geometric methods and deep learning for robust visual odometry; 2) an effective strategy to mitigate the impact of dynamic feature points on prediction outcomes; 3) a fly-out mask technique to resolve the fly-out padding issue in monocular depth prediction networks. Experimental results show that our GD-MVO not only exhibits excellent stability in high-dynamic scenes but also maintains reliable running performance on our specially customized low-texture dataset. In the KITTI odometry benchmark, GD-MVO outperforms both DF-VO and TSformer-VO across all metrics. Most impressively, it achieves a translation error of merely 1.778%, surpassing DF-VO (6.747%) and RAUM-VO (3.676%). Source code available at https://github.com/lilili996/GD-MVO}, archive = {J_ACCESS}, author = {Zhuang Li and Jianjun Chen and Abdul Waheed and Fei Wang and Yan Fan}, doi = {10.1109/ACCESS.2025.3562956}, journal = {IEEE Access}, pages = {71586-71597}, shortjournal = {IEEE Access}, title = {GD-MVO: Monocular geometric visual odometry using image depth prediction}, volume = {13}, year = {2025}, }
• (2025). Development and training strategy of badminton action recognition system under the background of artificial intelligence. ACCESS, 13, 71577-71585. (www)

  @article{ , abstract = {To improve the intelligence level of badminton training and match analysis, this study discusses a badminton action recognition system based on deep learning. It optimizes spatiotemporal feature extraction, multimodal data fusion, and computational efficiency to address current issues such as insufficient recognition accuracy, high computational resource consumption, and limited real-time performance. In the experimental section, the performance of three mainstream models—Spatial-Temporal Graph Convolutional Network (ST-GCN), Vision-Attention Transformer for Real-time Motion Recognition (VATRM), and Multi-Modal Network for Sports Action Recognition (MM-Net)—is compared from two dimensions: recognition performance and computational efficiency. The experimental results show that the optimized system achieves accuracies of 0.943, 0.967, and 0.912, higher than other comparison models. Additionally, in terms of F1 score, the optimized system scores 0.902 in the defense and transition action groups, significantly outperforming MM-Net (0.864) and ST-GCN (0.878). In terms of computational efficiency, the inference times of the optimized system are 12.4ms, 10.8ms, and 13.1ms, faster than ST-GCN and MM-Net, with memory usage as low as 487MB, 452MB, and 501MB, making it suitable for real-time mobile applications. Therefore, this study contributes to intelligent sports training, sports data analysis, and badminton technique optimization.}, archive = {J_ACCESS}, author = {Hongjun Ma and Fan Zhang and Ni Liang}, doi = {10.1109/ACCESS.2025.3563302}, journal = {IEEE Access}, pages = {71577-71585}, shortjournal = {IEEE Access}, title = {Development and training strategy of badminton action recognition system under the background of artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Detection of AI-generated texts: A bi-LSTM and attention-based approach. ACCESS, 13, 71563-71576. (www)

  @article{ , abstract = {This paper presents a novel algorithm that leverages cutting-edge machine-learning techniques to accurately and efficiently detect AI-generated texts. Rapid advancements in natural language processing models have led to the generation of text closely resembling human language, making it increasingly difficult to differentiate between human and AI-generated content. However, misuse of such texts presents a serious and imminent threat to the quality of academic publishing. This underscores the urgent need for robust detection mechanisms to ensure information quality, maintain trust, and preserve the integrity of research publications. Our proposed model outperformed existing algorithms for accuracy with less computational complexity. The proposed model is a feature-based hybrid deep learning network that leverages part-of-speech tagging and integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks with Attention modules. The initial module extracts local contextual features using convolutional layers, followed by Bi-LSTM layers that capture long-term dependencies from past and future sequences. An attention mechanism highlights critical sequence components, enhancing the model’s focus on relevant data. The outputs from the attention and initial modules are concatenated through a residual connection, ensuring comprehensive feature representation. This combination is then fed into dense layers for final classification, effectively balancing feature richness and computational efficiency. The proposed model was evaluated on two benchmark datasets, achieving 85.00% and 88.00% accuracy, respectively.}, archive = {J_ACCESS}, author = {John Blake and Abu Saleh Musa Miah and Krzysztof Kredens and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3562750}, journal = {IEEE Access}, pages = {71563-71576}, shortjournal = {IEEE Access}, title = {Detection of AI-generated texts: A bi-LSTM and attention-based approach}, volume = {13}, year = {2025}, }
• (2025). Dynamic wireless power transfer system for indoor AGV with a distance-measurement-based position detection technique. ACCESS, 13, 71551-71562. (www)

  @article{ , abstract = {Inductive dynamic wireless power transfer (WPT) for moving objects, such as automated guided vehicles (AGVs), has become of special interest. For efficient and leakage-flux-reduced WPT for an AGV, only one transmitting coil (or a small array of transmitting coils) should be energized simultaneously; therefore, the position of the receiving coil should be detected precisely. Conventional AGV position detection techniques are often based on position detection sensors (magnetic or optical) placed near or inside each transmitting coil. However, this approach is expensive, relatively unreliable and it has high level of complexity. Therefore, in this study, a less expensive, more reliable and less complex technique for position detection of low-speed and moderate-speed AGVs (speed <1.5 m/s) is proposed and verified experimentally using a scaled-down AGV prototype. The technique is based on the ultrasonic distance measurement of a moving AGV to energize the correct transmitting coil. As shown in the comparative measurements, the performance of the dynamic WPT system with the proposed detection technique for low-to-moderate-speed AGVs is similar to that of the dynamic WPT system with conventional position detection techniques. However, the cost and level of complexity of the position detection system are moderately lower and the proposed position detection system is immune to optical radiation and magnetic fields. To extend applications of the ultrasonic techniques to high-speed AGVs, another AGV position detection technique based on the combined application of the maximum transmitting coil current detection and ultrasound-based AGV motion direction detection was also proposed.}, archive = {J_ACCESS}, author = {Deniss Stepins and Janis Zakis and Aleksandrs Sokolovs and Oleksandr Husev and Ryszard Strzelecki and Rik W. de Doncker}, doi = {10.1109/ACCESS.2025.3562134}, journal = {IEEE Access}, pages = {71551-71562}, shortjournal = {IEEE Access}, title = {Dynamic wireless power transfer system for indoor AGV with a distance-measurement-based position detection technique}, volume = {13}, year = {2025}, }
• (2025). Analysis and control of ball displacement in air levitation experiment by using a disturbance rejection-based technique. ACCESS, 13, 71530-71550. (www)

  @article{ , abstract = {This work describes the application of a disturbance rejection technique for controlling ball position in an air levitation experiment. The prototype consists of a light-mass ball that moves freely inside a vertical transparent cylindrical tube. The control objective is to force the ball position to track a desired vertical trajectory by defining a voltage control law for an air blower actuator. The system’s nonlinear mathematical model is presented and discussed. To deal with model uncertainties, nonlinearities and non measurable states, the design and the analysis of a Modified-Plant Active Disturbance Rejection Controller (MP-ADRC) are performed. ADRC-based controllers have been studied for decades, and their main characteristics are the use of an extended state observer (ESO) to estimate the non-measurable signals of the plant and a state-feedback control law in standard form fed by that observer. The key advantage of the adopted variant MP-ADRC, which contrasts with the basic methodology, is that it only requires knowledge of the sign $(+/-)$ of plant’s control coefficient, rather than its exact value. This improves the well-known robustness property of the method, allowing its application to a general class of dynamical systems with uncertain parameters. By adopting and using such a control methodology, the resulting closed-loop scheme exhibits several advantageous characteristics: (i) it is robust to parametric uncertainties; (ii) it can compensate for external disturbances and unmodeled dynamics; (iii) it can deal with plants with slowly time-varying control coefficient; (iv) even for nonlinear plants, mathematical analysis using Laplace transform can always be performed. This work also presents and discusses simulation and experimental results obtained with the proposed control strategy applied to a real air levitation prototype.}, archive = {J_ACCESS}, author = {Alessandro R. L. Zachi and Josiel A. Gouvêa and Carlos A. M. Correia and Milena F. Pinto and Mariane R. Petraglia and Diego B. Haddad and Fabio A. A. Andrade}, doi = {10.1109/ACCESS.2025.3560323}, journal = {IEEE Access}, pages = {71530-71550}, shortjournal = {IEEE Access}, title = {Analysis and control of ball displacement in air levitation experiment by using a disturbance rejection-based technique}, volume = {13}, year = {2025}, }
• (2025). Predicting dry matter intake in lactating crossbred cows under semi-arid conditions using an error-based evolving takagi-sugeno fuzzy model. ACCESS, 13, 71521-71529. (www)

  @article{ , abstract = {Accurately predicting dry matter intake (DMI) in lactating crossbred cows under semi-arid conditions is a critical challenge in dairy farming. Precise DMI predictions are essential for optimizing nutrient utilization and enhancing animal performance, particularly in semi-arid environments, where complex and dynamic conditions prevail. This paper introduces a novel Error-Based Evolving Takagi-Sugeno (EBETS) Fuzzy Model designed to address these challenges by dynamically adapting to real-time data and leveraging both current and historical information to enhance prediction accuracy. The EBETS model employs fuzzy set theory and multivariate Gaussian functions to model complex, nonlinear relationships between variables, offering a more robust and flexible approach to DMI prediction. In experiments involving data from 114 crossbred cows under semi-arid conditions, the EBETS model was compared to traditional models and an artificial neural network. It achieved a mean squared error of 0.25, which is more than 90% lower than that of all the other models, while demonstrating a higher correlation coefficient with the expected values, indicating superior predictive performance and reliability. These results underscore the EBETS model’s substantial superiority over existing methods, highlighting its potential to significantly improve dairy farming productivity by providing precise and reliable DMI predictions in challenging environmental conditions. The model’s ability to continuously learn and adapt to changing conditions makes it a promising tool for various applications in agricultural science.}, archive = {J_ACCESS}, author = {Dijair B. Leal and Rafael A. Soares and Vicente R. Rocha Júnior and Flávio P. Monção and Murilo O. Camargos and Marcos F. S. V. D’Angelo and Luis Miralles-Pechuán}, doi = {10.1109/ACCESS.2025.3562133}, journal = {IEEE Access}, pages = {71521-71529}, shortjournal = {IEEE Access}, title = {Predicting dry matter intake in lactating crossbred cows under semi-arid conditions using an error-based evolving takagi-sugeno fuzzy model}, volume = {13}, year = {2025}, }
• (2025). Advanced prediction of recurrent fragility fractures using large language models. ACCESS, 13, 71503-71520. (www)

  @article{ , abstract = {Recurrent fragility fractures are a big challenge in managing bone health, especially in older adults and people with osteoporosis or related disorders. The prediction of such fractures depends on the overall understanding of various clinical and lifestyle factors contributing to bone fragility. The goal of this analysis is to carry out advanced predictive analytics related to the problem of recurrent fragility fractures concerning several key features about each patient: age, sex, body mass index, physical activities, smoking status, and several others: T-score, along with biomarkers such as Vitamin D3, calcium levels, and the rest. Indeed, the results show that the features most indicative of recurrent fractures include age, T-score, physical activity, and glucocorticoid usage; greater emphasis has been laid on bone mineral density and lifestyle factors. The detailed feature importance analysis showed that age and T-score have the highest important values for fracture recurrence prediction, followed by physical activity and glucocorticoid treatment. The study finds that the proposed models achieved an accuracy of 91.3% (LLaMA 7GB) and 90.2% (Gamma 7GB), with ROC AUC scores of 93% and 92%, respectively. The study highlights that age, T-score, and glucocorticoid usage were the most significant predictive factors. These findings enable improved clinical decision-making for fracture prevention. This model can facilitate the identification of high-risk patients with recurrent fragility fractures by clinicians, thus allowing for targeted intervention and personalized treatment to reduce the risk of future fractures. The findings contribute to the development of fracture prediction, incorporating a wide array of clinical data that, in turn, will improve patient outcomes and their quality of life.}, archive = {J_ACCESS}, author = {Mohammad Alshraideh and Arafat Al-Dhaqm and Ahmad Alshammari and Abedalrahman Alshraideh and Bahaaldeen Alshraideh and Bayan Mohamed Al-Fayoumi and Maged Nasser}, doi = {10.1109/ACCESS.2025.3562259}, journal = {IEEE Access}, pages = {71503-71520}, shortjournal = {IEEE Access}, title = {Advanced prediction of recurrent fragility fractures using large language models}, volume = {13}, year = {2025}, }
• (2025). Assessment of input-output dead times impact on tuning procedures and grouping policies for output soft constraints in MPC. ACCESS, 13, 71481-71502. (www)

  @article{ , abstract = {Model Predictive Control (MPC) is an Advanced Process Control technique able to handle constrained multivariable processes characterized by dead times. In these processes, dead times information covers a key role for control systems design and for the associated tuning procedures. This paper aims to assess the impact of input-output dead times on tuning procedures and grouping policies associated to output soft constraints in MPC based on linear models. An MPC problem characterized by hard constraints on the inputs magnitude and on the inputs slew rate and by soft constraints on the outputs magnitude is considered for the Description of the proposed approach. Window parameters, tuning parameters associated to slack variables and grouping policies for constraints relaxation represent the key points investigated in the work. In addition, a characterization of the proposed methods based on different input-output configurations and on the control matrix to be used at each control instant is presented. Simulation results based on first-order plus deadtime models show how the proposed methods can improve the effectiveness and the efficiency of MPC systems design.}, archive = {J_ACCESS}, author = {Crescenzo Pepe and Silvia Maria Zanoli}, doi = {10.1109/ACCESS.2025.3562779}, journal = {IEEE Access}, pages = {71481-71502}, shortjournal = {IEEE Access}, title = {Assessment of input-output dead times impact on tuning procedures and grouping policies for output soft constraints in MPC}, volume = {13}, year = {2025}, }
• (2025). Redefining OSINT software architecture with system-centric architecture design: A framework shaped by QAW, ADD, and ATAM. ACCESS, 13, 71456-71480. (www)

  @article{ , abstract = {This study develops a novel software architecture for Open Source Intelligence (OSINT). The primary architectural drivers of the OSINT architecture are identified using the Quality Attribute Workshop (QAW), and an end-to-end OSINT software architecture design is implemented in accordance with Attribute-Driven Design (ADD). The architecture is extensively analyzed with metric evaluations and the Architecture Tradeoff Analysis Method (ATAM), confirming critical quality attributes such as performance, reliability, functional suitability, and security. The design decisions taken within this architectural framework are detailed in the article through module view, component and connector view, and allocation view representations. The proposed architecture uses an on-premise Large Language Model (LLM) to explore the potential for deeper and more reliable information processing capabilities in OSINT analyses and presents a framework that enhances semantic depth and analytical capabilities. The architecture not only amplifies the semantic and analytical capabilities of OSINT systems but also sets a precedent for future architectural endeavors in intelligence systems design. This paper presents a framework that not only meets contemporary needs but also anticipates future demands in the rapidly evolving field of OSINT.}, archive = {J_ACCESS}, author = {Gökhan Yurtalan and Serdar Arslan}, doi = {10.1109/ACCESS.2025.3562718}, journal = {IEEE Access}, pages = {71456-71480}, shortjournal = {IEEE Access}, title = {Redefining OSINT software architecture with system-centric architecture design: A framework shaped by QAW, ADD, and ATAM}, volume = {13}, year = {2025}, }
• (2025). Multiphysics analysis of thermal deformation effects on a waveguide bandpass filter. ACCESS, 13, 71447-71455. (www)

  @article{ , abstract = {In this paper, we propose a multiphysics analysis of thermal deformation effects on the frequency response of a Chebyshev bandpass filter designed for C-band satellite communications (SATCOM). Indeed, the design of microwave devices for SATCOM must account for the effects of thermal gradients on frequency response and incorporate suitable compensation techniques. The proposed analysis consists of appropriate sequence of electromagnetic, thermal, and mechanical simulations to evaluate the filter thermal profile as a function of input power, the resulting mechanical deformations, and the consequent effect on the filter frequency response. The results obtained are related to the filter equivalent circuit model to obtain deeper insight of the involved physical phenomena and useful information for the design of the most suitable compensation strategy. Finally, to demonstrate the effectiveness of multiphysics analysis in the optimal design of devices robust to thermal gradients, a thermally compensated bandpass filter using passive heat sinks is designed and validated.}, archive = {J_ACCESS}, author = {Davide Guarnera and Giorgio S. Mauro and Santi Concetto Pavone and Tommaso Isernia and Gino Sorbello}, doi = {10.1109/ACCESS.2025.3561350}, journal = {IEEE Access}, pages = {71447-71455}, shortjournal = {IEEE Access}, title = {Multiphysics analysis of thermal deformation effects on a waveguide bandpass filter}, volume = {13}, year = {2025}, }
• (2025). Improving upper limb movement classification from EEG signals using enhanced regularized correlation-based common spatio-spectral patterns. ACCESS, 13, 71432-71446. (www)

  @article{ , abstract = {Detection of movement from electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) systems, particularly in rehabilitating individuals with disabilities. This study focuses on decoding two types of ipsilateral movements (right arm and thumb) and the resting state from EEG signals—a challenging task due to the reduced signal discrimination between ipsilateral movements. To address this challenge, we propose a novel framework that combines precise segmentation of EEG signals during movement with an improved feature extraction method. First, we detect accurate segmentation of EEG signals by using the teager-kaiser energy operator for electromyographic (EMG) signals, which allows for precise detection of the onset and end of movements. Next, for feature extraction, we developed the regularized correlation-based common spatio-spectral patterns (RCCSSP) algorithm, which improves the traditional common spatial patterns (CSP) by incorporating regularization based on correlation. RCCSSP employs spatio-spectral canonical correlation analysis (SS-CCA) with an advanced regularization approach. Specifically, this method calculates the correlation between two classes for each channel, assigning higher weights to channels with lower correlation to increase their impact while minimizing the effect of noisy channels with higher correlation. Classification is then performed using distance-weighted k-nearest neighbor and support vector machine algorithms. Experimental results from 15 healthy subjects demonstrate that the proposed approach achieves an average classification accuracy of 88.94%, representing a significant 11.66% improvement over the best-reported method. This work highlights the potential of precise movement segmentation and robust feature extraction in decoding ipsilateral movements for BCI applications.}, archive = {J_ACCESS}, author = {Amin Besharat and Nasser Samadzadehaghdam}, doi = {10.1109/ACCESS.2025.3563417}, journal = {IEEE Access}, pages = {71432-71446}, shortjournal = {IEEE Access}, title = {Improving upper limb movement classification from EEG signals using enhanced regularized correlation-based common spatio-spectral patterns}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of collaborative data caching in edge computing. ACCESS, 13, 71408-71431. (www)

  @article{ , abstract = {Edge computing has gained significant attention due to the swift development of wireless communication technology. As more smart, data-intensive applications and Internet of Things (IoT) devices are used, the amount of data traffic grows at an exponential rate. This means that we need to manage data effectively so that services can be scalable, bandwidth-efficient, and latency-sensitive. Collaborative data caching has emerged as an essential technique in this context to meet the storage and retrieval needs of edge computing systems. We thoroughly examine the current collaborative caching techniques in edge computing, with a focus on recent developments and emerging approaches. We have categorized collaborative caching strategies into four main types: models based on stochastic, game theoretic, and mathematical methods to deal with uncertainty in network conditions and optimize resource allocation; machine learning-based models that employ artificial intelligence (AI) for content popularity prediction and cache optimization; heuristic models providing lightweight solutions for cache placement and replacement; and hybrid models combining multiple strategies. Each of these models is intended to maximize performance under various network and data conditions. We conclude by looking into some of the open problems and difficulties with edge caching to promote further research in this field.}, archive = {J_ACCESS}, author = {Abdul Tawab Akrami and Girija Attigeri and Manjula C. Belavagi}, doi = {10.1109/ACCESS.2025.3563407}, journal = {IEEE Access}, pages = {71408-71431}, shortjournal = {IEEE Access}, title = {Comprehensive review of collaborative data caching in edge computing}, volume = {13}, year = {2025}, }
• (2025). Estimation of system-level reliability functions for the power grid using probabilistic modeling and monte carlo simulation. ACCESS, 13, 71388-71407. (www)

  @article{ , abstract = {Reliability Modeling for Power Systems is a very challenging task due to the high complexity of the interactions among its various components. In this paper, we develop a simple probabilistic method for modeling power system reliability based on the knowledge of the system size, transmission line capacities, and the failure rate type of the transmission lines in the system. Using Monte Carlo Simulation, we show that the probability distribution of the system failure rate is typically similar in shape to the failure distribution of the transmission lines in the system, with variations stemming from the system size, transmission line capacity, and the type of failure rate. Our method provides a very simple formula for describing system level reliability despite the high complexity of its interconnections, and provides a mechanism to develop similar functions for other complex systems, including different types of networks or critical infrastructures, and can pave the way towards better modeling for the more intelligent future grids.}, archive = {J_ACCESS}, author = {Ayman Faza}, doi = {10.1109/ACCESS.2025.3563427}, journal = {IEEE Access}, pages = {71388-71407}, shortjournal = {IEEE Access}, title = {Estimation of system-level reliability functions for the power grid using probabilistic modeling and monte carlo simulation}, volume = {13}, year = {2025}, }
• (2025). Robust scream detection and scream temporal interval prediction using CNN-transformer and windowing CNN. ACCESS, 13, 71374-71387. (www)

  @article{ , abstract = {Detecting screams in noisy environments remains an important challenge in emergency detection. This work presents a new hybrid architecture combining Convolutional Neural Networks (CNN)-Transformer and Windowing CNN for simultaneous scream detection and temporal localization. The proposed system employs parallel CNN blocks for robust feature extraction alongside a Transformer block that captures temporal dependencies in the audio signal. A specialized Windowing CNN with Huber Loss regression enables precise temporal predictions of scream intervals. Experimental results demonstrate significant advancements over existing approaches, achieving approximately 3 percentage point improvement in F-measure and a remarkable 10-fold enhancement in Equal Error Rate (EER). To enhance real-world applicability, we developed an extensive dataset with various noise types at equal Signal-to-Noise Ratio (SNR), incorporating Time Shifting augmentation techniques. The model was evaluated across diverse acoustic scenarios including crowd, factory, white, and road noise environments. The proposed architecture effectively addresses both detection accuracy and temporal localization challenges under real-world noise conditions, offering substantial reliability improvements for practical security applications.}, archive = {J_ACCESS}, author = {Youngjun Kim and Dalwon Jang and Seok-Pil Lee}, doi = {10.1109/ACCESS.2025.3556729}, journal = {IEEE Access}, pages = {71374-71387}, shortjournal = {IEEE Access}, title = {Robust scream detection and scream temporal interval prediction using CNN-transformer and windowing CNN}, volume = {13}, year = {2025}, }
• (2025). The internet of things in elderly healthcare applications: A systematic review and future directions. ACCESS, 13, 71335-71373. (www)

  @article{ , abstract = {The increase in the elderly population globally and the prevalence of chronic diseases among the elderly have resulted in rising healthcare costs worldwide. Utilizing the Internet of Things (IoT) and smart sensors to reduce healthcare costs and enhance the quality of life for the elderly is a highly effective and valuable solution. Effective monitoring can improve resource management in IoT-based healthcare systems. We conducted a systematic literature review (SLR) for elderly healthcare on the IoT to evaluate its various facets and key areas. The taxonomy of elderly healthcare studies in the IoT comprises six categories: smart homes, security and privacy, remote healthcare system, wearable devices, smartphone, healthcare and healthcare costs. Management of chronic diseases has the highest percentage of evaluation statistics in the field of smart homes, at 61%. In addition, 26% of smart homes in healthcare are devoted to behavioral characteristics and 13% to biological characteristics. Finally, this study highlights the visions and challenges of elderly healthcare articles and future work.}, archive = {J_ACCESS}, author = {Naser Shirvanian and Maryam Shams and Amir Masoud Rahmani and Jan Lansky and Stanislava Mildeova and Mehdi Hosseinzadeh}, doi = {10.1109/ACCESS.2025.3562147}, journal = {IEEE Access}, pages = {71335-71373}, shortjournal = {IEEE Access}, title = {The internet of things in elderly healthcare applications: A systematic review and future directions}, volume = {13}, year = {2025}, }
• (2025). Efficient real-time pathfinding for visually impaired individuals. ACCESS, 13, 71323-71334. (www)

  @article{ , abstract = {This paper presents a novel computer vision system, which enables real-time pathfinding for individuals with visual impairments. The navigation experience for visually impaired individuals has significantly improved “in traditional segmentation methods and deep learning techniques”. Traditional methods usually focus on the detection of specific patterns or objects, requiring custom algorithms for each object of interest. In contrast, deep learning models such as instance segmentation and semantic segmentation allow for independent recognition of different elements within a scene. In this research, deep convolutional neural networks are employed to perform semantic segmentation of camera images, thereby facilitating the identification of patterns across the image’s feature space. Motivated by a unique concept of a two-branch core architecture, we propose utilizing semantic segmentation to support navigation for visually impaired individuals. The “demarcation path” captures spatial details with wide channels and shallow layers, while the “path with rich features” extracts categorical semantics using deep layers. By providing awareness of both “obstacles” and “paths” in the surrounding vicinity, this method enhances the perceptual understanding of visually impaired individuals. We try to prioritize real-time performance and low computational overhead to ensure timely and responsive assistance. With a wearable assistive system, we demonstrate that semantic segmentation provides a comprehensive understanding of the surroundings to those with visual impairments. The experimental results showcase an impressive accuracy of 72.6% in detecting paths, path objects, and path boundaries.}, archive = {J_ACCESS}, author = {Tadeh Ghahremanians and Hossein Mahvash Mohammadi}, doi = {10.1109/ACCESS.2025.3562247}, journal = {IEEE Access}, pages = {71323-71334}, shortjournal = {IEEE Access}, title = {Efficient real-time pathfinding for visually impaired individuals}, volume = {13}, year = {2025}, }
• (2025). Explainable artificial intelligence driven segmentation for cervical cancer screening. ACCESS, 13, 71306-71322. (www)

  @article{ , abstract = {Cervical cancer remains an important global health challenge among women. Early and accurate identification of abnormal cervical cells is crucial for effective treatment and improved survival rates. This paper addresses the development of a novel weakly supervised segmentation framework that combines binary classification, Explainable Artificial Intelligence (XAI) techniques, and GraphCut to automate cervical cancer screening. Unlike traditional segmentation methods that rely on pixel-level annotations of medical images, which are costly, laborious, and require expertise in medical imaging, our approach leverages classification-driven insights to segment the nucleus, cytoplasm, and background regions. A key innovation of our framework is the use of XAI techniques such as Grad-CAM++ and LRP combined with GraphCut, to enable annotation-free segmentation using only classification-level labels. This represents a pioneering application of explainability techniques in the context of cervical cancer screening. Among the classification models explored, including fine-tuned variants of VGGNet and XceptionNet, VGG16-Adapted128 achieved the highest performance, marked by an accuracy of 0.94, precision of 0.94, recall of 0.94, and an F1 score of 0.94. This novel segmentation framework employed LRP and GradCAM++ as XAI techniques to gain insight into the decision-making process of classification models, with GradCAM++ demonstrating greater effectiveness. The performance of these XAI methods was assessed through both visual inspection and quantitative metrics, including entropy and pixel flipping. This innovative approach to segmentation is formally introduced through two algorithms detailed in this paper. The weakly supervised segmentation framework achieved a Dice Similarity Coefficient (DSC) of 62.05% and an Intersection over Union (IoU) of 61.89%. In addition, it has received high satisfaction ratings from expert evaluations and has been seamlessly integrated into a user-friendly Web application, offering clinicians a transparent and reliable tool to improve the precision of decision-making in the detection of cervical cancer. Although this work represents an early step, it lays a strong foundation for advancing XAI-driven, weakly supervised segmentation techniques in medical imaging, particularly in resource-constrained cervical cancer screening contexts.}, archive = {J_ACCESS}, author = {Niruthikka Sritharan and Nishaanthini Gnanavel and Prathushan Inparaj and Dulani Meedeniya and Pratheepan Yogarajah}, doi = {10.1109/ACCESS.2025.3561178}, journal = {IEEE Access}, pages = {71306-71322}, shortjournal = {IEEE Access}, title = {Explainable artificial intelligence driven segmentation for cervical cancer screening}, volume = {13}, year = {2025}, }
• (2025). Dynamic mapping and 3D perception using voxel grid and modified artificial potential fields for indoor locomotion. ACCESS, 13, 71288-71305. (www)

  @article{ , abstract = {This paper proposes an advanced 3D indoor navigation system for a mobile robot. The proposed method integrates RTAB-Map with Voxel Grid Filters and Joint Probabilistic Data Association (JPDA) to generate surrounding environment map efficiency. Additionally, the local path planner combines pure pursuit with a modified Artificial Potential Field (APF) method to improve navigation capability. It generates steering commands and desired velocities and adjusts the attractive potential force equation to maintain balance and operational efficiency. This modification improves safety, pedestrian avoidance, and comfort by minimizing unnecessary rotations while ensuring smooth navigation. The proposed system improves the locomotion ability by reducing roll, pitch, and yaw fluctuations by approximately 30% compared to traditional APF methods. Voxel grid filtering enhances computational efficiency, reducing processing time per iteration by up to 73% - from 0.247 seconds (raw LiDAR) to 0.067 seconds (voxel size of 0.9) - while maintaining obstacle detection accuracy. The integration of JPDA ensures safe multi-target detection, with minimum safe distances of 0.94 meters from dynamic actors and a Threat Level Index (TLI) peaking at 0.24. In a scenario comparing two robots with different map knowledge, the robot with map knowledge reached the waypoint 20% faster, following an efficient path. However, despite lacking prior knowledge, the second robot reached the waypoint, demonstrating the system’s adaptability. These quantitative results confirm the proposed method’s capability to enhance safety, efficiency, and human comfort, making it suitable for real-time indoor navigation in dynamic environments.}, archive = {J_ACCESS}, author = {Trihastuti Agustinah and Yurid Eka Nugraha and Aqil Rabbani Nurhadi and Vincentius Charles Maynad}, doi = {10.1109/ACCESS.2025.3563484}, journal = {IEEE Access}, pages = {71288-71305}, shortjournal = {IEEE Access}, title = {Dynamic mapping and 3D perception using voxel grid and modified artificial potential fields for indoor locomotion}, volume = {13}, year = {2025}, }
• (2025). An improved ensemble method with data resampling for credit risk prediction. ACCESS, 13, 71275-71287. (www)

  @article{ , abstract = {The increasing complexity and dynamic nature of financial data present significant challenges in accurately predicting credit risk, a critical task in the banking and finance sector. The application of machine learning (ML) in credit risk prediction has been hindered by the imbalanced nature of credit datasets. This study proposes an improved approach for predicting credit risk using a stacked ensemble method combined with a hybrid data resampling technique. The ensemble comprises random forests, logistic regression, and a convolutional neural network (CNN) as base learners, with the multilayer perceptron (MLP) serving as a meta-learner. To address the data imbalance, the Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTE-ENN) technique were applied. The proposed approach is benchmarked against other well-performing classifiers, including random forest, logistic regression, MLP, and CNN. The integration of hybrid data resampling with a robust stacking ensemble significantly enhanced credit risk prediction, with the proposed approach achieving sensitivity and specificity of 0.921 and 0.946 for the Australian dataset and 0.928 and 0.891 for the German dataset. Also, the stacked classifier achieved a sensitivity and specificity of 0.000 and 1.000 before data resampling for the Credit Risk Classification dataset with an accuracy of 0.7644. After data resampling, the accuracy, sensitivity, and specificity are 0.8056, 0.7989 and 0.8125, respectively. On the other hand, using the credit risk analysis for the extended banking loans dataset, the accuracy, sensitivity and specificity of the stacked classifier before data resampling are 0.8429, 0.6316, and 0.9216, respectively. After data resampling, the accuracy, sensitivity and specificity scores of the stacked classifier trained using the credit risk analysis for the extended banking loans dataset are 0.9632, 1.0000, and 0.9242, respectively. This shows that after data resampling, the performance of the stacked classifier trained using the credit risk analysis for the extended banking loans dataset outperformed other models.}, archive = {J_ACCESS}, author = {Idowu Aruleba and Yanxia Sun}, doi = {10.1109/ACCESS.2025.3563432}, journal = {IEEE Access}, pages = {71275-71287}, shortjournal = {IEEE Access}, title = {An improved ensemble method with data resampling for credit risk prediction}, volume = {13}, year = {2025}, }
• (2025). Raya: A bio-inspired AUV for inspection and intervention of underwater structures. ACCESS, 13, 71253-71274. (www)

  @article{ , abstract = {This work presents a bio-inspired Autonomous Underwater Vehicle (AUV) concept called Raya that enables high manoeuvrability required for close-range inspection and intervention tasks, while fostering endurance for long-range operations by enabling efficient navigation. The AUV has an estimated terminal velocity of 0.82 m/s in an optimal environment, and a capacity to acquire visual data and sonar measurements in all directions. Raya was designed with the potential to incorporate an electric manipulator arm of 6 degrees of freedom (DoF) for free-floating underwater intervention. Smart and biologically inspired principles applied to morphology and a strategic thruster configuration assure that Raya is capable of manoeuvring in all 6 DoFs even when equipped with a manipulator with a 5 kg payload. Extensive experiments were conducted using simulation tools and real-life environments to validate Raya’s requirements and functionalities. The stresses and displacements of the rigid bodies were analysed using finite element analysis (FEA), and an estimation of the terminal forward velocity was achieved using a dynamic model. To assess the accuracy of the perception system, a reconstruction task took place in an indoor pool, resulting in a 3D reconstruction with average length, width, and depth errors below 1. 5%. The deployment of Raya in the ATLANTIS Coastal Testbed and Porto de Leixões allowed the validation of the propulsion system and the gathering of valuable 2D and 3D data, thus proving the suitability of the vehicle for operation and maintenance (O&M) activities of underwater structures.}, archive = {J_ACCESS}, author = {Pedro Pereira and Renato Silva and João V. Amorim Marques and Raul Campilho and Aníbal Matos and Andry Maykol Pinto}, doi = {10.1109/ACCESS.2025.3560507}, journal = {IEEE Access}, pages = {71253-71274}, shortjournal = {IEEE Access}, title = {Raya: A bio-inspired AUV for inspection and intervention of underwater structures}, volume = {13}, year = {2025}, }
• (2025). Manhattan correlation attention network for metal part anomaly classification. ACCESS, 13, 71240-71252. (www)

  @article{ , abstract = {Abnormal classification of metal components plays an important role in industrial product manufacturing. However, it is difficult to detect metal anomalies due to the following issues: 1) Some defects of metal parts areas are small; 2) The abnormal metal regions are relatively similar to the normal ones. To address these problems, a Manhattan Correlation Attention Network (MCA-Net) is proposed for anomaly classification from the metal part, where the Manhattan Retentive Attention (MRA) module is designed to search for small anomaly regions by global information modeling, and the Structural Contextual Attention (SCA) module is devised to discriminate the similar abnormal regions from the normal ones by aggregating contextual structured dependency. Experiments on the benchmark verify the effectiveness of the proposed MCA-Net for metal part anomaly classification, achieving the performance of 91.76%, 91.76%, 91.69%, 91.35% on the accuracy, precision, specificity, and F1-score, respectively, further assisting in metal part classification in manufacturing.}, archive = {J_ACCESS}, author = {Zijiao Sun and Yanghui Li and Fang Luo and Zhiliang Zhang and Jiaqi Huang and Qiming Zhang}, doi = {10.1109/ACCESS.2025.3562093}, journal = {IEEE Access}, pages = {71240-71252}, shortjournal = {IEEE Access}, title = {Manhattan correlation attention network for metal part anomaly classification}, volume = {13}, year = {2025}, }
• (2025). ESLiteU²-net: A lightweight u²-net for road extraction from high-resolution remote sensing images. ACCESS, 13, 71223-71239. (www)

  @article{ , abstract = {Extracting road information from high-resolution remote sensing images has become a research hotspot in remote sensing image processing due to its cost-effectiveness and efficiency. Current road extraction methods generally face challenges such as large parameter sizes and limited accuracy when dealing with roads at different scales. To overcome these limitations, this study proposes a novel lightweight attention network model (ESLiteU2-Net) to improve both efficiency and accuracy of road extraction. Based on U2-Net, the proposed model reduces complexity by a channel reduction strategy and introduces an Efficient Spatial and Channel Attention Module (ESCA). This innovative design enables the model to better capture and reinforce road features across both spatial and channel dimensions, resulting in significant improvements in extraction accuracy and robustness while maintaining a lightweight structure. Experimental results demonstrate that ESLiteU2-Net outperforms existing methods on the CHN6-CUG and Massachusetts road datasets. Compared to U2-Net, the proposed model not only achieves superior accuracy but also reduces computational load and parameter number by 30.98% and 81.91%, respectively, achieving a balanced combination of lightweight design, efficiency, and accuracy for road extraction.}, archive = {J_ACCESS}, author = {Rui Xu and Zhenxing Zhuang and Renzhong Mao and Yihui Yang}, doi = {10.1109/ACCESS.2025.3563459}, journal = {IEEE Access}, pages = {71223-71239}, shortjournal = {IEEE Access}, title = {ESLiteU²-net: A lightweight u²-net for road extraction from high-resolution remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Automated endurance characterization of phase change memory. ACCESS, 13, 71214-71222. (www)

  @article{ , abstract = {Phase change memory cells are outstanding candidates for processing-in-memory and neuromorphic computing. The high endurance, low cycle-to-cycle variability, and low read noise especially suit many applications, whereas the high cell-to-cell variability poses a challenge, since each cell serves slightly different results. Therefore, automated characterization is necessary to create a sufficient statistical database to thoroughly study the switching behavior. This paper introduces a sophisticated algorithm for the endurance measurement of phase change memory cells using the aixMATRIX setup by aixACCT Systems. The algorithm is able to perform endurance measurements on devices over an entire sample, adjusting the biasing parameters according to the switching behavior of each cell and sorting out nonfunctional cells. The stepping between the cells is performed automatically. We highlight the benefits of our algorithm by providing an in-depth analysis of all devices on one phase change memory in a bridge geometry sample.}, archive = {J_ACCESS}, author = {Henriette Padberg and Abbas Espiari and Kristoffer Schnieders and Alexander Kiehn and Abdur Rehman Jalil and Rainer Waser and Stephan Menzel and Stefan Wiefels}, doi = {10.1109/ACCESS.2025.3562434}, journal = {IEEE Access}, pages = {71214-71222}, shortjournal = {IEEE Access}, title = {Automated endurance characterization of phase change memory}, volume = {13}, year = {2025}, }
• (2025). Analysis of force characteristics in foot operations using isometric device. ACCESS, 13, 71197-71213. (www)

  @article{ , abstract = {The lower limbs, which have multiple degrees of freedom, can be used to manipulate a robot or an object in a virtual environment. Such manipulation can be achieved by measuring the direction of force exerted by the lower limbs using an isometric device equipped with a force sensor. This study experimentally investigates the differences between the intended and actual directions of force exerted by the lower limbs when operating an isometric device. The results of experiments conducted using 48 force directions indicate that there are five prominent characteristics in the differences between the intended and actual force directions. For example, when the intended force is the backward direction, an upward force component is also generated. In addition, we conducted a musculoskeletal analysis to analyze the causes of the differences between the intended and actual force directions and calculated the metabolic energy consumption ( $E_{m}$ ). The results confirm that some force directions result in lower $E_{m}$ and show that the actual force directions measured in the experiment tend to be close to the force directions that resulted in lower $E_{m}$ . This suggests that reducing $E_{m}$ is one of the causes of the differences between the intended and actual force directions.}, archive = {J_ACCESS}, author = {Yang Li and Rintaro Iio and Shoun Nishioka and Tatsuro Terakawa and Masaharu Komori}, doi = {10.1109/ACCESS.2025.3562377}, journal = {IEEE Access}, pages = {71197-71213}, shortjournal = {IEEE Access}, title = {Analysis of force characteristics in foot operations using isometric device}, volume = {13}, year = {2025}, }
• (2025). An improved power factor correction converter for switched reluctance motor drive performance analysis. ACCESS, 13, 71187-71196. (www)

  @article{ , abstract = {Power Factor Correction (PFC) is crucial for the efficient operation of Switched Reluctance Motor (SRM) converters, as it enhances energy utilization and minimizes system losses. Conventional PFC techniques often rely on passive components or simple active methods, which may not fully exploit the dynamic characteristics of SRM operation. Low power factors in SRM converters lead to increased reactive power consumption and potential grid disturbances, necessitating effective PFC strategies to optimize energy efficiency, enhance system performance, and ensure compliance with standards. Therefore, this paper proposes a buck-boost converter to efficiently regulate the DC voltage supplied to the load, enabling voltage levels higher or lower than the input voltage while improving PFC in SRM converters. The objective is to address current challenges and push the boundaries of efficiency and performance. The proposed control strategies leverage the unique characteristics of SRMs to achieve superior PFC performance by dynamically adjusting the converter’s operation in real time based on motor load conditions. This approach ensures optimal PFC across a wide range of operating conditions. The proposed model is developed and validated using MATLAB/Simulink. Simulation and experimental results assess the motor’s performance under both static and dynamic load conditions. Additionally, the effectiveness of the converter is evaluated by analyzing the total harmonic distortion of the input current.}, archive = {J_ACCESS}, author = {Deepak Mohanraj and Devakirubakaran Samithas and Praveen Kumar Balachandran and Muhammad Ammirrul Atiqi Mohd Zainuri}, doi = {10.1109/ACCESS.2025.3561335}, journal = {IEEE Access}, pages = {71187-71196}, shortjournal = {IEEE Access}, title = {An improved power factor correction converter for switched reluctance motor drive performance analysis}, volume = {13}, year = {2025}, }
• (2025). A recommendation algorithm incorporating adaptive gating mechanisms and knowledge graph enhancements. ACCESS, 13, 71173-71186. (www)

  @article{ , abstract = {Aiming at the problem that existing knowledge graph-based recommendation algorithms do not fully utilize the interaction information between users and items, this paper proposes a recommendation algorithm called Gated Adaptive and Knowledge-Enhanced Recommendation (GAKR) that integrates an adaptive gating mechanism with knowledge graph enhancement. First, GAKR uses a Multilayer Perceptron (MLP) in the recommendation module to process the user’s initial feature vector and extract potentially compressible features. Then, for the item feature vector, the model dynamically adjusts the weights of Collaborative Filtering (CF) and Knowledge Graph (KG) through the gating mechanism, generating a more expressive item representation. The gating mechanism calculates the gating value through the sigmoid function, allowing user’s behavioral characteristics in different scenarios to more accurately impact item recommendation. Next, in the Knowledge Graph Embedding (KGE) module, an embedding interaction mechanism is designed, which updates the entity embeddings by using Mobius addition and mapping, capturing higher-order interaction information between entities and relations. To prevent overfitting, GAKR employs L2 regularization during the optimization process, which enhances the model’s generalization ability when handling complex interactions. Finally, a normalized inner product operation is used as an evaluation function to predict the user’s preferences for items. The experimental results on three public datasets from different domains-MovieLens-1M, Book-Crossing and Last. FM-show that the GAKR model outperforms other benchmark models in terms of the evaluation metrics such as AUC, F1-score and recall rate in the CTR (Click Through Rate) and Top-K recommendation scenarios.}, archive = {J_ACCESS}, author = {Yi Yu and Teng Lv}, doi = {10.1109/ACCESS.2025.3561808}, journal = {IEEE Access}, pages = {71173-71186}, shortjournal = {IEEE Access}, title = {A recommendation algorithm incorporating adaptive gating mechanisms and knowledge graph enhancements}, volume = {13}, year = {2025}, }
• (2025). SCM-DL: Split-combine-merge deep learning model integrated with feature selection in sports for talent identification. ACCESS, 13, 71148-71172. (www)

  @article{ , abstract = {In sports, identifying athletes with high potential to excel in sports schools is pivotal. In the literature, this process is called Talent Identification (TID) and is defined as “to know the players participating in the sport with the potential to be perfect.” The problem discussed in this paper focuses on the early identification of an athlete’s talented sports branch before they are assigned to a specific branch. This determination process is based on the evaluation of general performance tests and assessments. TID solutions in the literature use AI-driven methods (i.e., Machine Learning, Neural Nets, etc.). However, they could not beat the following deficiencies: they cannot be used with the dataset features having complex and non-linear relationships, are not scalable in the number of features, are not adaptable to hierarchical data, cannot generalize the solution, depend on any predefined thresholds or prior assumptions, are not adaptable to the other datasets, are not tolerable to the incomplete inputs. A two-stage TID solution has been introduced to address the deficiencies above and resolve the TID challenge. In the first stage (TID1), the admitted athletes are determined. In the second stage (TID2), athletes are classified into their talented branches (Football, basketball, volleyball, or athletics). TID1 uses our Shallow Deep learning (SDL) model to classify the admitted. In this stage, a remarkable performance was obtained with 98.85%. In TID2, nine different feature selection methods (four RFE-related methods, three SelectKBest-related methods, and Lasso and Boruta) are applied to reduce the number of features. After feature selection, our novel SCM-DL deep learning classifier model (apart from the architectures in literature, this model is constructed internally with parallel layers and carries a combinatorial layer that is beyond the combination of existing techniques) is applied and compared with Random Forest, Decision Tree, Extra Tree, and Support Vector Classifiers. The SCM-DL integrated with the RFE_DTC feature selection method achieved the highest performance for six features, yielding an accuracy rate of 97.40% and a Matthews Correlation Coefficient performance rate of 96.6%. By this result, our model guided the coaches by indicating which features to focus on in talent identification.}, archive = {J_ACCESS}, author = {Didem Abidin and Muhammed G. Erdem}, doi = {10.1109/ACCESS.2025.3562551}, journal = {IEEE Access}, pages = {71148-71172}, shortjournal = {IEEE Access}, title = {SCM-DL: Split-combine-merge deep learning model integrated with feature selection in sports for talent identification}, volume = {13}, year = {2025}, }
• (2025). Ultrasonic transmit beam steering using slitted transducer design. ACCESS, 13, 71133-71147. (www)

  @article{ , abstract = {Advancements in ultrasonic imaging sensors (UISs), such as improvements in cost-effectiveness, low power consumption, and suitability for challenging environments, have been useful for drone navigation. However, existing microelectromechanical systems (MEMS) transducer arrays suffer from limitations due to the non-uniform stress distribution of the thin-film layers, which changes the resonant frequency of devices. To address this, a novel sector slitted transducer design was proposed, which enhances the acoustic pressure output while maintaining mechanical stability and reduces the sensitivity of the resonant frequency to residual stresses. In this study, finite element analysis was used to model the slitted transducer design, which demonstrated minimal variation in resonant frequencies under different stress conditions. The MEMS transducer array simulations have displayed effective beam-steering capabilities using different phase profiles and validation of performance metrics through the Fresnel and Fraunhofer diffraction propagations. Both the imaging resolution and signal-to-noise ratio (SNR) of the array were evaluated over various distances. The figures-of-merit (FoM) of the sector-slitted design indicate that the transducer array is 1.6 to 30 times smaller than existing designs while being able to generate 26 to 965 times greater acoustic pressure than the other designs reported in the literature. Thus, the array promises enhanced performance in 3D range-finding and imaging applications for drones and mobile devices, offering compactness and efficiency. Future applications could leverage these advancements to improve autonomous navigation and environmental sensing in complex scenarios.}, archive = {J_ACCESS}, author = {Yinuo Enoch Zhao and Zheng Jie Tan and Wei-Bin Ewe and Wai Siang Yeoh and Rahul Singaram Senthilkumar and Viet Phuong Bui and Daniel Ssu-Han Chen and Xing Haw Marvin Tan}, doi = {10.1109/ACCESS.2025.3561805}, journal = {IEEE Access}, pages = {71133-71147}, shortjournal = {IEEE Access}, title = {Ultrasonic transmit beam steering using slitted transducer design}, volume = {13}, year = {2025}, }
• (2025). Government digital transformation: A tailor-made digital maturity assessment framework. ACCESS, 13, 71120-71132. (www)

  @article{ , abstract = {Governments seek to implement digital transformation initiatives to improve the effectiveness of their public services. To achieve this, governments need to assess their IT environment and evaluate their digital maturity. The conventional digital maturity assessment frameworks, mainly constructed by consulting firms, often fail when applied to the public sector due to the differences between the business and government sectors in terms of their operational missions and activities. Therefore, a maturity assessment framework that accommodates the unique requirements of governments and their operating environments is necessary. The novelty of this research lies in its contribution to digital transformation in the public sector by presenting a framework specifically designed to assess the digital transformation maturity of government organizations. This research is based on analyzing the factors of maturity assessment models developed by industry leaders from three leading global consulting firms. Insights from the academic literature on these factors were extracted, and a comprehensive survey was conducted with 88 IT experts from government institutes across six countries to ensure their relevance and applicability in real-world government settings. In this research, we tested our hypothesis on whether there is a significant relationship between the importance of digital transformation factors and their implementation. The analysis indicated that there is no strong statistical relationship between the importance of digital transformation maturity factors and their implementation. The framework helps assess digital maturity and supports the formulation of effective strategies to drive successful digital transformation initiatives in government.}, archive = {J_ACCESS}, author = {Muna Alfadhli and Murat Kucukvar and Nuri C. Onat and Somaya Al-Maadeed and Abdelhamid Abdessadok}, doi = {10.1109/ACCESS.2025.3560999}, journal = {IEEE Access}, pages = {71120-71132}, shortjournal = {IEEE Access}, title = {Government digital transformation: A tailor-made digital maturity assessment framework}, volume = {13}, year = {2025}, }
• (2025). Robust UWB positioning system using switching tracking filters with estimated states-based non-line-of-sight classifier. ACCESS, 13, 71101-71119. (www)

  @article{ , abstract = {We present a novel UWB-based positioning system that incorporates a deep learning-based non-line-of-sight (NLOS) classifier and a switching tracking filter. Unlike conventional methods, the proposed classifier determines whether a measured distance was obtained under LOS or NLOS conditions by using not only the channel impulse response (CIR) but also the distance measurement and a previously estimated distance sequence. This combination enhances the detection of abrupt changes in distance, thereby improving classification accuracy. Additionally, we propose switching filters that operate using different noise statistics based on the classification result. Specifically, we explore combinations of a particle filter and a robust Kalman filter to mitigate the impact of significant errors in NLOS signals. In experiments, the proposed classifier significantly outperformed conventional classifiers. Furthermore, the switching filter-based methods demonstrated substantial improvements in localization performance compared to traditional approaches. Among the switching filters, the switching particle filter provided the best localization performance, though at the cost of higher computational complexity, whereas the switching robust Kalman filter was the fastest, albeit with slightly lower localization performance.}, archive = {J_ACCESS}, author = {Miseon Han and Jeongtae Kim}, doi = {10.1109/ACCESS.2025.3561842}, journal = {IEEE Access}, pages = {71101-71119}, shortjournal = {IEEE Access}, title = {Robust UWB positioning system using switching tracking filters with estimated states-based non-line-of-sight classifier}, volume = {13}, year = {2025}, }
• (2025). FlexiNet: An adaptive feature synthesis network for real-time ego vehicle speed estimation. ACCESS, 13, 71082-71100. (www)

  @article{ , abstract = {Ego vehicle speed estimation is critical for autonomous driving and advanced driver-assistance systems (ADAS), but traditional methods often fail in accuracy and computational efficiency under dynamic conditions. To address these challenges, we propose FlexiNet, a novel adaptive feature synthesis network that leverages monocular camera data to perform real-time speed estimation. FlexiNet integrates five key components, the Contextual Motion Analysis Block, Adaptive Feature Transformer, Spatial Feature Extraction Module, Motion Feature Extraction Module, and Dynamic Integration Gate, to effectively extract and fuse spatial and temporal features, thereby overcoming limitations of previous approaches by mitigating noise and capturing subtle motion dynamics. Comprehensive evaluations on the KITTI and nuImages datasets demonstrate FlexiNet’s superior performance. On the nuImages dataset, our model achieves an RMSE of 1.1358 m/s and an MAE of 0.9599 m/s, while on the KITTI dataset it records an RMSE of 1.9542 m/s and an MAE of 1.0610 m/s—reductions in error of up to 27.6% and 75.5% compared to baseline methods. These results validate the technical soundness and real-time capability of FlexiNet for deployment on embedded automotive platforms. By addressing critical gaps in previous research, FlexiNet makes a significant contribution toward the development of safer and more efficient autonomous vehicle technologies. The source code for FlexiNet is publicly available at here https://github.com/Geekgineer/FlexiNet}, archive = {J_ACCESS}, author = {Abdalrahaman Ibrahim and Kyandoghere Kyamakya and Wolfgang Pointner}, doi = {10.1109/ACCESS.2025.3562229}, journal = {IEEE Access}, pages = {71082-71100}, shortjournal = {IEEE Access}, title = {FlexiNet: An adaptive feature synthesis network for real-time ego vehicle speed estimation}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Analysis and modeling of direct ammonia fuel cells for solar and wind power leveling in smart grid applications”. ACCESS, 13, 71081. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Analysis and Modeling of Direct Ammonia Fuel Cells for Solar and Wind Power Leveling in Smart Grid Applications”).}, archive = {J_ACCESS}, author = {Miswar A. Syed and Osamah Siddiqui and Mehrdad Kazerani and Muhammad Khalid}, doi = {10.1109/ACCESS.2025.3561984}, journal = {IEEE Access}, pages = {71081}, shortjournal = {IEEE Access}, title = {Corrections to “Analysis and modeling of direct ammonia fuel cells for solar and wind power leveling in smart grid applications”}, volume = {13}, year = {2025}, }
• (2025). A terrain-aided navigation method incorporating terrain matching and terrain suitability analysis. ACCESS, 13, 71066-71080. (www)

  @article{ , abstract = {Underwater terrain-aided navigation system can effectively correct the drift error of inertial navigation system that gradually increases with the accumulation of time, among which, underwater terrain suitability analysis is an important part of underwater terrain-aided navigation system. Traditional approaches view terrain matching and terrain suitability analysis as separate processes, which leaves terrain suitability analysis devoid of the knowledge used through navigation and terrain matching in particular. Whereas terrain matching can often provide useful information for TSA. This study attempts to use a deep learning model to deal with the terrain matching problem and terrain suitability analysis problem in the dimension of abstract features using a unified feature encoder. We propose a framework for sharing knowledge during the terrain analysis and matching phases, and construct a fitted probabilistic nonlinear classification network for matching. We validate the effectiveness of the method by performing suitability analysis in terrain regions with different geomorphic differences. After that, the good performance of the method is further verified by the navigation results in regions with different suitability. Finally, the feasibility and effectiveness of the terrain-assisted navigation system proposed in this paper are verified and analyzed as a whole in the form of semi-physical simulation.}, archive = {J_ACCESS}, author = {Gang Fan and Xiaojin Liu and Yun Li and Jia Li and Yan Han and Long He and Pengyun Chen}, doi = {10.1109/ACCESS.2025.3562768}, journal = {IEEE Access}, pages = {71066-71080}, shortjournal = {IEEE Access}, title = {A terrain-aided navigation method incorporating terrain matching and terrain suitability analysis}, volume = {13}, year = {2025}, }
• (2025). LM-CLIP: Adapting positive asymmetric loss for long-tailed multi-label classification. ACCESS, 13, 71053-71065. (www)

  @article{ , abstract = {Accurate multi-label image classification is essential for real-world applications, especially in scenarios with long-tailed class distributions, where some classes appear frequently while others are rare. This imbalance often leads to biased models that struggle to accurately recognize underrepresented classes. Existing methods either trade off performance between head and tail classes or rely on image captions, limiting adaptability. To address these limitations, we propose LM-CLIP, a novel framework built around a unified loss function. Our Balanced Asymmetric Loss (BAL) extends traditional asymmetric loss by emphasizing the gradients of rare positive samples where the model is uncertain, mitigating bias toward dominant classes. This is complemented by a contrastive loss that pushes negative samples further from the decision boundary, creating a more optimal embedding space even in long-tailed scenarios. These loss functions together ensure balanced performance across all classes. Our framework is built on pre-trained models utilizing textual and visual features from millions of image-text pairs. Furthermore, we incorporate a dynamic sampling strategy that prioritizes rare classes based on their occurrence, which ensures effective training without compromising overall performance. Experiments conducted on VOC-MLT and COCO-MLT benchmarks demonstrate the effectiveness of our approach, achieving +4.66% and +8.14% improvements in mean Average Precision (mAP) over state-of-the-art methods. Our code is publicly available at https://github.com/damilab/lm-clip.}, archive = {J_ACCESS}, author = {Christoph Timmermann and Seunghyeon Jung and Miso Kim and Woojin Lee}, doi = {10.1109/ACCESS.2025.3561581}, journal = {IEEE Access}, pages = {71053-71065}, shortjournal = {IEEE Access}, title = {LM-CLIP: Adapting positive asymmetric loss for long-tailed multi-label classification}, volume = {13}, year = {2025}, }
• (2025). Neural network-based imitation learning for approximating stochastic battery management systems. ACCESS, 13, 71041-71052. (www)

  @article{ , abstract = {Lithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. Traditional predictive control methods are limited by their reliance on precise models, which are often hindered by uncertainties in battery parameters due to aging, production variability, and operational conditions. While stochastic predictive control policies can address these uncertainties by incorporating them directly into the optimization process, they typically introduce considerable computational complexity. In response to this challenge, this paper presents a novel approach that adapts imitation learning to efficiently approximate stochastic predictive control strategies, thus significantly reducing the computational burden through offline training. Specifically, the proposed method leverages the Dataset Aggregation algorithm to overcome the issue of distributional shift, a common limitation in imitation learning frameworks. Simulations based on a detailed electrochemical model demonstrate the effectiveness of the method, adhering to probabilistic constraints while offering a scalable and computationally efficient solution for advanced battery management systems.}, archive = {J_ACCESS}, author = {Andrea Pozzi and Alessandro Incremona and Daniele Toti}, doi = {10.1109/ACCESS.2025.3563300}, journal = {IEEE Access}, pages = {71041-71052}, shortjournal = {IEEE Access}, title = {Neural network-based imitation learning for approximating stochastic battery management systems}, volume = {13}, year = {2025}, }
• (2025). Light field diffusion for single-view novel view synthesis. ACCESS, 13, 71027-71040. (www)

  @article{ , abstract = {Single-view novel view synthesis, the task of generating images from new viewpoints based on a single reference image, is important but challenging in computer vision. Recent advancements in novel view synthesis have leveraged Denoising Diffusion Probabilistic Models for their exceptional ability to produce high-fidelity images. However, current diffusion-based methods typically utilize camera pose matrices to globally and implicitly enforce 3D constraints, which can lead to inconsistencies in images generated from varying viewpoints, particularly in regions with complex textures and structures. To address these limitations, we present Light Field Diffusion, a novel conditional diffusion-based approach that transcends the conventional reliance on camera pose matrices. Starting from the camera pose matrices, Light Field Diffusion transforms them into light field encodings, with the same shape as the reference image, to describe the direction of each ray. By integrating the light field encoding with the reference image, our method imposes local pixel-wise constraints within the diffusion process, fostering enhanced view consistency. Our approach not only involves training Image Light Field Diffusion on the ShapeNet Car dataset but also includes fine-tuning a pre-trained latent diffusion model on the Objaverse dataset, which enables our Latent Light Field Diffusion model to exhibit remarkable zero-shot generalization capabilities across out-of-distribution datasets like the Ray-traced multi-view synthetic dataset, Google Scanned Objects dataset, and in-the-wild images. Experiments demonstrate that Light Field Diffusion produces high-fidelity images and achieves superior consistency in complex regions, outperforming existing novel view synthesis methods.}, archive = {J_ACCESS}, author = {Yifeng Xiong and Haoyu Ma and Shanlin Sun and Kun Han and Hao Tang and Xiaohui Xie}, doi = {10.1109/ACCESS.2025.3563313}, journal = {IEEE Access}, pages = {71027-71040}, shortjournal = {IEEE Access}, title = {Light field diffusion for single-view novel view synthesis}, volume = {13}, year = {2025}, }
• (2025). Modified swarm-based artificial intelligence optimization for optimal coordination of directional overcurrent relays in power system. ACCESS, 13, 71007-71026. (www)

  @article{ , abstract = {The coordination of directional overcurrent relays (DOCRs) plays a critical role in ensuring the reliability and robustness of modern electrical power protection systems. Achieving optimal relay coordination in multi-loop power networks is a complex optimization challenge requiring the minimization of relay operating times and achieve optimal tuning of time dial settings (TDS) and plug settings (PS) while considering the impact of DG integration. The proposed method employs a Quantum-Inspired Adaptive Walrus Optimization Algorithm (QIAWOA), a modified swarm-based artificial intelligence technique (AI) that incorporate rates quantum-inspired principles, such as adaptive quantum rotation gates, to enhance search dynamics and facilitate precise relay coordination. The performance of QIAWOA is validated using the IEEE 3, 8, and 15-bus systems, as well as the CEC 2020 benchmark suite, which includes multimodal and multi-objective optimization functions (MMOOF). QIAWOA demonstrates superior capabilities in identifying globally optimal solutions, significantly reducing relay operating times, and achieving robust coordination. Comprehensive statistical analyses, including empirical cumulative distribution functions (CDF), boxplots, histograms, probability plots, and quantile-quantile (QQ) plots, underscore the reliability and efficiency of the proposed method. Comparative evaluations with state-of-the-art nature-inspired techniques further highlight the enhanced performance of QIAWOA, establishing it as a powerful tool for improving the protection system performance in complex power networks.}, archive = {J_ACCESS}, author = {Abdul Wadood and Hani Albalawi and Aadel Mohammed Alatwi and Herie Park}, doi = {10.1109/ACCESS.2025.3563338}, journal = {IEEE Access}, pages = {71007-71026}, shortjournal = {IEEE Access}, title = {Modified swarm-based artificial intelligence optimization for optimal coordination of directional overcurrent relays in power system}, volume = {13}, year = {2025}, }
• (2025). Supervised anomaly detection in univariate time-series using 1D convolutional siamese networks. ACCESS, 13, 70980-71006. (www)

  @article{ , abstract = {In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.}, archive = {J_ACCESS}, author = {Ayan Chatterjee and Vajira Thambawita and Michael A. Riegler and Pål Halvorsen}, doi = {10.1109/ACCESS.2025.3561375}, journal = {IEEE Access}, pages = {70980-71006}, shortjournal = {IEEE Access}, title = {Supervised anomaly detection in univariate time-series using 1D convolutional siamese networks}, volume = {13}, year = {2025}, }
• (2025). A multilevel inverter with a single battery source and a high-frequency link for electric vehicles. ACCESS, 13, 70964-70979. (www)

  @article{ , abstract = {Multilevel inverter topologies with cascaded H-bridges fed by asymmetrical direct-current (DC) voltage sources have higher output voltage levels than symmetrical ones and are preferred in electric vehicles (EVs). However, these converters are difficult to incorporate in electric vehicles because the system requires a significant number of isolated DC supplies. This study presents a novel multilevel inverter drive topology, which is powered by a single battery source and uses a small, affordable high-frequency link (HFL) to generate isolated DC sources across H-bridges. The HFL consists of a Single-Input Multiple-Output (SIMO) flyback converter and a Bidirectional DC-DC (BDC) converter, which enables dynamic voltage control with a finite number of levels. This study focuses on a 27-level inverter fed induction motor drive with a cross-regulated DC link. In addition, the proposed multilevel drive system enables a smooth transition from motoring to regenerative charging of the battery with three-level rectifier operation of the cascaded H-bridge converter. The hybrid nearest level control (HNLC) modulation scheme is deployed in the proposed drive to control the inverter voltage over a wide range of speed variations without compromising the number of voltage levels. The proposed topology was simulated using MATLAB/Simulink and validated using hardware experiments.}, archive = {J_ACCESS}, author = {S. A. Kannan and G. Jagadanand and Nikhil Sasidharan}, doi = {10.1109/ACCESS.2025.3562351}, journal = {IEEE Access}, pages = {70964-70979}, shortjournal = {IEEE Access}, title = {A multilevel inverter with a single battery source and a high-frequency link for electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Reduced inter-element interference mmWave MIMO antenna and its application in WBAN. ACCESS, 13, 70947-70963. (www)

  @article{ , abstract = {Multi-input multi-output (MIMO) antennas operating in the millimeter wave (mmWave) band face challenges related to inter-element interference, limited isolation due to close spacing, and mutual coupling, all of which degrade antenna performance. To address these, this study introduces an advanced technique for enhancing inter-element isolation and minimizing mutual coupling. The proposed approach employs a combination of defective ground structures (DGS) and frequency-selective surfaces (FSS) to achieve effective isolation enhancement. Initially, a mmWave dual-band antenna was designed by incorporating a modified elliptical patch with rotating arms. Subsequently, a six-element dual-band MIMO antenna, measuring $1.97 \times 3.39 \lambda ^{2}$ , was developed to operate in the 23.63–32.90 GHz and 36.68–40 GHz bands, covering both ISM and 5G NR bands. The proposed inter-element isolation technique successfully achieves a coupling reduction of 33 dB between the MIMO elements. Furthermore, the designed dual-band MIMO antenna maintains a broadside radiation pattern, with maximum realized gains of 9.12 dBi, 9.19 dBi, 8.80 dBi, and 8.77 dBi at 26 GHz, 28 GHz, 30 GHz, and 38 GHz, respectively. It also demonstrates excellent MIMO diversity performance, including a total active reflection coefficient of $\lt -10$ dB, an envelope correlation coefficient of <0.04, and a diversity gain of >9.91 dB. A specific absorption rate (SAR) analysis confirmed that the design complies with safety standards for both 1g and 10g tissue models. Additionally, a communication link scenario for wireless body area network (WBAN) applications was investigated. The combination of high isolation, dual-band functionality, broadside radiation, excellent diversity performance, and acceptable link margin validates the suitability of the proposed MIMO antenna for WBAN communication applications.}, archive = {J_ACCESS}, author = {Jawad Ahmad and Mohammad Hashmi}, doi = {10.1109/ACCESS.2025.3562632}, journal = {IEEE Access}, pages = {70947-70963}, shortjournal = {IEEE Access}, title = {Reduced inter-element interference mmWave MIMO antenna and its application in WBAN}, volume = {13}, year = {2025}, }
• (2025). Relayer aggregation using chainless multi-layer consensus. ACCESS, 13, 70928-70946. (www)

  @article{ , abstract = {The increasing adoption of blockchain technology has underscored the need for cross-chain systems that enable seamless communication among multiple BC networks. Achieving cross-chain interoperability, which ensures secure and efficient data storage and transfer across BCs, remains a critical technical challenge. Among existing solutions, the Inter-Blockchain Communication (IBC) protocol within the Cosmos ecosystem is a prominent framework that facilitates cross-chain communication using light clients and continuous monitoring. However, IBC faces limitations due to its reliance on the consensus algorithms and block generation intervals of participating blockchains. Cross-chain transactions are processed sequentially, requiring approval at each stage, which reduces efficiency. Furthermore, the increasing number of relayers introduces scalability and operational challenges. To address these issues, this study proposes a novel framework called Relayer Aggregation (RA), which aims to enhance cross-chain communication by employing a chainless multi-layer consensus mechanism. RA enables parallel transaction processing to improve performance and scalability. Experimental nodes were developed, and comparative performance evaluations of RA and IBC were conducted to validate the proposed approach. The results demonstrate that RA significantly reduces the number of required relayer nodes and enhances processing efficiency through parallelization. By overcoming the sequential processing limitations of IBC, RA offers a scalable and efficient solution for cross-chain interoperability. This study contributes to advancing blockchain ecosystems by addressing key bottlenecks in cross-chain systems and providing a foundation for future optimization in distributed environments.}, archive = {J_ACCESS}, author = {Takaaki Yanagihara and Akihiro Fujihara}, doi = {10.1109/ACCESS.2025.3561243}, journal = {IEEE Access}, pages = {70928-70946}, shortjournal = {IEEE Access}, title = {Relayer aggregation using chainless multi-layer consensus}, volume = {13}, year = {2025}, }
• (2025). A study on improving of adaptive sensorless control performance via funnel control. ACCESS, 13, 70919-70927. (www)

  @article{ , abstract = {Model-based sensorless control, which estimates the position and speed of a motor via mathematical analysis, has been studied using various methods because of its simple formula and adequate performance without additional power consumption. Model-based sensorless control estimates the position and speed of an actual motor by interpreting the error between the values calculated by the observer and obtaining the data from the actual motor as an error due to position and speed. Motor parameters can vary depending on temperature and current. This creates additional errors by the observer, which results in a reduction in sensorless estimation performance. To solve these problems, an online parameter estimation method using the recursive least squares (RLS) algorithm or by creating a parameter lookup table through experiments has been studied. However, these approaches have the disadvantage of requiring overly complex formulas or excessive time and effort. In this paper, we propose a new adjustable model that includes parameter errors and adopt a sensorless method that compensates for additional parameter errors via funnel control. This method is more accurate and stable than the existing methods when parameter errors exist. The stability criterion of the algorithm is based on the theory of hyperstability in nonlinear feedback systems. The effectiveness and verification of the proposed algorithm are achieved through simulations and experiments in MATLAB.}, archive = {J_ACCESS}, author = {Jun-Seo Han and Gyung-Jae Cho and Hyeon-Ji Jin and Geun-Ho Lee}, doi = {10.1109/ACCESS.2025.3562619}, journal = {IEEE Access}, pages = {70919-70927}, shortjournal = {IEEE Access}, title = {A study on improving of adaptive sensorless control performance via funnel control}, volume = {13}, year = {2025}, }
• (2025). Activation-guided low-rank parameter adaptation for efficient model fine-tuning. ACCESS, 13, 70909-70918. (www)

  @article{ , abstract = {Fine-tuning large language models is computationally expensive, and while existing parameter-efficient methods like Low-Rank Adaptation (LoRA) reduce computational costs, they are limited by suboptimal initialization strategies. We introduce Activation-Guided LoRA (AG-LoRA), a novel approach that initializes LoRA modules using Singular Value Decomposition (SVD) guided by activation patterns. Our method employs pre-trained weights combined with activation-based weighting factors and implements a new global rank assignment strategy that accounts for activation outliers. Experimental evaluations on LLaMA and CLIP models show that AG-LoRA achieves superior performance while reducing GPU memory usage compared to existing methods. In tests with LLaMA 7B models, AG-LoRA reached 75.9% accuracy across various tasks, surpassing both LoRA and DoRA baselines. AG-LoRA demonstrates significant improvements in parameter-efficient fine-tuning of large language models, offering enhanced performance and reduced computational requirements. These advances make it a promising solution for efficient model adaptation across diverse applications.}, archive = {J_ACCESS}, author = {Qingchen Wang and Shengyu Shen}, doi = {10.1109/ACCESS.2025.3533701}, journal = {IEEE Access}, pages = {70909-70918}, shortjournal = {IEEE Access}, title = {Activation-guided low-rank parameter adaptation for efficient model fine-tuning}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning for precise individual tree segmentation in airborne LiDAR point clouds. ACCESS, 13, 70895-70908. (www)

  @article{ , abstract = {Segmenting individual trees from airborne LiDAR point cloud data is critical for forest management, urban planning, and ecological monitoring but remains challenging due to complex natural environments, diverse tree architectures, and dense canopies. Traditional supervised methods rely on extensive, manually annotated datasets that are often impractical to obtain. In this study, we propose a novel self-supervised learning framework that eliminates the need for manual labeling by integrating transformation-invariant feature extraction, an energy-based segmentation loss, and soft clustering. The framework operates in two stages: a pretext task applies geometric transformations—rotation (from –45° to +45°), translation (between –1 and 1 units), and scaling (between 0.5 and 2.0)—to learn robust features, while an unsupervised segmentation step leverages an energy function that combines height, density, and slope attributes to cluster points corresponding to individual trees. We evaluated our approach on a high-density LiDAR dataset acquired from the Estonian Land Board (LAS format, version 1.4) comprising over 850,000 points. Our method achieves up to 87% convexity, 78% solidity, and an elliptical fit error as low as 0.12, substantially reducing over-segmentation compared to baseline clustering techniques. These results demonstrate that our self-supervised framework offers a scalable, label-free solution for precise tree segmentation, with significant advantages in accuracy and efficiency over traditional supervised methods.}, archive = {J_ACCESS}, author = {Lama Shaheen and Bader Rasheed and Manuel Mazzara}, doi = {10.1109/ACCESS.2025.3563363}, journal = {IEEE Access}, pages = {70895-70908}, shortjournal = {IEEE Access}, title = {Self-supervised learning for precise individual tree segmentation in airborne LiDAR point clouds}, volume = {13}, year = {2025}, }
• (2025). Enhancing weakly-supervised video anomaly detection with temporal constraints. ACCESS, 13, 70882-70894. (www)

  @article{ , abstract = {Anomaly Detection has been a significant field in Machine Learning since it began gaining traction. In the context of Computer Vision, the increased interest is notorious as it enables the development of video processing models for different tasks without the need for a cumbersome effort with the annotation of possible events, that may be under represented. From the predominant strategies, weakly and semi-supervised, the former has demonstrated potential to achieve a higher score in its analysis, adding to its flexibility. This work shows that using temporal ranking constraints for Multiple Instance Learning can increase the performance of these models, allowing the focus on the most informative instances. Moreover, the results suggest that altering the ranking process to include information about adjacent instances generates best-performing models.}, archive = {J_ACCESS}, author = {Francisco Caetano and Pedro Carvalho and Christina Mastralexi and Jaime S. Cardoso}, doi = {10.1109/ACCESS.2025.3560767}, journal = {IEEE Access}, pages = {70882-70894}, shortjournal = {IEEE Access}, title = {Enhancing weakly-supervised video anomaly detection with temporal constraints}, volume = {13}, year = {2025}, }
• (2025). Encrypting images using the sum of functions of a real variable. ACCESS, 13, 70872-70881. (www)

  @article{ , abstract = {A combined cryptosystem with symmetric keys for encrypting messages is proposed. Its unique feature is the use of functions of a real variable as keys instead of natural numbers. Eight key functions correspond to the eight binary digits in the ASCII character code, with each binary digit assigned a specific key function. The cipher for each character is represented as a one-dimensional array of real numbers. The elements of the array are generated by summing selected key functions, where the amplitudes are chosen randomly. Only the key functions corresponding to binary digit “1” in the ASCII code of the character being encrypted are summed. Each array is subsequently encrypted by calculating the integral disproportion function using either a new key function or one employed in the initial stage. Decryption is similarly performed in two stages. Examples are provided to illustrate the robustness of this cryptographic system.}, archive = {J_ACCESS}, author = {Viktor V. Avramenko and Junzo Watada and Mykyta O. Bondarenko and Nataliya Kalashnykova and Mohd Hilmi Hasan}, doi = {10.1109/ACCESS.2025.3556696}, journal = {IEEE Access}, pages = {70872-70881}, shortjournal = {IEEE Access}, title = {Encrypting images using the sum of functions of a real variable}, volume = {13}, year = {2025}, }
• (2025). EEMLCR: Energy-efficient machine learning-based clustering and routing for wireless sensor networks. ACCESS, 13, 70849-70871. (www)

  @article{ , abstract = {Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for data routing and the cluster-based organization of sensor nodes.}, archive = {J_ACCESS}, author = {Muhammad Akram and Sibghat Ullah Bazai and Muhammad Imran Ghafoor and Saira Akram and Qazi Mudassar Ilyas and Abid Mehmood and Sajid Iqbal and Muhammad Asim Rafique}, doi = {10.1109/ACCESS.2025.3562368}, journal = {IEEE Access}, pages = {70849-70871}, shortjournal = {IEEE Access}, title = {EEMLCR: Energy-efficient machine learning-based clustering and routing for wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Improvised spectral efficiency and channel estimation parameters in visible light vehicular communication by integrating simulation of urban mobility data. ACCESS, 13, 70828-70848. (www)

  @article{ , abstract = {Visible Light Communication (VLC) has emerged as a promising technology for vehicular communication due to its high data rates, low latency, and immunity to electromagnetic interference. However, optimizing spectral efficiency and channel estimation in dynamic vehicular environments remains a key challenge. This paper proposes an OFDM-QAM-based VLC system to enhance spectral efficiency and improve channel estimation accuracy in vehicular networks. To achieve this, we leverage Simulation of Urban Mobility (SUMO) data, which provides detailed insights into vehicular movement and traffic patterns. By integrating SUMO with VLC system simulations, we optimize modulation schemes, multiple access techniques, and spatial reuse strategies to adapt to varying traffic conditions. Additionally, a mathematical framework is developed to evaluate the impact of vehicular mobility on spectral efficiency and bit error rate (BER) performance across different QAM modulation schemes. Simulation results demonstrate that the proposed VLC system significantly improves spectral efficiency, achieving values up to 1.4 bits/s/Hz, while maintaining low BER across varying Signal to Noise Ratio (SNR) levels. These findings confirm the effectiveness of the proposed methodology in enhancing Visible Light Communication (VLC) performance for vehicular applications, making it a viable solution for next-generation intelligent transportation systems.}, archive = {J_ACCESS}, author = {Ali H. Wheeb and Sudhanshu Maurya and Shaik Karimullah and Fahimuddin Shaik and Subrata Chowdhury and B. V. V. Siva Prasad and Anurag Sinha and Adamya Sood and Syed Abid Hussain}, doi = {10.1109/ACCESS.2025.3558697}, journal = {IEEE Access}, pages = {70828-70848}, shortjournal = {IEEE Access}, title = {Improvised spectral efficiency and channel estimation parameters in visible light vehicular communication by integrating simulation of urban mobility data}, volume = {13}, year = {2025}, }
• (2025). A hybrid control framework for swing-up and stabilization of the double inverted pendulum on a cart. ACCESS, 13, 70811-70827. (www)

  @article{ , abstract = {This paper presents a comprehensive study on the application of advanced control methods to the Double Inverted Pendulum on a Cart (DIPC) system. The focus is on the design and implementation of swing-up control laws, balancing strategies, and disturbance rejection techniques to achieve robust system stability. The control scheme integrates energy-based controllers (EC) with stabilizing controllers based on sliding mode control (SMC), including a variant incorporating the H-infinity norm, to address the challenges of swinging up and stabilizing the pendulums and the overall DIPC system. Simulation results demonstrate the effectiveness of these controllers in bringing the DIPC system from the Down-Down position to the Up-Up position. During the transition from Down-Down to Up-Down, the SMC controller combined with the H-infinity norm has shown superior performance in reducing disturbance effects compared to traditional SMC controllers. The proposed control framework is expected to contribute significantly to the control of nonlinear and complex systems like DIPC, with potential applications in various fields.}, archive = {J_ACCESS}, author = {Dinh-Hong Nguyen and Duc-Binh Pham and Thi-van-Anh Nguyen and Ngoc-Tam Bui}, doi = {10.1109/ACCESS.2025.3562945}, journal = {IEEE Access}, pages = {70811-70827}, shortjournal = {IEEE Access}, title = {A hybrid control framework for swing-up and stabilization of the double inverted pendulum on a cart}, volume = {13}, year = {2025}, }
• (2025). The effect of AI gamification on students’ engagement and academic achievement in malaysia: SEM analysis perspectives. ACCESS, 13, 70791-70810. (www)

  @article{ , abstract = {Gamification and Artificial Intelligence (AI) have transformed the educational environment, offering innovative approaches to enhance student engagement and learning outcomes. By using the power of gamification and AI, educators can create immersive, personalized, and interactive learning experiences that increase motivation, participation, and academic achievement. This study investigates the relationships between gamification, artificial intelligence, student engagement, and achievement in educational contexts. A quantitative approach was employed, combining online surveys and structural equation modeling (SEM) analysis with 290 students from Malaysia. SEM analysis was performed using SmartPLS 4 software. The findings reveal significant positive relationships between gamification and student achievement, gamification and student engagement, gamification and artificial intelligence, and AI with both student engagement and achievement. Furthermore, student engagement was found to influence student achievement positively. The study’s implications suggest that educators and instructional designers should adopt these technologies to create intelligent, interactive, and personalized learning environments. Future research directions include exploring long-term effects and contextual factors influencing student outcomes to promote student success in the digital age. However, the study is limited to a single country context (Malaysia), and relies on self-reported survey data, which may influence generalizability and introduce potential biases.}, archive = {J_ACCESS}, author = {Nisar Ahmed Dahri and Noraffandy Yahaya and Waleed Mugahed Al-Rahmi and Latifah Almuqren and Abeer S. Almgren and Abdulrahman Alshimai and Ahmad Samed Al-Adwan}, doi = {10.1109/ACCESS.2025.3560567}, journal = {IEEE Access}, pages = {70791-70810}, shortjournal = {IEEE Access}, title = {The effect of AI gamification on students’ engagement and academic achievement in malaysia: SEM analysis perspectives}, volume = {13}, year = {2025}, }
• (2025). Temporal-aware transformer approach for violence activity recognition. ACCESS, 13, 70779-70790. (www)

  @article{ , abstract = {The need for effective violence detection in public spaces has intensified with increasing antisocial behavior and violence. Traditional surveillance systems, which are relying on human operators, face delays and resource challenges. Using advances in artificial intelligence (AI) and computer vision, this research presents a scalable deep learning architecture for real-time violence detection using two approaches. In the first approach, Convolutional Neural Networks (CNN) and bidirectional long-short-term memory (BiLSTM) networks are combined, where MobileNetV2 is used for spatial feature extraction and BiLSTM for temporal pattern recognition, achieving an accuracy of 95.6%. The second approach incorporates a spatial-temporal transformer (TransformerSeq) in place of BiLSTM, improving performance to 97.2% by capturing spatiotemporal relationships in video data more effectively through self-attention for temporal feature learning. The lightweight SOTA MobileNetV2, along with the proposed MobileTransformerSeq, enables the effective differentiation between violent and non-violent activities, demonstrating the potential to enhance public safety in diverse settings.}, archive = {J_ACCESS}, author = {Rajdeep Chatterjee and Ritabrata Roy Choudhury and Mahendra Kumar Gourisaria and Sreejata Banerjee and Soumik Dey and Manoj Sahni and Ernesto León-Castro}, doi = {10.1109/ACCESS.2025.3560828}, journal = {IEEE Access}, pages = {70779-70790}, shortjournal = {IEEE Access}, title = {Temporal-aware transformer approach for violence activity recognition}, volume = {13}, year = {2025}, }
• (2025). Wearable multimodal sensor probe for monitoring core body temperature, electrocardiogram, heart rate, and sweat rate. ACCESS, 13, 70769-70778. (www)

  @article{ , abstract = {Global warming and the associated increase in heatwaves have made the prevention of heat-related illnesses such as heatstroke a critical global challenge. Monitoring physiological indicators such as core body temperature, heart rate, and sweat rate is beneficial for preventing excessive accumulation of heat stress and ensuring safe activities in hot environments. However, existing methods typically require multiple separate sensors, limiting their practicality for real-world applications. In this study, we describe the development of a compact and easy-to-use wearable multimodal sensor probe capable of simultaneously measuring the core body temperature, electrocardiogram, heart rate, and sweat rate. The prototype features an innovative design that integrates a heat flux probe for simultaneous measurement of the core body temperature and cardiac potentials with a computational model for estimating the sweat rate. Validation experiments were conducted with ten healthy adult men performing cycling exercises under controlled environmental conditions, with the accuracy of each measurement obtained from the prototype evaluated by comparison with reference values. The results showed root mean squared errors of 0.087°C for core body temperature and 131 g for total water loss, both surpassing the accuracy of previous studies. However, the prototype exhibited a tendency to overestimate the core body temperature and sweat rate during the exercise and post-exercise recovery phases compared to the resting condition before exercise. This was attributed to thermoregulatory responses, such as evaporative heat loss from sweating and increased skin blood flow, during exercise and recovery. Despite some remaining challenges in validation, this study represents a significant advancement in wearable sensor technology by offering a practical and comprehensive solution for real-time monitoring in hot environments. The proposed system has the potential to contribute to heat stress prevention in occupational, sports, and everyday settings.}, archive = {J_ACCESS}, author = {Yuki Hashimoto and Kota Noto and Soto Tada and Yoshifumi Nishida}, doi = {10.1109/ACCESS.2025.3561288}, journal = {IEEE Access}, pages = {70769-70778}, shortjournal = {IEEE Access}, title = {Wearable multimodal sensor probe for monitoring core body temperature, electrocardiogram, heart rate, and sweat rate}, volume = {13}, year = {2025}, }
• (2025). Utilizing footstep sound event detection by using CNN techniques for assuring property security. ACCESS, 13, 70760-70768. (www)

  @article{ , abstract = {The advanced camera systems developed for property security prove inadequate when faced with the ease of disabling the cameras. Therefore, incorporating sound data from potential threats can also play a significant role in ensuring property security. This sound event-based approach enables a more compact installation and allows for the use of affordable microphones, offering an alternative to visual-based security systems. We propose deep learning based on a convolutional neural network (CNN) algorithm for classification of footstep sound events. The study includes two consecutive stages: ReaLISED dataset and refined ReaLISED supported by Epidemic Sound data. Sound event files are transformed into Mel-Frequency Cepstral Coefficients (MFCC) and a CNN is fed with the represented images of MFCC. By optimizing the model parameters, our unique model detected footstep sound events with 98% accuracy among 17 other sound events. Validation through Repeated Stratified K-Fold Cross-Validation (5 folds, 10 repetitions) and comparisons with state-of-the-art architectures demonstrated robust performance, with F1-Scores ranging from 0.905 to 0.992 and a mean of 0.960. The strategic incorporation of diverse open-source data fosters transparency and reproducibility, enhancing the model’s adaptability and reliability in handling real-world audio patterns, as evidenced by its commendable 1% error rate in precise identification.}, archive = {J_ACCESS}, author = {Furkan Y. Yavuz and Nejat Yumusak}, doi = {10.1109/ACCESS.2025.3561076}, journal = {IEEE Access}, pages = {70760-70768}, shortjournal = {IEEE Access}, title = {Utilizing footstep sound event detection by using CNN techniques for assuring property security}, volume = {13}, year = {2025}, }
• (2025). PFW-YOLO lightweight helmet detection algorithm. ACCESS, 13, 70752-70759. (www)

  @article{ , abstract = {Helmet recognition, as an important means to ensure personnel safety in high-risk operating environments, requires the deployment of recognition models to edge-end devices to achieve real-time and portability. However, due to the limited arithmetic and storage resources of edge-end devices, and the traditional detection algorithms have problems such as the number of parameters and large computational volume, the detection algorithms are difficult to be deployed practically. Therefore, a lightweight helmet detection algorithm PFW YOLO is proposed in this paper. Firstly, a multi-scale feature fusion module is designed to reconstruct the Bottleneck structure in C2f, which finally forms the C2f-PMSFF module to enhance the feature expression ability of the model and optimize the computational efficiency. Second, in order to further reduce the size of the model while ensuring the detection accuracy, Feature Interaction Shared Detection Head (FISH) is introduced. Finally, Wise-Inner-Shape-IoU is used to optimize the bounding box regression loss function, which is used to enhance the detection accuracy and accelerate the convergence speed. The final experimental results indicate that, compared to the original YOLOv8n algorithm, the PFW-YOLO algorithm achieves a 53% reduction in parameters, a 42% decrease in computational effort, and a 51% reduction in model size, while enhancing the mean average precision (mAP) by 0.4%.}, archive = {J_ACCESS}, author = {Yue Hong and Hao Wang and Shuo Guo}, doi = {10.1109/ACCESS.2025.3561156}, journal = {IEEE Access}, pages = {70752-70759}, shortjournal = {IEEE Access}, title = {PFW-YOLO lightweight helmet detection algorithm}, volume = {13}, year = {2025}, }
• (2025). A feature selection and enhanced reuse framework for detection and classification of dental diseases in panoramic dental X-rays images. ACCESS, 13, 70741-70751. (www)

  @article{ , abstract = {Panoramic dental X-ray images are crucial tools in dental diagnosis, and accurate detection of teeth and related lesions is essential for clinical decision-making. However, the complexity of tooth structures, variability in image quality, and scarcity of annotated data make achieving precise automatic target detection challenging in this field. In this study, a dental X-ray disease target detection framework based on feature selection and enhanced reuse was proposed to address these challenges. An improved convolutional neural network architecture was designed and implemented by combining selective receptive field fusion with shape-sensitive multi-scale feature extraction modules to enhance the ability to detect targets of different scales and shapes. In addition, a novel feature reuse and skip fusion technique was introduced to further improve the utilization of features by the backbone network. To address the current lack of annotation for impacted teeth positions, an impacted tooth location image dataset named DENIMPACT is also presented, which significantly addresses the shortcomings of current deep learning object detection algorithms in the clinical diagnosis of dental impacted teeth on panoramic X-rays. Through experiments on our dataset, our method achieved a significant improvement in detection accuracy, with the mAP50 increasing from 0.410 to 0.631. The experimental results demonstrate that our model achieves state-of-the-art performance in tooth and lesion detection tasks, providing new solutions for the automated analysis of oral medical images. The dataset will be released at: https://github.com/hexiaomo624/DENIMPACT.}, archive = {J_ACCESS}, author = {Zhiyuan Wu and Huangjian and Zhengsong}, doi = {10.1109/ACCESS.2025.3561362}, journal = {IEEE Access}, pages = {70741-70751}, shortjournal = {IEEE Access}, title = {A feature selection and enhanced reuse framework for detection and classification of dental diseases in panoramic dental X-rays images}, volume = {13}, year = {2025}, }
• (2025). Shifting of research trends in fault detection and estimation of location in power system. ACCESS, 13, 70721-70740. (www)

  @article{ , abstract = {Fault detection is a critical process in ensuring the reliability and safety of modern power systems. With the evolvement of the grid in the modern paradigm, the detection of faults has become complex, particularly due to the incorporation of renewable based distributed generation. Thus, a detailed study of the available fault detection methods needs to be addressed while developing a fault detection model for the modern power system. In line with this, the paper provides a comprehensive overview of different fault detection techniques. The fundamentals of each technique have been elucidated in detail. Moreover, a through comparative analysis of each technique has been done on the basis of several terms to highlight the strengths and shortcomings of each method. Based on the review of these techniques, several prospective research gaps have been identified which will help the researchers to advance the study of power system protection.}, archive = {J_ACCESS}, author = {Mallinath and Soham Dutta and Jayalakshimi N. S. and Vinay Kumar Jadoun}, doi = {10.1109/ACCESS.2025.3562330}, journal = {IEEE Access}, pages = {70721-70740}, shortjournal = {IEEE Access}, title = {Shifting of research trends in fault detection and estimation of location in power system}, volume = {13}, year = {2025}, }
• (2025). Adaptive beam tracking in 5G/6G mmWave networks: A clustered federated learning approach. ACCESS, 13, 70705-70720. (www)

  @article{ , abstract = {Millimeter wave (mmWave, 30–100 GHz) communication is essential for meeting the high data throughput demands of 5G/6G networks. However, mmWave signals are highly susceptible to attenuation and blockage, necessitating directional beamforming antennas and efficient beam tracking algorithms. Traditional machine learning-based approaches, such as centralized learning (CL) and federated learning (FL), face significant challenges. While CL achieves fast convergence, it suffers from high computational costs and privacy concerns. Conversely, FL addresses these issues by enabling distributed model training but struggles with slow convergence and suboptimal accuracy due to data heterogeneity. To overcome these limitations, this paper introduces a novel clustered federated learning (CFL) framework for beam tracking. CFL leverages the benefits of FL while grouping users with similar data distributions, enabling the training of a single model per cluster. This approach reduces communication overhead, accelerates training, and improves accuracy. We analyze key attributes influencing user clustering and their impact on learning efficiency. Numerical results demonstrate that CFL significantly outperforms traditional CL and FL methods, achieving an 18% accuracy improvement over FL (specifically, the FedAvg algorithm) and an 11% enhancement over CL. These findings highlight the potential of CFL for enhancing beam tracking in mmWave systems, offering a more adaptive and privacy-preserving solution for future wireless networks.}, archive = {J_ACCESS}, author = {Amjad Ali and Yevgeni Koucheryavy}, doi = {10.1109/ACCESS.2025.3563435}, journal = {IEEE Access}, pages = {70705-70720}, shortjournal = {IEEE Access}, title = {Adaptive beam tracking in 5G/6G mmWave networks: A clustered federated learning approach}, volume = {13}, year = {2025}, }
• (2025). Local fault tolerance analysis in strictly nonblocking clos and multi-LogqN networks. ACCESS, 13, 70698-70704. (www)

  @article{ , abstract = {Fault tolerance is a fundamental consideration in the design of multistage interconnection networks (MINs). While extensive research has been conducted on the fault tolerance of blocking networks, nonblocking networks have received less attention. Nonblocking networks, which guarantee the accommodation of all possible permutations without blocking, present greater analytical challenges but are essential for high-reliability applications. Among MINs, the nonblocking properties of Clos and multi-Log ${}_{q} N$ networks, commonly used in parallel and high-performance computing systems, have been widely studied. However, fault tolerance has been explored only for rearrangeably nonblocking (RNB) MINs, which include Clos networks and specific cases of multi-Log ${}_{q} N$ networks, leaving strictly nonblocking (SNB) MINs underexplored. SNB MINs are highly attractive for applications demanding robust and uninterrupted operation. Though they have higher hardware requirements than RNB MINs, they ensure new connections can be established without requiring rearrangements, thereby eliminating potential connection interruptions. This paper addresses the gap in the research of SNB MINs by analyzing the fault tolerance capabilities of SNB Clos and multi-Log ${}_{q} N$ networks. The analysis is particularly challenging due to the complexity introduced by the extensive routing scenarios inherent to SNB networks. To address these challenges, we propose a deterministic method for evaluating the local fault tolerance of SNB Clos and multi-Log ${}_{q} N$ networks. Specifically, we derive the maximum number of tolerable non-contact faults within each shell s, which includes both the sth node stage and its symmetric counterpart, while ensuring the feasibility of all possible permutations. Our findings demonstrate that the SNB property is preserved in multi-Log ${}_{q} N$ networks with at least one additional node stage, even under numerous non-contact faults. In contrast, Clos networks lose their SNB capability with even a single non-contact fault. These results offer valuable insights into the fault tolerance characteristics of SNB networks and provide a robust foundation for designing resilient interconnection architectures in high-performance computing systems.}, archive = {J_ACCESS}, author = {Bey-Chi Lin}, doi = {10.1109/ACCESS.2025.3560675}, journal = {IEEE Access}, pages = {70698-70704}, shortjournal = {IEEE Access}, title = {Local fault tolerance analysis in strictly nonblocking clos and multi-LogqN networks}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in agriculture: A systematic review of crop yield prediction and optimization. ACCESS, 13, 70691-70697. (www)

  @article{ , abstract = {Agriculture is key to global food security, and Artificial Intelligence is emerging as a catalyst for its modernization. This article explores how advancements are transforming resource management in agriculture and decision-making for crop yield prediction and agricultural optimization. It is based on a Systematic Literature Review (SLR) of 25 selected studies from an initial set of 776 to identify key algorithms, critical variables, and evaluation metrics. The bibliometric analysis reveals a recent increase in publications on the topic, with a focus on algorithms such as SVM, KNN, and XGBoost. Important variables considered include soil data, climate data, and crop characteristics. Challenges identified include model complexity, data quality, and the need for appropriate pre-processing methods. The ultimate goal is to optimize agricultural practices and improve productivity through accurate predictions.}, archive = {J_ACCESS}, author = {Claudia R. Screpnik and Eduardo Zamudio and Laura I. Gimenez}, doi = {10.1109/ACCESS.2025.3560631}, journal = {IEEE Access}, pages = {70691-70697}, shortjournal = {IEEE Access}, title = {Artificial intelligence in agriculture: A systematic review of crop yield prediction and optimization}, volume = {13}, year = {2025}, }
• (2025). New composite chaotic map applied to an image encryption scheme in cybersecurity applications. ACCESS, 13, 70676-70690. (www)

  @article{ , abstract = {Chaotic systems have long played a crucial role in cryptographic applications due to their inherent unpredictability and sensitivity to initial conditions. Building on this foundation, this paper introduces an Improved Composite Chaotic Map (ICCM), inspired by classical quadratic and cubic chaotic maps. The proposed ICCM is rigorously validated through Lyapunov exponents and bifurcation diagrams, demonstrating its enhanced dynamical complexity and suitability for secure applications. Designed to expand the chaotic range, ICCM incorporates an additional parameter that significantly enlarges the keyspace, thereby improving the robustness and adaptability of encryption methods. To demonstrate its practical utility, ICCM is integrated into a novel opto-digital encryption scheme aimed at enhancing sensitivity and ensuring superior encryption performance. The effectiveness of this scheme is evaluated using a comprehensive suite of tests, including sensitivity analysis, histogram analysis, the NIST statistical test suite ( $15/15$ passed), and key security metrics. A high mean square error (MSE) value ( $\gt 10^{4}$ ) indicates a significant difference between the original and encrypted images, while correlation coefficients ( $Cr \approx 0.0001$ to 0.0028) and a low peak signal-to-noise ratio (PSNR <10 dB) further confirm its robustness. Keyspace analysis ( $2^{674}$ ) ensures resilience against brute-force attacks. Simulation results reveal that the ICCM-based encryption scheme achieves a Lyapunov exponent of 21.42 (compared to 19 in prior works) and withstands data loss up to 50%. These findings position ICCM as a robust defense against statistical, differential, and chosen-plaintext attacks. In conclusion, the proposed ICCM and its integration into the opto-digital encryption scheme advance the security of encryption systems, setting a new benchmark for chaos-based cryptography. The results underscore the model’s potential to address modern data security challenges and inspire further research in this critical field.}, archive = {J_ACCESS}, author = {Imane Kouadra and Tewfik Bekkouche and Lahcene Ziet and Imane Mehidi and Samah Alshathri and Walid El-Shafai}, doi = {10.1109/ACCESS.2025.3558948}, journal = {IEEE Access}, pages = {70676-70690}, shortjournal = {IEEE Access}, title = {New composite chaotic map applied to an image encryption scheme in cybersecurity applications}, volume = {13}, year = {2025}, }
• (2025). MSU-net: A synthesized U-net for exploiting multi-scale features in OCT image segmentation. ACCESS, 13, 70663-70675. (www)

  @article{ , abstract = {The U-Net architecture is widely recognized as a prominent algorithm for choroidal segmentation in optical coherence tomography (OCT) images. However, conventional U-Net implementations exhibit two critical limitations. First, the backbone employs uniform-sized convolutional kernels to process feature maps across all channels within the same layer, resulting in homogeneous receptive fields and a single-scale bottleneck that impedes global contextual feature extraction. Second, the skip connections are restricted to same-scale feature maps between encoder and decoder, failing to exploit cross-semantic hierarchical feature interactions. To address these issues, this study introduces MSU-Net, a novel neural network for OCT-based choroidal segmentation. The proposed framework enhances performance through two innovations: 1) replacement of standard encoder blocks with a multi-branch module combining heterogeneous convolutions to achieve multi-scale receptive field diversification; 2) redesign of skip connections through a pyramid fusion module with spatial attention for adaptive multi-level feature weighting. This architecture enables progressive refinement of low-level features guided by high-level semantics, significantly improving feature discriminability. Experimental results demonstrate superior performance with metrics of 99.5% (accuracy), 96.7% (sensitivity), 94.7% (Dice), and 94.6% (MIoU), surpassing the baseline by 0.4%, 3.7%, 2.8%, and 2.9% respectively. Notably, the model shows consistent advantages in segmenting indistinct choroidal boundaries compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Dejie Chen and Xiangping Chen and Hao Gu and Su Zhao and Hao Jiang}, doi = {10.1109/ACCESS.2025.3557800}, journal = {IEEE Access}, pages = {70663-70675}, shortjournal = {IEEE Access}, title = {MSU-net: A synthesized U-net for exploiting multi-scale features in OCT image segmentation}, volume = {13}, year = {2025}, }
• (2025). Tunnel crack segmentation algorithm based on feature enhancement. ACCESS, 13, 70650-70662. (www)

  @article{ , abstract = {Tunnel cracks, as one of the early indicators of tunnel damage, have a significant impact on the safe operation of tunnels. However, due to the complex lighting conditions, background noise, and diverse nature of cracks inside tunnels, traditional image segmentation algorithms often struggle to achieve sufficient accuracy in crack segmentation tasks. To address these challenges, this paper proposes a tunnel crack detection method. Firstly, a novel Dynamic Feature Enhancement Network (DFEN) module is designed to extract preliminary features using a feature extractor and selectively enhance feature representations through a gating mechanism. Additionally, a new Multi-Head Interaction Learning (MHIL) is introduced, facilitating feature sharing by incorporating information exchange across different attention heads, thereby improving feature representation capabilities. Lastly, the Adaptive Switchable Atrous Convolution (ASAC) module is introduced, combining the advantages of adaptive convolution and deformable convolution while incorporating Switchable Atrous Convolution (SAC) to enhance multi-scale feature capturing capabilities. Ablation experiments, through both qualitative and quantitative evaluations, demonstrate the effectiveness of the proposed method. Comparative analysis with existing semantic segmentation methods further confirms the superiority of the proposed method in tunnel crack detection.}, archive = {J_ACCESS}, author = {Lihua Feng and Aijun Yao and An Huang}, doi = {10.1109/ACCESS.2025.3562926}, journal = {IEEE Access}, pages = {70650-70662}, shortjournal = {IEEE Access}, title = {Tunnel crack segmentation algorithm based on feature enhancement}, volume = {13}, year = {2025}, }
• (2025). Text classification to detect interpretese in bidirectional simultaneous interpreting: Improved TF-IDF and stacking. ACCESS, 13, 70640-70649. (www)

  @article{ , abstract = {The study utilizes text classification (TC) to observe “interpretese” in simultaneous interpreting (SI) at United Nations Security Council conferences. “Interpretese” is a term coined to describe the distinctive linguistic patterns interpreters employ. A text vectorization method known as TF-IDF is improved with Shannon’s entropy and used to convert interpreted and non-interpreted target language speeches into vectors. Subsequently, stacking ensemble learning classifies the vectors reduced in dimensions into two labeled categories: interpreted speech and non-interpreted speech. Accurate classifications would support the interpretese hypothesis. To explore the universality of interpretese, this study detects interpretese in bidirectional SI when interpreters work from their first to second languages in one direction and from their second to first languages in the other direction. The results demonstrate successful classifications in the two interpreting directions, thereby supporting the interpretese hypothesis. Notably, a higher classification accuracy score is yielded when the interpreters work into their first language than into their second language, suggesting interpretese is more pronounced in the former direction, and interpreting directions impact interpreters’ language processing. Different classification algorithms vary in terms of their performance in the classification tasks, underscoring the importance of using stacking for ensemble learning to achieve reliable results and justify algorithm selection.}, archive = {J_ACCESS}, author = {Dan Feng Huang and Dennis Tay and Andrew K. F. Cheung}, doi = {10.1109/ACCESS.2025.3563148}, journal = {IEEE Access}, pages = {70640-70649}, shortjournal = {IEEE Access}, title = {Text classification to detect interpretese in bidirectional simultaneous interpreting: Improved TF-IDF and stacking}, volume = {13}, year = {2025}, }
• (2025). Evaluating the physical appeal of elderly companion robots: A study of cuteness versus anthropomorphism. ACCESS, 13, 70626-70639. (www)

  @article{ , abstract = {The extent of anthropomorphism in robot design has long been a topic of scholarly interest. However, the question of whether the elderly prefer robots that are “cute” or “human-like” remains unanswered. This study introduces the concept of cuteness, the degree of anthropomorphism, and other physical attributes as reference points. Using grey relational analysis, we evaluate the correlation between five appearance features of six companion robots and five affective evaluation indicators of the elderly. The study concludes that cuteness significantly correlates with perceptions of warmth and friendliness. The ranking of the correlation between the five affective evaluations and each appearance feature is as follows: brightness > glossiness > cuteness > anthropomorphism > saturation. This suggests that elderly individuals prefer companion robots that are cute rather than anthropomorphic. The findings of this study provide guidance for the design of companion robots for the elderly and offer new insights and methods for similar product design evaluations.}, archive = {J_ACCESS}, author = {Qi Li and Leyao Wang}, doi = {10.1109/ACCESS.2025.3558896}, journal = {IEEE Access}, pages = {70626-70639}, shortjournal = {IEEE Access}, title = {Evaluating the physical appeal of elderly companion robots: A study of cuteness versus anthropomorphism}, volume = {13}, year = {2025}, }
• (2025). Frequency response assessment of distribution feeders with capacitor banks for voltage harmonic mitigation: A practical approach with field measurements. ACCESS, 13, 70616-70625. (www)

  @article{ , abstract = {Harmonic distortions are major power quality issues observed in distribution systems, causing overloading, overheating, increased losses, reduced equipment lifespan, malfunctioning of devices, and, in extreme cases, service interruptions and shutdowns of industrial processes. Since determining the causes of voltage harmonic distortions is challenging, and responsibility is not always easily assigned, this paper proposes mitigating this power quality phenomenon in distribution feeders by modifying their impedance matrix for the h-th harmonic order through frequency scanning process, without the need to assign responsibility or require the installation of harmonic filters. This approach suggests that the feeder’s frequency response can be adjusted to improve voltage harmonic levels solely by relocating existing capacitor banks. Applying this concept to a real case study, the appropriate locations for relocating capacitor banks were identified, and power quality measurements taken at an affected consumer’s point of service confirmed the effectiveness of this approach, ensuring the original reactive power compensation of the feeder.}, archive = {J_ACCESS}, author = {Ricardo Fonseca Buzo and Heliton de Oliveira Vilibor and Tiago Rodarte Ricciardi and José Filho da Costa Castro and Ana Claudia Golzio and Mariana Matulovic and Flávio José de Oliveira Morais}, doi = {10.1109/ACCESS.2025.3562733}, journal = {IEEE Access}, pages = {70616-70625}, shortjournal = {IEEE Access}, title = {Frequency response assessment of distribution feeders with capacitor banks for voltage harmonic mitigation: A practical approach with field measurements}, volume = {13}, year = {2025}, }
• (2025). Navigating social media: How offline ethics, online etiquette, and protection behavior shape self-disclosure. ACCESS, 13, 70598-70615. (www)

  @article{ , abstract = {Self-disclosure is one of the inevitable risky behaviors when using social media, which could lead to grave repercussions. Though self-disclosing benefits are abundant, risks like privacy invasion, security breaches, victim blaming, and cyber victimization await. However, the existing literature mainly discusses privacy-related factors as self-disclosure protection factors; although there have been several discussions regarding acting ethically to ensure cyber safety, it has yet to be investigated in relevance to self-disclosure behavior. Therefore, this study examines the relationship between social media etiquette, protection behavior, and self-disclosure on social media. We also discuss the relationship between offline ethical behavior through the Islamic lens and social media etiquette. Using a quantitative method, we assess the effect of social media etiquette and protection behavior on social media self-disclosures. We surveyed Muslim Malaysian adult social media users and analyzed the findings using partial least squares structural equation modeling (PLS-SEM). Results show that offline ethical behavior significantly affects social media etiquette. We also found that social media etiquette and perceived vulnerability negatively relate to social media self-disclosure. However, perceived severity, self-efficacy, and response efficacy do not significantly affect social media self-disclosures. This study sheds light on the importance of offline ethical behavior in shaping social media etiquette. Consequently, good social media etiquette has an impact on self-disclosure behavior. Thus, these findings offer essential insights for academics, practitioners, and social media platforms on future mitigation efforts for risky social media self-disclosures.}, archive = {J_ACCESS}, author = {Azma Alina Ali Zani and Azah Anir Norman and Norjihan Abdul Ghani and Riswan Septriayadi Sianturi}, doi = {10.1109/ACCESS.2025.3555548}, journal = {IEEE Access}, pages = {70598-70615}, shortjournal = {IEEE Access}, title = {Navigating social media: How offline ethics, online etiquette, and protection behavior shape self-disclosure}, volume = {13}, year = {2025}, }
• (2025). ABS based on sliding-mode control and on force and moment measurement. ACCESS, 13, 70581-70597. (www)

  @article{ , abstract = {The aim of this paper is to introduce a novel Anti-lock Brake System (ABS) based on Sliding Mode Control (SMC). The control makes use of forces and moments provided by a novel sensorized hub carrier with sensorized brake caliper. SMC ABS performance has been assessed by comparing it with a rule-based ABS logic. The comparison is performed via simulation, using a complex 14-dof vehicle model that is intended to represent a high-performance sports car. The SMC ABS relies on the vertical force, lateral force, longitudinal force at the tire and brake torque. Such measurements are used to estimate the friction coefficient between the tire and the road surface. A full brake maneuver has been simulated in multiple scenarios either with dry tarmac, with wet tarmac or $\mu $ -split. The results indicate that the novel ABS allows a reduction in braking distance compared to the traditional ABS, particularly on low-friction roads. This improvement is attributed to the system’s ability to rapidly identify the road’s friction coefficient and adjust the brake torque in order to maximize the longitudinal tire performance.}, archive = {J_ACCESS}, author = {Matteo Amadini and Massimiliano Milivinti and Giampiero Mastinu and Massimiliano Gobbi}, doi = {10.1109/ACCESS.2025.3560238}, journal = {IEEE Access}, pages = {70581-70597}, shortjournal = {IEEE Access}, title = {ABS based on sliding-mode control and on force and moment measurement}, volume = {13}, year = {2025}, }
• (2025). A new approach for online transient instability assessment and prediction via tracing the variation trend of synchronous generator energy. ACCESS, 13, 70567-70580. (www)

  @article{ , abstract = {Transient Instability Prediction (TIP) of the synchronous generator offers an opportunity to mitigate out-of-step damages through early tripping or facilitating remedial actions to prevent instability. Analyzing transient stability is inherently complex due to the diverse components and controllers within the power system, and it must be conducted within a limited timeframe of less than one second. This study introduces an innovative method for predicting the instability of synchronous machines, focusing on the rate of generator energy variations. The energy is calculated using the integral of the electrical power of the generator measured over time. This energy increases during a fault and subsequently decreases in post-fault. The increase in the rate of change of energy in the energy-releasing area indicates that the machine is progressing toward an unstable condition. This novel criterion is implemented locally on each generator within the network, enabling effective prediction of transient instability through the sole measurement of the machine’s electrical power. The proposed approach is online, conceptual, and independent of fault data, network configuration, and system parameters. Furthermore, the reliability level of the proposed method can be determined based on the required accuracy level or prediction speed using a rule-based mechanism. Simulation results across various scenarios in the IEEE 39-bus and Iran power systems demonstrate that the proposed approach effectively predicts the instability condition quickly and precisely. The algorithm is implemented in MATLAB, and Power Factory software is employed as the stability laboratory.}, archive = {J_ACCESS}, author = {Parva Soori and Alireza Sobbouhi and Mohamma Reza Aghamohammadi and Mohamma Sadegh Sepasian}, doi = {10.1109/ACCESS.2025.3557212}, journal = {IEEE Access}, pages = {70567-70580}, shortjournal = {IEEE Access}, title = {A new approach for online transient instability assessment and prediction via tracing the variation trend of synchronous generator energy}, volume = {13}, year = {2025}, }
• (2025). Intraoperative surgical navigation and instrument localization using a supervised learning transformer network. ACCESS, 13, 70548-70566. (www)

  @article{ , abstract = {Accurate tracking of surgical instruments is critical for the effectiveness of computer-assisted interventions to enhance visual information and reduce tissue injury risks. Previous approaches often modeled instrument trajectories rigidly, failing to capture the dynamic nature of surgical procedures, particularly in scenarios where instruments move outside the camera’s field of view or temporarily exit the body. Addressing these challenges, multi-instrument tracking is essential in the domain of surgical instrument detection and tracking. It plays a pivotal role in ensuring precise localization and identity preservation of instruments throughout surgical procedures. However, traditional multi-object tracking (MOT) methods, which decouple the detection and re-identification (re-ID) processes, frequently suffer from long-term reID in non-gaussian movement. To mitigate these issues, we propose SurgTrackNet, a unified framework specifically designed for surgical instrument tracking that integrates detection and tracklet association using transformer-based models. SurgTrackNet leverages a spatiotemporal memory network that stores previous observations of tracked surgical instruments, enhancing the system’s capacity to maintain consistent instrument identities and accurately predict their trajectories over time. The framework’s Instrument sequence creation network, utilizes a transformer encoder-decoder to generate instrument candidates, while a memory decoder integrates these proposals with track vectors to predict instrument locations and class, significantly minimizing the need for extensive post-processing. The memory encoding-decoding mechanism aggregates instrument features and associates them across frames, enabling consistent and robust detection and identity preservation of instruments throughout the surgical procedure. SurgTrackNet outperforms existing models, including other Transformer-based approaches, with a 78.7 MOTA and 75.5 IDF1 score on the CholecTrack20 dataset, and a 76.3 MOTA and 78.1 IDF1 score on the ATLAS Dione dataset.}, archive = {J_ACCESS}, author = {Junjun Huang and Tianran He and Juan Xu and Weiting Wu and Wei Wu}, doi = {10.1109/ACCESS.2025.3561769}, journal = {IEEE Access}, pages = {70548-70566}, shortjournal = {IEEE Access}, title = {Intraoperative surgical navigation and instrument localization using a supervised learning transformer network}, volume = {13}, year = {2025}, }
• (2025). Subsea nodule recognition and deployment detection method based on improved YOLOv8s. ACCESS, 13, 70533-70547. (www)

  @article{ , abstract = {An improved small-target detection model based on YOLOv8s is proposed to address the challenges associated with deep-sea polymetallic nodule detection, such as complex target shapes, small sizes, and strong environmental interference. This model is capable of real-time deployment on the Orange Pi 5 development board and is designed to efficiently and accurately identify seafloor polymetallic nodules. The enhanced model incorporates an adaptive image preprocessing module and an EMA attention mechanism into the YOLOv8s framework. Additionally, SK-Conv convolution modules replace certain Conv and C2f layers in the backbone network, while the Focal SIoU loss function replaces the original CIoU loss function. These modifications enhance feature extraction capabilities in the presence of uneven lighting and background interference, optimizing nodule segmentation in complex backgrounds and improving small-target detection performance. Experimental results demonstrate that the improved YOLOv8s model offers significant advantages in terms of detection accuracy and robustness, achieving precision, recall, and mean average precision rates of 94.88%, 93.89%, and 96.2%, respectively. The deployed YOLOv8s-OR model strikes a balance between recognition accuracy and speed by maintaining an average frame rate of 37.5 FPS with a latency of 22 ms, providing an efficient and precise solution for automated deep-sea polymetallic nodule detection with practical value.}, archive = {J_ACCESS}, author = {Jixin Li and Junchao Li and Bin Su and Yuxin Cui}, doi = {10.1109/ACCESS.2025.3534992}, journal = {IEEE Access}, pages = {70533-70547}, shortjournal = {IEEE Access}, title = {Subsea nodule recognition and deployment detection method based on improved YOLOv8s}, volume = {13}, year = {2025}, }
• (2025). The linear diophantine fuzzy Z-numbers set: Development and application to decision making in textile engineering using the bonferroni mean operator. ACCESS, 13, 70512-70532. (www)

  @article{ , abstract = {In this research, we introduce a novel approach to multi-attribute decision making by defining the Linear Diophantine Fuzzy Z-number set and generalizing the Bonferroni mean operator to this framework. Fuzzy Z-numbers are known for their ability to handle imprecise information by considering both a restriction (fuzzy number) and a reliability (degree of confidence). By extending these to linear Diophantine fuzzy sets, we incorporate more complex relationships between attributes, allowing for a deeper exploration of decision scenarios that involve uncertainty and interdependencies. The proposed generalized Bonferroni mean operator captures interactions between multiple attributes, providing a more robust aggregation mechanism in decision-making processes. We have applied this methodology to a real-world problem in textile engineering, selecting the best fabrics for designing new cricket uniforms. Textile selection involves multiple conflicting criteria such as durability, comfort, moisture absorption, and aesthetics. Our method handles these complexities efficiently to allow decision-makers to compare fabrics in an uncertain environment while taking trade-offs between different criteria into account. It can potentially lead to skills applications that match a variety of manufacturing industries, where one of the key factors for function and competitiveness is the efficient use of materials. The designed framework is capable of improving decision-making and providing a more sophisticated framework for evaluation of alternatives under uncertainty which has implications in both fuzzy logic theory and textile engineering fields.}, archive = {J_ACCESS}, author = {Muhammad Umar Mirza and Rukhshanda Anjum and Hong Min and Badr S. Alkahtani and Mohd Anjum}, doi = {10.1109/ACCESS.2025.3561136}, journal = {IEEE Access}, pages = {70512-70532}, shortjournal = {IEEE Access}, title = {The linear diophantine fuzzy Z-numbers set: Development and application to decision making in textile engineering using the bonferroni mean operator}, volume = {13}, year = {2025}, }
• (2025). Seg-swin: A dual-attention transformer model for advanced AMD classification and lesion detection using color fundus imaging. ACCESS, 13, 70489-70511. (www)

  @article{ , abstract = {Age-related macular degeneration (AMD) is a prevalent retinal disorder in the elderly, often leading to significant vision impairment. The diagnosis of AMD is confirmed through various medical imaging modalities, with color fundus photography (CFP) being a primary tool. The detection and staging of AMD severity depend on several factors, including the number and size of drusen, the presence of pigmentary changes, geographic atrophy, and neovascularization, all of which are identifiable through CFP. In this study, we introduce an innovative dual-vision transformer-based network designed to automatically detect AMD and classify its severity into either dry AMD or wet AMD using CFP. Early diagnosis and accurate staging of AMD are crucial in mitigating the progression of the disease, making this work particularly valuable. Our proposed model, Seg-Swin, leverages a dual attention-based transformer network architecture, comprising two key stages. The first stage employs the SegFormer transformer model for the precise detection of AMD-related lesions, while the second stage utilizes the Swin transformer model to classify the detected lesions into dry or wet AMD. Our extensive experimental results demonstrate that the Seg-Swin model outperforms existing approaches, achieving remarkable diagnostic accuracy with metrics such as 98.7% accuracy, 99% sensitivity, 97.95% F1-score, and 98.24% specificity. By combining the strengths of advanced transformer models in both identification and classification tasks, the Seg-Swin model offers a comprehensive and powerful solution for detecting and staging AMD. The integration of these dual attention mechanisms allows the model to more precisely interpret complex retinal images, which is crucial for early diagnosis and accurate staging of AMD.}, archive = {J_ACCESS}, author = {Niveen Nasr El-Den and Mohamed Elsharkawy and Ibrahim Saleh and Ali H. Mahmoud and Mohammed Ghazal and Ashraf Khalil and Ashraf Sewelam and Hani Mahdi and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3562135}, journal = {IEEE Access}, pages = {70489-70511}, shortjournal = {IEEE Access}, title = {Seg-swin: A dual-attention transformer model for advanced AMD classification and lesion detection using color fundus imaging}, volume = {13}, year = {2025}, }
• (2025). 3-D unbalanced coordinate transformation with super-twisting control and observation for unbalance and disturbance load voltage mitigations in stand alone four-leg inverters. ACCESS, 13, 70464-70488. (www)

  @article{ , abstract = {This paper presents an enhanced dual-loop voltage vector control method for standalone power supply systems using a four-leg voltage source inverter (4LVSI). The proposed method aims to optimize the quality of load voltage and 4LVSI output current waveforms under unbalanced three-phase loads, enhance load voltage disturbance rejection during unbalanced load changes, and improve the system’s dynamic response during transients. Its purpose is to increase the robustness of both the inner 4LVSI output current and outer load voltage control loops against system parameter uncertainties while reducing the number of sensors. In the inner current control loop, a second-order sliding mode control combined with a 3-D unbalanced transformation is proposed. This transformation is adopted herein for its ability to transform the three-phase unbalanced inverter currents into direct current quantities in the synchronous reference frame based on the dynamic estimation of their unbalanced phases and amplitudes. The second-order sliding mode control is adopted in this control loop for its robustness and its ability to reduce the chattering problem and improve the robustness of the inner current control loop against parameter uncertainties. For the outer three-phase load voltage control loop, a proportional-integral controller incorporated with a super-twisting observer for load disturbance observation is designed not only to get rid of sensors but also to ameliorate the load voltage dynamics. It permits to enhance the load disturbance rejection and reliability, as well as decrease the system’s cost and size. In this control loop, the super-twisting sliding mode observer is suggested not only for the load current disturbance estimation but also to build up the current feed-forward control scheme to estimate and reject the load disturbance quickly. The most significant advantages of the designed technique over existing strategies are that it can overcome the control issue of periodic currents caused by unbalanced loads, provides high robustness against parameter uncertainties and high load disturbance rejection ability. It also improves the transient responses, lowers the ripples, and reduces the required number of sensors. Suggested method’s effectiveness is confirmed through various real-time simulation tests under different loading conditions using OPAL-RT-OP5700.INDEX TERMS Four-leg voltage source inverter, dual-loop voltage vector control, 3-D unbalanced transformation, super-twisting sliding mode control, super-twisting observer, unbalance load voltage elimination, load voltage disturbance rejection.}, archive = {J_ACCESS}, author = {Ali Chebabhi and Said Barkat and Djamal Ziane and Habib Benbouhenni and Mohamed Fouad Benkhoris and Z. M. S. Elbarbary and Mohammed Karim Fellah and Toufik Rekioua}, doi = {10.1109/ACCESS.2025.3558869}, journal = {IEEE Access}, pages = {70464-70488}, shortjournal = {IEEE Access}, title = {3-D unbalanced coordinate transformation with super-twisting control and observation for unbalance and disturbance load voltage mitigations in stand alone four-leg inverters}, volume = {13}, year = {2025}, }
• (2025). Enhancing BVR air combat agent development with attention-driven reinforcement learning. ACCESS, 13, 70446-70463. (www)

  @article{ , abstract = {This study explores the use of Reinforcement Learning (RL) to develop autonomous agents for Beyond Visual Range (BVR) air combat, addressing the challenges of dynamic and uncertain adversarial scenarios. We propose a novel approach that introduces a task-based layer, leveraging domain expertise to optimize decision-making and training efficiency. By integrating multi-head attention mechanisms into the policy model and employing an improved DQN algorithm, agents dynamically select context-aware tasks, enabling the learning of efficient emergent behaviors for variable engagement conditions. Evaluations in single- and multi-agent BVR scenarios against adversaries with diverse tactical characteristics demonstrate superior training efficiency and enhanced agent capabilities compared to leading RL algorithms commonly applied in similar domains, including PPO, DDPG, and SAC. A robustness study underscores the critical role of diverse enemy selection in the RL process, showing that adversaries with variable tactical behaviors are essential for developing robust agents. This work advances RL methodologies for autonomous BVR air combat and provides insights applicable to other problems with challenging adversarial scenarios.}, archive = {J_ACCESS}, author = {Andre R. Kuroswiski and Annie S. Wu and Angelo Passaro}, doi = {10.1109/ACCESS.2025.3561250}, journal = {IEEE Access}, pages = {70446-70463}, shortjournal = {IEEE Access}, title = {Enhancing BVR air combat agent development with attention-driven reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Frequency-aware learned image compression using channel-wise attention and restormer. ACCESS, 13, 70436-70445. (www)

  @article{ , abstract = {In recent years, learned image compression has leveraged convolutional neural networks (CNNs) and achieved notable advancements in coding efficiency. However, learned image compression has a limitation of balancing global context and local texture because the global structure easily ignores local redundancy, especially for the non-repetitive textures, affecting the reconstruction performance. In this paper, we propose a frequency-aware learned image compression network based on channel-wise attention and Restormer. In encoder, we perform locality-aware encoding based on window-based attention. In decoder, we combine Restormer for global context and channel attention for local texture, forming a mixed attention module. Moreover, we design a frequency-aware network based on the discrete wavelet transform (DWT) in a hyperprior network to learn both spatial and frequency features, while removing distortion and capturing significant context in the low frequency domain. Considering the training instability and convergence difficulty of Restormer, we propose a novel training strategy based on knowledge transfer to optimize compression models during training. Experimental results demonstrate that the proposed method reconstructs high quality images with outstanding coding efficiency and achieves average BD-rate gains of 9.88% and 3.24% in terms of PSNR and MS-SSIM on the Kodak24 dataset over the auto-regressive hyperprior model proposed by Cheng et al., respectively.}, archive = {J_ACCESS}, author = {Hanwen Zhang and Cheolkon Jung and Xu Liu}, doi = {10.1109/ACCESS.2025.3559937}, journal = {IEEE Access}, pages = {70436-70445}, shortjournal = {IEEE Access}, title = {Frequency-aware learned image compression using channel-wise attention and restormer}, volume = {13}, year = {2025}, }
• (2025). (r, k, ε)-anonymization: Privacy-preserving data publishing algorithm based on multi-dimensional outlier detection, k-anonymity, and ε-differential privacy. ACCESS, 13, 70422-70435. (www)

  @article{ , abstract = {In recent years, there has been a tremendous rise in both the volume and variety of big data, providing enormous potential benefits to businesses that seek to utilize consumer experiences for research or commercial purposes. The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals’ personal information and placed many limits. Data anonymization technologies have become an important solution for businesses trying to generate value from data while adhering to GDPR limitations. To address these challenges, researchers have developed various methods, including k-anonymity and $\varepsilon $ -differential privacy, offering solutions for both industry and academia. However, protecting individuals’ privacy against diverse attack attempts presents significant challenges for anonymization models that rely solely on a single technique, highlighting the need for more adaptable and hybrid approaches. In this study, a new hybrid anonymization algorithm called (r, k, $\varepsilon $ )-anonymization has been proposed, which combines k-anonymity and $\varepsilon $ -differential privacy models in a consistent framework and provides stronger privacy guarantees compared to existing privacy-preserving models. The proposed algorithm is capable of overcoming well-known shortcomings of the k-anonymity and $\varepsilon $ -differential privacy models, and it has been confirmed by extensive tests on real-world datasets. The proposed (r, k, $\varepsilon $ )-anonymization algorithm outperforms k-anonymity and $\varepsilon $ -differential privacy in terms of the average error rate measure, achieving data utility increases of 31.74% and 26.99%, respectively.}, archive = {J_ACCESS}, author = {Burak Cem Kara and Can Eyupoglu and Oktay Karakuş}, doi = {10.1109/ACCESS.2025.3559410}, journal = {IEEE Access}, pages = {70422-70435}, shortjournal = {IEEE Access}, title = {(r, k, ε)-anonymization: Privacy-preserving data publishing algorithm based on multi-dimensional outlier detection, k-anonymity, and ε-differential privacy}, volume = {13}, year = {2025}, }
• (2025). Wearable sensors and computational intelligence in alpine skiing analysis. ACCESS, 13, 70414-70421. (www)

  @article{ , abstract = {The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computational intelligence in this domain enhances motion analysis, injury prevention, and performance optimization. This study highlights the unifying role of digital signal processing, which uses similar mathematical tools across various applications.}, archive = {J_ACCESS}, author = {Aleš Procházka and Hana Charvátová}, doi = {10.1109/ACCESS.2025.3562686}, journal = {IEEE Access}, pages = {70414-70421}, shortjournal = {IEEE Access}, title = {Wearable sensors and computational intelligence in alpine skiing analysis}, volume = {13}, year = {2025}, }
• (2025). Feature-separated lightweight model for road extraction in wild environments. ACCESS, 13, 70401-70413. (www)

  @article{ , abstract = {Extracting road information from high-resolution remote sensing images is a critical challenge, particularly in complex environments with vegetation occlusion, shadow interference, and similar terrain textures causing discontinuous or incomplete road extraction. Existing methods often suffer from high computational costs and limited accuracy. To address these issues, we propose a lightweight road extraction model based on an encoder-decoder architecture, employing CSPDarknet53 as the backbone to reduce redundant computations. A Feature Separation and Enhancement Module (FSEM) independently processes spatial and channel features, while a Feature Aggregation Module (FAM) integrates multi-scale features for improved road continuity and accuracy. This design enhances prediction performance without significantly increasing parameters, reducing computational demands and making the model more adaptable for large-scale remote sensing tasks. Experimental results demonstrate that the proposed model achieves an mIoU improvement of 1.60% and 3.15% on the DeepGlobe and Mountain Road datasets, respectively, along with over 40% runtime reduction compared to other approaches. The proposed model combines high accuracy and efficiency, offering a scalable solution for complex road extraction tasks.}, archive = {J_ACCESS}, author = {Che Shi and Yuefeng Cen and Gang Cen}, doi = {10.1109/ACCESS.2025.3558087}, journal = {IEEE Access}, pages = {70401-70413}, shortjournal = {IEEE Access}, title = {Feature-separated lightweight model for road extraction in wild environments}, volume = {13}, year = {2025}, }
• (2025). TWEMTCRM with B-tree-based cluster vehicles on the VLAM for resource sharing. ACCESS, 13, 70378-70400. (www)

  @article{ , abstract = {Vehicular Ad Hoc Networks (VANETs) facilitate communication between vehicles and Roadside Units (RSUs). Intelligent Transportation Systems (ITS) utilize cutting-edge technologies such as artificial intelligence (AI), knowledge mining, and depth fusion to offer convenient, affordable, and environmentally friendly services to drivers and passengers. RSUs are essential for enabling efficient and dependable data transmission between vehicles with future VANET technology. Sharing of resources between vehicles is difficult because of the acceleration or movement of the vehicles. The proposed Two-Way Extended Minute Time Constraint Recollection Model (TWEMTCRM-VLAM) with Dual Vehicle Analysis facilitates resource sharing. The proposed model contains two staging processes: one to collect all the neighbor group vehicle information, and the second to collect valid log resource information based on time stamp and resource availability using B-tree. A group of vehicles within the transmission range shares two log files: one that contains its own resource information and another that provides details about the resources of neighboring vehicles. RSUs regularly update using new log data, ensuring the maintenance of extensive vehicle resource information. Ensuring secure routing methods is crucial for the exchange of data. This technique entails vehicles exchanging log data when requested, which includes information about their own resources and the addresses of nearby vehicles. We regularly update log files to reflect resource utilization, guaranteeing the availability of the most up-to-date data. The proposed model takes into account various network ranges for vehicles, using extended time limits to efficiently retrieve and analyze log files.}, archive = {J_ACCESS}, author = {Spandana Mande and Nandhakumar Ramachandran}, doi = {10.1109/ACCESS.2025.3553964}, journal = {IEEE Access}, pages = {70378-70400}, shortjournal = {IEEE Access}, title = {TWEMTCRM with B-tree-based cluster vehicles on the VLAM for resource sharing}, volume = {13}, year = {2025}, }
• (2025). Palmprint recognition using bifurcation line direction coding. ACCESS, 13, 70366-70377. (www)

  @article{ , abstract = {Palmprint recognition is a challenging task due to the variability in image quality, scale, and angle. Traditional methods often rely on single line features, which may not effectively capture the local bifurcation information critical for accurate recognition. To address these challenges, this paper proposes the Bifurcation Line Direction Coding (BLDC) method, which better captures the local bifurcation characteristics of palmprint images. Our approach leverages an improved Gabor filter for image preprocessing, where the main direction guides the branch direction. By considering both the maximum and minimum direction subscripts of the main direction, we generate robust feature codes that accurately represent the palmprint’s unique structure. Extensive experiments across five challenging palmprint datasets demonstrate that BLDC outperforms existing methods in recognition rate and feature extraction speed, providing a more effective solution for palmprint recognition in practical applications.}, archive = {J_ACCESS}, author = {Hongxia Wang and Xulei Cao}, doi = {10.1109/ACCESS.2025.3562648}, journal = {IEEE Access}, pages = {70366-70377}, shortjournal = {IEEE Access}, title = {Palmprint recognition using bifurcation line direction coding}, volume = {13}, year = {2025}, }
• (2025). Real-time anomaly detection in IoMT networks using stacking model and a healthcare- specific dataset. ACCESS, 13, 70352-70365. (www)

  @article{ , abstract = {The Internet of Medical Things (IoMT) connects medical devices to enable real-time monitoring and personalized care, significantly enhancing patient health and well-being. However, this connectivity also introduces substantial cybersecurity risks, including various attack types that compromise data integrity and availability, jeopardizing patient safety and healthcare service reliability. This study addresses these challenges by proposing a real-time anomaly detection model based on machine learning (ML) techniques, designed to detect and mitigate diverse cyber threats effectively. This paper proposes a new medical dataset for anomaly detection, inspired by the UNSW-NB15 dataset, and enriched with healthcare-relevant attack types, including falsification and DoS attacks, to reflect real-world IoMT scenarios. The dataset comprises 253680 records, with 60% anomalous data distributed across multiple attack types, offering a more challenging and realistic environment for evaluating ML models. Seven machine learning algorithms, including Random Forest, XGBoost, and Artificial Neural Networks (ANN), were rigorously tested, leading to the development of a novel stacking ensemble model. This model integrates XGBoost as the meta-learner with Random Forest and ANN as base models, leveraging their strengths to optimize anomaly detection. The proposed model was evaluated on both the UNSW-NB15 and the new medical dataset, achieving significant improvements across key metrics such as accuracy, precision, recall, and F1-score. A real-time prediction analysis further demonstrated its ability to detect anomalies efficiently during live data transmission, validating its suitability for detecting anomalies in real-time scenarios.}, archive = {J_ACCESS}, author = {Hadjer Goumidi and Samuel Pierre}, doi = {10.1109/ACCESS.2025.3563158}, journal = {IEEE Access}, pages = {70352-70365}, shortjournal = {IEEE Access}, title = {Real-time anomaly detection in IoMT networks using stacking model and a healthcare- specific dataset}, volume = {13}, year = {2025}, }
• (2025). TALIU: A novel decoder and augmentation strategy for boosting tampered document image detection. ACCESS, 13, 70340-70351. (www)

  @article{ , abstract = {In modern information exchange, document images are vital, often embedding sensitive data. The emergence of advanced image editing tools and generative AI models has elevated the risks associated with document forgery and tampering, representing considerable security concerns. Although many studies have been developed recently, they focus on images saved with compression factors. For lossless compression formats like PNG images, current methods based on segmentation exhibit poor performance. Thus, this study tackles the specific challenges of identifying tampered text within lossless compression factor images. We introduce a lightweight and efficient model for document image tampering detection, named TALIU, which is based on segmentation networks. Our model employs an encoder-decoder architecture with a novel streamlined iterative upsampling decoder specifically designed for the context of document tampering. Moreover, we present an innovative data augmentation strategy, tampered region augmentation, intended to enhance sample diversity during the training phase. Extensive experiments utilizing the DocTamper dataset, the largest of its kind, show that our TALIU model surpasses current state-of-the-art methods in both detection accuracy and computational efficiency for detecting tampered text in lossless compression images.}, archive = {J_ACCESS}, author = {Anh D. Nguyen and Hye-Young Kim and Hoa N. Nguyen}, doi = {10.1109/ACCESS.2025.3560360}, journal = {IEEE Access}, pages = {70340-70351}, shortjournal = {IEEE Access}, title = {TALIU: A novel decoder and augmentation strategy for boosting tampered document image detection}, volume = {13}, year = {2025}, }
• (2025). A bearing fault diagnosis method based on hierarchical belief rule base with power set. ACCESS, 13, 70324-70339. (www)

  @article{ , abstract = {As one of the most critical components in rotating machinery, bearings play a vital role in ensuring the stable operation of the equipment. Due to the influence of complex environments, there is uncertainty in the process of bearing fault diagnosis. The Belief Rule Base (BRB) is a modeling method that can effectively handle uncertainty issues. However, in the process of bearing fault diagnosis, with the increase of bearing use time, bearing wear will become more and more serious. When extracting data feature values, different types of fault feature values have similarities, which makes it difficult to distinguish and represent the type of fault in the diagnosis process, resulting in local ignorance. The traditional BRB model cannot solve the local ignorance problem, and with the increase of the number of rules, it will lead to the explosion of rule combinations. To solve the above problems, this paper proposes a Hierarchical Belief Rule Base with power set, Firstly, a Hierarchical Belief Rule Base (HBRB) model is selected to solve the problem of combination rule explosion, and then HBRB is extended to the power set identification framework. Because the power set recognition framework can more effectively represent the ignorant information in the complex system, and solve the local ignorance problem in the bearing fault diagnosis, then, the evidential reasoning algorithm is used to establish the reasoning process of the model. Finally, the parameters of the model are optimized using the projection covariance matrix adaptive evolutionary strategy algorithm. The experimental results show that the proposed method can identify different fault categories with high accuracy, effectively solve the local ignorance problem in bearing faults diagnosis, and has good generalization ability.}, archive = {J_ACCESS}, author = {Xingchi Yan and Ning Li and Yan Yu and Wei He and Duo Wang}, doi = {10.1109/ACCESS.2025.3558798}, journal = {IEEE Access}, pages = {70324-70339}, shortjournal = {IEEE Access}, title = {A bearing fault diagnosis method based on hierarchical belief rule base with power set}, volume = {13}, year = {2025}, }
• (2025). Adaptive fuzzy control of USV based on improved finite-time command-filtered backstepping method. ACCESS, 13, 70313-70323. (www)

  @article{ , abstract = {This article introduces a path tracking control method for unmanned surface vehicle (USV), which uses finite-time adaptive fuzzy technology combined with command filter. Divide the path tracking control problem into the design of line of sight (LOS) guidance law and controller. Firstly, in response to disturbances in the marine environment, a side slip angle update rate and expected guidance angle are designed. Secondly, an adaptive fuzzy control strategy is proposed to approximate and compensate for model uncertainty and changes in state variables, in order to improve system robustness. Simultaneously utilizing an improved finite-time command filter and backstepping method to control the required guidance angle for yaw angle tracking. Finally, design an improved compensation mechanism to make the tracking control of the system more precise. The effectiveness of this method is verified through MATLAB simulation results.}, archive = {J_ACCESS}, author = {Jiayu Liu and Baofang Wang}, doi = {10.1109/ACCESS.2025.3562579}, journal = {IEEE Access}, pages = {70313-70323}, shortjournal = {IEEE Access}, title = {Adaptive fuzzy control of USV based on improved finite-time command-filtered backstepping method}, volume = {13}, year = {2025}, }
• (2025). Model selection methods for model-bridge simulation calibration. ACCESS, 13, 70298-70312. (www)

  @article{ , abstract = {Computer simulations are ubiquitous in many scientific communities analyzing complex phenomena, such as physics, material science, medicine, and others. For simulations to yield credible insight, they must accurately represent key aspects of their real-world counterparts, making the calibration of simulation parameters crucial. Often, manual calibration is time-consuming, error-prone, and dependent on expert knowledge. Therefore, many algorithmic approaches have been explored, from heuristic-based and Bayesian methods to search and genetic algorithms. Such attempts often obtain good parameters, though at a high computational cost, as they require running the simulation many times to explore the parameter space. In contrast, the recently proposed model-bridge framework significantly speeds up this process by machine learning on a set of previous observations. In model-bridge framework, a complex simulation is represented by a simpler, uninterpretable surrogate; then, using past simulation data, a bridge model is trained to map predictions of the uninterpretable surrogate model to calibrated, interpretable simulation parameters. However, this approach introduces another problem: designing surrogate models and choosing their parameters. In this paper, we evaluate cross-validation and information theory-based model selection strategies for choosing the optimal surrogate models. Through experiments on synthetic signal and fluid dynamics simulations based on the finite element method, we show that model selection and the choice of the surrogate are essential to enabling high model-bridge performance, comparable to and surpassing established calibration approaches, at a fraction of the computational cost and time. Further, our experiments show that information theory-based methods such as Akaike information criterion (AIC) can obtain close to optimal models several orders of magnitude faster than cross-validation strategies. Finally, we discuss theoretical requirements which a surrogate model should satisfy to allow the use of information theory-based methods.}, archive = {J_ACCESS}, author = {Bojan Batalo and Lincon S. Souza and Keisuke Yamazaki}, doi = {10.1109/ACCESS.2025.3562627}, journal = {IEEE Access}, pages = {70298-70312}, shortjournal = {IEEE Access}, title = {Model selection methods for model-bridge simulation calibration}, volume = {13}, year = {2025}, }
• (2025). FPGA acceleration with hessian-based comprehensive intra-layer mixed-precision quantization for transformer models. ACCESS, 13, 70282-70297. (www)

  @article{ , abstract = {Recent advancements in using FPGAs as co-processors for language model acceleration, particularly for energy efficiency and flexibility, face challenges due to limited memory capacity. This limitation hinders the deployment of transformer-based language models. To address this challenge, we propose a novel software-hardware co-optimization framework that integrates Hessian-based intra-layer mixed-precision quantization with a runtime bit-configurable FPGA accelerator. Our proposed Hessian-based row-wise weight quantization addresses hardware inefficiencies in traditional parameter-wise and channel-wise approaches by enabling mixed-precision weight matrices to be split into two uniform-precision matrices, thereby simplifying hardware requirements. Additionally, our Query-Key coupled attention activation quantization optimally aligns precision within each outer product pair in attention calculations, reducing hardware complexity and memory management overhead. Our concurrent quantization method balances the benefits of row-wise weight quantization and Query-Key coupled activation quantization while maximizing energy efficiency through multi-precision optimization. To support this algorithm, we design a multi-precision FPGA accelerator capable of handling both 2n-based and non-2n mixed-precision operations. It is implemented on a single Xilinx ZCU102 FPGA board, operating at 200MHz with a power consumption of 15.08W during inference on the 110-million-parameter BERT-Base and 345-million-parameter GPT-2 Medium transformer models. Coupled with the proposed algorithm and dataflow optimization, it enables on-chip storage of all necessary parameters, minimizing off-chip memory access. Experimental results demonstrate that our FPGA accelerator significantly outperforms existing solutions, achieving energy efficiency improvements ranging from $2.22\times $ to $17.23\times $ over state-of-the-art FPGA accelerators.}, archive = {J_ACCESS}, author = {Woohong Byun and Jongseok Woo and Saibal Mukhopadhyay}, doi = {10.1109/ACCESS.2025.3563196}, journal = {IEEE Access}, pages = {70282-70297}, shortjournal = {IEEE Access}, title = {FPGA acceleration with hessian-based comprehensive intra-layer mixed-precision quantization for transformer models}, volume = {13}, year = {2025}, }
• (2025). Hybrid EXGBStackQoE classifier and stackelberg game-theoretic approaches for enhanced QoE in video services over 5G wireless networks. ACCESS, 13, 70269-70281. (www)

  @article{ , abstract = {Video streaming applications have experienced significant growth in recent years, driving an increase in global internet traffic. This rapid expansion underscores the critical need to ensure a high Quality of Experience (QoE) for users, as subpar video QoE can result in considerable financial losses for telecommunication providers. Traditional approaches, such as the Mean Opinion Score (MOS), while widely used for assessing QoE, are inherently subjective and require significant manual effort. This research addresses these limitations by introducing an advanced framework integrating multiple models and methodologies to enhance video QoE in 5G networks. The proposed framework features a novel Hybrid EnhancedXGBStackQoE (EXGBStackQoE) analytical model that applies a two-level stacking technique combined with 5-fold cross-validation to mitigate overfitting. At the initial level, various machine learning (ML) models are trained using the entire dataset, while the subsequent level leverages meta-features generated from the initial predictions to improve overall accuracy. This hybrid model demonstrates an accuracy improvement of 5–7% compared to traditional models, establishing a new standard in QoE evaluation. Additionally, the framework incorporates a Stackelberg Game-based Software-Defined Networking (SbSG) model designed for efficient data offloading in 5G Macro Base Stations (MBS). This model employs economic incentives and traffic load balancing to strategically select users for offloading, prioritizing those with lower QoS based on the Received Signal Strength Indicator (RSSI). By optimizing data distribution between MBS and Heterogeneous Networks (HetNets), the SbSG model enhances both network throughput and service quality. This comprehensive framework addresses critical challenges in video QoE optimization, providing a robust and automated solution tailored to the demands of next-generation networks.}, archive = {J_ACCESS}, author = {K. B. Ajeyprasaath and Vetrivelan Pandu}, doi = {10.1109/ACCESS.2025.3562645}, journal = {IEEE Access}, pages = {70269-70281}, shortjournal = {IEEE Access}, title = {Hybrid EXGBStackQoE classifier and stackelberg game-theoretic approaches for enhanced QoE in video services over 5G wireless networks}, volume = {13}, year = {2025}, }
• (2025). EcoptiAI: E-commerce process optimization and operational cost minimization through task automation using agentic AI. ACCESS, 13, 70254-70268. (www)

  @article{ , abstract = {The rapid expansion of e-commerce has increased the complexity of operational processes, making it challenging to manage tasks such as product cataloging, customer responses, delivery updates, and feedback collection efficiently. These challenges often result in elevated operational costs and decreased customer satisfaction. This paper introduces EcoptiAI, an agentic AI-powered framework leveraging a transformer-based model as an intelligent agent to automate essential e-commerce processes. EcoptiAI minimizes manual effort, streamlines workflows, and optimizes procedural costs by handling tasks such as product description generation, catalog updates, personalized customer communication, and empathetic delivery status updates. The system employs an empathetic tone for delay notifications, distinguishing itself from standard cold responses, and automates customer feedback analysis, ensuring an enhanced customer experience. The transformer model underlying EcoptiAI has been trained using a uniquely structured dataset created from diverse e-commerce-related sources, including product catalogs, customer reviews, and operational logs. The experimental analysis demonstrates that EcoptiAI reduces procedural costs by 52.7% on average and achieves high-performance metrics, with an accuracy of 92.42%, precision of 92.44%, recall of 92.40%, and an F1-score of 92.41%. The findings indicate the transformative potential of agentic AI in driving cost-effective, automated e-commerce operations while enhancing customer satisfaction. This paper provides a comprehensive evaluation of EcoptiAI’s design, implementation, and impact, paving the way for scalable and intelligent e-commerce automation solutions.}, archive = {J_ACCESS}, author = {Olivia-Roxana Alecsoiu and Nuruzzaman Faruqui and Andreea Anamaria Panagoret and Ceauşescu Aurelian Ionuţ and Dragoş Mihai Panagoret and Rares-Vladimir Nitu and Miruna Angela Mutu}, doi = {10.1109/ACCESS.2025.3560549}, journal = {IEEE Access}, pages = {70254-70268}, shortjournal = {IEEE Access}, title = {EcoptiAI: E-commerce process optimization and operational cost minimization through task automation using agentic AI}, volume = {13}, year = {2025}, }
• (2025). Predicting diabetic retinopathy and nephropathy complications using machine learning techniques. ACCESS, 13, 70228-70253. (www)

  @article{ , abstract = {Diabetes and its complications, especially Diabetic Retinopathy (DR) and Diabetic Nephropathy (DN) is a big challenge to the global healthcare system and needs accurate predictive models to help in early diagnosis and intervention. In this study we used a dataset from a reputed medical center in India with 767 patient records and 22 attributes including demographic details, clinical markers and treatment plans. We used a suite of advanced machine learning algorithms—Random Forest, XGBoost, LightGBM, CatBoost, Neural Networks and ensemble approaches like Voting and Stacking Classifiers to see their performance on original, oversampled and undersampled datasets. Through feature engineering, sampling strategies and hyperparameter tuning the models performed well on all the datasets. Surprisingly the models performed well even on the original imbalanced dataset which can be attributed to the power of the models and hyperparameter tuning. Ensemble methods like Voting and Stacking Classifiers performed better and achieved near perfect metrics (AUC = 1.0) in oversampled datasets. Hyperparameter tuning further improved the performance, reduced RMSE and log loss and increased accuracy and recall in all the configurations. This shows the importance of model optimization in real world clinical datasets which are imbalanced and noisy. This paper shows the possibility of machine learning based frameworks in diabetic complication management by predicting accurately and in time. These models can be integrated into clinical decision support systems (CDSS) to give insights to clinicians, improve patient outcomes through personalized interventions and optimize resource allocation. Future work will be to validate this on different populations, include longitudinal patient data and integrate real time electronic health records (EHR) for deployment in hospitals.}, archive = {J_ACCESS}, author = {D. R. Manjunath and J. J. Lohith and S. Selva Kumar and Abhijit Das}, doi = {10.1109/ACCESS.2025.3562483}, journal = {IEEE Access}, pages = {70228-70253}, shortjournal = {IEEE Access}, title = {Predicting diabetic retinopathy and nephropathy complications using machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). Construction method of CNC machine tool digital twin model based on the four-layer framework. ACCESS, 13, 70211-70227. (www)

  @article{ , abstract = {Digital twin (DT) has become a key technology to promote the development of intelligent manufacturing and has been widely used in the manufacturing industry. Computer numerical control (CNC) machine tool as an important equipment of intelligent manufacturing, due to its complex structure and complicated working conditions, DT technology is troubled by model consistency in application. To construct the DT model more effectively and accurately, a four-layer modeling framework is proposed based on the working characteristics of CNC machine tools, including requirement layer, representation layer, interaction layer and implementation layer. By taking the construction of the DT system of CNC machine tool for machining process as an example, the collection and processing of multi-source heterogeneous data and the monitoring of data-driven twin models are realized, a real-time dynamic cutting algorithm is developed on the basis of collision detection and mesh generation, the physical properties and working states of the model are verified by Fuzzy Analytic Hierarchy Process (FAHP) and Martens’ Distance Judgment, and the detection of tool wear status is completed by CNN-LSTM-Attention network model. Finally, the effectiveness and feasibility of the proposed framework are verified by a case of DT system construction of CNC lathe.}, archive = {J_ACCESS}, author = {Fengye Pei and Suifan Chen and Qipeng Li and Shixiong Guo and Yuyuan Xi and Pengju Huang}, doi = {10.1109/ACCESS.2025.3560647}, journal = {IEEE Access}, pages = {70211-70227}, shortjournal = {IEEE Access}, title = {Construction method of CNC machine tool digital twin model based on the four-layer framework}, volume = {13}, year = {2025}, }
• (2025). Balancing illumination and communication in indoor VLC: Impact of multiple LED configurations on system performance. ACCESS, 13, 70195-70210. (www)

  @article{ , abstract = {This study explores the influence of multiple luminaire arrangements on the dual functionality of visible light communication systems (VLC), focusing on their impact on illumination uniformity and communication performance. A comprehensive analysis of various luminaire distributions, ranging from single to multiple luminaires, is performed to identify configurations that optimize lighting quality and data transmission efficiency. In particular, the study examines three luminaire arrangements—circular, square, and circular-square—implemented in configurations of 1 to 12 luminaires, and includes theoretical modeling and experimental validation in indoor environments, covering single-input single-output (SISO), multiple-input single-output (MISO), and multiple-input multiple-output (MIMO) configurations up to $3 \times 2$ systems. Orthogonal frequency division multiplexing (OFDM) with repetition coding (RC) is employed, which distributes data across subcarriers while replicating signals across transmitters to enhance spatial diversity. Key performance metrics, such as illumination, uniformity, quality factor ( $F_{a}$ ), and coefficient of variation (CV(RMSE)), are evaluated for illumination, while communication performance is assessed using bit error rate (BER) metrics. The results demonstrate that the overall performance is improved as the number of luminaries increases, however a saturation point exists beyond which additional luminaires yield diminishing returns. Our findings highlight the complex interplay between illumination uniformity and communication performance in VLC systems, providing valuable insights for designing high-performance MIMO systems in smart indoor environments.}, archive = {J_ACCESS}, author = {David Esteban Farfán-Guillén and Paulo Pereira Monteiro and Juan Camilo Castellanos Rodriguez and Alexandre De Almeida Prado Pohl}, doi = {10.1109/ACCESS.2025.3562801}, journal = {IEEE Access}, pages = {70195-70210}, shortjournal = {IEEE Access}, title = {Balancing illumination and communication in indoor VLC: Impact of multiple LED configurations on system performance}, volume = {13}, year = {2025}, }
• (2025). Fault prediction of double toothed roller crusher failure in open pit mines based on digital twinning technique of ASL-CatBoost algorithm. ACCESS, 13, 70179-70194. (www)

  @article{ , abstract = {The bearings of double toothed roller crushers in open pit mines are prone to failure during prolonged operation, resulting in reduced productivity and shortened equipment life. Therefore, a bearing fault prediction model based on asymmetric loss function gradient boosting algorithm and digital twin has been proposed for effective fault prediction of double toothed roller crusher bearings. First, the fault signal is extracted by Wigner-Ville time-frequency distribution and empirical mode decomposition. Then, the asymmetric loss function gradient boosting algorithm is used to model and predict the signal characteristics. Finally, a digital twin model is constructed to visualize bearing fault information by combining sensor data and historical fault data. The experimental results showed that the fault prediction model achieved a precision rate of 98%, a recall rate of 99%, and an F1 value of 98.5% in predicting bearing rolling element failures. Among them, the running time of fault prediction was 1 minute and 49s. The experimental results demonstrate that the proposed fault prediction model can learn complex feature representations from large amounts of data, with good fault prediction performance and efficiency, and has certain practical application value. The research results contribute to improving the accuracy and intelligence level of fault prediction methods, thereby ensuring the stable operation of open-pit mine double toothed roller crushers.}, archive = {J_ACCESS}, author = {Pubo Gao and Sihai Zhao}, doi = {10.1109/ACCESS.2025.3557378}, journal = {IEEE Access}, pages = {70179-70194}, shortjournal = {IEEE Access}, title = {Fault prediction of double toothed roller crusher failure in open pit mines based on digital twinning technique of ASL-CatBoost algorithm}, volume = {13}, year = {2025}, }
• (2025). Deep learning-augmented evolutionary strategies for intelligent global optimization. ACCESS, 13, 70138-70178. (www)

  @article{ , abstract = {This paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. SIRO integrates deep learning into its initialization and parameter setting to enhance its efficiency, enabling intelligent and adaptive behavior. This hybridization improves solution quality, accelerates convergence, enhances robustness, and reduces computational costs. The algorithm’s performance was evaluated using CEC 2017 benchmark functions, demonstrating superior results in hybrid functions (C1-C28) despite moderate performance on traditional CEC1-CEC14 functions. Friedman’s test ranked SIRO 4th overall, with SSA as the top-performing algorithm. Additionally, SIRO was tested on real-world optimization problems, including mechanical engineering design, hyperparameter tuning, and feature selection for medical image classification. In the classification task, SIRO-enhanced CNN achieved an accuracy of 0.86 at the 5th epoch, outperforming CNN (0.66), CNN-GA (0.76), and CNN-WOA (0.75). Furthermore, SIRO reported a precision of 0.96, recall of 1.0, and F1-score of 0.98, highlighting its effectiveness. These results validate the benefits of integrating a learning mechanism into SIRO, yielding superior precision, computational efficiency, and performance over conventional optimization approaches.}, archive = {J_ACCESS}, author = {Absalom El-Shamir Ezugwu and Olaide Nathaniel Oyelade and Jeffrey Ovre Agushaka and Apu Kumar Saha}, doi = {10.1109/ACCESS.2025.3562367}, journal = {IEEE Access}, pages = {70138-70178}, shortjournal = {IEEE Access}, title = {Deep learning-augmented evolutionary strategies for intelligent global optimization}, volume = {13}, year = {2025}, }
• (2025). The game relationship of metro station connection mode: The competition and complementarity of bike-sharing and bus. ACCESS, 13, 70128-70137. (www)

  @article{ , abstract = {Previous studies have revealed that the relationship between bike-sharing and the bus system in connecting metro stations is substitution and supplement. However, the choice integration mode of users between bike-sharing and bus for the last-mile connection to the urban metro has not been sufficiently studied. This paper aims to address this gap and explore to promote stable connection integration while easing the financial pressure of governments. To achieve this, evolutionary game theory was employed to examine the complex behavior interaction between governments and operations, taking into account subsidy policy phase-outs. This paper defined the ideal event and analyzed the impact of critical factors on the dynamic evolution process, and gained valuable insights. Subsequently, the game models and primary conclusions using actual traffic data were validated. Additionally, a sensitivity analysis of factors based on the dynamics simulation model was conducted. The results demonstrate that the adjustment of factors can promote the integration model optimization on subsidy phasing-out. The integration decision of factors with real data provides a valuable reference for governments and operations. The findings hold significant meaning in promoting connection integration and enhancing the overall attractiveness of metro transportation.}, archive = {J_ACCESS}, author = {Tingzhao Chen and Yanyan Chen and Zhenhua Mou and Xiaohua Yu}, doi = {10.1109/ACCESS.2025.3562275}, journal = {IEEE Access}, pages = {70128-70137}, shortjournal = {IEEE Access}, title = {The game relationship of metro station connection mode: The competition and complementarity of bike-sharing and bus}, volume = {13}, year = {2025}, }
• (2025). Semantic segmentation of assembly images combining deep learning and ontological reasoning. ACCESS, 13, 70116-70127. (www)

  @article{ , abstract = {With the advancement of industrial intelligence, semantic segmentation of assembly images is increasingly widely used in automated production and other fields. Aiming at the problem that traditional image segmentation methods often neglect the semantic association and structural information between objects in the image, this paper proposes a semantic segmentation method for assembly body images that combines deep learning and ontology reasoning. DeepLabV3+ is used as the base model, and MobileNetV2 backbone network and Convolutional Block Attention Module (CBAM) are introduced to accelerate the computational speed of the network and at the same time enhance the fusion ability of multi-scale features of the network to further improve the semantic segmentation accuracy. However, due to the poor interpretability and reliability of the data-driven deep learning method, an ontology module with inference capability is introduced and semantic rules are written based on the relevant knowledge of the domain to correct the misclassification of deep learning. Finally, we make the reducer model through Solidworks as the dataset for the experiment. The experimental results show that the improved network model improves 11.92% and 6.53% over the original network model in mIoU and mPA, respectively. The constructed ontology model of assembly information is then referenced and the misclassified parts are corrected by reasoning. The effectiveness of the semantic segmentation method proposed in this paper is verified.}, archive = {J_ACCESS}, author = {Han Zhang and Xiaolin Shi and Haisong Xu and Yi Li and Liping Ma}, doi = {10.1109/ACCESS.2025.3561038}, journal = {IEEE Access}, pages = {70116-70127}, shortjournal = {IEEE Access}, title = {Semantic segmentation of assembly images combining deep learning and ontological reasoning}, volume = {13}, year = {2025}, }
• (2025). Fixed/Prescribed-time synchronization of fuzzy inertial memristive neural networks with time-varying delay: Interval matrix method and PI control. ACCESS, 13, 70105-70115. (www)

  @article{ , abstract = {This paper investigates the fixed/prescribed-time synchronization issues for fuzzy inertial memristive neural networks (FIMNNs) with time-varying delay, which integrate fuzzy logic, inertial terms, memristors, and time-varying delays simultaneously. The interval matrix method is employed firstly to deal with the state-dependent switching parameters, which is distinguished from the existing maximum absolute value approach. Consequently, the FIMNNs with time-varying delay are transformed into a type of delayed fuzzy systems, with coefficients characterized by a series of interval matrices. Following this, within the framework of Filippov solutions and differential inclusions, fixed/prescribed-time synchronization is comprehensively discussed. Two novel PI feedback controllers are synthesized through the resolution of a series of linear matrix inequalities (LMIs), followed by numerical simulations to demonstrate their effectiveness.}, archive = {J_ACCESS}, author = {Xin Ya Wang and Qing Zhang and Guici Chen}, doi = {10.1109/ACCESS.2025.3561116}, journal = {IEEE Access}, pages = {70105-70115}, shortjournal = {IEEE Access}, title = {Fixed/Prescribed-time synchronization of fuzzy inertial memristive neural networks with time-varying delay: Interval matrix method and PI control}, volume = {13}, year = {2025}, }
• (2025). Tropical cyclone intensity prediction using spatio-temporal data fusion. ACCESS, 13, 70095-70104. (www)

  @article{ , abstract = {Tropical cyclone is a sea storm that causes important life and economic losses in the coastal regions in the tropical zone around the equator of the earth. Tropical cyclone intensity is an important characteristic used to estimate the strength of the tropical cyclone. This study aims to improve the tropical cyclone intensity prediction by concatenating the spatial and temporal features of tropical cyclones. The proposed methodology utilized the deep learning based approach for handling 3D and 2D features for 24h early intensity prediction. In the first phase, a dynamic grid-based approach is utilized to extract the spatial features in a ( $3\times 3$ ) grid format from the eye of the TC. These spatial features are extracted for four different components (u,v,t,r) and 37 different isobaric planes. In the second step, multiple convolutional layers are used to process each spatial component separately, and a fusion method is used to combine the spatial and temporal features. The proposed method achieved state-of-the-art results by reducing the MAE up to 3.31% overall and 8.5%,14.78%, 5.67% for u,v, and (u,v) add fusion components, respectively. The proposed methodology outperformed the state-of-the-art Saf-net model by 8.5 %,14.78%,5.67% for u,v, and (u,v) add fusion, respectively. A performance comparison on four real-time tropical cyclones (Bavi 2015, AERE 2016, NANMADOL 2017, HECTOR 2018) is also performed. The proposed model achieved MAE 2.92, 2.99, 2.46, 3.95 that are 10.08%, 34.35%, 23.65%, and 3.2% lower than state-of-the-art spatio-temporal models, respectively.}, archive = {J_ACCESS}, author = {Kalim Sattar and Malik Muhammad Saad Missen and Najia Saher and Rab Nawaz Bashir and Syeda Zoupash Zahra and Muhammad Faheem and Amjad Rehman Khan}, doi = {10.1109/ACCESS.2025.3561355}, journal = {IEEE Access}, pages = {70095-70104}, shortjournal = {IEEE Access}, title = {Tropical cyclone intensity prediction using spatio-temporal data fusion}, volume = {13}, year = {2025}, }
• (2025). A complexity research of percolation and permeation behavior in cluster groups. ACCESS, 13, 70080-70094. (www)

  @article{ , abstract = {In this study, a novel artificial potential-driven computational framework is proposed for studying macroscopic percolation and permeation phenomena in dynamic group systems. This framework extends the traditional static percolation theory to the analysis of dynamic porous media, thereby promoting the expansion of the former to the latter. The proposed model generates links between individuals of the groups through an adaptive interaction mechanism, where the individuals exhibit time-evolving equilibrium dimensions and heterogeneous spatial constraints. This dynamic coupling spontaneously generates self-organizing interference patterns, resulting in, for example, parceling or barrier effects in the groups. These effects fundamentally affect the permeation paths of the evolving group structure. In contrast to the traditional approach, which assumes a static structure, the study reveals how the individual time-varying equilibrium radius and the collective spatial organization dynamics dominate the percolation and permeation processes of the macroscopic group. This includes key factors such as porosity evolution, permeation dynamics, and critical percolation transitions. The experimental results demonstrate that groups of agents with time-varying equilibrium radius can significantly improve the percolation characteristics of the groups, and the different structures of tightly clustered groups can also directly affect the percolation and permeation of the groups.From the perspective of dynamic porous media mechanics, the method proposed in this paper provides an innovative theoretical framework for analyzing biological communities, active substance assemblages and group dynamics.}, archive = {J_ACCESS}, author = {Yufei Yuan and Peng Yu and Jian He and Xue-Bo Chen}, doi = {10.1109/ACCESS.2025.3562693}, journal = {IEEE Access}, pages = {70080-70094}, shortjournal = {IEEE Access}, title = {A complexity research of percolation and permeation behavior in cluster groups}, volume = {13}, year = {2025}, }
• (2025). Enhancing power density in switched reluctance generators through split axial segmentation. ACCESS, 13, 70055-70079. (www)

  @article{ , abstract = {The main contribution of this paper is to propose a methodology for the electromagnetic design of split axial segmentation switched reluctance generators (SRGs) to optimize power and torque. This paper investigates the impact of generator geometry segmentation on performance, addressing the longstanding challenges of torque ripple and power density. The hypothesis suggests that dividing the stator magnetization at each rotor angle can counteract the opposing machine torque, thereby improving torque ripple and enhancing power density. A mixed-methods approach combining literature review, simulation, and experimental validation was used to evaluate torque retarding effects and machine power density. The results demonstrate a significant reduction in torque ripple (59.3%) and an increase in power density $603.3 \frac {kW}{m^{3}} $ across all four segments. Specifically, axial separation segmentation markedly enhanced power density and design efficiency compared to conventional SRGs designs. This paper provides novel insights into improving torque ripple and power output while maintaining the same overall machine dimensions at 1206.6 W. Additionally, the simpler fabrication process of axial segmentation—where each segment maintains the same diameter—offers distinct advantages over radial segmentation, making it more practical for manufacturing. These findings highlight the potential for harvesting low-power driving energy from natural sources, contributing to advancing renewable energy technologies. So, the improvements in torque ripple reduction and power density provide valuable directions for optimizing SRGs, with further potential for enhancing driving turbine performance and utilizing better materials for greater energy harvesting efficiency.}, archive = {J_ACCESS}, author = {Parlin Siagian and Ira Devi Sara and Tarmizi Tarmizi and Nasaruddin Nasaruddin}, doi = {10.1109/ACCESS.2025.3562436}, journal = {IEEE Access}, pages = {70055-70079}, shortjournal = {IEEE Access}, title = {Enhancing power density in switched reluctance generators through split axial segmentation}, volume = {13}, year = {2025}, }
• (2025). A transformer-based multi-scale deep learning model for lung cancer surgery optimization. ACCESS, 13, 70044-70054. (www)

  @article{ , abstract = {Lung cancer surgery presents significant challenges due to its complexity and the need for precise risk stratification to improve patient outcomes. This study presents a Transformer-based multi-scale deep learning framework that integrates imaging, clinical, and genomic data to optimize decision-making surrounding surgery. By using the self-attention mechanism in Transformers and multi-scale feature extraction, the model expertly explores different data modalities. Therefore, it enables a precise prediction of surgical risks, such as delayed extubation and mortality; besides, it further performs risk stratification, having the model improve resource utilization by identifying high- and low-risk patients, ensuring that intervention is matched accordingly and resources are not wasted on unnecessary measures. Thorough evaluations, including ablation experiments, case analyses, and error analyses, prove the model’s robustness and practical applicability in a clinical setting. This study demonstrates the game-changing potential of advanced deep learning techniques in the field of precision medicine and provides a concrete framework for personalized treatment in lung cancer surgery, laying the foundation for broader healthcare applications.}, archive = {J_ACCESS}, author = {Pengfei Zhu and Tingmin Wang and Fan Yang and Meng Wang and Yunjie Zhang}, doi = {10.1109/ACCESS.2025.3561948}, journal = {IEEE Access}, pages = {70044-70054}, shortjournal = {IEEE Access}, title = {A transformer-based multi-scale deep learning model for lung cancer surgery optimization}, volume = {13}, year = {2025}, }
• (2025). Joint prediction of U.S. rice yields and methane emissions: A machine learning approach. ACCESS, 13, 70018-70043. (www)

  @article{ , abstract = {Despite the United States’s major role as a rice exporter with significant economic and environmental impacts, most remote sensing and machine learning research on rice yield prediction has focused on Asian production regions. Moreover, while rice cultivation generates substantial methane emissions, few studies have explored the trade-offs between productivity and environmental impact. This study leverages remote sensing and machine learning techniques to predict U.S. county-level rice yields and methane emissions from 2008 to 2022 across 67 counties in six major rice-producing states. We use eight different machine learning models for predictions. XGBoost and EBM emerge as top performers, accurately predicting yields and emissions, individually, without overfitting. A key finding reveals that these models excel at out-of-season forecasts, accurately predicting yields as early as April-June of the growing season. Feature importance analysis highlights soil properties, particularly pH and texture at various depths, as critical predictors for both yield and emissions. Most importantly, this study advances an integrated economic-environmental modeling in agriculture by analyzing yield-emissions trade-offs through the Non-dominated Sorting Genetic Algorithm II (NSGA-II), revealing an unexpected synergy where practices that improve economic productivity also reduce environmental impact, as higher yields correlate with lower methane emissions.}, archive = {J_ACCESS}, author = {Jameson Augustin and Munisamy Gopinath and Berna Karali and Yuhan Rao}, doi = {10.1109/ACCESS.2025.3562397}, journal = {IEEE Access}, pages = {70018-70043}, shortjournal = {IEEE Access}, title = {Joint prediction of U.S. rice yields and methane emissions: A machine learning approach}, volume = {13}, year = {2025}, }
• (2025). Security issues in multi-cloud: A systematic literature review. ACCESS, 13, 70006-70017. (www)

  @article{ , abstract = {This study aims to systematically review the current literature regarding security concerns in multi-cloud environments. The main objective is to identify critical security issues, current solutions, investigate challenges, and propose recommendations for future research. This study uses a systematic literature review methodology adopting the Kitchenham method, which includes a literature search across leading academic databases including IEEE Xplore, ScienceDirect, ACM, SpringerLink, Emerald Insight, Nature, Taylor & Francis, Scopus, and ProQuest. A formal protocol was followed to conduct an automated search of relevant articles published between 2021 and 2024, and filters were applied. Based on the initial search results, 2038 studies were found, then the selection criteria process (inclusion and exclusion) and quality assessment were carried out, and finally 30 studies were selected for further research. The results show that most of the multi-cloud security issue trends fall into the areas of data, cryptography, authentication and authorization, followed by areas of service availability, confidential computing, and non even fall into the area of virtualization. In addition, this study systematically analyzes the relevant literature and identifies problems, solutions, challenges, and recommendations for future research. The findings of this review are expected to provide a strong overall picture for further research in multi-cloud environments.}, archive = {J_ACCESS}, author = {Drajat Cahya Diningrat and Suhardi and Budi Rahardjo}, doi = {10.1109/ACCESS.2025.3561352}, journal = {IEEE Access}, pages = {70006-70017}, shortjournal = {IEEE Access}, title = {Security issues in multi-cloud: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A novel VNA-enhanced technique for ka-band insertion loss measurement in microstrip lines. ACCESS, 13, 69998-70005. (www)

  @article{ , abstract = {A novel measurement technique has been developed to enhance the accuracy of insertion loss measurements using vector network analyzers (VNAs), addressing the challenge of data fluctuations — a critical factor in determining circuit bandwidth and ensuring high-frequency signal integrity. The proposed method employs a Point-wise Least Squares (PLS) approach, eliminating the need for manual selection of smoothing bandwidth and simplifying the measurement process with a dedicated flowchart. This technique accurately differentiates insertion loss variations in microstrip lines, even across the Ka-band frequency range, ensuring reliable and consistent data by reducing deviations, particularly at band edges. The improved measurement accuracy supports the design and optimization of high-performance communication systems.}, archive = {J_ACCESS}, author = {Ching-Wen Tang and Wei-Lun Tsai and Chieh Lee and You-Lin Luo}, doi = {10.1109/ACCESS.2025.3561738}, journal = {IEEE Access}, pages = {69998-70005}, shortjournal = {IEEE Access}, title = {A novel VNA-enhanced technique for ka-band insertion loss measurement in microstrip lines}, volume = {13}, year = {2025}, }
• (2025). An NVMe-based secure computing platform with FPGA-based TFHE accelerator. ACCESS, 13, 69980-69997. (www)

  @article{ , abstract = {In this study, we introduce a new approach to secure computing by implementing a platform that utilizes a non-volatile memory express (NVMe)-based system with an FPGA-based Torus fully homomorphic encryption (TFHE) accelerator, solid state drive (SSD), and middleware on the host-side. Our platform is the first to offer completely secure computing capabilities for TFHE by using an FPGA-based accelerator. We defined secure computing instructions to evaluate 14-bit to 14-bit functions using TFHE. Our middleware allows for the communication of ciphertexts, keys, and secure computing programs while invoking secure computing programs through NVMe commands with metadata. Our performance evaluation demonstrates that our secure computing platform outperforms CPU-based and GPU-based platforms by 15 to 120 times and 2.5 to 3 times, respectively, in gate bootstrapping execution time. Additionally, our platform uses 7 to 12 times less electric energy consumption during the gate bootstrapping execution time than CPU-based platforms and 4.95 times less than a GPU-based platform. The performance of a machine learning application running on our platform shows that bootstrapping accounts for more than 80% of ciphertext learning time.}, archive = {J_ACCESS}, author = {Yoshihiro Ohba and Tomoya Sanuki and Claude Gravel and Kentaro Mihara and Asuka Wakasugi and Kenta Adachi}, doi = {10.1109/ACCESS.2025.3561728}, journal = {IEEE Access}, pages = {69980-69997}, shortjournal = {IEEE Access}, title = {An NVMe-based secure computing platform with FPGA-based TFHE accelerator}, volume = {13}, year = {2025}, }
• (2025). LLD-YOLO: A low-light object detection algorithm based on dynamic weighted fusion of shallow and deep features. ACCESS, 13, 69967-69979. (www)

  @article{ , abstract = {Object detection in low-light scenarios has a wide range of applications, but existing algorithms often struggle to preserve the scarce low-level features in dark environments and exhibit limitations in localization accuracy for blurred edges and occluded objects, leading to suboptimal performance. To address these challenges, we propose an improved neck structure, SRB-FPN, to achieve fine-grained cross-level semantic alignment and feature fusion, while also optimizing the regression loss function to develop LLD-YOLO, a detector specifically designed for low-light conditions. To enhance the representation of key feature units and dynamically optimize the fusion weights between shallow and deep features, we introduce the SDFBF module. To improve the diversity of receptive fields and strengthen the network’s multi-scale feature capture capability, we incorporate the DBB-C2f module. Furthermore, we integrate the hard-sample focusing property of Focaler IoU with the geometric perception advantages of MPDIoU, proposing Focal MPDIoU Loss to refine the localization of difficult samples and precisely capture bounding box variations. Ultimately, LLD-YOLO achieves an mAP50 of 70.0% on the ExDark dataset, outperforming the baseline by 2.7 percentage points. Extensive experiments on three public datasets, ExDark, NOD, and RTTS, further validate the superior performance of the proposed method in low-light conditions and its strong adaptability to foggy environments.}, archive = {J_ACCESS}, author = {Wenhao Cai and Yajun Chen and Xiaoyang Qiu and Meiqi Niu and Jianying Li}, doi = {10.1109/ACCESS.2025.3558574}, journal = {IEEE Access}, pages = {69967-69979}, shortjournal = {IEEE Access}, title = {LLD-YOLO: A low-light object detection algorithm based on dynamic weighted fusion of shallow and deep features}, volume = {13}, year = {2025}, }
• (2025). On equivalent circuit model-based state-of-charge estimation for lithium-ion batteries in electric vehicles. ACCESS, 13, 69950-69966. (www)

  @article{ , abstract = {The State-of-Charge (SoC) of Lithium-Ion Batteries (LIBs) is a crucial parameter for Battery Management Systems (BMSs) used in Electric Vehicles (EVs). This paper presents a comprehensive study on the SoC estimation of LIBs using advanced model-based methods. The practical implications of this research are significant, as they provide a reliable and efficient approach to SoC estimation, enhancing the performance and lifespan of LIBs in real-world applications, particularly EVs. A third-order equivalent circuit model is employed for the LIB based on electrochemical impedance spectra test results, with model parameters identified using a particle swarm optimization algorithm. Two real-time model-based estimation algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are compared for SoC estimation. A hybrid approach based on UKF and EKF is presented. The results demonstrate that the UKF outperforms the EKF in SoC estimation, with the root mean squared error (RMSE) and maximum error for SoC estimation being 1.06% and 1.15%, respectively. The hybrid EKF-UKF approach provides the best performance for SoC estimation, achieving the lowest root mean squared error (RMSE) of 0.2% and a maximum error of 0.5% for SoC estimation. This approach leverages the strengths of EKF and UKF, offering superior accuracy and robustness in real-time battery monitoring in EV applications.}, archive = {J_ACCESS}, author = {Fatma Ahmed and Khalid Abualsaud and Ahmed M. Massoud}, doi = {10.1109/ACCESS.2025.3560065}, journal = {IEEE Access}, pages = {69950-69966}, shortjournal = {IEEE Access}, title = {On equivalent circuit model-based state-of-charge estimation for lithium-ion batteries in electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Mamba as a motion encoder for robotic imitation learning. ACCESS, 13, 69941-69949. (www)

  @article{ , abstract = {Recent advancements in imitation learning, particularly with the integration of Large Language Model (LLM) techniques, are set to significantly improve robots’ dexterity and adaptability. This paper proposes using Mamba, a state-of-the-art architecture with potential applications in LLMs, for robotic imitation learning, highlighting its ability to function as an encoder that effectively captures contextual information. By reducing the dimensionality of the state space, Mamba operates similarly to an autoencoder. It effectively compresses the sequential information into state variables while preserving the essential temporal dynamics necessary for accurate motion prediction. Experimental results in multiple tasks demonstrate that Mamba achieves smaller estimation errors and superior success rates compared to Transformers in practical task execution. This performance is attributed to Mamba’s structure, which encompasses the state space model. Additionally, the study investigates Mamba’s capacity to serve as a real-time motion generator with a limited amount of training data.}, archive = {J_ACCESS}, author = {Toshiaki Tsuji}, doi = {10.1109/ACCESS.2025.3561283}, journal = {IEEE Access}, pages = {69941-69949}, shortjournal = {IEEE Access}, title = {Mamba as a motion encoder for robotic imitation learning}, volume = {13}, year = {2025}, }
• (2025). Cityscape-adverse: Benchmarking robustness of semantic segmentation with realistic scene modifications via diffusion-based image editing. ACCESS, 13, 69921-69940. (www)

  @article{ , abstract = {Recent advancements in generative AI, particularly diffusion-based image editing, have enabled the transformation of images into highly realistic scenes using only text instructions. This technology offers significant potential for generating diverse synthetic datasets to evaluate model robustness in real-world applications, especially in the era of autonomous driving. In this paper, we introduce Cityscape-Adverse, a benchmark that employs diffusion-based image editing to simulate eight adverse conditions, including variations in weather, lighting, and seasons, while preserving the original semantic labels. We evaluate the reliability of diffusion-based models in generating realistic scene modifications and assess the performance of state-of-the-art CNN and Transformer-based semantic segmentation models under these challenging conditions. Additionally, we analyze which modifications have the greatest impact on model performance and explore how training on synthetic datasets can improve robustness in real-world adverse scenarios. Our results demonstrate that all tested models, particularly CNN-based architectures, experienced significant performance degradation under extreme conditions, while Transformer-based models exhibited greater resilience. We verify that models trained on Cityscape-Adverse show significantly enhanced resilience when applied to unseen domains. Code and datasets will be released at https://github.com/naufalso/cityscape-adverse.}, archive = {J_ACCESS}, author = {Naufal Suryanto and Andro Aprila Adiputra and Ahmada Yusril Kadiptya and Thi-Thu-Huong Le and Derry Pratama and Yongsu Kim and Howon Kim}, doi = {10.1109/ACCESS.2025.3537981}, journal = {IEEE Access}, pages = {69921-69940}, shortjournal = {IEEE Access}, title = {Cityscape-adverse: Benchmarking robustness of semantic segmentation with realistic scene modifications via diffusion-based image editing}, volume = {13}, year = {2025}, }
• (2025). Research on book recommendation integrating book category features and user attribute information. ACCESS, 13, 69910-69920. (www)

  @article{ , abstract = {Aiming at the problem of poor recommendation effect caused by data sparsity in traditional collaborative filtering algorithm in book recommendation, this study proposes a personalized recommendation model (BCF-UAI) that combines book category features and user attribute information, aiming to improve the accuracy and generalization ability of the recommendation system. By introducing user attribute information (gender, age, occupation) and book category features, Embedding technology is used to map discrete attributes into continuous low-dimensional vectors to construct feature representations of users and books. A feature fusion strategy is designed to vectorize and fuse the multi-dimensional attributes of users and the multi-category features of books to generate a unified high-dimensional feature vector. Based on cosine similarity, the potential correlation between users and books is calculated to generate a personalized recommendation list. The experiment uses the Douban book dataset to compare the UCF, ICF and MF, and verifies the effectiveness of the feature module through ablation experiments. The results show that the proposed method is significantly better than the traditional method in RMSE and MAE, and the ablation experiment shows that the contribution of book category features is higher (Precision increased by 3.38%). The research proves that the fusion of user attributes and book categories can effectively mine the deep association of data and alleviate the problem of data sparsity. The research verifies the necessity of attribute information to capture users’ personalized needs, and provides theoretical and practical reference for the optimization of intelligent book recommendation system.}, archive = {J_ACCESS}, author = {Yan Chen and Eric Blancaflor and Mideth Abisado}, doi = {10.1109/ACCESS.2025.3562061}, journal = {IEEE Access}, pages = {69910-69920}, shortjournal = {IEEE Access}, title = {Research on book recommendation integrating book category features and user attribute information}, volume = {13}, year = {2025}, }
• (2025). A novel formulation for adaptive computed torque control enabling low feedback gains in highly dynamical tasks. ACCESS, 13, 69898-69909. (www)

  @article{ , abstract = {Computed Torque Control is a widely used control strategy for ensuring precise trajectory tracking and impedance behavior in robotic manipulators. However, because it relies on feedback linearization using the robot’s dynamic model, any inaccuracies in the model can adversely affect tracking performance. This effect is even more visible when low feedback gains are used to impose compliance in the robot’s behavior. Adaptive Computed Torque Control addresses this issue by updating the dynamic model parameters to achieve asymptotic stability. Nevertheless, the classical update law requires the inversion of the estimated mass matrix, potentially leading to numerical stability problems. Several approaches, such as Adaptive Inertia-Related Control, were proposed to overcome this problem. However, they have problems in guaranteeing the desired impedance behaviour. In this work, we present a novel adaptive computed torque control law formulated both in joint space and Cartesian space, and we provide theoretical proofs of its asymptotic stability. We validate the proposed controller through simulations and real-robot experiments involving various dynamic motions. Finally, we demonstrate its effectiveness in a real dynamic task: throwing an unknown object.}, archive = {J_ACCESS}, author = {Giorgio Simonini and Marco Baracca and Tommaso V. Cavaliere and Antonio Bicchi and Paolo Salaris}, doi = {10.1109/ACCESS.2025.3561635}, journal = {IEEE Access}, pages = {69898-69909}, shortjournal = {IEEE Access}, title = {A novel formulation for adaptive computed torque control enabling low feedback gains in highly dynamical tasks}, volume = {13}, year = {2025}, }
• (2025). Capturing fine-grained food image features through iterative clustering and attention mechanisms. ACCESS, 13, 69888-69897. (www)

  @article{ , abstract = {Food image recognition is a crucial initial step for food detection, nutritional analysis, and dietary recommendations. However, it is highly challenging due to the diverse appearance of food, which includes variations in color, texture, and presentation. We believe that effective food image recognition requires exploiting the characteristics of food images, with color playing a dominant role in feature extraction. This paper proposes a novel color-cluster attention network model for food recognition, which integrates Kohonen networks-based clustering methods with iterative feature refinement. First, the model leverages Kohonen networks-based clustering methods to effectively group similar features, which helps in capturing the complex compositions of food ingredients. Then, we apply an iterative strategy to the feature extraction module to gradually capture finer image details, and utilize a self-attention mechanism for weighting to further enhance feature representation. Extensive experiments conducted on various food image datasets, including ETH Food-101, Food11, Food new and CAFD to evaluate the effectiveness of the proposed model. The results show that the proposed model significantly improves the recognition accuracy and validate the generalization ability of the method on multiple food datasets.}, archive = {J_ACCESS}, author = {Wei He and Mengmeng Liu and Qingqi Liang}, doi = {10.1109/ACCESS.2025.3561465}, journal = {IEEE Access}, pages = {69888-69897}, shortjournal = {IEEE Access}, title = {Capturing fine-grained food image features through iterative clustering and attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Multi-output regression for the prediction of world-class performances in women’s handball. ACCESS, 13, 69873-69887. (www)

  @article{ , abstract = {The purpose of this study was to investigate the relationship between workload and in-game technical and athletic performance. To achieve this,A modeling approach that predicts multiple numerical output variables simultaneously, particularly useful when these outputs are correlated (multi-output regression) models were used to predict 7 performance indicators based on previous training and game athletic workloads measured by Inertial Measurement Units (IMU) indicators, previous in-game actions annotated by staff members and game contextual factors. We compared 4 single-output models (kNN, regression tree, random forest and(NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance analysis (neural networks)), their multi-output counterparts and aA baseline model predicting future performance as the average of each player’s past performance, serving as a simple reference for comparison with more complex models (dummy baseline) (predicting the average performance of each player over the last month) in terms of average(Root Mean Squared Error) A measure of the quadratic difference between predicted and actual values in regression models (RMSE) (aRMSE) during aAn evaluation method where past training and game data are used sequentially to predict performance of the next game (chronological evaluation) where previous trainings and games data are used to train models to predict the next game performances. Overall, the use of multi-output regression models enabled a decrease of the average predictive error (A metric for prediction error that evaluates model accuracy in terms of average squared errors across multiple outputs in a multi-output models (aRMSE) = 4.23) in regards to their single-output counterparts (aRMSE = 4.35) while providing a significant decrease of average computation times (4.75 to 0.82 seconds). Among the 4 multi-output models, only the kNN (aRMSE = 3.852) and random forest (aRMSE = 3.888) performed better than the dummy regressor (aRMSE = 3.944). These results point towards that physical training may have a limited impact on game performance.}, archive = {J_ACCESS}, author = {Rayane Elimam and Nicolas Sutton-Charani and Jacques Prioux and Jacky Montmain and Stéphane Perrey}, doi = {10.1109/ACCESS.2025.3560838}, journal = {IEEE Access}, pages = {69873-69887}, shortjournal = {IEEE Access}, title = {Multi-output regression for the prediction of world-class performances in women’s handball}, volume = {13}, year = {2025}, }
• (2025). Deep learning for dust accumulation analysis on desert solar panels: A CNN-transformer approach. ACCESS, 13, 69857-69872. (www)

  @article{ , abstract = {In the energy transition towards sustainability, photovoltaic power is increasingly valued for its eco-friendly and renewable attributes. Northern and northwestern China’s deserts, abundant in solar energy and vast in land, are prime areas for photovoltaic generation. High winds and dust storms in these regions cause dust buildup on PV panels, reducing efficiency and potentially harming the equipment. In water-scarce deserts, cleaning photovoltaic panels is particularly difficult. Therefore, developing robust and precise dust accumulation detection techniques is crucial to ensure the stable operation of PV power plants in deserts. To address this challenge, this study proposes a solution based on the convolutional neural network (CNN) structure UTran-ResNet50, a model capable of accurately recognizing images with various levels of dust coverage on photovoltaic panels. By combining ResNet50 and Transformer architectures, their strengths in global and local feature extraction are effectively exploited. In addition, the study incorporates the Attention U-Net module to reduce the recognition error and introduces the Positional Attention module to enhance the feature recognition capability. It is particularly noteworthy that the experimental validation conducted with a homemade dataset, despite its limited sample size, demonstrates the high efficacy of the proposed network, achieving an accuracy of 96.41%. It exemplifies the model’s robustness in dealing with small sample datasets, highlighting its superior performance in accurately detecting dust accumulation on photovoltaic panels.}, archive = {J_ACCESS}, author = {Ming Li and Yun Wang}, doi = {10.1109/ACCESS.2025.3561363}, journal = {IEEE Access}, pages = {69857-69872}, shortjournal = {IEEE Access}, title = {Deep learning for dust accumulation analysis on desert solar panels: A CNN-transformer approach}, volume = {13}, year = {2025}, }
• (2025). CLS-3D: Content-wise LiDAR-camera fusion and slot reweighting transformer for 3D object detection in autonomous vehicles. ACCESS, 13, 69840-69856. (www)

  @article{ , abstract = {Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods.}, archive = {J_ACCESS}, author = {Husnain Mushtaq and Sohaib Latif and Muhammad Saad Bin Ilyas and Syed Muhammad Mohsin and Mohammed Ali}, doi = {10.1109/ACCESS.2025.3558780}, journal = {IEEE Access}, pages = {69840-69856}, shortjournal = {IEEE Access}, title = {CLS-3D: Content-wise LiDAR-camera fusion and slot reweighting transformer for 3D object detection in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Quality of experience optimization for AR service in an MEC federation system. ACCESS, 13, 69821-69839. (www)

  @article{ , abstract = {Augmented reality (AR) in the internet of things requires ultra-low latency, high-resolution video, and fairness in multi-user environments, which pose challenges for traditional cloud and edge computing. To address this shortcoming, we studied AR subtask offloading and resource allocation in a multi-hop, multi-access edge computing federation. Our approach improves the quality of experience (QoE) by optimizing video quality and reducing delay while ensuring fairness, which is modeled as the ratio between provided and required quality. Instead of sequential execution, we adopt parallel AR subtask dependency processing to minimize latency. We propose an improved deep deterministic policy gradient algorithm for efficient solution exploration. Additionally, we implement strict training process monitoring to optimize resource usage and ensure sustainability. Experiments demonstrate that our method improves QoE by nearly 8% compared with TD3 while cutting training time in half.}, archive = {J_ACCESS}, author = {Huong Mai do and Tuan Phong Tran and Myungsik Yoo}, doi = {10.1109/ACCESS.2025.3562618}, journal = {IEEE Access}, pages = {69821-69839}, shortjournal = {IEEE Access}, title = {Quality of experience optimization for AR service in an MEC federation system}, volume = {13}, year = {2025}, }
• (2025). AI-driven innovation using multimodal and personalized adaptive education for students with special needs. ACCESS, 13, 69790-69820. (www)

  @article{ , abstract = {This study provides an in-depth exploration of the use of multimodal techniques in developing adaptive learning systems designed for students with special needs using various neural network models: a Baseline Neural Network, Convolutional Neural Network, Attention Model, LSTM, GRU, and Transformer models. Adaptive learning systems play a major role in the customization of the educational process in such a way as to ensure the specific cognitive, behavioural and emotional needs of students with special educational needs are catered for. The models were trained using a sizeable amount of multimodal data that recorded factors such as academic achievement, behavioral interactions, and environmental factors. From our observations, it can be noted that transformer models and attention mechanisms allowed for higher performance in predicting and fulfilling the needs of students, and were more effective than simpler neural architectures. These findings suggest that more advanced and intelligent neural networks can improve how students with different learning abilities consume content and, consequently, provide better educational answers and solutions to the market. To identify the important features affecting student performance, various Explainable AI (XAI) techniques were utilized, including SHAP (Shapley Additive Explanations) values, logistic regression-based feature importance analysis, LIME (Local Interpretable Model-Agnostic Explanations), partial dependency plots, and whose global surrogate model was a Decision Tree Regressor. These XAI instruments allowed the model’s decision to be explained and visualized so that teachers could understand the main beneficiaries of the improved educational results. Such XAI methods enable transparency in how models make decisions, thus allowing educators to understand the reasons for certain recommended learning paths.}, archive = {J_ACCESS}, author = {Nesren Farhah and Muhammad Adnan and Ahmed Abdullah Alqarni and M. Irfan Uddin and Theyazn H. H. Aldhyani}, doi = {10.1109/ACCESS.2025.3560863}, journal = {IEEE Access}, pages = {69790-69820}, shortjournal = {IEEE Access}, title = {AI-driven innovation using multimodal and personalized adaptive education for students with special needs}, volume = {13}, year = {2025}, }
• (2025). Global-local ensemble detector for AI-generated fake news. ACCESS, 13, 69779-69789. (www)

  @article{ , abstract = {With the continuous evolution of advanced large language models like GPT, the proliferation of AI-generated fake news presents growing challenges to information dissemination. Traditional text classification methods face difficulties in accurately detecting such content, due to their limited capacity to differentiate between authentic and fabricated news. To address this issue, this paper introduces a novel “Global-Local News Detection Model”, which combines BERT, Bidirectional Long Short-Term Memory (BiLSTM) networks, Text Convolutional Neural Networks (TextCNN), and attention mechanisms to enhance the detection of AI-generated fake news. A new dataset, generated using GPT-4 and covering 42 news categories, was developed to serve as a comprehensive and diverse foundation for training and evaluating the model. Experimental results indicate that the proposed model achieves an accuracy and F1 score of 0.82, surpassing traditional approaches.}, archive = {J_ACCESS}, author = {Yujia Wang and Wen Long}, doi = {10.1109/ACCESS.2025.3562154}, journal = {IEEE Access}, pages = {69779-69789}, shortjournal = {IEEE Access}, title = {Global-local ensemble detector for AI-generated fake news}, volume = {13}, year = {2025}, }
• (2025). Spatiotemporal epidemiological similarity based on patient trajectories. ACCESS, 13, 69760-69778. (www)

  @article{ , abstract = {Healthcare-associated infections are a significant public health concern. This study presents the Spatiotemporal Epidemiological Similarity based on Patient Trajectories (StESPT) method to provide clinicians with support in investigating the epidemiological relationships between infected patients, making detecting outbreaks and identifying possible transmission routes. To this end, the StESPT retrieves the movements of infected patients through the hospital and transforms them into trajectories. Then, a Trajectory Distance Measure Algorithm (TDMA) was used to quantify the spatiotemporal similarity between the trajectories of each pair of patients. Finally, based on the results of each TDMA, the k-means clustering algorithm was used to group patients, which can help understand the spread of the infection. We compared the suitability of three commonly used TDMAs (Dynamic Time Warping (DTW), Spatiotemporal Linear Combine similarity (STLC), and Spatiotemporal Longest Common Subsequence (ST-LCSS)) that were modified to measure similarity instance of distance. For each TDMA, we also proposed a version that adapts better to the semantics of our problem. The StESPT method was tested with a synthetic simulation of Clostridium difficile infection in a hospital. The results of the modified version of the ST-LCSS-WTW best reflected the spatiotemporal relationships between patients and led to the k-means clustering revealing two potential outbreaks.}, archive = {J_ACCESS}, author = {Lorena Pujante-Otalora and Manuel Campos and Jose M. Juarez}, doi = {10.1109/ACCESS.2025.3562095}, journal = {IEEE Access}, pages = {69760-69778}, shortjournal = {IEEE Access}, title = {Spatiotemporal epidemiological similarity based on patient trajectories}, volume = {13}, year = {2025}, }
• (2025). Adaptive depth-wise pruning for efficient environmental sound classification. ACCESS, 13, 69751-69759. (www)

  @article{ , abstract = {Overparameterization of pretrained transformers often leads to inefficiencies in diverse Environmental Sound Classification (ESC) tasks, where excessive computation limits deployment in resource-constrained scenarios. To address this issue, we propose Adaptive Depth-wise Pruning (ADP), a task-adaptive and architecture-agnostic model compression framework for efficient ESC. ADP decomposes a general classification model into hierarchical depth-wise modular blocks and adaptively prunes less important blocks based on depth-wise classification performance. By incorporating Self-distillation (SD) into a global optimization framework, ADP efficiently performs depth-wise classification while maintaining a shared feature extraction backbone, and selects the most compact yet effective subnetwork within an acceptable performance degradation range. We evaluate ADP on the Audio Spectrogram Transformer (AST) with the ESC-50 dataset, achieving a 50.68% reduction in parameters with less than a 2% accuracy drop, demonstrating its ability to adaptively balance compression and performance within an acceptable degradation margin. The source code is available at https://github.com/youngwhite/ADP for reproducibility.}, archive = {J_ACCESS}, author = {Changlong Wang and Akinori Ito and Takashi Nose}, doi = {10.1109/ACCESS.2025.3561590}, journal = {IEEE Access}, pages = {69751-69759}, shortjournal = {IEEE Access}, title = {Adaptive depth-wise pruning for efficient environmental sound classification}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of solar photovoltaic systems: Scope, technologies, applications, progress, challenges, and recommendations. ACCESS, 13, 69723-69750. (www)

  @article{ , abstract = {The paradigm for energy systems has shifted in the last several years from non-renewable energy sources to renewable energy sources (RESs). Leveraging RESs seeks to meet local demand while reducing complexity, expense, and inefficiencies and promoting energy independence, local resilience, and the transition to zero CO2 emissions. One of the RES technologies that could help create clean, dependable, scalable, and reasonably priced energy is solar energy. This article offers a detailed analysis of solar photovoltaic (PV) technology. It examines the distinct qualities and developments of the three generations of solar PV technologies: first-generation crystalline silicon, second-generation thin-film, and third-generation developing technologies, including perovskite and organic cells. The study also looks at the many diverse applications of solar photovoltaics, such as energy communities, microgrids, transportation systems, telecommunications, and agriculture. Moreover, it highlights the deployment of the solar PV system in Italy by overseeing the installed capacity, electricity generation, and energy consumption with different types of energy sources after focusing on global and European scenarios. From the data, it is seen that China, the USA, and India have led the world in capacity growth for solar PV, while Europe continues to lead the way in policy-driven solar adoption. Italy has seen great growth in Europe after Germany in 2023, as compared to previous years. Apart from this, the challenges are also seen, which could result in hindrances in the deployment of solar PV growth. So, the article also offers recommendations for the future like the establishment of a balanced policy environment for distributed PV, encouragement of off-grid electrification system adoption, maintenance of the solar PV cost reduction trajectory, and encouraging the creation of more intelligent inverter systems to help with balance-of-system cost reductions and grid integration, friendly relation with community and supportive policies in the country.}, archive = {J_ACCESS}, author = {Shoaib Ahmed and Amjad Ali and Jamshed Ahmed Ansari and Sikandar Abdul Qadir and Laveet Kumar}, doi = {10.1109/ACCESS.2025.3558539}, journal = {IEEE Access}, pages = {69723-69750}, shortjournal = {IEEE Access}, title = {A comprehensive review of solar photovoltaic systems: Scope, technologies, applications, progress, challenges, and recommendations}, volume = {13}, year = {2025}, }
• (2025). H-UDMIR: A hybrid unsupervised deep learning framework for deformable medical image registration. ACCESS, 13, 69705-69722. (www)

  @article{ , abstract = {The study presents H-UDMIR, a novel hybrid medical unsupervised deep learning (DL) framework for deformable image registration (DIR) that integrates U-Net architecture and Spatial Transformer Networks (STNs). The U-Net initially predicts coarse deformation fields, which are then enhanced by cascaded STNs to dynamically register local image regions while tolerating a loss function to effectively balance deformation smoothness and image fidelity. This hybrid approach exhibits enhanced registration performance and resilience without the necessity for annotated training data. Experimental results using H-UDMIR highlight its precise registration capabilities across varied anatomical structures and imaging modalities: kidney computed tomography (CT) scans and brain magnetic resonance imaging (MRI) datasets. Notably, H-UDMIR achieves a Dice similarity coefficient of 92.72% and a Hausdorff distance of 881.9244 on low- and high-dose kidney CT scans, surpassing VoxelMorph’s 84.04% Dice score. A comparative analysis against state-of-the-art approaches such as VoxelMorph underscores H-UDMIR’s superior performance.}, archive = {J_ACCESS}, author = {Hossam Magdy Balaha and Ali Mahmoud and Mohamed Abou El-Ghar and Mohammed Ghazal and Sohail Contractor and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3562092}, journal = {IEEE Access}, pages = {69705-69722}, shortjournal = {IEEE Access}, title = {H-UDMIR: A hybrid unsupervised deep learning framework for deformable medical image registration}, volume = {13}, year = {2025}, }
• (2025). Toward 100% renewable power grids: A review. ACCESS, 13, 69690-69704. (www)

  @article{ , abstract = {The transition to a 100% renewable power grid remains beset by significant technical challenges. This paper critically examines the obstacles arising from the variability and unpredictability of renewable energy sources (RES), which complicate the real-time balancing of power generation and load demand. In addition to traditional stability concerns (e.g., rotor angle, voltage, and frequency stability), the increasing penetration of inverter-based resources (IBRs) introduces novel challenges, including resonance stability issues and converter-driven dynamics. Moreover, the dynamic behavior and fault response of IBRs diverge markedly from those of conventional synchronous generators, rendering traditional protection schemes increasingly inadequate. This paper reviews state-of-the-art solutions in power balancing, grid flexibility, stability enhancement, and advanced protection strategies, discussing their implications for future grid design. The analysis provides a comprehensive assessment of recent technological advancements, thereby outlining critical research directions essential for achieving a resilient, 100% renewable power grid.}, archive = {J_ACCESS}, author = {Yayu Yang and Yan Wen and Zhengfa Zhang and Yuqing Dong and Chong Shen and Yilu Liu}, doi = {10.1109/ACCESS.2025.3558772}, journal = {IEEE Access}, pages = {69690-69704}, shortjournal = {IEEE Access}, title = {Toward 100% renewable power grids: A review}, volume = {13}, year = {2025}, }
• (2025). A distributed photovoltaic operation and maintenance cloud platform for PV aerial inspections with sparse industrial data. ACCESS, 13, 69677-69689. (www)

  @article{ , abstract = {Distributed photovoltaic (DPV) power sites in industrial parks are characterized by dispersed layouts, practical fault detection environments, and high safety requirements. Conventional manual DPV O&M systems using handheld sensors are inefficient, expensive, and struggle with fault detection due to sparse industrial data and uni-modal information limitations. To this, this paper proposes an innovative advanced algorithm for DPV fault detection in industrial parks, utilizing a new sparse industrial dataset, “SolarPark,” collected via multi-modal UAVs and annotated through a multi-expert process with uncertainty scoring. By fusing the Convolutional Block Attention Module (CBAM), Bidirectional Feature Pyramid Network (BiFPN), Ghost modules, the algorithm enhances attention to critical photovoltaic fault-related channel information, strengthens multi-scale photovoltaic fault feature fusion, and achieves lightweight efficiency. Combined with multi-modal UAV videos, the proposed industrial DPV fault detection algorithm achieves a precision of 95.4%, effectively ensuring the efficiency of DPV power sites in data-scarce industrial scenarios. Extensive experiments on the developed cloud platform confirm the proposed algorithm’s efficient, cost-effective, and easy to deploy for aerial inspections of DPV O&M systems.}, archive = {J_ACCESS}, author = {Chengwu Liang and Songqi Jiang and Jie Yang and Wei Hu and Yalong Liu and Peiwang Zhu and Guofeng He and Chunlei Shi}, doi = {10.1109/ACCESS.2025.3561234}, journal = {IEEE Access}, pages = {69677-69689}, shortjournal = {IEEE Access}, title = {A distributed photovoltaic operation and maintenance cloud platform for PV aerial inspections with sparse industrial data}, volume = {13}, year = {2025}, }
• (2025). Multi-UAV cooperative suspension control strategy considering variable rope length and load posture coupling effect. ACCESS, 13, 69660-69676. (www)

  @article{ , abstract = {This paper addresses the issues of insufficient model accuracy and poor control performance in traditional unmanned aerial vehicle (UAV) lifting operations by proposing an improved control strategy for multi-UAV cooperative lifting operations and optimizing the dynamic model. Firstly, the efficiency of the operation is enhanced, by introducing multi-UAV cooperative lifting control. On this basis, the traditional dynamic model is improved, with a focus on the impact of variable rope length, analyzing the dynamic coupling relationship between rope length and swing angle, and constructing related coupling error functions to improve the stability and control accuracy of the system. At the same time, the interaction between the load and the swing is studied, considering the velocity relationship between the suspension point and the load, and taking into account the coupled effect of the load attitude. Finally, a Lagrangian dynamic model that includes multiple-UAV, variable rope length, and load-coupled effects is established. Based on the improved model, this paper proposes a coupled error function, designs a nonlinear controller based on the Lyapunov method, which is non-passive, and realizes precise position tracking of the UAV and suppression of the load swing, while maintaining the rope length within the desired range. Finally, the stability and convergence of the closed-loop system are proven through Lyapunov stability analysis and the LaSalle invariance principle. The simulation results show that the proposed method exhibits significant advantages in UAVs’ position, rope length tracking, and load swing suppression compared to the traditional lifting control strategy, and has strong robustness against uncertain parameters and external disturbances, verifying the effectiveness and practical value of the proposed method.}, archive = {J_ACCESS}, author = {Yi Wang and Kang Li and Xuefu Li and Jingyu Wang and Pengbo Yu}, doi = {10.1109/ACCESS.2025.3561954}, journal = {IEEE Access}, pages = {69660-69676}, shortjournal = {IEEE Access}, title = {Multi-UAV cooperative suspension control strategy considering variable rope length and load posture coupling effect}, volume = {13}, year = {2025}, }
• (2025). Sleep staging using compressed vision transformer with novel two-step attention weighted sum. ACCESS, 13, 69650-69659. (www)

  @article{ , abstract = {Automatic sleep staging is crucial for diagnosing sleep disorders, however, existing inter-epoch feature extraction schemes such as RNN-based networks or transformers often struggle with long sleep sequences due to overfitting. This study presents a novel automatic sleep staging method utilizing a pre-trained vision transformer with compression as a sequence encoder and a two-step attention to enhance the sleep-stage classification performance. In contrast to existing transformer-based methods, the pre-trained transformer with compression can handle long sequences covering a sleep cycle, leveraging robust feature extraction capabilities with substantially fewer parameters. Furthermore, an epoch encoder based on a bidirectional temporal convolutional network with a multi-head two-step attention mechanism is proposed to improve the efficiency of epoch-level feature extraction. The performance of the proposed method is evaluated using three publicly available datasets: SleepEDF-20, SleepEDF-78, and SHHS. Numerical experiments show notable performance enhancement of the proposed scheme in comparison with the state-of-the-art algorithms, particularly for small training datasets, which validates the resilience of the proposed method against overfitting. These results suggest that with appropriate regularization, transformer-based models can effectively capture long-term contextual information across a complete sleep cycle.}, archive = {J_ACCESS}, author = {Hyounggyu Kim and Moogyeong Kim and Wonzoo Chung}, doi = {10.1109/ACCESS.2025.3561310}, journal = {IEEE Access}, pages = {69650-69659}, shortjournal = {IEEE Access}, title = {Sleep staging using compressed vision transformer with novel two-step attention weighted sum}, volume = {13}, year = {2025}, }
• (2025). Securing LLM workloads with NIST AI RMF in the internet of robotic things. ACCESS, 13, 69631-69649. (www)

  @article{ , abstract = {The Internet of Robotic Things (IoRT) is revolutionizing industries by enabling autonomous, AI-driven robotic systems to perform complex and collaborative tasks, such as precision agriculture, disaster response, and logistic shipping operations. However, integrating AI into IoRT introduces significant challenges, including security vulnerabilities, adversarial attacks, data integrity risks, and operational disruptions in dynamic and high-stakes environments. This paper addresses these challenges by integrating and enhancing the NIST AI Risk Management Framework (AI RMF) for IoRT systems, providing a structured approach to identify, assess, and mitigate risks specific to IoRT ecosystems. We introduce a novel Large Language Model (LLM)-based approach for translating natural language commands into secure and precise robotic operations, enabling seamless collaboration and enhancing safety and reliability in mission-critical scenarios. Using a flood recovery scenario in precision agriculture, we demonstrate the practical application of these solutions, where swarm robots equipped with AI inference engines collaborate to navigate hazards, locate individuals, assess infrastructure damage, and mitigate risks. A comprehensive threat analysis is presented, mapping identified vulnerabilities to the NIST AI RMF, and tailored security controls are proposed to mitigate these threats effectively. We propose critical enhancements to the framework, including advanced quantitative risk assessment methods, subsystem governance strategies for interconnected IoRT networks, and robust auditing mechanisms to address unique IoRT-specific challenges. This work establishes a robust foundation for aligning AI governance frameworks with the complex and dynamic demands of IoRT systems. By addressing security, operational, and ethical considerations, it fosters secure, efficient, and trustworthy deployment across diverse applications, paving the way for sustainable and impactful IoRT innovations.}, archive = {J_ACCESS}, author = {Hassan Karim and Deepti Gupta and Sai Sitharaman}, doi = {10.1109/ACCESS.2025.3561235}, journal = {IEEE Access}, pages = {69631-69649}, shortjournal = {IEEE Access}, title = {Securing LLM workloads with NIST AI RMF in the internet of robotic things}, volume = {13}, year = {2025}, }
• (2025). An open-source IT tool for energy forecast of renewable energy communities. ACCESS, 13, 69619-69630. (www)

  @article{ , abstract = {Renewable Energy Communities (RECs) are envisaged as a key enabler for a citizen-driven energy transition. The pooling of renewable energy resources at a local level and the active involvement of public entities, Small and Medium-sized Enterprises (SMEs) and private customers can lower the energy costs of end-consumers and increase public acceptance of renewable projects. At the same time, the RECs can support power system operation by leveraging their flexibility and providing ancillary services to the grid. While a significant amount of research has focused on optimizing the operation of existing RECs to maximize their performance and economic benefits, fewer studies have analysed the forecast of future REC performance to support the planning of new communities. The present paper tackles this research gap by developing a novel tool for the forecast of techno-economic performance of REC. The tool relies on two different techniques (based on statistical random sampling and neural networks, respectively) to predict the energy behaviour of RECs on the basis of their fundamental planning parameters (type/number of members, installed generation, geographical location). In order to reach a larger audience of practitioners, a web-based open-source implementation of the tool has been developed and made available to the general public, designing a graphical interface that facilitates the use of the tool by non-technical experts.}, archive = {J_ACCESS}, author = {Antonio De Paola and Emma Musiari and Lara Fortunati and Francesco Gregori and Gian Piero Anselmi and Nikoleta Andreadou and Evangelos Kotsakis and Gianluca Fulli}, doi = {10.1109/ACCESS.2025.3561937}, journal = {IEEE Access}, pages = {69619-69630}, shortjournal = {IEEE Access}, title = {An open-source IT tool for energy forecast of renewable energy communities}, volume = {13}, year = {2025}, }
• (2025). ST-3DView: Multi-scale contrast-enhanced 3D point cloud reconstruction of single-view objects from video scene transition. ACCESS, 13, 69596-69618. (www)

  @article{ , abstract = {3D object tracking in monocular video relies on understanding the scene content to improve the continuity of the tracking signal. Reconstructing 3D shapes of single-view objects is essential for capturing object depth, orientation, and position within the scene. While existing deep learning-based methods excel in 3D reconstruction and tracking, they primarily focus on object feature semantics in normal frames, neglecting scene transition (ST) frames. This limitation leads to object information loss and discontinuity during tracking. This paper proposes a novel method for 3D reconstruction of single-view objects in monocular video scenes, focusing on fade scene transitions. First, large video datasets are pre-processed and segmented into sequences using cut transition detection via adaptive histogram equalization (AHE), and Euclidean distance estimation (EDE). Second, fade transition sequences are detected and classified into fade-in, fade-out, and mixed-fade scene transitions using pixel intensity-based adaptive threshold. Third, contrast enhancement is applied to fade transition frames using contrast-limited adaptive histogram equalization (CLAHE) to improve object feature extraction. Fourth, a modified DeepLabv3+ network is employed to generate multi-scale features for semantic foreground object and background segmentation. Finally, the segmented objects are processed through the proposed Point-wise multilayer perceptron (MLP) network, which reconstructs 3D object point clouds from segmented 2D single-view object pixels. Experimental evaluations on object categories “Chair,” “Car,” and “Airplane” from the benchmark TRECVID, Pix3D, ShapeNet, and Multimedia datasets achieved an accuracy improvement of 6.52% for fade transition detection and satisfactory results in 3D point cloud reconstruction.}, archive = {J_ACCESS}, author = {Dipanita Chakraborty and Werapon Chiracharit and Kosin Chamnongthai}, doi = {10.1109/ACCESS.2025.3561456}, journal = {IEEE Access}, pages = {69596-69618}, shortjournal = {IEEE Access}, title = {ST-3DView: Multi-scale contrast-enhanced 3D point cloud reconstruction of single-view objects from video scene transition}, volume = {13}, year = {2025}, }
• (2025). Lightweight gaussian process-based visual servoing for autonomous wheelchair sidewalk navigation. ACCESS, 13, 69582-69595. (www)

  @article{ , abstract = {The personal mobility of wheelchair users is pivotal to their overall well-being. Navigating electric wheelchairs through confined spaces poses notable challenges, particularly for individuals with disabilities. In response, this research introduces a pioneering vision-based approach for sidewalk navigation that leverages tactile paving features and advanced machine learning models. While many existing solutions rely on expensive sensors or GPU-accelerated deep networks, we address this limitation by employing only a low-cost monocular camera and a lightweight GP model. Running at 7 Hz on a Raspberry Pi, our approach offers real-time autonomy without requiring specialized or high-end hardware. The proposed methodology includes creating a specialized dataset, formulating a custom control law, and deploying a lightweight real-time Gaussian Process (GP) model on a Raspberry Pi platform. Thorough experimentation substantiates the efficacy of the presented approach, demonstrating accurate and dependable autonomous wheelchair navigation on sidewalks. Beyond its technical contributions, the proposed solution offers a cost-effective alternative to conventional sensor-dependent systems, profoundly enhancing user mobility and quality of life. By equipping wheelchair users with enhanced navigation capabilities, this research strives to foster greater independence and inclusion in their daily lives.}, archive = {J_ACCESS}, author = {A. H. Abdul Hafez and Ismail Haj Osman and Efgan Uğur and Tolgay Kara}, doi = {10.1109/ACCESS.2025.3561467}, journal = {IEEE Access}, pages = {69582-69595}, shortjournal = {IEEE Access}, title = {Lightweight gaussian process-based visual servoing for autonomous wheelchair sidewalk navigation}, volume = {13}, year = {2025}, }
• (2025). Transmission line anomaly detection and real-time monitoring system combining edge computing and EfficientDet. ACCESS, 13, 69573-69581. (www)

  @article{ , abstract = {Accurately identifying aberrant issues on transmission lines is crucial to guaranteeing the power system operates safely and steadily. Transmission lines are a crucial part of the power system. In response to the current issues of low recognition accuracy and low image contrast in abnormal detection of power transmission lines. This paper develops a transmission line anomaly detection and real-time monitoring system combining edge computing and improved Efficient Det based on the characteristics of foreign object images. Firstly, by collecting abnormal images of transmission channels, the Retinex algorithm is used to enhance the input images. Secondly, in order to improve the speed of model detection, the convolution operation process for obtaining data features based on Ghost lightweight module is reconstructed. In the target recognition part, an improved EfficientDet algorithm is adopted as the main body, and the aspect ratio of anchor boxes in the algorithm is optimized using K-means clustering algorithm. At the same time, a gradient equalization mechanism is added to the loss function. The experimental results show that the proposed method effectively improves recognition accuracy and recall rate, achieving over 88% on different types of transmission lines. The ablation experiment shows that adding K-means clustering algorithm and gradient equalization mechanism can significantly improve recognition accuracy.}, archive = {J_ACCESS}, author = {Menghao Lin and Yang Ding and Tianle Wang and Yang Liu and Zewei Li}, doi = {10.1109/ACCESS.2025.3561745}, journal = {IEEE Access}, pages = {69573-69581}, shortjournal = {IEEE Access}, title = {Transmission line anomaly detection and real-time monitoring system combining edge computing and EfficientDet}, volume = {13}, year = {2025}, }
• (2025). FaceArchive: Facial image tokenizer for identity and attribute preserving face generation. ACCESS, 13, 69562-69572. (www)

  @article{ , abstract = {Current approaches in face image generation typically focus on preserving identity or attributes exclusively. Identity preservation techniques maintain facial identity while enabling attribute manipulation, primarily serving personalized image generation applications. Conversely, attribute preservation methods retain facial characteristics while altering identity, commonly employed in face de-identification or dataset anonymization tasks. This dichotomy has created a significant gap in applications that require the simultaneous preservation of both aspects. In this paper, we present FaceArchive, a novel face tokenization framework that successfully disentangles and preserves both identity and attribute information from face images. Our architecture enables unprecedented control over face generation, allowing for either identity-preserving or attribute-preserving generation while maintaining high fidelity in both domains.}, archive = {J_ACCESS}, author = {Fabianaugie Jametoni and In Kyu Park}, doi = {10.1109/ACCESS.2025.3562074}, journal = {IEEE Access}, pages = {69562-69572}, shortjournal = {IEEE Access}, title = {FaceArchive: Facial image tokenizer for identity and attribute preserving face generation}, volume = {13}, year = {2025}, }
• (2025). Mixture of experts for code-switching and mitigating catastrophic forgetting in automatic speech recognition: A case study in thai colonoscopic reporting. ACCESS, 13, 69547-69561. (www)

  @article{ , abstract = {During colonoscopy procedures, gastroenterologists operate equipment with both hands, making real-time documentation of abnormal findings impractical. This reliance on memory increases the risk of missed details and prolongs report generation. Speech recognition technology offers a potential solution, but existing models struggle with Thai-English code-switching and suffer from overfitting when fine-tuned on small datasets. To address these challenges, we propose an Automatic Speech Recognition (ASR) model enhanced with the Mixture of Experts (MoE) technique within Transformer decoder layers. This approach enables domain specialization in gastroenterology (GI) while preserving Thai language knowledge, improving code-switching performance and mitigating catastrophic forgetting. Additionally, our Named Entity Recognition (NER) model extracts and categorizes GI terms from transcriptions to streamline colonoscopic reporting. Experimental results show that our MoE-ASR model achieves low word error rates (WER) (GI: 1.31%, Thai: 2.06%), with high medical term recall (MTR: 96.53%) and non-medical term recall (NTR: 95.85%), outperforming baseline models. The NER model also demonstrates strong performance, achieving precision (96.16%), recall (96.06%), and F1-score (95.11%). These results highlight the effectiveness of our approach in improving real-time documentation, reducing reliance on memory, and enhancing the efficiency of colonoscopic reporting.}, archive = {J_ACCESS}, author = {Arpanant Saeng-Xuto and Pasit Jakkrawankul and Krittamate Tiankanon and Kasenee Tiankanon and Natawut Nupairoj and Peerapon Vateekul}, doi = {10.1109/ACCESS.2025.3562146}, journal = {IEEE Access}, pages = {69547-69561}, shortjournal = {IEEE Access}, title = {Mixture of experts for code-switching and mitigating catastrophic forgetting in automatic speech recognition: A case study in thai colonoscopic reporting}, volume = {13}, year = {2025}, }
• (2025). Symbiotic positioning, navigation, and timing via game theory and reinforcement learning. ACCESS, 13, 69532-69546. (www)

  @article{ , abstract = {Precise positioning, navigation, and timing (PNT) capabilities are essential for numerous critical infrastructure systems and advanced location-dependent applications. The challenges related to the reliability and accuracy of traditional Global Navigation Satellite Systems (GNSS) have driven the pursuit of innovative alternative PNT methodologies. This paper presents a novel approach inspired by the concept of biological symbiosis and leveraging the advanced capabilities of Reconfigurable Intelligent Surfaces (RISs). The proposed framework establishes a cooperative interaction between targets with unknown positions and collaborator nodes with approximate location estimates. These interactions are supported by the RISs and the anchor nodes with known positions. The objective is to minimize errors in positioning and timing for both the targets and the collaborators. This challenge is modeled as a non-cooperative game, and the existence of a Nash Equilibrium is demonstrated using potential game theory. To solve the game, Best Response Dynamics and a log-linear Reinforcement Learning (RL)-based approach are developed to identify the equilibrium state. The proposed system is thoroughly evaluated through simulations, in order to demonstrate its performance and the key trade-offs between game-theoretic strategies and the RL-based solutions.}, archive = {J_ACCESS}, author = {Md Sadman Siraj and Eirini Eleni Tsiropoulou and Symeon Papavassiliou and Jim Plusquellic}, doi = {10.1109/ACCESS.2025.3561790}, journal = {IEEE Access}, pages = {69532-69546}, shortjournal = {IEEE Access}, title = {Symbiotic positioning, navigation, and timing via game theory and reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based life expectancy prediction in developed and developing regions. ACCESS, 13, 69520-69531. (www)

  @article{ , abstract = {The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging countries. XGBoost, or Extreme Gradient Boosting, is a robust machine-learning technique renowned for its precision and efficiency in handling structured data. The objective is to develop a precise prediction model and identify the primary factors influencing life expectancy. The experimental findings indicate that our XGBoost-based model effectively forecasts life expectancy in both industrialized and developing nations. Mean Absolute Error, Mean Squared Error, and R2 score are used for model selection. XGBoost is selected because to its mean squared error of 2.45, mean absolute error of 1.03, and R2 scores of 0.99 for the training set and 0.97 for the testing set. In this a feature significance analysis was conducted to identify the most relevant factors affecting life expectancy. Our results underscore the significance of variables such as HIV/AIDS, income composition, and adult mortality in influencing life expectancy. The model’s findings may assist lawmakers and healthcare professionals in making decisions to enhance public health outcomes, allocate resources efficiently, and formulate focused solutions.}, archive = {J_ACCESS}, author = {Shaik Shakeel Ahamad and K. Praveen Kumar and Sankuratri Sri Ganesh and Fayez Alharbi and Sultan Awad Alharby and Venkata S. P. Dendukuri and Suvarna Vani Koneru}, doi = {10.1109/ACCESS.2025.3560890}, journal = {IEEE Access}, pages = {69520-69531}, shortjournal = {IEEE Access}, title = {Machine learning-based life expectancy prediction in developed and developing regions}, volume = {13}, year = {2025}, }
• (2025). Mathematical model for delay locked loop discriminator for 5G demodulation reference signal. ACCESS, 13, 69513-69519. (www)

  @article{ , abstract = {The vulnerable nature of satellite-based positioning systems under certain conditions such as urban canyons and indoors has given rise to the need for alternative positioning system. Cellular signal based positioning is gaining popularity and development of an accurate cellular signal-based positioning receiver is in progress. Among the cellular signals, 5G is the most preferable due to its high carrier frequency, high bandwidth, high power, ease of access and low infrastructure cost. A review of the literature suggests that 5G based positioning receiver uses a Delay Locked Loop (DLL) to track the phase of the demodulation reference signal (DMRS) of the received 5G signal. An essential part of the DLL is the discriminator, which is used to estimate the phase difference between the received DMRS and the replica of transmitted DMRS. In order to understand the working involved in the discriminator, it is ideal to study its mathematical model. The discriminator’s mathematical model includes the mathematical representation of early, prompt and late DMRS symbols, the correlations between received DMRS symbols and early, prompt and late DMRS symbols, and the S-curve. These mathematical representations, available in the literature, are analyzed in this work using the MATLAB plots. It is observed that the plots do not accurately depict the theory. Therefore, the need for an improved mathematical model arises. This work is an effort to obtain improved mathematical model for the DLL discriminator processing which is also analyzed using MATLAB plots. The plots show that the improved mathematical model accords with the theory involved in the discriminator.}, archive = {J_ACCESS}, author = {Freeha M. Amjad and Kamaludin Mohamad Yusof and Belgacem Bouallegue and Amir A. A. Gasouma}, doi = {10.1109/ACCESS.2025.3561159}, journal = {IEEE Access}, pages = {69513-69519}, shortjournal = {IEEE Access}, title = {Mathematical model for delay locked loop discriminator for 5G demodulation reference signal}, volume = {13}, year = {2025}, }
• (2025). Gated-LNN: Gated liquid neural networks for accurate water quality index prediction and classification. ACCESS, 13, 69500-69512. (www)

  @article{ , abstract = {Surface water quality is of utmost significance to ensure public health and facilitate sustainable economic development. Traditional water quality assessment methods are typically time-consuming and labor-intensive and require numerous field measurements and laboratory analyses, which are costly and impractical to implement in large-scale water quality monitoring. Recent advances in machine learning (ML) have brought new approaches to predicting water quality index (WQI) and classifying water quality in real time to enhance decision-making in environmental management. In this study, we propose a novel gated liquid neural network (gated-LNN) that can predict WQI and classify water quality with high accuracy. As opposed to typical ML models, the proposed gated-LNN includes a gating mechanism that enhances temporal learning and noise robustness, making it well-suited for dynamic environmental data. For ascertaining the effectiveness of the proposed approach, we conducted rigorous experiments on a publicly available water quality dataset with 1897 examples collected from varied water bodies of India between the years 2005 and 2014. The dataset comprises seven most significant parameters of water quality, i.e., dissolved oxygen, pH, conductivity, biological oxygen demand, nitrate, fecal coliform, and total coliform. The proposed gated-LNN model achieved a high R2 of 0.9995 for WQI prediction and 99.74% accuracy for three-class water quality classification into “Good,” “Poor,” and “Unsuitable” classes, outperforming state-of-the-art models in both regression and classification tasks. While these results highlight the model’s potential as a highly accurate and efficient tool for real-time water quality assessment, its generalizability to different regions remains an important consideration. Future work will focus on enhancing computational efficiency and conducting generalization tests on datasets from diverse geographic regions and time periods to evaluate adaptability.}, archive = {J_ACCESS}, author = {Sreeni Chadalavada and Suleyman Yaman and Abdulkadir Sengur and Abdul Hafeez-Baig and Ru-San Tan and Prabal Datta Barua and Ravinesh C. Deo and Makiko Kobayashi and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3561593}, journal = {IEEE Access}, pages = {69500-69512}, shortjournal = {IEEE Access}, title = {Gated-LNN: Gated liquid neural networks for accurate water quality index prediction and classification}, volume = {13}, year = {2025}, }
• (2025). Polynomial and differential networks for end-to-end autonomous driving. ACCESS, 13, 69492-69499. (www)

  @article{ , abstract = {This study introduces a novel model for predicting control variables in end-to-end autonomous driving by leveraging polynomial and differential networks. Recent advancements in autonomous driving have predominantly focused on methods that incorporate additional supervisory data, such as attention mechanisms and bird’s-eye view images. However, these approaches are often hindered by issues related to computational efficiency and the high costs of data acquisition for real-world applications. In contrast, the proposed method enhances the performance by integrating polynomial and differential networks, facilitating efficient learning while accounting for the physical properties inherent in the data. The results of experiments conducted using the CARLA simulator demonstrate that the proposed model outperforms existing state-of-the-art approaches. The model weights and training code used in these experiments are available at https://github.com/choys0401/polydiff.}, archive = {J_ACCESS}, author = {Youngseong Cho and Kyoungil Lim}, doi = {10.1109/ACCESS.2025.3562666}, journal = {IEEE Access}, pages = {69492-69499}, shortjournal = {IEEE Access}, title = {Polynomial and differential networks for end-to-end autonomous driving}, volume = {13}, year = {2025}, }
• (2025). The voltage control strategy for doubly salient electromagnetic generator with active rectifier based on optimal current distribution. ACCESS, 13, 69478-69491. (www)

  @article{ , abstract = {The doubly salient electromagnetic generator (DSEG) has the advantages of simple structure, high-temperature and high-speed environment adaptability, high reliability, which can build a competitive aerospace brushless DC starting/generation system. The dynamic response of the power generation system with diode rectifier (DR) is limited by the DC-link capacitor and the large time constant of the excitation winding. The adoption of active rectifier (AR) can increase the output power, reduce the copper loss, and improve the system efficiency. Either the excitation current or the armature current can be adjusted to control the output voltage in the generation system with AR. However, adjusting only one of the two currents cannot achieve optimal performance in terms of dynamic response and copper loss. This paper proposes an optimal current distribution control (OCDC) strategy for the active rectifier power generation system, which is based on the back electromotive force (EMF) oriented vector control (BEFOVC) strategy. By using an armature current inner loop to replace the excitation current inner loop and controlling the excitation current according to the minimum copper loss trajectory (MCLT), the OCDC strategy improves the dynamic performance and reduces the copper loss of the DSEG system. Simulations and experiments comparing the traditional voltage control strategy with the proposed strategy verify the feasibility and superiority of the OCDC strategy.}, archive = {J_ACCESS}, author = {Weifeng Liu and Yulong Tao and Xiaoli Meng}, doi = {10.1109/ACCESS.2025.3561782}, journal = {IEEE Access}, pages = {69478-69491}, shortjournal = {IEEE Access}, title = {The voltage control strategy for doubly salient electromagnetic generator with active rectifier based on optimal current distribution}, volume = {13}, year = {2025}, }
• (2025). Vision transformers (ViTs) for feature extraction and classification of AI-generated visual designs. ACCESS, 13, 69459-69477. (www)

  @article{ , abstract = {Deep learning has become a cornerstone of modern Artificial Intelligence (AI), enabling machines to process and interpret complex visual information with unprecedented accuracy. As AI-generated content becomes more realistic, the ability to distinguish between machine-created and human-created images is increasingly important. This challenge extends beyond technical concerns, influencing digital media credibility, intellectual property rights, and the integrity of visual communication. Developing robust classification models to accurately attribute image origins is crucial for ensuring transparency, preventing misinformation, and upholding artistic authenticity in an era of rapidly evolving generative AI technologies. This study addresses the critical need to differentiate between AI-generated and human-generated aesthetic images through the application of advanced deep learning models. We investigate the effectiveness of advanced deep learning architectures including High Resolution Networks (HRNet), and Vision Transformers (ViT) which are generally accurate when used to infer the creative characteristics of human visual art. The proposed model ViT, employing a mechanism of self-attention to process images as sequences of patches for feature extraction, is examined for its potential to capture global contextual relationships within images, which is essential for recognizing the nuanced differences between AI and human artistry. ViT achieves 97% accuracy shows that superior performance validates its ability, using its transformer structure, to analyze and learn about the complex features of images which disclose their origin as compared to HRNet model of 95%. This research highlights the potential of using sophisticated deep learning techniques to address the challenges of content authenticity in digital media. By leveraging the unique strengths of each model, we provide insights into their applicability and effectiveness in distinguishing between different forms of digital creation, marking a significant step forward in the field of digital forensics and content verification.}, archive = {J_ACCESS}, author = {Qing Yun}, doi = {10.1109/ACCESS.2025.3562130}, journal = {IEEE Access}, pages = {69459-69477}, shortjournal = {IEEE Access}, title = {Vision transformers (ViTs) for feature extraction and classification of AI-generated visual designs}, volume = {13}, year = {2025}, }
• (2025). FAViTNet: An effective deep learning framework for non-invasive fetal arrhythmia diagnosis. ACCESS, 13, 69438-69458. (www)

  @article{ , abstract = {Analyzing fetal electrocardiograms (fECG) to classify fetal arrhythmia is a challenging task; still, it is indispensable for evaluating fetal cardiac health status. This study intends to develop a framework for the effective discernment of fetal arrhythmia that assists obstetricians in diagnosing whether the fetuses have any abnormality in cardiac rhythm. In this work, authors aim to develop a three-stage fetal arrhythmia detection framework comprised of 1) fECG extraction from abdominal Electrocardiogram (aECG) signal based on functional link neural network adaptive filter, 2) denoising of extracted fECG exploiting a generative adversarial network where the generator and the discriminator follow robust architectures, and 3) fetal arrhythmia detection based on FAViTNet architecture. The proposed work deeply focuses on the last stage, where we have unfolded a deep convolutional architecture that detects fetal arrhythmia utilizing high-resolution spectral images acquired from the Stockwell transform. The classification stage demonstrates an architecture involving spectral image tokenization and transformer encoder blocks. The spectral image tokenization module involves ghost bottleneck blocks, which are utilized to generate feature maps from the spectral images, subsequently transformed into 1D token embeddings. Channel-wise calibration is utilized to attain more attention to the deep feature maps acquired from the ghost network. The transformer encoder block effectively attains the long-term dependencies from the 1D embedded features with gated linear unit (GLU)-based multi-head self-attention where deep features are learned to discern fetal arrhythmia effectively. The proposed algorithm shows an accuracy of 96.85%, sensitivity of 96.69%, specificity of 96.98%, and precision of 96.48%.}, archive = {J_ACCESS}, author = {Bipin Samuel and Malaya Kumar Hota}, doi = {10.1109/ACCESS.2025.3561848}, journal = {IEEE Access}, pages = {69438-69458}, shortjournal = {IEEE Access}, title = {FAViTNet: An effective deep learning framework for non-invasive fetal arrhythmia diagnosis}, volume = {13}, year = {2025}, }
• (2025). Hybrid YOLOv8 and fast R-CNN for accurate schematic detection in power distribution networks. ACCESS, 13, 69425-69437. (www)

  @article{ , abstract = {Accurate schematic detection in Power Distribution Networks (PDNs) is critical for effective fault detection, asset management, and predictive maintenance. Conventional edge detection methods often struggle with the complexity and scale of modern PDNs, while standalone deep learning approaches face challenges in balancing real-time performance and high precision. To address these limitations, this paper introduces a hybrid detection framework that synergizes YOLOv8 and Fast R-CNN, leveraging their complementary strengths. YOLOv8 enables rapid initial detections with real-time capabilities, while Fast R-CNN refines these outputs to enhance contextual accuracy. A key contribution of this work is the integration of ensemble techniques—Soft Voting, Hard Voting, and Weighted Average Voting— which further optimize detection performance by effectively aggregating predictions from both models. Using a curated dataset of 3,304 schematic images, the proposed method achieves state-of-the-art results, including a precision of 96.59%, recall of 97.27%, and mean Average Precision at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) of 99.06% with the Hard Voting ensemble. These findings underscore the robustness, scalability, and applicability of the proposed framework in automating schematic analysis. Furthermore, the method demonstrates strong potential for practical deployment within PLN Indonesia, particularly in real-time fault detection, technician training, and predictive maintenance, contributing to enhanced reliability and operational efficiency in national power distribution systems.}, archive = {J_ACCESS}, author = {Aviv Yuniar Rahman and Zuhaina Zakaria}, doi = {10.1109/ACCESS.2025.3561279}, journal = {IEEE Access}, pages = {69425-69437}, shortjournal = {IEEE Access}, title = {Hybrid YOLOv8 and fast R-CNN for accurate schematic detection in power distribution networks}, volume = {13}, year = {2025}, }
• (2025). Grid operation enhancement via network topology optimization with sparse tableau formulation. ACCESS, 13, 69413-69424. (www)

  @article{ , abstract = {This study delves into network topology optimization (NTO) as a corrective switching mechanism, designed to effectively manage congestion within power system networks. Our proposed model employs an exact node-breaker representation of the network by utilizing sparse tableau formulation (STF), and incorporates a full set of network switching actions that include both bus and line switching. NTO is formulated with nonlinear AC power flow constraints, capturing the physical behavior and engineering limits of the network comprehensively. Solving the NTO problem that incorporates AC power flow constraints presents significant challenges. To tackle this computational issue, a modified STF representation has been introduced, which reduces both the number of variables and constraints and also eliminates the non-convexity inherent in power balance equations. This modification, which alters the geometry of the STF, reduces the computational complexity of the problem. The revised NTO model is subsequently tackled through an iterative algorithm. Our numerical results demonstrate the effectiveness of the proposed model in managing network congestion by reducing congestion/overloads in the branches, under both base case and contingency scenarios, through strategic bus/line switching actions. In particular, it shows that the proposed method has a better convergence rate compared to the existing STF-based method. Furthermore, the reconfigured network topologies proposed by our method result in fewer congested branches than those proposed by other exiting techniques examined in this study.}, archive = {J_ACCESS}, author = {Sogol Babaeinejadsarookolaee and Byungkwon Park and Bernard Lesieutre and Christopher L. DeMarco}, doi = {10.1109/ACCESS.2025.3561252}, journal = {IEEE Access}, pages = {69413-69424}, shortjournal = {IEEE Access}, title = {Grid operation enhancement via network topology optimization with sparse tableau formulation}, volume = {13}, year = {2025}, }
• (2025). High gain narrow beam MIMO array antenna operating at n260 band for millimeter wave applications. ACCESS, 13, 69395-69412. (www)

  @article{ , abstract = {This paper introduces a novel four-port antenna array designed for n260 band operation, specifically addressing challenges like path loss fading and multipath effects commonly encountered in millimeter-wave frequencies in urban settings. The proposed $4 \times 4$ MIMO antenna array operates in the n260 band, offering high gain and a narrow beam, with an extended design enabling spatial and pattern diversity to mitigate multipath effects effectively. The single-element antenna combines elliptical and circular rings fed by a quarter-wave transformer. Its fundamental resonance frequency of 30 GHz is suppressed by integrating circular rings in the radiator and a slot in the ground plane, which enhances its first harmonic at 38.5 GHz while generating vertical polarization. The array antenna improves the fractional bandwidth (FB) to 8.4%, with a frequency range of 36.76-39.92 GHz, and achieves dual broadside beams at ±37° angles with a gain of 16.7 dBi. Additionally, it exhibits exceptionally low cross-polarization (−80 dB), minimizing cross-talk effects. The MIMO configuration demonstrates excellent isolation ( $\vert $ S $21\vert \gt 26$ dB and 31.2 dB) while maintaining similar FB and radiation pattern characteristics as that of the array antenna. This robust design, incorporating both spatial and pattern diversity, makes it highly suitable for 5G wireless applications.}, archive = {J_ACCESS}, author = {B. G. Parveez Shariff and Tanweer Ali and Pallavi R. Mane and Sameena Pathan and Saad Hassan Kiani and Bal S. Virdee and Hala Mostafa}, doi = {10.1109/ACCESS.2025.3559977}, journal = {IEEE Access}, pages = {69395-69412}, shortjournal = {IEEE Access}, title = {High gain narrow beam MIMO array antenna operating at n260 band for millimeter wave applications}, volume = {13}, year = {2025}, }
• (2025). Attention-driven emotion recognition in EEG: A transformer-based approach with cross-dataset fine-tuning. ACCESS, 13, 69369-69394. (www)

  @article{ , abstract = {Emotion recognition from EEG (electroencephalogram) signals is crucial in mental health diagnostics and human-computer interaction but is often hindered by high dimensionality, noise, and complex temporal dependencies in the data. This paper presents a novel approach that integrates transformer models, attention mechanisms, and transfer learning to enhance emotion recognition accuracy from EEG signals. The proposed methodology consists of two phases: Attention Enhanced Base Model Development (AE-BMD) and Cross-Dataset Fine Tuning Adaptation (CD-FTA). In the AE-BMD phase, the base model is developed and trained on the SEED-IV dataset (15 participants, 62 EEG channels), achieving an accuracy of 84%, with an average precision of 84.75%, recall of 84% and F1-score of 84%. This phase employs optimized feature extraction from key EEG frequency bands (Delta, Theta, Alpha, Beta, Gamma) using techniques such as MFCC, GFCC, power spectral density, and Hjorth parameters. A transformer encoder with integrated spectral and temporal attention mechanisms captures intricate patterns and long-range dependencies within the EEG signals. In the CD-FTA phase, the model undergoes fine-tuning on the SEED-V dataset (20 participants, 62 channels) leading to an improved accuracy of 90%, with an average precision of 90.6%, recall of 90.6%, and F1-score of 90.6%. The model’s generalization is further validated on the MPED dataset (23 participants, 62 channels, seven emotion classes), achieving 79%, with an average precision of 79.3%, recall of 79.3% and F1-score of 79.1% across diverse emotional states. This cross-dataset adaptation leverages transfer learning to enhance the model’s generalization across different emotional states and EEG datasets. Experimental results show that the proposed approach outperforms traditional methods, achieving superior accuracy and robustness in emotion recognition tasks. This work advances emotion recognition systems by addressing challenges in EEG signal processing, feature extraction, and model generalization, positioning it as a promising tool for applications in healthcare, affective computing, and emotional state analysis.}, archive = {J_ACCESS}, author = {Ghulam Ghous and Shaheryar Najam and Mohammed Alshehri and Abdulmonem Alshahrani and Yahya AlQahtani and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3561137}, journal = {IEEE Access}, pages = {69369-69394}, shortjournal = {IEEE Access}, title = {Attention-driven emotion recognition in EEG: A transformer-based approach with cross-dataset fine-tuning}, volume = {13}, year = {2025}, }
• (2025). Peer-to-peer energy resource sharing in rural communities: Enabling technologies, applications, and challenges. ACCESS, 13, 69353-69368. (www)

  @article{ , abstract = {Standalone energy supply system has been in the limelight, especially in rural areas since it can provide cheap electrical supply to the local communities. Energy supply is always a problematic condition in many developing countries that significantly affects progress and inclusive economic development. Furthermore, excess energy generated is wasted due to insufficient battery storage. To confront these recurring problems, peer-to-peer energy resource sharing has emerged as a promising solution, offering a decentralized system and approach to reduce reliance on the grid energy resource. The system enables prosumers to share excess energy with consumers, reducing dependence on grid and lowering the cost of electricity. However, peer-to-peer energy resource sharing is still a relatively new technology in developing countries, especially in rural communities, needing enabling technologies, support, and sustainable policies and financing models. To this end, this study aims to provide comprehensive understanding of the state of the art of peer-to-peer energy resource sharing in rural communities.}, archive = {J_ACCESS}, author = {Alexander A. Hernandez and Sook Yee Yip and Kam Meng Goh and Arlene R. Caballero and Michelle Lei S. Victorino}, doi = {10.1109/ACCESS.2025.3561701}, journal = {IEEE Access}, pages = {69353-69368}, shortjournal = {IEEE Access}, title = {Peer-to-peer energy resource sharing in rural communities: Enabling technologies, applications, and challenges}, volume = {13}, year = {2025}, }
• (2025). CSMA: A standalone and ImageJ-compatible tool for enhanced wound healing assay analysis. ACCESS, 13, 69341-69352. (www)

  @article{ , abstract = {Accurate quantification of wound closure in cell migration assays is crucial yet challenging. Still, existing methods often underperform due to omitting cell detection within the wound area, resulting in biased outcomes. We developed the CSMA standalone and ImageJ-compatible tool, which utilizes advanced image processing techniques, including contrast enhancement, edge detection, and morphological operations, to precisely identify and quantify cells in the wound region. CSMA offers user-friendly features and adjustable parameters to accommodate different imaging conditions, ensuring robust performance across diverse experimental setups. Validation against conventional tools confirms CSMA’s superior ability to delineate wound boundaries and provide accurate estimations of area and width at every time point. As applied to SW480-ADH colon cancer cells treated with various compounds, CSMA proves valuable in biomedical research. CSMA represents a significant advancement in wound healing assay analysis, providing researchers with a simple and reliable tool for studying cell migration dynamics with enhanced precision and reproducibility. CSMA is available as a standalone and ImageJ-compatible tool with its source code at https://github.com/AminaSagymbayeva/CSMA_WoundHealing}, archive = {J_ACCESS}, author = {Tri Thanh Pham and Amina Sagymbayeva and Timur Elebessov and Zhadyra Onzhanova and Ferdinand Molnár}, doi = {10.1109/ACCESS.2025.3561607}, journal = {IEEE Access}, pages = {69341-69352}, shortjournal = {IEEE Access}, title = {CSMA: A standalone and ImageJ-compatible tool for enhanced wound healing assay analysis}, volume = {13}, year = {2025}, }
• (2025). DPN-GAN: Inducing periodic activations in generative adversarial networks for high-fidelity audio synthesis. ACCESS, 13, 69324-69340. (www)

  @article{ , abstract = {In recent years, generative adversarial networks (GANs) have made significant progress in generating audio sequences. However, these models typically rely on bandwidth-limited mel-spectrograms, which constrain the resolution of generated audio sequences, and lead to mode collapse during conditional generation. To address this issue, we propose Deformable Periodic Network based GAN (DPN-GAN), a novel GAN architecture that incorporates a kernel-based periodic ReLU activation function to induce periodic bias in audio generation. This innovative approach enhances the model’s ability to capture and reproduce intricate audio patterns. In particular, our proposed model features a DPN module for multi-resolution generation utilizing deformable convolution operations, allowing for adaptive receptive fields that improve the quality and fidelity of the synthetic audio. Additionally, we enhance the discriminator network using deformable convolution to better distinguish between real and generated samples, further refining the audio quality. We trained two versions of the model: DPN-GAN small (38.67 M parameters) and DPN-GAN large (124M parameters). For evaluation, we use five different datasets, covering both speech synthesis and music generation tasks, to demonstrate the efficiency of the DPN-GAN. The experimental results demonstrate that DPN-GAN delivers superior performance on both out-of-distribution and noisy data, showcasing its robustness and adaptability. Trained across various datasets, DPN-GAN outperforms state-of-the-art GAN architectures on standard evaluation metrics, and exhibits increased robustness in synthesized audio.}, archive = {J_ACCESS}, author = {Zeeshan Ahmad and Shudi Bao and Meng Chen}, doi = {10.1109/ACCESS.2025.3561857}, journal = {IEEE Access}, pages = {69324-69340}, shortjournal = {IEEE Access}, title = {DPN-GAN: Inducing periodic activations in generative adversarial networks for high-fidelity audio synthesis}, volume = {13}, year = {2025}, }
• (2025). Strategic integration of battery energy storage systems for effective EV charging demand management in transactive energy markets. ACCESS, 13, 69307-69323. (www)

  @article{ , abstract = {The increasing penetration of electric vehicles (EVs) and photovoltaic (PV) systems poses significant challenges to distribution grid performance and reliability. Battery energy storage systems (BESS) offer a promising solution to mitigate these challenges; however, most existing BESS optimization strategies fail to simultaneously enhance grid performance and maximize economic benefits for BESS owners. This research proposes an optimized BESS dispatch strategy that balances both grid performance and BESS bid pricing in the transactive energy (TE) market through a multi-criteria decision-making (MCDM) method. The strategy aims to reduce energy procurement costs and create a more favorable system for EV users by minimizing their charging expenses. Using the DIgSILENT PowerFactory power system software for modeling and simulation, the active and reactive power dispatch of BESS is optimized via a differential evolution (DE) algorithm. The experimental validation of the proposed approach demonstrates significant improvements in grid performance, reduced energy costs, and enhanced flexibility for distribution network operators (DNOs) in prioritizing either grid performance or financial considerations, or both. Overall, this novel approach provides an effective framework for DNOs to enhance grid performance while strategically incorporating the economic aspects of BESS, ultimately lowering EV charging costs compared to existing methods.}, archive = {J_ACCESS}, author = {T. H. M. Sumon Rashid and Md. Rabiul Islam and Md Muminur Rahman Sonic}, doi = {10.1109/ACCESS.2025.3562124}, journal = {IEEE Access}, pages = {69307-69323}, shortjournal = {IEEE Access}, title = {Strategic integration of battery energy storage systems for effective EV charging demand management in transactive energy markets}, volume = {13}, year = {2025}, }
• (2025). Theoretical and experimental methodology for studying the reduction of evaporation in open mining plant reservoirs using a floating photovoltaic system. ACCESS, 13, 69292-69306. (www)

  @article{ , abstract = {Photovoltaic modules on floats in reservoirs, forming a floating photovoltaic (FPV) system, are proving to be relevant solutions for reducing evaporation and generating electricity. This paper presents a theoretical and experimental methodology to evaluate the reduction of evaporation rates using FPV, applied at Hydro Paragominas’ bauxite mine in Pará, Brazil, in a location with a humid equatorial climate. Two test reservoirs were constructed, with and without FPV coverage. The results obtained from models for estimating the evaporation rate based on measurements of meteorological parameters at the site and a reference climate file for the location, indicate a reduction of around 49% in water evaporation with the partial covering of the test reservoir by FPV. An extrapolation to a water surface area of 450,000 m2, corresponding to the basins present in the mining company, shows an additional 43,600.5 m3 of water conserved in the month of highest evaporation and 465,961.5 m3 annually, increasing recirculation by approximately 2.9%. This enhances water savings and reduces new water intake, contributing to environmental conservation.}, archive = {J_ACCESS}, author = {Claudomiro F. O. Barbosa and Marcos A. B. Galhardo and Arthur C. da Fonseca and Ivan R. P. Junior and Victor P. O. Alves and José A. A. V. Filho and Luana O. de Albuquerque and Weslley L. Monteiro and Orlando L. L. Silva and Amanda I. M. Damasceno and Hélcio J. P. Filho and Débora M. Chácara and Maria L. Pires and Geraldo M. P. Duarte and Wilson N. Macêdo}, doi = {10.1109/ACCESS.2025.3561608}, journal = {IEEE Access}, pages = {69292-69306}, shortjournal = {IEEE Access}, title = {Theoretical and experimental methodology for studying the reduction of evaporation in open mining plant reservoirs using a floating photovoltaic system}, volume = {13}, year = {2025}, }
• (2025). Radar-based hand gesture recognition with feature fusion using robust CNN-LSTM and attention architecture. ACCESS, 13, 69281-69291. (www)

  @article{ , abstract = {In Human-Computer Interaction (HCI), seamless hand gesture recognition is essential for intuitive and natural interactions. Gestures act as a universal language, bridging the gap between humans and machines. Radar-based recognition surpasses traditional optical methods, offering robust interaction capabilities in diverse environments. This article introduces a novel deep learning approach for hand gesture recognition, leveraging convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms. CNNs extract spatial features from radar signals, while LSTMs model the temporal dependencies crucial for dynamic gestures. Additionally, attention mechanisms enhance feature selection, ultimately improving recognition performance. We evaluate our method on the UWB-Gestures dataset, which has 12 gestures from eight people that were recorded using three X-Thru X4 UWB impulse radar sensors. Our processing pipeline integrates feature extraction, LSTM-attention blocks, and dense layers for final classification. Early fusion techniques, which combine spatial and temporal features in the initial stages, yield superior results, achieving an overall accuracy of 98.33% and outperforming intermediate fusion methods across gesture classes. To enhance the model’s robustness, we evaluated its performance under common contributors of radar-specific noise scenarios in practical applications, including Gaussian noise, signal inversion, and multipath interference. Our model demonstrates high resilience, maintaining performance despite adverse conditions. As compared to state-of-the-art approaches, our approach delivers competitive accuracy and enhanced robustness, offering a reliable solution for noise-resilient radar-based hand gesture recognition in real-world applications.}, archive = {J_ACCESS}, author = {Irshad Khan and Young-Woo Kwon}, doi = {10.1109/ACCESS.2025.3558293}, journal = {IEEE Access}, pages = {69281-69291}, shortjournal = {IEEE Access}, title = {Radar-based hand gesture recognition with feature fusion using robust CNN-LSTM and attention architecture}, volume = {13}, year = {2025}, }
• (2025). TouchWIM: Object manipulation in AR spatial design with world in miniature and hybrid user interface. ACCESS, 13, 69269-69280. (www)

  @article{ , abstract = {In this study, we propose a novel interaction method, TouchWIM, which combines World in Miniature (WIM) and Hybrid User Interface (HUI) to enhance the efficiency of object manipulation and reduce the workload in spatial design by using Augmented Reality (AR). WIM provides an additional overview perspective in AR by displaying a miniature representation of a room, and HUI enables an accurate and easy input by combining a head-mounted display (HMD) with a tablet. Our system allows the placement and manipulation of objects within a real space by touch interaction with the miniature representation of the room displayed on the tablet. To evaluate TouchWIM, we conducted user studies using a prototype spatial design system, comparing it with existing methods such as Hand-Ray + Direct Touch and WIM alone. The results demonstrated that TouchWIM is the most efficient and reduces the workload for the task of creating a specified spatial layout. This interaction method provides new insights into object manipulation and spatial design in AR.}, archive = {J_ACCESS}, author = {Shintaro Imatani and Kensuke Tobitani and Kyo Akabane}, doi = {10.1109/ACCESS.2025.3562253}, journal = {IEEE Access}, pages = {69269-69280}, shortjournal = {IEEE Access}, title = {TouchWIM: Object manipulation in AR spatial design with world in miniature and hybrid user interface}, volume = {13}, year = {2025}, }
• (2025). Enhancing smart grid reliability: Fault detection in phasor measurement unit images with deep learning. ACCESS, 13, 69257-69268. (www)

  @article{ , abstract = {Stable operations of modern power networks depend on the reliability of smart grids. The expansion of modern power systems combined with renewable energy applications and decentralized networks creates difficulties for traditional fault detection methods because of high-dimensional data combined with measurement noise and delayed fault identification processes. A deep learning solution based on Phasor Measurement Unit (PMU) image data detection for smart grid faults is presented in this work. The proposed method achieves accurate and efficient fault classification through the conversion of PMU data to images and then utilization of advanced deep learning models VGG16 combined with Convolutional Neural Networks (CNNs). The experimental findings show that VGG16 reaches 98.75% accuracy in fault classification while surpassing CNNs that delivered 94.44% accuracy. In addition to reporting high classification accuracy, the study proposes a new hybrid deep learning framework that combines transfer learning and adaptive thresholding techniques. The image-based deep learning system extracts features automatically, reducing the need for human preprocessing to effectively detect fault patterns. The methodology enables immediate fault detection, allowing operators to make critical decisions to avoid major power outages. According to the research, artificial intelligence has the potential to significantly improve smart grid resilience, reduce economic losses, and improve power system operational efficiency. The research advances AI developments in energy management by developing advanced, powerful fault detection methods that will guide future changes to power system networks.}, archive = {J_ACCESS}, author = {Stephanie Ness}, doi = {10.1109/ACCESS.2025.3561587}, journal = {IEEE Access}, pages = {69257-69268}, shortjournal = {IEEE Access}, title = {Enhancing smart grid reliability: Fault detection in phasor measurement unit images with deep learning}, volume = {13}, year = {2025}, }
• (2025). A multi-faceted deep learning approach for student engagement insights and adaptive content recommendations. ACCESS, 13, 69236-69256. (www)

  @article{ , abstract = {In an era where technology shapes education, effectively engaging students remains a cri- tical challenge. Student engagement significantly impacts academic performance, yet traditional assessment methods fail to capture its multidimensional nature. This study proposes a novel Engagement Level Classification Framework (ELCF) that employs deep learning models to classify engagement into five distinct levels (H1, H2, M, L2, L1) based on behavioral indicators, emotional cues, and academic performance. A multi-modal dataset (N entries), integrating facial expressions, student behaviors, and cognitive performance, enables a holistic engagement assessment. The system further employs real-time AI-driven analysis to personalize course recommendations, adapting dynamically to student engagement states. The proposed framework was evaluated using deep learning architectures such as EfficientNetB0, CNN, and ensemble models, achieving up to 94% accuracy in engagement classification. The adaptive recommendation system attained an F1-score of 84% and a 92% hit rate, demonstrating its effectiveness in aligning content with student engagement levels. However, certain limitations remain, including potential biases in facial emotion recognition, privacy concerns in real-time monitoring, and dataset scalability constraints. Future research will focus on bias mitigation, expanding dataset diversity, and refining adaptive interventions to enhance system reliability and inclusivity. These findings contribute to the academic discourse on AI-driven student engagement monitoring, offering quantifiable evidence for the development of fair, transparent, and ethically responsible technology-enhanced learning environments.}, archive = {J_ACCESS}, author = {Rabeeya Saleem and Muhammad Aslam}, doi = {10.1109/ACCESS.2025.3561459}, journal = {IEEE Access}, pages = {69236-69256}, shortjournal = {IEEE Access}, title = {A multi-faceted deep learning approach for student engagement insights and adaptive content recommendations}, volume = {13}, year = {2025}, }
• (2025). Understanding information propagation in online social networks: A systematic mapping study. ACCESS, 13, 69194-69235. (www)

  @article{ , abstract = {Online Social Networks (OSNs) have become a significant research focus across various fields. The increase in their use has prompted numerous studies, particularly on the complex Information Propagation (IP) process, which researchers have approached from different perspectives and lines of investigation. The work presented in this article aims to analyse the state of the art on IP in OSNs, mapping the models, methods, algorithms, tools, and techniques developed in this domain. In particular, we have conducted a Systematic Mapping Study (SMS). To our knowledge, this is the first study to address this issue. The SMS collected 424 studies and analysed 175 primary studies, and the results reveal that most studies are model proposals, the most researched topic is Influence Maximisation (IM), and Twitter (now X) is the most commonly used resource in experiments. Also, the SMS reveals that there is no formal classification of the terms to refer to propagated information. In addition, we also found several proposals to mitigate or control IP. However, there is no common methodological framework to reduce IP. To conclude the study, we propose groups of features/attributes of users during IP and a propagated information classification. This research provides a general and organised overview for the scientific community regarding studies on IP in OSNs.}, archive = {J_ACCESS}, author = {Eleana Jerez-Villota and Francisco Jurado and Jaime Moreno-Llorena}, doi = {10.1109/ACCESS.2025.3558768}, journal = {IEEE Access}, pages = {69194-69235}, shortjournal = {IEEE Access}, title = {Understanding information propagation in online social networks: A systematic mapping study}, volume = {13}, year = {2025}, }
• (2025). Research on current sharing characteristics of 2-phase 90° interleaved parallel LLC resonant converter based on variable resonant capacitor. ACCESS, 13, 69182-69193. (www)

  @article{ , abstract = {This paper proposes a novel approach to address the issues of imbalanced 2-phase resonant currents and output currents in traditional 2-phase parallel LLC resonant converters, as well as the excessive output current ripple caused by the conventional 2-phase 180° interleaved current-sharing method. Specifically, a small variable resonant capacitor was parallel across each of the two resonant capacitors in the 2-phase LLC resonant converter, and a 2-phase 90° interleaved current sharing strategy is adopted. This method integrates the advantages of the adjustable resonant capacitor and 90° interleaved current sharing. Variable resonant capacitors are utilized to adjust the slightly mismatched 2-phase resonant tanks to a symmetrical state, thereby achieving current sharing in the converter and enhancing its efficiency. On the other hand, the 2-phase 90° interleaved current sharing enables the 2-phase output current ripples to be temporally offset and mutually cancelled, effectively reducing the total output current ripple. This, in turn, decreases the required output filter capacitance for the 2-phase LLC resonant converter and increases the power density of the converter. The working principle and current-sharing characteristics are analyzed, circuit simulation is conducted, experimental prototype with 100kHz operating frequency, 2kW output power is built. The rationality of the design is demonstrated through both simulation and experiments.}, archive = {J_ACCESS}, author = {Haichao Li and Yugang Yang and Chunguang Ren and Xinwei Wei and Runquan Meng}, doi = {10.1109/ACCESS.2025.3557915}, journal = {IEEE Access}, pages = {69182-69193}, shortjournal = {IEEE Access}, title = {Research on current sharing characteristics of 2-phase 90° interleaved parallel LLC resonant converter based on variable resonant capacitor}, volume = {13}, year = {2025}, }
• (2025). LLM agentic workflow for automated vulnerability detection and remediation in infrastructure-as-code. ACCESS, 13, 69175-69181. (www)

  @article{ , abstract = {This paper presents a multi-agent, AI-driven strategy employing Large Language Models (LLMs), retrieval-augmented generation, and a continuously updated knowledge base for the detection and remediation of security vulnerabilities win cloud frameworks. By examining Infrastructure as Code (IaC) templates alongside pertinent best-practice snippets, the system discerns context-specific misconfigurations commonly overlooked by static tools, achieving a detection rate of 85% with some occurrences of false positives. Automated remediation guidance, anchored in current security standards, provides actionable solutions that seamlessly integrate into standard continuous integration/continuous development (CI/CD) workflows. Experimental results indicate the solution’s efficacy and scalability, heralding a proactive, context-aware approach to IaC security.}, archive = {J_ACCESS}, author = {Dheer Toprani and Vijay K. Madisetti}, doi = {10.1109/ACCESS.2025.3560911}, journal = {IEEE Access}, pages = {69175-69181}, shortjournal = {IEEE Access}, title = {LLM agentic workflow for automated vulnerability detection and remediation in infrastructure-as-code}, volume = {13}, year = {2025}, }
• (2025). A novel community detection algorithm based on node propagation and attraction. ACCESS, 13, 69161-69174. (www)

  @article{ , abstract = {High-quality community detection in complex networks typically relies on the similarity between nodes to select communities. However, when a node exhibits the same similarity with different communities, it becomes ambiguous to determining the optimal community assignment for that node, potentially leading to incorrect predictions and affecting the accuracy of community assignments for other nodes. To address this problem, this paper proposes a novel community detection algorithm based on node propagation and attraction. The algorithm first introduces a node propagation strategy, which identifies high-propagation nodes based on their connectivity and core attributes. These high-propagation nodes form the initial backbone and framework of communities, minimizing the interference of low-propagation nodes on community structure. Subsequently, an attraction definition is introduced, which starts from high-propagation nodes to select partitioning methods based on their relationships with neighboring nodes, using the attraction among these nodes facilitate community organization and propagation, aiming to enhance community quality. Finally, in the community optimization phase, the algorithm effectively improves the accuracy of partitions that result from similar attractions by considering the association degree between nodes and communities. Experimental results demonstrate that, on both synthetic and real networks, the proposed algorithm outperforms various existing community detection algorithms.}, archive = {J_ACCESS}, author = {Lidong Fu and Xinxing Xie and Zengfa Dou and Yun Bai and Xi Zhao}, doi = {10.1109/ACCESS.2025.3559700}, journal = {IEEE Access}, pages = {69161-69174}, shortjournal = {IEEE Access}, title = {A novel community detection algorithm based on node propagation and attraction}, volume = {13}, year = {2025}, }
• (2025). Expanding AI’s role in healthcare applications: A systematic review of emotional and cognitive analysis techniques. ACCESS, 13, 69129-69160. (www)

  @article{ , abstract = {This systematic literature review (SLR) analyzes the various applications of artificial intelligence (AI) in healthcare, with a particular emphasis on the integration of emotive and cognitive analytical frameworks. The primary aim of this investigation is to thoroughly evaluate the influence of AI technology on patient care by analyzing emotional processes and enabling patient-centered solutions. In this research, we investigate the cognitive and emotional approaches to sentiment analysis and other modeling and forecasting methods using AI. Primary sources include patients’ reviews, online health exchanges and doctors’ narratives. Key aspects of the present state of affairs are advances in the development of machine learning algorithms for emotion recognition, intracellular fusion of cognitive and affective modes of analysis, and the application of artificial intelligence for the enhancement of clinical support systems. Moreover, these technologies have significantly improved individualized clinical approaches, expedited the early identification of mental health problems, and strengthened the rationale for therapeutic treatments. Despite recent advancements, the discipline still faces numerous persistent obstacles. Pressing issues include the ethical implications of using artificial intelligence, the need to protect patient privacy, and the complexity of detecting biases in algorithms. Nevertheless, the impact of AI on healthcare practices is indisputable, indicating a future marked by a more intelligent, efficient, empathetic, and patient-centered healthcare system. This study examines the consequences of artificial intelligence in healthcare by analyzing its importance in emotional and cognitive computing, tracking ongoing developments, and promoting the use of AI in healthcare while considering individual requirements.}, archive = {J_ACCESS}, author = {Prashant Kumar Nag and Amit Bhagat and R. Vishnu Priya}, doi = {10.1109/ACCESS.2025.3562131}, journal = {IEEE Access}, pages = {69129-69160}, shortjournal = {IEEE Access}, title = {Expanding AI’s role in healthcare applications: A systematic review of emotional and cognitive analysis techniques}, volume = {13}, year = {2025}, }
• (2025). Proof-of-diversity (PoD): A framework for equitable blockchain governance. ACCESS, 13, 69116-69128. (www)

  @article{ , abstract = {This paper introduces the Proof-of-Diversity (PoD) protocol, a new consensus mechanism that enhances decentralization, security, and energy efficiency using demographic, geographic, and computational diversity in validator selection. By using a multi-dimensional entropy-based approach, PoD shows high resistance to Sybil attacks, fosters inclusion, and ensures fair participation. Comparative analysis with Tendermint Proof-of-Stake (PoS) and Algorand Proof-of-Stake (Algorand) shows that PoD is more effective in various key metrics, including transaction finality, validator engagement, diversity entropy, energy use, and adaptability. In particular, PoD achieves a shortest average transaction finality time of 72.84 ms over a given period, a notable improvement compared to both Algorand at 215.37 ms and Tendermint PoS at 278.42 ms. In addition, PoD achieves a validator engagement of 85.42%, strengthening its ability to maintain decentralization. PoD also achieves a diversity score of 0.79, better than Tendermint PoS and Algorand, indicating a more fair and inclusive validator selection process. In terms of energy use, PoD achieves a mere 0.0132 kWh per transaction per second (TPS), a considerable improvement compared to its counterparts. In addition, PoD shows better adaptability to changes in step parameters and changes in benefit-cost ratios, further improving validator selection and network optimization. Overall, these results make PoD a scalable and sustainable consensus system that balances diversity, security, and performance in blockchain networks.}, archive = {J_ACCESS}, author = {M. Erdem Isenkul}, doi = {10.1109/ACCESS.2025.3562391}, journal = {IEEE Access}, pages = {69116-69128}, shortjournal = {IEEE Access}, title = {Proof-of-diversity (PoD): A framework for equitable blockchain governance}, volume = {13}, year = {2025}, }
• (2025). DeepEarthMY: A remote sensing dataset for tropical land-cover segmentation. ACCESS, 13, 69103-69115. (www)

  @article{ , abstract = {Land-cover mapping is essential for applications such as urban planning, natural resource management, and environmental monitoring. However, tropical equatorial regions pose unique challenges to land-cover classification due to dense vegetation, persistent cloud cover, and spectral similarity across land-cover types. Moreover, existing land-cover classification models often perform poorly in these regions as they are primarily trained on datasets from non-equatorial climates. To address this gap, we introduce DeepEarthMY, a high-resolution land-cover mapping dataset specifically designed for equatorial regions. The dataset contains 4,007 meticulously annotated images from 52 diverse regions across Malaysia, representing key land-cover types such as forests, buildings, roads, water bodies, agricultural lands, and barren land. We evaluated the dataset by performing land-cover segmentation on selected state-of-the-art semantic segmentation models, including DC-Swin, HRNet, and UNetFormer. The experimental results reveal that the DC-Swin model achieves the best performance, with a mIoU score of 80.23%. To further evaluate the significance of region-specific datasets for land-cover classification, we performed cross-dataset testing on two datasets: DeepEarthMY from the equatorial region and LoveDA from the temperate climate, using five-fold cross-validation. HRNet models trained on DeepEarthMY achieved a mIoU of 77.2% on DeepEarthMY but only 33.2% on the LoveDA test set. Conversely, models trained on LoveDA achieved 51.4% and 43% on the LoveDA and DeepEarthMY test sets, respectively. This significant performance gap highlights the need for region-specific datasets to advance land-cover research, particularly in equatorial climates where land-cover datasets are scarce. In conclusion, the DeepEarthMY dataset can be an invaluable resource for remote sensing in the equatorial region. The dataset is available at https://zenodo.org/records/14242124.}, archive = {J_ACCESS}, author = {Shaaban Mohammed Najib and Chih-Yang Pee and Choo-Yee Ting and Lai-Kuan Wong}, doi = {10.1109/ACCESS.2025.3556746}, journal = {IEEE Access}, pages = {69103-69115}, shortjournal = {IEEE Access}, title = {DeepEarthMY: A remote sensing dataset for tropical land-cover segmentation}, volume = {13}, year = {2025}, }
• (2025). Ultrasound-based visual servoing for out-of-plane longitudinal needle tracking in robot-aided percutaneous nephrolithotomy. ACCESS, 13, 69090-69102. (www)

  @article{ , abstract = {Percutaneous nephrolithotomy (PCNL) is a minimally invasive procedure to remove large renal calculi through a small incision in the patient’s back. Ultrasound (US) imaging is commonly used to guide the needle to the kidney during this procedure. However, it requires an advanced level of dexterity to coordinate the US probe and the needle to keep the needle visible in the images at all times. Failure to maintain needle-probe alignment can result in inadvertent injury, bleeding, and other complications. The use of robotic assistance can alleviate the surgeon’s cognitive workload by enabling autonomous positioning of the US probe and accurate needle tracking. This paper presents a new US-guided visual servoing (VS) algorithm for needle tracking using longitudinal US images of a needle subjected to out-of-plane motion. The ultrasound probe can move in 4 degrees-of-freedom (DOF), that is, two translations and one rotation in the imaging plane, and one rotation out of the imaging plane. Unlike previously reported VS algorithms, 4-DOF tracking is achieved using only 2D-US images and without any additional position sensor or prior knowledge of the needle trajectory. The algorithm is validated extensively in three different experimental scenarios using a water tank, a tissue phantom, and ex-vivo porcine tissue. Results obtained from several trials confirm the algorithm’s ability to track the needle and maintain needle-probe alignment with an average error of 1.5 mm, despite an out-of-plane average needle deflection of 7 mm along a 60 mm insertion depth.}, archive = {J_ACCESS}, author = {Hoorieh Mazdarani and Ben Sainsbury and James Watterson and Rebecca Hibbert and Carlos Rossa}, doi = {10.1109/ACCESS.2025.3562078}, journal = {IEEE Access}, pages = {69090-69102}, shortjournal = {IEEE Access}, title = {Ultrasound-based visual servoing for out-of-plane longitudinal needle tracking in robot-aided percutaneous nephrolithotomy}, volume = {13}, year = {2025}, }
• (2025). Lightweight probe cover for wearable thermal device designed through topology optimization. ACCESS, 13, 69081-69089. (www)

  @article{ , abstract = {Core body temperature (CBT) is a useful indicator of physiological changes. Wearable CBT sensors have been developed, including those based on the heat flux method, which is noninvasive but sensitive to environmental changes. An improved method that uses a high-thermal-conductivity material to cover the probe has been recently developed. However, the probe cover increases the sensor weight. Here, we develop a lightweight probe cover that maintains high measurement accuracy. The cover shape was designed using topology optimization. The total probe weight is 15 g, which is significantly lower than that reported in our previous study and is comparable to the weight of a commercial wearable sensor (12 g). Numerical simulation demonstrates that a sensor with the proposed cover can achieve a high accuracy of $0.009~^{\circ }$ C, exceeding the tolerance error of $0.1~^{\circ }$ C of an electronic thermometer. A human experiment shows that the wearable CBT sensor can measure temperature with an accuracy ( $0.027~\pm ~0.016~^{\circ }$ C) higher than the tolerance error of an electronic thermometer, although not as high as the designed accuracy. The results demonstrate that the sensor has potential for practical applications.}, archive = {J_ACCESS}, author = {Yuki Hashimoto and Soto Tada and Yoshifumi Nishida}, doi = {10.1109/ACCESS.2025.3561285}, journal = {IEEE Access}, pages = {69081-69089}, shortjournal = {IEEE Access}, title = {Lightweight probe cover for wearable thermal device designed through topology optimization}, volume = {13}, year = {2025}, }
• (2025). Perceptive locomotion through whole-body MPC and optimal region selection. ACCESS, 13, 69062-69080. (www)

  @article{ , abstract = {Real-time synthesis of legged locomotion maneuvers in challenging industrial settings is still an open problem, requiring simultaneous determination of footsteps locations several steps ahead while generating whole-body motions close to the robot’s limits. State estimation and perception errors impose the practical constraint of fast re-planning motions in a model predictive control (MPC) framework. We first observe that the computational limitation of perceptive locomotion pipelines lies in the combinatorics of contact surface selection. Re-planning contact locations on selected surfaces can be accomplished at MPC frequencies (50-100 Hz). Then, whole-body motion generation typically follows a reference trajectory for the robot base to facilitate convergence. We propose removing this constraint to robustly address unforeseen events such as contact slipping, by leveraging a state-of-the-art whole-body MPC (Croccodyl). Our contributions are integrated into a complete framework for perceptive locomotion, validated under diverse terrain conditions, and demonstrated in challenging trials that push the robot’s actuation limits, as well as in the ICRA 2023 quadruped challenge simulation.}, archive = {J_ACCESS}, author = {Thomas Corbères and Carlos Mastalli and Wolfgang Merkt and Jaehyun Shim and Ioannis Havoutis and Maurice Fallon and Nicolas Mansard and Thomas Flayols and Sethu Vijayakumar and Steve Tonneau}, doi = {10.1109/ACCESS.2025.3558093}, journal = {IEEE Access}, pages = {69062-69080}, shortjournal = {IEEE Access}, title = {Perceptive locomotion through whole-body MPC and optimal region selection}, volume = {13}, year = {2025}, }
• (2025). Derived power streams for fault detection and condition-based maintenance. ACCESS, 13, 69051-69061. (www)

  @article{ , abstract = {Nonintrusive electrical monitoring enables fault detection and diagnostics for power systems from an aggregate monitoring point. Faulty electromechanical equipment often produces unrecognizable electrical signatures that confound data-driven techniques. Fault signatures are typically unknown during training. For some loads, analytical models and simulation can predict fault signatures, facilitating their inclusion in the training set. However, this approach scales unfavorably with the number of parameters associated with faults. An alternate outlook recognizes that faults often change the power system conditions and the quality of power delivered. Assumptions about the character and quality of the local grid should be called into question during faulty load behavior. The objectives of this work are to correlate these changing grid behaviors with load and fault signatures as a step towards automatic fault detection. Our methodology involves creating supplemental “derived” data streams distilled from nonintrusive electrical measurements that can correlate changes in grid conditions and quality with load operation. Using real fault data from shipboard microgrids, our key findings illustrate that combining these derived streams with nonintrusive power data enables both nonintrusive load identification and fault diagnosis. Finally, this work presents a vision for implementing an integration of these streams with physics-based fault models in an automatic fault detection system.}, archive = {J_ACCESS}, author = {Aaron W. Langham and Thomas C. Krause and Steven B. Leeb}, doi = {10.1109/ACCESS.2025.3561723}, journal = {IEEE Access}, pages = {69051-69061}, shortjournal = {IEEE Access}, title = {Derived power streams for fault detection and condition-based maintenance}, volume = {13}, year = {2025}, }
• (2025). SVM-based control of solar PV-fed b3 inverter for variable speed induction motor drives. ACCESS, 13, 69039-69050. (www)

  @article{ , abstract = {Renewable energy sources (RES), particularly photovoltaic (PV) systems, have become increasingly essential for providing sustainable power solutions, especially in compact inverters designed for low-power applications such as household appliances. This paper presents two novel space-vector pulse-width modulation (SV-PWM) techniques for controlling a delta inverter coupled with an induction motor (IM) powered by PV panels. The study begins by formulating the reference voltage vector and identifying three intrinsic voltage vectors from the delta inverter. We then develop SV-PWM techniques by approximating the reference vector as a linear combination of these intrinsic vectors, followed by a detailed approach for controlling the power switches. The performance of the proposed techniques was thoroughly evaluated through comparative studies based on both simulation results and experimental measurements. The results demonstrate a significant improvement in performance, including a notable reduction in harmonic distortion, smoother electromagnetic torque ripple, and enhanced overall efficiency, compared to traditional methods, particularly during steady-state operation of the delta inverter and induction motor system.}, archive = {J_ACCESS}, author = {Imen Nouira El Badsi and Bassem El Badsi}, doi = {10.1109/ACCESS.2025.3560693}, journal = {IEEE Access}, pages = {69039-69050}, shortjournal = {IEEE Access}, title = {SVM-based control of solar PV-fed b3 inverter for variable speed induction motor drives}, volume = {13}, year = {2025}, }
• (2025). Arabic cyberbullying detection: A comprehensive review of datasets and methodologies. ACCESS, 13, 69021-69038. (www)

  @article{ , abstract = {The freedom of speech in online spaces has substantially promoted engagement on social media platforms, where cyberbullying has emerged as a significant consequence. While extensive research has been conducted on cyberbullying detection in English, efforts in the Arabic language remain limited. To address this gap, the current study provides a comprehensive, state-of-the-art review of datasets and methodologies specifically focused on Arabic cyberbullying detection. It systematically reviews different relevant studies from six academic databases, examining their methodologies, dataset characteristics, and performance in terms of classification accuracy and limitations. The paper critically evaluates existing Arabic cyberbullying datasets according to criteria such as dataset size, dialectal diversity, annotation processes, and accessibility. Additionally, this review identifies critical limitations, including dataset scarcity, dialectal imbalance, annotation subjectivity, and methodological constraints. By synthesizing current knowledge, identifying research gaps, and suggesting future directions, this review supports the development of more robust, effective, and linguistically inclusive analytical methods. Ultimately, this work contributes significantly to natural language processing research and advances the creation of safer online environments for Arabic-speaking users.}, archive = {J_ACCESS}, author = {Huda Aljalaoud and Kia Dashtipour and Ahmed Y. Al-Dubai}, doi = {10.1109/ACCESS.2025.3561132}, journal = {IEEE Access}, pages = {69021-69038}, shortjournal = {IEEE Access}, title = {Arabic cyberbullying detection: A comprehensive review of datasets and methodologies}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based image watermarking using catalan transform and non-negative matrix factorization. ACCESS, 13, 68995-69020. (www)

  @article{ , abstract = {In the digital age, embedding imperceptible data into images for authenticating ownership and content integrity through watermarking has gained immense importance. Existing watermarking methods struggle to balance imperceptibility, robustness, and security, resulting in suboptimal peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), normalized correlation (NC) values, along with higher time complexity. To overcome these constraints, this study proposes a new method for image watermarking that uses deep learning and operates within the YIQ color space. The main contribution of the proposed method is its new combination of the Catalan transform (CT) and non-negative matrix factorization (NMF) with an artificial neural network (ANN) for enhanced watermark embedding and extraction. CT enhances pixel intensity while NMF extracts the most significant features from each (m x m) block (with m= 2 in simulations), improving the method’s robustness and imperceptibility. Before embedding, the watermark undergoes a new scrambling technique combining adaptive diffusion and non-linear transform, significantly enhancing security. A new embedding equation integrates the watermark into the non-negative elements of the host image blocks. Statistical features from watermarked blocks are used to create a dataset for extraction, and an ANN model trained on this dataset enables accurate watermark recovery. The proposed method achieves a PSNR of 66.43 to 77.02 dB (31.91% improvement), SSIM of 0.99 to 1.00 (1.02% improvement), and NC of 0.99 to 1.00 (1.88% improvement). Its computational complexity is $O(n^{2})$ , a 33.33% reduction compared to traditional $O(n^{3})$ methods. Security metrics include RE = 0.3094, UACI = 34.21%, and NPCR = 99.15%, demonstrating superior robustness and efficiency over existing approaches.}, archive = {J_ACCESS}, author = {Md. Fahim Hossain Saikat and Md. Al-Mamun Provath and Kaushik Deb and Pranab Kumar Dhar and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3558121}, journal = {IEEE Access}, pages = {68995-69020}, shortjournal = {IEEE Access}, title = {Deep learning-based image watermarking using catalan transform and non-negative matrix factorization}, volume = {13}, year = {2025}, }
• (2025). A novel high boost five-level inverter with wide range of input voltage variations for photovoltaic applications. ACCESS, 13, 68984-68994. (www)

  @article{ , abstract = {This article introduces a new single-stage boost five-level inverter with minimum components, consisting of six switches, one diode and two capacitors. The proposed topology has benefits, such as boosted output voltage, compactness, and lower impulse charge currents due to soft charging. The benefits of the proposed topology are elucidated through a comparative analysis with recently developed single-stage boost five-level inverter topologies. The required switching sequence pattern is generated by single-carrier pulse-width modulation. Through simulation and a laboratory prototype model, the proposed 400 W topology is validated, demonstrating its suitability across a range of modulation indices and dynamically changing loads.}, archive = {J_ACCESS}, author = {V. Kubendran and M. Jagabar Sathik and Mamdouh L. Alghaythi and Meshari S. Alshammari and K. Vijayakumar}, doi = {10.1109/ACCESS.2025.3562121}, journal = {IEEE Access}, pages = {68984-68994}, shortjournal = {IEEE Access}, title = {A novel high boost five-level inverter with wide range of input voltage variations for photovoltaic applications}, volume = {13}, year = {2025}, }
• (2025). Generalized tolerance optimization for robust system design by adaptive learning of gaussian processes. ACCESS, 13, 68959-68983. (www)

  @article{ , abstract = {A novel formulation of robust optimization is presented which is more general than existing ones. The solution of the associated optimization problem provides the largest tolerance set within the feasible design space which is defined by black-box performance measures. A learning-based optimization algorithm is introduced in which the feasibility of the tolerance set is evaluated based on Gaussian process models of the performance measures. The Gaussian process models are adaptively learned during the optimization, where new samples of promising tolerance sets are acquired by solving a constrained optimization problem by utilizing methods from semi-infinite programming. The usefulness of the proposed method is demonstrated by numerical studies. First, the achievable accuracy is investigated for a non-convex test function the analytical solution of which is known. Second, the methods are applied to challenging engineering problems, namely the automatic tuning of nonlinear model-predictive controllers, where the control plant is subject to parametric uncertainties. Here it is shown that the proposed methods can greatly outperform a tuning by human experts.}, archive = {J_ACCESS}, author = {Julia Stecher and Lothar Kiltz and Knut Graichen}, doi = {10.1109/ACCESS.2025.3561222}, journal = {IEEE Access}, pages = {68959-68983}, shortjournal = {IEEE Access}, title = {Generalized tolerance optimization for robust system design by adaptive learning of gaussian processes}, volume = {13}, year = {2025}, }
• (2025). A novel PAPR reduction scheme based on deep autoencoder network for FBMC systems. ACCESS, 13, 68948-68958. (www)

  @article{ , abstract = {Filter bank multicarrier (FBMC) is a crucial complementary waveform to orthogonal frequency-division multiplexing (OFDM) in future communication systems. However, FBMC systems also suffer from the drawback of an excessively high peak-to-average power ratio (PAPR). Moreover, PAPR reduction methods designed for OFDM systems cannot be directly applied to FBMC systems due to the overlapping nature of FBMC symbols. In this paper, we propose a novel deep learning (DL)-based PAPR reduction scheme for FBMC systems. This innovative approach employs a deep denoising autoencoder (DAE) network in the time domain to suppress the PAPR of FBMC signals and reconstruct the ideal signal. Simulation results demonstrate that the proposed DAE-PAPR scheme achieves a PAPR reduction gain of approximately 2.3 dB to 4.5 dB compared to conventional state-of-the-art methods, while maintaining excellent bit error rate (BER) performance and out-of-band energy leakage characteristics.}, archive = {J_ACCESS}, author = {Xing Cheng and Deli Chen and Shuaishuai Li and Shanbao He and Dejin Kong}, doi = {10.1109/ACCESS.2025.3562066}, journal = {IEEE Access}, pages = {68948-68958}, shortjournal = {IEEE Access}, title = {A novel PAPR reduction scheme based on deep autoencoder network for FBMC systems}, volume = {13}, year = {2025}, }
• (2025). Embedded hardware-efficient FPGA architecture for SVM learning and inference. ACCESS, 13, 68930-68947. (www)

  @article{ , abstract = {Edge computing allows to do AI processing on devices with limited resources, but the challenge remains high computational costs followed by the energy limitations of such devices making on-device machine learning inefficient, especially for Support Vector Machine (SVM) classifiers. Although SVM classifiers are generally very accurate, they require solving a quadratic optimization problem, making their implementation in real-time embedded devices challenging. While Sequential Minimal Optimization (SMO) has enhanced the efficiency of SVM training, traditional implementations still suffer from high computational cost. In this paper, we propose Parallel SMO, a new algorithm that selects multiple violating pairs in each iteration, allowing batch-wise updates that enhance convergence speed and optimize parallel computation. By buffering kernel values, it minimizes redundant computations, leading to improved memory efficiency and faster SVM training on FPGA architectures. In addition, we present a embedded hardware-efficient FPGA architecture for the integrated SVM learning based on Parallel SMO with SVM inference. It consists of SVM controller that schedules the operations of each clock cycle such that computations and memory access happen concurrently. The dynamic pipeline scheduling employ parameterized modules to schedule linear or nonlinear kernels and produce dimension-based reconfigurable blocks. A configuration signal turns on corresponding sub-blocks and clock-gating unused ones, thus enhancing resource utilization efficiency, energy efficiency, and overall performance. In several benchmarking data sets, the scheme reduces clock cycles per iteration consistently and improves throughput (up to 2427 iterations per second). It achieves up to 98% accuracy in classification with low power consumption, as reflected by training power of $47 mW$ and high energy efficiency (up to $51.64e+3$ iterations per joule). With the assistance of an adaptive kernel datapath, parallel error update execution, and best-pair selection, the scheme facilitates faster convergence, higher throughput, and on-chip inference with resource efficiency maintained.}, archive = {J_ACCESS}, author = {B. B. Shabarinath and Muralidhar Pullakandam}, doi = {10.1109/ACCESS.2025.3562453}, journal = {IEEE Access}, pages = {68930-68947}, shortjournal = {IEEE Access}, title = {Embedded hardware-efficient FPGA architecture for SVM learning and inference}, volume = {13}, year = {2025}, }
• (2025). Distributed fixed-time nonsingular terminal sliding mode time-varying formation control of ASVs based on hierarchical control mechanism. ACCESS, 13, 68920-68929. (www)

  @article{ , abstract = {This paper presents a distributed fixed-time nonsingular terminal sliding mode (FxTNTSM) time-varying formation control scheme based on a hierarchical control mechanism (HCM) for autonomous surface vessels (ASVs) with model parameter uncertainties and unknown environment disturbances. The HCM includes a distributed estimation layer and a local time-varying formation control layer. Specifically, in the distributed estimation layer, a distributed fixed-time estimator (DFxTEO) is designed to estimate the leader’s state information. In the local time-varying formation control layer, a fixed-time integral sliding mode disturbance observer (FxTISMDO) is designed to estimate the compound disturbances lumped by uncertain dynamics and unknown ocean environment disturbances within a fixed settling time. A distributed time-varying formation control law for multiple ASVs is developed by incorporating the DFxTEO and the FxTISMDO into the FxTNTSM technique. Theoretical analyses and simulation studies show that the developed formation control law enables the multiple ASVs to achieve the desired formation control, and the formation errors can converge to the origin within a fixed settling time.}, archive = {J_ACCESS}, author = {Xiaolong Wang and Zhen Wang and Zunmin Li and Yanliang Bi}, doi = {10.1109/ACCESS.2025.3561925}, journal = {IEEE Access}, pages = {68920-68929}, shortjournal = {IEEE Access}, title = {Distributed fixed-time nonsingular terminal sliding mode time-varying formation control of ASVs based on hierarchical control mechanism}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based framework for predicting mild cognitive impairment progression in neurology using longitudinal MRI. ACCESS, 13, 68903-68919. (www)

  @article{ , abstract = {Alzheimer’s disease (AD), a leading neurodegenerative disorder, progresses from an intermediary stage known as Mild Cognitive Impairment (MCI), characterized by measurable cognitive decline with retained functional independence. Accurate prediction of MCI progression to AD is critical for timely interventions. Existing deep learning-based methods for structural MRI (sMRI) analysis predominantly utilize either Convolutional Neural Networks (CNNs), which effectively capture local features but neglect global context, or Transformer architectures that model global dependencies yet require extensive data and computational resources. Additionally, many methods inadequately leverage longitudinal imaging data, limiting their sensitivity to subtle temporal changes in brain morphology. To overcome these limitations, we introduce EffiSwin-MCI, a novel hybrid deep learning framework integrating EfficientNet and Swin Transformer architectures, specifically designed for longitudinal sMRI analysis. The primary novelty of EffiSwin-MCI lies in its sliding-window attention mechanism, inspired by the Swin Transformer, which effectively integrates localized spatial dependencies within 2D sMRI slices, combined with temporal attention blocks that fuse spatial-temporal features across longitudinal scans at two distinct time points (T1 and T2). EfficientNet-B2 serves as a computationally efficient backbone, extracting hierarchical spatial features crucial for detailed morphological characterization. This alternating spatial and temporal attention strategy uniquely captures progressive local and global structural changes indicative of cognitive decline. Comprehensive experiments conducted on the Alzheimer’s Disease ADNI dataset demonstrate the proposed model’s superior performance compared to state-of-the-art CNN and Transformer-based approaches, achieving an accuracy of 81.69%, recall of 80.27%, precision of 84.35%, and F1-score of 82.27%. EffiSwin-MCI’s interpretability is further validated through Grad-CAM visualizations, highlighting critical neurodegenerative biomarkers such as the hippocampus and amygdala, reinforcing its clinical relevance for early prediction and intervention strategies in MCI management.}, archive = {J_ACCESS}, author = {Guojiang Zheng and Yang Lu and Hui Chen}, doi = {10.1109/ACCESS.2025.3562432}, journal = {IEEE Access}, pages = {68903-68919}, shortjournal = {IEEE Access}, title = {Deep learning-based framework for predicting mild cognitive impairment progression in neurology using longitudinal MRI}, volume = {13}, year = {2025}, }
• (2025). Reliability analysis of EEG microstate features in patients with major depressive disorder at different electrode configurations. ACCESS, 13, 68891-68902. (www)

  @article{ , abstract = {Electroencephalogram (EEG) microstate analysis is a promising spatiotemporal approach widely used to investigate brain activity in psychiatric disorders. However, the reliability of the results can vary depending on the choice of data configuration. This study aimed to examine the influence of electrode numbers on the reliability of microstates and to recommend a minimum electrode number for microstate analysis in psychiatric disorders. Microstate topologies and parameters were compared across different electrode configurations (105-, 59-, 27-, 19-, and 9-channel) in health controls (HC) and patients with major depressive disorder (MDD), respectively. Similarity and Cronbach $\alpha $ were employed to quantify the reliability of microstate topologies and parameters for both HC and MDD groups. Statistical comparisons between the HC and MDD groups were conducted to assess the stability of microstate differences across different electrode configurations, with Cronbach $\alpha $ calculated for reliability. The results revealed minimally significant differences in microstate features between the 105- and 59-channel configurations in both HC and MDD groups. Cronbach $\alpha $ also indicated that the agreement between the 105- and 59-channel configurations was superior to that of the other electrode configurations. Furthermore, the significance of the differences between the HC and MDD groups remained stable with the 105- and 59-channel configurations. Cronbach $\alpha $ demonstrated the highest agreement between the 105-channel and 59-channel configurations. This study concludes that resting EEG microstate analysis in MDD patients is highly reliable with a minimum of 59 electrodes.}, archive = {J_ACCESS}, author = {Lei Wang and Yang Shen and Chen Wang and Shuo Yang}, doi = {10.1109/ACCESS.2025.3560797}, journal = {IEEE Access}, pages = {68891-68902}, shortjournal = {IEEE Access}, title = {Reliability analysis of EEG microstate features in patients with major depressive disorder at different electrode configurations}, volume = {13}, year = {2025}, }
• (2025). Toward a better understanding of IoT domain names: A study of IoT backend. ACCESS, 13, 68871-68890. (www)

  @article{ , abstract = {In this paper, we study Internet of Things (IoT) domain names, the domain names of backend servers on the Internet that are accessed by IoT devices. We investigate how they compare to non-IoT domain names based on their statistical and DNS properties and the feasibility of classifying these two classes of domain names using machine learning (ML). We construct a dataset of IoT domain names by surveying past studies that used testbeds with real IoT devices. For the non-IoT dataset, we use two lists of top-visited websites. We study the statistical and DNS properties of the domain names. We also leverage machine learning and train six models to perform the classification between the two classes of domain names. The word embedding technique we use to get the real-valued vector representation of the domain names is Word2vec. Our statistical analysis highlights significant differences in domain name length, label frequency, and compliance with typical domain name construction guidelines, while our DNS analysis reveals notable variations in resource record availability and configuration between IoT and non-IoT DNS zones. As for classifying IoT and non-IoT domain names using machine learning, Random Forest achieves the highest performance among the models we train, yielding the highest accuracy, precision, recall, and $F_{1}$ score. Our work offers novel insights to IoT, potentially informing protocol design and aiding in network security and performance monitoring.}, archive = {J_ACCESS}, author = {Ibrahim Ayoub and Martine S. Lenders and Benoît Ampeau and Sandoche Balakrichenan and Kinda Khawam and Thomas C. Schmidt and Matthias Wählisch}, doi = {10.1109/ACCESS.2025.3561521}, journal = {IEEE Access}, pages = {68871-68890}, shortjournal = {IEEE Access}, title = {Toward a better understanding of IoT domain names: A study of IoT backend}, volume = {13}, year = {2025}, }
• (2025). Superimposed AC signal frequency droop assisted scheme for decentralized power management of grid-forming controlled DC MG including multiple PMSG-WTs, DSs, and BESS units. ACCESS, 13, 68857-68870. (www)

  @article{ , abstract = {Effective power management in DC microgrids (MGs) with both dispatchable and non-dispatchable energy sources is essential for enhancing reliability and optimizing performance. This paper presents an integrated control strategy for decentralized power management (PM) in a DC MG featuring PMSG-based wind turbines (WTs), droop-controlled dispatchable sources (DSs), and battery energy storage system (BESS). Inspired by frequency signaling methods used in AC microgrids, our approach introduces a corrective AC signal superimposed on the reference DC output voltages of the controllers. The frequency of this signal serves as a global coordination indicator, enabling the establishment of unified multi-part P/ $\omega $ droop curves that facilitate effective PM across various sources while ensuring appropriate SOC management for BESS. Key findings include: (i) The drooping frequency of the AC signal relative to each source’s DC output power defines unified droop curves for decentralized PM and SOC control; (ii) DC-DC converters operate as controlled voltage sources in grid-forming mode, allowing autonomous transitions between power and voltage regulation states; and (iii) All generation and storage units contribute to generating the AC signal frequency, ensuring robust performance even with resource loss. This innovative control strategy enhances power balance within the MG while enabling regulated charging and discharging of BESS, demonstrating significant potential for improving the operational efficiency of DC microgrids.}, archive = {J_ACCESS}, author = {Sajad Ghadiriyan and Mohsen Rahimi}, doi = {10.1109/ACCESS.2025.3560805}, journal = {IEEE Access}, pages = {68857-68870}, shortjournal = {IEEE Access}, title = {Superimposed AC signal frequency droop assisted scheme for decentralized power management of grid-forming controlled DC MG including multiple PMSG-WTs, DSs, and BESS units}, volume = {13}, year = {2025}, }
• (2025). JCN: Joint constraint-based human pose refinement networks. ACCESS, 13, 68841-68856. (www)

  @article{ , abstract = {2D human pose estimation has essential applications in traffic prediction and human-computer interaction. We propose a pose refinement network for refining human pose features to improve human pose detection accuracy. We propose our approach to two critical problems in pose estimation, occlusion and background interference, for which the model often needs local information for inference. In contrast, for the background interference problem, the model needs more non-local semantic information to guide the model to make semantic judgments. Specifically, we propose Joint Constraint-Based Human Pose Refinement Networks, which explicitly and implicitly model the relationship between critical points through graph neural networks and self-attention mechanisms to capture local and non-local information of the pose features and refine the keypoints responses. In order to avoid mutual conflicts between explicit and implicit modeling, this paper chooses a parallel structure. It selects an appropriate feature fusion strategy to fuse parallel branch features to improve model robustness. Meanwhile, to make the model pay more attention to the hard-to-identify vital points, this paper adopts the focal loss function to optimize the model and improve the data’s long-tailed distribution. We validate our work on the publicly available dataset MPII with the MSCOCO dataset. The results show that our proposed method has the effect of refining the keypoints response, with a PCKh@0.5 of 92.51% on the official MPII validation dataset, and achieved a result of 78% AP on the official MSCOCO validation dataset, which is comparable to the performance of existing state-of-the-art models.}, archive = {J_ACCESS}, author = {Yuru Zhang and Jiayuan Zhao and Xiaodong Su and Hongyan Xu and Meijian Jin}, doi = {10.1109/ACCESS.2024.3407852}, journal = {IEEE Access}, pages = {68841-68856}, shortjournal = {IEEE Access}, title = {JCN: Joint constraint-based human pose refinement networks}, volume = {13}, year = {2025}, }
• (2025). CBGF: Callback coverage guided fuzzing. ACCESS, 13, 68831-68840. (www)

  @article{ , abstract = {Callback functions are widely used across programming languages, libraries, and operating systems. While offering flexible software design, these mechanisms introduce inherently complex execution flows that can serve as potential attack vectors for security vulnerabilities. Despite the continuous reporting and active exploitation of callback-related vulnerabilities in real-world environments, traditional coverage-guided greybox fuzzing approaches typically instrument the entire codebase uniformly without prioritizing callback functions, thereby insufficiently capturing callback-specific execution flows and limiting the effectiveness in detecting these vulnerabilities. We propose Callback Coverage Guided Fuzzing (CBGF), a novel approach that leverages user-defined callback functions to strategically insert instrumentation code, enabling precise monitoring of callback execution flows. The proposed instrumentation approach facilitates the comprehensive identification of callback execution paths while maintaining seamless compatibility with existing fuzzing frameworks. Our evaluation using CPython demonstrated the effectiveness of CBGF, by discovering seven bugs, including five callback-related vulnerabilities.}, archive = {J_ACCESS}, author = {Ho Hwang and Daesung Moon}, doi = {10.1109/ACCESS.2025.3561135}, journal = {IEEE Access}, pages = {68831-68840}, shortjournal = {IEEE Access}, title = {CBGF: Callback coverage guided fuzzing}, volume = {13}, year = {2025}, }
• (2025). RSTHFS: A rough set theory-based hybrid feature selection method for phishing website classification. ACCESS, 13, 68820-68830. (www)

  @article{ , abstract = {Phishing is a pervasive form of cybercrime where malicious websites deceive users into revealing sensitive information, e.g., passwords and credit card details. Despite advances in cybersecurity, accurately detecting phishing websites remains challenging due to the absence of universally accepted identification parameters. This study introduces a novel feature selection method, Rough Set Theory-based Hybrid Feature Selection (RSTHFS), to enhance phishing website detection using Machine Learning (ML) techniques. Our approach was evaluated using three diverse datasets containing 2,456, 10,000, and 88,647 instances. The RSTHFS method demonstrated a significant improvement by maintaining an average accuracy rate of 95.48% while reducing the number of features by 69.11% on average. Performance was further assessed using three advanced classifiers: Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), and Categorical Boosting (CatBoost), with CatBoost emerging as the most efficient, achieving the highest accuracy. Additionally, RSTHFS reduced the runtime by 61.43%, highlighting its efficiency. These findings indicate that RSTHFS is not only effective in identifying phishing websites but also accelerates ML processes, providing a reliable and swift approach to feature selection. This work contributes to the field by presenting a robust methodology that enhances the accuracy and speed of phishing detection systems.}, archive = {J_ACCESS}, author = {Jahanggir Hossain Setu and Nabarun Halder and Ashraful Islam and M. Ashraful Amin}, doi = {10.1109/ACCESS.2025.3561237}, journal = {IEEE Access}, pages = {68820-68830}, shortjournal = {IEEE Access}, title = {RSTHFS: A rough set theory-based hybrid feature selection method for phishing website classification}, volume = {13}, year = {2025}, }
• (2025). OntoMat 4.0: An ontology framework for enhanced industry 4.0 maturity assessment. ACCESS, 13, 68801-68819. (www)

  @article{ , abstract = {Industry 4.0 (I4.0) is expected to revolutionize the manufacturing process and business model and offer a significant competitive advantage. The Industry 4.0 Maturity Model (I4.0MM) is applied to guide organizations in I4.0 adoption. Nevertheless, most present models have certain limitations, including the absence of standardization, limited scope or narrow focus, and difficulty of use, which makes them less efficient. To address these gaps, this study presents OntoMat 4.0, an ontology that enables interoperability, knowledge sharing, and I4.0 maturity assessment. It was created following a four-step iterative process based on well-established ontology development frameworks, such as LOT and NeOn. The novelty of Ontomat 4.0 includes its innovative functionality through the ability to work alongside established I4.0 ontologies, which enables users to navigate multidimensional I4.0 aspects and extend interoperability while promoting the reuse of relevant and widely recognized ontologies. It also delivers prioritized actionable insights that guide strategic decisions during I4.0 transformation initiatives. Ontomat 4.0 was evaluated and tested through real-world strategic planning and benchmarking applications, and the effectiveness of Ontomat 4.0 was demonstrated in aiding organizations in making informed decisions. It brings the possibility of integrating the technical and nontechnical factors of I4.0 and can be a standard solution for measuring I4.0 maturity. Despite its limitations, this ontology has been shown to fill in the gaps in current models and promote consistency and interoperability.}, archive = {J_ACCESS}, author = {Linda Salma Angreani and Annas Vijaya and Hendro Wicaksono}, doi = {10.1109/ACCESS.2025.3561229}, journal = {IEEE Access}, pages = {68801-68819}, shortjournal = {IEEE Access}, title = {OntoMat 4.0: An ontology framework for enhanced industry 4.0 maturity assessment}, volume = {13}, year = {2025}, }
• (2025). ABA-IDS: Attention-based autoencoder for intrusion detection in assistive mobility robotic network. ACCESS, 13, 68785-68800. (www)

  @article{ , abstract = {Innovative mobility aids like smart wheelchairs and walkers are transforming the lives of individuals with mobility challenges. These devices use sensors and in-vehicle network technologies, such as Controller Area Network (CAN), to enable real-time data exchange. While CAN is cost-effective, robust, and low-latency, it is vulnerable to cyber-attacks due to issues like message transmission on a shared bus, ID-based priority arbitration, and lack of encryption or authentication mechanisms. To mitigate these vulnerabilities, researchers have developed machine and deep learning-based intrusion detection systems (IDS). However, these systems often struggle with low feature learning and high model complexity, making them unsuitable for resource-constrained CAN networks. This paper introduces ABA-IDS, a low-footprint IDS that uses a ConvLSTM autoencoder with an attention mechanism to detect anomalies in assistive mobility networks. The model’s design allows it to efficiently capture key spatial and temporal features of the network. By preprocessing CAN data based on CAN ID and transmission frequency, ABA-IDS combines frequency-based and content-based approaches to enhance anomaly detection. Anomaly scores are generated from reconstruction errors, and testing on a benchmark dataset showed that ABA-IDS outperformed existing models with fewer parameters. This makes ABA-IDS effective for deployment in resource-constrained in-vehicle networks, significantly improving the security of assistive mobility devices against cyber threats. The link to the project repository can be found here.}, archive = {J_ACCESS}, author = {Daniel Ayo Oladele and Elisha Didam Markus}, doi = {10.1109/ACCESS.2025.3560453}, journal = {IEEE Access}, pages = {68785-68800}, shortjournal = {IEEE Access}, title = {ABA-IDS: Attention-based autoencoder for intrusion detection in assistive mobility robotic network}, volume = {13}, year = {2025}, }
• (2025). Optimizing transmission network expansion planning using search space reduction. ACCESS, 13, 68773-68784. (www)

  @article{ , abstract = {Transmission network expansion planning is a critical and complex problem related to the operation and development of electrical power systems. It is typically formulated as a mixed-integer nonlinear programming (MINLP) problem with combinatorial characteristics. Various mathematical models have been proposed to better approximate real-world system behavior, but even the most relaxed formulations remain computationally challenging. This paper introduces a search space reduction strategy to reduce the gap between the optimal solution of the MINLP model and its relaxed counterpart by strategically considering surrogate constraints. This approach enhances computational efficiency, significantly reducing processing time when using an optimization solver. By applying this method, we successfully determined the previously unknown optimal solution for the Brazilian north-northeast system.}, archive = {J_ACCESS}, author = {Marco Meneses and Harol Zamora and Leonardo H. Macedo and Rubén Romero}, doi = {10.1109/ACCESS.2025.3561109}, journal = {IEEE Access}, pages = {68773-68784}, shortjournal = {IEEE Access}, title = {Optimizing transmission network expansion planning using search space reduction}, volume = {13}, year = {2025}, }
• (2025). Multi-modal social media analysis via SHAP-based explanation: A framework for public art perception. ACCESS, 13, 68753-68772. (www)

  @article{ , abstract = {Jiangsu Province’s rapid urbanization presents unprecedented opportunities for examining evolving cultural landscapes through public art perception patterns. We propose a multi-modal framework integrating visual and linguistic analysis: visual content undergoes ResNet-152 feature extraction and Qwen-7B-Chat captioning, while textual data is processed through hierarchical topic modeling and sentiment classification. Through analysis of 103,427 geo-tagged Weibo posts across thirteen administrative regions, this study employs fine-tuned Qwen-7B-Chat architecture integrated with spatial random forest modeling to decode urban-rural variations in artistic appreciation. The methodology reveals counterintuitive relationships between institutional density and cultural reception. Notably, technological firm concentration demonstrates negative correlation with digital art appreciation, while cultural venue density emerges as the dominant predictor for traditional art sentiment. Second-tier cities exhibit sophisticated hybrid indices, with Nantong and Changzhou developing balanced traditional-digital reception patterns independent of metropolitan centers. Educational attainment shows varying impacts across artistic modalities, suggesting differentiated mechanisms of cultural capital formation. These empirical patterns establish a polycentric model of cultural development where mid-sized urban centers cultivate autonomous artistic ecosystems characterized by distinctive institutional-technological configurations. The findings advance theoretical frameworks for understanding cultural diffusion in rapidly urbanizing regions while providing evidence-based recommendations for territorially-calibrated cultural policy implementation.}, archive = {J_ACCESS}, author = {Tianhao Guo and Zhengyang Lu and Feng Wang}, doi = {10.1109/ACCESS.2025.3560433}, journal = {IEEE Access}, pages = {68753-68772}, shortjournal = {IEEE Access}, title = {Multi-modal social media analysis via SHAP-based explanation: A framework for public art perception}, volume = {13}, year = {2025}, }
• (2025). Toward an IoT framework for the new generation of XR applications. ACCESS, 13, 68726-68752. (www)

  @article{ , abstract = {In recent years, industry and academia have grown interested in Extended Reality (XR) and the development of creative and immersive applications that take advantage of it. This interest has led to new forms of interaction and immersion being considered for this type of application. However, many of these applications are still tied to traditional input devices, such as a mouse, touchscreen, or game controller, and output to mundane output devices, such as screens or projectors. With the democratization of affordable Internet of Things (IoT) solutions, there is an opportunity to integrate these devices into the next generation of creative applications. However, such integrations may be costly or involve much custom work to develop. This work aims to bridge this gap and create a framework that can be used by developers of such applications to leverage IoT devices for the creation of digital twins or as input and output devices for their applications, opening up software development to a whole new world of possibilities. Throughout this research work, a framework to enable the integration of any IoT device into the next generation of XR applications was proposed, as well as a detailed proposal of a pairing scheme for IoT devices that allows them to be matched with confidence to clients automatically. Performance studies conducted using the industry standard IoT communication protocol MQTT proved the viability of the proposed framework in providing reliable means of low-latency communication for wireless input devices.}, archive = {J_ACCESS}, author = {João A. Dias and Nathan P. Campos and Miguel Boavida}, doi = {10.1109/ACCESS.2025.3560547}, journal = {IEEE Access}, pages = {68726-68752}, shortjournal = {IEEE Access}, title = {Toward an IoT framework for the new generation of XR applications}, volume = {13}, year = {2025}, }
• (2025). Optimizing resource allocation and task offloading in multi-UAV MEC networks. ACCESS, 13, 68710-68725. (www)

  @article{ , abstract = {Uncrewed aerial vehicle (UAV)-assisted multi-access edge computing (MEC) is increasingly being adopted to meet the rising demand for low-latency and efficient data processing, particularly in environments with limited ground infrastructure. However, optimizing task offloading and resource allocation in such networks remains a significant challenge as the number of UAVs and connected devices grows. To address this, we propose a Trajectory-Based Task Offloading in UAV-Assisted MEC (TB-TUAV) scheme, which leverages UAV mobility to enhance resource utilization and reduce latency. Unlike existing methods, TB-TUAV integrates a deep reinforcement learning (DRL) framework based on a Markov Decision Process (MDP) to dynamically optimize task offloading and resource allocation in multi-UAV networks. Our approach effectively balances exploration and exploitation, improving learning stability and ensuring fast convergence. By incorporating trajectory optimization through random path exploration, the proposed scheme efficiently distributes computational tasks while mitigating processing delays. Simulation results demonstrate that TB-TUAV significantly improves resource efficiency and reduces latency compared to state-of-the-art baseline methods. This research presents a scalable and adaptive solution for real-time MEC applications in dynamic multi-UAV environments, ensuring improved performance even under resource constraints.}, archive = {J_ACCESS}, author = {Manzoor Ahmed and Noor Fatima and Salman Raza and Hamid Ali and Abdul Qayum and Wali Ullah Khan and Muhammad Sheraz and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3562102}, journal = {IEEE Access}, pages = {68710-68725}, shortjournal = {IEEE Access}, title = {Optimizing resource allocation and task offloading in multi-UAV MEC networks}, volume = {13}, year = {2025}, }
• (2025). Preliminary study on real-time phonocardiogram signal acquisition and analysis using machine learning and IoMT for digital stethoscope. ACCESS, 13, 68682-68709. (www)

  @article{ , abstract = {Cardiovascular diseases currently pose the greatest threat to human health and future predicament is uncertain. Since most heart-related problems are reflected by the small variations in the heart’s sounds, quality research into heart rhythm will provide valuable health information that may identify and treat cardiac-related issues and disorders. Driven by the critical need for early and precise cardiac diagnostics, this study tackles the challenges of detecting subtle variations in heart sounds, such as S3 and S4, which are essential for identifying complex cardiovascular diseases (CVDs). This study introduces a real-time integrated system for heart sound detection, acquisition, analysis, and remote communication. A digital stethoscope was developed to facilitate computer-assisted auscultation, capturing heart sounds from the skin’s surface. These sounds are filtered, amplified, and transmitted to healthcare providers through the Internet of Medical Things (IoMT), enabling remote visualization and auscultation. Advanced signal segmentation and classification were performed using machine learning algorithms, including K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The results demonstrate that SVM and Naive Bayes Classifier (NBC) achieve high classification accuracies of up to 92.5% for normal heart sounds and 98.63% for abnormal sounds. This system enhances diagnostic precision, supports remote cardiac monitoring, and reduces the need for highly specialized personnel during preliminary assessments. By leveraging technological advancements, this research establishes a foundation for scalable and cost-effective solutions in cardiac care.}, archive = {J_ACCESS}, author = {M. Kalimuthu and C. Hemanth}, doi = {10.1109/ACCESS.2025.3560763}, journal = {IEEE Access}, pages = {68682-68709}, shortjournal = {IEEE Access}, title = {Preliminary study on real-time phonocardiogram signal acquisition and analysis using machine learning and IoMT for digital stethoscope}, volume = {13}, year = {2025}, }
• (2025). Electronic warfare cyberattacks, countermeasures, and modern defensive strategies of UAV avionics: A survey. ACCESS, 13, 68660-68681. (www)

  @article{ , abstract = {Unmanned Aerial Vehicles (UAVs) play a pivotal role in modern autonomous air mobility, and the reliability of UAV avionics systems is critical to ensuring mission success, sustainability practices, and public safety. The success of UAV missions depends on effectively mitigating various aspects of electronic warfare, including non-destructive and destructive cyberattacks, transponder vulnerabilities, and jamming threats, while rigorously implementing countermeasures and defensive aids. This paper provides a comprehensive review of UAV cyberattacks, countermeasures, and defensive strategies. It explores UAV-to-UAV coordination attacks and their associated features, such as dispatch system attacks, Automatic Dependent Surveillance-Broadcast (ADS-B) attacks, Traffic Alert and Collision Avoidance System (TCAS)-induced collisions, and TCAS attacks. Additionally, the paper examines UAV-to-command center coordination attacks, as well as UAV functionality attacks. The review also covers various countermeasures and defensive aids designed for UAVs. Lastly, a comparison of common cyberattacks and countermeasure approaches is conducted, along with a discussion of future trends in the field.}, archive = {J_ACCESS}, author = {Aaron Yu and Iuliia Kolotylo and Hashim A. Hashim and Abdelrahman E. E. Eltoukhy}, doi = {10.1109/ACCESS.2025.3561068}, journal = {IEEE Access}, pages = {68660-68681}, shortjournal = {IEEE Access}, title = {Electronic warfare cyberattacks, countermeasures, and modern defensive strategies of UAV avionics: A survey}, volume = {13}, year = {2025}, }
• (2025). Multi-level pre-training for encrypted network traffic classification. ACCESS, 13, 68643-68659. (www)

  @article{ , abstract = {With the recent growth of network environments, the use of encrypted traffic has increased, making traditional methods less effective. Consequently, research on encrypted traffic analysis using Machine Learning (ML) and Deep Learning (DL) techniques has become more widespread, with pre-training methods gaining particular attention mechanism. Network traffic typically occurs in the form of packets composed of multiple bytes, and each packet consists of various fields with specific meanings. These field information plays a crucial role in identifying the type or purpose of the traffic, but most of the studies have focused solely on the byte and packet level. In this paper, we propose a Multi-Level Pre-training for Encrypted Traffic Classification (MLETC) model. This model applies two pre-training strategies that consider multi-level traffic representations, including byte, field, and packet. By employing these pre-training strategies, we achieve comprehensive training on traffic data, which enables the development of a robust pre-trained model. We conducted experiments using three public datasets to validate the performance of MLETC. The results show that MLETC outperforms existing ML and DL-based models in most downstream tasks and achieves high performance comparable to existing pre-trained models. Additionally, we demonstrate that a pre-trained model considering bytes, fields, and packets together outperforms a pre-trained model that only considers bytes and packets.}, archive = {J_ACCESS}, author = {Jee-Tae Park and Yang-Seo Choi and Bu-Seung Cho and Seung-Hae Kim and Myung-Sup Kim}, doi = {10.1109/ACCESS.2025.3559068}, journal = {IEEE Access}, pages = {68643-68659}, shortjournal = {IEEE Access}, title = {Multi-level pre-training for encrypted network traffic classification}, volume = {13}, year = {2025}, }
• (2025). Model between network quality and XR user’s experience in terms of video stalling for cloud rendering XR. ACCESS, 13, 68632-68642. (www)

  @article{ , abstract = {At present, extended reality (XR) is a hotspot of research around the world. The cloud rendering XR based on mobile communication system provides a significant solution to reducing the demand of head-mounted display (HMD) for computing power, thus the scope of application for XR can be expanded from indoors to outdoors. However, cloud rendering XR has more demanding requirements on network quality, failure to meet these requirements leads to poor user experience. In particular, video streaming stalling is the most critical factor affecting the immersive experience, and it is closely related to network quality. Therefore, it is essential that the network is capable of predicting the user experience of stalling for precise resource scheduling and allocation. In order to reveal the relationship between network quality and stalling, a frame-level data transmission framework with two-layer mapping model is proposed in this paper. The framework uses indicators like packet loss, latency, throughput, and forward error correction (FEC) to model video frame loss in the lower layer. The upper layer then uses this information to predict stalling probability. Furthermore, simulation and experiment are implemented to evaluate the accuracy and feasibility of the proposed model in the 5th generation mobile communication technology (5G). The simulation results show that the data packet loss of less than 1.4% can ensure user to experience without stalling. The experimental results indicate that for 60 Mbps and 30 Mbps encoding rate, the data packet loss for no stalling are measured to be less than 1% and 2%.}, archive = {J_ACCESS}, author = {Yuhong Huang and Lei Cao and Hongbiao Zhang and Luting Kong and Jingwen Xu and Chenguang Jin and Xintai Wang}, doi = {10.1109/ACCESS.2025.3557518}, journal = {IEEE Access}, pages = {68632-68642}, shortjournal = {IEEE Access}, title = {Model between network quality and XR user’s experience in terms of video stalling for cloud rendering XR}, volume = {13}, year = {2025}, }
• (2025). Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network. ACCESS, 13, 68622-68631. (www)

  @article{ , abstract = {The steady-state visually evoked magnetic field (SSVEF) is a promising modality in brain-computer interference (BCI), which has the advantages of being non-invasive and non-contact. The combination of optically pumped magnetometers (OPM) and artificial intelligence technology makes SSVEF measurements more portable, accurate, and cost-effective. This paper examines the distribution of the human brain visually evoked magnetic field experimentally and then presents an SSVEF measurement system based on an OPM. A three-block temporal convolutional neural network (3B-TCN) is developed to classify brain magnetic signals. A data augmentation method based on statistical analysis of SSVEF signals is proposed, which generates 30,000 sets of data to train the 3B-TCN. The SSVEF signal classification accuracies of the 3B-TCN network are 96.61%, 92.36%, and 86.75% for 10 s, 5 s, and 2 s time length data, respectively. The impact of visually fatigued states on BCI is studied. The accuracy of controlling the character in the game is above 90% when the subjects are in a normal state, but it decreases considerably when the subjects are visually fatigued. The experimental results demonstrate the feasibility of realizing BCI using an OPM sensor and a convolutional neural network.}, archive = {J_ACCESS}, author = {Yutong Wei and Fudan Zhao and Fengwen Zhao and Shiqiang Zheng and Chaofeng Ye and Liangyu Liu}, doi = {10.1109/ACCESS.2024.3524397}, journal = {IEEE Access}, pages = {68622-68631}, shortjournal = {IEEE Access}, title = {Steady-state visually evoked magnetic signal classification and BCI evaluation based on a convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Parallel local and global context modeling of deep learning-based monaural speech source separation techniques. ACCESS, 13, 68607-68621. (www)

  @article{ , abstract = {The novel deep learning-based time domain single channel speech source separation methods have shown remarkable progress. Recent studies achieve either successful global or local context modeling for monaural speaker separation. Existing CNN-based methods perform local context modeling, and RNN-based or attention-based methods work on the global context of the speech signal. In this paper, we proposed two models which parallelly combine CNN-RNN-based and CNN-attention-based separation modules and perform parallel local and global context modeling. Our models keep maximum global or local context value at a particular time step. These values help our models to separate the speaker signals more accurately. We have conducted the experiments on Libri2mix and Libri3mix datasets. The experimental data demonstrates that our proposed models have outperformed the state-of-the-art methods. Our proposed models remarkably improve SDR and SI-SDR values on Libri2mix and Libri3mix datasets. The proposed parallel CNN-RNN-based and CNN-attention-based separation models achieve average SDR improvement of 2.10 dB and 2.21 dB, respectively, and SI-SDR improvement of 2.74 dB and 2.78 dB, respectively, on the Libri2mix dataset. However, on the Libri3mix dataset, the proposed models achieve 0.57 dB and 0.87 dB average SDR improvement for parallel CNN-RNN-based separation module, and 0.88 dB and 1.4 dB average SI-SDR improvement for CNN-attention-based separation models. Our work indirectly contributes to SDG Goal 10 (Reduced Inequalities) by improving communication tools for diverse linguistic communities. Furthermore, this technology aids SDG Goal 9 (Industry, Innovation, and Infrastructure) by advancing AI-powered assistive technologies, fostering innovation, and building resilient communication systems.}, archive = {J_ACCESS}, author = {Swati Soni and Lalita Gupta and Rishav Dubey}, doi = {10.1109/ACCESS.2025.3562343}, journal = {IEEE Access}, pages = {68607-68621}, shortjournal = {IEEE Access}, title = {Parallel local and global context modeling of deep learning-based monaural speech source separation techniques}, volume = {13}, year = {2025}, }
• (2025). Perceptual error logarithm: An efficient and effective analytical method for full-reference image quality assessment. ACCESS, 13, 68587-68606. (www)

  @article{ , abstract = {In this paper, we propose a new perceptual analytical method for full-reference image assessment. Techniques used in multimedia applications often introduce distortions, which can significantly degrade image and video quality. To mitigate these distortions, image quality assessment (IQA) methods have been employed to enhance these techniques. However, efficiency and effectiveness are difficult to achieve simultaneously when designing perceptual IQA methods. To face this challenge, we introduced a new method, named the namely Perceptual Error Logarithm (PEL). Initially, luminance and chrominance maps are generated after the reduction and spatial color conversion applied to the input images. Through the use of chrominance and luminance maps, simple absolute difference maps are generated, as well as special feature maps. The special maps are derived from perceptual feature extractions applied to gradient magnitude maps. These maps are generated immediately following the application of local standard deviation filtering to the luminance maps. Finally, to calculate the PEL score, a logarithmic function is applied to the perceptual error map, which combines the simple and special maps, besides adjusts this combination based on the weight of the superpixel similarity and local energy maps. Experimental results and discussions on eight databases prove the high level of effectiveness of the proposed method, which presents, according to the application of evaluation metrics, much more consistency with subjective evaluations than all the nineteen state-of-the-art IQA methods that were compared. Moreover, PEL is highly efficient because its processing is performed in real-time.}, archive = {J_ACCESS}, author = {Sergio A. C. Bezerra and Sérgio A. C. Bezerra and José L. De S. Pio and José R. H. De Carvalho and Keiko V. O. Fonseca}, doi = {10.1109/ACCESS.2025.3560918}, journal = {IEEE Access}, pages = {68587-68606}, shortjournal = {IEEE Access}, title = {Perceptual error logarithm: An efficient and effective analytical method for full-reference image quality assessment}, volume = {13}, year = {2025}, }
• (2025). Optimized path planning for autonomous guided vehicle through fusion of improved a* and dynamic window approach. ACCESS, 13, 68577-68586. (www)

  @article{ , abstract = {This paper addresses the issues of slow speed, safety concerns, and non-smooth paths in traditional A* algorithm-based path planning for Autonomous Guided Vehicle (AGV) in complex environments. The study proposes an improved fusion algorithm combining the A* and Dynamic Window Approach (DWA). The enhanced A* algorithm first plans the global path, and then the DWA algorithm handles local path planning under the guidance of the global path. First, by introducing the concept of obstacle density, the study integrates environmental information, optimizes the evaluation function, refines the child node selection process, and applies bidirectional smoothness optimization to the paths. These improvements enhance search efficiency, ensure adequate safety distances from obstacles, and strengthen the safety margin and rationality of the paths, making them more aligned with the robot’s needs. Next, the integration of the DWA algorithm allows for dynamic obstacle identification and local path regeneration for avoidance, resulting in more realistic paths for dynamic obstacle navigation. Finally, key nodes from the global path planning of the A* algorithm are extracted to serve as references for local planning in the DWA algorithm, ensuring that local paths are more closely aligned with global paths. Simulations and practical experiments confirm the effectiveness of the proposed algorithm, providing a more efficient and feasible solution to the path planning problem.}, archive = {J_ACCESS}, author = {Yixuan Luo and Shusen Lin and Yifan Wang and Kai Liang}, doi = {10.1109/ACCESS.2025.3561005}, journal = {IEEE Access}, pages = {68577-68586}, shortjournal = {IEEE Access}, title = {Optimized path planning for autonomous guided vehicle through fusion of improved a* and dynamic window approach}, volume = {13}, year = {2025}, }
• (2025). Zero-shot 3D scene representation with invertible generative neural radiance fields. ACCESS, 13, 68561-68576. (www)

  @article{ , abstract = {Generative Neural Radiance Fields (NeRFs) have recently enabled efficient synthesis of 3D scenes by training on unposed real image sets. However, existing methods for generating multi-view images of specific input images have limitations, such as requiring camera parameters or additional components for estimating them. In this paper, we propose ZIGNeRF, a novel learning-based approach for zero-shot 3D Generative Adversarial Network (GAN) inversion that generates multi-view images from a single input image without requiring camera parameters. Our method introduces a novel inverter that maps out-of-distribution images into the latent space of the 3D generator without needing additional training steps. We demonstrate the efficacy of ZIGNeRF on multiple real-world image datasets, including Cats, AFHQ, CelebA-HQ, CompCars, and CUB-200-2011. For example, ZIGNeRF achieves an FID of 14.77 for face image generation when trained on the CelebA-HQ dataset. Furthermore, ZIGNeRF is capable of performing 3D operations such as 360-degree rotation and spatial translations by disentangling objects from the background. It can also generate style-mixed images by combining characteristics from two distinct input images, which is a pioneering attempt in 3D-scene synthesis. Our approach opens up new possibilities for flexible and controllable 3D image generation from real-world data.}, archive = {J_ACCESS}, author = {Kanghyeok Ko and Sungyup Kim and Minhyeok Lee}, doi = {10.1109/ACCESS.2025.3561923}, journal = {IEEE Access}, pages = {68561-68576}, shortjournal = {IEEE Access}, title = {Zero-shot 3D scene representation with invertible generative neural radiance fields}, volume = {13}, year = {2025}, }
• (2025). Log anomaly detection method based on transformer and temporal convolutional networks. ACCESS, 13, 68547-68560. (www)

  @article{ , abstract = {As record files generated during system operation, logs play a vital role in ensuring system stability. However, the rapid development of distributed technologies has led to the increasing scale and complexity of log data, resulting in existing methods facing difficulties in fully mining log features and information loss during parsing. To overcome these difficulties, this study proposes TranConvAD, a global and local feature extraction method for log anomaly detection. The method extracts multidimensional features of logs from three perspectives: time, risk level, and message content, and designs FastText and BERT based multi-type word embedding methods for generating log representation vectors, aiming to minimize noise introduction and information loss. In addition, TranConvAD combines for the first time the global dependency capture of Transformer with the local temporal modeling of temporal convolutional networks. Furthermore, a separable self-attention mechanism and pointwise convolution are introduced to optimize the model. Experimental results demonstrate that TranConvAD achieves a maximum precision of 99.8% on the HDFS dataset, outperforming LogAnomaly by 3.8%, and 98.6% on the BGL dataset, surpassing LightLog by 3.4%, thereby providing a more efficient and scalable solution.}, archive = {J_ACCESS}, author = {Niandong Liao and Zihan Liu}, doi = {10.1109/ACCESS.2025.3561669}, journal = {IEEE Access}, pages = {68547-68560}, shortjournal = {IEEE Access}, title = {Log anomaly detection method based on transformer and temporal convolutional networks}, volume = {13}, year = {2025}, }
• (2025). CIELO: Class-incremental continual learning for overcoming catastrophic forgetting with smartphone-based indoor localization. ACCESS, 13, 68536-68546. (www)

  @article{ , abstract = {With dynamically evolving indoor environments, class-incremental learning (CIL) plays a crucial role in enabling indoor localization systems to adapt to new indoor areas. However, CIL poses additional challenges such as catastrophic forgetting, where patterns from previously learned paths are overwritten by data from new paths, and high data storage demands on the edge server, which must retain extensive localization data, resulting in high memory and power consumption overheads. To address these challenges, an effective solution must support CIL with indoor paths while mitigating catastrophic forgetting and reducing storage overheads on the edge server. To the best of our knowledge, CIELO is the first framework to address these challenges in the domain of indoor localization. It introduces a novel CIL approach that integrates an innovative representation memory management (RMM) policy with crowdsourcing to enable high-accuracy localization while significantly reducing catastrophic forgetting and data storage requirements. Through extensive experimental evaluations conducted across multiple real-world paths and devices, our results demonstrate that CIELO improves indoor localization accuracy by up to $29.4\times $ with up to 60 newly introduced classes (locations) across paths, reduces data storage by up to $1.75\times $ , and power consumption by up to $1.69\times $ on the edge server, compared to state-of-the-art solutions.}, archive = {J_ACCESS}, author = {Akhil Singampalli and Danish Gufran and Sudeep Pasricha}, doi = {10.1109/ACCESS.2025.3561103}, journal = {IEEE Access}, pages = {68536-68546}, shortjournal = {IEEE Access}, title = {CIELO: Class-incremental continual learning for overcoming catastrophic forgetting with smartphone-based indoor localization}, volume = {13}, year = {2025}, }
• (2025). Multilevel operation in scaled back-end-of-line ferroelectric FETs with a metal interlayer. ACCESS, 13, 68525-68535. (www)

  @article{ , abstract = {Multi-level operation, conventionally obtained in ferroelectric devices thanks to a domain-dependent inhomogeneous polarization, poses a big challenge for highly-scaled ferroelectric devices, where the number of ferroelectric domains is drastically reduced. In this work, we study a highly scaled back-end-of-line (BEOL) compatible, ferroelectric field-effect transistor (FeFET) that integrates a metal interlayer in the gate stack. Through analytical models and calibrated TCAD simulations, we show how this device can achieve a multi-level operation exploiting the interplay between the ferroelectric polarization and the charge in the metal interlayer. Such a working principle does not rely on a domain-dependent inhomogeneous polarization, and the device operation is thus ensured also for a homogeneous ferroelectric material. We also demonstrate that the charge in the interlayer can effectively stabilize the ferroelectric polarization even in the absence of a high concentration of trapped charges in the gate stack. The potentiation and depression curves for the readout conductance confirm that the proposed device can be operated as a memristor for neuromorphic computing applications. Moreover, we show how the choice of the dielectric in the metal-ferroelectric-dielectric-metal gate stack can be used as a design knob to reduce the device operation voltage.}, archive = {J_ACCESS}, author = {Chiara Rossi and Daniel Lizzit and David Esseni}, doi = {10.1109/ACCESS.2025.3561255}, journal = {IEEE Access}, pages = {68525-68535}, shortjournal = {IEEE Access}, title = {Multilevel operation in scaled back-end-of-line ferroelectric FETs with a metal interlayer}, volume = {13}, year = {2025}, }
• (2025). Risk assessment of UAS events from the perspective of heterogeneous graphs. ACCESS, 13, 68509-68524. (www)

  @article{ , abstract = {With the widespread application of Unmanned Aerial Systems (UAS) in the aviation sector, accidents caused by drones have become increasingly frequent. As a result, numerous studies have been conducted by scholars on the risk assessment of UAS accidents. To address the challenge of evaluating the accident risks arising from the ever-changing operational environments and diverse tasks of UAS, this paper proposes a risk prediction framework for UAS accidents based on Heterogeneous Graph Embedding (HGE). By integrating various node attributes and the relationships between different factors, the framework constructs a heterogeneous graph from UAS aviation incident data. The complex heterogeneous relationships are transformed into homogeneous subgraphs through different path patterns, which are used to represent the data. Random walks are performed on each homogeneous subgraph to capture semantic information, and the Skip-Gram method is employed to embed the semantic attention-weighted walk results into high-dimensional vectors. These vectors are then integrated with the original graph embedding vectors to update the representations of incident nodes, enabling accurate prediction of accident severity and risk types. Furthermore, a meta-path-based filter module is designed to evaluate the key factors contributing to UAS accident risk by comparing the contributions of different meta-paths under various path patterns. Experimental results on a UAS incident dataset demonstrate that the proposed method effectively predicts UAS operational risks in real time, achieving an accuracy of 97. 56% for accident severity prediction. Additionally, the method provides stable predictions when dealing with complex heterogeneous data. From the perspective of a novel graph structure, this research offers a new approach for the safety management of future UAS systems.}, archive = {J_ACCESS}, author = {Yu An and Qingpei Yang and Hao Liu and Liang Geng}, doi = {10.1109/ACCESS.2025.3559527}, journal = {IEEE Access}, pages = {68509-68524}, shortjournal = {IEEE Access}, title = {Risk assessment of UAS events from the perspective of heterogeneous graphs}, volume = {13}, year = {2025}, }
• (2025). A camera-embedded self-adaptable finger with multi-modal sensing capabilities for robotic manipulation. ACCESS, 13, 68492-68508. (www)

  @article{ , abstract = {This work introduces a novel vision-based sensing approach for self-adaptable soft fingers using an embedded camera, demonstrating proprioceptive and exteroceptive capabilities. The system achieves normal force estimation, Z-displacement estimation, slip detection, and force application position classification by integrating the Pi Camera V3 sensor into the structure of an additively manufactured self-adaptable soft finger. Our approach avoids the deformation disambiguation required in elastomer-based sensors by enabling direct deformation sensing without additional sensing layers like a silicon pad that alters the finger’s compliant characteristics. The system also overcomes the occlusion limitations inherent in external camera-based methods by observing structural changes through strategically placed dot patterns within the finger’s internal geometry, which are visible to the embedded camera. The camera mounting position was verified using Finite Element Analysis (FEA) while ensuring finger compliance and as much deformation as possible without touching the camera. We combined two algorithms: an optical flow-based method for reliably detecting slip events and a Convolutional Neural Network (CNN) for force, displacement estimation, and position classification. The slip detection algorithm uses a dual-threshold approach that combines the Median Absolute Deviation (MAD) and standard deviation. Customized rigs integrated with a Universal Testing Machine (UTM) were used for sensor characterization, dataset acquisition, and slip detection parameter optimization. Furthermore, the system was validated through integration with an industrial gripper in an experimental sequence on different object types to mimic real-world manipulation, where both algorithms were integrated and deployed on Raspberry Pi 5 to achieve proprioceptive and exteroceptive sensing. Reliable performance was achieved with slip detection operating at 28.5 Hz and CNN update rate of 8 Hz. The experimental results demonstrate the effectiveness of the proposed system for both sensing modalities. This study presents a compact, integrated solution that maintains the inherent compliant characteristics of the self-adaptable finger while providing feedback for real-world manipulation tasks.}, archive = {J_ACCESS}, author = {Muhammad Usman Khalid and Hafiz Muizz Ahmed Sethi and Nirmal Kumar Ravikumar and Halar Haleem and Alessandro Seitone and Mattia Frascio and Perla Maiolino and Matteo Zoppi}, doi = {10.1109/ACCESS.2025.3561582}, journal = {IEEE Access}, pages = {68492-68508}, shortjournal = {IEEE Access}, title = {A camera-embedded self-adaptable finger with multi-modal sensing capabilities for robotic manipulation}, volume = {13}, year = {2025}, }
• (2025). Differential and common-mode model-based controller for a grid-tied three-phase NPC converter. ACCESS, 13, 68481-68491. (www)

  @article{ , abstract = {This paper introduces a controller designed for transformerless inverters connected to the grid via an inductor-capacitor-inductor (LCL) output filter. The controller incorporates a virtual ground (VG) connection, providing a straightforward solution to mitigate common-mode current (CMC), i.e., to reduce the leakage ground current issue. Specifically, the focus is on a three-level, three-phase NPC inverter with split DC-link capacitors. The VG is established by connecting the star point of the LCL filter capacitors to the midpoint of the DC-link. This topology presents specific challenges, including voltage imbalances in the DC-link capacitors and resonances in the homopolar components of the signals within the LCL filter. The proposed controller not only addresses the goal of current injection but also ensures, on average, the balance of DC-link capacitor voltages (or equivalently, the regulation of the neutral point). Additionally, it provides the necessary damping for the homopolar components of the LCL filter signals. The performance of the proposed control scheme has been experimentally tested in a 2 kW three-phase NPC inverter prototype.}, archive = {J_ACCESS}, author = {Gerardo Escobar and Jorge L. Lopez-Sarabia and Andres A. Valdez-Fernandez and Edgar D. Silva-Vera and Jesus E. Valdez-Resendiz and Jonathan C. Mayo-Maldonado}, doi = {10.1109/ACCESS.2025.3561458}, journal = {IEEE Access}, pages = {68481-68491}, shortjournal = {IEEE Access}, title = {Differential and common-mode model-based controller for a grid-tied three-phase NPC converter}, volume = {13}, year = {2025}, }
• (2025). Fine-grained feature extraction in key sentence selection for explainable sentiment classification using BERT and CNN. ACCESS, 13, 68462-68480. (www)

  @article{ , abstract = {Online product reviews provide valuable insights into customer sentiment toward products; however, they often contain multiple sentences with redundant and non-essential content, making it harder to extract critical information. Identifying key sentences that directly impact sentiment along with fine-grained feature extraction is essential for reliable multi-class sentiment classification with greater explainability. Existing studies on document-level sentiment analysis with sentence-wise learning often overlook important local feature extraction at both the sentence and document levels for multi-class sentiment classification, limiting the selection of key sentences with greater transparency. These models rely on sequential processing of text rather than parallel processing, which can lead to missed important fine-grained features and long-range dependencies spread across sentences. Additionally, sequential models place more emphasis on the latter part of the text, assuming that the emotional aspects are concentrated at the end of the review. To address this issue, we introduce a novel Bidirectional Encoder Representations from Transformers (BERT) based dilated sentence attention deep learning model (Bert_DSENT_Att) for important fine-grained local feature extraction by capturing long-range dependencies within sentences, thereby enabling the selection of key sentences that directly impact multi-class sentiment classification. First, the BERT pre-trained language model captures global features through a feature vector representation. Next, a multi-layered Convolutional Neural Network (CNN) with varying kernels and dilation rates extracts long range local features at the sentence level. We employ within-sentence attention and between-sentence attention to extract important fine-grained features from the hierarchical representation. The experimental results show that our model outperforms seven baselines across three datasets in terms of accuracy, precision, recall, and F1 measure. A thorough ablation study demonstrated the contribution of each component to overall performance.}, archive = {J_ACCESS}, author = {Thennakoon Mudiyanselage Anupama Udayangani Gunathilaka and Jinglan Zhang and Yuefeng Li}, doi = {10.1109/ACCESS.2025.3560482}, journal = {IEEE Access}, pages = {68462-68480}, shortjournal = {IEEE Access}, title = {Fine-grained feature extraction in key sentence selection for explainable sentiment classification using BERT and CNN}, volume = {13}, year = {2025}, }
• (2025). MobilitApp: A deep learning-based tool for transport mode detection to support sustainable urban mobility. ACCESS, 13, 68439-68461. (www)

  @article{ , abstract = {The visible effects of climate change in urban areas are driving a paradigm shift in societal and political priorities. Urban planners, public transport providers, and traffic managers are increasingly focused on redesigning cities to promote sustainable mobility and create green spaces for pedestrians, cyclists, and scooter users. In alignment with these objectives, the European Climate Law mandates a minimum 55% reduction in greenhouse gas emissions by 2030 and climate neutrality by 2050. Achieving these targets requires robust tools to collect and analyze mobility data, enabling the evaluation of citizens’ travel habits and the planning of sustainable urban infrastructure. This study presents MobilitApp, a tool developed based on a deep learning (DL) model for real-time detection of transportation modes using smartphone sensor data. Our approach leverages a hierarchical model combining convolutional neural networks (CNNs) for feature extraction and long short-term memory (LSTM) layers for temporal processing, enhanced by skip connections. To ensure computational efficiency on mobile devices, the system integrates statistical techniques for early motion detection, minimizing reliance on DL models. The model was trained on a dataset of multimodal trips in Barcelona, achieving over 80% accuracy for most transport modes and a weighted average accuracy of 88%. These results highlight the effectiveness of our approach for accurately predicting users’ transport modes during their trips. The MobilitApp tool provides an intuitive platform for collecting and analyzing urban mobility data. By analyzing travel patterns, transport modes, and mode-switching behaviors, it delivers actionable insights to city planners, aiding in the enhancement of urban mobility, promotion of sustainable development, and transition to greener cities.}, archive = {J_ACCESS}, author = {Gerard Caravaca Ibañez and Luis J. de la Cruz Llopis and Adrian Catalín Diaconeasa and Alberto Bazán Guillén and Mónica Aguilar Igartua}, doi = {10.1109/ACCESS.2025.3561238}, journal = {IEEE Access}, pages = {68439-68461}, shortjournal = {IEEE Access}, title = {MobilitApp: A deep learning-based tool for transport mode detection to support sustainable urban mobility}, volume = {13}, year = {2025}, }
• (2025). Enhanced alzheimer’s disease prediction through integration of protein-protein interaction data and meta-learning. ACCESS, 13, 68422-68438. (www)

  @article{ , abstract = {Association between proteins and diseases have been widely studied to understand disease triggers and identify potential therapeutic targets. Deciphering the complexity of gene networks is crucial for understanding diseases. Node embedding offers a powerful approach, revealing latent patterns that can predict novel gene-disease associations. In this study, we applied a meta-learning approach to predict protein-disease associations with a focus on Alzheimer’s disease. Our approach utilized curated data from the Disease Gene Network Database (DisGeNET), a database of gene-disease associations, and protein-protein interaction data from the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) dataset. We generated embeddings for Protein-Protein Interactions(PPI) using the node2vec algorithm, capturing interaction patterns in a low-dimensional space. HybridBoost, a two-layered model architecture, was then employed, with random forest and CatBoost classifiers in the first layer and CatBoost as the meta-learner in the second layer. Our model achieved an accuracy of 96% and with precision, recall, and F1 scores also outperforming individual models like random forest, support vector machine, AdaBoost, and CatBoost. These results demonstrate the effectiveness of the proposed meta-learning model in predicting protein-disease associations for Alzheimer’s disease.}, archive = {J_ACCESS}, author = {Hansa J. Thattil and M. N. Arunkumar and Francis Antony}, doi = {10.1109/ACCESS.2025.3560216}, journal = {IEEE Access}, pages = {68422-68438}, shortjournal = {IEEE Access}, title = {Enhanced alzheimer’s disease prediction through integration of protein-protein interaction data and meta-learning}, volume = {13}, year = {2025}, }
• (2025). Building damage visualization through three-dimensional reconstruction. ACCESS, 13, 68410-68421. (www)

  @article{ , abstract = {Assessing building damage caused by natural disasters, such as earthquakes and typhoons, involves an initial survey to measure wall inclination and determine risk levels. Traditional plumb line surveying methods can be influenced by external factors, such as wind and vibration, and require physical contact with the damaged buildings, posing a risk of collapse during the survey. This study proposes a method using a camera capable of simultaneously capturing three-dimensional (3D) point clouds and images. This method extracts the entire wall surface of a building through point cloud processing and calculates its inclination. In the experiments, the accuracy of the system was first verified using a simple storage shed, followed by validation of its effectiveness on an actual house. The results demonstrated that, compared with the conventional plumb line method, the proposed method could remotely measure the building without physical contact, ensuring the safety of surveyors. The proposed method achieved measurements with less error and provided a measurement accuracy comparable to or better than the plumb line method. The assessed inclination was used to determine the risk level of the wall surfaces, enabling an intuitive and visual evaluation by color-coding the entire wall surface according to the risk level.}, archive = {J_ACCESS}, author = {Ittetsu Kuniyoshi and Sachie Sato and Itsuki Nagaike and Yue Bao}, doi = {10.1109/ACCESS.2025.3548102}, journal = {IEEE Access}, pages = {68410-68421}, shortjournal = {IEEE Access}, title = {Building damage visualization through three-dimensional reconstruction}, volume = {13}, year = {2025}, }
• (2025). The improved RRT integrated with the artificial potential field path planning algorithm. ACCESS, 13, 68398-68409. (www)

  @article{ , abstract = {The traditional informed rapidly exploring random tree * algorithm (IRRT*) has several drawbacks, including low efficiency, numerous ineffective samples, strict requirements of the environment, and slow convergence of path length in narrow passages. This study proposes an improved IRRT* integrated with an artificial potential field path planning algorithm (IRRT*-APF) by introducing a Narrow Space Map Recognition Strategy (NSMR), Sampling Guidance Strategy (SG), and Gravitational Elimination Strategy (GE) to address the efficiency problems of the traditional IRRT* algorithm when traversing narrow passages. NSMR identifies narrow passage regions by traversing the grid map and generating a special bias point in these regions. The SG was introduced at special bias points. When random sampling points fall within the range centered on the bias point, they quickly iterate to the specific bias point under the combined effect of gravity and repulsion, generating a large number of effective sample points, thus enabling the robot to efficiently navigate through narrow passages. The GE was used to eliminate the bias point when the robot passed. In the simulation section, data such as the optimal path length, average path length, and algorithm efficiency are analyzed. When comparing IRRT*-APF with IRRT*, the former shows significant advantages in terms of navigation efficiency and the number of sample points when traversing narrow passages.}, archive = {J_ACCESS}, author = {Zhifeng Yao and Zhicheng Liu and Chunsong Han}, doi = {10.1109/ACCESS.2025.3561348}, journal = {IEEE Access}, pages = {68398-68409}, shortjournal = {IEEE Access}, title = {The improved RRT integrated with the artificial potential field path planning algorithm}, volume = {13}, year = {2025}, }
• (2025). FreqWave-TranDuD: A multivariate time series anomaly detection method based on wavelet and fourier transforms. ACCESS, 13, 68384-68397. (www)

  @article{ , abstract = {Time series anomaly detection (AD) plays a crucial role in network systems. It enables the timely detection of anomalies and root cause analysis, helping to prevent unnecessary losses. Existing methods have not fully exploited the frequency information embedded in time series data, limiting their ability to effectively capture global and periodic patterns. In this paper, we propose a novel time series anomaly detection method based on frequency domain feature extraction, referred to as FreqWave-TranDuD. The method adopts an encoder-decoder deep learning architecture, integrating Fourier Transform and Wavelet Transform to extract comprehensive time-frequency information. Fast Fourier Transform (FFT) is used to capture global periodic patterns, while Wavelet Transform captures local patterns. Additionally, LSTM is employed to capture temporal dependencies in the time series. The extracted features are then preliminarily concatenated and fed into the encoder-decoder architecture for accurate anomaly detection. Extensive experiments on six public datasets from domains such as aerospace detection and water treatment demonstrate that our method, FreqWave-TranDuD, outperforms other advanced baseline models.}, archive = {J_ACCESS}, author = {Yashi Huang and Peishun Liu and Rongjia Han and Tian Yin and Quanjie Dou and Yibao Song and Mengqi Luo}, doi = {10.1109/ACCESS.2025.3557571}, journal = {IEEE Access}, pages = {68384-68397}, shortjournal = {IEEE Access}, title = {FreqWave-TranDuD: A multivariate time series anomaly detection method based on wavelet and fourier transforms}, volume = {13}, year = {2025}, }
• (2025). Optimize decision model for urban land development using disc fermatean fuzzy information with Z-numbers. ACCESS, 13, 68370-68383. (www)

  @article{ , abstract = {Urban land development choices manifest complex, ambiguous situations where multiple criteria are involved, making it hard for traditional evaluation methods to address them effectively. The present study introduces an advanced methodological view called the Disc Fermatean Fuzzy Z-Number (D-FFZN) method developed to enhance the accuracy and reliability of urban planning decisions by skillfully addressing uncertainties. The D-FFZN approach was applied in the selection of optimal land development alternatives in a case study through newly formulated aggregation operators based on the weighted averaging and weighted geometric methods. To confirm the method’s performance, a comparative analysis was undertaken between the D-FFZN method and a more renowned multi-criteria decision-making technique, the Combined Compromise Solution (CoCoSo). The findings suggested that D-FFZN offered a more comprehensive and balanced assessment framework that leads to more accurate and reliable decision outcomes. Sensitivity analysis demonstrated that small changes in the parameter value produced minor alterations in the ranking order of alternatives but the D-FFZN model provided a robust optimal solution regardless, thus proving its sturdiness. These findings indicate that the D-FFZN technique has a potential to substantially improve urban land-use planning by equipping stakeholders, policymakers, and urban planners with invaluable guidance in selecting optimal and sustainable development alternatives.}, archive = {J_ACCESS}, author = {Wei Xia and Yang Liu and Ya Pi}, doi = {10.1109/ACCESS.2025.3560685}, journal = {IEEE Access}, pages = {68370-68383}, shortjournal = {IEEE Access}, title = {Optimize decision model for urban land development using disc fermatean fuzzy information with Z-numbers}, volume = {13}, year = {2025}, }
• (2025). “Lure the enemy in deep”: Confronting rogue UAV through diverse hybrid jamming. ACCESS, 13, 68351-68369. (www)

  @article{ , abstract = {This research addresses the challenge in capturing rogue uncrewed aerial vehicle (UAV) strategies based on communication and navigation jamming technology in the absence of observable UAV communication performance parameters. The jamming involves remote communication suppressing (RCS) power control, as well as navigation suppressing (NS) and navigation deception (ND) combination, hence named as the diverse Hybrid jamming (DHJ) strategy. Firstly, we construct the DHJ model, which analyzes the dynamic impact mechanism of DHJ on UAV flight control systems based on common control protocols. Subsequently, we introduce the concept of the Situation Estimation Ring (SER) to overcome reward-driven deficiencies arising from incomplete observation and optimize features of the state space, which essentially considers the flight status feedback mechanism of the UAV to DHJ attacks and will be used to construct the DHJ Markov Decision Process (DHJ-MDP) mathematical framework. Then, the DHJ attack process against UAV is formulated as a game framework, where the counter-UAV system (CUS) generates DHJ strategy solely based on spatial positional states to deceive and capture the UAV, and the UAV adopts flight control strategies to evade jamming based on communication quality and navigation positioning information. Finally, the analysis of Nash equilibrium (NE) properties explain that the SER-based dynamic spatial positioning of UAV can be used for CUS to derive DHJ equilibrium strategies, thereby providing a theoretical foundation for solving these strategies using knowledge-based deep reinforcement learning (DRL) algorithms, with the algorithm’s complexity also being proven. Simulation and comparison experiments demonstrate that the solved DHJ strategy effectively drives UAV toward deceptive destinations under incomplete observation states, achieve the ‘lure the enemy in deep’ effect and the proposed SER-base algorithms enhances algorithm’s convergence speed.}, archive = {J_ACCESS}, author = {Xiaomeng Ma and Meiguo Gao}, doi = {10.1109/ACCESS.2025.3559659}, journal = {IEEE Access}, pages = {68351-68369}, shortjournal = {IEEE Access}, title = {“Lure the enemy in deep”: Confronting rogue UAV through diverse hybrid jamming}, volume = {13}, year = {2025}, }
• (2025). Adaptive RFID data scheduling using proximal policy optimization for reducing data processing latency. ACCESS, 13, 68340-68350. (www)

  @article{ , abstract = {Radio Frequency Identification (RFID) technology has become integral in various industries for automating processes and tracking items in real-time. However, efficiently offloading the large volume of data generated by RFID tags to readers poses significant challenges, particularly in dynamic environments where static or rule-based offloading methods fall short. This paper presents a novel approach for dynamically offloading data using deep reinforcement learning, specifically employing the Proximal Policy Optimization (PPO) algorithm. The proposed method utilizes a central controller equipped with the PPO model to make intelligent, real-time reader selection decisions based on environmental factors such as reader load, tag mobility, and network conditions. The reward function is designed to minimize data processing latency while maintaining balanced reader utilization, resulting in enhanced system efficiency. Extensive experiments demonstrate that the proposed PPO-based strategy significantly reduces average data processing latency by 30% and improves reader load balancing by 16% compared to conventional scheduling methods compared to conventional scheduling methods.}, archive = {J_ACCESS}, author = {Guowei Guo and Xinsen Yang and Ziwei Liang and Zeli Xi and Ximei Zhan and Peisong Li}, doi = {10.1109/ACCESS.2025.3562127}, journal = {IEEE Access}, pages = {68340-68350}, shortjournal = {IEEE Access}, title = {Adaptive RFID data scheduling using proximal policy optimization for reducing data processing latency}, volume = {13}, year = {2025}, }
• (2025). YOLO-SRW: An enhanced YOLO algorithm for detecting prohibited items in X-ray security images. ACCESS, 13, 68323-68339. (www)

  @article{ , abstract = {To address the challenges of false positives and false negatives in prohibited item detection within X-ray security images, caused by complex backgrounds, poor image quality, and varying scales, this paper proposes an improved algorithm based on YOLOv8, named YOLO-SRW, to improve the accuracy of detecting prohibited items. First, inspired by the receptive field attention mechanism and integrating the Large Separable Kernel (LSK) attention mechanism, we designed the Receptive Field Large Separable Kernel Attention (RFLSKA) module. This module dynamically adjusts the spatial receptive field to capture contextual information at different scales, thereby enhancing the understanding and the response to small objects and providing a more comprehensive scene interpretation. Then, we integrate the Shallow Robust Feature Downsampling (SRFD) module to enhance the shallow feature extraction in YOLOv8, enhancing the model’s ability to extract features from low-resolution and feature-sparse targets, thus reducing object information loss. Finally, by combining SCYLLA-IoU (SIoU) and Wise-IoUv3 losses, we design the Wise-SIoU loss function to reduce false negatives and false positives in Prohibited item detection, enhancing the model’s generalization ability. Experimental results on the SIXray dataset demonstrate that YOLO-SRW achieves an mAP of 92.8%, surpassing the original YOLOv8 by 2.4%. Compared to existing mainstream algorithms, YOLO-SRW offers higher detection accuracy, effectively reducing false negatives and false positives for small and multi-scale objects in X-ray security images.}, archive = {J_ACCESS}, author = {Minwei Chen and Zhixian Zhang and Nian Jiang and Xingxing Li and Xin Zhang}, doi = {10.1109/ACCESS.2025.3560840}, journal = {IEEE Access}, pages = {68323-68339}, shortjournal = {IEEE Access}, title = {YOLO-SRW: An enhanced YOLO algorithm for detecting prohibited items in X-ray security images}, volume = {13}, year = {2025}, }
• (2025). Federated synergy: Hierarchical multi-agent learning for sustainable edge computing in IIoT. ACCESS, 13, 68311-68322. (www)

  @article{ , abstract = {The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. This paper introduces the Hierarchical Multi-Agent Federated Actor-Critic (HMAFAC) algorithm. This novel approach integrates hierarchical reinforcement learning with multi-agent federated learning to enhance decision-making in dynamic edge computing environments. It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. A Deep Q-Network (DQN) agent manages task offloading decisions based on real-time system states, ensuring efficient task execution. To maintain data privacy and reduce communication overhead, federated learning aggregates model updates at the edge server, eliminating the need for raw data exchange. Additionally, a hierarchical framework enables adaptive scheduling, allowing flexible computational scaling from edge to cloud servers based on task complexity and network conditions. By effectively reducing latency and energy consumption, enhancing system adaptability, and maintaining computational efficiency, HMAFAC overcomes key limitations of existing IIoT frameworks. Simulation results validate its superior performance compared to baseline methods regarding resource utilization, energy conservation, and model convergence. These findings establish HMAFAC as a robust and scalable framework for optimizing IIoT applications in distributed and resource-constrained environments. Future work will further explore advanced federated learning techniques, enhanced privacy mechanisms, and real-world deployment in industrial systems to improve efficiency and scalability.}, archive = {J_ACCESS}, author = {S. Benila and K. Devi}, doi = {10.1109/ACCESS.2025.3560781}, journal = {IEEE Access}, pages = {68311-68322}, shortjournal = {IEEE Access}, title = {Federated synergy: Hierarchical multi-agent learning for sustainable edge computing in IIoT}, volume = {13}, year = {2025}, }
• (2025). Exon: An oblivious exactly-once messaging protocol with reliable delegation. ACCESS, 13, 68290-68310. (www)

  @article{ , abstract = {TCP is the default transport protocol of choice, namely for message-oriented middleware protocols (e.g., ZMTP, AMQP, MQTT) or distributed language runtimes (e.g., distributed Erlang), where exactly-once (EO) messaging is paramount. However, EO is only guaranteed within the TCP session, since reality shows that TCP connections can fail under many circumstances. Ensuring EO delivery ends up at the middleware layer, at the cost of higher complexity and lack of obliviouness—due to the use of permanent per-peer state. Moreover, using TCP at scale in highly concurrent systems leads to the need for TCP connection multiplexing, and possibly drastic performance loss due to head-of-line blocking. This paper introduces Exon, an oblivious exactly-once messaging protocol, and a corresponding lightweight (requiring no persistent storage, minimal memory, and low computation) library implementation over UDP. Exon uses a novel strategy of a per-message four-way protocol to ensure oblivious exactly-once messaging, with on-demand protocol-level “soft half-connections”, established when needed and safely discarded. Obliviousness here refers to the protocol’s ability to discard connection-specific state between incarnations, although some global information is retained. Exon achieves simultaneously: correctness with no timing assumptions, obliviousness, and performance through merging and pipelining basic protocol messages. Exon also employs a reliable delegation technique to handover the sending responsibility to a mediating node, without violating EO, when the sender the receiver are directly unreachable to each other and even if the message had already been delivered. The empirical evaluation of Exon demonstrates significant improvements (40%) over TCP in throughput and latency under packet loss, while maintaining a negligible (8%) overhead in healthy networks.}, archive = {J_ACCESS}, author = {Ziad Kassam and Paulo Sérgio Almeida and Ali Shoker}, doi = {10.1109/ACCESS.2025.3562332}, journal = {IEEE Access}, pages = {68290-68310}, shortjournal = {IEEE Access}, title = {Exon: An oblivious exactly-once messaging protocol with reliable delegation}, volume = {13}, year = {2025}, }
• (2025). Torque estimation in switched reluctance machines: A comprehensive approach involving inductance modeling techniques. ACCESS, 13, 68270-68289. (www)

  @article{ , abstract = {This work highlights advances in the torque estimation method for Switched Reluctance Machines (SRMs), focusing on a high-precision torque estimator that integrates classical and modern modeling techniques. The employed method uses cubic splines and Lagrange polynomials to model the inductance surfaces in order to optimize the estimated instantaneous torque. This approach optimizes drive systems, making SRMs more efficient for critical industrial applications, such as electric vehicle propulsion and renewable energy systems. The method, validated through simulations and experiments, presents an accuracy of about 97% in the reconstruction of the inductance surface, which guarantees the high performance of the estimated torque. The presented results indicate that the high detail of the inductance variations in SRMs contributes positively to the real-time and low-computation torque estimation. Thus, this work contributes to the development of more efficient electric drive control systems, which allow advances in sustainable, accurate and effective industrial applications.}, archive = {J_ACCESS}, author = {Ricardo Tirone Fidelis and Ghunter Paulo Viajante and Eric Nery Chaves and Carlos E. Tavares and Augusto W. F. V. Da Silveira and Luciano Coutinho Gomes}, doi = {10.1109/ACCESS.2025.3562495}, journal = {IEEE Access}, pages = {68270-68289}, shortjournal = {IEEE Access}, title = {Torque estimation in switched reluctance machines: A comprehensive approach involving inductance modeling techniques}, volume = {13}, year = {2025}, }
• (2025). A new extended kumaraswamy generalized pareto distribution with rainfall application. ACCESS, 13, 68259-68269. (www)

  @article{ , abstract = {In this paper, we introduce a new form of Generalized Pareto (GP) distribution which is an extension of Kumaraswamy generalized Pareto (KumGP) distribution based on KM transformation, referred to as KMKGP distribution. The new distribution uncovers more flexibility for data fitting. The maximum likelihood estimation of all parameters and return levels of KMKGP distribution, as well as their confidence intervals, are illustrated. Due to the complexity of likelihood functions, Newton–Raphson procedure is then implemented via simulation study to find the estimated values of all parameters. For goodness-of-fit test, the proposed distribution is also compared to other distributions through actual datasets. The result reveals that KMKGP distribution indicates improvement over other distributions.}, archive = {J_ACCESS}, author = {Natthinee Deetae and Pannawit Khamrot and Katechan Jampachaisri}, doi = {10.1109/ACCESS.2025.3561150}, journal = {IEEE Access}, pages = {68259-68269}, shortjournal = {IEEE Access}, title = {A new extended kumaraswamy generalized pareto distribution with rainfall application}, volume = {13}, year = {2025}, }
• (2025). Semantic template recognition of human activities in wearable sensor data using siamese network. ACCESS, 13, 68245-68258. (www)

  @article{ , abstract = {Human activity recognition plays a pivotal role in various fields, such as healthcare monitoring, smart environments, and human-computer interaction. In this study, we propose a novel approach for sensor-based human activity recognition.The key contribution of our work consists of first, defining activity representations we call “semantic templates”, which represent prototypical activity patterns of different human activity classes; second, designing and implementing a novel lightweight and versatile classifier for sensor-based HAR that leverages template matching through a deep-learning Siamese network. Through a series of rigorous experiments conducted on three distinct public datasets, we also demonstrate that the proposed approach yields enhanced performance in recognizing human activities when compared to a traditional deep multi-class classifier for resource-constrained devices. Furthermore, we showcase how our approach outperforms previous works by up to 20% in classifying previously unseen activities, paving the way for developing class-incremental continuous learning techniques.}, archive = {J_ACCESS}, author = {Emanuele Lattanzi and Chiara Contoli and Lorenzo Calisti and Giacomo Di Fabrizio and Nicholas Kania and Susanna Peretti}, doi = {10.1109/ACCESS.2025.3561697}, journal = {IEEE Access}, pages = {68245-68258}, shortjournal = {IEEE Access}, title = {Semantic template recognition of human activities in wearable sensor data using siamese network}, volume = {13}, year = {2025}, }
• (2025). Optimal computation offloading decisions based on system utility and cost balance. ACCESS, 13, 68232-68244. (www)

  @article{ , abstract = {Edge computing provides terminal users with computing resources and data processing capabilities by deploying edge nodes near Internet of Things (IoT) devices to meet the processing demands of terminal applications. In this study, we address the offloading problem of partitionable tasks in collaborative edge computing systems and propose a new evaluation model that jointly optimizes the time and energy costs along with the system utility. By calculating the full load rate of edge server clusters, we describe and evaluate the operational efficiency of the system, aiming to design a computation offloading decision scheme that not only reduces costs, but also enhances system utility. Based on the new evaluation model, an objective function is constructed to minimize the system’s cost-utility ratio, and an improved particle swarm optimization algorithm with enhanced search capabilities is proposed to solve it by designing a computation offloading strategy. Experiments conducted in a simulation based on the edge system of an industrial park verified that the cost-utility balanced optimization model achieves at least 7% reduction in the cost-utility ratio across varying user scales compared to traditional cost optimization models, while achieving over 20% reduction in optimization efficiency under different edge server scales. The computation offloading efficiency is significantly improved, effectively boosting the overall system performance through the collaborative optimization of resource efficiency and execution costs.}, archive = {J_ACCESS}, author = {Bingxue Zhang and Xisheng Li and Jia You}, doi = {10.1109/ACCESS.2025.3560291}, journal = {IEEE Access}, pages = {68232-68244}, shortjournal = {IEEE Access}, title = {Optimal computation offloading decisions based on system utility and cost balance}, volume = {13}, year = {2025}, }
• (2025). Fully distributed low-complexity UAV formation control with fixed-time full-state error performance. ACCESS, 13, 68224-68231. (www)

  @article{ , abstract = {This paper presents a fully distributed, low-complexity UAV formation controller design with fixed-time full-state error performance, which is able to address the difficulties in obtaining global information and suppressing external disturbances without observer or adaptive law in existing solutions. The position of neighboring UAVs is the only information to be exchanged, and desired state observer is not required, thereby reducing communication costs, which is well-suited to larger-scale UAV formation systems. The proposed control scheme has the capability to handle external disturbances without observer or adaptive law, simplifying the controller’s design and implementation process, reducing computational burden. Moreover, by imposing fixed-time performance constraints on tracking error and virtual error, fixed-time formation tracking error and virtual velocity tracking error can be achieved, which also improves the response speed and accuracy of UAV formation, making it more capable of meeting the time requirements of actual tasks and adapting to dynamically changing environments. Additionally, all closed-loop signals are rigorously proven to be semi-globally ultimately uniformly bounded. Finally, numerical simulation is conducted to verify the effectiveness of proposed control scheme.}, archive = {J_ACCESS}, author = {Pengfei Yu and Qing Li}, doi = {10.1109/ACCESS.2025.3557921}, journal = {IEEE Access}, pages = {68224-68231}, shortjournal = {IEEE Access}, title = {Fully distributed low-complexity UAV formation control with fixed-time full-state error performance}, volume = {13}, year = {2025}, }
• (2025). Sensorless deadbeat control strategy with adaptive capability for PMSM drive machines. ACCESS, 13, 68212-68223. (www)

  @article{ , abstract = {This research work designs an adaptive sensorless deadbeat control (AS-DBC) method for permanent magnet synchronous motors (PMSMs). The proposed AS-DBC effectively merges the merits of deadbeat control (DBC) for ensuring fast dynamic response while incorporating the outstanding features of adaptive control to enforce satisfactory control performance of PMSM drive systems such as insensitive to system perturbation, system mismatches, and parameter alterations, etc. Hence, merging these two methods elevates the robustness and enhances steady-state and dynamic responses. As the DBC contributes to improved dynamic response, the adaptive compensating items satisfy the practicality of the system to tolerate nonlinear behavior inherent in PMSM drives. The adaptive control terms are dynamically updated online to effectively suppress system uncertainties under severe conditions; for instance, low-speed region. The proposed AS-DBC method is then validated leveraging the PSIM simulation tool and experimentally tested using a PMSM drive setup, exhibiting superior performance in terms of precisely tracking capability covering low-, medium-, and high-speeds, and reduced estimated speed ripples compared to classical control methods.}, archive = {J_ACCESS}, author = {Sadeq Ali Qasem Mohammed and Mohammad M. Almuhaini and Kyo-Beum Lee}, doi = {10.1109/ACCESS.2025.3561731}, journal = {IEEE Access}, pages = {68212-68223}, shortjournal = {IEEE Access}, title = {Sensorless deadbeat control strategy with adaptive capability for PMSM drive machines}, volume = {13}, year = {2025}, }
• (2025). Enhanced blood loss quantification using a novel multi-wavelength imaging approach. ACCESS, 13, 68201-68211. (www)

  @article{ , abstract = {Current clinical methods for quantifying blood loss have limitations, often leading to inaccurate assessments, such as overestimations caused by non-blood fluids mixing with blood. Given blood’s ability to absorb Near Infrared (NIR) light, this wavelength is widely utilized in blood spectroscopy and advanced techniques incorporating computer vision and machine learning. This study then investigates a multi-wavelength approach that extracts and integrates features from images captured under visible (Vis) light, NIR light, and a combination of both (Vis-NIR), which leverages unique information provided by individual wavelengths. The findings reveal that using color features from the multi-wavelength method significantly reduced errors by 56% compared to relying on a single wavelength. Further incorporating spectral and spatial features alongside hyperparameter tuning decreased errors by an additional 23%. A mean absolute percentage error (MAPE) of 8.475% was achieved, falling well within the acceptable error threshold of 20% in clinical blood loss estimations. Lastly, an individualized interpretation for each surgical test gauze is provided, a useful tool to understand how the machine learning model predicts the absorbed blood volume.}, archive = {J_ACCESS}, author = {Ara Abigail E. Ambita and Laura T. David and Catherine Sim Co and Prospero C. Naval}, doi = {10.1109/ACCESS.2025.3561835}, journal = {IEEE Access}, pages = {68201-68211}, shortjournal = {IEEE Access}, title = {Enhanced blood loss quantification using a novel multi-wavelength imaging approach}, volume = {13}, year = {2025}, }
• (2025). Evaluating and optimizing MySejahtera app analytics for sustainable digital government services. ACCESS, 13, 68180-68200. (www)

  @article{ , abstract = {Numerous government entities have implemented digital strategies to manage the COVID-19 crisis throughout the pandemic. Malaysia has enhanced its initiatives through its official mobile applications (apps), MySejahtera. MySejahtera plays an essential role in managing COVID-19, providing valuable preferences to guide health strategies, and has remained the principal application endorsed by the Ministry of Health of Malaysia. In contrast to over 200 other applications created by various Malaysian government agencies, MySejahtera stands out as the most extensively downloaded and persistently utilized application among users, suggesting a marked discrepancy in user adoption and engagement levels with other government apps in Malaysia. Existing research is deficient in examining valuable data resources and user experience in government app optimization. This study undertakes a comprehensive evaluation of MySejahtera through the meticulous analysis of application analytics and user feedback, aiming to assess its operational efficacy, patterns of user engagement, and performance indicators. Through the implementation of both statistical and thematic analyses, this study presents critical factors that affect the app’s sustainability and its effectiveness within the realm of Malaysia digital governmental services. By integrating the Technology Acceptance Model (TAM) with sustainability theories, this study offers a basis for appraising user engagement and application efficacy. Furthermore, this comprehensive study endeavor meticulously proposes a variety of optimization strategies that are fundamentally rooted in the best practices that have been observed and documented in the MySejahtera application, and these strategies encompass a wide array of enhancements to its key feature insights, emphasizing key feature insights, frequent updates, response to app reviews, user engagement, emerging technology adoption, geographical, demographic, and language diversity, enhanced security and data privacy, optimize technical specifications, observing performance issues, and strategizing organizational efforts to address the dynamic requirements of citizens that can evolve in tandem with the needs of the user base and the technological landscape. The findings derived from this study significantly contribute to the existing body of knowledge surrounding software measurement methodologies by effectively demonstrating the critical role that app analytics play in the ongoing refinement and enhancement of mobile applications within the public sector, thereby providing a framework for evaluating their efficacy and impact. This study further offers a series of strategic recommendations that are vital for ensuring the long-term sustainability of mobile government applications, not only within the context of Malaysia but also in a broader, global perspective, thereby highlighting the importance of a forward-thinking approach in the development and maintenance of such technological initiatives.}, archive = {J_ACCESS}, author = {Adawiyah Md Ashar and Nur Fazidah Elias and Ruzzakiah Jenal and Meng Chun Lam and Maheshwara Rao Appannan and Siti Aishah Iskandar}, doi = {10.1109/ACCESS.2025.3561357}, journal = {IEEE Access}, pages = {68180-68200}, shortjournal = {IEEE Access}, title = {Evaluating and optimizing MySejahtera app analytics for sustainable digital government services}, volume = {13}, year = {2025}, }
• (2025). Method for mitigating attention to inappropriate content based on attention dynamics model. ACCESS, 13, 68172-68179. (www)

  @article{ , abstract = {The expansion of the attention economy has led to the growing issue of inappropriate content being posted by profit-driven users. Previous countermeasures against inappropriate content have relied on moderation, which raises ethical concerns, or information diffusion control, which requires considering larger scale networks, including general users. This study proposes an imitation strategy as an intervention method that does not rely on moderation and focuses on a relatively smaller scale competitive network of information disseminators rather than the entire social network. The imitation strategy is a novel approach that utilizes increased competition among information disseminators through imitation to reduce attention to inappropriate content. Through theoretical analysis and numerical simulations, this paper demonstrates that the imitation strategy is more effective when nodes with higher eigenvector centrality are selected as targets and nodes with lower eigenvector centrality are chosen as imitators.}, archive = {J_ACCESS}, author = {Naoki Hirakura}, doi = {10.1109/ACCESS.2025.3561496}, journal = {IEEE Access}, pages = {68172-68179}, shortjournal = {IEEE Access}, title = {Method for mitigating attention to inappropriate content based on attention dynamics model}, volume = {13}, year = {2025}, }
• (2025). AI-driven resource allocation for RIS-assisted NOMA in IoT networks. ACCESS, 13, 68152-68171. (www)

  @article{ , abstract = {Internet of Things (IoT) is playing a significant role in wireless communication for future applications such as smart home, smart cities, intelligent transportation, telecare, and various other applications. However, the emergence of IoT on a large scale has introduced numerous challenges to current wireless communication system connectivity, coverage and energy dissipation. We propose a Reconfigurable Intelligent Surface (RIS)-assisted downlink Non-Orthogonal Multiple Access (NOMA) for Internet of Things (IoT) network, where we address the challenge of optimizing power allocation, RIS phase shifts, and energy efficiency. In our approach, users are first clustered based on channel gain differences and then performed optimization of resources. The primary objective is to maximize system performance through a series of optimization techniques. Initially, joint optimization of power allocation and RIS phase shifts is carried out to enhance energy efficiency, addressing the non-convexity of the problem through alternating optimization and fractional programming. Subsequently, an alternative optimization strategy is employed using the Karush-Kuhn-Tucker (KKT) conditions to further refine power allocation and RIS phase shifts, aiming to maximize the effective throughput across the transmission period. The deployment of machine learning (ML) is critically important for addressing the challenges posed by the explosive growth in data volume and computational complexity, particularly in the optimization of smart 6G networks. In the final phase, we introduce a deep learning (DL) and reinforcement learning (RL) approach to jointly optimize power allocation and RIS phase shifts in dynamic environments. The DL approach demonstrates superior performance in terms of system sum rate, especially under varying network conditions, while the RL approach excels in long-term reward optimization. Numerical results validate the proposed framework, showing significant improvements in both sum rate and energy efficiency.}, archive = {J_ACCESS}, author = {Syed M. Hamedoon and Jawwad Nasar Chattha and Umair Rashid and S. M. Ahsan Kazmi and Manuel Mazzara}, doi = {10.1109/ACCESS.2025.3559231}, journal = {IEEE Access}, pages = {68152-68171}, shortjournal = {IEEE Access}, title = {AI-driven resource allocation for RIS-assisted NOMA in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Research on automatic guidance technology of tube-sheet welding based on vision and laser displacement sensor. ACCESS, 13, 68141-68151. (www)

  @article{ , abstract = {Utilizing intelligent sensors to obtain the coordinates of the tube bundle in the robot coordinate system during the tube-sheet welding process represents a current trend in the automated guidance technology for tube-sheet welding. In this paper, a surface array camera and a laser displacement sensor are integrated to establish the three-dimensional pose relationship among the robot, the tube-sheet plane, and the tube bundle, and an automatic welding guidance process based on this fused sensor is proposed. Taking the six-axis robot automatic welding system as an example, the key links affecting the final guidance accuracy are optimized, and ultimately a set of automated guidance technology for tube-sheet welding is developed. The tube bundle positioning process is divided into two steps, namely coarse positioning and fine positioning, to enhance the positioning accuracy. This paper presents the working process and the calibration method of the fused sensor and verifies the accuracy of the sensor. Compensation grids are divided into the motion space of the six-axis robot to increase its absolute positioning accuracy. Experiments indicate that after spatial grid compensation, the accuracy of the tube-sheet welding guidance can increase by 61%,demonstrating the feasibility of the automated guidance technology proposed in this paper.}, archive = {J_ACCESS}, author = {Yanyu Ding and Han Su and Ruobing Zhang and Wenkai Zhao and Xiyao Cui}, doi = {10.1109/ACCESS.2025.3560490}, journal = {IEEE Access}, pages = {68141-68151}, shortjournal = {IEEE Access}, title = {Research on automatic guidance technology of tube-sheet welding based on vision and laser displacement sensor}, volume = {13}, year = {2025}, }
• (2025). Time-frequency analysis and neural network-based multipath mitigation method for precise positioning. ACCESS, 13, 68127-68140. (www)

  @article{ , abstract = {Multipath errors caused by signal reflection are challenging to correct when compared to other errors in radio navigation. Specifically, multipath error for indoor positioning is a major error because various materials that surround the environment can cause errors through reflected signals. This study focuses on mitigating multipath in single-transmitter-based indoor positioning, which employs a transmitter emitting multi-channel signals and uses carrier-phase measurements for precise positioning with low noise. Owing to the difficulty in accurately modelling reflected signals mathematically, this study proposes a neural-network-based technique to detect and categorize multipath-distorted signals using short-time Fourier transform (STFT) to extract features in the time-frequency domain. The STFT magnitudes in the spectrogram are used to train and validate the neural networks that detect and isolate multipath-distorted measurements to minimize positioning errors. This paper also presents a novel method that employs a trained neural network to classify the most contaminated signals by considering a single-transmitter-based positioning system with an antenna array. Experiments conducted under challenging indoor conditions demonstrate an approximately 30% reduction in the positioning error after applying the proposed multipath mitigation methods.}, archive = {J_ACCESS}, author = {Min-Ji Kim and O-Jong Kim and Jikang Lee and Changdon Kee and Juhyun Maeng}, doi = {10.1109/ACCESS.2025.3561290}, journal = {IEEE Access}, pages = {68127-68140}, shortjournal = {IEEE Access}, title = {Time-frequency analysis and neural network-based multipath mitigation method for precise positioning}, volume = {13}, year = {2025}, }
• (2025). EEG-based detection of induced relaxation. ACCESS, 13, 68111-68126. (www)

  @article{ , abstract = {Relaxation, which refers to the absence of tension and stress, is important for health because extended periods of tension may lead to illness. This study detected induced relaxation through electroencephalography (EEG) for the real-time monitoring of brain activity. The Multivariate Empirical Mode Decomposition with Dynamic Phase-Synchronized Hilbert-Huang Transform (MEMD-DPS-HHT) algorithm was utilized to extract dynamic brain wave patterns from the EEG data of participants exposed to the blended essential oils. Preprocessing included artifact removal using independent component analysis (ICA), bandpass filtering within the 4-12 Hz range, and normalization. The EEG data were decomposed into five intrinsic mode functions (IMFs) using multivariate empirical mode decomposition (MEMD). The average frequencies of IMF1 (8.56 Hz) and IMF2 (4.91 Hz) are within the designated frequency range. Dynamic phase synchronization and instantaneous frequency analysis employ an adaptive time-frequency-energy representation. The phase synchronization analysis across channels indicated a maximum coherence of 0.87. The dynamic Hilbert spectra and relaxation indices, pre- and post-stimulation, showed an average maximum of 0.16, aligning with the paired t-test results ( $p \ge 0.05$ ), suggesting no significant difference in the Theta and Alpha bands (TA band) frequencies and stimulation. Two-way repeated measures ANOVA indicated $p =1$ , implying no significant effect of stimulation on the TA band. The results demonstrate the ability of the MEMD-DPS-HHT to analyze cross-channel, multi-dimensional, dynamic EEG data associated with relaxation. The average relaxation index from this dataset was relatively low, suggesting that the impact of this blended essential oil stimulation may differ among individuals.}, archive = {J_ACCESS}, author = {Bunyarat Umsura and Wanus Srimaharaj and Phakkharawat Sittiprapaporn and Nina Bencheva and Ratchuporn Suksathan and Roungsan Chaisricharoen}, doi = {10.1109/ACCESS.2025.3561142}, journal = {IEEE Access}, pages = {68111-68126}, shortjournal = {IEEE Access}, title = {EEG-based detection of induced relaxation}, volume = {13}, year = {2025}, }
• (2025). Enhancing crowdfunding success with machine learning and visual analytics: Insights from chinese platforms. ACCESS, 13, 68097-68110. (www)

  @article{ , abstract = {Crowdfunding helps SMEs and entrepreneurs secure financing, but many projects fail. This study examines how technological features of crowdfunding platforms, specifically advanced analytics and visual tools, influence project success. Using data from 2,285 projects (2022–2024) on Chinese platforms Xiaomi Youpin, Modian, and Zaodian Xinhuo, we apply a Random Forest model to identify key predictors of success. We employ the Investment Attractiveness Matrix to visualize relationships among project innovativeness, risk, and success rates. Our findings indicate that technological features significantly enhance success rates. The Random Forest model highlights critical factors such as project quality signals and social capital. The Investment Attractiveness Matrix aids creators in strategically positioning their campaigns. This research contributes by integrating machine learning and visual analytics into crowdfunding studies, offering a novel approach. It extends the Resource-Based View by demonstrating that technological resources are critical capabilities for platforms. Applying Signaling and Social Capital theories, we show how presentation and networks impact success. Practically, the study provides insights for platforms and project initiators to optimize strategies through advanced analytics.}, archive = {J_ACCESS}, author = {Wang Yunfeng and Othman Ibrahim}, doi = {10.1109/ACCESS.2025.3546265}, journal = {IEEE Access}, pages = {68097-68110}, shortjournal = {IEEE Access}, title = {Enhancing crowdfunding success with machine learning and visual analytics: Insights from chinese platforms}, volume = {13}, year = {2025}, }
• (2025). Interpretability-oriented adjustment of K-means: A multiple-objective particle swarm optimization framework. ACCESS, 13, 68084-68096. (www)

  @article{ , abstract = {Clustering is an unsupervised machine learning technique used to partition unlabeled data into different groups. However, traditional clustering methods only provide a set of results without any explanations. While numerous methods in the literature attempt to explain clustering results, the explanation rules provided by these models are fixed and sacrificing clustering quality for the sake of making the rules easier to understand. To address this, this paper proposes a feature selection model to balance the interpretability and clustering quality. The model consists of two stages. In the first stage, the concept of explanatory radius is defined, and the explanatory radius of each cluster in the initial clustering results is calculated. In the second stage, a feature subspace is selected, and the dataset is re-clustered in this subspace using a proposed formula to obtain a new clustering result. Three objective functions are designed to evaluate the performance of the new clustering results in terms of cluster quality and interpretability. Subsequently, the model utilizes a multi-objective optimization framework to optimize the three objective functions in the solution space formed by different feature subspaces, resulting in a set of Pareto solutions. Each solution in the Pareto set represents a trade-off between clustering quality and interpretability. Finally, the contribution of features during the generation process of each cluster is used to represent the interpretability of each cluster’s result. The proposed method is validated on both artificial datasets and real-world datasets, and comparative analyses are conducted with the latest explainable clustering algorithms in the literature. The analysis results demonstrate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Liang Chen and Leming Sun and Caiming Zhong}, doi = {10.1109/ACCESS.2025.3558716}, journal = {IEEE Access}, pages = {68084-68096}, shortjournal = {IEEE Access}, title = {Interpretability-oriented adjustment of K-means: A multiple-objective particle swarm optimization framework}, volume = {13}, year = {2025}, }
• (2025). Content-based image retrieval for multi-class volumetric radiology images: A benchmark study. ACCESS, 13, 68066-68083. (www)

  @article{ , abstract = {With the growing number of images generated daily in radiological practices and the digitization of historical studies, we face large databases where metadata can be incomplete or incorrect. Content-based image retrieval (CBIR) can help to manage these datasets by efficiently locating and retrieving relevant information by only using image data as a query. Recent studies have shown the potential use of pre-trained vision embeddings for CBIR in the context of radiology image retrieval for specific tasks like single-organ retrieval or distinguishing between normal and abnormal tissues. However, a comprehensive benchmark for the retrieval of 3D volumetric medical images is still lacking. In this study, we establish a benchmark for multi-class volumetric retrieval for different retrieval modes. The retrieval modes are defined based on end-point use-cases, which include Full-Scan Retrieval, Targeted-Region Retrieval, and Precise-Localization Retrieval. We utilize the TotalSegmentator dataset, comprising 104 detailed anatomical structures, to evaluate the performance of embeddings derived from pre-trained models across the proposed retrieval modes. We show the potential of CBIR for organ retrieval by achieving a retrieval recall of 1.0 for diverse anatomical regions with a wide size range. The methodology presented in this paper utilizes publicly available image data and embedding models without requiring any task-specific fine-tuning. This benchmark aims to enable further development and allow for a more consistent evaluation of CBIR approaches in the context of 3D medical imaging.}, archive = {J_ACCESS}, author = {Farnaz Khun Jush and Steffen Vogler and Tuan Truong and Matthias Lenga}, doi = {10.1109/ACCESS.2025.3561594}, journal = {IEEE Access}, pages = {68066-68083}, shortjournal = {IEEE Access}, title = {Content-based image retrieval for multi-class volumetric radiology images: A benchmark study}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of image super-resolution for cavity detection in irradiated materials. ACCESS, 13, 68052-68065. (www)

  @article{ , abstract = {Radiation-induced swelling in structural materials is a significant challenge for the safe operation of nuclear power plants. One of the promising solutions to accelerate the understanding of swelling is the development of machine learning tools for accelerated characterization of irradiated microstructures, focusing on cavities which contribute to a materials swelling response. In this paper, we examine an object detection model for cavities, YOLOv8, for cavity detection using the datasets from the Canadian Nuclear Laboratory (CNL) and Nuclear Oriented Materials & Examination (NOME). Specifically, we explored the use of Image Super-Resolution (ISR) techniques to enhance the detection performance either in the underfocused or overfocused condition for improved small cavity detection. The overall F1-score increase of 6.8% via ISR, highlights the effectiveness of ISR techniques in enhancing the model’s performance in detecting irradiation-induced cavities across all modalities. Furthermore, this study presents a comparative evaluation of YOLOv8 and Faster R-CNN, discussing their detection trade-offs and suitability for different imaging conditions. In addition to evaluating overall detection accuracy, this work assesses the impact of ISR on both models’ precision and recall for cavity detection.}, archive = {J_ACCESS}, author = {John Olamofe and Lijun Qian and Kevin G. Field}, doi = {10.1109/ACCESS.2025.3559443}, journal = {IEEE Access}, pages = {68052-68065}, shortjournal = {IEEE Access}, title = {Performance evaluation of image super-resolution for cavity detection in irradiated materials}, volume = {13}, year = {2025}, }
• (2025). Prompt tuning techniques for chinese idiom recommendation. ACCESS, 13, 68037-68051. (www)

  @article{ , abstract = {Chinese idioms pose significant challenges in natural language processing (NLP) due to their complex, non-compositional nature and frequent embedded cultural and historical meanings. This study investigates prompt tuning techniques for Chinese idiom recommendation, exploring multiple-choice (MC) prompts, binary classification (BC) prompts, and prompt ensembling strategies. We also introduce an innovative dynamic candidate sampling strategy (DCSS) technique designed to mitigate the overfitting issues commonly encountered with prompt tuning methods on Chinese idiom datasets. Our experimental results demonstrate that prompt tuning combined with ensembling methods significantly improves model performance across multiple datasets. The proposed methods outperform the state-of-the-art (SOTA) methods while maintaining training and inference efficiency. Moreover, we show that prompt tuning can effectively be generalized for other NLP tasks, such as sentiment analysis.}, archive = {J_ACCESS}, author = {Shun-Ming Wang and I-Fang Su and Yu-Chi Chung}, doi = {10.1109/ACCESS.2025.3561188}, journal = {IEEE Access}, pages = {68037-68051}, shortjournal = {IEEE Access}, title = {Prompt tuning techniques for chinese idiom recommendation}, volume = {13}, year = {2025}, }
• (2025). Assessment of virtual reality as a pedagogical tool using ARCS and TAM in a latin american context. ACCESS, 13, 68020-68036. (www)

  @article{ , abstract = {Virtual reality (VR) tools represent current interactive computerized simulation tools. The application of VR can provide immersive simulations by facilitating hands-on exploration and analysis, fostering a deeper understanding of complex phenomena. VR can also permit the development of competencies in different contexts, such as in education. This article investigates the potential of VR as an educational tool at two educational levels in Chile: high school and university students. Given the positive experience results, this article aims to generalize in academic contexts about how the implementation of VR can support learning in the academic field and analyze the associated benefits and challenges. The experimental study was carried out with groups of students from the two educational levels, applying a standard instrumental model to identify VR’s advantages and limitations concerning improving students’ learning, participation, and motivation to continue learning with similar experiences. We highlight that the results obtained provided valuable information for understanding the impact of VR in education and offering recommendations for its effective implementation in educational settings. The primary limitation of this study is the existing hardware limitations that prevent this experience from being repeated in other academic settings. However, technology has reduced costs and is more accessible than before. Concerning the students’ experience, they expressed their satisfaction and motivation regarding experiencing VR and related learning. Hence, without a doubt, VR represents a transformative tool in education.}, archive = {J_ACCESS}, author = {Jorge Serrano-Malebrán and Cristian Vidal-Silva and Franco Campos-Nuñez and Romina Valeria Gómez López and Constanza Araya-Márquez and Katalina Plaza-Carvajal}, doi = {10.1109/ACCESS.2025.3560478}, journal = {IEEE Access}, pages = {68020-68036}, shortjournal = {IEEE Access}, title = {Assessment of virtual reality as a pedagogical tool using ARCS and TAM in a latin american context}, volume = {13}, year = {2025}, }
• (2025). Enhancing LLM reasoning capabilities through brokered multi-expert reflection. ACCESS, 13, 67993-68019. (www)

  @article{ , abstract = {Large Language Models (LLMs) have found increasing application in tasks requiring multi-step reasoning, yet challenges such as hallucinations and inconsistencies in the generated responses persist. This study presents an innovative methodology to enhance the reasoning capabilities of LLMs by brokering and integrating multiple expert LLMs within a reflection layer to provide targeted feedback on the reasoning trajectories of the base LLM. The approach employs a foundational pre-trained LLM as the base model, which is further supported by agents to promote cognitive assistance for specific task types. In instances where conclusions are deemed incorrect or reasoning is interrupted, these instances are forwarded to the expert LLM layer, which includes systems such as Claude-3 haiku for intricate contexts and MedAlpaca for medical reasoning, to deliver feedback on the base model’s reasoning paths. This feedback forms a ‘reflection pool,’ enabling the base LLM to amend and enhance its reasoning trajectories in subsequent iterations. The experiments conducted across diverse datasets, including HotPotQA, SimpleQA, and PubmedQA, underscore the proposed architecture’s efficacy in augmenting success signals, Rouge-L scores (indicative of quality and precision), and CTRLEval Consistency Scores (indicative of coherence and consistency). The architecture effectively addresses the issues of hallucinations and inconsistencies that frequently occur in multi-step reasoning. Importantly, the approach exhibits considerable potential in tackling domain-specific tasks, underscoring the importance of achieving correct and reliable conclusions. To facilitate further investigation and validation of our proposed brokered multi-expert reflection framework for non-commercial use, the source code of our system is available at https://github.com/WiZY936/Brokered-Multi-Expert-Reflection}, archive = {J_ACCESS}, author = {Tejasvee Sheokand and Garveet Jain and Arshdeep Bahga and Vijay K. Madisetti}, doi = {10.1109/ACCESS.2025.3561693}, journal = {IEEE Access}, pages = {67993-68019}, shortjournal = {IEEE Access}, title = {Enhancing LLM reasoning capabilities through brokered multi-expert reflection}, volume = {13}, year = {2025}, }
• (2025). Edge AI-based detection for defective coffee beans using deep learning and streamlit framework. ACCESS, 13, 67977-67992. (www)

  @article{ , abstract = {Coffee is one of the most popular beverages nowadays. To ensure the quality of coffee beans, farmers must go through a sorting process to distinguish between normal and defective beans. Manual sorting by the farmer is time-consuming, prone to errors due to fatigue, and results in inconsistent quality. To solve these problems, this paper makes a three-fold contribution. First, an Edge AI-based detection for defective coffee beans using the USK-Coffee dataset, which contains a total of 8,000 images and is divided into 4 classes, is proposed. To the best of our knowledge, our USK-Coffee dataset is currently the most comprehensive green coffee bean dataset. To be specific, the edge device used is the NVIDIA (Registered trademark) Jetson Orin Nano (Trademark) 8GB. The model outputs the confidence percentage of defective coffee beans. For visual analysis, we built a GUI-friendly web interface through the Streamlit framework. Second, this study provides an extensive comparison of deep learning models using famous CNN and Transformer-based architectures in order to detect normal and defective coffee beans, namely ResNet-34, VGG-16, EfficientNet-B7, DenseNet121, InceptionV4, ViT, Swin Transformer, ConvNeXt, and FocalNet. These architectures are compared according to several performance evaluation metrics, including accuracy, recall, specificity, F-score, and running time. Finally, we implemented Grad-CAM heatmaps with increasing generalizability for CNN architecture interpretation to analyze the impact of CNN deep learning models on heatmap creation and visually identified the defect location. In the case of normal coffee beans, the heatmap is dispersed and does not highlight specific areas of the coffee bean image. The USK-Coffee dataset can be found through this link: https://coffee.comvislab-usk.org/.}, archive = {J_ACCESS}, author = {Kahlil Muchtar and Yayang Hafifah and Alifya Febriana and Rahmad Dawood and Ahmadiar Ahmadiar and Al Bahri and Chih-Yang Lin and Ervin Yohannes}, doi = {10.1109/ACCESS.2025.3561189}, journal = {IEEE Access}, pages = {67977-67992}, shortjournal = {IEEE Access}, title = {Edge AI-based detection for defective coffee beans using deep learning and streamlit framework}, volume = {13}, year = {2025}, }
• (2025). Multiscale structural information-based laplacian generative adversarial network representation learning. ACCESS, 13, 67966-67976. (www)

  @article{ , abstract = {In recent years, network representation learning (NRL) has attracted increasing attention due to its efficiency and effectiveness to analyze network structural data. NRL aims to learn low-dimensional representations of nodes while preserving their structural information, and preserving multiscale structural information of nodes is important for NRL. Deep learning-based algorithms are popular owing to their good performance to learn network representations, but they lack sufficient interpretability as closed boxes. In this study, we propose a novel algorithm called Multiscale structural information-based Laplacian generative adversarial Network Representation Learning (MLNRL). This algorithm consists of two components: 1) multiscale structural information preserving component, where a shift positive pointwise mutual information matrix (SPPMI) is calculated for storing multiscale structural information; 2) Laplacian generative adversarial learning component, where the ideas of Laplacian pyramid and generative adversarial networks are leveraged to generate robust and meaningful representations. We apply our model to three downstream tasks on real-world datasets for evaluation, and the results show that our model outperforms the baselines in almost all cases. Then, we perform an ablation study and verified the necessity of both components. We also investigate the hyperparameter sensitivity to prove the robustness of MLNRL.}, archive = {J_ACCESS}, author = {Yan Liu and Xi Chen and Zheng Lu and Ziyue Wu}, doi = {10.1109/ACCESS.2025.3561047}, journal = {IEEE Access}, pages = {67966-67976}, shortjournal = {IEEE Access}, title = {Multiscale structural information-based laplacian generative adversarial network representation learning}, volume = {13}, year = {2025}, }
• (2025). Attribute-guided alignment model for person re-identification with feature distillation and enhancement. ACCESS, 13, 67952-67965. (www)

  @article{ , abstract = {This paper proposes an attribute-guided alignment model for person re-identification with feature distillation and enhancement. The proposed attribute-guided part-level distillation model learns spatial body-part information, enabling the alignment of attribute features with their corresponding body regions. Hence, the localization ability of the attention mechanism in spatial information is fully exploited to address the feature misalignment issue in person re-identification tasks. Furthermore, an attribute-guided part-level feature enhancement method is proposed, which re-weights local features by selecting the top-3 most distinctive features for person re-identification during the inference phase. Experimental results on person re-identification datasets demonstrate the effectiveness of the proposed model.}, archive = {J_ACCESS}, author = {Rongnan Huang and Simeng Peng and Yaotao Xiahou and Xiaochao Li}, doi = {10.1109/ACCESS.2025.3561201}, journal = {IEEE Access}, pages = {67952-67965}, shortjournal = {IEEE Access}, title = {Attribute-guided alignment model for person re-identification with feature distillation and enhancement}, volume = {13}, year = {2025}, }
• (2025). A single-phase five-level switched-capacitor boost inverter with improving voltage gain. ACCESS, 13, 67942-67951. (www)

  @article{ , abstract = {In this paper, a new topology of single-phase five-level switched-capacitor boost inverter (5L-SCBI) is introduced to improve voltage gain in comparison with existing impedance-source inverters (ISIs). With switched-capacitor (SC) structure used in this topology, boost factor is increased to twice that in existing ISIs, and capacitor voltages are self-balanced. Unlike conventional SC-based inverters, charged/discharged currents of capacitors are limited by input current which significantly reduces current stresses of capacitors and switching devices. In this topology, the output of DC-DC stage has two voltage levels, which helps to create five levels at the output of H-bridge circuit. A new modified pulse-width modulation (PWM) technique is developed to control the proposed inverter. With the introduced control algorithm, modulation index utilization is improved compared to existing ISIs. Consequently, component voltage stresses are improved. Moreover, one switch of boost circuit can obtain zero-voltage switching (ZVS) on/off, which helps to reduce the switching loss of this switch. Experimental results are conducted by 1-kW laboratory prototype to verify the proposed inverter.}, archive = {J_ACCESS}, author = {Duc-Tri Do and Thuan-Thien Vu and Khai M. Nguyen and Vinh-Thanh Tran}, doi = {10.1109/ACCESS.2025.3560985}, journal = {IEEE Access}, pages = {67942-67951}, shortjournal = {IEEE Access}, title = {A single-phase five-level switched-capacitor boost inverter with improving voltage gain}, volume = {13}, year = {2025}, }
• (2025). Evidence of diseconomies of scale in subscription-based video on demand services: The case of netflix. ACCESS, 13, 67925-67941. (www)

  @article{ , abstract = {This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video-on-demand (SVOD) service provider. This challenges the common belief in prevalent economies of scale for such e-businesses. Our analysis is based on a comprehensive dataset, where we have collected and combined publicly available and audited financial data, primarily sourced from Netflix’s 10-K reports. A second contribution of this study is the user-cost models employed in our analysis—specifically, a baseline linear model, a power law model, an exponential model, and a logarithmic model. Despite their clear relevance, such models are largely absent from discussions on SVOD business models, a gap we address and exemplify here. Applying these mathematical models to Netflix’s financial data reveals a super-linear increase in costs relative to user growth, indicating rising per-user costs characteristic of diseconomies of scale. Management strategies differ significantly between firms operating under the assumption of economies of scale and those experiencing diseconomies of scale—an important distinction, particularly as firm size increases. Our findings offer critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.}, archive = {J_ACCESS}, author = {Gabriel A. Szalkowski and Christian Johansen}, doi = {10.1109/ACCESS.2025.3561718}, journal = {IEEE Access}, pages = {67925-67941}, shortjournal = {IEEE Access}, title = {Evidence of diseconomies of scale in subscription-based video on demand services: The case of netflix}, volume = {13}, year = {2025}, }
• (2025). Airport capacity prediction and optimization based on semi-varying coefficient and dynamic programming. ACCESS, 13, 67908-67924. (www)

  @article{ , abstract = {Air traffic congestion has emerged as a critical challenge in modern air traffic management. Adverse weather conditions and other factors frequently lead to reduced airport arrival capacity, resulting in flight delays. Accurate capacity prediction and optimization are essential for maximizing airport utilization and minimizing flight delays. This study introduces a capacity prediction model based on the semi-varying coefficient approach, utilizing real-world data from Guangzhou Baiyun Airport collected over a specific time period. Through 5-fold cross-validation, the semi-varying coefficient model is compared to the varying-coefficient model, demonstrating superior out-of-sample prediction capabilities. Subsequently, we establish and solve a flight arrival and departure flow allocation optimization model using dynamic programming to enhance traffic flow coordination. Analysis of case study results demonstrates that this method can generate optimal strategies for allocating arrival and departure traffic flows within airport capacity constraints, thereby minimizing overall flight delay costs.}, archive = {J_ACCESS}, author = {Huan Xia and Weirui Yao and Yi Zheng and Chaozhe Jiang and Chenyang Zhang}, doi = {10.1109/ACCESS.2025.3558640}, journal = {IEEE Access}, pages = {67908-67924}, shortjournal = {IEEE Access}, title = {Airport capacity prediction and optimization based on semi-varying coefficient and dynamic programming}, volume = {13}, year = {2025}, }
• (2025). A framework for evaluating driving patterns of automated vehicle based on on-road driving records. ACCESS, 13, 67894-67907. (www)

  @article{ , abstract = {In this study, we propose a methodology for driving pattern analysis based on a hybrid clustering algorithm that combines the self-organizing map (SOM) and the K-means++ algorithm. The methodology aims to identify automated vehicle (AV) driving behaviors, highlight road sections with risky behaviors, and evaluate these behaviors using real-world driving data from automated shuttle operations. By employing a hybrid clustering algorithm grounded in Artificial Neural Network (ANN) principles, the approach enhances reliability compared to traditional clustering techniques. The analysis identifies both normal and risky driving behaviors, delineating specific driving patterns and visualizing them on maps. This enables the development of adaptable models for various AV types. Applied in the Pangyo area, the methodology reveals six distinct clusters, uncovering three risky behaviors and six unique driving patterns. This practical approach supports monitoring AV behaviors and improving road safety in mixed-traffic environments with AVs and conventional vehicles. Moreover, the methodology is adaptable to any region with high-definition (HD) map data, offering valuable insights for automated driving traffic control centers.}, archive = {J_ACCESS}, author = {Younghoon Seo and Donghyun Lim and Jooyoung Lee and Jaehyun So and Hyungjoo Kim}, doi = {10.1109/ACCESS.2025.3556299}, journal = {IEEE Access}, pages = {67894-67907}, shortjournal = {IEEE Access}, title = {A framework for evaluating driving patterns of automated vehicle based on on-road driving records}, volume = {13}, year = {2025}, }
• (2025). Ground landscape urban element extraction method based on optimized convolutional neural network technology. ACCESS, 13, 67882-67893. (www)

  @article{ , abstract = {With the continuous advancement of modernization and urbanization, the importance of ground landscape design in urban environments is becoming increasingly prominent. To carry out reasonable design and planning of urban landscapes, this study uses a convolutional neural network to construct a DeepLab-v3+ ground landscape design and planning model. The polarized self-attention mechanism is adopted to improve the accuracy of the model in extracting ground landscape elements in complex environments. Meanwhile, this study utilizes transfer learning to optimize the model and improve its efficiency. The results showed that the maximum accuracy, recall, and F1 value of the research model reached 94.02%, 91.93%, and 93.45%, respectively. The landscape recognition accuracy of the model in practical applications reached 92.3%, with a video memory usage of 8743 MB and a prediction speed of 1.2 FPS. In summary, the ground landscape design and planning method based on optimized convolutional neural network technology can accurately and quickly classify urban remote sensing images, which is helpful for the actual planning and design of urban ground landscapes.}, archive = {J_ACCESS}, author = {Yixin Zhang and Jinyong Wu}, doi = {10.1109/ACCESS.2025.3560687}, journal = {IEEE Access}, pages = {67882-67893}, shortjournal = {IEEE Access}, title = {Ground landscape urban element extraction method based on optimized convolutional neural network technology}, volume = {13}, year = {2025}, }
• (2025). STMS-net: Spatial temporal multi-spectral network for sEMG gesture recognition. ACCESS, 13, 67867-67881. (www)

  @article{ , abstract = {Accurate gestures recognition of sEMG plays an important role in the field of human-robot interaction. Currently, researchers commonly extracted feature from sEMG signals though spatial or temporal views. This paper introduces a novel Spatial Temporal Multi-Spectral Network (STMS-Net) to improve the recognition capability by leveraging the spatial, temporal and spectral features of sEMG signals. First of all, sEMG signal is filtered by a filter bank to obtain a multi-spectral data, then it is segmented into several fragments. Afterwards, for each fragment, a Multilayer Perceptron (MLP) Combining Max Pooling Network (MCMP-Net) is employed to extract the local spatial features. After that, the spatial features undergo two self-attention based modules to aware both the temporal and spectral interdependence within them, in those two modules, the spatial features are regarded as tokens of attention layer from spatial or spectral views, and dimension rearrangement is employed to streamline the network operations. At last, MLP and Softmax layer are operated as classification head. We evaluate our model on eight benchmark datasets: NinaPro DB1, DB2, DB4, DB5, CapgMyo DB-a, DB-b, and DB-c, and BandMyo, the accuracy on them are 91.9%, 90.0%, 90.1%, 96.1%, 94.1%, 93.8%, 95.1%, 76.9%, respectively, the results show that STMS-Net achieves or exceeds state-of-art performance.}, archive = {J_ACCESS}, author = {Wang Sijin and Tan Jianjun and Zhou Bingtao and Hu Tao and Zhu Li and Xiang Mian}, doi = {10.1109/ACCESS.2025.3560665}, journal = {IEEE Access}, pages = {67867-67881}, shortjournal = {IEEE Access}, title = {STMS-net: Spatial temporal multi-spectral network for sEMG gesture recognition}, volume = {13}, year = {2025}, }
• (2025). Design and performance optimization of a high-frequency broadband transducer based on P(VDF-TrFE). ACCESS, 13, 67856-67866. (www)

  @article{ , abstract = {To address the increasing demand for bandwidth expansion in hydroacoustic transducers, this study presents a high-frequency broadband transducer design that utilizes a novel piezoelectric material, poly(vinylidene fluoride-co-trifluoro ethylene)copolymer (P(VDF-TrFE)). The low mechanical quality factor of P(VDF-TrFE) effectively mitigates resonance effects and significantly enhances the bandwidth performance of the transducer. In this study, the effects of piezoelectric material thickness, lateral dimension, and double-layer nonuniform structure on the acoustic performance of the transducer are systematically investigated through frequency-domain finite element simulations using COMSOL Multiphysics. The results indicate that increasing the lateral dimension effectively suppresses nontarget modes and improves the smoothness of the frequency response, while the incorporation of a double-layer nonuniform structure enhances the transmit voltage response and broadens the effective bandwidth. Furthermore, the influence of backing materials composed of epoxy resin mixed with varying mass fractions of hollow glass microspheres is analyzed. It is observed that bandwidth enhancement becomes more significant when the acoustic impedance of the backing material is closer to that of the piezoelectric element. The experimental results indicate that the developed transducer prototype achieves a maximum transmit voltage response of 151.9 dB and -3 dB bandwidth of 585 kHz within an operating frequency range of 620-1205 kHz. The proposed design scheme based on P(VDF-TrFE) material holds significant application value in high-frequency broadband aquatic acoustic transducers and serves as a crucial reference for advancements in underwater communication technology.}, archive = {J_ACCESS}, author = {Yu Nie and Yuebing Wang}, doi = {10.1109/ACCESS.2025.3561232}, journal = {IEEE Access}, pages = {67856-67866}, shortjournal = {IEEE Access}, title = {Design and performance optimization of a high-frequency broadband transducer based on P(VDF-TrFE)}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving deep learning: A survey on theoretical foundations, software frameworks, and hardware accelerators. ACCESS, 13, 67821-67855. (www)

  @article{ , abstract = {Deep Learning as a Service (DLaaS) has become a cornerstone in enabling access to deep learning capabilities, allowing users to train models or leverage pre-trained ones through APIs. This paradigm significantly lowers the barrier to entry for deploying complex AI systems, making cutting-edge technologies accessible to a broader audience. However, the growing reliance on DLaaS poses significant privacy concerns, mainly when sensitive data is involved. Addressing these challenges necessitates Privacy-Preserving Deep Learning (PPDL), an emerging field focused on safeguarding data and model privacy during training and inference without compromising utility or performance. This paper comprehensively reviews the current PPDL landscape from theoretical, software, and hardware perspectives. We analyze over 100 recent works in areas such as Homomorphic Encryption, Functional Encryption, Multi-Party Computation, Trusted Execution Environments, Federated Learning, and Differential Privacy. We detail core methodologies, technologies, and their applications, comparing approaches to highlight strengths and weaknesses. Visual summaries of key contributions are presented to aid understanding and provide an accessible overview of the field. Furthermore, we discuss the limitations of existing techniques and identify future research directions.}, archive = {J_ACCESS}, author = {Eric Jahns and Milan Stojkov and Michel A. Kinsy}, doi = {10.1109/ACCESS.2025.3561721}, journal = {IEEE Access}, pages = {67821-67855}, shortjournal = {IEEE Access}, title = {Privacy-preserving deep learning: A survey on theoretical foundations, software frameworks, and hardware accelerators}, volume = {13}, year = {2025}, }
• (2025). STFDGCN: Spatial–Temporal fusion time delayed simulation graph convolutional network for traffic flow forecasting. ACCESS, 13, 67808-67820. (www)

  @article{ , abstract = {Accurate prediction of traffic flow is fundamental to building intelligent transportation systems. However, most existing research methods fail to fully account for the potential spatiotemporal correlations, heterogeneity, and time delays present in traffic flow data. In addressing these concerns, this work introduces a novel Spatio-Temporal Fusion Delay Graph Convolutional Network (STFDGCN) model. The STFDGCN model features a Spatio-Temporal Delay Graph Convolution Module (STDGCM) that simulates time delays in traffic flow through displacement operations following local graph convolutions, and captures spatiotemporal heterogeneity by employing multiple STDGCMs. The inclusion of a self-attention mechanism allows the model to effectively capture global spatiotemporal correlations. Additionally, the model incorporates an Information Fusion Module (IFM) that integrates local information with global information captured by the self-attention mechanism, offering a more comprehensive analysis. Finally, the model’s comprehensive capability is further improved by stacking multiple STFDGCL layers. Extensive tests on three real-world datasets validate the usefulness of the proposed STFDGCN model by demonstrating its superior prediction accuracy compared to other baselines, highlighting the model’s robustness and its potential to enhance traffic flow forecasting in various practical scenarios.}, archive = {J_ACCESS}, author = {Keke Guo and Gonghao Duan and Juan Ma and Jie Fu}, doi = {10.1109/ACCESS.2025.3559662}, journal = {IEEE Access}, pages = {67808-67820}, shortjournal = {IEEE Access}, title = {STFDGCN: Spatial–Temporal fusion time delayed simulation graph convolutional network for traffic flow forecasting}, volume = {13}, year = {2025}, }
• (2025). Physical resilience in a multiport converter for prosumer systems: Proposal and analysis. ACCESS, 13, 67795-67807. (www)

  @article{ , abstract = {This research article presents a proposal and analysis of a multi-port converter designed to enhance electrical resilience for residential users located at the edge of the public service grid. The converter, which is capable of generating alternating current, is designed to decouple undesired effects at the common connection point with the electrical grid, particularly those commonly encountered in weak nodes. This objective is achieved through a decoupling stage that absorbs undesired dynamics by regulating a direct current link. Furthermore, by incorporating a self-generation and storage port, the system adds electrical energy management capability that allows the provision of auxiliary functions to the electrical grid and the maintenance of electrical supply during extended periods of grid unavailability. The validity of this proposal is supported by a reliability analysis based on Markov chains, and is experimentally validated through a prototype. The prototype is subjected to significant fluctuations in load to incorporate the worst-case scenario, as depicted by the CBEMA performance characteristic curve. The results demonstrate the ability of the converter to maintain a stable output voltage, improving the MTBF and ensuring continuous operation during grid disconnection.}, archive = {J_ACCESS}, author = {Juan Gonzalez-Rivera and Victor Cardenas and Fernando Quiroz-Vazquez and Mario Gonzalez-Garcia and Rafael Hernandez}, doi = {10.1109/ACCESS.2025.3561102}, journal = {IEEE Access}, pages = {67795-67807}, shortjournal = {IEEE Access}, title = {Physical resilience in a multiport converter for prosumer systems: Proposal and analysis}, volume = {13}, year = {2025}, }
• (2025). An application of uplink RSMA technique for communication of patrol UAVs. ACCESS, 13, 67784-67794. (www)

  @article{ , abstract = {Rate-splitting multiple access (RSMA) is gaining significant research attention due to its flexibility in optimizing system capacity and meeting specific application goals. This paper proposes a solution for applying RSMA to transmit data from cameras mounted on unmanned aerial vehicles (UAVs) during patrol missions. Our approach yields deterministic results in the simplest case and can be easily extended to more complex scenarios. Our solution aligns more closely with the intended objectives than the non-orthogonal multiple access (NOMA) method. More specifically, the simulation results show that the proposed technique can achieve high values of achievable rate while still maintaining the power fairness in comparison with conventional techniques. Moreover, our solution has the ability to enhance massive multiple-input multiple-output (MIMO) systems, particularly when multiple user equipments (UEs) share the same spatial angle relative to the base station. In this context, the power domain UE separation technique can effectively complement the spatial angle domain UE separation technique.}, archive = {J_ACCESS}, author = {Ha Xuan Son and Pham Thi Quynh Trang and Le Tran Manh and Trinh Anh Vu}, doi = {10.1109/ACCESS.2025.3560639}, journal = {IEEE Access}, pages = {67784-67794}, shortjournal = {IEEE Access}, title = {An application of uplink RSMA technique for communication of patrol UAVs}, volume = {13}, year = {2025}, }
• (2025). Research on diffusion kurtosis imaging of the brain based on deep learning. ACCESS, 13, 67769-67783. (www)

  @article{ , abstract = {Precise evaluation of the deviation of water molecule diffusion in the brain tissue from the Gaussian diffusion model is crucial for diagnosing brain disorders. Diffusion kurtosis imaging (DKI) is a technique that may accurately describe the non-Gaussian diffusion characteristics of water molecules in brain tissue. The traditional method for achieving diffusion kurtosis imaging is constrained weighted linear least squares estimation. However, the traditional constrained weighted linear least squares estimation method has greatly reduced its clinical practicability due to its excessively long data acquisition time. Consequently, we propose using a deep learning-based approach instead of the traditional constrained weighted linear least-squares estimation method. We suggest utilizing the DKI-Net model, which relies solely on 2D convolution, and the DKI-Transformer model, which has an improved self-attention mechanism module, to estimate DKI-derived metrics. DKI-Net with residual properties extracts accurate and rich blocks of feature information and precisely estimates voxels. The DKI-Transformer model can extract global voxel correlation characteristics, the estimation results have a high structural similarity index compared to the reference labeling and exhibit distinct boundaries of microscopic features. Compared with traditional methods, these methods utilize the strong fitting capabilities of deep learning to estimate high-quality rotationally invariant scalar measures using a small number of DWI data points, significantly decreasing the number of scans and the duration of scanning exponentially. This approach provides a perspective that promises to address the problem of long imaging times in diffusion kurtosis imaging (DKI), facilitating the resolution of the clinical limitations of DKI. Consequently, it is anticipated that the range of applications of DKI will broaden.}, archive = {J_ACCESS}, author = {Rui Chen and Jingwen Yue and Rong Li and Zijian Jia}, doi = {10.1109/ACCESS.2025.3561035}, journal = {IEEE Access}, pages = {67769-67783}, shortjournal = {IEEE Access}, title = {Research on diffusion kurtosis imaging of the brain based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Sectoring approach for performance enhancement of MTT and its application on JPDA algorithm. ACCESS, 13, 67758-67768. (www)

  @article{ , abstract = {Recent advancements in multi-target tracking (MTT) technologies and algorithms have led to improved accuracy and efficiency in tracking multiple objects in dynamic environments. However, as the number of targets increases, MTT methods often struggle with computational demands and tracking capacity. This paper presents a novel sectoring approach on the Joint Probabilistic Data Association (JPDA) algorithm to enhance its tracking performance in MTT scenarios. The sectoring method divides the tracking area into distinct sectors and assigns a separate JPDA tracker for each sector, yielding two primary benefits in environments with large numbers of targets. First, it increases the capacity of the JPDA tracker to handle a larger number of targets. Second, it reduces computational complexity when tracking a high number of targets. On the other hand, the approach introduces a trade-off by slightly increasing computational complexity in scenarios with fewer targets. Experimental results were obtained through simulations of 10 different scenarios, each with 37 varying numbers of targets and three different sector configurations, amounting to a total of 1110 simulations. The findings demonstrate that the sectoring approach achieves up to a 95% reduction in computational complexity and increases the number of tracked targets from 24 to 40 in randomly generated scenarios. Results also revealed that the effects of the sectoring approach become more pronounced as the number of sectors increases.}, archive = {J_ACCESS}, author = {Mustafa Latif Cobankaya and Alaa B. Elrouby}, doi = {10.1109/ACCESS.2025.3560601}, journal = {IEEE Access}, pages = {67758-67768}, shortjournal = {IEEE Access}, title = {Sectoring approach for performance enhancement of MTT and its application on JPDA algorithm}, volume = {13}, year = {2025}, }
• (2025). Novel leakage-based capacity enhancement scheme for multi-user OAM communications. ACCESS, 13, 67748-67757. (www)

  @article{ , abstract = {This paper explores a novel leakage-based capacity enhancement scheme in an orbital angular momentum (OAM) integrated multiuser MIMO system, where the leakage refers to the signal interference affecting other users. We aim to optimize a precoder using signal-to-leakage-and-noise ratio (SLNR) to maximize capacity and minimize interference and noise. The proposed SLNR-based precoding is compared to regularized zero-forcing (RZF), block-diagonalization (BD), and zero-forcing (ZF) techniques, all of which focus on interference mitigation or regularization. This comparison is conducted in indoor multiuser OAM-MIMO systems utilizing a uniform circular array for OAM mode generation. Simulation results show that the SLNR-based multiuser OAM-MIMO outperforms the traditional multiuser MIMO, RZF, BD, and ZF-based multiuser OAM-MIMO in the context of capacity. Notably, the SLNR method achieves capacity improvements of approximately 26% over ZF, 12% over BD, and 7% over RZF.}, archive = {J_ACCESS}, author = {Aye Yadanar Win and Merhawit Berhane Teklu and Yeon Ho Chung}, doi = {10.1109/ACCESS.2025.3561451}, journal = {IEEE Access}, pages = {67748-67757}, shortjournal = {IEEE Access}, title = {Novel leakage-based capacity enhancement scheme for multi-user OAM communications}, volume = {13}, year = {2025}, }
• (2025). A novel decision-making algorithm for the assessment of creative product value transformation based on customer preferences using bipolar intuitionistic hesitant fuzzy information. ACCESS, 13, 67729-67747. (www)

  @article{ , abstract = {Assessing factors affecting creative product value transformation is crucial for businesses aiming to remain competitive and customer-centric. It enables companies to identify and prioritize key influences, such as customer preferences and market trends. By understanding these factors, businesses can align their offerings with consumer expectations, enhance perceived value, and build stronger brand loyalty. This process also supports efficient resource allocation, adaptation to changing market dynamics, and the development of sustainable practices. This paper introduces Bipolar Intuitionistic Hesitant Fuzzy Sets (BIHFSs) as a novel and comprehensive framework to enhance decision-making processes, particularly in evaluating creative products where ambiguity and uncertainty arise from diverse customer preferences and attributes. By integrating Bipolar fuzzy sets with intuitionistic hesitant fuzzy sets, the proposed model overcomes limitations in existing methods, offering a more generalized approach capable of handling complex, uncertain data. Additionally, a series of power aggregation operators, including the Bipolar Intuitionistic Hesitant Fuzzy Weighted Averaging (BIHFWA) and Bipolar Intuitionistic Hesitant Fuzzy Weighted Geometric (BIHFWG) operators, were developed with their properties. These aggregation operators improve the model’s ability to assess multiple product attributes, such as aesthetic appeal, durability, functionality, and price, providing valuable insights for optimizing product design. An algorithm was also developed to aggregate BIHFS information for Multi-Criteria Decision-Making (MCDM), guiding decision-makers in selecting the best alternative. The paper also presents a comparative analysis of the proposed and existing methods, discussing their benefits and limitations. The results demonstrate that the BIHFS model aligns well with existing techniques, validating its effectiveness and applicability in real-world scenarios.}, archive = {J_ACCESS}, author = {Jing Wang}, doi = {10.1109/ACCESS.2025.3554637}, journal = {IEEE Access}, pages = {67729-67747}, shortjournal = {IEEE Access}, title = {A novel decision-making algorithm for the assessment of creative product value transformation based on customer preferences using bipolar intuitionistic hesitant fuzzy information}, volume = {13}, year = {2025}, }
• (2025). A super-resolution approach for image resizing of infant fingerprints with vision transformers. ACCESS, 13, 67718-67728. (www)

  @article{ , abstract = {Digital biometrics is one of the most widespread and reliable means of recognizing human identity. Despite being well-established for adults, digital biometrics for newborns constitutes a considerable open challenge in the literature. The very small size of the fingers exposes subtle features that can neither be visually inspected nor computationally captured. This leads conventional biometric matchers, which generally rely on the distance between ridges, not to work as expected for newborns. To some extent, this problem can be mitigated by increasing the scanner’s resolution, which is an expensive strategy. Besides, the saturation limit for resolution is unknown beforehand. Another alternative is to resize images using interpolation, but this can introduce unexpected noises or highlight preexisting ones. This paper proposes an approach for image resizing based on Super-Resolution networks that train Vision Transformer (ViT) how to handle newborn fingerprints, avoiding the introduction of noises while keeping and improving important details for biometric recognition. For biometric identification and verification, results show an increase in the order of 10% for resized images in comparison with traditional resizing algorithms.}, archive = {J_ACCESS}, author = {João H. P. Machado and Barbara De O. Koop and Marcelo Filipak and Marco A. C. Barbosa and Jefferson T. Oliva and Luiz Fernando Puttow Southier and Dalcimar Casanova and Marcelo Teixeira}, doi = {10.1109/ACCESS.2025.3561206}, journal = {IEEE Access}, pages = {67718-67728}, shortjournal = {IEEE Access}, title = {A super-resolution approach for image resizing of infant fingerprints with vision transformers}, volume = {13}, year = {2025}, }
• (2025). Accelerating the k-means++ algorithm by using geometric information. ACCESS, 13, 67693-67717. (www)

  @article{ , abstract = {Clustering is a fundamental task in data analysis with applications across a wide range of fields, such as computer vision, pattern recognition, and data mining. Real-world use cases include social network analysis, medical imaging, market segmentation, and anomaly detection, to name a few. In this paper, we propose an acceleration of the exact k-means++ algorithm using geometric information, specifically the Triangle Inequality and additional norm filters, along with a two-step sampling procedure. Our experiments demonstrate that the accelerated version outperforms the standard k-means++ version in terms of the number of visited points and distance calculations, achieving greater speedup as the number of clusters increases. The version utilizing the Triangle Inequality is particularly effective for low-dimensional data, while the additional norm-based filter enhances performance in high-dimensional instances with greater norm variance among points. Additional experiments show the behavior of our algorithms when executed concurrently across multiple jobs and examine how memory performance impacts practical speedup.}, archive = {J_ACCESS}, author = {Guillem Rodríguez Corominas and Maria J. Blesa and Christian Blum}, doi = {10.1109/ACCESS.2025.3561293}, journal = {IEEE Access}, pages = {67693-67717}, shortjournal = {IEEE Access}, title = {Accelerating the k-means++ algorithm by using geometric information}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on tackling cyber threats for cyber logistic chain and conceptual frameworks for robust detection mechanisms. ACCESS, 13, 67661-67692. (www)

  @article{ , abstract = {The Fourth Industrial Revolution (4IR) has significantly transformed the cyber logistic chain through advancements in automation, fueled by the Internet of Things (IoT) and artificial intelligence (AI). Despite these innovations, increased connectivity has brought new vulnerabilities, such as issues with trust, open networks, and insufficient security measures, which have led to a surge in cybercrime, including ransomware attacks. This study has systematically reviewed 61 studies published between 2015 and 2024, sourced from six significant databases, to examine cybersecurity challenges and solutions in the cyber supply chain (CSC). This review has highlighted critical confidentiality, integrity, and availability (CIA) issues while evaluating preventive strategies and access control mechanisms. To address these challenges, this study introduces the CyberChainGuard framework that incorporates access control mechanisms, blockchain technology, and the Clark-Wilson model to combat ransomware and strengthen CSC security. The findings aimed to provide a benchmark for researchers and practitioners by offering actionable insights that could enhance cybersecurity in the digital logistics chain.}, archive = {J_ACCESS}, author = {Bashayr Alshammari and Manmeet Mahinderjit Singh}, doi = {10.1109/ACCESS.2025.3552689}, journal = {IEEE Access}, pages = {67661-67692}, shortjournal = {IEEE Access}, title = {A systematic literature review on tackling cyber threats for cyber logistic chain and conceptual frameworks for robust detection mechanisms}, volume = {13}, year = {2025}, }
• (2025). Improving byzantine fault tolerance in blockchain networks with dynamic clustering. ACCESS, 13, 67652-67660. (www)

  @article{ , abstract = {In recent years, blockchain technology, which enables transactions to be distributed across multiple computers and managed in an immutable and secure manner, has garnered significant attention. Within blockchain networks, consensus mechanisms ensure the consistent sharing of ledger information when new blocks are added. In hybrid blockchains, typically employed by a limited number of organizations, the Practical Byzantine Fault Tolerance (PBFT) protocol is widely used. PBFT is designed to tolerate Byzantine nodes—nodes that may be compromised or malfunctioning—by achieving consensus as long as fewer than one-third of the total nodes are Byzantine. However, PBFT relies on the assumption that at least two-thirds of the nodes behave correctly, making consensus challenging when the number of malicious nodes exceeds this threshold. Previous research has explored the use of clustering to enhance throughput, but these methods are static and unsuited to dynamic environments. Moreover, clustering techniques aimed at bolstering Byzantine resistance remain underexplored. This paper presents a novel method for constructing clusters within a blockchain network to resist Byzantine nodes. By employing clustering, we estimate the locations of potential attackers, thereby enhancing the system’s resilience to Byzantine faults.}, archive = {J_ACCESS}, author = {Teppei Okada and Noriaki Kamiyama and Akihiro Fujihara}, doi = {10.1109/ACCESS.2025.3561194}, journal = {IEEE Access}, pages = {67652-67660}, shortjournal = {IEEE Access}, title = {Improving byzantine fault tolerance in blockchain networks with dynamic clustering}, volume = {13}, year = {2025}, }
• (2025). Optimizing current ratios for multi-tx WPT systems with non-identical coils. ACCESS, 13, 67641-67651. (www)

  @article{ , abstract = {In a wireless power transfer (WPT) system, efficiency is the most important factor because a low-efficiency system leads to problems such as energy waste, heat generation problems, and reduced system performance. For optimal efficiency in a multiple-transmitter (multi-Tx) WPT system, the Tx currents must be supplied proportionately to the mutual inductance between the transmitting and receiving coils. However, this conventional current condition is derived under the assumption that all transmitting coils are identical. In a multi-Tx structure, if the specifications of all transmitters are not the same, the conventional optimum current condition does not guarantee achieving optimal efficiency. This study presents a novel current ratio condition that significantly reduces power losses and optimizes efficiency in multi-Tx WPT systems by accounting for mutual inductance and parasitic resistance. To verify the proposed current ratio condition, we implemented a multi-Tx WPT system consisting of transmitting coils made from litz wire and a COTS receiving coil. As a result of applying the conventional and the proposed Tx current conditions, we confirmed that the proposed Tx current condition could improve the power transfer efficiency with small currents over that with the conventional condition.}, archive = {J_ACCESS}, author = {Hyungchul Kim and Dong-Wook Seo}, doi = {10.1109/ACCESS.2025.3561392}, journal = {IEEE Access}, pages = {67641-67651}, shortjournal = {IEEE Access}, title = {Optimizing current ratios for multi-tx WPT systems with non-identical coils}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of cooperative quadrature spatial modulation system with hybrid decode-amplify-forward relaying protocol. ACCESS, 13, 67632-67640. (www)

  @article{ , abstract = {This paper introduces the cooperative quadrature spatial modulation (CQSM) system with hybrid decode-amplify-forward (HDAF) relaying protocol. Compered with the traditional CQSM system which only uses amplify-and-forward (AF) protocol and decode-and-forward (DF) protocol, the system has enhanced the spectral efficiency and the performance of the average bit error probability (ABEP). In the proposed CQSM system, the information bit selects the relay to forward the information, while the relay index carries the information. At the relay, the amplify-and-forward (AF) protocol and the adaptive decode-and-forward (ADF) protocol are combined. The relay uses the AF protocol instead of remaining silent in the ADF protocol when the relay decodes signal incorrectly. Meanwhile, the closed-form expression of the ABEP for the proposed scheme is derived in detail. Furthermore, Monte Carlo simulation results verify the correctness of the theoretical ABEP derivation and the superiority of the proposed CQSM system with HDAF protocol over other existing schemes in terms of ABEP.}, archive = {J_ACCESS}, author = {Xiangyan Bu and Ke Pang}, doi = {10.1109/ACCESS.2025.3561187}, journal = {IEEE Access}, pages = {67632-67640}, shortjournal = {IEEE Access}, title = {Performance analysis of cooperative quadrature spatial modulation system with hybrid decode-amplify-forward relaying protocol}, volume = {13}, year = {2025}, }
• (2025). Enhancing model robustness in noisy environments: Unlocking advanced mono-channel speech enhancement with cooperative learning and transformer networks. ACCESS, 13, 67616-67631. (www)

  @article{ , abstract = {Enhancing mono-channel speech signals in scenarios involving complex spectral mapping poses a formidable challenge in the domain of audio signal processing. To tackle this challenge head-on, this paper introduces a novel rapid speech enhancement network that harnesses the combined strengths of Convolutional Neural Networks (ConvNets) and Transformers. The proposed network seamlessly integrates ConvNets for robust feature extraction and Transformers for comprehensive long-term sequence modeling, resulting in a substantial enhancement in speech quality. In the encoding-decoding layers of the network, we introduce the Unified Discovery Unit (UDU), employing cooperative learning techniques to augment feature extraction. Through collaborative learning across network layers, the UDU facilitates the extraction of richer feature spaces from speech data, thereby enhancing the network’s ability to capture pertinent information. Building upon recent advancements, we introduce the Temporal-Spectral Cross-Attention Component (TSCAC) in the transference layer. This innovative component, comprising temporal and spectral attention Transformers, adeptly processes both subband and entire band speech information, effectively capturing temporal and spectral dependencies to preserve crucial speech characteristics. Recognizing the significance of channel-specific features in speech signals, we integrate a channel attention segment and develop the teachable Twin-Segment Attention Integration Component (TSAIC). This component extracts semantic features from a spatial-channel perspective, further bolstering the network’s capacity to attend to relevant information. Additionally, we introduce a Gaussian-Weighted Progressive Structure (GWPS) to compensate for any lost detailed features in deeper network layers, thereby enhancing the fidelity of the enhanced speech signal and mitigating information loss. Extensive experimental evaluations conducted on datasets of varying scales and languages under diverse noise conditions underscore the effectiveness and robustness of the proposed method. Our approach showcases significant improvements in speech enhancement performance, paving the way for advancements in audio signal processing and real-world applications.}, archive = {J_ACCESS}, author = {Wei Hu and Yan Wu}, doi = {10.1109/ACCESS.2025.3561359}, journal = {IEEE Access}, pages = {67616-67631}, shortjournal = {IEEE Access}, title = {Enhancing model robustness in noisy environments: Unlocking advanced mono-channel speech enhancement with cooperative learning and transformer networks}, volume = {13}, year = {2025}, }
• (2025). Optimization of the traceability of perishable products through light blockchain and IoT in the food industry. ACCESS, 13, 67599-67615. (www)

  @article{ , abstract = {Traceability in the supply chain of perishable products faces significant challenges due to the limitations of traditional systems, such as centralized databases and conventional blockchain technologies. These solutions are often insufficient to ensure fast response times, energy efficiency, and scalability in dynamic scenarios, negatively impacting the quality and safety of food products. Furthermore, compliance with international regulations, such as those of the FDA and the European Union, requires implementing more efficient and transparent systems. This work proposes an innovative system that combines lightweight blockchain with IoT devices for traceability in the food industry. The system architecture is based on blockchain nodes optimized using lightweight consensus mechanisms, such as Proof of Authority, and integration with IoT sensors through efficient protocols such as MQTT. This combination ensures the collection and immutable recording of real-time data, including critical parameters such as temperature and location. The results show that the system reduces average response times to 105 ms in configurations with 10 nodes and 50 sensors, achieving transaction rates per second of up to 720. It also features an average energy consumption of 9.6 mJ per transaction and failure recovery times of just 5 seconds. These metrics highlight the system’s ability to operate in demanding environments with high data density, significantly improving existing technologies.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Jaime Govea and Pablo Santiago Moncayo-Moncayo and Alexandra Maldonado Navarro and Carlos Chavez-Pirca}, doi = {10.1109/ACCESS.2025.3561596}, journal = {IEEE Access}, pages = {67599-67615}, shortjournal = {IEEE Access}, title = {Optimization of the traceability of perishable products through light blockchain and IoT in the food industry}, volume = {13}, year = {2025}, }
• (2025). Multiplex network-based approach to gas sensing. ACCESS, 13, 67588-67598. (www)

  @article{ , abstract = {We present a multiplex network framework for analyzing sensor array data, integrating information from multiple network layers corresponding to different sensing conditions. Principal Component Analysis (PCA) is employed to reduce the dimensionality of the data and then used to construct the multiplex networks where each layer of the network is derived from correlation-based planar maximally filtered graphs (PMFG) for each distinct gas. The topological properties of the network layers are studied over different time frames. Clustering coefficients show a persistent local connectivity for ethanol and ethylene (stabilizing near 0.71) but a decreasing activity from 0.70 to 0.56 for acetone over time. We observe the highest path length for the initial phase of the sensor lifecycle, and a general decrease in path length is observed for most gases over time, indicating a decreasing sensor sensitivity. The analysis of multidegrees across different gases reveals both unique and shared connectivity patterns within sensor responses. Each gas shows some distinctive features in multi-degree, which decreases over time, and increase in significant overlapping features between gas combinations. For example, a high multidegree (001110) indicates simultaneous unique features in sensor response for ammonia, acetaldehyde, and acetone these features are not present in any other gas or their combination. We also observe a strong similarity in sensor response patterns between ethylene and ammonia. The shared features among certain gases indicate similar interaction patterns with sensors, potentially due to common chemical properties or response behaviors. We find that sensor drift significantly affects sensor responses, leading to evolving topological properties over time. Gas-specific network layers exhibit distinct trends, with some gases maintaining strong connectivity while others weaken due to drift. The methodology can be extended to other sensor arrays and applications beyond gas sensing, making it a versatile tool for multivariate sensor data analysis.}, archive = {J_ACCESS}, author = {Pradeep Bhadola and Ajit Khosla}, doi = {10.1109/ACCESS.2025.3559509}, journal = {IEEE Access}, pages = {67588-67598}, shortjournal = {IEEE Access}, title = {Multiplex network-based approach to gas sensing}, volume = {13}, year = {2025}, }
• (2025). Analysis and design of holographic reflectarray antennas using collective polarizability tensors of patch scatterers. ACCESS, 13, 67578-67587. (www)

  @article{ , abstract = {This paper presents an analytical approach for designing holographic reflectarray (HRA) antennas using polarizability tensors of patch scatterers. First, the behavior of patch scatterers is described using collective polarizability tensors as their characteristic parameters. The polarizability components of the patch scatterers are then extracted using a retrieval approach. Next, the analytical input admittance of HRA elements, containing patch scatterers, a dielectric substrate, and a ground plane, is derived as a function of incident angle and collective polarizabilities of patch scatterers, which depend on the patch dimensions. This is achieved by applying the collective polarizability tensors of patch scatterers in an equivalent circuit model. A design procedure is then developed to determine the distribution of scatterers on the reflectarray surface. This approach utilizes a closed-form formulation to calculate input admittances, expressed as a function of the incident angle and polarizability components. It takes into account the variations in incident angles for elements positioned at different locations. By adopting this approach, both the computational load and the time required for input admittance calculations are significantly reduced compared to previous methods based on numerical approaches. Additionally, in this method, increasing the number of elements or altering the feed antenna’s position has no impact on the calculation time. Finally, a practical example of an HRA antenna is designed using the proposed approach. Since the proposed approach requires neither extensive full-wave simulations nor an optimization procedure, it can pave the way for fast and accurate design of HRAs. To validate the proposed approach, a $20\,cm \times 20\,cm$ linearly polarized HRA is manufactured and tested at 15 GHz. The simulation and experimental results show good agreement. The experimental peak gain and aperture efficiency at 15 GHz are found 26.8 dBi and 38%, respectively. Additionally, 1-dB gain bandwidth is 22.22%, from 14 GHz to 17.5 GHz.}, archive = {J_ACCESS}, author = {Shakiba Paloudi and Nader Komjani and Mohammad Yazdi}, doi = {10.1109/ACCESS.2025.3554631}, journal = {IEEE Access}, pages = {67578-67587}, shortjournal = {IEEE Access}, title = {Analysis and design of holographic reflectarray antennas using collective polarizability tensors of patch scatterers}, volume = {13}, year = {2025}, }
• (2025). Empirical comparison of four stereoscopic depth sensing cameras for robotics applications. ACCESS, 13, 67564-67577. (www)

  @article{ , abstract = {Depth sensing is an essential technology in robotics and many other fields. Many depth sensing (or RGB-D) cameras are available on the market and selecting the best one for your application can be challenging. In this work, we tested four stereoscopic RGB-D cameras that sense the distance by using two images from slightly different views. We empirically compared four cameras (Intel RealSense D435, Intel RealSense D455, StereoLabs ZED 2, and Luxonis OAK-D Pro) in three scenarios: (i) planar surface perception, (ii) plastic doll perception, (iii) household object perception (YCB dataset). We recorded and evaluated more than 3,000 RGB-D frames for each camera. For table-top robotics scenarios with distance to objects up to one meter, the best performance is provided by the D435 camera that is able to perceive with an error under 1 cm in all of the tested scenarios. For longer distances, the other three models perform better, making them more suitable for some mobile robotics applications. OAK-D Pro additionally offers integrated AI modules (e.g., object and human keypoint detection). ZED 2 is overall the best camera which is able to keep the error under 3 cm even at 4 meters. However, it is not a standalone device and requires a computer with a GPU for depth data acquisition. All data (more than 12,000 RGB-D frames) are publicly available at https://rustlluk.github.io/rgbd-comparison}, archive = {J_ACCESS}, author = {Lukas Rustler and Vojtech Volprecht and Matej Hoffmann}, doi = {10.1109/ACCESS.2025.3560810}, journal = {IEEE Access}, pages = {67564-67577}, shortjournal = {IEEE Access}, title = {Empirical comparison of four stereoscopic depth sensing cameras for robotics applications}, volume = {13}, year = {2025}, }
• (2025). SDN controller for optical network control. ACCESS, 13, 67548-67563. (www)

  @article{ , abstract = {The ability of centralized programming flow tables in switches and routers to perform programmable resource control and management from the central controller under the Software Defined Networking (SDN) concept has introduced a new approach to controlling and managing network resources. The main principles of the SDN concept, as defined by the Open Networking Foundation (ONF), apply primarily to packet networks, making the usage of SDN for optical networks an area of ongoing work. However, ITU-T standardization recommendations have described the reference SDN controller architecture applicable to optical transport networks in terms of abstract components provided by the Automatically Switched Optical Network (ASON) approach. The paper regards the problem of an SDN controller for optical network control according to the G.7702 ITU-T recommendation approved in 2022. The proposition of the SDN controller presented in the paper is compliant with the guidelines outlined in ITU-T recommendations, while ensuring compatibility with the standards established for OpenFlow. The integration of the SDN concept with optical network architecture is performed based on an ASON/GMPLS optical network. In the presented solution, the SDN controller is built with ASON controllers, and dedicated OpenFlow messages are proposed based on ITU-T and ONF guidelines. The proposed solution is evaluated with the simulation model in the OMNeT++ environment. The performance of the proposed solution under specific traffic conditions and different SDN controller placements is presented.}, archive = {J_ACCESS}, author = {Sylwester Kaczmarek and Magdalena Młynarczuk and Maciej Sac and Patryk Miklaszewski}, doi = {10.1109/ACCESS.2025.3560210}, journal = {IEEE Access}, pages = {67548-67563}, shortjournal = {IEEE Access}, title = {SDN controller for optical network control}, volume = {13}, year = {2025}, }
• (2025). BLE channel sounding: Novel method for enhanced ranging accuracy in vehicle access. ACCESS, 13, 67531-67547. (www)

  @article{ , abstract = {The recent attempts for vehicle thefts using Relay Station or Man in Middle attacks have triggered the need for precise and secure distance measurement between the owner device and vehicle. Ultra-Wide Band offers high accuracy ranging; it is expensive and hence could not be deployed in vehicle access. Bluetooth (Register Trademark) Low Energy Channel Sounding is excellent cost effective and mass scale alternative for enhanced ranging and vehicle access solution. The motivation for this research work lies in minimising power consumption thereby maximising battery life for the handheld owner device such as keyfob or smartphone; whilst offering unparalleled security against Man in the Middle or Relay Station Attacks. The proposed work combines the conventional low power Received Signal Strength Indicator based approach with the Bluetooth (Register Trademark) Low Energy Channel Sounding framework for accurate distance estimation. The coarse ranging offered by conventional Received Signal Strength Indicator approach is embedded into proximity based secure pairing as well as a pre-check before establishing connection between paired devices. The proposed work performs bidirectional measurement of values of Received Signal Strength Indicator, Phase Based Ranging and Round-Trip Time and cross-correlation of them to enhance accuracy using novel method. The development process utilized ANSYS HFSS 3D High Frequency Simulation for dual antenna design, enabling precise phase difference calibration. The implementation was achieved using a chipset with an ARM Cortex M33 core, hardware accelerators, and a BLE Channel Sounding stack integrated through a Software Development Kit. This work demonstrates a practical, scalable, and secure solution for BLE-based ranging in modern vehicle access systems. Besides security, proposed work provides opportunities for many more use cases such as distance based welcome feature where vehicle will recognise approaching owner and welcome with step by step turning ON of functions such as approach light, ambient light, unlocking the doors etc. The experimental results demonstrate that the proposed method achieves ranging accuracy comparable to existing LF and RF technologies.}, archive = {J_ACCESS}, author = {Suresh D. and Prashant V. Joshi and Parag Parandkar and Ameya Gambhir and K. M. Sudharshan}, doi = {10.1109/ACCESS.2025.3561028}, journal = {IEEE Access}, pages = {67531-67547}, shortjournal = {IEEE Access}, title = {BLE channel sounding: Novel method for enhanced ranging accuracy in vehicle access}, volume = {13}, year = {2025}, }
• (2025). Dimensional-based methods for topological management in underwater wireless sensor networks: A comprehensive survey. ACCESS, 13, 67511-67530. (www)

  @article{ , abstract = {Most topology control algorithms consider a 2-D plane in terrestrial Wireless Sensor Networks. However, this is not the case in Underwater Wireless Sensor Networks (UWSN). Due to the underwater current, the node’s positions constantly flux. It is a significant challenge to satisfy the deployment of nodes precisely underwater for reliable communication. A three-dimensional (3-D) network is created by deploying the sensors in water at different levels of depth. This article presents a comprehensive review of node deployment strategies based on topological management. There are two ways to deploy the nodes in an underwater environment either a dense or sparse network. The last six years of extensive research are summarized in tabular form. There are distinct limitations when nodes are deployed as sparse and dense networks. The fundamental concepts used in sparse and dense network solutions are elaborated for user readability. The topology control solutions are addressed through mathematical analysis in both cases. At last, the authors offer their perspective on the open challenges with some suggestions for future directions.}, archive = {J_ACCESS}, author = {Meena Pundir and Jasminder Kaur Sandhu and Sapna Juneja and Deepali Gupta and Ghadir Ali Altuwaijri and Ali Nauman}, doi = {10.1109/ACCESS.2025.3546978}, journal = {IEEE Access}, pages = {67511-67530}, shortjournal = {IEEE Access}, title = {Dimensional-based methods for topological management in underwater wireless sensor networks: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). An eight-channel MRI head coil array for enhanced MRI-guided transcranial focused ultrasound imaging performance. ACCESS, 13, 67500-67510. (www)

  @article{ , abstract = {Magnetic resonance guided focused ultrasound (MRgFUS) thermal ablation is a new method of non-invasive neurosurgery that can benefit from improved MR imaging performance. The ultrasound transducer shields the radio-frequency field of the body coil, reducing transmit and receive sensitivity as well as reducing transmit homogeneity. To improve the quality of structural imaging and the MR thermometry used to guide lesioning, we propose a dedicated 8-element receive array designed to work within the water bath of the ultrasound transducer helmet. The array consists of dipoles, consisting of flexible twin lead conductors, that pierce the water bath interface membrane, which forms a watertight seal around the patient’s head. In transmit mode, with the array detuned, no change in the transmit efficiency or homogeneity was observed, and simulation demonstrated no significant change in the 10g averaged specific absorption rate (SAR). In receive mode, the mean receive sensitivity within the brain was 5.7 times higher than that of the body coil. FUS heating is unaffected by the coil array, as demonstrated by an unaltered spread of the temperature at the FUS focal point. Finally, the precision of thermometry was improved by a factor of 2.25 in results from phantom imaging and a similar amount in in-vivo imaging.}, archive = {J_ACCESS}, author = {Adam Maunder and Melany Mclean and Samuel Pichardo and G. Bruce Pike and Fraser J. L. Robb and Ashwin K. Iyer and Nicola de Zanche}, doi = {10.1109/ACCESS.2025.3559052}, journal = {IEEE Access}, pages = {67500-67510}, shortjournal = {IEEE Access}, title = {An eight-channel MRI head coil array for enhanced MRI-guided transcranial focused ultrasound imaging performance}, volume = {13}, year = {2025}, }
• (2025). Using BLE signals to estimate objective and subjective crowdedness levels on fixed-route buses. ACCESS, 13, 67488-67499. (www)

  @article{ , abstract = {Accurately estimating the crowdedness inside a fixed-route bus is essential for improving transportation system efficiency and enhancing passenger comfort. While methods using cameras or sensors installed at bus entrances to count passengers have been proposed, these methods present challenges in terms of passenger privacy, installation costs, and placement. These approaches typically use the number of passengers as an objective indicator to evaluate crowdedness. However, even with the same number of passengers, the subjective crowdedness level experienced by each passenger can vary. Thus, it is important to estimate the objective crowdedness and the subjective crowdedness level perceived by bus passengers. In this study, we developed a method for estimating objective and subjective crowdedness levels using only Bluetooth Low Energy (BLE) information collected by the existing bus location tracking system installed in fixed-route buses to reduce privacy and installation costs. Specifically, Bluetooth device (BD) addresses obtained from BLE scans are filtered based on occurrence frequency and average RSSI to distinguish between passenger and surrounding BD addresses. The number of passenger BD addresses, along with their differences and rates of change, are used as features to estimate the number of passengers and the subjective crowdedness level using machine learning models. An experiment to evaluate the BLE method produced an accuracy of 0.653 for the objective crowdedness level (number of passengers) and 0.513 for the subjective crowdedness level, indicating that BLE signal information can capture the general trend of objective and subjective crowdedness.}, archive = {J_ACCESS}, author = {Takumi Ikenaga and Yuki Matsuda and Ippei Goto and Kentaro Ueda and Hirohiko Suwa and Keiichi Yasumoto}, doi = {10.1109/ACCESS.2025.3558988}, journal = {IEEE Access}, pages = {67488-67499}, shortjournal = {IEEE Access}, title = {Using BLE signals to estimate objective and subjective crowdedness levels on fixed-route buses}, volume = {13}, year = {2025}, }
• (2025). Onto proximality in non negative matrix factorization for recommender systems. ACCESS, 13, 67476-67487. (www)

  @article{ , abstract = {Recommender Systems have become integral to most e-commerce applications and online platforms. The recommended suggestions heavily impact customer retention and business performance. One of the essential parameters in large-scale recommender systems is the time required to present a recommendation. The more time it takes, the more it loses the customer’s attention and interest. It is currently necessary for recommenders to be time-efficient and optimal. Collaborative filtering-based Matrix Factorization approaches have proven to be powerful for recommender systems. The standard approach uses the Singular Value Decomposition-based recommender systems with Gradient Descent optimizer and its advanced variants. These models provide good accuracy for recommenders. However, they are time-intensive. To alleviate these issues, the proximal gradient descent optimizer-based Nonnegative Matrix Factorization model is adapted for recommender systems to improve their performance in terms of time and accuracy. There has been no research on integrating proximal descent models in Nonnegative matrix factorization for recommender systems. These novel adaptations are analyzed with six other baseline recommender models on two datasets. The experimental analysis proves that these novel recommender models are the preferable choice for online recommenders and work well when the data is not smooth.}, archive = {J_ACCESS}, author = {Rachana Mehta and Shakti Mishra and Snehanshu Saha}, doi = {10.1109/ACCESS.2025.3557945}, journal = {IEEE Access}, pages = {67476-67487}, shortjournal = {IEEE Access}, title = {Onto proximality in non negative matrix factorization for recommender systems}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning model for UAV path planning in dynamic environments. ACCESS, 13, 67459-67475. (www)

  @article{ , abstract = {Integrating Unmanned Aerial Vehicles (UAVs) into military operations has become increasingly significant in recent years. In practical missions, UAVs are usually operated in unpredictable conditions, requiring robust planning to adapt to real-time changes. Thus, an effective path-planning algorithm is essential for navigating UAVs through complex terrains. However, the commonly used Rapidly-exploring Random Tree (RRT) algorithm for UAV path planning often generates suboptimal paths that require extensive post-processing to improve. While its variants can converge to the optimal solution, they suffer from more memory and computation consumption. On the other hand, purely learning-based methods could result in planning failures or excessively long paths generated for obstacle avoidance. In this work, we introduce a UAV path planning model that integrates diverse sensor data through a neural network architecture featuring CNN, LSTM, and an attention mechanism. Then, the deep learning-enhanced RRT algorithm combines the neural module with the informed sampling methods for adaptive and efficient pathfinding in dynamic environments. After carefully considering the trade-offs among path optimality, computational efficiency, and fewer turning angles, our approach exhibits superior overall performance compared to Informed RRT*, Batch Informed Trees (BIT*) algorithms, and the state-of-the-art Layered RQN.}, archive = {J_ACCESS}, author = {Junchi Zhang and Yanning Xian and Xun Zhu and Hongtao Deng}, doi = {10.1109/ACCESS.2025.3557394}, journal = {IEEE Access}, pages = {67459-67475}, shortjournal = {IEEE Access}, title = {A hybrid deep learning model for UAV path planning in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Modeling puerto rico grid’s sequential failures for hurricanes using electric grid resilience & assessment system (EGRASS) tool and dynamic contingency analysis tool (DCAT). ACCESS, 13, 67446-67458. (www)

  @article{ , abstract = {The increasing intensity and frequency of extreme events have raised significant concerns about the electrical grid’s resilience. Numerous extreme events such as hurricanes, tornadoes, earthquakes, geomagnetic storms, high-altitude electromagnetic pulses, ice storms, cyber-attacks, and physical attacks, among others, threaten the entire electrical grid and its various components. Fragility curves play a crucial role in assessing asset vulnerability having a significant impact in all studies that evaluate the power system resilience for extreme events. Real-world data for such extreme events are often significantly limited, and thus in this paper a method for calibrating fragility curves with limited real-world data is presented. This paper also introduces a methodology to generate likely temporal sequences of failures during a hurricane event leveraging the newly calibrated fragility curves and the geospatial knowledge utilizing the electric grid resilience & assessment system (EGRASS) tool. Furthermore, the methodology evaluates the sequential contingencies using the dynamic contingency analysis tool (DCAT) for power system dynamic simulations. The main contribution of this work is the development and analysis of the methodology that enables dynamic simulations of power systems under climate-related extreme events using limited real-world data. The methodologies and case studies presented in this paper focus on the hurricanes faced by Puerto Rico’s grid as an extreme event but are agnostic of the type of extreme event and can be applied to other such events.}, archive = {J_ACCESS}, author = {Fernando Bereta Dos Reis and Patrick Royer and Xinda Ke and Xiaoyuan Fan and Vishvas Hiren Chalishazar and Marcelo Elizondo and Jeffery Dagle and Alexandre Nassif and Olga Soto and Daniel Haughton}, doi = {10.1109/ACCESS.2025.3555144}, journal = {IEEE Access}, pages = {67446-67458}, shortjournal = {IEEE Access}, title = {Modeling puerto rico grid’s sequential failures for hurricanes using electric grid resilience & assessment system (EGRASS) tool and dynamic contingency analysis tool (DCAT)}, volume = {13}, year = {2025}, }
• (2025). Deep fusion of neurophysiological and facial features for enhanced emotion detection. ACCESS, 13, 67434-67445. (www)

  @article{ , abstract = {The fusion of facial and neurophysiological features for multimodal emotion detection is vital for applications in healthcare, wearable devices, and human-computer interaction, as it enables a more comprehensive understanding of human emotions. Traditionally, the integration of facial expressions and neurophysiological signals has required specialized knowledge and complex preprocessing. With the rise of deep learning and artificial intelligence (AI), new methodologies in affective computing allow for the seamless fusion of multimodal signals, advancing emotion recognition systems. In this paper, we present a novel multimodal deep network that leverages transformers to extract comprehensive features from neurophysiological data, which are then fused with facial expression features for emotion classification. Our transformer-based model analyzes neurophysiological time-series data, while transformer-inspired methods extract facial expression features, enabling the classification of complex emotional states. We compare single modality with multimodal systems, testing our model on Electroencephalography (EEG) signals using the DEAP and Lie Detection datasets. Our hybrid approach effectively captures intricate temporal and spatial patterns in the data, significantly enhancing the system’s emotion recognition accuracy. Validated on the DEAP dataset, our method achieves near state-of-the-art performance, with accuracy rates of 97.78%, 97.64%, 97.91%, and 97.62% for arousal, valence, liking, and dominance, respectively. Furthermore, we achieved a precision of 97.9%, a ROC AUC score of 97.6%, an F1-score of 98.1%, and a recall of 98.2%, demonstrating the model’s robust performance. We demonstrated the effectiveness of this method, specifically for EEG caps with a limited number of electrodes, in emotion detection for wearable devices.}, archive = {J_ACCESS}, author = {Farshad Safavi and Vikas Reddy Venkannagari and Dev Parikh and Ramana Kumar Vinjamuri}, doi = {10.1109/ACCESS.2025.3555934}, journal = {IEEE Access}, pages = {67434-67445}, shortjournal = {IEEE Access}, title = {Deep fusion of neurophysiological and facial features for enhanced emotion detection}, volume = {13}, year = {2025}, }
• (2025). Impact of electro-UV aging on the insulation performance of PVC/ZnO nanocomposites for outdoor applications. ACCESS, 13, 67415-67433. (www)

  @article{ , abstract = {Polymers are prone to degradation when exposed to UV radiation and electrical stress in operational environments. This study examines the performance of PVC nanocomposites reinforced with ZnO nanoparticles at concentrations of 0, 1, 3, 5, and 7 wt% under combined Electro-UV aging conditions. The nanocomposites were prepared through melt mixing and compression molding into 1.5 mm thick circular samples. X-ray diffraction (XRD) analysis confirmed the uniform dispersion of ZnO nanoparticles within the PVC matrix, which significantly influenced the structural, thermal, and electrical properties of the composites. Additional tests, including hydrophobicity, contact angle, leakage current, and temperature- and frequency-dependent analyses, were conducted during 450 hours of UV exposure at 5.11 mW/cm2, with measurements taken every 150 hours. The results showed that ZnO-reinforced PVC nanocomposites demonstrated enhanced resistance to UV-induced degradation, reduced surface deterioration, and controlled leakage current compared to unmodified PVC. The superior performance of the composites is attributed to the increased structural integrity and surface energy provided by the ZnO nanoparticles, making them suitable for applications in demanding outdoor environments.}, archive = {J_ACCESS}, author = {Wasim Ullah and Muhammad Farasat Abbas and Salman Khan and Aymen Flah and Claude Ziad El-Bayeh and Kamel A. Shoush and Bander Saman}, doi = {10.1109/ACCESS.2025.3559243}, journal = {IEEE Access}, pages = {67415-67433}, shortjournal = {IEEE Access}, title = {Impact of electro-UV aging on the insulation performance of PVC/ZnO nanocomposites for outdoor applications}, volume = {13}, year = {2025}, }
• (2025). On the implementation of a transverse wave-based routing technique for a TSP circular instance. ACCESS, 13, 67400-67414. (www)

  @article{ , abstract = {This study introduces the Transverse Waves Algorithm (TWA), a novel heuristic approach devised to address the Travelling Salesperson Problem (TSP) within a unique instance. Inspired by natural phenomena where energy disperses in waves from a central point and interacts with its environment, TWA is designed for areas structured in concentric circles and segmented into equal parts. The algorithm mimics wave behaviors, when disrupted by obstacles and reflected by surfaces, to optimize routing. It systematically calculates routes initiating from a central hub, extending along each circle’s circumference, visiting each location once, and progressing through strategically chosen adjacent points to minimize the total traveled distance. Experimental results indicate that TWA significantly enhances route optimization, outperforming traditional methods in specific instances. The findings underscore TWA’s potential contributions to the field of logistics and operational research.}, archive = {J_ACCESS}, author = {Lazaros S. Savvidis and Constandinos X. Mavromoustakis and George N. Mastorakis and Evangelos K. Markakis}, doi = {10.1109/ACCESS.2025.3560579}, journal = {IEEE Access}, pages = {67400-67414}, shortjournal = {IEEE Access}, title = {On the implementation of a transverse wave-based routing technique for a TSP circular instance}, volume = {13}, year = {2025}, }
• (2025). Electronic wedge brakes: A review of design, modeling, control strategies, and testing methodologies. ACCESS, 13, 67378-67399. (www)

  @article{ , abstract = {This review examines Electronic Wedge Brake (EWB) technology, a revolutionary vehicle braking system that uses a self-reinforcing wedge mechanism to increase braking force. We demonstrate the transformative potential of this system to improve performance, efficiency, and integration with advanced vehicle safety technologies by charting the transition from hydraulic and electro-mechanical brakes (EMBs) to EWBs. This review analyses state-of-the-art EWB design, modelling, and control contributions from 2002 to 2024. Our research shows that this sector has received little attention despite its importance in vehicular technology. We carefully study EWBs, focussing on the brake factor and how wedge geometry, actuation methods, and material selections affect braking. The examination addresses reverse braking, backlash, wear, Caliper rigidity, and mathematical modelling methods such as state-space representations, system identification, and lead screw, motor, and wedge dynamics. We also examine prototype development and validation methods such real-world vehicle testing, hardware-in-the-loop (HIL) simulations, and dynamometer testing to resolve the experimental-simulation mismatch. EWB control techniques such conventional PID, advanced sliding mode control, ADRC, fuzzy logic, and LQR are prioritised to maintain system stability around the optimal operating point. This evaluation synthesises EWB design, modelling, testing, and control and identifies important problems and future research areas to promote mainstream use, unlike earlier evaluations. The forward-thinking perspective of this study underlines the possibility for hybrid/electric vehicles to seamlessly combine EWB with Anti-lock Braking System (ABS), electronic speed control (ESC), and regenerative braking systems. This integration will enable next-generation intelligent braking solutions. This evaluation emphasises Electronic Wedge Brake technology’s contribution to safer, more efficient, and sustainable transportation and UN Sustainable Development Goals including affordable and clean energy for sustainable cities and communities.}, archive = {J_ACCESS}, author = {Mehrullah Soomro and Mohd Khair Hassan and Ghulam E. Mustafa Abro and Maran Marimuthu}, doi = {10.1109/ACCESS.2025.3560756}, journal = {IEEE Access}, pages = {67378-67399}, shortjournal = {IEEE Access}, title = {Electronic wedge brakes: A review of design, modeling, control strategies, and testing methodologies}, volume = {13}, year = {2025}, }
• (2025). A dual-servo stage with 6 D.O.F. magnetically levitated fine stage. ACCESS, 13, 67366-67377. (www)

  @article{ , abstract = {A novel dual-servo stage using the magnetic levitation technology for semiconductor lithography to achieve high-precision and high-acceleration simultaneously is shown in this paper. The dual-servo is one of the methods to realize high-precision with a large stroke. The proposed fine stage magnetically levitates by six degrees of freedom (D.O.F.) and is composed of a coarse stage without mechanical connections. The proposed dual-servo stage is vacuum compatible and cost-effective. A 6 D.O.F. magnetic-levitated fine stage is also proposed for the dual-servo stage. Designed fine stage levitates and actuates using a voice coil actuator with the Halbach magnet array. The magnetic flux is analytically modeled and verified via FEM simulations. Optimization is also performed to determine optimal design values. Two degrees of freedom of the stack type coarse stage are proposed for providing a large stroke and thrust force. Control strategies for the proposed dual-servo stage are introduced. The master-slave type control block diagram is used for overall control. For compensating the reaction force, a force observer is proposed. The sensor kinematics and actuator kinetics are designed. A dual-servo stage is manufactured with optimized design values. For levitating, 6 LVDTs are used for the homing control. After levitating, sensor switching was performed. The out-of-plane motion is fine controlled by 3 capacitance sensors and the in-plane motion is fine controlled by 3 laser interferometers. The moving ranges are 400 mm for the X- and Y-axis. The maximum speed is 175 mm/s, and the in-position stability is ±4.5 nm.}, archive = {J_ACCESS}, author = {Myeonghyeon Kim and Jae-Heon Jeong}, doi = {10.1109/ACCESS.2025.3560346}, journal = {IEEE Access}, pages = {67366-67377}, shortjournal = {IEEE Access}, title = {A dual-servo stage with 6 D.O.F. magnetically levitated fine stage}, volume = {13}, year = {2025}, }
• (2025). Surrogate-assisted multi-objective snow goose algorithm with gaussian process for cold chain logistics. ACCESS, 13, 67350-67365. (www)

  @article{ , abstract = {This study presents a novel optimization framework, the Gaussian Process-Based Surrogate-Assisted Multi-Objective Snow Goose Algorithm (GP-SAMOSGA), aimed at optimizing cold chain logistics (CCL) by addressing dual objectives: minimizing transportation costs and maximizing user satisfaction. User satisfaction is quantified through two key dimensions—time window compliance, which reflects delivery punctuality, and product freshness preservation, which evaluates the maintenance of perishable goods’ quality. The optimization is conducted within essential operational constraints, such as temperature regulation, vehicle loading capacities, and specified delivery time windows. To address the computational complexity and high evaluation costs of the objective functions, the GP-SAMOSGA integrates a Gaussian Process (GP) surrogate model with the Multi-Objective Snow Goose Algorithm (MOSGA). The GP surrogate model approximates the expensive objective functions, reducing the number of true function evaluations. Meanwhile, the MOSGA utilizes the migration and foraging behaviors of snow geese to balance exploration and exploitation in the search space. This integration allows the GP-SAMOSGA framework to efficiently identify Pareto-optimal solutions and form a well-distributed Pareto front that captures the trade-offs between competing objectives. The performance of the GP-SAMOSGA framework is validated through experiments on benchmark problems and a real-world CCL case study. The results demonstrate the algorithm’s high solution quality, computational efficiency, and scalability. Moreover, the GP-SAMOSGA outperforms traditional multi-objective optimization algorithms, making it a promising tool for complex logistics optimization tasks. This research contributes to the field by combining surrogate modeling with bio-inspired optimization, offering a robust and efficient approach for multi-objective CCL optimization.}, archive = {J_ACCESS}, author = {Jian Wu and Zong-Juan Yang and Zhi-Gang Du and Ai-Qing Tian}, doi = {10.1109/ACCESS.2025.3560731}, journal = {IEEE Access}, pages = {67350-67365}, shortjournal = {IEEE Access}, title = {Surrogate-assisted multi-objective snow goose algorithm with gaussian process for cold chain logistics}, volume = {13}, year = {2025}, }
• (2025). Driver-centric design: Empirical strategies for optimizing the visualization of driving information on vehicle liquid crystal display dashboards. ACCESS, 13, 67334-67349. (www)

  @article{ , abstract = {With the continuous advancement of smart car and vehicle display technologies, liquid crystal display (LCD) dashboards have become the mainstream medium for displaying driving information. This makes the reading performance and user research of LCD vehicle dashboards critically important. In this study, we aim to evaluate the impact of the human-machine interface (HMI) design of vehicle LCD dashboards on driver readability and user experience. Twelve experts participated in a clustering experiment of vehicle dashboards, and 32 drivers participated in a simulated driving environment test of resident display information and temporary display information. The results for resident display information indicate that design type makes a significant difference in both reading performance and visual search efficiency. In addition, only the unconventional-shaped (L-shaped) dual-dial design achieves the “desired” rating in the user experience evaluations. Regarding temporary display information, findings indicate that icons positioned at the top of the dashboard interface are more readable than those at the bottom. High-prioritized or frequently used icons should be placed at the top of the screen. Alternatively, if icons must be positioned at the bottom, they should be no smaller than 8 mm in size. This research will help reduce the driver’s visual workload and off-road sight time, reduce the risk of traffic accidents, and improve the driver’s user experience.}, archive = {J_ACCESS}, author = {Jin-Long Lin and Meng-Cong Zheng and Tao Wang and Peng Liu}, doi = {10.1109/ACCESS.2025.3560681}, journal = {IEEE Access}, pages = {67334-67349}, shortjournal = {IEEE Access}, title = {Driver-centric design: Empirical strategies for optimizing the visualization of driving information on vehicle liquid crystal display dashboards}, volume = {13}, year = {2025}, }
• (2025). Research on an improved stepwise feature matching algorithm for UAV indoor localization. ACCESS, 13, 67323-67333. (www)

  @article{ , abstract = {Indoor unmanned aerial vehicles (UAVs) localization faces challenges such as insufficient feature matches, high false match rates, and low accuracy under low-light conditions, viewpoint changes, and occlusions. To address these issues, we propose an adaptive FAST (Features from Accelerated Segment Test) corner detection thresholding method that adapts to indoor lighting and texture variations. Building upon the FAPP algorithm, our approach optimizes threshold selection during fine matching when correct and incorrect matching pairs are unevenly distributed. The proposed algorithm demonstrates superior robustness and efficiency in feature matching. In actual indoor UAV flight experiments, the feature matching rate reached 96.5%, and the indoor localization accuracy reached 0.02 meters. Compared to existing literature, both the average correct matching rate and localization accuracy were significantly improved, effectively solving the problem of precise indoor UAV localization.}, archive = {J_ACCESS}, author = {Yong He and Xiaochuan He}, doi = {10.1109/ACCESS.2025.3559989}, journal = {IEEE Access}, pages = {67323-67333}, shortjournal = {IEEE Access}, title = {Research on an improved stepwise feature matching algorithm for UAV indoor localization}, volume = {13}, year = {2025}, }
• (2025). Resilience enhancement strategies for energy systems in the face of natural calamities and cyber threats: A comprehensive review. ACCESS, 13, 67301-67322. (www)

  @article{ , abstract = {Energy systems and their related technologies are susceptible to natural extreme events, categorized as high-impact low-probability (HILP) events, posing a significant threat to their reliable functioning. Gas-to-power (G2P) and power-to-gas (P2G) technologies establish a bidirectional interface between these energy systems, leading to the creation of integrated power and natural gas systems (IPGS) as energy systems. Due to their extensive geographical coverage, energy systems are particularly vulnerable to severe damage from natural calamities. Given the growing interest and research focus on energy systems resilience, this comprehensive review meticulously navigates the intricate terrain of resilience differentiation, offering a detailed roadmap fortified by insights from significant instances of grid failures and weather-driven contingencies. The review examines preemptive cyber security fortifications and strategic planning imperatives, scrutinizing each aspect with conviction and clarity. Temporally stratified into long-term and short-term horizons, it not only delineates prevailing approaches and methodologies but also identifies emerging trends poised to shape the future landscape of resilience enhancement. This paper provides an exhaustive review of existing research on the resilience of energy systems, introducing a visual framework for comparing different studies and facilitating easy understanding through multiple figures. Since uncertainties play a crucial role in decision-making within this field, this paper broadly explores methods for addressing them as presented in previous studies. Furthermore, the literature is meticulously classified to offer a clear and organized overview, highlighting the impact of HILP events, such as natural disasters and cyber-attacks, on energy systems resilience. This review underscores the critical need to fortify energy systems, emphasizing its importance as a crucial component of integrated energy systems in preparing for the continuous impact of natural disasters in future research.}, archive = {J_ACCESS}, author = {Mostafa Shabanian-Poodeh and Rahmat-Allah Hooshmand and Miadreza Shafie-Khah and Pierluigi Siano}, doi = {10.1109/ACCESS.2025.3556233}, journal = {IEEE Access}, pages = {67301-67322}, shortjournal = {IEEE Access}, title = {Resilience enhancement strategies for energy systems in the face of natural calamities and cyber threats: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Hosting-capacity improvement using rooftop photovoltaics and decentralized BESS in low voltage grids. ACCESS, 13, 67286-67300. (www)

  @article{ , abstract = {Voltage regulation in low-voltage (LV) distribution grids is essential to increase the penetration of photovoltaic (PV) rooftop units. Hence, PV inverters are commonly embedded with volt-var (VV) and volt-watt (VW) control functions to support the grid voltage regulation within the grid codes. Such an approach usually increases the LV grid hosting capacity (HC) by 1.5 to 2 times at the cost of energy curtail. Thus, the use of decentralized battery energy storage systems (BESS) near to nodes sensible to overvoltage become appealing, as it stores energy without creating current circulation within the grid. However, there are still gaps in sizing of decentralized BESSs and in quantifying their improvement in terms of HC. Thus, this paper contributes with a comprehensive study that evaluates the HC of a generic LV grid comprised of PV units with embedded VV and VW control functions, and proposes a methodology to guide the sizing of small decentralized BESS used to increase the grid HC. Using OpenDSS software, the VV and VW control functions are evaluated and compared with a base scenario where PV units operate at a unity power factor. The active power curtailment is used to size decentralized BESS to absorb excess energy, avoiding PV active power derating. Results show that BESS units, sized at about 5-10% of the PV power capacity, can reduce revenue impacts and increase HC by over 30% in LV grids.}, archive = {J_ACCESS}, author = {Helio M. A. Antunes and Hugo Rodrigues Torquato and João Marcus Soares Callegari and Lucas Savoi Araujo and Danilo I. Brandao}, doi = {10.1109/ACCESS.2025.3560484}, journal = {IEEE Access}, pages = {67286-67300}, shortjournal = {IEEE Access}, title = {Hosting-capacity improvement using rooftop photovoltaics and decentralized BESS in low voltage grids}, volume = {13}, year = {2025}, }
• (2025). A review of human synchronization datasets. ACCESS, 13, 67269-67285. (www)

  @article{ , abstract = {Human synchronization refers to the coordination in time of individuals’ actions, movements, or physiological processes, often considered within a rhythmical context. This phenomenon can occur in various forms such as matching rhythmic movements, aligning speech patterns, or synchronizing biological rhythms. It plays a crucial role in social interactions, effective communication, sports, and musical performance. This work aims to reference accessible online datasets related to human synchronization, to increase their visibility and availability. Notably, it compiles 75 directly accessible datasets and 26 with indirect access to study synchronization in various contexts. It reviews all types of synchronization tasks and contexts found in the datasets. Results are clustered into four categories: discontinuous movement, continuous movement, social interactions, and musical performance. Then, this review explores the signals and acquisition methods used across human synchronization datasets, ranging from simple touchpads, and kinematic sensors, to expansive motion capture and hyperscanning technologies. This paper surveys objective measures of synchronization used in the gathered papers and reveals some heterogeneity in the quantification of synchronization. Furthermore, it points out a lack of annotated datasets, which limits the development of machine learning methods. By compiling accessible datasets, this review centralizes valuable experimental data, providing a foundation for more collaborative, comprehensive, and standardized approaches to studying human synchronization.}, archive = {J_ACCESS}, author = {Théo Velletaz and Stefan Janaqi and Sébastien Harispe and Julien Lagarde and Patrice Guyot}, doi = {10.1109/ACCESS.2025.3560424}, journal = {IEEE Access}, pages = {67269-67285}, shortjournal = {IEEE Access}, title = {A review of human synchronization datasets}, volume = {13}, year = {2025}, }
• (2025). An end-to-end secure communication framework for smart homes environment using consortium blockchain system. ACCESS, 13, 67250-67268. (www)

  @article{ , abstract = {The Internet of Things (IoT) technology is widely employed in several fields, such as smart cities, intelligent transportation systems, smart power grids, smart homes, and digital healthcare solutions, to enhance the quality of life, promote sustainability, and foster social interactions. The significant challenges related to IoT encompass the legitimacy verification of intelligent devices, handling cryptographic keys for edge devices with limited resources, guaranteeing the confidentiality and integrity of transmitted IoT data, and securely storing that data in the cloud system. The Blockchain system, characterized by its distributed, decentralized, and irreversible ledger, has gained recognition for its efficacy in ensuring security for sensitive smart home applications. This paper presents a comprehensive security solution for smart homes, comprising smart sensor authentication, key management, data confidentiality, data integrity, secure storage, and access control for cloud-based data. A robust and secure end-to-end structure for smart homes is developed in two phases. During the initial stage, a private Blockchain system is established on the fog nodes to facilitate the authentication and access control mechanisms for smart home edge devices. This private Blockchain can distribute and manage keys, enabling it to securely transfer data from intelligent sensors to the cloud environment using fog nodes. A consortium Blockchain is established during the next phase to simulate the cloud service providers. This Blockchain enables the provision of secured storage and data access control within the cloud, enhancing secure cloud storage even in a hostile environment. The novel contribution of this article relies on the integration of private and consortium Blockchain networks with a streamlined collaboration. This amalgamation achieves the efficient and distributed control of multiple Blockchain validators. A secure prototype has been developed to visualize and engage with a smart home environment. This prototype effectively displays data using various graphical and interactive techniques. The performance metrics, including attack resilience, latency and throughput of the network proves the efficiency of proposed framework as compared to state-of-the-art works. In future, we focus on the cross-chain interactions in multi-Blockchain environments and address the existing limitations such as inconsistencies, interoperability issues, and inefficient security measures.}, archive = {J_ACCESS}, author = {Suresh Babu Erukala and Dimitar Tokmakov and Aswani Devi Aguru and Rajesh Kaluri and Anna Bekyarova-Tokmakova and Nevena Mileva}, doi = {10.1109/ACCESS.2025.3559070}, journal = {IEEE Access}, pages = {67250-67268}, shortjournal = {IEEE Access}, title = {An end-to-end secure communication framework for smart homes environment using consortium blockchain system}, volume = {13}, year = {2025}, }
• (2025). Noise processing method of MEMS tilt sensor using improved kalman filter based on quantum particle swarm optimization. ACCESS, 13, 67239-67249. (www)

  @article{ , abstract = {This paper introduces a novel approach combining quantum particle swarm optimization (QPSO) and Kalman filter to enhance the anti-noise performance of micro-electro-mechanical system (MEMS) tilt sensors, which are susceptible to external environmental noise interference, affecting their measurement accuracy and reliability. The parameters of Kalman filter are adaptively optimized by QPSO, which addresses the issues of local optima and premature convergence during the optimization and correction process of prediction system. Then Kalman filter is carried out with the optimal parameters to achieve the effect of denoising. To validate the denoising performance of proposed algorithm, an experimental scene was set up with a six-dimensional space vibration test bench. According to the experimental findings, the proposed method exhibits a superior noise reduction effect, as evidenced by its smaller mean absolute error (MAE) and mean square error (MSE) compared to alternative techniques such as variational mode decomposition (VMD) combined with wavelet transform (WT), back propagation (BP) neural network optimized Kalman filter and particle swarm optimization (PSO) improved Kalman filter. The contribution lies in the innovative integration of QPSO with Kalman filtering, addressing a critical need in engineering by fortifying the anti-noise capabilities of MEMS tilt sensors, thereby bolstering their measurement accuracy and reliability in challenging environmental conditions.}, archive = {J_ACCESS}, author = {Yutong Ge and Weizheng Ren and Kaile Yu and Yiran Zhang and Yuxiao Li}, doi = {10.1109/ACCESS.2025.3552945}, journal = {IEEE Access}, pages = {67239-67249}, shortjournal = {IEEE Access}, title = {Noise processing method of MEMS tilt sensor using improved kalman filter based on quantum particle swarm optimization}, volume = {13}, year = {2025}, }
• (2025). Enhancing hajj and umrah services through predictive social media classification. ACCESS, 13, 67220-67238. (www)

  @article{ , abstract = {Each year, millions of individuals embark on the sacred journeys of Hajj and Umrah to Saudi Arabia. Given the diverse needs of these pilgrims and the continuous efforts to enhance their experience, we propose an advanced social media classification system based on predictive deep learning. The primary objective of this system is to efficiently classify and analyze social media content related to Hajj and Umrah services. To improve the effectiveness of this classification model, we introduce a predictive optimization strategy that employs a deep neural network as the learning module and utilizes particle swarm optimization to refine the weighting parameters. Leveraging real-time data from various microblogging platforms Twitter, blogging websites, Facebook, and Instagram, our model classifies individual posts using natural language processing techniques. The classification is based on relevant attributes such as service-level scores. If the dataset contains non-English text, it is first translated into English. Tokenization and preprocessing are then applied to categorize posts into five key areas: religious rites, management, safety, well-being, and services. The labeled posts are subsequently used to train a deep learning model. By incorporating a service-level score algorithm based on the TextBlob NLP library, each post is accurately classified and utilized as a feature in a supervised machine-learning classification system. The model’s performance is evaluated using standard metrics, including F-measure, Precision, and Recall. The ultimate objective is to achieve high-accuracy classification, enabling precise evaluation and improved analysis of social media content related to the pilgrimage experience.}, archive = {J_ACCESS}, author = {Samia Allaoua Chelloug and Mohammed Saleh Ali Muthanna and Faisal Jamil and Mehdhar S. A. M. Al-Gaashani and Soha Alhelaly and Ahmed Aziz and Ammar Muthanna}, doi = {10.1109/ACCESS.2025.3559204}, journal = {IEEE Access}, pages = {67220-67238}, shortjournal = {IEEE Access}, title = {Enhancing hajj and umrah services through predictive social media classification}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of modular neural network-based PI-controller ensemble with karush-kuhn-tucker conditions for grid-connected photovoltaic systems under ground fault conditions. ACCESS, 13, 67196-67219. (www)

  @article{ , abstract = {The increasing penetration of grid-connected solar PV system necessitates roboust and adaptive control strategies to mitigate the adverse effects of fluctuating grid voltages, variable solar irradiance, and ground faults. Traditional PI controllers through their widely used simplicity lack adaptability under non-linear and time varying conditions, leading to poor dynamic performance, increased total harmonic distortion, and compromised grid stability. To address these challenges, this paper proposes a Modular Neural Network (MNN)-based PI controller, which dynamically tunes proportional and integral gains in real time, ensuring optimal system performance. This work bridges the gap between fixed-gain PI controllers and heuristic optimization techniques by introducing a data-driven, real-time adaptive control methodology. Experimental validation through hardware implementation demonstrates a 45% reduction in steady-state error, a 30% reduction in THD, and improved reactive power compensation and grid synchronization. The findings establish the MNN-PI controller as a superior alternative for enhancing the reliability, efficiency, and grid compliance of SPV systems, particularly under dynamic operating conditions and fault scenarios.}, archive = {J_ACCESS}, author = {Burla Sridhar and Soorya Prakash Shukla and Ritesh Dash and Arpan Dwivedi and G. C. Biswal and Nivedita Singh and Mukesh Kumar Chandrakar and Kalvakurthi Jyotheeswara Reddy and Bhabasis Mohapatra and Binod Kumar Sahu}, doi = {10.1109/ACCESS.2025.3559139}, journal = {IEEE Access}, pages = {67196-67219}, shortjournal = {IEEE Access}, title = {Design and analysis of modular neural network-based PI-controller ensemble with karush-kuhn-tucker conditions for grid-connected photovoltaic systems under ground fault conditions}, volume = {13}, year = {2025}, }
• (2025). FPES-IDS: Forecast-path and exponential smoothing-based intrusion detection system to mitigate blackhole attack in RPL-based 6LoWPANs. ACCESS, 13, 67170-67195. (www)

  @article{ , abstract = {The Routing Protocol for Low-power and Lossy Networks (RPL) is vulnerable to Blackhole Attacks (BHA), where malicious nodes drop packets, isolating nodes or groups from the network. While traditional Intrusion Detection Systems (IDS) offer some level of protection, the expansion of the Internet of Things (IoT) in Low-Power and Lossy Networks (LLNs) requires more adaptive and resource-efficient solutions. This paper presents the Forecast-Path and Exponential Smoothing-based Intrusion Detection System (FPES-IDS) to detect and mitigate BHA in RPL-based IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs). FPES-IDS reduces overhead, employs Forecast-Path-based monitoring, dynamically computes thresholds, and uses environment-based smoothing parameters to predict packet arrivals. FPES-IDS operates on the principle that successful packet reception confirms the integrity of all nodes along the Forecast-Path, while packet delays indicate potential threats. FPES-IDS is an effective, lightweight, and adaptive IDS solution for securing RPL-based 6LoWPANs. FPES-IDS is structured into four modules: Pre-Detection (identifies leaf nodes and computes Forecast-Paths), Forecast (predicts packet arrival times), Monitoring and Detection (detects anomalies), and Incident Handler (mitigates threats by isolating malicious nodes and reconfiguring routes). Performance evaluations show FPES-IDS excels in detection, with a True Positive Rate between 99.01% and 99.99%, a Packet Delivery Ratio of 99.02% to 99.96%, and a throughput of 3370 to 3510 bps. FPES-IDS maintains a low False Positive Rate (0.03% to 0.88%), consumes minimal power (0.9456 mW to 0.9931 mW), and exhibits low energy consumption (5264 mJ to 6421 mJ). Additionally, FPES-IDS manages network overhead (1.15% to 1.74%) and minimises packet loss (0.04% to 0.48%).}, archive = {J_ACCESS}, author = {V. R. Rajasekar and S. Rajkumar}, doi = {10.1109/ACCESS.2025.3557670}, journal = {IEEE Access}, pages = {67170-67195}, shortjournal = {IEEE Access}, title = {FPES-IDS: Forecast-path and exponential smoothing-based intrusion detection system to mitigate blackhole attack in RPL-based 6LoWPANs}, volume = {13}, year = {2025}, }
• (2025). LLM-based topic modeling for dark web Q&A forums: A comparative analysis with traditional methods. ACCESS, 13, 67159-67169. (www)

  @article{ , abstract = {Topic modeling is a critical tool for understanding the thematic structures of unstructured text data, particularly in specialized domains like the dark web. This study compares the effectiveness of large language models (LLMs) and traditional topic modeling techniques in analyzing dark web Q&A forums, characterized by short, informal, and context-specific posts. We evaluate two LLMs—GPT and Gemini—against traditional methods, TF-IDF (scikit-learn) and Latent Dirichlet Allocation (LDA, Gensim), in their ability to generate meaningful and coherent topics. Our findings suggest that LLMs consistently outperform traditional methods, capturing contextually relevant themes such as “scam,” “bitcoin,” and “hacking” with an average semantic similarity score of 0.724 between GPT and Gemini, compared to 0.477 with Gensim. Additionally, LLMs produced significantly lower Levenshtein distances, with an average of 13.49 between GPT and Gemini, compared to 18.22 with scikit-learn and 17.97 with Gensim, indicating greater alignment in topic extraction. In exploring the impact of topic granularity, we found that while single-word topics effectively capture the main themes of dark web discussions, two-word topics provide additional context and specificity, enhancing the interpretability of complex discussions. For instance, topics like “bitcoin wallet” and “hacking tools” illustrate how two-word labels can convey nuanced meanings that single-word topics may overlook. This qualitative analysis underscores the importance of selecting appropriate topic modeling techniques based on the characteristics of the text and the requirements of the analysis, highlighting their potential as valuable tools for analyzing complex and unstructured text data in practical applications for law enforcement and research.}, archive = {J_ACCESS}, author = {Luis de-Marcos and Adrián Domínguez-Díaz}, doi = {10.1109/ACCESS.2025.3560543}, journal = {IEEE Access}, pages = {67159-67169}, shortjournal = {IEEE Access}, title = {LLM-based topic modeling for dark web Q&A forums: A comparative analysis with traditional methods}, volume = {13}, year = {2025}, }
• (2025). Semantic similarity of words in a taxonomy: A fuzzy and multiple-context approach. ACCESS, 13, 67149-67158. (www)

  @article{ , abstract = {In this paper, the semantic similarity of words organized according to a taxonomy is investigated. For this purpose, word senses are fundamental, and word senses depend on contexts. In general, in the literature, the methods for disambiguating the meaning of words are costly, have some limitations, often require human intervention, and sometimes identifying word senses is an ambiguous task also for human beings. Due to the frequent cases in which word disambiguation is inherently subjective, in this paper, we argue for the importance of considering all possible contexts and relative senses of words in these contexts. In our proposal, contexts and word senses are extracted from BabelNet, and words are fuzzy sets compared according to the Jaccard’s fuzzy similarity measure. This proposal has been applied to some of the most representative state-of-the-art taxonomy-based similarity measures and experimented with the Miller and Charles ( $M\& C$ ) golden dataset. Such a dataset is particularly relevant to test our proposal in that it stems from an in-depth analysis of the meaning of a word and the contexts in which the word is used. The experiment is very encouraging because it shows an improvement of the Pearson’s correlation values with human judgment for all the selected similarity measures.}, archive = {J_ACCESS}, author = {Anna Formica and Ida Mele and Francesco Taglino}, doi = {10.1109/ACCESS.2025.3560151}, journal = {IEEE Access}, pages = {67149-67158}, shortjournal = {IEEE Access}, title = {Semantic similarity of words in a taxonomy: A fuzzy and multiple-context approach}, volume = {13}, year = {2025}, }
• (2025). Decentralized data-driven voltage control for clustered PV inverters with local deviation priority. ACCESS, 13, 67136-67148. (www)

  @article{ , abstract = {This paper presents a decentralised, data-driven voltage control strategy designed to coordinate multiple photovoltaic (PV) inverters operating as a cluster, with a focus on mitigating local voltage deviations. The proposed framework is fully data-driven, obviating the need for prior knowledge of system parameters. A power-sharing mechanism is integrated to facilitate effective coordination among PV inverters within the cluster, dynamically adapting to both local and global voltage conditions. Simulation results on an actual distribution network confirm the method’s effectiveness in maintaining voltage regulation at the point of common coupling (PCC) while ensuring that all local voltages remain within permissible operational limits. The approach exhibits robust adaptability to system variations, addressing challenges posed by high PV penetration and dynamic network changes. Numerical simulations conducted in MATLAB/Simulink highlight the method’s potential to enhance grid stability and support the integration of renewable energy into modern distribution networks.}, archive = {J_ACCESS}, author = {David S. Pacheco-Cherrez and Jonathan C. Mayo-Maldonado and Gerardo Escobar and Daniel Guillen and Jesus Daniel Davalos Soto}, doi = {10.1109/ACCESS.2025.3560312}, journal = {IEEE Access}, pages = {67136-67148}, shortjournal = {IEEE Access}, title = {Decentralized data-driven voltage control for clustered PV inverters with local deviation priority}, volume = {13}, year = {2025}, }
• (2025). Low-power subgraph isomorphism at the edge using FPGAs. ACCESS, 13, 67127-67135. (www)

  @article{ , abstract = {Subgraph matching is a significant problem in several fields, including like social network analysis, chemical compound search, and fraud detection. While current solutions using CPU, graphics processing units (GPUs), and data center field-programmable gate arrays (FPGAs) deliver high performance, they consume significant power, making them unsuitable for resource-constrained edge environments. This article introduces a novel low-power FPGA accelerator that enables efficient subgraph matching using a small FPGA-based accelerator through three main innovations: a flexible two-level hash table architecture that minimizes memory accesses, a caching mechanism that reduces on-chip memory requirements, and a dynamic first-in first-out (FIFO) queue that efficiently buffers partial results. Experimental results on five real-world graphs show that the accelerator reduces energy consumption by an average of ${75 \,\, \times }$ compared to state-of-the-art CPU solutions, and ${107 \,\, \times }$ compared to GPU solutions, demonstrating that complex graph operations can be performed efficiently using even a small accelerator implemented on a reconfigurable platform.}, archive = {J_ACCESS}, author = {Roberto Bosio and Giovanni Brignone and Teodoro Urso and Mihai T. Lazarescu and Luciano Lavagno and Paolo Pasini}, doi = {10.1109/ACCESS.2025.3560405}, journal = {IEEE Access}, pages = {67127-67135}, shortjournal = {IEEE Access}, title = {Low-power subgraph isomorphism at the edge using FPGAs}, volume = {13}, year = {2025}, }
• (2025). Phase wise classification of abnormal gait in children with cerebral palsy using hybrid neural TEMPODE deep learning techniques. ACCESS, 13, 67112-67126. (www)

  @article{ , abstract = {The neurological condition known as Cerebral Palsy which affects motor control, frequently results in abnormal gait patterns that have a major negative influence on mobility and quality of life. It is essential to analyse and categorise these abnormalities of gait in order to implement tailored therapeutic interventions. The proposed study presents two Hybrid Neural TEMPODE (Temporal Ordinary Differential Equations) architectures for phase-wise classification of abnormal gait in children with cerebral palsy that combines Temporal Convolutional Networks (TCN) with Neural Ordinary Differential Equations (NODE) and Temporal Fusion Transformers (TFT) with Neural Ordinary Differential Equations. The suggested models classified the abnormal gait classes when the gait data is swing, stance and complete gait data, outperforming TCN, TFT and traditional deep learning methods like Long Short Term Memory model (LSTM) and Gated Recurrent Unit (GRU). Phase-specific feature modelling and strong hybrid architectures are two important advancements that achieved maximum accuracies of 94.61%, 94.40% and 94.29% for stance, swing and full gait cycle data, respectively. The findings highlight how well hybrid neural TEMPODE captures minute changes in gait, opening the door for more sophisticated clinical diagnostic treatments and a deeper comprehension of gait abnormalities in CP.}, archive = {J_ACCESS}, author = {Yelle Kavya and S.Sofana Reka}, doi = {10.1109/ACCESS.2025.3561061}, journal = {IEEE Access}, pages = {67112-67126}, shortjournal = {IEEE Access}, title = {Phase wise classification of abnormal gait in children with cerebral palsy using hybrid neural TEMPODE deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). AITtrack: Attention-based image-text alignment for visual tracking. ACCESS, 13, 67095-67111. (www)

  @article{ , abstract = {Vision-Language Models (VLMs) have recently advanced the Visual Object Tracking (VOT) performance. In VLMs, a vision encoder is employed to obtain visual representation, and a text encoder is employed to estimate the textual embeddings using natural language descriptions. By aligning the visual and textual representations, the VLMs achieve robust performance in complex and diverse tracking scenarios, efficiently handling dynamic target appearances such as motion blur, occlusion, fast motion, and similar object distractors. However, the input textual description in many existing VLM-based trackers incorporates class and semantics details without any contextual information. This is addressed in some recent VLM-based State Of The Art (SOTA) trackers by implicitly predicting some important attributes of the target object and encoding them as textual descriptions within the tracking paradigm. However, the SOTA methods neglect the contextual relationship among the predicted attributes. In this work, we propose an Attention-based Image-Text alignment Tracker (AITrack) for robust VOT tasks. AITrack simplifies the process of VLM-based tracking using attention-based visual and textual alignment modules. AITrack utilizes a region-of-interest (ROI) text-guided encoder that leverages existing pre-trained language models to implicitly extract and encode textual features and a simple image encoder to encode visual features. A simple alignment module is implemented to combine both encoded visual and textual features, thereby inherently exposing the semantic relationship between the template and search frames with their surroundings, providing rich encodings for improved tracking performance. We employ a simple decoder that takes past predictions as spatiotemporal clues to effectively model the target appearance changes without the need for complex customized post-processings and prediction heads. Extensive experiments are performed on six publicly available VOT benchmark datasets demonstrating the strong capabilities of our AITtrack by gaining an average success rate of 2.0%. Our code will be publicly available on: https://github.com/BasitAlawode/AITrack.}, archive = {J_ACCESS}, author = {Basit Alawode and Sajid Javed}, doi = {10.1109/ACCESS.2025.3555816}, journal = {IEEE Access}, pages = {67095-67111}, shortjournal = {IEEE Access}, title = {AITtrack: Attention-based image-text alignment for visual tracking}, volume = {13}, year = {2025}, }
• (2025). Integrating AI models for voltage and current monitoring in autonomous mobile robots to prevent power system blackouts. ACCESS, 13, 67079-67094. (www)

  @article{ , abstract = {Continuous operations in extreme environments require a reliable energy supply for autonomous systems. The proposed research presents a novel AI-driven system that averts power system blackouts by utilizing autonomous robots trained in artificial intelligence. The robots monitor power system signals in real-time to detect anomalies while employing their intelligence to select optimal distribution methods. This research combined multiple machine learning models utilizing power system attack data from Mississippi State University and Oak Ridge National Laboratory to ascertain their efficacy in anomaly detection and enhancing energy resilience. In power system threat detection, the ExtraTree Classifier and Random Forest Classifier demonstrate optimal accuracy at 95%, confirming their ability to ensure energy stability. In addition to XGBoost (88%), Bagging (91%), and Decision Tree (86%), the ExtraTree Classifier demonstrates superior performance; however, Gradient Boosting (77%) and Logistic Regression (63%) need improvement to ensure reliable performance in critical applications. The proposed method implements XAI techniques to make the system explainable while improving decision-making reliability. Future research will build on this work by augmenting data availability, integrating renewable energy systems into the research framework, and enhancing self-healing capabilities and robotic mobility to further improve power grid resilience. The developed findings on AI-driven energy automation of this mathematical applicability exploration created scalable intelligent solutions to tackle power stability challenges in unpredictable environmental conditions.}, archive = {J_ACCESS}, author = {Stephanie Ness}, doi = {10.1109/ACCESS.2025.3560088}, journal = {IEEE Access}, pages = {67079-67094}, shortjournal = {IEEE Access}, title = {Integrating AI models for voltage and current monitoring in autonomous mobile robots to prevent power system blackouts}, volume = {13}, year = {2025}, }
• (2025). Learning multi-attribute differential graphs with non-convex penalties. ACCESS, 13, 67065-67078. (www)

  @article{ , abstract = {We consider the problem of estimating differences in two multi-attribute Gaussian graphical models (GGMs) which are known to have similar structure, using a penalized D-trace loss function with non-convex penalties. The GGM structure is encoded in its precision (inverse covariance) matrix. Existing methods for multi-attribute differential graph estimation are based on a group lasso penalized loss function. In this paper, we consider a penalized D-trace loss function with non-convex [log-sum and smoothly clipped absolute deviation (SCAD)] penalties. Two proximal gradient descent methods are presented to optimize the objective function. Theoretical analysis establishing sufficient conditions for consistency in support recovery, convexity and estimation in high-dimensional settings is provided. We illustrate our approaches with numerical examples based on synthetic and real data.}, archive = {J_ACCESS}, author = {Jitendra K. Tugnait}, doi = {10.1109/ACCESS.2025.3559883}, journal = {IEEE Access}, pages = {67065-67078}, shortjournal = {IEEE Access}, title = {Learning multi-attribute differential graphs with non-convex penalties}, volume = {13}, year = {2025}, }
• (2025). Measurement of optical path difference based on phase modulation of light source for meter-scale and kilometer-scale optical fiber interferometers. ACCESS, 13, 67056-67064. (www)

  @article{ , abstract = {It is crucial in a wide range of applications based on optical fiber interferometry to precisely measure the length difference. In this article, a method of measuring the optical path difference between two arms of an optical fiber interferometer system based on the phase modulation of light source is proposed. This method enables measurement with the resolution of centimeter level and is suitable for both meter-scale and kilometer-scale optical fiber interference systems, which has a broad prospect in engineering applications. We present a low-cost and easily implementable method for measuring and compensating the length difference of an in-service optical fiber interferometer.}, archive = {J_ACCESS}, author = {Xin Lai and Yuchen Song and Lai Zhang and Yixiao Ma and Qiuheng Song and Qian Xiao and Hongyan Wu and Bo Jia}, doi = {10.1109/ACCESS.2025.3557903}, journal = {IEEE Access}, pages = {67056-67064}, shortjournal = {IEEE Access}, title = {Measurement of optical path difference based on phase modulation of light source for meter-scale and kilometer-scale optical fiber interferometers}, volume = {13}, year = {2025}, }
• (2025). A design framework for scalable and adaptive multi-agent coordination in dynamic environments: Addressing concurrent agent and environment interactions. ACCESS, 13, 67029-67055. (www)

  @article{ , abstract = {In dynamic environments, such as box-pushing tasks, multi-agent systems (MAS) face significant challenges in coordinating agents within high-density settings while managing uncertainties arising from fluctuations in agent configurations and environmental dynamics. In this study, we explore the integration of surrogate response surface modeling (SRSM) with optimization algorithms, comparing Stochastic Gradient Descent (SGD) with a fixed learning rate, and adaptive learning rate (ALR) optimizers—including Adaptive Moment Estimation (ADAM) and Adaptive Approximate Direction Method Algorithm (AADMA)—to enhance MAS performance metrics, such as Agent Collision Rate (ACR), Agent Movement Frequency (AMF), and Task Completion Time (TCT). Through systematic experimentation across five scenarios, SRSM is employed to uncover key trends in MAS performance and identify configurations that improve scalability and adaptability. From the analysis of simulation data, it has been observed that SGD struggles significantly in dynamic environments, while ADAM demonstrates moderate improvements. However, AADMA consistently outperforms both by reducing loss, lowering collision rates, increasing movement efficiency, and achieving shorter task completion times. Performance comparison charts and loss function graphs emphasize AADMA’s superiority in addressing the complexities of real-time coordination and adaptability. Through this study, we highlight the critical role of combining SRSM with ARL to design an MAS that is capable of thriving in complex, dynamic, and high-density environments. By addressing key scalability and adaptability challenges, the proposed framework significantly advances MAS design, paving the way for improved multi-agent coordination in real-world applications.}, archive = {J_ACCESS}, author = {Raza Muhammad Kazim and Guoxin Wang and Zhenjun Ming and Jinhui Cao and Janet K. Allen and Farrokh Mistree}, doi = {10.1109/ACCESS.2025.3560988}, journal = {IEEE Access}, pages = {67029-67055}, shortjournal = {IEEE Access}, title = {A design framework for scalable and adaptive multi-agent coordination in dynamic environments: Addressing concurrent agent and environment interactions}, volume = {13}, year = {2025}, }
• (2025). EFM-ResNet: A feature enhanced network for tobacco strips classification. ACCESS, 13, 67017-67028. (www)

  @article{ , abstract = {Tobacco strips are the core raw material in tobacco products, and the accuracy of their classification directly impacts product quality and production efficiency. In this paper, we propose a tobacco strip image classification model based on multi-scale fusion attention mechanism and feature enhancement improved ResNet, named EFM-ResNet, to address the issues of detail information loss during feature extraction and the difficulty in capturing long-range dependencies. First, we introduce a multi-scale fusion attention mechanism that enables the model to capture key information in the image, thereby enriching feature details. Second, we adopt a feature enhancement module to further improve the feature representation capability, allowing the model to capture more key information. Finally, a WFL loss function is used to address the limitations of the traditional cross-entropy loss function. Experimental results show that the proposed method achieves an accuracy of 95.87% in the tobacco strips classification task, significantly outperforming traditional methods and existing deep learning models. Therefore, the classification method proposed in this paper can substantially improve tobacco strips classification accuracy and provide strong technical support for the intelligent production of the tobacco industry.}, archive = {J_ACCESS}, author = {Bo Zhang and Shanshan Zheng and Leyuan Han and Yucan Qiu}, doi = {10.1109/ACCESS.2025.3560079}, journal = {IEEE Access}, pages = {67017-67028}, shortjournal = {IEEE Access}, title = {EFM-ResNet: A feature enhanced network for tobacco strips classification}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of interpolation models for IoT-enabled RF-EMF exposure mapping in urban areas. ACCESS, 13, 67006-67016. (www)

  @article{ , abstract = {The ubiquity of wireless communications in our daily environments has raised public concern about exposure to radio frequency (RF) electromagnetic fields (EMF). Manual collection of RF-EMF measurements is a time-consuming and labor-intensive task, while IoT-based monitoring systems still have quite limited coverage. Electromagnetic field strength maps have been employed to estimate EMF values at locations where a sensing/measuring infrastructure does not exist, inferring their values from measurements made at close distances, thus providing wider coverage in a cost-effective manner. However, such maps have only been created for indoor settings and relatively small geographical areas. The objective of this study is to identify the most optimal geospatial interpolation method to construct an electromagnetic field strength map of large-scale urban areas. We employed five different models to create an electromagnetic exposure map, with four of them being Gaussian process regression methods (Kriging), and the fifth being the classical weighted average method of nearest neighbors. The map was constructed for the city of Paris with a dataset consisting of 3632 measurements. Our results showed that Kriging, especially with the exponential model, outperformed the nearest-neighbor method in handling spatial autocorrelation in EMF data. In particular, removing outliers from the data set significantly improved all interpolation methods, reducing the mean square error (MSE) and variability.}, archive = {J_ACCESS}, author = {Theodor Panagiotakopoulos and Yiannis Kiouvrekis and Agapi Ploussi and Ellas Spyratou and Efstathios P. Efstathopoulos}, doi = {10.1109/ACCESS.2025.3555199}, journal = {IEEE Access}, pages = {67006-67016}, shortjournal = {IEEE Access}, title = {Comparative analysis of interpolation models for IoT-enabled RF-EMF exposure mapping in urban areas}, volume = {13}, year = {2025}, }
• (2025). Behavioral modeling of SiC MOSFET static and dynamic characteristics based on particle swarm optimization algorithm. ACCESS, 13, 66994-67005. (www)

  @article{ , abstract = {Silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) have been widely applied in electronic equipment, owing to the rapidly switching speed and superior thermal performance. Under high-frequency switching, the parasitic parameters in MOSFET driving circuit and power circuit will cause problems such as voltage overshoot and oscillation, which limit the application of power devices. To improve the stability and reliability of electronic circuit, considering that the operating frequency of the MOSFET reaches several hundred kHz, an accurate high-frequency model of the SiC MOSFET should be established, including static and dynamic models. This study proposes a SiC MOSFET behavioral model with parasitic parameters based on the particle swarm optimization (PSO) algorithm. The PSO algorithm is used to extract the parameters of the improved Enz-Krummnacher-Vittoz model and a static characteristic behavioral model of the SiC MOSFET is established, avoiding a large amount of simulation or calculation time for circuit and datasheet-drive models, the efficiency is increased by 60%, and the model accuracy is 98% compared to the datasheet. On the basis of the static characteristics, a nonlinear capacitance model is established using the PSO algorithm, and the parasitic inductance of the MOSFET is extracted through a finite element analysis. The fourth-order Runge-Kutta method is employed to solve the state equation, and the dynamic characteristic behavioral model of the MOSFET is established based on the static characteristics, parasitic capacitance and inductance, device and circuit parameters, which is simpler and easier to implement than the physical modeling method. A pulse testing experimental circuit is constructed to validate the accuracy of the dynamic model, compared with the sample model and experimental results, the errors of the behavioral model are less than 3%. This study provides valuable insights for MOSFET modeling and optimization, contributing to the dynamic analysis and reliability research of electronic circuits.}, archive = {J_ACCESS}, author = {Zhibo Zhu and Yang Zhao and Wei Yan}, doi = {10.1109/ACCESS.2025.3542060}, journal = {IEEE Access}, pages = {66994-67005}, shortjournal = {IEEE Access}, title = {Behavioral modeling of SiC MOSFET static and dynamic characteristics based on particle swarm optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). On the RANSAC performance for quadric surfaces in noisy conditions. ACCESS, 13, 66984-66993. (www)

  @article{ , abstract = {Random sample consensus-based algorithm (RANSAC) is one of the standard approaches in the field of robust estimation of parametric objects. There are numerous versions of RANSAC that are tailored to specific problems such as homography estimation, point cloud registration, plane estimation and others. On the other hand, numerous attempts were made towards the development of modifications of RANSAC that are extremely robust to noise or highly efficient in terms of the computational demands. However, the general premise of these approaches remains all the same: instead of trying to fit a single model to all the data, a number of small models is produced and evaluated in terms of their ability to describe the data. In this work, an empirical study is carried out that is purposely limited to a specific application of RANSAC. Particularly, the dependency of the output quality on the noise is examined for the quadric surface evaluation. The data was presented in the form of 3D point clouds. A method for synthetic data generation was developed, allowing to mimic the geometric principles of the point cloud formation. The dependency of the error on the noise was examined on the synthetic point clouds. Finally, numerical experiments on real point clouds were carried out in the agricultural setting. The code can be found in the repository (https://github.com/aidagroup/SCUF/) and the proposed method can be used as a module (https://pypi.org/project/scuf/).}, archive = {J_ACCESS}, author = {Ilya Ryakin and Sina Moghimi and Vladimir Guneavoy and Sergei Davidenko and Mikhail Patrikeev and Ilya Osokin and Pavel Osinenko}, doi = {10.1109/ACCESS.2025.3556757}, journal = {IEEE Access}, pages = {66984-66993}, shortjournal = {IEEE Access}, title = {On the RANSAC performance for quadric surfaces in noisy conditions}, volume = {13}, year = {2025}, }
• (2025). DDU-net: A domain decomposition-based CNN for high-resolution image segmentation on multiple GPUs. ACCESS, 13, 66967-66983. (www)

  @article{ , abstract = {The segmentation of ultra-high resolution images poses challenges such as loss of spatial information or computational inefficiency. In this work, a novel approach that combines encoder-decoder architectures with domain decomposition strategies to address these challenges is proposed. Specifically, a domain decomposition-based U-Net (DDU-Net) architecture is introduced, which partitions input images into non-overlapping patches that can be processed independently on separate devices. A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context. Experimental validation is performed on a synthetic dataset that is designed to measure the effectiveness of the communication network. Then, the performance is tested on the DeepGlobe land cover classification dataset as a real-world benchmark data set. The results demonstrate that the approach, which includes inter-patch communication for images divided into $16\times 16$ non-overlapping subimages, achieves a 2-3% higher intersection over union (IoU) score compared to the same network without inter-patch communication. The performance of the network which includes communication is equivalent to that of a baseline U-Net trained on the full image, showing that our model provides an effective solution for segmenting ultra-high-resolution images while preserving spatial context. The code is available at https://github.com/corne00/DDU-Net}, archive = {J_ACCESS}, author = {Corné Verburg and Alexander Heinlein and Eric C. Cyr}, doi = {10.1109/ACCESS.2025.3561033}, journal = {IEEE Access}, pages = {66967-66983}, shortjournal = {IEEE Access}, title = {DDU-net: A domain decomposition-based CNN for high-resolution image segmentation on multiple GPUs}, volume = {13}, year = {2025}, }
• (2025). Development of a low-cost UAV-based system for measuring mobile network signal power using open-source technologies. ACCESS, 13, 66944-66966. (www)

  @article{ , abstract = {Measurement of mobile network signal power, among other features, is an activity of major importance in the telecommunications industry due to several features associated with it, such as network optimization, Quality of Service or infrastructure planning. Unfortunately, major issues happen when those measurements must be taken in unconventional terrains such as remote, mountainous or rural areas, as traditional methods to gather information (drive or walk testing, crowdsourced data, usage of static measurement stations) quickly become ineffective. By employing Unmanned Aerial Vehicles (UAVs) equipped with signal receivers, this manuscript presents development works that aim to provide a scalable and cost-effective solution for collecting and processing signal measurements. The performed activities aim to use a UAV combined with open-source systems and standardized protocols to obtain and process signal measurements, as well as state-of-the-art software infrastructure that will be used to store and display such data readings in an inexpensive, secure and user-friendly manner. This approach not only enhances the understanding of signal distribution in various environments but also supports the optimization of mobile network performance, ultimately benefiting both public and private sectors in their efforts to improve connectivity and service quality.}, archive = {J_ACCESS}, author = {Vittorio Buggiani and Jesús Rodríguez-Molina and Margarita Martínez-Núñez and Juan Garbajosa}, doi = {10.1109/ACCESS.2025.3557538}, journal = {IEEE Access}, pages = {66944-66966}, shortjournal = {IEEE Access}, title = {Development of a low-cost UAV-based system for measuring mobile network signal power using open-source technologies}, volume = {13}, year = {2025}, }
• (2025). FML-swin: An improved swin transformer segmentor for remote sensing images. ACCESS, 13, 66931-66943. (www)

  @article{ , abstract = {Semantic segmentation of urban remote sensing images is a very challenging task. Due to the complex background, occlusion overlap and small scale target of urban remote sensing image, the semantic segmentation results have some defects such as target confusion and similarity, target boundary ambiguity, and small scale target omission. To solve the above problems, a feature-interactive fusion and multi-scale detail sensing lightweight enhanced Swin Transformer (FML-Swin) is proposed. The model includes several key components: feature interactive fusion transformer (FIFT) module, which enhances the model’s focus on current channel features; multi-scale detail sensing (MSDS) module, specifically designed to capture small scale features and details in remote sensing images; and lightweight enhanced squeeze excitation (LESE) module, which enriches the semantic feature information contained in the input image while maintaining a lightweight design. With limited training rounds, the model achieves a mIoU accuracy of 78.58 on the multi-class semantic segmentation task of the Potsdam dataset, exceeding SegNeXt 0.49. In addition, on the multi-class semantic segmentation task of the Vaihingen dataset, the mIoU accuracy of the model is 74.75, which is higher than SegNeXt 0.17. These results demonstrate the validity of the model.}, archive = {J_ACCESS}, author = {Tianren Wu and Wenqin Deng and Rui Lin and Junzhe Jiang and Xueyun Chen}, doi = {10.1109/ACCESS.2025.3561325}, journal = {IEEE Access}, pages = {66931-66943}, shortjournal = {IEEE Access}, title = {FML-swin: An improved swin transformer segmentor for remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Building bridges instead of putting up walls: An educational tool to facilitate instructors in adopting active learning methodologies for teaching programming. ACCESS, 13, 66907-66930. (www)

  @article{ , abstract = {Teaching programming is a challenging task, as it requires instructors to guide students in developing complex skills such as real-world abstraction, problem-solving, and logical reasoning. However, the traditional teaching approach is often ineffective in achieving these objectives. Evidence suggests that Active Learning Methodologies (ALMs) can provide a more conducive environment for skill and competency development. Nonetheless, instructors’ adoption rate of ALMs remains relatively low due to various barriers and factors, particularly in programming education. To assist instructors in facing this challenge, we present in this article CollabProg, an open collaborative repository designed to support instructors in identifying and selecting the appropriate ALMs for their teaching context and specific classroom needs. Additionally, CollabProg provides a set of practical guidelines, offering a step-by-step guide to assist instructors in adopting ALMs. We adopted the Design Science Research Methodology (DSRM) to systematically address the research problem and guide the development, evaluation, and evolution of CollabProg. Furthermore, we present two case studies to evaluate the acceptance and feasibility of using CollabProg from the perspective of instructors at different educational institutions in Brazil. The evidence demonstrates that CollabProg effectively supports instructors in adopting active learning methodologies while identifying limitations and opportunities for improvement. We also found that CollabProg helped instructors identify and choose suitable ALMs for their teaching context to meet their specific classroom needs. The guidelines provided by the repository were useful and highly practical for lesson planning in adopting ALMs. The adoption of CollabProg underscores the need for effective strategies to support instructors in teaching programming and motivating students to learn. These strategies are particularly important in collaborative learning contexts, where social interaction is key. CollabProg’s versatility in supporting such contexts is important for successful instruction.}, archive = {J_ACCESS}, author = {Ivanilse Calderon and Williamson Silva and Eduardo Feitosa}, doi = {10.1109/ACCESS.2025.3554156}, journal = {IEEE Access}, pages = {66907-66930}, shortjournal = {IEEE Access}, title = {Building bridges instead of putting up walls: An educational tool to facilitate instructors in adopting active learning methodologies for teaching programming}, volume = {13}, year = {2025}, }
• (2025). IoT-powered mental healthcare: Sleep state analyzing and monitoring based on EEG signals. ACCESS, 13, 66888-66906. (www)

  @article{ , abstract = {Nowadays, people frequently suffer from sleep deficiency. When this state continues for a long time, it undermines their physical health and mental health. With recent advancement in Internet of Thing (IoT), artificial intelligence (AI), and neuroscience, there is more room for developing efficient healthcare systems. In this paper, we propose an architecture for effectively building a mental healthcare system to analyze and monitor users’ sleep states, from which their mental health can be protected. The architecture consists of three main components, Edge Device, Cloud Server, and UI Application. The Edge Device is responsible for tracking electroencephalogram (EEG) signals, i.e., brainwave signals. The Cloud Server is employed to process the collected EEG signals and relies on various modern AI models to analyze and predict EEG activities and sleep stages. The UI Application allows the users to access and interact with the results of their mental health. The proposed architecture under the support of IoT and AI has successfully demonstrated its efficiency, accuracy, and convenience through a prototype system.}, archive = {J_ACCESS}, author = {Cong Thuan do and Nguyen Duy Khai and Tuan Dat Nguyen and Nguyen Tri Thanh and Pham Hoang Minh Chau and Anh Nam Le and Van Duc Nguyen and Trinh van Chien}, doi = {10.1109/ACCESS.2025.3559932}, journal = {IEEE Access}, pages = {66888-66906}, shortjournal = {IEEE Access}, title = {IoT-powered mental healthcare: Sleep state analyzing and monitoring based on EEG signals}, volume = {13}, year = {2025}, }
• (2025). Dielectric loaded antenna with conformal waterbolus and improved SAR for hyperthermia treatment of localized superficial cancer. ACCESS, 13, 66878-66887. (www)

  @article{ , abstract = {Selective increase in tumor temperature to 40 – $44~^{\circ }$ C during hyperthermia (HT) treatment is known to improve cancer treatment outcomes. However, existing HT applicators widely reported for large area diffused superficial cancers have challenges in selectively heating localized superficial tumors of 2-3 cm diameter located at curved surfaces of body. In this work, we report a compact water loaded 434 MHz antenna with 13.68 cm2 aperture area and an integrated conformal water bolus for HT treatment of small localized superficial cancers. The conformal water bolus was designed to provide good surface contact with the contoured tissue surface. Applicator power deposition and heating characteristics were characterized in phantoms as per the guidelines for superficial HT devices. The fabricated applicator has >98% power coupling at 434 MHz with 16.10 MHz bandwidth, 21.00 cm2 EFS, and 3.00 cm effective penetration depth in planar layered phantom. Thermal penetration depth and effective field size of 3.00 cm and 25.20 cm2, respectively measured in phantom demonstrate localized heating characteristics. Localized heating with >96% power coupling at 434 MHz was observed in cylindrical tissue phantoms and contoured surfaces of ex-vivo chicken and bovine tissues. It is concluded that the proposed applicator could be used for HT treatment of small localized superficial cancers.}, archive = {J_ACCESS}, author = {Rahul Choudhary and Kavitha Arunachalam}, doi = {10.1109/ACCESS.2025.3558614}, journal = {IEEE Access}, pages = {66878-66887}, shortjournal = {IEEE Access}, title = {Dielectric loaded antenna with conformal waterbolus and improved SAR for hyperthermia treatment of localized superficial cancer}, volume = {13}, year = {2025}, }
• (2025). Application of improved multi-fractal trend removing wave model in the analysis of multi-fractal characteristics of harmonic signals. ACCESS, 13, 66865-66877. (www)

  @article{ , abstract = {Accurate identification and quantification of harmonic signals are crucial in ensuring the stable operation of the power grid and improving power quality in power system analysis. However, traditional single fractal analysis is difficult to accurately capture the multi-fractal local characteristics of harmonic signals. To address this challenge, this paper constructs a novel method for analyzing the multi-fractal features of harmonic signals by integrating multi-fractal detrended fluctuation models, wavelet transform, and empirical mode decomposition techniques. Experiments have shown that the harmonic signals collected by the research belong to the type of low frequency and weak signal strength, and their frequency distribution is mainly concentrated in the range of 0Hz to 210Hz. The second harmonic (100Hz) and the fourth harmonic (200Hz) are the main harmonic components. As the number of decomposition layers increases, the frequency of the signal will gradually decrease and the fluctuations will gradually level off, which helps to clearly identify the low-frequency features in the signal, including the trend and periodic changes. And the amplitude of the high-frequency component will gradually decrease, indicating that the higher frequency components of the signal are gradually extracted. When the sampling frequency was set to 1kHz, the maximum bandwidth of the harmonic signal was 500Hz. Compared with other methods, the performance of the research method has been improved by 5% to 10%. In the analysis of even harmonics, the computational efficiency, resource consumption, stability, and parameter sensitivity of the research method were 96.6%, 31.2%, 93.7%, and 98.7%. In odd harmonic analysis, these indicators were 95.3%, 30.8%, 94.1%, and 97.9%. In contrast, the performance of the proposed method has enhanced by about 5% to 10%. These data indicate that the proposed method is innovative in technology and demonstrates excellent performance in practical utilization, providing an efficient, stable, and resource efficient solution for the multi fractal feature analysis of harmonic signals.}, archive = {J_ACCESS}, author = {Jiebin Wen}, doi = {10.1109/ACCESS.2025.3560381}, journal = {IEEE Access}, pages = {66865-66877}, shortjournal = {IEEE Access}, title = {Application of improved multi-fractal trend removing wave model in the analysis of multi-fractal characteristics of harmonic signals}, volume = {13}, year = {2025}, }
• (2025). Integration of C-V2X technology in hydrogen storage and pressure monitoring systems for fuel cell electric vehicles (FCEVs). ACCESS, 13, 66855-66864. (www)

  @article{ , abstract = {Hydrogen storage systems in fuel cell electric vehicles (FCEVs) face significant safety and efficiency challenges that require more advanced monitoring solutions. We describe the rationale and key role of hydrogen storage systems used in FCEVs currently on the market. Then, the operation principle of cellular vehicle-to-everything (C-V2X) technology and its application in different scenarios are expounded. Based on the review and analysis of relevant literature, the concept of combining C-V2X technology with hydrogen storage system in FCEV is proposed. C-V2X technology has great advantages in real-time data transmission, anomaly detection, response mechanism, data integration and analysis, which plays a key role in improving the efficiency of hydrogen storage system. Finally, the scientific aim of the work is outlines the future research direction of optimizing hydrogen storage system and promoting the application of C-V2X technology.}, archive = {J_ACCESS}, author = {Han Liang and Yuanyuan Song and Minggang Zheng}, doi = {10.1109/ACCESS.2025.3560401}, journal = {IEEE Access}, pages = {66855-66864}, shortjournal = {IEEE Access}, title = {Integration of C-V2X technology in hydrogen storage and pressure monitoring systems for fuel cell electric vehicles (FCEVs)}, volume = {13}, year = {2025}, }
• (2025). CIMF-net: A change indicator-enhanced multiscale fusion network for remote sensing change detection. ACCESS, 13, 66843-66854. (www)

  @article{ , abstract = {Remote sensing change detection (RSCD) is essential for monitoring land use, urban expansion, and environmental changes. Despite advancements in deep learning, existing methods exhibit limitations such as discontinuous edges and internal gaps in detected change regions, primarily due to insufficient feature fusion and limited contextual understanding. In this paper, we propose a novel change indicator-enhanced multiscale fusion network, called CIMF-Net, which integrates multiscale feature fusion modules (MFFM) and cascade guided attention modules (CGAM) to overcome these limitations. MFFM facilitates the extraction of semantic information through multiscale features, while CGAM employs hierarchical attention mechanisms to refine feature representation and enhance change region localization. Extensive experiments on three benchmark datasets highlight the superior performance of CIMF-Net compared to baseline network, achieving higher F1-scores, IoU, and overall accuracy. Furthermore, ablation studies confirm the effectiveness of MFFM and CGAM in enhancing feature fusion and hierarchical guidance. Our method not only delivers robust results in challenging scenarios but also presents a new perspective and technology for change detection.}, archive = {J_ACCESS}, author = {Jiting Zhou and Pu Zhang and Zhihao Zhou}, doi = {10.1109/ACCESS.2025.3560591}, journal = {IEEE Access}, pages = {66843-66854}, shortjournal = {IEEE Access}, title = {CIMF-net: A change indicator-enhanced multiscale fusion network for remote sensing change detection}, volume = {13}, year = {2025}, }
• (2025). Modeling and optimization of two-sided disassembly line balancing problem considering interactions among precedence free tasks. ACCESS, 13, 66829-66842. (www)

  @article{ , abstract = {In disassembly process, task time interactions between two tasks may occur when they do not have any precedence relationship, and this will increase the disassembly time and affect productivity. Therefore, this paper provides an attempt to study a multi-objective two-sided disassembly line balancing problem considering interactions among precedence free tasks, and establishes a mathematical model from efficiency and environment concerns. Due to NP-hard nature of the presented problem, a novel hybrid discrete artificial bee colony algorithm is proposed based on the problem knowledge of the problem to obtain satisfactory solutions within a short computational time. A new encoding scheme based on one-dimensional integer array is designed to represent feasible solutions. A random initialization method based on Logistic chaotic mapping is used to produce an initial population with diversity. The INSERT, SWAP and MUTATION operators developed according to the characteristics of the problem are employed to construct the set of neighborhoods for the VNS method to exploit new solutions. Instead of randomly exploring new solutions in the whole solution space, a MUTATION operator performed on the current best solution is designed to avoid inefficiency in the exploration progress. The effectiveness and superiority of the proposed algorithm are verified by a series of instances. The analysis results indicate that the proposed algorithm is suitable to solve the presented problem as well as its variants and shows superior performance. Moreover, different disassembly solutions are compared to illustrate the important of balancing the tasks among the disassembly line.}, archive = {J_ACCESS}, author = {Shuwei Wang and Jia Liu and Guoxun Xu}, doi = {10.1109/ACCESS.2025.3559059}, journal = {IEEE Access}, pages = {66829-66842}, shortjournal = {IEEE Access}, title = {Modeling and optimization of two-sided disassembly line balancing problem considering interactions among precedence free tasks}, volume = {13}, year = {2025}, }
• (2025). Online course curriculum design for subject matter experts: An agile methodology. ACCESS, 13, 66809-66828. (www)

  @article{ , abstract = {Curriculum design is a critical component of course development, facilitating the creation of meaningful learning experiences across delivery methods and learner types. Existing curriculum design methodologies often assume that educators possess prior knowledge and experience in curriculum development. However, these frameworks do not account for Subject Matter Experts (SMEs) who, with limited or no curriculum design experience, seek to develop online courses. Furthermore, current methodologies lack a learner-centered process model that is agile and responsive to external changes. With their inherent flexibility and adaptability, Agile methodologies offer potential solutions by allowing for quick adaptation to shifting educational environments. This paper introduces Agile Online Course Curriculum Design (AOCCD). This framework empowers SMEs to create learner-centered online curricula through iterative development cycles incorporating continuous learner feedback. The AOCCD was validated through a mixed-method study in which 12 SMEs applied this approach to develop their online course curricula, assessing its utility and effectiveness. Results demonstrate that AOCCD enables SMEs, regardless of prior curriculum experience, to create well-structured and scaffolded learner-centered courses, addressing the need for structure and responsiveness in online education design.}, archive = {J_ACCESS}, author = {Luis Rojas and Katrina Sorbello and Cristian Rusu and John W. Castro and Paulina González-Martínez and Claudio Alvarez and Fei Luo}, doi = {10.1109/ACCESS.2025.3559387}, journal = {IEEE Access}, pages = {66809-66828}, shortjournal = {IEEE Access}, title = {Online course curriculum design for subject matter experts: An agile methodology}, volume = {13}, year = {2025}, }
• (2025). Enhancing the efficacy of short-term prediction models for solar photovoltaic systems: An influence examination of chronological and meteorological factors. ACCESS, 13, 66787-66808. (www)

  @article{ , abstract = {The increasing energy demand, driven by population growth and technological advancements, necessitates a shift from traditional fossil fuel-based power generation to renewable energy systems. Solar photovoltaic panels offer a promising alternative; however, their energy output fluctuations present challenges in maintaining a reliable power source. Accurate estimation of solar energy generation is critical for enhancing the competitiveness of solar power plants and fostering social and economic progress. This paper aims to develop a machine learning model that utilizes meteorological and time-related data to predict solar photovoltaic power generation. It investigates how various factors, such as humidity and seasonal variations, impact prediction accuracy. Three different ML models were used and compared: Support vector regression, XGBoost, and HistGradient Boost. To improve the performance of these models hyperparameter tuning techniques is used. The findings reveal that radiation, humidity levels, and seasonal variations are the most influential factors in predicting photovoltaic solar power generation. Additionally, incorporating seasonal and humidity data can enhance the machine-learning model’s accuracy by 17.9%. XGBoost model outperforms existing methods, achieving the highest accuracy of 95.76% when incorporating humidity and seasonal factors. Its lowest MAPE of 4.23 and highest $R^{2}$ of 99.1. to assess model precision, an uncertainty analysis was performed using p-factor and q-factors. The results confirm model robustness and reliability in predictive performance.}, archive = {J_ACCESS}, author = {Ghalia Nassreddine and Amal El Arid and Mohamad Nassereddine and Obada Al-Khatib and Anas Arram and Abir El Abed}, doi = {10.1109/ACCESS.2025.3559587}, journal = {IEEE Access}, pages = {66787-66808}, shortjournal = {IEEE Access}, title = {Enhancing the efficacy of short-term prediction models for solar photovoltaic systems: An influence examination of chronological and meteorological factors}, volume = {13}, year = {2025}, }
• (2025). Real-time human action anomaly detection through two-stream spatial-temporal networks. ACCESS, 13, 66774-66786. (www)

  @article{ , abstract = {Human Action Anomaly Detection is an advanced technology that leverages computer vision and machine learning to identify unusual or suspicious human activities in real-time video stream and sensor data. By continuously monitoring environments such as public spaces, workplaces, and residential areas, this system can promptly detect and respond to potential threats and safety violations. The core components of this technology include data collection from video surveillance and sensors, preprocessing techniques for feature extraction, model training using normal action patterns, and real-time anomaly detection algorithms. Due to the hardware limitations in many industrial scenarios, we would like to explore the use of the CPU alone to detect abnormal human action. In this paper, we present a two-stream spatial-temporal transformer network for predicting human-object interactions in real-time. Initially, human skeleton information was extracted using a human pose detector. A human hand detector and tracker are utilized to localize the human hand and detect its joints in greater detail. Concurrently, an object detector was employed to spatially and semantically localize and classify the objects with which the worker in the image was interacting. With extracted information as input, we model human action anomaly detection in operations using both spatial and temporal dimensions. We validated our work with the composed network on our own collected dataset and experimentally proved that our work can significantly identify incorrect work steps in a work scenario.}, archive = {J_ACCESS}, author = {Chuan Peng and Zebin Jiang and Mao Lin and Hongbin Hu and Cen Qing and Yuankai Wu and Xiao Xu}, doi = {10.1109/ACCESS.2025.3560703}, journal = {IEEE Access}, pages = {66774-66786}, shortjournal = {IEEE Access}, title = {Real-time human action anomaly detection through two-stream spatial-temporal networks}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based probabilistic approach for non-destructive testing of aircraft components using laser ultrasonic data. ACCESS, 13, 66761-66773. (www)

  @article{ , abstract = {Composite structures are commonly used in complex applications such as automotive and aerospace due to their high strength-to-weight ratio. Although strictly supervised and inspected, they are often subject to dynamic events during their useful life that can cause invisible failures that extend and severely compromise their performance over time. Detecting these defects preventively and repairing them could avoid dramatic accidents. Here, we present a deep learning-based method for the non-destructive detection of defects in composite samples based on a laser ultrasonic system (LUT). Laser ultrasonic technology is a promising non-destructive testing (NDT) method for detecting inner defects in a non-contact way, as it does not require liquid coupling media. We investigated a composite laminate specimen containing six programmed defects as a test sample. We show that training deep learning-based models as autoencoders makes it possible to extract features that can be used to discern defective areas from non-defective ones in the US C-scan maps. The results demonstrate high detection accuracies (above 90% balanced accuracy and $75\%~F_{1}$ -score), indicating a promising and effective approach to NDT on composite materials.}, archive = {J_ACCESS}, author = {Adriano Liso and Cosimo Patruno and Angelo Cardellicchio and Pierfrancesco Ardino and Nicola Gallo and Giuseppe del Prete and Valerio Dentico and Veronica Vespini and Sara Coppola and Pietro Ferraro and Vito Renò}, doi = {10.1109/ACCESS.2025.3557200}, journal = {IEEE Access}, pages = {66761-66773}, shortjournal = {IEEE Access}, title = {A deep learning-based probabilistic approach for non-destructive testing of aircraft components using laser ultrasonic data}, volume = {13}, year = {2025}, }
• (2025). Experimental implementation of enhanced security coherent one-way quantum key distribution. ACCESS, 13, 66752-66760. (www)

  @article{ , abstract = {Coherent One-Way (COW) is a simple and widely utilized quantum key distribution (QKD) protocol, favored in both experimental and commercial applications. In this paper, we compare the information that an eavesdropper, Eve, can acquire in both the 2-decoy and 1-decoy COW protocols. Our findings demonstrate that the 2-decoy protocol offers enhanced security compared to the original 1-decoy COW, all while preserving the simplicity of implementation. Furthermore, we have experimentally implemented the improved security COW-QKD (2-decoy protocol), achieving a secure key rate exceeding 0.5 kbps over a distance of 110 km (22 dB loss) in optical fiber, with an interference visibility greater than 80%. We also emphasize that monitoring the click rates of the data line detector for each state individually serves as a powerful method for detecting zero-error attacks.}, archive = {J_ACCESS}, author = {Amirhosein Dadahhani and Soheil Hajibaba and Hamid Asgari and Majid Khodabandeh and Fatemeh Rezazadeh and Azam Mani and Seyed Ahmad Madani}, doi = {10.1109/ACCESS.2025.3558944}, journal = {IEEE Access}, pages = {66752-66760}, shortjournal = {IEEE Access}, title = {Experimental implementation of enhanced security coherent one-way quantum key distribution}, volume = {13}, year = {2025}, }
• (2025). Space-vector pulse width modulation for four-leg voltage source inverter under line-to-line short-circuit faults. ACCESS, 13, 66742-66751. (www)

  @article{ , abstract = {This paper propose a novel space vector pulse-width modulation (SVPWM) to achieve fault tolerance (FT) under line-to-line (LL) short-circuit (SC) conditions for three-phase four-leg (3P4L) inverter. The primary objective of the proposed method is to maintain normal voltage supply to the all phases while ensuring that zero voltage is applied to the faulted line during an LL fault. To achieve this, a new coordinate transformation method is introduced to accurately extract the magnitude of unbalanced three-phase voltages. The proposed coordinate transformation handles a three-dimensional (3-D) plane under LL fault, enabling conversion to a two-dimensional (2-D) plane via transformation along the $\gamma $ - axis. This transformation remains effective even under LL fault, ensuring reliable voltage magnitude detection and control through the synchronous reference frame (SRF) transformation. Additionally, an SVPWM switching strategy tailored for fault conditions is developed. By analyzing the 3P4L inverter’s switching vector diagram, a specific combination of switching vectors is proposed to apply zero voltage to the faulted line. The effectiveness of the proposed strategy is validated through experimental results from a 30 kW-class 3P4L inverter.}, archive = {J_ACCESS}, author = {Chang-Gyun An and Kyoung-Min Choo}, doi = {10.1109/ACCESS.2025.3560548}, journal = {IEEE Access}, pages = {66742-66751}, shortjournal = {IEEE Access}, title = {Space-vector pulse width modulation for four-leg voltage source inverter under line-to-line short-circuit faults}, volume = {13}, year = {2025}, }
• (2025). Predictive PID control for automated guided vehicles using genetic algorithm and machine learning. ACCESS, 13, 66726-66741. (www)

  @article{ , abstract = {The integration of automated guided vehicles (AGVs) in industrial automation demands precise and adaptive control systems for efficient path tracking. This study introduces a hybrid framework combining traditional Proportional-Integral-Derivative (PID) control with advanced machine learning to optimize AGV performance. A genetic algorithm (GA) was employed to generate ground truth PID parameters for diverse track configurations, ensuring superior path-tracking accuracy. However, the computational intensity of GA inspired the adoption of Support Vector Regression (SVR) as a scalable and efficient alternative for predicting PID gains. A custom simulator, designed with Webots, facilitated realistic testing environments, and comprehensive dataset with systematically varied track geometries was curated for training and evaluation. Results demonstrate the proposed framework’s ability to maintain high tracking accuracy while significantly reducing computational costs, with SVR predictions closely approximating the GA-derived benchmarks. Specifically, the GA-optimized true PID values achieved a mean error of 11.8402, while the SVR-predicted PID values had a mean error of 15.4438, with SVR attaining a recall of 86.55%. This synergy between heuristic optimization and machine learning advances the scalability and real-time adaptability of AGV control systems, paving the way for more robust industrial automation solutions.}, archive = {J_ACCESS}, author = {Kinza Nazir and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3559072}, journal = {IEEE Access}, pages = {66726-66741}, shortjournal = {IEEE Access}, title = {Predictive PID control for automated guided vehicles using genetic algorithm and machine learning}, volume = {13}, year = {2025}, }
• (2025). Post-quantum wireless-based key encapsulation mechanism via CRYSTALS-kyber for resource-constrained devices. ACCESS, 13, 66714-66725. (www)

  @article{ , abstract = {We consider the problem of adapting a Post-Quantum cryptosystem to be used in resource-constrained devices, such as those typically used in Device-to-Device and Internet of Things systems. In particular, we propose leveraging the characteristics of wireless communications channels to minimize the complexity of implementation of a Post-Quantum public key encryption scheme, without diminishing its security. To that end, we focus on the adaptation of a well-known cryptosystem, namely CRYSTALS-Kyber, so as to enable its direct integration into the lowest layer of the communication stack, the physical layer, defining two new transport schemes for CRYSTALS-Kyber to be used in Device-to-Device communications, both of which are modeled under a wireless channel subject to Additive White Gaussian Noise, using a 4 Quadrature Amplitude Modulation constellation and a BCH-code to communicate CRYSTALS-Kyber’s polynomial coefficients. Simulation results demonstrate the viability of the adapted Kyber algorithm due to its low key error probability, while maintaining the security reductions of the original Kyber by considering the error distribution imposed by the channel on the cipher.}, archive = {J_ACCESS}, author = {M. A. González de la Torre and I. A. Morales Sandoval and G. T. Freitas de Abreu and L. Hernández Encinas}, doi = {10.1109/ACCESS.2025.3560023}, journal = {IEEE Access}, pages = {66714-66725}, shortjournal = {IEEE Access}, title = {Post-quantum wireless-based key encapsulation mechanism via CRYSTALS-kyber for resource-constrained devices}, volume = {13}, year = {2025}, }
• (2025). Injection sensitivity-based damping improvement model using V2G and cooperative game theory. ACCESS, 13, 66700-66713. (www)

  @article{ , abstract = {With growing concerns about global climate change, the traditional utility grid is transitioning towards cleaner and greener energy generation through renewable energy resources. However, integrating these renewable resources into the utility grid presents significant challenges, adding operational complexities such as low inertia, intermittent generation, and locational inflexibility. Among these, locational inflexibility affects the damping of grid oscillations, particularly low-frequency electromechanical ones. Poorly damped oscillations can lead to reduced Area Transfer Capability (ATC), malfunctioning of protection devices due to erratic power swings, and in the worst case, large-scale outages and blackouts within the power network. This paper proposes a solution to improve the damping of oscillations by introducing an active power injection sensitivity-based damping enhancement using Plug-in Electric Vehicles (PEVs). A cooperative game-theoretic approach is employed to balance the interests of both the Grid and the PEVs while addressing the critical issue of maintaining the oscillation damping factor within a desirable range for secure grid operation. The effectiveness of the proposed method was validated using the standard IEEE 16-Machine 68-Bus test system, as well as a modified version of this test bed. The modification involved integrating wind energy to assess the performance of the proposed method under the influence of renewable energy sources. This adjustment enabled the method to effectively capture scenarios ranging from conventional power grids with minimal renewable integration to modern power systems with high levels of renewable energy integration. The results demonstrate that the method can significantly improve the damping of inter-area oscillations in both scenarios.}, archive = {J_ACCESS}, author = {Faheem Ul Haq and Pratyasa Bhui and Panagiotis N. Papadopoulos}, doi = {10.1109/ACCESS.2025.3557944}, journal = {IEEE Access}, pages = {66700-66713}, shortjournal = {IEEE Access}, title = {Injection sensitivity-based damping improvement model using V2G and cooperative game theory}, volume = {13}, year = {2025}, }
• (2025). NeuAFG: Neural network-based analog function generator for inference in CIM. ACCESS, 13, 66686-66699. (www)

  @article{ , abstract = {Resistive Random-Access Memory (RRAM)-based Compute-in-Memory (CIM) architectures offer promising solutions for energy-efficient deep neural network (DNN) inference. However, conventional CIM accelerators suffer from high energy consumption due to frequent analog-to-digital (AD) and digital-to-analog (DA) signal conversions, especially when computing nonlinear activation functions (NAFs). This paper presents NeuAFG, a time-domain analog function generator designed to directly compute arbitrary NAFs in the analog domain. NeuAFG utilizes a one-hidden-layer ReLU neural network (ReLUNet) to approximate a variety of activation functions, incorporating two novel optimization algorithms: Low-Discrepancy Search (LDSearch) for efficient parameter initialization and KDE-FocusSampler for focused sampling of regions that are difficult to approximate. The hardware implementation integrates RRAM-based components and employs a Pulse-In-Pulse-Out method for robust time-domain computation. Experimental results demonstrate that NeuAFG reduces energy consumption in mainstream CIM accelerators such as ISAAC and RAELLA by $1.03\times $ to $2.20\times $ , with less than 1% accuracy loss in DNN inference tasks.}, archive = {J_ACCESS}, author = {Pengcheng Feng and Yihao Chen and Jinke Yu and Zhelong Jiang and Junjia Su and Qian Zhou and Hao Yue and Zhigang Li and Haifang Jian and Huaxiang Lu and Wan’Ang Xiao and Gang Chen}, doi = {10.1109/ACCESS.2025.3535786}, journal = {IEEE Access}, pages = {66686-66699}, shortjournal = {IEEE Access}, title = {NeuAFG: Neural network-based analog function generator for inference in CIM}, volume = {13}, year = {2025}, }
• (2025). The adaptive model reference PID and split-range control of a MIMO nonlinear revolute-joint robot arm. ACCESS, 13, 66674-66685. (www)

  @article{ , abstract = {To research on the kinematic and dynamic features of the robot arm, it is primarily important to research on the mechatronic system. In this article, the author introduces the research on the system-level design and systematic control of a novel robot arm. At first, the dynamic analysis of the actuator based on the joint and link design is presented. Then the adaptive model reference PID AMRPID control method implemented on the joint motor is proposed. In addition, the Split-Range Control System based on the multiple-input-multiple-output MIMO framework with parallel harmonics vibration decoupling. What’s more, the Denavit-Hartenburg D-H coordinate system and transformation matrix of the robot arm are also given for the trajectory control of the end-effector. In the next, the electrical-system-level ESL design of the robot arm control system is illustrated. After that, the program and test are proceeded with the proposed robot arm on different actuators. Through the experiment, it has been demonstrated that the robot arm is able to achieve quantitative motion control, trajectory control and dynamic control. Compared to the conventional PID and AMRPI control method, distributed control system, single-input-single-output SISO system framework, the proposed control system has demonstrated its feasibility, advancement and stability in controlling the novel robot arm. Future research based on current research results and phenomenon has also been addressed.}, archive = {J_ACCESS}, author = {Haokai Liao}, doi = {10.1109/ACCESS.2025.3558862}, journal = {IEEE Access}, pages = {66674-66685}, shortjournal = {IEEE Access}, title = {The adaptive model reference PID and split-range control of a MIMO nonlinear revolute-joint robot arm}, volume = {13}, year = {2025}, }
• (2025). CPFT-MOSA: A comprehensive parallel fault-tolerant multi-objective simulated annealing framework for UAV-assisted edge computing in smart city traffic management. ACCESS, 13, 66646-66673. (www)

  @article{ , abstract = {UAV-assisted edge computing has emerged as a critical technology for real-time monitoring and control in smart city traffic management systems. A key challenge in these systems is efficiently distributing computational tasks across available edge nodes while ensuring system reliability and performance, particularly for resource-intensive applications like object detection. This paper aims to address these challenges by proposing CPFT-MOSA (Comprehensive Parallel Fault-Tolerant Multi-Objective Simulated Annealing), a novel task allocation optimization framework specifically designed for UAV-assisted edge computing in traffic management scenarios. Our approach makes three primary contributions: 1) a multi-objective optimization framework that minimizes active nodes, optimizes energy distribution, and reduces execution time while ensuring fault tolerance through parallel task execution; 2) an integrated YOLO-based real-time task prioritization system that dynamically adapts to traffic conditions under resource and bandwidth constraints; and 3) a feasibility-driven task assignment strategy that maintains computational balance while meeting system constraints. The proposed method decouples YOLO-based object detection tasks into several parallel modules within each UAV processing block and implements a dynamic resource management mechanism to balance processing efficacy and reliability. An experimental evaluation conducted across 17 nodes managing 45 full layers (comprising 270 total layer assignments) demonstrates that CPFT-MOSA performs superiorly to traditional methods. Our results show quantitative improvements in three key metrics: efficient resource utilization with an average of 16.8 active nodes; a 21% improvement in energy distribution efficiency with a value of 0.063; and an 8.5% reduction in execution time to 1.6 units compared to conventional approaches. The framework’s ability to balance multiple competing objectives while maintaining fault tolerance makes it particularly suitable for real-world traffic management applications where reliability and performance are crucial for time-sensitive decision-making in dynamic urban environments.}, archive = {J_ACCESS}, author = {Ahmed Shamil Mustafa and Salman Yussof and Nurul Asyikin Mohamed Radzi}, doi = {10.1109/ACCESS.2025.3558851}, journal = {IEEE Access}, pages = {66646-66673}, shortjournal = {IEEE Access}, title = {CPFT-MOSA: A comprehensive parallel fault-tolerant multi-objective simulated annealing framework for UAV-assisted edge computing in smart city traffic management}, volume = {13}, year = {2025}, }
• (2025). A survey of smart campus resource information management in internet of things. ACCESS, 13, 66622-66645. (www)

  @article{ , abstract = {The rapid development of information technology, especially the advancements in fields such as the Internet of Things (IoT), is driving significant changes in campuses. This change has a wide impact, covering many fields, such as educational resource allocation, student services, and campus security. In this context, smart campus resource information management (SCRIM) has emerged as a key strategy to facilitate these transformations. SCRIM leverages IoT to collect resource information on campus space, environment, transportation, energy, emissions, water, and various events. By comprehensively analyzing this resource information, SCRIM can implement measures such as energy-saving control, optimized space management and allocation, and disaster management to achieve the best utilization of campus resources. Although it has gradually gained popularity, current research lacks a comprehensive research summary. This study mainly selected SCRIM-related literature for investigation between 2019 and 2024. Evaluation criteria were established through campus resource information, security and privacy, and SCRIM framework. This paper introduces SCRIM, detailing its models and technologies, and emphasizes the importance of occupancy detection and the integration of knowledge graphs to facilitate more efficient data analysis. It provides a detailed comparison, analysis, and discussion of various SCRIM frameworks, highlighting their current challenges and issues. Finally, this paper provides improvement suggestions for SCRIM frameworks and outlines future work directions.}, archive = {J_ACCESS}, author = {Li Shihao and Dahlila Putri Dahnil and Saidah Saad}, doi = {10.1109/ACCESS.2025.3558900}, journal = {IEEE Access}, pages = {66622-66645}, shortjournal = {IEEE Access}, title = {A survey of smart campus resource information management in internet of things}, volume = {13}, year = {2025}, }
• (2025). Advanced optimization methods for nonlinear backstepping controllers for quadrotor-slung load systems. ACCESS, 13, 66607-66621. (www)

  @article{ , abstract = {Developments of computational platforms and metaheuristic algorithms increase the efficiency of various critical devices, including uncrewed aerial vehicles (UAVs), employed for monitoring, delivery, inspection, agriculture, military, and logistics applications. Consequently, many carriers prefer air transport for quicker delivery than land transport. This article introduces a nonlinear backstepping control (NBC) strategy for an underactuated quadrotor slung load system, tackling the challenge of trajectory tracking for a cable-suspended load. It models the quadrotor and slung load as rigid bodies and point mass, respectively. Using Lyapunov theory and the backstepping technique, it designs the controller optimally with precise thrust and angular velocity control laws to ensure the closed-loop system remains asymptotically stable. To achieve the stability mentioned, this article frames the task of finding the optimal parameters for the backstepping controller gains through an optimization problem where the minimization of the integral time squared error (ITSE) of the load position is considered as the objective function for the optimal design of the NBC parameters of the test system. Then the formulated optimization problem is then solved by employing two efficient metaheuristic algorithms, the improved grey wolf optimizer (IGWO) and the whale optimization algorithm (WOA). The effectiveness of the optimized controller is validated through simulation on the MATLAB/Simulink platform, and the results were compared with conventional NBC and literature-reported strategies. It is observed that the proposed NBC-IGWO and NBC-WOA achieve improved performance compared to the conventional and literature-reported strategy in the convergence of the load position trajectory to the desired path. The presented results confirm the efficacy of the proposed metaheuristic strategies.}, archive = {J_ACCESS}, author = {Muhammad Maaruf and Sulaiman S. Ahmad and Waleed M. Hamanah and Abdullah M. Baraean and Md Shafiul Alam and Mohammad A. Abido and Md Shafiullah}, doi = {10.1109/ACCESS.2025.3558615}, journal = {IEEE Access}, pages = {66607-66621}, shortjournal = {IEEE Access}, title = {Advanced optimization methods for nonlinear backstepping controllers for quadrotor-slung load systems}, volume = {13}, year = {2025}, }
• (2025). Forest fire detection based on enhanced feature information capture and long-range dependency. ACCESS, 13, 66593-66606. (www)

  @article{ , abstract = {Accurate and timely forest fire detection is critical for early warnings and effective wildfire containment, safeguarding both ecological systems and human lives. However, in the early stages of a fire, the flames are typically small, and the smoke often bears a close resemblance to mist or fog, which makes prompt and accurate detection difficult and frequently causing the loss of the optimal response window. To tackle this challenge, we present an enhanced single-stage object detector, that excels at identifying fires, especially in their nascent stages. First, we incorporate a contrast enhancement technique that leverages an atmospheric scattering model and entropy optimization to clarify the blurred smoke edges. Next, we integrate deformable convolutions into the original feature extraction module, enabling more effective capture of long-range dependencies and spatial relationships, thus improving multi-scale fire detection accuracy. Additionally, we introduce a novel Hybrid Spatial Pyramid Pooling (HSPP) structure, which uses parallel average and max pooling operations to enrich feature extraction, facilitating better recognition of small-scale early fires. Finally, we propose a Dynamic Channel Attention (DCA) module to adaptively optimize cross-channel interactions across multiple feature extraction layers, bolstering cross-scale feature representations. Extensive experimental results show that on the M4SFWD and FLAME public datasets, our method achieves a 1% improvement in both precision and recall compared to the SOTA methods, while reducing the number of parameters by about 40%, making it more suitable for practical application.}, archive = {J_ACCESS}, author = {Wangke Lin and Jun Gao and Jingde Huang}, doi = {10.1109/ACCESS.2025.3560192}, journal = {IEEE Access}, pages = {66593-66606}, shortjournal = {IEEE Access}, title = {Forest fire detection based on enhanced feature information capture and long-range dependency}, volume = {13}, year = {2025}, }
• (2025). Can knowledge distillation enable seismic interpolation, super-resolution, and denoising simultaneously?. ACCESS, 13, 66574-66592. (www)

  @article{ , abstract = {In seismic data processing, interpolation, super-resolution, and denoising are three key issues to improve data quality. While deep learning methods have successfully addressed one or two of these tasks, simultaneously resolving all three within a single network faces difficulties. These include conflicting objectives and complex network design. This paper proposes a heterogeneous knowledge distillation architecture comprising a teacher network (TNet) and a student network (SNet). TNet implements super-resolution and denoising tasks, while SNet implements interpolation task. This separation avoids goal conflicts between the networks. SNet retains TNet’s structure but reduces channels by half, simplifying both architectures. SNet combines data consistency loss and distillation loss. The data consistency loss integrates $\ell _{1}$ and multi-scale structural similarity. The distillation loss employs synchronous positive-negative learning. Consequently, SNet inherits TNet’s capabilities and further refines them during optimization. For comparison, we combine three interpolation algorithms with three super-resolution algorithms and select the one with the best performance. Experimental results on four field data show the superiority of our method over the counterpart in terms of data quality at a lower computational cost.}, archive = {J_ACCESS}, author = {Benwen Zhang and Linrong Wang}, doi = {10.1109/ACCESS.2025.3560294}, journal = {IEEE Access}, pages = {66574-66592}, shortjournal = {IEEE Access}, title = {Can knowledge distillation enable seismic interpolation, super-resolution, and denoising simultaneously?}, volume = {13}, year = {2025}, }
• (2025). Fusion of image filtering and knowledge-distilled YOLO models for root canal failure diagnosis. ACCESS, 13, 66557-66573. (www)

  @article{ , abstract = {Root canal treatment involves the removal of inflamed pulp from infected teeth and sealing them to prevent re-entry of bacteria and infection. Early and accurate identification of root canal issues is crucial for improving treatment outcomes and minimizing complications. This study proposes a novel method for efficiently detecting failures in root canal treatments by incorporating image filtering techniques into deep learning models. A custom dataset has been collected from Square Hospital Ltd of Dhaka, Bangladesh, comprising 1,600 annotated, filtered radiographs. These images are processed using denoising algorithms (mean, median, Gaussian, contourlet transform, or Bayesian wavelet), non-local means, and BM3D. The YOLO models used for detection included YOLOv5, YOLOv7, and YOLOv8, with YOLOv5 achieving the highest accuracy of 90.6% on the filtered datasets. This work integrates an autodistillation technique with Grounded SAM as the base model for bounding box generation, YOLOv8 as an intermediate model to bridge architectural differences, and YOLOv5 as the target model due to its lightweight design, fewer parameters, and superior performance on the filtered dataset. Knowledge distillation was employed to optimize performance on lightweight models, resulting in precision and recall values of 99.16% and 97.29%, respectively, for YOLOv5. The filtered datasets outperformed raw images, with the Bayesian wavelet and contourlet transform yielding the best results on YOLOv5. This pipeline demonstrates the potential to develop one of the most accurate and clinically applicable diagnostic tools for root canal failure detection by combining deep learning and knowledge distillation techniques with image filtering. This study demonstrates how combining deep learning with advanced image enhancement techniques can provide a highly accurate, clinically relevant tool for the early detection of root canal treatment failures, potentially aiding dentists in making faster and more consistent diagnoses.}, archive = {J_ACCESS}, author = {Afifa Zain Apurba and Md Fahim Shahoriar Titu and Mahir Afser Pavel and Intisar Tahmid Naheen and Riasat Khan}, doi = {10.1109/ACCESS.2025.3560998}, journal = {IEEE Access}, pages = {66557-66573}, shortjournal = {IEEE Access}, title = {Fusion of image filtering and knowledge-distilled YOLO models for root canal failure diagnosis}, volume = {13}, year = {2025}, }
• (2025). Data augmentation techniques for fMRI data: A technical survey. ACCESS, 13, 66529-66556. (www)

  @article{ , abstract = {The application of machine learning to fMRI data classification, prediction, and analysis tasks has experienced rapid growth in recent years. However, its implementation has been limited by the relatively small size of labeled fMRI datasets. Data augmentation, a technique that artificially increases the size and diversity of the training dataset, can help mitigate this issue. In this technical survey, we outline the main existing techniques for augmenting fMRI data, primarily represented as image, time series, and graph data. For each of these representations, we explore specific data augmentation methods. We provide a structured framework for understanding and comparing these principal fMRI data augmentation techniques. We define and summarize examples for each category, including image augmentation, time-series augmentation, graph augmentation, and additionally cover simulation and modeling approaches. For each main technique, we offer a general definition, discuss the technical details, and provide schematic illustrations. The final section provides a critical analysis of all methods, offering recommendations on which methods to use in different scenarios, and includes a comparative table of all techniques. This survey aims to serve as a valuable resource for researchers working on fMRI-based machine learning applications, guiding them in selecting appropriate augmentation techniques and inspiring novel approaches to enhance the performance and generalizability of their models.}, archive = {J_ACCESS}, author = {Valentina Sánchez and Çiçek Güven and Gonzalo Nápoles and Marie Šafář Postma}, doi = {10.1109/ACCESS.2025.3560395}, journal = {IEEE Access}, pages = {66529-66556}, shortjournal = {IEEE Access}, title = {Data augmentation techniques for fMRI data: A technical survey}, volume = {13}, year = {2025}, }
• (2025). A GPSR-based sparse ECG signal recovery approach for hybrid compressive sensing framework and its classification using deep convolution neural network. ACCESS, 13, 66505-66528. (www)

  @article{ , abstract = {Research on Compressive Sensing (CS) framework for ECG signals are of 3 categories. Category 1) involves CS for a single frame of ECG signal and performance analysis of reconstructed frame. Category 2) involves CS for every frame of the complete ECG signals and arrhythmia classification using deep learning approach on the reconstructed ECG signals. Category 3) is same as category 1) but the classification involves direct compressed measurements without reconstruction. The key merit of category 2) is that the signal can also be visually inspected by doctors. This paper falls in category 2) and proposes a hybrid CS algorithm for ECG signal reconstruction using discrete wavelet transform (DWT) and discrete cosine transform (DCT) both as sensing and sparsifying operator and the classification of reconstructed signal is achieved using DCNN for two different image representations (a) CWT and (b) STFT. Using MIT-BIT ECG dataset, for the proposed hybrid CS implementation the reconstruction is achieved with PRD=2.591, SNR=36.530dB and SSIM=0.745587 and the classification accuracy achieved for binary class problem with CR=0.5 is 93% for CWT image representation and 98.4% for STFT image representation. The proposed hybrid CS method performs better than the conventional non hybrid method for all the performance metrics and the classification results are better than other existing state of art methods for category 2) & 3). The same experiment is extended to a 3-class DCNN classification problem and the accuracy is 93.33% for the STFT image.}, archive = {J_ACCESS}, author = {Subramanyam Shashi Kumar and Prakash Ramachandran}, doi = {10.1109/ACCESS.2025.3560544}, journal = {IEEE Access}, pages = {66505-66528}, shortjournal = {IEEE Access}, title = {A GPSR-based sparse ECG signal recovery approach for hybrid compressive sensing framework and its classification using deep convolution neural network}, volume = {13}, year = {2025}, }
• (2025). Toward automated air leak localization: A machine learning-enhanced ultrasonic and LiDAR-SLAM framework for industrial environments. ACCESS, 13, 66492-66504. (www)

  @article{ , abstract = {Pressurized air leaks are the source of a significant amount of energy loss in industrial installations. Automating the detection process of these air leaks can help prevent this loss of energy, as detecting and localizing them is the first necessary step in efficient maintenance. Typically ultrasonic microphone arrays are used to detect the leaks, since the escaping air causes turbulences around the leak opening. When the inspection is done manually, the operator of the ultrasonic sensor can evaluate whether or not an ultrasonic sound is originating from an air leak. However, during the automated process of pressurized air leak detection using ultrasonic microphone arrays, many interfering sources arise which lead to the detection of false-positive leak locations. While some of these locations can be easily filtered out using known information of the environment, such as the locations of the compressed air network, many others are more cumbersome to filter out. In order to efficiently suppress these spurious leak signals, we propose the usage of machine learning algorithms to perform classification of the signal sources. To this end, we trained a series of classifiers using real-world experiment data, and show that, by using the best performing classifiers in our leak detection algorithm, we are able to drastically reduce the amount of false-positive leaks, without any meaningful impact on the true-positive leak detection performance. We demonstrate the efficacy of the proposed approach using real-world experimental data gathered in an operating industrial setting.}, archive = {J_ACCESS}, author = {Anthony Schenck and Walter Daems and Jan Steckel}, doi = {10.1109/ACCESS.2025.3558361}, journal = {IEEE Access}, pages = {66492-66504}, shortjournal = {IEEE Access}, title = {Toward automated air leak localization: A machine learning-enhanced ultrasonic and LiDAR-SLAM framework for industrial environments}, volume = {13}, year = {2025}, }
• (2025). FedRecSys: A federated learning-based recommender system for concept drift adaptation. ACCESS, 13, 66478-66491. (www)

  @article{ , abstract = {A new field called deep learning can be used to handle large amounts of multidimensional data in different e-commerce platforms. Big Data refers to this type of information, which is accessible in an unstructured and complicated format with respect to volume, velocity, validity, veracity, variety, and variability. To better meet the needs of customers and win their loyalty, it is necessary to examine the vast amount of hidden information present in the volume of data created by online platforms like e-commerce portals. To address this issue and produce the best recommendations depending on user requirements, numerous Recommendation Engines (RE) have been put forth. One such difficulty in large E-commerce based REs is Concept Drift (CD), which mentions about the continuous changing of data in such platforms. Additionally, the data utilised by these e-platforms is made public in several locations, which compromises the security and private information of the user. “Federated Learning (FL)” is a new level of the Deep Learning Approach that has been proposed to address the issue of user privacy security. Federated Learning improves system accuracy by offering security and privacy preservation for user data. This study addresses the CD problem in REs, the overview of REs, and the suggested hybrid method KalADWIN2 for CD detection. Along with the creation of FedRecSys—a mix of RE and FL (for CD adaption & correct recommendations) using Movielens 1M & Book datasets. The paper also focuses on FL, and its various types. The study goes on to detail with the outcomes and a comparative analysis of the Books and Movielens 1M datasets as FedRecSys. It ends with several FL applications, conclusion, and References.}, archive = {J_ACCESS}, author = {Anagha Chaudhari and A. A. Hitham Seddig and Roshani Raut and Aliza Sarlan}, doi = {10.1109/ACCESS.2025.3557045}, journal = {IEEE Access}, pages = {66478-66491}, shortjournal = {IEEE Access}, title = {FedRecSys: A federated learning-based recommender system for concept drift adaptation}, volume = {13}, year = {2025}, }
• (2025). A 3-DOF redundant parallel mechanism without parasitic motion for spatial nanopositioning. ACCESS, 13, 66468-66477. (www)

  @article{ , abstract = {This study proposed a piezoelectric stick-slip actuator based 2-PPS/PPPS redundant non-parasitic parallel mechanism for nanorobotic positioning. In particular, the proposed design has an asymmetric configuration of the limb planes with redundant joints and the symmetric arrangement of the limb rotation centers, capable of compensating parasitic motion and enabling continuous rotation around a fixed axis. To better describe the kinematics of the parallel motion system with the additional redundant joints, we derived an analytical model for the inverse kinematics instead of employing the numerical methods (such as the Denavit-Hartenberg method), which also helped to evaluate the workspace by considering physical joint constraints. The prototype system was fabricated and the experimental test platform was established, where comprehensive experiments demonstrated that the proposed design achieved the nanopositioning performance without parasitic motion, with a spatial motion resolution of $1.87 \mu \text {rad} \times 1.99 \mu \text {rad} \times 4.39 \text {nm}$ . The results also indicated great potential of the proposed mechanism for nanorobotic positioning and manipulating, with excellent cross-scale motion capability and effective mitigation of parasitic motions.}, archive = {J_ACCESS}, author = {Haiyang Yu and Peng Yan}, doi = {10.1109/ACCESS.2025.3561029}, journal = {IEEE Access}, pages = {66468-66477}, shortjournal = {IEEE Access}, title = {A 3-DOF redundant parallel mechanism without parasitic motion for spatial nanopositioning}, volume = {13}, year = {2025}, }
• (2025). A 16.8 fJ/c-s 8 b 500 MS/s asynchronous three-comparator SAR ADC with background comparator-swapping offset calibration in 28 nm CMOS LPP. ACCESS, 13, 66458-66467. (www)

  @article{ , abstract = {This paper presents a novel comparator-swapping background offset calibration method for SAR ADCs, addressing the limitations of previous calibration techniques. The proposed method uses three comparators to eliminate reset time and calibrates offset mismatch based on the LSB conversion, leading to rapid offset convergence and reduced dependence on input signal statistics. By alternating the roles of the comparators and eliminating the need for an additional calibration cycle, the method achieves efficient calibration without the overhead of extra reference comparators. A prototype 8-bit SAR ADC implemented in a 28 nm CMOS LPP process demonstrates the effectiveness of the proposed technique, achieving a measured SNDR of 44.3 dB and SFDR of 58.4 dB at 500 MS/s, with a power consumption of 1.13 mW. The ADC occupies only 0.0033 mm2, with a Walden FoM of 16.8 fJ/conversion-step. The results show that the proposed calibration method is competitive with state-of-the-art techniques, offering a highly accurate and efficient solution for background offset mismatch calibration in SAR ADCs.}, archive = {J_ACCESS}, author = {Seunghyun Kim and Yang Azevedo Tavares and Sewon Lee and Minjae Lee}, doi = {10.1109/ACCESS.2025.3560321}, journal = {IEEE Access}, pages = {66458-66467}, shortjournal = {IEEE Access}, title = {A 16.8 fJ/c-s 8 b 500 MS/s asynchronous three-comparator SAR ADC with background comparator-swapping offset calibration in 28 nm CMOS LPP}, volume = {13}, year = {2025}, }
• (2025). An open-access repository of synchrophasor data quality examples: Curation and example applications. ACCESS, 13, 66445-66457. (www)

  @article{ , abstract = {Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced in these measurements due to various factors such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. To test the performance of algorithms in the presence of bad data encountered in field measurements, researchers need access to real-world examples of such data artifacts. To address this need, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest (RF) classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. With optimally tuned class weights, the RF classifier achieves an F1 score of 0.999. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). It is found that the introduction of quantization noise and intermittent data anomalies can increase the root mean square error for event start time determination by 1-4 seconds. The use cases demonstrate how the repository curated in this work can be valuable for developing and testing tools aimed at analyzing synchrophasor measurements.}, archive = {J_ACCESS}, author = {Shuchismita Biswas and Tianzhixi Yin and Syed Ahsan Raza Naqvi and Jim Follum and Antos Cheeramban Varghese and Tawsif Ahmad and Pavel Etingov}, doi = {10.1109/ACCESS.2025.3560602}, journal = {IEEE Access}, pages = {66445-66457}, shortjournal = {IEEE Access}, title = {An open-access repository of synchrophasor data quality examples: Curation and example applications}, volume = {13}, year = {2025}, }
• (2025). Leveraging super-resolution technology in drone imagery for advanced plant disease diagnosis and prognosis. ACCESS, 13, 66432-66444. (www)

  @article{ , abstract = {The economy of developing nations is heavily reliant on agriculture. For agriculture to remain sustainable and productive, a number of important challenges must be addressed. Plant diseases are among the most alarming issues. To address the challenge of identifying plant diseases from drone-captured images, which often lack sufficient resolution and may lead to mis-classification, we propose Vision-based Image Generation and Analysis Model (VIGAM) framework as a solution. In this study, we introduce the VIGAM framework, a deep learning model-based solution that shows promise as a useful instrument in a variety of plant protection strategy. This comprehensive method using computer vision-based models aids in the interpretation of plant images taken by drones, which are then used to detect plant diseases and offer accurate diagnosis. Using the proposed approach, farmers can quickly respond with tailored treatments or the removal of afflicted plants. Our method is feasible for real-world agricultural applications, and through experiment, we show that the suggested framework, which incorporates a high-resolution approach for field photos, efficiently improves plant identification and disease detection.}, archive = {J_ACCESS}, author = {Vignesh A. Palan and Shivam Thakur and N. Sumith}, doi = {10.1109/ACCESS.2025.3550257}, journal = {IEEE Access}, pages = {66432-66444}, shortjournal = {IEEE Access}, title = {Leveraging super-resolution technology in drone imagery for advanced plant disease diagnosis and prognosis}, volume = {13}, year = {2025}, }
• (2025). Adaptive transmission control considering available channels difference among terminals in satellite IoT systems using CRDSA/IRSA. ACCESS, 13, 66420-66431. (www)

  @article{ , abstract = {The use of Low Power Wide Area (LPWA) networks in the 920-MHz band, which are compatible with terrestrial network devices, is being explored for a satellite Internet-of-Things (IoT) system. In this system, terminals cannot use the channels the terrestrial networks use to avoid contention with them. Thus, terminals are limited to different channels depending on their location, resulting in throughput degradation. This study proposes an adaptive transmission control scheme to mitigate throughput degradation caused by different available channels for the Contention Resolution Diversity Slotted ALOHA (CRDSA)/Irregular Repetition Slotted ALOHA (IRSA). The proposed scheme reduces the estimation error of transmitting load at the base station, updates the transmission probability at the terminal to maximize the throughput by considering the characteristics of CRDSA/IRSA, and introduces smoothing of the transmission probability. This proposed scheme enables adaptive transmission control without the base station knowing the terminals’ available channels. We evaluated the proposed transmission control through simulation and clarified that it can achieve high throughput by preventing the influence of the available channel difference.}, archive = {J_ACCESS}, author = {Hiraku Okada and Kansuke Kumazawa and Munehiro Matsui and Koyo Tategami and Daisuke Goto and Kiyohiko Itokawa and Fumihiro Yamashita}, doi = {10.1109/ACCESS.2025.3560759}, journal = {IEEE Access}, pages = {66420-66431}, shortjournal = {IEEE Access}, title = {Adaptive transmission control considering available channels difference among terminals in satellite IoT systems using CRDSA/IRSA}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral imaging and advanced vision transformers for identifying pure and pesticide-coated apples. ACCESS, 13, 66405-66419. (www)

  @article{ , abstract = {The classification of hyperspectral images has become an essential task in agricultural analysis, as it assesses the quality, chemical composition, and overall health of produce. This study concentrates on the analysis of the hyperspectral images of apples to distinguish and classify pure apples and those subjected to fertilizers in different concentration levels. This classification is fundamental to ensuring food quality and optimizing fertilizer input and precision agriculture practices. The dataset did not initially satisfy requirements owing to the small sample size, which rendered it difficult to train any robust generalizable model. A conditional GAN variation (CGAN) has been proposed for data augmentation. This approach effectively handled class imbalance by generating high-quality, category-specific synthetic images that enriched the dataset with realistic and diverse samples. For classification, the ConvNeXt architecture was employed, integrated with the Simple Attention Module (SimAM) to enhance feature refinement and extraction. The SimAM module allowed the model to focus on the most relevant areas of hyperspectral images while suppressing unimportant information, leading to a highly refined representation of features. The proposed classification model achieved an impressive accuracy of 99.75%, showcasing its exceptional ability to distinguish between different categories of apples, including untreated ones and those with varying fertilizer concentrations. This high level of accuracy underscores the model’s robustness and reliability in agricultural image analysis.}, archive = {J_ACCESS}, author = {Ayesha Shafique and Mohammad Siraj and Benmao Cheng and Saif A. Alsaif and Tariq Sadad}, doi = {10.1109/ACCESS.2025.3559945}, journal = {IEEE Access}, pages = {66405-66419}, shortjournal = {IEEE Access}, title = {Hyperspectral imaging and advanced vision transformers for identifying pure and pesticide-coated apples}, volume = {13}, year = {2025}, }
• (2025). Lightweight detection methods for multi-scale targets in complex scenarios. ACCESS, 13, 66393-66404. (www)

  @article{ , abstract = {Detecting multi-scale objects in complex scenes is crucial for real-time applications in building safety, where accurate monitoring of safety equipment under challenging conditions is essential. In this paper, we propose LAP-YOLO (Lightweight Aggregate Perception based on “You Only Look Once”), which integrates a Feature-Aware Aggregation (FAA) module to enhance feature representation and a Lightweight Information Diffusion (LID) detection head to improve small-object detection efficiency with minimal computational overhead. Experimental results demonstrate that LAP-YOLO surpasses other real-time models on the safety equipment dataset, achieving a 39% reduction in parameters and a 57% decrease in FLOPs compared to YOLOv8, while maintaining detection accuracy under the same conditions. This lightweight and effective approach presents a promising solution for enhancing construction site safety.}, archive = {J_ACCESS}, author = {Anjun Yu and Zhichao Rao and Yonghua Xiong and Jinhua She}, doi = {10.1109/ACCESS.2025.3560664}, journal = {IEEE Access}, pages = {66393-66404}, shortjournal = {IEEE Access}, title = {Lightweight detection methods for multi-scale targets in complex scenarios}, volume = {13}, year = {2025}, }
• (2025). CUNet-CLSTM: A novel fusion of CUNet and CLSTM for superior liver cancer detection in CT scans. ACCESS, 13, 66373-66392. (www)

  @article{ , abstract = {Liver cancer remains one of the main causes of mortality worldwide, requiring advancements in diagnostic systems for early detection and improved patient outcomes. Computed tomography (CT) scans are crucial in the diagnosis of liver cancer, providing detailed images for medical analysis. Despite the triumph of convolutional neural networks in medical image analysis, challenges such as overfitting, limited labeled data, and complex tumor morphology hinder accurate detection of liver cancer. This study proposes a novel architecture, the cascaded UNet convolutional long short-term model (CUNet-CLSTM), which leverages the strengths of UNet and convolutional long short-term memory to improve liver segmentation and tumor detection in CT images. The anticipated CUNet-CLSTM model evaluated on two benchmark datasets: LiTS and 3DICARDB. The experimental results show excellent performance, achieving the best DICE values of 91.3% for liver segmentation and 96.1% for tumor detection on the LiTS dataset, and 96.3% for liver segmentation and 98.3% for tumor detection on the 3DICARDB dataset. Compared to state-of-the-art models, the proposed CUNet-CLSTM not only improves segmentation accuracy, but also mitigates overfitting through sequential feature learning and optimizes computational efficiency by providing an end-to-end framework, reducing the complexity of multi-stage pipelines. This research contributes to addressing the urgent need for accurate methods for detecting liver cancer, facilitating early diagnosis, and customized treatment strategies.}, archive = {J_ACCESS}, author = {K. Vijayaprabakaran and Padmanaban Ramalingam and Rajakumar Ramalingam and A. Ilavendhan and R. Vedhapriyavadhana}, doi = {10.1109/ACCESS.2025.3559592}, journal = {IEEE Access}, pages = {66373-66392}, shortjournal = {IEEE Access}, title = {CUNet-CLSTM: A novel fusion of CUNet and CLSTM for superior liver cancer detection in CT scans}, volume = {13}, year = {2025}, }
• (2025). Real-time EEG signal analysis for microsleep detection: Hyper-opt-ANN as a key solution. ACCESS, 13, 66354-66372. (www)

  @article{ , abstract = {Microsleeps are brief lapses in awareness that pose significant risks, particularly in activities requiring continuous attention, such as driving. These episodes are common in sleep-deprived individuals and can lead to catastrophic outcomes. Electroencephalography (EEG) is a promising technique for detecting microsleeps due to its high temporal resolution, allowing real-time brain activity monitoring. The study aims to develop a lightweight version of the model to reduce computational costs and provide faster detection, enabling quicker intervention to prevent accidents in safety-critical environments. We propose a customized deep learning model, Hyper-Opt-ANN, designed to detect microsleep episodes from EEG signals. The model is evaluated across five time windows (1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds), with the 4 seconds window showing the best performance. The Hyper-Opt-ANN model achieved a significant accuracy of 97.33%, demonstrating its efficacy and potential for accurate microsleep detection using EEG signals. This method significantly outperforms traditional approaches and has potential applications in safety-critical domains. This study demonstrates the feasibility of using EEG signals and advanced deep learning models for detecting microsleep and enhancing safety in high-risk environments.}, archive = {J_ACCESS}, author = {Md Mahmudul Hasan and Md Nahidul Islam and Norizam Sulaiman and Mirza Mahfuj Hossain and Jorge M. Mendes}, doi = {10.1109/ACCESS.2025.3559619}, journal = {IEEE Access}, pages = {66354-66372}, shortjournal = {IEEE Access}, title = {Real-time EEG signal analysis for microsleep detection: Hyper-opt-ANN as a key solution}, volume = {13}, year = {2025}, }
• (2025). Fast reflective beamforming with situational awareness in IRS assisted communication systems. ACCESS, 13, 66338-66353. (www)

  @article{ , abstract = {In this paper, we investigate reflective beamforming methods for intelligent reflecting surface (IRS)-assisted communication systems. In our envisioned system, the IRS is equipped with computer vision to acquire situational awareness. Leveraging this information, we develop a gradient descent-based reflective beam optimization method that significantly reduces beamforming overhead. Through simulations, we demonstrate that the proposed method achieves a higher achievable rate compared to existing hierarchical codebook-based beamforming methods. Furthermore, to design prediction models for situational awareness in IRS-assisted communication systems, we investigate the impact of situational awareness estimation errors on the proposed method.}, archive = {J_ACCESS}, author = {Masaaki Miura and Koya Sato and Ning Zhang and Katsuya Suto}, doi = {10.1109/ACCESS.2025.3560183}, journal = {IEEE Access}, pages = {66338-66353}, shortjournal = {IEEE Access}, title = {Fast reflective beamforming with situational awareness in IRS assisted communication systems}, volume = {13}, year = {2025}, }
• (2025). Scrum4DO178C: An agile process to enhance aerospace software development for DO-178C Compliance—A case study at criticality level a. ACCESS, 13, 66318-66337. (www)

  @article{ , abstract = {The development of safety-critical systems is heavily governed by domain-specific standards. In the aerospace industry, the DO-178C—Software Considerations in Airborne Systems and Equipment Certification—serves as the primary certification standard used by agencies such as the FAA and EASA to review and approve software-based systems. Although DO-178C aims to ensure system safety while providing evidence for certification, it does not prescribe a specific software development process, allowing flexibility for traditional Waterfall, Agile, or hybrid methods with appropriate adaptations for the aerospace context. This study proposes Scrum4DO178C, an Agile process based on Scrum, to meet the demanding requirements of aerospace software, including safety, robustness, reliability, and integrity. Scrum4DO178C introduces novel process enhancements specifically tailored to meet these criticality needs, while aligning with the standard. Unlike previous proposals that lack detail, this research presents a comprehensive, validated process applied in a real-world industry project at the highest criticality level (Level A - Catastrophic), offering insights beyond theoretical scenarios. The findings demonstrated that the Scrum4DO178C process improves project performance, allows frequent and manageable requirement changes, reduces Verification & Validation (V&V) effort, and increases efficiency while maintaining full compliance with DO-178C. The study also identifies areas for further improvement and suggests exploring the process in additional case studies, both within the aerospace industry and other domains with similarly stringent safety-critical requirements. Finally, it confirms that appropriate automation, namely for documentation production, is a central element to further improve the process.}, archive = {J_ACCESS}, author = {J. Eduardo Ferreira Ribeiro and João Gabriel Silva and Ademar Aguiar}, doi = {10.1109/ACCESS.2025.3559803}, journal = {IEEE Access}, pages = {66318-66337}, shortjournal = {IEEE Access}, title = {Scrum4DO178C: An agile process to enhance aerospace software development for DO-178C Compliance—A case study at criticality level a}, volume = {13}, year = {2025}, }
• (2025). Leveraging AI for intrusion detection in IoT ecosystems: A comprehensive study. ACCESS, 13, 66290-66317. (www)

  @article{ , abstract = {The widespread adoption of Internet of Things (IoT) devices has brought about unprecedented connectivity and convenience, but it has also ushered in new security challenges. As IoT systems become integral parts of various domains, protecting them from malicious intrusions is crucial. This paper thoroughly examines how Artificial Intelligence (AI) techniques are applied to develop practical Intrusion Detection Systems (IDS) specifically designed for IoT environments. The examination comprehensively analyzes current state-of-the-art AI methodologies utilized in IoT-based IDS, including machine learning, deep learning, and anomaly detection algorithms. The paper delves into IoT systems’ distinctive characteristics and vulnerabilities that demand specialized intrusion detection mechanisms. It assesses various AI-based approaches’ performance, accuracy, and scalability in identifying and mitigating diverse cyber threats targeting IoT ecosystems. Beyond reviewing existing literature, the paper delves into the challenges of implementing AI-driven IDS in IoT, such as resource constraints, device heterogeneity, and dynamic network topologies. It also emphasizes potential research directions and solutions to tackle these challenges and strengthen the resilience of IoT systems against cyber threats. This all-encompassing survey aims to be a valuable resource for researchers, practitioners, and policymakers seeking to comprehend the current landscape of AI-based intrusion detection methods in IoT security. This paper contributes to the continuous efforts to fortify IoT ecosystems against evolving cybersecurity threats by shedding light on these approaches’ strengths, limitations, and prospects.}, archive = {J_ACCESS}, author = {Shashi Bhushan Sharma and Amit Kumar Bairwa}, doi = {10.1109/ACCESS.2025.3550392}, journal = {IEEE Access}, pages = {66290-66317}, shortjournal = {IEEE Access}, title = {Leveraging AI for intrusion detection in IoT ecosystems: A comprehensive study}, volume = {13}, year = {2025}, }
• (2025). A distributed economic power dispatch strategy considering state of charge for microgrids. ACCESS, 13, 66276-66289. (www)

  @article{ , abstract = {This paper proposes a distributed economic power dispatch strategy considering state of charge(SoC) for microgrids, aiming at unreasonable and untimely power distribution of different nodes in isolated microgrids. The proposed strategy is fully distributed. Every node estimates the average SoC of all battery energy storage systems(BESSs) in a microgrid and determines the power of the local BESS and microturbine (MT) autonomously to achieve SoC balancing and economic dispatch with component constraints and power balance constraints by peer-to-peer communication with its neighbors. To improve the effectiveness of power distribution, an improved average consensus algorithm(IACA) based on particle swarm optimization (PSO) is proposed to adjust the weight matrix of communication networks, which reduces the number of iterations to estimate the average SoC. Numerical experiments demonstrate that the proposed strategy can achieve SoC balance and have lower costs than traditional distributed economic power sharing strategy.}, archive = {J_ACCESS}, author = {Kaicheng Lu and Shufeng Dong and Yonghua Song}, doi = {10.1109/ACCESS.2025.3559715}, journal = {IEEE Access}, pages = {66276-66289}, shortjournal = {IEEE Access}, title = {A distributed economic power dispatch strategy considering state of charge for microgrids}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis and optimal design of hybrid renewable energy system for rural electrification in west papua, indonesia. ACCESS, 13, 66250-66275. (www)

  @article{ , abstract = {The use of renewable energy is becoming the most suitable solution to meet the growing energy needs in developing and developed countries. This is because fossil fuel-based energy sources significantly contribute to greenhouse gas emissions. As an alternative solution to fossil fuel, microgrids with hybrid renewable energy sources are being built to electrify isolated locations where distribution network expansion is not practical or cost-effective. Despite this, hybrid renewable energy systems face numerous challenges concerning resource variability, load demand assessments, and economic trade-offs to guarantee the effective and sustainable sizing of resources. This study aimed to determine the ideal hybrid using solar, diesel, hydro, and battery energy sources to address the load requirements of Anggi District, Indonesia. The experiment was carried out using HOMER software to size the main electricity components, analyze economics, and estimate greenhouse gas emissions. The best model was evaluated using the Net Present Cost (NPC), the Cost of Energy (COE), the Renewable Fraction (RF), excess energy, and fuel cost. A detailed sensitivity analysis was also conducted using fossil fuel costs, as well as flow, discount, and inflation rates to examine model variations. Different configurations of hybrid renewable energy systems, such as Diesel Generator (DG) Only, DG/Hydro, DG/Photovoltaic (PV)/Battery, DG/Hydro/PV/Battery, and PV/Hydro/Battery, are examined and contrasted through the optimization of diverse energy sources. The results showed that the off-grid DG/Hydro/PV/Battery hybrid system was the most technically, economically, and environmentally optimal configuration, comprising 450 kW DG, 157 kW hydro, 15.7 kW PV, and 7 battery storage. The system had the lowest value across all variables, including RF of 67.7%, NPC, and COE at ${\$}$ 6.30M and ${\$}$ 0.311/kWh. It also emitted 4,372 kg/year less than the DG system, serving as a greener system. Hybrid Renewable Energy System (HYRES) improved energy access, jobs, education, public health, economic, and social fairness. In conclusion, the proposed hybrid system showed the potential to be implemented in rural areas as well as in any location worldwide.}, archive = {J_ACCESS}, author = {Elias Kondorura Bawan and Fransisco Danang Wijaya and Husni Rois Ali and Juan C. Vasquez}, doi = {10.1109/ACCESS.2025.3556020}, journal = {IEEE Access}, pages = {66250-66275}, shortjournal = {IEEE Access}, title = {Comprehensive analysis and optimal design of hybrid renewable energy system for rural electrification in west papua, indonesia}, volume = {13}, year = {2025}, }
• (2025). Evaluation and classification of accident-inducing and risk propagation in airport apron networks. ACCESS, 13, 66238-66249. (www)

  @article{ , abstract = {Airport apron operations involve complex interactions among personnel, equipment, and environmental factors, posing significant safety challenges. To address these risks, this study establishes an innovative dual evaluation system combining static and dynamic methods to assess accident-inducing potential and risk propagation capability. Based on accident investigation reports from 2015 to 2022, a risk network with 82 nodes and 244 edges was constructed. Two static indicators—Target Oriented Centrality (TOC) and Accident Neighbor Risk Rate (ANRR)—were used to evaluate accident-inducing potential, while risk propagation capability was dynamically simulated using the Susceptible-Infectious-Recovered-Susceptible (SIRS) model, with Average Peak Infection Proportion (APIP) and Infection Cycle (APIC) as key metrics. Risk nodes were categorized into four clusters using spectral clustering, which was selected after comparing multiple algorithms with the Calinski-Harabasz (CH) index. Nodes were prioritized into three tiers based on accident-inducing potential and risk propagation capabilities. Results show that Tier 1 nodes (personnel and equipment) exhibit the highest accident-inducing potential, while Tier 2 nodes (management) play significant roles in risk propagation. Tier 3 nodes (environment) have limited direct impacts but may pose long-term risks. This system provides actionable insights to prioritize safety interventions, offering a robust framework for enhancing apron safety.}, archive = {J_ACCESS}, author = {Ruxin Wang and Hong Yan and Rui Kang and Xiaolei Feng}, doi = {10.1109/ACCESS.2025.3560750}, journal = {IEEE Access}, pages = {66238-66249}, shortjournal = {IEEE Access}, title = {Evaluation and classification of accident-inducing and risk propagation in airport apron networks}, volume = {13}, year = {2025}, }
• (2025). IterDBR: Iterative generative dataset bias reduction framework for NLU models. ACCESS, 13, 66228-66237. (www)

  @article{ , abstract = {Bias in natural language understanding (NLU) models often arises from reliance on spurious correlations in datasets. Existing dataset refinement methods frequently depend on predefined biased features, limiting their ability to address hidden biases. To address this, we introduce IterDBR, an Iterative Generative Dataset Bias Reduction Framework for NLU Models, which reduces dataset bias without the need for predefined features. Our approach utilizes an iteratively expanded sample pool where a shallow model assesses the bias levels of samples, leveraging large language models (LLMs) to generate pseudo samples with fewer bias. Various strategies, such as zero-shot prompting, few-shot prompting, and fine-tuning, were explored for optimizing the generator’s performance. Notably, combining these strategies improved the generator’s ability to create diverse and low-bias samples. Experimental results on three NLU tasks demonstrate that IterDBR surpasses state-of-the-art methods in performance and compatibility with model-centric techniques, showcasing its effectiveness in mitigating biases in diverse datasets.}, archive = {J_ACCESS}, author = {Xiaoyue Wang and Xin Liu}, doi = {10.1109/ACCESS.2025.3552610}, journal = {IEEE Access}, pages = {66228-66237}, shortjournal = {IEEE Access}, title = {IterDBR: Iterative generative dataset bias reduction framework for NLU models}, volume = {13}, year = {2025}, }
• (2025). Does AI adoption in financial management enhance corporate risk resilience?. ACCESS, 13, 66211-66227. (www)

  @article{ , abstract = {As artificial intelligence (AI) becomes increasingly integrated into financial decision-making, its impact on corporate risk resilience remains an open question. Addressing this gap, our study empirically investigates how AI adoption influences firms’ financial stability by examining a large-scale dataset of 23,700 firm-year observations from Chinese A-share listed companies (2013–2022). Using text mining techniques, we construct an AI-related lexicon from annual reports to quantify AI adoption and employ fixed-effects regression models alongside instrumental variable estimation to assess AI’s impact on corporate financial leverage as a proxy for risk resilience. Our empirical results indicate that a one-standard-deviation increase in AI adoption is associated with a 10.6% reduction in financial leverage ( $\beta = -0.106$ , p <0.01), reinforcing AI’s role in enhancing financial resilience. Furthermore, executives’ technical expertise significantly strengthens this effect ( $\beta = -0.083$ , p <0.05), while international background shows a relatively modest influence. These findings provide new insights into the interplay between AI adoption, financial risk management, and executive characteristics, offering practical implications for firms leveraging AI to enhance financial stability. Furthermore, this study introduces senior executives’ international backgrounds, technical expertise, and innovation orientation as moderating variables. Empirical analysis reveals differences in their moderating effects: executives’ technical proficiency demonstrates the strongest positive impact on the AI-risk resilience relationship, followed by innovation orientation, with overseas experience showing a relatively modest influence. This study enhances the theoretical understanding of AI applications in financial management, offering practical insights for companies to improve risk resilience and financial decision-making. Given the ongoing advancements and deeper integration of AI, its influence on corporate financial decision-making and risk management is expected to grow, warranting further exploration.}, archive = {J_ACCESS}, author = {Peng Yang and Shanyue Jin}, doi = {10.1109/ACCESS.2025.3560397}, journal = {IEEE Access}, pages = {66211-66227}, shortjournal = {IEEE Access}, title = {Does AI adoption in financial management enhance corporate risk resilience?}, volume = {13}, year = {2025}, }
• (2025). The analysis of body emotion recognition in new media art exhibition space-based artificial intelligence. ACCESS, 13, 66197-66210. (www)

  @article{ , abstract = {This study aims to explore the relationship between Artificial Intelligence (AI)-driven new media art exhibition space design and immersive experiences, incorporating body-based emotional recognition methods to optimize and innovate the design of exhibition spaces from an emotional perspective. The objectives include investigating the feasibility and effectiveness of body-based emotional recognition methods in art exhibition space design and developing an AI-based new media art exhibition space to enhance viewers’ immersive experiences. The validity and practicality of the proposed methods are verified through experiments and evaluations. The results demonstrate that: 1) The proposed method excels in emotional recognition tasks. On the Emotions in Context (EMOTIC) dataset, the accuracy of the proposed method is 0.897, and the F1-Score is 0.887, significantly outperforming the Vision Transformer (ViT) and Swin Transformer models, which have accuracy scores of 0.872 and 0.889, respectively. Precision and recall are also impressive, at 0.890 and 0.885, respectively. On the GroupWalk dataset, the proposed method achieves an accuracy of 0.885 and an F1-Score of 0.877, whereas the ViT and Swin Transformer models have accuracy scores of 0.857 and 0.874, respectively. These results highlight the efficiency and robustness of the proposed method in handling body language emotional recognition. 2) Through practical application testing in immersive exhibition spaces, the average recognition rates for pleasure, immersion, and awe are observed to be 0.900, 0.855, and 0.890, respectively. The average recognition rates for disgust, disappointment, surprise, and anticipation are 0.082, 0.075, 0.875, and 0.842, respectively. These data indicate that immersive exhibition space design effectively enhances viewers’ feelings of pleasure, immersion, and awe while reducing feelings of disgust and disappointment. These findings validate the effectiveness and superiority of the proposed method in emotional recognition tasks and provide significant insights for further research and application of emotional recognition technology.}, archive = {J_ACCESS}, author = {Weinan Liu and Hyung-Gi Kim}, doi = {10.1109/ACCESS.2025.3560422}, journal = {IEEE Access}, pages = {66197-66210}, shortjournal = {IEEE Access}, title = {The analysis of body emotion recognition in new media art exhibition space-based artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Numerical integration error in linear ordinary differential Equations—Characteristic root distortion by integration. ACCESS, 13, 66188-66196. (www)

  @article{ , abstract = {Obtaining solutions to large-scale linear ordinary differential equations (ODEs) inevitably relies on numerical integration methods that have developed in various contexts to ultimately approximate the differentiation of continuous-time values using differences in discrete values. This approximation typically induces integration error, which we hypothesize as arising from the deviation of the characteristic roots from those of the original ODEs. Herein, this hypothesis was proved by numerical experiments on integration of undamped vibration equation, which yielded error amplification over long-term integration. We numerically integrated second-order single and eighth order multiple undamped vibrational equations using various formulas and analyzed the results both qualitatively and quantitatively. In the quantitative analysis, we estimated the transfer function from the integrated numerical solutions and compared its roots with those of the original equation. This approach revealed that the numerical solutions corresponded to the solution of the ODEs with roots different from those of the original ODE. For example, integrating a vibration equation with (roots) $\times $ (step width) $= 0~\pm ~1$ i using the backward Euler method resulted in solutions of the ODE with (roots) $\times $ (step width) = – $0.3466~\pm ~0.7854$ i, which were the roots of damped vibration with a lower angular frequency than that of the original ODE. This phenomenon was clearly observed for an eighth-order vibrational ODE. As the product of the roots and step width decreases, the values approach the roots of the original ODE.}, archive = {J_ACCESS}, author = {Takuya Yamakawa and Yosuke Kurihara and Kazuyuki Kobayashi and Kajiro Watanabe}, doi = {10.1109/ACCESS.2025.3554351}, journal = {IEEE Access}, pages = {66188-66196}, shortjournal = {IEEE Access}, title = {Numerical integration error in linear ordinary differential Equations—Characteristic root distortion by integration}, volume = {13}, year = {2025}, }
• (2025). Explainable anomaly detection based on operational sequences in industrial control systems. ACCESS, 13, 66170-66187. (www)

  @article{ , abstract = {Traditionally, industrial control systems (ICS) are closed structures that rely on specialized hardware and software with proprietary control protocols. However, with the advancement of technology and the digitalization of ICS, the attack space available to malicious actors has significantly expanded. Anomaly detection systems, initially implemented for detecting device faults or failures, have increasingly become the focus of research aimed at identifying attack patterns as cyberattack techniques become more sophisticated and intelligent. Many anomaly detection algorithms based on deep learning models have good performance but often involve complex neural network structures, creating a black-box issue where users cannot interpret the decisions made by the models. The black-box issue hinders the adoption of artificial intelligence (AI) systems that can ensure efficient and secure operation in ICS environments. Various attempts have been made to address the black box issue but limited to identifying the features that caused the anomaly. In detecting target anomalies in industrial control system operational data, it is important not only to identify feature importance, but also to consider correlations between variables, and to provide intuitive explanations to enhance the understanding of operators. To satisfy these major values, this paper proposes a method for explainable anomaly detection for ICS.}, archive = {J_ACCESS}, author = {Ka-Kyung Kim and Joon-Seok Kim and Ieck-Chae Euom}, doi = {10.1109/ACCESS.2025.3560260}, journal = {IEEE Access}, pages = {66170-66187}, shortjournal = {IEEE Access}, title = {Explainable anomaly detection based on operational sequences in industrial control systems}, volume = {13}, year = {2025}, }
• (2025). An extendable 17-level inverter with suppressed inrush current and reduced component count. ACCESS, 13, 66149-66169. (www)

  @article{ , abstract = {This paper proposes a switched-capacitor-based 17-level inverter to reduce the number of components, limit inrush current, and minimize the components’ total standing voltage (TSV). The proposed structures circuit combines switched-capacitor (SC) units, including a series-parallel (SPSC) unit, a double unit, a flying capacitor (FC), and two inverted half-bridges (HBs) for generating positive and negative polarities. The proposed structure, with a low number of devices, reduced components with maximum blocking voltage to only two switches ( ${\mathrm{N}}_{\mathrm {MBV}}=2$ ), inrush current limitation, four-times boosting capability, and no need for unidirectional current switches, is a suitable choice in the field of multi-level inverters based on a switched capacitor. The proposed structure comprises ten switches, three diodes, and four capacitors. The capacitors in the structure are capable of automatic voltage balancing using the proposed switching method based on level-shift pulse width modulation. The flying capacitor in the proposed structure can automatically balance at half the voltage of the DC link (0.5E). The proposed structure requires only five switching signals and simple control of the intrinsic voltage balance of the capacitors, which reduces the size of the power switch driver and decreases the system cost. The proposed structure is compared with other 17-level SC-based structures, demonstrating its advantages. The performance of The proposed structure, the pulse width modulation method, capacitor voltage balancing, charging current stress, and losses have been thoroughly analyzed. The simulation results are thoroughly presented, showcasing the circuit’s performance metrics under various conditions. Additionally, experimental results are included to validate these findings, demonstrating that the circuit operates as intended.}, archive = {J_ACCESS}, author = {Majid Hosseinpour and Hossein Mansourizadeh and Ali Seifi and Shahab Sajedi}, doi = {10.1109/ACCESS.2025.3559000}, journal = {IEEE Access}, pages = {66149-66169}, shortjournal = {IEEE Access}, title = {An extendable 17-level inverter with suppressed inrush current and reduced component count}, volume = {13}, year = {2025}, }
• (2025). A survey of machine learning techniques leveraging brightness indicators for image analysis in biomedical applications. ACCESS, 13, 66122-66148. (www)

  @article{ , abstract = {This paper presents a comprehensive survey of machine-learning techniques that leverage brightness indicators for image analysis within biomedical applications. By examining commonalities and challenges in brightness-based analysis, this survey provides insights into machine learning (ML) methods that enhance interpretability, noise reduction, and feature detection in the biomedical field. We explore how brightness indicators aid in medical image segmentation, classification, and enhancement, analysing methods that improve diagnostic accuracy and efficiency. By categorising recent techniques, this survey highlights the strengths and limitations of each method and current open research questions, as well as promising directions for integrating brightness-based ML approaches in clinical and research settings.}, archive = {J_ACCESS}, author = {Hajer Ghodhbani and Suvendi Rimer and Khmaies Ouahada and Adel M. Alimi}, doi = {10.1109/ACCESS.2025.3552593}, journal = {IEEE Access}, pages = {66122-66148}, shortjournal = {IEEE Access}, title = {A survey of machine learning techniques leveraging brightness indicators for image analysis in biomedical applications}, volume = {13}, year = {2025}, }
• (2025). Smart irrigation systems: Overview. ACCESS, 13, 66109-66121. (www)

  @article{ , abstract = {Countries are collaborating to make agriculture more efficient by combining new technologies to improve its procedures. Improving irrigation efficiency in agriculture is thus critical for the survival of sustainable agricultural production. Smart irrigation methods can enhance irrigation efficiency, especially with the introduction of wireless communication systems, monitoring devices, and enhanced control techniques for efficient irrigation scheduling. The study compared a wide range of study subjects to investigate scientific approaches for smart irrigation. As a result, this project included a wide range of topics related to irrigation methods, decision-making, and the technology used. Information was gathered from a variety of scientific papers. So, our research relied on several published documents, the majority of which were published during the last four years, and authors from all over the world. In the meantime, various irrigation initiatives were given special attention. Following that, the evaluation focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting.}, archive = {J_ACCESS}, author = {Yomna Gamal and Ahmed Soltan and Lobna A. Said and Ahmed H. Madian and Ahmed G. Radwan}, doi = {10.1109/ACCESS.2023.3251655}, journal = {IEEE Access}, pages = {66109-66121}, shortjournal = {IEEE Access}, title = {Smart irrigation systems: Overview}, volume = {13}, year = {2025}, }
• (2025). Leveraging AI technology for coding the classroom observation record form of flanders interaction analysis. ACCESS, 13, 66094-66108. (www)

  @article{ , abstract = {This paper addresses several challenges in classroom interaction analysis, including incomplete data collection, coarse-grained behavior classification, subjectivity in analysis, and the difficulty of aligning traditional methods with the evolving demands of modern education for precise and efficient teaching evaluations. We propose an automatic coding method for the Flanders Interaction Analysis (FIA) classroom observation record form (CORF) by integrating various artificial intelligence technologies to tackle these issues. Initially, audio is extracted from recorded classroom videos to isolate the speech of both teachers and students. Next, we employ the Paraformer model for speaker separation and speech recognition, converting the extracted audio into corresponding text and timestamp information for each speaker. Simultaneously, a pre-trained BERT module is fine-tuned on all spoken text. Finally, this fine-tuned BERT model is used to classify the text into ten distinct categories of interaction behavior, effectively generating the coding necessary for the CORF of FIA. Experimental results reveal that our proposed method achieves an accuracy of 94.63% in speaker separation, 95.32% in speech recognition, and 87.95% in agreement with manual annotations during speech behavior analysis. These findings indicate that our method is highly effective for automating the coding process of the CORF for the FIA system.}, archive = {J_ACCESS}, author = {Ziyang Wang and Hongwu Yang and Xiangguo Li and Xioalong Bu and Yanshan He and Di Zhang}, doi = {10.1109/ACCESS.2025.3560311}, journal = {IEEE Access}, pages = {66094-66108}, shortjournal = {IEEE Access}, title = {Leveraging AI technology for coding the classroom observation record form of flanders interaction analysis}, volume = {13}, year = {2025}, }
• (2025). Detection of hepatocellular carcinoma using optimized miRNA combinations and interpretable machine learning models. ACCESS, 13, 66078-66093. (www)

  @article{ , abstract = {Early screening to improve the survival rate of hepatocellular carcinoma (HCC) patients remains a critical clinical challenge. However, current methods for early cancer detection still lack sufficient sensitivity and specificity. In this study, we developed an interpretable machine learning (ML) model for HCC prediction using miRNA-seq data from TCGA-LIHC. Five feature selection algorithms and 11 classifiers were employed to identify key miRNAs for HCC diagnosis, with the Sarsa-CatBoost combination yielding the best performance, achieving an accuracy of 96.0%, a matthews correlation coefficient of 89.2%, and an AUC of 97.6%. Three interpretability techniques—permutation feature importance, Partial Dependence Plots (PDP), and Shapley Additive Explanations (SHAP)—were integrated into the pipeline to enhance model transparency. Both permutation feature importance and SHAP analysis identified hsa-miR-93 and hsa-miR-139 as key miRNAs for HCC prediction. SHAP and PDP further revealed how these miRNAs influence HCC development and their potential synergistic interactions. Subsequently, survival analysis, target prediction, and enrichment analysis were performed to assess the biological significance of these miRNAs. The enrichment analysis suggested that these miRNAs may be implicated in the onset and progression of HCC. These miRNAs hold promise as potential biomarkers for HCC diagnosis and prognosis, warranting further investigation into their mechanistic roles and clinical applicability.}, archive = {J_ACCESS}, author = {Zhengwu Long and Lisheng Zhang}, doi = {10.1109/ACCESS.2025.3559105}, journal = {IEEE Access}, pages = {66078-66093}, shortjournal = {IEEE Access}, title = {Detection of hepatocellular carcinoma using optimized miRNA combinations and interpretable machine learning models}, volume = {13}, year = {2025}, }
• (2025). AB-VIP: Attack method using brightness information for visibility improvement of adversarial perturbations on the NMS algorithm. ACCESS, 13, 66065-66077. (www)

  @article{ , abstract = {As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS) algorithms have recently been highlighted. As a representative adversarial attack method targeting the NMS algorithm, PhantomSponges disrupts the normal operation of the NMS algorithm by using universal perturbations. However, since PhantomSponges focuses solely on the attack success rate, the adversarial examples it generates are visually noticeable. In this paper, we propose a simple yet effective attack method called Attack method using Brightness Information for Visibility Improvement(AB-VIP), to address the limitation of adversarial attack methods targeting the NMS algorithm. To improve the invisibility of adversarial examples, the proposed method adjusts universal perturbation using the brightness information of each target image. From the experimental results under various conditions, we demonstrate that the proposed method improves the invisibility of the existing NMS-targeted attack method while maintaining a high attack success rate. When applying the proposed method, the SSIM metric improved by 13.23%, while the L2 metric decreased by 66.23%.}, archive = {J_ACCESS}, author = {Gwang-Nam Kim and Han-Ju Lee and Han-Jin Lee and Hyeon-Jin Jung and Seok-Hwan Choi}, doi = {10.1109/ACCESS.2025.3558526}, journal = {IEEE Access}, pages = {66065-66077}, shortjournal = {IEEE Access}, title = {AB-VIP: Attack method using brightness information for visibility improvement of adversarial perturbations on the NMS algorithm}, volume = {13}, year = {2025}, }
• (2025). GSF-YOLOv8: A novel approach for fire detection using gather-distribute mechanism and SimAM attention. ACCESS, 13, 66051-66064. (www)

  @article{ , abstract = {To address the current challenges in fire detection algorithms, including insufficient feature extraction, high computational complexity, limited deployment on resource-constrained devices, missed detections, false detections, and low accuracy, we developed a high-precision algorithm named GSF-YOLOv8. The GSF-YOLOv8 consists of three main parts: Firstly, we introduced the Gather-Distribute (GD) feature aggregation module, which enhances the recognition accuracy of fire features by fusing and extracting multi-scale information. Secondly, we integrated the SimAM attention module to optimize the collaborative learning of feature map dimensions and to reduce reliance on hyperparameter tuning through precise weight allocation, thereby improving the model’s rapid response capability to fires. Lastly, we proposed a Focal-DIoU Loss to replace the original loss function, optimizing bounding box regression and improving localization accuracy. It also focuses on hard samples to reduce the impact of easily misclassified samples, thereby enhancing the model’s performance in complex fire detection. We tested our method on the D-Fire dataset. The experimental results showed that GSF-YOLOv8 achieved higher mAP (80.65%) and faster detection speed (118 FPS). Meanwhile, the model’s complexity and number of parameters have been significantly optimized. The GSF-YOLOv8 not only significantly improves the efficiency and accuracy of fire detection but also provides a more reliable and accurate solution for real-time detection in similarly complex environments.}, archive = {J_ACCESS}, author = {Caixiong Li and Dali Wu and Xing Zhang and Peng Wu}, doi = {10.1109/ACCESS.2025.3560393}, journal = {IEEE Access}, pages = {66051-66064}, shortjournal = {IEEE Access}, title = {GSF-YOLOv8: A novel approach for fire detection using gather-distribute mechanism and SimAM attention}, volume = {13}, year = {2025}, }
• (2025). Low-power, low-energy, static, contention-free, TSPC dual-edge triggered flip-flops. ACCESS, 13, 66040-66050. (www)

  @article{ , abstract = {The dual edge-triggered flip-flop samples the data on both the positive and negative edges of the clock. Hence, it can lead to lower clock relative power consumption as compared to the single-edge triggered flip-flop while maintaining the same data throughput. In this paper, we present two low-power, low-energy dual-edge triggered TSPC flip-flops based on latch-mux type methodology. These two flip-flops, Low-Power at Low Data Activity (LPLD-DET), and Low-Power at High Data Activity (LPHD-DET) are suitable for low-power application. These flip-flops are fully static and contention-free. The post-layout simulation results in TSMC CMOS 65 nm technology suggest that the proposed LPLD-DET is the most power-efficient dual-edge triggered flip-flop for low data activities up to 30%, and LPHD-DET is the most power-efficient dual-edge triggered flip-flop for higher data activities from 45% compared to the other state-of-the-art dual-edge triggered TSPC flip-flops.}, archive = {J_ACCESS}, author = {Yugal Kishore Maheshwari and Manoj Sachdev}, doi = {10.1109/ACCESS.2025.3560850}, journal = {IEEE Access}, pages = {66040-66050}, shortjournal = {IEEE Access}, title = {Low-power, low-energy, static, contention-free, TSPC dual-edge triggered flip-flops}, volume = {13}, year = {2025}, }
• (2025). Ship-DETR: A transformer-based model for EfficientShip detection in complex maritime environments. ACCESS, 13, 66031-66039. (www)

  @article{ , abstract = {With the widespread application of ship detection in civilian and military domains, ship detection in optical remote sensing images has become an important research direction. The core task is to determine the presence of ship targets in remote sensing images and to perform detection, classification, and localization. However, ship targets in remote sensing images exhibit multi-scale characteristics, complex backgrounds, and varying meteorological conditions, leading to challenges such as low detection accuracy, false positives, and missed detections, particularly for small-scale ships. To address these issues, we propose an effective ship detection algorithm—Ship Detection Transformer (Ship-DETR). First, we introduce the high-low frequency (HiLo) attention into the intra-scale feature interaction module to enhance the extraction of both high- and low-frequency features, reduce computational complexity, and improve detection performance. To strengthen the model’s multi-scale fusion capability for ship features of different scales, we incorporate the bidirectional feature pyramid network (BiFPN) to optimize cross-scale feature fusion and add the $S_{2}$ features to further enhance the representation of small-scale ship features. Additionally, we replace traditional downsampling operations with the Haar wavelet-based downsampling (HWD) module, which reduces the spatial resolution of feature maps while preserving edge and texture details as much as possible, thereby minimizing feature information loss. Experimental results on the LEVIR-Ship dataset and the HRSC2016-MS dataset demonstrate that Ship-DETR improves the ship detection accuracy while maintaining a small number of parameters and computational load. Ship-DETR achieved a mean average precision of 75.7% and 73.1% on the two datasets, respectively, representing an improvement of 3.5% and 1.9% compared to the baseline model. Compared with other mainstream object detection algorithms, our proposed model shows superior detection performance.}, archive = {J_ACCESS}, author = {Yi Wang and Xiang Li}, doi = {10.1109/ACCESS.2025.3559107}, journal = {IEEE Access}, pages = {66031-66039}, shortjournal = {IEEE Access}, title = {Ship-DETR: A transformer-based model for EfficientShip detection in complex maritime environments}, volume = {13}, year = {2025}, }
• (2025). A dynamic sliding window encoding for secured DNA data storage compliant with biological and indexing constraints. ACCESS, 13, 66009-66030. (www)

  @article{ , abstract = {In this paper, we propose a novel dynamic sliding window encoding to store encrypted data in DNA form, taking into account biological constraints (G-C content and homopolymers) and prohibited nucleotide motifs used for data indexing. The originality of our approach is twofold. First, it is based on a new dynamic encoding (DE) which takes advantage of variable-length DNA codewords to encode binary data while avoiding homopolymers longer than N bases. Second, DE is combined with a sliding window strategy to detect whether a prohibited motif is about to appear. In such cases, our method inserts a non-coding base into the sequence to prevent the encoding of the motif. Unlike existing schemes, our approach scales extremely easily for high values of homopolymers and of prohibited motifs. It is furthermore independent of the cryptosystem. We provide the theoretical information rate of our proposal for a given number of prohibited motifs and a maximum homopolymer length. In general, it offers significantly higher performances, particularly in terms of information rate, than existing schemes while maintaining G-C content within the desired range with extremely high probability. Moreover, experiments conducted with a recent DNA storage chain simulator show that our method requires a similar amount of sequence copies as other encoding strategies for error-free DNA sequence recovery.}, archive = {J_ACCESS}, author = {Chloé Berton and Gouenou Coatrieux and Héléne Gasnier and Sahar Haddad and Reda Bellafqira and Dominique Lavenier}, doi = {10.1109/ACCESS.2025.3560387}, journal = {IEEE Access}, pages = {66009-66030}, shortjournal = {IEEE Access}, title = {A dynamic sliding window encoding for secured DNA data storage compliant with biological and indexing constraints}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based sentiment analysis in english literature: Using deep learning models to analyze emotional and thematic content in texts. ACCESS, 13, 65997-66008. (www)

  @article{ , abstract = {This paper proposes a hybrid deep learning approach combining Bidirectional Long Short-Term Memory (BiLSTM) networks and an attention mechanism to extract sentiment and thematic content from literary texts. The model is designed to capture complex emotional nuances and themes in literature by processing text data from both forward and backward directions, while the attention mechanism enables the model to focus on the most important sections of the text. Hyperparameter optimization is performed using the Improved Particle Swarm Optimization (IPSO) algorithm to fine-tune the model for efficient sentiment extraction. A case study using a dataset of 500 English novels spanning various genres demonstrated the effectiveness of the proposed approach. The model achieved high accuracy and F1 scores in sentiment classification and thematic extraction, outperforming traditional methods like CNNs. The analysis revealed key emotional themes such as joy, fear, and sorrow, and the thematic content included love, betrayal, and revenge. The results highlight the potential of deep learning to advance literary analysis by providing deeper insights into both emotional and thematic layers of literary works. Future directions include exploring multimodal data integration and expanding the application of deep learning in the humanities.}, archive = {J_ACCESS}, author = {Jie Yu and Chunhong Qi}, doi = {10.1109/ACCESS.2025.3553386}, journal = {IEEE Access}, pages = {65997-66008}, shortjournal = {IEEE Access}, title = {Machine learning-based sentiment analysis in english literature: Using deep learning models to analyze emotional and thematic content in texts}, volume = {13}, year = {2025}, }
• (2025). Coordinated control of flywheel and battery energy storage systems for frequency regulation in diesel generator-based microgrid. ACCESS, 13, 65980-65996. (www)

  @article{ , abstract = {Due to the inherent slow response time of diesel generators within an islanded microgrid (MG), their frequency and voltage control systems often struggle to effectively manage rapid load changes. To mitigate this challenge, energy storage systems (ESSs) emerge as pivotal solutions. Flywheel energy storage systems (FESSs) are well-suited for handling sudden power fluctuations because they can quickly deliver or absorb large amounts of electricity. On the other hand, battery energy storage systems (BESSs) excel at storing large amounts of energy for extended periods and can handle gradual changes in power demand. This research introduces a coordinated control mechanism for a mixed energy storage setup that combines BESS and FESS elements to manage the frequency of a standalone MG. A new frequency detection method is employed to assign high, medium, and low-frequency deviations in power distribution to FESS, BESS, and diesel generators, respectively. Simulations are carried out to determine the exact performance of the proposed control method. The MATLAB/Simulink software platform is used to run these simulations. Furthermore, dynamic load changes are precisely emulated using a programmable load emulator enhancing the reliability and relevance of our analysis.}, archive = {J_ACCESS}, author = {Mahdi Faraji and Mohammad Saeed Mahdavi and Venera Nurmanova and Gevork B. Gharehpetian and Mehdi Bagheri}, doi = {10.1109/ACCESS.2025.3559722}, journal = {IEEE Access}, pages = {65980-65996}, shortjournal = {IEEE Access}, title = {Coordinated control of flywheel and battery energy storage systems for frequency regulation in diesel generator-based microgrid}, volume = {13}, year = {2025}, }
• (2025). How information contributors establish high-credibility online health information. ACCESS, 13, 65966-65979. (www)

  @article{ , abstract = {Purpose - This study aims to explore the credibility establishment strategies utilized by health information contributors when creating content to guide them in establishing high-credibility and user-trusted health information. Design/methodology/approach - This study conducts in-depth interviews with 86 online health information contributors to investigate the creative strategies they adopt to establish credibility. This study also integrates mathematical models to assess the contribution of each strategy to enhancing the credibility of online health information. Findings - Results confirm the information contributors use credibility establishment strategies that can be broadly categorized into 13 types with 52 specific strategies. The most significant contributors are the focus, review, supportive information search, credibility enhancement awareness, and specific writing strategy types. When evaluating the contributions of these strategies, users tend to prioritize creative strategies that enhance the expression and attractiveness of articles, while experts lean toward strategies that emphasize the professionalism and authenticity of information content. Originality/value - From the perspective of health information contributors, conducting an exploratory analysis of their credibility establishment strategies can guide them in producing online health information that is highly credible and trusted by users.}, archive = {J_ACCESS}, author = {Junping Qiu and Yutian Fu and Qinze Mi and Tingyong Zhang}, doi = {10.1109/ACCESS.2025.3557740}, journal = {IEEE Access}, pages = {65966-65979}, shortjournal = {IEEE Access}, title = {How information contributors establish high-credibility online health information}, volume = {13}, year = {2025}, }
• (2025). On-chip CMOS shorted bow-tie antenna enhanced by 3D printed parasitic resonator operating around 246 GHz. ACCESS, 13, 65955-65965. (www)

  @article{ , abstract = {This work describes the design process, manufacturing, and measurement of an antenna system consisting of an on-chip feeding element enhanced by 3D printed parasitic resonators operating around 246 GHz. The antennas are intended to be fed by the differential output of a wideband binary phase shift keying (BPSK) transmitter. The state-of-the-art is evaluated, and multiple possible complementary metal-oxide-metal (CMOS) back-end of line (BEOL) antenna structures are identified and compared against each other. The best option, in the form of a shorted bow-tie antenna, is selected. A parasitic resonator structure based on 3D printing and metallization is designed and improved using common mode analysis. The design and optimization process is detailed and explained. The realized designs are measured and compared against a similar concept using metallic resonators on a glass substrate as parasitic resonators. This is the first demonstration of a direct 3D printed structure on a CMOS antenna operating around 246 GHz.}, archive = {J_ACCESS}, author = {Joachim Hebeler and Pascal Maier and Alexander Kotz and Christian Koos and Akanksha Bhutani and Thomas Zwick}, doi = {10.1109/ACCESS.2025.3559218}, journal = {IEEE Access}, pages = {65955-65965}, shortjournal = {IEEE Access}, title = {On-chip CMOS shorted bow-tie antenna enhanced by 3D printed parasitic resonator operating around 246 GHz}, volume = {13}, year = {2025}, }
• (2025). Automated models for predicting software defects in hybrid message passing interface (MPI) and open multi-processing (OpenMP) parallel programs using deep learning. ACCESS, 13, 65939-65954. (www)

  @article{ , abstract = {Hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) parallel programs are pivotal for scalability and efficiency in high-performance computing (HPC), especially as systems approach exa-scale operations. These programs leverage distributed and shared memory systems, making them indispensable for scientific simulations, data analysis, and numerical computations. However, synchronization defects such as deadlocks and race conditions pose significant challenges to reliability and performance, often eluding traditional static and dynamic analysis tools due to the complexity of hybrid systems. This study introduces a deep learning-based models for automated defect prediction in hybrid MPI and OpenMP programs. Using a balanced dataset of 1,500 C++ files, three neural architectures—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model—were evaluated. To preprocess the code, Abstract Syntax Tree (AST)-based token extraction was employed, capturing structural and semantic relationships critical for detecting defects. Token extraction methods included detailed C++ syntax analysis via the Clang library and a custom regular expression approach. The results reveal that Clang-token-based representation provided the most effective input for defect prediction, enabling CNN models to achieve an accuracy of 97%. By integrating AST-based structural insights with the predictive power of deep learning, this research offers a scalable solution for enhancing the reliability of hybrid parallel programs. The findings establish a foundation for future advancements in automated defect detection, addressing the pressing needs of high-performance computing systems.}, archive = {J_ACCESS}, author = {Amani Saad Althiban and Hajar M. Alharbi and Lama A. Al Khuzayem and Fathy Elbouraey Eassa}, doi = {10.1109/ACCESS.2025.3559807}, journal = {IEEE Access}, pages = {65939-65954}, shortjournal = {IEEE Access}, title = {Automated models for predicting software defects in hybrid message passing interface (MPI) and open multi-processing (OpenMP) parallel programs using deep learning}, volume = {13}, year = {2025}, }
• (2025). A label propagation approach for missing data imputation. ACCESS, 13, 65925-65938. (www)

  @article{ , abstract = {Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines.}, archive = {J_ACCESS}, author = {Filipe Loyola Lopes and Arthur Dantas Mangussi and Ricardo Cardoso Pereira and Miriam Seoane Santos and Pedro Henriques Abreu and Ana Carolina Lorena}, doi = {10.1109/ACCESS.2025.3559772}, journal = {IEEE Access}, pages = {65925-65938}, shortjournal = {IEEE Access}, title = {A label propagation approach for missing data imputation}, volume = {13}, year = {2025}, }
• (2025). The design of discrete-time systems with prescribed probability distribution and power spectrum poles. ACCESS, 13, 65900-65924. (www)

  @article{ , abstract = {This paper addresses the problem of designing nonlinear discrete-time dynamical systems for prospective use in low-complexity random signal generators. Drawing upon ergodic systems theory, we derive a novel method for designing nonlinear systems with zero-input responses that simultaneously satisfy two user-specified statistical metrics as specifications. These metrics are a piecewise-constant probability density function and a power spectral density expressed as a rational function with an arbitrary number of prescribed non-repeating poles. This new method configures a memoryless nonlinearity (i.e., a map) with a low-complexity hat-like structure as a recursive system with adjustable parameters, thereby yielding a simplified and explicit relationship between the system’s parameters and the statistical metrics of its response. This yields a matrix design approach that, unlike existing methods, affords the freedom to prescribe all of the poles as a set of non-repeating complex values without resorting to a numerical search over the parameter space for a candidate solution. Simulations are presented of elementary systems constructed using the novel method, and the link between the maps’ structure and the observed statistical behavior is expounded. More sophisticated systems with richer statistical metrics are constructed as examples that demonstrate the method’s versatility. We also demonstrate the independent adjustment of the center frequency and bandwidth of power spectrum modes during system design, which affords greater freedom than existing methods to select the power spectral density. We anticipate that the proposed method will find application in the construction of efficient random signal generators for Monte Carlo simulation in domains such as mobile communications, radar engineering, data encryption and optimization.}, archive = {J_ACCESS}, author = {André Martin McDonald and Michaël Antonie van Wyk and Jacques Étienne Cilliers}, doi = {10.1109/ACCESS.2025.3558799}, journal = {IEEE Access}, pages = {65900-65924}, shortjournal = {IEEE Access}, title = {The design of discrete-time systems with prescribed probability distribution and power spectrum poles}, volume = {13}, year = {2025}, }
• (2025). A multi-market data-driven stock price prediction system: Model optimization and empirical study. ACCESS, 13, 65884-65899. (www)

  @article{ , abstract = {As global financial markets become increasingly interconnected, relying solely on historical data from a single market is insufficient for achieving high prediction accuracy in stock price forecasting. It is essential to consider the impact of cross-market transactions on China’s stock market fluctuations. This study investigates this impact by constructing a comprehensive dataset that includes stock market, bond market, and foreign exchange market data from multiple countries. A deep learning algorithm, combined with the grid search method, was employed to compare different structural hyperparameters of four models: LSTM, GRU, MLP, and Transformer. The results indicate that the GRU model outperformed the others in terms of prediction accuracy. Consequently, a GRU-Arbitrage trading system based on optimal structural hyperparameters was developed. This system effectively integrates international and domestic information for stock price prediction, providing investors with a practical and accurate tool. The research findings show that the mean squared error (MSE) of stock prices predicted using international market transaction information for the Chinese stock market is 0.0069, followed by 0.0035 for the bond market and 0.0102 for the foreign exchange market. These results verify the significant impact of cross-market data on predicting China’s stock market fluctuations.}, archive = {J_ACCESS}, author = {Le Yu and Yaoquan Chen and Yanting Zheng}, doi = {10.1109/ACCESS.2025.3559169}, journal = {IEEE Access}, pages = {65884-65899}, shortjournal = {IEEE Access}, title = {A multi-market data-driven stock price prediction system: Model optimization and empirical study}, volume = {13}, year = {2025}, }
• (2025). A novel and efficient multi-target backdoor attack for deep learning-based wireless signal classifiers. ACCESS, 13, 65863-65883. (www)

  @article{ , abstract = {Deep learning (DL)-based wireless signal classification is increasingly becoming important and deployed on edge devices (e.g., IoT, smartphones, etc.) to enhance communication reliability and efficiency. However, these DL models are vulnerable to security threats due to the broadcast nature of radio frequency (RF) signals. This work proposes a novel and practical backdoor attack on DL models for automatic modulation classification of RF signals after deployment in wireless devices. Our attack method is practically realizable and does not require access to the target model’s training data or architecture, which is a less explored area in DL-based wireless systems. We propose an efficient method to design multiple backdoor triggers based on Hadamard matrices that are orthogonal to each other, which can be easily injected into the received RF signals. Further, the attack mechanism utilizes a model named BackdoorNet that includes a denoising autoencoder to extract multiple triggers and a fully-connected deep neural network to generate misclassification signals based on the extracted triggers. The BackdoorNet is a lightweight model that can be injected into various target DL models without resource-intensive training, making the attack computationally efficient. We thoroughly investigate the attack’s effectiveness based on modulation classification accuracy (MCA), attack success rate (ASR), trigger false alarm rate (FAR), effect on signal characteristics, and generalization of attack performance across different target DL models. The results suggest that, across the investigated target DL models, our method can achieve an ASR of 99% on multiple target labels without affecting the MCA and the FAR on clean signals, emphasizing the method’s multi-target attack capability, stealthiness, and computational efficiency compared to recent backdoor attacks. Furthermore, we analyze defense techniques against the proposed backdoor attack and show the effectiveness of activation clustering in distinguishing between clean and triggered signals. The extensive experimentation and detailed analysis of the proposed backdoor attack mechanism show a significant security threat to the DL-based wireless classifiers, necessitating further research on other DL-based wireless applications.}, archive = {J_ACCESS}, author = {Nayan Moni Baishya and B. R. Manoj and Snehashish Karmakar}, doi = {10.1109/ACCESS.2025.3558276}, journal = {IEEE Access}, pages = {65863-65883}, shortjournal = {IEEE Access}, title = {A novel and efficient multi-target backdoor attack for deep learning-based wireless signal classifiers}, volume = {13}, year = {2025}, }
• (2025). Explainable AI for unraveling the significance of visual cues in high stakes deception detection. ACCESS, 13, 65839-65862. (www)

  @article{ , abstract = {Deception, a widespread aspect of human behavior, has significant implications in fields like law enforcement, security, judicial proceedings, and social areas. Detecting deception accurately, especially in high-stakes environments, is critical for ensuring justice and security. Recently, machine learning has significantly enhanced deception detection capabilities by analyzing various behavioral and visual cues. However, machine learning models often operate as opaque “black boxes,” offering high predictive accuracy without explaining the reasoning behind the decisions. This lack of transparency necessitates the integration of Explainable Artificial Intelligence to make the models’ decisions understandable and trustworthy. This study proposes the implementation of existing model-agnostic Explainable Artificial Intelligence techniques—Permutation Importance, Partial Dependence Plots, and SHapley Additive exPlanations—to showcase the contributions of visual features in deception detection. Using Real-Life Trial dataset, recognized as the most valuable high-stake dataset, we demonstrate that Multi-layer Perceptron achieved the highest accuracy of 88% and a recall of 92.86%. Along with the aforementioned existing techniques, Real-Life Trial dataset inspired us to develop a novel technique: ‘set-of-features permutation importance’. Additionally, this study is novel in the sense of that it extensively applies XAI techniques in the field of deception detection on Real-Life Trial dataset. Experimental results shows that the visual cues related to eyebrow movements are most indicative of deceptive behavior. Along with the new findings, our work underscores the importance of making machine learning models more transparent and explainable, thereby enhancing their utility for human-in-loop AI and ethical acceptability.}, archive = {J_ACCESS}, author = {Suhaib Salah and Hagar Elbatanouny and Abrar Sobuh and Eqab Almajali and Wasiq Khan and Haya Alaskar and Adel Binbusayyis and Taimur Hassan and Jawad Yousaf and Abir Hussain}, doi = {10.1109/ACCESS.2025.3558875}, journal = {IEEE Access}, pages = {65839-65862}, shortjournal = {IEEE Access}, title = {Explainable AI for unraveling the significance of visual cues in high stakes deception detection}, volume = {13}, year = {2025}, }
• (2025). Territorial space optimization method based on multi-objective genetic algorithm and FLUS model. ACCESS, 13, 65823-65838. (www)

  @article{ , abstract = {The rational planning of territorial space is related to the speed of economic development and the protection of ecology. There is an urgent need for a more advanced integrated territorial space layout method. For additional spatial layout design, the study suggests a multi-objective genetic algorithm based on spatial data prediction and coupled with land simulation modeling in the context of big data and machine learning development. The innovation of the research lies in the optimization of the multi-objective genetic algorithm by improving the crossover and mutation process of genetic operators, enhancing the adaptability of the algorithm in dynamic environments, and improving the prediction accuracy of the spatial quantity structure of the national territory. The outcomes revealed that this approach was applied to the prediction of populations of different sizes 400 and 600 with an average accuracy of 67%, which was 17% higher than that of the traditional genetic algorithm. The difference between the predicted and actual future spatial population values using the proposed algorithm was less than 2%, which was 56.5% lower than the other two prediction algorithms on average. The proposed land simulation model reached the highest accuracy at 100 iterations with an average fitness of $2.4\times 10 ^{11}$ , which was 13% and 27% higher than the other two traditional neural network algorithms, respectively. In the two simple functions of f1 and f2, the highest convergence accuracy reached $10^{-30}$ and $10^{-10}$ , respectively. In the two more complicated functions of f3 and f4, the optimal solutions were approximated in the ranges of [ $10^{2}$ , $10^{12}$ ] and [ $10^{-2}$ , $10^{6}$ ] without significant fluctuations. Therefore, the proposed algorithm can effectively predict the number of territorial space in macro and micro simulation, and has high feasibility and accuracy. This provides a reliable basis for the government to carry out land resource planning, and promotes the sustainable development of ecology and economy.}, archive = {J_ACCESS}, author = {Lin Ge}, doi = {10.1109/ACCESS.2025.3560096}, journal = {IEEE Access}, pages = {65823-65838}, shortjournal = {IEEE Access}, title = {Territorial space optimization method based on multi-objective genetic algorithm and FLUS model}, volume = {13}, year = {2025}, }
• (2025). Paraphrase identification with deep learning: A review of datasets and methods. ACCESS, 13, 65797-65822. (www)

  @article{ , abstract = {The rapid advancement of Natural Language Processing (NLP) has greatly improved text-generation tools like ChatGPT and Claude, offering significant utility but also posing risks to media credibility through paraphrased plagiarism—a subtle yet widespread form of content misuse. Despite progress in automated paraphrase detection, inconsistencies in training datasets often limit their effectiveness. This study examines traditional and modern approaches to paraphrase identification, revealing how the under-representation of certain paraphrase types in widely-used datasets, including those for training Large Language Models (LLMs), undermines plagiarism detection accuracy. To address these issues, we introduce and validate ReParaphrased, a refined paraphrase typology, and extend the Extended Typology Paraphrase Corpus (ETPC) with meticulous manual annotations to enhance reliability. Using the augmented ETPC, we fine-tune the LLama3.1-7B-instruct model, uncovering significant disparities in paraphrase type distribution across existing datasets. A detailed analysis of the MRPC benchmark dataset further highlights critical distributional issues and their implications. We propose four key solutions to address dataset limitations, providing both theoretical and practical guidance for improving dataset quality. These contributions aim to establish a more robust foundation for NLP model training and evaluation. Finally, we outline future research directions and suggest improvements for dataset development to advance AI-driven paraphrase detection.}, archive = {J_ACCESS}, author = {Chao Zhou and Cheng Qiu and Lizhen Liang and Daniel E. Acuna}, doi = {10.1109/ACCESS.2025.3556899}, journal = {IEEE Access}, pages = {65797-65822}, shortjournal = {IEEE Access}, title = {Paraphrase identification with deep learning: A review of datasets and methods}, volume = {13}, year = {2025}, }
• (2025). Handling sensor faults in hydroponics: A deep learning imputation technique for accurate tomato yield prediction. ACCESS, 13, 65776-65796. (www)

  @article{ , abstract = {Sensor faults in hydroponic systems pose significant challenges for precision agriculture by compromising the nutrient monitoring accuracy and yield prediction reliability. Current imputation methods lack domain-specific agricultural pattern-preservation capabilities. This paper presents a novel Deep Learning Precision Imputation Model (DLPIM) based on a generative adversarial network (GAN) architecture to recover missing agricultural data in IoT-based hydroponic monitoring systems. DLPIM introduces a feature encoder-based generator with a 4-headed self-attention mechanism and a Crop Growth Rate (CGR) guided temporal processor to capture complex time-series patterns while maintaining physiological consistency. The discriminator implements dual validity and feature matching pathways, enhanced by minibatch discrimination and spectral normalization. A dataset comprising 1092 data points related to tomato growth and nutrient analysis was utilized to develop and evaluate the proposed model, demonstrating the superior performance of DLPIM compared to nine state-of-the-art imputation methods across different missing data scenarios (10-60%). The proposed model achieved optimal accuracy (MSE =2.367-2.664, MAE =0.795-0.851, R ${}^{2} = 0.990$ -0.992), while maintaining consistent performance across all missing rates. Ablation studies confirmed the effectiveness of architectural innovations, with CGR removal resulting in the most substantial performance decline (R ${}^{2} = 0.851$ at a 60% missing rate). The DLPIM framework establishes a new benchmark for agricultural time series imputation, enabling robust decision-making in precision agriculture.}, archive = {J_ACCESS}, author = {Viji Venugopal and Paresh Tanna and Ramesh Karnati}, doi = {10.1109/ACCESS.2025.3555875}, journal = {IEEE Access}, pages = {65776-65796}, shortjournal = {IEEE Access}, title = {Handling sensor faults in hydroponics: A deep learning imputation technique for accurate tomato yield prediction}, volume = {13}, year = {2025}, }
• (2025). Context-aware embedded language transformers for evaluating climate change-based sustainable development goals. ACCESS, 13, 65757-65775. (www)

  @article{ , abstract = {This research work addresses the pressing issue of climate change and the urgent need for a comprehensive and reliable dataset that can assist stakeholders, policymakers and researchers in making informed and data-driven decisions. We propose the process for leveraging publicly available raw data such as news articles and social media posts and help to create meaningful ontologies. The study demonstrates the use of advanced Natural Language Processing and Deep Learning techniques to transform raw data into potential insights. Additionally, we present a methodology for utilizing these generated ontologies to anticipate the impacts of decisions on climate change. The primary objective of this research is to present a Context-Aware Embedded Language Transformers model that can be easily integrated into various pipelines by generating meaningful ontologies to support more informed decision-making. These ontologies will serve as knowledge graphs and contribute to a large dataset for future research in the field and address the current lack of comprehensive resources.}, archive = {J_ACCESS}, author = {Pratham Mishra and Senthil Kumar Narayanasamy and Kathiravan Srinivasan}, doi = {10.1109/ACCESS.2025.3559548}, journal = {IEEE Access}, pages = {65757-65775}, shortjournal = {IEEE Access}, title = {Context-aware embedded language transformers for evaluating climate change-based sustainable development goals}, volume = {13}, year = {2025}, }
• (2025). AI-driven haptic cues for haptic object reproduction. ACCESS, 13, 65737-65756. (www)

  @article{ , abstract = {The potential of haptic technology is enormous, and haptics plays a crucial role in various fields. Recent studies on haptics have primarily focused on granting robots a sense of touch to enhance their performance in manipulation tasks and unlock barriers in the virtual world. Most haptic studies have relied on model-based approaches to interpret haptic interactions, and conventional spring-damper systems are often used. However, these methods have limitations in accurately reflecting real, nonlinear haptic interactions. Although force sensors have been widely used in these studies, they have drawbacks such as limited bandwidth, susceptibility to signal noise, complexity, non-collocation, and instability. Interestingly, force measurements based on the sensorless force control mechanism using DOB (Disturbance Observer) and RFOB (Reaction Force Observer) have proven to provide more accurate results than traditional force sensors. Haptic studies have often relied on motion parameters to detect haptic feedback, potentially failing to capture the full range of factors that can influence haptic sensation. In recent years, AI (Artificial Intelligence) approaches have demonstrated superior results in many areas. However, it is crucial to clearly understand the dataset to achieve more reliable results through AI. Therefore, this study introduces an AI-based approach to recreate vivid force sensations in a virtual model, replicating the actual environment using information abstracted through DOB and RFOB-based sensorless force control mechanism. The validity of this approach is discussed by comparing the haptic sensations produced by the AI-based model with those produced by both the conventional object model and the actual object.}, archive = {J_ACCESS}, author = {Praveena W. Dewapura and A. M. Harsha S. Abeykoon}, doi = {10.1109/ACCESS.2025.3555929}, journal = {IEEE Access}, pages = {65737-65756}, shortjournal = {IEEE Access}, title = {AI-driven haptic cues for haptic object reproduction}, volume = {13}, year = {2025}, }
• (2025). Task decomposition and self-evaluation mechanisms for home healthcare robots using large language models. ACCESS, 13, 65726-65736. (www)

  @article{ , abstract = {A system leveraging Large Language Models (LLMs), is proposed to address the limitations of current models primarily used for conversational purposes. While user interactions are excelled by ChatGPT, instability and safety issues are encountered when generating control codes for manipulator. This study focuses on the field of home healthcare services and proposes a new approach to control robots equipped with self-developed dexterous hand. The objective of this paper is to develop and evaluate a new system, Elderly Care GPT, aimed at improving the control of robots for home healthcare services. The system utilizes the Chain-of-Thought (CoT) prompting technique to enhance the accuracy of model-generated control codes. In conjunction with a self-evaluation mechanism, it ensures that the control codes undergo an accuracy evaluation before execution, thereby ensuring safety. Additionally, the system decomposes tasks into basic groups to improve the robot’s task execution capabilities. The performance of the model is evaluated across eight real-world scenarios, covering classic home care tasks. In real-world evaluations, Elderly Care GPT is compared with LLaMA3 and LLaMA3+ Prompt Words, and a comparison is made with mainstream models used for controlling robots on the market. The results show that Elderly Care GPT achieves a task success rate of up to 97.3%, demonstrating exceptional performance. This paper concludes that Elderly Care GPT outperforms current models used for robot control in home healthcare services, achieving high task success rates and effectively handling complex tasks. The use of Chain-of-Thought prompting, a self-evaluation mechanism, and task decomposition significantly contributes to the model’s success.}, archive = {J_ACCESS}, author = {Liteng Liu and Sen Zhang and Yangmin Jiang and Jingzhen Guo and Wenlong Zhao}, doi = {10.1109/ACCESS.2025.3559076}, journal = {IEEE Access}, pages = {65726-65736}, shortjournal = {IEEE Access}, title = {Task decomposition and self-evaluation mechanisms for home healthcare robots using large language models}, volume = {13}, year = {2025}, }
• (2025). Characterizing data visualization literacy for standardization: A systematic literature review. ACCESS, 13, 65704-65725. (www)

  @article{ , abstract = {With the advent of the data era and of new, more intelligent interfaces for supporting decision-making, there is a growing need to define, model and assess human ability and data visualization usability for better encoding and decoding of data patterns. Data Visualization Literacy (DVL) is the ability to encode and decode data into and from a visual language. Although this ability and its measurement are crucial for advancing human knowledge and decision capacity, they have seldom been investigated, let alone systematically. To address this gap, this paper presents a systematic literature review comprising 45 reports on DVL, analyzed using the PRISMA methodology. Our results include the identification of the purposes of DVL, its satellite aspects, the models proposed, and the assessments designed to evaluate the degree of DVL of people. Eventually, we devise many research directions including, among the most challenging, the definition of a (standard) unifying construct and protocol for DVL.}, archive = {J_ACCESS}, author = {Sara Beschi and Davide Falessi and Silvia Golia and Angela Locoro}, doi = {10.1109/ACCESS.2025.3559298}, journal = {IEEE Access}, pages = {65704-65725}, shortjournal = {IEEE Access}, title = {Characterizing data visualization literacy for standardization: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A framework to identify the catalog of important tasks reflecting interorganizational characteristics regarding the deployment of portable equipment. ACCESS, 13, 65690-65703. (www)

  @article{ , abstract = {One key difference between the traditional probabilistic safety assessment (PSA) and multi-unit PSA (MU-PSA) is that the MU-PSA must consider interorganizational characteristics arising from deploying portable equipment. In this study, a systematic framework is proposed for the detailed sub-task analysis of human failure events (HFEs) involving portable equipment through utilization of STAMP (Systems-Theoretic Accident Model and Processes) and STPA (Systems-Theoretic Process Analysis). A case study was performed for a typical multi-unit emergency response involving a portable diesel generator. Through the proposed framework, detailed sub-tasks were identified for the HFE (“Failure of deploying the portable diesel generator”) in a form of unsafe control actions through a systemic approach. The results showed that the potential hazards that may not currently be analyzed in the traditional HRA enough include failed convocation of offsite workers, interorganizational communication between important EROs, and multi-unit decision making error on installing PDGs if there are conflict due to limited number of PDGs available. Results of the detailed HFE analysis using the proposed framework may be used as a basis to allow more realistic application of the HRA involving deployment of portable equipment (e.g., multi-unit accidents and beyond design-basis external events) that are better representative of the interorganizational characteristics.}, archive = {J_ACCESS}, author = {Seong Woo Kang and Jaewhan Kim and Jinkyun Park and Jong Woo Park and Sung-Min Shin}, doi = {10.1109/ACCESS.2025.3559627}, journal = {IEEE Access}, pages = {65690-65703}, shortjournal = {IEEE Access}, title = {A framework to identify the catalog of important tasks reflecting interorganizational characteristics regarding the deployment of portable equipment}, volume = {13}, year = {2025}, }
• (2025). STOP: Studying time-series of preeclamptic emergencies. ACCESS, 13, 65672-65689. (www)

  @article{ , abstract = {The management of public health in a nation is a crucial matter. The emergency departments are very packed, as in many other countries. To explore in-depth the monthly number of emergency room arrivals of preeclampsia patients during the period 2019–2023 at the “IESS Hospital del Día Sur Valdivia” in Guayaquil, Ecuador, we use descriptive, diagnostic, predictive, and prescriptive (DDPP) analytics together. The descriptive phase considered the benefits of statistics for data characterization. The diagnostic phase is in which the relationships of the trend, seasonality, stationarity, autocorrelation, and anomalies of the time series are reviewed. The predictive phase uses deep learning and statistical models to predict emergency arrivals. The multilayer perceptron model (MLP) achieved the best performance (a mean absolute percentage error of 17.21%), selecting it to forecast the number of preeclamptic emergencies during 2024. Finally, in the prescriptive phase, possible solutions are analyzed using two scenarios presented. The results show each phase of the DDPP analyzes, providing valuable information to improve hospital management. This work can serve as a basis for future studies on the joint application of all DDPP analyses to univariate time series, providing a step-by-step guide on how to analyze such data and introducing a systematic procedure for their analysis for those who may lack statistical expertise.}, archive = {J_ACCESS}, author = {Franklin Parrales-Bravo and Rosangela Caicedo-Quiroz and Elena Tolozano-Benites and Leonel Vasquez-Cevallos and Lorenzo Cevallos-Torres}, doi = {10.1109/ACCESS.2025.3558888}, journal = {IEEE Access}, pages = {65672-65689}, shortjournal = {IEEE Access}, title = {STOP: Studying time-series of preeclamptic emergencies}, volume = {13}, year = {2025}, }
• (2025). Research on classification and recognition of micro milling tool wear based on improved DenseNet. ACCESS, 13, 65659-65671. (www)

  @article{ , abstract = {Due to the small size of micro milling tools and wear sizes even in the micrometer range, monitoring tool wear is extremely challenging. The wear state of cutting tools directly affects machining quality and productivity, becoming a key factor restricting the development of micro milling tools. This article proposes and establishes a method for identifying and monitoring the wear status of micro milling tools based on machine vision and deep learning models. The deep learning model improves the DenseNet-121 network by using deformable convolution, depthwise separable convolution, and partial convolution instead of standard convolution, reducing the number of model parameters and computational complexity. We have developed a new DLDenseBlock and feature transfer method. In order to better utilize shallow features and reduce the risk of feature loss, the output of DLDenseBlock is processed by EMA attention mechanism and input into the feature pyramid for feature fusion. Four types of micro milling tool wear image datasets were created based on micro milling tool milling and wear evaluation criteria. Using this dataset, existing network models and the improved model proposed in this paper were trained and tested. The experimental results show that the proposed wear state evaluation criteria are reasonable and accurate. Compared with existing network models, the improved model in this paper can effectively identify the wear state of micro milling tools with an accuracy rate of up to 100%.}, archive = {J_ACCESS}, author = {Lei Feng and Shuai Zhang and Zhaoxiang Li and Xin Feng}, doi = {10.1109/ACCESS.2025.3557057}, journal = {IEEE Access}, pages = {65659-65671}, shortjournal = {IEEE Access}, title = {Research on classification and recognition of micro milling tool wear based on improved DenseNet}, volume = {13}, year = {2025}, }
• (2025). Leveraging large language models for discrepancy value prediction in custody transfer systems: A comparative analysis of probabilistic and point forecasting approaches. ACCESS, 13, 65643-65658. (www)

  @article{ , abstract = {Discrepancies in custody transfer systems in the oil and gas industry pose significant financial, regulatory, and operational risks. Accurate prediction of these discrepancies is critical to optimizing operations and minimizing potential losses. This study evaluates the effectiveness of Large Language Models (LLMs), specifically the Chronos-FineTuning Amazon Chronos T5 Small model, alongside statistical, machine learning, and deep learning models, in both probabilistic and point forecasting tasks. The evaluation covers metrics such as Weighted Quantile Loss (WQL), Scaled Quantile Loss (SQL), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Root Mean Square Error (RMSE). The results highlight the superior performance of the Chronos model in both forecasting paradigms, demonstrating its ability to capture uncertainty and deliver precise predictions. This research offers valuable insights into selecting forecasting methodologies to improve custody transfer operations, underscoring the transformative potential of LLMs in industrial applications.}, archive = {J_ACCESS}, author = {Fiki Hidayat and Arbi Haza Nasution and Fajril Ambia and Dike Fitriansyah Putra and Mulyandri}, doi = {10.1109/ACCESS.2025.3560254}, journal = {IEEE Access}, pages = {65643-65658}, shortjournal = {IEEE Access}, title = {Leveraging large language models for discrepancy value prediction in custody transfer systems: A comparative analysis of probabilistic and point forecasting approaches}, volume = {13}, year = {2025}, }
• (2025). Synergy-CLIP: Extending CLIP with multi-modal integration for robust representation learning. ACCESS, 13, 65630-65642. (www)

  @article{ , abstract = {Multi-modal representation learning has become a pivotal area in artificial intelligence, enabling the integration of diverse modalities such as vision, text, and audio to solve complex problems. However, existing approaches predominantly focus on bimodal interactions, such as image-text pairs, which limits their ability to fully exploit the richness of multi-modal data. Furthermore, the integration of modalities in equal-scale environments remains underexplored due to the challenges of constructing large-scale, balanced datasets. In this study, we propose Synergy-CLIP, a novel framework that extends the contrastive language-image pre-training (CLIP) architecture to enhance multi-modal representation learning by integrating visual, textual, and audio modalities. Unlike existing methods that focus on adapting individual modalities to vanilla-CLIP, Synergy-CLIP aligns and captures latent information across three modalities equally. To address the high cost of constructing large-scale multi-modal datasets, we introduce VGG-sound+, a triple-modal dataset designed to provide equal-scale representation of visual, textual, and audio data. Synergy-CLIP is validated on various downstream tasks, including zero-shot classification, where it outperforms existing baselines. Additionally, we introduce a missing modality reconstruction task, demonstrating Synergy-CLIP’s ability to extract synergy among modalities in realistic application scenarios. These contributions provide a robust foundation for advancing multi-modal representation learning and exploring new research directions. The code is available at https://github.com/JoSangYeon/Synergy-CLIP}, archive = {J_ACCESS}, author = {Sangyeon Cho and Jangyeong Jeon and Mingi Kim and Junyeong Kim}, doi = {10.1109/ACCESS.2025.3559663}, journal = {IEEE Access}, pages = {65630-65642}, shortjournal = {IEEE Access}, title = {Synergy-CLIP: Extending CLIP with multi-modal integration for robust representation learning}, volume = {13}, year = {2025}, }
• (2025). Optimal scheduling of power systems using asymmetric nash cooperative game based on bankruptcy theory under extreme weather conditions. ACCESS, 13, 65617-65629. (www)

  @article{ , abstract = {In recent years, large-scale power outages due to climate change have occurred frequently worldwide, causing significant economic losses and severe social impacts. Against the backdrop of extremely cold weather in northeastern China. This paper presents a new operating model using asymmetric Nash cooperative game theory and resource bankruptcy theory to minimize power system operating costs under extreme weather conditions. Firstly, a fair power allocation model based on the principle of minimum variance is used to determine the boundary conditions of power resource allocation. Secondly, an asymmetric Nash cooperative game model based on bankruptcy theory is constructed for two load response modes. Then, a comprehensive bargaining weight calculation method is proposed for the negotiation of power resource allocation among various types of users by taking into account the actual demands of various users and economic factors. According to the load response game, a cost minimization power system scheduling model is derived by combining thermal power, wind power, photovoltaic power, and pumped storage. Four scenarios are investigated in the case study. The results show that the proposed model can improve fairness and satisfaction while reducing load response costs.}, archive = {J_ACCESS}, author = {Yanbo Zhan and Zhigang Lu and Liangce He and Jiangfeng Zhang and Xueping Li and Xiaoqiang Guo}, doi = {10.1109/ACCESS.2025.3557735}, journal = {IEEE Access}, pages = {65617-65629}, shortjournal = {IEEE Access}, title = {Optimal scheduling of power systems using asymmetric nash cooperative game based on bankruptcy theory under extreme weather conditions}, volume = {13}, year = {2025}, }
• (2025). TCI-net: Structural feature enhancement and multi-level constrained network for reliable thin crack identification on concrete surfaces. ACCESS, 13, 65604-65616. (www)

  @article{ , abstract = {Accurately identifying thin cracks in concrete surfaces is a key technology for the automated maintenance of structures, with widespread applications in intelligent inspection tasks such as road, bridge, and building façade inspection. Existing crack identification methods, based on encoder-decoder architectures, suffer from inaccuracies and insufficient reliability for diverse concrete surface cracks. This study proposes a reliable crack identification method using the U-Net semantic segmentation network, incorporating image structural feature enhancement and multi-level consistency constraints. This method builds on a pre-trained multi-scale semantic feature encoding network, integrating various crack edge structure information extraction operators, and a lightweight crack spatial detail feature extraction module is constructed. Through an attention mechanism, the extracted multi-scale semantic features and high-resolution structural features are adaptively fused, enabling the reliable and intelligent identification of thin cracks in complex environments. Extensive experiments are conducted on the largest publicly available crack identification dataset, CrackSeg9k, for which the proposed method achieves an F1-score of 74.7%, surpassing state-of-the-art approaches. Furthermore, to assess its practical effectiveness, the trained model is validated on real-world data collected from complex tunnel environments. The results demonstrate that the proposed approach accurately identifies fine cracks and exhibits enhanced robustness against challenging conditions such as illumination variations and environmental interference. The source code is publicly available at https://github.com/SmileLeeCN/ThinCrackIdentification.}, archive = {J_ACCESS}, author = {Shiming Li and Fengtao Yan and Zhijie Li and Qingfeng Hu and Shiqian Xu and Shilian Liu}, doi = {10.1109/ACCESS.2025.3558409}, journal = {IEEE Access}, pages = {65604-65616}, shortjournal = {IEEE Access}, title = {TCI-net: Structural feature enhancement and multi-level constrained network for reliable thin crack identification on concrete surfaces}, volume = {13}, year = {2025}, }
• (2025). Convolutional bi-LSTM for automatic personality recognition from social media texts. ACCESS, 13, 65582-65603. (www)

  @article{ , abstract = {Automatic Personality Recognition (APR) has received much attention in recent years due to its wide range of important applications across various fields. The growing use of online social networks provides valuable opportunities for APR, as a strong correlation has been found between what users post on these platforms and their personality traits. Consequently, various APR models have been developed to infer the Big Five personality traits from social media user-generated texts. However, most of these models heavily relied on hand-crafted features, which are unable to capture deep contextual information and learn complex patterns from texts. More importantly, the performance of text-based APR is still unsatisfactory, especially at the level of each personality dimension. To tackle this issue, we propose a new model, called APR_ConvLSTM, that aims to improve text-based APR performance by integrating two robust deep learning architectures: CNN and Bi-LSTM. Unlike existing APR models, the APR_ConvLSTM is a unified end-to-end model where all personality traits are predicted simultaneously and effectively without a need for laborious feature engineering. We also developed a new labeled Big Five personality dataset, called X-Big5, which has been in need for a long time in the APR field. Extensive experiments on the X-Big5 and a publicly available benchmark dataset (PAN-2015 Author Profiling) demonstrate the promising performance of our model over its contenders. Overall, the proposed model achieved the highest Accuracy and F-1 score of 79.51% and 86.54% on the PAN-2015 dataset and 87.95% and 81.35%, respectively, on the X-Big5 dataset. Moreover, it shows promising performance over its competitors, with the highest average Accuracy and F-1 score of 79.01% and 80.56%, respectively, on the combined dataset. The model reached competitive results in predicting Openness, Extraversion, Agreeableness, and Neuroticism traits with the highest F1 scores of 88.60%, 77.35%, 76.16%, and 74.52%, respectively, on the combined dataset. The proposed model can positively impact the analysis of social media text generated by different users and help identify their personality traits.}, archive = {J_ACCESS}, author = {Nouf M. Aljuhani and Abdullah Al-Malaise Al-Ghamdi and Hanan S. Alghamdi and Farrukh Saleem}, doi = {10.1109/ACCESS.2025.3558714}, journal = {IEEE Access}, pages = {65582-65603}, shortjournal = {IEEE Access}, title = {Convolutional bi-LSTM for automatic personality recognition from social media texts}, volume = {13}, year = {2025}, }
• (2025). GenePixKolor (GPK) fusion: A novel evolutionary algorithm-based optimized NFT card generation and rarity ranking method for gaming tokenomics. ACCESS, 13, 65566-65581. (www)

  @article{ , abstract = {This study introduces GenePixKolor (GPK) Fusion, an innovative approach to non-fungible token (NFT) generation and rarity ranking tailored for the gaming industry and tokenomics ecosystems. GPK Fusion leverages genetic algorithms, image processing and machine learning to create a comprehensive, four-stage system that optimizes both the trait generation and visual appeal of NFTs, while providing an advanced rarity ranking mechanism. The GPK Fusion’s rarity ranking method uniquely combines trait-based and pixel-based evaluations. Pixel rarity was assessed using color distribution for overall aesthetic appeal, and trait rarity was assessed using trait combinations. Fusing pixel rarity with trait rarity provides a more holistic assessment of an NFT’s uniqueness, balancing functional value with visual attractiveness. This approach addresses the limitations of existing rarity calculation methods, offering a more refined and comprehensive evaluation of the NFT. Empirical comparisons demonstrate that GPK Fusion consistently produces NFTs with superior trait combinations and enhanced visual appeal compared to traditional methods. Its rarity ranking shows a strong correlation with practical valuation strategies in real-time trading environments and enhances the NFT marketplaces. This research contributes to the evolving field of NFT design and valuation by providing game developers and tokenomics strategists with a powerful tool for creating, evaluating and ranking digital assets. GPK Fusion’s methodology opens new ways for creating more engaging, visually striking and balanced NFTs. GPK potentially revolutionizes asset creation and valuation in the rapidly growing intersection of gaming and blockchain technologies.}, archive = {J_ACCESS}, author = {J. Guruprakash and P. Pradeep and L. B. Krithika}, doi = {10.1109/ACCESS.2025.3558833}, journal = {IEEE Access}, pages = {65566-65581}, shortjournal = {IEEE Access}, title = {GenePixKolor (GPK) fusion: A novel evolutionary algorithm-based optimized NFT card generation and rarity ranking method for gaming tokenomics}, volume = {13}, year = {2025}, }
• (2025). Design of a dual-mode SIW bandpass filter with multi-transmission zeros. ACCESS, 13, 65552-65565. (www)

  @article{ , abstract = {In the paper, a method for cascading two dual-mode substrate integrated waveguide (SIW) filters by loading slots is proposed, resulting in two pairs of transmission zeros (TZs) observable on both sides of the passband. A corner of the single-mode SIW cavity is folded inward, and a perturbation via is placed diagonally to achieve the conversion between single-mode and dual-mode operation. The dual-mode SIW filter primarily operates in the TE102 and TE201 modes. By adjusting the width of the coupling window and the length of the two slots, the resonance points within the passband can be tuned. The positions of the out-of-band TZs can be modified by changing the offset distance between the input/output ports and the centerline, as well as the distance between the slots. The two dual-mode SIW cavities achieve magnetic coupling through a coupling window structure, while electric coupling is realized by loading slot structures between the coupling windows. The combined effects of magnetic and electric coupling create two pairs of TZs on both sides of the passband, enhancing frequency selectivity. A fourth-order SIW filter with two pairs of TZs was simulated, fabricated, and measured. The measured frequency response closely matches the simulated results, with the center frequency $f_{0}$ of 29.3 GHz, the insertion loss of 0.81 dB, and the return loss better than 16.8 dB. Four TZs were observed at 23.05, 27.11, 31.46, and 37.74 GHz, with suppression better than 25 dB. This filter is applicable to microwave and radio-frequency circuits and systems.}, archive = {J_ACCESS}, author = {Yawen He and Zhonghua Ma and Mengnan Wang and Jingyao Huang and Xiang Chen}, doi = {10.1109/ACCESS.2025.3556252}, journal = {IEEE Access}, pages = {65552-65565}, shortjournal = {IEEE Access}, title = {Design of a dual-mode SIW bandpass filter with multi-transmission zeros}, volume = {13}, year = {2025}, }
• (2025). A dynamic compensation scheme for multi-load WPT system based on parity-time symmetry. ACCESS, 13, 65540-65551. (www)

  @article{ , abstract = {In multi-load wireless power transfer (WPT) systems, it is a challenging problem to ensure that each load obtains constant output power and maintains high efficiency when the coupling conditions change or the resonant frequencies of the loads are different. This paper proposes a dynamic compensation scheme for the multi-load parity-time symmetric wireless power transmission (PT-WPT) system. First, based on the analysis of the multi-load PT-WPT circuit model, a switch capacitor circuit (SCC) is proposed to replace the traditional compensation capacitor. The SCC consists of capacitors and transistors, which can dynamically adjust the equivalent capacitance by changing the conduction angle to make the system work in resonance and adapt to different frequency multi-load conditions. Secondly, to calculate the SCC conduction angle more accurately in the case of multi-coil cross-coupling. A dynamic compensation control strategy is proposed based on model predictive control (MPC). The system state is predicted by detecting the phase difference between the transmitter voltage and current, and the SCC is controlled to generate the appropriate equivalent capacitance for rapid dynamic compensation. Finally, an experimental prototype of the 50 W dual-load PT-WPT system was fabricated. The experimental results show that when the system supplies loads with resonant frequencies of 150 kHz and 200 kHz simultaneously, the transmission efficiency of the system is improved from 87% to 90.8% by using MPC dynamic predictive control. The system can maintain transmission efficiency of 90% or above within a certain transmission distance.}, archive = {J_ACCESS}, author = {Yan Liu and Jiaming Wei and Minghui Han}, doi = {10.1109/ACCESS.2025.3537992}, journal = {IEEE Access}, pages = {65540-65551}, shortjournal = {IEEE Access}, title = {A dynamic compensation scheme for multi-load WPT system based on parity-time symmetry}, volume = {13}, year = {2025}, }
• (2025). GIS-based fuzzy-AHP framework for identifying suitable hubs for offshore wind and clean hydrogen production. ACCESS, 13, 65526-65539. (www)

  @article{ , abstract = {Integrating offshore wind power with clean hydrogen production in sustainable hubs offers a promising approach to enhancing the economic viability and sustainability of renewable energy projects. Such energy hubs are strategies to combine different infrastructures in specific regions, thus enabling synergies among them regions and make more efficient use of available resources. However, identifying suitable hubs requires a comprehensive evaluation of spatial, technical, financial, and environmental factors. This paper presents a Geographic Information System (GIS)-based Fuzzy Analytic Hierarchy Process (AHP) framework to assist stakeholders in identifying the most suitable sustainable hubs. The fuzzy-AHP technique, which integrates fuzzy logic with AHP, is particularly effective in managing the subjective and imprecise nature of decision-making criteria. Applied to the Brazilian coastline, the framework highlights the northeast as the most favorable region for sustainable hydrogen production hubs, with techno-economic-environmental feasibility exceeding 73% of all analyzed locations. The southeast also shows strong potential, with techno-economic-environmental feasibility above 64%, largely due to its proximity to industrial centers and spatial characteristics, which result in reduced Capital Expenditure (CAPEX) values. These findings show the effectiveness of the GIS-based Fuzzy-AHP framework in providing a nuanced ranking of potential hubs. Thus, offering energy companies and policymakers a robust tool for planning and investing in offshore wind and clean hydrogen production. Furthermore, this tool supports global efforts to reduce carbon emissions and promote sustainable energy transitions.}, archive = {J_ACCESS}, author = {Karen F. Paula and Hayro A. Pumalloclla Castilla and Mahdi Pourakbari-Kasmaei and Fabian Heymann and Djalma M. Falcão and Joel D. Melo}, doi = {10.1109/ACCESS.2025.3553704}, journal = {IEEE Access}, pages = {65526-65539}, shortjournal = {IEEE Access}, title = {GIS-based fuzzy-AHP framework for identifying suitable hubs for offshore wind and clean hydrogen production}, volume = {13}, year = {2025}, }
• (2025). LMD_YOLO: A lightweight and efficient model for pavement defects detection. ACCESS, 13, 65510-65525. (www)

  @article{ , abstract = {Pavement defects detection is vital for ensuring safety and extending infrastructure lifespan. Traditional manual inspection methods are inefficient and insufficient for the increasing demand for road maintenance. While automated detection systems are essential, existing approaches face challenges such as complex road environments, diverse defect types, and high computational requirements for real-time processing. These methods often sacrifice either accuracy or efficiency. To address these challenges, a novel model, LMD_YOLO, is proposed. The model incorporates several innovations: the Diverse Branch Block enhances the detection head, improving accuracy; the mg_conv module replaces conventional convolution layers in the neck network, optimizing feature fusion without increasing computational cost; and improvements to the backbone network further enhance efficiency. These advancements enable LMD_YOLO to achieve high detection accuracy and robustness in complex conditions while maintaining computational efficiency. Experimental results show that LMD_YOLO outperforms existing models, with a 3.7% increase in mAP50 and 3.2% in mAP50-95. In terms of speed, LMD_YOLO achieves 63 frames per second, fully meeting real-time detection requirements. Additionally, its computational cost (FLOPs) is only 46.3% of YOLOv8n, demonstrating superior performance while significantly reducing resource consumption.}, archive = {J_ACCESS}, author = {Shuai He and Ye Yuan and Bingyang Yin}, doi = {10.1109/ACCESS.2025.3557938}, journal = {IEEE Access}, pages = {65510-65525}, shortjournal = {IEEE Access}, title = {LMD_YOLO: A lightweight and efficient model for pavement defects detection}, volume = {13}, year = {2025}, }
• (2025). A generative adversarial network-based extractive text summarization using transductive and reinforcement learning. ACCESS, 13, 65490-65509. (www)

  @article{ , abstract = {Text summarization is crucial in various sectors, such as engineering and healthcare, because it enhances efficiency in terms of time and costs. Current extractive text summarization methods struggle with challenges such as greedy selection, model generalization limitations, and high computational demands. To solve these problems, this research introduces a novel extractive text summarization method that uniquely integrates a Generative Adversarial Network (GAN), Transductive Long Short-Term Memory (TLSTM), and DistilBERT for sentence embedding. Our technique uses GANs, which include generator and discriminator components, with the core design based on TLSTM. TLSTM utilizes transductive learning to improve accuracy by focusing on samples geographically closer to the test data. In our model, the generator considers whether to include a sentence in the summary while the discriminator critically reviews the generated summary. This GAN model reduces greedy sentence selection, enhancing summary coherence and quality. We implement a Reinforcement Learning (RL)-based strategy to address an imbalance caused by more fake than real samples in the discriminator. This RL approach, novel in the context of GANs for summarization, views training as a sequence of interconnected decisions, treating each sample as a unique scenario. The network, acting as the decision-making agent, assigns greater rewards or penalties to the minority class to correct the imbalance. The effectiveness of our model was evaluated using the well-regarded CNN/Daily Mail dataset, achieving ROUGE-1, ROUGE-2, and ROUGE-L scores of 52.45, 26.46, and 44.85, respectively. Compared to existing methods, our results demonstrate a significant improvement in summarization quality and operational efficiency, as measured by the ROUGE metric.}, archive = {J_ACCESS}, author = {Jing Yang and Haoshen Qin and Yiping Sun and Hao Wang and Abdullah Ayub Khan and Lip Yee Por and Roohallah Alizadehsani and Paweł Pławiak}, doi = {10.1109/ACCESS.2025.3558266}, journal = {IEEE Access}, pages = {65490-65509}, shortjournal = {IEEE Access}, title = {A generative adversarial network-based extractive text summarization using transductive and reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Neural genome sequence encoding for downy mildew pathogen detection: A spatial pyramid and dual-attention approach. ACCESS, 13, 65478-65489. (www)

  @article{ , abstract = {A worldwide crop loss ranging from approximately 6 to 60% in pearl millet has been reported due to downy mildew caused by Sclerospora graminicola. This disease is transmitted through soil or seeds carrying oospores. Field screening, greenhouse experiments, and laboratory techniques are effective in preventing downy mildew in pearl millet; however, these methods are expensive, time-consuming, and require expertise in plant pathology. To address these challenges, researchers have turned to artificial intelligence, employing deep learning techniques such as long-short term memory, and recurrent neural networks, for disease identification using genomic sequences of pathogens. However, existing methods have limitations in interpreting lengthy genomic sequences, preserving the relative positioning of nucleotides, and identifying the minimum sequence length necessary to differentiate one genomic sequence from similar ones. Additionally, these models typically involve numerous training parameters and require significant computation time. Furthermore, none of the existing models specifically focus on recognizing Sclerospora graminicola. In this manuscript, we propose a Neural Genome Sequence Encoder (NeuGSE) model for early prediction of downy mildew in pearl millet using genomic sequences of the fungus Sclerospora graminicola. We also demonstrate the impact of genomic sequence length, positional encoding, and convolutional block attention modules on the performance of the proposed model. Furthermore, we apply two classifiers to prevent halt in training the model due to local minima. Thus, the model achieves the highest precision of 98.04% and an F1 score of 98% on genomic sequence of length 10,000. Moreover, the average F1 score of the proposed model is 19.53% and 13.81% higher than long-short term memory and bidirectional long-short term memory models, respectively, on the same dataset. Additionally, the model can recognize Sclerospora graminicola even in a genomic sequence of length 500. Thus, it may prove valuable in early screening for downy mildew in pearl millet, even before sowing seeds, potentially mitigating crop losses.}, archive = {J_ACCESS}, author = {Geeta Rani and Ankit Misra and Nidhi Kundu and Vijaypal Singh Dhaka and Pankaj Lather}, doi = {10.1109/ACCESS.2025.3558394}, journal = {IEEE Access}, pages = {65478-65489}, shortjournal = {IEEE Access}, title = {Neural genome sequence encoding for downy mildew pathogen detection: A spatial pyramid and dual-attention approach}, volume = {13}, year = {2025}, }
• (2025). StarCD-net: A remote sensing change detection method combining StarNet and differential operators. ACCESS, 13, 65466-65477. (www)

  @article{ , abstract = {In the past decades, remote sensing image change detection techniques have rapidly developed and achieved remarkable success in several fields. Although deep learning models have demonstrated good performance in change detection tasks, they still face challenges when dealing with some complex scenes, such as possible insufficient feature extraction and pixel loss at the target edges. To overcome these problems, we propose a new model, StarCD-Net, that combines StarNet and differential operators, which uses StarNet for feature extraction and aggregates the extracted features by Multi-Layer Feature Aggregation Module (MLFA) to obtain rich information. To address the problem of loss of edge details, we design the Detailed Feature Enhancement (DFE) Module to enhance the feature edges. In addition, the Hierarchical Feature Fusion Module (HFF) fuses the current layer with deeper features to obtain the global semantic information of the change object. StarCD-Net was tested on three challenging remote sensing image datasets, and its F1 and IOU reached 91.48% and 84.3% in LEVIR-CD, respectively, and its F1 was 0.94% and 0.59% higher than the second place in WHU-CD and SYSU-CD, respectively, which is significantly better than other change detection techniques and shows quite an outstanding performance.}, archive = {J_ACCESS}, author = {Caijia Zeng and Xiaorong Xue and Chuanlu Li and Xingbiao Xu and Siyue Zhao and Yifan Xu}, doi = {10.1109/ACCESS.2025.3559992}, journal = {IEEE Access}, pages = {65466-65477}, shortjournal = {IEEE Access}, title = {StarCD-net: A remote sensing change detection method combining StarNet and differential operators}, volume = {13}, year = {2025}, }
• (2025). Total link service rate estimation with packet metadata. ACCESS, 13, 65455-65465. (www)

  @article{ , abstract = {Guaranteeing end-to-end network latency bounds is required in many applications. A necessary condition for ensuring end-to-end latency bounds is to keep the total service rate for flows on each link lower than the capacity of the link. An admission control with setup messages before data transmission is necessary to check whether the total service rate remains within the capacity in each link through which the flow passes. Current practices of admission control require every node to maintain flow state information including the service rates of individual flows, which is not practical in large-scale networks with high link speeds and a massive number of flows. Instead, determining the total service rate of a link without maintaining any flow state information is highly desired. This study proposes a method to accurately infer the total service rate of the link using only packet metadata so that admission control can be performed without setup messages or state maintenance for each flow at the core node. By specifying the exact time of inference, the service rate can be inferred at a desired time. The effectiveness of the proposed method is proven through simulations. The simulation was conducted on a simple topology with four types of flows, each differing in service rate and service period.}, archive = {J_ACCESS}, author = {Juhyeok Kwon and Jinoo Joung and Jeong-Dong Ryoo and Taesik Cheung}, doi = {10.1109/ACCESS.2025.3559009}, journal = {IEEE Access}, pages = {65455-65465}, shortjournal = {IEEE Access}, title = {Total link service rate estimation with packet metadata}, volume = {13}, year = {2025}, }
• (2025). Identification of arbitrary length shellcode for the intel x64 architecture as a NOP sled. ACCESS, 13, 65438-65454. (www)

  @article{ , abstract = {A NOP (no-operation) sled is used as part of binary exploitation code to provide flexibility for exploitation accuracy and evade signatures before and after the exploitation has occurred and to transfer execution to the malicious code. The NOP sled requires that the code be executable and effectively “do nothing.” More specifically, “do nothing” means that the execution context is not disrupted to a point where the payload fails to execute. We enforce a zero-difference policy during validation for the components of the execution context we are analyzing. This paper uses the Ghidra reverse engineering tool to disassemble, emulate, and analyze a sequence of bytes to determine if they are an “effective NOP.” An effective NOP leaves the execution state unchanged after an arbitrary number of instructions. The execution state consists of a collection of registers and their values and a list of memory locations used. The proposed algorithm in this paper uses Ghidra to emulate instructions for NOP sleds and return a boolean true or false value based on the difference between the original and final execution context. The results from this paper are successful for the different types of constructed samples, polymorphic NOP sleds, and real-world data used to validate the artifact. We create an algorithm to calculate the differences between execution contexts, create an artifact to automatically process byte sequences to search for NOP sleds that satisfy our zero-difference policy, identify third-party NOP generators that did not produce NOP byte sequences that met this research’s standard and published the source code to an open-source repository.}, archive = {J_ACCESS}, author = {Austin Norby and Bhaskar P. Rimal and Bramwell Brizendine}, doi = {10.1109/ACCESS.2025.3560209}, journal = {IEEE Access}, pages = {65438-65454}, shortjournal = {IEEE Access}, title = {Identification of arbitrary length shellcode for the intel x64 architecture as a NOP sled}, volume = {13}, year = {2025}, }
• (2025). Comparing posture classification: A human lying posture pressure-map dataset. ACCESS, 13, 65420-65437. (www)

  @article{ , abstract = {We discuss methods and algorithms for classifying the posture of a patient in their bed. The actual classification tasks are performed with a measurement chain including an in-house designed pressure mattress, a matrix of $30 \times 11$ sensing spots, a data concentrator, and a cloud-based service. Utilizing a survey of open-source datasets that facilitate such classifying operations, we designed a relevant experiment. A Human Lying Posture Pressure-Map dataset (HLPPDat) formed during the research is publicly available. Involving 20 subjects in 64 defined postures, the classification output was separated into four basic groups included prone posture. The data enabled us to analyze multiple classification methods developed with the state-of-the-art concepts of Machine Learning (ML), sparse representation, and artificial intelligence represented by Transfer Learning (TL). The analysis included both data measured and data experimentally corrupted in a manner that would most probably occur due to a partial measurement error. Regarding the techniques and options tested, feature extraction via the Histogram of Oriented Gradient (HOG) and the K-Nearest Neighbors (KNN) tools appeared to be the most beneficial, yielding an accuracy of over 99.5% in the leave-one-subject-out crossvalidation. The research confirmed that an accurate classification is feasible at a matrix sensor resolution markedly lower than the limits regularly presented in the literature. The system allows monitoring how long a bedridden person has remained in the same posture, and thus it has a potential to help prevent decubitus in both hospitals and the home care.}, archive = {J_ACCESS}, author = {Michal Husák and Ondrej Mihálik and Jakub Arm and Michaela Mesárošová and Václav Kaczmarczyk and Zdeněk Bradáč}, doi = {10.1109/ACCESS.2025.3559764}, journal = {IEEE Access}, pages = {65420-65437}, shortjournal = {IEEE Access}, title = {Comparing posture classification: A human lying posture pressure-map dataset}, volume = {13}, year = {2025}, }
• (2025). Reduced-order decomposition and coordination approach for markov-based stochastic unit commitment with distributed wind farms. ACCESS, 13, 65403-65419. (www)

  @article{ , abstract = {States in the US increasingly integrate renewable energies to facilitate the transition to carbon neutrality. However, there are still many operational challenges to their efficient integration. This paper presents an innovative approach to solve the day-ahead Markov-based stochastic unit commitment (MSUC) problem with distributed wind generation. The MSUC utilizes a Markovian uncertainty set to represent the uncertainties in wind and other distributed intermittent renewable energies. While the Markovian uncertainty set intricately captures the stochasticity via states and state transitions, the set grows exponentially under the multi-dimensional uncertainties of distributed intermittent resources, thus rendering the MSUC intractable for systems with five or more such resources. In order to reduce the MSUC’s complexity, the problem is decomposed into approximate area problems (AAP), where each AAP utilizes a reduced, area-centric uncertainty set created using principal component analysis. Furthermore, the AAPs consider the binary commitment variables of only the local units, thus rendering much reduced complexity than the original MSUC. The AAPs are iteratively solved, and their solutions are coordinated in a novel Surrogate Absolute-Value Lagrangian Relaxation-based framework. Numerical testing is performed on test cases based on the IEEE 118-bus system with up to ten distributed wind farms, and solutions are validated via Monte Carlo simulations with typical and extreme wind generation scenarios. The simulation results demonstrate the ability of our method to obtain robust and cost-effective solutions to the MSUC in a constrained time frame.}, archive = {J_ACCESS}, author = {Niranjan Raghunathan and Zongjie Wang and Mikhail A. Bragin and Peter B. Luh and Bing Yan and Tianqiao Zhao and Meng Yue}, doi = {10.1109/ACCESS.2025.3553410}, journal = {IEEE Access}, pages = {65403-65419}, shortjournal = {IEEE Access}, title = {Reduced-order decomposition and coordination approach for markov-based stochastic unit commitment with distributed wind farms}, volume = {13}, year = {2025}, }
• (2025). Microstrip-based anti-aliasing filters for beyond-50 GHz-bandwidth mix-filter-sample analog-to-digital-converters. ACCESS, 13, 65389-65402. (www)

  @article{ , abstract = {High performance anti-aliasing filters (AAF) are critical components of advanced frequency interleaved (FI) analog-to-digital converter (ADC) structures, which are capable of overcoming the sample-clock jitter limit for ultra-high speed power efficient ADCs exceeding 100GS/s as is necessary in future radio frequency systems. Employing printed circuit board (PCB) microstrip filters is a promising approach as they offer a highly precise low-cost solution. In face of the specific requirements of the FI-ADC on the AAF amplitude response, the stepped impedance approach is shown to be much more suitable than other common approaches for wide stop band filters. The solution to neutralizing the spurious pass bands by placing them in the samplers’ mute frequency regions is introduced. Due to the high cutoff frequencies, typically beyond 8GHz, parasitics and lateral resonances become significant however. Hence, a novel compensation method is presented that integrates well with the required amplitude response, by reinterpreting the longitudinal low-Z-sections as individual lateral microstrips. To prove the proposed method, the design, simulation, and physical implementation of such AAFs are carried out in the context of a state-of-the-art beyond-20GS/s 7bit sub-ADC core and a baseband processing bandwidth of 7GHz. The measurement results confirm the behavior predicted by the analytical models and EM simulations, and therefore the suitability of the method for designing AAFs for FI-ADCs.}, archive = {J_ACCESS}, author = {Anton Lorenz and Christian D. Matthus and Frank Ellinger}, doi = {10.1109/ACCESS.2025.3560462}, journal = {IEEE Access}, pages = {65389-65402}, shortjournal = {IEEE Access}, title = {Microstrip-based anti-aliasing filters for beyond-50 GHz-bandwidth mix-filter-sample analog-to-digital-converters}, volume = {13}, year = {2025}, }
• (2025). B-RAN-based control method of user-equipment accommodation for multi-radio environment. ACCESS, 13, 65376-65388. (www)

  @article{ , abstract = {Wireless traffic is expected to increase mainly due to the demand for high-quality services. One approach to solving this problem is temporarily sharing private base stations (BSs) installed by individuals or companies. A blockchain is considered a key technology for achieving secure sharing of private BSs. In the blockchain-based radio access network (B-RAN), user equipment (UE) and BS owners can make communication contracts using blockchains. The B-RAN enables UEs to temporarily and securely share private BSs autonomously in a multi-radio environment without needing a centralized control station. However, since the selection of an access BS and making a communication contract are autonomously decentralized, the number of UE accommodations in each BS may be biased, and the overall BS-utilization efficiency and fairness of provided downlink throughput may decrease. We propose an autonomous decentralized method for improving the fairness of downlink throughput and overall BS-utilization efficiency by controlling the BS offer price using blockchain ledger information. Simulation results indicate that when determining the access BS for each UE without taking into account the authentication delays, the proposed method improves the median downlink throughput by about 2.2 times, fairness index of downlink throughput by about 7.6 times, and overall BS-utilization efficiency by about 1.2 times that of a conventional method in a multi-radio environment. The proposed method also improves the median downlink throughput by about 2.6 times, fairness index of downlink throughput by about 4.9 times, and overall BS-utilization efficiency by about 1.07 times that of a conventional method in a single-radio environment. This paper also evaluates the case in which authentication delay considerations exist and multiple UEs must be accommodated simultaneously. The evaluation results indicate that the proposed method can also improve downlink throughput regardless of the number of UEs accommodated simultaneously.}, archive = {J_ACCESS}, author = {Takeru Fukushima and Motoharu Sasaki and Toshiro Nakahira and Daisuke Murayama and Takatsune Moriyama and Yasushi Takatori}, doi = {10.1109/ACCESS.2025.3560024}, journal = {IEEE Access}, pages = {65376-65388}, shortjournal = {IEEE Access}, title = {B-RAN-based control method of user-equipment accommodation for multi-radio environment}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection in IoT networks using dynamic graph modeling and graph-based neural networks. ACCESS, 13, 65356-65375. (www)

  @article{ , abstract = {The rapid expansion of Internet of Things (IoT) networks has significantly increased security vulnerabilities, exposing critical infrastructures to sophisticated cyberattacks. Traditional Intrusion Detection Systems, based mainly on signature matching and predefined rules, present limitations in identifying emerging threats and distributed attacks due to their inability to analyze complex interactions within IoT networks. To address this problem, this study proposes a graph-based intrusion detection model using Graph Neural Networks (GNNs), leveraging a dynamic representation of IoT network traffic. In this model, devices are represented as nodes and communications as weighted edges, integrating features such as communication frequency, transmitted data volume, and protocol type. The proposed method was evaluated using a customized dataset from a simulated IoT network to reflect real-world attack scenarios, including Denial of Service, Spoofing, and Man-in-the-Middle. Experimental results demonstrate that our GNN-based model significantly outperforms traditional machine learning methods, achieving an F1-Score of 0.95 and an AUC-ROC of 0.98, compared to values between 0.84 and 0.91 for Support Vector Machines and Random Forests. Furthermore, the system reduces the false positive rate by 40% compared to signature-based IDS, improving its applicability in operational environments. The model also proved scalable, maintaining an inference time of 2.5 ms per sample on graphs of up to 10,000 nodes, making it viable for real-time deployment. These findings confirm that graph-based anomaly detection is a promising approach for securing large-scale IoT infrastructures, providing increased precision, adaptability, and robustness against emerging cyber threats.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Jaime Govea and Alexandra Maldonado Navarro and Pablo Palacios Játiva}, doi = {10.1109/ACCESS.2025.3559325}, journal = {IEEE Access}, pages = {65356-65375}, shortjournal = {IEEE Access}, title = {Intrusion detection in IoT networks using dynamic graph modeling and graph-based neural networks}, volume = {13}, year = {2025}, }
• (2025). Inverter-based MicroLED pixel circuit using LTPO TFTs achieving submicrosecond pulse-width modulation with low power consumption through dynamic SWEEP generation. ACCESS, 13, 65348-65355. (www)

  @article{ , abstract = {In this paper, we propose a pixel circuit for micro light-emitting diode displays based on low-temperature poly-Si oxide thin-film transistors. The proposed pixel circuit employs a dynamic SWEEP generation (DSG) scheme with an inverter, which steepens the slope of the internally generated SWEEP signal as the gray level decreases. Simulation results demonstrate that the proposed circuit achieves pulse-width modulation (PWM) down to a submicrosecond level, approximately $0.6~\mu $ s, with a falling time of $0.05~\mu $ s. This nearly ideal PWM operation enables the accurate expression of the lowest gray level down to 3 G. The proposed circuit also exhibited enhanced current uniformity and reduced inverter power consumption in low-gray levels. The average current error rate was less than 0.5% at 3 G, and the average inverter power in the mid- to low-gray levels was significantly reduced. This operational advancement was attributed to the adoption of a DSG scheme in the pixel circuit.}, archive = {J_ACCESS}, author = {Kyeong-Soo Kang and Ji-Hwan Park and Chanjin Park and Ji-Ho Lee and Soo-Yeon Lee}, doi = {10.1109/ACCESS.2025.3559546}, journal = {IEEE Access}, pages = {65348-65355}, shortjournal = {IEEE Access}, title = {Inverter-based MicroLED pixel circuit using LTPO TFTs achieving submicrosecond pulse-width modulation with low power consumption through dynamic SWEEP generation}, volume = {13}, year = {2025}, }
• (2025). Evaluation of generative AI models in python code generation: A comparative study. ACCESS, 13, 65334-65347. (www)

  @article{ , abstract = {This study evaluates leading generative AI models for Python code generation. Evaluation criteria include syntax accuracy, response time, completeness, reliability, and cost. The models tested comprise OpenAI’s GPT series (GPT-4 Turbo, GPT-4o, GPT-4o Mini, GPT-3.5 Turbo), Google’s Gemini (1.0 Pro, 1.5 Flash, 1.5 Pro), Meta’s LLaMA (3.0 8B, 3.1 8B), and Anthropic’s Claude models (3.5 Sonnet, 3 Opus, 3 Sonnet, 3 Haiku). Ten coding tasks of varying complexity were tested across three iterations per model to measure performance and consistency. Claude models, especially Claude 3.5 Sonnet, achieved the highest accuracy and reliability. They outperformed all other models in both simple and complex tasks. Gemini models showed limitations in handling complex code. Cost-effective options like Claude 3 Haiku and Gemini 1.5 Flash were budget-friendly and maintained good accuracy on simpler problems. Unlike earlier single-metric studies, this work introduces a multi-dimensional evaluation framework that considers accuracy, reliability, cost, and exception handling. Future work will explore other programming languages and include metrics such as code optimization and security robustness.}, archive = {J_ACCESS}, author = {Dominik Palla and Antonin Slaby}, doi = {10.1109/ACCESS.2025.3560244}, journal = {IEEE Access}, pages = {65334-65347}, shortjournal = {IEEE Access}, title = {Evaluation of generative AI models in python code generation: A comparative study}, volume = {13}, year = {2025}, }
• (2025). An ensemble formal approach to improve energy efficiency and data aggregation in smart agriculture. ACCESS, 13, 65316-65333. (www)

  @article{ , abstract = {The word Internet of Things (IoT) is designed for diverse sensing devices that are considered to capture real-world data and initiate corresponding actions. Sensor nodes consumed their built-in battery capacity allowing them to perform several tasks and act together with each other. Optimizing energy and extending the lifetime of wireless sensors are highly concerned with data transmission in a network. The use of energy at the cluster level to prolong the network’s life, and the sensors’ battery needs a mechanism during the transmission of data in a wireless network. There is also multiple research techniques presented for modelling IoT-based smart systems, but energy savings with efficiency and to validate smart agriculture systems have not been earlier adopted and focused. Our primary purpose is to save, enhance, and optimize the network life of wireless networks in smart agriculture by using an ensemble formal approach with IoT. Our research is based on three phases: firstly, we proposed an algorithm for saving energy consumption and data aggregation in a smart agriculture case to use the NS2 Simulator for experimental results. Secondly, we also developed a model using an activity diagram and transformed it into the formal language of TLA+ (Temporal Logic of Action). Lastly, the correctness properties of a model are verified by using the TLC (Temporal Logic Checker) with the model-checking capability of the TLA+ toolbox. The result of our proposed technique has been evaluated and shows an efficient performance in terms of energy consumption, delay, and network life.}, archive = {J_ACCESS}, author = {Shahbaz Ahmad and Nadeem Akhtar and Adeel Ahmed and Saman Iftikhar and Shaikhan Bawazeer and Saima Abdullah}, doi = {10.1109/ACCESS.2025.3558551}, journal = {IEEE Access}, pages = {65316-65333}, shortjournal = {IEEE Access}, title = {An ensemble formal approach to improve energy efficiency and data aggregation in smart agriculture}, volume = {13}, year = {2025}, }
• (2025). A mechatronic shape-shifting reflector system with true independent reflection magnitude and phase control for dynamic beamforming. ACCESS, 13, 65301-65315. (www)

  @article{ , abstract = {A novel shape-shifting mechatronic reflector system is proposed and demonstrated experimentally with a variety of real-time beam-forming examples. The reflector system consists of a 1D array of panels of sub-wavelength split ring resonators (SRR) loaded with PIN diodes for reflection magnitude control and a stepper motor array that de-embeds each panel to achieve column-by-column phase control, thus offering excellent decoupling between magnitude and phase. A complete system prototype is built including a printed circuit board (PCB) based resonator array forming the reflector front end, a stepper motor array assembly, a custom-built chassis using 3D-printed parts, and a fully programmable analog and digital electronic back-end for PIN diode biasing and motor control. The real-time beam-forming feature of the proposed reflector system is further experimentally demonstrated using illustrative examples of single beam steering with gain control and the generation of asymmetric dual beam radiation patterns where the two beams are independently controlled - a capability unlocked through achieving true decoupling between reflection magnitudes and phases.}, archive = {J_ACCESS}, author = {Keigan Macdonell and Debidas Kundu and Shulabh Gupta}, doi = {10.1109/ACCESS.2025.3555197}, journal = {IEEE Access}, pages = {65301-65315}, shortjournal = {IEEE Access}, title = {A mechatronic shape-shifting reflector system with true independent reflection magnitude and phase control for dynamic beamforming}, volume = {13}, year = {2025}, }
• (2025). ST-D3QN: Advancing UAV path planning with an enhanced deep reinforcement learning framework in ultra-low altitudes. ACCESS, 13, 65285-65300. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) have increasingly become indispensable instruments for surveillance, reconnaissance, and various missions due to their advanced capabilities. In intricate and dynamic operational contexts, especially during ultra-low altitude missions, inadequate communication and coordination impede UAVs’ capacity to avoid other UAVs and moving objects. In this instance, attaining accurate path planning for UAVs constitutes a significant technological problem that requires immediate attention. This paper introduces an innovative algorithm, ST-D3QN, which leverages deep reinforcement learning (DRL) to optimize UAV trajectories based on optimizing the Dueling Double Deep Q-Network (D3QN) and A* (A-star) algorithm. Our approach refines the optimization of sparse rewards by toggling between the two methods, thereby maximizing the long-term benefits for UAVs. The greedy strategy has been refined to prevent the algorithm from becoming ensnared in local optima. Our proposed method is benchmarked against state-of-the-art path planning algorithms through meticulously designed simulation tests. The experimental results indicate that the ST-D3QN algorithm achieves the fastest convergence rate during training and excels in optimizing sparse rewards. It also significantly reduces the risk of collisions and enhances the success rates of UAV path planning, establishing itself as an efficient and effective solution for determining the shortest path for UAVs in complex environments.}, archive = {J_ACCESS}, author = {Anping Yang and Jing Huan and Qi Wang and Hualong Yu and Shang Gao}, doi = {10.1109/ACCESS.2025.3559129}, journal = {IEEE Access}, pages = {65285-65300}, shortjournal = {IEEE Access}, title = {ST-D3QN: Advancing UAV path planning with an enhanced deep reinforcement learning framework in ultra-low altitudes}, volume = {13}, year = {2025}, }
• (2025). Dynamic data updates and weight optimization for predicting vulnerability exploitability. ACCESS, 13, 65266-65284. (www)

  @article{ , abstract = {With the purpose of managing efficiency in a large number of published vulnerabilities, the time-intensive process requires significant effort and efficient vulnerability prioritization procedures. In order to enhance the cybersecurity defense posture, it becomes imperative to ensure alignment with risk tolerance and business needs, making vulnerability management critical for reducing cybersecurity risks, avoiding system breaches, and optimizing organizational resources. However, traditional methods often struggle with accuracy due to static datasets and inconsistent scoring methodologies, leading to challenges in prioritizing vulnerabilities and exposing systems to significant risks. This study aims to propose a novel approach for predicting the exploitability of vulnerabilities using dynamic, continuously updated datasets and advanced learning models. A novel exploitability scoring equation is introduced and rigorously assessed through four methods: the Analytic Hierarchy Process, regression analysis, and two supervised learning models. The methodology incorporates machine learning and deep learning models to compute and predict exploitability scores, following the initial data processing and scoring calculation steps. The research framework integrates data ingestion, cleaning, and correlation processes to ensure data accuracy. It employs a dynamic dataset that is continuously updated with enriched data from diverse sources such as the National Vulnerability Database, ExploitDB, MITRE ATT&CK, and Vulners. Experimental results demonstrate a significant improvement in model training, with an accuracy of 84%. Furthermore, the predictive model achieved 82.9% accuracy in classifying vulnerabilities as exploitable or not, highlighting the importance of dynamic datasets and the comprehensive framework used to enhance vulnerability.}, archive = {J_ACCESS}, author = {K. Bennouk and D. Mahouachi and N. Ait Aali and Y. El Bouzekri El Idrissi and B. Sebai and A. Z. Faroukhi}, doi = {10.1109/ACCESS.2025.3558990}, journal = {IEEE Access}, pages = {65266-65284}, shortjournal = {IEEE Access}, title = {Dynamic data updates and weight optimization for predicting vulnerability exploitability}, volume = {13}, year = {2025}, }
• (2025). Deriving usability evaluation criteria for threat modeling tools. ACCESS, 13, 65246-65265. (www)

  @article{ , abstract = {As the domestic and international landscape rapidly changes, the importance of implementing security measures in response to the growing threats that businesses face has increased. In this context, the need for Security by Design (SbD), integrating security into the earlier phases of software development lifecycle, is becoming more obvious, with threat modeling recognized as a fundamental element of SbD. In particular, as part of the Shift Left strategy—which focuses on saving costs and time by detecting and resolving security threats early—personnel with limited security expertise, such as software developers, are required to engage in threat modeling. Although various automated threat modeling tools have been released, their lack of usability for users with limited security expertise poses challenges in effectively conducting threat modeling. To address this, we analyzed the usability of threat modeling tools based on criteria derived from the GQM (Goal-Question-Metric) approach. An expert survey was conducted to derive the importance of each criterion and utilize it as a weighting factor. We performed usability evaluations of five threat modeling tools (MS TMT, OWASP TD, PyTM, IriusRisk, SPARTA), and concluded that IriusRisk is the most usable one among them. This study proposes criteria of usability for software to assist personnel with limited security expertise in effectively performing threat modeling, thereby fostering a supportive environment.}, archive = {J_ACCESS}, author = {Inno Hwang and Hyunsuk Cho and Seungjoo Kim}, doi = {10.1109/ACCESS.2025.3559917}, journal = {IEEE Access}, pages = {65246-65265}, shortjournal = {IEEE Access}, title = {Deriving usability evaluation criteria for threat modeling tools}, volume = {13}, year = {2025}, }
• (2025). Vision transformers versus convolutional neural networks: Comparing robustness by exploiting varying local features. ACCESS, 13, 65232-65245. (www)

  @article{ , abstract = {Robustness of deep learning models is a crucial point that should be evaluated in computer vision tasks. Deep learning models must be able to produce the same results under different conditions such as brightness, image rotation, etc. Deep learning models must have high level robustness to be reliable to address real world problems. Unreliable systems may be harmful for critical systems such as security, healthcare, and military. In this study, it is aimed to evaluate image rotation robustness of Convolutional Neural Networks (CNNs) and Transformer-based deep learning models: MobileNetV2, Residual Network 18 (ResNet18), Visual Geometry Group 16 (VGG16), EfficientNetB1, Vision Transformer (ViT), Data-Efficient Image Transformers (DeiT), and Pooling-based Vision Transformer (PiT). Canadian Institute for Advanced Research 100 classes (CIFAR100), Canadian Institute for Advanced Research 10 classes (CIFAR10) and California Institute of Technology 256 classes (Caltech256) datasets were used to evaluate the performance of the models. All deep learning models were trained with original train datasets. Image embeddings of train, test datasets, and their rotated forms were extracted. A Support Vector Classifier (SVC) was trained with embeddings of original dataset and concatenation of original and rotated form of train dataset. Performances on original test dataset and their rotated forms were evaluated. 10, 50, and 90 degrees rotated form of the datasets were used for rotated test dataset. F1-score metrics were used compare model performances. In conclusion, Transformer-based classification algorithms are more robust to image rotation than the CNN-based models.}, archive = {J_ACCESS}, author = {Muhammed Cihad Arslanoglu and Abdulkadir Albayrak and Huseyin Acar}, doi = {10.1109/ACCESS.2025.3559794}, journal = {IEEE Access}, pages = {65232-65245}, shortjournal = {IEEE Access}, title = {Vision transformers versus convolutional neural networks: Comparing robustness by exploiting varying local features}, volume = {13}, year = {2025}, }
• (2025). Adaptive policy-oriented cybersecurity: A decentralized framework using message passing algorithms for dynamic threat mitigation. ACCESS, 13, 65216-65231. (www)

  @article{ , abstract = {In the face of increasingly sophisticated and dynamic cyber threats, policy-based cybersecurity deployment has emerged as a critical strategy for securing modern, complex network infrastructures. This approach enables organizations to enforce adaptable, scalable and context-aware security policies that can dynamically respond to evolving threats and operational demands. However, the growing complexity of distributed systems and the interdependencies between diverse security components present significant challenges in ensuring consistent and efficient policy enforcement. Static and centralized security models are no longer sufficient to address these challenges, underscoring the need for intelligent, distributed solutions that can optimize policy deployment in real-time. This paper introduces a novel framework based on Message Passing algorithms that enables decentralized, adapted and optimized policy deployment across complex cybersecurity infrastructures. More specifically, we extend the traditional Min-Sum algorithm by incorporating dynamic trust weights that influence policy enforcement decisions, allowing more reliable nodes to have a greater impact while mitigating the risks posed by untrusted entities. The proposed framework is designed to handle large-scale network environments with minimal computational overhead, relying on localized message exchanges instead of centralized computations. We benchmark our framework against state-of-the-art approaches, demonstrating that the proposed framework achieves lower total enforcement cost while significantly improving convergence speed, particularly in heterogeneous trust environments. Finally, we provide an in-depth evaluation of how trust heterogeneity influences security policy enforcement. Extensive simulations were conducted to evaluate the framework’s effectiveness in optimizing policy deployment for varying network topologies, demonstrating significant improvements in threat detection accuracy, policy consistency and resource efficiency across diverse and dynamic network environments.}, archive = {J_ACCESS}, author = {Spyridon Evangelatos and Eleni Veroni and Mariza Konidi and Sophia Karagiorgou and Georgia Dede and Anargyros Baklezos and Christos Nikolopoulos and Panagiotis Sarigiannidis and Sotirios K. Goudos}, doi = {10.1109/ACCESS.2025.3559428}, journal = {IEEE Access}, pages = {65216-65231}, shortjournal = {IEEE Access}, title = {Adaptive policy-oriented cybersecurity: A decentralized framework using message passing algorithms for dynamic threat mitigation}, volume = {13}, year = {2025}, }
• (2025). A novel multiband low mutual coupling quad-element MIMO antenna for advanced communication systems. ACCESS, 13, 65198-65215. (www)

  @article{ , abstract = {This study presents a compact multi-band fork-shaped multiple input multiple output (MIMO) antenna design that integrates Carrel’s log-periodic concept with the planar log-periodic array (PLPA) mechanism to improve C, Ku, K, Ka, and Q band performance. The suggested MIMO antenna full size is ${}\,54{\times }44 {\times }1.58\,{}$ mm ${}\ 3$ . The study empirically shows improved isolation and high gain of wideband MIMO antenna, addressing the requirements for modern wireless communication. The suggested self-multi-band coupling counteraction, characterized by a straightforward antenna design and extensive self-decoupling capabilities, is expected to offer considerable application value for compact and wideband MIMO systems. The observed data supports the assertion that the proposed MIMO antenna is designed to operate across a variety of frequency bands, including the C-band (4.4-5.21 GHz), Ku-band (14.30-17.64 GHz), K-band (20.10-28.10 GHz), Ka-band (28.95-30.76 GHz) and (32.38-36.42 GHz), and a segment of the Q-band (38.34-39.55 GHz). The proposed MIMO antenna’s wideband performance is achieved by applying the Carrel log-periodic theory, which prescribes the geometric scaling of radiating elements. Moreover, it was found that a microstrip feed line with a diameter of ${}\,50{\Omega }$ was noteworthy in conjunction with the carrel concept. The microstrip feed line is connected to two orthogonal branches. Decoupling structures eliminate the necessity for the MIMO antenna to achieve its maximum gain of 7.21 to 9.53 dBi and exceptional element isolation of 21.54 dB. MIMO performance measurements, encompassing envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL), demonstrate favorable characteristics and field correlation performance over the operational spectrum band. The design suggested suits Ku/K-band and multi-band MIMO applications across diverse wireless systems in communication.}, archive = {J_ACCESS}, author = {Ijaz Khan and Chaoyun Song and Habib Ullah and Xiaozhi Qi and Kuang Zhang and Mian Muhammad Kamal and Yizhi Shao and Tao Gong and Salahuddin Khan and Patrizia Livreri and Mohammad Alibakhshikenari}, doi = {10.1109/ACCESS.2025.3558518}, journal = {IEEE Access}, pages = {65198-65215}, shortjournal = {IEEE Access}, title = {A novel multiband low mutual coupling quad-element MIMO antenna for advanced communication systems}, volume = {13}, year = {2025}, }
• (2025). Aerial vehicle detection and DBSCAN-based interchange region identification for urban traffic analysis. ACCESS, 13, 65183-65197. (www)

  @article{ , abstract = {As urban traffic becomes increasingly complex, this study examines the dynamics at the Puente del Guambra Interchange in Quito, Ecuador, employing a novel methodology that integrates YOLO for vehicle detection, Voronoi analysis for spatial segmentation, and DBSCAN for clustering. Using drone-captured video footage, the study effectively identifies and distinguishes the intrinsic characteristics of the interchange’s upper and lower passes. A custom tracking solution is developed to accurately follow vehicles across multiple frames, addressing challenges such as missed detections and high-speed anomalies caused by off-center bounding boxes. The system consolidates multiple IDs for the same vehicle and calculates velocities, applying thresholds to correct false high-speed instances. A vehicle traffic inventory of the interchange is conducted. The findings reveal significant variations in vehicle counts and velocities across key urban arteries, particularly Av. 10 de Agosto and Av. Patria. This integrated approach not only enhances the accuracy of traffic assessments but also provides valuable insights for urban planners and traffic managers. The results underscore the potential of utilizing advanced analytical techniques in traffic monitoring and management to improve urban mobility.}, archive = {J_ACCESS}, author = {Ana Medina and Arthur Garon and Daniel Díaz-Bedoya and Mario González-Rodríguez}, doi = {10.1109/ACCESS.2025.3558717}, journal = {IEEE Access}, pages = {65183-65197}, shortjournal = {IEEE Access}, title = {Aerial vehicle detection and DBSCAN-based interchange region identification for urban traffic analysis}, volume = {13}, year = {2025}, }
• (2025). Aspect-based sentiment analysis: A comprehensive review and open research challenges. ACCESS, 13, 65138-65182. (www)

  @article{ , abstract = {The social web provides a facility for common people to share their views, comments, feedback, and experiences on various social media platforms. Due to these platforms now communication has become easier and it has provided us with opportunities to use social media channels for various businesses. The survival of an e-commerce business highly relies on customers’ opinions or feedback extensively articulated on internet-based social media platforms or social networking sites. Eventually, analysis of these public opinions from these platforms to identify and demonstrate the cumulative meaningful information is the prime objective of Sentiment Analysis (SA). Summarization of this informative knowledge is advantageous for companies, organizations, and industrialist analysts to improve the quality of their products or services. In this scenario, Aspect-Based Sentiment Analysis (ABSA) has proven to be a powerful companion for companies, organizations, and producers to specify the consumers’ attitudes and opinions towards products and brands’ impressive features. Various efforts have contributed to aspect extraction and sentiment classification over the last few decades. In this review study, we first focus on two diverse research tasks, aspect extraction, and aspect sentiment analysis and then we present a comprehensive review of existing studies in various classifications such as lexicon-based, graph data, topic models, machine learning, and deep learning. This diverse analysis provides pros and cons for various research approaches and comparative analysis. We also discuss various sources and details of datasets, which are used in this research study. We also present the research gaps including open research challenges as a guide for future researchers in the form of future research. Moreover, we also present the bibliometric analysis of the aspect-based sentiment analysis.}, archive = {J_ACCESS}, author = {Waqas Ahmad and Hikmat Ullah Khan and Fawaz Khaled Alarfaj and Mohammed Alreshoodi}, doi = {10.1109/ACCESS.2025.3555744}, journal = {IEEE Access}, pages = {65138-65182}, shortjournal = {IEEE Access}, title = {Aspect-based sentiment analysis: A comprehensive review and open research challenges}, volume = {13}, year = {2025}, }
• (2025). Sensing as a service reinvented: A unified and sustainable sensing framework for smart x development. ACCESS, 13, 65121-65137. (www)

  @article{ , abstract = {This paper introduces a Unified and Sustainable Sensing as a Service (USSenaaS) framework that integrates Internet of Things (IoT) technologies and Sensing as a Service (SenaaS) to enhance the efficiency, scalability, and sustainability of Smart-X applications. Motivated by the increasing demand for scalable, interoperable, and resource-efficient sensing solutions to support sustainable and data-driven decision-making across various domains, the proposed model addresses critical challenges in dependability, impacts scalability, interoperability, data security, and resource optimization by seamlessly combining sensor networks, connectivity, data management, and energy-efficient systems. To demonstrate its practical applicability, the framework is implemented in an agricultural monitoring system deployed in a sugarcane plantation in Subang City, Indonesia. This implementation highlights the system’s capability to enable real-time environmental monitoring, precision agriculture, and optimized crop management, thereby improving productivity and sustainability in agricultural practices. A comprehensive performance evaluation validates the system’s dependability, with key metrics demonstrating high levels of availability (0.9866), reliability (0.9989), integrity (0.9947), safety (0.9091), and maintainability (0.9310). These results underscore the system’s robustness, efficiency, and adaptability to real-world conditions. By aligning with global sustainability goals, this research contributes to the development of intelligent, resilient, and environmentally responsible Smart-X ecosystems. The framework’s scalability and versatility also pave the way for broader applications in smart cities, energy management, and industrial automation, offering a transformative approach to addressing the growing demands for sustainable and data-driven solutions in diverse domains.}, archive = {J_ACCESS}, author = {Mindit Eriyadi and Suhono Harso Supangkat and Fadhil Hidayat}, doi = {10.1109/ACCESS.2025.3558736}, journal = {IEEE Access}, pages = {65121-65137}, shortjournal = {IEEE Access}, title = {Sensing as a service reinvented: A unified and sustainable sensing framework for smart x development}, volume = {13}, year = {2025}, }
• (2025). Experimental low-cost magnetic scanning for non-destructive testing with hall sensors: Determining material’s thickness. ACCESS, 13, 65105-65120. (www)

  @article{ , abstract = {Non-destructive testing (NDT) is a highly regarded area on the medical, space and defense sectors but, also on heavy equipment susceptible to corrosion and hidden structural defects. One of the mature NDT methods for steel structures is magnetic flux leakage (MFL) detection. The current technique implementations require expensive equipment, specialist knowledge and custom data analysis tools whereas, the method uses large strong magnets on the same plane with the sensor while rolling on the inspected area, exerting a high pressure due to its magnets’ attraction and heavy body system, making it suitable only for a reduced number of investigation scenarios. This work experimentally investigates a complementary setup to classical MFL approach, useful for detection of a thin steel thickness variations from 0.7 mm up to 3.5 mm, while using a light low cost Hall sensor array setup built with accessible off-the-shelf components. Instead of the horseshoe magnet moved simultaneously with the detectors, this work uses small strong magnets tiled together and static as referred to the sensor array’s scanning movement. In this way, analyzing the sensor behavior at different spatial points in the complex magnetic field resulted from the low cost setup, this study delivers high valuable insights for MFL research.}, archive = {J_ACCESS}, author = {Vlad Marsic and Petar Igic and Vartika Pandey and Wenzhi Zhou and Soroush Faramehr}, doi = {10.1109/ACCESS.2025.3560025}, journal = {IEEE Access}, pages = {65105-65120}, shortjournal = {IEEE Access}, title = {Experimental low-cost magnetic scanning for non-destructive testing with hall sensors: Determining material’s thickness}, volume = {13}, year = {2025}, }
• (2025). Comprehensive energy efficiency analysis in buildings using drone thermal imagery, real-time indoor monitoring, and deep learning techniques. ACCESS, 13, 65094-65104. (www)

  @article{ , abstract = {This paper presents a comprehensive approach to building energy efficiency analysis by integrating outdoor drone thermal imagery with real-time indoor heat envelope monitoring using deep learning techniques. The study builds upon our previous work that employed a YOLOv4 model to identify heat envelopes from drone-captured thermal images. To enhance the scope and robustness of the analysis, we introduce a complementary indoor monitoring system that utilizes a thermal camera for capturing real-time images of interior building spaces. Due to the scarcity of labeled indoor thermal data, we employ web scraping techniques to collect a diverse dataset for model training. The trained model is then evaluated on real-time indoor thermal images, demonstrating its effectiveness in identifying heat envelopes and potential energy inefficiencies. The combination of outdoor and indoor monitoring provides a holistic view of building energy performance, enabling targeted interventions for improved efficiency. This research advances the field of energy efficiency analysis by leveraging state-of-the-art deep learning algorithms and diverse data sources, offering a scalable and data-driven approach to building energy management. The proposed methodology has significant implications for reducing energy consumption, costs, and environmental impact in both residential and commercial buildings.}, archive = {J_ACCESS}, author = {Koundinya Challa and Issa W. AlHmoud and A. K. M. Kamrul Islam and Corey A. Graves and Balakrishna Gokaraju}, doi = {10.1109/ACCESS.2025.3557804}, journal = {IEEE Access}, pages = {65094-65104}, shortjournal = {IEEE Access}, title = {Comprehensive energy efficiency analysis in buildings using drone thermal imagery, real-time indoor monitoring, and deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). The state of retinal image analysis: Deep learning advances and applications. ACCESS, 13, 65066-65093. (www)

  @article{ , abstract = {This study explores deep learning techniques that can help diagnose eye conditions, focusing on diabetic retinopathy (DR). By using cutting-edge technology in image processing and artificial intelligence, it aims to better understand and classify various eye issues like microaneurysms, hemorrhages, and others. Despite progress, challenges like poor image quality and noise persist. The paper stresses the need for improved methods to detect small blood vessels while reducing interference. It also addresses the complexity of uneven lighting in extracting blood vessel images from the retina. The main contribution of this study is its thorough examination of existing methods and its detailed investigation into using deep learning for detecting retinal blood vessels. It highlights the effectiveness of different deep learning models and their potential in accurately classifying DR grades. This research underscores the importance of grading methods in analyzing medical images, especially retinal images, to enhance the accuracy of diagnosing eye diseases.}, archive = {J_ACCESS}, author = {Toufique Ahmed Soomro and Faisal Bin Ubaid and Shehnila Zardari and Ahmed Ali Shah and Marwah M. Alansari and Shimaa A. Nagro}, doi = {10.1109/ACCESS.2025.3556765}, journal = {IEEE Access}, pages = {65066-65093}, shortjournal = {IEEE Access}, title = {The state of retinal image analysis: Deep learning advances and applications}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review: Classifying IoT botnet data features based on its lifecycle. ACCESS, 13, 65040-65065. (www)

  @article{ , abstract = {As the Internet of Things (IoT) becomes increasingly indispensable across various domains, the connectivity between humans, machines, and devices intensifies. With the surge in IoT devices deployed in numerous fields, security risks have escalated, particularly the proliferation of botnets. Due to IoT devices’ limited resources and performance, they are easily exploited by malware to form part of IoT botnets. Numerous studies have attempted to detect IoT botnets using artificial intelligence, yielding promising results. However, challenges such as poor interpretability, high false positive rates, and difficulty in adapting to new and evolving threats persist. This paper presents a systematic literature review of IoT botnet detection research conducted over the past five years, initially filtering 143 studies and ultimately reviewing 134 based on a quality assessment. It examines the IoT botnet lifecycle, which describes the formation of a botnet, and evaluates various detection methods, with a focus on the types of features extracted for detection and how they relate to different phases of the botnet lifecycle. Additionally, an IoT botnet malicious activity map is presented to address the challenges faced by current detection models. This study also highlights key limitations and future research directions, offering valuable insights to improve the performance of IoT botnet detection systems.}, archive = {J_ACCESS}, author = {Shihao Liu and Fariza Fauzi and Ven Jyn Kok}, doi = {10.1109/ACCESS.2025.3559635}, journal = {IEEE Access}, pages = {65040-65065}, shortjournal = {IEEE Access}, title = {A systematic literature review: Classifying IoT botnet data features based on its lifecycle}, volume = {13}, year = {2025}, }
• (2025). Series-cascaded AC microgrids: An inclusive review of architectures and control methodologies. ACCESS, 13, 65016-65039. (www)

  @article{ , abstract = {The modern utility grid is transitioning from a centralized to a distributed structure, facilitated by integrating distributed generation sources (DGs) and loads. Microgrids (MGs) have emerged as a key solution, with different topologies such as parallel AC microgrids, series-cascaded AC microgrids (SC-ACMGs), and series-parallel ACMGs, each offering distinct advantages for specific applications. Parallel ACMGs provide fault tolerance and well-established protection schemes, making them widely adopted in industrial and urban power grids. Meanwhile, SC-ACMGs offer modular scalability and efficient voltage stacking, making them suitable for renewable energy sources (RESs) integration, battery energy storage, off-grid electrification, and electric vehicle charging infrastructure. Series-parallel ACMGs combine features of both but introduce additional control complexity. With increasing interest in SC-ACMGs for applications requiring direct voltage stacking and reduced power conversion stages, this paper provides an inclusive review of SC-ACMG architectures and their control methodologies. While SC-ACMGs enable cost-effective and flexible control, they face challenges such as power sharing, frequency synchronization, and voltage regulation, particularly with higher penetration of RESs. This paper explores different SC-SCMG configurations, technical challenges, and control methodologies, providing a comparative analysis of existing methodologies. Furthermore, it identifies research gaps, future trends, and technical barriers to large-scale deployment while suggesting potential solutions. Finally, it discusses emerging technologies to enhance SC-ACMG integration with smart grid features, serving as a valuable resource and roadmap for researchers working on next-generation power systems.}, archive = {J_ACCESS}, author = {Salman Ali and Santiago Bogarra Rodríguez and Muhammad Mansoor Khan and Felipe Córcoles and Yung-Cheol Byun and Josep M. Guerrero}, doi = {10.1109/ACCESS.2025.3555497}, journal = {IEEE Access}, pages = {65016-65039}, shortjournal = {IEEE Access}, title = {Series-cascaded AC microgrids: An inclusive review of architectures and control methodologies}, volume = {13}, year = {2025}, }
• (2025). Analyzing relationships between success factors of project management in global software development. ACCESS, 13, 65004-65015. (www)

  @article{ , abstract = {In current era of technological advancement, global software development (GSD) is expanding to an increasing extent. This rise is driven by the advantages of low-cost development, access to expert resources, proximity to markets and clients, as well as options for innovation and the exchange of best practices. However, the various key aspects of GSD, including geographical dispersion, work ethics, time zone differences and cultural variability make project management more complex in a GSD setting. This study harnesses the critical success factors (CSFs) approach to identify the model for successful management of GSD projects. Sixteen CSFs have been determined using literature review, expert judgement, and exhaustive interviews. The interrelationship among the identified CSFs has been modeled using interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. The proposed model has been validated using detailed interviews, conducted with twelve professionals from industry and academia. The expert participants were carefully chosen based on criteria which include working experience, roles, and participation in GSD projects. This research computes CSFs of software project management in GSD by assessing their driving and relative strengths. Furthermore, these CSFs are categorized using MICMAC analysis. The previous research studies lack in exploring the mutual influence of CSFs and their hierarchical relationships. With the application of ISM and MICMAC analysis, the proposed study offers understanding of these interdependencies, providing great insights into their driving and dependent function. These insights enhance knowledge in GSD project management by allowing practitioners to prioritize key criteria and formulate potent management strategies.}, archive = {J_ACCESS}, author = {Ansar Siddique and Noorulhasan Naveed Quadri and Naim Ahmad and Hashim Elshafie}, doi = {10.1109/ACCESS.2025.3558459}, journal = {IEEE Access}, pages = {65004-65015}, shortjournal = {IEEE Access}, title = {Analyzing relationships between success factors of project management in global software development}, volume = {13}, year = {2025}, }
• (2025). Energy consumption optimization strategy for port cranes based on pontryagin’s minimum principle and filtering strategies. ACCESS, 13, 64991-65003. (www)

  @article{ , abstract = {This paper is concerned with developing an energy management strategy for port cranes, specifically Ship-to-Shore (STS) cranes. The objective is to optimize the crane’s energy consumption and reduce its energy consumption from the grid. The objective is achieved by applying a battery-supercapacitor hybrid energy storage system (H-ESS) and a combination of the filtering and the Pontryagin’s Minimum Principle (PMP) energy management strategies (EMSs) to the port crane. The port crane model is developed using the graphical energetic macroscopic formalism (EMR). The filtering-based EMS manages the power split between the grid and the H-ESS, while the PMP-based EMS manages the power split between the battery and supercapacitor inside the H-ESS. Overall, the proposed EMS for this study has a simple analytical cost function, is computationally fast, and is easier to implement while producing optimal and effective results. The EMR-based port crane model comprises the mathematical equations describing the crane systems, the cranes’ local control system, and the MATLAB Simulink simulation model. The experimental validation of the EMR-based port crane model is done by comparing the cranes’ EMR-based MATLAB Simulink software model with data recorded from a real-world crane. The results show that the port crane energy consumption from the utility decreased by 29%, and its peak power consumption decreased by 29.6%. During the crane load handling cycle, the supercapacitor and the battery get discharged and recharged by 26% and 8%, respectively. Furthermore, the EMS enhances the performance of the H-ESS by boosting efficiency, improving reliability, and extending its lifespan, making H-ESS more dependable and cost-efficient. Due to STS cranes being the largest port cranes with about 2MW power demand and the common drivetrain for many types of port and industry cranes, the EMS developed in this study can be adopted to other types of cranes.}, archive = {J_ACCESS}, author = {Rofhiwa Takalani and Lesedi Masisi}, doi = {10.1109/ACCESS.2025.3559006}, journal = {IEEE Access}, pages = {64991-65003}, shortjournal = {IEEE Access}, title = {Energy consumption optimization strategy for port cranes based on pontryagin’s minimum principle and filtering strategies}, volume = {13}, year = {2025}, }
• (2025). Analysis and modeling of the nonlinear characteristics of the insulation medium in capacitor cores and the electric thermal coupling mechanism of the main insulation structure of high-voltage bushings. ACCESS, 13, 64976-64990. (www)

  @article{ , abstract = {The valve side bushing of ultra-high voltage converter transformers is used in multiple fields. Due to the temperature gradient distribution inside the capacitor core, classical theoretical formulas cannot quantitatively describe the influence of the nonlinear characteristics of the insulation material in the capacitor core on the distribution of electric field strength. Therefore, it is urgent to introduce the nonlinear characteristics of insulating media into the optimization design of valve side bushings, and improve their operational reliability on the basis of fully considering the thermal coupling mechanism of electrical factors. In order to solve the above problems, this article first conducts a discrete spectral analysis of the rheological values of typical valve side bushings, introduces the physical structure, constituent materials, and operating conditions of the ultra-high voltage side bushing, analyzes the influence of temperature field on the nonlinear characteristics of the bushing capacitor core, and elucidates the mechanism of space charge electric field at the interface of the converter. Secondly, an optimal isochoric design method that is more suitable for the working environment of the epoxy impregnated dry-type sleeve valve side shell of $\pm \; 800$ kV converter transformers was proposed, achieving uniform distribution of electrical performance parameters in the core of the valve side shell and improving its operational reliability. Finally, the proposed nonlinear model of electric thermal coupling was validated in practical scenarios, and a rheological value side shrinking sleeve was manufactured based on it. The results showed that the proposed nonlinear mathematical model of electric thermal coupling had little impact on the geometric dimensions and layout of sleeve capacitors, and had good theoretical and engineering application value.}, archive = {J_ACCESS}, author = {Yongsheng He and Shiling Zhang and Zongxiang Lu and Sidun Fang and Huaisheng Liu}, doi = {10.1109/ACCESS.2025.3556226}, journal = {IEEE Access}, pages = {64976-64990}, shortjournal = {IEEE Access}, title = {Analysis and modeling of the nonlinear characteristics of the insulation medium in capacitor cores and the electric thermal coupling mechanism of the main insulation structure of high-voltage bushings}, volume = {13}, year = {2025}, }
• (2025). Design, simulation, and construction of a novel adjustable wideband and multi-polarized unit cell of reflect-array antenna by one or two control bits in x and ku bands. ACCESS, 13, 64962-64975. (www)

  @article{ , abstract = {The purpose of this paper is to design and simulate a unit cell with wideband and multi-polarization for a reflect-array antenna. The bandwidth of the proposed structure is greater than 70% of X and Ku bands for satellite applications. This structure includes two printed dipoles and an asymmetric arrowhead shape for multiple electric and magnetic resonances. Moreover, for enhancing the bandwidth, a thicker substrate is used by stacking different layers. The full-wave simulation results show good polarization conversion over the frequency range of 8-17 GHz. Depending on the antenna usage, the proposed structure can provide dual or triple polarization switching using one or two control bits. Among the innovations of this structure, compared to previous designs, is the switching capability to support multiple polarizations occurring in a common bandwidth. In other words, the desired frequency range is the same for each switching states. The proposed design has a new and simple geometry. In different switching modes, this design maintains or converts the main polarization of the feed antenna to orthogonal polarization and even circular polarization. Two measurement methods are used that include using OMT (Ortho-Mode Transducer) with waveguide for narrow bandwidth and using feed antenna in anechoic chamber for wide bandwidth. These methods confirm the construction of the proposed unit cell by comparing the simulation and measurement results, which show good agreement.}, archive = {J_ACCESS}, author = {Mohammad Mansourinia and Ramezan Ali Sadeghzadeh}, doi = {10.1109/ACCESS.2025.3558909}, journal = {IEEE Access}, pages = {64962-64975}, shortjournal = {IEEE Access}, title = {Design, simulation, and construction of a novel adjustable wideband and multi-polarized unit cell of reflect-array antenna by one or two control bits in x and ku bands}, volume = {13}, year = {2025}, }
• (2025). A non-isolated extendable boost topology with continuous input and output currents. ACCESS, 13, 64948-64961. (www)

  @article{ , abstract = {This paper presents a new expendable boost DC-DC converter design with continuous input and output currents. The voltage gain and voltage-current stresses on the semiconductors are the same as those in the conventional boost converter. The proposed converter has lower conduction loss in the inductors compared to the traditional boost converter, resulting in greater efficiency across all duty cycle ranges and under identical conditions. Additionally, the maximum voltage gain in the new topology surpasses that of the conventional boost converter. The continuity of the output current eliminates the non-minimum phase behavior of the traditional topology of boost, making it faster than the conventional boost converter. A new family of converters is derived from the proposed topology. These newly suggested topologies have higher gains than the basic topology. The paper analyzes the proposed converter in both ideal and non-ideal modes, outlines the requirements for continuous conduction mode (CCM) operation, and investigates non-ideal voltage gain and efficiency sensitivity. The potential extensions of the proposed topology are discussed. Notably, all these extensions have a higher voltage gain while retaining all the advantages of the proposed primary topology. Simulation and experimental tests are carried out for the main topology. Moreover, the voltage gain tests based on experimental results are discussed for the extensions of the main topology and compared with their corresponding theoretical predictions. The results confirm the feasibility of the proposed converter.}, archive = {J_ACCESS}, author = {Hossein Gholizadeh and Alireza Poursalan and Reza Sharifi Shahrivar and Saeed Amini and Mohammadreza Salehi and Lazhar Ben-Brahim}, doi = {10.1109/ACCESS.2025.3557897}, journal = {IEEE Access}, pages = {64948-64961}, shortjournal = {IEEE Access}, title = {A non-isolated extendable boost topology with continuous input and output currents}, volume = {13}, year = {2025}, }
• (2025). A probabilistic multi-topic model learned from aesthetic quality communities for dress colors recommendation. ACCESS, 13, 64930-64947. (www)

  @article{ , abstract = {We have developed a Probabilistic Multi-Topic Model that segments large-scale online clients, such as those on e-commerce platforms or social media, into distinct aesthetic quality communities based on their shared preferences for dress colors. This method leverages a geometry-based feature selection strategy to effectively capture and model each client’s aesthetic tendencies, characterized by a diverse set of features, including those learned under weak supervision. These features are further refined through a feature selection process. The model represents each client’s color preferences as a probabilistic multi-topic distribution within a hidden feature space, offering a comprehensive understanding of their aesthetic inclinations. To uncover the underlying connections between clients with similar preferences, we construct a graph-based framework and employ a specialized algorithm to identify tightly connected subgroups, or aesthetic quality communities. These communities are defined by shared dress color preferences, such as an affinity for bold, vibrant hues or subtle, neutral tones. To enhance personalization, the model integrates a ranking mechanism that recommends dress colors and styles aligned with the identified communities. By learning from these aesthetic quality communities, the system ensures that recommendations are both relevant and deeply resonant with the client’s unique aesthetic profile. We validated the effectiveness of this approach using a dataset comprising over one million clients. Results demonstrate that our model accurately identifies and classifies distinct aesthetic quality communities, enabling internet-scale personalized dress color recommendations that are aesthetically aligned and client-centric.}, archive = {J_ACCESS}, author = {Chuanying Liu and Jinjiao Zhuang}, doi = {10.1109/ACCESS.2025.3559670}, journal = {IEEE Access}, pages = {64930-64947}, shortjournal = {IEEE Access}, title = {A probabilistic multi-topic model learned from aesthetic quality communities for dress colors recommendation}, volume = {13}, year = {2025}, }
• (2025). Designing inclusive smartwatch interfaces: Guidelines for enhancing usability and adoption among older adults. ACCESS, 13, 64901-64929. (www)

  @article{ , abstract = {Aging introduces sensory, motor, and cognitive challenges and limited familiarity with digital interfaces, often hindering older adults’ adoption of new technologies. Smartwatches, with their compact size and health monitoring features, promise to improve older adults’ quality of life. However, their small screens and complex interfaces create significant usability barriers. While guidelines for mobile and web interfaces exist, frameworks for smartwatch design still need to be explored. This study addresses this gap by proposing smartwatch-specific design guidelines for older adults. Through an analysis of user challenges, existing design principles, and smartwatch constraints, the research formulates actionable recommendations to enhance usability and user experience. The contributions include identifying key obstacles older adults face with smartwatches, evaluating the applicability of established guidelines, creating tailored design principles for small screens, and developing a design system that balances simplicity, usability, and functionality. These contributions aim to facilitate smartwatch adoption and improve the inclusivity of digital technologies for older adults.}, archive = {J_ACCESS}, author = {Rui P. Duarte and Valter N. N. Alves and Mickael Mota}, doi = {10.1109/ACCESS.2025.3559726}, journal = {IEEE Access}, pages = {64901-64929}, shortjournal = {IEEE Access}, title = {Designing inclusive smartwatch interfaces: Guidelines for enhancing usability and adoption among older adults}, volume = {13}, year = {2025}, }
• (2025). Ring-based planning approach for intelligent reflecting surface in 6G networks. ACCESS, 13, 64890-64900. (www)

  @article{ , abstract = {Intelligent reflecting surface (IRS) is regarded as a promising technique of establishing virtual line-of-sight paths for mmWave signals. Existing literature on the IRS deployment focuses on the placing location of an IRS between a macrocell and a user or some users. The orientation of the deployed IRS and the deployment of multiple IRSs and macrocells are missed but challenging and vital to an ultra-dense network for 6G. In this paper, the deployment problem of an ultra-dense network with multiple macrocells and multiple IRSs is considered and solved. On the basis of the ring-based algorithm proposed in our previous paper, a novel deployment algorithm is proposed to adjust IRS’s orientation for the better achievable rate of all served users, i.e. the Ring-based Sector algorithm. The ring-based algorithm yielded notable achievable rate but failed to serve more users with IRS’s reflection signals. By changing IRS’s orientation, the elements on IRS receive macrocell’s signal perpendicularly along with higher received signal power. On the other hand, a user-side IRS deployment method is further proposed to improve users’ achievable rate, i.e. the Ring-weight Dominating algorithm. Simulations are conducted in one square kilometer of planned network topology with 200 candidate position and 500 user equipment as an ultra-dense network scenario, where macrocell’s carrier frequency of mmWave and its bandwidth is 30 GHz and 100 MHz respectively. Compared to benchmark algorithms, our approach achieves a significant 20.5% increase in average achievable rate and a 20.2% improvement in network capacity performance.}, archive = {J_ACCESS}, author = {Fan-Hsun Tseng and Hao-Kai Hong and Chi-Yuan Chen and Hsin-Hung Cho}, doi = {10.1109/ACCESS.2025.3556877}, journal = {IEEE Access}, pages = {64890-64900}, shortjournal = {IEEE Access}, title = {Ring-based planning approach for intelligent reflecting surface in 6G networks}, volume = {13}, year = {2025}, }
• (2025). Domain-generalized emotion recognition on german text corpora. ACCESS, 13, 64867-64889. (www)

  @article{ , abstract = {Text-based emotion recognition plays a crucial role in various domains and applications due to its significance in understanding human behavior and improving communication systems. Although extensive research has been conducted for texts in English, only few studies deal with non-English languages. Especially for the German language, previous work is limited due to the focus on specific text domains and the usage of lexicon-based approaches, which lack the ability to consider contextual information. In this paper, an approach for emotion recognition on German text corpora is presented, which addresses these challenges with domain-generalized and context-sensitive methodologies. To achieve domain generalization, neural machine translation and weak supervision techniques are combined with multi-view learning, in which various data sources from different domains are utilized to improve the generalizability and to overcome the problem of lack of data. Using the BERT model as the primary architecture to capture contextual information, an overall F1-score of 65.5% and an accuracy of 68.1% are achieved while maintaining well-balanced results on the metrics over all considered domains and emotions. To ensure the legitimacy and reliability of the findings, further comprehensive evaluations and comparisons are conducted. Given the absence of an established benchmark specifically for emotion recognition on German text corpora, the used datasets, evaluations, and results can serve for benchmarking purposes.}, archive = {J_ACCESS}, author = {Oweys Momenzada and Michael Palk and Stefan Voss}, doi = {10.1109/ACCESS.2025.3559135}, journal = {IEEE Access}, pages = {64867-64889}, shortjournal = {IEEE Access}, title = {Domain-generalized emotion recognition on german text corpora}, volume = {13}, year = {2025}, }
• (2025). OMS-DNN: A surrogate model for forward design of mirror structure and optical system. ACCESS, 13, 64853-64866. (www)

  @article{ , abstract = {As demand grows for real-time diagnostics and control in mirror structure and optical system design, traditional computation-heavy methods face efficiency bottlenecks. To address this, we propose OMS-DNN, a fully data-driven deep learning framework that replaces resource-intensive finite element analysis and ray tracing, while integrating optimization algorithms for forward design. OMS-DNN models complex nonlinear relationships using deep neural networks and captures parameter topologies through a graph convolutional network (GCN). We evaluate OMS-DNN on large-scale datasets, including 20,000 structural and 100,000 Three-Mirror Anastigmat (TMA) optical design samples. It achieves mean absolute percentage errors (MAPE) of 0.98%, 4.09%, and 1.66% across three key metrics. To meet forward design requirements, we integrated the proposed architecture with an optimization algorithm. Experimental results demonstrate that our method can rapidly generate constraint-compliant parameter design solutions tailored to various needs, thereby lowering the entry barrier for designers. These findings validate the effectiveness of OMS-DNN and its potential to transform traditional design workflows.}, archive = {J_ACCESS}, author = {Dongxue Zhang and Xiaoqin Deng and Wei Sha and Jianzhao Wang and Hongwei Lou and Lei Sheng and Ye Wang and Junqing Zhu}, doi = {10.1109/ACCESS.2025.3559481}, journal = {IEEE Access}, pages = {64853-64866}, shortjournal = {IEEE Access}, title = {OMS-DNN: A surrogate model for forward design of mirror structure and optical system}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of the new flexible double weight (FDW) code with various detection techniques. ACCESS, 13, 64841-64852. (www)

  @article{ , abstract = {In this paper, Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system performance is analyzed using a Flexible Double Weight (FDW) code with various detection techniques. In theoretical analysis, various types of noise such as Phase-Induced Intensity Noise (PIIN), shot noise, thermal noise, and Multiple-Access Interference (MAI) effects were considered. System performance was characterized by the Signal-to-Noise Ratio (SNR) and bit error rate (BER). The analysis results obtained using the Spectral direct detection (SDD) technique were compared with those obtained using the AND subtraction and Modified AND subtraction detection techniques. The comparison results revealed that the FDW code with the SDD technique can support more active users and can improve system performance compared with the AND subtraction and Modified AND subtraction detection techniques. The FDW code is compared to the previous OCDMA code, known as Enhanced Double Weight (EDW) code, in terms of the number of simultaneous users it can support. The FDW code can accommodate up to 52 simultaneous users which is a significant improvement over the EDW code’s capacity of 30 users. This represents a 73.33% improvement achieved through the use of SDD techniques. In simulation analysis, the results were exemplified by the simulation experiment of the BER performance in the system with the new FDW code. The BERs of the AND subtraction and Modified AND subtraction detection techniques were $1.63 \times 10^{-05}$ and $1.69 \times 10^{-08}$ , respectively, at a distance of 60 km, whereas the SDD technique’s BER was $2.29548 \times 10^{-12}$ , which could reach up to 60 km with bit rate of 10 Gbps.}, archive = {J_ACCESS}, author = {Muhammad Amirul Anuar and Syed Mohammad Ammar and Rosdisham Bin Endut and Syed Alwee Aljunid Syed Junid and Norshamsuri Ali and Mohamad Naufal Saad and Hamza Mohammed Ridha Al-Khafaji}, doi = {10.1109/ACCESS.2025.3559712}, journal = {IEEE Access}, pages = {64841-64852}, shortjournal = {IEEE Access}, title = {Performance analysis of the new flexible double weight (FDW) code with various detection techniques}, volume = {13}, year = {2025}, }
• (2025). RAI-net: Tomato plant disease classification using residual-attention-inception network. ACCESS, 13, 64832-64840. (www)

  @article{ , abstract = {Pests and pathogens cause significant losses to tomato crop yield, resulting in billion-dollar economic impact worldwide. Early and accurate detection of tomato plant disease is important for an effective intervention and improved crop yield. In this work, a deep learning-based RAI (Residual-Attention-Inception)-Net model has been proposed for an effective feature extraction by deploying channel attention on the output obtained from fine-tuned ResNet18 model for improved feature extraction. Inception module has been integrated with the ResNet18 model augmented with channel attention module enhances the multi-scale feature analysis capability of the proposed model for tomato plant leaf disease detection task. The interpretability of the proposed network has been improved with the Gradient-weighted Class Activation Mapping (Grad-CAM) method which highlights the regions focused by the proposed RAI-Net model in making its predictions. RAI-Net achieves an accuracy of 97.88% on a test set comprising 4595 images across ten different classes of tomato disease, demonstrating its effectiveness in automated detection of tomato leaf diseases.}, archive = {J_ACCESS}, author = {Ritesh Maurya and Lucky Rajput and Satyajit Mahapatra}, doi = {10.1109/ACCESS.2025.3559804}, journal = {IEEE Access}, pages = {64832-64840}, shortjournal = {IEEE Access}, title = {RAI-net: Tomato plant disease classification using residual-attention-inception network}, volume = {13}, year = {2025}, }
• (2025). Meta-transformers: A hybrid approach for medical image segmentation with U-net and meta-learning. ACCESS, 13, 64822-64831. (www)

  @article{ , abstract = {The increasing demand for accurate and efficient medical image segmentation has driven the development of advanced Artificial Intelligence (AI) models to assist in diagnosing and planning treatments. In this paper, we propose a novel approach that combines U-Net architecture with attention mechanisms and Model-Agnostic Meta-Learning (MAML) to enhance segmentation performance, particularly in scenarios with limited annotated data. By applying this method to the AMOS dataset for multi-organ segmentation in CT and MRI scans, our model significantly improves generalization across different patients and imaging modalities. The use of attention mechanisms refines feature extraction, while class weighting effectively addresses class imbalance. The proposed model achieves a state-of-the-art Dice score of 98.09%, pixel accuracy of 98.39%, precision of 98.58% and recall of 98.27% outperforming existing methods and demonstrating the potential for rapid adaptation in clinical applications. This combination of meta-learning and deep learning offers a robust solution for addressing the challenges of medical image segmentation.}, archive = {J_ACCESS}, author = {Neha Tirpude and Tausif Diwan and Meera Dhabu}, doi = {10.1109/ACCESS.2025.3559446}, journal = {IEEE Access}, pages = {64822-64831}, shortjournal = {IEEE Access}, title = {Meta-transformers: A hybrid approach for medical image segmentation with U-net and meta-learning}, volume = {13}, year = {2025}, }
• (2025). Deep learning innovations for greenhouse climate prediction: Insights from a spanish case study. ACCESS, 13, 64810-64821. (www)

  @article{ , abstract = {Accurate prediction of a greenhouse temperature is essential for effective climate control and optimal crop production. In this study, we focus on the performance of deep learning (DL) models and the proposed Power Long Short-Term Memory (PLSTM) model introduced in our early research for predicting internal temperatures using a database from Spain. By analyzing DL architectures such as Gated Recurrent Units (GRU), Artificial Neural Networks (ANN), Long Short-Term Memory with Artificial Neural Network (LSTM-ANN), and Long Short-Term Memory with Recurrent Neural Network (LSTM-RNN), we aim to benchmark their performance against the proposed PLSTM model. Additionally, this study explores the correlation between internal temperature and other key environmental factors and evaluates how well the models generalize these relationships. The results show that the PLSTM model consistently outperforms the evaluated DL models, achieving an R2 of 0.9999 with a significantly lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) demonstrating its robustness in handling time-series forecasting for greenhouse conditions. This research underscores the potential of PLSTM as a key tool for improving precision climate control in agriculture and offers valuable insights for the development of intelligent greenhouse systems.}, archive = {J_ACCESS}, author = {Salma Ait Oussous and Dauris Madama Lail and Rachid El Bouayadi and Aouatif Amine}, doi = {10.1109/ACCESS.2025.3559232}, journal = {IEEE Access}, pages = {64810-64821}, shortjournal = {IEEE Access}, title = {Deep learning innovations for greenhouse climate prediction: Insights from a spanish case study}, volume = {13}, year = {2025}, }
• (2025). Distributed adaptive control of nonlinear multi-agent systems-based controller dynamic linearization method. ACCESS, 13, 64796-64809. (www)

  @article{ , abstract = {The leader-follower consensus tracking control and containment control problems for nonlinear nonaffine multi input multi output (MIMO) multi-agent systems without a leader or with a leader-follower are investigated. Different from the conventional controller based compact form dynamic linearization (CFDL) method for this problem, a new distributed model free adaptive control method (CFFDL-DMFAC) is proposed by using the controller based full form dynamic linearization (CFFDL) method and a new time-varying pseudo Jacobian matrix. This method does not require a mathematical model of the system during the controller design process, and only uses real time I/O data between neighboring agents to achieve the common goal of consensus tracking of each agent. At the same time, the use of consensus error dynamic linearization methods enables the algorithm design and stability analysis of containment control to be analyzed in a unified manner with leaderless and leader-follower consensus control. Finally, rigorous mathematical proofs and simulation results are used to verify the convergence and effectiveness of the proposed control scheme.}, archive = {J_ACCESS}, author = {Wenchao Wu and Lixia Fu and Han Li}, doi = {10.1109/ACCESS.2025.3559729}, journal = {IEEE Access}, pages = {64796-64809}, shortjournal = {IEEE Access}, title = {Distributed adaptive control of nonlinear multi-agent systems-based controller dynamic linearization method}, volume = {13}, year = {2025}, }
• (2025). Assessment of network security alerts based on expert experience. ACCESS, 13, 64783-64795. (www)

  @article{ , abstract = {The network architecture of the public security video private network is relatively complex, and the types of devices it carries are different from those of general information networks. It mainly consists of video surveillance IoT devices, as well as numerous streaming media servers, video scheduling servers, structured computing power, big data computing power, storage devices, and network devices. These devices have strong regional dispersion. In such a network environment, when conducting alarm analysis and assessment for network intrusions, malicious code, and abnormal logs, the collected security logs have strong characteristics of multi-source and heterogeneity. Traditional log processing methods are prone to alarm fatigue, high false positive rates, and poor adaptability to complex traffic. To improve the accuracy and efficiency of alarm analysis and assessment, this paper proposes a model that integrates expert experience and supervised learning, called FEESLM, aiming to enhance the ability to judge network security alarms. The FEESLM model consists of three major components: the expert experience modeling component, the supervised learning modeling component, and the assessment result decision component. The expert experience modeling component summarizes the expert’s assessment experience to form rules, statistics, and association models. The supervised learning modeling component uses the XGBoost algorithm to learn from the data processed by expert experience, constructing a assessment model. The assessment result decision component uses the maximum entropy model to calculate the most credible results from the outputs of expert and supervised learning modeling as the final assessment result. Experimental results show that the FEESLM model outperforms single expert experience modeling and supervised learning modeling in terms of accuracy, recall rate, and F1-score, effectively improving the accuracy of network security alarm assessment.}, archive = {J_ACCESS}, author = {Tao Wang and Ruowei Pang and Xu Wang and Weihai Shen and Bingqian Huang and Bin Xie and Tian Tan and Yang Yang}, doi = {10.1109/ACCESS.2025.3559709}, journal = {IEEE Access}, pages = {64783-64795}, shortjournal = {IEEE Access}, title = {Assessment of network security alerts based on expert experience}, volume = {13}, year = {2025}, }
• (2025). A hybrid graph-based risk assessment and attack path detection model for IoT systems. ACCESS, 13, 64770-64782. (www)

  @article{ , abstract = {The rapid expansion of Internet of Things (IoT) technology has led to a proliferation of smart devices and interconnected systems. Critical factors such as production limitations, cost constraints, and insufficient technical capabilities have rendered these devices more vulnerable and at higher risk compared to traditional devices. The extensive data processing and communication requirements due to the increasing number of devices and connections have also introduced significant security challenges. Consequently, security risk assessment methodologies have gained relevance for a wide range of IoT systems. However, identifying vulnerable nodes within the system, individually assessing devices, and performing compact and efficient analyses of the entire topology remain underexplored areas. To address these gaps, this paper presents a quantitative assessment approach based on risk and vulnerability metrics. By integrating computational metrics from existing literature, we conduct a host-based attack probability assessment and extend this analysis to devices, communication paths, and the overall graph within a security context. Beyond establishing a mathematical framework, we refine the IOTA approach, typically used for attack path detection and graph generation, into a hybrid risk-based model to enhance search-domain efficiency. Our proposed approach targets high-vulnerability components within the system through risk-weighted backtracking, thereby facilitating more efficient attack path detection and filtering. The developed method is evaluated in a border security case scenario, with comparisons made in terms of algorithmic and asymptotic complexity. Simulation results demonstrate that our hybrid approach for detecting potential attack paths achieves an average runtime improvement of 16.9% compared to existing methods.}, archive = {J_ACCESS}, author = {Ferhat Arat and Sedat Akleylek and Zaliha Yüce Tok}, doi = {10.1109/ACCESS.2025.3558985}, journal = {IEEE Access}, pages = {64770-64782}, shortjournal = {IEEE Access}, title = {A hybrid graph-based risk assessment and attack path detection model for IoT systems}, volume = {13}, year = {2025}, }
• (2025). CAMP: Counterexamples, abstraction, MDPs, and policy refinement for enhancing safety, stability, and rewards in reinforcement learning. ACCESS, 13, 64750-64769. (www)

  @article{ , abstract = {Reinforcement learning (RL) has demonstrated exceptional performance across various real-world applications such as autonomous driving, robotic control, and finance. However, challenges surrounding safety and stability continue to limit its practical deployment. Specifically, effectively blocking transitions to failure states, maintaining consistent policy action selection, and optimizing rewards simultaneously remain key challenges in RL. This study addresses these issues by proposing a novel approach that combines counterexample-driven cluster refinement with a Markov Decision Process-Proximal Policy Optimization (MDP-PPO) hybrid policy. The proposed method enhances computational efficiency in complex state spaces through state abstraction and strengthens policy safety by employing the PRISM model checker to quantitatively analyze transitions to failure states. Furthermore, the approach is designed to optimize RL’s primary objective: reward maximization. Experimental evaluations in the CartPole, LunarLander, and Highway environments demonstrate significant improvements over the PPO-only baseline across all metrics. For example, in the LunarLander environment, the failure probability was reduced to 0.001352 (representing a reduction of over 90% relative to the baseline), while in the Highway environment, the standard deviation of rewards decreased by 85.27%, indicating a substantial gain in stability. In the CartPole environment, the average reward increased by 78.34%, reflecting the method’s ability to achieve superior performance in reward optimization. These quantitative results, combined with qualitative insights into enhanced policy safety and reliability, establish a novel framework for balancing safety, stability, and reward optimization in RL policy design. The integration of state abstraction and model checking plays a critical role in ensuring the reliability of policies, even in complex environments. Future work will focus on extending the approach to real-time applications and assessing its generalizability across diverse real-world scenarios.}, archive = {J_ACCESS}, author = {Ryeonggu Kwon and Gihwon Kwon}, doi = {10.1109/ACCESS.2025.3556125}, journal = {IEEE Access}, pages = {64750-64769}, shortjournal = {IEEE Access}, title = {CAMP: Counterexamples, abstraction, MDPs, and policy refinement for enhancing safety, stability, and rewards in reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Adoption of home service robots: Exploring household differences in perceived usefulness. ACCESS, 13, 64737-64749. (www)

  @article{ , abstract = {As service robots increasingly penetrate households, researchers have identified the factors that influence users’ intention to adopt them. Although sociodemographic aspects such as age and gender have been widely studied, gaps remain in considering household composition, particularly between single- and multi-person households. This study addresses this gap through a case study conducted in South Korea that examines how the perceived usefulness of home service robots for various tasks—such as basic and complex household chores, personal care, leisure and companionship, and healthcare—affects the intention to adopt them. An online survey of 400 participants was analyzed using partial least-squares structural equation modeling. The results indicate that the perceived usefulness of robots in performing basic household chores ( $\beta =0.34$ , p-value <0.01) and leisure and companionship tasks ( $\beta =0.32$ , p-value <0.01) significantly affects intention to adopt across household types, whereas their perceived usefulness in handling complex household chores ( $\beta = -0.33$ , p-value <0.01) and healthcare tasks ( $\beta =0.24$ , p-value <0.05) varies depending on household composition. This study contributes to the literature by addressing an understudied area and emphasizing the unique needs of the home context. By using household type as a moderating variable, it provides a novel framework for understanding how consumer acceptance of home service robots is influenced by evolving household dynamics. These findings lay the groundwork for future research on the development and implementation of home service robots in the context of changing household compositions.}, archive = {J_ACCESS}, author = {Minjun Kim}, doi = {10.1109/ACCESS.2025.3559060}, journal = {IEEE Access}, pages = {64737-64749}, shortjournal = {IEEE Access}, title = {Adoption of home service robots: Exploring household differences in perceived usefulness}, volume = {13}, year = {2025}, }
• (2025). Convolution smooth: A post-training quantization method for convolutional neural networks. ACCESS, 13, 64727-64736. (www)

  @article{ , abstract = {Convolutional neural network (CNN) quantization is an efficient model compression technique primarily used for accelerating inference and optimizing resources. However, existing methods often apply different quantization strategies to activations and weights, without considering their interplay. To address this issue, this paper proposes a new method called Convolution Smooth, which aims to balance the quantization difficulty of activations and weights. This method effectively mitigates the significant accuracy drop typically observed in activation quantization with traditional methods. By appropriately scaling the tensors, the method shifts quantization complexity from activations to weights, ensuring a reasonable distribution of quantization difficulty between activations and weights. Experimental results show that the proposed method is applicable to a wide range of network models and can be seamlessly integrated into multiple post-training quantization (PTQ) methods. Through tensor scaling and the fusion of factors, the network achieves significant accuracy improvements in most cases, particularly when there is a significant discrepancy between the activation values and weight quantization bit-widths. This study provides a new theoretical foundation and technical support for CNN quantization compression.}, archive = {J_ACCESS}, author = {Yongyuan Chen and Zhendao Wang}, doi = {10.1109/ACCESS.2025.3558956}, journal = {IEEE Access}, pages = {64727-64736}, shortjournal = {IEEE Access}, title = {Convolution smooth: A post-training quantization method for convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). An ultra-low-power fully-static contention-free single-phase-clock flip-flop with low area. ACCESS, 13, 64719-64726. (www)

  @article{ , abstract = {This paper introduces a Fully-Static, Contention-Free Single-Phase-Clock Flip-Flop for IoT devices. The design objectives are accomplished through three main components. Firstly, the elimination of invalid toggling of internal nodes significantly reduces power consumption. Secondly, a Fully-Static and a Contention-Free structure are achieved by eliminating floating nodes and contention paths, respectively, enhancing robustness. Lastly, area is reduced through logic merging and topology compression. This Flip-Flop is implemented in a 28nm process. Post-simulation results indicate that, at 0.9V, 1GHz and activity rate of 10%/20%, dynamic power of this design is only $0.44~\mu $ W/ $0.88~\mu $ W, resulting in 86.95%/76.44% power reduction compared to traditional MSFF. This design focuses on static power. Static power of this design achieves 0.07nW/0.77nW at 10MHz, 0%, 0.4V/0.9V, 98.91%/97.59% less than MSFF. 10K Monte Carlo simulation confirms that this design maintains 100% functional integrity within the voltage range of 0.4-0.9V. Layout results indicate this design achieves an extremely low area.}, archive = {J_ACCESS}, author = {Yinglei Dong and Jia Yuan and Shushan Qiao and Ye Zhao}, doi = {10.1109/ACCESS.2025.3553240}, journal = {IEEE Access}, pages = {64719-64726}, shortjournal = {IEEE Access}, title = {An ultra-low-power fully-static contention-free single-phase-clock flip-flop with low area}, volume = {13}, year = {2025}, }
• (2025). Self and target locating with cooperation of heterogeneous unmanned vehicles in the denial environment. ACCESS, 13, 64699-64718. (www)

  @article{ , abstract = {The growing reliance on unmanned vehicles, such as drones and autonomous vehicles, has revolutionized both military and civilian applications, particularly in challenging environments where traditional reconnaissance methods fail. These systems are essential for tasks such as self-localization and target localization, particularly in denial environments where GPS and communication networks are compromised. However, effective cooperation of heterogeneous unmanned systems under these conditions remains a significant challenge. Without external positioning systems, ensuring reliable navigation and mission execution requires innovative approaches to localization, mapping, and path planning. This paper proposes an integrated system that enhances the cooperation between ground robots and aerial drones for self-localization and target localization in GPS-denied environments. The system incorporates advanced techniques such as the Extended Kalman Filter (EKF) for localization, G-mapping for environment mapping, Dijkstra’s algorithm for global path planning, and the Dynamic Window Approach (DWA) for real-time local path replanning. Through simulations involving Husky robots and Hector quadcopters, the system demonstrates its ability to maintain accurate navigation and obstacle avoidance in communication-limited environments. The results demonstrate that the proposed system can successfully enable autonomous vehicles to cooperate and perform tasks reliably under challenging conditions. Future work will focus on expanding the framework to support a wider range of unmanned vehicles, improving control algorithms, and testing the system in even more complex denial environments to ensure continued robustness and adaptability.}, archive = {J_ACCESS}, author = {Muhammad Amjad and Md Sahin Ali and Shouwen Yao and Md Faishal Rahaman and Changsong Zheng and Raza Muhammad Kazim and Bilal Zouaoui}, doi = {10.1109/ACCESS.2025.3558873}, journal = {IEEE Access}, pages = {64699-64718}, shortjournal = {IEEE Access}, title = {Self and target locating with cooperation of heterogeneous unmanned vehicles in the denial environment}, volume = {13}, year = {2025}, }
• (2025). GIRH-unet: Improved residual tobacco segmentation algorithm based on GhostNetV3-unet. ACCESS, 13, 64687-64698. (www)

  @article{ , abstract = {Cigarette production presents an industrial environment with significant light and shadow interference. The residual tobacco contains fine particles that exhibit random distribution and varying edge morphologies. These factors contribute to the reduced accuracy and robustness of visual detection technologies based on segmentation algorithms within tobacco intelligent production systems, highlighting the need for a targeted segmentation model. In this study, we propose an enhanced lightweight segmentation model, GhostNetV3-Unet, designed explicitly for residual tobacco segmentation. Our approach utilizes an improved GhostNetV3 to bolster feature extraction capabilities. Additionally, we optimize the residual modules and activation functions to enhance the extraction of detailed features and the deep transmission of information. Furthermore, we enhance the semantic representation of fine residue features amidst complex backgrounds by incorporating a hybrid self-attention mechanism that integrates spatial information and channels attention within the decoding phase. We employ Focal Loss combined with Intersection over Union (IoU) loss to address the class imbalance challenge between positive and negative pixel classes, thereby improving the segmentation performance in regions with morphological and size variations. Moreover, we designed an image enhancement method based on Poisson fusion to mitigate the difficulties associated with sample labeling. This method employs a combination of randomly cropped, combined sample images and Poisson-fused background images to enhance edge contour clarity while effectively expanding the tobacco dataset. The experimental results demonstrate that the constructed artificial data samples can effectively substitute the original data samples, significantly reducing the labeling costs associated with sample preparation. On the tobacco dataset, the interaction rate of the proposed GhostNetV3-Unet model reaches 94.63%, with a checking rate of 97.91%, while maintaining a model parameter count of 17.88 million. Additionally, on the bird and light-and-shadow datasets, the model achieves interaction rates of 92.03% and 92.86% and checking rates of 95.22% and 96.22%, respectively. Practical application in the production environment confirms that the proposed method successfully segments fine residual tobacco in complex backgrounds and meets the detection requirements relevant to the production site.}, archive = {J_ACCESS}, author = {Jianhua Ye and Yunda Zhang and Pan Li and Ze Guo}, doi = {10.1109/ACCESS.2025.3559588}, journal = {IEEE Access}, pages = {64687-64698}, shortjournal = {IEEE Access}, title = {GIRH-unet: Improved residual tobacco segmentation algorithm based on GhostNetV3-unet}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid item-based similarity method to mitigate the effects of data sparsity in multi-criteria collaborative filtering. ACCESS, 13, 64660-64686. (www)

  @article{ , abstract = {Data sparsity presents a significant challenge for Recommendation Systems, particularly in neighborhood-based approaches that rely on co-ratings to compute similarity. As co-ratings decrease, these methods often struggle to generate accurate recommendations. Addressing the persistent challenge of data sparsity, a novel similarity method, hybrid item-based, for multi-criteria collaborative filtering is proposed in this study. The proposed approach introduces a local similarity measure that combines a modified Jaccard similarity with a distance-based method, enhancing item-to-item similarity calculations. Additionally, it incorporates $\alpha $ -divergence as a global similarity measure, leveraging the probability density distribution of ratings to compute similarities even when user overlap is minimal or absent. This dual approach allows our method to produce reliable similarity measures in sparse data environments. The results of experimental evaluations conducted on multiple datasets illustrate that the proposed approach demonstrates a superior performance than existing traditional and hybrid approaches in terms of accuracy and achieves notable improvements in recall and F1-measure. Furthermore, the proposed method exhibits high diversity and competitive novelty, striking an optimal balance that enhances user engagement. These results underline the proposed method’s potential as a comprehensive solution, offering improved accuracy, diversity, and relevance, particularly in sparse data contexts.}, archive = {J_ACCESS}, author = {Burcu Demirelli Okkalioglu}, doi = {10.1109/ACCESS.2025.3559398}, journal = {IEEE Access}, pages = {64660-64686}, shortjournal = {IEEE Access}, title = {A novel hybrid item-based similarity method to mitigate the effects of data sparsity in multi-criteria collaborative filtering}, volume = {13}, year = {2025}, }
• (2025). Outage probability of mixed THz-UWOC transmission systems with hardware impairments. ACCESS, 13, 64653-64659. (www)

  @article{ , abstract = {In this paper, we propose a mixed dual-hop communication system consisting of terahertz/underwater wireless optical communication (THz-UWOC) links with hardware impairments. In particular, considering the heterodyne detection, we assume that the THz link obeys $\alpha $ - $\mu $ fading with pointing errors (PEs), while the UWOC channel experiences a mixture exponential-generalized gamma (EGG) fading with pointing errors. Assuming a fixed gain amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols, the closed-form expressions of the outage probability, in terms of the bivariate Fox’s H-function is derived. Finally, the Monte Carlo simulation verifies the analytical results, which demonstrates the effect of turbulence conditions, different fading, and pointing errors on the system performance.}, archive = {J_ACCESS}, author = {Shenghai Chen and Lili Wan and Xianwu Mi}, doi = {10.1109/ACCESS.2025.3555412}, journal = {IEEE Access}, pages = {64653-64659}, shortjournal = {IEEE Access}, title = {Outage probability of mixed THz-UWOC transmission systems with hardware impairments}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient aggressive duty-cycling of V-band power amplifiers. ACCESS, 13, 64638-64652. (www)

  @article{ , abstract = {In addition to implementing the core functionality, a design goal for modern circuits is reduced energy consumption. When options for improving active state efficiency are eventually exhausted, the next step is to aggressively minimize the duration a circuit is active to reduce overall energy consumption. Consequently, this article presents a top-down study on efficient duty-cycling of power amplifiers (power amplifiers), spanning system-level considerations down to experimental measurements of a proposed V-band PA. A thorough analysis of the duty-cycling characteristics in a wireless transmitter reveals the significance of fast and efficient switching, particularly if minimal latency is desired and if energy consumption should scale linearly with required data rate. To be able to assess power amplifiers in that regard, the related key characteristics as well as transient RF power measurements are discussed. To enable aggressive duty-cycling at circuit level, the switching process is studied for integrated millimeter-wave (mmWave) class-E power amplifiers. Despite intrinsic switched mode operation at RF, transitions from sleep to active state are not instantaneous, but two phases are revealed: operating point switching (OPS) and large-signal rise in the load network. A kick-starter circuit for speeding up this transition is investigated. To prove these concepts, a two-stage 51 GHz 14 dBm SiGe PA capable of fast and efficient operational state switching is designed and fabricated. Its duty-cycling performance is measured in detail for both single-tone and modulated signals showing turn-on times in the nanosecond range. The results demonstrate highly efficient duty-cycling of data transmission at a cycle duration of just $\mathrm {1~\mu \text {s} }$ for minimum latency. This article aims to allow system and circuit designers to better evaluate requirements for and performance of switchable mmWave power amplifiers in context of duty-cycled communications.}, archive = {J_ACCESS}, author = {Maximilian Gottfried Becker and Marco Gunia and Diego Mendez and Frank Ellinger}, doi = {10.1109/ACCESS.2025.3558546}, journal = {IEEE Access}, pages = {64638-64652}, shortjournal = {IEEE Access}, title = {Energy-efficient aggressive duty-cycling of V-band power amplifiers}, volume = {13}, year = {2025}, }
• (2025). A robust finite-time fault-tolerant tracking control for quadrotor attitude system with stochastic actuator faults and input delays. ACCESS, 13, 64627-64637. (www)

  @article{ , abstract = {Quadrotor unmanned aerial vehicles have become very popular for doing numerous tasks; however, due to their small size and weight, they are very sensitive to parameter uncertainties and external disturbances. In this paper, a new robust fault-tolerant approach is introduced to track the attitude trajectory of a quadrotor in finite time that suffers from stochastic actuator faults, system parameter uncertainties, and unknown external disturbances. Furthermore, the effects of time-varying input delay are considered in the control input. Based on the Lyapunov-Krasovskii functional approach, two theorems are given to derive some delay-dependent conditions regarding the design of a controller that guarantees the finite-time contractive stability of the attitude tracking error system with the prescribed ${\mathrm{H}}_{\infty } $ performance index. This means that the tracking error states converge to a defined bound within a fixed time interval which is smaller than the initial state bound, and the acceptable ${\mathrm{H}}_{\infty } $ disturbance attenuation level is achieved. Finally, the proposed method is applied to a quadrotor facing the external disturbance and stochastic actuator fault. The simulation results verify the effectiveness and superiority of the proposed control scheme as compared to the asymptotic stability approach and the finite-time boundedness stability model.}, archive = {J_ACCESS}, author = {V. T. Mai and Farzaneh Jani and Khalid A. Alattas and Ebrahim Ghaderpour and Ardashir Mohammadzadeh}, doi = {10.1109/ACCESS.2025.3559180}, journal = {IEEE Access}, pages = {64627-64637}, shortjournal = {IEEE Access}, title = {A robust finite-time fault-tolerant tracking control for quadrotor attitude system with stochastic actuator faults and input delays}, volume = {13}, year = {2025}, }
• (2025). Multi-layered distributed control for collective movement and coverage of robot swarms in unknown structured environments. ACCESS, 13, 64610-64626. (www)

  @article{ , abstract = {In this study, we propose a novel multi-layered distributed control (MDC) framework for multi-robot deployment and coverage strategies in unknown structured environments. The MDC is structured with strategic function-based layers of swarm deployment, coverage, and withdrawal built on top of the underlying behavior-based motion control layer. The behavior-based motion control layer is responsible for preserving the network integrity and guaranteeing collision avoidance for mobile robots. To enable adaptability and flexibility of control strategies in various unknown structured environments, the behavior suppression mechanism is created to modulate and substitute individual behaviors for appropriate swarm strategies including swarm movement in aggregation, one-chain configuration, collective coverage with target-tracking motions. We have examined and evaluated our proposed method through both simulation and real-world experiments. The results demonstrate that the MDC achieves high performance in both task completion and coverage rate while maintaining flexibility and scalability, highlighting its potential for real-world applications.}, archive = {J_ACCESS}, author = {Pham Duy Hung and Trung Dung Ngo}, doi = {10.1109/ACCESS.2025.3559424}, journal = {IEEE Access}, pages = {64610-64626}, shortjournal = {IEEE Access}, title = {Multi-layered distributed control for collective movement and coverage of robot swarms in unknown structured environments}, volume = {13}, year = {2025}, }
• (2025). Radio spectrum sensing framework for large-scale, high-density IoT sensor networks. ACCESS, 13, 64594-64609. (www)

  @article{ , abstract = {The rapid growth of the Internet of Things (IoT) requires innovative solutions to address the challenges posed by constrained radio spectrum resources in Wireless Sensor Networks (WSNs). This paper presents an Artificial Intelligence (AI) based spectrum sensing framework designed to detect and classify various IoT communication technologies and generate advanced communication channel analytics, such as the technology in use, central frequency and the bandwidth of the radio channel, time-on-air parameter, channel usage patterns, and radio spectrum occupancy degree. In contrast to most existing other methods, the proposed framework follows an end-to-end approach that integrates dataset design, AI model training, and inference evaluation using IoT sensors controlled by a traffic orchestrator that emulates a large-scale, high-density WSN architecture. The system follows a complete design approach, automatically creating and enhancing datasets consisting of spectrogram images from both controlled tests and real-world scenarios (6,190 unique radio spectrograms). Using the YOLOv8 architecture, the AI model demonstrates high performance in detecting multiple narrow-band radio signals, including LoRa and Sigfox technologies, achieving precision and recall scores of 0.983 and 0.947, respectively. In live deployment tests, the framework processes spectrograms with an average latency of 30.12 ms. Through an integrated detection tracking mechanism, it efficiently generates advanced radio channel analytics, enabling better use of spectral resources and interference mitigation in large-scale IoT networks, making it a valuable tool for optimizing IoT deployments. In conclusion, the proposed framework offers significant advances in spectrum sensing methodologies, paving the way for improved scalability and reliability in the evolving IoT landscape.}, archive = {J_ACCESS}, author = {Partemie-Marian Mutescu and Alexandru Lavric and Adrian-Ioan Petrariu and Alin-Mihai Cailean and Valentin Popa}, doi = {10.1109/ACCESS.2025.3558746}, journal = {IEEE Access}, pages = {64594-64609}, shortjournal = {IEEE Access}, title = {Radio spectrum sensing framework for large-scale, high-density IoT sensor networks}, volume = {13}, year = {2025}, }
• (2025). Semantic fusion-oriented bi-typed multi-relational heterogeneous graph neural network. ACCESS, 13, 64584-64593. (www)

  @article{ , abstract = {Compared to traditional heterogeneous graph (HG) data, Bi-typed Multi-relational Heterogeneous Graph (BMHG) not only have various edge relationships between different types of nodes but also connections among the same type of nodes, which increases modeling complexity. However, existing Heterogeneous Graph Neural Networks (HGNN) primarily focus on HGs with single relationships and are ineffective for BMHGs. These methods often rely on meta-paths to capture node features, which leads to the loss of intermediate node information. To address this, this paper proposes a Semantic Fusion-based Bi-typed Multi-relational Heterogeneous Graph model (SF-BMHG), aimed at solving the challenges of multiple edge relationships and complex semantic information processing among nodes in BMHGs. SF-BMHG first generates new features with intermediate nodes through a feature fusion strategy to avoid the loss of intermediate node information. It then captures semantic information in BMHGs using the new node features and meta-relation paths, integrating features from different semantic paths with a semantic-level attention mechanism. Additionally, it employs relational convolutions to capture relationship features within different types and fuses different relationship features through a relational-level attention mechanism. Ultimately, SF-BMHG combines intra-type relationship features and inter-type semantic features to produce rich feature embeddings. Experimental results on real-world constructed datasets demonstrate that, compared to existing baseline methods, SF-BMHG achieves superior performance across various tasks.}, archive = {J_ACCESS}, author = {Yifan Sun and Jing Yan and Lilei Lu and Hongbo Zhang and Yanhong Shang}, doi = {10.1109/ACCESS.2025.3559720}, journal = {IEEE Access}, pages = {64584-64593}, shortjournal = {IEEE Access}, title = {Semantic fusion-oriented bi-typed multi-relational heterogeneous graph neural network}, volume = {13}, year = {2025}, }
• (2025). Multimodal sentiment Analysis—A comprehensive survey from a fusion methods perspective. ACCESS, 13, 64556-64583. (www)

  @article{ , abstract = {Multimodal sentiment analysis (MSA) has emerged as a crucial domain within artificial intelligence, aimed at accurately interpreting human emotions by integrating information from multiple data modalities such as text, audio, and visual signals. Traditional sentiment analysis (SA) predominantly focused on unimodal data, restricting the capability to interpret subtle emotional expressions. Leveraging the progress in machine learning and deep learning, multimodal fusion plays a crucial role in harnessing the complementary features of diverse modalities, thereby improving SA performance. This paper provides a comprehensive survey from a perspective of fusion methods, encompassing early fusion, late fusion, hybrid fusion, tensor fusion, contextual fusion, hierarchical fusion, graph fusion and attention mechanism-based approaches. We also present detailed statistics on popular datasets. We categorize three types of feature extraction techniques critical for MSA. Furthermore, this work emphasizes key challenges, including figurative language recognition, dataset biases, dataset preparation, intermodal imbalance, noise removal, modalities missing, synchronization issues, increased dimensionality and selection of fusion method. Through an in-depth exploration of these issues, we highlight future directions for advancing fusion methodologies and propose strategies for developing more robust, adaptive, and generalizable models. This paper provides valuable insights serving as a valuable resource for practitioners and researchers, guiding the development of innovative solutions for MSA across diverse application domains.}, archive = {J_ACCESS}, author = {Kai Zhao and Mingsheng Zheng and Qingguan Li and Jianing Liu}, doi = {10.1109/ACCESS.2025.3554665}, journal = {IEEE Access}, pages = {64556-64583}, shortjournal = {IEEE Access}, title = {Multimodal sentiment Analysis—A comprehensive survey from a fusion methods perspective}, volume = {13}, year = {2025}, }
• (2025). Wideband oscillation mechanism analysis in VSC-HVDC systems with virtual synchronous machine in master-slave mode. ACCESS, 13, 64546-64555. (www)

  @article{ , abstract = {Virtual synchronous machine (VSM) has been applied in voltage source converter high voltage direct current (VSC-HVDC) system. This paper proposes a stability evaluation model for ‘rate of change of frequency-transient frequency excursion’ as well as ‘rate of change of voltage-transient voltage excursion’ for clarifying the physical essence of instability of the DC voltage and AC frequency in the VSC-HVDC system with virtual synchronous machines. The proposed model framework can help understanding the mechanism and physical significance of wideband oscillation in VSC-HVDC systems. In addition, a comprehensive analysis is developed for wideband oscillations and loss of synchronization in VSC-HVDC system. It is found that the grid stiffness impacts the wideband oscillation and loss of synchronization stability simultaneously, where it is also demonstrated by simulations. Finally, the theoretical analysis is validated by simulations.}, archive = {J_ACCESS}, author = {Chang Li and Bingbing Shao and Xun Mao and Wangchao Dong and Kai Lyu}, doi = {10.1109/ACCESS.2025.3553687}, journal = {IEEE Access}, pages = {64546-64555}, shortjournal = {IEEE Access}, title = {Wideband oscillation mechanism analysis in VSC-HVDC systems with virtual synchronous machine in master-slave mode}, volume = {13}, year = {2025}, }
• (2025). Assessing notification timing strategies for improved micro-learning engagement. ACCESS, 13, 64534-64545. (www)

  @article{ , abstract = {Micro-learning has been widely adopted in recent years as an approach to improve online learning, particularly on mobile applications. Micro-learning is a method of dividing learning content into smaller lessons that can be completed within a short period (e.g., from ten to 15 minutes). While micro-learning is often seen as a way to boost user engagement, strategies to optimize engagement within these applications remain underexplored. This study aims to examine the impact of notification delay times to enhance user engagement in language learning activities within micro-learning platforms. Specifically, we developed a notification system designed to encourage users to transition from social media usage to educational activities: in this case, the Duolingo application. Over a three-week period, we conducted a within-subject experiment with 10 different users, with each week dedicated to one of three notification delay times - the period of time our proposed system waits before sending a notification once a social media usage activity is recognized: immediate (0 minutes), 15 minutes, and 30 minutes. Our findings reveal that notifications sent after a delay of 15 and 30 minutes - significantly increase both the response rate and the duration of learning sessions compared to immediate notifications. This suggests that a strategic delay in sending study reminders can substantially enhance engagement and prolong the duration of learning, offering valuable insights into designing educational technologies that foster sustained learning habits.}, archive = {J_ACCESS}, author = {H. T. Nguyen Tran and Wan Jou She and Shogo Kajimura and Yu Shibuya}, doi = {10.1109/ACCESS.2025.3559521}, journal = {IEEE Access}, pages = {64534-64545}, shortjournal = {IEEE Access}, title = {Assessing notification timing strategies for improved micro-learning engagement}, volume = {13}, year = {2025}, }
• (2025). Fairness-aware graph neural networks for ICU length of stay prediction in IoT-enabled environments. ACCESS, 13, 64516-64533. (www)

  @article{ , abstract = {This study introduces a novel end-to-end framework designed to optimize patient outcomes and operational efficiency in intensive care units (ICUs) through fairness-aware and accurate length of stay (LOS) predictions within an IoT-enabled environment. Motivated by the concept of “fairness through unawareness,” our proposed framework employs a demographic feature exclusion strategy, preventing access to potentially discriminatory information, and thus enforcing an inductive bias that redirects learning toward non-discriminatory pattern dependencies. To address the loss of static information, we introduce a custom graph neural network that dynamically reconstructs patient relationships over time, adapting from static demographics to evolving inter-patient correlations via multi-modal embeddings (e.g., medications, procedures, vitals, conditions) and learned feature-driven edge formation. We evaluate our approach on MIMIC-IV across diverse time-series formats and demonstrate its superiority over top performing LOS prediction methods, showcasing notable performance improvements while maintaining comparable computational complexity. Additionally, we present a performance analysis highlighting our approach’s computational and predictive scalability across different graph sizes at inference, while underscoring its intrinsic ability to improve performance when deprived of demographic features by dynamically readjusting its expressive power, unlike all evaluated methodologies in this study. Finally, we present a comparative study against the literature’s top-performing LOS prediction approaches that highlights our method’s predictive superiority, demonstrating significant performance gains in predicting LOS exceeding 3 and 7 days, with improvements of up to 13.1% AUC and 10.3% ARP, and 14.1% AUC and 42.0% ARP, respectively. The code is available at https://github.com/icsa-hua/mimic_fairness_aware_gnn}, archive = {J_ACCESS}, author = {Angelos Christos Maroudis and Konstantina Karathanasopoulou and Charithea C. Stylianides and George Dimitrakopoulos and Andreas S. Panayides}, doi = {10.1109/ACCESS.2025.3560180}, journal = {IEEE Access}, pages = {64516-64533}, shortjournal = {IEEE Access}, title = {Fairness-aware graph neural networks for ICU length of stay prediction in IoT-enabled environments}, volume = {13}, year = {2025}, }
• (2025). Securing software defined networks: A comprehensive analysis of approaches, applications, and future strategies against DoS attacks. ACCESS, 13, 64473-64515. (www)

  @article{ , abstract = {Software Defined Networks (SDN) offer advantages over traditional networks, such as programmability, flexibility, and scalability, making them ideal for implementing and managing new networks while lowering associated expenses. In this article, we will examine and assess various approaches, looking at the benefits and limitations of each solution based on factors such as efficiency, user satisfaction, delay, and other relevant factors. In addition, we have conducted extensive analysis on SDN technology, including the most recent research and applications in areas such as 5G, Wi-Fi networks, IoT-based automated vehicle technology, satellite networks, smart grids, green and renewable energy, and AI. However, we should remember that even with all these applications, these networks are still susceptible to Denial of Service (DoS) and Distributed DoS (DDoS) attacks, which can cause serious disruption to network operations. This article also provides a thorough overview of the threat landscape for SDN and DoS attacks, highlighting the various attacks and their potential impact on network operations and sensitive data security. To mitigate the risks associated with these attacks, it is crucial to have effective solutions in place. We must constantly research and develop new strategies and approaches to counter DoS attacks in SDN as attackers continually discover new vulnerabilities in these networks. Furthermore, we highlight various detection and mitigation strategies for DoS attacks in SDN and emphasize the importance of constantly innovating and developing new approaches to secure SDN. Moreover, we delve into the future of SDN security and provide valuable insights for network administrators, security professionals, and researchers in devising effective strategies to protect SDNs from DoS attacks.}, archive = {J_ACCESS}, author = {Nemalikanti Anand and M. A. Saifulla and Raveendra Babu Ponnuru and Goutham Reddy Alavalapati and Rizwan Patan and Amir H. Gandomi}, doi = {10.1109/ACCESS.2024.3520478}, journal = {IEEE Access}, pages = {64473-64515}, shortjournal = {IEEE Access}, title = {Securing software defined networks: A comprehensive analysis of approaches, applications, and future strategies against DoS attacks}, volume = {13}, year = {2025}, }
• (2025). Next-gen internet of drones: Federated learning and digital twin synergy for energy-efficient task allocation and seamless service migration. ACCESS, 13, 64459-64472. (www)

  @article{ , abstract = {The computing-intensive tasks generated by Internet of Things devices cannot be handled alone by themselves due to limitations in battery and processing power. An appropriate approach to this problem is the Internet of Drones (IoDs) with edge computing capabilities, which can offload the created tasks from IoT devices to IoDs. To improve sustainability by maximizing energy efficiency, minimizing duplicate service migrations, and guaranteeing dynamic task offloading in UAV-supported IoD networks, this paper proposes a Federated Digital aided Internet of Drones (FD-IoD) architecture. The proposed framework guarantees the long-term viability of IoD-based edge networks by combining digital twin technology with federated deep reinforcement learning. The FD-IoD framework integrates energy harvesting algorithms and optimizes mobility-aware resource management to extend drone lifespan and reduce unnecessary computational overheads. To adjust to various IoT environments, the framework uses a dual-layer optimization approach that combines local agent learning with global decision-making via digital twin. The framework outperforms current benchmarks by up to 40% in energy efficiency, lower service migration rates, and faster task completion rates, as shown by extensive simulations. Additionally, the proposed framework guarantees decreased latency, efficient resource use, and queue stability even in heavy demand.}, archive = {J_ACCESS}, author = {Ahmad Arsalan and Tariq Umer and Rana Asif Rehman and Byung-Seo Kim}, doi = {10.1109/ACCESS.2025.3558439}, journal = {IEEE Access}, pages = {64459-64472}, shortjournal = {IEEE Access}, title = {Next-gen internet of drones: Federated learning and digital twin synergy for energy-efficient task allocation and seamless service migration}, volume = {13}, year = {2025}, }
• (2025). Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words. ACCESS, 13, 64412-64458. (www)

  @article{ , abstract = {We show that n-gram-based distributional models fail to distinguish unrelated words due to the noise in semantic spaces. This issue remains hidden in conventional benchmarks but becomes more pronounced when orthographic similarity is high. To highlight this problem, we introduce OSimUnr, a dataset of nearly one million English and Turkish word-pairs that are orthographically similar but semantically unrelated (e.g., grammar – crammer). These pairs are generated through a graph-based WordNet approach and morphological resources. We define two evaluation tasks—unrelatedness identification and relatedness classification—to test semantic models. Our experiments reveal that FastText, with default n-gram segmentation, performs poorly (below 5% accuracy) in identifying unrelated words. However, morphological segmentation overcomes this issue, boosting accuracy to 68% (English) and 71% (Turkish) without compromising performance on standard benchmarks (RareWords, MTurk771, MEN, AnlamVer). Furthermore, our results suggest that even state-of-the-art LLMs, including Llama 3.3 and GPT-4o-mini, may exhibit noise in their semantic spaces, particularly in highly synthetic languages such as Turkish. To ensure dataset quality, we leverage WordNet, MorphoLex, and NLTK, covering fully derivational morphology supporting atomic roots (e.g., ‘-co_here+ance+y’ for ‘coherency’), with 405 affixes in Turkish and 467 in English.}, archive = {J_ACCESS}, author = {Gökhan Ercan and Olcay Taner Yildiz}, doi = {10.1109/ACCESS.2025.3557086}, journal = {IEEE Access}, pages = {64412-64458}, shortjournal = {IEEE Access}, title = {Grammar or crammer? the role of morphology in distinguishing orthographically similar but semantically unrelated words}, volume = {13}, year = {2025}, }
• (2025). How can digital twins support the economic, environmental, and social sustainability of healthcare systems: A systematic review focused on the triple bottom line. ACCESS, 13, 64390-64411. (www)

  @article{ , abstract = {Digital twins (DTs) are transforming healthcare systems (HSs) by enabling real-time, data-driven decision-making. Despite their potential, research on DTs’ role in long-term HS sustainability remains nascent. This study systematically reviews DT use cases in HSs through the lens of the triple-bottom-line framework of sustainability, identifying their economic, environmental, and social contributions. Additionally, it maps these use cases to the United Nations (UN) Sustainable Development Goals (SDGs) to assess their alignment with global sustainability policies. A systematic literature review following the PRISMA framework was conducted across four databases (Scopus, Web of Science, Engineering Village, and PubMed), identifying 81 peer-reviewed studies. DT use cases were categorized into sustainability dimensions and qualitatively mapped to UN SDGs. We identify 28 unique DT use cases supporting HS sustainability – 13 contributing to economic (e.g., precision medicine, early diagnosis), 8 to environmental (e.g., energy-efficient hospital operations, waste management), and 7 to social sustainability (e.g., provider burnout prevention, equitable access). Our mapping reveals that DTs could support 11 of 17 UN SDGs, including SDG 3 (good health and well-being), SDG 8 (economic growth), SDG 9 (innovation), and SDGs 12–15 (environmental impact mitigation), among others. This study documents the significant potential of DTs to enhance HS sustainability across economic, environmental, and social dimensions while supporting multiple SDGs. However, most existing DT studies overlook explicit sustainability linkages, with limited attention to assessing or prioritizing DTs’ impact on HS sustainability. Future research should develop standardized sustainability metrics, conduct empirical studies, and create frameworks linking DT outcomes to SDGs.}, archive = {J_ACCESS}, author = {Md Doulotuzzaman Xames and Taylan G. Topcu}, doi = {10.1109/ACCESS.2025.3559502}, journal = {IEEE Access}, pages = {64390-64411}, shortjournal = {IEEE Access}, title = {How can digital twins support the economic, environmental, and social sustainability of healthcare systems: A systematic review focused on the triple bottom line}, volume = {13}, year = {2025}, }
• (2025). Adaptive boom barrier management at ETC toll plazas using predictive analytics. ACCESS, 13, 64364-64389. (www)

  @article{ , abstract = {The management of barrier systems in Electronic Toll Collection (ETC) lanes is crucial for optimizing toll plaza operations by balancing traffic flow, congestion, revenue, and environmental impacts. Open-barrier systems, while reducing vehicle queues and carbon emissions by enabling continuous flow, may increase the risk of revenue loss due to unauthorized crossings. Closed-barrier systems (CBB), on the other hand, improve payment compliance and safety but can lead to longer queues and higher emissions due to idling vehicles. This study proposes an innovative adaptive barrier management framework that integrates predictive modeling to optimize toll booth operations, particularly during peak traffic periods. The framework utilizes real-time traffic data to predict both service time and Average Queue Length (AQL). A key component of the system involves the prediction of service times under open-barrier conditions (OBB) using machine learning models and the use of Gated Recurrent Unit (GRU) networks combined with the Optuna framework for predicting AQL under both CBB and OBB. This work explores the novel application of these techniques to the specific problem of toll plaza operations, thereby contributing new insights into optimizing barrier systems for ETC toll lanes. The study evaluates the trade-offs between operational efficiency, carbon reduction, and revenue loss in open-barrier and closed-barrier systems. Our results show that allowing a certain percentage of unauthorized crossings can significantly improve operational efficiency, reducing AQL by 36.62%/day on weekdays and 31.88%/day on weekends in ETC lanes, as well as decreasing carbon emissions by 617.43 kg ${\mathrm {CO}}_{2}$ equivalent/day on weekdays and 383.08 kg ${\mathrm {CO}}_{2}$ equivalent/day on weekends by reducing idle times. However, exceeding established thresholds for unauthorized crossings leads to an increase in revenue loss, which can negatively affect toll plaza performance. The feasibility analysis further quantifies these environmental and financial trade-offs, proposing a strategy for dynamically adjusting barrier management to minimize revenue loss while maintaining high operational efficiency. This research contributes to the advancement of intelligent toll systems by offering a framework for dynamic barrier management that balances operational efficiency, carbon reduction, and economic sustainability, supporting the development of low-carbon, efficient toll infrastructure and paving the way for future innovations in adaptive traffic management.}, archive = {J_ACCESS}, author = {Pattarapon Klaykul and Wilaiporn Lee and Kanabadee Srisomboon and Luepol Pipanmekaporn and Akara Prayote}, doi = {10.1109/ACCESS.2025.3558502}, journal = {IEEE Access}, pages = {64364-64389}, shortjournal = {IEEE Access}, title = {Adaptive boom barrier management at ETC toll plazas using predictive analytics}, volume = {13}, year = {2025}, }
• (2025). Multi-step method for axial-flux permanent-magnet motor to reduce performance analysis time. ACCESS, 13, 64353-64363. (www)

  @article{ , abstract = {This paper proposes a novel multi-step analysis (MSA) method to reduce the analysis time and improve the accuracy of finite element analysis (FEA) for axial-flux permanent-magnet motors (AFPMM). Unlike the conventional method of performing full-step analysis (FSA) over one period of electrical angle, the MSA accurately derives the average torque and fundamental voltage results by utilizing only two steps. To validate the performance of the MSA, the analysis accuracy and reduction in analysis time for torque-beta, voltage-beta, and torque-speed curves are compared between the MSA, FSA, and quasi-3D FEA which is another analysis time reduction method across the four cases of pole-slot combinations of yokeless and segmented armature (YASA) type AFPMM. The MSA confirms its overwhelming performance compared to quasi-3D FEA, and compared to FSA, it achieved a maximum error rate of 0.26% for torque-beta, 0.32% for voltage-beta, and 0.30% for torque-speed, along with a 94% reduction in analysis time. Furthermore, the effectiveness of the analysis method is validated through the experimental results of a 20pole-18slot YASA type AFPMM. The proposed analysis method is expected to be widely utilized by motor designers and engineers in the initial design and characteristic curve derivation stages, as it can be universally applied to complex geometries and radial flux permanent magnet motors.}, archive = {J_ACCESS}, author = {Ji-Sung Lee and Dong-Kuk Lim and Dong-Hee Lee and Peel-Joong Kim and Han-Kyeol Yeo}, doi = {10.1109/ACCESS.2025.3559177}, journal = {IEEE Access}, pages = {64353-64363}, shortjournal = {IEEE Access}, title = {Multi-step method for axial-flux permanent-magnet motor to reduce performance analysis time}, volume = {13}, year = {2025}, }
• (2025). BiMER: Design and implementation of a bimodal emotion recognition system enhanced by data augmentation techniques. ACCESS, 13, 64330-64352. (www)

  @article{ , abstract = {In today’s world, accurately understanding and interpreting emotions in human-computer interaction is important. In this context, this study has adopted a detailed approach to the emotion recognition problem on both speech and text data using the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. First, the problem of datasets with limited number of records and unbalanced distribution across classes was addressed. For this purpose, a dataset obtained from records created as improvised in the IEMOCAP dataset was used and data augmentation methods were applied for both speech and text data. Using datasets that were balanced by applying data augmentation, single-mode emotion recognition experiments were performed with models developed for Speech Emotion Recognition (SER) and Textual Emotion Recognition (TER). Subsequently, the features obtained from these two single modalities were combined with the intermediate fusion method to provide more comprehensive emotion recognition and accuracy, and the Bimodal Emotion Recognition (BiMER) system was developed. The ResNet50-CRNN+AT model, which we obtained the highest accuracy from the three different models developed for SER, creates the speech mode of BiMER, while the Bidirectional Encoder Representations from Transformers (BERT) model used for TER creates the text mode of BiMER. In this way, BiMER was supported with data augmentation methods and the robustness and generalization ability of the model were improved, reaching 88.33% accuracy. Finally, the developed BiMER system was implemented as a real-time web application using the Flask framework, and the capacity of this application to recognize emotions interactively through the user interface was tested.}, archive = {J_ACCESS}, author = {Emrah Dikbiyik and Onder Demir and Buket Dogan}, doi = {10.1109/ACCESS.2025.3559339}, journal = {IEEE Access}, pages = {64330-64352}, shortjournal = {IEEE Access}, title = {BiMER: Design and implementation of a bimodal emotion recognition system enhanced by data augmentation techniques}, volume = {13}, year = {2025}, }
• (2025). DermaTransNet: Where transformer attention meets U-net for skin image segmentation. ACCESS, 13, 64305-64329. (www)

  @article{ , abstract = {Skin, the largest organ of the human body, serves as a protective barrier against the external environment, safeguarding the internal organs. It is composed of layers, namely the epidermis, dermis, and hypodermis. The analysis and detection of diseases heavily rely on identifying the penetration of biomarkers within each layer, thus highlighting the significance of accurate segmentation of each layer in disease diagnosis. In skin disease diagnosis, haematoxylin and eosin-stained whole slide images are the most commonly employed imaging modality. To enhance the efficacy of analysis and assist pathologists, computer-aided diagnostic systems have been proposed to automate the process of disease detection and classification. A crucial initial step in skin disease diagnosis is the accurate segmentation of skin layers, as it facilitates the identification of regions of interest for subsequent processing. This study presents a novel attention-based encoder-decoder architecture comprising a multi-axis encoder with an attention-mixing decoder to precisely segment the skin layers (Epidermis, Dermis, Hypodermis, Keratin) from stained whole slide image samples. The proposed Transformer-based encoder block has helped the model preserve global and local features, which are then passed to the decoder block through an attention-based gated skip connection. The attention mixing decoder module has combined multi-head self-attention, spatial attention, and squeeze excitation modules to enhance the spatial information gain. Additionally, the encoder block aids in extracting high and low-level features, which are further weighted before passing to the decoder block via a gated attention mechanism, thus helping the model acquire the relevant details from all spatial dimensions. The proposed architecture has been evaluated on a publicly available dataset against various evaluation metrics i.e., F1-Score, Accuracy, Precision, Recall, and Hausdorff Distance. The model yielded the highest accuracy of 95.5% when compared with state-of-the-art segmentation models. Additionally, compared with the baseline framework, the model has resulted in an increased overall accuracy of 14.20%.}, archive = {J_ACCESS}, author = {Anum Abdul Salam and Muhammad Usman Akram and Muhammad Haroon Yousaf and Babar Rao}, doi = {10.1109/ACCESS.2025.3556889}, journal = {IEEE Access}, pages = {64305-64329}, shortjournal = {IEEE Access}, title = {DermaTransNet: Where transformer attention meets U-net for skin image segmentation}, volume = {13}, year = {2025}, }
• (2025). Robust model predictive sliding mode control of linear parameter varying systems. ACCESS, 13, 64291-64304. (www)

  @article{ , abstract = {The stability and performance of nonlinear systems under uncertainty are guaranteed by this paper’s robust model predictive control (MPC) approach, which combines sliding mode control (SMC) with a polytopic linear parameter varying (PLPV) framework. The approach begins with the development of a PLPV integral SMC aimed at effectively managing model uncertainties and external disturbances. Subsequently, a PLPV MPC is formulated using a Lyapunov function along with an ellipsoidal terminal constraint to enhance system reliability. This means, after ensuring robust performance in the face of uncertainties, the MPC is designed, incorporating the integral SMC. This implies that the MPC is designed online to optimize system performance and account for system constraints, with robustness provided offline by the SMC. The suggested predictive controller is based on a nominal system and ensures robustness in the offline process with SMC. Compared to existing robust MPC approaches, such as tube-based MPC and max-min MPC, it demonstrates reduced computational burden and conservatism. Finally, the effectiveness of the proposed framework is validated through numerical examples and experimental tests, in comparison to existing robust MPC and traditional SMC.}, archive = {J_ACCESS}, author = {Mohammadsadegh Baneshi and Mohsen Farbood and Anthony Ja Rossiter and Saleh Mobayen and Paweł skruch and Ruggero Carli}, doi = {10.1109/ACCESS.2025.3558973}, journal = {IEEE Access}, pages = {64291-64304}, shortjournal = {IEEE Access}, title = {Robust model predictive sliding mode control of linear parameter varying systems}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Gradual variation-based dual-stream deep learning for spatial feature enhancement with dimensionality reduction in early alzheimer’s disease detection”. ACCESS, 13, 64290. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Gradual Variation-Based Dual-Stream Deep Learning for Spatial Feature Enhancement With Dimensionality Reduction in Early Alzheimer’s Disease Detection”).}, archive = {J_ACCESS}, author = {Najmul Hassan and Abu Saleh Musa Miah and Taro Suzuki and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3558660}, journal = {IEEE Access}, pages = {64290}, shortjournal = {IEEE Access}, title = {Corrections to “Gradual variation-based dual-stream deep learning for spatial feature enhancement with dimensionality reduction in early alzheimer’s disease detection”}, volume = {13}, year = {2025}, }
• (2025). Computer-assisted noise pareidolia tests through patient emulation. ACCESS, 13, 64271-64289. (www)

  @article{ , abstract = {Pareidolia is a psychiatric phenomenon similar to visual illusions or hallucinations, serving as a distinguishing feature of psychiatric disorders. The so-called noise pareidolia test is a typical diagnosis method for pareidolia. In the test, ambiguous noise-like images are shown to patients, to see if patients misunderstand them as faces. However, the existing testing method is very time-consuming for both patients and experts. Furthermore, it has low accuracy in distinguishing some psychiatric disorders with similar characteristics. This research presents a machine learning-based framework to assist in assessing the pareidolia phenomenon. This is a challenging task because analyzing psychiatric disorders primarily involves observing behavioral changes. This is in contrast to other medical imaging tasks which rely on physically visible changes. Our framework emulates the behavior of patients in noise pareidolia tests. To achieve this, we fine-tune face detection models on patient data to misunderstand ambiguous patterns in the same way a patient would do. Then, we can compare new patients to the behavior of existing patients to assess the pareidolia phenomenon, through two classification approaches: a distance function-based approach and a multilayer perceptron-based approach. The proposed framework is evaluated on the data from 48 real patients. The result shows a performance superior to baseline methods in distinguishing pareidolia from other similar visual illusions. Moreover, our framework introduces two sampling methods to reduce the number of images shown to the patient while reaching a comparable high accuracy, improving the efficiency and making the framework feasible for a clinical test.}, archive = {J_ACCESS}, author = {Zhaohui Zhu and Marc A. Kastner and Kazuki Nakajima and Shota Furukawa and Momoko Kitazawa and Taishiro Kishimoto and Shin’Ichi Satoh}, doi = {10.1109/ACCESS.2025.3558512}, journal = {IEEE Access}, pages = {64271-64289}, shortjournal = {IEEE Access}, title = {Computer-assisted noise pareidolia tests through patient emulation}, volume = {13}, year = {2025}, }
• (2025). GEODES: Geometric descriptors for the assessment of global and local flexibility of proteins during molecular dynamics simulation. ACCESS, 13, 64259-64270. (www)

  @article{ , abstract = {Molecular dynamics simulations offer insights into macromolecular structures and functions through extensive time-series atomic data. Two widely used metrics for assessing these simulations are RMSD and RMSF. While RMSD measures conformational convergence, it suffers from degeneracy, where different conformations can produce identical values relative to a reference. RMSF indicates relative mobility but lacks temporal specificity. As both metrics provide only global perspectives, there is a pressing need for novel metrics to capture local flexibility with temporal resolution. We introduce GEODES, a novel complementary 3D geometrical descriptor approach, and compare its effectiveness with conventional analyses using RMSD and RMSF. Through molecular dynamics simulations of the vitamin D receptor trimeric complex, we demonstrate that GEODES significantly enhances the molecular dynamics analysis workflow, offering deeper insights into the structural dynamics and interactions of this complex. This innovative and versatile approach holds great promise for applications in drug discovery, structural biology, and bioinformatics. The GEODES Python3 script toolbox is available at https://github.com/rinnifox/GEODES}, archive = {J_ACCESS}, author = {Karina Pats and Igor Glukhov and Stepan Petrosian and Maria Mamaeva and Alexey Sergushichev and Marie-Dominique Devignes and Ferdinand Molnár}, doi = {10.1109/ACCESS.2025.3558781}, journal = {IEEE Access}, pages = {64259-64270}, shortjournal = {IEEE Access}, title = {GEODES: Geometric descriptors for the assessment of global and local flexibility of proteins during molecular dynamics simulation}, volume = {13}, year = {2025}, }
• (2025). Suitability analysis of inverter topologies and their modulation strategies for two-phase bearingless motors. ACCESS, 13, 64246-64258. (www)

  @article{ , abstract = {Different from the traditional three-phase form of the bearingless motor (BM), the two-phase form of BM has unique advantages in sensor assembly and signal interpretation. As the current link directly determines the torque/levitation characteristics, the selection of the inverter topology and the design of the modulation scheme are the keys to determining the current control performance of BM. The available inverter topologies (full bridge topology, staggered parallel topology and split capacitor topology) and their corresponding modulation schemes for two-phase BM, which are sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM), are compared and analyzed in detail from the perspective of four dimensions, i.e., system cost, control performance, voltage utilization, and algorithm complexity. The staggered parallel topology is selected as the inverter topology for two-phase BM in this paper for its low system cost and high control performance, and an improved SPWM scheme, which increases the voltage utilization from 50% to 71%, is proposed. It is worth mentioning that the idea of carrier phase shifting is applied to the two-phase BM drive system in this paper to improve the system efficiency further. Finally, based on a set of 4000rpm/100W two-phase BM prototype platform, the inverter topology and its modulation scheme explored in this paper are given a complete comparison of simulation and experimental quantitative data to demonstrate the effectiveness and superiority of the proposed improved two-phase BM SPWM drive scheme based on the staggered parallel topology.}, archive = {J_ACCESS}, author = {Qiang Ding and Xucong Bao}, doi = {10.1109/ACCESS.2025.3559449}, journal = {IEEE Access}, pages = {64246-64258}, shortjournal = {IEEE Access}, title = {Suitability analysis of inverter topologies and their modulation strategies for two-phase bearingless motors}, volume = {13}, year = {2025}, }
• (2025). KPI-based evaluation framework for cyber resilience of ships. ACCESS, 13, 64226-64245. (www)

  @article{ , abstract = {Advancements in information and communication technologies have improved operational efficiency in the maritime industry through the integration of operational technology and information technology systems. However, this integration increases external network interfaces, exposing ships to greater cyber threats. Operating in isolated environments far from ports, ships face challenges in responding to cyberattacks and securing timely technical support. As a result, cyber resilience has become a critical concept in the maritime domain. To address these challenges, organizations like the International Maritime Organization and classification societies have issued guidelines. The International Association of Classification Societies introduced Unified Requirements E26, specifying minimum technical requirements to enhance ship cyber resilience. However, Unified Requirements E26 falls short of adequately reflecting the operational environment of ships and does not sufficiently address managerial and operational factors, limiting its effectiveness in evaluating ship cyber resilience. This paper proposes a KPI-based framework to evaluate ship cyber resilience. Seven evaluation domains—Govern, Identify, Protect, Detect, Respond, Recover, and Adapt—were identified to encompass technical, managerial, and operational aspects. Using the SMART methodology, 18 measurable Key Performance Indicators were developed to enable comprehensive resilience evaluation. The proposed framework addresses existing guidelines’ limitations and provides a systematic tool for assessing and improving maritime cyber resilience. This contribution enhances the safety and security of the maritime industry by aligning resilience evaluation with ship-specific operational characteristics.}, archive = {J_ACCESS}, author = {Areum Ko and Ju Hyeon Lee and Jung Taek Seo}, doi = {10.1109/ACCESS.2025.3550501}, journal = {IEEE Access}, pages = {64226-64245}, shortjournal = {IEEE Access}, title = {KPI-based evaluation framework for cyber resilience of ships}, volume = {13}, year = {2025}, }
• (2025). High isolation auxiliary power source for high voltage converters employing wireless inductive power transfer. ACCESS, 13, 64216-64225. (www)

  @article{ , abstract = {A study of a wireless power source operating at high switching frequency to feed gate drivers of high voltage converter switches is presented. Wireless Inductive Power Transfer is chosen for its high isolation between primary and secondary windings, ensuring isolation from control to power circuits. Series-series reactive power compensation is applied due to the low coupling factor, which causes elevated circulation of reactive power and thus losses can be increased. The resonant circuit is powered by a controlled current source instead of a voltage source, ensuring appropriate voltage regulation without a voltage loop control and the need of control signal transmission between primary and secondary windings. A Proportional Resonant control scheme is implemented to apply a sinusoidal input current source, increasing the power factor and reducing reactive power. This paper presents the operation, modeling and control of proposed topology. A prototype with input voltage of 50 V, output voltage of 24 V, rated output power of 100 W and 125 kV isolation between primary and secondary stages was built and its experimental results are included in this paper.}, archive = {J_ACCESS}, author = {Leonardo F. Pacheco and Gustavo C. Bernardi and André L. Kirsten}, doi = {10.1109/ACCESS.2025.3559762}, journal = {IEEE Access}, pages = {64216-64225}, shortjournal = {IEEE Access}, title = {High isolation auxiliary power source for high voltage converters employing wireless inductive power transfer}, volume = {13}, year = {2025}, }
• (2025). Spectrum hole geolocation for database-driven IoT-enabled dynamic spectrum access. ACCESS, 13, 64199-64215. (www)

  @article{ , abstract = {The effective realization of dynamic spectrum access (DSA) subsumes that, when a secondary terminal seeks a vacant band, the geolocation of the candidate spectrum hole coincides with the position of the terminal. Otherwise, stemming from a spectrum hole detection not corresponding to the terminal location, harmful interference to the primary network may occur. Non-cooperative spectrum sensing can inherently deliver accurate spectrum hole geolocation, as it corresponds to the position of the spectrum sensor. However, performance is degraded due to propagation-induced phenomena in the environment. While the performance of cooperative spectrum sensing is less prone to such phenomena, the accurate geolocation of the spectrum hole is compromised. This is because a vacant band is determined by a collective of spectrum sensors whose dispersed spatial distribution prevents from achieving high resolution concerning where in space the band is in fact vacant. This article delves into the problem of spectrum hole geolocation for DSA. As a solution, it is introduced a cooperative spectrum sensing scheme based on overlapped-clustering, integrated with a database-driven Internet of Things-enabled DSA framework. It is shown that the proposed solution achieves a high rate of correct estimations of spectrum hole geolocation, with small variance and under diverse system conditions.}, archive = {J_ACCESS}, author = {Dayan Adionel Guimarães}, doi = {10.1109/ACCESS.2025.3560213}, journal = {IEEE Access}, pages = {64199-64215}, shortjournal = {IEEE Access}, title = {Spectrum hole geolocation for database-driven IoT-enabled dynamic spectrum access}, volume = {13}, year = {2025}, }
• (2025). Comprehensive load regulation strategy considering outage risk in power tight balance scenarios. ACCESS, 13, 64186-64198. (www)

  @article{ , abstract = {With the steadily growing penetration of renewable energy, considerable uncertainties and intermittency are introduced to the power grid. On the other hand, controllable load plays an increasingly important role in the system power balance. Therefore, load regulation strategy is more critical than before to the reliability and safety of power system. A framework of comprehensive load regulation strategy with consideration of outage risk is proposed in this work to deal with power tight balance and power deficiency. Firstly, the orderly electricity consumption (OEC) and demand response (DR) based on time of use (TOU) pricing program are collaboratively employed. Then, orderly consumption power (OCP) is determined by power balance analyzing and flexible resource accessing, reasonably allocated over the subsystems by considering the load types and energy efficiencies, and further specified by components of peak shifting, peak averting and power rationing. Secondly, load-shedding risk evaluation algorithm based on multiple samplings with short-term chronological sequences (MSSCS) is proposed to accurately evaluate load-shedding risk without prohibitive computational burdens. Furthermore, the clear pictures of outage risks over OCP are depicted by calculating incremental cost-effectiveness ratios. Comprehensive case studies are implemented and confirm the validity of the proposed methodology.}, archive = {J_ACCESS}, author = {Shunjiang Wang and Zhongwei Li and Rongmao Wang and Huan Ma}, doi = {10.1109/ACCESS.2025.3559658}, journal = {IEEE Access}, pages = {64186-64198}, shortjournal = {IEEE Access}, title = {Comprehensive load regulation strategy considering outage risk in power tight balance scenarios}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of a grid connected wind turbine-based PMSG using effective predictive control algorithm. ACCESS, 13, 64160-64185. (www)

  @article{ , abstract = {The paper introduces a study in which an effective control method is designed to enhance the dynamic performance of a wind driven permanent magnet synchronous generator (PMSG) connected to the utility grid. The proposed control method is formulated based on the predictive control and backstepping theories taking the advantages that these theories provide. The system components under consideration are modeled and described in details. The theoretical analysis of the proposed control is presented and described in a systematic approach. In order to evaluate the performance of the proposed predictive controller, a comprehensive comparative analysis is performed between four control techniques and the proposed one. The four control techniques are the vector control, predictive current control, predictive voltage based on PI and predictive voltage based on deadbeat theory. The captured results show that the generator’s dynamics are improved significantly with the proposed controller through maintaining reduced ripples and current harmonics, faster dynamics and reduced computational burden as well. The captured results also confirm that the proposed controller outperforms predictive controllers that stand on a similar operating principle (i.e. predictive voltage control), which provides better performance for the wind turbine based generation system.}, archive = {J_ACCESS}, author = {Mahmoud A. Mossa and Rasha A. Mohamed and Ameena Saad Al-Sumaiti}, doi = {10.1109/ACCESS.2025.3557194}, journal = {IEEE Access}, pages = {64160-64185}, shortjournal = {IEEE Access}, title = {Performance enhancement of a grid connected wind turbine-based PMSG using effective predictive control algorithm}, volume = {13}, year = {2025}, }
• (2025). 1-ϕ seven-level switched-capacitor boost multilevel inverter topology with optimized number of components. ACCESS, 13, 64148-64159. (www)

  @article{ , abstract = {Increasing the voltage of energy sources, such as photovoltaic (PV), fuel cells, and battery storage units, requires a voltage-boosting technique. This paper introduces an efficient switched-capacitor multilevel inverter (SC-MLI) capable of generating a boosted seven-level output voltage. The key advantages of this topology include the inherent self-balancing of capacitor voltages without the need for auxiliary balancing technique. Additionally, the proposed SC-MLI offers numerous features such as lower voltage stress, improved efficiency, and reduced overall cost. The proposed topology has been evaluated based on parameters such as component count, THD, cost, and efficiency. Experimental validation was conducted on a 1.0 kW prototype setup under varying modulation index and sudden load changes. Finally, a thorough comparison with state-of-the-art seven-level multilevel inverters highlighted the features of the proposed topology.}, archive = {J_ACCESS}, author = {Ahmed Awadelseed and Arkadiusz Lewicki and Dmytro Kondratenko}, doi = {10.1109/ACCESS.2025.3558617}, journal = {IEEE Access}, pages = {64148-64159}, shortjournal = {IEEE Access}, title = {1-ϕ seven-level switched-capacitor boost multilevel inverter topology with optimized number of components}, volume = {13}, year = {2025}, }
• (2025). SalsaNext+: A multimodal-based point cloud semantic segmentation with range and RGB images. ACCESS, 13, 64133-64147. (www)

  @article{ , abstract = {Advances in sensor fusion techniques are redefining the landscape of 3D point cloud semantic segmentation, particularly for autonomous driving applications. We propose an enhanced approach that leverages the complementary strengths of LiDAR and multi-camera systems. This study introduces two extensions to the state-of-the-art SalsaNext model based only in LiDAR: SalsaNext+RGB, which integrates RGB data into range-view (RV) images, and SalsaNext+PANO, incorporating panoramic images built from multi-camera setups. The proposed methods are evaluated using the SemanticKITTI and Panoptic nuScenes datasets, showing notable improvements in segmentation accuracy. Results indicate that RGB fusion boosts performance with minimal latency, while panoramic integration offers additional gains at the expense of higher computational load. Comparative analyses highlight significant mIoU gains, demonstrating the potential of multimodal sensor fusion for intricate driving scene understanding.}, archive = {J_ACCESS}, author = {Fabio Sánchez-García and Santiago Montiel-Marín and Miguel Antunes-García and Rodrigo Gutiérrez-Moreno and Ángel Llamazares Llamazares and Luis M. Bergasa}, doi = {10.1109/ACCESS.2025.3559580}, journal = {IEEE Access}, pages = {64133-64147}, shortjournal = {IEEE Access}, title = {SalsaNext+: A multimodal-based point cloud semantic segmentation with range and RGB images}, volume = {13}, year = {2025}, }
• (2025). Enhanced workload prediction in data centers using two-stage decomposition and hybrid parallel deep learning. ACCESS, 13, 64115-64132. (www)

  @article{ , abstract = {Workload prediction is one of the most basic requirements in developing cost and energy-efficient Cloud Data Centers (CDCs). Most traditional approaches have suffered from noise and failed to capture the complex dynamic patterns in workload data, reducing their accuracy. To improve this, we introduce CVCBM which blends signal processing techniques Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) with advanced deep learning models like Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. CVCBM utilizes a hierarchical two-stage decomposition process, beginning with CEEMDAN. We use the capability of CEEMDAN for denoising by additive white noise to clean the noisy workload data and further decompose it into several Intrinsic Mode Functions (IMFs), ranging from high to low frequencies. Then, we propose a partitional clustering approach based on Sample Entropy (SE) to select components of similar complexity to increase the effectiveness of the second-stage denoising. After that, VMD is a method based on the center frequency applied to decompose further the high-frequency components, which may contain fluctuations that obscure underlying trends, thus enhancing the model’s overall accuracy. A novel hybrid model is utilized to forecast future workloads, incorporating two sets of three distinct parallel Conv1D layers with varying kernel sizes. These layers extract patterns from the input data, capturing short-term, medium-term, and long-term workload information, allowing the model to learn variations at different scales. Following this, Bi-LSTM layers capture the temporal dependencies within the patterns identified by the Conv1D layers. Extensive experiments on various real-world datasets from Google and Alibaba show that CVCBM significantly outperforms the other methods, which makes it a solution for future workload prediction in cloud data centers.}, archive = {J_ACCESS}, author = {Dalal Alqahtani and Hamidreza Imani and Tarek El-Ghazawi}, doi = {10.1109/ACCESS.2025.3558743}, journal = {IEEE Access}, pages = {64115-64132}, shortjournal = {IEEE Access}, title = {Enhanced workload prediction in data centers using two-stage decomposition and hybrid parallel deep learning}, volume = {13}, year = {2025}, }
• (2025). Exploring the potential of offline LLMs in data science: A study on code generation for data analysis. ACCESS, 13, 64087-64114. (www)

  @article{ , abstract = {Large Language Models (LLMs) have recently attracted considerable attention from the scientific community, due to their advanced capabilities and potential to serve as vital tools across various industries and academic fields. An important implementation domain for LLMs is Data Science, in which they could enhance the efficiency of Data Analysis and Profiling tasks. With the utilization of LLMs in Data Analytics tools, end-users could directly issue data analysis queries in natural language, bypassing the need for specialized user interfaces. However, due to the sensitive nature of certain data in some organizations, it is unwise to consider using established, cloud-based LLMs. This article explores the feasibility and effectiveness of a standalone, offline LLM in generating code for performing data analytics, given a set of natural language queries. A methodology tailored to a code-specific LLM is presented, evaluating its performance in generating Python Spark code and successfully producing the desired result. The model is assessed on its efficiency and ability to handle natural language queries of varying complexity, exploring the potential for wider adoption of offline LLMs in future data analysis frameworks and software solutions.}, archive = {J_ACCESS}, author = {Anastasios Nikolakopoulos and Antonios Litke and Alexandros Psychas and Eleni Veroni and Theodora Varvarigou}, doi = {10.1109/ACCESS.2025.3556973}, journal = {IEEE Access}, pages = {64087-64114}, shortjournal = {IEEE Access}, title = {Exploring the potential of offline LLMs in data science: A study on code generation for data analysis}, volume = {13}, year = {2025}, }
• (2025). Analysis of interferometric millimeter-wave target detection technology based on active illumination echoes. ACCESS, 13, 64076-64086. (www)

  @article{ , abstract = {In this article, a novel method for interferometric target detection that employs coherent echoes and spatial frequency sampling is proposed. Originating from radio astronomy, interferometric passive microwave imaging has become widely applied for the passive microwave remote sensing of Earth. The proposed technique, termed Interferometric Coherent Echo Detection, is a novel active imaging method that capitalizes on coherent echoes to sample the spatial frequency domain. In comparison with traditional active imaging systems, the proposed approach is characterized by an autonomous transceiver system that operates independently. This system negates the requirement for phase synchronization and eliminates the need for specific modulation techniques to be applied to the emitted waveform. Compared to passive imaging techniques, the technique of interferometric target detection using echoes obtained from active transmission reduces the system’s receiver sensitivity requirements by using transmitter. The detection methodology presented herein is designed to suppress the spatial coherence of the echo by optimizing the positioning of the transmitter. The theoretical derivation and subsequent simulation validation confirm the feasibility of the proposed echo interference measurement technique. Furthermore, an experimental apparatus incorporating automotive radar chips and a Field-Programmable Gate Array (FPGA) successfully identified multiple target points.}, archive = {J_ACCESS}, author = {Mengyao Jiang and Ziyang Zhang and Donghao Han and Hao Liu}, doi = {10.1109/ACCESS.2025.3557517}, journal = {IEEE Access}, pages = {64076-64086}, shortjournal = {IEEE Access}, title = {Analysis of interferometric millimeter-wave target detection technology based on active illumination echoes}, volume = {13}, year = {2025}, }
• (2025). How much power is needed for RIS to beat relays? a sustainability framework. ACCESS, 13, 64061-64075. (www)

  @article{ , abstract = {Future wireless networks are expected to utilise reconfigurable intelligent surfaces (RISs) to enhance coverage and communication quality. However, to ensure efficient RIS deployment and operation, it is crucial to understand the operational costs of RIS-assisted communication systems. This study aims to investigate the potential of RIS integration to improve the sustainability and energy efficiency of wireless communication networks, focusing on quantifying and minimising the operational cost of RIS-assisted communications. We present an in-depth analysis of power dissipation in RIS circuitry, emphasising both static and dynamic elements and offering a thorough breakdown of the operational costs associated with RIS-assisted communications. Our study examines control circuit components, control boards, and other factors contributing to dynamic power dissipation. We also develop a precise optimisation problem that addresses RIS power dissipation, aiming to minimise total power consumption in RIS-assisted communication systems while meeting essential communication constraints. In particular, we consider the rate requirements and the phase shift constraints, which enables our optimisation approach to be both theoretically valid and applicable to practical network scenarios. To address the optimisation problem effectively, we have developed a robust algorithm based on the particle swarm optimisation approach (PSO). This algorithm chooses the optimum number of RIS elements and adjusts the phase shifts matrix to reduce power dissipation while maintaining the channel gain required to achieve service requirements. Also, we present a comparison with active solutions in terms of power requirements, spectral efficiency, and environmental impact. Our analysis provides valuable insights into the advantages of RIS deployment, especially at higher data rates. It also highlights the importance of considering alternative relay techniques when tailoring wireless communication systems to specific rate requirements and operating conditions.}, archive = {J_ACCESS}, author = {Mohammad Abualhayja'a and Mahmoud Wagih and Anthony Centeno and Muhammad Ali Imran and Lina Mohjazi}, doi = {10.1109/ACCESS.2025.3560131}, journal = {IEEE Access}, pages = {64061-64075}, shortjournal = {IEEE Access}, title = {How much power is needed for RIS to beat relays? a sustainability framework}, volume = {13}, year = {2025}, }
• (2025). A survey on hybrid free space optical and radio frequency systems: Classification, progress, observations, and challenges. ACCESS, 13, 63994-64060. (www)

  @article{ , abstract = {Data rate, security, spectral efficiency, energy efficiency and reliability represent the main requirements in most of communication systems. Different wireless technologies can achieve different levels of these requirements, where a specific wireless technology cannot individually attain the best performance in all of these system requirements. Free-Space Optical (FSO) and Radio Frequency (RF) communications are fundamental and popular wireless technologies. Although FSO systems can achieve high data rates, maintain high security levels and minimize energy consumption, they suffer from low reliability in some weather conditions (such as fog and haze), require Line-of-Sight (LoS) connections, and are limited to point-to-point communication systems. On the other hand, RF technology has been shown as a reliable system under diverse weather conditions, allows for users and data multiplexing and represents an efficient option for non-LoS communication scenarios. However, compared to FSO, the achievable data rate and security level by RF are limited. To this end, combining both technologies (i.e., FSO and RF) has been widely nominated as an efficient hybrid communication system that exploits the advantages of both technologies in different scenarios and conditions. In the literature, a wide set of hybrid FSO/RF systems has been presented to accomplish different objectives including improving throughput, boosting reliability, maintaining connectivity and enhancing security. This survey paper provides a comprehensive overview of hybrid FSO/RF systems, presenting a classification framework of the different hybrid setups proposed in the literature, and identifying the main design objectives of hybrid FSO/RF systems. The survey includes technical revisions of related literature, followed by a set of the observations made and another set of challenges and open issues in the field of hybrid FSO/RF systems.}, archive = {J_ACCESS}, author = {Heyam Hassan and Saud Althunibat and Amneh Al-Mbaideen and Mazen Hasna and Khalid A. Qaraqe}, doi = {10.1109/ACCESS.2025.3558500}, journal = {IEEE Access}, pages = {63994-64060}, shortjournal = {IEEE Access}, title = {A survey on hybrid free space optical and radio frequency systems: Classification, progress, observations, and challenges}, volume = {13}, year = {2025}, }
• (2025). FD-YOLO11: A feature-enhanced deep learning model for steel surface defect detection. ACCESS, 13, 63981-63993. (www)

  @article{ , abstract = {Steel surface defect detection plays a critical quality control role in industrial manufacturing. However, the existing methods struggle to balance accuracy and efficiency, especially in complex defect environments, where significant challenges persist. To address this challenge, FD-YOLO11, which is a YOLO11-based deep learning model with enhanced feature extraction and fusion mechanisms for attaining improved detection performance, is proposed in this paper. To enhance the multiscale feature extraction process, self-calibrated convolution is integrated into the C3k2 module. Additionally, an FSPPF structure is designed to optimize the process of fusing local and global information, improving the defect recognition ability of the model in complex backgrounds. Furthermore, the DySample mechanism replaces the traditional upsampling method, effectively refining the feature fusion process and minimizing the semantic information loss. The NEU-DET and GC10-DET datasets are used for evaluation purposes, and the experimental results demonstrate that FD-YOLO11 achieves 4.6% and 4.0% mAP@0.5 improvements over YOLO11s while maintaining an inference speed comparable to that of YOLO11s. This ensures a compromise between detection performance and computational efficiency. The model exhibits enhanced recognition capabilities and greater robustness in complex defect detection tasks. This research indicates that FD-YOLO11 provides a high-precision solution for metal surface defect detection, with broad application potential in intelligent manufacturing and industrial inspection scenarios.}, archive = {J_ACCESS}, author = {Zichen Dang and Xingshuo Wang}, doi = {10.1109/ACCESS.2025.3559733}, journal = {IEEE Access}, pages = {63981-63993}, shortjournal = {IEEE Access}, title = {FD-YOLO11: A feature-enhanced deep learning model for steel surface defect detection}, volume = {13}, year = {2025}, }
• (2025). Design of radix-8 unsigned bit pair recoding algorithm-based floating-point multiplier for neural network computations. ACCESS, 13, 63969-63980. (www)

  @article{ , abstract = {The neural network computations for Artificial Intelligence (AI) applications demand high speed, low power and area-efficient Floating-Point (FP) multiplication. In this work, we propose an efficient unsigned Bit Pair Recoding (BPR) algorithm for area, power, and speed improved FP unsigned mantissa multiplication. The partial product rows are reduced from n to $\frac {n}{4}$ for $n\times n$ binary multiplier using the BPR algorithm with parallel processed partial product reduction. The new algorithm performs partial product row reduction without the 2’s complement, Negative Encoding (NE), and Sign Extension (SE) are required for Booth recoded-based multiplication but these computations are not required for floating point unsigned multiplication. The computational cost of determining a 2’s complementary circuit and neglecting the sign bit extension of each partial product row in the Modified Booth Encoding (MBE) algorithm is effectively eliminated by BPR algorithm. The unsigned mantissa multiplication using partial product array reduction with the BPR technique uses 27.5% less area, 18% less power, and 33.33% improved speed for generating one partial product row than conventional Booth multipliers. The $8\times 8$ and $16\times 16$ multipliers are used to verify the BPR binary multipliers on TSMC 65nm 1.1 V CMOS standard cell library and the synthesis reports are compared with the conventional and best-reported improved Booth multipliers. Finally, the MAC design uses 16-bit FP arithmetic with an $8\times 8$ mantissa multiplier for the CNN accelerator is developed, and it is validated with suitable error metrics like Mean Relative Error (MRE) to assess the suggested architecture for AI applications.}, archive = {J_ACCESS}, author = {J. Jean Jenifer Nesam and S. Sankar Ganesh}, doi = {10.1109/ACCESS.2025.3559226}, journal = {IEEE Access}, pages = {63969-63980}, shortjournal = {IEEE Access}, title = {Design of radix-8 unsigned bit pair recoding algorithm-based floating-point multiplier for neural network computations}, volume = {13}, year = {2025}, }
• (2025). New heuristic approaches for the Min–Max regret traveling salesman problem. ACCESS, 13, 63952-63968. (www)

  @article{ , abstract = {The traveling salesman problem (TSP) is widely known as one of the most important NP-hard combinatorial optimization problems. In this paper, we consider the min-max regret traveling salesman problem (MMR-TSP) with interval travel costs. The MMR-TSP aims to find a tour that minimizes the difference between its objective function value and the optimal value of the case in which the real scenario is known in advance. We examine five methods: a Benders-like decomposition method, a branch-and-cut algorithm, a fixed-scenario method, an iterated dual substitution method, and a gravitational search algorithm. For the iterated dual substitution method, we discuss several possible implementations based on different mathematical models for the classical TSP. We further propose a new heuristic approach, which we call the core selection algorithm. The experimental results show that the proposed core selection algorithm achieved superior performance compared to all other methods we examined for benchmark instances up to 80 cities. Furthermore, for the instances with 1000 cities, our new algorithm obtained high-quality solutions with an average optimality gap of 2.7%, whereas existing methods failed to obtain feasible solutions or only obtained solutions with an average optimality gap above 100%.}, archive = {J_ACCESS}, author = {Kazuki Hasegawa and Wei Wu and Mutsunori Yagiura}, doi = {10.1109/ACCESS.2025.3558359}, journal = {IEEE Access}, pages = {63952-63968}, shortjournal = {IEEE Access}, title = {New heuristic approaches for the Min–Max regret traveling salesman problem}, volume = {13}, year = {2025}, }
• (2025). Enhancing secure energy efficiency of SWIPT IoT network considering IRS and artificial-noise: A deep learning approach. ACCESS, 13, 63942-63951. (www)

  @article{ , abstract = {Internet of things (IoT) networks connect sensing devices to the Internet for tasks like tracking, monitoring, and data analysis. However, the large data generated, especially in resource-heavy services, challenges energy efficiency (EE) and security. The use of intelligent reflecting surfaces (IRS) in the physical layer security of IoT devices shows promise in improving secrecy performance and EE. This paper addresses IRS and AN-aided secure communication against eavesdroppers, where eavesdroppers are IoT devices equipped with simultaneous wireless information and power transfer (SWIPT) using power splitting policy. Our goal is to maximize secure EE, where the secure EE is defined as the secrecy rate over the power consumption of the user. We aim to jointly optimize phase-shift of IRS, transmit power of jammer and user, to maximize the objective function while ensuring that the secrecy rate is greater than the predefined threshold while allowing the eavesdroppers to harvest energy using SWIPT. We first formulate a joint optimization model of IRS and AN aided secure communication, which is a non-convex optimization problem with linear and non-linear constraints. To solve this problem, we propose a deep neural network (DNN) algorithm that utilizes a loss function derived from Lagrange duality function. The simulation results show that the proposed DNN achieves near-global optimal solutions. In addition, the results also prove the effectiveness of implementing IRS to enhance the secure EE of the user.}, archive = {J_ACCESS}, author = {Kimchheang Chhea and Jung-Ryun Lee}, doi = {10.1109/ACCESS.2025.3559981}, journal = {IEEE Access}, pages = {63942-63951}, shortjournal = {IEEE Access}, title = {Enhancing secure energy efficiency of SWIPT IoT network considering IRS and artificial-noise: A deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Learning the hit-or-miss transform-based morphological neural networks. ACCESS, 13, 63935-63941. (www)

  @article{ , abstract = {Mathematical morphology is well suited for learning interpretable shapes because of its structure-based operations. In this article, we propose techniques to improve the learning of the hit-or-miss transform, a morphological operation developed to detect object shapes by simultaneously matching the foreground and background. First, we reformulate the definition of the transform for better intuition and interpretation. Next, we enhance the selection of the “Don’t Care” elements-which are neither foreground nor background-by using an interval instead of a point, zero, proposed in prior work. We then apply linear operations to these Don’t Care elements that are by definition excluded from the transform, allowing them to be included in the parameter update during the optimization process. Finally, we propose to piece-wise linearize the hit-or-miss transform to make it robust against variations and noises. Experiment results using a deep neural network architecture, MobileNetV2, on Cifar-10 and FashionMNIST datasets demonstrate the efficacy of the proposed methods. In particular, the piecewise linear hit-or-miss transform improved the baseline accuracy for Cifar-10 by 10.7%.}, archive = {J_ACCESS}, author = {Muhammad Aminul Islam and Sireesha Chimbili}, doi = {10.1109/ACCESS.2025.3559012}, journal = {IEEE Access}, pages = {63935-63941}, shortjournal = {IEEE Access}, title = {Learning the hit-or-miss transform-based morphological neural networks}, volume = {13}, year = {2025}, }
• (2025). High-level reconfigurable embedded system design based on heterogeneous models of computation. ACCESS, 13, 63918-63934. (www)

  @article{ , abstract = {One of the main challenges of embedded system design lies in the natural heterogeneity of these systems. We can say that embedded systems are electronic systems designed and programmed to tackle a specific application. Each application has its requirements, although embedded systems often combine many domain-specific subsystems. Considering this context, the design of embedded systems can be extremely challenging, including system modeling, simulation, formal verification, and the synthesis to a correct implementation. To manage the complexity of such systems, the design should start at higher levels of abstraction, based on formal models, without considering the low-level characteristics of the underlying software or hardware. These high-level formal meta-models, named models of computation (MoC), define a set of rules that dictate how computation should be performed and how they should communicate with each other, along with other information such as the notion of time. In this paper, we present as the main contribution a set of rules and interfaces that enable the proper mixing of different MoC domains in a framework for complex embedded system design, thus allowing a heterogeneous system composition at a high abstraction level, including the synchronous reactive, synchronous dataflow, and scenario-aware dataflow MoCs. We model both part of an avionic system and a reconfigurable RISC-V processor using these MoCs and the proposed interfaces as a case study showing the applicability brought by our proposal.}, archive = {J_ACCESS}, author = {Gabriel C. Duarte and Denis S. Loubach and Ingo Sander}, doi = {10.1109/ACCESS.2025.3559695}, journal = {IEEE Access}, pages = {63918-63934}, shortjournal = {IEEE Access}, title = {High-level reconfigurable embedded system design based on heterogeneous models of computation}, volume = {13}, year = {2025}, }
• (2025). Creating a novel approach to examine how king saud university’s arts college students utilize AI programs. ACCESS, 13, 63895-63917. (www)

  @article{ , abstract = {One of the cutting-edge ideas that is essential to improving students’ behavioral intention for using artificial intelligence (AI) and attitude toward it (ATAI) at higher education institutions is artificial intelligence learning. Artificial intelligence education is not widely available in Saudi Arabian universities. The study’s goal is to ascertain how perceptions of assessment quality, perceived risk, price value, artificial intelligence anxiety, educational quality, and perceived AI intelligence affect attitudes toward and perceived usefulness of employing artificial intelligence. When attempting to encourage the use of artificial intelligence (AI) in higher education, take into account factors such as perceived utility, attitude toward AI, behavioral intention to use, user happiness, and general mindset regarding AI. Additionally considered is the mediating function that behavioral intention and user pleasure have when utilizing artificial intelligence (USAI). A survey of Saudi universities was conducted in order to meet the study’s goal. 183 questionnaires were distributed around the universities to collect student data. Partial least squares (PLS) analysis and structural equation modeling were utilized to examine the data once it was gathered. User satisfaction in using artificial intelligence is the most crucial element in promoting student adoption of AI in higher education ( $\beta = 0.646$ , t-value = 10.402). Perceived usefulness of using artificial intelligence ( $\beta = 0.309$ , t-value = 4.240; $\beta = 0.275$ , t-value = 2.194; and $\beta = 0.473$ , t-value = 7.619, respectively) and attitude toward artificial intelligence ( $\beta = 0.285$ , t-value = 2.584; and $\beta = 0.151$ , t-value = 2.453, respectively) are significant factors in transferring the positive effects of behavioral intention to use and user satisfaction in using artificial intelligence. A crucial part of communicating the positive impacts of AI adoption in higher education is also played by user satisfaction ( $\beta = 0.646$ , t-value = 10.402, respectively) and behavioral intention to use AI ( $\beta = 0.174$ , t-value = 2.817, respectively)”. We can better comprehend Saudi undergraduates’ motivation to use artificial intelligence (AI) for learning in higher education thanks to this study, which combines the TAM model and UTAUT theory. Future research in educational environments can make use of this theoretical framework. Furthermore, this study provides educators and educational institutions with some recommendations on how to motivate students to use AI to achieve better outcomes.}, archive = {J_ACCESS}, author = {Abeer S. Almogren}, doi = {10.1109/ACCESS.2025.3553631}, journal = {IEEE Access}, pages = {63895-63917}, shortjournal = {IEEE Access}, title = {Creating a novel approach to examine how king saud university’s arts college students utilize AI programs}, volume = {13}, year = {2025}, }
• (2025). Deep learning empowered service selection in FANETs: A 5G-enabled UAV-cloud architecture. ACCESS, 13, 63877-63894. (www)

  @article{ , abstract = {Flying Ad-Hoc Networks (FANETs), commonly referred to as drones or Unmanned Aerial Vehicles (UAVs), are increasingly transforming various industries by supporting numerous applications. In this context, a UAV can function as a cloud service provider, offering services such as memory, computing, and network connectivity to consumer UAVs. This work introduces an innovative 5G-enabled UAV-Cloud Architecture empowered by deep learning for service selection. The architecture integrates two main modules: (i) A game theory-based module, which establishes an interactive game between provider and consumer UAVs, generating datasets to guide consumer UAVs in selecting suitable providers; and (ii) A deep learning-based module, leveraging the generated datasets to predict optimal service providers. Numerical simulations validate the proposed architecture, highlighting its superior prediction accuracy, reduced time and message complexity, and minimized service discovery and consumption delays compared to existing methods. These advancements are attributed to the synergy of game theory, deep learning, and 5G communication capabilities.}, archive = {J_ACCESS}, author = {Mohamed Ben Bezziane and Messaoud Abbas and Hessa Alfraihi and Bouziane Brik and Yacine Khaldi and Oussama Aiadi and Hasan Siham}, doi = {10.1109/ACCESS.2025.3555871}, journal = {IEEE Access}, pages = {63877-63894}, shortjournal = {IEEE Access}, title = {Deep learning empowered service selection in FANETs: A 5G-enabled UAV-cloud architecture}, volume = {13}, year = {2025}, }
• (2025). Transforming brain tumor detection empowering multi-class classification with vision transformers and EfficientNetV2. ACCESS, 13, 63857-63876. (www)

  @article{ , abstract = {Brain Tumor is a significant health challenge, with early, precise detection crucial for improving patient outcomes. Traditional diagnostics methods depend on expert radiologists, leading to subjectivity and time-intensive evaluations. In recent advancements, AI and DL have demonstrated remarkable medical imaging potential, yet many existing models are limited to binary classification, failing to distinguish between multiple tumor types. This limitation restricts their clinical applicability and effectiveness in comprehensive diagnosis. We propose a deep learning-based multi-class classification approach for brain tumor detection, leveraging EfficientNetV2 and Vision Transformer (ViT) models to address this challenge. EfficientNetV2, known for its optimized convolutional architecture, achieves high accuracy while maintaining computational efficiency, whereas ViT, a transformer-based model, effectively captures global contextual information in medical images. Our approach was evaluated on a brain tumor MRI dataset containing 7,023 images, divided into training and testing sets. EfficientNetV2 achieved an accuracy of 95% with a loss of 0.13, alongside an F1-score, precision, and recall of 0.96. In comparison, ViT attained 90% accuracy with a loss of 0.30 and an F1-score, precision, and recall of 0.89. The highest accuracy 96%, was achieved by the proposed model using the geometric mean ensemble learning technique. The results demonstrate that our proposed system outperforms many existing deep learning models, offering a robust solution for multi-class brain tumor classification. Our approach enhances diagnostic accuracy by integrating convolutional and transformer-based architectures, aiding radiologists in early and automated tumor detection. This research contributes to the advancement of AI-driven medical diagnostics and has the potential to improve clinical decision-making in brain tumor assessment.}, archive = {J_ACCESS}, author = {Anees Tariq and Muhammad Munwar Iqbal and Muhammad Javed Iqbal and and Iftikhar Ahmad}, doi = {10.1109/ACCESS.2025.3555638}, journal = {IEEE Access}, pages = {63857-63876}, shortjournal = {IEEE Access}, title = {Transforming brain tumor detection empowering multi-class classification with vision transformers and EfficientNetV2}, volume = {13}, year = {2025}, }
• (2025). A proactive caching approach to boost network offloading in urban areas. ACCESS, 13, 63841-63856. (www)

  @article{ , abstract = {The rapid growth of 6G networks and the Internet of Things (IoT) has increased the need for advanced and adaptive data management. In response, our research brings together multiple devices, including smartphones, bicycles, and in-vehicle systems, under a single user preference model to propose a novel proactive caching framework that surpasses traditional single-device approaches that give each device a unique set of preferences. Exploiting the advantages of both Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, our proposed approach minimizes core network congestion and increases spectrum efficiency. At its core is the Cognitive File Caching Algorithm (CFCA), a proactive, scalable, and adaptive approach designed to address complex challenges involving varying file popularity in cities and user movement patterns. Our proposed CFCA improves caching efficiency by clustering users based on shared zones of interest and behavioral data, achieving a 27% improvement in network offloading and a 24% increase in cache hit ratio over existing benchmarks. Furthermore, the study explores the economic implications of proactive caching, establishing a strategic balance that ensures long-term profitability while minimizing high-cost caching inefficiencies. This study marks a paradigm advancement in proactive caching, setting the groundwork for future-ready 6G networks by combining user-centric data models, scalable algorithms, and adaptive file exchange mechanisms to address ever-increasing traffic needs.}, archive = {J_ACCESS}, author = {Ahmed Hassan and Khaled Metwally and Tamer Mekkawy and Ashraf Mahran}, doi = {10.1109/ACCESS.2025.3559465}, journal = {IEEE Access}, pages = {63841-63856}, shortjournal = {IEEE Access}, title = {A proactive caching approach to boost network offloading in urban areas}, volume = {13}, year = {2025}, }
• (2025). Multi-task faces (MTF) data set: A legally and ethically compliant collection of face images for various classification tasks. ACCESS, 13, 63827-63840. (www)

  @article{ , abstract = {Human facial data offers valuable potential for tackling classification problems, including face recognition, age estimation, gender identification, emotion analysis, and race classification. However, recent privacy regulations, particularly the EU General Data Protection Regulation, have restricted the collection and usage of human images in research. As a result, several previously published face data sets have been removed from the internet due to inadequate data collection methods and privacy concerns. While synthetic data sets have been suggested as an alternative, they fall short of accurately representing the real data distribution. Additionally, most existing data sets are labeled for just a single task, which limits their versatility. To address these limitations, we introduce the Multi-Task Face (MTF) data set, designed for various tasks including face recognition and classification by race, gender, and age, as well as for aiding in training generative networks. The MTF data set comes in two versions: a non-curated set containing 132,816 images of 640 individuals, and a manually curated set with 5,246 images of 240 individuals, meticulously selected to maximize their classification quality. Both data sets were ethically sourced, using publicly available celebrity images in full compliance with copyright regulations. Along with providing detailed descriptions of data collection and processing, we evaluated the effectiveness of the MTF data set in training five deep learning models across the aforementioned classification tasks, achieving up to 98.88% accuracy for gender classification, 95.77% for race classification, 97.60% for age classification, and 79.87% for face recognition with the ConvNeXT model. Both MTF data sets can be accessed through the following link. https://github.com/RamiHaf/MTF_data_set}, archive = {J_ACCESS}, author = {Rami Haffar and David Sánchez and Josep Domingo-Ferrer}, doi = {10.1109/ACCESS.2025.3559310}, journal = {IEEE Access}, pages = {63827-63840}, shortjournal = {IEEE Access}, title = {Multi-task faces (MTF) data set: A legally and ethically compliant collection of face images for various classification tasks}, volume = {13}, year = {2025}, }
• (2025). Designing a digital transformation strategy for hydropower generation in colombia. ACCESS, 13, 63815-63826. (www)

  @article{ , abstract = {Energy transition is driving substantial changes in the global electricity industry, yet large-scale digital transformation strategies for hydropower remain underexplored. This paper presents a case study on the design and early implementation of a digital transformation strategy for hydropower generation for one of the largest power generation companies in the Colombian electricity market. The research addresses the gap in study cases detailing digital transformation strategies and concrete digital transformation architecture for hydropower generators at the countrywide level. In this paper, we provide a detailed characterization of the Colombian electricity market while revealing the derivable benefits of digitally transformed electricity generation. While the designed strategy is expected to improve the efficiency and effectiveness of the company’s power generation and maintenance processes, it can also increase the flexibility of the electric grid to enable wind-solar energy incorporation in the Colombian electricity market. The project is currently under implementation and is expected to be fully operational by 2025.}, archive = {J_ACCESS}, author = {Edgar Guevara Pabón and Bladhimir Cortés González and Iván Camilo Durán Tovar and Luis Daniel Benavides Navarro and Agustin Marulanda Guerra}, doi = {10.1109/ACCESS.2025.3555537}, journal = {IEEE Access}, pages = {63815-63826}, shortjournal = {IEEE Access}, title = {Designing a digital transformation strategy for hydropower generation in colombia}, volume = {13}, year = {2025}, }
• (2025). Examining the mediating effects of game user experience satisfaction on continuance intention: A case study of league of legends. ACCESS, 13, 63792-63814. (www)

  @article{ , abstract = {This research critically examines the relationship between game user experience satisfaction and continuance intention, focusing on dividing game user experience satisfaction into design and psychological factors and examining their mediating roles. Data were collected from 524 players of League of Legends and League of Legends: Wild Rift via online surveys. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (PLS-MGA), the study examines factors such as usability/playability, narratives, play engrossment, enjoyment, creative freedom, audio aesthetics, personal gratification, social connectivity, visual aesthetics, and continuance intention. The findings demonstrate that desktop version players are more likely to have higher educational attainment and more extended gaming experience than mobile version players, with a partial mediating relationship between game user experience satisfaction and continuance intention. A significant observation is the differences between casual and core players in user experience satisfaction. In contrast, audio aesthetics have a more pronounced effect on enjoyment for casual players. Additionally, for core players, visual aesthetics have a more substantial impact on continuance intention, mediated by enjoyment, compared to casual players. However, no significant differences were observed across hypothesized paths based on gaming platforms. These insights underscore the importance of game user experience satisfaction in the mediating influence on players’ continuance intention. Key design elements, such as enhancing audio and visual aesthetics, are identified as practical strategies to improve player engagement and loyalty, providing valuable insights for developers optimizing gaming experiences across platforms.}, archive = {J_ACCESS}, author = {Li-Hsun Peng and Ming-Han Bai}, doi = {10.1109/ACCESS.2025.3559632}, journal = {IEEE Access}, pages = {63792-63814}, shortjournal = {IEEE Access}, title = {Examining the mediating effects of game user experience satisfaction on continuance intention: A case study of league of legends}, volume = {13}, year = {2025}, }
• (2025). Exploring features and products in E-commerce on consumers behavior using cognitive affective. ACCESS, 13, 63774-63791. (www)

  @article{ , abstract = {Technology advancement has given rise to a new online purchasing paradigm known as e-commerce. Online shopping in e-commerce platform has altered consumer behavior and purchasing decisions. However, a few studies have conducted by considering cognitive-affective process in making purchase decision on e-commerce. Therefore, the current study examines consumer behavior from perspective of stimulus-organism-response theory and the internal perception process (cognitive and affective) to understand consumer’s purchase decisions. We use Structural Equation Model-Partial Least Square technique to analyze the data of 308 consumers in Indonesia. Our findings report that two dimensions of the stimulus environment (credibility and product usefulness) significantly contributed to perceived trust; convenience has an insignificant impact on perceived trust and perceived value. In comparison, three dimensions of the stimulus environment (convenience, informativeness, and product price) contribute to consumers’ perceived trust. Furthermore, the stimulus environment indicators (credibility, informativeness, product price, and product usefulness) effect on perceived value, while convenience has an insignificant on consumers’ perceived value. The internal perception process or cognitive and affective process, perceived trust has an insignificant impact on perceived pleasure, while perceived value has a significant impact on perceived pleasure. Lastly, perceived pleasure significantly influences consumer purchase decisions in online shopping and e-commerce. The findings of this study can expand the research on e-commerce how consumer’s consider their cognitive and affective factor on product purchase decision. The study suggests that the optimize the features and products as way to improve the consumer purchase decision, and it affects toward cognitive and affective components such as perceive trust, value, and pleasure. This study has contributed both theoretically and practically to stakeholders, policy makers, and practitioner in e-commerce.}, archive = {J_ACCESS}, author = {Pahrudin Pahrudin and Rezzy Eko Caraka and Li-Wei Liu and Prana Ugiana Gio and Nur Anita Yunikawati and Bens Pardamean}, doi = {10.1109/ACCESS.2025.3553192}, journal = {IEEE Access}, pages = {63774-63791}, shortjournal = {IEEE Access}, title = {Exploring features and products in E-commerce on consumers behavior using cognitive affective}, volume = {13}, year = {2025}, }
• (2025). Bridging the digital divide in health for older adults: A repeated cross-sectional study of mHealth in saudi arabia. ACCESS, 13, 63757-63773. (www)

  @article{ , abstract = {The COVID-19 pandemic significantly accelerated the adoption of mobile health (mHealth) applications, reshaping healthcare delivery while exposing critical barriers to usability and accessibility for older adults in Saudi Arabia. This study examines these challenges during and after the pandemic using a repeated cross-sectional design, focusing on issues such as limited digital literacy, accessibility concerns, and the evolving role of mHealth applications. To capture these dynamics, an initial survey (n = 397) was conducted during the pandemic to understand how families of older adults navigated mHealth applications under mandatory usage requirements. This was complemented by direct interviews (n = 5) and unstructured observations of real-world interactions to provide a nuanced view of older adults’ experiences. To evaluate the long-term impact of mHealth adoption post-pandemic, a second survey (n = 129) was conducted, accompanied by a review of 21 mHealth applications. The findings reveal persistent barriers that hinder independent use, exacerbate reliance on others, and deepen digital inequities. These challenges highlight systemic design and infrastructure shortcomings that undermine equitable access. At the same time, the study highlight the role of families in bridging these gaps, fostering collaboration, and enabling older adults to access digital healthcare. As mHealth applications become integral to healthcare ecosystems, this study calls for the development of inclusive, user-centered designs and targeted interventions to enhance digital literacy, reduce disparities, and empower older adults in accessing equitable healthcare.}, archive = {J_ACCESS}, author = {Sultan A. Alharthi}, doi = {10.1109/ACCESS.2025.3558368}, journal = {IEEE Access}, pages = {63757-63773}, shortjournal = {IEEE Access}, title = {Bridging the digital divide in health for older adults: A repeated cross-sectional study of mHealth in saudi arabia}, volume = {13}, year = {2025}, }
• (2025). Differential signals-of-opportunity timing back-up method on VHF band for critical infrastructures. ACCESS, 13, 63743-63756. (www)

  @article{ , abstract = {This paper describes a back-up method for GNSS-free timing for power lines which is currently achieved using fibre links. The method is based on software-defined-radio technologies for monitoring low-frequency signals of opportunity (SoOp) serving as an alternative timing signal. Our solution utilizes FM broadcasts on VHF band to provide the SoOp and aims to distribute information to a large number of geographical locations. As time synchronisation capabilities are limited between two ends of a fibre link due to long distance, we propose utilizing the electrical length of a test probe at both ends as a baseline for the arrival time of the signal on multiple frequencies. After calibration, difference between the baselines accounts only for the error in the timing signal (SoOp). Making this value available online would serve a similar purpose as differential global navigation satellite system (DGNSS). We present results of a method validation in the laboratory. The slope of the baseline shows good agreement with a theoretical value. The values for the slope of the fitted regression lines are -3.06°/MHz and -5.04°/MHz for the theoretical and measured electrical length, respectively. We also demonstrate a method to improve the accuracy of estimating the environmental timing errors by introducing a mitigation method of the multipath effect at both ends. Then, the slope becomes -4.42°/MHz, which is closer to the expected, theoretical value. Thus, utilizing SoOp at both ends of fibre links, the error of open-air signals can be estimated with an increased accuracy.}, archive = {J_ACCESS}, author = {Martta-Kaisa Olkkonen and Maija Mäkelä and Sanna Kaasalainen}, doi = {10.1109/ACCESS.2025.3558991}, journal = {IEEE Access}, pages = {63743-63756}, shortjournal = {IEEE Access}, title = {Differential signals-of-opportunity timing back-up method on VHF band for critical infrastructures}, volume = {13}, year = {2025}, }
• (2025). Capacity optimization and location of BESS-SC hybrid system for grid inertia support with high wind power penetration. ACCESS, 13, 63729-63742. (www)

  @article{ , abstract = {The high penetration of renewable energy leads to reduced system inertia, which has caused considerable concerns over the stability of frequency and oscillations in power systems. This paper investigates the use of a hybrid system consisting of a Battery Energy Storage System (BESS) and a Synchronous Condenser (SC) to enhance the frequency response of power systems with low inertia. This paper firstly proposes a sizing strategy based on techno-economic analysis using the Genetic Algorithm (GA) optimization technique, which can minimize the cost of the BESS-SC hybrid system while maintaining the Rate of Change of Frequency (RoCoF) within an acceptable limit. Then this paper proposes a locating strategy for identifying the most suitable location for the BESS-SC hybrid system, which utilizes the Short Circuit Ratio (SCR) to identify the location and is further verified by the Prony method employing eigenvalues and damping ratio analysis. The proposed sizing and locating strategies for the BESS-SC hybrid system are demonstrated on the modified Kundur 4-Machine 2-Area system using MATLAB/Simulink. The effectiveness and feasibility of the proposed sizing and locating approaches are verified in three case studies, showing notable enhancements in the RoCoF and frequency nadir using the proposed sizing and locating strategies for the BESS-SC hybrid system.}, archive = {J_ACCESS}, author = {Shami Ahmad Assery and Nan Chen and Xiao-Ping Zhang}, doi = {10.1109/ACCESS.2025.3558481}, journal = {IEEE Access}, pages = {63729-63742}, shortjournal = {IEEE Access}, title = {Capacity optimization and location of BESS-SC hybrid system for grid inertia support with high wind power penetration}, volume = {13}, year = {2025}, }
• (2025). UAV path planning for precision multi-target localization. ACCESS, 13, 63715-63728. (www)

  @article{ , abstract = {Localization of radio-tagged wildlife is essential in environmental research and conservation. Recent advancements in Uncrewed Aerial Vehicles (UAVs) have expanded the potential for improving this process. However, a key challenge lies in the optimal choice of waypoints for UAVs to localize animals with high precision. This study addresses the intelligent selection of waypoints for UAVs assigned to localize multiple stationary Very High Frequency (VHF)-tagged wildlife simultaneously, with a primary emphasis on minimizing localization uncertainty in the shortest possible time. At each designated waypoint, the UAV obtains bearing measurements to tagged animals, considering the associated uncertainty. The algorithm then intelligently recommends subsequent locations that minimize predicted localization uncertainty while accounting for constraints related to mission time, keeping the UAV within signal range, and maintaining a suitable distance from targets to avoid disturbing the wildlife. The evaluation of the algorithm’s performance includes comprehensive assessments, featuring the analysis of uncertainty reduction throughout the mission, comparison of estimated animal locations with ground truth data, and analysis of mission time using Monte Carlo simulations.}, archive = {J_ACCESS}, author = {Mahsa Mohammadi and Michael W. Shafer}, doi = {10.1109/ACCESS.2025.3558700}, journal = {IEEE Access}, pages = {63715-63728}, shortjournal = {IEEE Access}, title = {UAV path planning for precision multi-target localization}, volume = {13}, year = {2025}, }
• (2025). SDPSNet: An efficient 3D object detection based on spatial dynamic pruning sparse convolution and self-attention feature diffusion. ACCESS, 13, 63703-63714. (www)

  @article{ , abstract = {In the field of autonomous driving, the technology of 3D object detection using LiDAR point clouds has been widely implemented. However, existing detectors are faced with challenges due to redundant overhead when processing large amounts of background points in the depth perception field, and insufficient feature mapping in the central regions of large objects. To address these issues, this paper proposes a novel 3D object detection model, SDPSNet, which combines spatial dynamic pruning and self-attention feature diffusion to reduce data redundancy and improve the representation of central features. SDPSNet introduces a spatial dynamic pruning sparse convolution strategy to optimise the inference efficiency and data redundancy of the detector. Meanwhile, the self-attention feature diffusion module is used to effectively diffuse the features of the object edges to the central region, providing a richer and more accurate representation for modelling the centre of large objects. Extensive experimental results have been conducted on the Waymo Open, nuScenes and Argoverse2 datasets. On Argoverse2, SDPSNet achieves a 2.8% increase in mAP over the previous hybrid detector, HEDNet, while being $2.4\times $ faster. It outperforms the previously popular sparse detector FSDv2 by 2.3% in mAP and is $1.5\times $ faster. It also shows a 0.2% improvement in mAP and is $1.2\times $ faster than the current best-performing sparse detector, SAFDNet.}, archive = {J_ACCESS}, author = {Meng Wang and Qianlei Yu and Haipeng Liu}, doi = {10.1109/ACCESS.2025.3559541}, journal = {IEEE Access}, pages = {63703-63714}, shortjournal = {IEEE Access}, title = {SDPSNet: An efficient 3D object detection based on spatial dynamic pruning sparse convolution and self-attention feature diffusion}, volume = {13}, year = {2025}, }
• (2025). AKTMD: Attention-KAN-based neural networks for transportation mode detection. ACCESS, 13, 63690-63702. (www)

  @article{ , abstract = {In recent years, with the development of sensor technology and the popularity of smartphones, transportation mode detection has become increasingly integrated into people’s lives, assisting various mobile intelligent services in improving personal health, optimizing urban traffic, etc. Although previous studies have addressed fine-grained transportation mode detection, challenges including complex temporal correlations, non-linearities and interpretability required to further addressed. To this end, we propose a novel attention-KAN (Kolmogorov-Arnold Networks) based deep model for transportation mode detection, which primarily consists of three modules: convolutional KAN, multi-head attention and linear KAN. By introducing learnable B-spline functions, KAN can express more abundant feature information with fewer parameters which strengthens the non-linearities modeling and interpretability. Specifically, we employ convolutional KAN, which combines the advantages of KAN and convolutional neural network (CNN) to capture the short-term temporal correlations for distinct sensor features. Then we exploit multi-head attention to effectively capture long-term temporal correlations for linked sensor features in parallel, resulting in better trade-off between accuracy and efficiency. Extensive experimental results demonstrate that our proposed AKTMD model outperforms the state-of-the-art TMD-BERT model among the compared baselines, achieving an accuracy of 98.12% on the SHL dataset and 95.52% on the HTC dataset, with improvements of 5.05% and 3.29% over the TMD-BERT model on each dataset.}, archive = {J_ACCESS}, author = {Rui Li and Xueyi Song and Yunmei Wu and Xubo Yu and Hua Huang}, doi = {10.1109/ACCESS.2025.3559417}, journal = {IEEE Access}, pages = {63690-63702}, shortjournal = {IEEE Access}, title = {AKTMD: Attention-KAN-based neural networks for transportation mode detection}, volume = {13}, year = {2025}, }
• (2025). Balanced-simplified spatiotemporal memory attention for image captioning. ACCESS, 13, 63671-63689. (www)

  @article{ , abstract = {Visual attention and memory-based attention methods have been effectively utilized in image captioning to focus on the most relevant areas of an image during the language generation process. Nevertheless, they face significant challenges, as they are solely guided by the hidden state of the LSTM, leading to attention focused on less relevant areas at various time steps. Furthermore, many approaches apply a uniform focus across all visual information within an image, lacking a mechanism for adjustment of focus intensity. Additionally, the complexity of memory-based attention methods highlights the necessity of developing a simplified memory-based attention mechanism for more efficient and effective image captioning. To address these challenges, a novel attention method for image captioning, named Balanced-Simplified Spatiotemporal Memory Attention (BS-STMA), is proposed. The proposed attention mechanism captures spatiotemporal relationships by combining the advantages of LSTM and visual attention in a simple and effective manner. The combination of LSTM memory and the attention mechanism significantly enhances the model’s capacity to retain, convey, and utilize relevant visual information throughout the captioning process. Additionally, an Intensity Balancing Controller (IBC) is introduced and integrated into BS-STMA to enhance its efficiency. IBC allows for adjustments of attention intensity, enabling the model to capture visual information more accurately over time. Extensive experiments on the MSCOCO dataset demonstrate that the method significantly improves image captioning performance, surpassing recent approaches in various evaluation metrics by effectively capturing spatiotemporal.}, archive = {J_ACCESS}, author = {Majjed Al-Qatf and Ali Abdullah Yahya and Ammar Hawbani and Saeed Hamood Alsamhi and Junming Jiang and Edward Curry}, doi = {10.1109/ACCESS.2025.3552217}, journal = {IEEE Access}, pages = {63671-63689}, shortjournal = {IEEE Access}, title = {Balanced-simplified spatiotemporal memory attention for image captioning}, volume = {13}, year = {2025}, }
• (2025). Gradient boosting feature selection for integrated fault diagnosis in series-compensated transmission lines. ACCESS, 13, 63640-63670. (www)

  @article{ , abstract = {Series-compensated power transmission lines are integral to modern power grids and enhance the system reliability and stability. However, they introduce challenges such as voltage inversion, harmonic distortion, and nonlinear dynamics, complicating fault diagnosis in contemporary power systems. This study introduces an innovative method to analyze the waveforms of fault signals, utilizing advances in Internet and sensor technologies that provide extensive voltage and current data in a time-series form. By optimizing each phase of data processing from feature extraction to model learning, the proposed system effectively addresses fault detection, classification, and localization as a multi-classification problem. The integration of feature extraction with efficient gradient-boosting feature selection ensures high accuracy, speed, and computational efficiency, thus outperforming techniques that require heavy preprocessing. The methodology is implemented using four ensemble classifiers: Adaptive Boosting (AB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Extreme Gradient Boosting (XGB), and is rigorously evaluated on various standard models of differing complexity and system configurations under dynamic conditions. This approach achieves high accuracy, adaptability, and robustness, overcoming practical constraints, such as high noise immunity, resource efficiency, and scalability for real-world applications. The results showed that RF achieved the highest accuracy of 99.94%, whereas XGB exhibited the fastest computational time of 0.2790 s for binary classification. RF achieved perfect accuracy (100%) for binary fault classification, whereas XGB achieved perfect accuracy for tri-class fault localization. RF achieved 99.89% accuracy in the five-class classification. LGBM demonstrates the highest noise immunity with 99% accuracy at 10 dB in ten-class multi-classification, whereas RF maintains 94.52% accuracy under extreme noise conditions (5 dB) and unforeseen signal distortions. These findings underscore the effectiveness of the proposed method, particularly the use of gradient boosting classifiers, and validate its potential for practical applications in modern power systems.}, archive = {J_ACCESS}, author = {Rab Nawaz and Abdul Wadood and Khawaja Khalid Mehmood and Syed Basit Ali Bukhari and Hani Albalawi and Aadel Mohammed Alatwi and Muhammad Sajid}, doi = {10.1109/ACCESS.2025.3558796}, journal = {IEEE Access}, pages = {63640-63670}, shortjournal = {IEEE Access}, title = {Gradient boosting feature selection for integrated fault diagnosis in series-compensated transmission lines}, volume = {13}, year = {2025}, }
• (2025). A hierarchical path planning approach using waypoint visibility-based target planner. ACCESS, 13, 63627-63639. (www)

  @article{ , abstract = {This paper presents a hierarchical path planning framework for efficient and practical robot navigation in environments with unknown obstacles that are not registered in a given map. While existing path planning approaches primarily focus on separately enhancing the performance of either the local or global planner, we propose a novel framework that employs a target planner to integrate the two, enabling adaptive navigation toward the final goal position via reachable waypoints. The proposed framework integrates a visibility-based target planner to dynamically evaluate the distance and heading visibility of the current target waypoint along a global path computed by the global planner. By dynamically assessing visibility, the visibility-based target planner autonomously updates the robot’s target waypoint if the current waypoint becomes unreachable owing to obstacles, thereby ensuring that the local planner efficiently navigates the robot to the updated target. Experiments were conducted to validate the proposed framework against a conventional sequential waypoint assignment approach. The results demonstrate that, compared to the conventional approach, the hierarchical framework using the visibility-based target planner reduces the overall navigation time by 49.6 % and 52.6 % in two different experiments while requiring fewer control commands. These improvements indicate that the proposed framework effectively minimized unnecessary detours and enabled the robot to reach the final goal position more efficiently, even in the presence of unmapped obstacles.}, archive = {J_ACCESS}, author = {Hyejeong Ryu}, doi = {10.1109/ACCESS.2025.3559234}, journal = {IEEE Access}, pages = {63627-63639}, shortjournal = {IEEE Access}, title = {A hierarchical path planning approach using waypoint visibility-based target planner}, volume = {13}, year = {2025}, }
• (2025). Enhanced detection of hand gestures from sEMG signals using stacking ensemble with particle swarm optimization and meta-classifier. ACCESS, 13, 63611-63626. (www)

  @article{ , abstract = {Hand gesture recognition is a key aspect of Human Computer Interaction (HCI), that enables recognition of hand movements or gestures through sensors and cameras. Surface Electromyography (sEMG) is a technique used to measure electrical signals generated by human muscle activity. Analyzing sEMG signals are highly influenced by the factors like electrode placement, muscle contraction patterns etc. Existing methodologies for detecting sEMG signals face challenges in interpreting signals and frequency feature variations, primarily due to factors such as noise and motion artifacts, which significantly limit the accuracy and reliability when applied to large sEMG datasets. To overcome these challenges, a cutting-edge deep learning framework is proposed, combining a convolutional autoencoder with a stacking ensemble method that integrates CNN and LSTM models, optimized through particle swarm optimization to fine-tune the hyperparameters for superior training performance. Further to reduce the prediction error, the model employs a Meta classifier technique which incorporates Random Forests approach for improved accuracy. It is evaluated on the NinaPro DB1 dataset with 27 gestures from 52 subjects and a window length of 500 ms. It achieves high accuracy of 85% and effectively mitigates the impact of erroneous data patterns for advancing the robustness of gesture recognition systems.}, archive = {J_ACCESS}, author = {Umesh Chandra Sakinala and S. Abinaya}, doi = {10.1109/ACCESS.2025.3559182}, journal = {IEEE Access}, pages = {63611-63626}, shortjournal = {IEEE Access}, title = {Enhanced detection of hand gestures from sEMG signals using stacking ensemble with particle swarm optimization and meta-classifier}, volume = {13}, year = {2025}, }
• (2025). Advancing perimeter security: Integrating DAS and CNN for object classification in fiber vicinity. ACCESS, 13, 63600-63610. (www)

  @article{ , abstract = {This paper presents an advanced perimeter protection system that integrates phase-sensitive Optical Time-Domain Reflectometry ( $\Phi $ -OTDR) with Convolutional Neural Networks (CNNs) for real-time event classification near optical fibers. The proposed approach enhances traditional security methods by providing robust monitoring in challenging environments, such as low visibility and large-scale areas. We evaluated multiple signal preprocessing techniques, including Fast Fourier Transform (FFT), Redundant Discrete Fourier Transform (RDFT), Discrete Wavelet Transform (DWT), and Mel-Frequency Cepstral Coefficients (MFCC), to optimize classification accuracy and computational efficiency. While MFCC achieved the highest accuracy (85.61%), RDFT provided the best balance between performance (85.47%) and real-time feasibility, making it the preferred method for deployment. The system successfully differentiates events such as vehicle movement, fence manipulation, and construction work, while anomaly detection capabilities further enhance security by identifying irregular activities with minimal error. These findings demonstrate the potential of integrating fiber-optic sensing with deep learning to develop scalable, real-time perimeter protection solutions for critical infrastructure, border surveillance, and urban security.}, archive = {J_ACCESS}, author = {Adrian Tomasov and Pavel Zaviska and Petr Dejdar and Ondrej Klicnik and Francesco da Ros and Tomas Horvath and Petr Munster}, doi = {10.1109/ACCESS.2025.3558594}, journal = {IEEE Access}, pages = {63600-63610}, shortjournal = {IEEE Access}, title = {Advancing perimeter security: Integrating DAS and CNN for object classification in fiber vicinity}, volume = {13}, year = {2025}, }
• (2025). A novel approach with enhanced accuracy and efficiency for image segmentation. ACCESS, 13, 63587-63599. (www)

  @article{ , abstract = {Image segmentation is a fundamental and critical task in computer vision, with applications across various domains, such as medical imaging, autonomous driving, and satellite image analysis. However, existing methods often face challenges in achieving both high accuracy and computational efficiency simultaneously. In this paper, we propose a novel approach to image segmentation that addresses these challenges. Our method is based on the sophisticated U-Net deep learning architecture which integrates multiple advanced techniques. We introduce residual connections to mitigate the vanishing gradient problem, thereby enhancing the stability and convergence speed of the training process. Dilated convolutions are incorporated to expand the receptive field of the network without significantly increasing the number of parameters, enabling the model to capture more contextual information. Additionally, attention mechanisms are integrated to allow the model to focus its computational resources on the most relevant and salient regions of the image, resulting in more precise segmentation. A unique multi-scale feature fusion strategy is employed to combine features from different levels of the network, enriching the feature representation and improving the model’s ability to handle objects of various sizes. Furthermore, we design a novel loss function that combines traditional cross-entropy loss with a structural similarity index measure (SSIM) loss. The cross-entropy loss ensures accurate pixel classification, while the SSIM loss preserves the structural integrity and consistency of the segmented images. Extensive experiments on several benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO-Stuff, demonstrate that our method outperforms numerous state-of-the-art approaches in terms of both segmentation accuracy and computational efficiency. The results show that our method achieves higher mean intersection over union (mIoU), better pixel accuracy, and improved boundary F-score compared to competing methods. Additionally, our approach exhibits greater robustness in handling noisy and occluded images, further validating its practical applicability in real-world scenarios. Overall, our novel approach represents a significant advancement in image segmentation and holds great potential for a variety of practical applications.}, archive = {J_ACCESS}, author = {Jinliang Liang}, doi = {10.1109/ACCESS.2025.3552588}, journal = {IEEE Access}, pages = {63587-63599}, shortjournal = {IEEE Access}, title = {A novel approach with enhanced accuracy and efficiency for image segmentation}, volume = {13}, year = {2025}, }
• (2025). Understanding the role of diversity in ensemble-based AutoML methods for classification tasks. ACCESS, 13, 63566-63586. (www)

  @article{ , abstract = {Ensemble-based Automated Machine Learning (AutoML) methods have gained prominence for their ability to combine diverse machine learning models, achieving superior generalization performance. Despite their empirical success, the underlying mechanisms driving this performance, particularly the role of model diversity, are not yet adequately understood. This study uses novel theoretical frameworks related to the role of diversity in ensembles, which were recently proposed, to shed light on this issue. In this work, we focus on AutoML methods for classification tasks. We use AUTO-SKLEARN (a widely used AutoML ensemble-based method) as a basis. More specifically, we examine how individual model diversity and performance evolves across the four key phases of AUTO-SKLEARN (base-learners, meta-learning, Bayesian Optimization (BO), and Caruana Ensemble). We also examine how they contribute to the diversity and performance of the final ensemble produced by the AutoML method. Using datasets from the AutoML benchmark, we empirically validate these insights by analyzing error rates and diversity measures across the mentioned phases. Our findings highlight the trade-off between individual model accuracy and ensemble diversity, showing that phases like BO improve the mean error rate of classifiers by nearly 50% percent but reduce their mean diversity by 20%. However, the Caruana phase increases the diversity by a 50% compared to the BO phase, allowing better generalization despite the higher mean error rate of the selected individual models (48% higher than BO). This work provides theoretical and empirical evidence that diversity is critical to the success of ensemble-based AutoML methods and a deeper understanding of diversity’s impact on generalization performance and the role of the different AutoML phases. These findings can contribute to advance the development of more robust and theoretically grounded AutoML frameworks.}, archive = {J_ACCESS}, author = {Salomey Osei and Andrés R. Masegosa and Antonio D. Masegosa}, doi = {10.1109/ACCESS.2025.3554093}, journal = {IEEE Access}, pages = {63566-63586}, shortjournal = {IEEE Access}, title = {Understanding the role of diversity in ensemble-based AutoML methods for classification tasks}, volume = {13}, year = {2025}, }
• (2025). Experimental demonstration of 73 gb/s QPSK and 150 gb/s QAM-32 wireless data links in the sub-THz band through frequency selective surface filters. ACCESS, 13, 63556-63565. (www)

  @article{ , abstract = {Investigations on the beneficial effects of microfabricated frequency selective surfaces (FSS) filters in terahertz (THz) wireless links have been recently proposed for amplitude-only on-off keying modulation schemes. Preliminary results on the error performance of amplitude-phase modulations were obtained and compared with a simple model to serve as a testbed for future, more accurate analyses. Here, we further increase the data rate of wireless data links based on FSS filters while maintaining an error below the forward error correction limit. This improvement is achieved by resorting to modulation schemes with higher spectral efficiency such as 4-level phase shift keying, and 32-level quadrature amplitude modulation, measuring 73 Gb/s and 150 Gb/s, with error vector magnitudes (EVM) lower than 16% and 10% respectively. Measurement results for the EVM are predicted through an innovative use of the k-means algorithm which is here suitably modified to deal with FSS-based communications. A remarkable agreement is observed, which makes the proposed tool a promising candidate for the calibration of future wireless links based on FSS filters for next-generation high data rate THz communications.}, archive = {J_ACCESS}, author = {Walter Fuscaldo and Daniele Pirrone and Dimitrios C. Zografopoulos and Antonio Ferraro and Pascal Szriftgiser and Guillaume Ducournau and Romeo Beccherelli}, doi = {10.1109/ACCESS.2025.3558880}, journal = {IEEE Access}, pages = {63556-63565}, shortjournal = {IEEE Access}, title = {Experimental demonstration of 73 gb/s QPSK and 150 gb/s QAM-32 wireless data links in the sub-THz band through frequency selective surface filters}, volume = {13}, year = {2025}, }
• (2025). HitShield: Enhanced swap eviction scheme for multi-generational LRU. ACCESS, 13, 63546-63555. (www)

  @article{ , abstract = {The least recently used (LRU) algorithm is one of the page replacement algorithms used in the swap mechanism of the Linux kernel. The LRU algorithm has evolved through various modifications and developments, ranging from pseudo LRU to LRU/2, and the multi-generational LRU (MGLRU) is currently being discussed and implemented in recent kernels. MGLRU is a variant of the LRU algorithm that improves upon the limitation of using previous memory access records in traditional inactive and active lists. It was officially merged into the kernel 6.1 and continues to be actively improved since then. This paper introduces the HitShield mechanism, which addresses one of the drawbacks of MGLRU: the lack of consideration for the state of the folio during eviction. HitShield is designed by modifying the internal functions of MGLRU, where a counter is maintained in the function that updates the generation of the folio. When eviction occurs, if HitShield of the folio is activated, it is protected from eviction once by promoting it to the youngest generation, similar to a second-chance algorithm. The implementation of HitShield has demonstrated performance improvements ranging from 1.79% to 6.21% in the situation with insufficient memory of the 7-Zip benchmark, from 2.63% to 12.1% in kernel build tasks, and from 0.83% to 9.02% across workload A to F in Yahoo! cloud serving benchmark (YCSB). Additionally, in conjunction with the observed performance improvements, a noticeable reduction in swap-in and swap-out operations was also observed.}, archive = {J_ACCESS}, author = {Minwoo Jo and Binwon Song and Hayong Jeong and Heeseung Jo}, doi = {10.1109/ACCESS.2025.3558527}, journal = {IEEE Access}, pages = {63546-63555}, shortjournal = {IEEE Access}, title = {HitShield: Enhanced swap eviction scheme for multi-generational LRU}, volume = {13}, year = {2025}, }
• (2025). DOG: An object detection adversarial attack method. ACCESS, 13, 63532-63545. (www)

  @article{ , abstract = {This study presents an object detection adversarial attack method (DOG) based on the dynamic optimization of a multi-scale feature grid cluster, aimed at addressing the challenges of poor transferability in white-box attacks and long generation cycles in black-box attacks within the current adversarial example generation techniques. The DOG leverages the C&W adversarial example generation framework, DAG model activation value selection, and R-AP object label resetting. It introduces the maximum/minimum dynamic target category biasing, background resetting, and PSNR(Peak Signal-to-Noise Ratio) rollback mechanisms. By constructing a specialized loss function, the algorithm can iteratively generate adversarial noise, enabling the rapid production of adversarial examples with high attack and transfer rates. Experimental results demonstrate that the DOG performs exceptionally well across the three image classification networks and six object detection networks. In white-box conditions, the attack success rate exceeds 97%, whereas in black-box conditions, it reaches over 51%, showing good generality and efficiency. Additionally, ablation experiments were used to analyze the impact of each module on attack effectiveness, providing a theoretical basis and practical guidance for future research. This study offers new ideas and methods for further exploration of adversarial example generation.}, archive = {J_ACCESS}, author = {Jinpeng Li and Xiaoyu Ji and Wenyuan Xu and Yushi Cheng}, doi = {10.1109/ACCESS.2025.3543171}, journal = {IEEE Access}, pages = {63532-63545}, shortjournal = {IEEE Access}, title = {DOG: An object detection adversarial attack method}, volume = {13}, year = {2025}, }
• (2025). Integrating multimodal AI techniques and MRI preprocessing for enhanced diagnosis of alzheimer’s disease: Clinical applications and research horizons. ACCESS, 13, 63519-63531. (www)

  @article{ , abstract = {A progressive neurological disorder, Alzheimer’s disease has a significant social and economic impact and has become more problematic for world health. Early and precise diagnosis is critical to enable timely intervention to improve patient outcomes. This study introduces a new multimodal AI framework integrating advanced MRI preprocessing techniques and speech analysis for the enhancement of the detection of AD. A strong preprocessing pipeline, including noise reduction, normalization, skull stripping, and segmentation, is utilized to prepare the MRI data. Cognitive and acoustic biomarkers are extracted through spectrograms and pre-trained linguistic models to create speech features. The architecture involves the application of Vision Transformers for spatial analysis and a hybrid CNN- RNN architecture to derive contextual insights, making the framework applicable to multimodal fusion for holistic diagnosis. The framework achieved an accuracy of 94.2% with precision, recall, and F1-scores higher than 92% in the experimental evaluation on a diverse dataset. Comparative analysis with several recent studies further emphasizes that the framework provides better diagnostic performance on key metrics. This work advances early AD detection by incorporating complementary data modalities, addressing existing gaps in unimodal approaches, and providing a scalable, interpretable solution for clinical application.}, archive = {J_ACCESS}, author = {Bandaru A Chakravarthi and Gandla Shivakanth}, doi = {10.1109/ACCESS.2025.3557533}, journal = {IEEE Access}, pages = {63519-63531}, shortjournal = {IEEE Access}, title = {Integrating multimodal AI techniques and MRI preprocessing for enhanced diagnosis of alzheimer’s disease: Clinical applications and research horizons}, volume = {13}, year = {2025}, }
• (2025). Efficient implementation of radar imaging techniques including time gating clutter removal for through the walls applications. ACCESS, 13, 63504-63518. (www)

  @article{ , abstract = {Short-range radars are widely used for automotive applications, through-the-wall imaging and to monitor the elderly activity into their home. Given the relevance of such applications, some imaging algorithms are reformulated using a consistent formalism applied with a very efficient region-of-interest grid step. Moreover, to reduce the computation time, the numerical procedures are structured in such a way to employ fast Fourier transform or inverse fast Fourier transform algorithms whenever it results possible, while to reduce the artifact effects due to antenna and wall reflections, the time domain gating technique is implemented. The adoption of the combined technique (reconstruction algorithms and time gating) allows to remove the artifacts on both simulated and measured data with performances comparable with those of the background subtraction technique. The reported test cases evidence the ability of the considered algorithms to operate with different fractional bandwidth and with very fast execution times.}, archive = {J_ACCESS}, author = {Orlandino Testa and Stefano Pisa and Renato Cicchetti}, doi = {10.1109/ACCESS.2025.3557636}, journal = {IEEE Access}, pages = {63504-63518}, shortjournal = {IEEE Access}, title = {Efficient implementation of radar imaging techniques including time gating clutter removal for through the walls applications}, volume = {13}, year = {2025}, }
• (2025). Optimized renewable energy integration: Advanced modeling, control, and design of a standalone microgrid using hybrid FA-PSO. ACCESS, 13, 63486-63503. (www)

  @article{ , abstract = {The increasing environmental impacts and limited nature of fossil fuels have accelerated the growth of renewable energy sources (RESS). This study addresses the challenges associated with combining renewable energy sources, such as wind, solar, and tidal energy, into power systems, and it focuses on the design and optimization of a hybrid renewable microgrid that uses battery energy storage systems (BESS) to balance supply and demand while considering issues related to battery degradation. Battery degradation is a crucial constraint within the optimization framework. A hybrid optimization technique combining the Firefly Algorithm and Particle Swarm Optimization (FA-PSO) is proposed to enhance system reliability, known as loss of load probability (LPSP), and minimize the net present cost (NPC) of the system. The results and statistical analysis reveal that the proposed hybrid method outperforms the common algorithms used in the literature like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and firefly algorithm (FA). This work contributes to the literature by integrating tidal energy into renewable management and emphasizing realistic battery degradation considerations.}, archive = {J_ACCESS}, author = {Shiva Talebi and Hamed H. Aly}, doi = {10.1109/ACCESS.2025.3559725}, journal = {IEEE Access}, pages = {63486-63503}, shortjournal = {IEEE Access}, title = {Optimized renewable energy integration: Advanced modeling, control, and design of a standalone microgrid using hybrid FA-PSO}, volume = {13}, year = {2025}, }
• (2025). Hybrid 3B net and EfficientNetB2 model for multi-class brain tumor classification. ACCESS, 13, 63465-63485. (www)

  @article{ , abstract = {Brain tumor classification and diagnosis are critical for timely and effective treatment, as brain tumors can severely impact patient health and survival. This paper introduces an enhanced method for the multiclass classification of brain tumors using a novel three-branch convolutional neural network integrated with EfficientNetB2 feature fusion. We systematically compared multi-branch architectures, ranging from single to six branches, to identify the optimal configuration for accurate tumor classification. The three-branch network (3B Net) consistently yielded superior performance across multiple classification tasks. Our experiments, conducted across three distinct datasets, involved binary classification (tumor vs. normal), three-class classification (meningioma, glioma, and pituitary), and four-class classification (non-tumor, meningioma, glioma, and pituitary). The proposed method achieved a binary classification accuracy of 99.50%, a three-class classification accuracy of 94.95% without augmentation, 98.72% with augmentation, and a four-class classification accuracy of 97.80%. Applying data augmentation techniques to the three-class classification dataset led to a marked improvement in classification accuracy. Blind test evaluations confirmed the robustness of the model for both binary and four-class classification, while five-fold cross-validation for the augmented three-class classification dataset further validated its performance. The 3B Net demonstrated the highest accuracy and robustness, significantly outperforming other network configurations. The integration of EfficientNetB2 for feature extraction further enhanced the model’s classification capabilities. These results underline the effectiveness of our 3B Net architecture, suggesting its potential for deployment in automated brain tumor diagnosis and improvement in clinical decision-making processes.}, archive = {J_ACCESS}, author = {R. Preetha and M. Jasmine Pemeena Priyadarsini and J. S. Nisha}, doi = {10.1109/ACCESS.2025.3558411}, journal = {IEEE Access}, pages = {63465-63485}, shortjournal = {IEEE Access}, title = {Hybrid 3B net and EfficientNetB2 model for multi-class brain tumor classification}, volume = {13}, year = {2025}, }
• (2025). Improving artistic design with KFDeformNet: Single-frame three-dimensional cartoon face recovery. ACCESS, 13, 63448-63464. (www)

  @article{ , abstract = {We propose KFDeformNet, a novel framework for enhancing 3D cartoon face recovery from single-frame inputs. Existing methods struggle to capture the fine details and exaggerated characteristics common in artistic cartoon designs. KFDeformNet addresses this with two key components: the Multi-level Attention Keypoint Estimator (MAKEstimator) and the Fourier Representation Deformer (FRDeformer). MAKEstimator improves 2D facial keypoint estimation by utilizing multi-level channel-wise aggregation and the Swin Transformer, overcoming challenges in keypoint localization. FRDeformer uses Fourier representation to project 3D template mesh points to high-order space, preserving geometric details and enriching the 3D representation. We evaluate KFDeformNet on various cartoon face datasets, demonstrating that MAKEstimator outperforms state-of-the-art methods in keypoint precision and FRDeformer achieves better 3D recovery with reduced MSE and faster convergence, aided by the Unified Layer Hyper-Network (ULHN) structure. Our results show that KFDeformNet surpasses existing methods in both accuracy and efficiency, enabling improved 3D cartoon face recovery for creative applications.}, archive = {J_ACCESS}, author = {Tong Sun and Xiaohui Wang and Yichen Qi}, doi = {10.1109/ACCESS.2025.3551397}, journal = {IEEE Access}, pages = {63448-63464}, shortjournal = {IEEE Access}, title = {Improving artistic design with KFDeformNet: Single-frame three-dimensional cartoon face recovery}, volume = {13}, year = {2025}, }
• (2025). Super multi-view near-eye display using pinhole arrays of timing-polarizing characteristics. ACCESS, 13, 63439-63447. (www)

  @article{ , abstract = {Among the various indicators of near-eye display devices, a compact form factor is crucial for the viewing experience of users but also a difficult goal to achieve. The pinhole-based light field display technology is regarded as an effective method to achieve this goal. Despite the numerous advantages, there are problems such as crosstalk and the vergence-accommodation conflict (VAC). This paper proposes a super multi-view near-eye light field display device to solve these two problems simultaneously. By endowing the pinhole array and the display screen with timing-polarizing characteristics, crosstalk-free near-eye four-view light field display is achieved. A proof-of-concept display system with a field of view (FOV) of $22.6^{\circ } \times 22.6^{\circ }$ and a depth of field (DOF) of defocus blur of 650mm was implemented using two $3\times 3$ pinhole arrays of timing-polarizing characteristics.}, archive = {J_ACCESS}, author = {Ruitao Liao and Lilin Liu and Dongdong Teng}, doi = {10.1109/ACCESS.2025.3555746}, journal = {IEEE Access}, pages = {63439-63447}, shortjournal = {IEEE Access}, title = {Super multi-view near-eye display using pinhole arrays of timing-polarizing characteristics}, volume = {13}, year = {2025}, }
• (2025). Benchmarking consistency levels of cloud-distributed NoSQL databases using YCSB. ACCESS, 13, 63428-63438. (www)

  @article{ , abstract = {Distributed database systems offer scalability and fault tolerance by replicating databases across geographically dispersed nodes. This redundancy aims to ensure data availability even during failures and allows for backups in case of disasters. However, maintaining strong data consistency, where all nodes reflect the latest data simultaneously, becomes a challenge in such geographically distributed setups since database systems need to prioritize availability or performance over strong data consistency. This study examines how different data consistency configurations affect the performance of popular NoSQL (Not only SQL) databases, namely Cassandra, MongoDB, and Redis, in a multi-region cloud environment. We adopt the Yahoo! Cloud Serving Benchmark (YCSB) tool to simulate various workloads, measure performance metrics, and compare the results. Our findings reveal significant performance degradation associated with strong data consistency configurations. For instance, in Cassandra, the number of writing/reading operations processed per second can decrease by up to 95% for specific workloads. Similarly, enforcing strong data consistency in Redis can result in execution times that are over 20 times slower on writing/reading operations.}, archive = {J_ACCESS}, author = {Saulo Ferreira and Júlio Mendonça and Bruno Nogueira and Willy Tiengo and Ermeson Andrade}, doi = {10.1109/ACCESS.2025.3558923}, journal = {IEEE Access}, pages = {63428-63438}, shortjournal = {IEEE Access}, title = {Benchmarking consistency levels of cloud-distributed NoSQL databases using YCSB}, volume = {13}, year = {2025}, }
• (2025). Adaptive linear state estimation for unbalanced distribution system. ACCESS, 13, 63417-63427. (www)

  @article{ , abstract = {The inclusion of PMU functionality in distribution relays enables the implementation of a linear state estimator (LSE) in Distribution Systems (DS). However, the unbalanced topology and phase coupling in distribution lines necessitate modifications to the LSE formulation. Additionally, the higher fault frequency in distribution systems requires a state estimation approach that is resilient to contingencies. This work proposes an adaptive linear state estimation algorithm tailored for unbalanced distribution systems with single-phase and two-phase laterals. Furthermore, a modified Optimal PMU Placement (OPP) strategy is introduced to ensure full observability in distribution systems with single-phase and two-phase buses. To maintain adaptability to topology changes, the state estimator incorporates circuit breaker status data provided by PMUs, ensuring robust performance during topology changes triggered by faults. The performance of the algorithm is verified on the IEEE 13-bus, 34-bus, and 123-bus systems.}, archive = {J_ACCESS}, author = {Yijie Zhou and Virgilio Centeno}, doi = {10.1109/ACCESS.2025.3559792}, journal = {IEEE Access}, pages = {63417-63427}, shortjournal = {IEEE Access}, title = {Adaptive linear state estimation for unbalanced distribution system}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of control techniques for various mechanisms of parallel robots. ACCESS, 13, 63381-63416. (www)

  @article{ , abstract = {This study provides a literature review of control schemes associated with parallel robots. Various types of parallel robots have been discussed, including the Delta Robot, 2 DOF Parallel Robot, 3 DOF Parallel Robot, 4 DOF Parallel Robot, the R4 Redundantly Parallel Robot, the 5R Parallel Robot, 6 DOF Parallel Robot (Hexa Robot), Cable-Driven Parallel Robot, and Parallel Quadruped Robot. Additionally, an overview of the control structuring within these systems has been provided. This study covers the developments occurring between 2008 and 2024. These controllers are PI Control, PID Control, Genetic Algorithm Optimized PID Control, Extended PD Control, Augmented Nonlinear PD Control, Model-Based PD Control, Synchronous PD Control Based on a Time-Delay Estimator, Identification-Based Control, Image-Based Control, Lagrangian Formulation-Based Control, Observers-Based Control, Optimal PID Control, Anti-Windup Based PID Control, Model-Based Control, PLC-Based Control Architecture, Feedback PID Control, and Computed Torque Control, Hedge Algebras Control, B-Spline Neural Network Control, Fuzzy Logic Control, Model-Based control, Cooperative Control, Brain Emotional Learning-Based Intelligent Control, Nonlinear Model Predictive Control, Fuzzy Logic-Based Sliding Surface Control, Fuzzy PID Control, Fuzzy- Based PID Control, Interval Type-2 Fuzzy Logic Control Fuzzy Logic based Neural Network Control, Nonlinear Model Predictive Control, Synchronous Sliding Mode Control, Observer-Based Sliding Mode Control, Sliding Mode-Based on Backstepping Control, LQ Optimal Control, Adaptive Fuzzy Control, Rise Feedback Control, Dual-Space Feedforward Control, Dual-Space Feedforward Control in the Cartesian Space, Dual-Space Feedforward Control, Dual Mode Adaptive Control, Nonlinear Dual Mode Adaptive Control, Optimized Fuzzy Adaptive PID Control, Adaptive Position-Force Control, Adaptive Admittance Control, and Robust Nonlinear Adaptive Control, Adaptive Robust Control, Adaptive Control, and Adaptive Terminal Sliding Mode Control, Robust control, fault tolerant controllers, and passivity control. The primary contribution is a literature review of control-based strategies for parallel robots, designed to inform those interested in control-oriented parallel robot research.}, archive = {J_ACCESS}, author = {Shaymaa Mahmood Mahdi and Ahmed I. Abdulkareem and Amjad Jaleel Humaidi and Ammar K. Al Mhdawi and Hamed Al-Raweshidy}, doi = {10.1109/ACCESS.2025.3557937}, journal = {IEEE Access}, pages = {63381-63416}, shortjournal = {IEEE Access}, title = {Comprehensive review of control techniques for various mechanisms of parallel robots}, volume = {13}, year = {2025}, }
• (2025). Mapping the intellectual structure of AI-driven economic forecasting: A scientometric analysis from 1991 to 2024. ACCESS, 13, 63367-63380. (www)

  @article{ , abstract = {This study presents a comprehensive bibliometric analysis of artificial intelligence (AI) applications in economic forecasting from 1991 to 2024. Through systematic examination of 746 publications from the Web of Science Core Collection, we employ collaboration network analysis, co-citation analysis, and keyword co-occurrence analysis to map the intellectual structure and development trajectory of this rapidly growing field. Our findings reveal distinct patterns in international research collaboration, with China and the United States emerging as primary contributors, while European institutions demonstrate strong centrality in global research networks. The co-citation analysis identifies five major research clusters, highlighting the field’s theoretical foundations in explainable AI, deep learning applications, support vector regression, specialized forecasting domains, and behavioral finance integration. Temporal analysis of keyword co-occurrence patterns indicates an evolution from basic neural network applications to sophisticated hybrid approaches incorporating multiple AI techniques. The study provides novel insights into emerging research frontiers, particularly in areas of explainable AI, privacy-preserving computation, and adaptive modeling for economic forecasting. This comprehensive analysis contributes to the literature by mapping the field’s intellectual landscape and highlighting promising future research directions, providing valuable guidance for researchers, practitioners, and policymakers working at the intersection of AI and economic forecasting.}, archive = {J_ACCESS}, author = {Xiaoyuan Yu and Peng Wang and Nuzul Azam Haron}, doi = {10.1109/ACCESS.2025.3558887}, journal = {IEEE Access}, pages = {63367-63380}, shortjournal = {IEEE Access}, title = {Mapping the intellectual structure of AI-driven economic forecasting: A scientometric analysis from 1991 to 2024}, volume = {13}, year = {2025}, }
• (2025). Analytical optimal control of delayed worm propagation model in heterogeneous IoT systems. ACCESS, 13, 63349-63366. (www)

  @article{ , abstract = {This paper presents a mathematical model for worm propagation, where infectivity is influenced by latency within heterogeneous Internet of Things (IoT) systems. The model incorporates the heterogeneity of susceptible-exposed-infected-recovered (SEIR) compartments and considers the varying negative impacts of worms spread across these groups. Sufficient conditions for the persistence of worm propagation are derived using the optimistic equilibrium point. By selecting latency as a bifurcation parameter, the study reveals a specific latency value critical for maintaining worm propagation’s stability in these systems. The normal form approach and central manifold theory are employed to analyze the direction and stability of Hopf bifurcation. Furthermore, this study addresses strategies for mitigating the spread of worms by employing best practices to minimize the number of devices exposed and infected across systems. We analyze the effects of control measures, such as vaccination and treatment, which should be applied promptly during a worm proliferation outbreak and gradually scaled down over time as the outbreak decreases. Numerical findings expose that latency significantly impacts system stability, however, optimally managing the latency below a deterministic threshold may maintain system stabilization.}, archive = {J_ACCESS}, author = {B. S. N. Murthy and V. Madhusudanan and M. N. Srinivas and L. Guerrini and Anwar Zeb and Nhu-Ngoc Dao and Sungrae Cho}, doi = {10.1109/ACCESS.2025.3559146}, journal = {IEEE Access}, pages = {63349-63366}, shortjournal = {IEEE Access}, title = {Analytical optimal control of delayed worm propagation model in heterogeneous IoT systems}, volume = {13}, year = {2025}, }
• (2025). Stochastic technique on assigning adaptive minimal code bits to locally frequent symbols in continuous data stream. ACCESS, 13, 63334-63348. (www)

  @article{ , abstract = {As increasing demands for recognizing social environment and/or human activity using sensory devices and video cameras, streaming data has become one of major data types. The applications process the data stream and generate new knowledge due to recent AI technologies. During the scenario, it is important to consider how fast the data stream is transferred from the sensing equipment to the computing platform. Lossless compression is one of the solutions for the fast communication, which reduces the amount of data. This paper focuses on realtime encoding mechanism for data stream by employing lossless encoding technique. Conventional encoding mechanisms such as Huffman coding use data entropy based on frequency counters of data symbols. According to the ranking of the frequencies of data unit, that is, for lower entropy, shorter code is assigned to the corresponding symbol to compress a given dataset. However, we found that the method with frequency counters cannot identify actual instant entropy because its accumulated frequency includes past entropy. We also found that an encoder can assign shorter code to the locally frequent symbols suddenly appeared in data stream, and that it results more data reduction than the conventional methods. In this paper, we propose a novel stochastic method with Markov model to respond the locally frequent symbols. The method uses the entropy calculations from the state machine of Markov model with/without a frequency counting. From experimental evaluations, we show the technique follows instant entropy changes of data stream and compresses the symbols to appropriate shorter codes. Through experimental evaluations, we show validity of the technique with describing the algorithm. The experimental evaluations show improvements up to 17% better compression ratios due to the instant response to the locally frequent symbols.}, archive = {J_ACCESS}, author = {Taiki Kato and Shinichi Yamagiwa}, doi = {10.1109/ACCESS.2025.3558983}, journal = {IEEE Access}, pages = {63334-63348}, shortjournal = {IEEE Access}, title = {Stochastic technique on assigning adaptive minimal code bits to locally frequent symbols in continuous data stream}, volume = {13}, year = {2025}, }
• (2025). Enhancing SR-tree for nearest neighbor search. ACCESS, 13, 63324-63333. (www)

  @article{ , abstract = {Nearest neighbor search, also known as k-nearest neighbors (kNN), has become one of the essential backbones in machine learning and data mining tasks, particularly in multidimensional dynamic and evolving data environments. While dynamic index structures have been extensively utilized for indexing dynamic data, their performance in nearest neighbor search can further be improved. This paper aims to enhance SR-Tree as it has been shown to be effective for indexing high dimensional clustered data. We propose enhancements to SR-Tree structure to better handle multidimensional data and reduce query processing time. First, we improve SR-Tree using a variance-based bulk-loading algorithm to build an efficient tree structure. Second, we apply the approximation of the smallest enclosing ball and leaf-level pruning methods to improve performance of kNN queries. Third, we adapt the dual-tree algorithm for SR-Tree that outperforms sequential queries for the all-nearest neighbors problem in low-to-medium dimensional large datasets. These improvements were evaluated using real-world datasets across varying dimensionalities by comparing against several freely available indexing structure frameworks. Experimental results indicated considerable speedup over the state-of-the-art dynamic index implementation in nearest neighbor retrieval across high-dimensional conditions. This work also provides an open-source implementation of our enhancements to SR-Tree for the Rust ecosystem.}, archive = {J_ACCESS}, author = {Kayumov Abduaziz and Shincheol Lee and Ji Sun Shin}, doi = {10.1109/ACCESS.2025.3559478}, journal = {IEEE Access}, pages = {63324-63333}, shortjournal = {IEEE Access}, title = {Enhancing SR-tree for nearest neighbor search}, volume = {13}, year = {2025}, }
• (2025). Nested FOPI and PI controller performance comparison for electric vehicle traction system. ACCESS, 13, 63310-63323. (www)

  @article{ , abstract = {Electric Vehicles (EVs) are increasing in popularity due to climate change concerns, and pollution caused by fossil fuels across the globe. This stimulated advanced research in motor control algorithms for improving efficiency and performance of EVs. Induction Motors (IMs) are still the backbone of EVs drive systems due to their many qualities and cost effectiveness. This work investigates the use of nested Fractional-Order Proportional Integral (FOPI) and conventional integer order Proportional Integral (PI) controllers, and examines the effect of having these controllers in the inner and outer loops. Ziegler-Nichols (ZN) and Cohen-Coon (CC) rules are used for tuning the PI combinations on a prototype EV traction system consisting of an Indirect Field-Oriented Control (IFOC) based induction motor drive system. The speed regulation and power consumption for seven test cases with different combinations of FOPI, PI controllers used in these loops, are analyzed and compared. The findings of this research may help IM-based electric drives designers find an appropriate nested-configuration for the required applications when PI, FOPI controllers are involved.}, archive = {J_ACCESS}, author = {Faris AtaAllah and Mazen Elsaadany and Muhammad Qasim Elahi and Shayok Mukhopadhyay and Habibur Rehman}, doi = {10.1109/ACCESS.2025.3556174}, journal = {IEEE Access}, pages = {63310-63323}, shortjournal = {IEEE Access}, title = {Nested FOPI and PI controller performance comparison for electric vehicle traction system}, volume = {13}, year = {2025}, }
• (2025). Enhancing wi-fi 7: Traffic flow intelligence and multi-link operation for optimal efficiency. ACCESS, 13, 63298-63309. (www)

  @article{ , abstract = {As the proliferation of Wi-Fi-enabled devices continues to accelerate, there is a pressing need for efficient wireless connectivity solutions. To address this challenge, the introduction of Multi-Link Operation (MLO) in Wi-Fi 7 (IEEE 802.11be) enables clients to establish connections with access points across multiple frequency bands, thereby enhancing throughput and reducing latency at the expense of increased power consumption. To balance the benefits of MLO with its potential impact on smartphone battery life and user experience, we propose an innovative approach that integrates machine learning-based network service detection for Traffic Flow Intelligence with intelligent link adaptation for MLO. Our solution utilizes machine learning to detect latency-sensitive applications with up to 99% accuracy and optimizes link settings based on the Quality of Experience (QoE) of the user relative to the application, detected by our QoE Monitoring Classifier with 99.7% recall for bad QoE and 88% accuracy overall. Notably, our approach enables users to enjoy up to 40 minutes more of high-quality video calls compared to an always-on MLO configuration on the Samsung Galaxy S24 Ultra.}, archive = {J_ACCESS}, author = {Neha Dawar and Khuong N. Nguyen and Abhishek Sehgal and Yuming Zhu and Boon Loong Ng and Junsu Choi}, doi = {10.1109/ACCESS.2025.3557435}, journal = {IEEE Access}, pages = {63298-63309}, shortjournal = {IEEE Access}, title = {Enhancing wi-fi 7: Traffic flow intelligence and multi-link operation for optimal efficiency}, volume = {13}, year = {2025}, }
• (2025). Mental health safety and depression detection in social media text data: A classification approach based on a deep learning model. ACCESS, 13, 63284-63297. (www)

  @article{ , abstract = {Social media expansion enables real-time mental health monitoring particularly for depression detection through its platforms. Traditional depression detection techniques, however, struggle to handle the informal, often ambiguous nature of language used in social media, including emojis, slang, and context-dependent expressions. To address this limitation, this research proposes a novel framework for depression detection that integrates advanced text preprocessing with a deep learning model. The preprocessing pipeline contains two key components where emojis are converted into text by emoji normalization then slang expressions are replaced through a customized dictionary. Emotion scores serve as additional components to identify depression-related emotional cues within the text. The model combines BERT’s contextual embeddings with BiLSTM’s sequential processing power to effectively represent emotional content in social media posts. The proposed approach demonstrates superior performance compared to traditional baseline models by achieving better results in accuracy, precision, recall, F1-score, AUC and RGA. This approach shows exceptional performance in detecting depression through informal texts which serves as a new benchmark for depression detection in dynamic social media environments.}, archive = {J_ACCESS}, author = {Shiwen Zhou and Masnizah Mohd}, doi = {10.1109/ACCESS.2025.3559170}, journal = {IEEE Access}, pages = {63284-63297}, shortjournal = {IEEE Access}, title = {Mental health safety and depression detection in social media text data: A classification approach based on a deep learning model}, volume = {13}, year = {2025}, }
• (2025). A review of short-term, long-term, and memory consolidation mechanisms in the hippocampus and cerebral cortex. ACCESS, 13, 63248-63283. (www)

  @article{ , abstract = {Learning and memory have long been a focal point of research across neuroscience and artificial intelligence. A deep understanding of the neural mechanisms underlying learning and memory is necessary to advance artificial intelligence capabilities. Considering this, we review neuroscience principles about learning and memory in the hippocampus and cerebral cortex (neocortex). Specifically, we present the different stages of learning and memory, including short-term memory (STM), long-term memory (LTM), and memory consolidation. Moreover, this work aims to translate neuroscience’s mechanisms of learning and memory to intelligent agents, enabling machines with human-like learning, memory, cognitive, and decision-making functions. We discuss a foundational overview of cutting-edge research on continuous learning and memory in artificial intelligence. In addition, we analyze the limitations and research prospects in long-term and short-term memory for continuous learning, providing a solid theoretical groundwork for future advancements.}, archive = {J_ACCESS}, author = {Zuomin Yang and Anis Salwa Binti Mohd Khairuddin and Joon Huang Chuah and Wei Ru Wong and Hafiz Muhammad Fahad Noman and Tri Wahyu Oktaviana Putri and Xin Xu and Md Minhazul Haq}, doi = {10.1109/ACCESS.2025.3555539}, journal = {IEEE Access}, pages = {63248-63283}, shortjournal = {IEEE Access}, title = {A review of short-term, long-term, and memory consolidation mechanisms in the hippocampus and cerebral cortex}, volume = {13}, year = {2025}, }
• (2025). Forecasting energy consumption of a mining plant using artificial neural networks. ACCESS, 13, 63237-63247. (www)

  @article{ , abstract = {This study addresses the issue of forecasting active and reactive power consumption at a mining and processing plant, aiming to improve the efficiency of energy resource management. It explores existing approaches to modelling and analysing electricity consumption, including methods for forecasting active power to accurately assess the energy needs of industrial enterprises, as well as methods for estimating reactive power required to compensate for reactive components and stabilize grid parameters. Using real electricity consumption data from an industrial enterprise, the completed training and research demonstrates the potential to forecast energy consumption for the next 24 to 48 hours using artificial neural networks with nonlinear autoregressive architecture. It also provides rationale for pre-processing initial data to enhance forecast accuracy. These approaches contribute to reducing capital and operating costs, improving the reliability and stability of energy systems, and optimising operating efficiency.}, archive = {J_ACCESS}, author = {Mahmoud Al-Suod and Busher Victor and Tytiuk Valerii and Chorna Olha and Sivyakova Galina and Mohammad Zannon and Zhuk Dmytro}, doi = {10.1109/ACCESS.2025.3558445}, journal = {IEEE Access}, pages = {63237-63247}, shortjournal = {IEEE Access}, title = {Forecasting energy consumption of a mining plant using artificial neural networks}, volume = {13}, year = {2025}, }
• (2025). Advancing agile software cost estimation through data synthesis: A comparative analysis of five generation techniques. ACCESS, 13, 63219-63236. (www)

  @article{ , abstract = {Agile has been used in software development for over 20 years and is the preferred development method for more than 85% of software companies. However, cost estimation in agile development remains a significant challenge. This is reflected in the fact that the accuracy of estimation still needs improvement, and most cost estimation techniques still rely on the team’s experience and knowledge. While machine learning algorithms have performed better in this area, the lack of sufficient agile cost data hinders large-scale training and in-depth research. To address this issue, this study selected five data generation techniques—Variational Autoencoder (VAE), Wasserstein Generative Adversarial Network (WGAN), Synthetic Minority Over-sampling Technique for Nominal and Continuous Features (SMOTE-NC), Data Augmentation for Tabular Data (Augmentation), and Tabular Data Diffusion Probabilistic Models (TabDDPM)—based on the characteristics of agile cost data. Using cost data from 75 agile projects, these techniques were employed to generate three sets of data with sizes of 200, 500, and 1000. A performance evaluation model was created based on consistency, authenticity, diversity, and effectiveness to verify the performance of these generated data. The experimental results show that WGAN consistently scored 16 out of 20 points across all three data sets, excelling in data consistency and authenticity. SMOTE-NC and Augmentation Were followed. SMOTE-NC scored 15 out of 20 points for all data sizes and performed best in terms of effectiveness, with an MMRE of 88.16% and a PRED (0.2) of 84.5%. Augmentation performed the best when generating 1000 data points. These findings highlight the potential of data generation technologies, particularly WGAN, in enhancing agile cost estimation and providing guidance on selecting the appropriate amount of data. This lays a foundation for further development of machine learning algorithms in this field and offers valuable insights for other researchers.}, archive = {J_ACCESS}, author = {Xiaoyan Zhao and Zulkefli Mansor and Rozilawati Razali and Mohd Zakree Ahmad Nazri and Xin Xiong}, doi = {10.1109/ACCESS.2025.3558715}, journal = {IEEE Access}, pages = {63219-63236}, shortjournal = {IEEE Access}, title = {Advancing agile software cost estimation through data synthesis: A comparative analysis of five generation techniques}, volume = {13}, year = {2025}, }
• (2025). PHARFF: A pipeline-based graceful live migration and checkpointing framework for achieving high availability and reliability in FPGA applications on cloud. ACCESS, 13, 63203-63218. (www)

  @article{ , abstract = {FPGAs have long been used in cloud environments for hardware acceleration, significantly improving service speed. However, while performance benefits are well-established, managing FPGA applications remains a challenge. Specifically, ensuring high availability and reliability requires live migration and checkpointing of hardware state registers with minimal downtime. Existing approaches focus on reducing migration time, but this has inherent limits in minimizing downtime. In this work, we propose a pipeline-based live migration and checkpointing mechanism that decouples downtime from migration time, effectively reducing it to nearly zero. This approach is realized through a novel framework, PHARFF, which is demonstrated with four benchmark applications: systolic array-based Matrix Multiplication, SSearch, FIR filter, and AES-128. Results show that PHARFF achieves the highest service availability, with zero downtime and minimal checkpoint sizes (zero for memoryless applications). Compared to existing methods, our approach also improves design frequency by 16% and optimizes hardware resource utilization, reducing LUT requirements by 27% and minimizing register usage by 2.5 times.}, archive = {J_ACCESS}, author = {Yame Asfia and Sajid Gul Khawaja and Muhammad Usman Akram and Muhammad Asad and Shoab A. Khan and Alina Mirza}, doi = {10.1109/ACCESS.2025.3557303}, journal = {IEEE Access}, pages = {63203-63218}, shortjournal = {IEEE Access}, title = {PHARFF: A pipeline-based graceful live migration and checkpointing framework for achieving high availability and reliability in FPGA applications on cloud}, volume = {13}, year = {2025}, }
• (2025). Assessing transformer aging using dielectric oil characteristics: Influence of operating voltage levels and purification. ACCESS, 13, 63186-63202. (www)

  @article{ , abstract = {Dielectric characteristic testing of transformer oil is a crucial diagnostic tool for assessing transformer health. While previous studies have investigated the correlation between oil quality parameters and aging factors, limited research has examined the direct influence of operating voltage levels on dielectric oil degradation. Additionally, the effect of oil purification on aging assessments is often overlooked, leading to potential misinterpretations in condition assessment. This study analyzes historical dielectric test data from 4315 oil samples collected from power transformers operating at 500 kV, 150 kV, and 70 kV within Indonesia’s electrical grid. The data is categorized into two groups: one without considering purification and another combining purified and unpurified oil samples. Statistical analysis using linear regression is employed to assess correlations among key parameters. The findings reveal that higher operating voltage levels accelerate the degradation of dielectric oil, with transformers operating at 500 kV exhibiting the most rapid decline in oil quality. Furthermore, purification significantly alters certain dielectric parameters, particularly BDV and water content, whereas the color scale remains largely unaffected. The study also establishes that parameters such as acidity, water content, and interfacial tension (IFT) are reliable indicators of transformer aging only in unpurified oil samples. Conversely, the color scale proves to be a more consistent indicator of overall insulation system aging, making it a valuable parameter for maintenance decision-making. These results provide a refined approach for utilities in transformer condition assessment, ensuring that the impact of purification is accounted for to avoid misleading aging evaluations. The results also highlight the necessity for voltage-specific maintenance strategies to enhance transformer reliability and lifetime.}, archive = {J_ACCESS}, author = {Muhammad A. Akmal Putra and Suwarno and Rahman Azis Prasojo}, doi = {10.1109/ACCESS.2025.3558408}, journal = {IEEE Access}, pages = {63186-63202}, shortjournal = {IEEE Access}, title = {Assessing transformer aging using dielectric oil characteristics: Influence of operating voltage levels and purification}, volume = {13}, year = {2025}, }
• (2025). Developer recommendation and team formation in collaborative crowdsourcing platforms. ACCESS, 13, 63170-63185. (www)

  @article{ , abstract = {The Competitive Crowdsourcing Software Development (CSE) environment is a dynamic and evolving field that interests researchers and the software industry. This ecosystem ensures the timely delivery of cost-effective, innovative, and high-quality solutions. As this environment grows in popularity, it faces the crucial challenge of developer recommendation and team formation, which must be addressed to ensure its ongoing success and progress. This research offers a novel method for assembling teams and recommending developers. It combines keyword-based embeddings and fuzzy logic to match developers with appropriate tasks with exceptional accuracy. The technique utilizes KeyBERT to extract keywords and perform embedding to capture relevant skills and task requirements without emphasizing common words. The embeddings are optimized using a fuzzy logic framework. This framework categorizes the quality of developer-task pairings into three distinct levels: “Strong,” “Average,” and “Weak.” This approach enables developer recommendations and team formation, balancing precision, recall and F1-score across varying team sizes to achieve high overall accuracy by a percentage increase of 4.19%. and TF score increased by 4.05%. The proposed method consistently outperforms existing methods, allowing the formation of capable and reliable teams of varying sizes. This ensures the creation of high-performing, well-balanced teams that can effectively handle diverse tasks with the percentage improvement from Content-Based Filtering (CBF). The results in percentage increase (precision, recall and f1-score) of the proposed approach (with k=5) from CBF improve by 30.29%, 36.49%, and 33.45%, and with User-based Collaborative Filtering (UCF) and Random Recommendation (RR) performances increase by (19.58%, 15.98%, and 17.92%) and (160.17%, 215.07%, and 188.31%), respectively.}, archive = {J_ACCESS}, author = {Yasir Munir and Qasim Umer and Muhammad Faheem and Sheeraz Akram and Arfan Jaffar}, doi = {10.1109/ACCESS.2025.3558557}, journal = {IEEE Access}, pages = {63170-63185}, shortjournal = {IEEE Access}, title = {Developer recommendation and team formation in collaborative crowdsourcing platforms}, volume = {13}, year = {2025}, }
• (2025). Performance analysis and model improvements for electromagnetic transient state estimators. ACCESS, 13, 63157-63169. (www)

  @article{ , abstract = {State estimation techniques for the study of electromagnetic transients use measurement records to analyze the propagation of such effects to other unmonitored locations within an electrical grid. Among the approaches mentioned in the literature, strategies based on numerical derivatives and integrative substitutions stand out because of their implementation facilities and effective responses. However, since these procedures typically rely on simplified component models that omit parameters essential for transient phenomena analysis, inaccurate estimator responses can result. Due to the lack of comparative studies on state estimation methods for electromagnetic transients, this article presents a comparative evaluation of two widely recognized approaches: Numerical Integration Substitution (NIS) and Numerical Derivative Substitution (NDS). In addition, improvements in transformer modeling are proposed for both methods by incorporating previously neglected parameters, such as winding resistances and stray capacitances. Both methods are assessed through computational and experimental simulations, highlighting their performance in terms of accuracy and numerical stability. The results demonstrate that the proposed improvements enhance the estimation accuracy of both methods. Moreover, the improved NDS approach consistently outperforms NIS, exhibiting better convergence under conditions involving high-frequency transients.}, archive = {J_ACCESS}, author = {Humberto Cunha de Oliveira and Vinicius Henrique Faria Brito and José Carlos de Oliveira and Isaque Nogueira Gondim}, doi = {10.1109/ACCESS.2025.3557286}, journal = {IEEE Access}, pages = {63157-63169}, shortjournal = {IEEE Access}, title = {Performance analysis and model improvements for electromagnetic transient state estimators}, volume = {13}, year = {2025}, }
• (2025). A statistical analysis of the relationship between meme stocks and social media. ACCESS, 13, 63143-63156. (www)

  @article{ , abstract = {Meme stocks, driven by viral social media trends, have added new complexities to financial markets. Prior studies have explored meme stock price dynamics and investor sentiment, but the interplay between social media activity and market movements, along with the structural and linguistic features of online discussions, remains understudied. To address this, we integrate econometric and NLP-based techniques, combining correlation analysis, Granger causality testing, BSADF-based bubble detection, and textual analysis. Our results reveal a strong correlation between trading volume and social media engagement, with Granger causality confirming a feedback loop between market fluctuations and online discussions. BSADF analysis demonstrates that social media-based detection complements price-based methods by identifying explosive periods they may miss. Additionally, network analysis indicates that meme stock discussions exhibit distinct structural patterns, while linguistic analysis highlights unique word choices and emoji usage. Sentiment analysis shows that bullish sentiment dominates during speculative surges, reinforcing the emotionally driven nature of meme stock trading. These findings provide investors with a complementary tool for risk assessment by integrating sentiment with traditional market indicators, while helping regulators monitor online sentiment to identify early signs of speculative excess and market instability.}, archive = {J_ACCESS}, author = {Seungju Lee and Yunyoung Lee and Jaewook Lee and Hoki Kim}, doi = {10.1109/ACCESS.2025.3557460}, journal = {IEEE Access}, pages = {63143-63156}, shortjournal = {IEEE Access}, title = {A statistical analysis of the relationship between meme stocks and social media}, volume = {13}, year = {2025}, }
• (2025). Quad-tree-based driver classification using deep learning for mild cognitive impairment detection. ACCESS, 13, 63129-63142. (www)

  @article{ , abstract = {Given GPS points on a transportation network, the goal of the Quad-tree Based Driver Classification (QBDC) problem is to identify whether drivers have Mild Cognitive Impairment (MCI). The QBDC problem is challenging due to the large volume and complexity of the data. This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. The experimental results demonstrate the effectiveness of the proposed algorithm, achieving an F1 score of 95% that significantly outperforms the baseline models. These results highlight the potential of geo-regional quad-tree structures to extract interpretable features and describe complex driving patterns. This approach offers significant implications for driver classification, with the potential to improve road safety and cognitive health monitoring.}, archive = {J_ACCESS}, author = {Seyedeh Gol Ara Ghoreishi and Charles Boateng and Sonia Moshfeghi and Muhammad Tanveer Jan and Joshua Conniff and Kwangsoo Yang and Jinwoo Jang and Borko Furht and David Newman and Ruth Tappen and Monica Rosselli and Kelley L. Jackson}, doi = {10.1109/ACCESS.2025.3558706}, journal = {IEEE Access}, pages = {63129-63142}, shortjournal = {IEEE Access}, title = {Quad-tree-based driver classification using deep learning for mild cognitive impairment detection}, volume = {13}, year = {2025}, }
• (2025). Lunar power grid: Network structure and spontaneous synchronization. ACCESS, 13, 63120-63128. (www)

  @article{ , abstract = {Achieving stable synchronized operation in an alternating current power network is critical to the continuity and reliability of energy delivery. In this paper, we study a dynamic model for synchronization in the proposed power network which the National Aeronautic and Space Agency plans to build on the lunar surface to support continuous human presence on the Moon and a lunar economy. This network is quite remarkable in the sense that it is expected to be the first power network to operate at the unprecedented operating frequency in the kHz range. The particular structure of this network allows us to derive the necessary and sufficient conditions guaranteeing the existence of a unique locally stable synchronized mode, which will provide a passive control mechanism for the system. Furthermore, we study the bifurcation process leading to the loss of synchronization when the system parameters fall outside of the stable regime. Our results have broader implications for many complex networks with parametric heterogeneity to enhance stability and resilience.}, archive = {J_ACCESS}, author = {Mark S. Walth and Amirhossein Sajadi and Marc A. Carbone and Bri-Mathias Hodge}, doi = {10.1109/ACCESS.2025.3558692}, journal = {IEEE Access}, pages = {63120-63128}, shortjournal = {IEEE Access}, title = {Lunar power grid: Network structure and spontaneous synchronization}, volume = {13}, year = {2025}, }
• (2025). Resource allocation for federated learning with highly distorted model. ACCESS, 13, 63108-63119. (www)

  @article{ , abstract = {Information loss has emerged and escalated as the information bottleneck of a deep encryption model surpasses the entropy of the data and reduces the data reconstruction efficiency at the decoder (i.e., lossy compression and high data encryption). Therefore, existing communication-effective federated learning (FL) approaches (e.g., model quantization, data sparsification, and model compression) incurred a considerable trade-off between communication efficiency and global convergence rate when an extreme encryption rate is applied. Nonetheless, the trade-off becomes less severe as the FL network expands. By utilizing this fact, we formulate an optimization problem for encryption-aided FL that captures the relationship between the distortion rate, the number of participating Internet-of-Things (IoT) devices, and the convergence rate. The purpose of the formulated FL optimization problem is to simultaneously optimize both the energy efficiency and the FL performance at once while using various model encryption techniques. Thereafter, our theoretical analysis shows that by actively controlling the number of participating IoT devices, we can avoid the training divergence of encryption-assisted FL while maintaining communication efficiency.}, archive = {J_ACCESS}, author = {Ryu Junewoo and Nguyen Xuan Tung and Minh-Duong Nguyen and Quang-Vinh do and Won-Joo Hwang}, doi = {10.1109/ACCESS.2025.3556780}, journal = {IEEE Access}, pages = {63108-63119}, shortjournal = {IEEE Access}, title = {Resource allocation for federated learning with highly distorted model}, volume = {13}, year = {2025}, }
• (2025). Synergistic integration of image-text modalities in telemedicine: VAE-infused graph convolutional networks for diagnostic decision-making. ACCESS, 13, 63093-63107. (www)

  @article{ , abstract = {The fusion of multimodal data in telemedicine diagnosis plays a crucial role in improving diagnostic accuracy and enabling comprehensive analysis. While integrating multimodal pathological images is relatively straightforward, effectively integrating non-image modalities remains challenging, leading to the underutilization of the complementary characteristics present in multimodal data. We introduce a novel fusion method that addresses this challenge by integrating image and text modalities. Our proposed method leverages a Variational Autoencoder (VAE) to extract hidden states from images. These states are subsequently fused with patient-specific text attributes-age, gender, and diagnostic keywords-during the feature fusion stage. The fused features are structured as a graph and then input into a Graph Convolutional Neural Network (GCN) for classification tasks. Our approach establishes connections between the text and image modalities, exploring the multimodal relationship using GCN. Our method exploits similarities among samples with shared diagnostic keywords, effectively utilizing information present in both modalities. The experimental results show that our fusion scheme outperforms advanced image classification models and multimodal models in terms of classification accuracy. Specifically, in the experiments on the dataset, our method Hidden State Graph Convolutional Network (HSGCN) performed the best in performance metrics such as Precision, Recall, F1 score, Accuracy, and AUC, with Precision reaching 78.42%, significantly higher than other methods. In addition, compared with single modal image classification models and multimodal models, HSGCN also demonstrates higher accuracy in Top-k performance, with average performance for each category exceeding that of single modal classification models, and overall accuracy being 2-4% higher. These results highlight the effectiveness of our method in utilizing supplementary information from image and text patterns to improve remote medical diagnosis.}, archive = {J_ACCESS}, author = {Yirui Kuang and Hui Jin and Jiayu Yang and Mengxue Pang and Jianqiang Ma and Wei Ou}, doi = {10.1109/ACCESS.2025.3558499}, journal = {IEEE Access}, pages = {63093-63107}, shortjournal = {IEEE Access}, title = {Synergistic integration of image-text modalities in telemedicine: VAE-infused graph convolutional networks for diagnostic decision-making}, volume = {13}, year = {2025}, }
• (2025). Risk assessment method for autonomous vehicles violating safety common sense based on driving behavior. ACCESS, 13, 63076-63092. (www)

  @article{ , abstract = {As autonomous driving technology continues to evolve, ensuring decision-making safety in complex traffic environments has become a major challenge. This study proposes a novel risk assessment method based on driving behaviors that violate safety common sense. Driving behaviors that violate safety common sense are defined and categorized, and a comprehensive risk assessment framework is established. By analyzing real-time trajectories and driving behaviors, risk indicators are quantified to form the basis of the risk assessment model. Combined with environment safety entropy, the comprehensive risk assessment is obtained using the subjective Analytic Hierarchy Process and the objective entropy weight method to enhance assessment accuracy. The comprehensive risk levels are classified using a Gaussian Mixture Model (GMM) optimized by K-means. Finally, simulations and real-world road tests validate the effectiveness of the model. This method addresses the limitations of existing approaches by providing a more adaptive and accurate framework for assessing unconventional yet risky driving behaviors. It offers new perspectives and strategies for improving the safety of autonomous driving systems and provides valuable references for future regulations and standards.}, archive = {J_ACCESS}, author = {Zhaowen Pang and Zhenbin Chen and Jiayi Lu and Bin Sun and Tianyang Gong and Xinjie Feng and Yu Wang and Shichun Yang and Yaoguang Cao}, doi = {10.1109/ACCESS.2025.3552833}, journal = {IEEE Access}, pages = {63076-63092}, shortjournal = {IEEE Access}, title = {Risk assessment method for autonomous vehicles violating safety common sense based on driving behavior}, volume = {13}, year = {2025}, }
• (2025). Robust position estimator for sensorless control of traction motor in electric vehicles. ACCESS, 13, 63066-63075. (www)

  @article{ , abstract = {Although the sensorless control of traction motor can improve the mileage, cost, and reliability of electric vehicles (EVs), its practical use remains challenging due to the vulnerability of conventional position estimator for sensorless control to severe load torque. However, this limitation can be overcome by utilizing the kinematic and dynamic sensors embedded in the latest EVs. Therefore, this study proposes a new position estimator that utilizing the total torque and speed error as the inputs by reprocessing the vehicle acceleration information to improve the robustness against the load torque. In addition, gains of proposed position estimator are determined to nullify errors that may occur during the reprocessing of information. As the results, the proposed position estimator can achieve the high estimation bandwidth, while improving the overall estimation performance. Furthermore, the practical effectiveness of proposed position estimator is verified by both the experimental tests and hardware-in-the-loop (HIL) tests under various speed and load torque conditions.}, archive = {J_ACCESS}, author = {Myeong-Won Kim and Issac Kim and Seung-Mook Baek and Jung-Wook Park}, doi = {10.1109/ACCESS.2025.3557032}, journal = {IEEE Access}, pages = {63066-63075}, shortjournal = {IEEE Access}, title = {Robust position estimator for sensorless control of traction motor in electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Developments and trends in water level forecasting using machine learning Models—A review. ACCESS, 13, 63048-63065. (www)

  @article{ , abstract = {Water level forecasting in rivers, lakes, and reservoirs is crucial for effective water resource management, flood control, and environmental planning. This review examines the latest developments and trends in water level forecasting research from 2011-2024. A wide range of methods are explored, including traditional statistical models (ARIMA, regression) and advanced techniques like artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), and deep learning models (LSTM). The study assesses the performance and accuracy of these applied models, analyzing their strengths and limitations in capturing water system dynamics and uncertainties. It investigates how data sources (hydrological, meteorological, historical) and variables (rainfall, evaporation, inflow) impact forecast accuracy. The significance of different variables for improving model predictive capabilities is determined. Spatiotemporal aspects are explored, examining model applicability across local, regional, and global scales. Approaches to quantifying and communicating uncertainties associated with probabilistic forecasting for decision-making are evaluated. Detailed analysis identifies proven model efficiencies, potential challenges, and suggests future research directions. By comprehensively reviewing recent water level forecasting literature, this study provides state-of-the-art knowledge on applying machine learning models for reservoir water level prediction. It guides water resource strategies, flood mitigation measures, and decision-making for sustainable water systems management. This review is a valuable resource for researchers and practitioners in hydrology and related fields.}, archive = {J_ACCESS}, author = {Abdus Samad Azad and Nahina Islam and Md Nurun Nabi and Hifsa Khurshid and Mohammad Ashraful Siddique}, doi = {10.1109/ACCESS.2025.3557910}, journal = {IEEE Access}, pages = {63048-63065}, shortjournal = {IEEE Access}, title = {Developments and trends in water level forecasting using machine learning Models—A review}, volume = {13}, year = {2025}, }
• (2025). Einstein aggregation operator technique for linguistic q-rung orthopair fuzzy hypersoft set with application to sustainable construction industry. ACCESS, 13, 63029-63047. (www)

  @article{ , abstract = {The unclear and vague information, subjective assessments, and the languages spoken with diversity in these domains are some of the linguistic features that complicate the decision-making process in the construction industry. Language-specific terminology, context-specific terms, and terms requiring a common translation all provide challenges. Furthermore, it is challenging to compile linguistic information from several sources with a cohesive framework due to interpretation variability. To address these challenges, in this article, we propose a novel theory term as linguistic q-rung orthopair fuzzy hypersoft set (Lq-ROFHS). To aggregate the various information, we defined some new weighted averaging and geometric operators by using Einstein t-norm operations. The fundamental properties of all these stated operators are derived in detail. To illustrate the method, a multi-criteria group decision making algorithm is proposed by using the stated operators and apply them to the case study related to the selection of the best construction company. To demonstrate the efficiency of the proposed algorithm, a comparative analysis between the proposed and the several existing studies is done by comparing the order of preference. The proposed approach is a significant advancement that will enable decision-makers to navigate the complexities of their choice with greater assurance and precision using a single tool.}, archive = {J_ACCESS}, author = {Muhammad Saqlain and Harish Garg and Wiyada Kumam and Rana Muhammad Zulqarnain}, doi = {10.1109/ACCESS.2025.3555015}, journal = {IEEE Access}, pages = {63029-63047}, shortjournal = {IEEE Access}, title = {Einstein aggregation operator technique for linguistic q-rung orthopair fuzzy hypersoft set with application to sustainable construction industry}, volume = {13}, year = {2025}, }
• (2025). GPU accelerated trilateral filter for MR image restoration. ACCESS, 13, 63013-63028. (www)

  @article{ , abstract = {Medical image processing demands essential image restoration techniques to handle both blurred and noisy images. The image capture process frequently causes these degradations. The restoration of medical images such as MRI scans holds essential value for better diagnosis and improved treatment accuracy. MRI serves as a non-invasive imaging tool to diagnose brain diseases yet the noise produced during its acquisition phase degrades image quality so that diagnosis accuracy suffers. Reduction of noise becomes essential for both clinical diagnostic procedures and computer-assisted medical analysis practices which require tissue classification and registration and segmentation algorithms. Detecting and eliminating noise from magnetic resonance images represents a complex medical imaging challenge. Multiple noise filtering methods operate on MR images with distinct performance benefits. This work presents a trilateral filter specifically designed for image restoration which provides both precision and speed performance. The bilateral filter served as foundation to develop this filter while adding another intensity comparison function to deal with brain MR image noise. A weighting control function based on intensity entropy is established for intensity-based weight calculations. The implementation of GPU parallel computing techniques together with optimization of memory and threads leads to faster computation. A combination of texture-based image analysis with advanced computational algorithms powers the automated filtration process. The approach uses forward selection to identify 98 texture attributes while refining the selection process to find optimal regularity features. A two-phase classification system trains automation parameters using artificial neural networks together with support vector machines. Research findings show that trilateral filtering yields superior noise reduction alongside better definition of MR image features than alternative techniques.}, archive = {J_ACCESS}, author = {Suthir Sriram and Nivethitha Vijayaraj and T. Srilekha and M. Praveena and Thangavel Murugan}, doi = {10.1109/ACCESS.2025.3556747}, journal = {IEEE Access}, pages = {63013-63028}, shortjournal = {IEEE Access}, title = {GPU accelerated trilateral filter for MR image restoration}, volume = {13}, year = {2025}, }
• (2025). Reversible data hiding in encrypted images based on edge-directed prediction and multi-MSB self-prediction. ACCESS, 13, 63000-63012. (www)

  @article{ , abstract = {With the development of cloud computing, reversible data hiding in encrypted images (RDH-EI) technology has gained significant attention in ensuring data security and privacy protection. This paper proposes a high-capacity RDH-EI method that employs a dual prediction strategy, including edge-directed prediction and multiple most significant bits (multi-MSB) self-prediction. First, the image owner utilizes the edge-directed property of least-square optimization to enhance the accuracy of pixel prediction. Subsequently, each pixel undergoes self-prediction starting from the MSB and progressing towards an adaptive high-order bit-plane, plane by plane. A binary location map marks the prediction errors for each bit-plane. After being losslessly compressed using the Joint Bi-level Image Experts Group (JBIG) algorithm, the location maps are embedded into the encrypted image as auxiliary data, thereby creating ample room for embedding additional data. Next, the data hider retrieves the auxiliary data from the encrypted image and embeds the additional data into the reserved embedding room, resulting in a marked encrypted image. Finally, authorized recipients with different keys can either accurately extract data or precisely restore the image from the marked encrypted image without any errors. The experimental results show that the proposed method achieves average pure embedding rates of 4.240 bpp for BOSSBase dataset and 4.088 bpp for BOWS-2 dataset, respectively, outperforming many state-of-the-art RDH-EI methods.}, archive = {J_ACCESS}, author = {Yingqiang Qiu}, doi = {10.1109/ACCESS.2025.3558369}, journal = {IEEE Access}, pages = {63000-63012}, shortjournal = {IEEE Access}, title = {Reversible data hiding in encrypted images based on edge-directed prediction and multi-MSB self-prediction}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of voltage source converters-based FACTS controllers in hybrid microgrids. ACCESS, 13, 62961-62999. (www)

  @article{ , abstract = {This article presents a comprehensive review of voltage source controller-based Flexible Alternating Current Transmission System (FACTS) controllers and their important role in enhancing the performance of hybrid microgrids. The primary objectives of this review are to explore the various applications of voltage source controllers, investigate advanced control strategies used in their integration, and evaluate their impact on power quality, stability, and overall efficiency in hybrid microgrid scenarios. The review exploit a systematic approach, synthesizing existing literature on FACTS controllers, with a focus on Static Synchronous Compensators (STATCOMs), Static Synchronous Series Compensator (SSSC), Interline Power Flow Controller (IPFC), and Unified Power Flow Controllers (UPFCs). This review emphasizes the significant role of VSC-based FACTS controllers in enhancing the stability, reliability, and efficiency of hybrid microgrids. The review covers various control strategies, integration challenges, and optimization techniques, offering insights into the latest advancements and future research directions. The capability of STATCOMs to regulate voltage and reactive power, while SSSC concentrates on series compensation for enhanced power transfer capability. UPFCs are perfect for complex microgrid management because they provide complete control over both reactive and real power. IPFCs, designed for multi-terminal operation, further enhance controllability. The focus is on acknowledging the development of voltage source controllers, starting from conventional control techniques to the integration of more advanced methods like optimization strategies, adaptive control, and machine learning-based approaches. This review highlights the significance of FACTS controllers, particularly VSCs, in ensuring reliable and efficient operation of hybrid microgrids, hence further empowering the wider adoption of renewable energy sources.}, archive = {J_ACCESS}, author = {Maloth Hussen and Thirumalaivasan Rajaram}, doi = {10.1109/ACCESS.2025.3557961}, journal = {IEEE Access}, pages = {62961-62999}, shortjournal = {IEEE Access}, title = {A comprehensive review of voltage source converters-based FACTS controllers in hybrid microgrids}, volume = {13}, year = {2025}, }
• (2025). Application of artificial intelligence in obsessive-compulsive disorder detection and response to treatment: A systematic review. ACCESS, 13, 62930-62960. (www)

  @article{ , abstract = {Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder that causes unwanted thoughts and repetitive behaviors in a person’s life. Clinical data and neuroimaging technologies are common methods used to diagnose OCD disorders and predict treatment responses. This study reviews the application of artificial intelligence (AI), particularly traditional machine learning (ML) and deep learning (DL), in the detection of OCD and the prediction of responses to various treatments from clinical data and neuroimaging modalities, including electroencephalography (EEG), magnetic resonance imaging (MRI), functional MRI (fMRI) and diffusion tensor imaging data (DTI). A comprehensive search was performed utilizing Google Scholar and PubMed for published papers up to 2024. The search utilized the following keywords: “obsessive-compulsive disorder,” “OCD + prediction response to treatment,” “OCD + electroencephalography (EEG),” “OCD classification,” “EEG + OCD diagnosis,” “OCD + machine learning,” “neuroimaging + OCD + predict,” and “OCD + predictor.” This paper summarizes and discusses the 88 selected papers. Our findings demonstrate the significant role of AI in OCD diagnosis and predicting treatment response. Support Vector Machines methods have been used more among AI methods. Moreover, extracting and selecting features from EEG is more common than other neuroimaging modalities. Some clinical characteristics, such as age, gender, and the severity of the obsession, along with neuroimaging-derived factors like EEG frequency band power, EEG complexity, and white matter volume, help diagnose OCD and predict treatment response. Future studies should aim to develop deep learning to facilitate the rapid diagnosis of OCD and accurately predict treatment responses.}, archive = {J_ACCESS}, author = {Faezeh Ghasemi and Ahmad Shalbaf and Ali Esteki}, doi = {10.1109/ACCESS.2025.3558845}, journal = {IEEE Access}, pages = {62930-62960}, shortjournal = {IEEE Access}, title = {Application of artificial intelligence in obsessive-compulsive disorder detection and response to treatment: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Integrated THz/mmWave transmission method for enhanced URLLC communications. ACCESS, 13, 62914-62929. (www)

  @article{ , abstract = {Ultra-Reliable and Low-Latency Communication (URLLC) is considered one of the key use cases in the current 5G and future 6G mobile communication networks. In this work, a hybrid transmission scheme is proposed to improve reliability in Terahertz (THz) bands employing adaptive diversity combining at the destination. The proposed scheme combines two links, the first link is a THz link (offering higher capacity but less reliable connectivity) as the main link, and the second link is a millimetre wave (mmWave) link (offering lower capacity but more reliable connectivity) as the backup link. The proposed scheme utilises both links dynamically exploiting the benefits of transmitting over both frequency bands by changing between the main link only and joint main/backup links based on the instantaneous quality of the channel for the main link. The received signals are combined at the destination using Selection Combining (SC) and Maximal Ratio Combining (MRC). The performance of the proposed scheme is evaluated analytically considering the Fluctuating Two-Ray (FTR) and $\alpha -\mu $ models for the mmWave and THz links, respectively. The obtained results show that the proposed hybrid transmission scheme achieves the same reliability provided by the continuous dual-link transmission scheme, where both links (THz and mmWave links) are used continuously for transmission, thus making the proposed scheme an appropriate solution to provide URLLC applications in a resource-efficient manner.}, archive = {J_ACCESS}, author = {Moawiah Alhulayil and Mohammad Abu Aqoulah and Miguel López-Benítez and Mamoun F. Al-Mistarihi and Mohammed Alammar and Abdulrahman Al Ayidh}, doi = {10.1109/ACCESS.2025.3558842}, journal = {IEEE Access}, pages = {62914-62929}, shortjournal = {IEEE Access}, title = {Integrated THz/mmWave transmission method for enhanced URLLC communications}, volume = {13}, year = {2025}, }
• (2025). Construction and application of GIS-based public transport transfer network. ACCESS, 13, 62903-62913. (www)

  @article{ , abstract = {The transfer issue has always been a hot topic in the field of public transport, and improving transfer efficiency is crucial for effectively guiding public travel and optimizing the organization of public transportation. This paper proposes a new method for constructing transfer networks based on the advantages and disadvantages of the Space-P model and network analysis methods. Taking Lhasa as a case study, we used ArcGIS software to construct the public transport transfer network of Lhasa and employed network analysis methods to validate the effectiveness of the constructed transfer network. Based on 851 bus stops in the study area, a total of 723,350 valid routes were obtained. According to the validation results, after data organization and spatial representation using ArcGIS, the constructed transfer network can effectively measure passenger dwell time, transfer coefficients, and transfer routes in the public transport network, demonstrating the network’s effectiveness. This paper aims to provide a new perspective for the construction of public transport transfer models and offers scientific basis for the planning and design of public transport systems.}, archive = {J_ACCESS}, author = {Hang Su and Leishan Guo and Yunpeng Li and Shijie Liu and Junde Fan and Cong Du and Jianqing Wu}, doi = {10.1109/ACCESS.2025.3555751}, journal = {IEEE Access}, pages = {62903-62913}, shortjournal = {IEEE Access}, title = {Construction and application of GIS-based public transport transfer network}, volume = {13}, year = {2025}, }
• (2025). Aerial display method using a flying screen and long range dynamic projection mapping. ACCESS, 13, 62893-62902. (www)

  @article{ , abstract = {In recent years, technologies that enable visual representation of images in the air, such as drone art, have been gaining attention. However, drone art requires the use of hundreds of light-equipped drones, which are extremely costly and difficult to use for small-scale events. In order to realize an aerial display at low cost with fewer drones, we propose an aerial display method using dynamic projection mapping with a high-brightness projector and a screen suspended from a drone. The proposed method consists of a screen structure suitable for suspension from a drone, an infrared marker structure that enables estimation of the center of the screen, and a center estimation method based on the marker. To evaluate the proposed method, we conducted a proof-of-concept experiment using a simple laser projector and an experiment using a high-brightness projector to project an image onto a dynamic screen at a distance. The results of the experiments demonstrated the effectiveness of the aerial display with our proposed method.}, archive = {J_ACCESS}, author = {Yuito Hirohashi and Hiromasa Oku}, doi = {10.1109/ACCESS.2025.3557000}, journal = {IEEE Access}, pages = {62893-62902}, shortjournal = {IEEE Access}, title = {Aerial display method using a flying screen and long range dynamic projection mapping}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Effective gait abnormality detection in parkinson patients for multi-sensors surveillance system”. ACCESS, 13, 62892. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Effective Gait Abnormality Detection in Parkinson Patients for Multi-Sensors Surveillance System”).}, archive = {J_ACCESS}, author = {Abdulwahab Alazeb and Mouazma Batool and Naif Al Mudawi and Mohammed S. Alshehri and Sultan Almakdi and Nouf Abdullah Almujally and Asaad Algarni}, doi = {10.1109/ACCESS.2025.3558379}, journal = {IEEE Access}, pages = {62892}, shortjournal = {IEEE Access}, title = {Corrections to “Effective gait abnormality detection in parkinson patients for multi-sensors surveillance system”}, volume = {13}, year = {2025}, }
• (2025). Graph neural networks: Architectures, applications, and future directions. ACCESS, 13, 62870-62891. (www)

  @article{ , abstract = {In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.}, archive = {J_ACCESS}, author = {Valerio Ponzi and Christian Napoli}, doi = {10.1109/ACCESS.2025.3558752}, journal = {IEEE Access}, pages = {62870-62891}, shortjournal = {IEEE Access}, title = {Graph neural networks: Architectures, applications, and future directions}, volume = {13}, year = {2025}, }
• (2025). Fusion+: A multi-sensor image fusion for very high-resolution satellite imagery. ACCESS, 13, 62856-62869. (www)

  @article{ , abstract = {A very detailed image, such as very high-resolution satellite imagery, is necessary for many applications. By enhancing the image’s detail and reducing its color distortion, we can optimize its use. Image fusion is a method to achieve this goal. This paper proposes a novel method of multi-sensor image fusion called Fusion+. This method enhances spatial resolution, sharpens the image using the kernel of image enhancement, and preserves radiometric consistency using the histogram specification. In addition, the Fusion+ has a shift adjustment between the input images to address the geometric issue, especially for images from multiple sensors. The proposed method can be used for both single-sensor and multi-sensor image fusion. The fused image obtained from the proposed method also increased the image’s detail and was sharper than the other methods. The universal image quality index, peak signal-to-noise ratio, and structural similarity index measure are also sufficient to maintain the radiometric consistency of the original image. The results also showed that the fused image had maximum detail increase, minimum color distortion, and a natural color appearance. Fusion+ obtained an average SSIM score of 0.779, an average PSNR of 29.342, and an average UQI of 0.973 on single-sensor fused images, as well as SSIM averages of 0.556, an average PSNR of 27.896, and an average UQI of 0.947 on multi-sensor fused images. The Fusion+ method demonstrates the potential for improved image fusion quality, which can contribute to improved accuracy and reliability in the analysis of very high-resolution remote sensing data, especially in multi-sensor applications.}, archive = {J_ACCESS}, author = {Yohanes Fridolin Hestrio and Danang Surya Candra and Randy Prima Brahmantara and Yudhi Prabowo and Anjar Dimara Sakti and MGS M. Luthfi Ramadhan and Yong Loong Yap and Kurniawati Azizah and Muhammad Hafizhuddin Hilman and Stuart Phinn and Sin Liang Lim and Wisnu Jatmiko}, doi = {10.1109/ACCESS.2025.3558558}, journal = {IEEE Access}, pages = {62856-62869}, shortjournal = {IEEE Access}, title = {Fusion+: A multi-sensor image fusion for very high-resolution satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Industrial maturity of machine learning solutions within the food industry. ACCESS, 13, 62831-62855. (www)

  @article{ , abstract = {Ensuring food security is a crucial challenge becoming increasingly complex for society on a global level. Machine learning technology can help to overcome this challenge, however its successful deployment in practice is a mandatory prerequisite and currently achieved only to a limited extent. Therefore, this systematic literature review aims at determining the current state of industrial maturity of machine learning-based approaches in the context of food industry, evaluating their readiness for operational use and deployment. An initial framework for assessment of industrial technology readiness consisting of six technical and human- and process-related dimensions is developed. Existing solutions are categorized according to the addressed process step within the food value chain and the covered dimension of the maturity framework. As the findings demonstrate, the industrial maturity degree is mainly located in the lower to middle range. Regarding all considered dimensions and phases within the food value chain, however particularly regarding the dimensions integrability and usability as well as the phase packaging and logistics, huge progress is required to achieve an overall high or very high industrial maturity degree. Thus, this work highlights the importance of a holistic perspective realized e.g. by cooperation between research and industry in order to achieve application-ready machine learning models with high levels of industrial maturity.}, archive = {J_ACCESS}, author = {Laura Gradl and Luisa Reis and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3558091}, journal = {IEEE Access}, pages = {62831-62855}, shortjournal = {IEEE Access}, title = {Industrial maturity of machine learning solutions within the food industry}, volume = {13}, year = {2025}, }
• (2025). NeuroFuzDetect–A neural and fuzzy inference system for fake news classification. ACCESS, 13, 62820-62830. (www)

  @article{ , abstract = {The widespread dissemination of fake news in the digital era significantly impacts public opinion, economies, and political outcomes. Traditional fake news detection methods often struggle to balance accuracy and interpretability, limiting their effectiveness. To address this challenge, this paper introduces NeuroFuzDetect, an Intelligent Fuzzy-Neural Network that integrates Fuzzy Logic and Long Short-Term Memory (LSTM) to enhance both accuracy and transparency in fake news detection. NeuroFuzDetect leverages advanced computational reasoning to analyze uncertainty and ambiguity in textual data by evaluating critical linguistic features, including credibility, emotional tone, and language patterns, ensuring interpretability by providing justifications for its predictions. The model was evaluated on a widely recognized dataset, demonstrating an accuracy of 87.2%, along with significant improvements in precision, recall, and F1-score. Future research will focus on refining the model by incorporating multimodal analysis, integrating visual and contextual cues, and adapting it for real-time detection on digital platforms while enhancing robustness against adversarial attacks and extending applicability across diverse linguistic and cultural contexts. By achieving a balance between efficiency and transparency, NeuroFuzDetect presents a scalable and reliable solution for combating fake news in real-world applications.}, archive = {J_ACCESS}, author = {Borra Vineetha and Munirathinam Nirmala}, doi = {10.1109/ACCESS.2025.3547123}, journal = {IEEE Access}, pages = {62820-62830}, shortjournal = {IEEE Access}, title = {NeuroFuzDetect–A neural and fuzzy inference system for fake news classification}, volume = {13}, year = {2025}, }
• (2025). Bayesian optimized GridDehazeNet for adaptive haze removal in real-world applications. ACCESS, 13, 62807-62819. (www)

  @article{ , abstract = {Haze removal is essential for improving image visibility in applications like autonomous vehicles and surveillance. While models like GridDehazeNet enhance dehazing performance, our research emphasizes that selecting appropriate image data and application methods is even more crucial. We propose an adaptive haze removal system that integrates Bayesian Optimization with GridDehazeNet to automatically find the optimal network width and height for specific environments. Our experiments show that the optimal architectural parameters of the neural network—specifically, the height (h) and width (w) of the network architecture—vary for images captured by fixed-position cameras in real-world settings. By employing our algorithm, we effectively identify the best architectural parameters, achieving higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to models without this optimization. Notably, fine-tuning the model for specific environments yields a 36% average boost in PSNR over the baseline while reducing the model parameter count by 63%. This is a pivotal outcome, as it addresses two critical demands in dehazing: improved performance and computational efficiency. By demonstrating that a smaller, optimized network can surpass a larger, generic baseline in both accuracy and speed, our work suggests a practical pathway toward real-time dehazing solutions, particularly in resource-constrained or edge-based scenarios.}, archive = {J_ACCESS}, author = {Sungkwan Youm}, doi = {10.1109/ACCESS.2025.3558632}, journal = {IEEE Access}, pages = {62807-62819}, shortjournal = {IEEE Access}, title = {Bayesian optimized GridDehazeNet for adaptive haze removal in real-world applications}, volume = {13}, year = {2025}, }
• (2025). On the generation of synthetic invoices for training machine learning models. ACCESS, 13, 62798-62806. (www)

  @article{ , abstract = {Currently, the problem of generating synthetic financial documents is particularly acute. Extending recent research on the topic, we present an enhanced tool for invoice generation. The primary motivation is the need for invoice corpora for machine learning in accounting automation. The generation produces synthetic invoices with randomized layouts and contents. As content fields are generated, annotations for supervised machine learning are saved along with the generated invoice, thus solving the problem of labor-intensive annotation tasks. The content and layout diversity is evaluated and compared to empirical and synthetic invoice corpora using SELF-BLEU, Alignment, and Overlap metrics. We have validated the stability of the modeling statistically. The modeling is consistent and reproducible. The final assessment is that the diversity of the generated invoices is on par with the real-world ones and, by most metrics, exhibits superiority over the foregoing ones.}, archive = {J_ACCESS}, author = {Rolandas Gricius and Igoris Belovas}, doi = {10.1109/ACCESS.2025.3555155}, journal = {IEEE Access}, pages = {62798-62806}, shortjournal = {IEEE Access}, title = {On the generation of synthetic invoices for training machine learning models}, volume = {13}, year = {2025}, }
• (2025). MIE-SPD: A new and highly efficient chaos-based multiple image encryption technique with synchronous permutation diffusion. ACCESS, 13, 62773-62797. (www)

  @article{ , abstract = {Due to the advancement of computing technologies and the Internet of Things, there has been a notable rise in image data in different sectors. Having safety mechanisms in place is crucial to protect image data from unauthorised access. This article introduces a new and effective technique for securing multiple images concurrently. The suggested encryption method varies from other multiple image encoding approaches because of its unique synchronous permutation diffusion (SPD) functioning and fast circular shifting operation based on diffusion and permutation. SPD involves evaluating diffusion and permutation processes at the same time. When encrypting, the colour image components are merged to build a single big image. After that, the big picture is segregated into distinct cellblocks that do not overlap. Furthermore, every single block undergoes the SPD process. The encryption method is fast and efficient as it combines circular shift and SPD techniques. The method is also more efficient due to the two types of permutation operations: block-selection and circular shift. Moreover, the method is effective in both software and hardware as it utilises a single fixed type 1D chaotic map for both diffusion and permutation stages. Using the SHA-256 hash method, the input image hashes are utilised to create algorithmic key parameters, providing resistance to chosen-ciphertext and plaintext attacks. Based on the results of the security study and simulation, the proposed method is quick, highly effective in encryption, and enhances the security of cipher images.}, archive = {J_ACCESS}, author = {Bibhudendra Acharya and Javvadi Venkata Sravan and Durga Ram Janaki Potnuru and K. Abhimanyu Kumar Patro}, doi = {10.1109/ACCESS.2025.3558943}, journal = {IEEE Access}, pages = {62773-62797}, shortjournal = {IEEE Access}, title = {MIE-SPD: A new and highly efficient chaos-based multiple image encryption technique with synchronous permutation diffusion}, volume = {13}, year = {2025}, }
• (2025). Multi-fidelity surrogate modeling via hierarchical kriging with infinite-width bayesian neural network correlation function. ACCESS, 13, 62753-62772. (www)

  @article{ , abstract = {Hierarchical Kriging (HK) is a promising surrogate model to fuse multi-fidelity data. In theory, HK can serve as predictor for problems with any number of input dimensions. In practice, for a problem with more than 1000 variables, it is often not affordable to build such HK model due to the high demand on the computer memory to store the spatial distance matrix and the heavy computational burden relating to the frequent inversion of the large correlation matrix. To mitigate the curse of dimensionality of HK for high-dimensional problems, an infinite-width Bayesian neural network correlation function is developed and incorporated to the HK model. Theoretical analysis demonstrates that the proposed correlation function is independent on the spatial distance and has no hyperparameters to be fine-tuned. Therefore, the demand on the consumption of computer memory and computility for the model construction can be reduced significantly. Real engineering problems, including the aerodynamic performance prediction of single axial flow compressor rotor with 31 variables, 3-stage compressor with 144 variables, and 10.5-stage compressor of 1512 variables, are established along with the collected problems to test the performance of the proposed method. Comparison among the proposed method and alternative methods are further conducted. It is observed that the proposed method performs competitive in terms of the modeling accuracy and significantly better with respect to modeling efficiency. On the 1512-dimensional problem of the prediction for the compressor pressure ratio, the proposed method can build most accurate model within 50 seconds on a personal laptop with a data set consisting of 15120 low-fidelity and 7560 high-fidelity sample data.}, archive = {J_ACCESS}, author = {Youwei He and Jiangshuo Cui}, doi = {10.1109/ACCESS.2025.3559167}, journal = {IEEE Access}, pages = {62753-62772}, shortjournal = {IEEE Access}, title = {Multi-fidelity surrogate modeling via hierarchical kriging with infinite-width bayesian neural network correlation function}, volume = {13}, year = {2025}, }
• (2025). An effective genetic algorithm for mixed precision. ACCESS, 13, 62739-62752. (www)

  @article{ , abstract = {The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic algorithm to determine the maximum number of variables that can sustain precision alterations without compromising the desired error threshold. The proposed approach is evaluated across various benchmark programs, analyzing the effects of precision tuning under increasing error thresholds. Our findings reveal that, for a majority of these programs, reducing precision through partitioning leads to significant performance enhancements, with improvements of up to 15%.}, archive = {J_ACCESS}, author = {Wanyu Zhang and Yu Shang and Min Tsao and Yiwei Li and Xiaoyu Song}, doi = {10.1109/ACCESS.2025.3557505}, journal = {IEEE Access}, pages = {62739-62752}, shortjournal = {IEEE Access}, title = {An effective genetic algorithm for mixed precision}, volume = {13}, year = {2025}, }
• (2025). Roadside sensor systems for vulnerable road user protection: A review of methods and applications. ACCESS, 13, 62717-62738. (www)

  @article{ , abstract = {This review surveyed infrastructure-based sensor technologies for protecting vulnerable road users (VRUs) in traffic safety systems, focusing on four key components of the roadside system: calibration methods, sensor fusion approaches, trajectory prediction, and risk analysis frameworks. The paper analyzes different sensor types, discussing their characteristics, advantages, and limitations. Key challenges in sensor calibration for roadside deployment are addressed, alongside various fusion strategies at data, feature, and decision levels. The review covers trajectory prediction methods, from classical approaches to deep learning architectures, examining their applications in VRU behavior analysis. A comprehensive evaluation of safety assessment methodologies using surrogate safety measures is provided, considering both single and multi-sensor implementations. Technical challenges including scalability, real-time processing, and sensor synchronization are discussed, while identifying opportunities in emerging technologies such as advanced AI and vehicle-to-everything (V2X) integration. The paper concludes by addressing future research directions and ethical implications for improving urban VRU safety.}, archive = {J_ACCESS}, author = {Tianya Zhang and Lei Cheng and Tam Bang and Lihao Guo and Mustafa Hajij and Siyang Cao and Austin Harris and Mina Sartipi}, doi = {10.1109/ACCESS.2025.3558174}, journal = {IEEE Access}, pages = {62717-62738}, shortjournal = {IEEE Access}, title = {Roadside sensor systems for vulnerable road user protection: A review of methods and applications}, volume = {13}, year = {2025}, }
• (2025). Beyond axial symmetry: Tracking vectorial motion enhances nanoparticles mapping with magnetomotive ultrasound. ACCESS, 13, 62704-62716. (www)

  @article{ , abstract = {Magnetomotive ultrasound is an imaging technique that maps the distribution of magnetic nanoparticles within tissues by detecting motion induced by a time-varying magnetic field, overcoming the inherent limitations of ultrasound in nanoparticle detection. However, a major challenge in this technique is reducing the halo effect, which is the induced motion observed in tissues surrounding nanoparticles even when those regions are nanoparticle-free. This effect remains unresolved in current experimental methods, as they primarily rely on axial displacements due to the high resolution of ultrasound transducers in that direction. This study introduces a vectorial motion approach to enhance the detection of magnetic nanoparticles in magnetomotive ultrasound. Experiments were conducted on tissue-mimicking phantom, ex-vivo bovine liver, and a murine model, all labeled with iron oxide nanoparticles. A mathematical framework was established to describe the relationship between the magnetic properties of nanoparticles, the applied magnetic field, and the resulting tissue strains and displacements. Additionally, the Lucas-Kanade method is introduced as a reliable tool for analyzing the magnetically induced displacement field. Results demonstrate that vector-valued displacement analysis improves the interpretation of magnetomotive ultrasound data. However, displacement data alone proved insufficient for accurately resolving nanoparticle distribution. By incorporating external magnetic field information, the halo effect was effectively reduced, decreasing overestimation by 38% in phantom, 41% in ex-vivo tissues, and 58% in preliminary in-vivo experiment. Furthermore, this approach reduces dependence on axial symmetry between the excitation coil and transducer, offering greater flexibility in experimental setups, which is particularly beneficial for in-vivo applications.}, archive = {J_ACCESS}, author = {David A. Collazos-Burbano and Jose E. Freire and Ariane F. Sanches and Nicholas Zufelato and Joao H. Uliana and Maria S. Brassesco and Antonio A. O. Carneiro and Theo Z. Pavan}, doi = {10.1109/ACCESS.2025.3557679}, journal = {IEEE Access}, pages = {62704-62716}, shortjournal = {IEEE Access}, title = {Beyond axial symmetry: Tracking vectorial motion enhances nanoparticles mapping with magnetomotive ultrasound}, volume = {13}, year = {2025}, }
• (2025). Forecasting tunnel-induced ground settlement: A hybrid deep learning approach and traditional statistical techniques with sensor data. ACCESS, 13, 62691-62703. (www)

  @article{ , abstract = {Accurately forecasting tunnel-induced ground settlement is crucial for mitigating risks to urban infrastructure and ensuring the safety of tunnelling operations. This study introduces advanced predictive frameworks that incorporate enhancements to both deep learning (DL) models and statistical techniques to handle the intricate, nonlinear, and dynamic characteristics of settlement data. The proposed DL models, Convolutional Long Short-Term Memory (Conv-LSTM2D) and Convolutional Gated Recurrent Unit (Conv-GRU2D), extend traditional LSTM and GRU architectures with 2D convolutional mechanisms to capture complex spatiotemporal dependencies. Additionally, the statistical Autoregressive Integrated Moving Average (ARIMA)/Seasonal ARIMA (SARIMA) models were enhanced through seasonality removal, automated model selection using the auto_arima algorithm, and parameter fine-tuning via grid search to improve their predictive accuracy. Extensive evaluations using real-world sensor datasets from tunnelling projects demonstrated that the proposed Conv-GRU2D model achieved superior performance, with a R-squared of 0.94, an RMSE of 0.12, and MAPE of 0.32, surpassing both Conv-LSTM2D and optimized ARIMA/SARIMA models. These results highlight the effectiveness of convolutional mechanisms in deep learning and the advantage of optimized ARIMA/SARIMA models in capturing complex temporal patterns and improving forecasting precision for optimizing ground settlement. Notably, our findings demonstrate that the proposed models outperform existing methods, providing superior prediction accuracy and computational efficiency, even with limited training data. This underscores the potential of these innovative methods to significantly enhance the reliability of ground settlement forecasts in urban tunnel construction, offering valuable insights that can further advance geotechnical engineering practices and contribute to safer and more efficient tunnelling operations in urban environments.}, archive = {J_ACCESS}, author = {Syed Mujtaba Hussaine and Linlong Mu and Yimin Lu and Syed Sajid Hussain}, doi = {10.1109/ACCESS.2025.3557815}, journal = {IEEE Access}, pages = {62691-62703}, shortjournal = {IEEE Access}, title = {Forecasting tunnel-induced ground settlement: A hybrid deep learning approach and traditional statistical techniques with sensor data}, volume = {13}, year = {2025}, }
• (2025). A peek into the minds of game developers and neuroscience researchers collaborating on cognition assessing games. ACCESS, 13, 62674-62690. (www)

  @article{ , abstract = {Cognition-assessing games (CAGs) are an evolving area of research, with collaboration between game developers and neuroscience researchers playing a critical role in their development. Game developers bring valuable expertise in game design and development, while neuroscience researchers contribute their knowledge of cognitive assessment. However, CAGs differ from both mainstream entertainment games (game developers’ expertise) and the traditional neuropsychological assessments (neuroscience researchers’ expertise). Given this context, it is essential to address the ideological challenges and conflicts that may arise between game developers and neuroscience researchers as they collaborate on CAG development. In this viewpoint, we present the perspectives of both game developers and neuroscience researchers on ten key themes relevant to CAGs: game intent, game tutorial, game art style and user interface, learning effects, ecological validity, game scoring, game realism, game testing, commercialization, and game platform. These themes are identified based on our over four years of collaborative work and are further supported by existing literature on CAGs. Our viewpoint is grounded in practical examples, offering concrete insights to facilitate impactful development of CAGs. The ten themes provide a structured framework for decision-making in CAGs emphasizing the importance of a systematic approach to CAG development. Finally, we hope that the findings from our viewpoint will assist game developers and neuroscience researchers in creating successful, translatable CAGs.}, archive = {J_ACCESS}, author = {Yesoda Bhargava and S. V. Sumanth and Anirban Deshmukh and Veeky Baths}, doi = {10.1109/ACCESS.2025.3557454}, journal = {IEEE Access}, pages = {62674-62690}, shortjournal = {IEEE Access}, title = {A peek into the minds of game developers and neuroscience researchers collaborating on cognition assessing games}, volume = {13}, year = {2025}, }
• (2025). CALFUSE-KAN: A multi-scale feature fusion network for aircraft trajectory prediction. ACCESS, 13, 62660-62673. (www)

  @article{ , abstract = {Aircraft trajectory prediction using Deep Learning models has become a significant challenge in the field of civil aviation. Traditional Convolutional Neural Network (CNN) models are difficult to capture the global features of the aircraft trajectories due to the fixed size of the receptive field of the convolution kernel, whereas attentional mechanism based Transformer, despite being able to handle long-term dependencies, has a high computational complexity and lacks local inductive bias. This paper proposes a multi-scale feature fusion architecture for aircraft trajectory prediction, called CALFUSE-KAN. This method employs CNN to extract local spatial features, multi-head self-attention to capture global spatial information, and an adaptive weighted feature fusion block for dynamic feature balancing, subsequently utilizing Long Short-Term Memory (LSTM) to effectively capture temporal dependencies. Moreover, integrating Kolmogorov-Arnold Network (KAN) to model high-dimensional nonlinear relationships further enhances prediction accuracy. Experiment demonstrates that the CALFUSE-KAN model reduces the average prediction error by 80.02%, 63.38%, 38.39%, and 33.49% compared to the LSTM, Transformer, CNN-KAN, and CNN-BiGRU models, respectively. These results indicate that the proposed hybrid model achieves high accuracy and robustness in predicting flight trajectories.}, archive = {J_ACCESS}, author = {Yalan Zhang and Ruxin Sun and Fang Liu and Yongchao Wei}, doi = {10.1109/ACCESS.2025.3558519}, journal = {IEEE Access}, pages = {62660-62673}, shortjournal = {IEEE Access}, title = {CALFUSE-KAN: A multi-scale feature fusion network for aircraft trajectory prediction}, volume = {13}, year = {2025}, }
• (2025). Highly efficient perovskite heterojunction solar cell with dual absorber layers for state of art photovoltaic technologies. ACCESS, 13, 62648-62659. (www)

  @article{ , abstract = {Perovskite solar cells (PSCs) face significant challenges, including instability, high recom- bination losses, poor charge transport, and suboptimal band alignment, which limit their efficiency and commercial viability. The previous studies relied on MoS2 as an ETL, which has poor conduction band alignment with CsPbI3, resulting in high resistance and inefficient charge extraction. This study overcomes these limitations and objectives by introducing MoSe2 as a dual absorber, leveraging advantageous characteristics such as its strong infrared absorption, high carrier mobility, enhanced band alignment, optimal layer thickness, controlled defect levels, tailored doping concentrations, and minimized interface defects, all of which improve charge carrier transport and optimize generation and recombination dynamics. Also, defect passivation at the MoSe2/CsPbI3 interface lowers the number of trap states, which lowers recombination and raises Voc. The replacement of organic HTLs with CFTS further enhances device stability and longevity. The simulation yielded remarkable photovoltaic parameters, achieving an open-circuit voltage (Voc) of 1.40 V, a short-circuit current density (Jsc) of 35.81 mA/cm2, a fill factor (FF) of 82.9%, and an exceptional power conversion efficiency (PCE) of 41.86%, approaching the Shockley-Queisser theoretical efficiency limit for heterojunction solar cells. The proposed architecture offers a roadmap for future experimental work, semiconductor device simulation, modeling in high-efficiency and stable perovskite heterojunction solar cells for next-generation photovoltaic technology.}, archive = {J_ACCESS}, author = {G. Venkateswarlu and Umakanta Nanda}, doi = {10.1109/ACCESS.2025.3558867}, journal = {IEEE Access}, pages = {62648-62659}, shortjournal = {IEEE Access}, title = {Highly efficient perovskite heterojunction solar cell with dual absorber layers for state of art photovoltaic technologies}, volume = {13}, year = {2025}, }
• (2025). Adaptive compensation for bilateral teleoperation systems with asymmetric time-varying delays using neural network in p+d control framework. ACCESS, 13, 62633-62647. (www)

  @article{ , abstract = {Bilateral teleoperation systems face significant challenges in achieving accurate master-slave position convergence, especially under the influence of time-varying communication delays. This document presents an adaptive neural network compensation strategy within a proportional-derivative-like (P+d) control framework. The proposed controller addresses uncertainties associated with time delays and system dynamics using online adaptation of neural networks. Furthermore, a rigorous theoretical analysis of the closed-loop teleoperation system is presented and validated through numerical simulations on two-degree-of-freedom manipulators. Finally, the results demonstrate that adaptive neural networks can significantly improve the operational reliability of bilateral teleoperation systems in dynamic environments.}, archive = {J_ACCESS}, author = {Marco Moran-Armenta and Kleber Patiño and Emanuel Slawiñski and Francisco G. Rossomando and Vicente Mut and Javier Moreno-Valenzuela}, doi = {10.1109/ACCESS.2025.3558119}, journal = {IEEE Access}, pages = {62633-62647}, shortjournal = {IEEE Access}, title = {Adaptive compensation for bilateral teleoperation systems with asymmetric time-varying delays using neural network in p+d control framework}, volume = {13}, year = {2025}, }
• (2025). Contrast limited adaptive local histogram equalization method for poor contrast image enhancement. ACCESS, 13, 62600-62632. (www)

  @article{ , abstract = {Contrast limited adaptive histogram equalization (CLAHE) is a widely utilised method for image enhancement due to its speed and simplicity. However, this method faces two major limitations, namely, the requirement of manual parameter setting (i.e. clip limit and window size) and the use of a single fixed weight for enhancement, which lead to artifacts in some regions of the enhanced images. A number of approaches are used to overcome these limitations. However, each approach has its gaps, such as complexity, parameter tuning sensitivity, dependence on initial image quality and long computational time. Furthermore, most methods may perform poorly well on images with different types. This paper proposes a new modified version of CLAHE, called Contrast Limited Adaptive Local Histogram Equalization (CLALHE), to address its limitations. This method is designed to enhance image contrast locally and adaptively, without the need for user input. The CLALHE approach consists of several steps: First, multiple enhancements are applied to identify the optimal enhancement parameters. Second, these parameters are determined adaptively. Third, the original image is divided into subimages, and the optimal parameters are applied to each subimage independently, emphasizing and enhancing local features. Finally, the enhanced subimages are combined to produce the resulting image. The effects of local and adaptive concepts were systematically explored to identify the optimal parameters and validate the performance using three datasets (DIARETDB1, Pasadena-Houses 2000 and Faces 1999). Qualitative assessments demonstrated the excellent performance of CLALHE and showcased enhanced images with improved contrast, better-defined details and less time consumption. Quantitative evaluation further confirmed the efficacy of CLALHE, which surpasses other methods in terms of peak signal-to-noise ratio, entropy, absolute mean brightness error, structure similarity index, contrast improvement index and root mean square error, across various image types.}, archive = {J_ACCESS}, author = {Ibrahim Majid Mohammed and Nor Ashidi Mat Isa}, doi = {10.1109/ACCESS.2025.3558506}, journal = {IEEE Access}, pages = {62600-62632}, shortjournal = {IEEE Access}, title = {Contrast limited adaptive local histogram equalization method for poor contrast image enhancement}, volume = {13}, year = {2025}, }
• (2025). Tire-wear estimation system using semantic segmentation and its deployment. ACCESS, 13, 62591-62599. (www)

  @article{ , abstract = {Tire-wear significantly impacts vehicle operation and passenger safety, making tire-wear monitoring a critical task. Traditional manual groove-depth measurements are precise but impractical for general drivers. Existing sensor and intelligent tire-based methods also have limitations that require additional equipment. This paper presents a novel Tire-Wear Estimation (TWE) system using mobile phone cameras, which leverages close-up tire videos to estimate individual groove-depths. Due to the lack of an existing dataset that captures tire grooves, we have collected a large number of tire videos with the ground truths of groove-depths to build and evaluate our system. For the semantic segmentation model, we select one from the U-Net family by considering complexity as well as output quality, and develop a post-processing method to improve the quality of masks (segmentation results). After obtaining frame-wise tire-masks from the input video, we measure the width and depth of each dent (the indented parts in the masks). By tracking the dimensions of dents over the video frames, we estimate the actual depth of grooves. Additionally, we implement a model lifecycle-based service to improve the performance of our TWE system. Since it is not feasible to inspect all user inputs and their results, we have also developed a mask quality pre-screening method based on mask generation to facilitate the data validation process. The proposed TWE system has shown an absolute error of $0.94~mm$ , with an average latency of 2.44 seconds, to obtain results from tire videos of around 10 seconds.}, archive = {J_ACCESS}, author = {Min Jun Kang and Kyung Beom Kwon and Hyung Il Koo}, doi = {10.1109/ACCESS.2025.3557381}, journal = {IEEE Access}, pages = {62591-62599}, shortjournal = {IEEE Access}, title = {Tire-wear estimation system using semantic segmentation and its deployment}, volume = {13}, year = {2025}, }
• (2025). Multi-USV task assignment based on NSGA II-MC. ACCESS, 13, 62577-62590. (www)

  @article{ , abstract = {With the development of artificial intelligence technology, the level of autonomy of unmanned platforms is continuously improving. They are capable of completing certain specific tasks based on external information inputs and can achieve battlefield situational awareness. However, when USVs (Unmanned Surface Vehicles) search for underwater targets, the problem of target motion dispersion is particularly prominent. To address the multi-USV task allocation optimization problem, an allocation optimization model for USV clusters has been constructed, which aims to maximize the probability of target detection and minimize the collaborative time of USV clusters. An improved task allocation optimization algorithm, NSGA II-MC (Non-dominated Sorting Genetic Algorithm II-Monte Carlo), has been proposed. Firstly, a more realistic detection model was selected. Secondly, the encoding strategy and crossover mutation operations of the algorithm were adjusted. An adaptive crossover and mutation probability mechanism was introduced, and the fitness was calculated using the Monte Carlo method. Experimental results show that compared with the traditional NSGA-II algorithm, the proposed algorithm generated two more solutions in the model solving process and exhibited better convergence. The research results enrich the methods of multi-objective optimization algorithms and multi-USV task allocation, providing strong support for more efficient underwater target search.}, archive = {J_ACCESS}, author = {Yonghao Zhang and Xueman Fan and Zhuo Cheng and Changyou Xue}, doi = {10.1109/ACCESS.2025.3557582}, journal = {IEEE Access}, pages = {62577-62590}, shortjournal = {IEEE Access}, title = {Multi-USV task assignment based on NSGA II-MC}, volume = {13}, year = {2025}, }
• (2025). A clause-based data augmentation method for low-resource neural machine translation. ACCESS, 13, 62567-62576. (www)

  @article{ , abstract = {Transformer-based neural machine translation (NMT) systems have achieved remarkable success with high-resource bilingual corpora. However, their performance deteriorates significantly in low-resource environments due to the scarcity of training data. To mitigate this issue, this paper proposes a novel clause-based data augmentation (DA) approach for NMT, aimed at expanding the training set by leveraging valuable information from the original data. The proposed method commences with the development of a clause extraction algorithm to extract clauses from the target sentences. Subsequently, a target-to-source language NMT model is utilized to generate translations for these clauses. To further enrich the training set, two DA strategies are employed. The efficacy of the proposed approach is validated through experiments conducted on four open translation tasks with limited resources. Experimental results demonstrate that our method consistently outperforms the baseline model and several other DA approaches, highlighting its potential to improve the translation quality in low-resource scenarios.}, archive = {J_ACCESS}, author = {Fuxue Li and Mingzhi Shao and Hong Yan and Chuncheng Chi}, doi = {10.1109/ACCESS.2025.3555742}, journal = {IEEE Access}, pages = {62567-62576}, shortjournal = {IEEE Access}, title = {A clause-based data augmentation method for low-resource neural machine translation}, volume = {13}, year = {2025}, }
• (2025). A machine learning-based real-time remaining useful life estimation and fair pricing strategy for electric vehicle battery swapping stations. ACCESS, 13, 62555-62566. (www)

  @article{ , abstract = {The increasing adoption of electric vehicles (EVs) has led to the widespread implementation of battery swapping stations. However, ensuring fairness in battery pricing remains a significant challenge since variations in battery health and performance among swapped batteries can result in user dissatisfaction and operational inefficiencies. This paper introduces a novel approach to enhance fairness in battery swapping by integrating a machine learning-based real-time prediction model with a pricing strategy based on remaining useful life (RUL) estimation to address this issue. The proposed solution comprises a real-time RUL estimation system and a dynamic pricing mechanism that ensures fair pricing based on battery health and performance. This integrated approach aims to improve user satisfaction and the operational efficiency of swapping stations. The paper evaluates various machine learning algorithms for real-time RUL estimation regarding accuracy, computation time, and memory usage. The results suggest that XGBoost provides the most suitable balance between accuracy and efficiency, making it an effective solution for real-world applications. Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. The proposed approach has the potential to accelerate the adoption of electric vehicles and contribute to sustainability goals by promoting efficient battery utilization and fair pricing mechanisms.}, archive = {J_ACCESS}, author = {Seyit Alperen Celtek and Seda Kul and A. Ozgur Polat and Hamed Zeinoddini-Meymand and Farhad Shahnia}, doi = {10.1109/ACCESS.2025.3554682}, journal = {IEEE Access}, pages = {62555-62566}, shortjournal = {IEEE Access}, title = {A machine learning-based real-time remaining useful life estimation and fair pricing strategy for electric vehicle battery swapping stations}, volume = {13}, year = {2025}, }
• (2025). Decentralized fuzzy polynomial observer synthesis for large-scale nonlinear system with arbitrary faults and uncertainties. ACCESS, 13, 62543-62554. (www)

  @article{ , abstract = {This paper proposes a method to synthesize a decentralized polynomial observer for the large-scale nonlinear system. The polynomial Takagi-Sugeno (T-S) fuzzy framework is employed to model the large-scale nonlinear system to increase the accuracy and decrease the complexity with respect to applying T-S model or linearization method in previous studies. The faults and uncertainties are taken into consideration in this paper. The faults and uncertainties in this work are arbitrary and do not need to satisfy the bounded constraints that will make the observer synthesis for large-scale nonlinear systems much more challenging. Additionally, the large-scale polynomial T-S fuzzy system will be modified to a new framework before starting the observer synthesis. The method for designing the decentralized fuzzy polynomial observer is proposed in this paper. The decentralized fuzzy polynomial observer not only completely eliminates the impact of uncertainties and faults but also estimates unknown states and reconstructs the faults of large-scale nonlinear systems. The sufficient conditions for designing the decentralized polynomial observer will be presented in the theorems. Finally, an illustrative example is provided to show the superiority and success of the proposed method.}, archive = {J_ACCESS}, author = {Van-Phong Vu and Van-Thuyen Ngo and Yi Ding}, doi = {10.1109/ACCESS.2025.3557714}, journal = {IEEE Access}, pages = {62543-62554}, shortjournal = {IEEE Access}, title = {Decentralized fuzzy polynomial observer synthesis for large-scale nonlinear system with arbitrary faults and uncertainties}, volume = {13}, year = {2025}, }
• (2025). Variability-aware machine learning model selection: Feature modeling, instantiation, and experimental case study. ACCESS, 13, 62527-62542. (www)

  @article{ , abstract = {The emergence of machine learning (ML) has led to a transformative shift in software techniques and guidelines for building software applications that support data analysis process activities such as data ingestion, modeling, and deployment. Specifically, this shift is impacting ML model selection, which is one of the key phases in this process. Model selection is the process of selecting a model or a set of models for the analysis. There have been several advances in model selection from the standpoint of core ML methods, including basic probability measures and resampling methods. However, from a software engineering perspective, this selection is still an ad hoc and informal process. It is not supported by a design approach and representation formalism that captures the selection process and cannot support the specification of existing model selection procedures (e.g., heuristics). Further, it is not interpretable in the sense of explaining why a model has been selected and does not take into account the contextual factors and their interdependencies in the experimental evaluation that leads to a specific technique selection. In general, although the current literature provides a wide variety of ML techniques and algorithms, there is a lack of design approaches to support algorithm selection. In this paper, we present a variability-aware ML algorithm selection approach that takes into account the commonalities and variations in the model selection process. The selection adapts to the variety of contextual factors that affect the model selection, such as data characteristics, number of features, prediction type, and their intricate dependencies. The applicability of the approach is illustrated by an experimental case study based on the Scikit-Learn heuristics, in which existing model selections presented in the literature are compared with selections suggested by the approach. The proposed approach can be seen as a step towards the provision of a more explicit, adaptive, transparent, interpretable, and automated basis for model selection.}, archive = {J_ACCESS}, author = {Cristina Tavares and Nathalia Nascimento and Paulo Alencar and Donald Cowan}, doi = {10.1109/ACCESS.2025.3558218}, journal = {IEEE Access}, pages = {62527-62542}, shortjournal = {IEEE Access}, title = {Variability-aware machine learning model selection: Feature modeling, instantiation, and experimental case study}, volume = {13}, year = {2025}, }
• (2025). Energy urban domain: Personalized evaluation of expert and non-expert stakeholder interaction with artificial intelligence through ChatGPT using the VIRTSI model. ACCESS, 13, 62506-62526. (www)

  @article{ , abstract = {This paper explores the application of Generative AI, specifically ChatGPT, in urban energy management, assessing human users’ trust in AI systems that, while often accurate, can still make mistakes. It examines how different stakeholder groups—AI experts, energy domain experts, and non-experts—develop trust, distrust, or overtrust in AI-generated outputs and highlights the risks associated with these trust states. While overtrust can lead to blind reliance on incorrect AI outputs, distrust can result in the unnecessary rejection of accurate AI recommendations, ultimately reducing the effectiveness of AI-assisted decision-making. Using the VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent Systems) methodology, this research monitors human-AI trust evolution through Deterministic Finite Automata (DFA) and quantifies trust behaviors using user-adapted Confusion Matrices, addressing a critical gap in AI trust dynamics that traditional acceptance models overlook. The findings validate VIRTSI’s ability to track trust transitions. The study reveals that AI experts exhibit skepticism due to their awareness of AI’s limitations, energy experts tend to overtrust AI, likely influenced by its confident and seemingly reliable responses, and non-experts display inconsistent trust, highlighting decision-making challenges. These findings confirm VIRTSI’s premise that trust in AI is dynamic, varies by user expertise, and must be continuously monitored and assessed. Ultimately, this study strengthens VIRTSI as a necessary framework for assessing and optimizing trust in AI-driven sustainability solutions, ensuring that AI systems are not only trusted but also used effectively and responsibly in energy applications. Unlike other models of technology acceptance that focus solely on adoption, VIRTSI provides a continuous and quantifiable approach to trust calibration, identifying harmful trust patterns and guiding improvements in AI-human interaction over time.}, archive = {J_ACCESS}, author = {George A. Tsihrintzis and Elissaios Sarmas and Vangelis Marinakis and Dimitrios P. Panagoulias and Evangelia-Aikaterini Tsichrintzi and Maria Virvou}, doi = {10.1109/ACCESS.2025.3557907}, journal = {IEEE Access}, pages = {62506-62526}, shortjournal = {IEEE Access}, title = {Energy urban domain: Personalized evaluation of expert and non-expert stakeholder interaction with artificial intelligence through ChatGPT using the VIRTSI model}, volume = {13}, year = {2025}, }
• (2025). Multi-channel multi-protocol quantum key distribution system for secure image transmission in healthcare. ACCESS, 13, 62476-62505. (www)

  @article{ , abstract = {Quantum key distribution (QKD) is a viable technique for safeguarding image transmission against numerous attacks. To address the limitations of existing methods in securing electronic medical records, we have explored an encryption technique using a set of real-time medical images along with existing standard images using QKD protocols including BB84 (Bennett-Brassard 1984), CASCADE (Cascading Error Correction Algorithm for Data Exchange), and differential-phase-shift (DPS) under the fiber-based (OFC) and free-space (FSO) channels. It utilizes the quantum bit-plane representation of the Real Ket (QBRK) model and a logistic chaotic system with XOR operation. The performance of the developed framework is tested via visual illustration and 11 different quantitative assessments. The average entropy of the scrambled image for the medical source image is 7.9896, which is quite near to the optimum value of 8. The UACI (Unified Averaged Changed Intensity) and the NPCR (Number of Pixels Change Rate) metric values lie between 33.7879 to 33.9711 and 99.6014 to 99.8000 for the FSO channel. The BB84 QKD protocol achieves a QBER (Quantum Bit Error Rate) of 0.0028 for an FSO channel with DCR (Dark Count Rate), which improves to 0.0012 when DCR is ignored for a 10 km link range. In addition, we analyzed and compared the effect of OFC on system output as the quantum channel. From several experiments, it is observed that the BB84 provides better results against CASCADE and DPS. The proposed work is validated using the OptiSystem 15.1 software under FSO and for OFC.}, archive = {J_ACCESS}, author = {Bandana Mallick and Priyadarsan Parida and Chittaranjan Nayak and Tarek Khalifa and Manoj Kumar Panda and Nawaf Ali and Gajanan Uttam Patil and Bibhu Prasad}, doi = {10.1109/ACCESS.2025.3558294}, journal = {IEEE Access}, pages = {62476-62505}, shortjournal = {IEEE Access}, title = {Multi-channel multi-protocol quantum key distribution system for secure image transmission in healthcare}, volume = {13}, year = {2025}, }
• (2025). Data-driven uplift modeling. ACCESS, 13, 62462-62475. (www)

  @article{ , abstract = {Uplift modeling studies the impact of different treatments on an outcome and has several applications, such as targeted marketing and medical therapy assessment. Usually, such studies are retrospective in nature rather than controlled experiments; hence, pre-existing bias in the observed data can distort the true effect of treatment. Previous studies on uplift modeling has largely overlooked this bias, focusing instead on model-based solutions for specific applications. In this paper, we introduce a new data-driven framework for uplift modeling based on matching. We represent individuals in treatment and control groups using weighted bipartite graphs, and bipartite graph matching is then employed to retain similar individuals, thereby reducing bias. Subsequently, appropriate datasets are generated from the matched pairs for learning predictive models for individuals. Our proposed model-independent framework facilitates robust inferences, making it adaptable to a wide range of applications and settings. In particular, we demonstrate how uplift modeling can be applied to discrimination discovery and prevention in discrimination-aware data mining. We evaluate our framework on three datasets representing three different applications. The results reveal that our framework produces better prediction performances in comparison to others and is easy to apply in practice.}, archive = {J_ACCESS}, author = {Anam Zahid and Faisal Kamiran and Samar Abbas and Bilal Qureshi and Asim Karim}, doi = {10.1109/ACCESS.2025.3557468}, journal = {IEEE Access}, pages = {62462-62475}, shortjournal = {IEEE Access}, title = {Data-driven uplift modeling}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review on the exploitation of advanced memory optimization strategies to improve performance for convolutional and spiking neural networks in medical imaging using hardware accelerators. ACCESS, 13, 62449-62461. (www)

  @article{ , abstract = {Advanced memory optimization techniques are reviewed to enhance the performance of Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs) on hardware accelerators, addressing the real-world challenges in medical imaging. This review evaluates various platforms: In-Memory Computing (IMC), Field-Programmable Gate Array (FPGA), Python Productivity for Zynq (PYNQ-Z2), Graphics Processing Unit (GPU), and Application-Specific Integrated Circuit (ASIC) concerning overcoming memory bottlenecks, minimizing latency, and reducing energy consumption in Magnetic Resonance Imaging (MRI) reconstruction, Computed Tomography (CT) scan analysis, and real-time diagnostics. It will analyze techniques like memory compression, tiling, hierarchical memory management, and neural network pruning to improve computation efficiency. In addition, in-memory computing will be a key focus to mitigate the inefficiency of data movement, adaptability of Field-Programmable Gate Array (FPGA) for custom workloads, parallel processing by Graphics Processing Unit (GPU), and domain-specific optimizations of Application-Specific Integrated Circuit (ASIC). This review addresses the challenges of high-resolution image processing and energy constraints to provide a comprehensive guide to scalable, efficient hardware accelerators for neural networks in medical imaging.}, archive = {J_ACCESS}, author = {N. Srikanth Prasad and S. Sundar}, doi = {10.1109/ACCESS.2025.3541931}, journal = {IEEE Access}, pages = {62449-62461}, shortjournal = {IEEE Access}, title = {Comprehensive review on the exploitation of advanced memory optimization strategies to improve performance for convolutional and spiking neural networks in medical imaging using hardware accelerators}, volume = {13}, year = {2025}, }
• (2025). Feasibility study of carbon nanotube winding for permanent magnet synchronous generator considering coupled electromagnetic- thermal aspects. ACCESS, 13, 62434-62448. (www)

  @article{ , abstract = {The increasing demand for lightweight, high-efficiency electrical machines has driven research into copper-carbon nanotube (Cu-CNT) composites as alternatives to copper windings. While Cu-CNT-based conductors show promise, their bi-directional electromagnetic-thermal behavior in permanent magnet synchronous generators (PMSGs) remains underexplored. This study evaluated the feasibility of Cu-CNT windings to enhance power density, thermal management, and efficiency through a coupled analysis using 2D electromagnetic finite element analysis integrated with a lumped parameter thermal network. Two Cu-CNT materials with a resistivity of $7.12\times 10^{-8}~\Omega $ .m (Cu-CNT #1) and $2.12\times 10^{-8}~\Omega $ .m (Cu-CNT #2) were tested over speeds ranging from 1000 to 4800 RPM with parametric variations in axial length and outer diameter. Results show that Cu-CNT #2 delivered higher specific power and, at high temperatures, achieved better efficiency than the copper-based model. Due to limited composite material samples from current processing and manufacturing, a copper coil prototype was manufactured for validation. The good agreement between the simulation and the test results of the copper-based confirms the accuracy of the coupled electromagnetic-thermal analysis. As a result, the correctness of the Cu-CNT-based model is validated.}, archive = {J_ACCESS}, author = {Vu-Khanh Tran and Thanh-Tuan Nguyen and Tung Nguyen and Jae-Beom Kang and Ji-Young Lee and Yon-do Chun}, doi = {10.1109/ACCESS.2025.3557500}, journal = {IEEE Access}, pages = {62434-62448}, shortjournal = {IEEE Access}, title = {Feasibility study of carbon nanotube winding for permanent magnet synchronous generator considering coupled electromagnetic- thermal aspects}, volume = {13}, year = {2025}, }
• (2025). Direction of arrival estimation based on AR modeling in the presence of colored noise. ACCESS, 13, 62427-62433. (www)

  @article{ , abstract = {This paper investigates the problem of estimating the direction of arrival considering colored noise at the sensors. To this aim, we model the recieved signal at each time instant with an AR system with rank equal to the number of sources and we simultaneously estimated signal and noise parameters by solving a quadratic eigenvalue problem. Simulation results demonstrate that utilizing root mean square error as the accuracy metric, the proposed method outperforms existing subspace-based approaches, particularly when the targets are close to eachother.}, archive = {J_ACCESS}, author = {Sina Bakhshandeh Babarsad and Alimorad Mahmoudi and Ehsan Namjoo}, doi = {10.1109/ACCESS.2025.3555516}, journal = {IEEE Access}, pages = {62427-62433}, shortjournal = {IEEE Access}, title = {Direction of arrival estimation based on AR modeling in the presence of colored noise}, volume = {13}, year = {2025}, }
• (2025). Evaluation of forklift workers’ perception accuracy of the carried cargo weight using a virtual reality simulator. ACCESS, 13, 62412-62426. (www)

  @article{ , abstract = {This study evaluated the accuracy of weight perception of cargo carried on forklifts with the aim of developing a virtual reality training simulator for forklift work safety and efficiency. In the developed simulator, users with a head-mounted display can experience virtual forklift operations using the operating equipment in a cab mock-up. The virtual forklift has parameters that determine the behaviors of the fork and vehicle body, which are set based on actual measurements; therefore, it is highly reproducible. The simulator is the first to incorporate cargo weight perception based on fork behavior and seat tilt. We conducted an experiment in which 13 workers and 13 beginners compared the weights of two cargos on the simulator. The results showed that, under the same conditions, workers and beginners had an average correct response rate of 76.9% and 64.0%, respectively. The incorrect response rate for weight perception decreased exponentially as the employment term increased. Workers perceived cargo weight through the fork behavior and seat tilt, with dominance varying among individuals, categorizing them into three groups: seven with dominant fork behavior, four with dominant seat tilt, and two with both dominant behaviors. The cross-modal effect of combining fork behavior and seat tilt was confirmed. It was suggested the accuracy may decrease if the operator is not aware of which sense was more important. These findings indicate that forklift workers can improve their work safety and efficiency by understanding their own perceptual characteristics.}, archive = {J_ACCESS}, author = {Nobuyoshi Hashimoto and Wataru Wakita and Susumu Okumura and Takuma Saga}, doi = {10.1109/ACCESS.2025.3558329}, journal = {IEEE Access}, pages = {62412-62426}, shortjournal = {IEEE Access}, title = {Evaluation of forklift workers’ perception accuracy of the carried cargo weight using a virtual reality simulator}, volume = {13}, year = {2025}, }
• (2025). A drone-deployable remote current sensor for non-invasive overhead transmission lines monitoring. ACCESS, 13, 62393-62411. (www)

  @article{ , abstract = {The remote monitoring of current flow in overhead transmission cables is recognized as a significant challenge, yet reliable access to these measurements is regarded as essential for ensuring grid stability and system reliability. Accordingly, this paper introduces an innovative approach to remote current flow measurement in overhead Transmission Lines (TLs). The proposed solution features a drone-deployable sensor box with uniquely designed internal signal processing units that filter noise and safeguard signals before forwarding them to an Arduino Due. The Arduino Due processes the signals and transmits the Root Mean Square (RMS) current values to a remote computer via an HC-06 Bluetooth module. On the remote computer, equipped with a Bluetooth range extender, MATLAB is used to generate timestamped curves of the received data. Laboratory tests demonstrated a maximum average error of 5.22% under extreme conditions (e.g., Total Harmonic Distortions (THD) of 40% in the injected current signals) and a minimum error of 2.54% under standard TL operating conditions. The system supports current measurements up to 5 kA RMS and allows for 6 hours of continuous second-wise data recording on a single battery charge. The communication between the sensor box and MATLAB remained stable within a 60-meter radius in open air and 15 meters in a laboratory setting with physical barriers. This topology offers a promising solution for remote current monitoring in TLs, contributing to smart grid applications. The paper also outlines potential future work to enhance the system’s performance and applicability.}, archive = {J_ACCESS}, author = {Khaled Osmani and Detlef Schulz}, doi = {10.1109/ACCESS.2025.3554570}, journal = {IEEE Access}, pages = {62393-62411}, shortjournal = {IEEE Access}, title = {A drone-deployable remote current sensor for non-invasive overhead transmission lines monitoring}, volume = {13}, year = {2025}, }
• (2025). Identifying suicidal ideation through automatic extraction of emotional traces in suicide notes. ACCESS, 13, 62378-62392. (www)

  @article{ , abstract = {Suicide is a critical mental health issue and one of the leading causes of death worldwide. Several studies in suicidology claim that emotional dysregulation is a factor triggering suicidal behavior. In today’s digital age, individuals often express suicidal ideation on social media platforms to seek help, empathy, or advice. The primary objective of this study is to classify suicide notes based on their emotional content using machine and deep learning algorithms. We propose an innovative approach to automatically identify emotional changes in a suicide note’s content, leveraging these shifts as key indicators of suicidal ideation. The goal is to automatically identify the correlation between latent emotional states in suicide notes and their classification as either suicidal or non-suicidal. We employed a long short-term memory (LSTM) neural network to analyze emotional patterns and address various binary classification scenarios. Results demonstrated an F-measure exceeding 80% in all suicide note classification scenarios. Our analysis revealed that the vocabulary used in suicide notes collected from social media often differs from that found in notes written by individuals who have actually completed suicide. Additionally, we observed that suicide attempters typically do not directly express suicidal ideation but instead use non-death-related vocabulary, which presents a greater challenge in identifying genuine suicidal thoughts.}, archive = {J_ACCESS}, author = {Ángel Hernández-Castañeda and René Arnulfo García-Hernández and Yulia Ledeneva}, doi = {10.1109/ACCESS.2025.3558693}, journal = {IEEE Access}, pages = {62378-62392}, shortjournal = {IEEE Access}, title = {Identifying suicidal ideation through automatic extraction of emotional traces in suicide notes}, volume = {13}, year = {2025}, }
• (2025). Directed graph neural networks for anomaly detection of smart ponzi schemes. ACCESS, 13, 62367-62377. (www)

  @article{ , abstract = {Ponzi schemes remain a significant challenge to financial security, particularly on blockchain platforms such as Ethereum, where the autonomy of smart contracts facilitates fraudulent activities. Existing detection methods, typically framed as binary classification tasks, often face the challenge of extreme class imbalance, while conventional graph-based detection methods fail to capture asymmetric transaction dynamics. To address these limitations, we introduce Directed Graph Neural Networks for Anomaly Detection of Smart Ponzi Schemes (DGAD-SPS), a novel approach that leverages directed graph analysis to detect Ponzi schemes in Ethereum’s transactional data. By formulating the problem as an anomaly detection task on a directed graph, DGAD-SPS captures the asymmetrical and directional nature of Ethereum transactions, enabling a more accurate differentiation between fraudulent and legitimate contracts. The proposed model employs a self-supervised learning paradigm that combines contrastive and generative learning to derive node embeddings without relying on labeled data, making it particularly well-suited for imbalanced datasets. Experimental validation confirms DGAD-SPS’s effectiveness in real-world Ponzi scheme detection through explicit modeling of directional transaction relationships and robust performance under severe data imbalance conditions.}, archive = {J_ACCESS}, author = {Xiaofang Jiang and Wei-Tek Tsai}, doi = {10.1109/ACCESS.2025.3558589}, journal = {IEEE Access}, pages = {62367-62377}, shortjournal = {IEEE Access}, title = {Directed graph neural networks for anomaly detection of smart ponzi schemes}, volume = {13}, year = {2025}, }
• (2025). Clustering algorithm by boundary detection base on entropy of KNN. ACCESS, 13, 62353-62366. (www)

  @article{ , abstract = {Clustering analysis has been widely applied in various fields, and boundary detection based clustering algorithms have shown effective performance. In this work, we propose a clustering algorithm by boundary detection based on entropy of KNN (CBDEK). A border point contains only the nearest neighbors within a specific directional range. Thus, we define entropy of KNN (EK) to accurately identify the boundary of the cluster. Since entropy has the property of measuring uncertainty, it can be used to quantify the possibility that a point is a border point. A lower EK indicates an uneven neighbor distribution, increasing the possibility of the point being a border point. Then, the border points are clustered based on the directional similarity of their nearest neighbors. Specifically, if a border point is a neighbor of another border point and most of their nearest neighbors show directional similarity, they are considered to belong to the same cluster. Furthermore, we assign the label of a border point to the interior points located within the maximum nearest neighbor sub-block of this border point to facilitate an efficient allocation for the remaining points (interior points). In addition, our algorithm incorporates noise mitigation techniques using average distance and box plot analysis. The effectiveness of CBDEK is proven by a comparative evaluation of nine algorithms on 24 datasets.}, archive = {J_ACCESS}, author = {Jiaman Ding and Jinyuan Yin and Lianyin Jia and Xiaodong Fu and Hongbin Wang}, doi = {10.1109/ACCESS.2025.3555772}, journal = {IEEE Access}, pages = {62353-62366}, shortjournal = {IEEE Access}, title = {Clustering algorithm by boundary detection base on entropy of KNN}, volume = {13}, year = {2025}, }
• (2025). Threat hunting the shadows: Detecting adversary lateral movement with elasticsearch. ACCESS, 13, 62341-62352. (www)

  @article{ , abstract = {This research investigates the elusive tactic of lateral movement employed by adversaries within a compromised network. The focus is on identifying the mechanisms and techniques used for lateral movement, with a particular emphasis on credential access. The study leverages a custom-designed Security Information and Event Management (SIEM) system built upon Elasticsearch, coupled with powerful KQL (Kibana Query Language) and Lucene search queries. Employing a realistic dataset, the research simulates an adversary’s TTPs (Tactics, Techniques, and Procedures) to dive deep into the critical area of credential access. This unique approach allows for the identification of indicators of compromise (IoCs) and the construction of targeted search queries to uncover signs and traces of lateral movement within the simulated environment. The findings contribute valuable insights into detection methodologies and highlight the effectiveness of an SIEM system in conjunction with advanced search functionalities for proactively countering lateral movement attempts.}, archive = {J_ACCESS}, author = {Naif Alsharabi and Akashdeep Bhardwaj and Talal Sarheed Alshammari and Shoayee Alotaibi and Dhahi Alshammari and Amr Jadi}, doi = {10.1109/ACCESS.2025.3556184}, journal = {IEEE Access}, pages = {62341-62352}, shortjournal = {IEEE Access}, title = {Threat hunting the shadows: Detecting adversary lateral movement with elasticsearch}, volume = {13}, year = {2025}, }
• (2025). A picture guide to cancer progression and evolutionary accumulation models: Systematic critique, plausible interpretations, and alternative uses. ACCESS, 13, 62306-62340. (www)

  @article{ , abstract = {Cancer progression and evolutionary accumulation models were developed to discover dependencies in the irreversible acquisition of binary traits from cross-sectional data. They have been used in computational oncology and virology but also in widely different problems such as malaria progression. These methods have been applied to predict future states of the system, identify routes of feature acquisition, and improve patient stratification, and they hold promise for evolutionary-based treatments. New methods continue to be developed. But these methods have shortcomings, which are yet to be systematically critiqued, regarding key evolutionary assumptions and interpretations. After an overview of the available methods, we focus on why inferences might not be about the processes we intend. Using fitness landscapes, we highlight difficulties that arisearising from bulk sequencingand reciprocal sign epistasis, from conflating lines of descent, path of the maximum, and mutational profiles, and from ambiguous use of the idea of exclusivity. We show that bulk sequencing can severely limit interpretation as inferring within-cell restrictions is only warranted under very strong assumptions. We examine how the previous concerns change when bulk sequencing is explicitly considered; We suggest that limitations and opportunities from bulk sequencing be recognised and exploited explicitly; the methods remain valuable for patient stratification, and we underline opportunities for addressing dependencies due to frequency-dependent selection. This review identifies major standing issues, recommends turning inferences into actionable insights for translational impact —which requires careful consideration of empirical entities for causally-justified interpretations—, assessing method performance with entity-appropriate simulations, and should encourage the use of these methods in other areas with a better alignment between entities and model assumptions.}, archive = {J_ACCESS}, author = {Ramon Diaz-Uriarte and Iain G. Johnston}, doi = {10.1109/ACCESS.2025.3558392}, journal = {IEEE Access}, pages = {62306-62340}, shortjournal = {IEEE Access}, title = {A picture guide to cancer progression and evolutionary accumulation models: Systematic critique, plausible interpretations, and alternative uses}, volume = {13}, year = {2025}, }
• (2025). Enhanced computational efficiency and accuracy of discrete-time PID-DPWM equation. ACCESS, 13, 62288-62305. (www)

  @article{ , abstract = {The technique of using the duty cycle output of a proportional integral derivative controller as the input for a count-based digital pulse width modulation module to process digital PWM signals is widely applied across various industrial fields, including automotive, power control, and motor control. This paper refers to this approach as the classic PID-DPWM equation. Classic PID-DPWM equations have been studied with the perspective that the raw-data measured input, and the integer-value duty value are integers. This study presents the improved PID-DPWM equations that start with the initial data scaling equation and perform integer-based computations and one floating-point operation. Consequently, the proposed equations become equivalent to the classic PID-PWM equations, enhancing computational efficiency and accuracy. The enhancement in computational efficiency was verified mathematically, and the improvement in accuracy was validated using performance index tests. A validation test of a closed-loop system in MATLAB/Simulink demonstrated that the improved standard PID-DPWM equation outperformed the classic standard PID-DPWM equation, achieving improvements in performance indices (ISE = 0.0001%, IAE = 0.27%, IE = 0.035%, ITSE = 0.012%, and ITAE = 2.85%). Furthermore, the improved difference PID-DPWM equation demonstrates further improvements over the classic difference PID-DPWM equation, achieving improvements in performance indices (ISE = 0.98%, IAE = 4.79%, IE = 15.87%, ITSE = 4.25%, and ITAE = 26.89%). Overall, the improved standard PID-DPWM equation exhibited enhanced performance in ISE, IAE, and ITAE, whereas the improved difference PID-DPWM equation exhibited superior performance in IE and ITSE.}, archive = {J_ACCESS}, author = {Eungnam Kim and Jin-Young Choi}, doi = {10.1109/ACCESS.2025.3555125}, journal = {IEEE Access}, pages = {62288-62305}, shortjournal = {IEEE Access}, title = {Enhanced computational efficiency and accuracy of discrete-time PID-DPWM equation}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches for morphological classification of intestinal organoids. ACCESS, 13, 62267-62287. (www)

  @article{ , abstract = {Organoids, derived from primary donor or stem cells, closely replicate the composition and function of their in vivo counterparts. This quality makes them a reliable model for validating hypotheses on disease-related biological processes and mechanisms. To date, the classification of organoids is performed manually by microscope and, therefore, in a data-driven application, is time-consuming, inaccurate, and difficult to morphological analysis process. The use of deep learning (DL) in organoid image analysis becomes crucial to handle complexity, variability, and large amounts of data efficiently and accurately, overcoming the limitations of traditional image processing approaches. In this paper, five CNN-based DL models such as MobileNet, DenseNet, ResNet, Inception, VGG, and the very recent Vision Transformers (ViT) were analyzed using a publicly available dataset for the morphological classification of intestinal organoids. Additionally, traditional ML models, such as SVM and RF, were tested for comparison using a feature set similar to conventional image processing tools. The systematic performance evaluation is designed to guide users in choosing the most suitable model for processing organoid images. Among all models, ViT achieved the highest accuracy of 86.95%, demonstrating its effectiveness in organoid classification. Inception and DenseNet also exhibited strong performance, with accuracy values of 86.10% and 86.47%, respectively. Rather, SVM and RF performed significantly worse, showing an accuracy approximately 20% lower than the selected DL models. Considering efficiency, ViT had the highest accuracy but required more resources (0.0437 sec/image, 343 MB), while MobileNet, the lightest model (35.6 MB), had the fastest inference time (0.0063 sec/image). The findings highlight the potential of DL models in enhancing the accuracy of organoid classification while emphasizing the importance of balancing performance with computational efficiency for real-time applications.}, archive = {J_ACCESS}, author = {Giovanni Cicceri and Sebastiano Di Bella and Simone Di Franco and Giorgio Stassi and Matilde Todaro and Salvatore Vitabile}, doi = {10.1109/ACCESS.2025.3558621}, journal = {IEEE Access}, pages = {62267-62287}, shortjournal = {IEEE Access}, title = {Deep learning approaches for morphological classification of intestinal organoids}, volume = {13}, year = {2025}, }
• (2025). ESFormer: A pillar-based object detection method based on point cloud expansion sampling and optimised swin transformer. ACCESS, 13, 62254-62266. (www)

  @article{ , abstract = {In recent years, with the wide application of autonomous driving, surveillance, and robotics, the demand for accurate object detection in efficient object scenarios has surged. However, traditional object detection methods often face the challenge of difficulty in balancing detection accuracy and processing speed when dealing with the dynamic characteristics of fast-moving objects. To this end, this study proposes an innovative target detection method based on Pillar point cloud, by designing a point cloud enhancement module called the Point Cloud Expansion Sampling Tool (PCES-Tool) and combining it with a Swin Transformer architecture based on optimized patch partition (Swin-T+). to solve the above problems. By adopting the inflated point cloud approach, the method enhances the spatial representation of the object to achieve faster object detection. And Swin Transformer further enhances the model’s ability to capture multi-scale features through its hierarchical design, enabling it to recognize objects at different scales and distances. The method is evaluated on the KITTI, NuScenes and WOD standard datasets, and the results show that significant improvements are achieved in the detection accuracy with both three-dimensional intersection and merger ratio (3D IoU) and two-dimensional intersection and merger ratio (2D IoU). The experimental results prove that the method performs well in high-speed detection scenarios, indicating that the combination of the PCES-Tool and Swin Transformer can effectively solve the challenges in point cloud object detection. This study provides important technical support for the future development of real-time object detection systems.}, archive = {J_ACCESS}, author = {Zhuoxuan Zhao and Qianchun Li and Shengyu Hao and Xinyan Huang and Fan Zhang}, doi = {10.1109/ACCESS.2025.3554839}, journal = {IEEE Access}, pages = {62254-62266}, shortjournal = {IEEE Access}, title = {ESFormer: A pillar-based object detection method based on point cloud expansion sampling and optimised swin transformer}, volume = {13}, year = {2025}, }
• (2025). Arabic fake news dataset development: Humans and AI-generated contributions. ACCESS, 13, 62234-62253. (www)

  @article{ , abstract = {The extensive use of social media platforms has promoted the rapid spread of fake news on the internet, such as fake reviews, rumors, and propaganda. Although these terminologies have different objectives, they share the aim of causing harm in the form of fake news. This study presents an Arabic fake news detection framework to overcome the widespread fake news phenomenon. The proposed framework introduces the first Arabic fake news dataset compiled by passing through strict guidelines to produce fake articles composed by humans and the generative pre-trained transformer (GPT). First, we performed human-based experiments to evaluate the ability of humans to distinguish real news articles from fake news articles. Our findings reveal that humans could roughly identify half of the fake articles from humans or GPT, raising concerns about their ability to detect fake news. This highlights the growing concern surrounding fake news, especially because GPT demonstrates the ability to generate fake news that closely resembles human-created content, further amplifying the issue. To address this issue, we performed the same task using Deep Learning (DL) and transformer-based methods with different word embeddings. Across all the employed models, the study revealed that the innovative transformer-based model, ARBERT, outperformed the DL models, reaching an accuracy of 78% in classifying real and fake news generated by humans and GPT. The findings suggest effective techniques for addressing and resolving this issue.}, archive = {J_ACCESS}, author = {Hanen Himdi and Nuha Zamzami and Fatma Najar and Mada Alrehaili and Nizar Bouguila}, doi = {10.1109/ACCESS.2025.3556376}, journal = {IEEE Access}, pages = {62234-62253}, shortjournal = {IEEE Access}, title = {Arabic fake news dataset development: Humans and AI-generated contributions}, volume = {13}, year = {2025}, }
• (2025). Exploring dynamic hierarchical fusion for multi-view clustering. ACCESS, 13, 62222-62233. (www)

  @article{ , abstract = {Multi-view clustering is effective at uncovering the latent structures within different views or modalities. However, existing approaches often oversimplify the problem by treating the contribution and granularity of information from all views as uniform, neglecting the semantic richness and diversity inherent in different views. To address this limitation, we propose a dynamic hierarchical fusion method that not only integrates multi-granularity representations from multiple views but also dynamically computes and adjusts the contribution of each view’s representation for the clustering task through weighted fusion. Specifically, we design a multi-view hierarchical features fusion module that adaptively maps the intermediate representations from all view-specific encoders into a unified representation space, enabling effective multi-scale fusion. This process yields a set of intermediate representations that transition from coarse to fine granularity across multiple views and scales. Additionally, we introduce a multi-view gated fusion module, which utilizes a set of learnable normalized parameters to dynamically compute the contribution of each view’s representation and its multi-scale features. This weighted fusion produces a unified clustering representation that captures the most relevant information for the clustering task. Experimental results on benchmark datasets, such as Scene-15 Dataset, show that our method outperforms existing state-of-the-art methods by up to 1.88% in clustering accuracy (e.g., 41.08% compared to 39.20%). Ablation studies demonstrate the importance of the multi-view gated fusion module in learning the relative contributions of different views, which significantly enhances clustering performance.}, archive = {J_ACCESS}, author = {Zhenshan Chen and Xuran Tian and Lizhen Gao and Gengbin Liao and Yang Shen and Kainan Zheng and Jianglin Fang}, doi = {10.1109/ACCESS.2025.3550923}, journal = {IEEE Access}, pages = {62222-62233}, shortjournal = {IEEE Access}, title = {Exploring dynamic hierarchical fusion for multi-view clustering}, volume = {13}, year = {2025}, }
• (2025). A systematic review on application of multimodal learning and explainable AI in tuberculosis detection. ACCESS, 13, 62198-62221. (www)

  @article{ , abstract = {Physicians rely on various data sources when diagnosing Tuberculosis (TB). This includes the patient’s historical data, demographic data, clinical laboratory results, and imaging data. Traditionally, the application of machine learning and deep learning in detecting TB has focused more on using single modes of data. This constrains the capabilities of the artificial intelligence (AI) techniques to replicate the clinical practice of incorporating multiple sources of information in decision-making. Recent advancements in deep learning and machine learning have enabled the integration of multimodal data which has led to the development of applications that more accurately reflect the clinician’s approach. However, the operations of deep learning techniques are still blackbox in nature, which makes it hard to understand their internal work mechanisms. As a result, it is necessary to incorporate explainable AI techniques to assist AI model users understand how the models make decisions. In this paper, we carried out a systematic review of two areas: First, we reviewed recent studies on the application of multimodal learning in TB detection. Here we have provided a summary of the public datasets used in the studies, data modalities used, the fusion techniques, and finally identified AI techniques that can be used with multimodal data. Then we looked at papers that used explainable AI techniques in TB diagnosis and prognosis. This study followed PRISMA guidelines to ensure replicability and accurate reporting of the main findings of the reviewed studies. To stay up-to-date with the state of the art, we specifically examined papers published between 2019 and June 2024. We reviewed thirty-one journal and conference papers we found using Web of Science, Scopus and Pubmed databases. The review indicated that models trained on multiple data modalities outperformed those trained on single data modalities. This is due to the additional information extracted from each data modality. Therefore, multimodal learning can improve clinical decision-making and TB diagnostic precision, but faces challenges like insufficient datasets and interpretability issues in model prediction processes.}, archive = {J_ACCESS}, author = {Barbara Nansamba and Joyce Nakatumba-Nabende and Andrew Katumba and David Patrick Kateete}, doi = {10.1109/ACCESS.2025.3558878}, journal = {IEEE Access}, pages = {62198-62221}, shortjournal = {IEEE Access}, title = {A systematic review on application of multimodal learning and explainable AI in tuberculosis detection}, volume = {13}, year = {2025}, }
• (2025). The influence of higher-order disturbance estimation on wind power generation of WECS using SMC with sensorless wind speed estimation. ACCESS, 13, 62179-62197. (www)

  @article{ , abstract = {The increasing demand for environmentally friendly, renewable, and clean energy sources as a substitute to fossil fuels, which emit greenhouse gases, continues to rise. Nonetheless, extracting the maximum wind power through the wind energy conversion system (WECS) hinges on accurately measuring the generator’s reference speed and the aerodynamic torque. This paper introduces an approach using an exponential disturbance estimator to extract the WECS’s maximum power by estimating aerodynamic torque ( $T_{a}$ ) and wind speed (v). Unlike most existing literature that assumes the behavior of the torque to change slowly, this paper offers a thorough investigation of the impact of fast-varying $T_{a}$ on WECS’s power harnessing using observer-based super-twisting sliding mode control (STSMC). Simulation results of reduced-intensity, fast-varying, and extremely fast-varying wind speeds for the zero-order (ZO), first-order (FO), and second-order (SO) $T_{a}$ estimations were investigated. Though the incorporation of higher-order estimations improved the speed tracking performance and increased the maximum power extraction by about 6.5% for extremely varying wind profiles (from 182.15 kW to 193.92 kW), the ZO estimation almost extracted the same amount of power for fast-changing wind speed (151.87 kW vs 152.08 kW), and low-intensity wind force (6.86 kW vs 6.89 kW). Ultimately, the analyses suggested that in scenarios where the wind speed is not extremely changing, the ZO estimation proves effective, as the higher-order estimations will increase noise and the computational burden on the WECS.}, archive = {J_ACCESS}, author = {Ahmad Bala Alhassan and Ton Duc Do}, doi = {10.1109/ACCESS.2025.3558398}, journal = {IEEE Access}, pages = {62179-62197}, shortjournal = {IEEE Access}, title = {The influence of higher-order disturbance estimation on wind power generation of WECS using SMC with sensorless wind speed estimation}, volume = {13}, year = {2025}, }
• (2025). Multi-microgrid system optimization addressing the uncertainties in generation and load demand. ACCESS, 13, 62165-62178. (www)

  @article{ , abstract = {A complete graph network imposes high costs for power routing in a multi-microgrid system when considering a separate connection between every two microgrids. Clustering highly interacting microgrids into subsets yields a more cost-effective substitute for the complete graph assumption. In this paper, we develop a mathematical optimization framework to capture the interactions between multiple microgrids in a multi-microgrid energy management system. The framework aims to achieve optimal profit while meeting the load demand for the system. Each microgrid is subject to various sources of energy generation that entail uncertainty. Moreover, this paper proposes a model that could cluster these microgrids through optimization, in which the microgrids can transact energy within their respective clusters. Our model has yielded savings that could prove to be beneficial in the long run. Also, this project provides a stepping stone into dynamic pricing, which could be implemented to manipulate the consumers’ behavior in terms of energy consumption. This body of work has proven to come up with economical, environmentally, and more reliable energy transactions.}, archive = {J_ACCESS}, author = {Lena Abu-El-Haija and Basel E’Layan and Yousef Refai and Mohamed Kenan Alsafadi and Mohammad J. Abdel-Rahman and Ahmad I. Abu-El-Haija}, doi = {10.1109/ACCESS.2025.3555927}, journal = {IEEE Access}, pages = {62165-62178}, shortjournal = {IEEE Access}, title = {Multi-microgrid system optimization addressing the uncertainties in generation and load demand}, volume = {13}, year = {2025}, }
• (2025). Multi-pathway 3D CNN with conditional random field for automated segmentation of multiple sclerosis lesions in MRI. ACCESS, 13, 62154-62164. (www)

  @article{ , abstract = {Multiple Sclerosis (MS) is a chronic and autoimmune disease that causes lesions in the central nervous system. It is diagnosed based on accurate identification and segmentation of lesions in magnetic resonance imaging (MRI). The structure and dimension of the lesions provide useful information about the course and status of the disease. Manual detection of lesions is labor intensive, highly time-consuming, and prone to error. One of the challenges in automatic MS lesion segmentation is the high variability of the lesion’s size and shape. In this work, a novel hybridization of the multi-scale features extraction, multi-pathway 3D convolutional neural network (CNN), and Conditional Random Field (CRF) is employed for an automated MS lesion detection and segmentation. To capture regions of interest of various shapes and sizes, we extracted multi-scale features using multi-resolution 3D input images for accurate MS lesion segmentation. To reduce over-segmentation, we employed the CRF as a post-processing step to refine the MS lesion segmentation by minimizing false positives. The CNN model is trained with 5 subjects with a mean of 4.4 time points taken from the ISBI 2015 MS lesion segmentation challenge. The model is tested on 14 subjects with a mean of 4.4 time points in the ISBI 2015 dataset. The results showed that the devised model obtained a Total Weighted Score of 91.1%, which is higher than the human rater Score of 89.4%.}, archive = {J_ACCESS}, author = {Reeda Saeed and Shahab U. Ansari and Muhammad Hanif and Kamran Javed and Usman Haider and Iffat Maab and Saeed Mian Qaisar and Pawel Plawiak}, doi = {10.1109/ACCESS.2025.3556885}, journal = {IEEE Access}, pages = {62154-62164}, shortjournal = {IEEE Access}, title = {Multi-pathway 3D CNN with conditional random field for automated segmentation of multiple sclerosis lesions in MRI}, volume = {13}, year = {2025}, }
• (2025). Exploring the effects of personality traits on customer perceived value based on text decomposition. ACCESS, 13, 62137-62153. (www)

  @article{ , abstract = {Customer perceived value (CPV) is pivotal for hospitality competitiveness, but the impact of personality traits on CPV remains underexplored. This study examines how personality traits influence CPV in the hotel industry using advanced text mining and NLP techniques. By analyzing TripAdvisor reviews, we propose a framework that integrates LDA topic modeling, multi-level sentiment classification with an improved Doc2Vec-IOVO strategy, and a CNN-LIWC based personality trait recognition model to extract perceptual factors and infer reviewers’ Big Five personality traits. Our regression analysis reveals significant relationships between personality traits and CPV dimensions. Finally, we explore the predictive value of the personality measures. Incorporating new features into the model increases the recognition accuracy by up to 2.66%. Among the Big Five personality traits, extraversion, conscientiousness, and openness positively affect perceived value, whereas neuroticism has a negative effect. Extraversion and neuroticism exert a stronger influence than the other traits. The results demonstrate the significant predictive value of personality indicators. Compared with traditional surveys, NLP-based personality identification from online reviews offers a more efficient approach, enabling personalized recommendations to enhance customer satisfaction in the hotel industry.}, archive = {J_ACCESS}, author = {Zihan Jiang and Xuhui Zheng and Wei Yu and Junpeng Chen}, doi = {10.1109/ACCESS.2025.3558374}, journal = {IEEE Access}, pages = {62137-62153}, shortjournal = {IEEE Access}, title = {Exploring the effects of personality traits on customer perceived value based on text decomposition}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based approach for cell segmentation in phase-contrast images. ACCESS, 13, 62121-62136. (www)

  @article{ , abstract = {Cell segmentation in Phase-Contrast (PhC) microscopy is crucial for non-invasive analysis of live-cell imaging data. However, traditional segmentation methods often struggle with challenges such as low contrast, overlapping cells, and artifacts that are unique to PhC imaging. In this study, we proposed a deep learning-based model utilizing the Mask Regional Convolutional Neural Network (Mask R-CNN) architecture for segmentation of cells in PhC images. To optimize the performance, three optimizers have been evaluated including Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), and Ranger. The models have been trained and tested on two PhC datasets, PhC-C2DH-U373 and PhC-C2DL-PSC, derived from the ISBI cell tracking challenge. Models were tested with ResNet50, ResNet101, and ViT-B/16 backbones, and ablation studies assessed the effects of backbone architectures and layer fine-tuning strategies. Results demonstrated that CNN-based backbones with ResNet101 achieved higher performance compared to the ResNet50 and ViT-B/16 backbones. The Ranger optimizer outperformed Adam and SGD with an Intersection over Union (IoU) of 94.3% and an F1 score of 97.1% on the PhC-C2DH-U373 dataset, as well as an IoU of 85.3% and F1 score of 92% on the PhC-C2DL-PSC dataset, both with data augmentation. Statistical validation using the Wilcoxon signed-rank test confirmed the significance of these results (P-Value < 0.01). Furthermore, an IoU improvement of over 10% is observed for densely packed cells as in the second dataset compared to traditional and benchmark approaches. Additionally, we introduced a generalized model by combining the two datasets to increase model reliability and reduce computational complexity. The findings highlight the potential of Ranger and the generalized training model to enhance cell segmentation across different microscopy datasets.}, archive = {J_ACCESS}, author = {Basma A. Mohamed and Nancy M. Salem and Walid Al-Atabany and Lamees N. Mahmoud}, doi = {10.1109/ACCESS.2025.3557970}, journal = {IEEE Access}, pages = {62121-62136}, shortjournal = {IEEE Access}, title = {A deep learning-based approach for cell segmentation in phase-contrast images}, volume = {13}, year = {2025}, }
• (2025). Automated detection of deviations in bankruptcy processes using process mining. ACCESS, 13, 62103-62120. (www)

  @article{ , abstract = {This study investigates the application of process mining (PM) techniques for the analysis of bankruptcy processes based on digital trace logs. The primary objective is to improve transparency, efficiency, and regulatory compliance within bankruptcy proceedings. An automated framework is proposed that addresses three key tasks: 1) the detection of deviations in process execution, 2) the identification of root causes behind these deviations, and 3) the verification of process conformance to predefined regulatory standards. Event logs are used to identify inefficiencies, bottlenecks, and instances of non-compliance with expected process flows, such as delays, irregularities, and resource misallocations. Root cause analysis is employed to pinpoint factors influencing process outcomes, while conformance checking ensures alignment with regulatory models. The framework is evaluated using real-world bankruptcy case data, demonstrating its capability to detect and analyze process inefficiencies.}, archive = {J_ACCESS}, author = {Ivan P. Malashin and Igor S. Masich and Daniel A. Ageev and Dmitriy A. Evsyukov and Andrei P. Gantimurov and Vladimir A. Nelyub and Aleksei S. Borodulin and Vadim S. Tynchenko}, doi = {10.1109/ACCESS.2025.3558998}, journal = {IEEE Access}, pages = {62103-62120}, shortjournal = {IEEE Access}, title = {Automated detection of deviations in bankruptcy processes using process mining}, volume = {13}, year = {2025}, }
• (2025). Rank fusion-based crime scene shoeprint image retrieval. ACCESS, 13, 62088-62102. (www)

  @article{ , abstract = {The primary objective of shoeprint retrieval is to identify shoeprints with comparable patterns among the numerous shoeprints collected from different crime scenes. This task becomes challenging when trying to find the most similar shoeprints from a large number of deteriorated prints. Most existing shoeprint retrieval methods focus on using either holistic or local features for matching, which may lead to reduced accuracy, especially with degraded shoeprints obtained from crime scenes. In this study, we propose a framework for retrieving crime scene shoeprint images by fusing handcrafted and deep features. The proposed approach allows us to prioritize candidate shoeprints by assigning them top ranks. Experiments are conducted on the publicly accessible FID-300 dataset, which consists of crime scene shoeprint images. The results demonstrate that the proposed approach can effectively deal with real crime scene shoeprint images, achieving a cumulative match score of nearly 98% within the top 20 percent.}, archive = {J_ACCESS}, author = {Yanjun Wu and Xianling Dong and Guochao Shi and Xiaolei Zhang and Yanli Liu and Zhongxiao Wang and Congzhe Chen and Shiqi Xu}, doi = {10.1109/ACCESS.2025.3557609}, journal = {IEEE Access}, pages = {62088-62102}, shortjournal = {IEEE Access}, title = {Rank fusion-based crime scene shoeprint image retrieval}, volume = {13}, year = {2025}, }
• (2025). Adaptive intelligent reflecting surfaces for enhanced wireless communication via multi-agent deep reinforcement learning. ACCESS, 13, 62071-62087. (www)

  @article{ , abstract = {This research introduces an innovative model-free framework for Intelligent Reflecting Surface (IRS) control in multi-user wireless communication systems using Multi-Agent Reinforcement Learning (MARL). Departing from traditional methods that rely heavily on detailed channel state information (CSI), this approach emphasizes the dynamic adaptation of IRS phase shifts to optimize overall system performance, measured in bits per second per Hertz (bps/Hz). The MARL framework enables decentralized decision-making among multiple agents, offering superior adaptability to complex and rapidly evolving channel conditions. Comparative analyses with established methods, including Multi-Armed Bandit (MAB), Random Selection, Deep Q-Network (DQN), and Hierarchical Reinforcement Learning (HRL), reveal substantial improvements in system performance. In particular, for a base station (BS) configuration with two antennas ( $N_{B} = 2$ ), MARL achieves superior performance compared to baseline methods, demonstrating its robustness in limited-antenna settings. Similarly, an IRS deployment with 32 elements enhances MARL’s efficiency by leveraging improved signal reflections, leading to significant performance gains. Furthermore, the impact of batch size and K-factor on MARL’s adaptability is evaluated, confirming its ability to optimize phase shift adjustments across diverse network conditions. Simulation results confirm MARL’s efficacy in optimizing resource allocation and phase shift adjustments, demonstrating its scalability and potential for real-time deployment in large-scale wireless networks.}, archive = {J_ACCESS}, author = {Sakhshra Monga and Abhishake Bansal and Ibrahim Aljubayri and Nitin Saluja and Chander Prabha and Shivani Malhotra and Prakash Srivastava and Mohammad Zubair Khan}, doi = {10.1109/ACCESS.2025.3557008}, journal = {IEEE Access}, pages = {62071-62087}, shortjournal = {IEEE Access}, title = {Adaptive intelligent reflecting surfaces for enhanced wireless communication via multi-agent deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Formal specification and verification of smart contract-based loan management system using TLA+. ACCESS, 13, 62060-62070. (www)

  @article{ , abstract = {Smart contracts provide convenience to the financial industry by automating complex transactions without intermediaries. In the context of decentralized finance, ensuring the correctness, reliability, and efficiency of these automated systems is crucial, especially for loan management processes. This paper presents a formal specification of a smart contract-based loan management system using TLA+. The system is designed to handle various types of loans, including credit-based and collateral-backed loans, managing both regular and early repayments, as well as handling late payments and collateral forfeiture. Our specification defines key components such as loan states, repayment calculations, and collateral management, ensuring that all aspects of the loan lifecycle are rigorously modeled and verified. Safety properties, such as preventing negative loan balances and ensuring proper collateral ownership transfer, are enforced alongside liveness properties that guarantee the system reaches a termination state where all loans are either fully repaid or defaulted. We used the TLC model checker to verify the correctness of the system across all possible states and transitions. The verification process confirmed that the system consistently adheres to its formal specification under a variety of operational conditions. Through the formal specification and comprehensive verification with TLA+, this work enhances the dependability of smart contract based financial systems, providing a secure, verifiable, and resilient loan management framework ready for deployment in real-world DeFi environments.}, archive = {J_ACCESS}, author = {Seongho Yoon and Jin-Young Choi}, doi = {10.1109/ACCESS.2025.3558081}, journal = {IEEE Access}, pages = {62060-62070}, shortjournal = {IEEE Access}, title = {Formal specification and verification of smart contract-based loan management system using TLA+}, volume = {13}, year = {2025}, }
• (2025). Challenges and advances in materials and fabrication technologies for the development of p-GaN gated E-mode AlGaN/GaN power HEMTs: A critical review. ACCESS, 13, 62045-62059. (www)

  @article{ , abstract = {Industries including energy production, automotive and aerospace has seen a tremendous increase in interest in high temperature electronics. There has been a lot of investigations done on wide bandgap materials because of the difficulties traditional electronics suffer at high temperatures, such as poor stability and increased leakage current. GaN-based power high electron mobility transistors (HEMTs) are a popular choice for a variety of power electronic applications due to their excellent efficiency and ability to deliver high power density. When it comes to power HEMTs, p-GaN gate technology is a reliable and affordable way to accomplish normally off (E-Mode) operation and guarantee fail-safe standards. This article critically reviews the advances and challenges in fabrication technologies, recent developments in materials, impact of gate first/gate last fabrication processes, gate contact effects, advances in etching techniques and annealing/plasma treatment effects in the development of p-GaN Gated E-Mode AlGaN/GaN RF Power HEMTs.}, archive = {J_ACCESS}, author = {J. Ajayan and Vakkalakula Bharath Sreenivasulu and N. Aruna Kumari and S. Sreejith and Subhajit Das}, doi = {10.1109/ACCESS.2025.3555845}, journal = {IEEE Access}, pages = {62045-62059}, shortjournal = {IEEE Access}, title = {Challenges and advances in materials and fabrication technologies for the development of p-GaN gated E-mode AlGaN/GaN power HEMTs: A critical review}, volume = {13}, year = {2025}, }
• (2025). Advanced noise reduction for in-cylinder combustion pressure data using ICEEMDAN and optimal wavelet selection. ACCESS, 13, 62029-62044. (www)

  @article{ , abstract = {This study introduces a robust approach for denoising pressure signals by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), Continuous Mean Square Error (CMSE) analysis, optimal wavelet selection, and wavelet thresholding techniques. ICEEMDAN decomposes noisy signals into Intrinsic Mode Functions (IMFs), which are classified via CMSE to distinguish noise-dominated IMFs from signal-dominated IMFs. A key innovation is the sparsity-based optimal wavelet selection strategy, which dynamically identifies the most effective wavelet basis and decomposition levels for each noise-dominated IMF. These IMFs undergo wavelet thresholding for noise suppression, while signal-dominated IMFs are preserved to ensure signal integrity during reconstruction. The proposed approach was validated on simulated signals and experimental data from lean syngas/air combustion at equivalence ratios. Results consistently demonstrate superior denoising performance across various metrics compared to traditional infinite impulse response (IIR) filters, empirical mode decomposition (EMD) and conventional ICEEMDAN-wavelet methods. By dynamically adapting wavelet selection and decomposition levels to varying signal and noise characteristics, this method achieves optimal noise suppression with high signal fidelity. Heursure and Minimaxi methods use hard thresholding and reveal consistent outperformance compared to Sqtwolog and Rigrsure in both simulated and real-world scenarios. This approach offers significant advancements in engine performance optimization, fuel efficiency improvement, combustion diagnostics, and industrial pressure monitoring. By bridging theoretical innovation with practical applications, the proposed method provides a scalable and precise solution for signal reconstruction in complex environments.}, archive = {J_ACCESS}, author = {Van-Trung Nguyen and Minh-Tien Nguyen}, doi = {10.1109/ACCESS.2025.3558428}, journal = {IEEE Access}, pages = {62029-62044}, shortjournal = {IEEE Access}, title = {Advanced noise reduction for in-cylinder combustion pressure data using ICEEMDAN and optimal wavelet selection}, volume = {13}, year = {2025}, }
• (2025). Modern deep learning techniques for big medical data processing in cloud. ACCESS, 13, 62005-62028. (www)

  @article{ , abstract = {The recent advancements in Machine Learning (ML) and Deep Learning (DL) provide a new dimension in biomedical big data analysis, while the cloud computing technologies present the breakthroughs of handling massive data from hardware, software, and storage. Therefore, in this paper, an examination of the characteristics and challenges of big data is conducted with, highlighting the need for sophisticated analytical approaches. The paper then explores a wide spectrum of ML techniques, including unsupervised, supervised, and reinforcement learning methods, and their applications in big data contexts. This study explores recent advancement in DL architectures, while examining their effectiveness. Furthermore, we will show main challenges and limitations associated with using ML and DL in big data settings, including computational complexity, issues of data quality, model interpretability, and ethical concerns. Finally, it concludes by discussing the trends and future directions that are upcoming, including federated learning, explainable AI, and edge computing. Additionally, an experimental case study is presented to validate our approach, demonstrating its effectiveness in real-world healthcare scenarios through improved diagnostic accuracy and reduced computational time. Results have shown that the incorporation of DL techniques, such as CNN and RNN, forms the basis for large-scale analytics in medical data analytics due to their great contribution toward the development of accuracy and scalability. In specific research on disease prediction and medical image diagnosis within the healthcare industry, there was an improvement in diagnostics by up to 20%, with a reduction in computation time achieved via cloud-based architecture. These developments demonstrate the high extent of integration between DL and cloud computing in handling the challenge of bulk medical datasets and providing practical insight for both researchers and health professionals.}, archive = {J_ACCESS}, author = {Mohammed Y. Shakor and Mustafa Ibrahim Khaleel}, doi = {10.1109/ACCESS.2025.3556327}, journal = {IEEE Access}, pages = {62005-62028}, shortjournal = {IEEE Access}, title = {Modern deep learning techniques for big medical data processing in cloud}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Predictive energy management for docker containers in cloud computing: A time series analysis approach”. ACCESS, 13, 62004. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Predictive Energy Management for Docker Containers in Cloud Computing: A Time Series Analysis Approach”.}, archive = {J_ACCESS}, author = {Abdulmohsen Algarni and Iqrar Shah and Ali Imran Jehangiri and Mohammed Alaa Ala’Anzy and Zulfiqar Ahmad}, doi = {10.1109/ACCESS.2025.3557477}, journal = {IEEE Access}, pages = {62004}, shortjournal = {IEEE Access}, title = {Corrections to “Predictive energy management for docker containers in cloud computing: A time series analysis approach”}, volume = {13}, year = {2025}, }
• (2025). Trajectory tracking of fixed-wing UAV using ANFIS-based sliding mode controller. ACCESS, 13, 61986-62003. (www)

  @article{ , abstract = {This paper proposes a robust Adaptive Neuro-Fuzzy Inference System-based Sliding Mode Controller (ANFIS-SMC) for trajectory tracking in fixed-wing unmanned aerial vehicles (FWUAVs). FWUAVs are autonomous and versatile, finding applications in many fields such as defense, surveillance, and logistics. However, their dynamic model is highly complex due to nonlinearity and coupling effects. In this paper the decoupling is held based on extracting dominant inputs, and all other inputs are considered uncertainties, for designing controllers. The proposed ANFIS-SMC integrates the adaptability of the ANFIS with the robustness of SMC to overcome issues such as the chattering effect and shortcomings of traditional fuzzy logic controllers. ANFIS is trained with the sliding surface as an input data and SMC’s discontinuous control effort as an output data. Through such training, ANFIS can effectively approximate the discontinuous control action without losing smoothness and adaptability of control while keeping the robustness of the traditional SMC strategy. The Lyapunov theory ensures the finite time convergence of both reaching and sliding phases of sliding mode controller. The proposed ANFIS-SMC controller is stronger and more adaptive compared to FSMC and SMC. During mass variation along the x-axis, ITAE increased from 10.94 to 63.52 using ANFIS-SMC, from 17.39 to 128.3 using FSMC, and from 38.28 to 830.1 using SMC. This is an improvement of 50% over FSMC and 90% over SMC, which demonstrates improved trajectory tracking performance for fixed-wing UAVs in dynamic environments. Simulations on MATLAB®/Simulink® confirm that the proposed ANFIS-SMC ensures stable flight and accurate trajectory tracking, even in challenging flight conditions.}, archive = {J_ACCESS}, author = {Feleke Tsegaye Yareshe and Nigatu Wanore Madebo and Chala Merga Abdissa and Lebsework Negash Lemma}, doi = {10.1109/ACCESS.2025.3557472}, journal = {IEEE Access}, pages = {61986-62003}, shortjournal = {IEEE Access}, title = {Trajectory tracking of fixed-wing UAV using ANFIS-based sliding mode controller}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning for vehicle noise prediction with limited labeled data. ACCESS, 13, 61972-61985. (www)

  @article{ , abstract = {With advancements in artificial intelligence, its application for developing quieter vehicles is increasingly researched in the automotive industry. Vehicle noise is highly impacted by the acceleration from electric power steering (EPS); therefore, determining the relationship between EPS-induced acceleration and vehicle noise is important. However, collecting labeled data for noise prediction is challenging because it requires expensive acceleration sensors attached to the EPS, and experts must measure the noise directly by ear. In contrast, obtaining unlabeled acceleration data from environments similar to those of the EPS is manageable. This study proposes an autoencoder-based self-supervised learning with information maximization (ASSIM) method to predict vehicle noise. ASSIM allows robust feature learning via pretraining to reconstruct unlabeled data with necessary augmentations. The experimental results demonstrate that ASSIM performs better than the other comparative methods in predicting vehicle noise in environments with less labeled data. The proposed method may aid in the design of quieter vehicles while reducing the data collection time.}, archive = {J_ACCESS}, author = {Sunhyeok Hwang and Seoung Bum Kim}, doi = {10.1109/ACCESS.2025.3558172}, journal = {IEEE Access}, pages = {61972-61985}, shortjournal = {IEEE Access}, title = {Self-supervised learning for vehicle noise prediction with limited labeled data}, volume = {13}, year = {2025}, }
• (2025). Dual hackathon based on immersive simulation: A multidisciplinary approach for engineering training and emergency management. ACCESS, 13, 61958-61971. (www)

  @article{ , abstract = {Engineering education worldwide is facing two major challenges: a shortage of students and a high dropout rate in this field of study. The main objective of this study is to identify new and improved teaching methodologies that motivate students, enhance their essential professional skills, and provide insight into the societal impact of engineering work. In order to achieve this, the study focused on a topic of great concern to the general population: hydrometeorological emergencies resulting from climate change. These emergencies are increasing in frequency and intensity, posing significant challenges where engineering can contribute solutions to mitigate their severe effects. A dual hackathon was designed, incorporating a real-world immersion experience. Graduate students in engineering disciplines participated in a simulation of flooding in a nearby town, experiencing firsthand the planning and execution of interventions, the application of technology for various rescue operations, and the management of interoperability among resources and teams. Following this immersive experience, students took part in a hackathon where they developed innovative solutions in diverse fields to improve the mitigation of human and economic damages. The competency evaluations conducted after the dual hackathon revealed improvements ranging from 20% to 70%, with key technical skills such as “preventive planning” and “audits and certifications” increasing by 33% and 21%, respectively. Paired t-tests confirmed these gains (p <0.005), with large and moderate effect sizes (Cohen’s d =0.87, 0.59). Moreover, civil protection and risk management competencies saw increases of over 70% (p <0.001, Cohen’s d >1.4), reinforcing the strong impact of this approach. These results consolidate the dual hackathon as a comprehensive, attractive and effective teaching methodology that significantly improves the training of Engineering 5.0 students.}, archive = {J_ACCESS}, author = {Sonia Zaragoza Fernandez and Daniel Lorenzo Mirón and Antonio Couce Casanova and Feliciano Fragela Díaz}, doi = {10.1109/ACCESS.2025.3556815}, journal = {IEEE Access}, pages = {61958-61971}, shortjournal = {IEEE Access}, title = {Dual hackathon based on immersive simulation: A multidisciplinary approach for engineering training and emergency management}, volume = {13}, year = {2025}, }
• (2025). Resource-efficient personalization in federated learning with closed-form classifiers. ACCESS, 13, 61928-61957. (www)

  @article{ , abstract = {Statistical heterogeneity in Federated Learning (FL) often leads to client drift and biased local solutions. Prior work in the literature shows that client drift particularly affects the parameters of the classification layer, hindering both convergence and accuracy. While Personalized FL (PFL) addresses this by allowing client-specific models, it can overlook valuable global knowledge. This paper introduces Federated Recursive Ridge Regression (Fed3R), a fast and efficient method to construct a closed-form classifier that effectively incorporates global knowledge while being inherently robust to statistical heterogeneity. Fed3R leverages a pre-trained feature extractor and a recursive ridge regression formulation to achieve exact aggregation of local classifiers and recover the centralized solution. We demonstrate that Fed3R serves as a robust initialization for further fine-tuning with various FL and PFL algorithms, accelerating convergence and boosting performance. Furthermore, we propose Only Local Labels (OLL), a novel PFL technique that simplifies local classifiers by focusing only on locally relevant classes, preventing misclassifications and improving efficiency. Our empirical evaluation on real-world cross-device datasets shows that Fed3R, combined with OLL, significantly improves performance and reduces training costs in heterogeneous FL and PFL scenarios.}, archive = {J_ACCESS}, author = {Eros Fanì and Raffaello Camoriano and Barbara Caputo and Marco Ciccone}, doi = {10.1109/ACCESS.2025.3556587}, journal = {IEEE Access}, pages = {61928-61957}, shortjournal = {IEEE Access}, title = {Resource-efficient personalization in federated learning with closed-form classifiers}, volume = {13}, year = {2025}, }
• (2025). A probabilistic model predictive control approach for PV-diesel hybrid systems in ghana’s health sector using seamless state prediction methods. ACCESS, 13, 61890-61927. (www)

  @article{ , abstract = {In Ghana, unreliable public grid infrastructure greatly impacts rural healthcare, where diesel generators are commonly used despite their high financial and environmental costs. Photovoltaic (PV)-hybrid systems offer a sustainable alternative, but require robust, predictive control strategies to ensure reliability. This study proposes a sector-specific Model Predictive Control (MPC) approach, integrating advanced load and meteorological forecasting for optimal energy dispatch. The methodology includes a long-short-term memory (LSTM)-based load forecasting model with probabilistic Monte Carlo dropout, a customized Numerical Weather Prediction (NWP) model based on the Weather Research and Forecasting (WRF) framework, and deep learning-based All-Sky Imager (ASI) nowcasting to improve short-term solar predictions. By combining these forecasting methods into a seamless prediction framework, the proposed MPC optimizes system performance while reducing reliance on fossil fuels. This study benchmarks the MPC against a traditional rule-based dispatch system, using data collected from a rural health facility in Kologo, Ghana. Results demonstrate that predictive control greatly reduces both economic and ecological costs. Compared to rule-based dispatch, diesel generator operation and fuel consumption are reduced by up to 61.62% and 47.17%, leading to economical and ecological cost savings of up to 20.7% and 31.78%. Additionally, system reliability improves, with battery depletion events during blackouts decreasing by up to 99.42%, while wear and tear on the diesel generator and battery are reduced by up to 54.93% and 37.34%, respectively. Furthermore, hyperparameter tuning enhances MPC performance, introducing further optimization potential. These findings highlight the effectiveness of predictive control in improving energy resilience for critical healthcare applications in rural settings.}, archive = {J_ACCESS}, author = {Samer Chaaraoui and Stefanie Meilinger and Sebastian Houben and Thorsten Schneiders and Windmanagada Sawadogo}, doi = {10.1109/ACCESS.2025.3556980}, journal = {IEEE Access}, pages = {61890-61927}, shortjournal = {IEEE Access}, title = {A probabilistic model predictive control approach for PV-diesel hybrid systems in ghana’s health sector using seamless state prediction methods}, volume = {13}, year = {2025}, }
• (2025). Adaptive global super-twisting sliding mode control for trajectory tracking of two-link flexible manipulators. ACCESS, 13, 61876-61889. (www)

  @article{ , abstract = {This paper proposes a new adaptive supertwisting global sliding mode control method for the nonlinear two-link flexible manipulator’s exponentially changing trajectory tracking control. This technique consists of an adaptive super-twisting to prevent chattering and the robustness issue, and a global sliding mode control structure to eliminate the reaching phase of the tip trajectory tracking of the flexible manipulator with unknown disturbances and variation in the payloads. The tip trajectory tracking control and the stability analysis of the system is guaranteed using Lyapunov method for the convergence to zero in finite time while mitigating the traditional drawback of the high frequency chattering. By tuning the gains of the new adaptive method, the controller can handle the flexible manipulator’s parametric variations and avoid the aggressive control inputs. Moreover, the upper boundaries of the perturbation of the system information are not necessary when utilising the adaptation laws. Lesser tracking error in terms of integral absolute error, quick tip deflection suppression in terms of 2-norm, no chattering, no reaching phase, lesser input torque energy in terms of 2-norm and total variance are the effective matrices used to validate the effectiveness of the suggested method. The effectiveness of the proposed controller is assessed by means of simulation validation and compared with other controllers of the state of art in the literature. It is shown that our proposed control technique ensures robustness with low tracking error, reduced tip deflection, lower control inputs, no chattering, and smoother control, while achieving 94.92% energy efficiency, 57.64% improved tracking accuracy, and stability under increased payloads.}, archive = {J_ACCESS}, author = {Kshetrimayum Lochan and Lakmal Seneviratne and Irfan Hussain}, doi = {10.1109/ACCESS.2025.3557202}, journal = {IEEE Access}, pages = {61876-61889}, shortjournal = {IEEE Access}, title = {Adaptive global super-twisting sliding mode control for trajectory tracking of two-link flexible manipulators}, volume = {13}, year = {2025}, }
• (2025). Infrared and visible image fusion based on image enhancement and target extraction. ACCESS, 13, 61862-61875. (www)

  @article{ , abstract = {In order to improve the detail visibility and target contrast of fused images, an infrared and visible image fusion method based on image enhancement and target extraction is proposed. The proposed method is divided into three parts, including enhancement stage, target extraction stage, and fusion stage. In the enhancement stage, to efficiently decompose the visible images, a decomposition method based on improved guided filter is proposed by using the deep feature as the guided image. Based on the characteristics of low and high frequency layers, brightness correction function and detail adjustment function are designed to improve the global and local contrast, respectively. In target extraction, morphological operation and background subtraction are introduced to achieve coarse target extraction efficiently. The feature distribution of redundant background is optimized to obtain more accurate infrared targets. In the fusion stage, the infrared target is injected into the enhanced visible image by compression ratio. A fused image with clear local details and high contrast thermal target is obtained while the exposure is suppressed. In order to prove the effectiveness of the proposed method, experiments are performed on different datasets. Moreover, the proposed method is compared with several mainstream methods based on six evaluation metrics. The results show that the fusion effect of the proposed method is better as compared to other methods.}, archive = {J_ACCESS}, author = {Haoran Zhu and Wenying Zhang}, doi = {10.1109/ACCESS.2025.3557799}, journal = {IEEE Access}, pages = {61862-61875}, shortjournal = {IEEE Access}, title = {Infrared and visible image fusion based on image enhancement and target extraction}, volume = {13}, year = {2025}, }
• (2025). Feasibility and economic assessment of a PV-wind hybrid charging station in the bronx, NY. ACCESS, 13, 61841-61861. (www)

  @article{ , abstract = {This work presents a technical and economic feasibility assessment of a photovoltaic (PV)-wind hybrid charging station located in the South Bronx, New York, with a 100-kW demand. The study evaluates the technical requirements for sizing the hybrid system, including determining the appropriate capacity of solar PV panels, wind turbines, and battery storage to meet the energy demand of the charging station. The analysis also explores the economic feasibility by considering key financial parameters such as capital costs, operation and maintenance (O&M) costs, system efficiency, and the system’s lifetime. A financial model is developed to estimate the costs, payback period, and overall economic viability, incorporating sensitivity analysis on the Net Present Value (NPV) and Return on Investment (ROI). Environmental impact metrics, including carbon emission reduction, energy efficiency gains, renewable energy utilization, and air quality improvement, are also examined to assess the broader environmental benefits of the hybrid system. The study highlights the potential of renewable energy-based charging stations in urban areas, providing valuable insights into the technical, economic, and environmental aspects of hybrid energy systems for electric vehicle (EV) infrastructure.}, archive = {J_ACCESS}, author = {Khairy Sayed and Mohammed M. Elsayed and Ahmed Mohamed}, doi = {10.1109/ACCESS.2025.3555158}, journal = {IEEE Access}, pages = {61841-61861}, shortjournal = {IEEE Access}, title = {Feasibility and economic assessment of a PV-wind hybrid charging station in the bronx, NY}, volume = {13}, year = {2025}, }
• (2025). Depth distribution and correlation analysis of ambient noise based on underwater acoustic glider measurements. ACCESS, 13, 61833-61840. (www)

  @article{ , abstract = {There have been a lot of research results on the characteristics of ambient noise distribution with depth, but the correlation between the two is rarely reported. In order to reveal the cross-correlation between noise spectrum level and reception depth, three-day ambient noise measurements were conducted in the northern South China Sea using two underwater acoustic gliders. The acoustic system of underwater acoustic glider collects the noise signal in all stages except for the glider surface diving preparation stage and at the maximum working depth in the floating stage. The data processing results show that the ambient noise level fluctuates with the depth of glider platform, and it diminishes with the increase in receiving depth. At frequencies below 200 Hz, the cross-correlation coefficient between ambient noise level and depth is approximately 0.5, reaching a moderate correlation. At frequencies above 500 Hz, the correlation coefficient is greater than 0.8, indicating high correlation.}, archive = {J_ACCESS}, author = {Chao Wang and Meng Yuan}, doi = {10.1109/ACCESS.2025.3556134}, journal = {IEEE Access}, pages = {61833-61840}, shortjournal = {IEEE Access}, title = {Depth distribution and correlation analysis of ambient noise based on underwater acoustic glider measurements}, volume = {13}, year = {2025}, }
• (2025). Haar wavelet-based representation learning for unpaired image-to-image translation. ACCESS, 13, 61821-61832. (www)

  @article{ , abstract = {In recent years, there have been numerous attempts to achieve unpaired image-to-image translation. Many algorithms have especially incorporated the contrastive learning framework into unpaired image-to-image translation. This paper presents an innovative approach to applying contrastive learning to unpaired image-to-image translation using the Haar wavelet transform, called HAWAII. Specifically, we investigated how mutual and domain-specific information can be optimally chosen for contrastive learning. This leads to a novel definition of typical and non-typical features by using features from specific frequency bands that are separated using the Haar wavelet transform. By utilizing the typical and non-typical features for contrastive learning, the generator can effectively account for which information should be preserved and which should be modified for image translation. Since contrastive learning is effective for the classification task, we applied it not only to the generator but also to the discriminator. The proposed regularization method enables the model to efficiently identify the information that should be retained in the given image and the translated image in the generator and to better distinguish the features of the source and target domains in the discriminator. Extensive experiments on various tasks demonstrate the feasibility of the proposed method, which achieves state-of-the-art performance in terms of many metrics.}, archive = {J_ACCESS}, author = {Soobin Park and Seohyeon Yoo and Nabin Jeong and Eunju Cha}, doi = {10.1109/ACCESS.2025.3556217}, journal = {IEEE Access}, pages = {61821-61832}, shortjournal = {IEEE Access}, title = {Haar wavelet-based representation learning for unpaired image-to-image translation}, volume = {13}, year = {2025}, }
• (2025). SMART based multi-point matching assisted approximation of renewable interconnected power system. ACCESS, 13, 61805-61820. (www)

  @article{ , abstract = {In the interconnected power systems, the renewable based hydro power generating plants are generally used as one of the effective renewable energy source due to its capability of high response time against the variable loading conditions in comparison with other renewable based energy sources. In this presented work, the hydro power plant (HPP) system of order six is approximated to a reduced-order (RO) model of order three with the help of simple multi-attribute rating technique (SMART) based systematic weight ascertainment approach employing multi-point matching method. The mathematically modeled sixth order higher-order (HO) HPP system is matched around multi-points to achieve the better response of RO model with respect to HO HPP system. The errors between expansion parameters obtained in terms of coefficients of HO HPP system and its RO model are minimized around selected multi-points, further. For the error minimization, objective function is framed. The objective function is then converted in the form of weighted objective function by employing SMART based weight determination approach. The weighted objective function is then minimized with the help of brown-bear optimization algorithm. In this minimization, the steady-state constraint and Hurwitz stability criterion are satisfied for a comparatively better and stable RO model with zero steady-state error. In the support of the ascertained RO model of order three for sixth order HO HPP system, step, Bode, and impulse responses are depicted. In addition, tabular comparisons of specifications of time domain values are provided in inclusion with error response and performance error values.}, archive = {J_ACCESS}, author = {Umesh Kumar Yadav and V. P. Singh and Luigi Fortuna and Umesh Kumar Sahu}, doi = {10.1109/ACCESS.2025.3556895}, journal = {IEEE Access}, pages = {61805-61820}, shortjournal = {IEEE Access}, title = {SMART based multi-point matching assisted approximation of renewable interconnected power system}, volume = {13}, year = {2025}, }
• (2025). Trajectory optimization for non-prehensile manipulation of space robots. ACCESS, 13, 61793-61804. (www)

  @article{ , abstract = {The increasing demand for on-orbit servicing tasks has driven advancements in space robotics. Traditional capture-based manipulation methods are limited by the need for customized grasping mechanisms and target-specific designs, reducing adaptability for uncooperative or docking-less targets. Non-prehensile manipulation through contact offers a promising alternative for OOS, especially in low-gravity environments. However, the complexity of contact interactions have posed significant challenges to progress in this area. This paper proposes a mode-invariant trajectory optimization method for non-prehensile manipulation. The proposed approach combines complementarity constraints with a direct trajectory optimization framework and employs sequential quadratic programming solvers to generate contact-aware trajectories for non-prehensile manipulations. Simulations involving a 7-degree-of-freedom space robot reorienting a target object demonstrate the method’s feasibility and effectiveness. The results show the capability to produce physically realistic and efficient trajectories, enabling complex tasks without relying on capturing mechanisms.}, archive = {J_ACCESS}, author = {Chen Li and Yayun Wu and Zixuan Zheng}, doi = {10.1109/ACCESS.2025.3556010}, journal = {IEEE Access}, pages = {61793-61804}, shortjournal = {IEEE Access}, title = {Trajectory optimization for non-prehensile manipulation of space robots}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Combining convolutional neural networks for fungi classification”. ACCESS, 13, 61792. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Combining Convolutional Neural Networks for Fungi Classification”.}, archive = {J_ACCESS}, author = {Anuruk Prommakhot and Jakkree Srinonchat}, doi = {10.1109/ACCESS.2025.3557469}, journal = {IEEE Access}, pages = {61792}, shortjournal = {IEEE Access}, title = {Corrections to “Combining convolutional neural networks for fungi classification”}, volume = {13}, year = {2025}, }
• (2025). Transmission power grid hardening strategies against hurricane-induced extreme weather conditions: A comprehensive review. ACCESS, 13, 61768-61791. (www)

  @article{ , abstract = {High Impact Low Probability (HILP) natural events like hurricanes have become a matter of serious concern in recent times. These catastrophic events not only cause severe damage to residential and industrial infrastructures but also engender massive losses in the economy due to large-scale power outages. Such cascaded long-term power interruptions are usually triggered by individual or simultaneous failure in generation, transmission, and distribution components. In this context, high voltage (HV) transmission lines, insulators, and towers are the most vulnerable components of the transmission system, frequently sustaining the most severe damage imposed by HILP events. In this paper, the underlying reasons for wide-scale power outages are investigated from the perspective of transmission failure. Based on the literature review, this paper contextualizes the idea of grid hardening and emphasizes the importance of adopting transmission power grid hardening strategies. Following this, a brief overview is presented of different failure probability assessment models of these components exposed to intense wind loads. In addition to commonly practiced hardening techniques, this paper provides an elaborative discussion on various robust design and reinforcement aspects of the abovementioned components with an aim toward reduction in large-scale power outages due to wind-induced adverse conditions during hurricanes.}, archive = {J_ACCESS}, author = {Md Jahid Hasan Rifat and Mona Ghassemi}, doi = {10.1109/ACCESS.2025.3558195}, journal = {IEEE Access}, pages = {61768-61791}, shortjournal = {IEEE Access}, title = {Transmission power grid hardening strategies against hurricane-induced extreme weather conditions: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). 2D pre-beamforming control method via analog radio over fiber for wireless communication systems with massive planar array antenna. ACCESS, 13, 61760-61767. (www)

  @article{ , abstract = {To rapidly deploy large capacity wireless communication services using high frequency bands, a system is being considered that use radio-over-fiber (RoF) technology to separate the wireless base station function into a central station (CS) with signal processing functions and remote radio units (RRU) with antenna functions. In this system, remote beamforming, in which the CS performs beamforming control, is desirable for simplifying the RRU. We have proposed a fixed wavelength allocation beamforming scheme and a pre-beamforming (pre-BF) control method as a means of remote beamforming suitable for high frequency band RoF systems. However, when these methods are applied to RoF systems with a massive planar array antenna, the beam may not be formed properly. In this paper, the performance of applying these methods to massive planar array antennas is evaluated in simulations. This evaluation shows that the phase spacing irregularities due to the wavelength spacing expanding as the number of antenna elements increase causes BF gain reducing by beam misshaping and directional shifts. To solve this problem, we propose a two-dimensional (2D) pre-BF control method, which extends the pre-BF control method to two dimensions. The proposed method compensates for the phase spacing irregularities due to the wavelength spacing expanding. Thus, high accuracy beamforming with the fixed wavelength allocation beamforming scheme can be realized even for planar array antennas. We show that the 2D pre-BF control method can conduct high accuracy beamforming by reducing the degradation from the ideal beamforming gain to about 0.2 dB with a 100-element planar array antenna.}, archive = {J_ACCESS}, author = {Mizuki Suga and Yuta Takahashi and Yasuyoshi Yamamoto and Hideki Toshinaga and Naoki Kita and Takeshi Onizawa}, doi = {10.1109/ACCESS.2025.3556124}, journal = {IEEE Access}, pages = {61760-61767}, shortjournal = {IEEE Access}, title = {2D pre-beamforming control method via analog radio over fiber for wireless communication systems with massive planar array antenna}, volume = {13}, year = {2025}, }
• (2025). FrameBoost: Advanced video analytics with inference trigger frame selection via tracking error estimation. ACCESS, 13, 61748-61759. (www)

  @article{ , abstract = {For real-time video analytics, the inference trigger frame selection problem is crucial. While continuous video streams offer rich data, processing every frame is computationally intensive. Thus, modern systems strategically analyze the frames to choose frames for inference, i.e., inference trigger frames (ITF). Between ITFs, they use tracking to leverage temporal coherence. Consequently, the ITF selection probelm serves as the cornerstone technology for video analytics, directly impacting both accuracy and efficiency. Beyond selecting critical frames for system accuracy, a major requirement for ITF selection algorithms is efficiency. They must be able to extract informative frames while imposing minimal computational overhead on the analytics pipeline. Current approaches like ‘frame differencing’ methods, while promising, have fundamental limitations. These methods rely on simple inter-frame changes that often correlate poorly with actual tracking quality, particularly for differently sized objects. We introduce $\mathsf{FrameBoost}$ , an intelligent ITF selection method that adopts aggregate tracking error (ATE) as its metric, based on object-wise IoU predictions. $\mathsf{FrameBoost}$ addresses a key limitation of existing methods: their tendency to overemphasize large object movements while undervaluing smaller objects that are more prone to tracking errors. We identify key factors affecting IoU prediction performance and develop a lightweight solution that requires minimal computational resources. By directly measuring tracking quality, $\mathsf{FrameBoost}$ makes more sophisticated decisions about when new detections are truly necessary. Through comprehensive evaluations across diverse scenarios, we demonstrate that $\mathsf{FrameBoost}$ achieves superior accuracy with comparable or lower computational overhead than existing approaches, making it ideal for various video analytics systems with different system-level requirements.}, archive = {J_ACCESS}, author = {Jin Mo Yang and Sunwook Hwang and Jeongjun Park and Saewoong Bahk}, doi = {10.1109/ACCESS.2025.3558251}, journal = {IEEE Access}, pages = {61748-61759}, shortjournal = {IEEE Access}, title = {FrameBoost: Advanced video analytics with inference trigger frame selection via tracking error estimation}, volume = {13}, year = {2025}, }
• (2025). Single-node power demand during AI training: Measurements on an 8-GPU NVIDIA h100 system. ACCESS, 13, 61740-61747. (www)

  @article{ , abstract = {The expansion of artificial intelligence (AI) applications has driven substantial investment in computational infrastructure, especially by cloud computing providers. Quantifying the energy footprint of this infrastructure requires models parameterized by the power demand of AI hardware during training. In this work, we measured the instantaneous power draw of an 8-GPU NVIDIA H100 HGX node during the training of open-source image classifier (ResNet) and large-language models (Llama2-13b). We characterize power demand for a single node configuration, providing foundational data for future multi-node studies. The maximum observed power draw was approximately 8.4 kW, 18% lower than the manufacturer-rated 10.2 kW, even with GPUs near full utilization. Holding model architecture constant, increasing batch size from 512 to 4096 images for ResNet reduced total training energy consumption by a factor of 4. These findings can inform capacity planning for data center operators and energy use estimates by researchers. Future work will investigate the impact of cooling technology and carbon-aware scheduling on AI workload energy consumption.}, archive = {J_ACCESS}, author = {Imran Latif and Alex C. Newkirk and Matthew R. Carbone and Arslan Munir and Yuewei Lin and Jonathan Koomey and Xi Yu and Zhihua Dong}, doi = {10.1109/ACCESS.2025.3554728}, journal = {IEEE Access}, pages = {61740-61747}, shortjournal = {IEEE Access}, title = {Single-node power demand during AI training: Measurements on an 8-GPU NVIDIA h100 system}, volume = {13}, year = {2025}, }
• (2025). Causal physics-infused hybrid learning (CPIHL) framework for next-gen battery health forecasting. ACCESS, 13, 61728-61739. (www)

  @article{ , abstract = {A novel hybrid model, denoted by Causal Physics-Informed Hybrid Learning Neural Networks (CPIHL), is developed in this study to significantly enhance the accuracy, interoperability, and real-time feasibility of battery health predictions. The model incorporates the effects of temperature and voltage on internal resistance, effectively capturing the non-linear electrochemical behaviors that drive battery degradation. The proposed framework is rigorously validated using two open-source datasets: the Samsung INR21700-50E and the Forklift Battery Degradation datasets. The CPIHL model demonstrates exceptional performance, achieving an R2 score of 0.9994, a mean absolute error of 0.0007, and a root mean square error of 0.0025, outperforming all baseline machine learning and deep learning models, including Random Forest, Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Units. The CPIHL framework exhibits robust, interpretable, and scalable behavior, making it highly effective for predictive maintenance tasks. It provides actionable insights for battery management, enabling the optimization of operational strategies to extend battery life. By improving battery health monitoring, this work contributes to sustainable energy usage, enhancing efficiency and reducing battery disposal waste.}, archive = {J_ACCESS}, author = {Sahar Qaadan and Aiman Alshare and Rami Alazrai and Alexander Popp and Benedikt Schmuelling}, doi = {10.1109/ACCESS.2025.3556314}, journal = {IEEE Access}, pages = {61728-61739}, shortjournal = {IEEE Access}, title = {Causal physics-infused hybrid learning (CPIHL) framework for next-gen battery health forecasting}, volume = {13}, year = {2025}, }
• (2025). XR-driven robotic system training for occupational health, safety, and maintenance. ACCESS, 13, 61708-61727. (www)

  @article{ , abstract = {The integration of Extended Reality (XR) technologies into Industry 5.0 offers innovative solutions for industrial training by overcoming the limitations of traditional methods, such as high costs, inefficiencies, and production disruptions. These technologies enable immersive and scalable training environments, providing workers with hands-on experience while minimizing risks and resource constraints. This study presents an XR-based training platform designed to enhance workforce skills in health and safety, robotic system operations, and maintenance through immersive Virtual Reality (VR) and Augmented Reality (AR) scenarios. Evaluated by 22 participants from diverse professional backgrounds, the platform achieved a 50% reduction in training time compared to conventional methods, while significantly improving skill acquisition and task efficiency. Participants rated immersive VR environments highly (4.4/5) for engagement and knowledge retention, with usability and instructions also scoring positively (4/5). Scenario-based performance showed the average scores in health and safety tasks (72 points), while robotic system operations (60 points) and maintenance (54 points) reflected increasing task complexity. Despite moderate cognitive demands, the platform effectively balanced mental effort with engagement, providing an accessible and effective training experience. These findings underscore the potential of XR technologies to transform industrial training by enhancing efficiency, safety, and scalability, aligning with the evolving needs of Industry 5.0.}, archive = {J_ACCESS}, author = {Yunus Emre Esen and Berk Kaan Çetincan and Kübra Yayan and Hüseyin Güray Gürlek and Uğur Yayan}, doi = {10.1109/ACCESS.2025.3556699}, journal = {IEEE Access}, pages = {61708-61727}, shortjournal = {IEEE Access}, title = {XR-driven robotic system training for occupational health, safety, and maintenance}, volume = {13}, year = {2025}, }
• (2025). BuildWin-SAM: An improved SAM-based method for extracting building windows from street view images. ACCESS, 13, 61696-61707. (www)

  @article{ , abstract = {Building facade segmentation provides critical support for urban information management, precise 3D reconstruction, and energy consumption analysis. Window, as a pivotal component of building facades, plays a central role in these applications. However, accurately identifying windows in diverse urban environments poses significant challenges due to dataset limitations and variability in model performance. This study presents two primary contributions: first, we develop the Street View Building Window (SVBW) segmentation dataset, comprising 1,172 images that represent diverse urban contexts and window types, with a total of 50,321 meticulously annotated window instances. This dataset addresses existing gaps in segmenting irregular building facades. Second, we propose BuildWin-SAM, a model for window extraction based on the Segment Anything Model (SAM) architecture, which is trained on the SVBW dataset. Comparative analysis with CNN-based semantic segmentation models and SAM demonstrates that BuildWin-SAM achieves improvements across key evaluation metrics, including Intersection over Union (IoU), F1 score, precision, and recall. Specifically, BuildWin-SAM achieves an IoU of 80.70%, precision of 89.43%, recall of 89.20%, and an F1 score of 88.52%, demonstrating precise window localization and delineation capabilities. To further validate its robustness, we conduct evaluations on three public datasets featuring multi-scale and multi-scene images with building window annotations. BuildWin-SAM achieves Recall rates exceeding 72% and Precision rates mainly above 87% across these datasets. These results demonstrate BuildWin-SAM’s potential to significantly enhance building window recognition in diverse urban environments, ultimately contributing to advancements in building information management and other relevant applications. The SVBW dataset will be provided at https://github.com/zhengnanle/svbw.}, archive = {J_ACCESS}, author = {Zhengnan Li and Yizhen Yan and Bo Huang}, doi = {10.1109/ACCESS.2025.3556738}, journal = {IEEE Access}, pages = {61696-61707}, shortjournal = {IEEE Access}, title = {BuildWin-SAM: An improved SAM-based method for extracting building windows from street view images}, volume = {13}, year = {2025}, }
• (2025). Design and research of a parallel cable-driven force feedback master hand. ACCESS, 13, 61685-61695. (www)

  @article{ , abstract = {Teleoperation systems enable operators to interact with remote environments or robots, allowing for remote control and task execution even when not physically present at the task location. Teleoperation robots with force feedback capabilities significantly enhance task success rates and reduce the risks associated with extreme operations. This paper proposes a parallel cable-driven force feedback master hand. Compared to traditional linkage-based force feedback master hands, cable-driven systems use lightweight elastic ropes or cables to transmit force and motion, offering greater flexibility and adaptability. This approach allows for simpler and more precise simulation of the forces encountered by the slave end in contact with the environment, providing operators with a more realistic experience. This study focuses on the development of a force feedback-enabled teleoperation master hand. Initially, a generative design of a six-cable parallel-driven force feedback master hand is presented. This master hand features a five-degree-of-freedom serial structure and integrates six motors to provide force feedback. The system’s functional modules are designed based on the overall control scheme. Subsequently, kinematic and dynamic analyses are conducted to derive the cable tension distribution scheme. A master-slave control system for the force feedback master hand is then established using Multibody dynamics, and force control simulations are performed based on planned trajectories to verify the accuracy of the kinematic analysis and the correctness of the tension distribution algorithm. Finally, displacement and force feedback experiments are conducted using the developed platform to validate the feasibility of the overall control scheme.}, archive = {J_ACCESS}, author = {Duanjiao Li and Wenxing Sun and Junwen Yao and Yilong Chen and Yuhui Chen and Jieren Zheng and Yupeng Zou}, doi = {10.1109/ACCESS.2025.3531226}, journal = {IEEE Access}, pages = {61685-61695}, shortjournal = {IEEE Access}, title = {Design and research of a parallel cable-driven force feedback master hand}, volume = {13}, year = {2025}, }
• (2025). A spatio-temporal tensor graph neural network-based method for node-link prediction in port networks. ACCESS, 13, 61675-61684. (www)

  @article{ , abstract = {Port network information security has received extensive attention in recent years, in which the prediction of node links in the network is significant. A Port network is a dynamic network, and its structure changes continuously with time. Therefore, to effectively utilize the information of the dynamic network and improve the prediction efficiency as well as the prediction accuracy, this paper proposes a spatio-temporal tensor graph neural network model, which learns graph structural features from both spatial and temporal aspects to capture the evolution of the dynamic network. Firstly, the model extracts the corresponding spatial features for the port network node links using the graph compression technique and updates the spatial features based on GraphSAGE by sampling and aggregating the neighbor node features. Secondly, for the port network temporal links, this paper introduces an exponential decay model to extract the corresponding temporal features and obtains the temporal edge embedding vectors by convolution method based on the GCN model. The updated spatial features and temporal edge embedding vectors are then weighted for feature fusion to predict the security of port network links. Finally, this paper conducts experiments on seven publicly available dynamic graph datasets, and the results show that the prediction accuracy of the spatio-temporal tensor graph neural network model is better than the baseline models, such as GC-LSTM, HTGN, and GAT, in general.}, archive = {J_ACCESS}, author = {Zhixin Xia and Zhangqi Zheng and Feiyang Wei and Yongshan Liu and Lu Yu}, doi = {10.1109/ACCESS.2025.3555264}, journal = {IEEE Access}, pages = {61675-61684}, shortjournal = {IEEE Access}, title = {A spatio-temporal tensor graph neural network-based method for node-link prediction in port networks}, volume = {13}, year = {2025}, }
• (2025). Color-based lifetime estimation of LEDs using spectral power distribution prediction through analytical and machine learning models. ACCESS, 13, 61665-61674. (www)

  @article{ , abstract = {LEDs have seen widespread adoption across various applications in recent years. However, reliability continues to be a major concern. Conventional methods estimate LED lifetime using lumen data and an exponential model, which are suitable for lumen-based evaluations but inadequate for applications where color stability is equally critical. To address this limitation, this paper presents a reliability analysis of LEDs by evaluating performance metrics such as chromaticity shifts $\Delta u'v'$ , correlated color temperature (CCT), and chromaticity coordinates ( $u'$ and $v'$ ) through spectral power distribution (SPD) prediction. The study also compares chromaticity coordinate projections with the TM-35-19 projection method and explores lifetime predictions using CCT binning, as outlined in ANSI C78.377, to assess long-term stability. Both analytical and machine learning (ML) models are employed for SPD prediction, with the support vector machine demonstrating superior performance. The results show that both models reliably estimate LED lifetime by predicting $\Delta u'v'$ values, with errors remaining below 10%. The models offer high predictive accuracy, with minimal deviations in CCT and chromaticity coordinates, validating their effectiveness in reliability assessments with accepted methods widely. This work highlights the potential of ML-based approaches to enhance LED performance prediction through colorimetric parameters, and reduce testing time.}, archive = {J_ACCESS}, author = {J. Lokesh and Savitha G. Kini and M. G. Mahesha and Anjan N. Padmasali}, doi = {10.1109/ACCESS.2025.3558559}, journal = {IEEE Access}, pages = {61665-61674}, shortjournal = {IEEE Access}, title = {Color-based lifetime estimation of LEDs using spectral power distribution prediction through analytical and machine learning models}, volume = {13}, year = {2025}, }
• (2025). Low complexity CS/CB techniques for aerial assisted cellular network with imperfect CSI. ACCESS, 13, 61651-61664. (www)

  @article{ , abstract = {The inclusion of aerial base stations (ABSs) into cellular networks has many potential benefits, including low-cost network connectivity for remote rural areas and quick deployment to provide coverage in emergencies and enhance capacity at hotspots. However, their strong line-of-sight links exacerbate network interference particularly at terrestrial base stations. To mitigate this interference, this paper proposes coordinated scheduling/coordinated beamforming (CS/CB) with low complexity. Through simulations, the performance and complexity for three low complexity beamforming algorithms are compared for a multi antenna aerial assisted cellular network in a rural environment under perfect and imperfect channel state information (CSI) scenarios. The beamforming algorithms are zero forcing beamforming (ZFBF), block diagonalization using singular value decomposition (BD SVD) and block diagonalization using Householder transformation (BD HH). The results show that CS/CB improves coverage over non coordination. For an 8 antenna system the maximum increment in coverage probability for ZFBF is 16% and 44% for cooperating cluster sizes of 3 and 6 respectively against 29% and 42% for the BD techniques. As the CSI error variance increases from 0.1 to 0.3, the coverage probability drops at a rate of 0.6 for ZFBF and 0.2 for the BD techniques while the average rate deteriorates at 6 Mbps for ZFBF and 2 Mbps for the BD techniques. BD HH gives the lowest overall complexity and together with its resilience to CSI error is the most pragmatic beamforming technique for an aerial assisted cellular network.}, archive = {J_ACCESS}, author = {Sheila N. Mugala and Jonathan Serugunda and Dorothy K. Okello}, doi = {10.1109/ACCESS.2025.3532384}, journal = {IEEE Access}, pages = {61651-61664}, shortjournal = {IEEE Access}, title = {Low complexity CS/CB techniques for aerial assisted cellular network with imperfect CSI}, volume = {13}, year = {2025}, }
• (2025). Improve LADRC strategy for variable air gap permanent magnetic levitation system. ACCESS, 13, 61641-61650. (www)

  @article{ , abstract = {Aiming to address the shortcomings of the single-point variable air gap permanent magnetic levitation system in terms of dynamic performance and anti-disturbance capabilities, an improved linear active disturbance rejection control (ILADRC) strategy is proposed. First, based on the system dynamics model, a conventional linear active disturbance rejection control (LADRC) controller is designed, with the total perturbation observed and compensated for in real time by a linear expanded state observer (LESO). Building upon this, the known part of the model is embedded into the LESO framework to enhance the accuracy of total perturbation observation, and the ILADRC controller is designed to further improve the system’s stability and perturbation resistance. The stability and tracking performance of the ILADRC are then verified through theoretical analysis. Finally, the ILADRC strategy is compared with proportional-integral-derivative (PID) control, traditional LADRC control, and auxiliary model-based linear active disturbance rejection control (AMLADRC) in both simulation and experimental settings. The results show that the ILADRC strategy outperforms the other three control strategies, significantly improving the system’s dynamic performance and disturbance resistance.}, archive = {J_ACCESS}, author = {Hongkui Zhang and Qinwei Zhang and Hang Shen and Yipeng Lan and Jiaqi Wen and Chuan Zhao}, doi = {10.1109/ACCESS.2025.3549146}, journal = {IEEE Access}, pages = {61641-61650}, shortjournal = {IEEE Access}, title = {Improve LADRC strategy for variable air gap permanent magnetic levitation system}, volume = {13}, year = {2025}, }
• (2025). Explainable artificial intelligence (XAI) for malware analysis: A survey of techniques, applications, and open challenges. ACCESS, 13, 61611-61640. (www)

  @article{ , abstract = {Machine learning (ML) has rapidly advanced in recent years, revolutionizing fields such as finance, medicine, and cybersecurity. In malware detection, ML-based approaches have demonstrated high accuracy; however, their lack of transparency poses a significant challenge. Traditional ML models often fail to provide interpretable justifications for their predictions, limiting their adoption in security-critical environments where understanding the reasoning behind a detection is essential for threat mitigation and response. Explainable AI (XAI) addresses this gap by enhancing model interpretability while maintaining strong detection capabilities. This survey presents a comprehensive review of state-of-the-art ML techniques for malware analysis, with a specific focus on explainability methods and research mainly from 2018 to 2024. We examine existing XAI frameworks, their application in malware classification and detection, and the challenges associated with making malware detection models more interpretable. Additionally, we explore recent advancements and highlight open research challenges in the field of explainable malware analysis. By providing a structured overview of XAI-driven malware detection approaches, this survey serves as a valuable resource for researchers and practitioners seeking to bridge the gap between ML performance and explainability in cybersecurity.}, archive = {J_ACCESS}, author = {Harikha Manthena and Shaghayegh Shajarian and Jeffrey C. Kimmell and Mahmoud Abdelsalam and Sajad Khorsandroo and Maanak Gupta}, doi = {10.1109/ACCESS.2025.3555926}, journal = {IEEE Access}, pages = {61611-61640}, shortjournal = {IEEE Access}, title = {Explainable artificial intelligence (XAI) for malware analysis: A survey of techniques, applications, and open challenges}, volume = {13}, year = {2025}, }
• (2025). Fault-tolerant control of multi-UAV attitude based on predictive control in uncertain network environment. ACCESS, 13, 61594-61610. (www)

  @article{ , abstract = {This paper analyzes the simultaneous occurrence of actuator faults (AF) and sensor faults (SF) in a Multi-Unmanned Aerial Vehicle (UAV) system operating in an unreliable network environment, and designs a distributed fault-tolerant control (FTC) scheme based on predictive control theory. First, the existence of Markov-based uncertain time delay in the system is considered, and generalized to obtain a Markov Jump System (MJS). Secondly, after analyzing the system with input constraints, sufficient conditions for stochastically stabilizing the augmented system are derived using the Lyapunov function; then, the matrix to be solved is computed after decoupling the sufficient conditions; Finally, the effectiveness of the proposed method is demonstrated through numerical simulations.}, archive = {J_ACCESS}, author = {Jiacheng Bin and Fang Liu and Qingnan Huang and Qiqi Wu and Xianming Xie}, doi = {10.1109/ACCESS.2025.3555821}, journal = {IEEE Access}, pages = {61594-61610}, shortjournal = {IEEE Access}, title = {Fault-tolerant control of multi-UAV attitude based on predictive control in uncertain network environment}, volume = {13}, year = {2025}, }
• (2025). Predicting priority factors for rural E-commerce development using tri-stage integrated attention residual transformer. ACCESS, 13, 61578-61593. (www)

  @article{ , abstract = {E-commerce’s quick expansion has changed global marketplaces, creating rural development potential. Rural e-commerce performance relies on technology infrastructure, socio-economic circumstances, and government policy. Despite increased interest in rural e-commerce, current models generally fail to combine these various aspects. This research introduces a new Tri-Stage Integrated Attention Residual Transformer (TIART) model to anticipate rural e-commerce development priorities. Attention mechanisms, residual learning, and transformer topologies improve feature extraction and prediction. The model forecasts key parameters including family income, internet penetration, and logistical performance that are essential for rural e-commerce adoption. The unbalanced, incomplete, and noisy aspects of rural data have challenged traditional models. A three-stage design in the TIART model tackles these issues—residual connections enhance feature stability, attention layers prioritize critical variables, and dimensional mixing improves cross-feature interactions. The model is evaluated on a five-year dataset from rural Bavaria, Germany, consisting of over 150,000 records and 21 features. TIART achieves a prediction accuracy of 98.7% $R^{2}$ , with a root mean square error (RMSE) of 0.98, outperforming XGBoost, EfficientNet, and GhostNet by 4–7% across key metrics. It also reduces execution time by up to 35% compared to deep CNN models. This study shows that powerful machine learning may maximize rural e-commerce growth and equip policymakers and entrepreneurs with actionable insights. The model provides a scalable framework to guide rural intervention and infrastructure development.}, archive = {J_ACCESS}, author = {He Li and Naiyu Shi}, doi = {10.1109/ACCESS.2025.3558391}, journal = {IEEE Access}, pages = {61578-61593}, shortjournal = {IEEE Access}, title = {Predicting priority factors for rural E-commerce development using tri-stage integrated attention residual transformer}, volume = {13}, year = {2025}, }
• (2025). Hybrid reinforcement learning-based collision avoidance algorithm for autonomous vehicle clusters. ACCESS, 13, 61564-61577. (www)

  @article{ , abstract = {Nowadays, collaborative collision avoidance for autonomous vehicle clusters has become the key to ensure traffic safety. Aiming at the complex and changeable traffic environment, this paper proposes a novel collision avoidance method for Autonomous Vehicle Clusters based on hybrid reinforcement learning. The method combines the adaptive capability of reinforcement learning with the feature extraction capability of deep learning to improve the collision avoidance performance in complex traffic scenarios. A hybrid reinforcement learning framework is designed, which consists of a deep neural network structure and a reinforcement learning structure. The feature extraction key from environmental perception data and predict possible collision risks. While the latter learns how to adjust vehicle motion parameters based on these features and the historical performance of collision avoidance strategies. Convolutional neural network is used to process image data to capture spatial information in traffic scenes, and time series data is combined with long and short time memory network to capture the time dependence of vehicle motion. Through a large number of simulation experiments and field tests from KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute), nuScenes and INTERACTION, we verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm in this study showed a high success rate of collision avoidance and a low average reaction time under most collision avoidance times.}, archive = {J_ACCESS}, author = {Chubing Guo and Jianshe Wu and Panzheng Luo and Zhigang Wang and Kai Zhang and Ziyi Yang and Zengfa Dou and Kan Song}, doi = {10.1109/ACCESS.2025.3553968}, journal = {IEEE Access}, pages = {61564-61577}, shortjournal = {IEEE Access}, title = {Hybrid reinforcement learning-based collision avoidance algorithm for autonomous vehicle clusters}, volume = {13}, year = {2025}, }
• (2025). Distributed model predictive cross-dimensional formation control for heterogeneous multiagent systems. ACCESS, 13, 61549-61563. (www)

  @article{ , abstract = {In this paper, a distributed predefined-time model predictive formation control method is proposed for second-order multidimensional cross-dimensional heterogeneous multiagent systems (HMASs) with collision avoidance, switching topology and formation transformation. First, a distributed cross-dimensional formation control protocol based on the full or partial information of neighboring agents is proposed for multidimensional heterogeneous multiagent systems. Second, a formation cooperative control approach that can quickly achieve the desired formation and formation transformation is designed by combining the predefined-time control strategy with the auxiliary control law of Lyapunov-based distributed model predictive control (DMPC). In addition, collision avoidance among agents can be achieved by adding a collision penalty mechanism to the cost function. Finally, an application to heterogeneous multiagent unmanned surface vehicle and unmanned underwater vehicle (USV-UUV) systems, through which the effectiveness of the proposed results has been demonstrated, and the simulation results are analyzed to evaluate the effectiveness and reliability of the proposed method.}, archive = {J_ACCESS}, author = {Haitao Liu and Zhaoxin Li and Qingqun Mai and Haochun Liu and Xuehong Tian}, doi = {10.1109/ACCESS.2025.3555976}, journal = {IEEE Access}, pages = {61549-61563}, shortjournal = {IEEE Access}, title = {Distributed model predictive cross-dimensional formation control for heterogeneous multiagent systems}, volume = {13}, year = {2025}, }
• (2025). Integrating cognitive intelligence and VANET for effective traffic congestion detection in smart urban mobility. ACCESS, 13, 61538-61548. (www)

  @article{ , abstract = {The delay in transporting essential goods is primarily attributed to widespread traffic congestion globally. This issue not only results in significant time and fuel wastage but also poses a considerable challenge in efficiently disseminating traffic information and managing road conditions for authorities. Addressing these challenges, vehicular ad hoc networks (VANETs) have emerged as a crucial component of the cognitive intelligent transportation system (C-ITS). To tackle this issue effectively, vehicle-to-vehicle (V2V) communication plays a crucial role in fostering cooperation and optimizing route management within transportation networks. This paper proposes an innovative congestion detection system that integrates the fuzzy k-means (FKM) clustering technique with the fuzzy analytical hierarchy process (FAHP). Utilizing the simulation of urban mobility (SUMO) simulator, a detailed transport network is modeled where vehicle parameters indicative of congestion are collected, integrated using sensor fusion, and analyzed. These parameters are processed using FKM clustering and a mathematical mean algorithm to identify key congestion indicators. Subsequently, FAHP prioritizes these collected parameters, pinpointing congestion hotspots within specific routes. By incorporating cognitive intelligence, the system continuously refines congestion detection and response strategies, enhancing traffic flow efficiency and enabling proactive congestion avoidance. This approach promises a more effective congestion detection methodology with minimal installation costs. Moreover, it can be effortlessly integrated into vehicles to facilitate congestion avoidance strategies, thereby enhancing overall traffic flow efficiency and mitigating the negative impacts of traffic congestion on transportation networks globally.}, archive = {J_ACCESS}, author = {Anita Mohanty and Ambarish G. Mohapatra and Subrat Kumar Mohanty and Tiansheng Yang and Rajkumar Singh Rathore and Ahmed Alkhayyat and Deepak Gupta}, doi = {10.1109/ACCESS.2025.3557276}, journal = {IEEE Access}, pages = {61538-61548}, shortjournal = {IEEE Access}, title = {Integrating cognitive intelligence and VANET for effective traffic congestion detection in smart urban mobility}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Intelligent ultrasound imaging for enhanced breast cancer diagnosis: Ensemble transfer learning strategies”. ACCESS, 13, 61537. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies”.}, archive = {J_ACCESS}, author = {Kuncham Sreenivasa Rao and Panduranga Vital Terlapu and D. Jayaram and Kalidindi Kishore Raju and G. Kiran Kumar and Rambabu Pemula and M. Venu Gopalachari and S. Rakesh}, doi = {10.1109/ACCESS.2025.3557513}, journal = {IEEE Access}, pages = {61537}, shortjournal = {IEEE Access}, title = {Corrections to “Intelligent ultrasound imaging for enhanced breast cancer diagnosis: Ensemble transfer learning strategies”}, volume = {13}, year = {2025}, }
• (2025). Lightweight pyramidal multi-scale attention residual network for plant disease recognition. ACCESS, 13, 61526-61536. (www)

  @article{ , abstract = {Earlier plant disease recognition plays a crucial role in determining agricultural yield. Therefore, developing an effective method for accurately identifying plant disease categories has become a significant concern within the agricultural domain. In this article, we propose a lightweight model for plant disease recognition called Lightweight Pyramidal Multi-scale Attention Residual Network (LPMA-ResNet). Our model incorporates a dual pyramidal structure to capture comprehensive information and utilizes an adaptive selection attention mechanism to identify informative features from pyramid multi-scale features while suppressing irrelevant information. To evaluate the performance of our model, extensive experiments were conducted on the soybean diseases dataset and plant village dataset. The experimental results obtained from these plant diseases datasets demonstrate that our model outperforms other state-of-the-art methods by achieving superior performance with fewer parameters utilized. LIMA-ResNet ( $\times 1.0$ ) could achieve over 2.12% accuracy improvement compared to ShuffleNet-v2( $\times 2.0$ ) while using only about 2/3 of its parameters on the soybean dataset. Meanwhile, we provide visualizations illustrating the impact of our proposed method on enhancing model performance to validate its effectiveness. Based on these empirical experiments, we demonstrate the effectiveness of our approach to plant disease recognition.}, archive = {J_ACCESS}, author = {Wenjie Liu and Guoqing Wu and Han Wang and Fuji Ren}, doi = {10.1109/ACCESS.2025.3552140}, journal = {IEEE Access}, pages = {61526-61536}, shortjournal = {IEEE Access}, title = {Lightweight pyramidal multi-scale attention residual network for plant disease recognition}, volume = {13}, year = {2025}, }
• (2025). Power theft detection in smart grids using quantum machine learning. ACCESS, 13, 61511-61525. (www)

  @article{ , abstract = {Electricity theft can lead to enormous economic losses and cause operational and security problems for electricity networks and utilities. Most current research has focused on electricity theft detection in the consumption sector. However, the high penetration rate of distributed generation (DG) can lead to an increase in power theft attacks in this sector via smart meter manipulation. This study is an extension of prior works focused on electricity theft detection in the consumption and generation domains of a smart grid environment with DG. This study proposes a novel electricity theft detection framework based on quantum machine learning (QML). The elegant field of QML has been used to demonstrate that data classification becomes more efficient in higher-dimensional spaces. An extensive numerical study was conducted to determine the type of QML architecture that can perform well and efficiently in electricity theft detection cases. The technique presented here has not yet been extensively studied in the domain of energy theft detection. Extensive experiments were conducted to assess this approach, and an accuracy of 0.87 was achieved with respect to the classical consumption domain, whereas an accuracy of 0.977 was achieved with respect to the net metering domain.}, archive = {J_ACCESS}, author = {Konstantinos Blazakis and Nikolaos Schetakis and Mahmoud M. Badr and Davit Aghamalyan and Konstantinos Stavrakakis and Georgios Stavrakakis}, doi = {10.1109/ACCESS.2025.3558143}, journal = {IEEE Access}, pages = {61511-61525}, shortjournal = {IEEE Access}, title = {Power theft detection in smart grids using quantum machine learning}, volume = {13}, year = {2025}, }
• (2025). GuardianML: Anatomy of privacy-preserving machine learning techniques and frameworks. ACCESS, 13, 61483-61510. (www)

  @article{ , abstract = {Machine learning has become integral to our lives, finding applications in nearly every aspect of our daily routines. However, using personal information in machine learning applications has raised concerns about user data privacy and security. As concerns about data privacy grow, algorithms and techniques for achieving robust privacy-preserving machine learning (PPML) have become a pressing technical challenge. Privacy-preserving machine learning PPML aims to safeguard the confidentiality of both data and models and ensure that sensitive information remains protected during training and inference processes. Different techniques, protocols, libraries, and frameworks have been advanced to enable privacy-preserving machine learning, including implementation trade-offs, computational efficiency, communication overhead minimization, security guarantees, and scalability. However, choosing the proper technique, framework, and corresponding algorithmic or system parameters for a specific deployment instance can be difficult. Various techniques, protocols, libraries, and frameworks have been proposed for PPML, but choosing the right combination along with the appropriate algorithmic or system parameters for a specific deployment instance can be very difficult. In this work, we introduce GuardianML, an open-source recommendation system for selecting the correct parameters and suitable framework for specific use cases of privacy-preserving machine learning PPML. GuardianML allows users to search through a wide range of privacy-preserving machine learning PPML frameworks, techniques, protocols, libraries, and more based on a set of objectives. GuardianML filters potential frameworks based on user-defined criteria, such as the number of parties involved in multi-party computation or the need to minimize communication costs in homomorphic encryption scenarios. The system’s recommendations and optimizations are formulated as a maximization problem using linear integer programming to identify the most suitable solution for various use cases. Moreover, this work thoroughly analyzes and presents seventy relevant frameworks in the system’s database. Additionally, we offer an open-source repository containing practical examples and documentation for some of the frameworks.}, archive = {J_ACCESS}, author = {Nges Brian Njungle and Eric Jahns and Zhenqi Wu and Luigi Mastromauro and Milan Stojkov and Michel A. Kinsy}, doi = {10.1109/ACCESS.2025.3557228}, journal = {IEEE Access}, pages = {61483-61510}, shortjournal = {IEEE Access}, title = {GuardianML: Anatomy of privacy-preserving machine learning techniques and frameworks}, volume = {13}, year = {2025}, }
• (2025). MelanoDet: Multimodal imaging system for screening and analysis of cutaneous melanoma. ACCESS, 13, 61469-61482. (www)

  @article{ , abstract = {Cutaneous melanoma is one of the most aggressive skin cancers, and early detection is critical for effective treatment. Although traditional imaging techniques such as dermoscopy improve diagnostic accuracy, many cases remain undetected. Recent advancements in non-invasive imaging, including multispectral imaging and infrared thermal imaging, offer new opportunities for early diagnosis. In this study, we introduce MelanoDet, a multimodal imaging system integrating three cameras corresponding to different spectra: visible, near-infrared, and long-wavelength infrared. To ensure accurate lesion inspection, an analytical method based on the optical cameras parameters estimated the cameras’ common field of view, to capture the same region of interest. Since the acquired images differ in resolution, angle, and orientation, a second refined registration step has been applied. A standardized image acquisition protocol was established in collaboration with an experienced team of dermatologists from Elias Emergency University Hospital, Bucharest. The MelanoDet system, along with its protocol and image processing techniques, was validated on 25 cases of suspicious nevi, demonstrating its potential for improving melanoma screening.}, archive = {J_ACCESS}, author = {Alina Elena Sultana and Maria Oniga and Paul Florin Rus and Andra Laura Dobre and Olguta Anca Orzan}, doi = {10.1109/ACCESS.2025.3557628}, journal = {IEEE Access}, pages = {61469-61482}, shortjournal = {IEEE Access}, title = {MelanoDet: Multimodal imaging system for screening and analysis of cutaneous melanoma}, volume = {13}, year = {2025}, }
• (2025). Digital transformation for institution operations in higher education: A literature review. ACCESS, 13, 61457-61468. (www)

  @article{ , abstract = {In recent years, higher education institutions (HEIs) have increasingly embraced digital technology to optimize admissions, enhance student recruitment through digital marketing, and facilitate online learning. The COVID-19 pandemic has significantly accelerated this shift, reshaping education through digital transformation (DT) in HEI. This study examines the relationships between digital transformation leadership, digital agility, and the execution of smart campus models. It delineates the principal problems and opportunities HEIs encounter in the adoption of new technology. This study presents a comprehensive literature analysis that outlines the critical elements of efficient smart management, including resource management, institutional collaboration, and enhancement of digital competencies among educators. These findings emphasize the importance of stakeholders adopting systematic procedures while implementing digital transformation strategies to improve operational efficiency and stakeholder involvement. Addressing issues such as digital culture, leadership dedication, and resource allocation is critical for higher education institutions to meet rising academic requirements and remain competitive in a global setting. This report is a valuable resource for Indonesian HEIs seeking to navigate changes and improve educational quality in the digital era.}, archive = {J_ACCESS}, author = {Daniel Jahja Surjawan and Armein Z. R. Langi and Radiant Victor Imbar}, doi = {10.1109/ACCESS.2025.3557446}, journal = {IEEE Access}, pages = {61457-61468}, shortjournal = {IEEE Access}, title = {Digital transformation for institution operations in higher education: A literature review}, volume = {13}, year = {2025}, }
• (2025). Ride-sharing determinants: Spatial and spatio-temporal bayesian analysis for chicago service in 2022. ACCESS, 13, 61440-61456. (www)

  @article{ , abstract = {Ridesharing services have revolutionized transportation, transforming the manner in which individuals navigate urban environments. The estimation of ride-sharing demand is crucial for enhancing service utilization, reliability, and mitigating traffic congestion. In this research, we employ spatial and spatiotemporal Bayesian models to estimate ride-sharing demand in the 77 Chicago community areas. We investigate various explanatory variables, including demographic, socio-economic, transportation, and land-use features. The Bayesian models adjust for random and structured errors to account for spatial correlations. Additionally, we utilize spatially correlated priors for explanatory variables to improve model precision. Model diagnostics demonstrate the absence of residual spatial structure in the spatial regression, confirming effective management of spatial correlation. The spatiotemporal model yields a squared correlation of 0.95 between observed ride-shares and fitted values, indicating robust predictive power. Demographic factors, including population size and crime rate, emerge as the primary determinants positively influencing ride-sharing demand. Higher income levels, increased numbers of economically active citizens, and higher proportions of car-free households are associated with increased demand. Public transit availability and walkability also play significant roles in ride-sharing patterns in Chicago. Our study demonstrates that ride-sharing demand recovered in 2022 following COVID-19-related closures, particularly during weekends. This research provides planners and policymakers with insights to enhance first and last mile services, identify underserved areas, and enable ride-sharing to address public transportation gaps. Such knowledge has the potential to enhance mobility for individuals with disabilities and those residing in rural areas. This study, utilizing Bayesian models, offers deeper insights into ride-sharing demand, thereby contributing to improved urban transportation planning.}, archive = {J_ACCESS}, author = {Mohamed Elkhouly and Taqwa Alhadidi}, doi = {10.1109/ACCESS.2025.3556350}, journal = {IEEE Access}, pages = {61440-61456}, shortjournal = {IEEE Access}, title = {Ride-sharing determinants: Spatial and spatio-temporal bayesian analysis for chicago service in 2022}, volume = {13}, year = {2025}, }
• (2025). Two-layer security for image encryption: A switched system approach. ACCESS, 13, 61429-61439. (www)

  @article{ , abstract = {This paper proposes an image encryption-decryption scheme with two-layer security. In the first layer, an image is encrypted by a switched system, using the concept from control theory. Also, from this switched system, a random key matrix will be generated while encrypting in the first layer. This key matrix is used to diffuse the image pixels from the first layer, providing the second layer of security. Then, in the decryption stage, the random key matrix plays a role as a cryptographic key and the optimization method will be used to decrypt the second layer encrypted image. The flatness property of the switched system that makes the plain image to be recovered from the encrypted image of the first layer. This scheme is to enhance the security in image encryption and also fortifies the encryption process against sophisticated attacks while maintaining computational efficiency. Finally, the effectiveness of the proposed scheme is evaluated by simulation, then assessing it to different types of cryptanalytic analyses such as information entropy analysis, adjacent pixel correlation, differential attack analysis, key sensitivity analysis, and histogram analysis. The numerical values that can achieved by the proposed schemes are entropy 7.996, adjacent pixel correlation for horizontal -0.0012, vertical -0.0039, diagonal 0.0005; and differential attack analysis with Number of Pixels Change Rate 0.9963 and Unified Average Changing Intensity 0.3341.}, archive = {J_ACCESS}, author = {Uyen L. P. Nguyen and Long Tonthat and Vo-Tan Phuoc and Lap Luat Nguyen}, doi = {10.1109/ACCESS.2025.3555520}, journal = {IEEE Access}, pages = {61429-61439}, shortjournal = {IEEE Access}, title = {Two-layer security for image encryption: A switched system approach}, volume = {13}, year = {2025}, }
• (2025). CADA: A flexible and elastic DDoS mitigation architecture. ACCESS, 13, 61416-61428. (www)

  @article{ , abstract = {The rigidity of traditional hardware DDoS mitigation frameworks is insufficient for handling complex and varied DDoS attacks. To address this, we introduce the Cloud-native Anti-DDoS Architecture (CADA), which is designed to ensure flexibility and elasticity while enhancing the efficiency of DDoS attack mitigation. CADA leverages software-driven concepts to abstract traditional hardware-based DDoS mitigation functions into capabilities. By utilizing an overlay network, CADA integrates capabilities into service chains based on user requests to formulate tailored DDoS mitigation strategies. These service chains are deployed on a cloud-native network, thereby overcoming the constraints of physical hardware. The selection of deployment strategies affects the efficiency of CADA’s DDoS mitigation. Therefore, we propose a genetic algorithm-based quantitative deployment algorithm that generates deployment strategies for each service chain through a fitness function that quantifies response time and resource utilization. Experimental results demonstrate that CADA significantly improves acceptance rates, reduces response times, and enhances the resource-time product compared to traditional hardware-based DDoS mitigation frameworks.}, archive = {J_ACCESS}, author = {Junrui Wu and Zhige He and Wenyong Wang and Haoran Luo and Shuisong Hu and Yu Miao and Yubo Li and Jun Deng}, doi = {10.1109/ACCESS.2025.3554782}, journal = {IEEE Access}, pages = {61416-61428}, shortjournal = {IEEE Access}, title = {CADA: A flexible and elastic DDoS mitigation architecture}, volume = {13}, year = {2025}, }
• (2025). Driving toward inclusion: A systematic review of AI-powered accessibility enhancements for people with disability in autonomous vehicles. ACCESS, 13, 61384-61415. (www)

  @article{ , abstract = {This paper provides a comprehensive and, to our knowledge, the first review of inclusive human-computer interaction (HCI) within autonomous vehicles (AVs) and human-driven cars with partial autonomy, emphasizing accessibility and user-centered design principles. We explore the current technologies and HCI systems designed to enhance passenger experience, particularly for individuals with accessibility needs. Key technologies discussed include brain-computer interfaces, anthropomorphic interaction, virtual reality, augmented reality, mode adaptation, voice-activated interfaces, haptic feedback, etc. Each technology is evaluated for its role in creating an inclusive in-vehicle environment. Furthermore, we highlight recent interface designs by leading companies and review emerging concepts and prototypes under development or testing, which show significant potential to address diverse accessibility requirements. Safety considerations, ethical concerns, and adoption of AVs are other major issues that require thorough investigation. Building on these findings, we propose an end-to-end design framework that addresses accessibility requirements across diverse user demographics, including older adults and individuals with physical or cognitive impairments. This work provides actionable insights for designers, researchers, and policymakers aiming to create safer and more comfortable environments in autonomous and regular vehicles accessible to all users.}, archive = {J_ACCESS}, author = {Ashish Bastola and Hao Wang and Sayed Pedram Haeri Boroujeni and Julian Brinkley and Ata Jahangir Moshayedi and Abolfazl Razi}, doi = {10.1109/ACCESS.2025.3555923}, journal = {IEEE Access}, pages = {61384-61415}, shortjournal = {IEEE Access}, title = {Driving toward inclusion: A systematic review of AI-powered accessibility enhancements for people with disability in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Learning to adapt deep corrosion assessment models from indoor to outdoor image domains. ACCESS, 13, 61368-61383. (www)

  @article{ , abstract = {Corrosion of materials impacts critical economic sectors from infrastructure to transportation. The development of safe, corrosion-inhibiting materials is thus an important area of study in materials science. Traditional corrosion science, preparing and monitoring materials under adverse conditions, is labor intensive and extremely costly. While deep learning has become popular in automating engineering tasks, the development of deep models for corrosion assessment is lacking. We study the unique problem of deep domain adaptation (DA) for automated corrosion assessment of corrosion-inhibiting materials. Corrosion data, i.e., photographic images of corroding materials, is abundant when produced in an artificially controlled laboratory, while controlled images from natural outdoor environments are limited. We thus leverage the more readily available artificial-environment indoor data to train a corrosion assessment classifier to transfer it via domain adaptation. In doing so, we can perform well on the smaller, yet more realistic, outdoor corrosion data, without requiring target labels. We empirically evaluate 5 popular DA models on real-world corrosion image data. Further, we design 8 strategies for ensembling these models. Our study finds across evaluation metrics of accuracy, F1-score, and balanced accuracy that ensembled models incorporating both predictions and confidence scores from each DA model outperform individual DA models. They achieve 41% relative improvement in test accuracy compared to a no-DA baseline. Additionally, we perform a failure analysis study of our model to explain its performance.}, archive = {J_ACCESS}, author = {Nicholas Josselyn and Biao Yin and Thomas A. Considine and John Kelley and Berend Rinderspacher and Robert Jensen and James F. Snyder and Walter Gerych and Ziming Zhang and Elke A. Rundensteiner}, doi = {10.1109/ACCESS.2025.3555118}, journal = {IEEE Access}, pages = {61368-61383}, shortjournal = {IEEE Access}, title = {Learning to adapt deep corrosion assessment models from indoor to outdoor image domains}, volume = {13}, year = {2025}, }
• (2025). Optimizing problem-solving in technical education: An adaptive learning system based on artificial intelligence. ACCESS, 13, 61350-61367. (www)

  @article{ , abstract = {The increasing complexity of educational challenges in technical disciplines highlights the need for personalized learning systems to address diverse student needs. Traditional methods, often relying on static activities or predefined rules, limit their ability to adapt to individual progress, hindering the development of critical skills such as problem-solving. Based on rules or machine learning, existing adaptive systems offer varying levels of personalization and efficiency but face significant scalability and computational demand barriers. This study proposes an adaptive learning system powered by deep learning algorithms designed to optimize problem-solving skills in technical college students. The system dynamically adjusts the difficulty of activities based on real-time performance data, ensuring a personalized and practical learning experience. A controlled experimental study was conducted with 200 students over eight weeks, divided into pretest, intervention, and posttest phases. The experimental group, which used the adaptive system, showed a 14% improvement in precision (from 71.8% to 85%) compared to 5% for the control group. In addition, the experimental group reduced its average time per activity by 15%, achieving 105 seconds compared to 124 seconds for the control group. These results demonstrate the system’s ability to improve precision, efficiency, and motivation in problem-solving tasks. By balancing computational efficiency with high personalization, this proposal offers a scalable and innovative solution that responds to current limitations in adaptive learning technologies.}, archive = {J_ACCESS}, author = {Rommel Gutierrez and William Eduardo Villegas-Ch and Alexandra Maldonado Navarro and Sergio Luján-Mora}, doi = {10.1109/ACCESS.2025.3557281}, journal = {IEEE Access}, pages = {61350-61367}, shortjournal = {IEEE Access}, title = {Optimizing problem-solving in technical education: An adaptive learning system based on artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Tracing student learning outcome at historically black colleges and universities via deep knowledge tracing. ACCESS, 13, 61340-61349. (www)

  @article{ , abstract = {Historically Black Colleges and Universities (HBCUs) in the United States play a vital role in higher education, providing educational opportunities and training, particularly for African American students. Deep Knowledge Tracing (DKT) has emerged as an advanced approach to enhancing higher education outcomes by enabling personalized learning experiences and more accurate assessments of student knowledge mastery. This study is the first to leverage DKT techniques for student learning outcome (SLO) tracing at an HBCU, focusing on Science, Technology, Engineering, and Mathematics (STEM) education at Prairie View A&M University (PVAMU). To systematically validate DKT-based SLO tracing, we construct a comprehensive dataset and employ multiple state-of-the-art (SOTA) DKT models to evaluate SLO tracing performance. The dataset consists of 352,148 learning records from $17,181$ undergraduate students across eight colleges at PVAMU. Experimental results demonstrate the effectiveness of DKT models in effectively predicting SLO. Specifically, Dynamic Key-Value Memory Network (DKVMN) outperforms other models due to its advanced mechanisms for capturing student learning patterns. These findings highlight the potential of DKT in identifying students at risk of underperforming on SLO, enabling proactive interventions to support academic progress and success at HBCUs.}, archive = {J_ACCESS}, author = {Ming-Mu Kuo and Xiangfang Li and Pamela H. Obiomon and Lijun Qian and Xishuang Dong}, doi = {10.1109/ACCESS.2025.3557316}, journal = {IEEE Access}, pages = {61340-61349}, shortjournal = {IEEE Access}, title = {Tracing student learning outcome at historically black colleges and universities via deep knowledge tracing}, volume = {13}, year = {2025}, }
• (2025). Non-redundant feature extraction in mobile edge computing. ACCESS, 13, 61329-61339. (www)

  @article{ , abstract = {Extracting discriminative features of data on Internet of Things (IoT) devices can reduce the amount of data uploaded by IoT devices to edge/cloud servers, thereby reducing the response time, which has attracted widespread attention from industry and academia. However, many existing related approaches have the following two limitations: (i) extracting a large number of redundant features, resulting in a waste of bandwidth resources, and (ii) extracting harmful features, resulting in low performance. To this end, this paper proposes a feature extractor generation algorithm for extracting non-redundant features, namely the Nor algorithm. The Nor algorithm analyzes existing discriminative feature extractors and finds that they contain a large number of redundant vectors. It further analyzes the locations where the redundant vectors appear and proposes how to remove them. The goal of this paper is to reduce the amount of data uploaded by IoT devices to the cloud/edge server by removing redundant vectors of the feature extractor, thereby reducing the data transmission time and the time it takes for the cloud/edge server to process features, and improving image recognition accuracy. Experimental results show that the Nor algorithm can effectively remove redundant vectors in the feature extractor and reduce the amount of feature data uploaded by IoT devices to the cloud/edge server. For example, in the Yale dataset, we can reduce the amount of feature data that needs to be uploaded to the edge server by 68.8% compared to the WAPL algorithm.}, archive = {J_ACCESS}, author = {Xiaojun Chen and Qi Wang and Chuntao Ding}, doi = {10.1109/ACCESS.2025.3552479}, journal = {IEEE Access}, pages = {61329-61339}, shortjournal = {IEEE Access}, title = {Non-redundant feature extraction in mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). Leveraging large language models for enhanced back-translation: Techniques and applications. ACCESS, 13, 61322-61328. (www)

  @article{ , abstract = {Cross-cultural studies are prevalent in academia, yet challenges arise in conducting objective research due to linguistic and cultural disparities. Rigorous international comparative research requires appropriate questionnaires that can be used in all countries, and translation becomes an extremely important process. Brislin’s back-translation method is widely recognized, but it usually requires many skilled bilingual translators and is both time-consuming and expensive. This study aims to overcome these limitations by using Large Language Model (LLM) AI technology. We utilized the Application Programming Interfaces (APIs) of well-known commercial LLM models such as ChatGPT3.5, ChatGPT4o, Google-Gemini, and Anthropic-Claude 3. The entire program was built using the Python programming language, and the user interface was built using the Streamlit library. This pilot study’s results confirm the feasibility of LLM-assisted back-translation, particularly for complex topics like carbon footprint reduction planning. This represents a significant advance over traditional back-translation methods, offering substantial time and cost savings.}, archive = {J_ACCESS}, author = {Ji-Bum Chung and Taehyun Kim}, doi = {10.1109/ACCESS.2025.3557014}, journal = {IEEE Access}, pages = {61322-61328}, shortjournal = {IEEE Access}, title = {Leveraging large language models for enhanced back-translation: Techniques and applications}, volume = {13}, year = {2025}, }
• (2025). GRE2-MDCL: Graph representation embedding enhanced via multidimensional contrastive learning. ACCESS, 13, 61312-61321. (www)

  @article{ , abstract = {Graph representation learning aims to preserve graph topology when mapping nodes to vector representations, enabling downstream tasks like node classification and community detection. However, most graph neural network models require extensive labelled data, limiting their practical applicability. To address this, researchers have explored Graph Contrastive Learning (GCL), which uses enhanced graph data and contrastive learning to better capture graph structure and features, providing new avenues for solving real-world problems with limited labelled data. Building on this, this work proposes Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning (GRE2-MDCL). GRE2-MDCL first globally and locally augments the input graph using SVD and LAGNN. The enhanced data is then fed into a triple network with a multi-head attention GNN as the core model. Finally, GRE2-MDCL constructs a multidimensional contrastive loss, incorporating cross-network, cross-view, and neighbor contrast, to optimize the model. Evaluated on Cora, Citeseer, and PubMed, GRE2-MDCL achieves average accuracies of 83.1%, 72.6%, and 82.7%, outperforming baseline GCL models. Visualizations also show tighter intra-cluster aggregation and clearer inter-cluster boundaries, demonstrating the framework’s effectiveness in improving upon the baseline.}, archive = {J_ACCESS}, author = {Quanjun Li and Weixuan Li and Xiya Zheng and Junhua Zhou and Wenming Zhong and Xuhang Chen and Chao Long}, doi = {10.1109/ACCESS.2025.3553862}, journal = {IEEE Access}, pages = {61312-61321}, shortjournal = {IEEE Access}, title = {GRE2-MDCL: Graph representation embedding enhanced via multidimensional contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Optimizing fairness and spectral efficiency with shapley-based user prioritization in semantic communication. ACCESS, 13, 61299-61311. (www)

  @article{ , abstract = {Efficient and fair resource allocation is a critical challenge in modern semantic communication systems, particularly in scenarios where numerous users compete for limited resources. Traditional methods, such as the Hungarian algorithm and greedy allocation, primarily focus on maximizing spectral efficiency, often neglecting fairness in resource distribution. This paper introduces a Shapley value-based framework that integrates Semantic Spectral Efficiency (S-SE) with fairness-driven prioritization. By leveraging Shapley values, the framework quantifies each user’s marginal contribution to overall system performance, enabling equitable and context-aware resource allocation. Semantic similarity is embedded directly into the allocation process, allowing the proposed method to intelligently manage resources based on channel conditions and the semantic relevance of transmitted information. Experimental evaluations demonstrate the framework’s effectiveness, achieving significant improvements in fairness, as measured by Jain’s Fairness Index, without compromising spectral efficiency. The Shapley-based approach outperforms established methods, including the Hungarian algorithm, reinforcement learning algorithms like Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), as well as conventional 4G and 5G resource allocation strategies. Notably, as the number of channels increases, S-SE stabilizes while fairness continues to improve, approaching optimal levels in diverse system configurations. Although the Shapley value calculation introduces a moderate increase in computational cost, this trade-off is justified by the robust balance achieved between fairness, efficiency, and overall system performance. These results highlight the potential of the proposed framework to advance next-generation wireless communication systems by prioritizing semantic relevance and equitable resource allocation.}, archive = {J_ACCESS}, author = {Moirangthem Tiken Singh and Adnan Arif and Rabinder Kumar Prasad and Bikramjit Choudhury and Chandan Kalita and Sikdar Md. S. Askari}, doi = {10.1109/ACCESS.2025.3558107}, journal = {IEEE Access}, pages = {61299-61311}, shortjournal = {IEEE Access}, title = {Optimizing fairness and spectral efficiency with shapley-based user prioritization in semantic communication}, volume = {13}, year = {2025}, }
• (2025). EEG-based multi-level mental state classification using partial directed coherence and graph convolutional networks: Impact of binaural beats on stress mitigation. ACCESS, 13, 61284-61298. (www)

  @article{ , abstract = {This study addresses limitations in EEG-based stress detection research by developing a novel approach to differentiate multiple mental states in different stress baseline population samples. Utilizing EEG signals, graph convolutional neural networks (GCNs), and binaural beats stimulation (BBs), the research investigates stress detection and reduction in two population sample groups with distinct baselines (group 1: low daily baseline, and group 2: stressed daily baseline). The experiment comprises four phases: rest state, control alertness, stress induction, and stress mitigation. Mental states were assessed using behavioral data: reaction time to stimuli (RT) and target detection accuracy, subjective reports: Perceived Stress Scale scores (PSS-10), biochemical indicators: salivary cortisol levels, and neurophysiological measure: EEG effective connectivity via Partial Directed Coherence (PDC). BBs significantly improved target detection accuracy by 31.6% and 22.8% for low and high-stress groups, respectively. PDC connectivity showed a shift to the temporal region during mitigation, indicating a return to a more balanced state. GCN classification achieved accuracies of $76.43~\pm ~9.01$ % and $76.32~\pm ~7.79$ % for each group, and $76.37~\pm ~8.40$ % for a common baseline. While 16-Hz BBs enhanced focusing abilities they did not significantly reduce subjective stress scores. This study highlights the complex relationship between cognitive performance, perceived stress, and neurophysiological measures, emphasizing the need for multifaceted stress research and management approaches.}, archive = {J_ACCESS}, author = {Yara Badr and Fares Al-Shargie and M. N. Afzal Khan and Nour Faris Ali and Usman Tariq and Fadwa Almughairbi and Fabio Babiloni and Hasan Al-Nashash}, doi = {10.1109/ACCESS.2025.3553932}, journal = {IEEE Access}, pages = {61284-61298}, shortjournal = {IEEE Access}, title = {EEG-based multi-level mental state classification using partial directed coherence and graph convolutional networks: Impact of binaural beats on stress mitigation}, volume = {13}, year = {2025}, }
• (2025). Projection-free methods for online distributed quantized optimization with strongly pseudoconvex cost functions. ACCESS, 13, 61274-61283. (www)

  @article{ , abstract = {This paper investigates online distributed optimization within multi-agent systems under inequality constraints. Agents are allowed to exchange local data with their immediate neighbors through a time-varying digraph and perform computations, aiming to minimize a collective objective function. Considering that the communication capacity of multi-agent systems is often limited in practical applications, a random quantizer is introduced to reduce the transmission bits when agents exchange information over the network. In this study, we specifically consider the scenario where the cost function is strongly pseudoconvex. To handle these problems, a quantized distributed online projection-free optimization algorithm is developed for the strongly pseudoconvex problem with an inequality constraint set. The performance of the algorithm is evaluated using the expectation of dynamic regret. Provided that the graph satisfies certain mild conditions, it has been demonstrated that the bound for each dynamic regret function increases at a sublinear rate. A simulation example is presented to illustrate the validity of our theoretical results.}, archive = {J_ACCESS}, author = {Xiaoxi Yan and Yu Li and Muyuan Ma}, doi = {10.1109/ACCESS.2025.3556719}, journal = {IEEE Access}, pages = {61274-61283}, shortjournal = {IEEE Access}, title = {Projection-free methods for online distributed quantized optimization with strongly pseudoconvex cost functions}, volume = {13}, year = {2025}, }
• (2025). Enhancing collaborative learning environments: A multi-feature fusion model for disruptive talk detection. ACCESS, 13, 61261-61273. (www)

  @article{ , abstract = {Effective detection and identification of disruptive talk is of significant practical value for improving collaborative learning quality and optimizing the online education environment. However, existing research on disruptive talk detection, which often relies on features from a single dimension, struggles to comprehensively capture the semantic information of talk and learner characteristics, thereby limiting the detection effectiveness. To address this, we propose a multi-feature fusion model. Based on social cognitive theory, this model captures learner features from multiple dimensions, including the week of discussion, talk length, sentiment polarity of talk, the learner’s demographic factor and pre-test knowledge level. The model integrates both Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (Bi-LSTM) models. This integration achieves deep fusion of talk semantics and time-series features, thereby enabling more accurate identification of disruptive talk. Experimental results on real classroom datasets show that our method outperforms existing baseline models across multiple metrics, fully demonstrating its effectiveness and practical value. This research not only advances the development of disruptive talk detection but also provides feasible insights for optimizing collaborative learning environments. Future work will explore the scalability of this model in different educational settings, and investigate its integration with real-time learning platforms.}, archive = {J_ACCESS}, author = {Shuang Yu and Junmin Ye and Chen Zhang and Qingtang Liu and Sheng Luo and Mengting Nan and Qi Xu and Xinghan Yin}, doi = {10.1109/ACCESS.2025.3556369}, journal = {IEEE Access}, pages = {61261-61273}, shortjournal = {IEEE Access}, title = {Enhancing collaborative learning environments: A multi-feature fusion model for disruptive talk detection}, volume = {13}, year = {2025}, }
• (2025). Quantifying objective and perceived image quality through EEG and eye-tracking. ACCESS, 13, 61250-61260. (www)

  @article{ , abstract = {Image quality evaluation of electronic displays is inherently subjective. To date, user studies in focus groups and viewers’ self-reports have been the primary sources of feedback used as judgment criteria for the display quality. However, little has been known whether the measured responses to visual stimuli, other than self-reports, reflect the objective and subjective levels of image quality on displays. Here, we used electroencephalogram (EEG) and eye-tracking to investigate whether individuals’ neural and physiological responses to visual stimuli track three objectively discerned levels of image quality, as well as their self-ratings on vividness. Our findings reveal that event-related potentials (ERPs) at 200-300 msec after stimulus onset in the frontocentral brain region, as well as saccade and blink frequencies, significantly tracked (cluster-based permutation p <0.05) the objective perspective of image quality. In contrast, ERPs at 600 msec in the frontolateral region and saccade peak velocity tracked (cluster-based permutation p <0.05) the subjective evaluation of image quality. Individuals’ pupil diameter successfully tracked (cluster-based permutation p <0.05) both objective and subjective perspectives of image quality. These patterns highlight both the shared characteristics and measurable distinctions between objective image quality and subjective vividness ratings. This study demonstrates the effectiveness of EEG and eye-tracking as quantitative tools for assessing objective image quality disparities and the subjective affective nuances of users’ visual display experiences, potentially reducing reliance on self-reports.}, archive = {J_ACCESS}, author = {Changhyun Lim and Hyun-Jun Jeon and Woojae Jung and Youngshin Kwak and Myong Young Lee and Jung Hyun Ham and Hyun Bin Joo and Oh-Sang Kwon and Dongil Chung}, doi = {10.1109/ACCESS.2025.3556391}, journal = {IEEE Access}, pages = {61250-61260}, shortjournal = {IEEE Access}, title = {Quantifying objective and perceived image quality through EEG and eye-tracking}, volume = {13}, year = {2025}, }
• (2025). A novel security threat model for automated AI accelerator generation platforms. ACCESS, 13, 61237-61249. (www)

  @article{ , abstract = {In recent years, the design of Artificial Intelligence (AI) accelerators has gradually shifted from focusing solely on standalone accelerator hardware to considering the entire system, giving rise to a new AI accelerator design paradigm that emphasizes full-stack integration. Systems designed based on this paradigm offer a user-friendly, end-to-end solution for deploying pre-trained models. While previous studies have identified vulnerabilities in individual hardware components or models, the security of this paradigm has not yet been thoroughly evaluated. This work, from an attacker’s perspective, proposes a threat model based on this paradigm and reveals the potential security vulnerabilities of systems by embedding malicious code in the design flow, highlighting the necessity of protection to address this security gap. In exploration and generation, it maliciously leverages the exploration unit to identify sensitive parameters in the model’s intermediate layers and insert hardware Trojan (HT) into the accelerator. In execution, malicious information is concealed within the control instructions, triggering the HT. Experimental results demonstrate that the proposed method, which manipulates sensitive parameters in a few selected kernels across the middle convolutional layers, successfully misclassifies input images into specified categories with high misclassification rates across various models: 97.3% in YOLOv8 by modifying only three parameters per layer in three layers, 99.2% in ResNet-18 by altering four parameters per layer in three layers and 98.1% for VGG-16 by changing seven parameters per layer in four layers. Additionally, the area overhead introduced by the proposed HT occupies no more than 0.39% of the total design while maintaining near-original performance as in uncompromised designs, which clearly illustrates the concealment of the proposed security threat.}, archive = {J_ACCESS}, author = {Chao Guo and Youhua Shi}, doi = {10.1109/ACCESS.2025.3558072}, journal = {IEEE Access}, pages = {61237-61249}, shortjournal = {IEEE Access}, title = {A novel security threat model for automated AI accelerator generation platforms}, volume = {13}, year = {2025}, }
• (2025). SSCD-YOLO: Semi-supervised cross-domain YOLOv8 for pedestrian detection in low-light conditions. ACCESS, 13, 61225-61236. (www)

  @article{ , abstract = {To mitigate the domain differences between infrared and visible light images and address the challenges of data annotation and poor pedestrian detection performance in low-light environments, we propose a semi-supervised cross-domain YOLOv8 pedestrian detection model SSCD-YOLO for low-light environments. First, a CDF-CycleGAN cross-domain image fusion method is designed, which improves the input of CycleGAN using a diffusion model, integrates the self-attention mechanism into the residual network of the CycleGAN generator to design the SA-Block, replaces the linear layer in the CycleGAN discriminator with an autoencoder, and improves the training method of CycleGAN. These enhancements achieve complementary information exchange between infrared and visible images, thereby improving the robustness and accuracy of the cross-domain object detection model. Second, the Mean Teacher approach is combined with the YOLOv8 detector, the EMA module is added to the output part of the YOLOv8 detection head, and the Swin Transformer block is used to enhance the C2f module of YOLOv8. Additionally, a cross-domain distillation loss function is designed to improve the performance and efficiency of multi-scale object detection. Experimental results show that CDF-CycleGAN outperforms CycleGAN on various metrics across the LLVIP and M3FD datasets. The overall detection performance of SSCD-YOLO on both LLVIP and M3FD datasets surpasses other comparative algorithms while meeting real-time detection requirements.}, archive = {J_ACCESS}, author = {Fangliang Cao and Kai Yan and Hongliang Chen and Zhen Wang and Yunliang Du and Zekang Zheng and Kefan Li and Baozhu Qi and Mingjia Wang}, doi = {10.1109/ACCESS.2025.3557387}, journal = {IEEE Access}, pages = {61225-61236}, shortjournal = {IEEE Access}, title = {SSCD-YOLO: Semi-supervised cross-domain YOLOv8 for pedestrian detection in low-light conditions}, volume = {13}, year = {2025}, }
• (2025). An optimized route assignment for preventing traffic jams. ACCESS, 13, 61207-61224. (www)

  @article{ , abstract = {Frequent traffic jams are a common problem in large urban areas and despite real-time operation, contemporary traffic control (i.e., navigation) systems do not fully succeed in solving this problem. This situation motivates traffic assignment research aimed at optimal usage of infrastructure, that is, minimization of travel time. However, achieving this goal in real-time requires a departure from traditional approach and an enhancement of the predictive and preventive abilities of control and navigation systems. To this aim, the paper presents an efficient solution for an optimized route assignment given the set of available routes between two city zones. Unlike other methods, mostly working on link (graph edge) level, the proposed solution works on route (graph path) level and is based on estimation of the expected (average) travel times. This enables the application of two efficient algorithms for optimization of vehicle distribution according to two important criteria, that is, both Wardrop’s principles. The most prominent characteristics of the proposed algorithms are linear complexity with respect to the number of vehicles (traffic flow), linear and quadratic complexity with respect to the number of routes for user equilibrium and system optimization, respectively, simplicity, and absence of any numerical issues, which makes them suitable for real-time traffic management. The theoretical results have been confirmed by diverse evaluations using synthetic models and in a case study using the city of Zagreb as a real world example. The proposed concept is universal, inexpensive, and quickly implementable as an upgrade to the existing traffic control or navigation systems.}, archive = {J_ACCESS}, author = {Nikica Hlupić and Danko Basch and Edouard Ivanjko and Jakov Prister}, doi = {10.1109/ACCESS.2025.3553492}, journal = {IEEE Access}, pages = {61207-61224}, shortjournal = {IEEE Access}, title = {An optimized route assignment for preventing traffic jams}, volume = {13}, year = {2025}, }
• (2025). CGI-based synthetic data generation and detection pipeline for small objects in aerial imagery. ACCESS, 13, 61192-61206. (www)

  @article{ , abstract = {Small object detection in drone-captured imagery presents critical challenges across environmental monitoring, urban planning, and disaster management. Current approaches struggle with accurate landmark identification due to varying altitudes, diverse landscapes, and object scale variability. This research proposes an innovative two-stage object detection pipeline specifically designed for aerial imagery analysis. Our approach integrates advanced data generation techniques with a novel detection methodology. Experimental results demonstrate significant performance improvements: precision increased by 29.3% (from 0.616 to 0.796), recall improved by 24% (0.699 to 0.867), and mean Average Precision (mAP@0.5) enhanced by 23.2% (0.677 to 0.834) compared to traditional YOLO models. The pipeline combines Computer-Generated Imagery (CGI) for synthetic data creation with a two-stage detection approach. The first stage employs YOLOv9 for efficient region of interest localization, while the second stage utilizes a ResNet-based model for precise classification. By addressing challenges associated with small object detection, our methodology provides a scalable solution for automated landmark recognition in diverse aerial environments.}, archive = {J_ACCESS}, author = {Rudra Patel and Divyam Chandalia and Ashalatha Nayak and Andrew Jeyabose and David Jijo}, doi = {10.1109/ACCESS.2025.3553530}, journal = {IEEE Access}, pages = {61192-61206}, shortjournal = {IEEE Access}, title = {CGI-based synthetic data generation and detection pipeline for small objects in aerial imagery}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of a self-calibration paper counting system using capacitive sensing and curve fitting. ACCESS, 13, 61181-61191. (www)

  @article{ , abstract = {This paper presents a novel self-calibrating paper counting device that integrates an embedded processor with a capacitance sensor chip. The device operates on the principle of capacitance measurement, detecting variations between two copper-coated plates as paper sheets are inserted. The capacitance data, acquired via an I2C interface, undergoes filtering and averaging to enhance stability and accuracy. Further processing involves scaling and transformation to refine the data, which is then utilized in curve fitting to determine the precise sheet count. The final result is displayed on an LCD screen, accompanied by an audible signal upon measurement completion. Experimental results demonstrate that the incorporation of data fusion and curve fitting significantly improves measurement accuracy, maintaining it consistently above 97%. This highlights the proposed system’s high precision and robustness, making it a promising solution for paper counting applications.}, archive = {J_ACCESS}, author = {Junyuan Zheng and Lei Chen and Xuxin Zhao and Ming Lei and Xiaoping Chen and Xiaochao Ma}, doi = {10.1109/ACCESS.2025.3554857}, journal = {IEEE Access}, pages = {61181-61191}, shortjournal = {IEEE Access}, title = {Design and implementation of a self-calibration paper counting system using capacitive sensing and curve fitting}, volume = {13}, year = {2025}, }
• (2025). HSoMLSDP: A hybrid swarm-optimized machine learning framework for software defect prediction. ACCESS, 13, 61161-61180. (www)

  @article{ , abstract = {This research aims to design a hybrid swarm-optimized machine learning software defect prediction (HSoMLSDP) framework to predict software defects. We strive to do this by designing a swarm-optimized machine learning defect prediction (SoMLDP) model within the HSoMLSDP framework. In pursuit of enhancing the defect prediction accuracy of the SoMLDP model, this paper designed two novel hybrid swarm-optimization algorithms (SOAs) referred to as gravitational force grasshopper optimization algorithm-artificial bee colony (GFGOA-ABC), and levy flight grasshopper optimization algorithm-artificial bee colony (LFGOA-ABC) algorithms. By combining the enhanced exploration features of LFGOA and GFGOA with the robust exploitation capacity of the artificial bee colony (ABC), the LFGOA-ABC and GFGOA-ABC algorithms are proposed. Prior to validating the HSoMLSDP framework, the LFGOA-ABC and GFGOA-ABC algorithm’s efficacy is first confirmed by experimenting on 19 benchmark functions (BFs) to assess their mean, standard deviation (SD) of optimal values, convergence rate, and convergence rate improvements. Following BFs verification, the second experiment tunes the hyperparameters of the ML models (artificial neural network, XGBOOST) to improve the defect accuracy of the SoMLDP model. The outcomes of the experiments justify a more rapid convergence rate for BFs and notable enhancements of 0.01-0.28 in software defect prediction (SDP) accuracy for NASA defect datasets when compared with state-of-the-art methods. As an enhancement of accuracy justifies the correctness of the SoMLDP model, thus validating the HSoMLSDP framework.}, archive = {J_ACCESS}, author = {Madhusmita Das and Biju R. Mohan and Ram Mohana Reddy Guddeti}, doi = {10.1109/ACCESS.2025.3557532}, journal = {IEEE Access}, pages = {61161-61180}, shortjournal = {IEEE Access}, title = {HSoMLSDP: A hybrid swarm-optimized machine learning framework for software defect prediction}, volume = {13}, year = {2025}, }
• (2025). OSBF: One-sided box filter for edge-preserving image processing. ACCESS, 13, 61149-61160. (www)

  @article{ , abstract = {Box filter is well-known for the image smoothing task, thanks to its effectiveness and computation efficiency. However, it can NOT preserve edges. In contrast, edge-preserving methods can NOT achieve the high computation performance as the box filter. To tackle this issue, in this paper, we present a one-sided box filter that can preserve edges much better than the box filter. Meanwhile, it has a similar high computation performance as the box filter. More specifically, we perform the box filter on nine one-sided local windows, and then select the most possible candidate as the result. Such selection imposes the non-linearity, which preserves the edges and corners. Several numerical experiments are conducted to confirm this edge-preserving property. At the same time, it has a similar computation performance as the box filter. It inherits the constant computation complexity $O(1)$ and the linear complexity $O(N)$ from the box filter with respect to the window size and the total number of pixels, respectively. We numerically confirm that this filter is the fastest method among the edge-preserving methods, including the classical and the state of the art approaches. It is at least $10 \times $ faster than other edge-preserving methods. Thanks to the edge-preserving property and the high computation performance, the proposed one-sided box filter can be deployed in a large range of applications where the edge-preserving and high performance is required, such as real-time video processing, augmented reality and view synthesis.}, archive = {J_ACCESS}, author = {Yuanhao Gong}, doi = {10.1109/ACCESS.2025.3555434}, journal = {IEEE Access}, pages = {61149-61160}, shortjournal = {IEEE Access}, title = {OSBF: One-sided box filter for edge-preserving image processing}, volume = {13}, year = {2025}, }
• (2025). Improving implicit sentiments analysis via explanations of multiple perspectives. ACCESS, 13, 61136-61148. (www)

  @article{ , abstract = {Implicit sentiment expressions convey emotions indirectly, through context or factual statements, rather than explicit opinion words. Recent research on implicit sentiment analysis overlooks the fact that various individuals can interpret the same implicit expressions in different manners and experience different sentiments. Additionally, most previous research mainly focuses on implicit sentiment classification, neglecting the reasons behind the results. It hinders the deep understanding of the complexities involved in human emotions and limits the application of sentiment analysis. In this work, we introduce a new task, Abisa-Ex, which aims at both sentiment classification and explanation generation. We re-labeled the previous aspect-based implicit sentiment analysis dataset, incorporating new (sentiment, explanation) pair labels provided by various annotators. Based on the new dataset, we design frameworks to allow models to learn to predict sentiments from different perspectives and provide reasonable explanations jointly. Notably, our work shows that learning explanations from various viewpoints not only allows the model to generate the logical process behind sentiment analysis, but also significantly improves the model’s sentiment classification performance.}, archive = {J_ACCESS}, author = {An Wang and Huidong Jiang and Youmi Ma and Junfeng Jiang and Ao Liu and Naoaki Okazaki}, doi = {10.1109/ACCESS.2025.3556762}, journal = {IEEE Access}, pages = {61136-61148}, shortjournal = {IEEE Access}, title = {Improving implicit sentiments analysis via explanations of multiple perspectives}, volume = {13}, year = {2025}, }
• (2025). DialFill: Utilizing dialogue filling to integrate retrieved knowledge in responses. ACCESS, 13, 61123-61135. (www)

  @article{ , abstract = {In knowledge-based dialogue systems, generating responses that are both contextually relevant and factually accurate requires efficient and precise integration of external knowledge. Pre-trained language models (LM-based) leverage extensive general knowledge but often struggle with accuracy in domain-specific or time-sensitive contexts due to their reliance on implicit knowledge. Conversely, knowledge-based approaches (KB-based) retrieve relevant information from external sources before response generation, yet they frequently fail to incorporate the retrieved content effectively, leading to responses that may omit critical information. To address these limitations, we propose DialFill, a novel response generation framework that reframes dialogue generation as a Dialogue Filling task. DialFill constructs an intermediate masked response that explicitly integrates the retrieved knowledge, subsequently predicting the missing components to ensure the final response incorporates all relevant information seamlessly. We validate DialFill on both unstructured (Wizard-of-Wikipedia) and structured (OpenDialKG) knowledge benchmarks, demonstrating competitive performance against state-of-the-art methods. In large language model experiments, DialFill significantly reduces the rate of retrieved content that is ignored, decreasing the number of ignored knowledge instances from 17.8% to 0.2%. These results highlight DialFill’s potential to enhance the accuracy, reliability, and adaptability of knowledge-based dialogue systems, marking a significant advancement in the field.}, archive = {J_ACCESS}, author = {Qiang Xue and Tetsuya Takiguchi and Yasuo Ariki}, doi = {10.1109/ACCESS.2025.3555650}, journal = {IEEE Access}, pages = {61123-61135}, shortjournal = {IEEE Access}, title = {DialFill: Utilizing dialogue filling to integrate retrieved knowledge in responses}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A hybrid network analysis and machine learning model for enhanced financial distress prediction”. ACCESS, 13, 61122. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction”).}, archive = {J_ACCESS}, author = {Saba Taheri Kadkhoda and Babak Amiri}, doi = {10.1109/ACCESS.2025.3557462}, journal = {IEEE Access}, pages = {61122}, shortjournal = {IEEE Access}, title = {Corrections to “A hybrid network analysis and machine learning model for enhanced financial distress prediction”}, volume = {13}, year = {2025}, }
• (2025). A comparison of approaches for handling concept drifts in data processed with machine learning. ACCESS, 13, 61109-61121. (www)

  @article{ , abstract = {In the realm of machine learning models, the pursuit of achieving favorable metrics is undeniably significant. However, these models confront phenomena that can diminish their effectiveness if left unaddressed-notably, the phenomenon of concept drift. Concept drift materializes when unforeseen alterations in the statistical properties of the target variable transpire over time. This article introduces a comprehensive analysis of classical treatment methods, examining the behavior of machine learning models across diverse datasets. Various concept drift detection algorithms are employed, facilitating a holistic assessment. The study encompasses a comparative exploration of different classification algorithms within the scikit-multiflow framework. These algorithms integrate adaptive strategies to contend with concept drifts. Through this generalized analysis, the performance of distinct classification algorithms is contrasted. The overarching aim is to facilitate the selection of optimal classification methods aligned with specific types of concept drift. Ultimately, this study provides a pivotal toolkit for the judicious selection of classification methods, enhancing model adaptability in the presence of concept drifts. In addition to shedding light on the behavior of machine learning models under concept drift, the findings empower practitioners and researchers to make informed decisions to optimize model robustness.}, archive = {J_ACCESS}, author = {Emanuel Valério Pereira and Wendley Souza da Silva}, doi = {10.1109/ACCESS.2025.3557229}, journal = {IEEE Access}, pages = {61109-61121}, shortjournal = {IEEE Access}, title = {A comparison of approaches for handling concept drifts in data processed with machine learning}, volume = {13}, year = {2025}, }
• (2025). Usability and perceived efficiency of an adaptive route optimization solution for commercial vehicles. ACCESS, 13, 61091-61108. (www)

  @article{ , abstract = {This paper details the outcome of a European scientific research and development project, funded to prototype a commercial solution for vehicle fleets. While current route optimization solutions have data sources such as real-time road traffic and weather conditions, our solution was developed to offer companies a proposed plan with adapted optimized routes, considering critical operational requirements, local conditions, and drivers’ driving experience. The iRoute solution consists of a module for receiving vehicle positions from GPS devices, an integration layer for end-customer applications, a route optimization engine, and applications for web and mobile users. Unlike other approaches, this innovative solution leverages historical, well-known, and familiar route data to enhance, refine, and adapt the optimized operational routes. This way, the drivers benefit from a more effective daily plan to follow based on business requirements such as locations, quantities, time, and distance. At the end of the day, adding these adjustments results in achieving up to a 20% improvement in the accuracy of executed routes, in terms of distance and time, compared to the planned route, outperforming standard optimization algorithms. This allows business owners to rely more confidently on the optimization results, providing a safer itinerary for their drivers. Also, due to lower incident rates on these known and preferred itineraries, the overall business will benefit from increased vehicle fleet availability and fewer days in service, with lower maintenance costs. The paper evaluates the usability and perceived efficiency value of the proposed solution by statistically analyzing feedback from dispatchers and business owners.}, archive = {J_ACCESS}, author = {Florian Anghelache and Nicolae Goga and Constantin Viorel Marian and Dan Alexandru Mitrea and Ionel-Bujorel Pavaloiu}, doi = {10.1109/ACCESS.2025.3556891}, journal = {IEEE Access}, pages = {61091-61108}, shortjournal = {IEEE Access}, title = {Usability and perceived efficiency of an adaptive route optimization solution for commercial vehicles}, volume = {13}, year = {2025}, }
• (2025). OPTICALS: A novel framework for optimizing predictive trading indicators in cryptocurrency using advanced learning simulations. ACCESS, 13, 61078-61090. (www)

  @article{ , abstract = {Cryptocurrencies have reshaped finance with secure, decentralized trading, attracting investor interest due to high volatility and potential returns. Accurate price forecasting is essential for optimizing returns and managing risks in digital markets. This study introduces OPTICALS, a novel framework for daily cryptocurrency price forecasting, focusing on transparency, robust performance assessment, and interpretability in machine and deep learning models. Unlike existing methods, OPTICALS provides detailed insights into model predictions by optimizing hyperparameters and identifying each model’s strengths and limitations. The framework evaluates five models-XGBoost, LightGBM, LSTM, Bi-LSTM, and GRU-on three major cryptocurrencies: Ethereum, Binance, and Solana, known for high trading volumes and distinct characteristics. OPTICALS incorporates a “Look-back window” hyperparameter, using recent historical prices to predict next-day trends through Moving Averages analysis. This parameter refines lagged feature engineering to enhance trend capture and predictive accuracy. Models underwent rigorous evaluation, including multiple simulations and hyperparameter tuning. Gradient Boosting models were tuned via GridSearchCV and regularization to improve performance through diverse ensembles. RNN models were optimized by adjusting neurons, stacks, epochs, batch sizes, and optimizers. Predictions were validated against one-week-ahead prices to ensure robust accuracy. Findings show that GRU and XGBoost excel at predicting real-time trends, with GRU supporting day trading and XGBoost benefiting swing trading. This study advances cryptocurrency analytics, providing practical forecasting tools for traders, investors, and institutions to navigate volatility and manage risks effectively.}, archive = {J_ACCESS}, author = {Hasib Shamshad and Fasee Ullah and Syed Adeel Ali Shah and Muhammad Faheem and Beena Shamshad}, doi = {10.1109/ACCESS.2025.3556881}, journal = {IEEE Access}, pages = {61078-61090}, shortjournal = {IEEE Access}, title = {OPTICALS: A novel framework for optimizing predictive trading indicators in cryptocurrency using advanced learning simulations}, volume = {13}, year = {2025}, }
• (2025). AED-net: A high-resolution remote sensing image road extraction method integrating atrous spatial pyramid pooling and efficient channel attention mechanism. ACCESS, 13, 61067-61077. (www)

  @article{ , abstract = {High-resolution remote sensing imagery plays a pivotal role in road extraction due to its abundant semantic information. However, traditional road extraction methods are time-consuming, labor-intensive, and susceptible to subjective factors, leading to inaccurate extraction results. Therefore, this study aims to explore an efficient, accurate, and automated road extraction method. We propose a road extraction model named AED-Net, which employs a lightweight MobileNet v2 as the feature extractor, combines the multi-scale feature extraction capability of ASPP with an encoder-decoder structure, and integrates an ECA mechanism to enhance feature learning ability. During the training process, the BCE-DL loss function is adopted to optimize model performance. Experimental validation on the Massachusetts Roads dataset and Ottawa Road dataset demonstrates that AED-Net outperforms the latest Attention U-Net (AU-Net) and ECA-DeeplabV3+ models. On the Massachusetts Roads dataset, AED-Net achieves an overall accuracy (OA) of 97.60% and a mean intersection over union (MIoU) of 85.29%. Similarly, on the Ottawa Road dataset, AED-Net exhibits superior performance compared to classical semantic segmentation networks such as SegNet, U-Net, and Deeplab V3+, achieving an OA of 98.83% and an MIoU of 88.74%. Furthermore, ablation studies validate the effectiveness and necessity of the proposed improvements. In summary, AED-Net has made significant progress in multi-scale feature extraction, boundary information restoration, and critical feature learning, offering new insights and solutions for the development of road network extraction technologies. Its high accuracy and robustness render AED-Net promising for a wide range of applications in remote sensing image processing, intelligent transportation systems, and beyond.}, archive = {J_ACCESS}, author = {Jintong Ren and Lizhi Liu and Zixuan Xia and Yang Liu}, doi = {10.1109/ACCESS.2025.3548262}, journal = {IEEE Access}, pages = {61067-61077}, shortjournal = {IEEE Access}, title = {AED-net: A high-resolution remote sensing image road extraction method integrating atrous spatial pyramid pooling and efficient channel attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Short-term voltage stability prediction for power systems based on a dominant koopman operator-enhanced MLE. ACCESS, 13, 61056-61066. (www)

  @article{ , abstract = {Short-term voltage stability (STVS) prediction is a critical technology for modern power systems with high penetration of renewable energy resources. To address the limitations of the traditional maximum Lyapunov exponent (MLE) in handling short-time window data during transient dynamics, this paper proposes a novel dominant Koopman operator-enhanced MLE (DKO-MLE) method for STVS prediction. First, the dominant Koopman operator is extracted using a proposed approach that combines the Hankel matrix with the extended dynamic mode decomposition (HEDMD) method. Subsequently, the extracted dominant Koopman operator is predicted in the time domain and utilized for MLE calculation. The proposed method leverages the data dimensionality expansion capability of the Hankel matrix and the time-domain prediction ability of the dominant Koopman operator to effectively generate sufficient data for online STVS prediction. Case studies conducted on the Nordic32 system and a standard test system provided by the China Electric Power Research Institute (CEPRI) demonstrate the accuracy and rapidity of the proposed method in predicting short-term voltage stability.}, archive = {J_ACCESS}, author = {Han Gao and Deyou Yang and Yanling Lv and Lixin Wang}, doi = {10.1109/ACCESS.2025.3548609}, journal = {IEEE Access}, pages = {61056-61066}, shortjournal = {IEEE Access}, title = {Short-term voltage stability prediction for power systems based on a dominant koopman operator-enhanced MLE}, volume = {13}, year = {2025}, }
• (2025). Rapid dataset-free self-calibration for heat flux-based core body temperature sensors. ACCESS, 13, 61048-61055. (www)

  @article{ , abstract = {This study presents a reference-free calibration method for wearable core body temperature sensors based on the single-heat-flux approach, eliminating the need for a pre-constructed dataset and reducing the required calibration time. In conventional single-heat-flux methods, calibration relies on invasive measurements or numerically generated datasets and typically requires at least 1 h, posing practical challenges. In contrast, the proposed method identifies calibration parameters directly from measured temperature data within approximately 1500 s, which is much quicker than the 3600 s required by existing methods. We validated our method through numerical simulations considering factors such as skin thickness and acceptable measurement errors. These simulations confirmed that the proposed approach reliably achieves adequate calibration without prior dataset construction. Furthermore, we conducted a human subject experiment under resting conditions, where ambient and core body temperature fluctuations, along with heat tolerance responses, were minimal. The results demonstrate that our method can achieve clinically sufficient accuracy in estimating core body temperature, even in the presence of environmental perturbations such as introduced airflow. While the proposed method currently assumes stable conditions and may face challenges in highly dynamic environments involving intense physical activity or significant thermal changes, it marks a critical step toward more accessible and practical wearable core body temperature monitoring. Future work will focus on validating the method with a larger cohort of subjects and integrating additional physiological sensors to enhance reliability and broaden applicability.}, archive = {J_ACCESS}, author = {Yuki Hashimoto and Soto Tada and Yoshifumi Nishida}, doi = {10.1109/ACCESS.2025.3556405}, journal = {IEEE Access}, pages = {61048-61055}, shortjournal = {IEEE Access}, title = {Rapid dataset-free self-calibration for heat flux-based core body temperature sensors}, volume = {13}, year = {2025}, }
• (2025). Anchor-based explainable and causal artificial intelligence for enhancing financial predictions of future earnings. ACCESS, 13, 61026-61047. (www)

  @article{ , abstract = {Accurate prediction of future earnings is crucial for stakeholders. However, existing machine learning models often operate as “black boxes,” offering high accuracy but minimal interpretability. Prior approaches focus on correlational patterns without establishing genuine causal relationships or providing straightforward rule-based explanations. This lack of transparency and causal insight limits the actionable value of current financial prediction models. We propose an anchor-based explainable and causal AI framework for earnings prediction. It integrates an optimized XGBoost classifier (with RENN undersampling to address class imbalance) for high-performance prediction, the Anchor XAI method to generate human-readable “if-then” rules explaining model decisions, and the DoWhy causal inference tool to validate genuine cause-and-effect factors in the financial data. The optimized XGBoost with the RENN model achieved an overall accuracy of ~93.3%, with precision, recall, and F1-scores ranging from 93% to 94%, outperforming other classifiers. Key features such as Inventory/Total Assets, % $\Delta $ Net Profit Margin, and Cash Dividends/Cash Flows emerged as the most influential factors. Coordinated adjustments in these variables yielded significantly better predictive outcomes than isolated changes. Furthermore, DoWhy-based analysis confirms that improvements in these factors causally drive earnings growth, as verified by robustness checks like placebo tests. The proposed framework effectively bridges the gap between predictive accuracy and interpretability. It provides financial decision-makers with reliable earnings predicting and transparent, actionable insights for strategic planning and management, making the predictive model trustworthy and informative.}, archive = {J_ACCESS}, author = {Siriporn Sawangarreerak and Putthiporn Thanathamathee and Pankaewta Lakkanawanit and Nor Shaipah Abdul Wahab}, doi = {10.1109/ACCESS.2025.3557264}, journal = {IEEE Access}, pages = {61026-61047}, shortjournal = {IEEE Access}, title = {Anchor-based explainable and causal artificial intelligence for enhancing financial predictions of future earnings}, volume = {13}, year = {2025}, }
• (2025). Recursive forward-backward EDMD: Guaranteed algebraic search for koopman invariant subspaces. ACCESS, 13, 61006-61025. (www)

  @article{ , abstract = {The implementation of the Koopman operator on digital computers often relies on the approximation of its action on finite-dimensional function spaces. This approximation is generally done by orthogonally projecting on the subspace. Extended Dynamic Mode Decomposition (EDMD) is a popular, special case of this projection procedure in a data-driven setting. Importantly, the accuracy of the model obtained by EDMD depends on the quality of the finite-dimensional space, specifically on how close it is to being invariant under the Koopman operator. This paper presents a data-driven algebraic search algorithm, termed Recursive Forward-Backward EDMD, for subspaces close to being invariant under the Koopman operator. Relying on the concept of temporal consistency, which measures the quality of the subspace, our algorithm recursively decomposes the search space into two subspaces with different prediction accuracy levels. The subspace with lower level of accuracy is removed if it does not reach a satisfactory threshold. The algorithm allows for tuning the level of accuracy depending on the underlying application and is endowed with convergence and accuracy guarantees.}, archive = {J_ACCESS}, author = {Masih Haseli and Jorge Cortés}, doi = {10.1109/ACCESS.2025.3554154}, journal = {IEEE Access}, pages = {61006-61025}, shortjournal = {IEEE Access}, title = {Recursive forward-backward EDMD: Guaranteed algebraic search for koopman invariant subspaces}, volume = {13}, year = {2025}, }
• (2025). Effect of explainable artificial intelligence on trust of mental health professionals in an AI-based system for suicide prevention. ACCESS, 13, 60987-61005. (www)

  @article{ , abstract = {Artificial Intelligence (AI)-based systems have been proposed to aid Mental Health Professionals (MHPs) in various tasks, including the prevention of suicide by identifying Suicidal Ideation (SI). However, these systems may lack transparency and thereby create mistrust among MHPs. Explainable Artificial Intelligence (XAI) methods can elucidate how features influence system predictions, aiding MHPs in understanding them. This exploratory study aims to investigate how MHPs’ trust is influenced by AI explanations (educational intervention and XAI methods) and other factors (professional background, knowledge of AI and computing, and reported system misclassification). We conducted an experiment using Boamente, an AI-powered clinical decision support system designed to assist MHPs in suicide prevention. Boamente identifies SI in Brazilian Portuguese texts typed on smartphones by leveraging a Large Language Model (LLM) for analysis. The results demonstrate that professional background, knowledge of AI and computing, and educational intervention had no statistically significant effect on trust. In contrast, trust was affected by factors such as LLM prediction explanations, the quality of explanations, and reported misclassification. Therefore, providing prediction explanations to understand the inner workings of AI models led MHPs to be more critical in relation to predictions, while there was an overtrust on MHPs when no explanations were provided. Furthermore, disagreement with LLM classifications and perceptions of system vulnerabilities also affected trust.}, archive = {J_ACCESS}, author = {Adonias Caetano de Oliveira and João Pedro Cavalcanti Azevedo and Lívia Ruback and Rayele Moreira and Silmar Silva Teixeira and Ariel Soares Teles}, doi = {10.1109/ACCESS.2025.3556245}, journal = {IEEE Access}, pages = {60987-61005}, shortjournal = {IEEE Access}, title = {Effect of explainable artificial intelligence on trust of mental health professionals in an AI-based system for suicide prevention}, volume = {13}, year = {2025}, }
• (2025). A unified approach to video anomaly detection: Advancements in feature extraction, weak supervision, and strategies for class imbalance. ACCESS, 13, 60969-60986. (www)

  @article{ , abstract = {This paper explores advancements in Video Anomaly Detection (VAD), combining theoretical insights with practical solutions to address model limitations. Through comprehensive experimental analysis, the study examines the role of feature representations, sampling strategies, and curriculum learning in enhancing VAD performance. Key findings include the impact of class imbalance on the Cross-Modal Awareness-Local Arousal (CMALA) architecture and the effectiveness of techniques like pseudo-curriculum learning in mitigating noisy classes, such as “Car Accident.” Novel strategies like the Sample-Batch Selection (SBS) dynamic segment selection and pre-trained image-text models, including Contrastive Language-Image Pre-training (CLIP) and ViTamin encoder, significantly improve anomaly detection. The research underscores the potential of multimodal VAD, highlighting the integration of audio and visual modalities and the development of multimodal fusion techniques. To support this evolution, the study proposes a Unified WorkStation 4 VAD (UWS4VAD) to streamline research workflows and introduces a new VAD benchmark incorporating multimodal data and textual information. The work envisions enhanced anomaly interpretation and performance by leveraging joint representation learning and Large Language Models (LLMs). The findings set the stage for future advancements, advocating for large-scale pre-training on audio-visual datasets and shifting toward a more integrated, multimodal approach to VADs. Source code of the project available at https://github.com/zuble/uws4vad}, archive = {J_ACCESS}, author = {Rui Z. Barbosa and Hugo S. Oliveira}, doi = {10.1109/ACCESS.2025.3557948}, journal = {IEEE Access}, pages = {60969-60986}, shortjournal = {IEEE Access}, title = {A unified approach to video anomaly detection: Advancements in feature extraction, weak supervision, and strategies for class imbalance}, volume = {13}, year = {2025}, }
• (2025). Rethinking technological investment and cost-benefit: A software requirements dependency extraction case study. ACCESS, 13, 60955-60968. (www)

  @article{ , abstract = {Machine Learning (ML) is widely used for different purposes within Software Engineering. It can substantially improve the efficiency and effectiveness of organizations. While various methods and techniques exist, all of them have strengths and weaknesses under varying scenarios and contexts. Thus far, the selection and implementation of ML techniques rely almost exclusively on accuracy criteria. This narrow perspective ignores crucial considerations of anticipated costs of the ML activities versus the projected benefits gained from applying the results. Thus, in this study we introduce a return-on-investment (ROI) perspective to evaluate ML techniques in Software Engineering, offering a novel lens to assess their true value beyond traditional benchmarks. We present findings for an approach that addresses this gap by enhancing the accuracy criterion with return on investment (ROI) considerations. Specifically, we extract dependencies from textual descriptions of software requirements and analyze the performance of two state-of-the-art ML techniques: Random Forest and Bidirectional Encoder Representations from Transformers (BERT), a encoder only Large Language Model. Drawing upon two publicly available data sets, we compare decision-making based on 1) exclusively on accuracy and 2) on ROI analysis to provide decision support for the selection and usage of ML classification methods. As such, our results showed that, 1) chasing model accuracy improvisation through increased annotated data does not generate expected returns in traditional ML methods. 2) For complex ML algorithms, the need for larger annotated dataset investment cost is justified by the higher returns, however, the trade-offs between accuracy and ROI become evident.}, archive = {J_ACCESS}, author = {Gouri Ginde and Guenther Ruhe and Chad Saunders}, doi = {10.1109/ACCESS.2025.3556313}, journal = {IEEE Access}, pages = {60955-60968}, shortjournal = {IEEE Access}, title = {Rethinking technological investment and cost-benefit: A software requirements dependency extraction case study}, volume = {13}, year = {2025}, }
• (2025). Exploring road traffic accidents hotspots using clustering algorithms and GIS-based spatial analysis. ACCESS, 13, 60944-60954. (www)

  @article{ , abstract = {This study conducts a comprehensive spatial analysis of road traffic accidents (RTAs) in Najran, a city emblematic of rapid urbanization in Saudi Arabia, which is facing significant public safety challenges due to an increase in vehicular traffic. By means of a dataset from 2022, we explore the spatial distribution of RTAs across the city’s districts by employing advanced clustering algorithms, including Density-based Spatial Clustering of Applications with Noise (DBSCAN) and Hierarchical Agglomerative Clustering (HAC), as well as GIS-based density analysis, proximity analysis, and spatial interpolation, to unveil accident hotspots and disparities in emergency service coverage. Our findings reveal that (1) the HAC model, based on the Silhouette and Calinski-Harabasz Scores, performs better in identifying accident hotspots; (2) significant concentrations of accidents are observed along major highways and arterial roads, pinpointing critical hotspots within the city’s fabric; (3) proximity analysis indicates gaps in the coverage of ambulance services and public hospitals relative to high-incident areas; (4) through spatial interpolation, detailed visualizations of RTA distributions are provided, revealing diverse accident patterns across Najran. The study highlights the critical role of spatial analysis in identifying high-risk areas and provides valuable insights for transport planners and public safety officials, supporting the development of targeted strategies to improve road safety and enhance emergency service responses.}, archive = {J_ACCESS}, author = {Hussien Kamh and Saleh H. Alyami and Afaq Khattak and Mana Alyami and Hamad Almujibah}, doi = {10.1109/ACCESS.2024.3443245}, journal = {IEEE Access}, pages = {60944-60954}, shortjournal = {IEEE Access}, title = {Exploring road traffic accidents hotspots using clustering algorithms and GIS-based spatial analysis}, volume = {13}, year = {2025}, }
• (2025). Correlation between depression-associated genes and cancer types: Predicting cancer based on mutation frequencies. ACCESS, 13, 60930-60943. (www)

  @article{ , abstract = {This study examines the potential interconnection between genetic mutations frequently observed in various types of cancer and depression, aiming to elucidate shared genetic factors that may influence the etiology and treatment of both conditions. Genomic profiles from cancer patients were extracted using cBioPortal to perform comparisons with a subset of individuals carrying genetic variants linked to depression. The analysis employed advanced methodologies, including HJ biplot K-means and DBSCAN clustering algorithms for pattern grouping in 2D. This process generated a dataset, enabling the training and testing of machine learning and deep learning classification models. Clustering results highlighted genes associated with depression and mutation patterns across different cancer types, such as skin, uterine, cervical, stomach, and prostate cancer. Furthermore, in classifying cancers with similar characteristics potentially related to depression, an F1 score of 0.95 or higher was achieved using random forest, KNN, and neural network models. The implementation details and source code for this research are available in https://github.com/Aptroide/Genes_Protocol, providing transparency and facilitating further studies.}, archive = {J_ACCESS}, author = {Fernando Patricio Carranco-Avila and Clayanela Zambrano-Caicedo and Jonathan Javier Loor-Duque and Ariana Deyaneira Jiménez-Narváez and Iván Galo Reyes-Chacón and Paulina Vizcaíno and Isidro Rafael Amaro Martín and Manuel Eugenio Morocho-Cayamcela}, doi = {10.1109/ACCESS.2025.3556798}, journal = {IEEE Access}, pages = {60930-60943}, shortjournal = {IEEE Access}, title = {Correlation between depression-associated genes and cancer types: Predicting cancer based on mutation frequencies}, volume = {13}, year = {2025}, }
• (2025). Toward deep semi-supervised continual learning: A unified survey for scalable and adaptive AI. ACCESS, 13, 60903-60929. (www)

  @article{ , abstract = {The integration of Deep Semi-Supervised Learning (DSSL) with Continual Learning (CL) holds significant promise for advancing artificial intelligence systems capable of learning from limited labeled data while continuously adapting to new tasks. This review explores recent progress in combining DSSL and CL, referred to as Deep Semi-Supervised Continual Learning (DSCL), focusing on the potential to develop models capable of learning efficiently in dynamic environments through both labeled and unlabeled data while mitigating catastrophic forgetting. Our analysis highlights several critical applications including image classification, cybersecurity, and natural language processing, while also identifying key challenges that prevent its broader adoption in real-world scenarios. Integration challenges such as catastrophic forgetting, handling noisy, unlabeled, and imbalanced data, and managing the stability-plasticity trade-off are discussed in detail. Moreover, the importance of open-world learning, lightweight architectures for on-device learning, enhanced scalability, and interpretable models is emphasized to ensure DSCL’s applicability in real-world, high-stakes domains. However, the current reliance on benchmark datasets, while valuable for evaluation, may limit generalization to complex tasks like medical imaging. To address these challenges, future research should prioritize leveraging domain-specific datasets to enhance real-world applicability, integrating transfer learning for better adaptability, and developing domain-agnostic frameworks and task-free continual learning. Additionally, exploring techniques like Reinforcement Learning from Human Feedback (RLHF) could enhance interpretability and trustworthiness. By addressing these gaps, DSCL can evolve to provide more flexible, scalable, and reliable solutions, contributing significantly to the development of adaptable and intelligent systems across diverse domains.}, archive = {J_ACCESS}, author = {Mona Ebadi Jalal and Adel Elmaghraby}, doi = {10.1109/ACCESS.2025.3556569}, journal = {IEEE Access}, pages = {60903-60929}, shortjournal = {IEEE Access}, title = {Toward deep semi-supervised continual learning: A unified survey for scalable and adaptive AI}, volume = {13}, year = {2025}, }
• (2025). Iterative assessment of edge criticality: Efficiency enhancement or hidden insufficiency detection. ACCESS, 13, 60889-60902. (www)

  @article{ , abstract = {The assessment of edge criticality ranking in complex networks is a challenging issue in network science and has numerous applications, including network decomposition and, conversely, enhancing the resilience and redundancy of complex systems. Two main approaches are commonly used to rank edges based on their importance for maintaining network connectivity. The first is the Static approach, which relies on a single evaluation of topological features. The second is the Optimization-based approach, which treats network decomposition as an integral process and optimizes the edge sequence for network decomposition using genetic-like algorithms. While the Static approach is computationally efficient, the Optimization-based approach potentially yields the best decomposition pattern. In the present work, we propose the Iterative approach, which bridges the gap between these two methods. The Iterative approach involves a loop of identifying the most critical edge using selected ranking algorithms, removing it from the network, and then re-assessing edge criticality based on the modified network topology. As a result, the ranking of edge criticality depends not only on the initial topology of the network but also on its continuous modifications caused by edge removal. To evaluate the efficiency of the Iterative approach, we analyze the decomposition of sixteen well-known real-world benchmark networks using seven widely recognized edge ranking algorithms. The results demonstrate, first, that the Iterative approach can achieve a tenfold increase in the efficiency of network decomposition. Second, the analysis reveals hidden inner insufficiency in edge ranking for some algorithms, as evidenced by the fact that algorithm iterations can reduce decomposition efficiency. Additionally, we discuss the time complexity of the Iterative approach and strategies for its reduction. We also outline a potential framework for combining the Static and Iterative approaches during the network decomposition process to further enhance its efficiency.}, archive = {J_ACCESS}, author = {Vasily Lubashevskiy and Hamza Ejjbiri and Ihor Lubashevsky}, doi = {10.1109/ACCESS.2025.3557852}, journal = {IEEE Access}, pages = {60889-60902}, shortjournal = {IEEE Access}, title = {Iterative assessment of edge criticality: Efficiency enhancement or hidden insufficiency detection}, volume = {13}, year = {2025}, }
• (2025). An in-depth tutorial on BJTU-RAO bogie datasets for fault diagnosis. ACCESS, 13, 60879-60888. (www)

  @article{ , abstract = {The reliability and safety of trains have always been the top priority in the railway industry. As the critical subsystem of trains, the health states of bogie transmission systems directly affect the operation safety of trains. Train fault diagnosis, predictive maintenance, and algorithms evaluation become crucial to ensure operational safety. Thus, there is an urgent need for high-quality fault datasets of train transmission systems. This tutorial introduces the first publicly available fault datasets of train transmission systems, referred to as Beijing Jiaotong University - Rail Autonomous Operations (BJTU-RAO) bogie datasets. The datasets contain multi-sensor data streams of transmission systems of subway train bogies including a health state and 50 faulty states, which are acquired by fault simulation experiments and are released at the 2024 Global Reliability and Prognostics and Health Management (PHM) Conference. For each type of state, samples under nine different working conditions are collected to represent the different operating conditions of trains, and the signals are recorded at a sampling frequency of 64 kHz. The datasets are collected, organized, and made publicly available by the research team from the State Key Lab of Advanced Rail Autonomous Operation at BJTU, aiming to encourage scholars and engineers to investigate and validate their fault detection or diagnosis algorithms for key components of train transmission systems, such as driving gearboxes, axle boxes, and traction motors.}, archive = {J_ACCESS}, author = {Yong Qin and Yiran Wang and Zhaojun Steven Li and Biao Wang and Ao Ding and Chengcheng Wang and Yuanjing Qin and Yang Wang}, doi = {10.1109/ACCESS.2025.3551603}, journal = {IEEE Access}, pages = {60879-60888}, shortjournal = {IEEE Access}, title = {An in-depth tutorial on BJTU-RAO bogie datasets for fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Enhanced multi-pill detection and recognition using VFI augmentation and auto-labeling for limited single-pill data. ACCESS, 13, 60859-60878. (www)

  @article{ , abstract = {This study presents a method for object detection and recognition to identify the positions and types of pills in images containing multiple pills using a small-scale dataset of single-pill images for training. The proposed approach aims to detect multiple pills at the final stage despite the initial training data, which includes only single pills. The method consists of three primary steps. First, a data augmentation technique is introduced to prevent overfitting and improve learning efficiency. This augmentation uses a video frame interpolation (VFI) technique based on the latent diffusion model (LDM). A capturing system is developed for this purpose, and differences between images are used as additional information in weight maps to train the LDM. Second, an automatic labeling system is proposed to generate label data for the paired dataset efficiently. Accurate labeling requires the position and type of pills as training data, but manually labeling the augmented dataset of 61,440 images would be costly. Therefore, an automatic labeling system using an attention map and a deep U-Net is proposed to generate the label data efficiently. Third, a method is presented to detect the position and type of multiple pills based on a training dataset containing only single-pill images. Reliable detection and recognition of multiple pills usually require datasets containing various pill combinations. However, as the number of classes increases, the possible combinations grow exponentially. To address this, we propose a system that learns from single-pill images to detect multiple pills accurately. This study uses a dataset containing 40 types of pills for experimentation, and the results demonstrate superior precision, recall, individual pill accuracy, and image accuracy compared to other methods.}, archive = {J_ACCESS}, author = {Seung-Hwan Lee and Dong-Min Son and Sung-Hak Lee}, doi = {10.1109/ACCESS.2025.3557569}, journal = {IEEE Access}, pages = {60859-60878}, shortjournal = {IEEE Access}, title = {Enhanced multi-pill detection and recognition using VFI augmentation and auto-labeling for limited single-pill data}, volume = {13}, year = {2025}, }
• (2025). Spiking neural networks for people counting based on FMCW radar. ACCESS, 13, 60846-60858. (www)

  @article{ , abstract = {This paper presents an innovative method for indoor people counting using Spiking Neural Networks (SNNs) exclusively with radar data, effectively addressing privacy concerns associated with camera-based systems. When compared to established neural network-based algorithms like VGG16 (F1-Score=0.8003), ResNet50 (F1-Score=0.6669), and MobileNet (F1-Score=0.7258), the SNN (F1-Score=0.8284) exhibits superior performance in the task of counting people indoors. Furthermore, the energy efficiency of the SNN is a strong advantage, particularly for hardware deployment. This characteristic not only reduces computational demands but also aligns with the increasing emphasis on energy conservation in modern technology. The combination of privacy preservation, accuracy, and energy efficiency positions the SNN as a promising choice for a wide range of real-world applications, including security, transportation, and smart spaces, offering a comprehensive solution for people counting in indoor environments.}, archive = {J_ACCESS}, author = {Alberto Martin-Martin and Marta Verona-Almeida and Rubén Padial-Allué and Borja Saez and Javier Mendez and Encarnación Castillo and Luis Parrilla}, doi = {10.1109/ACCESS.2025.3557317}, journal = {IEEE Access}, pages = {60846-60858}, shortjournal = {IEEE Access}, title = {Spiking neural networks for people counting based on FMCW radar}, volume = {13}, year = {2025}, }
• (2025). On BESS capacity optimization of hybrid coal-fired generator and BESS power station for secondary frequency regulation. ACCESS, 13, 60833-60845. (www)

  @article{ , abstract = {Integrating battery energy storage systems (BESS) into a coal-fired generator can enhance power systems’ secondary frequency regulation capability. To this end, this paper proposes a policy to coordinate the BESS and the coal-fired generator to meet the automatic generation control (AGC) requirements and subsequently investigates the optimal BESS capacity to maximize the net profit gained from the frequency regulation. Firstly, probability characteristics of AGC instruction duration period, interval period, regulation rate, and regulation direction are investigated according to the real AGC datalog. The AGC regulation direction would change randomly. Directly switching the BESS between charging and discharging to match the requirement would significantly deplete the lifetime of the battery. Therefore, this paper divides the BESS into two groups, which will be controlled stay in charging and discharging states to respectively respond to the AGC requirement of “up” and “down”. To obtain a cost-effective BESS investment, this paper develops a new sizing method, which optimizes the BESS capacity by simulating the operation of the hybrid coal-fired generator and BESS power station (HCGBPS) over an example day. Finally, the case studies justified the developed sizing method could make satisfying BESS investment decisions to ensure a maximum net profit in operation.}, archive = {J_ACCESS}, author = {Xinsong Zhang and Ziyun Ma and Beiping Gu and Lurui Fang and Xiuyong Yu}, doi = {10.1109/ACCESS.2025.3557853}, journal = {IEEE Access}, pages = {60833-60845}, shortjournal = {IEEE Access}, title = {On BESS capacity optimization of hybrid coal-fired generator and BESS power station for secondary frequency regulation}, volume = {13}, year = {2025}, }
• (2025). Human daily indoor action (HDIA) dataset: Privacy-preserving human action recognition using infrared camera and wearable armband sensors. ACCESS, 13, 60822-60832. (www)

  @article{ , abstract = {Human Activity Recognition (HAR) plays a vital role in applications such as healthcare, smart homes, and robotics, particularly in supporting the elderly. However, most existing HAR datasets focus on general human activities and are typically collected using RGB cameras in controlled environments with fixed angles—conditions that limit their real-world applicability. In this study, we introduce the Human Daily Indoor Actions (HDIA) dataset, specifically designed to capture natural indoor activities performed by elderly individuals. The dataset includes 48 daily actions, recorded using non-invasive infrared (IR) cameras and wearable armband sensors positioned at various angles to ensure diverse and realistic activity representation. The use of IR sensors enhances privacy, making the dataset ethically suitable for long-term monitoring. To demonstrate its utility, we implemented a fusion-based HAR model that integrates data from both IR and Inertial Measurement Unit (IMU) sensors. This model achieves strong activity recognition performance while minimizing the risk of identity exposure. By focusing on privacy-aware data collection and the daily routines of elderly individuals, the HDIA dataset offers a valuable resource for advancing real-world HAR research.}, archive = {J_ACCESS}, author = {Jongbum Park and Kyoung Ok Yang and Sunme Park and Jun Won Choi}, doi = {10.1109/ACCESS.2025.3556001}, journal = {IEEE Access}, pages = {60822-60832}, shortjournal = {IEEE Access}, title = {Human daily indoor action (HDIA) dataset: Privacy-preserving human action recognition using infrared camera and wearable armband sensors}, volume = {13}, year = {2025}, }
• (2025). An enhanced path loss model for maritime communication channels. ACCESS, 13, 60814-60821. (www)

  @article{ , abstract = {This study proposes a new ocean propagation loss model, the curved ground loss (CGL) model, to improve the existing models by considering the effects of sea surface roughness, earth curvature, and sea wave height on signal fading at the reflection point. The CGL model integrates a variety of loss factors, including air absorption loss, which has a significant impact on high-frequency and long-distance communications, reflection loss (including diffuse reflection effect and shadow fading), and diffraction loss caused by earth curvature. By comparing with measurement data, the accuracy of the CGL model is verified, achieving an RMSE of approximately 4–5 dB, with the best agreement with the measurement data for four cases: high frequencies, low antennas, the reflection effect and the distance exceeding 60% of the radius of the first Fresnel Zone ( $D_{06}$ ).}, archive = {J_ACCESS}, author = {Chongquan Wang and Shengming Jiang}, doi = {10.1109/ACCESS.2025.3557824}, journal = {IEEE Access}, pages = {60814-60821}, shortjournal = {IEEE Access}, title = {An enhanced path loss model for maritime communication channels}, volume = {13}, year = {2025}, }
• (2025). Predictive probability density mapping for search and rescue using an agent-based approach with sparse data. ACCESS, 13, 60802-60813. (www)

  @article{ , abstract = {Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-based model designed to replicate diverse psychological profiles of lost persons, allowing these agents to navigate real-world landscapes while making decisions autonomously without the need for location-specific training. The probability distribution map depicting the potential location of the lost person emerges through a combination of Monte Carlo simulations and mobility-time-based sampling. Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model. This allows generalization of the data to sample initial starting points for the agents during validation. Comparative analysis with historical data showcases promising outcomes relative to alternative methods. This work introduces a flexible agent that can be employed in search and rescue operations, offering adaptability across various geographical locations.}, archive = {J_ACCESS}, author = {Jan-Hendrik Ewers and David Anderson and Douglas Thomson}, doi = {10.1109/ACCESS.2025.3557693}, journal = {IEEE Access}, pages = {60802-60813}, shortjournal = {IEEE Access}, title = {Predictive probability density mapping for search and rescue using an agent-based approach with sparse data}, volume = {13}, year = {2025}, }
• (2025). Breast cancer detection using mammography: Image processing to deep learning. ACCESS, 13, 60776-60801. (www)

  @article{ , abstract = {Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals.}, archive = {J_ACCESS}, author = {Shahzad Ahmad Qureshi and Aziz-Ul-Rehman and Lal Hussain and Touseef Sadiq and Syed Taimoor Hussain Shah and Adil Aslam Mir and Muhammad Amin Nadim and Darnell K. Adrian Williams and Tim Q. Duong and Qurat-Ul-Ain Chaudhary and Natasha Habib and Asrar Ahmad and Syed Adil Hussain Shah}, doi = {10.1109/ACCESS.2024.3523745}, journal = {IEEE Access}, pages = {60776-60801}, shortjournal = {IEEE Access}, title = {Breast cancer detection using mammography: Image processing to deep learning}, volume = {13}, year = {2025}, }
• (2025). Watch your callback: Offline anomaly detection using machine learning in ROS 2. ACCESS, 13, 60763-60775. (www)

  @article{ , abstract = {Robotic systems are increasingly prevalent across various fields, necessitating a strong focus on safety and security. The evolution from ROS 1 to ROS 2 has addressed many security concerns; however, it has also introduced new challenges, particularly in anomaly detection due to its decentralized architecture. This shift emphasizes the need for robust methods to monitor and analyze system behavior, as traditional techniques tailored for centralized systems may overlook subtle anomalies. In this paper, we propose an offline anomaly detection using machine learning in ROS 2. Our methodology leverages the widely adopted ros2_tracing framework for efficient and automated data collection, and employs Turtlebot3 running Cartographer SLAM in a Gazebo simulation environment that closely mimics real-world conditions. We further enhance our evaluation by applying controlled fault injection and data augmentation techniques to simulate both malicious and benign anomalies. In our experiments, we utilize multiple machine learning models, including Isolation Forest, OCSVM, and Autoencoder, to analyze the temporal characteristics of callbacks, focusing on response time and invocation frequency. Based on our evaluation, Isolation Forest achieved an F1 score of up to 0.94 with a false positive rate of 1.3%, OCSVM reached an F1 score of up to 0.98 with a false positive rate of around 1.7%, and the Autoencoder attained an F1 score of up to 0.99 with a false positive rate as low as 1.0%.}, archive = {J_ACCESS}, author = {Jeonghwan Kang and Kyounghwan Kim and Donghyun Kwon}, doi = {10.1109/ACCESS.2025.3556864}, journal = {IEEE Access}, pages = {60763-60775}, shortjournal = {IEEE Access}, title = {Watch your callback: Offline anomaly detection using machine learning in ROS 2}, volume = {13}, year = {2025}, }
• (2025). Computationally efficient neural architecture search for image denoising. ACCESS, 13, 60743-60762. (www)

  @article{ , abstract = {In image denoising, deep neural networks require suitable architectural selection and noise-representative datasets for learning, often leading to significant computational demand. In this paper, this challenge is addressed in two ways. First, deep reinforcement learning is leveraged with weight-sharing for optimal architecture search, creating a shared model that enables efficient weight reuse across architectural configurations. This weight-sharing mechanism significantly reduces memory requirements. Second, by utilizing a representative subset of a dataset and examining empirical distributions of SSIM and PSNR values, it is demonstrated that comparable performance is achievable while reducing computational costs, provided that the subset is sufficiently sampled as measured by the Jensen-Shannon Divergence. This sufficiently sampled subset thus captures the characteristics of the full dataset. This combined approach of intelligent sampling and weight-shared reinforcement learning yields results equivalent to full-dataset training but with significantly reduced training time, demonstrating that efficient neural architecture search coupled with smart sampling can maintain high-quality denoising performance while substantially decreasing computational demand.}, archive = {J_ACCESS}, author = {Esau Alain Hervert Hernandez and Yan Cao and Nasser Kehtarnavaz}, doi = {10.1109/ACCESS.2025.3557691}, journal = {IEEE Access}, pages = {60743-60762}, shortjournal = {IEEE Access}, title = {Computationally efficient neural architecture search for image denoising}, volume = {13}, year = {2025}, }
• (2025). Recognizing supporting standpoints for online restaurant reviews: Evidence from tripadvisor. ACCESS, 13, 60734-60742. (www)

  @article{ , abstract = {This article presents a practical framework that analyzes a real dataset from Tripadvisor with 2330 restaurant reviews. The reviews are written in English, and the structure of the reviews is free, not following any specific form. We conducted four experiments that exploited different artificial intelligence models and natural language processing systems. Our results indicate that an online restaurant review often shares several standpoints with another review of the same restaurant. Our results also illustrate that the number of agreed standpoints between reviews is not related to the rating of the respective reviews. Likewise, the results imply that the pattern of variation in the number of reviews (identified as supported with a specific evidence intensity) is unrelated to the reviews’ rating, where “evidence intensity” refers to the number of shared standpoints between a review and its supporter review. Additionally, if a restaurant review is found unsupported, the cause is mainly related to how the review was structured and written. Our framework can be utilized by online review platforms, such as Tripadvisor, to accredit or rank reviews based on supporting evidence from other related reviews.}, archive = {J_ACCESS}, author = {Samer Nofal}, doi = {10.1109/ACCESS.2025.3557526}, journal = {IEEE Access}, pages = {60734-60742}, shortjournal = {IEEE Access}, title = {Recognizing supporting standpoints for online restaurant reviews: Evidence from tripadvisor}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Performance of spatially coupled LDPC codes over the underwater wireless optical channel with strong turbulence and pointing errors”. ACCESS, 13, 60733. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Performance of Spatially Coupled LDPC Codes Over the Underwater Wireless Optical Channel With Strong Turbulence and Pointing Errors”).}, archive = {J_ACCESS}, author = {Sravan Kumar Padala and John D’Souza}, doi = {10.1109/ACCESS.2025.3557463}, journal = {IEEE Access}, pages = {60733}, shortjournal = {IEEE Access}, title = {Corrections to “Performance of spatially coupled LDPC codes over the underwater wireless optical channel with strong turbulence and pointing errors”}, volume = {13}, year = {2025}, }
• (2025). Challenges and perspectives in interpretable music auto-tagging using perceptual features. ACCESS, 13, 60720-60732. (www)

  @article{ , abstract = {In the era of music streaming platforms and recommendation systems, the automatic music auto-tagging task has gained a lot of traction and has motivated researchers to develop methods for solving the task focusing on improving performance metrics on baseline datasets. The majority of recent approaches rely on deep neural networks, which despite their impressive performance, are opaque, meaning it is difficult to explain their output on a given input. While the problem of interpretability has been highlighted in other domains, such as medicine, it has not been a priority for music-related tasks. In this work, we explored the usefulness of interpretability for music auto-tagging. We developed a pipeline incorporating three types of information extraction procedures: 1) symbolic knowledge, 2) auxiliary deep neural networks, and 3) signal processing, to extract perceptual features of audio files, which were then used to train an explainable machine learning model to predict tags. We experimented on three datasets the MTG-Jamendo dataset, the GTZAN dataset, and the MagnaTagATune dataset. Our method outperforms baseline models in all tasks and in some cases is competitive with the state-of-the-art. We conducted a human survey to evaluate user trust in our methodology and in a state-of-the-art model, concluding that while the state-of-the-art model offers better performance, there are use cases where the slight deterioration in accuracy is outweighed by the increased trust and value provided by interpretability.}, archive = {J_ACCESS}, author = {Vassilis Lyberatos and Spyridon Kantarelis and Edmund Dervakos and Giorgos Stamou}, doi = {10.1109/ACCESS.2025.3555741}, journal = {IEEE Access}, pages = {60720-60732}, shortjournal = {IEEE Access}, title = {Challenges and perspectives in interpretable music auto-tagging using perceptual features}, volume = {13}, year = {2025}, }
• (2025). Distributed control strategies for microgrids: A critical review of technologies and challenges. ACCESS, 13, 60702-60719. (www)

  @article{ , abstract = {The surging growth of the global energy demand, rapid depletion of fuel reserves, and, most importantly, increasing global warming trends have been the major concerns for decision-makers for the last couple of decades. The current prevalent renewable and clean power sources such as wind, hydro, and solar energy provide workable solutions to the foregoing problems via (DGs). Microgrids (MGs) are essential for interfacing the major portion of renewable energy sources and decision-making regarding the control and operation modes. Recent MG research trends focused on integrating those affordable and cost-effective resources into the energy mix at a consumer level. Still, due to the complexity of DG integration into the MG, many research efforts have been made to examine and develop distributed control (DCT) systems for alternating current, direct current, and hybrid MGs, and adherents of this approach, the distributed controllers have numerous advantages compared with centralized and decentralized control methods, such as increased reliability, flexibility, controllability, possibility to initiate operations independently, and robustness to communication failures. This research critically reviews the DCT strategies developed for MGs, presents various MG control strategies, and delves into different approaches to designing distributed controllers. Besides, this article highlights existing challenges and possible ways of mitigation, most especially by using artificial intelligence approaches. It also reviews the multi-microgrid concept to shed light on modern technologies and their potential applications in MGs. It is expected that the decision-makers and the researchers will find the critical analysis of this article useful while delving into various control strategies for MG.}, archive = {J_ACCESS}, author = {Ghali Ahmad and Abdulbasit Hassan and Asif Islam and Md Shafiullah and Mohammad A. Abido and Mujahed Al-Dhaifallah}, doi = {10.1109/ACCESS.2025.3552940}, journal = {IEEE Access}, pages = {60702-60719}, shortjournal = {IEEE Access}, title = {Distributed control strategies for microgrids: A critical review of technologies and challenges}, volume = {13}, year = {2025}, }
• (2025). Lateral phishing with large language models: A large organization comparative study. ACCESS, 13, 60684-60701. (www)

  @article{ , abstract = {The emergence of Large Language Models (LLMs) has heightened the threat of phishing emails by enabling the generation of highly targeted, personalized, and automated attacks. Traditionally, many phishing emails have been characterized by typos, errors, and poor language. These errors can be mitigated by LLMs, potentially lowering the barrier for attackers. Despite this, there is a lack of large-scale studies comparing the effectiveness of LLM-generated lateral phishing emails to those crafted by humans. Current literature does not adequately address the comparative effectiveness of LLM and human-generated lateral phishing emails in a real-world, large-scale organizational setting, especially considering the potential for LLMs to generate more convincing and error-free phishing content. To address this gap, we conducted a pioneering study within a large university, targeting its workforce of approximately 9,000 individuals including faculty, staff, administrators, and student workers. Our results indicate that LLM-generated lateral phishing emails are as effective as those written by communications professionals, emphasizing the critical threat posed by LLMs in leading phishing campaigns. We break down the results of the overall phishing experiment, comparing vulnerability between departments and job roles. Furthermore, to gather qualitative data, we administered a detailed questionnaire, revealing insights into the reasons and motivations behind vulnerable employee’s actions. This study contributes to the understanding of cyber security threats in educational institutions and provides a comprehensive comparison of LLM and human-generated phishing emails’ effectiveness, considering the potential for LLMs to generate more convincing content. The findings highlight the need for enhanced user education and system defenses to mitigate the growing threat of AI-powered phishing attacks.}, archive = {J_ACCESS}, author = {Mazal Bethany and Athanasios Galiopoulos and Emet Bethany and Mohammad Bahrami Karkevandi and Nicole Beebe and Nishant Vishwamitra and Peyman Najafirad}, doi = {10.1109/ACCESS.2025.3555500}, journal = {IEEE Access}, pages = {60684-60701}, shortjournal = {IEEE Access}, title = {Lateral phishing with large language models: A large organization comparative study}, volume = {13}, year = {2025}, }
• (2025). Offline versus real-time grasp prediction employing a wearable high-density lightmyography armband: On the control of prosthetic hands. ACCESS, 13, 60672-60683. (www)

  @article{ , abstract = {Several studies over the last decades have investigated the use of myoelectric upper-arm prostheses to restore lost functionality and perform complex activities of daily life. Muscle-machine interfacing methods employing electromyography, sonomyography, forcemyography, and mechanomyography, have been developed offering users intuitive control of prostheses. However, these methods suffer their own drawbacks and the practical, robust, real-time control of prostheses in the execution of complex everyday life tasks remains difficult to accomplish. In this study, a wearable high-density lightmyography armband is proposed, and the offline and real-time grasp prediction schemes are compared in an attempt to deepen our understanding in real-time decoding employing lightmyography signals. Thus bringing lightmyography closer to advanced real-time prosthetic control scenarios. Offline experiments were conducted where models decoding 10 classes were trained and tested, achieving accuracies exceeding 91% with a mean of 94.11% when a Random Forest model was employed and a mean of 95.87% when a Convolutional Neural Networks model was employed. However, as models were deployed in real-time, a decline in performance was notable, reducing the accuracies to 60-70%. Other limitations were observed in this transition. Although average accuracies were high, the precision and recall for a small fraction of grasps were often low, hindering overall real-time performance. Moreover, prolonged usage led to decreased decoding accuracy, with accuracies dropping to below 35%, indicating that fatigue, sweat, or sensor shift may have affected the signals over time. Thus for the real-time application of lightmyography, it is recommended to discriminate between a smaller number of classes for which accuracies are higher (around 80% for 6 classes without prolonged use).}, archive = {J_ACCESS}, author = {Bonnie Guan and Ricardo V. Godoy and Mojtaba Shahmohammadi and Anany Dwivedi and Minas Liarokapis}, doi = {10.1109/ACCESS.2025.3556920}, journal = {IEEE Access}, pages = {60672-60683}, shortjournal = {IEEE Access}, title = {Offline versus real-time grasp prediction employing a wearable high-density lightmyography armband: On the control of prosthetic hands}, volume = {13}, year = {2025}, }
• (2025). Blockchain security in focus: A comprehensive investigation into threats, smart contract security, cross-chain bridges, and vulnerabilities detection tools and techniques. ACCESS, 13, 60643-60671. (www)

  @article{ , abstract = {The advancement and comprehensive adoption of blockchain technology has led to momentous progression in data-critical areas such as DeFi, health sector, defense, supply chain, and beyond. Nevertheless, beside this growth, there has been a distinguished rise in both the prevalence and diversification of security vulnerabilities. Recent attacks have exploited these vulnerabilities in targeting different layers of the blockchain architecture. This paper centralizes on the security vulnerabilities, attacks, and challenges faced by blockchain systems, presenting an exhaustive taxonomical classification of blockchain layered security. It classifies diverse security issues across the different layers of blockchain architecture. With the widespread adoption of blockchain in enterprise applications, smart contracts have become a vital target for attackers, leading to significant financial and data losses. Additionally, as divergent applications are deployed across multiple blockchains, the demand for cross-chain contracts is getting progressively imperative. Investigating the security of smart contracts and cross-chain interactions is important for defending decentralized applications against vulnerabilities, assuring the secure transfer of assets among blockchains, and embracing trust and stability within the extensive blockchain ecosystem. The paper also examines different tools and techniques available in the literature for improving blockchain security, including techniques for vulnerability detection along with the surging impact of deep learning in vulnerability detection. This work presents beneficial insights into the prevailing state of blockchain security and provides a progressive prospect on the capability of intelligent, high-dimensional deep learning-based solutions to target future threats in the blockchain security space.}, archive = {J_ACCESS}, author = {Deepa Mishra and Shraddha Phansalkar}, doi = {10.1109/ACCESS.2025.3556499}, journal = {IEEE Access}, pages = {60643-60671}, shortjournal = {IEEE Access}, title = {Blockchain security in focus: A comprehensive investigation into threats, smart contract security, cross-chain bridges, and vulnerabilities detection tools and techniques}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning and multi-access edge computing for 6G-based underwater wireless networks. ACCESS, 13, 60627-60642. (www)

  @article{ , abstract = {6G networks are envisioned to dramatically enhance the connectivity landscape by integrating communication across ground, air, and sea environments. In the aquatic domain, the Internet of Underwater Things (IoUT) represents a global network of intelligent underwater devices designed to capture, interpret, and share data. Although Underwater Acoustic Communications (UAC) has become widespread as a solution for transmitting information, data collection from Underwater Sensor Nodes (USNs) to the surface results in extensive delays and higher energy consumption. Edge communication emerges as a solution to address these challenges. In this approach, Autonomous Underwater Vehicles (AUVs) bring edge computing as close as possible to the source devices. This paper proposes an innovative AUV-based Multi-Access Edge Computing (MEC) system where cluster-heads that collect data from IoUT devices offload their associated computational tasks to local AUVs. These AUVs are strategically positioned to execute tasks either fully locally, partially, or by offloading them entirely to a more resource-equipped AUV (AUV MEC). We achieve this by jointly optimizing the task offloading strategy, resource allocation, and the trajectories of the AUVs. We formulate a non-convex optimization problem to minimize the weighted sum of service delays for all local AUVs and their energy consumption. To address the NP-hard nature of this problem, we employ a deep reinforcement learning algorithm, Deep Deterministic Policy Gradient (DDPG), to solve it. Extensive simulations have been conducted to evaluate the effectiveness of our proposed communication system. The results show that our proposed algorithm outperforms the Total Offloading (Offloading), Local Execution (Locally), and Actor-Critic (AC) algorithms.}, archive = {J_ACCESS}, author = {Juan Carlos Cepeda-Pacheco and Mari Carmen Domingo}, doi = {10.1109/ACCESS.2025.3557158}, journal = {IEEE Access}, pages = {60627-60642}, shortjournal = {IEEE Access}, title = {Reinforcement learning and multi-access edge computing for 6G-based underwater wireless networks}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review: Fourier analysis as a mathematical tool for robot navigation. ACCESS, 13, 60613-60626. (www)

  @article{ , abstract = {The introduction of Fourier analysis or series in robot navigation algorithms greatly improves the capability to find solutions to many inherent applications and tasks of robotic systems. This paper presents a systematic literature review on using Fourier analysis as a mathematical tool for robot navigation, analyzing a total of 51 articles from selected databases. The review identifies and clusters the articles based on operational attributes, providing insights into the usage of Fourier analysis in various application fields, including the benefits of applying such approaches for future control developments and designs. Research integrating Fourier analysis into the robotic applications of pick-and-place, transportation, configuration space and exploration, spacecraft trajectory and medical purposes has highlighted multiple potentials of the mathematical tool. Fourier analysis is capable of uncertainty compensation in systems, deliverance in repetitive and contact tasks, operating in partially known systems, and optimizing paths. The systematic review also reveals future directions that can be addressed in the field of robot navigation using the Fourier analysis.}, archive = {J_ACCESS}, author = {Prithvi Narayan and Sandeep Ameet Kumar and Avinesh Prasad and Bibhya Nand Sharma}, doi = {10.1109/ACCESS.2025.3556121}, journal = {IEEE Access}, pages = {60613-60626}, shortjournal = {IEEE Access}, title = {A systematic literature review: Fourier analysis as a mathematical tool for robot navigation}, volume = {13}, year = {2025}, }
• (2025). Extended state observer-based robust model predictive velocity control for permanent magnet synchronous motor. ACCESS, 13, 60602-60612. (www)

  @article{ , abstract = {This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). We develop a new PMSM model that consists of velocity and acceleration to lump the system information and an external disturbance into a disturbance. The extended state observer (ESO) is designed to estimate the velocity, acceleration, and disturbance. By estimating the state variables and disturbance using the ESO, the model predictive control (MPC) finds the optimal control input by predicting future system behavior. Additionally, the direct current controller is designed so that the direct current converges to zero. Because the proposed method is not designed based on the cascade structure from the viewpoint of velocity control, the optimization control for the velocity and currents can be defined. Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.}, archive = {J_ACCESS}, author = {Eunji Lee and Yonghao Gui and Sesun You and Jun Moon and Wonhee Kim}, doi = {10.1109/ACCESS.2025.3555245}, journal = {IEEE Access}, pages = {60602-60612}, shortjournal = {IEEE Access}, title = {Extended state observer-based robust model predictive velocity control for permanent magnet synchronous motor}, volume = {13}, year = {2025}, }
• (2025). Improving multi-label emotion classification on imbalanced social media data with BERT and clipped asymmetric loss. ACCESS, 13, 60589-60601. (www)

  @article{ , abstract = {This research addresses the challenge of multi-label emotion classification on imbalanced datasets using a BERT-based model. Emotion classification, essential for applications like social media analysis and sentiment monitoring, often suffers from class imbalance, which hinders the detection of rare emotions. To address this, our model incorporates a clipped asymmetric loss function to prioritize minority classes while mitigating the dominance of frequent classes. We conducted extensive experimentation on GoEmotions and SemEval-2018 Task 1C datasets to demonstrate the model’s effectiveness in achieving improved precision, recall, and F1-scores across various taxonomies, including GoEmotions, Ekman, and sentiment-grouped levels. Our approach achieved a notable improvement in macro-average F1-scores, increasing from 0.46 (baseline) to 0.54 on the GoEmotions dataset and 0.59 on the SemEval-2018 dataset. The results indicate significant advancements over standard BERT implementations and state-of-the-art models, particularly in recognizing rare emotions, making the model a robust solution for real-world, multi-label emotion classification tasks under imbalanced settings.}, archive = {J_ACCESS}, author = {Sandhya Ramakrishnan and L. D. Dhinesh Babu}, doi = {10.1109/ACCESS.2025.3557091}, journal = {IEEE Access}, pages = {60589-60601}, shortjournal = {IEEE Access}, title = {Improving multi-label emotion classification on imbalanced social media data with BERT and clipped asymmetric loss}, volume = {13}, year = {2025}, }
• (2025). Lyapunov-controlled quadrotor landings on dynamic mobile platforms. ACCESS, 13, 60570-60588. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) have become indispensable in diverse applications, including delivery, disaster relief, and surveillance. However, achieving precise autonomous landing on mobile platforms, such as moving vehicles or ships, poses significant challenges due to dynamic platform motion, constrained landing spaces, and the nonlinear nature of UAV dynamics. This paper proposes a Lyapunov-based control framework for ensuring accurate and robust quadrotor landings on mobile platforms. By leveraging Lyapunov theory’s stability and convergence principles, the developed control laws guarantee safe navigation and precision landing under dynamic conditions. The methodology incorporates artificial potential fields to manage collision avoidance and workspace constraints, enhancing system reliability. Extensive simulations validate the effectiveness and robustness of the proposed approach, demonstrating its potential for practical implementation in industries requiring UAV operations on moving platforms.}, archive = {J_ACCESS}, author = {Darmesh Kumar and Jai Raj and Krishna Raghuwaiya and Jito Vanualailai}, doi = {10.1109/ACCESS.2025.3557372}, journal = {IEEE Access}, pages = {60570-60588}, shortjournal = {IEEE Access}, title = {Lyapunov-controlled quadrotor landings on dynamic mobile platforms}, volume = {13}, year = {2025}, }
• (2025). Enhancing FMCW radar gesture classification with physically interpretable data augmentation. ACCESS, 13, 60556-60569. (www)

  @article{ , abstract = {This study introduces a novel, physically interpretable data augmentation framework that improves the robustness and accuracy of hand gesture recognition using Frequency-Modulated Continuous Wave (FMCW) radar and Convolutional Neural Networks (CNN). The proposed reconfigurable and parametric method modifies specific characteristics of five time-series features, namely range, velocity, azimuth and elevation angles, and signal magnitude, to generate synthetic gesture samples with realistic variations. By simulating variations in hand gesture distance, angle, duration, and noise, the framework improves model generalization while reducing the need for extensive and costly data collection. The augmentation techniques employed in this research include time scaling, range and angle transformation, and noise injection, effectively simulating different gesture speeds, orientations, distances, and interference levels. Additionally, applying augmentation at the feature level, rather than on raw radar data, reduces data size significantly, leading to faster training, lower memory requirements, and improved scalability. Experimental results demonstrate significant performance gains in a 1D-CNN classifier deployed on an ARM Cortex-M4 microcontroller after applying the proposed augmentation techniques. Specifically, the combination of time scaling, range transformation, and noise injection improves accuracy by 16.67%, precision by 6.4%, recall by 15.07%, and F1 score by 13.12% in the extended range scenario, 1 meter to 1.2 meter, compared to the baseline model trained on unaugmented data.}, archive = {J_ACCESS}, author = {Alessandra Fusco and Zain Amir Zaman and Souvik Hazra and Lorenzo Servadei and Robert Wille}, doi = {10.1109/ACCESS.2025.3556565}, journal = {IEEE Access}, pages = {60556-60569}, shortjournal = {IEEE Access}, title = {Enhancing FMCW radar gesture classification with physically interpretable data augmentation}, volume = {13}, year = {2025}, }
• (2025). Multi-stage deep learning for intrusion detection in industrial internet of things. ACCESS, 13, 60532-60555. (www)

  @article{ , abstract = {The Industrial Internet of Things (IIoT) facilitates enhanced automation, predictive maintenance, real-time monitoring, and data analytics across various sectors, including manufacturing, energy, transportation, agriculture, and supply chain management, thereby improving productivity, efficiency, and operational safety. However, as IIoT networks continue to expand, it is imperative to secure them against increasingly sophisticated cyber threats. Deep Learning (DL) techniques have been extensively utilized for intrusion detection within IIoT systems. Nevertheless, addressing the class imbalance problem remains a significant challenge. The underrepresentation of certain attack types in training data frequently results in the development of DL models that struggle to accurately detect these categories of malicious activities. This limitation represents considerable risks to the security of IIoT networks, as undetected attacks and false alarms may lead to severe operational disruptions. In this paper, we propose a multi-stage deep learning (MSDL) method specifically designed to enhance intrusion detection within IIoT networks by addressing the class imbalance issue. We assessed the effectiveness of our approach utilizing two highly imbalanced datasets: X-IIoTID and WUSTL-IIoT. Our experimental findings indicate that the proposed MSDL method surpasses the baseline DL models as well as state-of-the-art oversampling and undersampling techniques. Specifically, the MSDL method exhibits significant improvements in recognizing minority-class attacks that are frequently misclassified. Consequently, the implementation of the MSDL for intrusion detection is anticipated to strengthen the overall security and resilience of IIoT systems, providing stronger protection against a diverse array of cyber threats in industrial applications.}, archive = {J_ACCESS}, author = {Segun I. Popoola and Yakubu Tsado and Abimbola A. Ogunjinmi and Erika Sanchez-Velazquez and Yonghong Peng and Danda B. Rawat}, doi = {10.1109/ACCESS.2025.3557959}, journal = {IEEE Access}, pages = {60532-60555}, shortjournal = {IEEE Access}, title = {Multi-stage deep learning for intrusion detection in industrial internet of things}, volume = {13}, year = {2025}, }
• (2025). Improvement of adaptive cruise control system performance on sloped roads based on adaptive neuro-fuzzy inference system. ACCESS, 13, 60519-60531. (www)

  @article{ , abstract = {Adaptive cruise control is a system that maintains an appropriate distance from vehicles ahead while driving. Although Adaptive cruise control systems operate stably on flat roads, they can become unstable on sloped roads. This study presents a method to enhance the performance of Adaptive cruise control systems on sloped roads using fuzzy logic control techniques and the adaptive neuro-fuzzy inference system. First, an Adaptive cruise control system is described based on state-space equations. Then, a fuzzy logic controller is designed and applied to the Adaptive cruise control system, and improved Adaptive cruise control performance is verified via computer simulations. The two inputs of the fuzzy logic controller are the speed error and the distance between vehicles. The output is the improved control force. In addition, to achieve enhanced performance under varying road conditions, the adaptive neuro-fuzzy inference system is applied. The adaptive neuro-fuzzy inference system is designed by training the designed fuzzy logic controller with data extracted from scenarios in which the road gradient angle changes. Simulations are performed by applying the adaptive neuro-fuzzy inference system applied to the Adaptive cruise control system. These simulations demonstrate that improved results are obtained even under new road conditions. This paper demonstrates that the design of the adaptive neuro-fuzzy inference system controller can effectively enhance the performance of the Adaptive cruise control system on sloped roads.}, archive = {J_ACCESS}, author = {Kang-Hyeon Choi and Hyuk-Jun Chang}, doi = {10.1109/ACCESS.2025.3557089}, journal = {IEEE Access}, pages = {60519-60531}, shortjournal = {IEEE Access}, title = {Improvement of adaptive cruise control system performance on sloped roads based on adaptive neuro-fuzzy inference system}, volume = {13}, year = {2025}, }
• (2025). Sentiment-driven bitcoin price range forecasting: Enhancing CART decision trees with high-dimensional indicators and twitter dynamics. ACCESS, 13, 60508-60518. (www)

  @article{ , abstract = {In this study, I propose a method for forecasting the next-day Bitcoin price range using a CART decision tree model, which integrates 124 high-dimensional technical indicators with Twitter-roBERTa sentiment analysis as the 125th feature to enhance prediction accuracy. The experiments utilize Bitcoin market data from the past six years (2019 to 2024) and approximately 58 million Twitter posts. The results demonstrate that the enhanced model, incorporating sentiment analysis, improves the average accuracy from 0.56 in the baseline model—trained solely on 124 technical indicators—to 0.62, with win rates increasing significantly by up to 45%. Sensitivity analysis further optimizes the sentiment feature weight, confirming the model’s robustness, and provides an innovative perspective for cryptocurrency market prediction, with future applications extensible through multi-source data fusion.}, archive = {J_ACCESS}, author = {Lei Shang}, doi = {10.1109/ACCESS.2025.3557186}, journal = {IEEE Access}, pages = {60508-60518}, shortjournal = {IEEE Access}, title = {Sentiment-driven bitcoin price range forecasting: Enhancing CART decision trees with high-dimensional indicators and twitter dynamics}, volume = {13}, year = {2025}, }
• (2025). Quantifying security in volume-hiding searchable symmetric encryption schemes with a novel scoring metric. ACCESS, 13, 60492-60507. (www)

  @article{ , abstract = {Size pattern leakage remains a critical issue in oblivious RAM (ORAM)-based Searchable Symmetric Encryption (SSE) schemes. Despite efforts to define security notions against size pattern leakage, existing studies either overly restrict analysis by focusing on maximum padding strategies or fail to offer meaningful quantitative comparisons across distinct schemes. In this paper, we introduce a novel scoring metric, Response Length Obfuscation (RLO), which fundamentally redefines how to assess volume-hiding schemes by measuring the hardness of guessing keywords based on the response lengths of queries. The proposed RLO-scoring utilizes Shannon entropy to measure the inability of guessing keywords for every feasible response length, providing a comprehensive measurement of security. Our main finding is that size pattern leakage should be perceived not merely as a binary categorization of leaked versus not leaked, but rather as a quantifiable continuum. This enables deeper evaluation of the security properties in various SSE schemes. Moreover, we propose how to capture adversarial attempts with size pattern leakage under an adaptive threat model, contrasting with previous work that depends on a selective model. We rigorously demonstrate the general applicability of the RLO-scoring through both theoretical analysis and experimental validation on diverse padding strategies with real-world Enron dataset and Ubuntu dataset corpus.}, archive = {J_ACCESS}, author = {Kangmo Ahn and Chaewon Kwak and Dongyoung Koo and Changhee Hahn and Junbeom Hur}, doi = {10.1109/ACCESS.2025.3557784}, journal = {IEEE Access}, pages = {60492-60507}, shortjournal = {IEEE Access}, title = {Quantifying security in volume-hiding searchable symmetric encryption schemes with a novel scoring metric}, volume = {13}, year = {2025}, }
• (2025). Electric boats and electric vehicles data-driven approach for enhanced resilience in power distribution networks. ACCESS, 13, 60470-60491. (www)

  @article{ , abstract = {This paper proposes a novel hierarchical optimization framework to enhance power distribution network resilience against extreme events by incorporating electric boats (E-Boats) as complementary assets to electric vehicles (EVs). Unlike EVs reliant on potentially compromised roadways, E-Boats navigate unaffected waterways, offering superior resilience during disaster response in regions with rivers and canals. The approach employs a two-phase methodology evaluated on a modified IEEE 123-node test feeder with actual infrastructure data, enabling large-scale integration of non-pre-determined EVs/E-Boats. Phase-I emphasizes proactive planning and anticipatory optimization, leveraging extensive data integration and a developed exchange architecture enabling efficient EV/E-Boat distribution through novel selection and pairing algorithms. Phase-II focuses on adaptive restoration and coordination optimization by employing sophisticated model layers. These include advanced EV and E-Boat dispatch models that use changing geospatial data to approximate motion conditions and charging/discharging requirements, enabling energy-efficient and optimized dispatch. Detailed simulations with comprehensive performance analysis demonstrate the proposed algorithm’s superiority in achieving optimal solutions within shorter computational times. This work presents an effective solution for enhancing power network resilience using a combined EV/E-Boat network with geospatial data for EV and E-Boat locations, state-of-charge updates, and adaptive path optimization algorithms. Results highlight faster restoration, minimized costs, and reduced energy consumption.}, archive = {J_ACCESS}, author = {Abdullah Ali M. Alghamdi and Dilan Jayaweera}, doi = {10.1109/ACCESS.2025.3558095}, journal = {IEEE Access}, pages = {60470-60491}, shortjournal = {IEEE Access}, title = {Electric boats and electric vehicles data-driven approach for enhanced resilience in power distribution networks}, volume = {13}, year = {2025}, }
• (2025). Adaptive control strategy for automotive magnetorheological dampers based on artificial neural networks. ACCESS, 13, 60455-60469. (www)

  @article{ , abstract = {In vehicle systems, the suspension is fundamental in achieving acceptable levels of ride comfort, while ensuring ride safety. Semi-active suspensions allow changing the suspension damping to prioritize different performance metrics. Multiple control techniques have been developed to find a suitable trade-off between comfort and road holding. This manuscript proposes the use of standard (proportional-integral-derivative) controllers tuned in real time by an artificial neural network. The formulation of the controller considers a magnetorheological damper represented through the Bouc-Wen model. A stochastic gradient descent algorithm with backward propagation is used to train the artificial neural network that then selects the controller gains in real time. This technique is tested numerically through quarter and full car models, with the latter one running on the automotive simulation software CarSim. The obtained results highlight significant improvements of the proposed approach in comparison to state-of-the-art controllers. Furthermore, the study proves the viability of running four controllers on a real-time embedded hardware platform through processor-in-the-loop tests.}, archive = {J_ACCESS}, author = {C. Steven Diaz-Choque and Luis C. Felix-Herran and Renato Galluzzi and Riccardo Cespi and Jorge de J. Lozoya-Santos and Ricardo A. Ramirez-Mendoza}, doi = {10.1109/ACCESS.2025.3554483}, journal = {IEEE Access}, pages = {60455-60469}, shortjournal = {IEEE Access}, title = {Adaptive control strategy for automotive magnetorheological dampers based on artificial neural networks}, volume = {13}, year = {2025}, }
• (2025). YOLO-MES: An effective lightweight underwater garbage detection scheme for marine ecosystems. ACCESS, 13, 60440-60454. (www)

  @article{ , abstract = {Marine pollution significantly impacts the sustainable development of marine ecosystems and the marine economy. Accurate detection of marine debris is essential for effective pollution control. However, existing high-precision detection algorithms are challenging to deploy on performance-constrained IoT underwater devices due to their large computational complexity and model size. To address this issue, this paper introduces YOLO-MES, an innovative underwater debris detection algorithm that integrates a lightweight design and a feature enhancement mechanism to enable efficient model deployment while preserving high accuracy. Unlike traditional YOLO models, YOLO-MES incorporates MobileNetV3 into underwater target detection, replacing the CSPDarknet backbone network, and optimizes the C3 and Conv layers through the bneck structure, significantly reducing computational demands and parameter scale at the architectural level. Additionally, YOLO-MES embeds the Efficient Channel Attention (ECA) module within the bneck structure to form the MECAneck module, which enhances adaptive feature extraction, significantly improving the network’s cross-channel feature capture and target recognition capabilities. This paper also proposes a streamlined Slim-neck design strategy, which effectively reduces the number of parameters in the neck network while maintaining multi-scale feature fusion accuracy. Experimental results indicate that YOLO-MES achieves 95.8% accuracy on the dataset, while reducing model size and computational complexity by 64% and 67%, respectively. Compared to existing mainstream detection algorithms, YOLO-MES offers significant advantages in lightweight design and computational efficiency, providing a practical and deployable solution for underwater target detection on mobile devices.}, archive = {J_ACCESS}, author = {Chengxu Huang and Wenyuan Zhang and Beitian Zheng and Jiahao Li and Bochen Xie and Ruisi Nan and Zongming Tan and Baohua Tan and Neal N. Xiong}, doi = {10.1109/ACCESS.2025.3552090}, journal = {IEEE Access}, pages = {60440-60454}, shortjournal = {IEEE Access}, title = {YOLO-MES: An effective lightweight underwater garbage detection scheme for marine ecosystems}, volume = {13}, year = {2025}, }
• (2025). Neuro-based adaptive PID controller for marine satellite tracking systems. ACCESS, 13, 60424-60439. (www)

  @article{ , abstract = {Marine Satellite Tracking Antenna (MSTA) systems are used to communicate between marine vessels and geostationary satellites. These systems enable the use of critical communication channels such as telephone, TV and internet communication in marine vehicles such as boats, yachts and ships. For this reason, improving the performance of systems that can react in real time against the disruptive effects of sea waves is gaining importance. In this study, the design and implementation of an adaptive control system that trains a neural-based proportional integral derivative (PID-NN) controller by modeling with a real-time indirect neural network has been realized. The prominent feature of this work is that it offers an advantageous design in terms of price performance ratio as well as having a unique control design. In this study, all hardware and software components of a high-performance embedded system that, even in 20 degrees/second sea waves, can broadcast continuously were physically implemented with a single-core standard microcontroller unit (MCU) such as Arm Cortex-M4 Core, without the need for special peripherals such as neural processing unit (NPU). In addition, the performance results and implementation methods obtained from this study are discussed in comparison with a commercial product working in a similar field and other academic studies. While the maximum elevation error of the compared commercial product reaches up to 2 degrees in waves of similar intensity, it is observed that the error does not exceed 0.7 degrees under the same conditions in the design presented in this study.}, archive = {J_ACCESS}, author = {Cihan Yıldırım and Gökhan Böcekçi}, doi = {10.1109/ACCESS.2025.3558032}, journal = {IEEE Access}, pages = {60424-60439}, shortjournal = {IEEE Access}, title = {Neuro-based adaptive PID controller for marine satellite tracking systems}, volume = {13}, year = {2025}, }
• (2025). BedEye: A bed exit and bedside fall warning system based on skeleton recognition technology for elderly patients. ACCESS, 13, 60403-60423. (www)

  @article{ , abstract = {Falls are an important medical safety issue, and patients older than 65 years are the most prone to falling in hospitals. According to a previous study, approximately 80% of falls occur near hospital beds. Although many visual devices can be used to detect and prevent falls from a bed, these devices cannot accurately recognize and separate all movements of getting out of a bed. To solve this problem, this study proposes a skeleton identification technology-based early warning system named BedEye to detect bed exits and bedside falls in older patients. The main novelty of the proposed BedEye system lies in its application of skeleton recognition technology to accurately detect bed exits and prevent bedside falls among elderly patients. Traditional methods, including wearable and nonwearable systems, often face challenges such as high false alarm rates and difficulties in recognizing complex movements such as getting in and out of bed. The proposed BedEye system addresses these issues by combining AI-based skeleton identification with edge computing to ensure high accuracy in detecting all postures during bed exit movements. The proposed BedEye system innovatively utilizes OpenPose-light, which is a lightweight version of the OpenPose model optimized for edge computing. The proposed BedEye system processes real-time images captured by an RGB sensor, which are then fed into a deep learning model running locally on an Nvidia Jetson Xavier-NX edge computing device. This ensures faster processing with minimized delays and without reliance on cloud computing, which reduces network bandwidth use. Moreover, we develop an algorithm for determining bed posture using the posture of patients leaving the bed. BedEye was validated through both laboratory-based experiments and clinical human trials. Compared with previous solutions, the proposed BedEye system demonstrated effectiveness, with an accuracy rate of 97.4% for detecting bed exits and bedside falls in elderly patients, significantly improving the prediction of patient movements. This validation confirms the system’s high reliability in real-world healthcare settings.}, archive = {J_ACCESS}, author = {Liang-Bi Chen and Wan-Jung Chang and Tzu-Chin Yang}, doi = {10.1109/ACCESS.2025.3558004}, journal = {IEEE Access}, pages = {60403-60423}, shortjournal = {IEEE Access}, title = {BedEye: A bed exit and bedside fall warning system based on skeleton recognition technology for elderly patients}, volume = {13}, year = {2025}, }
• (2025). A compact and multi-coupled MIMO antenna system applicable to various electronic devices for sub6 GHz design. ACCESS, 13, 60387-60402. (www)

  @article{ , abstract = {This paper presents a multi-coupled Multiple-Input Multiple-Output (MIMO) antenna system designed for laptops, routers, and base stations, characterized by multi-band capability, compact size, low manufacturing complexity, cost efficiency, and broad applicability. The system integrates multiple antennas of identical structure, implemented on a single-layer FR4 substrate, and employs feed coupling through multiple microstrip line branches to support low-frequency bands, LTE, Sub-6 GHz, 5G FR1, and WiFi 6E. The antenna ensures stable performance across varying device sizes and placement conditions. With a compact dimension of $59\times 10$ mm2, the design is simplified, requiring no additional passive components, thereby reducing manufacturing costs. This makes it suitable for a wide range of applications, including laptops, routers, and base station devices. The MIMO antenna system achieves isolation below -10 dB, a maximum gain of 3.63 dBi, and a maximum efficiency of 85.3%, with an Envelope Correlation Coefficient (ECC) below 0.5 within the operating frequency bands, demonstrating excellent independent operation and spatial diversity. Simulated Specific Absorption Rate (SAR) and Power Density (PD) results indicate compliance with safety standards, posing no radiation hazard to human health. Experimental results validate the system’s adaptability to various devices, offering a stable, reliable, and cost-effective solution for multi-band wireless communication applications.}, archive = {J_ACCESS}, author = {Ming-An Chung and Shang-Jui Huang and Chia-Chun Hsu and Chia-Wei Lin}, doi = {10.1109/ACCESS.2025.3557608}, journal = {IEEE Access}, pages = {60387-60402}, shortjournal = {IEEE Access}, title = {A compact and multi-coupled MIMO antenna system applicable to various electronic devices for sub6 GHz design}, volume = {13}, year = {2025}, }
• (2025). Why consumers resist urban air mobility: A hybrid SEM-fsQCA approach. ACCESS, 13, 60374-60386. (www)

  @article{ , abstract = {Urban Air Mobility (UAM), represented by drones and electric Vertical Take-off and Landing (eVTOLs), has facilitated the emergence of a nascent and more sustainable intra- and inter-city transportation modality. Nevertheless, extant research has predominantly concentrated on consumers’ intentions to embrace UAM from the viewpoints of technology acceptance and diffusion, disregarding the crucial aspect of resistance. To address this disparity, this study put forward a framework integrating the Innovation Resistance Theory (IRT) and prior experience to examine the antecedents and potential strategies regarding UAM resistance. Data were collected via an online survey and subsequently analysed using a symmetrical PLS path model and an asymmetrical fuzzy set qualitative comparative analysis (fsQCA). It was disclosed that usage, value, risk, and tradition barriers, in conjunction with AV and trust, were significant antecedents of consumers’ resistance intention towards UAM. The results of fsQCA offered six sufficient solutions that might account for the resistance to UAM. All four perceived barriers constitute a sufficient condition for UAM, with the risk barrier being the most persuasive one, while Autonomous Vehicle (AV) experience and trust are only the peripheral causal conditions. The findings are anticipated to assist manufacturers, operators, and the government in mitigating consumers’ resistance towards UAM.}, archive = {J_ACCESS}, author = {Chuanhui Liao and Siqi Wang and Rengang Guo and Shuang Lin}, doi = {10.1109/ACCESS.2025.3555411}, journal = {IEEE Access}, pages = {60374-60386}, shortjournal = {IEEE Access}, title = {Why consumers resist urban air mobility: A hybrid SEM-fsQCA approach}, volume = {13}, year = {2025}, }
• (2025). Simulation and analysis of an integrated cardiopulmonary gas exchange model under different pathological conditions. ACCESS, 13, 60357-60373. (www)

  @article{ , abstract = {The development of individualised diagnosis and treatment plans depends on accurate modelling of cardiopulmonary dynamics. A comprehensive cardiopulmonary model that incorporates gas exchange, lung mechanics and cardiovascular interactions has been provided in this work. This model shows high accuracy after being carefully assessed against existing model and clinical observations. The essential physiological parameters are altered to imitate pathological situations such as hypoxia, hypercapnia, chronic obstructive pulmonary disease and pulmonary fibrosis. The findings provide quantitative insights into the progression of the disease by revealing apparent changes in ventilatory response, blood flow dynamics and alveolar gas exchange. These results help practitioners to diagnose diseases in advance.}, archive = {J_ACCESS}, author = {K. Anjana and Shantanu Saha and N. Selvaganesan}, doi = {10.1109/ACCESS.2025.3556023}, journal = {IEEE Access}, pages = {60357-60373}, shortjournal = {IEEE Access}, title = {Simulation and analysis of an integrated cardiopulmonary gas exchange model under different pathological conditions}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Fog offloading and task management in IoT-fog-cloud environment: Review of algorithms, networks and SDN application”. ACCESS, 13, 60355-60356. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Fog Offloading and Task Management in IoT-Fog-Cloud Environment: Review of Algorithms, Networks and SDN Application”).}, archive = {J_ACCESS}, author = {Mohammad Reza Rezaee and Nor Asilah Wati Abdul Hamid and Masnida Hussin and Zuriati Ahmad Zukarnain}, doi = {10.1109/ACCESS.2025.3554257}, journal = {IEEE Access}, pages = {60355-60356}, shortjournal = {IEEE Access}, title = {Corrections to “Fog offloading and task management in IoT-fog-cloud environment: Review of algorithms, networks and SDN application”}, volume = {13}, year = {2025}, }
• (2025). An optimized LSTM model for diagnosis prediction of lower respiratory tract infections using a minimalistic data set. ACCESS, 13, 60341-60354. (www)

  @article{ , abstract = {Data scarcity presents a significant challenge in developing effective data-driven diagnostic systems, particularly in healthcare settings where comprehensive feature capture is often limited. In medical contexts, vital features may be absent or unmeasurable due to constraints in laboratory capabilities and limited data collection infrastructure. This study addresses these limitations by deriving a minimalistic feature set to predict lower respiratory tract infections (LRTI’s), including diseases like pneumonia, bronchitis, and tuberculosis, using essential but widely available features. We employ age, gender, and initial diagnosis at admission—key indicators extracted from the MIMIC-III dataset—to train a Long Short-Term Memory (LSTM) model, demonstrating that minimal but relevant features can still yield accurate predictive outcomes. Compared to conventional models like Bi-LSTM, Random Forest, and XGBoost, our LSTM approach achieves superior training and validation performance, with an F1 score of 90% even without extensive optimization. The study validates our model across both LRTI and non-LRTI cases, underscoring its potential as an efficient, reliable diagnostic tool for resource-limited healthcare environments.}, archive = {J_ACCESS}, author = {Lukman Bukenya and Odongo Steven Eyobu and Tonny J. Oyana}, doi = {10.1109/ACCESS.2025.3540359}, journal = {IEEE Access}, pages = {60341-60354}, shortjournal = {IEEE Access}, title = {An optimized LSTM model for diagnosis prediction of lower respiratory tract infections using a minimalistic data set}, volume = {13}, year = {2025}, }
• (2025). Small force estimation and velocity limitation control for a tele-operated 6-DoF eye surgery robot. ACCESS, 13, 60327-60340. (www)

  @article{ , abstract = {When performing eye surgery, surgeons traditionally rely on visual feedback through needle insertion to sense tissue. Particularly, in vitreoretinal surgery, the contact force exerted by a surgical robot to avoid damaging healthy tissue is very small and can be below that which a surgeon is able to sense. In this paper, we introduce the small frequency sliding perturbation observer (SFSPO), which is a technique for estimating the very small contact force exerted when the tip of a surgical tool of a 6-Degree of Freedom (DoF) eye surgery robot touches tissue. This contact force is estimated without using any force/torque sensor, and the small feedback force is amplified, meaning the surgeon can feel it as a large reaction force. Furthermore, during lengthy microscopic surgery procedures, surgeons can experience fatigue and tiredness due to the intense focus required. This may result in damage to healthy tissue due to sudden changes in manipulation and the exertion of excessive force. To avoid this problem, force and velocity limitation control is applied. Simulations are conducted, and the experimental results indicate that very small forces below the perception of the surgeon can be sensed using the SFSPO. We have estimated minimum force which is 0.001 N by using SFSPO which is underneath the perception of surgeon. The results also verify successful implementation of the force and velocity limitation control.}, archive = {J_ACCESS}, author = {Karam Dad Kallu and Chenyu Wang and Gowoon Jeong and Seong Young Ko and Amad Zafar and Muhammad Umair Ali and Hamza Khan}, doi = {10.1109/ACCESS.2025.3554011}, journal = {IEEE Access}, pages = {60327-60340}, shortjournal = {IEEE Access}, title = {Small force estimation and velocity limitation control for a tele-operated 6-DoF eye surgery robot}, volume = {13}, year = {2025}, }
• (2025). A comparative study on various au wire rinse compositions and their effects on the electronic flame-off errors of wire-bonding semiconductor package. ACCESS, 13, 60317-60326. (www)

  @article{ , abstract = {In this study, we identify the origin of electronic flame-off (EFO) errors based on the type of rinse applied for the surface treatment of gold (Au) wires in the wire-bonding semiconductor packaging process. Hydrocarbon and silicone surfactants are commonly used for Au wire surface treatment, and five different 1.0 wt% rinse solutions with varying compositions are applied to the Au wire surface. To determine the origin of EFO errors, an experiment is conducted to examine the reactivity of the rinse-coated Au wires with silicon (Si)-containing dust, which is inevitably generated in the wire-bonding process. Optical microscopy and field-emission scanning electron microscopy show that a large amount of dust is adsorbed onto the surface of the Au wire coated with a Si-containing rinse. This is attributed to the Si component in the rinse, which interacts with the electric arc during the EFO process. Si-CH3 bonds break at a high thermal energy, thereby enabling the formation of low-activation-energy nonpolar $\sigma $ bonds with Si-containing dust. As these defects negatively impact productivity and reliability, we suggest using Si-free organic rinses for Au wire surface treatment in the wire-bonding process to reduce the defect rates associated with contamination.}, archive = {J_ACCESS}, author = {Jisu Lim and Wonbin Kim and Suk Jekal and Jiwon Kim and Ha-Yeong Kim and Zambaga Otgonbayar and Jeoung Han Kim and Jinsung Rho and Woo-Jin Song and Chang-Min Yoon}, doi = {10.1109/ACCESS.2025.3551884}, journal = {IEEE Access}, pages = {60317-60326}, shortjournal = {IEEE Access}, title = {A comparative study on various au wire rinse compositions and their effects on the electronic flame-off errors of wire-bonding semiconductor package}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection over multi-relational graphs using graph structure learning and multi-scale meta-path graph aggregation. ACCESS, 13, 60303-60316. (www)

  @article{ , abstract = {Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly detection problems on real-world datasets presents several challenges that significantly hinder performance: the structural noise and inconsistencies inherent in real-world graph data, difficulties in aggregating information across multiple relation types, and the imbalanced class labels. To address these limitations, we introduce a graph structure learning layer designed to refine the original, noisy graph structure, enhancing the representation of node relationships. This enables our model to effectively handle inconsistencies and structural noise present in the original graph data with the augmented graph topology. To further exploit the semantic information embedded within the multi-relational graph data, we developed a multi-scale aggregation layer that facilitates information propagation and fusion through multi-relational levels, utilizing automatically and implicitly extracted meta-path graphs. Additionally, to handle the impact of imbalanced class labels, which often degrades the classification performance of GNN-based approaches, we introduce a score reweighting mechanism that balances probability scores between minority and majority classes. This strategy improves the model’s learning ability, leading to improved class distinction. Comprehensive experiments are conducted using two popular real-world review datasets to validate the effectiveness of our proposed model. Empirical results demonstrate that our approach consistently outperforms state-of-the-art baselines across three evaluation metrics, namely Average Precision, AUC and GMean.}, archive = {J_ACCESS}, author = {Chi Zhang and Junho Jeong and Jin-Woo Jung}, doi = {10.1109/ACCESS.2025.3554407}, journal = {IEEE Access}, pages = {60303-60316}, shortjournal = {IEEE Access}, title = {Anomaly detection over multi-relational graphs using graph structure learning and multi-scale meta-path graph aggregation}, volume = {13}, year = {2025}, }
• (2025). Location allocation model for community centers using GIS and multi-objective goal programming in war weary area: A case study in syria. ACCESS, 13, 60284-60302. (www)

  @article{ , abstract = {This research addresses the critical challenges faced by communities in Idleb, Syria, particularly the barriers children encounter in accessing basic services due to the ongoing conflict. We propose a comprehensive methodology that integrates Geographic Information Systems (GIS), multi-objective goal programming (using both weighted and preemptive approaches), and the Analytic Hierarchy Process (AHP) to optimize the location allocation of community centers. These centers aim to provide essential support and rehabilitation services for affected children. By engaging local stakeholders, the proposed approach ensures practical alignment with on-ground realities. The findings, validated through sensitivity analyses conducted on the GAMS platform, demonstrate the model’s effectiveness and offer a practical framework for humanitarian interventions in similar crisis-affected regions globally. This GIS-based multi-objective goal programming model, integrated with AHP, optimizes community center locations in conflict zones by maximizing coverage and minimizing costs, serving as a valuable tool for humanitarian logistics in comparable global contexts.}, archive = {J_ACCESS}, author = {Abdulhamit Ertuğrul and Süleyman Mete and Eren Özceylan}, doi = {10.1109/ACCESS.2025.3553816}, journal = {IEEE Access}, pages = {60284-60302}, shortjournal = {IEEE Access}, title = {Location allocation model for community centers using GIS and multi-objective goal programming in war weary area: A case study in syria}, volume = {13}, year = {2025}, }
• (2025). Toward real-time recognition of continuous indian sign language: A multi-modal approach using RGB and pose. ACCESS, 13, 60270-60283. (www)

  @article{ , abstract = {Sign language recognition (SLR) involves translating visual gestures into meaningful text or speech, bridging the communication gap between signers and non-signers. However, real-time recognition remains a critical challenge due to variability in signing speed, subtle hand gestures, and the computational complexity of processing video data in real time. Existing methods often rely on encoding RGB videos into latent representations, making them unsuitable for real-time interaction. To address these challenges, we present SignFlow, a network for real-time recognition of continuous sign gestures. Our approach introduces a novel pre-processing technique to down-sample video streams, ensuring compatibility with varying frame rates across different devices. For the first time, the core components of our network are pre-trained on domain-specific Indian Sign Language (ISL) data. The CNN is pre-trained using ISL word videos, while the Transformer is pre-trained on Mediapipe pose estimates from ISL videos. This pretraining effectively captures the nuances of hand shapes and body movements unique to ISL, significantly enhancing sentence recognition. SignFlow combines a pre-trained CNN for feature extraction and a Transformer for learning the temporal dynamics of continuous signing. The framework is fine-tuned end-to-end using Connectionist Temporal Classification. For evaluating the real-time models, we have introduced a detection rate metric, which measures how accurately individual gestured words are recognized within a sequence, regardless of their order. SignFlow achieves a Word Error Rate (WER) of 19 on the Continuous ISL dataset, demonstrating its effectiveness for real-time ISL recognition. Additionally, it shows competitive performance on the German Phoenix 2014 and Phoenix 2014T datasets.}, archive = {J_ACCESS}, author = {M. Geetha and Neena Aloysius and Darshik A. Somasundaran and Amritha Raghunath and Prema Nedungadi}, doi = {10.1109/ACCESS.2025.3554618}, journal = {IEEE Access}, pages = {60270-60283}, shortjournal = {IEEE Access}, title = {Toward real-time recognition of continuous indian sign language: A multi-modal approach using RGB and pose}, volume = {13}, year = {2025}, }
• (2025). Binarized neural networks for resource-efficient spike sorting. ACCESS, 13, 60258-60269. (www)

  @article{ , abstract = {Deep learning is fastly gaining ground in neuroscience. In the field of implantable brain computer interfaces, a fundamental application of deep learning is to sort action potentials (known as spikes), measured with extracellular electrodes, according to their origin neurons. This enables the generation of precise modulatory patterns of neuronal circuits. Deep learning-based spike sorting algorithms are based on power-intensive dot products, which poses challenges for on-chip processing with resource-constrained devices. In contrast, binarized neural networks offer great potential for on-chip sorting, mainly relying on bitwise operations and accumulations. However, recently published binarized models perform significantly worse than deep full-precision networks and fail on challenging neural data. This work presents a binarized neural network for spike sorting that narrows the performance gap between recently developed binarized models and more accurate full-precision models. The novelty of this work resides in the developed network architecture. In comparison to previous research, this work presents a deep binarized neural network featuring two hidden layers, each containing 256 units to effectively capture the spike characteristics of complex neural data. Before training, spikes were pre-sorted in an unsupervised way to generate pseudo-labels. Subsequently, the deep binarized model and an equally sized full-precision model were trained and evaluated using experimentally obtained and synthetic spike waveforms. The proposed binarized model could achieve results close to more advanced network types, such as convolutional and long short-term memory networks, which is remarkable considering that the binarized model was primarily designed to maintain a balance between resource consumption and accuracy. The equally sized full-precision model could even outperform the aforementioned models, despite its much lighter architecture.}, archive = {J_ACCESS}, author = {Luca M. Meyer and Majid Zamani and Andreas Demosthenous}, doi = {10.1109/ACCESS.2025.3552008}, journal = {IEEE Access}, pages = {60258-60269}, shortjournal = {IEEE Access}, title = {Binarized neural networks for resource-efficient spike sorting}, volume = {13}, year = {2025}, }
• (2025). Numerical modeling of PMSM noise reduction by harmonic current injection. ACCESS, 13, 60245-60257. (www)

  @article{ , abstract = {In this paper, harmonic components are injected into the phase current of a Permanent Magnet Synchronous Motor (PMSM) used in automotive applications to improve the Noise Vibration and Harshness (NVH) performance. The study proposes and validates a numerical method for modeling this phenomenon. The strategy was first applied experimentally. Subsequently, the experimental currents were measured and used as input for the numerical models developed in Flux2D (Registered trademark) and Abaqus (Registered trademark). The harmonic contributions of the experimental currents, obtained from the inverter control strategy, are analysed and adopted as inputs to the electromagnetic model to calculate the forces. These are applied to the Finite Element Model (FEM), which returns the values of vibration and acoustic emission of the motor as the injection strategy is varied. The numerical results are then compared and validated with the experimental data. The strategy is applied as an NVH countermeasure to a specific time order that is particularly noisy for the analyzed PMSM. This order turns out to be dominated by a spatial 0 mode also referred to as the breathing mode. The phase of the injected current harmonic is varied between 0 and $2\pi $ . For a specific phase angle, a minimum of the acoustic emission level is reached. The application of the strategy experimentally shows a maximum reduction of 29 dB and 27 dB from the vibration and acoustic levels respectively. Numerical models are able to capture the physics of the system, reproducing the variation in acoustic emission as a function of the phase angle of the injected current. Numerical results are close to the experimental data, with a maximum difference of 3dB on the optimal vibration reduction condition.}, archive = {J_ACCESS}, author = {Federico Soresini and Dario Barri and Federico Ballo and Massimiliano Gobbi}, doi = {10.1109/ACCESS.2025.3556334}, journal = {IEEE Access}, pages = {60245-60257}, shortjournal = {IEEE Access}, title = {Numerical modeling of PMSM noise reduction by harmonic current injection}, volume = {13}, year = {2025}, }
• (2025). PFML: Self-supervised learning of time-series data without representation collapse. ACCESS, 13, 60233-60244. (www)

  @article{ , abstract = {Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process. In contrast to supervised learning, which depends on external labels, SSL utilizes the inherent characteristics of the data to produce its own supervisory signal. However, one frequent issue with SSL methods is representation collapse, where the model outputs a constant input-invariant feature representation. This issue hinders the potential application of SSL methods to new data modalities, as trying to avoid representation collapse wastes researchers’ time and effort. This paper introduces a novel SSL algorithm for time-series data called Prediction of Functionals from Masked Latents (PFML). Instead of predicting masked input signals or their latent representations directly, PFML operates by predicting statistical functionals of the input signal corresponding to masked embeddings, given a sequence of unmasked embeddings. The algorithm is designed to avoid representation collapse, rendering it straightforwardly applicable to different time-series data domains, such as novel sensor modalities in clinical data. We demonstrate the effectiveness of PFML through complex, real-life classification tasks across three different data modalities: infant posture and movement classification from multi-sensor inertial measurement unit data, emotion recognition from speech data, and sleep stage classification from EEG data. The results show that PFML is superior to a conceptually similar SSL method and a contrastive learning-based SSL method. Additionally, PFML is on par with the current state-of-the-art SSL method, while also being conceptually simpler and without suffering from representation collapse. The code is freely available at https://github.com/SPEECHCOG/PFML.}, archive = {J_ACCESS}, author = {Einari Vaaras and Manu Airaksinen and Okko Räsänen}, doi = {10.1109/ACCESS.2025.3556957}, journal = {IEEE Access}, pages = {60233-60244}, shortjournal = {IEEE Access}, title = {PFML: Self-supervised learning of time-series data without representation collapse}, volume = {13}, year = {2025}, }
• (2025). An attention mechanism-based interpretable model for epileptic seizure detection and localization with self-supervised pre-training. ACCESS, 13, 60213-60232. (www)

  @article{ , abstract = {Epilepsy, characterized by unpredictable seizures caused by abnormal brain activity, presents significant diagnostic challenges due to the complexity of interpreting electroencephalogram (EEG) signals. Current EEG analysis methods rely heavily on manual annotations by neurophysiologists, limiting scalability and efficient utilization of vast clinical datasets. In this study, we propose a self-supervised learning (SSL) model for seizure detection and localization, designed to reduce dependence on labeled data while maintaining high performance. The pretext task of signal transformation recognition enables the model to learn generalized spatiotemporal features from EEG data, effectively leveraging unlabeled recordings. This is followed by fine-tuning with a limited set of labeled data for precise seizure detection. Our model integrates an attention mechanism that enhances interpretability by focusing on the most relevant EEG electrodes, improving both detection accuracy and seizure localization. The proposed solution demonstrates competitive performance on the CHB-MIT dataset, achieving a sensitivity of 93.1% and an AUC of 91% with only 30% labeled data, and consistent localization accuracy of 83.33%. Extensive experiments across multiple datasets and under various configurations validate the robustness and generalizability of the proposed approach. This work not only introduces a data-efficient and interpretable solution for EEG-based seizure detection but also sets the stage for future research on applying SSL to broader neurological disorders, opening new avenues for trustworthy and optimal medical diagnostics.}, archive = {J_ACCESS}, author = {Ghita Amrani and Amina Adadi and Mohammed Berrada}, doi = {10.1109/ACCESS.2025.3556811}, journal = {IEEE Access}, pages = {60213-60232}, shortjournal = {IEEE Access}, title = {An attention mechanism-based interpretable model for epileptic seizure detection and localization with self-supervised pre-training}, volume = {13}, year = {2025}, }
• (2025). Forward kinematics for parallel platforms based on the integration of residual networks with self-attention and error compensation. ACCESS, 13, 60200-60212. (www)

  @article{ , abstract = {Parallel platforms are widely used in modern industry due to their superior dynamic and static properties. However, different parallel platforms have different closure structures, and their forward kinematics equations have high nonlinearity, leading to poor generalization, low efficiency, and precision in solving these equations. This study presents a novel RNSA-EC algorithm designed to enhance the computational precision and efficiency of forward kinematics for parallel platforms. The algorithm integrates a Self-Attention optimized Residual Network (RNSA) with an Error Compensation (EC) method. The RNSA model initially achieves high-precision nonlinear mapping from joint coordinates to pose coordinates of parallel platforms. Subsequently, the inverse kinematic model is employed to develop an EC method, which further reduces computational errors in the forward kinematics of the parallel platform by iteratively updating the inputs to the RNSA model. We applied the RNSA-EC algorithm to solve the forward kinematics of two 6-degree-of-freedom (6-DOF) parallel platforms with distinct structures. The results demonstrate that the algorithm is approximately 60%, 50%, and 45% more efficient than the Newton-Raphson (NR), the hybrid Backpropagation Neural Network (BPNN)-NR, and the Iterative Artificial Neural Network (IANN) methods, respectively, on the two parallel platforms. Furthermore, the algorithm can be extended to solve forward kinematics for parallel platforms with different structures and DOFs.}, archive = {J_ACCESS}, author = {Yi Hu and Fan Chen and Bo Zhu and Jin Jiang Liu and Wei Bing Cheng and Zhong Qing Zhang and Shijie Su}, doi = {10.1109/ACCESS.2025.3549796}, journal = {IEEE Access}, pages = {60200-60212}, shortjournal = {IEEE Access}, title = {Forward kinematics for parallel platforms based on the integration of residual networks with self-attention and error compensation}, volume = {13}, year = {2025}, }
• (2025). Complex path planning for manipulators based on an improved NSGA-III algorithm with hopfield network. ACCESS, 13, 60186-60199. (www)

  @article{ , abstract = {In various production applications across industries such as manufacturing and agriculture, manipulators often face complex trajectories composed of numerous locally discrete paths during movement. To enhance planning efficiency and convergence accuracy, this paper proposes an improved NSGA-III multi-objective optimization algorithm based on Hopfield neural network. First, we construct an optimization objective function considering three dimensions: the length of the manipulator’s motion path, motion time, and joint impact. We introduce a Hopfield neural network to replace the initial random sampling of the NSGA-III algorithm, allowing for the rapid generation of an initial optimized population. Based on non-dominance relationships, we use reference points for population crossover, mutation, and selection, thereby accelerating convergence to the optimal solution. Additionally, we employ seventh-degree polynomial smooth interpolation to improve the geometric properties of the trajectory. The main contributions include: By utilizing the energy gradient of the Hopfield neural network, we rapidly reduce the random sampling space formed by the multi-objective functions along the gradient direction towards the suboptimal solution set. This approach effectively eliminates a significant number of inferior solutions during the iterations of NSGA-III, leading to a marked improvement in the efficiency of generating the Pareto front. Specifically, the optimization efficiency is enhanced by 45.86%, the length of the robot’s motion path is reduced by 77.9%, motion efficiency is increased by 51.89%, and joint motion stability is improved by 69.45%. The feasibility of the algorithm has been validated through simulations and experiments in complex three-dimensional motion scenarios.}, archive = {J_ACCESS}, author = {Xiaoxiang Sun and Jue Wang and Wei Wang}, doi = {10.1109/ACCESS.2025.3556586}, journal = {IEEE Access}, pages = {60186-60199}, shortjournal = {IEEE Access}, title = {Complex path planning for manipulators based on an improved NSGA-III algorithm with hopfield network}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on coverage path planning for mobile robots in dynamic environments. ACCESS, 13, 60158-60185. (www)

  @article{ , abstract = {Coverage Path Planning (CPP) is a fundamental aspect of mobile robotics, enabling robots to navigate dynamic environments efficiently while ensuring thorough coverage of all areas of interest and avoiding obstacles. CPP is pivotal in diverse applications, from everyday tasks like vacuum cleaning, lawn mowing, and window cleaning to specialized operations such as demining hazardous areas, autonomous underwater imaging, and inspecting complex structures. This survey reviews recent advancements in CPP, focusing on algorithms and methodologies tailored for dynamic environments. It highlights the challenges posed by environmental variability, obstacle dynamics, and real-time computational demands. By synthesizing insights from existing research, this survey aims to guide future developments in CPP, paving the way for smarter and more adaptive robotic systems capable of handling the complexities of real-world scenarios.}, archive = {J_ACCESS}, author = {K. P. Jayalakshmi and Vishnu G. Nair and Dayakshini Sathish}, doi = {10.1109/ACCESS.2025.3556446}, journal = {IEEE Access}, pages = {60158-60185}, shortjournal = {IEEE Access}, title = {A comprehensive survey on coverage path planning for mobile robots in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Optimal control of intelligent vehicle path tracking. ACCESS, 13, 60148-60157. (www)

  @article{ , abstract = {Path tracking control is a core technology of intelligent vehicles, and the accuracy of tracking performance is a key element of this technology. In order to address the issue of low accuracy in traditional methods for solving intelligent vehicle path tracking problems, a fast and high-precision solution method was design. This method discretized the continuous optimal control problem at collocation points and approximated the state and control variables through global interpolation polynomials, thereby transforming the optimal control problem of intelligent vehicle path tracking into a nonlinear programming NLP problem for solution. Finally, the real vehicle test verified the effectiveness of the method. The simulation results and comparison with traditional methods show that this method can solve the intelligent vehicle path tracking problem with high accuracy and has good engineering application potential.}, archive = {J_ACCESS}, author = {Enhao Wang and Yingjie Liu and Chenglian Xie}, doi = {10.1109/ACCESS.2025.3557215}, journal = {IEEE Access}, pages = {60148-60157}, shortjournal = {IEEE Access}, title = {Optimal control of intelligent vehicle path tracking}, volume = {13}, year = {2025}, }
• (2025). Cross-method overview of fleet-based machine health estimation and prediction: A practical guide for industrial applications. ACCESS, 13, 60131-60147. (www)

  @article{ , abstract = {A reliable assessment of industrial machine health is crucial for economical and safe operation. To this end, data-driven approaches have gained prominence owing to the advancements in data acquisition and machine learning techniques. However, practical applications of these approaches often confront the challenge of data scarcity, due to heterogeneity among machines. To address the data scarcity problem, this study delves into health estimation and prediction methods that utilize fleet data. Unlike existing review papers that mainly focus on one specific fleet-based method, this work offers a cross-method overview. The methods are classified into six categories. All share three steps: data selection, model development, and model adjustment. This work also provides a step-by-step guide for industry practitioners to incorporate fleet knowledge, which emphasizes business requirements and highlights an iterative method development process. It helps industrial practitioners navigate through the complexities of various approaches to utilize fleet knowledge, paving the way to bring advanced methods to industrial implementations.}, archive = {J_ACCESS}, author = {Xuqian Yan and Janis Woelke and Boris Bensmann and Christoph Eckert and Richard Hanke-Rauschenbach and Astrid Nieße}, doi = {10.1109/ACCESS.2025.3556251}, journal = {IEEE Access}, pages = {60131-60147}, shortjournal = {IEEE Access}, title = {Cross-method overview of fleet-based machine health estimation and prediction: A practical guide for industrial applications}, volume = {13}, year = {2025}, }
• (2025). The mixed partition dimension: A new resolvability parameter in graph theory. ACCESS, 13, 60122-60130. (www)

  @article{ , abstract = {In this article, we introduce a novel graph-theoretical parameter called the mixed partition dimension and apply it to the path graph and the hexagonal network. This parameter builds on the concept of resolvability in graphs, integrating vertex-based partition dimensions with edge-oriented strategies to characterize the complexity of graph structures. It is the extension of the mixed metric dimension and partition dimension. Suppose Let $R = \{W_{1}, W_{2}, {\dots }, W_{k}\}$ be a partition of the vertex set $V(G)$ of a graph $G = (V, E)$ , where $W_{1} \cup W_{2} \cup {\dots } \cup W_{k} = V(G)$ and $ W_{i} \cap W_{j} = \emptyset ~ \text {for}~ i \neq j$ . Each subset $W_{i}$ is non-empty, mutually disjoint, and collectively covers all vertices. The partition set ${R}_{mp}$ is called mixed resolving partition set if it satisfies the condition. For any two distinct vertices $u, v \in V(G)$ , there exists $W_{i} \in R$ such that: $d(u, W_{i}) \neq d(v, W_{i})$ , for any two distinct edges $e_{1}, e_{2} \in E(G)$ , there exists $W_{i} \in R$ such that: $d(e_{1}, W_{i}) \neq d(e_{2}, W_{i})$ and for any vertex $u \in V(G)$ and edge $e \in E(G)$ , there exists $W_{i} \in R$ such that: $d(u, {W}_{i}) \neq d(e, W_{i})$ . The mixed partition dimension of G is the minimum number of subsets in a mixed resolving partition set ${R}_{mp}$ . This parameter provides a unified measure of a graph’s complexity by accounting for both vertex and edge distinguishability, offering new insights into the structure of complex networks.}, archive = {J_ACCESS}, author = {Siti Norziahidayu Amzee Zamri and Sikander Ali and Muhammad Azeem and Husam A. Neamah and Bandar Almohsen}, doi = {10.1109/ACCESS.2025.3534819}, journal = {IEEE Access}, pages = {60122-60130}, shortjournal = {IEEE Access}, title = {The mixed partition dimension: A new resolvability parameter in graph theory}, volume = {13}, year = {2025}, }
• (2025). An optimal two-step approach for defense against poisoning attacks in federated learning. ACCESS, 13, 60108-60121. (www)

  @article{ , abstract = {Federated learning (FL) has gained widespread adoption for training artificial intelligence (AI) models while ensuring the confidentiality of client data. However, this privacy-preserving nature of FL also makes it vulnerable to poisoning attacks. To counter these attacks, several defense methods have been developed to identify and filter out poisoned local models before the aggregation process. Nevertheless, these defense methods demonstrate sub-optimal performance in retaining benign local models while discarding poisoned local models, primarily due to inadequate filtering strategies. Consequently, these defense methods filter out large proportions of benign local models that are not poisoned, resulting in high false rejection rates or low detection accuracy, which leads to test accuracy degradation of the global model as well. In this paper, we propose the Two-step Defense Framework for Poisoning Attacks Detection (TDF-PAD), which first identifies the obvious-poisoned, obvious-benign, and ambiguous local models by utilizing the inter-quartile range method. The second step employs the Z-score method to classify ambiguous local models into benign or poisoned local models based on their performance history. Through extensive experimentation on three real-world benchmark datasets, we demonstrate that TDF-PAD outperforms state-of-the-art defense methods by achieving a 0% false positive rate on these benchmark datasets, showing it is generally applicable to any dataset.}, archive = {J_ACCESS}, author = {Yasir Ali and Kyung Hyun Han and Abdul Majeed and Joon S. Lim and Seong Oun Hwang}, doi = {10.1109/ACCESS.2025.3556906}, journal = {IEEE Access}, pages = {60108-60121}, shortjournal = {IEEE Access}, title = {An optimal two-step approach for defense against poisoning attacks in federated learning}, volume = {13}, year = {2025}, }
• (2025). Research on the factors and pathways affecting users’ willingness to use AR animations for science popularization: An extension of the technology acceptance model. ACCESS, 13, 60088-60107. (www)

  @article{ , abstract = {The rise of augmented reality (AR) technology offers a new viewpoint and innovative approach to the sharing and understanding of science education. AR animations for science popularization transform educational content into animated formats and utilize interactive presentation techniques made possible by AR. This approach improves users’ comprehension, enjoyment, and motivation, encouraging them to pursue further learning, exploration, and engagement in experiences. Nonetheless, research on the factors that shape users’ experiences with AR animations for science popularization is limited, and examining the elements that influence users’ willingness to engage and enhance their behavioral intentions is an area that necessitates further exploration. This study proposes an extended Technology Acceptance Model (TAM) that integrates user perception, psychological experiences, and technology acceptance-related factors to predict the engagement of AR animations for science popularization. The study investigates the pathways connecting these factors and puts forward 15 associated hypotheses. Data analysis from 347 respondents indicates that entertainment, interactivity, visual appeal, self-efficacy, perceived usefulness, perceived ease of use, and attitude are significant factors affecting users’ willingness to engage. The research results not only validate the impact of these factors on users’ willingness to use but also provide practical and feasible improvement strategies with practical insights for the design of AR animations for science popularization, the teaching practices of educators, and the innovative directions for technology developers. Especially in the field of science education, the findings of this study have strong practical value and provide theoretical support and practical evidence for the future promotion of AR technology in education.}, archive = {J_ACCESS}, author = {Xu Zhang and Kai Zhang and Lei Liang and Ji Zhang and Junming Chen}, doi = {10.1109/ACCESS.2025.3557006}, journal = {IEEE Access}, pages = {60088-60107}, shortjournal = {IEEE Access}, title = {Research on the factors and pathways affecting users’ willingness to use AR animations for science popularization: An extension of the technology acceptance model}, volume = {13}, year = {2025}, }
• (2025). Predict+Optimize problem in renewable energy scheduling. ACCESS, 13, 60064-60087. (www)

  @article{ , abstract = {Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.}, archive = {J_ACCESS}, author = {Christoph Bergmeir and Frits de Nijs and Evgenii Genov and Abishek Sriramulu and Mahdi Abolghasemi and Richard Bean and John Betts and Quang Bui and Nam Trong Dinh and Nils Einecke and Rasul Esmaeilbeigi and Scott Ferraro and Priya Galketiya and Robert Glasgow and Rakshitha Godahewa and Yanfei Kang and Steffen Limmer and Luis Magdalena and Pablo Montero-Manso and Daniel Peralta and Yogesh Pipada Sunil Kumar and Alejandro Rosales-Pérez and Julian Ruddick and Akylas Stratigakos and Peter Stuckey and Guido Tack and Isaac Triguero and Rui Yuan}, doi = {10.1109/ACCESS.2025.3555393}, journal = {IEEE Access}, pages = {60064-60087}, shortjournal = {IEEE Access}, title = {Predict+Optimize problem in renewable energy scheduling}, volume = {13}, year = {2025}, }
• (2025). Reliability prediction based on multi-state dynamic bayesian network structure learning for complex system with time-lagged correlation. ACCESS, 13, 60040-60063. (www)

  @article{ , abstract = {Due to the complicated interaction in complex systems, correlations between modules are nonlinear and relate to time intervals. In this paper, a novel correlation analysis method based on cross correlation analysis and using two parameter matrixes to describe correlation with time-lag appropriately in multistate complex systems is proposed. Then the judgement criterion of independent, simultaneous correlation and time-lagged correlation is given. To structure the model to describe the time-lagged correlation, the Time-lagged Dynamic Bayesian Network (TDBN) is built, meanwhile the modelling and calculation method are provided. According to the parameter comparation among time-lagged correlation analysis and Maximum Likelihood Estimation (MLE), the correlation judgment is deduced. To make sure the time-lagged correlation analysis is compatible with structure learning methods used currently, the data preprocessing method, corresponding model transformation method and restoration method are proposed. Through the analysis of the data of dismantled smart electronic meters, the correlation estimated method, the TDBN model, and the data progressing method proposed for electronic system in this paper are proved. It also proposed ideas for system reliability modeling in other industrial scenarios.}, archive = {J_ACCESS}, author = {Kaixin Liu and Weilu Chen}, doi = {10.1109/ACCESS.2025.3554333}, journal = {IEEE Access}, pages = {60040-60063}, shortjournal = {IEEE Access}, title = {Reliability prediction based on multi-state dynamic bayesian network structure learning for complex system with time-lagged correlation}, volume = {13}, year = {2025}, }
• (2025). Optimal power flow: A review of state-of-the-art techniques and future perspectives. ACCESS, 13, 60012-60039. (www)

  @article{ , abstract = {The Optimal Power Flow (OPF) problem has become increasingly pivotal in the planning and operation of modern power systems. With the expansion of the grid scale, the advent of smart grid technologies, and the unpredictable nature of renewable energy sources (RESs), interest in OPF has surged. These challenges with new energy storage have introduced a heightened level of uncertainty into the power system’s operation as well as planning. Because of this, OPF is seen as an important tool for achieving different goals, such as optimizing the distribution of resources, making electrical networks more efficient, and so on. However, the OPF problem is inherently difficult to solve because of its non-linear characteristics. Different constraints and limitations intrinsic to real power system grids further accentuate this complexity. Moreover, modern power systems have incorporated new constraints, which make the OPF problem more complex in terms of mathematical formulation and solution. This paper offers a comprehensive and foundational review of OPF, covering the main concept, mathematical formulation, OPF types, comprehensive OPF optimization problem concepts, and the various methods developed to solve it. Additionally, it explores the evolution of these methods from conventional approaches to advanced and recent techniques, including mathematical methods and artificial intelligence methods, which include metaheuristic (search-based) and machine learning algorithms (data-driven). The paper also discusses various types of convex relaxation methods in depth. Ultimately, the paper highlights key gaps, challenges, and opportunities for future research.}, archive = {J_ACCESS}, author = {Ahmed Babiker and Sulaiman S. Ahmad and Ijaz Ahmed and Muhammad Khalid and Mohammad A. Abido and Fahad Saleh Al-Ismail}, doi = {10.1109/ACCESS.2025.3556168}, journal = {IEEE Access}, pages = {60012-60039}, shortjournal = {IEEE Access}, title = {Optimal power flow: A review of state-of-the-art techniques and future perspectives}, volume = {13}, year = {2025}, }
• (2025). Effects of varying operating conditions of different renewable energy sources on steady state, fault performance, and transient stability of power systems. ACCESS, 13, 59991-60011. (www)

  @article{ , abstract = {Performance issues may arise with the increased integration of renewable energy sources (RESs). In this paper, ETAP analysis tools are used to examine IEEE 9-bus system by integrating various RESs, including photovoltaic (PV) panels, two types of wind turbines (WTs), and hybrid configurations. To assess steady-state performance, the operating conditions are varied including changes in RES types, penetration levels, power factors, and topology. Improved voltage stability and reduced power losses can be attained by implementing optimal operation techniques and utilizing RESs with controllable volt/var capabilities to adjust the power factor. Further, fault analysis at different buses reveals that the presence of RESs can significantly alter fault currents, potentially causing maloperation of protective devices. When RESs are distributed among multiple buses, fault currents in the lines close to RESs increase while those in distant lines decrease by 50% in certain cases. Additionally, the behavior of the system under transient events-including faults, loss of generation units, and sudden load changes-is examined. The findings indicate that while RES integration improves the overall stability, notable differences exist between PV- and wind-based sources. Specifically, the power system may overcompensate for the lack of inertia-support in PV-based sources, leading to a speed drop below the nominal value of the swing bus. Therefore, adaptive protection schemes and inertia-support capabilities are required when RESs are integrated into existing grids to provide accurate protection operations and stability support particularly during major disturbances.}, archive = {J_ACCESS}, author = {Sultan Sh. Alanzi and Rashad M. Kamel and Yazen Kh. Abualia}, doi = {10.1109/ACCESS.2025.3555847}, journal = {IEEE Access}, pages = {59991-60011}, shortjournal = {IEEE Access}, title = {Effects of varying operating conditions of different renewable energy sources on steady state, fault performance, and transient stability of power systems}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of hybrid bio-inspired and deep learning algorithms in gene selection and cancer classification. ACCESS, 13, 59977-59990. (www)

  @article{ , abstract = {Cancer classification based on gene expression data is a critical challenge in modern bioinformatics, requiring efficient and accurate feature selection methods. This study explores the performance of hybrid bio-inspired algorithms and deep learning techniques for gene selection and cancer classification. Hybrid bio-inspired methods, inspired by natural optimization processes, have demonstrated significant advantages in navigating high-dimensional genomic data. Meanwhile, deep learning models excel in pattern recognition and automated feature extraction, offering a complementary approach to traditional gene selection techniques. This paper systematically evaluates both approaches, highlighting their strengths and limitations in terms of classification accuracy, computational efficiency, and feature selection effectiveness. Our findings reveal that hybrid bio-inspired methods, such as Grey Wolf Optimizer and Harris Hawks Optimization, achieve high classification accuracy with minimal selected genes, making them computationally efficient for clinical applications. Conversely, deep learning models, including convolutional neural networks and autoencoders, demonstrate superior feature extraction but often require larger datasets and higher computational resources. By providing a comparative analysis, this study aims to guide researchers and clinicians in selecting the most suitable approach for cancer classification tasks. The results underscore the potential of hybrid methodologies in advancing precision oncology while identifying opportunities for future improvements in deep learning-based feature selection.}, archive = {J_ACCESS}, author = {Shahad S. Alkamli and Hala M. Alshamlan}, doi = {10.1109/ACCESS.2025.3556816}, journal = {IEEE Access}, pages = {59977-59990}, shortjournal = {IEEE Access}, title = {Performance evaluation of hybrid bio-inspired and deep learning algorithms in gene selection and cancer classification}, volume = {13}, year = {2025}, }
• (2025). Domain generalized open-set fault detection and diagnosis for belt conveyor systems with prototype learning. ACCESS, 13, 59959-59976. (www)

  @article{ , abstract = {Belt conveyor systems are essential across various industries but are prone to faults due to their distinctive design and challenging operational environments. Various approaches have been explored for fault detection and diagnosis (FDD) in belt conveyor systems, but research specifically addressing the unique features of these systems and providing a comprehensive analysis remains limited. In this paper, we propose a deep learning-based two-stage FDD approach specifically tailored to address the challenges associated with belt conveyor systems. Challenges in applying deep learning-based FDD include managing the vast number of features collected from large-scale systems, the scarcity of diverse fault data, and distribution shifts between training and testing environments. To address these issues, we developed a FDD approach that integrates open-set recognition and domain generalization, leveraging multivariate sensor data. Our two-stage design effectively mitigates the limitations posed by insufficient faulty samples while enhancing overall FDD performance. To properly assess the proposed method, we utilized datasets collected from a testbed that simulates the actual operational conditions of belt conveyor systems. The evaluation results demonstrate that the proposed FDD method exhibits robust performance in real-world applications on belt conveyor systems.}, archive = {J_ACCESS}, author = {Jinyou Kim and Ilkyu Yi and Young-Joo Suh}, doi = {10.1109/ACCESS.2025.3555984}, journal = {IEEE Access}, pages = {59959-59976}, shortjournal = {IEEE Access}, title = {Domain generalized open-set fault detection and diagnosis for belt conveyor systems with prototype learning}, volume = {13}, year = {2025}, }
• (2025). Analysis and design of ADRC for the LCC-LCC WPT system. ACCESS, 13, 59949-59958. (www)

  @article{ , abstract = {This paper analyzes the dynamic characteristics of wireless power transfer systems based on LCC-LCC topology. By controlling the input current of the primary compensation network, the Active Disturbance Rejection Control (ADRC) Algorithm is used to realize the voltage output of the secondary compensation network indirectly, which improves the dynamic response of load voltage and anti-interference ability. Firstly, the small signal model of the system is obtained by using the generalized state-space average method. On this basis, the transient characteristics of the Wireless Power Transfer (WPT) system are analyzed and the consistency of the transient performance of the primary current and the secondary voltage is demonstrated. Secondly, the Active Disturbance Rejection Control (ADRC) strategy based on the primary side current of the Wireless Power Transfer (WPT) system is proposed and the parameter calculation rules of the controller are given. Finally, an experimental platform was built to demonstrate the theoretical analysis. The experimental result demonstrated that the proposed Active Disturbance Rejection Control (ADRC) strategy can achieve a stable output of load voltage with the response time of about 1.61ms and the voltage fluctuation of about 2V.}, archive = {J_ACCESS}, author = {Wencong Huang and Wentao Lin and Peng Gao and Yufang Chang}, doi = {10.1109/ACCESS.2025.3552402}, journal = {IEEE Access}, pages = {59949-59958}, shortjournal = {IEEE Access}, title = {Analysis and design of ADRC for the LCC-LCC WPT system}, volume = {13}, year = {2025}, }
• (2025). Explainable AI-driven dew point forecasting with attention-based temporal convolutional networks. ACCESS, 13, 59923-59948. (www)

  @article{ , abstract = {Climate change poses global challenges, including rising temperatures and extreme weather, severely affecting regions such as Bangladesh. Predictions of dew point temperature, a key indicator for climate change, are vital for climate adaptation, agriculture, energy planning, weather forecasting, and public health. This research analyzed dew point temperature data from 35 weather stations across Bangladesh to find patterns and trends over four decades since 1980. This study developed an attention-based Temporal Convolutional Networks (TCN) model for predicting dew point temperature. The model was enhanced with Explainable AI (XAI) techniques to facilitate feature selection and provide interpretable insights into the factors influencing the predictions. In order to assess the performance of the proposed model this work utilized different performance measures and achieved test loss of 0.002, validation loss of 0.00212, Mean Absolute Error (MAE) of 0.032, and Root Mean Squared Error (RMSE) of 0.044, $R^{2}$ and adjusted $R^{2}$ values of 0.938, demonstrating higher prediction accuracy compared to other models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Bi-directional Long Short-Term Memory (BiLSTM), LSTM_CNN hybrid, and Gated Recurrent Unit (GRU). Consistent performance across datasets from other weather stations confirmed the robustness of the suggested model. The findings provide valuable insights into dew point trends and contribute a reliable predictive model for practical applications in climate-sensitive domains.}, archive = {J_ACCESS}, author = {Md Abdullah and Sajjad Waheed and Md Mahmodul Hasan and Monir Morshed}, doi = {10.1109/ACCESS.2025.3557263}, journal = {IEEE Access}, pages = {59923-59948}, shortjournal = {IEEE Access}, title = {Explainable AI-driven dew point forecasting with attention-based temporal convolutional networks}, volume = {13}, year = {2025}, }
• (2025). Automated processing of speech recordings for dietary assessment: Evaluation in the LLMIC context. ACCESS, 13, 59911-59922. (www)

  @article{ , abstract = {Novel methods of using smartphones to collect speech records of dietary intake facilitate self-reporting of intake in free-living conditions, and may further reduce biases affecting reliable capture such as complexity and reactivity. However, the processing of speech records for dietary assessment is a time-consuming task for analysts and thus restricted by research infrastructure. Low- and Low-Middle Income Countries have been disproportionately affected by barriers to dietary assessment (e.g., due to varying literacy or limited research infrastructure). As such, speech records could be a promising avenue to address the research gap in Low and Low-Middle Income regions. While recent advances in speech recognition and natural language processing technology have facilitated automation of this process, no existing studies have evaluated the effectiveness of this technology when applied to an LLMIC context. To this end we adapt the methods identified in a review of the literature to this new context and evaluate their performance on a data set of Khmer speech recordings describing dietary data captured in a free-living context in Cambodia.}, archive = {J_ACCESS}, author = {Connor T. Dodd and Marc T. P. Adam and Janelle L. Windus and Megan E. Rollo}, doi = {10.1109/ACCESS.2025.3555998}, journal = {IEEE Access}, pages = {59911-59922}, shortjournal = {IEEE Access}, title = {Automated processing of speech recordings for dietary assessment: Evaluation in the LLMIC context}, volume = {13}, year = {2025}, }
• (2025). Advancing four-tier diagnostic assessments: A novel approach to mapping engineering students’ conceptual understanding in microwave engineering course. ACCESS, 13, 59886-59910. (www)

  @article{ , abstract = {Diagnosing students’ misconceptions in engineering education is a crucial task for designing targeted instructional interventions. This study presents a new approach to the development and validation of Four-Tier Diagnostic Test as an instructional tool for assessing engineering students’ conceptual understanding and misconceptions. The study aimed to evaluate the psychometric properties of the developed instrument and to identify the level of engineering students’ conceptual understanding and misconceptions in microwave engineering courses. A cohort of 141 undergraduate engineering students who took part in the course were administered with the developed 28-item Four-tier Diagnostic Test. Results of the study indicated that 24.7% of students demonstrated scientific knowledge, while 20.3% exhibited misconceptions. Regression analysis revealed a logarithmic relationship between students’ misconceptions and their overall performance, with misconceptions being present even among higher-performing students. Item response analysis using the mirt package showed that test items were effective in discriminating between students with different levels of conceptual understanding. While satisfactory KR-20 reliability coefficients for scientific knowledge (KR- $20=0.739$ ) and misconceptions (KR- $20=0.749$ ) were achieved respectively, the overall scale marginal conditional reliability ( $\rho \gt 0.907$ ) indicates high precision of the instrument in measuring students’ conceptual attributes. This study contributes to the literature by demonstrating how multi-tiered diagnostic assessment coupled with advanced psychometric approaches can enhance the precision of educational diagnostics test.}, archive = {J_ACCESS}, author = {Nafiu Salele and MD Shahadat Hossain Khan and Mahbub Hasan and Sanjida Ali}, doi = {10.1109/ACCESS.2025.3555432}, journal = {IEEE Access}, pages = {59886-59910}, shortjournal = {IEEE Access}, title = {Advancing four-tier diagnostic assessments: A novel approach to mapping engineering students’ conceptual understanding in microwave engineering course}, volume = {13}, year = {2025}, }
• (2025). Optimal participation in manual frequency restoration reserve-based demand response programs for large consumers. ACCESS, 13, 59866-59885. (www)

  @article{ , abstract = {The increasing penetration of intermittent, weather-dependent generation has heightened the need for flexibility in power systems. Traditionally passive, consumers are now being encouraged to actively support ancillary services to help balance generation and consumption. This work examines the participation of a large electricity consumer in manual frequency restoration reserve-based demand response programs. In particular, the Active Demand Response Service (ADRS), recently introduced in Spain, is addressed. Participation involves submitting power-price bids in an annual auction, indicating the amount of grid consumption the consumer is willing to reduce upon request by the system operator. The problem is modeled as a three-stage stochastic optimization, considering three key decisions: the power bid submitted to the auction, the operational decisions of the consumer before ADRS participation, and operational adjustments when ADRS is activated. A cement producer serves as the case study, with several scenarios comparing different electricity procurement options: grid-only supply, photovoltaic self-generation, and battery storage. The results show that consumers with alternative energy sources, such as solar or batteries, are less inclined to participate in ADRS due to the requirement to maintain a minimum level of grid consumption. Nevertheless, participation in ADRS can offer substantial economic benefits, especially at auction prices observed in previous editions. In this regard, the cement producer considered in this case study could achieve an expected cost savings of up to 30% annually by participating in this service. Moreover, the fixed remuneration from the ADRS could significantly offset the plant’s operational costs, covering between 28% and 42% when participating with approximately 5 MW in ADRS.}, archive = {J_ACCESS}, author = {José Arellano and Álvaro García-Cerezo and Miguel Carrión}, doi = {10.1109/ACCESS.2025.3557284}, journal = {IEEE Access}, pages = {59866-59885}, shortjournal = {IEEE Access}, title = {Optimal participation in manual frequency restoration reserve-based demand response programs for large consumers}, volume = {13}, year = {2025}, }
• (2025). An ensemble of vision-language transformer-based captioning model with rotatory positional embeddings. ACCESS, 13, 59841-59865. (www)

  @article{ , abstract = {Image captioning is a dynamic and crucial research area focused on automatically generating image textual descriptions. Traditional models, primarily employing an encoder-decoder framework with Convolutional Neural Networks (CNNs), often struggle to capture the complex spatial and sequential relationships inherent in visual data. This gap in performance underscores the necessity for more sophisticated solutions. The proposed work introduces a groundbreaking ensemble model that integrates CNN, Graph Convolutional Network (GCN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer architectures. Our approach achieves an outstanding 97% increase in CIDEr scores on the Flickr30K dataset and a remarkable 28.6% improvement on the Flickr8K dataset, thanks to the innovative implementation of Rotary Positional Encoding (RoPE). By strategically incorporating GCN and BiLSTM layers, our model adeptly captures essential relationships within the data. This groundbreaking research effectively addresses the challenges of image captioning, leveraging a powerful combination of advanced architectures. As a result, our model significantly enhances the generation of accurate and contextually rich captions, positioning it as a game-changer for automated image-to-text applications. The proposed Ensemble model with RoPE, achieved impressive performance on the Flickr8k and Flickr30k datasets, with scores of 80.62 and 95.0 for BLEU-1, 72.01 and 90.51 for BLEU-2, 63.12 and 81.24 for BLEU-3, 48.32 and 68.8 for BLEU-4, 74.26 and 81.89 for METEOR, 80.24 and 84.29 for ROUGE-L, 118.94 and 155.77 for CIDEr, and 48.7 and 39.0 for SPICE, respectively.}, archive = {J_ACCESS}, author = {K. B. Sathyanarayana and Dinesh Naik}, doi = {10.1109/ACCESS.2025.3556449}, journal = {IEEE Access}, pages = {59841-59865}, shortjournal = {IEEE Access}, title = {An ensemble of vision-language transformer-based captioning model with rotatory positional embeddings}, volume = {13}, year = {2025}, }
• (2025). Fault current limiter and mechanical DC CB-based protection against valve-side SPG faults in HB-MMC bipolar system. ACCESS, 13, 59826-59840. (www)

  @article{ , abstract = {Valve-side single-phase-to-ground (SPG) faults are atypical yet critical for the operation of half-bridge (HB) Modular Multilevel converter (MMC) bipolar or asymmetrical monopolar systems. For the impacted converter station, these faults may result in non-zero crossing currents on the connected AC grid and cause significant overvoltage on upper arm submodule (SM) capacitors. This article presents an SPG valve-side fault mitigation strategy using a power electronic (PE) fault current limiter (FCL) installed at the grounded terminal of the MMC converter, along with a mechanical DC breaker on the DC transmission line. The FCL transfers converter ground current to a resistive branch during the SPG fault, resulting in zero crossing currents on the AC side of the grid, thus ensuring fault current clearance by grid-side AC circuit breakers. Moreover, the proposed topology limits the fault current magnitude, reducing the impact of SPG valve-side faults on the AC grid. The discontinuous DC line current is cleared by a fast mechanical DC circuit breaker, halting the charging of the upper arm SM capacitor and limiting the overvoltage stress on converter insulation. In addition, the severity of upper arm SM overvoltage in case of SPG fault in long distance HB-MMC bipolar links has been assessed, and the prospect of a passive oscillation DC circuit breaker to curb such overvoltage has been demonstrated. The effectiveness of the protection topology has been verified through PSCAD/EMTDC software and compared with previously proposed SPG fault clearance methods.}, archive = {J_ACCESS}, author = {Muhammad Usman and Yu-Jin Kwak and Bang-Wook Lee}, doi = {10.1109/ACCESS.2025.3556936}, journal = {IEEE Access}, pages = {59826-59840}, shortjournal = {IEEE Access}, title = {Fault current limiter and mechanical DC CB-based protection against valve-side SPG faults in HB-MMC bipolar system}, volume = {13}, year = {2025}, }
• (2025). Scalable mixed-criticality safety concepts for a stationary battery management system (BMS) with integrated machine learning (ML). ACCESS, 13, 59811-59825. (www)

  @article{ , abstract = {Battery management systems (BMSs) play a crucial role in controlling and supervising the safe operation of lithium-ion batteries in stationary energy storage systems. The integration of mixed-criticality functions (combining safety functions of different criticality and non-safety-related functions) into these electronic embedded systems offers significant potential benefits but also introduces technical and certification challenges. Additionally, modern BMSs are increasingly integrating battery models based on machine learning (ML). This publication contributes with the example-based design and analysis of scalable computing architectures for the development of a mixed-criticality BMS that integrates ML battery models in stationary applications. The safety concept focuses on the computing architecture, where mixed-criticality functions and ML models are integrated, and on analyzing the advantages and disadvantages of the proposed approaches. The computing architectures discussed range from widely used lockstep and single-core processor solutions to high-performance computing platforms such as multicore devices. The system-on-a-chip with hypervisor is identified as the preferred architecture for a BMS that is flexible to efficiently adapt and integrate non-safety-related functions that require intensive computing resources without compromising safety requirements.}, archive = {J_ACCESS}, author = {Xabier Arraztoa-Lazkanotegi and David Marcos and Maitane Garmendia and Eñaut Muxika Olasagasti and Jon Perez-Cerrolaza}, doi = {10.1109/ACCESS.2025.3555969}, journal = {IEEE Access}, pages = {59811-59825}, shortjournal = {IEEE Access}, title = {Scalable mixed-criticality safety concepts for a stationary battery management system (BMS) with integrated machine learning (ML)}, volume = {13}, year = {2025}, }
• (2025). Multi-modal emotion detection and sentiment analysis. ACCESS, 13, 59790-59810. (www)

  @article{ , abstract = {In the digital era, the proliferation of online reviews through videos has been meteoric and driven by recent technological advancements. The sentiments expressed in these videos drive consumer reliance, decision-making and may shape perception building of general public. The sentiment classification of videos is emerging as a vital area of research in Natural Language Processing (NLP) and is challenging due to complexity of emotions and temporal aspects of modalities. These challenges become more complex for low-resourced languages such as Urdu. Addressing the need for a comprehensive study in this realm, we introduce a generic framework, Urdu Multi-modal Sentiment Analysis (UMSA), for emotion detection and sentiment classification of videos.UMSA highlights that incorporating additional modalities and cross-modal interactions significantly enhances the analysis. Our research culminates in the creation of the Urdu Sentiments Dataset (USD), a comprehensive collection of Urdu video reviews. In this study, we classify videos using a two-phase approach that incorporates early fusion and ensembling. After fusion, we perform ensembling of two models for each modality: audio, text, and frames. We utilize Long Short-Term Memory (LSTM) networks and Random Forest Classifier for audio. Text-based analysis is conducted using Logistic Regression and the Bidirectional Encoder Representations from Transformers (BERT) model. For frames, we employ Random Forest and Convolutional Neural Networks (CNN). Afterwards, we implement model ensembling across the three modalities. This multi-modal integration proves essential in providing a clearer and more comprehensive understanding of the sentiments conveyed and achieved more than 80% accuracy. The validation of UMSA is reinforced through a comprehensive case study approach. This independent validation highlights its robustness and adaptability to real-world scenarios.}, archive = {J_ACCESS}, author = {Shoaib Sikunder Malik and Muhammad Ilyas and Yasin Ul Haq and Rabia Sana and Muhamamd Saad Razzaq and Fahad Maqbool and Muhammad Salman Pathan}, doi = {10.1109/ACCESS.2025.3552475}, journal = {IEEE Access}, pages = {59790-59810}, shortjournal = {IEEE Access}, title = {Multi-modal emotion detection and sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Design and development of research level microgrid controller for resilient and sustainable energy management. ACCESS, 13, 59780-59789. (www)

  @article{ , abstract = {Due to the sustainability requirements demanded by the world community, renewable energy-based power supply is highly appreciated at the consumer level. Also, the resiliency of the power supply is considered to ensure power system security. Microgrids are becoming role models of novel grid improvement and extension strategies due to their high controllability and ability to implement high-level grid functions, subjected to sustainability and resiliency. One of the key methods of establishing microgrids is by upgrading the existing distribution system into a controllable microgrid with required functionalities. Since this type of microgrid can be operated in either grid-connected or islanded modes, the microgrid controller plays a crucial role inside the microgrid setup. Smart Grid Research Lab (SGRL) of the University of Moratuwa is facilitated with 30kW research-level microgrid components and this paper discusses how the controlling structure of that microgrid is developed. This study comprises of two major components: simulation and hardware development. The simulation is conducted using a Real-Time Digital Simulator (RTDS). Furthermore, the hardware-level developments for establishing the microgrid controllers are based on the simulated outputs obtained from various case studies.}, archive = {J_ACCESS}, author = {O. V. Gnana Swathika and M. A. Sarith Wasitha and K. T. M. Udayanga Hemapala and Sitharthan Ramachandran}, doi = {10.1109/ACCESS.2025.3554771}, journal = {IEEE Access}, pages = {59780-59789}, shortjournal = {IEEE Access}, title = {Design and development of research level microgrid controller for resilient and sustainable energy management}, volume = {13}, year = {2025}, }
• (2025). Summarization, prediction, and analysis of turkish constitutional court decisions with explainable artificial intelligence and a hybrid natural language processing method. ACCESS, 13, 59766-59779. (www)

  @article{ , abstract = {The use of artificial intelligence in legal analysis represents a significant transformation process in modern legal practices. The literature review showed that the number of studies on Turkish court decisions was limited, and only classification applications were developed. This study aims to summarize, predict, and analyze the Turkish Constitutional Court’s decision texts. In this context, first a new, unique dataset was created. The dataset included class labels, original court decision texts, and summary court decision texts created by expert lawyers. Then, a hybrid summary model was established, combining extractive and abstract summarization to summarize long court decisions automatically. With this model, the summarization efficiency was increased, and the token limitation problem common in existing transformative models was eliminated. The model showed good performance for summarization by obtaining Rouge-1, Rouge-2, and Rouge-L scores of 0.6129, 0.5884, and 0.5891 respectively. After the summarization phase, the Legal Judgment Prediction applications were developed. Separate classification models were developed for court decision texts and summary court decision texts, obtained using the hybrid model. When the models were compared, the XGBoost model achieved the best performance in legal judgment prediction tasks, with an accuracy rate of 93.84% for full texts and 62.30% for summary texts. In the final stage of the study, the model results were explained using the SHapley Additive exPlanations method. The findings of this study emphasize the superiority of hybrid approaches to legal document analysis. They highlighted the vital role of explainable techniques in improving transparency and reliability in legal processes.}, archive = {J_ACCESS}, author = {Tülay Turan and Ecır Uğur Küçüksılle}, doi = {10.1109/ACCESS.2025.3556725}, journal = {IEEE Access}, pages = {59766-59779}, shortjournal = {IEEE Access}, title = {Summarization, prediction, and analysis of turkish constitutional court decisions with explainable artificial intelligence and a hybrid natural language processing method}, volume = {13}, year = {2025}, }
• (2025). Accelerated aging of dual-layer cables system used in nuclear power plants: A degradation pathway study. ACCESS, 13, 59750-59765. (www)

  @article{ , abstract = {This study explores the degradation behavior of cable insulation under simulated aging conditions, specifically focusing on monitoring thermal properties. The insulations, used in nuclear power plants (NPPs), are a two-layer system composed of chlorinated polyethylene elastomer (CPE) for the bonded jacket (L2) and ethylene propylene rubber (EPR) for the insulator (L1). Degradation was monitored via Fourier-transform infrared spectroscopy (FTIR) spectroscopy and Differential Scanning Calorimetry (DSC). Our results revealed a gradient degradation between the external and internal sides of the bonded jacket layer, L2, while L1 demonstrated a homogeneous degradation pattern. Notably, the external surface of L2 exhibited significant oxidation, while the internal surface showed an increase in methylene groups, due to low molecular mass degradation products from the external surface. These findings provide crucial insights into cable degradation processes, highlighting that, multiple phenomena, such as oxidation, chain scission, and cross-linking occur simultaneously. The results have implications for the safety and maintenance of NPP (e.g., cable aging management programs during long-term operations).}, archive = {J_ACCESS}, author = {Samuel Escobar Veras and Stephanie S. Watson and Gerardo Morell and Brad R. Weiner}, doi = {10.1109/ACCESS.2025.3556837}, journal = {IEEE Access}, pages = {59750-59765}, shortjournal = {IEEE Access}, title = {Accelerated aging of dual-layer cables system used in nuclear power plants: A degradation pathway study}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A comprehensive review and analysis of the allocation of electric vehicle charging stations in distribution networks”. ACCESS, 13, 59749. (www)

  @article{ , archive = {J_ACCESS}, author = {T. Yuvaraj and K. R. Devabalaji and J. Anish Kumar and Sudhakar Babu Thanikanti and Nnamdi I. Nwulu}, doi = {10.1109/ACCESS.2025.3554026}, journal = {IEEE Access}, pages = {59749}, shortjournal = {IEEE Access}, title = {Corrections to “A comprehensive review and analysis of the allocation of electric vehicle charging stations in distribution networks”}, volume = {13}, year = {2025}, }
• (2025). GOME-NGU: Visual navigation under sparse reward via goal-oriented memory encoder with never give up. ACCESS, 13, 59737-59748. (www)

  @article{ , abstract = {In this paper, we propose the Goal-Oriented Memory Encoder (GOME) with Never Give Up (NGU) algorithm to enhance visual navigation in sparse-reward environments. Our approach addresses the critical need for efficient exploration and exploitation when dealing with high-dimensional visual data and limited spatial information. In our proposed GOME-NGU, we first utilize NGU for sufficient exploration, and then apply GOME to effectively leverage the information acquired during the exploration phase for exploitation. GOME stores information about goals and their associated rewards obtained during the exploration phase as pairs. During the exploitation phase, the stored goals are prioritized based on the current location of the agent and arranged in order of proximity to enable the agent to act optimally. To demonstrate the efficiency of the proposed GOME-NGU, two experimental categories were considered. Specifically, 1) to validate the sufficiency of exploration using NGU, we measured the states of the environment and recorded the number of times the agent visited each state, and 2) to confirm that GOME optimizes the paths to the goals nearest the location of the agent during the exploitation phase, we measured the frequency of the agent reaching nearby goals at least twice. For both 1) and 2), training was conducted using NVIDIA Isaac Gym, and validation was performed by migrating to Isaac Sim. In addition, for 2), we validated the efficiency of the proposed GOME-NGU using a Husky robot in a real-world setting. Based on the experimental results, the proposed GOME-NGU demonstrated enhanced performance in both exploration and exploitation in sparse-reward environments.}, archive = {J_ACCESS}, author = {Ji Sue Lee and Jun Moon}, doi = {10.1109/ACCESS.2025.3556894}, journal = {IEEE Access}, pages = {59737-59748}, shortjournal = {IEEE Access}, title = {GOME-NGU: Visual navigation under sparse reward via goal-oriented memory encoder with never give up}, volume = {13}, year = {2025}, }
• (2025). Generating synthetic malware samples using generative AI. ACCESS, 13, 59725-59736. (www)

  @article{ , abstract = {Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in malware. However, collecting a diverse set of malware samples with various obfuscation techniques is challenging and often takes years, especially for newly developed malware. This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. In this paper, we propose a new system for generating synthetic malware samples to augment imbalanced malware dataset. Our approach decomposes malware binary samples into mnemonic opcode sequences, leveraging natural language processing to extract contextual meaning behind malware opcode features to aid the learning of generative AI (GenAI) employed in this paper, Generative Adversarial Networks (GAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), and a modified Diffusion model. The experiment results show that augmenting training data with Diffusion-based synthetic data significantly improves classification performance for minor classes by up to 60% on average. This enhancement ultimately leads to an overall malware classification performance of 96%, an 8% improvement. These findings demonstrate the high quality and fidelity of the synthetic data, its robustness, and its potential applications in malware analysis. Specifically, synthetic malware data proves effective in improving the classification of minor malware classes and detection rates, even though the size of known malware data is significantly small.}, archive = {J_ACCESS}, author = {Tiffany Bao and Kylie Trousil and Quang Duy Tran and Fabio Di Troia and Younghee Park}, doi = {10.1109/ACCESS.2025.3556704}, journal = {IEEE Access}, pages = {59725-59736}, shortjournal = {IEEE Access}, title = {Generating synthetic malware samples using generative AI}, volume = {13}, year = {2025}, }
• (2025). SmartClus: Mobility and energy-aware autonomous aerial vehicles’ clustering for B5G cellular networks. ACCESS, 13, 59713-59724. (www)

  @article{ , abstract = {Autonomous aerial vehicles (AAVs) are key enablers for various beyond-5G (B5G) applications, including remote sensing, monitoring, emergency response, and delivery systems. However, communication with AAVs faces challenges such as energy constraints, dynamic environments, weather sensitivity, and high mobility. Ensuring connectivity for fast-moving AAVs while minimizing energy consumption to extend network lifetime remains critical. This paper presents SmartClus, a lightweight and dynamic clustering framework designed to address AAV group mobility and resource sharing. To enable efficient cluster formations, a multi-metric cluster head (CH) fitness and mobility similarity score process is formulated, that considers communication quality, spatial proximity, velocity, and direction. The extensive performance evaluation in the NS3 network simulator shows that SmartClus outperforms two state-of-the-art schemes. The results demonstrated that the proposed approach nearly doubled the average throughput, and reduced transmission delays by 2 to 4 seconds. Moreover, SmartClus achieved a packet delivery ratio (PDR) of 70% to 90% compared to previous schemes with 50% to 70% and 60% to 80%, and extended average CH lifetime (being CH longer and requiring no re-clustering).}, archive = {J_ACCESS}, author = {Rabia Ahmed and Farooque Hassan Kumbhar and Wessam Mesbah and Anis Elgabli and Farrukh Hasan Syed}, doi = {10.1109/ACCESS.2025.3554747}, journal = {IEEE Access}, pages = {59713-59724}, shortjournal = {IEEE Access}, title = {SmartClus: Mobility and energy-aware autonomous aerial vehicles’ clustering for B5G cellular networks}, volume = {13}, year = {2025}, }
• (2025). Dual-loop fractional-order IMC structure for unstable systems to handle ramp input signals. ACCESS, 13, 59704-59712. (www)

  @article{ , abstract = {One major concern in control engineering is the problem of introducing an unstable system. Such systems are even more sensitive to ramp input changes, either set-point or disturbance. We have proposed an extended fractional-order IMC (FOIMC) control for an unstable system exhibiting a time delay. The complete design involves only three adjustable parameters, such as PID tuning. In the proposed structure, the inner loop control stabilises the system, whereas the fractional IMC filter improves the overall performance, together to tackle the ramp inputs. A systematic approach is developed to tune the required design parameters to obtain the desired peak of the sensitivity function and stability margins. The proposed control is simple and can easily calculate the FOIMC parameters from the explicit formulae. The method works under practical considerations, such as process parameter perturbations and load disturbances. The developed scheme is also tested on the nonlinear continuous stirred tank reactor system. The proposed control method results in a percentage enhancement of 61.9% under the perfectly ideal condition (when the process model is equal to the actual plant) whereas 81.3% enhancement is obtained when the process parameter variations are considered for the CSTR system.}, archive = {J_ACCESS}, author = {Sanjay Kumar and Moina Ajmeri and Utkal Mehta}, doi = {10.1109/ACCESS.2025.3553242}, journal = {IEEE Access}, pages = {59704-59712}, shortjournal = {IEEE Access}, title = {Dual-loop fractional-order IMC structure for unstable systems to handle ramp input signals}, volume = {13}, year = {2025}, }
• (2025). Predictive modeling for fetal health: A comparative study of PCA, LDA, and KPCA for dimensionality reduction. ACCESS, 13, 59687-59703. (www)

  @article{ , abstract = {Pregnancy complications significantly impact maternal and fetal health, requiring accurate and timely diagnostic methods for life-saving interventions. Traditional manual analysis of cardiotocography (CTG) tests, a commonly used technique for fetal health monitoring, is labor-intensive and prone to variability. This study addresses this issue by applying dimensionality reduction techniques—Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Kernel Principal Component Analysis (KPCA)—to improve the performance of machine learning (ML) models in predicting fetal health using CTG data. Using a dataset of 2,126 records, our goal was to reduce the feature space while preserving essential information, resulting in improved classification accuracy. The results show that PCA with XGBoost achieved 98% accuracy, LDA with K-nearest Neighbors achieved 91%, and KPCA with XGBoost achieved 97%. These findings highlight the importance of dimensionality reduction and feature selection in developing robust ML models for fetal health assessment, emphasizing their potential impact on improving clinical diagnostics and medical decision-making.}, archive = {J_ACCESS}, author = {Ariana Deyaneira Jiménez-Narváez and Victor David Casa Vaca and Jonathan Javier Loor-Duque and Isidro Rafael Amaro Martín and Iván Galo Reyes-Chacón and Paulina Vizcaíno and Manuel Eugenio Morocho-Cayamcela}, doi = {10.1109/ACCESS.2025.3553110}, journal = {IEEE Access}, pages = {59687-59703}, shortjournal = {IEEE Access}, title = {Predictive modeling for fetal health: A comparative study of PCA, LDA, and KPCA for dimensionality reduction}, volume = {13}, year = {2025}, }
• (2025). Compound mask for divergent wave imaging in medical ultrasound. ACCESS, 13, 59675-59686. (www)

  @article{ , abstract = {Divergent wave imaging with coherent compounding allows obtaining broad field-of-view and higher frame rate with respect to line-by-line insonification. However, the spatial and contrast resolution crucially depends on the weights applied in the compound phase, whose optimization is often cumbersome and based on trial and error. This study addresses these limitations by introducing a closed-form approach that maps the transmit apodization weights used in synthetic aperture imaging into the compound mask applied to divergent wave imaging. The approach draws inspiration from a successful technique developed for plane wave imaging, leveraging synthetic aperture imaging as a reference due to its superior image quality. Unlike the previous work on plane waves, the proposed approach is not limited to linear probe geometries but works seamlessly also with convex ones, thus expanding the scope of applicability of divergent wave imaging. Moreover, it handles arbitrary spatial arrangements of virtual sources generating divergent waves. The approach has been validated through simulated data using both linear and convex probes, demonstrating that the Full Width at Half Maximum (FWHM) in Divergent Wave Linear Array (DWLA) increased by 7.5% at 20 mm and 9% at 30 mm compared to Synthetic Aperture Linear Array (SALA). For Divergent Wave Convex Array (DWCA), the increase was 1.64% at 20 mm and 26.56% at 30 mm compared to Synthetic Aperture Convex Array (SACA), witnessing the method’s effectiveness.}, archive = {J_ACCESS}, author = {Zahraa Alzein and Marco Crocco and Daniele D. Caviglia}, doi = {10.1109/ACCESS.2025.3557085}, journal = {IEEE Access}, pages = {59675-59686}, shortjournal = {IEEE Access}, title = {Compound mask for divergent wave imaging in medical ultrasound}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in crime prediction: A survey with a focus on explainability. ACCESS, 13, 59646-59674. (www)

  @article{ , abstract = {Crime prediction has become a valuable tool for enhancing predictive policing, enabling law enforcement agencies to allocate resources more effectively and implement proactive crime prevention strategies, particularly in high-crime areas. The use of artificial intelligence (AI) has revolutionized this field by analyzing vast amounts of data to identify patterns and anticipate criminal activities with unprecedented accuracy. This paper aims to review the literature on AI-based crime prediction, analyzing 142 studies that focus on crimes against individuals, society, and property. Despite the promising potential of AI in crime prediction, significant challenges remain, particularly regarding the trustworthiness of AI systems, which is essential for their social acceptance. To address these issues, this review explores the explainability of AI-based prediction models, with a specific focus on the role of explainable AI (XAI). The findings highlight the importance of XAI in building trust in these models by offering more transparent and interpretable insights into how AI systems make decisions. However, the review also reveals that the integration of XAI remains underdeveloped in the current literature. By improving the transparency of AI systems, XAI has the potential to lead to more accurate, trustworthy, and fair crime predictions, ultimately facilitating more effective and equitable crime prevention efforts.}, archive = {J_ACCESS}, author = {Filiz Ersöz and Taner Ersöz and Francesco Marcelloni and Fabrizio Ruffini}, doi = {10.1109/ACCESS.2025.3553934}, journal = {IEEE Access}, pages = {59646-59674}, shortjournal = {IEEE Access}, title = {Artificial intelligence in crime prediction: A survey with a focus on explainability}, volume = {13}, year = {2025}, }
• (2025). Maximizing measurement visual-inertial odometry for low-light environment through online image pre-processing. ACCESS, 13, 59636-59645. (www)

  @article{ , abstract = {Recently, the interest in exploring unknown areas such as caves and tunnels has increased, and Visual-Inertial Odometry (VIO) can provide a suitable localization solution thanks to its lightweight and low cost advantages for mobile robots or drones. However, in such environments, lighting conditions are often not guaranteed due to lack of infrastructure. Although, attached light sources, such as body-mounted lights on robots and headlights on autonomous vehicles, can be used to address this problem, it is difficult to obtain images of consistent quality since the direction of the light changes as the robot moves. This can adversely affect the extraction and tracking of feature points, resulting in insufficient measurements. To solve this issue, we propose Maximizing Measurement VIO, named MMVIO, to ensure consistent image quality and rich measurements, which enables prolonged feature tracking in low-light environments. We employ two image pre-processing techniques, $\gamma $ -correction and CLAHE, whose parameters are estimated online using Bayesian Optimization (BO). The objective function of BO is to maximize the number of feature points extracted by the FAST corner detector, effectively increasing the measurements. For a detailed analysis, we conducted experiments using public datasets to validate our approach. The results show that MMVIO achieves the improved tracking time of the feature points and superior localization performance compared to other approaches that use different image quality metrics as objective functions. Moreover, we present the results that the localization accuracy of MMVIO is not only improved by online image pre-processing, but also is comparable to or outperforms the state-of-the-art VIOs.}, archive = {J_ACCESS}, author = {Suyong Lee and Hanyeol Lee and Chan Gook Park}, doi = {10.1109/ACCESS.2025.3555319}, journal = {IEEE Access}, pages = {59636-59645}, shortjournal = {IEEE Access}, title = {Maximizing measurement visual-inertial odometry for low-light environment through online image pre-processing}, volume = {13}, year = {2025}, }
• (2025). X-band dielectric rod antenna with pre-select bandpass filter. ACCESS, 13, 59627-59635. (www)

  @article{ , abstract = {This paper presents a dielectric rod antenna integrated with a pre-select filter at X-band. The antenna and the pre-select filter are both 3D-printed in a single pass using ABS filament with $\varepsilon = 2.6$ and $\tan (\delta)=0.0025$ . The pre-select filter section is designed based on variations in the waveguide’s cross-section, which is shielded by conductive paint, while the antenna section consists of a rectangular cross-section dielectric rod antenna. All sections of the pre-select frequency antenna are made to WR-90 standard waveguide dimensions, and prototypes of the antenna and pre-select frequency antenna have been built and measured. A passband bandwidth of 10% at a center frequency of 10.5 GHz for the pre-select frequency antenna is achieved, with the incorporated pre-select filter limiting the bandwidth of the X-band antenna, showing a considerable off-band rejection. The prototype shows a maximum peak realized gain of 13.1 dBi at 11 GHz, with a minimum side-lobe level of 12.29 dB.}, archive = {J_ACCESS}, author = {Mehdi Babazadeh and Carlos E. Saavedra}, doi = {10.1109/ACCESS.2025.3556547}, journal = {IEEE Access}, pages = {59627-59635}, shortjournal = {IEEE Access}, title = {X-band dielectric rod antenna with pre-select bandpass filter}, volume = {13}, year = {2025}, }
• (2025). Customizable preference models for prosumers in peer-to-peer energy trading. ACCESS, 13, 59610-59626. (www)

  @article{ , abstract = {The transition toward decentralized energy systems necessitates advanced mechanisms for optimizing energy-sharing frameworks and dynamic supplier-consumer matching that aligns energy transactions with individual preferences. To this aim, this paper introduces a preference-driven approach for supplier-consumer matching in peer-to-peer (P2P) energy-sharing systems, addressing limitations in previous models that fail to capture the diversity and dynamism of consumer preferences. The proposed method introduces three customizable preference models and employs a multi-objective optimization model to evaluate suppliers based on critical attributes: cost, energy assurance, and security. Experimental findings validate the robustness of the proposed approach, demonstrating its ability to efficiently rank suppliers and accommodate consumer preferences across diverse scenarios involving large supplier pools and multiple attributes. The approach proves adaptable to varying consumer demands, balancing computational efficiency with responsiveness to consumer needs. The results underscore the potential of this approach to enhance energy-sharing systems by enabling more personalized and scalable supplier-consumer interactions.}, archive = {J_ACCESS}, author = {Bahareh Abolhasanzadeh and Masoud Barati}, doi = {10.1109/ACCESS.2025.3556955}, journal = {IEEE Access}, pages = {59610-59626}, shortjournal = {IEEE Access}, title = {Customizable preference models for prosumers in peer-to-peer energy trading}, volume = {13}, year = {2025}, }
• (2025). A novel dual-stream attention-based hybrid network for solar power forecasting. ACCESS, 13, 59596-59609. (www)

  @article{ , abstract = {Photovoltaic (PV) power forecasting is essential for providing accurate data on future power production, ensuring secure power grid operations, and reducing solar energy operation expenses. This research introduces a novel dual-steam hybrid model that uses Bidirectional Long-Short Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) to predict PV power production. The proposed model employs a parallel processing technique, with BiLSTM and CNN analyzing input data independently to detect temporal and spatial features. These features are then combined and passed to the multihead attention layer to further identify the most desirable features for PV power forecasts. The proposed model’s performance is thoroughly assessed by a series of experiments that include various window sizes, four seasons, and different weather conditions. Subsequently, the predictive accuracy of the developed model is compared with three single and five hybrid deep learning models. The findings show that the dual-stream attention-based hybrid network can precisely predict future PV production across various meteorological, seasonal, and climatic conditions.}, archive = {J_ACCESS}, author = {Rafiq Asghar and Michele Quercio and Lorenzo Sabino and Assia Mahrouch and Francesco Riganti Fulginei}, doi = {10.1109/ACCESS.2025.3555810}, journal = {IEEE Access}, pages = {59596-59609}, shortjournal = {IEEE Access}, title = {A novel dual-stream attention-based hybrid network for solar power forecasting}, volume = {13}, year = {2025}, }
• (2025). A bi-directional missing data imputation scheme based on CNN and short-term window sliding for thermal power units data. ACCESS, 13, 59576-59595. (www)

  @article{ , abstract = {Under the global clean energy and low-carbon development trend, the thermal power generation industry faces multiple pressures, including technological upgrades, industrial adjustments, and environmental protection. High-quality data recording of thermal power units is crucial for ensuring efficient economic dispatch of electricity. However, data loss is common due to sensor failures and equipment maintenance. Although data missingness has been extensively studied in other fields, there is limited in-depth research in the field of thermal power units, which mainly relies on traditional statistical methods or machine learning techniques. To address this, this paper proposes an innovative method that combines Convolutional Neural Networks, bidirectional imputation, and short-period window sliding techniques (CNN-BIS). This method integrates the powerful modeling capabilities of deep learning with the characteristics of thermal power unit data. Using unit data from a power plant for validation, we found that the results show the performance of the CNN-BIS model is significantly improved compared to baseline models across datasets with different missing rates and time periods. In modeling the flue gas oxygen content before and after data imputation, the prediction performance of various models is significantly enhanced, with the Root Mean Square Error (RMSE) reduced by 0.59% to 16.53%.}, archive = {J_ACCESS}, author = {Xiaoying Zhang and Yaqi Zhang and Hong Liu and Hua Li}, doi = {10.1109/ACCESS.2025.3554843}, journal = {IEEE Access}, pages = {59576-59595}, shortjournal = {IEEE Access}, title = {A bi-directional missing data imputation scheme based on CNN and short-term window sliding for thermal power units data}, volume = {13}, year = {2025}, }
• (2025). An effective CDS heuristic for the fuzzy flow shop scheduling problem with ordered precedence constraints using pentagonal fuzzy processing time. ACCESS, 13, 59560-59575. (www)

  @article{ , abstract = {In today’s modern manufacturing environment, effective production scheduling has a major impact on constructing schedules that meet customer demands efficiently, with uncertainties in production processes, such as variable job processing times and dynamic demand. This study presents an effective CDS heuristic for the n-job m-machine flow shop scheduling problem with ordered precedence constraints and fuzzy logic, aimed at determining a sequence that minimizes the fuzzy makespan. Job processing times are represented using pentagonal fuzzy numbers, and a new robust ranking method is employed for the defuzzification process. A key contribution of this research is the integration of these two methods to address the scheduling problem. Extensive numerical tests were conducted to validate the effectiveness of the proposed model. The results showed that the fuzzy makespan achieved its minimum value when ordered precedence constraints were applied. Additionally, the paper assesses the performance of these methods in terms of solution quality, utilizing test problems ranging from 10 to 40 jobs across various machines. The findings indicate that the proposed methods consistently yield better results.}, archive = {J_ACCESS}, author = {K. Ranjith and K. Karthikeyan}, doi = {10.1109/ACCESS.2025.3554021}, journal = {IEEE Access}, pages = {59560-59575}, shortjournal = {IEEE Access}, title = {An effective CDS heuristic for the fuzzy flow shop scheduling problem with ordered precedence constraints using pentagonal fuzzy processing time}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of ns-3 real-time emulation. ACCESS, 13, 59544-59559. (www)

  @article{ , abstract = {With its real-time mode and tap-bridges, the network simulator ns-3 offers the possibility to integrate real applications or even hardware into a testbed in which ns-3 emulates the network topology. This eliminates the need to develop separate simulation models for the applications. However, there are several differences compared to a model-based discrete event simulation, where applications and network topology run together in the simulation process without real-time constraints. When using ns-3 to build an emulation environment, it needs to run in real-time mode, which results in considerable limitations with regard to the achievable throughput and the complexity of the simulated topology. This paper compares the performance that researchers can expect from pure model-based simulations and ns-3-based emulation environments. For this, we evaluate the impact of differently complex network topologies and applications on the simulator throughput in a minimal emulation setup. All measurements are compared with a pure model-based simulation setup. The performance of our setup is measured by the number of possible TCP and UDP flows. With complex network topologies and applications, only a few flows can be handled. In contrast, with simple topologies and applications, the measured throughput of our emulation setup is between 14 Mbps and 120 Mbps. We demonstrate that the achievable throughput can be increased by using optimized ns-3 build profiles and modern CPUs, highlighting, in particular, the impact of packet throughput as a limiting factor for the simulation process.}, archive = {J_ACCESS}, author = {Martin Ottens and Jörg Deutschmann and Kai-Steffen Hielscher and Reinhard German}, doi = {10.1109/ACCESS.2025.3555478}, journal = {IEEE Access}, pages = {59544-59559}, shortjournal = {IEEE Access}, title = {Performance evaluation of ns-3 real-time emulation}, volume = {13}, year = {2025}, }
• (2025). A FixMatch framework for alzheimer’s disease classification: Exploring the trade-off between supervision and performance. ACCESS, 13, 59531-59543. (www)

  @article{ , abstract = {Alzheimer’s Disease (AD) poses a major challenge for healthcare systems worldwide, as timely and accurate diagnosis is crucial for patient management and outcome improvement. While experienced medical professionals can often identify AD through conventional assessment methods, limited resources and growing patient populations make large-scale and rapid screening increasingly necessary. In this work, we explore whether the FixMatch algorithm—a semi-supervised learning approach—can aid in classifying Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) by using the ADNI fMRI dataset of 5,182 images. This approach supplements rather than replaces clinical expertise, offering a faster, more standardized classification process where expert labeling is limited. We first assessed various supervised models and determined that VGG19-FFT provided the strongest balance of classification accuracy and computational efficiency. Integrating VGG19-FFT into FixMatch as the teacher model, initial tests using 10% labeled and 90% unlabeled data yielded modest results. A more systematic examination of different labeled-to-unlabeled data splits revealed that a 60:40 ratio enabled FixMatch to achieve classification accuracies of 100% for AD, 99% for CN, and 99% for MCI—on par with fully supervised training. This outcome highlights the potential of FixMatch to significantly reduce labeling requirements, a particular advantage in resource-constrained settings where expert annotations are costly. By striking an effective balance between labeling effort and model performance, the identified 60:40 ratio helps make advanced diagnostic methods both feasible and practical in real-world clinical applications.}, archive = {J_ACCESS}, author = {Al Hossain and Umme Hani Konok and MD Tahsin and Raihan Ul Islam and Mohammad Rifat Ahmmad Rashid and Mohammad Shahadat Hossain and Karl Andersson}, doi = {10.1109/ACCESS.2025.3556964}, journal = {IEEE Access}, pages = {59531-59543}, shortjournal = {IEEE Access}, title = {A FixMatch framework for alzheimer’s disease classification: Exploring the trade-off between supervision and performance}, volume = {13}, year = {2025}, }
• (2025). Ship magnetic signature classification using GRU-based recurrent neural networks. ACCESS, 13, 59514-59530. (www)

  @article{ , abstract = {Magnetic signatures represent the magnetic field generated by a ship’s ferromagnetic components and provide valuable information for identifying vessels not only in naval operations, but also in civil passages. The topic of accurate modelling of these signatures is relevant to this day, but also the complexity of the model necessary to accurately predict the ship’s magnetic field. This paper presents the implementation of a deep, recurrent neural network (RNN) designed for classification of compliance between the original magnetic signature of a ship and the one obtained from a model. Therefore, the quality of the model can be analyzed using a classifier during the modeling process. The necessity to introduce a tool for signature compliance classification arose during numerical modeling of a ship in Finite Element Method (FEM) environment as well as during reverse modeling based on data coming from measurements. Another application is the use of a shallow RNN for classifying ships by their size and type. A sufficient amount of data is rarely available and therefore data augmentation solution is necessary. The process of obtaining a large dataset of signals from a multi-dipole model and using an interpolation technique for generating training, validation and test data is comprehensively described. Methods used for selecting the best network structure and hyperparameter tuning using grid search and random search in order to achieve a satisfactory classification accuracy are thoroughly explained. Features, advantages and limitations of developed algorithms are derived strictly from the nature of neural networks.}, archive = {J_ACCESS}, author = {Kajetan Zielonacki and Jarosław Tarnawski and Miroslaw Woloszyn}, doi = {10.1109/ACCESS.2025.3557331}, journal = {IEEE Access}, pages = {59514-59530}, shortjournal = {IEEE Access}, title = {Ship magnetic signature classification using GRU-based recurrent neural networks}, volume = {13}, year = {2025}, }
• (2025). Prescribed performance trajectory tracking control for under-actuated underwater vehicles with adaptive dynamic programming. ACCESS, 13, 59505-59513. (www)

  @article{ , abstract = {This work studies the trajectory tracking control problem for under-actuated autonomous underwater vehicles (AUVs). In order to solve the under-actuated problem, an error definition method is used, which transformed the trajectory tracking control problem into the prescribed performance tracking control problem. The continuous dynamics of the trajectory tracking error, for which the actuated velocities are virtual control inputs, are formulated. By using the backstepping, a virtual control velocity is designed to guarantee the prescribed performance. Then the dynamic of velocity tracking error is formulated and a cost function is given. The optimal control law is designed by ADP. By combining the backstepping and ADP, not only an optimal control law is desgined, but also the prescribed performance is guaranteed. Moreover, since there is no need to select performance functions at dynamic level, it makes the design of the control scheme more easily. The Lyapunov theory is used to demonstrate that all error variables and the under-actuated states are uniformly ultimately bounded (UUB). The effectiveness of the proposed control scheme is validated using simulation results.}, archive = {J_ACCESS}, author = {Xiumei Han and Yahan Zhang and Mengnan Hao and Yupeng Jia}, doi = {10.1109/ACCESS.2025.3557066}, journal = {IEEE Access}, pages = {59505-59513}, shortjournal = {IEEE Access}, title = {Prescribed performance trajectory tracking control for under-actuated underwater vehicles with adaptive dynamic programming}, volume = {13}, year = {2025}, }
• (2025). Enhancing predictive maintenance in mining mobile machinery through a hierarchical inference network. ACCESS, 13, 59480-59504. (www)

  @article{ , abstract = {Mining mobile machinery in non-stationary operations faces high levels of wear and unpredictable stress, posing significant challenges for predictive maintenance (PdM). This paper introduces a hierarchical inference network for PdM consisting on edge sensor devices, gateways, and cloud services for real-time condition monitoring. The system dynamically can adjusts inference locations – on-device, on-gateway, or on-cloud – based on trade-offs between real-time demands and conditions such as accuracy, latency, and battery range. The edge-based architecture enables rapid decision-making directly on-sensor or on-gateway, achieving classification accuracies above 90% while reducing latency up to 30% and power consumption on sensor nodes by approximately 45% regarding the cloud inference mode. This is critical to ensure machinery uptime in remote, rugged environments. The use of Tiny-Machine-Learning (TinyML) optimization approaches allow optimal accuracy and model compression for efficient deployment of deep learning models on IoT edge devices with limited hardware resources. The ESN-PdM hierarchical framework offers a scalable and adaptive solution for reliable condition monitoring and anomaly detection, contributing to advancing technology in PdM frameworks for real-world industrial applications.}, archive = {J_ACCESS}, author = {Raúl de la Fuente and Luciano Radrigan and Anibal S. Morales}, doi = {10.1109/ACCESS.2025.3557405}, journal = {IEEE Access}, pages = {59480-59504}, shortjournal = {IEEE Access}, title = {Enhancing predictive maintenance in mining mobile machinery through a hierarchical inference network}, volume = {13}, year = {2025}, }
• (2025). Multi-scale feature pyramid network with camera artifact augmentation for pedestrian detection. ACCESS, 13, 59469-59479. (www)

  @article{ , abstract = {Nowadays, pedestrian detection is considered as an important area of applications in automated driving and surveillance systems. This paper presents a novel hybrid model termed as multi-scale feature pyramid network with camera artifact augmentation (MSFPN-CAA) that integrates Feature Pyramid Networks (FPN) with a fine-tuned YOLOv10 model for enhanced pedestrian detection. The model is augmented using various camera motion artifacts to simulate real-world dynamic environments. FPN is employed to extract multi-scale features. The fine-tuned YOLOv10 model predicts the bounding box regions. Non-maximum suppression (NMS) is applied to remove redundant detections. The paper presented the result with different batch size of the model, with different variants of YOLOV10. From the result analysis it was observed that batch size 16 is optimal batch size and variant YOLOV10n is the most accurate and YOLOV10s is the fastest version. Then result was compared with YOLOV9 and proposed fine- tuned YOLOV10 outperforms better as compared to YOLOV9. Then the result was presented for different camera artifacts. With all artifacts included, the model achieved a mAP of 72.9% with an average execution time of 4.5ms. Finally, the paper presented a comparative analysis with state-of-art models and achieved better performance.}, archive = {J_ACCESS}, author = {Ankit Shrivastava and Shanmugam Poonkuntran}, doi = {10.1109/ACCESS.2025.3556756}, journal = {IEEE Access}, pages = {59469-59479}, shortjournal = {IEEE Access}, title = {Multi-scale feature pyramid network with camera artifact augmentation for pedestrian detection}, volume = {13}, year = {2025}, }
• (2025). A genetic-algorithms matrix-factorization-based recommender system model using tags. ACCESS, 13, 59458-59468. (www)

  @article{ , abstract = {Recommender systems (RSs) are useful technology that can alleviate the problem of overload of information provided to users. In this research, we build a new RS, and we name it ETagMF. ETagMF is an Evolutionary-based Tags-based Matrix Factorization (MF) model. Recently, researchers have introduced several MF-based approaches to improve the performance of the recommendation. MF is a multiplication of the items and user preference matrices in order to predict the unknown rating of items. ETagMF replaces the latent factors with the tags. It then uses Genetic Algorithms to predict the unknown rated items. It aims at improving accuracy, speeding up the recommendation process, increasing transparency and interaction. As far as we know, we have not found in the literature any similar work that applies the evolutionary algorithm and the tag-based MF techniques to predict values for the unknown items. Experimentally, we use Movielens dataset, and we show that ETagMF achieves good results. Furthermore, it outperforms the other competitive and similar state-of-the-art evolutionary-based collaborative filtering RSs. We compare ETagMF versus the Evolutionary Based Matrix Factorization (EMF) method done by Navgran using 500 latent factors for EMF and 500 tags for ETagMF. EMF is similar to ETagMF except that ETagMF uses tags and EMF uses latent factors. We show that ETagMF outperforms EMF, and it is 2.41 times faster than EMF and 12 times faster than the traditional MF method.}, archive = {J_ACCESS}, author = {Manal Almaayah and Yousef Kilani and Ayoub Alsarhan and Mohammad Aljaidi and Mohammed Alshammari and Aadil Alshammari}, doi = {10.1109/ACCESS.2025.3550296}, journal = {IEEE Access}, pages = {59458-59468}, shortjournal = {IEEE Access}, title = {A genetic-algorithms matrix-factorization-based recommender system model using tags}, volume = {13}, year = {2025}, }
• (2025). Multi-band NIR colorization via dual-teacher color and structure distillation. ACCESS, 13, 59446-59457. (www)

  @article{ , abstract = {Near-infrared (NIR) imaging captures more details and textures with less noise in low-light environments compared to RGB, making it widely used in such scenarios. However, the lack of color in NIR poses challenges for human cognition and computer vision, necessitating its colorization. We propose a multi-band NIR imaging approach with dual-teacher knowledge distillation to better estimate original color and structure. The dual-teacher network, with color- and structure-teacher, separately instructs the student network on color and structural qualities. To fuse these features, the Color Guided Structure (CGS) and the Color Embedding (CE) modules are applied. The CGS module enhances correlation by synchronizing color and structure under the guidance of the color feature, while the CE module effectively fuses them. Our model retains color consistency and detailed structure information of objects. The source code and datasets will be available at https://github.com/ymin2570/Multi-Band-NIR-colorization/.}, archive = {J_ACCESS}, author = {Tae-Sung Park and Young-Min Jeong and Jong-Ok Kim}, doi = {10.1109/ACCESS.2025.3555632}, journal = {IEEE Access}, pages = {59446-59457}, shortjournal = {IEEE Access}, title = {Multi-band NIR colorization via dual-teacher color and structure distillation}, volume = {13}, year = {2025}, }
• (2025). IIoT’s risk odyssey: Navigating the risk propagation of illegal information flows. ACCESS, 13, 59422-59445. (www)

  @article{ , abstract = {Industrial Internet of Things (IIoT) refers to a broad network of low-cost, interconnected devices, including actuators, programmable logic controllers (PLCs), and sensors. Such environments are characterized by the vast amount of data exchanged among a wide range of devices, applications, and services. The scalability and decentralized nature of IIoT introduces considerable challenges for traditional security mechanisms. As a result, it is crucial to establish more robust security measures, enforce more effective access control policies, and efficiently manage information flows within business processes. In our prior research, we introduced a methodology for the assessment of information flows in IIoT environments and the detection of the illegal ones. Specifically, we utilized a risk-based methodology to model complex business processes as directed graphs. This approach enabled us to thoroughly analyze the interdependencies among participating objects. Through this analysis, we aimed to identify objects that are susceptible to initiating or being influenced by illegal information flows. In our current study, we investigate the propagation of the risk of illegal information flows within and across business processes. Finally, we apply centrality metrics to identify critical objects that require more efficient access control rules and policies in order to mitigate illegal information flows within the IIoT network. To the best of our knowledge, no previous research has explored the concept of risk-based detection of illegal information flows and examined potential propagation of risk in industrial environments.}, archive = {J_ACCESS}, author = {Argiro Anagnostopoulou and Ioannis Mavridis and Michael Athanasopoulos and Alexios Mylonas and Dimitris Gritzalis}, doi = {10.1109/ACCESS.2025.3555873}, journal = {IEEE Access}, pages = {59422-59445}, shortjournal = {IEEE Access}, title = {IIoT’s risk odyssey: Navigating the risk propagation of illegal information flows}, volume = {13}, year = {2025}, }
• (2025). Enhancing user experience in interactive virtual museums for cultural heritage learning through extended reality: The case of sanxingdui bronzes. ACCESS, 13, 59405-59421. (www)

  @article{ , abstract = {As a digital extension of physical museums, virtual museums overcome the limitations of space and resources, meet the demand for remote access, and play an important role in cultural heritage preservation and education. However, traditional virtual museums are often limited to simple navigation and display functions, lacking in-depth interaction and struggling to provide personalized learning experiences. To address this, this paper designs an interactive virtual museum (IVM) system, using the Sanxingdui culture as an example, which includes various types of serious games in nine applications. The aim is to provide a more immersive and personalized cultural heritage learning experience. We designed and implemented three types of IVMs: a personal computer-based virtual museum (PC-IVM), an augmented reality virtual museum (AR-IVM), and a mixed reality virtual museum (MR-IVM), and evaluates their impact on the museum educational experience. In a within-subjects design experiment, 30 school participants and 12 museum visitors were recruited to measure 9 user experience dimensions, including learning motivation, engagement, flow experience, etc. The quantitative results showed that MR-IVM significantly outperformed other forms in all dimensions and demonstrated higher usability. Qualitative analyses further indicated that the majority of participants enjoyed experiencing the MR-IVM platform, and was more popular in action-adventure and puzzle-solving games. However, AR-IVM was more favored by participants in casual-competitive games. The study suggests that the designed extended reality (XR) system has a positive impact on museum education for cultural heritage, effectively complementing the physical museum experience and enhancing user engagement. Through gamified learning, it further improves learning outcomes. The results provide valuable insights for the future application of XR technology in cultural heritage education and open new directions for the development of virtual museums and the promotion of game-based learning.}, archive = {J_ACCESS}, author = {Xin Huang and Yuzhi Li and Feng Tian}, doi = {10.1109/ACCESS.2025.3556938}, journal = {IEEE Access}, pages = {59405-59421}, shortjournal = {IEEE Access}, title = {Enhancing user experience in interactive virtual museums for cultural heritage learning through extended reality: The case of sanxingdui bronzes}, volume = {13}, year = {2025}, }
• (2025). A novel recursive methodology for power flow analysis based on the holomorphic embedding method using the total multiplication of polynomials. ACCESS, 13, 59390-59404. (www)

  @article{ , abstract = {This paper introduces an innovative recursive methodology for ultra-fast load flow analysis. The approach is based on the Holomorphic Embedding Load Flow Method (HELM) and employs the Total Multiplication of Polynomials (TMP) to enhance computational efficiency and accuracy. Unlike traditional HELM, the proposed procedure bypasses the need for analytical continuation, directly solving the load flow by simple polynomial multiplication. This approach provides solutions with reduced computational time compared to alternative methods. The methodology deduces general expressions for calculating higher-order terms of the Maclaurin series, eliminating the need for time-consuming matrix inversions or comparisons with Padé approximants. To demonstrate the effectiveness of the proposed method, computational simulations were performed using a wide variety of test systems, including the IEEE 118-bus transmission system and the equivalent 476-bus Brazilian distribution system. The main contribution is the TMP procedure, which accelerates the recursive solution process. Due to a significantly reduced computational time for higher-order term calculations, the proposed method is well suited for real-time applications, including ill-conditioned and overloaded power networks. This feature enables an accurate calculation of the maximum loadability point in the context of voltage stability assessment, allowing the determination of the exact solution even in cases where the traditional Newton-Raphson load flow fails to converge.}, archive = {J_ACCESS}, author = {Glaucus R. S. Lima and Edimar J. Oliveira and Leonardo W. Oliveira and Igor D. Melo}, doi = {10.1109/ACCESS.2025.3556368}, journal = {IEEE Access}, pages = {59390-59404}, shortjournal = {IEEE Access}, title = {A novel recursive methodology for power flow analysis based on the holomorphic embedding method using the total multiplication of polynomials}, volume = {13}, year = {2025}, }
• (2025). Emotion recognition method using U-net neural network with multichannel EEG features and differential entropy characteristics. ACCESS, 13, 59377-59389. (www)

  @article{ , abstract = {To improve accuracy in EEG-based emotion recognition and address current research limitations, providing a more robust framework for scientific study and mental health support. This paper presents an emotion recognition method tailored for small sample training data, effectively addressing the issue of emotion classification based on multichannel continuous electroencephalogram (EEG) signals. The proposed method first extracts differential entropy features from EEG signals and maps them into two-dimensional matrices based on their spectral characteristics. These preprocessed original data and five different frequency band matrices are then stacked into three-dimensional multichannel data, which are fed into the 3D-EEGU-Net model for classification. Experiments conducted on the public SEED dataset demonstrate an emotion recognition accuracy of 92.34%. Furthermore, to enhance the accuracy of emotion recognition for specific populations, the size of data blocks (patches) and model hyperparameters were dynamically adjusted. In the valence-based emotion ternary classification task conducted on the DEAP public dataset, the method achieved an emotion recognition accuracy of 72.02%. Comparative experiments show that the proposed method exhibits significant superiority in emotion recognition performance on both datasets. This research not only provides a new approach to EEG emotion recognition but also holds substantial potential for applications in detecting psychological disorders and physical conditions in individuals from specialized occupational groups.}, archive = {J_ACCESS}, author = {Haitao Huang and Ying Deng and Bowen Hao and Wenguang Liu and Xiaoyu Tu and Guojun Zeng}, doi = {10.1109/ACCESS.2024.3497160}, journal = {IEEE Access}, pages = {59377-59389}, shortjournal = {IEEE Access}, title = {Emotion recognition method using U-net neural network with multichannel EEG features and differential entropy characteristics}, volume = {13}, year = {2025}, }
• (2025). Hybrid PI and fuzzy logic control for energy optimization in train operations. ACCESS, 13, 59366-59376. (www)

  @article{ , abstract = {This paper presents a Fuzzy logic-based train control algorithm designed to enhance energy efficiency across a complete railway route. The proposed framework dynamically integrates practical information such as curvature data, gradient profiles, and station schedules to optimize acceleration, coasting, and braking phases. By using Fuzzy logic, the algorithm adaptively balances energy consumption, travel time, and stopping precision. Key features include predictive speed scanning and adaptive PI control, enabling comprehensive energy optimization. Simulation results confirm significant energy savings or voltage regulation while maintaining operational reliability and precision. This study highlights the potential of Fuzzy-based control systems in advancing sustainable railway operations.}, archive = {J_ACCESS}, author = {Hwan-Hee Cho and Jae-Won Kim and Min-Sup Song and Chi-Myeong Yun and Gyu-Jung Cho and Zhongbei Tian}, doi = {10.1109/ACCESS.2025.3555592}, journal = {IEEE Access}, pages = {59366-59376}, shortjournal = {IEEE Access}, title = {Hybrid PI and fuzzy logic control for energy optimization in train operations}, volume = {13}, year = {2025}, }
• (2025). Reactive power controller for under-excitation limiter with regard to electromechanical oscillations. ACCESS, 13, 59339-59365. (www)

  @article{ , abstract = {This paper presents a novel tuning algorithm for the adaptive reactive power (Var) PI controller as a control part of under-excitation limiter (UEL). The proposed algorithm is designed to adapt to changes in the synchronous generator (SG) plant dynamics without requiring prior knowledge of generator and system parameters. The algorithm is based on the reduced first-order linear plant model, whose parameters are estimated using the Recursive least-squares (RLS) algorithm. Considering that the SG is close to its stability limit when the UEL is operational, if the Var controller is not tuned well, the UEL can generate a negative damping torque on electromechanical oscillations and even lead to SG instability. The proposed algorithm solves this problem by targeting the specific frequency bandwidth of the Var control system that excludes the detected frequency of electromechanical oscillations. This mitigates the negative impact of the UEL on the damping of electromechanical oscillations and prevents overlapping of UEL action with the power system stabilizer (PSS) action. Once the plant model parameters are estimated, and the targeted control system frequency band is identified based on the measured electromechanical oscillation frequency, the controller parameters are tuned using pole-zero cancellation method. The effectiveness and performance of the proposed tuning procedure, encompassing online identification of reduced linear plant model parameters, online measurement of electromechanical oscillation frequency, and subsequent online tuning of the UEL Var controller, have been demonstrated through both simulation and experimental studies. Simulations utilized nonlinear and linear models of a 240 MVA SG connected to an infinite bus and Kundur’s two-area power system model. Experimental validation was conducted in a laboratory environment using a reduced-scale, low-power SG. Dynamic performance indices obtained from various test cases demonstrate the robustness and stability of the Var control system incorporating the UEL controller proposed in this paper.}, archive = {J_ACCESS}, author = {Dušan Joksimović and Djordje Stojić and Slavko Veinović and Darko Marčetić}, doi = {10.1109/ACCESS.2025.3555960}, journal = {IEEE Access}, pages = {59339-59365}, shortjournal = {IEEE Access}, title = {Reactive power controller for under-excitation limiter with regard to electromechanical oscillations}, volume = {13}, year = {2025}, }
• (2025). Automatic detection and classification of dental anomalies and tooth types using transformer-based yolo with GA optimization. ACCESS, 13, 59326-59338. (www)

  @article{ , abstract = {Dental pathology detection and tooth type categorization are crucial yet often error-prone and time-consuming tasks in dental diagnostics. This paper presents a novel approach using Transformer-Based You Only Look Once (YOLO) for automating dental pathology detection and tooth type categorization from panoramic X-ray images. Manual dental examinations are error-prone and time consuming. Therefore automated systems are required. Preprocessing techniques such as image resizing, rotation, Gaussian smoothing, and histogram scaling are used to enhance image quality and analysis. A significant enhancement is the integration of a multi-head self-attention block into the Cross Stage Partial Darknet (CSPDarknet) backbone within the YOLOv4 architecture. This addition enabled the model to capture comprehensive contextual information and learn distinct feature representations from the dental images. Inspired by Transformer models, the multi-head self-attention mechanism improves the capacity of the model to intricate spatial relationships in images, enhancing detection and classification performance. In addition, the system employs Genetic Algorithm (GA) optimization for hyperparameter tuning, which fine-tunes model parameters to maximize performance metrics and enhance accuracy and robustness in dental diagnostics. The system achieved an average precision, recall rate of 99.5% and F1 score of 99.41% and a detection and classification accuracy of 99.31%. These results highlight the effectiveness of advanced preprocessing, deep learning enhancements, and GA optimization for precise dental pathology detection and tooth type categorization using panoramic radiography.}, archive = {J_ACCESS}, author = {Sanabam Bineshwor Singh and Anuradha Laishram and Khelchandra Thongam and Khumanthem Manglem Singh}, doi = {10.1109/ACCESS.2025.3556523}, journal = {IEEE Access}, pages = {59326-59338}, shortjournal = {IEEE Access}, title = {Automatic detection and classification of dental anomalies and tooth types using transformer-based yolo with GA optimization}, volume = {13}, year = {2025}, }
• (2025). Integrated time series analysis, clustering, and forecasting for energy efficiency optimization and tariff management. ACCESS, 13, 59309-59325. (www)

  @article{ , abstract = {This manuscript explores an advanced approach to the analysis of power demand and energy consumption, focusing on anomaly detection and the characterization of consumption patterns in different energy consumer units in the state of Ceará, Brazil. The research employs time series techniques and clustering algorithms to identify atypical consumption profiles and regular patterns based on average monthly consumption and contracted power demand. Through the application of specialized algorithms, consumer groups are identified, revealing trends and seasonality, providing insights into consumption behaviors. Additionally, the study incorporates consumption forecasting methods to estimate the behavior of new consumer units or those with limited historical data, optimizing the definition of contracted demand and promoting energy efficiency. Databases with daily measured values of energy consumption and power demand for 185 energy consumer units in the state of Ceará, Brazil, were analyzed. The research demonstrated that energy costs for the analyzed public units could be reduced by approximately 2.7 million reais. To ensure the reliability of the clustering process, different techniques were applied and compared. The K-Means and Mini-Batch K-Means algorithms were used to segment consumer units based on contracted demand and average monthly consumption. Additionally, the BIRCH algorithm was evaluated in combination with dimensionality reduction methods, such as Principal Component Analysis (PCA) and Autoencoders, to enhance clustering performance. These comparative analyses provided a deeper understanding of the clustering effectiveness and the suitability of each approach for identifying patterns in energy consumption. The findings reinforce the importance of using machine learning techniques to improve tariff selection, anomaly detection, and demand forecasting, contributing to better energy management and efficiency.}, archive = {J_ACCESS}, author = {Anderson Jhones Passos Nascimento and Menaouar Berrehil El Kattel and José Antônio Fernandes de Macêdo and Fernando Luiz Marcelo Antunes}, doi = {10.1109/ACCESS.2025.3554756}, journal = {IEEE Access}, pages = {59309-59325}, shortjournal = {IEEE Access}, title = {Integrated time series analysis, clustering, and forecasting for energy efficiency optimization and tariff management}, volume = {13}, year = {2025}, }
• (2025). Material segmentation using 1-D convolutional neural network with transient histogram. ACCESS, 13, 59295-59308. (www)

  @article{ , abstract = {This study introduces a method for material classification using transient histograms obtained via a single-photon avalanche diode (SPAD) sensor. Temporal resolution in optical sensing plays a crucial role in material classification and surface segmentation, particularly for distinguishing materials with similar visual properties. In this study, SPAD sensors were utilized to capture transient histograms with temporal resolutions ranging from 13 picoseconds to 208 picoseconds, enabling precise extraction of temporal signatures for various materials. A comparative evaluation of classification techniques, including one-dimensional convolutional neural networks (1-D CNN), random forest (RF), support vector classifier (SVC), and k-nearest neighbors (KNN), was conducted to assess the impact of temporal resolution and exposure time on classification accuracy. 1-D CNN achieved the highest classification accuracy of 99.25% at a temporal resolution of 13 ps and an exposure time of 0.09 s, significantly outperforming other methods. Additionally, the proposed SPAD-based system was evaluated for material segmentation on non-planar surfaces. In a real-world experiment, 1-D CNN achieved an overall accuracy of 87.5% in differentiating visually similar materials, demonstrating the effectiveness of transient histograms for material classification where conventional RGB-based methods fail. These findings highlight the potential of SPAD sensors combined with advanced classification techniques to enhance material classification and segmentation, providing a versatile framework for applications in robotics, computer vision, and optical sensing.}, archive = {J_ACCESS}, author = {Yohanssen Pratama and Kazuya Kitano and Takahiro Kushida and Yuki Fujimura and Takuya Funatomi and Yasuhiro Mukaigawa}, doi = {10.1109/ACCESS.2025.3554776}, journal = {IEEE Access}, pages = {59295-59308}, shortjournal = {IEEE Access}, title = {Material segmentation using 1-D convolutional neural network with transient histogram}, volume = {13}, year = {2025}, }
• (2025). Alleviating local optima in bridge bidding via diverse PPO ensembling. ACCESS, 13, 59285-59294. (www)

  @article{ , abstract = {Bridge, as a strategic card game, has a bidding phase that critically influences the final outcome. However, optimizing the policy during this phase is extremely challenging owing to incomplete information. Models often converge to local optima, which can severely limit their overall performance, particularly in the context of bridge bidding, where such convergence can result in suboptimal bidding decisions. To address this, we propose Diverse PPO (Proximal policy optimization) Ensembling, a method that improves policy updates by incorporating diverse constraints, including different clipping functions and regularized entropy, to promote exploration and mitigate the risk of becoming trapped in local optima. These improvements are further integrated through ensemble learning to create a robust and efficient strategic framework. Additionally, techniques such as policy initialization accelerate the self-play process, whereas search methods utilize pruning to filter out irrelevant situations, further enhancing the model performance. Our model outperformed WBridge5 by 0.73 IMPs (International Match Points) during the deep reinforcement learning phase, further improving to a 0.99 IMP advantage after incorporating search methods.}, archive = {J_ACCESS}, author = {Yi Zhang and Fengyi Song and Chaohui Zhang and Tao Zhang and Weiguo Deng}, doi = {10.1109/ACCESS.2025.3555303}, journal = {IEEE Access}, pages = {59285-59294}, shortjournal = {IEEE Access}, title = {Alleviating local optima in bridge bidding via diverse PPO ensembling}, volume = {13}, year = {2025}, }
• (2025). Voltage stability assessment through modal analysis using a smooth formulation of the power flow problem based on sigmoid functions. ACCESS, 13, 59275-59284. (www)

  @article{ , abstract = {This paper introduces a voltage stability analysis based on an innovative formulation of the power flow problem designed to address and eliminate discontinuities associated with reactive power limits from synchronous generators, synchronous compensators, and static VAr compensators. By replacing these discontinuities with appropriately tuned sigmoid functions, we achieve smooth behavior without compromising accuracy. A key advantage of this formulation is its ability to convert limit-induced bifurcations into saddle-node bifurcations. This conversion allows the voltage security studies to focus solely on identifying saddle-node bifurcations, leading to significant computational efficiency. In the present context of modern power systems, characterized by growing demand and uncertainties in generation capacity, this new approach is relevant for ensuring the computational feasibility of highly uncertain scenarios. The effectiveness of the proposed formulation is validated using a modal analysis of the reduced Jacobian matrix, which focuses mainly on the smooth control equations incorporated into the solution of the problem using Newton’s method. Our findings are substantiated by simulations on four test systems, including the IEEE 118-Bus system, which shows the conversion process of bifurcations as described. The results presented validate and indicate the effectiveness of the proposed technique.}, archive = {J_ACCESS}, author = {João Pedro Peters Barbosa and João Alberto Passos Filho and Rodrigo Andrade Ramos and Luís Fernando Costa Alberto}, doi = {10.1109/ACCESS.2025.3555935}, journal = {IEEE Access}, pages = {59275-59284}, shortjournal = {IEEE Access}, title = {Voltage stability assessment through modal analysis using a smooth formulation of the power flow problem based on sigmoid functions}, volume = {13}, year = {2025}, }
• (2025). The ferro-power MOSFET: Enhancing short-circuit robustness in power switches with a ferroelectric gate stack. ACCESS, 13, 59264-59274. (www)

  @article{ , abstract = {Power Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) are essential in modern electronics, enabling efficient power conversion and control in a wide range of applications. Wide bandgap semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) have been shown to boost device performance, by providing higher efficiency and faster switching for high-power, high-frequency applications. Although optimised for standard operation, their performance under short circuit (SC) events remains critical mostly because of the higher current density. Notably, SiC MOSFETs can only withstand SC conditions for a few microseconds, necessitating larger layouts or faster control electronics to prevent the catastrophic failure of the device. This paper introduces a novel power device, the Ferro-Power MOSFET, that integrates a ferroelectric material into the gate stack of a power SiC MOSFET. This innovative approach leverages the temperature-dependent dielectric constant of ferroelectrics to effectively reduce the temperature rise during short-circuit events without altering the basic layout of the device neither the control electronics. TCAD simulations and design optimisation of a 1.2 kV SiC MOSFET reveal substantial enhancements, achieving temperature and current reductions of up to 31% and 42%, respectively, without compromising current conduction during normal operation. It stands to reason that this concept is general and can be broadly applied to any power MOSFET. Moreover, it is bolstered by recent achievements in ferroelectricity in CMOS-compatible hafnium oxide (HfO2), thus prospecting concrete experimental developments in power semiconductors.}, archive = {J_ACCESS}, author = {Marco Boccarossa and Luca Maresca and Alessandro Borghese and Michele Riccio and Giovanni Breglio and Andrea Irace and Giovanni A. Salvatore}, doi = {10.1109/ACCESS.2025.3555931}, journal = {IEEE Access}, pages = {59264-59274}, shortjournal = {IEEE Access}, title = {The ferro-power MOSFET: Enhancing short-circuit robustness in power switches with a ferroelectric gate stack}, volume = {13}, year = {2025}, }
• (2025). Modeling and simulation of insulin pump set failures based on the power flow method. ACCESS, 13, 59254-59263. (www)

  @article{ , abstract = {The insulin pump is a critical device for intensive insulin therapy in diabetic patients. Failures in insulin pump sets such as blockage and leakage can disrupt normal insulin delivery, potentially causing hyperglycemia or life-threatening diabetic ketoacidosis. Developing a mathematical model to accurately describe the failure mechanisms of insulin pump sets is essential for fault diagnosis and performance optimization. However, modeling these systems is challenging due to the interplay of rigid and elastic constraints (e.g., needles and hoses) and multi-domain interactions (e.g., fluid insulin and solid components). To address these challenges, this study establishes a mathematical model for insulin pump sets with blockage and leakage faults based on the power flow method. The model is used to investigate the effects of these faults on insulin fluid dynamics, with a focus on impacts of failures at various positions. Numerical results demonstrate a maximum relative error of 0.57% compared to simulation data. Findings reveal that leakage faults closer to the flow source significantly reduce the output flow rate and chamber pressure. Conversely, as blockage approach the flow source, the impact on reducing flow rate diminishes, while their influence on increasing chamber pressure becomes more pronounced. Notably, when the blockage thickness exceeds 0.6 mm, both flow rate and pressure are markedly affected, with the effects intensifying as the thickness increases.}, archive = {J_ACCESS}, author = {Weijie Wang and Dinghui Guo and Long Quan and Shiwei Liu}, doi = {10.1109/ACCESS.2025.3554261}, journal = {IEEE Access}, pages = {59254-59263}, shortjournal = {IEEE Access}, title = {Modeling and simulation of insulin pump set failures based on the power flow method}, volume = {13}, year = {2025}, }
• (2025). Sketch face recognition method based on local-global adapter. ACCESS, 13, 59244-59253. (www)

  @article{ , abstract = {The objective of sketch-based face identification is to match a target individual’s facial features from a collection of photographs using a sketched portrait as the search query. The method based on Contrastive Language-Image pre-training (CLIP) brings matched sketch and optical images closer together through learnable text tokens, therefore improving recognition performance. However, the existing sketch face datasets are relatively small. Training the CLIP with a large number of parameters on these datasets makes model overfit, leading to suboptimal performance. To address the above issue, we propose a sketch face recognition method based on Local-Gobal Adapter (LGAdapter). The method uses a transformer to capture facial details, and we segment the features through H windows, thus limiting the self-attention mechanism to localized windows, which better captures detailed information. In order to enhance the extraction of more comprehensive representational characteristics, we incorporate a streamlined Graph Convolutional Network (GCN) module subsequent to the local module. In addition, we propose a two-stage training strategy to fully utilize the LGAdapter to obtain more accurate visual features. In the first phase, only text tokens are optimized; in the second phase, only the LGAdapter is fine-tuned. The outcomes of our experiments reveal that our approach surpasses existing leading-edge methods across all three widely recognized datasets, namely UoM-SFGS, CUFSF, and PRIP-VSCG.}, archive = {J_ACCESS}, author = {Huanyu Bian and Boqian Lv and Yanan Guo and Benkui Zhang and Kangning Du}, doi = {10.1109/ACCESS.2025.3553314}, journal = {IEEE Access}, pages = {59244-59253}, shortjournal = {IEEE Access}, title = {Sketch face recognition method based on local-global adapter}, volume = {13}, year = {2025}, }
• (2025). A rapid concrete crack detection method based on improved YOLOv8. ACCESS, 13, 59227-59243. (www)

  @article{ , abstract = {An improved YOLOv8 (You Only Look Once version 8) model is proposed to tackle the challenges of low detection accuracy and slow speed resulting from the complex background and shape diversity of concrete cracks. Firstly, a lightweight feature fusion module, GE_Conv, is designed by integrating the Ghost Module and Efficient Channel Attention in series. This module is embedded into the neck network to preserve more feature information during downsampling and accelerate the model’s inference speed. Secondly, the DBB_Bottleneck is introduced into the C2f module, combining the lightweight GE_Conv with the structurally re-parameterized Diverse Branch Block, enhancing the model’s multi-scale feature extraction capability. Furthermore, the introduction of the GF_Detect detection head significantly reduces the number of model parameters while improving detection performance. Finally, the WIoUv3 loss function is employed to dynamically assign anchor boxes of varying qualities, thereby enhancing the accuracy of anchor box positioning. Experimental results demonstrate that the proposed model achieves a detection precision of 92.9% and a mAP@50 (mean average precision at an Intersection over Union threshold of 0.5) of 77.8%. Compared to state-of-the-art algorithms such as Faster R-CNN, SSD, RetinaNet, YOLOv5s, YOLOv7-tiny, and the original YOLOv8s, the proposed model exhibits superior performance in both detection accuracy and generalization capability. Additionally, the model achieves an average detection time of 10.20 ms per image, demonstrating its practical feasibility. This study not only improves the accuracy and speed of crack detection but also significantly reduces the computational complexity of the model, advancing the development of lightweight and practical crack detection algorithms. The proposed model can be widely applied to crack inspection tasks for roads, bridges, coal mine shafts, and other scenarios, providing efficient and reliable technical support for crack maintenance and management.}, archive = {J_ACCESS}, author = {Yongzhen Wang and Jiacong He}, doi = {10.1109/ACCESS.2025.3555825}, journal = {IEEE Access}, pages = {59227-59243}, shortjournal = {IEEE Access}, title = {A rapid concrete crack detection method based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Optimal sizing, techno-economic feasibility and reliability analysis of hybrid renewable energy system: A systematic review of energy storage systems’ integration. ACCESS, 13, 59198-59226. (www)

  @article{ , abstract = {One of the most significant ways to improve energy reliability and lessen reliance on fossil fuels is to combine renewable energy sources with energy storage systems. Using wind, solar, and battery storage as case studies, the article examines hybrid renewable energy system (HRES) size, optimization, techno-economic potential, and reliability in extensive detail. In order to minimize expenses and emissions while satisfying energy demands, the sizing process consists of evaluating the appropriate capacity of each component. In order to tackle the growing demand with a low carbon footprint, this study investigates the prospect of leveraging hybrid renewable energy (HRE) storage. System demands, budget, and performance indicators are some of the most critical considerations when selecting an energy storage system (ESS) for a renewable energy system. Whether or not the storage option is appropriate for HRE systems depends on the setup requirements. The selection of an ESS technology necessitates taking into account aspects like energy and power requirements, efficiency, cost-effectiveness, versatility, and reliability. The investigation also considers factors such as electricity long-term viability, dependability, technical and financial feasibility, and ecological sustainability when evaluating hybrid renewable energy power-producing technologies. Improving dependability and smoothing down power output are two major benefits of ESS integration. The findings show that integrating HRES with ESS can lead to more sustainable energy systems, providing a long-term, reliable, and cost-effective solution. Findings emphasize the need for further study of optimization methods, meta-heuristic algorithm strategies, system components, design constraints, and desired techniques.}, archive = {J_ACCESS}, author = {Akhlaque Ahmad Khan and Ahmad Faiz Minai and Rakesh Kumar Godi and Vijay Shankar Sharma and Hasmat Malik and Asyraf Afthanorhan}, doi = {10.1109/ACCESS.2025.3535520}, journal = {IEEE Access}, pages = {59198-59226}, shortjournal = {IEEE Access}, title = {Optimal sizing, techno-economic feasibility and reliability analysis of hybrid renewable energy system: A systematic review of energy storage systems’ integration}, volume = {13}, year = {2025}, }
• (2025). AERU-net: Adaptive edge recovery and attention U-shaped network for remote sensing image super-resolution. ACCESS, 13, 59177-59197. (www)

  @article{ , abstract = {Remote sensing image super-resolution (RSISR) has recently garnered significant attention, with major advances achieved through deep learning neural networks. Remote sensing images (RSIs) contain rich, recurring textural and structural patterns that vary substantially across different classes of RSIs. While state-of-the-art RSISR methods have begun incorporating attention mechanisms to explore and recover multi-scale global features, they face challenges in identifying the most relevant details and recovering complex edge patterns. In this research, we propose a novel Adaptive Edge Recovery and Attention U-shaped Network (AERU-Net), which integrates an Edge Recovery Block (ERB), Channel Attention Module (CAM) and Spatial Attention Module (SAM) within a three-level encoder-decoder architecture. Furthermore, we introduce a novel adaptive Cross-scale Interaction (CSI) module along with a Laplacian Edge Preserver (LEP) block to connect encoders and decoders across different network scales (levels). Our design facilitates the gradual acquisition of contextual information while enhancing the retention of high-frequency edge details. Performance analysis conducted on four RSI datasets (UCMerced, RSCNN7, AID, and WHU-RS19) in three up-sampling scales ( $\times 2$ , $\times 3$ , and $\times 4$ ) demonstrates that AERU-Net achieves significant improvements over state-of-the-art models in PSNR, SSIM, SCC, and SAM. Our model strikes an effective balance between accuracy and network complexity. The code will be released on GitHub and can be accessed at https://github.com/CUEE-MDAP/aeru-net.}, archive = {J_ACCESS}, author = {Amir Hajian and Supavadee Aramvith}, doi = {10.1109/ACCESS.2025.3556312}, journal = {IEEE Access}, pages = {59177-59197}, shortjournal = {IEEE Access}, title = {AERU-net: Adaptive edge recovery and attention U-shaped network for remote sensing image super-resolution}, volume = {13}, year = {2025}, }
• (2025). A semantic weight adaptive model based on visual question answering. ACCESS, 13, 59170-59176. (www)

  @article{ , abstract = {Visual Question Answering (VQA) is an advanced artificial intelligence task that combines computer vision and natural language processing technologies. Its core objective is to enable computers to accurately answer natural language questions posed by users about image content, with these questions being either open-ended or closed-ended. For instance, the system must address closed-ended questions such as “Does the image contain 11 goats?” and open-ended ones like “Where was this photo taken?” To accomplish this task, computers must not only deeply analyze image content but also precisely comprehend and respond to complex natural language expressions.However, current VQA models often struggle when dealing with questions requiring deep semantic analysis due to their inability to fully capture the semantic information within the questions. This limitation significantly hinders the models’ capacity to decipher complex relationships between objects in images and perform high-level semantic reasoning.To address this challenge and recognizing the differing natures of open-ended and closed-ended tasks, we innovatively propose a conditional reasoning model called the Semantic Weight Adaptive Model Network (SWAMN). The crux of this model lies in its ability to automatically extract task-relevant information from questions to dynamically guide the fusion process of multimodal features. This means that SWAMN can more intelligently integrate image and language information to provide more accurate answers to user questions.To validate the effectiveness of the SWAMN model, we conducted extensive ablation studies on the benchmark dataset VQA V2.0. Through both qualitative and quantitative evaluations, we not only delved into the fundamental reasons for the model’s outstanding performance but also demonstrated that SWAMN achieved an overall accuracy of 70.82% on test-dev, significantly surpassing current state-of-the-art models and setting a new milestone in the field of VQA.}, archive = {J_ACCESS}, author = {Li Huimin and Li Xuan and Chen Yan}, doi = {10.1109/ACCESS.2024.3442129}, journal = {IEEE Access}, pages = {59170-59176}, shortjournal = {IEEE Access}, title = {A semantic weight adaptive model based on visual question answering}, volume = {13}, year = {2025}, }
• (2025). Selective reading for arabic sentiment analysis. ACCESS, 13, 59157-59169. (www)

  @article{ , abstract = {This work introduces a novel deep learning method for Arabic sentiment analysis, arguing that reading the entire input sequence is not always necessary. Many texts can be accurately classified without processing all input tokens. The method employs a reinforcement learning agent that selects relevant tokens using a selection policy network. Instead of predicting sentiment polarity from the entire input, the model focuses only on tokens chosen by the policy network. To empirically evaluate the proposed method, experiments were carried out on three Arabic sentiment analysis datasets: Large Arabic Book Reviews (LABR), Hotels Arabic Reviews Data (HARD), and Arabic Sentiment Tweets Dataset (ASTD). The results demonstrate a significant improvement in Arabic sentiment classification with the selective reading method, achieving state-of-the-art accuracy while using only a fraction of the tokens. However, the approach introduces additional computational cost due to the reinforcement learning component, and its scalability to larger datasets might require further optimization.}, archive = {J_ACCESS}, author = {Mohamed Zouidine and Mohammed Khalil}, doi = {10.1109/ACCESS.2025.3556976}, journal = {IEEE Access}, pages = {59157-59169}, shortjournal = {IEEE Access}, title = {Selective reading for arabic sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Opportunistic dynamic architecture for class-incremental learning. ACCESS, 13, 59146-59156. (www)

  @article{ , abstract = {Continual learning has attracted increasing attention over the last few years, as it enables to continually learn new tasks over time, which has significant implication to many real-world applications. A large number of continual learning techniques are proposed and achieve promising performance; however, many of them commit to a fixed, large architecture at the beginning, which can waste the memory space and incur high training cost. To directly tackle this challenge, we propose an Opportunistic Dynamic Architecture, ODA, based on mixture of experts. ODA can automatically grow with more experts for new incoming tasks and opportunistically shrink by merging experts with similar weights. We evaluated ODA on three commonly used datasets: CIFAR-100, CUB, and iNaturalist, and compared against eight existing continual learning techniques. ODA not only outperforms these techniques but does so with a parameter size that is slightly smaller on average, maintaining memory efficiency without compromising accuracy. Furthermore, ODA achieves this with only around 16% of the training time across all datasets when updating for each new task, making it a highly resource-efficient solution for continual learning applications.}, archive = {J_ACCESS}, author = {Fahrurrozi Rahman and Andrea Rosales Sanabria and Juan Ye}, doi = {10.1109/ACCESS.2025.3556471}, journal = {IEEE Access}, pages = {59146-59156}, shortjournal = {IEEE Access}, title = {Opportunistic dynamic architecture for class-incremental learning}, volume = {13}, year = {2025}, }
• (2025). Pre-assembly core loss measurement of electric motor cores: A review of conventional and advanced technologies. ACCESS, 13, 59130-59145. (www)

  @article{ , abstract = {Accurate core loss measurement considering manufacturing effects is essential for improving motor efficiency and reliability. This paper explores pre-assembly core loss measurement techniques in electric motors, focusing on the influence of manufacturing processes and the efficacy of core loss measurement methods. Traditional methods such as the Epstein frame, Single Sheet Tester (SST), and toroidal core measurement are discussed, along with recent advancements in measurement techniques. Experimental methods with an excitation yoke placed inside a stacked stator for core loss measurement, mainly under an alternating magnetic field, are presented. The application of advanced core loss measurement in industrial settings highlights that automation plays a vital role in enhancing measurement efficiency. The gaps in the existing literature and specific areas where further research is needed are discussed.}, archive = {J_ACCESS}, author = {Sudesh V. Pathirannahalage and Lukasz Mierczak and Berker Bilgin}, doi = {10.1109/ACCESS.2025.3556905}, journal = {IEEE Access}, pages = {59130-59145}, shortjournal = {IEEE Access}, title = {Pre-assembly core loss measurement of electric motor cores: A review of conventional and advanced technologies}, volume = {13}, year = {2025}, }
• (2025). Metrics and algorithms for identifying and mitigating bias in AI design: A counterfactual fairness approach. ACCESS, 13, 59118-59129. (www)

  @article{ , abstract = {The rapid advancements in artificial intelligence (AI) have revolutionized industries such as healthcare, finance, and education. However, these advancements have also intensified ethical concerns regarding bias, fairness, and accountability in AI systems. Traditional fairness evaluation methods primarily focus on dataset-level biases, overlooking biases arising from model decision-making processes. This study introduces a novel framework for identifying, evaluating, and mitigating biases in AI models using counterfactual fairness, a robust approach that simulates alternative outcomes to minimize discriminatory effects. The proposed methodology integrates fairness-aware data preprocessing, feature selection, and model optimization strategies, ensuring equitable treatment across demographic groups. To validate the framework, we conducted empirical experiments using random forest and eXtreme Gradient Boosting models on the xAPI-Edu-Data dataset. Our results demonstrate significant improvements in demographic parity and equal opportunity fairness metrics while maintaining high predictive performance. Furthermore, comparative analysis with existing bias mitigation techniques confirms that our approach effectively reduces bias propagation in AI decision-making processes. By incorporating counterfactual fairness into AI design, this study provides a scalable and adaptable solution for ensuring ethical AI deployments aligned with regulatory standards.}, archive = {J_ACCESS}, author = {Dongsoo Moon and Seongjin Ahn}, doi = {10.1109/ACCESS.2025.3556082}, journal = {IEEE Access}, pages = {59118-59129}, shortjournal = {IEEE Access}, title = {Metrics and algorithms for identifying and mitigating bias in AI design: A counterfactual fairness approach}, volume = {13}, year = {2025}, }
• (2025). Spectral-spatial convolutional hybrid transformer for hyperspectral image classification. ACCESS, 13, 59102-59117. (www)

  @article{ , abstract = {The combination of Convolutional neural networks (CNNs) and Transformers has achieved excellent performance in hyperspectral image classification due to the characteristics of local features extraction and long-range dependencies capture. However, how to integrate the spectral and spatial features effectively after dimensionality reduction while retaining key information remains to be optimized further. To address this issue, the Spectral-spatial convolutional hybrid Transformer (SSCHFormer) hyperspectral classification model is proposed in this article. First, the spectral pyramid 3D convolution and 2D convolution are combined to extract joint and detailed spectral-spatial features. Then, the hybrid convolutional transposed attention mechanism is introduced to enhance the correlation between the local and global features further and perceive the complex spatial and spectral information while reducing computational complexity. Furthermore, the gated feedforward network is designed to enhance the ability to distinguish subtle differences between different classes. Extensive experiments are implemented on four public datasets and demonstrate the superior classification performances of the proposed SSCHFormer to those of other advanced networks.}, archive = {J_ACCESS}, author = {Haixin Sun and Jingwen Xu and Fanlei Meng and Mengdi Cheng and Qiuguang Cao}, doi = {10.1109/ACCESS.2025.3555593}, journal = {IEEE Access}, pages = {59102-59117}, shortjournal = {IEEE Access}, title = {Spectral-spatial convolutional hybrid transformer for hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). A cascaded ensemble framework using BERT and graph features for emotion detection from english poetry. ACCESS, 13, 59085-59101. (www)

  @article{ , abstract = {Researchers have developed complex deep-learning models to extract emotions from poetry, opting for these over lightweight models. However, this approach requires a high volume of resources, which can be a significant limitation. Moreover, they often suffer from overfitting, making them less effective in real-time scenarios. This work introduces a novel cascaded ensemble framework that combines the strengths of BERT and Graph features (CP Net). The framework is designed in a tiered approach, classifying the majority of emotions in poetry, thereby reducing the need to invoke more complex models later on. This strategic arrangement enables efficient resource allocation and minimizes the usage of complex models. The basic model may not classify all so that a later model will classify residual unclassified emotions in the poetry. While the basic models in the first phase classify the majority of emotions in poetry, the remaining unclassified emotions are then passed on to the subsequent stages of the model, where more complex models can further process and classify them. This work performs feature fusion only in case of failed input samples. The performance of the proposed model is evaluated and compared against four baseline models using word embedding models, including Glove and Fast Text. The proposed CP Net model demonstrated exceptional performance, surpassing all other models by achieving an impressive 95% accuracy on the CAPEMO dataset and an outstanding 98% accuracy on the BAPEMO dataset. CP Net achieved state-of-the-art results with a minimum computational time of 0.274 to 0.332 milliseconds, outperforming other models.}, archive = {J_ACCESS}, author = {Praveen K. Kazipeta and Venkatrama Phani Kumar Sistla and Venkata Krishna Kishore Kolli}, doi = {10.1109/ACCESS.2025.3555897}, journal = {IEEE Access}, pages = {59085-59101}, shortjournal = {IEEE Access}, title = {A cascaded ensemble framework using BERT and graph features for emotion detection from english poetry}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning powered by synthetic data from diffusion models: Application to X-ray images. ACCESS, 13, 59074-59084. (www)

  @article{ , abstract = {Synthetic data offers a compelling solution to the challenges associated with acquiring high-quality medical data, which is often constrained by privacy concerns and limited accessibility. This study explores the efficacy of synthetic data generated using diffusion models for training deep learning models within a self-supervised learning framework. The primary objective is to evaluate whether synthetic data can effectively preserve critical medical biomarkers and support reliable downstream tasks such as classification and segmentation. Using chest X-ray images as a case study, the results reveal that models pretrained on synthetic data achieve performance comparable to or surpassing those pretrained on real data. Specifically, in pneumonia classification task, the model trained on synthetic data outperformed established benchmarks, achieving an Area Under the Curve of 99.1 and an F1-score of 96.1%. Similarly, for segmentation tasks, the model trained on synthetic data demonstrated robust performance, attaining a Dice score of 0.85. These findings underscore a significant advancement in the generation of synthetic medical images, providing a viable approach to creating realistic, biomarker-preserving datasets that ensure patient confidentiality and enable diverse applications in medical imaging.}, archive = {J_ACCESS}, author = {Abdullah Hosseini and Ahmed Serag}, doi = {10.1109/ACCESS.2025.3555619}, journal = {IEEE Access}, pages = {59074-59084}, shortjournal = {IEEE Access}, title = {Self-supervised learning powered by synthetic data from diffusion models: Application to X-ray images}, volume = {13}, year = {2025}, }
• (2025). Touch of privacy: A homomorphic encryption-powered deep learning framework for fingerprint authentication. ACCESS, 13, 59057-59073. (www)

  @article{ , abstract = {Deep learning and fully homomorphic encryption (FHE) are integrated for privacy-preserving fingerprint recognition. Convolutional neural network (CNN) extract fingerprint features encrypted using the Cheon-Kim-Kim-Song (CKKS) FHE scheme. TenSEAL ensures all computations occur in the encrypted domain, preventing raw biometric data exposure during authentication. A subset of the SOCOFing dataset, comprising 7,200 altered fingerprints from 90 individuals across three difficulty levels, is used for training (80%), validation (10%), and testing (10%). One real fingerprint per user is encrypted and stored for authentication, reducing computational complexity. The CNN classifies encrypted features without decryption, ensuring secure authentication. The system, utilizing Euclidean similarity, achieves 99.06% test accuracy with a loss of 0.1692, a True Acceptance Rate (TAR) of 99.19%, a False Rejection Rate (FRR) of 0.81%, a False Acceptance Rate (FAR) of 0%, and a True Rejection Rate (TRR) of 100%, with a minimal Equal Error Rate (EER) of 0.40%. Encrypted templates require only 31 MB for 90 users, averaging 344 KB per fingerprint per user. Despite a $157.26\times $ encryption overhead, remains feasible for real-time applications, completing an encrypted comparison in 0.025 seconds with a total processing time of 0.136 seconds per fingerprint. Adhering to ISO/IEC 24745 biometric protection standards, the system ensures irreversibility, unlinkability, and renewability of biometric templates. Decryption occurs only on the client side, keeping raw fingerprint data inaccessible to the server. The results confirm the feasibility of FHE for secure fingerprint authentication in cloud-based environments, benefiting applications in healthcare, banking, and access control.}, archive = {J_ACCESS}, author = {U. Sumalatha and K. Krishna Prakasha and Srikanth Prabhu and Vinod C. Nayak}, doi = {10.1109/ACCESS.2025.3555311}, journal = {IEEE Access}, pages = {59057-59073}, shortjournal = {IEEE Access}, title = {Touch of privacy: A homomorphic encryption-powered deep learning framework for fingerprint authentication}, volume = {13}, year = {2025}, }
• (2025). To combine or not to combine? the influence of combining training datasets on the robustness of deep learning models: An analysis for optical character recognition of handwriting. ACCESS, 13, 59039-59056. (www)

  @article{ , abstract = {The present manuscript addresses the question of how training data should be sampled for deep learning models by analyzing and evaluating the impact of training data representation and complexity on the performance and robustness of deep learning models. To address this open question, we take a combinatorial approach and train three architecturally identical deep learning models on three combinations of handwritten digit datasets of varying complexity: EMNIST Digits, DIDA, and a newly composed third dataset combining the first two datasets. Each model was evaluated using withheld test data from all three datasets. We find that models trained exclusively on either EMNIST Digits or DIDA performed well on their respective datasets but poorly on unfamiliar datasets. However, the model trained on both datasets showed an overall solid performance, although not quite reaching the accuracy of the specialized models on their respective datasets. We conclude that while specializing in the training dataset can increase accuracy, a more diverse dataset enhances model robustness. In practice, deep learning models should thus be trained on data that represents the actual application environment as closely as possible or, if such data is not available, on diverse data.}, archive = {J_ACCESS}, author = {Leopold Fischer-Brandies and Lucas Müller and Benjamin Rebholz and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3556582}, journal = {IEEE Access}, pages = {59039-59056}, shortjournal = {IEEE Access}, title = {To combine or not to combine? the influence of combining training datasets on the robustness of deep learning models: An analysis for optical character recognition of handwriting}, volume = {13}, year = {2025}, }
• (2025). Asynchronous fault detection filter for discrete singular markov jump systems via event-triggered scheme. ACCESS, 13, 59031-59038. (www)

  @article{ , abstract = {This paper discusses the asynchronous fault detection filter (FDF) problem for discrete singular Markov jump systems (SMJSs) via event-triggered scheme (ETS). A hidden Markov model (HMM) is adopted to describe the asynchronization behavior between the system and the FDF. An event-triggered scheme (ETS) is used to reduce the cost of calculation. Moreover, the FDF is designed to detect when the failure occurs to the system. Firstly, sufficient conditions are given to assure that the system is stochastically admissible (SA) with ${\mathcal {H}}_{\infty } $ performance. Secondly, based on the linear matrix inequality technique (LMI), the FDF and the event-triggered parameters are designed. Finally, a dynamic Leontief model of economic systems (DLMES) is verified to show the usefulness of the design method.}, archive = {J_ACCESS}, author = {Qian Zhang and Yang Yue and Changchang Pan and Jingang Ding}, doi = {10.1109/ACCESS.2025.3556942}, journal = {IEEE Access}, pages = {59031-59038}, shortjournal = {IEEE Access}, title = {Asynchronous fault detection filter for discrete singular markov jump systems via event-triggered scheme}, volume = {13}, year = {2025}, }
• (2025). Optimized 3D printed wireless charging infrastructure for underwater autonomous vehicle. ACCESS, 13, 59020-59030. (www)

  @article{ , abstract = {In this paper, an innovative charging system for autonomous underwater vehicles is introduced. The proposed architecture aims to guarantee high conversion efficiencies even in the presence of seawater. For this purpose, the structure geometry has been designed and optimised to ensure a strong anchorage even in rough sea conditions. This has also been achieved by utilizing Neodymium magnets. The geometries of the primary and secondary coils have been optimized to maximize the magnetic coupling through iterative FEM simulations, by identifying the best position of Ferrite Powder. The structure has finally been prototyped using additive manufacturing, to get a lightweight structure, with a high strength-to-weight ratio, which is beneficial for underwater applications. The performance of the proposed system has been experimentally evaluated under several environmental conditions by making the device work in air, fresh water and salt water.}, archive = {J_ACCESS}, author = {Fabio Corti and Michele Quercio and Gabriele Maria Lozito and Francesco Riganti Fulginei and Luca Pugi and Alberto Reatti}, doi = {10.1109/ACCESS.2025.3556205}, journal = {IEEE Access}, pages = {59020-59030}, shortjournal = {IEEE Access}, title = {Optimized 3D printed wireless charging infrastructure for underwater autonomous vehicle}, volume = {13}, year = {2025}, }
• (2025). Land-cover semantic segmentation for very-high-resolution remote sensing imagery using deep transfer learning and active contour loss. ACCESS, 13, 59007-59019. (www)

  @article{ , abstract = {An accurate land-cover segmentation of very-high-resolution aerial images is essential for a wide range of applications, including urban planning and natural resource management. However, the automation of this process remains a challenge owing to the complexity of images, variability in land surface features, and noise. In this study, a method for training convolutional neural networks and transformers to perform land-cover segmentation on very-high-resolution aerial images in a regional context was proposed. We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. Our experiments conducted using the OpenEarthMap dataset, which includes images from 44 countries, demonstrate the superior performance of U-Net-scSE models with the EfficientNet-V2-XL and MiT-B4 encoders, achieving an mIoU of over 0.80 on a test dataset of urban and rural images from Peru.}, archive = {J_ACCESS}, author = {Miguel Chicchon and Francisco James Leon Trujillo and Ivan Sipiran and Ricardo Madrid}, doi = {10.1109/ACCESS.2025.3556632}, journal = {IEEE Access}, pages = {59007-59019}, shortjournal = {IEEE Access}, title = {Land-cover semantic segmentation for very-high-resolution remote sensing imagery using deep transfer learning and active contour loss}, volume = {13}, year = {2025}, }
• (2025). LSTM-MM: Efficient LSTM-based mobility management for power inspection vehicles in smart grids. ACCESS, 13, 58992-59006. (www)

  @article{ , abstract = {The smart grid achieves efficient power management and resource allocation through various advanced technologies, among which the Internet of Vehicles communication technology is particularly important. This technology enables power inspection vehicles to seamlessly connect to the smart grid system, allowing for real-time acquisition and transmission of critical data, thereby further enhancing the efficiency and safety of power inspections. However, the high-speed movement of power inspection vehicles and frequent base station handovers pose challenges to their mobility management. To address the cost issues arising from frequent base station handovers in dense networks, this paper proposes an efficient mobility management solution based on trajectory prediction, suitable for Software-Defined Vehicular Networks. This scheme leverages Software-Defined Networking (SDN) technology to centrally manage vehicle communication and network traffic, integrating trajectory prediction algorithms to forecast the future movements of power inspection vehicles, optimizing route planning, and improving operational efficiency and network performance. Through detailed theoretical analysis and simulation experiments, the results indicate that this scheme significantly outperforms existing solutions in terms of signaling costs, handover delays, and packet delivery costs, substantially enhancing the efficiency of mobility management for power inspection vehicles.}, archive = {J_ACCESS}, author = {Xiaoyan Wang and Xianfeng Xie and Shengnan Zhao}, doi = {10.1109/ACCESS.2025.3556712}, journal = {IEEE Access}, pages = {58992-59006}, shortjournal = {IEEE Access}, title = {LSTM-MM: Efficient LSTM-based mobility management for power inspection vehicles in smart grids}, volume = {13}, year = {2025}, }
• (2025). A novel labeling method for radar pulse data with complex modulations and parametric overlaps. ACCESS, 13, 58978-58991. (www)

  @article{ , abstract = {This paper aims to solve a problem: using azimuthal information to label radar pulse data with complex inter-pulse modulations and overlaps on electromagnetic parameters. In these tough conditions, azimuthal information is the key for data labeling instead of parameter-based methods. According to the measurement principle, the azimuthal information from the identical emitter shares the same positional relationship between the radiation source and the receivers. We propose a transformation, the inv-Hough transform, to reveal this positional relationship. The data from the identical emitter show highly linearly correlated distributions in the transformed space. Through transformation, the problem becomes a clustering problem. Dirichlet Process Mixture Models (DPMM) are widely used to address clustering problems, as they can automatically estimate the number of clusters. However, the outliers deteriorate clustering performances. Therefore, two improvements based on DPMM are proposed: adding a pre-clustering step to improve the outlier adaptation and distributed inference to improve the federated learning capability. The experimental results show that our method outperforms parameter-based methods when complex interpulse modulations and overlaps on electromagnetic parameters occur. In addition, the pre-clustering step significantly improves outlier adaptation and parametric stability for our method. And the method can work in federated learning scenarios.}, archive = {J_ACCESS}, author = {Youtan Wei and Yang Shen}, doi = {10.1109/ACCESS.2025.3556054}, journal = {IEEE Access}, pages = {58978-58991}, shortjournal = {IEEE Access}, title = {A novel labeling method for radar pulse data with complex modulations and parametric overlaps}, volume = {13}, year = {2025}, }
• (2025). Model input-output configuration search with embedded feature selection for sensor time-series and image classification. ACCESS, 13, 58960-58977. (www)

  @article{ , abstract = {Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional machine learning algorithms rely on well-defined input and output variables; however, there are scenarios where the separation between the input and output variables and the underlying, associated input and output layers of the model are unknown. Feature Selection (FS) and Neural Architecture Search (NAS) have emerged as promising solutions in such scenarios. This paper proposes MICS-EFS, a Model Input-Output Configuration Search with Embedded Feature Selection. The methodology explores internal dependencies in the complete input parameter space for classification tasks involving both 1D sensor time-series and 2D image data. MICS-EFS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate MICS-EFS’s superior performance compared to other FS algorithms. Across all tested datasets, MICS-EFS delivered an average accuracy improvement of 1.5% over baseline models, with the accuracy gains ranging from 0.5% to 5.9%. Moreover, the algorithm reduced feature dimensionality to just 2–5% of the original data, significantly enhancing computational efficiency. These results highlight the potential of MICS-EFS to improve model accuracy and efficiency in various machine learning tasks. Furthermore, the proposed method has been validated in a real-world industrial application focused on machining processes, underscoring its effectiveness and practicality in addressing complex input-output challenges.}, archive = {J_ACCESS}, author = {Anh Tuan Hoang and Zsolt János Viharos}, doi = {10.1109/ACCESS.2025.3555379}, journal = {IEEE Access}, pages = {58960-58977}, shortjournal = {IEEE Access}, title = {Model input-output configuration search with embedded feature selection for sensor time-series and image classification}, volume = {13}, year = {2025}, }
• (2025). Investigation of a novel approach to securing data exchanges within industrial environments. ACCESS, 13, 58942-58959. (www)

  @article{ , abstract = {The paper examines a novel and robust approach for safeguarding Industrial Control Systems (ICSs) from cyberattacks. The derived method, named the Symmetric Analog Licence Transfer (SALT) Method, combines analog, on-the-wire signal transfer with symmetric key data encryption. Analog exchanges, commonly used in the industry among various types of Programmable Logic Controllers (PLCs) and other equipment, are difficult to intercept and interpret, both over the Internet and on-site, hence are suitable for periodic and secure randomly generated key transfers. Once exchanged securely, the keys can be used for symmetric data encryption. The method has been built to address some of the key challenges the OT community faces in ICS cybersecurity. These challenges, which often hinder the implementation of effective cyber defences, include the inherent complexity of ICSs, financial constraints, and a general reluctance within OT to adopt new technologies in operational environments. Moreover, existing solutions often require complex modifications to communication protocols and costly external protection mechanisms or devices, causing even highly effective cyber defence solutions to be easily overlooked by the OT community. As a result, vulnerabilities in data exchange protocols can expose unencrypted process data to malicious cyber activities. The proposed method is designed to be affordable, scalable, adaptable, backward compatible, and straightforward to maintain. It can be implemented alongside existing process codes and functionalities across various ICS devices, requiring no major protocol modifications or specific ICS equipment. Deployment, modification, and maintenance operations are carried out within the ICS software tools (simple drag-and-drop action), making the method highly accessible and intuitive for OT engineers. The ease of use, robustness, and, as demonstrated through experiments, high effectiveness against plaintext-altering cyberattacks make the SALT method an interesting and viable novel approach for ICS protection against cyber threats.}, archive = {J_ACCESS}, author = {Marko Slunjski}, doi = {10.1109/ACCESS.2025.3554983}, journal = {IEEE Access}, pages = {58942-58959}, shortjournal = {IEEE Access}, title = {Investigation of a novel approach to securing data exchanges within industrial environments}, volume = {13}, year = {2025}, }
• (2025). “Diwan”: Constructing the largest annotated corpus for arabic poetry. ACCESS, 13, 58927-58941. (www)

  @article{ , abstract = {In recent years, Arabic natural language processing has achieved remarkable advancements, particularly with the advent of large language models that have enhanced the analysis of diverse Arabic texts, including literary and artistic works such as poetry. However, these models encounter specific challenges when dealing with Arabic poetry, which is characterized by complex metrical structures, varied themes, and unique linguistic intricacies. Currently available poetry corpora are often limited in scope and depth, and typically lack the comprehensive coverage required for sophisticated computational analysis tasks. To address this gap, this paper introduces “Diwan,” the largest and most precise annotated Arabic poetry dataset/corpus, designed to support scientific research and facilitate the development of AI applications in this domain. “Diwan” comprises approximately 14 million verses of poetry across 16 major categories and includes detailed annotations related to poetic genres, prosodic structures, thematic content, linguistic patterns, and poet-specific metadata. This dataset/corpus has been meticulously curated using advanced data collection methods, rigorous normalization and annotation protocols, and expert oversight from specialists in Arabic prosody and poetry. By leveraging intelligent annotation algorithms, Diwan serves as a foundational resource and benchmark dataset for advancing research in fields such as automatic poetry generation, metrical analysis, thematic classification, and plagiarism detection. “Diwan” has been compared against 4 leading corpora for Arabic poetry; this comparison proves that “Diwan” outperforms all of them in terms of scope, and depth. Besides, “Diwan” is constructed and structured in a way enables AI-powered analysis and deep-learning-based analysis to work accurately. By providing an unprecedented foundation for computational exploration of Arabic poetry, Diwan opens up new avenues and possibilities for intelligent applications and promotes the digital analysis of this rich literary heritage.}, archive = {J_ACCESS}, author = {Badriyya B. Al-Onazi and Wadee A. Nashir and Asma A. Al-Shargabi}, doi = {10.1109/ACCESS.2025.3551161}, journal = {IEEE Access}, pages = {58927-58941}, shortjournal = {IEEE Access}, title = {“Diwan”: Constructing the largest annotated corpus for arabic poetry}, volume = {13}, year = {2025}, }
• (2025). Path planning based on combination of sampling and learning-from-demonstration for static obstacle avoidance of autonomous vehicles. ACCESS, 13, 58913-58926. (www)

  @article{ , abstract = {This paper presents a sampling-based path planning algorithm for autonomous vehicles with the existence of static obstacles. Conventional sampling-based path planning algorithms pose challenges in balancing driving performance and sample efficiency. To overcome this issue, Learning-from-Demonstration (LfD) approach has been combined with the sampling approach based on expert driving data for efficient path planning with enhanced driver acceptance. First, a neural network is designed to probabilistically estimate proposal points of the ego vehicle’s future path using environmental information and vehicle sensor information. The network is trained with expert driving data. Second, a driving path is planned by sampling path primitives in spatial domain. To generate the primitives, terminal points of the primitives are sampled within reliable ranges centered around the proposal points which are derived from the neural network. The driving path is determined by the evaluation of primitives according to cost function and feasibility constraints. To enhance the driving performance, two-piece path primitives are used to compose the driving path, rather than a single-piece path primitive. Simulation studies have been conducted to compare driving performance and efficiency of the proposed algorithm with other approaches. Vehicle tests have been conducted based on autonomous driving. Test results show that the proposed combination of LfD and sampling approach provides feasible and acceptable path planning results with enhanced computational efficiency.}, archive = {J_ACCESS}, author = {Youngmin Yoon and Ara Jo}, doi = {10.1109/ACCESS.2025.3556865}, journal = {IEEE Access}, pages = {58913-58926}, shortjournal = {IEEE Access}, title = {Path planning based on combination of sampling and learning-from-demonstration for static obstacle avoidance of autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). A proof-of-concept digital twin for real-time simulation: Leveraging a model-based systems engineering approach. ACCESS, 13, 58899-58912. (www)

  @article{ , abstract = {This paper presents a proof-of-concept digital twin designed to enhance industrial process monitoring and maintenance through real-time and bidirectional communication. The integrated approach combines a physical twin—constructed using LEGO ${}^{\unicode {0x00A9}} $ Spike Prime kits and multiple Raspberry Pi 4 microcontrollers—with a digital twin modeled in FlexSim discrete-event simulation software package. Data is continuously synchronized via the MQTT protocol, allowing the digital twin to accurately mirror key operational parameters such as object sorting status, conveyor speed, and splitter position. The system leverages Model-Based Systems Engineering (MBSE) techniques, implemented with Capella and the Arcadia methodology, to support a robust design and integration of physical and digital components. Experimental scenarios, including normal operation and simulated malfunctions, demonstrate the system’s ability to trigger corrective actions and provide valuable insights for predictive maintenance. The results confirm that such a modular digital twin approach can improve real-time simulation, fault detection, and operational efficiency in industrial applications.}, archive = {J_ACCESS}, author = {Bulent Soykan and Grégoire Blanc and Ghaith Rabadi}, doi = {10.1109/ACCESS.2025.3557367}, journal = {IEEE Access}, pages = {58899-58912}, shortjournal = {IEEE Access}, title = {A proof-of-concept digital twin for real-time simulation: Leveraging a model-based systems engineering approach}, volume = {13}, year = {2025}, }
• (2025). Energy storage impact on CO2 emissions reduction in an islanded microgrid. ACCESS, 13, 58887-58898. (www)

  @article{ , abstract = {The increasing deployment of distributed generation (DG) presents considerable challenges for system operators. The impact is even more pronounced in the case of islanded grids, such as those of small islands. Their electrical networks are usually supplied by a central station with diesel generators and many distributed sources from renewable energies. In these networks, storage systems are installed to help network stability. An optimization algorithm is presented in this paper to use these storage systems to reduce fuel consumption of diesel generators and the consequent CO2 emissions. More in detail, to control the energy systems, an approach is proposed to manage the different sources of a microgrid to obtain the minimum solution in terms of CO2 emissions. The proposed method utilizes an optimization algorithm within a day-based scheduling strategy. The decision strategy is based on a multi-stage process to both handle the network constraints of the distribution system and the operating specifications of both sources and storage systems.}, archive = {J_ACCESS}, author = {Nicola Panzavecchia and Giuseppe Caravello and Antonio Cataliotti and Valentina Cosentino and Giovanni Tinè and Dario Di Cara}, doi = {10.1109/ACCESS.2025.3555883}, journal = {IEEE Access}, pages = {58887-58898}, shortjournal = {IEEE Access}, title = {Energy storage impact on CO2 emissions reduction in an islanded microgrid}, volume = {13}, year = {2025}, }
• (2025). Overcurrent limitation and enhanced fault current utilization by grid-forming inverter using hybrid methods during short circuit faults. ACCESS, 13, 58870-58886. (www)

  @article{ , abstract = {The next-generation grid-forming (GFM) inverters face significant over-current and over-voltage challenges during grid faults due to their inherent voltage source behaviour behind impedance. These challenges are mitigated by simultaneously reconstructing power, voltage, or current references fed to different control layers in a coordinated way, known as the hybrid method. However, existing hybrid methods fail to 1) ensure maximum fault current utilization and 2) provide improved post-fault recovery. This paper proposes two new hybrid methods to address these limitations. The first method employs a novel non-linear transient virtual resistance (NLTVR) combined with reconstructed power references for symmetrical faults. An extension of NLTVR, referred to as unified NLTVR (UNLTVR), is introduced to handle both symmetrical and asymmetrical faults. By integrating NLTVR or UNLTVR with reconstructed power references based on grid fault severity, the inverter operates near its maximum current limit during faults while preserving GFM functionality, ensuring smooth post-fault power recovery without exceeding reference values. The second method combines the sinusoidal current reference limiter (SCRL) with the proposed UNLTVR. The proposed schemes are implemented in a droop-controlled GFM inverter modelled in the stationary reference frame. The effectiveness of the proposed methods is demonstrated through MATLAB/Simulink simulations of symmetrical and asymmetrical faults, supported by a Controller Hardware-in-the-Loop (CHIL) experiment. The proposed methods are also compared with conventional current-limiting methods. It is revealed that the SCRL achieves maximum fault current capability faster, while the combined efforts of the UNLTVR help the GFM inverter to experience a seamless post-fault recovery.}, archive = {J_ACCESS}, author = {Jack Benjamin and Animesh Kumar Sahoo and Pratyasa Bhui}, doi = {10.1109/ACCESS.2025.3555817}, journal = {IEEE Access}, pages = {58870-58886}, shortjournal = {IEEE Access}, title = {Overcurrent limitation and enhanced fault current utilization by grid-forming inverter using hybrid methods during short circuit faults}, volume = {13}, year = {2025}, }
• (2025). A new elliptical model for device-free localization. ACCESS, 13, 58863-58869. (www)

  @article{ , abstract = {Device-free localization (DFL) is a technology that identifies and tracks individuals by analyzing fluctuations in received signal strength (RSS), thus eliminating the requirement for any devices. As a rapidly developing and significant technology within WSNs, radio tomographic imaging (RTI) has garnered growing interest. However, there is significant potential for improving the accuracy of localization in RTI. To address this issue, we propose a novel DFL algorithm to improve the localization accuracy: this algorithm incorporates an enhanced method for channel choosing to gather data, along with a novel weighting model. The distance-based channel choosing method selects channels with stronger Pearson correlation, which enhances resilience to environmental fluctuations. The weighting model that has been proposed is based on the spatial positioning of voxels relative to sensors. The experimental results indicate that the proposed algorithm can enhance positioning accuracy by up to 26% relative to certain leading RTI approaches.}, archive = {J_ACCESS}, author = {Qian Lei and Mengna Fan and Shuilong Zou and Zunhai Gao}, doi = {10.1109/ACCESS.2025.3555569}, journal = {IEEE Access}, pages = {58863-58869}, shortjournal = {IEEE Access}, title = {A new elliptical model for device-free localization}, volume = {13}, year = {2025}, }
• (2025). CNN issues in skin lesion classification: Data distribution and quantity. ACCESS, 13, 58848-58862. (www)

  @article{ , abstract = {Convolutional Neural Networks (CNNs) have become indispensable tools in skin cancer classification, aiding clinical experts to achieve earlier and more accurate diagnoses, improving treatment outcomes, and driving advancements in medical research. Despite their pivotal role, the most popular CNN architecture families exhibit a critical issue related to the distribution and quantity of available data, potentially compromising their performance and generalization abilities. This challenge is commonly overlooked in most skin lesion classification papers, which mainly rely on weighted classification techniques. Directly using appropriately dataset balancing or Transfer Learning (TL) methods, as suggested in recent studies, has the potential to deliver more satisfactory results, providing a more effective approach to addressing this issue. In the effort to tackle this problem, we provide a comprehensive quantitative evaluation aimed at identifying the most critical new emerging computational aspects and the related effective techniques. Specifically, we propose twelve Computational Models (CMs) based on four prominent CNN models with increasing structural complexity. We assess their effectiveness in both pretrained and unpretrained versions, incorporating TL strategies as well. Our experiments focus on the ISIC 2018 image dataset, a benchmark widely recognized for its extensive application in skin cancer research yet challenged by significant class imbalance issues. To mitigate this, we also randomly extracted a balanced image subset from ISIC 2018 for evaluation purposes. The experimental results, regarding four different scenarios, provide valuable insights into the design and utilization of CNNs for skin lesion classification, laying the groundwork for further investigations.}, archive = {J_ACCESS}, author = {Giuliana Ramella and Luca Serino}, doi = {10.1109/ACCESS.2025.3556479}, journal = {IEEE Access}, pages = {58848-58862}, shortjournal = {IEEE Access}, title = {CNN issues in skin lesion classification: Data distribution and quantity}, volume = {13}, year = {2025}, }
• (2025). Research on life attenuation of lithium-ion batteries based on IC curves and voltage hybrid evaluation algorithm. ACCESS, 13, 58835-58847. (www)

  @article{ , abstract = {Due to the gradual degradation of lithium batteries during use, simple and accurate evaluation of their performance is crucial for the optimization of Battery Management Systems. Traditional battery performance evaluations often struggle to accurately reflect the actual state of battery degradation due to the indistinct features of voltage and current data. To overcome this issue, it is proposed a hybrid input method that combines IC curves with voltage data, and employs a Long Short-Term Memory (LSTM) deep learning model to assess battery degradation. To improve evaluation accuracy, utilized the LSTM model, leveraging its capability to process time-series data, effectively capturing the dynamic characteristics of battery performance as it changes over time. By integrating a mixed input approach with voltage data, this method demonstrated higher accuracy in a series of validation tests, with evaluation precision controlled within 2%. This finding offers a simple and efficient technical solution for optimizing battery management systems, promising to enhance battery performance monitoring and management processes.}, archive = {J_ACCESS}, author = {Xiaorong Huang and Wanwei Wang and Zhijun Guo and Xiliang Dai and Maoquan Ye and Danyu Tian}, doi = {10.1109/ACCESS.2024.3520250}, journal = {IEEE Access}, pages = {58835-58847}, shortjournal = {IEEE Access}, title = {Research on life attenuation of lithium-ion batteries based on IC curves and voltage hybrid evaluation algorithm}, volume = {13}, year = {2025}, }
• (2025). An NN framework for target number detection in FMCW radar systems via hankelization: A low-dimensional data representation perspective. ACCESS, 13, 58823-58834. (www)

  @article{ , abstract = {In this paper, we introduce a novel neural network (NN)-based algorithm that significantly improves the target number detection in frequency modulated continuous wave (FMCW) radar systems. By integrating the mathematical processes of Hankelization and singular value extraction, we can perform input data manipulation for effective target number detection, resulting in constructing an efficient NN framework. This is based on the following mathematical properties: 1) A sequence obtained by uniform sampling of the superposition of K radio waves can be represented as a superposition of K geometric sequences; 2) A Hankelized matrix formed by the superposition of K geometric sequences exhibits low-rank characteristics; and 3) In an FMCW radar system with K targets, if the received signal, which is represented as a matrix, is ideal, the vectors obtained by extracting this matrix in row, column, diagonal, and anti-diagonal patterns can all be modeled as a superposition of K geometric sequences. The proposed NN framework showcases remarkable improvements in accuracy and efficiency for target number detection, leveraging a small sized dataset and a compact NN design to achieve unprecedented performance levels. Numerical results validate the superiority of our method across various scenarios, establishing a new benchmark for low-dimensional data representation in radar systems.}, archive = {J_ACCESS}, author = {Jung-Hwan Kim and Gyung-Eun Kim and Woong-Hee Lee}, doi = {10.1109/ACCESS.2025.3557161}, journal = {IEEE Access}, pages = {58823-58834}, shortjournal = {IEEE Access}, title = {An NN framework for target number detection in FMCW radar systems via hankelization: A low-dimensional data representation perspective}, volume = {13}, year = {2025}, }
• (2025). Responsible AI framework for autonomous vehicles: Addressing bias and fairness risks. ACCESS, 13, 58800-58822. (www)

  @article{ , abstract = {Autonomous Vehicles (AVs) hold immense potential to revolutionize transportation, yet their deployment raises significant concerns regarding safety, security, and ethical considerations. Furthermore, the increased use of automation, edge computing, and Artificial Intelligence (AI), fueled by generative AI technology, has increasingly elevated the risks of AI. Most of the existing research only covers ethical components and lacks the breadth of Responsible AI (RAI), while some have presented components such as justice and solidarity but do not provide an approach or mechanism to implement those in an AI-based system. This paper addresses these gaps by proposing a comprehensive RAI framework for AVs with four key contributions. First, it provides an in-depth analysis of AI risks in AVs, encompassing safety, security, ethical, and legal domains, offering a structured classification to guide developers and regulators. Second, it introduces a holistic RAI framework that spans the AI lifecycle, identifying and mitigating risks at each AV system development and deployment stage. Third, the paper focuses on bias and fairness within AV systems, outlining precise techniques for bias detection and mitigation across data collection, algorithm design, and real-time decision-making. Lastly, the research presents bias removal simulations using publicly available AV datasets, evaluating mitigation strategies such as synthetic data generation and algorithmic fairness analysis. The proposed framework and simulations provide a practical foundation for integrating RAI principles into AV development, ensuring safer, fairer, and more accountable AI-driven mobility solutions.}, archive = {J_ACCESS}, author = {Abhinav Tiwari and Hany E. Z. Farag}, doi = {10.1109/ACCESS.2025.3556781}, journal = {IEEE Access}, pages = {58800-58822}, shortjournal = {IEEE Access}, title = {Responsible AI framework for autonomous vehicles: Addressing bias and fairness risks}, volume = {13}, year = {2025}, }
• (2025). Study on the influencing factors of direct current dipole array for tunnel advance detection and its application. ACCESS, 13, 58790-58799. (www)

  @article{ , abstract = {The direct current resistivity method is a primary technique for detecting water-bearing structures ahead of tunnel and coal mine excavation faces. We propose a dipole array advance detection device based on the DC method, and quantified the advance detection effect and the optimum device parameters using numerical simulation, and then verified it on field application. Firstly, the corresponding methods for data gathering and processing are introduced. Then, according to simulation of several geoelectric models, we optimized the device parameters on the detection effectiveness, such as the current electrode numbers, electrode spacing, the shape and position of the anomalous body. The optimal device parameters are then selected based on these considerations. Using the optimized parameters, we finally conducted a field test to obtain the electrical response characteristics ahead of the tunneling face, and the results were validated with the transient electromagnetic method. The device successfully detects low-resistance anomalies at distances of 20 to 50 m ahead of the tunnel face, validated by borehole drilling. It illustrates that the dipole array advance detection device can at least detect low-resistance anomalies up to 40 m ahead of the tunneling face, with an electrode spacing of 4 m and four current electrodes. The device’s high detection accuracy and construction efficiency significantly reduce labor and material costs in field applications, offering both theoretical and practical value.}, archive = {J_ACCESS}, author = {Xiaozhen Teng and Jianhua Yue and Shuanggui Hu and Chao Sun and Kailiang Lu and Herui Zhang and Danyang Xi}, doi = {10.1109/ACCESS.2025.3553694}, journal = {IEEE Access}, pages = {58790-58799}, shortjournal = {IEEE Access}, title = {Study on the influencing factors of direct current dipole array for tunnel advance detection and its application}, volume = {13}, year = {2025}, }
• (2025). Ultra short-term charging load forecasting based on improved data decomposition and hybrid neural network. ACCESS, 13, 58778-58789. (www)

  @article{ , abstract = {Ultra-short-term charging load prediction is of crucial importance for the real-time scheduling and stable operation of power systems. To address the problem of the strong nonlinearity of power load sequences, this study proposes an ultra-short-term charging load prediction model that combines a Time Convolutional Network-Bidirectional Gated Recurrent Unit (TCN-Bi-GRU)and uses the Osprey Optimization Algorithm (OOA) based on the opposition-based learning strategy to optimize Variational Mode Decomposition (VMD). First, an OOA incorporating an opposition-based learning strategy was introduced to optimize the key parameters of the VMD. The permutation entropy is utilized as the fitness function to iteratively obtain the optimal combination of fitness parameters.Secondly, VMD with the optimal parameter combination is used to decompose the original charging load sequence into multiple relatively stable components, reducing the non-stationarity and complexity of the sequence. Then, the components and external data are divided into training sets to train TCN-Bi-GRU. After the model training was completed, the test sets of each component were predicted separately to fully explore the spatiotemporal characteristics of the dataset. The experimental results show that compared with single models, the proposed model performs better in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared in three different scenarios, proving that the model has high prediction accuracy and good robustness in ultra-short-term charging load prediction.}, archive = {J_ACCESS}, author = {Shaoyang Yin and Zhaohui Chen and Wanyuan Liu and Zhiwen Su}, doi = {10.1109/ACCESS.2025.3555737}, journal = {IEEE Access}, pages = {58778-58789}, shortjournal = {IEEE Access}, title = {Ultra short-term charging load forecasting based on improved data decomposition and hybrid neural network}, volume = {13}, year = {2025}, }
• (2025). DeepGuard: Enhancing violence detection in smart cities through deep learning. ACCESS, 13, 58759-58777. (www)

  @article{ , abstract = {Video violence detection is essential to ensure public safety in smart cities, particularly in the context of political violence. Existing datasets and traditional methods for video violence detection often fall short of capturing the complexities of political violence, limiting the effectiveness of current models. To address these gaps, we propose a comprehensive approach that includes the development of a new dataset specifically focused on political violence, comprising 480 labeled video clips across four distinct categories. This dataset provides a broader understanding of political violence and is a benchmark for future research. We fine-tuned the Mobile Video Network (MoViNet-A0) to achieve an accuracy of 92.86% with 1.904M parameters. In addition, a custom keyframe extraction algorithm was designed, using temporal and pixel-level features to improve stability and accuracy. Our approach outperforms state-of-the-art methods, demonstrating high accuracy across multiple benchmark datasets. These results underscore the effectiveness of our system in detecting political violence, which offers significant implications for improving security measures and informing policy decision-making.}, archive = {J_ACCESS}, author = {Md. Al-Mamun Provath and Musfequa Rahman and Kaushik Deb and Pranab Kumar Dhar and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3541059}, journal = {IEEE Access}, pages = {58759-58777}, shortjournal = {IEEE Access}, title = {DeepGuard: Enhancing violence detection in smart cities through deep learning}, volume = {13}, year = {2025}, }
• (2025). ET-GNN: Ensemble transformer-based graph neural networks for holistic automated essay scoring. ACCESS, 13, 58746-58758. (www)

  @article{ , abstract = {Essay writing tasks are crucial for assessing students’ writing skills, but manual evaluation can be time-consuming and prone to inconsistencies. Automated Essay Scoring (AES) offers a solution by automatically evaluating essays, reducing the need for human intervention. This paper presents a hybrid method, called Ensemble Transformer-Based Graph Neural Networks (ET-GNN), which integrates Transformer-based models with Graph Convolutional Networks (GCNs) for holistic AES. Three Transformer models, DistilBERT, RoBERTa, and DeBERTaV3, were individually fine-tuned to generate contextual embeddings for each essay. The GCNs process these embeddings, effectively capturing relevant semantic information and inter-essay similarities. Additionally, ensemble methods are used to combine the DistilBERT-GCN, RoBERTa-GCN, and DeBERTaV3-GCN models employing averaging for regression tasks, majority voting for classification tasks, and a weighted ensemble method for both types of tasks. The proposed ET-GNN method enhances the performance and robustness of AES systems, achieving Quadratic Weighted Kappa (QWK) scores of 0.835 and 0.841 on the ASAP and AES 2.0 datasets, respectively. These results outperform other state-of-the-art models based on Transformer or GCNs architectures for the AES task.}, archive = {J_ACCESS}, author = {Hind Aljuaid and Areej Alhothali and Ohoud Alzamzami and Hussein Assalahi and Tahani Aldosemani}, doi = {10.1109/ACCESS.2025.3556352}, journal = {IEEE Access}, pages = {58746-58758}, shortjournal = {IEEE Access}, title = {ET-GNN: Ensemble transformer-based graph neural networks for holistic automated essay scoring}, volume = {13}, year = {2025}, }
• (2025). Depth-enhanced tumor detection framework for breast histopathology images by integrating adaptive multi-scale fusion, semantic depth calibration, and boundary-guided detection. ACCESS, 13, 58735-58745. (www)

  @article{ , abstract = {Multiple modalities offer the advantage of providing complementary visual information that enhances overall understanding. This capability is particularly valuable in the domain of autonomous tumor detection. However, the challenge of occlusions in autonomous tumor perception—especially in cases of tumor-to-tumor occlusion—hinders the effective utilization of occlusion-related features. As a result, these limitations lead to a decline in the accuracy of object detection. We propose a novel framework for tumor detection in histopathological images by integrating multi-scale RGB features and depth-enhanced semantic information. The method comprises three primary modules: the Adaptive Multi-Scale Fusion Module (AMSF), Semantic Depth Integration and Calibration Module (SDICM), and Depth-Guided Tumor Detection Module (DG-TDM). AMSF combines RGB histopathology image channels, binary masks, and multi-scale convolution outputs using attention mechanisms to generate a fused feature map that captures occlusion information and boundary relevance. SDICM constructs dense depth maps by fusing RGB, semantic, and sparse depth data through bidirectional feature aggregation, enhancing spatial continuity and edge clarity. DG-TDM refines tumor boundary detection by combining depth and RGB features using spatial and channel-wise attention mechanisms, which highlight boundary differences and reduce redundancy from background noise. The proposed loss function optimizes RGB-depth feature alignment, balancing contributions from each modality for robust tumor detection. This approach addresses challenges such as overlapping boundaries, occlusions, and poor contrast in histopathology images, enabling precise localization of tumor regions. Experimental results demonstrate that the proposed framework significantly improves detection accuracy by 98% and boundary delineation compared to existing methods. This robust and efficient methodology provides a promising solution for advancing tumor detection in medical imaging.}, archive = {J_ACCESS}, author = {A. Robert Singh and Suganya Athisayamani and Hariharasitaraman S and Faten Khalid Karim and José Varela-Aldás and Samih M. Mostafa}, doi = {10.1109/ACCESS.2025.3554342}, journal = {IEEE Access}, pages = {58735-58745}, shortjournal = {IEEE Access}, title = {Depth-enhanced tumor detection framework for breast histopathology images by integrating adaptive multi-scale fusion, semantic depth calibration, and boundary-guided detection}, volume = {13}, year = {2025}, }
• (2025). Partial discharge diagnosis using semi-supervised learning and complementary labels in gas-insulated switchgear. ACCESS, 13, 58722-58734. (www)

  @article{ , abstract = {Deep neural networks have proven to be highly efficient in fault detection and classification using partial discharges (PDs) in gas-insulated switchgear (GIS). However, previous studies have not fully addressed the issue of limited labeled training data, which significantly impacts the performance of these models. Existing semi-supervised learning (SSL) approaches typically discard low-confidence pseudo-labels from unlabeled PD samples, because these unreliable labels can mislead the model. This gap in current research overlooks the potential value of low-confidence samples, which, despite their uncertain class, are unlikely to belong to the classes with the lowest probabilities. In this study, we aim to overcome this limitation by proposing a novel semi-supervised contrastive complementary learning (SCCL) method. Our SCCL approach generates a larger set of reliable negative pairs using complementary labels, allowing us to utilize the entire set of unlabeled PD samples effectively. We validate the feasibility of SCCL using phase-resolved PDs (PRPDs) and onsite noise data collected through an ultrahigh-frequency (UHF) PD measurement system. Experimental results indicate that the SCCL method achieves an impressive accuracy of 96.23% by effectively leveraging low-confidence unlabeled PD samples to improve classification performance in GIS under restricted labeling conditions.}, archive = {J_ACCESS}, author = {Ho Trong Tai and Young-Woo Youn and Hyeon-Soo Choi and Yong-Hwa Kim}, doi = {10.1109/ACCESS.2025.3556353}, journal = {IEEE Access}, pages = {58722-58734}, shortjournal = {IEEE Access}, title = {Partial discharge diagnosis using semi-supervised learning and complementary labels in gas-insulated switchgear}, volume = {13}, year = {2025}, }
• (2025). Calculating depth for dark images using self-supervised learning: An investigation of the role of edge maps as input. ACCESS, 13, 58710-58721. (www)

  @article{ , abstract = {Depth estimation from a single image is a challenging problem that has received significant attention recently. In low-light conditions and with multiple light sources where the pixel intensities are not uniform, the problem becomes even more difficult due to the lack of visual information in the image. In response to the lack of suitable datasets that adequately represent these specific issues, we undertook the task of collecting our own dataset. This initiative was driven by the need for a more accurate and comprehensive representation of the problem space. Leveraging this custom dataset, which we call the dark depth dataset, we developed and applied a novel self-supervised approach to effectively address the problem. In this paper, we propose a novel method for depth estimation in low-light conditions that utilizes edge information. We have used edge information since they are prominent even in low-light conditions and gives a better understanding of the scene than simple images. Our approach is based on the self-supervised monocular depth estimation framework and extends it by incorporating edge information for improved accuracy. We have tested the edge input at different network parts to see the effect on accuracy. With this research, edge information is crucial for depth estimation as most of the points in an object are usually at the same distance, and edges can give boundaries to identify objects. However, the edge information gets lost during deep learning processes while going through different layers, so we provided the edge map at different layers to check where this edge information is most effective for the depth estimation process. Our experiment shows that giving edge information with edge-concatenation gives the best results with dark dataset. We achieved an improvement of nearly 50% over the base model in terms of $\delta $ values.}, archive = {J_ACCESS}, author = {Shwetang Dubey and Satvik Watts and Ashok Malhotra and Kunal Prajapati and Pavan Chakraborty}, doi = {10.1109/ACCESS.2025.3556610}, journal = {IEEE Access}, pages = {58710-58721}, shortjournal = {IEEE Access}, title = {Calculating depth for dark images using self-supervised learning: An investigation of the role of edge maps as input}, volume = {13}, year = {2025}, }
• (2025). Augmented graph convolutional network for enhancing label reachability. ACCESS, 13, 58699-58709. (www)

  @article{ , abstract = {Graph Convolutional Networks (GCNs) have emerged as a leading approach for semi-supervised node classification. However, due to the uneven distribution of labeled nodes in graphs, only a limited subset of unlabeled nodes can directly access the labeled nodes. This disconnection between the labeled and unlabeled nodes significantly hinders GCNs performance. To address this challenge, we propose an Augmented Graph Convolutional Network for enhancing Labeled Reachability (AGCN-LR), which is designed to improve information flow to low-degree nodes by strengthening their connectivity with labeled nodes. The core of AGCN-LR comprises two parts. First, we selectively connect low-degree nodes to labeled nodes by calculating feature similarity and shortest path distances to generate augmented graphs, thereby enhancing label reachability. Second, we leverage GCNs to learn these augmented graphs and integrate embeddings from these graphs through an attention mechanism to bolster overall performance. Furthermore, to capture consistent information across augmented graphs, we incorporate a tailored contrastive loss function, facilitating consistent contextual learning across different augmented graphs. Experimental results demonstrate that AGCN-LR substantially enhances performance in semi-supervised node classification tasks across various benchmark datasets, exhibiting stronger advantages in sparse label and noisy graph scenarios.}, archive = {J_ACCESS}, author = {Xiangyi Wang and Fengjun Zhang and Wei Teng and Baoda Liu}, doi = {10.1109/ACCESS.2025.3555997}, journal = {IEEE Access}, pages = {58699-58709}, shortjournal = {IEEE Access}, title = {Augmented graph convolutional network for enhancing label reachability}, volume = {13}, year = {2025}, }
• (2025). DWE: Research on water quality inversion and scalability in small watersheds using UAV remote sensing data. ACCESS, 13, 58680-58698. (www)

  @article{ , abstract = {Urban rivers are faced with serious water quality deterioration, and an efficient, comprehensive and accurate dynamic monitoring method for urban water quality is urgently needed. Remote sensing water quality inversion model based on spectral features has been widely used in water quality monitoring. However, traditional water quality inversion models ignore the complex optical properties of water bodies, resulting in the performance of models that are often limited and cannot be reused across time and space. Therefore, this study applies the idea of integrated modeling to remote sensing inversion of water quality parameters (WQPs), and proposes an adaptive dynamic weighted ensemble learning (DWE) model, aiming to integrate the advantages of different machine learning models, improve the accuracy of WQP inversion, and adaptively assign dynamic weights to different machine learning models, thereby achieving the scalability of the model in time and space. In addition, to address the limitations of using drone data to infer small-scale water WQP due to spatial resolution limitations and limited input features, we propose an ASO-GSA feature selection method for selecting the optimal feature combination from potential feature set data. The results show that the DWE model based on optimal features has high accuracy and robustness in WQP prediction, with coefficients of determination ( $R^{2}$ ) of 0.8472, 0.8547, 0.8973, and 0.8393 for chlorophyll a (Chl-a), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), respectively, which improved the prediction accuracy by nearly 30% compared with the traditional inversion model. In order to evaluate the spatiotemporal scalability of the model, we applied the model to unmanned aerial vehicle (UAV) remote sensing data acquired from the Nanxintang River during the first half of the year from 2024 to 2025 (nearly one year later) and the Shangheng River between 2023 and 2024 to assess the scalability of the model. The results show that our model can be extended across time and space( $R^{2}$ of 0.8012, 0.8387,0.8462 and 0.7863; $R^{2}$ of 0.7892, 0.8153,0.7587 and 0.7664). The results of this paper can further inform water quality monitoring and water resource management in urban rivers.}, archive = {J_ACCESS}, author = {Hao Li and Zhijun Xie and Xing Jin and Changchun Peng and Cheng Ren and Yunfei Mao and Kefan Zhou and Yingying Li}, doi = {10.1109/ACCESS.2025.3554253}, journal = {IEEE Access}, pages = {58680-58698}, shortjournal = {IEEE Access}, title = {DWE: Research on water quality inversion and scalability in small watersheds using UAV remote sensing data}, volume = {13}, year = {2025}, }
• (2025). Wavelet-enhanced hybrid LSTM-XGBoost model for predicting time series containing unpredictable events. ACCESS, 13, 58671-58679. (www)

  @article{ , abstract = {Accurate electricity consumption forecasting is essential for effective power management, especially in the presence of unpredictable events that disrupt typical consumption patterns. Using the COVID-19 pandemic as a case study for such unpredictable events, this study proposes an improved hybrid LSTM-XGBoost model with adapted wavelets to capture complex, irregular fluctuations in energy demand. The model first applies wavelet decomposition to the original data, extracting multiple frequency components that highlight short-term variations and long-term trends. By incorporating these wavelet coefficients as features, the model is sensitized to anomalous events, resulting in more accurate forecasts over a more extended period without the need for frequent retraining. The hybrid approach takes advantage of the LSTM’s ability to model temporal sequences and uses XGBoost to adjust for residual errors. Experimental results show that the model can effectively forecast energy demand with minimal error, especially on regular weekdays, and achieves robust performance in the face of unforeseen anomalies. This methodology shows a promising aspect for improving the reliability of energy forecasting models with potential applications in smart grid management and sustainable energy planning.}, archive = {J_ACCESS}, author = {Ali Ajder and Hisham A. A. Hamza and Ramazan Ayaz}, doi = {10.1109/ACCESS.2025.3556540}, journal = {IEEE Access}, pages = {58671-58679}, shortjournal = {IEEE Access}, title = {Wavelet-enhanced hybrid LSTM-XGBoost model for predicting time series containing unpredictable events}, volume = {13}, year = {2025}, }
• (2025). Vulnerability time series forecasting: A comparative study of hierarchical and non-hierarchical approaches. ACCESS, 13, 58658-58670. (www)

  @article{ , abstract = {Cybersecurity has become an increasing priority in large organizations due to the rapid evolution of digital threats. With increasingly sophisticated attacks, vulnerability management emerges as one of the main strategies to protect systems and data. However, the complexity and dynamics of threats make management processes challenging, requiring efficient and adaptable approaches. Artificial Intelligence (AI) and Machine Learning (ML) are promising tools to anticipate and mitigate these threats, offering predictive capabilities based on historical data. This article proposes the use of time series in a hierarchical manner for the forecasting of vulnerabilities in systems. The proposed methodology aims to deal with the complexity of the data, allowing a hierarchical structure to understand and capture the interdependencies and patterns among different levels more completely. The evaluation is carried out with different ML models, such as LSTM, RNN, MLP, among others, comparing the performance of hierarchical and non-hierarchical approaches. The results indicate that, in a hierarchical structure, especially LSTM, shows superior performance in vulnerability forecasting. However, in some scenarios, when using reconcilers, models like N-BEATS also demonstrate good results.}, archive = {J_ACCESS}, author = {Juscimara G. Avelino and Bruna A. O. Brito and Thiago T. de Oliveira and Jean M. M. de Lima and André M. Gurgel and Ramon S. Malaquias and Itamir de M. B. Filho}, doi = {10.1109/ACCESS.2025.3555860}, journal = {IEEE Access}, pages = {58658-58670}, shortjournal = {IEEE Access}, title = {Vulnerability time series forecasting: A comparative study of hierarchical and non-hierarchical approaches}, volume = {13}, year = {2025}, }
• (2025). Enhancing QoS in 6G networks through multi-IAB relaying strategies with optimal path selection. ACCESS, 13, 58649-58657. (www)

  @article{ , abstract = {The rapid increase in global data traffic requires a new generation of mobile communication technologies to enable seamless and immersive high bandwidth services, with improved Quality of Service (QoS), to the end-user terminals. To achieve this goal, the combination of Millimeter Wave (mmWave) links with Integrated-Access-and-Backhauling (IAB) technology, offers a promising solution. With IAB, the wireless Backhaul Link (BL) between donor Next Generation Node B (gNB) and IAB nodes simplifies the ultra-densification of network elements, reducing the cell sizes and increasing the chances of having a Line-of-Sight (LoS) condition in the last wireless hop towards the User Equipment (UE), which is known as an Access Link (AL). This paper studies the end-to-end (e2e) data rate performance of an IAB-enhanced cellular network deployed in a Manhattan-like urban setting, proposing an optimal path selection scheme to improve the experience of mobile users in dense urban environments. This selection scheme allows a mobile UE to connect to a gNB or an IAB node, ensuring improved Signal-to-Interference-and-Noise-Ratio (SINR) levels and, consequently, higher e2e data rates. Simulation results show notable improvements with the optimal path selection scheme, achieving data rate gains of 4.9-6.6 bps/Hz at the 50th percentile, across various IAB relaying strategies when using mmWave frequency bands with a few hundred MHz of bandwidth. This study emphasizes the significance of mmWave IAB deployments in dense urban areas, with optimized path selection to improve end-user experience in dense urban scenarios in which the probability of LoS condition reduces notably with distance even in small cell network deployments.}, archive = {J_ACCESS}, author = {Inam Ullah and Hesham El Sayed and Alexis A. Dowhuszko and Umerulfaruque Shehseid and Manzoor Ahmed Khan and Jyri Hämäläinen}, doi = {10.1109/ACCESS.2025.3555208}, journal = {IEEE Access}, pages = {58649-58657}, shortjournal = {IEEE Access}, title = {Enhancing QoS in 6G networks through multi-IAB relaying strategies with optimal path selection}, volume = {13}, year = {2025}, }
• (2025). Enhancing maritime data integration for platform services with sequence-to-sequence models and statistical refinement. ACCESS, 13, 58636-58648. (www)

  @article{ , abstract = {The increasing adoption of IoT devices on ships and the expansion of platform services present a critical challenge in integrating heterogeneous ship data models into a unified platform data model. The task involves mapping ship Domain-Specific Language (DSL) descriptions to platform indices, complicated by variability and class imbalance. To address these challenges, this paper proposes a framework that combines a sequence-to-sequence model with statistical vectorization techniques. The model generates structured mapping classes, offering flexibility to accommodate diverse equipment and attributes, while training exclusively on connected data mitigates class imbalance. Subsequently, statistical vectorization techniques are applied to identify the correct match among the classified candidates, while ensuring that unconnected data is excluded. This two-step approach enhances recall and guarantees accurate relationships between ship DSLs and platform data indices. The proposed framework is validated using real-world data from 52 ships. Experimental results demonstrate that the sequence-to-sequence model with statistical refinement outperformed single-step and discriminative methods in handling class imbalance and variations when mapping ship DSLs to a unified platform data model. Our method achieved a recall of 89.14 and an $\text {F}_{\beta }$ -Score of 87.12, which are 4.15 and 1.91 points higher, respectively, than the reference classification method.}, archive = {J_ACCESS}, author = {Hyoseong Hwang and Richard Wong and Ducsun Lim and Jonggu Kang and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3555272}, journal = {IEEE Access}, pages = {58636-58648}, shortjournal = {IEEE Access}, title = {Enhancing maritime data integration for platform services with sequence-to-sequence models and statistical refinement}, volume = {13}, year = {2025}, }
• (2025). Cluster path planning method in mountainous areas based on optimal timeliness. ACCESS, 13, 58612-58635. (www)

  @article{ , abstract = {This study addresses the path planning problem of road roller unmanned aerial vehicle swarms in mountainous terrains, aiming to improve compaction and optimize operational efficiency. Global scheduling is based on environmental information and task planning, while local path planning adjusts paths using real-time sensor data to ensure safety and efficiency without requiring global optimality. A dynamic spatial optimal path method is proposed, considering construction quality, swarm efficiency, and path safety. A dynamic spatial grid map is used to represent the roller’s surface information, and collision prediction is based on obstacle occupancy and temporal trends. A model for key mountainous highway scenarios is established to predict collisions between unmanned rollers and surrounding obstacles. The study also investigates the generation and optimization of dynamic grid maps for roadwork conditions. Experimental results show significant improvements in the following areas: 1) elimination of sharp turns in the trajectory, 2) path tracking accuracy of up to 5 cm, and 3) improved compaction uniformity with density distribution concentrated between 98.8%-99.8%, and a 47% reduction in variability. These results demonstrate the effectiveness of the proposed dynamic path planning method in complex mountainous environments.}, archive = {J_ACCESS}, author = {Zhufeng Shao and Dunwei Gong and Chuansheng Liu}, doi = {10.1109/ACCESS.2025.3556398}, journal = {IEEE Access}, pages = {58612-58635}, shortjournal = {IEEE Access}, title = {Cluster path planning method in mountainous areas based on optimal timeliness}, volume = {13}, year = {2025}, }
• (2025). AURA-depth: Attention-based uncertainty reduction and feature aggregation depth network. ACCESS, 13, 58600-58611. (www)

  @article{ , abstract = {Reliable depth information is crucial in autonomous driving technology. However, monocular depth estimation often faces inherent limitations such as scale ambiguity and lack of depth cues. To address these issues, we propose a novel approach that fuses two methods: triangulated depth estimation derived from the optical flow of consecutive frames, and dense depth estimation from a single image. In the initial task, we use a dense depth network to extract a depth map from the current frame and calculate a triangulated depth map through the optical flow and relative pose differences between previous frames. While triangulated depth estimation may lack detail in object-specific regions, it provides high accuracy for the overall scene depth. Conversely, dense depth estimation excels at extracting the relative depth of objects, making these methods complementary. The main task employs a multi-branch architecture to process RGB image, dense depth map, and triangulated depth map. The output of each encoder branch’s residual module is fused using a Multimodal Cross Attention (MCA) module, guided by the uncertainty calculated from the absolute difference between dense depth features and triangulated depth features. This process effectively integrates the features of each branch. The final depth map is generated by a decoder that processes the fused features. Our approach significantly improves depth estimation accuracy and reliability by leveraging the complementary strengths of uncertainty-guided dense depth maps and triangulated depth maps, overcoming the limitations of traditional monocular methods. This method demonstrates substantial improvements in various computer vision tasks, providing a robust solution.}, archive = {J_ACCESS}, author = {Youngtak Na and Sungho Woo and Soonyoung Lee and Byung Jun Kang}, doi = {10.1109/ACCESS.2025.3556062}, journal = {IEEE Access}, pages = {58600-58611}, shortjournal = {IEEE Access}, title = {AURA-depth: Attention-based uncertainty reduction and feature aggregation depth network}, volume = {13}, year = {2025}, }
• (2025). Systematic literature review for detecting intrusions in unmanned aerial vehicles using machine and deep learning. ACCESS, 13, 58576-58599. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs), known as drones, have significantly impacted the agricultural, police, military, and commercial sectors, aiming to enhance the quality of life; however, they are exposed to significant risks from the adversarial side, thereby gaining benefits from security vulnerabilities, including insecure communication channels, authorization risks, hardware, software, and network risks, to perform various attacks. One of those attacks is intrusion malware, which uses malicious programs, signal spoofing, denial of services, targeting integrity, confidentiality, and availability of the system. Detecting these intrusions has recently gained attention in academia and industrial fields for addressing existing threats and developing detection frameworks, such as utilizing machine and deep learning algorithms. Because of its importance in this field, this survey aims to provide a background for researchers interested in detecting malware in drones, discuss recent approaches, depict a taxonomy of constructing approaches, identify existing problems, and explore trends in future work.}, archive = {J_ACCESS}, author = {Abdulaziz A. Alzubaidi}, doi = {10.1109/ACCESS.2025.3552329}, journal = {IEEE Access}, pages = {58576-58599}, shortjournal = {IEEE Access}, title = {Systematic literature review for detecting intrusions in unmanned aerial vehicles using machine and deep learning}, volume = {13}, year = {2025}, }
• (2025). Toward an integrated intelligent framework for crowd control and management (IICCM). ACCESS, 13, 58559-58575. (www)

  @article{ , abstract = {Managing large-scale gatherings, such as global festivals, sporting events, and religious congregations, presents substantial challenges in ensuring crowd safety and control. Innovative frameworks are essential to address these complexities effectively. The Integrated Intelligent Crowd Control and Management (IICCM) framework combines cutting-edge technologies, including Computer Vision (CV), Artificial Intelligence (AI), and the Internet of Things (IoT), to enhance participant safety and optimize crowd management. CV enables precise real time identification and tracking, AI analyzes crowd behavior to anticipate risks, and IoT gathers environmental data to improve crowd flow, alleviate congestion, and provide timely assistance. Additionally, the framework facilitates emergency evacuation planning by modeling crowd dynamics and identifying safe, efficient escape routes. Although suitable for diverse events, the Hajj pilgrimage—a uniquely large and dynamic annual gathering—provides a rigorous test case for the IICCM framework. Managing millions of participants from varied cultural and linguistic backgrounds highlights the system’s adaptability and robustness. By effectively addressing Hajj specific challenges, the IICCM framework demonstrates its scalability and applicability to other large-scale events. This research offers valuable insights for decision-makers seeking to implement advanced crowd management technologies.}, archive = {J_ACCESS}, author = {Tarik Alafif and Mohammad Jassas and Alaa E. Abdel-Hakim and Ghada Alfattni and Hassan Althobaiti and Mohammed Ikram and Amirah Alharbi and Hussam Alsharif and Mazin AlShamrani and Ebtisam Alharbi and Tahani Alsubait and Abdullah Alhawsawi and Badr Alsolami and Khalid Khayyat}, doi = {10.1109/ACCESS.2025.3555154}, journal = {IEEE Access}, pages = {58559-58575}, shortjournal = {IEEE Access}, title = {Toward an integrated intelligent framework for crowd control and management (IICCM)}, volume = {13}, year = {2025}, }
• (2025). Developing an automated model-based software testing tool from the design phase. ACCESS, 13, 58548-58558. (www)

  @article{ , abstract = {Software testing involves a number of labor-intensive tasks that, to save resources, should be automated. Model-based software testing is one of the most widely used methods of software quality assurance in the business world is model-based software testing. A potential starting point for creating test paths for testing the target software system is the UML model, which can intuitively explain the dynamic behaviors of a software system under testing. Unfortunately, the lack of strong tool support and automated generating methodologies has resulted in the dearth of practical model-based testing methods for industry practitioners. To address this issue, we suggest automated generation paths using a support tool. The present study proposes a tool for automatically generating test paths from IDEF and UML models to facilitate automated and model-based software testing. This is because early-stage software development process model-based testing was shown to be more efficient; the IDEF model, along with the rule flow model, was utilized in this study, which has never been used to create software test paths. The models were transformed into an XMI file for input, and nodes parser was utilized to construct the test scenarios and assess the active performance of the system. Next came the drawing of a control graph and, finally, the creation of test paths. The test paths generated were measured using a variety of coverage criteria, including edges, actions, branches, and paths. Using the electricity billing system as a case study, the proposed tool achieved high accuracy within authorized coverage limits.}, archive = {J_ACCESS}, author = {Rasha Gh. Alsarraj and Atica M. Altaie and Esra Zuhair Majeed}, doi = {10.1109/ACCESS.2025.3553967}, journal = {IEEE Access}, pages = {58548-58558}, shortjournal = {IEEE Access}, title = {Developing an automated model-based software testing tool from the design phase}, volume = {13}, year = {2025}, }
• (2025). A survey on the design, detection, and prevention of pre-silicon hardware trojans. ACCESS, 13, 58509-58547. (www)

  @article{ , abstract = {The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs. This has resulted in a significant corpus of hardware Trojan research including Trojan design and benchmarking efforts and development of corresponding metrics and detection and prevention techniques, over the last two decades. In this survey, we first highlight efforts in Trojan design and benchmarking, followed by a cataloging of seminal and recent works in Trojan detection and prevention and their accompanied metrics. Given the volume of literature in this field, this survey considers only pre-silicon techniques. We make this distinction between pre- and post-silicon to properly scope and provide appropriate context into the capabilities of existing hardware Trojan literature. Each major section (design, prevention, and detection) is accompanied by insights, and common pitfalls, which we highlight can be addressed by future research.}, archive = {J_ACCESS}, author = {Jonathan Cruz and Jason Hamlet}, doi = {10.1109/ACCESS.2025.3555166}, journal = {IEEE Access}, pages = {58509-58547}, shortjournal = {IEEE Access}, title = {A survey on the design, detection, and prevention of pre-silicon hardware trojans}, volume = {13}, year = {2025}, }
• (2025). Broadband operation of binary metasurfaces: Limitations and perspectives. ACCESS, 13, 58496-58508. (www)

  @article{ , abstract = {In this paper, physical prerequisites of the broadband operation of reflective intelligent surfaces (RIS) implemented as periodically non-uniform metasurfaces (MSs) of binary type are elucidated. The general relation for the maximal operational band of a binary MS following from these prerequisites is deduced. Based on this relation, the general rules for engineering the periodical RISs based on the binary MSs with the maximally broad frequency band are formulated. Recent studies in which this maximum was approached are discussed. Finally, we present a numerical example of an original binary MS designed with these guidelines which grants for restricted incidence angles an ultra-broadband operation.}, archive = {J_ACCESS}, author = {Constantin R. Simovski and Javad Shabanpour}, doi = {10.1109/ACCESS.2025.3556467}, journal = {IEEE Access}, pages = {58496-58508}, shortjournal = {IEEE Access}, title = {Broadband operation of binary metasurfaces: Limitations and perspectives}, volume = {13}, year = {2025}, }
• (2025). Enhancing defect classification in solar panels with electroluminescence imaging and advanced machine learning strategies. ACCESS, 13, 58481-58495. (www)

  @article{ , abstract = {Electroluminescence (EL) imaging is the most widely used diagnostic technique for identifying flaws at every stage of the production, installation, and operation of solar modules. This method can potentially reduce power outages by locating and fixing solar module faults such microcracks and breaks in the finger lines. The EL test is a reliable inspection method, however, because of complex fault patterns and heterogeneous backgrounds, interpreting EL images can be difficult. As a result, assessing damaged cells and determining the severity of an issue necessitates specialized knowledge, which makes manually executing these methods for each cell time-consuming. Because of this, automated visual inspection of solar cells becomes very important. In this work, a novel system for automatically identifying and categorizing solar cell faults is presented. A strong CNN model created from scratch is used to extract deep features. Utilizing the recently developed RSWS classification method, the deep characteristics are evaluated. The popular ELPV dataset with two and four classes is used to test the suggested methodology. For the two-class classification problem, the classification performance is 98.17%, and for the four-class classification problem, it is 97.02%.}, archive = {J_ACCESS}, author = {Fatih Demir}, doi = {10.1109/ACCESS.2025.3551749}, journal = {IEEE Access}, pages = {58481-58495}, shortjournal = {IEEE Access}, title = {Enhancing defect classification in solar panels with electroluminescence imaging and advanced machine learning strategies}, volume = {13}, year = {2025}, }
• (2025). Detecting throat cancer from speech signals using machine learning: A scoping literature review. ACCESS, 13, 58465-58480. (www)

  @article{ , abstract = {Cases of throat cancer are rising worldwide. With survival decreasing significantly at later stages, early detection is vital. Artificial intelligence (AI) and machine learning (ML) have the potential to detect throat cancer from patient speech, facilitating earlier diagnosis and reducing the burden on overstretched healthcare systems. However, no comprehensive review has explored the use of AI and ML for detecting throat cancer from speech. This review aims to fill this gap by evaluating how these technologies perform and identifying issues that need to be addressed in future research. We conducted a scoping literature review across three databases: Scopus, Web of Science, and PubMed. We included articles that classified speech using ML and specified the inclusion of throat cancer patients in their data. Articles were categorised based on whether they performed binary or multi-class classification. We found 27 articles fitting our inclusion criteria, 12 performing binary classification, 13 performing multi-class classification, and two that do both binary and multi-class classification. The most common classification method used was neural networks, and the most frequently extracted feature was mel-spectrograms. We also documented pre-processing methods and classifier performance. We compared each article against the TRIPOD-AI checklist, which showed a significant lack of open science, with only one article sharing code and only three using open-access data. Open-source code is essential for external validation and further development in this field. Our review indicates that no single method or specific feature consistently outperforms others in detecting throat cancer from speech. Future research should focus on standardising methodologies and improving the reproducibility of results.}, archive = {J_ACCESS}, author = {Mary Paterson and James Moor and Luisa Cutillo}, doi = {10.1109/ACCESS.2025.3555767}, journal = {IEEE Access}, pages = {58465-58480}, shortjournal = {IEEE Access}, title = {Detecting throat cancer from speech signals using machine learning: A scoping literature review}, volume = {13}, year = {2025}, }
• (2025). Recent results on the use of artificial intelligence techniques applied to wireless power transfer systems. ACCESS, 13, 58443-58464. (www)

  @article{ , abstract = {This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. With the growing demand for wireless charging applications such as electric vehicles (EVs), IoT devices, and medical implants, WPT systems face challenges in terms of coil alignment, foreign object detection, and power efficiency. The use of ML algorithms, particularly neural networks and reinforcement learning has emerged as a promising solution to address these challenges. We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. Additionally, ML is being used for foreign object detection (FOD) to ensure safety by identifying metallic and living objects that may interfere with power transmission. The article discusses various approaches, including supervised learning, regression models, and Q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. By reviewing recent advancements and ongoing research, this paper provides a comprehensive overview of the potential and limitations of integrating ML into WPT systems, paving the way for smarter, safer, and more efficient wireless charging technologies.}, archive = {J_ACCESS}, author = {Federico Amadei and Michele Quercio and Francesco Riganti Fulginei}, doi = {10.1109/ACCESS.2025.3556235}, journal = {IEEE Access}, pages = {58443-58464}, shortjournal = {IEEE Access}, title = {Recent results on the use of artificial intelligence techniques applied to wireless power transfer systems}, volume = {13}, year = {2025}, }
• (2025). Corrections to “YOLO-DLHS-P: A lightweight behavior recognition algorithm for captive pigs”. ACCESS, 13, 58442. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “YOLO-DLHS-P: A Lightweight Behavior Recognition Algorithm for Captive Pigs”).}, archive = {J_ACCESS}, author = {Changhua Zhong and Hao Wu and Junzhuo Jiang and Chaowen Zheng and Hong Song}, doi = {10.1109/ACCESS.2025.3552810}, journal = {IEEE Access}, pages = {58442}, shortjournal = {IEEE Access}, title = {Corrections to “YOLO-DLHS-P: A lightweight behavior recognition algorithm for captive pigs”}, volume = {13}, year = {2025}, }
• (2025). On combining deep neural network classifiers for source device identification. ACCESS, 13, 58425-58441. (www)

  @article{ , abstract = {This paper proposes combining deep neural network classifiers while simultaneously optimizing the networks. The proposed combination scheme enhances the accuracy of each classifier, which, in turn, boosts the overall combined accuracy during a post-processing step. The proposed classification scheme is thoroughly evaluated on a dataset specifically designed for multimedia forensics research. The combined classifiers include shallow and deep neural networks, with input data comprising original and manipulated content processed through online social networks such as YouTube, WhatsApp, and Facebook. The experimental results demonstrate promising performance, proving the usability of the proposed classifier combination scheme. Specifically, it is observed that the accuracy of shallow neural networks improves significantly when combined with deep neural networks. This performance enhancement is particularly notable when the combined classifiers are trained on data manipulated by online social network platforms.}, archive = {J_ACCESS}, author = {Ioannis Tsingalis and Constantine Kotropoulos}, doi = {10.1109/ACCESS.2025.3555141}, journal = {IEEE Access}, pages = {58425-58441}, shortjournal = {IEEE Access}, title = {On combining deep neural network classifiers for source device identification}, volume = {13}, year = {2025}, }
• (2025). Multi-modal deep embedded clustering (MM-DEC): A novel framework for mineral detection using hyperspectral imagery. ACCESS, 13, 58407-58424. (www)

  @article{ , abstract = {Identification of minerals through remote sensing imagery plays an important role in mineral exploration, especially when ground truth data or field work is not accessible. Hyperspectral imaging, which collects information from hundreds of narrow spectral bands, is a powerful tool that provides intricate surface information, but presents challenges due to its high-dimensionality and the complex structure of the data. To this end, we propose Multi-Modal Deep Embedded Clustering (MM-DEC) approach, an innovative unsupervised learning framework that integrates Convolutional Autoencoders(CAEs), Variational Autoencoders (VAEs), and Gray Level Co-occurrence Matrix (GLCM) based texture extraction that is able to exploit the spatial, spectral, and texture features of mineral in consideration We demonstrate the MM-DEC potential to identify hematite prospects in the mineralized Kiriburu area of Jharkhand, India using EO-1 Hyperion hyperspectral data. Preprocessing pipeline includes denoising using Machine Learning(ML) and statistical techniques, followed by major land cover classification based on spectral indices including Normalized Difference Vegetation Index (NDVI); Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI). By using the widely applied Pixel Purity Index (PPI), pure spectral pixels are extracted for refining and providing a high-confidence target point of hematite detection. Evaluation metrics based on cluster quality such as Silhouette Score, and Davies-Bouldin Index (DBI), show that MM-DEC is a competent model, as it is able to produce relatively compact and well-defined clusters as compared to conventional clustering methods. We achieved a similarity score of 98.69% in reference to the hematite spectral signatures. With accurate cluster labels, geographical coordinates, and spectral similarity scores, our method establishes a precedent in mineral extraction and resource planning based on remote sensing advancements.}, archive = {J_ACCESS}, author = {Priyanka Nair and Devesh Kumar Srivastava and Roheet Bhatnagar}, doi = {10.1109/ACCESS.2025.3554837}, journal = {IEEE Access}, pages = {58407-58424}, shortjournal = {IEEE Access}, title = {Multi-modal deep embedded clustering (MM-DEC): A novel framework for mineral detection using hyperspectral imagery}, volume = {13}, year = {2025}, }
• (2025). Efficient multi-stage self-supervised learning for pathology image analysis via masking. ACCESS, 13, 58394-58406. (www)

  @article{ , abstract = {Annotated large-scale datasets are crucial for pathology image analysis, yet creating such datasets is challenging. Self-supervised learning (SSL) offers a potential solution to reduce the need for extensive annotations. However, applying SSL methods designed for natural images directly to pathology images may not yield optimal efficiency and performance due to the unique characteristics of pathological images. In this paper, we explore the strengths and weaknesses of two widely used SSL categories, including generation-based SSL and contrastive SSL in pathology tasks. Our findings indicate that pathology images exhibit a high degree of information redundancy, making the direct application of these SSL categories inefficient and suboptimal. To address this, we propose the Rapid Histopathology Pre-training (RHP) framework, which employs a simple yet effective method for training vision Transformers. RHP uses a dual-branch random masking strategy to integrate the characteristics of pathological images with the advantages of both self-supervised schemes. Additionally, an extra multi-stage strategy can enhance model performance while mitigating domain discrepancy issues. This approach enables rapid training on pathological images, yielding superior performance compared to baseline methods. For our experiments, we extracted 4,821,041 multi-scale pathological images from 37,378 pathology slides for pre-training. We evaluated RHP’s performance on four downstream tasks, including patch classification, nucleus segmentation, gland segmentation, and whole slide image classification across eight public datasets. The results demonstrate that our RHP-based visual representation achieved state-of-the-art performance on each dataset with high efficiency, proving to be more robust compared to other SSL methods and ImageNet transfer learning. Our code is publicly released on GitHub (https://github.com/AndrewTal/RHP).}, archive = {J_ACCESS}, author = {Ming Feng and Weiquan Huang and Liang Hu}, doi = {10.1109/ACCESS.2025.3556435}, journal = {IEEE Access}, pages = {58394-58406}, shortjournal = {IEEE Access}, title = {Efficient multi-stage self-supervised learning for pathology image analysis via masking}, volume = {13}, year = {2025}, }
• (2025). A bibliometric exposition and review on leveraging LLMs for programming education. ACCESS, 13, 58364-58393. (www)

  @article{ , abstract = {The world is experiencing an AI revolution, with large language models (LLMs) transforming various industries, including education. Academics are striving to harness the potential of LLMs while also contending with their risks. This paper presents the first bibliometric analysis focused on LLM research in programming education, identifying leading countries, authors, and institutions while analyzing key terms and popular keywords in this field. Additionally, it highlights influential studies on topics such as introductory programming, computer science, computing, programming education, and prompt engineering, discussing key insights from these works. Findings indicate that LLMs could play a significant role in programming education and may be integrated into computer science curricula. However, careful consideration is needed to ensure their benefits outweigh their risks across various use cases. This study specifically examines ChatGPT as a representative LLM, exploring its benefits and limitations as both a learning aid for students and a support tool for professionals. It also evaluates the quality of ChatGPT-generated code and its effectiveness in simplifying programming concepts for beginners. Furthermore, the ethical implications of increasing reliance on LLMs for programming tasks, including concerns about dependency, plagiarism, and potential effects on critical thinking, are addressed. By contributing to the ongoing discourse on integrating AI tools like ChatGPT in programming education, this research emphasizes the importance of responsible and ethical usage to maximize benefits for students, educators, and the broader educational community.}, archive = {J_ACCESS}, author = {Joanah Pwanedo Amos and Oluwatosin Ahmed Amodu and Raja Azlina Raja Mahmood and Akanbi Bolakale Abdulqudus and Anies Faziehan Zakaria and Abimbola Rhoda Iyanda and Umar Ali Bukar and Zurina Mohd Hanapi}, doi = {10.1109/ACCESS.2025.3554627}, journal = {IEEE Access}, pages = {58364-58393}, shortjournal = {IEEE Access}, title = {A bibliometric exposition and review on leveraging LLMs for programming education}, volume = {13}, year = {2025}, }
• (2025). A latent multi-scale residual transformer approach for cross-modal medical image synthesis. ACCESS, 13, 58351-58363. (www)

  @article{ , abstract = {Cross-modal generation has emerged as a crucial method for addressing the challenge of filling in missing modalities in medical imaging. Existing approaches predominantly utilize convolutional neural networks (CNNs) or vision transformers (ViTs) and their variants as model backbones. Consequently, issues arise concerning limited receptive fields and significant increases in computational costs. This paper proposes a latent feature space-based multi-scale residual ViT generative adversarial model (LMRT-NET), which leverages the global sensitivity of ViTs and the local precision of CNNs while reducing computational costs. The generator of LMRT-NET comprises an encoder-decoder architecture that enhances performance and lowers computational expenses through the use of multi-scale dynamic aggregation residual ViT (DART) blocks in the latent feature space. This module consists of two layers of residual convolutional blocks and transformer blocks of different scales, where the transformer blocks assist the convolutional blocks in capturing contextual features, and lower-level blocks support higher-level blocks in learning high-dimensional global information. Additionally, a multi-level information fusion (MIF) module is integrated into the encoder-decoder and latent feature space, consisting of a dual-scale selective fusion (DSF) module that adaptively aggregates multi-scale information to generate target modality images. Extensive experimental results across three different datasets demonstrate that LMRT-NET outperforms baseline methods in terms of image generation quality and generalization capability. Our code will be released on https://github.com/ffan14/LMRT-NET.}, archive = {J_ACCESS}, author = {Xinmiao Zhu and Yang Li}, doi = {10.1109/ACCESS.2025.3555879}, journal = {IEEE Access}, pages = {58351-58363}, shortjournal = {IEEE Access}, title = {A latent multi-scale residual transformer approach for cross-modal medical image synthesis}, volume = {13}, year = {2025}, }
• (2025). Metaverse unbound: A survey on synergistic integration between semantic communication, 6G, and edge learning. ACCESS, 13, 58302-58350. (www)

  @article{ , abstract = {With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool.}, archive = {J_ACCESS}, author = {Mohammad Z. Aloudat and Abdulla Aboumadi and Abdelrahman Soliman and Hasan Abbas Al-Mohammed and Muhammed Al-Ali and Asma Mahgoub and Mahmoud Barhamgi and Elias Yaacoub}, doi = {10.1109/ACCESS.2025.3555753}, journal = {IEEE Access}, pages = {58302-58350}, shortjournal = {IEEE Access}, title = {Metaverse unbound: A survey on synergistic integration between semantic communication, 6G, and edge learning}, volume = {13}, year = {2025}, }
• (2025). A reduced-parameter mathematical surrogate model for improving measurement accuracy of mechanical systems using experimental techniques. ACCESS, 13, 58281-58301. (www)

  @article{ , abstract = {Modeling of a larger mechanical system is inherently difficult due to the complexity of individual processes and the interaction between multiple processes. Using a polynomial model based on experimental results to obtain an input-output relationship offers an easier option than establishing a mathematical model in the case of an Open Loop Controller. Such input-output relationship is workable but sometimes experiences computational burden due to a higher degree of the polynomial. Surrogate modeling is commonly used as a method to overcome this difficulty through an approximation by reducing the degree of complexity of the relationship. Surrogate models based on statistical principles, which correlate multiple inputs and required outputs, are opaque to the system parameters. These system parameters can be optimized to improve the measurement performance and hence input-output relationship by either the polynomial model or its surrogate does not serve the purpose of optimization. A surrogate of a mathematical model based on experimentation is proposed here. The method involves developing mathematical models of subsystems using minimum parameters. The complexity of each subsystem is reduced by retaining dominant parameters and eliminating non-influential parameters. To improve the measurement accuracy of the optimization, model an experimental method has evolved where parameters are attuned appropriately to accommodate the effect of eliminated parameters. This technique is demonstrated by establishing a mathematical model for a pendulum-ball system.}, archive = {J_ACCESS}, author = {Pranav Vijay Deosant and H. P. Jawale and H. T. Thorat and Ankur Jaiswal}, doi = {10.1109/ACCESS.2025.3555556}, journal = {IEEE Access}, pages = {58281-58301}, shortjournal = {IEEE Access}, title = {A reduced-parameter mathematical surrogate model for improving measurement accuracy of mechanical systems using experimental techniques}, volume = {13}, year = {2025}, }
• (2025). SCA-CVENet: A spatial-channel-attention collaborative cost volume enhancement network for high-quality depth reconstruction. ACCESS, 13, 58264-58280. (www)

  @article{ , abstract = {Accurate and complete depth map prediction from a set of overlapping multi-view stereo images has received extensive attention in the fields of photogrammetry and computer vision. Despite the tremendous efforts made in multi-view depth map reconstruction in recent years, the accuracy and completeness of the reconstructed depth map in matching difficulty areas needs to be further improved. In this paper, to address this issue, we propose SCA-CVENet, which is an efficient spatial-channel-attention collaborative cost volume enhancement network for high-quality depth map reconstruction. The main contributions of SCA-CVENet are the channel attention weight (CAW)-based cost volume enhancement module that can effectively filter the redundant channel information of the feature volumes to boost the feature representation of the cost volumes, and the spatial attention weight (SAW)-based cost volume enhancement module that can significantly improve the robustness of the cost volumes in textureless and repetitive texture areas, to produce more accurate and complete depth map reconstruction results. First, a weight-sharing hierarchical feature extraction subnetwork is adopted to extract robust multi-scale feature maps. Second, the SAW generation subnetwork is adopted, which incorporates the group-wise correlation (GWC)-based cost volume adaptive fusion module and 3D hourglass module to produce an accurate SAW. Finally, the coarse-to-fine cascade depth prediction subnetwork incorporates the CAW- and SAW-based cost volume enhancement modules into the cascade network architecture to predict a high-quality depth map. Three publicly available benchmark datasets—the DTU, BlendedMVS, and Tanks & Temples datasets—were utilized to validate the performance of SCA-CVENet, and the comprehensive experimental results demonstrate that SCA-CVENet obtains a superior performance on all the datasets in terms of accuracy and completeness.}, archive = {J_ACCESS}, author = {Mao Tian and Xudong Zhao and Xiong Lv}, doi = {10.1109/ACCESS.2024.3525302}, journal = {IEEE Access}, pages = {58264-58280}, shortjournal = {IEEE Access}, title = {SCA-CVENet: A spatial-channel-attention collaborative cost volume enhancement network for high-quality depth reconstruction}, volume = {13}, year = {2025}, }
• (2025). State-of-the-art in responsible, explainable, and fair AI for medical image analysis. ACCESS, 13, 58229-58263. (www)

  @article{ , abstract = {Integrating responsible, explainable, and fair artificial intelligence (REF-AI) into medical image analysis has gained significant attention in recent years. This has been driven by the pressing need for ethical, trustworthy, and transparent implementation of AI systems in healthcare. The following review provides a concise overview of REF-AI in the context of medical image analysis. It begins with the fundamental concepts of AI responsibility, explainability, and fairness, followed by a comprehensive taxonomy of over 35 key algorithms and strategies. In addition, it compares methodologies, strengths, and limitations, such as the alignment of AI models with medical standards and the development of interpretable and actionable results for clinicians. Finally, it highlights current trends and proposes directions for future research to further advance the responsible, explainable, and fair application of AI in medical imaging.}, archive = {J_ACCESS}, author = {Soheyla Amirian and Fengyi Gao and Nickolas Littlefield and Jonathan H. Hill and Adolph J. Yates and Johannes F. Plate and Liron Pantanowitz and Hooman H. Rashidi and Ahmad P. Tafti}, doi = {10.1109/ACCESS.2025.3555543}, journal = {IEEE Access}, pages = {58229-58263}, shortjournal = {IEEE Access}, title = {State-of-the-art in responsible, explainable, and fair AI for medical image analysis}, volume = {13}, year = {2025}, }
• (2025). YOLO-SAD: Enhancing small aircraft detection with multi-scale context and improved gradient flow. ACCESS, 13, 58211-58228. (www)

  @article{ , abstract = {Small object detection, especially for fast-moving targets like small aircrafts, faces significant challenges due to limited texture information, motion blur, and complex background environments. In this study, we propose YOLO-SAD, a novel object detection framework designed to enhance the accuracy and efficiency of small aircraft detection through innovative mechanisms. YOLO-SAD comprises three core modules: The Cross-Scale Attention Kernel Aggregation Module enhances small object feature representation by leveraging complementary channel information through a cross-attention mechanism, improving feature fusion and context enhancement; The Feature Highway Aggregation Structure seamlessly integrates features from different network levels while preserving critical information and improving gradient flow behavior in the shallow layers by utilizing auxiliary training branches during training; The Mixed Spectral Feature Compression Module combines multiple downsampling strategies, introduces multi-scale contextual factors, and retains more original details, effectively reducing information loss. Experimental results on the Det-Fly dataset show that YOLO-SAD achieves a 3.9% improvement in AP and a 3.0% increase in recall compared to YOLOv11L. Additionally, generalization performance on the VisDrone2019-DET dataset indicates a 1.8% improvement in small object AP and a 14% reduction in GFLOPS. These results emphasize the robustness and efficiency of YOLO-SAD, establishing a new performance benchmark for small object detection in complex real-world scenarios.}, archive = {J_ACCESS}, author = {Xin Ning and Hongchang Ding and Xiaoqiang Guo}, doi = {10.1109/ACCESS.2025.3555538}, journal = {IEEE Access}, pages = {58211-58228}, shortjournal = {IEEE Access}, title = {YOLO-SAD: Enhancing small aircraft detection with multi-scale context and improved gradient flow}, volume = {13}, year = {2025}, }
• (2025). An expert hybrid federated learning and trust management for security, efficiency, and power optimization in smart health systems. ACCESS, 13, 58191-58210. (www)

  @article{ , abstract = {Health care systems play an important role in smart city infrastructure and seem very beneficial to citizens. The large numbers of health devices are connected with each other and share the patient’s data, AI doctors analyze the data and give recommendations to patients. The challenges associated with the integration of the health system bring significant security and privacy issues to the forefront, especially with respect to sensitive patient information. Ensuring the security and privacy of the health system is necessary. To overcome these challenges, the author proposed a novel and practical model consisting of a hybrid federated SVM and trust management model. First, the system computes the trust, using the parameters of cooperativeness, honesty, and community trust. The proposed model achieves an overall accuracy of 95%, linear kernel accuracy of 95%, RBF kernel accuracy of 93%, and polynomial kernel accuracy of 95% against anomaly detection and provides security and privacy to the health system. The proposed approach is lightweight and reduces 52.5% computational. Our design also promotes savings on unnecessary energy consumption and computational overhead. As a result, our novel strategy opens the door to enhancing the security of smart health infrastructures, ensuring optimal performance and economical use of resources.}, archive = {J_ACCESS}, author = {Sohrab Khan and Nayab Imtiaz and Arnab Kumar Biswas and Zeeshan Bin Siddique and Qaisar Ali Khan}, doi = {10.1109/ACCESS.2025.3556628}, journal = {IEEE Access}, pages = {58191-58210}, shortjournal = {IEEE Access}, title = {An expert hybrid federated learning and trust management for security, efficiency, and power optimization in smart health systems}, volume = {13}, year = {2025}, }
• (2025). Industrial camera-based powder bed defect detection in laser powder bed fusion: Dataset construction and model evaluation. ACCESS, 13, 58175-58190. (www)

  @article{ , abstract = {Laser Powder Bed Fusion (LPBF) is a crucial additive manufacturing technique that builds complex geometries by selectively melting metal powders. However, it faces challenges from defects such as ditches, stacking, insufficient spreading, and craters, which can affect the mechanical properties and quality of the final product. To enhance manufacturing quality and stability, we developed a specialized dataset for detecting these defects, comprising 420 images of ditches, 800 of insufficient spreading, 657 of craters, and 600 of stacking. We evaluated 16 state-of-the-art deep learning models on this dataset and implemented data augmentation techniques, including rotation, scaling, and noise addition, to increase the dataset size fivefold to 12,385 images. Our results showed that data augmentation significantly improved model performance. The YOLOv10 series excelled in post-processing speed at 0.7 milliseconds, while YOLOv8-n had strong inference capabilities at 12.4 milliseconds. YOLOv9-t achieved the fastest preprocessing at 0.3 milliseconds, and YOLOv9-e attained the highest mAP@0.50 score, with precision and recall rates of 0.965 and 0.975, respectively. This study provides a high-quality dataset for powder bed defect detection and validates the efficacy of advanced deep learning models in this field.}, archive = {J_ACCESS}, author = {Zihan Yang and Xiangyu Lu and Junlai Zhao and Wei Wang and Chen Zhang and Jianhui Zhao and Jiachen Yu and Sheng Liu and Fang Dong}, doi = {10.1109/ACCESS.2025.3553264}, journal = {IEEE Access}, pages = {58175-58190}, shortjournal = {IEEE Access}, title = {Industrial camera-based powder bed defect detection in laser powder bed fusion: Dataset construction and model evaluation}, volume = {13}, year = {2025}, }
• (2025). Self-supervised spatiotemporal representation learning for skeleton-based human action recognition. ACCESS, 13, 58164-58174. (www)

  @article{ , abstract = {Skeleton-based human action recognition (HAR) plays an important role in video analytics and recognition systems, with the goal of accurately identifying human actions in videos. However, large-scale action annotation is costly, which has led to the growing interest in HAR research using self-supervised learning (SSL). While existing SSL studies have focused on extracting global information from skeleton sequences, they often overlook local information that captures the relationships between joints and their subtle movements over time. In this study, we propose an SSL-based HAR framework called coarse-to-fine spatiotemporal representation masking (CFSEM) that effectively learns global, local, and temporal information within skeletal. CFSEM captures not only global information in the skeleton using body- and part-level masking but also fine-grained movements using hand masking. In addition, temporal-axis shuffling is introduced into the proposed framework to account for temporal patterns inherent in skeleton sequences. To further enhance the learning process, the loss function is redefined using a cross-correlation matrix, introducing a non-contrastive SSL approach. Experiments on various datasets were conducted to evaluate the proposed framework against baseline methods. Experimental results showed the superior performance of CFSEM and highlighted the possibility of training HAR models using less labeled data, offering the potential to effectively develop HAR models for various industries.}, archive = {J_ACCESS}, author = {Jinhyeok Park and Seoung Bum Kim}, doi = {10.1109/ACCESS.2025.3555953}, journal = {IEEE Access}, pages = {58164-58174}, shortjournal = {IEEE Access}, title = {Self-supervised spatiotemporal representation learning for skeleton-based human action recognition}, volume = {13}, year = {2025}, }
• (2025). Development of lower limb exoskeleton rehabilitation robot framework based on multi-modal motion intent detection. ACCESS, 13, 58152-58163. (www)

  @article{ , abstract = {The number of patients with knee injuries caused by strokes, spinal cord injuries, cerebral palsy or other related diseases is increasing worldwide. Robotic devices such as knee exoskeletons have been studied and adopted in gait rehabilitation as they can provide effective gait training for patients. In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the patients take part in. Most of the signals used to measure the patients’ participation are EMG (Electromyography) signals or oxygen consumption, which increase the cost and the complexity of the robotic device. To achieve an exoskeleton that provides intelligent, effective, and comfortable assistance to the wearer, it is essential to acquire different types of motion data from the human-exoskeleton system during movement. The measured motion data can be used to identify the wearer’s movement intentions, analyze movement states and gait patterns, and evaluate motor performance. This paper proposed a framework for human intention recognition based on motion imagery with multi-mode integration. The framework was applied to the active training mode of the LLER (Lower limb exoskeleton rehabilitation robot), and it consists of two main parts: an EEG (Electroencephalography) intent signal acquisition framework based on motion imagery and an EMG-based motion command correction framework. Among them, the MI (Motor Imagery) based EEG intention signal acquisition framework relies on the passive training in the pre-rehabilitation period to generate effective EEG signals to drive the LLER robot to execute the pre-programmed trajectory training. Moreover, combined with the constant stimulation of the patient’s brain by visual instruments of HMI rehabilitation, the accuracy of motor imagery is reinforced. The EMG-based motor command correction framework involves the EMG dry electrode sensor immobilized to the muscle areas of the affected limb where activation is possible. By detecting muscle activation with the EMG sensor, the framework corrects the intentional control commands after EEG acquisition and processing. The control command of the LLER robot is valid as long as both the EEG drive command and the EMG muscle activation command are satisfied; otherwise, it is considered an invalid control command. Based on a rehabilitation robot dynamics model, a robust adaptive PD control system is developed, and the accurate signals of this multimodal fusion human intention recognition framework based on motor imagery are used as input signals to the system.}, archive = {J_ACCESS}, author = {Yuefei Wang and Yudong Fang and Wujia Huang and Zhen Liu and Chenglong Zhao and Muxin Du and Liucun Zhu}, doi = {10.1109/ACCESS.2025.3550500}, journal = {IEEE Access}, pages = {58152-58163}, shortjournal = {IEEE Access}, title = {Development of lower limb exoskeleton rehabilitation robot framework based on multi-modal motion intent detection}, volume = {13}, year = {2025}, }
• (2025). A computationally efficient model predictive control energy management strategy for hybrid vehicles considering driving style. ACCESS, 13, 58136-58151. (www)

  @article{ , abstract = {In this study, a computationally efficient energy management strategy for model predictive control for hybrid vehicles considering the driving style is proposed. Driving data were collected through driver-in-the-loop simulation experiments, a certain number of feature parameters related to driving styles were analyzed, and the final feature parameters were determined through two screenings. Driving data were labeled according to unsupervised clustering, and the performance of multiple machine learning classifiers was compared under the driving style recognition task. The extra Tree, which had the highest accuracy rate, was selected as the driving style recognition model for online applications. A driving style adaptive speed predictor was designed, and the prediction accuracy of the proposed predictor was simulated and compared with that of some common predictors on different driving style data. The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). The performance of the rule-based, Pontryagin’s minimum principle (PMP), dynamic programming (DP-MPC), Pontryagin’s minimum principle for model predictive control (PMP-MPC), and the strategy proposed in this study are compared through simulation on test data of four driving styles, and it is verified that the strategy in this study can achieve good overall performance, including fuel economy and computational efficiency.}, archive = {J_ACCESS}, author = {Yalian Yang and Yuqi Chen and Changdong Liu}, doi = {10.1109/ACCESS.2024.3420077}, journal = {IEEE Access}, pages = {58136-58151}, shortjournal = {IEEE Access}, title = {A computationally efficient model predictive control energy management strategy for hybrid vehicles considering driving style}, volume = {13}, year = {2025}, }
• (2025). Uncertainty avoider defuzzification of general type-2 multi-layer fuzzy membership functions for image segmentation. ACCESS, 13, 58120-58135. (www)

  @article{ , abstract = {General type two fuzzy models are rarely used in practical problems due to their computational complexity, especially during fuzzification and defuzzification. This is why the main purpose of this article is to present a simple and effective method with a closed formula to increase their use in real-time tasks. For this purpose, we interpreted the general type two membership function as a set of infinite horizontal layers of uncertainty footprints, and then a closed formula for defuzzification is calculated using the uncertainty avoidance method for the triangular shape of secondary membership degree forms. One of the abilities that is expected from Fuzzy logic, particularly General Type-2 Fuzzy Systems (GT2FS), is their effective role in noise handling. In this article, we emphasize providing a method that can be used more consciously to handle noise. To demonstrate the effectiveness of the proposed multilayer approach, it has been applied to image segmentation, a critical aspect of image processing often hindered by challenges like noise, low contrast, and blurred features. The proposed multilayer type-2 fuzzy method based on the uncertainty avoider method has shown very good performance in image segmentation, especially in the case of high-noise images, compared to type one Fuzzy C-Means (FCM) and type-2 FCM methods as well as hybrid methods presented recently in the literature.}, archive = {J_ACCESS}, author = {Sirwan Mohammad Kekshar and Sadegh Abdollah Aminifar}, doi = {10.1109/ACCESS.2025.3556139}, journal = {IEEE Access}, pages = {58120-58135}, shortjournal = {IEEE Access}, title = {Uncertainty avoider defuzzification of general type-2 multi-layer fuzzy membership functions for image segmentation}, volume = {13}, year = {2025}, }
• (2025). UAV-assisted superposition coded cooperation: Hybrid relaying and nakagami- m channel for UL/DL. ACCESS, 13, 58108-58119. (www)

  @article{ , abstract = {In future wireless communication systems reliability and throughput are the major concern for the users in an infrastructure less scenarios. These requirements are fulfill by the unmanned aerial vehicle (UAV)-assisted superposition coded cooperation (UA-SP-CC) system. However, the traditional relaying scheme (amplify-and-forward (AF)/decode-and-forward (DF)) used at the UAV is no longer sufficient in practical scenarios because it applies the received signal without comparing the received instantaneous signal-to-noise ratio (SNR) with the predefined threshold value. The other important issue in UA-SP-CC system is the same channel (Rayleigh or Rician) consideration for the whole network. However, in practical circumstances, the channel gain varies with UAV height. Taking these concerns into account, we consider a hybrid relaying at UAV and Nakagami-m channel whose gain is a function of UAV heights while deciding the channel between uplink (UL)/downlink (DL) in a UA-SP-CC scenario. The height-dependent path loss exponent is also consider for modeling the large scale fading between UL/DL. Taking urban scenarios into account, this paper considers non-line-of-sight (NLoS) paths among the ground users, and Rayleigh fading utilize for implementing these NLoS components. The expressions for the superposition coded noise (SPCN) and the variance of SPCN are developed for UA-NC-CC system using the proposal. The expressions of spectral efficiency, probability of outage, time complexity analysis, and throughput are developed for the proposal. Algorithm 1 is developed for evaluating the performance metrics (spectral efficiency and probability of outage) of our proposed work and compared the results with the existing state-of-the-art (Rician and Rayleigh fading channel model).}, archive = {J_ACCESS}, author = {Pankaj Kumar and Nikita Goel and Manoj Tolani and Santhosha Kamath}, doi = {10.1109/ACCESS.2025.3555120}, journal = {IEEE Access}, pages = {58108-58119}, shortjournal = {IEEE Access}, title = {UAV-assisted superposition coded cooperation: Hybrid relaying and nakagami- m channel for UL/DL}, volume = {13}, year = {2025}, }
• (2025). SMOTEHashBoost: Ensemble algorithm for imbalanced dataset pattern classification. ACCESS, 13, 58092-58107. (www)

  @article{ , abstract = {Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority class. The majority class is often favored by conventional classifiers, which can lead to biases from improper oversampling or subpar performance when detecting instances of the minority class. Consequently, there is growing concern about algorithmic fairness. Thus, the goal is to improve the ability to identify different discriminatory patterns in the data by creating a large number of test cases, which will result in more fair and unbiased model performance. Here, we introduce SMOTEHashBoost, an ensemble learning approach that overcomes class imbalance by combining three core methods: SMOTE oversampling, hash-based undersampling, and AdaBoost boosting. The proposed approach combines weak classifiers to increase the model’s ability to generalize while generating a balanced and representative dataset. The effectiveness of SMOTEHashBoost is demonstrated by improved performance in handling imbalanced datasets, which makes it a dependable solution for skewed class distributions in real-world classification problems.}, archive = {J_ACCESS}, author = {Seema Yadav and Dhruvanshu Joshi and Soham Mulye and Sandeep S. Udmale and Girish P. Bhole}, doi = {10.1109/ACCESS.2025.3555565}, journal = {IEEE Access}, pages = {58092-58107}, shortjournal = {IEEE Access}, title = {SMOTEHashBoost: Ensemble algorithm for imbalanced dataset pattern classification}, volume = {13}, year = {2025}, }
• (2025). Heuristic XAI: AIME and HMM integration improved-feature-importance assessment through heuristics. ACCESS, 13, 58075-58091. (www)

  @article{ , abstract = {The explainability of AI and machine learning models has become crucial, drawing significant attention to explainable AI (XAI) technologies. Global feature importance extraction is key to understanding the behavior of these models. However, existing XAI techniques often overlook the effects of multicollinearity in data, limiting their accuracy. We propose a method that combines Approximate Inverse Model Explanations (AIME) with Hidden Markov Models (HMMs). The HMMs are used to extract hidden states, and the local feature importance for each state is calculated. These values are then used to generate heuristics that adjust the importance of global features. We evaluated our method using the Wine Quality, Census Income, Gas Sensor Array Drift at Different Concentrations, and Breast Cancer Wisconsin datasets. The results showed that our method effectively handles strong multicollinearity and provides approximately 20% more accurate feature importance assessments than previous methods depending on the dataset. Therefore, our approach can better represent XAI contributions using algorithmic or human-supplied heuristics. Our study confirms that this approach is valid for XAI and enables the integration of algorithmic or human-supplied heuristics to realize XAI, which can significantly benefit various fields by enhancing model interpretability and decision-making processes.}, archive = {J_ACCESS}, author = {Takafumi Nakanishi and Ponlawat Chophuk and Krisana Chinnasarn}, doi = {10.1109/ACCESS.2025.3555626}, journal = {IEEE Access}, pages = {58075-58091}, shortjournal = {IEEE Access}, title = {Heuristic XAI: AIME and HMM integration improved-feature-importance assessment through heuristics}, volume = {13}, year = {2025}, }
• (2025). RAPO: An automated performance optimization tool for redis clusters in distributed storage metadata management. ACCESS, 13, 58060-58074. (www)

  @article{ , abstract = {Distributed storage systems are well-equipped to address the demands of massive data storage and rapid access in the era of big data. However, as the scale of these systems continues to expand, challenges related to data localization, performance optimization, and secure access have become increasingly prominent, making effective metadata management critically important. Redis, a memory-based NoSQL (Not only SQL) database, has emerged as an ideal choice for metadata management in distributed storage systems due to its high performance, flexible data structures, excellent scalability, and robust data persistence mechanisms. This study employs Redis as the caching layer for metadata services in distributed storage systems and develops the Redis-cluster Automatic Performance Optimizer (RAPO) to enhance performance. By implementing load balancing among main nodes and read-write separation between main and secondary nodes, the tool aims to improve the metadata service performance of Redis clusters during system maintenance and in high-concurrency scenarios. To address load balancing among main nodes, the research defines the Load Balance Index (LBI) as a metric for evaluating the degree of load distribution within the cluster. Greedy and random iterative search algorithms are designed to migrate hash slots from selected nodes, thereby balancing the node loads and enhancing overall cluster performance. By optimizing with the greedy and random iterative search algorithms, the cluster’s LBI was reduced by an average of 29.36% and 25.20% respectively during random read operations and by 24.98% and 24.03% respectively during imbalanced write operations. A load threshold is defined to achieve read-write separation between main and secondary nodes. Random allocation, round-robin, weighted round-robin, and smooth weighted round-robin are employed to optimize the read-write separation. Experimental results demonstrate that the weighted round-robin and smooth weighted round-robin strategies reduce read response times by an average of 27.53% and 30.75%, respectively.}, archive = {J_ACCESS}, author = {Yunkai Zhu and Tian Xia and Te Zhu and Zhoujie Zhao and Kexin Li and Xingbo Hu}, doi = {10.1109/ACCESS.2025.3556240}, journal = {IEEE Access}, pages = {58060-58074}, shortjournal = {IEEE Access}, title = {RAPO: An automated performance optimization tool for redis clusters in distributed storage metadata management}, volume = {13}, year = {2025}, }
• (2025). Generative AI for analog integrated circuit design: Methodologies and applications. ACCESS, 13, 58043-58059. (www)

  @article{ , abstract = {Electronic Design Automation (EDA) in analog Integrated Circuits (ICs) has been the focus of extensive research; however, unlike its digital counterpart, it has not achieved widespread adoption. In this systematic review, we discuss recent contributions in the last five years, highlighting methods that address data scarcity, topology exploration, process-voltage-temperature (PVT) variations, and layout parasitics. Our goal is to support researchers new to this domain by creating a comprehensive collection of references and practical application guidelines. We provide a methodological review of state-of-the-art machine learning (ML) approaches, including graph neural networks (GNNs), large language models (LLMs), and variational autoencoders (VAEs), which have been successfully applied to analog circuit sizing tasks. To the best of authors’ knowledge, this is the first review to comprehensively explore the application of generative AI models in analog IC circuit design. We conclude that future research could focus on few-shot learning with domain-adaptation training of generative AI methods to simplify the design tasks such as human-tool interaction or guided design space exploration.}, archive = {J_ACCESS}, author = {Danial Noori Zadeh and Mohamed B. Elamien}, doi = {10.1109/ACCESS.2025.3553743}, journal = {IEEE Access}, pages = {58043-58059}, shortjournal = {IEEE Access}, title = {Generative AI for analog integrated circuit design: Methodologies and applications}, volume = {13}, year = {2025}, }
• (2025). Error-based restarted simpler QGMRES for image denoising problem. ACCESS, 13, 58034-58042. (www)

  @article{ , abstract = {QGMRES is an effective approach for solving quaternion linear systems. Without solving upper Hessenberg minimization problem, SQGMRES is the simpler version of QGMRES. Similar to restarted GMRES and SGMRES, the residual vectors at the end of each restart cycle in SQGMRES also alternate direction in a cyclic manner. Motivated by LGMRES and SLGMRES, in this paper, we present an acceleration strategy for SQGMRES by disrupting the alternation, and the convergence theory of new algorithm is provided. Finally, we apply new algorithms to color image denoising problems, and the experimental results showed a significant decrease in both the number of iterations and the required time compared to QGMRES.}, archive = {J_ACCESS}, author = {Wendi Yao and Hongxiu Zhong and Juan Song}, doi = {10.1109/ACCESS.2025.3556066}, journal = {IEEE Access}, pages = {58034-58042}, shortjournal = {IEEE Access}, title = {Error-based restarted simpler QGMRES for image denoising problem}, volume = {13}, year = {2025}, }
• (2025). Healthcare economics-vital threat detection system efficiency: A business process analysis methodology. ACCESS, 13, 58025-58033. (www)

  @article{ , abstract = {Continuous monitoring of vital signs is a significant challenge in healthcare, particularly for individuals requiring precise and frequent follow-ups, such as elderly and chronically ill patients often resulting in delayed responses to emergencies. This research uses a business process analysis approach to propose and present “SafePulse,” a solution that utilizes a smartwatch to collect vital data such as blood pressure, heart rate, and glucose levels through the Internet of Things (IoT). The data is sent to the cloud, where it is stored and analyzed using artificial intelligence (AI) algorithms to predict critical health conditions. When any abnormalities are detected, immediate alerts are sent to family members or caregivers via mobile applications, enabling a quick response. The system provides high flexibility and scalability to accommodate a large number of users, improving healthcare quality through proactive monitoring and real-time information. After realizing and analyzing the data of KPI extracted from the “SafePulse” process, the IoT solution emerges as an effective and adaptable solution for addressing the challenges of vital sign monitoring; and this answers the main research question of this work; which is; how a business process methodology is used to analyze, implement and monitor continuous improvements of the process of continuous monitoring of vital threat signs.}, archive = {J_ACCESS}, author = {Salma Elhag and Amal Alghamdi and Amal Alahmari and Abeer Alharbi}, doi = {10.1109/ACCESS.2025.3553447}, journal = {IEEE Access}, pages = {58025-58033}, shortjournal = {IEEE Access}, title = {Healthcare economics-vital threat detection system efficiency: A business process analysis methodology}, volume = {13}, year = {2025}, }
• (2025). Renal biopsy pathological tissue segmentation: A comprehensive review and experimental analysis. ACCESS, 13, 58008-58024. (www)

  @article{ , abstract = {Traditional renal biopsy pathological analysis relies heavily on subjective judgment by pathologists, which is time-consuming and susceptible to inter-observer variability, especially in segmenting glomeruli, renal tubules, and renal vessels. This study aims to develop an efficient and accurate segmentation method for renal pathological tissues to support intelligent diagnostic workflows. By integrating classic models such as Unet, U2net, and EfficientNet, along with the state-of-the-art Transformer-based SwinUnet, this research conducts a comprehensive comparison of different architectures in terms of accuracy, speed, and computational resource consumption. Experimental results show that while SwinUnet achieves an F1 score of 84.02 with an inference time of 630s, the proposed EfficientNet-b4+Unet model reaches a higher F1 score of 84.26 with a significantly reduced inference time of 420s, demonstrating its superior efficiency. A complete pipeline is proposed, encompassing data preprocessing, model training, and validation, including multi-scale feature extraction and optimized loss function configurations. Moreover, to enhance segmentation accuracy at tissue boundaries, this study introduces a novel boundary processing strategy, where boundaries are treated as a separate class and assigned higher weights. Experimental validation confirms the effectiveness of this approach. Results on datasets with PAS, PASM, H&E, and Masson staining demonstrate that the improved EfficientNet-b4+Unet model achieves the best balance between performance and speed while exhibiting strong generalization across different staining methods. This study provides a novel technical framework for precise renal pathological tissue segmentation and lays the foundation for future lesion detection and intelligent diagnostic applications.}, archive = {J_ACCESS}, author = {Yan Liu and Taiping Wang and Xinxin He and Wei Wang and Qiang He and Juan Jin}, doi = {10.1109/ACCESS.2025.3555654}, journal = {IEEE Access}, pages = {58008-58024}, shortjournal = {IEEE Access}, title = {Renal biopsy pathological tissue segmentation: A comprehensive review and experimental analysis}, volume = {13}, year = {2025}, }
• (2025). Ultra-low-power 0.35-V 0.63-nW/kHz multi-stacked clock oscillator with adjustable frequency and duty-cycling. ACCESS, 13, 57994-58007. (www)

  @article{ , abstract = {This paper presents the analysis, design, and characterization of an on-chip ultra-low-power clock oscillator for sensor nodes, implemented in 130nm BiCMOS technology. The design employs a multi-stacked inverter-based ring oscillator topology which utilizes variable RC features to tune the oscillation frequency. It is demonstrated that incorporating resistive coupling in the inverter stages helps to minimize frequency deviation around 12.1-13.5% for 10% of power supply variations and consequently achieves frequency stability improvement. To the best knowledge of the authors, this is the first time that a multi-stacked technique is utilized in the inverter stages of an oscillator to make the leakage current negligible reducing power consumption. With an adaptive delay line, the design is enabled to produce an adjustable duty-cycling output. The proposed oscillator consumes 510pW from a 0.35V power supply at 810 Hz operation frequency and achieves a power efficiency of 0.63 nW/kHz, positioning it among the most efficient Hz-to-kHz oscillators. The design occupies an area of 0.038mm2.}, archive = {J_ACCESS}, author = {Seyyedmohsen Seyyedrezaei and Jens Wagner and Frank Ellinger}, doi = {10.1109/ACCESS.2025.3555502}, journal = {IEEE Access}, pages = {57994-58007}, shortjournal = {IEEE Access}, title = {Ultra-low-power 0.35-V 0.63-nW/kHz multi-stacked clock oscillator with adjustable frequency and duty-cycling}, volume = {13}, year = {2025}, }
• (2025). Multi-targeted textual backdoor attack: Model-specific misrecognition via trigger position and word choice. ACCESS, 13, 57983-57993. (www)

  @article{ , abstract = {Deep neural networks excel in tasks like image recognition and natural language processing, but they are susceptible to backdoor attacks. These attacks involve training a target model with samples that include specific triggers, causing the model to misclassify these triggered samples while still correctly classifying regular samples. While many studies have concentrated on single-target misclassifications caused by backdoor attacks, this paper introduces a new method for multi-targeted backdoor attacks in text. This approach causes misclassifications in models based on both the words and positions of the triggers. We targeted the Bidirectional Encoder Representations from Transformers (BERT) model using four trigger words: “ATTACK,” “WOW, ” “HELP,” and “GOOD,” placed at the beginning, end, and middle of sentences. Our experiments were conducted using the AG News dataset. The results demonstrate that the attacker can manipulate a specific model to misclassify based on the trigger position and the specific trigger word. Our experimental results indicate that with a backdoor sample size of 2% and triggers placed at the beginning or end of sentences, the backdoor sample was misclassified by the targeted model with an average success rate of 98.92%, while maintaining an average accuracy of 94.15% for the original samples. Additionally, when the backdoor sample size was increased to 5% and triggers were placed in the middle of sentences, the backdoor sample was misclassified with an average success rate of 95.46%, with the original sample accuracy at 93.93%, using the targeted model.}, archive = {J_ACCESS}, author = {Taehwa Lee and Soojin Lee and Hyun Kwon}, doi = {10.1109/ACCESS.2024.3474088}, journal = {IEEE Access}, pages = {57983-57993}, shortjournal = {IEEE Access}, title = {Multi-targeted textual backdoor attack: Model-specific misrecognition via trigger position and word choice}, volume = {13}, year = {2025}, }
• (2025). A survey of ransomware detection methods. ACCESS, 13, 57943-57982. (www)

  @article{ , abstract = {Ransomware attacks continue to pose a significant challenge to cybersecurity, causing substantial financial and reputational damage to individuals and organizations. These attacks typically encrypt user data and demand a ransom for its release. There is a growing need for more effective and dynamic detection methods, especially for zero-day and unknown ransomware variants. This survey focuses on ransomware-detecting methods published from 2019 to 2025. One hundred thirty-five papers were reviewed and filtered based on their scope and publication venue. For in-depth analysis, this survey selected 45 papers focusing on ransomware detection for Windows or Android operating systems. This paper aims to comprehensively review existing ransomware detection methods, focusing on their effectiveness, limitations, and applicability. The detection methods are categorized into machine-learning-based and non-machine-learning-based methods, discussing the advantages and drawbacks of each. The paper also highlights ransomware-as-a-service, explaining what it is, how it works, and how it affects the increasing number of ransomware attacks in recent years. It also studies the datasets used in the reviewed literature, listing their structures and limitations. This survey identifies gaps in current research and suggests future directions for developing more robust ransomware detection systems.}, archive = {J_ACCESS}, author = {Saleh Alzahrani and Yang Xiao and Sultan Asiri and Jianying Zheng and Tieshan Li}, doi = {10.1109/ACCESS.2025.3556187}, journal = {IEEE Access}, pages = {57943-57982}, shortjournal = {IEEE Access}, title = {A survey of ransomware detection methods}, volume = {13}, year = {2025}, }
• (2025). Efficient path planning in multi-agent environment of AAVs with payloads. ACCESS, 13, 57932-57942. (www)

  @article{ , abstract = {The development of Autonomous Aerial Vehicle (AAV) technology is considered promising for applications such as product delivery, surveillance, and search and rescue operations. However, efficient navigation through unknown and dynamic environments is one of the major challenges for such systems. Especially when AAVs are armed with payloads, one needs to consider additional factors like capacity, weight, fuel consumption, etc, which further fosters the complexity of the problem. This research proposes a multi-path framework to improve the intelligence-of-path planning algorithm for AAVs while navigating through new terrain to achieve their missions successfully. The proposed framework integrates a Constrained A* (CA*) algorithm for path planning, a Constraint Tree (CT), and a Conflict Avoidance Table (CAT) for resolving conflicts. Integrating conflict avoidance principles enables intelligent and proactive AAV behavior, allowing for the wide deployment of multi-AAV systems in various operational scenarios. This model adapts dynamic path adjustments based on real-time environmental data and predictive modeling to suit AAV trajectories under changing conditions. The performance evaluation demonstrates that the proposed model surpasses the state-of-the-art algorithms, especially in unknown environments.}, archive = {J_ACCESS}, author = {Annapurna Jonnalagadda and Yuva Sai Verma and M. V. Bharat and E. Z. Ushus}, doi = {10.1109/ACCESS.2025.3554546}, journal = {IEEE Access}, pages = {57932-57942}, shortjournal = {IEEE Access}, title = {Efficient path planning in multi-agent environment of AAVs with payloads}, volume = {13}, year = {2025}, }
• (2025). Model-free predictive control based on RLS algorithm for grid-forming inverters with virtual synchronous generator. ACCESS, 13, 57921-57931. (www)

  @article{ , abstract = {As a key element to connect renewable energy sources, grid-forming inverters (GFIs) mostly use droop control as an outer loop, but it can cause a swift change in the system frequency when disturbances arise. In the role of the inner loop control, the traditional model predictive control (MPC) is widely employed in converters, but it is heavily dependent on model parameters. To overcome the above drawbacks, a model-free predictive control with virtual synchronous generator (VSG-MFPC) is proposed in this study. The scheme uses an autoregressive MFPC based on an auto-regressive with exogenous input (ARX) model as an inner-loop control structure for voltage prediction, combined with a simplified virtual synchronous generator (VSG) as an outer-loop control structure for power sharing and inertial support control systems. The implementation of MFPC improves the parameter robustness of the system. Building on this, the improvement schemes of algorithmic parameter initialization, delay compensation and current limiting capability are further investigated. Additionally, incorporating the VSG into the outer loop strengthens the stability of the system’s frequency and voltage while also enhancing the system’s dynamic characteristics. The aforementioned advantages, along with the efficacy and feasibility of the proposed control method, are validated through simulation and experimental results.}, archive = {J_ACCESS}, author = {Min Huang and Huiying Zhang and Kangan Wang and Zhilei Yao and Weimin Wu}, doi = {10.1109/ACCESS.2025.3556089}, journal = {IEEE Access}, pages = {57921-57931}, shortjournal = {IEEE Access}, title = {Model-free predictive control based on RLS algorithm for grid-forming inverters with virtual synchronous generator}, volume = {13}, year = {2025}, }
• (2025). Robotic integration of massage and real-time tissue assessment: A technological framework. ACCESS, 13, 57910-57920. (www)

  @article{ , abstract = {Compared with traditional soft tissue assessment devices, the massage robot equipped with a stiffness assessment system that leverages integrated sensors, enables real-time monitoring of soft tissue conditions during massage, thus integrates detection and treatment seamlessly. In this study, we investigate the foundation and applications of the technique for utilizing a massage robot for soft tissue assessment. Specifically, we investigate the technique for stiffness evaluation using finger pressing actions, analogous to the indentation test. Furthermore, machine learning algorithms are employed to classify materials with varying stiffness levels during finger rubbing actions. The results demonstrate the technical superiority of the system in soft tissue assessment and its promising potential for future clinical applications.}, archive = {J_ACCESS}, author = {Yizhong Zhang and Hui Zhang and Chen Qiu and Shaosong Xie and Min Tang and Yue-Guang Fan}, doi = {10.1109/ACCESS.2025.3549430}, journal = {IEEE Access}, pages = {57910-57920}, shortjournal = {IEEE Access}, title = {Robotic integration of massage and real-time tissue assessment: A technological framework}, volume = {13}, year = {2025}, }
• (2025). Developing an intelligent speech technology-assisted teaching activity framework to improve the mandarin proficiency among ethnic students: An action research study. ACCESS, 13, 57898-57909. (www)

  @article{ , abstract = {In China, students from ethnic regions often face significant challenges in mastering Mandarin, primarily due to the influence of their native languages. This struggle is further compounded by various factors, including inconsistent use of Mandarin by teachers, a lack of practical classroom resources, and a one-size-fits-all teaching approach, which hinder students’ spoken Mandarin proficiency and communication skills. To address these challenges, we propose an initial teaching activity framework that integrates advanced speech technologies across three key phases of the Mandarin teaching process: Motivating, enabling, and assessing, based on the Production-Oriented Approach theory. Then, we conducted three rounds of action research with 42 students to refine the proposed framework. At last, the effectiveness of the enhanced framework was evaluated using a univariate repeated ANOVA to analyze the results in Mandarin expression proficiency, complemented by comparative assessments of quantitative data and qualitative classroom observations collected throughout the research. The results indicate that the intelligent speech technology-assisted Mandarin teaching framework significantly enhances students’ Mandarin proficiency and effectively enables teachers to integrate technology into their teaching practices. This proposed framework has considerable potential for widespread application in initiatives to acquire Mandarin as a second language, equipping teachers with vital tools to improve their teaching strategies.}, archive = {J_ACCESS}, author = {Hongwu Yang and Hongwen Xu and Chunhui Shang and Weitong Guo and Yanshan He and Xiaolong Bu}, doi = {10.1109/ACCESS.2025.3552620}, journal = {IEEE Access}, pages = {57898-57909}, shortjournal = {IEEE Access}, title = {Developing an intelligent speech technology-assisted teaching activity framework to improve the mandarin proficiency among ethnic students: An action research study}, volume = {13}, year = {2025}, }
• (2025). Robust 3D human avatar reconstruction from monocular videos using depth optimization and camera pose estimation. ACCESS, 13, 57886-57897. (www)

  @article{ , abstract = {This paper presents a novel approach for 3D human avatar reconstruction from monocular RGB videos, overcoming the limitations of existing template-based methods such as BANMo. We introduce a two-fold optimization framework: first, using RelPose++ for accurate camera pose estimation and second, incorporating depth maps for enhancing 3D shape reconstruction. Our method minimizes so-called intra-frame and inter-frame distances, optimizing both detailed frame-level accuracy and maintaining temporal coherence across multiple video frames. Extensive experiments on the MEAD, Multiface and FEED datasets demonstrate the superiority of our approach in generating realistic, deformable 3D avatars, achieving significant improvements in Chamfer distance and F-score compared to existing methods. This framework is particularly effective in complex scenarios, such as bust-shot videos with partial views of subjects, offering robust and high-quality 3D reconstructions.}, archive = {J_ACCESS}, author = {Kyung Min Kim and Byung Cheol Song}, doi = {10.1109/ACCESS.2025.3556445}, journal = {IEEE Access}, pages = {57886-57897}, shortjournal = {IEEE Access}, title = {Robust 3D human avatar reconstruction from monocular videos using depth optimization and camera pose estimation}, volume = {13}, year = {2025}, }
• (2025). A novel strategy for enhancing performance of rectangular pulse generators: Simulation and optimization of blumlein pulse forming networks. ACCESS, 13, 57878-57885. (www)

  @article{ , abstract = {This paper presents a novel strategy to enhance the performance of rectangular pulse generators by simulating and optimizing Blumlein pulse forming networks (PFNs). The proposed approach imitates the Blumlein transmission line structure and synthesis methods of an LC ladder network to set up Blumlein PFNs as a series of inductor and capacitor combinations, initially. Subsequently, an optimization design algorithm is utilized to derive the optimal device parameters. The effectiveness of this strategy is demonstrated through simulations, which reveal significant improvements in output pulse characteristics such as flat top, pulse width, and peak power. The optimized Blumlein PFNs exhibit better performance compared to traditional PFNs, making them a promising solution for boosting the functionality and efficiency of rectangular pulse generators. This research contributes to the development of advanced pulse generation techniques and can be applied across various fields, including high-power microwave, laser, radar, X-ray and high-voltage testing equipment.}, archive = {J_ACCESS}, author = {Chuanwei Wang and Hongtao Li and Jinshui Xiao}, doi = {10.1109/ACCESS.2024.3420071}, journal = {IEEE Access}, pages = {57878-57885}, shortjournal = {IEEE Access}, title = {A novel strategy for enhancing performance of rectangular pulse generators: Simulation and optimization of blumlein pulse forming networks}, volume = {13}, year = {2025}, }
• (2025). Protocol-agnostic and packet-based intrusion detection using a multi-layer deep-learning architecture at the network edge. ACCESS, 13, 57867-57877. (www)

  @article{ , abstract = {Intrusion Detection (ID) faces multiple challenges, including the diversity of intrusion types and the risk of false positives and negatives. In an edge computing context, resource constraints further complicate the process, particularly during the training phase, which is computationally intensive. This paper presents a novel approach to ID in network traffic within edge computing environments using a Neural Network (NN) model. The proposed model is designed to align with the layered structure of network packets and has been trained and evaluated on the widely used CIC-IDS2017 cybersecurity dataset. Its protocol-agnostic design and customized preprocessing method enable it to efficiently detect network attacks across multiple protocols while preserving the original packet structure. Unlike existing approaches that transform packets into alternative representations such as images or NLP-based techniques, which introduce additional overhead, our method processes packets directly, eliminating the need for complex components like Recurrent Neural Networks (RNNs) or convolutional layers. Our model is optimized for edge computing by employing a centralized training approach that minimizes resource consumption while allowing flexible deployment on edge devices. Experimental results demonstrate that our approach outperforms existing methods in terms of accuracy, F1-score, recall, and precision when evaluated on a real-world dataset. This work highlights the potential of deep learning in enhancing network security while respecting edge computing constraints.}, archive = {J_ACCESS}, author = {Rodolphe Picot and Felipe Gohring de Magalhães and Ahmad Shahnejat Bushehri and Maroua Ben Atti and Gabriela Nicolescu and Alejandro Quintero}, doi = {10.1109/ACCESS.2025.3555201}, journal = {IEEE Access}, pages = {57867-57877}, shortjournal = {IEEE Access}, title = {Protocol-agnostic and packet-based intrusion detection using a multi-layer deep-learning architecture at the network edge}, volume = {13}, year = {2025}, }
• (2025). Design of a full-directional and light-weight electric vibrator. ACCESS, 13, 57858-57866. (www)

  @article{ , abstract = {Vibrators are widely used in agriculture, such as for vibrating trees to harvest fruits and nuts, or for vibrating screens to separate different materials (e.g. plants and soil or grain and debris) in the harvesting process. Traditional vibrators are bulky and configured with fixed mechanical transmission, so they cannot be precisely controlled and cannot adapt to different conditions, causing negative effects such as ineffective vibration or damaging tree barks. In this paper, a full-directional and lightweight electric vibrator is designed. The unidirectional vibration force is produced through the utilization of two centrifugal forces that are generated by the eccentric mass rotation of two motors. Firstly, the vibration direction can be adjusted to any direction by adjusting the meeting position of the two centrifugal forces. Secondly, the vibration force can be adjusted by changing the motor speed, as the centrifugal force is proportional to the square of the rotation speed. The vibrator is tested with laboratory bench experiment and with agricultural application for vibrating a tree. The prototype vibrator can produce 680N with the weight of 7.2kg, the force can be further improved by increasing the eccentric mass, increasing the rotation speed or decreasing the rotation arm length. The vibrator can be applied to smart agriculture, such as nut and fruit harvesting, or adaptive vibration screening.}, archive = {J_ACCESS}, author = {Baochao Wang and Chenyuan Lu and Qingtao Zong and Dongwei Wang and Alim Pulatov and Shuqi Shang}, doi = {10.1109/ACCESS.2025.3534896}, journal = {IEEE Access}, pages = {57858-57866}, shortjournal = {IEEE Access}, title = {Design of a full-directional and light-weight electric vibrator}, volume = {13}, year = {2025}, }
• (2025). Combining the improved RGB water-filling algorithm with penumbra removal technique for shadow removal from digitized images. ACCESS, 13, 57847-57857. (www)

  @article{ , abstract = {This study proposes a novel and highly efficient approach for the comprehensive removal of shadows from digitized images that is applicable to a broad spectrum of image types, from document scans to natural scenes. The proposed method introduces an RGB water-filling algorithm specifically designed to address soft shadows, optimized with matrix operations and a streamlined processing workflow that substantially enhance the computational efficiency over existing methods. This improved efficiency facilitates real-time applications such as vision systems in automated guided vehicles, which often contend with shadow interference in natural environments. In addition to soft shadows, the proposed method addresses hard shadows, which can severely degrade the image quality and leave residual shadow boundaries that are difficult to eliminate using conventional techniques. To overcome this, the proposed method combines the RGB water-filling algorithm with the penumbra removal technique that not only removes shadow boundaries but also reconstructs the underlying image background. A comparative analysis demonstrates that the proposed method significantly reduces the root mean squared error (RMSE) by 70% and the processing time by 99%, while achieving the highest peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), outperforming previous approaches for both soft and hard shadow removal.}, archive = {J_ACCESS}, author = {You-Chang Liu and Cheng-Ta Chuang}, doi = {10.1109/ACCESS.2025.3555302}, journal = {IEEE Access}, pages = {57847-57857}, shortjournal = {IEEE Access}, title = {Combining the improved RGB water-filling algorithm with penumbra removal technique for shadow removal from digitized images}, volume = {13}, year = {2025}, }
• (2025). A review of block ciphers and its post-quantum considerations. ACCESS, 13, 57834-57846. (www)

  @article{ , abstract = {Recent advances in cryptography aim to address threats posed by quantum computers. However, these advances have focused disproportionately on asymmetric schemes, while symmetric primitives like block ciphers have received less attention. The common assumption is that doubling block cipher key sizes provides adequate quantum resistance, but this oversimplifies the challenge. This paper provides a systematic examination of block cipher security in the quantum computing era. The paper first establishes a formal framework for understanding block cipher security, covering their mathematical foundations and essential security properties. Building on this foundation, we examine how quantum computing fundamentally challenges these security assumptions, going beyond the simple impact on key lengths. Our review of recent research reveals that quantum attacks can exploit structural vulnerabilities in block ciphers and their modes of operation, requiring more comprehensive defenses than key length adjustments alone. We analyze emerging approaches in quantum-resistant design and identify critical areas requiring further research to ensure block cipher security in a post-quantum world.}, archive = {J_ACCESS}, author = {R. V. Chethana and Jayavrinda Vrindavanam and Subarna Roy and P. C. Deshmukh}, doi = {10.1109/ACCESS.2025.3554602}, journal = {IEEE Access}, pages = {57834-57846}, shortjournal = {IEEE Access}, title = {A review of block ciphers and its post-quantum considerations}, volume = {13}, year = {2025}, }
• (2025). Smart farming solutions: A user-friendly GUI for maize tassel estimation using YOLO with dynamic and fixed labelling, featuring video support. ACCESS, 13, 57809-57833. (www)

  @article{ , abstract = {The integration of Autonomous Aerial Vehicles (AAVs) has significantly advanced image processing and remote sensing, particularly in precision agriculture. These technologies enhance data collection and agricultural yield estimation, benefiting banks, insurance companies, and government agencies in decision-making for budget allocation and quality assessments. This study addresses the challenge of accurately quantifying corn production by developing an enhanced YOLO-v8-based deep learning model, incorporating dynamic and fixed labeling techniques, tested on 810 images and video data for real-time detection. The research utilized two primary datasets totaling 570 images. The evaluation process comprised four distinct tests: Test 1, conducted on Dataset 1 with 200 images, assessed seven attention mechanisms (SE, CBAM, GA, LKA, CA, SA, and TA) using deep learning metrics (Precision, Recall, mAP50, mAP50-95, F1-score) and statistical methods (Duncan’s test). Test 2 validated model performance on 370 images from external sources, where YOLO.SA achieved 97.48% accuracy, outperforming YOLO.LKA (95.13%). Test 3, comparing with the MTDC benchmark dataset, confirmed YOLO.SA’s accuracy at 95.93%, exceeding previous reports, while YOLO.LKA achieved 95.71%. Finally, Test 4, utilizing video-based evaluation via a developed GUI, demonstrated YOLO.SA’s superiority (95.77%) over YOLO.LKA (95.48%) and YOLO-v5 (95.72%), significantly outperforming the standard YOLO model (72.79%). This study advances computer vision in agriculture, offering a scalable, high-accuracy model for corn yield estimation, with broad applications in farming optimization, financial planning, and policy-making.}, archive = {J_ACCESS}, author = {Ata Jahangir Moshayedi and Zhonghua Wang and Maryam Sharifdoust and Arash Sioofy Khoojine and Wei Zhang and Amin Kolahdooz and Jiandong Hu}, doi = {10.1109/ACCESS.2025.3554984}, journal = {IEEE Access}, pages = {57809-57833}, shortjournal = {IEEE Access}, title = {Smart farming solutions: A user-friendly GUI for maize tassel estimation using YOLO with dynamic and fixed labelling, featuring video support}, volume = {13}, year = {2025}, }
• (2025). Fast digital circuit synthesis framework based on deep reinforcement learning using scoreboard. ACCESS, 13, 57798-57808. (www)

  @article{ , abstract = {In a field of circuit design that uses deep learning such as layout optimization, parameter optimization, and circuit synthesis automation, the proposed framework, fast-GateRL, addresses the digital circuit synthesis automation based on deep reinforcement learning at a CMOS transistor level. The fast-GateRL considers the threshold voltage and avoids shoot-through current paths, whereas the previous GateRL deals with transistors as ideal switches. It also achieves high-speed circuit extraction by mask generation of the prohibited and meaningless connections, intermediate rewards based on a scoreboard, and initial state selection with the trimmed circuit connections. The new connection is generated by the deep neural network to support a deep Q-network. The fast-GateRL is evaluated on 16 two-input digital logic circuits that are verified by SPICE simulation. In addition, the ablation test confirms that scoreboard and initial state selection help the proposed framework reduce the search time for final logic circuits substantially.}, archive = {J_ACCESS}, author = {Hyeonseong Cho and Yeonhak Pyo and Tae Woong Lee and Jiyoung Lee and Hyunyoung Choi and Hyung Uk Cho and Hyoungsik Nam}, doi = {10.1109/ACCESS.2025.3555771}, journal = {IEEE Access}, pages = {57798-57808}, shortjournal = {IEEE Access}, title = {Fast digital circuit synthesis framework based on deep reinforcement learning using scoreboard}, volume = {13}, year = {2025}, }
• (2025). Multimodal ensemble fusion deep learning using histopathological images and clinical data for glioma subtype classification. ACCESS, 13, 57780-57797. (www)

  @article{ , abstract = {Glioma is the most common malignant tumor of the central nervous system, and diffuse Glioma is classified as grades II-IV by world health organization (WHO). In the the cancer genome atlas (TCGA) Glioma dataset, grade II and III Gliomas are classified as low-grade glioma (LGG), and grade IV Gliomas as glioblastoma multiforme (GBM). In clinical practice, the survival and treatment process with Glioma patients depends on properly diagnosing the subtype. With this background, there has been much research on Glioma over the years. Among these researches, the origin and evolution of whole slide images (WSIs) have led to many attempts to support diagnosis by image analysis. On the other hand, due to the disease complexities of Glioma patients, multimodal analysis using various types of data rather than a single data set has been attracting attention. In our proposed method, multiple deep learning models are used to extract features from histopathology images, and the features of the obtained images are concatenated with those of the clinical data in a fusion approach. Then, we perform patch-level classification by machine learning (ML) using the concatenated features. Based on the performances of the deep learning models, we ensemble feature sets from top three models and perform further classifications. In the experiments with our proposed ensemble fusion AI (EFAI) approach using WSIs and clinical data of diffuse Glioma patients on TCGA dataset, the classification accuracy of the proposed multimodal ensemble fusion method is 0.936 with an area under the curve (AUC) value of 0.967 when tested on a balanced dataset of 240 GBM, 240 LGG patients. On an imbalanced dataset of 141 GBM, 242 LGG patients the proposed method obtained the accuracy of 0.936 and AUC of 0.967. Our proposed ensemble fusion approach significantly outperforms the classification using only histopathology images alone with deep learning models. Therefore, our approach can be used to support the diagnosis of Glioma patients and can lead to better diagnosis.}, archive = {J_ACCESS}, author = {Satoshi Shirae and Shyam Sundar Debsarkar and Hiroharu Kawanaka and Bruce Aronow and V. B. Surya Prasath}, doi = {10.1109/ACCESS.2025.3556713}, journal = {IEEE Access}, pages = {57780-57797}, shortjournal = {IEEE Access}, title = {Multimodal ensemble fusion deep learning using histopathological images and clinical data for glioma subtype classification}, volume = {13}, year = {2025}, }
• (2025). RADNet: Adaptive spatial-dilation learning for efficient road crack detection. ACCESS, 13, 57767-57779. (www)

  @article{ , abstract = {Road crack detection is crucial for infrastructure maintenance and traffic safety, yet existing methods struggle to balance detection accuracy and computational efficiency due to complex texture similarities between cracks and road surfaces. In this paper, we propose RADNet, a lightweight framework for efficient road crack detection through adaptive spatial-dilation learning. Our approach introduces three key innovations: an adaptive spatial learning module (ASDown) for efficient feature extraction, a multi-scale dilation strategy (C2f-MSD) for comprehensive crack pattern capture, and a lightweight detection enhancement mechanism. Experiments on the RDD 2022 dataset demonstrate that RADNet achieves state-of-the-art performance with $86.6\%$ precision, 78.7% recall, and 83.8% mAP50, while maintaining a lightweight architecture of 2.47M parameters and 9.4 GFLOPs at 370 FPS. Ablation studies show that the integration of ASDown and C2f-MSD significantly enhances both detection accuracy and computational efficiency, improving mAP50 by 5.6% over the YOLOv8n baseline while reducing computational complexity.}, archive = {J_ACCESS}, author = {Kehao Du and Yifan Dai}, doi = {10.1109/ACCESS.2025.3555032}, journal = {IEEE Access}, pages = {57767-57779}, shortjournal = {IEEE Access}, title = {RADNet: Adaptive spatial-dilation learning for efficient road crack detection}, volume = {13}, year = {2025}, }
• (2025). IoMT and blockchain synergy: A novel approach to health data validation and storage. ACCESS, 13, 57753-57766. (www)

  @article{ , abstract = {The increasing adoption of Internet of Medical Things (IoMT) devices is revolutionizing healthcare, facilitating enhanced remote patient monitoring and data-driven clinical decision-making. However, a significant challenge remains: the fragmentation of sensitive medical data across diverse healthcare infrastructures. This separation of medical data seriously hinders seamless data exchange and interoperability, preventing healthcare providers from obtaining a comprehensive understanding of patient health and limiting the scope of data-driven research for improved treatments. To address this critical issue, we have developed a novel blockchain-based framework carefully designed to unify separate IoMT data sources within a secure platform. This framework aims to enhance data accessibility, ensure robust data security and patient privacy, and significantly improve the depth of clinical understanding, ultimately leading to enhanced patient care. Our system employs Hyperledger Fabric, a permissioned blockchain architecture, selected for its security, scalability, and suitability for handling sensitive healthcare data. We further integrate edge computing principles to optimize data processing efficiency and reduce network latency, establishing a decentralized architecture that prioritizes data integrity and confidentiality. This innovative approach to IoMT data integration aims to enable secure, real-time data sharing and improve interoperability across diverse healthcare ecosystems. By facilitating seamless data flow and establishing a trustworthy and auditable data infrastructure, our framework supports enhanced clinical decision-making in crucial areas such as remote patient monitoring, chronic disease management, and preventative care. Rigorous performance evaluations demonstrate the potential of our framework to transform healthcare workflows, improve operational efficiency, and maximize the utility of IoMT data for improved healthcare delivery and patient-centric models of care. This research represents a significant step toward realizing more interconnected, secure, and efficient healthcare systems for the future.}, archive = {J_ACCESS}, author = {Abbas Syed Muhammad Ali and Sikandar Ali and Khan Ziaullah and Moon-Il Joo and Hee-Cheol Kim}, doi = {10.1109/ACCESS.2025.3555289}, journal = {IEEE Access}, pages = {57753-57766}, shortjournal = {IEEE Access}, title = {IoMT and blockchain synergy: A novel approach to health data validation and storage}, volume = {13}, year = {2025}, }
• (2025). APD-BayNet: Jakarta air quality index prediction using bayesian optimized tabnet. ACCESS, 13, 57734-57752. (www)

  @article{ , abstract = {The Air Quality Index (AQI) is a critical measure for assessing air pollution levels and their impact on public health and the environment. Jakarta, the capital of Indonesia, has consistently ranked among the world’s most polluted cities. Various machine learning-based studies have attempted to predict AQI levels in Jakarta, demonstrating promising results. However, existing studies suffer from several limitations, including the use of outdated datasets, lack of dataset normalization, suboptimal hyperparameter tuning, absence of k-fold cross-validation in certain experiments, and limited exploration of deep learning models for AQI detection. Consequently, the robustness of existing air pollution detection systems remains inadequate. To address these challenges, this study introduces APD-BayNet, an advanced air quality detection system that integrates the TabNet deep learning architecture with Bayesian Optimization (BO) for enhanced predictive performance. Furthermore, we compare APD-BayNet with two widely used deep learning models: Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our methodology consists of four key stages: data preprocessing, model development, hyperparameter tuning using BO, and performance evaluation through 5-fold cross-validation, applied consistently across all models. Extensive experiments demonstrate that APD-BayNet consistently outperforms LSTM and CNN in AQI prediction. The TabNet model within APD-BayNet achieves high performance on the training set, with precision, recall, F1-score, and accuracy of 96.64%, 95.28%, 95.79%, and 98.17%, respectively. On the test set, these metrics remain strong at 94.30%, 94.99%, 94.36%, and 98.17%, respectively. These findings highlight the effectiveness of APD-BayNet in providing a robust and scalable solution for air quality monitoring. Future research could explore its adaptability to other geographical regions, enhancing its applicability on a global scale.}, archive = {J_ACCESS}, author = {Raey Faldo and Satria Mandala and Rina Pudji Astuti and Ary Setijadi Prihatmanto and Mohd Soperi Mohd Zahid}, doi = {10.1109/ACCESS.2025.3555961}, journal = {IEEE Access}, pages = {57734-57752}, shortjournal = {IEEE Access}, title = {APD-BayNet: Jakarta air quality index prediction using bayesian optimized tabnet}, volume = {13}, year = {2025}, }
• (2025). Traffic urgency model: A novel approach to prioritize complaint texts using enhanced named entity recognition for bahasa indonesia cases. ACCESS, 13, 57720-57733. (www)

  @article{ , abstract = {Addressing traffic complaints effectively requires a priority system capable of accommodating urgency levels based on critical factors. This study introduces the Traffic Urgency Model (TUM), which quantitatively assesses the urgency of traffic complaints by identifying and measuring key variables. The research analyzes 23,361 traffic complaint records using a redesigned Named Entity Recognition (NER) approach with specialized tags tailored to Indonesian-language complaint texts. The NER development process enhances information extraction, capturing relevant elements such as time, location, reported conditions, involved parties, and traffic-related objects, which serve as potential variables. Furthermore, the study employs a Causal Loop Diagram (CLD) to map causal relationships among variables and construct a comprehensive model structure. Variable evaluation integrates normalization techniques and weighting based on historical data and expert assessments, resulting in a data-driven model framework that supports objective prioritization in handling complaints. The findings include a new entity design for NER development to identify entities in traffic complaint texts and a novel method for managing complaint texts based on urgency levels that encompass various traffic criteria. The results demonstrate that TUM provides a more comprehensive approach by considering quantitative data and inter-variable relationships to prioritize traffic complaints with greater specificity according to urgency values.}, archive = {J_ACCESS}, author = {Berlian Rahmy Lidiawaty and Erma Suryani and Retno Aulia Vinarti}, doi = {10.1109/ACCESS.2025.3556423}, journal = {IEEE Access}, pages = {57720-57733}, shortjournal = {IEEE Access}, title = {Traffic urgency model: A novel approach to prioritize complaint texts using enhanced named entity recognition for bahasa indonesia cases}, volume = {13}, year = {2025}, }
• (2025). A hybrid brain tumor classification using FL with FedAvg and FedProx for privacy and robustness across heterogeneous data sources. ACCESS, 13, 57705-57719. (www)

  @article{ , abstract = {Data privacy and heterogeneity among healthcare settings present fundamental challenges to machine learning (ML) brain tumor classification (BTC) model development based on local data. In this paper, we outline the need to develop an effective brain tumor diagnosis model while ensuring data security. We propose a distributed learning framework based on Federated Learning (FL) to build such a system. This framework combines Federated Averaging (FedAvg) and Federated Proximal (FedProx) to train Convolutional Neural Networks (CNN) on data hosted by multiple clients. FedAvg saves these locally trained models from the patients to make a global model while keeping sensitive patient data decentralized and private. Nevertheless, traditional FL suffers from model instability and divergence due to the disparity of data distributions among clients. FedProx addresses this challenge by introducing a proximal regularization term that penalizes divergence from the global model to stabilize local training considering data heterogeneity. In this work, we utilized the Brain Tumor MRI Dataset from Kaggle and trained the model to classify four brain tumors (BT), namely glioma, meningioma, pituitary tumor, and no-tumor class. Experiments were conducted for ten communication rounds, with each client training the model independently based on its local dataset and without sharing data. Results revealed that the method consistently outperformed accuracy, recall and F1-score, further validating the capabilities of the proposed framework in effectively handling data heterogeneity while conserving data privacy. The global model outperformed models that had been centralized and trained on a smaller, common dataset observed in FL scenarios to improve the potential for collaborative medical diagnosis. This method is especially suitable for medical applications with strict privacy regulations, as it provides an approach to linking heterogeneous data from multiple healthcare institutions.}, archive = {J_ACCESS}, author = {N. Sivakumar and Ahmad Raza Khan and Syed Umar and R. N. Ravikumar and I. Bremnavas and Munindra Lunagaria and Krunal Vaghela and Ghanshyam G. Tejani and Sunil Kumar Sharma}, doi = {10.1109/ACCESS.2025.3549440}, journal = {IEEE Access}, pages = {57705-57719}, shortjournal = {IEEE Access}, title = {A hybrid brain tumor classification using FL with FedAvg and FedProx for privacy and robustness across heterogeneous data sources}, volume = {13}, year = {2025}, }
• (2025). Adapter with textual knowledge graph for zero-shot sketch-based image retrieval. ACCESS, 13, 57692-57704. (www)

  @article{ , abstract = {Zero-shot sketch-based image retrieval (ZS-SBIR) refers to retrieving natural images with similar contours and semantics using query sketches in zero-shot scenarios. Existing works mainly focus on using teacher networks to mitigate catastrophic forgetting of models. They aim to retain the knowledge learned by pre-trained models on large-scale datasets, thereby improving the zero-shot generalization ability of models. However, naively using teacher networks is difficult to effectively mitigate catastrophic forgetting and leads to overfitting risks in some cases. In addition, previous methods ignore the explicit utilization of structural knowledge and fail to guide the model to fully mine the structural knowledge of category semantic information, thereby neglecting the learning of inter-class relations. To address these issues, we propose an adapter with textual knowledge graph (ATKG) for ZS-SBIR. Specifically, we employ a dual constraint of prompt learning strategies and teacher networks to suppress catastrophic forgetting and reduce overfitting risks. Additionally, to effectively learn inter-class relationships, we introduce an adapter with textual knowledge graph. This method treats category semantic information and visual features as nodes, computing their edges using cosine similarity. Subsequently, a graph convolutional network (GCN) is used to mine the structural knowledge between nodes, further effectively learning relationships among different categories. Extensive experiments on the Sketchy, TU-Berlin, and QuickDraw datasets demonstrate that our proposed method achieves better retrieval performance than state-of-the-art competitors. Specifically, on Sketchy split1, TU-Berlin and QuickDraw datasets, mAP@all is improved by 3.2%, 4.3% and 5.1% compared to IVT or CLIP-all, respectively. On the more challenging Sketchy split2 dataset, mAP@200 is improved by 2% compared to Dr.CLIP.}, archive = {J_ACCESS}, author = {Jie Zhang and Jiali Tang}, doi = {10.1109/ACCESS.2025.3553690}, journal = {IEEE Access}, pages = {57692-57704}, shortjournal = {IEEE Access}, title = {Adapter with textual knowledge graph for zero-shot sketch-based image retrieval}, volume = {13}, year = {2025}, }
• (2025). Recovery of E-SRS with 2 × (2oo2) system architecture and its impact on the safety integrity of a safety function. ACCESS, 13, 57676-57691. (www)

  @article{ , abstract = {All electronic safety-related systems (E-SRS) must undergo qualitative and quantitative evaluation to determine the required safety integrity level (SIL) or similar safety evaluation metrics. Various methods, such as fault tree analysis (FTA) and Markov chain analysis (MC), are available for evaluating system safety parameters. While existing studies extensively document the safety characteristics of electronic safety-related systems, there is a lack of research focusing specifically on the impact of recovery mechanisms in $ 2 \times (2 oo 2) $ system architectures. This study utilizes homogeneous continuous-time Markov chains (CTMC) to assess the safety and reliability properties of electronic safety-related systems with cold standby. Qualitative and quantitative analyses are performed on the E-SRS architecture, with a focus on identifying potential subsystem failure combinations that could lead the system into a dangerous state. The practical analysis is divided into three parts: first, the safety and reliability of the E-SRS without recovery are evaluated; second, recovery mechanisms are incorporated using different approaches; and third, results from systems with and without recovery are compared. The analysis reveals that recovery mechanisms slightly reduce safety, with a negative impact of 3. 36% observed in the calculations with our parameters compared to system architecture without recovery. However, recovery significantly improves reliability, underscoring its practical advantages despite minor safety trade-offs. The implications of this study can be used to determine the optimal way to employ qualified personnel to perform recovery of the system.}, archive = {J_ACCESS}, author = {L’ubomír Hrmo and Juraj Ždánsky and Karol Rástočný and Jozef Hrbček}, doi = {10.1109/ACCESS.2025.3556533}, journal = {IEEE Access}, pages = {57676-57691}, shortjournal = {IEEE Access}, title = {Recovery of E-SRS with 2 × (2oo2) system architecture and its impact on the safety integrity of a safety function}, volume = {13}, year = {2025}, }
• (2025). Efficient switched-beam detection for dynamic spectrum sharing in 6G wireless networks with full duplex technology at the THz band. ACCESS, 13, 57662-57675. (www)

  @article{ , abstract = {The demand for higher data rates and bandwidth-intensive applications in the digital age is driving the need for cutting-edge technologies in future 6G systems, such as terahertz (THz) and full duplex (FD) communications. These technologies promise exceptional performance enhancement when combined with a dynamic spectrum management approach based on Cognitive Radio (CR). This paper explores spectrum sensing (SS) methods that do not require any prior information suitable for FD-THz communications, addressing challenges posed by residual self-interference (RSI) and molecular noise, which characterize THz communications. Both molecular re-radiation and RSI are sources of colored noise that dominate additive white Gaussian noise, complicating the operations of SS algorithms that do not require prior information based on the eigenvalues of the sample covariance matrix. To address this issue, we propose a low-complexity solution using a switched beam technique, along with a whitening matrix derived from a dataset containing RSI, molecular noise, and two largest eigenvalue (LE) detectors. The LE detector is introduced as an upper bound applicable only when perfect knowledge of the variances of all the noise components is available, which is unlikely in most practical scenarios. Therefore, we propose a novel, low-complexity detector not relying on prior information, based on scaled LE (SLE), which estimates the molecular noise and the AWGN variance online. The algorithm also includes a heuristic to adjust the contribution of the RSI and molecular noise components, ensuring that the detector retains an approximately constant false alarm rate (CFAR) property under typical operating conditions. Numerical simulations show that the proposed SS method is feasible and robust. Compared to the recursive least squares (RLS) adaptive filtering benchmark, it offers a more robust false alarm probability, particularly in the low-AWGN regime, with detection performance similar to or slightly lower than the RLS detector, but with significantly lower computational complexity.}, archive = {J_ACCESS}, author = {Andrea Tani and Dania Marabissi}, doi = {10.1109/ACCESS.2025.3554606}, journal = {IEEE Access}, pages = {57662-57675}, shortjournal = {IEEE Access}, title = {Efficient switched-beam detection for dynamic spectrum sharing in 6G wireless networks with full duplex technology at the THz band}, volume = {13}, year = {2025}, }
• (2025). A search-based domain adaptation network for fault diagnosis of rotating machinery under cross-operating conditions. ACCESS, 13, 57650-57661. (www)

  @article{ , abstract = {Most domain adaptation diagnostic methods focus only on minimizing the distribution discrepancy between domains but fail to learn discriminative features among different classes of data. This often results in data being statistically similar but lacking discriminative power. To solve this problem, this paper proposes a Searcher Mechanism and a Supervisor Regularization Mechanism. The Searcher Mechanism learns domain-invariant features and inter-class discrepancy by round trip search for homogeneous samples between different domains. The Supervisor regularization mechanism improves generalization and prevents overfitting by ensuring that the searcher explores all target domain samples with equal probability. First, we employ a wide-kernel deep residual network as a feature extractor to extract features from the data of different domains. Subsequently, the Searcher Mechanism continuously aligns the distributions between different domains while learning discriminative features for different classes. Additionally, to ensure that the Searcher Mechanism searches all target domain samples fairly, a supervisor regularization mechanism is introduced to supervise the search process. Through learning discriminative features, the classifier can form more accurate decision boundaries. Experimental results on two datasets under varying operating conditions validate the cross-domain diagnostic performance of the proposed method. The related code is on https://github.com/jiaqilearning/searcherFD.}, archive = {J_ACCESS}, author = {Jiaqi Zhang and Hua Sun and Tong Yuan}, doi = {10.1109/ACCESS.2025.3555992}, journal = {IEEE Access}, pages = {57650-57661}, shortjournal = {IEEE Access}, title = {A search-based domain adaptation network for fault diagnosis of rotating machinery under cross-operating conditions}, volume = {13}, year = {2025}, }
• (2025). Multiscale modeling and reconstruction of joint motion: Finite element optimization based on particle swarm algorithm. ACCESS, 13, 57639-57649. (www)

  @article{ , abstract = {This study employs finite element analysis (FEA) to address the challenge of motion reconstruction in three-dimensional (3D) human body modeling, specifically focusing on joint movement. High-resolution CT and MRI scans of the knee joint were utilized to construct an accurate 3D reconstruction model, while the particle swarm algorithm was implemented to optimize joint positioning. Experimental evaluations were conducted to assess the influence of varying unit numbers and joint rotation angles on reconstruction accuracy. The results demonstrate that increasing the number of units significantly improves the model’s continuity and precision. Notably, with 64 units, the discontinuities in the knee region were nearly eliminated, and the entire reconstruction process was completed in just 6.69 milliseconds. Moreover, adjustments in joint rotation angles effectively reduced reconstruction errors, bringing them down to 1.317 cm at a sampling frequency of 1/60. Additionally, iterative reconstruction decreased the error in the knee region by approximately 30% compared to non-iterative methods. The optimization process facilitated by the particle swarm algorithm revealed that most particles achieved high fitness levels after the initial iteration, and a considerable proportion shifted to the foreground region during the second iteration once fitness values dropped below 0.2. Ultimately, the study substantiates the accuracy and reliability of the proposed method for joint area reconstruction. These findings indicate that the FEA-based 3D reconstruction framework is highly effective in capturing complex joint movements and deformations, providing valuable insights for both biomechanical research and clinical applications.}, archive = {J_ACCESS}, author = {Jiaju Zhu and Zhong Zhang and Runnan Liu and Meixue Ren and Guodong Ma}, doi = {10.1109/ACCESS.2025.3553469}, journal = {IEEE Access}, pages = {57639-57649}, shortjournal = {IEEE Access}, title = {Multiscale modeling and reconstruction of joint motion: Finite element optimization based on particle swarm algorithm}, volume = {13}, year = {2025}, }
• (2025). Blockchain-enhanced attribute-based encryption architecture with feasibility analysis. ACCESS, 13, 57629-57638. (www)

  @article{ , abstract = {In today’s digital landscape, data security is critical, particularly in the Internet of Things (IoT), where large volumes of sensitive data are exchanged. Traditional encryption methods like RSA and AES face challenges in balancing security and performance, exposing systems to advanced cyber threats. To address these issues, blockchain technology offers decentralized, tamper-resistant data protection that enhances trust and transparency. Attribute-Based Encryption (ABE) schemes have been developed, often combining asymmetric and symmetric encryption for efficiency and security. However, gaps remain in practical deployment due to underexplored network architectures and limited feasibility simulations. This study proposes an end-to-end security architecture integrating ABE with Linear Secret Sharing Scheme (LSSS) access policies and blockchain-based distributed key management. The system’s feasibility was evaluated using Network Simulator 3 (NS3) within a simulated IoT network. Results demonstrate a lightweight and scalable solution suitable for constrained environments. Numerical simulations showed consensus times as low as 0.25 seconds for key agreement and 0.7 seconds for message consensus, even in resource-constrained settings. For large networks, consensus times reached as low as 0.75 seconds. The system also achieved an average throughput of 0.3 transactions per second in low-resource environments. These outcomes highlight the system’s potential for secure, efficient data transmission in IoT and other distributed systems.}, archive = {J_ACCESS}, author = {Agustin Ferrer-Rojas and Bodhaswar T. Maharaj and Mduduzi C. Hlophe}, doi = {10.1109/ACCESS.2025.3554643}, journal = {IEEE Access}, pages = {57629-57638}, shortjournal = {IEEE Access}, title = {Blockchain-enhanced attribute-based encryption architecture with feasibility analysis}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based segmentation and localization in CT angiography for coronary heart disease diagnosis. ACCESS, 13, 57615-57628. (www)

  @article{ , abstract = {The most prevalent cause of mortality worldwide is coronary artery disease (CAD), and coronary angiography is the gold standard for its diagnosis. There are still a limited number of rapid and efficient automated methods for measuring the disease and localizing the coronary anatomy, even though over 2 million procedures are performed annually globally. This study proposes a deep learning-based framework for diagnosing coronary heart disease through segmentation and localization, called multi-stream attention-guided coronary analysis network (MACAN). The proposed MACAN employs a dual-stream model combining segmentation and localization tasks, enhanced by channel and spatial attention modules, to improve feature extraction and accuracy. During data pre-processing, several enhancement techniques are employed to augment image quality. The proposed model incorporates dual-stream processing with channel and spatial attention mechanisms to improve feature extraction. The automatic region-based coronary artery disease diagnostics using the x-ray angiography images (ARCADE) dataset was utilized for model training and evaluation. The proposed model achieved a classification accuracy of 0.6154 and 0.5827 on validation sets of phase 1 and phase 2, whereas it achieved F1-scores of 0.4287 and 0.4126 on validation sets of phase 1 and phase 2, respectively. The proposed MACAN model is compared with state-of-the-art methods, including FPN, PAN, DeepLabV3, UNet++ and Ensemble model, where the F1-score remains between 18% to 39%. In contrast, the proposed model achieved 43% F1-score or phase 1 validation data and 41% on phase 2 validation data. This study demonstrates the capability of automated technologies to improve the precision of stent injections in clinical environments, facilitate treatments, and aid in identifying CAD.}, archive = {J_ACCESS}, author = {Bo Zhao and Jianjun Peng and Ce Chen and Yongyan Fan and Kai Zhang and Yang Zhang}, doi = {10.1109/ACCESS.2025.3555991}, journal = {IEEE Access}, pages = {57615-57628}, shortjournal = {IEEE Access}, title = {Deep learning-based segmentation and localization in CT angiography for coronary heart disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Smooth guided adversarial fully test-time adaptation. ACCESS, 13, 57604-57614. (www)

  @article{ , abstract = {Fully test-time adaptation (FTTA) refers to a specific type of domain adaptation that involves adjusting a pre-trained machine learning model to work with a new target domain, without accessing any data from the source domain. The lack of access to source data makes it more difficult to adjust to the target domain. Therefore, it is generally considered as a more challenging task than unsupervised domain adaptation. To improve the performance of FTTA, we propose an adversarial fully test-time training approach based on smoothness enhancing formulations. In our approach, we investigate how smoothness enhancing formulations affect fully test-time adversarial training, which aims to minimize a combination of entropy minimization loss and adversarial terms. We observe that achieving a smooth minima in terms of the adversarial loss leads to better performance on the target domain, as compared to achieving a smooth minima w.r.t entropy minimization loss, which leads to sub-optimal generalization on the target domain. Moreover, since we only have access to target domain data during adversarial training, we propose a pseudo labeling scheme to train the domain discriminator. Experimentally, SAFTTA demonstrates state-of-the-art results, improving error rate by 0.7% on the CIFAR-10-C benchmark and achieving a 1.1% reduction in error rate on the ImageNet-C benchmark compared to existing methods. These results underscore the effectiveness and robustness of our method for FTTA.}, archive = {J_ACCESS}, author = {Dong Li and Panfei Yang}, doi = {10.1109/ACCESS.2024.3432197}, journal = {IEEE Access}, pages = {57604-57614}, shortjournal = {IEEE Access}, title = {Smooth guided adversarial fully test-time adaptation}, volume = {13}, year = {2025}, }
• (2025). Novel hybrid UNet++ and LSTM model for enhanced attack detection and classification in IoMT traffic. ACCESS, 13, 57589-57603. (www)

  @article{ , abstract = {The Internet of Medical Things (IoMT) transforms healthcare by allowing real-time monitoring, diagnosis, and treatment using interconnected medical devices and sensors. However, the rapid growth of IoMT brings significant security and privacy challenges due to its critical vulnerability to cyber-attacks. This paper introduces a novel Deep Learning (DL) approach designed to analyze IoMT traffic and identify malicious activities. By leveraging the CIC IoMT dataset, we improved an existing neural network model to enhance prediction accuracy. Our approach combines UNet++ and Long Short-Term Memory (LSTM) models to extract network traffic features effectively. Experimental results show that the proposed model outperforms traditional algorithms, achieving an accuracy of 99.92% in anomaly detection and 87.96% in attack categorization. Finally, we highlight the main limitations as well as possibilities for real-world implementation of the approach.}, archive = {J_ACCESS}, author = {Anzhelika Mezina and Jari Nurmi and Aleksandr Ometov}, doi = {10.1109/ACCESS.2025.3553966}, journal = {IEEE Access}, pages = {57589-57603}, shortjournal = {IEEE Access}, title = {Novel hybrid UNet++ and LSTM model for enhanced attack detection and classification in IoMT traffic}, volume = {13}, year = {2025}, }
• (2025). A multimodal deep learning model integrating CNN and transformer for predicting chemotherapy-induced cardiotoxicity. ACCESS, 13, 57568-57588. (www)

  @article{ , abstract = {Chemotherapy-induced cardiotoxicity presents a major risk to cancer patients, often leading to severe cardiac complications such as heart failure, myocardial infarction, and arrhythmias. Early detection is crucial for preventing long-term damage and improving patient outcomes, yet existing diagnostic methods struggle to effectively capture the complexity of multimodal medical data and often lack interpretability. In this study, we propose an innovative approach that integrates multimodal deep learning with Explainable AI (XAI) techniques to enhance early cardiotoxicity detection. Our model combines clinical data (e.g., age and cardiovascular metrics) with Tissue Doppler Imaging (TDI), a functional imaging technique that captures myocardial velocity during the cardiac cycle. To overcome data limitations, we employed Conditional Generative Adversarial Networks (cGANs) and Conditional Tabular Generative Adversarial Networks (CTGANs) to augment the dataset, improving its diversity and balance for better model training. We developed three architectures that integrate Convolutional Neural Networks (CNNs) for feature extraction from TDI images with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to capture temporal dependencies and enhance prediction accuracy. Additionally, we incorporated SHapley Additive Explanations (SHAP) to interpret the contribution of input features, increasing model transparency and clinical applicability. Our Transformer-based model achieved the highest accuracy of 96%, outperforming the GRU (94%) and LSTM (89%) models, significantly surpassing traditional approaches. These findings highlight the potential of transformer-based architectures in multimodal deep learning for precise cardiotoxicity prediction, supporting early intervention and personalized treatment strategies while improving interpretability through XAI techniques such as SHAP.}, archive = {J_ACCESS}, author = {Ahmed Bouatmane and Abdelaziz Daaif and Abdelmajid Bousselham and Bouchra Bouihi and Omar Bouattane}, doi = {10.1109/ACCESS.2025.3556700}, journal = {IEEE Access}, pages = {57568-57588}, shortjournal = {IEEE Access}, title = {A multimodal deep learning model integrating CNN and transformer for predicting chemotherapy-induced cardiotoxicity}, volume = {13}, year = {2025}, }
• (2025). State-of-the-art review: Electronic warfare against radar systems. ACCESS, 13, 57530-57567. (www)

  @article{ , abstract = {The electromagnetic spectrum (EMS) is emerging as a sixth battlespace domain in military applications and is essential for wireless communications and remote sensing. Due to the demand for EMS, it is increasingly contested, congested, constrained, and shared. Evolving threats, doctrine, and tactics drive significant advancements in the battlespace. Electronic Warfare (EW) or Electromagnetic Warfare products need to keep pace by leveraging advances in technology driven by Industry 4.0, artificial intelligence (AI), direct radio frequency (RF) sampling, RF System on Chip (RFSoC), and ultrawideband antenna arrays. These trends drive a need for technology-intensive innovation in EW products underpinned by a coherent business-product-technology strategy. This article reviews advancements in Electronic Warfare against radar systems using a novel approach that integrates quantitative bibliometric and patentometric analyses with a qualitative assessment of EW architectures, Electronic Attack (EA) algorithms, and Electronic Support (ES) algorithms. This article marks the first instance of such a methodology being applied to systematically assess the landscape of research publications and technological innovations in the field of EW against radar systems. A taxonomy of EW System Types by military domain against radar and their role in the modern battlefield is presented together with their typical role and real-world examples. The quantitative and qualitative insights are synthesized to outline a notional next-generation EW architecture and a technology roadmap for its realization that researchers can use to guide the development of innovative EW technologies and methodologies and by EW practitioners to inform system design, operational deployment, and capability enhancements.}, archive = {J_ACCESS}, author = {Reeshen Reddy and Saurabh Sinha}, doi = {10.1109/ACCESS.2025.3555493}, journal = {IEEE Access}, pages = {57530-57567}, shortjournal = {IEEE Access}, title = {State-of-the-art review: Electronic warfare against radar systems}, volume = {13}, year = {2025}, }
• (2025). Lightweight CNN for resource-constrained BCD system using knowledge distillation. ACCESS, 13, 57504-57529. (www)

  @article{ , abstract = {Breast cancer (BC) remains a leading cause of mortality among women worldwide, with over two million new cases annually. Early detection improves survival rates significantly, yet resource limitations in low- and middle-income countries (LMICs) hinder access to advanced diagnostic tools. While deep learning (DL) models have shown high accuracy in breast cancer detection (BCD), their computational complexity and hardware requirements make them impractical for deployment on low-power devices. To address this, we propose a lightweight convolutional neural network (CNN) for BCD, leveraging knowledge distillation (KD) to transfer knowledge from a complex teacher model (TM) to a smaller student model (SM). Our approach achieves up to 99.3% accuracy, 100% precision, and 99% recall while reducing the number of trainable parameters by 87% compared to conventional deep models. The proposed model successfully runs on a Raspberry Pi 4B with an inference time of 500 ms and memory usage of just 12% (of 8GB RAM), demonstrating its suitability for telemedicine and mobile diagnostics. Additionally, resource utilization experiments confirm that inference remains stable at 10% CPU usage and 40°C when using a heat sink and fan, ensuring sustained deployment. Future work will explore federated learning for decentralized training, integration of multimodal data for enhanced diagnosis, and cloud-based model updates using delay-tolerant networks (DTNs) for remote healthcare applications.}, archive = {J_ACCESS}, author = {Falmata Modu and Rajesh Prasad and Farouq Aliyu}, doi = {10.1109/ACCESS.2025.3556517}, journal = {IEEE Access}, pages = {57504-57529}, shortjournal = {IEEE Access}, title = {Lightweight CNN for resource-constrained BCD system using knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Unbalancing datasets to enhance CNN models learnability: A class-wise metrics-based closed-loop strategy proposal. ACCESS, 13, 57485-57503. (www)

  @article{ , abstract = {Developing deep learning models often involves working with balanced datasets. However, in real-world scenarios, class equilibrium is rarely observed. To address this, oversampling and undersampling techniques are commonly employed, with appropriate monitoring and countermeasures to mitigate the risk of overfitting. Yet, even when datasets are balanced, they can still introduce biases, leading to better classification performance for certain classes over others. One significant cause of such biases is class underrepresentation, which is challenging to control and often arises from the acquisition of uneven feature distributions influenced by endogenous factors and environmental conditions. In such cases, random data augmentation can inadvertently amplify these discrepancies, exacerbating the issue of underrepresentation. To address this challenge, this paper proposes a metrics-based closed-loop (MbCL) approach that strategically unbalances the dataset to enhance class-wise performance and model generalization in line with the training process. The proposed method iteratively adjusts and improves model performance across different classes by employing data augmentation techniques, including both classical and GAN-based methods, to mitigate classes’ underrepresentation. To compare regular (linear) and MbCL-based approaches, a couple of contextually distinct datasets were considered to broaden consistency analysis across some diversity. Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures, initial learning rates, and optimizers – were initially trained and then evaluated against imagery test sets. In this first assessment, MbCL methods matched or outperformed linear training in approximately 85% of the cases. Additionally, all the models were tested on an external image set acquired under different contextual conditions but with matching labels to the training datasets. The results showed that the proposed MbCL methods consistently outperformed linear training, achieving top-accuracies of 32% and 72% for both the encompassed dataset-related contexts, therefore, also indicating that classic and GAN-based augmentation approaches had a positive impact on inference performances as resampling strategies. Furthermore, complementary indicators, including gradient-weighted class activation mapping (Grad-CAM) and intersection over union (IoU), were analyzed to explore the relationship between performance and visualizable features.}, archive = {J_ACCESS}, author = {Somayeh Shahrabadi and Victor Alves and Emanuel Peres and Raul Morais Dos Santos and Telmo Adão}, doi = {10.1109/ACCESS.2025.3554395}, journal = {IEEE Access}, pages = {57485-57503}, shortjournal = {IEEE Access}, title = {Unbalancing datasets to enhance CNN models learnability: A class-wise metrics-based closed-loop strategy proposal}, volume = {13}, year = {2025}, }
• (2025). Quantization-aware training with dynamic and static pruning. ACCESS, 13, 57476-57484. (www)

  @article{ , abstract = {The evolution of deep neural networks (DNNs) naturally leads to an increase in model size. This necessitates various model compression techniques, such as pruning and quantization, to reduce memory usage and power consumption. In particular, combining these compression techniques can achieve significant cost savings. However, we found that methods using dynamic pruning and quantization suffer from instability in training and poor generalization performance due to the effects of the two Straight Through Estimators (STE). To address this problem, we propose a Quantization-aware training with Dynamic and Static pruning (QADS) method that takes advantage of both pruning and quantization by performing STE operations only during quantization from a certain point in time. In our experiments, the proposed method exhibits more stable training compared to existing techniques and achieves performance improvements on the CIFAR-10/100, ImageNet, and Google Speech Command datasets. The code is provided at https://github.com/Ahnho/Quantization-aware-training-with-Dynamic-and-Static-Pruning.}, archive = {J_ACCESS}, author = {Sangho An and Jongyun Shin and Jangho Kim}, doi = {10.1109/ACCESS.2025.3556629}, journal = {IEEE Access}, pages = {57476-57484}, shortjournal = {IEEE Access}, title = {Quantization-aware training with dynamic and static pruning}, volume = {13}, year = {2025}, }
• (2025). PNMLR: Enhancing route recommendations with personalized preferences using graph attention networks. ACCESS, 13, 57465-57475. (www)

  @article{ , abstract = {In the modern landscape, millions of users rely on mapping services daily to plan trips of various kinds. While these services traditionally focus on providing the shortest routes, individual preferences often influence real-world route choices. However, most existing solutions neglect the impact of such preferences, limiting their ability to deliver truly personalized recommendations. This paper presents the Personalized Neuro-MLR (PNMLR) model, which enhances route recommendations by embedding user-specific preferences into the prediction process. Built on the Neuro-MLR (NMLR) framework, PNMLR leverages Graph Attention Networks (GAT) to integrate factors like user ID, time of day, and transport mode. This approach captures the variability in user behavior, offering personalized predictions for the most likely route. Extensive experiments on the Geolife GPS dataset show significant improvements across key metrics, including F1-score, precision, recall and route reachability, compared to models that do not consider user preferences. These results demonstrate the potential of PNMLR to transform route recommendation systems from generic solutions to user-centric models.}, archive = {J_ACCESS}, author = {Valerio Ponzi and Ludovico Comito and Christian Napoli}, doi = {10.1109/ACCESS.2025.3555049}, journal = {IEEE Access}, pages = {57465-57475}, shortjournal = {IEEE Access}, title = {PNMLR: Enhancing route recommendations with personalized preferences using graph attention networks}, volume = {13}, year = {2025}, }
• (2025). Software defect prediction based on effective fusion of multiple features. ACCESS, 13, 57455-57464. (www)

  @article{ , abstract = {In the field of software engineering, the goal of software defect prediction is to accelerate the detection of potential defective modules in products, thereby enhancing the efficiency of allocating testing resources effectively. Traditional software defect prediction models typically rely on a single feature or perform simple aggregation of multiple features during training. However, the former overlooks the potential contributions of other features in defect prediction, while the latter fails to fully exploit the value of different features in the model. Therefore, this paper proposes a software defect prediction model based on the effective fusion of multiple features, named DP-SSCT (Semantics, Source Code, and Tradition). The model integrates traditional features, source code features, and semantic features. To determine the importance of each feature in the model, a gate fusion mechanism and attention mechanism are introduced to capture significant information in the fused features. The proposed model is evaluated on nine open-source Java project samples from the PROMISE dataset. Experimental results demonstrate that the model outperforms existing prediction models, exhibiting superior performance and predictive capability.}, archive = {J_ACCESS}, author = {Chaozheng Zhang and Junhua Wu}, doi = {10.1109/ACCESS.2024.3409709}, journal = {IEEE Access}, pages = {57455-57464}, shortjournal = {IEEE Access}, title = {Software defect prediction based on effective fusion of multiple features}, volume = {13}, year = {2025}, }
• (2025). Compact fully-packaged hybrid CPWG/SIDGS for 1-to-4 in-Phase/Out-of-phase power splitter with low Amplitude/Phase imbalances, low radiation loss, and good isolation. ACCESS, 13, 57442-57454. (www)

  @article{ , abstract = {In this paper, two miniaturized 1-to-4 in-phase/out-of-phase power splitters are proposed using fully-packaged hybrid coplanar waveguide with ground (CPWG) and substrate-integrated defected ground structure (SIDGS). The broadside-coupled stepped-impedance CPWG resonator and a pair of C-shape SIDGS resonators with fully-packaged scheme are used to implement the 1-to-4 in-phase/out-of-phase power splitter, which achieves the in-phase and out-of-phase power splitters responses simultaneously with low insertion and radiation loss. Meanwhile, a lowpass unit (LPU) embedded in the CPWG input feed-line is introduced to extend the stopband with enhanced rejection level. Then, to verify aforementioned mechanisms, an ultra-compact 1-to-4 in-phase/out-of-phase power splitter centered at 9.35 GHz with fractional bandwidth (FBW) of 26.7% is designed and fabricated. To further improve the isolation of proposed power splitter, a parallel isolation network (i.e., parallel quarter-wavelength ( $\lambda $ /4) lines with complex isolation impedance RC) located at the output ports is developed. Such a 1-to-4 in-phase/out-of-phase power splitter is then implemented and fabricated, which operates at 9.7 GHz with FBW of 47.4% and achieves 18 dB stopband rejection level up to 40 GHz. The isolations of in-phase and out-of-phase power splitters are greater than 12 dB and 15 dB, respectively, while the radiation loss remains 7% up to 40 GHz. Moreover, the passband amplitude/phase imbalances of in-phase and out-of-phase power splitters are ±0.13 dB/±0.7° and ±0.2 dB/±1.1°, respectively.}, archive = {J_ACCESS}, author = {Qiqi Luo and Jie Zhou and Xun Luo}, doi = {10.1109/ACCESS.2025.3554197}, journal = {IEEE Access}, pages = {57442-57454}, shortjournal = {IEEE Access}, title = {Compact fully-packaged hybrid CPWG/SIDGS for 1-to-4 in-Phase/Out-of-phase power splitter with low Amplitude/Phase imbalances, low radiation loss, and good isolation}, volume = {13}, year = {2025}, }
• (2025). Wind farm meteorological prediction model based on frequency domain feature extraction fusion mechanism. ACCESS, 13, 57426-57441. (www)

  @article{ , abstract = {Given the increasing significance of wind energy in the worldwide energy composition, the effective administration of wind farms has emerged as a crucial element in guaranteeing the reliability of the energy provision. Precise forecasting of meteorological conditions is essential for optimizing wind farm operations in this scenario. However, the current prediction model has certain limitations, namely the problem of inaccurate prediction for a long period and the problem of insufficient ability to extract frequency domain information. Among them, we found that frequency domain features are the key to affecting the above problems. Therefore, this study aims to enhance the ability of existing models to capture frequency domain feature information by introducing an innovative frequency domain feature extraction fusion mechanism, thereby significantly improving the accuracy of meteorological condition forecasting. Specifically, this study addresses the issue of long-term dependency in the model by incorporating the Frequency Enhanced Attention module. This integration improves the model’s ability to reliably forecast wind farm meteorological conditions over extended periods. Furthermore, this study utilizes the LoRA (Low-Rank Adaptation) fine-tuning strategy to improve the model’s generalization capacity across various wind power circumstances. In the experimental part, we verify the model’s high-precision forecasting ability on coarse-grained seasonal changes. At the same time, we have extensively verified the model in four different regions, where our model outperformed existing mainstream models (Autoformer, Informer, and Transformer) by an average of 28.9%, 22.7%, and 27.0% respectively, demonstrating the broad applicability of our model. The model prediction results are better than the current mainstream models in critical indicators, proving the model’s effectiveness. These experiments validate our model’s efficacy and offer novel technical resources for forecasting meteorological conditions and managing energy in wind farms. This is highly significant in advancing the efficient utilization of wind energy and promoting the sustainable development of the wind power industry.}, archive = {J_ACCESS}, author = {Yichen Liao and Ziqi Gao and Xufeng Li}, doi = {10.1109/ACCESS.2024.3450569}, journal = {IEEE Access}, pages = {57426-57441}, shortjournal = {IEEE Access}, title = {Wind farm meteorological prediction model based on frequency domain feature extraction fusion mechanism}, volume = {13}, year = {2025}, }
• (2025). Data-driven hybrid CNN-LSTM neural networks for predicting drying kinetics of green tomatoes (Physalis ixocarpa) integrating mathematical models. ACCESS, 13, 57413-57425. (www)

  @article{ , abstract = {This study investigates the drying kinetics of green tomatoes under controlled conditions, comparing 14 mathematical models including Newton, Page, Modified Page, Henderson and Pabis, Logarithmic, Two-Term, Wang and Singh, Thomson, Approximate Diffusion, Verma, Modified Henderson and Pabis, Two-Term Exponential, Simplified Fick’s Diffusion, and Midilli et al. The Verma, Two-Term Exponential, and Modified Page models demonstrated superior accuracy (R2 > 0.998, RMSE 0.01268 - 0.01345). A novel approach integrating a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) optimized drying process prediction, significantly enhancing moisture content prediction over time. This hybrid CNN-LSTM model, coupled with the accurate mathematical models (especially the Two-Term Exponential), offers a robust framework for predicting and optimizing food dehydration.}, archive = {J_ACCESS}, author = {Carolina Livier Recio-Colmenares and Roxana Berenice Recio-Colmenares and Robin Fernando Conchas-Cedano and Isaac Pilatowsky-Figueroa and César Alejandro García García}, doi = {10.1109/ACCESS.2025.3554492}, journal = {IEEE Access}, pages = {57413-57425}, shortjournal = {IEEE Access}, title = {Data-driven hybrid CNN-LSTM neural networks for predicting drying kinetics of green tomatoes (Physalis ixocarpa) integrating mathematical models}, volume = {13}, year = {2025}, }
• (2025). Maximizing opinion polarization using double deep Q-learning in social networks. ACCESS, 13, 57398-57412. (www)

  @article{ , abstract = {Social media networks, such as Facebook and X (formerly Twitter), have become crucial platforms for public discourse, but they also contribute to the rise of misinformation and opinion polarization. This polarization has profound social, economic, and political implications, making it a critical area of study. In this research, we investigate the impact of automated adversary agents on social media networks by introducing a novel approach using Double-Deep Q-learning reinforcement learning (RL) to deliberately increase polarization within social media networks. The RL agent requires minimal interaction with the system and can flexibly adapt to changes within the network, allowing it to effectively exploit structural vulnerabilities and amplify divisions among users. By learning how to influence the flow of information, the agent intensifies polarization and disagreement with fewer interventions compared to traditional methods. Through simulations and experiments with real-world datasets, we demonstrate that our RL-based approach significantly outperforms conventional techniques in escalating polarization. These findings underscore the risks posed by such techniques and the urgent need to develop safeguards against malicious manipulation of social media platforms by intelligent adversary agents.}, archive = {J_ACCESS}, author = {Mohamed N. Zareer and Rastko R. Selmic}, doi = {10.1109/ACCESS.2025.3537397}, journal = {IEEE Access}, pages = {57398-57412}, shortjournal = {IEEE Access}, title = {Maximizing opinion polarization using double deep Q-learning in social networks}, volume = {13}, year = {2025}, }
• (2025). Impact of mixed traffic flow involving connected automated vehicles and human-driven vehicles at roll-on and roll-off ports. ACCESS, 13, 57387-57397. (www)

  @article{ , abstract = {This study investigates the impact of mixed traffic flow involving connected automated vehicles (CAVs) and human-driven vehicles (HDVs) at roll-on and roll-off (Ro-Ro) ports. Using the simulation of urban mobility with a traffic control interface, we developed port environments incorporating various car-following models. We analysed the efficiency of port operations under mixed traffic scenarios with increasing CAV penetration rates. The findings demonstrate significant improvements in productivity and reductions in emissions and fuel consumption as CAV penetration increases. Specifically, 100% penetration of CAVs leads to a productivity increase of up to 71.14%, a substantial decrease in carbon monoxide, hydrocarbons and nitrogen oxide emissions as well as a reduction in fuel consumption by over 70% in a double-ramp scenario. This study provides valuable insights into the practical implementation and phased transition towards fully automated vehicle operations in Ro-Ro ports.}, archive = {J_ACCESS}, author = {Sang Hyung Park and Sihyun Kim}, doi = {10.1109/ACCESS.2025.3555308}, journal = {IEEE Access}, pages = {57387-57397}, shortjournal = {IEEE Access}, title = {Impact of mixed traffic flow involving connected automated vehicles and human-driven vehicles at roll-on and roll-off ports}, volume = {13}, year = {2025}, }
• (2025). Lung-AttNet: An attention mechanism-based CNN architecture for lung cancer detection with federated learning. ACCESS, 13, 57369-57386. (www)

  @article{ , abstract = {Lung cancer is one of the fatal diseases whose early diagnosis is essential to mitigate the death rate. Computed Tomography (CT) scans are widely used for lung cancer diagnosis, but manual interpretation by health professionals can lead to inconsistent results. To address this, we propose Lung-AttNet, a novel lightweight convolutional neural network (CNN) model enhanced with an attention mechanism. Lung-AttNet incorporates a convolutional block with a Lightweight Global Attention Module (LGAM) to effectively distinguish between lung cancer types. The convolutional block extracts both low- and high-dimensional features, while LGAM captures feature dependencies across channel and spatial dimensions. The model is evaluated using the Kaggle CT scan dataset, which includes four classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal. Extensive experiments, including ablation studies, 5-fold cross-validation, and explainable AI (XAI) techniques such as Grad-CAM and LIME, demonstrate that Lung-AttNet achieves an average accuracy of 91.5%. Furthermore, to address medical data sensitivity and privacy concerns, the model is deployed in a Federated Learning (FL) framework, where the global model is trained using weights from local models rather than sharing raw data. In the FL environment, Lung-AttNet achieves an accuracy of 92% with 2 and 3 clients, underscoring its robustness and adaptability for real-world applications.}, archive = {J_ACCESS}, author = {Chamak Saha and Somak Saha and Md. Asadur Rahman and Md. Mahmudul Haque Milu and Hiroki Higa and Mohd Abdur Rashid and Nasim Ahmed}, doi = {10.1109/ACCESS.2025.3554744}, journal = {IEEE Access}, pages = {57369-57386}, shortjournal = {IEEE Access}, title = {Lung-AttNet: An attention mechanism-based CNN architecture for lung cancer detection with federated learning}, volume = {13}, year = {2025}, }
• (2025). CP-YOLO: An algorithm for cigarette pack defects detection based on CCD images. ACCESS, 13, 57354-57368. (www)

  @article{ , abstract = {In cigarette production, detecting cigarette pack defects is crucial for ensuring that products meet quality standards. The failure to detect defective packs promptly may affect production efficiency and material consumption. Hence, in this study, we used Charge-Coupled Device (CCD) cameras to collect many defect images from real industrial production lines. A relatively comprehensive cigarette pack defect dataset, called CigPack, was established based on complex defect features of different sizes and structures. Consequently, this paper proposes an improved CP-YOLO algorithm based on YOLOv5 to address the characteristics of cigarette pack defects including large size variations and complex foreground-background information. The algorithm integrates multi-scale aggregate convolution into the BottleneckCSP architecture to form the C3MSAC module. This module extracts and fuses grouped features at multiple scales to enhance the multi-scale representation of input feature maps. Additionally, a balanced-domain loss function, which introduces normalized Wasserstein distance to optimize the guidance of the network for bounding boxes with different geometric characteristics, is proposed. Experimental results demonstrate that CP-YOLO achieved average accuracy improvements of 2.9% and 2.5% on the CigPack dataset, without increasing computational complexity. The model also demonstrated excellent detection accuracy and robustness on steel surface defect dataset and printed circuit board defect dataset.}, archive = {J_ACCESS}, author = {Peng Dong and Weihua Feng and Rui Wang and Mingyan Zhang and Qunye Hong and Yongsheng Wang and Di Wang and Guohao Zong}, doi = {10.1109/ACCESS.2025.3554989}, journal = {IEEE Access}, pages = {57354-57368}, shortjournal = {IEEE Access}, title = {CP-YOLO: An algorithm for cigarette pack defects detection based on CCD images}, volume = {13}, year = {2025}, }
• (2025). Traffic impact analysis of expressway accident in mixed traffic environment of connected and autonomous considering multi-factor coupling. ACCESS, 13, 57342-57353. (www)

  @article{ , abstract = {Connected automated vehicle (CAVs) and human-driven Vehicles (HVs) are expected to coexist for an extended period, potentially introducing new safety challenges and altering traffic flow characteristics. This study examines the traffic impacts of accidents in mixed-traffic environments under various conditions, employing the Simulation of Urban Mobility (SUMO) platform. The Intelligent Driver Model (IDM) and Cooperative Adaptive Cruise Control (CACC) are utilized to model car-following behavior for HVs and CAVs, respectively. The LC2013 lane-changing model in SUMO is employed to describe lane-changing behavior for HVs and CAVs, with distinct parameter values. A sensitivity analysis of key factors, such as the penetration rate of autonomous vehicles, traffic flow, accident duration and occupancy rate of accident-affected roads, is conducted, focusing safety and efficiency aspects. The results show that higher CAV penetration rates significantly reduce delays and collision risks when a single lane is occupied. However, when two lanes are occupied, delays and collision risks initially increase before decreasing as the CAV penetration rate rises. Additionally, high traffic flow rate and long durations result in more delays, and collision risks increase significantly at high flow rates. This study provides valuable insights for optimizing mixed-traffic operations in the event of accidents and offers practical guidance for the effective deployment of CAVs.}, archive = {J_ACCESS}, author = {Yaqin He and Yang Kong}, doi = {10.1109/ACCESS.2025.3547236}, journal = {IEEE Access}, pages = {57342-57353}, shortjournal = {IEEE Access}, title = {Traffic impact analysis of expressway accident in mixed traffic environment of connected and autonomous considering multi-factor coupling}, volume = {13}, year = {2025}, }
• (2025). A hybrid CNN-LSTM model with attention mechanism for improved intrusion detection in wireless IoT sensor networks. ACCESS, 13, 57322-57341. (www)

  @article{ , abstract = {Wireless Internet of Things (IoT) Sensor Networks (WIoTSNs) are frequently deployed in resource-constrained environments where security threats pose significant challenges. Existing intrusion detection systems (c) often struggle with scalability and efficiency under the unique demands of IoT networks. This work introduces an Intrusion Detection System (IDS) framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks in a hybrid architecture, enhanced by an attention mechanism to improve feature extraction and classification accuracy. To address computational demands, an enhanced Particle Swarm Optimization (PSO) algorithm is implemented for dynamic feature selection, thereby optimizing the system’s efficiency in high-dimensional data environments characteristic of IoT networks. The proposed model enhances IoT intrusion detection by integrating a novel hybrid CNN-LSTM with an attention mechanism, thereby improving feature extraction and temporal pattern recognition. Additionally, the improved dynamic PSO algorithm optimizes feature selection in real time, enhancing classification accuracy and adaptability to evolving IoT network threats. This combination ensures more efficient and robust intrusion detection in dynamic IoT environments. Experimental evaluations using a standard IoT intrusion dataset indicate that the proposed model achieves notable accuracy rates of 98.73% with CNN, 99.87% with LSTM, 99.12% with CNN-LSTM, and 98.88% with the enhanced CNN-LSTM with attention, demonstrating an improvement over existing techniques. The framework’s resilience and adaptability underscore its potential for enhancing network security in real-world IoT applications by addressing evolving threats and computational constraints.}, archive = {J_ACCESS}, author = {Pendukeni Phalaagae and Adamu Murtala Zungeru and Abid Yahya and Boyce Sigweni and Selvaraj Rajalakshmi}, doi = {10.1109/ACCESS.2025.3555861}, journal = {IEEE Access}, pages = {57322-57341}, shortjournal = {IEEE Access}, title = {A hybrid CNN-LSTM model with attention mechanism for improved intrusion detection in wireless IoT sensor networks}, volume = {13}, year = {2025}, }
• (2025). Generative diffusion network for creating scents. ACCESS, 13, 57311-57321. (www)

  @article{ , abstract = {This paper introduces a novel application of generative diffusion networks for creating scents. The research presents a generative diffusion network designed to create new aromas with specified, required odor descriptors using essential oils as the basic components. The model uses mass spectrometry data of essential oils as latent embedding space of the essential oils. The generative network outputs mass spectrometry data as the primary output. These generated mass spectrometry profiles are then processed by non-negative least squares to create essential oils recipes that have the required odor descriptors. The results demonstrate the model’s ability to produce diverse and new aroma profiles, which are validated by sensory tests. The method can create new scents by mixing essential oils, making automated aroma design possible. This approach shows major progress in aroma design. These results suggest many uses in industries like perfumery and food and beverage, improving efficiency and creativity in making many different fragrances.}, archive = {J_ACCESS}, author = {Manuel Aleixandre and Dani Prasetyawan and Takamichi Nakamoto}, doi = {10.1109/ACCESS.2025.3555273}, journal = {IEEE Access}, pages = {57311-57321}, shortjournal = {IEEE Access}, title = {Generative diffusion network for creating scents}, volume = {13}, year = {2025}, }
• (2025). Advanced biosignal-RGB fusion with adaptive neurofuzzy classification for high-precision action recognition. ACCESS, 13, 57287-57310. (www)

  @article{ , abstract = {In the domain of action recognition using multisensory data, the integration of RGB and signal-based modalities offers a promising approach to enhance the accuracy of action classification systems. Our system was developed through experimentation on three benchmark datasets: UTD-MHAD (University of Texas at Dallas Multimodal Human Action Dataset), HWU-USP and LaRa. Initially, the data undergoes preprocessing, where gaussian and butterworth filters are applied to the RGB and signal data, respectively. Following this, windowing/segmentation is applied to signals and RGB data. After that, features are extracted from the signal data, including auto-regression, MFCC (Mel-frequency Cepstral Coefficients), and transient detection principle, while the RGB (Red Green Blue) was processed as a combined input to extract features such as angles, velocity, full-body elliptical modeling, fiducial points, and a 2.5D point cloud of the entire body. These features are then fused, followed by the application of the Yeo-Johnson power optimizer to refine the data. The optimized data is subsequently classified using a Neurofuzzy classifier to recognize different actions. This classifier is chosen for its ability to adapt to the heterogeneous nature of multimodal data, where features are spread across different domains, making traditional classifiers less effective. The Neurofuzzy model employs cross-validation for training and testing to ensure reliable results. The results also suggest that the proposed model yields a higher accuracy than the existing models. More specifically, in the HWU-USP dataset, the accuracy amounts to mean 89%, in the LaRa, to mean 91% and 88% over the UTD-MHAD dataset. The system under study effectively distinguishes related actions, but its efficiency is hindered by the complexity of individual actions and the increased noise in the dataset.}, archive = {J_ACCESS}, author = {Iqra Aijaz Abro and Haifa F. Alhasson and Shuaa S. Alharbi and Mohammed Alatiyyah and Dina Abdulaziz AlHammadi and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3553196}, journal = {IEEE Access}, pages = {57287-57310}, shortjournal = {IEEE Access}, title = {Advanced biosignal-RGB fusion with adaptive neurofuzzy classification for high-precision action recognition}, volume = {13}, year = {2025}, }
• (2025). An internet-of-things-integrated deep learning model for fault diagnosis in industrial rotating machines. ACCESS, 13, 57266-57286. (www)

  @article{ , abstract = {Rotating machines are crucial in industries for reliable system operation, but unexpected failures can result in significant financial losses and personnel damage. Hence, fault diagnosis is important. Among the common types of faults in rotating machines, imbalance, and misalignment are important types of faults in operation but are rarely studied. This research utilizes data from four distinct operating conditions: normal operation, imbalanced condition, imbalanced condition combined with horizontal misalignment, and imbalanced condition combined with vertical misalignment. This study proposed a method for fault diagnosis using Continuous Wavelet Transform (CWT) for extracting vibration signals into RGB (Red – Green - Blue) images and using Convolutional Neural Network (CNN) models for fault diagnosis. Additionally, Internet of Things (IoT) integration is implemented using the Message Queuing Telemetry Transport (MQTT) protocol, enabling the update of fault status to the cloud for efficient monitoring. A mobile application provides an intuitive platform for visualizing and interacting with fault data. Moreover, several machine learning (ML) models are also utilized for comparison with the proposed method such as Random Forest (RF), Multilayer Perceptron (MLP), and Xtreme Gradient Boosting (XGBoost). The results indicate that CNN yields an overall accuracy of 93.27% compared to other ML methods.}, archive = {J_ACCESS}, author = {Tuan M. Le and Hieu M. Tran and Ke Wang and Hung V. Pham and Son V. T. Dao}, doi = {10.1109/ACCESS.2025.3553155}, journal = {IEEE Access}, pages = {57266-57286}, shortjournal = {IEEE Access}, title = {An internet-of-things-integrated deep learning model for fault diagnosis in industrial rotating machines}, volume = {13}, year = {2025}, }
• (2025). Explainable feature-injected diffusion model for medical image translation. ACCESS, 13, 57255-57265. (www)

  @article{ , abstract = {The integration of computed tomography (CT) and magnetic resonance (MR) imaging is crucial for accurate medical diagnosis and treatment planning. However, translating images between CT and MR remains challenging due to significant differences in imaging modalities. To address this problem, we propose an Explainable Feature-Injected Diffusion Model (EIDM) for unsupervised CT-to-MR image translation. EIDM comprises a feature synthesis module and a diffusion-based latent space learning framework. This model captures frequency representations of the original CT images using the Fast Fourier Transform and applies high-pass filters to restore anatomical structures lost during diffusion. It also integrates weighted heatmaps generated by explainable AI models and utilizes a cross-attention mechanism to achieve unbiased image synthesis. We quantitatively evaluated EIDM and recent approaches using four metrics for comparison. Experimental results demonstrate that EIDM outperforms latest Generative Adversarial Networks (GANs) and diffusion models, generating realistic MR images that preserve anatomical integrity, as evidenced by enhanced scores across evaluation metrics. This work highlights the effectiveness of jointly learning explainable features and contour regions in achieving the goal of translating CT to MR images.}, archive = {J_ACCESS}, author = {Jung Su Ahn and Ki Hoon Kwak and Young-Rae Cho}, doi = {10.1109/ACCESS.2025.3555585}, journal = {IEEE Access}, pages = {57255-57265}, shortjournal = {IEEE Access}, title = {Explainable feature-injected diffusion model for medical image translation}, volume = {13}, year = {2025}, }
• (2025). BS-BGM: Leveraging vision language models and shot boundary for beat-synced video background music generation. ACCESS, 13, 57241-57254. (www)

  @article{ , abstract = {Short videos have emerged as a powerful medium for self-expression and background music (BGM) plays a crucial role in enhancing audience immersion. Existing video-to-audio generation methods struggle to achieve precise alignment with beat-synced dynamic rhythms tailored to video content. To address this challenge, we introduce BS-BGM500, a curated dataset comprising 500 short videos with meticulous synchronization between shot boundaries and audio rhythm changes. This dataset includes comprehensive annotations such as shot boundaries, captions, and rhythm information. Additionally, we propose BS-BGM, a diffusion-based model designed for generating BGMs. By integrating visual and textual features while leveraging shot boundary information as a Boundary Rhythm Bias (BRB), the model achieves dynamic rhythm transitions and ensures seamless alignment with video content. Extensive evaluations on BS-BGM500 and the widely-used BGM909 dataset demonstrate that our method significantly outperforms previous approaches in terms of audio quality, emotional alignment, and beat-synced consistency. This work represents a substantial advancement in automated BGM generation for short videos, bridging the gap between video dynamics and music generation.}, archive = {J_ACCESS}, author = {Yuji Sun and Yunbin Luo and Hao Chen}, doi = {10.1109/ACCESS.2025.3555297}, journal = {IEEE Access}, pages = {57241-57254}, shortjournal = {IEEE Access}, title = {BS-BGM: Leveraging vision language models and shot boundary for beat-synced video background music generation}, volume = {13}, year = {2025}, }
• (2025). Exploring the intersection of big data and AI with CRM through descriptive, network, and contextual methods. ACCESS, 13, 57223-57240. (www)

  @article{ , abstract = {As artificial intelligence (AI) continues to gain prominence, understanding its application in customer relationship management (CRM) has become increasingly critical. Advances in computing power, big data availability, AI methodologies, and the growing adoption of CRM systems have driven significant interest in AI-based CRM from both academia and industry. However, comprehensive reviews of this rapidly evolving domain remain limited. Using a systematic review approach, this study analyzes 840 documents from the Web of Science (WoS) database, following the PRISMA framework for study selection. Through descriptive, network, and contextual analyses, we identify key research clusters, including Enhancing Customer Experience with AI, predictive analytics in CRM, AI-CRM adoption and digital transformation, and Emerging AI Techniques in CRM. The findings highlight a significant shift toward AI-powered hyper-personalization, explainable AI, federated learning, and IoT-enhanced CRM. This study contributes by mapping the research landscape, uncovering emerging trends, and providing future research directions on the adoption and effectiveness of AI in CRM, AI ethics, the integration of IoT with AI in predictive CRM and AI-driven sentiment analysis, offering valuable insights for scholars and practitioners.}, archive = {J_ACCESS}, author = {Dervis Ozay and Mohammad Jahanbakth and Shouyi Wang}, doi = {10.1109/ACCESS.2025.3554549}, journal = {IEEE Access}, pages = {57223-57240}, shortjournal = {IEEE Access}, title = {Exploring the intersection of big data and AI with CRM through descriptive, network, and contextual methods}, volume = {13}, year = {2025}, }
• (2025). BiFormer for scene graph generation based on VisionNet with taylor hiking optimization algorithm. ACCESS, 13, 57207-57222. (www)

  @article{ , abstract = {Scene Graph Generation (SGG) plays a vital role in determining the graph structure of an image by classifying objects based on their pairwise visual relationships. In the SGG, visually grouped graphs are generated by considering edges as visual relationships between objects and nodes as object classes. Various schemes have been developed to generate scene graphs; however, these techniques require significant computational resources and time for the SGG. In this study, a deep learning-based optimization model, VisionNet_Taylor Hiking Optimization Algorithm (VisionNet_THOA), was introduced to generate high-quality scene graphs from noisy samples. Here, objects were detected by performing semantic segmentation using dynamic routing. The attention areas and actions were determined using the BiFormer method. The nodes in the graph are signified as detected objects and the detected action is represented by edges. Prediction head classification was performed to measure the accuracy of predicting object labels and relationships using VisionNet. The superiority of VisionNet is increased by training the hyperparameters using the Taylor hiking optimization algorithm (THOA). Furthermore, extensive experimental results were obtained using VisionNet_THOA, where VisionNet_THOA attained an accuracy of 94.867%, a True Negative Rate (TNR) of 93.877%, and a True Positive Rate (TPR) of 96.654%, a Precision of 91.765%, and F-Measure of 94.146%.}, archive = {J_ACCESS}, author = {S. Monesh and N. C. Senthilkumar}, doi = {10.1109/ACCESS.2025.3555230}, journal = {IEEE Access}, pages = {57207-57222}, shortjournal = {IEEE Access}, title = {BiFormer for scene graph generation based on VisionNet with taylor hiking optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). A set-based method for planning coordinated trajectories for skid-steered robotic units subject to constraints, uncertainties, and external disturbances. ACCESS, 13, 57191-57206. (www)

  @article{ , abstract = {This paper introduces a method for planning coordinated trajectories for a group of remotely-controlled, skid-steered robotic units with limited motion capabilities, subject to skidding and slipping phenomena. The proposed solution aims to define optimal trajectories that can be simultaneously tracked by the robots while ensuring collision-free motion, even in the presence of unavoidable tracking errors due to the limited authority of the control algorithm. A key feature is that, at the cost of certain degree of conservatism, the robotic units do not require collision avoidance capabilities to solve online coordination problems during motion. From a methodological point of view, the problem of planning robust and coordinated trajectories that ensure collision-free robot movements is formulated as a graph search problem involving semidefinite programming with constraints in the form of linear matrix inequalities. Finally, in order to show the effectiveness of the proposed solution and highlight some of its potential limitations, the results of several numerical and experimental simulations are discussed.}, archive = {J_ACCESS}, author = {Valerio Scordamaglia and Alessia Ferraro}, doi = {10.1109/ACCESS.2025.3555494}, journal = {IEEE Access}, pages = {57191-57206}, shortjournal = {IEEE Access}, title = {A set-based method for planning coordinated trajectories for skid-steered robotic units subject to constraints, uncertainties, and external disturbances}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient prediction in textile manufacturing: Enhancing accuracy and data efficiency with ensemble deep transfer learning. ACCESS, 13, 57177-57190. (www)

  @article{ , abstract = {Traditional textile factories consume substantial energy, making energy-efficient production optimization crucial for sustainability and cost reduction. Meanwhile, deep neural networks (DNNs), which are effective for factory output prediction and operational optimization, require extensive historical data—posing challenges due to high sensor deployment and data collection costs. To address this, we propose Ensemble Deep Transfer Learning (EDTL), a novel framework that enhances prediction accuracy and data efficiency by integrating transfer learning with an ensemble strategy and a feature alignment layer. EDTL pretrains DNN models on data-rich production lines (source domain) and adapts them to data-limited lines (target domain), reducing dependency on large datasets. Experiments on real-world textile factory datasets show that EDTL improves prediction accuracy by 5.66% and enhances model robustness by 3.96% compared to conventional DNNs, particularly in data-limited scenarios (20%–40% data availability). This research contributes to energy-efficient textile manufacturing by enabling accurate predictions with fewer data requirements, providing a scalable and cost-effective solution for smart production systems.}, archive = {J_ACCESS}, author = {Yan-Chen Chen and Wei-Yu Chiu and Qun-Yu Wang and Jing-Wei Chen and Hao-Ting Zhao}, doi = {10.1109/ACCESS.2025.3551798}, journal = {IEEE Access}, pages = {57177-57190}, shortjournal = {IEEE Access}, title = {Energy-efficient prediction in textile manufacturing: Enhancing accuracy and data efficiency with ensemble deep transfer learning}, volume = {13}, year = {2025}, }
• (2025). POSES: Patch optimization strategies for efficiency and stealthiness using eXplainable AI. ACCESS, 13, 57166-57176. (www)

  @article{ , abstract = {Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence. While adversarial examples have primarily focused on digital-world attacks, recent research has proposed adversarial patches as the focus expands to physical-world attacks. Unlike traditional adversarial examples that use small perturbations, adversarial patches employ large perturbations to bypass existing defense mechanisms against adversarial attacks. Adversarial patches have been shown to be highly effective in causing deep learning models to misclassify. However, existing adversarial patches are often limited by their noticeable appearance and the high computational cost of generating them. To solve these problems, we propose a new adversarial patch generation method called Patch Optimization Strategies for Efficiency and Stealthiness (POSES). POSES uses a two-step optimization architecture that employs an eXplainable AI-based method to optimize the location and size of adversarial patches. Experimental results on benchmark datasets demonstrate that POSES enhances the stealthiness of adversarial patches while maintaining a high attack success rate. We also show that POSES improves attack efficiency by reducing the number of iterations required.}, archive = {J_ACCESS}, author = {Han-Ju Lee and Jin-Seoung Kim and Han-Jin Lee and Seok-Hwan Choi}, doi = {10.1109/ACCESS.2025.3555044}, journal = {IEEE Access}, pages = {57166-57176}, shortjournal = {IEEE Access}, title = {POSES: Patch optimization strategies for efficiency and stealthiness using eXplainable AI}, volume = {13}, year = {2025}, }
• (2025). Machine learning and digital-twins-based internet of robotic things for remote patient monitoring. ACCESS, 13, 57141-57165. (www)

  @article{ , abstract = {The digital twins (DTs) paradigm creates a virtual replica of a physical artifact that represents its real-world status in a virtual space. When implemented in the Internet of Robotic Things (IoRT), it develops a virtual model of the physical robot that receives real-time sensor data, acting like an actual robot. The DTs-based IoRT allows remote patient monitoring (RPM) that improves treatment plans and reduces the burden of gathering vital signs on health carers by automating repeated tasks. However, RPM procedures generate large amounts of sensor data, which are difficult to examine. Furthermore, health carers cannot forecast abnormalities based on health data. Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. This research leverages the advantages of ML and DTs-based IoRT to remotely monitor chronic or infectious patients and predict their future status. The proposed system is an extension of our previously published work. It uses the virtual twin (VT) to navigate the physical twin (PT) for collecting data from the patient-mounted sensors and applies ML techniques to predict health anomalies. It evaluated six ML algorithms to determine the most accurate model. The models were trained and tested by using a dataset combining two public datasets and real-world data collected by healthcare providers. Results confirmed that the K Nearest-Neighbor (KNN) has the best accuracy i.e. 97%. The system was also tested in the clinical setting to collect patient data and the best-performing algorithm (KNN) was used for status prediction, obtaining 98% accuracy.}, archive = {J_ACCESS}, author = {Sehat Ullah and Sangeen Khan and David Vanecek and Inam Ur Rehman}, doi = {10.1109/ACCESS.2025.3555495}, journal = {IEEE Access}, pages = {57141-57165}, shortjournal = {IEEE Access}, title = {Machine learning and digital-twins-based internet of robotic things for remote patient monitoring}, volume = {13}, year = {2025}, }
• (2025). Winograd transform-based fast detection of heart disease using ECG signals and chest X-ray images. ACCESS, 13, 57119-57140. (www)

  @article{ , abstract = {In resource-constrained environments, efficient feature extraction is crucial for applications in classification and prediction tasks. This study investigates a fast, DFT-based, one-dimensional Winograd Transform (WT) to extract convolution-based features from 1-D ECG signals. For two-dimensional (2-D) Chest X-Ray (CXR) images, 2-D DFT-based convolution is employed to generate features. Traditional multi-stage convolution methods for feature extraction can be slow and computationally intensive. Therefore, to improve speed and accuracy in heart disease (HD) detection, WT-based convolution methods for both 1-D and 2-D data are applied to extract features from ECG signals and CXR images. These features serve as inputs for AI-based detection models, with six machine learning (ML) and four deep learning (DL) models developed for HD detection. Using standard datasets, extensive simulations were conducted, yielding various performance metrics that were analyzed and compared. Additionally, the feature extraction and model training times were evaluated and compared. The comparative analysis demonstrates that WT-based feature extraction significantly reduces processing time for both 1-D and 2-D data types. The WT-based method achieved speedups of x times for 1-D and y times for 2-D feature extraction relative to traditional convolution methods. Performance metrics, including classification accuracy (0.94) and AUC scores (0.98), remained consistently high across models, confirming that WT-based circular convolution offers a practical and effective solution for real-time heart disease detection. This approach enhances diagnostic capabilities in healthcare by enabling resource-efficient, real-time feature extraction in medical image and signal processing.}, archive = {J_ACCESS}, author = {Rishabh Anand and Adyasha Rath and Prabodh Kumar Sahoo and Prince Jain and Ganapati Panda and Xinhong Wang and Haipeng Liu}, doi = {10.1109/ACCESS.2025.3555518}, journal = {IEEE Access}, pages = {57119-57140}, shortjournal = {IEEE Access}, title = {Winograd transform-based fast detection of heart disease using ECG signals and chest X-ray images}, volume = {13}, year = {2025}, }
• (2025). Multiple-time reentrant processes optimization of robotic dual-arm cluster tools with cleaning operations. ACCESS, 13, 57101-57118. (www)

  @article{ , abstract = {Cluster tools are the key equipment in semiconductors manufacturing. Such a tool has been used for some reentrant wafer fabrication processes, such as atomic layer deposition and plasma enhanced chemical vapor deposition. With reentrant processes, wafers need to visit some processing modules for multiple times. This makes a cluster tool no longer a flow-shop system. To ensure the wafer quality, cleaning operations are required by process modules to eliminate the residual gas and chemicals, which complicates the scheduling problem of a cluster tool for reentrant processes. This work focuses on dealing with the challenging scheduling problem of dual-arm cluster tools with cleaning operation for multiple-time reentrant processes. To do so, by analyzing the robot tasks of a tool under the conventional swap strategy, it is found that significant robot delay time is caused by cleaning operation during the swap operation. Thus, to reduce such robot delay time, two novel scheduling strategies are proposed. Based on them, two efficient algorithms are developed to obtain the makespan for completing a given number of wafers. Finally, experiments are carried out to demonstrate the effectiveness of the proposed methods.}, archive = {J_ACCESS}, author = {Tan Li and Yiming Lai and Yonghua Shao and Sijun Zhan and Yan Qiao}, doi = {10.1109/ACCESS.2025.3555111}, journal = {IEEE Access}, pages = {57101-57118}, shortjournal = {IEEE Access}, title = {Multiple-time reentrant processes optimization of robotic dual-arm cluster tools with cleaning operations}, volume = {13}, year = {2025}, }
• (2025). Advancements in context recognition for edge devices and smart eyewear: Sensors and applications. ACCESS, 13, 57062-57100. (www)

  @article{ , abstract = {Edge devices have garnered significant attention for their ability to process data locally, providing low-latency, context-aware services without the need for extensive reliance on cloud computing. This capability is particularly crucial in context recognition, which enables dynamic adaptation to a user’s real-time environment. Applications range from health monitoring and augmented reality to smart assistance and social interaction analysis. Among edge devices, smart eyewear has emerged as a promising platform for context recognition due to its ability to unobtrusively capture rich, multi-modal sensor data. However, the deployment of context-aware systems on such devices presents unique challenges, including real-time processing, energy efficiency, sensor fusion, and noise management. This manuscript provides a comprehensive survey of context recognition in edge devices, with a specific emphasis on smart eyewear. It reviews the state-of-the-art sensors and applications for context inference. Furthermore, the paper discusses key challenges in achieving reliable, low-latency context recognition while addressing energy and computational constraints. By synthesizing advancements and identifying gaps, this work aims to guide the development of more robust and efficient solutions for context recognition in edge computing.}, archive = {J_ACCESS}, author = {Francesca Palermo and Luca Casciano and Lokmane Demagh and Aurelio Teliti and Niccolò Antonello and Giacomo Gervasoni and Hazem Hesham Yousef Shalby and Marco Brando Paracchini and Simone Mentasti and Hao Quan and Riccardo Santambrogio and Cédric Gilbert and Manuel Roveri and Matteo Matteucci and Marco Marcon and Diana Trojaniello}, doi = {10.1109/ACCESS.2025.3555426}, journal = {IEEE Access}, pages = {57062-57100}, shortjournal = {IEEE Access}, title = {Advancements in context recognition for edge devices and smart eyewear: Sensors and applications}, volume = {13}, year = {2025}, }
• (2025). A novel approach for tweet similarity in a context-aware fake news detection model. ACCESS, 13, 57043-57061. (www)

  @article{ , abstract = {In today’s information-driven world, identifying fake news is more critical than ever. The spread of false information threatens societal stability, eroding trust in institutions and fostering community polarization. While numerous algorithms and methods have been developed to detect fake news, their effectiveness varies depending on the context and data used. Recent advancements, particularly those leveraging transformer-based models like BERT, have significantly improved detection performance, especially in multimodal environments. Social media platforms are the primary focus of fake news research, as they serve as fertile grounds for the unchecked spread of misinformation. Their rapid dissemination capabilities and global reach make them ideal channels for propagating disinformation. Addressing these challenges requires innovative approaches and robust solutions. In response to these essential issues, this study presents a formal framework for detecting fake news using a multilayered approach. The proposed three-tiered model includes the topic, social, and context layers, providing a comprehensive approach to identifying fake news. One of the critical tasks within the topic layer involves assessing the similarity between two messages. To achieve this, we developed a novel method for evaluating the similarity of two tweets by extending a state-of-the-art model. Our approach leverages FastText vectors, cosine similarity, and word positions within sentences to improve accuracy. For verification, we utilized the STSBenchmark dataset and employed correlation values to measure our model’s quality. We conducted a rigorous 50-fold evaluation of the validation data. Ultimately, our model achieved a superior correlation value (median 0.673528) compared to another leading model, which had previously delivered the best results in the field. This paper presents one of the important elements of a comprehensive tool for detecting fake news, with some components already published in other works by the authors or planned for future development (e.g. multimodal content processing or multi-lingual similarity).}, archive = {J_ACCESS}, author = {Jose Fabio Ribeiro Bezerra and Adrianna Kozierkiewicz and Marcin Pietranik}, doi = {10.1109/ACCESS.2025.3554540}, journal = {IEEE Access}, pages = {57043-57061}, shortjournal = {IEEE Access}, title = {A novel approach for tweet similarity in a context-aware fake news detection model}, volume = {13}, year = {2025}, }
• (2025). Self-interference cancellation for MIMO full-duplex downlink systems: A constrained MMSE-based precoding approach. ACCESS, 13, 57030-57042. (www)

  @article{ , abstract = {This paper introduces a new spatial domain-based self-interference cancellation (SIC) precoding method named constrained minimum mean square error (C-MMSE) for a undersized massive multiple-input multiple-output (mMIMO) full-duplex (FD) system, where the FD receiver (RX) only receives a single signal of interest (SOI). To tackle this, the main idea is to translate the commonly used singular value decomposition (SVD)-based null-space projection approach, which is unfeasible in our considered system model, into an optimization problem under MMSE criterion, where additional constraints are introduced to perform SIC. Theoretical derivation of the C-MMSE precoder is presented, followed by performance comparison with conventional MMSE precoding, where no constraints are added for SIC. We theoretically show that the C-MMSE scheme outperforms the conventional one in terms of SIC, and allows the FD system to work under an almost interference-free environment. Additionally, we also assess the performance of the proposed method under imperfect channel state information (CSI), to further evaluate the robustness of our spatial precoder in more realistic conditions. We show that the C-MMSE precoder outperforms MMSE in terms of interference suppression ratio (ISR), even under CSI imperfection. Additionally, the C-MMSE achieves the same spectral efficiency (SE) as a hypothetical perfect SIC in a wide range of SNRs.}, archive = {J_ACCESS}, author = {Xuan Chen and Vincent Savaux and Matthieu Crussière and Patrick Savelli and Koffi-Clément Yao}, doi = {10.1109/ACCESS.2025.3555505}, journal = {IEEE Access}, pages = {57030-57042}, shortjournal = {IEEE Access}, title = {Self-interference cancellation for MIMO full-duplex downlink systems: A constrained MMSE-based precoding approach}, volume = {13}, year = {2025}, }
• (2025). Real-time assembly task validation using deep learning-based object detection and operator’s hand-joints trajectory classification. ACCESS, 13, 57009-57029. (www)

  @article{ , abstract = {Real-time monitoring for evaluating manual assembly work is essential for improving operator efficiency and product quality. Recent applications detect and prevent operator errors with instant feedback by visually recognizing actions in a monitored assembly scene. However, prior studies often disregarded hand-object interactions and lacked the modeling of fine-grained hand movements. Because industrial assembly tasks are primarily performed by hand, the focus should be on the hands and their interactions with manipulated tools and objects. This paper proposes a real-time fine-grained assembly task recognition system using 3-dimensional hand skeleton data extracted from streaming 2-dimensional video frames. The hybrid task recognition system consists of a You-Only-Look-Once (YOLO) deep object detection method and a Long Short-Term Memory (LSTM) based classifier working in an integrated approach. First, from the streaming data, the possible starting point for each sequential task is determined using the object detection method. Time series skeleton data were then captured using a pose estimation algorithm from the possible starting point until YOLOv8 detected a different start point. Subsequently, the proposed LSTM-based network classifies the time series of the hand joint coordinates to comply with the corresponding fine-grained assembly task. Sequential tasks at an industrial assembly station are used to create an operator-centric video dataset with annotations to evaluate the proposed system. The proposed hybrid system significantly improved the operator efficiency for sequential assembly tasks, achieving 85.23% accuracy in real-time task recognition. The real-world industrial assembly dataset used in our study was also shared as open access for the assembly task recognition community.}, archive = {J_ACCESS}, author = {Öznur Ay and Erdal Emel}, doi = {10.1109/ACCESS.2025.3554263}, journal = {IEEE Access}, pages = {57009-57029}, shortjournal = {IEEE Access}, title = {Real-time assembly task validation using deep learning-based object detection and operator’s hand-joints trajectory classification}, volume = {13}, year = {2025}, }
• (2025). Proposal of applying transaction server to each shard in ethereum. ACCESS, 13, 56994-57008. (www)

  @article{ , abstract = {A sharding framework has been proposed by Ethereum 2.0, and researchers have tried to enhance its applicability and scalability to real blockchain networks. Each shard can execute transactions requested by users, so the number of transactions dealt with by the shard-based blockchain grows as the number of shards increases. Dealing with cross-shard transactions, however, is a major hindrance to blockchain performance, because each such transaction requires cooperation among different shard validators in the network. Given this background, this paper proposes a novel cross-shard architecture in which each shard has its own transaction server (TS) and each TS has the role of dealing with transaction requests/results coming from different users or different shards. In this architecture, it is possible for each validator to concentrate on dealing with the transactions in its own shard, so there is no need to multicast transaction information to the other shard validators. We describe the proposed cross-shard framework in detail and demonstrate how a cross-shard transaction can be executed using the hierarchically connected smart contract functions. In the proposed architecture, the interactions among different shard validators can be reduced by having each validator select its neighbors from among members of its own shard after each epoch. This frequent change of neighbors for each validator makes it robust against eclipse attacks. In addition, this architecture speeds up the validation process of transactions/blocks in each shard; we show the effect by measuring the block transmission delays in the proposed architecture in a simulation environment.}, archive = {J_ACCESS}, author = {Hiroshi Matsuura and Takayuki Fujii}, doi = {10.1109/ACCESS.2025.3556616}, journal = {IEEE Access}, pages = {56994-57008}, shortjournal = {IEEE Access}, title = {Proposal of applying transaction server to each shard in ethereum}, volume = {13}, year = {2025}, }
• (2025). Joint class and domain continual learning for decentralized federated processes. ACCESS, 13, 56982-56993. (www)

  @article{ , abstract = {Continual learning (CL) is attracting attention to learn in dynamic and non-stationary environments, typical of many real applications, such as those related to Internet of things (IoT). So far, learning in IoT scenarios has almost exclusively been studied in a centralized fashion, but CL can be used in decentralized settings involving a multitude of nodes to learn, adapt, and generalize from their streams of data, even with limited communication and computation capabilities. In this work, we formulate a joint Class and Domain Continual Learning problem, where local data at the nodes might include multiple classes belonging to different domains. In our solution, we adopt knowledge distillation (KD) to share knowledge extracted from local models trained with the data at each node and to incrementally generate a global model, while preserving privacy and avoiding centralization bottlenecks. Thanks to KD, the learning process can be performed without any prior information about the model architecture, i.e., every local model can be based on different architectures, which can also differ from the global. The learning process is also agnostic to domain and classes’ distribution across the nodes. With these features, our distributed learning solution may assist in privacy preservation and save communication resources, which are fundamental challenges in IoT scenarios. Compared against two baseline losses based on state-of-the-art CL solutions and using different datasets (MNIST, USPS, DomainNet), our proposal outperforms different baselines in terms of accuracy, spanning from a gain of 15.9%, up to 95.4%, by limiting catastrophic forgetting.}, archive = {J_ACCESS}, author = {Chiara Lanza and Tinne Tuytelaars and Marco Miozzo and Eduard Angelats and Paolo Dini}, doi = {10.1109/ACCESS.2025.3555750}, journal = {IEEE Access}, pages = {56982-56993}, shortjournal = {IEEE Access}, title = {Joint class and domain continual learning for decentralized federated processes}, volume = {13}, year = {2025}, }
• (2025). Hybrid beamforming design on riemannian manifolds for wideband MIMO systems. ACCESS, 13, 56958-56981. (www)

  @article{ , abstract = {This paper proposes a novel, unified approach to design both radio frequency (RF) and baseband beamforming (BF) solutions for wideband hybrid multiple-input multiple-output (MIMO) systems using the Riemannian manifold optimization. The proposed two-stage optimization framework effectively addresses various power constraints of the wideband hybrid MIMO systems, including total sum power, per-RF-chain, and per-antenna power constraints. By transforming constrained optimization problems into unconstrained ones on Riemannian submanifolds, we achieve notable improvements in spectral efficiency (SE) and power consumption. In addition, we account for the log-determinant structure of the objective function, which forms an unconstrained solution subspace. This allows for a more efficient retraction process towards the manifold, representing the set of solutions that satisfy the constraints. This unified design approach provides a more accurate and efficient solution to non-convex optimization problems with complex constraints. Simulation results demonstrate the superior performance of the proposed approach over conventional approaches.}, archive = {J_ACCESS}, author = {Hyunwoo Nam and Girim Kwon and Hyuncheol Park}, doi = {10.1109/ACCESS.2025.3555296}, journal = {IEEE Access}, pages = {56958-56981}, shortjournal = {IEEE Access}, title = {Hybrid beamforming design on riemannian manifolds for wideband MIMO systems}, volume = {13}, year = {2025}, }
• (2025). Proximal LSSVR of gauss-laplacian with mixed-noise-characteristics and its applications for short-term wind-speed forecasting. ACCESS, 13, 56946-56957. (www)

  @article{ , abstract = {Proximal least squares support vector regression (PLSSVR) is a novel regression machine that combines the advantages of proximal support vector regression (PSVR) and least squares support vector regression (LSSVR). It possesses the traits of high efficiency, simplicity, and good generalization ability. This article establishes a new regression model by utilizing the above model frameworks, called PLSSVR model of heteroscedastic Gauss-Laplacian with mixed-noise-characteristics (PLSSVR-GLMH). The least square method is introduced and the regularization terms ${(1/2)}\cdot b_{t} ^{2}$ are added in model PLSSVR-GLMH respectively. It converts inequality constraint problems into simpler equality constraint problems and employs the Augmented Lagrange multiplier method to solve the proposed model. This not only improves the training speed and generalization ability, but also effectively enhances the prediction accuracy. Because the wind-speed forecasting error approximately conforms to the Gauss-Laplacian mixture noise distribution, in the real-world wind-speed dataset, the prediction accuracy of the PLSSVR-GLM model was significantly improved compared to the PLSSVR and LSSVR-GLM models. The experimental results validate the efficacy and practicality of the proposed method.}, archive = {J_ACCESS}, author = {Ting Zhou and Shiguang Zhang}, doi = {10.1109/ACCESS.2025.3555580}, journal = {IEEE Access}, pages = {56946-56957}, shortjournal = {IEEE Access}, title = {Proximal LSSVR of gauss-laplacian with mixed-noise-characteristics and its applications for short-term wind-speed forecasting}, volume = {13}, year = {2025}, }
• (2025). Enhancing the educational model using mixed reality technologies with meta quest 3: A usability analysis using IBM-CSUQ. ACCESS, 13, 56930-56945. (www)

  @article{ , abstract = {In today’s education landscape, there is a lack of effective digital tools for hands-on teaching of PC and smartphone hardware assembly, especially in environments where physical resources are limited. This challenge is critical because practical learning in hardware is essential for developing technical skills in engineering students and related fields. Traditional alternatives, such as simulators or online tutorials, have not managed to provide an immersive and efficient learning experience. To address this need, Build_3D was developed—a mixed reality (MR) application designed for learning PC and smartphone hardware assembly, experienced through the Meta Quest 3 headset. The use of MR in an educational context is a topic of considerable debate, given the necessity for a comprehensive analysis of its potential advantages and disadvantages. This research proposes an evaluation of Build_3D’s usability using the Computer System Usability Questionnaire (IBM-CSUQ), focusing on three key dimensions: interface quality (INTERQUIAL), information quality (INFOQUIAL), and system usability (SYSUSE). The results indicate high user satisfaction, with interface quality rated the highest, followed by information quality and system usability. These findings suggest that this application provides an immersive and effective educational experience, minimizing cognitive load without compromising the quality of practical learning. The implications of this solution point to an improvement in hands-on hardware teaching through MR technologies, offering a viable and scalable alternative in educational environments with limited access to physical labs. Finally, this facilitates autonomous and motivating acquisition of technical skills for students.}, archive = {J_ACCESS}, author = {Santiago Criollo-C and Andrea Guerrero-Arias and Agariadne Dwinggo Samala and Yunifa Miftachul Arif and Sergio Luján-Mora}, doi = {10.1109/ACCESS.2025.3555930}, journal = {IEEE Access}, pages = {56930-56945}, shortjournal = {IEEE Access}, title = {Enhancing the educational model using mixed reality technologies with meta quest 3: A usability analysis using IBM-CSUQ}, volume = {13}, year = {2025}, }
• (2025). White blood cell detection based on FBDM-YOLOv8s. ACCESS, 13, 56918-56929. (www)

  @article{ , abstract = {White blood cells, also known as immune cells, help the body resist infectious diseases and foreign pathogens as part of the immune system. This article proposes a white blood cell detection method based on an improved YOLOv8s to enhance the accurate detection of different categories of normal white blood cells. In the YOLOv8s network, this study replaces the main framework of YOLOv8 with the Fasternet module to reduce computational redundancy and memory access. Subsequently, a BiLevel Routing Attention mechanism is proposed to achieve precise localization of white blood cells. Additionally, to attain diversified feature extraction, this article introduces the DBBNCSPELAN4 module, which can perform diversified feature extraction in a multi-branch manner while lightweighting the model, reducing the number of parameters, and speeding up computation. Finally, the MultiSEAM module is integrated with Detect to address detection errors caused by occlusion and connected white blood cells. Experiments were conducted on a dataset of five types of normal cells provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). The FBDM-YOLOv8s significantly outperforms other advanced object detection algorithms in terms of performance, with a 2.1% increase in mAP compared to the baseline YOLOv8s.We will release the source code on https://github.com/SSRR-LLL/FBDM-YOLOv8.git.}, archive = {J_ACCESS}, author = {Borui Sun and Xiangsuo Fan and Jie Meng and Jinfeng Wang and Huajin Chen and Lei Liu}, doi = {10.1109/ACCESS.2025.3553139}, journal = {IEEE Access}, pages = {56918-56929}, shortjournal = {IEEE Access}, title = {White blood cell detection based on FBDM-YOLOv8s}, volume = {13}, year = {2025}, }
• (2025). Open-vocabulary action localization with iterative visual prompting. ACCESS, 13, 56908-56917. (www)

  @article{ , abstract = {Video action localization aims to find the timings of specific actions from a long video. Although existing learning-based approaches have been successful, they require annotating videos, which comes with a considerable labor cost. This paper proposes a training-free, open-vocabulary approach based on emerging off-the-shelf vision-language models (VLMs). The challenge stems from the fact that VLMs are neither designed to process long videos nor tailored for finding actions. We overcome these problems by extending an iterative visual prompting technique. Specifically, we sample video frames and create a concatenated image with frame index labels, allowing a VLM to identify the frames that most likely correspond to the start and end of the action. By iteratively narrowing the sampling window around the selected frames, the estimation gradually converges to more precise temporal boundaries. We demonstrate that this technique yields reasonable performance, achieving results comparable to state-of-the-art zero-shot action localization. These results support the use of VLMs as a practical tool for understanding videos. Sample code is available at (https://microsoft.github.io/VLM-Video-Action-Localization/).}, archive = {J_ACCESS}, author = {Naoki Wake and Atsushi Kanehira and Kazuhiro Sasabuchi and Jun Takamatsu and Katsushi Ikeuchi}, doi = {10.1109/ACCESS.2025.3555167}, journal = {IEEE Access}, pages = {56908-56917}, shortjournal = {IEEE Access}, title = {Open-vocabulary action localization with iterative visual prompting}, volume = {13}, year = {2025}, }
• (2025). Grid impact of unbalanced phase integration of PV generation, electrified mobility, and heating in LV distribution grids. ACCESS, 13, 56887-56907. (www)

  @article{ , abstract = {Low-carbon technologies (LCTs) such as Electric vehicles (EVs), heat pumps (HPs), and PV systems increase phase unbalance due to uneven phase distribution. Phase unbalance can lead to overheating, suboptimal capacity utilization, and power losses. This work analyzes the unbalance impact inflicted by the grid integration of PVs, HPs, and EVs under different combinations and penetration levels. The main novelty of this study is the use of different types of real-world distribution grids (rural, suburban, and urban) for the LCT unbalance impact comparison, while simultaneously considering the influence of several unbalance factors; LCT phase connections and grid distributions, the seasonal effect, and the power and consumption levels, the latter of which have been evaluated as mitigation strategies. The results showed that the combined integration of PVs, EVs, and HPs can cause high voltage unbalance, especially in grids with high existing loading. The seasonal effect was the most impactful unbalance factor, intensifying unbalance by the integration of PVs-HPs and PVs-EVs combinations during Winter and Summer, respectively. Furthermore, reductions in the power and consumption levels of the LCTs decreased the unbalance total violation duration in a range between 11% and 25% for all distribution grids. Reducing the LCT consumption levels also decreased the unbalance magnitude, which reached up to 15% for the urban grid under 100% HP and PV penetration. Finally, it was found that consumption duration enhances unbalance, such as the peak power levels, because it increases the simultaneity of technologies operating in different phases.}, archive = {J_ACCESS}, author = {Yme Wesseling and Nikolaos Damianakis and Gautham Ram Chandra Mouli and Pavol Bauer}, doi = {10.1109/ACCESS.2025.3555169}, journal = {IEEE Access}, pages = {56887-56907}, shortjournal = {IEEE Access}, title = {Grid impact of unbalanced phase integration of PV generation, electrified mobility, and heating in LV distribution grids}, volume = {13}, year = {2025}, }
• (2025). LLM-driven, self-improving framework for security test automation: Leveraging karate DSL for augmented API resilience. ACCESS, 13, 56861-56886. (www)

  @article{ , abstract = {Modern software architectures heavily rely on APIs, yet face significant security challenges, particularly with Broken Object Level Authorization (BOLA) vulnerabilities, which remain the most critical API security risk according to OWASP. This paper introduces Karate-BOLA-Guard, an innovative framework leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to automate security-focused test case generation for APIs. Our approach integrates vector databases for context retrieval, multiple LLM models for test generation, and observability tools for process monitoring. Initial experiments were carried out on three deliberately vulnerable APIs (VAmPI, Crapi, and OWASP Juice Shop), with subsequent validation on fifteen additional production APIs spanning diverse domains including social media, version control systems, financial services, and transportation services. Our evaluation metrics show Llama 3 8B achieving consistent performance (Accuracy: 3.1-3.4, Interoperability: 3.7-4.3) with an average processing time of 143.76 seconds on GPU. Performance analysis revealed significant GPU acceleration benefits, with 20-25x improvement over CPU processing times. Smaller models demonstrated efficient processing, with Phi-3 Mini averaging 69.58 seconds and Mistral 72.14 seconds, while maintaining acceptable accuracy scores. Token utilization patterns showed Llama 3 8B using an average of 36,591 tokens per session, compared to Mistral’s 25,225 and Phi-3 Mini’s 31,007. Our framework’s effectiveness varied across APIs, with notably strong performance in complex platforms (Instagram: A = 4.3, I = 4.4) while maintaining consistent functionality in simpler implementations (VAmPI: A = 3.6, I = 4.3). The iterative refinement process, evaluated through comprehensive metrics including Accuracy (A), Complexity (C), and Interoperability (I), represents a significant advancement in automated API security testing, offering an efficient, accurate, and adaptable approach to detecting BOLA vulnerabilities across diverse API architectures.}, archive = {J_ACCESS}, author = {Emil Marian Pasca and Daniela Delinschi and Rudolf Erdei and Oliviu Matei}, doi = {10.1109/ACCESS.2025.3554960}, journal = {IEEE Access}, pages = {56861-56886}, shortjournal = {IEEE Access}, title = {LLM-driven, self-improving framework for security test automation: Leveraging karate DSL for augmented API resilience}, volume = {13}, year = {2025}, }
• (2025). A novel framework for saraiki script recognition using advanced machine learning models (YOLOv8 and CNN). ACCESS, 13, 56843-56860. (www)

  @article{ , abstract = {In recent years, a lot of local languages require careful consideration in terms of knowledge exchange. Each language creates a sophisticated understanding system that allows users to interact, communicate, express ideas, and define demands. With an emphasis on language’s structure, usage, and social dimensions, linguistics examines these systems holistically. Linguistics’ fundamental goal is to comprehend how language functions in composition and context. On the other hand, machine learning is the study of how to teach machines to learn and predict using data instead of explicit programming. By combining these two domains, machine learning has emerged as a potent instrument in linguistics, improving our capacity to comprehend semantics, analyze verbal patterns, and even simulate human-like replies. This work represents a major breakthrough in the use of machine learning models for the detection and replication of Saraiki handwritten text as Saraiki is one of the important language. Accurate and reliable OCR systems tailored to the Saraiki script were the aim. The study used Convolutional Neural Networks (CNNs) in conjunction with YOLOv8 models to address the problems of recognizing Saraiki alphabets’ primary and secondary components. Several data augmentation approaches were used to improve the performance of these models, which had been painstakingly trained. After four distinct models A, B, C, and D were trained, Model D continuously performed better than the others. Additionally, the study introduced the “Saraiki Handwritten Characters” Dataset (SHC), which features well-labeled segmented characters evaluated by annotators. This research provides insight into Saraiki script machine learning models and opens the door to Saraiki language text processing, transcription, and document digitization, contributing to OCR and showing how machine learning can preserve linguistic diversity.}, archive = {J_ACCESS}, author = {Hafiz Muhammad Raza Ur Rehman and Syed Arfan Haider and Hiba Faisal and Kook-Yeol Yoo and M. Z. Jhandir and Gyu Sang Choi}, doi = {10.1109/ACCESS.2025.3551747}, journal = {IEEE Access}, pages = {56843-56860}, shortjournal = {IEEE Access}, title = {A novel framework for saraiki script recognition using advanced machine learning models (YOLOv8 and CNN)}, volume = {13}, year = {2025}, }
• (2025). Blockchain and RL-based secured task offloading framework for software-defined 5G edge networks. ACCESS, 13, 56820-56842. (www)

  @article{ , abstract = {5G (Fifth Generation) technology represents a significant advancement in telecommunications, facilitating industry innovation and advancement for applications across various sectors. It enhances high-speed, low-latency communication, making it ideal for task offloading in resource-constrained mobile devices. By leveraging task offloading, 5G networks maximize the efficiency of both computational and network resources, ensuring faster, more reliable data delivery and enabling high-performance requirements of modern applications. However, dynamic and secure task offloading remains a challenge due to fluctuating network conditions and trust concerns. This paper proposes SAGE (Secured Adaptive Generalized Edge), a reinforcement learning-based task offloading framework that leverages contextual bandits for adaptive decision-making in Multi-Access Edge Computing (MEC) environments. By integrating blockchain smart contracts for security and SDN-based orchestration for dynamic resource management, SAGE ensures robust, low-latency offloading. Experimental evaluations demonstrate that SAGE reduces task offloading delays by 36% and task durations by 30% compared to baseline methods under varying load and energy constraints.}, archive = {J_ACCESS}, author = {N. Sethu Subramanian and Prabhakar Krishnan and Kurunandan Jain and K. B. Aneesh Kumar and Tulika Pandey and Rajkumar Buyya}, doi = {10.1109/ACCESS.2025.3554638}, journal = {IEEE Access}, pages = {56820-56842}, shortjournal = {IEEE Access}, title = {Blockchain and RL-based secured task offloading framework for software-defined 5G edge networks}, volume = {13}, year = {2025}, }
• (2025). Multi-purpose graphene-based terahertz metamaterial and its equivalent circuit model. ACCESS, 13, 56808-56819. (www)

  @article{ , abstract = {A tunable 2-bit encoder, switch, and absorber using a graphene-based terahertz (THz) metamaterial were developed to operate within 1-4 THz range. The metamaterial comprises three resonators in each unit cell: one split ring resonator containing four gaps and two finite parallel graphene ribbon resonators. Simulations are done by the finite element method (FEM) in the frequency-domain solver of CST Software. In addition, computationally efficient equivalent circuit models of the metamaterial were implemented by MATLAB code. The equivalent circuit model (ECM) was used to confirm the CST simulation results. By tuning the Fermi levels of the central split ring resonator and the side ribbon resonators, the 2-bit encoder and switch were obtained. The maximum modulation depth (MD), extinction ratio (ER), and insertion loss (IL) reach 99.56%, 54.25 dB, and 0.41 dB, respectively. The metamaterial can also work as a dual-band absorber with maximum absorption of 99.58 and 99.44 in the first and second resonances. Therefore, these results reveal that our work is significant in terahertz encoders, optical switches, and absorbers.}, archive = {J_ACCESS}, author = {Somayyeh Asgari and Tapio Fabritius}, doi = {10.1109/ACCESS.2025.3554530}, journal = {IEEE Access}, pages = {56808-56819}, shortjournal = {IEEE Access}, title = {Multi-purpose graphene-based terahertz metamaterial and its equivalent circuit model}, volume = {13}, year = {2025}, }
• (2025). Instantaneous metabolic energetics: Data-driven modeling using function-based surrogates and gradient boosting. ACCESS, 13, 56793-56807. (www)

  @article{ , abstract = {Objective: Current methods for measuring metabolic energy expenditure (MEE) constrain experiment design and only provide time-averaged values. We propose a novel two-stage predictive model using surrogate learners in the first stage for physiological dynamics and gradient-boosted regression trees in the second stage to learn a generalized representation of instantaneous, whole-body MEE. Methods: Kinematic, kinetic, metabolic, and surface electromyograph data were recorded for nine human subjects in level, over-ground walking at 100%, 70%, 85%, 115%, and 130% subject-preferred speeds. We use surrogate learners to encode fundamental information about the time-varying properties of MEE. A gradient-boosted machine-learning model was then trained on the surrogate functions’ outputs. For robustness, an information-theoretic data selection step was added during model training. The trained model uses joint torques and angular velocities to predict instantaneous, whole-body MEE during walking. Results: The model accurately predicts instantaneous MEE without subject-specific input parameters. Shapley Additive Explanations were used to investigate energetic features of the learned MEE function and demonstrate alignment with literature. We find similarities between the model’s MEE predictions, muscle mechanical work rate, and normal ground reaction forces, suggesting a link between MEE and the work required to raise the center of mass. Conclusion: The proposed approach provides an alternative to experimental MEE measurement while balancing the generalizability and complexity trade-off typically imposed on existing computational, predictive models. Significance: Evaluating MEE of human motion can provide insight into underlying biomechanics and inform clinical and engineering practices.}, archive = {J_ACCESS}, author = {Christopher Buglino and William Z. Peng and Stacy Ashlyn and Hyunjong Song and Howard J. Hillstrom and Joo H. Kim}, doi = {10.1109/ACCESS.2025.3555182}, journal = {IEEE Access}, pages = {56793-56807}, shortjournal = {IEEE Access}, title = {Instantaneous metabolic energetics: Data-driven modeling using function-based surrogates and gradient boosting}, volume = {13}, year = {2025}, }
• (2025). Multi-user sparse vector coding for eXtreme ultra-reliable low-latency communication in beyond 5G. ACCESS, 13, 56780-56792. (www)

  @article{ , abstract = {Short A short packet transmission scheme, such as Sparse Vector Coding (SVC), is a primary candidate for achieving ultra-low latency and high-reliability communication (URLLC). This paper proposes a spectral-efficient multi-user SVC (MU-SVC) scheme for achieving next-generation URLLC or eXtreme URLLC (xURLLC) in beyond 5G (B5G) communications. The key idea is to transmit multiple user information within a single sparse vector where the users are segregated into far users (FU) and near users (NU) depending on the distance from the base station. The classification into FU and NU paves way to optimize resource allocation, user fairness, manage interference, ensure reliable communication and quality of service requirements. Firstly, the FU binary data is converted into a sparse vector and secondly, the NU data is modulated and embedded into the non-zero positions of the sparse vector to form an MU-SVC. On transmission, the FU data is obtained through sparse demapping, while the NU adopts symbol detection techniques like the maximum likelihood detector. A new performance metric, called position error rate (PoER), is introduced to study the performance of the FU since it is based on the correct identification of the non-zero positions. Theoretical analyses of PoER and symbol error rate (SER) were carried out for FU and NU, respectively and the results are also validated through Monte-Carlo simulations. Further, the bit error rate, complexity, spectral and latency analyses are performed for MU-SVC and compared with the SVC and enhanced SVC schemes. The simulation results demonstrate an improved spectral efficiency and low latency with high reliability for the proposed MU-SVC scheme, thus, achieving xURLLC with reduced complexity in the multi-user scenario for B5G.}, archive = {J_ACCESS}, author = {Sundaresan Sabapathy and Surendar Maruthu and Dushantha Nalin K. Jayakody}, doi = {10.1109/ACCESS.2025.3551398}, journal = {IEEE Access}, pages = {56780-56792}, shortjournal = {IEEE Access}, title = {Multi-user sparse vector coding for eXtreme ultra-reliable low-latency communication in beyond 5G}, volume = {13}, year = {2025}, }
• (2025). Toward greener matrix operations by lossless compressed formats. ACCESS, 13, 56756-56779. (www)

  @article{ , abstract = {Sparse matrix-vector multiplication (SpMV) is a fundamental operation in machine learning, scientific computing, and graph algorithms. In this paper, we investigate the space, time, and energy efficiency of SpMV using various compressed formats for large sparse matrices, focusing specifically on Boolean matrices and real-valued vectors. Through extensive analysis and experimentation on server and edge devices, we observed that different matrix compression formats entail distinct trade-offs among space efficiency, execution time, and energy consumption. Notably, selecting the appropriate compressed format can reduce energy consumption by an order of magnitude on both servers and single-board computers. Moreover, our experiments reveal that while data parallelism can improve execution speed and energy efficiency, optimizing both simultaneously poses interesting challenges. Specifically, we demonstrate that for certain compression schemes, the optimal degree of parallelism for minimizing execution time does not coincide with that for energy efficiency. This finding aligns with recent results in the literature, thus challenging the prevailing assumption of a straightforward linear correlation between execution time and energy usage. Overall, our findings advocate for software engineers to adopt compressed data structures as important instruments in the development of more energy-efficient software components, extending beyond SpMV.}, archive = {J_ACCESS}, author = {Francesco Tosoni and Philip Bille and Valerio Brunacci and Alessio de Angelis and Paolo Ferragina and Giovanni Manzini}, doi = {10.1109/ACCESS.2025.3555119}, journal = {IEEE Access}, pages = {56756-56779}, shortjournal = {IEEE Access}, title = {Toward greener matrix operations by lossless compressed formats}, volume = {13}, year = {2025}, }
• (2025). Optimization of a novel FOPIDN-(1+PIDN) controller for renewable integrated multi-area load frequency control system with non-linearities. ACCESS, 13, 56736-56755. (www)

  @article{ , abstract = {The evolution of multi-regional interconnected power systems has been driven by the swift technological advancements of power-producing technologies and the increasing demand from power consumers. To meet the growing power demand, the power system relies on renewable energy sources, but their unpredictable nature influences the dynamic performance of the power system, especially frequency regulation. In response to this challenge, a robust cascade controller called FOPIDN (1+PIDN) has been recommended for the Load Frequency Control (LFC) system. This system encompasses two distinct areas that integrate a non-reheat thermal system with renewable energy sources such as solar photovoltaic and wind energy systems. Additionally, the study has introduced nonlinear constraints like Governor Dead Band (GDB) and Generation Rate Constraints (GRC) to enhance the realism of the analysis. The optimal parameters for the controller have been fine-tuned using the Grey Wolf optimization (GWO) technique by minimizing the ITAE performance index. The findings unequivocally show better performance in terms of performance indices, settling time, overshoots, and undershoots of the proposed controller over existing controllers such as PID, PIDN, FOPID, and FOPIDN. At last, a robustness study has been conducted to validate the efficiency of the proposed controller, assessing its performance across different load scenarios and under extreme variations in system parameters.}, archive = {J_ACCESS}, author = {Shreekanta Kumar Ojha and Maddela Chinna Obaiah}, doi = {10.1109/ACCESS.2025.3555846}, journal = {IEEE Access}, pages = {56736-56755}, shortjournal = {IEEE Access}, title = {Optimization of a novel FOPIDN-(1+PIDN) controller for renewable integrated multi-area load frequency control system with non-linearities}, volume = {13}, year = {2025}, }
• (2025). Predicting and understanding disorders of consciousness of sleep patients through multimodal data fusion and temporal attention mechanisms. ACCESS, 13, 56717-56735. (www)

  @article{ , abstract = {This study introduces MTAMA-DoC (Multimodal Temporal Attention Multitask Analyzer for Disorders of Consciousness), a novel deep learning approach that leverages sleep-related physiological data to predict consciousness states in patients with Disorders of Consciousness (DoC). Employing a transformer-based architecture with task-aware guided masking and joint-optimization training, MTAMA-DoC integrates time series data (heart rate and breath rate) with structured clinical information, addressing the limitations of traditional assessment methods. In a comparative analysis involving 170 patients, our model significantly outperformed existing methods in consciousness state classification (binary classification accuracy: 0.87, F1-score: 0.82; ternary classification accuracy: 0.72, F1-score: 0.64) and demonstrated competitive performance in Glasgow Coma Scale score prediction. The model’s interpretability, achieved through attention mechanisms and feature importance analysis, provides valuable insights into the temporal dynamics of physiological signals and the relative importance of clinical factors in consciousness assessment, highlighting the critical role of cardiovascular and respiratory metrics, as well as sleep architecture features. By demonstrating the efficacy of sleep-related data in consciousness evaluation, MTAMA-DoC not only enhances diagnostic capabilities but also contributes to the theoretical understanding of the sleep-consciousness relationship, offering a non-invasive, continuous monitoring approach that complements traditional clinical methods. This advancement opens new avenues for AI-driven approaches in neurology and sleep medicine, potentially improving diagnosis and care for patients with disorders of consciousness, while paving the way for future investigations into the complex interplay between sleep, consciousness, and brain function.}, archive = {J_ACCESS}, author = {Haixia Pan and Haotian Geng and Pingshu Zhang and Xiaodong Yuan}, doi = {10.1109/ACCESS.2025.3555521}, journal = {IEEE Access}, pages = {56717-56735}, shortjournal = {IEEE Access}, title = {Predicting and understanding disorders of consciousness of sleep patients through multimodal data fusion and temporal attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Understanding how AI chatbots influence EFL learners’ oral english learning motivation and outcomes: Evidence from chinese learners. ACCESS, 13, 56699-56716. (www)

  @article{ , abstract = {Artificial intelligence is undergoing rapid transformations, and its application in education has gradually expanded. In particular, the educational effect of artificial intelligence chatbots in English learning has steadily become prominent. This study explores the application status of using artificial intelligence chatbots for spoken language learning in China. It also discusses the impact of human likeness and self-efficacy, social presence concerning EFL learners’ motivation, and the distribution of learning effects. Moreover, whether artificial intelligence technical proficiency impacts learning motivation. 351 EFL students who had experience acquiring spoken English using an AI chatbot participated in the questionnaire survey of this study. After collecting the survey data, the data analysis was conducted using a structural equation model (SEM). The study found that human likeness, self-efficacy, and social presence strongly predict learning motivation. The human likeness of AI chatbots and their social presence can significantly affect learners’ self-efficacy. In AI technology education, learning motivation can still significantly predict learning outcomes. Through the analysis of the moderating effect of PROCESS, this study concluded that when learners have high technical proficiency, the moderating effect of technical proficiency on learning motivation is more significant. Through the actual survey data output and theory construction, this study deeply discusses the factors that influence the interaction process of AI chatbots for language learning and provides both data and theoretical support. Designers can use them to optimize language learning programs for AI chatbots.}, archive = {J_ACCESS}, author = {Wenbo Wei and An Zhao and Hongning Ma}, doi = {10.1109/ACCESS.2025.3554545}, journal = {IEEE Access}, pages = {56699-56716}, shortjournal = {IEEE Access}, title = {Understanding how AI chatbots influence EFL learners’ oral english learning motivation and outcomes: Evidence from chinese learners}, volume = {13}, year = {2025}, }
• (2025). Vehicle painting robot path planning using hierarchical optimization. ACCESS, 13, 56682-56698. (www)

  @article{ , abstract = {In vehicle production factories, the vehicle painting process employs multiple robotic arms to simultaneously apply paint to car bodies advancing along a conveyor line. Designing paint paths for these robotic arms, which involves assigning car body areas to arms and determining paint sequences for each arm, remains a time-consuming manual task for engineers, indicating the demand for automation and design time reduction. The unique constraints of the painting process hinder the direct application of conventional robotic path planning techniques, such as those used in welding. Therefore, this paper formulates the design of paint paths as a hierarchical optimization problem, where the upper-layer subproblem resembles a vehicle routing problem (VRP), and the lower-layer subproblem involves detailed path planning. This approach allows the use of different optimization algorithms at each layer, and permits flexible handling of constraints specific to the vehicle painting process through the design of variable representation, constraints, repair operators, and an initialization process at the upper and lower layers. Experiments with three commercially available vehicle models demonstrated that the proposed method can automatically design paths that satisfy all constraints for vehicle painting with quality comparable to those created manually by engineers.}, archive = {J_ACCESS}, author = {Yuya Nagai and Hiromitsu Nakamura and Narito Shinmachi and Yuta Higashizono and Satoshi Ono}, doi = {10.1109/ACCESS.2025.3556230}, journal = {IEEE Access}, pages = {56682-56698}, shortjournal = {IEEE Access}, title = {Vehicle painting robot path planning using hierarchical optimization}, volume = {13}, year = {2025}, }
• (2025). Error rate analysis of quantum-annealing-aided multi-user detection in power-domain nonorthogonal multiple access. ACCESS, 13, 56671-56681. (www)

  @article{ , abstract = {We present the error rate performance of quantum-annealing-aided multiuser detection (QA-aided MUD), a signal detection approach that combines quantum annealing (QA) with conventional iterative multiuser detection (MUD). This method utilizes QA to compute log-likelihood ratios (LLR), improving signal detection and enhancing connectivity in power-domain nonorthogonal multiple access (PD-NOMA) systems. However, owing to the limited computational resources of the QA hardware, a detailed error-rate analysis was not performed. In this study, we extend QA-aided MUD to digital annealing techniques and evaluate the error rate performance of uplink PD-NOMA systems. Block error rate (BLER) analysis demonstrates that QA-aided MUD outperforms the conventional successive interference cancellation (SIC) method when difference of received signal strength corresponding to devices at base station is small. Moreover, we revealed that tuning the annealing parameters is crucial to obtain such better BLER performance. Additionally, we conducted evaluation on a bit error rate (BER) with a few instances to compare the performance of the QA hardware and digital annealing techniques. The results indicate that signal detection can be achieved using all the annealing techniques. Finally, we present the latest challenges, including an over-the-air (OTA) experiment using QA-aided MUD.}, archive = {J_ACCESS}, author = {Kouki Yonaga and Kenichi Takizawa and Masaru Inaba}, doi = {10.1109/ACCESS.2025.3555531}, journal = {IEEE Access}, pages = {56671-56681}, shortjournal = {IEEE Access}, title = {Error rate analysis of quantum-annealing-aided multi-user detection in power-domain nonorthogonal multiple access}, volume = {13}, year = {2025}, }
• (2025). Auto-induced downlink radiofrequency electromagnetic field exposure at 3.5 GHz with focusing near the head. ACCESS, 13, 56659-56670. (www)

  @article{ , abstract = {The auto-induced downlink exposure at 3.5 GHz is evaluated in networks where the power density is focused at the user equipment (UE), leading to a highly localized exposure. To achieve this focusing, various precoding techniques can be applied. Two of these are compared using Finite-Difference Time-Domain simulations. For each technique, three different exposure scenarios are considered: UE located next to the ear (phone call), UE located in front of the eyes, and the nose (video call). The exposure is evaluated in terms of the localized specific absorption rate using a female and a male phantom. We find that the exposure highly depends on the location of the UE and precoding technique. According to the International Commission on Non-Ionizing Radiation protection (ICNIRP)’s guidelines, this exposure should be normalized to the maximum incident power density. In this work, we suggest normalization strategies of this highly focused exposure and compare these. We show that the choice of normalization can create a situation where the ICNIRP basic restrictions can be exceeded while complying with the reference levels. This is important because compliance with the reference levels should ideally ensure compliance with the basic restrictions.}, archive = {J_ACCESS}, author = {Hanne Herssens and Arno Thielens}, doi = {10.1109/ACCESS.2025.3555388}, journal = {IEEE Access}, pages = {56659-56670}, shortjournal = {IEEE Access}, title = {Auto-induced downlink radiofrequency electromagnetic field exposure at 3.5 GHz with focusing near the head}, volume = {13}, year = {2025}, }
• (2025). Automated code assessment and feedback: A comprehensive model for improved programming education. ACCESS, 13, 56642-56658. (www)

  @article{ , abstract = {Programming skills are essential in nearly every job today. To prepare students for the growing demand for programming expertise, they must be proficient in coding. This poses a challenge for educators who must ensure students are well-equipped to meet these demands. In Today’s time, most of the schools and universities have introduced advanced curricula and syllabi with programming courses, thus an effective and conventional model to evaluate the codes submitted by students in a timely manner is very much required. Traditional grading systems were often slow, biased, and lacked the ability to provide meaningful feedback, which is crucial for student improvement. This study proposes an extensive automated code assessment and feedback model that addresses these issues and provides timely feedback to students. The model evaluates different aspects of the codes like originality, accuracy, structure, and syntax. By using the Abstract Syntax Tree (AST) analysis for code structure, plagiarism detection, and syntax error identification, the model offers an improved approach to evaluating student’s coding skills. To evaluate the model, a group of 60 engineering students participated, and the model demonstrated a 40% increase in grading efficiency, reducing grading time while maintaining quality. The feedback on scores provided students with valuable insights about their understanding of programming concepts. This model not only supports skill development through actionable feedback but also promotes larger educational goals of accessibility and inclusivity by offering a sustainable solution for quality learning.}, archive = {J_ACCESS}, author = {R. Parvathy and M. G. Thushara and Jinesh M. Kannimoola}, doi = {10.1109/ACCESS.2025.3554838}, journal = {IEEE Access}, pages = {56642-56658}, shortjournal = {IEEE Access}, title = {Automated code assessment and feedback: A comprehensive model for improved programming education}, volume = {13}, year = {2025}, }
• (2025). MDCNN: Multi-teacher distillation-based CNN for news text classification. ACCESS, 13, 56631-56641. (www)

  @article{ , abstract = {News text classification is crucial for efficient information acquisition and dissemination. While deep learning models, such as BERT and BiGRU, excel in accuracy for text classification, their high complexity and resource demands hinder practical deployment. To address these challenges, we proposed MDCNN (Multi-teacher Distillation-based CNN), which leverages knowledge distillation with BERT and BiGRU as teacher models and TextCNN as the student model. Experiments on three benchmark news dataset demonstrate that MDCNN improves classification accuracy by nearly 2% while significantly reducing computational overhead, offering a practical solution for real-world applications.}, archive = {J_ACCESS}, author = {Xiaolei Guo and Qingyang Liu and Yanrong Hu and Hongjiu Liu}, doi = {10.1109/ACCESS.2025.3555224}, journal = {IEEE Access}, pages = {56631-56641}, shortjournal = {IEEE Access}, title = {MDCNN: Multi-teacher distillation-based CNN for news text classification}, volume = {13}, year = {2025}, }
• (2025). Efficient-proto-caps: A parameter-efficient and interpretable capsule network for lung nodule characterization. ACCESS, 13, 56616-56630. (www)

  @article{ , abstract = {Deep learning techniques have demonstrated significant potential in computer-assisted diagnosis based on medical imaging. However, their integration into clinical workflows remains limited, largely due to concerns about interpretability. To address this challenge, we propose Efficient-Proto-Caps, a lightweight and inherently interpretable model that combines capsule networks with prototype learning for lung nodule characterization. Additionally, an innovative Davies-Bouldin Index with multiple centroids per cluster is employed as a loss function to promote clustering of lung nodule visual attribute representations. When evaluated on the LIDC-IDRI dataset, the most widely recognized benchmark for lung cancer prediction, our model achieved an overall accuracy of 89.7 % in predicting lung nodule malignancy and associated visual attributes. This performance is statistically comparable to that of the baseline model, while utilizing a backbone with only approximately 2 % of the parameters of the baseline model’s backbone. State-of-the-art models achieved better performance in lung nodule malignancy prediction; however, our approach relies on multiclass malignancy predictions and provides a decision rationale aligned with globally accepted clinical guidelines. These results underscore the potential of our approach, as the integration of lightweight and less complex designs into accurate and inherently interpretable models represents a significant advancement toward more transparent and clinically viable computer-assisted diagnostic systems. Furthermore, these findings highlight the model’s potential for broader applicability, extending beyond medicine to other domains where final classifications are grounded in concept-based or example-based attributes.}, archive = {J_ACCESS}, author = {Eduardo M. Rodrigues and Margarida Gouveia and Hélder P. Oliveira and Tania Pereira}, doi = {10.1109/ACCESS.2025.3555428}, journal = {IEEE Access}, pages = {56616-56630}, shortjournal = {IEEE Access}, title = {Efficient-proto-caps: A parameter-efficient and interpretable capsule network for lung nodule characterization}, volume = {13}, year = {2025}, }
• (2025). DPHNet: Dual-path hybrid network for blurry face image super-resolution. ACCESS, 13, 56607-56615. (www)

  @article{ , abstract = {Face images captured in the real world are usually corrupted by motion blur, which inevitably degrades the performance of current face super-resolution methods. To remedy this, in this study, a dual-path hybrid network with a CNN branch and a Transformer branch is developed. Specifically, the Transformer branch captures global features while the CNN branch focuses on extracting local features. Then, features extracted from these two branches are aggregated through a convolutional block attention module to capture spatio-channel correlation. With global features, blur cues can be well captured to facilitate clean images to be reconstructed. Experiments on the CelebA and FFHQ datasets demonstrate that the proposed network outperforms existing methods with notable margins.}, archive = {J_ACCESS}, author = {Tailai Qiu and Yubao Yan}, doi = {10.1109/ACCESS.2024.3407152}, journal = {IEEE Access}, pages = {56607-56615}, shortjournal = {IEEE Access}, title = {DPHNet: Dual-path hybrid network for blurry face image super-resolution}, volume = {13}, year = {2025}, }
• (2025). A decade in software-based side and covert channel attacks and countermeasures: A survey. ACCESS, 13, 56587-56606. (www)

  @article{ , abstract = {As technology continues to evolve, it provides significant benefits, such as seamless access to information, while also introducing critical security challenges, including privacy vulnerabilities and the rapid spread of misinformation. One major class of security threats involves software-based attacks, divided in side-channel attacks, which exploit unintended information leakage, and covert-channel attacks, which establish unauthorized communication pathways to bypass security policies. This survey provides a comprehensive review of the most cited attacks of these types in the past decade, along with their associated countermeasures. We classify the attacks and countermeasures based on the targeted system component and fundamental attack mechanisms. In addition, we conduct a comparative analysis of the attacks, considering key aspects such as attack methodology, targeted algorithms, victim system environment, reliability, performance impact, and mitigation strategies. Furthermore, we examine defense mechanisms, assessing their effectiveness, computational overhead, and applicability across various attack scenarios. Finally, we discuss emerging trends in attack techniques and countermeasures, highlighting open challenges and future research directions to secure modern computing systems.}, archive = {J_ACCESS}, author = {Teodora Vasilas and Remus Brad}, doi = {10.1109/ACCESS.2025.3555139}, journal = {IEEE Access}, pages = {56587-56606}, shortjournal = {IEEE Access}, title = {A decade in software-based side and covert channel attacks and countermeasures: A survey}, volume = {13}, year = {2025}, }
• (2025). A faster fixed-time fault tolerant sliding mode control for robot manipulators with mismatched disturbances. ACCESS, 13, 56576-56586. (www)

  @article{ , abstract = {Mismatched disturbances and actuator failures are primary challenges in the field of robotic manipulators. This research proposes a faster disturbance observer-based fixed-time sliding manifold with accelerated convergence rate developing a comprehensive strategy capable of addressing both of these limitations, which have garnered little attention from researchers so far. First, a disturbance observer is introduced to estimate fault information, matched and mismatched disturbances. With the estimated information, a novel faster fixed-time nonsingular terminal sliding manifold is designed, and a control law is formulated to compensate for the faults and disturbances. Additionally, a fixed-time adaptive algorithm is developed to approximate the upper bound of observer errors. The Lyapunov function is utilized to ensure fixed-time stability and faster convergence of the proposed approach. Finally, simulations and comparative analyses are conducted to confirm the robustness and superiority of the proposed scheme despite the presence of mismatched disturbances and actuator faults. This main contributions of this research lies in a novel disturbance observer-based terminal sliding manifold with faster convergence is proposed, developing a comprehensive strategy capable of handling both mismatched disturbances and actuator failures.}, archive = {J_ACCESS}, author = {Wendong Li and Zhuokun Zhang and Ping Wang and Haicheng Wan}, doi = {10.1109/ACCESS.2025.3554177}, journal = {IEEE Access}, pages = {56576-56586}, shortjournal = {IEEE Access}, title = {A faster fixed-time fault tolerant sliding mode control for robot manipulators with mismatched disturbances}, volume = {13}, year = {2025}, }
• (2025). Development of random walks strategy-based dandelion optimizer and its application to engineering design problems. ACCESS, 13, 56547-56575. (www)

  @article{ , abstract = {The objective of this paper is to enhance the swarm-based metaheuristic dandelion optimizer (DO) algorithm by incorporating various strategies to address early convergence and mitigate the risk of becoming trapped in local optima. This integration is intended to yield optimal and favorable outcomes for real-world optimization problems. To achieve this objective, a novel hybrid algorithm called the random walks dandelion optimizer (RW-DO) was introduced. This new algorithm addresses the limitations of the dandelion optimizer DO when handling optimization problems by incorporating at random walks strategy. By leveraging the random walks strategy, the RW-DO algorithm addresses the issue of premature convergence. This strategy enhances the diversity of solutions, thereby preventing the DO algorithm from becoming trapped in the local optima during the exploitation phase. To assess its performance, the RW-DO algorithm was compared with alternative algorithms by using the CEC 2020 and CEC 2019 function sets. Across all the test sets, the RW-DO algorithm consistently generateds more advantageous solutions. For the CEC 2020 function set, the RW-DO algorithm demonstrated superior performance compared with the DO algorithm from 3% to 11% in 5-dimensional problems. In 30-dimensional problems, the RW-DO algorithm exhibited superior performance compared to the DO algorithm from 14% to 46%. For the CEC 2020 function set in 50-dimensional problems, the RW-DO algorithm demonstrated superior performance compared to the DO algorithm from 11% to 188%. In the CEC 2019 function set, this ratio ranges from 3% to 38%. In engineering problems, the RW-DO algorithm also achieved superiority over the DO algorithm from 2% to 3%. Statistical analyses were performed to validate the superiority of RW-DO. The Kolmogorov-Smirnov normality test was used to select appropriate statistical tests for evaluating the performance of the algorithm based on the CEC 2020 and CEC 2019 function sets. Because the data set is not normally distributed, non-parametric tests such as the Wilcoxon signed-rank test and Kolmogorov-Smirnov for two sample tests were employed. These tests confirm that RW-DO yields distinct and superior solutions compared with the different data sets. Furthermore, the effectiveness of the RW-DO algorithm in solving real-world problems was demonstrated through its application to six engineering design problems. The experimental results highlight its competency in comparison to other algorithms. Overall, this research demonstrates the enhanced capabilities of the RW-DO algorithm in optimizing complex problems and its competitiveness when pitted against alternative methods.}, archive = {J_ACCESS}, author = {Erdal Eker}, doi = {10.1109/ACCESS.2025.3554505}, journal = {IEEE Access}, pages = {56547-56575}, shortjournal = {IEEE Access}, title = {Development of random walks strategy-based dandelion optimizer and its application to engineering design problems}, volume = {13}, year = {2025}, }
• (2025). Active and reactive power multi-objective control of multi-microgrid in on- and off-grid modes. ACCESS, 13, 56531-56546. (www)

  @article{ , abstract = {In this paper, a multi-microgrid (MMG) system consisting of three microgrids (MGs), each with three nano grids (NGs) and one central battery storage unit, is modeled to pursue multiple objectives in on-grid and off-grid modes by implementing control strategies in the d-q frame. When the MMG is connected to the upstream grid, all three MGs are responsible for injecting active power into the grid while supplying their internal loads. On the other hand, MG 1 can inject reactive power into the grid to mitigate voltage sags, while MG 2 can absorb reactive power from the grid to address voltage swells. Additionally, MG 3 focuses on recovering fault voltage by improving its low voltage ride-through (LVRT) capability during a single-line-to-ground (SLG) fault. In off-grid mode, the MGs pursue additional objectives to ensure that the load of MG 2, considered the critical MG, remains powered under all operating conditions. If the central battery of MG 2 becomes disconnected, the central batteries of MGs 1 and 3 will provide support, thereby improving the resilience of the DC bus. Furthermore, the AC bus in MG 2 is designed to ensure that if the inverter supplying the internal loads of any of NGs 1 to 3 encounters an outage, the inverters of NGs 1 to 3 in MGs 1 and 3 will provide support. Results obtained from nonlinear simulations in MATLAB/SIMULINK show that the proposed control strategies in on-grid mode successfully injected active powers of 15 kW, 20 kW, and 20 kW for MGs 1, 2, and 3, respectively. During a voltage sag, MG 1 injected 65 kVAr of reactive power into the grid, while MG 2 absorbed 60 kVAr of reactive power to stabilize the voltage profile during a voltage swell. In the event of an SLG fault, MG 3 recovered the voltage by injecting 90 kVAr of reactive power, with a maximum three-phase current of 200 A into the fault location. When an SLG fault coincided with voltage sags, the voltage profile improved from 0.6 per unit (pu) to over 0.9 pu due to the simultaneous operation of MGs 1 and 3. Also, in off-grid mode, the proposed control strategies successfully injected 20 kW through MG 1 and 25 kW through MG 3 to support MG 2 when its central battery went out of circuit. Additionally, when the inverter of NG 1 in MG 2 was disconnected, 13 kW of auxiliary power was transferred from NG 1 of MG 1 to NG 1 of MG 2, ensuring that the three-phase load remained powered.}, archive = {J_ACCESS}, author = {Hossien Faraji and Gevork B. Gharehpetian and Amir Khorsandi and Seyed Hossein Hosseinian and Roya AhmadiAhangar and Argo Rosin}, doi = {10.1109/ACCESS.2025.3553402}, journal = {IEEE Access}, pages = {56531-56546}, shortjournal = {IEEE Access}, title = {Active and reactive power multi-objective control of multi-microgrid in on- and off-grid modes}, volume = {13}, year = {2025}, }
• (2025). Artifacts extraction from video head impulse test data using time series classification methods and VOR gain analysis. ACCESS, 13, 56520-56530. (www)

  @article{ , abstract = {The video head impulse test (vHIT) has become an essential tool in the examination of patients with dizziness and other balance disorders, providing significant data on all six semicircular canals. The clinical interpretation of the vestibulo-ocular reflex (VOR) dynamic function of the human brain in vertigo and balance disorders using the vHIT method poses a considerable challenge. We utilize VOR gain measurements to ascertain the health of the patient’s vestibular system. However, all methods have inherent limitations due to the presence of noise and artifacts in the data, which can significantly affect the gain values of normal and abnormal impulses, leading to inaccuracies. This paper presents a comprehensive study, where we have created a dataset using vHIT data from 5,782 clinical patients from the Department of Otorhinolaryngology, College of Medicine, Inha University. We apply time series classification (TSC) algorithms to identify and filter artifact-affected impulses, ensuring more reliable VOR gain calculations. The encoder model achieved a classification accuracy of 94%, surpassing previous approaches such as SSNHLV (92%) and AI-based stroke (88%) classification. Statistical analysis confirms the significance of our method, with p-values (<0.05) demonstrating a clear distinction between normal, abnormal, and artifact impulses. By improving impulse classification, our approach enhances the precision of VOR gain calculations, contributing to more accurate clinical diagnoses of vestibular disorders.}, archive = {J_ACCESS}, author = {Shokhrukh Baydadaev and Saidrasul Usmankhujaev and Kyu Sung Kim and Jang Woo Kwon}, doi = {10.1109/ACCESS.2025.3553714}, journal = {IEEE Access}, pages = {56520-56530}, shortjournal = {IEEE Access}, title = {Artifacts extraction from video head impulse test data using time series classification methods and VOR gain analysis}, volume = {13}, year = {2025}, }
• (2025). Parametric design studies of multilayer ceramic capacitors for enhanced reliability under mechanical stress in electric vehicles. ACCESS, 13, 56509-56519. (www)

  @article{ , abstract = {Multilayer Ceramic Capacitors (MLCCs), composed of alternating layers of nickel electrodes and BaTiO3 dielectric, are critical electronic components in electric vehicles (EVs). This study focuses on optimizing MLCC design for EV applications by analyzing a 3225-size MLCC with 361 layers using a text-based modeling approach. Key design parameters, including termination length (TL) and Cu pad width (CPW), were identified as critical for reducing mechanical stress. Experimental validation through board-flex tests confirmed that increasing TL and reducing CPW effectively minimize crack formation, which aligns with numerical analysis. The findings provide practical guidelines for improving MLCC durability in EVs, to help develop more robust capacitors capable of enduring the demanding conditions of automotive environments.}, archive = {J_ACCESS}, author = {Ji Min Shin and Sung Jin Hong and Changwoon Han}, doi = {10.1109/ACCESS.2025.3554765}, journal = {IEEE Access}, pages = {56509-56519}, shortjournal = {IEEE Access}, title = {Parametric design studies of multilayer ceramic capacitors for enhanced reliability under mechanical stress in electric vehicles}, volume = {13}, year = {2025}, }
• (2025). A novel transformer-based approach for adult’s facial emotion recognition. ACCESS, 13, 56485-56508. (www)

  @article{ , abstract = {Adult facial expression recognition (FER) is essential for human-computer interaction, mental health assessment, and social robotics applications because it improves user experiences and emotional well-being. This study presents a novel attention mechanism-based transformer approach designed to capture detailed patterns in facial features and dynamically focus on the most relevant regions for enhanced accuracy. Unlike conventional deep learning approaches, our method integrates an adaptive attention mechanism and dynamic token pruning, which optimizes computational efficiency while maintaining high accuracy. The model is evaluated on five widely used datasets: FER2013, CK+, AffectNet, RAF-DB, and AFEW. It achieves state-of-the-art performance, with accuracies of 98.67% on FER2013, 99.52% on CK+, 99.3% on AffectNet, 96.3% on AFEW, and 98.45% on RAF-DB. An ablation study further validates the contribution of each model component, and comparisons with CNN-based and transformer-based approaches confirm the effectiveness of the model. These findings establish the proposed method as a significant advancement in FER, which offers a scalable and efficient solution for real-world applications.}, archive = {J_ACCESS}, author = {Uzma Nawaz and Zubair Saeed and Kamran Atif}, doi = {10.1109/ACCESS.2025.3555510}, journal = {IEEE Access}, pages = {56485-56508}, shortjournal = {IEEE Access}, title = {A novel transformer-based approach for adult’s facial emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Application placement in the cloud continuum with resource overbooking. ACCESS, 13, 56471-56484. (www)

  @article{ , abstract = {The pervasiveness of IoT devices in many real-world domains, such as healthcare, agriculture, manufacturing, transportation, offers new interesting opportunities, but at the same time opens new challenges. In fact, to ensure the effectiveness of the innovative smart applications being deployed nowadays in these ecosystems, the mapping between applications and resources of the cloud continuum plays a key role. In this context, we propose a lightweight placement policy aimed at minimizing the network delays experienced by IoT applications by fully exploiting the resources of the cloud continuum. For this purpose, our heuristic tries to allocate application modules as close as possible to their data sources. In addition, by introducing the concept of resource overbooking, a module can be allocated to a resource even if the desired processing capacity of the module exceeds to some extent the available processing capacity of the resource. The results of the extensive simulation experiments have clearly demonstrated the benefits of our policy. For example, as the overbooking factor increases, the network delay tends to decrease even significantly. In contrast, the potential contentions caused by resource overbooking introduce a slight increase in the processing time although it does not generally exceed the desired processing time of the applications, thus ensuring their QoS requirements.}, archive = {J_ACCESS}, author = {Ivan Mongiardo and Luisa Massari and Maria Carla Calzarossa and Belen Bermejo and Daniele Tessera}, doi = {10.1109/ACCESS.2025.3554995}, journal = {IEEE Access}, pages = {56471-56484}, shortjournal = {IEEE Access}, title = {Application placement in the cloud continuum with resource overbooking}, volume = {13}, year = {2025}, }
• (2025). Trust verification method for leveraging secure task completion in mobile crowd outsourcing processes. ACCESS, 13, 56454-56470. (www)

  @article{ , abstract = {Mobile crowdsourcing (MCS) is an autonomous paradigm that integrates humans, technology, devices, and computing methods for cost-effective task completion. The tasks are outsourced from service providers, organizations, etc. for ease of completion. Such a process is concerned with trust and privacy-dependent security considerations. This article provides a solution for mitigating illegitimate workers causing malicious injections in heterogeneous task processing instances. The method named Rendezvous Trust-enabled Task Processing (RTTPM) recommends workers and devices based on individual highness value. This highness represents the trust observed from the previous task completion intervals. In this trust computing and verification process two distinct phases of Q-learning are utilized; the transitions between false data injection and task completion are independently validated for highness value derivations. Learning offers a best-fit reward with updates based on the maximum possible highness derivatives from the transitions. If the derivatives are diverging from the rendezvous maximum reward, then the worker/device is malicious, and its trust is nullified. Such nullified workers/devices are thwarted from the current task handling interval preventing further malicious injections. The reward computation is halted for the same from which a new transition phase is initiated. This process is active until all the tasks are processed and their precise completion is maximized.}, archive = {J_ACCESS}, author = {Abdullah Alharthi}, doi = {10.1109/ACCESS.2025.3552550}, journal = {IEEE Access}, pages = {56454-56470}, shortjournal = {IEEE Access}, title = {Trust verification method for leveraging secure task completion in mobile crowd outsourcing processes}, volume = {13}, year = {2025}, }
• (2025). FLFT: A large-scale pre-training model distributed fine-tuning method that integrates federated learning strategies. ACCESS, 13, 56439-56453. (www)

  @article{ , abstract = {In recent years, large-scale pre-trained models have developed rapidly in general fields, but there are few practically viable technical solutions for application scenarios in specialized fields, especially in sensitive areas such as healthcare, finance, and even military domains that require precise, professional, and credible knowledge for decision-making assistance. Concentrating data on cloud servers and then efficiently fine-tuning large-scale pre-trained models is impractical for industries with high data privacy protection requirements. This paper proposes a distributed training strategy for large-scale pre-trained models that integrates federated learning strategies. First, local data is fine-tuned offline on various edge devices. Then, the parameters of the efficiently fine-tuned small-scale models (e.g., LORA) are uploaded for aggregation on the server. Finally, intelligent services and solutions are provided for complex scenarios in specialized fields.}, archive = {J_ACCESS}, author = {Yu Tao and Ruopeng Yang and Kaisheng Zeng and Changsheng Yin and Yiwei Lu and Wenxin Lu and Yongqi Shi and Bo Wang and Bo Huang}, doi = {10.1109/ACCESS.2025.3549819}, journal = {IEEE Access}, pages = {56439-56453}, shortjournal = {IEEE Access}, title = {FLFT: A large-scale pre-training model distributed fine-tuning method that integrates federated learning strategies}, volume = {13}, year = {2025}, }
• (2025). An advanced local current-only protection for microgrids: Deep-learning-based approach. ACCESS, 13, 56428-56438. (www)

  @article{ , abstract = {The integrity of protection actions in microgrids (MGs) is often compromised by varying operational conditions and contingencies. Notably, the operation of on-load tap-changers (OLTCs) can lead to the maloperation of overcurrent relays during both internal and external faults due to the altered fault current levels. This issue, combined with diverse operational conditions and other N-1 contingencies, further broadens the scope of undesirable operations. A deep-learning (DL) assisted fault detection method is introduced in this paper, which addresses the impact of OLTC operation on overcurrent protection in MGs under different operational scenarios. The proposed method utilizes a long short-term memory (LSTM) model to detect faults, taking into account the OLTC operation, on/off-grid status of the MG, outages of distributed generations (DGs), and capacitor bank switching. Additionally, various fault locations and resistances are incorporated into all simulated fault scenarios to enhance the training performance of the method. The ability of the LSTM model to interpret temporal dependencies in time-series signals allows the proposed method to rely solely on single-sourced current measurements at the relaying point, thereby reducing costs and improving module security. Simulation results on the low-voltage section of the IEEE 14-bus test system demonstrate the effectiveness of the proposed method in fault detection compared to existing approaches.}, archive = {J_ACCESS}, author = {Amin Yazdaninejadi and Hossein Ebrahimi and Sajjad Golshannavaz}, doi = {10.1109/ACCESS.2025.3555123}, journal = {IEEE Access}, pages = {56428-56438}, shortjournal = {IEEE Access}, title = {An advanced local current-only protection for microgrids: Deep-learning-based approach}, volume = {13}, year = {2025}, }
• (2025). Single-camera-based 3D drone trajectory reconstruction for surveillance systems. ACCESS, 13, 56413-56427. (www)

  @article{ , abstract = {Drones have been utilized in various fields, but the number of drones being used illegally and for illegal and hazardous purposes has recently increased. These misuses have given rise to the development of an anti-drone system, C-UAS, comprising two key steps: engagement and detection. This detection step plays a crucial role in reconstructing the drone’s trajectory for the subsequent engagement step. Therefore, we focus on a trajectory reconstruction approach that utilizes external data, such as CCTV view images, instead of internal drone data. While numerous methods have been explored for estimating 3D drone trajectory using multiple sensors, they are often unsuitable for surveillance systems. In this study, we use a calibrated single camera suitable for surveillance systems, leveraging the relationship between 2D and 3D spaces. We use a drone tracker to automatically track 2D images. The tracked images are used to estimate the 2D rotation of the drone in the image through principal component analysis (PCA). By combining the estimated 2D drone positions with the actual length, we geometrically infer the 3D drone trajectories. We also develop synthetic 2D and 3D drone datasets to address the lack of public drone datasets. Additionally, a real-world 3D dataset is generated and made available for public use. The experimental results demonstrate that the proposed method can accurately reconstruct drone trajectories in 3D space. With a MAE of 5.66 and RMSE of 7.93 showing low error rates, these findings validate the practical value and potential of our framework as a single-camera-based surveillance system.}, archive = {J_ACCESS}, author = {Seo-Bin Hwang and Yeong-Jun Cho}, doi = {10.1109/ACCESS.2025.3555321}, journal = {IEEE Access}, pages = {56413-56427}, shortjournal = {IEEE Access}, title = {Single-camera-based 3D drone trajectory reconstruction for surveillance systems}, volume = {13}, year = {2025}, }
• (2025). Time series forecasting in marine environment monitoring sensor networks based on edge computing. ACCESS, 13, 56399-56412. (www)

  @article{ , abstract = {Marine sensor networks, essential for obtaining marine environmental data, face new challenges as marine environment monitoring extends into the deep sea and the demand for long-term in-situ time series analysis increases. Traditional centralized computing mode is inadequate for deep-sea monitoring and intelligent marine observation due to its limitations in communication and real-time processing. Moreover, long-term time series analysis models are typically complex and unable to provide in-situ real-time services. To overcome these challenges, this paper integrates edge computing into marine sensor networks and combines it with a lightweight time series forecasting model based on the Transformer structure, MarineEdgeformer. This presented model utilizes a linear attention mechanism and an optimized decoder structure, significantly reducing the model’s computational costs and improving inference speed while maintaining forecasting accuracy. Experimental evaluations using a real-world dataset with a Raspberry Pi as the edge computing node demonstrate that MarineEdgeformer effectively reduces computational costs and achieves reliable long-term forecasting accuracy. These results not only confirm the potential of edge computing in enhancing the real-time processing capabilities, reliability, and intelligence of marine sensor networks but also provide a technical solution for the development of future marine environment monitoring.}, archive = {J_ACCESS}, author = {Jiajia Teng and Rufu Qin and Changwei Xu}, doi = {10.1109/ACCESS.2025.3554178}, journal = {IEEE Access}, pages = {56399-56412}, shortjournal = {IEEE Access}, title = {Time series forecasting in marine environment monitoring sensor networks based on edge computing}, volume = {13}, year = {2025}, }
• (2025). Co-optimization of ancillary services providing primary frequency control in the day-ahead market. ACCESS, 13, 56385-56398. (www)

  @article{ , abstract = {Large-scale integration of inverter-based resources (IBRs) challenges primary frequency control because IBRs don’t typically respond to frequency fluctuations, which is partially due to a lack of financial incentives. To address this, we consider four ancillary service products for primary frequency control: Primary Frequency Response (PFR) reserve, which accommodates droop control, Fast Frequency Response (FFR) reserve, Virtual Inertia (VI) reserve, and synchronous inertia. The available frequency response reserve requirement is proposed, which ensures sufficient available PFR and FFR reserve to cover the largest contingency. To ensure that PFR reserve is available fast enough to contribute to primary frequency control, we propose enforcing novel rate-based PFR reserve limits that vary with inertia and FFR reserve and are constructed by a simulation-based heuristic. The proposed requirements are incorporated into the unit-commitment problem and are compatible with the equivalency ratio requirement used by the Electric Reliability Council of Texas (ERCOT). Results are provided using an ERCOT test case. We illustrate that PFR reserve limits currently used by ERCOT may be insufficient due to generator ramping limitations, while the addition of our rate-based limits would not significantly increase cost. We also illustrate that including VI reserve allows storage devices to fully utilize their power and energy capacities.}, archive = {J_ACCESS}, author = {Manuel Garcia and J. Kyle Skolfield and Felipe Wilches-Bernal and Ross Baldick}, doi = {10.1109/ACCESS.2025.3551254}, journal = {IEEE Access}, pages = {56385-56398}, shortjournal = {IEEE Access}, title = {Co-optimization of ancillary services providing primary frequency control in the day-ahead market}, volume = {13}, year = {2025}, }
• (2025). A review of machine learning approaches for non-invasive cognitive impairment detection. ACCESS, 13, 56355-56384. (www)

  @article{ , abstract = {This review paper explores recent advances in machine learning approaches with emphasis on deep learning techniques for non-invasive cognitive impairment detection. We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility. The paper provides an overview of relevant datasets, feature-extracting techniques, and deep-learning architectures applied to this domain. The paper comprises 60 peer-reviewed papers that mainly utilize deep learning models to detect cognitive impairment conditions. We have analyzed the performance of different methods across modalities and observed that speech and language-based methods generally achieved the highest detection performance. Studies combining acoustic and linguistic features tended to outperform those using a single modality. Facial analysis methods showed promise for visual modalities but were less extensively studied. Most papers focused on binary classification (impaired vs. non-impaired), with fewer addressing multi-class or regression tasks. Transfer learning and pre-trained language models emerged as popular and effective techniques, especially for linguistic analysis. Despite significant progress, several challenges remain, including data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation. Lastly, we propose future research directions, such as investigating language-agnostic speech analysis methods, developing multi-modal diagnostic systems, and addressing ethical considerations in AI-assisted healthcare. By synthesizing current trends and identifying key obstacles, this review aims to guide further development of deep learning-based cognitive impairment detection systems to improve early diagnosis and ultimately patient outcomes.}, archive = {J_ACCESS}, author = {Muath Alsuhaibani and Ali Pourramezan Fard and Jian Sun and Farida Far Poor and Peter S. Pressman and Mohammad H. Mahoor}, doi = {10.1109/ACCESS.2025.3555176}, journal = {IEEE Access}, pages = {56355-56384}, shortjournal = {IEEE Access}, title = {A review of machine learning approaches for non-invasive cognitive impairment detection}, volume = {13}, year = {2025}, }
• (2025). Visual explanation with action query transformer in deep reinforcement learning and visual feedback via augmented reality. ACCESS, 13, 56338-56354. (www)

  @article{ , abstract = {Deep Reinforcement Learning (DRL) agents possess powerful control capabilities and have potential applications in robotics and other fields. However, the closed box properties of DRL agent models still make it difficult to interpret their decision-making processes. One research area that addresses this challenge is eXplainable Reinforcement Learning (XRL). Conventional visual explanation methods in XRL focus only on the rationale behind a single selected action and are insufficient for a more comprehensive analysis of an agent’s decision making. In addition, visualizing attention only as an image is problematic in real-world environments such as robotics because it does not clearly map attention to physical space, limiting user understanding. To overcome these limitations, we propose Action Q-Transformer (AQT), an XRL method that uses a transformer encoder-decoder architecture with action information as a query. In this way, AQT calculates explicit attentions for each action that an agent may select, resulting in a DRL agent model that is easier to interpret. Furthermore, we introduce a visual feedback method using augmented reality (AR) to project these attentions directly into the physical environment. Through experiments on the Atari 2600 video game strategy task and a robot control task in an indoor environment, we demonstrate that AQT can analyze agent decision making in detail. Also, user evaluation in a robot task confirms that AR-based visual feedback effectively improves the understanding of the agent’s behavior.}, archive = {J_ACCESS}, author = {Hidenori Itaya and Wantao Yin and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi and Komei Sugiura}, doi = {10.1109/ACCESS.2025.3554493}, journal = {IEEE Access}, pages = {56338-56354}, shortjournal = {IEEE Access}, title = {Visual explanation with action query transformer in deep reinforcement learning and visual feedback via augmented reality}, volume = {13}, year = {2025}, }
• (2025). Low-cost multifunctional assistive device for visually impaired individuals. ACCESS, 13, 56326-56337. (www)

  @article{ , abstract = {Visually impaired individuals face challenges in mobility, object recognition, and text reading. Existing assistive tools provide partial solutions but lack full integration. This paper presents a low-cost, multifunctional assistive device in the form of wearable glasses, integrating real-time text recognition, obstacle detection, and remote assistance. Built around a Raspberry Pi Zero 2W, the system features an Arducam IMX519 autofocus camera and a tactile button interface for user-friendly control. Real-time audio feedback enhances situational awareness, promoting greater independence. The device was tested at the Rehabilitation Center for the Visually Impaired in Levoča. Performance evaluations confirm high text recognition accuracy and improved navigation support. A comparative analysis highlights its affordability and functionality over commercial alternatives. While effective, the device requires processing speed optimization and hardware miniaturization. Future improvements will explore haptic feedback and AI-driven scene analysis to enhance usability. This research contributes to affordable and inclusive assistive technology, improving accessibility for visually impaired individuals.}, archive = {J_ACCESS}, author = {Richard Král and Patrik Jacko and Tibor Vince}, doi = {10.1109/ACCESS.2025.3554366}, journal = {IEEE Access}, pages = {56326-56337}, shortjournal = {IEEE Access}, title = {Low-cost multifunctional assistive device for visually impaired individuals}, volume = {13}, year = {2025}, }
• (2025). Distilling wisdom: A review on optimizing learning from massive language models. ACCESS, 13, 56296-56325. (www)

  @article{ , abstract = {In the era of Large Language Models (LLMs), Knowledge Distillation (KD) enables the transfer of capabilities from proprietary LLMs to open-source models. This survey provides a detailed discussion of the basic principles, algorithms, and implementation methods of knowledge distillation. It explores KD’s impact on LLMs, emphasizing its utility in model compression, performance enhancement, and self-improvement. Through the analysis of practical examples such as DistilBERT, TinyBERT, and MobileBERT, the paper demonstrates how knowledge distillation can markedly enhance the efficiency and applicability of large language models in real-world scenarios. The discussion encompasses the varied applications of KD across multiple domains, including industrial systems, embedded systems, Natural Language Processing (NLP), multi-modal processing, and vertical domains, such as medicine, law, science, finance, and materials science. This survey outlines current KD methodologies and future research directions, highlighting its role in advancing AI technologies and fostering innovation across different sectors.}, archive = {J_ACCESS}, author = {Dingzong Zhang and Devi Listiyani and Priyanka Singh and Manoranjan Mohanty}, doi = {10.1109/ACCESS.2025.3554586}, journal = {IEEE Access}, pages = {56296-56325}, shortjournal = {IEEE Access}, title = {Distilling wisdom: A review on optimizing learning from massive language models}, volume = {13}, year = {2025}, }
• (2025). Enhancing emotion recognition in speech based on self-supervised learning: Cross-attention fusion of acoustic and semantic features. ACCESS, 13, 56283-56295. (www)

  @article{ , abstract = {Speech Emotion Recognition has gained considerable attention in speech processing and machine learning due to its potential applications in human-computer interaction, mental health monitoring, and customer service. However, state-of-the-art models for speech emotion recognition use many parameters, which leads to computational complexity. In this paper, we introduce a novel deep-learning model to enhance the accuracy of emotional content detection in speech signals while maintaining a lightweight architecture compared to state-of-the-art models. The proposed model incorporates a feature encoder that significantly improves the emotional representation of acoustic features and a cross-attention mechanism to fuse acoustic features, such as Spectrograms, with semantic features extracted from the pre-trained self-supervised learning framework, enriching the emotional representation of speech. An extensive experimental study demonstrates that the proposed model achieves a weighted accuracy of 74.6% on the IEMOCAP dataset, competitive with the state-of-the-art baselines. In addition, our proposed model achieves a latency of 24 milliseconds on moderate devices while containing up to three times fewer parameters.}, archive = {J_ACCESS}, author = {Bashar M. Deeb and Andrey V. Savchenko and Ilya Makarov}, doi = {10.1109/ACCESS.2025.3554454}, journal = {IEEE Access}, pages = {56283-56295}, shortjournal = {IEEE Access}, title = {Enhancing emotion recognition in speech based on self-supervised learning: Cross-attention fusion of acoustic and semantic features}, volume = {13}, year = {2025}, }
• (2025). Discrete predefined-time terminal sliding mode controller for current control of PMSM drives. ACCESS, 13, 56273-56282. (www)

  @article{ , abstract = {This paper focuses on the discrete sliding mode control (SMC) method adopted in the permanent magnet synchronous motor (PMSM) current loop control system. Based on the predefined-time terminal sliding mode (PTSM) method, a discrete PTSM and its sufficient conditions for convergence are proposed. Considering that the PMSM current loop control system can be rewritten as a typical discrete first-order multiple-input multiple-output (MIMO) system in practical applications, a discrete PTSM law is further proposed. The effectiveness of the proposed discrete PTSM controller is verified through numerical simulations and experiments on a PMSM drive control test bench. Experiments are conducted to compare different current loop control schemes, which fully demonstrates that the discrete PTSM method proposed in this paper can achieve better dynamic performance of discrete state variables during the convergence sliding process.}, archive = {J_ACCESS}, author = {Haibo Xue and Xinghua Liu}, doi = {10.1109/ACCESS.2025.3555483}, journal = {IEEE Access}, pages = {56273-56282}, shortjournal = {IEEE Access}, title = {Discrete predefined-time terminal sliding mode controller for current control of PMSM drives}, volume = {13}, year = {2025}, }
• (2025). MICAAL: A domain-specific language for microservices in ambient assisted living. ACCESS, 13, 56255-56272. (www)

  @article{ , abstract = {The rise of the Internet of Things (IoT) has driven innovation across various sectors, one of its benefiting areas is healthcare. Within this domain, Ambient Assisted Living (AAL) aims to improve the wellbeing and independence of vulnerable populations, particularly seniors, by using IoT technologies. As AAL environments become increasingly complex and diverse, traditional monolithic architectures struggle to offer the flexibility and scalability required for sustainable solutions. In response, paradigms such as microservices architectures emerge as a powerful approach to overcome these challenges. This paper presents MICAAL, a Domain-Specific Language (DSL), to model microservices-oriented architectures for AAL environments. MICAAL empowers users to design modular, scalable systems by enabling the easy selection and deployment of microservices ensuring that IoT devices in AAL environments are efficiently supported and adaptable to changing needs. We demonstrate the practical usability of MICAAL through a case study of a real-world AAL system. We also present an empirical evaluation, using the Technology Acceptance Model (TAM), assessing user satisfaction and intention to adopt MICAAL for modeling microservice-based architectures for AAL. The results confirm that MICAAL simplifies the modeling process and promotes efficient, future-proof AAL systems.}, archive = {J_ACCESS}, author = {Wilson Valdez Solis and Priscila Cedillo and Attila Kertesz}, doi = {10.1109/ACCESS.2025.3555831}, journal = {IEEE Access}, pages = {56255-56272}, shortjournal = {IEEE Access}, title = {MICAAL: A domain-specific language for microservices in ambient assisted living}, volume = {13}, year = {2025}, }
• (2025). RanViz: Ransomware visualization and classification based on time-series categorical representation of API calls. ACCESS, 13, 56237-56254. (www)

  @article{ , abstract = {Ransomware continues to pose a significant threat to individuals and organizations worldwide, causing disruptions, financial losses, and reputational damage. As ransomware attacks grow in sophistication, understanding their behaviour through effective analysis has become increasingly critical for mitigation and prevention. However, ransomware analysis presents several challenges. First, the sheer volume of Application Programming Interface (API) call data generated by ransomware during execution can overwhelm traditional analysis methods. Second, the temporal and categorical nature of this data makes identifying meaningful patterns complex. Third, the integration of machine learning (ML) models, which are essential for accurate classification, is hindered by the difficulty of modelling intricate API call behaviours. Without effective tools to address these issues, analysts risk missing critical behavioural indicators. To overcome these challenges, the proposed Ransomware Visualization (RanViz) system was developed to provide a comprehensive visual analytics and classification platform designed to enhance ransomware analysis. RanViz employs advanced visualization techniques to represent categorical API call time-series data, enabling analysts to intuitively understand ransomware behaviours that might otherwise remain obscured. The system incorporates ML models based on API call frequency, temporal interval, and sequence to classify unknown samples as either benign or ransomware. The models collectively achieve an accuracy of over 95% in detecting ransomware. By providing a unified platform that combines powerful visualization tools with high-performing ML models, RanViz simplifies ransomware analysis and offers a robust framework for accurate classification. This makes it an invaluable tool for digital forensics and cybersecurity professionals tasked with addressing the ever-evolving ransomware threat.}, archive = {J_ACCESS}, author = {Vhuhwavho Mokoma and Avinash Singh}, doi = {10.1109/ACCESS.2025.3555163}, journal = {IEEE Access}, pages = {56237-56254}, shortjournal = {IEEE Access}, title = {RanViz: Ransomware visualization and classification based on time-series categorical representation of API calls}, volume = {13}, year = {2025}, }
• (2025). Attention-based deep neural network for high-dimensional microwave modeling of non-reciprocal bandpass filters. ACCESS, 13, 56220-56236. (www)

  @article{ , abstract = {This paper presents attention-based deep neural networks for high-dimensional microwave modeling to predict behavior of spatio-temporal modulated (STM) non-reciprocal bandpass filters (NR-BPFs). The proposed method integrates convolutional neural networks (CNN), joint channel-spatial attention, and residual connections to enhance prediction accuracy and model generalization. The combined channel-spatial attention approach allows the model to concentrate on essential features, while residual connections improve learning by reducing overfitting and preventing vanishing gradients, ensuring efficient gradient flow. For experimental validation of the proposed model, high-dimensional microwave modeling of four types of NR-BPF (example 1: third-order single-band NR-BPF, example 2: fourth-order single-band NR-BPF, example 3: third-order dual-band NR-BPF, example 4: microstrip line third-order single-band NR-BPF) are performed. Experimental results show that the proposed model exhibits superior performances for four types of NR-BPFs, achieving lower root mean square error (RMSE) and higher R-squared as compared to conventional deep multilayer perceptron (MLP) and standard CNN models. The results indicate that the proposed approach provides a robust solution for accurately predicting complex input-output relationships in STM-based NR-BPFs, outperforming conventional deep MLP and CNN models in both prediction accuracy and model robustness.}, archive = {J_ACCESS}, author = {Girdhari Chaudhary and Yao Meng and Dong Sun Park and Yongchae Jeong}, doi = {10.1109/ACCESS.2025.3555583}, journal = {IEEE Access}, pages = {56220-56236}, shortjournal = {IEEE Access}, title = {Attention-based deep neural network for high-dimensional microwave modeling of non-reciprocal bandpass filters}, volume = {13}, year = {2025}, }
• (2025). Comparing generative AI literature reviews versus human-led systematic literature reviews: A case study on big data research. ACCESS, 13, 56210-56219. (www)

  @article{ , abstract = {Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are transforming research methodologies, including Systematic Literature Reviews (SLRs). While traditional, human-led SLRs are labor-intensive, AI-driven approaches promise efficiency and scalability. However, the reliability and accuracy of AI-generated literature reviews remain uncertain. This study investigates the performance of GPT-4-powered Consensus in conducting an SLR on Big Data research, comparing its results with a manually conducted SLR. To evaluate Consensus, we analyzed its ability to detect relevant studies, extract key insights, and synthesize findings. Our human-led SLR identified 32 primary studies (PSs) and 207 related works, whereas Consensus detected 22 PSs, with 16 overlapping with the manual selection and 5 false positives. The AI-selected studies had an average citation count of 202 per study, significantly higher than the 64.4 citations per study in the manual SLR, indicating a possible bias toward highly cited papers. However, none of the 32 PSs selected manually were included in the AI-generated results, highlighting recall and selection accuracy limitations. Key findings reveal that Consensus accelerates literature retrieval but suffers from hallucinations, reference inaccuracies, and limited critical analysis. Specifically, it failed to capture nuanced research challenges and missed important application domains. Precision, recall, and F1 scores of the AI-selected studies were 76.2%, 38.1%, and 50.6%, respectively, demonstrating that while AI retrieves relevant papers with high precision, it lacks comprehensiveness. To mitigate these limitations, we propose a hybrid AI-human SLR framework, where AI enhances search efficiency while human reviewers ensure rigor and validity. While AI can support literature reviews, human oversight remains essential for ensuring accuracy and depth. Future research should assess AI-assisted SLRs across multiple disciplines to validate generalizability and explore domain-specific LLMs for improved performance.}, archive = {J_ACCESS}, author = {Davide Tosi}, doi = {10.1109/ACCESS.2025.3554504}, journal = {IEEE Access}, pages = {56210-56219}, shortjournal = {IEEE Access}, title = {Comparing generative AI literature reviews versus human-led systematic literature reviews: A case study on big data research}, volume = {13}, year = {2025}, }
• (2025). Identifying AC control patterns from a massive longitudinal log dataset using deep clustering. ACCESS, 13, 56195-56209. (www)

  @article{ , abstract = {An air conditioner is a household appliance and one of the most innovative inventions, primarily used to cool indoor temperatures and lower humidity to provide a comfortable environment. The sale of air conditioners increased continuously, reaching approximately 135 million units annually, with nearly 1.6 billion devices currently in operation. While it is important to devise and develop technologies for improving their energy efficiency, it is even more important to analytically investigate the performance of its main objective, user experience, or comfortableness. Here, we collected a massive set of logs collected in a longitudinal manner during the summer season of the year 2021 from approximately 1.47 million AC devices manufactured by LG Electronics and located in South Korea. Among the many features in the log, the current temperature, target temperature, humidity, energy consumption, and wind strength features were used to identify clusters embedding meaningful time-course feature patterns. We particularly focused on identifying patterns and user groups who either actively or inactively controlled the target temperature since such behavior likely indicates a discomforting air state. As a result, a total of 10 distinctive patterns and 14 representative user groups were searched. Among the 10 patterns, five showed active target temperature control and the other five inactive control. Regardless of control activity, all patterns are highly correlated with the progressing weather conditions during the summer. The feature patterns of the actively controlled group significantly differed from those of the inactively controlled group. Further investigation of the semantic difference between the groups would provide valuable research directions for improving the user experience of AC usage. It may also be used to devise an AI-driven automatic AC controller to minimize user intervention while providing an optimal air state.}, archive = {J_ACCESS}, author = {Seunghwan Bae and Donghee Kim and Seokhwan Seong and Jaeyeon Jang and Young-Kyoon Suh and Honghyun Kim and Dongwoo Han and Inuk Jung}, doi = {10.1109/ACCESS.2025.3554749}, journal = {IEEE Access}, pages = {56195-56209}, shortjournal = {IEEE Access}, title = {Identifying AC control patterns from a massive longitudinal log dataset using deep clustering}, volume = {13}, year = {2025}, }
• (2025). Time series classification using federated convolutional neural networks and image-based representations. ACCESS, 13, 56180-56194. (www)

  @article{ , abstract = {Time series classification (TSC) has numerous applications across various domains. This research introduces a federated hybrid TSC method that combines image-based time series representation techniques with Convolutional Neural Networks (CNNs) in a decentralized framework. Specifically, three image representation techniques—Motif representation of numerical time series (NMotif), Motif representation of fuzzy time series (FTSMotif), and Polar—are utilized to convert raw data into image representations, which are then used as inputs for CNNs. Federated Learning (FL) enables collaborative training across distributed clients while ensuring data privacy. The method’s effectiveness is evaluated using 40 datasets from the UCR Archive. The results show that the FL model surpasses traditional baselines by up to 1.9% while enabling collaborative learning and preserving privacy. Although centralized state-of-the-art (SOTA) models such as Rocket and ResNet achieve higher overall accuracy, the proposed approach offers a competitive and practical alternative for real-world scenarios where centralized training is infeasible due to privacy constraints. These findings highlight the potential of our proposed federated image-based TSC as a scalable and privacy-aware solution for time series analysis.}, archive = {J_ACCESS}, author = {Felipe A. R. Silva and Omid Orang and Fabricio Javier Erazo-Costa and Petrônio C. L. Silva and Pedro H. Barros and Ricardo P. M. Ferreira and Frederico Gadelha Guimarães}, doi = {10.1109/ACCESS.2025.3554097}, journal = {IEEE Access}, pages = {56180-56194}, shortjournal = {IEEE Access}, title = {Time series classification using federated convolutional neural networks and image-based representations}, volume = {13}, year = {2025}, }
• (2025). Unsupervised video anomaly detection using video vision transformer and adversarial training. ACCESS, 13, 56169-56179. (www)

  @article{ , abstract = {Surveillance cameras have been recently introduced in various locations to maintain public safety. However, it is tedious for security personnel to continue observing videos obtained by surveillance cameras because abnormal events rarely occur. Consequently, this study aims to develop an unsupervised anomaly detection method for surveillance videos based on the prediction of future frame and optical flow magnitude using adversarial networks. Our database consists of two public datasets for anomaly detection: UCSD Pedestrian 2 (UCSD Ped2) and ShanghaiTech Campus. The proposed network consists of a generator and a U-Net-based discriminator. The generator has an encoder based on a video vision transformer and two decoders that independently predict the future frame and optical flow magnitude using feature maps extracted from the encoder at different resolutions. The U-Net-based discriminator identifies at the pixel level whether the image is a “real” or “false” frame predicted by the generator. The generator and U-Net-based discriminator in the proposed network are trained through adversarial training. Finally, anomaly detection is performed based on element-wise subtraction between the predicted future frame and the optical flow magnitude and their corresponding ground truth frames. The areas under the receiver operating characteristic curve (AUC) with the proposed network were 97.5% for UCSD Ped2 and 85.8% for ShanghaiTech Campus. The AUC for the proposed network was also greater than those of well-known anomaly detection methods.}, archive = {J_ACCESS}, author = {Shimpei Kobayashi and Akiyoshi Hizukuri and Ryohei Nakayama}, doi = {10.1109/ACCESS.2025.3554813}, journal = {IEEE Access}, pages = {56169-56179}, shortjournal = {IEEE Access}, title = {Unsupervised video anomaly detection using video vision transformer and adversarial training}, volume = {13}, year = {2025}, }
• (2025). Power module lifetime consumption in robotic manufacturing application. ACCESS, 13, 56160-56168. (www)

  @article{ , abstract = {This paper investigates the impact of industrial robot operation on the estimated lifetime consumption of hardware components, with a focus on power electronics modules. By converting operational hours to electrical stress and analyzing various robot program types and their corresponding parameters, the study examines how different operational modes can affect the thermal loading and mechanical stress on these power electronics components. These variations can lead to accelerated wear and potential failure. The paper introduces an innovative approach to estimate lifetime consumption based on motor current profiles, enabling real-time analysis of operational data to account for varying robot tasks and stresses. A key contribution of this paper is the translation of motor current data into predictive models for power electronics wear, which allows for more accurate optimization of derating strategies and extends component lifespan. This method enhances predictive maintenance, providing valuable feedback for improving the reliability and efficiency of industrial robots. By assessing the thermal and mechanical impacts on power electronics, the study offers valuable feedback for enhancing the design and operational efficiency of robotic systems in industrial applications. This research contributes to the development of more resilient and durable industrial robots, ensuring better performance and reduced downtime in manufacturing environments.}, archive = {J_ACCESS}, author = {Oskars Bormanis and Janis Zakis and Leonids Ribickis and Frede Blaabjerg}, doi = {10.1109/ACCESS.2025.3551608}, journal = {IEEE Access}, pages = {56160-56168}, shortjournal = {IEEE Access}, title = {Power module lifetime consumption in robotic manufacturing application}, volume = {13}, year = {2025}, }
• (2025). Using deep convolutional neural networks for earthquake and explosion classification. ACCESS, 13, 56144-56159. (www)

  @article{ , abstract = {Accurate classification of seismic events is essential to prevent confusion in catalogs, which can distort seismic statistics and hazard analyses. Monitoring small-scale explosions aids in safety supervision and the detection of illegal mining, while rapid event identification facilitates timely emergency responses. With the proliferation of seismic monitoring stations and increased human activities such as mining, tunnel construction, and island development, the number of non-natural seismic events recorded by seismic networks has surged. Therefore, developing automatic classification methods for seismic events is vital for enhancing safety and disaster prevention efforts. This study utilized 28,421 seismic events and 172,214 waveform data from the Fujian Seismic Network (2010-2022). Three neural network models, VGG, AlexNet, and ResNet, were used to develop a deep-learning model for seismic event classification. We analyzed factors influencing model accuracy, such as signal-to-noise ratio, epicentral distance, magnitude, and source depth. The Grad-CAM and Activation Maximization method was employed to identify the primary features learned by the models. All three networks achieved over 95% accuracy, with VGG reaching 97%. In classifying earthquake events in Fujian in 2023, the model achieved 94.9% accuracy by individual stations and 98.8% by network average. Natural earthquake recall was 94.1%, and explosion recall was 95%. Generalization tests showed 85.9% accuracy in Shandong and 87.9% in Utah. This study demonstrates deep convolutional neural networks’ high accuracy in classifying earthquake events from raw waveforms, with good generalization capabilities.}, archive = {J_ACCESS}, author = {Mingquan Hong and Hongcai Zhang and Lihua Wu and Jialiang Chen and Lijin Dai and Lujun Wang and Tengchao Dong and Jinling Yang and Lihua Fang}, doi = {10.1109/ACCESS.2025.3552127}, journal = {IEEE Access}, pages = {56144-56159}, shortjournal = {IEEE Access}, title = {Using deep convolutional neural networks for earthquake and explosion classification}, volume = {13}, year = {2025}, }
• (2025). DAHNet: Dilated attention hybrid network for intracerebral hemorrhage segmentation. ACCESS, 13, 56129-56143. (www)

  @article{ , abstract = {Intracerebral hemorrhage (ICH) is caused by the rupture of blood vessels in the brain, leading to the accumulation of blood in the brain tissue, which compresses normal brain tissue and causes a range of neurological problems. Detecting ICH based on brain CT images presents several challenges, particularly the difficulty in precisely detecting small or irregular hemorrhage regions. To address these issues, we present an innovative model, named the Dilated Attention Hybrid Network (DAHNet), which incorporates an encoder-decoder architecture. In the encoder stage, we introduces a Dilated Convolution Pooling (DCP) module, an innovative block combining dilated convolution and pooling operations. By adjusting the dilation rate, DCP expands the receptive field while maintaining image resolution, overcoming the limitations of traditional methods in handling hemorrhage regions of varying shapes and sizes. Dilated convolution enables the network to capture larger region information within a smaller receptive field, allowing DCP to better adapt to complex hemorrhage regions and meet the demands of high-precision small-target segmentation. Additionally, we employ a self-attention mechanism to extract global contextual information from the ICH images. These processed features are then integrated to accurately locate and guide low-level information. In the decoder stage, we design decoder blocks that incorporate upsampling operations, residual networks, and channel and spatial attention mechanisms for joint optimization. The experimental results demonstrate that DAHNet outperforms existing methods in key metrics, such as the Dice coefficient, Jaccard index, and sensitivity, especially in the detection of small hemorrhagic regions. Moreover, DAHNet shows excellent generalization ability on out-of-distribution validation datasets, further validating its robustness and adaptability. These results highlight DAHNet’s significant advantages in the task of intracerebral hemorrhage segmentation. Our code is available at https://github.com/hpguo1982/DAHNet.}, archive = {J_ACCESS}, author = {Haiyan Liu and Yu Zeng and Xiaoyan Yang and Hao Li and Jianjun Chang and Huaping Guo}, doi = {10.1109/ACCESS.2025.3550189}, journal = {IEEE Access}, pages = {56129-56143}, shortjournal = {IEEE Access}, title = {DAHNet: Dilated attention hybrid network for intracerebral hemorrhage segmentation}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal analysis of urban leisure consumption activities based on autoencoder and multi-source data: A case study of chongqing, china. ACCESS, 13, 56112-56128. (www)

  @article{ , abstract = {Urban leisure consumption activities (ULCA) are key indicators of urban vitality, and analyzing their spatio-temporal distribution is essential for understanding the structure and dynamics of urban spaces. This study conducts a quantitative analysis of leisure consumption behavior in the main urban area of Chongqing based on multi-source data (including mobile signaling data, POI data, and traffic analysis zone) and proposes a novel spatio-temporal layout analysis method. The study focuses on the spatial layout of ULCA venues, the spatio-temporal distribution of activity behaviors, and their interrelationships. An autoencoder model is employed to predict behavioral patterns and visualize leisure consumption dynamics. Specifically, five key indicators—land diversity, population density, transportation accessibility, environmental livability, and site convenience—are defined to quantify and evaluate urban leisure consumption vitality. Subsequently, spatio-temporal trends of leisure consumption are analyzed using the autoencoder, and trajectory predictions of human behavior are performed to reveal vitality patterns across different areas of Chongqing. Finally, cluster analysis and tag cloud visualizations are used to identify ULCA hotspots and explore their spatial characteristics. The results indicate significant spatio-temporal variation in ULCA distribution, reflecting disparities in urban vitality across regions. This study provides valuable data support for urban planning and leisure facility design, while also offering new perspectives and methodological insights for future urban vitality research.}, archive = {J_ACCESS}, author = {Ziyu Hu and Moran Zhang and Li Liu and Haijia Wu}, doi = {10.1109/ACCESS.2025.3555745}, journal = {IEEE Access}, pages = {56112-56128}, shortjournal = {IEEE Access}, title = {Spatio-temporal analysis of urban leisure consumption activities based on autoencoder and multi-source data: A case study of chongqing, china}, volume = {13}, year = {2025}, }
• (2025). An enhanced residual architecture for automated detection of bone metastasis on whole-body SPECT images. ACCESS, 13, 56100-56111. (www)

  @article{ , abstract = {Single-photon emission computed tomography (SPECT) is a vital technology for diagnosing bone metastases and other skeletal diseases, playing a critical role in the early diagnosis and treatment of bone-related conditions. To address the challenges of physicians dedicating considerable time and effort to analyzing SPECT images, as well as the overly complex preprocessing steps in existing automatic diagnosis systems, an enhanced residual model that is better suited for clinical applications was proposed in this work. By incorporating a hybrid convolutional attention module and a pyramid pooling module into the backbone, the proposed model achieves comparable performance to the state-of-the-art methods while substantially simplifying the architecture. The experimental results show that the model achieved accuracy, recall, proportion of true positive and F-1 score of 0.8721, 0.7951, 0.9349 and 0.8594 respectively on our self-built dataset, MetaLung-SPECT. The results of the comparative experiments conducted on the public BS-80K dataset further corroborate that our proposed model achieves performance comparable to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Jianwei Li and Yusheng Hao and Qiang Lin and Yongchun Cao and Zhengxing Man and Weilan Wang}, doi = {10.1109/ACCESS.2025.3554622}, journal = {IEEE Access}, pages = {56100-56111}, shortjournal = {IEEE Access}, title = {An enhanced residual architecture for automated detection of bone metastasis on whole-body SPECT images}, volume = {13}, year = {2025}, }
• (2025). Analyzing partial shading in PV systems using wavelet packet transform and empirical mode decomposition techniques. ACCESS, 13, 56085-56099. (www)

  @article{ , abstract = {Partial shading in solar photovoltaic (PV) modules typically reduces the output current of the shaded PV module due to the reduction in the irradiance level. Although this phenomenon is temporary in nature, it is considered an intermittent fault, and it is essential for a protection system to differentiate it from other fault conditions to avoid unnecessary tripping. The main problem in identifying partial shading in a PV system is the difficulty of extracting its features under different shading conditions. To solve this difficulty, this article proposes a novel approach combining Wavelet Packet Transform (WPT) along with Empirical Mode Decomposition (EMD) to extract the features of PV panel output voltage and string current signals during partial shading conditions. In the first stage, the WPT is used to split the PV voltage and string currents into specific sub-band frequencies, and then EMD is used to decompose the selected frequency bands into a number of intrinsic mode functions (IMFs) and a residual. The generated IMF components are then fed into the Random Forest (RF) algorithm designed for shading detection and classification. This proposed hybrid technique provides a high-resolution representation of the array voltage and string currents without loss in the time-frequency resolution, aiding in the detection of partial shading and differentiation of its strength. The results indicate that the proposed approach achieved a detection accuracy of 98.43% and a classification accuracy of 97.6%.}, archive = {J_ACCESS}, author = {Kais Abdulmawjood and Walid G. Morsi}, doi = {10.1109/ACCESS.2025.3552733}, journal = {IEEE Access}, pages = {56085-56099}, shortjournal = {IEEE Access}, title = {Analyzing partial shading in PV systems using wavelet packet transform and empirical mode decomposition techniques}, volume = {13}, year = {2025}, }
• (2025). An augmented reality framework for education: Deep learning integration and impact evaluation. ACCESS, 13, 56067-56084. (www)

  @article{ , abstract = {We propose a novel augmented reality (AR) framework that transforms education with immersive and interactive experiences that enhance student engagement and comprehension. However, existing AR-based learning solutions are built upon generic object detectors that struggle with recognizing domain-specific educational materials, thus limiting their effectiveness. To address this challenge, we introduce a novel fully automated image dataset generation pipeline that synthesizes high-fidelity images from 3D models by varying lighting, camera angles, and background occlusion. This variation enhances the diversity of the dataset, enabling robust training of domain-specific object detectors. The proposed pipeline stands out from existing methods because it provides scalability and adaptability features that allow researchers to build customized educational datasets. The study involved generating a dataset and evaluating it using four state-of-the-art object detection models: Faster R-CNN, SSD, YOLOv5n, and YOLOv7. The YOLOv7 detection model reached an accuracy of 97.2% with 99.5% mAP@0.5 and performed at a real-time speed of 45 frames per second (FPS) making it the best choice for AR applications. To assess the educational impact of our system, we conducted a pilot study involving 210 elementary students. The results showed notable improvements in learning outcomes: fourth graders’ scores increased from $68.78~\pm ~10.85$ to $90.96~\pm ~11.70$ , while fifth graders improved from $62.43~\pm ~11.53$ to $75.43~\pm ~12.53$ . Comprehensive statistical analyses, including ANOVA, regression, and paired t-tests, confirmed that our approach significantly enhances both academic performance and student engagement when compared to traditional learning methods. These findings demonstrate that our domain-focused data pipeline and optimized object detection framework effectively bridge the gap between deep learning research and AR real-world classroom implementation, offering a highly scalable and transformative solution for AR-based education.}, archive = {J_ACCESS}, author = {R. Kaviyaraj and Uma Mohan}, doi = {10.1109/ACCESS.2025.3551656}, journal = {IEEE Access}, pages = {56067-56084}, shortjournal = {IEEE Access}, title = {An augmented reality framework for education: Deep learning integration and impact evaluation}, volume = {13}, year = {2025}, }
• (2025). EEG multi-mode oscillatory brain state allocation using switching spectral gaussian processes. ACCESS, 13, 56053-56066. (www)

  @article{ , abstract = {We propose a new model for the non-stationary brain state allocation problem from electroencephalography (EEG) data, based on spectral features and their interaction. Spontaneous EEG data are modeled as continuous Gaussian Processes (GPs) emissions governed by discrete states, represented by a hidden semi-Markov model, that switch in time (HsMM-SGP). The GPs are defined by multivariate spectral kernels, covariance functions parameterized in the frequency domain. The multivariate spectral kernels describe oscillatory modes at specific frequencies and their interactions across channels, encapsulating periodicity, amplitude, and spread. Multivariate spectral kernels enable the GPs to represent temporal patterns with fine-grained frequency-specific structures and interactions, a unique spectral “fingerprint” per state, making it particularly suited for capturing non-stationary oscillatory behaviour in the neural time series. The model parameters were estimated using the Expectation-Maximization approach. The inference scheme was validated on data generated from the HsMM-SGP generative model to evaluate the accuracy in recovering the ground truth parameters. Next, we generated time-series from a metastable connectome-connected whole brain network to demonstrate the HsMM-SGP’s capability to infer meaningful oscillatory modes that reflect the changes in the underlying dynamics due to varying structural connectivity parameters. Finally, a practical application of the HsMM-SGP is illustrated using EEG data from a healthy control and an AD patient. We show that the inferred brain states exhibit distinct spectral properties across both conditions, with the AD states marked slower frequencies. We conclude that the proposed HsMM-SGP offers a method for estimating physiologically meaningful dynamical brain states.}, archive = {J_ACCESS}, author = {Yunier Prieur-Coloma and Felipe A. Torres and Nelson J. Trujillo-Barreto and Wael El-Deredy}, doi = {10.1109/ACCESS.2025.3554137}, journal = {IEEE Access}, pages = {56053-56066}, shortjournal = {IEEE Access}, title = {EEG multi-mode oscillatory brain state allocation using switching spectral gaussian processes}, volume = {13}, year = {2025}, }
• (2025). An SDR-based GNSS-R CubeSat payload: Hardware development and optimization of the onboard processing. ACCESS, 13, 56045-56052. (www)

  @article{ , abstract = {Recent developments in high-performance Software Defined Radios (SDRs) and their utilization in CubeSat payloads are transforming Earth Observation (EO), including Microwave Radiometers, Global Navigation and Satellite System – Radio Occultations (GNSS-RO), and – Reflectometry (GNSS-R). In recent years, GNSS-R has been increasingly used in land and marine environmental monitoring, with applications expanding to other emerging fields in EO. The so called Delay Doppler Map (DDM) is the primary observable of GNSS-R receivers, providing information on surface properties, i.e. dielectric constant and surface roughness. Efficient on-board processing is essential in CubeSat-based GNSS-R missions, due to the large volume of raw data and the constraints of limited downlink bandwidth. However, limited on-board computational resources present challenges, as DDM generation requires intensive Fast Fourier Transform (FFT) operations. This study presents the design and development of a cost-effective and compact 0.5U GNSS-R CubeSat payload that optimizes the GNSS-R data processing technique by using the auxiliary data from the reference signals, such as Pseudo-Random Noise (PRN) codes, and their Doppler frequencies in order to reduce the search space. This way the payload selectively processes the raw data, significantly reducing the computational load. The payload processing unit is implemented in Analog Devices ADRV9364 with a dual-core ARM Cortex-A9 processor with a Zynq-7000 Field-Programmable Gate Array (FPGA).}, archive = {J_ACCESS}, author = {Shah Zahid Khan and Yasir M. O. Abbas and Edwar Edwar and Abdul-Halim Jallad and Adriano Camps}, doi = {10.1109/ACCESS.2025.3546358}, journal = {IEEE Access}, pages = {56045-56052}, shortjournal = {IEEE Access}, title = {An SDR-based GNSS-R CubeSat payload: Hardware development and optimization of the onboard processing}, volume = {13}, year = {2025}, }
• (2025). Design and empirical validation of a low-cost outdoor mmWave altimetry test bench for UAS. ACCESS, 13, 56034-56044. (www)

  @article{ , abstract = {The increasing adoption of unmanned aerial systems (UAS) in diverse urban applications necessitates reliable and precise altimetry solutions for situational awareness and landing safety. Despite significant advancements in the realm of waveform design and signal processing, empirical validation of millimeter wave (mmWave) Frequency Modulated Continuous Wave (FMCW) waveforms for altimetry use case remains a critical gap in literature. This study argues the necessity of empirical validation of candidate FMCW waveforms by proposing an inexpensive and practical outdoor test bench. The multipath propagation model for the said test bench is presented along with a simplified testing methodology suitable for implementation on contemporary mmWave automotive radars. The specifics of the test bench are deliberated where a flat concrete wall with a sufficiently large surface area is employed as a static target. The underpinnings established by this body of work shall serve dynamic testing on a drone platform against various terrain types in the next phase of research. The experimental results demonstrate the potential of practical use of mmWave altimetry on UAS in urban deployment.}, archive = {J_ACCESS}, author = {Mohammad Derawi and Maaz Ali Awan and Adem Sahin and Yaser Dalveren and Ibrahim Ozdur and Ali Kara}, doi = {10.1109/ACCESS.2025.3554733}, journal = {IEEE Access}, pages = {56034-56044}, shortjournal = {IEEE Access}, title = {Design and empirical validation of a low-cost outdoor mmWave altimetry test bench for UAS}, volume = {13}, year = {2025}, }
• (2025). A self-decoupled MIMO patch antenna system for V2X communications. ACCESS, 13, 56021-56033. (www)

  @article{ , abstract = {This article presents a new method for self-decoupling in multiple-input-multiple-output (MIMO) circular patch antenna (CPA) system. In the presented method, three shorting vias are strategically placed in the E-plane of the probe-fed CPA resonating in TM11 mode to create a weak-current density region on the ground plane where the adjacent antenna element can be placed. Initially, a two-element MIMO antenna system is designed, developed, and tested for vehicle-to-everything (V2X) communication in the IEEE 802.11p band. High inter-port isolation of 78 dB across the entire 5.82 to 5.98 GHz operating band is obtained. Both antenna pairs exhibit identical radiation properties, with a peak gain of 7.4 dBi. Furthermore, a 10-element MIMO antenna system is developed, showing promising results for V2X MIMO communications.}, archive = {J_ACCESS}, author = {Amit Kumar and Sandeep Rana and Akhilesh Mohan and Gunjan Srivastava and Sachin Kumar and Kang Wook Kim}, doi = {10.1109/ACCESS.2025.3554151}, journal = {IEEE Access}, pages = {56021-56033}, shortjournal = {IEEE Access}, title = {A self-decoupled MIMO patch antenna system for V2X communications}, volume = {13}, year = {2025}, }
• (2025). Performance improvement of grid-connected PV-wind hybrid systems using adaptive neuro-fuzzy inference system and fuzzy FOPID advanced control with OPAL-RT. ACCESS, 13, 55996-56020. (www)

  @article{ , abstract = {This paper presented enhancing hybrid energy systems, specifically those combining photovoltaic (PV) and wind turbine sources, linked to the electrical grid with the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) control unit during implementation. Our study focuses on the comparative performance of the ANFIS controller against traditional Fuzzy Fraction-Order Proportional-Integral-Derivative, (FOPID) and Fuzzy Proportional-Integral, (PI) controllers. Hybrid energy systems present unique challenges due to renewable energy sources’ intermittent and non-linear nature. Conventional controllers, such as Fuzzy FOPID and Fuzzy PI, often struggle to manage these complexities effectively. The ANFIS controller, nevertheless, blends fuzzy logic’s qualitative reasoning with neural networks’ capacity for adaptive learning, offering a more robust and flexible solution. Through extensive simulations and real-world testing, we demonstrate that the ANFIS controller significantly outperforms both Fuzzy FOPID and Fuzzy PI controllers in key performance metrics. These include improved voltage regulation, lower total harmonic distortion (THD), and enhanced overall system stability and efficiency under varying load and environmental conditions. The findings highlight ANFIS’s potential as a better hybrid energy system control method, enabling more dependable and effective grid integration of renewable energy sources. This research contributes to advancing smart grid technologies and promoting sustainable and resilient energy infrastructure. Using the OPAL-RT 4512 platform, this paper generates a thorough real-time simulation and investigation of a hybrid PV/wind energy system. Real-time integration of intricate MATLAB/Simulink models is made possible by the OPAL-RT 4512, which makes it easier to accurately simulate real-world operational situations. At the beginning time the values and percentage modified ANFIS and Fuzzy FOPID give good controller compared Fuzzy PI, at time (0 to 0.4 sec) Fuzzy PI decrease in most results values percentage 20% compared two controller ANFIS and Fuzzy FOPID.}, archive = {J_ACCESS}, author = {Moayed Mohamed and Zuhair Muhammed Alaas and Badr Al Faiya and Hossam Youssef Hegazy and Wael I. Mohamed and Saad A. Mohamed Abdelwahab}, doi = {10.1109/ACCESS.2025.3548926}, journal = {IEEE Access}, pages = {55996-56020}, shortjournal = {IEEE Access}, title = {Performance improvement of grid-connected PV-wind hybrid systems using adaptive neuro-fuzzy inference system and fuzzy FOPID advanced control with OPAL-RT}, volume = {13}, year = {2025}, }
• (2025). Leveraging pre-built catalogs and object-level scheduling to eliminate I/O bottlenecks in HPC environments. ACCESS, 13, 55984-55995. (www)

  @article{ , abstract = {Modern High-Performance Computing (HPC) environments face mounting challenges due to the shift from large to small file datasets, along with an increasing number of users and parallelized applications. As HPC systems rely on Parallel File Systems (PFS), such as Lustre for data processing, performance bottlenecks stemming from Object Storage Target (OST) contention have become a significant concern. Existing solutions, such as LADS with its object-level scheduling approach, fall short in large-scale HPC environments due to their inability to effectively address metadata I/O bottlenecks and the growing number of I/O processes. This study highlights the pressing need for a comprehensive solution that tackles both OST contention and metadata I/O challenges in diverse HPC workloads. To address these challenges, we propose SwiftLoad, an object-level I/O scheduling framework that leverages a metadata catalog to enhance the performance and efficiency of parallel HPC utilities. The adoption of the metadata catalog mitigates the metadata I/O bottlenecks that commonly occur in HPC utilities, a challenge that is particularly pronounced in object-level I/O scheduling. SwiftLoad addresses OST contention and the uneven distribution of I/O processes across different OSTs through mathematical modeling and incorporates a Loader Configuration Module to regulate the number of I/O processes. Evaluated with two representative utilities—data deduplication profiling and data augmentation—SwiftLoad achieved performance improvements of up to $5.63\times $ and $11.0\times $ , respectively, on a production supercomputer.}, archive = {J_ACCESS}, author = {Seoyeong Lee and Junghwan Park and Yoochan Kim and Safdar Jamil and Awais Khan and Seung Woo Son and Jae-Kook Lee and Do-Sik An and Taeyoung Hong and Youngjae Kim}, doi = {10.1109/ACCESS.2025.3553311}, journal = {IEEE Access}, pages = {55984-55995}, shortjournal = {IEEE Access}, title = {Leveraging pre-built catalogs and object-level scheduling to eliminate I/O bottlenecks in HPC environments}, volume = {13}, year = {2025}, }
• (2025). Novel physical unclonable function implementation for microcontrollers and field programmable gate arrays. ACCESS, 13, 55970-55983. (www)

  @article{ , abstract = {Physical Unclonable Functions (PUFs) are frequently exploited for security purposes. In particular, they are adopted to generate unique authentication passwords and cryptographic keys, with no need to store any secret information in non-volatile memories that can be easily attacked. In this paper, we present a novel PUF based structure, that can be implemented at low costs in microcontrollers and FPGAs, by exploiting the circuitry that is already present in their General Purpose Input-Output (GPIO) interface. The proposed PUF based scheme has been implemented on five different FPGA devices, in order to experimentally verify their correct operation under various realistic operating conditions. We will show that our proposed PUF based structure features a good uniqueness (of the 49.75%), a uniformity close to the ideal value of the 50%, an average steadiness of the 99.33%, and a maximum reliability of the 99.61%, for a temperature ranging from 5°C to 65°C.}, archive = {J_ACCESS}, author = {Marco Grossi and Martin Omaña and Cecilia Metra and Andrea Acquaviva}, doi = {10.1109/ACCESS.2025.3555438}, journal = {IEEE Access}, pages = {55970-55983}, shortjournal = {IEEE Access}, title = {Novel physical unclonable function implementation for microcontrollers and field programmable gate arrays}, volume = {13}, year = {2025}, }
• (2025). Optimizing group activity recognition with actor relation graphs and GCN-LSTM architectures. ACCESS, 13, 55957-55969. (www)

  @article{ , abstract = {The challenge of understanding and recognizing group activities through human behavior and interactions is a prominent issue in the realm of computer vision research. This area boasts a wide range of applications, including security surveillance, healthcare monitoring, and human-computer interaction systems. However, accurately deciphering complex group dynamics continues to pose significant difficulties. Traditional video-based activity recognition methods often grapple with persistent obstacles such as environmental noise, background clutter, and the inter-class similarities inherent in activity patterns. Recent advancements in this field have largely focused on spatial feature extraction, which alone proves inadequate for thorough group activity analysis. In response to these limitations, we propose a novel deep learning framework that effectively captures both temporal and spatial characteristics of group interactions. Our architecture employs a Convolutional Neural Network (CNN) with Inception-V3 as the foundational model for initial feature extraction. This is complemented by the development of an Actor Relation Graph (ARG) utilizing Zero Normalized Cross Correlation (ZNCC), which adeptly illustrates both appearance-based and positional relationships among participants. By integrating the ARG with a hybrid model that combines Graph Convolutional Network (GCN), Long Short-Term Memory (LSTM), and Attention mechanisms, our approach significantly enhances the extraction of spatial and relational features compared to conventional techniques. Experimental evaluations conducted on two benchmark datasets—the Collective Activity Dataset (CAD) and the Volleyball dataset—demonstrate the efficacy of our framework. Our proposed model achieves state-of-the-art performance, attaining prediction accuracies of 94.32% and 94.47% on the CAD and Volleyball datasets, respectively, thereby surpassing existing methodologies in the realm of group activity recognition.}, archive = {J_ACCESS}, author = {M. R. Tejonidhi and C. V. Aravinda and S. V. Aruna Kumar and C. K. Madhu and A. M. Vinod}, doi = {10.1109/ACCESS.2025.3552668}, journal = {IEEE Access}, pages = {55957-55969}, shortjournal = {IEEE Access}, title = {Optimizing group activity recognition with actor relation graphs and GCN-LSTM architectures}, volume = {13}, year = {2025}, }
• (2025). Virtual reality and user experience: Current trends and future challenges. ACCESS, 13, 55939-55956. (www)

  @article{ , abstract = {Virtual reality is an emerging and versatile technology that is gaining increasing interest from the scientific community, particularly regarding user experience. A review of the literature highlights the need for updated analyses that comprehensively address current trends in this field. The objective of this study is to analyze the evolution of scientific publications related to virtual reality and user experience, identifying trends, the most influential authors, relevant journals, and international collaborations, to provide a comprehensive overview. A bibliometric analysis was conducted using the Web of Science Core Collection database, identifying a total of 1,848 relevant publications. Network analysis techniques and Bradford’s law were applied, allowing for the examination of the distribution and impact of publications, as well as collaboration between authors and institutions. The results show an exponential growth in scientific output, particularly since 2019. While the United States leads in the number of publications, most of the influential authors are from Europe, with notable scientific output in Spain and Portugal. Current research hotspots include virtual reality applications in education and healthcare (over 70% of the analyzed publications), which align with Sustainable Development Goals, particularly 3 and 4. Trends indicate a shift towards integrating virtual reality with artificial intelligence, adaptive systems, and multisensory interaction to enhance immersion and usability. Challenges include a lack of standardized methodologies to assess immersion, presence, and cognitive load, which limits comparisons across studies and hinders the development of best practices. Future research should focus on refining interaction paradigms, improving accessibility, and developing ethical frameworks that respect data privacy and protect the user from cybersickness and the psychological effects of prolonged virtual reality exposure.}, archive = {J_ACCESS}, author = {Fátima Avilés-Castillo and Jorge Buele and Guillermo Palacios-Navarro}, doi = {10.1109/ACCESS.2025.3554434}, journal = {IEEE Access}, pages = {55939-55956}, shortjournal = {IEEE Access}, title = {Virtual reality and user experience: Current trends and future challenges}, volume = {13}, year = {2025}, }
• (2025). Pseudolite self-interference cancellation without additional GNSS signal distortion. ACCESS, 13, 55927-55938. (www)

  @article{ , abstract = {The goal of this paper is to solve the low-orbit pseudolite self-interference problem. The low-orbit pseudolite navigation signals will interference satellite navigation as they broadcast at the same frequency and same time. However, the low-orbit pseudolite also needs satellite navigation to get continuous and effective high-precision positioning. So, while effectively filtering the self-interference, the GNSS navigation signals need low distortion. In this paper, we propose an anti-self-interference method which ensures that GNSS navigation signals are not distorted. Specifically, the method uses the known characteristics of self-interference signals to establish an optimization problem. By solving this problem, self-interference filtering can be achieved without estimating the direction of satellite navigation signal and without the assistance of the receiver channel amplitude-frequency and phase-frequency characteristics. The simulation test results show that the algorithm can filter self-interference without additional distortion of the satellite navigation signals. The application test results show that, the algorithm can filter self-interference with high-precision carrier observation of the satellite navigation signal which statistical standard deviation is only 0.01 times of the carrier wavelength. The method could help in achieving high-precision satellite navigation and positioning of pseudolite terminals and providing high-precision position information for low-orbit pseudolites.}, archive = {J_ACCESS}, author = {Lichao Gao and Jin He and Yuanxin Wu and Wenxian Yu}, doi = {10.1109/ACCESS.2025.3554355}, journal = {IEEE Access}, pages = {55927-55938}, shortjournal = {IEEE Access}, title = {Pseudolite self-interference cancellation without additional GNSS signal distortion}, volume = {13}, year = {2025}, }
• (2025). Advanced optimization in E-commerce logistics: Combining matheuristics with random forests for hub location efficiency. ACCESS, 13, 55915-55926. (www)

  @article{ , abstract = {The rapid growth of e-commerce has introduced significant challenges in optimizing logistics and distribution networks, particularly in determining hub locations to balance cost efficiency and service quality. This study presents a novel framework integrating matheuristics with Random Forest machine learning to address the Hub Location Problem (HLP) in e-commerce logistics. Leveraging real-world data from a global logistics company, the methodology combines predictive analytics with robust optimization techniques to dynamically adjust hub configurations based on fluctuating demand and operational constraints. Experimental results demonstrate substantial improvements over traditional methods, including an improvement of 7.8% in delivery time compliance, a 9.4% increase in fulfillment rate and enhanced network robustness by 16%. The findings highlight the strategic advantages of integrating advanced analytics into logistics planning, offering actionable insights for industry practitioners.}, archive = {J_ACCESS}, author = {Kassem Danach and Abbas Rammal and Imad Moukadem and Hassan Harb and Abbass Nasser}, doi = {10.1109/ACCESS.2025.3550560}, journal = {IEEE Access}, pages = {55915-55926}, shortjournal = {IEEE Access}, title = {Advanced optimization in E-commerce logistics: Combining matheuristics with random forests for hub location efficiency}, volume = {13}, year = {2025}, }
• (2025). Modeling higher-order interactions in graphs through combinatorial arc-transitive structure using graph convolutional network. ACCESS, 13, 55899-55914. (www)

  @article{ , abstract = {The analysis of networks, including social, citation, biological, and traffic networks, has become a critical research area, enabling deeper insights into complex systems across diverse fields. Traditional Graph Convolutional Networks (GCNs) have demonstrated success in graph representation learning, particularly in homophilic networks where nodes share similar features. However, these models struggle in heterophilic networks, where connected nodes exhibit dissimilar properties, leading to performance degradation due to ineffective feature propagation. Existing approaches have attempted to address these limitations by incorporating higher-order neighborhood aggregation and signed message passing, yet they often fail to preserve network topology while maintaining computational efficiency. This research addresses the fundamental challenge of learning robust representations in heterophilic graphs by introducing Higher-Order Graph Convolutional Network (HiGCN), a novel framework that effectively models both homophilic and heterophilic interactions through a structured Petal-Complex (PC) model. The proposed approach employs a two-step random walk mechanism between core and petal regions, facilitating bidirectional information transfer while preserving the inherent graph structure. Furthermore, adaptive spectral filters across distinct Petal-Complex Laplacian spectral domains enable the effective capture of both localized and global structural patterns in combinatorial arc-transitive complexes. Our experimental evaluations on various benchmark graph datasets—including both homogeneous and heterogeneous structures—demonstrate that HiGCN significantly outperforms state-of-the-art models in node classification and graph classification tasks, particularly in domains such as bioinformatics, social networks, and recommendation systems. The findings highlight HiGCN’s potential to enhance higher-order graph learning, making it a promising solution for complex relational modeling in diverse applications.}, archive = {J_ACCESS}, author = {Qingwei Wen}, doi = {10.1109/ACCESS.2025.3555011}, journal = {IEEE Access}, pages = {55899-55914}, shortjournal = {IEEE Access}, title = {Modeling higher-order interactions in graphs through combinatorial arc-transitive structure using graph convolutional network}, volume = {13}, year = {2025}, }
• (2025). Efficient technique for joint parameter estimation of mixed far field targets and near field jammers in phased array radar. ACCESS, 13, 55887-55898. (www)

  @article{ , abstract = {Parameter estimation of the impinging signal in the sensor arrays is one of the challenging issues in Phased Array Radar (PAR). Most often, a PAR operates in the presence of jammers deployed in its near field. In order to mitigate the jammers signals, it is important that PAR must know the exact Direction Of Arrival (DOA) and ranges of the jammers along with the DOAs of the actual targets located in the far field. This information is crucial for the PAR to steer the mainbeam and nulls in the desired and undesired (jammers) directions, respectively. In this work, we consider a PAR that simultaneously receives the desired plane waves and the undesired spherical waves from the far field targets and near field jammers respectively. The plane waves are the function of the unknown DOA, whereas the spherical waves depend on the DOAs and ranges of the jammers. A novel approach based on the Gradient Based Optimizer (GBO) algorithm and Marine Predators Algorithm (MPA) is proposed to estimate jointly the unknown parameters related to the far field targets and near field jammers. The GBO uses two operators called Gradient Search Rule (GSR) and Local Escaping Operator (LEO). The GSR enhances the convergence rate and improves the exploration tendency to obtain the better solution in the search space, while the LEO helps the algorithm avoid getting stuck in the local optima. The MPA uses different velocities ratios based on predators and prey movement in the ocean to achieve better exploration and exploitation in the search domain. A cost function is developed and optimized using GBO and MPA, which is based on the Penalty Function (PF) and Mean Square Errors (MSE) of the system. The PF controls the deviation in the MSE from the desired result during the optimization process. The proposed schemes are not only compared with each other but also with the state-of-the-art MUSIC algorithm. Extensive Monte Carlo simulations are enacted for diverse scenarios to justify the legitimacy of the proposed algorithms.}, archive = {J_ACCESS}, author = {Fawad Zaman and Sadiq Akbar and Zohaib Akhtar and Gridsada Phanomchoeng}, doi = {10.1109/ACCESS.2025.3554172}, journal = {IEEE Access}, pages = {55887-55898}, shortjournal = {IEEE Access}, title = {Efficient technique for joint parameter estimation of mixed far field targets and near field jammers in phased array radar}, volume = {13}, year = {2025}, }
• (2025). Improved U-net++ semantic segmentation method for remote sensing images. ACCESS, 13, 55877-55886. (www)

  @article{ , abstract = {Remote sensing image semantic segmentation has extensive applications in land resource planning and smart cities. Due to the problems of unclear boundary segmentation and insufficient Semantic information of small targets in high-resolution remote sensing images, an improved network TU net based on U-net++ is proposed. Secondly, the attention aggregation module of the base Transformer is introduced to capture global contextual information, replacing the original multi-level skip connections of U-net++. A cross-window interaction module is designed, which significantly reduces computational complexity and achieves a lightweight model. Finally, a dynamic feature fusion block is designed at the end of the decoder to obtain multi-class and multi-scale Semantic information and enhance the final segmentation effect. TU-net conducted experiments on two datasets, where OA, mIoU, and mF1 scores were higher than mainstream models. The IoU and F1 scores of small-sized target cars in the Vaihingen dataset were 0.896 and 0.962, respectively, which were 5% and 15.8% higher than the suboptimal model; The IoU and F1 scores of the trees in the Potsdam dataset are 0.913 and 0.936, respectively, which are 6.3% and 4.3% higher than the suboptimal model. The experimental results show that the model can more accurately segment small-sized targets and target boundaries.}, archive = {J_ACCESS}, author = {Yang Xu and Bin Cao and Hui Lu}, doi = {10.1109/ACCESS.2025.3552581}, journal = {IEEE Access}, pages = {55877-55886}, shortjournal = {IEEE Access}, title = {Improved U-net++ semantic segmentation method for remote sensing images}, volume = {13}, year = {2025}, }
• (2025). Adaptive DDoS attack detection: Entropy-based model with dynamic threshold and suspicious IP reevaluation. ACCESS, 13, 55858-55876. (www)

  @article{ , abstract = {DDoS constitutes a significant andger to network security, frequently employing anomalous traffic patterns to impede services. DDoS detection can be executed by an entropy-based anomaly detection approach, which juxtaposes the entropy value with the threshold $\delta $ . Nonetheless, prior research indicates that the threshold $\delta $ with a static k as the threshold sensitivity parameter is inadequate for detecting attacks on dynamic traffic patterns. This study presents two significant innovations: the re-evaluation of suspect IPs and the dynamic adjustment of the threshold via the parameter $k_{\text {dynamic}}$ . Reevaluation is utilised to address dubious IPs that evade initial identification due to erratic traffic patterns, whereas $k_{\text {dynamic}}$ is engineered to enhance detection sensitivity by automatic adaptability to traffic fluctuations. The experimental results indicate that the method incorporating re-evaluation of suspect IPs enhances detection accuracy. Concurrently, the method utilising $k_{\text {dynamic}}$ demonstrates enhanced detection efficacy while minimising the necessity for human modification of the k parameter. The suggested method, through these advances, surmounts the limits of prior systems, facilitating more efficient and adaptive detection of complicated attack traffic patterns.}, archive = {J_ACCESS}, author = {Juri Pebrianto and Vera Suryani}, doi = {10.1109/ACCESS.2025.3553144}, journal = {IEEE Access}, pages = {55858-55876}, shortjournal = {IEEE Access}, title = {Adaptive DDoS attack detection: Entropy-based model with dynamic threshold and suspicious IP reevaluation}, volume = {13}, year = {2025}, }
• (2025). Robust framework for PMU placement and voltage estimation of power distribution network. ACCESS, 13, 55841-55857. (www)

  @article{ , abstract = {This paper proposes a robust optimization methodology for phasor measurement unit (PMU) placement and voltage estimation in power distribution networks. The suggested method uses a hybrid Multi-Objective Particle Swarm Optimization and Differential Evolution (MOPSO-DE) algorithm to find the best PMU positions and the Weighted Least Squares (WLS) method to estimate voltage magnitude. The framework improves network observability with a minimum of PMUs, lowering deployment costs while retaining excellent estimation accuracy. The proposed approach reduces voltage estimation errors significantly by strategically placing PMUs in a distribution network. Furthermore, the method indicates high robustness in dealing with uncertainties associated with photovoltaic (PV) generation, ensuring accurate voltage estimation under different operational conditions. A Pareto front analysis yields key insights into the trade-off between system observability and estimation accuracy, allowing grid operators to make better decisions about sensor location. In addition, the computational efficiency of the MOPSO-DE method enables faster convergence and lower complexity than typical optimization techniques, making it a viable alternative for large-scale distribution networks. The findings support the proposed approach as a cost-effective, scalable, and practical solution for modern power networks with high renewable energy penetration. Future research will look into real-time adaptive state estimation utilizing machine learning-based predictive modelling, as well as expanding the framework to account for communication constraints and dynamic grid topology changes to improve its practical usefulness.}, archive = {J_ACCESS}, author = {Nida Khanam and Mohd. Rihan and Salman Hameed}, doi = {10.1109/ACCESS.2025.3554522}, journal = {IEEE Access}, pages = {55841-55857}, shortjournal = {IEEE Access}, title = {Robust framework for PMU placement and voltage estimation of power distribution network}, volume = {13}, year = {2025}, }
• (2025). Communication-aware graph neural network for multi-agent reinforcement learning. ACCESS, 13, 55832-55840. (www)

  @article{ , abstract = {Multi-agent reinforcement learning (MARL) requires effective communication strategies to solve complex control tasks over uncertain communication channels. This paper explores a communication-aware graph neural network (GNN) approach for MARL, where the interactions between agents are modeled as a dynamic directed graph that explicitly considers time-varying lossy links. We integrate communication aspects into MARL by combining the self-attention-based coordination graph and a graph convolution with zero-input compensation to migrate the information losses over multi-hop networks. We evaluate our approach on two challenging tasks: the predator-prey and the coverage problems. We show 1) the operational benefits of communication-aware GNN with sensing range, node density, and task complexity, 2) the robust performance of the proposed scheme to support the graph convolution over various ranges of packet loss probabilities of links, and 3) the effectiveness of the residual connection of the GNN model on the overall performance and the communication architecture.}, archive = {J_ACCESS}, author = {Youngrok Choi and Piergiuseppe Di Marco and Pangun Park}, doi = {10.1109/ACCESS.2025.3554736}, journal = {IEEE Access}, pages = {55832-55840}, shortjournal = {IEEE Access}, title = {Communication-aware graph neural network for multi-agent reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). CAFU-net: A context-aware feature aggregation network for lung nodule segmentation. ACCESS, 13, 55815-55831. (www)

  @article{ , abstract = {Accurate segmentation of pulmonary nodules from CT images is of significant importance for the early diagnosis and treatment of lung cancer and related diseases. However, the segmentation task faces challenges due to the overlap between pulmonary nodules and surrounding anatomical structures, as well as variations in size, shape, and density. These challenges are further compounded by low contrast and complex structural backgrounds. To address these issues, this paper proposes an improved U-Net network—CAFU-Net. The network significantly enhances the accuracy and robustness of pulmonary nodule segmentation by integrating context-aware information based on a gating mechanism, a shared-weight triple attention mechanism, and a dual-scale selective convolution kernel structure. Quantitative and qualitative experiments were conducted on the publicly available LIDC (The Lung Image Database Consortium) dataset and the private MID-FAHGMU (The Fourth Affiliated Hospital of Guangxi Medical University Medical Imaging Department) dataset. The experimental results show that, compared to 10 advanced medical image segmentation methods, CAFU-Net achieves an IoU of 86.51% on the LIDC dataset, surpassing the best-performing comparative method, BRAU-Net++, by 0.55%. The Dice coefficient is 91.71%, the F1-score is 91.78%, the F2-score is 91.78%, and the F0.5-score is 92.39%, all significantly outperforming other methods. On the MID-FAHGMU dataset, CAFU-Net achieves an IoU of 63.52%, a Dice coefficient of 76.21%, an F1-score of 76.47%, an F2-score of 77.06%, and an F0.5-score of 77.74%, exceeding most comparative methods in several metrics. These experimental results fully validate the superiority and robustness of CAFU-Net in the task of pulmonary nodule segmentation. The source code and segmentation images will be released at https://github.com/XiaoBai-Lyq/CAFU-Net.}, archive = {J_ACCESS}, author = {Jiachen Hou and Yingqi Lu and Zhougui Ling and Tao Li and Xiangsuo Fan and Yanna Qin and Qingnan Huang}, doi = {10.1109/ACCESS.2025.3555489}, journal = {IEEE Access}, pages = {55815-55831}, shortjournal = {IEEE Access}, title = {CAFU-net: A context-aware feature aggregation network for lung nodule segmentation}, volume = {13}, year = {2025}, }
• (2025). ShadowFPN-YOLO: A real-time NMS-free detector for remote sensing ship detection. ACCESS, 13, 55801-55814. (www)

  @article{ , abstract = {The deployment of advanced satellites has significantly enhanced computer vision applications in remote sensing. Among these, ship detection plays a vital role in ensuring maritime safety and managing ship traffic. However, the overhead perspective in remote sensing images often results in ship bounding boxes containing substantial background or overlapping ships, which can obscure key features necessary for accurate detection. To address these challenges, this paper proposes a real-time detector, ShadowFPN-YOLO, featuring the novel ShadowFPN module. ShadowFPN dynamically applies shadows to multi-scale feature maps by randomly masking contiguous regions, effectively reducing background interference and enhancing the network’s focus on the essential features of target ships. Furthermore, the backbone network integrates generalized efficient layer aggregation network with reparameterized cross stage partial modules to further enhance performance. Additionally, the adoption of a dual-head strategy eliminates the dependency on non-maximum suppression (NMS) for managing overlapping boxes, offering a more robust and accurate solution for object detection in densely packed scenarios. Experimental results demonstrate the strong performance of our method, achieving mAP values of 55.70 at 430 FPS on the DIOR-Ship dataset and 55.85 at 497 FPS on the HRSID dataset, all while maintaining the fewest parameters and the lowest computational cost compared to the latest YOLO models. These results underscore the efficiency and effectiveness of our approach for real-time remote sensing ship detection.}, archive = {J_ACCESS}, author = {Xiao Yang and Ahmad Sufril Azlan Mohamed and Chuanchuan Wang}, doi = {10.1109/ACCESS.2025.3554018}, journal = {IEEE Access}, pages = {55801-55814}, shortjournal = {IEEE Access}, title = {ShadowFPN-YOLO: A real-time NMS-free detector for remote sensing ship detection}, volume = {13}, year = {2025}, }
• (2025). Lightweight DDoS attack detection using bayesian space-time correlation. ACCESS, 13, 55769-55800. (www)

  @article{ , abstract = {DDoS attacks are still one of the primary sources of problems on the Internet and continue to cause significant financial losses for organizations. To mitigate their impact, detection should preferably occur close to the attack origin, e.g., at home routers or edge servers. However, relying on packet inspection may bring serious privacy and scalability issues. We propose a lightweight system for DDoS detection that solely employs byte and packet counts from off-the-shelf home routers. To detect attacks with such a limited amount of information, our key insight consists in defining two detection layers: 1) a ML classifier trained with data from real home user and malware; 2) and a Bayesian hierarchical model that exploits the synchronized nature of DDoS attacks by correlating alarms from multiple homes to check the approach in the wild. We collect data on DDoS attacks by generating real attack traffic from the homes of a selected group of volunteers, utilizing authentic malware source code. In that experiment, conducted using the residences of volunteers and over one month, our system detected 99.1% of all DDoS attacks launched, with no false alarms.}, archive = {J_ACCESS}, author = {Gabriel Mendonça and Rosa M. M. Leão and Edmundo De Souza E. Silva and Don Towsley}, doi = {10.1109/ACCESS.2025.3553742}, journal = {IEEE Access}, pages = {55769-55800}, shortjournal = {IEEE Access}, title = {Lightweight DDoS attack detection using bayesian space-time correlation}, volume = {13}, year = {2025}, }
• (2025). Unveiling the evolution of educational technologies: A patent-based clustering and life cycle analysis. ACCESS, 13, 55740-55768. (www)

  @article{ , abstract = {The rapid evolution of educational technology (EdTech) has transformed learning environments, offering unprecedented opportunities for personalized, adaptive, and immersive education. Despite the increasing adoption of these innovations, the EdTech field lacks comprehensive, data-driven analyses to systematically categorize proprietary technologies and assess their developmental trajectories. Addressing this gap, this study employs a dataset of 85,267 patents to map the technological landscape of EdTech through patent mining, clustering, and technology life cycle analysis. Using natural language processing and clustering techniques, 15 distinct technology clusters were identified, including Learning Management Systems, Virtual and Augmented Reality, Adaptive Learning Systems, and Educational Robotics. Each cluster was further analyzed using logistic regression to determine its stage within the technology life cycle—emerging, growth, maturity, or saturation. Key findings indicate that Interactive Display Technologies have reached saturation, while Adaptive Learning Systems and Virtual and Augmented Reality are in active growth, highlighting their transformative potential for future innovations. The study also integrates the SAMR model to evaluate how these technologies align with pedagogical goals, distinguishing between those that enhance existing practices and those that redefine educational experiences.}, archive = {J_ACCESS}, author = {Navid Mohammadi and Mehrdad Maghsoudi and Mohammad Sabet and Mahdi Soghi}, doi = {10.1109/ACCESS.2025.3554741}, journal = {IEEE Access}, pages = {55740-55768}, shortjournal = {IEEE Access}, title = {Unveiling the evolution of educational technologies: A patent-based clustering and life cycle analysis}, volume = {13}, year = {2025}, }
• (2025). Online fault tolerant RUL prediction strategy for lithium-ion batteries using machine learning. ACCESS, 13, 55727-55739. (www)

  @article{ , abstract = {The deterioration of lithium-ion batteries can lead to electrical system failures and potentially catastrophic consequences. Consequently, predicting the remaining useful life (RUL) of batteries is essential to prevent such failures and related issues. Reliable, accurate, and straightforward RUL prediction is crucial for effective power management in electric vehicles and to mitigate the risk of battery failure. This study introduces a highly available fault-tolerant prediction framework designed to forecast the RUL of lithium-ion batteries in challenging scenarios where key features such as voltage, current, and temperature are unavailable. The framework utilizes an Improved Convolutional Long Short-Term Memory Deep Network (Imp-CLD), a hybrid machine learning algorithm integrating Deep Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory networks. Performance metrics including Mean Absolute Error, Absolute Error, Relative Error, and Root Mean Square Error are used to assess the accuracy of RUL predictions. The model’s ability to maintain accuracy is influenced by the point at which predictions begin, as earlier predictions introduce higher levels of uncertainty due to accumulating errors over time. The proposed method is experimentally validated using datasets from the Massachusetts Institute of Technology and the Center for Advanced Life Cycle Engineering. The results indicate that this approach demonstrates greater resilience in predicting RUL, thereby enhancing lifetime control strategies and the safety monitoring function of the battery. This framework maintains prediction accuracy even when all features are missing at the prediction time, potentially preventing short-term catastrophes.}, archive = {J_ACCESS}, author = {Brahim Zraibi and Mohamed Mansouri and Chafik Okar and Hicham Chaoui}, doi = {10.1109/ACCESS.2025.3554720}, journal = {IEEE Access}, pages = {55727-55739}, shortjournal = {IEEE Access}, title = {Online fault tolerant RUL prediction strategy for lithium-ion batteries using machine learning}, volume = {13}, year = {2025}, }
• (2025). Personalized gamification a technological approach for student education: A systematic literature review. ACCESS, 13, 55712-55726. (www)

  @article{ , abstract = {Gamification has become an increasingly popular approach in education, applying game elements to increase student motivation and engagement. However, generalized applications often do not consider individual needs, making personalized of gamification important. Personalized aims to tailor game elements to students learning styles and preferences, thereby improving intrinsic motivation and learning outcomes. This research uses the Systematic Literature Review (SLR) method to review gamification personalized trends, methods, framework and provide guidelines for effective implementation in education. This research focuses on several key questions. RQ1: What are the current trends and advancements in personalized gamification. RQ2: Why does student education need personalized gamification. RQ3: How is personalized gamification implemented in student education. This research provides an up-to-date overview of previous research studies related to Personalized Gamification by conducting a Systematic Literature Review (SLR) summarizing 59 relevant journal articles. The research results show that gamification trends in education have steadily increased since 2015, with the adoption of technologies such as Artificial Intelligent (AI), Augmented Reality (AR), mobile, and websites. Personalized gamification has proven effective in enhancing student motivation and engagement by adjusting game elements to individual needs. Various methods such as the Gamification User Type Hexad and Game-Based Learning are employed to support personalized gamification for personalized learning.}, archive = {J_ACCESS}, author = {Suma Danu Ristianto and Atina Putri and Yusep Rosmansyah}, doi = {10.1109/ACCESS.2025.3552826}, journal = {IEEE Access}, pages = {55712-55726}, shortjournal = {IEEE Access}, title = {Personalized gamification a technological approach for student education: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral detection of pesticide residues in black vegetable based on multi-classifier entropy weight method. ACCESS, 13, 55701-55711. (www)

  @article{ , abstract = {This study addresses the issue of rapid, non-destructive detection of pesticide residues in black vegetable (Brassica narinosa L.H. Bailey) by proposing a multi-classifier entropy weighted method algorithm that combines hyperspectral technology and the entropy weight method. 10 black vegetable samples were sprayed with each of the four different pesticides (trichlorfon, propargite, cypermethrin, and imidacloprid) at concentrations of 0.10, 2.00, 0.20, and 2.00 mg/kg, respectively. Hyperspectral data were acquired using a hyperspectral imaging system. Savitzky-Golay convolutional smoothing was used for spectral preprocessing. Three dimensionality reduction techniques, competitive adaptive reweighted sampling, random frog leaping, and successive projections algorithm, were compared. Models were built using eXtreme gradient boosting, random forest, and support vector machine algorithms. The entropy weight method was then used to optimize model weights, developing the multi-classifier entropy weighted method algorithm to improve detection accuracy and robustness. Experimental results show that competitive adaptive reweighted sampling significantly improved overall classification accuracy and Kappa coefficient after dimensionality reduction. The multi-classifier entropy weighted method algorithm outperformed both single classifiers and other fusion algorithms in pesticide residue detection, achieving an overall accuracy of 94.20% and a Kappa coefficient of 0.93. Thus, integrating hyperspectral technology, machine learning, and the entropy weight method effectively leverages the strengths of each classifier, overcoming the “curse of dimensionality” and improving detection accuracy. This method offers an effective solution for the rapid detection of pesticide residues in black vegetable.}, archive = {J_ACCESS}, author = {Rongchang Jiang and Guoqiang Zhuang and Shijie Xie and Yang Wang and Guoqi Zhang and Dandan Qu and Wanzhi Wen}, doi = {10.1109/ACCESS.2025.3553384}, journal = {IEEE Access}, pages = {55701-55711}, shortjournal = {IEEE Access}, title = {Hyperspectral detection of pesticide residues in black vegetable based on multi-classifier entropy weight method}, volume = {13}, year = {2025}, }
• (2025). Exploring the dynamics of social media addiction and depression models with discrete and distributed delays. ACCESS, 13, 55682-55700. (www)

  @article{ , abstract = {Web-based networking significantly influences daily interactions and user well-being. This study analyzes social media addiction and depression models that incorporate distribution delays to enhance control strategies. Unlike previous deterministic models, our approach integrates both discrete (Dirac-delta) and distributed (gamma) delay distributions to assess the linear stability of disease-free and endemic equilibria and the occurrence of Hopf bifurcation. We find that while both equilibria maintain stability under short delays, increased mean delays lead to instability through Hopf bifurcation across both delay types. Notably, the gamma distribution demonstrates a stability switch; the endemic equilibrium initially remains stable, destabilizes as delays lengthen, and restabilizes with further delay increases. Analytical results confirm the direction and stability of these bifurcations, supported by numerical validations. This research fills a significant gap by combining discrete and distributed delays, providing insights crucial for developing effective interventions and shaping public health policies to mitigate the adverse effects of social media addiction on mental health.}, archive = {J_ACCESS}, author = {V. Madhusudanan and L. Guerrini and B. S. N. Murthy and Nhu-Ngoc Dao and Sungrae Cho}, doi = {10.1109/ACCESS.2025.3555306}, journal = {IEEE Access}, pages = {55682-55700}, shortjournal = {IEEE Access}, title = {Exploring the dynamics of social media addiction and depression models with discrete and distributed delays}, volume = {13}, year = {2025}, }
• (2025). Effective data augmentation for active sonar classification using attention-based complementary learning with uncertainty measure. ACCESS, 13, 55670-55681. (www)

  @article{ , abstract = {We enhance the performance of active sonar classification by integrating a validated attention-based complementary learning model (ABCL) with an active learning (AL) framework, following modifications to adapt the model for AL. Acoustic data from active sonar systems strongly depend on ocean environmental conditions and deep learning (DL) models trained on limited active sonar data often demonstrate poor performance when tested on data from new environments. To enhance performance, a small number of test data samples with high uncertainties are selected to augment the existing training set. A deep ensemble approach is employed to measure the uncertainty of each sample, quantified as the variance of predictions from models with independently optimized weights. ABCL, initially designed for robust generalization with limited active sonar data, was modified to produce two predictions per sample. This modification enhances the reliability of uncertainty measurement and is referred to as deeper ABCL (DABCL). Two datasets from different experimental conditions serve as the training and test sets. Most high-uncertainty test samples identified through AL are found in regions with a mix of target and non-target instances, allowing DABCL to adapt effectively to the shifted test data distribution. This approach achieves superior performance compared to other DL models, including VGG16, ResNet18, and Swin Transformer, both before and after applying AL. Although mislabeling occurs in 20 percent of the uncertainty samples during data augmentation, the fine-tuned DABCL still outperforms the version without AL.}, archive = {J_ACCESS}, author = {Youngsang Hwang and Geunhwan Kim and Wooyoung Hong and Youngmin Choo}, doi = {10.1109/ACCESS.2025.3555420}, journal = {IEEE Access}, pages = {55670-55681}, shortjournal = {IEEE Access}, title = {Effective data augmentation for active sonar classification using attention-based complementary learning with uncertainty measure}, volume = {13}, year = {2025}, }
• (2025). Neural network-based safety analysis of driving behavior in desert areas. ACCESS, 13, 55657-55669. (www)

  @article{ , abstract = {In order to explore the influence of sand accumulation road surface on traffic safety under wind and sand environment. This paper relies on the desert environment real-vehicle test to study the correlation between drivers’ EEG data and driving behavior under different road alignments and different sand thicknesses, constructs behavioral spectra, uses K-means to classify dangerous driving behaviors into four grades, and applies MATLAB software to construct the BP and RBF neural network recognition model. The study shows that 1) driver’s EEG $\delta $ and $\beta $ wave PSD integral values are positively correlated with the thickness of sand on the road and the complexity of the road alignment; 2) drivers tend to change the speed to reduce the driving risk when there is no sand accumulation to 0-2mm and the flat straight road to the flat curved road, and drivers tend to change the driving direction to avoid the dangerous driving environment and reduce the driving risk when there is a combination of road sand thickness of 4-6mm and curved and sloped road; 3) spectrum of risky driving behavior characteristics is mainly distributed in the (0, 0.2] interval, and the risk of serious driving increases significantly in the $\alpha $ / $\beta =0.81$ and $\alpha $ / $\beta =0.89$ states, which account for 2.16 times and 5.28 times more than the $\alpha $ / $\beta =1.01$ state, respectively. The results of the study can provide a basis for driving safety and security in desert areas, and reduce the rate of traffic accidents in desert areas to a certain extent.}, archive = {J_ACCESS}, author = {Fang Wang and Siping Huang and Weiguo Sun and Shixiao Liu and Huan Zhang}, doi = {10.1109/ACCESS.2025.3554758}, journal = {IEEE Access}, pages = {55657-55669}, shortjournal = {IEEE Access}, title = {Neural network-based safety analysis of driving behavior in desert areas}, volume = {13}, year = {2025}, }
• (2025). Research on the impact of energy-use right trading system driving green technology innovation in energy-intensive enterprises. ACCESS, 13, 55648-55656. (www)

  @article{ , abstract = {The previous economic development model based on scale expansion has caused serious environmental pollution and resource constraints, so it is particularly important to take energy conservation and environmental protection as the core of green innovation development. The introduction of the energy-use right trading system is of great significance to the impact of green innovation on energy-intensive enterprises. This paper takes the 2017 energy-use right trading pilot policy as a “quasi-natural experiment”, based on the data of listed companies from 2012 to 2022, and applies the PSM-DID method to test the impact of the energy-use right trading system on the green innovation of energy-intensive enterprises and its mechanism of action. The study finds that the energy-use rights trading system promotes the improvement of green innovation level, and the promotion effect gradually increases with time. The mechanism test shows that the energy-use right trading system promotes the green innovation level by increasing the R&D investment of enterprises, and the business environment of the region plays a positive moderating role in this process. The heterogeneity test shows that the energy-use right trading system is more effective in regions with more stringent state-owned enterprises and environmental regulations. Meanwhile, from the current point of view, the energy-use right trading system mainly promotes the strategic green innovation of enterprises, and does not drive the substantive innovation of enterprises enough. This paper provides theoretical support for the implementation of the energy-use right trading system, as well as empirical evidence and policy implications for its green innovation effects.}, archive = {J_ACCESS}, author = {Zihao Fu and Haixia Cao and Yan Li and Gang Wang}, doi = {10.1109/ACCESS.2025.3554176}, journal = {IEEE Access}, pages = {55648-55656}, shortjournal = {IEEE Access}, title = {Research on the impact of energy-use right trading system driving green technology innovation in energy-intensive enterprises}, volume = {13}, year = {2025}, }
• (2025). Breaking away from AI: The ontological and ethical evolution of machine learning. ACCESS, 13, 55627-55647. (www)

  @article{ , abstract = {Machine Learning (ML) has historically been associated with Artificial Intelligence (AI) but has developed into an independent discipline. This paper argues for the ontological independence of ML, driven by its unique methodologies, applications, and ethical considerations. A bibliometric analysis reveals that ML research output (494,572 publications from 2017–2023) surpasses AI (283,762 publications) by 74%, reflecting its rapid growth and specialization. Unlike AI’s pursuit of general intelligence and symbolic reasoning, ML focuses on data-driven performance optimization, with impactful applications in computer vision, natural language processing (NLP), and autonomous systems. The study highlights ethical challenges—such as addressing algorithmic bias (50 occurrences), fairness (2,778 publications), and environmental sustainability (283 related works)—which emphasize the need for dedicated ethical frameworks tailored to ML. These findings propose a conceptual and practical separation between ML and AI to enable targeted research, interdisciplinary collaboration, and solutions to challenges like explainability, transparency, and sustainability. The paper underscores the importance of recognizing ML’s independence in advancing both fields.}, archive = {J_ACCESS}, author = {Enrico Barbierato and Alice Gatti and Alessandro Incremona and Andrea Pozzi and Daniele Toti}, doi = {10.1109/ACCESS.2025.3553032}, journal = {IEEE Access}, pages = {55627-55647}, shortjournal = {IEEE Access}, title = {Breaking away from AI: The ontological and ethical evolution of machine learning}, volume = {13}, year = {2025}, }
• (2025). Adaptive virtual inertia in doubly fed induction generators for enhanced frequency regulation. ACCESS, 13, 55616-55626. (www)

  @article{ , abstract = {The integration of large-scale renewable energy sources, such as wind and photovoltaic power, into the electrical grid significantly reduces system inertia, thereby diminishing the grid’s capability to resist frequency deviations. During the frequency regulation process, the fixed virtual inertia provided by Doubly Fed Induction Generators is inadequate to support the system inertia requirements under large-scale integration of renewable energy. To address this issue, it is necessary to enhance the virtual inertia control of Doubly Fed Induction Generators by developing a fuzzy adaptive virtual control strategy. Under varying wind speeds and frequency fluctuations, the wind turbine is capable of adaptively adjusting the virtual inertia coefficient, enabling the renewable energy system to actively and adaptively participate in frequency regulation. Moreover, the pitch control strategy in the high wind speed region can be employed to provide additional frequency regulation power for wind turbines. During the frequency regulation process, the output relationship between the wind power system and the energy storage system should be carefully considered to achieve coordinated operation between the two systems. The whale optimization algorithm is utilized to determine the optimal output ratio, ensuring enhanced coordination and regulation performance. In a wind-storage frequency domain system model developed using MATLAB/Simulink simulation software, this strategy improves performance by at least 30% across different wind speed intervals compared to traditional control strategies, with enhancements of 38%, 24%, and 17% under various step loads. This approach effectively enhances the frequency characteristics of renewable energy systems.}, archive = {J_ACCESS}, author = {Xianxu Ding and Yihong Zhang}, doi = {10.1109/ACCESS.2025.3555443}, journal = {IEEE Access}, pages = {55616-55626}, shortjournal = {IEEE Access}, title = {Adaptive virtual inertia in doubly fed induction generators for enhanced frequency regulation}, volume = {13}, year = {2025}, }
• (2025). Battery state of health estimation methods: Implementation and comparison of 11 algorithms. ACCESS, 13, 55599-55615. (www)

  @article{ , abstract = {Battery State of Health (SOH) estimation is critical for ensuring the safety, performance, and longevity of batteries, particularly in applications such as electric vehicles and renewable energy systems. This study systematically reviews and implements 11 SOH estimation algorithms, categorized into direct measurement, adaptive, data-driven, and hybrid methods. Unlike previous research, this work emphasizes empirical validation using real-world battery datasets and evaluates each algorithm based on predictive accuracy, computational complexity, and practical applicability. Notably, these foundational algorithms serve as critical building blocks for advanced hybrid approaches that combine their unique strengths to enhance accuracy and robustness. Through detailed comparative analysis, this study not only guides researchers and practitioners in selecting optimal SOH estimation techniques but also lays the groundwork for innovative hybrid algorithm development to address limitations in current battery management systems. These findings contribute to advancing sustainable battery technologies and their integration into modern energy solutions.}, archive = {J_ACCESS}, author = {Eojin Kim and Sunghun Jung}, doi = {10.1109/ACCESS.2025.3541631}, journal = {IEEE Access}, pages = {55599-55615}, shortjournal = {IEEE Access}, title = {Battery state of health estimation methods: Implementation and comparison of 11 algorithms}, volume = {13}, year = {2025}, }
• (2025). Semi-supervised approach for automatic counting of whiteflies with small annotated dataset. ACCESS, 13, 55586-55598. (www)

  @article{ , abstract = {Insect counting is key action for pests’ control in agriculture. Automatic insect counting would allow a fast and accurate characterization of the infestation degree which would lead to a better choice of insecticide dose and, consequently, more effective treatments. Recently, an approach that automatically counts the insects in the wild has been proposed by Bereciartua-Pérez et al. That method is based on density map estimation with deep learning and has proven to offer very good results. Deep learning techniques, however, still present one big drawback: they rely on lots of annotated data. In the case of insect counting by density map estimation, the annotation process is a very tedious and time-consuming task and it entails an important bottleneck in the development of the model. In this paper, a new semi-supervised method is proposed for automatic counting of whiteflies with a small annotated dataset. Semi-supervised learning is based on leveraging not annotated data during training. Our semi-supervised method is based on the design and implementation of a pseudo-annotation algorithm that requires few annotated data. The pseudo-annotations obtained from this algorithm might be noisy but they help during the training of the whitefly counting model allowing to reduce the manual annotations needed and, therefore, reducing the effort and time needed to get a usable deep learning based solution for the task. Our new semi-supervised approach using only 48 manually annotated images achieves similar results as the fully supervised approach trained with 474 manually annotated images.}, archive = {J_ACCESS}, author = {Laura Gomez-Zamanillo and Arantza Bereciartua-Perez and Andoni Elola and Artzai Picon and Aitor Alvarez-Gila and Itziar Egusquiza and Ramon Navarra-Mestre}, doi = {10.1109/ACCESS.2025.3552195}, journal = {IEEE Access}, pages = {55586-55598}, shortjournal = {IEEE Access}, title = {Semi-supervised approach for automatic counting of whiteflies with small annotated dataset}, volume = {13}, year = {2025}, }
• (2025). AI-driven smart shopping carts with real-time tracking and inventory forecasting for enhanced retail efficiency. ACCESS, 13, 55576-55585. (www)

  @article{ , abstract = {This paper presents an AI-driven smart shopping cart system designed to enhance retail efficiency and customer experience through real-time data analytics and machine learning. Traditional shopping carts lack capabilities for adaptive tracking, inventory management, and personalized customer interaction. Our system addresses these gaps with a multi-layered architecture that integrates person-specific tracking, reinforcement learning (RL) for navigation, and Long Short-Term Memory (LSTM) networks for demand forecasting, alongside seamless Point-of-Sale (POS) integration for automated billing. The architecture comprises real-time data capture, edge computing for low-latency decisions, and cloud processing for customer profiling and inventory management. Experimental results demonstrate notable improvements in tracking accuracy, navigation efficiency, inventory forecasting, and customer satisfaction, highlighting AI’s transformative potential in retail.}, archive = {J_ACCESS}, author = {Muhammad Imran Zulfiqar and Ayesha Khalid and Abubakr Siddig and Muhammad Junaid Nawaz and Salim Saay}, doi = {10.1109/ACCESS.2025.3553854}, journal = {IEEE Access}, pages = {55576-55585}, shortjournal = {IEEE Access}, title = {AI-driven smart shopping carts with real-time tracking and inventory forecasting for enhanced retail efficiency}, volume = {13}, year = {2025}, }
• (2025). Enhanced detection of narrow-band leakage gas infrared image based on bilateral filtering and U-net. ACCESS, 13, 55559-55575. (www)

  @article{ , abstract = {Uncooled infrared image detection methods with a narrow-band filter have the advantages of low cost and can efficiently distinguish gas types; however, they also introduce nonuniform patterns to the image and reduce the target radiation flux and signal-to-noise ratio (SNR), resulting in low gas extraction accuracy. In this study, first, a temperature control device is designed in a part containing a filter and a detector array to effectively suppress dark current noise, which is positively correlated with temperature and eliminates nonuniform patterns, such as the lid effect, using the two-point correction method. Second, according to the infrared image characteristics of the leaked gas, a leakage gas infrared image enhancement algorithm based on bilateral filtering is developed. This algorithm considerably reduces the time frame noise of image sequences, improves the contrast between the gas plume region and background, and improves the SNR. Next, based on an improved fuzzy Gaussian background model, U-Net is used to separate the gas and non-gas foreground. Experimental results demonstrate that the proposed method exhibits high gas plume detection accuracy.}, archive = {J_ACCESS}, author = {Kangjun Dong and Weiqi Jin and Minghe Wang and Xia Wang and Shiwei Xu}, doi = {10.1109/ACCESS.2025.3554881}, journal = {IEEE Access}, pages = {55559-55575}, shortjournal = {IEEE Access}, title = {Enhanced detection of narrow-band leakage gas infrared image based on bilateral filtering and U-net}, volume = {13}, year = {2025}, }
• (2025). Point cloud registration for lava tube surface reconstruction using curvature-optimized projection. ACCESS, 13, 55545-55558. (www)

  @article{ , abstract = {The surface reconstruction of point clouds is a key technology in lunar exploration. The generation of high-precision three-dimensional models of lunar lava tubes via surface reconstruction provides technical support for subsequent exploration. To address the problem of detailed feature loss in the surface reconstruction of natural lava tubes, our study proposed a curvature-optimized point cloud surface reconstruction method. First, the normal vectors were estimated and normalized on an unstructured point cloud using Principal Component Analysis. Subsequently, the point cloud was projected using the Moving Least Squares method based on an anisotropic weighting function to obtain curvature-optimized point cloud data. Finally, implicit surface reconstruction and triangular mesh construction were achieved using the Poisson algorithm. Our study analyzed the morphological parameters of the Indian lava tube and investigated the impact of the parameter settings on registration, resampling, and surface reconstruction. The results show that our method can achieve resampling and surface reconstruction with Root Mean Square Error of less than 1.4 cm and 6.8 cm, respectively. Compared with Farthest Point Sampling, Normal Space Sampling, Uniform Sampling, and Voxel Sampling methods, our method has a higher reconstruction surface accuracy and is suitable for high-precision surface reconstruction of natural lava tube point clouds. It can be used to construct high-precision three-dimensional models of the lunar lava tubes.}, archive = {J_ACCESS}, author = {Jiaqi Yao and Yanqiu Wang and Wen Li and Yuan Han and Hognxu Ai and Zhenchen Ji and Fu Zheng and Zhibin Sun}, doi = {10.1109/ACCESS.2025.3550497}, journal = {IEEE Access}, pages = {55545-55558}, shortjournal = {IEEE Access}, title = {Point cloud registration for lava tube surface reconstruction using curvature-optimized projection}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches for continuous sign language recognition: A comprehensive review. ACCESS, 13, 55524-55544. (www)

  @article{ , abstract = {Sign language uses hand gestures as a visual mode of communication, along with body actions and facial expressions. Due to the increasing incidence of hearing deficiencies, the field of Continuous Sign Language Recognition (CSLR) has seen a considerable increase in research, which involves identifying consecutive signs in video streams without previous information of their sequential limitations. However, existing research often lacks a unified framework for integrating spatial, temporal, and alignment approaches, while critical challenges such as real-time processing, diverse datasets, and signer variability remain unresolved. This survey uniquely contributes by presenting a novel framework that integrates these dimensions into a unified taxonomy for CSLR systems. It critically analyzes numerous studies, organizing them into a comprehensive taxonomy covering aspects such as sign language, data collection, input method, gesture signals, identification methods, applied data collections, and comprehensive efficiency. The article further categorizes deep-learning CSLR models according to spatial, temporal, and alignment approaches, highlighting their benefits and drawbacks. Furthermore, it explores various research aspects, such as the challenges of CSLR, the significance of nonverbal elements in CSLR systems, and the gaps in the body of current research. By emphasizing the role of real-time processing and diverse datasets, this survey provides actionable insights for advancing CSLR systems in practical applications. This classification serves as a helpful tool for researchers developing and organizing cutting-edge CSLR methods. The study highlights the effectiveness of deep learning systems in capturing different sign language signals. On the other hand, several challenges remain, such as the need for diverse, naturalistic datasets, improved signer diversity, and real-time CSLR systems. Addressing these gaps will be essential for advancing CSLR’s real-world applications and developing more robust, efficient models for the future. The conclusions give a wider Comprehension of sign language recognition and set the groundwork for future studies focused on addressing the current challenges and issues in this developing area.}, archive = {J_ACCESS}, author = {Asma Khan and Seyong Jin and Geon-Hee Lee and Gul E. Arzu and L. Minh Dang and Tan N. Nguyen and Woong Choi and Hyeonjoon Moon}, doi = {10.1109/ACCESS.2025.3554046}, journal = {IEEE Access}, pages = {55524-55544}, shortjournal = {IEEE Access}, title = {Deep learning approaches for continuous sign language recognition: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Improving hydroponic systems by using ARUCO markers for leaf detection: Focus on tomato plants. ACCESS, 13, 55512-55523. (www)

  @article{ , abstract = {Over the past few years, overpopulation has become a major issue, raising the demand for basic agricultural necessities. Even with the introduction of closed-environment systems aiming to provide an alternative for space requirements, the initial setup costs for such systems are high, which serves as a barrier for farmers and industries to set up a profitable system. Hydroponics is a soil-less cultivation method that aims to replace the soil in agriculture. In traditional soil-based agriculture, plants obtain their nutrients from the soil, which acts as a reservoir for essential minerals, physical support, and water retention. However, soil contains undesirable components that reduce plant growth efficiency by absorbing plant nutrients. This makes it unpredictable, even in a controlled environment. Compared to its water counterpart, the water medium provides numerous advantages, such as water conservation, efficient nutrient delivery, reduced pest and disease risk, faster growth, reduced environmental impact, and much more. Hence, by eliminating soil, this work eliminates unpredictability and creates a better-controlled environment that performs better than its soil counterpart. However, the initial setup costs of hydroponic systems can often be prohibitive for small-scale farmers or resource-constrained communities. This research work aims to grow tomato plants hydroponically so that tomatoes are available regardless of the season. We use a combination of Nutrient Film Technique (NFT), Deep Water Culture (DWT) to analyze the growth of tomatoes in outdoor weather and other environmental conditions in southern India. Further, ARUCO markers are used for leaf identification and also to estimate the distance, pose and coordinates of the leaves by image processing technique. This serves as a method to achieve optimal leaf picture from the environment.}, archive = {J_ACCESS}, author = {P. Tharun and S. Muthulakshmi and N. Subhashini and P. Vishnuram and R. Kesavan}, doi = {10.1109/ACCESS.2025.3554598}, journal = {IEEE Access}, pages = {55512-55523}, shortjournal = {IEEE Access}, title = {Improving hydroponic systems by using ARUCO markers for leaf detection: Focus on tomato plants}, volume = {13}, year = {2025}, }
• (2025). Leaf level ash dieback disease detection and online severity estimation with UAVs. ACCESS, 13, 55499-55511. (www)

  @article{ , abstract = {Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone species threatens biodiversity and may lead to significant habitat degradation. Since no cure exists, early detection and removal of infected trees are critical to slowing the spread of the disease. Traditional identification methods rely on visual assessments of canopy loss, which are inefficient and impractical for large-scale monitoring. Leveraging advancements in computer vision and deep learning, our key objective is to develop a tool to detect ash dieback symptoms at the leaf level, classifying leaves into three categories: healthy, early-stage infection, and mid-stage infection. Since there is no known available dataset for ash dieback at the leaf level, we generated a new synthetic dataset and trained a YOLOv5 single-stage object detection model. The final model achieves mean Average Precision (mAP) scores of above 90% for each category. Evaluations on real ash tree leaf footage captured using uncrewed aerial vehicles (UAVs) show strong alignment between the model’s detections and expert annotations. Our tool demonstrates the potential of integrating advanced computer vision techniques into tree health monitoring platforms. In the near future, this can provide conservationists and researchers with a novel, efficient means of early disease identification.}, archive = {J_ACCESS}, author = {Elizabeth Bates and Marija Popović and Conor Marsh and Ronald Clark and Mirko Kovac and Basaran Bahadir Kocer}, doi = {10.1109/ACCESS.2025.3541980}, journal = {IEEE Access}, pages = {55499-55511}, shortjournal = {IEEE Access}, title = {Leaf level ash dieback disease detection and online severity estimation with UAVs}, volume = {13}, year = {2025}, }
• (2025). Integration of fourier and wavelet transform for adsorption mechanism of cesium study based on X-ray diffraction and absorption spectroscopy. ACCESS, 13, 55487-55498. (www)

  @article{ , abstract = {The safety and storage of nuclear disposal plays a critical challenge in the utilization of radiology applications such as for nuclear medicine, radiotherapy, nuclear energy, particularly concerning the environmental behavior of radio-materials. One of the most concerning materials is radiocalcium due to its physical and chemical mechanism and half-life period. In this study, several minerals were collected, including basalt, argillite, mudrock and granite for the batch tests. In analysis stage, X-ray diffraction and absorption spectroscopy were used for radio-Cs LIII-edge adsorption study. The batch test process shows that basalt, argillite, and mudrock exhibited strong adsorption effectiveness for Cs, with distribution coefficient values ( $\ge 700$ mL/g). In contrast, granite from two different locations showed lower distribution coefficient values (<15 mL/g) after adsorption, with pH also affecting the adsorption efficiency. The coordination number ratio confirmed the adsorption mechanisms: basalt and argillite exhibited inner-sphere (IS) complexation, granite showed a transition from ion exchange to IS complexation due to pH effects, and mudrock exhibited IS complexation after Cs+ dehydration. A novel EXAFS fitting strategy is proposed to deliver reasonable fitting parameters. Moreover, a 2D wavelet transform with XANES could deliver another viewpoint of the adsorption mechanisms. Based on the comparison with adsorption parameters of the standard CsNO3 sample, an iterative check algorithm is proposed then to yield the reasonable outcomes. The preliminary results found that the hydration layer adsorption mechanism of argillite and mudrock are similar to CsNO3, while granite-W and mudrock displayed similar structural information for outer-sphere (OS) complexes, indicating IS complexation caused by Cs+ dehydration.}, archive = {J_ACCESS}, author = {Chi-Wen Hsieh and Zih-Shiuan Chiou and Chuan-Pin Lee and Shih-Chin Tsai and Wei-Hsiang Tseng and Yu-Hung Wang and Hui-Min Chiu and Qi-Rui Sun and Chun-Liang Yeh}, doi = {10.1109/ACCESS.2025.3554742}, journal = {IEEE Access}, pages = {55487-55498}, shortjournal = {IEEE Access}, title = {Integration of fourier and wavelet transform for adsorption mechanism of cesium study based on X-ray diffraction and absorption spectroscopy}, volume = {13}, year = {2025}, }
• (2025). Experimental validation of a multi-functional metasurface for 5G and satellite communication. ACCESS, 13, 55476-55486. (www)

  @article{ , abstract = {In this article, a single layer, ultrathin, multiband, and multifunctional reflective polarization converting metasurface (PCMS) is presented that can perform linear to linear (LTL) orthogonally with respect to the incident polarization, also called cross polarization conversion (CPC), and linear to circular (LTC) polarization conversion (PC). It consists of a periodic arrangement of an inclined H-shaped metallic patch, on the top of a F4B grounded dielectric substrate. The proposed configuration converts linear polarization (LP) to an orthogonal polarization in five frequency bands, i.e., 9-10.6, 15.9-16, 18.6-22.9, 23.7-23.8, and 25.9-26.9 GHz with more than 90% polarization conversion ratio (PCR). Furthermore, the proposed metasurface design accomplishes circular polarization (CP) in eight different frequency bands namely, 8.6-8.8, 10.9-11.4, 15.6-15.8, 16.8-17.7, 22.9-23, 23.3-23.4, 25.6-25.7, and 27.3-27.5 GHz, having the required axial ratio (AR $\le 3$ dB). Multiple surface plasmonic resonances are the reason behind the different conversions. The experimental results are in good agreement with the simulated results. The ultrathin, simple structure, multiband, multifunctional characteristics, and compact size of the polarization converter make it an excellent option for 5G wireless systems, satellite communication, and stealth technologies.}, archive = {J_ACCESS}, author = {Hilal Rauf and Babar Kamal and Sadiq Ullah and Usman Ali and Ladislau Matekovits}, doi = {10.1109/ACCESS.2025.3555129}, journal = {IEEE Access}, pages = {55476-55486}, shortjournal = {IEEE Access}, title = {Experimental validation of a multi-functional metasurface for 5G and satellite communication}, volume = {13}, year = {2025}, }
• (2025). MsF-AT: A study on ship SAR image classification based on multi-scale feature and attention mechanism. ACCESS, 13, 55467-55475. (www)

  @article{ , abstract = {Ship Synthetic Aperture Radar (SAR) images are a critical component of remote sensing imaging. Leveraging the all-weather and round-the-clock capabilities of radar detection, ship SAR has found extensive application in marine remote sensing monitoring and management. Despite achieving relatively high resolution at present, the Automatic Target Recognition (ATR) accuracy of ship SAR remains suboptimal due to limited image features and inherent SAR imaging characteristics, which affect its performance and broader adoption. In recent years, the rapid development of deep learning methods has significantly enhanced classification performance through innovative classifier design strategies and automatic feature extraction mechanisms. This paper proposes a novel deep neural network for classifying ship SAR images by integrating attention mechanisms and multi-scale feature fusion techniques within deep convolutional networks. The aim is to enhance feature channels and effectively leverage low-level features. Experimental results demonstrate that this model outperforms traditional methods, achieving 1.64% higher accuracy on three-category classification and 3.97% higher accuracy on six-category classification compared to the second-best method on the OpenSARShip dataset.}, archive = {J_ACCESS}, author = {Jianli Zheng and Jianjun Cao and Xin Hu}, doi = {10.1109/ACCESS.2025.3554047}, journal = {IEEE Access}, pages = {55467-55475}, shortjournal = {IEEE Access}, title = {MsF-AT: A study on ship SAR image classification based on multi-scale feature and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A study on the feasibility of LiFi in an intra-vehicular data transmission application”. ACCESS, 13, 55466. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Study on the Feasibility of LiFi in an Intra-Vehicular Data Transmission Application”).}, archive = {J_ACCESS}, author = {Hongyu Guan and Manolo Dulva Hina}, doi = {10.1109/ACCESS.2025.3554921}, journal = {IEEE Access}, pages = {55466}, shortjournal = {IEEE Access}, title = {Corrections to “A study on the feasibility of LiFi in an intra-vehicular data transmission application”}, volume = {13}, year = {2025}, }
• (2025). Graph-CAM: Explainable image features via graph neural network in fourier domain. ACCESS, 13, 55456-55465. (www)

  @article{ , abstract = {Even though Fourier decomposition plays a foundational role in imaging, modern deep learning tools naturally elude direct inputs in the form of spectra, as the frequency content in each layer can be ruined by the pixel-processing operations (e.g., by a convolution). To construct an ‘all-frequency’ neural network, we propose a pipeline with tiling and frequency isoclines, embedded as nodes in a trainable graph network. This new pipeline ensures that spatial relations between frequency bands are preserved, addressing key limitations of traditional deep learning architectures in frequency domain applications. Our method, Graph-CAM, proves competitive for analyzing both natural and medical datasets, the latter being of special value, as various medical imaging modalities acquire power spectra as their raw data. Regardless of the source of image data, the method can highlight the most important frequency bands and the image features responsible for a given prediction, which is a new interpretable knowledge discovery pathway.}, archive = {J_ACCESS}, author = {Vladimir Shaposhnikov and Iaroslav Bespalov and Dmitry V. Dylov}, doi = {10.1109/ACCESS.2025.3554970}, journal = {IEEE Access}, pages = {55456-55465}, shortjournal = {IEEE Access}, title = {Graph-CAM: Explainable image features via graph neural network in fourier domain}, volume = {13}, year = {2025}, }
• (2025). In-depth analysis of ka-band integrated commercial beamformer non-idealities in phased array calibration and pattern prediction methods for 5G scenarios. ACCESS, 13, 55442-55455. (www)

  @article{ , abstract = {This paper comprehensively examines the non-idealities inherent in commercially available beamformers and explores their implications in the calibration procedures for phased array antennas. The non-ideal nature of the beamformer is meticulously modeled and assessed through extensive S-Parameter measurements to discern its impact on analyzed excitation retrieval methods. The Rotating-element Electric-field Vector (REV) and Control Circuit Encoding (CCE) methods are simulated using a synthetic array with spherical wave expansion driven by output coefficients measured from the beamformer. Simulations are executed to compare the REV method with a Selective-REV variant, designed to enhance coefficient prediction accuracy by leveraging a priori knowledge of phase-shifter amplitude deviations. Additionally, amplitude, phase, and complex CCE encodings are employed to determine the encoding technique that best accommodates beamformer non-idealities. Finally, anechoic chamber measurements assess the pattern prediction capabilities of the analyzed methods, and an iterative calibration scheme is proposed to compensate for the studied beamformer errors due to its behavior.}, archive = {J_ACCESS}, author = {Jorge Calatayud Maeso and Xiaoliang Sun and Alfonso Tomás Muriel-Barrado and Fernando Rodriguez Varela and Pablo Sánchez-Olivares and José-Manuel Fernández-González and Manuel Sierra Castañer}, doi = {10.1109/ACCESS.2025.3555140}, journal = {IEEE Access}, pages = {55442-55455}, shortjournal = {IEEE Access}, title = {In-depth analysis of ka-band integrated commercial beamformer non-idealities in phased array calibration and pattern prediction methods for 5G scenarios}, volume = {13}, year = {2025}, }
• (2025). Enhancing sports team management through machine learning. ACCESS, 13, 55431-55441. (www)

  @article{ , abstract = {Sports like basketball and baseball have seen significant improvements through the effective use of sports analytics. However, research on machine learning in football has primarily focused on prediction. There is a need to explore whether machine learning can contribute to generating more in-depth and accurate insights into football analysis. This essay presents a descriptive analysis of player performance in football matches. Typically, player performance in football is evaluated by consulting a panel of experts, though the criteria they use for evaluation remain undisclosed. By applying machine learning algorithms and pruning techniques, several classifications were developed to identify the most critical functional attributes of players. The functional metrics that delivered the best results in rating predictions had an average error of 0.17. The most significant performance data for each position on the field were closely aligned with the factors experts consider when grading players. The 34 performance characteristics identified achieved a prediction accuracy of 63.4% for match outcomes.}, archive = {J_ACCESS}, author = {Ling Zhang and Yifan An}, doi = {10.1109/ACCESS.2025.3551889}, journal = {IEEE Access}, pages = {55431-55441}, shortjournal = {IEEE Access}, title = {Enhancing sports team management through machine learning}, volume = {13}, year = {2025}, }
• (2025). A novel quad-core double-steel optical fiber solution for landslide monitoring. ACCESS, 13, 55416-55430. (www)

  @article{ , abstract = {This study presented the design and performance evaluation of a novel quad-core optical fiber cable with lateral double steel wire support for landslide detection. The cable offered a multipurpose solution for both landslide monitoring and traditional data transmission. We investigated the mechanical behavior, durability, sensitivity, and signal integrity of single- and double-steel optical fiber cables (OFCs) under various mechanical stresses. Simulations were conducted using SOLIDWORKS 2023 SP1, while experimental simulations of landslide forces were performed with a Universal Testing Machine (UTM)-Instron, and measurements were evaluated using an Optical Time Domain Reflectometer (OTDR). The results showed that the quad-core double steel cable exhibited significantly lower localized and overall signal losses, improved mechanical strength, and greater sensitivity compared to the 1-core single steel cable. Notably, the quad-core design demonstrated a 76% lower load threshold, a 95% reduction in overall signal loss, a 20% reduction in loss at load points, and a 315% increase in attenuation. These advantages made the quad-core cable ideal for landslide detection, environmental monitoring, and subsurface pipeline movement, offering a more reliable solution for geohazard and infrastructure monitoring.}, archive = {J_ACCESS}, author = {Merlin Asha Manoah Johnson and Swee King Phang and Wei Ru Wong and Ari Happonen}, doi = {10.1109/ACCESS.2025.3555734}, journal = {IEEE Access}, pages = {55416-55430}, shortjournal = {IEEE Access}, title = {A novel quad-core double-steel optical fiber solution for landslide monitoring}, volume = {13}, year = {2025}, }
• (2025). GT-BDI model: A combined game-theoretic and BDI-based computational model for emergency evacuation with search and rescue robots. ACCESS, 13, 55398-55415. (www)

  @article{ , abstract = {The catastrophic impact of disasters on affected populations necessitates effective management practices to minimize the societal and economical damages caused by disasters. This pertains planning effective measures to find and rescue trapped victims in time. Search and rescue in general is very challenging, as the number of the trapped victims may be unknown and their behaviour while trying to evacuate the disaster area is prone to variations, depending on their individual cognition and social interactions. Evacuation robots have attracted attention for their role in assisting search and rescue teams to locate and save victims. A behavioural model of the victims can provide insights for both the staff and the robots in search and rescue missions on how the trapped victims act during an evacuation, and to plan the search and rescue mission accordingly. Such a model, after being validated, can also be used for analysis of the influence of the robots in search and rescue missions. This paper proposes a novel evacuation model that integrates game theory and the belief-desire-intention (BDI) framework, in order to incorporate both the interactions of the trapped victims and their cognitive processes at the individual level. The model is validated using existing benchmark models for evacuation behaviour. Furthermore, the validated model is used to assess the effectiveness of the evacuation robots within the evacuation procedure. It is found that the presence of evacuation robots does reduce the evacuation time, as a function of the trust of the victims in these robots.}, archive = {J_ACCESS}, author = {Laura Wilmes and Anahita Jamshidnejad}, doi = {10.1109/ACCESS.2025.3555870}, journal = {IEEE Access}, pages = {55398-55415}, shortjournal = {IEEE Access}, title = {GT-BDI model: A combined game-theoretic and BDI-based computational model for emergency evacuation with search and rescue robots}, volume = {13}, year = {2025}, }
• (2025). EAD-YOLOv10: Lightweight steel surface defect detection algorithm research based on YOLOv10 improvement. ACCESS, 13, 55382-55397. (www)

  @article{ , abstract = {In response to the issues of low detection accuracy (DA), slow speed, and missed detections caused by the complex texture background and diverse shapes of surface defects (SD) in steel, this paper designs an improved lightweight YOLOv10 model called EAD-YOLOv10. By incorporating an Adaptive Downsampling (Adown) structure to replace traditional convolutional layers, this module reduces the model’s computational complexity and accelerates inference speed while enhancing the retention of semantic information through a lightweight design. A Dynamic Upsample (DySample) module is embedded in the neck network, which adaptively adjusts sampling weights based on the occurrence frequency of various samples in the dataset, improving the model’s recognition accuracy for minority class samples. Finally, the designed C2f_EMSCP method is integrated into the backbone and neck networks, effectively merging multi-scale convolutional networks and position-aware modules to enhance the model’s sensitivity and detection capability for multi-scale targets by fusing feature maps of different scales. The experimental results show that the improved EAD-YOLOv10 network model achieves an average precision of 94.2% for detecting six types of defects in the NEU-DET dataset, which is an improvement of 7.6% over the baseline model, with a 9.75% reduction in model size and a 12.5% decrease in computational load, outperforming other mainstream object detection algorithms and meeting the requirements for SD detection in industrial production. Additionally, generalization experiments on actual industrial defect datasets confirm that this algorithm has good generalization capabilities.}, archive = {J_ACCESS}, author = {Hu Haoyan and Tong Jinwu and Wang Haibin and Lu Xinyun}, doi = {10.1109/ACCESS.2025.3552683}, journal = {IEEE Access}, pages = {55382-55397}, shortjournal = {IEEE Access}, title = {EAD-YOLOv10: Lightweight steel surface defect detection algorithm research based on YOLOv10 improvement}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Self-supervised adaptive learning algorithm for multi-horizon electricity price forecasting”. ACCESS, 13, 55381. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting”.}, archive = {J_ACCESS}, author = {Muhammad Ahsan Zamee and Yeongsang Lee and Dongjun Won}, doi = {10.1109/ACCESS.2025.3554390}, journal = {IEEE Access}, pages = {55381}, shortjournal = {IEEE Access}, title = {Corrections to “Self-supervised adaptive learning algorithm for multi-horizon electricity price forecasting”}, volume = {13}, year = {2025}, }
• (2025). Research on long-distance snow depth measurement method based on improved YOLOv8. ACCESS, 13, 55370-55380. (www)

  @article{ , abstract = {Real-time monitoring of snow depth is crucial in meteorological forecasting, transportation management, disaster warning, and ecological protection. With the development of deep learning, the YOLO model has become widely used for automated snow depth detection due to its efficient and accurate instance segmentation. However, the standard YOLOv8-seg model still faces challenges, such as insufficient detection accuracy and blurred edges, when dealing with snow detection in complex backgrounds. To address these issues, this paper proposes a snow depth detection method based on an improved YOLOv8-seg model, named YOLOv8-AE. First, an efficient multiscale attention (EMA) module is incorporated into the C2f module to enhance feature extraction capabilities. Second, the introduction of the variable kernel convolution (AKConv) module improves the adaptability of convolutional operations, boosting the model’s performance in snow depth detection. Comparative experimental results show that, compared with the YOLOv8-seg model, the YOLOv8-AE model increases the ratio of snow depths that deviate by no more than 2 cm from the reference value, from 84% to 88% on average. The RMSE decreases by 0.74, and the MAE decreases by 0.93, significantly improving the accuracy of snow depth measurements.}, archive = {J_ACCESS}, author = {Jia-Wen Wang and Yu Cao and Zong-Kai Guo and Cheng Xu}, doi = {10.1109/ACCESS.2025.3551727}, journal = {IEEE Access}, pages = {55370-55380}, shortjournal = {IEEE Access}, title = {Research on long-distance snow depth measurement method based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). High-order and interactive perceptual feature learning for medical image retargeting. ACCESS, 13, 55358-55369. (www)

  @article{ , abstract = {Computed tomography (CT) imaging is a valuable technique for detecting lesions and injuries in internal structures of the human body. Visual retargeting focuses on resizing less significant areas, such as normal organs, either horizontally or vertically, while preserving the dimensions of key regions, such as affected organs. This process enhances the display of CT images, which can be particularly beneficial for applications like medical education, enabling optimal presentation of CT images to students. This study explores an innovative approach to CT image retargeting by effectively fusing gaze shift path (GSP) features using a hypergraph framework to address the complex spatial layout of CT images. To capture both semantically and visually significant elements in each CT image, the BING objectness descriptor is employed to rapidly and accurately localize objects or their components across multiple scales. Each identified object-aware patch is then described using multi-channel low-level features. To model human attention and its perception of important elements within a CT image, a locality-preserved and interactive active learning (LIAL) approach is introduced. This method sequentially generates GSPs for each CT image, which represent human gaze trajectories. Given the high dimensionality of these features, a hypergraph fusion enables the representation of higher-order relationships among features, capturing complex dependencies and interactions between the GSP features. Through this framework, the CT image retargeting process benefits from enhanced feature fusion, leading to more accurate identification of critical areas. Extensive empirical evaluations demonstrate the efficacy of this hypergraph-based method in improving CT image retargeting outcomes, offering significant advancements for medical imaging and educational applications.}, archive = {J_ACCESS}, author = {Mingjuan Ma and Yuehong Zhang}, doi = {10.1109/ACCESS.2025.3555570}, journal = {IEEE Access}, pages = {55358-55369}, shortjournal = {IEEE Access}, title = {High-order and interactive perceptual feature learning for medical image retargeting}, volume = {13}, year = {2025}, }
• (2025). Fairness-aware computation offloading for mobile edge computing with energy harvesting. ACCESS, 13, 55345-55357. (www)

  @article{ , abstract = {Mobile edge computing (MEC) improves network performance by minimizing latency and assigning computing tasks to edge servers. Nonetheless, delegating computations in environments with high device density poses considerable difficulties. Ensuring fairness in resource distribution among users is essential for preserving network stability and user satisfaction in these contexts. This research formulates the Fairness-aware Computation Offloading Optimization (FACOO) algorithm. The Lyapunov approach and sequential least squares quadratic programming (SLSQP) are used to ascertain the best offloading ratio, transmission power, and CPU frequency while complying with signal-to-interference-plus-noise ratio (SINR) limitations. Energy harvesting (EH) is built into FACOO to prolong device battery life and to ensure that MEC systems, which have limited resources, are more sustainable. The results show that FACOO greatly improves throughput and fairness while using significantly less energy, especially in settings with numerous nodes dispersed across large areas. Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.}, archive = {J_ACCESS}, author = {Dedi Triyanto and I. Wayan Mustika and Widyawan and Praphan Pavarangkoon}, doi = {10.1109/ACCESS.2025.3552498}, journal = {IEEE Access}, pages = {55345-55357}, shortjournal = {IEEE Access}, title = {Fairness-aware computation offloading for mobile edge computing with energy harvesting}, volume = {13}, year = {2025}, }
• (2025). Design and architecture for anti-hidden faults multiagent protection system in smart grids. ACCESS, 13, 55332-55344. (www)

  @article{ , abstract = {The sensitivity of modern day smart devices and the continuously evolving power system into smart grid has raised the level of requirements from protection systems to zero-tolerance for hidden faults. Researchers have continuously proposed various protection schemes to enhance the performance of protection systems. However, high resistance hidden faults could still pass undetected for most of the proposed schemes or could be detected but suffer from high requirements of phasor measurement units (PMUs) or low confidence in the scheme decision. In this paper, an Anti-hidden fault multiagent protection system (AHF-MPS) is proposed that detects hidden faults with minimal PMU requirements and high confidence level in the detection decision. The AHF-MPS employs PMUs as primary agents which detects the periodic change in the phase angle of positive sequence currents that arises due to fault incidents. The second step of AHF-MPS is to initiate an accelerating factor to overcurrent protection devices acting as secondary agents. The system was tested upon IEEE 34 testing system and the results showed successful detection of hidden faults with operating times ranging from 77 ms up to 432 ms depending on fault location. That proved its ability in detecting hidden faults with minimal requirements proving its superiority to its peers.}, archive = {J_ACCESS}, author = {Tamer Eliyan and Saad F. Al-Gahtani and Z. M. S. Elbarbary and Fady Wadie}, doi = {10.1109/ACCESS.2025.3555487}, journal = {IEEE Access}, pages = {55332-55344}, shortjournal = {IEEE Access}, title = {Design and architecture for anti-hidden faults multiagent protection system in smart grids}, volume = {13}, year = {2025}, }
• (2025). Nonlinear model predictive control for internet of things using particle swarm optimization and cloud computing. ACCESS, 13, 55324-55331. (www)

  @article{ , abstract = {This paper proposes a novel model predictive control design for Internet of Things (IoT) applications by using the generalized opposition-based learning particle swarm optimization algorithm and cloud computing. The proposed nonlinear optimal control algorithm significantly generalize existing algorithms, which are tailored only for linearized or multi-model plant descriptions. The first key aim is to present a nonlinear model predictive control design based on the generalized opposition-based learning particle swarm optimization algorithm, which can stabilize the nonlinear control system without deriving the stability conditions. The second relevant contribution assumes that the complex computations involved in determining the optimal control input for proposed controller are performed by a cloud computing system, enabling efficient handling of computational tasks. Third, the communication between the actuators and transmitters in the control unit is established through IoT, ensuring seamless exchange of information. Finally, the efficacy of the proposed methodology is illustrated via simulations showcasing the implementation of distributed model predictive control on a dynamic nonlinear mobile robot model.}, archive = {J_ACCESS}, author = {B. Devanathan and P. Selvaraj and A. Suyampulingam}, doi = {10.1109/ACCESS.2025.3554719}, journal = {IEEE Access}, pages = {55324-55331}, shortjournal = {IEEE Access}, title = {Nonlinear model predictive control for internet of things using particle swarm optimization and cloud computing}, volume = {13}, year = {2025}, }
• (2025). A leader-follower control system for a package-delivery drone. ACCESS, 13, 55313-55323. (www)

  @article{ , abstract = {This paper proposes a feasible strategy to design a control system to guide drones in delivering packages autonomously. It is shown that the leader-follower control paradigm fulfills all the requirements for the delivery mission, thus being a good strategy for designing the control system. The logic behind the problem is that the quadrotor takes off from a ground vehicle (a delivery truck, for instance) or even from a warehouse, goes to the delivery point, returns to the ground vehicle or warehouse, and lands on it. In each of those steps, the drone should compose a leader-follower formation, meaning it should have a leader and a relative position to such a leader in each step. The advantage of the leader-follower control paradigm is that just one controller is necessary for the delivery drone, the follower. It is required to define the leader in each task step, which is perfectly possible using such a control paradigm, as the paper shows. Such a logic relies on the drone disregarding the ground vehicle as its leader when flying toward the delivery point and resuming its leadership after delivering the parcel. In other words, the delivery drone will follow two leaders at two different moments: the delivery point in the delivery step and the ground vehicle when returning to it. The delivery drone should also avoid obstacles, such as buildings and trees, to which the well-known potential field theory is adopted, which can be easily inserted into the leader-follower control system. Results of an experiment on a lab scale are presented and discussed, which validates the control system designed under the strategy discussed here. Consequently, the correctness of the hypothesis that the leader-follower control paradigm is a feasible solution to control a delivery drone in accomplishing package delivery is confirmed. This is the main contribution of the study. Moreover, a possible generalization of the proposed control system for a set of delivery drones is outlined.}, archive = {J_ACCESS}, author = {Emanuele Dos Santos Cardoso and Vinícius Pacheco Bacheti and Mário Sarcinelli-Filho}, doi = {10.1109/ACCESS.2025.3555031}, journal = {IEEE Access}, pages = {55313-55323}, shortjournal = {IEEE Access}, title = {A leader-follower control system for a package-delivery drone}, volume = {13}, year = {2025}, }
• (2025). Recent trends in localization, routing, and security for wireless sensor networks. ACCESS, 13, 55290-55312. (www)

  @article{ , abstract = {Wireless sensor networks (WSN) are an essential component of modern systems that enable ubiquitous sensing and data collection in a wide range of contexts, including healthcare, smart cities, and environmental monitoring. This article thoroughly covers the three pillars of WSNs i.e. localization, routing, and security along with the integration of WSN with IoT. Localization methods impact the accuracy of data and applications, which in turn influence the determination of the geographical coordinates of sensor nodes. Localization methods, such as range-based and range-free systems, influence data accuracy and energy conservation. This article will also discuss about Routing protocols, which primarily establish energy-efficient and resource-optimal communication paths between nodes in WSNs and popular routing methods like flat, hierarchical, and location-based protocols. It also measures how well they work. In addition to localization and routing, our study delves deep into the most recent security protocols and policies to secure WSNs. Data interception, network interruption, and node penetration are just a few of the many attacks that WSNs are vulnerable to, making security a top priority. Methods like authentication, encryption, and intrusion detection are the main focus points. Similarly, 6TiSCH protocols and their impact in WSN-IoT systems is also discussed. This study highlights the continuous need of improving the security, efficiency, and reliability of WSNs for IoT applications. The detailed survey on three pillars of WSN provides an insight into the existing research gaps which has to be addressed.}, archive = {J_ACCESS}, author = {H. N. Vishwas and T. K. Ramesh}, doi = {10.1109/ACCESS.2025.3555280}, journal = {IEEE Access}, pages = {55290-55312}, shortjournal = {IEEE Access}, title = {Recent trends in localization, routing, and security for wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Generating automatically Print/Scan textures for morphing attack detection applications. ACCESS, 13, 55277-55289. (www)

  @article{ , abstract = {The focus of Morphing Attack Detection (MAD) is to identify unauthorised attempts to use a legitimate identity. One common scenario involves creating altered images and using them in passport applications. Currently, there are limited datasets available for training the MAD algorithm due to privacy concerns and the challenges of obtaining and processing a large number of printed and scanned images. A larger and more diverse dataset representing passport application scenarios, including various devices and resulting printed, scanned, or compressed images, is needed to enhance the detection capabilities and identify such morphing attacks. However, generating training data that accurately represents the variety of attacks is a labour-intensive task since the training material is created manually. This paper presents two methods based on texture transfer techniques for the automatic generation of digital print and scan facial images, which are utilized to train a Morphing Attack Detection algorithm. Our proposed methods achieve an Equal Error Rate (EER) of 3.84% and 1.92% on the FRGC/FERET database when incorporating our synthetic and texture-transferred print/scan images at 600 dpi alongside handcrafted images, respectively.}, archive = {J_ACCESS}, author = {Juan E. Tapia and Maximilian Russo and Christoph Busch}, doi = {10.1109/ACCESS.2025.3555922}, journal = {IEEE Access}, pages = {55277-55289}, shortjournal = {IEEE Access}, title = {Generating automatically Print/Scan textures for morphing attack detection applications}, volume = {13}, year = {2025}, }
• (2025). The role of artificial intelligence in boosting cybersecurity and trusted embedded systems performance: A systematic review on current and future trends. ACCESS, 13, 55258-55276. (www)

  @article{ , abstract = {As technology becomes increasingly interconnected, ensuring the security of cyber and embedded systems is critical due to escalating vulnerabilities and sophisticated cyber threats. Researchers are exploring artificial intelligence (AI) to improve security mechanisms, yet there is a lack of a comprehensive technical, AI-focused analysis detailing the integration of AI into existing security hardware and frameworks. To address this gap, this article systematically reviews 63 articles on AI in cybersecurity and trusted embedded systems. The reviewed articles are categorized into four application domains: 1) Intrusion Detection and Prevention (IDPS), 2) Malware Detection, 3) Industrial Control and Cyber-Physical Systems (CPS) and 4) Distributed Denial-of-Service (DDoS) Detection and Prevention. We investigated current trends in integrating AI into security domains by summarizing the hardware used, the AI methodologies adopted, and the statistical distribution by publication year and region. The key findings of our review indicate that AI significantly enhances security measures by enabling capabilities such as detection, classification, feature selection, data privacy preservation, model combination, data generation, output interpretation, optimization, and adaptation. In addition, the benefits and challenges identified in these studies provide insight into the future potential of AI integration in security. Suggested directions for future work include improving generalization and scalability, exploring continuous or real-time monitoring, and improving AI model performance. This analysis serves as a foundation for advancing AI applications in the effective securing of cyber and embedded systems effectively.}, archive = {J_ACCESS}, author = {Ahmed Oun and Kaden Wince and Xiangyi Cheng}, doi = {10.1109/ACCESS.2025.3554739}, journal = {IEEE Access}, pages = {55258-55276}, shortjournal = {IEEE Access}, title = {The role of artificial intelligence in boosting cybersecurity and trusted embedded systems performance: A systematic review on current and future trends}, volume = {13}, year = {2025}, }
• (2025). Microstrip patch antenna design using a four-layer feed forward artificial neural network trained by levenberg-marquardt algorithm. ACCESS, 13, 55244-55257. (www)

  @article{ , abstract = {This study proposes an efficient Artificial Neural Network (ANN) model for predicting the dimensions and feed point of microstrip patch antennas with three types of geometrical shape. The geometrical shapes investigated are square, triangular, and trapezoidal. Initially, the dimensions of these patches are varied using Advanced Design System (ADS) electromagnetic field simulator. The simulated performance parameters named resonance frequency, gain, and reflection coefficient are recorded and exported to the MATLAB workspace. Subsequently, the ANN is designed and trained using these performance parameters as input variables, while the dimensions and feed point serve as output variables. The Levenberg-Marquardt (LM) algorithm is employed for training due to its efficiency and faster convergence compared to other algorithms. The proposed ANN predicts not only the dimensions but also the feed point of the patch antennas. Therefore, patch antennas with three fundamental geometrical shapes can be designed using the same ANN removing computational complexity for the designers. The ANN contains a multi-layered network architecture that learns and generalizes complex patterns through the LM algorithm and weight optimization based on the datasets without any feature extraction like Deep Neural Network (DNN). Finally, the proposed ANN’s prediction results are represented in terms of mean-squared-errors (MSE) and regression plots. The ANN’s best obtained MSE and regression values are $10^{-6}$ and 0.99999, respectively, which show improvement over some recent models indicating robust accuracy and precision in predictions. To validate the performance of the proposed ANN, a trapezoidal patch antenna is simulated and fabricated based on one of the output values predicted by the ANN. It is found that the simulated and fabricated results approximately match, thereby validating the performance of the proposed ANN.}, archive = {J_ACCESS}, author = {Jitu Prakash Dhar and Maodudul Hasan and Eisuke Nishiyama and Ichihiko Toyoda}, doi = {10.1109/ACCESS.2025.3553913}, journal = {IEEE Access}, pages = {55244-55257}, shortjournal = {IEEE Access}, title = {Microstrip patch antenna design using a four-layer feed forward artificial neural network trained by levenberg-marquardt algorithm}, volume = {13}, year = {2025}, }
• (2025). SWRVM: Sliding window recurrent vision mamba model for long-term spatial-temporal prediction. ACCESS, 13, 55231-55243. (www)

  @article{ , abstract = {Spatial-temporal prediction, to predict future spatial data for a period with past data, is widely used in precipitation prediction, target motion prediction, and traffic flow forecasting. The data for these tasks typically exhibits multi-scale variability which imposes a great deal of difficulty for long-term prediction. A deep learning model, named sliding window recurrent vision mamba (SWRVM), is proposed for exploiting spatial and long-term temporal information accurately and dexterously to perform effective long-term spatial-temporal prediction in this paper. The proposed SWRVM model combines improved embedding module and sliding window recurrent mechanisms into vision mamba, while the improved embedding module is to retain more spatial and temporal feature, the sliding window recurrent mechanism is the key structure for long-term prediction, and the vision mamba model gives effective results by global receptive field and computational efficiency. We perform extensive experiments on four public datasets and one private dataset in three situations of 10 in 10 out, 20 out, and 40 out. The quantitative and qualitative visualization results demonstrate the SWRVM model outperforms the state-of-the-arts (SOTA) models in multi-scale variations long-term spatial-temporal prediction tasks.}, archive = {J_ACCESS}, author = {Ge Peng and Yujing Zhong}, doi = {10.1109/ACCESS.2025.3554254}, journal = {IEEE Access}, pages = {55231-55243}, shortjournal = {IEEE Access}, title = {SWRVM: Sliding window recurrent vision mamba model for long-term spatial-temporal prediction}, volume = {13}, year = {2025}, }
• (2025). From GPS to AR: Leveraging augmented reality and grid-based systems for improved indoor navigation. ACCESS, 13, 55211-55230. (www)

  @article{ , abstract = {With the rapid growth of technology in intelligent city networks, navigating inside complex buildings has become increasingly difficult. The indoor Global Positioning System (GPS), which operates within an ultra-high radio frequency range, suffers from signal degradation due to thick walls. The lack of GPS signals and the inability to maintain a line of sight with orbiting satellites make indoor navigation more challenging than outdoor environments. Although Radio Frequency (RF) signals, Assisted GPS, and sensor-based solutions are used to track users indoors, they each have limitations. This paper introduces the Enhanced Smart Indoor Navigation System Using Augmented Reality (ESINS_AR), which offers a solution to these challenges using available and accessible resources. ESINS_AR facilitates easy location of places like shops within shopping malls, specific hotel rooms, or university lecture rooms by providing users with reasonably accurate visual assistance through their smartphones. Inspired by Augmented Reality (AR) and 2-D visual markers, the system uses several markers throughout the building. When scanned using the application, these markers display arrows on the screen, guiding users to their destination. The application allows users to select and change their desired destination as needed.}, archive = {J_ACCESS}, author = {Ala Saleh Alluhaidan and Latifa Abdullah Almusfar and Ahmed Younes Shdefat and Ahmed Elsayed Mansour and Diaa Salama Abdelminaam and Yasmin Alkady}, doi = {10.1109/ACCESS.2025.3552319}, journal = {IEEE Access}, pages = {55211-55230}, shortjournal = {IEEE Access}, title = {From GPS to AR: Leveraging augmented reality and grid-based systems for improved indoor navigation}, volume = {13}, year = {2025}, }
• (2025). Experimental study of the stability of a low-cost C-band active reflector using sentinel-1 imagery. ACCESS, 13, 55202-55210. (www)

  @article{ , abstract = {The application of Synthetic Aperture Radar (SAR) interferometry (InSAR) techniques when in the imaged areas the coherence is low and persistent scatterers are missing often demands the installation of artificial reflectors, usually represented by passive corner reflectors (PCR). For missions based on C band sensors such as Sentinel-1, passive corner reflectors capable of providing a high phase accuracy are cumbersome and heavy, and their installation in hard places can be difficult and costly. The installation of active reflectors, compact and smaller with respect to passive corner reflectors, can sometimes represent a valid alternative but the use of these devices has not yet largely spread due to their high sensitivity to the seasonal temperature variation which can reduce their reliability both in amplitude and phase stability. This paper focuses on the analysis of the performance of a low-cost active reflector designed to operate with C band spaceborne radars, tested in a real field campaign using Sentinel-1 imagery. The goal is to assess through an experimental based approach the stability of this device, using data acquired during the monitoring campaign of a landslide based on InSAR. The stability of the Active Reflector (AR) installed in a stable area is analyzed to evaluate its performance when used as a reference in interferometric processing. The results of this study show a stability over a temporal lapse of almost three years equivalent in deformation measurement to a few millimeters accuracy, which can be considered a satisfactory goal for this InSAR application.}, archive = {J_ACCESS}, author = {Guido Luzi and Qi Gao and Pedro Espín-López}, doi = {10.1109/ACCESS.2025.3554305}, journal = {IEEE Access}, pages = {55202-55210}, shortjournal = {IEEE Access}, title = {Experimental study of the stability of a low-cost C-band active reflector using sentinel-1 imagery}, volume = {13}, year = {2025}, }
• (2025). Optimizing power control in cellular and cell-free massive MIMO systems: A SVM/RBF approach. ACCESS, 13, 55187-55201. (www)

  @article{ , abstract = {This paper explores the optimization of power control in both cellular (CL) and cell-free (CF) massive MIMO (mMIMO) systems using a hybrid approach combining support vector machine (SVM) and radial basis function (RBF). The traditional WMMSE method, while effective, exhibits high computational complexity and suboptimal convergence in large-scale systems. The proposed SVM/RBF method addresses these challenges by significantly reducing the computational overhead, as detailed in the computational complexity analysis in Proposed SVM/RBF Methods. To address these challenges, we propose an SVM/RBF-based method for power control (PC) that leverages SVM regression to predict optimal PC vectors and utilizes RBF kernels to enhance prediction accuracy by transforming input features into higher-dimensional spaces. The proposed method dynamically adjusts transmission power levels of user devices based on real-time channel conditions, thereby optimizing resource utilization and system performance. Simulation results demonstrate that the SVM/RBF approach significantly outperforms the WMMSE method in both spectral efficiency and computational efficiency. In terms of Area Under the Curve (AUC) metric, the SVM-RBF method shows a substantial performance gain with AUC values of 24,931 for CL-mMIMO systems compared to 12,698 for WMMSE. Additionally, the SVM-RBF method reduces execution time by approximately 30% in both CL and CF-mMIMO scenarios. This paper confirms that the SVM/RBF method offers a robust, efficient, and scalable solution for optimizing PC in complex wireless communication environments.}, archive = {J_ACCESS}, author = {Neda Ahmadi and Gholamreza Akbarizadeh}, doi = {10.1109/ACCESS.2025.3554433}, journal = {IEEE Access}, pages = {55187-55201}, shortjournal = {IEEE Access}, title = {Optimizing power control in cellular and cell-free massive MIMO systems: A SVM/RBF approach}, volume = {13}, year = {2025}, }
• (2025). End-to-end deployment of the educational AI hub for personalized learning and engagement: A case study on environmental science education. ACCESS, 13, 55169-55186. (www)

  @article{ , abstract = {This study introduces an end-to-end framework for deploying conversational AI-enabled educational assistants, focusing on personalized support for students across diverse subject areas, including Business, Culture, Environmental Sciences, History, Politics, and Science, as outlined in our evaluation framework. The system leverages advanced conversational AI technologies to provide targeted, course-specific learning experiences by facilitating access to complex data and integrating seamlessly with Learning Management Systems (LMS) like Canvas. Key metrics—information retrieval accuracy, question-answering accuracy, and hallucination accuracy—were selected to rigorously evaluate the system’s ability to retrieve relevant contexts, generate accurate responses, and identify unanswerable questions. Additionally, the Educational AI Hub agents utilize innovative document parsing methods, such as the Nougat technique, to interpret content accurately, enabling adaptable academic support tailored to individual learning needs and extending to quantitative fields through code execution capabilities. This study also emphasizes the importance of accessibility, inclusivity, and user privacy. The results showcase the potential for enhanced engagement and improved understanding of environmental concepts and software tools, demonstrating the significant impact of conversational AI in educational settings, especially in disciplines involving complex data interactions. A case study, presented at the 12th International Congress on Environmental Modelling and Software, illustrates the Educational AI Hub’s effectiveness in enhancing student engagement and delivering personalized learning experiences in environmental sciences education.}, archive = {J_ACCESS}, author = {Ramteja Sajja and Yusuf Sermet and Ibrahim Demir}, doi = {10.1109/ACCESS.2025.3554222}, journal = {IEEE Access}, pages = {55169-55186}, shortjournal = {IEEE Access}, title = {End-to-end deployment of the educational AI hub for personalized learning and engagement: A case study on environmental science education}, volume = {13}, year = {2025}, }
• (2025). Identification of depression patients using LIF spiking neural network model from the pattern of EEG signals. ACCESS, 13, 55156-55168. (www)

  @article{ , abstract = {Interpreting electroencephalography signals and the abnormality of the signals can help to find the specific pattern for specific diseases like depression. A Spiking Neural Network is a machine learning approach that emphasizes the data value and manipulates the value to find the particular signal feature. Finding the specific abnormal features of electroencephalography signals can help to detect depression patients. Since a vast number of individuals are suffering from depression and the treatment of depression is possible by detecting depression patients earlier, different deep learning and conventional machine learning approaches were proposed. But speed, accuracy, and reality with less time and space complexity are essential factors in detecting depression patients in our society. We have proposed a leaky integrate and fire spiking neural network model for interpreting the electroencephalography signals of depression patients. The electroencephalography signals of a sixty-channel dataset of 121 subjects are taken for the experiment where frequency for each channel of a subject is recorded for 2 mins in 2-second time intervals, and the dataset contains 4,35,600 data with 121 instances and 3600 attributes. A leaky integrate and fire model is applied to the electroencephalography signals to find the spike sequences and potentials. Then, a three-layered neural network approach is stacked to generate a classifier. The performance of the classifier is shown to be approximately 98% accuracy. Generating a noble classifier and implementing it with a mask of metal disk benefited society for easily and quickly detecting a depression patient, and corresponding treatment can be started. Besides, more experiments are needed on different and more depression datasets with spiking neural network models to identify depression patients and finalize a robotic classifier.}, archive = {J_ACCESS}, author = {Rekha Sahu and Prasant Kumar Pattnaik and Kalaiarasi Sonai Muthu Anbananthen and Saravanan Muthaiyah}, doi = {10.1109/ACCESS.2025.3552619}, journal = {IEEE Access}, pages = {55156-55168}, shortjournal = {IEEE Access}, title = {Identification of depression patients using LIF spiking neural network model from the pattern of EEG signals}, volume = {13}, year = {2025}, }
• (2025). Some concepts of interval valued pythagorean fuzzy graphs and analysis of socioeconomic conditions of communities. ACCESS, 13, 55140-55155. (www)

  @article{ , abstract = {The concepts of covering and matching in fuzzy graphs have played a significant role in addressing problems within uncertain and imprecise environments. Numerous real-world problems in decision-making, network optimization etc have been investigated through these concepts in the domains of fuzzy graphs, intuitionistic fuzzy graphs etc. However, the concepts of covering and matching have not yet been explored within the framework of interval-valued Pythagorean fuzzy graphs (IVPyFGs). To fill this gap, we extend these notions within IVPyFGs and utilize them to analyze socioeconomic conditions across different communities. Initially, we introduce several key terms related to covering such as strong arcs (SAs), strong node cover (SNC) and strong node covering number (SNCN), and characterized different kinds of IVPyFGs including complete-IVPyFGs and complete bipartite IVPyFGs based on these terms. Following this, we introduce the concepts of matching in IVPyFGs and explore several characterizations several specific types of IVPyFGs. To demonstrate the effectiveness of this study, we apply these newly established concepts to analyze the socioeconomic conditions of various communities. Finally, a comparative study is conducted to verify that our proposed model is more efficient than existing models.}, archive = {J_ACCESS}, author = {Waheed Ahmad Khan and Izhar Ahmed Shah and Trung Tuan Nguyen and Minh Hoan Pham and Hai van Pham}, doi = {10.1109/ACCESS.2025.3553351}, journal = {IEEE Access}, pages = {55140-55155}, shortjournal = {IEEE Access}, title = {Some concepts of interval valued pythagorean fuzzy graphs and analysis of socioeconomic conditions of communities}, volume = {13}, year = {2025}, }
• (2025). Interior innovation design using ResNet neural network and intelligent Human–Computer interaction. ACCESS, 13, 55130-55139. (www)

  @article{ , abstract = {This study aims to optimize the interior design generation process while enhancing personalization and efficiency through the development of a hybrid Residual Network-Human-Computer Interaction (ResNet-HCI) framework. It employs Residual Network (ResNet) neural networks, which leverage residual learning to improve training stability and feature extraction capabilities in deep networks. This allows for efficient feature reuse and optimization of model performance. Additionally, Human-Computer Interaction (HCI) technologies, such as voice commands, gesture control, and Virtual Reality/Augmented Reality (VR/AR), are integrated to enhance user interaction with the design system, thereby improving the intelligence and personalization of the interior design workflow. The Large-Scale Scene Understanding dataset is used for model training to evaluate system performance under varying training steps, hyperparameter configurations, and noise conditions. The experimental results show the following: 1) Significant performance variations are observed across different models under conditions such as increased training iterations, noise interference, and design scoring. In the iteration experiment, model performance generally improves with more training steps. ResNet50 consistently outperforms other models, achieving an F1 score of 0.935 after 20 iterations, demonstrating exceptional feature learning and stability. In the noise robustness analysis, ResNet50 and ResNeXt show minimal performance degradation under Gaussian noise, indicating strong noise robustness. 2) Regarding interior design scoring, hybrid layouts generated using ResNet models combined with HCI technologies excel in multiple dimensions, achieving the highest overall satisfaction scores and emerging as the optimal design solutions. These findings validate the exceptional performance and versatility of ResNet50 and ResNeXt in both deep learning and HCI applications. This study provides both theoretical and practical support for the intelligent transformation of the interior design field, while also offering insights into broader applications in creative industries.}, archive = {J_ACCESS}, author = {Kejing Guo and Jinxin Ma}, doi = {10.1109/ACCESS.2025.3554553}, journal = {IEEE Access}, pages = {55130-55139}, shortjournal = {IEEE Access}, title = {Interior innovation design using ResNet neural network and intelligent Human–Computer interaction}, volume = {13}, year = {2025}, }
• (2025). UHD video encoding in CPU versus GPU: Quality and performance trade-offs. ACCESS, 13, 55115-55129. (www)

  @article{ , abstract = {In the contemporary digital media environment, video encoding is of paramount importance for applications such as streaming and video conferencing. With the increasing demand for higher resolutions and immersive video, optimizing videos for Internet consumption necessitates the use of the latest codecs. Additionally, live event broadcasts require fast and efficient codecs, making GPU codecs essential. This paper analyses the coding efficiency of video encoders on CPUs and GPUs with 4K content. We have selected three families of encoders (H264/AVC, HEVC, and AV1) and have computed the Bjøntegaard Delta (BD) using a set of 15 UHD videos to compare the results of the CPU implementation (libx264, libx265, and librav1e) with their GPU version (h264_nvenc, h265_nvenc, and av1_nvenc) of each family. Besides, we have measured the mean encoding time per frame (as the test videos have different duration). The BD rate analysis shows that the CPU implementations of the codecs for H264 and AV1 (libx264 and librav1e) require in mean more than 20% bitrate (for both metrics PSNR and VMAF) compared to the GPU implementations (h264_nvenc and av1_nvenc). However, the CPU implementation of the H265 (libx265) codec required around 7.5% less bitrate (for both metrics) than the GPU implementation (hevc_nvenc). The encoding times reveal that the mean encoding time per frame for the GPU encoders is very similar (109 ms, 108.39 ms, and 117.77 ms) and they achieve a great time reduction compared to the CPU version (1.3 times faster for H264, 2.35 times faster for HEVC, and 68.14 times faster for AV1). Therefore, the GPU implementations of these encoders reduce the encoding time drastically, specially in the case of AV1, and lead to a noticeable reduction of the bitrate (except for HEVC).}, archive = {J_ACCESS}, author = {Andoni Salcedo-Navarro and Juan Gutiérrez-Aguado and Miguel Garcia-Pineda}, doi = {10.1109/ACCESS.2025.3553634}, journal = {IEEE Access}, pages = {55115-55129}, shortjournal = {IEEE Access}, title = {UHD video encoding in CPU versus GPU: Quality and performance trade-offs}, volume = {13}, year = {2025}, }
• (2025). Corrections to “An integrated queuing network model for optimizing multi-level AVS/RS performance in multi-floor manufacturing environments”. ACCESS, 13, 55113-55114. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “An Integrated Queuing Network Model for Optimizing Multi-Level AVS/RS Performance in Multi-Floor Manufacturing Environments”).}, archive = {J_ACCESS}, author = {Xuanrui Chen and Xiao-Peng Liu and Ai-Lin Yu}, doi = {10.1109/ACCESS.2025.3552249}, journal = {IEEE Access}, pages = {55113-55114}, shortjournal = {IEEE Access}, title = {Corrections to “An integrated queuing network model for optimizing multi-level AVS/RS performance in multi-floor manufacturing environments”}, volume = {13}, year = {2025}, }
• (2025). Wafer region yield prediction: Employing majority under-sampling and output binarization on low-yield threshold. ACCESS, 13, 55104-55112. (www)

  @article{ , abstract = {The Wafer Acceptance Test (WAT) is a vital process that measures the manufacturing quality of the wafer using a series of parametric measurements to ensure that the devices manufactured are within design specification. The current framework when using WAT in Yield Prediction (YP) is to use the average value of all WAT measured on the wafer to predict the average wafer yield. This framework causes significant loss of information and does not provide the correct yield information that is more often used in practice, which is the wafer yield loss pattern. Knowledge on the wafer yield loss pattern will provide important insights such as possible process issues and the yield loss nature. To address this, a wafer region yield prediction model is proposed by using the WAT site data found within each region to predict whether the region is high or low yield. Each wafer region WAT site data will be pre-processed with majority under-sampling and the yield of each site is determined based on the shortest distance between the die to the site location. To change it to a classification problem, the yield output for each site will be binarized by finding the low yield threshold which is determined by domain experts using k-means clustering with gap statistic to find the optimal number of clusters for each site. Each region model will be trained using an eXtreme Gradient Boost (XGB) model and Support Vector Machine (SVM) using f-beta scoring to determine the best prediction threshold across different precision emphasis. Each of the five region’s prediction model can achieve a higher precision score while achieving a relatively high f-beta score.}, archive = {J_ACCESS}, author = {Martin Lim Ying Song and Anurag Sharma and Cheng Siong Chin}, doi = {10.1109/ACCESS.2025.3554525}, journal = {IEEE Access}, pages = {55104-55112}, shortjournal = {IEEE Access}, title = {Wafer region yield prediction: Employing majority under-sampling and output binarization on low-yield threshold}, volume = {13}, year = {2025}, }
• (2025). Preset-time tracking control for nonlinear cyber-physical systems against cyber attacks. ACCESS, 13, 55094-55103. (www)

  @article{ , abstract = {In this article, we developed a novel preset-time adaptive fuzzy tracking control strategy to dispose of the preset-time tracking control issue for a family of nonlinear cyber-physical systems (CPSs), which are subject to spiteful actuator attacks occurring in controller-actuator (C-A) channel. Particularly, a new Gaussian radial basis function neural network (RBFNN) is designed as a neural estimator acting in an on-line pattern to estimate the vitriolic attacks. The designed control strategy can guarantee that the output tracking error enters a predefined small region around the equilibrium point, but also all the other states of the closed-loop system keep semi-globally practical finite-time stable (SGPFS), where the regulation time and tracking accuracy level remain prior known and could be preassigned. Significantly, the initial value of each system state is independent of the developed tracking controller and can be chosen in the constrained region specified by the designed finite-time performance function at random. Finally, a typical application instance is presented to attest the feasibility and effectiveness of the designed control approach.}, archive = {J_ACCESS}, author = {Zhiliang Chen and Wei Zhang and Ting Yu and Yijie Liu and Wang Ren and Kaixiong Zhu and Shilin Li}, doi = {10.1109/ACCESS.2025.3552438}, journal = {IEEE Access}, pages = {55094-55103}, shortjournal = {IEEE Access}, title = {Preset-time tracking control for nonlinear cyber-physical systems against cyber attacks}, volume = {13}, year = {2025}, }
• (2025). Zero-shot prediction of conversational derailment with large language models. ACCESS, 13, 55081-55093. (www)

  @article{ , abstract = {Online discussion platforms often show a tendency for conversations to stray from the topic and devolve into personal attacks. Previous studies have trained machine learning algorithms to detect conversational derailment using supervised methods. However, creating the datasets required for supervised training is very costly. To address this challenge, we focus on the zero-shot performance of large language models (LLMs), which have advanced rapidly in recent years. This study aims to evaluate the zero-shot prediction performance of conversational derailment using LLMs. First, we measured the performance of the most commonly used LLMs in predicting conversational derailments and found that the zero-shot prediction performance is comparable to that of traditional fine-tuning approaches. Secondly, we explored the effect of inserting prior knowledge into the prompts on the behavior of the LLMs. We discovered that this practice does not necessarily improve the performance of LLMs, resulting in unexpected changes in prediction timing. This study’s contributions are as follows: We demonstrate that zero-shot inference with LLMs is an effective method for predicting conversational derailment in the absence of training data. Additionally, we found that altering the prompt to include prior knowledge leads to unintended results in the conversational derailment prediction task. This observation emphasizes the need to evaluate the impact of prompt changes from various perspectives, going beyond simple performance metrics.}, archive = {J_ACCESS}, author = {Kenya Nonaka and Mitsuo Yoshida}, doi = {10.1109/ACCESS.2025.3554548}, journal = {IEEE Access}, pages = {55081-55093}, shortjournal = {IEEE Access}, title = {Zero-shot prediction of conversational derailment with large language models}, volume = {13}, year = {2025}, }
• (2025). Optimum selection of lithium iron phosphate battery cells for electric vehicles. ACCESS, 13, 55070-55080. (www)

  @article{ , abstract = {This paper presents a systematic approach to selecting lithium iron phosphate (LFP) battery cells for electric vehicle (EV) applications, considering cost, volume, aging characteristics, and overall performance. A battery selection algorithm is developed, and to investigate its functionality, a case study to evaluate four different LFP battery cell models based on their long-term behavior in a 40 kWh battery pack is conducted. The algorithm integrates a vehicle energy consumption model to better account for the aging impacts of different cell choices, where battery performance is analyzed based on the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) over a 10-year simulated period, considering five driving cycles per day. In order to ensure a fair assessment, the model accounts for variations in battery pack weight as the sole influencing factor on vehicle dynamics. The results compare vehicle range, battery pack mass, cost, cell degradation, and volume for each battery option. The case study findings indicate that the developed method found A123 Systems ANR 26650m1 battery cell superior among the considered four options offering the best trade-off between longevity and cost-effectiveness, making it a highly suitable choice for durable and efficient EV battery packs. This study underscores the importance of considering several critical factors including aging based on detailed driving cycles, together for the most suitable battery selection in designing cost-effective, long-lasting EV energy storage solutions.}, archive = {J_ACCESS}, author = {Arda Akyildiz and Bati E. Ergun and Ege Uzun and Mustafa A. Zehir and Cavit F. Kucuktezcan and Ilhan Kocaarslan and Mehmet O. Gulbahce}, doi = {10.1109/ACCESS.2025.3553081}, journal = {IEEE Access}, pages = {55070-55080}, shortjournal = {IEEE Access}, title = {Optimum selection of lithium iron phosphate battery cells for electric vehicles}, volume = {13}, year = {2025}, }
• (2025). A 2.1 GS/s 2-channel pipeline-SAR ADC with speed-enhanced bootstrap switch and low-latency SAR logic. ACCESS, 13, 55061-55069. (www)

  @article{ , abstract = {This paper introduces a 12-bit 2-channel interleaved Pipelined Successive Approximation Register (Pipelined-SAR) Analog-to-Digital Converter (ADC) capable of operating at 2.1 GS/s. A speed-enhanced bootstrap switch that enhances the sampling speed is featured so that the overall speed bottleneck of the 3-stage pipelined SAR structure can be alleviated. In addition, an innovative SAR logic structure, compatible with multiple comparators in a loop-unrolled fashion, is presented. N/P MOS unbalanced sizing technique is implemented in SAR logic to further reduce the propagation delay of both the clocking path and the Capacitive Digital-to-Analog Converter (CDAC) driving path. This work exploits the circuits architectures and design techniques and tradeoffs to achieve multi-Giga sampling frequency without interleaving a large number of channels. Implemented in a 28 nm CMOS process, the ADC achieves an SNDR of 59.1 dB and an SFDR of 76.1 dB at the Nyquist input operating at 2.1 GS/s. With 17.5 mW power consumption at a 0.9 V power supply, it achieves a Walden FOM of 11.4 fj/conv.-step and a Schreier FOM of 166.9 dB.}, archive = {J_ACCESS}, author = {Tao Fu and Xianshan Wen and Liang Fang and Ping Gui}, doi = {10.1109/ACCESS.2025.3551975}, journal = {IEEE Access}, pages = {55061-55069}, shortjournal = {IEEE Access}, title = {A 2.1 GS/s 2-channel pipeline-SAR ADC with speed-enhanced bootstrap switch and low-latency SAR logic}, volume = {13}, year = {2025}, }
• (2025). Continual test-time adaptation for robust remote photoplethysmography estimation. ACCESS, 13, 55049-55060. (www)

  @article{ , abstract = {Remote photoplethysmography (rPPG) estimation has made considerable progress by leveraging deep learning, yet its performance remains highly susceptible to the domain shifts caused by lighting, skin tone and movement, particularly during inference. Moreover, continuous adaptation across multiple domains is also challenging due to the dynamic environmental changes such as lighting transitions or continuous motions. Domain adaptation has been widely investigated, mostly focusing on classification tasks with labels. Prior arts to address domain shifts in rPPG estimation, which is a regression task, rely on labeled data, require pretraining on target domains, and focus on single-domain test-time adaptation (TTA). However, there are still remaining challenges in TTA for their applicability of rPPG estimation in real-world scenarios such as no label during inference, continuous adaptation over multiple domains, and potential catastrophic forgetting when re-adapting to the source domain. In this work, we recast an rPPG TTA problem as a continual learning and propose an efficient continual TTA method that mitigates significant domain shifts in multiple target domains without labels by leveraging the non-contrastive unsupervised learning loss with selective updates of the batch normalization layers only as well as alleviates catastrophic forgetting in source domain by adopting the learning without forgetting (LwF) regularization in the frequency domain. Our method without target labels consistently yielded improved performance in challenging continual adaptation scenarios, including adapting to multiple new domain datasets over several cycles. This approach not only mitigates catastrophic forgetting in the source domain, but also ensures robust performance across different domains.}, archive = {J_ACCESS}, author = {Hyunwoo Lee and Haechang Lee and Byung Hyun Lee and Se Young Chun}, doi = {10.1109/ACCESS.2025.3554508}, journal = {IEEE Access}, pages = {55049-55060}, shortjournal = {IEEE Access}, title = {Continual test-time adaptation for robust remote photoplethysmography estimation}, volume = {13}, year = {2025}, }
• (2025). Unsupervised inductive node representation learning for dynamic graphs. ACCESS, 13, 55034-55048. (www)

  @article{ , abstract = {Graph-structured data are pivotal for modeling evolving systems such as social interactions or biological networks. While graph embedding techniques map nodes into low-dimensional spaces to capture relational patterns, most methods focus on static graphs, failing to adapt to the temporal dynamics inherent in real-world scenarios. Existing dynamic approaches often rely on supervised signals or lack scalability, limiting their practicality for large, unlabeled networks. To bridge this gap, we proposed Unsupervised Dynamic graph neural network, an unsupervised framework for inductive node representation learning on Discrete-Time Dynamic Graphs (DTDGs). Our key contribution is a parameter learning strategy that trains Graph Neural Networks (GNNs) without labeled data, coupled with a scalable edge sampling mechanism to handle large graphs efficiently. Our model generates temporally consistent embeddings by aligning structural changes across time steps while preserving the ability to adapt to abrupt topological shifts. We evaluate our model on five real-world datasets spanning social, communication, and biological networks (e.g., protein-protein interactions). Experiments demonstrate that our model outperforms 10 state-of-the-art static and dynamic baselines, showcasing superior robustness to graph dynamics. Notably, our edge sampling strategy reduces memory overhead for larger graphs. This work advances unsupervised representation learning for dynamic graphs, offering a practical solution for applications requiring real-time adaptation, such as anomaly detection in human interaction networks or protein function prediction. Code and data are available at: https://github.com/khushnood/UnsupervisedInductiveNodeRepresentationForDynamicGraphs.}, archive = {J_ACCESS}, author = {Wen-Gang Zhou and Khushnood Abbas}, doi = {10.1109/ACCESS.2025.3553377}, journal = {IEEE Access}, pages = {55034-55048}, shortjournal = {IEEE Access}, title = {Unsupervised inductive node representation learning for dynamic graphs}, volume = {13}, year = {2025}, }
• (2025). Few-shot transfer learning-based fault classification in wireless sensor networks. ACCESS, 13, 55017-55033. (www)

  @article{ , abstract = {This paper introduces a few-shot transfer learning approach to fault classification in wireless sensor networks (WSNs) with a minimal number of fault samples. WSNs are susceptible to various faults, such as drift, stuck, bias, and spike faults, erratic behavior, and data loss, which can compromise system reliability. Conventional deep learning fault diagnosis methods have achieved promising results, however, the majority of these approaches require a substantial amount of labeled training data, which is not available in real-world scenarios. To address this, we propose a novel method that combines convolutional neural networks (ResNet-18, VGG-16, and MobileNetV2 backbone architectures) with prototypical networks for fault diagnosis in WSNs with only a few fault samples. By transforming time-series data into Gramian Angular Field images, our approach leverages pre-trained deep learning models to extract feature-rich embeddings. These embeddings are then classified using a prototypical network, which enhances the system’s ability to diagnose faults even with a limited amount of labeled data. The proposed model is lightweight and deployable on Internet of Things (IoT) devices, ensuring efficient fault classification with minimal computational resources. Experimental results demonstrate the model’s high accuracy and robustness across various fault types, highlighting its potential for scalable and adaptive IoT applications.}, archive = {J_ACCESS}, author = {Nouman Ijaz and Md. Nazmul Hasan and Insoo Koo}, doi = {10.1109/ACCESS.2025.3554748}, journal = {IEEE Access}, pages = {55017-55033}, shortjournal = {IEEE Access}, title = {Few-shot transfer learning-based fault classification in wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). A review of link prediction applications in network biology. ACCESS, 13, 54997-55016. (www)

  @article{ , abstract = {In the domain of network biology, the interactions among heterogeneous genomic and molecular entities are represented through networks. Link prediction (LP) methodologies are instrumental in inferring missing or prospective associations within these biological networks. This review systematically dissects the attributes of local, centrality and embedding-based LP approaches applied to static and dynamic biological networks. We examine the current applications of LP metrics for predicting links between diseases, genes, proteins, RNA, microbiomes, drugs, and neurons. We carry out comprehensive performance evaluations on established biological network datasets to show the practical applications of standard LP models. Moreover, we compare the similarity in prediction trends among the models and the specific network attributes that contribute to effective link prediction, before underscoring the role of LP in addressing the formidable challenges prevalent in biological systems, ranging from noise, bias, and data sparseness to interpretability. We conclude the review with an exploration of the essential characteristics expected from future LP models, poised to advance our comprehension of the intricate interactions governing biological systems.}, archive = {J_ACCESS}, author = {Ahmad F. Al Musawi and Satyaki Roy and Preetam Ghosh}, doi = {10.1109/ACCESS.2025.3553732}, journal = {IEEE Access}, pages = {54997-55016}, shortjournal = {IEEE Access}, title = {A review of link prediction applications in network biology}, volume = {13}, year = {2025}, }
• (2025). Neural network models for predicting magnetization surface switched reluctance motor: Classical, radial basis function, and physics-informed techniques. ACCESS, 13, 54987-54996. (www)

  @article{ , abstract = {Neural networks have increasingly been utilized in electric drive systems to enhance modeling, control, and optimization. These data-driven techniques enable accurate predictions of complex nonlinear behaviors, including the magnetization characteristics of electric machines. This paper investigates the use of neural networks for predicting magnetization surfaces in switched reluctance motors, a key aspect of their design and operational efficiency. Three neural networks-based methods are studied: classical neural networks, radial basis function neural networks, and physics-informed neural networks. Experimental data from a 7.5 kW switched reluctance motor are used to assess the capabilities of each approach. Moreover, the study evaluates predictive accuracy, computational requirements, and the ability to reflect physical dynamics. Results demonstrate that classical neural networks and radial basis function networks can model magnetization surface, but with inaccuracy due to failure to comply with flux behavior, with radial basis function networks excelling in computational efficiency. Physics-informed neural networks achieve the highest accuracy by integrating physical laws into the learning process. This research highlights the potential of neural networks techniques in advancing switched reluctance motors modeling, paving the way for improved electric drive systems.}, archive = {J_ACCESS}, author = {Galina Demidova and Jackson J. Justo and Dmitry Lukichev and Nikolai Poliakov and Alecksey Anuchin}, doi = {10.1109/ACCESS.2025.3540626}, journal = {IEEE Access}, pages = {54987-54996}, shortjournal = {IEEE Access}, title = {Neural network models for predicting magnetization surface switched reluctance motor: Classical, radial basis function, and physics-informed techniques}, volume = {13}, year = {2025}, }
• (2025). Online learning agents for group cooperative control systems. ACCESS, 13, 54976-54986. (www)

  @article{ , abstract = {This paper introduces a novel approach to group cooperative control in MASs by designing an online learning neural network for follower agents. The proposed neural network is capable of dynamically adjusting its structure and parameters in real-time based on the agents’ current states and input data. This feature allows the network to accurately approximate and continuously learn the complex nonlinear components present in the control systems, thereby achieving higher accuracy and stronger robustness in consensus tracking. Unlike traditional control methods, our approach leverages the adaptive capabilities of online learning to effectively handle dynamic changes and uncertainties, significantly enhance the overall performance and stability of the cooperative control system. Simulation results and experimental validations confirm the effectiveness of the proposed method, demonstrating its superiority in tracking accuracy and robustness.}, archive = {J_ACCESS}, author = {Ping Wang and Min Gao and Junyu Li}, doi = {10.1109/ACCESS.2025.3551883}, journal = {IEEE Access}, pages = {54976-54986}, shortjournal = {IEEE Access}, title = {Online learning agents for group cooperative control systems}, volume = {13}, year = {2025}, }
• (2025). Deep classification of algarrobo trees in seasonally dry forests of peru using aerial imagery. ACCESS, 13, 54960-54975. (www)

  @article{ , abstract = {Seasonally dry forests require tailored strategies to address the challenges posed by deforestation and desertification, particularly for the conservation of protected tree species. Efforts to defend protected flora involve maintaining accurate records of superior specimens of seed-producer trees (plus trees), to support informed management decisions in designated areas. In this paper, a methodology is proposed for the automated classification of plus algarrobo trees (Neltuma pallida), based on RGB aerial imagery and deep learning classifiers. As a proof of concept, three state-of-the-art approaches were evaluated in selected zones of interest, and a new application-specific architecture called AlgarroboNet was proposed for efficient classification of plus algarrobo trees. Initially, a dataset containing the geographic and phenological characteristics of algarrobo trees was provided by the Peru National Forest Service. Subsequent in-situ evaluations retrieved detailed morphometric measurements of plus trees, along with aerial imagery for both plus and non-plus specimens. After correction and segmentation, a balanced database was prepared to evaluate the four deep classifiers. The performance of each approach was summarized using both accuracy and $F_{1}$ -measure, following a hold-out validation strategy with 30 trials. The results reveal that state-of-the-art approaches show a $F_{1}$ -measure that ranges between 0.92 and 0.99 among the models, presenting a trade-off between computational resources and accuracy. Through a detailed analysis, the proposed methodology shows potential for large-scale monitoring and characterization of plus algarrobo trees, and suggest being suitable for implementation, aiming to maintain an up-to-date inventory and enforce reforestation programs.}, archive = {J_ACCESS}, author = {Wilson Castro and Himer Avila-George and William Nauray and Roenfi Guerra and Jorge Rodriguez and Jorge Castro and Miguel de-la-Torre}, doi = {10.1109/ACCESS.2025.3553752}, journal = {IEEE Access}, pages = {54960-54975}, shortjournal = {IEEE Access}, title = {Deep classification of algarrobo trees in seasonally dry forests of peru using aerial imagery}, volume = {13}, year = {2025}, }
• (2025). SoK: Grouping spam and phishing email threats for smarter security. ACCESS, 13, 54938-54959. (www)

  @article{ , abstract = {Emails are a vital form of communication, owing to their open nature, which allows any individual to send emails to anyone else without centralized monitoring. While this has facilitated the widespread adoption of email, it has also inadvertently facilitated malicious activities, such as spam and phishing attacks, which pose a serious threat to the security of organizations worldwide. The volume of such emails is growing at an alarming rate, leading to security researchers finding new ways to protect their organizations. To develop effective protection, it’s essential to identify commonalities among emails, such as whether they originate from the same attacker, contain similar wording, or promote nearly identical products. The commonalities used in research to group emails can vary significantly. While the range of research is laudable, the absence of consistent language, datasets, and features can make understanding the results and limitations of this field very challenging. In this systematic literature survey, we looked at 23 research articles on grouping spam and phishing emails, focusing on two foundational aspects (definition of a group and use case) and four methodological aspects (dataset, input features, clustering or grouping algorithms, and evaluation strategies). We propose three definitions of “campaign” representing how researchers approach the groupings: source-based, scam-based, and response-based. Furthermore, we discuss the various features and algorithms that have been utilized in relation to the goals of the researchers and highlight the key takeaways and recommendations for future work.}, archive = {J_ACCESS}, author = {Tarini Saka and Kami Vaniea and Nadin Kökciyan}, doi = {10.1109/ACCESS.2025.3555157}, journal = {IEEE Access}, pages = {54938-54959}, shortjournal = {IEEE Access}, title = {SoK: Grouping spam and phishing email threats for smarter security}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based MRI brain tumor segmentation with EfficientNet-enhanced UNet. ACCESS, 13, 54920-54937. (www)

  @article{ , abstract = {Medical image segmentation plays a critical role in the field of medical image processing. Precisely delineating brain tumor areas from multimodal MRI scans is crucial for clinical diagnosis and predicting patient outcomes. However, challenges arise from similar intensity patterns, varying tumor shapes, and indistinct boundaries, which complicate brain tumor segmentation. Traditional segmentation networks like UNet face difficulties in capturing comprehensive long-range dependencies within the feature space due to the limitations of CNN receptive fields. This limitation is particularly significant in tasks requiring detailed predictions such as brain tumor segmentation. Inspired by these constraints, this study suggests incorporating EfficientNet as an encoder within UNet, with a thorough reassessment of its fundamental components: the encoder, bottleneck, and skip connections. EfficientNet replaces UNet’s encoder, initially frozen to retain learned features from pre-trained weights, adept at extracting detailed features crucial for precise segmentation like brain tumors from MRI scans. Preserving UNet’s bottleneck compresses EfficientNet’s outputs, while skip connections maintain spatial integrity during decoder upsampling. The decoder reconstructs the original image size by merging encoder-decoder features, refining boundaries with convolutional layers for accurate clinical insights. The study conducted multiclass operations on the Brain-Tumor.npy dataset from Kaggle, consisting of 3064 T1-weighted contrast-enhanced images from 233 patients with meningioma (708 slices), glioma (1426 slices), and pituitary tumor (930 slices). Experimental findings in brain tumor segmentation tasks show that the proposed model achieves performance on par with or better than recent CNN or Transformer models. Specifically, the model achieves an accuracy of 0.9925 and a loss of 0.2991 on the dataset.}, archive = {J_ACCESS}, author = {Pradeep Kumar Tiwary and Prashant Johri and Alok Katiyar and Mayur Kumar Chhipa}, doi = {10.1109/ACCESS.2025.3554405}, journal = {IEEE Access}, pages = {54920-54937}, shortjournal = {IEEE Access}, title = {Deep learning-based MRI brain tumor segmentation with EfficientNet-enhanced UNet}, volume = {13}, year = {2025}, }
• (2025). Competency-based hybrid learning: A modern approach to teaching programming and digital technologies subjects. ACCESS, 13, 54892-54919. (www)

  @article{ , abstract = {The COVID-19 pandemic prompted the global education sector to experiment with various forms of online learning as institutions rapidly transitioned to remote formats. This article presents a comprehensive overview of a competency-based hybrid learning methodology, developed and implemented during the pandemic. In addition to detailing the methodology itself, the article also shares the experiences of educators at the authors’ institution, who observed significant improvements in educational outcomes, surpassing even pre-pandemic standards. The methodology highlights the limitations of directly replicating traditional in-person instruction in an online format using existing materials and approaches. Instead, it advocates for carefully designed adaptations tailored to the digital environment, leveraging asynchronous components, interactive tools, and newly created e-learning resources to optimize effectiveness. This approach also requires increased interaction between educators and students beyond scheduled classes, ensuring timely support and guidance. Although this methodology may not suit all course types, it has proven particularly effective in advanced information and communication technologies (ICT) and digital technology subjects, such as programming, artificial intelligence, digital or electronic marketing, video editing, 3D engine work, etc. The positive student feedback further underscores the potential of this model to enhance educational quality and outcomes in these domains.}, archive = {J_ACCESS}, author = {Erik Kučera and Oto Haffner}, doi = {10.1109/ACCESS.2025.3555333}, journal = {IEEE Access}, pages = {54892-54919}, shortjournal = {IEEE Access}, title = {Competency-based hybrid learning: A modern approach to teaching programming and digital technologies subjects}, volume = {13}, year = {2025}, }
• (2025). An exploration of controllability in symbolic music infilling. ACCESS, 13, 54873-54891. (www)

  @article{ , abstract = {This study uses a transformer model to enhance the controllability of generative symbolic music models, specifically related to the infilling task. We introduce a novel Symbolic Music representation with Explicit Rest notation (SMER) encoding incorporating five basic duration types and explicit rest note tokens similar to standard music notation. We compare this approach with another event-based symbolic music encoding called “REMI” (REvamped MIDI-derived events) regarding controllability over bar-level tension and track-level texture, which refers to how musical elements such as melody and harmony are combined in a musical composition. The SMER encoding is compared with another controllable infilling model, Multi-Track Music Machine (MMM), for track-level density controllability. The findings confirm that the proposed SMER demonstrates superior controllability and generates music stylistically more similar to the original music than that generated by MMM. We propose strategies to further enhance track-level texture control by training two models, controlling each bar’s texture (SMER BAR), and predicting each bar’s texture after each bar’s generation (SMER Pre). Those two models with bar-level texture control effectively increase track-level texture control. To explore the interaction of the controllability of different controls, we thoroughly analyzed the controllability of different types and levels of texture controls. Finally, we implemented an interactive interface to facilitate interactive music composition with AI to help bridge the gap between the AI model and musicians.}, archive = {J_ACCESS}, author = {Rui Guo and Dorien Herremans}, doi = {10.1109/ACCESS.2025.3554648}, journal = {IEEE Access}, pages = {54873-54891}, shortjournal = {IEEE Access}, title = {An exploration of controllability in symbolic music infilling}, volume = {13}, year = {2025}, }
• (2025). Prefix tuning using residual reparameterization. ACCESS, 13, 54866-54872. (www)

  @article{ , abstract = {Fine-tuning large language models for specific tasks requires updating and storing all parameters, leading to significant computational and storage cost issues. To address these challenges, parameter-efficient learning such as prefix tuning has gained attention. However, prefix tuning can suffer from sensitivity to prefix length. This is considered to be due to different prefix tokens being required for each task. In this paper, we propose improving the robustness and performance of prefix tuning through residual reparameterization. We add residual connections to the prefix module, providing more flexibility to the model. Additionally, we propose a gate mechanism to assign weights to prefix tokens, allowing for focus on more important tokens. Our experiments on the GLUE benchmark and E2E dataset demonstrate that our methods lead to improved and stabilized performance across various prefix lengths. The residual connections enable faster convergence during training, while the gate mechanism helps balance prefix tokens and find more optimized parameters. Our approach shows particular effectiveness when combining residual connections with the gate mechanism, outperforming original prefix tuning, especially with longer prefix lengths, while remaining parameter-efficient. We also provide an analysis of gate weight trends during training, offering insights into how the model uses prefix tokens for different prefix lengths.}, archive = {J_ACCESS}, author = {Youngjun Jung and Hyunsun Hwang and Changki Lee}, doi = {10.1109/ACCESS.2025.3554513}, journal = {IEEE Access}, pages = {54866-54872}, shortjournal = {IEEE Access}, title = {Prefix tuning using residual reparameterization}, volume = {13}, year = {2025}, }
• (2025). Neural hierarchical interpolation time series (NHITS) for reservoir level multi-horizon forecasting in hydroelectric power plants. ACCESS, 13, 54853-54865. (www)

  @article{ , abstract = {Energy planning in systems heavily influenced by hydroelectric power is based on assessing the availability of water in the future. In Brazil, based on the soil moisture active passive, the National Electricity System Operator defines electricity dispatch concerning a stochastic optimization problem. Currently, machine learning models are an alternative for improving forecasts, and could be a promising solution for predicting reservoir levels at hydroelectric dams. In this paper, neural hierarchical interpolation for time series (NHITS) is applied to improve forecasts and thus help decision-making in the management of electric power systems. The NHITS model achieved a root mean square error of $4.64\times 10^{-4}$ for a 1-hour forecast horizon, and $1.03\times 10^{-3}$ for a 10-hour forecast horizon, being superior to multilayer perceptron (MLP) neural network, long short-term memory (LSTM), convolutional neural network with long short-term memory (CNN-LSTM), recurrent neural network (RNN), Dilated RNN, temporal convolutional neural (TCN), neural basis expansion analysis for interpretable time series forecasting (N-BEATS), and deep non-parametric time series forecaster (DeepNPTS) deep learning approaches.}, archive = {J_ACCESS}, author = {Stefano Frizzo Stefenon and Laio Oriel Seman and Cristina Keiko Yamaguchi and Leandro Dos Santos Coelho and Viviana Cocco Mariani and João Pedro Matos-Carvalho and Valderi Reis Quietinho Leithardt}, doi = {10.1109/ACCESS.2025.3554446}, journal = {IEEE Access}, pages = {54853-54865}, shortjournal = {IEEE Access}, title = {Neural hierarchical interpolation time series (NHITS) for reservoir level multi-horizon forecasting in hydroelectric power plants}, volume = {13}, year = {2025}, }
• (2025). Modeling the error of caliper measurements in animal experiments. ACCESS, 13, 54836-54852. (www)

  @article{ , abstract = {Cancer prevention and treatment is one of the most significant public health challenges of the 21st century. Cancer is a serious health problem, and it is the second leading cause of death following cardiovascular diseases. This requires a reliable virtual patient model, which is usually created based on animal studies that precede human studies. Preclinical drug testing often involves mouse experiments, where tumors are implanted under the skin. Up until now, the most widespread tumor measurement method is caliper measurement, which involves a large measurement error, especially if the tumor is small. We present a noise model that can be used to model the measurement noise in animal experiments where tumor size is measured with calipers. Accurate in silico measurement is essential, as animal studies are costly, time-consuming, and strictly regulated. By incorporating a noise model, in silico experiments can better reflect real-world measurement uncertainties, improving experimental reproducibility and the reliability of virtual patient modeling. In order to model the noise, we use data from preclinical experiments measured using MRI and digital calipers, and we use a nonlinear transformation to whiten the noise. Finally, based on the Anderson-Darling test, we find the distributions that fit the noise best. We show that virtually generated measurements based on the noise model produce similar results to the original measurement noise, thus the noise model can be used to create virtual patients and model realistic experimental setups for in silico experiments.}, archive = {J_ACCESS}, author = {Melánia Puskás and Dániel András Drexler}, doi = {10.1109/ACCESS.2025.3555148}, journal = {IEEE Access}, pages = {54836-54852}, shortjournal = {IEEE Access}, title = {Modeling the error of caliper measurements in animal experiments}, volume = {13}, year = {2025}, }
• (2025). A power control algorithm for V2V communication networks based on dynamic multi-objective optimization. ACCESS, 13, 54823-54835. (www)

  @article{ , abstract = {Vehicular communication networks have the nature of high dynamics and consist of communication links with different or even conflicting transmission objectives and quality of service (QoS) requirements. Therefore, it is extremely challenging to make optimal transmission decisions in vehicular communication networks. In this paper, we study a sequential transmission design problem in a typical vehicle-to-vehicle (V2V) communication network and formulate the problem as a dynamic multi-objective optimization (DMO) problem with the aim to trade-off transmission objectives and guarantee QoS requirements through power control. We propose a prediction-based dynamic multi-objective optimization evolutionary algorithm (DMOEA) that facilitates the evolution of the solution population by predicting the centroid of the power allocation decision set in a new environment, so that transmission decisions can be made to adapt to the highly dynamic environment. Extensive simulation experiments demonstrate the effectiveness and advantages of the proposed algorithm.}, archive = {J_ACCESS}, author = {Mengyu Ma and Zihao Gu and Chao Wang and Zuxing Li and Jianyao Hu and Fuqiang Liu}, doi = {10.1109/ACCESS.2025.3554402}, journal = {IEEE Access}, pages = {54823-54835}, shortjournal = {IEEE Access}, title = {A power control algorithm for V2V communication networks based on dynamic multi-objective optimization}, volume = {13}, year = {2025}, }
• (2025). Secrecy capacity and efficiency outage analysis of cooperative PHY-secure wireless systems and secrecy capacity-based RIS design. ACCESS, 13, 54808-54822. (www)

  @article{ , abstract = {Physical layer (PHY) security (PLS) leverages the inherent randomness of wireless fading channels to provide enhanced secrecy capacity. In this work, we consider a four-node, dual hop, eavesdropper-aware cooperative PHY-security model. Considering probabilistic relay selection and relaying in the presence of hybrid fading channels, we develop an insightful analysis for the probability of PHY-secrecy capacity outage (PSCO) and PHY-secrecy efficiency outage (PSEO). Specifically, we derive closed form expressions for these performance measures and evaluate them numerically to obtain valuable qualitative insights. We also develop an insightful comparative study to show that the cooperative PLS relay model having a destination node equipped with multiple antennas and performing selection combining delivers superior PHY-secrecy outage performance. We extend the analysis to the reconfigurable intelligent surface (RIS)-assisted cooperative PLS system. Specifically, we address the design problem of N, the number of reflecting elements in RIS. We develop insightful criteria based on secrecy capacity to derive a closed form lower bound on N. This insightful result provides the values of N that could achieve superior PHY-secrecy capacity than the relay-assisted cooperative PLS system. Our analysis of the former cooperative PLS model and its extension to RIS design is useful for next generation cooperative PLS relay and RIS-assisted wireless systems and networks.}, archive = {J_ACCESS}, author = {Tarun Jain and Sainath Bitragunta and Ashutosh Bhatia}, doi = {10.1109/ACCESS.2025.3553540}, journal = {IEEE Access}, pages = {54808-54822}, shortjournal = {IEEE Access}, title = {Secrecy capacity and efficiency outage analysis of cooperative PHY-secure wireless systems and secrecy capacity-based RIS design}, volume = {13}, year = {2025}, }
• (2025). ASAP: Automated style-aware similarity measurement for selection of annotated pre-training datasets in 2D biomedical imaging. ACCESS, 13, 54794-54807. (www)

  @article{ , abstract = {Medical imaging scenarios are characterized by varying image modalities, several organs/cell shapes, and little annotated data because of the expertise required for labeling. The successful use of state-of-the-art deep-learning approaches requires a large amount of annotated data or a pre-trained model. Despite the constant publication of new annotated datasets and pre-trained models, a vast subset of them remains untapped, owing to the challenges in effectively applying transfer learning or domain adaptation across varying scenarios. In this paper, we propose an automated style-aware framework for predicting the similarity value of a new biomedical dataset with respect to the state-of-the-art annotated datasets, selecting the most suitable annotated dataset for transfer learning or domain adaptation. Our pipeline, consisting of an autoencoder trained with self-supervised learning through a comprehensive loss function that considers the image reconstruction, style features, and dataset membership, does not need any kind of labels in training and test stages. The resulting 2D latent space represents a similarity measurement, which is demonstrated to correlate with the pre-training results in a task of binary semantic segmentation, and can provide the dataset that offers the optimal results for pre-labeling or pre-training a new biomedical task. Our results demonstrate the superior performance of this measurement with respect to manual selection and the state-of-the-art approaches. Therefore, ASAP can speed up the deployment processes of new biomedical applications. Our code is publicly available at https://github.com/miguel55/ASAP.}, archive = {J_ACCESS}, author = {Miguel Molina-Moreno and Marcel P. Schilling and Markus Reischl and Ralf Mikut}, doi = {10.1109/ACCESS.2025.3555020}, journal = {IEEE Access}, pages = {54794-54807}, shortjournal = {IEEE Access}, title = {ASAP: Automated style-aware similarity measurement for selection of annotated pre-training datasets in 2D biomedical imaging}, volume = {13}, year = {2025}, }
• (2025). Performance testing and analysis of a new GNSS spoofing detection method in different spoofing scenarios. ACCESS, 13, 54779-54793. (www)

  @article{ , abstract = {This paper addresses the vulnerability of Global Navigation Satellite Systems (GNSS) to spoofing signal attacks by proposing a spoofing detection method based on multi-parameter features and an optimized random forest algorithm. Traditional methods, which rely on a single parameter, struggle to adapt to diverse spoofing strategies and suffer from limitations in reliability and robustness. To overcome these shortcomings, this study extracts multi-dimensional parameters from observational data. By improving the RF algorithm and introducing a weighted voting mechanism to optimize the classification decision process, a high-precision classification model is constructed. Experiments were conducted using the Texas Spoofing Test Battery (TEXBAT) dataset and a self-built spoofing platform to evaluate the performance of the proposed method under various spoofing scenarios. The results demonstrate that the optimized Random Forest achieves a detection probability exceeding 97% in both single and mixed scenarios. Furthermore, the method exhibits stable performance in dynamic generated spoofing scenarios, with a detection accuracy rate surpassing 99%. The study proves that the combination of multi-parameter joint analysis and optimized Random Forest effectively enhances the generalization capability and anti-interference performance of GNSS spoofing detection, providing a new approach for navigation security in complex environments.}, archive = {J_ACCESS}, author = {Li Junzhi and Wu Haitao and Gao Jinfeng and Liu Fang and Zhang Yu and Li Gangqiang and He Yu}, doi = {10.1109/ACCESS.2025.3553475}, journal = {IEEE Access}, pages = {54779-54793}, shortjournal = {IEEE Access}, title = {Performance testing and analysis of a new GNSS spoofing detection method in different spoofing scenarios}, volume = {13}, year = {2025}, }
• (2025). Fake news detection landscape: Datasets, data modalities, AI approaches, their challenges, and future perspectives. ACCESS, 13, 54757-54778. (www)

  @article{ , abstract = {Social media platforms have transformed the world into a global village by providing a unique platform for unrestricted communication and opinion sharing. However, this freedom is used to spread misinformation and disrupt societal harmony. To combat misinformation and fake news on social media platforms, multifarious AI applications have been developed to detect such content in various languages and data modalities, including text, images, and videos. To establish a distinctive platform that fosters the rapid development of AI-driven fake news detectors, researchers have published several review articles in recent years. However, many of these articles are outdated, and lack comprehensiveness, particularly regarding recent trends, public datasets, representation learning methods, and classifiers details. The limited scope of articles hindered their ability to provide in-depth information on predictors’ language specificity, data modality focus, and state-of-the-art performance across diverse languages. This paper offers a unique platform with detailed information on these aspects to address this gap. It offers a detailed roadmap for understanding the scope, strengths, and limitations of existing review articles on the subject. To support the development of new benchmark datasets and more accurate predictors, it conducts an in-depth analysis of the definitions and perspectives of fake news within the context of existing literature. This analysis provides a more comprehensive definition and basic concepts of fake news. In addition, 310 fake news detection articles published in the last 8 years have been thoroughly investigated. Within the landscape of these articles, the study presents details of languages, datasets, data modalities, predictor architecture designs, and their performance metrics.}, archive = {J_ACCESS}, author = {Fiza Gulzar Hussain and Muhammad Wasim and Seemab Hameed and Abdur Rehman and Muhammad Nabeel Asim and Andreas Dengel}, doi = {10.1109/ACCESS.2025.3553909}, journal = {IEEE Access}, pages = {54757-54778}, shortjournal = {IEEE Access}, title = {Fake news detection landscape: Datasets, data modalities, AI approaches, their challenges, and future perspectives}, volume = {13}, year = {2025}, }
• (2025). Grasping causality for the explanation of criticality for automated driving. ACCESS, 13, 54739-54756. (www)

  @article{ , abstract = {Safeguarding automated driving systems at SAE levels 4 and 5 is a multi faceted challenge, for which classical distance-based approaches become infeasible. To alleviate this, contemporary scenario-based approaches suggest a decomposition into scenario classes combined with the statistical analysis of these classes regarding their criticality. Unfortunately, relying solely on associative statistics may fail to recognize the causalities leading to critical scenarios. These scenarios are prerequisite for the scenario-based development of safe automated driving systems. As to incorporate causal knowledge within the development process, this work introduces a formalization of causal queries. Answering these facilitates a causal understanding of safety-relevant influencing factors. This formalized causal knowledge can be used to specify and implement safety principles that provably reduce their associated criticality. Based on Judea Pearl’s causal theory, we define a causal relation as a causal structure together with a context, both related to a suitable domain ontology. The focus lies on modeling the effect of such influencing factors on criticality as measured by appropriate criticality metrics. Our main example is a causal relation for the influencing factor ‘reduced coefficient of friction’ and its effect on the Brake-Threat-Number. As availability and quality of data are important to answer the causal queries, we also discuss requirements on real-world and synthetic data acquisition. Overall, this work contributes to establish formal causal considerations within the safety process for automated driving systems.}, archive = {J_ACCESS}, author = {Tjark Koopmann and Lina Putze and Lukas Westhofen and Roman Gansch and Ahmad Adee and Christian Neurohr}, doi = {10.1109/ACCESS.2025.3555177}, journal = {IEEE Access}, pages = {54739-54756}, shortjournal = {IEEE Access}, title = {Grasping causality for the explanation of criticality for automated driving}, volume = {13}, year = {2025}, }
• (2025). A concise dataset for intelligent behaviors in domestic tasks. ACCESS, 13, 54722-54738. (www)

  @article{ , abstract = {For service robots operating in domestic environments, high-level intelligent behaviors require a comprehensive understanding of objects through visual perception. The random placement of objects introduces variations that impact the accuracy of object detection and recognition. This study presents a novel method for automatically generating a concise image dataset, named the Object Dataset Federal University of Technology (ODUTF), to enhance intelligent behaviors in service robots to focus on domestic tasks. The dataset is produced using an automatic multicapture device that gathers RGB images, stereo information, depth images, and point-cloud data. This device has two degrees of freedom to adjust both the orientation of objects and the camera’s viewpoint. The method creates a precise and detailed visual description of objects, which improves a service robot’s ability to approach and pick up objects. This approach is evaluated within the context of the RoboCup@Home Brazil League, part of the international RoboCup competition dedicated to domestic service robots. This league involves diverse tasks, emphasizing object detection and recognition. The use of high-level intelligent behaviors is critical for overcoming domestic challenges, and ODUTF facilitates the deployment of more reliable deep neural network methods for tracking objects during pick-up tasks. Furthermore, ODUTF can be dynamically adapted using post-processing scripts to incorporate artificial features like varying backgrounds, luminosity, and noise.}, archive = {J_ACCESS}, author = {Marlon de Oliveira Vaz and Ronnier Frates Rohrich and João Alberto Fabro and André Schneider de Oliveira}, doi = {10.1109/ACCESS.2025.3554607}, journal = {IEEE Access}, pages = {54722-54738}, shortjournal = {IEEE Access}, title = {A concise dataset for intelligent behaviors in domestic tasks}, volume = {13}, year = {2025}, }
• (2025). Revisiting the control systems of autonomous vehicles in the agricultural sector: A systematic literature review. ACCESS, 13, 54686-54721. (www)

  @article{ , abstract = {The primary objective of this article is to systematically review and categorize the diverse control algorithms applied in autonomous vehicles within the agricultural sector from 2000 to 2023. This systematic literature review (SLR) was conducted using Scopus and Web of Science databases to ensure a comprehensive coverage of peer-reviewed research. The geographical scope of this review is global, encompassing studies from various regions to present a holistic perspective on the technological advancements in autonomous agricultural vehicles. By employing a systematic literature review (SLR) methodology, this study meticulously analyzed published articles to identify, extract, and synthesize data on various control algorithms, which include their application and effectiveness in enhancing agricultural productivity and sustainability. The findings reveal a significant evolution in autonomous vehicle control systems, highlighting a trend towards integrating artificial intelligence (AI)-based control algorithms. These advancements suggest potential navigation and operational efficiency improvements, contributing towards sustainable development goals (SDGs) related to sustainable agriculture. This research presents a novel systematic categorization of control algorithms for autonomous agricultural vehicles by integrating control strategies into a multi-dimensional framework based on algorithmic type (linear, nonlinear, AI-based), application context (path tracking, stability control, obstacle avoidance), and agricultural field type (dry, paddy). Unlike previous reviews that primarily classify algorithms based on technical specifications alone, this study uniquely maps these algorithms to real-world agricultural challenges, providing a structured framework that aligns control methodologies with practical implementation scenarios. This approach enhances clarity in understanding algorithm suitability, adaptability, and scalability across different agricultural settings. The study’s broad implications suggest that enhanced control systems could revolutionize the agricultural sector by improving precision farming techniques. Future research directions include further exploration of AI and machine learning integration with control algorithms and their scalability across various agricultural settings. This SLR provides foundational knowledge and direction for future innovation in the farming sector’s autonomous vehicle technology.}, archive = {J_ACCESS}, author = {Vinayambika S. Bhat and Yong Wang}, doi = {10.1109/ACCESS.2025.3555142}, journal = {IEEE Access}, pages = {54686-54721}, shortjournal = {IEEE Access}, title = {Revisiting the control systems of autonomous vehicles in the agricultural sector: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). PAPR reduction for OFDM mobile wireless communications by constant-amplitude signal decomposition. ACCESS, 13, 54672-54685. (www)

  @article{ , abstract = {This paper proposes a constant-amplitude (CA) modulation scheme for orthogonal frequency-division multiplexing (OFDM) over mobile wireless channels. To cope with the high peak-to-average power ratio (PAPR) of OFDM signals, the proposed scheme decomposes one OFDM signal into two CA signals, and then transmits these two signals at different timings. Since CA signals give off a considerable amount of out-of-band emission (OOBE), the decomposed CA signals are passed into a low-pass filter (LPF) of which outputs are sequentially transmitted from one antenna. On the receiver side, the received signals are fed into a frequency-domain equalizer and then, combining the two time-domain equalizer’s outputs that originate from those two CA signals can regenerate the original OFDM signal. Especially when the channel is time-invariant without phase noise, lower complexity recovery of the original OFDM signal is to combine and equalize the two received signals that correspond to those two CA signals. As a conventional CA OFDM approach, the constant envelope OFDM (CE-OFDM) scheme transforms the OFDM signal into a phase modulated signal and is compared with the proposed scheme by computer simulation. It is demonstrated that the proposed scheme with LPF is superior to CE-OFDM in terms of both bit error rate (BER) performance and OOBE characteristics. Computational complexity of the proposed scheme is almost the same as that of CE-OFDM without any unwrappers in the phase demodulator, but the former can require a less amount of complexity than that of the latter with an accurate unwrapper being implemented.}, archive = {J_ACCESS}, author = {Yuyuan Chang and Kazuhiko Fukawa}, doi = {10.1109/ACCESS.2025.3554660}, journal = {IEEE Access}, pages = {54672-54685}, shortjournal = {IEEE Access}, title = {PAPR reduction for OFDM mobile wireless communications by constant-amplitude signal decomposition}, volume = {13}, year = {2025}, }
• (2025). Network design for efficient video data transmission with real-time traffic constraints in networked control systems. ACCESS, 13, 54663-54671. (www)

  @article{ , abstract = {This paper addresses the challenge of efficiently transmitting video data in networked control systems while meeting stringent real-time constraints such as low latency and minimal packet loss. The objective is to develop a simple yet scalable network architecture that minimizes end-to-end delays for critical watchdog traffic, ensuring system reliability and performance. The proposed design includes three interconnected workcells with sensors, controllers, actuators, and cameras, and is capable of managing bandwidth-intensive video data alongside time-sensitive control traffic. Using delay calculations and Riverbed simulations, the study evaluates transmission, propagation, queuing, and processing delays. By implementing traffic management strategies like phase-shifting camera traffic, the solution achieves a significant improvement in delay reduction, decreasing end-to-end delays for watchdog traffic by over 95%, demonstrating its effectiveness in mitigating congestion and ensuring timely data delivery.}, archive = {J_ACCESS}, author = {Moustafa M. Awad and Tarek Refaat and Ramez Daoud and Hany M. Elsayed and Hassanein H. Amer}, doi = {10.1109/ACCESS.2025.3552317}, journal = {IEEE Access}, pages = {54663-54671}, shortjournal = {IEEE Access}, title = {Network design for efficient video data transmission with real-time traffic constraints in networked control systems}, volume = {13}, year = {2025}, }
• (2025). 7.7-kW inductive coupling three-phase coil wireless power transfer pads for electric vehicles charging. ACCESS, 13, 54648-54662. (www)

  @article{ , abstract = {Modern electric vehicle (EV) wireless charging systems (WCSs) rely heavily on Resonant Inductive Power Transfer (RIPT), which offers a flawless integration of safety, dependability, and automated functionality. In RIPT-based WCS, the design of magnetic pads is a critical factor that significantly influences power transfer efficiency. This component is essential for optimizing the system’s performance. Three-phase couplers (TPCs) surpass single-phase designs by offering higher energy transfer capacity, along with benefits such as rotating magnetic flux and lessened use of ferrite materials. This article provides an in-depth analysis of a TPC with a circular design, referred to as the “Three Half Circular Coil” (3HCC) pad. Three half-circular coils are positioned in a circle to form the 3HCC pad, delivering advantages such as improved angular misalignment tolerance, optimized ferrite utilization, and simplified construction. The effectiveness of the 3HCC design is evaluated using Matlab and FEA tools for a 7.7 kW WCS, with comparisons made against tripolar, three-phase rectangular, and traditional single-phase circular coil designs. To validate the 3HCC design, a 7.7 kW lab prototype was developed, focusing on coupling and cross-coupling effects influenced by various coil attributes like misalignment, coil position, and the number of turns. The research emphasizes the vital role of RIPT in EV charging and demonstrates the superiority of TPCs compared to their single-phase equivalents. Furthermore, it demonstrates the proposed 3HCC design’s effectiveness in improving charging efficiency.}, archive = {J_ACCESS}, author = {Mahesh Aganti and Bharatiraja Chokkalingam and Rajesh Verma and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3552825}, journal = {IEEE Access}, pages = {54648-54662}, shortjournal = {IEEE Access}, title = {7.7-kW inductive coupling three-phase coil wireless power transfer pads for electric vehicles charging}, volume = {13}, year = {2025}, }
• (2025). Finger vein recognition based on vision transformer with feature decoupling for online payment applications. ACCESS, 13, 54636-54647. (www)

  @article{ , abstract = {Biometric recognition plays a pivotal role in enhancing the security of online payment systems. Nevertheless, practical challenges such as image translation caused by user behavior and illumination variations in real-world environments can significantly degrade recognition performance. To address these issues, this study proposes a Global-Local Attention Model based on Feature Decoupling (GLA-FD), which integrates two key components: the Feature Decoupling and Reconstruction Module (FDRM) and the Global-Local Attention Module (GLAM). The FDRM module decouples finger vein images into background information and texture features, subsequently reconstructing them to generate enhanced vein feature maps. This process ensures robust recognition performance even under varying illumination conditions. Building upon the enhanced vein feature maps, the GLAM module extracts both global and local representations, thereby strengthening the model’s ability to capture spatial correlations and effectively mitigating the adverse effects of image translation. Rigorous testing across diverse public databases validates the enhanced efficacy of the newly developed GLA-FD model, outperforming state-of-the-art approaches in cross-domain scenarios, including FV-USM, NUPT-FPV, UTFVP, MMCBNU-6000, PLUSVein-FV3 (LED), and PLUSVein-FV3 (Laser). The model achieved remarkable Correct Identification Rates (CIRs) of 99.95%, 99.96%, 96.45%, 99.71%, 99.72%, and 99.20%, respectively. Furthermore, in terms of Equal Error Rate (EER), the GLA-FD model exhibited consistently low values of 0.04%, 0.05%, 3.69%, 0.29%, 0.23%, and 0.69% across the same datasets. These results highlight the model’s exceptional stability and generalization capabilities, ensuring reliable performance even in challenging scenarios such as blurred images, broad age distributions, limited sample sizes, large-scale identity classes, and diverse acquisition sensors. The versatility of the GLA-FD model underscores its potential for a wide range of real-world applications. The source code of the proposed GLA-FD model is publicly accessible at: https://github.com/liangying-Ke/GLA-FD, licensed under MIT Lab. at NIU, Taiwan for academic and non-commercial use.}, archive = {J_ACCESS}, author = {Liang-Ying Ke and Yi-Chen Lin and Chih-Hsien Hsia}, doi = {10.1109/ACCESS.2025.3552075}, journal = {IEEE Access}, pages = {54636-54647}, shortjournal = {IEEE Access}, title = {Finger vein recognition based on vision transformer with feature decoupling for online payment applications}, volume = {13}, year = {2025}, }
• (2025). Sustainable AI with quantum-inspired optimization: Enabling end-to-end automation in cloud-edge computing. ACCESS, 13, 54622-54635. (www)

  @article{ , abstract = {The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.}, archive = {J_ACCESS}, author = {Andreas Andreou and Constandinos X. Mavromoustakis and Evangelos K. Markakis and Athina Bourdena and George Mastorakis}, doi = {10.1109/ACCESS.2025.3554024}, journal = {IEEE Access}, pages = {54622-54635}, shortjournal = {IEEE Access}, title = {Sustainable AI with quantum-inspired optimization: Enabling end-to-end automation in cloud-edge computing}, volume = {13}, year = {2025}, }
• (2025). Exploring beyond logits: Hierarchical dynamic labeling based on embeddings for semi-supervised classification. ACCESS, 13, 54611-54621. (www)

  @article{ , abstract = {In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with noisy and biased data, the model’s representation network is more reliable than the classification network. Additionally, label generation methods based on model predictions often show poor adaptability across different datasets, necessitating customization of the classification network. Therefore, we propose a Hierarchical Dynamic Labeling (HDL) algorithm that does not depend on model predictions and utilizes image embeddings to generate sample labels. We also introduce an adaptive method for selecting hyperparameters in HDL, enhancing its versatility. Moreover, HDL can be combined with general image encoders (e.g., CLIP) to serve as a fundamental data processing module. We extract embeddings from datasets with class-balanced and long-tailed distributions using pre-trained semi-supervised models. Subsequently, samples are re-labeled using HDL, and the re-labeled samples are used to further train the semi-supervised models. Experiments demonstrate improved model performance, validating the motivation that representation networks are more reliable than classifiers or predictors. Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.}, archive = {J_ACCESS}, author = {Jiayi Chen and Yanbiao Ma and Wei Dai and Xiaohua Chen and Shuo Li}, doi = {10.1109/ACCESS.2025.3554583}, journal = {IEEE Access}, pages = {54611-54621}, shortjournal = {IEEE Access}, title = {Exploring beyond logits: Hierarchical dynamic labeling based on embeddings for semi-supervised classification}, volume = {13}, year = {2025}, }
• (2025). MF-YOLO: Mask wearing detection algorithm for dense environments. ACCESS, 13, 54601-54610. (www)

  @article{ , abstract = {To address the challenges of false positives and missed detections in face mask detection within dense environments, we propose the MF-YOLO face mask detection model. First, a feature map convolution approach is introduced to transform the feature map information into the initial weights of convolutional kernels, accelerating the convergence of model training. Additionally, a residual self-attention module is designed to capture fine edge position features by combining global feature extraction with sliding window-based information interaction, effectively reducing missed detections, especially for small objects. Furthermore, we introduce a mixed attention mechanism that integrates both channel and spatial attention, enabling the model to focus more effectively on key features and better target the objects of interest. Finally, an enhanced feature-fusion strategy is employed to mitigate the loss of feature information during layer-by-layer transmission, improving the model’s ability to fuse information at the same scale. Experimental results in the public MASK dataset and the self-built SFMD dataset demonstrate that the proposed algorithm significantly accelerates convergence while improving accuracy by 2.1% and 1.3%, respectively, compared to the original network. Our code and datasets are publicly released on GitHub https://github.com/wenpengshishuaige/MF-YOLO.}, archive = {J_ACCESS}, author = {Peng Wen and Zhengyi Yuan and Junhu Zhang and Haitao Li}, doi = {10.1109/ACCESS.2025.3551892}, journal = {IEEE Access}, pages = {54601-54610}, shortjournal = {IEEE Access}, title = {MF-YOLO: Mask wearing detection algorithm for dense environments}, volume = {13}, year = {2025}, }
• (2025). Sunrise in the desert: Leveraging big data analytics for predictive solar energy production in saudi arabia. ACCESS, 13, 54585-54600. (www)

  @article{ , abstract = {This research addresses the critical need for accurate solar energy forecasting in Saudi Arabia’s renewable energy transition. Leveraging the comprehensive Solar Resource Monitoring Stations dataset (2017–2021) from King Abdullah City for Atomic and Renewable Energy, this study presents the first extensive analysis of solar stations across Saudi Arabia with a systematic classification of solar stations into five distinct geographical groups, accounting for regional climate variations and solar radiation patterns. Using meteorological variables, including air conditions, wind patterns, relative humidity, and barometric pressure from 44 stations, we evaluated three machine learning models—Linear Regression (LR), Support Vector Regression (SVR), and Decision Tree (DT)—for predicting daily solar radiation. The results demonstrate that LR achieved superior performance with an RMSE of 139.71 and MAE of 98.32, significantly outperforming other models, particularly the Decision Tree model, which showed the highest error rate RMSE: 504.67. Through detailed regional analysis, identified northern and central regions exhibit consistently high Global Horizontal Irradiance (GHI), marking these areas as optimal locations for residential photovoltaic system deployment. These findings provide valuable insights for optimizing solar resource allocation and integration in Saudi Arabia’s power grid, facilitating the country’s transition to sustainable energy. This study’s results contribute to a better understanding solar resource distribution and variability, supporting informed decision-making for the Kingdom’s sustainable energy objectives.}, archive = {J_ACCESS}, author = {Da’ad Albahdal and Maha Almousa and Wijdan Aljebreen and Aeshah A. Almutairi}, doi = {10.1109/ACCESS.2025.3551271}, journal = {IEEE Access}, pages = {54585-54600}, shortjournal = {IEEE Access}, title = {Sunrise in the desert: Leveraging big data analytics for predictive solar energy production in saudi arabia}, volume = {13}, year = {2025}, }
• (2025). Understanding flaky tests through linguistic diversity: A cross-language and comparative machine learning study. ACCESS, 13, 54561-54584. (www)

  @article{ , abstract = {Software development is significantly impeded by flaky tests, which intermittently pass or fail without requiring code modifications, resulting in a decline in confidence in automated testing frameworks. Code smells (i.e., test case or production code) are the primary cause of test flakiness. In order to ascertain the prevalence of test smells, researchers and practitioners have examined numerous programming languages. However, one isolated experiment was conducted, which focused solely on one programming language. Across a variety of programming languages, such as Java, Python, C++, Go, and JavaScript, this study examines the predictive accuracy of a variety of machine learning classifiers in identifying flaky tests. We compare the performance of classifiers such as Random Forest, Decision Tree, Naive Bayes, Support Vector Machine, and Logistic Regression in both single-language and cross-language settings. In order to ascertain the impact of linguistic diversity on the flakiness of test cases, models were trained on a single language and subsequently tested on a variety of languages. The following key findings indicate that Random Forest and Logistic Regression consistently outperform other classifiers in terms of accuracy, adaptability, and generalizability, particularly in cross-language environments. Additionally, the investigation contrasts our findings with those of previous research, exhibiting enhanced precision and accuracy in the identification of flaky tests as a result of meticulous classifier selection. We conducted a thorough statistical analysis, which included t-tests, to assess the importance of classifier performance differences in terms of accuracy and F1-score across a variety of programming languages. This analysis emphasizes the substantial discrepancies between classifiers and their effectiveness in detecting flaky tests. The datasets and experiment code utilized in this study are accessible through an open source GitHub repository to facilitate reproducibility is available at: https://github.com/PELAB-LiU/FlakyCrossLanguage. Our results emphasize the effectiveness of probabilistic and ensemble classifiers in improving the reliability of automated testing, despite certain constraints, including the potential biases introduced by language-specific structures and dataset variability. This research provides developers and researchers with practical insights that can be applied to the mitigation of flaky tests in a variety of software environments.}, archive = {J_ACCESS}, author = {Azeem Ahmad and Xin Sun and Muhammad Rashid Naeem and Yasir Javed and Mohammad Akour and Kristian Sandahl}, doi = {10.1109/ACCESS.2025.3553626}, journal = {IEEE Access}, pages = {54561-54584}, shortjournal = {IEEE Access}, title = {Understanding flaky tests through linguistic diversity: A cross-language and comparative machine learning study}, volume = {13}, year = {2025}, }
• (2025). A low-cost environment-interactive patrol inspection system with navigation based on sensor-fusion and robotic arm contact pose feedback. ACCESS, 13, 54547-54560. (www)

  @article{ , abstract = {In this paper, we introduce a cost-effective mobile-robot-based patrol inspection system that navigates using sensor fusion and arm contact feedback, bypassing the need for 3D LiDAR, physical odometry, and external positioning systems. Our system utilizes a height-adjustable, four-wheel platform equipped with a 6-DOF robotic arm, achieving nine degrees of freedom with the platform’s height adjustability and planar movement. Within the Robot Operating System (ROS) framework, the system employs 2D LiDAR and a depth camera for SLAM-based mapping and pose estimation. The primary challenge during the implementation of this system is to obtain reliable pose updates of the mobile platform without physical odometry and a direct positioning source while maintaining affordability. To address this challenge, a lightweight deep neural network (DNN) object detection model is trained to identify the specific interactive items at checkpoints. By integrating a contact sensor and knowing the position of the button on the map, the acquisition of the pose of the end effector is achieved upon contact. This allows a precise update of the position of the mobile platform on the map through transforms. Experimental results indicate that our system can efficiently patrol designated routes, interact with the environment at checkpoints, and recalibrate pose using robotic arm feedback. In real-world evaluations, the system achieves a 24.35% improvement in positional accuracy and a 26.70% improvement in orientation accuracy, demonstrating its effectiveness and robustness.}, archive = {J_ACCESS}, author = {Zhesheng Zhang and Long Chen}, doi = {10.1109/ACCESS.2025.3553521}, journal = {IEEE Access}, pages = {54547-54560}, shortjournal = {IEEE Access}, title = {A low-cost environment-interactive patrol inspection system with navigation based on sensor-fusion and robotic arm contact pose feedback}, volume = {13}, year = {2025}, }
• (2025). A multiphysics dataset generation procedure for the data-driven modeling of traction electric motors. ACCESS, 13, 54534-54546. (www)

  @article{ , abstract = {This paper presents the work done to address two main challenges in the simulation and design of electric machines for traction applications. On one hand, the modeling process is becoming increasingly complex as the demand for higher efficiency, high power density, and low cost pushes the speed and compactness of the motor to high levels. As a result, the interactions between multiple physical domains (e.g., electromagnetic, thermal, structural, etc.) can no longer be neglected, even in preliminary designs. Consequently, research into new modeling solutions in this area is currently active and widespread. On the other hand, new computational methodologies based on data-driven machine learning are becoming increasingly widespread as the computational power available for this task increases. However, to assess their performance and realize their potential in surrogate and meta-modeling electrical machines, a standardized benchmark for comparing these new approaches is needed. To address these challenges, the paper presents an open-source dataset that provides a reliable foundation for the multi-physical analysis of electric motors used in traction applications. One of the main novelties of this approach is that geometrical and physical data of the motor configuration are shared among different analysis codes. Attention is focused on tailoring the numerical discretization so that the same mesh can be used in different domains, avoiding data conversions and possible numerical inaccuracies. The paper thoroughly explains the workflow developed to create the database, detailing the methodological aspects. Ultimately, the resulting database is made available as an open resource for other researchers in the field. The resulting dataset represents a tool for benchmarking advanced computational methodologies and promoting reproducibility in research.}, archive = {J_ACCESS}, author = {Simone Ferrari and Luigi Solimene and Riccardo Torchio and Costanza Anerdi and Fabio Freschi and Luca Giaccone and Gianmarco Lorenti and Francesco Lucchinizz and Piergiorgio Alotto and Gianmario Pellegrino and Maurizio Repetto}, doi = {10.1109/ACCESS.2025.3554147}, journal = {IEEE Access}, pages = {54534-54546}, shortjournal = {IEEE Access}, title = {A multiphysics dataset generation procedure for the data-driven modeling of traction electric motors}, volume = {13}, year = {2025}, }
• (2025). Improving investment forecasting: A comparative analysis of machine learning models as key GDP indicators. ACCESS, 13, 54517-54533. (www)

  @article{ , abstract = {Investment is a crucial driver of economic growth and a fundamental component of Gross Domestic Product (GDP). Accurate investment forecasting is essential for informed policymaking and economic planning. However, traditional econometric models often struggle to capture economic data’s intricate and non-linear dynamics, indicating a need for more robust and adaptable approaches. This study addresses this need by employing machine learning techniques to enhance investment forecasting accuracy, particularly the Gated Recurrent Unit (GRU) model. Historical investment data from fifteen leading GDP countries (1990–2020) was analyzed using seven models: GRU, ARIMA, ETS, SVR, XGBoost, CNN, and LSTM. Key performance metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—demonstrated that the GRU model achieved superior accuracy. The GRU model consistently outperformed other models across all performance metrics. It achieved the lowest MAE, RMSE, and MAPE values, highlighting its effectiveness in capturing complex temporal dependencies. Compared to traditional econometric models, GRU delivered significantly more accurate forecasts, emphasizing its potential for improving investment predictions. The results emphasize the potential of advanced machine learning models in capturing complex temporal dependencies, leading to more reliable investment predictions. This study addresses a significant gap in economic forecasting by highlighting the advantages of GRU and its implications for policymaking and future research.}, archive = {J_ACCESS}, author = {Cheng-Hong Yang and Tshimologo Molefyane and Borcy Lee and Ting-Jen Hsueh and Yu-da Lin}, doi = {10.1109/ACCESS.2025.3554641}, journal = {IEEE Access}, pages = {54517-54533}, shortjournal = {IEEE Access}, title = {Improving investment forecasting: A comparative analysis of machine learning models as key GDP indicators}, volume = {13}, year = {2025}, }
• (2025). SHPNeXt: A novel method of multi-scale and variable resolution AI-based tongue image segmentation. ACCESS, 13, 54504-54516. (www)

  @article{ , abstract = {In the domain of Traditional Chinese Medicine, accurately segmenting tongue images is fundamental for computer-assisted diagnosis. Yet, current models often falter with images of diverse scales and clarity, impeding their widespread application. To address this challenge, we propose SHPNeXt, an innovative network designed to accurately segment tongue images across different scales and resolutions. This model blends PoolFormer and Hire-MLP to adeptly discern both overarching and nuanced details, ensuring accurate segmentation across varying tongue image sizes. Furthermore, SHPNeXt’s precision was further enhanced by integrating a Nuclear-Norm Non-negative Matrix Factorization (NMF) approach, which robustly counters noise in lower quality images. Experiments on three benchmark datasets demonstrate SHPNeXt’s superior performance, achieving mean Intersection over Union (mIoU) scores of 99.64%, 97.05%, and 96.82%. Balancing efficiency and accuracy, SHPNeXt’s architecture comprises 5.984 million parameters and operates at 1.22 GFLOPs, rendering it an exceptionally effective tool for real-world tongue diagnosis in TCM. The code has been released on github: (https://github.com/Kuanzhaipcx/SHPNeXt.git).}, archive = {J_ACCESS}, author = {Chong-Xiao Peng and Zhi-Jun Gao and Jin-Huan Wang and Xin Yue and Yi Li and Li-Li Sun and Yin-Huan Sun and Fu-Quan Du}, doi = {10.1109/ACCESS.2025.3554487}, journal = {IEEE Access}, pages = {54504-54516}, shortjournal = {IEEE Access}, title = {SHPNeXt: A novel method of multi-scale and variable resolution AI-based tongue image segmentation}, volume = {13}, year = {2025}, }
• (2025). Approximation of First–Order complex resonators in the Frequency–Domain. ACCESS, 13, 54494-54503. (www)

  @article{ , abstract = {A novel approach for implementing first-order complex filters without the requirement of two separate signal paths, for the real and imaginary parts of the transfer function, is introduced in this work. This is achieved by employing a curve-fitting based method to approximate both the gain and phase responses of the prototype complex filter. The resulting rational integer-order transfer function can be realized using conventional filter design techniques. The behavior of the proposed complex resonator configuration is evaluated through experimental results, derived using a Field Programmable Analog Array (FPAA) device. An application example is provided, in which the complex resonator is employed to extract vowels from the corresponding glottal pulse trains.}, archive = {J_ACCESS}, author = {Julia Nako and Georgia Tsirimokou and Costas Psychalinos and Ahmed S. Elwakil}, doi = {10.1109/ACCESS.2025.3553629}, journal = {IEEE Access}, pages = {54494-54503}, shortjournal = {IEEE Access}, title = {Approximation of First–Order complex resonators in the Frequency–Domain}, volume = {13}, year = {2025}, }
• (2025). Sum-rate improvement with power distribution scheme for low-complexity decoding rate splitting. ACCESS, 13, 54474-54493. (www)

  @article{ , abstract = {The combination of Non-Orthogonal Multiple Access (NOMA) and Multiple Input Multiple Output (MIMO) technologies has been identified as a powerful strategy to achieve high spectral efficiency and improved wireless services. Imperfect Channel State Information at Transmitter (CSIT) can lead to multiuser interference issues that pose obstacles for wireless networks utilizing MIMO technology. Instead of maintaining the accurate CSIT, Rate Splitting (RS) directly addresses the issue of imperfect CSIT by dividing user messages into private and common messages. Generalized RS is designed to maximize the achievable rate and Quality of Service (QoS) at the cost of transceiver complexity. The increasing number of users served by the Generalized RS raises the number of Successive Interference Cancellation (SIC) processes; consequently, this can place a more significant burden on users in carrying out the SIC process. This paper aims to improve RS performance by employing power distribution adjustment without increasing the number of SICs, which would burden users in the overloaded network. We apply power distribution adjustment to 1-layer RS and 2-layer Hierarchical RS (HRS), called 1-layer RSPDA and 2-layer HRSPDA, which require one or two SICs per user. The simulation results demonstrate the capabilities of the proposed methods to enhance the total sum rate of the system for low-complexity decoding RS. 1-layer RSPDA and 2-layer HRSPDA achieve an increase of 13.68 % and 12.64% in sum rate compared to Generalized RS at Signal-to-Noise Ratio (SNR) of 30 dB, respectively.}, archive = {J_ACCESS}, author = {Desti Madya Saputri and Iskandar and Mohammad Sigit Arifianto and Aloysius Adya Pramudita}, doi = {10.1109/ACCESS.2025.3554226}, journal = {IEEE Access}, pages = {54474-54493}, shortjournal = {IEEE Access}, title = {Sum-rate improvement with power distribution scheme for low-complexity decoding rate splitting}, volume = {13}, year = {2025}, }
• (2025). E-waste management challenges in india from the perspective of producer responsibility organizations. ACCESS, 13, 54462-54473. (www)

  @article{ , abstract = {Producer Responsibility Organizations (PROs) are professional organizations that take responsibility for collecting and treating E-waste in an eco-friendly way on behalf of producers. However, they face several challenges in effective e-waste management (EWM). The current study aims to identify and model key challenges in EWM from PROs’ perspective in the Indian context. We identified key challenges to EWM through a literature review and discussions with experts. The expert team validated and mapped key challenges into the EWM process, from collection to disposal. We employed the DEMATEL method to model key challenges. Nine key challenges were identified. Findings suggest that a knowledge-sharing platform is a crucial step in EWM as it allows PROs to share best practices. In addition, collaboration among stakeholders, usage of tracking systems, and eco-design could play a key role in sustainable and effective EWM. This study provides various mitigation strategies to overcome key challenges in the adoption of sustainable EWM. Furthermore, PROs can use the proposed conceptual framework to identify challenges that may be faced during different EWM process stages.}, archive = {J_ACCESS}, author = {Tejendra Singh Gaur and Vinod Yadav and Sameer Mittal and Shailender Singh and Muztoba Ahmad Khan}, doi = {10.1109/ACCESS.2025.3553203}, journal = {IEEE Access}, pages = {54462-54473}, shortjournal = {IEEE Access}, title = {E-waste management challenges in india from the perspective of producer responsibility organizations}, volume = {13}, year = {2025}, }
• (2025). Time-optimal stabilization of asynchronous boolean control networks under state and control constraints. ACCESS, 13, 54450-54461. (www)

  @article{ , abstract = {This paper investigates the problem of time-optimal stabilization in Boolean control networks (BCNs) with an asynchronous update scheme, subject to state and control constraints. BCNs are fundamental models for various applications, including biological regulatory systems. While asynchronous updates provide a more realistic representation, they introduce nondeterministic state transitions, posing significant challenges for control strategy design. We propose a systematic approach based on an iterative algorithm that efficiently computes the basin of attraction (BoA) of a steady state and constructs a state feedback control law to ensure time-optimal stabilization from any initial state in the BoA to the steady state. The algorithm explicitly accounts for state and control constraints. The correctness and time complexity of the proposed approach are rigorously analyzed. Notably, the time complexity remains relatively low since our algorithm only requires one-step state transition computations, avoiding the expensive matrix products commonly used in existing algebraic methods. To validate our approach, we apply it to an asynchronous BCN model of the myeloid differentiation process involving 11 genes. The results demonstrate the effectiveness and efficiency of our method in the theoretical analysis and control of asynchronous BCNs particularly modelling gene regulatory systems. Our algorithm code is publicly accessible at https://gitee.com/shuhuagao/tos-abcn.}, archive = {J_ACCESS}, author = {Haonan Wang and Shuping Wang and Ruohai Xi and Shuhua Gao}, doi = {10.1109/ACCESS.2025.3553040}, journal = {IEEE Access}, pages = {54450-54461}, shortjournal = {IEEE Access}, title = {Time-optimal stabilization of asynchronous boolean control networks under state and control constraints}, volume = {13}, year = {2025}, }
• (2025). CR-ATTACKER: Exploiting crash-reporting systems using timing gap and unrestricted file-based workflow. ACCESS, 13, 54439-54449. (www)

  @article{ , abstract = {Software vendors widely adopt crash-reporting systems to automate the collection of crash reports, enabling efficient diagnosis and management of software failures. However, these reports often contain detailed memory snapshots of crashed processes, which may include sensitive user data (e.g., credentials and cryptographic keys). Ensuring the security of crash-reporting systems is, therefore, critical to prevent information leakage and potential exploitation. This paper analyzes the security of common crash-reporting system architectures for Linux and identifies two novel attack vectors: 1) a timing gap vulnerability during partial privilege de-escalation and 2) a file-based workflow exploitation between crash-reporting system components. By leveraging these attack vectors, we demonstrate that unprivileged attackers can extract arbitrary memory contents from other processes or manipulate the behavior of crash-reporting systems, leading to information leakage and system compromise. To mitigate these threats, we propose practical defense mechanisms that effectively neutralize both attack vectors, thereby enhancing the overall security of crash-reporting systems. We validate our findings through real-world evaluations on widely used open-source crash-reporting systems, which resulted in the discovery of four new CVEs related to ASLR bypass, arbitrary code execution, and denial-of-service (DoS) attacks. These findings highlight the urgent need for strengthened security measures in modern crash-reporting systems.}, archive = {J_ACCESS}, author = {Seong-Joong Kim and Myoungsung You and Seungwon Shin}, doi = {10.1109/ACCESS.2025.3553746}, journal = {IEEE Access}, pages = {54439-54449}, shortjournal = {IEEE Access}, title = {CR-ATTACKER: Exploiting crash-reporting systems using timing gap and unrestricted file-based workflow}, volume = {13}, year = {2025}, }
• (2025). Ensuring zero trust security in consumer internet of things using federated learning-based attack detection model. ACCESS, 13, 54423-54438. (www)

  @article{ , abstract = {The rising adoption of Consumer Internet of Things (CIoT) devices brings heightened concerns about security vulnerabilities and privacy breaches. Given their connection to daily routine and data collection abilities, it is vital to protect individual data against unauthorized entry and possible leaks proactively. Data protection’s maximum communication and storage charge will gradually challenge the typical ecology of integrated over-the-cloud learning and handling for IoT environments. Federated learning (FL) is developed as the most reasonable method for this challenge. In FL, training data-driven machine learning (ML) approaches is a form of association among several users with no need for data carried to a fundamental point; therefore, improving communication and storage charge offers a great degree of consumer-level secrecy. FL is an advanced ML that performs appropriately by inherently prioritizing confidentiality, circumventing requirements for centralized data collection, and supporting the defence. FL improves security in IoT by enabling decentralized model training without centralizing sensitive data, preserving privacy, and reducing communication costs. Integrating FL into attack detection models ensures zero-trust security, minimizing vulnerabilities and enhancing protection against cyber threats while keeping data on-device. This study develops an enhanced artificial gorilla troop optimizer with an FL-based attack detection and classification (EGTO-FLADC) approach on the CIoT environment. The EGTO-FLADC approach aims to improve security in the CIoT environment via FL and the attack detection process. Primarily, the EGTO-FLADC technique utilizes the FL model for collaborative training of combined models with many devices with no data-sharing process. In addition, the EGTO-FLADC technique uses a min-max scalar for data normalization. For attack detection, the EGTO-FLADC technique employs the temporal convolution networks with a gated recurrent unit (TCN-GRU) model and its hyperparameters are chosen by the EGTO model. The EGTO technique involves the integration of the conventional GTO model with the marine predator’s algorithm (MPA) model. The performance evaluation of the EGTO-FLADC approach is examined under the EdgeIIoTset dataset. The experimental validation of the EGTO-FLADC approach portrayed a superior accuracy value of 97.11% over existing models.}, archive = {J_ACCESS}, author = {Ali M. Al-Sharafi and Fatma S. Alrayes and Nuha Alruwais and Mohammed Maray and Asma Alshuhail and Abdulbasit A. Darem and Shoayee Dlaim Alotaibi and Mohammed Abdullah Al-Hagery}, doi = {10.1109/ACCESS.2025.3551212}, journal = {IEEE Access}, pages = {54423-54438}, shortjournal = {IEEE Access}, title = {Ensuring zero trust security in consumer internet of things using federated learning-based attack detection model}, volume = {13}, year = {2025}, }
• (2025). Convolutional autoencoder with sequential and channel attention for robust ECG signal denoising with edge device implementation. ACCESS, 13, 54407-54422. (www)

  @article{ , abstract = {Electrocardiograms (ECG) are vital for diagnosing various cardiac conditions but are often corrupted by noise from multiple sources, which can hinder accurate interpretation. Denoising ECG signals is particularly challenging because noise usually overlaps with the frequency range of the signal of interest. We proposed a convolutional autoencoder with sequential and channel attention (CAE-SCA) to address this issue. Sequential attention (SA) is based on long short-term memory (LSTM), which captures causal-temporal relationships. Meanwhile, channel attention (CA) is used to emphasize important features within channels. SA is applied to the skip connection of each encoder block, and CA is applied after each decoder block. We validated the CAE-SCA using the MIT-BIH and SHDB-AF databases as clean ECG signals, with the MIT-BIH Noise Stress Test Database as the noise source. Experimental results give an average SNR value of 16.187 dB, RMSE of 0.059, and PRD value of 18.529 in the MIT-BIH database. While in the SHDB-AF dataset, the model obtained 15.308 dB of SNR, 0.049 of RMSE, and 19.220 of PRD. These results demonstrate our CAE-SCA outperforms all the state-of-the-art methods across all tested metrics. For efficiency, CAE-SCA achieved competitive results in the metrics of floating-point operations (FLOPs), inference time, and total parameters. This allowed CAE-SCA to be implemented in edge devices as we tested using our custom ECG acquisition circuit. A significance test further confirms a statistically significant improvement in SNR values achieved by the CAE-SCA compared to baseline models, suggesting the CAE-SCA’s potential for advancing ECG processing in healthcare applications.}, archive = {J_ACCESS}, author = {Alif Wicaksana Ramadhan and Syifa Kushirayati and Salsabila Aurellia and Mgs M. Luthfi Ramadhan and Muhammad Hannan Hunafa and Muhammad Febrian Rachmadi and Aprinaldi Jasa Mantau and Siti Nurmaini and Satria Mandala and Wisnu Jatmiko}, doi = {10.1109/ACCESS.2025.3550949}, journal = {IEEE Access}, pages = {54407-54422}, shortjournal = {IEEE Access}, title = {Convolutional autoencoder with sequential and channel attention for robust ECG signal denoising with edge device implementation}, volume = {13}, year = {2025}, }
• (2025). A wideband circularly polarized antenna array using irregularly shaped hexagonal slot optimized by iterative taguchi’s method. ACCESS, 13, 54394-54406. (www)

  @article{ , abstract = {This study proposes a novel 2.45 GHz wideband $2\times 2$ circularly polarized (CP) slot antenna array, which consists of irregularly hexagonal slot CP antenna elements and a sequentially rotated phase feed network. The CP antenna element has a single-layer, single-fed, simple structure. Designing the CP antenna element does not require stacked layers, dual-feed networks, or additional resonant/perturbing components. The antenna element, including its hexagonal slot, is optimized using the iterative Taguchi’s optimization method to broaden the antenna’s impedance and axial ratio bandwidths. Detailed CP antenna design approaches, optimization settings, CP wave mechanisms, and the element’s and antenna array’s results, are discussed. Good agreements between measured and simulated results are revealed, confirming the validity of the proposed designs. The proposed wideband $2\times 2$ CP irregularly shaped hexagonal slot antenna array provides a maximum boresight gain of 15.3 dBic and a CP bandwidth of 47.6% from 2.13 to 3.46 GHz. At the center frequency of 2.45 GHz, the cross-polarization level in the main beam direction is less than –23.6 dB; the front-to-back ratio is larger than 40 dB; and the back-lobe level is less than –22 dB. A comparison of the proposed array with other $2\times 2$ wideband CP antenna arrays in the literature shows that it has a broader CP bandwidth and higher gain than those of all the compared antenna arrays. This study also demonstrates that irregular radiating geometries provide a novel approach to antenna design.}, archive = {J_ACCESS}, author = {Wei-Chung Weng and Chi-Keong Wong}, doi = {10.1109/ACCESS.2025.3554048}, journal = {IEEE Access}, pages = {54394-54406}, shortjournal = {IEEE Access}, title = {A wideband circularly polarized antenna array using irregularly shaped hexagonal slot optimized by iterative taguchi’s method}, volume = {13}, year = {2025}, }
• (2025). Identification of earthquake precursors origin and AI framework for automatic classification for one of these precursors. ACCESS, 13, 54383-54393. (www)

  @article{ , abstract = {Many precursory signal patterns arise before the initial P-wave arrival. The origin of these patterns can be traced to either instrumental or natural ground effects. Identifying these signals’ origin and nature helps to enhance automatic P-wave identification and earthquake source parameter determination. Several investigations have been accomplished to define the origin of these precursory signals, starting from installing a temporary station with 50 and 200 sampling rates for 17 months, in addition to the original station with a sampling rate of 100 samples per second at the New Abu Dabbab station (NADB). From these two stations, 429 earthquakes were recorded at three sampling rates: 50, 100, and 200 samples per second. Then, a spectral analysis was performed, where it was found that there was a clear relationship between the first pattern and the acquisition sampling rate. This proved that the first pattern (the ramping pattern) is an instrumental artifact that occurs from the finite impulse response (FIR) filter in the analog-to-digital conversion stage of the digitizer. The other type of precursory signals was found to have varying spectra independent of the used sampling rate. This led to the differentiation of the natural ground origin and instrumental artifacts-based signals in front of these earthquakes. By automating the classification of these patterns, the true P-wave arrival can be determined in real-time processing, reducing the error in P-wave arrival timing. The current study also introduces this automatic classification by developing various machine learning (ML) and Convolutional Neural Network (CNN) models to highlight the features characterizing each pattern. The classifier topology distinguishes these patterns, and then the automatic P-wave detector can decide the arrival of the P-wave depending on the type of precursory. In instrumental artifacts, the arrival is taken after the precursory and before it in the case of the natural ground-based pattern. The examined classification topologies are Logistic Regression (LR), K-nearest neighbors Classifier (KNN), Support Vector Machine (SVM), Decision Tree Classifier (DT), Random Forest Classifier (RF), XGB Classifier, Naïve Bayes (NB), Voting Classifier and Convolutional Neural Network (CNN). Out of these methodologies, the CNN outperformed with a classification accuracy of 97.67% because of its capability in feature extraction.}, archive = {J_ACCESS}, author = {Ghada Ali and Lotfy Samy and Omar M. Saad and Ali G. Hafez and El-Sayed Hasaneen and Kamal AbdElrahman and Ibrahim Salah and Mohammed S. Fnais and Hamed Nofel and Ahmed M. Mohamed}, doi = {10.1109/ACCESS.2025.3550167}, journal = {IEEE Access}, pages = {54383-54393}, shortjournal = {IEEE Access}, title = {Identification of earthquake precursors origin and AI framework for automatic classification for one of these precursors}, volume = {13}, year = {2025}, }
• (2025). Parabolic weighting mechanism in information retrieval: A mathematical analogy to lenz’s law. ACCESS, 13, 54367-54382. (www)

  @article{ , abstract = {Conventional term-weighting techniques in information retrieval, such as term frequency-inverse document frequency and the probabilistic ranking framework, often place excessive emphasis on extreme term frequencies, which can distort document ranking. Inspired by Lenz’s Law in electromagnetism, which naturally resists sudden fluctuations in magnetic flux, this work introduces a novel parabolic weighting mechanism. By applying a parabolic function to reduce the impact of excessively frequent terms while enhancing moderately occurring words, the proposed method achieves a balanced contribution of term frequency. The mathematical formulation promotes equilibrium in term weighting by integrating term frequency with inverse document frequency. Experiments conducted on benchmark datasets, including BBC News and 20 Newsgroups, demonstrate that the parabolic weighting mechanism outperforms traditional techniques, yielding measurable improvements in accuracy with classification models such as the support vector classifier (0.44 percent increase) and logistic regression (0.30 percent increase). Furthermore, statistical validation using Cohen’s effect size measure confirms the significance of performance improvements, while bootstrap analysis ensures the reliability of the observed gains. These results establish a strong foundation for future integration with neural information retrieval models and highlight the potential of the proposed approach in domain-specific applications, such as legal document analysis and biological literature search.}, archive = {J_ACCESS}, author = {Krishnan Batri and S. Lakshmi and R. Sowrirajan}, doi = {10.1109/ACCESS.2025.3550964}, journal = {IEEE Access}, pages = {54367-54382}, shortjournal = {IEEE Access}, title = {Parabolic weighting mechanism in information retrieval: A mathematical analogy to lenz’s law}, volume = {13}, year = {2025}, }
• (2025). An evolutionary toolchain for morphological filter mapping on many-core architectures. ACCESS, 13, 54350-54366. (www)

  @article{ , abstract = {Many-core systems are systolic architectures consisting of an arbitrarily large number of processing nodes connected by a point-to-point communication network. Their architecture makes them ideally suited for the implementation of data-flow algorithms, of which Mathematical Morphology (MM) filters are a typical example. However, the performance of data-flow applications on many-core systems is highly dependent on the quality of the mapping of the application tasks to the computational cores. Decomposing the structuring elements of morphological operations improves their performance, however, performing such decomposition on many-core systems leads to increased communication. The need to find a balance between performance and communication is a representative example of the general problem of mapping optimizations. The approach presented in this paper explores a two-phase design-time optimization toolchain based on evolutionary algorithms: a front-end single-objective algorithm decomposes a MM filter using smaller operators, while a back-end multi-objective (fault-tolerance, energy, and communication) algorithm searches for optimal mappings of the filter on a specific many-core system, taking into account the architectural parameters of the hardware. The output of the toolchain is a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. A set of standard benchmark applications was used to determine the optimal parameters for the algorithms, which were then validated on two real-world application examples involving the detection of features in high-resolution PCB images. Two application mapping experiments focusing on energy constraints were conducted, in which the proposed procedure was compared to deterministic mapping techniques. The evolutionary procedure was observed to offer a significant advantage over the deterministic approach, with percentage gains of up to 73.78% for smaller grids.}, archive = {J_ACCESS}, author = {Emerson C. Pedrino and Denis P. Lima and Igor F. Gallon and Valentin O. Roda and Naijia Liu and Gianluca Tempesti}, doi = {10.1109/ACCESS.2025.3554478}, journal = {IEEE Access}, pages = {54350-54366}, shortjournal = {IEEE Access}, title = {An evolutionary toolchain for morphological filter mapping on many-core architectures}, volume = {13}, year = {2025}, }
• (2025). Identifying AI hazards and responsibility gaps. ACCESS, 13, 54338-54349. (www)

  @article{ , abstract = {With the expansion of artificial intelligence (AI) in general, but especially in self-driving cars in the United States, there is an increasing need for new hazard analysis approaches to determine if and how AI contributes to accidents. The original Swiss Cheese model widely used for hazard analyses focuses uniquely on human activities that lead to accidents but cannot address accidents where AI is a possible causal factor. To this end, the Taxonomy for AI Hazard Analysis (TAIHA) is proposed that introduces layers focusing on the oversight, design, maintenance and testing of AI. Three case studies situated in the self-driving domain demonstrate how TAIHA can be used to better understand how AI contributes to accidents. Resulting TAIHA analyses suggest responsibility gaps are emerging in the self-driving car community in the US where humans, either unknowingly or on purpose, sidestep accountability, leading to serious consequences. Recommendations for mitigating these gaps include more regulatory involvement, as well as the need for company-appointed senior lead test engineers, who should become the new first-line actors in any autonomous system operation.}, archive = {J_ACCESS}, author = {Mary L. Cummings}, doi = {10.1109/ACCESS.2025.3552200}, journal = {IEEE Access}, pages = {54338-54349}, shortjournal = {IEEE Access}, title = {Identifying AI hazards and responsibility gaps}, volume = {13}, year = {2025}, }
• (2025). Enhanced privacy and communication efficiency in non-IID federated learning with adaptive quantization and differential privacy. ACCESS, 13, 54322-54337. (www)

  @article{ , abstract = {Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian-based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian-based DP. We propose a simple and efficient global bit-length scheduler using round-based cosine annealing, along with a client-based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non-IID data distributions across varying client counts, bit-length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32-bit float training while maintaining competitive model accuracy and ensuring robust privacy through DP.}, archive = {J_ACCESS}, author = {Emre Ardıç and Yakup Genç}, doi = {10.1109/ACCESS.2025.3554138}, journal = {IEEE Access}, pages = {54322-54337}, shortjournal = {IEEE Access}, title = {Enhanced privacy and communication efficiency in non-IID federated learning with adaptive quantization and differential privacy}, volume = {13}, year = {2025}, }
• (2025). An analytical cost function design and implementation for predictive control of induction machine drives. ACCESS, 13, 54313-54321. (www)

  @article{ , abstract = {In finite control set-model predictive torque control (FCS-MPTC) of induction machine (IM), the optimal design of weighting factors for the cost function has always been a research difficulty in community of scholars. Generally, for weighting factors of FCS-MPTC, the rating method or cut-and-trial method are often utilized. These methods adopts the fixed value for weighting factor, which can not adapt to multiple operating modes of IM. In addition, the cut-and-trial method is cumbersome and difficult to achieve multi-objective balanced regulation. To solve this problem, this paper proposes an analytical cost function design for predictive control of induction machine drives (abbreviated as ACF-MPTC). According to the internal electromagnetic relationship of IM, the analytical expression of weighting factor is obtained through theoretical derivation. The control performances of traditional method and ACF-MPTC, which includes root mean square (RMS) of electromagnetic torque, RMS of stator flux and total harmonic distortion (THD) of stator current, for different operating modes of IM is studied. The parameter sensitivity of ACF-MPTC is analyzed, and a robust ACF-MPTC based on online parameter identification technology is proposed. The experimental results verify the effectiveness of the proposed algorithm.}, archive = {J_ACCESS}, author = {Wei Wei and Liming Yan and Shun Tian and Xisheng Xu and Keke Sun}, doi = {10.1109/ACCESS.2025.3553839}, journal = {IEEE Access}, pages = {54313-54321}, shortjournal = {IEEE Access}, title = {An analytical cost function design and implementation for predictive control of induction machine drives}, volume = {13}, year = {2025}, }
• (2025). EffiDenseGenOp: Ensemble transfer learning with hyperparameter tuning using genetic algorithm optimization for PCOS detection from ultrasound sonography images. ACCESS, 13, 54285-54312. (www)

  @article{ , abstract = {In this research, we present the revolutionary ‘EffiDenseGenOp’ framework for Polycystic Ovary Syndrome (PCOS) detection, leveraging the amalgamation of Ensembled Transfer Learning Models. By harnessing the synergies of Ensembled EfficientNetB7 and DenseNet201, our approach transcends conventional models, offering a robust solution for PCOS detection. Notably, we introduce a Genetic Algorithm-based hyperparameter tuning mechanism, optimizing the model configuration to ensure superior generalization and performance. Our contributions encompass a meticulous comparative analysis, pitting machine learning models, deep learning models, transfer learning models, and our proposed Ensembled Transfer Learning Models against each other. The ensemble technique strategically captures complementary patterns and features from each base model, significantly amplifying the overall predictive power. Moreover, we conduct a comprehensive exploration of hyperparameters, employing extensive tuning to enhance model performance and generalization. The efficiency of Genetic Algorithm is underscored through a rigorous comparative analysis. Additionally, a novel Fuzzy Inference System is introduced for image quality enhancement, designed after meticulous examinations of image behavior under varying noise levels and membership functions. Our model undergoes rigorous training through diverse image variations, employing data augmentation techniques and noise addition. Performance evaluation reveals superior accuracy (99.58%), precision (98.87%), recall (99.20%), F1-score (99.01%), and AUC-ROC score (98.97%), substantiated by detailed analyses including confusion matrices and AUC-ROC curves. Compared to existing models, our proposed model outperforms several state-of-the-art techniques, such as VGGNet16, PCOSNet, and ResNet50, with an accuracy of 99.95%, highlighting a significant improvement in PCOS detection performance. Robustness is ensured through exhaustive K-fold cross-validation, while visualization techniques like Class Activation Maps shed light on the model’s interpretability. The research attains highly efficient PCOS detection, achieving elevated accuracy levels validated through a detailed ablation study.}, archive = {J_ACCESS}, author = {Ananya Ghosh and Kathiravan Srinivasan}, doi = {10.1109/ACCESS.2025.3553895}, journal = {IEEE Access}, pages = {54285-54312}, shortjournal = {IEEE Access}, title = {EffiDenseGenOp: Ensemble transfer learning with hyperparameter tuning using genetic algorithm optimization for PCOS detection from ultrasound sonography images}, volume = {13}, year = {2025}, }
• (2025). Corrections to “SSDWSN: A scalable software-defined wireless sensor networks”. ACCESS, 13, 54284. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “SSDWSN: A Scalable Software-Defined Wireless Sensor Networks”.}, archive = {J_ACCESS}, author = {Mohammed Alsaeedi and Mohd Murtadha Mohamad and Anas Al-Roubaiey}, doi = {10.1109/ACCESS.2025.3554084}, journal = {IEEE Access}, pages = {54284}, shortjournal = {IEEE Access}, title = {Corrections to “SSDWSN: A scalable software-defined wireless sensor networks”}, volume = {13}, year = {2025}, }
• (2025). Innovative approaches for PCB image reconstruction: Tailored datasets, metrics, and models. ACCESS, 13, 54267-54283. (www)

  @article{ , abstract = {The reconstruction of PCB (Printed Circuit Board) images is vital for quality control in electronic manufacturing, enabling fault analysis, reverse engineering, and repair. However, this task faces significant challenges due to the high complexity and precision required, compounded by densely packed layouts and image degradation from physical damage, contamination, low-light conditions, or tampered components. Existing methods often fall short due to the lack of specialized datasets, insufficient validation against real-world degradation, limited robustness to diverse defect scenarios, and evaluation metrics that fail to capture the fine details critical for PCB functionality. To address these challenges, this study introduces a data-centric approach for PCB image reconstruction, featuring three major contributions: 1) A rigorously validated dataset, PCB_Reconstruction, comprising three subsets—PCB_Mask, PCB_Blur, and PCB_Replace—systematically simulates physical damage, contamination effects, and tampered components. The dataset’s realism is validated through Structural Similarity Index Measure (SSIM) analysis and a CNN-based classification test, confirming its alignment with real-world PCB defects. 2) A new evaluation metric, Detail Reconstruction Quality (DRQ), designed to measure edge reconstruction precision, addressing the limitations of traditional metrics like PSNR and MSE. The validity of DRQ is further demonstrated by benchmarking it against SSIM and PSNR on diverse datasets, including DIV2K and Kodak, where DRQ achieves superior fine-detail reconstruction performance. 3) Comprehensive benchmarking across seven models, providing a robust evaluation of both traditional and state-of-the-art approaches. Among these, the Non-Autoregressive Transformer (NAT) stands out for its lightweight architecture and superior edge restoration performance, achieving a 11.2% DRQ improvement over U-Net on PCB_Mask and a 9.8% DRQ increase over MAE on PCB_Blur. Retraining traditional models (e.g., U-Net and EDSR) on the PCB_Reconstruction subsets further enhances DRQ scores by an average of 15%, demonstrating the dataset’s capacity to improve robustness and generalization. Experimental results confirm that the proposed datasets, evaluation framework, and benchmarking approach significantly advance the precision and reliability of PCB image reconstruction. The datasets, evaluation metrics, and results will be made publicly available at https://github.com/Wangq180/PCB_Research.}, archive = {J_ACCESS}, author = {Qianyue Wang}, doi = {10.1109/ACCESS.2025.3554509}, journal = {IEEE Access}, pages = {54267-54283}, shortjournal = {IEEE Access}, title = {Innovative approaches for PCB image reconstruction: Tailored datasets, metrics, and models}, volume = {13}, year = {2025}, }
• (2025). Set membership adaptive non parametric identification of non-linear systems. ACCESS, 13, 54254-54266. (www)

  @article{ , abstract = {Adaptation is a desirable feature when dealing with the identification of complex systems. However, this property can be difficult to achieve when non-convex model structures such as neural networks are employed to parametrize the unknown system. This work introduces a novel approach for dynamically adapting the data set that defines the model in non-parametric Set Membership identification methods. The proposed solution constructs a nonparametric, nonlinear model of a discrete-time dynamical system by exploring the data set, assuming the system follows a Nonlinear Auto-Regressive model with exogenous Inputs (NARX) structure. The identification data are assumed to be affected by unknown but bounded noise. Specifically, two strategies are proposed to adapt the identification data set while preserving system performance dynamically. The first strategy allows the data set to incorporate new data as novel modeling information becomes available, while redundant information can be eliminated when memory conditions are reached. The second strategy introduces new information sequentially; once an auxiliary memory vector in the data set reaches its desired cardinality, the method orderly replaces the oldest data with newer dynamics. These strategies enable the identified models to adapt in response to unmodeled behaviors arising from time-varying dynamics or limited initial data sets, minimizing the need for extensive experimentations and allowing to dynamically reconstruct the data set for developing data-driven models. The effectiveness of the proposed approaches is demonstrated through the experimental modeling of a nonlinear mechatronic system. Performance is benchmarked against neural network models and a static Set Membership identification strategy. Results indicate that the proposed dynamic data set generation approach improves the accuracy and robustness of the model when using non-informative experimental data sets as starting point for the estimation, improving the overall performance of the data-driven modeling task and facilitating the use of these modeling techniques in real environments.}, archive = {J_ACCESS}, author = {Juan Alejandro Castano and Fernando Quevedo and Fredy Ruiz}, doi = {10.1109/ACCESS.2025.3554187}, journal = {IEEE Access}, pages = {54254-54266}, shortjournal = {IEEE Access}, title = {Set membership adaptive non parametric identification of non-linear systems}, volume = {13}, year = {2025}, }
• (2025). DYMRO-SLAM: A robust stereo visual SLAM for dynamic environments leveraging mask R-CNN and optical flow. ACCESS, 13, 54240-54253. (www)

  @article{ , abstract = {Simultaneous localization and mapping (SLAM) enables robots to localize in uncertain environments and has been widely used in the field of robotics. However, traditional vision SLAM systems usually assume that the observation environment is stationary, which results in unsatisfactory performance in dynamic scenarios. To overcome this challenge, we propose a DYMRO-SLAM system which incorporates a Mask R-CNN instance segmentation network and an optical flow tracking algorithm based on ORB-SLAM3 to enhance the localization performance in dynamic environments. Specifically, the system separates dynamic targets from the stationary background during image processing stage to improve the accuracy of its localization in dynamic scenes; When processing key and non-keyframes, the system adopts feature point tracking and optical flow tracking, respectively, which greatly reduces computation time of descriptors and thus improves the real-time performance. Finally, we validate the performance on datasets from TUM, EuRoC and our own datasets, and the experiment results verifies that our DYMRO-SLAM system has significant performance gains in terms of localization accuracy and runtime efficiency.}, archive = {J_ACCESS}, author = {Hongjie Cui and Xin Zhao and Guanyao Luo}, doi = {10.1109/ACCESS.2025.3555156}, journal = {IEEE Access}, pages = {54240-54253}, shortjournal = {IEEE Access}, title = {DYMRO-SLAM: A robust stereo visual SLAM for dynamic environments leveraging mask R-CNN and optical flow}, volume = {13}, year = {2025}, }
• (2025). Evolutionary algorithm for the traveling salesman problem with innovative encoding on hybrid quantum-classical machines. ACCESS, 13, 54223-54239. (www)

  @article{ , abstract = {The Traveling Salesman Problem (TSP) is a widely studied NP-complete optimization challenge with significant theoretical and practical implications. This study proposes a hybrid quantum-classical framework using a Quantum-Inspired Evolutionary Algorithm (QEA) with Sort Gray Binary Encoding to solve the TSP. The proposed method guarantees the generation of valid TSP tours by eliminating invalid solutions. It employs quantum superposition with intrinsic randomness to enhance computational efficiency and scalability. The framework was implemented on cloud-based NISQ platforms, including IBM Quantum and AWS Braket, demonstrating its practicality and effectiveness. Experimental evaluations revealed that the proposed framework successfully solved TSP instances with up to 15 cities, achieving superior performance compared to classical methods and showcasing its ability to scale under NISQ constraints. The results also highlight the potential of hybrid quantum-classical approaches to overcome hardware limitations in current quantum systems. This study establishes a robust hybrid methodology for solving combinatorial optimization problems. It also sets a benchmark for leveraging the capabilities of NISQ-era quantum devices in real-world applications, thereby providing a foundation for future research in hybrid quantum-classical optimization techniques.}, archive = {J_ACCESS}, author = {Ravi Saini and Ashish Mani and M. S. Prasad and Siddhartha Bhattacharyya and Jan Platos}, doi = {10.1109/ACCESS.2025.3554690}, journal = {IEEE Access}, pages = {54223-54239}, shortjournal = {IEEE Access}, title = {Evolutionary algorithm for the traveling salesman problem with innovative encoding on hybrid quantum-classical machines}, volume = {13}, year = {2025}, }
• (2025). Hardware-aware iterative one-shot neural architecture search with adaptable knowledge distillation for efficient edge computing. ACCESS, 13, 54204-54222. (www)

  @article{ , abstract = {The growing demand for edge applications calls for efficient and optimized deep neural network models. Neural Architecture Search (NAS) is instrumental in designing such models, but achieving optimal architectures quickly remains a key challenge. To address this, we propose Hardware-aware Iterative One-shot NAS (HIO-NAS), a highly efficient approach that iteratively explores architectures across predefined search spaces for depth, filter, and width, all while respecting hardware constraints. HIO-NAS operates in four main steps: full training, random search, hardware verification, and retraining with adaptable knowledge distillation, repeated for each search space. The hardware-aware mechanism incorporates a lookup table to evaluate and filter subnetworks sampled during random search, ensuring only the most promising candidates proceed to hardware verification. The top-performing subnetwork(s) are then deployed on the target hardware for further validation. The adaptable knowledge distillation technique dynamically adjusts the teacher model’s influence based on deviations in the cost function during training. By progressively refining search spaces, HIO-NAS reduces computational overhead and avoids convergence issues. Its hypernetwork design emphasizes chain-like structures, where layers maintain connectivity. Applied to MobileNet v2 on CIFAR-100 and YOLOv7 on BDD100K, HIO-NAS delivered significant improvements: a 2.1% accuracy gain and 20.8% reduction in latency for MobileNet v2, and better performance over YOLOv7 and its variants. Interestingly, the findings highlight that unit-sharing topologies excel in depth searches, whereas unit-non-sharing topologies perform better in filter and width searches. Overall, HIO-NAS showcases a robust capability for efficiently discovering high-performance, hardware-optimized architectures, making it ideal for edge applications.}, archive = {J_ACCESS}, author = {Oscal Tzyh-Chiang Chen and Yu-Xuan Chang and Chih-Yu Chung and Ya-Yun Cheng and Manh-Hung HA}, doi = {10.1109/ACCESS.2025.3554185}, journal = {IEEE Access}, pages = {54204-54222}, shortjournal = {IEEE Access}, title = {Hardware-aware iterative one-shot neural architecture search with adaptable knowledge distillation for efficient edge computing}, volume = {13}, year = {2025}, }
• (2025). A novel learning-based MPC method via basic-residual cooperative model. ACCESS, 13, 54192-54203. (www)

  @article{ , abstract = {This study proposes a novel model predictive control (MPC) method based on the basic-residual cooperative model. Compared to existing learning-based MPC methods that rely on a single network model as prediction models for either static feature capture or dynamic adaptation, which often result in insufficient adaptability or compromised computational efficiency, the proposed method integrates a dual-network architecture: a Long Short-Term Memory (LSTM) network to capture static system features, and a self-attention feed-forward neural network to adapt to dynamic aspects. The convergence and stability of the resulting control system are proven through theoretical analysis. The effectiveness of proposed method is validated through numerical simulations and experiments. Experimental results show that the proposed MPC method can reduce the prediction model’s root mean square error by about 70% compared to classical static model-based MPC and cuts computational time by about 30% compared to classical dynamic model-based MPC. The proposed method significantly enhances the model adaptability and computational efficiency of nonlinear dynamic systems, such as autonomous vehicles and robots.}, archive = {J_ACCESS}, author = {Yuesheng Liu and Zhongxian Xu and Ning He and Lile He and Fuan Cheng}, doi = {10.1109/ACCESS.2025.3554168}, journal = {IEEE Access}, pages = {54192-54203}, shortjournal = {IEEE Access}, title = {A novel learning-based MPC method via basic-residual cooperative model}, volume = {13}, year = {2025}, }
• (2025). An RNN-based adaptive hybrid time series forecasting model for driving data prediction. ACCESS, 13, 54177-54191. (www)

  @article{ , abstract = {In this paper, we propose a hybrid time series forecasting model, named as the Adaptive Multivariate Exponential Smoothing-Recurrent Neural Networks (AMES-RNN), which enables accurate prediction for time series data with non-seasonal and additive trend characteristics. The AMES-RNN follows a hybrid approach in which each of the statistical and deep learning models predicts particular time series components and then merges their output. To enhance prediction performance, the optimal smoothing coefficients of the Exponential Smoothing (ES) model are estimated and updated online. Here, the coefficient estimation is performed through a deep learning-based regression model, and a method for training the regression model is presented. In addition, the prediction model utilizes future-implying information as additional input if available in order to improve prediction accuracy. The effectiveness of the proposed model was validated through multistep forecast tests using vehicle driving data that has non-seasonal and additive trend characteristics. The results show that the prediction accuracy of the proposed model was improved at least 23.0% compared to those of the existing prediction model. Additionally, we demonstrated that AMES-RNN requires low computational resources, making it feasible to perform online predictions.}, archive = {J_ACCESS}, author = {Ji Hwan Seo and Kyoung-Dae Kim}, doi = {10.1109/ACCESS.2025.3554803}, journal = {IEEE Access}, pages = {54177-54191}, shortjournal = {IEEE Access}, title = {An RNN-based adaptive hybrid time series forecasting model for driving data prediction}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection workflow using random forest regressor in large-scale photovoltaic power plants. ACCESS, 13, 54168-54176. (www)

  @article{ , abstract = {In large-scale photovoltaic (PV) plants, anomalies can affect performance, and maintenance schedules, and compromise long-term reliability, reducing the financial returns from the PV plant. Although the importance of anomaly detection in this context is clear, it poses a significant challenge due to the vast amount of data generated by the plant. Therefore, tools that assist in the anomaly detection process, minimizing human intervention, are essential. This paper proposes a workflow using a random forest regressor, with the application of a dynamically modeled mathematical threshold for anomaly detection. The model predicts the target power using features such as plane-of-array (POA) irradiance and temperature. To this end, Random Forest was selected after testing with other presented models, followed by the implementation of a mathematical model to establish a dynamic threshold that classifies the data as normal, alert, or anomaly. The threshold is calculated by comparing the mean absolute difference between predicted and actual power and adjusting it based on a dynamic multiplier to establish alert and anomaly limits. Subsequent tests were conducted on different inverters, applying various dataset splits. The workflow achieved excellent accuracy in detecting anomalies and alerts, with a total accuracy of 99.69%. The model successfully predicted anomalies in different inverters within the same plant. Thus, it is expected that the application of prediction methods, combined with the dynamic threshold, will serve as a support tool for anomaly detection in large-scale PV power plants.}, archive = {J_ACCESS}, author = {João Lucas de Souza Silva and Marcelo Vinícius de Paula and Juliana de Souza Granja Barros and Tárcio André Dos Santos Barros}, doi = {10.1109/ACCESS.2025.3554162}, journal = {IEEE Access}, pages = {54168-54176}, shortjournal = {IEEE Access}, title = {Anomaly detection workflow using random forest regressor in large-scale photovoltaic power plants}, volume = {13}, year = {2025}, }
• (2025). Federated learning for fall detection with multimodal residual fusion and pareto-optimized client selection. ACCESS, 13, 54148-54167. (www)

  @article{ , abstract = {With the increasing aging population and the prevalence of chronic diseases, fall detection has become a critical component in elderly healthcare monitoring. However, challenges such as multimodal data integration and joint analysis in Internet of Medical Things (IoMT) environments and data heterogeneity across sources hinder efficient and accurate fall detection. This paper proposes a Federated Learning-based framework with Multimodal Residual Fusion and Pareto-optimized Client Selection (FLPCS-MRF). Firstly, the framework incorporates a multimodal feature fusion network with a residual mechanism, which adaptively learns the optimal fusion scheme through residual connections, dynamically suppressing noise interference from redundant modalities. Secondly, to address variations in data modalities, distributions, and quality across clients, by considering all client factors rather than treating clients as independent, five innovative evaluation metrics are designed to assess the convergence and generalization performance of the local models. Finally, a Pareto-optimized client selection method is introduced to efficiently select reliable clients for global aggregation, ensuring both the stability and robustness of the global model. Extensive experiments on the UP Fall dataset demonstrate the effectiveness of the proposed approach, achieving 95.27% accuracy and 95.42% F1-score, outperforming existing methods. Additionally, it demonstrates strong robustness in complex scenarios involving imbalanced data distributions and missing modalities.}, archive = {J_ACCESS}, author = {Bao-Quan Wang and Fan Yang and Yi Wang and Fan Zhao and Yun-Fei Han and Yu-Peng Ma}, doi = {10.1109/ACCESS.2025.3553419}, journal = {IEEE Access}, pages = {54148-54167}, shortjournal = {IEEE Access}, title = {Federated learning for fall detection with multimodal residual fusion and pareto-optimized client selection}, volume = {13}, year = {2025}, }
• (2025). Research progress and prospects of pre-training technology for electromagnetic signal analysis. ACCESS, 13, 54130-54147. (www)

  @article{ , abstract = {The pre-training technology can overcome the problem of label dependence and improve the generalization ability of the model, it has achieved remarkable results in natural language processing (NLP) and computer vision (CV), and extensive exploration has been carried out in the analysis of time series data (TS) and spatio-temporal data (STD). Electromagnetic signal is a special time series, and the time series analysis pre-training technology has important reference significance for the electromagnetic signal analysis pre-training. Therefore, this survey compares and analyzes the time series data and electromagnetic signals, summarizes time series analysis pre-training methods, and reviews the research progress of electromagnetic signal analysis pre-training technology. The open source data of electromagnetic signals are comprehensively summarized, which provides a massive data basis for the construction of the pre-training basic model for electromagnetic signal analysis, and the performances of the pre-training technology for electromagnetic signal analysis are evaluated. In addition, the challenges and future development directions of electromagnetic signal analysis pre-training technology are analyzed.}, archive = {J_ACCESS}, author = {Xuemin Lu and Qizhen Li and Huimin Long and Lianggang Wang and Xianming Liang and Wenjie Chen and Xihui Zhang and Xiangyu Zeng and Yong Liu}, doi = {10.1109/ACCESS.2025.3553739}, journal = {IEEE Access}, pages = {54130-54147}, shortjournal = {IEEE Access}, title = {Research progress and prospects of pre-training technology for electromagnetic signal analysis}, volume = {13}, year = {2025}, }
• (2025). Improving wind turbine gearbox reliability: A hybrid deep learning approach using bispectrum image analysis. ACCESS, 13, 54116-54129. (www)

  @article{ , abstract = {Wind turbine gearboxes are widely used in wind power plants, whose operating conditions are characterized by fluctuating wind speeds and loads, introducing noise and complexity into vibration signals, complicating accurate fault diagnosis in wind turbine components. This study explores the potential of bispectrum image analysis combined with Convolutional Neural Network (CNN), CNN-Long Short-Term Memory (LSTM), and CNN-Bidirectional LSTM networks for enhanced fault detection and predictive maintenance for wind turbine systems. Bispectrum images are used to identify nonlinearities, harmonics, and interactions among various frequency components, thereby enhancing the robustness and accuracy of fault diagnosis. Results demonstrate high accuracy ¿98% for all three models. However, CNN-BiLSTM consistently achieves the highest classification accuracy, exhibiting superior generalization capabilities and minimal misclassification. This superior performance is attributed to the CNN-BiLSTM’s ability to discern bidirectional temporal patterns within the bispectrum image data, effectively capturing the complex dynamics of gearbox failures. This research demonstrates the promise of bi-spectrum analysis as an effective method for detecting abnormalities and trends that may signify emerging defects, facilitating timely maintenance and averting catastrophic failures.}, archive = {J_ACCESS}, author = {Muhammad Zacky Asy’Ari and Jun Kondoh and Benyamin Kusumoputro}, doi = {10.1109/ACCESS.2025.3553122}, journal = {IEEE Access}, pages = {54116-54129}, shortjournal = {IEEE Access}, title = {Improving wind turbine gearbox reliability: A hybrid deep learning approach using bispectrum image analysis}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Pest identification based on fusion of self-attention with ResNet”. ACCESS, 13, 54115. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Pest Identification Based on Fusion of Self-Attention With ResNet”.}, archive = {J_ACCESS}, author = {SK Mahmudul Hassan and Arnab Kumar Maji}, doi = {10.1109/ACCESS.2025.3553995}, journal = {IEEE Access}, pages = {54115}, shortjournal = {IEEE Access}, title = {Corrections to “Pest identification based on fusion of self-attention with ResNet”}, volume = {13}, year = {2025}, }
• (2025). Spatial resolution ensured configuration planning for visual inspection using pan-tilt-zoom camera-equipped robots. ACCESS, 13, 54105-54114. (www)

  @article{ , abstract = {We propose a novel pan-tilt-zoom (PTZ) camera configuration planning method to improve data collection for structure visual inspections. This method plans a set of configurations for PTZ camera-equipped robots to collect visual data of target surfaces at the required spatial resolution. Unlike previous methods that focus solely on optimizing camera positions to cover different areas of the target, our method fully leverages all the degrees of freedom of PTZ cameras. Our method allows the robot to completely cover targets with far less movement by adjusting the camera direction and zoom level. Experiments on bridge inspections using a PTZ ground inspection platform have validated that our method ensured both the completeness and spatial resolution of the collected data. Furthermore, drone-based inspection simulations have demonstrated that our method avoids unnecessary robot movement and reduces inspection time by up to 38% compared to the previous algorithm, significantly enhancing inspection efficiency.}, archive = {J_ACCESS}, author = {Weitong Wu and Masaya Haneda and Yuki Funabora and Shinji Doki and Kae Doki}, doi = {10.1109/ACCESS.2025.3553517}, journal = {IEEE Access}, pages = {54105-54114}, shortjournal = {IEEE Access}, title = {Spatial resolution ensured configuration planning for visual inspection using pan-tilt-zoom camera-equipped robots}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based differential diagnosis of parkinson’s disease using kinematic feature extraction and selection. ACCESS, 13, 54090-54104. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is the second most common neurodegenerative disorder and is characterized by dopaminergic neuron loss and the accumulation of abnormal synuclein. PD presents both motor and non-motor symptoms that progressively impair daily functioning. The severity of these symptoms is typically assessed using the MDS-UPDRS rating scale, which is subjective and dependent on the physician’s experience. Additionally, PD shares symptoms with other neurodegenerative diseases, such as progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), complicating accurate diagnosis. We propose a machine learning-based system for differential diagnosis of PD, PSP, MSA, and healthy controls (HC) to address these diagnostic challenges. This system utilizes a kinematic feature-based hierarchical feature extraction and selection approach. Initially, 18 kinematic features are extracted, including two newly proposed features: Thumb-to-index vector velocity and acceleration, which provide insights into motor control patterns. In addition, 41 statistical features were extracted here from each kinematic feature, including some new approaches such as Average Absolute Change, Rhythm, Amplitude, Frequency, Standard Deviation of Frequency, and Slope. Feature selection is performed using One-way ANOVA to rank features, followed by Sequential Forward Floating Selection (SFFS) to identify the most relevant ones, aiming to reduce the computational complexity. The final feature set is used for classification, achieving a classification accuracy of 66.67% for each dataset and 88.89% for each patient, with particularly high performance for the MSA and HC groups using the SVM algorithm. This system shows potential as a rapid and accurate diagnostic tool in clinical practice, though further data collection and refinement are needed to enhance its reliability.}, archive = {J_ACCESS}, author = {Masahiro Matsumoto and Abu Saleh Musa Miah and Nobuyoshi Asai and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3553528}, journal = {IEEE Access}, pages = {54090-54104}, shortjournal = {IEEE Access}, title = {Machine learning-based differential diagnosis of parkinson’s disease using kinematic feature extraction and selection}, volume = {13}, year = {2025}, }
• (2025). Opacity verification for a class of modular discrete event systems. ACCESS, 13, 54080-54089. (www)

  @article{ , abstract = {The verification of opacity of discrete event systems (DESs) is subjected to the curse of dimensionality because this issue has been proven to be EXPSPACE-complete. Therefore, how to improve the efficiency of opacity verification in DESs is crucial. We discuss in this paper the verification of several classes of opacity in modular discrete event systems (modular DESs) formalized as Cartesian composition of deterministic finite state automata. We derive the sufficient and necessary conditions for these opacity notions in modular DESs. Many examples are also used to explain the results obtained in this article. These results greatly reduce the computational complexity of verifying the opacity for modular DESs.}, archive = {J_ACCESS}, author = {Jingkai Yang and Weilin Deng}, doi = {10.1109/ACCESS.2025.3554636}, journal = {IEEE Access}, pages = {54080-54089}, shortjournal = {IEEE Access}, title = {Opacity verification for a class of modular discrete event systems}, volume = {13}, year = {2025}, }
• (2025). Wireless powered NOMA assisted full-duplex cooperative U2U communications system with fluctuating two-ray channel. ACCESS, 13, 54069-54079. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) are used for search and rescue, transportation, easy relocation, and stability. UAVs can also be utilized as aerial base stations to enhance the coverage for ground cellular users. This paper proposes non-orthogonal multiple access (NOMA) assisted full-duplex (FD) cooperative UAV-to-UAV (U2U) communications system wherein the UAV transmitter acts as an FD relay for cellular downlink signal. In addition, the UAVs employ an energy harvesting framework consisting of simultaneous wireless information and power transfer (SWIPT) and wireless power transfer (WPT) protocols to improve energy efficiency. The links between the communicating nodes are modeled by the fluctuating two-ray (FTR) fading, and a probabilistic mathematical model is developed for end-to-end cellular and U2U links. The closed-form outage expressions for U2U and cellular links are derived by performing an asymptotic analysis over a high transmit signal-to-noise ratio (SNR) regime. The impact of transmit-SNR, NOMA power allocation factor, and power splitting factor on the outage performance of the cellular and U2U links are analyzed. Results show that the cellular outage probability for a high SNR regime is independent of the FTR parameter, whereas U2U outage probability decreases monotonically and converges to zero.}, archive = {J_ACCESS}, author = {Rahul Bajpai and Afraj Shabbir Shaikh and Naveen Gupta}, doi = {10.1109/ACCESS.2025.3551909}, journal = {IEEE Access}, pages = {54069-54079}, shortjournal = {IEEE Access}, title = {Wireless powered NOMA assisted full-duplex cooperative U2U communications system with fluctuating two-ray channel}, volume = {13}, year = {2025}, }
• (2025). EFFResNet-ViT: A fusion-based convolutional and vision transformer model for explainable medical image classification. ACCESS, 13, 54040-54068. (www)

  @article{ , abstract = {The rapid advancement of medical imaging technologies requires the development of advanced, automated, and interpretable diagnostic tools for clinical decision-making. Although convolutional neural networks (CNNs) have shown significant promise in medical image analysis, they have limitations in capturing the global context and lack interpretability, thereby hindering their clinical adoption. This study presents EFFResNet-ViT, a novel hybrid deep learning (DL) model designed to address these challenges by combining EfficientNet-B0 and ResNet-50 CNN backbones with a vision transformer (ViT) module. The proposed architecture employs a feature fusion strategy to integrate the local feature extraction strengths of CNNs with the global dependency modeling capabilities of transformers. The extracted features are further refined through a post-transformer CNN and a global average pooling layer to enhance the classification performance. To improve interpretability, EFFResNet-ViT incorporates Grad-CAM visualization techniques to highlight regions contributing to classification decisions and employs t-distributed stochastic neighbor embedding for feature space analysis, providing insights into class separability. The proposed model was evaluated on two benchmark datasets: brain tumor (BT) CE-MRI for BT classification and a retinal image dataset for ophthalmological diagnosis. EFFResNet-ViT achieved state-of-the-art performance, with accuracies of 99.31% and 92.54% on the BT CE-MRI and retinal datasets, respectively. Comparative analyses demonstrate the superior classification performance and interpretability of EFFResNet-ViT over existing ViT and CNN-based hybrid models. The explainable design of EFFResNet-ViT addresses the critical need for transparency in artificial intelligence-driven medical diagnostics, facilitating its potential integration into clinical workflows to improve decision-making and patient outcomes.}, archive = {J_ACCESS}, author = {Tahir Hussain and Hayaru Shouno and Abid Hussain and Dostdar Hussain and Muhammad Ismail and Tatheer Hussain Mir and Fang Rong Hsu and Taukir Alam and Shabnur Anonna Akhy}, doi = {10.1109/ACCESS.2025.3554184}, journal = {IEEE Access}, pages = {54040-54068}, shortjournal = {IEEE Access}, title = {EFFResNet-ViT: A fusion-based convolutional and vision transformer model for explainable medical image classification}, volume = {13}, year = {2025}, }
• (2025). An optimal permanent magnet motor for pumps in Electric/Hybrid vehicles: Design and experimentation. ACCESS, 13, 54028-54039. (www)

  @article{ , abstract = {This paper presents the optimal design of a 1/2 HP Surface-Mounted Permanent Magnet Motor (SMPM). The motor complies with the latest department of Energy (DoE) standards for water pump applications, released in January 2020. A design procedure is performed to optimize the cogging torque and total harmonic distortion (THD), through parametric analysis. The design is executed using the ANSOFT Maxwell 3D software and proven using the lab pump test rig. The proposed design aims to produce a back-EMF close to a sinusoidal waveform, maximize the power density and reduce the cogging torque. To attain this sinusoidal back-EMF along with a reduced cogging torque, techniques like optimizing the skewing angle and modifying the windings configuration have been used. To decrease the harmonics in the back EMF, the pole pitch is calculated such that it eliminates the significant harmonics to achieve high power factor. The proposed SMPM based pump cost is ${\$}$ 72, which is less than the current price of the induction machine-based pump of ${\$}$ 113 for the same power rating. The test rig readings prove the proposed design has a better working efficiency of 2% compared to the best market pump circulators with an excellent power factor at its rated load.}, archive = {J_ACCESS}, author = {Mohamed Z. Youssef and Asjad Elahi and Abir Ihsan}, doi = {10.1109/ACCESS.2025.3551606}, journal = {IEEE Access}, pages = {54028-54039}, shortjournal = {IEEE Access}, title = {An optimal permanent magnet motor for pumps in Electric/Hybrid vehicles: Design and experimentation}, volume = {13}, year = {2025}, }
• (2025). Machine learning for long COVID inference based on the acute phase: A case study in healthcare professionals. ACCESS, 13, 54019-54027. (www)

  @article{ , abstract = {Since 2021, the COVID-19 pandemic has affected global health, economies, and societal structures, leading to great attention to new research and awareness. In this context, Long COVID refers to the persistent symptoms some individuals experience following acute infection with the SARS-CoV-2 virus. Early diagnosis is crucial for managing these symptoms and possible consequences. In this study, we explore several Machine Learning (ML) techniques to estimate the development of long COVID from a serological study with 53 healthcare professionals, including IgA and IgG antibody data conducted before the availability of a COVID-19 vaccine. Four cases were analyzed by combining information on specific symptoms (e.g., fever, pain, fatigue, and/or loss of smell and paladar), comorbidity, and possibly antibody information. In addition to five ML (i.e., models such asRandom Forest, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Multilayer Perceptron), we applied dimensionality reduction techniques such as Principal Components Analysis, Linear Discriminant Analysis, and Feature Selection. The feature selection procedure based on specific thresholds (0.1 and 0.2) of the Gini index was the most suitable dimension reduction method, with KNN highlighting the best-balanced accuracy (greater than 74%) for Case 1, when no antibody is used, and SVM when only IgG is required. For Cases 3 and 4 that require IgA, RF performance also goes up 82% with the same thresholds in these smaller datasets. These findings emphasize the potential of ML as a decision-support tool in inferring long COVID symptoms developed weeks after an acute infection.}, archive = {J_ACCESS}, author = {Caio B. S. Maior and Sandrely P. Silva and Isis D. Lins and Ana Lisa Gomes and Marcio C. Moura}, doi = {10.1109/ACCESS.2025.3553783}, journal = {IEEE Access}, pages = {54019-54027}, shortjournal = {IEEE Access}, title = {Machine learning for long COVID inference based on the acute phase: A case study in healthcare professionals}, volume = {13}, year = {2025}, }
• (2025). Evaluation of uplink video streaming QoE in 4G and 5G cellular networks using real-world measurements. ACCESS, 13, 53996-54018. (www)

  @article{ , abstract = {In this study, we propose a comprehensive method for evaluating the Quality of Experience (QoE) for uplink video streaming in commercial 4G and 5G cellular networks. Uplink video streaming is becoming increasingly important due to applications such as live content creation and remote operations. However, commercial networks are primarily optimized for downlink performance, posing significant challenges for achieving high uplink throughput. We investigate the factors affecting uplink QoE, including signal strength, network congestion, carrier aggregation, and coverage. Our methodology involves comprehensive data collection from network elements and streaming servers, encompassing RF parameters, IP packet data, and performance metrics such as throughput and latency. We employ full-reference metrics such as Video Multi-Method Assessment Fusion (VMAF), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR) to assess video quality and address challenges in frame alignment for accurate evaluation. Our dataset includes video recordings, streamed video files, QoE metrics, IP packet data, and Radio Access Network (RAN) measurements, enabling robust regression analysis and machine learning for QoE prediction. We propose a system architecture for real-time data collection and streaming, integrating live video capture, streaming server setup, and network measurement tools. The results highlight the relationship between network conditions and video quality, demonstrating the impact of factors like path loss and Physical Resource Block (PRB) allocation and handover on QoE. This study provides valuable insights and practical solutions for optimizing uplink video streaming QoE in current and future cellular networks.}, archive = {J_ACCESS}, author = {Darius Chmieliauskas and Šarūnas Paulikas}, doi = {10.1109/ACCESS.2025.3554340}, journal = {IEEE Access}, pages = {53996-54018}, shortjournal = {IEEE Access}, title = {Evaluation of uplink video streaming QoE in 4G and 5G cellular networks using real-world measurements}, volume = {13}, year = {2025}, }
• (2025). YOLO network-based extraction of target geometry from time-frequency representation. ACCESS, 13, 53980-53995. (www)

  @article{ , abstract = {In response to challenges in target geometric parameter extraction from radar echoes, an improved YOLOv8 neural network model is proposed in this paper to recognize target types and extract geometric parameters of targets from time-frequency representation (TFR). Compared with the existing network model, the ability to extract geometric parameters is enhanced by improving the network structure, integrating the attention mechanism, and introducing deformable convolution operators. The attributed scattering center (ASC) models for targets are established to generate the TFRs. This effectively addresses the challenge of generating target datasets with varying geometric parameters for the neural network. The robustness of the network is verified through comparison experiments and ablation experiments for different types of targets. Experimental results, including metric functions and data on reconstructed geometric structures, demonstrate good consistency between the extracted and actual geometric parameters, and the trained network model has a good generalization ability for more complicated structural targets. This study explores the possibility of applying neural networks with TFR to extract target parameters.}, archive = {J_ACCESS}, author = {Jingyuan Han and Kunyi Guo and Xinqing Sheng}, doi = {10.1109/ACCESS.2025.3550888}, journal = {IEEE Access}, pages = {53980-53995}, shortjournal = {IEEE Access}, title = {YOLO network-based extraction of target geometry from time-frequency representation}, volume = {13}, year = {2025}, }
• (2025). Performance comparison of LCC and VSC HVDC systems in minimizing long-term voltage collapse. ACCESS, 13, 53965-53979. (www)

  @article{ , abstract = {High-voltage direct current (HVDC) transmission helps transmit large energy blocks over long distances. Line-Commutated Converters (LCC) and Voltage Source Converters (VSC) can be used for this purpose. LCC transmission is a mature technology with some drawbacks regarding reactive power consumption. At the same time, VSC transmission is increasing due to its advantages, such as flexible control and reactive power support. LCC and VSC have been compared, but their impact on the performance of large power systems has not been directly analyzed, especially to prevent long-term voltage instabilities. In this paper, a direct comparison of LCC and VSC during an AC fault that leads to voltage collapse will be evaluated in the Nordic32 power system, a case study used for voltage collapse analysis. LCC and VSC technologies will be applied to verify their capacity to prevent system collapse and assess their impact on bus voltages, generator current limiters, and transformer tap changing. A scenario with no active power transmitted by the HVDC will also be analyzed, highlighting the VSC’s capacity to function as a STATCOM. Steady-state and electromechanical analyses will be conducted using PV curves and time-domain simulations. A total of six scenarios were analyzed and results show that both the LCC and VSC technologies were able to prevent long-term voltage collapse when transferring active power through the DC link, although the VSC presented a better performance, being able to prevent voltage collapse even when the active power transferred was set to zero. The VSC was also able to reduce the transformer’s tap changing and prevent generators from reaching their overexcitation limiters.}, archive = {J_ACCESS}, author = {Renan Pinto Fernandes and Fabricio Lucas Lirio and Bráulio César de Oliveira and Antonio Carlos Siqueira de Lima}, doi = {10.1109/ACCESS.2025.3554448}, journal = {IEEE Access}, pages = {53965-53979}, shortjournal = {IEEE Access}, title = {Performance comparison of LCC and VSC HVDC systems in minimizing long-term voltage collapse}, volume = {13}, year = {2025}, }
• (2025). Corrections to “On the in vivo recognition of kidney stones using machine learning”. ACCESS, 13, 53964. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “On the In Vivo Recognition of Kidney Stones Using Machine Learning”).}, archive = {J_ACCESS}, author = {Francisco Lopez-Tiro and Vincent Estrade and Jacques Hubert and Daniel Flores-Araiza and Miguel Gonzalez-Mendoza and Gilberto Ochoa-Ruiz and Christian Daul}, doi = {10.1109/ACCESS.2025.3554065}, journal = {IEEE Access}, pages = {53964}, shortjournal = {IEEE Access}, title = {Corrections to “On the in vivo recognition of kidney stones using machine learning”}, volume = {13}, year = {2025}, }
• (2025). Multi-task prediction method based on GGCN for object centric event logs. ACCESS, 13, 53949-53963. (www)

  @article{ , abstract = {Event logs constitute the fundamental data for predictive process monitoring research, and the quality and format of these logs are crucial for predictive analysis. Existing process prediction methods are primarily based on flattened event logs, which overlook multi-object interactions and complex dependencies, thereby limiting their ability to model complex processes. In contrast, object-centric event logs associate events with multiple objects, thereby enabling the modeling of multi-object interactions and complex dependencies within processes. Object-centric event logs frequently employ graph neural networks for predictive analysis. To systematically analyze and compare the performance of existing predictive models designed for object centric event logs, this paper proposes a multi-task prediction model, Graph-based Relational Graph Convolutional Network (GGCN), which is based on relational graph convolutional networks and gated recurrent units. The proposed GGCN model embeds the multi-relational aspects of object-centric event logs into a graph structure and inputs this structure into the model, thereby achieving three tasks: next activity prediction, next timestamp prediction, and remaining time prediction. The graph embedding structure of the GGCN model is capable of extracting complex multi-relational associations and temporal dependency features between events. Finally, we conducted multi-task process prediction experiments using four publicly available datasets. The results demonstrate that, compared with baseline models such as Graph Convolutional Networks (GCN), Graph Isomorphism Networks (GIN), and Graph Attention Networks (GAT), the proposed GGCN model exhibits superior predictive performance and data robustness. Furthermore, in relation to the impact of subgraph size in graph embeddings on prediction performance, the experimental results indicate a significant correlation between subgraph size and model performance.}, archive = {J_ACCESS}, author = {Li Ke and Fang Huan and Xu Yifei and Shao Chifeng}, doi = {10.1109/ACCESS.2025.3553618}, journal = {IEEE Access}, pages = {53949-53963}, shortjournal = {IEEE Access}, title = {Multi-task prediction method based on GGCN for object centric event logs}, volume = {13}, year = {2025}, }
• (2025). Dynamic state estimation in power electronics-dominated grids using a digital twin approach. ACCESS, 13, 53938-53948. (www)

  @article{ , abstract = {This paper proposes a digital twin (DT) based approach to improve dynamic state estimation (DSE) in power electronics-dominated grids (PEDGs), ultimately contributing to a more reliable, efficient, and sustainable power system by enabling better integration of renewable energy sources and reducing grid instability. Traditional DSE methods struggle with the complexities of PEDGs, such as high penetration of distributed generation and energy storage, leading to inaccurate state estimations. Our approach leverages a real-time, high-fidelity virtual model of a microgrid, including detailed models of DG units, power electronics and load dynamics. This enables accurate state estimation even during transient events. The proposed approach is validated using a hardware-in-the-loop test bench which demonstrates the DT’s accuracy in tracking grid dynamics under various operating conditions. Furthermore, a sensitivity analysis highlights the importance of accurate parameter estimation and minimal communication delays for robust DSE. This research provides a framework for developing reliable and accurate DSE solutions for modern PEDGs, enabling advanced grid control, optimization, and predictive maintenance strategies.}, archive = {J_ACCESS}, author = {Ildar Idrisov and Ilya Veretennikov and Federico M. Ibanez}, doi = {10.1109/ACCESS.2025.3554258}, journal = {IEEE Access}, pages = {53938-53948}, shortjournal = {IEEE Access}, title = {Dynamic state estimation in power electronics-dominated grids using a digital twin approach}, volume = {13}, year = {2025}, }
• (2025). A novel method for quick diagnosis of MS by analyzing demyelinated axon pathway and changes in brain volumes in MRIs over time. ACCESS, 13, 53919-53937. (www)

  @article{ , abstract = {In a multiple sclerosis (MS) patient’s brain, plaques form in the axon pathway and discharge a portion of the neural signals from the axon, leading to signal losses. An axon transmits neural signals generated by several dendrites connected to the cell body. In a healthy brain, these signals have the same frequency and no inference in a circular pathway because they are aligned with the closed simple geodesics and transmitted into the axons by the cell body. Based on the ergodic theory of the earthquake flow, these signals seem to be ergodic along the axon pathway. This paper extracted changes in the volumes of the gray matter, white matter, and cerebrospinal fluid (CSF) of MS patients from magnetic resonance (MR) image slices over time by comparing MR images with the earlier MR images of the same patients. The volume changes arising from the passing of signals through the demyelinated axons were observed to be directly correlated with MS progression and inversely correlated with signal variation in demyelinated axons. The demyelinated axon pathway was modeled with the transmission lines of signal losses, and these lines were formulated in the form of Bessel functions of the first kind. The function curves were then intersected with the volumetric analysis samples of MS patients over time, obtaining two unique curves for MS progression and signals passing through the demyelinated axons for each patient. The statistical indices of the MR images of 53 patients taken were analyzed over a period, and MS and its progression were demonstrated to be ergodic. MS can be diagnosed by considering not only changes in the brain volumes but also the statistical indices of MR images over time.}, archive = {J_ACCESS}, author = {Alireza Banitalebidehkordi and Mohammad Mahdi Khalilzadeh and Farzan Khatib and Mahdi Azarnoosh}, doi = {10.1109/ACCESS.2025.3544524}, journal = {IEEE Access}, pages = {53919-53937}, shortjournal = {IEEE Access}, title = {A novel method for quick diagnosis of MS by analyzing demyelinated axon pathway and changes in brain volumes in MRIs over time}, volume = {13}, year = {2025}, }
• (2025). A K-band VCO with AC-shorted transformer tank for 76–81 GHz FMCW radars. ACCESS, 13, 53909-53918. (www)

  @article{ , abstract = {This paper presents an 18.5–20.6 GHz voltage-controlled oscillator (VCO) that achieves a wide tuning range and low phase noise, suitable for frequency-modulated continuous-wave (FMCW) radar systems. The VCO’s resonator consists of a transformer in which the primary attaches to the core, the secondary attaches to a varactor bank with the center tap providing one polarity of the tuning voltage, and a capacitor shorts the primary to the secondary for AC signals. Compared to capacitive-coupled varactors with resistive chokes, this AC-shorted transformer tank permits larger varactors without an associated noise penalty. This work analyzes the capacitive and inductive coupling to find their optimal values. A Colpitts VCO is implemented in 130 nm SiGe BiCMOS to illustrate the achievable wideband and low-noise performance. The circuit has a phase noise of −112 dBc/Hz at 1 MHz offset across a continuous tuning range of 10.4%. When used with two frequency doublers within a 76–81 GHz FMCW automotive radar transceiver, the local oscillator can fulfill both the phase noise requirement of long-range radar and the tuning range requirement of short-range radar applications in a single circuit.}, archive = {J_ACCESS}, author = {Weihu Wang and Brian A. Floyd}, doi = {10.1109/ACCESS.2025.3553353}, journal = {IEEE Access}, pages = {53909-53918}, shortjournal = {IEEE Access}, title = {A K-band VCO with AC-shorted transformer tank for 76–81 GHz FMCW radars}, volume = {13}, year = {2025}, }
• (2025). Real-time decision support system for dynamic optimization in multi-product process manufacturing. ACCESS, 13, 53895-53908. (www)

  @article{ , abstract = {The implementation of adaptive optimization in multi-product process manufacturing is a crucial strategy for the reduction of unplanned downtime and the enhancement of overall productivity. Nevertheless, the availability of real-time decision support tools for dynamic adjustments in large-scale industries is currently limited. In response to this challenge, we propose a novel model that processes data collected from extensive manufacturing operations. By leveraging Explainable Artificial Intelligence, we developed a real-time decision support system designed to dynamically adjust process parameters following varying input variables. The proposed model achieved a capture rate of 62% of the minority of products that cause micro-stoppages due to non-compliance with specifications. This approach provides a robust framework for adaptive optimization in complex and large-scale manufacturing environments, enhancing productivity and resilience against unplanned disruptions.}, archive = {J_ACCESS}, author = {Mustapha Belmouadden and Camélia Dadouchi and Robert Pellerin}, doi = {10.1109/ACCESS.2025.3553034}, journal = {IEEE Access}, pages = {53895-53908}, shortjournal = {IEEE Access}, title = {Real-time decision support system for dynamic optimization in multi-product process manufacturing}, volume = {13}, year = {2025}, }
• (2025). On the vulnerability of UMOSFETs in terrestrial radiation environments. ACCESS, 13, 53885-53894. (www)

  @article{ , abstract = {The vulnerability of prominent silicon-based U-shaped Metal-Oxide-Semiconductor Field Effect Transistors (UMOSFETs) to destructive radiation effects when operating in terrestrial atmospheric environments is investigated. Secondary particles from nuclear reactions between atmospheric neutrons and the constituent materials of electronic devices can trigger Single-Event Burnout (SEB), a destructive failure in power MOSFETs. The susceptibility of UMOSFETs to SEBs induced by atmospheric neutrons in accelerated tests is compared to that of similarly rated traditional Double-diffused MOSFETs (DMOSFETs) counterparts. Computational simulations are conducted to elucidate the failure mechanisms and propose strategies to potentially enhance the survivability of next-generation UMOSFETs in high-reliability power systems operating on Earth.}, archive = {J_ACCESS}, author = {Saulo G. Alberton and Alexis C. V. Bôas and Jeffery Wyss and Vitor A. P. Aguiar and Matheus S. Pereira and Luca Silvestrin and Serena Mattiazzo and Alessandro Paccagnella and Carlo Cazzaniga and Maria Kastriotou and Christopher D. Frost and Nilberto H. Medina}, doi = {10.1109/ACCESS.2025.3553758}, journal = {IEEE Access}, pages = {53885-53894}, shortjournal = {IEEE Access}, title = {On the vulnerability of UMOSFETs in terrestrial radiation environments}, volume = {13}, year = {2025}, }
• (2025). MicroCrystalNet: An efficient and explainable convolutional neural network for microcrystal classification using scanning electron microscope petrography. ACCESS, 13, 53865-53884. (www)

  @article{ , abstract = {Morphological characterization of microcrystalline rock textures typically relies upon the visual interpretation and manual measurement of scanning electron microscopy (SEM) imagery: a practice fraught with subjectivity, inefficiency, sampling bias, and data loss. We introduce a state-of-the-art computer vision pipeline, built on deep learning architectures, for segmenting and classifying individual microcrystals from SEM images. Initially applied to low-Mg calcite carbonate rocks, instance segmentation is achieved using a custom-tuned version of Meta’s Segment Anything Model (SAM). To train and test the classifier, we utilized 48 SEM images of diverse carbonate microtextures composed of Low-Mg calcite from studies performed worldwide. Each individual microcrystal (1852 in total) was labelled according to a bipartite classification scheme, encompassing both crystal shape (rhombic, polyhedral, amorphous, and spherical), and degree of crystal facet definition (euhedral to subhedral, anhedral), with a total of four distinct classes. MicroCrystalNet: our proposed classification model employs a convolutional neural network architecture, incorporating advanced feature map processing (feature normalization, dimensionality reduction, and sparse feature selection), integrated within a novel Normalized Sparse Reduction block. Performance metrics reveal excellent average precision scores (AP = 0.93-0.98) and Area Under Receiver-Operator Curve values (AUC = 0.95-0.99) across all classes, with visual comparison to manual ground truth images demonstrating powerful inter-class discriminatory power, even in the presence of occlusions.}, archive = {J_ACCESS}, author = {Mohammed Yaqoob and Mohammed Yusuf Ansari and Mohammed Ishaq and Issac Sujay Anand John Jayachandran and Mohammed S. Hashim and Thomas Daniel Seers}, doi = {10.1109/ACCESS.2025.3552626}, journal = {IEEE Access}, pages = {53865-53884}, shortjournal = {IEEE Access}, title = {MicroCrystalNet: An efficient and explainable convolutional neural network for microcrystal classification using scanning electron microscope petrography}, volume = {13}, year = {2025}, }
• (2025). New mathematical modeling to optimize the frequency of bus routes. ACCESS, 13, 53849-53864. (www)

  @article{ , abstract = {The public transport system must be structured to ensure the constancy of activities, the maintenance of demand, and financial stability, in which the frequency of service plays a crucial role. This paper introduces a new mathematical model and two optimization techniques to improve bus route frequency. The objective is to minimize social, operational, and environmental costs. The model comprises a mixed-integer nonlinear formulation, and the optimization techniques involve linearization and a heuristic approach, which is based on sequentially solving relaxed mixed-integer subproblems and fixing integer variables throughout the process. As a case study, we consider the bus routes connecting the central and oceanic regions of Niterói, Brazil. The results indicate that the combined approach has good application in real-world scenarios, positively contributing to sustainable mobility by making the best fleet allocation based on existing demand.}, archive = {J_ACCESS}, author = {Joyce Azevedo Caetano and Francisco Glaubos Nunes Clímaco and Glaydston Mattos Ribeiro and Laura Bahiense}, doi = {10.1109/ACCESS.2025.3552007}, journal = {IEEE Access}, pages = {53849-53864}, shortjournal = {IEEE Access}, title = {New mathematical modeling to optimize the frequency of bus routes}, volume = {13}, year = {2025}, }
• (2025). Coupling heterogeneous neural networks with memristors. ACCESS, 13, 53835-53848. (www)

  @article{ , abstract = {This paper proposes a novel approach for cascading Hopfield and Hindmarsh-Rose neural networks using memristive synapses. The model integrates two distinct neural networks, each consisting of two identical neurons. First, we design a memristor and analyze its memristive characteristics. We then develop the Hopfield and Hindmarsh-Rose networks with dual neurons, utilizing memristors to couple these sub-networks, forming a new four-neuron neural network. The equilibrium points and system stability are thoroughly analyzed. The dynamic behavior of the network is examined through bifurcation diagrams, two-parameter bifurcation plots, Lyapunov exponents, and phase diagrams, revealing phenomena such as chaos, periodicity, period-doubling bifurcations, and alternating-period chaos. To validate the numerical simulations, we implement the phase diagram on an STM32 hardware platform. Finally, we propose a DNA-based image encryption algorithm leveraging chaotic sequences generated by the network. The performance analysis of encrypted images demonstrates that the algorithm offers strong robustness during transmission and effective resistance against attacks, ensuring privacy and security.}, archive = {J_ACCESS}, author = {Li Zhang and Rongli Jiang and Yike Ma and Xiangkai Pu and Zhongyi Li}, doi = {10.1109/ACCESS.2025.3549978}, journal = {IEEE Access}, pages = {53835-53848}, shortjournal = {IEEE Access}, title = {Coupling heterogeneous neural networks with memristors}, volume = {13}, year = {2025}, }
• (2025). A security and privacy-preserving consortium blockchain-based accessing control in mobile crowdsensing. ACCESS, 13, 53815-53834. (www)

  @article{ , abstract = {In current mobile crowdsensing (MCS) systems, there is limited attention given to the security threats associated with access to the profile records (PR) of participating mobile devices. For example, most existing studies consider stakeholders of MCS applications as fully trusted entities, which granting them unlimited authorization to access the Participated Mobile Devices’ PR for the purpose of collecting sensing data. From this point, hackers may exploit this trusted point to gain unlimited authorization access to a particular participated mobile devices. They can achieve this by launching attacks, such as creating counterfeit applications as a trusted MCS applications and then posing as legitimate stakeholders to request access to the targeted devices. Thus, the hacker will gain full authorized access to the participating mobile devices’ PR, in which will result in the disclosure of security and privacy-related information of their participated devices. Therefore, the blockchain paradigm is recommended as the optimal solution for ensuring data access, owing to its advantages of immutability. However, because the blockchain is a decentralized database, a malicious MCS-server will be able to disclose the privacy of participating mobile devices by linking multiple blocks generated for the same device while it performs different tasks. Based on the aforementioned issue, this work designs a consortium blockchain-based access control system to protect the privacy rights of participating mobile devices in MCS. Furthermore, an efficient searchable keyword encryption methodology is proposed to link between the consortium blockchain and the privacy blockchain, thereby enhancing system security and access control. Finally, a security analysis and performance evaluation are conducted to demonstrate the efficiency of the proposed protocol.}, archive = {J_ACCESS}, author = {Abdulrahman Alamer}, doi = {10.1109/ACCESS.2025.3554600}, journal = {IEEE Access}, pages = {53815-53834}, shortjournal = {IEEE Access}, title = {A security and privacy-preserving consortium blockchain-based accessing control in mobile crowdsensing}, volume = {13}, year = {2025}, }
• (2025). 100 gbps low-latency protocol processing and FPGA implementation. ACCESS, 13, 53804-53814. (www)

  @article{ , abstract = {With the ongoing evolution of integrated space-air-ground networks, space laser communications face increasingly stringent demands for low latency and high throughput in high-speed data transmission. To meet these requirements, this paper presents a low-latency protocol stack processing architecture based on Field Programmable Gate Array (FPGA) and provides an in-depth investigation into its implementation at the hardware level. By employing a nonindependent four-channel data interface, the proposed architecture significantly enhances the alignment efficiency during the data framing and deframing processes, thereby mitigating the overhead associated with large-volume data handling. In addition, an adaptive inter-frame interval control mechanism is introduced to effectively reduce the data processing latency. Experimental validation on a Xilinx Virtex UltraScale VCU108 development board, equipped with a 100G CFP2-DCO optical module, demonstrates that the system achieves a throughput of up to 98.1 Gbps, with an end-to-end latency of approximately 300 ns. Compared to other FPGA-based solutions, which achieve a best-reported protocol processing latency of approximately $1.3~\mu $ s in existing research, our method achieves at least a 4x improvement in overall data processing speed.}, archive = {J_ACCESS}, author = {Liangwei Lei and Funan Zhu and Shaowen Lu and Yaohui Du and Jiawei Li and Xia Hou}, doi = {10.1109/ACCESS.2025.3549947}, journal = {IEEE Access}, pages = {53804-53814}, shortjournal = {IEEE Access}, title = {100 gbps low-latency protocol processing and FPGA implementation}, volume = {13}, year = {2025}, }
• (2025). Gyromagnetics of the electron clock. ACCESS, 13, 53772-53803. (www)

  @article{ , abstract = {The Dirac equation is presented as a complete theory of the electron as a gyromagnetic particle clock with precise physical interpretation for all degrees of freedom. The electron is modeled as a point charge with toroidal zitter and variable zilch. That is to say, the charge oscillates at the speed of light on a torus centered on a circular orbit around its center of mass $\mathbf {z}=\mathbf {z}(t)$ , with an axis at a variable angle $\beta =\beta (\mathbf {z}(t))$ with respect to its spin vector $\mathbf {s}=\mathbf {s}(t)$ . The Dirac wave function $\Psi =\Psi (ct+\mathbf {x})$ has a unique factorization $\Psi =(\rho e^{i\beta })^{\scriptstyle \frac {1}{2}}U$ , where $U=U(ct+\mathbf {x})$ is a spatial rotor with magnetic degrees of freedom, and the quantity $\Psi \widetilde {\Psi }=\rho e^{i\beta }$ specifies an embedding of electron paths in the vacuum, where the zilch function $\beta =\beta (ct+\mathbf {x}-\mathbf {z}(t))$ is a measure of electron energy density. It culminates in a new synthesis of Dirac electron theory with Maxwell’s electrodynamics by identifying zilch as a common factor that binds them together.}, archive = {J_ACCESS}, author = {David Hestenes}, doi = {10.1109/ACCESS.2025.3544654}, journal = {IEEE Access}, pages = {53772-53803}, shortjournal = {IEEE Access}, title = {Gyromagnetics of the electron clock}, volume = {13}, year = {2025}, }
• (2025). Techno-economic, predictive modeling, and demand response analysis of a renewable energy-based microgrid for residential applications. ACCESS, 13, 53748-53771. (www)

  @article{ , abstract = {This study develops a grid-connected solar PV–biogas microgrid for a five-story residential building in Rajshahi, Bangladesh, optimized using HOMER Pro. Among six configurations, the optimal configuration, integrating solar PV and a biogas generator, achieves a 59.4% renewable fraction, reducing CO2 emissions by 46% and lowering the cost of energy to ${\$}$ 0.0306/kWh. The system’s net present cost is ${\$}$ 46,813, with an annual operating cost of ${\$}$ 1,187. It generates 31,168 kWh/year from solar PV and 3,040 kWh/year from biogas, with grid purchases and sales of 21,951 kWh/year and 13,411 kWh/year, respectively. Predictive modeling ( $R^{2} = 1.0$ ) enhances power estimation accuracy. MATLAB-based stability analysis confirms seamless voltage and frequency integration. A demand response strategy achieves 2,730.44 kWh/year savings, reducing non-renewable reliance. The analysis results show the microgrid’s high reliability, with negligible Energy Not Supplied (ENS) is $4.31\times 10^{-14}$ kWh and an almost zero Loss of Power Supply Probability (LPSP) is $1.06\times 10^{-18}$ , indicating stable and uninterrupted power supply. Sensitivity analysis identifies solar irradiance as the dominant cost factor. The results establish a scalable, cost-effective model for residential renewable microgrid adoption globally.}, archive = {J_ACCESS}, author = {Md. Feroz Ali and Md. Rafiqul Islam Sheikh and Abdullah Al Mamun and Md. Jakir Hossen}, doi = {10.1109/ACCESS.2025.3552056}, journal = {IEEE Access}, pages = {53748-53771}, shortjournal = {IEEE Access}, title = {Techno-economic, predictive modeling, and demand response analysis of a renewable energy-based microgrid for residential applications}, volume = {13}, year = {2025}, }
• (2025). Adaptive synchronverter algorithm using Q-learning for microgrid stability control. ACCESS, 13, 53731-53747. (www)

  @article{ , abstract = {The integration of renewable energy sources into microgrids often leads to stability challenges, primarily due to the absence of inertia typically provided by conventional generators. This lack of inertia can be mitigated through the use of synchronverters, which emulate the dynamic characteristics of synchronous generators. However, traditional synchronverter control systems rely on fixed values for virtual inertia and voltage control gain, which limits their adaptability to changing grid conditions. To address this limitation, this research proposes a novel control strategy for synchronverters based on reinforcement learning (RL), enabling adaptive adjustment of both virtual inertia and voltage control gain. The Q-learning algorithm is selected for this purpose, as it is capable of handling continuous observation spaces and discrete action spaces, making it well-suited for optimization in complex systems. The proposed Q-learning-based synchronverter control algorithm adjusts the inverter output dynamically, ensuring both frequency and voltage stability within the microgrid. The results demonstrate that the Q-learning-based synchronverter, with its adaptive control parameters, achieves better stability performance compared to conventional synchronverters, offering enhanced flexibility and robustness in maintaining microgrid stability.}, archive = {J_ACCESS}, author = {Mohd Brado Frasetyo and Fransisco Danang Wijaya and Husni Rois Ali and Muhammad Zharfan Wiranata}, doi = {10.1109/ACCESS.2025.3551287}, journal = {IEEE Access}, pages = {53731-53747}, shortjournal = {IEEE Access}, title = {Adaptive synchronverter algorithm using Q-learning for microgrid stability control}, volume = {13}, year = {2025}, }
• (2025). Traffic crash countermeasure recommendations using deep neural network: A decision support tool for traffic safety engineer. ACCESS, 13, 53718-53730. (www)

  @article{ , abstract = {Various measures have been implemented to reduce the number of traffic crashes, which can cause significant social and economic damage. Several studies have identified the factors that cause traffic crashes and recommended effective countermeasures for specific types of traffic crashes. However, only a few studies support decision making by comprehensively presenting the causes and countermeasures of crashes. Various policy making stakeholders are emphasizing the need to establish a traffic safety management knowledge database that presents comprehensive information on crash causes and countermeasures. Therefore, this study aims to support reasonable decision making by recommending appropriate countermeasures based on crash types using a deep neural network (DNN). The algorithm automatically recommends countermeasures suitable to each type of traffic crash. The DNN-based multilabel classification label model learns the recommended countermeasure use-case results of each matched traffic crash use-case using logic. The algorithm is validated by applying the K-fold cross-validation. A performance evaluation of the model reveals that its accuracy reaches 93%, and other evaluation indicators, such as precision, recall and f1-score exhibit excellent results. An algorithm that automatically recommends traffic crash countermeasures can help policymakers make rational decisions based on the established database. An algorithm that automatically recommends traffic crash countermeasures can help policymakers make rational decisions based on the established database. The algorithm supports policymakers, such as public officials in transportation departments and government agencies, responsible for implementing traffic safety measures. It overcomes the limitations of fragmented reports by providing data-driven recommendations, enabling efficient resource use and implementation of optimal safety strategies.}, archive = {J_ACCESS}, author = {Haneul Park and Jiyeon Kim and Minseok Kim and Jinsu Yoon and Kyeongseung Hwang and Juneyoung Park and Jaehyun So}, doi = {10.1109/ACCESS.2025.3543641}, journal = {IEEE Access}, pages = {53718-53730}, shortjournal = {IEEE Access}, title = {Traffic crash countermeasure recommendations using deep neural network: A decision support tool for traffic safety engineer}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Fuzzy logic-based substitution-box for robust medical image encryption in telemedicine”. ACCESS, 13, 53717. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine”.}, archive = {J_ACCESS}, author = {Abdul Razaq and Louai A. Maghrabi and Musheer Ahmad and Farrah Aslam and Wei Feng}, doi = {10.1109/ACCESS.2025.3553991}, journal = {IEEE Access}, pages = {53717}, shortjournal = {IEEE Access}, title = {Corrections to “Fuzzy logic-based substitution-box for robust medical image encryption in telemedicine”}, volume = {13}, year = {2025}, }
• (2025). Information processing via human soft tissue: Soft tissue reservoir computing. ACCESS, 13, 53706-53716. (www)

  @article{ , abstract = {Physical reservoir computing refers to the concept of using nonlinear physical systems as computational resources to achieve complex information processing. This approach exploits the intrinsic properties of physical systems such as their nonlinearity and memory to perform computational tasks. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity that satisfy the requirements of physical reservoir computing. This study evaluates the potential of human soft biological tissues as physical reservoirs for information processing. Particularly, it determines the feasibility of using the inherent dynamics of human soft tissues as a physical reservoir to emulate nonlinear dynamic systems. In this concept, the deformation field within the muscle, which is obtained from ultrasound images, represented the state of the reservoir. The findings indicate that the dynamics of human soft tissue have a positive impact on the computational task of emulating nonlinear dynamic systems. Specifically, our system outperformed the simple LR model for the task. Simple LR models based on raw inputs, which do not account for the dynamics of soft tissue, fail to emulate the target dynamical system (relative error on the order of $10^{-2}$ ). By contrast, the emulation results obtained using our system closely approximated the target dynamics (relative error on the order of $10^{-3}$ ). These results suggest that the soft tissue dynamics contribute to the successful emulation of the nonlinear equation. This study suggests that human soft tissues can be used as a potential computational resource. Soft tissues are found throughout the human body. Therefore, if computational processing is delegated to biological tissues, it could lead to a distributed computation system for human-assisted devices.}, archive = {J_ACCESS}, author = {Yo Kobayashi}, doi = {10.1109/ACCESS.2025.3553140}, journal = {IEEE Access}, pages = {53706-53716}, shortjournal = {IEEE Access}, title = {Information processing via human soft tissue: Soft tissue reservoir computing}, volume = {13}, year = {2025}, }
• (2025). Neural networks for phase shift optimization of reconfigurable intelligent surfaces under imperfect channel state information. ACCESS, 13, 53694-53705. (www)

  @article{ , abstract = {Reconfigurable intelligent surface (RIS) is a key enabling technology for the sixth generation (6G) of mobile networks. It can focus the signal at an intended location (e.g., a user hotspot) through dynamically adjusting the phase shifts of its passive reflecting elements, thereby enhancing the signal quality and network coverage. However, the optimal configuration of the phase shift profile of RIS is challenging since it requires accurate channel state information (CSI), which is prohibitively expensive to acquire in practice because the number of reflecting elements in RIS is usually large. To address this limitation, in this paper, we train and test a fully-connected neural network (FCN) that estimates the optimal phase shift profile of RIS from noisy CSI measurements. We evaluate the performance of the proposed Machine Learning (ML) model in terms of different key performance indicators (KPIs), including the system bit error rate (BER) and throughput, phase shift estimation mean square error (MSE), and the training time of the neural network itself. Simulation results demonstrate that our proposed technique can significantly improve the performance in RIS-assisted wireless networks, reducing the gap to the optimal network throughput to below 1 %.}, archive = {J_ACCESS}, author = {Pablo Fondo-Ferreiro and Firooz B. Saghezchi and Felipe Gil-Castiñeira and Jonathan Rodriguez}, doi = {10.1109/ACCESS.2025.3552065}, journal = {IEEE Access}, pages = {53694-53705}, shortjournal = {IEEE Access}, title = {Neural networks for phase shift optimization of reconfigurable intelligent surfaces under imperfect channel state information}, volume = {13}, year = {2025}, }
• (2025). Innovative load frequency control: Integrating adaptive backstepping and disturbance observers. ACCESS, 13, 53673-53693. (www)

  @article{ , abstract = {Load frequency control (LFC) in large interconnected power systems is crucial for balancing electricity supply and demand while minimizing frequency deviations. Traditional methods like proportional-integral-derivative (PID)controllers and advanced techniques such as evolutionary algorithms and artificial intelligence (AI) have limitations, including computational complexity, sensitivity to parameter changes, and high resource demands. This paper introduces a novel decentralized observer-based backstepping control (DOBC) strategy to overcome these challenges. In our work, each area controller utilizes local measurements and feedback signals to regulate its own area frequency. This approach inherently reduces the reliance on centralized communication and minimizes the impact of potential communication failures, such as packet losses and delays. The proposed method synergistically combines backstepping and disturbance observer techniques, resulting in rapid and stable system responses with reduced control effort, while a noncertainty equivalent adaptive approach ensures exponential disturbance estimation and maintains system stability under time-varying disturbances. Unlike conventional sliding mode control, the proposed method eliminates chattering, making it suitable for sensitive applications. Simulations validate its effectiveness under time delays, parametric uncertainties, nonlinearities, and load disturbances. Results show superior transient response, better oscillation damping, and lower control effort compared to adaptive neuro-fuzzy inference system based fractional-order PID-acceleration controller (ANFIS-FOPIDA), Second-Order sliding mode control (SOSMC), and Deep Reinforcement Learning (DRL). The paper concludes with a rigorous stability and robustness analysis, demonstrating the method’s resilience to parametric uncertainties and time-varying disturbances. This highlights its practical applicability and advantages in modern power systems.}, archive = {J_ACCESS}, author = {Javad Ansari and Mohamadreza Homayounzade and Ali Reza Abbasi}, doi = {10.1109/ACCESS.2025.3554141}, journal = {IEEE Access}, pages = {53673-53693}, shortjournal = {IEEE Access}, title = {Innovative load frequency control: Integrating adaptive backstepping and disturbance observers}, volume = {13}, year = {2025}, }
• (2025). Safety monitoring system of stamping presses based on YOLOv8n model. ACCESS, 13, 53660-53672. (www)

  @article{ , abstract = {Industrial safety is crucial for business stability, as it affects worker health, productivity, and social stability. With the rise of Industry 4.0, smart manufacturing and automation have made safety even more important. The metal processing industry, which is vital to the economy, still faces serious safety risks, such as workers’ hands getting caught in stamping presses. These accidents threaten worker safety and impact efficiency, costs, and social responsibility. To address these issues, this study proposes a safety monitoring system for stamping presses based on YOLOv8n and advanced image recognition technology. The system is designed to detect potential dangers in real time and respond immediately. Experimental results show that YOLOv8n outperforms other models, with a mean average precision (mAP50) of 99.4%, a precision of 99.7%, and a recall rate of 99.9%, while maintaining a high processing speed of 95 frames per second (FPS). In practical tests, the system effectively stops machine operation when it detects danger, ensuring worker safety and greatly reducing the risk of accidents.}, archive = {J_ACCESS}, author = {Tsoi-Na Fung and Yao-Hsuan Ku and Yu-Wei Chou and He-Shiang Yu and Jin-Fa Lin}, doi = {10.1109/ACCESS.2025.3553845}, journal = {IEEE Access}, pages = {53660-53672}, shortjournal = {IEEE Access}, title = {Safety monitoring system of stamping presses based on YOLOv8n model}, volume = {13}, year = {2025}, }
• (2025). Three-stage precoding scheme based on nullforming and subarray partitioning for Aerial–Terrestrial spectrum sharing. ACCESS, 13, 53640-53659. (www)

  @article{ , abstract = {Non-terrestrial networks have been recognized as a key technology for providing a ubiquitous coverage in 5G, 6G, and beyond, where high-altitude platform stations (HAPSs) are an indispensable node. Spectrum sharing in integrated HAPS-terrestrial networks is an important task for achieving high spectral efficiency. This study proposes a novel three-stage precoding scheme for HAPSs to suppress the interference with terrestrial users. Specifically, an outer precoder reduces the interference with neighboring service areas based on nullforming and subarray partitioning. A middle precoder mitigates the interference with terrestrial cells, and an inner precoder forms user-specific beams toward HAPS users. In addition, three precoders with different subarray structures are developed based on the proposed scheme and compared with conventional precoders under the coexistence scenario of seven HAPSs with multiple terrestrial cells. For the case of 12 spatially-multiplexed HAPS users with increasing number of terrestrial cells, the results demonstrate that the proposed precoders reduce the interference with other service areas by over 15 dB, which improves the signal-to-interference-plus-noise ratio (SINR) of HAPS users by at most 7 dB. Moreover, the degradation in the SINR of terrestrial cell users caused by the proposed precoders is only 2.8 dB. Consequently, the total capacity of a HAPS and terrestrial cells improves by 61%–119%, compared with the conventional schemes. Additional evaluation results are provided to verify the effectiveness of the proposed scheme, focusing on the increasing HAPS users, performance with imperfect channels, and computational complexity. This study shows that the significant capacity enhancement and coverage extension achieved by the proposed scheme contribute to successful aerial-terrestrial spectrum sharing.}, archive = {J_ACCESS}, author = {Koji Tashiro and Kenji Hoshino and Atsushi Nagate}, doi = {10.1109/ACCESS.2025.3554007}, journal = {IEEE Access}, pages = {53640-53659}, shortjournal = {IEEE Access}, title = {Three-stage precoding scheme based on nullforming and subarray partitioning for Aerial–Terrestrial spectrum sharing}, volume = {13}, year = {2025}, }
• (2025). A dual output temporal convolutional network with attention architecture for stock price prediction and risk assessment. ACCESS, 13, 53621-53639. (www)

  @article{ , abstract = {In this paper, we propose a novel deep learning model that integrates a Temporal Convolutional Network (TCN) with an Attention mechanism to predict stock prices and assess risk for MasterCard (MA) and Visa (V). The model is designed with a dual output to forecast future stock prices (Open, Close, High, Low) while simultaneously predicting risk metrics, including volatility and the Sharpe Ratio. By utilizing dilated convolutions, the TCN captures both short-term and long-term dependencies in the stock price data, and the attention layer focuses on critical time steps for enhanced predictive accuracy. On a dataset covering over 15 years (2008-2024), the TCN with attention model achieved a mean absolute error (MAE) of 1.23 for MasterCard and 1.45 for Visa, outperforming LSTM and ARIMA baselines. Additionally, the model demonstrated strong performance in predicting risk metrics, with an MAE of 0.012 for volatility and 0.065 for the Sharpe Ratio. Backtesting on unseen data further validated the model’s robustness, with a backtest MAE of 1.25 for MasterCard and 1.50 for Visa. This dual-output architecture provides an accurate and interpretable solution for both stock price forecasting and risk assessment, offering a valuable tool for investors and risk managers.}, archive = {J_ACCESS}, author = {Arindam Kishor Biswas and Md Shariful Alam Bhuiyan and Md Nazmul Hossain Mir and Ashifur Rahman and M. F. Mridha and Md Rashedul Islam and Yutaka Watanobe}, doi = {10.1109/ACCESS.2025.3551307}, journal = {IEEE Access}, pages = {53621-53639}, shortjournal = {IEEE Access}, title = {A dual output temporal convolutional network with attention architecture for stock price prediction and risk assessment}, volume = {13}, year = {2025}, }
• (2025). EF-StrongSORT: An enhanced feature StrongSORT model for multi-object tracking. ACCESS, 13, 53608-53620. (www)

  @article{ , abstract = {Multi-object tracking (MOT) faces persistent challenges owing to the complexities introduced by occlusions, dynamic appearance variations, and the rapid motion of objects within a scene. These issues are further complicated by the need for robust identity management and consistent object re-identification across frames. To improve the performance of multi-object tracking, this study introduces EF-StrongSORT, which extends the StrongSORT model, incorporating advanced object detection, efficient feature extraction, and identity management techniques. The EF-StrongSORT demonstrates an improvement over conventional tracking methods by achieving higher accuracy and robustness in challenging scenarios. The experimental results show that the EF-StrongSORT enhances the performance of multi-object tracking techniques and is better than existing approaches on the MOT17, MOT20 and DanceTrack benchmarks. On the MOT17 dataset, EF-StrongSORT outperformed StrongSORT with improvements of +5.5 in HOTA, +7.7 in MOTA, +4 in IDF1, and a decrease of 828 in IDS. On the MOT20 dataset, EF-StrongSORT showed improvements of +2 in HOTA, +6.3 in MOTA, +1.1 in IDF1, and a reduction of 16 in IDS compared to StrongSORT. On the DanceTrack dataset, EF-StrongSORT achieved improvements of +4.3 in HOTA, +2.2 in MOTA, and +1.4 in AssA compared to the latest state-of-the-art model, LQTTrack. These results emphasize the contributions of the proposed model to the improvement of quality and efficiency of multi-object tracking systems targeted for specific problems, including object appearance changes and occlusions.}, archive = {J_ACCESS}, author = {Miar Mamdouh Khalil and Sherine Nagy Saleh and Noha S. Tawfik and Mazen Nabil Elagamy}, doi = {10.1109/ACCESS.2025.3554706}, journal = {IEEE Access}, pages = {53608-53620}, shortjournal = {IEEE Access}, title = {EF-StrongSORT: An enhanced feature StrongSORT model for multi-object tracking}, volume = {13}, year = {2025}, }
• (2025). An ultra-low-leakage microcontroller with configurable power management for energy harvesting IoT devices. ACCESS, 13, 53594-53607. (www)

  @article{ , abstract = {This paper presents a power management unit (PMU) architecture designed for energy-harvesting IoT devices, integrating a dual-capacitor system, an ultra-low-leakage balloon-based microcontroller (MCU), and a fully digital controller. The PMU dynamically switches between an active capacitor, an idle capacitor, and a backup battery, optimizing energy use in different operational modes. This configuration optimizes capacitor voltages for various operational modes, applying a higher voltage to the active capacitor for enhanced performance and a lower voltage to the idle capacitor for ultra-low-leakage data retention. Additionally, a fully digital PMU controller replaces conventional analog components, leveraging a digital voltage monitor and converter-free voltage regulation. The test chip is designed and fabricated using a 28-nm FD-SOI technology. The balloon-based MCU measurement shows that it can retain data during idle mode with a power consumption of only 3nW at 0.6V, achieving a $4400 \times $ reduction in leakage power compared to conventional MCUs without balloon memory. Furthermore, the results of the system measurement demonstrate a substantial reduction in battery usage from 88% in traditional setups to 0%, while ensuring continuous operation and data retention.}, archive = {J_ACCESS}, author = {Shima Sedighiani and Bram van Bolderik and Barry de Bruin and Kamlesh Singh and Roel Jordans and Pieter Harpe and José Pineda de Gyvez}, doi = {10.1109/ACCESS.2025.3552943}, journal = {IEEE Access}, pages = {53594-53607}, shortjournal = {IEEE Access}, title = {An ultra-low-leakage microcontroller with configurable power management for energy harvesting IoT devices}, volume = {13}, year = {2025}, }
• (2025). The impact of solar angle and cloud shadows on 3D reconstruction of rolling stock cargo. ACCESS, 13, 53581-53593. (www)

  @article{ , abstract = {Meeting the relentless demand for more efficient air cargo transportation is of paramount importance for commercial needs and military missions. This study describes an experiment to test an innovative approach that harnesses cutting-edge stereoscopic vision technology to create 3D point clouds of rolling stock cargo across varying solar angles and cloud shadow conditions. Virtual cargo point clouds are generated by calibrating and systematically organizing the depth and location points from an RGB-D camera and then reprojecting them in a virtual environment. Measurement accuracy was rigorously tested across six camera positions in various combinations of weather conditions against physical ground truth measurements. The high accuracy of such systems offers detailed, real-time insights into optimized cargo loading under unpredictable outdoor conditions. The findings, informed by both quantitative and qualitative analyses, reveal the impact of solar position, cloud coverage, and camera placement on the completeness of the point clouds, quality of the depth points, and accuracy of the measurements. This study provides insights to push the boundaries of cargo logistics possibilities in challenging outdoor environments.}, archive = {J_ACCESS}, author = {Carlina M. Ostrand and Adam D. Reiman and Frank W. Ciarallo and Scott L. Nykl and Clark N. Taylor and Joshua F. Krutz}, doi = {10.1109/ACCESS.2025.3554409}, journal = {IEEE Access}, pages = {53581-53593}, shortjournal = {IEEE Access}, title = {The impact of solar angle and cloud shadows on 3D reconstruction of rolling stock cargo}, volume = {13}, year = {2025}, }
• (2025). A 20-bit SAR-assisted extended counting incremental ADC with a clock-controlled PGA buffer. ACCESS, 13, 53568-53580. (www)

  @article{ , abstract = {To optimize small signal acquisition and conversion, analog-to-digital converter (ADC) often requires a programmable gain amplifier (PGA) and buffer. However, there still remains trade-offs among noise, power consumption and drive capability, which present a significant challenge for further improvement. This paper introduces a novel clock-controlled buffer that operates only during the initial 1/4 time of the sampling period, enabling rapid signal establishment while minimizing noise overhead. To achieve high resolution, an incremental ADC (IADC) with extended counting has been employed. The circuit primarily consists of an incremental delta-sigma ADC and the proposed 9-bit successive approximation register (SAR) ADC for extended counting. Fabricated in a standard $0.25~\mu $ m CMOS process, the proposed ADC achieves a 20.58 effective number of bits (ENOB) with extended counting from a 3.3 V supply. The proposed buffer enhanced the overall dynamic performance of the system by facilitating rapid establishment, leading to a 14.75% increase in signal-to-noise ratio (SNR) and a 30% improvement in spurious-free dynamic range (SFDR).}, archive = {J_ACCESS}, author = {Xueke Xu and Yunpeng Li and Zirui Cui and Qingqing Sun and Hao Zhu}, doi = {10.1109/ACCESS.2025.3553312}, journal = {IEEE Access}, pages = {53568-53580}, shortjournal = {IEEE Access}, title = {A 20-bit SAR-assisted extended counting incremental ADC with a clock-controlled PGA buffer}, volume = {13}, year = {2025}, }
• (2025). Modality-guided refinement learning for multimodal emotion recognition. ACCESS, 13, 53558-53567. (www)

  @article{ , abstract = {Multimodal emotion recognition (MER) aims to understand human emotions by leveraging multiple modalities. Previous MER methods have focused on learning enhanced multimodal representations through various interaction and fusion mechanisms, utilizing different types of features from individual modalities. However, these methods often fail to account for the varying contributions of each modality to emotion, leading to suboptimal representations. To address this, we propose a modality-guided refinement learning framework that enhances multimodal representations by incorporating modality information. Specifically, we decouple multimodal representations into modality-invariant and modality-specific components by introducing shared and private encoders, which are learned by leveraging the distributional properties of the representations in their latent subspaces, guided by a modality classifier. Our method introduces margin constraints to further refine these decoupled representations, adaptively considering the contribution of each modality during the decoupling and multimodal learning processes. This optimization reduces information loss and corruption, resulting in more robust and discriminative multimodal representation learning. We evaluate our proposed method through experiments on two benchmark MER datasets: the CMU Multimodal Corpus of Sentiment Intensity (CMU-MOSI) and the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI). Comprehensive experiments demonstrate that our method outperforms several baseline models in multimodal emotion recognition.}, archive = {J_ACCESS}, author = {Sunyoung Cho}, doi = {10.1109/ACCESS.2025.3554708}, journal = {IEEE Access}, pages = {53558-53567}, shortjournal = {IEEE Access}, title = {Modality-guided refinement learning for multimodal emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Semantic flow control for task-oriented position tracking. ACCESS, 13, 53547-53557. (www)

  @article{ , abstract = {Scalable position tracking systems are essential for ensuring efficient operation in dynamic and resource-constrained environments, particularly in applications requiring real-time accuracy and adaptability. Ultra-Wide-Band (UWB) communications facilitate high tracking accuracy with high beacon rate. However, this limits the number of parallel position tracking applications due to reduced communication effectiveness. We solve this problem by introducing Semantic Feedback to the mobile tag. By incorporating prediction accuracy into the Semantic Feedback, the mobile tag can adjust its beacon interval, reducing the frequency of transmissions when the localization system has high prediction confidence. To further improve this confidence, mobile tags also send their IMU (Inertial Measurement Unit) data to the Position Tracking System (PTS). By controlling the information flow in a task-oriented manner without incorporating IMU data, Semantic Flow Control improves communication effectiveness by 19.3% with slight reduction in localization accuracy, compared to using only a Kalman Filter. When integrated with IMU data, communication effectiveness further increases by additional 16.2% while transferred data decreases by 19.4%.}, archive = {J_ACCESS}, author = {T. Tolga Sari and Büşra Bayram and Byung-Seo Kim and Gökhan Seіntі}, doi = {10.1109/ACCESS.2025.3552927}, journal = {IEEE Access}, pages = {53547-53557}, shortjournal = {IEEE Access}, title = {Semantic flow control for task-oriented position tracking}, volume = {13}, year = {2025}, }
• (2025). Corrections to “New power interface based on multi-dimensional golden section search algorithm for power-hardware-in-the-loop applications”. ACCESS, 13, 53546. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “New Power Interface Based on Multi-Dimensional Golden Section Search Algorithm for Power-Hardware-in-the-Loop Applications”}, archive = {J_ACCESS}, author = {Juan Constantine and Kuo Lung Lian and You Fang Fan and Chu Ying Xiao and Zhao-Peng He}, doi = {10.1109/ACCESS.2025.3553774}, journal = {IEEE Access}, pages = {53546}, shortjournal = {IEEE Access}, title = {Corrections to “New power interface based on multi-dimensional golden section search algorithm for power-hardware-in-the-loop applications”}, volume = {13}, year = {2025}, }
• (2025). MHealth case study presenting design SynMeth, a rapid prototyping MBSE methodology, by advancing specific OPM-to-SysML mapping. ACCESS, 13, 53531-53545. (www)

  @article{ , abstract = {Designing IoT-based cognitive assessment and monitoring devices for older adults poses critical challenges in managing trade-offs between accessibility and functionality. With the global aging population to exceed 2 billion by 2050, an increasing number of older adults will require Active Assisted Living (AAL) technologies to support independent living. Cognitive impairments make standard interfaces difficult to use, necessitating user-centered design approaches. Effective solutions must address the transition from document-centric to model-based design, incorporating co-design with users and caregivers, iterative modeling cycles, and continuous model evolution. This study highlights key factors that call for a hybrid approach, blending the flexibility of rapid prototyping with the accuracy and robustness of precision engineering. This study demonstrates a successful implementation of a cognitive detection device within an AAL European project by presenting Design SynMeth, a novel blended Model-Based Systems Engineering methodology that maps Object-Process Methodology (OPM) to Systems Modeling Language (SysML) using a modified MagicGrid framework. This approach bridges early and late design phases, integrating OPM’s strength in conceptual modeling and SysML’s rigor in technical specifications. Design SynMeth enhances system design efficiency and adaptability to IoT challenges. The case study reveals how Design SynMeth methodology models the architecture of a mobile health well-being device for detecting cognitive issues, supporting seniors’ autonomy. It highlights the dynamic interplay between problem and solution domains, leveraging OPM diagrams for problem domain and SysML diagrams for requirements and solution domain. This work advances the state of the art in IoT-based cognitive monitoring and promotes innovative, human-centered engineering for aging societies.}, archive = {J_ACCESS}, author = {Cristian Vizitiu and Kevin Dominey and Alexandru Nistorescu and Adrian Dinculescu and Mihaela Marin}, doi = {10.1109/ACCESS.2025.3553945}, journal = {IEEE Access}, pages = {53531-53545}, shortjournal = {IEEE Access}, title = {MHealth case study presenting design SynMeth, a rapid prototyping MBSE methodology, by advancing specific OPM-to-SysML mapping}, volume = {13}, year = {2025}, }
• (2025). A strict minimal siphon-based necessary and sufficient condition for liveness verification in general petri nets. ACCESS, 13, 53517-53530. (www)

  @article{ , abstract = {To describe deadlocks occurring in general nets, some characterizations have been proposed to establish a relationship between liveness and siphons. Among them, the most prominent characterizations are max-controlled siphons and deadly marked siphons. Nevertheless, the former is only a sufficient condition while the latter may lead to some unreasonable operations, i.e., modified markings are necessary if applicable. In this paper, we elaborately discuss the related concepts of deadly marked siphons when liveness analysis is conducted. Subsequently, a new siphon-based characterization of liveness, i.e., undermarked siphons that explain liveness in the original markings, is proposed to demonstrate its more general applicability. Finally, a necessary and sufficient condition is developed to show the relationship between the liveness and undermarked siphons in general nets.}, archive = {J_ACCESS}, author = {Tian Ma and Jipeng Wang and Hesuan Hu}, doi = {10.1109/ACCESS.2025.3551396}, journal = {IEEE Access}, pages = {53517-53530}, shortjournal = {IEEE Access}, title = {A strict minimal siphon-based necessary and sufficient condition for liveness verification in general petri nets}, volume = {13}, year = {2025}, }
• (2025). A yarn quality prediction method based on M-ESTIMATOR robust broad learning system with tightly cascaded feature layers. ACCESS, 13, 53506-53516. (www)

  @article{ , abstract = {Aiming at the problem that multilayer neural networks rely on large datasets and broad learning system (BLS) cannot cope well with outliers in data of yarn production, which leads to low accuracy and stability when used for predicting yarn quality, we propose a robust broad learning system with the ability to resist the interference of outliers and optimize its ability to extract features. First, the feature mapping groups of the BLS were connected in a tightly cascaded manner to improve the model’s ability to express the features of the parameters of raw cotton and machine operation. Second, five M-ESTIMATOR regression loss functions, such as the Tukey function with robust characteristics, were proposed to replace the L2 norm squared loss function of the BLS. Using the iterative reweighted least-squares idea, weights were assigned to samples with different errors, and the output weights were updated to alleviate the error feedback to the error of the outliers so that the model could better fit the data. The experimental data were obtained from a medium-sized textile factory. Experimental results showed that the error of the new model was less than 3.5% when predicting the yarn strength and 4.5% when predicting the hairiness H-value. The accuracy and stability were improved compared to those of the multilayer neural network and BLS. The prediction effect of the new model satisfy the application requirements of textile enterprises, thus providing a new method for textile enterprises to predict yarn quality, which can guide production practices to a certain extent.}, archive = {J_ACCESS}, author = {Baowei Zhang and Zhilin Guo and Yonghua Wang}, doi = {10.1109/ACCESS.2025.3553103}, journal = {IEEE Access}, pages = {53506-53516}, shortjournal = {IEEE Access}, title = {A yarn quality prediction method based on M-ESTIMATOR robust broad learning system with tightly cascaded feature layers}, volume = {13}, year = {2025}, }
• (2025). Nation’s defense: A comprehensive review of anti-drone systems and strategies. ACCESS, 13, 53476-53505. (www)

  @article{ , abstract = {The rapid advancement of drone technology has revolutionized both the civilian and military sectors, enabling a wide range of applications, from commercial deliveries to battlefield reconnaissance. However, the misuse of drones has introduced significant security threats, necessitating the development of robust anti-drone systems. This paper provides a comprehensive review of anti-drone technologies and defense strategies. It explores the rise of drones, their dual-use potential, and the growing need for countermeasures. The study examines the evolution of anti-drone technologies, classifies existing systems, and analyzes case studies of drone-related incidents. It also highlights the correlation between drone threats and the use of anti-drone solutions. Furthermore, this review differentiates between military and civilian defense frameworks, emphasizing the impact of drone misuse on national security and public safety. This study discusses current defense strategies, technological challenges, and complex regulatory landscapes. Finally, it outlines future directions for developing and deploying anti-drone technologies, aiming to raise awareness among policymakers and technologists about the critical role that these systems play in national defense.}, archive = {J_ACCESS}, author = {Dharmendra Chauhan and Harshil Kagathara and Hiren Mewada and Sagar Patel and Sagar Kavaiya and Gordana Barb}, doi = {10.1109/ACCESS.2025.3550338}, journal = {IEEE Access}, pages = {53476-53505}, shortjournal = {IEEE Access}, title = {Nation’s defense: A comprehensive review of anti-drone systems and strategies}, volume = {13}, year = {2025}, }
• (2025). Corrections to “PMUT and CMUT devices for biomedical applications: A review”. ACCESS, 13, 53475. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “PMUT and CMUT Devices for Biomedical Applications: A Review”.}, archive = {J_ACCESS}, author = {Elisabetta Moisello and Lara Novaresi and Eshani Sarkar and Piero Malcovati and Tiago L. Costa and Edoardo Bonizzoni}, doi = {10.1109/ACCESS.2025.3554027}, journal = {IEEE Access}, pages = {53475}, shortjournal = {IEEE Access}, title = {Corrections to “PMUT and CMUT devices for biomedical applications: A review”}, volume = {13}, year = {2025}, }
• (2025). Evolutionary synthesis of high-capacity reconfigurable multilayer road networks using a multiagent hybrid clustering-assisted genetic algorithm. ACCESS, 13, 53448-53474. (www)

  @article{ , abstract = {Modern requirements for urban traffic management and control call for the design of high-capacity reconfigurable multilayer road networks (RMRNs). This paper discusses the proposed evolutionary synthesis approach, a promising method for finding the best configurations of RMRNs, aiming to create road networks with optimized layouts that maximize vehicle outflow. As the complexity of RMRNs increases, due to the addition of overpasses and tunnels, the expenses for building these road networks also rise significantly. Therefore, it is essential to find a balance when choosing the optimal topological solution for an RMRN. These solutions need to maximize traffic flow while minimizing the complexity of the RMRN. To achieve this goal, a new multiagent hybrid clustering-assisted genetic algorithm (MA-HCAGA). The proposed algorithm combines the use of binary-coded crossovers and mutations as genetic operators, and biobjective discrete particle swarm optimization (BODPSO) techniques to improve the evolutionary search process. In addition, the algorithm combines the use of finite-state machines (FSMs) to control the transitions between the states of agent-processes and the fuzzy clustering technique (FCA) to estimate the swarm and select clusters for interaction among the groups of agent-processes and particle swarms. The superior performance of the MA-HCAGA algorithm in evolutionary synthesis of RMRNs has been demonstrated through comparisons with other well-known multiobjective optimization methods. MA-HCAGA has been successfully applied in the evolutionary synthesis of RMRNs, allowing a decision maker to select the optimal RMRN topologies along the approximate Pareto front by selecting specific solutions. A traffic flow simulation model, aggregated with the MA-HCAGA algorithm, has been developed to simulate vehicle flow at various configurations of RMRNs. The results of this study show the effectiveness of the proposed method for configuring RMRNs in order to optimize vehicle outflow and reduce the complexity of RMRNs.}, archive = {J_ACCESS}, author = {Andranik S. Akopov and Levon A. Beklaryan}, doi = {10.1109/ACCESS.2025.3554054}, journal = {IEEE Access}, pages = {53448-53474}, shortjournal = {IEEE Access}, title = {Evolutionary synthesis of high-capacity reconfigurable multilayer road networks using a multiagent hybrid clustering-assisted genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Global and local context fusion in heterogeneous graph neural network for summarizing lengthy scientific documents. ACCESS, 13, 53433-53447. (www)

  @article{ , abstract = {The primary objective of text summarization is to condense a document’s length while preserving its essential content. Extractive summarization methods are commonly used due to their effectiveness and straightforward presentation. However, a significant challenge lies in segmenting documents into distinct concepts and understanding how sentences interact, especially in complex materials such as scientific articles. This process entails identifying relationships between sentences and determining the most significant and informative content within extensive text collections. Traditional techniques often utilize pre-trained models like BERT, known for their ability to capture word context. Nonetheless, these models have limitations, including constrained input lengths and the computational intensity of self-attention mechanisms, which hinder their effectiveness in processing large-scale scientific texts. To address these challenges, we propose a computationally efficient Heterogeneous Graph Neural Network (HGNN) for the extractive summarization of lengthy scientific texts. This framework combines GloVe embeddings with Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) encoders. GloVe offers simple yet effective word embeddings, CNNs focus on capturing local word structures, and BiLSTMs identify long-range dependencies, allowing for flexible encoding of extensive texts. For global context and topic modeling, we utilize an enhanced version of Latent Dirichlet Allocation (LDA) to retain essential document attributes. In this model, words, sentences, and topics are represented as nodes in a heterogeneous graph, with TF-IDF values illustrating the relationships between edges. The graph is processed using a Graph Attention Network (GAT), which refines node representations by integrating both local and global information. This study represents the first instance of combining LDA with CNN and BiLSTM encoders in a Graph Attention-based model for summarizing scientific texts. Experimental results demonstrate that the proposed framework outperforms both BERT-based and non-BERT approaches on publicly available datasets from arXiv and PubMed. Our model achieves a ROUGE-1 score of 46.31, a ROUGE-2 score of 19.98, and a ROUGE-L score of 40.21 on the arXiv dataset. It performs even better on the PubMed dataset, attaining a ROUGE-1 score of 48.85, a ROUGE-2 score of 21.78, and a ROUGE-L score of 42.12.}, archive = {J_ACCESS}, author = {Muhammad Umair and Atif Khan and Fasee Ullah and Atef Masmoudi and Muhammad Faheem}, doi = {10.1109/ACCESS.2025.3553755}, journal = {IEEE Access}, pages = {53433-53447}, shortjournal = {IEEE Access}, title = {Global and local context fusion in heterogeneous graph neural network for summarizing lengthy scientific documents}, volume = {13}, year = {2025}, }
• (2025). SPOC: Model metrics and inference scalability. ACCESS, 13, 53420-53432. (www)

  @article{ , abstract = {The growth of deep learning has increased the challenge of understanding model performance and scalability. Model metrics are coarse-grained values that help to capture the complexity of the model. In this research, we propose SPOC (size, parameters, operations, and Critical Datapath Length) as a suite of metrics that describe much of the complexity in deep-learning architecture and therefore help to characterize their scalability. We utilize small, specially structured “micro-models” to showcase overall scalability trends across four devices: the ARM Cortex-A76 embedded CPU, the AMD EPYC 9374F server CPU, the NVIDIA Jetson Orin Nano embedded GPU, and the NVIDIA A100 server GPU. Through these micro-models, we vary components of SPOC and highlight different scaling behavior across these classes of devices. Finally, we provide examples of how SPOC can be used with standard CNNs and MobileViT to capture expected and measured performance differences during inference. Through this study, we provide deep-learning practitioners with an overview of how metrics can guide our understanding of inference performance.}, archive = {J_ACCESS}, author = {Calvin B. Gealy and David Langerman and Marika E. Schubert and Alan D. George}, doi = {10.1109/ACCESS.2025.3553071}, journal = {IEEE Access}, pages = {53420-53432}, shortjournal = {IEEE Access}, title = {SPOC: Model metrics and inference scalability}, volume = {13}, year = {2025}, }
• (2025). Efficient fault localization in smart grid through analysis of the wave matrix image using convolutional neural networks. ACCESS, 13, 53407-53419. (www)

  @article{ , abstract = {This article presents a methodology for fault localization in electric power distribution systems through the analysis of wave matrix image using Convolutional Neural Networks (CNN). Ensuring a continuous and high-quality supply of electric power is crucial for the efficient operation of a Power System. However, the extensive coverage of the Electric System makes it susceptible to various disturbances caused by factors such as adverse weather conditions, equipment failures, presence of animals in the networks, and human errors. These disturbances can result in faults, characterized as short-circuits or abnormal currents between conductors or to the ground. With approximately 80% of power supply interruptions attributed to Distribution System faults, the need for effective fault localization methods is evident. By extracting voltage signal characteristics at measurement points during disturbances and transforming them into a panoramic representation of the system in the form of an image, the panoramic image generation, derived from simulated short-circuit values at each system bus, provides a comprehensive visualization of the network, enabling precise fault localization. The results demonstrate the accuracy of the CNN method in fault localization. This approach offers scalability, efficient transformation into images, and precise fault localization in electric power distribution systems.}, archive = {J_ACCESS}, author = {José Carlos Filho and Francisco C. M. Abreu and Maurício Silva and Fabbio Borges and Ricardo A. L. Rabelo}, doi = {10.1109/ACCESS.2025.3553455}, journal = {IEEE Access}, pages = {53407-53419}, shortjournal = {IEEE Access}, title = {Efficient fault localization in smart grid through analysis of the wave matrix image using convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Cloud detection challenge-methods and results. ACCESS, 13, 53394-53406. (www)

  @article{ , abstract = {Accurate cloud detection is critical for advancing atmospheric monitoring and meteorological forecasting. This paper presents the Cloud Detection Challenge, an initiative aimed at enhancing cloud detection through innovative solutions using lidar-based ceilometer data. This initiative was hosted by IEEE MetroXRAINE 2024, and 11 teams participated in this initiative. Participants were provided with a novel dataset of backscatter profiles converted into time-height plots, offering unique insights into atmospheric conditions beyond conventional imagery. Data collection employed a Lufft CHM 15k ceilometer, capturing cloud dynamics every 15 seconds located near Mt. Etna, an active volcano in Italy. The dataset includes 1568 hourly labeled backscatter profiles, serving as a benchmark for state-of-the-art deep learning models. The challenge sets a baseline performance of 89.57% accuracy, 92.73% F1-score, 89.82% precision, and 95.84% recall, inviting participants to develop models to exceed these results. Submissions proposed a wide-range of AI-based approaches, including Transformer and Convolutional Neural Network architectures, showcasing the potential of advanced image analysis techniques in lidar-based cloud detection. This paper details the challenge framework, as well as the methodologies proposed by top-performing teams, offering a comparative evaluation of their effectiveness. Our initiative advances cloud detection technologies and underscores their broader implications for environmental monitoring, agriculture, and satellite imaging. The insights and dataset presented herein lay the groundwork for future advancements in leveraging lidar data for atmospheric analysis.}, archive = {J_ACCESS}, author = {Alessio Barbaro Chisari and Luca Guarnera and Alessandro Ortis and Wladimiro Carlo Patatu and Bruno Casella and Luca Naso and Giuseppe Puglisi and Vincenzo del Zoppo and Mario Valerio Giuffrida and Sebastiano Battiato}, doi = {10.1109/ACCESS.2025.3553422}, journal = {IEEE Access}, pages = {53394-53406}, shortjournal = {IEEE Access}, title = {Cloud detection challenge-methods and results}, volume = {13}, year = {2025}, }
• (2025). Applying machine learning on big data with apache spark. ACCESS, 13, 53377-53393. (www)

  @article{ , abstract = {The exponential growth of data in the digital age has necessitated the development of frameworks capable of efficiently handling and processing vast datasets. This paper explores the application of machine learning (ML) models within the Apache Spark ecosystem, focusing on the performance and scalability of these models in big data environments. Through comprehensive experiments on three diverse datasets, namely NYC Taxi Trip Duration, Netflix Prize, and Higgs Boson, we address both regression and classification tasks. For the regression tasks using the NYC Taxi Trip Duration and Netflix Prize datasets, we evaluated models including Linear Regression (LinR), Random Forest (RF), Gradient-Boosted Trees (GBT), Support Vector Regressor (SVR), and K-Nearest Neighbors (KNN). For the classification task using the Higgs Boson dataset, we assessed models such as Logistic Regression (LR), RF, GBT, Support Vector Machines (SVM), and KNN. The study employed key performance metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for regression and Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC) for classification. Our findings indicate that Apache Spark’s in-memory processing and distributed computing capabilities provide effective scalability, allowing these models to handle large-scale data with linear increases in processing time. Finally, that study highlights the importance of model selection and resource optimization in big data contexts and provides valuable insights into the practical integration of ML models within the Spark framework.}, archive = {J_ACCESS}, author = {Elias Dritsas and Maria Trigka}, doi = {10.1109/ACCESS.2025.3552042}, journal = {IEEE Access}, pages = {53377-53393}, shortjournal = {IEEE Access}, title = {Applying machine learning on big data with apache spark}, volume = {13}, year = {2025}, }
• (2025). Retinal-ESRGAN: A hybrid GAN model approach for retinal image super-resolution coupled with reduced training time and computational resources for improved diagnostic accuracy. ACCESS, 13, 53366-53376. (www)

  @article{ , abstract = {Medical Image Super-Resolution has always been a subject of interest in medical image processing. However, super-resolved retinal images are a requisite tool for doctors to properly diagnose and treat ophthalmic diseases. The acquisition of high-quality images is challenging owing to several factors including technical hardware limitations, high cost, operator skills, data compatibility, and maintenance issues. This paper proposes Retinal-ESRGAN, a novel hybrid unsupervised GAN model particularly designed for retinal image super-resolution. The model incorporates architectural modifications with respect to generator and discriminator network using Google Colaboratory and TensorFlow 2.0 facilitating limited resource usage. To address resource constraints, a training strategy involving pausing and resuming in batches is implemented. The experiments conducted have demonstrated Retinal-ESRGAN’s potential that achieved an average PSNR of 35.22 dB and SSIM of 0.916, outperforming both SRGAN and ESRGAN showing PSNR metric improvement of 4.8% over SRGAN and 10.5% improvement over ESRGAN. Also, a 5.7% improvement over SRGAN and 22.4% improvement over ESRGAN in SSIM metric, Inception score of 6.02, Fréchet Inception Distance of 25.31 and accuracy of 94.98% utilizing significantly less training time and computational resources.}, archive = {J_ACCESS}, author = {K. Deepthi and Aditya K. Shastry}, doi = {10.1109/ACCESS.2025.3553457}, journal = {IEEE Access}, pages = {53366-53376}, shortjournal = {IEEE Access}, title = {Retinal-ESRGAN: A hybrid GAN model approach for retinal image super-resolution coupled with reduced training time and computational resources for improved diagnostic accuracy}, volume = {13}, year = {2025}, }
• (2025). UAV-assisted IRS system with energy harvesting: Enhanced reliability in critical scenarios for 5G/6G wireless communication. ACCESS, 13, 53356-53365. (www)

  @article{ , abstract = {This paper proposes an UAV-assisted intelligent reflecting surface (IRS) system for improving the network performance between ground-to-ground (G2G) users in 5G/6G wireless communication. This work may be applicable for packet delivery in bustling urban areas, especially where G2G links are in deep fade. For energizing the IRS elements and controlling the processing units of the UAV, radio-frequency (RF)-based energy harvesting (EH) is proposed at UAV, where the dynamic power splitting factor depends on the height-dependent Nakagami-m shaping parameter (m). The mathematical framework for the proposal includes the height-dependent Nakagami-m fading to represent small-scale fading and height-dependent path-loss exponent to model large-scale fading. We develop the expression for spectral efficiency and derive a closed-form expression of an outage probability for the proposed scenario by considering Nakagami-m channel among A2G links. The analytical results are validated with simulation results, and the work is compared with the existing state-of-the-art.}, archive = {J_ACCESS}, author = {Pankaj Kumar and Santhosha Kamath and Sam Darshi and Jen-Yi Pan and Muhammad Iqbal and Samar Shailendra and Vijay Mohan}, doi = {10.1109/ACCESS.2025.3552485}, journal = {IEEE Access}, pages = {53356-53365}, shortjournal = {IEEE Access}, title = {UAV-assisted IRS system with energy harvesting: Enhanced reliability in critical scenarios for 5G/6G wireless communication}, volume = {13}, year = {2025}, }
• (2025). High-speed CMOS synchronous binary counter with constant counting rate. ACCESS, 13, 53347-53355. (www)

  @article{ , abstract = {In VLSI design, the counter stands as one of the most fundamental and widely utilized components. It finds extensive applications across various fields, including analog-to-digital converters, time-to-digital converters, frequency dividers, phase-locked loops, frequency synthesizers, and IoT networks. As technology advances, many applications necessitate high-speed counters supporting wider bit-widths. Nevertheless, the conventional synchronous binary counter, renowned for its simplicity, encounters a decline in counting rate as the counter size increases, primarily due to the lengthy signal chain. To overcome this limitation, a pre-scaled structure has been presented, ensuring a consistent counting rate even with increased sizes. In our pursuit of creating a high-speed counter, we incorporate this pre-scaled structure and introduce an early state detection technique to reduce the critical path affecting the counting rate. This enhancement expedites the generation of the enable signal for the subsequent sub-counter. Furthermore, to minimize the delay caused by T-flip-flops in the conventional binary counter, the sub-counters are intelligently designed based on the Johnson counter. In a 28nm CMOS process, the proposed counter achieves an impressive counting rate of 3.7 GHz, and maintains a nearly constant counting rate regardless of counter sizes, showcasing its robust performance.}, archive = {J_ACCESS}, author = {Sungho Kim and Jeongmin Kim and In-Cheol Park}, doi = {10.1109/ACCESS.2025.3553580}, journal = {IEEE Access}, pages = {53347-53355}, shortjournal = {IEEE Access}, title = {High-speed CMOS synchronous binary counter with constant counting rate}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of machine learning techniques for server health monitoring using time series data against DDOS attacks. ACCESS, 13, 53321-53346. (www)

  @article{ , abstract = {In today’s digitally driven business environment, the continuous operation of online enterprises relies heavily on resilient server infrastructures. The increasing frequency and sophistication of cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, pose significant challenges to maintain server health. DDoS attacks are malicious attempts to disrupt normal traffic to a server by overwhelming it with fake requests. Their impact has grown alarmingly, with a 117% year-over-year increase in network-layer DDoS attacks observed in Q4 of 2023. This study introduces a novel two-stage server health monitoring system designed to detect and assess the severity of such attacks. In Stage I: DDoS Attack Detection, binary classification techniques—machine learning methods that divide data into two groups—analyze network traffic to differentiate normal traffic from DDoS attack traffic. The light-gradient boosting model (LGBM) demonstrated exceptional performance on the testing set with an accuracy of 0.9988, AUC of 0.9999, and other metrics such as recall, precision, and F1 score of 0.9988, indicating high reliability. Once a DDoS attack is detected, the system moves to Stage II: Attack Severity Assessment, where a multiclass classification method categorizes the attack’s severity as low, moderate, or high. The gradient boosting classifier (GBC) emerged as the best-performing model in this stage, achieving an accuracy of 0.9994, AUC of 1, recall, precision, and F1 score of 0.9994 on the testing set. This research provides a robust approach for detecting attack severity, enabling cybersecurity professionals to allocate resources efficiently and implement timely mitigation to safeguard server health.}, archive = {J_ACCESS}, author = {Sajja Ratan Kumar and Valli Kumari Vatsavayi}, doi = {10.1109/ACCESS.2025.3553558}, journal = {IEEE Access}, pages = {53321-53346}, shortjournal = {IEEE Access}, title = {Performance analysis of machine learning techniques for server health monitoring using time series data against DDOS attacks}, volume = {13}, year = {2025}, }
• (2025). An enhanced approach for remaining useful life prediction of bearings using incomplete lifecycle data. ACCESS, 13, 53305-53320. (www)

  @article{ , abstract = {Accurately predicting the remaining useful life (RUL) of rolling bearings is crucial for ensuring optimal equipment operation and timely maintenance. This study addresses key limitations of traditional deep learning approaches, including limited lifecycle data and a significant reliance on supervised learning. To address these challenges, we propose the Incomplete Lifecycle Data Enhanced Network (ILDENet), a self-supervised learning network incorporating contrastive learning to improve feature discrimination. The network operates in three distinct phases: first, feature parameters indicative of bearing degradation trends are extracted across time, frequency, and time-frequency domains. Second, pre-training is performed in a mask self-supervised learning mode with contrastive learning on extensive incomplete lifecycle data, effectively distinguishing similar and dissimilar degradation patterns. Finally, the model undergoes fine-tuning in a supervised phase using complete lifecycle data. To further improve prediction accuracy, we employ a channel-independent transformer network, while RUL estimation is accomplished by integrating multi-source features through a multi-layer perceptron (MLP) layer. The proposed method is rigorously evaluated on both the IEEE PHM 2012 and XJTU-SY datasets and benchmarked against state-of-the-art approaches. Experimental results demonstrate that the proposed algorithm effectively captures the complex dynamic behavior of rolling bearings, achieving high accuracy and generalization in RUL prediction.}, archive = {J_ACCESS}, author = {Xunmeng An and Chao Zhang and Caiye Liu and Wentao Zhao}, doi = {10.1109/ACCESS.2025.3553279}, journal = {IEEE Access}, pages = {53305-53320}, shortjournal = {IEEE Access}, title = {An enhanced approach for remaining useful life prediction of bearings using incomplete lifecycle data}, volume = {13}, year = {2025}, }
• (2025). Threat modeling for the defense industry: Past, present, and future. ACCESS, 13, 53276-53304. (www)

  @article{ , abstract = {In today’s interconnected world, enhancing defense cooperation through effective information sharing among allied nations is critical. A cybersecurity incident in one country can quickly cascade across all allied nations. As a result, the U.S. has mandated stronger cybersecurity practices within the defense sector among its allies. This directive has led many nations to develop their own security risk management frameworks, often modeled after the Risk Management Framework (RMF) employed by the U.S. Department of Defense (DoD). A key component of the U.S. DoD standards is the mandatory use of threat modeling techniques—a methodology for identifying and mitigating threats to a system—during the performing of the RMF. However, the problem is that threat modeling methods used in the defense field are mostly based on old-fashioned techniques. This study seeks to modernize threat modeling methodologies in the defense industry by incorporating the latest technological advancements. First, a systematic literature review is conducted to identify the most recent innovations in threat modeling. Then, we analyze the specific needs of the defense sector that these methodologies must address. Finally, we evaluate the feasibility of integrating cutting-edge technologies into existing threat modeling frameworks and propose future directions. Ultimately, this paper presents a structured approach for applying modern threat modeling techniques that can be adopted globally across the defense industry.}, archive = {J_ACCESS}, author = {Hyunsuk Cho and Seungjoo Kim}, doi = {10.1109/ACCESS.2025.3550337}, journal = {IEEE Access}, pages = {53276-53304}, shortjournal = {IEEE Access}, title = {Threat modeling for the defense industry: Past, present, and future}, volume = {13}, year = {2025}, }
• (2025). Optimization of railway transportation planning by combining TST model and genetic algorithm. ACCESS, 13, 53266-53275. (www)

  @article{ , abstract = {Railway transportation, a key long - distance freight transport method, faces challenges due to the rapid growth of global logistics demand. These challenges include high transportation costs, low punctuality rates, and inefficient resource utilization. Traditional static optimization methods cannot adapt to dynamic changes and multi-objective optimization requirements. The study proposes an integrated method that combines the Temporal-Spatial Tunnel (TST) model with the Genetic Algorithm (GA). The TST model describes railway transportation changes dynamically by integrating temporal and spatial dimensions. The GA uses its global search ability to optimize train routing and timetabling. The proposed method enhances the efficiency and flexibility of the railway transportation system. It addresses the issues of low punctuality, inefficient resource utilization, and lack of adaptability to dynamic changes and multi - objective optimization in traditional methods. Experimental results show the superiority of this approach. In urban network scenarios, it achieves a punctuality rate of 94.87%, resource utilization of 89.78%, and a response time of 280.12 seconds. In freight - priority scenarios, the maximum punctuality rate reaches 95.45%. Compared to traditional methods, it significantly improves transportation efficiency and flexibility in multi - objective optimization, offering an effective solution for railway transportation planning under dynamic demands and valuable references for logistics system scheduling optimization.}, archive = {J_ACCESS}, author = {Wei Cao and Fan Chen}, doi = {10.1109/ACCESS.2025.3554677}, journal = {IEEE Access}, pages = {53266-53275}, shortjournal = {IEEE Access}, title = {Optimization of railway transportation planning by combining TST model and genetic algorithm}, volume = {13}, year = {2025}, }
• (2025). Analysis of output characteristics of electrically excited doubly salient generator based on back EMF oriented vector control strategy. ACCESS, 13, 53254-53265. (www)

  @article{ , abstract = {Compared to using uncontrolled rectifier, the use of active rectifier can significantly improve the output power of doubly salient electric generator (DSEG). The traditional six-state-angle-control (SSAC) strategy has limited effectiveness in improving the output power of DSEG. A mathematical model of DSEG in a rotating coordinate system oriented by back electromotive force (EMF) vector was established based on the principle of instantaneous reactive power. The reason why SSAC strategy is difficult to further improve the output power of DSEG was analyzed through this model. To address this issue, this paper proposed a back EMF oriented vector control (BEFOVC) strategy. Compared with SSAC strategy, BEFOVC can further optimize the output performance of DSEG, reduce copper loss, phase current peak and THD, and suppress the torque ripple of DSEG. Finally, the feasibility and effectiveness of the proposed method were verified through simulations and experiments on a 12/8-pole DSEG.}, archive = {J_ACCESS}, author = {Weifeng Liu and Yulong Tao and Xiaoli Meng}, doi = {10.1109/ACCESS.2025.3553902}, journal = {IEEE Access}, pages = {53254-53265}, shortjournal = {IEEE Access}, title = {Analysis of output characteristics of electrically excited doubly salient generator based on back EMF oriented vector control strategy}, volume = {13}, year = {2025}, }
• (2025). ECMS-based MPC strategy for multi-mode hybrid electric trucks. ACCESS, 13, 53241-53253. (www)

  @article{ , abstract = {To address the adaptive energy management optimization challenge of a hybrid powertrain system consisting of an internal combustion engine and dual electric motors, an Equivalent Consumption Minimization Strategy (ECMS) is introduced into the Model Predictive Control (MPC) framework. This paper proposes an MPC-ECMS energy management strategy tailored for multi-mode hybrid electric trucks. First, a genetic algorithm is employed to optimize a radial basis function neural network, enhancing its predictive performance for short-term, high-precision vehicle speed forecasting based on historical data. Next, the ECMS is combined with MPC to solve for the minimum objective function value within the prediction horizon, thus obtaining the corresponding power distribution results. This enables real-time adaptive adjustment of the equivalent factor, with the prediction horizon rolling forward for continuous optimization. Simulation results demonstrate that the proposed control strategy outperforms the ECMS control strategy with a fixed optimal equivalent factor under the same operating conditions, achieving a 4.2% improvement in fuel consumption and reaching 93% of the fuel consumption performance of a dynamic programming-based approach.}, archive = {J_ACCESS}, author = {Longjian Li and Changqing Du and Xiangyu Gongye and Jinhai Wang}, doi = {10.1109/ACCESS.2025.3552834}, journal = {IEEE Access}, pages = {53241-53253}, shortjournal = {IEEE Access}, title = {ECMS-based MPC strategy for multi-mode hybrid electric trucks}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based rapid fire detection in buildings using acoustic wave travel time information. ACCESS, 13, 53227-53240. (www)

  @article{ , abstract = {Rapid fire detection in buildings is essential for effective fire emergency management. Moreover, locating the fire source provides critical evidence for subsequent investigations into the fire’s cause. Acoustic tomography temperature measurement technology can be employed to monitor building fires, as fires create anomalies in the temperature field. This study proposes a fully AI-driven tomographic method to achieve rapid temperature field inversion. In the forward problem, we simulate the time of flight (TOF) of sound waves between transceivers using ray tracing techniques with unstructured meshes, which offers higher accuracy than methods that utilize straight-line ray paths between emission and reception points. To address the inverse problem, we employ a deep learning residual neural network (ResNet) named ResNet50 as the backbone of our AI model, using TOF as input parameters and producing the acoustic velocity fields of the monitored area as output. After training, the AI model can perform high-quality predictions in 0.91 seconds for scenarios involving 169 TOF data points and 1700 velocity field meshes. The temperature fields are derived from the velocity fields using an empirical formula that describes their relationship. The fire source is identified as the location with the highest temperature. Our validation dataset indicates that the prediction errors compared to ground truth measurements are acceptable, even with high noise levels (e.g., 25%). The proposed method presents a promising performance in both accuracy and efficiency. This research serves as a preliminary investigation into the feasibility of a one-step, fully AI-driven approach for fire monitoring and detection in buildings.}, archive = {J_ACCESS}, author = {Mengyao Sun and Cewen Liu}, doi = {10.1109/ACCESS.2025.3554256}, journal = {IEEE Access}, pages = {53227-53240}, shortjournal = {IEEE Access}, title = {Deep learning-based rapid fire detection in buildings using acoustic wave travel time information}, volume = {13}, year = {2025}, }
• (2025). Lightweight image super-resolution reconstruction guided by gated feedback and LatticeFormer. ACCESS, 13, 53214-53226. (www)

  @article{ , abstract = {Despite the excellent performance of Transformers in image super-resolution reconstruction, their high complexity and single forward-feed information flow limit the effective mining of deep information. To address this issue, we propose a lightweight image super-resolution reconstruction model guided by gated feedback and LatticeFormer. Firstly, the reconstruction effect is significantly improved without adding extra parameters by proposing a gated feedback-LatticeFormer block as the basic block in the feedback block. Then, a gating block is designed to integrate deep global information from the previous iteration to enhance the expression of shallow features. Finally, continuous LatticeFormer blocks are constructed to improve the network’s feature extraction capability further using an attention mechanism, thus achieving effective acquisition of global features at different levels. Compared with other existing lightweight image super-resolution reconstruction methods, our method on datasets such as Set5, Set14, BSD100, Urban100, and Manga109 for $2 \times $ , $3 \times $ , and $4 \times $ tasks reduces the model parameters by 3%, 5%, and 7% respectively, while increasing the Peak Signal-to-Noise Ratio (PSNR) by 0.18-1.05 dB, 0.17-0.82 dB, and 0.18-1.00 dB respectively. Additionally, in the $2 \times $ and $4 \times $ tasks of the Thangka dataset, the PSNR is increased by 0.89 dB and 0.60 dB respectively. Experimental results show that our method can generate high-quality reconstructed images with complete structures and clear texture details.}, archive = {J_ACCESS}, author = {Yao Fan and Min Jiang and Zhaoyang Liu and Zhiwei Zhao and Lizhi Zheng}, doi = {10.1109/ACCESS.2025.3553852}, journal = {IEEE Access}, pages = {53214-53226}, shortjournal = {IEEE Access}, title = {Lightweight image super-resolution reconstruction guided by gated feedback and LatticeFormer}, volume = {13}, year = {2025}, }
• (2025). Exercise assessment based on human pose estimation and relative phase for real-time remote exercise system. ACCESS, 13, 53203-53213. (www)

  @article{ , abstract = {Human pose estimation (HPE) technology, a vital tool for assessing exercise posture by extracting the three-dimensional coordinates of each joint, has been applied in many studies. It is effective for determining static postures, such as yoga; however, it still presents challenges in assessing dynamic exercise that involves considering each joint’s velocity. Although the HPE technology enables the derivation of position and velocity from each joint, assessing exercise posture using multiple time-series data requires time consumption and expert knowledge. Therefore, this study addressed this challenge by introducing a method for determining the velocity-based exercise posture, which combines the coordinates of significant-extracted joints using HPE with the relative-phase method. The relative phase angle ( $\Delta \phi _{Angle}$ ) is valuable for assessing the combination of position and velocity. This study added the relative phase distance ( $\Delta \phi _{Distance}$ ). An experiment was conducted to compare the exercise postures of experts and beginners during barbell back squats using a constructed dataset of time-series data for the positions and velocities of each joint and the relative phase Angle and Distance. Training and prediction were performed using a one-dimensional deep learning model. The results demonstrated the effectiveness of the proposed index in velocity-based exercise assessment with over 95% accuracy and confirmed the robustness of the method without requiring expert knowledge in real time. This study has significant implications for practical application in sports science and biomechanics. It has the potential to revolutionize the assessment and improvement of exercise posture.}, archive = {J_ACCESS}, author = {Yeongju Woo and Hieyong Jeong}, doi = {10.1109/ACCESS.2025.3551834}, journal = {IEEE Access}, pages = {53203-53213}, shortjournal = {IEEE Access}, title = {Exercise assessment based on human pose estimation and relative phase for real-time remote exercise system}, volume = {13}, year = {2025}, }
• (2025). Learning observers’ gaze dynamics: An efficient and mobile sport scenery recognition pipeline. ACCESS, 13, 53188-53202. (www)

  @article{ , abstract = {This study addresses the challenge of semantically sorting complex scenes in a mobile environment by processing multimodal visual inputs to create detailed landscape representations. Central to the approach is a streamlined multi-layer hierarchical model that mimics human attention dynamics, using the BING objectness metric to quickly identify significant areas by recognizing objects across different scales and contexts. To enhance feature extraction, time-sensitive and manifold-guided selectors are employed to prioritize high-quality visual features, while a low-rank active learning (LAL) algorithm simulates human-like focus on key visual zones, specifically in sports scenes. The model generates a Gaze Shift Path (GSP), which directs the collection of composite CNN features, ultimately classifying the scenes into distinct landscape types using a support vector machine (SVM). Experimental results on seven scene image sets have shown that our method outperforms the others by $2\% \sim 5\%$ . Additionally, our calculated deep GSP features can greatly facilitate image clustering. Last but not least, our visualized GSPs are over 90% consistent with real-world human gaze behaviors, which explains the competitiveness of our method.}, archive = {J_ACCESS}, author = {Huiting Lv and Jiashun Gao and Yu Li and Hongcheng Li}, doi = {10.1109/ACCESS.2025.3554704}, journal = {IEEE Access}, pages = {53188-53202}, shortjournal = {IEEE Access}, title = {Learning observers’ gaze dynamics: An efficient and mobile sport scenery recognition pipeline}, volume = {13}, year = {2025}, }
• (2025). Federated learning with sailfish-optimized ensemble models for anomaly detection in IoT edge computing environment. ACCESS, 13, 53171-53187. (www)

  @article{ , abstract = {The Internet of Things (IoT) has transformed cyber-physical systems by enabling seamless connectivity and automation. However, IoT devices face resource constraints, making anomaly detection challenging. Traditional centralized approaches suffer from computational inefficiencies, increased latency, and privacy concerns, making them unsuitable for real-time anomaly detection in distributed IoT environments. To address these challenges, this paper proposes a privacy-preserving anomaly detection framework that integrates Federated Learning (FL) with an optimized Isolation Forest model. FL enables decentralized training on IoT devices, reducing the risk of data breaches. However, anomaly detection performance is often hindered by suboptimal parameter selection. To overcome this, the Sailfish Optimization Algorithm (SFO) is incorporated to fine-tune the Isolation Forest model’s parameters dynamically, balancing exploration and exploitation. This optimization enhances accuracy while maintaining data confidentiality. Additionally, the framework is evaluated against leading FL-based and traditional anomaly detection models, including Local Outlier Factor (LOF), Generative Adversaria (GAN), and Variational autoencoder (VAE), demonstrating superior performance in recall and F1-score. Extensive experiments on benchmark datasets confirm that the proposed method achieves higher anomaly detection efficiency with a lower error rate than existing methods. The results establish this framework as a scalable, privacy-preserving, and computationally efficient solution for anomaly detection in IoT edge environments, addressing critical limitations in security, latency, and data privacy in real-world applications.}, archive = {J_ACCESS}, author = {Aravam Babu and A. Bagubali}, doi = {10.1109/ACCESS.2025.3554301}, journal = {IEEE Access}, pages = {53171-53187}, shortjournal = {IEEE Access}, title = {Federated learning with sailfish-optimized ensemble models for anomaly detection in IoT edge computing environment}, volume = {13}, year = {2025}, }
• (2025). XRGuard: A model-agnostic approach to ransomware detection using dynamic analysis and explainable AI. ACCESS, 13, 53159-53170. (www)

  @article{ , abstract = {Ransomware remains a persistent and evolving cybersecurity threat, demanding advanced and adaptable detection strategies. Traditional methods often fall short as signature-based systems are easily circumvented by emerging ransomware variants, while techniques like obfuscation and polymorphism add complexity to the detection process. Although machine learning and deep learning techniques present viable solutions, the opacity of complex black-box models can hinder their application in critical security environments. This paper introduces XRGuard, a novel ransomware detection framework that utilizes machine learning techniques to analyze Event Tracing for Windows (ETW) logs, identifying critical file I/O patterns indicative of ransomware attacks. By incorporating XAI techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), XRGuard bridges the trust gap associated with complex machine learning models by providing transparent and interpretable explanations for its decisions. Experimental results demonstrate that XRGuard achieves a 99.69% accuracy rate with an exceptionally low false positive rate of 0.5%. By enhancing detection accuracy and offering clear explanations of its operations, XRGuard not only improves security but also fosters trust and a deeper understanding of ransomware behaviors.}, archive = {J_ACCESS}, author = {M. Adnan Alvi and Zunera Jalil}, doi = {10.1109/ACCESS.2025.3553562}, journal = {IEEE Access}, pages = {53159-53170}, shortjournal = {IEEE Access}, title = {XRGuard: A model-agnostic approach to ransomware detection using dynamic analysis and explainable AI}, volume = {13}, year = {2025}, }
• (2025). Robust and unified semi-supervised unmixing of hyperspectral imaging for linear and multilinear models. ACCESS, 13, 53140-53158. (www)

  @article{ , abstract = {The spectral unmixing paradigm is an important analysis tool for hyperspectral (HS) images which allows one to decompose the 2D spatial information from the basic spectral signatures or end-members. In this work, we introduce a semi-supervised perspective for spectral unmixing, where some end-members are known a priori, while the rest are estimated from the HS image. The proposal is relevant in unmixing scenarios where there is only available partial information of end-members, or when the known end-members are not fully representative of the scene. Our formulation simultaneously addresses linear and multilinear mixing models in a unified fashion. The proposed algorithms are referred as ESSEAE (Extended Semi-Supervised End-members and Abundance Extraction) for the linear model, and NESSEAE (Non-linear Extended Semi-Supervised End-members and Abundance Extraction) for the multilinear one. The estimation process is presented as a weighted optimal approximation problem with regularization terms for abundances, end-members and sparse noise components, which is solved by a cyclic coordinate descent optimization (CCDO) scheme. In this work, we derive closed-solutions at each step of the CCDO scheme, and just for the multilinear model, the end-members estimation involves a gradient descent scheme with optimal linear search. We validate first our contributions with synthetic HS images that include Gaussian and sparse noise components to evaluate their robustness, and compare them with supervised and unsupervised perspectives. In addition, we validated the linear scheme with a breast histological sample, and the multilinear approach with the Urban dataset. The use of two datasets from different fields guarantees the generalizability of the proposed formulation. In general, our semi-supervised spectral unmixing schemes provide accurate and robust results with a fast computational time, and as expected, present an overall performance in between the supervised and unsupervised approaches. All scripts for the proposed algorithms are freely available in https://github.com/Nicothe4th/ESSEAE-NESSEAE.}, archive = {J_ACCESS}, author = {Daniel Ulises Campos-Delgado and Juan Nicolás Mendoza-Chavarría and Omar Gutierrez-Navarro and Laura Quintana-Quintana and Raquel Leon and Samuel Ortega and Himar Fabelo and Carlos López and Marylène Lejeune and Gustavo M. Callico}, doi = {10.1109/ACCESS.2025.3552439}, journal = {IEEE Access}, pages = {53140-53158}, shortjournal = {IEEE Access}, title = {Robust and unified semi-supervised unmixing of hyperspectral imaging for linear and multilinear models}, volume = {13}, year = {2025}, }
• (2025). Knowledge-enriched recommendations: Bridging the gap in alloy material selection with large language models. ACCESS, 13, 53124-53139. (www)

  @article{ , abstract = {Navigating materials performance databases efficiently is a persistent challenge in materials science and engineering, particularly in the selection of alloy materials. While recommendation systems address information overload, traditional approaches relying on historical user data face limitations such as data sparsity and cold-start issues. This study presents a novel recommendation model that integrates domain-specific knowledge graphs with large language models (LLMs) to enhance recommendation accuracy in alloy material selection. A knowledge graph for alloys is developed, encapsulating technical material data and relationships to improve retrieval and recommendation outcomes. LLMs are employed for label clustering and natural language-based instruction-following to craft user profiles and enhance data representation. Two graph enhancement strategies, integrated with attention mechanisms, effectively capture user preferences. Experimental results on a ferroalloy dataset demonstrate the model’s superior performance compared to baseline methods, significantly addressing data sparsity while offering personalized, accurate recommendations. This research bridges the gap between knowledge graphs and LLMs in recommendation systems, contributing a flexible, intelligent solution to streamline material selection processes.}, archive = {J_ACCESS}, author = {Tongwei Wu and Shiyu Du and Yiming Zhang and Honggang Li}, doi = {10.1109/ACCESS.2025.3554125}, journal = {IEEE Access}, pages = {53124-53139}, shortjournal = {IEEE Access}, title = {Knowledge-enriched recommendations: Bridging the gap in alloy material selection with large language models}, volume = {13}, year = {2025}, }
• (2025). Capacitance classification for supercapacitors using machine learning. ACCESS, 13, 53116-53123. (www)

  @article{ , abstract = {Supercapacitors are considered key components in many industrial and technological applications, due to their high ability to store energy and rapid charging and discharging. In this study, machine learning techniques were used for analyzing specific capacitance data of supercapacitors and developing a classification technique that helps to improve the efficiency of systems that rely on supercapacitors. Kernel Naïve Bayes model was used in this study to classify the data of supercapacitors to improve understanding of their performances. MATLAB program was used to apply the model to a dataset of supercapacitors, taking into account specific capacitance, voltage window, electrode materials, and electrolyte used. Supercapacitors were divided into five classes according to the level of their specific capacitance values. The results showed high accuracy in classifying the data – more than 95% – as the model was able to distinguish between different categories effectively while displaying the percentage of correct and incorrect predictions for each class.}, archive = {J_ACCESS}, author = {Ali Almuamri and Furkan Soysal and Sıtkı kocaoğlu}, doi = {10.1109/ACCESS.2025.3553187}, journal = {IEEE Access}, pages = {53116-53123}, shortjournal = {IEEE Access}, title = {Capacitance classification for supercapacitors using machine learning}, volume = {13}, year = {2025}, }
• (2025). Enhanced transmission expansion planning with a high confidence strategy considering fluctuations in solar PV generation and electricity demand. ACCESS, 13, 53100-53115. (www)

  @article{ , abstract = {Transmission Expansion Planning (TEP) is a popular approach to support the electricity demand increase and accommodate the solar photovoltaic systems increase in the transmission system. In state-of-the-art, maximum and minimum demand situations are often used in the TEP problem which is defined as a conventional TEP. However, modern TEP typically considers several uncertain scenarios, especially through Monte Carlo Simulation (MCS). Moreover, the metaheuristic algorithm is mostly applied to solve the TEP problem. These lead to an exponential increase in computational time as additional situations are included. To address this issue, this work proposes an enhanced TEP-based 24-hour situation optimized by an improved Binary Differential Evolution (BDE) algorithm assisted by the logistic map for adjusting the mutation factor and crossover rate. Zhao’s Point Estimation Method (PEM) combined with the Nataf Transformation (NT) is used instead of MCS to generate effective uncertain scenarios. Moreover, power system operation constraints are calculated through a probabilistic power flow with a 95% confidence level. The modified IEEE 24-bus system is used to test the proposed method. The simulation results show that Zhao’s PEM combined with NT can provide the mean and standard deviation of the desired parameters close to those from the MCS, while reducing computational time by 99.91%. Additionally, the improved BDE outperforms the conventional BDE by providing faster convergence in both conventional and proposed TEP problems. Furthermore, the proposed TEP solved by the improved BDE can achieve an optimal solution without violations for all scenarios, whereas the conventional TEP fails to support all scenarios.}, archive = {J_ACCESS}, author = {Siripat Somchit and Niphon Kaewdornhan and Rongrit Chatthaworn}, doi = {10.1109/ACCESS.2025.3553979}, journal = {IEEE Access}, pages = {53100-53115}, shortjournal = {IEEE Access}, title = {Enhanced transmission expansion planning with a high confidence strategy considering fluctuations in solar PV generation and electricity demand}, volume = {13}, year = {2025}, }
• (2025). Improving local fidelity and interpretability of LIME by replacing only the sampling process with CVAE. ACCESS, 13, 53084-53099. (www)

  @article{ , abstract = {Knowing the basis of decisions is essential for using Machine Learning (ML) in various regions, such as medical diagnosis, finance, and organizational decision-making. The Local Interpretable Model agnostic Explanation (LIME) algorithm is an Explainable AI (XAI) method that can be applied to many black -box models. However, there is a problem in that the local fidelity of the interpretable model decreases. Therefore, local fidelity must be increased, and the more interpretability of the local surrogate model, the better. Moreover, the fewer the changes, the easier it is to apply them to other studies and the more significant the contribution to the XAI area. Therefore, there are studies that improve fidelity by changing only the sampling. However, no studies have been identified that improve local fidelity and interpretability by modifying only the sampling. To this end, we use a conditional variational autoencoder (CVAE) to modify the sampling of LIME in the classification task to improve local fidelity and interpretability. We conducted a study to improve local fidelity and interpretability by modifying only the sampling part and clarifying the mechanism behind this improvement. Results show that the proposed method improves local fidelity while increasing interpretability compared to existing methods and the factors contributing to this improvement. We also conducted experiments on stability, stability against noise, and execution time, and confirmed that the proposed method performs well, although not as well as methods specific to these issues. Our code is publicly released on GitHub (https://github.com/YasuiDaisuke/CVAE-LIME.git) under the Apache 2.0 License.}, archive = {J_ACCESS}, author = {Daisuke Yasui and Hiroshi Sato}, doi = {10.1109/ACCESS.2025.3553505}, journal = {IEEE Access}, pages = {53084-53099}, shortjournal = {IEEE Access}, title = {Improving local fidelity and interpretability of LIME by replacing only the sampling process with CVAE}, volume = {13}, year = {2025}, }
• (2025). Classification and time-frequency localization of arbitrary LPWAN signals with radial deformable DETR. ACCESS, 13, 53065-53083. (www)

  @article{ , abstract = {With the increasing adoption of Internet-of-Things (IoT) technologies, numerous devices utilizing protocols such as Sigfox and LoRa are now widely available inexpensively and operate in unlicensed ISM bands. However, challenges such as inventory management, unauthorized usage, and network performance must be addressed. Future adoption of emerging IoT protocols with various modulation schemes, bandwidth, and data rates can further complicate this. Therefore, it is important not only to classify but also to localize the frequency, bandwidth, and time of these LPWAN signals on the air for management, security, or band planning purposes. SOTA algorithms usually look through the whole received signal on the time domain or frequency domain only to perform classification tasks, without finding out the corresponding time-frequency location of the signal. This paper proposes to classify and localize time-frequency locations of LPWAN signals by an enhanced version of Deformable DEtection TRansformer (Deformable DETR). We devise an attention radius suitable for processing Low Power Wide Area Network (LPWAN) Spectrogram traces extracted from Software Defined Radios (SDRs) IQ data with Short-Time Fourier Transform (STFT). Inspired by Large Language Models (LLMs), sequences of STFT vectors from SDR IQs can leverage attention mechanisms, and finding out LPWAN signals in spectrograms is similar to object detection tasks in computer vision. Our method eliminates the need for hand-crafting CNN layers or signal processing pipelines for different LPWAN protocols provided that sufficient training samples are available. Therefore, we build a fully annotated dataset for Lora and Sigfox in multiple frequencies, bandwidths, packet data, and time, as well as data augmentation techniques that serve both training and validation datasets for our modified Deformable DETR model. The experimental results demonstrate an average precision of over 89.5% for LoRa signals and over 79.8% when mixed with ultra-narrow-band signals.}, archive = {J_ACCESS}, author = {Chun Ho Kong and Haibo Hu}, doi = {10.1109/ACCESS.2025.3554080}, journal = {IEEE Access}, pages = {53065-53083}, shortjournal = {IEEE Access}, title = {Classification and time-frequency localization of arbitrary LPWAN signals with radial deformable DETR}, volume = {13}, year = {2025}, }
• (2025). YOLO-GCOF: A lightweight low-altitude drone detection model. ACCESS, 13, 53053-53064. (www)

  @article{ , abstract = {The maturity of the drone industry and the open access to low-altitude airspace have resulted in massive use of low-altitude drones. While the use of drones has brought economic efficiency, the illegal use of drones has raised concerns about increased drone trespass in the no-fly zone and countermeasure strategies. These concerns underscore the urgent need for low-altitude drone object detection. Traditional radar detection methods often struggle to detect small drones at low altitudes. To address this issue, we propose a lightweight model named YOLO-GCOF, leveraging an optimized version of the YOLOv8 architecture to enhance accuracy and enable efficient deployment on embedded devices. YOLO-GCOF incorporates the GSConv-Integrated Dynamic Group Convolution Shuffle Transformer (GI-DGCST) module as the feature extraction module, which captures fine-grained details and improves the detection of small-scale features. The integration of multi-level feature information is achieved by incorporating the Coordinate-Scale Attention High-level Screening-Feature Fusion Pyramid Networks (CSA-HSFPN) into the neck layer. The HSFPN architecture enables efficient multi-scale feature fusion, and the CSA module optimizes feature selection across scales, improving the model’s ability to detect key features using both scale and coordinate information. The Detect-Olive Group Convolution Head (Detect-OGCH) module, a lightweight parameter-sharing design, is integrated into the head layer to significantly reduce the model’s GFLOPs and parameter count while maintaining detection performance. Lastly, the original Complete Intersection over Union (CIoU) is replaced with the Focaler-SCYLLA IoU (Focaler-SIoU) to improve detection accuracy. The YOLO-GCOF model outperforms the original YOLOv8n, as demonstrated by a 1.1% improvement in mAP@50, alongside reductions in parameter count, computational overhead, and model size by 60%, 49.4%, and 55.1%, respectively. Additionally, the YOLO-GCOF model achieved 91.5% mAP@50 on the DUT-Anti-UAV dataset. These results and successful deployment on Raspberry Pi 4B demonstrate that the YOLO-GCOF is highly effective for lightweight, high-performance low-altitude drone detection.}, archive = {J_ACCESS}, author = {Wanjun Yu and Kongxin Mo}, doi = {10.1109/ACCESS.2025.3553477}, journal = {IEEE Access}, pages = {53053-53064}, shortjournal = {IEEE Access}, title = {YOLO-GCOF: A lightweight low-altitude drone detection model}, volume = {13}, year = {2025}, }
• (2025). Condition monitoring of photovoltaic panels through electrical impedance spectroscopy and machine learning focusing on temperature, dust, and microcracks. ACCESS, 13, 53039-53052. (www)

  @article{ , abstract = {The efficiency of Photovoltaic (PV) systems depends on environmental conditions that include temperature variations, dust accumulation and microcracks. These factors severely affect the output power of PV systems particularly where PV is installed domestically for which monitoring and maintenance are challenging due to cost constraints. Traditional numerical schemes are limited because PV systems exhibit nonlinear behavior due to environmental factors. To address this issue, electrical impedance spectroscopy (EIS) combined with machine learning is studied, modeling different scenarios to monitor the impact of temperature variations, dust and microcracks on photovoltaic (PV) systems.The experiment was conducted in a controlled environment, generating datasets for machine learning using EIS on solar panels through direct current (DC) lines over a frequency range of 1 Hz to 25 MHz. The light gradient boosting machine (LGBM) demonstrated great performance, achieving an F1 score of 0.856 for temperature classification and 0.975 for detection of dust and microcracks. The results of this study suggest the integration of EIS with machine learning schemes presents a cost effective and viable solution for real time monitoring of PV systems. This approach not only mitigates the effect of environmental factors but also reduces the cost of maintenance and increases predictive maintenance ensuring enhanced PV system performance.}, archive = {J_ACCESS}, author = {Saeed Iqbal and Syed Maaz Hasan and Yasar Ayaz and Emad Ud Din and Adeel Waqas and Muhammad Sajid}, doi = {10.1109/ACCESS.2025.3550738}, journal = {IEEE Access}, pages = {53039-53052}, shortjournal = {IEEE Access}, title = {Condition monitoring of photovoltaic panels through electrical impedance spectroscopy and machine learning focusing on temperature, dust, and microcracks}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based medical object detection: A survey. ACCESS, 13, 53019-53038. (www)

  @article{ , abstract = {Recent advancements in medical object detection (MOD) have been propelled by the rapid evolution of deep learning (DL) technologies, revolutionizing medical imaging and diagnostic workflows. This survey comprehensively reviews various studies across diverse imaging modalities, including X-Ray, CT, MRI, Ultrasound, and Histopathology. Notable improvements include integrating You Only Look Once (YOLO)-based architectures, Vision Transformers (ViT), and hybrid attention mechanisms, significantly enhancing detection accuracy and efficiency. Standout models, such as YOLOv8m, Hybrid YOLO-NAS, and YOLOv4+ViT, have demonstrated exceptional performance, achieving mean average precision (mAP) scores between 98.6% and 99.5%. These advancements leverage sophisticated features like Cross-Stage Partial (CSP) networks, Spatial Pyramid Pooling (SPP), and Bi-Directional Feature Pyramid Networks (BiFPN) to improve feature extraction and detection in medical images. Despite these successes, challenges remain in adapting these models to resource-limited settings and ensuring their outputs are interpretable for clinicians. This survey aims to bridge the gap between theoretical progress and practical implementation by aligning cutting-edge technological developments with clinical demands. It provides a certain roadmap for future innovation in MOD, with the overarching goal of improving patient care through enhanced diagnostic capabilities.}, archive = {J_ACCESS}, author = {Mohammadreza Saraei and Mehrshad Lalinia and Eung-Joo Lee}, doi = {10.1109/ACCESS.2025.3553087}, journal = {IEEE Access}, pages = {53019-53038}, shortjournal = {IEEE Access}, title = {Deep learning-based medical object detection: A survey}, volume = {13}, year = {2025}, }
• (2025). On control of delta-CHB STATCOM. ACCESS, 13, 53007-53018. (www)

  @article{ , abstract = {Static Synchronous Compensators (STATCOMs) are a widespread solution for reactive power compensation and offer advantages such as fast response, smaller footprint, and voltage disturbance support. The Delta-CHB converter is one of the most preferred medium- and high-voltage STATCOM topologies in the industry. However, this topology poses challenges for those researchers who intend to develop new modulation strategies, power loss reduction techniques, capacitance reduction schemes, and fault-tolerant methods due to the relatively complex control structure with several objectives required. Previous publications present control strategies for this topology, but they still lack approaches where the system modeling and the control tuning are performed straightforwardly with enough details and validity. This paper presents the dynamic model of the Delta-CHB STATCOM that allows the employment of well-known and analytical control tuning strategies for all control loops involved. Although the individual control loops are somehow covered in previous publications, this work consolidates key design details into a single reference. It also provides insights regarding the normalization strategy employed and its effects on the circulating current. The proposed modeling dynamic response is compared with the simulation results of a real 90 MVA STATCOM plant. This paper also provides experimental results demonstrating the adopted approach’s effectiveness, which reaches all control objectives (capacitor voltage control, capacitor voltage balancing, circulating current suppression, and reactive power control at the point of common coupling). Furthermore, the normalization strategy reduced the experimental ripple of the circulating current by 29.95%.}, archive = {J_ACCESS}, author = {João Victor Guimarães França and Dayane Do Carmo Mendonça and Seleme Isaac Seleme and Heverton Augusto Pereira and Allan Fagner Cupertino}, doi = {10.1109/ACCESS.2025.3552552}, journal = {IEEE Access}, pages = {53007-53018}, shortjournal = {IEEE Access}, title = {On control of delta-CHB STATCOM}, volume = {13}, year = {2025}, }
• (2025). Dual-channel dynamic gated spatio-temporal graph for traffic flow forecasting. ACCESS, 13, 52995-53006. (www)

  @article{ , abstract = {Traffic flow forecasting is a critical and essential technology in the field of Intelligent Transportation Systems (ITS), as it plays a pivotal role in optimizing traffic management, improving road safety, and enhancing the overall efficiency of transportation networks. However, current research neglects the relationships between the local and global traffic flow data. Additionally, the predefined static graph structure fails to adequately capture the dynamic spatial features of traffic flow. To address the these challenges, this paper proposes a Dual-Channel Dynamic Gated Spatio-Temporal graph network (DC-DGST) for traffic flow prediction. We consider hourly slices as the local feature and daily slices to be the global feature of traffic flow. The DC-DGST framework employs a dual-channel structure to capture spatiotemporal dependencies between global and local features. It transforms the predefined static graph into a dynamic graph, enabling the establishment of connections between input data and historical information. Furthermore, we design gated spatio-temporal blocks based on residual structures within the spatio-temporal module. Specifically, we utilize Graph Gated Neural Networks (GGNNs) to learn and integrate both static and dynamic graphs, while Transformer encoders are used to capture long-range dependencies in the temporal sequence. We conducted a series of experiments on four publicly available benchmark datasets: PEMS03, PEMS04, PEMS07, and PEMS08. The results demonstrate that our model significantly outperforms baseline models. Moreover, the dual-channel structure effectively captures the correlation between local and global traffic flow features, while the dynamic graph enhances the model’s overall performance.}, archive = {J_ACCESS}, author = {Chao Wang and Jun-Feng Hao and He Huang and Wang Zou and Xia Sun and Ting Peng}, doi = {10.1109/ACCESS.2025.3553535}, journal = {IEEE Access}, pages = {52995-53006}, shortjournal = {IEEE Access}, title = {Dual-channel dynamic gated spatio-temporal graph for traffic flow forecasting}, volume = {13}, year = {2025}, }
• (2025). Flexible multi-battery system with active balancing and voltage regulation. ACCESS, 13, 52983-52994. (www)

  @article{ , abstract = {This paper presents a flexible multi-battery system with active balancing and voltage regulation. The presented battery system has two main features: (1) active balancing can be achieved between batteries connected in series/parallel using a balancing converter, and (2) the presented battery system can operate with a smaller number of batteries while still achieving balancing between the operating (existing) batteries and regulating the load voltage using the balancing converter. These features make the presented system suitable for applications where energy supply continuity is highly desired. The operation principle of the flexible battery system is presented in detail. A comprehensive steady-state analysis and voltage balancing control diagrams are presented for different operating scenarios. Moreover, expressions for the balancing converter inductor currents and batteries currents are derived to provide insights on the system’s operation, and to show how the balanced condition is achieved under different operating scenarios. Experimental results obtained from a laboratory prototype are presented to evaluate the features of the battery system.}, archive = {J_ACCESS}, author = {Mohammad K. Al-Smadi and Jaber A. Abu Qahouq}, doi = {10.1109/ACCESS.2025.3552502}, journal = {IEEE Access}, pages = {52983-52994}, shortjournal = {IEEE Access}, title = {Flexible multi-battery system with active balancing and voltage regulation}, volume = {13}, year = {2025}, }
• (2025). Detection of potential breeding sites based on AAV remote sensing imagery and MultiDCCSP-YOLO network. ACCESS, 13, 52972-52982. (www)

  @article{ , abstract = {Detection and control of potential breeding sites (PBS) for Aedes albopictus are considered among the most effective methods for managing mosquito-borne diseases. Due to the high adaptability and rapid reproduction of mosquitoes, PBS are widely distributed. Traditional manual investigation methods are inefficient and would increase the risk of exposure. Autonomous Aerial Vehicle (AAV) remote sensing technology, with its speed and broad coverage, can capture large-scale images quickly. However, current PBS detection methods primarily focus on the detection of large, regular containers. To address the issue for detecting diverse PBS in complex environments, we propose a method called MultiDCCSP-YOLO. This approach integrates several modules to improve the PBS detection accuracy from AAV remote sensing images with challenging backgrounds. We introduce the Multi-DC Block and use it to build the CSPDCStage module. This module captures multi-scale features while reducing computational demands without compromising the detection accuracy. The FasterELAN module minimizes overfitting risks and enhances the detection of PBS targets with limited-sample classes. Additionally, we incorporate the Adaptive Threshold Focal Loss (ATFL) function to emphasize small, hard-to-detect targets, thereby improving multi-scale PBS detection performance. The MultiDCCSP-YOLO method enables efficient and accurate PBS detection in complex environments, facilitating large-scale monitoring efforts.}, archive = {J_ACCESS}, author = {Zichen Gao and Qinmei Liu and Yibin Zhou and Qing Liu and Zu Ma and Huaiyue Ding and Bin Ren and Deliang Xiang and Hengduan Zhang}, doi = {10.1109/ACCESS.2025.3543858}, journal = {IEEE Access}, pages = {52972-52982}, shortjournal = {IEEE Access}, title = {Detection of potential breeding sites based on AAV remote sensing imagery and MultiDCCSP-YOLO network}, volume = {13}, year = {2025}, }
• (2025). Agricultural text classification method based on ERNIE 2.0 and multi-feature dynamic fusion. ACCESS, 13, 52959-52971. (www)

  @article{ , abstract = {Under the rapid development of agricultural information technology, there has been an explosive growth in agricultural Q&A texts on the Internet, making the accurate classification of these texts a critical research topic in this field. However, the dispersion, complexity, and difficulty in collecting agricultural data pose significant challenges to achieving efficient and accurate text classification. To address this, this paper proposes a text classification model based on multi-feature dynamic fusion—ERNIE 2.0_BiGRU_CapsNet_Att. The model uses the ERNIE 2.0 pre-training framework to encode the dataset to learn the dynamic semantic representations of words; it combines Bidirectional Gated Recurrent Units (Bi-GRU) and Capsule Networks to capture high-dimensional sequential and local features of the text; meanwhile, it enriches feature inputs by constructing artificial numerical feature vectors. With the introduction of an attention mechanism, the model can dynamically adjust feature weights to achieve effective multi-feature fusion. We validated the effectiveness of this fusion model on a self-constructed agricultural Q&A dataset. Experimental results, averaged over five runs, show that the proposed model achieved 95.1% precision (±0.1), 94.8% recall (±0.2), and 95.2% F1-score (±0.1), representing a 1.1% improvement in precision, a 0.6% improvement in recall, and a 0.7% improvement in F1-score compared to the best-performing baseline model. These results demonstrate that the proposed model significantly outperforms traditional classification models and achieves robust performance across experiments.}, archive = {J_ACCESS}, author = {Xuliang Duan and Yuhai Liu and Zhenjia You and Zhiyao Li}, doi = {10.1109/ACCESS.2025.3537277}, journal = {IEEE Access}, pages = {52959-52971}, shortjournal = {IEEE Access}, title = {Agricultural text classification method based on ERNIE 2.0 and multi-feature dynamic fusion}, volume = {13}, year = {2025}, }
• (2025). Time-division-multiplexed energy harvesting from quasi-distributed fiber bragg grating arrays (FBGAs) sensing networks. ACCESS, 13, 52949-52958. (www)

  @article{ , abstract = {This work presents a new approach to energy harvesting (EH) from a quasi-distributed sensing network of fiber Bragg grating arrays (FBGAs). While maintaining accurate FBGA temperature sensing, our approach collects the typically unused transmitted power from the broadband light across an FBGA sensing network and converts and stores it as electrical energy to power up electronic-based sensors (EBSs). To demonstrate this concept, we reported on a quasi-distributed FBGA network topology consisting of two different FBGAs: one with 5 FBGs and the other with 10 FBGs. The system employs time-division multiplexing (TDM) via an optical switch to alternate the light between both FBGAs. Both FBGAs were calibrated for temperature sensing using their reflected spectra, showing typical sensitivity values of 11.72 pm/°C-12.43 pm/°C for FBGA1 and 12.86 pm/°C-14 pm/°C for FBGA2. The untapped power transmitted through both FBGAs was harvested using EH units based on supercapacitors. The EH process was investigated for different switching times (1 s, 100 s, 600 s, and 1000 s). The cumulative harvested power ranged from ~6.56-7.06 mW, corresponding to the overall conversion efficiency of ~25.8-27.8% for the entire system after leaving it for 60 min of temperature sensing. These results validate the potential of using quasi-distributed FBGA networks for simultaneous sensing and EH, providing a sustainable solution for autonomous multi-parameter hybrid sensing applications such as remote underwater or underground EBS.}, archive = {J_ACCESS}, author = {Alaaeddine Rjeb and Juan M. Marin and Chun Hong Kang and Ibrahim G. Alsayoud and Islam Ashry and Tien Khee Ng and Boon S. Ooi}, doi = {10.1109/ACCESS.2025.3554139}, journal = {IEEE Access}, pages = {52949-52958}, shortjournal = {IEEE Access}, title = {Time-division-multiplexed energy harvesting from quasi-distributed fiber bragg grating arrays (FBGAs) sensing networks}, volume = {13}, year = {2025}, }
• (2025). A somatotype classification approach based on generative AI and frontal images of individuals. ACCESS, 13, 52939-52948. (www)

  @article{ , abstract = {Somatotype is a definition that classifies human bodies according to their shape, and its determination is crucial for evaluating and improving athletic performance. However, the traditional method used for its estimation has some limitations: measurements are directly collected from the human body, specialists are required, and it is time-consuming. In this paper, two approaches are evaluated: GPT-4’s performance in classifying the predominant somatotype using only human body frontal images, and a proposed multimodal architecture that combines the textual descriptions provided by GPT-4 with these images, using pre-trained MiniLM models for the textual information and pre-trained Vision Transformer (ViT) with transfer learning for images. Frontal images were collected from 50 volunteers aged between 18 and 65 years, with 10 anthropometric measurements following Heath and Carter protocol. The results show that GPT-4 achieved an average accuracy of 78% in somatotype classification for the first approach. By combining the textual descriptions with the frontal images in the multimodal model, the average accuracy was of 97%, with a precision of 94% and recall of 89%. These results indicate that combining multimodal language models with deep learning improved accuracy in automatic somatotype classification compared to GPT-4 classification. The results are promising and open up new possibilities in the classification of somatotypes using only digital images.}, archive = {J_ACCESS}, author = {Antonio R. A. Brasil and Fabian T. Amaral and Lícia Cristina de Lima Oliveira and Girlandia A. Brasil and Klaus F. Côco and Patrick Marques Ciarelli}, doi = {10.1109/ACCESS.2025.3553797}, journal = {IEEE Access}, pages = {52939-52948}, shortjournal = {IEEE Access}, title = {A somatotype classification approach based on generative AI and frontal images of individuals}, volume = {13}, year = {2025}, }
• (2025). Evaluating coding proficiency of large language models: An investigation through machine learning problems. ACCESS, 13, 52925-52938. (www)

  @article{ , abstract = {Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains, but their effectiveness in coding workflows, particularly in machine learning (ML), requires deeper evaluation. This paper investigates the coding proficiency of LLMs such as GPT and Gemini by benchmarking their performance on three ML problems: Titanic, MNIST, and Steel Defect. These problems were chosen to encompass a range of challenges, including handling missing data, feature engineering, deep learning architectures, and multi-label classification. Using systematic prompts, we evaluated the LLMs’ abilities in data preprocessing, hyperparameter tuning, and classifier generation, comparing their outputs with those of human developers and AutoML frameworks. Experimental results indicate that the human developer outperformed untuned LLMs in data preprocessing, maintaining a 3–5% accuracy advantage across datasets. However, GPT’s hyperparameter tuning improved model performance by up to 6.3% in Titanic and 3.33% in Steel Defect, surpassing human-tuned models in some cases. In contrast, Gemini exhibited only marginal tuning improvements (0.19–1.78%) and failed to compensate for preprocessing inefficiencies. These findings show that while LLMs can assist with ML coding tasks, they exhibit varying levels of efficiency depending on task complexity and preprocessing requirements. GPT demonstrated superior hyperparameter tuning capabilities, whereas both LLMs struggled with intuitive data preprocessing, particularly in feature selection and transformation. This study provides practical insights into the strengths and limitations of LLMs in ML workflows, offering guidance for their effective integration into real-world applications.}, archive = {J_ACCESS}, author = {Eunbi Ko and Pilsung Kang}, doi = {10.1109/ACCESS.2025.3553870}, journal = {IEEE Access}, pages = {52925-52938}, shortjournal = {IEEE Access}, title = {Evaluating coding proficiency of large language models: An investigation through machine learning problems}, volume = {13}, year = {2025}, }
• (2025). A multi-factor-fusion framework for efficient prediction of pedestrian-level wind environment based on deep learning. ACCESS, 13, 52912-52924. (www)

  @article{ , abstract = {Efficient and accurate assessment of the Pedestrian-Level Wind Environment is essential to maintain a healthy and safe urban living environment. Numerical simulations, such as computational fluid dynamics and multi-scale modeling techniques, are commonly used for wind environment analysis. However, they are computationally intensive and time-consuming, particularly when dealing with the complexities of urban landscapes. This study proposes a novel Multi-Factor-Fusion (MFF) framework that leverages deep learning techniques. This framework integrates Graph Convolutional Networks and Long Short-Term Memory networks to extract and fuse multiple factors and create an end-to-end neural network model capable of directly predicting wind fields. By avoiding the need for grid division and iterative calculations, the framework significantly enhances the efficiency of wind environment analysis. Furthermore, multi-scale simulation data is used to train the model and correct the predictive results, ensuring the accuracy of the final results. This innovative approach has the potential to revolutionize the Pedestrian-Level Wind Environment prediction by achieving a trade-off between efficiency and accuracy.}, archive = {J_ACCESS}, author = {Zhen-Zhong Hu and Yan-Tao Min and Shuo Leng and Sunwei Li and Jia-Rui Lin}, doi = {10.1109/ACCESS.2025.3553490}, journal = {IEEE Access}, pages = {52912-52924}, shortjournal = {IEEE Access}, title = {A multi-factor-fusion framework for efficient prediction of pedestrian-level wind environment based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Toward automatic streetside building identification with an integrated YOLO model for building detection and a vision transformer for identification. ACCESS, 13, 52901-52911. (www)

  @article{ , abstract = {In an era of widespread digital imagery and advancing machine vision technologies, automated methods to precisely locate photographed buildings are crucial across various sectors. This research initiates the development of an automated Streetside Building Identification System (SBIS). Leveraging the comprehensive coverage of Google Street View images across major cities worldwide, the research integrates a YOLO model for building detection with a Vision Transformer (ViT) model for building identification, supported by Transfer Learning. This innovative approach aims to pinpoint exact building coordinates in urban environments, overcoming challenges associated with insufficient supporting data. Utilizing Google Street View datasets that cover entire urban landscapes, the proposed method offers efficiency and scalability, simplifying data acquisition and avoiding logistical complexities of manual interaction for targeted collections. Furthermore, it ensures a more inclusive representation of diverse urban environments, recognizing buildings of every shape and architectural style. The system can scan areas covered by Google Street View even without commercial or business data, navigating through limited information scenarios and marking a significant progression from previous studies. The initial system version provides insights into its implementation and discusses potential improvements. Tests against privately collected images show a current accuracy of 94.23%, offering a promising foundation for further refinements. The primary objective is to develop an automated solution capable of creating a comprehensive database for building recognition tasks, eliminating the laborious manual search process for extensive datasets.}, archive = {J_ACCESS}, author = {Ossama Krawi and Lavdie Rada}, doi = {10.1109/ACCESS.2025.3552928}, journal = {IEEE Access}, pages = {52901-52911}, shortjournal = {IEEE Access}, title = {Toward automatic streetside building identification with an integrated YOLO model for building detection and a vision transformer for identification}, volume = {13}, year = {2025}, }
• (2025). Genetic algorithm-optimized diode-based temperature control actuation system. ACCESS, 13, 52889-52900. (www)

  @article{ , abstract = {The temperature control of silicon diodes as actuators was studied from both theoretical and experimental perspectives. Genetic algorithms were employed to optimize diode distributions for effective temperature regulation. Temperature fluctuations can significantly affect the performance of high-precision electronic equipment, necessitating robust temperature control methods. By integrating series diodes into the temperature control system alongside the traditional proportional-integral-differential (PID) control mode, precise regulation of high-precision component temperatures in power supplies was achieved. The diodes’ minimal voltage variation ensures linear power and current characteristics, enabling effective temperature rise control through current adjustments and mitigating temperature overshoot issues. Diodes also offer advantages such as ease of installation and high safety margins against open circuits. Simulation-validated optimization of diode positions using genetic algorithms demonstrated their effectiveness in achieving optimal configurations through selection, crossover, and mutation operations. This approach not only reduces the number of diodes but also meets diverse temperature control requirements, enhancing system responsiveness and power output stability. The study underscores the potential of diodes as temperature control actuators, particularly in regulating high-precision power components in power supplies.}, archive = {J_ACCESS}, author = {Yajie Huang and Donglai Zhang and Xueli Zhu and Anshou Li and Yi Wang}, doi = {10.1109/ACCESS.2025.3553476}, journal = {IEEE Access}, pages = {52889-52900}, shortjournal = {IEEE Access}, title = {Genetic algorithm-optimized diode-based temperature control actuation system}, volume = {13}, year = {2025}, }
• (2025). A new parameter-changing integral ZNN model with nonlinear activation for solving inequality constraint time-varying quadratic programming. ACCESS, 13, 52874-52888. (www)

  @article{ , abstract = {The conventional zeroing neural network (ZNN) model faces significant challenges in handling time-varying noise, with its convergence speed being highly sensitive to initial conditions. In this paper, we propose a new parameter-changing integral ZNN model with nonlinear activation (NAPCIZNN) to effectively tackle time-varying quadratic programming problems with inequality constraints (IC-TVQP). By integrating a nonlinear activation function and dynamic parameter adjustment, the proposed NAPCIZNN model exhibits superior convergence speed and robust noise tolerance. We rigorously derive the theoretical upper bound for convergence time under noisy environments, providing a strong foundation for the model’s reliability. Comprehensive numerical simulations demonstrate that NAPCIZNN significantly outperforms traditional ZNN variants—including the original ZNN, nonlinear activated ZNN, integral ZNN, and piecewise variable parameter ZNN—in solving time-varying quadratic programming problems. Moreover, the practical application of the NAPCIZNN model in controlling the PUMA560 robotic manipulator showcases its robustness and precision in real-world scenarios. Empirical evidence from these applications validates the model’s exceptional capability in executing complex butterfly trajectory tracking controls with high accuracy and reliability.}, archive = {J_ACCESS}, author = {Chongbiao Tang and Yongmei Ding}, doi = {10.1109/ACCESS.2025.3548004}, journal = {IEEE Access}, pages = {52874-52888}, shortjournal = {IEEE Access}, title = {A new parameter-changing integral ZNN model with nonlinear activation for solving inequality constraint time-varying quadratic programming}, volume = {13}, year = {2025}, }
• (2025). A novel chaotic system generator architecture based on optimization algorithm. ACCESS, 13, 52863-52873. (www)

  @article{ , abstract = {Chaotic systems have always been of critical importance for researchers as a factor that triggers science and engineering studies. For this reason, understanding the real world more effectively by exploring new chaotic systems has always been a hot research topic. However, discovering new chaotic systems is not an easy process. In this study, a new approach is proposed to address this difficult problem. The original aspect of the proposed study is to develop a new chaotic system based on existing chaotic systems. The proposed theoretical architecture combines the components of existing chaotic systems with a new approach using the advantages of optimization algorithms. The success of the theoretical architecture proposed in the study has been verified on one-dimensional chaotic systems. It has been demonstrated through detailed analysis that the chaotic properties of the chaotic systems produced fulfill the basic requirements most effectively, and it is thought that this will have serious potential in many practical applications in the future.}, archive = {J_ACCESS}, author = {T. Cıgal and E. Tanyildizi}, doi = {10.1109/ACCESS.2025.3554308}, journal = {IEEE Access}, pages = {52863-52873}, shortjournal = {IEEE Access}, title = {A novel chaotic system generator architecture based on optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Robust current sensor fault-tolerant controller using third order super-twisting sliding mode observer and controller for induction motors. ACCESS, 13, 52841-52862. (www)

  @article{ , abstract = {This paper aims to enhance the performance and reliability of three-phase IMs in the event of current sensor failures by introducing a robust sensor Fault Tolerant Controller (FTC). The proposed FTC integrates a Third Order Super Twisting Sliding Mode Observer (TOSTSMO) with a novel Direct Field Oriented Control (DFOC) based on a Third Order Super Twisting Sliding Mode Controller (TOSTSMC) for the IM. The first objective is to replace conventional PI controllers with TOSTSMC, which offers superior system dynamics, faster speed and torque responses, and greater robustness against load and rotor resistance variations, outperforming other control methods. TOSTSMC also mitigates the chattering associated with First-Order Sliding Mode Control (FOSMC) while maintaining simplicity, robustness, and accuracy. The second objective is to enhance the Super-Twisting Sliding Mode Observer by incorporating third-order dynamics, which improves estimation accuracy and robustness, particularly in systems with higher-order uncertainties and disturbances. Integrating the TOSTSMO into the proposed FTC system enables precise and robust state estimation of the induction machine, even in the presence of current sensor failures. This ensures that the control system can maintain high performance and reliability despite sensor faults. Simulation results, conducted in the MATLAB/Simulink environment, demonstrate the effectiveness of the proposed algorithm.}, archive = {J_ACCESS}, author = {Rakia Tarkhani and Saber Krim and Majdi Mansouri and Mouhamed Faouzi Mimouni}, doi = {10.1109/ACCESS.2025.3553367}, journal = {IEEE Access}, pages = {52841-52862}, shortjournal = {IEEE Access}, title = {Robust current sensor fault-tolerant controller using third order super-twisting sliding mode observer and controller for induction motors}, volume = {13}, year = {2025}, }
• (2025). HGAPSO-based third order-SMC, ST-SMC, and SMC strategy for AAV control: A comparative analysis. ACCESS, 13, 52821-52840. (www)

  @article{ , abstract = {The study of the control of quadrotors was addressed extensively in different AAV research areas. In this work, the nonlinear dynamic model of the quadrotor has been developed using the Newton-Euler technique. By considering the system’s under-actuated and strongly coupled characteristics, the controllers have been then designed. As a main objective, design and comparative analysis of conventional sliding mode controller (SMC), supper twisting sliding mode controller (ST-SMC), and third order sliding mode controller (TO-SMC) schemes are realized for a nonlinear model of a quadrotor. The controller’s performance is highly dependent on the sliding surface coefficients and the controller constants. Therefore, a hybrid type of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), HGAPSO, has been formulated to find the best controllers’ parameters. The effectiveness of GA, PSO, and HGAPSO are investigated with TO-SMC in terms of trajectory tracking and robustness. The control algorithms are focused on the position, altitude, and attitude states manipulated by four control inputs. The performance comparison of the controllers is evaluated under the effect of wind disturbance. Model development and simulation of the proposed system are carried out using MATLAB Simulink and a set of simulations are conducted to illustrate the states and analysis the performance of the proposed controllers. Finally, the HGAPSO tuned TO-SMC has performance supremacy in trajectory tracking, chattering elimination, settling time, rise time, ITAE minimization, and robustness over HGAPSO tuned ST-SMC and SMC. In addition, the HGAPSO reveals attractive performance over PSO and GA alone.}, archive = {J_ACCESS}, author = {Dawit Kefale Wassie and Lebsework Negash Lemma and Abrham Tadesse Kassie}, doi = {10.1109/ACCESS.2025.3553146}, journal = {IEEE Access}, pages = {52821-52840}, shortjournal = {IEEE Access}, title = {HGAPSO-based third order-SMC, ST-SMC, and SMC strategy for AAV control: A comparative analysis}, volume = {13}, year = {2025}, }
• (2025). Sinusitis diagnosis in temporal bone CT using extremal intensity transform-based inflated 3-D ConvNet. ACCESS, 13, 52807-52820. (www)

  @article{ , abstract = {Despite being primarily used for ear assessment, temporal bone CT (TBCT) can be an effective diagnostic tool for sinusitis because it captures parts of paranasal sinuses, especially given the strong link between ear diseases and sinusitis. In this work, we propose a TBCT-based sinusitis diagnosis method using deep learning. As a pioneering research problem, the available training data for this is limited besides the high dimensionality of 3D TBCT volumes, causing overfitting. For improved training, we propose an extremal intensity transform to simplify the model input and incorporate an inflated 3D convolutional network for effective pre-training. We evaluated our diagnostic method on a private dataset of 100 TBCT volumes collected from a local hospital. The proposed approach achieved an average area under the receiver operating characteristic curve (AUC) of 0.95 and an accuracy of 0.90, significantly reducing overfitting while enhancing diagnostic accuracy by 5% compared to standard deep learning-based diagnostic models.}, archive = {J_ACCESS}, author = {Walid Abdullah Al and Il Dong Yun and Yun Jung Bae}, doi = {10.1109/ACCESS.2025.3552094}, journal = {IEEE Access}, pages = {52807-52820}, shortjournal = {IEEE Access}, title = {Sinusitis diagnosis in temporal bone CT using extremal intensity transform-based inflated 3-D ConvNet}, volume = {13}, year = {2025}, }
• (2025). Operational and planning perspectives on battery swapping and wireless charging technologies: A multidisciplinary review. ACCESS, 13, 52775-52806. (www)

  @article{ , abstract = {The electrification of the transportation system is considered one of the most viable solutions to address the pressing need to shift towards sustainable development. However, one of the major challenges to the rapid adoption of Electric Vehicles (EVs) is the lack of the right charging infrastructure where and when it is needed. Motivated by the necessity for a multidisciplinary approach, this study addresses the complex operational and planning challenges involved in integrating transportation and electrical networks for effective EV charging. Thus, this research aims to survey the literature to identify the adopted technologies, and the application and allocation of the right mix of the technologies to better serve the seamless adoption of EVs. That problem is multifaced where transportation network requirements and the electrical grid are important to support the charging loads at the needed time. The literature survey adopts the PRISMA methodology, contributing to the existing literature by highlighting the following shortcomings: 1) the problem of allocating the charging technology is either purely viewed from a transportation or an electrical perspective, 2) there is a gap in adopting both networks’ requirements comprehensively, 3) the literature predominately focuses on a single technology either battery swapping or wireless charging, 4) introducing new technologies highlights the impact of each on both networks in terms of return of investment, traffic flows, and power grids operational conditions, 5) research direction is focused more towards operation and routing while service allocation is relatively new and has not yet been extensively explored, and 6) transportation network research focuses on a static representation of the transportation network and EV demand. This study’s main contribution, besides identifying critical gaps in the existing literature and possible future research directions, is the proposal of a novel framework that integrates multiple charging technologies. This framework is designed to optimize infrastructure deployment, enhancing the efficiency and economic viability of EV charging systems across both transportation and electrical networks.}, archive = {J_ACCESS}, author = {Sarah M. Kandil and Akmal Abdelfatah and Maher A. Azzouz}, doi = {10.1109/ACCESS.2025.3554336}, journal = {IEEE Access}, pages = {52775-52806}, shortjournal = {IEEE Access}, title = {Operational and planning perspectives on battery swapping and wireless charging technologies: A multidisciplinary review}, volume = {13}, year = {2025}, }
• (2025). DynaOOD-net: Dynamic progressive feature fusion and energy balancing for robust out-of-distribution detection. ACCESS, 13, 52758-52774. (www)

  @article{ , abstract = {In the field of object detection in open environments, models are required to simultaneously recognize predefined categories and accurately distinguish unknown categories from non-object regions, all while addressing the challenges of imbalanced data distributions and multi-scale targets. To meet these demands, this paper proposes DynaOOD-Net, a novel detection framework designed to enhance model generalization performance through dynamic feature integration and energy-balanced regularization strategies. DynaOOD-Net consists of two core components: the Dynamic Progressive Feature Pyramid Network (DP-FPN) and the Balanced Energy Constraint Loss (BECL). DP-FPN incorporates a Multi-Branch Dynamic Fusion Block (MBDF) to enable dynamic feature interactions and progressive optimization, ensuring robust feature representation across complex scenarios. Meanwhile, BECL dynamically adjusts category constraints, effectively addressing the indistinct energy boundary between known and unknown objects and the class imbalance problem, thus improving the model’s ability to distinguish between in-distribution (ID) and out-of-distribution (OOD) samples. Additionally, Dynamic Progressive Feature Pyramid Network-Lite (DP-FPN-Lite) is proposed, which reduces computational and storage overhead by simplifying the network architecture while preserving the core concepts of DP-FPN. Experimental results on seven datasets demonstrate that DynaOOD-Net significantly improves the mAP for known categories and consistently outperforms existing state-of-the-art methods in unknown object detection metrics. These findings establish DynaOOD-Net as a robust and efficient solution to the challenges inherent in open-set object detection. At the same time, DynaOOD-Lite utilizes DP-FPN-Lite to retain strong OOD detection capabilities while significantly reducing model parameters, making it suitable for deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Jiting Zhou and Zhihao Zhou and Pu Zhang}, doi = {10.1109/ACCESS.2025.3552796}, journal = {IEEE Access}, pages = {52758-52774}, shortjournal = {IEEE Access}, title = {DynaOOD-net: Dynamic progressive feature fusion and energy balancing for robust out-of-distribution detection}, volume = {13}, year = {2025}, }
• (2025). Practical quantum circuit implementation for simulating coupled classical oscillators. ACCESS, 13, 52738-52757. (www)

  @article{ , abstract = {Simulating large-scale coupled-oscillator systems presents substantial computational challenges for classical algorithms, particularly when pursuing first-principles analyses in the thermodynamic limit. Motivated by the quantum algorithm framework proposed by Babbush et al. (2023), we present and implement a detailed quantum circuit construction for simulating one-dimensional spring–mass systems. Our approach incorporates key quantum subroutines, including block encoding, quantum singular value transformation (QSVT), and amplitude amplification, to realize the unitary time-evolution operator associated with simulating classical oscillators dynamics. In the uniform mass–spring setting, our circuit construction requires a gate complexity of $\mathcal {O}\bigl (\log _{2}^{2} N\,\log _{2}(1/\varepsilon )\bigr )$ , where N is the number of oscillators and $\varepsilon $ is the target accuracy of the approximation. For more general, heterogeneous mass–spring systems, the total gate complexity is $\mathcal {O}\bigl (N\log _{2} N\,\log _{2}(1/\varepsilon )\bigr )$ . Both settings require $\mathcal {O}(\log _{2} N)$ qubits. Numerical simulations agree with classical solvers across all tested configurations, indicating that this circuit-based Hamiltonian simulation approach can substantially reduce computational costs and potentially enable larger-scale many-body studies on future quantum hardware.}, archive = {J_ACCESS}, author = {Natt Luangsirapornchai and Peeranat Sanglaor and Apimuk Sornsaeng and Stéphane Bressan and Thiparat Chotibut and Kamonluk Suksen and Prabhas Chongstitvatana}, doi = {10.1109/ACCESS.2025.3551308}, journal = {IEEE Access}, pages = {52738-52757}, shortjournal = {IEEE Access}, title = {Practical quantum circuit implementation for simulating coupled classical oscillators}, volume = {13}, year = {2025}, }
• (2025). QRS-trs: Style transfer-based image-to-image translation for carbon stock estimation in quantitative remote sensing. ACCESS, 13, 52726-52737. (www)

  @article{ , abstract = {Forests serve as vital carbon reservoirs, reducing atmospheric CO2 and mitigating climate change. Monitoring carbon stocks typically combines ground-based data with satellite remote sensing, yet accuracy remains a challenge. This study analyzes Huize County, China, using GF-1 WFV and Landsat TM images and introduces the Quantitative Remote Sensing Transformer (QRS-Trs), which leverages style transfer and attention mechanisms to enhance carbon stock estimation as an image-to-image translation task. QRS-Trs demonstrates three advantages: 1) Swin-Pix2Pix effectively reduces inter-domain discrepancies caused by sensor and lighting variations while excelling in de-clouding, outperforming Pix2Pix. 2) It incorporates a median filter to eliminate anomalies and a mask module to exclude non-target areas, achieving MAE =16.29 Mg/ha, RMSE =29.38 Mg/ha, $R^{2} =0.71$ , and SSIM =0.75. 3) Applied to multi-year data, from 2005 to 2020, 44.04% of the area showed increased carbon stock, 10.22% decreased, and 45.74% remained unchanged. While QRS-Trs performs well, its generalization to diverse ecological conditions depends on high-quality training data. Nevertheless, this study provides a robust approach for high-resolution carbon stock estimation, contributing to improved forest carbon sink management.}, archive = {J_ACCESS}, author = {Zhenyu Yu and Jinnian Wang and Hanqing Chen and Mohd Yamani Idna Idris}, doi = {10.1109/ACCESS.2025.3554045}, journal = {IEEE Access}, pages = {52726-52737}, shortjournal = {IEEE Access}, title = {QRS-trs: Style transfer-based image-to-image translation for carbon stock estimation in quantitative remote sensing}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Smarter world living lab as an integrated approach: Learning how to improve quality of life”. ACCESS, 13, 52725. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Smarter World Living Lab as an Integrated Approach: Learning How to Improve Quality of Life”).}, archive = {J_ACCESS}, author = {Suhono Harso Supangkat and Hendra Sandhi Firmansyah and Rezky Kinanda and Irma Rizkia}, doi = {10.1109/ACCESS.2025.3551100}, journal = {IEEE Access}, pages = {52725}, shortjournal = {IEEE Access}, title = {Corrections to “Smarter world living lab as an integrated approach: Learning how to improve quality of life”}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning for optimize coverage in art gallery problem using Q-learning based in grid world. ACCESS, 13, 52711-52724. (www)

  @article{ , abstract = {This paper presents a novel approach to solving the Art Gallery Problem (AGP) using a grid-based system and Reinforcement Learning (RL) in a two-dimensional space. The algorithm converts complex polygons into grids to simplify coverage calculations, allowing for scalable extensions. The method employs a Q-learning agent that interacts with the environment to optimize guard placement by balancing the number of guards and the coverage area. Experiments show that finer grid densities improve accuracy, while parameter adjustments ( $\alpha $ , $\gamma $ , $\varepsilon $ ) significantly impact performance. The algorithm effectively handles varying map complexities, demonstrating robust and efficient coverage solutions.}, archive = {J_ACCESS}, author = {Yuan-Hsun Liao and Po-Chun Chang and Hsiao-Hui Li}, doi = {10.1109/ACCESS.2025.3553036}, journal = {IEEE Access}, pages = {52711-52724}, shortjournal = {IEEE Access}, title = {Reinforcement learning for optimize coverage in art gallery problem using Q-learning based in grid world}, volume = {13}, year = {2025}, }
• (2025). Simul-RL portfolio framework: Black-scholes-merton and reinforcement learning for asset allocation. ACCESS, 13, 52697-52710. (www)

  @article{ , abstract = {Asset allocation method using reinforcement learning is being actively researched. However, the existing asset allocation methods do not consider the following viewpoints in solving the asset allocation problem. First, State design without considering portfolio management and financial market characteristics. Second, Model Overfitting. Third, Model training design without considering the statistical structure of financial time series data. To solve these problems, we propose a new Reinforcement Learning asset allocation method. First, financial market state and agent state. Second, Monte Carlo simulation data are used to increase training data complexity. Third, Monte Carlo simulation data are created considering various statistical structures of financial markets. We show experimentally that our method outperforms the benchmark at several test intervals.}, archive = {J_ACCESS}, author = {Jungyu Ahn and Hyoung-Goo Kang}, doi = {10.1109/ACCESS.2025.3552713}, journal = {IEEE Access}, pages = {52697-52710}, shortjournal = {IEEE Access}, title = {Simul-RL portfolio framework: Black-scholes-merton and reinforcement learning for asset allocation}, volume = {13}, year = {2025}, }
• (2025). A modified MobileNetv3 coupled with inverted residual and channel attention mechanisms for detection of tomato leaf diseases. ACCESS, 13, 52683-52696. (www)

  @article{ , abstract = {An early detection of tomato leaf diseases is crucial for ensuring high-quality and abundant crop production. There is a growing need for an Android application that can identify these diseases and alert farmers to take timely and effective measures, thereby reducing crop yield losses. While deep learning models have been instrumental in detecting plant leaf diseases, they often involve complex models and significant computational demands to achieve optimal performance. This study introduces a feasible lightweight model optimized for an Android application to detect diseased areas within images. This research focuses on enhancing the efficiency and accuracy of tomato leaf disease detection by modifying mobile-based Convolutional Neural Networks (CNNs). This model employs two parallel network streams based on the core principles of MobileNetv3, utilizing inverted residual blocks (IRBs) to improve accuracy at both low and high-level features, operating across different image dimensions. Additionally, inverted residual connections are incorporated to expand the model’s receptive field. To maximize feature utilization, cross-layer connections are introduced between the two parallel streams, integrating the Efficient Channel Attention (ECA) module to reduce the number of parameters. The model is trained using transfer learning with specific adjustments to minimize detection errors and is fine-tuned using a tomato leaf disease image dataset extracted from Plant Village. This feature integration and data analysis scheme are then deployed to provide a tomato leaf disease detection Android app. Evaluation of the model’s performance is conducted through correct predictions and quality metrics, with the average training accuracy for the ten classes found to be 98.77%. The research aims to assess the model’s effectiveness through comprehensive experiments, ultimately contributing to advancements in agricultural applications.}, archive = {J_ACCESS}, author = {Rubina Rashid and Waqar Aslam and Romana Aziz and Ghadah Aldehim}, doi = {10.1109/ACCESS.2025.3550205}, journal = {IEEE Access}, pages = {52683-52696}, shortjournal = {IEEE Access}, title = {A modified MobileNetv3 coupled with inverted residual and channel attention mechanisms for detection of tomato leaf diseases}, volume = {13}, year = {2025}, }
• (2025). Techno-economic and environmental analysis of solar PV system at sher-e-bangla national cricket stadium: A comprehensive case study. ACCESS, 13, 52658-52682. (www)

  @article{ , abstract = {The proposed rooftop solar photovoltaic (PV) system at the Sher-e-Bangla National Cricket Stadium (SBNCS) demonstrates a sustainable energy solution addressing Bangladesh’s energy challenges. The system has a capacity of 83.2 kWp and is estimated to generate 129.5 MWh of energy annually. This deployment reduces reliance on fossil fuels and contributes to global Sustainable Development Goal 7 (SDG7). The performance evaluation reveals a Performance Ratio (PR) of 79.4%, ensuring efficient solar resource utilization. Economically, the project involves a total investment of ${\$}$ 101,031, achieving annual energy cost savings of ${\$}$ 5,370. Financial feasibility metrics include a Net Present Value (NPV) of ${\$}$ 99,131.5, an Internal Rate of Return (IRR) of 6%, and a Payback Period (PBP) of 13 years. Furthermore, the system reduces 50 tons of CO2 emissions annually, resulting in a Social Cost of Carbon (SCC) savings of ${\$}$ 77,064 over its 25-year lifespan. The project’s Levelized Cost of Energy (LCOE) is calculated as ${\$}$ 0.03/kWh, reflecting its long-term cost-effectiveness. This analysis highlights the economic, environmental, and performance benefits of implementing a rooftop solar PV system at SBNCS, offering a scalable model for integrating renewable energy into and other stadiums and large infrastructure. This study can aid in the integration of renewable energy into the grid and assist policymakers in facilitating the future energy storage systems and expanding grid-tied operations for enhanced sustainability.}, archive = {J_ACCESS}, author = {Mohammad Ariful Islam Rafi and Md Sajid Hasan and Imam-Ur-Rashid and Md Manzurul Hasan and Jawadul Alam Chowdhury and Moshiur Rahman Sohan and Nahid A. Jahan and M. Mofazzal Hossain}, doi = {10.1109/ACCESS.2025.3553636}, journal = {IEEE Access}, pages = {52658-52682}, shortjournal = {IEEE Access}, title = {Techno-economic and environmental analysis of solar PV system at sher-e-bangla national cricket stadium: A comprehensive case study}, volume = {13}, year = {2025}, }
• (2025). Modulation recognition algorithm of multipath channel signal for orthogonal frequency division multiplexing. ACCESS, 13, 52644-52657. (www)

  @article{ , abstract = {As wireless communication technology rapidly develops, multipath channel effects pose a severe challenge to the effectiveness of orthogonal frequency division multiplexing systems. The signal distortion caused by multipath channel effects seriously affects the performance of the system. The objective of the research is to design a deep learning-based orthogonal frequency division multiplexing system signal modulation recognition method to improve recognition accuracy in multipath channel environments. The study first uses a subspace imperceptive channel estimation algorithm to perform preliminary processing on the received signal, eliminating the influence of some multipath effects. Then, the minimum mean square error equalization algorithm is used to further balance the signal and reduce signal distortion. Finally, an improved convolutional neural network is used to construct a classifier for modulation recognition of preprocessed signals, thereby establishing an intelligent signal modulation recognition method. The results denoted that the average recognition accuracy of the research algorithm for six modulation modes, namely 16QAM, 32QAM, 64QAM, BPSK, QPSK, and 8PSK, was 0.931, and the recognition accuracy was higher at 10SNR. The testing time was $1.61\times 10^{-4}$ s, and the practicality and modulation recognition accuracy were better than other methods. The research results provide a new solution for signal modulation recognition in orthogonal frequency division multiplexing systems, which can effectively improve the overall performance of communication systems, reduce error rates, and enhance the system’s anti-interference ability.}, archive = {J_ACCESS}, author = {Jinyu Guo}, doi = {10.1109/ACCESS.2025.3551812}, journal = {IEEE Access}, pages = {52644-52657}, shortjournal = {IEEE Access}, title = {Modulation recognition algorithm of multipath channel signal for orthogonal frequency division multiplexing}, volume = {13}, year = {2025}, }
• (2025). Forecasting stock market volatility using housing market indicators: A reinforcement learning-based feature selection approach. ACCESS, 13, 52621-52643. (www)

  @article{ , abstract = {This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. We propose a sophisticated Early Warning System (EWS) designed to forecast stock market instability by leveraging the predictive power of housing market bubbles. Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. To directly address these issues, our innovative approach utilizes a spatial attention-based Transductive Long Short-Term Memory (TLSTM) model combined with a Reinforcement Learning (RL) strategy, which is further enhanced by a novel scope loss function for refined feature selection and an Artificial Bee Colony (ABC) algorithm for hyperparameter optimization. The TLSTM model surpasses traditional LSTM models by effectively capturing subtle temporal shifts and prioritizing data points proximate to the test sample, thereby enhancing model generalization. The RL component actively refines feature selection through continuous data interaction, ensuring the model captures the most significant features and effectively mitigates the risk of overfitting. The introduction of the scope loss function strategically manages the trade-off between exploiting known data and exploring new patterns, thereby maintaining a healthy balance between accuracy and generalizability. Additionally, the customized ABC algorithm specifically optimizes hyperparameters to increase the adaptability and performance of the model under varying market conditions. We validated our EWS using data from the Korean market, achieving an impressive accuracy of 90.427%. This validation demonstrates the robust capability of the system to forecast market dynamics. Our study significantly contributes to financial analytics by providing deeper insights into the interactions between housing and stock markets, particularly during periods of market bubbles. This research not only enhances predictive accuracy but also aids in understanding complex market behaviors, thereby offering valuable tools for financial risk management and decision-making.}, archive = {J_ACCESS}, author = {Pourya Zareeihemat and Samira Mohamadi and Jamal Valipour and Seyed Vahid Moravvej}, doi = {10.1109/ACCESS.2025.3554224}, journal = {IEEE Access}, pages = {52621-52643}, shortjournal = {IEEE Access}, title = {Forecasting stock market volatility using housing market indicators: A reinforcement learning-based feature selection approach}, volume = {13}, year = {2025}, }
• (2025). Early detection via EEG signals of self-initiated reaching and grasping movements performed with the subject’s selected upper extremity. ACCESS, 13, 52607-52620. (www)

  @article{ , abstract = {Recognizing movement intention through electroencephalography (EEG) signals in decision-making tasks remains a challenge for developing systems that translate brain activity into commands for assistive applications. By understanding and incorporating this information, these systems could come closer to replicating human movement’s natural and voluntary behavior. This research analyzed brain activity before self-initiated reach-and-grasp movements performed with a participant-selected upper limb to identify relevant information regarding movement onset and configuration. A classification analysis was then conducted to assess the performance of a prospective assistive system in detecting movement onset, identifying the limb used, and distinguishing the type of movement executed. EEG signals were recorded from 20 healthy participants aged 18–45 in an environment insulated from external noise and visual stimuli to minimize evoked potentials. When evaluating the upper limb used, the analyses indicated relevant information and significant differences in signals acquired from different electrodes, especially in the Alpha and Beta frequency bands. However, these differences diminished when comparing vertical and lateral movements. For movement onset classification, an average accuracy of 82% was achieved, while the limb classification reached 50% accuracy in a three-class scenario. For the classification of movement type, the average accuracy was 45%. These results suggest that detecting movement intention is feasible and decoding movement intentions could contribute to developing more natural and intuitive device-assisted neurorehabilitation therapies.}, archive = {J_ACCESS}, author = {Jorge A. Sanchez-Bautista and Javier M. Antelis and Omar Mendoza-Montoya}, doi = {10.1109/ACCESS.2025.3553554}, journal = {IEEE Access}, pages = {52607-52620}, shortjournal = {IEEE Access}, title = {Early detection via EEG signals of self-initiated reaching and grasping movements performed with the subject’s selected upper extremity}, volume = {13}, year = {2025}, }
• (2025). Dual scalable annealing processing system that scales number of spins and interaction bit width simultaneously. ACCESS, 13, 52592-52606. (www)

  @article{ , abstract = {Annealing processors (APs) specialized for solving combinatorial optimization problems (COPs) have been gaining attention. Among them, APs that use a fully-coupled Ising model are in demand because of their versatility. However, due to the complexity of the spin couplings, challenges remain in achieving systems that balance both capacity (number of spins) and precision (interaction bit width). Previous studies have implemented annealing systems that only increase the number of spins using a scalable structure by distributing the calculation across multiple chips. This study proposes a dual scalable annealing processing system (DSAPS) that can simultaneously scale both capacity and precision using the same scalable structure. This demonstrates a new direction that combines both the number of spins and interaction bit width. A board implementation utilized spin threads to create two types of DSAPS: DSAPS #1, with 2048 spins, 10-bit interactions and four threads, and DSAPS #2, with 1024 spins, 37-bit interactions and two threads. Scaled MAX-CUT problems were used for operational validation, achieving an accuracy of over 99% compared with theoretical values. In another validation using the 0-1 knapsack problem, DSAPS #1 achieved an average deviation of over 99% relative to the knapsack capacity, whereas DSAPS #2 reduced the average deviation to 0.73%. This result is close to the average deviation of 0.40% obtained with an annealing emulation on a CPU (64-bit), demonstrating the significance of implementing DSAPSs suited to the characteristics of COPs.}, archive = {J_ACCESS}, author = {Dong Cui and Taichi Megumi and Akari Endo and Takayuki Kawahara}, doi = {10.1109/ACCESS.2025.3553542}, journal = {IEEE Access}, pages = {52592-52606}, shortjournal = {IEEE Access}, title = {Dual scalable annealing processing system that scales number of spins and interaction bit width simultaneously}, volume = {13}, year = {2025}, }
• (2025). ECG biometrics on mobile devices: High-accuracy authentication using i-vectors and cepstral coefficients. ACCESS, 13, 52572-52591. (www)

  @article{ , abstract = {In recent years, the growing importance of personal data security has led to a rapid increase in demand for biometric authentication systems. This study proposes a biometric authentication method based on electrocardiogram (ECG) data that holds potential for use in mobile devices. The proposed approach integrates Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) features with the i-vector technique to effectively capture the biometric information present in the ECG signal within the 1–35 Hz range. In particular, the complementary effect of GFCC at low frequencies enhances the robustness of the MFCC-based features, thereby providing a more reliable biometric representation. The method was evaluated using the ECG-ID, Heartprint, and a dataset collected specifically for this study. While all datasets were obtained at different time intervals, the Heartprint dataset also includes recordings from multiple sessions. Performance metrics obtained from the Heartprint dataset—namely, 94.39% accuracy, 93.17% true acceptance rate (TAR), and 5.61% false acceptance rate (FAR)—demonstrate that the i-vector-based approach yields results on large datasets that are comparable to those achieved by deep learning and conventional machine learning methods. The use of a brief 5-second ECG signal minimizes memory and processing power requirements, enabling rapid data processing and real-time authentication. The data collected specifically for this study were acquired using the cardiochip BMD101; this sensor offers easy integration with wearable devices such as smartwatches or phone cases, thereby supporting the feasibility of applying the proposed method on mobile platforms. In conclusion, the integration of MFCC and GFCC features with the i-vector technique provides an effective and mobile-compatible solution for ECG-based biometric authentication, while also establishing a solid foundation for future developments in biometric systems.}, archive = {J_ACCESS}, author = {F. Saba Koçkan and Bülent Bolat}, doi = {10.1109/ACCESS.2025.3553423}, journal = {IEEE Access}, pages = {52572-52591}, shortjournal = {IEEE Access}, title = {ECG biometrics on mobile devices: High-accuracy authentication using i-vectors and cepstral coefficients}, volume = {13}, year = {2025}, }
• (2025). Neural network-based genetic algorithm for complex circuit design of high-power vacuum electron device. ACCESS, 13, 52563-52571. (www)

  @article{ , abstract = {The circuits of high-power vacuum electron devices (HPVEDs) typically possess complex topologies that are crucial for efficiently converting electron beam energy to microwave energy. Due to the highly nonlinear beam-wave interactions, designing HPVED circuits generally relies on extensive particle-in-cell (PIC) simulations, making it a computationally intensive task. Especially for circuits with frequency tuning capabilities, the simulation workload is even one to two orders of magnitude higher than that of conventional circuits. To reduce the reliance on PIC simulations, this paper investigates the capability of artificial neural networks (ANNs) for modeling HPVED circuits. Given that the advantageous gene patterns are retained and recombined during the iterations of genetic algorithm, a method for HPVED circuit modeling using process data from the genetic algorithm is designed. This method avoids generating an extensive dataset for ANN pre-training before optimization. Testing on a dataset obtained by a simple genetic algorithm (SGA) shows that the ANN has good modeling capabilities for power, model evaluation, and tuning performance. Accordingly, this paper proposes a neural network-based genetic algorithm (NNGA), which significantly reduces the dependency on PIC simulations during optimization and enhances the efficiency of HPVED circuit optimization design. Preliminary tests on optimization tasks for HPVED circuits with one and two tuning parameters yielded excellent results, achieving tuning bandwidths of over 17% and 20%, respectively. In the tests, NNGA achieved optimization results comparable to SGA with half the simulation workload and better optimization results with the same simulation workload.}, archive = {J_ACCESS}, author = {Dongyang Wang and Yonggang Che and Hongfei Yu and Yan Teng}, doi = {10.1109/ACCESS.2025.3553547}, journal = {IEEE Access}, pages = {52563-52571}, shortjournal = {IEEE Access}, title = {Neural network-based genetic algorithm for complex circuit design of high-power vacuum electron device}, volume = {13}, year = {2025}, }
• (2025). Resilient placement of network functions in a WMN-based NFV infrastructure. ACCESS, 13, 52538-52562. (www)

  @article{ , abstract = {A Wireless Mesh Network (WMN)-based disaster network integrates Network Function Virtualisation (NFV) for dynamic service provisioning by utilising node hardware to establish an NFV infrastructure (NFVI). This NFVI provides two kinds of network functions: virtual network functions (VNFs) to enable required network services and distributed Management and Orchestration (MANO) components to manage and orchestrate the NFVI and its resources. The network infrastructure faces challenges such as node and link failures and end-user movement, potentially leading to network function loss, connectivity issues, or even network partitions and traffic congestion. This publication proposes a resilient placement concept for the network functions required in the WMN-based NFVI, improving their resilience regarding the possible challenges. The concept enforces a geo-distributed redundant deployment of the network functions based on the end-user positions and different connectivity metrics, including node-individual connectivity and the overall network connectivity, including potentially existing network partitions. Simulations using an Integer Linear Programming (ILP)-based optimisation objective show that the concept significantly decreases the likelihood of losing a network function instance to a network partition, reducing this overall probability by up to 30% compared to a performance-based placement approach and by up to 50% compared to a random-based approach. At the same time, it only slightly increases the traffic load in the network, with the average hop-count increasing by just 0.5 compared to performance-based placement.}, archive = {J_ACCESS}, author = {Gregor Frick and Armin Lehmann and Ulrich Trick and Bogdan Ghita}, doi = {10.1109/ACCESS.2025.3553810}, journal = {IEEE Access}, pages = {52538-52562}, shortjournal = {IEEE Access}, title = {Resilient placement of network functions in a WMN-based NFV infrastructure}, volume = {13}, year = {2025}, }
• (2025). Multi-level cell structure for capacitor-less 1T DRAM with SiGe-based separated data storing regions. ACCESS, 13, 52528-52537. (www)

  @article{ , abstract = {One-transistor dynamic random-access memory (1T DRAM) offers significant advantages in fabrication process and scalability over the traditional one-transistor one-capacitor (1T-1C) DRAM due to its simplified structure that eliminates the need for capacitors. However, a limitation arises from its single-bit data storage capability, which necessitates scaling down to improve integration density. In this paper, we propose a multi-level cell structure for 1T DRAM to overcome and improve upon these limitations. Through technology computer-aided design (TCAD) simulations, the memory operation of the proposed device is validated, and it is confirmed that using Si0.8Ge0.2 in the data storing region significantly enhances the sensing margin compared to Si. Additionally, the proposed structure is shown to offer advantages over the conventional structure in terms of current variation.}, archive = {J_ACCESS}, author = {Eungi Hwang and Jang Hyun Kim and Sangwan Kim and Garam Kim}, doi = {10.1109/ACCESS.2025.3553802}, journal = {IEEE Access}, pages = {52528-52537}, shortjournal = {IEEE Access}, title = {Multi-level cell structure for capacitor-less 1T DRAM with SiGe-based separated data storing regions}, volume = {13}, year = {2025}, }
• (2025). FetalMovNet: A novel deep learning model based on attention mechanism for fetal movement classification in US. ACCESS, 13, 52508-52527. (www)

  @article{ , abstract = {Automated classification of fetal movements in ultrasound (US) videos is critical for assessing fetal well-being and detecting potential complications during pregnancy. This study introduces FetalMovNet, a novel deep learning model that incorporates an attention mechanism to improve the classification of fetal movement in US video sequences. The model integrates convolutional neural networks (CNN) for feature extraction and an attention mechanism to capture spatio-temporal patterns, significantly improving classification performance of fetal movements. To evaluate FetalMovNet, we construct a new dataset containing fetal movements in US across seven different anatomical structures-head, body, arm, hand, heart, leg, and foot. Experimental results show that FetalMovNet achieves an accuracy of 0.9887, precision of 0.9871, recall of 0.9910, and an F1-score of 0.9891, outperforming state-of-the-art CNN and CNN-LSTM architectures. Ablation studies confirm the effectiveness of the attention mechanism, with FetalMovNet achieving an area under curve (AUC) score of 0.9957, compared to 0.9471 for CNN and 0.9543 for CNN-LSTM. The proposed FetalMovNet model provides a robust and clinically applicable tool for real-time fetal movement monitoring, reducing the need for manual assessment and improving prenatal care.}, archive = {J_ACCESS}, author = {Musa Turkan and Emre Dandil and Furkan Erturk Urfali and Mehmet Korkmaz}, doi = {10.1109/ACCESS.2025.3553548}, journal = {IEEE Access}, pages = {52508-52527}, shortjournal = {IEEE Access}, title = {FetalMovNet: A novel deep learning model based on attention mechanism for fetal movement classification in US}, volume = {13}, year = {2025}, }
• (2025). Bit-mapping based security-aware energy- efficient MAC protocol for LoRaWAN. ACCESS, 13, 52497-52507. (www)

  @article{ , abstract = {Low energy consumption and high security are two key challenges in achieving efficient data transmission in LoRa-based wireless communication. The present research focuses on enhancing the efficiency and security of data transmission with low energy consumption in wireless sensor networks by introducing security-aware methods and features. A Bit Mapping-based channel allocation method is proposed, distinguishing between critical and non-critical nodes to ensure prioritized communication for time-sensitive data. A comprehensive mathematical model is developed to evaluate the system performance by enabling the analysis of energy consumption at the end nodes, gateways, and overall system levels. In addition, a security-aware feature is integrated into the application layer using Advanced Encryption Standard (AES)-128 and AES-256 encryptions, dynamically adjusting security levels based on the criticality of the sensor node while ensuring minimal impact on energy consumption. The channel access is optimized by prioritizing energy management according to the criticality of the information processed, ensuring energy-efficient and secure communication. The performance of the proposed protocol is analyzed with respect to standard TDMA-based LoRa Medium access Control (MAC) protocols. The result analysis proves the superiority of the proposed protocol.}, archive = {J_ACCESS}, author = {K. Tushar Vikash and Gaurav Suman and Manoj Tolani and Akshay Teja Tumunuri and Pankaj Kumar}, doi = {10.1109/ACCESS.2025.3552842}, journal = {IEEE Access}, pages = {52497-52507}, shortjournal = {IEEE Access}, title = {Bit-mapping based security-aware energy- efficient MAC protocol for LoRaWAN}, volume = {13}, year = {2025}, }
• (2025). Fuzzy rule-based combination model for the fire pixel segmentation. ACCESS, 13, 52478-52496. (www)

  @article{ , abstract = {Color-feature-based wildfire pixel segmentation has become a challenging task extensively addressed in various research studies. Rule-based models aim to identify fire pixels in a binary manner by determining whether the pixel intensity exceeds a specified threshold value. The authors determine the thresholds by analyzing diverse collections of images that contain wildfires. This has resulted in a lack of consensus on the thresholds determined by various researchers, even when the same color space is used during the examination process. Additionally, determining fire pixels in a binary manner complicates the handling of uncertainty and vagueness in color information. This research aims to enhance fire-pixel segmentation by integrating color-based rule models with a fuzzy set approach, which effectively addresses uncertainty and vagueness. The proposed approach automatically learns the optimal set of fuzzy operators and rules for fire detection to construct a combined model. To address the limitations of combining binary class labels, this approach modifies the rule form proposed by various authors to obtain a fuzzy set of data, such as a grayscale fire map, instead of a crisp set of data, such as a binary fire map. In addition, our proposal uses a genetic algorithm approach to construct the best combination model. The final binary form of the fire map is calculated using the widely used Otsu method. The presented method is evaluated qualitatively and quantitatively in a well-accepted dataset designed for wildfire pixel segmentation tasks. The model obtained outperforms state-of-the-art rules and traditional strategies for combining binary labels in the F-measure and IoU metrics.}, archive = {J_ACCESS}, author = {Alberto Lopez-Alanis and Hector de-la-Torre-Gutierrez and Arturo Hernández-Aguirre and María T. Orvañanos-Guerrero}, doi = {10.1109/ACCESS.2025.3554140}, journal = {IEEE Access}, pages = {52478-52496}, shortjournal = {IEEE Access}, title = {Fuzzy rule-based combination model for the fire pixel segmentation}, volume = {13}, year = {2025}, }
• (2025). YOLOv8-AM: YOLOv8 based on effective attention mechanisms for pediatric wrist fracture detection. ACCESS, 13, 52461-52477. (www)

  @article{ , abstract = {Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for surgery based on the analysis of the radiologists. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection as computer-assisted diagnosis (CAD) tools. Ultralytics presented the latest version of the YOLO models in 2023, which has been employed for detecting fractures across various parts of the body. Attention mechanism is one of the most popular methods to improve the model performance. This work proposes YOLOv8-AM, which incorporates the attention modules into the YOLOv8 architecture. Specifically, we respectively employ four different attention modules, ResBlock with Convolutional Block Attention Module (ResCBAM), Shuffle Attention (SA), Efficient Channel Attention (ECA), and ResBlock with Global Attention Mechanism (ResGAM), to improve the model architecture, and train these models on the GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean Average Precision at IoU 50 (mAP@50) of one of the variants of YOLOv8-AM model (i.e., YOLOv8+ResCBAM) increased from 63.6% to 65.8%, which achieves the state-of-the-art (SOTA) performance. In addition, the proposed YOLOv8-AM models can detect the single-label category “fracture” with mAP@50 value of 95.7% in pediatric wrist trauma X-ray images. The implementation code for this work is available on GitHub at https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8.}, archive = {J_ACCESS}, author = {Chun-Tse Chien and Rui-Yang Ju and Kuang-Yi Chou and Enkaer Xieerke and Jen-Shiun Chiang}, doi = {10.1109/ACCESS.2025.3549839}, journal = {IEEE Access}, pages = {52461-52477}, shortjournal = {IEEE Access}, title = {YOLOv8-AM: YOLOv8 based on effective attention mechanisms for pediatric wrist fracture detection}, volume = {13}, year = {2025}, }
• (2025). TONE: A 3-tiered ONtology for emotion analysis. ACCESS, 13, 52446-52460. (www)

  @article{ , abstract = {Emotion ontologies facilitate better communication, support mental health interventions, and advance research in fields such as psychology, artificial intelligence, and social sciences. Existing emotion ontologies often fall short of capturing the complexity and fluidity of human emotions, limiting their effectiveness in varied fields. Major issues include the oversimplification of emotions into rigid categories, inadequate representation of emotional transitions, and cultural biases hindering global applicability. Models like Ekman’s basic emotions or Plutchik’s wheel provide valuable insights but fail to account for the nuanced relationships between emotions, such as how one emotion can trigger or intensify another. To address these limitations, this paper proposes an emotion ontology, TONE, based on Gerrod Parrott’s hierarchical classification of emotions. This classification allows for greater granularity, capturing subtle variations and acknowledging the interrelatedness of emotions using various dependencies. By leveraging Parrott’s model, TONE offers a flexible, culturally adaptable framework that more accurately reflects the dynamic nature of emotional experiences. This ontology is available at https://github.com/srishtigupta253/TONE.git.}, archive = {J_ACCESS}, author = {Srishti Gupta and Piyush Kumar Garg and Sourav Kumar Dandapat}, doi = {10.1109/ACCESS.2025.3553107}, journal = {IEEE Access}, pages = {52446-52460}, shortjournal = {IEEE Access}, title = {TONE: A 3-tiered ONtology for emotion analysis}, volume = {13}, year = {2025}, }
• (2025). Effects of impedance control on haptic perception of object properties: A qualitative and quantitative approach. ACCESS, 13, 52432-52445. (www)

  @article{ , abstract = {Previous research on haptic feedback in virtual reality often relies on single quantitative measures, potentially overlooking the diversity of user perceptions regarding sensory experiences. To address this gap, we conducted user interviews and thematic analysis to uncover how users perceive physical properties when interacting with varying levels of motion impedance, such as changes in inertia, viscosity, and elasticity. Through evaluation experiments using the semantic differential method, we identified four key perceptual dimensions—size, viscosity, weight, and flexibility—related to changes in impedance. Furthermore, we analyzed and examined how each impedance parameter influences these perceptual dimensions. Our findings offer a framework for designing haptic feedback systems that can better capture a variety of user perceptions, leading to more personalized and immersive virtual reality experiences.}, archive = {J_ACCESS}, author = {Takeru Hashimoto and Shigeo Yoshida and Takuji Narumi}, doi = {10.1109/ACCESS.2025.3553042}, journal = {IEEE Access}, pages = {52432-52445}, shortjournal = {IEEE Access}, title = {Effects of impedance control on haptic perception of object properties: A qualitative and quantitative approach}, volume = {13}, year = {2025}, }
• (2025). Dynamic optimization of user and cell association in UAV-assisted THz 6G networks. ACCESS, 13, 52406-52431. (www)

  @article{ , abstract = {The dynamic optimization of user and cell association in UAV-assisted THz 6G networks ensures high reliability, spectral efficiency, and real-time adaptability in high-mobility environments. However, beam misalignment, Doppler effects, and path delay distortions significantly degrade network performance. This paper introduces a novel machine learning-driven framework that integrates advanced optimization techniques for beam alignment, Doppler compensation, and user association. The proposed framework consist of three key algorithms: the Deep Reinforced Hybrid Angle Estimation Algorithm (DRHAEA), which employed deep reinforcement learning and transformer-based beamforming to dynamically adjust azimuth and elevation angles, mitigating beam squint effects and ensuring precise beam alignment; the Adaptive Neural Doppler Refinement Algorithm (ANDRA), which integrates Recurrent Neural Networks (RNN) and Multi-Stage Neural Networks (MSNN) to refine Doppler shift estimation and compensate for path delay distortions, thereby enhancing synchronization even in complex urban environments and the Neurodynamic Channel Tracker Algorithm (NCTA), which utilizes Deep Neural Networks (DNN), RNN, and Graph Neural Networks (GNN) for real-time channel tracking, efficient user clustering, and scalable connectivity. By mitigating beam squint, optimizing Doppler shift compensation, and improving user association, the proposed framework significantly enhanced spectral efficiency, reduced signal distortions, and improved UAV-ground communication reliability. Simulation results demonstrated that adaptive angle estimation and Doppler compensation effectively minimize beam misalignment and synchronization errors, achieving low-latency, high-throughput, and stable UAV communications. The framework achieved near-optimal spectral efficiency at SNR levels above 5 dB, outperforming conventional approaches. Comprehensive benchmarking against IEEE 802.15.3d and 6G performance metrics confirms its superior adaptability in dynamic THz network environments, making it a promising solution for next-generation UAV-assisted THz 6G networks.}, archive = {J_ACCESS}, author = {Workeneh Geleta Negassa and Demissie Jobir Gelmecha and Ram Sewak Singh}, doi = {10.1109/ACCESS.2025.3552539}, journal = {IEEE Access}, pages = {52406-52431}, shortjournal = {IEEE Access}, title = {Dynamic optimization of user and cell association in UAV-assisted THz 6G networks}, volume = {13}, year = {2025}, }
• (2025). Development of an fNIRS-based neurofeedback system to modulate prefrontal asymmetry. ACCESS, 13, 52397-52405. (www)

  @article{ , abstract = {Functional near-infrared spectroscopy (fNIRS) is promising for neurofeedback (NF) applications and warrants further investigation. In this study, we developed an fNIRS-based NF system to modulate the hemodynamic asymmetry of the prefrontal cortex, which may help improve performance under pressure. The system was designed as a compact fNIRS system with two channels located in the prefrontal cortex and controlled using a smartphone. It calculates the dynamic laterality index (LI), indicating frontal asymmetry (a positive LI value when the left side is dominant), which is transformed into a white noise volume mixed with the music stimuli the participant listens to as feedback information. As the LI increases in the positive direction, the volume of the white noise reduces, making the music clearer. With this as the objective, the participants aimed to achieve a left-dominant state in the prefrontal cortex. We evaluated the NF system using a double-masked crossover design with eight participants. Analysis comparing the average LI in the NF and placebo conditions showed that the LI in the NF condition was significantly higher than that in the placebo condition. This finding demonstrates the feasibility of our NF system in modulating prefrontal asymmetry.}, archive = {J_ACCESS}, author = {Ryosuke Hiyama and Tatsuo Hirata and Hiroki Sato}, doi = {10.1109/ACCESS.2025.3552625}, journal = {IEEE Access}, pages = {52397-52405}, shortjournal = {IEEE Access}, title = {Development of an fNIRS-based neurofeedback system to modulate prefrontal asymmetry}, volume = {13}, year = {2025}, }
• (2025). Height-adaptive deformable multi-modal fusion for 3D object detection. ACCESS, 13, 52385-52396. (www)

  @article{ , abstract = {LiDAR-Camera fusion has demonstrated remarkable potential in 3D object detection for autonomous vehicles, leveraging complementary information from both modalities. Recent state-of-the-art approaches primarily make use of projection matrices to achieve cross-modal data alignment. However, these methods often struggle with poor performance when faced with sensor misalignment or calibration errors, resulting in suboptimal fusion quality and limited robustness. In this paper, we propose a novel framework for 3D object detection, called Height-Adaptive Deformable Multi-Modal Fusion, which leverages Deformable Attention to enhance the fusion process. Specifically, we introduce a Deformable-based Cross-Modal Spatial Attention that dynamically fuse image features through learnable offsets, allowing for more flexible and precise alignment between the LiDAR and camera modalities. To further improve the fusion quality, we design a Height-Adaptive Aggregation strategy that mitigates the risk of incorrect fusion from background points while emphasizing the aggregation of foreground object features. In addition, we introduce projection noise to simulate misalign scenarios. To tackle these issues, an extra supervision loss is added. Extensive experiments on the nuScenes benchmark demonstrate the effectiveness and robustness of our proposed framework. Specifically, our methods significantly outperforms the LiDAR-only method and exhibits reduced precision degradation under sensor misalignment, outperforming other fusion-based approaches. Our results validate the potential of proposed framework for improving 3D object detection accuracy, particularly in real-world, imperfect sensor environments.}, archive = {J_ACCESS}, author = {Jiahao Li and Lingshan Chen and Zhen Li}, doi = {10.1109/ACCESS.2025.3553372}, journal = {IEEE Access}, pages = {52385-52396}, shortjournal = {IEEE Access}, title = {Height-adaptive deformable multi-modal fusion for 3D object detection}, volume = {13}, year = {2025}, }
• (2025). IoT-based traffic prediction for smart cities. ACCESS, 13, 52369-52384. (www)

  @article{ , abstract = {This study explores the integration of Convolutional Neural Networks (CNNs) with Particle Swarm Optimization (PSO) to enhance traffic management in smart cities. The primary objective was to develop a predictive model that improves traffic forecasting accuracy, reduces congestion, and optimizes real-time traffic management. The methodology involved combining CNNs’ powerful spatial feature extraction capabilities with PSO’s optimization strengths. Traffic data collected from IoT sensors were processed to extract relevant spatial features using CNNs, and PSO was employed to fine-tune the CNN hyperparameters and feature selection. Key findings indicate that the CNN-PSO model significantly improves traffic prediction accuracy, with an average increase from 76.5% to 92.0% across various urban areas. The model demonstrated a 20.0% reduction in average traffic delay and a 25.0% enhancement in traffic flow efficiency. Real-time metrics revealed high prediction accuracy and low processing times, indicating the model’s effectiveness in timely traffic forecasting. These results underscore the model’s potential for optimizing traffic management systems, reducing congestion, and improving urban mobility. The implications for smart city development are substantial, as the model offers a robust solution for managing complex traffic patterns and enhancing real-time decision-making. The study suggests that integrating advanced machine learning techniques like CNNs and PSO can lead to more efficient and adaptive traffic management solutions, contributing to the development of smarter, more resilient urban environments. Future research could expand on these findings by incorporating additional data sources and testing the model’s scalability and real-world applicability.}, archive = {J_ACCESS}, author = {Zhinong Miao and Qilong Liao}, doi = {10.1109/ACCESS.2025.3552276}, journal = {IEEE Access}, pages = {52369-52384}, shortjournal = {IEEE Access}, title = {IoT-based traffic prediction for smart cities}, volume = {13}, year = {2025}, }
• (2025). Optimized multi-scale detection and numbering of teeth in panoramic radiographs using DentifyNet. ACCESS, 13, 52353-52368. (www)

  @article{ , abstract = {Manual tooth detection and numbering in panoramic radiographs are time-consuming and prone to human errors, negatively impacting diagnostic accuracy and treatment outcomes. These challenges necessitate robust automated solutions to improve efficiency and precision in dental imaging. This study introduces DentifyNet, a novel deep learning architecture designed for automatic tooth detection and numbering in panoramic radiography images. DentifyNet integrates a customized Faster R-CNN framework with Feature Pyramid Networks (FPN), flexible anchor structures, and RoI Align to enhance detection precision. The model was trained and evaluated on 468 panoramic radiographs annotated by dental experts using the FDI numbering system. Experimental results demonstrate that DentifyNet achieved state-of-the-art performance with an Intersection over Union (IoU) of 86.42%, precision of 97.52%, recall of 97.49%, F1-score of 97.51%, and accuracy of 97.50%. The architecture effectively detects challenging cases, such as adjacent similar teeth and missing teeth. These findings suggest that DentifyNet surpasses standard Faster R-CNN architectures, offering a reliable solution for automated tooth detection and numbering. Future research will focus on utilizing broader datasets and architectural advancements to address current limitations and expand clinical applications.}, archive = {J_ACCESS}, author = {Salih Taha Alperen Özçelik and Hüseyіn Üzen and Abdulkadir Sengur and Muammer Türkoğlu and Adalet Çelebi and Nebras M. Sobahi}, doi = {10.1109/ACCESS.2025.3553228}, journal = {IEEE Access}, pages = {52353-52368}, shortjournal = {IEEE Access}, title = {Optimized multi-scale detection and numbering of teeth in panoramic radiographs using DentifyNet}, volume = {13}, year = {2025}, }
• (2025). A systematic review of the comparison of different types of card sorting. ACCESS, 13, 52334-52352. (www)

  @article{ , abstract = {Card sorting is a commonly used technique to extract mental models from users in computer science, especially in the field of Human Computer Interaction (HCI) to structure and arrange the functionality or content of interactive systems. It is a well-liked and reasonably priced method for figuring out customer preferences and designing efficient information architecture. This systematic review aimed to provide a review of the existing literature examining the different card sorting techniques under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Systematic reviews and meta-analyses were sought in electronic databases, encompassing studies that explored the different card sorting techniques. Fifty eligible reviews were identified based on the inclusion criteria, and relevant information was extracted from the review and qualitatively compiled. Findings show a clear understanding of how these card sorting can contribute to different classifications based on their individual characteristics or their strengths. The paper ends with the mention of a few problems identified from the systematic review so that researchers in this field may pick these open problems for future research and hence contribute to the growing literature on card sorting.}, archive = {J_ACCESS}, author = {Elinda Tchivi and Bibhya Sharma and Sione Paea}, doi = {10.1109/ACCESS.2025.3552949}, journal = {IEEE Access}, pages = {52334-52352}, shortjournal = {IEEE Access}, title = {A systematic review of the comparison of different types of card sorting}, volume = {13}, year = {2025}, }
• (2025). Smartphone enabled wearable diabetes monitoring system. ACCESS, 13, 52324-52333. (www)

  @article{ , abstract = {Glucose is a vital biomarker involved in numerous physiological and pathological processes, making its detection a key factor in diabetes monitoring. To enhance convenience, real-time tracking, comfort, and accuracy, glucose measurement techniques are rapidly advancing towards smartphone integrated wearable, non-invasive, and low power systems. In this work, a diabetes monitoring system is developed which can be used as a wearable health tracker. The device offers dual ways of monitoring glucose levels in diabetic patients; first by glucose measurement via blood using a screen-printed electrode sensor (SPES), second by volatile organic compounds (VOCs) measurement via breath analysis using an ethanol sensor. Additionally, an oximetry sensor is included in the system to monitor important human vitals such as heart rate and blood oxygen saturation (SpO2), similar to commercial health trackers. The system is fabricated on a printed circuit board and its size is 3 cm $\times 3$ cm which is suitable as a wearable device. It also consumes low power and has capabilities of wired/wireless charging and wired/wireless communication. Furthermore, customized Windows and Android applications have been developed to visualize the obtained data in user’s computer or smartphone. The conducted experiments aimed at validating the performance of each sensors have yielded promising results. These findings indicate that the developed system has significant potential to be utilized as a point-of-care testing device for real-time diabetes monitoring.}, archive = {J_ACCESS}, author = {Shanthala Lakshminarayana and Mrudula Ranganatha and Yuze Sun and Hyusim Park}, doi = {10.1109/ACCESS.2025.3552731}, journal = {IEEE Access}, pages = {52324-52333}, shortjournal = {IEEE Access}, title = {Smartphone enabled wearable diabetes monitoring system}, volume = {13}, year = {2025}, }
• (2025). A fast and accurate inertia identification approach for dual three-phase PMSM drives. ACCESS, 13, 52308-52323. (www)

  @article{ , abstract = {There is a contradictory relationship between the convergence speed and the steady-state accuracy of the conventional model reference adaptive system-based (MRAS-based) inertia identification method. To solve this issue, this paper proposes a fast and accurate inertia identification approach for dual three-phase permanent magnet synchronous machine (DTP-PMSM). The conventional MRAS-based inertia identification method for DTP-PMSM is given, and the detailed analysis points out that the selection of its gain coefficient requires a tradeoff between the convergence speed and the steady-state accuracy. An error-adaptive gain coefficient based on the integral increment of the mechanical angular speed error is proposed, which resolves the contradictory relationship between the convergence speed and the steady-state accuracy. Then the effects of switching coefficient, period of the error integral, and amplification coefficient on the inertia identification results are analyzed thoroughly, based on which the coefficient selection process of the proposed approach is provided. Furthermore, the block diagram of the DTP-PMSM drives with the proposed inertia identification is presented. Finally, the feasibility and effectiveness of the proposed approach are verified through a prototype DTP-PMSM experimental platform.}, archive = {J_ACCESS}, author = {Li Liu and Yashan Hu and Xu Liu and Faxuan Chen and Xin Guo}, doi = {10.1109/ACCESS.2025.3552788}, journal = {IEEE Access}, pages = {52308-52323}, shortjournal = {IEEE Access}, title = {A fast and accurate inertia identification approach for dual three-phase PMSM drives}, volume = {13}, year = {2025}, }
• (2025). Extended kalman filter-based state estimation and adaptive control of cable-driven parallel robots. ACCESS, 13, 52284-52307. (www)

  @article{ , abstract = {Cable-Driven Parallel Robots (CDPRs) are used in ever-changing, unstructured, and long-term autonomous operations; however, they require precise component assembly to achieve high positioning accuracy. This article presents an adaptive control framework for CDPRs that addresses actuator position uncertainty in all types of CDPRs without relying on vision-based sensing. The core concept of the developed adaptive control scheme involves employing an Extended Kalman Filter (EKF) to estimate system states, including the uncertain actuator positions and the end-effector pose, and replacing the uncertain parameters in the feedback controller with their estimates. Monte Carlo Simulations (MCSs) are also conducted to evaluate the robustness and stability of the proposed estimation method under the anchor point uncertainties. Moreover, the proposed controller incorporates a robust term to compensate for the unmodeled dynamics and payload changes. The results demonstrate that the adaptive control design effectively reduces the actuator position uncertainty, enhances the end-effector positioning accuracy, and successfully compensates for the payload changes. The performance comparisons of the proposed adaptive controller over its non-adaptive counterpart and PID controller highlight its superior performance in managing the anchor point uncertainties and adapting to the payload changes.}, archive = {J_ACCESS}, author = {Gokhan Gungor and Mitchell Rushton and Baris Fidan and William Melek}, doi = {10.1109/ACCESS.2025.3553592}, journal = {IEEE Access}, pages = {52284-52307}, shortjournal = {IEEE Access}, title = {Extended kalman filter-based state estimation and adaptive control of cable-driven parallel robots}, volume = {13}, year = {2025}, }
• (2025). A novel color difference-based method for palette extraction and evaluation using images of birds. ACCESS, 13, 52270-52283. (www)

  @article{ , abstract = {Color palettes are important sources of inspiration for designers. In most coloring studies, designers and interior architects use ready-made color palettes that are known to be harmonious and popular with clients. In this study, a new color palette extraction and evaluation method is proposed that differs from existing methods because it accelerates designers’ product coloring processes by identifying the color-area and color-neighborhood relationship. Furthermore, attractive color palettes will be identified by focusing on living creatures, primarily colorful birds. For this purpose, a new data set of images of birds was created. In the literature, the success of color palettes is usually evaluated through questionnaires, while measurements are made without relying on numerical metrics. In this study and, for the first time, a new metric based on CIEDE2000 and the Hungarian algorithm to measure the accuracy of color palettes is demonstrated. In the proposed method birds are first segmented from background using Mask R-CNN and PointRend models. Then, Fuzzy c-means (FCM), Gaussian Mixture Model (GMM), k-means, Mean Shift, and mini batch k-means algorithms were used to extract the color palette of each bird image. Color reduction based on the CIEDE2000 was performed on the color palettes using various threshold values, resulting in the final color palettes. In the last step, color-area and color-neighborhood relationships are shown through graph-based color palettes. Experimental results showed that the PointRend method, with a 93.92% success rate, produced the most successful results. However, k-means and GMM methods were more successful in extracting the colors of birds. The evaluation of the palettes was performed with the proposed numerical metric rather than questionnaires, resulting in more robust and repeatable findings. Furthermore, the proposed graph-based color palette results in considerably simplified coloring process by reducing the computational complexity for an industrial product.}, archive = {J_ACCESS}, author = {Melike Bektas Kösesoy and Seçkin Yilmaz}, doi = {10.1109/ACCESS.2025.3553252}, journal = {IEEE Access}, pages = {52270-52283}, shortjournal = {IEEE Access}, title = {A novel color difference-based method for palette extraction and evaluation using images of birds}, volume = {13}, year = {2025}, }
• (2025). Aberration effects of planar layers in near-field multistatic millimeter-wave imaging. ACCESS, 13, 52260-52269. (www)

  @article{ , abstract = {This paper presents the analysis on the effects of aberration induced by planar layers in the near-field millimeter-wave imaging. A point spread function (PSF) simulator, based on the matched filtering method combined with ray tracing technique, is introduced to quantitatively investigate the effect of aberrations caused by multiple planar layers in between a multistatic aperture and an imaging target. Furthermore, based on the simulation results, we provide the relation between the thickness and refractive indices of aberrating layers and the positional error and brightness reduction in a reconstructed image. The presented simulator and theoretical analysis would provide a basis for further research on the aberration effects in millimeter-wave imaging for objects concealed within a thick and heterogenous medium.}, archive = {J_ACCESS}, author = {Hakseok Ko and Mooseok Jang}, doi = {10.1109/ACCESS.2025.3553177}, journal = {IEEE Access}, pages = {52260-52269}, shortjournal = {IEEE Access}, title = {Aberration effects of planar layers in near-field multistatic millimeter-wave imaging}, volume = {13}, year = {2025}, }
• (2025). Identification of spambots and fake followers on social network via interpretable AI-based machine learning. ACCESS, 13, 52246-52259. (www)

  @article{ , abstract = {Social networking platforms like X (Twitter) serve as hubs for open human interaction, but they are also increasingly infiltrated by automated accounts masquerading as human users. These bots often engage in activities such as spreading fake news and manipulating public opinion during politically sensitive times like elections. Most of the current bot detection methods rely on black-box algorithms, raising concerns about their transparency and practical usability. This study aims to address these limitations by developing a novel methodology for the detection of spambots and fake followers using annotated data. To this end, we propose an interpretable machine learning (ML) framework, leveraging multiple ML algorithms with hyperparameters optimized through cross-validation, to enhance the detection process. Furthermore, we analyze several features and provide a unique feature set that is optimized to offer excellent performance for bot detection. Moreover, we utilize multiple interpretable AI techniques which include Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). SHAP will help to display the effects of particular characteristics on the model’s prediction which will help in determining whether an account is a bot or a legitimate user. LIME will help to comprehend the model’s predictions, offering clarity regarding the traits or attributes that drive the classification conclusion. LIME allows researchers to detect bot-like activity in social networks by generating locally faithful explanations for each prediction. Our model offers enhanced interpretability by clearly highlighting the impact of various features used for spam and fake follower detection when compared to existing state-of-the-art social network bot detection systems. The results showcase the model’s ability to identify key distinguishing attributes between bots and legitimate users which offers a transparent and effective solution for social network bot detection. Additionally, we utilize two comprehensive datasets including Cresci-15 and Cresci-17, which serve as robust baselines for comparison. Our model showcases its effectiveness by outperforming other methods while providing interpretability which increases performance and reliability for the task of bot detection.}, archive = {J_ACCESS}, author = {Danish Javed and Noor Zaman Jhanjhi and Navid Ali Khan and Sayan Kumar Ray and Arafat Al-Dhaqm and Victor R. Kebande}, doi = {10.1109/ACCESS.2025.3551993}, journal = {IEEE Access}, pages = {52246-52259}, shortjournal = {IEEE Access}, title = {Identification of spambots and fake followers on social network via interpretable AI-based machine learning}, volume = {13}, year = {2025}, }
• (2025). Data-driven motion planning: A survey on deep neural networks, reinforcement learning, and large language model approaches. ACCESS, 13, 52195-52245. (www)

  @article{ , abstract = {Motion planning is a fundamental challenge in robotics, involving the creation of trajectories from start to goal states while meeting constraints like collision avoidance and joint limits. Its complexity increases with the number of robot joints. Several traditional approaches tackle this problem, such as sampling motion planning, grid-based methods, potential fields, and optimization techniques. Recent advancements in deep neural networks, reinforcement learning, and large language models enable new possibilities for solving motion planning problems by improving sampling efficiency, optimizing control policies, and enabling task planning through natural language prompts. This survey comprehensively reviews these novel approaches, providing background knowledge, analyzing key contributions, and identifying common patterns, limitations, and research gaps. Our work is the first to integrate all three major data-driven approaches, discussing their applications and future research directions.}, archive = {J_ACCESS}, author = {Gabriel Peixoto de Carvalho and Tetsuya Sawanobori and Takato Horii}, doi = {10.1109/ACCESS.2025.3552225}, journal = {IEEE Access}, pages = {52195-52245}, shortjournal = {IEEE Access}, title = {Data-driven motion planning: A survey on deep neural networks, reinforcement learning, and large language model approaches}, volume = {13}, year = {2025}, }
• (2025). Towards a new energy reserves market: The role of EV aggregators. ACCESS, 13, 52176-52194. (www)

  @article{ , abstract = {The efficient participation of storage and demand aggregators in electricity markets is hindered by a misalignment between the markets’ bidding language and these new players’ technical characteristics. Specifically, we focus on day-ahead (DA) provision of frequency restoration reserves, where each market participant is asked to declare its reserve capacity for each timeslot of the following day. However, flexible assets bear cross-temporal characteristics (namely, limited energy) that cannot be communicated within the market’s existing bidding language and may leave such assets unable to deliver their allocated reserves. To this end, this paper adopts a novel reserve product, termed Energy Reserves, which effectively addresses such service availability issues. Specifically, in this paper, we elaborate on how this product would enable Electric Vehicle (EV) aggregators to accurately communicate their flexibility capabilities to System Operators (SOs). We present the relevant mathematical formulations and illustrative examples of how the new framework would function. Furthermore, we present a Six-Criteria filtering method, through which the SO can pre-select which aggregators will be asked to contribute, so that the SO’s administrative burden is drastically reduced. Our simulations demonstrate that this reduction in the communication overhead comes without an analogous reduction in the dispatch efficiency. Specifically, the 25% of top-ranked EV aggregators can contribute, on average, to about 80% of selected, real imbalance scenarios.}, archive = {J_ACCESS}, author = {Christoforos Menos-Aikateriniadis and Iason-Iraklis Avramidis and Pavlos S. Georgilakis and Isidoros Kokos and Georgios Tsaousoglou}, doi = {10.1109/ACCESS.2025.3552838}, journal = {IEEE Access}, pages = {52176-52194}, shortjournal = {IEEE Access}, title = {Towards a new energy reserves market: The role of EV aggregators}, volume = {13}, year = {2025}, }
• (2025). Amodal instance segmentation for mealworm growth monitoring using synthetic training images. ACCESS, 13, 52157-52175. (www)

  @article{ , abstract = {Automatic dimensioning of mealworms based on computer vision is challenging due to occlusions. Amodal instance segmentation (AIS) could be a viable solution, but the acquisition of annotated training data is difficult and time-consuming. This work proposes a new method to prepare data for training AIS models that reduces the human annotation effort significantly. Instead of acquiring the occluded images directly, only images of fully visible larvae are acquired and processed, allowing obtaining their contours via automatic segmentation. Next, synthetic images with occlusions are generated from the database of automatically extracted instances. The generation procedure uses simple computer graphics tools and is computationally inexpensive, yet yields images that allow training off-the-shelf AIS models. Since those models need to be tested on real data, which requires manual annotation, a data acquisition method that significantly simplifies the test set annotation process is demonstrated. Results are reported in terms of the amodal segmentation quality as well as the accuracy of larvae dimensioning, measured using the histogram intersection metric. The best-performing model achieves a mean average precision of 0.41 and a histogram intersection of 0.77, confirming the effectiveness of the proposed method of data acquisition and generation. The method is not specific to mealworm detection and could be applied to other similar problems where object occlusions pose a challenge.}, archive = {J_ACCESS}, author = {Przemysław Dolata and Paweł Majewski and Piotr Lampa and Maciej Ziȩba and Jacek Reiner}, doi = {10.1109/ACCESS.2025.3550780}, journal = {IEEE Access}, pages = {52157-52175}, shortjournal = {IEEE Access}, title = {Amodal instance segmentation for mealworm growth monitoring using synthetic training images}, volume = {13}, year = {2025}, }
• (2025). Depression and anxiety screening for pregnant women via free conversational speech in naturalistic condition. ACCESS, 13, 52149-52156. (www)

  @article{ , abstract = {Depression and anxiety during pregnancy are critical public health concerns, impacting both maternal and fetal well-being. The onset of these mental health conditions during pregnancy can lead to adverse outcomes, such as preterm birth and impaired infant development. Traditional diagnostic and monitoring methods, reliant on self-reporting and clinical evaluations, are often hindered by stigmatization, subjective bias, and accessibility issues, leading to underdiagnosis and inadequate management of these conditions. This study addresses the challenge by making use of free conversational speech recordings via smartphones as an approach for screening depression and anxiety in pregnant women. By leveraging machine learning techniques to analyze speech patterns, this research seeks to offer a non-invasive, objective, and accessible method for early detection and intervention. The research involved collecting conversational speech samples from pregnant women attending a high-risk pregnancy outpatient service, employing smartphones to capture naturalistic speech in everyday contexts. Machine learning algorithms were utilized combined with different audio feature sets to analyze these recordings in conjunction with PHQ-4 questionnaire scores. The combination of Mel Frequency Cepstral Coefficients and OpenSmile feature sets analyzed through LightGBM classifiers emerged as the most effective, achieving an accuracy of 75% and an Area Under the Curve of 0.71. These findings highlight the potential of smartphone-based speech analysis as a viable screening tool for mental health in pregnant women, suggesting a significant shift towards accessible, real-time monitoring. This technological intervention could enhance maternal health by enabling early detection and timely care, ultimately improving pregnancy outcomes and child development.}, archive = {J_ACCESS}, author = {Rafael T. Sousa and Gustavo D. M. Silva and Paula L. L. Pinto and Juliano Backes and Amanda S. Mota and Thiago M. Paixão and Maria G. S. Teixeira and Wilian H. Hisatugu and Anilton S. Garcia and Kelly S. Prado and Rodrigo S. Dias and Marco A. Galletta and Hermano Tavares}, doi = {10.1109/ACCESS.2025.3552659}, journal = {IEEE Access}, pages = {52149-52156}, shortjournal = {IEEE Access}, title = {Depression and anxiety screening for pregnant women via free conversational speech in naturalistic condition}, volume = {13}, year = {2025}, }
• (2025). Handwritten amharic character recognition through transfer learning: Integrating CNN models and machine learning classifiers. ACCESS, 13, 52134-52148. (www)

  @article{ , abstract = {Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance recognition accuracy. Transfer learning is applied using four CNN architectures: AlexNet, VGG16, VGG19, and ResNet50 as feature extractors. Initially, their performance is evaluated with softmax classifiers. Subsequently, the softmax layer is replaced with machine learning classifiers, including Random Forest, XGBoost, and Support Vector Machine (SVM), while freezing the pretrained feature extractors. The Hybrid ResNet50 + SVM model achieves the highest accuracy of 91.89%, with a precision of 92.46%, recall of 91.15%, and an F1-score of 91.80%. These results indicate that SVM serves as a potential alternative to softmax, offering robust classification performance for complex handwritten scripts. This research contributes to advancements in handwritten character recognition systems for underrepresented languages.}, archive = {J_ACCESS}, author = {Natenaile Asmamaw Shiferaw and Zefree Lazarus Mayaluri and Prabodh Kumar Sahoo and Ganapati Panda and Prince Jain and Adyasha Rath and Md. Shabiul Islam and Mohammad Tariqul Islam}, doi = {10.1109/ACCESS.2025.3553199}, journal = {IEEE Access}, pages = {52134-52148}, shortjournal = {IEEE Access}, title = {Handwritten amharic character recognition through transfer learning: Integrating CNN models and machine learning classifiers}, volume = {13}, year = {2025}, }
• (2025). Sustainability-based strategic framework for digital transformation in the oil and gas industry. ACCESS, 13, 52114-52133. (www)

  @article{ , abstract = {The oil and gas industry is transforming profoundly, driven by integrating digital technologies to enhance sustainability, operational efficiency, and competitiveness. However, the sector continues to tackle persistent challenges, including fluctuating oil prices, the demand for a stable fuel supply, and increasingly stringent environmental regulations. This paper introduces a sustainability-centered strategic framework for digital transformation within the sector, showcasing the deployment of cutting-edge technologies like data analytics, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT). These advancements address key industry challenges, including operational inefficiencies, safety hazards, and the pressing need to reduce carbon emissions. The research examines the hurdles faced by hydrocarbon-dependent countries, focusing on Qatar’s oil and gas sector, in adopting digital transformation. Through a qualitative case study method involving semi-structured interviews with industry experts, the study identifies critical barriers to digital transformation, organizational resistance to change, a shortage of skilled workers, cybersecurity concerns, and the complexities of integrating new technologies with legacy systems. The findings underscore the pivotal role of digital technologies in improving efficiency, safety, and sustainability, and they highlight the importance of strong leadership and workforce development. The proposed sustainability-based framework provides actionable insights for overcoming these barriers, offering a holistic approach to fostering innovation, enhancing resilience, and aligning digital initiatives with sustainable and business objectives. These results offer valuable guidance for industry leaders and policymakers navigating the complexities of digital transformation in a competitive and sustainable conscious global market.}, archive = {J_ACCESS}, author = {Abdulla Al-Hajri and Abdel Magid Hamouda and Galal M. Abdella}, doi = {10.1109/ACCESS.2025.3548960}, journal = {IEEE Access}, pages = {52114-52133}, shortjournal = {IEEE Access}, title = {Sustainability-based strategic framework for digital transformation in the oil and gas industry}, volume = {13}, year = {2025}, }
• (2025). Long trip charging planning of battery electric vehicle considering vehicle waiting time forecast at fast charging stations: A mixed-integer dynamic programming approach. ACCESS, 13, 52100-52113. (www)

  @article{ , abstract = {Battery electric vehicles have shared concerns such as range anxiety and long charging times compared to conventional vehicles. To consider an electric vehicle as a replacement for a conventional vehicle, it is essential to reduce the cost of ownership and encourage widespread adoption without range concerns. It is appropriate that charging planning should be utilized to tackle such problems. It can also be helpful to have information from charging planning services to apprise incoming vehicles for charging to prepare their trip itinerary. For long-distance road trips, choosing appropriate charging stations can be instrumental in achieving the optimization goal. Selecting the right charging station can affect the total trip time. To optimize charging planning, we utilize mixed-integer dynamic programming to achieve the global optimal solution. The information on vehicle waiting time is used and parameterized from open-source US household travel survey information. Probability distributions of daily trips, daily mileage, and probability distribution of arrival vehicle SOC can be derived. Monte Carlo simulation generates the waiting time at the charging station based on the probability distributions and station configuration. The presented optimal charging plan method is validated by multiple test cases. The test cases are designed based on US Alternate Fuel Corridors utilizing interstate highways. For the selected test scenarios up to 9.6% of time saving is observed when there is no waiting time at the charging stations. The validation tests with waiting time encountered at the charging station, time saving of up to 15.1% is achieved with energy level closer to the minimum limit at the end of the trip. This charging planning method significantly enhances the viability of EVs for long-distance travel by addressing core concerns like range anxiety and lengthy trip time.}, archive = {J_ACCESS}, author = {Pradeep Krishna Bhat and Zhouquan Wu and Bo Chen}, doi = {10.1109/ACCESS.2025.3552730}, journal = {IEEE Access}, pages = {52100-52113}, shortjournal = {IEEE Access}, title = {Long trip charging planning of battery electric vehicle considering vehicle waiting time forecast at fast charging stations: A mixed-integer dynamic programming approach}, volume = {13}, year = {2025}, }
• (2025). A novel multimodal deep learning approach with loss function for detection of sleep apnea events. ACCESS, 13, 52085-52099. (www)

  @article{ , abstract = {Sleep apnea, a common and potentially serious sleep disorder, is characterized by repeated pauses in breathing during sleep. These pauses, known as apneas, can last from a few seconds to minutes and may occur multiple times per hour. This disorder poses significant health risks, including increased likelihoods of developing cardiovascular complications such as hypertension, heart disease, and stroke. Detecting sleep apnea accurately and efficiently presents several challenges, including variability in physiological signals among individuals and class imbalance for apnea events. In this study, we propose a novel deep-learning approach for automatic sleep apnea detection using physiological signals. Our method utilizes a convolutional neural network (CNN) with gated recurrent units (GRU) and an attention mechanism to capture both spatial and temporal information. We introduce a multi-domain feature extractor (MDFE) block with a Squeeze and Excitation (SE) block for enhanced feature representation and channel-wise attention. To promote model diversity and robustness, we further introduce a novel loss function that encourages the network’s key modules to learn disparate feature representations. This approach is trained and validated on the Sleep Heart Health Study (SHHS) dataset, achieving high accuracy and robustness (AUC: 91.94, Accuracy: 83.87). We used the K-fold cross-validation technique to show the effectiveness of our method, suggesting its potential for real-time monitoring and personalized treatment strategies in sleep disorders.}, archive = {J_ACCESS}, author = {Alireza Fakhim Babaei and Jafar Tanha and Mohammad Ali Balafar and Seyedehsan Roshan}, doi = {10.1109/ACCESS.2025.3552254}, journal = {IEEE Access}, pages = {52085-52099}, shortjournal = {IEEE Access}, title = {A novel multimodal deep learning approach with loss function for detection of sleep apnea events}, volume = {13}, year = {2025}, }
• (2025). Fed-DPSDG-WGAN: Differentially private synthetic data generation for loan default prediction via federated wasserstein GAN. ACCESS, 13, 52069-52084. (www)

  @article{ , abstract = {Lenders typically conduct thorough credit checks to mitigate credit default risk before lending money. Proactively predicting loan defaulters has become increasingly important. However, creating robust deep-learning algorithms that classify loan defaulters requires abundant data, potentially compromising individuals’ privacy. We investigate the potential of replacing the original data with statistically similar synthetic data to be used as training data to predict loan defaulters. Generative adversarial networks stand out with the best privacy utility trade-off among the many approaches that can generate synthetic data. However, limited data and membership inference attacks still significantly affect the privacy of the synthetic data generated. This paper uses federated learning to create synthetic data to improve loan defaulter prediction. By allowing multiple institutions to collaboratively train a shared model without directly exchanging sensitive loan data, this privacy-preserving approach allows for the utilization of more extensive and diverse datasets, potentially leading to significant improvements in prediction accuracy compared to traditional, siloed approaches. This method also builds a layer of privacy to safeguard against membership inference attacks using a differentially private optimizer layer in the WGAN architecture. The proposed framework utilizes a Min-Max Module and Autoencoders for global representation of distributed loan data, followed by a Federated WGAN, enhanced with a Gaussian noise-added Adagrad optimizer, to generate high-fidelity, privacy-preserved synthetic credit risk data. Furthermore, we use CTGAN to mitigate class imbalance in the synthetic dataset. Finally, we test the generated synthetic dataset using a model trained on the real dataset to check its utility. With this novel framework, we could attain a privacy-utility tradeoff in a federated environment. Our extensive experimentation, which includes a variety of communication rounds, client populations, and privacy budgets, demonstrates that the proposed framework achieves an impressive 95% accuracy. The proposed model marks a significant improvement over both a non-federated setup, which achieves 82% accuracy, and FLIGAN with 73% accuracy, which possesses the same functionalities with different techniques.}, archive = {J_ACCESS}, author = {Padmaja Ramachandra and Santhi Vaithiyanathan}, doi = {10.1109/ACCESS.2025.3552487}, journal = {IEEE Access}, pages = {52069-52084}, shortjournal = {IEEE Access}, title = {Fed-DPSDG-WGAN: Differentially private synthetic data generation for loan default prediction via federated wasserstein GAN}, volume = {13}, year = {2025}, }
• (2025). A novel similarity score for link prediction approach using financial transaction networks and firms’ attribute. ACCESS, 13, 52051-52068. (www)

  @article{ , abstract = {Financial transaction networks represent inter-firm relationships, where firms and transactions act as nodes and edges, respectively. Link prediction in these networks aims to identify potential future or missing transactions or links, providing valuable insights for decision-making and market analysis. While several link prediction studies exist for general networks, limited research has specifically addressed the unique characteristics of financial transaction networks. Existing studies often overlook important features such as the direction of transactions between firms, the hierarchical nature of transaction networks, and the significance of node attributes, thereby hindering accurate link prediction. In this study, we propose a novel similarity score, the “Attribute-Transaction Similarity (ATS) Score,” for link prediction in financial transaction networks. The ATS Score integrates both transaction network topology and firm attributes, such as the Standard Industrial Classification (SIC) codes, to predict unobserved links between firms. Our method not only forecasts future transactions but also preserves the hierarchical structure of transaction networks. By leveraging both network topology and firm attribute frequencies, our method results in more accurate and reliable predictions. Experimental evaluations on real-world financial transaction network datasets demonstrate that the ATS Score-based link prediction method outperforms existing similarity-based link prediction techniques, achieving superior results in terms of the area under the receiver operating characteristic curve (AUC). This highlights the effectiveness of the ATS Score in capturing the intricate relationships and dynamics of financial transaction networks.}, archive = {J_ACCESS}, author = {Aparajita Bose and Byunghoon Kim}, doi = {10.1109/ACCESS.2025.3553795}, journal = {IEEE Access}, pages = {52051-52068}, shortjournal = {IEEE Access}, title = {A novel similarity score for link prediction approach using financial transaction networks and firms’ attribute}, volume = {13}, year = {2025}, }
• (2025). Nonlinear control of brushless dual-fed induction generator with a flywheel energy storage system for improved system performance. ACCESS, 13, 52036-52050. (www)

  @article{ , abstract = {Managing fluctuations in wind energy to ensure a stable and reliable energy supply remains a significant challenge in the operation of wind generators. Various solutions have been developed to address this problem and mitigate the impact of wind energy fluctuations, such as the deployment of Flywheel Energy Storage Systems (FESSs). This study focuses on developing an advanced control strategy for a Brushless Dual-Fed Induction Generator (BDFIG) integrated with a Nonlinear Energy Storage System. In the first stage, a robust Sliding Mode Control (SMC)-based nonlinear decoupled control algorithm is designed to efficiently regulate BDFIG operation. The system is further enhanced with Optimal Torque Control (OTC) and Maximum Power Point Tracking (MPPT) to maximize wind energy extraction. In the second stage, a Synergetic Control (SC) strategy is implemented for FESS management, integrated seamlessly into the overall system. SC is selected for its ability to handle parametric and nonparametric uncertainties, offering a robust solution that ensures fast response times and asymptotic stability across varying operating conditions. To optimize parameter tuning within the control system, the Grasshopper Optimization Algorithm (GOA) is applied, ensuring optimal performance of the proposed framework. After optimization, the SMC settling time was significantly reduced from 0.7 seconds to 19.97 milliseconds, achieving a 96.9% improvement in response speed, while its steady-state error decreased from 0.48 to 0.06, marking an 87.5% reduction in tracking error. Similarly, the SC settling time improved from 0.85 seconds to 0.3 seconds, resulting in a 64.7% faster response, and its steady-state error was minimized from 0.044 to 0.03, enhancing accuracy by 31.8%. These enhancements contributed to a rapid dynamic response, reduced tracking error, and improved overall system stability under variable wind conditions. The proposed system was extensively simulated on MATLAB/Simulink under various wind generator operating conditions. These findings confirm the robustness and effectiveness of the proposed methodologies in delivering reliable and efficient energy management under different operating conditions.}, archive = {J_ACCESS}, author = {Mohammed Hamidat and Ahmed M. Massoud and Katia Kouzi}, doi = {10.1109/ACCESS.2025.3552703}, journal = {IEEE Access}, pages = {52036-52050}, shortjournal = {IEEE Access}, title = {Nonlinear control of brushless dual-fed induction generator with a flywheel energy storage system for improved system performance}, volume = {13}, year = {2025}, }
• (2025). Cyber-biosecurity challenges in next-generation sequencing: A comprehensive analysis of emerging threat vectors. ACCESS, 13, 52006-52035. (www)

  @article{ , abstract = {Next-generation sequencing (NGS) has transformed genomic research and healthcare by enabling the rapid and cost-effective sequencing of DNA and RNA, surpassing traditional techniques such as Sanger sequencing. This technological leap has had a profound impact on fields including biomedical research, personalised medicine, cancer genomics, agriculture, and forensic sciences. With its widespread adoption, NGS has made genomic information more accessible, facilitating the sequencing of millions of genomes. However, the growing reliance on NGS has also brought significant challenges related to cyber-biosecurity, particularly the protection of genomic data against cyber threats such as unauthorised access, data breaches, and exploitation. Genomic data is inherently sensitive, and vulnerabilities in NGS technologies, software, data-sharing practices, and open-access databases expose it to risks concerning data confidentiality, integrity, and privacy. While NGS data plays an indispensable role across numerous sectors, research addressing the cyber-biosecurity of these technologies remains fragmented. Most existing studies focus narrowly on specific areas, such as microbial sequencing or system architecture, and fail to provide a holistic perspective on the security challenges that span the entire NGS workflow. Additionally, the lack of interdisciplinary collaboration between the biotechnology and cybersecurity communities further exacerbates these gaps. This paper seeks to bridge these gaps by thoroughly examining cyber-biosecurity threats throughout the NGS workflow. It introduces a tailored taxonomy specifically designed for NGS, aimed at increasing stakeholder awareness of potential vulnerabilities and threats. Key insights include identifying vulnerabilities at various stages of the NGS process—from data generation to analysis and storage—and categorising these threats systematically. The study highlights critical gaps in current research, underscoring the need for interdisciplinary collaboration between experts in biotechnology and cybersecurity. It calls for focused efforts to mitigate risks associated with unauthorised access, data misuse, and exploitation. Failure to address these vulnerabilities could result in severe consequences, such as breaches of medical confidentiality, ethical concerns, and the potential for misuse in malicious applications like genetic warfare or bioterrorism. By providing a comprehensive analysis, this paper advocates for intensified research efforts and collaborative strategies to protect genomic data and ensure its ethical and secure use.}, archive = {J_ACCESS}, author = {Nasreen Anjum and Hani Alshahrani and Asadullah Shaikh and Mahreen-Ul-Hassan and Mehreen Kiran and Shah Raz and Abu Alam}, doi = {10.1109/ACCESS.2025.3552069}, journal = {IEEE Access}, pages = {52006-52035}, shortjournal = {IEEE Access}, title = {Cyber-biosecurity challenges in next-generation sequencing: A comprehensive analysis of emerging threat vectors}, volume = {13}, year = {2025}, }
• (2025). Contextual regularization-based energy optimization for segmenting breast tumor in DCE-MRI. ACCESS, 13, 51986-52005. (www)

  @article{ , abstract = {Accurate breast tumor segmentation is crucial for precise diagnosis, effective treatment planning, and the development of automated decision-support systems in clinical practice. The imprecision of trending segmentation models in differentiating tumors from their surrounding tissues, particularly in weighing the boundary pixels across tumor regions poses a significant challenge in precise tumor delineation. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) effectively captures tumor vascularity and perfusion dynamics and is a reliable modality for extracting the region of interest (ROI). Nevertheless, the intricate intensity variations in DCE-MRI owing to heterogeneous tumor morphology pose considerable challenges in tumor delineation, necessitating a highly adaptive and robust model for precise tumor segmentation. Accordingly, this manuscript presents a Contextual Regularization-Based Energy Optimization (CRBEO) model that effectively captures these intensity variations in the form of energies contributed by data fidelity and regularization terms. The formulated non-linear energy-based convex optimizer is adaptively tuned by a variational Minimax principle to achieve the desired solution. An iterative gradient descent algorithm is engaged to minimize the energy-based cost function, obtaining stable convergence towards the optimal solution. The extensive relative analysis of CRBEO on complex breast DCE-MRI datasets including QIN breast DCE-MRI, TCGA-BRCA, BreastDM, RIDER, and ISPY1 has recorded significant dice improvements of 30.16%, 11.48%, 20.66%, 1.012%, and 28.107%, respectively on par with trending SOTA methods. The complexity analysis of CRBEO with time and space has justified its extension to real-time clinical diagnosis.}, archive = {J_ACCESS}, author = {Priyadharshini Babu and Mythili Asaithambi and Sudhakar Mogappair Suriyakumar}, doi = {10.1109/ACCESS.2025.3553035}, journal = {IEEE Access}, pages = {51986-52005}, shortjournal = {IEEE Access}, title = {Contextual regularization-based energy optimization for segmenting breast tumor in DCE-MRI}, volume = {13}, year = {2025}, }
• (2025). Sperm detection and tracking model using HDE transformer with spatio-temporal deformable attention for sperm analysis automation. ACCESS, 13, 51978-51985. (www)

  @article{ , abstract = {Infertility is a common problem, affecting approximately one in six adults worldwide. Several studies have shown that the male factor is responsible for infertility in up to 50% of couples. Elaborate sperm analysis is needed to solve this problem. Manual evaluation of sperm motility requires precise microscopic observation. In addition, the rapid movement of sperm in the microscopic field of view requires a large amount of human and time labor. To address this issue, the adoption of Computer-Assisted Sperm Analysis (CASA) systems has been increasing in clinical settings as a replacement for manual evaluations conducted by experts. To automate sperm analysis, it is necessary to detect and track target sperm from microscopic videos. However, a baseline has not been established for an end-to-end model that performs sperm detection and tracking simultaneously. To contribute to the automation of sperm evaluation, we propose HDE-Track, a model that considers both the detection of blurry sperm and the movement of sperm. Through experiments conducted using VISEM-Tracking(a human spermatozoa tracking dataset), HDE-Track achieved 2.0% improvement in HOTA and 5.2% improvement in MOTA compared to the existing model TransTrack. The proposed method, HDE-Track, contributes to the automation of sperm analysis by integrating with conventional CASA systems. HDE-Track can serve as a stronger baseline for future research on temporal modeling and Transformer-based sperm detection and tracking models.}, archive = {J_ACCESS}, author = {Shoui Kosuge and Tomoki Hamagami}, doi = {10.1109/ACCESS.2025.3552792}, journal = {IEEE Access}, pages = {51978-51985}, shortjournal = {IEEE Access}, title = {Sperm detection and tracking model using HDE transformer with spatio-temporal deformable attention for sperm analysis automation}, volume = {13}, year = {2025}, }
• (2025). A design framework for reconfigurable intelligent metasurfaces enabling full-space beamforming using auxiliary surface waves. ACCESS, 13, 51969-51977. (www)

  @article{ , abstract = {Electromagnetic metasurfaces (MTSs) and Reconfigurable Intelligent Surfaces (RISs) have revolutionized wave manipulation by enabling precise control over the reflected and transmitted waves. While traditional MTSs rely on static designs, RISs incorporate tunable elements, allowing dynamic adaptation to external illuminations for real-time wavefront optimization. This paper provides a complete design framework for RISs based on the Method of Moments (MoM) to establish a multiport network and an efficient optimization scheme. By incorporating auxiliary surface waves and accounting for mutual coupling, precise control over amplitude and phase can be achieved. As a result, our designs enable wide-angle beamsteering of up to 70° in a single plane, as well as out-of-plane anomalous reflection. To further demonstrate the capabilities of the design framework for complex wave manipulation, a Chebyshev pattern with a -20 dB sidelobe level, as well as sector patterns in a single plane and across a hemisphere, are achieved using the designed RIS. The effectiveness of the proposed method is validated through full-wave simulations, supplemented by an additional measurement of a static MTS exhibiting out-of-plane anomalous reflection.}, archive = {J_ACCESS}, author = {Tianke Qiu and George V. Eleftheriades}, doi = {10.1109/ACCESS.2025.3552755}, journal = {IEEE Access}, pages = {51969-51977}, shortjournal = {IEEE Access}, title = {A design framework for reconfigurable intelligent metasurfaces enabling full-space beamforming using auxiliary surface waves}, volume = {13}, year = {2025}, }
• (2025). A hybrid method combining variational mode decomposition and deep neural networks for predicting PM2.5 concentration in china. ACCESS, 13, 51956-51968. (www)

  @article{ , abstract = {Accurately predicting air pollutants, especially PM2.5 (particles with a diameter of 2.5 micrometers or less) concentration, is crucial for pollution control and public health management. A single deep learning architecture often struggles to achieve satisfactory results for PM2.5 prediction due to the difficulty in accurately capturing hidden spatio-temporal patterns within time series. To address this issue, this study introduces a hybrid parallel method (VDPS) that combines variational mode decomposition (VMD) with single deep neural networks for PM2.5 concentration prediction. The VMD algorithm, which can decompose signals adaptively and possesses a certain level of robustness to noise and interference, decomposes complex time series into intrinsic mode functions (IMFs), which are then used as inputs for training a deep neural network prediction model. The cumulative predictions from all deep network models determine the final PM2.5 predictions for VDPS. The deep neural structures used include recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). To demonstrate the effectiveness of VDPS, we conducted comparative evaluations of different models’ performance on many experimental datasets of PM2.5 concentrations in four cities: Beijing, Shanghai, Guangzhou, and Chengdu. The results confirm that the proposed hybrid structure outperforms single deep neural prediction structures in terms of performance metrics. Specifically, the hybrid structure reduces both the root mean square error (RMSE) and the mean absolute error (MAE) by approximately 20-30% when predicting PM2.5 concentrations. And the combined accuracy (CA) index value has decreased by an average of about 10 percentage points. The results suggest that the proposed hybrid method is effective in capturing the intrinsic spatio-temporal characteristics of time series data compared with many single deep neural structures. This can assist governments in making informed decisions and protecting public health.}, archive = {J_ACCESS}, author = {Senlin Li and Bo Tang and Xiaowu Deng}, doi = {10.1109/ACCESS.2025.3552229}, journal = {IEEE Access}, pages = {51956-51968}, shortjournal = {IEEE Access}, title = {A hybrid method combining variational mode decomposition and deep neural networks for predicting PM2.5 concentration in china}, volume = {13}, year = {2025}, }
• (2025). An automotive body-in-white welding stud flexible and efficient recognition system. ACCESS, 13, 51938-51955. (www)

  @article{ , abstract = {The traditional stud welded inspection method suffers from low efficiency and a lack of precision, which makes it difficult to meet the demand for high efficiency and precision in modern automobile production lines. In this paper, a flexible recognition system incorporating a deep learning model is proposed for the inspection of welded studs in the whole car body-in-white of an automobile, including the composition of the recognition system, error analysis, path planning, and the deep learning model used. The proposed system has made significant improvements to both accuracy and detection efficiency. It can detect welded studs in a white car body with high accuracy and efficiency. Its overall detection accuracy of 99.36% demonstrates its potential to automate the production line and reduce production costs.}, archive = {J_ACCESS}, author = {Hong Huang and Xiangqian Peng and Shiyu Wu and Wenchu Ou and Xiaoping Hu and Lifeng Chen}, doi = {10.1109/ACCESS.2025.3553691}, journal = {IEEE Access}, pages = {51938-51955}, shortjournal = {IEEE Access}, title = {An automotive body-in-white welding stud flexible and efficient recognition system}, volume = {13}, year = {2025}, }
• (2025). Introduction to OmniPES: A novel modelica library for power systems modeling and analysis. ACCESS, 13, 51922-51937. (www)

  @article{ , abstract = {This paper introduces OmniPES, a Modelica library tailored for the swift prototyping of small to medium-scale power systems, facilitating analysis in both steady-state and transient conditions. The primary contribution of this new library is to enable the development and enhancement of models in an effective and efficient manner within a Modelica integrated development environment, without the need for external computational software. Notable features include sigmoid-based switches that enforce reactive power constraints in power sources during steady-state or quasi-steady-state operation. Moreover, the library incorporates a transient stability modeling framework, offering a structure akin to those utilized in production-grade power systems programs. These features are illustrated through a tutorial system operating under a secondary voltage regulation loop. The presented results validate and clearly demonstrate the accuracy and effectiveness of the proposed library.}, archive = {J_ACCESS}, author = {Marcelo Aroca Tomim and Ricardo Mota Henriques and João A. Passos Filho}, doi = {10.1109/ACCESS.2025.3553782}, journal = {IEEE Access}, pages = {51922-51937}, shortjournal = {IEEE Access}, title = {Introduction to OmniPES: A novel modelica library for power systems modeling and analysis}, volume = {13}, year = {2025}, }
• (2025). Real-time long-wave infrared semantic segmentation with adaptive noise reduction and feature fusion. ACCESS, 13, 51911-51921. (www)

  @article{ , abstract = {Semantic segmentation in the Long-Wave Infrared (LWIR) domain is critical for a wide range of applications including emergency response, industrial safety monitoring, and public building security. However, LWIR images often suffer from inherent pattern noise (known as fixed-pattern noise) and ambiguous object boundaries that hinder accurate segmentation. To address these challenges, this study presents a novel real-time semantic segmentation framework specifically designed for LWIR images. The framework incorporates a Stripe noise Denoising Module (SDM) and a Boundary Enhancement Module (BEM), which leverage frequency-domain filtering and learnable weights to adaptively process noisy input data and improve boundary prediction. In addition, a Multi-Stream Fusion Module (MSFM) integrates multi-scale semantic features with boundary information, thereby enhancing segmentation accuracy across diverse object scales. The proposed method demonstrates state-of-the-art performance in both accuracy and efficiency on multiple datasets, including KERInha and SODA. Extensive qualitative and quantitative evaluations further validate its robustness, particularly in scenarios where RGB imagery is unavailable. By eliminating the need for supplementary information such as depth data, this approach facilitates precise indoor and outdoor segmentation tasks with lightweight computation, making it highly suitable for real-world applications. Code is available at https://github.com/saha3jet/pytorch\_efficientirseg.}, archive = {J_ACCESS}, author = {Haejun Bae and Dong-Goo Kang and Minhye Chang and Kye Young Jeong and Byung Cheol Song}, doi = {10.1109/ACCESS.2025.3552782}, journal = {IEEE Access}, pages = {51911-51921}, shortjournal = {IEEE Access}, title = {Real-time long-wave infrared semantic segmentation with adaptive noise reduction and feature fusion}, volume = {13}, year = {2025}, }
• (2025). Enhanced harmony search algorithm for maximum power point tracking under partial shading in PV systems. ACCESS, 13, 51900-51910. (www)

  @article{ , abstract = {Multiple power peaks are introduced under nonuniform irradiation conditions in photovoltaic (PV) systems. However, conventional maximum power point tracking (MPPT) algorithms, such as the perturb-and-observe (P&O) algorithm, are only effective under homogeneous irradiation conditions. Therefore, there is a need for an efficient method for global maximum power point (GMPP) tracking under partial shading conditions (PSCs). This paper proposes a novel MPPT algorithm based on harmony search (HS). The proposed algorithm not only overcomes the power loss induced by a wide search but also avoids falling into local maximum power points under PSCs. A parameter-setting-free (PSF) HS algorithm is introduced, which is further improved for the MPPT problem. Furthermore, the algorithm integrates a reduced-range (RR) operator to reduce the convergence time. To validate the performance of the proposed algorithm, experiments are performed using a 250-W PV system with a PV emulator under different PSCs. The results show that the proposed algorithm outperforms conventional HS and particle swarm optimization algorithms in terms of the number of iterations and convergence time, while maintaining a similar level of accuracy.}, archive = {J_ACCESS}, author = {Geon Hee Lee and Zong Woo Geem and Jehyuk Won}, doi = {10.1109/ACCESS.2025.3552805}, journal = {IEEE Access}, pages = {51900-51910}, shortjournal = {IEEE Access}, title = {Enhanced harmony search algorithm for maximum power point tracking under partial shading in PV systems}, volume = {13}, year = {2025}, }
• (2025). Dual-mode ISGW bandpass filters with TZs based on mode controlling and coupling. ACCESS, 13, 51891-51899. (www)

  @article{ , abstract = {A dual-mode bandpass filters based on the technology of Integrated Substrate Gap Waveguide (ISGW) is proposed for millimeter wave system in this paper. Initially, the resonant cavity of ISGW filter is introduced and modelled in an equivalent rectangular waveguide cavity with equivalent relative permittivity. Two types of feeding structure for ISGW cavity, microstrip ridge and coaxial probe, are designed and analyzed. Then, the design method of dual-mode bandpass filter with transmission zeros (TZs) is given in detail, which is based on the technology of coupling topology and mode controlling. The dual-mode of ${\mathrm {TE}}_{102}$ and ${\mathrm {TE}}_{201}$ of ISGW resonant cavity is excited. Finally, the dual-mode bandpass ISGW filters using both feeding structures is realized with two TZs, one on the left and another on the right of the passband. The measured results of the filter prototype are in good agreement with the simulated results, demonstrating a rectangular bandpass filtering characteristic. The measured bandpass filter circuit fed by microstrip ridge exhibits a passband centered at 26.93 GHz with 3-dB fractional bandwidth of 3.71%, two out-of-band TZs, and stopband power-attenuation levels above 20 dB for 23.7-26.1 GHz and 28.4-32.2 GHz.}, archive = {J_ACCESS}, author = {Jianpei Chen and Dongya Shen}, doi = {10.1109/ACCESS.2025.3539031}, journal = {IEEE Access}, pages = {51891-51899}, shortjournal = {IEEE Access}, title = {Dual-mode ISGW bandpass filters with TZs based on mode controlling and coupling}, volume = {13}, year = {2025}, }
• (2025). Airport clearance detection based on vision transformer and multi-scale feature fusion. ACCESS, 13, 51874-51890. (www)

  @article{ , abstract = {With the improvement of the ecological environment and technology, the possibility of foreign object invasion over airports is increasing, which seriously affects the flight safety of aircraft. Most airports use independent bird repelling devices to drive away birds, but their effectiveness will gradually deteriorate over time. In addition, these devices cannot effectively repel foreign objects such as drones. Therefore, a complete airport clearance system is needed, and the core part of this system is the airport clearance detection which requires accurate identification of birds, drones, and foreign objects in the airspace to ensure aviation safety. Faced with airport complex scenes and small object detection, there are still certain limitations to the traditional object detection algorithm. To overcome the defects in detection, this paper proposes an airport clearance detection algorithm based on Vision Transformer and multi-scale feature fusion to address the problems of poor real-time performance, low accuracy, and large parameter quantity in existing airport clearance detection systems. Firstly, to enrich the feature representation, replace the last C2f of the neck with C2fCIB. Secondly, to improve the feature extraction ability, partial convolution is replaced with dynamic convolution, and attention is introduced to the convolution kernel from four dimensions. Then, add the Vision Transformer module to capture more contextual information. Finally, improve the loss function to enhance the ability of bounding box regression processing. The experimental results show that the model achieves high detection accuracy which has reached mAP@0.5 at 93.7%, an improvement of 5.4% compared to YOLOv8n.}, archive = {J_ACCESS}, author = {Yutong Chen and Yufen Liu and Zhixiong Guo and Qiang Gao}, doi = {10.1109/ACCESS.2025.3549427}, journal = {IEEE Access}, pages = {51874-51890}, shortjournal = {IEEE Access}, title = {Airport clearance detection based on vision transformer and multi-scale feature fusion}, volume = {13}, year = {2025}, }
• (2025). Fixed-time prescribed performance synchronization for complex networks via aperiodically intermittent event-triggered pinning control. ACCESS, 13, 51862-51873. (www)

  @article{ , abstract = {This paper deals with the fixed-time prescribed performance synchronization of complex networks using intermittent event-triggered pinning control. A fixed-time prescribed performance function is employed to guarantee the specified steady and transient performance of synchronization error. Different from classical controllers with fixed sets of pinning nodes, an appropriate event-triggered aperiodic intermittent pinning controller (ETAIPC) is designed via the average control rate. Several necessary conditions are derived to ensure the fixed-time synchronization. A convenient algorithm is adopted to renew pinning set. The Zeno behavior is also excluded through a rigorous analysis. The theoretical results are applied to Chua’s circuit system, and numerical simulation are made to show the feasibility of the proposed methods.}, archive = {J_ACCESS}, author = {Junru Zhang and Jian-An Wang and Jie Zhang and Mingjie Li}, doi = {10.1109/ACCESS.2025.3550717}, journal = {IEEE Access}, pages = {51862-51873}, shortjournal = {IEEE Access}, title = {Fixed-time prescribed performance synchronization for complex networks via aperiodically intermittent event-triggered pinning control}, volume = {13}, year = {2025}, }
• (2025). SPLAS: An autonomous lightweight and versatile image classification robustness benchmark. ACCESS, 13, 51848-51861. (www)

  @article{ , abstract = {Image classification is one of the most fundamental vision tasks and remarkable progress in prediction accuracy has been made with deep neural networks and vision transformers in the last decade. However, in real-world applications, these models face many challenges, especially data quality issues. Unexpected behaviors are often observed in such degraded situations. The researchers began to comprehend the robust design of the classification models. However, there is a lack of compact and comprehensive benchmarks that quickly and precisely evaluate the robustness of a model in multiple dimensions. This work proposes a lightweight and versatile robustness benchmark called SPLAS that has five components: SuperHard, PartialInfo, LowResolution, AdversarialAttack and SpeckleNoise. It contains $50\,000$ images evenly distributed over $1\,000$ classes and evaluates a classification model in five aspects: accuracy on intrinsically difficult images, images with partial information, robustness against low resolution, adversarial attacks, and speckle noise. In addition, there is no human decision intervention in the inclusion of SPLAS image. Instead, benchmark images are selected on the basis of the vote of a group of surrogate models. We tested 80 renowned and widely utilized models on SPLAS. Among them, a vision transformer, ViT-L/16, secures the best score of 51.16 out of 100 while 74 models achieve a score under 30. The whole benchmark is publicly available on Google Drive and IEEE DataPort.}, archive = {J_ACCESS}, author = {Yiming Bian and Arun K. Somani}, doi = {10.1109/ACCESS.2025.3552669}, journal = {IEEE Access}, pages = {51848-51861}, shortjournal = {IEEE Access}, title = {SPLAS: An autonomous lightweight and versatile image classification robustness benchmark}, volume = {13}, year = {2025}, }
• (2025). Blind carrier and noise power and roll-off factor estimation for PCMA signals. ACCESS, 13, 51832-51847. (www)

  @article{ , abstract = {The Paired Carrier Multiple Access (PCMA) technology is adopted in modern satellite communication systems. It establishes duplex links between two ground stations while enhancing the bandwidth efficiency by assigning two single-carrier (SC) signals to the same time-frequency resource. For a blind receiver for the satellite downlink signal in such system, in contrast to the cooperating transceivers of the duplex link, no information about signaling, training sequences or data is available. Yet, it requires accurate knowledge about the signal and channel parameters because the task of jointly demodulating and detecting the data of both included SC signals is rather challenging. In this paper, we present several statistics based estimators for the respective powers of the SC signals and the noise from the mixture signal. We illustrate the benefits of the respective power estimation approaches for different modulation schemes by providing a performance evaluation based on numerical simulations. To provide the estimators with the knowledge of the respective roll-off factors that are used by the transmitters to generate the SC signals, we introduce a neural network classifier for roll-off factor estimation.}, archive = {J_ACCESS}, author = {Andreas Feder and Adela Vagollari and Rodrigo Fischer and Martin Hirschbeck and Wolfgang Gerstacker}, doi = {10.1109/ACCESS.2025.3552186}, journal = {IEEE Access}, pages = {51832-51847}, shortjournal = {IEEE Access}, title = {Blind carrier and noise power and roll-off factor estimation for PCMA signals}, volume = {13}, year = {2025}, }
• (2025). MultiModalGraphSearch: Intelligent massive-scale SubGraph discovery for multi-category financial pattern mining. ACCESS, 13, 51813-51831. (www)

  @article{ , abstract = {This study introduces a pipeline to categorize a rich set financial clients (or investors) into multiple financial communities (we call them circles), by leveraging a regularized topic model to describe each client’s financial behavior. Such discovered financial circles can be applied in many tasks. For example, different financial circles can reveal the different risk-tolerant levels or investment tendency. We can recommend different investment plans to different financial circles. We propose a geometry-based feature selector to extract high-quality features that represent the transactional nature of each record, followed by a probabilistic model that maps transaction attributes as distributions within a latent space. A graph-based approach is then employed to capture transactional similarities, using an advanced algorithm to identify densely connected subgraphs and cluster transactions into circles based on shared behaviors. Experimental results on diverse financial datasets demonstrate the robustness of this method in accurately distinguishing and characterizing distinct financial behavior types, providing a valuable tool for enhancing the analysis of transactional strategies and patterns.}, archive = {J_ACCESS}, author = {Yaohan Qi}, doi = {10.1109/ACCESS.2025.3553560}, journal = {IEEE Access}, pages = {51813-51831}, shortjournal = {IEEE Access}, title = {MultiModalGraphSearch: Intelligent massive-scale SubGraph discovery for multi-category financial pattern mining}, volume = {13}, year = {2025}, }
• (2025). Why to fail fast and often: A strategy for OT safety and security evaluation. ACCESS, 13, 51793-51812. (www)

  @article{ , abstract = {As the Operational Technology (OT) environment becomes increasingly interconnected and integrates diverse technologies, traditional models often struggle to accurately represent the complex interactions and dependencies of the underlying systems. Factors like changes in operational conditions, software updates, and the introduction of new devices can significantly impact the system’s risk profile. This paper presents a methodology to bridge the gap between manual and automated safety and security requirements in Industry 4.0 OT environments. First, a meta-model is developed to capture OT infrastructure components and relationships. This is then transformed into a C#-based GUI, enabling tasks like network scanning, application and interface identification, and AI-powered data extraction. Next, compliance checks and risk assessments are conducted using standards such as IEC 62443-3-3 and methods like LOPA, SEFR (HAZID), STRIDE, and DREAD. Finally, the data is converted into system models (e.g., OWL, AutomationML) for visualization. This approach reduces complexity and time by 83.72%, though it faces challenges like platform dependency and resource constraints.}, archive = {J_ACCESS}, author = {Mukund Bhole and Thilo Sauter and Sabrina Semper and Wolfgang Kastner}, doi = {10.1109/ACCESS.2025.3553011}, journal = {IEEE Access}, pages = {51793-51812}, shortjournal = {IEEE Access}, title = {Why to fail fast and often: A strategy for OT safety and security evaluation}, volume = {13}, year = {2025}, }
• (2025). (Q, s, r)-dissipativity analysis of large-scale networked singular systems with time delays. ACCESS, 13, 51781-51792. (www)

  @article{ , abstract = {This paper investigates the strictly (Q, S, R)-dissipative issues of large-scale networked singular time-delay systems composed of multiple subsystems in discrete time. These subsystems are interconnected arbitrarily and possess distinct dynamic characteristics. The existing lumped analysis methods encounter significant computational challenges when dealing with such systems. By fully leveraging the system structure, sufficient conditions for the strict dissipativity are derived. Building upon this foundation, a sufficient condition is provided further that depends solely on the parameters of the individual subsystem, greatly enhancing computational efficiency. Through numerical simulations and a case study, the derived conditions demonstrate effectiveness and superiority in analyzing the strict dissipativity of large-scale networked singular time-delay systems.}, archive = {J_ACCESS}, author = {Lingze Zhang and Huabo Liu}, doi = {10.1109/ACCESS.2025.3553131}, journal = {IEEE Access}, pages = {51781-51792}, shortjournal = {IEEE Access}, title = {(Q, s, r)-dissipativity analysis of large-scale networked singular systems with time delays}, volume = {13}, year = {2025}, }
• (2025). Development of a severity-based attack mitigation system in cognitive radio networks using blockchain and GFHQDC. ACCESS, 13, 51765-51780. (www)

  @article{ , abstract = {Cognitive Radio Networks (CRNs) are a form of wireless communication in which sensing devices continuously monitor the network to identify spectrums in use and those that are available. However, existing research has largely overlooked the mitigation of attacks based on their severity levels within CRNs. This oversight leads to issues such as degradation in quality of service, inefficient resource utilization, network instability, and reduced spectrum efficiency. To address these challenges, we propose an effective severity-based attack mitigation system in CRNs, utilizing Logistic Rational Quadratic Fuzzy (LRQF) and Grid Search-based Frequency Hopping with Quantum Debrock Cryptography (GFHQDC). Our approach develops a robust attack severity analysis system for network spectrum threats, mitigating them according to their severity levels. Initially, Primary Users (PUs) and Secondary Users (SUs) are registered with user IDs and passwords, and cryptographic keys are generated and stored in a blockchain. Spectrum sensing is then performed, and PUs are detected. If a PU is present, the spectrum is not allocated; otherwise, spectrum features are extracted and reduced using dimensionality reduction techniques. Outliers are detected, and the attack types and severity levels are identified using LRQF. High-severity attacks are blocked, while low and medium-level attacks are mitigated using GFHQDC. Finally, the optimal SU is selected based on proximity to the PU, and the spectrum is allocated. Our results demonstrate the superiority of the proposed model, achieving a 98.42% security level in attack mitigation, thereby ensuring robust and efficient spectrum sharing within the CRN.}, archive = {J_ACCESS}, author = {V. Saraswathi and R. Dayana}, doi = {10.1109/ACCESS.2025.3553628}, journal = {IEEE Access}, pages = {51765-51780}, shortjournal = {IEEE Access}, title = {Development of a severity-based attack mitigation system in cognitive radio networks using blockchain and GFHQDC}, volume = {13}, year = {2025}, }
• (2025). Machine learning approaches to code similarity measurement: A systematic review. ACCESS, 13, 51729-51764. (www)

  @article{ , abstract = {Source code similarity measurement, which involves assessing the degree of difference between code segments, plays a crucial role in various aspects of the software development cycle. These include but are not limited to code quality assurance, code review processes, code plagiarism detection, security, and vulnerability analysis. Despite the increasing application of ML technique in this domain, a comprehensive synthesis of existing methodologies remains lacking. This paper presents a systematic review of Machine Learning techniques applied to code similarity measurement, aiming to illuminate current methodologies and contribute valuable insights to the research community. Following a rigorous systematic review protocol, we identified and analyzed 84 primary studies on a broad spectrum of dimensions covering application type, devised Machine Learning algorithms, used code representations, datasets, and performance metrics, as well as performance evaluations. A deep investigation reveals that 15 applications for code similarity measurement have utilized 51 different machine learning algorithms. Additionally, the most prevalent code representation is found to be the abstract syntax tree (AST). Furthermore, the most frequently employed dataset across various code similarity research applications is BigCloneBench. Through this comprehensive analysis, the paper not only synthesizes existing research but also identifies prevailing limitations and challenges, shedding light on potential avenues for future work.}, archive = {J_ACCESS}, author = {Zixian Zhang and Takfarinas Saber}, doi = {10.1109/ACCESS.2025.3553392}, journal = {IEEE Access}, pages = {51729-51764}, shortjournal = {IEEE Access}, title = {Machine learning approaches to code similarity measurement: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable and highly-miniaturized low-noise amplifiers based on tunable filtering loads. ACCESS, 13, 51718-51728. (www)

  @article{ , abstract = {This article reports on new types of reconfigurable and multifunctional low-noise amplifier filters (LNAF) for sub-6 GHz New Radio (NR) S-band applications. The proposed LNAF concepts are based on complex-terminated bandpass filter (BPF)-type output matching networks that facilitate quasi-elliptic transfer functions that can be tuned in terms of bandwidth (BW), center frequency ( $f_{cen}$ ), and can be intrinsically switched-off. To validate the proposed approaches, four proof-of-concept LNAF prototypes were realized by means of static and varactor-loaded microstrip resonators. Further size reduction is introduced in one of the prototypes through a co-designed filtering-based LNAF using tapped line resonators. For practical validation purposes, four alternative prototypes, namely LNAF I-IV, were manufactured and tested. They exhibit the following features: i) LNAF I: passband mode with $f_{cen}$ tuning: 2.64-3.25 GHz, fractional bandwidth (FBW) tuning: 11.9-31.6% (2.65:1), gain: 16.5 dB, noise figure (NF) <0.86 dB, third order output intercept point (OIP3): 10.87 dBm, and an intrinsically switched-off mode with isolation >39 dB; ii) LNAF II: passband mode with $f_{cen}$ tuning: 3.61-3.82 GHz, FBW: 6.3%, gain: 10.2-12.6 dB, NF <0.95 dB, OIP $3\gt 11.15$ dB, and an intrinsically switched-off mode with isolation >37.07 dB; iii) LNAF III: passband mode with $f_{cen}$ : 3.99 GHz, FBW: 6.48%, gain: 14.3 dB, NF <1.1 dB, OIP $3\gt 13.2$ dB, and footprint reduction of 49% with respect to LNAF II. A 2-stage LNAF prototype, named LNAF IV, is implemented to achieve higher gain of 22.6 dB and NF <1.1 dB at 3.98 GHz with 15% less area than LNAF II.}, archive = {J_ACCESS}, author = {Andrés Fontana and Steven Matthew Cheng and Dimitra Psychogiou}, doi = {10.1109/ACCESS.2025.3548311}, journal = {IEEE Access}, pages = {51718-51728}, shortjournal = {IEEE Access}, title = {Reconfigurable and highly-miniaturized low-noise amplifiers based on tunable filtering loads}, volume = {13}, year = {2025}, }
• (2025). The cross section of AI and sustainability: Extensive topic modeling of ten years of research. ACCESS, 13, 51702-51717. (www)

  @article{ , abstract = {The rapid advancements in artificial intelligence (AI) are driving innovation and providing new tools to enhance sustainable practices across various domains. However, the swift progression of AI also presents new sustainability challenges, particularly concerning its carbon footprint and resource consumption. To explore the complex research landscape at the intersection of AI and sustainability, this study utilized topic modeling on research papers in the field. Using an AI-based topic modeling tool, 4,541 research abstracts published from 2015 to 2024 were processed. This approach identified 67 distinct topics, each characterized by unique keywords and associated publications. The findings illustrate a consistent expansion in research areas and a significant recent surge in publications, particularly in topics such as ‘Large Language Models’ and ‘Generative Art and Design.’ By organizing topics into the quadrants of a BCG-like matrix considering both topic size and growth, the study offers balanced insights into the dynamic landscape of the field. Furthermore, hierarchical clustering was applied to the 67 topics to reveal the interrelationships among them and identify broader and major research themes. This study advances our understanding of the relationship between AI and sustainability by highlighting both established and emerging areas of research and elucidating the evolving nature of the field.}, archive = {J_ACCESS}, author = {Hyung Jun Ahn}, doi = {10.1109/ACCESS.2025.3552974}, journal = {IEEE Access}, pages = {51702-51717}, shortjournal = {IEEE Access}, title = {The cross section of AI and sustainability: Extensive topic modeling of ten years of research}, volume = {13}, year = {2025}, }
• (2025). RAT-CC: A recurrent autoencoder for time-series compression and classification. ACCESS, 13, 51693-51701. (www)

  @article{ , abstract = {The growth of interconnected devices has led to an enormous volume of temporal data that requires specialized compression models for efficient storage. Besides this, most applications need to classify these data efficiently, and having to reconstruct the original data from the compressed representation to then classify them is not optimal. For this reason, we propose a Recurrent Autoencoder for Time-series Compression and Classification, termed RAT-CC, that allows to perform any classification task on the compressed representation without needing to reconstruct the original time-series data. RAT-CC leverages a Long Short-Term Memory (LSTM) recurrent autoencoder with a dual-loss function: the standard reconstruction loss to minimize reconstruction error; and an embedding loss to preserve relative distances in the compressed embedding space. This combined loss ensures that the learned embeddings remain meaningful for classification tasks while preserving the necessary information for reconstruction. We assess the compression and classification performance of RAT-CC on four datasets taken from different domains. RAT-CC is implemented in Keras and freely available at (https://github.com/ChJ4m3s/RAT-CC).}, archive = {J_ACCESS}, author = {Giacomo Chiarot and Sebastiano Vascon and Claudio Silvestri and Idoia Ochoa}, doi = {10.1109/ACCESS.2025.3553056}, journal = {IEEE Access}, pages = {51693-51701}, shortjournal = {IEEE Access}, title = {RAT-CC: A recurrent autoencoder for time-series compression and classification}, volume = {13}, year = {2025}, }
• (2025). Phase code integration using interpulse techniques for enhanced radar performance. ACCESS, 13, 51680-51692. (www)

  @article{ , abstract = {Phase coding plays an important role in radar systems, affecting signal modulation, target detection, and interference rejection. This study assesses various phase codes, including Gold, Kasami, Barker, Frank, and Chaotic codes, by analyzing performance metrics like cross-correlation and peak-to-sidelobe ratio (PSLR). The study highlights certain limitations, suggesting the need for new waveform solutions. It integrates component-based and Kronecker product-based interpulse coding to combine different codes and improve overall system performance. By merging phase codes effectively, interpulse coding leverages the strengths of individual codes to produce a composite waveform with enhanced autocorrelation and cross-correlation properties, improving radar performance. An optimization problem is formulated using a weighted performance metric to identify the best waveform, considering factors such as cross-correlation and PSLR. The approach, utilizing simple Kronecker and element-wise multiplication techniques, offers significant benefits with minimal complexity. The Barker-Gold interpulse code stands out as highly effective for spread spectrum-based applications due to its favorable correlation properties.}, archive = {J_ACCESS}, author = {Basmah Ahmad and Faran Awais Butt and Ijaz Haider Naqvi and Saqib Ejaz and Saqib Ali and Ali Hussein Muqaibel and Saleh A. Alawsh and Muhammad Arif Anwar}, doi = {10.1109/ACCESS.2025.3553308}, journal = {IEEE Access}, pages = {51680-51692}, shortjournal = {IEEE Access}, title = {Phase code integration using interpulse techniques for enhanced radar performance}, volume = {13}, year = {2025}, }
• (2025). Fractional-order sliding mode control for a remotely operated vehicle with saturated input constraints. ACCESS, 13, 51669-51679. (www)

  @article{ , abstract = {A fractional-order sliding mode controller (FOSMC) is proposed for the trajectory tracking problem of a remotely operated vehicle (ROV) in the presence of external disturbance and input saturation constraints. Firstly, the ROV dynamics model is established. Secondly, based on the ROV dynamics model, an equivalent mathematical model that facilitates the design of the FOSMC is derived. Subsequently, the fractional-order differential operator is introduced into the sliding mode surface and control law of the traditional sliding mode control (SMC) algorithm. Utilizing the fast convergence properties of the fractional-order differential operator, a FOSMC was developed. Proof of the stability of the control system is given according to the Lyapunov theorem. Finally, the simulation results show that, compared to the Proportional-Integral-Derivative (PID) controller and traditional SMC, FOSMC offers superior control accuracy, strong robustness against external disturbances, and lower energy consumption under input saturation constraints.}, archive = {J_ACCESS}, author = {Liping Deng and Jianguo Tao}, doi = {10.1109/ACCESS.2025.3552464}, journal = {IEEE Access}, pages = {51669-51679}, shortjournal = {IEEE Access}, title = {Fractional-order sliding mode control for a remotely operated vehicle with saturated input constraints}, volume = {13}, year = {2025}, }
• (2025). Examining the impact of fingerprint vector size on similarity determination and clustering performance of a pattern-based similarity metric. ACCESS, 13, 51660-51668. (www)

  @article{ , abstract = {The correlation-based context similarity coefficient (CSC) metric is a pattern-based fingerprint similarity metric that has gained interest in fingerprint database clustering operations. The performance of the traditional distance-based metric in fingerprint vector similarity determination has been known to be influenced by the size of the fingerprint vector. However, as for the correlation-based CSC metric, there is no comprehensive research on how fingerprint vector size affects its performance in similarity determination and subsequently clustering performance. As such, this paper examines the impact of fingerprint vector size on the similarity determination performance of the correlation-based CSC metric with the k-medoids algorithm employed for clustering. The analysis is performed across four synthetic and two experimentally generated fingerprint databases with varying fingerprint vector sizes. The impact analysis is carried out with the clustering algorithm set to generate K $\in $ [3, 5] clusters. Additionally, the results are compared against three distance-based metrics: squared Euclidean, Manhattan, and cosine. With silhouette score as the clustering performance metric, the simulation result shows that the size of the fingerprint vector influences the similarity determination performance of the correlation-based CSC metric. Additionally, the number of clusters in which the clustering algorithm is set to generate also contributes to how the correlation-based CSC metric performs in similarity determination. The similarity determination time complexity of the correlation-based CSC metric increases with fingerprint vector size, making efficient clustering more challenging as fingerprint vector sizes increase. For optimal performance, the correlation-based CSC metric is recommended for us as a similarity metric on a database with a fingerprint vector size of N $\le 4$ and a clustering algorithm configured to generate no more than 3 clusters.}, archive = {J_ACCESS}, author = {Abdulmalik Shehu Yaro and Filip Malý and Pavel Prazak}, doi = {10.1109/ACCESS.2025.3553510}, journal = {IEEE Access}, pages = {51660-51668}, shortjournal = {IEEE Access}, title = {Examining the impact of fingerprint vector size on similarity determination and clustering performance of a pattern-based similarity metric}, volume = {13}, year = {2025}, }
• (2025). Conformal prediction for uncertainty quantification and reliable HER2 status classification in breast cancer IHC images. ACCESS, 13, 51645-51659. (www)

  @article{ , abstract = {Accurate assessment of Human Epidermal growth factor receptor 2 (HER2) status in breast cancer is crucial for determining treatment eligibility for targeted therapies. Although machine learning approaches have shown promise in automating HER2 status classification from immunohistochemistry (IHC) images, they struggle to maintain reliable accuracy in equivocal cases. This study introduces a novel application of Conformal Prediction (CP) framework that, instead of making potentially incorrect predictions for uncertain cases, explicitly identifies them for further examination. The CP framework achieves this by generating prediction sets whose size reflects uncertainty, inherently aligning with the clinical practice of identifying cases that require confirmatory testing. Using dual in situ hybridization (DISH) results as the gold standard for HER2 status, we developed our system using handcrafted features—including color intensity, local binary patterns, and Haralick features—combined with tree-based classifiers. In experiments on 2,570 IHC images (94 cases) from the Institute of Pathology, Bangkok, Thailand, our framework demonstrated the ability to make highly accurate predictions on unambiguous cases while automatically filtering out uncertain ones. At a significance level of 0.05, the XGBoost-based conformal predictor achieved 90.1% (95% CI: 87.4-92.2%) accuracy on the equivocal cases, while identifying 69.8% (95% CI: 67.7-71.9%) of cases as requiring additional testing. This selective prediction approach represents a practical step toward reliable automated HER2 assessment by focusing on high-confidence predictions. The source code of our implementation is available at: https://github.com/Surayuth/her2cp.git.}, archive = {J_ACCESS}, author = {Surayuth Pintawong and Shanop Shuangshoti and Tikamporn Jitpasutham and Somruethai Shuangshoti and Kulachet Wiwatwarayos and Thananop Kobchaisawat and Thanarat H. Chalidabhongse}, doi = {10.1109/ACCESS.2025.3552934}, journal = {IEEE Access}, pages = {51645-51659}, shortjournal = {IEEE Access}, title = {Conformal prediction for uncertainty quantification and reliable HER2 status classification in breast cancer IHC images}, volume = {13}, year = {2025}, }
• (2025). A multifunctional wind power conversion system with sensorless operation and power quality improvement features. ACCESS, 13, 51632-51644. (www)

  @article{ , abstract = {The paper proposes a model reference adaptive system with fourth order generalized integrator (MRAS-FOGI) for power quality improvement in wind power systems. With the effect of a model-reference speed estimator in conjunction with the generalized integrator, the system offers robustness towards significant fluctuations in wind speed while minimizing control intricacy and improving grid current transient response while ensuring the various grid connection standards such as IEEE 519 and IEC61400-21. Utilizing a fourth order generalized integrator ensures balanced grid currents, unity power factor, harmonic mitigation, and precise rotor speed estimation under grid deviations, load unbalances, and variation in wind system. The proposed system offers a viable solution for sensorless wind power integration while maintaining the power quality, such a system is seldom reported. Additionally, a thorough closed-loop stability is analyzed here. Simulation results highlight its superior performance during steady-state and dynamic conditions. Real-time testing on the Opal-RT OP4510 platform confirms its effectiveness under practical scenarios. Comprehensive comparative analysis with some recent effective state-of-the-art systems demonstrates the system’s superior performance even under abnormal grid voltages irrespective of local nonlinear load, the presented wind power system is observed to adhere to various renewable energy integration standards strictly.}, archive = {J_ACCESS}, author = {Abdoalateef Alzhrani and Pranay Krishna Sahay and Sreeram Naveen Chandra and Vedantham Lakshmi Srinivas}, doi = {10.1109/ACCESS.2025.3552989}, journal = {IEEE Access}, pages = {51632-51644}, shortjournal = {IEEE Access}, title = {A multifunctional wind power conversion system with sensorless operation and power quality improvement features}, volume = {13}, year = {2025}, }
• (2025). SatDiff: A stable diffusion framework for inpainting very high-resolution satellite imagery. ACCESS, 13, 51617-51631. (www)

  @article{ , abstract = {Satellite image inpainting is a critical task in remote sensing, requiring accurate restoration of missing or occluded regions for reliable image analysis. In this paper, we present SatDiff, an advanced inpainting framework based on diffusion models, specifically designed to tackle the challenges posed by very high-resolution (VHR) satellite datasets such as DeepGlobe and the Massachusetts Roads Dataset. Building on insights from our previous work, SatInPaint, we enhance the approach to achieve even higher recall and overall performance. SatDiff introduces a novel Latent Space Conditioning technique that leverages a compact latent space for efficient and precise inpainting. Additionally, we integrate Explicit Propagation into the diffusion process, enabling forward-backward fusion for improved stability and accuracy. Inspired by encoder-decoder architectures like the Segment Anything Model (SAM), SatDiff is seamlessly adaptable to diverse satellite imagery scenarios. By balancing the efficiency of preconditioned models with the flexibility of postconditioned approaches, SatDiff establishes a new benchmark in VHR satellite datasets, offering a scalable and high-performance solution for satellite image restoration. The code for SatDiff is publicly available at: https://github.com/kaopanboonyuen/SatDiff.}, archive = {J_ACCESS}, author = {Teerapong Panboonyuen and Chaiyut Charoenphon and Chalermchon Satirapod}, doi = {10.1109/ACCESS.2025.3551782}, journal = {IEEE Access}, pages = {51617-51631}, shortjournal = {IEEE Access}, title = {SatDiff: A stable diffusion framework for inpainting very high-resolution satellite imagery}, volume = {13}, year = {2025}, }
• (2025). QoS aware task scheduling using hybrid genetic algorithm in cloud computing. ACCESS, 13, 51603-51616. (www)

  @article{ , abstract = {Simultaneous improvement of Quality of Service (QoS) parameters is a challenge in the Cloud Computing (CC) environment if the overall QoS provided is not sufficient for end-users. In this paper, the Smart Message Passing Interface Approach (SMPIA) is combined with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms for CC application. PSO-SMPIA and GA-SMPIA algorithms are presented for task scheduling and resource allocation with the aim of reducing makespan and total execution time, and increasing resource utilization in CC. The main contribution of this paper is to calculate the maximum cost for each transaction flow which has not been addressed in previous studies. This new multipurpose function includes flow load amount, load amount on makespan, capacity of Virtual Machines (VMs) and execution speed parameter. Telecommunication deals and tenders are categorized based on the type of flow. The transaction flow allocation matrix to the VMs is obtained with appropriate values. According to the selected matrix, the transaction flow is sent to the selected VMs. Also, we analyze the effect of makespan and total execution time on resource utilization. The results show that PSO-SMPIA performs better than Optimized-SMPIA (O-SMPIA), Fuzzy SMPIA (FSMPIA), SMPIA and GA-SMPIA in terms of average resource utilization. But FSMPIA and O-SMPIA perform more efficiently than other algorithms in terms of improving makespan and total execution time, respectively. The total execution time and makespan are reduced significantly compared to other algorithms which in turn increases the QoS delivered to end-users. Thus, GA-SMPIA outperforms other algorithms through simultaneous improvement of the total execution time and resource utilization besides further improvement (minimizing) of the makespan.}, archive = {J_ACCESS}, author = {Keyvan Atbaee Tabary and Homayun Motameni and Behnam Barzegar and Ebrahim Akbari and Hossien Shirgahi and Mehran Mokhtari}, doi = {10.1109/ACCESS.2024.3520412}, journal = {IEEE Access}, pages = {51603-51616}, shortjournal = {IEEE Access}, title = {QoS aware task scheduling using hybrid genetic algorithm in cloud computing}, volume = {13}, year = {2025}, }
• (2025). Deadline-aware redundant transmission on multi-paths for real-time service. ACCESS, 13, 51589-51602. (www)

  @article{ , abstract = {This study addresses the challenge of meeting deadlines in real-time applications such as video streaming and online gaming, in which data block delivery is time sensitive. Traditional loss-recovery solutions such as retransmission are inadequate, particularly in multi-path networks with long round-trip times, resulting in delays and missed deadlines. The proposed deadline-aware redundant transmission (DART) approach proactively transmits redundant packets across multiple paths, considering the next transmission time and one-way delay on each path to meet deadlines with minimal overhead. It also addresses deadlocks caused by insufficient network capacity by prioritizing the transmission of the smallest data block in the queue. DART, which operates independently of existing schedulers, simplifies implementation and reduces disturbances to existing schedulers. Simulation results demonstrate that DART increases the block transmission success rates by 5% to 10%, depending on the network conditions, while requiring minimal overheads.}, archive = {J_ACCESS}, author = {Hyeonwoo Kang and Seungwoo Hong and Donghyun Kang and Donghyeok An}, doi = {10.1109/ACCESS.2025.3553539}, journal = {IEEE Access}, pages = {51589-51602}, shortjournal = {IEEE Access}, title = {Deadline-aware redundant transmission on multi-paths for real-time service}, volume = {13}, year = {2025}, }
• (2025). Landslide displacement prediction model based on optimal decomposition and deep attention mechanism. ACCESS, 13, 51573-51588. (www)

  @article{ , abstract = {Landslide displacement forecasting is crucial for disaster prevention and risk management, as it enables timely warnings and effective mitigation strategies. However, the highly nonlinear and complex nature of landslide displacement poses significant challenges for accurate prediction. To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search Algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. The trend component is modeled using the Autoregressive Integrated Moving Average (ARIMA) with a grid search strategy, while the periodic component is predicted using a Bidirectional Long Short-Term Memory network with an Attention mechanism (BiLSTM-Attention), which dynamically adjusts the contribution of influencing factors. Grey Relational Analysis (GRA) is further employed to identify key external driving factors, enhancing prediction accuracy. Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. Moreover, the Mean Absolute Scaled Error (MASE) analysis confirms the robustness of the model in capturing both short-term fluctuations and long-term trends. Given its superior predictive accuracy and practical applicability, this approach provides valuable technical support for landslide monitoring and early warning systems.}, archive = {J_ACCESS}, author = {Shuai Ren and Kamarul Hawari Ghazali and Yuanfa Ji and Samra Urooj Khan}, doi = {10.1109/ACCESS.2025.3551730}, journal = {IEEE Access}, pages = {51573-51588}, shortjournal = {IEEE Access}, title = {Landslide displacement prediction model based on optimal decomposition and deep attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Model-driven development of web augmenters for facilitating environmental open data consumption. ACCESS, 13, 51551-51572. (www)

  @article{ , abstract = {Citizens must consume open data published by public institutions to be aware of environmental issues of SDGs (Sustainable Development Goals). However, the existence of many related heterogeneous open data sets hampers citizens to access and consume them to raise environmental awareness. To overcome this scenario, in this paper, we introduce a model-driven development approach to automatically generate maps as web augmenters to allow citizens to surf the Web while they 1) homogeneously access environmental open data, and 2) easily visualize this data on the map. To show the feasibility of our approach, we focus on a case study of biodiversity maps, which are related to SDG 14 (life below water), and 15 (life on land). Through this case study, a web augmenter applied to Wikipedia is automatically generated from environmental open data related to “Mar Menor” coastal lagoon; a highly anthropized ecosystem located in Southeast Spain. A focus group is conducted to validate that our generated web augmenter is easy to use and useful for environmental open data consumption.}, archive = {J_ACCESS}, author = {Paula González-Martínez and César González-Mora and Irene Garrigós and Jose-Norberto Mazón and José M. Cecilia}, doi = {10.1109/ACCESS.2025.3553686}, journal = {IEEE Access}, pages = {51551-51572}, shortjournal = {IEEE Access}, title = {Model-driven development of web augmenters for facilitating environmental open data consumption}, volume = {13}, year = {2025}, }
• (2025). Machine learning and deep learning approaches for accent recognition: A review. ACCESS, 13, 51527-51550. (www)

  @article{ , abstract = {Accent recognition has attracted immense research interest owing to the advancements in automatic speech recognition (ASR) systems. Accent variations are an essential factor in speech, and can drastically decrease the performance of ASR systems. This study presents an extensive examination of various accent-recognition models. In this paper, we explain various preprocessing techniques, different feature extraction methods, and a detailed methodology based on the machine learning (ML) and deep learning (DL) approaches used for accent recognition. Papers were selected from Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, Springer, Research Gate, Scopus, and the Directory of Open Access. The search returned 103 papers, including (journals and conferences) covering the scope of this study from 2015 to 2023. Detailed discussions on the various characteristics of audio signals are presented for the development of an accent recognition system. This paper provides an overview of various DL techniques to highlight the current state-of-the-art approaches, along with the associated activation and loss functions. It also offers a concise discussion on commonly used datasets for accent recognition systems and examines the challenges posed by different auditory characteristics. Finally, we discuss the research gaps, ideas for improvement, and the current and future trends proposed by different authors.}, archive = {J_ACCESS}, author = {Muzaffar Ahmad Dar and Jagalingam Pushparaj}, doi = {10.1109/ACCESS.2025.3552935}, journal = {IEEE Access}, pages = {51527-51550}, shortjournal = {IEEE Access}, title = {Machine learning and deep learning approaches for accent recognition: A review}, volume = {13}, year = {2025}, }
• (2025). Learning electromagnetic metamaterial physics with ChatGPT. ACCESS, 13, 51513-51526. (www)

  @article{ , abstract = {Large language models (LLMs) such as ChatGPT, Gemini, LlaMa, and Claude are trained on massive quantities of text parsed from the internet and have shown a remarkable ability to respond to complex prompts in a manner often indistinguishable from humans. For all-dielectric metamaterials consisting of unit cells with four elliptical resonators, we present a LLM fine-tuned on up to 40,000 data that can predict the absorptivity spectrum given a text prompt that only specifies the metasurface geometry. Results are compared to conventional machine learning approaches including feed-forward neural networks, random forest, linear regression, and K-nearest neighbor (KNN). Remarkably, the fine-tuned LLM (FT-LLM) achieves a comparable performance across large dataset sizes with a deep neural network. We also explore inverse problems by asking the LLM to predict the geometry necessary to achieve a desired spectrum. LLMs possess several advantages over humans that may give them benefits for research, including the ability to process enormous amounts of data, find hidden patterns in data, and operate in higher-dimensional spaces. This suggests they may be able to leverage their general knowledge of the world to learn faster from training data than traditional models, making them valuable tools for research and analysis.}, archive = {J_ACCESS}, author = {Darui Lu and Yang Deng and Jordan M. Malof and Willie J. Padilla}, doi = {10.1109/ACCESS.2025.3552418}, journal = {IEEE Access}, pages = {51513-51526}, shortjournal = {IEEE Access}, title = {Learning electromagnetic metamaterial physics with ChatGPT}, volume = {13}, year = {2025}, }
• (2025). Impact of harmonic distortions due to scaled EV charging in residential distribution cable systems considering the filtering effect of distribution cable π structure. ACCESS, 13, 51499-51512. (www)

  @article{ , abstract = {The number of electric vehicles (EVs) has rapidly grown over the last decade, and their charging loads have caused a significant impact on the cable capacity of the power system. The effect of the EV charging load on cables would be overestimated with harmonics and underestimated without harmonics. To accurately evaluate the impact of EV charging loads on the cable capacity of the distribution system, this paper presents a cable model considering the cable inductance and capacitance filtering effect and a methodology to solve the harmonic power flow in the distribution system. Then, the maximum penetration of EV under the constraint of system voltage and current and the maximum allowable temperature of cable insulation is calculated. An IEEE-33 node distribution system is adopted as an example. The total number of vehicles in the distribution system is assumed to be 2300. Results show that the LC filtering effect of cables can reduce the harmonic voltage and harmonic current above 29th by more than 99% when the penetration of EV is 20% and 6.6 kW single-phase uncontrolled rectifiers are used. The LC filtering effect of the cable can be disregarded when charging with Pulse-Width Modulation (PWM) rectifier chargers. The method improves the accuracy of cable capacity assessment by EV charging loads.}, archive = {J_ACCESS}, author = {Yujie Shi and Zeyang Tang and Hang Li and Shengcong Wu and Hang Wang}, doi = {10.1109/ACCESS.2025.3552946}, journal = {IEEE Access}, pages = {51499-51512}, shortjournal = {IEEE Access}, title = {Impact of harmonic distortions due to scaled EV charging in residential distribution cable systems considering the filtering effect of distribution cable π structure}, volume = {13}, year = {2025}, }
• (2025). Evaluating ORB and SIFT with neural network as alternatives to CNN for traffic classification in SDN environments. ACCESS, 13, 51484-51498. (www)

  @article{ , abstract = {Software-Defined Networking (SDN) centralizes control and separates the control plane from the data plane, enhancing flexibility and efficiency but also increasing the attack surface. Consequently, effective traffic classification is crucial for identifying cyber threats within SDN infrastructures. To address this need, we explore the potential of Convolutional Neural Network (CNN), which is known for its superior performance in image classification tasks. To leverage CNN in this context, we propose converting traffic flow data into images using the flow-to-bar chart conversion method. We also employ two well-known methods for generating images from tabular data: the Image Generator for Tabular Data (IGTD) based on Euclidean distance and the IGTD based on Manhattan distance. As an alternative to CNN, we use ORB (Oriented FAST and Rotated BRIEF) or SIFT (Scale-Invariant Feature Transform) for feature extraction, combined with neural network for classification. This approach reduces computational overhead while maintaining high accuracy. Our approach, which incorporates flow-to-bar chart conversion along with ORB and a neural network (NN), achieved 97.14% accuracy on the InSDN dataset and 99.86% on the CIC-DDoS2019 dataset, demonstrating its effectiveness in traffic classification.}, archive = {J_ACCESS}, author = {Hicham Yzzogh and Hafssa Benaboud}, doi = {10.1109/ACCESS.2025.3553449}, journal = {IEEE Access}, pages = {51484-51498}, shortjournal = {IEEE Access}, title = {Evaluating ORB and SIFT with neural network as alternatives to CNN for traffic classification in SDN environments}, volume = {13}, year = {2025}, }
• (2025). Data-driven insights into EV charging patterns: Machine learning models reveal key predictors of station utilization in tennessee. ACCESS, 13, 51458-51483. (www)

  @article{ , abstract = {This study analyzes electric vehicle (EV) charging patterns and station utilization in Tennessee using machine learning (ML) techniques. While previous research has examined time series usage data, few studies have incorporated point of interest (POI) information or explored the relationship between various features and charging station (CS) utilization, particularly in the context of U.S. public charging infrastructure. Moreover, existing studies have often relied on limited feature sets and achieved relatively low coefficients of determination (R-squared). To address these gaps, we utilize a dataset consisting of 49,900 charging session records of Level 2 (L2) and direct current fast charging (DCFC) stations across Tennessee from 2018 to June 2024. To the best of our knowledge, our work is the first study which extensively analyzes utilization under various features, especially POI info. By employing a range of supervised ML models, including Ordinary Least Squares (OLS) regression, Gradient Boosting, XGBoost, Multi-layer perceptron (MLP), and Random Forest (RF), we predict daily charging station utilization with significantly improved accuracy. Our models achieve a 74% improvement in R-squared values for L2 stations and a 72% improvement for DCFC stations compared to the state-of-the-art research baseline. These results demonstrate that our approach outperforms the models used in previous studies.}, archive = {J_ACCESS}, author = {Seyedmehdi Khaleghian and Thanh-Nam Doan and Joe Knox and Austin Harris and Mina Sartipi}, doi = {10.1109/ACCESS.2025.3553086}, journal = {IEEE Access}, pages = {51458-51483}, shortjournal = {IEEE Access}, title = {Data-driven insights into EV charging patterns: Machine learning models reveal key predictors of station utilization in tennessee}, volume = {13}, year = {2025}, }
• (2025). Toward causal and evidential open-set temporal action detection. ACCESS, 13, 51440-51457. (www)

  @article{ , abstract = {Temporal action detection (TAD) is a critical task in video understanding. Nevertheless, most existing closed-set TAD methods often struggle to replicate their high performance when completely unseen or unknown actions emerge in an open-world test environment. To this end, the open-set temporal action detection (OSTAD) task has been recently proposed to relax the closed-set TAD condition to the unknown-aware open-set detection. Given only a limited number of known action classes available in model training, precisely localizing and rejecting the unknown action instances is extremely difficult and requires strong model generalization abilities. However, existing approaches are yet far from optimal in discriminative action feature learning and prediction uncertainty estimation, which may hamper model generalization to unknown action detection. To address these issues, this paper proposes a novel Causal and Evidential Open-set Temporal Action Detection model named CEO-TAD for improved OSTAD performance. It accomplishes expressive video feature pyramid extraction, discriminative causal action feature representation learning, and reliable EDL-based prediction uncertainty estimation with our tailored network architectures and modified loss functions. Experimental results show that our proposed method achieves state-of-the-art open-set temporal action detection performance on the THUMOS14 and ActivityNet1.3 benchmarks. Ablation studies verify the effectiveness of the proposed model components.}, archive = {J_ACCESS}, author = {Zhuoyao Wang and Rui-Wei Zhao and Rui Feng and Cheng Jin}, doi = {10.1109/ACCESS.2025.3553717}, journal = {IEEE Access}, pages = {51440-51457}, shortjournal = {IEEE Access}, title = {Toward causal and evidential open-set temporal action detection}, volume = {13}, year = {2025}, }
• (2025). Design of enhanced protograph codes and codebooks for IDD-SCMA systems. ACCESS, 13, 51429-51439. (www)

  @article{ , abstract = {This paper investigates the design of protograph low-density parity-check (PLDPC) codes and Gray-labeling codebooks (CBs) for the sparse code multiple access systems with iterative detection and decoding receiver (IDD-SCMA). Specifically, leveraging the protograph extrinsic information transfer (PEXIT) analysis, we first propose two types of PLDPC codes (i.e., an unpunctured Code-W and a punctured Code-V) which achieve the lowest decoding thresholds for the IDD-SCMA systems over AWGN and Rayleigh fading channels. To further enhance system performance, we focus on the critical role of Gray-labeling in the PLDPC-coded-IDD-SCMA systems, which are regarded as specific types of bit-interleaved coded modulation with iterative decoding (BICM-ID) systems. Subsequently, we devise a simple SCMA CB design scheme, referred to as the MPSK-based Gray-labeling CB design. This scheme is composed of the factor graph matrix design, the constellation set design, and Gray-labeling for the constellation set. Furthermore, with the aid of the PEXIT algorithm, the proposed CBs exhibit superior compatibility with the PLDPC-coded-IDD-SCMA systems. Analytical and simulation results demonstrate significant performance gains from the proposed PLDPC codes and CBs compared to existing counterparts. Consequently, the PLDPC-coded-IDD-SCMA systems with the proposed design emerge as a promising solution to satisfy the high-reliability requirements for SCMA applications.}, archive = {J_ACCESS}, author = {Jiaqin Chen and Huan Ma and Liang Lv and Wuyang Xue}, doi = {10.1109/ACCESS.2025.3553397}, journal = {IEEE Access}, pages = {51429-51439}, shortjournal = {IEEE Access}, title = {Design of enhanced protograph codes and codebooks for IDD-SCMA systems}, volume = {13}, year = {2025}, }
• (2025). The integrated adaptive fault tolerant control design with saturation for nonlinear system. ACCESS, 13, 51419-51428. (www)

  @article{ , abstract = {A integrated fault tolerant control design with adaptive fault estimation algorithm is proposed in this paper, which is used for the nonlinear system with saturation. Firstly, the fault model with actuator and sensor faults is built for the nonlinear system with saturation, to simplify the controller design procedure, taking the output tracking error as the augmented state, and the extended system model is obtained. Secondly, the fault tolerant control with adaptive estimation law is proposed, in order to solve the constraints caused by saturation in the controller design, the design algorithm through convex group description is presented, meanwhile, the robust control index is introduced in the controller design, and the controller calculating principle is given through linear matrix inequality(LMI). Finally, a flight numerical example is proposed to demonstrate the effectiveness of the method presented in this paper.}, archive = {J_ACCESS}, author = {Cong Liu}, doi = {10.1109/ACCESS.2025.3540166}, journal = {IEEE Access}, pages = {51419-51428}, shortjournal = {IEEE Access}, title = {The integrated adaptive fault tolerant control design with saturation for nonlinear system}, volume = {13}, year = {2025}, }
• (2025). Power decoupling of single-phase drive system through a symmetrical active power decoupling converter. ACCESS, 13, 51406-51418. (www)

  @article{ , abstract = {A new active power decoupling converter (APDC) applied in electrolytic capacitor-less (ECL) drive system is proposed. The APDC has a symmetrical structure and the same mathematical model when absorbing or releasing the grid pulsating power. The DC link voltage and the decoupling capacitor voltage can be independently controlled by one power switch, and the proposed APDC can be flexibly switched between energy storage and energy release modes. Therefore, the proposed APDC can limit the DC link ripple voltage to a small scope even under dynamic operating conditions. Moreover, the decoupling capacitor operates at a higher average and pulsating voltage levels, which further reduces the decoupling capacitors and increases the power density of the APDC. The APDC can effectively regulate the second-order pulsating power input from the grid side under both steady-state and dynamic operating conditions, significantly suppressing the resulting DC link voltage fluctuations, and thus ensuring the high-performance operation of the motor. Experiments have demonstrated the effectiveness of the proposed APDC.}, archive = {J_ACCESS}, author = {Hai Jin and Wenchao Zhu and Mengting Mao and Qiuyi Zhu and Chao Zhang and Hongping Jia}, doi = {10.1109/ACCESS.2025.3550355}, journal = {IEEE Access}, pages = {51406-51418}, shortjournal = {IEEE Access}, title = {Power decoupling of single-phase drive system through a symmetrical active power decoupling converter}, volume = {13}, year = {2025}, }
• (2025). A novel method for vessel trajectory clustering based on three-dimensional triangulation division algorithm. ACCESS, 13, 51388-51405. (www)

  @article{ , abstract = {Automatic identification system (AIS) performs vital functions in vessel collision avoidance and maritime security. The large-scale vessel trajectory data recorded by AIS has been facilitating vessel trajectory pattern mining, of which vessel trajectory clustering is a fundamental and indispensable approach. The precise and efficient measurement of trajectory similarity serves as the cornerstone of large-scale trajectory clustering. Most ship trajectory similarity measurement algorithms have issues of high time complexity, poor robustness and inability to distinguish the trajectories in opposite directions. This paper presents an improved measurement named three-dimensional Triangulation Division (3TD), which is based on area division in three-dimensional space with time axis incorporated, to ameliorate those deficiencies. In addition, a novel vessel trajectory clustering method is proposed by combining the 3TD algorithm with DBSCAN algorithm. AIS trajectory data in Yangshan Port area has been used to make experimental dataset. Experimental results indicate that the improved method in this paper has superior performance compared with some other existing methods in terms of clustering accuracy, robustness and computational efficiencies.}, archive = {J_ACCESS}, author = {Kai Xu and Rui Wang and Qikai Gao and Jingjing Tan}, doi = {10.1109/ACCESS.2025.3553138}, journal = {IEEE Access}, pages = {51388-51405}, shortjournal = {IEEE Access}, title = {A novel method for vessel trajectory clustering based on three-dimensional triangulation division algorithm}, volume = {13}, year = {2025}, }
• (2025). Architecture and operational control for resilient Microgrids—A university case study. ACCESS, 13, 51373-51387. (www)

  @article{ , abstract = {The growing frequency of natural disasters has led to an increase in occurrences in which critical infrastructure and small urban centres are damaged and isolated from the main utility grid. Microgrids, with their ability to operate independently of the utility grid, offer a practical solution to mitigate the damage caused by extreme weather events. Microgrid resiliency refers to the capability of a microgrid to minimize the effects of disruptive events and ensure a stable power supply under adverse conditions. This is particularly crucial for critical infrastructures such as hospitals, communication systems, data centres, and military bases. This paper aims to propose an algorithm for the management of electric microgrids, emphasizing resiliency in critical and disaster situations. The algorithm uses the available energy resources, demand response and load shedding to prolong the duration of critical energy supply and introduces flexibility to the microgrid by being able to adjust operation modes. Additionally, a novel resiliency index is introduced to assess the performance of a microgrid under various scenarios and conditions. As a result, the resiliency of critical loads and the duration of autonomous operation are significantly improved. The proposed solution was validated through an experimental microgrid implemented as a pilot project at the University of Coimbra.}, archive = {J_ACCESS}, author = {Alexandre F. M. Correia and Miguel António Gonçalves Neves and Ana Isabel Casal Dos Reis and António Paulo Coimbra and Tony Richard de Oliveira de Almeida and Pedro Moura and Aníbal T. de Almeida}, doi = {10.1109/ACCESS.2025.3553350}, journal = {IEEE Access}, pages = {51373-51387}, shortjournal = {IEEE Access}, title = {Architecture and operational control for resilient Microgrids—A university case study}, volume = {13}, year = {2025}, }
• (2025). Immersive digital imaging experience: An empirical study on audiences’ switching intention to metaverse online art museum. ACCESS, 13, 51355-51372. (www)

  @article{ , abstract = {The Metaverse Online Art Museum (MOAM) represents an innovative application of metaverse technology in the art field, with an increasing number of art galleries and museums transitioning to the metaverse. In the metaverse environment, audiences can overcome geographical and economic limitations, enjoying artworks anytime and anywhere, resulting in a more unique and immersive experience. However, there is still a lack of research on audience switching intention from traditional offline art museum (TOAM) to MOAM. To fill this academic gap, our study aims to explore and validate the key factors influencing Chinese audiences’ Switching Intentions (SWI) to MOAM. Specifically, we integrated Flow Theory (FT) and Self-Determination Theory (SDT) into the Push-Pull-Mooring framework to develop a new conceptual model, elucidating the internal mechanisms of push, pull, and mooring effects on switching intentions (SWI). Through an online questionnaire, we recovered 305 valid samples from the audience that experienced MOAM and systematically analyzed the data using Partial Least Squares Equation (PLS-SEM). The results of the study showed that eight of the ten hypotheses were valid and that two push factors (visiting inconvenience and dissatisfaction), five pull factors (concentrated attention, perceived enjoyment, presence, autonomy, and competence), and one mooring factor (visiting inertia) had a significant effect on SWI. But, Perceived risk and Relatedness did not significantly influence switching MOAM. The findings of this study not only contribute valuable theoretical insights for metaverse and MOAM, but also provide important insights for relevant service providers in optimizing audience experience and improving switching rate at the practical level. Additionally, it gives direction and reference to the government and enterprises in formulating specific policies and management measures. It also helps to promote the sustainable development of the metaverse art ecology.}, archive = {J_ACCESS}, author = {Le Xu and Huayuan Xu and Jiacheng Luo and Ru Zhang and Younghwan Pan and Junping Xu}, doi = {10.1109/ACCESS.2025.3552399}, journal = {IEEE Access}, pages = {51355-51372}, shortjournal = {IEEE Access}, title = {Immersive digital imaging experience: An empirical study on audiences’ switching intention to metaverse online art museum}, volume = {13}, year = {2025}, }
• (2025). TFC: A series of band selection methods for hyperspectral target detection. ACCESS, 13, 51332-51354. (www)

  @article{ , abstract = {The abundant spectral information provided by hyperspectral images (HSIs) greatly benefits target detection (TD), but it introduces a large amount of data redundancy, which greatly increases the complexity of data processing. Band selection (BS) is an important preprocessing step for HSI applications, and it plays an important role in reducing data volume and improving processing efficiency. Compared with BS methods that do not have specific applications, BS methods with specific applications often have clear objectives and better band selection results. Based on this idea, a series of BS methods called target feature constrained-based (TFC) used for the field of TD are developed and proposed in this paper. These methods can select the band subset that simultaneously enhance the desired targets and suppress the undesired targets. Its idea is derived from the target-constrained interference-minimized filter (TCIMF). By analyzing the detection principle of TCIMF, the BS problem can be transformed into a constrained optimization problem. According to the different method for solving constrained optimization problem, the TFC methods are divided into TFC-MP (TFC-Matching Pursuit), TFC-OMP (TFC-Orthogonal Matching Pursuit) and TFC-StOMP (TFC-Stagewise Orthogonal Matching Pursuit) methods. The experimental results show that, compared with several traditional BS methods, the TFC methods achieve better accuracy, efficiency and performance.}, archive = {J_ACCESS}, author = {Qianghui Wang and Xikui Miao and Enze Guo and Tao Zhao}, doi = {10.1109/ACCESS.2025.3548898}, journal = {IEEE Access}, pages = {51332-51354}, shortjournal = {IEEE Access}, title = {TFC: A series of band selection methods for hyperspectral target detection}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient hybrid adaptive clustering for dynamic MANETs. ACCESS, 13, 51319-51331. (www)

  @article{ , abstract = {Mobile ad hoc network (MANET) is a wireless, mobile node network in which the nodes move randomly and operate without centralized management. In MANETs, the network structure increases the energy consumption of the nodes, which shortens the network lifetime and affects packet transmission. The process of clustering in MANETs (Mobile Ad-hoc Network) can be achieved through the division of the network into virtual groups, known as clusters. The Cluster Head (CH) of each cluster is in charge of data transmission within the cluster. In this study, a two-stage Hybrid Adaptive Clustering Algorithm for Dynamics MANETs (HACADM) is proposed to improve the network performance in MANETs. In the first stage, based on the Weighted Clustering Algorithm (WCA) for selecting optimal CHs, criteria such as node degree, neighborhood distance, power of battery and mobility are optimized using the Gravity Search Algorithm (GSA). In the second phase, the clustering is executed by identifying the member nodes and their roles of the selected CHs using the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, which is one of the unsupervised learning methods. Moreover, this approach serves to reduce the load on the CHs and enhance the stability of the cluster by selecting gateway nodes for inter-cluster communication. This study represents a significant step towards optimizing energy efficiency and extending network lifetime by enhancing the adaptability of clustering processes in MANETs under dynamic network conditions. The proposed HACADM method has the potential to enhance the performance of MANETs by ensuring a more balanced load distribution compared to existing clustering approaches. The HACADM method was compared with the EE-WCA, E-MAVMMF, TSDR and MORS-ASO methods using critical performance metrics such as remaining energy, end-to-end delay, packet delivery ratio and throughput. For example, experimental results on remaining energy show that the average energy consumption improvements of HACADM compared with EE-WCA, E-MAVMMF, TSDR and MORS-ASO are 46.38%, 18.35%, 13.08% and 8.33% respectively. Other Performance evaluation results also show that HACADM significantly contributes to the effective management of MANETs, extends the network lifetime and maintains high performance under dynamic network conditions.}, archive = {J_ACCESS}, author = {Kudret Yilmaz and Resul Kara and Ferzan Katircioglu}, doi = {10.1109/ACCESS.2025.3552232}, journal = {IEEE Access}, pages = {51319-51331}, shortjournal = {IEEE Access}, title = {Energy-efficient hybrid adaptive clustering for dynamic MANETs}, volume = {13}, year = {2025}, }
• (2025). A compact solar thermic cooking solution: Design validation and performance analysis. ACCESS, 13, 51300-51318. (www)

  @article{ , abstract = {The increasing cost of cooking gas has led to the growing popularity of solar cooking as a renewable energy alternative. However, implementing effective renewable energy-based cooking systems in urban areas remains a significant challenge for industries. To address this, a novel compact solar thermic cooking device is proposed, designed specifically for domestic use in urban settings. The device utilizes a fluid-based heat transfer system, incorporating molten salt or mineral oil, and operates using four key modules: 1) a solar thermal concentrating system, 2) a thermal exchanger, 3) a heat dissipater, along 4) thermal Reservoir, utilizing a Fresnel lens-based Concentrated Solar Power (CSP) technique for energy conversion. Building on prior work that included a 3D CAD model and optimization, along with computational fluid dynamics (CFD) analysis and thermal simulations, this study details the fabrication and evaluation of the solar thermal cooking device, supported by real-time field-testing results. The findings demonstrate the system’s capability to meet the daily cooking energy needs of an average Indian household while promoting sustainable energy use. Future scope aims to facilitate the broader implementation of renewable solar cooking solutions to enhance long-term sustainability in urban areas.}, archive = {J_ACCESS}, author = {R. Jegadeeshwaran and K. Sudhan and G. Sakthivel and D. Saravana Kumar and S. Abirami and T. Nathezhtha and Y. S. Govardhan and P. Manickavasagam}, doi = {10.1109/ACCESS.2025.3553080}, journal = {IEEE Access}, pages = {51300-51318}, shortjournal = {IEEE Access}, title = {A compact solar thermic cooking solution: Design validation and performance analysis}, volume = {13}, year = {2025}, }
• (2025). Bubbling inception temperature in power Transformers—Part 1: Comparative study of kraft paper, thermally upgraded kraft paper, and aramid paper with mineral oil. ACCESS, 13, 51287-51299. (www)

  @article{ , abstract = {The bubbling inception temperature (BIT) of insulating materials used in transformers is critical for their performance and lifespan. This study, which represents the first part of a two-part series, provides a comparative analysis of the BIT for kraft paper, thermally upgraded kraft paper (TUK), and aramid paper impregnated with mineral oil. A customized experimental setup was used to measure the BIT under controlled laboratory conditions. The uniqueness of the setup lies in its precise control of dynamic load conditions via an autotransformer, real-time bubble detection, continuous moisture in oil and temperature monitoring using sensors, the use of capacitive measurement to assess moisture content in paper, and the flexibility to test different oils and insulation materials. This combination enables accurate analysis of bubble formation in oil-paper insulation systems under realistic conditions. Results show that TUK paper has the highest BIT, followed by kraft and aramid papers. Additionally, the study introduces new empirical equations for predicting BIT based on water content for each paper type, notably filling a gap for aramid paper. These equations are valuable for practical engineering applications. The research underscores the importance of moisture control in determining BIT and suggests future studies focused on standardizing methodologies and exploring different dielectric fluids. The findings contribute to improving the design, maintenance, and reliability of transformer insulation systems. Part 2 of this study further explores the long-term effects of thermal aging and alternative dielectric fluids on BIT.}, archive = {J_ACCESS}, author = {Ghada Gmati and Issouf Fofana and Patrick Picher and Fethi Meghnefi and Oscar Henry Arroyo-Fernàndez and Djamal Rebaine and Youssouf Brahami and Yapi Marie Lucia Kouba}, doi = {10.1109/ACCESS.2025.3550088}, journal = {IEEE Access}, pages = {51287-51299}, shortjournal = {IEEE Access}, title = {Bubbling inception temperature in power Transformers—Part 1: Comparative study of kraft paper, thermally upgraded kraft paper, and aramid paper with mineral oil}, volume = {13}, year = {2025}, }
• (2025). An effective summary preprocessing method for time series forecasting with multiple temporal granularities. ACCESS, 13, 51277-51286. (www)

  @article{ , abstract = {Time-series forecasting plays a pivotal role in decision-making. Recently, as deep learning models have shown exceptional performance in time-series forecasting, research in the field of time-series forecasting based on deep learning has been actively conducted. Deep learning models use a lot of past time-series data as a look-back window to effectively capture temporal information. However, increasing the size of a look-back window increases the resource demands, such as training time, GPU usage, and memory allocation, posing limitations to its extension. In response, we averaged the time-series data into summaries having multiple time granularities to accommodate a large length of time-series data in a look-back window. These constructed look-back windows have a similar effect as if the size of the look-back window were increased, even though an equal length of the look-back window was given. We employed six datasets and three time-series forecasting models to demonstrate the effectiveness of the proposed summary preprocessing method. Finally, the experimental results demonstrated an 11% improvement in forecasting accuracy along with a significant reduction in training time by 77% on average.}, archive = {J_ACCESS}, author = {Hyeseong Lee and Yunyeong Kim and Sungwon Jung}, doi = {10.1109/ACCESS.2025.3553154}, journal = {IEEE Access}, pages = {51277-51286}, shortjournal = {IEEE Access}, title = {An effective summary preprocessing method for time series forecasting with multiple temporal granularities}, volume = {13}, year = {2025}, }
• (2025). A malware classification method based on knowledge distillation and feature fusion. ACCESS, 13, 51268-51276. (www)

  @article{ , abstract = {TAs the number of web users continues to increase, the frequency of malicious attacks carried out by malware is also on the rise. The emergence of many new malware variants has resulted in relatively poor detection accuracy using traditional methods. To address issues such as poor accuracy and the inability to effectively handle malware obfuscation, this paper proposes a malware classification method based on improved ResNet50 and improved VGG16 feature fusion (MC-KDFF). This approach incorporates image texture features with enhanced Local Binary Pattern (LBP), providing insights into the local structure and layout of images and aiding the model in better understanding image details and internal structure, thus enhancing classification performance. In the feature extraction phase using improved ResNet50 and improved VGG16, asymmetric convolution is used and triple attention mechanism is used to improve the feature extraction in the center cross position part, capturing the importance of cross dimensional features in the tensor. In the classification phase, an attention mechanism (global attention block) is added to better extract features and improve model robustness.MCF-RV also uses knowledge distillation with vit (Vision Transformer) as a faculty modeler, which can effectively optimize and improve the performance of large models. The approach was evaluated using the Malimg malware image dataset, achieving an accuracy rate of 99.50%. Similarly, experiments conducted on the Microsoft BIG 2015 dataset yielded an accuracy rate of 97.52%.}, archive = {J_ACCESS}, author = {Xin Guan and Guodong Zhang}, doi = {10.1109/ACCESS.2025.3545762}, journal = {IEEE Access}, pages = {51268-51276}, shortjournal = {IEEE Access}, title = {A malware classification method based on knowledge distillation and feature fusion}, volume = {13}, year = {2025}, }
• (2025). A hybrid LDA and fuzzy CRITIC-MABAC model for smart city ranking. ACCESS, 13, 51255-51267. (www)

  @article{ , abstract = {A smart city is an urban development idea that seeks to efficiently incorporate diverse information and communication technology (ICT) solutions in a safe manner to properly oversee a city’s resources. The literature demonstrates an absence of a universally accepted and widely used definition. The essential criteria for assessing the development of smart cities and components is the prevailing paradigm in the context of smart cities are the focus of this research. The aforementioned inquiries constitute the aim of the proposed research. This study seeks to establish a complete evaluation framework that can adapt the variations in the concept of smart cities by incorporating advanced approaches, including latent-dirichlet-allocation (LDA), fuzzy criteria-importance-through-intercriteria-correlation (fuzzy CRITIC), and multi-attributive-border-approximation area comparison (MABAC), to assess and rank cities according to multi-dimensional criteria. Afterwards, the utilization of LDA, a technique for topic analysis, is employed to extract pertinent subjects and important insights. Subsequently, internet news pertaining to specific smart cities is methodically gathered and examined using LDA results, leading an enhanced objectivity. Next, the fuzzy CRITIC technique is utilized to calculate criteria weights. Weights are utilized in the MABAC method to rank the identified smart cities.}, archive = {J_ACCESS}, author = {İbrahim Miraç Eligüzel and Nazmiye Eligüzel}, doi = {10.1109/ACCESS.2025.3553674}, journal = {IEEE Access}, pages = {51255-51267}, shortjournal = {IEEE Access}, title = {A hybrid LDA and fuzzy CRITIC-MABAC model for smart city ranking}, volume = {13}, year = {2025}, }
• (2025). Entropy-based emotion recognition using EEG signals. ACCESS, 13, 51242-51254. (www)

  @article{ , abstract = {Automatic detection of emotional states using electroencephalogram (EEG) signals has emerged as an attractive research topic due to advancements in brain-computer interfaces (BCIs). Considering the non-stationary nature of EEG signals, extracting emotion-related features remains a challenging task. Additionally, practical applications demand a balance between reducing computational complexity and maintaining high classification accuracy. To address these challenges, this study pioneers the application of multiscale fluctuation-based dispersion entropy (MFDE) and refined composite MFDE (RCMFDE) for emotion recognition, marking the first exploration of these features in this context. By leveraging these advanced entropy-based methods, the proposed approach significantly enhances classification accuracy while maintaining computational efficiency. The method employs optimal channel selection and data balancing strategies to provide both superior EEG characterization and reduced computational times compared to other entropy-based methods. Comprehensive evaluations on the DEAP dataset achieved binary classification accuracies of 93.51% (HA/LA) and 92.91% (HV/LV), with a multi-class accuracy of 96.67% across four classes. These results demonstrate the effectiveness of MFDE and RCMFDE in addressing critical challenges in emotion recognition and showcase their potential for real-world BCI applications. The proposed method outperforms other state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Yeganeh Alidoost and Babak Mohammadzadeh Asl}, doi = {10.1109/ACCESS.2025.3553809}, journal = {IEEE Access}, pages = {51242-51254}, shortjournal = {IEEE Access}, title = {Entropy-based emotion recognition using EEG signals}, volume = {13}, year = {2025}, }
• (2025). Stabilization for p-normal nonlinear systems in presence of inverse dynamics and input time-delay. ACCESS, 13, 51231-51241. (www)

  @article{ , abstract = {This study concentrates on the state feedback stabilization for a class of more general p-normal nonlinear systems in presence of inverse dynamics and input time-delay under rational powers. A novel control input compensation signal comprising a finite integral of previous control values is presented, and an equivalent system with control input dependent-free time delay is obtained by introducing a variable transformation technique that includes the compensation signal. The event-triggered mechanism is utilized to overcome the resource consumption that occurs in the traditional control frameworks by adjusting the controller according to system, and overcomes the difficulties caused by high power $p_{n}$ , and effectively reduces the number of controller update compared without event-trigger mechanism. A suitable controller is designed in line with the homogeneous domination method to ensure the globally stability of the closed-loop system, and further simplifying the processing of nonlinearities during controller design. Finally, the effectiveness of the proposed control scheme is verified through a numerical simulation.}, archive = {J_ACCESS}, author = {Jiao-Jun Zhang}, doi = {10.1109/ACCESS.2025.3553365}, journal = {IEEE Access}, pages = {51231-51241}, shortjournal = {IEEE Access}, title = {Stabilization for p-normal nonlinear systems in presence of inverse dynamics and input time-delay}, volume = {13}, year = {2025}, }
• (2025). Cross-modality object detection based on DETR. ACCESS, 13, 51220-51230. (www)

  @article{ , abstract = {Cross-modality can integrate complementary information from different modalities to improve the reliability and robustness of object detection effectively. However, compared to processing uni-modality inputs, cross-modality may significantly increase the computational complexity of the model, which will lead to huge memory consumption. In this paper, we propose a novel lightweight Cross-Modality Hybrid Encoder (CHE) that maintains low computational consumption while enhancing the performance of the detection model, which includes two modules: the Attention-based Cross-Modality Feature Interaction (ACFI) module for feature interaction within and between modalities, and the Res-CNN-based Cross-Modality Feature Fusion (RCFF) module for feature association and enhancement. Our method is tested on LLVIP, FLIR, and M3FD datasets. The experiments demonstrates good performance. Our proposed approach outperforms the state-of-the-art methods on mAP with 2.6% on LLVIP, 1.1% on FLIR, and 0.4% on M3FD datasets. Additionally, our method requires only 79.7M parameters, significantly fewer than the 244.6M parameters of the SOTA method Fusion-Mamba, while maintaining high accuracy. This work explores DETR’s potential for cross-modality fusion. Our code and models are available at https://github.com/CVandDetect/Cross-Modality-Detect.}, archive = {J_ACCESS}, author = {Xinyi Huang and Guochun Ma}, doi = {10.1109/ACCESS.2025.3551947}, journal = {IEEE Access}, pages = {51220-51230}, shortjournal = {IEEE Access}, title = {Cross-modality object detection based on DETR}, volume = {13}, year = {2025}, }
• (2025). Hierarchical multi-task learning based on interactive multi-head attention feature fusion for speech depression recognition. ACCESS, 13, 51208-51219. (www)

  @article{ , abstract = {Depression, a severe psychiatric disorder with potential implications for suicide, has garnered increasing attention from researchers and the general public alike. Due to the accessibility of speech data, researchers have proposed many automatic depression recognition methods based on speech. Extracting effective features and designing an efficient recognition framework pose challenges in speech-based depression recognition. This study proposed a hierarchical multi-task learning framework based on interactive multi-head attention feature fusion (HMTL-IMHAFF), aiming to enhance the depression recognition accuracy by effectively leveraging the emotional and gender-related information embedded in speech. Firstly, we proposed a feature fusion approach based on interactive multi-head attention (IMHAFF) to comprehensively investigate the interplay among emotion valence, gender, and depression in order to obtain enhanced feature representation for depression recognition. Secondly, we developed a hierarchical multi-task learning framework with two levels. The first level consisted of sub-models related to emotion valence and gender to obtain the primary recognition results. The second level served as the final decision-making stage, which included a connection layer followed by a softmax layer that received input from the outputs of the first level. In addition, HMTL-IMHAFF used Long short-term memory (LSTM) model to capture the temporal relationship of speech feature sequences. Finally, the proposed method was comprehensively tested on the AVEC2014 dataset, and good results were achieved. Compared with the current related methods, the proposed method also stood out, obtaining an accuracy of 83.02%, which was 15% higher than the baseline method.}, archive = {J_ACCESS}, author = {Yujuan Xing and Ruifang He and Chengwen Zhang and Ping Tan}, doi = {10.1109/ACCESS.2025.3551549}, journal = {IEEE Access}, pages = {51208-51219}, shortjournal = {IEEE Access}, title = {Hierarchical multi-task learning based on interactive multi-head attention feature fusion for speech depression recognition}, volume = {13}, year = {2025}, }
• (2025). Generalized score comparison-based learning objective for deep speaker embedding. ACCESS, 13, 51194-51207. (www)

  @article{ , abstract = {In state-of-the-art speaker verification systems, speaker embeddings are trained to be closer to the target speaker prototype, which is either obtained from the other speech samples or constructed with trainable parameters. This can be considered a classification task since the network is trying to learn the features that are most relevant to the corresponding speaker from the input speech. Although classification-based learning demonstrates the ability to extract speaker-related information, it does not guarantee optimal speaker verification performance. In this paper, we propose a score comparison-based learning objective, which guides the training framework to be more consistent with the verification task, enforcing the embedding space to have lower intra-class variance compared to inter-class variance in terms of similarity scores. Furthermore, we propose a generalized loss function for score comparison-based learning, encompassing many conventional training losses and regularization techniques. The proposed technique is compared with the conventional methods using the VoxCeleb, VOiCES, CN-Celeb, and Common Voice datasets. Experimental results demonstrate that the proposed method can boost the performance and make the system more robust to over-fitting in speaker verification tasks.}, archive = {J_ACCESS}, author = {Min Hyun Han and Sung Hwan Mun and Nam Soo Kim}, doi = {10.1109/ACCESS.2025.3552790}, journal = {IEEE Access}, pages = {51194-51207}, shortjournal = {IEEE Access}, title = {Generalized score comparison-based learning objective for deep speaker embedding}, volume = {13}, year = {2025}, }
• (2025). Transchain: Blockchain-based management of allografts for enhancing data provenance. ACCESS, 13, 51182-51193. (www)

  @article{ , abstract = {Recent technological advances have enabled researchers to investigate various novel approaches utilized to manage allograft transplants and overcome the challenges of conventional centralized systems. The rising need for transparency, efficiency, and, especially, security in this highly sensitive medical procedure necessitates the use of decentralized solutions like blockchain rather than existing centralized approaches. However, the current state of research is theoretical and unproven, and allograft management lacks any reliable, cost-effective, or data-proven solution. In this paper, we propose an Ethereum blockchain-based allograft transplantation management system that can address all of those issues linked to the existing solutions. The proposed approach aims to enhance traceability, transparency, and data provenance across the entire allograft transplant process. We present six reliable and cost-efficient algorithms, as well as a comprehensive system architecture, to provide valuable insight into system implementation complexity. We have designed an efficient smart contract implementing the proposed algorithms to ensure flawless execution of allograft donation, transportation, and transplantation. We conduct thorough tests, validation, security, cost, throughput, and latency assessments of the system in order to contrast its effectiveness with existing solutions and results shows that our solution is cost-effective, as well as secure and efficient. We generalized the proposed solution so that, with minimal changes, it could be used for other problems and addressed some of the technical and ethical challenges.}, archive = {J_ACCESS}, author = {Rashid Ul Haq and Rahim Khan and Fahad Alturise and Shafrida Sahrani and Salem Alkhalaf and Mahidur R. Sarker}, doi = {10.1109/ACCESS.2025.3552576}, journal = {IEEE Access}, pages = {51182-51193}, shortjournal = {IEEE Access}, title = {Transchain: Blockchain-based management of allografts for enhancing data provenance}, volume = {13}, year = {2025}, }
• (2025). Real-time detection of forest fires using FireNet-CNN and explainable AI techniques. ACCESS, 13, 51150-51181. (www)

  @article{ , abstract = {This study presents FireNet-CNN, an advanced deep-learning model particularly designed for forest fire detection, which significantly surpasses existing methods in terms of reliability, efficiency, and interpretability. FireNet-CNN is compared to popular pre-trained models, including VGG16, VGG19, and Inception V3, across key performance metrics and consistently shows superior results, achieving 99.05% accuracy, 99.41% precision, and 98.28% recall. The model was evaluated using two augmented datasets: Dataset A and Dataset B, which consist of fire and non-fire images sourced from multiple video and image datasets. FireNet-CNN’s architecture, which includes 2.75 million parameters and a compact model size of 10.58 MB, has been meticulously optimized for fire detection tasks. As a consequence, the inference time of 0.95 seconds/image enables fast real-time deployment especially suitable for resource-constrained platforms like drones, remote sensors or other types of embedded systems in wooded regions. FireNet-CNN uses synthetic data augmentation based on Stable Diffusion to overcome the limitations of dataset size and class imbalance. This augmentation is critical as it helps the model accurately identify fire instances with a lower false positive rate, which is key for any real-time fire detection system where reliability and dependability are vital. To improve transparency and trust in safety-critical applications, FireNet-CNN incorporates the explainable AI (XAI) techniques, such as Grad-CAM and Saliency Maps. Despite encountering challenges such as reliance on synthetic data and issues of class imbalance, FireNet-CNN has demonstrated promising potential as a viable and effective solution for early wildfire detection. It offers significant insights for future research and practical applications in fire management and disaster response.}, archive = {J_ACCESS}, author = {Gazi Mohammad Imdadul Alam and Naima Tasnia and Tapu Biswas and Md. Jakir Hossen and Sharia Arfin Tanim and Md Saef Ullah Miah}, doi = {10.1109/ACCESS.2025.3552352}, journal = {IEEE Access}, pages = {51150-51181}, shortjournal = {IEEE Access}, title = {Real-time detection of forest fires using FireNet-CNN and explainable AI techniques}, volume = {13}, year = {2025}, }
• (2025). Efficient hybrid-robust approach for cancer biomarker discovery using omics data. ACCESS, 13, 51130-51149. (www)

  @article{ , abstract = {DNA Microarray datasets, also known as “omics” data, are important for the diagnosis of numerous diseases, including cancer and tumors. In the analysis of these data, feature selection techniques and classification algorithms are the workhorse for choosing candidate genes that serve as cancer biomarkers. However, microarray datasets present a challenge; they contain a greater number of features than the samples, which affects the performance of algorithms used in the analysis process. In order to extract precise information, it is necessary to employ a method that is both robust and performant. This paper emphasizes the importance of accurate and stable gene selection for the discovery of knowledge derived from high-dimensional data. A novel hybrid framework was put forth for consideration, comprising three distinct stages: Clustering, Parallel Filtering, and Hybrid-Parallel Optimization. In each step, a combination of techniques and algorithms is used to improve the results in terms of stability and/or accuracy. The proposal is evaluated and tested according to different scenarios; using thirteen gene expression datasets and two classifiers: Artificial Neural Network (ANN) and Naïve Bayes (NB). Comparison with related work demonstrates the efficacy of this approach, which enhances classification accuracy and stability while reducing the number of selected genes.}, archive = {J_ACCESS}, author = {Karima Sid and Soumia Zertal and Mohamed Batouche and Soumeya Zerabi}, doi = {10.1109/ACCESS.2025.3553078}, journal = {IEEE Access}, pages = {51130-51149}, shortjournal = {IEEE Access}, title = {Efficient hybrid-robust approach for cancer biomarker discovery using omics data}, volume = {13}, year = {2025}, }
• (2025). A survey of large language model for drug research and development. ACCESS, 13, 51110-51129. (www)

  @article{ , abstract = {Drug research and development (drug R&D) is a sophisticated, cost-intensive, and time-consuming procedure with historically low success rates. The advent of Artificial Intelligence (AI) technologies has introduced innovative methods into drug R&D, particularly by leveraging AI capabilities. Large language models (LLMs), a breakthrough in generative AI, have revolutionized drug discovery. With their extensive datasets, numerous parameters, and strong multitasking abilities, LLMs have significantly improved efficiency across various related domains, providing unparalleled support to drug R&D. These models have facilitated a deeper understanding of intricate disease mechanisms and the identification of novel therapeutic strategies, ushering in a new era in drug development and clinical applications. As a result, the advancement of LLMs is poised to drive significant transformations in drug R&D, emphasizing the importance of effectively leveraging this technology. This review provides insights into the architecture and characteristics of LLMs, explores their applications in drug R&D, and highlights their research implications in bioinformatics data, including proteins, genes, and chemical compounds. Furthermore, it investigates the practical strategies of LLMs in drug discovery, drug repositioning, and clinical inquiries, presenting an innovative approach to research and future advancements in this field.}, archive = {J_ACCESS}, author = {Huijie Guo and Xudong Xing and Yongjie Zhou and Wenjiao Jiang and Xiaoyi Chen and Ting Wang and Zixuan Jiang and Yibing Wang and Junyan Hou and Yukun Jiang and Jianzhen Xu}, doi = {10.1109/ACCESS.2025.3552256}, journal = {IEEE Access}, pages = {51110-51129}, shortjournal = {IEEE Access}, title = {A survey of large language model for drug research and development}, volume = {13}, year = {2025}, }
• (2025). Effects of current commutation methods on SiC MOSFET and si IGBT based 4-quadrant switches. ACCESS, 13, 51085-51109. (www)

  @article{ , abstract = {Silicon Carbide (SiC) MOSFETs are promising replacements for Silicon (Si) IGBTs in various power electronic topologies. While extensive research exists on SiC MOSFET based 2-quadrant switches, studies on 4-quadrant switch topologies remain limited. A 4-quadrant switch is typically formed by combining multiple 2-quadrant switches such as MOSFETs, IGBTs, or diodes. Designing such topologies presents challenges, including commutation methods (2-step and 4-step), finite switching speed constraints, variable ON state voltage drops, and quadrant-dependent transition (turn ON/OFF) times. This research systematically investigates these challenges through analysis, simulation, and experimental validation in a controlled FPGA based test environment. The study focuses on selecting an optimal commutation method, analysing the impact of 2-step and 4-step switching on transition times and ON state voltage drops, and evaluating the maximum switching frequency and efficiency of converters utilizing SiC MOSFET based 4-quadrant switches. The findings demonstrate the advantages of these converters in terms of reduced transition times, higher switching frequencies, and improved efficiency, establishing SiC MOSFET based 4-quadrant switches as a viable alternative to Si IGBT based designs.}, archive = {J_ACCESS}, author = {Nishant Anurag and Shabari Nath}, doi = {10.1109/ACCESS.2025.3553555}, journal = {IEEE Access}, pages = {51085-51109}, shortjournal = {IEEE Access}, title = {Effects of current commutation methods on SiC MOSFET and si IGBT based 4-quadrant switches}, volume = {13}, year = {2025}, }
• (2025). Data processing approach based on OPC UA architecture implementation and bluebird platform. ACCESS, 13, 51069-51084. (www)

  @article{ , abstract = {Process flexibility of automated workplaces, lines, and machinery is essential for optimizing, digitalizing and deploying critical principles based on IoT, IIoT and Industry 4.0/5.0. This potential also has considerable overlap and impetus in manufacturing and non-manufacturing areas. In addition, national and international directives, norms and standards are putting pressure on corporate structures to deploy these principles as much as possible. Therefore, the authors of this paper focused on creating an architecture for collecting, evaluating and sorting (process and state) data from a monitored automated workplace in such a way as to transform the existing research knowledge from a comprehensive solution that will be of significant benefit to both the scientific community and industrial practice. The proposed approach is based on implementing the OPC UA technology, the selected IBH Link UA communication gateway and the Bluebird visualization platform. By realizing measurements, tests, and remote management via dashboard windows, the paper intends to contribute a concrete example to the advanced supervision of the monitored running processes of automated lines and workplaces (at SCADA level and above). The requirements were seamless connectivity, immediate response and advanced diagnostics of the monitored processes. The universal language the authors want to communicate is the OPC UA structure, which unifies and prepares other solutions supporting MQTT protocols. As a result, we documented the performance of our approach in the range of 0.58 - 0.61 for the Capability Index (CpK) and 0.58 - 0.63 for the Capability Process (Cp) value. We uncovered potential opportunities for improving process and workplace consistency (stability). The paper’s parallel and supporting idea is to combine known (and working-proven) approaches without costly software and hardware replacement of the entire automated workplace equipment portfolio.}, archive = {J_ACCESS}, author = {Marek Vagas and Ondrej Majercak and Alena Galajdova and Robert Rakay and Jaroslav Romancik}, doi = {10.1109/ACCESS.2025.3552976}, journal = {IEEE Access}, pages = {51069-51084}, shortjournal = {IEEE Access}, title = {Data processing approach based on OPC UA architecture implementation and bluebird platform}, volume = {13}, year = {2025}, }
• (2025). Low complexity optimized AOR method for massive MIMO signal detection. ACCESS, 13, 51054-51068. (www)

  @article{ , abstract = {Massive multiple-input multiple-output (MIMO) efficiently resolves the contradiction between the rapid growth in capacity demand and the limited spectrum resources by deploying a large number of antennas. However, it involves certain obstacles such as designing low-complexity signal detectors. Linear minimum mean-square error (MMSE) achieves excellent performance for such large wireless systems but suffers from high computational complexity as the system dimensions increase. To overcome this problem, several iterative detectors with improved variants have been developed. However, these detectors suffer from performance loss and slow convergence when it comes to large system configurations. To address these challenges, we introduce an improved variant of traditional accelerated over-relaxation (AOR) called an optimized AOR (OAOR) signal detector. AOR performance is highly dependent on its acceleration and relaxation parameters. To determine these parameters systematically, we incorporate an innovative approach using the Nelder-Mead Simplex optimization algorithm. This heuristic optimization technique allows us to find the optimal acceleration and relaxation parameters, thereby achieving superior detection accuracy with faster convergence for challenging system configurations. We also propose a novel, low-complexity adaptive regularized initialization method for our detector. Initialization is crucial for signal detectors as it helps to enhance performance and achieve better results. This approach provides a better starting point that facilitates faster convergence and more accurate detection. These significant modifications amplify the efficiency of the OAOR method. Our proposed method improves signal detection accuracy while decreasing computational complexity from $O(K^{3})$ to $O(K^{2})$ . This makes it suitable for signal detection in large-scale antenna systems within modern wireless communication. Simulation results of the proposed method indicate superior performance compared to existing techniques in detection accuracy, error rates, and reduced computational complexity.}, archive = {J_ACCESS}, author = {Muhammad Nauman Irshad and Muhamamd Muzamil Aslam and Rardchawadee Silapunt}, doi = {10.1109/ACCESS.2025.3550272}, journal = {IEEE Access}, pages = {51054-51068}, shortjournal = {IEEE Access}, title = {Low complexity optimized AOR method for massive MIMO signal detection}, volume = {13}, year = {2025}, }
• (2025). SEMG-based complex human in-hand motion recognition for dexterous robotic manipulation. ACCESS, 13, 51042-51053. (www)

  @article{ , abstract = {Surface electromyography (SEMG) signals have emerged as a natural and intuitive control method, offering significant advantages for the operation of bionic hands. This technology has been widely applied in various fields, including medical rehabilitation and human-computer interaction. This paper proposes an innovative human in-hand motion (HIM) recognition system that leverages SEMG signals to identify 10 self-defined in-hand operations.Firstly, acknowledging that conventional gestures and postures are insufficient to capture the complexity of human hand operations, we designed 10 HIM sets,including classical motions such as transfer, translation, and rotation. The original SEMG signals were collected using high-precision sensors.Subsequently, the collected SEMG signals were denoised using the ensemble empirical mode decomposition (EEMD) algorithm, effectively mitigating noise interference and enhancing signal quality. Feature extraction was then performed using the multivariate autoregressive (MVAR) method, which provides detailed nonlinear data analysis and captures the intricate temporal dynamics of the SEMG signals. Finally, the gradient boosting decision tree (GBDT) algorithm was employed to analyze the extracted features comprehensively. The results were discussed from three perspectives: the accuracy of human in-hand motion recognition, the inter-subject variability in motion recognition rates, and the comparative performance of different machine learning methods.The experimental results demonstrate that the proposed SEMG-based HIM recognition system achieves a high accuracy of 92.78% in identifying the 10 different HIMs. This study highlights the potential of integrating advanced signal processing and machine learning techniques to enhance the functionality and precision of bionic hand control systems.}, archive = {J_ACCESS}, author = {Yaxu Xue and Feifei Ru and Haojie Du and Kaiyang Yin and Pengfei Li and Zhaojie Ju}, doi = {10.1109/ACCESS.2025.3550265}, journal = {IEEE Access}, pages = {51042-51053}, shortjournal = {IEEE Access}, title = {SEMG-based complex human in-hand motion recognition for dexterous robotic manipulation}, volume = {13}, year = {2025}, }
• (2025). Examining customer satisfaction through transformer-based sentiment analysis for improving bilingual E-commerce experiences. ACCESS, 13, 51027-51041. (www)

  @article{ , abstract = {As e-commerce continues its rapid expansion, accurately gauging and interpreting customer sentiment has become critical for optimizing user satisfaction and predicting purchasing behavior. This study advances feature-level sentiment analysis by examining Chinese e-commerce product reviews, translated into English, to uncover the contextual impact of sentiment-bearing keywords across diverse product categories. The research intends to discover trends, reasons, and broad conclusions associated with the volume, co-occurrence, and contextual use of keywords across various product types. Using sentiment intensity analysis, N-gram modeling, and machine learning, this research attempted to evaluate the performance of these models: ELECTRA, BERT, BiLSTM, Random Forest with XGBoost, and SVC with SGD. Empirical results demonstrate that ELECTRA yields the highest accuracy (98.09%) and F1 score (0.9711), illustrating superior performance in capturing nuanced sentiment cues, with low loss of 0.062. BiLSTM followed with 96.09% accuracy and F1 score of 0.9583. Although BERT performed strongly with an accuracy of 95.28% and an F1 score of 0.9528, it is extremely resource demanding. Ensemble methods like Random Forest with XGBoost and SVC with SGD had moderate performance, finding 82.43%, and 88.84% accuracy, respectively, but had better runtimes. The results supports the advantage of using transformer based models in attempting more sophisticated contextual analysis by uncovering paramount sentiment patterns, product specific issues, and customer needs. Furthermore, by translating Chinese reviews into English, the study highlights the potential for cross-lingual frameworks to enrich global e-commerce strategies. Future research directions include integrating cultural factors, extending analysis to additional languages, and refining computational approaches for resource-limited environments.}, archive = {J_ACCESS}, author = {Shizhong Shan and Jin Sun and Remson Mark C. Macawile}, doi = {10.1109/ACCESS.2025.3551666}, journal = {IEEE Access}, pages = {51027-51041}, shortjournal = {IEEE Access}, title = {Examining customer satisfaction through transformer-based sentiment analysis for improving bilingual E-commerce experiences}, volume = {13}, year = {2025}, }
• (2025). Business optimization of financial centers in pharmaceutical enterprises based on robotic process automation technology. ACCESS, 13, 51012-51026. (www)

  @article{ , abstract = {With the continuous advancement of technology and the rapid development of automation technology, pharmaceutical companies urgently need to optimize and upgrade their financial operations with higher efficiency. To optimize the business services of the financial center of pharmaceutical enterprises, the machine learning algorithm and robotic process automation technology are used to design suitable optimization methods. Based on the analysis of the departmental structure and business generation of the financial center of pharmaceutical enterprises, the optimization focus of business requirements is determined. Next, the decision tree algorithm is introduced to visually demonstrate the classification and decision-making process, optimizing the classification operation. Combined with robotic process automation technology, highly repetitive tasks are simplified to optimize business. The test results showed that the decision tree algorithm accurately classified different types of samples into the correct category when distinguishing them. The recall rate on reasonable financing schemes reached 0.69. The business processing time of robotic process automation technology was reduced by 50%, and the task completion rate was increased by 27%. The results indicate that the research designed business optimization method for pharmaceutical enterprise financial centers based on robot process automation technology significantly improves business processing efficiency, effectively controls costs, and enhances operational flexibility. In addition, the research provides theoretical support and practical guidance for the construction of intelligent financial center systems, promotes the automation and intelligent transformation of financial management, and makes important contributions to the development of this field.}, archive = {J_ACCESS}, author = {Yali Wang and Weiwei Zhou and Yingji Li and Jingqi Sun}, doi = {10.1109/ACCESS.2025.3550962}, journal = {IEEE Access}, pages = {51012-51026}, shortjournal = {IEEE Access}, title = {Business optimization of financial centers in pharmaceutical enterprises based on robotic process automation technology}, volume = {13}, year = {2025}, }
• (2025). Q-DMAC: Quality-aware distributed channel selection MAC protocol for enhancing multi-channel communication in directional sensor networks. ACCESS, 13, 50996-51011. (www)

  @article{ , abstract = {The performance of wireless networks has continuously improved using Directional Wireless Sensor Networks (DSNs). DSN achieves this by enhancing data sensing accuracy, maximizing spectrum use, decreasing interference and collisions, and lengthening the network’s operating lifetime. Within the domain of Directional Sensor Networks (DSNs), the primary difficulties are coordinating transmission and reception possibilities for a variety of sender-receiver pairs and offering traffic priority-aware channel access. To improve the quality and efficiency of multi-channel communication in DSNs, this research presents a quality-aware Directional MAC, Q-DMAC a novel medium access control protocol. The novelty of our protocol lies in its ability to leverage multi-channel resources effectively, enabling reuse through nodes equipped with directional antennas while ensuring the Quality of Service (QoS). Proposed Q-DMAC assigns a higher number of slots inside a data window to nodes carrying more packets waiting to be sent and prioritizes medium access to nodes carrying a significant amount of data packets. Also by employing regret matching of game theory Q-DMAC selects channels resulting in reduced channel switching cost as well as computation cost. Simulation results demonstrate that Q-DMAC outperforms existing protocols such as CAR MAC, 2D-CMD MAC, and DCDS-MAC, achieving up to 20% higher throughput, 15% lower latency, 12% better packet delivery ratio, 25% longer network lifetime, and 15% reduced overhead.}, archive = {J_ACCESS}, author = {Maheen Islam and Fernaz Narin Nur and Selina Sharmin and Sajeeb Saha and Majharul Haque and Ahsan Habib}, doi = {10.1109/ACCESS.2025.3551960}, journal = {IEEE Access}, pages = {50996-51011}, shortjournal = {IEEE Access}, title = {Q-DMAC: Quality-aware distributed channel selection MAC protocol for enhancing multi-channel communication in directional sensor networks}, volume = {13}, year = {2025}, }
• (2025). Locating building change via adaptive frequency enhancement. ACCESS, 13, 50980-50995. (www)

  @article{ , abstract = {Building change localization for very-high-resolution image is important in accurately tracking urbanization. However, the increase in resolution inevitably enhances the complex and variable background interference, which affects the accurate identification of building targets. This paper seeks to explore a computational intelligence approach for tackling the above challenges. Specifically, a spatial frequency adaptive enhancement is presented that takes into account the spatial frequency difference of different land objects, so as to formulate the spatial frequency attention network. It can adaptively enhance the information that favors the detection of architectural changes and suppresses irrelevant background noise interference. The entire network is designed with a classic U-shaped architecture, and two attention schemes are specifically designed, including spatial frequency attention module to regulate spatial-frequency weights in each level of feature maps, and the triple attention gate to comprehensively integrate spatial, channel, and frequency information. Experiments on three popular real datasets show that our proposal is able to obtain advanced performance, and visualization of features indicates the positive effect of our spatial-frequency attention.}, archive = {J_ACCESS}, author = {Lei Lu and Yuejie Li and Fei Yang and Haixiong Li and Guoqiang Wang and Kun Xie}, doi = {10.1109/ACCESS.2025.3552444}, journal = {IEEE Access}, pages = {50980-50995}, shortjournal = {IEEE Access}, title = {Locating building change via adaptive frequency enhancement}, volume = {13}, year = {2025}, }
• (2025). AI-based detection and simulation of hardware trojan attacks in industrial applications. ACCESS, 13, 50967-50979. (www)

  @article{ , abstract = {Industrial Internet of Things (IIoT) is becoming increasingly important for companies worldwide since it allows integrating data from different areas and sectors, thus creating new opportunities to raise efficiency, flexibility, and productivity. However, IIoT also exposes companies to new risks and threats, which implies new challenges related to cybersecurity. In this context, this paper analyzes Hardware Trojan (HT) attacks also characterized by possible malicious controlled white noises on sensors working in an IIoT scenario. Through a parametric analysis in a simulation environment, the impact and the sensitivity of the hardware attacks about the electrical signal deterioration is analyzed as a function of the variation of the HT resistance and capacitance, also superposing a white noise effect to increase the signal disturbance and complexity to find possible causes. The model also includes the possibility for the attackers to modulate and switch the white noise signal in accordance with the IIoT transmission triggering. Specifically, the paper analyzes the behavior of an attacked resistive humidity sensor by means of circuit simulations. Furthermore, the paper proposes a new approach for training commonly used Artificial Intelligence (AI) algorithms, such as Artificial Neural Networks (ANNs) and Random Forest (RF), to predict disturbed sensor signals. Simulation results show the proposed method is effective in detecting HT attacks, thus supporting the adoption of proper corrective actions. The AI-based data processing also demonstrates that circuit simulations provide an effective solution to train AI models tailored to a digital twin HT cybersecurity framework.}, archive = {J_ACCESS}, author = {Alessandro Massaro and Nicola Epicoco and Giuseppe Loseto and Carmelo A. Ardito}, doi = {10.1109/ACCESS.2025.3552112}, journal = {IEEE Access}, pages = {50967-50979}, shortjournal = {IEEE Access}, title = {AI-based detection and simulation of hardware trojan attacks in industrial applications}, volume = {13}, year = {2025}, }
• (2025). Optimizing QoE of real-time video using bandwidth sharing. ACCESS, 13, 50953-50966. (www)

  @article{ , abstract = {We consider the problem of optimizing Quality of Experience (QoE) of clients streaming real-time video. The clients are served by networks managed by different operators that can share bandwidth with each other. Abundance of real-time video traffic is evident in the popularity of applications like video conferencing and video streaming of live events, which have increased significantly since the recent pandemic. We model the problem as a joint optimization of resource allocation for the clients and bandwidth sharing across the operators, with special attention to how the resource allocation impacts clients’ perceived video quality and QoE. We propose an online policy as solution, which involves dynamically sharing a portion of one operator’s bandwidth with another operator. We provide strong theoretical optimality guarantees for the policy. We also use extensive simulations to demonstrate the policy’s substantial performance improvements (of up to ninety percent), and identify insights into key factors (e.g., imbalance in arrival rates or channel conditions of the operators) that dictate the improvements.}, archive = {J_ACCESS}, author = {Sushi Anna George and Vinay Joseph}, doi = {10.1109/ACCESS.2025.3552944}, journal = {IEEE Access}, pages = {50953-50966}, shortjournal = {IEEE Access}, title = {Optimizing QoE of real-time video using bandwidth sharing}, volume = {13}, year = {2025}, }
• (2025). Using deep learning transformers for detection of hedonic emotional states by analyzing eudaimonic behavior of online users. ACCESS, 13, 50931-50952. (www)

  @article{ , abstract = {Hedonic emotions represent a significant concept in the study of linguistics, encapsulates the patterns of positivity, pleasures, activities, and enjoyment. These emotions play a critical role in shaping eudaimonic behavior in that users reflect the personal growth of individual, personality, social connections, and values, leading to positive psychology. This behavior refers to taking actions and practices that contribute to a deep sense of purpose, meaning, and personal growth in life. The significance of detecting these emotions extends beyond psychological well-being, impacting fields such as marketing, mental health, and education. The area of hedonic emotion remains underexplored. In this study, for classification of hedonic emotions, the main aim to carry out an extensive empirical analysis by applying computational models of shallow machine learning, ensemble learning, and advanced Deep Learning (DL) algorithms. Moreover, diverse feature extraction techniques including textual features such as Term Frequency-Inverse Document Frequency (TF-IDF), Part-of-Speech (PoS) and word embeddings such as word2vec, GloVe will be explored. In addition, the state-of-the-art sentence embeddings have also been computed. The experimentation has been carried out on a standard dataset by analyzing the patterns of positive emotions and activities to understand the user’s behavior in the digital world. This empirical analysis of results measured with standard performance measures reveals the highest accuracy of 97% with transformer-based model RoBERTa using advanced embedding approach, to effectively detect and identify the hedonic emotion patterns from online textual content. By bridging the existing research gap, our research contributes to a more comprehensive understanding of emotional dynamics in digital interactions.}, archive = {J_ACCESS}, author = {Cailian Qu and Zhengli Yang}, doi = {10.1109/ACCESS.2025.3551228}, journal = {IEEE Access}, pages = {50931-50952}, shortjournal = {IEEE Access}, title = {Using deep learning transformers for detection of hedonic emotional states by analyzing eudaimonic behavior of online users}, volume = {13}, year = {2025}, }
• (2025). Human brain-inspired network using transformer and feedback processing for cell image segmentation. ACCESS, 13, 50918-50930. (www)

  @article{ , abstract = {Semantic segmentation of microscopy cell images by deep learning plays a crucial role in advancing medicine and cell biology research. We considered that Transformers, which have recently outperformed CNNs in image recognition, could also be improved and developed for cell image segmentation. Transformers tend to focus more on contextual information than on detailed information. This often results in blurred boundaries and difficulty in distinguishing small cellular structures. Hybrid models combining Transformers and CNNs have been proposed to address this issue, but they introduce high computational costs and architectural complexity. Therefore, to supplement or reinforce the missing information, we hypothesized that feedback processing in the visual cortex of the human brain could be highly effective. Our proposed Feedback Former is a novel architecture for semantic segmentation, inspired by the structure of the human brain. In the visual cortex of the human brain, the inference is made by feedforward processing from lower to upper layers, followed by the transfer of information in the reverse direction by feedback processing. In this process, specific information is emphasized or suppressed, and recognition is modified. This modification of recognition might occur in neural networks as well, so we incorporate feedback processing into a segmentation model that uses Transformers as an encoder. Feedback processing is implemented by directly connecting the output neighborhood of the model to the lower layers. Feeding back feature maps with detailed information near the output of the model obtained once inference is performed to the lower layers improves the accuracy of segmentation, especially in areas with complex textures and small objects, by enhancing the ability to extract features near boundaries and details. We further propose Lite Feedback Module, a computationally efficient alternative to conventional feedback modules. Unlike hybrid models that require additional CNN components, this module improves segmentation accuracy while maintaining lower computational costs. Experiments on three different cell image datasets confirmed that the proposed method surpasses the methods without feedback processing, demonstrating its superior accuracy in cell image segmentation. Our method achieved higher segmentation accuracy while consuming less computational cost than conventional feedback approaches. Our method enhanced accuracy more efficiently than simply increasing the model size or using a hybrid structure with CNNs.}, archive = {J_ACCESS}, author = {Hinako Mitsuoka and Kazuhiro Hotta}, doi = {10.1109/ACCESS.2025.3552847}, journal = {IEEE Access}, pages = {50918-50930}, shortjournal = {IEEE Access}, title = {Human brain-inspired network using transformer and feedback processing for cell image segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhanced MPP tracking in partial shading conditions for solar PV systems: A metaheuristic approach utilizing projectile search algorithm. ACCESS, 13, 50895-50917. (www)

  @article{ , abstract = {For solar Photovoltaic (PV) systems to function effectively amid dynamic conditions, it is imperative to achieve efficient power harvesting. Given the escalating demand for energy, the application of solar PV technology for electricity generation must be optimized to ensure optimal performance and cost-effectiveness. Maintaining an ample supply of power at the lowest cost is crucial in this regard. Partial Shading Conditions (PSCs) significantly reduce power transfer efficiency in solar PV systems, potentially leading to the formation of hotspots within the solar array. While the insertion of bypass diodes can address this issue, it often results in multiple power peaks on the Power vs Voltage (P-V) curve characteristics, complicating the process of maximum power tracking. To overcome this challenge and alleviate the computational load on the microcontroller, the application of metaheuristic techniques for Maximum Power Point Tracking (MPPT) proves beneficial. However, due to the distinctive operational characteristics of metaheuristic algorithms, continuous research in this domain is essential. Addressing the need for effective Maximum Power Point (MPP) capture in diverse partial shading scenarios, this study introduces a novel MPPT technique based on the Projectile Search Algorithm (PSA). The PSA, inspired by the projectile motion of physical objects, is a metaheuristic optimization algorithm tailored for solving optimization problems by simulating projectile motion within a search space to identify optimal solutions. To assess the performance of the proposed PSA-based approach, comparisons are made with Jaya, Cuckoo Search, Particle Swarm Optimization (PSO) and Perturb and Observe (P&O) algorithms. Real-time validation using the Typhoon Hardware in the Loop (HIL)-402 emulator is employed to verify the suggested approach, and MATLAB/Simulink software is utilized for evaluation. A comprehensive analysis of the results, considering tracking time, power tracking efficiency, and power fluctuations, demonstrates the superior performance of the proposed algorithm compared to existing methodologies.}, archive = {J_ACCESS}, author = {Md Tahmid Hussain and Mohammed Shahabuddin and Liang-Yin Huang and Adil Sarwar and Mohammed Asim and Shafiq Ahmad and Hwa-Dong Liu and Mohd Tariq}, doi = {10.1109/ACCESS.2025.3546351}, journal = {IEEE Access}, pages = {50895-50917}, shortjournal = {IEEE Access}, title = {Enhanced MPP tracking in partial shading conditions for solar PV systems: A metaheuristic approach utilizing projectile search algorithm}, volume = {13}, year = {2025}, }
• (2025). HiTS: Hierarchical text-guided stylization for face sketch-to-photo synthesis. ACCESS, 13, 50885-50894. (www)

  @article{ , abstract = {Face sketch-to-photo synthesis is crucial in law enforcement, converting forensic sketches into RGB images for criminal database matching. A major challenge is ensuring accurate color representation in synthesized images to avoid identification error caused by mismatched skin or eye color. However, direct sketch-to-photo translation struggles with proper color representation as it relies solely on grayscale sketches. While recent text-guided generative methods show promise for style adjustment based on text descriptions, they often produce mixed or exaggerated colors due to using a single representation for text prompts containing multiple entangled attributes. To address these challenges, we propose Hierarchical Text-guided Stylization (HiTS), a novel identity-preserving face sketch-to-photo synthesis method. HiTS categorizes text descriptions into intrinsic and mutable attributes, capturing both global and local color features. Using an encoder-decoder architecture, the encoder extracts global features from intrinsic attributes, while the decoder refines local styles via a semantic-textual embedding map. This map integrates text embeddings with facial parsing masks, enabling precise style adjustments for each facial component, even in small regions. Both quantitative and qualitative results demonstrate that HiTS achieves fine-grained stylization while preserving identity, leading to improved face recognition accuracy.}, archive = {J_ACCESS}, author = {Yeji Choi and Haksub Kim and Kwanghoon Sohn and Ig-Jae Kim}, doi = {10.1109/ACCESS.2025.3549102}, journal = {IEEE Access}, pages = {50885-50894}, shortjournal = {IEEE Access}, title = {HiTS: Hierarchical text-guided stylization for face sketch-to-photo synthesis}, volume = {13}, year = {2025}, }
• (2025). Real-time monitoring and simulation of multi-user electricity metering anomaly data based on distributed system. ACCESS, 13, 50876-50884. (www)

  @article{ , abstract = {Amidst the rapid development of smart grids and distributed energy systems, the volume and complexity of data within power systems have significantly increased, posing substantial challenges to traditional anomaly detection methods. To overcome these challenges, this study introduces a V-LSTM framework, an innovative approach that combines a variational autoencoder (VAE) with a long short-term memory (LSTM) network for anomaly detection in distributed power metering systems. The framework facilitates the extraction of features from multi-source data, generated by users’ electricity consumption behavior, via VAE, while LSTM conducts a meticulous time-series analysis of these features to enable high-precision anomaly detection in complex datasets. VAE proficiently mitigates data complexity, preserving essential anomalous information, whereas LSTM augments the model’s capacity to manage temporal dependencies. In empirical evaluations utilizing both public and proprietary datasets, the V-LSTM framework demonstrates superior performance in accuracy and AUC metrics, decisively surpassing traditional detection approaches. The experimental findings indicate that V-LSTM not only enhances the accuracy and dependability of power anomaly detection but also offers a novel technical trajectory for anomaly monitoring within the smart grid. This research outcome fosters the continued evolution of smart grid technology, providing more scientifically grounded management and decision support for power utilities and energy management entities.}, archive = {J_ACCESS}, author = {Kai Liu and Xuchao Jia and Junlong Wang and Xun Ma and Jiadong Li}, doi = {10.1109/ACCESS.2025.3552062}, journal = {IEEE Access}, pages = {50876-50884}, shortjournal = {IEEE Access}, title = {Real-time monitoring and simulation of multi-user electricity metering anomaly data based on distributed system}, volume = {13}, year = {2025}, }
• (2025). Isolated grid-forming control of wave energy converter for island electrification. ACCESS, 13, 50860-50875. (www)

  @article{ , abstract = {As the world transitions to renewable electrification to reduce CO2 emissions, remote island electrification remains a challenge. Although some islands are connected to the grid, many still rely on fossil fuels for electricity generation. Several studies indicate that renewable energy sources, such as wave energy, have the potential to make these islands self-reliant because of their substantial power potential. However, research on the control of power electronics converters for these systems remains limited. This paper proposes isolated grid-forming control for island electrification to address this gap using a wave energy converter and an energy storage system. Resistive loading control is implemented to optimize the power absorption of the generator. The result illustrates the establishment of the required AC voltage and 50 Hz frequency in the island load, ensuring harmonics compliance with the recommended standards. Experiments were conducted to test and validate the operation of different converter controls. The results also demonstrate the converter’s ability to black-start the island load and automatically transition the load current with varying loads in a few milliseconds. Furthermore, the power quality produced by the wave energy converter presents one of its significant challenges. Therefore, the performance of two distinct converter technologies was compared. The performance of the IGBT converter was evaluated against that of the SiC-based converter in terms of power quality. The study demonstrates that the use of SiC enhances power quality in all switching frequencies tested, achieving the most significant reduction of 78% in current THD and 92% in voltage THD at the 25 kHz switching frequency, thus validating its advantages for wave energy converter applications.}, archive = {J_ACCESS}, author = {Md Imran Ullah and Tuanku Badzlin Hashfi and Jessica S. Döhler and Vinicius M. de Albuquerque and Aisuluu Aitkulova and Johan Forslund and Cecilia Boström and Addy Wahyudie and Irina Temiz}, doi = {10.1109/ACCESS.2025.3552820}, journal = {IEEE Access}, pages = {50860-50875}, shortjournal = {IEEE Access}, title = {Isolated grid-forming control of wave energy converter for island electrification}, volume = {13}, year = {2025}, }
• (2025). ZPVQA: Visual question answering of images based on zero-shot prompt learning. ACCESS, 13, 50849-50859. (www)

  @article{ , abstract = {In recent years, the use of zero-shot learning to solve visual question-answering (VQA) problems has become a common strategy to address the challenges of complex interactions between visual and verbal modalities. Despite the significant progress of large-scale language models (LLMs) in language tasks, their application to visual question-answering tasks is still challenging because of the differences between visual and textual data. To alleviate this problem, we propose the zero-shot prompt learning VQA (ZPVQA) model, which mitigates the differences between visual and textual data through a prompt-based reasoning strategy and reduces the dependence on end-to-end training. The model devises a method of prompt learning by means of designed prompts that enable LLMs to generate caption prompts based on images and then combines the images with the generated caption prompts in order for the LLMs to generate questions and answers related to the images. In this study, we tested the performance of the ZPVQA model on multiple datasets, and achieve a performance improvement of 3.4% on the VQAv2 dataset and 2.6% on the OK-VQA dataset. The experimental results demonstrate that the prompt learning mechanism designed in this study improves the performance of the model in handling complex multi-modal tasks.}, archive = {J_ACCESS}, author = {Naihao Hu and Xiaodan Zhang and Qiyuan Zhang and Wei Huo and Shaojie You}, doi = {10.1109/ACCESS.2025.3550942}, journal = {IEEE Access}, pages = {50849-50859}, shortjournal = {IEEE Access}, title = {ZPVQA: Visual question answering of images based on zero-shot prompt learning}, volume = {13}, year = {2025}, }
• (2025). Convolutional variational autoencoder for anomaly detection in on-load tap changers. ACCESS, 13, 50838-50848. (www)

  @article{ , abstract = {Transformer outages significantly impact the reliability and cost efficiency of power systems. Studies indicate that approximately 30% of transformer failures stem from issues with on-load tap changers (OLTC), crucial components in transformer operation. Therefore, continuous monitoring of OLTCs is essential to enhance transformer serviceability. In this study, a vibro-acoustic signal analysis-based monitoring system is employed to assess the condition of OLTCs. This system has been operational since 2016 on three single-phase autotransformers within the Hydro-Québec network, continuously measuring vibration signals from their OLTCs. Notably, these transformers are equipped with sister OLTC units, and the system also records temperature and other pertinent parameters. To detect anomalies in OLTCs and analyze the generated vibration signals, a convolutional variational autoencoder (CVAE) is utilized, trained individually for each transformer family. This approach allows mapping the signal envelope into a two-dimensional latent space using the encoder component of the CVAE, facilitating visual investigation and analysis. The decoder component reconstructs the original input from data in the latent space. Several thresholds based on reconstruction errors are evaluated to detect anomalies, achieving optimal thresholds for each family. This results in anomaly detection rates of 4%, 5%, and 2%, respectively, when tested on data from within the same family not used in the training phase. Furthermore, when tested on data from the other two families, the anomaly detection rates are 99%, 99%, and 100%, respectively. These findings underscore the methodology’s accuracy and effectiveness in identifying anomalies in OLTC operations and distinguishing between different transformer families. Consequently, it holds promise for preemptively identifying potential future anomalies.}, archive = {J_ACCESS}, author = {Fataneh Dabaghi-Zarandi and Hassan Ezzaidi and Michel Gauvin and Patrick Picher and Issouf Fofana and Vahid Behjat}, doi = {10.1109/ACCESS.2025.3550097}, journal = {IEEE Access}, pages = {50838-50848}, shortjournal = {IEEE Access}, title = {Convolutional variational autoencoder for anomaly detection in on-load tap changers}, volume = {13}, year = {2025}, }
• (2025). A digital twin framework with bayesian optimization and deep learning for semiconductor process control. ACCESS, 13, 50821-50837. (www)

  @article{ , abstract = {This paper introduces an intelligent optimization framework that integrates Digital Twin (DT) technology, deep learning, and a tailored Multi-Restart Bayesian Optimization with Random Initialization (MRBORI) to enhance parameter control and yield in semiconductor manufacturing. The proposed framework synergizes XGBoost-based feature selection, which identifies critical parameters in high-dimensional spaces, with a custom deep learning surrogate model that captures complex nonlinear interactions. Building on these insights, the MRBORI strategy leverages multiple optimization restarts, each initialized randomly, to mitigate local minima risks and systematically explore broad parameter spaces. Experimental validation using real-world data from an epitaxial silicon carbide (Epi SiC) process demonstrates notably tighter thickness control and improved yield compared to traditional methods. By unifying DT-driven real-time insights with advanced machine learning and multi-restart optimization, this framework offers a robust and precise solution for tackling the complexities of modern semiconductor manufacturing.}, archive = {J_ACCESS}, author = {Chin-Yi Lin and Tzu-Liang Tseng and Tsung-Han Tsai}, doi = {10.1109/ACCESS.2025.3551332}, journal = {IEEE Access}, pages = {50821-50837}, shortjournal = {IEEE Access}, title = {A digital twin framework with bayesian optimization and deep learning for semiconductor process control}, volume = {13}, year = {2025}, }
• (2025). A novel dataset for polyp segmentation and detection using a vision-language model. ACCESS, 13, 50807-50820. (www)

  @article{ , abstract = {Colorectal Cancer (CRC) is caused by malignant polyps that develop on the colon walls, and early detection is crucial for prevention. Colonoscopy is one of the most effective methods for the detection of polyps, and deep learning algorithms can be utilized for colonoscopy image processing. The main challenge with using deep learning models is the lack of suitable data to train the models. To address this, we introduce a colorectal polyp dataset with a larger statistical population than previous datasets. Moreover, we provide the first clinical reports of colonoscopy related to patients that can be used for the purpose of training visual-language models. To enhance segmentation accuracy, we propose a deep learning model that integrates a Global Attention Module (GAM) for improved feature extraction and a Multi-Scale Aggregation Module (MSAM) for efficient fusion of information. The GAM is designed to extract features from different scales while utilizing low-level features to enhance high-level feature extraction. Furthermore, we employ a vision-language model to analyze the impact of different textual information on performance, which is crucial for report generation tasks and multimodal learning. Results indicate that the proposed model performs better than state-of-the-art studies by 2.7% and 2.5%, respectively, in mDice and mIoU. The dataset and code are available at: https://github.com/javadmozaffari/PolypDataset.}, archive = {J_ACCESS}, author = {Mahdiyeh Alilou and Javad Mozaffari and Abdollah Amirkhani and Shahriar B. Shokouhi and Alireza Khafaf}, doi = {10.1109/ACCESS.2025.3552613}, journal = {IEEE Access}, pages = {50807-50820}, shortjournal = {IEEE Access}, title = {A novel dataset for polyp segmentation and detection using a vision-language model}, volume = {13}, year = {2025}, }
• (2025). Overground, body weight supported gait training robots: A scoping review. ACCESS, 13, 50794-50806. (www)

  @article{ , abstract = {Gait rehabilitation is essential for improving mobility in patients with neurological impairments. Yet, conventional and treadmill-based robotic devices often fail to replicate natural walking conditions, which has been identified as one of the main drawbacks to achieving clinical efficacy. The scientific community is figuring out that a new generation of robotic devices is needed to enable impaired users to ambulate independently, navigate real-world environmental barriers, and maintain and train postural stability during walking. Unlike previous reviews, which focus on either standalone exoskeletons or treadmill-based rehabilitation systems, this review aims to analyze robotic devices for over-ground body-weight-supported gait training with lower limb guidance, filling a gap in a field that has not been systematically reviewed. From 240 screened articles, 15 different robotic devices were identified. Most devices combine exoskeleton-based lower-limb assistance, body-weight support (BWS), and mobile frame, aiming to facilitate ambulatory gait training while assisting leg motion in the sagittal plane. All devices featured assistance based on predefined joint kinematics. Only six devices have undergone clinical feasibility studies with their target population. Yet, despite the potential of this type of device, no randomized controlled trials have been conducted to investigate therapeutic efficacy. Future work should focus on addressing synchronization, and mechanical complexity issues through adaptive and real-time control strategies and the integration of sensor systems to improve and facilitate daily rehabilitation performance, and prioritize enabling robotic-assisted functional exercises, such as weight-bearing and dynamic balance tasks, to maximize rehabilitation outcomes. Furthermore, adequately designed clinical evaluations through large-scale, multi-center randomized controlled trials should also be conducted to ensure both the feasibility and effectiveness of this type of rehabilitation device.}, archive = {J_ACCESS}, author = {Jaime Ramos-Rojas and Emma Pérez-Martín and Antonio J. Del-Ama}, doi = {10.1109/ACCESS.2025.3552192}, journal = {IEEE Access}, pages = {50794-50806}, shortjournal = {IEEE Access}, title = {Overground, body weight supported gait training robots: A scoping review}, volume = {13}, year = {2025}, }
• (2025). DSI-ResCNN: A framework enhancing the error-tolerance capacity of DNA storage for images. ACCESS, 13, 50777-50793. (www)

  @article{ , abstract = {Traditional silicon-based storage technologies have reached a bottleneck with the dramatic increase in global data storage demands. High energy consumption, limited natural resources, and environmental concerns are some of the key reasons. DNA storage emerges as a promising alternative method, offering relatively high density and long-term stability. However, this storage method still faces considerable challenges. One such challenge pertains to errors introduced during the DNA storage process, which require the development of information subsystems with enhanced error-tolerance capabilities. In the present study, we propose the integration of two modules into information subsystems to enhance their error-tolerance capacities for DNA storage of images. These two modules, namely the residual convolutional neural network (ResCNN) module and the DNA sequence dropout control (SDC) module, collectively constitute a framework, named DNA storage of images with residual CNN (DSI-ResCNN). DSI-ResCNN enhances the fidelity of images recovered from DNA sequences generated under error-prone conditions, thereby substantiating its potential as an effective solution for mitigating the adverse impacts of errors.}, archive = {J_ACCESS}, author = {Cihan Ruan and Liang Yang and Rongduo Han and Shan Gao and Haoyu Wu and Qiming Yuan and Yanting Guo and Nam Ling}, doi = {10.1109/ACCESS.2025.3549673}, journal = {IEEE Access}, pages = {50777-50793}, shortjournal = {IEEE Access}, title = {DSI-ResCNN: A framework enhancing the error-tolerance capacity of DNA storage for images}, volume = {13}, year = {2025}, }
• (2025). A review of tactics, techniques, and procedures (TTPs) of MITRE framework for business email compromise (BEC) attacks. ACCESS, 13, 50761-50776. (www)

  @article{ , abstract = {The MITRE ATT&CK (Registered trademark) framework is a globally recognized repository of adversarial Tactics, Techniques, and Procedures (TTPs) based on real-world cyber threats. It provides a model for analyzing attack behaviors and enhancing incident attribution. Business Email Compromise (BEC) frauds, a growing cyber threat, exploit email communications for financial gain. However, the MITRE framework is not widely used for BEC, as no custom matrix has been developed specifically for these attacks. This study maps the TTPs used by BEC Threat Actors (TA) within the MITRE ATT&CK framework. The methodology included a review of academic literature, Cyber Threat Intelligence (CTI) reports, and real-world incident response data from INCIDE Digital Data S.L. A total of 10 tactics, 34 techniques, and 46 sub-techniques were identified, with 5 new sub-techniques proposed to address gaps, particularly in mailbox manipulation and defense evasion. Additionally, Privilege Escalation, Lateral Movement, and Credential Access tactics were merged due to overlapping techniques, while the Execution tactic was excluded as it is not central to BEC attacks. To demonstrate the utility of the framework, we characterized two real-world TAs: Cosmic Lynx, a sophisticated actor targeting multinational organizations, and Chiffon Herring, a smaller-scale attacker employing simpler methods. These case studies highlight the framework’s adaptability for analyzing diverse TA profiles and its potential to support improved incident attribution, detection, and prevention strategies.}, archive = {J_ACCESS}, author = {Sebastien Kanj Bonhard and Pau Garcia Villalta and Oriol Rosés and Josep Pegueroles}, doi = {10.1109/ACCESS.2025.3552523}, journal = {IEEE Access}, pages = {50761-50776}, shortjournal = {IEEE Access}, title = {A review of tactics, techniques, and procedures (TTPs) of MITRE framework for business email compromise (BEC) attacks}, volume = {13}, year = {2025}, }
• (2025). A hybrid chatbot model for enhancing administrative support in education: Comparative analysis, integration, and optimization. ACCESS, 13, 50741-50760. (www)

  @article{ , abstract = {The increasing number of students and the limited administrative resources in educational institutions have highlighted inefficiencies in traditional communication methods. Although chatbots offer potential solutions, current implementations are constrained by their architectures: rule-based chatbots lack flexibility, retrieval-based systems depend heavily on predefined datasets, and generative models risk inaccuracies. This study introduces a hybrid chatbot model designed to overcome these challenges by combining rule-based, retrieval-based, and generative approaches. The proposed model employs a Multinomial Naive Bayes classifier to intelligently route user queries to the most appropriate module, ensuring optimized performance across diverse query types. A domain-specific dataset from Sakarya University was developed, featuring 2,253 question-answer pairs categorized into seven administrative domains. Experimental results demonstrate that the hybrid chatbot model outperforms standalone approaches, achieving 97.57% accuracy and improved user satisfaction. Moreover, the model’s flexibility allows seamless adaptation to other sectors, such as healthcare and e-commerce. By significantly reducing administrative workload and enhancing engagement, the proposed hybrid chatbot provides an effective framework for scalable, robust, and adaptable conversational agents in educational settings and beyond.}, archive = {J_ACCESS}, author = {Kanaan Mikael and Cemil Öz and Tarik A. Rashid and Goran Saman Nariman}, doi = {10.1109/ACCESS.2025.3552501}, journal = {IEEE Access}, pages = {50741-50760}, shortjournal = {IEEE Access}, title = {A hybrid chatbot model for enhancing administrative support in education: Comparative analysis, integration, and optimization}, volume = {13}, year = {2025}, }
• (2025). Revisiting ONE simulator in IoV research: Seeing the forest through the trees. ACCESS, 13, 50727-50740. (www)

  @article{ , abstract = {The Internet of Vehicles (IoV) refers to a large-scale distributed system that can be considered as the convergence of the mobile Internet and the Internet of Things (IoT). IoV represents an advanced evolution of vehicular communication networks, transitioning from the primary principles of Vehicular Ad hoc Network (VANET) to a more comprehensive framework that supports the dynamic interaction of vehicles, traffic management systems, and other elements of the environment. This dynamic interaction is referred to as V2X, where the X refers to vehicles, pedestrians, network elements, etc. The IoV ecosystem, in general, is very complex due to the diverse entities in the network. In this article, the complexities of IoV simulation are analyzed through the metaphorical lens of a tree to make sense of IoV ecosystems as hierarchical and interdependent structures. The metaphor “seeing the forest through the trees” reflects the existing IoV framework and the taking of a holistic approach that addresses the complexities of the IoV simulation using different relevant KPIs as well as can inspire a set of principles that shape the simulation of the IoV ecosystem through the ONE simulator. However, during the last two decades or more, many researchers and automakers have dedicated their efforts to developing tools for the experimentation, verification, and validation of IoV protocols and their applications. This study introduces the Opportunistic Network Environment (ONE) simulator as a suitable tool in IoV research. In addition, we have argued that the modular structure of the ONE simulator opens up a great landscape for students/researchers interested in researching IoV.}, archive = {J_ACCESS}, author = {Hasan Mahmood Aminul Islam and Khalid Mahbub Khan and Mahamudur Rahman Maharaz and Angon Bhadra Antu and Farhad Billah and Shishir Majumder and Arham Khan}, doi = {10.1109/ACCESS.2025.3552026}, journal = {IEEE Access}, pages = {50727-50740}, shortjournal = {IEEE Access}, title = {Revisiting ONE simulator in IoV research: Seeing the forest through the trees}, volume = {13}, year = {2025}, }
• (2025). VGA*-RRT*: A mobile robot path planning algorithm for irregular and complex maps. ACCESS, 13, 50714-50726. (www)

  @article{ , abstract = {The aim of this paper is to propose a VGA*-RRT* planning model, which integrates the A* algorithm and the improved Rapidly Exploring Random Tree Star(RRT*) algorithm to address the path planning problem for mobile robots in irregular and complex environments. First, we present a practical obstacle map simplification strategy and utilize the A* algorithm to identify the optimal coarse path within the simplified grid maps. This step ensures that subsequent path generation remains consistently close to the actual optimal path. The optimal guidance vectors are then devised based on the direction vector information of the optimal path points. To reduce the sampling space, we define distinct sector sampling spaces, with the optimal guidance vectors serving as their median line reference. Additionally, we combine an improved vector field with the optimal guidance vector to create a new guidance vector that effectively directs sampling points away from local optima. After generating the initial path, multiple beta distribution points are inserted near the path points to enhance path optimization with higher probability. Finally, our adaptive rounded corner smoothing strategy enables effective rounding of corners while ensuring curvature continuity and producing a smooth overall path. To validate the effectiveness of the algorithm, we conducted simulation experiments using two irregular and complex maps with different levels of complexity. Compared to existing algorithms such as RRT*, Informed RRT*, and Sector Informed RRT*, the proposed method achieves approximately 20%-35% shorter path lengths across both map types while reducing planning time by approximately 70%-80%.}, archive = {J_ACCESS}, author = {Minghao Duan and Zhou Wang and Xiyang Shao and Guangge Ren}, doi = {10.1109/ACCESS.2025.3552785}, journal = {IEEE Access}, pages = {50714-50726}, shortjournal = {IEEE Access}, title = {VGA*-RRT*: A mobile robot path planning algorithm for irregular and complex maps}, volume = {13}, year = {2025}, }
• (2025). AI-driven ensemble classifier for jamming attack detection in VANETs to enhance security in smart cities. ACCESS, 13, 50687-50713. (www)

  @article{ , abstract = {Vehicular Ad-hoc Networks (VANETs) are integral to the fabric of Intelligent Transportation Systems (ITSs), facilitating essential vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, the rising prevalence of jamming attacks, characterized by the intentional disruption of communications through interference signals, presents a significant challenge to the security of VANETs and, consequently, public safety. This emerging threat highlights a critical research gap in the development of sophisticated, AI-driven security solutions for VANETs. In response to this challenge, our study introduces an innovative artificial intelligence (AI) model, meticulously engineered to detect jamming attacks in VANETs. This model represents a synergistic integration of an array of machine learning (ML) and deep learning (DL) classifiers, meticulously analyzing signal characteristics within VANET communication channels. Its primary aim is the effective identification of anomalous patterns signaling the presence of jamming attacks. Extensive simulations were conducted to rigorously test the model’s efficacy, which yielded encouraging results. Initially, we assessed the detection accuracy of 14 different ML classifiers and 4 DL classifiers. Subsequently, we proposed a voting-based ensemble AI classifier combining the most accurate ML and DL classifiers, namely Random Forest (RF), Extra Tree (ET), and fine-tuned Convolutional Neural Network (CNN). This ensemble classifier, RF+ET+CNN, achieved the highest detection accuracy, outperforming the individual classifiers. Specifically, the CNN algorithm demonstrated an exceptional detection accuracy of 99.133%, while the RF and ET classifiers were the most accurate among the ML algorithms tested, with accuracy rates of 97.4359% and 97.4357%, respectively. Notably, the proposed RF+ET+CNN ensemble classifier achieved an impressive detection accuracy of 99.8125%. These findings underscore the superiority of our proposed model over existing jamming detection models. The integration of this model into existing VANET security systems is anticipated to significantly enhance their capability to mitigate jamming attacks, thereby reinforcing the overall security and reliability of VANET communications. This advancement is particularly pertinent in the context of smart city infrastructures, where the safety and efficiency of transportation networks are paramount.}, archive = {J_ACCESS}, author = {Walid El-Shafai and Ahmad Taher Azar and Saim Ahmed}, doi = {10.1109/ACCESS.2025.3552544}, journal = {IEEE Access}, pages = {50687-50713}, shortjournal = {IEEE Access}, title = {AI-driven ensemble classifier for jamming attack detection in VANETs to enhance security in smart cities}, volume = {13}, year = {2025}, }
• (2025). A review on mechanical proving and formalization of mathematical theorems. ACCESS, 13, 50672-50686. (www)

  @article{ , abstract = {The field of artificial intelligence represents a frontier and a focal point of contemporary technological development. As an important embodiment of artificial intelligence applied to theoretical-level research, mechanical proving has been a subject of significant interest among researchers. The past few centuries have witnessed multiple periods of machine proof of mathematical theorems as computer technology has continued to evolve. From the initial proposal of “mechanization of human mental labor” being proposed to the present, the idea of “mechanization of mathematics” has been gradually being realized. Along with various formalization tools being developed, mechanical proving have ushered in a new era nowadays. This paper introduces and summarizes the history of mathematical formalization, the main formalization tools and the main theoretical achievements in different stages and fields. This paper also provides an overview of our group’s work in the formalization of a series of mathematical theorems using Coq, including Morse-Kelley axiomatic set theory and the foundations of analysis among others. Despite the considerable promise of formal tools in bolstering system reliability and mathematical rigorousness, it continues to encounter obstacles in practical applications. Although the formal tool holds considerable prospects in enhancing system reliability and mathematical rigorousness, it still meets with barriers in practical application. The paper concludes with a discussion of some of the current challenges associated with mechanical proving, as well as offers suggestions for future research directions.}, archive = {J_ACCESS}, author = {Si Chen and Wensheng Yu and Guowei Dou and Qimeng Zhang}, doi = {10.1109/ACCESS.2025.3552634}, journal = {IEEE Access}, pages = {50672-50686}, shortjournal = {IEEE Access}, title = {A review on mechanical proving and formalization of mathematical theorems}, volume = {13}, year = {2025}, }
• (2025). Picture fuzzy concept lattice models for knowledge structure analysis. ACCESS, 13, 50652-50671. (www)

  @article{ , abstract = {Knowledge space theory is a theory that uses mathematical language to evaluate learners’ knowledge and guide future learning, belonging to the research field of mathematical psychology. Existing research results mainly focus on classical knowledge spaces, while insufficient attention has been paid to the uncertainty of data in practical problems. Therefore, this paper introduces picture fuzzy sets into knowledge space theory and combines them with formal concept analysis. The relationship between formal contexts and picture fuzzy skill mappings is discussed, and two models, namely the knowledge space picture fuzzy concept lattice and the closure space picture fuzzy concept lattice, are constructed. Based on skill atomic granules and problem atomic granules, corresponding concept construction algorithms are developed respectively, and the relationships between different fuzzy concept lattices are deeply explored. Compared with traditional fuzzy and intuitionistic models, the picture fuzzy concept lattice models proposed in this paper have significant advantages in describing uncertain information. For example, in multi-attribute decision-making scenarios, traditional models may not be able to accurately distinguish between neutral and hesitant attitudes, while the picture fuzzy concept lattice models can effectively distinguish them through the degree of neutral membership, thus enabling more precise analysis and decision-making in complex situations.}, archive = {J_ACCESS}, author = {Chen Zhang and Zengtai Gong}, doi = {10.1109/ACCESS.2025.3552095}, journal = {IEEE Access}, pages = {50652-50671}, shortjournal = {IEEE Access}, title = {Picture fuzzy concept lattice models for knowledge structure analysis}, volume = {13}, year = {2025}, }
• (2025). Approximate bayesian computation for a dual-failure-modes models and its application to failure times data. ACCESS, 13, 50635-50651. (www)

  @article{ , abstract = {In this study, comprehensive approximate Bayesian computation (ABC) technique is explored, and develop for an innovative model. We practically demonstrate approximate Bayesian computation (ABC)-Gibbs scheme using Kolmogorov-Smirnov as distance function; this method is a simulation-based scheme, which the explicit formulation of the likelihood function is not required, hence, suitable for a complex model or big data, where the “accept-reject” mechanism is required for the posterior computation. The proposed ABC-Gibbs algorithm presents a remarkable computational efficiency and accuracy in simulation studies and practice. Additionally, we utilize an adaptable Weibull related lifetime distribution for the purpose of testing the proposed algorithm. The model was designed for the analysis of complex systems exhibiting bathtub-shaped failure rate, and its termed as the two-parameter-modified-Weibull-Chen (TMWC). Some vital reliability characteristics of the TMWC are derived, applied and thoroughly discussed. The TMWC model show its significance within reliability frameworks, notably presenting a bathtub-shaped failure rate with an extended-flat-region, presenting a distinctive burn-in and wear-out periods. Extensive simulation studies were conducted to evaluate the performance of ABC-Gibbs on TMWC. Results showed that incorporating the KS distance metric lead to significant improvement; as we decreases the tolerance threshold, the ABC posterior converges more accurately to the true posterior, though with a lower acceptance rate. Finally, a practical study involving the modeling of two failure datasets with a bathtub failure rate is presented-namely, the failure times of thirty devices, and the failure times of dielectric insulation in a generator armature. This study confirms the practical efficiency of the TMWC model and the ABC-Gibbs methodology in reliability engineering.}, archive = {J_ACCESS}, author = {Mustapha Muhammad and Badamasi Abba and Jinsen Xiao}, doi = {10.1109/ACCESS.2025.3552486}, journal = {IEEE Access}, pages = {50635-50651}, shortjournal = {IEEE Access}, title = {Approximate bayesian computation for a dual-failure-modes models and its application to failure times data}, volume = {13}, year = {2025}, }
• (2025). Count and teletraffic analysis of G/M/1 queueing systems with log-normal interarrival time of bursty IoT traffic. ACCESS, 13, 50611-50634. (www)

  @article{ , abstract = {In this paper, both performance analysis of finite and infinite buffer G/M/1 queueing systems and count analysis using Log-Normally distributed interarrival times to model highly variable Internet of Things (IoT) traffic are addressed. These mathematical analyses require either exact or approximate expressions for the Laplace-Stieltjes Transform (LT) of the interarrival time density function. Due to non-existence of closed-form LT for the Log-Normal (LN) distribution, count analysis is mathematically infeasible. Furthermore, analysis of finite buffer G/M/1/N queue systems is complex, with only asymptotic results available in the literature; and Markovian teletraffic analysis for LN interarrival time cannot be used. To address these challenges, two novel approaches are proposed. First, a new method to numerically calculate the parameter $\sigma $ (probability of experiencing delay) in the infinite buffer G/M/1 system is introduced. Second, the LN interarrival time distribution is approximated by Hyper-Exponential (HE) distributions of varying order. Then, a novel birth-and-death process-based teletraffic analysis for the HE/M/1/N system is developed to approximate the LN/M/1/N system’s performance. The mathematical expressions and performance evaluations presented here focus on relevant metrics, such as expected queue length, packet arrival rate, packet dropping probability, and mean packet delay, which were not addressed in prior related studies of finite buffer G/M/1 queue systems. Additionally, the HE approximation approach facilitates the development of analytical tools to quantify traffic burstiness. Numerical results show that 4th-order HE distribution provides performance metrics with less than 3% difference relative to those of the LN/M/1 queueing systems, and these metrics correlate strongly with count metrics.}, archive = {J_ACCESS}, author = {Sandra Lirio Castellanos-López and Felipe A. Cruz-Perez and Mario Eduardo Rivero-Ángeles and Genaro Hernández-Valdez}, doi = {10.1109/ACCESS.2025.3543460}, journal = {IEEE Access}, pages = {50611-50634}, shortjournal = {IEEE Access}, title = {Count and teletraffic analysis of G/M/1 queueing systems with log-normal interarrival time of bursty IoT traffic}, volume = {13}, year = {2025}, }
• (2025). An analytical hierarchy process-based technique for software requirements prioritization. ACCESS, 13, 50603-50610. (www)

  @article{ , abstract = {Software requirements prioritization is a crucial step in the requirements engineering process. It is applied to define the ordering of specific requirements according to schedule and budget constraints. Many requirements prioritization approaches have been proposed in the literature, but not all have been empirically investigated in real-life settings, cannot be used for all types of projects, and do not provide a hierarchical model for the requirements. Therefore, this study proposes an analytical hierarchy process-based technique to prioritize functional and quality requirements prioritization based on stakeholders’ perspectives. The proposed technique has been demonstrated in the organization’s quality management system and eliminates inconsistencies between business and technical stakeholder valuations. The results indicated that the consistency ratio for hierarchy was acceptable (0.089), thereby confirming the accuracy of the analysis. Notably, the effectiveness requirement (28%) was the highest among the functional and nonfunctional categories in this hierarchy.}, archive = {J_ACCESS}, author = {Thamer Ahmad Al-Rawashdeh and Feras Al'Azzeh and Feras Al-Tarawneh}, doi = {10.1109/ACCESS.2025.3552490}, journal = {IEEE Access}, pages = {50603-50610}, shortjournal = {IEEE Access}, title = {An analytical hierarchy process-based technique for software requirements prioritization}, volume = {13}, year = {2025}, }
• (2025). Zero-shot Recon2Recon: Data-free unsupervised denoiser learning for plug-and-play magnetic resonance imaging reconstruction. ACCESS, 13, 50590-50602. (www)

  @article{ , abstract = {This work presents a novel zero-shot learning method for undersampled magnetic resonance imaging (MRI) reconstruction. The proposed method utilizes a plug-and-play approach, wherein the denoiser neural network, serving as the image prior, is trained solely with the single acquired undersampled k-space data. Specifically, the training of the denoiser employs the Noise2Noise and Self2Self frameworks; the acquired k-space is divided into two disjoint subsets using complementary subsampling masks, which are then reconstructed to serve as the training data and target labels, respectively. We provide theoretical justification for the proposed method, demonstrating the feasibility of training an effective denoiser without the need for extra training data or fully sampled ground truths. The trained denoiser is subsequently utilized as a plug-and-play denoiser for undersampled MRI reconstruction. We have evaluated and compared the performance of our method with several other reconstruction techniques, thereby demonstrating the advantages of the proposed approach.}, archive = {J_ACCESS}, author = {Tae Hyung Kim}, doi = {10.1109/ACCESS.2025.3552853}, journal = {IEEE Access}, pages = {50590-50602}, shortjournal = {IEEE Access}, title = {Zero-shot Recon2Recon: Data-free unsupervised denoiser learning for plug-and-play magnetic resonance imaging reconstruction}, volume = {13}, year = {2025}, }
• (2025). Transient protection of UHV busbars based on standardized VMD-shannon entropy. ACCESS, 13, 50575-50589. (www)

  @article{ , abstract = {Transient bus protection exhibits ultra-high-speed action, yet displays low reliability under weak fault conditions. This paper proposes a novel multidimensional fault analysis framework to address this issue. By investigating the propagation characteristics of transient high-frequency components of fault traveling waves in a 3/2 bus system, a transient protection method using composite characteristics consisting of four fault features is developed, of which two Shannon entropies have increasing properties and one entropy difference has decreasing properties. This dual-dimensional approach minimizes misclassification. Additionally, standardized fault characteristics representation ensures robustness across diverse conditions, which differentiate 36.1% of cross-partial fault characterization data, while also resolving 33.5% of false activations and 11.1% of rejected activations. Finally, EMTP-RV is utilized to validate the accuracy of the proposed algorithm in identifying bus faults and out-of-area faults for a wide variety of scenarios including fault initial angles, fault resistances and fault types, as well as in correctly distinguishing between bus I faults and bus II faults through numerous simulations.}, archive = {J_ACCESS}, author = {Zhenwei Guo and Yongyan Jiang and Zebo Huang and Yangsheng Liu}, doi = {10.1109/ACCESS.2025.3552079}, journal = {IEEE Access}, pages = {50575-50589}, shortjournal = {IEEE Access}, title = {Transient protection of UHV busbars based on standardized VMD-shannon entropy}, volume = {13}, year = {2025}, }
• (2025). Constrained optimization of functional inspection based on delay time model and its application in aircraft maintenance. ACCESS, 13, 50559-50574. (www)

  @article{ , abstract = {Although functional inspection and delay time model are important maintenance policy, inaccuracy under some conditions and lacking of practical data of potential defect and delay time imposes serious restrictions on its application in engineering. According to practical failure degeneration process, a constraint optimization model of non-periodic functional inspection based on expected cost rate is created from delay time conception. Moreover, the indicators measured the accuracy of inspection policy are given. Considering an accepted fact that failure rate is much higher during delay time than before, change-point in failure rate is taken as starting point of delay time. Then a new information criterion of MLE for segmented distribution and hierarchical clustering based on KS test are proposed to estimate change-point. Subsequently, delay time samples can be analyzed from lifetime sample and then its distribution can be exact inferred. Finally, after the distribution of potential defect can be easily calculated by fast Fourier transform, the constraint optimization model of functional inspection is solved. The methodology in this paper is applied to aircraft maintenance, reasonable inspection interval is easily obtained ever if there are nothing but lifetime samples. From the accurate results, not only the constraint model is practicable, but also the proposed methodology is suitable for practical maintenance engineering.}, archive = {J_ACCESS}, author = {Huai Yuan Li and Wen Ming Gui and Wen Jun Chen and Ru Bin Gong}, doi = {10.1109/ACCESS.2025.3543455}, journal = {IEEE Access}, pages = {50559-50574}, shortjournal = {IEEE Access}, title = {Constrained optimization of functional inspection based on delay time model and its application in aircraft maintenance}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Could the use of AI in higher education hinder students with disabilities? a scoping review”. ACCESS, 13, 50556-50558. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”).}, archive = {J_ACCESS}, author = {Oriane Pierrès and Markus Christen and Felix M. Schmitt-Koopmann and Alireza Darvishy}, doi = {10.1109/ACCESS.2025.3534338}, journal = {IEEE Access}, pages = {50556-50558}, shortjournal = {IEEE Access}, title = {Corrections to “Could the use of AI in higher education hinder students with disabilities? a scoping review”}, volume = {13}, year = {2025}, }
• (2025). Advancing object detection: A narrative review of evolving techniques and their navigation applications. ACCESS, 13, 50534-50555. (www)

  @article{ , abstract = {Object detection plays a pivotal role in advancing computer vision systems by enabling machines to perceive and interact intelligently with their environments. Despite significant advancements, comprehensive exploration of its evolution and applications in navigation remains underrepresented. This review paper examines the evolution of object detection technologies, from early methodologies to contemporary advancements, and their critical role in navigation tasks. The emphasis was on the significance of contextual learning in enhancing object detection performance by leveraging spatial and temporal information. Furthermore, the limitations of conventional approaches that rely heavily on hand-engineered features are examined. It is then demonstrated that contextual learning facilitates automated feature extraction, resulting in improved accuracy exceeding a 50% increase and adaptability in diverse applications. The review concludes by outlining future trends and opportunities for further advancements in object detection and, underscoring its transformative impact on autonomous navigation and beyond. In summary, this review contributes to a comprehensive understanding of object detection technologies by offering insights into their evolution, highlighting their applications in navigation, and providing guidance for future research in context-aware systems.}, archive = {J_ACCESS}, author = {Shanelle Tennekoon and Nushara Wedasingha and Anuradhi Welhenge and Nimsiri Abhayasinghe and Iain Murray Am}, doi = {10.1109/ACCESS.2025.3551686}, journal = {IEEE Access}, pages = {50534-50555}, shortjournal = {IEEE Access}, title = {Advancing object detection: A narrative review of evolving techniques and their navigation applications}, volume = {13}, year = {2025}, }
• (2025). Recent advances in pediatric wearable lower-limb exoskeletons for gait rehabilitation: A systematic review. ACCESS, 13, 50511-50533. (www)

  @article{ , abstract = {Cerebral palsy (CP) is the most common physical disability affecting children, often resulting in reduced quality of life due to challenges with movement control, posture, balance, and increased energy expenditure required to walk. Lower-limb exoskeletons (LLEs) offer a promising alternative to traditional rehabilitation methods, with the potential to improve mobility in children with CP. This review aims to provide an updated overview of mechanical characteristics, control strategies, technological advancements, clinical efficacy, and limitations associated with pediatric LLEs for gait rehabilitation in children with CP. The main objectives were to evaluate the current state of technology, review the clinical outcomes of exoskeleton use, and identify emerging trends and challenges in this field. To achieve this, a comprehensive search was conducted using Scopus and Web of Science databases to identify relevant recent studies published from 2020 onward. A total of 11 studies were included based on pre-defined inclusion and exclusion criteria. The findings suggest that pediatric exoskeletons have shown promise in improving gait parameters, such as walking speed and joint kinematics while reducing the metabolic cost in children with CP. Despite these positive outcomes, challenges remain regarding device customization, long-term adherence, and integration into clinical practice. The review underscores the potential of pediatric exoskeletons to significantly improve mobility and quality of life in children with CP but also emphasizes the need for further research to optimize device design, long-term outcomes, and standardized guidelines and protocols to conduct and evaluate clinical trials. These findings have important implications for clinicians, researchers, and developers who seek to advance the field of pediatric rehabilitation technologies.}, archive = {J_ACCESS}, author = {Josué Pacheco-Chérrez and Juan C. Tudon-Martinez and Jorge de J. Lozoya-Santos}, doi = {10.1109/ACCESS.2025.3552757}, journal = {IEEE Access}, pages = {50511-50533}, shortjournal = {IEEE Access}, title = {Recent advances in pediatric wearable lower-limb exoskeletons for gait rehabilitation: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Evaluating the effectiveness of dimensionality reduction on machine learning algorithms in time series forecasting. ACCESS, 13, 50493-50510. (www)

  @article{ , abstract = {Time series analysis is a critical task across various scientific and industrial domains, enabling the extraction of valuable insights from temporal data. High dimensionality of time series data can lead to computational inefficiencies and increased complexity. Dimensionality reduction techniques play a crucial role in handling the high-dimensional nature of time series data essential information while reducing computational complexity. This study explores the impact of various dimensionality reduction techniques and machine learning models on enhancing the accuracy and efficiency of time series datasets. The effectiveness of different dimensionality reduction methods is evaluated based on their ability to simplify data while preserving essential features. Subsequently, several machine learning models—such as Autoregressive Integrated Moving Average, k-Nearest Neighbors, Random Forest, Least Absolute Shrinkage and Selection Operator, and Extreme Gradient Boosting are applied to the transformed data. The findings reveal that the choice of dimensionality reduction technique significantly influences the performance of these models. Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. These results highlight the importance of selecting an appropriate combination of dimensionality reduction and machine learning techniques based on the specific characteristics of the time series data. This can contributes to a deeper understanding of how these methods can be effectively integrated, thereby enhancing decision-making in areas such as finance, meteorology, and operational planning.}, archive = {J_ACCESS}, author = {Rida Zaheer and Muhammad Kashif Hanif and Muhammad Umer Sarwar and Ramzan Talib}, doi = {10.1109/ACCESS.2025.3551741}, journal = {IEEE Access}, pages = {50493-50510}, shortjournal = {IEEE Access}, title = {Evaluating the effectiveness of dimensionality reduction on machine learning algorithms in time series forecasting}, volume = {13}, year = {2025}, }
• (2025). Day-ahead scheduling model for high-penetration renewable energy power system considering energy storage for auxiliary peak shaving and frequency regulation. ACCESS, 13, 50480-50492. (www)

  @article{ , abstract = {In response to the increasing pressures of frequency regulation and peak shaving in high-penetration renewable energy power system, we propose a day-ahead scheduling model that incorporates the auxiliary role of energy storage systems in supporting frequency regulation and peak shaving operations. First, we derive the constraints related to steady-state power imbalance, transient frequency excursion risk, and quasi-steady-state frequency recovery risks associated with the participation of energy storage in these ancillary services. Then, we analyze in detail the compatibility between the regulation requirements at various stages of system operation and the corresponding actions of energy storage systems. Based on this analysis, we develop a comprehensive day-ahead active power and frequency security scheduling model to improve the economic efficiency and stability of high-penetration renewable energy power system. The improved IEEE 30-bus system was used as a case study to validate the effectiveness and necessity of the proposed model, which considers the entire dynamic peak shaving and frequency regulation process. Although the associated costs increased by 8.32%, 10.5%, and 2.55% compared to Scenario 1, Scenario 2, and Scenario 3, respectively, the model significantly enhanced the system’s frequency stability. Additionally, the role of energy storage resources in the overall dynamic peak shaving and frequency regulation process was confirmed. Furthermore, the superiority of the proposed day-ahead scheduling method in enhancing system kinetic energy and minimizing the lowest frequency point was also verified.}, archive = {J_ACCESS}, author = {Juan Ren and Wei Tian and Guiliang Gao and Peng Yao and Chaoshan Xin and Mengni Ye and Wenjin Cui}, doi = {10.1109/ACCESS.2025.3552758}, journal = {IEEE Access}, pages = {50480-50492}, shortjournal = {IEEE Access}, title = {Day-ahead scheduling model for high-penetration renewable energy power system considering energy storage for auxiliary peak shaving and frequency regulation}, volume = {13}, year = {2025}, }
• (2025). Optimizing artificial neural networks using mountain gazelle optimizer. ACCESS, 13, 50464-50479. (www)

  @article{ , abstract = {The performance of artificial neural networks heavily depends on the optimization of network parameters, specifically weights and biases, during the training process. Effectively adjusting these parameters is essential to minimize the error between predicted and actual outputs. While traditional training algorithms, such as gradient-based methods, have been widely used, they often face challenges like premature convergence and stagnation in local optima. Training an ANN can, therefore, be viewed as an optimization problem, where the goal is to fine-tune parameters to achieve accurate and efficient performance. In this study, we introduce a novel approach to optimizing neural network parameters using the Mountain Gazelle Optimizer (MGO), a nature-inspired metaheuristic algorithm that mimics the social hierarchy and behavioral patterns of wild mountain gazelles. The MGO algorithm leverages its unique features, including hierarchical social structure and adaptive movement strategies, to effectively navigate the complex parameter space of neural networks. The algorithm’s search mechanism integrates four key behavioral strategies: Territorial Solitary Males (TSM) for refining optimal solutions, Maternity Herds (MH) for balancing exploration and exploitation, Bachelor Male Herds (BMH) for global exploration, and Migration to Search for Food (MSF) for introducing randomness to prevent stagnation in local optima. These mechanisms work collaboratively, ensuring a dynamic and balanced optimization process throughout the training phase. To evaluate the effectiveness of the proposed approach, we conducted comprehensive experiments using various classification datasets from the UCI repository. The performance of the MGO-based neural network optimizer was compared with traditional backpropagation and several state-of-the-art optimization algorithms. Experimental results demonstrate that the proposed method exhibits superior performance in terms of convergence speed and avoiding local optima, suggesting that MGO is a promising alternative for optimizing artificial neural networks during training.}, archive = {J_ACCESS}, author = {Muhammed Abdulhamid Karabiyik and Bahaeddin Turkoglu and Tunc Asuroglu}, doi = {10.1109/ACCESS.2025.3552831}, journal = {IEEE Access}, pages = {50464-50479}, shortjournal = {IEEE Access}, title = {Optimizing artificial neural networks using mountain gazelle optimizer}, volume = {13}, year = {2025}, }
• (2025). The research on image adaptive block compressive sensing method based on underwater depth information. ACCESS, 13, 50450-50463. (www)

  @article{ , abstract = {Underwater imaging technology has been confronted with the challenge in computation, storage and transmission. Compressive sensing with advantage in reducing data redundancy is widely used in underwater imaging. However, compressive sensing with fixed sampling rate restricts reconstruction quality of the primary object. To address this issue, this paper innovatively proposes a compound depth-based adaptive block compressed sensing method (CD-ABCS). The compound depth matrix that is correlated with underwater depth information, image saliency and image variance is used to set sampling rate of the image block. According to the compound depth matrix, the original image is divided into multi-level image blocks to conduct sparse sampling. Reconstructed image blocks are stitched into a complete image. To verify propose method, experiments including method comparison, ablation study and parameter optimization are executed. Experimental results show that, the proposed method is certified to have a significant improvement in image quality by comparing with other adaptive block compressive sensing methods. Specifically, when the global sampling rate is 0.5, the peak signal-to-noise ratio (PSNR) is increased by 1dB, and the structural similarity (SSIM) improves by at least 0.015. Proposed method is capable of enhancing image quality at various global sampling rates.}, archive = {J_ACCESS}, author = {Shoubo Zhao and Jing Wang and Cheng Qian and Yue Yin}, doi = {10.1109/ACCESS.2025.3552555}, journal = {IEEE Access}, pages = {50450-50463}, shortjournal = {IEEE Access}, title = {The research on image adaptive block compressive sensing method based on underwater depth information}, volume = {13}, year = {2025}, }
• (2025). Addressing adversarial attacks in IoT using deep learning AI models. ACCESS, 13, 50437-50449. (www)

  @article{ , abstract = {Adversarial attacks, specialized attacks, pose a severe threat to AI model performance in various applications, including the Internet of Things (IoT). Various defense mechanisms have been proposed to counter these attacks. However, their primary limitation lies in their inability to effectively handle broader datasets derived from diverse applications. In this study, we utilize multiple AI models with adaptive weights applied at different neural network layers to achieve enhanced performance and more robust results. This study introduces a novel AI-based deep learning model to detect adversarial threats within IoT systems, optimizing data preprocessing, feature extraction, and classification through a holistic approach. A three-stage filtering technique featuring Adaptive Weights was applied to enhance the data preprocessing efficiency. A two-level adaptive feature extraction strategy was utilized to maximize feature learning performance. This is refined using adaptive dilated enriched convolution operations, whereas statistical attributes are optimized through a Quantum-inspired Coati Optimization Algorithm (Q-COA). A dual system based on self-attention combines a Restricted Boltzmann Machine (RBM) with a Recurrent Convolutional Neural Network (RCNN). This configuration effectively identifies adversarial attacks by linking classifiers via a self-attention-driven weight-sharing mechanism. The proposed two-level weight-sharing approach surpasses conventional classifiers and achieves superior classification accuracy. This comprehensive Artificial Intelligence (AI) model significantly improves the preprocessing efficiency, feature learning performance, and classification accuracy, offering an innovative and robust solution for adversarial attack detection in IoT systems. The performance metric, Area Under the Curve (AUC), achieves values of 0.95 and 0.97 for two datasets using the proposed model, highlighting its effectiveness compared to the models in the comparison.}, archive = {J_ACCESS}, author = {Sesibhushana Rao Bommana and Sreehari Veeramachaneni and Syed Ershad Ahmed and M. B. Srinivas}, doi = {10.1109/ACCESS.2025.3552529}, journal = {IEEE Access}, pages = {50437-50449}, shortjournal = {IEEE Access}, title = {Addressing adversarial attacks in IoT using deep learning AI models}, volume = {13}, year = {2025}, }
• (2025). Automatic modulation recognition method based on channel-enhanced convolution and linear-angular attention. ACCESS, 13, 50428-50436. (www)

  @article{ , abstract = {Automatic Modulation Recognition (AMR) is an electronic signal processing technology designed to automatically identify and classify the modulation type of radio signals. Existing AMR methods suffer from a significant decrease in classification accuracy when the Signal-to-Noise Ratio (SNR) degrades. To enhance the accuracy of AMR methods under low SNR conditions, this paper proposes a novel AMR method based on Channel-Enhanced Convolution and a Linear-Angular Attention Mechanism, named CCLANN (Channel-enhanced Convolutional Linear-Angular Attention Neural Network). This method first utilizes a channel-enhanced deep convolutional module to extract spatial information from the feature maps of the input signal across different channel dimensions. Subsequently, a linear-angular attention mechanism is introduced to effectively extract time-series features from the modulated signals. Experimental results on the public datasets RML2016.10a and RML2016.10b demonstrate that the proposed AMR method improves the average classification accuracy by 2.4% and 2.1%, respectively, compared to existing schemes in the SNR range of −4 dB to 4 dB, while maintaining robustness. Slight improvements in classification accuracy are also observed at SNRs above 0 dB.}, archive = {J_ACCESS}, author = {An Gong and Anxuan Jia and Shuhui Wu and Bitian Fan and Xintong Wei}, doi = {10.1109/ACCESS.2025.3552414}, journal = {IEEE Access}, pages = {50428-50436}, shortjournal = {IEEE Access}, title = {Automatic modulation recognition method based on channel-enhanced convolution and linear-angular attention}, volume = {13}, year = {2025}, }
• (2025). A dynamic pricing and clustering scheme for efficient home energy management system in a microgrid. ACCESS, 13, 50411-50427. (www)

  @article{ , abstract = {Home energy management systems (HEMS) in microgrids have played a vital role in revolutionizing the power system. Consumer participation in microgrid load scheduling is a key feature of modern HEMS. Much research has been conducted on minimizing the peak-to-average ratio and cost optimization. This paper proposes a community-based HEMS model incorporating dynamic pricing to achieve these objectives. The loads are dynamically clustered, a process where similar loads are grouped together based on their characteristics and scheduled for one day in 10-minute intervals. The resulting load profile is compared against existing algorithms, demonstrating notable improvements. Moreover, the application of dynamic pricing to the optimized load profile, leveraging resources such as battery-powered electric vehicles, solar PV, battery storage, and the primary grid or main grid, has shown promising results. Priority is given to the renewable and local energy storage to minimize cost. Three case studies, each considering weather-driven variations in appliance usage, demonstrate the potential for significant cost reductions. The surplus energy is returned to the primary grid, while in cases of insufficient microgrid supply, the power is drawn from the primary grid. Net metering with smart meters is used to ensures the accuracy and fairness of the billing process, providing consumers with a transparent and reliable system. The results show a substantial cost reduction of up to 36.2% in the spring and 7.6% in the summer and winter seasons, offering a hopeful outlook for the future of energy management in microgrids.}, archive = {J_ACCESS}, author = {Nabila Ahmad and Ayesha Abbasi and Hassan Abdullah Khalid and and Habibur Rehman}, doi = {10.1109/ACCESS.2025.3552633}, journal = {IEEE Access}, pages = {50411-50427}, shortjournal = {IEEE Access}, title = {A dynamic pricing and clustering scheme for efficient home energy management system in a microgrid}, volume = {13}, year = {2025}, }
• (2025). Comparing portability of FPGA high-level synthesis frameworks in the context of a highly-parallel application. ACCESS, 13, 50394-50410. (www)

  @article{ , abstract = {With the growing popularity of FPGA-based accelerators in HPC applications, new challenges have emerged, particularly in terms of programming and portability. This paper provides an overview of the current state of FPGA tools and their limitations. This study evaluates the performance and portability of two frameworks, SYCL and OpenCL, for developing HPC FPGA solutions. The case of porting a highly-parallel application to FPGAs is studied. First, naïve, low-development-effort implementations are presented using both ND-range and single-task types of kernels, and their performance is evaluated. Subsequently, an optimized FPGA-centric approach is presented and assessed using metrics from the compilation framework. Finally, the different approaches presented are implemented using OpenCL and SYCL and their performance is evaluated. Results reveal that ND-range kernels offer high portability for highly parallel applications, while single-task codes exhibit significantly lower portability. Additionally, SYCL struggles to generate efficient hardware architectures for this kind of application when described as single-task codes, although its performance when following the ND-range approach is surprisingly high.}, archive = {J_ACCESS}, author = {Manuel De Castro and Roberto R. Osorio and Francisco J. Andújar and Rocío Carratalá-Sáez and Yuri Torres and Diego R. Llanos}, doi = {10.1109/ACCESS.2025.3551428}, journal = {IEEE Access}, pages = {50394-50410}, shortjournal = {IEEE Access}, title = {Comparing portability of FPGA high-level synthesis frameworks in the context of a highly-parallel application}, volume = {13}, year = {2025}, }
• (2025). Understanding and addressing variability through recalibration in a Brain–Computer interface for intelligent vehicle control using long-term implanted human neural data: A case study. ACCESS, 13, 50379-50393. (www)

  @article{ , abstract = {This work involved human subject in its research. Approval of all ethical and experimental procedures and protocols was granted by the FDA and IRB (NCT01997125). Even when trained on sufficient neural data, the performance of a brain-computer interface (BCI) is susceptible to error due to both systematic and random noise. Error from both sources may increase over time with changes in the neural signal or recording process. In practice, systematic error could be addressed through recalibration of model parameters, where the frequency of recalibration is based on the rate of performance loss. Random prediction error could be addressed by incorporating more observations into each prediction. However, recalibration incurs computational overhead and time delay; increasing the number of observations per prediction increases the latency from observation to prediction. The needs of the BCI application will determine whether the necessary frequency of recalibration or increased observations per prediction are valid configurations. This study evaluates recalibration as a mechanism to address accumulating systematic error in the unique context of vehicle driving, using neural data recorded previously in a driving simulation environment. A prior study demonstrated vehicle control but required some modifications to the paradigm to address variability, such as increasing the amount of data used to make each inference. In this study, offline analysis revealed substantial variation in the neural data recorded during the original experiments. Adding recalibration using labels assigned by the route navigation system improved classification accuracy from 55% to over 90% and reduced the latency of the classifier output. These findings demonstrate the potential of recalibration in BCI as a mitigation for neural variability, with application to vehicle control by individuals with tetraplegia.}, archive = {J_ACCESS}, author = {Yicong Xiao and Spencer Kellis and Christopher. F Reiche and Florian Solzbacher}, doi = {10.1109/ACCESS.2025.3551294}, journal = {IEEE Access}, pages = {50379-50393}, shortjournal = {IEEE Access}, title = {Understanding and addressing variability through recalibration in a Brain–Computer interface for intelligent vehicle control using long-term implanted human neural data: A case study}, volume = {13}, year = {2025}, }
• (2025). Enhanced fault localization in energy systems using an improved MVO algorithm and multistrategy optimization. ACCESS, 13, 50367-50378. (www)

  @article{ , abstract = {With the advancement of energy systems toward intelligence and renewability, fault localization in active distribution networks (ADNs) has become a critical issue due to its direct impact on power supply reliability and system stability. Existing fault localization methods often suffer from low accuracy and slow convergence, particularly in complex network structures and under variable fault scenarios. This paper proposes an Improved Multiverse Optimization (IMVO) algorithm to address these challenges. The IMVO algorithm enhances the standard Multiverse Optimization (MVO) by introducing a three-phase search strategy—exploration, development, and hybrid—and dynamically adjusting key parameters such as the Travel Distance Ratio (TDR) and Wormhole Existence Probability (WEP) to balance global and local search capabilities. Additionally, elite retention and differential evolution strategies are integrated to improve population diversity and prevent premature convergence. Validation on the IEEE 33-node ADN model with distributed energy sources demonstrates that the IMVO algorithm achieves fault localization accuracies of 97.2% for single-point faults and 97.6% for multipoint faults, with average convergence within 6 and 8 generations, respectively. Further experiments on the IEEE 69-node network validate the algorithm’s scalability, achieving 96.8% and 97.0% accuracy for single-point and multipoint faults, respectively, with only a moderate increase in computational time. The results indicate that the IMVO algorithm maintains near-linear computational complexity as network size grows, making it a viable solution for real-time fault diagnosis in large-scale ADNs. These findings highlight the superior accuracy, convergence speed, and robustness of the proposed IMVO algorithm, demonstrating its potential as an efficient and reliable fault localization method for complex ADN environments and future smart grid applications.}, archive = {J_ACCESS}, author = {Ze Li}, doi = {10.1109/ACCESS.2025.3552761}, journal = {IEEE Access}, pages = {50367-50378}, shortjournal = {IEEE Access}, title = {Enhanced fault localization in energy systems using an improved MVO algorithm and multistrategy optimization}, volume = {13}, year = {2025}, }
• (2025). Distinct views improve generalization and robustness: Combinations of augmentations with different features. ACCESS, 13, 50353-50366. (www)

  @article{ , abstract = {Data augmentation is an effective method for improving deep learning model performance. In the vision domain, various augmentation studies have been conducted to enhance generalization ability and robustness against corruption. However, recent augmentation studies have focused on transforming data to be more diverse and challenging. This approach can prevent models from properly learning key features of objects, such as texture and shape. In response, unlike traditional methods that employ a single augmentation strategy, our method simultaneously utilizes three distinct augmentations, each with different characteristics. We transform the images into color-preserving, shape-preserving, and diversity-enhancing views. More specifically, to ensure the model still captures the key factors of visual information, we utilize two feature-preserving views, one with local color (texture) and the other with global shape information. The third view is transformed by an augmentation that enhances diversity. By utilizing these three distinct augmentations, DV (Distinct Views) helps the model effectively learn all the important features of visual information. To further improve robustness against corruption, we incorporate adversarial perturbations into the third (diversity-enhancing) view, unifying additional hardness and diversity. Experimental results show that DV considerably enhances generalization and robustness against corruption, achieving state-of-the-art performance on various image benchmark datasets. Furthermore, we confirm that DV is quite effective even for Transformer-based models, which typically underperform on small datasets.}, archive = {J_ACCESS}, author = {Keon Kim and Hyun Woo Kim and Yong Suk Choi}, doi = {10.1109/ACCESS.2025.3552110}, journal = {IEEE Access}, pages = {50353-50366}, shortjournal = {IEEE Access}, title = {Distinct views improve generalization and robustness: Combinations of augmentations with different features}, volume = {13}, year = {2025}, }
• (2025). Exploring the potential of tungsten disulfide interlayer for perovskite solar cell performance enhancement through numerical approach. ACCESS, 13, 50340-50352. (www)

  @article{ , abstract = {This study investigated the effect of an inorganic interlayer (IL) of WS2 on the performance of perovskite solar cells (PSCs). The device configuration with IL consisted of Au/CuSbS2/CH3NH3SnI3/WS2/ZnO/Glass structure. The chemical instability of ZnO causes rapid perovskite breakdown upon its contact with metal halide perovskites. This can be addressed by inserting the IL between these two materials. The effect of different parameters including the thickness of WS2 and perovskite, the defect density of perovskite, and interfaces were investigated. Moreover, the impact of doping density of WS2 and the electron affinities of ZnO, CuSbS2, and WS2 were also studied. The results showed that the device performance with IL enhanced remarkably as PCE of 34.38%, JSC of 33.30 mA/cm2, VOC of 1.13 V, and FF of 91.16% compared to the device without IL. The research highlights the significant potential of interlayer-based PSCs and provides valuable insights for their further development.}, archive = {J_ACCESS}, author = {Aqsa Islam and Nasim Ullah and Vojtech Blazek and Lukas Prokop and Hafeez Anwar}, doi = {10.1109/ACCESS.2025.3549606}, journal = {IEEE Access}, pages = {50340-50352}, shortjournal = {IEEE Access}, title = {Exploring the potential of tungsten disulfide interlayer for perovskite solar cell performance enhancement through numerical approach}, volume = {13}, year = {2025}, }
• (2025). AI-powered IoT: A survey on integrating artificial intelligence with IoT for enhanced security, efficiency, and smart applications. ACCESS, 13, 50296-50339. (www)

  @article{ , abstract = {The Internet of Things (IoT) and artificial intelligence (AI) enabled IoT is a significant paradigm that has been proliferating to new heights in recent years. IoT is a smart technology in which the physical objects or the things that are ubiquitously around us are networked and linked to the internet to deliver new services and enhance efficiency. The primary objective of the IoT is to connect all the physical objects or the things of the world under a common infrastructure, allowing humans to control them and get timely, frequent updates on their status. These things or devices connected to IoT generate, gather and process a massive volume of binary data. This massive volume of data generated from these devices is analyzed and learned by AI algorithms and techniques that aid in providing users with better services. Thus, AI-enabled IoT or artificial IoT (AIoT) is a hybrid technology that merges AI with IoT and is capable of simplifying complicated and strenuous tasks with ease and efficiency. The various machine learning (ML) and deep learning (DL) algorithms in IoT are necessary to ensure the IoT network’s improved security and confidentiality. Furthermore, this paper also surveys the various architectures that form the backbone of IoT and AIoT. Moreover, the myriad state-of-the-art ML and DL-based approaches for securing IoT, including detecting anomalies/intrusions, authentication and access control, attack detection and mitigation, preventing distributed denial of service (DDoS) attacks, and analyzing malware in IoT, are also enlightened. In addition, this work also reviews the role of AIoT in optimizing network efficiency, securing IoT infrastructures, and addressing key challenges. Furthermore, it explores cutting-edge technologies like blockchain, 6G-enabled AIoT, federated learning (FL), and hyperdimensional (HD) computing, indicating their potential in advancing IoT and AIoT-driven applications within sectors like healthcare, autonomous systems, and industrial automation. Therefore, based on the plethora of prevailing significant works, the objective of this manuscript is to provide a comprehensive survey that expounds on AIoT in terms of security, architecture, applications, emerging technologies, and challenges.}, archive = {J_ACCESS}, author = {Vivek Menon U and Vinoth Babu Kumaravelu and Vinoth Kumar C and Rammohan A and Sunil Chinnadurai and Rajeshkumar Venkatesan and Han Hai and Poongundran Selvaprabhu}, doi = {10.1109/ACCESS.2025.3551750}, journal = {IEEE Access}, pages = {50296-50339}, shortjournal = {IEEE Access}, title = {AI-powered IoT: A survey on integrating artificial intelligence with IoT for enhanced security, efficiency, and smart applications}, volume = {13}, year = {2025}, }
• (2025). Mobile location with multi-spot measurements model considering anchor node position errors. ACCESS, 13, 50282-50295. (www)

  @article{ , abstract = {Multi-spot measurements model has emerged as a powerful approach for enhancing positioning accuracy in wireless sensor networks (WSNs), particularly in dynamic environments like autonomous aerial vehicles (AAV) swarms. By leveraging multiple measurements collected over short intervals, this models can significantly improve localization performance compared to traditional single-measurement approaches. However, practical implementation of multi-spot techniques faces challenges when anchor nodes are subject to motion and position errors, which can substantially impact the accuracy of position estimation. This paper addresses this critical gap by deriving a new Cramér-Rao Lower Bound for sensor location estimation in multi-spot scenarios that explicitly accounts for anchor node movement and position uncertainties. Building on this theoretical foundation, we introduce novel closed-form solutions and iterative algorithms tailored for multi-spot measurements with imperfect anchor positions. Additionally, we propose a semi-definite programming (SDP) algorithm formulated on convex optimization principles to further enhance robustness and accuracy. Extensive simulation results validate the efficacy of our proposed methods against the derived CRLB, demonstrating the potential of enhanced multi-spot techniques to significantly improve localization performance in dynamic WSN environments, with particular relevance to AAV swarm applications. This work advances the practical implementation of multi-spot measurements models, paving the way for more accurate and reliable positioning systems in challenging real-world scenarios.}, archive = {J_ACCESS}, author = {Chenyu Yang and Rui Ren and Dongkun Jin and Jiyan Huang}, doi = {10.1109/ACCESS.2025.3551359}, journal = {IEEE Access}, pages = {50282-50295}, shortjournal = {IEEE Access}, title = {Mobile location with multi-spot measurements model considering anchor node position errors}, volume = {13}, year = {2025}, }
• (2025). Assessment of the impact of missing teeth on deep learning-based tooth numbering method from panoramic X-rays. ACCESS, 13, 50274-50281. (www)

  @article{ , abstract = {Automatically analyzing panoramic dental X-rays through AI-based approaches has attracted attention and concern among dentists with the recent advancement of AI techniques. Recent studies have shown that these approaches have achieved high accuracy in recognizing and numbering teeth. However, the datasets utilized in such studies contain only a small proportion of panoramic X-rays that include missing teeth, despite such X-rays often being observed in actual diagnosis. Accordingly, the suitability of these approaches for practical panoramic X-rays remains unconfirmed. This study verifies the efficacy of three powerful and efficient object detection models for tooth numbering on panoramic X-rays with varying numbers of missing teeth. The experimental results indicate that the performance of AI models is affected by the existing tooth count appearing in panoramic X-ray dataset images, and images with a lower tooth count tend to exhibit a lower tooth-numbering accuracy that reaches a precision of 62.2%. Additionally, our study highlights the features in low tooth-count images that influence this accuracy. Our results thus also verify the unsuitability of AI models in numbering on panoramic X-ray images with a low tooth count. Importantly, this study suggests that disclosing the tooth count distribution of datasets is indispensable for proving the effectiveness of AI-based approaches in the tooth numbering task.}, archive = {J_ACCESS}, author = {Zhongbo Tang and Tomoaki Mameno and Tomonori Hayami and Chonho Lee and Susumu Date and Kazunori Ikebe and Kazunori Nozaki}, doi = {10.1109/ACCESS.2025.3552203}, journal = {IEEE Access}, pages = {50274-50281}, shortjournal = {IEEE Access}, title = {Assessment of the impact of missing teeth on deep learning-based tooth numbering method from panoramic X-rays}, volume = {13}, year = {2025}, }
• (2025). An enhanced generative adversarial network prediction model based on LSTM and attention for corrosion rate in pipelines. ACCESS, 13, 50260-50273. (www)

  @article{ , abstract = {To address the pervasive issue of internal pipeline corrosion in the oil and gas industry, this paper proposes a hybrid intelligent model for predicting corrosion rates. This model integrates an improved Generative Adversarial Network with Grey Wolf Optimization and Support Vector Regression (LAGAN-GWO-SVR). In this model, the traditional Generative Adversarial Network is combined with the Long Short-Term Memory network and the Multi-head Attention mechanism. The Long Short-Term Memory is used to capture and analyze the sequential features in internal pipeline corrosion data, effectively uncovering latent relationships within the sequences. Meanwhile, the Multi-head Attention mechanism focuses on key features, further enhancing the model’s ability to concentrate on critical information. In addition, to more accurately predict the corrosion rate of internal pipeline in complex environments, this paper utilizes Grey Wolf Optimization to optimize the hyper-parameters in Support Vector Regression. Three sets of experiments were conducted, including different data augmentation algorithms, various improvement strategies, and comparisons with other benchmark models. The experimental results show that the model offers significant advantages in predicting internal pipeline corrosion rates. The LAGAN-GWO-SVR in this paper achieves a Root Mean Square Error of 0.013 and a coefficient of determination of 0.982.}, archive = {J_ACCESS}, author = {Pujun Long and Mi Liang and Hongjian Chen and Qin Yang}, doi = {10.1109/ACCESS.2025.3552096}, journal = {IEEE Access}, pages = {50260-50273}, shortjournal = {IEEE Access}, title = {An enhanced generative adversarial network prediction model based on LSTM and attention for corrosion rate in pipelines}, volume = {13}, year = {2025}, }
• (2025). Ultra-fast AR grading system: Real-time beef quality grading with micro attention U-net and VGG-16. ACCESS, 13, 50244-50259. (www)

  @article{ , abstract = {Speed of beef grading is crucial. Grade determination must be completed quickly, because all daily quantities must be graded within a set time each day. However, current grading system is slow and inconsistent, with grading times ranging from 2 to 5 minutes per beef carcass. Current system is passive, in which beef graders analyze the beef manually. Therefore, we propose Ultra-Fast AR Grading System (UFAR Grading System), an automated system for beef quality grading. The system consists of two parts: Segmentation and Classification. In the Segmentation part, the segmentation model operates when the beef grader captures a beef image with an AR device. The segmentation model segments the muscle area, which is crucial for determining the beef quality grades. In the Classification part, once this muscle area image is sent to the server, the classification model is activated to grade beef quality. To implement Ultra-Fast AR Grading System, a fast and lightweight segmentation model that is suitable for AR devices was required. Therefore, we developed Micro Attention U-Net (MAU-Net) model based on Attention U-Net architecture. MAU-Net achieved 0.25M parameters and 5.88ms inference speed. Furthermore, we aimed to find a classification model that could quickly and accurately classify grades using the muscle area images obtained from MAU-Net. We experimented with three classification models, ResNeXt-50, DenseNet-121, and VGG-16. Among these, VGG-16 exhibited the fastest inference speed of 2.59ms. And in combination with MAU-Net, it achieved a grading accuracy of 98.283%.}, archive = {J_ACCESS}, author = {Chaeyun Park and Eungyeol Song}, doi = {10.1109/ACCESS.2025.3551547}, journal = {IEEE Access}, pages = {50244-50259}, shortjournal = {IEEE Access}, title = {Ultra-fast AR grading system: Real-time beef quality grading with micro attention U-net and VGG-16}, volume = {13}, year = {2025}, }
• (2025). Retail resilience engine: An agentic AI framework for building reliable retail systems with test-driven development approach. ACCESS, 13, 50226-50243. (www)

  @article{ , abstract = {System reliability and operational resilience are two critical success factors in the retail industry that are directly connected to customer satisfaction and business sustainability. Staying competitive in today’s dynamic and rapidly evolving market requires rapid adaptability. However, it contradicts the reliability and resilience. This paper proposes an innovative solution, the Retail Resilience Engine (RRE), to establish a balance between these success factors and market demand. It is a unique framework that combines Test-Driven Development (TDD) with a Large Language Model (LLM). This framework follows the state-of-the-art Agentic-AI architecture. It effectively evaluates the decision-making process at rapid speed in retail by incorporating diverse factors, including inventory management, demand forecasting, and customer feedback. As a result, the system reliability is improved significantly. The experimental analysis of the proposed framework shows its decision-making is similar to human experts with a similarity index of 97.5%. It further proves the reliability of the system. The framework also scales effectively, maintaining high accuracy, precision, recall, and F1 scores across varying dataset sizes. The robustness analysis of the system demonstrates the agility enhancement across diverse retail domains, ensuring consistent performance with accuracy exceeding 90% across all tested scenarios. The integration of a creative filtering mechanism further enhances the performance of the RRE framework by preventing 98.2% of the irrelevant inputs. Overall, the proposed RRE framework demonstrates the impressive potential to transform retail systems by enhancing reliability, scalability, and decision-making quality through an Agentic-AI approach.}, archive = {J_ACCESS}, author = {Lalit Narayan Mishra and Biswaranjan Senapati}, doi = {10.1109/ACCESS.2025.3552592}, journal = {IEEE Access}, pages = {50226-50243}, shortjournal = {IEEE Access}, title = {Retail resilience engine: An agentic AI framework for building reliable retail systems with test-driven development approach}, volume = {13}, year = {2025}, }
• (2025). Transmission power optimization for hybrid NOMA-URLLC system with STAR-RIS assistance. ACCESS, 13, 50216-50225. (www)

  @article{ , abstract = {The simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged as a cutting-edge technology for future wireless networks. In this paper, we explore an uplink STAR-RIS-enabled ultra-reliable and low-latency communications (URLLC) system utilizing hybrid non-orthogonal multiple access (NOMA). To enhance system performance, we pair one transmitting user with one reflecting user as a NOMA pair. Using a time division multiple access (TDMA) protocol among different pairs, our goal is to minimize the overall transmission power while meeting the individual reliability requirements of each user. This is achieved through joint optimization of block length and transmit power of users alongside beamforming design at the STAR-RIS. This non-convex optimization problem is then effectively approximated by estimating the channel dispersion in both the high signal-to-interference-plus-noise ratio (SINR) regime and across the entire practical SINR range. Then, we address this power minimization problem using an alternating optimization approach. Simulation results demonstrate the advantages of the proposed framework over conventional approaches, such as conventional RIS-based and OMA-based systems. Moreover, beamforming design, block-length optimization, and the rising of the element number of STAR-RIS significantly impact power reduction performance.}, archive = {J_ACCESS}, author = {Tra Huong Thi Le and Nguyen van Dien and Luong Vuong Nguyen and Tri-Hai Nguyen and O-Joun Lee}, doi = {10.1109/ACCESS.2025.3528251}, journal = {IEEE Access}, pages = {50216-50225}, shortjournal = {IEEE Access}, title = {Transmission power optimization for hybrid NOMA-URLLC system with STAR-RIS assistance}, volume = {13}, year = {2025}, }
• (2025). Enhancing anti-money laundering frameworks: An application of graph neural networks in cryptocurrency transaction classification. ACCESS, 13, 50201-50215. (www)

  @article{ , abstract = {Cryptocurrency money laundering is a pressing issue, as it not only facilitates and hides criminal activities but also disrupts markets and the overall financial system. To respond this challenge, researchers are trying to develop robust Anti-Money Laundering (AML) frameworks. These efforts play a crucial role in promoting societal welfare by mitigating the impact of criminal activities. This paper explores the application of Graph Neural Networks (GNNs) for classifying Bitcoin transactions. The research specifically employs Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), the Chebyshev spatial convolutional neural networks, and GraphSAGE networks. Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.}, archive = {J_ACCESS}, author = {Stefano Ferretti and Gabriele D’Angelo and Vittorio Ghini}, doi = {10.1109/ACCESS.2025.3552240}, journal = {IEEE Access}, pages = {50201-50215}, shortjournal = {IEEE Access}, title = {Enhancing anti-money laundering frameworks: An application of graph neural networks in cryptocurrency transaction classification}, volume = {13}, year = {2025}, }
• (2025). A differential coupled-line-based active microwave sensor system for retrieving real permittivity of binary aqueous solution. ACCESS, 13, 50188-50200. (www)

  @article{ , abstract = {An active microwave sensor system, which is based on a differential coupled-line, is proposed. The sensor comprises a differential coupled-line, as well as RF circuits. The coupled-line with through and isolation ports short-circuited, and coupling port open-circuited, is used to design the coupled-line sensor. The phase of reflection coefficient of coupled-line is utilized to extract real permittivity of liquid sample, and in order to realize the maximum sensitivity of coupled-line sensor, it is necessary to obtain the optimal condition for achieving the maximum phase-variation rate with respect to real permittivity. The mathematical analysis is conducted to obtain the optimal geometrical parameters of coupled-line, and it is inferred that the values for odd- and even-mode impedances of coupled-line are smaller, the sensitivity would be higher with the electrical length of 90°. After determining the optimal parameters, a pair of coupled-line is mirrored to produce a differential sensor, with one line used as a reference and the other for testing. The employed differential structure can mitigate external interference and boost sensitivity. To eliminate the need for a vector network analyzer (VNA) and reduce costs, active microwave circuits are incorporated. With the coupled lines functioning as load networks and the PHEMT ATF34143, a pseudomorphic high electron mobility transistor, providing the negative impedance, the proposed unloaded differential reflective RF oscillator can attain a stable oscillation frequency of 1962.66 MHz. Some microwave circuits are added into reflective RF oscillator to form microwave sensor system. Notably, the measured average sensitivity is about 5.7%. Characterized by high sensitivity and low cost, the proposed differential active microwave sensor system stands out as a promising candidate for liquid sample characterization.}, archive = {J_ACCESS}, author = {Lina Shang and Guang Chen and Wen-Jing Wu and Wen-Sheng Zhao}, doi = {10.1109/ACCESS.2025.3551071}, journal = {IEEE Access}, pages = {50188-50200}, shortjournal = {IEEE Access}, title = {A differential coupled-line-based active microwave sensor system for retrieving real permittivity of binary aqueous solution}, volume = {13}, year = {2025}, }
• (2025). Advancement in graph neural networks for EEG signal analysis and application: A review. ACCESS, 13, 50167-50187. (www)

  @article{ , abstract = {Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation of rich spatial information. Graph neural networks (GNNs), as a new kind of deep learning method, can use EEG signals as graph vertices, capturing the hidden topological connections between signals. GNNs have made great progress in EEG studies due to the advantage. In this overview, we review the very new and fundamental models of GNNs and their modifications, such as graph regularized neural networks, graph convolutional neural networks, spatial-temporal graph neural networks, graph attention networks, and their variants in EEG signal analysis fields. The applications of GNNs are summarized in the domains of emotion detection, epilepsy seizure detection, stroke rehabilitation, Alzheimer’s disease diagnosis, motor imagery detection, neurological disease diagnosis, major depressive disorder, and driving fatigue detection. We employed a Systematic Literature Review (SLR) approach to select 79 papers for a comprehensive review. The current state is analyzed and forecasts are provided based on the available difficulties. We conclude by suggesting potential directions for future research in this rapidly developing topic.}, archive = {J_ACCESS}, author = {S. M. Atoar Rahman and Md Ibrahim Khalil and Hui Zhou and Yu Guo and Ziyun Ding and Xin Gao and Dingguo Zhang}, doi = {10.1109/ACCESS.2025.3549120}, journal = {IEEE Access}, pages = {50167-50187}, shortjournal = {IEEE Access}, title = {Advancement in graph neural networks for EEG signal analysis and application: A review}, volume = {13}, year = {2025}, }
• (2025). TriLiteNet: Lightweight model for multi-task visual perception. ACCESS, 13, 50152-50166. (www)

  @article{ , abstract = {Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To address the real-time execution needs of such perception models, this study introduces the TriLiteNet model. This model can simultaneously manage multiple tasks related to panoramic driving perception. TriLiteNet is designed to optimize performance while maintaining low computational costs. Experimental results on the BDD100k dataset demonstrate that the model achieves competitive performance across three key tasks: vehicle detection, drivable area segmentation, and lane line segmentation. Specifically, the TriLiteNetbase demonstrated a recall of 85.6% for vehicle detection, a mean Intersection over Union (mIoU) of 92.4% for drivable area segmentation, and an Acc of 82.3% for lane line segmentation with only 2.35M parameters and a computational cost of 7.72 GFLOPs. Our proposed model includes a tiny configuration with just 0.14M parameters, which provides a multi-task solution with minimal computational demand. Evaluated for latency and power consumption on embedded devices, TriLiteNet in both configurations shows low latency and reasonable power during inference. By balancing performance, computational efficiency, and scalability, TriLiteNet offers a practical and deployable solution for real-world autonomous driving applications. Code is available at https://github.com/chequanghuy/TriLiteNet.}, archive = {J_ACCESS}, author = {Quang-Huy Che and Duc-Khai Lam}, doi = {10.1109/ACCESS.2025.3552088}, journal = {IEEE Access}, pages = {50152-50166}, shortjournal = {IEEE Access}, title = {TriLiteNet: Lightweight model for multi-task visual perception}, volume = {13}, year = {2025}, }
• (2025). Triplet multi-kernel CNN for detection of pulmonary diseases from lung sound signals. ACCESS, 13, 50137-50151. (www)

  @article{ , abstract = {Recent studies have demonstrated the notable success of Convolutional Neural Networks (CNNs) to detect respiratory diseases from Lung Sound (LS) signals. However, traditional Single-Kernel CNN (SK-CNN) methods frequently face limitations when depending solely on a singular kernel type to extract essential information. To overcome these limitations, we present a Multi-Kernel CNN (MK-CNN), designed specifically to capture a wider range of information from LS signals, which increases the accuracy and reliability of Pulmonary Diseases (PDs) detection. We also present a Triplet MK-CNN (TMK-CNN) model that combines the benefits of multi-kernel feature extraction with a triplet-based architecture to enhance detection performance. The effectiveness of these models was evaluated using LS data from a publicly accessible dataset provided by King Abdullah University Hospital (KAUH). Experimental results show that the MK-CNN model achieves an accuracy of 93.94%, indicating a 1.17 percentage-point improvement over the SK-CNN baseline, which is at 92.77%. The TMK-CNN model enhances classification accuracy to 97.98%, achieving a 4.04 percentage points over MK-CNN and a 5.21 percentage-point improvement compared to SK-CNN. These findings indicate the significant potential of MK-CNN and TMK-CNN architectures in enhancing automated PD identification, allowing for more reliable, user-friendly, and clinically useful diagnostic tools.}, archive = {J_ACCESS}, author = {Pumin Duangmanee and Khomdet Phapatanaburi and Wongsathon Pathonsuwan and Talit Jumphoo and Atcharawan Rattanasak and Khwanjit Orkweha and Patikorn Anchuen and Monthippa Uthansakul and Peerapong Uthansakul}, doi = {10.1109/ACCESS.2025.3552108}, journal = {IEEE Access}, pages = {50137-50151}, shortjournal = {IEEE Access}, title = {Triplet multi-kernel CNN for detection of pulmonary diseases from lung sound signals}, volume = {13}, year = {2025}, }
• (2025). Vehicle-to-infrastructure multi-sensor fusion (V2I-MSF) with reinforcement learning framework for enhancing autonomous vehicle perception. ACCESS, 13, 50122-50136. (www)

  @article{ , abstract = {Traditional autonomous driving systems, which rely solely on vehicle onboard unit (OBU) sensors, often face challenges such as limited perception range and sensor occlusion, while systems dependent only on roadside infrastructure sensors may lack the fine-grained data required for accurate, vehicle-level decision-making. To address these challenges, this paper presents a Vehicle-to-Infrastructure Multi-Sensor Fusion (V2I-MSF) framework that integrates LiDAR and camera data from vehicle OBUs with sensor data from roadside infrastructure. This fusion enhances environmental visualization and supports more informed decision-making for autonomous vehicles. In addition, we propose an Actor-Critic reinforcement learning (RL) model designed to process the fused sensor data, enabling precise motion estimation, obstacle avoidance, and lane prediction. The proposed framework demonstrates significant improvements in accuracy, extended perception range, and robustness in decision-making, particularly in complex urban intersection scenarios. Simulation results validate the effectiveness of the V2I-MSF approach, showing its superiority over standalone sensor configurations in creating safer, more efficient driving environments.}, archive = {J_ACCESS}, author = {Daud Khan and Sawera Aslam and Kyunghi Chang}, doi = {10.1109/ACCESS.2025.3551367}, journal = {IEEE Access}, pages = {50122-50136}, shortjournal = {IEEE Access}, title = {Vehicle-to-infrastructure multi-sensor fusion (V2I-MSF) with reinforcement learning framework for enhancing autonomous vehicle perception}, volume = {13}, year = {2025}, }
• (2025). Graph-based prediction of spatio-temporal vaccine hesitancy from insurance claims data. ACCESS, 13, 50106-50121. (www)

  @article{ , abstract = {Growing vaccine hesitancy is contributing to the decline in immunization rates for highly contagious, vaccine-preventable childhood diseases. Therefore, there has been a significant interest in understanding how hesitancy is spreading at higher spatio-temporal resolutions, enabling more targeted interventions. Motivated by this, we study the problem of prediction of vaccine hesitancy at the ZIP Code level, referred to as the VaxHesitancy problem. A significant challenge for this problem is the lack of high-resolution data that indicates hesitancy. Here, we develop a hybrid VaxHesSTL framework that combines a Graph Neural Network (GNN) and a Recurrent Neural Network (RNN) to address the VaxHesitancy problem. The GNN uses a ZIP Code-level network to capture spatial signals from neighboring areas, while the RNN models the temporal dynamics present in the data. We train and evaluate VaxHesSTL using a large dataset, namely the All-Payer Claims Databases (APCD), for Virginia, consisting of insurance claims from over five million individuals for six years. We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. Experiments demonstrate that VaxHesSTL outperforms a range of state-of-the-art baselines, which rely solely on historical time series data without accounting for spatial relationships. Since hesitancy data at higher spatial resolution is often unavailable or hard to get, we incorporate an active learning approach with our VaxHesSTL framework to optimize the training set without compromising the prediction performance. We find that hesitancy data for only 18% of ZIP Codes selected by active learning allows us to forecast hesitancy for all the ZIP Codes in the Virginia.}, archive = {J_ACCESS}, author = {Sifat Afroj Moon and Rituparna Datta and Tanvir Ferdousi and Hannah Baek and Abhijin Adiga and Achla Marathe and Anil Vullikanti}, doi = {10.1109/ACCESS.2025.3550775}, journal = {IEEE Access}, pages = {50106-50121}, shortjournal = {IEEE Access}, title = {Graph-based prediction of spatio-temporal vaccine hesitancy from insurance claims data}, volume = {13}, year = {2025}, }
• (2025). OWNER — Toward unsupervised open-world named entity recognition. ACCESS, 13, 50077-50105. (www)

  @article{ , abstract = {Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP), traditionally addressed through supervised learning, which requires extensive annotated corpora. This requirement poses challenges, particularly in specialized domains with limited labeled data. In response, the field has shifted towards lower-resource approaches, such as few-shot and zero-shot learning, which reduce the dependency on annotated data. However, even zero-shot models require prior knowledge of entity types, limiting their applicability in exploratory scenarios. In this context, we introduce OWNER, our unsupervised and open-world NER model, designed to operate without annotated documents or predefined entity types. OWNER leverages Encoder-only Language Models like BERT to infer and organize entities into dynamic entity types through a two-step process: mention detection and entity typing. Mention detection employs a BIO sequence labeling approach to locate entities, while entity typing uses BERT-based embeddings, refined through contrastive learning, for clustering and naming entity types. This method allows OWNER to automatically identify and structure unknown entity types, offering advantages for exploratory dataset analysis and knowledge graph construction. Our experimental evaluation on 13 domain-specific datasets demonstrates that OWNER surpasses existing LLM-based open-world NER models and remains competitive with more supervised and closed-world zero-shot models. OWNER’s architecture provides a lightweight, easily deployable solution that advances the state of the art in unsupervised and open-world NER. The source code of OWNER is publicly available at https://github.com/alteca/OWNER, facilitating future research in this domain.}, archive = {J_ACCESS}, author = {Pierre-Yves Genest and Pierre-Edouard Portier and Előd Egyed-Zsigmond and Martino Lovisetto}, doi = {10.1109/ACCESS.2025.3552122}, journal = {IEEE Access}, pages = {50077-50105}, shortjournal = {IEEE Access}, title = {OWNER — Toward unsupervised open-world named entity recognition}, volume = {13}, year = {2025}, }
• (2025). A flexible framework for design and validation of rPPG methods. ACCESS, 13, 50059-50076. (www)

  @article{ , abstract = {Over the past decade, Remote Photoplethysmography (rPPG) has emerged as an unobtrusive alternative to wearable sensors for measuring physiological signals, such as heart rate. Despite advancements, its real-world scalability remains limited due to the absence of standardized benchmark methods for validation. This lack of standardization complicates proper comparisons between different approaches, creating inconsistencies in performance evaluation. To address this, we conducted a comprehensive review of recent rPPG methods, analyzing their pre- and post-processing algorithms, validation procedures, benchmark algorithms, datasets, evaluation metrics, data segmentation, and reported results. Our findings demonstrate significant variability in the reported Mean Absolute Error (MAE) of benchmark rPPG methods applied to the same public datasets, confirming the challenge of inconsistent evaluation. By examining the original implementations of established benchmark methods, we developed a flexible framework that optimally selects pre- and post-processing algorithms through an exhaustive search. Applying this framework to benchmark algorithms across three public datasets, we found that 80% of the refined methods ranked within the top 25th percentile in MAE, RMSE, and PCC, with 60% surpassing the highest reported accuracies. These refined methods provide a more rigorous foundation for evaluating novel rPPG techniques, addressing the standardization gap in the field. The codebase for this framework (frPPG) is available at [https://github.com/Building-Robotics-Lab/flexible_rPPG], offering a valuable tool for designing and benchmarking rPPG methods against the best-performing algorithms on a given dataset.}, archive = {J_ACCESS}, author = {Ilyas Dawoodjee and Ali Ghahramani}, doi = {10.1109/ACCESS.2025.3552585}, journal = {IEEE Access}, pages = {50059-50076}, shortjournal = {IEEE Access}, title = {A flexible framework for design and validation of rPPG methods}, volume = {13}, year = {2025}, }
• (2025). Correlation between wind turbine failures and environmental conditions: A machine learning approach. ACCESS, 13, 50043-50058. (www)

  @article{ , abstract = {Wind energy has emerged as a vital renewable source, competing with conventional energy due to its clean and inexhaustible nature. However, the global mass production of wind turbines often disregards the unique environmental conditions of installation sites, leading to performance and reliability challenges. This study applies machine learning methodologies to analyze the correlation between wind turbine failures and local environmental conditions. The research leverages Rough Set Theory to transform instances of undesirable turbine shutdowns—especially those influenced by incomplete tropicalization processes—into actionable decision rules. The findings provide practical insights applicable to wind farms worldwide, enabling optimized maintenance strategies and precise adjustments to protection parameters. These improvements contribute to reducing failure rates, enhancing energy conversion efficiency, and promoting the sustainable expansion of wind energy across diverse geographic and climatic contexts.}, archive = {J_ACCESS}, author = {Thadeu Carneiro da Silva and Fábio Augusto da Silva Antunes and Júlio Carlos Teixeira and Rodrigo Colnago Contreras}, doi = {10.1109/ACCESS.2025.3551241}, journal = {IEEE Access}, pages = {50043-50058}, shortjournal = {IEEE Access}, title = {Correlation between wind turbine failures and environmental conditions: A machine learning approach}, volume = {13}, year = {2025}, }
• (2025). A design of a 100-MHz fully integrated digital pulse width modulation three-level buck converter using package bond-wire inductor. ACCESS, 13, 50032-50042. (www)

  @article{ , abstract = {This paper presents a fully integrated digital pulse-width-modulation three-level DC-DC buck converter. An internal switching frequency of 100 MHz is selected to minimize the value of passive components, and the package bond-wire based inductor is selected. In the integrated voltage regulator, a three-level structure is used to reduce the output voltage ripple, and Digital pulse width modulation (DPWM) is employed to achieve efficient current and voltage control. This digitally configured control scheme is robust against external environmental factors, such as process, voltage, and temperature variations, and also achieves a fast load transient time. The proposed circuit is fabricated using a 130-nm BCDMOS process, and it can regulate the 5-V input voltage to the output voltage range of 3.3–3.7 V. The three-level buck converter with an active area of 1.035 mm2 shows a power conversion efficiency of 72.4% under the load current of 200 mA.}, archive = {J_ACCESS}, author = {Sung-Jae Lee and Ju Hyoung Kim and Yun-Gwan Kim and Jeong Seop Lee and Ji Hoon Song and Dong Gyun Kim and Jong-Wan Jo and Young-Gun Pu and Kang-Yoon Lee and Jun-Eun Park}, doi = {10.1109/ACCESS.2025.3552411}, journal = {IEEE Access}, pages = {50032-50042}, shortjournal = {IEEE Access}, title = {A design of a 100-MHz fully integrated digital pulse width modulation three-level buck converter using package bond-wire inductor}, volume = {13}, year = {2025}, }
• (2025). AIDFL: An information-driven anomaly detector for data poisoning in decentralized federated learning. ACCESS, 13, 50017-50031. (www)

  @article{ , abstract = {Decentralized Federated Learning eliminates central servers by enabling direct communication among clients. However, this structure introduces significant security challenges, as each client has access to the model parameters. Existing defense mechanisms face significantly reduced effectiveness under non-IID data distributions. To address these challenges, AIDFL is proposed to utilize conditional entropy and mutual information, which are independent of data distribution to detect and mitigate data poisoning attacks in DFL environments. Experimental results demonstrate that AIDFL achieves superior defense under non-IID settings under different poisoning configurations. In particular, this study not only enhances the robustness of DFL but also highlights the critical need for further research on advanced defense strategies against model poisoning attacks in decentralized frameworks. This work serves as a foundation for future exploration of secure DFL systems.}, archive = {J_ACCESS}, author = {Xiao Chen and Chao Feng and Shaohua Wang}, doi = {10.1109/ACCESS.2025.3552168}, journal = {IEEE Access}, pages = {50017-50031}, shortjournal = {IEEE Access}, title = {AIDFL: An information-driven anomaly detector for data poisoning in decentralized federated learning}, volume = {13}, year = {2025}, }
• (2025). Development of a novel real-time weighted surrogate model for wind turbine-based on DFIG: Enhancing computational efficiency in power systems with error bound. ACCESS, 13, 50000-50016. (www)

  @article{ , abstract = {In the realm of modern wind turbine engineering, where precision is paramount for stability, control, and observability, this article introduces a groundbreaking method leveraging time-weighted Gramians. The focal point of this work is the reduction of model order in wind turbines featuring a double-fed induction generator with time-varying rotational speeds. Employing sophisticated state-space representations, we establish a comprehensive and systematic framework for analyzing wind turbine performance, ensuring adherence to stringent grid standards. A distinctive aspect of our proposed approach lies in the utilization of time-weighted Gramians and an innovative balanced realization technique to reduce the dimensionality of large state-space models effectively. Stability and reduced approximation errors are guaranteed by creating a lower-order system. A significant improvement is the availability of an a priori formula for error-bound, which allows for more efficient and faster computations. The use of time-weighted Gramians allowed for the application of this groundbreaking technique to time-sensitive systems in the real world, such as wind turbines. The optimization of models utilizing vast simulation data is what makes our methodology better than current methods. Wind turbines that allow for real-time adjustment of rotating speeds are part of this dataset. A lot of consideration is given to stability and error calculation in this study, which makes it innovative. The use of time-weighted Gramians in practical, real-time systems has greatly improved the accuracy and efficiency of modeling approaches.}, archive = {J_ACCESS}, author = {Muhammad Latif and Hira Ambreen and Tang Diyin and Farrukh Hassan and Muhammad Imran and Saddam Hussain Abbasi and Muhammad Imran}, doi = {10.1109/ACCESS.2025.3550276}, journal = {IEEE Access}, pages = {50000-50016}, shortjournal = {IEEE Access}, title = {Development of a novel real-time weighted surrogate model for wind turbine-based on DFIG: Enhancing computational efficiency in power systems with error bound}, volume = {13}, year = {2025}, }
• (2025). Life cycle assessment of hybrid-electric aircraft Technologies—A review. ACCESS, 13, 49989-49999. (www)

  @article{ , abstract = {Decarbonizing aviation is a critical goal for mitigating climate change, as the sector contributes significantly to global greenhouse gas emissions. Hybrid electric aircraft (HEA) represent a promising step towards reducing the environmental impact of aviation. The paper presents a review of life cycle assessment analysis of hybrid electric aircraft designs as emerging technologies. The literature review is complemented by a presentation of the technologies developed within the HECATE (Hybrid Electric Regional Aircraft Distribution Technologies) EU project. Results of HECATE prove that for the HEA platforms, requirement for higher voltage and higher power distribution components is essential to reduce the environmental load. All the literature findings show that hybrid-electric aircraft configurations can reduce the carbon intensity per RPK (revenue passenger kilometer) from 49%-57% in the conservative scenario to 82%-88% in the best scenario when compared to fossil-fueled aircraft. The environmental impact of HEA could be further reduced by 7% if electricity for batteries charging is produced from renewable sources and battery has longer lifetime. LCA analysis shows that the main contributors to the environmental load produced by HEA are aircraft airframes and batteries, producing on average 15-40 kg CO2 eq./h.}, archive = {J_ACCESS}, author = {Aleksandra Ziemińska-Stolarska and Mariia Sobulska and Daniel Izquierdo and Deborah Neumann Dela Cruz and Monika Pietrzak and Ireneusz Zbicińśki}, doi = {10.1109/ACCESS.2025.3549924}, journal = {IEEE Access}, pages = {49989-49999}, shortjournal = {IEEE Access}, title = {Life cycle assessment of hybrid-electric aircraft Technologies—A review}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on human-computer interaction to support older adults’ physical and cognitive health. ACCESS, 13, 49966-49988. (www)

  @article{ , abstract = {Emerging hardware and software technologies have increased the development of technological solutions for people, particularly those focused on older adults’ healthcare. Human-computer interaction is a research area that uses motion capture devices as input interaction devices, e.g., touch screen devices, wearables, eye trackers, and more. Numerous primary studies have provided support and training tools for older people with physical and cognitive diseases. However, there is a need for more secondary studies that systematically summarize and evidence these solutions. To address this gap, a systematic literature review was conducted to identify and interpret all studies from 2006 to 2023 that present human-computer techniques, motion capture devices, tools, and methodologies for older adults’ physical and cognitive training and support. The review aims to answer the research question: “What devices and applications of Human-Computer Interaction (HCI) can be used to support the neuropsychological and motor condition of older adults?”. The review identified 1140 potential papers from automatic searches in three digital libraries and manual searches in specialized journals and conferences. Three domain experts (computer engineer, electronics engineer, and psychologist) reviewed the papers based on inclusion and exclusion criteria and selected 79 papers for the review. Additionally, Fleiss’ kappa measure was used to achieve agreement between raters. The results provide a comprehensive overview of the state of research in this area, which can assist researchers in identifying weaknesses, directing research efforts, and new research lines. Additionally, the results can serve as a reference guide for professionals in technical and health areas who can contribute to creating better and innovative solutions to aid the older population’s physical and cognitive training and support.}, archive = {J_ACCESS}, author = {Priscila Cedillo and Paúl Cárdenas-Delgado and Andrea Paulina Rodríguez Zúñiga and Lorena Encalada-Torres and Carlos Ramos-Galarza and Janio Jadan-Guerrero}, doi = {10.1109/ACCESS.2025.3551236}, journal = {IEEE Access}, pages = {49966-49988}, shortjournal = {IEEE Access}, title = {A systematic literature review on human-computer interaction to support older adults’ physical and cognitive health}, volume = {13}, year = {2025}, }
• (2025). Toward a unified understanding of cyber resilience: Concepts, strategies, and future directions. ACCESS, 13, 49945-49965. (www)

  @article{ , abstract = {In the digital age, the concept of cyber resilience has become a cornerstone for industries aiming to safeguard their operational integrity, data security, and service continuity against the backdrop of an ever-evolving cyber threat landscape. Businesses need cyber-resilient solutions that can anticipate, withstand, recover, or adapt to cyber-crisis situations affecting their operations. This paper provides a comprehensive view of cyber resilience concepts and techniques, offering a foundational understanding for both researchers and security experts. Additionally, it addresses prevalent discrepancies and conceptual ambiguities in resilience literature, specifically focusing with respect to cyber security i.e., cyber resilience. As the concept of cyber resilience is currently shrouded in ambiguity due to the absence of in-depth thorough consensus on its definition, this paper establishes a consolidated scientific definition of cyber resilience, refining theoretical constructs and operational paradigms in the field. By exploring key actors in cyberspace, delineating the characteristics, goals, and objectives, of cyber resilience, and distinguishing it from similar concepts, this paper enhances the foundational understanding of cyber resilience. Furthermore, it delivers critical insights, identifies limitations and challenges, and outlines future research directions in the domain of cyber resilience. This can guide organizations to comprehensively assess, measure, and build their cyber resilience to anticipate, withstand, recover, or adapt to cyber crises that threaten the survival of their organization across a broad range of business processes.}, archive = {J_ACCESS}, author = {Priyanka Verma and Thomas Newe and George D. O'Mahony and Dean Brennan and Donna O'Shea}, doi = {10.1109/ACCESS.2025.3551887}, journal = {IEEE Access}, pages = {49945-49965}, shortjournal = {IEEE Access}, title = {Toward a unified understanding of cyber resilience: Concepts, strategies, and future directions}, volume = {13}, year = {2025}, }
• (2025). Microgrid multi-time scale rolling optimization and modification scheduling considering the decision of air conditioning users. ACCESS, 13, 49934-49944. (www)

  @article{ , abstract = {Demand response is an effective load management strategy that helps balance power supply and demand and improve the stability and reliability of the power grid. Air conditioning plays a crucial role in power demand response. However, air conditioning users’ decisions are highly uncertain, leading to a significant deviation in the scheduling process. Therefore, this paper proposes a microgrid multi-time scale rolling optimization and modification scheduling considering the decision of air conditioning users. Firstly, to maximize the dispatchability and flexibility of air conditioning loads, the dynamic decisions of air conditioning users are considered, and a virtual energy storage model for variable-frequency air conditioning is developed. Secondly, by integrating optimization strategies across multiple time scales, a day-ahead scheduling model is proposed to minimize microgrid operational costs. Then, considering the deviation between the day-ahead time and intra-day time, as well as the dynamic decision probability of air conditioning users, the intra-day rolling modification method of microgrid penalty cost minimization is proposed, and the scheduling plan is corrected in real-time through this rolling strategy. Finally, the effectiveness of the microgrid integrated demand response and multi-time scale optimization strategy is demonstrated through simulation examples and comparative analysis.}, archive = {J_ACCESS}, author = {Shunjiang Wang and Jiayi Li and Tianfeng Chu and Peng Yu}, doi = {10.1109/ACCESS.2025.3551680}, journal = {IEEE Access}, pages = {49934-49944}, shortjournal = {IEEE Access}, title = {Microgrid multi-time scale rolling optimization and modification scheduling considering the decision of air conditioning users}, volume = {13}, year = {2025}, }
• (2025). Advancements in large-scale image and text representation learning: A comprehensive review and outlook. ACCESS, 13, 49922-49933. (www)

  @article{ , abstract = {Large-scale image and text representation learning is critical in determining the performance of multimodal tasks involving images and text, such as visual question answering and image captioning. Most existing research on large-scale image and text representation learning relies on Transformer networks for pre-training, i.e., learning generic semantic representations from large-scale image-to-text pairs. These representations are then fine-tuned and transferred to downstream multimodal tasks. This paper first provides a brief analysis of the advantages of pre-training models. It then comprehensively summarizes the relevant research on large-scale image and text representation learning based on pre-training. The focus is on pre-training model architectures, pre-training tasks, and image-text datasets. Finally, we provide a summary and outlook of large-scale image and text representation learning.}, archive = {J_ACCESS}, author = {Yang Qin and Shuxue Ding and Huiming Xie}, doi = {10.1109/ACCESS.2025.3541194}, journal = {IEEE Access}, pages = {49922-49933}, shortjournal = {IEEE Access}, title = {Advancements in large-scale image and text representation learning: A comprehensive review and outlook}, volume = {13}, year = {2025}, }
• (2025). A fuzzy logic-based energy management approach for fuel cell and photovoltaic powered electric vehicle charging station in DC microgrid operations. ACCESS, 13, 49905-49921. (www)

  @article{ , abstract = {Hydrogen and electric vehicles (EVs) stand out as two promising technologies with the potential to revolutionize the future of transportation. The adoption of hydrogen and EVs indicates a noteworthy shift in the transportation paradigm, offering the prospect of reshaping this sector. This paper introduces an energy management approach based on fuzzy logic for a charging station that combines fuel cell (FC) and photovoltaic (PV) to power electric vehicles (EVs). The study emphasizes developing and simulating a fuzzy logic-based energy management system tailored for DC microgrid operations, including on-grid and islanded microgrid schemes. The system is designed and simulated in a digital simulation environment. The findings indicate that the fuzzy logic approach significantly enhances the performance of PV and FC-based EV charging stations. This research contributes to developing sustainable and efficient solutions for integrating electric vehicles and hydrogen-powered supply systems.}, archive = {J_ACCESS}, author = {Recep Çakmak and Gökay Bayrak and Mehmet Koç}, doi = {10.1109/ACCESS.2025.3552253}, journal = {IEEE Access}, pages = {49905-49921}, shortjournal = {IEEE Access}, title = {A fuzzy logic-based energy management approach for fuel cell and photovoltaic powered electric vehicle charging station in DC microgrid operations}, volume = {13}, year = {2025}, }
• (2025). An effective temporal convolutional networks-based method for detecting android malware using dynamic extracted features. ACCESS, 13, 49891-49904. (www)

  @article{ , abstract = {With an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable. Therefore, it is necessary to continue proposing and investigating new methods for Android malware detection, including machine learning (ML) and deep learning (DL) based ones. A very highly effective DL model named temporal convolutional network (TCN) which makes use of causal convolution and dilation for sequential data processing, was yet to be implemented with various dynamically extracted features from Android malware applications. In several applications, TCN has proven to be more effective with sequential data, faster due to parallelism and less computationally exhaustive. This paper proposes a new and improved method using TCN for Android malware detection with dynamically extracted feature types including system calls, binder calls and composite behaviours. The proposed method achieves high effectiveness for dynamic Android malware detection with a validation F1-Score of 99.59% and has also shown higher effectiveness than other papers using the same (CICMalDroid 2020) dataset for dynamic Android malware detection.}, archive = {J_ACCESS}, author = {Abdurraheem Joomye and Mee Hong Ling and Muhammed Basheer Jasser and Athirah Mohd Ramly and Kok-Lim Alvin Yau}, doi = {10.1109/ACCESS.2025.3552070}, journal = {IEEE Access}, pages = {49891-49904}, shortjournal = {IEEE Access}, title = {An effective temporal convolutional networks-based method for detecting android malware using dynamic extracted features}, volume = {13}, year = {2025}, }
• (2025). A unified self-supervised learning framework for hyperspectral image classification. ACCESS, 13, 49874-49890. (www)

  @article{ , abstract = {Self-supervised learning (SSL) methods, including contrastive learning (CL) and masked image modeling (MIM), have shown commendable performance on various remote sensing visual tasks. However, learning comprehensive and complementary representations from unlabeled remote sensing images remains a challenging task due to their intricate spatial-spectral characteristics. To address this issue, we propose a Unified Self-supervised Learning Framework (USeLF) that incorporates both CL and MIM to enhance hyperspectral image (HSI) classification. USeLF employs an asymmetric Siamese network framework, designed to accommodate both CL and MIM. Leveraging the common characteristics of these two paradigms, we developed a hierarchical Transformer structure that integrates downsampling and upsampling modules to improve model generalization. Furthermore, image masking is devised as a novel data augmentation strategy to bridge the gap between CL and MIM. USeLF learns diverse and complementary features from both CL and MIM, resulting in superior performance for HSI classification. Experiments conducted on four widely used HSI datasets demonstrate that USeLF outperforms existing state-of-the-art (SOTA) methods. Notably, on the WHU-Hi-HongHu dataset—a complex agricultural scene with numerous crop classes—USeLF achieves a 5.3% improvement in overall accuracy compared to the current SOTA method.}, archive = {J_ACCESS}, author = {Guoyun Zhang and Ping Tan and Xiaoqi Fang and Xuhui Zhou and Lin Zhao}, doi = {10.1109/ACCESS.2025.3549277}, journal = {IEEE Access}, pages = {49874-49890}, shortjournal = {IEEE Access}, title = {A unified self-supervised learning framework for hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). Comparative study of portfolio optimization models for cryptocurrency and stock markets. ACCESS, 13, 49861-49873. (www)

  @article{ , abstract = {Since the introduction of Modern Portfolio Theory (MPT) in 1952, its practical applications, associated challenges, and computational efficiency on large and high-frequency datasets, particularly datasets not used to develop and optimize the model, have drawn extensive research interest. This study has examined the performance of various portfolio models that have explored the concept of MPT on U.S. stock and cryptocurrency markets, i.e., discrete Markowitz portfolio selection (DMPS), the optimal dynamic portfolio (ODP), the binary unconstrained ODP (BUODP) with a quantum annealing solver, and the 1/N naive diversification (ND). Their performance is then compared to the indices that measure the performance of corresponding market exchange-traded funds (ETFs) for stock markets. Our findings show that the DMPS and ODP perform better than other models, delivering better returns in a shorter period. Both run significantly faster than the BUODP (with quantum annealing) with computation time of approximately 0.5 seconds for the S&P 400, 500, and 600 markets whereas BUODP takes 30 seconds; they also mitigate risk and outperform ETFs and ND model for the out-of-sample test with diversified portfolios combining NASDAQ stocks and cryptocurrencies. Furthermore, we have analyzed the impact of data with different frequency intervals, i.e., weekly, daily, hourly, and one-minute, on portfolio performance of the stock markets. The results suggest that data collection frequencies do not make differences in portfolio selections and weights. This study contributes to the advancement of portfolio theory, providing insights and practical values, especially in addressing computational efficiency for high-frequency and large-scale datasets and saving computational costs.}, archive = {J_ACCESS}, author = {Bahram Alidaee and Haibo Wang and Wendy Wang}, doi = {10.1109/ACCESS.2025.3551628}, journal = {IEEE Access}, pages = {49861-49873}, shortjournal = {IEEE Access}, title = {Comparative study of portfolio optimization models for cryptocurrency and stock markets}, volume = {13}, year = {2025}, }
• (2025). Research on intelligent voltage regulation method within a station based on multi-agent reinforcement learning. ACCESS, 13, 49852-49860. (www)

  @article{ , abstract = {In response to the issues that existing voltage regulation methods cannot finely perceive the load data within the station area, have low regulation precision, and often use active power regulation leading to high electricity costs for users, a novel intelligent voltage regulation method within the station area based on multi-agent reinforcement learning is proposed. Firstly, a photovoltaic (PV) output prediction technology based on a hybrid neural network is proposed to predict the load for the previous day. Secondly, a strategy generation mechanism for voltage regulation within the station area that integrates active and reactive power control is designed, and based on this, an intelligent voltage regulation method within the station area using multi-agent reinforcement learning is proposed. Finally, the feasibility of this method is verified through the voltage regulation situation of the same station area at different times of the same day and the same times on different days. The experimental results show that the regulation algorithm can effectively achieve voltage regulation at the PV connection point and prevent voltage limit violations at the PV connection point.}, archive = {J_ACCESS}, author = {Zhe Zheng and Pan Li and Wenpeng Cui and Yu Liu and Mingyang Sun and Yingying Chi and Qingchen Yang and Yuzhe Chen}, doi = {10.1109/ACCESS.2025.3525777}, journal = {IEEE Access}, pages = {49852-49860}, shortjournal = {IEEE Access}, title = {Research on intelligent voltage regulation method within a station based on multi-agent reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Joint space-time interference cancellation for mmWave active array SDR platforms in coexistent sensing and communication systems. ACCESS, 13, 49836-49851. (www)

  @article{ , abstract = {This paper investigates the interference issue between sensing and communication millimeter-wave (mmWave) active arrays, such as the sensing array (SA) and communication array (CA), which share the same time and frequency resources at a base station (BS) in integrated sensing and communication (ISAC) systems. To mitigate interference and improve signal quality at the communication user station (CUS), we propose a joint space-time interference cancellation method. This method integrates spatial interference suppression for SA-BS using a deep nulling weight design with 6-bit phase shifters and temporal pre-weight cancellation for CA-BS. Specifically, for the SA-BS array, deep nulling beamforming is achieved through exhaustive search (ES) or genetic algorithm (GA) techniques, leveraging an initial minimum mean square error (MMSE) weight with a 2-bit least significant bit (LSB) precision. For the CA-BS array, interference is further canceled by regenerating an accurate pre-weighted version of the interference signal, necessitating the channel estimation for both SA-BS and CA-BS arrays. Experimental results from a software-defined radio (SDR) platform equipped with developed dual mmWave active arrays confirm that the proposed space-time interference cancellation method effectively eliminates interference in over-the-air (OTA) environments, thereby enhancing the overall performance of mmWave active array systems practically.}, archive = {J_ACCESS}, author = {Juinn-Horng Deng and Yuanzhang Xiao and Meng-Lin Ku and Soo Yong Lim and Jill S. K. Nakatsu and Magdy F. Iskander}, doi = {10.1109/ACCESS.2025.3552527}, journal = {IEEE Access}, pages = {49836-49851}, shortjournal = {IEEE Access}, title = {Joint space-time interference cancellation for mmWave active array SDR platforms in coexistent sensing and communication systems}, volume = {13}, year = {2025}, }
• (2025). Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication. ACCESS, 13, 49824-49835. (www)

  @article{ , abstract = {This paper investigates the probability distribution of the signal-to-interference noise ratio (SINR) for a 6G communication system comprising a multi-antenna transmitter, an intelligent reflecting surface (IRS) and a remote receiver station. A common assumption in the literature is that the density distribution function for SINR and signal-to-noise ratio (SNR) of an IRS-to-ground communication follows a Rayleigh and Rician distribution. This assumption is essential as it influences the derivation of the properties of the communication system such as the physical layer security models and the designs of IRS controller units. Therefore, in this paper, we present an analytical derivation for the density distribution functions of the SINR for an IRS-to-6G ground communication ameliorating the typical assumptions in the literature. We demonstrated that the SINR density function of an IRS-to-6G ground communication contains a hypergeometric function. We further applied the derived density distribution function to determine the average secrecy rate for passive eavesdropping.}, archive = {J_ACCESS}, author = {Christantus Obinna Nnamani and Chidera Linda Anioke and Saba Al-Rubaye and Antonios Tsourdos}, doi = {10.1109/ACCESS.2025.3549426}, journal = {IEEE Access}, pages = {49824-49835}, shortjournal = {IEEE Access}, title = {Signal-to-interference-noise-ratio density distribution for UAV-carried IRS-to-6G ground communication}, volume = {13}, year = {2025}, }
• (2025). Novel wearable device for non-invasive management of positional sleep apnea: A proof-of-concept. ACCESS, 13, 49817-49823. (www)

  @article{ , abstract = {Obstructive Sleep Apnea (OSA) is a serious health condition characterized by repetitive airway collapses during sleep, which can lead to cardiovascular and metabolic complications if untreated. Positional OSA (POSA), a subtype triggered by specific sleeping positions, presents additional therapeutic challenges. This paper introduces a novel wearable device designed to treat POSA non-invasively. The system employs a microcontroller, inertial sensors, and a quiet piezoelectric pump to dynamically adjust a pillow based on the user’s position. When the patient lies supine, the pillow inflates to encourage a lateral position, reducing nocturnal respiratory events. Experimental tests demonstrate the system’s low latency and minimal noise levels, making it a comfortable alternative to conventional OSA treatment, but also aiming to become the first-line treatment for POSA. The proposed device offers an effective, portable, and adaptable solution for patients seeking non-intrusive treatment options for sleep apnea. Future developments include integrating remote monitoring capabilities to enhance usability and clinical effectiveness.}, archive = {J_ACCESS}, author = {Elisabetta Leogrande and Pasquale Tondo and Donato Lacedonia and Francesco Dell'Olio}, doi = {10.1109/ACCESS.2025.3551662}, journal = {IEEE Access}, pages = {49817-49823}, shortjournal = {IEEE Access}, title = {Novel wearable device for non-invasive management of positional sleep apnea: A proof-of-concept}, volume = {13}, year = {2025}, }
• (2025). Factors influencing medical students’ acceptance of clinical virtual simulation experiments: A combined TAM and TPB approach. ACCESS, 13, 49801-49816. (www)

  @article{ , abstract = {Clinical virtual simulation experiments (CVSEs) have become increasingly important in medical education and are supported by platforms such as the National Virtual Simulation Experiment Teaching Center (iLAB-X) in China. Despite its potential, the adoption rate of CVSEs among Chinese medical students remains low, and few studies have explored the behavioral factors that influence their use. This study integrates the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine the behavioral intentions of clinical medicine undergraduates toward CVSE adoption. A total of 387 students participated in the study, and the data were collected through an online survey and analyzed via structural equation modeling. The results indicate that system quality and course quality significantly influence perceived usefulness and perceived ease of use, which in turn shape attitudes toward use and behavioral intentions. Furthermore, perceived behavioral control is a critical predictor of behavioral intention. However, subjective norms and computer self-efficacy had no significant effect. These findings advance our understanding of technology adoption by combining the TAM and TPB frameworks and provide actionable insights for educators, platform developers, and policymakers to enhance CVSE adoption not only in China but also in other countries. By focusing on improved system performance, content quality, and resource support, these strategies can address common challenges faced in diverse educational contexts, such as limited access to clinical training, resource disparities, and the need for scalable, technology-driven solutions in medical education.}, archive = {J_ACCESS}, author = {Hui Lv and Yu Ning and Xuebin Li and Lanqing Meng and Caimei Yang and Yongming Jiang and Xiaoxia Li and Lingjiao Tang and Xinzhou Li}, doi = {10.1109/ACCESS.2025.3547901}, journal = {IEEE Access}, pages = {49801-49816}, shortjournal = {IEEE Access}, title = {Factors influencing medical students’ acceptance of clinical virtual simulation experiments: A combined TAM and TPB approach}, volume = {13}, year = {2025}, }
• (2025). Joint modeling of prediction and behavioral patterns for reliable recommendation with implicit feedback. ACCESS, 13, 49788-49800. (www)

  @article{ , abstract = {Implicit feedback in recommender systems contains noisy interactions that do not reflect true user preferences, which diminishes recommendation accuracy. Current methods identify unreliable interactions using single indicators such as loss values or model disagreement, but fail to capture the complex patterns that separate genuine preferences from noise. In this paper, we present a method that estimates interaction reliability by analyzing two key signals: the prediction confidence of the recommender model and the consistency of user behavior across interaction sequences. Our approach uses a two-component two-dimensional Gaussian mixture model to estimate reliability by combining prediction scores with co-occurrence patterns that measure behavioral consistency. Instead of making binary decisions, we implement an adaptive weighting mechanism that allows interactions to influence training according to their estimated reliability. Evaluation on three public datasets (Yelp, Amazon-book, and ML-1M) across three recommendation architectures demonstrated that our method improved recommendation accuracy by up to 38% compared with standard training and 33% over existing denoising approaches. These improvements persisted across various models and evaluation metrics, thereby demonstrating the effectiveness of our reliability estimation approach. Our implementation is available at https://github.com/meruemon/JMPBP.}, archive = {J_ACCESS}, author = {Takuto Sugiyama and Soh Yoshida and Mitsuji Muneyasu}, doi = {10.1109/ACCESS.2025.3552102}, journal = {IEEE Access}, pages = {49788-49800}, shortjournal = {IEEE Access}, title = {Joint modeling of prediction and behavioral patterns for reliable recommendation with implicit feedback}, volume = {13}, year = {2025}, }
• (2025). Instrumentation system for monitoring of soil variables in precision agriculture applications. ACCESS, 13, 49777-49787. (www)

  @article{ , abstract = {The world faces a food crisis due to population growth, climate change, and social conflicts. Different solutions are being studied to increase food production and ensure food security. One approach is precision agriculture. This practice comprises a set of technologies that combine sensors, information systems, machinery, and information analysis to optimize agricultural production. Since each crop has different resource requirements, having a system to measure and visualize soil variables related to crop productivity is of great interest. The present work proposes a system to measure and visualize temperature, moisture, pH, Nitrate, and positive Potassium ions. The system design was divided into two stages. The first stage is an electronic instrumentation system to acquire, adapt, and transmit georeferenced soil variables. The second stage is a data processing system interpolating georeferenced information to generate estimated heatmaps for visualization. The built prototype characterization showed errors of less than 1% for temperature and moisture, 3% for pH, 17% for Nitrate, and 14% for positive Potassium ions. Despite presenting high levels of error in measuring nutrients, the sensors, manufactured using CMOS-MEMS technology, have high potential for integration and miniaturization. With data obtained from the prototype in a field trip to a rice crop, estimated heatmaps were generated using two interpolation techniques (IDW- Inverse Distance Weighting- and Kriging). Results showed that for the Kriging technique, MAE (mean absolute error) and RMSE (root-mean-square error) values were lower, confirming that this method is adequate for extrapolation and soil data visualization.}, archive = {J_ACCESS}, author = {Diana Rueda-Delgado and Fredy Cuellar-Torres and Damian Martinez and Margarita Sofia Narducci}, doi = {10.1109/ACCESS.2025.3550859}, journal = {IEEE Access}, pages = {49777-49787}, shortjournal = {IEEE Access}, title = {Instrumentation system for monitoring of soil variables in precision agriculture applications}, volume = {13}, year = {2025}, }
• (2025). Classroom student behavior recognition using an intelligent sensing framework. ACCESS, 13, 49767-49776. (www)

  @article{ , abstract = {Recognizing classroom student behavior is crucial for evaluating teaching quality and fostering effective learning environments. However, existing methods often rely on teachers’ direct observation or intelligent static image analysis, missing important temporal cues that reflect subtle interactive and collaborative processes in the classroom. To address this gap, this study proposes an Intelligent Sensing Framework based on a Multimodal Framework Transformer that integrates video and audio modalities. By capturing temporal features from video streams and leveraging audio signals, our approach accurately identifies nuanced student behaviors, thus overcoming the limitations of single-modality methods. We evaluate our framework on the newly introduced CSBR10 dataset, containing ten categories of student behavior with over 700 clips per category, and further validate its robustness on the UCF-101 dataset. Experimental results reveal that the proposed method improves temporal modeling over traditional image-based baselines and achieves significant accuracy gains when fusing audio and video modalities. By enabling real-time, accurate behavior analysis, our approach provides educators with actionable insights for personalized interventions and timely feedback to enhance engagement and improve academic outcomes. Consequently, this advanced intelligent sensing framework holds promise for elevating classroom engagement and learning outcomes. The code and trained models will be accessible at https://github.com/LiuHaiChuan0/2021-Deep-learning/tree/main/CSBR.}, archive = {J_ACCESS}, author = {Xian Min Zhao and Farrah Dina Yusop and Hai Chuan Liu and Yeni Nur Prilanita and Yi Xuan Chang}, doi = {10.1109/ACCESS.2025.3550921}, journal = {IEEE Access}, pages = {49767-49776}, shortjournal = {IEEE Access}, title = {Classroom student behavior recognition using an intelligent sensing framework}, volume = {13}, year = {2025}, }
• (2025). STARS—Deep diversified analysis of occlusion-resiliency for robot vision in medical contexts. ACCESS, 13, 49750-49766. (www)

  @article{ , abstract = {Occlusion is a critical challenge in Robot vision. Unfavorable circumstances including occlusions must be considered when integrating action recognition techniques into an autonomous robotic system. It becomes more critical when the recognition involves Medical scenarios. Occlusion of certain body parts can disrupt the geometric and temporal stability of recognition, significantly affecting accuracy. Despite its practical significance, occlusion in healthcare scenarios is rarely addressed by existing skeleton-based action recognition techniques. This study proposes STARS (Spatial Temporal Adaptive Recurrent Solution), a neural network-based technique designed for robust occlusion handling in medical robotics. The proposed method effectively handles temporal and spatial occlusions in medical scenarios, outperforming state-of-the-art techniques. The efficacy of the proposed model is evaluated using the NTU RGB+D dataset, processed through five schematic configurations: Fundamental, Temporal, Spatial, Calibration, and Appraisal. These configurations target occlusion in distinct ways. The findings reveal that occluded data samples impact recognition accuracy, and the proposed technique achieves results close to non-occluded scenarios. However, occlusion in medical activity detection requires further attention.}, archive = {J_ACCESS}, author = {Saima Sultana and Muhammad Mansoor Alam and Jawahir Che Mustapha and Mukesh Prasad and Sadia Nazim and Mazliham Mohd Su'Ud}, doi = {10.1109/ACCESS.2025.3550078}, journal = {IEEE Access}, pages = {49750-49766}, shortjournal = {IEEE Access}, title = {STARS—Deep diversified analysis of occlusion-resiliency for robot vision in medical contexts}, volume = {13}, year = {2025}, }
• (2025). Introducing a novel figure of merit for evaluating stability of perovskite solar cells: Utilizing long short-term memory neural networks. ACCESS, 13, 49735-49749. (www)

  @article{ , abstract = {This study introduces a novel figure of merit for evaluating the stability of perovskite solar cells (PSCs) by employing advanced Long Short-Term Memory (LSTM) neural networks to investigate degradation mechanisms. By harnessing the power of artificial intelligence and data analytics, we analyzed extensive datasets encompassing PSC parameters, experimental results, and environmental conditions, revealing critical insights into the degradation patterns affecting cell performance over time. Our findings indicate that the LSTM model effectively captures and predicts the complex relationships between key design parameters—efficiency, fill factor, and open-circuit voltage—and degradation-induced changes in PSCs. Specifically, we identified three degradation coefficients associated with the electron transport layer, hole transport layer, and perovskite active layer. These coefficients serve as a new figure of merit, facilitating numerical studies on degradation and stability in PSCs, mainly focusing on cesium lead halides. Furthermore, the enhanced LSTM architecture, featuring deeper layers, dropout for regularization, and batch normalization, demonstrated improved stability and training speed, leading to a test Mean Absolute Error (MAE) of 0.0354 and an R2 value of 0.9991, indicating near-perfect predictive accuracy. The comparative analysis of model complexity confirmed that increasing the sophistication of the LSTM model significantly enhances predictive accuracy and generalization capabilities. Identifying crucial design parameters offers actionable insights for optimizing PSC designs, materials selection, and operational conditions, ultimately contributing to enhanced long-term stability and efficiency of PSCs. Future research should prioritize using experimental datasets to achieve more realistic predictions, thereby driving innovation and unlocking the full potential of machine learning and deep learning in optimizing PSC design and performance.}, archive = {J_ACCESS}, author = {Zahraa Ismail and Ahmet Sait Alali and Ahmad Muhammad and Mahmoud Ashraf and Sameh O. Abdellatif}, doi = {10.1109/ACCESS.2025.3550658}, journal = {IEEE Access}, pages = {49735-49749}, shortjournal = {IEEE Access}, title = {Introducing a novel figure of merit for evaluating stability of perovskite solar cells: Utilizing long short-term memory neural networks}, volume = {13}, year = {2025}, }
• (2025). A systematic review of extended reality (XR) technologies in agriculture and related sectors (2022–2024). ACCESS, 13, 49721-49734. (www)

  @article{ , abstract = {The agricultural sector confronts critical challenges such as climate change, overpopulation, and resource shortages, necessitating urgent innovative solutions. In this context, Extended Reality (XR) technologies have emerged as a transformative technology, offering groundbreaking approaches to modern farming practices. This study conducts a thorough literature review to explore the integration of XR technologies in agriculture, livestock farming, and aquaculture. By categorizing studies based on focus areas, application types, and human-machine interaction dimensions, it identifies emerging trends, highlights critical gaps, and proposes future direction for advancing these fields. The analysis revealed a significant increase in XR technology adoption over the past two years, highlighting its potential within the sector. The findings suggest that XR technologies can enhance productivity, improve training methods, and facilitate remote collaboration in agricultural settings. However, future research should focus on addressing health, privacy, and security concerns to enhance seamless and immersive XR experiences, thereby maximizing the potential benefits of XR technology in the primary sector. Therefore, these technologies will transform the agriculture industry, fostering a resilient and sustainable food system to meet the demands of a growing global population.}, archive = {J_ACCESS}, author = {Maryam Bigonah and Fatemeh Jamshidi and Aparana Pant and Sanjaya Poudel and Sasikiran Reddy Nallapareddy and Atefeh Charmchian Langroudi and Daniela Marghitu}, doi = {10.1109/ACCESS.2025.3550891}, journal = {IEEE Access}, pages = {49721-49734}, shortjournal = {IEEE Access}, title = {A systematic review of extended reality (XR) technologies in agriculture and related sectors (2022–2024)}, volume = {13}, year = {2025}, }
• (2025). A dialogue response generation model with latent variable modeling and LWE attention based on the VW-BLEU evaluation metric. ACCESS, 13, 49710-49720. (www)

  @article{ , abstract = {In this page, We propose a novel dialogue response generation model that combines BART-large models with latent variable modeling and the Latent Weight Enhanced (LWE) Attention mechanism to improve the performance of dialogue response generation. Dialogue systems are increasingly utilized in various domains, such as customer service, medical consultations, and personal assistants. In these applications, generating natural and consistent responses is a critical challenge. However, existing models often struggle to maintain long-term dialogue coherence or capture hidden meanings and inferential relationships within conversations. To address these limitations, this research presents enhanced modeling techniques based on the Learning to Memorize Entailment and Discourse Relations (LMEDR) framework, effectively capturing contextual consistency and inferential relationships in dialogue. Additionally, a new evaluation metric, Vocabulary-Weighted BLEU (VW-BLEU), is introduced to overcome the limitations of traditional BLEU scores by assessing response diversity and naturalness. Experimental results demonstrate that the proposed model achieves superior performance on traditional metrics such as BLEU, METEOR, and ROUGE-L. In addition, our model exhibits higher lexical diversity as reflected by improved Distinct-1 (2.79 vs. 2.47) and Distinct-2 (14.95 vs. 13.82) scores and a significant boost in persona consistency with a Consistency Score of 39.41 compared to 25.31 from the state-of-the-art LMEDR. Furthermore, on the DSTC7-AVSD dataset, our model yields improvements of 13.4% in BLEU and 12.3% in METEOR, confirming its ability to generate responses with enhanced lexical richness and contextual fluency, as further validated by high VW-BLEU scores}, archive = {J_ACCESS}, author = {Jongwon Lee and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3549152}, journal = {IEEE Access}, pages = {49710-49720}, shortjournal = {IEEE Access}, title = {A dialogue response generation model with latent variable modeling and LWE attention based on the VW-BLEU evaluation metric}, volume = {13}, year = {2025}, }
• (2025). Integrating deep learning and SHAP for breast cancer classification and biomarker discovery using gene expression data. ACCESS, 13, 49693-49709. (www)

  @article{ , abstract = {This study explores the application of deep learning models combined with SHAP (SHapley Additive exPlanations) for breast cancer classification using gene expression data. Our model demonstrated exceptional performance, achieving a mean accuracy of 0.9964 across 5-fold cross-validation and perfect scores through ensemble learning. To further validate the model’s generalizability, we tested it on three independent datasets (GSE45827, GSE7904, and GSE42568), where it achieved an accuracy of 0.9867, 1.0 and 0.9914. SHAP analysis provided valuable insights into gene importance, identifying key genes such as DSCAM-AS1, KRT19, ESR1, and KRT5 as significant contributors to cancer prediction. Validation against the MalaCards database confirmed the relevance of most top-ranked genes, while novel candidates were also identified. Notably, some well-known breast cancer-related genes were not top-ranked, likely due to dataset limitations. This research underscores the potential of combining deep learning and SHAP for precise gene ranking and the discovery of novel biomarkers.}, archive = {J_ACCESS}, author = {Salah Eddine Aliouane and Hamza Chehili and Khaled Boulahrouf and Aya Abdelaziz and Nawres Khlifa and Mohamed Abdelhafid Hamidechi}, doi = {10.1109/ACCESS.2025.3552280}, journal = {IEEE Access}, pages = {49693-49709}, shortjournal = {IEEE Access}, title = {Integrating deep learning and SHAP for breast cancer classification and biomarker discovery using gene expression data}, volume = {13}, year = {2025}, }
• (2025). Simultaneous control of cost dynamics and transport in network systems. ACCESS, 13, 49683-49692. (www)

  @article{ , abstract = {Transportation networks, including infrastructures such as roads and power transmission, are crucial for supporting modern society. However, these networks are frequently exposed to risks such as natural disasters, e.g., earthquakes and hurricanes. In the event of such an incident, efforts are made to maintain connectivity as much as possible while striving to restore the transportation network. The optimal transport (OT) problem, which can efficiently manage transportation networks, presents a promising approach to control such situations. Although there has been considerable progress in the application of OT to transportation research, studies addressing both the control of transport operations and network structure, such as network restoration, are limited. This paper introduces a novel formulation of OT that incorporates network structure control. Specifically, we optimize the routing and restoration of the network after the damage. The network damage is parameterized as a significant increase in cost, and the restoration process is formulated as the cost dynamics. Ultimately, this is framed as the OT on the network using costs that include these parameters. Additionally, we propose an efficient algorithm for this problem, which utilizes the Markov property and Schrödinger bridge. Through a post-disaster simulation of food transport across the European transportation network, we confirmed that our approach outperforms both the node-degree-based restoration strategy and the strategy that manages transportation and restoration separately. Our method shows promise as a more effective strategy for the initial response to a damaged network.}, archive = {J_ACCESS}, author = {Koshi Oishi and Tomohiko Jimbo and Satoshi Kikuchi and Yuji Date and Hirotaka Kaji and Kenji Kashima}, doi = {10.1109/ACCESS.2025.3552040}, journal = {IEEE Access}, pages = {49683-49692}, shortjournal = {IEEE Access}, title = {Simultaneous control of cost dynamics and transport in network systems}, volume = {13}, year = {2025}, }
• (2025). Heterogeneous graph neural network model based on edge feature generation and meta-graph similarity for feature extraction. ACCESS, 13, 49672-49682. (www)

  @article{ , abstract = {Edges and nodes form the core elements of heterogeneous graphs (HGs). However, existing heterogeneous graph neural networks (HGNNS) largely rely on meta-paths to capture semantic information of nodes, often overlooking the features embedded in edges. This not only limits the expressiveness of the models but also the potential semantic isolation issues within meta-paths can affect the learning efficiency of the models, leading to suboptimal outcomes. To address these challenges, this paper proposes a novel heterogeneous graph neural network model—heterogeneous graph neural network model based on edge feature generation and meta-graph similarity for feature extraction (HGNN-ESFE)—which aims to optimize the feature extraction process through edge feature generation and meta-graph similarity. Given that edges in HGs typically lack direct feature descriptions, HGNN-ESFE innovatively adopts an averaging strategy to generate feature vectors for different types of edges. During this process, to ensure that edge features more comprehensively reflect the structural characteristics of the heterogeneous graph, HGNN-ESFE replaces the original features of the nodes at both ends of the edges with one-hot encoding. Subsequently, by randomly sampling a fixed number of edges from the generated edge features, HGNN-ESFE aggregates them to construct richer representations of edge features. To tackle the semantic isolation problem brought about by meta-paths, HGNN-ESFE introduces a similarity-based neighbor node screening mechanism. This mechanism effectively filters out noise information in the meta-graph, retaining only those neighbor nodes highly relevant to the target node for feature aggregation. This approach not only enhances the accuracy of feature extraction but also strengthens the model’s ability to understand complex relationships within heterogeneous graphs. Furthermore, by integrating feature information obtained from two distinct aggregation strategies, HGNN-ESFE utilizes a module-level attention mechanism to achieve effective fusion of these two parts of features, ultimately generating high-quality node embeddings. The experimental results demonstrate that, in tasks such as node classification, HGNN-ESFE achieves approximately a 1%-2% performance improvement compared to the state-of-the-art methods.}, archive = {J_ACCESS}, author = {Chengyuan Qian}, doi = {10.1109/ACCESS.2025.3552125}, journal = {IEEE Access}, pages = {49672-49682}, shortjournal = {IEEE Access}, title = {Heterogeneous graph neural network model based on edge feature generation and meta-graph similarity for feature extraction}, volume = {13}, year = {2025}, }
• (2025). FuzzyBin: Enhanced border binary identification by leveraging fuzzy hashing algorithms. ACCESS, 13, 49659-49671. (www)

  @article{ , abstract = {The rapid growth of IoT devices has led to the development of various methods to analyze firmware vulnerabilities and uncover potential security risks. One promising approach is to focus on binaries exploited as attack vectors, particularly those that handle user input, referred to as border binaries. However, existing methods often depend on narrowly defined identification rules. Consequently, they may fail to detect binaries that handle user input but lie outside the scope of these rules, resulting in overlooked vulnerabilities. In this paper, we introduce FuzzyBin, a novel approach for the comprehensive identification of border binaries. The key insight behind FuzzyBin is that border binaries share execution logic with well-known web server binaries. Building on this observation, FuzzyBin systematically identifies border binaries by examining their similarity to web server binaries. Specifically, FuzzyBin employs fuzzy hashing as a powerful technique for similarity analysis. Additionally, it incorporates a function filtering mechanism to focus on the primary functions within each binary, thereby enhancing detection accuracy. We evaluate FuzzyBin on 13 firmware samples from various vendors, demonstrating its superior detection coverage and processing time compared to state-of-the-art tools.}, archive = {J_ACCESS}, author = {Dongsoo Kim and Hyerean Jang and Youngjoo Shin}, doi = {10.1109/ACCESS.2025.3552751}, journal = {IEEE Access}, pages = {49659-49671}, shortjournal = {IEEE Access}, title = {FuzzyBin: Enhanced border binary identification by leveraging fuzzy hashing algorithms}, volume = {13}, year = {2025}, }
• (2025). Performance modeling of data storage systems using generative models. ACCESS, 13, 49643-49658. (www)

  @article{ , abstract = {High-precision systems modeling is one of the main areas of industrial data analysis. Models of systems, their digital twins, are used to predict their behavior under various conditions. In this study, we developed several models of a storage system using machine learning-based generative models to predict performance metrics such as IOPS and latency. The models achieve prediction errors ranging from 4%–10% for IOPS and 3%–16% for latency and demonstrate high correlation (up to 0.99) with observed data. By leveraging Little’s law for validation, these models provide reliable performance estimates. Our results outperform conventional regression methods, offering a vendor-agnostic approach for simulating data storage system behavior. These findings have significant applications for predictive maintenance, performance optimization, and uncertainty estimation in storage system design.}, archive = {J_ACCESS}, author = {Abdalaziz R. Al-Maeeni and Aziz Temirkhanov and Artem Ryzhikov and Mikhail Hushchyn}, doi = {10.1109/ACCESS.2025.3552409}, journal = {IEEE Access}, pages = {49643-49658}, shortjournal = {IEEE Access}, title = {Performance modeling of data storage systems using generative models}, volume = {13}, year = {2025}, }
• (2025). Importance sampling and feature fusion paradigm-boosted multi-modal convolutional neural networks: Deployment in composite curing process monitored by electro-mechanical impedance. ACCESS, 13, 49630-49642. (www)

  @article{ , abstract = {The increasing application of composite materials in various industrial sectors is driven by their lightweight nature, high strength-to-stiffness ratio, and corrosion resistance. Effective monitoring of the curing process is crucial for ensuring quality and performance. Electro-Mechanical Impedance (EMI) offers promising, non-destructive, real-time monitoring, but the complexity of EMI signals poses challenges. Convolutional Neural Networks (CNNs) have the potential to enhance EMI-based monitoring accuracy. However, training CNNs on multi-modal EMI signals requires addressing data heterogeneity, class imbalance, and computational complexity at present. This study develops the Importance Sampling Algorithm-optimized Multi-Modal CNNs (ISA-MM-CNNs) paradigm for EMI-based evaluation of composite curing processes. By prioritizing informative samples and capturing complementary information from diverse EMI signal modalities, we aim to improve the robustness and efficiency of CNNs in evaluating curing degrees. This study outlines EMI monitoring challenges, details the ISA-MM-CNNs paradigm, and discusses data preprocessing, network architecture, and training optimization. Experimental results demonstrate the superiority of the developed ISA-MM-CNNs and suggest further studies for the curing monitoring of composites.}, archive = {J_ACCESS}, author = {Xin Zhao and Zeyuan Gao and Meng Li and Zhibin Han and Jianjian Zhu}, doi = {10.1109/ACCESS.2025.3551508}, journal = {IEEE Access}, pages = {49630-49642}, shortjournal = {IEEE Access}, title = {Importance sampling and feature fusion paradigm-boosted multi-modal convolutional neural networks: Deployment in composite curing process monitored by electro-mechanical impedance}, volume = {13}, year = {2025}, }
• (2025). VSBL-based multi-person range estimation and vital signs monitoring via FMCW MIMO radar. ACCESS, 13, 49611-49629. (www)

  @article{ , abstract = {With the rising incidence of chronic diseases of the respiratory and cardiopulmonary systems, as well as the rapid development of medical instrumentation technology, non-contact vital signs monitoring (VSM) technology has emerged. The multiple-input-multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar can offer high accuracy and reliability. Therefore, a mathematical model for multi-person range estimation and VSM based on FMCW MIMO radar is proposed in this paper. Additionally, a variation sparse Bayesian learning (VSBL) technique for frequency estimation is provided to estimate the range, breathing rate, and heart rate from the phase signals of the target. It outperforms other techniques by further refining the grid based on the sparse Bayesian learning (SBL) to produce high-resolution frequency estimate results. The feasibility of the proposed method is verified by comparing the results based on the proposed mathematical model with those based on measured data. In addition, experiments show that the proposed model can effectively facilitate VSM in multi-person.}, archive = {J_ACCESS}, author = {Yanjiao Chen and Yuncan Sheng and Yuehao Guo and Xianpeng Wang}, doi = {10.1109/ACCESS.2025.3547215}, journal = {IEEE Access}, pages = {49611-49629}, shortjournal = {IEEE Access}, title = {VSBL-based multi-person range estimation and vital signs monitoring via FMCW MIMO radar}, volume = {13}, year = {2025}, }
• (2025). Cogging torque minimization strategies in interior permanent magnet synchronous motors. ACCESS, 13, 49597-49610. (www)

  @article{ , abstract = {Cogging torque in interior permanent magnet synchronous motors (IPMSMs) is caused by variation in magnetic reluctance between permanent magnets and stator slot openings. It can result in vibration, acoustic noise, speed ripple, and possible mechanical resonance in this family of electric machines. Minimization of cogging torque is of high importance in IPMSMs. In this paper, a detailed study of cogging torque minimization techniques based on a grid on/off search method applied to the rotor surface in an IPMSM is presented. Since the airgap is one of the most influential parameters on flux distribution, optimal sculpturing of the rotor outer surface can result in reduction of cogging torque and overall torque ripple. This approach utilizes a genetic algorithm (GA)-assisted grid on/off search method and introduces asymmetric rotor surface modifications, with experimental validation showing significant cogging torque reduction while maintaining average torque. Additionally, an iron-air partitioning approach for rotor surface optimization is applied, along with a hybrid optimization framework that enhances cogging torque reduction compared to conventional methods. Experimentally, the designs demonstrate reductions of up to 67.65% for asymmetric designs and 89.71% when combined with rotor skewing, without substantial reduction in average torque. The approach is further distinguished by its practical feasibility, validated through prototypes and performance testing, without introducing additional manufacturing complexity. Simulation and experimental results for several geometries, including symmetric rotor design, asymmetric rotor design, and skewing of symmetric rotor design, are compared with those from the original design.}, archive = {J_ACCESS}, author = {Milad Bahrami-Fard and Jingchen Liang and Pengyuan Chen and Colbey Hair and Mehdi Moallem and Steven Pekarek and Babak Fahimi}, doi = {10.1109/ACCESS.2025.3550797}, journal = {IEEE Access}, pages = {49597-49610}, shortjournal = {IEEE Access}, title = {Cogging torque minimization strategies in interior permanent magnet synchronous motors}, volume = {13}, year = {2025}, }
• (2025). Contrastive feature bin loss for monocular depth estimation. ACCESS, 13, 49584-49596. (www)

  @article{ , abstract = {Recently monocular depth estimation has achieved notable performance using encoder-decoder-based models. These models have utilized the Scale-Invariant Logarithmic (SILog) loss for effective training, leading to significant performance improvements. However, since the SILog loss is designed to reduce error variance, it may potentially mislead the model. To address this problem, we propose the Contrastive Feature Bin (CFB) loss as an additional regularization loss. CFB loss prevents the possibility of incorrect learning by ensuring that similar depths are learned similarly, and can be easily integrated into various encoder-decoder-based models and greatly enhances overall performance. Another problem commonly faced by existing monocular depth estimation models is that they sometimes demand a significant amount of memory resources during training. Nevertheless, reducing memory consumption by employing smaller batch sizes can result in a noticeable decline in performance, compromising reproducibility and practicality. CFB loss allows encoder-decoder-based models to achieve comparable or even superior performance with lower batch sizes, requiring only modest increases in training time. Our proposed approach demonstrates improvements in the performance of diverse monocular depth estimation models on datasets such as NYU Depth v2 and KITTI Eigen split. Notably, in scenarios with a small batch size, it achieves up to an 11% improvement in RMSE compared to existing methods. The code is available at Github.}, archive = {J_ACCESS}, author = {Jihun Song and Yoonsuk Hyun}, doi = {10.1109/ACCESS.2025.3551435}, journal = {IEEE Access}, pages = {49584-49596}, shortjournal = {IEEE Access}, title = {Contrastive feature bin loss for monocular depth estimation}, volume = {13}, year = {2025}, }
• (2025). 1DCNN-residual bidirectional LSTM for permanent magnet synchronous motor temperature prediction based on operating condition clustering. ACCESS, 13, 49567-49583. (www)

  @article{ , abstract = {With the rapid development of electric vehicle (EV) technology, accurate prediction of motor stator temperatures is essential to ensure safe operation and extend the service life of motors. However, traditional prediction methods have limitations in dealing with nonlinear and complex temperature data, making it difficult to provide highly accurate prediction results. To address this issue, this paper proposes a deep learning model based on one-dimensional convolutional neural network (1DCNN) and bidirectional long and short-term memory network (BiLSTM) with operating condition clustering for learning and accurately predicting the temperature of the motor stator directly from the raw data. Through training and validation with the existing dataset of the motor, the experimental results show that the proposed model has a significant improvement in prediction accuracy compared to the traditional methods, which provides a strong support for the health monitoring and maintenance of the motor.}, archive = {J_ACCESS}, author = {Jingrong Cheng and Feifan Ji and Chenglong Huang and Tong Wang and Yan Liu and Yanjun Li}, doi = {10.1109/ACCESS.2025.3549079}, journal = {IEEE Access}, pages = {49567-49583}, shortjournal = {IEEE Access}, title = {1DCNN-residual bidirectional LSTM for permanent magnet synchronous motor temperature prediction based on operating condition clustering}, volume = {13}, year = {2025}, }
• (2025). TrBot: A turkish deep learning chatbot utilizing Seq2Seq model. ACCESS, 13, 49552-49566. (www)

  @article{ , abstract = {In Natural Language Processing (NLP) and Artificial Intelligence (AI), chatbots, which are software programs designed to facilitate human-computer interaction through natural language, are becoming increasingly important. However, creating an effective chatbot remains a complex task, as it must accurately interpret user input and generate appropriate responses. This study presents TrBot, a general-purpose Turkish chatbot that utilizes deep learning techniques, specifically a seq2seq model with Long Short-Term Memory (LSTM) layers. This architecture allows TrBot to manage sequential dependencies and effectively generate coherent responses, offering advantages in handling the complex morphological structure of Turkish. In contrast to earlier Turkish chatbots that were application-specific, TrBot is designed for broad conversational use across various topics. In this study, we also proposed and created two comprehensive datasets: a QA dataset with 40,702 entries and a conversation dataset with 304,446 entries, both specifically designed to enhance TrBot’s performance. Trained on these datasets, TrBot achieved an accuracy of 80% on the QA dataset and 70% on the dialog dataset, with BLEU scores of 0.90 and 0.77 respectively, indicating substantial enhancements in response quality. In comparison, a Transformer-based model exhibited reduced training times but achieved lower accuracies of 60% on the QA dataset and 50% on the dialog dataset, with BLEU scores of 0.76 and 0.61 respectively, with the limited size of the datasets and available computational resources. The development of TrBot has significant implications, offering potential benefits in areas such as customer support, language learning, and other fields that require robust Turkish conversational capabilities. This study demonstrates that with adequate data and appropriate modeling techniques, it is possible to create effective conversational agents for complex languages like Turkish, paving the way for further advancements in this domain.}, archive = {J_ACCESS}, author = {Bilal Babayigit and Habibelahi Rahmani and Mohammed Abubaker}, doi = {10.1109/ACCESS.2025.3550852}, journal = {IEEE Access}, pages = {49552-49566}, shortjournal = {IEEE Access}, title = {TrBot: A turkish deep learning chatbot utilizing Seq2Seq model}, volume = {13}, year = {2025}, }
• (2025). RLS-based time delay control method for distributed asynchronous sheath current sensing in underground HV cable systems. ACCESS, 13, 49543-49551. (www)

  @article{ , abstract = {Distributed sheath current sensing in HV cable systems is vital for grounding fault detection. However, the signal is occasionally lost in underground sensing applications with previous synchronized methods based on satellite time transfer. In this paper, a time delay control method based on parallel Smith estimation and the coupling law between cables via RLS (Recursive Least Square) is proposed for asynchronous sheath current sensing in underground HV cable systems. This method is verified by the field test on the primary side high voltage cable of the traction transformer of 220 kV high-speed railway. The result shows that the proposed method converges to the minimum residual value point, the average residual is 0.321 A, the accurate time delay is 5.22 s, and the synchronization of distributed sheath current sensing can be realized.}, archive = {J_ACCESS}, author = {Jing Tu and Yongheng Ai and Zhanran Xia and Ruoxin Song and Bin Yang and Long Qiu and Yitao Yan and Shasha Zhou and Hang Wang}, doi = {10.1109/ACCESS.2025.3550963}, journal = {IEEE Access}, pages = {49543-49551}, shortjournal = {IEEE Access}, title = {RLS-based time delay control method for distributed asynchronous sheath current sensing in underground HV cable systems}, volume = {13}, year = {2025}, }
• (2025). Automated matching of ancient bone stick fragments: Integrating siamese networks with sequence similarity metrics for multi-feature fusion. ACCESS, 13, 49527-49542. (www)

  @article{ , abstract = {Ancient bone stick fragments excavated from the site of ancient Chang’an during the Western Han Dynasty present significant challenges for matching due to their sheer number, complex matching features, and the scarcity of reference samples. To address these challenges, this paper proposes a novel Automatic Matching Method for Bone Stick Fragments that integrates Siamese networks with Sequence Similarity Metric Algorithms. Our method reconstructs a Siamese feature extraction network by combining a Vision Transformer with a scaled-down version of EfficientNetV2, efficiently capturing both global and local features of the bone stick images. We further enhance the matching accuracy by coupling the Siamese network with the Fast Multiscale Derivative Dynamic Time Warping algorithm, which focuses on the intricate matching features on the image surface and Broken Edge Sequence Characteristics. Experimental results show that our approach achieves a Top-15 recall rate of 94.55%, significantly outperforming comparative algorithms. This advancement facilitates efficient and precise matching of bone stick fragments, offering a substantial improvement over existing methods.}, archive = {J_ACCESS}, author = {Shiyi Ren and Huiqin Wang and Ke Wang and Rui Liu and Zhan Wang}, doi = {10.1109/ACCESS.2025.3551604}, journal = {IEEE Access}, pages = {49527-49542}, shortjournal = {IEEE Access}, title = {Automated matching of ancient bone stick fragments: Integrating siamese networks with sequence similarity metrics for multi-feature fusion}, volume = {13}, year = {2025}, }
• (2025). Advanced quantum control with ensemble reinforcement learning: A case study on the XY spin chain. ACCESS, 13, 49514-49526. (www)

  @article{ , abstract = {This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. We comprehensively analyse the proposed ensemble learning, including algorithmic details, implementation specifics, and experimental results. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the ensemble approach in learning control strategies for manipulating quantum systems towards a random target state. The results highlight the potential of ensemble RL techniques in addressing the challenges of quantum control tasks, such as system noise and dynamics. By integrating multiple RL agents within an ensemble framework, We aim to advance current developments in quantum control and create a new path for the development of adaptive control systems for quantum systems. The performance of the ensemble model is assessed against Gradient Ascent Pulse Engineering (GRAPE) and robust Model Predictive Control (MPC) to demonstrate its efficiency in highly challenging and noisy environments.}, archive = {J_ACCESS}, author = {Farshad Rahimi Ghashghaei and Nebrase Elmrabit and Ayyaz-Ul-Haq Qureshi and Adnan Akhunzada and Mehdi Yousefi}, doi = {10.1109/ACCESS.2025.3551232}, journal = {IEEE Access}, pages = {49514-49526}, shortjournal = {IEEE Access}, title = {Advanced quantum control with ensemble reinforcement learning: A case study on the XY spin chain}, volume = {13}, year = {2025}, }
• (2025). DC series arc fault detection and extinguishing with modulation algorithm of non-inverting buck-boost converter for PV applications. ACCESS, 13, 49504-49513. (www)

  @article{ , abstract = {Power generation using a photovoltaic (PV) system is increasing to achieve the carbon neutrality. Module-level power electronics (MLPEs) can optimize power generation for each PV panel. However, it increases the possibility of DC series arc fault condition between the PV panel and MLPEs. The broken and loose connector and damaged cable can induce a DC series arc fault condition. Previously, frequency domain analysis using PV current, such as Fourier transform and wavelet transform, was widely employed for DC series arc fault detection. It requires the auxiliary device to detect the arc failure, which degrades the cost-effectiveness of PV systems. In addition, they cannot achieve the fault detection and extinguishing in the same time. This paper proposes a modulation algorithm of non-inverting buck-boost converter (NIBBC) to achieve the DC series arc fault detection and extinguishing. The proposed method uses the characteristics of PV, NIBBC, and arc fault to classify the normal and arc fault conditions. The operational principle is analyzed to design the modulation method according to operating modes. The 800-W prototype NIBBC is used to verify the performance of series arc fault detection and extinguishing.}, archive = {J_ACCESS}, author = {Byungki Kim and Taesic Kim and Wan Kim and Hwa-Pyeong Park}, doi = {10.1109/ACCESS.2025.3552028}, journal = {IEEE Access}, pages = {49504-49513}, shortjournal = {IEEE Access}, title = {DC series arc fault detection and extinguishing with modulation algorithm of non-inverting buck-boost converter for PV applications}, volume = {13}, year = {2025}, }
• (2025). Application of metamaterials in antennas for gain improvement: A study on integration techniques and performance. ACCESS, 13, 49489-49503. (www)

  @article{ , abstract = {Metamaterials, with their extraordinary electromagnetic properties, have transformed antenna design by offering advanced control over signal propagation and radiation patterns. This review emphasizes on the integration techniques of major types of metamaterials in antennas to achieve significant gain enhancement. It begins with an overview of metamaterials, discussing their distinct types, properties, and the fundamental principles that enable them to manipulate electromagnetic waves in novel ways. The review then delves into the core topic: the application of these major types of metamaterials in enhancing antenna gain. It thoroughly examines four primary integration techniques, detailing the physical structures. Furthermore, the review analyses how specific metamaterials are integrated with different antennas along with their performances in terms of gain enhancement. Additionally, the review briefly addresses the current challenges and limitations in the existing techniques of integration of metamaterials with antennas in the context of recent advancements, emphasizing scopes for future research in order to fully exploit the potential of metamaterial-enhanced antennas.}, archive = {J_ACCESS}, author = {Md Mirazur Rahman and Yang Yang and Shuvashis Dey}, doi = {10.1109/ACCESS.2025.3552023}, journal = {IEEE Access}, pages = {49489-49503}, shortjournal = {IEEE Access}, title = {Application of metamaterials in antennas for gain improvement: A study on integration techniques and performance}, volume = {13}, year = {2025}, }
• (2025). Shade artifact reduction in CBCT-to-MDCT: Fine-tuning based on style transfer and human feedback. ACCESS, 13, 49476-49488. (www)

  @article{ , abstract = {Cone beam computed tomography (CBCT) is widely used in dental treatment due to its low radiation dose and cost. However, it has lower image quality compared to Multi Detector Computed Tomography (MDCT), limiting its use in precision medical examinations. Recent research has focused on using generative models, particularly Cycle-GAN, to transform low-resolution CBCT images into high-resolution MDCT-like images. Although Cycle-GAN has shown promising results, it often produces shading artifacts that compromise image quality. In this paper, we propose a fine-tuning method that integrates human feedback with style transfer techniques to effectively address this issue, thereby improving the model’s adaptability and performance. Our method involves re-training the Cycle-GAN generator to learn and replicate the feature distribution of clean MDCT images, significantly reducing shading artifacts and preserving morphological structures. Experimental results demonstrate that our proposed fine-tuning method effectively reduces artifacts and enhances image quality. Additionally, our selective training technique ensures the preservation of local morphological structures, leading to better outcomes compared to non-selective training methods. This approach provides a reliable and efficient solution for generating high-resolution, artifact-free MDCT-like images from cost-effective CBCT scans, with the potential to greatly benefit clinical diagnostics and patient outcomes.}, archive = {J_ACCESS}, author = {Hyun-Cheol Park and Kiwan Jeon and Hyoung Suk Park and Sung Ho Kang}, doi = {10.1109/ACCESS.2025.3552063}, journal = {IEEE Access}, pages = {49476-49488}, shortjournal = {IEEE Access}, title = {Shade artifact reduction in CBCT-to-MDCT: Fine-tuning based on style transfer and human feedback}, volume = {13}, year = {2025}, }
• (2025). When poor-quality data meet anonymization models: Threats and countermeasures. ACCESS, 13, 49457-49475. (www)

  @article{ , abstract = {In the modern era, the avenues for data generation have significantly evolved, and therefore, large-scale and diverse types of data are being collected for downstream tasks. However, some automated tools can curate data with many vulnerabilities (e.g., missing values, wrong values, outliers, skewed distributions, missing labels, etc.) which can hamper its usage in underlying applications. For example, skewed data may lead to imbalanced learning when used in machine learning (ML) classifiers. Similarly, low-quality data can inadvertently propagate bias in ML decisions, leading to conflict or disputes. Data quality enhancement with the least possible cost has become a hot research area. In this paper, we demonstrate how poor-quality data can pose serious threats to anonymization models, consequently undermining privacy and utility requirements. We propose various countermeasures to inspect and improve data quality before anonymization so as to not lose guarantees of both privacy and utility. Specifically, we pinpoint eleven different threats from a small segment of data to underscore the relevance and urgency of such issues in the anonymization domain. We devise six practical countermeasures to provide resilience against these potential threats to enhance the performance and resilience of anonymization models. We aim to uncover the ways poor-quality datasets are handled by anonymization models, and what threats to both privacy and utility exist. Our work can guide the privacy and database community to improve the mainstream technologies used for privacy preservation to effectively resolve present-day privacy threats. To the best of the authors’ knowledge, this is the first work that highlights the threats posed by the poor quality data to the popular anonymization models and suggests countermeasures to overcome them along with reasonable experiments on two datasets.}, archive = {J_ACCESS}, author = {Abdul Majeed and Seong Oun Hwang}, doi = {10.1109/ACCESS.2025.3552412}, journal = {IEEE Access}, pages = {49457-49475}, shortjournal = {IEEE Access}, title = {When poor-quality data meet anonymization models: Threats and countermeasures}, volume = {13}, year = {2025}, }
• (2025). Bearing prognostics using the PRONOSTIA data: A comparative study. ACCESS, 13, 49433-49456. (www)

  @article{ , abstract = {Vibration-based condition monitoring is a well-developed and established field in maintenance engineering. Failure prognosis is a crucial aspect of condition monitoring, particularly Remaining Useful Life (RUL) prediction. Over the last decade, the PRONOSTIA bearing dataset has become the standard reference for testing these prognostic algorithms. However, the lack of standardized comparisons makes it difficult to objectively assess the relative performance of different methods. This paper systematically compares three established data-driven artificial intelligence approaches: classical machine learning, deep learning, and transfer learning approaches. We analyze their application to the PRONOSTIA dataset, providing detailed discussions of their relative strengths, limitations, and achieved performance. While this study primarily serves as a benchmark for testing new prognostic algorithms in vibration monitoring, we hope the insights will also broadly apply to other condition-monitoring techniques.}, archive = {J_ACCESS}, author = {Hariom Dhungana and Thorstein Rykkje and Alexander S. Lundervold}, doi = {10.1109/ACCESS.2025.3551772}, journal = {IEEE Access}, pages = {49433-49456}, shortjournal = {IEEE Access}, title = {Bearing prognostics using the PRONOSTIA data: A comparative study}, volume = {13}, year = {2025}, }
• (2025). Design methodology for optimized control of high-power quadratic buck converters using sliding mode and hybrid controllers. ACCESS, 13, 49416-49432. (www)

  @article{ , abstract = {This paper introduces a novel design methodology for optimizing the control of high-power quadratic Buck converters using a Sliding Mode Controller (SMC). The proposed methodology focuses on addressing the non-linearities and dynamic challenges inherent in such converters, offering a systematic approach to controller design that enhances both stability and efficiency. The SMC is rigorously validated through dual rapid prototyping methods, Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL), to ensure robust performance across diverse operating conditions. Additionally, a hybrid control strategy is explored, integrating traditional PID control with the SMC to leverage the strengths of both approaches. A comprehensive stability analysis, including root-locus diagrams, further confirms the reliability of the proposed controller. Comparative analysis against conventional PID control demonstrates the superiority of the SMC, particularly in managing complex operational scenarios. This work provides a significant advancement in the control of quadratic Buck converters, offering a comprehensive framework for implementing reliable and efficient control strategies, and paving the way for future applications in high-power systems.}, archive = {J_ACCESS}, author = {Rafael Antonio Acosta-Rodríguez and Fredy Hernán Martinez-Sarmiento and German Ardul Múñoz-Hernandez}, doi = {10.1109/ACCESS.2025.3551953}, journal = {IEEE Access}, pages = {49416-49432}, shortjournal = {IEEE Access}, title = {Design methodology for optimized control of high-power quadratic buck converters using sliding mode and hybrid controllers}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review: Impacts of extreme temperatures due to climate change on power grid infrastructure and operation. ACCESS, 13, 49375-49415. (www)

  @article{ , abstract = {The power grid is experiencing a multi-fold transformation while the global climate evolves with record-breaking extreme temperatures during heat domes, polar vortices, and severe ice. Over the decades, these extreme temperature events have increased in frequency, duration, and intensity. The power grid infrastructure is geographically spread over thousands of square miles with millions of small and large components, and the impact of extreme temperature operations on the grid infrastructure needs to be researched further. This paper reviews academic literature, standards, industry articles, and federal reports to identify the impacts of heat domes, polar vortices, and icing on all the T&D grid equipment, including substations (assets owned and operated by the utilities and independent system operators). This paper classifies the equipment into primary and auxiliary equipment and determines its vulnerability to extreme temperatures for a deeper analysis of a more critical and vulnerable set of grid equipment. For each equipment under consideration, its fundamental role in the system, the impact of extreme temperatures on its operation, available monitoring, and mitigation of these impacts are discussed. The paper develops insights on standards readiness and identifies gaps concerning extreme temperature definitions. The paper also develops summary tables to identify the critical failure modes for each type of equipment, failure influence diagrams, and cascading influence diagrams to highlight and aid in translating the equipment vulnerability information into power grid contingency definitions that need to be considered in grid planning and operations.}, archive = {J_ACCESS}, author = {Kishan Prudhvi Guddanti and Alok Kumar Bharati and Sameer Nekkalapu and Joseph Mcwherter and Scott L. Morris}, doi = {10.1109/ACCESS.2025.3548531}, journal = {IEEE Access}, pages = {49375-49415}, shortjournal = {IEEE Access}, title = {A comprehensive review: Impacts of extreme temperatures due to climate change on power grid infrastructure and operation}, volume = {13}, year = {2025}, }
• (2025). DeepCRC-net: An attention-driven deep learning network for colorectal cancer classification using xception and efficient lightweight local feature fusion networks. ACCESS, 13, 49362-49374. (www)

  @article{ , abstract = {Colorectal cancer (CRC) is one of the primary causes of cancer-related deaths globally, and early detection is critical for improving patient outcomes. Histopathological analysis of colorectal tissue remains the gold standard for diagnosis, however it is labour-intensive and prone to human error which could lead to misdiagnosis. Deep learning models offer a promising solution by automating the analysis and enhancing diagnostic accuracy. This research introduces DeepCRC-Net, a novel dual-track architecture designed to classify CRC histopathology images using the EBHI dataset. The first track leverages the Xception network to capture long-range dependencies using depth-wise separable convolutions and residual connections. The second track employs the Efficient Lightweight Local Feature-Fusion Network (ELLFFN), which integrates Efficient Semi-Local Attention Convolution (ESAC) and Dynamic Deformed Shuffle-Fusion Convolution (DDSFC) blocks to capture semi-local and local features efficiently. The extracted features from both tracks are concatenated and refined using Shuffle Attention, which enhances the ability of the model to focus on the most informative regions of the CRC images. The proposed model achieved an accuracy of 98.6% and an F1-score of 99.25% when tested on the EBHI dataset. DeepCRC-Net outperforms existing studies by 2.5% in accuracy and state-of-the-art CNNs by 4.2%.}, archive = {J_ACCESS}, author = {Akshaj Singh Bisht and Armaano Ajay and R. Karthik}, doi = {10.1109/ACCESS.2025.3550004}, journal = {IEEE Access}, pages = {49362-49374}, shortjournal = {IEEE Access}, title = {DeepCRC-net: An attention-driven deep learning network for colorectal cancer classification using xception and efficient lightweight local feature fusion networks}, volume = {13}, year = {2025}, }
• (2025). Longest-first search using bloom filter: Algorithm and FPGA implementation. ACCESS, 13, 49354-49361. (www)

  @article{ , abstract = {Due to the surge in Internet traffic and the rapid increase in forwarding table entries, achieving wire-speed packet forwarding in Internet routers demands both algorithmic enhancements and hardware innovations. In this paper, we propose the longest-first search algorithm with a Bloom filter that stores prefixes in a leaf-pushing trie. Our approach utilizes an on-chip Bloom filter to indicate the presence of prefixes within the trie, while an off-chip hash table stores the corresponding output port information for each prefix. For each incoming IP address, the Bloom filter query begins from the longest length, reducing the queried number of bits by one for each negative result. The off-chip hash table is only accessed when the query to the Bloom filter produces a positive result. Therefore, access to the slower off-chip memory is minimized to once, given a reasonable Bloom filter size. The proposed approach is simulated using C++ and constructed with Verilog for field programmable gate array (FPGA) implementation. The experimental results indicate that the proposed approach achieves the throughput of 0.8 million packets per second at a clock frequency of 100MHz.}, archive = {J_ACCESS}, author = {Jinsol Lee and Hyesook Lim}, doi = {10.1109/ACCESS.2025.3551748}, journal = {IEEE Access}, pages = {49354-49361}, shortjournal = {IEEE Access}, title = {Longest-first search using bloom filter: Algorithm and FPGA implementation}, volume = {13}, year = {2025}, }
• (2025). DDNet: A robust, and reliable hybrid machine learning model for effective detection of depression among university students. ACCESS, 13, 49334-49353. (www)

  @article{ , abstract = {In recent years, the detection of depression among university students has become an increasingly critical issue. This paper presents a depression detection network (DDNet), a novel approach utilizing a three-stage stacked ensemble model to address this challenge. The proposed model incorporates two different Multilayer Perceptron (MLP) and a Stochastic Gradient Descent (SGD) as the 1st stage base classifier, an MLP, and CatBoost (CB) as 2nd stage base classifiers, with a Lasso Regressor (LASSO) serving as the meta-classifier. The hyperparameter of the used models has been optimized using random search. The optimal configuration has been determined through extensive experimentation with various machine learning (ML) models and settings to ensure high performance. Two different datasets (Dataset-1, Dataset-2) of depression detection among university students have been used to evaluate the model. The model has achieved 98.98% and 99.16% accuracy in Dataset-1 and Dataset-2, respectively. Paired t-test has been performed to ensure the statistical significance of the proposed model. To guarantee the model’s transparency, SHapley Additive exPlanations (SHAP) were employed, providing interpretability of the predictive factors. Additionally, a variation of Monte Carlo Dropout (MCD) has been used to assess the uncertainty of the model’s predictions, ensuring reliability. The results indicate that the proposed model is a promising tool for mental health professionals seeking effective, interpretable, and reliable solutions for early depression detection in educational settings.}, archive = {J_ACCESS}, author = {Nasirul Mumenin and Mohammad Abu Yousuf and Madini O. Alassafi and Muhammad Mostafa Monowar and Md. Abdul Hamid}, doi = {10.1109/ACCESS.2025.3552041}, journal = {IEEE Access}, pages = {49334-49353}, shortjournal = {IEEE Access}, title = {DDNet: A robust, and reliable hybrid machine learning model for effective detection of depression among university students}, volume = {13}, year = {2025}, }
• (2025). Off-the-jaw tactile sensing system for tissue stiffness and thickness assessment in minimally invasive surgery. ACCESS, 13, 49320-49333. (www)

  @article{ , abstract = {The missing tactile sensation in minimally invasive surgery (MIS) presents significant challenges and safety risks in routine surgical procedures. Previous attempts to integrate tactile sensors onto the jaws of surgical tools encountered issues with sterilization, biocompatibility, and wiring complexities. This work introduces a novel “off-the-jaw” (OTJ) tactile sensing system, which tackles these limitations by integrating force and angle sensors onto the handle of MIS instruments to indirectly measure the grasping force and angle during tissue palpation while preserving the original design and functionality of the grasping jaws. A prototype was developed using a force-sensitive resistor and a rotary potentiometer integrated onto the handle of a laparoscopic grasper. Real-time data processing provides user-friendly visual feedback on tissue thickness and stiffness. Testing was performed using soft elastomeric samples designed to mimic human tissues of varying sizes and stiffness. Initial validation demonstrated a strong correlation between the force applied by the user’s thumb on the handle and the force exerted by the grasping jaws on the samples. The sensor-equipped handle successfully differentiated samples based on thickness and stiffness and integrated seamlessly with various surgical end effectors in a plug-and-play manner. Pre-clinical trials with both regular and surgical users revealed significant improvements in sample sorting and lump detection tasks, with up to 30% increase in speed and accuracy. The OTJ technology holds promising potential to improve surgical precision and patient care by restoring tactile feedback in MIS, ultimately contributing to safer and more effective surgical practices.}, archive = {J_ACCESS}, author = {Wael Othman and Kojo E. Vandyck and Ma-Sum Abdul-Hamid and Carlos Abril and Juan S. Barajas-Gamboa and Juan P. Pantoja and Mohammad A. Qasaimeh}, doi = {10.1109/ACCESS.2025.3550948}, journal = {IEEE Access}, pages = {49320-49333}, shortjournal = {IEEE Access}, title = {Off-the-jaw tactile sensing system for tissue stiffness and thickness assessment in minimally invasive surgery}, volume = {13}, year = {2025}, }
• (2025). A novel mixed-precision quantization approach for CNNs. ACCESS, 13, 49309-49319. (www)

  @article{ , abstract = {Model size and inference speed have brought about major challenges for the deployment of Convolutional Neural Networks (CNNs) in many applications. An effective approach to address this issue is model quantization, which achieves network compression and inference speedup by reducing the parameters bit-width during inference. Mixed-precision quantization assigns different quantization precision to different layers of a CNN. However, due to accuracy degradation, determining the precision of each layer remains a challenge. In this paper, we propose an efficient mixed-precision search method that tackles this problem. It first utilizes the average approximation trace of the Hessian matrix to determine the sensitivity of each convolutional layer to quantization. It then establishes the optimization objective of the mixed-bit-width search network, and combines the cosine distance of the outputs of the two neighboring layers in the search process in order to construct a micro-minimizable combination of mixed-bit-widths for the super-net search. It finally adds the conditional constraints to trade-off the training loss of the network and the time complexity of the model. Our experimental results on CIFAR-10 and ImageNet show that our proposed method confers advantages over several state-of-the-art methods.CIFAR-10 and ImageNet are two commonly used datasets in the field of computer vision for image classification tasks. CIFAR-10 is a smaller dataset suitable for fast experiments and lightweight models; ImageNet is a large-scale dataset suitable for complex models and deep learning research. In particular, compared to the recently proposed BP-NAS, it improves the compression rate by 2.15% and average bit-width by 0.77 bit on ResNet-50, while achieving the same accuracy, and compared to MBF, it improves the classification accuracy by 0.12% on ResNet-20 using the same compression rate and average bit-width conditions. Furthermore, we show on MobileNetV2 that our proposed method achieves 0.23% higher accuracy compared to HAQ.}, archive = {J_ACCESS}, author = {Dan Wu and Yanzhi Wang and Yuqi Fei and Guowang Gao}, doi = {10.1109/ACCESS.2025.3551802}, journal = {IEEE Access}, pages = {49309-49319}, shortjournal = {IEEE Access}, title = {A novel mixed-precision quantization approach for CNNs}, volume = {13}, year = {2025}, }
• (2025). A novel efficient and secure privacy-preserving scheme for smart grid based on HECC. ACCESS, 13, 49290-49308. (www)

  @article{ , abstract = {Smart Grid (SG) is an electricity distribution network that monitors energy flow from the point of generation to its final location. The SG can save electricity usage and improve service quality at the same time. However, there are several security issues associated with communication between SG nodes. For example, information breakout is a frequent security problem that arises during communication between two Smart Meters (SMs). Existing studies resolve the issue of information breakout by using numerous cryptographic techniques such as Elliptic Curve Cryptography, Advanced Encryption Standard, RSA, and Bilinear Pairing. Moreover, these techniques suffer due to their greater communication overhead and computational costs because of the enormous size of the parameters and key space. In this study, we proposed a novel, efficient, and privacy-preserving approach for secure data transferring between entities in the SG network to protect against adversaries’ attacks by using minimal resources. Our proposed system uses the standard key size of Hyper Elliptic Curve Cryptography (HECC) along with the Signcryption technique for secure communication among the SM, aggregator nodes, and distribution substation to protect sensitive information. The proposed technique ensures safe communication between SG entities while also reducing communication and computational expenses by using shorter key sizes along with other parameters of HECC. Furthermore, we employ the popular simulation program Automated Validation of Internet Security Protocols and Applications (AVISPA) to verify the security aspects of our suggested approach. Moreover, our proposed technique fulfills essential security characteristics like confidentiality, integrity, authenticity, authorization, non-repudiation, and unforgeability.}, archive = {J_ACCESS}, author = {Umair Habib and Mahwish Bano and Jawaid Iqbal and Syed Sajid Ullah}, doi = {10.1109/ACCESS.2025.3549050}, journal = {IEEE Access}, pages = {49290-49308}, shortjournal = {IEEE Access}, title = {A novel efficient and secure privacy-preserving scheme for smart grid based on HECC}, volume = {13}, year = {2025}, }
• (2025). Utilizing conditional GANs for synthesis of equilibrated hyperspectral data to enhance classification and mitigate majority class bias. ACCESS, 13, 49271-49289. (www)

  @article{ , abstract = {A land cover scene is described by vegetation, urban infrastructure, water bodies, bare soil, and many other physical materials. Satellite imagery and remote sensing techniques analyze the scene for the management of natural resources and environmental changes. Hyperspectral Imaging (HSI) plays a vital role in land cover classification due to its rich spectral information, which captures a broader range of wavelengths. This enhanced spectral data allows for more precise identification and differentiation of land cover types. Imbalanced class distributions in HSI classification hinder rare class performance, as these critical classes are often underrepresented in training data. GANs offer a solution by generating synthetic samples to address this scarcity. However, current GAN-based methods use a static up-sampling mechanism for minority classes and generate samples for even larger classes. Furthermore, these models use the largest class as the scaling factor for minority classes, leading to excessive synthetic sample generation for smaller classes. Although these methods achieve class balance, but often suffer from synthetic bias due to over-reliance on generated samples, especially for minority classes. As a result, this can cause dataset inflation, compromise the authenticity of minority class representations, and potentially degrade the model’s performance on real-world data. To overcome this limitation, we introduce a novel approach that dynamically balances class distributions by leveraging the smallest class size as a scaling factor. Our proposed model, Spectra Balance GAN (SB-GAN), ensures efficient and adaptive sample generation, producing only the necessary synthetic samples while preserving the integrity of the dataset and maintaining a manageable size. The primary novelty of this model lies in its adaptive nature of class balancing mechanism and an integrated conditional GAN to generate only the required samples, ensuring efficient and balanced hyperspectral image classification. The efficacy of the SB-GAN approach has been evaluated using three publicly available datasets: Indian Pines, Pavia University, and Salinas. The approach achieved an average classification accuracy of 99% across these datasets, demonstrating its potential to significantly improve the classification of rare classes in HSI scenes.}, archive = {J_ACCESS}, author = {Bibi Noor Asmat and Hafiz Syed Muhammad Bilal and M. Irfan Uddin and Faten Khalid Karim and Samih M. Mostafa}, doi = {10.1109/ACCESS.2025.3550057}, journal = {IEEE Access}, pages = {49271-49289}, shortjournal = {IEEE Access}, title = {Utilizing conditional GANs for synthesis of equilibrated hyperspectral data to enhance classification and mitigate majority class bias}, volume = {13}, year = {2025}, }
• (2025). Innovative tailored semantic embedding and machine learning for precise prediction of drug-drug interaction seriousness. ACCESS, 13, 49249-49270. (www)

  @article{ , abstract = {This study explores applying advanced machine-learning strategies, particularly improved semantic vectors, to predict the severity of drug-drug interactions (DDIs), a crucial element in pharmacovigilance. Based on the Adverse Event Reporting System (FAERS), our study aims to analyze the combination of advanced embedding techniques with state-of-the-art machine learning (ML) algorithms to identify and quantify DDI severity. The CatBoost Classifier is the center of our analysis, as it has emerged as the most effective model in the examined trials. We improved the performance by increasing the BioWordVec Indication Substance embedding specificity, a new creation constructed through transfer learning methodologies employed on the BioWordVec model. This approach employs not only the names of the drugs but also the indications for the drugs and the active substances, forming a highly semantic network capable of capturing multiple relations between drugs. Applying BioWordVec Indication Substance embedding combined with the CatBoost Classifier, especially using the contact-vectors method, provided the best F1 score of 73. 32% and an ROC AUC score of 84%. The results imply that this method effectively models and predicts severe consequences of DDIs using deep learning that comprehensively covers pharmacological and clinical aspects. Based on our results, we suggest incorporating semantic embedding and ML into the pharmacovigilance processes to improve the predictive potential of DDI evaluations. Thus, by enhancing the body of knowledge related to the analytical methods of assessing drug interactions, the present study substantially enhances the quality of clinical decision-making and patient protection. The novel embedding marks a significant step forward in the methodology, providing a more solid tool for the fine-grained dissection of the complexities needed in modern medicine, where multiple drug therapies are now the norm.}, archive = {J_ACCESS}, author = {Ayman Mohamed Mostafa and Alaa S. Alaerjan and Hisham Allahem and Bader Aldughayfiq and Meshrif Alruily and Alshaimaa A. Tantawy and Mohamed Ezz}, doi = {10.1109/ACCESS.2025.3552239}, journal = {IEEE Access}, pages = {49249-49270}, shortjournal = {IEEE Access}, title = {Innovative tailored semantic embedding and machine learning for precise prediction of drug-drug interaction seriousness}, volume = {13}, year = {2025}, }
• (2025). TIRDH: A novel three-shadow-image reversible data hiding algorithm using weight and modulo. ACCESS, 13, 49232-49248. (www)

  @article{ , abstract = {Reversible data hiding (RDH) in image media is crucial for securely embedding confidential information while ensuring complete recovery of both the cover image and the hidden data. Traditional RDH techniques often struggle to balance embedding capacity and image fidelity, limiting their practical effectiveness. This paper introduces TIRDH, a novel three-shadow-image RDH scheme that employs weight and modulo operations to achieve high-capacity embedding while preserving image quality. The method embeds an M-ary secret message within a shadow pixel cluster using a 3-tuple weight vector, modulo operations, and a variation table to guide pixel modifications. This guarantees data integrity without pixel underflow or overflow, thereby avoiding the falling-off boundary problem (FOBP) and ensuring reversibility. Additionally, the scheme offers flexible embedding rates, adjustable through a single parameter and weight vector, making it highly adaptable to various user requirements, image types, and application scenarios. Experimental results from three standard image databases demonstrate its effectiveness, achieving embedding rates ranging from 0.04 to 1.6814 bits per pixel while maintaining high image quality, with an average PSNR ranging from 47.65 to 62.65 dB. Furthermore, its prediction mechanism attains 98.2% accuracy, enabling precise performance estimation before actual message embedding. This predictive capability enhances efficiency by allowing users to anticipate the impact on image quality beforehand. Security assessments confirm strong resilience against RS steganalysis and pixel difference histogram attacks, providing enhanced protection for confidential data. Comparisons with 12 state-of-the-art methods show that the proposed approach outperforms competitors when embedding fewer than 50,000 secret bits and surpasses another set of 12 schemes when the embedding rate exceeds 1.0 bits per pixel. For 1,000 test images with an embedding rate of 1.4 bits per pixel, it consistently outperforms six leading RDH techniques, demonstrating robustness across diverse datasets. These findings establish our proposed TIRDH method as a comprehensive and reliable solution for RDH applications, offering superior embedding performance, high image quality, strong security, and practical adaptability across various real-world scenarios.}, archive = {J_ACCESS}, author = {Li-Chian Chin and Yu-Chen Li and Hung-Mo Hsieh and Chung-Ming Wang}, doi = {10.1109/ACCESS.2025.3552661}, journal = {IEEE Access}, pages = {49232-49248}, shortjournal = {IEEE Access}, title = {TIRDH: A novel three-shadow-image reversible data hiding algorithm using weight and modulo}, volume = {13}, year = {2025}, }
• (2025). Enhancing motor skills and coordination with visual-haptic feedback in ball sport training. ACCESS, 13, 49214-49231. (www)

  @article{ , abstract = {Traditional ball sport training methods often lack targeted feedback and personalized guidance, limiting beginners’ ability to develop the rapid responses and precise movements essential for such sports. This study investigates the effectiveness of integrating Visual-Haptic Feedback into simulated ball sport training to enhance body awareness and coordination without increasing cognitive load. By delivering tactile feedback to the wrists and ankles, we aim to improve beginners’ motor skills and overall performance. Our research reviews advancements in human-computer interaction focused on enhancing bodily awareness and discusses the application of tactile feedback in sports training. Experimental results demonstrate that Visual-Haptic Feedback significantly enhances beginners’ body awareness, enabling them to perceive and adjust their movements more accurately without imposing a cognitive burden. Furthermore, this approach effectively standardizes and coordinates motor performance, with a particularly notable improvement in lower limb movements. Additionally, Visual-Haptic Feedback reduces the error rate in executing complex tasks and improves the precision of limb movements. This study enriches the understanding of using Visual-Haptic Feedback in sports training and highlights its potential to improve skills and body coordination, offering valuable insights for future applications and developments in this field.}, archive = {J_ACCESS}, author = {Gang Ren and Zhihuang Huang and Shijiang You and Wenshuo Lin and Tianyang Huang and Gang Wang and Jee Hang Lee}, doi = {10.1109/ACCESS.2025.3547159}, journal = {IEEE Access}, pages = {49214-49231}, shortjournal = {IEEE Access}, title = {Enhancing motor skills and coordination with visual-haptic feedback in ball sport training}, volume = {13}, year = {2025}, }
• (2025). The evolution of VANET: A review of emerging trends in artificial intelligence and software-defined networks. ACCESS, 13, 49187-49213. (www)

  @article{ , abstract = {This paper presents an in-depth look at the evolution of Vehicular Ad Hoc Networks (VANET) and their increasing importance in the development of Intelligent Transportation Systems (ITS). Emerging technologies that shape the future of VANET are discussed, focusing on artificial intelligence (AI) and software-defined networking (SDN) as key solutions to the challenges inherent in these dynamic networks. Applications of AI, including machine learning (ML), deep learning, and swarm intelligence, for improving security, traffic efficiency, and information management in VANET were examined. Routing challenges in these environments are discussed and SDN is presented as a viable solution. SDN protocols, controllers, and architectures are described, and their advantages and disadvantages in the context of VANET are analyzed. It provides a comprehensive overview of the current state of VANET, the emerging technologies driving them, and the challenges and opportunities presented by their continued development. The convergence of AI, SDN, and other key technologies is crucial for intelligent transportation, paving the way for safer, more efficient, and more connected driving.}, archive = {J_ACCESS}, author = {Octavio José Salcedo Parra and Lewys Correa Sánchez and Jorge Gómez}, doi = {10.1109/ACCESS.2025.3548640}, journal = {IEEE Access}, pages = {49187-49213}, shortjournal = {IEEE Access}, title = {The evolution of VANET: A review of emerging trends in artificial intelligence and software-defined networks}, volume = {13}, year = {2025}, }
• (2025). An open-source tool-box for asset management based on the asset condition for the power system. ACCESS, 13, 49174-49186. (www)

  @article{ , abstract = {This Study introduces an open-source toolbox for asset management in power systems developed under the European ATTEST project. This paper focuses on presenting an open-source toolbox for Transmission and Distribution System Operators (TSOs and DSOs) to improve the reliability and efficiency of power networks, including a solution to the difficulties faced by the power industry, such as the aging infrastructure and the growing need for renewable energy integration. The toolbox uses predictive analytics and machine learning to evaluate the health of assets, enhance maintenance plans, and guarantee efficient resource distribution. It evaluates the condition of power grid assets through clustering (K-means, SOM) and reinforcement learning (Q-learning), providing actionable insights for improving asset management. This approach allows TSOs and DSOs to adopt proactive maintenance strategies, reducing the risk of failures, minimizing downtime, and extending the lifespan of critical infrastructure. The toolbox provides actionable insights for planning maintenance strategies and optimizing resource allocation. Scalability tests were conducted using a synthetic power grid of 600 transformers alongside real-world data from five European electrical companies. Due to space constraints, only the results from 92 transformers. This research contributes to achieving sustainable power systems and supporting the energy transition by focusing on intelligent asset management.}, archive = {J_ACCESS}, author = {Gopal Lal Rajora and Miguel A. Sanz-Bobi and Carlos Mateo Domingo and Lina Bertling Tjernberg}, doi = {10.1109/ACCESS.2025.3551663}, journal = {IEEE Access}, pages = {49174-49186}, shortjournal = {IEEE Access}, title = {An open-source tool-box for asset management based on the asset condition for the power system}, volume = {13}, year = {2025}, }
• (2025). Gibbs-BERTopic: A hybrid approach for short text topic modeling. ACCESS, 13, 49162-49173. (www)

  @article{ , abstract = {As a rich source of direct user needs, online reviews can be effectively analyzed through topic modeling to uncover user preferences and requirements. However, the short and unstructured nature of online reviews, along with their high dimensionality, noise, and complex semantic structure, poses challenges for traditional topic modeling techniques. The topic modeling technique BERTopic utilizes high-dimensional embeddings to capture text semantic information, but it faces the problems of sparsity, noise, and uncertainty when dealing with small-scale data. To reduce information barriers and improve the robustness and accuracy of topic models, we propose a new enhanced topic model—a hybrid method that integrates Gibbs sampling with BERTopic (Gibbs-BERTopic). Gibbs-BERTopic builds on BERTopic and iteratively optimizes and adjusts the topic distribution by Gibbs sampling the initial topic modeling results of BERTopic to improve the quality of the topic model. Experimental results show that Gibbs-BERTopic significantly outperforms the original BERTopic model, achieving over a 20% increase in silhouette coefficient and nearly a 20% improvement in topic coherence. It is verified that Gibbs-BERTopic can effectively reduce the impact of noise and data sparsity on topic extraction, optimize topic distribution, enhance topic clustering effect, and thus extract topics that are more in line with the actual data characteristics and potential structure.}, archive = {J_ACCESS}, author = {Yan Zhu and Yueying Liu}, doi = {10.1109/ACCESS.2025.3552221}, journal = {IEEE Access}, pages = {49162-49173}, shortjournal = {IEEE Access}, title = {Gibbs-BERTopic: A hybrid approach for short text topic modeling}, volume = {13}, year = {2025}, }
• (2025). Outdoor free space optical systems: Motivations, challenges, contributions in environmental conditions, and future Directions—A systematic survey. ACCESS, 13, 49121-49161. (www)

  @article{ , abstract = {This paper presents a new systematic survey of the free-space optical (FSO) system, highlighting its operations and applications in outdoor optical communication underlying its interaction with the environment. As the application portfolio of FSO technology grows, so does the need for a clear classification for FSO link configurations. Most existing surveys and classifications are single-level classifications using traditional survey methods, and thus not inclusive enough to accommodate recent and emerging changes and developments of different FSO link configurations and systems. To that end, this paper adopts a novel methodology based on statistical analysis using a natural language processing approach (NLP) to analyze, visualize, and interpret the researchers’ trends of 5075 relevant literature published documents (journal papers, conference papers, books, etc.) from 2017 to Feb-2024. The process of summarizing and analyzing is encompassed by adopting hierarchal multi-level classifications for the key performance evaluations, motivations, challenges, and contributions related to FSO systems. The survey is started by understanding the key performance of FSO such as matrices models and mathematical models. Then, the survey process is followed by investigating the outdoor FSO applications (urban, satellite, smart city, military, healthcare, disasters, and sensing applications). We have relied on listing and dividing the outdoor FSO networks into sub-networks based on the FSO’s architectural structures (point-to-multipoint, mobile network, long-range transmission and heterogeneous). In turn, outdoor impairments are classified into two categories: 1) environmental factors (weather and atmospheric conditions) and 2) component systems (source types and mechanical issues). We demonstrate the impact of these impairments in terms of the optical phenomena (effects), such as scattering, reflection, refraction, absorption, shadowing, and consequences (outcomes), such as pointing error, misalignments, beam divergence, etc. Finally, multi-technical solutions are listed and analyzed based on the researchers’ contributions, such as modulation, detection techniques, relay, array, and retroflectors. This paper is the first paper that adopts the statistical analysis approach for studying the FSO in outdoor applications, making this approach a good choice for future researchers to prioritize their decisions about future trends and target their improvements.INDEX TERMS Statistical analysis, free space optics (FSO), mobility, outdoor optical communication, point-to-multipoint coverage, subnetwork, weather conditions.}, archive = {J_ACCESS}, author = {Amjaad T. Altakhaineh and Sarah A. Alsarayreh and Rula Alrawashdeh and Ahed Aleid and Fatima Alsharari and Rowida Alodat and Amal Alhasanat and Abdullah Alhasanat and Moath Alsafasfeh and Mohanad Alhasanat and Kaled Rabie and Mustapha Benjillali}, doi = {10.1109/ACCESS.2025.3549776}, journal = {IEEE Access}, pages = {49121-49161}, shortjournal = {IEEE Access}, title = {Outdoor free space optical systems: Motivations, challenges, contributions in environmental conditions, and future Directions—A systematic survey}, volume = {13}, year = {2025}, }
• (2025). Demand side management considering Load–Voltage interdependence and optimal topology selection in active distribution networks. ACCESS, 13, 49107-49120. (www)

  @article{ , abstract = {In active electric distribution networks (ADN), demand-side management (DSM) optimizes energy consumption to ensure a better balance between power generation and demand and confines grid parameters within the tolerable band. Load management in the DSM is generally adopted to align instantaneous power demands towards its time average value, reducing peak and improving load factors. In three-phase, reconfigurable ADNs with a significant presence of voltage-dependent loads, overlooking topology-voltage-load interdependencies critically impact the optimality and efficiency of DSM implementation. This paper proposes an efficient grid managment scheme to combine DSM and optimal network selection that accommodates topology-voltage and voltage-load influences. In the first stage, node load factors are improved by optimizing the ‘voltage-dependent load factor index (VDLFI)’, an indicator designed to find the optimal load schedules for flexible loads. The second stage finds optimal topology among the archived feasible ones based on a sorting and ranking method, corresponding to minimal losses and no voltage violations. The second stage updates node voltages to the first stage, and the process is repeated iteratively until convergence to common optimal points. The proposed technique is validated with extensive case studies on a modified IEEE-34 bus system. Apart from eliminating all voltage violation instances, this novel approach reduced energy loss by 39.21% and peak demand by 19.21%, offering promising prospects for operational planning in modern ADNs.}, archive = {J_ACCESS}, author = {Vineeth Vijayan and Ali Arzani and Satish M. Mahajan}, doi = {10.1109/ACCESS.2025.3550836}, journal = {IEEE Access}, pages = {49107-49120}, shortjournal = {IEEE Access}, title = {Demand side management considering Load–Voltage interdependence and optimal topology selection in active distribution networks}, volume = {13}, year = {2025}, }
• (2025). Voltage control approach with dynamic voltage reference for PV distributed generation operating under cloud-induced shading conditions. ACCESS, 13, 49092-49106. (www)

  @article{ , abstract = {The integration of photovoltaic generation into distribution systems can deteriorate the system voltage and the conventional voltage regulation process based on step voltage regulators. The system operation under cloud conditions is much more challenging than the operation under clear sky conditions, requiring alternative voltage control approaches. This work proposes an adaptive voltage control approach for photovoltaic distributed generation units operating under cloud conditions. The control approach employs an adaptive voltage reference to cope with the dynamic reactive power margin and limited voltage regulation capability of photovoltaic distributed generation units. The proposed volt-var control is local, coordinates with step voltage regulators, and fills a gap in voltage control approaches by simultaneously considering dynamic models and realistic cloud shading conditions. The voltage regulation margin of each distributed generation unit is determined in real-time by a straightforward formulation also proposed in this work. The proposed control effectively mitigated the voltage fluctuations caused by stochastic variations of the photovoltaic generation and system loads, outperforming the conventional control approach based on fixed voltage reference. The proposed control reduced the number of tap changes by 63% and mitigated voltage fluctuations with magnitudes higher than 3%. The formulation to determine the voltage regulation margin effectively supported the voltage regulation process by providing a feasible range of values for the voltage reference of the distributed generation units.}, archive = {J_ACCESS}, author = {Glorio Arnaldo Landbrug and Géremi Gilson Dranka and Ricardo Vasques de Oliveira}, doi = {10.1109/ACCESS.2025.3551506}, journal = {IEEE Access}, pages = {49092-49106}, shortjournal = {IEEE Access}, title = {Voltage control approach with dynamic voltage reference for PV distributed generation operating under cloud-induced shading conditions}, volume = {13}, year = {2025}, }
• (2025). Spintronic content addressable memory with integrated boolean logic and arithmetic functions. ACCESS, 13, 49076-49091. (www)

  @article{ , abstract = {To address growing data processing demands, traditional von Neumann architectures face increased power consumption and delay issues. In response, this paper presents a novel latch-based hybrid CMOS/MTJ content-addressable memory (TCAM) designed to perform Boolean logic, as well as full adder (FA) and full subtractor (FS) operations, within a computing-in-memory (CIM) architecture. The proposed cell efficiently compares 4-bit encoded data in TCAM mode while supporting 8-bit data processing in computing mode, improving area efficiency. Additionally, the reduced number of active paths between the power supply and ground during the read phase significantly lowers power consumption. Complemented by peripheral circuits, a scalable array architecture has been developed to demonstrate the system’s feasibility. Post-layout simulations using 40 nm technology in Cadence Virtuoso and HSPICE show that the proposed design reduces power consumption and PDP by up to 35% in TCAM mode and up to 65% in COMPUTING mode compared to previous solutions. Furthermore, Monte Carlo simulations confirm the design’s robust resilience to process variations, highlighting its potential for reliable, energy-efficient operation. Also, the suggested design in the presence of temperature variations is investigated, and PDP results for temperatures range 0° C to 100° C show design resilience. In addition, our results indicate that the proposed strategy significantly reduces power consumption in neural network applications compared to its counterparts.}, archive = {J_ACCESS}, author = {Zahra Mehdizadeh Taheri and Sayed Masoud Sayedi and Mohammad Hossein Moaiyeri}, doi = {10.1109/ACCESS.2025.3551411}, journal = {IEEE Access}, pages = {49076-49091}, shortjournal = {IEEE Access}, title = {Spintronic content addressable memory with integrated boolean logic and arithmetic functions}, volume = {13}, year = {2025}, }
• (2025). Precision in aerial surveillance: Integrating YOLOv8 with PConv and CoT for accurate insulator defect detection. ACCESS, 13, 49062-49075. (www)

  @article{ , abstract = {Insulator defect detection using autonomous aerial vehicles (AAVs) images is a promising method for power transmission line inspections. However, varying sizes, orientations, and complex backgrounds of insulator defects result in high false negatives and low accuracy. Previous studies have not adequately incorporated self-attention mechanisms focusing on adjacent keys. To address this, we propose an improved YOLOv8-based detection algorithm. We added a Contextual Transformer module to the YOLOv8 backbone for better contextual understanding and introduced a Partial Convolution layer to reduce redundant calculations. Our model shows improvements over existing ones, achieving a precision of 97.5%, a mean average precision of 86.2%, and a recall of 81.1%, offering a robust solution for automated, precise power line inspections.}, archive = {J_ACCESS}, author = {Guoying Lu and Benhao Li and Yining Chen and Siyuan Qu and Tianyu Cheng and Junkun Zhou}, doi = {10.1109/ACCESS.2025.3551289}, journal = {IEEE Access}, pages = {49062-49075}, shortjournal = {IEEE Access}, title = {Precision in aerial surveillance: Integrating YOLOv8 with PConv and CoT for accurate insulator defect detection}, volume = {13}, year = {2025}, }
• (2025). Modeling 5G-NR-based positioning accuracy in urban environments. ACCESS, 13, 49050-49061. (www)

  @article{ , abstract = {Modeling phenomena influencing radiocommunication networks, such as 5G-NR, is a key aspect during their development and further optimization. In this paper, one proposes a model, for three different scenario types in urban environments, for user localization accuracy. The model is supported by measurements of real 5G-NR downlink signals carried out in three different urban environments, corresponding to the typical user localization. The main goal of the paper is to present a model for estimating the relation between the environment type, i.e., its statistical parameters, and the estimated localization accuracy in outdoor, light-indoor and deep-indoor environments. Results show that the localization accuracy may degrade from 5 to 295 m when the localization process is performed, just by moving the terminal from outside a building to its inside. This can make the localization service invalid, not only from a user-related perspective but also as a method to adjust beamforming parameters in downlink.}, archive = {J_ACCESS}, author = {Piotr Rajchowski and Luis M. Correia and Pedro Tareco and Krzysztof K. Cwalina}, doi = {10.1109/ACCESS.2025.3551841}, journal = {IEEE Access}, pages = {49050-49061}, shortjournal = {IEEE Access}, title = {Modeling 5G-NR-based positioning accuracy in urban environments}, volume = {13}, year = {2025}, }
• (2025). HAN: Hierarchical attention network for learning latent context-aware user preferences with attribute awareness. ACCESS, 13, 49030-49049. (www)

  @article{ , abstract = {Sequential recommendation has become a key role in many online services as user activities grow. With recent advancements in the prediction of users’ next interests through joint modeling of long-term and short-term preferences, recommendation performance has significantly improved. However, existing work largely overlooks the evolving nature of users’ preferences, directly integrating interactions between item-item or user-item features, with no proper understanding of what motivates the user in the selection of a sequence. Users usually make their decisions on a two-step basis: initiating an action on a specific intent and then selecting an item that satisfies the intent. Besides, the existing models fail to properly capture the hierarchical relationships between multiple attributes of items and the temporal dependencies of the items within sequences. In this paper, we introduce a novel architecture for a sequential recommendation that unifies users’ long- and short-term preferences and models the underlying user intentions. Our model employs a Hierarchical Attention Network (HAN), which can grasp fine-grained relationships between item attributes in sessions and incorporates both temporal and semantic dependencies among items. By explicitly modeling user intentions and their evolving preferences, the HAN illustrates better how users act in behavior. On two real datasets, we show that the proposed model HAN outperforms all other current state-of-the-art methods.}, archive = {J_ACCESS}, author = {Hadise Vaghari and Mehdi Hosseinzadeh Aghdam and Hojjat Emami}, doi = {10.1109/ACCESS.2025.3551402}, journal = {IEEE Access}, pages = {49030-49049}, shortjournal = {IEEE Access}, title = {HAN: Hierarchical attention network for learning latent context-aware user preferences with attribute awareness}, volume = {13}, year = {2025}, }
• (2025). Robust and efficient phase estimation in legged robots via signal imaging and deep neural networks. ACCESS, 13, 49018-49029. (www)

  @article{ , abstract = {Accurate phase estimation in legged robots, determining whether legs are swinging or in ground contact, is crucial for stability and performance, especially on uneven terrain and with dynamic obstacles. This paper presents a novel approach to phase estimation using signal imaging and lightweight deep neural networks. Time-series data from an inertial measurement unit (IMU) and joint encoders are fused into phase images in real-time using Stacked Channels Image (SCI) and Recurrence Plot (RP) encoding methods. These phase images are then processed for phase classification, eliminating the need for force measurement sensors, which are prone to noise and failure. Extensive experiments on a 3-DoF hopping robot leg, with contact sensor data as ground truth, show that EfficientNet-B0 deep model achieves 98.97% accuracy with RP imaging and an optimized inference time of 2 milliseconds. Comparative validation on an independent dataset from the MIT Mini Cheetah traversing eight terrains demonstrates our model’s generalizability. The EfficientNet-RP-Mode1 configuration achieves 96.51% accuracy, outperforming traditional model-based methods and matching or surpassing other deep learning-based approaches, despite requiring fewer sensor inputs. This research highlights the efficiency, robustness, and adaptability of the proposed method, offering a reliable solution for phase estimation in diverse robotic platforms and environments.}, archive = {J_ACCESS}, author = {Kamyab Yazdipaz and Nooshin Kohli and Seyed Ali Golestaneh and Mohammad Shahbazi}, doi = {10.1109/ACCESS.2025.3549165}, journal = {IEEE Access}, pages = {49018-49029}, shortjournal = {IEEE Access}, title = {Robust and efficient phase estimation in legged robots via signal imaging and deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation and application potential of small large language models in complex sentiment analysis tasks. ACCESS, 13, 49007-49017. (www)

  @article{ , abstract = {Sentiment analysis using Large Language Models (LLMs) has gained significant attention in recent research due to its outstanding performance and ability to understand complex texts. However, popular LLMs, such as ChatGPT, are typically closed-source and come with substantial API costs, posing challenges for resource-limited scenarios and raising concerns about privacy. To address this, our study evaluates the feasibility of using small LLMs (sLLMs) as alternatives to GPT for aspect-based sentiment analysis in Chinese healthcare reviews. We compared several Chinese sLLMs of varying sizes with GPT-3.5, using GPT-4o’s results as the benchmark, and assessed their classification accuracy by computing F1 scores for each individual aspect as well as an overall F1 score. Additionally, we examined sLLMs’ instruction-following capabilities, VRAM requirements, generation times, and the impact of temperature settings on the performance of top-performing sLLMs. The results demonstrate that several sLLMs can effectively follow instructions and even surpass GPT-3.5 in accuracy. For instance, InternLM2.5 achieved an F1 score of 0.85 with zero-shot prompting, while the smaller Qwen2.5-3B model performed well despite its minimal size. Prompt strategies significantly influenced smaller and older models like Qwen2.5-1.5B and ChatGLM3.5 but had limited impact on newer models. Temperature settings showed minimal effect, while older models generated responses faster, and newer models offered higher accuracy. This study underscores the potential of sLLMs as resource-efficient, privacy-preserving alternatives to closed-source LLMs in specialized domains. Our work demonstrates versatility, with potential applications across domains such as finance and education, and tasks like sentiment analysis, credit risk assessment, and learning behavior analysis, offering valuable insights for real-world use cases.}, archive = {J_ACCESS}, author = {Yunchu Yang and Jiaxuan Li and Jielong Guo and Patrick Cheong-Iao Pang and Yapeng Wang and Xu Yang and Sio-Kei Im}, doi = {10.1109/ACCESS.2025.3549733}, journal = {IEEE Access}, pages = {49007-49017}, shortjournal = {IEEE Access}, title = {Performance evaluation and application potential of small large language models in complex sentiment analysis tasks}, volume = {13}, year = {2025}, }
• (2025). Optimization of adversarial reprogramming for transfer learning on closed box models. ACCESS, 13, 48999-49006. (www)

  @article{ , abstract = {In this work, we optimise a transfer learning approach for predicting the Remaining Useful Life (RUL) of ball bearings, particularly in scenarios with limited data availability. Accurate RUL prediction is crucial for improving maintenance strategies, reducing downtime and improving machine reliability, making it highly relevant to industry. We use the Black Box Adversarial Reprogramming (BAR) algorithm to process target domain data in a source domain model through adversarial reprogramming. While it has been shown that this concept can work well in image classification, and a further modification has been developed to classify time series features, this work focuses primarily on the remaining key challenges such as the selection and comparison of appropriate loss functions, the optimisation of hyperparameters using Bayesian methods, and data labelling in the absence of ground truth. Our results show an increase in the performance of the BAR algorithm on the macro f1 score of 0.23 on the training set and up to 0.21 on the test set.}, archive = {J_ACCESS}, author = {Alexander Bott and Moritz Siems and Alexander Puchta and Jürgen Fleischer}, doi = {10.1109/ACCESS.2025.3552219}, journal = {IEEE Access}, pages = {48999-49006}, shortjournal = {IEEE Access}, title = {Optimization of adversarial reprogramming for transfer learning on closed box models}, volume = {13}, year = {2025}, }
• (2025). A systematic literature review on mining LTL specifications. ACCESS, 13, 48950-48998. (www)

  @article{ , abstract = {Linear Temporal Logic (LTL) specifications play a crucial role in the verification process of cyber-physical systems, increasing the guarantees of their correctness. These specifications are vital for ensuring that both hardware and software components behave as expected, especially in complex real-world scenarios. In the last decades, researchers have developed several methodologies and tools to automatically generate LTL specifications, creating an urgent need to organize and synthesize existing literature to ease entry into this field and guide future research efforts. Therefore, starting from a pool of over 3000 papers extracted from the Scopus database in the temporal range 2000–2024, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to produce a systematic review of mining LTL specifications of hardware and software systems. In particular, we provide a taxonomy of the methods and describe with significant detail all the relevant techniques present at the state of the art. Finally, we discuss the challenges of mining LTL specifications and explore potential directions and opportunities for future research.}, archive = {J_ACCESS}, author = {Samuele Germiniani and Daniele Nicoletti and Graziano Pravadelli}, doi = {10.1109/ACCESS.2025.3551607}, journal = {IEEE Access}, pages = {48950-48998}, shortjournal = {IEEE Access}, title = {A systematic literature review on mining LTL specifications}, volume = {13}, year = {2025}, }
• (2025). Path-based clustering approaches for a hybrid slepian-wolf compression and coded caching system. ACCESS, 13, 48935-48949. (www)

  @article{ , abstract = {Combining caching with source coding, a hybrid content delivery system further facilitates the shift towards Information-Centric Networks. This is a promising technology heralded as the next phase in network design. However, finding the optimal balance between the source coding gains and the computational complexity is itself an NP-hard problem. By modelling the problem using a path-based approach, this paper outlines iterative algorithms that can be tuned to provide control over this trade-off. So too, a necessary condition of optimality is derived. This condition can be applied repeatedly to improve the performance of the results from the iterative algorithms. The Ant Colony Optimisation family of meta-heuristic algorithms is adapted to solve this problem, providing a benchmark that outperforms the Genetic Algorithm presented in prior work. The iterative algorithms have a larger time complexity than other solutions, but still converge in polynomial time. When combined with the optimality condition, they outperform all of the currently proposed algorithms that solve this problem to date. More specifically, this approach produces results that are found to fall in the 99.97th percentile on average.}, archive = {J_ACCESS}, author = {Benjamin Rosen and Adnan M. Abu-Mahfouz and Ling Cheng}, doi = {10.1109/ACCESS.2025.3550790}, journal = {IEEE Access}, pages = {48935-48949}, shortjournal = {IEEE Access}, title = {Path-based clustering approaches for a hybrid slepian-wolf compression and coded caching system}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of solar grid-connected inverter with the approach of maximum power injection and THD reduction in partial shading conditions using cascaded repetitive controller. ACCESS, 13, 48923-48934. (www)

  @article{ , abstract = {In this article, an approach is presented to ensure that a rooftop solar power plant performs efficiently in the face of partial shading. A two-stage, five-level H-Bridge hardware structure has been implemented to achieve lower switching losses compared to conventional two-level inverters. This design reduces heat generation and enhances the efficiency of the power injected into the grid. To track the sinusoidal input signal effectively, a novel Repetitive Controller has been employed, minimizing error and thereby decreasing Total Harmonic Distortion (THD). The controller’s performance results in an error that approaches zero, leading to a significant reduction in THD and an increase in the power injected into the grid. Notably, this novel controller outperforms traditional Repetitive Controllers as well as conventional Proportional-Integral (PI) and Proportional-Resonant (PR) controllers, especially during changes in grid frequency or under conditions of partial shading on the input panels. Using this controller, the amplitude of $3^{\mathrm {rd}}$ , $5^{\mathrm {th}}$ , and $7^{\mathrm {th}}$ harmonics is minimized. In normal conditions, there is no phase shift between the output current of the inverter and the grid voltage at frequencies of 50.5 Hz, 50 Hz, or 49.5 Hz. The Total Harmonic Distortion current (THD $_{\mathrm {i}}$ ) is measured at 1.18% under normal conditions and 3.01% during partial shading—both values significantly lower than those achieved with other methods. This structure, along with a suitable maximum power tracking control on the side of the input converter, has resulted in significant performance improvements.}, archive = {J_ACCESS}, author = {Mahmoud Karimi and Ali Khaki-Sedigh}, doi = {10.1109/ACCESS.2025.3533602}, journal = {IEEE Access}, pages = {48923-48934}, shortjournal = {IEEE Access}, title = {Design and implementation of solar grid-connected inverter with the approach of maximum power injection and THD reduction in partial shading conditions using cascaded repetitive controller}, volume = {13}, year = {2025}, }
• (2025). Aircraft target detection in remote sensing images based on improved YOLOv7-tiny network. ACCESS, 13, 48904-48922. (www)

  @article{ , abstract = {To address the challenges posed by complex backgrounds and target scale variations in remote sensing aircraft detection, we propose an improved YOLOv7-tiny network designed to boost both detection accuracy and the model’s ability to generalize across varying conditions. Complex backgrounds hinder effective target feature extraction, while variations in target scale complicate the real-time detection of objects at different sizes. To address these issues, we introduce a multi-head dynamic joint self-attention mechanism that combines self-attention, channel attention, and spatial attention, thereby enhancing the model’s ability to capture crucial target features. Furthermore, we design an enhanced feature extraction module that expands the receptive field and improves the model’s ability to extract features from multi-scale targets. In addition, we constructed diverse remote sensing aircraft detection datasets and applied a range of data augmentation techniques to improve the robustness and adaptability of the model in real-world scenarios. The experimental results demonstrate that the enhanced YOLOv7-tiny model achieves a mAP of 93.41%, which is a 2.98% improvement over the original model. The model also achieves precision and recall rates of 90.44% and 87.81%, respectively, with a FPS rate of 23.31. These results highlight the model’s excellence in both precision and real-time performance, making it highly effective for remote sensing aircraft detection and fine-grained feature recognition, particularly in complex environments where real-time processing is crucial.}, archive = {J_ACCESS}, author = {Ruizhe Zhu and Hai Jin and Yonghua Han and Qiyang He and Haibo Mu}, doi = {10.1109/ACCESS.2025.3551320}, journal = {IEEE Access}, pages = {48904-48922}, shortjournal = {IEEE Access}, title = {Aircraft target detection in remote sensing images based on improved YOLOv7-tiny network}, volume = {13}, year = {2025}, }
• (2025). Emperor yu tames the flood: Water surface garbage cleaning robot using improved a* algorithm in dynamic environments. ACCESS, 13, 48888-48903. (www)

  @article{ , abstract = {Every year, the global production of marine debris reaches a staggering 400 million tons, and the amount of garbage continues to increase. Among this, about 14 million tons will eventually flow into the oceans, posing a significant threat to marine ecosystems. Apart from endangering marine ecology, this waste will ultimately come back to affect humans. To address the increasingly severe issue of marine debris, our team independently developed a surface garbage cleaning robot, DaYu No. 1, and designed a path planning algorithm specifically for DaYu No. 1, called DyNav. DaYu No. 1 aims to increase garbage cleaning efficiency and adaptability. The traditional A $^{\ast }$ algorithm performs well in static environments, but the ocean is a dynamic environment where marine debris constantly shifts due to ocean currents or tides, making it extremely challenging for robots to collect marine debris. Therefore, our proposed DyNav algorithm can adaptively adjust paths to cope with environmental changes. We proposed DyNav, a new path planning algorithm that takes ocean currents into account, and the DaYu No. 1 robot with YOLOv7 and IMA algorithms built in. These will make cleaning up marine debris much more efficient and flexible. Compared to traditional path planning algorithms, DyNav demonstrates superior performance. We have validated the effectiveness of the DyNav algorithm through practical experiments. The results show that in dynamic environments, the robot can clean surface debris more quickly and flexibly while avoiding obstacles. This not only contributes to environmental protection, but also improves cleaning efficiency, offering an innovative solution for water environment management.}, archive = {J_ACCESS}, author = {Ronghao Li and Bingying Zhang and Di Lin and Rong-Guei Tsai and Wenwen Zou and Shanna He and Ziqi Lin and Xinxin Chen and Yating Yao}, doi = {10.1109/ACCESS.2025.3551088}, journal = {IEEE Access}, pages = {48888-48903}, shortjournal = {IEEE Access}, title = {Emperor yu tames the flood: Water surface garbage cleaning robot using improved a* algorithm in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Improving trajectory tracking of differential wheeled mobile robots with enhanced GWO-optimized back-stepping and FOPID controllers. ACCESS, 13, 48872-48887. (www)

  @article{ , abstract = {Improving trajectory tracking of differential wheeled mobile robots (DWMRs) is crucial for enhancing their effectiveness in various applications, such as autonomous cleaning, mowing, and leader-following scenarios. These scenarios often involve navigating complex, nonlinear paths, requiring advanced control strategies for improved performance. This work presents a novel integration of a Backstepping Controller (BSC) and a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within a cascade closed-loop structure for DWMRs. The proposed BSC-FOPID controller addresses velocity saturations and nonlinearities, ensuring system stability and precise trajectory tracking. A key contribution is the enhanced Grey Wolf Optimization (GWO) strategy, termed GWO-SMA, which integrates GWO with the Slime Mould Algorithm (SMA). By leveraging opposition space and optimum cache concepts, the GWO-SMA improves fitness optimization in each iteration, enhancing both exploration and exploitation efficiency. This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize steady-state error and enhance responsiveness to larger deviations. Simulations demonstrate the superior performance of the proposed GWO-SMA algorithm compared to existing optimization techniques, such as Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), and its individual components, GWO and SMA, which have shown strong performance in recent literature for optimizing PID-type controllers. Additionally, simulation results using three distinct reference paths-lemniscate, square, and cloverleaf—demonstrate that the GWO-SMA-optimized BSC-FOPID controller outperforms both Adaptive Dynamic Compensation Control (ADCC) and the BSC-PID controller in position and posture tracking accuracy. Specifically, the BSC-FOPID controller achieves significant improvements, including average reductions of 55.65% in ISE and 38.25% in IAE for position control, as well as 62.12% and 38.95% improvements in ISE and IAE for posture control, respectively. These improvements highlight the controller’s enhanced responsiveness and smoother error convergence, particularly during maneuvers involving sharp curves.}, archive = {J_ACCESS}, author = {Li Qiang and Hooi Hung Tang and Nur Syazreen Ahmad}, doi = {10.1109/ACCESS.2025.3552312}, journal = {IEEE Access}, pages = {48872-48887}, shortjournal = {IEEE Access}, title = {Improving trajectory tracking of differential wheeled mobile robots with enhanced GWO-optimized back-stepping and FOPID controllers}, volume = {13}, year = {2025}, }
• (2025). Trajectory planning for autonomous cars in low-structured and unstructured environments: A systematic review. ACCESS, 13, 48841-48871. (www)

  @article{ , abstract = {Autonomous driving research has gained attention in recent years due to its great potential in reshaping transportation systems. It is highly noticeable that most research focuses on well-maintained urban roads, whereas much less effort is pushed for low or unstructured scenarios usually found in suburban or countryside areas of emerging countries. This study systematically reviews state-of-the-art trajectory planning for car-like vehicles in these environments. We analyzed 4601 papers published in the last 10 years and selected 75 studies. Based on the selected research, we summarize the techniques for global planning, local planning, motion control, and multi-vehicle collaborative planning. In particular, we highlight the strengths and weaknesses of the local planning approaches and discuss and identify common limitations, such as sensor faults, terrain variations, safety and regulatory risks. Moreover, we summarize the most adopted visual input sensors, their associated data structures that are used as search spaces for local planning, and sensor fusion techniques, which complement the advantages of different types of sensors. Furthermore, we provide a systematic schema that relates the dependencies between the visual sensors, data-representations, and local planning techniques. From this, we identify gaps in the current literature, suggesting possible directions for future work.}, archive = {J_ACCESS}, author = {Cristiano Souza de Oliveira and Rafael De S. Toledo and Vitor H. Tulux Oliveira Victorio and Aldo von Wangenheim}, doi = {10.1109/ACCESS.2025.3551453}, journal = {IEEE Access}, pages = {48841-48871}, shortjournal = {IEEE Access}, title = {Trajectory planning for autonomous cars in low-structured and unstructured environments: A systematic review}, volume = {13}, year = {2025}, }
• (2025). A hybrid K-means++ and particle swarm optimization approach for enhanced document clustering. ACCESS, 13, 48818-48840. (www)

  @article{ , abstract = {Document Clustering has attracted the interest of many researchers who have created several solutions to this problem by combining different techniques, models, and algorithms. While famous for its simplicity, the most commonly used algorithm, K-Means, suffers from issues such as finding the optimal value for k and random initialization of the centroids. In this paper, we propose a hybrid methodology combining K-Means++ with the metaheuristic algorithm PSO to overcome the challenges of both these algorithms. K-Means++ is a smart initialization technique that selects clusters based on probability function so that they are further away from each other. Two methods, Elbow Analysis, and Silhouette Score, have been used to find the optimal value of k. The research also attempts to assess the results of the best-performing feature extraction techniques and create a combined approach using their average. Although the combined approach did not outperform the existing methods, it paved the way for further advancement and exploration of merging feature extraction techniques. The experiments were performed on four datasets: 20 Newsgroups, Reuters, WebKB, and Wine, and the results were evaluated using three evaluation metrics: Purity, Silhouette Score, and Jaccard Index. The proposed approach achieved a Purity of 0.95, 0.91. 0.86, 0.89, the Silhouette Score was measured to be 0.5, 0.05, 0.05, 0.59, and the Jaccard Index was reported to be 0.04, 0.04, 0.05, 0.14 on Reuters, WebKB, 20 Newsgroups, and Wine datasets, respectively}, archive = {J_ACCESS}, author = {Eisha Hassan and Fazila Malik and Qazi Waqas Khan and Nadeem Ahmad and Muhammad Sardaraz and Faten Khalid Karim and Hela Elmannai}, doi = {10.1109/ACCESS.2025.3535226}, journal = {IEEE Access}, pages = {48818-48840}, shortjournal = {IEEE Access}, title = {A hybrid K-means++ and particle swarm optimization approach for enhanced document clustering}, volume = {13}, year = {2025}, }
• (2025). Enhancing power allocation in DAS: A hybrid machine learning and reinforcement learning model. ACCESS, 13, 48802-48817. (www)

  @article{ , abstract = {This paper presents a novel hybrid approach to optimize downlink power allocation in a Distributed Antenna System (DAS) with many Remote Access Units (RAUs) and User Equipment (UEs) randomly distributed in a single cell. The proposed method combines Machine Learning (ML) for predictive modeling with Multi-Agent Reinforcement Learning (MARL) for real-time coordination among RAUs. ML models utilize historical data to forecast optimal power levels, while MARL agents dynamically adjust power allocation based on real-time conditions, aiming to enhance spectral efficiency, minimize interference, and optimize energy efficiency. The hybrid approach achieves a mean Spectral Efficiency (SE) of 0.855 bits/s/Hz and a mean Energy Efficiency (EE) of 1.210 bits/Joule, significantly outperforming traditional optimization (mean SE: 0.700, mean EE: 1.00) and the k-NN algorithm (mean SE: 0.725, mean EE: 1.105). Unlike existing approaches, our method offers continuous learning and hierarchical control, adapting effectively to varying network dynamics. Simulation results demonstrate the hybrid approach’s superiority in diverse scenarios, underlining its potential for practical implementation. The study concludes with insights into the synergistic benefits of integrating ML and MARL in DAS environments and suggests directions for future research.}, archive = {J_ACCESS}, author = {S. Gnanasekar and K. C. Sriharipriya}, doi = {10.1109/ACCESS.2025.3551238}, journal = {IEEE Access}, pages = {48802-48817}, shortjournal = {IEEE Access}, title = {Enhancing power allocation in DAS: A hybrid machine learning and reinforcement learning model}, volume = {13}, year = {2025}, }
• (2025). A second-order sliding mode control scheme with fuzzy logic-based online sliding surface adjustment for buck converters. ACCESS, 13, 48792-48801. (www)

  @article{ , abstract = {This article presents a second-order sliding mode control-based scheme for the closed-loop control of DC-DC buck converters. The proposed control technique adjusts online the sliding surface of the second-order sliding mode controller to enhance the dynamic performance of the closed-loop system. The sliding surface adjustment is achieved by a single-input, single-output fuzzy logic inference system based on sliding variables. While maintaining its simplicity concerning design and implementation, the controller makes sure the system achieves the design requirements in terms of improved dynamic performance, stability, and robustness. Theoretical analysis confirms the global finite-time error convergence of the buck converter system. The performance of this novel control approach is compared with that of the conventional second-order sliding mode controller with a pre-defined sliding surface using SIMULINK-based simulations for a buck converter model. From the analysis of the simulation results, it is concluded that the proposed controller provides a remarkable improvement in the dynamic performance of the buck converter control system compared with the conventional second-order sliding mode controller with a pre-defined sliding surface.}, archive = {J_ACCESS}, author = {Abdul Kareem and V. P. Basheer and Varuna Kumara}, doi = {10.1109/ACCESS.2025.3551127}, journal = {IEEE Access}, pages = {48792-48801}, shortjournal = {IEEE Access}, title = {A second-order sliding mode control scheme with fuzzy logic-based online sliding surface adjustment for buck converters}, volume = {13}, year = {2025}, }
• (2025). Extraction-augmented generation of scientific abstracts using knowledge graphs. ACCESS, 13, 48775-48791. (www)

  @article{ , abstract = {Graph-to-text generation for specialized tasks, such as scientific abstract generation, is challenging due to the limited availability of structured knowledge graphs and the need to balance semantic accuracy with paragraph coherence. This motivates our proposal of an Extraction-Augmented Scientific Abstract Generation (EASAG) model which includes the processes of self-extraction, graph fusion, and abstract generation. The model performs self-determination of entities, followed by fine-grained extraction for each entity, predicting the target entity by specifying relations to construct semantic triples. The accumulated triples are then represented more logically through knowledge fusion using two proposed methods: Multi-hop Longest Subchain (MLS) and Label Ordering (LO). The former focuses on uncovering the core logical chain of the content, while the latter functionally segments sequences within the knowledge graph. Experimental results indicate that our model improves the quality of generated scientific abstracts through knowledge richness and the integration of discrete information. The two knowledge fusion methods are designed to enhance specific aspects, with one focusing on semantic accuracy and the other on maintaining paragraph structure integrity. Through fine-grained extraction, we reconstructed the Abstract Generation Dataset (AGENDA) and the newly developed ACL Abstract Graph Dataset (ACL-AGD) containing the latest Natural Language Processing (NLP) research, both datasets composed of graph-abstract pairs. Analysis reveals that these datasets exhibit richer relations, enhanced graph connectivity, and a more uniform distribution of relations.}, archive = {J_ACCESS}, author = {Haotong Wang and Yves Lepage}, doi = {10.1109/ACCESS.2025.3551756}, journal = {IEEE Access}, pages = {48775-48791}, shortjournal = {IEEE Access}, title = {Extraction-augmented generation of scientific abstracts using knowledge graphs}, volume = {13}, year = {2025}, }
• (2025). SwinIoT: A hierarchical transformer-based framework for behavioral anomaly detection in IoT-driven smart cities. ACCESS, 13, 48758-48774. (www)

  @article{ , abstract = {Internet of Things (IoT) devices become more and more important because they are useful in different applications, for example, traffic monitoring, public safety, and environmental management. However, the vastness and diversity of IoT data and the requirement for real-time decision-making create a big challenge for anomaly detection frameworks. In this work, we propose SwinIoT, a new framework with hierarchical and windowed attention mechanisms of Swin Transformer that is especially good for the purpose of behavioral anomaly detection in IoT settings. SwinIoT would solve important problems of class imbalance, noisy data, and heterogeneous devices through the introduction of custom attention models embedding real-time optimizations. The proposed framework was benchmarked on nine datasets such as ARAS, CASAS, WESAD, and UCF Crime compared to the above-mentioned state-of-the-art algorithms like Active Learning-Based Anomaly Detection, Deep Support Vector Data Description (DSVDD), Deep Support Vector Data Description Contractive Autoencoder (DSVDD-CAE), and Federated Principal Component Analysis (FedPCA). It is proven to be better than the above algorithms by attaining up to 96% accuracy, 97% Mean Average Precision $(mAP)$ , and excellent Area Under the Receiver Operating Characteristic curve (AUC-ROC) as well as Precision-Recall (PR) metrics, especially in low-resource and unbalanced data scenarios. The results indicate the potential of SwinIoT in scalable and accurate anomaly detection to the development of safer, smarter cities, ensuring reliability and security in critical systems.}, archive = {J_ACCESS}, author = {H. Mancy and Qamar H. Naith}, doi = {10.1109/ACCESS.2025.3551207}, journal = {IEEE Access}, pages = {48758-48774}, shortjournal = {IEEE Access}, title = {SwinIoT: A hierarchical transformer-based framework for behavioral anomaly detection in IoT-driven smart cities}, volume = {13}, year = {2025}, }
• (2025). Simulation-based investigation of wind turbine induced shadow flicker on IGBT reliability and energy yield in solar converters in hybrid wind-solar systems. ACCESS, 13, 48746-48757. (www)

  @article{ , abstract = {With the growing adoption of renewable energy, hybrid wind-solar plants are gaining interest because they allow the increase of energy yield per unit of surface area. However, the presence of wind turbines creates unique shading scenarios for the solar panels. This work presents a simulation-based approach to investigate the solar plant’s energy yield and converter IGBT lifetime in the presence of dynamic and fast-moving shadows created by the wind turbine blades. First, the need for a dynamic simulation as opposed to a static one is examined. Next, the sensitivity of Photovoltaic (PV) string orientation, PV string location, wind direction, Maximum Power Point Tracker (MPPT) control speed, and turbine rotor speeds on energy yield and IGBT lifetime are investigated in a case study. Results show that dynamic and static simulations can show vastly different results. MPPT can have difficulty with shadow flicker, resulting in different string voltages depending on the MPPT update speed and wind turbine rotor speed. The shadow flicker introduces additional temperature swings in the IGBT, but these are too small to add any significant lifetime consumption (<1.2%). Thus, the observed changes in lifetime consumption are caused mainly by controller behavior and static shadows. Finally, it is concluded that PV strings are better placed south, east, or west of the wind turbine for optimal energy yield and lifetime consumption. A northern placement can be suitable if there is a significant distance (+50 m) between the PV string and the wind turbine.}, archive = {J_ACCESS}, author = {Leander Van Cappellen and Martijn Deckers and Kristof Engelen and Georgi H. Yordanov and Michaël Daenen}, doi = {10.1109/ACCESS.2025.3548976}, journal = {IEEE Access}, pages = {48746-48757}, shortjournal = {IEEE Access}, title = {Simulation-based investigation of wind turbine induced shadow flicker on IGBT reliability and energy yield in solar converters in hybrid wind-solar systems}, volume = {13}, year = {2025}, }
• (2025). Design of an iterative method leveraging deep Q-networks for intrusion detection system operations. ACCESS, 13, 48720-48745. (www)

  @article{ , abstract = {The complexity and the level of sophistication in cyber-attacks that challenge conventional security measures make the need for advanced intrusion detection systems even more compelling. The major challenges in the conventional NIDS include high false-positive rates, poor adaptability to new and emerging attack vectors, and high computational demand, hence degrading effectiveness and scalability. In addressing these limitations, this work proposes a novel framework for deep reinforcement learning (DRL) algorithms in enhancing NIDS capabilities and efficiency. Five DRL techniques are used in the proposed model: DQN, DDPG, PPO, TD3, and SAC. Each of these algorithms is chosen for their specific strengths in handling the dynamic and adversarial nature of network security environments. DQN is used because of its excellence in addressing the discrete action space, making it very suitable for binary decision-making tasks in intrusion detection. DDPG extends the model to continuous action space, enabling the model to respond to complex threat scenarios. PPO is integrated into the model for its stability and sample efficiency essential for resource-constrained settings. TD3 addresses the overestimation bias in DDPG, thus improving the reliability of the generated security alerts. Finally, SAC brings an exploration-centric approach, ensuring robust adaptability to emerging threats. Initial results show a substantial increase in intrusion detection accuracy, reduced false positive rates, and better computational efficiency compared to traditional NIDS approaches when benchmarked. Thus, this research not only advances the state of NIDS but also contributes to scalable and adaptive security solutions necessary for defending against current and future cyber threats. The proposed model had a detection accuracy of 95.8%, which far outperformed the current approaches such as Random Forest with an accuracy of 91.2% and GAN-based approaches with 90.4%, at a false positive rate of just 2.5%. The model had a detection rate of 97.9% against DoS attacks and achieved an F1-score of 0.92 for U2R attacks. The system processed up to 2,400 transactions per second, showing better scalability and real-time applicability.}, archive = {J_ACCESS}, author = {Mr. Mahaboob Subhani Shaik Ch and Yamarthi Narasimha Rao}, doi = {10.1109/ACCESS.2025.3551718}, journal = {IEEE Access}, pages = {48720-48745}, shortjournal = {IEEE Access}, title = {Design of an iterative method leveraging deep Q-networks for intrusion detection system operations}, volume = {13}, year = {2025}, }
• (2025). A double deep learning-based solution for efficient event data coding and classification. ACCESS, 13, 48703-48719. (www)

  @article{ , abstract = {Event cameras have the ability to capture asynchronous per-pixel brightness changes, usually called “events”, offering advantages over traditional frame-based cameras for computer vision tasks. Efficiently coding event data is critical for practical transmission and storage, given the very significant number of events captured. This paper proposes a novel double deep learning-based solution for efficient event data coding and classification, using a point cloud-based representation for events. Moreover, since the conversions from events to point clouds and back to events are key steps in the proposed solution, novel tools are proposed and their impact is evaluated in terms of compression and classification performance. Experimental results show that it is possible to achieve a classification performance for decompressed events which is similar to the one for original events, even after applying a lossy point cloud codec, notably the recent deep learning-based JPEG Pleno Point Cloud Coding standard, with a clear rate reduction. Experimental results also demonstrate that events coded using the JPEG standard achieve better classification performance than those coded using the conventional lossy MPEG Geometry-based Point Cloud Coding standard for the same rate. Furthermore, the adoption of deep learning-based coding offers future high potential for performing computer vision tasks in the compressed domain, which allows skipping the decoding stage, thus mitigating the impact of compression artifacts.}, archive = {J_ACCESS}, author = {Abdelrahman Seleem and André F. R. Guarda and Nuno M. M. Rodrigues and Fernando Pereira}, doi = {10.1109/ACCESS.2025.3551073}, journal = {IEEE Access}, pages = {48703-48719}, shortjournal = {IEEE Access}, title = {A double deep learning-based solution for efficient event data coding and classification}, volume = {13}, year = {2025}, }
• (2025). A unified neural framework for real-time deepfake detection across multimedia modalities to combat misleading content. ACCESS, 13, 48683-48702. (www)

  @article{ , abstract = {The rise of deepfake technology poses significant threats to the authenticity of content on social media. This research introduces Sach-AI, a pioneering framework for detecting various deepfakes in video, audio, and image data. Leveraging the power of deep neural networks, Sach-AI utilizes Eulerian Video Magnification combined with the ResNext architecture for enhanced detection. For video deepfakes, Long Short-Term Memory (LSTM) networks are integrated to improve classification tasks. This combination allows Sach-AI to effectively address the evolving, multimodal nature of deepfakes. The framework has been rigorously evaluated using diverse datasets, such as Celeb-DF and FaceForensics++, demonstrating its robustness and accuracy. Sach-AI achieved 97.76% accuracy in video deepfake detection, surpassing Intel’s FakeCatcher, 99.13% accuracy in audio deepfake detection, and 93.64% accuracy in image deepfake detection. These results underscore Sach-AI’s reliability in safeguarding digital media integrity against deceptive synthetic content in an era increasingly dominated by artificial technologies.}, archive = {J_ACCESS}, author = {Ayan Sar and Subhangi Sati and Tanupriya Choudhury and Purvika Joshi and Roohi Sille and K. Srihari and Kritika Bansal}, doi = {10.1109/ACCESS.2025.3550770}, journal = {IEEE Access}, pages = {48683-48702}, shortjournal = {IEEE Access}, title = {A unified neural framework for real-time deepfake detection across multimedia modalities to combat misleading content}, volume = {13}, year = {2025}, }
• (2025). Thermal analysis and research of a yokeless and segmented armature axial flux in-wheel motor with an amorphous magnetic metal stator. ACCESS, 13, 48672-48682. (www)

  @article{ , abstract = {The in-wheel motor for electric vehicles is required to meet the demands for high power density and a compact axial length. Precise thermal design and effective heat dissipation are critical, as they significantly influence the reliability of vehicle operation. This paper presents a novel yokeless and segmented armature (YASA) axial flux in-wheel motor featuring a specifically designed amorphous magnetic metal stator. The electromagnetic and mechanical losses of the motor are calculated, and the thermal conduction properties of the amorphous magnetic metal stator are analyzed. Additionally, the equivalent thermal conduction coefficients for the windings and insulation are determined. A three-dimensional thermal finite element model of the motor is developed to obtain the temperature distribution. A prototype is manufactured, and an experimental platform is established to measure the temperature of each component. The measured data closely correlate with the calculated results, thereby validating the effectiveness of both the electromagnetic and thermal finite element models. The thermal characteristics of the prototype demonstrate compliance with the requirements for electric vehicles, providing valuable insights for the thermal design of the novel YASA in-wheel motor.}, archive = {J_ACCESS}, author = {Tao Li and Yuxiu Liang and Ye Yang and Jingyi Tian and Likang Fan}, doi = {10.1109/ACCESS.2025.3550362}, journal = {IEEE Access}, pages = {48672-48682}, shortjournal = {IEEE Access}, title = {Thermal analysis and research of a yokeless and segmented armature axial flux in-wheel motor with an amorphous magnetic metal stator}, volume = {13}, year = {2025}, }
• (2025). Speech translation from darija to modern standard arabic: Fine-tuning whisper on the darija-C corpus across six model versions. ACCESS, 13, 48656-48671. (www)

  @article{ , abstract = {This study explores the fine-tuning of six versions of the Whisper model for speech translation from Moroccan Darija to Modern Standard Arabic (MSA). We focus on fine-tuning Whisper across six different versions: small, medium, large, large-v2, large-v3, and turbo. Our primary goal is to evaluate how these model variants perform in translating Darija speech into accurate and coherent MSA text, shedding light on the trade-offs between model capacity and translation quality. The experiments are conducted on the Darija-C Corpus, a specialized dataset designed to capture the linguistic nuances of Darija and its relationship with MSA. We analyze factors such as computational efficiency, memory usage, and training time to offer a clear view of model deployment in resource-constrained environments. This study provides valuable insights for developing robust Darija-to-MSA speech translation systems and highlights the broader potential of fine-tuning Whisper for low-resource language pairs.}, archive = {J_ACCESS}, author = {Maria Labied and Abdessamad Belangour and Mouad Banane}, doi = {10.1109/ACCESS.2025.3551229}, journal = {IEEE Access}, pages = {48656-48671}, shortjournal = {IEEE Access}, title = {Speech translation from darija to modern standard arabic: Fine-tuning whisper on the darija-C corpus across six model versions}, volume = {13}, year = {2025}, }
• (2025). Enhancing tabular data generation with dual-scale noise modeling. ACCESS, 13, 48643-48655. (www)

  @article{ , abstract = {The generation of synthetic tabular data plays a critical role in applications such as data enhancement, privacy preservation, and model validation. However, the heterogeneity of tabular data—comprising both continuous and categorical features with intricate interdependencies—presents significant challenges in generating high-quality data. This study presents the Dual-Scale Diffusion Probabilistic Method (DSDDPM), a novel framework developed to improve the Denoising Diffusion Probabilistic Model (DDPM) performance for tabular data generation. DSDDPM tackles the challenge of capturing both global dependencies and local details through the use of a dual-scale noise-handling mechanism. It models the global structure at the coarse scale and the regional patterns at the fine scale throughout the forward and backward diffusion processes. Experimental evaluations on several benchmark tabular datasets show that DSDDPM outperforms existing state-of-the-art methods. Specifically, DSDDPM improves machine learning efficiency by an average of 5-7% compared to other methods. DSDDPM exhibits the lowest correlation deviation across all datasets regarding global dependency modeling. Moreover, regarding detail consistency, DSDDPM shows the highest alignment of feature distributions between the generated and actual data, significantly outperforming other methods. These results position DSDDPM as a robust and scalable solution for generating high-quality synthetic tabular data, enhancing fidelity and versatility across various applications.}, archive = {J_ACCESS}, author = {Xiaorong Zhang and Fei Li and Xuting Hu}, doi = {10.1109/ACCESS.2025.3551594}, journal = {IEEE Access}, pages = {48643-48655}, shortjournal = {IEEE Access}, title = {Enhancing tabular data generation with dual-scale noise modeling}, volume = {13}, year = {2025}, }
• (2025). Enhancing plant disease detection using attention-augmented residual networks and faster Region–Convolutional networks. ACCESS, 13, 48625-48642. (www)

  @article{ , abstract = {Rapid and accurate detection of plant diseases is crucial for agricultural productivity and food security. Traditional methods are labor-intensive and often unreliable. To overcome these limitations, this research introduces an innovative approach that integrates attention mechanisms into residual networks (ResNets) and utilizes Generative Adversarial Networks (GANs) for data augmentation. The method incorporates Attention-Augmented Residual Networks (AARN), which enhance feature extraction and classification by focusing on critical image regions. A Conditional GAN (cGAN) generates synthetic images of diseased and healthy plants, increasing dataset diversity. By combining AARN with Faster Region-Convolutional Neural Network (Faster-RCNN), detection capabilities are further enhanced. Training the AARN model on this augmented dataset improves generalization, achieving an impressive 98.78% accuracy in plant disease classification. The attention-augmented residuals boost the Faster-RCNN’s effectiveness by 23.84%, improving feature relevance and reducing overfitting. Comparative analysis shows that this method outperforms existing techniques in accuracy, precision, recall, and F1-score, offering a robust solution for plant disease detection. This integration of advanced deep learning techniques significantly improves automated plant disease identification, benefiting agricultural management practices.}, archive = {J_ACCESS}, author = {K. Sathya and Arunkumar Balakrishnan and P. Baskaran and Arun Kumar Ramamoorthy}, doi = {10.1109/ACCESS.2025.3551242}, journal = {IEEE Access}, pages = {48625-48642}, shortjournal = {IEEE Access}, title = {Enhancing plant disease detection using attention-augmented residual networks and faster Region–Convolutional networks}, volume = {13}, year = {2025}, }
• (2025). Identification and alleviation of false data injection within the cyber layer of an enhanced distributed secondary control in DC islanded microgrids. ACCESS, 13, 48605-48624. (www)

  @article{ , abstract = {As a result of the integration of communication links within secondary control to circumvent the drawbacks associated with centralized control, microgrids have evolved into cyber-physical systems which are susceptible to cybersecurity risks. This research addresses the repercussions of injecting fake data into the cyber layer within the framework of distributed secondary control in a DC-islanded microgrid. Kullback-Leibler divergence, also referred to as relative entropy, serves as a statistical metric between two probability distributions employed to compute a specific parameter tasked with mitigating the impact on the control system. This parameter comprises the multiplication of two components. The first component is an internal component derived from neighboring entities and provides insight into their security status, indicating whether they have been subjected to attacks. The second component is an external component computed locally and verifies the accuracy of the status received from each neighbor, ensuring its authenticity. Two medium voltage DC microgrids were examined through MATLAB Simulink simulations to corroborate the efficiency of the suggested approach in identifying and mitigating the consequences of false data injection. The results demonstrated that the cybersecurity threat’s influence could be proficiently detected and alleviated in both microgrid configurations, while maintaining the effectiveness of secondary control functions, which are, preserving voltage regulation and precise current distribution. Finally, one of the Microgrids was implemented using OPAL-RT, allowing for the observation of output voltages and currents on a physical hardware oscilloscope.}, archive = {J_ACCESS}, author = {Nada Mosaad and Omar Abdel-Rahim and Wesam Rohouma and Sobhy Mohamed Abdelkader}, doi = {10.1109/ACCESS.2025.3547724}, journal = {IEEE Access}, pages = {48605-48624}, shortjournal = {IEEE Access}, title = {Identification and alleviation of false data injection within the cyber layer of an enhanced distributed secondary control in DC islanded microgrids}, volume = {13}, year = {2025}, }
• (2025). Achieving high efficiency and privacy in IoT with federated learning: A verifiable horizontal approach. ACCESS, 13, 48587-48604. (www)

  @article{ , abstract = {The exponential growth of the Internet of Things (IoT) domain has raised the question of the immediate need for complex and privacy-sensitive data processing methodologies. Federated Learning (FL) makes learning decentralized and supports individual privacy; hence, it proves to be an optimistic approach. However, according to recent research, federated learning systems are vulnerable to attacks that may breach client privacy. Traditional federated learning models face issues such as verification of aggregated results and high computational and communication demands, making them less practical in large-scale IoT implementations. We propose a new federated learning framework: Verifiable Horizontal Federated Learning (VHFL). The main goals of VHFL include data privacy, reducing computational and communication-side overheads, and ensuring the accuracy of aggregated results. To improve the data’s privacy, VHFL uses single-mask encryption techniques together with group-key techniques. Another step will be to further integrate Latin Squares Design to reduce client-side computational and communication overheads. The system introduces a new verification scheme that generates Hamiltonian graphs from LSD to ensure that the VHFL aggregation result is correct. We tested the proposed system on several datasets, including MIMIC-III and HAR and compared to traditional federated learning models. More importantly, our proof-of-concept provided evidence that VHFL is robust in ensuring high efficiency and strong privacy within IoT environments. These experiments confirm that VHFL is efficient in handling the most demanding issues posed to federated learning and can be viable for both secure and efficient IoT applications.}, archive = {J_ACCESS}, author = {Jayabrabu Ramakrishnan}, doi = {10.1109/ACCESS.2025.3549708}, journal = {IEEE Access}, pages = {48587-48604}, shortjournal = {IEEE Access}, title = {Achieving high efficiency and privacy in IoT with federated learning: A verifiable horizontal approach}, volume = {13}, year = {2025}, }
• (2025). Adaptive and nonadaptive speed estimation for stability improvement of sensorless induction motor control for electric vehicles. ACCESS, 13, 48571-48586. (www)

  @article{ , abstract = {This study proposes a reactive torque flux control (RTFC) and modified speed estimation of a speed observer based on multiscalar control of an induction motor (IM) traction drive for electric vehicles (EVs). It employs an extended-speed adaptive and nonadaptive speed estimation with a stabilizing function to improve the low-speed stability of sensorless IMs. An approach for rotor speed estimation using machine models of multiscalar control plus the stabilizing function is used to control the IM. The proposed RTFC controls the flux of IM drives, in which the reactive torque component replaces the flux control target used in the Direct field-oriented control (DFOC). As a result, it resolves one PI tuning issue. The modified speed estimation design strategy for adaptive full-order observers to address the challenge is proposed to eliminate the unstable zone during a low-speed regenerative mode of operation. The pole placement method is used to select the stabilizing function gains to minimize anticipated rotor speed errors and increase resilience against parameter uncertainties at regenerating mode and low-speed operation. The proposed speed estimator performance is compared to motor speed throughout several driving profiles of EVs, including motoring, constant speed, low and zero speed, and regenerating modes of operation. The proposed RTFC approach requires only one PI controller for the reactive torque flux control. Though the scheme has a comparable performance to one with an additional flux controller, as in DFOC, it significantly reduces the computational burden. The results show that the proposed speed observer performs well across various driving profiles, and using an adaptive full-order observer (AFO) for modified speed estimation of sensorless control in EV is effective and stable. Experimental studies are carried out on a three-phase, 5.5 kW induction motor to validate the effectiveness and performance of the proposed control scheme.}, archive = {J_ACCESS}, author = {Lelisa Wogi and Marcin Morawiec and Tadele Ayana and Amal Baqais and Shabana Urooj}, doi = {10.1109/ACCESS.2025.3548269}, journal = {IEEE Access}, pages = {48571-48586}, shortjournal = {IEEE Access}, title = {Adaptive and nonadaptive speed estimation for stability improvement of sensorless induction motor control for electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Optimized vision transformers for superior plant disease detection. ACCESS, 13, 48552-48570. (www)

  @article{ , abstract = {Detecting plant diseases is vital for maintaining agricultural productivity and ensuring food security. Advances in computer vision, particularly with Vision Transformers (ViTs), have shown significant potential in improving the accuracy and efficiency of plant disease identification. This study provides a comprehensive evaluation of various ViT parameters to determine the most effective configuration for this purpose. Using the extensive PlantVillage dataset, we systematically analyzed the effects of patch sizes, image resolutions, embedding dimensions, the number of transformer blocks (depth), the number of heads in the multi-head attention layer, and the dimension of the MLP (FeedForward) layer on model performance. We introduced saliency map visualizations to enhance interpretability and evaluate the critical regions contributing to classification decisions, ensuring the approach’s transparency and robustness. Our experiments identified the optimal ViT configuration as follows: image size $ = 224 \times 224$ , patch size = 16, embedding dimension = 512, depth = 6, number of heads = 8, and MLP dimension = 1024. This configuration achieved an impressive accuracy of 99.77% on the PlantVillage dataset. In addition, we incorporated a novel cross-dataset transferability evaluation to validate the generalizability of the proposed model. Comparative analysis with traditional convolutional neural network architectures, such as VGG19 and AlexNet, revealed that our optimized ViT model not only surpasses these models in accuracy but also requires significantly fewer trainable parameters and storage space. The incorporation of a lightweight, domain-specific fine-tuning process ensures the model’s adaptability to new datasets with minimal computational overhead. Our findings highlight the scalability and adaptability of ViTs, emphasizing their ability to effectively handle varying image sizes and resolutions. Moreover, our approach outperforms recent state-of-the-art methods across multiple databases, underscoring the efficacy of the chosen ViT parameters.}, archive = {J_ACCESS}, author = {Abdelmalik Ouamane and Ammar Chouchane and Yassine Himeur and Sami Miniaoui and Shadi Atalla and Wathiq Mansoor and Hussain Al-Ahmad}, doi = {10.1109/ACCESS.2025.3547416}, journal = {IEEE Access}, pages = {48552-48570}, shortjournal = {IEEE Access}, title = {Optimized vision transformers for superior plant disease detection}, volume = {13}, year = {2025}, }
• (2025). An improved test method to investigate the power transformer winding buckling hoop stress. ACCESS, 13, 48543-48551. (www)

  @article{ , abstract = {Power transformers play a pivotal role in power grids, yet they are invariably exposed to short-circuit impacts. Precise assessment of the winding mechanical strength is crucial for ensuring power grid security. Nevertheless, traditional short-circuit tests face significant challenges in identifying critical stability states due to the high cost and destructive nature, which impedes the accurate assessment of winding strength. To address this issue, this paper proposes an improved buckling hoop stress investigation (IBHSI) test method that focuses on the inner windings. The novelty of this approach lies in its utilization of the non-equivalent force characteristics exhibited by winding disks along the height. Departing from conventional practices, it enables the accurate determination of the winding buckling hoop stress with a limited number of transformer samples and test times. Initially, a representative transformer is meticulously selected. By preliminarily estimating the winding’s critical withstand current, a series of short-circuit tests as described in this paper are implemented. Afterward, the transformer is disassembled to identify the location where gradual deformation occurs. Through a secondary simulation focused on this specific position, the buckling hoop stress can be precisely calculated. The IBHSI method not only significantly enhances the verification ability of short-circuit withstand capacity but also provides a novel and more precise means of assessing winding stability, effectively compensating for the inadequacies of traditional testing methods.}, archive = {J_ACCESS}, author = {Qiang Ou and Zongxi Zhang and Ruichi Zeng}, doi = {10.1109/ACCESS.2025.3548836}, journal = {IEEE Access}, pages = {48543-48551}, shortjournal = {IEEE Access}, title = {An improved test method to investigate the power transformer winding buckling hoop stress}, volume = {13}, year = {2025}, }
• (2025). Nonlinear and linear PID controllers-based hybrid flight control strategy for a quadcopter with slung load. ACCESS, 13, 48520-48542. (www)

  @article{ , abstract = {This paper proposes a new hybrid flight control strategy for a quadcopter with a slung load to achieve a user-defined trajectory following. The high-fidelity coupled nonlinear model involving the quadcopter and slung load is obtained by integrating the nonlinear slung load model with the nonlinear quadcopter model. The quadcopter’s nonlinear model is derived using the Euler-Newton method, whereas the slung load model is obtained using the Lagrangian methodology. The hybrid flight control strategy for this quadcopter with a slung load to accomplish trajectory tracking is developed using a conventional three-loop flight control structure. This control structure uses both linear proportional-integral-derivative (LPID) and nonlinear PID (NLPID) controllers. Here, NLPID controllers are employed in the outer loop for accomplishing trajectory tracking, whereas LPID controllers are used in the inner and middle loops to maintain stability and attitude tracking, respectively. Furthermore, the proposed technique uses a dedicated linear proportional-derivative controller to track the relative slung load positions with respect to the center of the quadcopter, thus reducing the adverse effect of the slung load oscillations. The effectiveness of the new hybrid flight control strategy is verified through different 8 degree-of-freedom (DOF) nonlinear numerical simulations.}, archive = {J_ACCESS}, author = {Abhishek Manalathody and Kishoor Santhana Krishnan and Jisnu Adindtra Subramanian and Sakthivel Thangavel and Jinraj V. Pushpangathan and Hariprasanth Paranjothy}, doi = {10.1109/ACCESS.2025.3551622}, journal = {IEEE Access}, pages = {48520-48542}, shortjournal = {IEEE Access}, title = {Nonlinear and linear PID controllers-based hybrid flight control strategy for a quadcopter with slung load}, volume = {13}, year = {2025}, }
• (2025). Runtime verification for visual deep learning systems with loss prediction. ACCESS, 13, 48502-48519. (www)

  @article{ , abstract = {Deep neural networks often demonstrate remarkable performance on benchmarks, but have been shown to readily fail once they leave lab-conditions and are deployed in real-world conditions. This can be attributed to their sensitivity to minor changes in the nature of the data, typically referred to as distributional shifts. To address this, a growing body of work has emerged implementing distributional shift detectors, a form of runtime verification intended to detect when a neural network encounters data for which it is unlikely to predict correctly. These methods generally involve extracting features which characterize the degree to which a given point is out-of-distribution and setting a suitable threshold for this feature which sufficiently separates in-distribution and out-of-distribution data. In this paper, we advocate for a significant methodological shift from framing the detection of distributional-shift as a classification problem to framing it as a regression problem. We first justify this view through an empirical analysis of several popular methods of distributional shift detection, where we show that optimal thresholds for one type and severity of shift does not necessarily generalize to other shifts and that classification generally lacks the granularity and expressivity for use as a form of runtime verification in deployment scenarios. We then show that the relationships between the features these classifiers are based upon and network loss are often fairly strong. Motivated by these results, we perform a feasibility study wherein we train a generalized linear model on synthetically-augmented data, which we assess on instances of organic distributional shift in several domain-adaptation datasets. We attain promising results, with for instance, an average percentage error of 5% when predicting network loss for a polyp segmentation benchmark, equivalent to predicting intersection-over-union scores with an average error of only 0.03. We discuss the implications this has for practitioners and advocate for the continued development of loss-regression models as runtime monitors for deep learning systems.}, archive = {J_ACCESS}, author = {Birk Torpmann-Hagen and Michael A. Riegler and Pål Halvorsen and Dag Johansen}, doi = {10.1109/ACCESS.2025.3552157}, journal = {IEEE Access}, pages = {48502-48519}, shortjournal = {IEEE Access}, title = {Runtime verification for visual deep learning systems with loss prediction}, volume = {13}, year = {2025}, }
• (2025). A unified framework for super-resolution via clean image prior. ACCESS, 13, 48488-48501. (www)

  @article{ , abstract = {Super-resolution (SR) methods aim to generate a clean, high-resolution (HR) image from a low-resolution (LR) image containing unknown degradations. Previous super-resolution approaches typically follow a two-stage framework, estimating degradation-related information, and then, restoring HR images based on the estimated information. However, such approaches lack generalization ability to various degradations and have difficulties in fully leveraging the information of degradation. This paper deals with the SR problem from a new perspective: learning clean image prior instead of degradation-specific information. Specifically, we introduce the clean feature extraction (CFE) module that generates clean image features from degraded inputs. Subsequently, the efficient frequency decomposition SR (EFDSR) module is applied to produce clean HR images from the extracted clean features. Our EFDSR module is designed to decompose feature into different frequency bands and focus on more informative components, resulting to produce fine SR results. Furthermore, we introduce a two-phase training pipeline for our framework, allowing the joint training in an end-to-end manner. Experimental results show that our SR framework outperforms state-of-the-art (SOTA) performance not only on LR images with unknown blur but also for various degradations such as noise and JPEG compression artifact, e.g., achieved 29.91dB PSNR on DIV2K validation dataset (with random anisotropic Gaussian blur), exceeding the previous SOTA method by 0.20dB. We further show that our SR framework produces the closest results to the true HR images, without unpleasant distortion, for severely degraded images by fully learning the mapping from degraded LR images to clean HR images.}, archive = {J_ACCESS}, author = {Songju Na and Yoonchan Nam and Suk-Ju Kang}, doi = {10.1109/ACCESS.2025.3551213}, journal = {IEEE Access}, pages = {48488-48501}, shortjournal = {IEEE Access}, title = {A unified framework for super-resolution via clean image prior}, volume = {13}, year = {2025}, }
• (2025). Aggregation-based ensemble classifier versus neural networks models for recognizing phishing attacks. ACCESS, 13, 48469-48487. (www)

  @article{ , abstract = {This contribution proposes a classifier designed to reduce the number of false positive detections. It is a self-tuning model, tested in the context of phishing link detection. In this prediction model diverse types of aggregation functions and time-series data periods are used. Aggregation functions are employed to integrate the prediction values of classification models applied in the email phishing problem. Dividing a dataset into subsets allows for incremental learning strategies. This makes it possible to gradually improve the model by using previously acquired knowledge when training on new data. The aim of the contribution is to discuss the problem of obtaining minimal value of FPR while simultaneously getting maximal value of TPR. We applied the proposed ensemble model and neural networks models which were adjusted to the incremental learning strategy (and as base models were applied typical examples of incremental learning models). The study analyzes the dataset provided by FreshMail company. The reason to consider this problem arose with the real-life problem of the Freshamil Company and the data provided by this Company. This dataset uniquely fulfills the criteria essential for our experiments. Unlike other phishing datasets, this data provides dates which is important for the incremental learning approach. The proposed approach of ensemble learning models, based on aggregation functions, is compared to the well-known neural network models which may be treated as state of the art models in recognizing phishing attacks. The main advantage of the proposed algorithm is achieving high numbers of true positives while simultaneously achieving relatively small number of false positives. According to the statistical tests, for some of the desired TPR levels, the proposed model obtained significantly better by a few percentage points results than neural network models. It mitigates the cost arising from the manual analysis of these cases by domain experts.}, archive = {J_ACCESS}, author = {Wojciech Gałka and Jan G. Bazan and Urszula Bentkowska and Kamil Szwed and Marcin Mrukowicz and Paweł Drygaś and Lech Zarȩba and Marcin Szpyrka and Piotr Suszalski and Sebastian Obara}, doi = {10.1109/ACCESS.2025.3551764}, journal = {IEEE Access}, pages = {48469-48487}, shortjournal = {IEEE Access}, title = {Aggregation-based ensemble classifier versus neural networks models for recognizing phishing attacks}, volume = {13}, year = {2025}, }
• (2025). Near miss detection using distancing monitoring and distance-based proximal indicators. ACCESS, 13, 48449-48468. (www)

  @article{ , abstract = {Despite efforts to improve road safety, accidents persist due to insufficient evidence from manual police reporting, non-optimized detection algorithms, and technical limitations in real-time video processing and modelling. This study focuses on detecting and tracking vehicles within a monitoring system and analyzing near-miss incidents (black spot and unseen area), specifically examining the influence of video quality on detection performance using advanced model detectors (YOLOv4-tiny, YOLOv5, YOLOv7, and YOLOv7+CNeB). The experiment employed methods for vehicle detection through the monitoring system. Near-miss detection was conducted using two approaches: manual observation (Social Distancing Monitoring and Bird’s Eye View) and automatic calculation (using DN indicators). Statistical methods, including descriptive statistics, and one-way ANOVA, were applied to compare datasets obtained from these indicators. The study concludes that YOLOv7+CNeB is effective for vehicle detection and near-miss analysis when video quality is considered in system design and implementation. YOLOv7+CNeB significantly reduces the time required to collect evidence from specific roads, provides visual reports, and addresses technical limitations in current algorithms. Future research should explore additional factors contributing to near-miss events, such as road environment, lane changes, and driver behaviours.}, archive = {J_ACCESS}, author = {Lek Ming Lim and Lu Yang and Wen Zhu and Ahmad Sufril Azlan Mohamed and Majid Khan Majahar Ali}, doi = {10.1109/ACCESS.2025.3548108}, journal = {IEEE Access}, pages = {48449-48468}, shortjournal = {IEEE Access}, title = {Near miss detection using distancing monitoring and distance-based proximal indicators}, volume = {13}, year = {2025}, }
• (2025). A rotated object detection model with feature redundancy optimization for coronary athero-sclerotic plaque detection. ACCESS, 13, 48431-48448. (www)

  @article{ , abstract = {Coronary atherosclerotic plaques are a major cause of cardiovascular disease (CVD). Although there have been significant advancements in plaque-assisted diagnosis by deep learning methods utilizing intravascular optical coherence tomography (IVOCT), images still contain redundant features, including residual blood, thrombus, and extensive black backgrounds. Furthermore, the feature fusion process produces redundant information. These redundant features interfere with plaque feature extraction, resulting in decreased performance and increased computational complexity. Current methods for addressing redundant features rely mostly on simple feature selection or reduction methods, overlooking the significance of redundant features in model training. The Redundancy Feature Diminishment Strategy (RFDS) is designed to address this issue by dynamically adjusting the importance of redundant features during the training process. Meanwhile, RFDS is introduced into spatial and channel reconstruction convolution (SCConv) to form the enhancement structure RFDS-SCConv, which refines feature extraction by adaptively regulating redundant features in both spatial and channel dimensions. Based on RFDS-SCConv, a lightweight plaque detection framework, YOLO-Plaque, is proposed. RFDS-SCConv and FasterBlock are integrated into the framework to substantially reduce the interference of redundant features during feature extraction and fusion. This study evaluates the robustness of the YOLO-Plaque model by tenfold cross-validation utilizing 2504 IVOCT images collected from a collaborative project between the School of Software at Shanxi Agricultural University and Shanxi Provincial Coal Centre Hospital. Experimental results demonstrate that YOLO-Plaque performs better than existing plaque detection models while remaining lightweight. The vital metric mAP50 has increased by 3.59% reaching 91.23%, while GFLOPs and parameters decreased to 7.2 and 2.55M, respectively.}, archive = {J_ACCESS}, author = {Xue Hao and Haza Nuzly Abdull Hamed and Qichen Su and Xin Dai and Linqiang Deng}, doi = {10.1109/ACCESS.2025.3538168}, journal = {IEEE Access}, pages = {48431-48448}, shortjournal = {IEEE Access}, title = {A rotated object detection model with feature redundancy optimization for coronary athero-sclerotic plaque detection}, volume = {13}, year = {2025}, }
• (2025). Please stop knocking on my door: An empirical study on opt-out of internet-wide scanning. ACCESS, 13, 48416-48430. (www)

  @article{ , abstract = {Internet-wide scanning is prevalent due to the availability and widespread adoption of high-speed scanning tools, e.g., ZMap and Masscan, which can be used to perform Internet census tasks. However, benign scanning traffic can create undesirable noise for network administrators or researchers monitoring network traffic for security-related events. To mitigate the negative effects, previous studies have proposed best practices to guide ethical and well-regulated Internet-wide scans. In this paper, we are the first to shed light on the practicality of these best practices, with a primary focus on opt-out practices. By analyzing large-scale darknet traffic, we identify 46 scan organizations, including some that have not been reported in previous studies. We found that nearly 70% of the scanners we considered to be for survey purposes did not reveal their identity. In addition, we demonstrated that among scanners with identifiable identities, approximately 50% did not implement effective opt-out measures, which suggests that the effectiveness of opt-out practices is limited. Furthermore, only seven scanners confirmed that an opt-out request was sent from a legitimate administrator, indicating a challenge in terms of verifying the authenticity of opt-out requests. Based on these findings and reactions from scanning organizations, we revisit best practices for scanning organizations and recipients to facilitate effective and sustainable Internet-wide scanning practices.}, archive = {J_ACCESS}, author = {Takahiro Kasama and Yukiko Endo and Masaki Kubo and Daisuke Inoue}, doi = {10.1109/ACCESS.2025.3551691}, journal = {IEEE Access}, pages = {48416-48430}, shortjournal = {IEEE Access}, title = {Please stop knocking on my door: An empirical study on opt-out of internet-wide scanning}, volume = {13}, year = {2025}, }
• (2025). AI-driven optimization of low-energy IoT protocols for scalable and efficient smart healthcare systems. ACCESS, 13, 48401-48415. (www)

  @article{ , abstract = {The Internet of Things (IoT) promises a hyperconnected world, integrating billions of devices across various domains, including smart healthcare systems. Central to this connectivity are low-energy communication protocols, essential for prolonging the battery life of resource-constrained IoT devices and ensuring efficient data exchange. This paper introduces a novel AI-driven optimization framework to enhance these protocols’ energy efficiency, scalability, and adaptability, specifically for smart healthcare applications. Unlike previous works that focus on protocol optimization in isolation, this study provides a comprehensive analysis of protocols, including Bluetooth Low Energy (BLE), Zigbee, Thread, Long Range (LoRa), Sigfox, Narrowband Internet of Things (NB-IoT), Wireless Smart Utility Network (Wi-SUN), and Weightless, highlighting their strengths and limitations. The framework utilizes advanced AI techniques, such as machine learning (ML), reinforcement learning (RL), and deep learning (DL), to optimize key metrics like range, data rate, and power consumption. Quantitative evaluations show significant improvements in performance and trade-offs, particularly for healthcare scenarios such as wearable devices and remote monitoring. This research bridges the gap between theoretical advancements and practical deployments, aligning optimizations with real-world constraints and healthcare needs and paving the way for scalable IoT solutions.}, archive = {J_ACCESS}, author = {Salma Rattal and Abdelmajid Badri and Mohamed Moughit and El Miloud Ar-Reyouchi and Kamal Ghoumid}, doi = {10.1109/ACCESS.2025.3551224}, journal = {IEEE Access}, pages = {48401-48415}, shortjournal = {IEEE Access}, title = {AI-driven optimization of low-energy IoT protocols for scalable and efficient smart healthcare systems}, volume = {13}, year = {2025}, }
• (2025). A review of metal surface defect detection technologies in industrial applications. ACCESS, 13, 48380-48400. (www)

  @article{ , abstract = {Surface defects, including cracks, scratches, and deformations, significantly affect the product performance and service life in industrial manufacturing. Accurate detection of metal surface defects ensures product quality and reliability. Traditional nondestructive testing (NDT) methods, such as ultrasonic and eddy current inspections, are effective but face challenges in terms of efficiency and scalability for industrial applications. This review examines the recent advancements in automated defect detection, focusing on image acquisition, image processing, and detection techniques. The study also evaluates state-of-the-art machine learning and deep learning methods used in defect detection, including the Symmetric Convolutional Network (SCN), Swin Transformer You Only Look Once (ST-YOLO), and Statistical Texture Feature Enhancement Network (STFE-Net), which achieve high accuracy in defect classification and detection. In addition, advanced three-dimensional (3D) detection methods such as photometric stereo, light-field imaging, and structured light have been explored for their potential to address challenges such as real-time detection, few-shot detection, and small-target detection. The study concludes by identifying current limitations and proposing future directions for enhancing industrial defect detection systems.}, archive = {J_ACCESS}, author = {Qi Qiao and Huiying Hu and Azlin Ahmad and Ke Wang}, doi = {10.1109/ACCESS.2025.3544578}, journal = {IEEE Access}, pages = {48380-48400}, shortjournal = {IEEE Access}, title = {A review of metal surface defect detection technologies in industrial applications}, volume = {13}, year = {2025}, }
• (2025). Presenting an order-aware multimodal fusion framework for financial advisory summarization with an exclusive video dataset. ACCESS, 13, 48367-48379. (www)

  @article{ , abstract = {Amidst the current digital era, global entrepreneurship and financial awareness dissemination has surged via online podcasts and videos showcasing insightful expertise from diverse financial domain professionals. However, existing financial summarization techniques predominantly focus on textual and numerical data, neglecting the potential of a time-saving multimodal data. Addressing this gap, we introduce FinMSG, a pioneering framework for generating concise and informative summaries from lengthy financial expert videos. Leveraging a multimodal transformer-based architecture and an ordered-aware fusion algorithm, FinMSG processes text, audio, and video features to distil key opinions and insights from diverse financial domains. Subsequently, we present FAV, a one-of-its-kind multimodal financial advice video corpus comprising 420 videos across diverse domains, with gold-standard summary annotations. Through extensive experimentation and human evaluation, we demonstrate the efficacy of FinMSG in producing high-quality financial summaries while also investigating the interplay between different modalities. In addition, we investigated the capabilities of widely recognized small language models, such as BART and T5, alongside advanced large language models, including LLaMA-2 and GPT-3.5, to evaluate their proficiency in handling financial tasks within a multimodal configuration. By offering a transparent and time-efficient means for laypeople to access and comprehend finance insights, our work represents a significant advancement in multimodal financial summarisation. Code and dataset are available at (https://github.com/sarmistha-D/Fin-OAF).}, archive = {J_ACCESS}, author = {Sarmistha Das and Samrat Ghosh and Abhisek Tiwari and R. E. Zera Marveen Lynghoi and Sriparna Saha and Zak Murad and Alka Maurya}, doi = {10.1109/ACCESS.2025.3551124}, journal = {IEEE Access}, pages = {48367-48379}, shortjournal = {IEEE Access}, title = {Presenting an order-aware multimodal fusion framework for financial advisory summarization with an exclusive video dataset}, volume = {13}, year = {2025}, }
• (2025). Polarization reconfigurable, single-layer, 2-d, monopulse array antenna with compact comparator circuit. ACCESS, 13, 48359-48366. (www)

  @article{ , abstract = {A single layered, planar array antenna is presented, aiming to replace the complex geometry of monopulse comparator circuits which are used in conventional corporate-fed monopulse antenna systems for direction-finding applications. This work investigates a new configuration of corporate-fed patch array antenna, in order to reduce the insertion loss of monopulse comparator circuit while utilizing only two 3 dB/180° hybrid couplers as monopulse comparator network. This antenna exhibits the sum ( $\Sigma $ ) pattern with dual (linear, slant and circular) polarization whereas the difference ( $\Delta $ ) patterns exhibit dual-linear polarization, without compromising the radiation performance and electrical size. The proposed monopulse comparator configuration leads to reduced insertion loss of only 3 dB. The measured peak gain is recorded to be 8.5 dBi at 10.04 GHz with minimum null-depth of -18 dB for the difference-patterns. The overall size of the antenna system is only $1.93\mathrm {\lambda }_{\mathrm {o}} \times 2.26\mathrm {\lambda }_{\mathrm {o}} \times 0.0254\mathrm {\lambda }_{\mathrm {o}}$ making the proposed single-layered structure a potential candidate for un-ambiguous, dual-axis monopulse direction-finding and polarimetric radar applications.}, archive = {J_ACCESS}, author = {Noman Ahmad and Muhammad Waseem and Nosherwan Shoaib and Haq Nawaz and Sultan Shoaib and Khaled A. Aljaloud and Rifaqat Hussain and Symeon Nikolaou}, doi = {10.1109/ACCESS.2025.3549971}, journal = {IEEE Access}, pages = {48359-48366}, shortjournal = {IEEE Access}, title = {Polarization reconfigurable, single-layer, 2-d, monopulse array antenna with compact comparator circuit}, volume = {13}, year = {2025}, }
• (2025). Leveraging cyberattack news tweets for advanced threat detection and classification using ensemble of deep learning models with wolverine optimization algorithm. ACCESS, 13, 48343-48358. (www)

  @article{ , abstract = {At present, cyber-attacks have become more critical and familiar, which appeals to a novel line of security defences to defend against them. Cyber Threat Intelligence (CTI) originated as a reputation in the frequently developing cybersecurity landscape, vital in safeguarding digital models. Understanding this domain develops over a complete study of intelligence’s numerous features and sources. Data sharing and analysis centres perform as alarms of collaboration, demonstrating the collective vigilance needed to oppose such developing attacks. Inspecting and gathering data about cyberattacks from tweets can effectively deliver critical perceptions of the threats, their effects, occurrence areas, and probable mitigation tactics. Existing study on cyberattack absences in establishing Artificial Intelligence (AI) based analytic solutions for delivering country-wide cyber-attack intelligence. Cyber planners at a domestic level need AI-based decision support models to determine a country’s cyber attitude or vigilance. This study designs and develops an Enhanced Threat Intelligence for Cybersecurity Using an Ensemble of Deep Leaning Models with Metaheuristic Optimization Algorithm (ETIC-EDLMOA) model. The presented ETIC-EDLMOA model’s main aim is to detect and mitigate network attacks in cybersecurity effectively. Initially, the ETIC-EDLMOA model undergoes a data pre-processing stage to ensure clean and structured input data for analysis. Besides, the Word2vec model is utilized for feature extraction. For the classification process, the ensemble of DL models is employed, including the recurrent neural network (RNN) method, long short-term memory (LSTM) model, and conditional variational autoencoders (CVAE) technique. Finally, the ensemble models’ hyperparameter fine-tuning process is performed using the Wolverine optimization algorithm (WoOA) technique. A comprehensive range of simulation analyses is conducted to ensure the improved performance of the ETIC-EDLMOA method on the CybAttT dataset. The comparison study of the ETIC-EDLMOA method illustrated a superior accuracy value of 98.51% over existing techniques.}, archive = {J_ACCESS}, author = {Sripada Nsvsc Ramesh and Bader Mohammed M. Al Fardan and C. S. S. Anupama and Kollati Vijaya Kumar and Seongsoo Cho and Srijana Acharya and Cheolhee Yoon}, doi = {10.1109/ACCESS.2025.3550378}, journal = {IEEE Access}, pages = {48343-48358}, shortjournal = {IEEE Access}, title = {Leveraging cyberattack news tweets for advanced threat detection and classification using ensemble of deep learning models with wolverine optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Cancer cell classification based on morphological features of 3D phase contrast microscopy using deep neural network. ACCESS, 13, 48335-48342. (www)

  @article{ , abstract = {3D phase contrast microscopy is one of the most common imaging modalities for the observation of long-term multicellular processes of living cells without phototoxicity and photobleaching, because the morphological features of cancer cells can be used as an indicator of metastasizing behavior. However, image features such as non-uniform illumination and phase contrast interference rings pose certain difficulties in analyzing these images. We propose a cancer cell classification methodology based on morphological features of 3D phase contrast microscopy and deep neural network with scaled principal component analysis. We initially apply non-uniform illumination correction based on the histogram information of images to correct unstable brightness problems in images and an image intensity-based global thresholding method to compensate for row-contrast artifacts via single-cell detection. We also extracted cross-sections to observe the morphological features using principal component analysis because of the nonsymmetric diffusion pattern of the interference that appeared around each cell. Then, the cell morphologies from an intensity gradient, considering local peaks as bright ring regions, were analyzed. The peak was calculated from the intensity profile from the center point of the cell area, which was the center of the extracted section, to the outer background. Based on the peak information, we extracted representative ten morphological features, applied a min-max scaler to convert the initial features, and used a deep neural network to classify active and inactive cancer cells. The proposed method achieved an area under the receiver operating characteristic curve value of 0.944 and an equal error rate of 0.091. We confirmed that the accuracy of classification using DNN with the proposed method was closer to the results of manual classification by experts, enabling a more precise analysis of cell morphology. This approach improves the accuracy of image-based cellular phenotypic profiling for assessing drug responses in patients.}, archive = {J_ACCESS}, author = {Misun Kang and Jungyoon Kim}, doi = {10.1109/ACCESS.2025.3547711}, journal = {IEEE Access}, pages = {48335-48342}, shortjournal = {IEEE Access}, title = {Cancer cell classification based on morphological features of 3D phase contrast microscopy using deep neural network}, volume = {13}, year = {2025}, }
• (2025). Transfer learning-based ensemble approach for rainfall class amount prediction. ACCESS, 13, 48318-48334. (www)

  @article{ , abstract = {Predicting short-term precipitation amounts is challenging, especially due to meteorological data scarcity. While deep learning-based models have been shown to be more effective in predicting precipitation amounts, their performance heavily relies on the size of the training datasets. This paper presents a multi-station-based transfer learning ensemble approach to mitigate the data scarcity problem by transferring knowledge learned from multiple meteorological station datasets to a single target station. To achieve this, multi-layer perceptron, convolutional neural networks, and long-short-term memory (LSTM) systems were trained on weather station datasets from the Lake Victoria Basin (LVB). From the experiments, the LSTM model outperformed other state-of-the-art models achieving high F1 scores across individual stations. Fine-tuning pre-trained models for the target station demonstrated improved accuracy, with performance gains of up to 5%. Additionally, the ensemble of these models further enhanced performance, delivering highly accurate classification results. Summarily, the proposed ensemble approach demonstrates significant improvements in predicting rainfall class amounts, offering a robust solution for precipitation forecasting in data-scarce regions like the LVB.}, archive = {J_ACCESS}, author = {Tumusiime Andrew Gahwera and Odongo Steven Eyobu and Mugume Isaac and Samuel Kakuba and Dong Seog Han}, doi = {10.1109/ACCESS.2025.3551737}, journal = {IEEE Access}, pages = {48318-48334}, shortjournal = {IEEE Access}, title = {Transfer learning-based ensemble approach for rainfall class amount prediction}, volume = {13}, year = {2025}, }
• (2025). Constructing composite indicators through extreme values reductions-ordered weighted averaging: Human development index. ACCESS, 13, 48306-48317. (www)

  @article{ , abstract = {The Human Development Index is a complex social phenomenon composed of multiple criteria. While many studies are concerned with dealing with outliers in the representation of this composite indicator, the problem of extreme values is overlooked. This study falls within the multi-criteria decision framework aimed at introducing a new approach to constructing composite indicators specifically that addresses the issue of extreme values, called the Extreme Values Reductions-Ordered Weighted Averaging approach. The study findings reveal that popular methods such as Principal Component Analysis, Benefit of the Doubt, Entropy, and Equal Weights ignore outliers and bias the composite indicator scores. The Extreme Value Reduction approach provides an unbiased representation of the Human Development Index, preventing extreme lower and higher values from uncovering weaknesses and unmasking strengths of the decision-making units. The possibility of adjusting the weights of the intermediate values also provides the opportunity to reveal nuances that are difficult to detect by controlling the coefficient of variation of the composite indicator scores.}, archive = {J_ACCESS}, author = {Matheus Pereira Libório and Petr Ekel and Marcos Flávio Silveira Vasconcelos D’Angelo and Luis Martínez and Hamid Rabiei}, doi = {10.1109/ACCESS.2025.3546529}, journal = {IEEE Access}, pages = {48306-48317}, shortjournal = {IEEE Access}, title = {Constructing composite indicators through extreme values reductions-ordered weighted averaging: Human development index}, volume = {13}, year = {2025}, }
• (2025). A study on the performance evaluation of F-type multilevel inverters employing phase disposition carrier PWM schemes. ACCESS, 13, 48286-48305. (www)

  @article{ , abstract = {This research presents a three-phase, three-level F-type inverter utilizing a four switch per leg and a DC link. The proposed structure necessitates less power components than traditional three-level inverter topologies, hence reducing switching stress and loss as thereby decreasing the topology cost. The suggested inverter employs a carrier-based pulse width modulation (PWM) technique, including Sine PWM, Sixty-Degree PWM, and Switching Frequency Optimal (SFO) PWM, to achieve the necessary output voltage. The inverter operates with various amplitude and frequency modulation indices (ma, m $_{\mathrm {f}}$ ), and its performance is evaluated based on total harmonic distortion (THD), fundamental output voltage, inverter losses, and efficiency. To demonstrate the efficacy of the proposed configuration, a 400 W prototype of the inverter is developed, and empirical findings are provided. The suggested inverter demonstrates exceptional performance with a modulation index of m $_{\mathrm {a}}=0.9$ and m $_{\mathrm {f}}=100$ under SFO PWM, enabling improved THD performance, increased output voltage, and greater efficiency. As the switching frequency increases, a greater portion of the harmonic spectrum shifts away from the fundamental frequency, so assuring that the SFO PWM technique requires reduced filter size.}, archive = {J_ACCESS}, author = {Meenakshi Madhavan and Chellammal Nallaperumal and Rajesh Verma and Bharatiraja Chokkalingam and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3549589}, journal = {IEEE Access}, pages = {48286-48305}, shortjournal = {IEEE Access}, title = {A study on the performance evaluation of F-type multilevel inverters employing phase disposition carrier PWM schemes}, volume = {13}, year = {2025}, }
• (2025). Boundary-aware cross-level multi-scale fusion network for RGB-D salient object detection. ACCESS, 13, 48271-48285. (www)

  @article{ , abstract = {Accurate salient object detection is of great importance in many computer vision applications. However, due to scale variation and complex backgrounds, achieving effective detection of objects at different scales in various scenes remains a challenging task. To address this, we propose a novel Boundary-Aware Cross-Level Multi-Scale Fusion Network (BCMNet), which enhances salient object detection by fully exploiting cross-level and multi-scale features. Specifically, we propose a Cross-Attention Fusion Module (CAFM) to fuse two modality features, generating modality fusion features. Next, a Boundary-Aware Module (BAM) combines low-level features with high-level features to learn boundary-aware features, which are integrated into each decoding unit during the decoding process. During the decoding stage, a Bidirectional Cross-Level Multi-Scale Module (BCMM) is introduced to effectively integrate cross-level features and perform multi-scale learning. Finally, the output of the BCMM, combined with boundary-aware features, generates saliency prediction maps. We conduct extensive experiments on six datasets, and the experimental results show that, compared to the state-of-the-art methods, the proposed model improves MAE, maxF, maxE, and S metrics by $0\sim 8$ %, $0\sim 1.34$ %, 0.11%~0.54%, and $0\sim 0.45$ %, respectively.}, archive = {J_ACCESS}, author = {Zhijun Zheng and Yanbin Peng}, doi = {10.1109/ACCESS.2025.3549945}, journal = {IEEE Access}, pages = {48271-48285}, shortjournal = {IEEE Access}, title = {Boundary-aware cross-level multi-scale fusion network for RGB-D salient object detection}, volume = {13}, year = {2025}, }
• (2025). Deep learning for facial expression and human activity recognition using smart glasses. ACCESS, 13, 48257-48270. (www)

  @article{ , abstract = {Facial Expression Recognition (FER) and Human Activity Recognition (HAR) are key areas in wearable computing, with applications in healthcare, fitness, and human-computer interaction. This study explores the use of Emteq’s OCOsense smart glasses for FER and HAR, which incorporate optomyographic sensors and a 9-axis inertial measurement unit (IMU). Using deep learning (DL) models, including CNNs, ConvLSTM, and STResNet variants, the study compares the performance of FER for six expression classes and HAR for seven different activities. We investigate the impact of the ConvBoost framework on model performance, extending its use beyond HAR with IMU data and introducing end-to-end learning for FER using OCOsense smart glasses. Our findings confirm that ConvBoost effectively improves performance of complex overfitting-prone models. Furthermore, the results also reveal that using ConvBoost in simpler models, such as the 1-dimensional convolutional neural network (CNN 1D), leads to a decrease in F1-score. The best performing models, Attention ConvLSTM for FER and ConvLSTM for HAR (in combination with ConvBoost), achieved a macro F1-score of 0.78 and 0.88, respectively. These results highlight the potential of smart glasses for FER and HAR, establishing benchmark results for future advancements in glasses-based wearable computing.}, archive = {J_ACCESS}, author = {Matea Marinova and Emilija Chona and Andrej Kotevski and Borjan Sazdov and Ivana Kiprijanovska and Simon Stankoski and Martin Gjoreski and Charles Nduka and Hristijan Gjoreski}, doi = {10.1109/ACCESS.2025.3551610}, journal = {IEEE Access}, pages = {48257-48270}, shortjournal = {IEEE Access}, title = {Deep learning for facial expression and human activity recognition using smart glasses}, volume = {13}, year = {2025}, }
• (2025). Forecasting soybean futures prices with adaptive AI models. ACCESS, 13, 48239-48256. (www)

  @article{ , abstract = {The volatility of soybean prices in international futures markets presents significant challenges for stakeholders, including policymakers, producers, and investors. Traditional econometric and time-series models often struggle to capture complex, nonlinear market dynamics, while deep learning models require extensive data and tuning for optimal performance. This study addresses this gap by developing adaptive machine learning models tailored for soybean price forecasting, leveraging historical data from 2020 to 2024, global market reports, and external economic indicators such as exchange rates, crude oil prices, and climate data. Advanced forecasting methods, including Gradient Boosting, Random Forest, ARIMAX, and LSTM, are evaluated alongside a naïve baseline model to benchmark improvements in predictive accuracy. A comparative analysis based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) identifies Gradient Boosting as the most accurate model, achieving the lowest MAPE of 5.8239%, significantly outperforming deep learning models such as LSTM (MAPE =10.5665%) and ANN (MAPE =26.6723%). Compared to the naïve forecasting model (MAPE =48.0720%), Gradient Boosting reduces error by 87.88%, demonstrating its superior capability in capturing price fluctuations and short-term trends. This study introduces a novel integration of external economic indicators with adaptive feature selection techniques (PCA and L1 Regularization) to improve model interpretability and computational efficiency. Additionally, a rolling update mechanism is implemented, allowing the models to dynamically adapt to evolving market conditions an approach not commonly explored in prior soybean price forecasting studies. These enhancements distinguish this research from existing static forecasting methodologies, improving both accuracy and practical applicability for decision-makers.}, archive = {J_ACCESS}, author = {Puntaree Srichaiyan and Korrakot Yaibuathet Tippayawong and Anuwat Boonprasope}, doi = {10.1109/ACCESS.2025.3546786}, journal = {IEEE Access}, pages = {48239-48256}, shortjournal = {IEEE Access}, title = {Forecasting soybean futures prices with adaptive AI models}, volume = {13}, year = {2025}, }
• (2025). An accurate deep key-point prediction model with low-level texture refinement and high-level semantic enhancement for bolt vertex detection in industrial machine systems. ACCESS, 13, 48226-48238. (www)

  @article{ , abstract = {The safety status of intelligent monitoring bolts is crucial for ensuring the stability of industrial building and structural systems. Considering the complexity of the industrial production environment, the collected bolt images may have diverse and complex features, which will seriously affect the recognition accuracy of bolt vertices and easily lead to false alarms and missed alarms of bolt looseness. To address these challenges, this paper proposes a bolt key-point detection method guided by masked-based dual-views model with texture refinement and semantic enhancement. Firstly, inspired by multimodal technology, a dual-views deep fusion method is designed based on the original and relief views to fully utilize their respective advantages to obtain robust key features. Secondly, low-level texture refinement and high-level semantic enhancement modules are designed to improve the edge texture and high-level semantic details of the bolt area, respectively. Finally, we established the gradient update of the above network for key-point recognition loss, target detection loss and cross entropy loss. In addition, we introduced a masked-based unsupervised pre-training paradigm based on convolutional structure to enhance the feature representation ability of the above model. In experiments and discussions, we analyzed the effectiveness of the proposed method in bolt vertex recognition tasks and achieved 0.987 AP and 0.743 Acc. In real-world application cases, a large number of experimental results have demonstrated the necessity of the proposed method.}, archive = {J_ACCESS}, author = {Jiaqi Liu and Yingbo Wang and Mingyue Lang and Fengyuan Zuo}, doi = {10.1109/ACCESS.2025.3547332}, journal = {IEEE Access}, pages = {48226-48238}, shortjournal = {IEEE Access}, title = {An accurate deep key-point prediction model with low-level texture refinement and high-level semantic enhancement for bolt vertex detection in industrial machine systems}, volume = {13}, year = {2025}, }
• (2025). Detailed architectural design of a multi-head self-attention model for lithium-ion battery capacity forecasting. ACCESS, 13, 48212-48225. (www)

  @article{ , abstract = {As the adoption of lithium-ion batteries increases, concerns over safety incidents and replacement costs have become increasingly pressing. Accurate battery state prediction is thereby vital for reducing costs and ensuring safety. This paper introduces a novel system based on a transformer encoder architecture, leveraging Multi-Head Self-Attention (MHSA) layers to enhance battery capacity prediction. To address variability in battery data collection, we implement robust preprocessing techniques and a sliding window method to standardize data input. Positional encoding is applied to embed sequence order information at the input stage, while residual connections and layer normalization between MHSA layers optimize the learning process. Final predictions are generated through a fully connected neural network (FNN). Experimental results on the NASA dataset, obtained from repeated charge-discharge cycles, demonstrate that the proposed model mostly achieves superior predictive accuracy across multiple cell datasets, as measured by MAPE and RMSE. The proposed model showed enhanced performance, achieving a MAPE of 0.9918% and an RMSE of 0.02542 in experiments on specific cells. Furthermore, compared to other models in most experiments, it showed a performance improvement of at least 41% in MAPE and 29% in RMSE. Notably, the model maintains excellent performance while reducing complexity. Additionally, to prevent overestimation of overall performance due to high accuracy in only the early stages, we conducted interval-based performance evaluations, confirming that the proposed model consistently provides accurate predictions with minimal variance across different stages compared to other models.}, archive = {J_ACCESS}, author = {Juyeon Park and Gyeong Ho Lee and Jangkyum Kim and Yoon-Sik Yoo and Il-Woo Lee}, doi = {10.1109/ACCESS.2025.3549402}, journal = {IEEE Access}, pages = {48212-48225}, shortjournal = {IEEE Access}, title = {Detailed architectural design of a multi-head self-attention model for lithium-ion battery capacity forecasting}, volume = {13}, year = {2025}, }
• (2025). Active disturbance rejection control with data-driven compensation for a novel extensible continuum robot. ACCESS, 13, 48202-48211. (www)

  @article{ , abstract = {Continuum robots (CRs) are widely used in minimally invasive surgery due to their inherent flexibility, compliance, and ease of miniaturization. To overcome the limitations of existing CRs in terms of operation dexterity, we design a novel extensible CR that combines spring structures and rod actuation. This design features a slim profile, large working channels, and the ability to independently control both bending length and curvature, enabling a wide range of configurations and enhanced dexterity. On the other hand, due to the nonlinear deformation and infinite degrees of freedom of the CR, controlling the robot presents many challenges, including nonlinearity and uncertainties. Thus, we propose an active disturbance rejection control with data-driven compensation (ADRC-DDC). This method uses a constant curvature model to capture the main dynamic behavior of the CR and combines data-driven disturbance estimation and disturbance estimation from an extended state observer (ESO). The proposed method can better handle system uncertainties, alleviate the burden on the ESO, and improve both robustness and control performance. Experimental results demonstrate the superiority of the designed extensible CR and the proposed ADRC-DDC method.}, archive = {J_ACCESS}, author = {Zefeng Liu and Wentuo Yang and Yongfeng Cao and Le Xie}, doi = {10.1109/ACCESS.2025.3550093}, journal = {IEEE Access}, pages = {48202-48211}, shortjournal = {IEEE Access}, title = {Active disturbance rejection control with data-driven compensation for a novel extensible continuum robot}, volume = {13}, year = {2025}, }
• (2025). Edge-YOLO: Lightweight multi-scale feature extraction for industrial surface inspection. ACCESS, 13, 48188-48201. (www)

  @article{ , abstract = {The increasing complexity of industrial quality control necessitates advanced defect detection systems capable of identifying small-scale surface anomalies with high precision. While deep learning methods, particularly You Only Look Once (YOLO) architectures, have shown promise in object detection, they frequently encounter limitations when detecting minute defects, primarily due to inadequate feature retention in shallow network layers and computational constraints in industrial settings. This study introduces Edge-YOLO, an innovative network architecture that enhances defect detection through a novel edge-enhanced backbone (Edge-backbone). The Edge-backbone systematically strengthens edge feature extraction and retention through three synergistic components: an Edge-Sensitive (EdgeS) module for optimized feature initialization, a Cross-Stage-Partial Edge Enhancement (C3E2) module that integrates edge information across network stages, and a Multi-Scale Dilated Convolution (MSDC) module that efficiently fuses multi-scale features through weight-sharing. These modules work in concert to create a comprehensive edge-aware feature extraction pipeline. Comprehensive evaluation across three industrial datasets demonstrates Edge-YOLO’s effectiveness, achieving mean Average Precision (mAP@50) improvements of 2.1%, 3.5%, and 10.3% on NEU-DET, GC10-DET, and GEAR-DET datasets respectively, compared to YOLO11. Notably, Edge-YOLO reduces computational complexity by 69.8% while maintaining competitive accuracy, making it particularly suitable for real-time industrial applications requiring both precision and efficiency.}, archive = {J_ACCESS}, author = {Wang Guiqiang and Chen Junbao and Li Chengzhang and Lu Shuo}, doi = {10.1109/ACCESS.2025.3550374}, journal = {IEEE Access}, pages = {48188-48201}, shortjournal = {IEEE Access}, title = {Edge-YOLO: Lightweight multi-scale feature extraction for industrial surface inspection}, volume = {13}, year = {2025}, }
• (2025). Validation for predictive maintenance of aircraft systems using AI models developed with rotor blade vibration data. ACCESS, 13, 48173-48187. (www)

  @article{ , abstract = {This study presents an approach for predictive maintenance of aircraft systems by developing an artificial intelligence (AI) model that utilizes rotor blade vibration data to detect mechanical faults early. A total of 4,921 data samples were collected, with 3,601 used for training and 1,320 for testing. The data consists of time-series structured data collected from 500 sensors, classified into normal 1) and abnormal (−1) states. Performance evaluation results indicate that, the convolutional Neural Network (CNN) model achieved an accuracy of 97%, precision of 98%, recall of 97%, and an F1 score of 97%, demonstrating the highest performance among the evaluated models. This research is expected to enhance the safety of aircraft operations and reduce maintenance costs.}, archive = {J_ACCESS}, author = {Kyu Young Lee and Khurshid Hussain and Inn-Yeal Oh}, doi = {10.1109/ACCESS.2025.3550822}, journal = {IEEE Access}, pages = {48173-48187}, shortjournal = {IEEE Access}, title = {Validation for predictive maintenance of aircraft systems using AI models developed with rotor blade vibration data}, volume = {13}, year = {2025}, }
• (2025). State-of-the-art fault detection and diagnosis in power transformers: A review of machine learning and hybrid methods. ACCESS, 13, 48156-48172. (www)

  @article{ , abstract = {Fault detection and diagnosis (FDD) in power transformers is essential for maintaining reliability and safety in modern power systems. Recent trends in transformer fault diagnosis include the use of advanced monitoring systems and data analysis techniques, and implementing effective FDD strategies can prevent costly repairs, minimize downtime, and enhance the overall reliability of power systems. In this work, a systematic review of FDD in power transformers, focusing on machine learning and hybrid methods applications is presented. The methodology comprised a detailed systematic process undertaken to gather, filter, and analyze relevant research papers using Scopus database, while VOSviewer, and bibliometrix tools were utilized for results analysis. The research findings indicate that there is clear progress in detecting transformer faults, moving from older methods like Dissolved Gas Analysis (DGA) to machine learning models such as Random Forest and Convolutional Neural Networks (CNN). Hybrid models combining machine learning with optimization have made detection more accurate. New tools, including optical sensors, now allow for real-time monitoring. Still, issues like limited data and complex models remain. This study contributes by reviewing how machine learning is applied to transformer fault detection, exploring hybrid methods that combine traditional techniques like DGA with advanced models for better accuracy. It identifies key research patterns, trends, and themes, while also highlighting gaps and offering suggestions for future research to improve diagnostics and monitoring.}, archive = {J_ACCESS}, author = {Lebo Dina Mashifane and Bongumsa Mendu and Bessie Baakanyang Monchusi}, doi = {10.1109/ACCESS.2025.3546861}, journal = {IEEE Access}, pages = {48156-48172}, shortjournal = {IEEE Access}, title = {State-of-the-art fault detection and diagnosis in power transformers: A review of machine learning and hybrid methods}, volume = {13}, year = {2025}, }
• (2025). Image segmentation method for water transport feature detection in fabrics via target-located strategy. ACCESS, 13, 48146-48155. (www)

  @article{ , abstract = {The comfort of apparel, particularly sportswear, is affected by the materials’ liquid water transport properties. Many studies have employed machine vision techniques (taking pictures to document changes in the wet zones of fabric) to investigate the transport characteristics of liquid water in fabrics with various topologies. Machine vision techniques fix the problems of manual detection in terms of cost and efficiency. Still, several issues, such as changes in lighting during the detection process, reflections from water droplets, and wrinkles in the fabric, can lower the segmentation accuracy. To easily differ the wet zones from dry parts of fabrics, a novel segmentation and detection method (called SAD) that combines detection models like YOLOv5 with segmentation models like SAM is proposed, after the comparison with SAD to a variety of detection models, including YOLOv5, DETR, and YOLOv3, YOLOv5 presents an optimal accurate. The results show that the accuracy of calculating the area of wet zone in fabric using the SAM-SAD method reaches 94.07%, which is the same as the manual marking method. It is better than traditional models such as Otsu, Canny, and Watershed. And the time-dependent curve is closer to the actual wetting and evaporation process of fabric, the SAD method is conducive to wet zone segmentation.}, archive = {J_ACCESS}, author = {Yucheng Wang and Huiping Wang and Hang Mao and Suwei Gao and Qiaofeng Wei and Shujing Li and Rangtong Liu and Boyang Xu}, doi = {10.1109/ACCESS.2025.3550260}, journal = {IEEE Access}, pages = {48146-48155}, shortjournal = {IEEE Access}, title = {Image segmentation method for water transport feature detection in fabrics via target-located strategy}, volume = {13}, year = {2025}, }
• (2025). Photogrammetry-driven detection of structural peeling in joints and corners of rigid pavements using an unsupervised learning meta-architecture. ACCESS, 13, 48132-48145. (www)

  @article{ , abstract = {This study proposes the SS-XceVNet meta-architecture, which integrates Xception and VGG16 to detect structural peeling in rigid pavements in an automated manner, thereby reducing the reliance on large, labeled datasets. With increasing traffic and exposure to environmental factors, failures in rigid pavement joints and corners are becoming increasingly frequent, making manual inspections time consuming, dangerous, and subjective. To address this problem, a method was developed in four phases. First, photogrammetry and data augmentation techniques are used to collect and increase the variety of images. Self-supervised learning was then applied to unlabeled data to reduce reliance on manual annotations. SS-XceVNet was then trained on labeled data, combining the robustness of Xception and VGG16 to refine the accuracy. Finally, the results were compared with those of the XceVNet-based model. Evaluations showed that SS-XceVNet achieved 80.43% accuracy, surpassing 47.83% of the base model. This underscores the usefulness of self-supervised learning and integration of advanced architectures in pavement failure detection. In conclusion, this approach improves efficiency in the functional evaluation of road infrastructures while reducing costs and occupational risks, laying the foundation for future research that contributes to the implementation of proactive maintenance systems. Automated detection thus strengthens the quality control of pavements, favoring road safety, and prolonging the useful life of roads.}, archive = {J_ACCESS}, author = {Jose Cruz and Helarf Calsina and Luis Huacasi and Wilson Mamani and Norman Beltran and Flavio Puma and Victor Yana-Mamani and Saul Huaquipaco}, doi = {10.1109/ACCESS.2025.3550021}, journal = {IEEE Access}, pages = {48132-48145}, shortjournal = {IEEE Access}, title = {Photogrammetry-driven detection of structural peeling in joints and corners of rigid pavements using an unsupervised learning meta-architecture}, volume = {13}, year = {2025}, }
• (2025). YOLOV8-MR: An improved lightweight YOLOv8 algorithm for tomato fruit detection. ACCESS, 13, 48120-48131. (www)

  @article{ , abstract = {As one of the widely planted fruit and vegetable crops, real-time monitoring of the tomato growth process is crucial for enhancing production and maintaining quality. To fulfill the requirement for real-time detection, we introduce an enhanced tomato target detection approach utilizing the YOLOv8n model, offering essential technical assistance for both real-time monitoring and automated tomato harvesting. Initially, we introduce Receptive-Field Attention Convolution (RFAConv) in the model’s backbone to replace the original Conv operation, enabling the model to process details and complex patterns in images more effectively. Secondly, to enhance the small target detection ability and feature extraction effect of the model, we design a non-local channel attention mechanism (MHNCA), which directs the model to focus more on important channel information during feature extraction and further enhances feature expression through cross-channel interaction. Finally, the RepNCSPELAN4_low module is used to replace the original Cf2 module in the head layer, which further reduces the model parameters, reduces calculation costs and speeds up the inference speed. To assess the efficacy of our enhanced model, we conducted comparisons with YOLOv7, YOLOv9, YOLOv10, and several other models. Experiments demonstrate that, compared with YOLOv8n, the improved YOLOv8n model shows a 1.7% boost in mAP% value, a 1.6% boost in accuracy, a reduction in model parameters from 3.01M to 2.83M, and a detection speed of 121.13 FPS, which meets the requirements for real-time detection. In comparison to other models, the improved model has enhanced overall performance, providing a theoretical foundation for the tomato detection system and enabling efficient and high quality harvesting.}, archive = {J_ACCESS}, author = {Xu Li and Changhan Cai and Yue Yang and Bo Song}, doi = {10.1109/ACCESS.2025.3533489}, journal = {IEEE Access}, pages = {48120-48131}, shortjournal = {IEEE Access}, title = {YOLOV8-MR: An improved lightweight YOLOv8 algorithm for tomato fruit detection}, volume = {13}, year = {2025}, }
• (2025). An efficient approach based on RAE-GAMI-NET for long range attack detection on blockchain. ACCESS, 13, 48106-48119. (www)

  @article{ , abstract = {Blockchain is a prominent and leading decentralized ledger technology that has gained global attention and adoption across various industries. Long-range attacks (LRAs) are when an adversary attempts to rewrite the blockchain’s history from a point far back in time. Since PoS Blockchain relies on validators’ stakes as a form of security, LRAs can potentially undermine the network’s security if not detected and prevented. In order to protect against long-range attacks, this research suggests a high-performance explainable neural network model that can accurately categorize nodes as malicious or non-malicious while maintaining interpretability. The proposed explainable neural network model includes Residual Auto Encoder (RAE) guided generalized additive models with incorporating structured interactions (RAE-GAMI-Net) for LRA detection in PoS Blockchain In this work, a wrapper-based Binary Orchard Algorithm (W-BOA) is used to find the best features to lessen the dimensionality of extracted Characteristics, and a global feature extraction has been implemented based on multi-scale Densenet (MDensenet) that assures early convergence and optimal performance by providing global optimal solution. Then, the transformed features are used to train the RAE-GAMI-Net-based model to detect the LR attack. The included RAE learns a compressed representation (latent) of the input features. Then, the latent features are classified with GAMI-Net, balancing the model interpretability and accuracy. The effectiveness of our proposed method is assessed using the Proof of Stake blockchain dataset and benchmarked against other deep learning techniques. Our approach yields significant enhancements in accuracy (0.962), precision (0.9614), and recall 0.9604, accompanied by a notably low Brier score of 0.038.}, archive = {J_ACCESS}, author = {Vasavi Chithanuru and Mangayarkarasi Ramaiah}, doi = {10.1109/ACCESS.2025.3551627}, journal = {IEEE Access}, pages = {48106-48119}, shortjournal = {IEEE Access}, title = {An efficient approach based on RAE-GAMI-NET for long range attack detection on blockchain}, volume = {13}, year = {2025}, }
• (2025). Monitoring method for GIS electrical contact state based on VMD-hilbert marginal spectrum energy entropy. ACCESS, 13, 48096-48105. (www)

  @article{ , abstract = {To diagnose the electrical contact state of gas-insulated switchgear (GIS), the monitoring method based on variational mode decomposition (VMD) and Hilbert marginal spectrum energy entropy is proposed. The multi-physical field coupling model of GIS is established, and vibration signals under various states and operating currents are simulated. The GIS prototype and laboratory test platform are developed, and vibration tests are conducted under four different electrical contact states. Experimental results indicate that at the same contact state, with the current increases, the vibration amplitude of the GIS contact shows an increasing trend; under the same current, with the deterioration of the contact state, the vibration amplitude of the GIS contact shows an increasing trend. Further, the method of GIS contact state diagnosis based on VMD-Hilbert spectrum energy entropy is proposed. The online monitoring experiment is carried out in the substation. The results indicate that, aiming at the abnormal contact state of GIS, the energy entropy of the intrinsic mode function 6(IMF6) is 0.704, below 0.7368, while that of IMF7 is 0.2176, exceeding 0.187, which indicates a mechanical fault state. The conclusions show that the monitoring method can accurately identify the electrical contact state of GIS.}, archive = {J_ACCESS}, author = {Chen Cao and Jialin Wang and Qingli Wang and Ying Li and Zheng Li and Jiayu Zhang}, doi = {10.1109/ACCESS.2025.3549034}, journal = {IEEE Access}, pages = {48096-48105}, shortjournal = {IEEE Access}, title = {Monitoring method for GIS electrical contact state based on VMD-hilbert marginal spectrum energy entropy}, volume = {13}, year = {2025}, }
• (2025). Enhanced E-commerce personalization through AI-powered content generation tools. ACCESS, 13, 48083-48095. (www)

  @article{ , abstract = {This paper proposes a new framework for personalizing e-commerce, which integrates multivariant user interfaces (MultiUI) with AI-generated content (AIGC). By utilizing customer behavioral data, our approach customizes both the visual layout and product descriptions for specific customer segments. This addresses the current research gap that often overlooks the synergy between UI design and content personalization. We conducted an empirical study to demonstrate the effectiveness of this integrated approach, showing that personalized user interface variants significantly improve customer engagement and conversion rates. In addition, we explore the potential of AIGC by using behavioral clusters to generate customized product descriptions. This showcases how AI can improve the relevance and appeal of product information, contributing to a more engaging and effective e-commerce experience. Although our initial findings using a simplified approach with ChatGPT are promising, future research will focus on refining AIGC models by incorporating domain-specific knowledge and leveraging comprehensive customer behavior data to generate highly tailored product descriptions. This research advances information processing in e-commerce by demonstrating how AI can be used to extract valuable insights from customer data, adapt UI designs, and generate personalized content, ultimately leading to more profitable online shopping experiences. Experimental studies showed that only about 10% of the most popular words were repeated in the product descriptions generated for the three different clusters. At the same time, two-thirds of the most popular words were dominant in only one of the clusters, confirming the satisfactory degree of matching descriptions to the specifics of customer groups.}, archive = {J_ACCESS}, author = {Adam Wasilewski and Yash Chawla and Ewa Pralat}, doi = {10.1109/ACCESS.2025.3550956}, journal = {IEEE Access}, pages = {48083-48095}, shortjournal = {IEEE Access}, title = {Enhanced E-commerce personalization through AI-powered content generation tools}, volume = {13}, year = {2025}, }
• (2025). FRAUD-X: An integrated AI, blockchain, and cybersecurity framework with early warning systems for mitigating online financial fraud: A case study from north macedonia. ACCESS, 13, 48068-48082. (www)

  @article{ , abstract = {Online financial fraud remains a pervasive threat, incurring billions of dollars in global losses annually. Mid-sized markets, such as North Macedonia, face acute challenges as digital adoption in the Banking, Financial Services, and Insurance (BFSI) sector outpaces the establishment of robust, multi-layered security systems. This paper introduces FRAUD-X, a unified framework merging artificial intelligence (AI)–based anomaly detection, blockchain-driven transaction verification, cybersecurity intrusion detection, and real-time early warning mechanisms into a single pipeline. Drawing upon three datasets—a Credit Card Fraud dataset (Kaggle), the PaySim Mobile Money dataset, and collected 50,000 anonymized local BFSI transactions from North Macedonia—FRAUD-X demonstrates a ~2–4% improvement in F1 compared to single-plane AI approaches, with ~90% recall for zero-day threats. Key enhancements include: 1) a permissioned blockchain for tamper-proof ledger entries, 2) synergistic AI-cybersecurity integration for dynamic risk scoring, and 3) real-time alerts that reduce reaction windows from hours to mere minutes. The framework runs at ~15–16 ms per transaction (~33% CPU usage), supporting near-real-time BFSI operations. Ablation studies confirm that each synergy layer (blockchain, cybersecurity, and early warning) significantly contributes to overall performance. A security analysis illustrates how FRAUD-X mitigates node compromise, collusion attempts, and advanced persistent threats (APT). By providing a replicable roadmap that balances high detection accuracy with operational feasibility, FRAUD-X offers practical value to BFSI entities in North Macedonia and comparable mid-scale markets.}, archive = {J_ACCESS}, author = {Bekim Fetaji and Majlinda Fetaji and Affan Hasan and Shpetim Rexhepi and Goce Armenski}, doi = {10.1109/ACCESS.2025.3547285}, journal = {IEEE Access}, pages = {48068-48082}, shortjournal = {IEEE Access}, title = {FRAUD-X: An integrated AI, blockchain, and cybersecurity framework with early warning systems for mitigating online financial fraud: A case study from north macedonia}, volume = {13}, year = {2025}, }
• (2025). Lightweight blockchain for authentication and authorization in resource-constrained IoT networks. ACCESS, 13, 48047-48067. (www)

  @article{ , abstract = {The Internet of Things (IoT) expansion has exposed connected devices to significant security vulnerabilities, particularly in terms of authentication and authorization. Traditional solutions, such as centralized servers or Proof of Work (PoW)–based blockchain, are unfeasible due to the resource limitations of IoT devices, such as their low processing capacity and dependence on batteries. This study proposes a lightweight blockchain system based on a simplified Proof of Stake (PoS) consensus mechanism designed to optimize energy consumption and improve resilience to attacks in IoT networks. The system implements a hierarchical network topology that improves data propagation and transmission times, significantly reducing latency compared to distributed topologies. In addition, it uses lightweight cryptographic algorithms such as ECDSA for authentication and AES-128 for authorization, ensuring transaction security without compromising the efficiency of IoT devices. The results show that the system reduces energy consumption by 54% compared to PoW solutions in high-load scenarios while maintaining an average latency below 30 ms. Furthermore, the system achieved a 92.5% attack detection rate under low malicious load, demonstrating its effectiveness in high-threat environments. This work offers a scalable and efficient solution that optimizes security and performance in IoT networks, opening new possibilities for its application in critical infrastructures and real-time sensor networks.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Rommel Gutierrez and Alexandra Maldonado Navarro and Aracely Mera-Navarrete}, doi = {10.1109/ACCESS.2025.3551261}, journal = {IEEE Access}, pages = {48047-48067}, shortjournal = {IEEE Access}, title = {Lightweight blockchain for authentication and authorization in resource-constrained IoT networks}, volume = {13}, year = {2025}, }
• (2025). Robust 3D human reconstruction system from sparse views in outdoor environments. ACCESS, 13, 48034-48046. (www)

  @article{ , abstract = {We propose a novel 3D human reconstruction system designed for diverse settings including outdoor environments. Existing methods face the challenges of portability, robustness, and adaptability to environmental variations, as they often rely on sensor data or handcrafted features for marker detection in camera calibration. Consequently, these methods are often limited in outdoor environments. To overcome these limitations, we employ a simple ball-wand as a marker and leverage visual foundation models for marker detection. Our approach enables precise multi-camera calibration without requiring bulky, sensor-specific calibration tools and dependence on controlled environments. Additionally, we introduce a framework that reconstructs 3D human model from sparse input views using occupancy and neural radiance fields. By using sparse views as input, our method efficiently captures both the geometry and appearance of humans in outdoor scenarios, eliminating the need for numerous cameras. We validated our system through extensive experiments on both synthetic human scan datasets and real-world outdoor datasets. The results demonstrate that our system accurately optimizes camera parameters and reconstructs high-quality 3D human models in both outdoor environments and synthetic scenarios.}, archive = {J_ACCESS}, author = {Hansoo Park and Soomin Kim and Bryan Heryanto and Hwasup Lim}, doi = {10.1109/ACCESS.2025.3544877}, journal = {IEEE Access}, pages = {48034-48046}, shortjournal = {IEEE Access}, title = {Robust 3D human reconstruction system from sparse views in outdoor environments}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Deep and shallow learning model-based sleep apnea diagnosis systems: A comprehensive study”. ACCESS, 13, 48033. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Deep and Shallow Learning Model-Based Sleep Apnea Diagnosis Systems: A Comprehensive Study”}, archive = {J_ACCESS}, author = {Roksana Akter Raisa and Ayesha Siddika Rodela and Mohammad Abu Yousuf and Akm Azad and Salem A. Alyami and Pietro Liò and Md Zahidul Islam and Ganna Pogrebna and Mohammad Ali Moni}, doi = {10.1109/ACCESS.2025.3551391}, journal = {IEEE Access}, pages = {48033}, shortjournal = {IEEE Access}, title = {Corrections to “Deep and shallow learning model-based sleep apnea diagnosis systems: A comprehensive study”}, volume = {13}, year = {2025}, }
• (2025). Anterior cruciate ligament (ACL) tear detection using hybrid CNN transformer. ACCESS, 13, 48019-48032. (www)

  @article{ , abstract = {Classifying medical images to detect ACL (Anterior Cruciate Ligament) tears is the medical image classification task, which requires significant importance to avoid misdiagnosis. However, this also poses some difficulties due to differences in formats of medical images and standard images. While Convolutional neural networks (CNN) and transformers are among the types of models that have been used for such tasks, they both have their weaknesses. For instance, CNNs have a tendency of extracting local features, hence have problems in understanding global context but on the other hand, transformers excel in understanding global information even if it implies losing some details in localization. Because of this, we introduce the MambaConvT (Mamba Convolutional Transformer) model that employs a hybrid strategy that combines CNNs and transformers giving room for the advantages of both. Firstly, MambaConvT utilizes multi-core convolutional networks to achieve higher extraction capability of the ACL tear specific local features from structural MR (Magnetic Resonance) images. Next, it uses a selective scanning module to implement depth separable convolution using Selective Scanning Module (SS2D) to achieve a larger distance of the receptive field therefore increasing the chances of getting important long-distance connections and essential fine details for the tear detection process. A hybrid model is utilized that integrates these hybrid features and after global feature modeling, attempts to enhance both local and global information representation. MambaConvT outperforms the current major classification models, according to a robust performance evaluation conducted on four public and two protected datasets. It also yields the best detection scores, taking into account all evaluation metrics, such as accuracy, recall, precision, F1 score, and area under the curve, for the identification of ACL tears in medical imaging.}, archive = {J_ACCESS}, author = {Suthir Sriram and Deependra K. Singh and D. V. Sairam and Nivethitha Vijayaraj and Thangavel Murugan}, doi = {10.1109/ACCESS.2025.3550188}, journal = {IEEE Access}, pages = {48019-48032}, shortjournal = {IEEE Access}, title = {Anterior cruciate ligament (ACL) tear detection using hybrid CNN transformer}, volume = {13}, year = {2025}, }
• (2025). A graphite ore grade recognition method based on improved inception-ResNet-v2 model. ACCESS, 13, 48007-48018. (www)

  @article{ , abstract = {With the rapid advancement of technology, intelligent identification of graphite ore grade in graphite mines has emerged as an essential requirement. To address the variability and low timeliness of traditional manual methods and the limited accuracy of deep learning due to image complexity and feature similarity, we propose an improved Inception-ResNet-v2 model for graphite ore grade recognition. Key improvements include: 1) To enhance the extraction of global feature information from graphite mine data, a global average pooling branch is incorporated into the Inception-resnet architecture. 2) Incorporating a $1\times 1$ convolutional layer at the tail of the model to control channel dimensions and employing the LeakyReLU activation function to address the limitations of the ReLU activation function. 3) Designing an LDP-Conv structure to replace certain $3\times 3$ convolutions and incorporating a channel attention mechanism to improve feature capture. 4) Optimizing the Stem module to expand the early-stage receptive field and reconstructing the network architecture. Experiments on the self-made graphite ore dataset showed improvements of 2.77% in Accuracy, 2.96% in Precision, 2.85% in Recall, and 2.89% in F1 Score. The model also demonstrated enhanced attention to graphite ore features. Compared with some mainstream CNN algorithms, our method offers significant advantages in accuracy and robustness, making it highly suitable for graphite ore grade recognition.}, archive = {J_ACCESS}, author = {Xueyu Huang and Renjie Pan and Jionghui Wang}, doi = {10.1109/ACCESS.2025.3550423}, journal = {IEEE Access}, pages = {48007-48018}, shortjournal = {IEEE Access}, title = {A graphite ore grade recognition method based on improved inception-ResNet-v2 model}, volume = {13}, year = {2025}, }
• (2025). Multi-view enhancement graph-level clustering network. ACCESS, 13, 47996-48006. (www)

  @article{ , abstract = {Graph-level clustering is a fundamental and significant task in data mining. The advancement of graph neural networks has provided substantial impetus to this area of research. However, existing graph-level clustering methods often focus exclusively on either graph structure or node attributes, which limit their ability to comprehensively capture graph-level features. To address this issue, we propose a Multi-view Enhancement Graph-level Clustering Network (ME-GCN), which consists of a Multi-view Feature Extraction Strategy (MFES) and a Graph-level Heterogeneous Enhancement Mechanism (GHEM) to generate high-quality graph-level representations, thus improving clustering performance. Specifically, the network extracts node attributes, subgraph structure, and global structure information through three personalized encoders with different receptive fields, respectively, to enrich feature representations from different views. In addition, it perceives and reasons heterogeneous features through GHEM, including multi-source information and hierarchical enhancement, which improves the compactness of multi-source representations. Extensive experiments on five benchmark datasets have demonstrated the superiority of ME-GCN, highlighting its effectiveness in leveraging multiple types of structure information for deep graph-level clustering.}, archive = {J_ACCESS}, author = {Zeyi Li and Renda Han and Tianyu Hu and Mengfei Li and Caimao Li}, doi = {10.1109/ACCESS.2025.3550185}, journal = {IEEE Access}, pages = {47996-48006}, shortjournal = {IEEE Access}, title = {Multi-view enhancement graph-level clustering network}, volume = {13}, year = {2025}, }
• (2025). Understanding and securing the risks of uncrewed aerial vehicle services. ACCESS, 13, 47955-47995. (www)

  @article{ , abstract = {Uncrewed Aerial Vehicles (UAV), are rapidly advancing across various sectors. However, this growth is accompanied by escalating security risks, as UAV become prime targets for sophisticated cyber and physical attacks. Although several surveys on UAV security exist in the literature, there is a lack of a practical guide to assist stakeholders in assessing and mitigating security risks in diverse UAV application sectors. Motivated by this gap, the main contribution of this tutorial is to describe a hands-on security assessment methodology for UAV services, by integrating fundamental background knowledge of UAV technology with actionable security strategies. The methodology is based on established standards, combining risk assessment with penetration testing. The goal is to assist practitioners and stakeholders of UAV services in understanding threat modeling, vulnerability analysis, and targeted risk mitigation. Initially, the underlying UAV technologies, architectures, and applications are presented. Then, a taxonomy of security attacks and countermeasures for UAV is provided. Based on the presented UAV characteristics and security needs, a practical step-by-step security assessment methodology for UAV services is presented. To demonstrate the practicality of the presented methodology, it is applied to a real-world-inspired scenario, showcasing its effectiveness in identifying attack vectors, assessing risks, and deploying countermeasures.}, archive = {J_ACCESS}, author = {Ioannis Anagnostis and Panayiotis Kotzanikolaou and Christos Douligeris}, doi = {10.1109/ACCESS.2025.3549861}, journal = {IEEE Access}, pages = {47955-47995}, shortjournal = {IEEE Access}, title = {Understanding and securing the risks of uncrewed aerial vehicle services}, volume = {13}, year = {2025}, }
• (2025). Multiobjective transit network design with travel time reliability for conventional and autonomous electric vehicles. ACCESS, 13, 47938-47954. (www)

  @article{ , abstract = {This study addresses the multiobjective transit network design and frequency setting problem, aiming to improve travel time reliability under urban congestion. Digital tachograph data collected from a large fleet are utilized to assess the variability of roadway travel times across the network. By integrating a multinomial logit mode choice model, this study explores the relationship between automobile demand and public transit use, highlighting the influence of urban congestion on transit performance. In addition, a comparative analysis between the operations of autonomous electric buses and those of conventional vehicles reveal potential improvements in operational benefits as well as enhancements in passenger experience. A sensitivity analysis reveals the network response to changes in operating unit costs and variations in total passenger demand. Experimental results from a large-scale, real-world transit network demonstrate notable improvements in operator’s net costs, passenger travel times, and overall system reliability, suggesting promising directions for future transit network planning and sustainable urban mobility.}, archive = {J_ACCESS}, author = {Su Jin Park and Seungmo Kang}, doi = {10.1109/ACCESS.2025.3546917}, journal = {IEEE Access}, pages = {47938-47954}, shortjournal = {IEEE Access}, title = {Multiobjective transit network design with travel time reliability for conventional and autonomous electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Outage-constrained secrecy rate maximization for STAR-RIS with energy-harvesting eavesdroppers and imperfect CSI. ACCESS, 13, 47927-47937. (www)

  @article{ , abstract = {This paper proposes a novel approach to enhancing secure wireless communication using a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in a multiple-input single-output system. Unlike conventional RIS, which is limited to half-space coverage, STAR-RIS enables full 360-degree coverage, making it a promising technology for next-generation secure wireless communication. In the presence of energy-harvesting eavesdroppers, this study aims to maximize the sum secrecy rate while ensuring strict energy harvesting constraints, an area that has not been widely explored, particularly under imperfect channel state information (CSI) conditions. To tackle this challenge, we formulate a complex non-convex optimization problem, which is efficiently solved using a penalty concave-convex procedure combined with an alternating optimization algorithm. This optimization framework jointly optimizes beamforming and STAR-RIS transmission and reflection coefficients, striking a balance between secure communication and energy harvesting requirements. Numerical simulations show that the proposed method outperforms conventional RIS-based approaches, demonstrating its effectiveness even in the presence of CSI uncertainty. Furthermore, the results confirm that optimizing STAR-RIS under imperfect CSI leads to superior security and energy efficiency compared to assuming perfect CSI, highlighting the practical significance of our approach in real-world wireless networks.}, archive = {J_ACCESS}, author = {Zahra Rostamikafaki and Francois Chan and Claude D’Amours}, doi = {10.1109/ACCESS.2025.3549450}, journal = {IEEE Access}, pages = {47927-47937}, shortjournal = {IEEE Access}, title = {Outage-constrained secrecy rate maximization for STAR-RIS with energy-harvesting eavesdroppers and imperfect CSI}, volume = {13}, year = {2025}, }
• (2025). A novel fractional slot concentrated winding yokeless counter-rotating dual-rotor permanent magnet machine. ACCESS, 13, 47912-47926. (www)

  @article{ , abstract = {Radial-flux fractional slot concentrated winding (FSCW) yokeless counter-rotating dual-rotor permanent magnet (YCDRPM) machines are proposed and investigated in this paper. Counter-rotating operation is required for special applications, such as washing machines, counter-rotating wind power generators, and underwater vehicles. The YCDRPM machine integrates magnetic gear and the FSCW PM machine. Its dual-rotor counter-rotating operation is achieved by utilising the magnetic field modulation theory in the FSCW PM machine, which is proved by the analytical method. The operation principle and three feasible slot/pole number combinations are investigated under different operation modes. The FSCW YDCRPM machine is analysed using an analytical method and validated by finite element analysis (FEA) and experiment. The FSCW YCDRPM machine not only operates as a counter-rotating DRPM machine but also can utilise the magnetic gearing effect to enhance the output torque. Experiments on a prototype FSCW YCDRPM machine validate the results. The proposed machine shows counter-rotating functionality and flexibility in rotational speed and torque. The machine used in the specific application will be researched in the future based on specific application requirements for practical implementation.}, archive = {J_ACCESS}, author = {Zhitong Ran and Zi-Qiang Zhu and Hai Xu and Yanjian Zhou and Liang Chen and Lei Yang}, doi = {10.1109/ACCESS.2025.3550017}, journal = {IEEE Access}, pages = {47912-47926}, shortjournal = {IEEE Access}, title = {A novel fractional slot concentrated winding yokeless counter-rotating dual-rotor permanent magnet machine}, volume = {13}, year = {2025}, }
• (2025). Leveraging highly approximated multipliers in DNN inference. ACCESS, 13, 47897-47911. (www)

  @article{ , abstract = {In this work, we present our control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our approach does not require retraining and significantly decreases the induced error due to approximate multiplications, improving the overall inference accuracy. As a result, control variate approximation enables satisfying tight accuracy loss constraints while boosting the power savings. Our experimental evaluation, across six different DNNs and several approximate multipliers, demonstrates the versatility of control variate technique and shows that compared to the accurate design, it achieves the same performance, 45% power reduction, and less than 1% average accuracy loss. Compared to the corresponding approximate designs without using our technique, the error-correction of the control variate method improves the accuracy by 1.9x on average.}, archive = {J_ACCESS}, author = {Georgios Zervakis and Fabio Frustaci and Ourania Spantidi and Iraklis Anagnostopoulos and Hussam Amrouch and Jörg Henkel}, doi = {10.1109/ACCESS.2025.3550520}, journal = {IEEE Access}, pages = {47897-47911}, shortjournal = {IEEE Access}, title = {Leveraging highly approximated multipliers in DNN inference}, volume = {13}, year = {2025}, }
• (2025). Guided-aloha for secondary access with spectrum prediction. ACCESS, 13, 47885-47896. (www)

  @article{ , abstract = {The demand for spectrum access continues to grow exponentially and dynamic spectrum access (DSA) is a candidate to alleviate issues with spectrum scarcity by adapting spectrum resources in real-time. Despite the benefits of intelligent DSA protocols, many algorithms are complex, and thus prohibitively difficult to implement in a real-time system. This paper proposes a contention-based access method named Guided-Aloha (or G-Aloha), that stems from the simple and well-known slotted-Aloha (S-Aloha) access method, for DSA in the context of primary users (PU) and secondary users (SU) of the network. The G-Aloha method is proposed for SU access to provide extended protection to the PU compared to the S-Aloha method by being guided by the knowledge from spectrum prediction (or spectrum forecasting), considering any potential regulatory requirements for protecting the PU. Using Markov modeling, an extensive theoretical treatment of the proposed G-Aloha method in terms of the success and conflict probabilities is presented along with simulation results. Our results show that the G-Aloha method provides superior protection for the PUs compared to the S-Aloha method and a very close match between the theoretical closed form expressions and the simulation results for the performance.}, archive = {J_ACCESS}, author = {Sithamparanathan Kandeepan and Madhulika Tripathi and Ke Wang and Don Gossink and Tharaka Samarasinghe and Chamath Divarathne}, doi = {10.1109/ACCESS.2025.3550533}, journal = {IEEE Access}, pages = {47885-47896}, shortjournal = {IEEE Access}, title = {Guided-aloha for secondary access with spectrum prediction}, volume = {13}, year = {2025}, }
• (2025). Design of observer-controller combination for nonholonomic mobile robots. ACCESS, 13, 47874-47884. (www)

  @article{ , abstract = {This paper presents a combined observer-controller for nonholonomic mobile robots with the immeasurable direction angle. To conquer the local stability caused by the dynamic feedback linearization method, an asymptotically global stable velocity control law is proposed by employing Lyapunov method. This control method removes the restriction of dimension of nonlinear system, and the controller design process is simplified by using virtual control inputs and derivable Lyapunov function. Furthermore, the pole placement method is designed to design the control gain scheduling method. Meanwhile, the angle-error observer is adopted to estimate the sine and cosine of orientation angle error by the available position information of mobile robot. The convergence of the controlled robot system is proved by the Lyapunov theory. Simulation and real experiment are implemented to validate the theoretical analysis.}, archive = {J_ACCESS}, author = {Mingyue Cui and Lei Zhou and Yiyuan Cheng and Wei Liu}, doi = {10.1109/ACCESS.2025.3551081}, journal = {IEEE Access}, pages = {47874-47884}, shortjournal = {IEEE Access}, title = {Design of observer-controller combination for nonholonomic mobile robots}, volume = {13}, year = {2025}, }
• (2025). Region-based self-segmentation guided diffusion model for thermal infrared to pseudo-color visible light image conversion. ACCESS, 13, 47860-47873. (www)

  @article{ , abstract = {Unlike traditional visible light cameras, thermal infrared cameras can be used to conduct clear reconnaissance and observations during both day and night. However, because of the imaging principle of thermal infrared cameras, thermal infrared images lack color features and appropriate detail information for human observation. This paper introduces RSDM, a novel Region-aware Semantic-guided Diffusion Model, for converting thermal infrared images into perceptually accurate pseudo-color visible light images. RSDM addresses the limitations of existing methods by incorporating a two-stage process: 1) non-uniformity correction to remove sensor artifacts; and 2) unique SEM-guided diffusion model for image generation. This approach leverages pre-trained diffusion models in conjunction with region segmentation techniques to achieve superior performance, particularly in preserving fine details and effectively handling small targets. We systematically evaluated the proposed RSDM method on the FLIR, M3FD, and self-collected datasets. The experimental results indicate that our approach yields significant enhancements in image quality, detail preservation, and object recognition performance compared to current state-of-the-art methods.}, archive = {J_ACCESS}, author = {Dian Sheng and Weiqi Jin and Minghe Wang and Jianguo Yang}, doi = {10.1109/ACCESS.2025.3550895}, journal = {IEEE Access}, pages = {47860-47873}, shortjournal = {IEEE Access}, title = {Region-based self-segmentation guided diffusion model for thermal infrared to pseudo-color visible light image conversion}, volume = {13}, year = {2025}, }
• (2025). Evaluation of K-means algorithm for faulted landforms extraction and offset measurement with an example from the eastern kunlun fault. ACCESS, 13, 47848-47859. (www)

  @article{ , abstract = {Accurate offset measurement is crucial for recovering the size of past earthquakes and understanding the recurrence patterns of strike-slip faults. Traditional methods, which rely on manual delineation of displaced geomorphic markers from satellite images, often introduce significant uncertainties. This study aims to develop a more objective and accurate method for identifying faulted landforms and measuring offsets. Although supervised deep learning methods have great potential for image recognition and segmentation, due to the absence of data sets, we apply the K-means algorithm, an easy and practical unsupervised machine learning method with minimal parameters, to extract displaced geomorphic markers. Our research is conducted in the Kusai Lake segment of the Eastern Kunlun Fault using high-resolution satellite imagery. Initially, we identify multiple well-preserved geomorphic markers along the fault traces and manually label some to form an imagery dataset. The K-means algorithm demonstrates its efficacy in extracting landforms clearly when the classification number is set to three. Furthermore, a quantitative comparison of several commonly used unsupervised algorithms confirmed that K-means performs best, achieving a recall of 0.70 and an accuracy of 0.906 on the dataset. The validity of our measurements is corroborated by examining two specific sites around Hongshui Gou. Quantitative analysis comparing our imagery-based measurements with field data reveals a strong agreement, evidenced by a Pearson’s correlation coefficient of 0.997. Repeated measurements of the same geomorphic markers across different images indicate that the geomorphic features obtained from the images significantly affect the accuracy of offset measurements. This study underscores the potential of our approach for both extracting faulted landforms and accurately measuring their offsets, emphasizing the importance of assessing the integrity of geomorphic markers when using satellite imagery for offset measurements.}, archive = {J_ACCESS}, author = {Shengchao Zhou and Zhou Lin}, doi = {10.1109/ACCESS.2025.3551121}, journal = {IEEE Access}, pages = {47848-47859}, shortjournal = {IEEE Access}, title = {Evaluation of K-means algorithm for faulted landforms extraction and offset measurement with an example from the eastern kunlun fault}, volume = {13}, year = {2025}, }
• (2025). Predicting missing links based on power-law distribution and information transmission efficiency. ACCESS, 13, 47833-47847. (www)

  @article{ , abstract = {Link prediction is essential in complex network analysis, aiming to predict missing or potential future connections based on network structure and attributes. Node similarity methods are commonly used to estimate the likelihood of forming links between unconnected nodes, typically relying on common neighbors. However, traditional methods have limitations: they ignore the differing influence of neighbors based on connectivity and miss potential links between nodes without shared neighbors. This paper proposes a novel algorithm that improves existing link prediction methods by considering both the contributions of common neighbors and the efficiency of information transmission within the network. We introduce a power-law distribution model to capture the contributions of neighbors and select relevant network features to construct indicators that reflect information transmission efficiency. The proposed method does not require manual parameter tuning, making it more efficient and adaptable to different networks. Through experiments on multiple real-world networks, the algorithm demonstrates high prediction accuracy and achieves impressive results when evaluated using standard metrics. The findings underscore the effectiveness of this method in accurately predicting missing links, making it a useful tool for network analysis.}, archive = {J_ACCESS}, author = {Yi Wang and Jun Ma}, doi = {10.1109/ACCESS.2025.3551252}, journal = {IEEE Access}, pages = {47833-47847}, shortjournal = {IEEE Access}, title = {Predicting missing links based on power-law distribution and information transmission efficiency}, volume = {13}, year = {2025}, }
• (2025). Newton’s cooling law-based oversampling technique for handling imbalanced data in software defect prediction. ACCESS, 13, 47820-47832. (www)

  @article{ , abstract = {Class imbalance presents considerable challenges for software defect prediction. However, software defect datasets exhibit additional complex characteristics, with class overlap being the most harmful to prediction performance. Currently, most techniques only focus on balancing data distributions. To this end, this study applies the previously proposed Newton’s Cooling Law-Based Oversampling Technique (NCLWO) to software defect prediction. The method first uses density and distance factors to identify hard-to-learn defect instances in SDP, such as boundary samples in overlapping distributions and small disjoints. Then, the density and distance factors are used to measure the initial heat of each defect instance. Newton’s Cooling Law is applied to expand the sampling area into a hypersphere until thermal equilibrium is achieved, moving non-defective instances out of the region to clean the overlapping area. Finally, weighted oversampling is performed within the expanded sampling area to generate defect instances, addressing within-class imbalance. Comparative experiments were conducted between NCLWO and ten state-of-the-art sampling methods on 30 software defect datasets. The experimental results reveal that the proposed method excels in performance and showcases significant robustness and versatility in handling the joint effects of class overlap and imbalance.}, archive = {J_ACCESS}, author = {Faqiang Wang and Liangliang Tao and Yage Liang and Lilin Zhang and Tianyu Luo}, doi = {10.1109/ACCESS.2025.3550583}, journal = {IEEE Access}, pages = {47820-47832}, shortjournal = {IEEE Access}, title = {Newton’s cooling law-based oversampling technique for handling imbalanced data in software defect prediction}, volume = {13}, year = {2025}, }
• (2025). A neural network-based approach to determining the mechanical design dimensions of asynchronous machines. ACCESS, 13, 47805-47819. (www)

  @article{ , abstract = {It is crucial to figure out the parameters of asynchronous machines, which have an essential function in industry, to guarantee secure operation, control, and analysis. In order to address the time-consuming calculations associated with conventional approaches, the shortcomings in the manufacturer’s documentation, and the interruptions caused by experimental studies, the methods presented primarily were concentrated on determining electrical parameters. However, research concerning the estimation of mechanical parameters was restricted to a minor quantity of parameters and utilized a sample size that is insufficient to establish broad conclusions. Hence, in this research, it is aimed at developing a machine learning-based, high-accuracy, and fast prediction system that surpasses this restricted range. This approach was specifically developed to estimate 17 mechanical dimensions by evaluating three prediction algorithms—Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM)—to choose the most effective one within a specified power range and enable parameter configuration in less than a minute for practical implementation. The RNN demonstrated the best performance by capturing dependencies effectively and achieving the highest accuracy, while MLP provided rapid results with a simpler structure but limited capacity for modeling complex relationships. LSTM, despite its theoretical advantages, fell short due to high computational demands and inconsistent test performance. A correlation coefficient of 0.99, mean absolute error values below 0.0025, and root mean square error values below 0.0045 were attained throughout the study, thereby signifying a statistically significant relationship between the variables. This research offers a remarkable framework for enhancing the design and operation of machines by improving a parameter determination approach.}, archive = {J_ACCESS}, author = {Sema Nur Ipek and Murat Taskiran and Nur Bekiroglu}, doi = {10.1109/ACCESS.2025.3550824}, journal = {IEEE Access}, pages = {47805-47819}, shortjournal = {IEEE Access}, title = {A neural network-based approach to determining the mechanical design dimensions of asynchronous machines}, volume = {13}, year = {2025}, }
• (2025). Electric traction emulator system. ACCESS, 13, 47794-47804. (www)

  @article{ , abstract = {This paper addresses the need for advanced research facilities to investigate electric traction systems, particularly control strategies, in the context of the increasing adoption of electric vehicles (EVs). The lack of accessible laboratory tools that accurately emulate real-world vehicle dynamics represents a significant gap in current research capabilities. To bridge this gap, this work presents the development of a traction system emulator for an electric vehicle based on an electrodynamometer. The emulator replicates vehicle dynamics, where the electric machine must overcome resistance forces such as aerodynamic drag and rolling resistance, which are critical in EV operation. Given the limited rated torque of laboratory machines, a vehicle dynamics scaling method is proposed, demonstrating that only specific vehicle parameters require adjustment to replicate full vehicle dynamics accurately. The paper further details the permanent magnet synchronous machine (PMSM) model, its control scheme, and the determination of Proportional-Integral (PI) gains using Nyquist’s stability criterion. Finally, simulation and experimental results validate the proposed emulator through the driving cycles SAE J227 and US06, contributing to a scalable and practical EV control system research approach.}, archive = {J_ACCESS}, author = {F. Pérez Carmona and Jaime J. Rodríguez Rivas and R. Ortega González and O. Carranza Castillo and D. Memije Garduño and E. Peralta Sánchez}, doi = {10.1109/ACCESS.2025.3550289}, journal = {IEEE Access}, pages = {47794-47804}, shortjournal = {IEEE Access}, title = {Electric traction emulator system}, volume = {13}, year = {2025}, }
• (2025). How do national innovation factors affect the planning knowledge flow?. ACCESS, 13, 47776-47793. (www)

  @article{ , abstract = {The efficacy of planning as a collaborative process is contingent upon the active flow and effective utilization of diverse knowledge, particularly that which pertains to planning. However, how to promote planning knowledge flow (PKF) remains to be studied. This article contributed to the issue. It identified national innovation factors (NIFs) and their linear and non-linear effects that were closely related to the PKF between 113 countries. The research methods applied included social network analysis, Pearson correlation analysis, multiple regression analysis, and an interpretable machine learning method, the XGBoost-SHAP model. Our findings included: 1) For linear relationships, the results showed that research & development (R&D) and trade, diversification and market scale (T, D&M) had the greatest impact on transnational PKF. 2) The country’s institutional environment, knowledge absorption and education, etc., also affected the transnational PKF. 3) For non-linear effects, this study revealed the specific intervals in which the relevant NIFs played a facilitating or inhibiting role. 4) It was worth noting that information and communication technologies (ICTs) facilitated transnational PKF lightly, and even suppressed the flow in case it was underdeveloped and highly developed. This study provided theoretical and empirical references to formulate relevant policies on promoting PKF for managers, policy makers and scholars.}, archive = {J_ACCESS}, author = {Bin Wu and Weifeng Qi and Xing Gao and Kang Cao}, doi = {10.1109/ACCESS.2025.3549497}, journal = {IEEE Access}, pages = {47776-47793}, shortjournal = {IEEE Access}, title = {How do national innovation factors affect the planning knowledge flow?}, volume = {13}, year = {2025}, }
• (2025). Sustaining front-seat passenger engagement: User experience and perceived interactivity of in-vehicle information systems. ACCESS, 13, 47763-47775. (www)

  @article{ , abstract = {This study aims to explore the user experience of front-seat passengers using In-Vehicle Infotainment (IVI) systems, focusing on how perceived interactivity influences their satisfaction and continuance intention through user experience. It examines how optimizing the user experience of front-seat passengers can enhance their satisfaction and continuance intention, which is of significant importance for car manufacturers and software developers in competitive market strategies. Based on a literature review to form key variables and hypotheses, data from 315 valid questionnaires were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that perceived playfulness, perceived personalization, and perceived control significantly impact the functional and emotional experiences of front-seat passengers, thereby affecting their satisfaction and continuance intention. These discoveries underscore the necessity of focusing on front-seat passengers’ personalized needs and enhancing their sense of control in the design of in-vehicle information systems to improve user experience and satisfaction.}, archive = {J_ACCESS}, author = {Lifan Yang and Shusheng Shi}, doi = {10.1109/ACCESS.2025.3549273}, journal = {IEEE Access}, pages = {47763-47775}, shortjournal = {IEEE Access}, title = {Sustaining front-seat passenger engagement: User experience and perceived interactivity of in-vehicle information systems}, volume = {13}, year = {2025}, }
• (2025). Energy-based reinterpretation of equal area criterion for generator instability assessment via local rotational speed and voltage frequency measurements. ACCESS, 13, 47748-47762. (www)

  @article{ , abstract = {To apply the physical concept of stability embedded in the equal area criterion (EAC) to large-scale power systems, this study proposes a measurement-based reinterpretation of the EAC from an energy perspective in the time domain. Based on this approach, a system for assessing generator instability is developed. The study reinterprets the primary state variables in the angle domain, which define the accelerating and decelerating areas in the EAC, by converting them into measurable quantities in the time domain, enabling direct measurement of accelerating and decelerating energy. The proposed method determines the accumulated accelerating energy and the decelerating energy released to the power system after fault clearance by measuring the generator’s terminal voltage and mechanical rotational speed. An algorithm for generator instability assessment in large-scale power systems using this measurement-based EAC is implemented, taking into account practical aspects for constructing an instability assessment system. The effectiveness of this energy-based reinterpretation is validated in multi-machine power systems through simulations on the IEEE 39-bus and IEEE 118-bus systems modeled in MATLAB/Simulink to assess its practical applicability. Time-domain simulations of various fault scenarios are conducted to analyze the performance of the proposed algorithm in assessing instability. Furthermore, a comparative analysis with previous studies evaluates the performance and practicality of the proposed method for application in multi-machine power systems.}, archive = {J_ACCESS}, author = {Jongju Kim}, doi = {10.1109/ACCESS.2025.3550538}, journal = {IEEE Access}, pages = {47748-47762}, shortjournal = {IEEE Access}, title = {Energy-based reinterpretation of equal area criterion for generator instability assessment via local rotational speed and voltage frequency measurements}, volume = {13}, year = {2025}, }
• (2025). ACMFed: Fair semi-supervised federated learning with additional compromise model. ACCESS, 13, 47734-47747. (www)

  @article{ , abstract = {One of the major drawbacks of federated learning (FL) is data imbalance and uneven reliability of clients, which adversely impacts model performance and generalization ability. Among these problems, data imbalance can be addressed through semi-supervised federated learning (SSFL), in which fully labeled, and fully unlabeled clients are jointly trained to derive a global model. Existing approaches work well when local clients have independent identically distributed (IID) data but fail to generalize under a more practical SSFL setting, namely fairness, non-IID. Since the non-IID problem stems from unfairness, to address this issue, we propose ACMFed, a fair and improved concurrent federated learning method that balances client connections, computing resources, data quality and quantity based on fairness. ACMFed reaches the compromise by performing fair multiple sampling of clients in models with largely deviating labeled and unlabeled clients. Each client distills the model through a multi-sub-sampling process without aggregating the local model to the central server, and builds a global model based on it. The additional compromise model applies a fair adjustment method of the new distance-based dynamic model to reduce data bias and optimize performance. ACMFed outperforms other federated learning methods and achieves faster convergence on six benchmark datasets. To code is available at https://github.com/kimdohyoung96/ACMFed.}, archive = {J_ACCESS}, author = {Dohyoung Kim and Kangyoon Lee and Youngho Lee and Hyekyung Woo}, doi = {10.1109/ACCESS.2025.3536639}, journal = {IEEE Access}, pages = {47734-47747}, shortjournal = {IEEE Access}, title = {ACMFed: Fair semi-supervised federated learning with additional compromise model}, volume = {13}, year = {2025}, }
• (2025). Optimization of new energy vehicle industry structure based on optimization link prediction similarity algorithm and CGE. ACCESS, 13, 47722-47733. (www)

  @article{ , abstract = {In light of the growing global concerns over energy and climate change, new energy vehicles are confronted with both opportunities and challenges posed by industrial structural transformation. To foster the optimization of the new energy vehicle industry structure while considering environmental impact, this paper first uses an improved link prediction algorithm to construct an industrial structure optimization model. Then, a computable general equilibrium model is used to explore the effect of a carbon tax on the new energy vehicle industry. The proposed model had the best area under the curve on the Karate network and FWFW network, which were 90.64% and 65.05%. In the Jazz network, the prediction accuracy was 92.65%. The prediction accuracy of the proposed model for the “Yangtze River Delta” and “Beijing Tianjin Hebei” regions was 86.37% and 85.62%. With the gradual rise of carbon tax rates and emission reductions, the demand for electricity, coal, and oil in the new energy vehicle manufacturing industry was gradually decreasing. The gross domestic product and total output of the manufacturing industry were gradually decreasing, and the total investment was gradually increasing. This result demonstrates the application effect of the proposed industrial structure optimization model and indicates that carbon tax collection will have an impact on carbon emissions and energy demand. This research will help promote the optimization of the new energy vehicle industry structure and promote the sustainable growth of the national economy.}, archive = {J_ACCESS}, author = {Zhenjun Xie}, doi = {10.1109/ACCESS.2025.3547735}, journal = {IEEE Access}, pages = {47722-47733}, shortjournal = {IEEE Access}, title = {Optimization of new energy vehicle industry structure based on optimization link prediction similarity algorithm and CGE}, volume = {13}, year = {2025}, }
• (2025). Enhancing smart city connectivity: Implementing channel-adaptive strategies for FSO-based drone-assisted mobile access networks in urban environments. ACCESS, 13, 47710-47721. (www)

  @article{ , abstract = {The global surge in urbanization has catalyzed the evolution of smart city infrastructures, characterized by expansive interconnected networks spanning vast urban landscapes. To address the connectivity challenges inherent in smart city environments, this study advocates for the utilization of autonomous aerial vehicles (AAVs) as an alternative to traditional mobile air base stations. Leveraging Free-Space Optical (FSO) links for downlink transmission from AAVs to users, the proposed system confronts atmospheric turbulence and pointing loss impairments. In this context, the study explores the average spectral efficiency (ASE) performance of the proposed FSO-based Drone-Assisted system employing various channel adaptive techniques, including Optimal Rate Adaptation, Channel Inversion with Fixed Rate, Optimal Power and Rate Adaptation, and Truncated Channel Inversion with Fixed Rate. Additionally, the investigation underscores the impact of factors such as link range, coverage distance, AAV orientation fluctuation standard deviation, AAV position vibration standard deviation, and beam divergence on system performance. By elucidating these aspects, the research aims to contribute valuable insights into the optimization of smart city connectivity, paving the way for enhanced communication infrastructure in urban environments. Notably, the study achieved a maximum ASE of 101 bits/s/Hz using the TIFR adaptation scheme, underscoring the efficacy of the proposed approach.}, archive = {J_ACCESS}, author = {G. Aarthi and L. Bhargava Kumar and Mohit Vaidya and Shreyash Nema and Prabu Krishnan and A. Sangeetha and Rajesh Natarajan and Karthikeyan Thangavel}, doi = {10.1109/ACCESS.2025.3549041}, journal = {IEEE Access}, pages = {47710-47721}, shortjournal = {IEEE Access}, title = {Enhancing smart city connectivity: Implementing channel-adaptive strategies for FSO-based drone-assisted mobile access networks in urban environments}, volume = {13}, year = {2025}, }
• (2025). Open-phase fault tolerant model predictive current controller for asymmetrical dual three-phase permanent magnet synchronous machine drive system. ACCESS, 13, 47693-47709. (www)

  @article{ , abstract = {Asymmetrical dual three-phase permanent magnet synchronous machine (DTP-PMSM) drives have attracted the attention of the scientific community and the industry for high-performance electromobility applications. Fault tolerant operation is a desirable feature to guarantee the continuous operation of such life-critical systems. Commonly, field oriented control (FOC) following the vector space decomposition (VSD) representation is used for current regulation purposes, incorporating proportional integral and resonant (PI-R) controllers in the harmonic frame xy to eliminate open-phase fault perturbations. As an alternative, a novel model predictive resonant controller (MPRC) is proposed in this work for fault tolerant operation. Multiple-Input Multiple-Output (MIMO) tools are used to tune the controller in the theoretical analysis, supported by the disk margin method. The aim of this work is to provide an MPC-based solution with the same functionalities as of the conventional PI-R based fault tolerant control method. It is demonstrated that, unlike conventional approaches, the MPRC can provide phase compensation intrinsically and, with a correct parameter tuning, it results in a satisfactory post-fault performance. Extensive simulations in a high-fidelity electric drive model are presented to compare the MPRC features with respect to conventional controllers. Finally, experimental results are provided, demonstrating the correctness of the proposal.}, archive = {J_ACCESS}, author = {Adriano Navarro-Temoche and Josu Jugo and Edorta Ibarra and Iñigo Kortabarria and Endika Robles}, doi = {10.1109/ACCESS.2025.3550654}, journal = {IEEE Access}, pages = {47693-47709}, shortjournal = {IEEE Access}, title = {Open-phase fault tolerant model predictive current controller for asymmetrical dual three-phase permanent magnet synchronous machine drive system}, volume = {13}, year = {2025}, }
• (2025). A robust kalman filter for heavy-tailed measurement noise based on gamma pearson VII mixture distribution. ACCESS, 13, 47680-47692. (www)

  @article{ , abstract = {This study develops an enhanced state estimation framework by integrating the Kalman filtering mechanism with a Gamma Pearson VII (GaPV) hybrid probability model to address non-stationary heavy-tailed noise characteristics in measurement systems. This innovative methodology synthesizes Gaussian morphological properties with Gamma’s supplementary parameters within the Pearson VII structural framework, establishing a unified probabilistic modeling paradigm. The proposed filter adaptively optimizes the Pearson VII formulation’s mean vector and covariance matrix, enabling precise characterization of data features in challenging environments exhibiting non-stationary behavior and heavy-tailed noise distributions. An Inverse Wishart distribution is introduced as the prior distribution of the measurement noise covariance to estimate it adaptively in response to unknown time-varying measurement noise. The variational Bayesian method is used to calculate the state and parameters jointly. Simulation results demonstrate that, compared to traditional Kalman filters based on the Gaussian assumption, the proposed filter exhibits higher precision and stronger robustness when dealing with non-stationary heavy-tailed measurement noise (HTMN). In summary, the Kalman Filter based on the Gamma Pearson VII distribution provides a new and effective tool for state estimation in complex noise environments, mainly showing significant advantages in applications requiring high precision and robustness.}, archive = {J_ACCESS}, author = {Shen Liang and Guoliang Xu and Zhenmei Qin}, doi = {10.1109/ACCESS.2025.3551075}, journal = {IEEE Access}, pages = {47680-47692}, shortjournal = {IEEE Access}, title = {A robust kalman filter for heavy-tailed measurement noise based on gamma pearson VII mixture distribution}, volume = {13}, year = {2025}, }
• (2025). A holistic optimization framework for optimal sizing, cost, and longer lifetime of DC-DC modular solid-state transformers in wind energy applications. ACCESS, 13, 47663-47679. (www)

  @article{ , abstract = {Offshore wind farms have been gaining momentum in recent years. As a result, industrial and research efforts are focusing on improving the performance of power electronics converters for offshore wind turbines. Solid-state transformers (SSTs) are regarded as a prominent alternative to the current energy conversion units in wind turbines. However, application specific performance evaluations for SSTs are missing in the literature. Due to this gap, it is not possible to evaluate the benefits of using SSTs in offshore wind applications. To address this issue, this paper presents the modeling and optimization of the efficiency, cost, and lifetime of a DC-DC SST for an offshore wind application. A direct current system is integrated between the wind turbines; therefore, the wind farm utilizes a DC collection system. The converter is designed with high voltage SiC (Silicon Carbide) MOSFETs (Metal-Oxide-Semiconductor Field-Effect Transistors) (3.3 kV) to achieve high efficiency and medium voltage rating. Dual active bridge topology and its variations are utilized to achieve high power density and isolation. A novel optimization framework is developed and employed to determine the best design parameters to achieve an optimal solution in terms of efficiency, cost, and lifetime. The final results have demonstrated that the efficiency of the optimal design can exceed 98.1%, and its converter material cost is 46.5 ke/MW. Furthermore, the expected lifetime can exceed 45 years. Lastly, a cost sensitivity analysis is conducted to evaluate the future cost of the system. This analysis indicates that the cost of the SiC based SST converter could match that of Si based solutions by the mid-2030s.}, archive = {J_ACCESS}, author = {Serhat Emir Ogan and Chaochao Song and Sajib Chakraborty and Thomas Geury and Pooya Davari and Omar Hegazy}, doi = {10.1109/ACCESS.2025.3550651}, journal = {IEEE Access}, pages = {47663-47679}, shortjournal = {IEEE Access}, title = {A holistic optimization framework for optimal sizing, cost, and longer lifetime of DC-DC modular solid-state transformers in wind energy applications}, volume = {13}, year = {2025}, }
• (2025). SCL-YOLOv11: A lightweight object detection network for low-illumination environments. ACCESS, 13, 47653-47662. (www)

  @article{ , abstract = {Object detection in low-light environments has been widely recognized as a critical research direction in the field of computer vision. In response to the challenges of reduced detection accuracy and high edge-deployment costs encountered by mainstream single-stage object detection models under low-light conditions, this paper proposes a lightweight object detection network based on YOLOv11, integrates StarNet, C3k2-Star, and a lightweight detail-enhanced convolution and shared convolutional detection head(LSDECD), so called SCL-YOLOv11 herein. First, the StarNet architecture is introduced into the Backbone to enhance the extraction of shallow image features and significantly reduce computational complexity. Next, Star Blocks from the StarNet framework are employed to optimize the C3k2 module in the Neck stage, thereby improving the localization accuracy of deep features without increasing network complexity. Meanwhile, the Minimum Point Distance based IoU(MPDIoU) loss function is adopted to mitigate gradient explosion risks while enhancing detection precision. Furthermore, a lightweight detail-enhancement convolution layer and a shared-convolution detection head are designed to improve the model’s capability in capturing fine-grained details. Finally, a knowledge distillation procedure is conducted using SCL-YOLOv11 as the student model and YOLOv8 as the teacher model, ensuring that the proposed SCL-YOLOv11 model achieves both low complexity and excellent detection performance. Experimental results on the ExDark dataset demonstrate that the proposed algorithm achieves 67.6% mAP@0.5 and 42.4% mAP@0.5:0.95, while reducing parameter count by 38.5% and computational cost by 25.4%. Consequently, the proposed model is highly suitable for edge deployment in industrial applications and embedded development systems.}, archive = {J_ACCESS}, author = {Shulong Zhuo and Hao Bai and Lifeng Jiang and Xiaojian Zhou and Xu Duan and Yiqun Ma and Zihan Zhou}, doi = {10.1109/ACCESS.2025.3550947}, journal = {IEEE Access}, pages = {47653-47662}, shortjournal = {IEEE Access}, title = {SCL-YOLOv11: A lightweight object detection network for low-illumination environments}, volume = {13}, year = {2025}, }
• (2025). PROMORE: A procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contexts. ACCESS, 13, 47632-47652. (www)

  @article{ , abstract = {Remote sensing (RS) is a rapidly evolving field that facilitates the study of phenomena on the Earth’s surface. Through various platforms, including satellites, manned aircraft, and remotely piloted aerial vehicles (RPAV), RS has been strategically applied to critical sectors like agriculture and forestry, which are essential for humanity’s sustenance. Key applications include crops classification, yield estimation and livestock monitoring and quantification. In the era of artificial intelligence (AI), the development of deep learning (DL) models for such applications often requires extensive field data collection and labor-intensive image labeling, which are both time-consuming and resource-intensive. To address these challenges, this paper presents Procedural Modeling of Rural Environments (PROMORE), a parameterizable, ontology-driven system designed to generate 3D virtual environments encompassing forestry, farmland – mainly focused on vineyards – and village settings. This system also implements functionalities to automate the extraction of training data for deep learning applications in remote sensing, with the declared aim of providing complementary capabilities to data augmentation techniques, encompassing both traditional methods (e.g., flips, rotations, zooming) and advanced approaches such as generative adversarial networks (GANs). By simulating RPAV flights and managing virtual object visibility, PROMORE enables the automatic labeling, delineation, and highlighting of elements of interest (e.g., vine plants, trees, buildings), facilitating the generation of datasets tailored for tasks such as semantic segmentation, and object detection.}, archive = {J_ACCESS}, author = {Telmo Adão and João Cerqueira and Miguel Adão and Nuno Silva and David Pascoal and Luís G. Magalhães and Tiago Barros and Cristiano Premebida and Urbano J. Nunes and Emanuel Peres and Raul Morais}, doi = {10.1109/ACCESS.2025.3548513}, journal = {IEEE Access}, pages = {47632-47652}, shortjournal = {IEEE Access}, title = {PROMORE: A procedural modeler of virtual rural environments with artificial dataset generation capabilities for remote sensing contexts}, volume = {13}, year = {2025}, }
• (2025). A hybrid optimization algorithm for a multi-objective aircraft loading problem with complex constraints. ACCESS, 13, 47617-47631. (www)

  @article{ , abstract = {The optimization of aircraft loading problems are critical for operational efficiency and safety in the aviation industry. Therefore, how to improve the convergence speed and solution quality for complex multi-objective optimization problems in aircraft loading raises widespread concerns. Existing solutions are monolithic and cannot optimize multiple performance simultaneously. In this paper, we propose a Hybrid Optimization Algorithm for Multi-objective Problems with Complex Constraints (HybridMOCC) to solve the aircraft loading problem. Specifically, we present a comprehensive analysis of the proposed HybridMOCC algorithm, detailing its theoretical foundations and operational intricacies. Through rigorous experimental setups using aircraft loading, the algorithm’s performance is juxtaposed against several state-of-the-art optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and others. The experimental results show that the HybridMOCC’s superior performance in terms of solution quality, convergence speed, and consistency. Furthermore, the research delves into the real-world challenges and limitations of implementing the HybridMOCC algorithm in dynamic and complex aviation transportation environments. Potential future directions, including adaptive parameter tuning, hybrid approaches, and real-time optimization, are also explored.}, archive = {J_ACCESS}, author = {Boliang Zhang and Yu Yao and H. Y. Kan and Mei-Pou Chan and Chan-Tong Lam and Sio-Kei Im}, doi = {10.1109/ACCESS.2025.3545478}, journal = {IEEE Access}, pages = {47617-47631}, shortjournal = {IEEE Access}, title = {A hybrid optimization algorithm for a multi-objective aircraft loading problem with complex constraints}, volume = {13}, year = {2025}, }
• (2025). Suggestion for camera location in monocular markerless 3D motion capture system: Focused on accuracy comparison with marker-based system for upper limb joints. ACCESS, 13, 47605-47616. (www)

  @article{ , abstract = {A markerless motion capture system that analyzes human movements without markers has emerged. However, most studies use 2D coordinates to track basic movements from fixed viewpoints, with limited research on joint movement accuracy based on camera angles. This study evaluates a monocular markerless 3D motion capture system’s accuracy from various RGB camera positions and identifies optimal placements by comparing it with a marker-based system. Ten male participants in their 20s performed clinical range of motion (ROM) tasks, and joint angles were measured from different camera positions. The mean absolute error (MAE) and Pearson correlation coefficient between the two systems were analyzed. The neck joint showed the highest accuracy at 0°, while 45° and 315° were viable for flexion/extension. The shoulder joint performed best at 135°, 180°, and 315°, while the elbow joint showed the highest accuracy at 90° and 135°. The wrist joint had the lowest accuracy, but considering its correlation with flexion/extension, the 0°, 90°, and 270° positions may still be viable. For comprehensive upper body analysis, positioning the camera at 45° or 315° ensures accurate neck assessment while enabling balanced evaluation of lateral flexion, abduction/adduction, and flexion/extension. Future research should improve estimation performance in cases of body occlusion by expanding datasets and refining algorithms. Additionally, analyzing tilt caused by distance variations and applying a correction ratio could help mitigate MediaPipe’s tilt issue. Identifying optimal camera placements can enhance the accuracy of monocular markerless 3D motion capture, supporting its broader clinical application.}, archive = {J_ACCESS}, author = {Taeu Kim and Myung-Chul Jung and Seung-Min Mo}, doi = {10.1109/ACCESS.2025.3547715}, journal = {IEEE Access}, pages = {47605-47616}, shortjournal = {IEEE Access}, title = {Suggestion for camera location in monocular markerless 3D motion capture system: Focused on accuracy comparison with marker-based system for upper limb joints}, volume = {13}, year = {2025}, }
• (2025). Predicting atmospheric dispersion of industrial chemicals using machine learning approaches. ACCESS, 13, 47587-47604. (www)

  @article{ , abstract = {This study presents an intelligent framework for assessing atmospheric dispersion in industrial accident scenarios involving chemical substances. The research focuses on modeling the dispersion of key chemicals, such as chlorine, methanol, and propane, under various accident conditions, including leaks, fires, and explosions. Atmospheric and contextual variables, such as wind speed, air temperature, tank specifications, and chemical release parameters, were thoroughly characterized to construct a robust database using experimental data and software simulations. Machine learning techniques were rigorously trained and tested to predict atmospheric dispersion, emphasizing hyperparameter optimization to enhance model performance. Dimensionality reduction methods, such as principal component analysis and correlation-based dimensionality reduction, were implemented to improve computational efficiency, reduce data noise, and maintain essential information. Results demonstrate the effectiveness of the proposed approach, with satisfactory predictions across all evaluated risk areas. Key contributions include the development of a replicable framework adaptable to diverse industrial scenarios, applying hyperparameter tuning to optimize model accuracy, and integrating dimensionality reduction techniques to streamline data processing. These advancements establish a foundation for future studies to incorporate additional chemicals and accident scenarios, improving the flexibility and reliability of atmospheric dispersion modeling. Future work will explore hybrid machine learning models and advanced dimensionality reduction methods to enhance the system’s applicability to complex industrial environments.}, archive = {J_ACCESS}, author = {María Valle and Jairo A. Cardona and César Viloria-Nuñez and Christian G. Quintero M.}, doi = {10.1109/ACCESS.2025.3551259}, journal = {IEEE Access}, pages = {47587-47604}, shortjournal = {IEEE Access}, title = {Predicting atmospheric dispersion of industrial chemicals using machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). Datasets, machine learning recognition performance, and key issues in nursing care activity recognition: A systematic review. ACCESS, 13, 47570-47586. (www)

  @article{ , abstract = {Countries experiencing rapid aging face severe caregiver shortages, necessitating effective workforce optimization. Nursing care activity recognition (NCAR) offers a promising solution by automatically detecting caregiving tasks over time and visualizing workflows. Based on this information, NCAR can facilitate improvements in caregiving processes. This review systematically assessed 32 studies following PRISMA guidelines, analyzing dataset characteristics and recognition performance of algorithms in NCAR to identify factors influencing recognition accuracy. The review revealed significant challenges, including a lack of publicly available datasets, class imbalance, and decreased recognition accuracy in real-world environments. Additionally, sensor configuration and annotation quality were found to significantly impact recognition performance. The limited availability of field data restricts model generalization, highlighting the need for novel algorithms and efficient data collection methods. Expanding datasets, designing appropriate classes, and improving algorithms are critical steps for advancing NCAR research and practical applications. The findings of this study demonstrate the potential of NCAR in optimizing caregiving workflows and mitigating workforce shortages.}, archive = {J_ACCESS}, author = {Yasuyuki Yoshida and Hiroyasu Miwa and Satoshi Nakae and Satoki Ogiso and Ryosuke Ichikari and Takashi Okuma}, doi = {10.1109/ACCESS.2025.3550579}, journal = {IEEE Access}, pages = {47570-47586}, shortjournal = {IEEE Access}, title = {Datasets, machine learning recognition performance, and key issues in nursing care activity recognition: A systematic review}, volume = {13}, year = {2025}, }
• (2025). BGL-PhishNet: Phishing website detection using hybrid model-BERT, GNN, and LightGBM. ACCESS, 13, 47552-47569. (www)

  @article{ , abstract = {Phishing attacks exploit human and technological vulnerabilities to steal sensitive information, posing a significant threat to online security. To address this challenge, this research introduces a new hybrid method for phishing detection using three advanced techniques: BERT for analyzing texts, Graph Neural Networks for analyzing URLs, and LightGBM for extracting key metadata features. The hybrid approach combines these techniques into a multi-layered model to improve detection accuracy by focusing on text, URL structure, and metadata. The hybrid model achieves an accuracy of 97.3%, outperforming current state-of-the-art models. Experimental results show that this method reduces false positives and enhances phishing detection across various online platforms. The system ensures better real-time performance by integrating outputs from BERT, GNNs, and LightGBM. It also addresses evolving phishing tactics by considering both lexical and structural URL features. This multi-level approach highlights the importance of strengthening online security through robust detection mechanisms.}, archive = {J_ACCESS}, author = {S. Remya and Manu J. Pillai and B. S. Aparna and Somula Rama Subbareddy and Yong Yun Cho}, doi = {10.1109/ACCESS.2025.3551542}, journal = {IEEE Access}, pages = {47552-47569}, shortjournal = {IEEE Access}, title = {BGL-PhishNet: Phishing website detection using hybrid model-BERT, GNN, and LightGBM}, volume = {13}, year = {2025}, }
• (2025). Damage detection method for road ancillary facilities integrating attention mechanism. ACCESS, 13, 47539-47551. (www)

  @article{ , abstract = {Anti-glare reflective stickers, as essential road ancillary facilities on highways, play a crucial role in ensuring driver visibility during nighttime and adverse weather conditions. However, existing detection methods often suffer from low accuracy, high computational complexity, and large model sizes when dealing with complex backgrounds, small targets, and long-distance conditions. To address these challenges, this paper proposes a damage detection model for road ancillary facilities that integrates an attention mechanism. The model first introduces the D-GhostNet V3Conv module, replacing the standard convolutional layers, significantly enhancing feature extraction capabilities while reducing computational costs. Additionally, the improved AR-BiFormer attention mechanism is integrated into the backbone network, enabling adaptive weight adjustment of feature maps through parallel processing of contextual information, thereby effectively improving the detection of small targets in complex scenes. Lastly, the WIoUv3 bounding box loss function is employed to optimize the regression performance of bounding boxes, ensuring higher localization accuracy under different scales and overlap conditions. The experimental results show that the improved model achieves a mAP of 89.78% and an FPS of 86.31 in complex road scenes, which are 1.57% and 1.82% higher than the original YOLOv8 model, respectively, which substantially improves the detection accuracy and real-time performance of the model, and it is especially suitable for highway monitoring tasks in edge computing environments.}, archive = {J_ACCESS}, author = {Shuang Yang and Huiqin Wang and Ke Wang and Nan Guo}, doi = {10.1109/ACCESS.2025.3546622}, journal = {IEEE Access}, pages = {47539-47551}, shortjournal = {IEEE Access}, title = {Damage detection method for road ancillary facilities integrating attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Improved results synchronization on secure communication in complex dynamical networks with time-varying delay using delay-dependent feedback control. ACCESS, 13, 47525-47538. (www)

  @article{ , abstract = {This article addresses the problem of synchronization on secure communications in complex dynamical networks (CDNs) with the time-varying coupling delay using delay-dependent feedback control. To enhance the security of communication between the main system and each node of CDNs, the $\aleph $ -shift cipher, a cryptographic technique designed to encrypt plain signals, and a public key are used to encrypt the original information signal transmitted from the main system. After fully accounting for the nonlinear function, a unique Lyapunov-Krasovskii functional (LKF) is developed. Subsequently, new criteria are derived using novel integral inequalities and the reciprocally convex combination method to handle the derivative of the LKF, which is finally formulated in terms of linear matrix inequality (LMI). These criteria recover the original information signal on each CDNs node and ensure stable synchronization, thus facilitating secure communication among all nodes of the main system and CDNs. The efficacy and merits of the synchronization criteria are demonstrated using Chua’s circuit in four distinct scenarios.}, archive = {J_ACCESS}, author = {Arthit Hongsri and Ovidiu Bagdasar and Narongsak Yotha}, doi = {10.1109/ACCESS.2025.3550589}, journal = {IEEE Access}, pages = {47525-47538}, shortjournal = {IEEE Access}, title = {Improved results synchronization on secure communication in complex dynamical networks with time-varying delay using delay-dependent feedback control}, volume = {13}, year = {2025}, }
• (2025). Robust model-free predictive torque control for induction motor drive based on an ultralocal model. ACCESS, 13, 47514-47524. (www)

  @article{ , abstract = {Model predictive torque control (PTC) is a high-performance control strategy for induction motor (IM) drives. However, its performance is highly dependent on the motor parameters. Besides, the controller suffers from a high computational burden. In this manuscript, a new robust model-free predictive torque control (MF-PTC) is proposed to avoid the effect of the machine parameters mismatch and also to reduce the computational burden. To form a model-free predictive controller, the ultralocal model is used instead of the simplified mathematical model of an IM. The unknown part of the ultralocal model is estimated using a linear extended state observer (ESO). However, tuning the local parameters of the ESO is a difficult task. Hence, a simple mathematical model is employed in this study to design the local parameters of the ESO. This model is completely independent of the motor parameters, and tuning the local parameters of the ESO is a less complex task. The experimental results confirm that the proposed MF-PTC is robust and computationally efficient in comparison with the conventional PTC.}, archive = {J_ACCESS}, author = {Md. Belal Hossen and Md. Habibullah}, doi = {10.1109/ACCESS.2025.3550924}, journal = {IEEE Access}, pages = {47514-47524}, shortjournal = {IEEE Access}, title = {Robust model-free predictive torque control for induction motor drive based on an ultralocal model}, volume = {13}, year = {2025}, }
• (2025). Active learning framework for improving knowledge graph accuracy. ACCESS, 13, 47500-47513. (www)

  @article{ , abstract = {Knowledge graphs are graph-structured data models that provide a robust scheme for representing real-world relational facts with structured triples. The structural and factual information in knowledge graphs are extensively leveraged in various downstream applications. Unfortunately, knowledge graphs often contain incorrect triples due to the automated extraction processes. Therefore, to ensure the reliability and usability of knowledge graphs, it is crucial to identify and rectify these incorrect triples. However, this remains a challenging task, as knowledge graphs are intricate structures encompassing a vast number of triples formed by diverse entities, relations, and their complex interconnections. This paper proposes an effective method to enhance knowledge graph accuracy by introducing an active learning framework. The proposed framework integrates the advantages of machine-based models and human involvement to enable efficient and reliable improvement in knowledge graph accuracy. Additionally, the proposed method includes sampling strategies that consider the relation distribution in knowledge graphs to maximize the effectiveness of the active learning framework. Extensive experimental results demonstrate the effectiveness of the proposed active learning framework and sampling strategies in improving knowledge graph accuracy. Furthermore, this paper provides an exploration of the level of human involvement and a discussion of practical approaches to improve knowledge graph accuracy in real-world scenarios.}, archive = {J_ACCESS}, author = {Donghyun Kim and Hyeongjun Yang and Seokju Hwang and Kyuhwan Yeom and Midan Shim and Kyong-Ho Lee}, doi = {10.1109/ACCESS.2025.3551370}, journal = {IEEE Access}, pages = {47500-47513}, shortjournal = {IEEE Access}, title = {Active learning framework for improving knowledge graph accuracy}, volume = {13}, year = {2025}, }
• (2025). Continual learning with quasi-newton methods. ACCESS, 13, 47485-47499. (www)

  @article{ , abstract = {Catastrophic forgetting remains a major challenge when neural networks learn tasks sequentially. Elastic Weight Consolidation (EWC) attempts to address this problem by introducing a Bayesian-inspired regularization loss to preserve knowledge of previously learned tasks. However, EWC relies on a Laplace approximation where the Hessian is simplified to the diagonal of the Fisher information matrix, assuming uncorrelated model parameters. This overly simplistic assumption often leads to poor Hessian estimates, limiting its effectiveness. To overcome this limitation, we introduce Continual Learning with Sampled Quasi-Newton (CSQN), which leverages Quasi-Newton methods to compute more accurate Hessian approximations. CSQN captures parameter interactions beyond the diagonal without requiring architecture-specific modifications, making it applicable across diverse tasks and architectures. Experimental results across four benchmarks demonstrate that CSQN consistently outperforms EWC and other state-of-the-art baselines, including rehearsal-based methods. CSQN reduces EWC’s forgetting by 50% and improves its performance by 8% on average. Notably, CSQN achieves superior results on three out of four benchmarks, including the most challenging scenarios, highlighting its potential as a robust solution for continual learning.}, archive = {J_ACCESS}, author = {Steven Vander Eeckt and Hugo Van Hamme}, doi = {10.1109/ACCESS.2025.3551146}, journal = {IEEE Access}, pages = {47485-47499}, shortjournal = {IEEE Access}, title = {Continual learning with quasi-newton methods}, volume = {13}, year = {2025}, }
• (2025). Forward-flyback converter with cockcroft-walton voltage multiplier in DCM: Averaged small-signal modeling and valley-switching control design to supply electrohydrodynamic devices. ACCESS, 13, 47475-47484. (www)

  @article{ , abstract = {The Forward-Flyback converter with Cockcroft-Walton Voltage Multiplier (FFCWVM) operating in discontinuous conduction mode (DCM) is validated in Serrano-Vargas et al. (2021) as a suitable topology to supply novel cooling systems based in Electrohydrodynamic (EHD) technology. More details about the topology analysis, the steady-state operation and the whole design and optimization of the FFCWVM are given in Serrano Vargas (2022), where this converter is validated at different specifications. Since the conductivity of the EHD devices is reduced over time due to the erosion of the electrodes, the output voltage must be accurately controlled and the design of the control stage becomes essential, which is the main focus of this paper. The valley-switching control technique is proposed to optimize the performance of the FFCWVM under high voltage and very low power specifications due to its inherent resonant operation. In addition, the main functionalities required to control the FFCWVM while supplying the EHD devices are described. Then, the averaged modeling of the FFCWVM operating in DCM is achieved based on a first-order equivalent circuit approximation and considering the effect of the parasitic capacitances and the resonant currents in the circuit. As a result, two different averaged small-signal models (a simplified and a more accurate one) are accomplished, allowing for calculating the control-to-output transfer functions. Based on them, the most representative bode plots are highlighted and validated by means of simulations, and a suitable Type-II regulator is designed. Finally, a prototype is built and experimental results are presented, achieving a 94.07% global efficiency (96.03% open loop efficiency) and validating the proposed averaged modeling analysis and the implemented valley switching controller.}, archive = {J_ACCESS}, author = {Juan Antonio Serrano-Vargas and Regina Ramos and Jesús A. Oliver and Pedro Alou}, doi = {10.1109/ACCESS.2025.3550813}, journal = {IEEE Access}, pages = {47475-47484}, shortjournal = {IEEE Access}, title = {Forward-flyback converter with cockcroft-walton voltage multiplier in DCM: Averaged small-signal modeling and valley-switching control design to supply electrohydrodynamic devices}, volume = {13}, year = {2025}, }
• (2025). OptiASAR: Optimized aspect-based sentiment analysis of reviews with BiLSTM-GRU and NER-BERT in healthcare decision-making. ACCESS, 13, 47459-47474. (www)

  @article{ , abstract = {In the healthcare sector, understanding patient feedback through Aspect-Based Sentiment Analysis (ABSA) is vital, as it provides detailed insights into various elements like doctor-patient interactions, treatment efficacy, and hospital amenities. Traditional sentiment analysis methods may not capture the diverse aspects and often miss these subtleties. This paper proposes an OptiASAR (Optimized Aspect-based Sentiment Analysis of Reviews) model integrating a Bidirectional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM-GRU) for enhanced sequence modeling, improving the capture of long-term dependencies in healthcare reviews. By using the Named Entity Recognition and Bidirectional Encoder Representations from Transformers (NER-BERT) embeddings, the model enhances the representation and contextual comprehension of aspect terms. Furthermore, a multi-headed self-attention mechanism is employed to focus on different parts of the input, thereby capturing complex relationships and dependencies more effectively. The study highlights significant improvements in sentiment analysis performance, particularly within the healthcare domain. The proposed OptiASAR model achieved promising performance in terms of accuracy compared with contemporary models, offering healthcare professionals and stakeholders a robust tool to understand sentiments related to various healthcare service aspects, thus, aiding informed decision-making and quality enhancement initiatives.}, archive = {J_ACCESS}, author = {Divanshu Singh and Sunita Shivprakash Barve and Amit Krishna Dwivedi}, doi = {10.1109/ACCESS.2025.3549303}, journal = {IEEE Access}, pages = {47459-47474}, shortjournal = {IEEE Access}, title = {OptiASAR: Optimized aspect-based sentiment analysis of reviews with BiLSTM-GRU and NER-BERT in healthcare decision-making}, volume = {13}, year = {2025}, }
• (2025). Intelligent resource allocation via hybrid reinforcement learning in 5G network slicing. ACCESS, 13, 47440-47458. (www)

  @article{ , abstract = {Manufacturers are focusing on reconfigurable, resilient environments for Industry 5.0 paradigms. Applications like digital twins and mobile robots require communication networks to meet latency, bandwidth, and reliability requirements. Beyond 5G (B5G) networks provide unprecedented communications performance and flexibility through virtualization and network slicing, which generates various logical partitions for particular applications with specific requirements. RAN slicing is an essential section of 5G network slicing due to its vulnerability to errors, affecting its ability to meet stringent reliability requirements. This paper presents a novel framework for optimizing resource allocation in 5G network slicing by integrating Double Deep Q-Network with Prioritized Experience Replay (DDQN-PER) and Pointer Network-based Long Short-Term Memory (PtrNet-LSTM). The proposed framework dynamically adjusts the attention coefficient, balancing Service Satisfaction Level (SSL) and Quality of Experience (QoE), improving system efficiency, spectrum efficiency, and user connectivity across diverse user scenarios. The experiment illustrates that the combined PtrNet-LSTM framework within DDQN-PER outperforms the baseline methods in terms of spectrum efficiency and user connectivity, demonstrating scalability and the potential to address challenges in dynamic wireless networks.}, archive = {J_ACCESS}, author = {Zahraa Zakariya Saleh and Maysam F. Abbod and Rajagopal Nilavalan}, doi = {10.1109/ACCESS.2025.3550518}, journal = {IEEE Access}, pages = {47440-47458}, shortjournal = {IEEE Access}, title = {Intelligent resource allocation via hybrid reinforcement learning in 5G network slicing}, volume = {13}, year = {2025}, }
• (2025). Enhancing segmentation: A comparative study of clustering methods. ACCESS, 13, 47418-47439. (www)

  @article{ , abstract = {With the increasing complexity of consumer preferences and behaviors, businesses face challenges to capture the dynamic nature of online consumer behavior, highlighting the need for advanced approaches. This study aims to enhance customer segmentation in e-marketing by analyzing and comparing various machine learning-based clustering methods, with a particular focus on unsupervised clustering techniques for predicting Customer Lifetime Value (CLV). While prior research has utilized unsupervised clustering for customer segmentation, this current study uniquely integrates K-Means++ with other clustering techniques to enhance segmentation accuracy and gain deeper insights into consumer behavior. This study adopts a structured, unsupervised clustering approach, enabling natural customer groupings without predefined labels, which is particularly suitable for customer segmentation in scenarios with limited labeled data. Several clustering techniques are investigated, including K-Means, K-Medoids, Agglomerative Clustering, DBSCAN, Fuzzy C-Means, K-Means++, Mini Batch K-Means, Mean Shift, and Gaussian Mixture Models (GMM). K-Means++ demonstrated superior performance in segmentation accuracy, outperforming other techniques under various conditions. Performance is evaluated using key metrics such as the Silhouette Score and Davies-Bouldin Index. Utilizing Kaggle datasets, the analysis follows a comprehensive preprocessing protocol comprising RFM (Recency, Frequency, Monetary) analysis, outlier removal, and data normalization to ensure data integrity and facilitate systematic identification of distinct consumer segments. This research highlights the potential and significance of machine learning in refining customer segmentation processes within e-marketing, ultimately aiding businesses in optimizing their marketing effectiveness and strategic planning. While focusing primarily on a limited selection of clustering methods, the study underscores the necessity for ongoing exploration in the realm of consumer segmentation. By utilizing advanced clustering methods such as K-Means++, businesses can enhance the marketing efforts to succeed in the competitive e-marketing landscape. Unlike previous studies that often relied on traditional techniques, which may not fully capture the complexities of consumer behavior, this study introduces a comprehensive approach that leverages multiple clustering methods to gain deeper insights into consumer behavior. Additionally, considering the study limitations, further research could explore additional clustering techniques, refine predictive modeling approaches and investigate the generalizability of findings to industries beyond e-marketing.}, archive = {J_ACCESS}, author = {Lew Sook Ling and Claireta Tang Weiling}, doi = {10.1109/ACCESS.2025.3550339}, journal = {IEEE Access}, pages = {47418-47439}, shortjournal = {IEEE Access}, title = {Enhancing segmentation: A comparative study of clustering methods}, volume = {13}, year = {2025}, }
• (2025). Cross-scale transformer-based matching network for generalizable person re-identification. ACCESS, 13, 47406-47417. (www)

  @article{ , abstract = {While the person re-identification (Re-ID) task has made significant progress in closed-set setting in recent years, its generalizability to unknown domains continues to be limited. To tackle the issue, the domain generalization (DG) Re-ID task has been proposed. The current state-of-the-art approach involves deep feature matching, where key regions of image pairs are matched at the same scale. However, the method does not take into account the variability of angles in real image acquisition. To resolve the problem, we propose an innovative deep image matching framework called Cross-scale Transformer-based Matching Network (CTMN) for DG Re-ID task. CTMN model matches two images through cross-scale local respondence rather than using fixed representations. The Transformer is specifically adjusted to enable effective local interactions between query and gallery images across different scales. Additionally, deformable convolution is incorporated to better segment the local regions of the person before the procedure for matching image pairs. Lastly, the Style Normalization Module (SNM) is added to remove identity-irrelevant features, improving the matching results. Extensive experiments on multiple DG Re-ID tasks demonstrate the advantages of our proposed method over existing state-of-the-arts.}, archive = {J_ACCESS}, author = {Junjie Xiao and Jinhua Jiang and Jianyong Huang and Wei Hu and Wenfeng Zhang}, doi = {10.1109/ACCESS.2025.3548321}, journal = {IEEE Access}, pages = {47406-47417}, shortjournal = {IEEE Access}, title = {Cross-scale transformer-based matching network for generalizable person re-identification}, volume = {13}, year = {2025}, }
• (2025). Non-destructive methods based on machine learning for the prediction of sweet potato leaf area: A comparative approach. ACCESS, 13, 47389-47405. (www)

  @article{ , abstract = {Leaf area is an essential parameter for studies of plant growth and physiology and is considered one of the main parameters for agricultural production. Leaf area determination methods are fundamental to understanding and predicting crop productivity. They can be classified as destructive or non-destructive. The study evaluated the performance of five methods for predicting the leaf area of sweet potato cultivars, including simple linear regression, artificial neural networks, support vector regression, adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF). The coefficient of determination (R2), relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean bias error (MBE) were used as criteria for choosing the best methods. The standardized Ranking Performance Index (sRPI) was used to classify the proposed methods. The ANFIS method performed better than the other methods analyzed (R $^{2} =0.8315$ ; RRMSE =0.0593; MAE =6.0789; MAPE =14.5741; MBE =0.000003; sRPI =1.00). Thus, the results indicated that the ANFIS method can be used as a non-destructive alternative for predicting the leaf area of sweet potato cultivars.}, archive = {J_ACCESS}, author = {João Everthon Da Silva Ribeiro and Ester Dos Santos Coêlho and Antonio Gideilson Correia Da Silva and Pablo Henrique De Almeida Oliveira and Elania Freire Da Silva and Gisele Lopes Dos Santos and Anna Kézia Soares De Oliveira and John Victor Lucas Lima and Walter Esfrain Pereira and Lindomar Maria Da Silveira and Aurélio Paes Barros}, doi = {10.1109/ACCESS.2025.3551270}, journal = {IEEE Access}, pages = {47389-47405}, shortjournal = {IEEE Access}, title = {Non-destructive methods based on machine learning for the prediction of sweet potato leaf area: A comparative approach}, volume = {13}, year = {2025}, }
• (2025). Stochastic geometry analysis of reconfigurable intelligent surface-assisted millimeter-wave energy harvesting networks. ACCESS, 13, 47375-47388. (www)

  @article{ , abstract = {Energy harvesting (EH) in millimeter-wave (mm-wave) cellular networks has gained significant attention due to the widespread use of large antenna arrays and dense base station (BS) deployments. However, mm-wave signals are highly susceptible to path attenuation caused by significant atmospheric and obstacle-induced absorption, which can limit coverage and degrade the performance of EH systems due to high path losses. This paper considers the use of reconfigurable intelligent surface (RIS)-assisted mm-wave networks as a solution to enhance EH performance. We propose an analytical framework based on stochastic geometry to evaluate the energy coverage probability (ECP) performance of user equipment (UE) in these networks, deriving a closed-form expression for the ECP. The analytical formula for average harvested energy (AHE) is also provided to help characterize system performance. The findings show that deploying RISs can significantly improve EH performance in mm-wave networks, even in challenging urban areas with significant path loss. The findings also show that dense deployment of passive RISs significantly improves EH comparable to active BS deployment. Furthermore, the findings indicate that adding passive reflectors is as effective as equipping the BS with additional active antenna elements to enhance ECP and AHE. This study provides valuable insights for designing future EH strategies tailored to end-UEs in RIS-assisted mm-wave networks, emphasizing the efficacy of RISs in improving network performance and EH capabilities.}, archive = {J_ACCESS}, author = {Mohammed Mehdi Saleh and Nor Aishah Muhammad and Norhudah Seman and Nur Ilyana Anwar Apandi}, doi = {10.1109/ACCESS.2025.3547837}, journal = {IEEE Access}, pages = {47375-47388}, shortjournal = {IEEE Access}, title = {Stochastic geometry analysis of reconfigurable intelligent surface-assisted millimeter-wave energy harvesting networks}, volume = {13}, year = {2025}, }
• (2025). The art of retention: Advancing sustainable management through age-diverse turnover modeling. ACCESS, 13, 47361-47374. (www)

  @article{ , abstract = {In the diverse modern workforce, understanding employee turnover across different age groups is critical for effective human resource management. Existing research often treats employees as a homogeneous group, potentially overlooking nuanced age-specific dynamics. In this study, we introduce a novel application of the advanced elastic data shared lasso (DSL2) method to simultaneously identify both shared and age-specific predictors from a large employee dataset from a multinational technology company. Our analysis reveals 18 key variables – including factors such as overtime, history of job hopping, and commute distance – that universally drive turnover across age cohorts. Moreover, the findings indicate that while competitive compensation and dynamic career opportunities exert a stronger influence on younger employees, job stability and long-term recognition prove more critical for older workers. The integrated turnover models achieve outstanding predictive performance with area under the curve (AUC) scores exceeding 96.86 percent across all age groups. These insights underscore the need for tailored retention strategies that address the unique needs of different age segments, thereby advancing turnover theory and practical human resource management.}, archive = {J_ACCESS}, author = {Yan-Qin Chen and Hai-Hui Huang}, doi = {10.1109/ACCESS.2025.3551338}, journal = {IEEE Access}, pages = {47361-47374}, shortjournal = {IEEE Access}, title = {The art of retention: Advancing sustainable management through age-diverse turnover modeling}, volume = {13}, year = {2025}, }
• (2025). Cache-aware timing analysis of limited preemption scheduling with fixed preemption points in set-associative mapping. ACCESS, 13, 47346-47360. (www)

  @article{ , abstract = {In a limited preemption real-time system with a cache architecture, scheduling analysis must not only consider the execution time of tasks and the blocking of lower-priority tasks, but also precisely analyze cache-related preemption delays (CRPD). In set-associative mapping, the existing schedulability analysis for limited preemption scheduling with fixed preemption points employs overly conservative strategies for calculating cache-related preemption delays, which leads to pessimistic results in schedulability analysis. In this paper, we propose a cache-aware timing analysis method utilizing the oldest memory block in cache sets for analyzing sporadic real-time tasks with fixed preemption points in set-associative mapping. This method can more accurately analyze the cache-related preemption delays by examining whether memory blocks in the cache have been evicted. Experimental results demonstrate that the method proposed in this paper surpasses the best existing approaches, achieving a 17% higher recognition rate for schedulable task sets under the group concatenated mapping strategy.}, archive = {J_ACCESS}, author = {Pengyu Zhou}, doi = {10.1109/ACCESS.2025.3550384}, journal = {IEEE Access}, pages = {47346-47360}, shortjournal = {IEEE Access}, title = {Cache-aware timing analysis of limited preemption scheduling with fixed preemption points in set-associative mapping}, volume = {13}, year = {2025}, }
• (2025). User authentication method for smart rings using active acoustic sensing. ACCESS, 13, 47337-47345. (www)

  @article{ , abstract = {Wearable devices, such as bracelets and rings, have gained widespread popularity due to advancements in semiconductor technology, the miniaturization of devices and sensors, and the development of data analysis techniques. While tablets, smartwatches, and laptops incorporate biometrics like facial recognition and fingerprint identification for user authentication, many wearable devices lack user authentication features. In particular, smart rings (ring-type devices) do not come equipped with authentication capabilities. When user authentication is necessary, a fingerprint or an alternative device is required. This paper proposes an automatic and continuous user authentication method that utilizes active acoustic sensing, employing a speaker and microphone integrated within the smart ring. The proposed method authenticates the wearer by analyzing the frequency response of the acoustic signal captured by the microphone and comparing it to the frequency response of a pre-registered individual. The method takes advantage of the constant contact between the smart ring and the finger, as each person’s finger has unique acoustic characteristics based on its shape and composition. Two types of devices were developed for this study: a tape device featuring a flexible piezoelectric element attached to Velcro tape, and a ring device with a flexible piezoelectric element mounted on a 3D-printed ring. The tape device was evaluated with seven participants in a relaxing hand state, and the observed average Equal Error Rate (EER) was 0.127. Additionally, the ring-shaped device was evaluated with thirteen participants in three different states: a relaxing hand, a gripping hand, and a typing hand. The observed average EER for the relaxing hand was 0.066, for the gripping hand was 0.026, and for the typing hand was 0.078.}, archive = {J_ACCESS}, author = {Shunsuke Iwakiri and Kazuya Murao}, doi = {10.1109/ACCESS.2025.3549278}, journal = {IEEE Access}, pages = {47337-47345}, shortjournal = {IEEE Access}, title = {User authentication method for smart rings using active acoustic sensing}, volume = {13}, year = {2025}, }
• (2025). Digital twin in industries: A comprehensive survey. ACCESS, 13, 47291-47336. (www)

  @article{ , abstract = {Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.}, archive = {J_ACCESS}, author = {Md Bokhtiar Al Zami and Shaba Shaon and Vu Khanh Quy and Dinh C. Nguyen}, doi = {10.1109/ACCESS.2025.3551532}, journal = {IEEE Access}, pages = {47291-47336}, shortjournal = {IEEE Access}, title = {Digital twin in industries: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Remote sensing image detection method combining dynamic convolution and attention mechanism. ACCESS, 13, 47271-47290. (www)

  @article{ , abstract = {Small object detection in remote sensing images is challenging. Traditional CNN downsampling often leads to the loss of small object details and missed detections. This paper proposes an improved YOLOv8 algorithm, incorporating adaptive feature extraction and multi-scale fusion modules to enhance feature representation, effectively capturing the details of small objects. We introduced the AFGCAttention mechanism to strengthen the network’s focus on key regions while suppressing irrelevant background information, improving the model’s ability to recognize small objects. To address the resolution loss issue in small object detection, we adopted the CARAFE (Content-Aware ReAssembly of FEatures) upsampling operator. By performing content-aware reassembly of feature maps, CARAFE avoids the blurriness and information loss commonly associated with traditional upsampling methods, demonstrating significant advantages in reconstructing the details of small objects, resulting in clearer and more accurate boundaries. Additionally, to improve the accuracy of bounding box regression, we integrated the GIoU loss function to optimize the geometric matching between ground truth and predicted boxes, addressing the problem of inaccurate bounding box localization for small objects and enhancing localization precision. Experimental results demonstrate that the proposed algorithm significantly improves the precision of small object detection and maintains robustness in complex backgrounds. The improved model achieved an mAP of 83.0%, with accuracy improvements of 85.0%, 2.0%, and 5.0% compared to the baseline. Compared with existing detection methods, this approach shows outstanding performance in detection accuracy, localization precision, and computational efficiency, particularly excelling in small object detection.}, archive = {J_ACCESS}, author = {Yunfei Zhang and Ming Chen and Cong Chen}, doi = {10.1109/ACCESS.2025.3550586}, journal = {IEEE Access}, pages = {47271-47290}, shortjournal = {IEEE Access}, title = {Remote sensing image detection method combining dynamic convolution and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Advanced EEG signal processing and deep Q-learning for accurate student attention monitoring. ACCESS, 13, 47260-47270. (www)

  @article{ , abstract = {Measuring student attention during learning remains a challenging task. Traditional methods, such as surveys and observations, are subjective and often inaccurate. While EEG can detect attentional changes, its interpretation is complicated by noise and artifacts. This study introduces an enhanced approach that leverages advanced data preprocessing techniques and a Double Deep Q-Network (DDQN) model, a specialized deep reinforcement learning (DRL) algorithm adept at handling complex, multidimensional EEG data. We employ wavelet transformations to generate robust frequency-based representations of raw multi-channel EEG signals, followed by Butterworth bandpass and notch filters to eliminate noise and artifacts. Dimensionality reduction and feature scaling are achieved employing Principal Component Analysis (PCA) alongside Independent Component Analysis (ICA), leading in cleaner and more representative EEG data. Our method significantly improves the accuracy of attention state classification, achieving a test accuracy of 98.4%. This advancement sets a new standard for utilizing EEG data to monitor attention in educational settings, showcasing the effectiveness of our preprocessing techniques in enhancing the DDQN’s neural decoding capabilities.}, archive = {J_ACCESS}, author = {Asad Ur Rehman and Noor Ul Eman and Farhan Ullah and Diletta Cacciagrano and Leonardo Mostarda and Nabeel Raza}, doi = {10.1109/ACCESS.2024.3523225}, journal = {IEEE Access}, pages = {47260-47270}, shortjournal = {IEEE Access}, title = {Advanced EEG signal processing and deep Q-learning for accurate student attention monitoring}, volume = {13}, year = {2025}, }
• (2025). Optimizing building performance: A grasshopper modeling case study of the king hussein mosque. ACCESS, 13, 47244-47259. (www)

  @article{ , abstract = {In Mediterranean regions, traditional courtyard designs are celebrated for their passive cooling potential, yet modern mosques often rely heavily on energy-intensive cooling systems due to underperforming architectural strategies. This study addresses this paradox by leveraging parametric modeling to optimize courtyard-driven energy efficiency in the King Hussein Mosque, Jordan. Using a novel Grasshopper-based workflow integrating Ladybug, Honeybee, and Butterfly plugins, we quantify how courtyard orientation, enclosure, and fenestration impact Energy Use Intensity (EUI) and thermal comfort. Simulations revealed a baseline EUI of 111.132 kWh/m2 (EPC Rating E), with cooling demands contributing 20.432 kWh/m2 despite three courtyards designed for natural ventilation. Parametric testing of interventions—including partial courtyard enclosures and optimized window-to-wall ratios—reduced EUI to 98.989 kWh/m2 (EPC Rating D), achieving an 11% energy savings while maintaining the mosque’s architectural integrity. Thermal comfort in the prayer hall improved by 15%, with UTCI thresholds shifting from “high heat stress” to “moderate comfort” during summer occupancy. This work pioneers the application of Grasshopper in mosque energy optimization, demonstrating its superiority over static simulation tools by enabling real-time, multi-variable adjustments tailored to intermittent prayer schedules. The findings challenge assumptions about courtyard efficacy in contemporary religious architecture, offering actionable retrofitting guidelines for designers: strategic partial enclosures, localized shading, and adaptive fenestration. By bridging computational design with cultural heritage preservation, this study provides a replicable framework for decarbonizing religious buildings in arid climates, contributing to UN Sustainable Development Goals (SDGs) 7 (Affordable Energy) and 11 (Sustainable Cities).}, archive = {J_ACCESS}, author = {Majd Albaik and Rabab Muhsen}, doi = {10.1109/ACCESS.2025.3551226}, journal = {IEEE Access}, pages = {47244-47259}, shortjournal = {IEEE Access}, title = {Optimizing building performance: A grasshopper modeling case study of the king hussein mosque}, volume = {13}, year = {2025}, }
• (2025). A refined model for check-in and security staff allocation at airports based on an improved bayesian neural network. ACCESS, 13, 47230-47243. (www)

  @article{ , abstract = {This study addresses the challenge of optimizing staff allocation in airport check-in and security areas by considering the inherent randomness and complexity of individual passenger characteristics, a factor often overlooked in traditional research. Unlike existing studies that treat passengers as a homogeneous group and rely on static models or monitoring data, this paper introduces a novel approach by leveraging passenger-specific characteristics to predict behavior and optimize staff allocation accordingly. The proposed model integrates Graph Neural Networks (GNN) within the genetic algorithm framework to enhance chromosome encoding, enabling more effective feature extraction and structure learning in Bayesian Neural Networks (BNN). The results show that the GNN-GA BNN model outperforms advanced models like DNN and XGBoost, improving accuracy by 7% and 2%, respectively, and enhancing AUROC by 3% and 4%. Additionally, the study introduces Conditional Rényi Entropy as a new metric for intervention analysis within BNNs, offering a more refined understanding of passenger behavior. A comprehensive passenger handling behavior prediction model and staff allocation strategy are developed, aiming to optimize staff requirements at airport terminals. A detailed case study further validates the model, demonstrating its ability to accurately predict passenger handling behavior and optimize staff allocation under varying conditions of gender, age, and luggage characteristics, which significantly impact service times.}, archive = {J_ACCESS}, author = {Yuchen Sun and Keqiang Dong}, doi = {10.1109/ACCESS.2025.3551130}, journal = {IEEE Access}, pages = {47230-47243}, shortjournal = {IEEE Access}, title = {A refined model for check-in and security staff allocation at airports based on an improved bayesian neural network}, volume = {13}, year = {2025}, }
• (2025). Machine learning evaluation metric discrepancies across programming languages and their components in medical imaging domains: Need for standardization. ACCESS, 13, 47217-47229. (www)

  @article{ , abstract = {This study evaluates metrics for tasks such as classification, regression, clustering, correlation analysis, statistical tests, segmentation, and image-to-image (I2I) translation in medical imaging domains. Metrics were compared across Python libraries, R packages, and Matlab functions to assess their consistency and highlight discrepancies. The findings underscore the need for a unified roadmap to standardize metrics, ensuring reliable and reproducible ML evaluations across platforms. This study examined a wide range of evaluation metrics across various tasks in medical imaging and found only some to be consistent across platforms, such as Accuracy, Balanced Accuracy, Cohens Kappa, F-beta Score, MCC, Geometric Mean, AUC, and Log Loss in binary classification; Accuracy, Cohens Kappa, and F-beta Score in multi-class classification; MAE, MSE, RMSE, MAPE, Explained Variance, Median AE, MSLE, and Huber in regression; Davies-Bouldin Index and Calinski-Harabasz Index in clustering; Pearson, Spearman, Kendall’s Tau, Mutual Information, Distance Correlation, Bicor, Percbend, Shepherd, and Partial Correlation in correlation analysis; Paired t-test, Chi-Square Test, ANOVA, Kruskal-Wallis Test, Shapiro-Wilk Test, Welch’s t-test, and Bartlett’s test in statistical tests; Accuracy, Precision, and Recall in 2D segmentation; Accuracy in 3D segmentation; MAE, MSE, RMSE, and R-Squared in 2D-I2I translation; and MAE, MSE, and RMSE in 3D-I2I translation. Given observation of discrepancies in a number of metrics (e.g. precision, recall and F1 score in binary classification, WCSS in clustering, and multiple statistical tests, amongst multiple metrics), this study concludes that ML evaluation metrics require standardization and recommends that future research use consistent metrics for different tasks to effectively compare ML techniques and solutions.INDEX TERMS 2D/3D medical images, consistency of evaluation metrics in multi-framework, evaluation metric roadmap, ML evaluation metrics.}, archive = {J_ACCESS}, author = {Mohammad R. Salmanpour and Morteza Alizadeh and Ghazal Mousavi and Saba Sadeghi and Sajad Amiri and Mehrdad Oveisi and Arman Rahmim and Ilker Hacihaliloglu}, doi = {10.1109/ACCESS.2025.3549702}, journal = {IEEE Access}, pages = {47217-47229}, shortjournal = {IEEE Access}, title = {Machine learning evaluation metric discrepancies across programming languages and their components in medical imaging domains: Need for standardization}, volume = {13}, year = {2025}, }
• (2025). Low-voltage alternating current series arc fault detection using periodic background subtraction and linear dividing lines. ACCESS, 13, 47201-47216. (www)

  @article{ , abstract = {To address the challenges in fully expressing the fault features of low-voltage series arcs and the limitations of existing detection algorithms, this paper proposes a novel method combining periodic background subtraction and linear dividing lines for detecting arc faults. During feature extraction, a periodic background subtraction method is introduced, which calculates the difference between the periodic current signal and the average of the first four current periods nearest to the signal. This approach effectively suppresses interference from normal current variations and environmental noise, enabling a more robust expression of arc fault characteristics. For fault detection, a method based on linear dividing lines is developed to determine the optimal dividing line between fault and non-fault states for both linear and nonlinear load types via adaptive learning of the logistic regression model, achieving effective arc fault detection.}, archive = {J_ACCESS}, author = {Xiaofei Zhang and Jinjie Li and Bangzheng Han and Wei Wang and Guofeng Zou}, doi = {10.1109/ACCESS.2025.3548091}, journal = {IEEE Access}, pages = {47201-47216}, shortjournal = {IEEE Access}, title = {Low-voltage alternating current series arc fault detection using periodic background subtraction and linear dividing lines}, volume = {13}, year = {2025}, }
• (2025). An evolutional topology optimization method based on kernel level set function. ACCESS, 13, 47181-47200. (www)

  @article{ , abstract = {Topology optimizations involving evolutionary algorithms are promising approaches to solve practical engineering design problems, since their use of derivation-free algorithms makes them applicable to any design problem. In some evolutional topology optimization methods, structures are determined through the use of spatially smooth parametric functions. This article, first, presents a new parametric level set function using kernel functions as a general formulation of spatially smooth function-based methods. It is called the kernel level set function, which is used to determine the distributions of materials in the design region. By using the evolutionary algorithm to modify the profile of the kernel level set function, topology optimizations can be realized for various design problems, and the shape expression ability of the kernel level set function can be customized by changing the kernel functions. Second, a multi-objective formulation considering an objective and structural complexity is introduced, and an optimization algorithm specialized for solving it is proposed. The combination of the kernel level set function and the present optimization algorithm makes it possible to solve various topology optimization problems, and Pareto fronts with respect to target performance and structural complexity can be obtained. The present method is applied to three topology optimization problems. The numerical results are used to examine the characteristics of the present method for various kernel functions. Furthermore, the present method is applied to an optimization problem of an interior permanent magnet motor for electric vehicles. It is shown that the present method can solve practical design problems that have various objectives and constraint conditions, thanks to the use of a derivation-free evolutionary algorithm.}, archive = {J_ACCESS}, author = {Takahiro Sato and Kota Watanabe and Hajime Igarashi}, doi = {10.1109/ACCESS.2025.3550528}, journal = {IEEE Access}, pages = {47181-47200}, shortjournal = {IEEE Access}, title = {An evolutional topology optimization method based on kernel level set function}, volume = {13}, year = {2025}, }
• (2025). LLM-CDM: A large language model enhanced cognitive diagnosis for intelligent education. ACCESS, 13, 47165-47180. (www)

  @article{ , abstract = {Cognitive diagnosis is a key component of intelligent education to assess students’ comprehension of specific knowledge concepts. Current methodologies predominantly rely on students’ historical performance records and manually annotated knowledge concepts for analysis. However, the extensive semantic information embedded in exercises, including latent knowledge concepts, has not been fully utilized. This paper presents a novel cognitive diagnosis model based on the LLAMA3-70B framework (referred to as LLM-CDM), which integrates prompt engineering with the rich semantic information inherent in exercise texts to uncover latent knowledge concepts and improve diagnostic accuracy. Specifically, this study first inputs exercise texts into a large language model and develops an innovative prompting method to facilitate deep mining of implicit knowledge concepts within these texts by the model. Following the integration of these newly extracted knowledge concepts into the existing Q matrix, this paper employs a neural network to diagnose students’ understanding of knowledge concepts while applying the monotonicity assumption to ensure the interpretability of model factors. Experimental results from an examination data set for course completion assessments demonstrate that LLM-CDM exhibits superior performance in both accuracy and explainability.}, archive = {J_ACCESS}, author = {Xin Chen and Jin Zhang and Tong Zhou and Feng Zhang}, doi = {10.1109/ACCESS.2025.3549309}, journal = {IEEE Access}, pages = {47165-47180}, shortjournal = {IEEE Access}, title = {LLM-CDM: A large language model enhanced cognitive diagnosis for intelligent education}, volume = {13}, year = {2025}, }
• (2025). Transformer-based multi-target object detection and tracking framework for robust spatio-temporal memory in dynamic environments. ACCESS, 13, 47146-47164. (www)

  @article{ , abstract = {Multiple object tracking (MOT) is a critical task in computer vision, applied in areas such as autonomous driving, sports analytics, and human activity recognition. Traditional online MOT approaches, which rely on separate detection and re-identification (re-ID) stages, often suffer from high computational costs and suboptimal accuracy. To address these challenges, we propose MOTTNet, a unified framework that integrates detection and tracklet mapping using transformer-based architectures. MOTTNet leverages a spatiotemporal memory network to store past observations of tracked objects, allowing for more robust identity preservation and trajectory prediction over time. The Target Proposal Module generates object candidates using a transformer encoder-decoder structure, and the memory decoder integrates both proposal and track vectors to predict object locations and identities in real-time, minimizing post-processing. The framework incorporates a multi-scale attention pyramid to handle variations in object scale and utilizes a deformable transformer for generating accurate object proposals. A memory encoding-decoding aggregates object features and associates them across frames, allowing robust detection and identity preservation over time. MOTTNet achieves IDF1 scores of 79.8 and 80.4, MOTA scores of 79.3 and 80.1, and HOTA scores of 73.2 and 72.7, respectively, on on the MOT17 and MOT20 datasets. Additionally, MOTTNet exhibits lower identity switch rates, significantly outperforming existing transformer-based and traditional tracking models.}, archive = {J_ACCESS}, author = {Tareq Mahmod AlZubi and Umar Raza Mukhtar}, doi = {10.1109/ACCESS.2025.3551672}, journal = {IEEE Access}, pages = {47146-47164}, shortjournal = {IEEE Access}, title = {Transformer-based multi-target object detection and tracking framework for robust spatio-temporal memory in dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Review of cyber-physical systems modeling with UML, SysML, and MARTE. ACCESS, 13, 47132-47145. (www)

  @article{ , abstract = {In an era marked by the swift progress of technology, Cyber-Physical Systems (CPS) conquer one of the top places, standing out through the ability to integrate physical components and processes with software capabilities to improve efficiency and adeptness across diverse domains. This paper investigates the use of three standard modeling languages: Unified Modeling Language (UML), Systems Modeling Language (SysML), and Modeling and Analysis of Real-Time and Embedded Systems (MARTE) in the context of the design and development of CPS. The goal is to analyze how they address various stakeholders’ concerns, to what extent they cover viewpoints essential for CPS, what development phases they may support through a collection of specific model kinds, and what are the application domains that make use of these languages. This review offers a perspective on UML, SysML, and MARTE’s overlapping and differentiative aspects, leading to the underlying reasons for choosing multiple languages in the development of a CPS.}, archive = {J_ACCESS}, author = {Veronica Opranescu and Anca Daniela Ionita}, doi = {10.1109/ACCESS.2025.3551117}, journal = {IEEE Access}, pages = {47132-47145}, shortjournal = {IEEE Access}, title = {Review of cyber-physical systems modeling with UML, SysML, and MARTE}, volume = {13}, year = {2025}, }
• (2025). EMNv4-YOLO: A high-efficiency target detection framework for robotic driving vehicle instrument reading. ACCESS, 13, 47123-47131. (www)

  @article{ , abstract = {In the domain of autonomous vehicle testing, a crucial challenge in implementing onboard information perception systems is the development of lightweight detection network models that can be efficiently deployed on edge devices. In this study, we collected and collated 6,168 images encompassing automotive dashboards, industrial instruments, and automotive simulation test instruments, which possess diverse features. Subsequently, we performed manual annotation on these images to construct the instrument detection dataset, namely Complex_Dashboard_Dataset. To satisfy the environmental perception requirements of mobile driving robots, this paper presents an enhanced feature recognition and detection model for automotive dashboards, named eMNv4-YOLO.This model combines the YOLO object detection framework with the lightweight deep learning model MobileNetV4. Moreover, a novel lightweight convolutional module, EPDConv, was introduced in this study to further reduce the model parameters, thereby yielding the refined eMNv4 model. In addition, a highly robust and efficient automotive dashboard reading recognition algorithm, SAGR, was developed by integrating machine vision image processing algorithms and template matching algorithms. The experimental results show that the eMNv4-YOLO model achieved an mAP50 detection result of 96.4% on the constructed dataset, whereas the model parameter count was reduced to only 2.0M, substantially reducing the model size and computational complexity. Finally, the verification of a real vehicle validated the effectiveness of the SAGR algorithm. In conclusion, the eMNv4-YOLO model designed in this study has extensive applicability to information perception systems on edge mobile devices, providing an effective object detection solution in the field of autonomous driving.}, archive = {J_ACCESS}, author = {Yin Lei and Chen Shan and Wancheng Ge and Xinhong Wang and Jianyao Hu}, doi = {10.1109/ACCESS.2025.3549298}, journal = {IEEE Access}, pages = {47123-47131}, shortjournal = {IEEE Access}, title = {EMNv4-YOLO: A high-efficiency target detection framework for robotic driving vehicle instrument reading}, volume = {13}, year = {2025}, }
• (2025). Enhancing cybersecurity through fusion of optimization with deep wavelet neural networks on denial of wallet attack detection in serverless computing. ACCESS, 13, 47111-47122. (www)

  @article{ , abstract = {A Denial of Wallet (DoW) attack is a cyberattack designed to drain financial resources by causing excessive charges or costs on a serverless computing or cloud platform. This attack is mainly associated with serverless frameworks due to features like auto-scaling, cost amplification, pay-as-you-go, and limited control. Serverless computing, or Function-as-a-Service (FaaS), is a cloud computing (CC) model that enables developers to build and run applications without managing traditional server infrastructure. Deep learning (DL) models show strong potential in detecting DoW attacks, where attackers disrupt services by exploiting system resources. By utilizing advanced neural network architectures, DL methods can identify complex patterns in network traffic, offering enhanced accuracy and resilience against such attacks. This study presents a Fusion of Optimization with Deep Wavelet Neural Networks on Denial of Wallet Attack Detection (FODWNN-DoWAD) approach in serverless computing. The main intention of the FODWNN-DoWAD approach is to enhance cybersecurity in DoW attack detection. The FODWNN-DoWAD method utilizes min-max normalization to scale the input network data into a beneficial format. Besides, the pair barracuda swarm optimization (PBSO) method is employed to select features optimally. To recognize a DoW attack, the FODWNN-DoWAD method utilizes a deep wavelet neural network (DWNN) model. Finally, the hyperparameter tuning of the DWNN classifier is performed using the hierarchical learning-based chaotic crayfish optimizer (HLCCO) model. Wide-ranging experiments are accomplished on a benchmark database to demonstrate the good classification outcome of the FODWNN-DoWAD model. The performance validation of the FODWNN-DoWAD approach portrayed a superior accuracy value of 99.05% over existing methods.}, archive = {J_ACCESS}, author = {P. Renukadevi and Sibi Amaran and A. Vikram and T. Prabhakara Rao and Mohamad Khairi Ishak}, doi = {10.1109/ACCESS.2025.3550735}, journal = {IEEE Access}, pages = {47111-47122}, shortjournal = {IEEE Access}, title = {Enhancing cybersecurity through fusion of optimization with deep wavelet neural networks on denial of wallet attack detection in serverless computing}, volume = {13}, year = {2025}, }
• (2025). A novel approach for efficient detection of lotus seedpod maturity using compressed models. ACCESS, 13, 47099-47110. (www)

  @article{ , abstract = {This study aims to achieve efficient and accurate detection of lotus seedpod maturity in complex environments. To address the challenges associated with existing object detection algorithms, which often involve numerous model parameters and high computational loads that hinder deployment on resource-limited mobile terminals, we propose an efficient lotus seedpod maturity detection method based on a compressed model. First, data augmentation techniques were employed to enhance the diversity of lotus seedpod image samples. Next, a model was established under the CSPDarknet53 framework, incorporating a fast spatial pyramid pooling module to detect the maturity of lotus seedpods. To simplify the model and improve detection speed, a channel pruning algorithm was utilized for model compression. Finally, the compressed model was fine-tuned to restore accuracy. Experimental results demonstrate that the compressed model reduced the number of parameters, model size, and inference time by 72.96%, 70.96%, and 26.92%, respectively, achieving a mean Average Precision (mAP) of 99.2%, representing only a 0.20% decrease compared to the original model. Compared to Faster R-CNN, SSD, YOLOv5, YOLOv7-tiny, YOLOv10 and YOLOv11 models, the proposed model significantly reduces parameter count, computational load, and model size while maintaining a high mAP, thus demonstrating feasibility for rapid and accurate detection of lotus seedpod maturity. Additionally, a prototype testing platform for lotus seedpod maturity detection was established, and the compressed model was deployed on an NVIDIA® Jetson Xavier NX mobile terminal. The testing results indicated that the compressed model size was 3.95 MB, with a detection speed of 416.67 frames per second, meeting real-time detection requirements and confirming its applicability for low-computational-capacity mobile terminals. This research provides valuable technical support for the subsequent development of lotus seedpod harvesting robots.}, archive = {J_ACCESS}, author = {Tao Tang and Min Jin and Gaohong Yu and Rui Feng and Bingliang Ye}, doi = {10.1109/ACCESS.2025.3551070}, journal = {IEEE Access}, pages = {47099-47110}, shortjournal = {IEEE Access}, title = {A novel approach for efficient detection of lotus seedpod maturity using compressed models}, volume = {13}, year = {2025}, }
• (2025). QUBO formulations and characterization of penalty parameters for the multi-knapsack problem. ACCESS, 13, 47086-47098. (www)

  @article{ , abstract = {The Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants—the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)—and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.}, archive = {J_ACCESS}, author = {Evren Güney and Joachim Ehrenthal and Thomas Hanne}, doi = {10.1109/ACCESS.2025.3550788}, journal = {IEEE Access}, pages = {47086-47098}, shortjournal = {IEEE Access}, title = {QUBO formulations and characterization of penalty parameters for the multi-knapsack problem}, volume = {13}, year = {2025}, }
• (2025). LCGD: Enhancing text-to-video generation via contextual LLM guidance and U-net denoising. ACCESS, 13, 47068-47085. (www)

  @article{ , abstract = {Diffusion models have emerged as a leading solution in computer vision and they excel at audio, image, and video generation by utilizing the Markov chain to map complex latent spaces. These models outperform other generative models such as GANs and VAEs, with their noising and denoising processes modeled after U-Net architecture, enabling high-quality text-to-image and text-to-video synthesis. However, existing research has largely focused on application rather than improving underlying architectures, leading to limitations, such as oversmoothing in approaches like FreeU. To address these gaps, we introduce LLM Contextual Guided Diffusion (LCGD), which integrates large language models (LLMs) into the noising and denoising phases to enhance semantic understanding, noise modulation, and feature selection. This approach improves the output realism and coherence, as demonstrated by our results, where SD+LCGD achieved 89.91% compared to 85.88% for SD+FreeU.}, archive = {J_ACCESS}, author = {Muhammad Waseem and Muhammad Usman Ghani Khan and Syed Khaldoon Khurshid}, doi = {10.1109/ACCESS.2025.3550945}, journal = {IEEE Access}, pages = {47068-47085}, shortjournal = {IEEE Access}, title = {LCGD: Enhancing text-to-video generation via contextual LLM guidance and U-net denoising}, volume = {13}, year = {2025}, }
• (2025). Assessing the influencing factors of locational marginal price in day-ahead hydro-thermal power spot market. ACCESS, 13, 47055-47067. (www)

  @article{ , abstract = {The quantitative analysis of electricity spot market clearing results is vital for independent system operators to monitor and mitigate market abnormalities that could potentially lead to the abuse of market power. The increasing complexity and expansion of spot markets, driven by growing market participation, have significantly amplified the challenge and importance of clearing result analysis. In this paper, a framework for decomposing the factors influencing the locational marginal price (LMP) is proposed, aiming to quantitatively assess the causes behind clearing results. A day-ahead security-constrained hydro-thermal economic dispatch model is established to minimize the total bidding price. Based on this model, the LMP at each node is deduced in detail using the optimization conditions, in which the LMP of non-marginal nodes can be decomposed into a linear combination of local sensitivity factors and the LMPs of marginal nodes, with each factor corresponding to a specific constraint affecting the LMP. To efficiently solve the proposed framework, an advanced matrix preconditioning method integrated with commercial optimization solver is developed to compute the local sensitivity factors, providing enhanced computational efficiency for large-scale power systems. Finally, the proposed framework is validated using the IEEE 30-node system and the significantly larger 2746-node Polish system.}, archive = {J_ACCESS}, author = {Wenjun Tang and Libao Shi and Peng Xu and Hao Luo and Linghao Li}, doi = {10.1109/ACCESS.2025.3550588}, journal = {IEEE Access}, pages = {47055-47067}, shortjournal = {IEEE Access}, title = {Assessing the influencing factors of locational marginal price in day-ahead hydro-thermal power spot market}, volume = {13}, year = {2025}, }
• (2025). MuRelSGG: Multimodal relationship prediction for neurosymbolic scene graph generation. ACCESS, 13, 47042-47054. (www)

  @article{ , abstract = {Neurosymbolic Scene Graph Generation (SGG) is a promising approach that jointly leverages the perception capabilities of deep neural networks and the reasoning capabilities of symbolic techniques for scene understanding and visual reasoning. SGG systematically captures semantic components, including objects and their relationships, in images, enabling structured representations of visual data. However, existing SGG methods exhibit constrained accuracy and limited expressiveness, particularly in long-tail relationship prediction. To address these limitations, we present MuRelSGG, a novel neurosymbolic SGG framework that integrates a Transformer-based multimodal relationship prediction pipeline with common sense knowledge enrichment. This synergistic combination encapsulates global context, long-range dependencies, and complex object interactions to enhance relationship prediction in SGG. The proposed neurosymbolic architecture begins with object detection via Faster R-CNN, followed by a cascade of Multi-Head Attention Transformers (M-HAT) and Vision Transformers (ViT) for relationship prediction. Subsequently, CSKG enrichment refines and augments visual relationships, improving both accuracy and expressiveness. We conduct extensive evaluations on both the Visual Genome (VG) and GQA datasets to assess performance and generalizability. MuRelSGG achieves substantial gains in recall rates (VG: R@ $100=43.2$ , mR@ $100=14.9$ ; GQA: R@ $100=42.1$ ), outperforming state-of-the-art SGG techniques. Ablation studies confirm the critical contributions of M-HAT, ViT, linguistic features, CSKG enrichment and embedding similarity thresholds, demonstrating the effectiveness of structured knowledge integration for long-tail relationship prediction. These findings underscore the potential of combining deep learning architectures with structured knowledge bases to advance visual scene representation and reasoning.}, archive = {J_ACCESS}, author = {Muhammad Junaid Khan and Adil Masood Siddiqui and Hamid Saeed Khan and Faisal Akram and M. Jaleed Khan}, doi = {10.1109/ACCESS.2025.3551267}, journal = {IEEE Access}, pages = {47042-47054}, shortjournal = {IEEE Access}, title = {MuRelSGG: Multimodal relationship prediction for neurosymbolic scene graph generation}, volume = {13}, year = {2025}, }
• (2025). Design of a robust adaptive control scheme using singular perturbation technique for grid-connected wind turbine systems. ACCESS, 13, 47022-47041. (www)

  @article{ , abstract = {The doubly-fed induction generator (DFIG)-based wind turbine system (DFIG-WTS) is one of the most renewable energy sources used for producing electricity. Generally, a back-to-back converter connects the DFIG-WTS to the power grid. However, the DFIG-WTS operates under a wide range of intermittent wind speeds, which may result in highly variable and nonlinear system dynamics. Hence, implementing a robust adaptive control strategy becomes essential to guarantee optimal performance and stability of the system. In this paper, a detailed design and implementation of a model reference adaptive control (MRAC) scheme, with control laws derived using Lyapunov’s stability method, is presented to control the rotor side converter (RSC). In the proposed control scheme, the MRAC is applied to regulate the machine rotor voltages. Due to the complexity and nonlinearity of the DFIG-WTS along with the uncertainty of wind conditions, a singular perturbation technique based on the Chang transformation method is used to reduce the studied model by decoupling it into slow and fast modes. The proposed adaptive control scheme is tested and validated on a DFIG-WTS where both the full and reduced models are studied. Simulation results demonstrate that the proposed control scheme significantly enhances system stability and dynamic performance, particularly in regulating rotor winding voltages, electromagnetic torque, rotor angular speed and flux dynamics, across a wide range of wind conditions.}, archive = {J_ACCESS}, author = {Khalid Alqunun and Wissem Bahloul and Tawfik Guesmi and Amer Alghadhban and Badr M. Alshammari and Kamel Tlijani and Hsan Hadj Abdallah and Ahmed Toumi}, doi = {10.1109/ACCESS.2025.3548858}, journal = {IEEE Access}, pages = {47022-47041}, shortjournal = {IEEE Access}, title = {Design of a robust adaptive control scheme using singular perturbation technique for grid-connected wind turbine systems}, volume = {13}, year = {2025}, }
• (2025). Intelligent lighting system incorporating two different data transmission methods. ACCESS, 13, 47013-47021. (www)

  @article{ , abstract = {In this paper, a novel lighting communication system is proposed that simultaneously transmits two types of data - communication data and visual information - while also providing an intelligent lighting function. For the communication data transmission function, each LED transmits different dynamic data in parallel by using Multiple-Input and Multiple-Output (MIMO) techniques. By assigning and modulating pseudo-noise (PN) codes to each LED, inter-LED interference is eliminated, and the effect of noise is reduced. In this paper, Modified Pseudo-Orthogonal M-sequence Sets (MPOMSs) are used as PN codes. For the visual information transmission function, a QR code embedded with static data is visually represented and transmitted by varying the color of the LEDs. The lighting function is achieved by switching the colors of the LEDs at brief intervals, ensuring the light is consistently perceived as a uniform color. Theoretical analyses are employed to evaluate both lighting and transmission performance. The proposed system achieves superior transmission performance compared to the conventional OOK-MIMO system. The lighting performance and the success rate of visual information transmission under various environments are also demonstrated through experiments.}, archive = {J_ACCESS}, author = {Tianhe Huang and Yusuke Kozawa and Hiromasa Habuchi}, doi = {10.1109/ACCESS.2025.3550447}, journal = {IEEE Access}, pages = {47013-47021}, shortjournal = {IEEE Access}, title = {Intelligent lighting system incorporating two different data transmission methods}, volume = {13}, year = {2025}, }
• (2025). Ensemble-based AI learning algorithm to predict drag force for particle clusters. ACCESS, 13, 47004-47012. (www)

  @article{ , abstract = {In this work, we propose a novel approach that uses an ensemble of deep neural networks (DNN) and convolutional neural networks (CNN) to predict the drag coefficient of small, irregular-shaped particles in low-Reynolds-number flows. In addition, a dataset containing 710 simulations of randomly shaped particle clusters are generated, on which the proposed model is trained upon. Existing literature within the field have mainly focused on symmetrical or regularly shaped objects. The DNN model is trained on tabular data, capturing flow characteristics and particle properties, whereas the CNN processes images of particles taken from three different angles. This combination allows the model to account for the different orientations and shapes of particles. By integrating visual information, the model provides a better understanding of particle geometry, which improves its predictive capabilities compared with methods that rely solely on tabular data. To provide a baseline, we implemented support vector regression (SVR) as a simpler approach, allowing us to assess the added value of our ensemble model. The comparison shows that our ensemble method achieves a notable improvement over SVR and DNN, effectively capturing the influence of the particle structure on drag behavior. The result is validated using k-folds cross-validation with ten folds resulting in only a small deviation from the model presented, which is expected due to each fold having less data to train on. This work demonstrates the potential of ensemble learning as surrogate modeling to address complex fluid dynamics problems with irregular-shaped particles, which are common in environmental and industrial contexts.}, archive = {J_ACCESS}, author = {Andreas Mark and Benjamin Elm Jonsson and Niclas Persson}, doi = {10.1109/ACCESS.2025.3550720}, journal = {IEEE Access}, pages = {47004-47012}, shortjournal = {IEEE Access}, title = {Ensemble-based AI learning algorithm to predict drag force for particle clusters}, volume = {13}, year = {2025}, }
• (2025). Traffic monitoring and control system for smart city pollution regulation using IoT and correlated capsule networks. ACCESS, 13, 46983-47003. (www)

  @article{ , abstract = {Organizational vehicle density management and automated interventions without humans are gaining traction with traffic monitoring and control systems based on the Internet of Things (IoT). Regarding controlling pollution and regulating vehicle density, the current traffic control technologies are ineffective regarding real-time adaptability, efficient classification techniques, and scalable infrastructure. Therefore, a system that uses the IoT is necessary for the real-time identification and regulation of automobiles that release excessive pollution. As a result, the study suggests a TCSP-PC, or Traffic Control System Process for Pollution Control, to manage waste in smart cities. This control system incorporates capsule networks for vehicle categorization based on emission behaviour and employs IoT sensors for data collecting. This system primarily seeks automobiles that cause much pollution during their travels by monitoring their emission infractions. To initiate initial pollution control, such vehicle data is used to detect infractions at any interval. The IoT paradigm aids this process through traffic control regulations. Additionally, capsule networks are used to learn the emission behaviours of both environmentally good and polluting automobiles on the roadside. Through capsule networks that categorize vehicles according to their emission behaviour on the roadside, traffic control systems can monitor and control autos. The vehicles may be categorized as either environmentally friendly or polluting. The system finds polluting vehicles, organizes them using capsule networks, and then suggests pollution restrictions based on the IoT. Utilizing pre-existing smart city resource data, this novel approach combines technological concerns with rules about traffic control and environmental impacts. Comparative analysis with existing models (VEQM, MPTC-PRP, MWIS, and CONV-BI-LSTM) demonstrates significant improvements in impact verification (+12.48%), classification rate (+15.36%), and traffic control efficiency (+121.5 vehicles/km). Additionally, the system achieves an average 9.77% enhancement in data analysis rate and a notable reduction in processing time (-2.06s), ensuring real-time responsiveness. These findings highlight the potential of TCSP-PC to optimize urban traffic flow, reduce emissions, and improve decision-making in smart city infrastructure.}, archive = {J_ACCESS}, author = {Ali Mohd Ali and Ahmad Al-Qerem and Mohammad R. Hassan and Ahmed Abu-Khadrah and Muath Jarrah}, doi = {10.1109/ACCESS.2025.3550966}, journal = {IEEE Access}, pages = {46983-47003}, shortjournal = {IEEE Access}, title = {Traffic monitoring and control system for smart city pollution regulation using IoT and correlated capsule networks}, volume = {13}, year = {2025}, }
• (2025). Quantum feasibility labeling for NP-complete vertex coloring problem. ACCESS, 13, 46972-46982. (www)

  @article{ , abstract = {Many important science and engineering problems can be converted into NP-complete problems which are of significant importance in computer science and mathematics. Currently, neither existing classical nor quantum algorithms can solve these problems in polynomial time. To address this difficulty, this paper proposes a quantum feasibility labeling (QFL) algorithm to label all possible solutions to the vertex coloring problem, which is a well-known NP-complete problem. The QFL algorithm converts the vertex coloring problem into the problem of searching an unstructured database where good and bad elements are labeled. The recently proposed variational quantum search (VQS) algorithm was demonstrated to achieve an exponential speedup, in circuit depth, up to 26 qubits in finding good element(s) from an unstructured database. Using the labels and the associated possible solutions as input, the VQS can find all feasible solutions to the vertex coloring problem. The number of qubits and the circuit depth required by the QFL each is a polynomial function of the number of vertices, the number of edges, and the number of colors of a vertex coloring problem. We have implemented the QFL on an IBM Qiskit simulator to solve a 4-colorable 4-vertex 3-edge coloring problem.}, archive = {J_ACCESS}, author = {Junpeng Zhan}, doi = {10.1109/ACCESS.2025.3545262}, journal = {IEEE Access}, pages = {46972-46982}, shortjournal = {IEEE Access}, title = {Quantum feasibility labeling for NP-complete vertex coloring problem}, volume = {13}, year = {2025}, }
• (2025). Square-shaped circularly polarized MIMO dipole antenna with dual-sense characteristic for 5G application. ACCESS, 13, 46956-46971. (www)

  @article{ , abstract = {A new approach of dual-sense circularly polarized (CP) MIMO dipole antenna design is presented in this study, which is suitable for Sub-6 GHz 5G applications, explicitly targeting the n78 frequency band (3.3–3.8 GHz). The proposed antenna comprises four printed dipole elements arranged orthogonally on a $105\times 105 \times 23$ mm3 substrate. Each balun-fed dipole pair is excited through a 3-dB branch-line coupler. This technique generates high purity in right-handed circular polarization (RHCP) and left-handed circular polarization (LHCP) modes. By simultaneously activating any two of the four ports of the MIMO antenna, the design accommodates multiple polarization configurations, offering advanced polarization diversity through RHCP-RHCP, LHCP-LHCP, RHCP-LHCP, and LHCP-RHCP modes. The proposed design achieves an impedance bandwidth of 3.08–4.03 GHz for RHCP and 3.12–4.06 GHz for LHCP, respectively, with CP performance in the entire operating frequency band. With peak gains of 7.15 dBic for RHCP and 6.80 dBic for LHCP, and axial ratio values below 1.32 dB across the operating band, the antenna exhibits exceptional performance. Furthermore, isolation levels exceed 23.8 dB for co-polarization ports and 30.1 dB for cross-polarization ports, ensuring minimal mutual coupling. The envelope correlation coefficient (ECC) is below 0.0021 for co-polarization ports and below 0.0007 for cross-polarization ports, confirming excellent MIMO performance. Simulated and measured results strongly agree, validating the design’s robustness and suitability for Sub-6 GHz 5G applications. Its compact structure, wide bandwidth, high gain, and superior CP purity make it an ideal candidate for next-generation wireless communication systems.}, archive = {J_ACCESS}, author = {Hanieh Hashemi Asl and Changiz Ghobadi and Javad Nourinia and Majid Shokri}, doi = {10.1109/ACCESS.2025.3547489}, journal = {IEEE Access}, pages = {46956-46971}, shortjournal = {IEEE Access}, title = {Square-shaped circularly polarized MIMO dipole antenna with dual-sense characteristic for 5G application}, volume = {13}, year = {2025}, }
• (2025). Event-triggered parallel distributed compensator design for nonlinear system with mixed delays and sampling input by T-S fuzzy approach. ACCESS, 13, 46941-46955. (www)

  @article{ , abstract = {In this article, the robust $H_{\mathrm {\infty }}$ and $H_{2}$ achievement is investigated by using the proposed scheme of event-triggered parallel distributed compensator (ETPDC) for T-S delay fuzzy model with sampling input. Some nonlinear practical systems can be approximated by T-S fuzzy models. In order to implement the parallel distributed compensator of a T-S fuzzy model, the network-based computation and hardware should be designed. The resource of communication of network can be saved by the proposed ETPDC scheme developed in this article. The proposed matrix presentation approach and some inequalities are used to show and improve our developed consequences. Finally, two nonlinear practical models for mass spring damper and thermal turbine synchronous generator are illustrated to exhibit the developed main consequences.}, archive = {J_ACCESS}, author = {Chang-Hua Lien and Hao-Chin Chang and Ker-Wei Yu and Hung-Chih Li and Cheng-Ruei Yu and Sundarapandian Vaidyanathan}, doi = {10.1109/ACCESS.2025.3548963}, journal = {IEEE Access}, pages = {46941-46955}, shortjournal = {IEEE Access}, title = {Event-triggered parallel distributed compensator design for nonlinear system with mixed delays and sampling input by T-S fuzzy approach}, volume = {13}, year = {2025}, }
• (2025). Advancements in speech recognition: A systematic review of deep learning transformer models, trends, innovations, and future directions. ACCESS, 13, 46925-46940. (www)

  @article{ , abstract = {The transformer is a Deep Learning (DL) model that revolutionized language processing with its self-attention mechanism, enabling parallel processing and improving model efficiency, which dramatically reshaped the landscape of speech recognition technology, based on the ability to efficiently manage the dynamic and context-rich nature of speech. The proposed systematic review in this article critically examines the impact of transformer models on speech recognition, covering the published research over the past seven years (from January 1, 2017, to May 15, 2024). The goals of this article are to synthesize the current knowledge, pinpoint emerging trends, and identify research gaps that could be beneficial for future investigations. From an initial pool of 2,838 publications sourced from leading digital libraries, a rigorous two-step screening process applied to distill high-quality studies relevant to the review criteria. We concentrated our analysis on seven pivotal areas as following: the environmental conditions (neutral versus noisy) addressed by the studies, methods of feature extraction employed, characteristics of the transformer models used, datasets utilized, variations in model efficiency, the influence of noise on model generalizability, and trends in self-supervised learning, ending up with 37 articles to review in this paper. Our findings underscore the transformative potential of transformers in enhancing the accuracy and robustness of speech recognition systems, especially in challenging acoustic environments. In addition, this review highlights areas where more research is needed to make speech recognition even better by using transformer technology.}, archive = {J_ACCESS}, author = {Yousef O. Sharrab and Hani Attar and Mohammad Ali H. Eljinini and Yasmin Al-Omary and Wala’A E. Al-Momani}, doi = {10.1109/ACCESS.2025.3550855}, journal = {IEEE Access}, pages = {46925-46940}, shortjournal = {IEEE Access}, title = {Advancements in speech recognition: A systematic review of deep learning transformer models, trends, innovations, and future directions}, volume = {13}, year = {2025}, }
• (2025). Our digital traces in cybersecurity: Bridging the gap between anonymity and identification. ACCESS, 13, 46909-46924. (www)

  @article{ , abstract = {The rapid expansion of digital interactions has led to the formation of unique digital traces, often referred to as digital fingerprints, which play a crucial role in modern cybersecurity. These fingerprints, constructed from data related to user devices, browsers, and behavioral patterns, serve to identify users across different platforms and sessions. This identification aids in enhancing security, such as fraud detection, identity verification, and multi-factor authentication. However, digital fingerprints also pose significant privacy challenges, as they can be used to track individuals without their knowledge or consent. This paper provides an in-depth analysis of digital fingerprints, tracing the evolution of tracking technologies from simple browser cookies to sophisticated methods like machine learning-enhanced fingerprinting. It explores how digital fingerprints are created, the kinds of data used, and the ways these traces are utilized to bridge the gap between ensuring digital security and maintaining user anonymity. Through detailed literature review, we investigate the application of digital fingerprints in various domains, such as web browsing, online banking, and e-commerce, revealing both the security benefits and the privacy risks. The paper also discusses emerging techniques for mitigating these risks and highlights the importance of balancing personalization and privacy. Our paper concludes by addressing the ongoing debate between privacy and security, emphasizing the need for privacy-enhancing technologies that can coexist with the benefits of digital fingerprints in today’s interconnected digital landscape.}, archive = {J_ACCESS}, author = {Arbena Musa and Kamer Vishi and Edlira Martiri and Blerim Rexha}, doi = {10.1109/ACCESS.2025.3551095}, journal = {IEEE Access}, pages = {46909-46924}, shortjournal = {IEEE Access}, title = {Our digital traces in cybersecurity: Bridging the gap between anonymity and identification}, volume = {13}, year = {2025}, }
• (2025). Digital twin-assisted online monitoring of elevator abnormal vibration using improved ShuffleNetV2 and domain adaptation. ACCESS, 13, 46897-46908. (www)

  @article{ , abstract = {Elevator’s vibration is closely linked to safe operation, which is crucial for social public safety. In order to achieve the online detection of the horizontal abnormal vibration and solve the problem of scarce labelled abnormal data for training intelligent models, a digital twin-assisted method using an improved lightweight ShuffleNetV2 triplet attention network (STAN) and deep subdomain adaptation network (DSAN) is proposed. According to the actual working conditions of elevator operation, a digital twin system was constructed, enabling the virtual-to-real mapping of the elevator operation states. The simulated data of abnormal horizontal vibration was augmented. The one-dimensional simulated data and measured data were converted into two-dimensional time-frequency map data by continuous wavelet transform (CWT). Feature extraction was performed by the improved lightweight STAN that incorporated grouped mixing blocks and attention mechanism. A DSAN method based on Gaussian kernel function was used for abnormal vibration detection by data fusion and feature alignment. Experiment results showed that the detection model had a size of 1.258 MB and required 0.152 G of floating-point operations (FLOPs), and the response time of the visualization platform was 0.11 s on average. The abnormal vibrations can be classified accurately during elevator operation. The digital twin-assisted online monitoring method combined with an improved lightweight STAN can be easily deployed in edge end to realize the online monitoring elevators with visualization and virtuality to reality interaction.}, archive = {J_ACCESS}, author = {Weiwei Ye and Xinchun Zhao and Yixun Wang and Hailong Chen and Luping Lin}, doi = {10.1109/ACCESS.2025.3550880}, journal = {IEEE Access}, pages = {46897-46908}, shortjournal = {IEEE Access}, title = {Digital twin-assisted online monitoring of elevator abnormal vibration using improved ShuffleNetV2 and domain adaptation}, volume = {13}, year = {2025}, }
• (2025). Controlling the effects of an internal arc fault and benefits of active arc protection in low-voltage switchgear assemblies. ACCESS, 13, 46887-46896. (www)

  @article{ , abstract = {Internal arcs in low-voltage switchgear assemblies are rare; however, when they appear, they produce high temperatures and pressures, and emit light, noise, and toxic gases. Different methods can be implemented to control the effects of an internal arc fault and to protect personnel and assemblies from the consequences of such faults. This paper describes methods that either contain the effects of an internal arc by the design of an assembly (passive arc protection) or limit the effects of an internal arc using specialized devices (active arc protection). Additionally, the results of the internal arc fault tests conducted in an ABB laboratory demonstrate the practical application of the methods described in this paper for two test cases. Moreover, this paper shows how the verification of low-voltage switchgear assemblies under conditions of internal arc fault evolved in the standardization documents IEC TR 61641 and IEC TS 63107. Finally, it focuses on the benefits of using active arc protection systems, which offer not only enhanced protection of personnel and assemblies but also ensure reduction of downtime after a fault and positive impact from sustainability and circularity point of view.}, archive = {J_ACCESS}, author = {Sebastian Berhausen and Rafal Burzynski}, doi = {10.1109/ACCESS.2025.3550847}, journal = {IEEE Access}, pages = {46887-46896}, shortjournal = {IEEE Access}, title = {Controlling the effects of an internal arc fault and benefits of active arc protection in low-voltage switchgear assemblies}, volume = {13}, year = {2025}, }
• (2025). 100-nW wake up receiver with -42.3 dBm sensitivity for real-time location systems. ACCESS, 13, 46881-46886. (www)

  @article{ , abstract = {This paper presents a Wake-up Receiver (WuR) with Ultra-Low Power (ULP) consumption for a Real-Time Location System (RTLS). A novel method for its always-on mode is proposed that operates without using any active amplifiers that results in an ULP operation. The proposed WuR employs a D-flip-flop configuration, a peak detector and clock comparator with a minimal power consumption. In addition, it uses an On-Off Keying (OOK) modulation scheme. The WuR is designed for an ULP OOK-based transceiver that requires a minimum sensitivity of -42.3 dBm and operates at a frequency of 2.4 GHz. To feature a wide input power supply voltage operation, a current-biasing technique is employed to ensure the required sensitivity, consistently, over a wide input power range. The proposed design achieves power consumption as low as 100 nW while it operates over a wide range power supply of 680–900 mV. Moreover, the proposed WuR occupies a small die area of $100~\mu $ m by $50~\mu $ m due to the lack of active amplifiers}, archive = {J_ACCESS}, author = {Kyung-Duk Choi and Reza E. Rad and Yu-Sub Sin and Jun-Eun Park and Keum-Cheol Hwang and Youngoo Yang and Kang-Yoon Lee}, doi = {10.1109/ACCESS.2025.3551134}, journal = {IEEE Access}, pages = {46881-46886}, shortjournal = {IEEE Access}, title = {100-nW wake up receiver with -42.3 dBm sensitivity for real-time location systems}, volume = {13}, year = {2025}, }
• (2025). SimHH: A versatile, multi-GPU simulator for extended hodgkin-huxley networks. ACCESS, 13, 46865-46880. (www)

  @article{ , abstract = {Computational neuroscience relies on complex mathematical models to simulate brain activity and decipher underlying biological processes. However, these simulations are computationally intensive, prompting the exploration of high-performance computing systems as a viable solution to enhance efficiency. In this work, we introduce SimHH, an extended-Hodgkin-Huxley simulator designed for versatility and high performance. Leveraging the OpenMPI library, SimHH exhibits exceptional scalability, catering to a wide spectrum of computing environments. Scalability is optimized through two distinct configurations: one that distributes all possible cell-compartment potentials among network nodes and another that shares compartment potentials only among relevant nodes, employing MPI Allgather and Alltoall. Seamless support for CUDA, CUDA-aware MPI, and NVLink further enhances performance, with communication overhead minimized through concurrent execution of compute kernels. Benchmarking against various neuron models, including the challenging Inferior-Olivary Nucleus, demonstrates SimHH’s potential, achieving remarkable results on up to 256 compute nodes. Notably, large-scale GPU clusters enable the simulation of highly biologically plausible networks exceeding 10 million cells. Comparative analyses against CPU- and FPGA-based solutions underscore SimHH’s superiority, boasting a speedup of approximately $150\times $ over single-threaded CPU implementations, $10\times $ over single-FPGA setups, and $10\times $ over multi-threaded CPU configurations with 128 threads, all for a fully connected network of approximately 7,000 IO cells. Additionally, a $7\times $ speedup is attained compared to the established NEST neurosimulator running on 32 nodes, simulating a network of 94,720 Hodgkin-Huxley neurons with gap junctions. These findings underscore SimHH’s efficacy in advancing computational-neuroscience research by facilitating efficient and scalable simulation of complex neuronal networks.}, archive = {J_ACCESS}, author = {Max C. W. Engelen and River Betting and Christos Strydis}, doi = {10.1109/ACCESS.2025.3550444}, journal = {IEEE Access}, pages = {46865-46880}, shortjournal = {IEEE Access}, title = {SimHH: A versatile, multi-GPU simulator for extended hodgkin-huxley networks}, volume = {13}, year = {2025}, }
• (2025). Semiconductor wafer defect recognition based on improved coordinate attention mechanism. ACCESS, 13, 46856-46864. (www)

  @article{ , abstract = {In the semiconductor wafer manufacturing process, it is necessary to inspect the electrical parameters and functions of the wafer to identify the defects in the chip manufacturing process. The inspection results are presented in the form of wafers; therefore, the accuracy of wafer defect recognition directly affects the chip yield. Traditional manual methods suffer from subjectivity, inefficiency, and diminished accuracy. With improvements in computing power, computer vision based on convolutional neural networks has demonstrated notable advantages in defect recognition. Nonetheless, with the development of Moore’s law and the continuous increase in wafer size, it continues to contend with challenges regarding the accurate identification of mixed, complex types of wafer defects and necessitates distinct training for each defect type, a process that is both laborious and time-intensive. In this study, we introduce a new deep learning model called the Deep Attention Pyramid Network (DAP-Net), which is based on depth-wise separable convolution and combines an improved coordinate attention mechanism with a multi-scale convolution structure to identify single and mixed types of wafer defects. Our model achieved an impressive recognition accuracy of 98.6% when evaluated on a mixed-type defect dataset (Mixed38WM), surpassing the performance of most previously reported deep learning models.}, archive = {J_ACCESS}, author = {Hao He and Yuanjie Wei and Xionghao Lin and Minmin Zhu and Haizhong Zhang}, doi = {10.1109/ACCESS.2025.3546781}, journal = {IEEE Access}, pages = {46856-46864}, shortjournal = {IEEE Access}, title = {Semiconductor wafer defect recognition based on improved coordinate attention mechanism}, volume = {13}, year = {2025}, }
• (2025). HMD2: Texture surface defect recognition with histogram matching and two stage denoising using a diffusion model. ACCESS, 13, 46838-46855. (www)

  @article{ , abstract = {Texture surface defect recognition plays a key role in ensuring both the aesthetic and functional quality of products, and its importance is further emphasized when considering the impact of defects on product performance and safety. However, existing defect recognition methods predominantly rely on image segmentation techniques, which have limitations in accurately detecting complex texture patterns and subtle defects. To address these challenges, this paper proposes a novel defect recognition method based on a Diffusion model utilizing a U-Net backbone network. The proposed approach introduces a loss function that effectively captures changes in color distribution not only in defect areas but also in normal regions, by incorporating histogram matching. Additionally, the method includes a process that removes high-frequency noise by retaining only low-frequency components. By combining these techniques, the proposed method is designed to significantly improve defect detection accuracy without the need for complex segmentation processes, enabling defect detection at the whole image level. The performance of the proposed method was evaluated using the texture categories of the MVTec-AD, resulting in an average AUC of 0.977 across all texture categories. These results demonstrate the effectiveness of the proposed method in detecting defects on complex texture surfaces by leveraging color distribution and low-pass filtering.}, archive = {J_ACCESS}, author = {Jongwook Si and Sungyoung Kim}, doi = {10.1109/ACCESS.2025.3551371}, journal = {IEEE Access}, pages = {46838-46855}, shortjournal = {IEEE Access}, title = {HMD2: Texture surface defect recognition with histogram matching and two stage denoising using a diffusion model}, volume = {13}, year = {2025}, }
• (2025). Study on improving detection performance of wildfire and non-fire events early using swin transformer. ACCESS, 13, 46824-46837. (www)

  @article{ , abstract = {The increasing prevalence of wildfires globally has resulted in significant loss of life and extensive property damage, underscoring the urgent need for improved detection systems. Recent research has prioritized minimizing false fire detections and reducing non-fire alarms using deep learning technologies. However, existing studies predominantly focus on fire detection after significant spread, with limited emphasis on early detection and the differentiation between fire and non-fire scenarios. This study introduces a wildfire detection model employing the Swin Transformer as the backbone for Mask-RCNN, enabling simultaneous detection of bounding boxes and segmentation masks. The proposed model enhances the management of non-fire alarms, reduces false fire detections, and significantly improves early fire detection capabilities. Evaluations conducted across various fire and non-fire scenarios—including clouds, fog/haze, chimney smoke, flames, and wildfire smoke—demonstrated the model’s superior performance. The Swin Transformer-based model achieved a Segm mAP50 of 0.842, surpassing the baseline ResNet-50 model (0.792). Furthermore, it exhibited robust detection across object sizes, with mAP_S, mAP_M, and mAP_L values of 0.166, 0.494, and 0.699, respectively. Confusion Matrix analysis highlighted the model’s reduced false positives for non-fire classes and improved detection of small-scale fire indicators, such as early wildfire smoke trails. These findings underscore the Swin Transformer’s effectiveness in enhancing wildfire detection, particularly during the critical early stages when timely intervention is crucial. Future research will focus on integrating the proposed model into one-stage architectures like YOLO, expanding datasets, and employing data augmentation techniques to further optimize its real-world applicability in wildfire management systems.}, archive = {J_ACCESS}, author = {Sugi Choi and Youngjoo Song and Haiyoung Jung}, doi = {10.1109/ACCESS.2025.3528983}, journal = {IEEE Access}, pages = {46824-46837}, shortjournal = {IEEE Access}, title = {Study on improving detection performance of wildfire and non-fire events early using swin transformer}, volume = {13}, year = {2025}, }
• (2025). A novel nonvolatile quaternary FPGA architecture based on magnetic tunnel junctions. ACCESS, 13, 46810-46823. (www)

  @article{ , abstract = {Field Programmable Gate Arrays (FPGAs) are valued for their reconfigurability and parallel processing capabilities. FPGAs struggle with inadequate resources and computing inefficiency despite these fascinating features when implementing large designs. Quaternary logic, a Multiple-Valued Logic (MVL) type compatible with binary systems, enables FPGAs to represent multiple states simultaneously, boosting computational efficiency through enhanced parallel processing. This paper proposes a novel nonvolatile quaternary FPGA (NQFPGA) architecture, integrating Magnetic Tunnel Junction (MTJ) with a Carbon Nanotube Field Effect Transistor (CNTFET) to improve FPGA performance and efficiency. MTJ adds nonvolatility, preserving data during power losses, while CNTFET offers tunable threshold voltage, which is essential for implementing MVL circuits. The proposed NQFPGA was designed using a novel Quaternary Look-Up Table (QLUT) and Quaternary Flip-Flop (QFF). The QLUT cuts average power by up to 85% and static power by 72%. The QFF also reduces average power by 44% and static power by 40%. Also, the QFF shows up to 99% improvement in Power-Delay-Area Product (PDAP) compared to previous designs. The evaluation used ISCAS’89 and Rosetta benchmark suites to ensure standardized and realistic scenarios. The results of these benchmarks indicate the efficiency, high performance, and cost-effectiveness of the proposed NQFPGA in various real-world scenarios.}, archive = {J_ACCESS}, author = {Motahareh BahmanAbadi and Mohammad Hossein Moaiyeri}, doi = {10.1109/ACCESS.2025.3550778}, journal = {IEEE Access}, pages = {46810-46823}, shortjournal = {IEEE Access}, title = {A novel nonvolatile quaternary FPGA architecture based on magnetic tunnel junctions}, volume = {13}, year = {2025}, }
• (2025). Constructing a lightweight fire and smoke detection through the improved GhostNet architecture and attention module mechanism. ACCESS, 13, 46794-46809. (www)

  @article{ , abstract = {Fire disasters have a serious impact on human life and property. Fire detection methods have been developed as early detection in preventing fire disasters, including implementing object detection models. However, the challenges in fire detection are random object shapes and complex backgrounds, which cause the model to be unable to detect fire optimally. In addition, to support practical application, the model must be efficient in memory-consuming. To address these issues, this study proposed FSP-YOLO as a fire detection algorithm that integrates the YOLOv8 model with the improved GhostNet (iGhostNet) and the attention module that can detect three classes, including fire, smoke, and people. The improvement of iGhostNet is implementing coordinate attention module and trained integrated learning, which has an impact on increasing accuracy and reducing computational costs. With this improvement, the proposed fire detection model could gain outstanding performance, reducing computational costs, and a lightweight architecture. Furthermore, to validate the performance of FSP-YOLO, several comparative experiments have been conducted, including single and multiple module experiments, evaluation under different YOLOv8, experiments with other object detection algorithms, and experiments in practical application. From these experiments, FSP-YOLO represents the optimal performance, which results in mAP0.5 of 0.977, precision of 0.952, and recall of 0.973. Based on weight or model size, compared to the other fire detection algorithms such as YOLOFM, FireRPG, Fine Tuned YOLOv8, Improved YOLOv8, YOLOv7-Fire, YOLOv9, and Improved-YOLOv7, FSP-YOLO has a lighter size of 53.18 MB. Therefore, FSP-YOLO is reliable in accuracy but reduces the computational cost.}, archive = {J_ACCESS}, author = {Wen-Tsai Sung and Indra Griha Tofik Isa and Ching-Hsiang Tseng and Sung-Jung Hsiao}, doi = {10.1109/ACCESS.2025.3550539}, journal = {IEEE Access}, pages = {46794-46809}, shortjournal = {IEEE Access}, title = {Constructing a lightweight fire and smoke detection through the improved GhostNet architecture and attention module mechanism}, volume = {13}, year = {2025}, }
• (2025). Sparse multi-view K-means clustering. ACCESS, 13, 46773-46793. (www)

  @article{ , abstract = {In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assigns equal importance to all features in a single view. Several multi-view clustering techniques have been developed over the last few decades, including multi-view k-means (MVKM). In MVKM, patterns can be detected more accurately, and clustering performance is improved by applying each view individually for pattern discovery. The demand for highly accurate and flexible methods in machine learning and artificial intelligence has increased the use of multi-view datasets. These datasets are complicated and pose challenges for researchers in real-life situations. A novel type of sparse k-means clustering algorithm, sparse MVKM (S-MVKM), is proposed in this study for multi-view data. The main aim of our proposed S-MVKM algorithm is to achieve sparsity in features and shrink unimportant features to exactly zero using Lasso (least absolute shrinkage and selection operator). For considering theoretic behaviors of the S-MVKM algorithm, we create a convergence theorem to ensure that any convergent subsequence in S-MVKM will ultimately tend to an optimal solution. The proposed S-MVKM algorithm is then compared with several existing algorithms, such as traditional MVKM, feature reduction MVKM (FR-MVKM), MVKM with adaptive sparse membership weight allocation (M-VASM), and redundant and sparse feature learning (RSF-MVKM). Based on the three clustering performance measures of accuracy rate, Rand index, and normalized mutual information, we make the comparisons between them by using several synthetic and real-world datasets. The comparative results demonstrate that the proposed S-MVKM clustering algorithm effectively surpasses these competing clustering algorithms in terms of these three performance measures. Finally, we make concluding remarks that provide potential guidelines for future research in multi-view clustering area.}, archive = {J_ACCESS}, author = {Miin-Shen Yang and Shazia Parveen}, doi = {10.1109/ACCESS.2025.3551160}, journal = {IEEE Access}, pages = {46773-46793}, shortjournal = {IEEE Access}, title = {Sparse multi-view K-means clustering}, volume = {13}, year = {2025}, }
• (2025). Single DC-link current sensor control of PMSM based on discrete space vector modulation model predictive control. ACCESS, 13, 46763-46772. (www)

  @article{ , abstract = {The single dc-link current sensor control of permanent magnet synchronous motor (PMSM) based on model predictive control (MPC) combines the advantages of high dynamic response of model prediction and low complexity of single current sensor. However, under traditional MPC strategy, the current reconstruction and tracking performance of single current control is inadequate, which can not meet the requirements of high performance control. In order to solve the above problems, a discrete space vector modulation-model predictive control (DSVM-MPC) method is designed. By dividing the control period into four equal parts, the number of alternative vectors is expanded, then the accuracy of current tracking has been improved. On this basis, by flexibly adjusting the action sequence of different vectors in the control period, the sampling moment and sampling interval of single current sensor are fixed, then the reconstruction error is reduced. In order to solve the problem of parameter mismatch under the MPC, an extended state observer (ESO) is introduced to observe the parameter disturbance and predictive current, which improves the parameter robustness and the accuracy of reconstructed current. Experimental results verify the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Peng Liu and Nailong Zong and Zhicheng Yin and Liwei Liu and Lisi Tian and Xianming Deng}, doi = {10.1109/ACCESS.2025.3536457}, journal = {IEEE Access}, pages = {46763-46772}, shortjournal = {IEEE Access}, title = {Single DC-link current sensor control of PMSM based on discrete space vector modulation model predictive control}, volume = {13}, year = {2025}, }
• (2025). Enhancing cardiovascular disease diagnosis: The power of optimized ensemble learning. ACCESS, 13, 46747-46762. (www)

  @article{ , abstract = {Cardiovascular disease, a leading cause of global mortality, is influenced by genetic, environmental, and lifestyle factors. Accurate diagnosis involves identifying key risk factors such as age, gender, family history, diabetes, obesity, and sedentary lifestyle, using medical history, physical exams, and lab tests. This study introduces an advanced method called GMM-based Dynamic Ensemble Learning Optimization using a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). This approach utilizes evolutionary resampling inspired by the Gaussian mixture model (GMM) to enhance classifier diversity, performance, and quantity, producing optimal datasets. Ensemble weight interpolation and dynamic ensemble techniques are applied to reduce overfitting. Tested on seven heart disease datasets with varying imbalance rates, the proposed method consistently surpasses established ensemble algorithms across multiple metrics, validated by McNemar’s and Wilcoxon tests. Additionally, Kaplan Meier Estimates analysis of a heart failure dataset provides deeper insights into heart disease prognosis. This research advances accurate heart disease prediction and prognosis through innovative data mining and ensemble learning techniques.}, archive = {J_ACCESS}, author = {Fatemeh Yazdi and Shahrokh Asadi}, doi = {10.1109/ACCESS.2025.3550015}, journal = {IEEE Access}, pages = {46747-46762}, shortjournal = {IEEE Access}, title = {Enhancing cardiovascular disease diagnosis: The power of optimized ensemble learning}, volume = {13}, year = {2025}, }
• (2025). Predicting student dropout through text and media content analysis of VKontakte profiles. ACCESS, 13, 46732-46746. (www)

  @article{ , abstract = {This paper presents a novel approach to predicting student dropout by analyzing publicly available data from VKontakte social network profiles. Unlike traditional methods that primarily rely on academic and institutional data, our method leverages publicly available content, including photos, videos, music preferences, and textual posts. Image and video content were analyzed using scene recognition models (Places365) and contextual alignment models (CLIP), while text data were processed using the BERTopic model for topic modeling and a pre-trained model for emotion analysis. Music tracks were categorized by genre. Additionally, we developed and implemented the Weighted Neighborhood Feature Aggregation method, which incorporates weighted features from a student’s friends to make classification models graph-aware, leveraging information from the friendship graph to enrich feature representation. Using this data, a comprehensive digital student profile was constructed and utilized to predict dropout risk through machine learning techniques. The CatBoost model achieved the highest prediction accuracy, with a ROC-AUC of 0.802 and a balanced accuracy of 0.749. Key predictors included indirect indicators of academic and extracurricular engagement, the emotional tone of posts, and leisure activity preferences. The findings demonstrate that social media data can effectively aid in the early identification of students at risk of dropping out, allowing for timely interventions. While this approach depends on publicly available VK data and may be limited by privacy settings, it still provides useful insights. This approach also holds promise for applications such as student loan risk assessment.}, archive = {J_ACCESS}, author = {Sergei S. Gorshkov and Dmitry I. Ignatov and Anastasia Yu. Chernysheva}, doi = {10.1109/ACCESS.2025.3550817}, journal = {IEEE Access}, pages = {46732-46746}, shortjournal = {IEEE Access}, title = {Predicting student dropout through text and media content analysis of VKontakte profiles}, volume = {13}, year = {2025}, }
• (2025). Identification of parkinson’s disease through facial image classification: A systematic review. ACCESS, 13, 46720-46731. (www)

  @article{ , abstract = {Parkinson’s Disease (PD) is one of the most prevalent neurological conditions, affecting a significant portion of the elderly population worldwide. Characterized by motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms like hypomimia (reduced facial expressiveness), PD presents significant diagnostic challenges, especially in its early stages. In this context, this systematic review (SR) aims to explore machine learning and deep learning techniques in detecting PD through facial image analysis. The study identifies the key works in the field, the computational techniques employed, and the datasets used. The main findings suggest that facial phenotyping can be a strong indicator for the early diagnosis of PD. Various approaches, including Convolutional Neural Networks (CNNs) and other deep learning models, have shown promise in detecting subtle changes in facial expressions associated with hypomimia. Integrating multimodal data sources, such as voice recordings, hand-drawn sketches, movement analysis, and facial images, has also demonstrated the potential to enhance diagnostic accuracy. This review highlights the potential of facial image analysis as an innovative approach for the early detection of PD, which could lead to more timely and effective interventions.}, archive = {J_ACCESS}, author = {José Nazareno Alves Rodrigues and Rodrigo De Melo Souza Veras and Laurindo De Sousa Britto Neto and Pedro Henrique Ximenes Ramalho Barros and Wilana Da Silva Moura and Kelson James Silva De Almeida and Kelson Romulo Teixeira Aires}, doi = {10.1109/ACCESS.2025.3549694}, journal = {IEEE Access}, pages = {46720-46731}, shortjournal = {IEEE Access}, title = {Identification of parkinson’s disease through facial image classification: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Complex-valued multi-domain features and its application in motor imagery classification. ACCESS, 13, 46710-46719. (www)

  @article{ , abstract = {The extraction of features from electroencephalography (EEG) signals is a vital step in the classification of motor imagery. However, traditional feature extraction methods for EEG signals either focus either on amplitude or frequency, or the rich phase information. This inability to simultaneously integrate phase information results in the loss of certain implicit details. Hence, effectively merging multiple pieces of features with phase data to establish a complex value feature extraction model is a significant challenge. Based on this, we propose a new hybrid framework combining complex-value multi-domain features (CVF) and complex-valued extreme learning machine network (CVELM), namely, CVF-CVELM to classify the motor imagery tasks. First of all, some multi-domain features are extracted. These features are then encoded into complex domain space based on phase-locked value, which captures the phase information of specific channels and time points. Furthermore, to align with the feature vectors in complex space, we developed a CVELM. Significance testing and separability analysis are employed for real-value features and CVF, the experimental results manifest the effectiveness of the CVF. Meanwhile, we evaluate our proposed method on two publicly available datasets from the representative fields of BCI, notably motor imagery decoding. The proposed model yielded better performance compared with existing state-of-the-art methods in terms of overall classification accuracy and kappa coefficient. The empirical evaluations demonstrate that the features in the complex domain contain richer information, which could be utilized in online BCI systems in the future.}, archive = {J_ACCESS}, author = {Yabing Li and Zhenbo Sun and Zhenhua Wang and Kun Song}, doi = {10.1109/ACCESS.2025.3540202}, journal = {IEEE Access}, pages = {46710-46719}, shortjournal = {IEEE Access}, title = {Complex-valued multi-domain features and its application in motor imagery classification}, volume = {13}, year = {2025}, }
• (2025). Securing networks against adversarial domain name system tunneling attacks using hybrid neural networks. ACCESS, 13, 46697-46709. (www)

  @article{ , abstract = {Domain name system tunneling is one of the emerging threats that use Domain name system (DNS) to transfer unwanted material, and it is usually undetected by conventional detection systems. Thus, the current paper proposes a double-architecture deep learning system built upon Long short-term memory (LSTM) and Deep Neural Networks (DNN) to detect and categorize adversarial Domain name system tunneling assaults. Limitations in the current Domain name system traffic classification techniques are overcome in the proposed model through temporal sequence modelling and feature extraction to distinguish clearly between normal, attack, and adversarial traffic. Based on the experiments conducted on a broad data set, the application of the proposed hybrid model increased the classification accuracy up to 85.2%, which is higher compared with basic machine learning algorithms. Moreover, the ablation analysis showed that downstream components, such as the Long short-term memory layer and exact dropout rate, are critical to the performance of the proposed model against adversarial perturbation. This work offers a solution for identifying intricate threats in a big and live manner; as such, it has broad applicability in sensitive areas of activity like finance, health care, and administration. Further work includes applying this approach to other network-based threats and improving the effectiveness of applying it to oligopolistic adversaries’ tactics.}, archive = {J_ACCESS}, author = {Stephanie Ness}, doi = {10.1109/ACCESS.2025.3550853}, journal = {IEEE Access}, pages = {46697-46709}, shortjournal = {IEEE Access}, title = {Securing networks against adversarial domain name system tunneling attacks using hybrid neural networks}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based ensemble framework for robust android malware detection. ACCESS, 13, 46673-46696. (www)

  @article{ , abstract = {The exponential growth of Android applications has resulted in a surge of malware threats, posing severe risks to user privacy and data security. To address these challenges, this study introduces a novel malware detection approach utilizing an ensemble of Convolutional Neural Networks (CNNs) for enhanced classification accuracy. The methodology incorporates a multi-phase process, starting with the extraction and preprocessing of APK (Android app) files. The preprocessing phase involves decompressing, decompiling, and transforming the APK files into bytecode and Dex files. The extracted byte data is converted into 1D vectors and reshaped into 2D grayscale images, enabling efficient feature learning through CNNs. The proposed ensemble of CNN-based models undergoes comprehensive training, validation, and evaluation, demonstrating superior performance compared to existing approaches. We used two popular Android datasets to evaluate the performance of our proposed model. Specifically, the model achieves an accuracy of 98.65%, F1-score of 96.43% on the Drebin dataset and attains 97.91% accuracy, 96.73% of F1-score on the AMD dataset. These results confirm the mode’s ability to effectively identify Android malware with high precision and reliability, outperforming traditional techniques. This research not only underscores the potential of our proposed approach in malware detection but also sets a foundation for future advancements. Future efforts will focus on real-time malware detection, integration with mobile security frameworks, and evaluation across diverse datasets to ensure adaptability to emerging malware threats.}, archive = {J_ACCESS}, author = {Sainag Nethala and Pronoy Chopra and Khaja Kamaluddin and Shahid Alam and Soltan Alharbi and Mohammad Alsaffar}, doi = {10.1109/ACCESS.2025.3551152}, journal = {IEEE Access}, pages = {46673-46696}, shortjournal = {IEEE Access}, title = {A deep learning-based ensemble framework for robust android malware detection}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral data driven solid waste classification. ACCESS, 13, 46659-46672. (www)

  @article{ , abstract = {Computer vision methods have been recently integrated into industrial facilities performing the automatic household waste sorting. In this research, we report on an approach based on hyperspectral cameras and consecutive data analysis in recycling plants for improve waste sorting processes in terms of waste detection and classification. The majority of existing methods have been conducted on static conveyor belt, and their findings may not be applicable to recycling systems. The study focuses on developing a real-time hyperspectral object classification system for sorting machines, addressing the challenge of differentiating objects that are unidentifiable to RGB cameras. The linear hyperspectral camera operates within the wavelength range of 900 nm to 1800 nm. The research includes the creation of a dataset with physically separated training and test data, automated pseudo-labeling of hyperspectral data. This allows for rapid expansion to new classes without waste of manual annotation. The dataset contains 18 classes of objects, including various types of plastics, films, textiles, tetrapack, and cellulose products. To improve the accuracy and inference speed, we experiment with normalization, dimensionality reduction, and model calibration. Experimental results show a promising F1-score of 71% on a moving conveyor belt.}, archive = {J_ACCESS}, author = {Gleb Mazanov and Anna Iliushina and Sergey Nesteruk and Andrey Pimenov and Anton Stepanov and Nadezhda Mikhaylova and Andrey Somov}, doi = {10.1109/ACCESS.2025.3551097}, journal = {IEEE Access}, pages = {46659-46672}, shortjournal = {IEEE Access}, title = {Hyperspectral data driven solid waste classification}, volume = {13}, year = {2025}, }
• (2025). A dual-stream deep learning architecture with adaptive random vector functional link for multi-center ischemic stroke classification. ACCESS, 13, 46638-46658. (www)

  @article{ , abstract = {One of the main causes of death and permanent disability is ischemic stroke, for which prompt and precise diagnosis is essential to successful treatment. This study introduces a novel dual-stream deep learning framework for ischemic stroke classification using Computed Tomography (CT) images, specifically addressing challenges in accuracy, computational efficiency, and clinical interpretability. Three significant innovations are included in the suggested architecture: (1) a hybrid Dual Attention Mechanism that combines Dynamic Routing and Cross-Attention for improved region-specific feature discrimination; (2) a Multi-Scale Feature Extraction Module with parallel convolutional pathways that captures both contextual and fine-grained features; and (3) an Adaptive Random Vector Functional Link layer that significantly reduces training time while maintaining high classification performance. When tested on a single-center dataset, the model achieves state-of-the-art classification accuracy of 98.83% across normal, acute and chronic stroke categories. We demonstrate the strong generalization capabilities of the proposed framework by achieving 92.42% accuracy on a diverse, multi-center dataset of 7,842 CT images. The integration of explainable Artificial Intelligence tools improve clinical trustworthiness by offering clear insight into the model’s decision-making process. These outcomes demonstrate the model’s potential to use in actual clinical settings for quick and accurate stroke diagnosis, along with its interpretability and computational efficiency.}, archive = {J_ACCESS}, author = {Mahesh Anil Inamdar and Anjan Gudigar and U. Raghavendra and Massimo Salvi and Raja Rizal Azman Bin Raja Aman and Nadia Fareeda Muhammad Gowdh and Izzah Amirah Binti Mohd Ahir and Mohd Salahuddin Bin Kamaruddin and Khairul Azmi Abdul Kadir and Filippo Molinari and Ajay Hegde and Girish R. Menon and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3550344}, journal = {IEEE Access}, pages = {46638-46658}, shortjournal = {IEEE Access}, title = {A dual-stream deep learning architecture with adaptive random vector functional link for multi-center ischemic stroke classification}, volume = {13}, year = {2025}, }
• (2025). Continuous monitoring and modeling of high-frequency EM pollution using cutting-edge regression techniques. ACCESS, 13, 46627-46637. (www)

  @article{ , abstract = {Electromagnetic (EM) sources have been of scientific interest for decades, primarily because of their intricate wave structures and the ways they travel through various media. Both national and international agencies regulate EM emissions across different frequency bands, making sure the public remains safe, mostly by setting limits on electric field intensity and power density. Here, we propose a precise predictive model for ongoing, high-frequency EM pollution, using data collected over six months in a mid-sized metropolitan area. Measurements were taken at a rate of 2 Hz, 24 hours a day. To forecast electric field intensity, we tested five regression models: Liquid Time-Constant Networks (LTC), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) Networks, Gated Recurrent Unit (GRU) Networks, and Kolmogorov-Arnold Networks (KAN). Among these, LTC achieved the top test accuracy of 97.95%, although the other models also proved highly reliable.}, archive = {J_ACCESS}, author = {Ismail Can Dikmen}, doi = {10.1109/ACCESS.2025.3548841}, journal = {IEEE Access}, pages = {46627-46637}, shortjournal = {IEEE Access}, title = {Continuous monitoring and modeling of high-frequency EM pollution using cutting-edge regression techniques}, volume = {13}, year = {2025}, }
• (2025). Toward low-resource languages machine translation: A language-specific fine-tuning with LoRA for specialized large language models. ACCESS, 13, 46616-46626. (www)

  @article{ , abstract = {In the field of computational linguistics, addressing machine translation (MT) challenges for low-resource languages remains crucial, as these languages often lack extensive data compared to high-resource languages. General large language models (LLMs), such as GPT-4 and Llama, primarily trained on monolingual corpora, face significant challenges in translating low-resource languages, often resulting in subpar translation quality. This study introduces Language-Specific Fine-Tuning with Low-rank adaptation (LSFTL), a method that enhances translation for low-resource languages by optimizing the multi-head attention and feed-forward networks of Transformer layers through low-rank matrix adaptation. LSFTL preserves the majority of the model parameters while selectively fine-tuning key components, thereby maintaining stability and enhancing translation quality. Experiments on non-English centered low-resource Asian languages demonstrated that LSFTL improved COMET scores by 1-3 points compared to specialized multilingual machine translation models. Additionally, LSFTL’s parameter-efficient approach allows smaller models to achieve performance comparable to their larger counterparts, highlighting its significance in making machine translation systems more accessible and effective for low-resource languages.}, archive = {J_ACCESS}, author = {Xiao Liang and Yen-Min Jasmina Khaw and Soung-Yue Liew and Tien-Ping Tan and Donghong Qin}, doi = {10.1109/ACCESS.2025.3549795}, journal = {IEEE Access}, pages = {46616-46626}, shortjournal = {IEEE Access}, title = {Toward low-resource languages machine translation: A language-specific fine-tuning with LoRA for specialized large language models}, volume = {13}, year = {2025}, }
• (2025). CBCTL-IDS: A transfer learning-based intrusion detection system optimized with the black kite algorithm for IoT-enabled smart agriculture. ACCESS, 13, 46601-46615. (www)

  @article{ , abstract = {Smart Agriculture(SA) stands as a critical application frontier for the evolution of the IoT, catalyzing the transformation of traditional agricultural practices into modern, data-driven, intelligent, and automated systems. This transition not only elevates the levels of automation and intelligence in agricultural production but also introduces a spectrum of cybersecurity challenges. In particular, the widespread deployment of interconnected devices within these systems poses significant risks to the security and integrity of data. As a pivotal defense mechanism against malicious activities and anomalous traffic, Network Intrusion Detection Systems (NIDS) play an indispensable role in ensuring the security of cyberspace. Nevertheless, existing methods for detecting anomalous traffic in IoT networks still exhibit limitations in accuracy, thereby hindering the efficiency of identifying potential threats. To enhance the capability of threat detection, this paper proposes a novel network intrusion detection method, CBCTL-IDS, based on transfer learning. This method integrates three core components: Convolutional Neural Networks (CNN), the Black Kite Algorithm (BKA), and a Confidence Averaging mechanism. By incorporating pre-trained models such as MobileNet, EfficientNet, Xception, VGG19, and Inception into the domain of IoT intrusion detection, CBCTL-IDS leverages transfer learning to achieve efficient feature extraction and classification, addressing the limitations of traditional methods in feature representation. Furthermore, the BKA is utilized to adaptively optimize the hyperparameters of multiple DL models, significantly enhancing their classification performance. Through ensemble learning, the top three performing models are selected, and the Confidence Averaging mechanism is applied to further improve the stability and reliability of detection results. Experimental results demonstrate that the CBCTL-IDS method achieves detection accuracy rates exceeding 99% on three IoT intrusion detection datasets: ToN-IoT, Edge-IIoTset, and WSN-DS significantly outperforming existing mainstream methods. This approach provides robust technical support for the security protection of IoT systems.}, archive = {J_ACCESS}, author = {Hai Zhou and Haojie Zou and Pinxi Zhou and Yue Shen and Di Li and Wei Li}, doi = {10.1109/ACCESS.2025.3550800}, journal = {IEEE Access}, pages = {46601-46615}, shortjournal = {IEEE Access}, title = {CBCTL-IDS: A transfer learning-based intrusion detection system optimized with the black kite algorithm for IoT-enabled smart agriculture}, volume = {13}, year = {2025}, }
• (2025). RLE-YOLO: A lightweight and multiscale SAR ship detection based on improved YOLOv8. ACCESS, 13, 46584-46600. (www)

  @article{ , abstract = {The background of SAR ship images is complex and prone to false detection and missed detection when facing multiscale ship targets. In addition, SAR ship detection places high demands on real-time performance and accuracy, which is a major challenge for the detection algorithm. In response to the above problems, we propose a lightweight and multiscale SAR ship detection model, RLE-YOLO, based on improved YOLOv8s. Firstly, a Reversible Multinets (RMNet) backbone network inspired by RevCol reduces the model calculation and parameters while enhancing robustness in complex backgrounds. Secondly, to address the issue of large size differences among ships in SAR images, a Lightweight Multiscale Convolution (LMSC) module is incorporated in the Neck section to improve the model’s detection performance for multiple-scale targets. Subsequently, the Efficient Shared Convolutional Detection Head (ESCDH), with the design of group normalization and shared convolution, serves to enhance the detection accuracy while effectively reducing the computational overhead. Experiments conducted on the HRSID and SSDD datasets show that RLE-YOLO achieves 98.4% and 93.9% mAP50, respectively, outperforming the benchmark model. It also reduces parameters by 43.9% and computation by 34.5%. Overall, RLE-YOLO has demonstrated clear advantages in terms of detection accuracy and computational effectiveness.}, archive = {J_ACCESS}, author = {Yifan Xu and Xiaorong Xue and Chuanlu Li and Siyue Zhao and Xingbiao Xu and Caijia Zeng}, doi = {10.1109/ACCESS.2025.3550541}, journal = {IEEE Access}, pages = {46584-46600}, shortjournal = {IEEE Access}, title = {RLE-YOLO: A lightweight and multiscale SAR ship detection based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Measurement of anti-debugging techniques on the windows and linux operating systems for the intel x86_64 architecture. ACCESS, 13, 46568-46583. (www)

  @article{ , abstract = {Malware authors and software protection frameworks often use anti-debugging techniques to hinder understanding of the underlying code. Companies use anti-debugging techniques to prevent intellectual property, including music, movies, and games, from being stolen or abused by malicious actors. In addition, the encryption of program data or private data can also be classified as an anti-debugging technique because it requires additional effort to reverse engineer or debug it. Malware authors also use these techniques to prevent their software from being analyzed, detected, or blocked. While anti-debugging techniques are not novel, there is minimal research on their performance. This paper conducted novel research on commonly used anti-debugging techniques by measuring their performance on both the Windows and Linux operating system while also creating performance data about the most common anti-debugging techniques, including implementations and statistical measures for six categories of anti-debugging techniques. The statistical results describe a low overhead for the API tests, timing tests, and debug register tests. As expected, the memory encryption test had the highest overhead. Lastly, the paper discusses these results and their statistics per technique and includes code samples for future research.}, archive = {J_ACCESS}, author = {Austin Norby and Bhaskar P. Rimal and Bramwell Brizendine}, doi = {10.1109/ACCESS.2025.3550009}, journal = {IEEE Access}, pages = {46568-46583}, shortjournal = {IEEE Access}, title = {Measurement of anti-debugging techniques on the windows and linux operating systems for the intel x86_64 architecture}, volume = {13}, year = {2025}, }
• (2025). Dempster shafer-empowered machine learning-based scheme for reducing fire risks in IoT-enabled industrial environments. ACCESS, 13, 46546-46567. (www)

  @article{ , abstract = {In high-risk IoT-enabled Industrial Environments such as oil and gas industries, early and accurate fire detection is crucial to prevent catastrophic damage. Traditional fire detection systems that rely on single IoT sensors, like heat or smoke detectors, are prone to inaccuracies and delayed responses, particularly in noisy or complex industrial environments. This research proposes an advanced fire prediction approach aiming to enhance decision-making accuracy with uncertain or incomplete fire sensor data in an edge computing IoT complex industrial environment that integrates multiple supervised machine learning algorithms for each sensor types and Dempster-Shafer theory (DST) with multi-sensor fusion. By combining data from multiple different types of sensors (RGB, thermal, gas, smoke, and flame), the proposed architecture enhances the reliability of fire prediction and detection as each sensor detects different parameters of a fire and this ensures every parameter is considered for fire detection ensuring early detection and it reduces false positives. By preprocessing sensor data and by combining machine learning algorithms (MobileNet, EfficientNet, Random Forest, SVM, and Decision Trees) where each ML processes different sensor data types, this supports providing more accurate data to DST. The D-S theory helps in handling uncertainty by fusing conflicting multi-sensor data to generate a more reliable decision. The results demonstrate that this multi-integrated methodology proposed significantly outperforms traditional methods in predicting and detecting fires early and accurately, even under challenging and complex environmental conditions of Oil and Gas Industries. The goal of this research contribute to the development of advanced fire prediction protocols offering a robust solution for managing and mitigating fire risks in dynamic, data-rich IoT environments. Predicting fire and reducing Fire in a critical oil and gas IoT industrial environment remains a critical challenge. The proposed method aims to enhance fire prediction and detection accuracy through algorithms developed using sensor preprocessing, machine learning, and Dempster Shafer fusion handling uncertain and varying multi-sensor data and demonstrates real-time, data-driven decision-making in critical industrial environments. The results show an accuracy of 98.2%, a precision of 98.5%, and a recall of 98.3% significantly confirming the prediction over other models.}, archive = {J_ACCESS}, author = {Jayameena Desikan and Sushil Kumar Singh and A. Jayanthiladevi and Saurabh Singh and Byungun Yoon}, doi = {10.1109/ACCESS.2025.3550413}, journal = {IEEE Access}, pages = {46546-46567}, shortjournal = {IEEE Access}, title = {Dempster shafer-empowered machine learning-based scheme for reducing fire risks in IoT-enabled industrial environments}, volume = {13}, year = {2025}, }
• (2025). PS-SLAM: A visual SLAM for semantic mapping in dynamic outdoor environment using panoptic segmentation. ACCESS, 13, 46534-46545. (www)

  @article{ , abstract = {Simultaneous Localization and Mapping (SLAM) suffers from localization and reconstruction challenges in large-scale outdoor environments due to dynamic features. Although methods that address dynamic features in scenes rely on semantic segmentation, they neglect the issue of unknown areas that arise during segmentation. We propose a semantic visual SLAM system tailored for large-scale outdoor environments. Our system integrates panoptic segmentation with epipolar geometry to effectively detect and filter out dynamic features. It also performs dynamic feature detection and filters out those unknown areas around dynamic objects generated by panoptic segmentation. We evaluated our method on the KITTI dataset and performed a comprehensive comparison with state-of-the-art methods. Experimental results demonstrate that our approach significantly enhances the accuracy of SLAM systems in dynamic environments, outperforming state-of-the-art methods in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) in some scenarios. In addition, we use a stereo matching model to generate disparity images and compute depth information based on camera parameters. When combined with panoptic segmentation semantic labels, our system enables the construction of comprehensive, detailed semantic maps in outdoor environments.}, archive = {J_ACCESS}, author = {Gang Li and Jinxiang Cai and Chen Huang and Hao Luo and Jian Yu}, doi = {10.1109/ACCESS.2025.3547002}, journal = {IEEE Access}, pages = {46534-46545}, shortjournal = {IEEE Access}, title = {PS-SLAM: A visual SLAM for semantic mapping in dynamic outdoor environment using panoptic segmentation}, volume = {13}, year = {2025}, }
• (2025). A topological-indicators-based k-means clustering algorithm and its application in time series data: A case study on sea level variability in peninsular malaysia. ACCESS, 13, 46514-46533. (www)

  @article{ , abstract = {Traditional k-means clustering is widely used to analyze regional and temporal variations in time series data, such as sea levels. However, its accuracy can be affected by limitations, particularly when applied to datasets with mixed groups or significant noise. In this study, we analyzed monthly sea level data derived from daily time series at 14 tide gauge stations along the coastline of Peninsular Malaysia. To enhance traditional k-means clustering, we propose a hybrid approach that combines clustering techniques with topological data analysis (TDA). Specifically, we integrate k-means and its variant, k-means++, with persistent homology, the primary tool in TDA, to capture topological insights from the datasets. The proposed approach clusters the 14 tide gauge stations based on predefined topological features, and the probability of data points from each station belonging to specific clusters is computed. The results demonstrate that our approach significantly improves the performance of traditional k-means clustering by incorporating topological information, compared to using clustering without such insights.}, archive = {J_ACCESS}, author = {Zixin Lin and Nur Fariha Syaqina Zulkepli and Mohd Shareduwan Bin Mohd Kasihmuddin and Rudrusamyr Gobithaasan}, doi = {10.1109/ACCESS.2025.3548558}, journal = {IEEE Access}, pages = {46514-46533}, shortjournal = {IEEE Access}, title = {A topological-indicators-based k-means clustering algorithm and its application in time series data: A case study on sea level variability in peninsular malaysia}, volume = {13}, year = {2025}, }
• (2025). Enhancing the efficiency of unsupervised network alignment using quotient graph. ACCESS, 13, 46495-46513. (www)

  @article{ , abstract = {Network alignment, a foundational technique for cross-domain applications such as recommendation systems and knowledge fusion, faces significant challenges in balancing computational efficiency and alignment accuracy. Although graph neural networks (GNNs) effectively capture structural and semantic relationships, their computational intensity—stemming from multi-layer matrix operations and high memory consumption—severely limits scalability. To address these limitations, this paper proposes ENAMOR (Efficient Network AlignMent via Quotient gRaph), an unsupervised framework incorporating three key components: 1) multi-scale representation learning that hierarchically aggregates local and global structural patterns through GNN layers; 2) embedding-driven graph coarsening via hashing-based quotient graph construction, reducing computational complexity by 60–80% while preserving topological and attribute information; and 3) Matched Neighborhood Consistency (MNC) optimization, which iteratively refines alignment matrices by enforcing structural congruence constraints. Extensive experiments on three real-world datasets (Douban Online-Offline, Allmovie-Imdb, ACM-DBLP) demonstrate that ENAMOR achieves a 13.47–94.56% reduction in runtime compared to state-of-the-art methods, alongside improvements of 0.27–13.47 percentage points in mean average precision (MAP) and 0.15–8.17 percentage points in precision@5. The framework effectively balances efficiency and accuracy, providing a scalable solution for cross-network analysis tasks.}, archive = {J_ACCESS}, author = {Lei Zhang and Feng Qian}, doi = {10.1109/ACCESS.2025.3549787}, journal = {IEEE Access}, pages = {46495-46513}, shortjournal = {IEEE Access}, title = {Enhancing the efficiency of unsupervised network alignment using quotient graph}, volume = {13}, year = {2025}, }
• (2025). Image classification of multimedia platforms with texture algorithms and neural networks. ACCESS, 13, 46478-46494. (www)

  @article{ , abstract = {In this paper, a methodology for real-time image classification on multimedia platforms has been developed. For this purpose, six feedforward neural network models were trained with images from two databases, which were preprocessed by three texture extraction methods: local binary pattern-uniform (LBP-U), gray level co-occurrence matrix (GLCM), and wavelet image scattering (WIS). The databases used consist of 157,448 images of the sections with the thumbnails of the platform content (mosaics) representing 14 classes and 38,214 images with the descriptions of the available content (descriptors) representing 11 classes, where all images have a resolution of $1280\times 720$ pixels. The six models (three for mosaics and three for descriptors) were validated with images from the databases, which were not part of the training process, to obtain their performance metrics. The training and validation process was performed 30 times, and the average results were compared. The most outstanding models for each database were the neural networks trained with the wavelet image scattering method, with metrics of $99.97\pm 0.01$ % accuracy, $99.99\pm 0.01$ % specificity, $99.84\pm 0.06$ % sensitivity, $99.59\pm 0.13$ % precision and $99.71\pm 0.~08$ % of F1 score with a response time of 0.7349 seconds for the model trained with mosaics and with metrics of $99.90\pm 0.03$ % of accuracy, $99.94\pm 0.02$ % of specificity, $99.58\pm 0.15$ % of sensitivity, $98.63\pm 0.55$ % of precision and $99.09\pm 0.30$ % of F1 score with a response time of 0.6227 seconds for the model trained with descriptor images. The results are very significant due to the high efficiency obtained and confirm the effectiveness of the models with the WIS method for the classification of multimedia platform images with the characteristics of the databases used. It is suggested that the remaining methods be adjusted to improve their performance.}, archive = {J_ACCESS}, author = {Mauricio Sauzameda-González and Josué Álvarez-Borrego and Esperanza Guerra-Rosas}, doi = {10.1109/ACCESS.2025.3550303}, journal = {IEEE Access}, pages = {46478-46494}, shortjournal = {IEEE Access}, title = {Image classification of multimedia platforms with texture algorithms and neural networks}, volume = {13}, year = {2025}, }
• (2025). Integrating time series anomaly detection into DevOps workflows. ACCESS, 13, 46459-46477. (www)

  @article{ , abstract = {Anomaly detection in the monitoring systems of DevOps environments is crucial for ensuring system reliability, preventing downtime, and maintaining the efficiency of continuous integration and continuous deployment pipelines. Artificial Intelligence (AI)-based solutions for automated anomaly detection in DevOps workflows are attracting growing research interest. However, challenges remain regarding the lack of ground truth data from DevOps systems, as well as difficulties in storing, processing, and visualizing the collected data. Furthermore, most of these datasets are unlabeled, making it unclear what constitutes anomalous system behavior, and no generalized approach exists for selecting the most suitable AI algorithms for anomaly detection in such contexts. To address these challenges, this paper publishes a comprehensive time series dataset from 19 different DevOps test systems, comprising 24 performance metrics sampled twice per minute over 30 days. Moreover, to benchmark the dataset, we first label a subset of the dataset based on feedback from the DevOps experts within the industry context. Then six different algorithms are employed on the dataset and their performance in anomaly detection is evaluated using three different Area Under the Curve (AUC) metrics. Additionally, this paper presents a tool for storing, visualizing, monitoring, and integrating anomaly detection algorithms and makes it available to the community. The performance evaluation results aiming to establish a baseline for further research show that AI-based anomaly detection can significantly benefit DevOps workflows but emphasize the need for algorithm selection and parameter tuning tailored to the specific industry context and dataset.}, archive = {J_ACCESS}, author = {Gustav Kånåhols and Shahriar Hasan and Per Erik Strandberg}, doi = {10.1109/ACCESS.2025.3550665}, journal = {IEEE Access}, pages = {46459-46477}, shortjournal = {IEEE Access}, title = {Integrating time series anomaly detection into DevOps workflows}, volume = {13}, year = {2025}, }
• (2025). Graph neural network with hessian-based locally linear embedding for cancer metastasis analysis in lymph nodes using DeepLab segmentation. ACCESS, 13, 46448-46458. (www)

  @article{ , abstract = {Breast cancer patients could benefit greatly from an automatic diagnosis of metastases in lymph nodes. In clinical settings, where whole-slide images of histological lymph nodes are involved, it is crucial to identify and detect breast cancer metastases. Patient survival rates increase significantly with early detection. In order to identify metastases, pathologists are depending more and more on microscopic research. However, the diagnostic process is time-consuming and sometimes results in missed diagnoses. Automated, reliable patient-level classification has the potential to minimize pathologists’ effort while improving diagnosis accuracy. The objective of this work is to propose and standardize a new framework that combines a Graph Neural Network (GNN) with Hessian-based Locally Linear Embedding (HLLE) to better understand cancer metastasis in lymph nodes, while DeepLab is used to effectively segment each location. The proposed framework integrates DeepLab, a highly advanced convolutional neural network for image segmentation, to segment the lymph node regions from high-resolution MRI and PET scans. These segmented regions are then modeled as graphs, where the nodes signify the segmented areas and the edges reflect spatial and functional coupling. To accomplish better preservation of the geometry of high-dimensional data and more effective feature extraction, the HLLE algorithm is applied. These features are then inputted into a GNN, which utilizes the graph structure to identify and analyze metastatic patterns. The method was tested on the CAMELYON17 dataset, which contains of imaging data from patients with dissimilar stages of cancer. Precisely, DeepLab was utilized to segment the lymph nodes, and the segmented regions were then converted into graphs. The meaningful features were obtained utilizing the proposed HLLE, after which the GNN conducted the classification and metastasis analysis. Overall, the precision and reliability of the established segmentation method, DeepLab, followed by HLLE and GNN, proved to be precise in classifying metastatic lymph nodes and their classification.}, archive = {J_ACCESS}, author = {Senthil Jayapal and R. Annamalai}, doi = {10.1109/ACCESS.2025.3546716}, journal = {IEEE Access}, pages = {46448-46458}, shortjournal = {IEEE Access}, title = {Graph neural network with hessian-based locally linear embedding for cancer metastasis analysis in lymph nodes using DeepLab segmentation}, volume = {13}, year = {2025}, }
• (2025). SLAM performance evaluation on uneven terrain under varying illuminance conditions and trajectory lengths. ACCESS, 13, 46426-46447. (www)

  @article{ , abstract = {Simultaneous Localization and Mapping (SLAM) algorithms based on visual and light detection and ranging (LiDAR) technologies have shown remarkable capabilities in robot navigation, particularly for mapping and navigating unfamiliar environments. However, the performance of visual and LiDAR based SLAM in challenging settings, such as uneven terrain with complex environmental conditions, remains insufficiently explored. To address this challenge, the feasibility of two visual-based SLAM algorithms (i.e. real-time appearance-based mapping (RTAB-Map) and neuro-inspired 4DoF (degree of freedom) SLAM (NeuroSLAM)), and two LiDAR-based SLAM algorithms (i.e. LiDAR inertial odometry via smoothing and mapping (LIO-SAM) and fast LiDAR-inertial odometry (FAST-LIO)) were investigated. The algorithms were tested on uneven terrain within a palm oil plantation under varying illuminance conditions (morning and noon) and across trajectories of different lengths, corresponding to map sizes of 400 m (small), 600 m (medium), 1000 m (large), and 1200 m (extra-large). Performance metrics, including distance error and loop closure detection, were used to assess the impact of trajectory length and illuminance. Additionally, the consistency and stability of the map estimation were analyzed. Ground-truth data were obtained using the GeoMax Zoom 10 Total Station to ensure high-precision reference measurement. Results revealed that LIO-SAM consistently achieved the lowest distance errors across all map sizes during morning condition of 0.17 m, 1.08 m, 0.52 m, and 0.71 m for small, medium, large, and extra-large maps, respectively. During noon, LIO-SAM also demonstrated superior performance for small, medium, and large map sizes, with distance error values of 0.11 m, 0.25 m, and 0.38 m, respectively. Furthermore, LIO-SAM recorded the highest successful loop closure rates by achieving 75 % in the morning and 100 % at noon. Overall, the findings highlights the superior performance of LiDAR-based SLAM algorithms over visual-based SLAM approaches in complex, uneven terrains under varying environmental conditions.}, archive = {J_ACCESS}, author = {Amirul Jamaludin and Syaimaa’ Solehah Mohd Radzi and Norhidayah Mohamad Yatim and Nazlee Azmeer Masuan and Norsharimie Mat Adam and Nur Aisyah Syafinaz Suarin and Pazli Mohd Sulong and Siti Sofiah Mohd Radzi}, doi = {10.1109/ACCESS.2025.3549385}, journal = {IEEE Access}, pages = {46426-46447}, shortjournal = {IEEE Access}, title = {SLAM performance evaluation on uneven terrain under varying illuminance conditions and trajectory lengths}, volume = {13}, year = {2025}, }
• (2025). Improving learning management system performance: A comprehensive approach to engagement, trust, and adaptive learning. ACCESS, 13, 46408-46425. (www)

  @article{ , abstract = {This paper examines the critical factors influencing the performance of Learning Management Systems (LMS), emphasizing user satisfaction and loyalty. Employing a quantitative approach, structural equation modeling (SEM) was used to analyze survey data from 266 university students actively utilizing LMS platforms. Findings reveal that system quality significantly impacts user satisfaction, emphasizing reliability and usability. Adaptive learning enhances satisfaction and engagement by tailoring educational content to individual needs. Trust, particularly regarding data security and system reliability, further contributes to satisfaction, underscoring the necessity of robust and transparent security measures. Although information quality positively affects user satisfaction, it is less impactful than system-related factors. Service quality, while exerting a smaller influence, highlights the importance of responsive technical support for overall functionality. Additionally, user satisfaction strongly predicts user loyalty, indicating that satisfied users are more likely to continue using and recommending LMS platforms. The study provides actionable insights for developers and educators, advocating for enhanced system performance, adaptive learning tools, and trust-building measures to foster satisfaction and loyalty.}, archive = {J_ACCESS}, author = {Latifa Abdullah Almusfar}, doi = {10.1109/ACCESS.2025.3550288}, journal = {IEEE Access}, pages = {46408-46425}, shortjournal = {IEEE Access}, title = {Improving learning management system performance: A comprehensive approach to engagement, trust, and adaptive learning}, volume = {13}, year = {2025}, }
• (2025). Entropy-driven dynamic block compressive sampling for underwater image compression in the context of IoUT: A research perspective. ACCESS, 13, 46395-46407. (www)

  @article{ , abstract = {The Internet of Underwater Things (IoUT) is a network of countless connected devices that monitor vast, uncharted water territories. These gadgets consists of cameras designed to capture images beneath the water’s surface. and then distribute it among themselves and save them in the cloud. However, the substantial amount of data produced can hinder the devices’ performance due to limited computational power and battery life. To tackle this, Block Compressed Sampling (BCS) can be used to compress data, but it may result in distorted images after recovery. To tackle this problem, the Dynamic Block Compressive Sampling (DBCS) technique is utilized. This study introduces the Entropy-based Dynamic Block Compressive Sampling (EDBCS) algorithm to enhance the sampling accuracy and visual clarity of the recovered image. Through this approach, blocks with greater entropy receive increased measurements, while those with lower energy receive fewer ones. The suggested method has outperformed existing techniques, yielding superior results.}, archive = {J_ACCESS}, author = {R. Monika and Samiappan Dhanalakshmi and R. Narayanamoorthi and Hossam Kotb and Amr Yousef}, doi = {10.1109/ACCESS.2025.3548443}, journal = {IEEE Access}, pages = {46395-46407}, shortjournal = {IEEE Access}, title = {Entropy-driven dynamic block compressive sampling for underwater image compression in the context of IoUT: A research perspective}, volume = {13}, year = {2025}, }
• (2025). System-in-the-loop test system with mixed-reality for autonomous ground vehicle (AGV) and military applications. ACCESS, 13, 46383-46394. (www)

  @article{ , abstract = {In this paper, we propose a novel System-In-the-Loop (SysIL) test system based on mixed reality, capable of generating various test cases in outdoor environments, such as rough terrain, for Autonomous Ground Vehicle (AGV) and military applications. SysIL refers to the integration of real systems and simulation environments, enabling more accurate and dynamic testing of system performance across various proving grounds. Implementing a mixed reality test system for military applications presents specific challenges due to the diverse nature of missions and the complexity of unpaved road environments. To address these challenges and develop a SysIL test system for military applications, we propose a novel real-time Light Detection and Ranging (LiDAR) augmentation algorithm and a pose estimation improvement process within a mixed reality framework. The LiDAR augmentation includes real-time occlusion and intensity augmentation algorithms, while the pose estimation improvement process incorporates a real-time algorithm utilizing an Extended Kalman Filter (EKF) with localization sensors (Inertial Measurement Unit (IMU) and odometry) and a 2.5D elevation map. The proposed architecture is designed for an autonomous platform that responds to virtual objects and obstacles generated by the Sensor Signal Generator with Mixed Reality (SSGMR). The SSGMR is positioned between the perception sensors and the controller (either autonomous or manual) of an AGV system, to generate mixed signals for LiDAR point cloud data and camera image data, which are representative sensor data in autonomous applications. The mixed-reality performance of the proposed system is validated through path planning and obstacle avoidance methods within the Robot Operating System (ROS). Finally, several further works are suggested to enhance the performance of the proposed system.}, archive = {J_ACCESS}, author = {Hyunsung Tae and Saedong Yeo and Sukhyun Hwang and Seogu Park and Gyuhwan Hwang}, doi = {10.1109/ACCESS.2025.3549372}, journal = {IEEE Access}, pages = {46383-46394}, shortjournal = {IEEE Access}, title = {System-in-the-loop test system with mixed-reality for autonomous ground vehicle (AGV) and military applications}, volume = {13}, year = {2025}, }
• (2025). An improved framework: NFMGBM for enhanced anomaly detection. ACCESS, 13, 46374-46382. (www)

  @article{ , abstract = {Anomaly detection is an intricate task, especially when faced with tabular data that combines categorical and numerical features. Recent advancements in deep anomaly detection methods have aimed to improve downstream anomaly detection by acquiring new feature representations. However, the learning process associated with these methods has proven to be inefficient, hindering their practicality. To overcome this challenge, we propose an innovative anomaly detection framework called NFMGBM, which combine NFM (Neural Factorization Machines) with GBDT (Gradient-Boosted Decision Trees). Unlike previous models, NFMGBM offers a more sophisticated approach for capturing both low and high-order feature interactions, enabling the model to handle complex, non-linear patterns more effectively. Through extensive experiments conducted on three publicly available datasets, we substantiate the superior effectiveness of NFMGBM. By seamlessly integrating both categorical and numerical features, NFMGBM surpasses well-established models such as LightGBM, NFM, and DeepGBM in terms of crucial performance metrics, including AUC, accuracy, and precision. NFMGBM not only effectively addresses the challenges posed by tabular data but also opens up new horizons and solutions within the realm of anomaly detection. Its ability to handle the complexities inherent in tabular data makes NFMGBM a highly promising advancement in the field.}, archive = {J_ACCESS}, author = {Shiyun Liu and Yan Song and Zikai Xu and Yuan Zhao and Bowen Pan and Duo Xu}, doi = {10.1109/ACCESS.2025.3549093}, journal = {IEEE Access}, pages = {46374-46382}, shortjournal = {IEEE Access}, title = {An improved framework: NFMGBM for enhanced anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Competition between store brands and national brands: Examining whether and when live streaming channels should be introduced. ACCESS, 13, 46346-46373. (www)

  @article{ , abstract = {With the vigorous promotion of store brands (SBs) by platform retailers, they have rapidly risen in the market, which not only meets the diversified needs of the market but also intensifies competition with national brands (NBs). In order to solidify their position in this brand competition, national brand manufacturers have begun to actively introduce live streaming sales models. In this context, we have delved into whether, by whom, and when live streaming channels (LSCs) should be introduced as brand competition intensifies. Taking into account the impact of live streaming sales, we analyzed the sales strategies of e-commerce platforms and manufacturers in traditional online channels (TOCs) and LSCs. In the absence of LSCs, e-commerce platforms sell SBs products through their TOCs, while manufacturers resell their NBs products through TOCs. However, when an LSC is introduced, both parties have the opportunity to collaborate with live streamers and further expand the market. To comprehensively study this issue, we compared three different strategies: both parties not introducing LSCs (SN strategy), only the manufacturer introducing LSCs (SL strategy), and both parties simultaneously introducing LSCs (LL strategy). The research results show that the introduction of an LSC does not always positively impact the demand and profits of both parties. This primarily depends on the fan base of the live streamer and the required slot fee. Specifically, when the slot fee is at a lower level, live streaming sales are beneficial for both manufacturers and e-commerce platforms. In addition, we further explored the profit differences between manufacturers and e-commerce platforms under two different strategies. Specifically, in the comparison between the LL strategy and the LN strategy, we found that these differences are closely related to the level of slot fees and show an inverse relationship. Specifically, in scenarios with low slot fees, manufacturers are more inclined to choose the LL strategy to maximize their profits, while e-commerce platforms perform better under the LN strategy; and vice versa. This finding reveals that in a constantly changing market environment, manufacturers and e-commerce platforms can optimize their respective interests by flexibly adjusting their LSCs strategies. It’s worth noting that our study also found that in a particularly competitive market, consumer surplus (CS) and social welfare (SW) reached their peak under the SL strategy, while they dropped to their lowest under the SN strategy. This discovery not only emphasizes the significant role of LSCs in enhancing brand competitiveness and expanding sales channels, but also provides strong theoretical support and practical guidance for brand owners to develop effective strategies in a complex and changing market environment.}, archive = {J_ACCESS}, author = {Jiqiong Liu and Jing Liu and Shuai Feng and Wanling Zhou and Xueqiao Yuan}, doi = {10.1109/ACCESS.2025.3549126}, journal = {IEEE Access}, pages = {46346-46373}, shortjournal = {IEEE Access}, title = {Competition between store brands and national brands: Examining whether and when live streaming channels should be introduced}, volume = {13}, year = {2025}, }
• (2025). Token-wise asymmetric contrastive learning in countering unknown attacks for face anti-spoofing. ACCESS, 13, 46334-46345. (www)

  @article{ , abstract = {Face anti-spoofing (FAS) techniques are crucial for safeguarding face recognition systems against an ever-evolving landscape of spoofing attacks. While existing methods have made strides in detecting known attacks, robustness against unknown, potentially more sophisticated attack types remains a critical and unresolved challenge in the field. In this paper, we propose a novel framework integrating token-wise asymmetric contrastive learning with angular margin loss to enhance the robustness of FAS models against unknown attack types. The key idea to resolve the problem is to learn a feature space where live face features are densely distributed, whereas spoof face features are more dispersed. This is achieved through two novel strategies: 1) asymmetric contrastive learning, encouraging the FAS model to learn a compact distribution for live face features while relaxing the constraint on the distribution of spoof face features, and 2) token-wise learning, focusing on capturing intrinsic liveness cues from local region rather than identity or facial-related features. Additionally, an angular margin loss is incorporated to enhance the discriminative power of the learned features. Extensive experiments on public benchmark datasets demonstrate the superiority of our FAS model over state-of-the-art methods in cross-attack scenarios, showcasing its strong robustness to unknown attacks while maintaining unseen domain generalization capability.}, archive = {J_ACCESS}, author = {Jimin Min and Yunho Jeon and Yonghyun Jeong and Youngjoon Yoo and Haneol Jang}, doi = {10.1109/ACCESS.2025.3550397}, journal = {IEEE Access}, pages = {46334-46345}, shortjournal = {IEEE Access}, title = {Token-wise asymmetric contrastive learning in countering unknown attacks for face anti-spoofing}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey on split-fed learning: Methods, innovations, and future directions. ACCESS, 13, 46312-46333. (www)

  @article{ , abstract = {In this work we presented Split-Fed Learning (SFL), a new framework that combines the concepts of Federated Learning (FL) and Split Learning (SL), to provide privacy-aware and scalable training of machine learning models in settings with distributed data. With organizations needing more and more to take advantage of decentralized data sources without compromising on security, the potential of using SFL becomes more apparent. We identified studies that assess SFL’s performance against FL (both conventional and naive techniques) and SL on several datasets. Experimental results in many research shows that SFL can reduce the communication overhead and training time by a large margin while achieving a competitive accuracy of final model. In contrast, SFL delivered 30% better training speed and 15% accuracy than baseline models particularly for non-IID data distributions challenges. Lastly, a thorough breakdown of the SFL architecture explains its novelty for separating model training between clients and servers with respect to model partitioning. Hence, this optimization not only improves the efficiency of resource utilization but also boosts the resiliency of learning process against data heterogeneity. These results demonstrate the potential of SFL as a scalable and efficient approach to privacy-preserving machine learning, providing an alternative solution to recent challenges faced by organizations that deal with sensitive data. This is the first paper establishing such type of literature for SFL, representing an initial step toward a theoretical background for future research for implementing distributed learning frameworks and next towards practical usage scenarios on testing SFL.}, archive = {J_ACCESS}, author = {Geetabai S. Hukkeri and R. H. Goudar and G. M. Dhananjaya and Vijayalaxmi N. Rathod and Shilpa Ankalaki}, doi = {10.1109/ACCESS.2025.3547641}, journal = {IEEE Access}, pages = {46312-46333}, shortjournal = {IEEE Access}, title = {A comprehensive survey on split-fed learning: Methods, innovations, and future directions}, volume = {13}, year = {2025}, }
• (2025). Improving visual question answering by image captioning. ACCESS, 13, 46299-46311. (www)

  @article{ , abstract = {Visual Question Answering (VQA) is a challenging task that bridges the computer vision and natural language processing communities. It provide natural language answers to questions related to an associated image. Most existing VQA methods focus on the fusion and inference of visual features with the textual question. However, visual features often lack the necessary semantic information required to answer the questions accurately. To address this limitation, we propose a novel approach called Question-Guided Parallel Attention (QGPA), which effectively leverages the semantic information provided by an embedded image captioning model to answer related questions. First, we introduce an Attention-Aware (AA) mechanism that extends the traditional attention mechanism, helping to filter out incorrect or irrelevant information during answer prediction. Second, QGPA incorporates AA, which simultaneously utilizes visual features and semantic information from the embedded image captioning model to answer questions. Experiments results demonstrate that the accuracy of “Overall” of our proposed model delivers 72.57% and 72.55% on the test-dev and test-std split set of VQA-v2.0 dataset, respectively, which outperforms most existing VQA methods. The experiment results and ablation studies demonstrate that the proposed method has good performance.}, archive = {J_ACCESS}, author = {Xiangjun Shao and Hongsong Dong and Guangsheng Wu}, doi = {10.1109/ACCESS.2025.3549478}, journal = {IEEE Access}, pages = {46299-46311}, shortjournal = {IEEE Access}, title = {Improving visual question answering by image captioning}, volume = {13}, year = {2025}, }
• (2025). TRUNC: A transfer learning unsupervised network for data clustering. ACCESS, 13, 46282-46298. (www)

  @article{ , abstract = {There is a demand for effective clustering methods, especially given the increasing complexity of data scenarios in modern applications. Motivated by the limitations of traditional clustering algorithms, particularly in handling noise, imbalanced data, and large-scale datasets, this work introduces TRUNC, a Transfer Learning Unsupervised Network for Data Clustering. TRUNC leverages a bio-inspired approach, utilizing a single-layer feed-forward winner-takes-all neural network enhanced with transfer learning to optimize clustering performance. The proposed algorithm is evaluated across a range of synthetic and real-world datasets, demonstrating its robustness and performance over conventional and state-of-the-art clustering methods. Key contributions include the integration of transfer learning into the clustering process, a detailed sensitivity analysis of the TL parameter, and extensive experimentation that confirms the efficiency and generalization capability of TRUNC. Results indicate that TRUNC improves clustering quality and convergence speed, offering a competitive and scalable solution for various clustering tasks. This proposal opens new avenues for the integration of TRUNC’s principles with other bio-inspired algorithms and its application across diverse domains.}, archive = {J_ACCESS}, author = {Rita Xavier and John Peller and Leandro Nunes de Castro}, doi = {10.1109/ACCESS.2025.3546787}, journal = {IEEE Access}, pages = {46282-46298}, shortjournal = {IEEE Access}, title = {TRUNC: A transfer learning unsupervised network for data clustering}, volume = {13}, year = {2025}, }
• (2025). Omnidirectional relay in wireless networks. ACCESS, 13, 46273-46281. (www)

  @article{ , abstract = {For wireless networks with multiple sources, an omnidirectional relay scheme is developed, where each node can simultaneously relay different messages in different directions. This is accomplished by the decode-forward relay strategy, with each relay binning the multiple messages to be transmitted, in the same spirit of network coding. Specially for the all-source all-cast problem, where each node is an independent source to be transmitted to all the other nodes, this scheme completely eliminates interference in the whole network, and the signal transmitted by any node can be used by any other node. For networks with some kind of symmetry, assuming no beamforming is to be performed, this omnidirectional relay scheme is capable of achieving the maximum achievable rate.}, archive = {J_ACCESS}, author = {Liang-Liang Xie}, doi = {10.1109/ACCESS.2025.3547326}, journal = {IEEE Access}, pages = {46273-46281}, shortjournal = {IEEE Access}, title = {Omnidirectional relay in wireless networks}, volume = {13}, year = {2025}, }
• (2025). SleepSatelightFTC: A lightweight and interpretable deep learning model for single-channel EEG-based sleep stage classification. ACCESS, 13, 46263-46272. (www)

  @article{ , abstract = {Sleep scoring by experts is necessary for diagnosing sleep disorders. To this end, electroencephalography (EEG) is an essential physiological examination. As manual sleep scoring based on EEG signals is time-consuming and labor-intensive, an automated method is highly desired. One promising automation technology is deep learning, which has performed well or better than experts in sleep scoring. However, deep learning lacks adequate interpretability, which is crucial for ensuring safety and accountability, especially for complex inference processes. We propose SleepSatelightFTC, a lightweight model that achieves comparable performance to state-of-the-art models with only one-third of their parameters. Based on the rules for sleep scoring, self-attention is applied to each of the time- and frequency-domain inputs, a raw EEG signal and its amplitude spectrum. The simple method of continuously connecting the intermediate outputs of the epoch-wise model has resulted in a highly lightweight architecture. On the Sleep-EDF-78 dataset, our model achieves an accuracy of 84.8% and a kappa coefficient of 0.787 while requiring significantly fewer parameters ( $0.47\times 10^{6}$ ) compared to existing models (1.3– $4.54\times 10^{6}$ ). The visualization of feature importance obtained from self-attention confirms that the proposed model learns representative waveform features, including K-complexes and sleep spindles.}, archive = {J_ACCESS}, author = {Aozora Ito and Toshihisa Tanaka}, doi = {10.1109/ACCESS.2025.3549436}, journal = {IEEE Access}, pages = {46263-46272}, shortjournal = {IEEE Access}, title = {SleepSatelightFTC: A lightweight and interpretable deep learning model for single-channel EEG-based sleep stage classification}, volume = {13}, year = {2025}, }
• (2025). Sim-to-real learning-based nonlinear MPC for UAV navigation and collision avoidance in unknown cluttered environments. ACCESS, 13, 46249-46262. (www)

  @article{ , abstract = {Quadrotor control in dynamic and unstructured environments requires robust and adaptive solutions to achieve agile and autonomous navigation. Nonlinear Model Predictive Control (NMPC) offers precise control through a nonlinear dynamical system model and online optimization over a short prediction horizon. However, its adaptability can be significantly enhanced through reinforcement learning. This study proposes a multimodal NMPC (MP-NMPC) policy, leveraging deep reinforcement learning to improve online optimization. Conditioned on the quadrotor’s local observations, the trained reinforcement learning policy dynamically selects adaptation parameters and feedforward control commands for the low-level NMPC controller. The search for adaptation variables and feedforward control signals is formulated as a Markov Decision Process (MDP), enabling reinforcement learning to address this optimization challenge. A multimodal deep reinforcement learning policy that combines depth images with inertial data is trained in simulation and transferred to a real quadrotor in a zero-shot fashion, facilitating online hyperparameter adaptation in highly dynamic unstructured environments. The significance of this approach is demonstrated through its application to challenging tasks such as agile navigation and obstacle avoidance, where it enables autonomous flight through complex environments and outperforms standard reinforcement learning methods.}, archive = {J_ACCESS}, author = {Oualid Doukhi and Deok-Jin Lee}, doi = {10.1109/ACCESS.2025.3539761}, journal = {IEEE Access}, pages = {46249-46262}, shortjournal = {IEEE Access}, title = {Sim-to-real learning-based nonlinear MPC for UAV navigation and collision avoidance in unknown cluttered environments}, volume = {13}, year = {2025}, }
• (2025). Glioma brain tumor classification using convolution neural network and majority voting. ACCESS, 13, 46228-46248. (www)

  @article{ , abstract = {Glioma brain tumors are malignant diseases for which early detection and instant treatment will increase the survival rate. Several studies have reported the efficiency of deep learning convolutional neural networks (CNN) in diagnosing brain tumors using magnetic resonance imaging (MRI). In this study, we investigated the potential of state-of-the-art classifiers to achieve the highest accuracy in the detection of brain tumors using MRI. For this purpose, we introduced a comparative study of eight state-of-the-art classifiers. The methodology comprised three different approaches: 1) an imbalanced dataset, 2) a balanced dataset using image augmentation, and 3) ensemble learning using the best of the top five models for majority hard and soft voting. The dataset comprised converted MRI data from the repository of molecular brain neoplasia data (REMBRANDT) and brain tumor segmentation 2021 (BraTS) databases. An increasing number of MRI images and datasets has prevented overfitting. Initially, a preprocessing stage morphological operation and contrast-limited adaptive histogram equalization (CLAHE) algorithms were used to remove skeletons and artifacts and optimize the image contrast for readiness classification. The stochastic gradient descent with the momentum algorithm option was used to train the network. The trained model was used to predict the testing dataset, and the results from each pretrained network were evaluated. The experimental results demonstrated that the prediction accuracy of the trained network was significantly improved using a balanced training dataset. The discriminative image region used to interpret the predicted result using the gradient-weighted class activation mapping (Grad-CAM) algorithm was proposed in the final stage for trustworthiness. The experimental results showed that the best approach was inceptionV3 with a balanced dataset. The accuracy, sensitivity, specificity, and area under the curve were 99.73%, 99.61%, 100%, and 1.00, respectively.}, archive = {J_ACCESS}, author = {Pongsak Pilaoon and Kazuhiko Hamamoto and Noppadol Maneerat}, doi = {10.1109/ACCESS.2025.3550012}, journal = {IEEE Access}, pages = {46228-46248}, shortjournal = {IEEE Access}, title = {Glioma brain tumor classification using convolution neural network and majority voting}, volume = {13}, year = {2025}, }
• (2025). Road pothole recognition and size measurement based on the fusion of camera and LiDAR. ACCESS, 13, 46210-46227. (www)

  @article{ , abstract = {In intelligent driving systems, accurate detection and measurement of the three-dimensional (3D) dimensions of road potholes are essential for optimizing decision-making processes, including deceleration and obstacle avoidance. To address the challenges posed by unreliable depth information from images and the impracticality of large LiDAR point cloud data in real-time applications—issues that can result in false positives or missed detections—we propose a fusion-based approach that integrates images and LiDAR point clouds for measuring pothole dimensions. Initially, we extract ground LiDAR point clouds from raw data through statistical filtering and ground segmentation. Subsequently, we generate a frustum as a two-dimensional (2D) region of interest to identify potential pothole areas within the ground LiDAR point clouds. We then utilize roughness feature description and the Mean-shift clustering algorithm to extract precise sets of pothole points, which allows us to determine the depth, length, width, and relative coordinates of the potholes with respect to the vehicle. Finally, experiments conducted using the open-source KITTI Road dataset and real vehicle data reveal that our method accurately delineates pothole contours, achieving a 27.4% improvement in accuracy over single LiDAR methods and reducing the average processing time by 88.2%. In practical scenarios, the relative error in size measurement is generally within 15%, with an average data processing time of 45.6 ms per frame, thereby satisfying the system’s real-time requirement of 100 ms per frame.}, archive = {J_ACCESS}, author = {Yongxiang Cai and Mingxing Deng and Xin Xu and Wei Wang and Xiaowei Xu}, doi = {10.1109/ACCESS.2025.3549835}, journal = {IEEE Access}, pages = {46210-46227}, shortjournal = {IEEE Access}, title = {Road pothole recognition and size measurement based on the fusion of camera and LiDAR}, volume = {13}, year = {2025}, }
• (2025). Bayesian network-based landslide susceptibility safe route assessment in the face of uncertain knowledge and various information. ACCESS, 13, 46190-46209. (www)

  @article{ , abstract = {Landslides are major natural hazards that pose significant threats to life, property, and economic stability, particularly in vulnerable regions such as China. Among various approaches for landslide prevention and control, landslide susceptibility assessment is the most commonly employed measure. However, this approach alone is insufficient. A critical issue remains: how to effectively and accurately use landslide susceptibility assessment results to identify safe evacuation or rescue routes in emergency situations when landslides occur. Unfortunately, little research has addressed this issue in depth. In this study, we propose an integrated framework that combines a Bayesian network (BN)-based landslide susceptibility map (LSM) with an improved A* algorithm for safe and efficient route planning. The BN model effectively integrates multi-source data and uncertainty knowledge to generate an accurate LSM, while the improved A* algorithm combines safety and efficiency considerations to optimize routes. The main contributions of this study include identifying the key factors influencing landslide hazards, developing a robust landslide susceptibility map (LSM), and improving the A* algorithm to ensure safe route planning in landslide-prone areas. Experimental results demonstrate that the improved A* algorithm significantly outperforms the original method by providing safer and more efficient routes. The proposed framework offers an integrated approach to landslide susceptibility assessment and emergency relief, providing valuable insights for strengthening early warning systems in high-risk areas and reducing economic losses and casualties.}, archive = {J_ACCESS}, author = {Xinyu Gao and Bo Wang and Wen Dai and Yuanmin Liu}, doi = {10.1109/ACCESS.2025.3550572}, journal = {IEEE Access}, pages = {46190-46209}, shortjournal = {IEEE Access}, title = {Bayesian network-based landslide susceptibility safe route assessment in the face of uncertain knowledge and various information}, volume = {13}, year = {2025}, }
• (2025). Enhancing the precision and interpretability of retrieval-augmented generation (RAG) in legal technology: A survey. ACCESS, 13, 46171-46189. (www)

  @article{ , abstract = {Retrieval-Augmented Generation (RAG) is a promising solution that can enhance the capabilities of large language model (LLM) applications in critical domains, including legal technology, by retrieving knowledge from external databases. Implementing RAG pipelines requires careful attention to the techniques and methods implemented in the different stages of the RAG process. However, robust RAG can enhance LLM generation with faithfulness and few hallucinations in responses. In this paper, we discuss the application of RAG in the legal domain. First, we present an overview of the main RAG methods, stages, techniques, and applications in the legal domain. We then briefly discuss the different information retrieval models, processes, and applied methods in current legal RAG solutions. Then, we explain the different quantitative and qualitative evaluation metrics. We also describe several emerging datasets and benchmarks. We then discuss and assess the ethical and privacy considerations for legal RAG and summarize various challenges, and propose a challenge scale based on RAG failure points and control over external knowledge. Finally, we provide insights into promising future research to leverage RAG efficiently and effectively in the legal field.}, archive = {J_ACCESS}, author = {Mahd Hindi and Linda Mohammed and Ommama Maaz and Abdulmalik Alwarafy}, doi = {10.1109/ACCESS.2025.3550145}, journal = {IEEE Access}, pages = {46171-46189}, shortjournal = {IEEE Access}, title = {Enhancing the precision and interpretability of retrieval-augmented generation (RAG) in legal technology: A survey}, volume = {13}, year = {2025}, }
• (2025). Investigating the influence of o2 on surface flashover characteristics and gas-solid interface compatibility in C4F7N/CO2 mixtures under negative DC voltage. ACCESS, 13, 46161-46170. (www)

  @article{ , abstract = {Previous studies have indicated that the addition of O2 can enhance the insulation performance and chemical stability of C4F7N/CO2 mixed gases during partial discharge to a certain extent. However, the influence of O2 on the surface flashover characteristics at gas-solid interfaces, as well as the compatibility and interaction mechanisms between gaseous and solid materials, remain poorly understood. Furthermore, there is a lack of empirical basis for determining the optimal proportion of O2 in most current ternary gas mixtures. To identify an appropriate O2 mixing ratio, we investigated the effects of different O2 concentrations on the flashover characteristics at the gas-solid interface of C4F7N/CO2 mixtures and analyzed the microscopic properties of the solid surfaces after flashover. Our findings show that O2 content has a minimal overall impact on the flashover voltage and current magnitude, suggesting that its influence on decomposition characteristics should be the primary consideration when developing mixture formulations. Notably, adding 2% to 4% O2 significantly suppresses the deposition on the solid surface, whereas increasing the O2 content beyond 6% leads to the formation of evident ablation pits or black solid deposits near the triple junction of gas, electrode, and insulation material, thereby compromising solid insulation. In summary, the addition of O2 intensifies the reaction between the epoxy resin and the C4F7N/CO2 mixture (also its decomposition products), thereby reducing the compatibility of gas-solid interface materials in the C4F7N/CO2 system.}, archive = {J_ACCESS}, author = {Dongwei Sun and Feng Wang and Kaibin Liang and Lipeng Zhong and Heng Yi and Nian Tang and Zhi Li}, doi = {10.1109/ACCESS.2025.3549742}, journal = {IEEE Access}, pages = {46161-46170}, shortjournal = {IEEE Access}, title = {Investigating the influence of o2 on surface flashover characteristics and gas-solid interface compatibility in C4F7N/CO2 mixtures under negative DC voltage}, volume = {13}, year = {2025}, }
• (2025). Novel machine learning-resistant RO-based PUF optimized for IoT device authentication. ACCESS, 13, 46147-46160. (www)

  @article{ , abstract = {In this work, a novel Physically Unclonable Function (PUF) based on Generalized Galois Ring Oscillators (GenGAROs) optimized for IoT device identification purposes has been proposed and analyzed. A GenGARO is composed of a certain number of logic gates with up to two inputs connected in cascade so that one input corresponds to the output of the previous gate and the other to the output of the last one. The bias of the signal of these oscillators has been used to construct a GenGARO-PUF. Firstly, the combination of logic gates which optimize the response of the GenGARO-PUF in terms of identifiability has been studied. Once the optimal configuration of GenGARO has been found, a GenGARO-PUF of 11 Look-Up Tables (LUTs) has been implemented on FPGA and its properties have been analyzed and compared to a conventional RO-PUF implemented in the exact same locations of the FPGA and using the same hard constraints. The proposal of this work shows an average Inter- Hamming Distance (HD) of 49.9 % and an Equal Error Rate $\textit {EER}={1.52}\cdot {10}^{\text {-12}}$ using 100-bit responses. The GenGARO-PUF has proven to outperform the conventional Ring Oscillator (RO) PUF in terms of spatial autocorrelation, uniqueness, uniformity, bit-aliasing, identifiability and resistance to modeling attacks. Furthermore, a 3-LUT GenGARO-PUF has been proposed maintaining the prediction accuracy of the 11-LUT GenGARO-PUF, and showing that a good identifiability can still be achieved using fewer resources of the FPGA.}, archive = {J_ACCESS}, author = {Raúl Aparicio-Téllez and Miguel Garcia-Bosque and Guillermo Díez-Señorans and Santiago Celma}, doi = {10.1109/ACCESS.2025.3550677}, journal = {IEEE Access}, pages = {46147-46160}, shortjournal = {IEEE Access}, title = {Novel machine learning-resistant RO-based PUF optimized for IoT device authentication}, volume = {13}, year = {2025}, }
• (2025). Code clone detection techniques based on large language models. ACCESS, 13, 46136-46146. (www)

  @article{ , abstract = {Code duplication, commonly known as code cloning, is a persistent challenge in software development. While reusing code fragments boosts productivity, excessive cloning poses challenges to maintenance and elevates the risk of bugs. Therefore, integrating code clone detection into the development process is crucial. The extensive code-related knowledge inherent in Large Language Models (LLMs) renders them high-potential candidates for addressing diverse software engineering challenges. However, the effectiveness of LLMs in the specific task of code clone detection requires precise evaluation. This paper proposes an innovative methodology leveraging few-shot instruction-tuned GPT-3.5 Turbo and GPT-4 to detect code clones across all types, focusing on complex clones (Type-3 and Type-4). Unlike conventional approaches confined to specific language pairs or tasks, our method employs versatile language models, showcases generalization strengths for semantic understanding, and leverages instruction tuning with few-shot inference for task-specific adaptability in code clone detection. A conversational dataset was crafted from BigCloneBench for instruction tuning, enhancing task alignment and performance. This study evaluates the proficiency of LLMs in identifying code clones, analyzing the impact of instruction tuning, and assessing the efficiency across various clone types. Experimental results demonstrate these models achieving competitive performance against existing tools for overall and complex clone detection. Integration into an Integrated Development Environment (IDE) enables real-time detection and automated refactoring, bridging the gap between theoretical advancements and practical usability. This work highlights the potential of generalized LLMs setting a new standard in a field traditionally dominated by specialized tools and demonstrates their adaptability for complex challenges in code analysis and maintainability.}, archive = {J_ACCESS}, author = {Afnan A. Almatrafi and Fathy A. Eassa and Sanaa A. Sharaf}, doi = {10.1109/ACCESS.2025.3549780}, journal = {IEEE Access}, pages = {46136-46146}, shortjournal = {IEEE Access}, title = {Code clone detection techniques based on large language models}, volume = {13}, year = {2025}, }
• (2025). Cross-modal semantic relations enhancement with graph attention network for image-text matching. ACCESS, 13, 46124-46135. (www)

  @article{ , abstract = {Image-text matching is a vital task in multi-modal intelligence. Recently, researchers have moved beyond simply aligning fragments between image regions and text words at a low level. They are now focusing on high-level alignment with semantic relations to enhance performance, and significant progress has been made. However, almost all existing research concentrates on extracting semantic relations between fragments within individual modalities, overlooking the interaction between modalities. This oversight results in a deficiency of high-level semantic and consistency information, ultimately diminishing performance. To address this, we argue that integrating intra-modal and inter-modal semantic relations between fragments could help improve feature quality and promote consistency in semantic space by enabling more comprehensive interactions between modalities. Therefore, we propose a novel Cross-modal Semantic Relations Enhancement Network (CSREN), which designs an implicit semantic relations graph and employs a graph attention network to adaptively mine semantic relations from both modalities, separately and collectively. Quantitative experiments on Flickr30K and MS-COCO demonstrate that our method significantly outperforms existing approaches, achieving improvements of 5.4% to 24.5% for text retrieval and 9.7% to 26.9% for image retrieval.}, archive = {J_ACCESS}, author = {Xiaocong Xi and Chee-Onn Chow and Joon Huang Chuah and Jeevan Kanesan}, doi = {10.1109/ACCESS.2025.3549781}, journal = {IEEE Access}, pages = {46124-46135}, shortjournal = {IEEE Access}, title = {Cross-modal semantic relations enhancement with graph attention network for image-text matching}, volume = {13}, year = {2025}, }
• (2025). BP-vot: Blockchain-based e-voting using smart contracts, differential privacy, and self-sovereign identities. ACCESS, 13, 46106-46123. (www)

  @article{ , abstract = {Election is the key process typically utilized for maintaining democracy in a given society. Recent technological advancements, such as Blockchain (BC), have been already deployed in previous works to realize non-conventional e-Voting systems. The main goal for such proposals is to provide the necessary level of security and reliability, while maintaining transparency, trust, and remote elections. However, the distributed and publicity nature of BC brought new challenges related to privacy and performance trade-off. This paper aims to address these issues by integrating smart contracts for reliability and transparency, Differential Privacy for enhancing vote anonymity, and Self-Sovereign Identities to unlock the potential of the Web3 framework for verifiable credentials and digital identities. Specifically, a novel ( $k,\epsilon $ )-differential privacy mechanism is developed, where a randomly selected candidate is pivoted from which retrievable votes are transferred to other candidates. Final election results are then statistically approximated. We evaluate the proposed methods for different arrival rates (10–80 TX/s), different total numbers of cast votes (10k–50k votes), and different numbers of elected candidates (2–8 candidates). To demonstrate the applicability of our proposal in real-life scenarios, we deploy our SC on a cloud-based permissioned BC network using Hyperledger Besu, with nodes set in Google’s EU and USA data centers. Our experimental results showed that BP-Vot could provide 24% enhancement in latency over state-of-the-art solutions ( $\approx 1$ s/TX compared to 1.24 s/TX). Additionally, using a standardized Min-Max regression mechanism, we show that BP-Vot could provide no less than 98% accuracy in votes approximation during all experiments, with a linearly increasing accuracy trend as a function of the total number of cast votes. Finally, we formally evaluate the proposed differential privacy method and prove that it is robust against reconstruction attacks.}, archive = {J_ACCESS}, author = {Hamza Baniata and Giovanny Caluna}, doi = {10.1109/ACCESS.2025.3548404}, journal = {IEEE Access}, pages = {46106-46123}, shortjournal = {IEEE Access}, title = {BP-vot: Blockchain-based e-voting using smart contracts, differential privacy, and self-sovereign identities}, volume = {13}, year = {2025}, }
• (2025). Evolutionary parameter tuning method for a graph-based path planning algorithm in agricultural robotic manipulators. ACCESS, 13, 46091-46105. (www)

  @article{ , abstract = {The successful execution of path planning algorithms can be substantially impacted by the selection of appropriate planner’s parameters. Identifying the optimal parameters suitable for a specific task and environment may require a heuristic process. For an agricultural robotic manipulator operating in a dynamic environment, achieving the optimal balance to satisfy many performance criteria can be challenging. This work addresses the issue of parameter tuning for a graph-based path planning algorithm called the Bi-directional Transition-based Rapidly Exploring Random Tree (BiTRRT) algorithm in a given high-dimensional search space environment. The objective of this work is to propose a highly efficient evolutionary tuning method, based on a genetic algorithm, that optimizes the parameters of the BiTRRT algorithm, taking into account the features and characteristics of a given task and working environment. The performance of the robotic manipulator is measured by the generated paths that minimize both planning and execution time. A genetic algorithm and a high-fidelity simulation are used together in the proposed method. This combines the benefits of a stochastic optimization approach with a substantial degree of real-world replication, facilitating the fine-tuning of the optimization process. The genetic algorithm was designed with a distinctive fitness function that encompasses the total path cycle time accumulated from various motion scenarios. A high-resolution three-dimensional occupancy grid map of a tree was created from stereo-depth camera pointcloud data for the high-fidelity simulation. Experimental results indicated that the BiTRRT algorithm, when optimized with optimal parameters, outperforms the baseline BiTRRT algorithm, yielding a 5.7% - 7.6% enhancement in the completion of path planning tasks varying in geometric complexity. Given the improvement in a robotic manipulator’s agricultural service life, operational productivity may substantially increase.}, archive = {J_ACCESS}, author = {Seyed Abdollah Vaghefi and Mohd Faisal Ibrahim and Mohd Hairi Mohd Zaman}, doi = {10.1109/ACCESS.2025.3550028}, journal = {IEEE Access}, pages = {46091-46105}, shortjournal = {IEEE Access}, title = {Evolutionary parameter tuning method for a graph-based path planning algorithm in agricultural robotic manipulators}, volume = {13}, year = {2025}, }
• (2025). Exploring the integration of generative AI tools in software testing education: A case study on ChatGPT and copilot for preparatory testing artifacts in postgraduate learning. ACCESS, 13, 46070-46090. (www)

  @article{ , abstract = {Software testing education is important for building qualified testing professionals. To ensure that software testing graduates are ready for real-world challenges, it is necessary to integrate modern tools and technologies into the curriculum. With the emergence of Large Language Models (LLMs), their potential use in software engineering has become a focus, but their application in software testing education remains largely unexplored. This study, conducted in the Capstone Project course of a postgraduate software testing program, was carried out over two semesters with two distinct groups of students. A custom-built Travel Application limited to a web platform was used in the first semester. In the second semester, a new set of students worked with an open-source application, offering a larger-scale, multi-platform experience across web, desktop, and mobile platforms. Students initially created preparatory testing artifacts manually as a group deliverable. Following this, they were assigned an individual assignment to generate the same artifacts using LLM tools such as ChatGPT 3.5 in the first semester and Microsoft Copilot in the second. This process directly compared manually created artifacts and those generated using LLMs, leveraging AI for faster outputs. After completion, they responded to a set of assigned questions. The students’ responses were assessed using an integrated methodology, including quantitative and qualitative assessments, sentiment analysis to understand emotions, and a thematic approach to extract deeper insights. The findings revealed that while LLMs can assist and augment manual testing efforts, they cannot entirely replace the need for manual testing. By incorporating innovative technology into the curriculum, this study highlights how Generative AI can support active learning, connect theoretical concepts with practical applications, and align educational practices with industry needs.}, archive = {J_ACCESS}, author = {Susmita Haldar and Mary Pierce and Luiz Fernando Capretz}, doi = {10.1109/ACCESS.2025.3545882}, journal = {IEEE Access}, pages = {46070-46090}, shortjournal = {IEEE Access}, title = {Exploring the integration of generative AI tools in software testing education: A case study on ChatGPT and copilot for preparatory testing artifacts in postgraduate learning}, volume = {13}, year = {2025}, }
• (2025). Robust blind watermarking to secure color medical images using multidimensional-FFT fusing LFSR-encryption and LZW compression. ACCESS, 13, 46054-46069. (www)

  @article{ , abstract = {This paper offers a new robust-blind watermarking scheme for medical image protection. In the digital era, protecting medical images is essential to maintain the confidentiality of patients and data reliability. Robust blind watermarking has surfaced as a practical option for secure image authentication and copyright protection. A new plan uses the multidimensional fast Fourier transform (MD-FFT) and combines LZW compression with linear feedback shift register (LFSR) encryption. Nevertheless, current watermarking methodologies frequently encounter difficulties balancing endurance, invisibility, and payload capacity. Numerous methodologies inadequately offer robust protection against diverse threats while ensuring imperceptibility and effective data management. The motivation behind this successful integration is to reinforce invisibility and sturdiness. The host image is initially divided into four equal portions for rapid processing, followed by encryption using the LFSR method to increase robustness. The Multi-FFT is applied to the encrypted parts to enhance their visual invisibility. The watermark is first encoded using LZW compression, then scrambled with Arnold encryption before being embedded in the modified MD-FFT magnitudes, strengthening the protection level. The watermark is fully and blindly extracted without referencing the host image. Compared to similar methods, the proposed scheme maintains a fundamental tradeoff between robustness and visual imperceptibility against many commonly encountered attacks. The extracted watermark remains original with minimal observable distortion. Empirical findings confirm that the proposed algorithm outperforms existing watermarking methods in terms of invisibility and robustness while maintaining an acceptable payload capacity.}, archive = {J_ACCESS}, author = {Mohamed W. Abo El-Soud and Mohamed Meselhy Eltoukhy and Mostafa M. Abdel-Aziz and Abdullah Alourani and Khalid M. Hosny}, doi = {10.1109/ACCESS.2025.3547614}, journal = {IEEE Access}, pages = {46054-46069}, shortjournal = {IEEE Access}, title = {Robust blind watermarking to secure color medical images using multidimensional-FFT fusing LFSR-encryption and LZW compression}, volume = {13}, year = {2025}, }
• (2025). Electronically controllable varactor loaded beam switchable 4-element array antenna for sub-6 GHz application. ACCESS, 13, 46042-46053. (www)

  @article{ , abstract = {In this paper, we present a 4-element varactor loaded switched beam array antenna, which is configured using a switch-line phase shifting mechanism and varactor diodes in the Sub-6 GHz band ranging from 5.8 to 6.6 GHz. A discrete beam steering at $\pm ~30{^{\circ}}$ is aimed to achieve by employing switch-line phase shifters into the feed, whereas a continuous beam steering around $\pm ~30{^{\circ}}$ is accomplished by using varactor diodes. The one-to-four-way corporate feeding network is used for the array which is connected through an aperture-coupled feeding system. The design is first validated using HFSS (High-Frequency Structure Simulator), confirming the discrete beam steering at $\pm ~24{^{\circ}}$ , with continuous steering possible between $\pm ~22{^{\circ}}$ and $\pm ~30{^{\circ}}$ . The discrete steering is found to be in agreement with measurements showing steering of +26° and -28°, which were close to its simulated outcomes i.e., $\pm ~24{^{\circ}}$ . The performance of this design, validated by HFSS simulations and “anechoic chamber” measurements are found in good agreement, with the broadside beam gain of 7.02 dBi with a Front-to-Back ratio of 17.71 dBi. This design can serve itself as a good candidate as a sub-unit for 5G base stations through hybrid beam forming.}, archive = {J_ACCESS}, author = {Pooja Panda and Rohit Mathur and Ashutosh Mahajan}, doi = {10.1109/ACCESS.2025.3550783}, journal = {IEEE Access}, pages = {46042-46053}, shortjournal = {IEEE Access}, title = {Electronically controllable varactor loaded beam switchable 4-element array antenna for sub-6 GHz application}, volume = {13}, year = {2025}, }
• (2025). WGAN-GP-based conditional GAN (cGAN) with extreme critic for precipitation downscaling in a key agricultural region of the northeastern U.S.. ACCESS, 13, 46030-46041. (www)

  @article{ , abstract = {This study develops a conditional Generative Adversarial Network with a multi-head Critic (cGAN_ext), under a Wasserstein GAN with gradient penalty (WGAN-GP) framework, to downscale coarse-resolution meteorological data into high-resolution precipitation fields. The model’s U-Net Generator combines large-scale atmospheric inputs—2 m temperature, total column water vapor, mean sea-level pressure, and downsampled precipitation—with a noise tensor, while the Critic enforces adversarial constraints and explicitly classifies extreme events. By focusing on rare high-intensity rainfall within the adversarial training loop, cGAN_ext captures crucial tail behavior that can be overlooked by conventional approaches. Experimental results reveal that cGAN_ext not only preserves fine-grained spatial details but also better represents heavy precipitation episodes, thereby improving essential metrics such as mean squared error, fractions skill scores for extremes, and temporal correlation. Visual analysis further confirms the model’s ability to reproduce sharp precipitation fronts and narrow bands, underscoring the benefits of integrating an extreme-classification objective into a WGAN-GP cGAN pipeline. This enhanced downscaling method offers more accurate and coherent high-resolution precipitation maps, supporting informed decision-making in agricultural planning and water resource management.}, archive = {J_ACCESS}, author = {Jangho Lee and Sun Young Park}, doi = {10.1109/ACCESS.2025.3549443}, journal = {IEEE Access}, pages = {46030-46041}, shortjournal = {IEEE Access}, title = {WGAN-GP-based conditional GAN (cGAN) with extreme critic for precipitation downscaling in a key agricultural region of the northeastern U.S.}, volume = {13}, year = {2025}, }
• (2025). A compact multi-band MIMO antenna with high isolation and low SAR for LTE and sub-6 GHz applications. ACCESS, 13, 46014-46029. (www)

  @article{ , abstract = {This study designs a multi-band antenna that covers LTE, WiFi, Sub-6 GHz, and WiFi 6E frequencies, applying it to multi-input multi-output (MIMO) antenna systems in laptops, base stations, and smartphones. The antenna employs a microstrip structure and can achieve multiple frequency bands without passive component support. Different multi-band MIMO antenna systems can be configured using the same multi-band antenna structure, thereby reducing design complexity and manufacturing costs. The multi-band MIMO antenna system covers multiple bands and achieves an isolation level of −11 dB, a peak gain of 1.76 dBi, and an envelope correlation coefficient below 0.5. In this study, the antenna prototype is fabricated and measured to verify the performance of the three proposed multi-band MIMO antenna systems, with results showing strong consistency with the simulations. SAR analysis was also conducted for the proposed multi-band MIMO antenna system. The simulation results show that the SAR values are well below the standard limits. The proposed multi-band antenna has a straightforward and robust structural design. Its performance is minimally affected by the size of the device and the amount of antennas, demonstrating excellent safety characteristics. In the future, this antenna design will be able to connect seamlessly with electronic devices of various sizes, offering greater flexibility and scalability.}, archive = {J_ACCESS}, author = {Ming-An Chung and Ming-Chun Hsieh and Chia-Chun Hsu and Chia-Wei Lin}, doi = {10.1109/ACCESS.2025.3550209}, journal = {IEEE Access}, pages = {46014-46029}, shortjournal = {IEEE Access}, title = {A compact multi-band MIMO antenna with high isolation and low SAR for LTE and sub-6 GHz applications}, volume = {13}, year = {2025}, }
• (2025). Location and intent privacy preservation for spatial range queries in a mobile network. ACCESS, 13, 45998-46013. (www)

  @article{ , abstract = {Location-based services (LBSs) in a mobile network environment that provide personalized and timely information to users entail privacy concerns due to the leakage of user locations to adversaries. In the literature, several cloaking-based privacy preservation approaches have been proposed by considering the P2P environment. However, existing spatial cloaking approaches form a large cluster of mobile users to cloak the user query location. Maintaining such structures in a highly dynamic mobile-P2P network is challenging. In addition to user’s location, the user’s intent is also an important concern and needs to be preserved from an adversary. This paper proposes a location privacy and intent privacy preservation scheme, especially for spatial range queries. The key contributions of our work are three-fold. First, we introduce the concept of ijk-anonymity to achieve improved location privacy. Second, we propose a location-based privacy-preservation approach, which we designate as ijkCloak, for spatial range queries in a mobile network environment. The proposed approach preserves user location and intent information from the LBS provider and nearby peers. Third, we conduct theoretical analysis and experiments on resistance to attacks. We show that ijkCloak effectively facilitates improved user location privacy and intent privacy by employing fewer peers w.r.t. existing approaches.}, archive = {J_ACCESS}, author = {Shadaab Siddiqie and P. Krishna Reddy and Srinivas Reddy Annappalli}, doi = {10.1109/ACCESS.2025.3547747}, journal = {IEEE Access}, pages = {45998-46013}, shortjournal = {IEEE Access}, title = {Location and intent privacy preservation for spatial range queries in a mobile network}, volume = {13}, year = {2025}, }
• (2025). Protecting industrial control systems from shodan exploitation through advanced traffic analysis. ACCESS, 13, 45985-45997. (www)

  @article{ , abstract = {The security of industrial control systems (ICS) remains a critical challenge for industry stakeholders and business owners who rely on these devices. Many legacy ICS systems still in operation today harbor numerous and severe security vulnerabilities. Shodan, a prominent online scanning tool, systematically scans the entire internet, typically on a weekly basis. By sending targeted requests and analyzing the responses, Shodan identifies internet-connected devices and provides its clients with detailed information, including security vulnerabilities. Unfortunately, this functionality has been exploited by hackers to pinpoint potential attack targets and their associated weaknesses. A key defense against such threats is preventing ICS devices from being indexed by Shodan. To achieve this, it is crucial to understand Shodan’s scanning methods. This research introduces protocol analysis as a novel feature in network traffic analysis, significantly improving detection accuracy over models that rely solely on traditional network features. Additionally, a dual-pronged approach combining customized honeypots with protocol analysis is proposed, enhancing detection capabilities by integrating decoy technologies with enriched traffic insights. Several machine learning algorithms were evaluated, including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting. The proposed model was tested on traffic data from three different Siemens honeypot devices—S7-300, S7-1200, and S7-1500. The traffic was categorized into three types: TCP traffic, UDP traffic, and Others, which includes non-standard or unidentified protocols. Gradient Boosting consistently outperformed other algorithms, achieving the highest F1-scores across all programmable logic controllers (PLC) models. For the S7-300 model, it achieved F1-scores of 0.96 (Others), 0.98 (TCP), and 0.66 (UDP). Similarly, for the S7-1200 model, it obtained F1-scores of 0.95 (Others), 0.98 (TCP), and 0.58 (UDP). The highest performance was observed in the S7-1500 model, with F1-scores of 0.99 (Others), 0.98 (TCP), and 0.74 (UDP). While Random Forest demonstrated competitive performance in Others and TCP traffic, it struggled with UDP classification, highlighting Gradient Boosting’s superior ability to handle diverse traffic types. These findings emphasize the effectiveness of Gradient Boosting in detecting Shodan-originating traffic and suggest its potential application in real-time intrusion detection and firewall automation for ICS environments.}, archive = {J_ACCESS}, author = {Sayed Reza Ghazinour Naeini and Alireza Shameli-Sendi and Masoume Jabbarifar}, doi = {10.1109/ACCESS.2025.3544906}, journal = {IEEE Access}, pages = {45985-45997}, shortjournal = {IEEE Access}, title = {Protecting industrial control systems from shodan exploitation through advanced traffic analysis}, volume = {13}, year = {2025}, }
• (2025). Predicting insomnia response to acupuncture with the development of innovative machine learning. ACCESS, 13, 45964-45984. (www)

  @article{ , abstract = {Insomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To address this, an innovative machine learning algorithm, Relief-NDPGWO-WSVM, is developed to predict insomnia response to acupuncture. Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. Experimental results demonstrate that this model outperforms existing models in terms of precision, recall, F1-score, accuracy, and area under the curve (AUC). These results highlight the model’s potential in accurately predicting acupuncture treatment outcomes for insomnia and suggest its broader applicability for enhancing clinical decision-making and optimizing healthcare resources for other related conditions.}, archive = {J_ACCESS}, author = {Qingyun Wan and Kai Liu and Yuyang Bo and Xiya Yuan and Mufeng Li and Xiaoqiu Wang and Chuang Chen and Lanying Liu and Wenzhong Wu}, doi = {10.1109/ACCESS.2025.3549630}, journal = {IEEE Access}, pages = {45964-45984}, shortjournal = {IEEE Access}, title = {Predicting insomnia response to acupuncture with the development of innovative machine learning}, volume = {13}, year = {2025}, }
• (2025). Blur CLIP context model-based image coding for machines framework with down and up sample pairs for multitasks. ACCESS, 13, 45951-45963. (www)

  @article{ , abstract = {Image Coding for Machines (ICM) aims to develop systems and frameworks for image compression and transmission tailored for computer vision tasks such as object detection and instance segmentation. This paper focuses on multi-task ICM not only for object detection and instance segmentation but also for image compression. In recent years, Contrastive Language-Image Pre-training (CLIP) has demonstrated its powerful capability in extracting text and image features and has been applied to various vision tasks. Inspired by CLIP, channel-wise context model and mask convolutional neural network (PixelCNN), we propose a Blur CLIP context model (BCcm) for reducing bitrate usage and a two-hyperprior multitask framework augmented by BCcm (TMFBC). Through experiments, we have observed that down and up sample resize pairs can significantly reduce the bitrate usage. We integrated a plug-and-play down and up sample resize pair at both ends of TMFBC. Additionally, we propose a Resnet Pyramid Hierarchical Feature Extractor (RPHFE) based on down and up sample resize pairs to endow decoded images with richer multi-scale features. We term the aforementioned pipeline as TMFBC with down and up Sample Pairs (TMFBC-SP). We compare the proposed TMFBC-SP with state-of-the-art (SOTA) methods and demonstrate that we can achieve higher object detection and instance segmentation accuracy, measured by mean average precision (mAP), using fewer bitrates than existing approaches. We also compare ours with coarse-to-fine and context models on image compression, ours achieves higher PSNR and uses less bitrate.}, archive = {J_ACCESS}, author = {Zhongpeng Zhang and Ying Liu}, doi = {10.1109/ACCESS.2025.3550335}, journal = {IEEE Access}, pages = {45951-45963}, shortjournal = {IEEE Access}, title = {Blur CLIP context model-based image coding for machines framework with down and up sample pairs for multitasks}, volume = {13}, year = {2025}, }
• (2025). Toward secure and transparent global authentication: A blockchain-based system integrating biometrics and subscriber identification module. ACCESS, 13, 45936-45950. (www)

  @article{ , abstract = {The growing reliance on e-government services necessitates robust and secure user authentication. Existing solutions often suffer from limitations such as lack of transparency, compromise of user privacy, and reliance on a central server, thus introducing a single point of failure (SPOF). This paper proposes B2-GAS, a novel Biometric and Blockchain-based Global Authentication System, that addresses these shortcomings. B2-GAS leverages user biometrics on smartphones for strong identification and isolates sensitive cryptographic operations within a secure enclave on a SIM card. This approach safeguards user privacy and data security. By employing blockchain technology, B2-GAS eliminates SPOFs, ensures tamper-proof transaction storage, and guarantees transparency. Unlike existing protocols, which often rely on theoretical analysis, B2-GAS utilizes an emulated environment to assess its performance under realistic conditions. This allows for a more practical evaluation compared to purely theoretical approaches. B2-GAS exerts multiple factors during authentication including biometrics, a password, and a secret parameter to further enhance security. Rigorous security proofs demonstrate B2-GAS’s resistance to user impersonation, offline password-guessing, replay attacks, and brute-force attempts. Evaluation using the emulated environment and blockchain simulations demonstrates B2-GAS security parameters, performance, and computational overheads. By combining biometrics, secure SIM enclaves, and blockchain, B2-GAS offers a unique and robust authentication solution for diverse e-government services in smart cities.}, archive = {J_ACCESS}, author = {Navid Khalili and Dimitrios Makrakis and Yaser Baseri and Abdelhakim Senhaji Hafid}, doi = {10.1109/ACCESS.2025.3550302}, journal = {IEEE Access}, pages = {45936-45950}, shortjournal = {IEEE Access}, title = {Toward secure and transparent global authentication: A blockchain-based system integrating biometrics and subscriber identification module}, volume = {13}, year = {2025}, }
• (2025). RCLT-net: An indoor panoramic room layout estimation framework based on consecutive dilated convolutions and transformer. ACCESS, 13, 45923-45935. (www)

  @article{ , abstract = {Current mainstream models for indoor panoramic room layout depth estimation primarily utilize deep residual networks and various Transformer-based modifications to reconstruct room boundaries from 2D images. However, these methods often encounter challenges such as limited receptive fields, insufficient multi-scale feature interaction and integration capabilities, and blurred boundary estimations in complex scenes. To overcome these challenges, we propose ResCDC-Net, a novel feature extraction model that expands the receptive field by incorporating continuous dilated convolutions within convolutional layers and applying varying dilation rates in residual blocks. This design enhances the perception of fine-grained multi-scale features. Additionally, we introduce the Local-Global Feature Interaction Shifting Window Transformer, which significantly improves the Transformer model’s ability to extract features by fostering robust interactions between local and global features, enabling more flexible and efficient feature representation. Furthermore, we employ a triplet loss function based on positive and negative anchor points to ensure clear boundary distinctions in complex object boundary regions. Extensive experiments validate that our proposed RCLT-Net surpasses SoTA models in depth estimation accuracy and demonstrates exceptional performance across multiple challenging public datasets.}, archive = {J_ACCESS}, author = {Qiucheng Sun and Zhengkai Deng and Jinlong Zhu}, doi = {10.1109/ACCESS.2025.3550164}, journal = {IEEE Access}, pages = {45923-45935}, shortjournal = {IEEE Access}, title = {RCLT-net: An indoor panoramic room layout estimation framework based on consecutive dilated convolutions and transformer}, volume = {13}, year = {2025}, }
• (2025). Pain classification using discrete wavelet transform feature extraction and machine learning techniques. ACCESS, 13, 45912-45922. (www)

  @article{ , abstract = {Electrodermal activity (EDA) measurement has been identified as a useful indicator for evaluating pain levels in patients. This study aims to find features of the EDA signal that influence pain classification. The proposed approach includes signal decomposition, feature extraction from EDA signals, and application of various machine learning algorithms for classification. EDA signals from different participants who experienced painful and non-painful stimuli will be extracted using DWT, domain amplitude, and frequency as features in the classification model. Machine learning uses three models, namely Random Forest (RF), Support Vector Machine (SVM), and k-nearest neighbors (k-NN), to classify pain levels. In this study, Forward Selection is also used as a feature selection method. Evaluation results show that the Random Forest model achieved the highest performance with accuracy, precision, recall, specificity, and an F1 score of 1.0 or 100%, indicating that the model can classify pain levels very well. However, the SVM and k-NN models also show good performance, with an accuracy of around 0.9988 or 99.88%, and evaluation results show some features that can influence the level of performance. The findings from this study indicate that EDA signals can be used to identify pain levels using machine learning models to perform classification.}, archive = {J_ACCESS}, author = {Sudimanto and Iman Herwidiana Kartowisastro and Agung Trisetyarso and Widodo Budiharto}, doi = {10.1109/ACCESS.2025.3545869}, journal = {IEEE Access}, pages = {45912-45922}, shortjournal = {IEEE Access}, title = {Pain classification using discrete wavelet transform feature extraction and machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). A high accuracy and large-scale detection for fixed-wing UAV autonomous ground landing with GNSS-denied. ACCESS, 13, 45898-45911. (www)

  @article{ , abstract = {This paper concentrates on the ground-guided autonomous landing of a fixed-wing Unmanned Aerial Vehicle (UAV) within Global Navigation Satellite System (GNSS) denied environments. A highly accurate and large-scale automatic landing system for fixed-wing UAVs in GNSS-denied environments is proposed, leveraging advanced multi-sensor fusion techniques. To address the challenge of low accuracy over extended ranges, a distance measurement module is introduced as a key component. Additionally, the integration of near-infrared and visible cameras ensures precise UAV detection, thereby significantly enhancing the system’s reliability and overall performance. To further facilitate large-scale range detection in GNSS-denied conditions, a landing guidance window is developed, enabling the efficient search for and tracking of UAVs. Moreover, a joint calibration method (JCM) is implemented to minimize discrepancies between the system’s outputs and GNSS data. This method involves a systematic two-step calibration process: first, the GNSS and pan-tilt unit (PTU) coordinate systems are aligned on the runway, and then the PTU and landing point coordinate systems are calibrated. At the same time, the near-infrared and visible cameras are calibrated simultaneously to ensure synchronized and accurate performance across the system. To demonstrate the generalizability and robustness of this approach, the proposed system is implemented on two UAV platforms tested across diverse and complex scenarios. When compared to state-of-the-art methods, the proposed system not only achieves superior accuracy but also exhibits exceptional scalability, making it particularly well-suited for GNSS-denied environments.}, archive = {J_ACCESS}, author = {Bin Fang and Zhiwei Zhong and Xiangjun Jiang}, doi = {10.1109/ACCESS.2025.3550127}, journal = {IEEE Access}, pages = {45898-45911}, shortjournal = {IEEE Access}, title = {A high accuracy and large-scale detection for fixed-wing UAV autonomous ground landing with GNSS-denied}, volume = {13}, year = {2025}, }
• (2025). Tongue image segmentation method based on the VDAU-net model. ACCESS, 13, 45885-45897. (www)

  @article{ , abstract = {Accurate segmentation of the tongue is fundamental to tongue diagnosis, yet existing methods are predicated on images captured under standard conditions. In practical tongue diagnosis, such controlled environments are not always feasible. This study, therefore, aims to develop a segmentation method capable of effectively isolating the tongue region from complex backgrounds, thereby enhancing the precision and efficiency of tongue diagnosis. Initially, we constructed a novel tongue image segmentation dataset with complex backgrounds, which comprises 1,600 images obtained through data augmentation techniques. Subsequently, We introduce VDAU-Net, an enhanced U-Net-based model for tongue segmentation, featuring three core innovations: 1) use of VGG16 as the encoder (V) to extract multi-level semantic features; 2) implementation of a dual-attention mechanism (DA) that integrates Position Convolution Block Attention Module (PCBAM) – focusing on tongue fissures and coating textures-with Shifted Window Attention (SWA) to enhance the continuity of tongue contours; and 3) realization of cross-scale feature fusion within the U-shaped encoder-decoder framework. Together, these innovations enable pixel-precise tongue segmentation against complex backgrounds. The VDAU-Net demonstrated exceptional performance on our dataset, achieving a Mean Intersection over Union of 96.73%. It effectively captured detailed tongue features and accurately localized the tongue region, even under challenging conditions. The VDAU-Net, by integrating advanced attention mechanisms and transfer learning, can effectively handle complex backgrounds and variable imaging conditions, making it a powerful tool for intelligent tongue diagnosis systems. Future research will focus on expanding the dataset and refining the algorithm to enhance its applicability across diverse environments.}, archive = {J_ACCESS}, author = {Fengdong Shi and Tongtong Liang and Song Cui}, doi = {10.1109/ACCESS.2025.3550670}, journal = {IEEE Access}, pages = {45885-45897}, shortjournal = {IEEE Access}, title = {Tongue image segmentation method based on the VDAU-net model}, volume = {13}, year = {2025}, }
• (2025). Android malware detection method combining multi-frequency features and convolutional neural networks. ACCESS, 13, 45874-45884. (www)

  @article{ , abstract = {With the rapid evolution of Android malware variants, traditional detection methods exhibit significant limitations in addressing complex patterns and adversarial attacks. This paper proposes a dual-modal feature extraction and fusion approach that integrates Convolutional Neural Networks (CNN) with Fourier frequency domain analysis for Android malware detection. Firstly, a novel feature extraction framework based on frequency domain analysis is constructed, which dynamically adjusts the boundary between high-frequency and low-frequency components through adaptive frequency selection. This framework effectively captures both the global patterns and local details of malware, significantly enhancing the robustness of frequency domain features. Secondly, a bidirectional recursive optimization-based feature fusion mechanism is designed, enabling deep integration of high-frequency, low-frequency, and spatial domain features through multiple rounds of iterative interaction. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of accuracy, recall, and adversarial sample detection capability. Notably, it exhibits outstanding stability and performance in handling malware variants, offering an efficient and reliable technical framework for Android malware detection.}, archive = {J_ACCESS}, author = {Jian Wang and Xue Hua Liu and Meng Yuan Huang and Pei Yi Zhou and Yuan Xu}, doi = {10.1109/ACCESS.2025.3550124}, journal = {IEEE Access}, pages = {45874-45884}, shortjournal = {IEEE Access}, title = {Android malware detection method combining multi-frequency features and convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). A comparative review: Floating photovoltaic, agrivoltaics, and ground-mounted PV systems. ACCESS, 13, 45853-45873. (www)

  @article{ , abstract = {Climate change is mostly caused by the production of electricity, which makes environmentally friendly energy solutions like floating solar photovoltaic (PV) systems necessary. The efficiency, cost-effectiveness, and environmental impact of ground-mounted, floating, and agrivoltaic PV systems are all thoroughly assessed and contrasted in this study. The benefits of installing PV systems on bodies of water, which can maximize land usage and improve energy output, are a major area of focus. The study also explores agrivoltaics, a potential strategy that tackles the problem of land use disputes by fusing solar power generation with agricultural practices. Performance comparisons, a thorough economic and environmental assessment, and a full analysis of agrivoltaic trials are all included in this paper. Additionally, it looks at how PV systems deteriorate and how to identify failures, offering preventative and remedial measures based on international research. The paper provides recommendations for improving the design, deployment, and long-term sustainability of PV plants, especially those that incorporate agrivoltaic systems, with an emphasis on current performance data and degradation trends. The findings aim to lead the future development of more efficient and environmentally friendly solar energy technologies.}, archive = {J_ACCESS}, author = {Inam-Ur-Raheem Khoja and Abdul Sattar Saand and Muhammad Ismail Jamali and Mohsin Ali Koondhar and Ghulam Sarwar Kaloi and Lutfi Albasha and Mouloud Aoudia and Ezzeddine Touti}, doi = {10.1109/ACCESS.2025.3547438}, journal = {IEEE Access}, pages = {45853-45873}, shortjournal = {IEEE Access}, title = {A comparative review: Floating photovoltaic, agrivoltaics, and ground-mounted PV systems}, volume = {13}, year = {2025}, }
• (2025). Analysis of power line communication for the use in automotive wiring systems. ACCESS, 13, 45846-45852. (www)

  @article{ , abstract = {This analysis describes the use of power line communication (PLC) for the simplification of automotive wiring systems. An electronic control unit (ECU) hardware that allows demonstration of basic functions like remote load switching is designed and set into operation. All necessary software routines for a system containing five individual control units are developed. Different simple wiring setups including twin wire and single wire are implemented for evaluation purposes. Immunity against electromagnetic interference (EMI) is tested and proven to comply with relevant standards in all of these different wiring setups. The analysis shows the suitability of PLC for automotive systems and retro-fit solutions even with a very cheap and basic setup.}, archive = {J_ACCESS}, author = {Baixiang Wang and Niklas Langmaack}, doi = {10.1109/ACCESS.2025.3549084}, journal = {IEEE Access}, pages = {45846-45852}, shortjournal = {IEEE Access}, title = {Analysis of power line communication for the use in automotive wiring systems}, volume = {13}, year = {2025}, }
• (2025). Novel modular buck-boost based multiport bidirectional DC–DC converter (MPBC) for hybrid electric vehicle application. ACCESS, 13, 45826-45845. (www)

  @article{ , abstract = {In order to increase the application of renewable energy sources in the hybrid electrical vehicle application and also for zero tailpipe emission, a typical DC power converter interface in addition of effective control of power flow is very important. On that aim, a novel four port dc-dc power converter with bidirectional power flow is proposed to integrate the two input ports for photovoltaic (PV) and battery to two output ports for motor load and light load. The MPBC is customized for operating in buck and boost states with power flow control in both directional through changing the switching combinations. This system is implemented in hybrid e-vehicles (HEVs), where different level of dc voltages at battery and motor needs to be connected with the capability of bi-directional flow of power in regenerative action of vehicle to charge the battery. The different states of operation, steady state analysis, control scheme to regulate the power are presented. Power loss analysis is carried out to do proper design with more efficiency. The proposed converter simulation is done using MATLAB/Simulink to verify the validity of design, its behaviors and hardware response in different states of operation.}, archive = {J_ACCESS}, author = {Jayamurugan Mookkan and Saravanan Kaliyaperumal and Z. M. S. Elbarbary and Saad F. Al-Gahtani and Hossam Kotb and Narayanamoorthi R}, doi = {10.1109/ACCESS.2025.3548883}, journal = {IEEE Access}, pages = {45826-45845}, shortjournal = {IEEE Access}, title = {Novel modular buck-boost based multiport bidirectional DC–DC converter (MPBC) for hybrid electric vehicle application}, volume = {13}, year = {2025}, }
• (2025). Leveraging the learning curve: Reusing existing architectural patterns to design and implement MAS. ACCESS, 13, 45809-45825. (www)

  @article{ , abstract = {Recent advancements in AI have led to the development of specialized systems related to multi-agent systems (MAS). However, the inherently collaborative nature of agents is often overlooked, and many of these specialized systems are used as components by other AI systems. From a software engineering perspective, this context can benefit from aligning the architectural characteristics of distributed systems with the inherently distributed nature of MAS. We propose that introducing a minimal set of agent-related concepts into the Distributed Systems (DS) domain can improve the engineering of modern MAS by leveraging techniques from DS engineering with established agent theory. In this study, we recapitulated the common origins of MAS and DS by drawing architectural parallels to establish a unified engineering approach. We then defined a minimal set of agent concepts to perform two practical studies on leveraging MAS development. First, we incorporated these concepts into a DS architectural pattern to design a distributed MAS. We then used these concepts in a graduate course to teach MAS engineering to students with no prior knowledge of agent theory. The learning outcomes from both courses included successful MAS implementation using DS tools and techniques. Although more than two-thirds of these students had no practical experience in developing distributed systems, the average final grade in both courses was above 80%, thus validating our approach. Finally, we discuss how this study supports the development of advanced systems using modern AI techniques consistently with established agent-related research while leveraging established DS techniques and concepts.}, archive = {J_ACCESS}, author = {Arthur Casals and Anarosa A. F. Brandão}, doi = {10.1109/ACCESS.2025.3546526}, journal = {IEEE Access}, pages = {45809-45825}, shortjournal = {IEEE Access}, title = {Leveraging the learning curve: Reusing existing architectural patterns to design and implement MAS}, volume = {13}, year = {2025}, }
• (2025). Comprehensive phasor domain short circuit analysis of bulk power transmission systems in presence of inverter-based resources. ACCESS, 13, 45795-45808. (www)

  @article{ , abstract = {Phasor domain fault analysis of bulk power systems uses a linear model, where generators are represented as Thevenin equivalents and loads are neglected. However, due to the highly nonlinear, current-limited, and proprietary behavior of controllers dictating the response of renewable sources to faults in the power grid, this model breaks down. This paper significantly advances the state of research in this field by proposing a framework that can correctly perform short circuit analysis in bulk power grids for any penetration of inverter-based resources, without neglecting loads. Unlike previous methods that approximate the total fault current as sum of the contributions of all source currents, this framework uses the fundamental properties of bus impedance matrix to accurately determine the part of source current that actually reaches the fault. Additionally, in cases where the voltage fluctuations in two successive iterations are large, the proposed framework breaks down this large change into small changes, which solves the convergence issue reported in literature for faults electrically farther away from the source. The method is tested for accuracy, convergence, and scalability by comparing its results with results from time-domain simulations of faults on the IEEE 39-bus transmission system.}, archive = {J_ACCESS}, author = {Dol Raj Kunwar and Sukumar Brahma}, doi = {10.1109/ACCESS.2025.3550297}, journal = {IEEE Access}, pages = {45795-45808}, shortjournal = {IEEE Access}, title = {Comprehensive phasor domain short circuit analysis of bulk power transmission systems in presence of inverter-based resources}, volume = {13}, year = {2025}, }
• (2025). Evaluating the impact of pair documentation on requirements quality in agile software development. ACCESS, 13, 45784-45794. (www)

  @article{ , abstract = {A fundamental deliverable of the requirements engineering process is the software requirements specification (SRS) document. A reliable SRS ensures that the final product aligns with the stakeholder’s expectations. Yet poorly documented requirements may lead to significant project risks, which include delays, cost overruns, and product failure. Currently, delivering a precise SRS remains a complex task which sometimes result in miscommunication and incomplete information. Such challenges are particularly seen in agile development where the iterative nature of projects requires continuous refinement of the requirements. Consequently, this empirical research aims to evaluate the impact of pair documentation on the quality and effectiveness of SRS documents within agile development teams. Twenty pairs of documentation writers worked into two groups where one group used the pair documentation, i.e., the experimental group, and the other one used conventional documentation i.e., the control group. Both groups were tasked with producing SRS documents for the same project. The outcomes are evaluated against three key criteria of completeness, ambiguity, and consistency of the documents. Our preliminary analysis indicates that the experimental group demonstrated an improvement in document quality with fewer ambiguities, more comprehensive coverage of requirements, and greater consistency across sections. Our research further suggests that collaborative efforts in pair documentation not only enhance the clarity and precision of SRS documents but also may mitigate risks associated with possible miscommunications. In addition, the findings of this study emphasize the potential of pair documentation as a strategy to deliver higher quality SRS documents in dynamic project environments.}, archive = {J_ACCESS}, author = {Nosheen Qamar and Nosheen Sabahat and Amir Mosavi}, doi = {10.1109/ACCESS.2025.3550123}, journal = {IEEE Access}, pages = {45784-45794}, shortjournal = {IEEE Access}, title = {Evaluating the impact of pair documentation on requirements quality in agile software development}, volume = {13}, year = {2025}, }
• (2025). Pseudo-LiDAR with two-dimensional instance for monocular three-dimensional object tracking. ACCESS, 13, 45771-45783. (www)

  @article{ , abstract = {Establishing a framework capable of performing 3D object tracking is crucial for various applications, such as autonomous driving and robot navigation. Monocular cameras offer economic and flexible advantages over LiDAR sensors; however, monocular camera-based 3D object detection is fraught with uncertainty owing to difficulty in depth estimation, visual occlusion, and ambiguous object appearance. This uncertainty poses additional challenges to object tracking as well, making performing stable multiple-object tracking on monocular 3D detection results more complex and difficult. To address these challenges in monocular 3D multiple-object detection and tracking, we propose the innovative framework pseudo-LiDAR-MOT, which accurately infers complete 3D bounding box information from 2D image sequences captured by a monocular camera and efficiently correlates and tracks moving objects in the temporal dimension. This framework increases the object detection accuracy and tracking stability of objects in dynamic environments via the following three methods. Monocular depth prediction and 2D object detection networks are leveraged to estimate pixelwise depth and object boundaries. Subsequently, a pseudo-LiDAR approach is employed to generate a 3D point cloud for each object, where 3D point clouds are transformed into 2D representations. Furthermore, a novel 3D detection network based on image-based detection is introduced, simplifying the complexity of conventional 3D point-cloud-based detection while increasing the accuracy of monocular image-based 3D object detection. A specialized association network is designed to enhance object trajectory tracking across frames, thereby improving 3D tracking performance. The effectiveness of our approach was validated through extensive experiments conducted on the KITTI dataset. Our proposed framework exhibits excellent performance in 3D object detection and tracking, outperforming conventional methods. This study introduces innovative ideas for 3D object detection and tracking and establishes a basis for advancing autonomous driving systems.}, archive = {J_ACCESS}, author = {Rui Gao and Junoh Kim and Phuong Minh Chu and Kyungeun Cho}, doi = {10.1109/ACCESS.2025.3549790}, journal = {IEEE Access}, pages = {45771-45783}, shortjournal = {IEEE Access}, title = {Pseudo-LiDAR with two-dimensional instance for monocular three-dimensional object tracking}, volume = {13}, year = {2025}, }
• (2025). The factors influencing university-driven technology transfer with SMEs: A systematic literature review. ACCESS, 13, 45753-45770. (www)

  @article{ , abstract = {Over the past 20 years, universities have become the major drivers to improve scientific research and socio-industrial development, while Small and medium-sized enterprises (SMEs) have become key players in regional GDP contribution and technological innovation. Despite extensive studies on university-industry collaboration (UIC), there is a gap in the research on the micro-framework of University-SMEs collaboration (USC). This study addresses this gap by focusing on the interaction between universities and SMEs, intending to provide a detailed understanding of the influencing factors between universities and SMEs. This study conducted a systematic literature review of articles on USC from 2010 to 2023 and explored the internal and external factors that influence the success of USC. In terms of influencing factor analysis, this study ultimately focused on four conceptual groups: University-SMEs relationship (USR), University innovation and cooperation management (UICM), Actors, resources and capabilities (ARC), and Innovative concepts and spirit (ICS). Simultaneously, sub-items were expanded based on the conceptual groups to derive an overall conceptual framework that outlines the influencing factors of USC. The findings are expected to significantly impact academic literature and management practices, offering insights to enhance university-driven technology transfer to SMEs.}, archive = {J_ACCESS}, author = {Yuejie Wang and Liyana Shuib and Azah Anir Norman and Farzana Parveen Tajudeen}, doi = {10.1109/ACCESS.2025.3526653}, journal = {IEEE Access}, pages = {45753-45770}, shortjournal = {IEEE Access}, title = {The factors influencing university-driven technology transfer with SMEs: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Optimal performance of PI controller for AC microgrid based on metaheuristic optimization algorithms. ACCESS, 13, 45733-45752. (www)

  @article{ , abstract = {Microgrids are small power systems with one or most distributed generating units (DGs). Frequency and voltage control are crucial for grid-independent operating. It is a complex problem and essential to provide reliability and stability. Thus, the operating control issue is one main problem for the microgrid that must be addressed during operation, which calls for developing an optimal control method. Among these techniques, droop control is a common method due to the lack of critical communication links between parallel inverters to regulate the distributed generating units inside a microgrid, which is used for optimizing power sharing among DGs sources. Unfortunately, this strategy isn’t accurate in keeping the system frequency and voltage near their nominal values. As a result, the parameter values must be carefully selected utilizing the optimization techniques. This work aims to improve the droop controller based on optimized PI controller parameters to control the frequency and voltage of microgrids under various conditions by using three metaheuristic optimization algorithms, Slime Mould Algorithm (SMA), Sine Cosine Algorithm (SCA), and Sparrow Search Algorithm (SSA), and compare with the conventional method. Simulation findings using Simulink in MATLAB demonstrate the performance of the suggested microgrid stability techniques. Finally, to investigate the effectiveness of the suggested control strategies, the study’s results are compared with those of the conventional control method. The simulation results demonstrated the significant superiority of the proposed methods, including the SMA, in terms of performance analysis standards, power deviations, frequency stability, voltage response, load-sharing balance, overshoot reduction, and improvement in settling time and rise time.}, archive = {J_ACCESS}, author = {Ruqaya Majeed Kareem and Mohammed Kh. Al-Nussairi and Ramazan Bayindir}, doi = {10.1109/ACCESS.2025.3546321}, journal = {IEEE Access}, pages = {45733-45752}, shortjournal = {IEEE Access}, title = {Optimal performance of PI controller for AC microgrid based on metaheuristic optimization algorithms}, volume = {13}, year = {2025}, }
• (2025). Research on the suppression strategy with SMES of SSO for the black-box DFIG system. ACCESS, 13, 45715-45732. (www)

  @article{ , abstract = {To solve the problem of sub-synchronous oscillations caused by the application of series compensation technology in long-distance wind power transmission, this paper proposes a sub-synchronous oscillation suppression strategy for the black-box Doubly-fed Induction Generator system, which makes use of a superconducting magnetic energy storage device with an additional sub-synchronous damping controller to provide positive damping to the system in order to suppress sub-synchronous oscillations of the system. First, a doubly fed induction generator system is constructed in the RTDS simulation software, and second, an impedance model of the entire system under different operating conditions is constructed using an impedance scanning technique to analyze the risk of subsynchronous oscillations occurring in the constructed wind farm model and superconducting magnetic energy storage model. Then, an additional damping controller of the energy storage device with the function of sub-synchronous oscillation suppression is designed, and the parameters are optimized using a genetic algorithm. Simulation verification shows that the designed energy storage device can effectively suppress the 8.40-11.80 Hz oscillation frequency range generated by this system in four quadrants. The results demonstrate that the proposed method provides an effective solution to the sub-synchronous oscillation problem.}, archive = {J_ACCESS}, author = {Xu Zhang and Hui Huang and Qiman Xie and Lintao Gao and Jun Ye and Shenbing Ma}, doi = {10.1109/ACCESS.2025.3545570}, journal = {IEEE Access}, pages = {45715-45732}, shortjournal = {IEEE Access}, title = {Research on the suppression strategy with SMES of SSO for the black-box DFIG system}, volume = {13}, year = {2025}, }
• (2025). Cloud telescope: An ephemeral, distributed, and cloud-native architecture for collecting internet background radiation. ACCESS, 13, 45682-45714. (www)

  @article{ , abstract = {For two decades, cyber security researchers have been looking to answer one major question: what threats affect the Internet at large? In addition, what malicious traffic patterns would emerge if we could sample the unsolicited traffic - termed Internet Background Radiation (IBR) - arriving at devices directly connected to the Internet? The standard approach to collecting malicious traffic is the Network Telescope: a computer device assigned with a public IP address range, configured to passively listen to incoming packets. The deployment of Network Telescopes has helped to detect and quantify major cyberspace outbreaks, from the rise of the Conficker malware, to uncovering massive botnet propagation activity, such as performed by Mirai and its variants, against the Internet-of-Things. This paper introduces the Cloud Telescope: an ephemeral, cloud-native architecture, described as Infrastructure-as-Code, enabling for geographically distributed capture of the IBR, along with a discussion of a 5-month-long validation experiment, in which a sensor fleet comprising 130 cloud instances was launched across twenty-six regions of the world. The result is a quantitative and qualitative analysis of 530 million captured packets. This includes traffic breakdown by protocol: TCP (80%), UDP (3%), ICMP (17%), and by source country. We also discuss traffic aggregation by destination country and by affected cloud region, enabling novel forms of geopolitical influence analysis.}, archive = {J_ACCESS}, author = {Fabricio Bortoluzzi and Barry Irwin and Carla Merkle Westphall}, doi = {10.1109/ACCESS.2025.3549623}, journal = {IEEE Access}, pages = {45682-45714}, shortjournal = {IEEE Access}, title = {Cloud telescope: An ephemeral, distributed, and cloud-native architecture for collecting internet background radiation}, volume = {13}, year = {2025}, }
• (2025). Universal synchronization loss optimization in DETR-based oriented and rotated object detection. ACCESS, 13, 45669-45681. (www)

  @article{ , abstract = {The network models based on Detection Transformer (DETR) have gained popularity in object detection tasks. However, their performance is hindered by the lack of explicit correlation between classification and localization losses, resulting in suboptimal optimization and convergence. To address this issue, we propose universal synchronization loss. It can associate different positioning losses to improve detection accuracy. Specifically, we introduce L1 distance on the oriented object detection dataset and control the GIOU loss through a function. In the rotated dataset, we introduce the L1 distance to control the Kullback-Leibler divergence loss. In addition, we propose a method to dynamically adjust the classification weights to further associate the relationship between classification loss and localization loss. Experimental results on the COCO and DOTA v1.0/v1.5/v2.0 dataset demonstrate that our proposed universal synchronization loss brings excellent performance, surpassing previous DETR models and other similar models. This work underscores the importance of synchronizing losses in improving the overall optimization and detection accuracy of DETR-based models. Our code is available at https://github.com/yuqingchen1/SL-DETR.}, archive = {J_ACCESS}, author = {Gang Liu and Qingchen Yu and Mingming Gong and Hao Yang}, doi = {10.1109/ACCESS.2025.3539874}, journal = {IEEE Access}, pages = {45669-45681}, shortjournal = {IEEE Access}, title = {Universal synchronization loss optimization in DETR-based oriented and rotated object detection}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient DC power traction network systems for urban mass transportation: A comparative study of optimization algorithms. ACCESS, 13, 45653-45668. (www)

  @article{ , abstract = {Urban electric rail systems, such as metro systems, are crucial in reducing air pollution and alleviating traffic congestion. However, their significant energy consumption necessitates careful system design. This paper addresses the optimal design and simulation of a direct current (DC) power-pulling network system (PTNS) for metro systems, to minimize costs and energy consumption while ensuring adequate power supply capacity (PSC). To model the complex dynamics of urban mass transport systems (UMTS), including variations in speed, acceleration, load, and train position, we employ a novel approach using the Gray Wolf Optimizer (GWO) algorithm. This optimization technique efficiently determines the governing equations of the network, enhancing computational efficiency and convergence speed. To validate the proposed method, we compare the performance of the GWO algorithm with other optimization techniques, such as the Teaching-Learning-Based Optimizer (TLBO) and Differential Evolution (DE). A real-world case study is simulated further to demonstrate the feasibility and robustness of the method. The results confirm the potential of the proposed approach to optimize energy consumption and system design in urban electric rail systems.}, archive = {J_ACCESS}, author = {Dongxian Yu and Zhixin Wu and Lei Wang and Zaihui Cao and Hamdolah Aliev}, doi = {10.1109/ACCESS.2025.3547626}, journal = {IEEE Access}, pages = {45653-45668}, shortjournal = {IEEE Access}, title = {Energy-efficient DC power traction network systems for urban mass transportation: A comparative study of optimization algorithms}, volume = {13}, year = {2025}, }
• (2025). OSPXAI: An explainable and quantifiable deep learning method for the detection and identification of serial radiation leakage signals. ACCESS, 13, 45634-45652. (www)

  @article{ , abstract = {Electromagnetic radiation leakage during serial communication poses critical security vulnerabilities in data transmission. This paper presents OSPXAI, a deep learning methodology that integrates feature extraction and model interpretability for serial radiation leakage signal detection. The architecture incorporates three primary contributions: 1) an Offset Scaling Pooling Layer (OSPLayer) that implements adaptive feature extraction through offsetting, scaling, and rotational positional coding mechanisms; 2) an enhanced Smooth Gradient-weighted Class Activation Mapping (Smooth Grad-CAM++) that generates interpretable feature visualization maps; and 3) a novel Signal Overlap Index (SOI) metric that quantifies the intersection-over-union between predicted and actual signal regions. Experimental validation on RS-232 serial signals demonstrates that OSPXAI achieves 99.41% classification accuracy in binary detection tasks. The model exhibits robust generalization across multi-class recognition tasks with varying baud rates (9600-115200), maintaining a more stable performance under complex noise conditions. Quantitative evaluation through SOI validates the model’s feature localization capabilities, while Smooth Grad-CAM++ visualization provides interpretable insights into the decision-making process. The proposed methodology advances both the accuracy and explainability of electromagnetic leakage detection, offering practical value for communication security applications.}, archive = {J_ACCESS}, author = {Youbin Huang and Jian Mao and Kai Huang and Jinming Liu and Ni Tang}, doi = {10.1109/ACCESS.2025.3549031}, journal = {IEEE Access}, pages = {45634-45652}, shortjournal = {IEEE Access}, title = {OSPXAI: An explainable and quantifiable deep learning method for the detection and identification of serial radiation leakage signals}, volume = {13}, year = {2025}, }
• (2025). Optimized epoch selection ensemble: Integrating custom CNN and fine-tuned MobileNetV2 for malimg dataset classification. ACCESS, 13, 45623-45633. (www)

  @article{ , abstract = {In this paper, we introduce the Optimized Epoch Selection Ensemble, a new model designed to address key challenges in the application of deep learning within network security, specifically focusing on the effective selection of ensemble members. Our model combines a custom Convolutional Neural Network (CNN) with a fine-tuned MobileNetV2, targeting the classification of the Malimg dataset. By meticulously selecting the best-performing epochs from the CNN training and pairing them with the top epoch from MobileNetV2, we employ a strategy based on analyzing true predictions to optimize ensemble composition. This approach not only enhances the ensemble’s decision-making process but also significantly boosts accuracy, achieving 99.05% compared to the previous state-of-the-art result of 98.5%. Our results highlight the potential of tailored ensemble methods to significantly improve performance in specialized applications such as network security.}, archive = {J_ACCESS}, author = {Akobir Musaev and Abdulaziz Anorboev and Jonghee M. Youn}, doi = {10.1109/ACCESS.2025.3547791}, journal = {IEEE Access}, pages = {45623-45633}, shortjournal = {IEEE Access}, title = {Optimized epoch selection ensemble: Integrating custom CNN and fine-tuned MobileNetV2 for malimg dataset classification}, volume = {13}, year = {2025}, }
• (2025). Event-based multi-task facial landmark and blink detection. ACCESS, 13, 45609-45622. (www)

  @article{ , abstract = {Facial landmark detection and blink detection are essential tasks in computer vision, with significant applications in behavioral analysis and human-computer interaction. Traditional frame-based cameras struggle with the high temporal resolution needed for accurate blink detection under challenging conditions. This paper presents a novel approach using event-based cameras, which offer superior temporal resolution and dynamic range. By leveraging the asynchronous nature of these cameras, we capture fine-grained motion information, enabling precise real-time detection of rapid eye blinks and facial landmarks. We propose a multi-task deep neural network that optimizes both tasks simultaneously, enhancing overall system performance through shared learning. Our method achieves state-of-the-art results on a public event dataset with a normalized mean error of 4.76% for five facial keypoints. Using a combined dataset of real and synthetic events, we scale the task from 5 facial landmarks to 98, while attaining a normalized mean error of 3.65%. This 98 landmark model simultaneously detects blinks with an F1 score of 0.905, setting a new state-of-the-art in event-based blink detection. With these findings we demonstrate the potential of event-based cameras for robust, real-time facial analysis.}, archive = {J_ACCESS}, author = {Paul Kielty and Cian Ryan and Waseem Shariff and Joe Lemley and Peter Corcoran}, doi = {10.1109/ACCESS.2025.3549910}, journal = {IEEE Access}, pages = {45609-45622}, shortjournal = {IEEE Access}, title = {Event-based multi-task facial landmark and blink detection}, volume = {13}, year = {2025}, }
• (2025). The implementation of a prediction system for sugarcane’s destruction rate from sugarcane stem borer via hybrid machine learning. ACCESS, 13, 45594-45608. (www)

  @article{ , abstract = {Sugarcane stem borer poses a significant threat to sugarcane production, causing extensive damage and economic losses in the agricultural sector. Accurately predicting the destruction rate caused by this pest is crucial for implementing timely and effective management strategies. This paper presents the development and implementation of a time series prediction system designed to estimate the short-time destruction rate of sugarcane in the near future caused by stem borer infestations. The proposed Hybrid system combines Adaptive and Batch machine learning techniques, allowing for continuous model updates and adaptation to new data while maintaining initial accuracy, thereby improving predictive accuracy over time. A dataset comprising historical pest infestation data, manually recorded from the fields, and environmental factors, retrieved online, was used to train the model to capture complex temporal dependencies, with features such as temperature, humidity, seasonal patterns, and pest density being key predictors. The performance of the model was evaluated using the MAE, AIC, BIC, and HQIC, which demonstrated its robustness and reliability in predicting the destruction rate. This proposed Hybrid prediction system offers a valuable tool for sugarcane farmers and agronomists, enabling proactive decision-making to mitigate the impact of stem borer infestations. The findings of this study contribute to the broader application of machine learning in agricultural pest management, highlighting the potential for Hybrid learning approaches to enhance prediction accuracy in dynamic environments.}, archive = {J_ACCESS}, author = {Wachirawit Pituckwanich and Daranee Hormdee and Apiwat Boonkong and Piroon Kaewfoongrungsi and Manuwat Tintarasara Na Ratchaseema and Vorraveerukorn Veerachit}, doi = {10.1109/ACCESS.2025.3549453}, journal = {IEEE Access}, pages = {45594-45608}, shortjournal = {IEEE Access}, title = {The implementation of a prediction system for sugarcane’s destruction rate from sugarcane stem borer via hybrid machine learning}, volume = {13}, year = {2025}, }
• (2025). Balancing control of DC-link capacitor voltages for five-level hybrid diode-clamped inverters with passive front-ends. ACCESS, 13, 45577-45593. (www)

  @article{ , abstract = {The paper presents a control strategy for balancing DC-link capacitor voltages in a five-level hybrid diode-clamped (5L-HDC) inverter, consisting of two half-bridges and diode-clamped cells. The inverter experiences capacitor voltage imbalance, particularly in the middle capacitors, which naturally discharge due to voltage drift under the level-shifted (LS) PWM scheme, especially with passive front-end elements like diode rectifiers. To mitigate this issue, a capacitor voltage balancing strategy using the carrier-overlapped PWM (COPWM) technique is proposed. This approach generates multiple voltage levels per carrier period, ensuring effective voltage regulation without requiring an auxiliary circuit. The proposed method is compared with two other balancing strategies: LSPWM scheme with an auxiliary circuit and the finite control set model predictive control (FCS-MPC) technique, in terms of THD and power losses. In addition, a comprehensive evaluation is conducted on a 5-kV/1-MVA three-phase system, comparing the 5L-HDC inverter with balancing controls against other five-level inverter topologies. The assessment includes component count, cost, THD, and power losses, highlighting performance and economic trade-offs. The 5L-HDC inverter achieves cost reductions of 14.2%, 15%, 25.4%, and 4.3% compared to the 5L-NPC, 5L-ANPC, 5L-DFC, and 5L-HFC inverters, respectively. The effectiveness of the proposed method in regulating DC-link capacitor voltages is validated through experimental results under various operating conditions.}, archive = {J_ACCESS}, author = {Min-Seok Kim and Dinh Du To and Dong-Choon Lee}, doi = {10.1109/ACCESS.2025.3549760}, journal = {IEEE Access}, pages = {45577-45593}, shortjournal = {IEEE Access}, title = {Balancing control of DC-link capacitor voltages for five-level hybrid diode-clamped inverters with passive front-ends}, volume = {13}, year = {2025}, }
• (2025). SpecBoost: Accelerating tiled sparse matrix multiplication via dataflow speculation. ACCESS, 13, 45568-45576. (www)

  @article{ , abstract = {Sparse matrix-sparse matrix multiplication (SpMSpM) is crucial in many fields such as scientific computing, sparse linear algebra, and machine learning due to its computational complexity in the large and extremely sparse datasets. Various applications dealing with the sparse matrix show a variety of sparse matrix patterns, so the inner product, outer product, and Gustavson (row-wise) methods have been selectively used for the acceleration of the sparse matrix computation. Previous works determine a fixed dataflow before the computation. However, such an approach cannot optimize all the input matrice types having various data patterns. To address these limitations, we propose a SpecBoost, a method that dynamically selects an optimal tile-level SpMSpM dataflow by analyzing the sparsity pattern within each matrix tile and speculating the best tiled dataflow scheme before the computational stage. We compared our method with the widely known previous methods (CSSpa, ExTensor, MatRaptor), and experimental results show that on average our method reduced memory accesses by a factor of ( $4.01\times $ , $2.86\times $ , $2.22\times $ ) and boosts the performance of prior works over the baseline by ( $4.62\times $ , $2.40\times $ , $1.59\times $ ).}, archive = {J_ACCESS}, author = {Gwanghwi Seo and Sungju Ryu}, doi = {10.1109/ACCESS.2025.3550414}, journal = {IEEE Access}, pages = {45568-45576}, shortjournal = {IEEE Access}, title = {SpecBoost: Accelerating tiled sparse matrix multiplication via dataflow speculation}, volume = {13}, year = {2025}, }
• (2025). Evolving pedagogy in digital signal processing education: AI-assisted review and analysis. ACCESS, 13, 45559-45567. (www)

  @article{ , abstract = {This survey provides a comprehensive review of the evolving pedagogical approaches in Digital Signal Processing (DSP) education. By employing an AI-assisted recursive search methodology, the paper systematically examines traditional and innovative teaching methods, ranging from classic lecture styles to modern, technology-enhanced, and interactive strategies. The focus is on evaluating the effectiveness of these methodologies in teaching complex DSP concepts and fostering practical skills. The integration of digital tools, online learning platforms, and laboratory experiences is critically analyzed, emphasizing their role in enhancing student engagement and comprehension. Through comparative analysis and insights derived from AI-assisted synthesis, this paper offers actionable recommendations for educators to optimize DSP teaching techniques in the rapidly advancing field of engineering education.}, archive = {J_ACCESS}, author = {Miguel Bordallo López}, doi = {10.1109/ACCESS.2025.3549477}, journal = {IEEE Access}, pages = {45559-45567}, shortjournal = {IEEE Access}, title = {Evolving pedagogy in digital signal processing education: AI-assisted review and analysis}, volume = {13}, year = {2025}, }
• (2025). Curriculum learning algorithms for reward weighting in sparse reward robotic manipulation tasks. ACCESS, 13, 45544-45558. (www)

  @article{ , abstract = {Robotic learning from sparse rewards can be a considerable challenge due to large amounts of data required for mastering a task. We explore the application of curriculum learning (CL) algorithms for automatic reward weighting to tackle learning from sparse rewards in robotic pick-and-place and stacking tasks. We take several state-of-the-art CL algorithms that were originally designed to generate curriculum by manipulating the environment and appropriate them to weigh multiple sparse reward functions instead. The reward functions are chosen in a way that facilitates staged learning of the task, and the two robotic tasks are designed so that the agent learns to generalize to any initial and goal object position in the scene. The results of our three implemented CL algorithms show large improvement over the naive and state-of-the-art baselines in terms of speed of convergence to a successful policy in experiments with multiple task variations. Various generalization tests showcase some strengths and weaknesses of our approach. Inspection of changes in reward weight values during training further reveals varying curricula generated by the employed approaches, and showcases shifting emphasis from auxiliary to the main reward as the training progresses.}, archive = {J_ACCESS}, author = {Benjamin Fele and Jan Babič}, doi = {10.1109/ACCESS.2025.3549639}, journal = {IEEE Access}, pages = {45544-45558}, shortjournal = {IEEE Access}, title = {Curriculum learning algorithms for reward weighting in sparse reward robotic manipulation tasks}, volume = {13}, year = {2025}, }
• (2025). An effective AFNIS-MPPT-based method for optimizing hybrid energy harvesting systems. ACCESS, 13, 45527-45543. (www)

  @article{ , abstract = {The increasing global demand for renewable energy, particularly from solar and wind sources, highlights the need for sophisticated control strategies to optimize power generation efficiency. Maximum Power Point Tracking (MPPT) techniques are essential for improving the performance of Hybrid Renewable Energy Systems by maximizing power extraction from variable sources. Among these techniques, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Logic Control (FLC) have shown considerable promise in enhancing MPPT for wind and solar energy systems. By employing membership functions and adaptive algorithms, ANFIS effectively reduces tracking errors and ensures the maintenance of the Maximum Power Point (MPP) under changing environmental conditions. Real-time simulations illustrate ANFIS’s superior performance compared to traditional methods, showcasing smoother output curves, higher voltage outputs, and improved power extraction, particularly in wind power applications. These findings highlight the effectiveness of ANFIS-based control strategies in boosting the efficiency and reliability of renewable energy systems, facilitating their integration into mainstream power grids, and contributing to global sustainability initiatives.}, archive = {J_ACCESS}, author = {Muhammad Saqib and Ghulam Sarwar Kaloi and Mehr Gul and Muhammad Shahzad Nazir and Mohsin Ali Koondhar and Lutfi Albasha and Faisal Alsaif}, doi = {10.1109/ACCESS.2025.3549794}, journal = {IEEE Access}, pages = {45527-45543}, shortjournal = {IEEE Access}, title = {An effective AFNIS-MPPT-based method for optimizing hybrid energy harvesting systems}, volume = {13}, year = {2025}, }
• (2025). Adaptive sliding mode control for quadrotor UAVs under disturbances using multi-layer perceptron. ACCESS, 13, 45518-45526. (www)

  @article{ , abstract = {This paper introduces a novel adaptive sliding mode control (SMC) framework for quadrotor unmanned aerial vehicles (UAVs) operating in the presence of external disturbances and parametric uncertainties. The proposed approach leverages a multi-layer perceptron (MLP) neural network to dynamically tune the SMC parameters in real-time. This synergistic integration of MLP with SMC enables adaptive, efficient, robust, and energy-efficient control, significantly enhancing system performance. By continuously adjusting the SMC controller parameters based on real-time system feedback, the MLP effectively mitigates the impact of external disturbances and parameter uncertainties, achieving optimal hyperparameter values for enhanced trajectory tracking accuracy. The neural network empowers the controller to adapt seamlessly to dynamic changes in system behavior and environmental conditions. Another key contribution of this research lies in the substantial reduction of the chattering phenomenon, a common limitation of traditional SMC systems. Through rigorous simulations, the proposed controller demonstrates superior stability and robustness under diverse external disturbances and dynamic operating conditions. The rapid adaptability of the controller to changing environments surpasses the performance of conventional methods. This work underscores the significant potential of integrating artificial intelligence techniques with classical control methodologies to enhance the resilience and adaptability of UAV control systems in complex and unpredictable scenarios.}, archive = {J_ACCESS}, author = {Mir Mikael Fatemi and Adel Akbarimajd}, doi = {10.1109/ACCESS.2025.3549356}, journal = {IEEE Access}, pages = {45518-45526}, shortjournal = {IEEE Access}, title = {Adaptive sliding mode control for quadrotor UAVs under disturbances using multi-layer perceptron}, volume = {13}, year = {2025}, }
• (2025). Rectifying adversarial examples using their vulnerabilities. ACCESS, 13, 45500-45517. (www)

  @article{ , abstract = {Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications. Hence, extensive research has been undertaken to develop defense mechanisms that mitigate their threats. Most existing methods primarily focus on discriminating AEs based on the input sample features, emphasizing AE detection without addressing the correct sample categorization before an attack. While some tasks may only require mere rejection on detected AEs, others necessitate identifying the correct original input category such as traffic sign recognition in autonomous driving. The objective of this study is to propose a method for rectifying AEs to estimate the correct labels of their original inputs. Our method is based on re-attacking AEs to move them beyond the decision boundary for accurate label prediction, effectively addressing the issue of rectifying minimally perceptible AEs created using white-box attack methods. However, challenge remains with respect to effectively rectifying AEs produced by black-box attacks at a distance from the boundary, or those misclassified into low-confidence categories by targeted attacks. By adopting a straightforward approach of only considering AEs as inputs, the proposed method can address diverse attacks while avoiding the requirement of parameter adjustments or preliminary training. Results demonstrate that the proposed method exhibits consistent performance in rectifying AEs generated via various attack methods, including targeted and black-box attacks. Moreover, it outperforms conventional rectification and input transformation methods in terms of stability against various attacks.}, archive = {J_ACCESS}, author = {Fumiya Morimoto and Ryuto Morita and Satoshi Ono}, doi = {10.1109/ACCESS.2025.3550024}, journal = {IEEE Access}, pages = {45500-45517}, shortjournal = {IEEE Access}, title = {Rectifying adversarial examples using their vulnerabilities}, volume = {13}, year = {2025}, }
• (2025). Eyewear-based ECG recording: A novel head-electrode approach for reliable cardiac signal acquisition. ACCESS, 13, 45488-45499. (www)

  @article{ , abstract = {Recent advances in wearable technology have enabled the recording of electrocardiogram (ECG) signals from unconventional body locations, including the head. Particularly on this last location, ECG recording is challenging due to the low signal-to-noise ratio (SNR), making fundamental the selection of relevant positions on the head to gather high-quality signals. In addition, traditional wet electrodes are unsuitable, necessitating the use of dry electrodes. In this work, we investigated the feasibility of recording the ECG using a novel eyewear-based setup, enabling a single-lead high-quality ECG acquisition. The prototype featured three dry electrodes, strategically placed on an eyewear prototype, one in the nose pad (reference electrode), the second electrode (-) in contact with the ear pad skin and the third electrode (+) exposed on the external surface of the frame to be touched by the fingertip. Among various materials, we identified conductive elastomers featuring the most favorable impedance characteristics through measurements performed on gelatin compounds and human skin. The prototype was systematically tested against standard chest ECG in 25 participants to verify the quality of the ECG signal and the derived heart-rate variability (HRV) indexes. The recorded ECG signals were classified as Excellent, Barely Acceptable, and Unacceptable in 72, 28 and 0% of the overall cases. HRV indices obtained from both the prototype and standard ECG showcased a strong correlation ( $R\gt 0.9$ ). This study highlighted the feasibility of high-quality ECG recording based on eyewear setups.}, archive = {J_ACCESS}, author = {Andrea Costanzo Palmisciano and Matteo Rossi and Andrea Farabbi and Niccolò Antonello and Diana Trojaniello and Tommaso Ongarello and Pietro Cerveri and Luca Mainardi}, doi = {10.1109/ACCESS.2025.3547230}, journal = {IEEE Access}, pages = {45488-45499}, shortjournal = {IEEE Access}, title = {Eyewear-based ECG recording: A novel head-electrode approach for reliable cardiac signal acquisition}, volume = {13}, year = {2025}, }
• (2025). Dimension-specific margins and element-wise gradient scaling for enhanced matryoshka speaker embedding. ACCESS, 13, 45473-45487. (www)

  @article{ , abstract = {Speaker embeddings trained with Matryoshka Representation Learning (MRL) provide embeddings of various dimensions with minimal overhead, adapting to different computational and storage constraints. Compared to single-dimensional models, MRL-based models show improved speaker verification performance in lower dimensions, but there is some degradation in higher dimensions. Analyzing learned embeddings, we observe an imbalance in the element-wise magnitudes of speaker embeddings trained with MRL. Specifically, the higher-dimensional elements exhibit extremely small values, which could reduce their contribution to cosine similarity and degrade speaker verification performance. To address this imbalance and improve performance consistency across all dimensions, we propose two methods: dimension-specific margins and element-wise gradient scaling. Dimension-specific margins stabilize training by adjusting the margin for each dimension to mitigate instability caused by excessively high values for given dimensions. Element-wise gradient scaling mitigates imbalance by scaling gradients propagated to each element, considering differences in embedding dimensionality and the number of loss functions influencing each element. Evaluation on the VoxCeleb benchmark shows that the proposed methods, when applied to MRL, improve speaker verification performance in higher-dimensional embeddings while maintaining performance in lower-dimensional embeddings. Additionally, an analysis of the element-wise magnitudes of learned embeddings visually demonstrates that element-wise gradient scaling effectively mitigates the magnitude imbalance.}, archive = {J_ACCESS}, author = {Sunchan Park and Hyung Soon Kim}, doi = {10.1109/ACCESS.2025.3550161}, journal = {IEEE Access}, pages = {45473-45487}, shortjournal = {IEEE Access}, title = {Dimension-specific margins and element-wise gradient scaling for enhanced matryoshka speaker embedding}, volume = {13}, year = {2025}, }
• (2025). Empty substrate integrated waveguide coupler and transition structure to WR-3 waveguide. ACCESS, 13, 45468-45472. (www)

  @article{ , abstract = {This paper proposes a sub-THz Riblet coupler based on the empty substrate integrated waveguide (ESIW) and a transition structure between ESIW and a WR-3 rectangular waveguide. The ESIW is fabricated through laser engraving and electroplating processes. The transition is achieved simply by a rectangular cavity and can be modeled by two coupled notch filters. The measured insertion loss of a single transition is 1.25 dB from 250 to 313 GHz. The amplitude imbalance of the coupler is about 2 dB and the phase difference is between 87 deg and 101 deg from 250 to 290 GHz.}, archive = {J_ACCESS}, author = {Ching Li and Yu-Hsiang Cheng}, doi = {10.1109/ACCESS.2025.3547729}, journal = {IEEE Access}, pages = {45468-45472}, shortjournal = {IEEE Access}, title = {Empty substrate integrated waveguide coupler and transition structure to WR-3 waveguide}, volume = {13}, year = {2025}, }
• (2025). Elevational synthetic aperture focusing for rotated array-based three-dimensional ultrasound imaging. ACCESS, 13, 45458-45467. (www)

  @article{ , abstract = {Three-dimensional (3D) ultrasound (US) imaging is widely used for real-time, non-ionizing, and cost-effective medical diagnostics. However, using a one-dimensional (1D) transducer often results in limited elevational resolution due to the inherent beam thickness. In this paper, we introduce an elevational Synthetic Aperture Focusing (SAF) algorithm specifically designed for rotational 3D US imaging. Unlike previous methods requiring channel data, our approach operates on in-plane beamformed radio-frequency (RF) data, making it more accessible on many commercial scanners. Through simulations and experiments, we demonstrate significant improvements in elevational resolution (up to 96.4%) and contrast (up to 274.7%). These findings highlight the potential of the proposed algorithm to enhance both research and clinical applications of rotational 3D US imaging.}, archive = {J_ACCESS}, author = {Ryo Murakami and Yang Wang and Yichuan Tang and Ryosuke Tsumura and Gregory S. Fischer and Haichong K. Zhang}, doi = {10.1109/ACCESS.2025.3549638}, journal = {IEEE Access}, pages = {45458-45467}, shortjournal = {IEEE Access}, title = {Elevational synthetic aperture focusing for rotated array-based three-dimensional ultrasound imaging}, volume = {13}, year = {2025}, }
• (2025). Development of in-memory computing device using positive feedback field effect transistor based on NAND flash array. ACCESS, 13, 45449-45457. (www)

  @article{ , abstract = {In this study, we propose a feedback field effect transistor (FBFET) in-memory computing device based on the string of NAND flash array. By combining the polysilicon-blocking oxide-silicon nitride-tunneling oxide-silicon (SONOS) cell structure and N+-P $^{-}$ -N $^{-}$ -P+ body doping structure of FBFET, the device is designed in a string structure. Our in-memory computing FBFET can perform Boolean logical operations by utilizing the threshold voltage modulation characteristics of the FBFET by adjusting the control gate voltage. Additionally, we present optimized hot carrier injection conditions tailored for the in-memory computing FBFET, enabling the successful writing of results for Boolean logic operations: OR, AND, NOR, NAND. These findings contribute a novel mechanism and direction for the field of in-memory computing research.}, archive = {J_ACCESS}, author = {Hangwook Jeong and Minseon Park and Min-Woo Kwon}, doi = {10.1109/ACCESS.2025.3548572}, journal = {IEEE Access}, pages = {45449-45457}, shortjournal = {IEEE Access}, title = {Development of in-memory computing device using positive feedback field effect transistor based on NAND flash array}, volume = {13}, year = {2025}, }
• (2025). TCCM: Trajectory converged chaff-based mix-zone strategy for enhancing location privacy in VANET. ACCESS, 13, 45436-45448. (www)

  @article{ , abstract = {Vehicles in vehicular ad-hoc networks (VANETs) are required to continuously broadcast sensitive data including coordinates and speeds. Such practices make sensitive data susceptible to eavesdroppers, undermining location privacy. Conventional strategies attempt to preserve location privacy through different privacy enhancing techniques such as encryption, silent periods, and chaff messages. However, existing approaches fail to simultaneously ensure driving safety and location privacy, particularly against semantic linking attacks by machine-learning-enabled adversaries. To this end, this paper proposes the trajectory converged chaff-based mix zone (TCCM) strategy. It generates chaff messages that imitate real vehicle trajectories to confuse eavesdroppers while maintaining low communication overhead, thereby enhancing location privacy without compromising vehicle safety. Additionally, the TCCM strategy incorporates a genetic algorithm to optimize mix zone placement and ensure a balance between privacy protection and resource efficiency. We implemented a VANET simulation and two adversary attack algorithms to evaluate the performance of our scheme. Reportedly, the TCCM strategy reduces trajectory traceability by at least 24.6% compared with conventional mix zone strategies while maintaining vehicle safety. Additionally, the chaff messages of the TCCM strategy incur 54% less communication overhead than existing chaff-based schemes.}, archive = {J_ACCESS}, author = {Yunheng Wu}, doi = {10.1109/ACCESS.2025.3550442}, journal = {IEEE Access}, pages = {45436-45448}, shortjournal = {IEEE Access}, title = {TCCM: Trajectory converged chaff-based mix-zone strategy for enhancing location privacy in VANET}, volume = {13}, year = {2025}, }
• (2025). Blockchain-based secured data sharing in healthcare: A systematic literature review. ACCESS, 13, 45415-45435. (www)

  @article{ , abstract = {Blockchain technology is increasingly recognised for its potential to enhance the security, reliability, and resilience of distributed systems. By ensuring data integrity and protecting against unauthorised access, blockchain creates immutable and transparent records, making it an appealing option for various applications. In the current healthcare landscape, secure data sharing is essential for improving patient outcomes, streamlining healthcare operations, and ensuring compliance with legal standards. This paper conducts a systematic literature review to explore how blockchain can be utilised as a security solution for health data sharing. We assess the strengths and weaknesses of blockchain-based solutions in areas such as secure data management, data sharing, access control, trust and transparency, and authentication and authorisation. Our findings indicate that blockchain has significant potential to secure health data sharing, although we identify certain limitations in blockchain-based access control systems and propose potential solutions. Additionally, this paper provides detailed insights into future research directions in this domain.}, archive = {J_ACCESS}, author = {Umair Ullah Tariq and Fariza Sabrina and Md Mamunur Rashid and Steven Gordon and Yufeng Lin and Zhenglin Wang and Salahuddin Azad}, doi = {10.1109/ACCESS.2025.3547953}, journal = {IEEE Access}, pages = {45415-45435}, shortjournal = {IEEE Access}, title = {Blockchain-based secured data sharing in healthcare: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Lung cancer detection utilizing mixed sensor based electronic nose. ACCESS, 13, 45400-45414. (www)

  @article{ , abstract = {The lungs are the most important organ of the respiratory system. The volatile organic compounds found in breath, which usually originate from the blood and enable us to observe different processes in the body, are carried to the lungs through the blood and then exhaled by breath. In this study, a mixed sensor based electronic nose circuit was developed using eight metal oxide semiconductors and 14 Quartz Crystal Microbalance gas sensors. A total of 100 volunteers participated in the study, including 20 healthy volunteers who did not smoke, 20 healthy volunteers who smoked, and 60 lung cancer volunteers. Throughout this study, 338 experiments were conducted using breath samples. Data dimension reduction was achieved using linear discriminant analysis and principal component analysis algorithms. The individual classification accuracies for the metal oxide semiconductor and quartz crystal microbalance sensor data are 81.54% and 73.18%, respectively. Upon combining the sensor data, a noticeable increase in accuracy of 85.26% was observed. In this study, the performance of the developed system was enhanced using principal component and linear discriminant analyses. While the highest classification accuracy increased to 88.56% with the feature matrix obtained using the principal component analysis method, this value was obtained with 94.58% accuracy using the linear discriminant analysis method.}, archive = {J_ACCESS}, author = {Umit Ozsandikcioglu and Ayten Atasoy and Yusuf Sevim}, doi = {10.1109/ACCESS.2025.3550453}, journal = {IEEE Access}, pages = {45400-45414}, shortjournal = {IEEE Access}, title = {Lung cancer detection utilizing mixed sensor based electronic nose}, volume = {13}, year = {2025}, }
• (2025). Sequential recommendation via joint modeling of temporal dynamics with LSTM and hawkes process. ACCESS, 13, 45391-45399. (www)

  @article{ , abstract = {The recommendation system serves as a pivotal component of numerous online systems, playing a crucial role in enhancing user experience. Nevertheless, the majority of these models make the assumption that the user interaction history sequence is continuous, without taking into account the event intervals between user behaviors during modeling. The combination of Long Short Term Memory (LSTM) and Hawkes process incorporating the power law decay characteristics, enables the modeling of complex temporal dynamics in sequential data. To achieve this, we leverage the capabilities of a LSTM to automatically acquire a representation of the influences derived from the behavior history. Through this hybrid model, we are able to predict the next item a user is likely to interact with, taking into consideration the temporal dimension of user-item interactions. We evaluate our model on three real-world datasets, demonstrating significant improvements in recommendation performance compared to state-of-the-art baseline methods. Additionally, the inclusion of the power-law feature within the Hawkes process provides deeper insights into the underlying temporal patterns of user behavior, which can be leveraged for more refined and timely recommendations.}, archive = {J_ACCESS}, author = {Yongjie Yan and Hui Xie}, doi = {10.1109/ACCESS.2025.3549636}, journal = {IEEE Access}, pages = {45391-45399}, shortjournal = {IEEE Access}, title = {Sequential recommendation via joint modeling of temporal dynamics with LSTM and hawkes process}, volume = {13}, year = {2025}, }
• (2025). Improving weakly-supervised video instance segmentation by leveraging spatio-temporal consistency. ACCESS, 13, 45377-45390. (www)

  @article{ , abstract = {The performance of Video Instance Segmentation (VIS) methods has improved significantly with the advent of transformer networks. However, these networks often face challenges in training due to the high annotation cost. To address this, unsupervised and weakly-supervised methods have been developed to reduce the dependency on annotations. This work introduces a novel weakly-supervised method called Eigen-Cluster VIS that, without requiring any mask annotations, achieves competitive accuracy compared to other VIS approaches. This method is based on two key innovations: a Temporal Eigenvalue Loss (TEL) and a clip-level Quality Cluster Coefficient (QCC). The TEL ensures temporal coherence by leveraging the eigenvalues of the Laplacian matrix derived from graph adjacency matrices. By minimizing the mean absolute error between the eigenvalues of adjacent frames, this loss function promotes smooth transitions and stable segmentation boundaries over time, reducing temporal discontinuities and improving overall segmentation quality. The QCC employs the K-means method to ensure the quality of spatio-temporal clusters without relying on ground truth masks. Using the Davies-Bouldin score, the QCC provides an unsupervised measure of feature discrimination, allowing the model to self-evaluate and adapt to varying object distributions, enhancing robustness during the testing phase. These enhancements are computationally efficient and straightforward, offering significant performance gains without additional annotated data. The proposed Eigen-Cluster VIS method is evaluated on the YouTube-Video Instance Segmentation (YouTube-VIS) 2019/2021 and Occluded Video Instance Segmentation (OVIS) datasets, demonstrating that it effectively narrows the performance gap between the fully-supervised and weakly-supervised VIS approaches.}, archive = {J_ACCESS}, author = {Farnoosh Arefi and Amir M. Mansourian and Shohreh Kasaei}, doi = {10.1109/ACCESS.2025.3550435}, journal = {IEEE Access}, pages = {45377-45390}, shortjournal = {IEEE Access}, title = {Improving weakly-supervised video instance segmentation by leveraging spatio-temporal consistency}, volume = {13}, year = {2025}, }
• (2025). Collaborative filtering techniques for predicting web service QoS values in static and dynamic environments: A systematic and thorough analysis. ACCESS, 13, 45350-45376. (www)

  @article{ , abstract = {In recent years, the rapid growth of Web Services (WSs) has led to a proliferation of functionally similar options, making Quality of Service (QoS) a crucial factor for users in selecting the most suitable services. Predicting QoS values and recommending optimal services remain challenging, particularly in dynamic environments. This study systematically reviews QoS prediction for web services, focusing on Collaborative Filtering (CF) techniques. Following PRISMA guidelines, 512 studies were initially identified from databases like IEEE Xplore, ACM Digital Library, and Google Scholar, using keywords such as “collaborative filtering,” “web services,” and “QoS prediction.” After rigorous screening, 146 studies underwent a full-text review. Key insights were gathered on algorithms, evaluation metrics, datasets, and performance outcomes, with a focus on CF methods and advancements in hybrid and context-aware models. Despite progress, challenges in dynamic WS environments persist, highlighting the need for adaptive and real-time prediction approaches.}, archive = {J_ACCESS}, author = {Ghizlane Khababa and Sadik Bessou and Fateh Seghir and Nor Hazlyna Harun and Abdulaziz S. Almazyad and Pradeep Jangir and Ali Wagdy Mohamed}, doi = {10.1109/ACCESS.2025.3550284}, journal = {IEEE Access}, pages = {45350-45376}, shortjournal = {IEEE Access}, title = {Collaborative filtering techniques for predicting web service QoS values in static and dynamic environments: A systematic and thorough analysis}, volume = {13}, year = {2025}, }
• (2025). Multi-kernel learning for heterogeneous data. ACCESS, 13, 45340-45349. (www)

  @article{ , abstract = {Multi-kernel learning is an excellent machine learning algorithm widely used in various learning tasks such as classification and regression. Traditional kernel methods mainly focus on numerical data and lack sufficient research on categorical and mixed data. However, mixed data is widely used in practical applications, and many unstructured data can be converted into mixed data through appropriate preprocessing. In this work, we propose a new Heterogeneous Multi-Kernel Learning (HMKL) algorithm for processing mixed data containing both categorical and numerical attributes. In HMKL, category attributes and numerical attributes are processed separately. Different similarity measurement methods are used to obtain different kernel matrices for category attributes, which are then fused with numerical kernel matrices to improve the classification performance of multi-kernel learning. We propose a new ratio Gaussian kernel function for category attributes, which can maintain a balance between the AND and OR operations of the matching kernel matrix. In addition, to address the curse of dimensionality caused by one-of-N encoding, we use the summation of matching kernel matrices to reduce the difficulty of preprocessing categorical attributes. The experiment shows that our proposed HMKL algorithm can effectively handle mixed data and has excellent performance.}, archive = {J_ACCESS}, author = {Chunlan Liao and Shili Peng}, doi = {10.1109/ACCESS.2025.3530396}, journal = {IEEE Access}, pages = {45340-45349}, shortjournal = {IEEE Access}, title = {Multi-kernel learning for heterogeneous data}, volume = {13}, year = {2025}, }
• (2025). Computer vision for clinical gait analysis: A gait abnormality video dataset. ACCESS, 13, 45321-45339. (www)

  @article{ , abstract = {Clinical gait analysis (CGA) using computer vision is an emerging field in artificial intelligence facing great challenges in obtaining accessible and annotated real-world data and clear task objectives. This paper lays the groundwork for advancing CGA, presenting vision-based methods and datasets tailored for gait analysis. We introduce the Gait Abnormality in Video Dataset (GAVD) in response to a review of over 150 existing gait-related datasets, designed to address the critical need for a large, clinically annotated gait dataset. GAVD contains 1,874 video sequences, capturing normal, abnormal, and pathological gait patterns. It features RGB data, clinically annotated from publicly available online sources, and encompassing over 400 subjects who have undergone clinical-grade visual screening. The dataset spans diverse environments, such as hospital clinics and uncontrolled urban settings. We validated GAVD through action recognition models, achieving abnormality detection rates of 94% and 92% with Temporal Segment Networks (TSN) and SlowFast networks, respectively. We further demonstrate GAVD’s clinical utility by testing on the Clinical Abnormality Simulated Dataset (CASD) achieving abnormality detection accuracy of 73% and 87%, respectively, with TSN and SlowFast networks. To facilitate further research, we provide a GitHub repository GAVD with URL links and clinically relevant annotation for over 450 videos featuring a variety of gait patterns and unique subjects.}, archive = {J_ACCESS}, author = {Rahm Ranjan and David Ahmedt-Aristizabal and Mohammad Ali Armin and Juno Kim}, doi = {10.1109/ACCESS.2025.3545787}, journal = {IEEE Access}, pages = {45321-45339}, shortjournal = {IEEE Access}, title = {Computer vision for clinical gait analysis: A gait abnormality video dataset}, volume = {13}, year = {2025}, }
• (2025). Statistical insights into machine learning models for predicting under-five mortality: An analysis from multiple indicator cluster survey (MICS). ACCESS, 13, 45312-45320. (www)

  @article{ , abstract = {Under-five mortality remains a significant public health challenge in the Central African Republic (CAR), yet research on the region-specific determinants of this issue is limited. This study explores the use of machine learning (ML) models to predict under-five mortality in CAR using data from the 2018–2019 Multiple Indicator Cluster Survey (MICS). Advanced ML models, including CatBoost, Light Gradient Boosting (LGB), Extreme Gradient Boosting (XGBoost), and Extreme Gradient Boosting Random Forest (XGBRF), were compared with traditional logistic regression, with model performance evaluated using accuracy metrics such as the Area Under the Curve (AUC), F1 score, Precision, Recall, and Matthews Correlation Coefficient (MCC). CatBoost outperformed all models, achieving an AUC of 0.973, accuracy of 0.946, F1 score of 0.951, and MCC of 0.897, demonstrating its robust predictive power. The study identified key factors influencing under-five mortality in CAR, such as birth type, maternal education, birth order, birth interval, and birth weight. It suggests that using advanced ML models, particularly CatBoost, can improve identifying region-specific risk factors and inform targeted interventions to reduce child mortality in CAR.}, archive = {J_ACCESS}, author = {Ali Satty and Gamal Saad Mohamed Khamis and Zakariya M. S. Mohammed and Ashraf F. A. Mahmoud and Faroug A. Abdalla and Mohyaldein Salih and Abaker A. Hassaballa and Elzain A. E. Gumma}, doi = {10.1109/ACCESS.2025.3549097}, journal = {IEEE Access}, pages = {45312-45320}, shortjournal = {IEEE Access}, title = {Statistical insights into machine learning models for predicting under-five mortality: An analysis from multiple indicator cluster survey (MICS)}, volume = {13}, year = {2025}, }
• (2025). Interpretable deep learning for efficient code smell prioritization in software development. ACCESS, 13, 45290-45311. (www)

  @article{ , abstract = {Code smells indicate potential design flaws in software systems that can impair maintainability and increase technical debt. While existing approaches have advanced code smell priortization, they often lack effective prioritization mechanisms and interpretability, hindering developers’ ability to make informed refactoring decisions. This paper presents a novel approach combining CodeBERT embeddings with Bidirectional Long Short-Term Memory (Bi-LSTM) networks for code smell prioritization, enhanced by Local Interpretable Model-agnostic Explanations (LIME) for model interpretability. The approach introduces specialized preprocessing for large-scale projects and implements a selectivity metric for validating explanation quality. Our comprehensive evaluation demonstrates that the Bi-LSTM approach consistently outperforms traditional architectures across various code smell types, achieving 0.90 across precision, recall, and F1-score metrics for complex class priortization, and precision of 0.88 with recall of 0.87 for feature envy priortization. The model also showed strong performance in identifying God classes, namely, 0.84 for precision, 0.77 for recall, and 0.89 for long methods. The integration of LIME provides developers with clear insights into the model’s decision-making process, enhancing trust and facilitating more effective refactoring decisions. This work contributes a framework that not only accurately detects and prioritizes code smells but also offers transparent, interpretable results applicable in real-world software development.}, archive = {J_ACCESS}, author = {Maaeda M. Rashid and Mohd Hafeez Osman and Khaironi Yatim Sharif and Hazura Zulzalil}, doi = {10.1109/ACCESS.2025.3543277}, journal = {IEEE Access}, pages = {45290-45311}, shortjournal = {IEEE Access}, title = {Interpretable deep learning for efficient code smell prioritization in software development}, volume = {13}, year = {2025}, }
• (2025). Deterministic uncertainty estimation for multi-modal regression with deep neural networks. ACCESS, 13, 45281-45289. (www)

  @article{ , abstract = {Prediction interval (PI) is a common method to represent predictive uncertainty in regression by deep neural networks. This paper proposes an extension of the prediction interval by using a union of disjoint intervals. Since previous PI methods assumed a single-interval PI (one lower and upper bound), it suffers from performance degradation in uncertainty estimation when the conditional density function is multi-modal. This paper demonstrates the need to include multi-modality in uncertainty estimation for regression. To address the issue, we propose a novel method that generates a union of disjoint PI’s. With UCI benchmark experiments, the proposed method is shown to improve over current state-of-the-art uncertainty quantification methods, reducing an average PI width by over $27~\%$ . With qualitative experiments, it is shown that multi-modality often exists in real-world datasets, and our method produces high-quality PI’s compared to existing PI methods.}, archive = {J_ACCESS}, author = {Jaehak Cho and Jae Myung Kim and Seungyub Han and Jungwoo Lee}, doi = {10.1109/ACCESS.2025.3547911}, journal = {IEEE Access}, pages = {45281-45289}, shortjournal = {IEEE Access}, title = {Deterministic uncertainty estimation for multi-modal regression with deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Dual-band with dual-linear polarization frequency selective surface utilizing aperture coupled technique. ACCESS, 13, 45272-45280. (www)

  @article{ , abstract = {This article investigates the design and development of a frequency selective surface (FSS) that achieves a dual-passband and dual-linear polarization. It consists of two independent corner-truncated square patch resonators to examine dual-passband responses, and a cross-coupled aperture is used to produce two independent linear polarizations (vertical and horizontal) for both passbands. The patch resonators are arranged back-to-back, separated by a metallic aperture-coupled layer located at the middle. The two passband responses correspond to Sub-section I (SS-I) and Sub-section II (SS-II). The first passband arises when a crossed aperture is etched into the middle metallic layer beneath the patch resonator in SS-I, while the second passband is achieved under similar conditions in SS-II. The incidence wave orientation to the designed FSS is either a vertically/horizontally/combination of both polarized waves. The FSS is designed, a prototype is fabricated, and its simulation results are compared with and validated by the measured results. The results are satisfactory and carry two passbands and dual linear polarization simultaneously with a low frequency band ratio and a maximum insertion loss of about 1.2 dB, which also indicates that the frequency performances of both designed FSSs are stable under different incident polarizations and angles (up to $30^{0}$ ). In addition, the proposed design is useful to improve the polarization diversity and enhance the frequency selectivity (allow or block the specific frequency) for dual passband by incorporating polarization conversion in various microwave applications.}, archive = {J_ACCESS}, author = {K. N. Mohan and Abhijit Bhowmick and Sudhanshu Arya and Yogesh Kumar Choukiker}, doi = {10.1109/ACCESS.2025.3549047}, journal = {IEEE Access}, pages = {45272-45280}, shortjournal = {IEEE Access}, title = {Dual-band with dual-linear polarization frequency selective surface utilizing aperture coupled technique}, volume = {13}, year = {2025}, }
• (2025). Extensive review of threat models for DevSecOps. ACCESS, 13, 45252-45271. (www)

  @article{ , abstract = {Amidst the rising tide of cyber threats, the software industry is gravitating toward the widespread adoption of DevSecOps as a crucial defense strategy. In response to the evolving application development needs, DevSecOps enables developers and enterprises to seamlessly integrate security and, fortify digital realms against modern cybersecurity challenges. This study attempts to identify and review various threat models that can be combined with components of DevSecOps, such as its principles, methodology, tools, and workflows. Underscoring the necessity of early security considerations in the software development life cycle, the discussion then delves into the details of adaptable strategies and best practices to mitigate potential threats proactively, while also emphasizing the pivotal role of monitoring and incident response in the prompt detection and mitigation of potential risks. Moreover, this study focuses on identifying the challenges associated with adopting traditional threat models in the contemporary landscape of multi-cloud architecture, a prevalent paradigm in current technological landscapes. Recognizing the intricacies of securing applications across diverse cloud environments, this scholarly work proposes a layered framework for multi-cloud, which can be adopted by practitioners to address the security challenges of the multi-cloud environment. Hence, this investigation furnishes valuable insights into the requirements, workflow and threat models of DevSecOps in single-cloud and emerging multi-cloud environment.}, archive = {J_ACCESS}, author = {S Nagasundari and Pratham Manja and Priyansh Mathur and Prasad B. Honnavalli}, doi = {10.1109/ACCESS.2025.3547932}, journal = {IEEE Access}, pages = {45252-45271}, shortjournal = {IEEE Access}, title = {Extensive review of threat models for DevSecOps}, volume = {13}, year = {2025}, }
• (2025). An auto-annotation approach for object detection and depth-based distance estimation in security and surveillance systems. ACCESS, 13, 45240-45251. (www)

  @article{ , abstract = {Existing object detection and annotation methods in surveillance systems often suffer from inefficiencies due to manual labeling and a lack of accurate distance estimation, which limits their effectiveness in large-scale environments. These limitations reduce the speed and accuracy required for real-time surveillance, especially in scenarios that necessitate simultaneous monitoring of multiple feeds. To address these challenges, this paper proposes a framework for automated object detection and annotation, specifically designed for surveillance applications. The framework incorporates both manual and automatic modes, offering flexibility in object labeling. A synthetic data is created by using the blender tool which emulates the real-world security and surveillance environments, is utilized to train a model for fast and accurate object recognition and identification. Moreover, for precise distance estimation a depth estimation model is used for calculating the distance for detected objects. The proposed architecture presents both manual and auto modes for the object detection by incorporating the both models in proposed framework. The auto mode of the proposed architecture increases the efficiency of the large surveillance and monitoring applications while lowering the manual labor. The model’s performance is evaluated with state-of-the-art models to assess its performance for auto detection and recognition. The precision and recall above 90% shows that our fine-tuned model demonstrates improved results on synthetic data. With real time surveillance and risk assessment as the foundation, this method seamlessly overthrows drawbacks of existing methods.}, archive = {J_ACCESS}, author = {Misbah Bibi and Muhammad Faseeh and Anam Nawaz Khan and Atif Rizwan and Qazi Waqas Khan and Rashid Ahmad and Do-Hyeun Kim}, doi = {10.1109/ACCESS.2025.3539310}, journal = {IEEE Access}, pages = {45240-45251}, shortjournal = {IEEE Access}, title = {An auto-annotation approach for object detection and depth-based distance estimation in security and surveillance systems}, volume = {13}, year = {2025}, }
• (2025). Theoretical analysis and experiment of the five DOF hybrid robot P(RPR/RP)RR. ACCESS, 13, 45227-45239. (www)

  @article{ , abstract = {To meet the demands of machining new energy vehicles aluminum alloy structures, this paper introduced a five degree-of-freedom (DOF) hybrid robot, P(RPR/RP)RR, and conducted a systematic investigation into its kinematics, stiffness, and preliminary calibration algorithms. The research began with the robot’s kinematic model establishment. By analyzing the relationship between the independent motion variables and the branch velocities of the parallel module, a mapping matrix between input and output velocities was derived. Subsequently, the robot’s stiffness was analyzed by the coefficient matrix method. Through scale optimization, the structural dimensions of the hybrid robot are obtained. Based on three-dimensional model of the hybrid robot P(RPR/RP)RR established by these dimensions, the kinematics and stiffness of the hybrid robot are simulated and verified. Finally, the research team constructed a prototype of the robot, and zero-point error parameter calibration and accuracy testing were completed using the closed-loop vector method. This research provides significant technical support for the precision machining of aluminum alloy structures in new energy vehicles.}, archive = {J_ACCESS}, author = {Xuejian Ma and Xiaoyu He and Yundou Xu and Jiantao Yao and Yongsheng Zhao}, doi = {10.1109/ACCESS.2025.3549746}, journal = {IEEE Access}, pages = {45227-45239}, shortjournal = {IEEE Access}, title = {Theoretical analysis and experiment of the five DOF hybrid robot P(RPR/RP)RR}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization for lightning protection in distribution networks: A novel approach based on design of experiments. ACCESS, 13, 45215-45226. (www)

  @article{ , abstract = {This paper focuses on the identification of a proper protection strategy against lightning effects on overhead distribution lines. Particularly, a multi-objective optimisation problem is formulated to contemporaneously take into account the flashover rate and the cost of the protection devices. The random nature of lightning events needs to be considered to find optimal protection. Traditional approaches typically use the Monte Carlo method to reproduce large series of lightning events that vary in location, current intensity, and current ramp rate. However, the high computational effort of the Monte Carlo approach makes it difficult to carry out the above analysis, especially when dealing with complex networks. Thus, in this paper, the Design of Experiments is proposed as the statistical tool to characterize lightning events. More specifically, full factorial design and Taguchi arrays are used which allow for significantly reducing the computational effort. Numerical applications on an actual distribution network located in Italy allowed a comparison of these methods to the Monte Carlo approach. The results evidenced the effectiveness of the proposed approach in terms of computational effort and accuracy of the analysis.}, archive = {J_ACCESS}, author = {Nagananthini Ravichandran and Daniela Proto and Fabio Mottola and Amedeo Andreotti}, doi = {10.1109/ACCESS.2025.3546490}, journal = {IEEE Access}, pages = {45215-45226}, shortjournal = {IEEE Access}, title = {Multi-objective optimization for lightning protection in distribution networks: A novel approach based on design of experiments}, volume = {13}, year = {2025}, }
• (2025). Fast, robust, and learned distribution-based sorting. ACCESS, 13, 45198-45214. (www)

  @article{ , abstract = {Despite a long history of research and achievements in designing sorting routines, new hardware features and application requirements pose advanced challenges that computer scientists are recently tackling through novel data-aware (learned) algorithms. This paper aims to better understand the strengths and limitations of learned sorters for numerical data, by addressing two main research and engineering questions: (Q1) Which is the best trade-off, in sorting items, between the efficacy of a “learned” model in approximating the distribution of the input data and its time-space complexity? and (Q2) Which algorithmic structure for distribution-based sorting is suited to leverage a learned model in order to achieve state-of-the-art performance? To answer Q1, we implement a variant of the best-known learned model (i.e., the two-layers RMI) and, also, design a fully new learned model that turns out to be space-time efficient and efficacious in approximating adversarial input distributions. We experimentally test them over 11 datasets of 200M and 800M 64-bit floating-point items, thus offering a comprehensive answer to Q1. We then address Q2 by plugging the best-learned models from above into a distribution-based sorting scheme that leads to three new sorters whose performance is tested over 33 datasets (real and synthetic) of sizes up to 800M items. Our experimental results will show that our sorters perform better than 6 (classic and learned) best-known sorters on 29 out of those 33 datasets, thus achieving new state-of-the-art tradeoffs.}, archive = {J_ACCESS}, author = {Paolo Ferragina and Mattia Odorisio}, doi = {10.1109/ACCESS.2025.3549626}, journal = {IEEE Access}, pages = {45198-45214}, shortjournal = {IEEE Access}, title = {Fast, robust, and learned distribution-based sorting}, volume = {13}, year = {2025}, }
• (2025). Optimized grid voxelization for obstacle avoidance in collaborative robotics. ACCESS, 13, 45187-45197. (www)

  @article{ , abstract = {Voxelization into a fixed voxel grid is used for simplicity of processing and data format for applications such as dynamic re-planing of a collaborative robot path around a dynamic obstacle. In this case, it may be undesirable that conventional voxelization methods generate more volume, which results in a longer generated path than needed if the real shape of the obstacle was used instead of voxels. The approach proposed in this paper utilizes triangular normals of the mesh formed by neighboring points in a depth image to adjust the occupancy of a fixed voxel grid, ensuring the voxels more accurately represent the true volume of the scanned object. An experiment with real hardware was performed to compare the proposed voxelization method with the conventional fixed grid voxelization method. The collected point clouds are voxelized using both methods, and the resulting voxel maps are then passed to a simple path solver to find the length of the robot path around the obstacle. The results indicate that our proposed method achieves better results in terms of shorter avoidance paths, as our method provides a tighter representation of the obstacle. The average increase in the length of the alternative avoidance path compared to the theoretical shortest path around the object was 15.7 % for the conventional method and 8.6 % for the presented method. Overall, the experimental results indicate the usefulness of the developed system, as it can shorten the robot cycle time when avoiding obstacles.}, archive = {J_ACCESS}, author = {Jakub Chlebek and Tomáš Kot and Petr Oščádal and Dominik Heczko and Jan Maslowski and Lorenzo Scalera and Aleš Vysocký}, doi = {10.1109/ACCESS.2025.3549622}, journal = {IEEE Access}, pages = {45187-45197}, shortjournal = {IEEE Access}, title = {Optimized grid voxelization for obstacle avoidance in collaborative robotics}, volume = {13}, year = {2025}, }
• (2025). The universal learning platform (ULP): A hybrid IoT-based learning environment for automatic device and circuit characterization. ACCESS, 13, 45168-45186. (www)

  @article{ , abstract = {This paper presents the design, both hardware and software, and hands-on assessment of an IoT Based Learning Environment, constructed around the Universal Learning Platform (ULP), with on-site and remote learning capabilities. The development was based on the urgent necessity of converting the “Electronic circuit and device automated characterization” laboratory, once with fully on-site presence, into a hybrid attendance one, with full remote capabilities, thus maintaining the same curricula, with a low impact on student and tutor performance. The ULP provides the foundation for several electronics curricula labs, offering the basic measurement instrumentation, multiplexing and circuit reconfiguration, as well as software control prerequisites, that enhance automatic measurements. Additional tailor made application boards to the lab curricular specifics, as well as the measuring instruments interchangeability, were implemented to ensure flexibility towards extensions of the setup by leveraging most of the system components. Circuit analysis allows each hardware and software test setup, to be reconfigured remotely or on-site to characterize desired circuit parameters, ensuring a seamless transition between operations. Additionally, extensive evaluations were conducted on the ULP platform, to verify each developed test setup expected functionality. The educational impact of the ULP-based learning environment was assessed over four years (2020-2024) with 499 bachelor students, showing a pass rate increase from 62% to 91%. A student survey highlighted the intuitive API, easy comprehension of hardware and software components, and a strong industry link in automatic circuit characterization. Students rated the learning environment’s complexity as moderate, consistently preferring on-site interactions. Development of the ULP has the prerequisites of versatility, ease of maintenance and curricula development, offering basic functionality for most of the device and circuit-based laboratories, due to the reduced cost approach. Maintaining the remote capabilities of the learning environment proves to be beneficial for providing unattended remote access to the teaching premises for leveraging research and extending the curricular activities.}, archive = {J_ACCESS}, author = {Alexandru Mihai Antonescu and Florin-Silviu Dumitru and Marius Enachescu}, doi = {10.1109/ACCESS.2025.3550022}, journal = {IEEE Access}, pages = {45168-45186}, shortjournal = {IEEE Access}, title = {The universal learning platform (ULP): A hybrid IoT-based learning environment for automatic device and circuit characterization}, volume = {13}, year = {2025}, }
• (2025). Electrical modeling of high voltage windings of power transformers for online partial discharge localization. ACCESS, 13, 45157-45167. (www)

  @article{ , abstract = {Partial discharge (PD) is one of the most critical factors contributing to the gradual destruction of transformer insulation, potentially leading to complete insulation failure if left unresolved. Accurate physical localization of PD faults within transformers is essential for effective maintenance and cost management. This paper presents an innovative online technique based on transfer function analysis for detecting and localizing PD within power transformers, which has been experimentally verified. Initially, the study considers transformer windings as single-layer disk windings with an air core. A suitable pulse generator is utilized to simulate PD signals in the laboratory environment. Voltage signals at the bushing and neutral ends are measured and used to derive mathematical equations and winding transfer functions via curve fitting techniques in MATLAB software. By comparing the frequency response with reference values, the precise location of PD is determined. The proposed method has been validated through experiments using a laboratory winding setup consisting of six disks.}, archive = {J_ACCESS}, author = {Abdolsamad Hamidi and Moslem Salehi and Alireza Setayeshmehr and Jacques M. Maritz}, doi = {10.1109/ACCESS.2025.3549634}, journal = {IEEE Access}, pages = {45157-45167}, shortjournal = {IEEE Access}, title = {Electrical modeling of high voltage windings of power transformers for online partial discharge localization}, volume = {13}, year = {2025}, }
• (2025). Foundations of measuring power and energy consumption in video communication. ACCESS, 13, 45142-45156. (www)

  @article{ , abstract = {Given the growing environmental concerns and significant resource consumption associated with video streaming on electronic devices, measuring the energy consumption is important to guide optimisation and to assess its relative environmental impact. In this paper, we provide comprehensive guidance to accurately measure the energy and power consumption in video communication technologies. We address the complexities inherent in measuring energy consumption across diverse software and hardware setups, with a focus on video communication tasks. We review current measurement techniques, identify limitations in existing practices, and propose a structured methodology that incorporates considerations for static and dynamic power consumption, appropriate sampling frequencies, and statistical rigor. Additionally, we introduce a reference workflow that is adaptable to various multimedia applications and demonstrate its applicability through a case study. By offering clear guidance and practical tools, this work aims to improve the reliability, reproducibility, and comparability of energy consumption measurements in video technologies, providing a strong foundation for the multimedia community to base decisions on.}, archive = {J_ACCESS}, author = {Christian Herglotz and Angeliki Katsenou and Xinyi Wang and Matthias Kränzler and Daniel Schien}, doi = {10.1109/ACCESS.2025.3545814}, journal = {IEEE Access}, pages = {45142-45156}, shortjournal = {IEEE Access}, title = {Foundations of measuring power and energy consumption in video communication}, volume = {13}, year = {2025}, }
• (2025). Inflation rate prediction model combining SMC-ABC algorithm and BVAR. ACCESS, 13, 45126-45141. (www)

  @article{ , abstract = {The inflation rate is an important indicator of the economic health of a country or region. To improve the accuracy of inflation rate prediction and to fill the shortcomings of the existing inflation rate forecasting model in terms of forecasting accuracy and adaptability, the study constructed a prediction model based on the technique of Bayesian vector auto-regression model. The experimental results revealed that the study’s improved Bayesian vector auto-regression model had a higher prediction accuracy than both the baseline and existing advanced models. Its mean absolute error and root mean square error were less than 0.001 and 0.015, respectively. In the cross-validation analysis, the generalization ability of the model was strong, with R-squared taking values of 0.962 and 0.984 for the test set and training set, respectively. In the goodness-of-fit test, the model had a significant advantage over the other models, with both Akaike information criterion and Bayesian information criterion in the minimum value interval of 0.1-0.3. Anticipation Likelihood Probability converged to a maximum value of 0.973, which was the most efficient in prediction. In the applied analysis, the method was confirmed by the Diebold-Mariano test to show predictive test P-values of 2.464 per cent and 3.486 per cent, respectively. The percentage deviation analysis of the sample’s percent bias had a small error, with all predictions being closest to the true value. The change trend and details of the prediction results were better grasped. The study scientifically and rationally aids policy formulation, helps to avoid the impact of inflation on the economic market, and promotes the stable and healthy development of the economy.}, archive = {J_ACCESS}, author = {Xiang Li and Hai Lan and Junren Ming}, doi = {10.1109/ACCESS.2025.3548575}, journal = {IEEE Access}, pages = {45126-45141}, shortjournal = {IEEE Access}, title = {Inflation rate prediction model combining SMC-ABC algorithm and BVAR}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Fine-tuned segment anything model (SAM) for reservoir extractions compared with popular CNNs: An experiment for space-borne synthetic-aperture radar images”. ACCESS, 13, 45125. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Fine-Tuned Segment Anything Model (SAM) for Reservoir Extractions Compared With Popular CNNs: An Experiment for Space-Borne Synthetic-Aperture Radar Images”.}, archive = {J_ACCESS}, author = {Nguyen Hong Quang and Hanna Lee and Eui-Myoung Kim and Gihong Kim}, doi = {10.1109/ACCESS.2025.3546708}, journal = {IEEE Access}, pages = {45125}, shortjournal = {IEEE Access}, title = {Corrections to “Fine-tuned segment anything model (SAM) for reservoir extractions compared with popular CNNs: An experiment for space-borne synthetic-aperture radar images”}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on cybersecurity of digital twins issues, challenges, and future research directions. ACCESS, 13, 45106-45124. (www)

  @article{ , abstract = {This systematic literature review examines existing literature to determine how Digital Twin (DT) technology can enhance security, with an emphasis on emerging issues, approaches, and proposals in cybersecurity. The review categorizes selected studies based on the techniques used to protect DTs: Machine Learning (ML) and non-machine learning techniques. DTs are classified into five types: component level, process level, asset level, system level, and network of systems level, each with unique applications and corresponding cybersecurity threats. The main conclusions highlight medium to very high perceived risks depending on the DT type and industry implementation. Results identify the most common types of attacks on DTs, various security enhancement techniques, and the sectors that utilize DTs. Future research should focus on developing more effective security paradigms, technologies, and integration schemes to reduce risks and maximize DT technology’s potential. This review presents the current status of DT-related cybersecurity and explores ways to enhance security against emerging cyber risks.}, archive = {J_ACCESS}, author = {Norah Alhamam and M. M. Hafizur Rahman and Ahmed Aljughaiman}, doi = {10.1109/ACCESS.2025.3545004}, journal = {IEEE Access}, pages = {45106-45124}, shortjournal = {IEEE Access}, title = {A comprehensive review on cybersecurity of digital twins issues, challenges, and future research directions}, volume = {13}, year = {2025}, }
• (2025). A novel structure of compact and high-efficiency dual-band rectifier with low complexity for wireless power transfer. ACCESS, 13, 45100-45105. (www)

  @article{ , abstract = {A novel methodology is presented for designing compact and high-efficiency dual-band (DB) rectifier with low insertion loss and complexity. The proposed network implements an opposite order of fundamental matching and harmonic control, as compared to the conventional approaches. Specifically, a typical T-type DB fundamental matching network with closed-form formulas is first used to match directly the diode impedance to the $50\Omega $ standard load. As analyzed, this matching circuit consistently generate a perfect DB matching due to the frequency-dependent diode impedance without any additional impedance tuning methods, yielding a low design complexity. Then, a DB harmonic control mechanism is provided by combination of a DB resonator and a $50\Omega $ tuning series line, which can yield precise class-R harmonic termination of second and third harmonics with only a total of three transmission lines leading a low insertion loss. A rectifier is designed, simulated and measured at two target frequencies of 2.45 and 5.8 GHz for verification. The measured results show a peak PCE of 80.6% at 2.33 GHz and 73.1% at 5.65 GHz while having a compact size of $4.6\times 3.1$ cm2, indicating achievement of a superior performance with a simplicity of the design process to the other similar designs.}, archive = {J_ACCESS}, author = {Dang-An Nguyen and Gia Thang Bui and Chulhun Seo}, doi = {10.1109/ACCESS.2024.3382714}, journal = {IEEE Access}, pages = {45100-45105}, shortjournal = {IEEE Access}, title = {A novel structure of compact and high-efficiency dual-band rectifier with low complexity for wireless power transfer}, volume = {13}, year = {2025}, }
• (2025). A pre-trained collaborative network leveraging multi-scale depth information for image deraining and defogging enhancement. ACCESS, 13, 45089-45099. (www)

  @article{ , abstract = {Rain and fog are prevalent weather conditions that negatively impact advanced computer vision tasks such as object detection and recognition. The recently proposed NeRD-Rain model attains state of the art performance in rain removal. However, it fails to comprehensively address the veiling effect in complex rainy environments. To enhance the NeRD-Rain model, we integrate a pre-trained depth prediction model, grounded in the atmospheric scattering model, into its architecture. This integration is implemented across multiple scales. Experimental evaluations on the RainCityScapes dataset, where rain and fog co-occur, demonstrate that our proposed network substantially improves the dehazing performance of NeRD-Rain under foggy conditions. Quantitative assessments reveal a 1.5% increase in the Structural Similarity Index Measure (SSIM), a 0.01 reduction in Laplacian variance, and a 1.41 decrease in the Frechet Inception Distance (FID).}, archive = {J_ACCESS}, author = {Dingdiao Mu and Changjiang Liu}, doi = {10.1109/ACCESS.2025.3549830}, journal = {IEEE Access}, pages = {45089-45099}, shortjournal = {IEEE Access}, title = {A pre-trained collaborative network leveraging multi-scale depth information for image deraining and defogging enhancement}, volume = {13}, year = {2025}, }
• (2025). An improved bagging of machine learning algorithms to predict motif structures from protein-protein interaction networks. ACCESS, 13, 45077-45088. (www)

  @article{ , abstract = {Protein-protein interaction (PPI) networks play a vital role in the functional organization of cellular systems. Recurring subgraph patterns with these networks, known as motif structures, can be used to uncover mechanisms in complex biological processes. However, predicting motif structures from PPI networks remains challenging because of the complex and dynamic nature of interactions. Hence, this research focuses on predicting the topological structure of protein-protein interaction networks, acknowledging the computational challenges of exact motif discovery for diverse protein interactions. This study proposes a hybrid ensemble classification algorithm that leverages Penalized Matrix Decomposition (PMD) for feature extraction and a bagging approach using a Support Vector Machine (SVM), Alternative Decision Tree (ADT), and Extreme Learning Machine (ELM) classifiers. The proposed hybrid classification algorithm was evaluated against the ACC-Motif, Combi-Motif, Minimum Spanning Tree (MST), and Slider on benchmark datasets to demonstrate its statistical significance. On benchmark datasets, the proposed method achieved 92.3% accuracy, 91.5% precision, 89.8% recall, and an F1-score of 90.6%, outperforming existing algorithms such as ACC-Motif, Combi-Motif, and MST by 8-10%. This study assessed the practicality of similar topological subnetworks in protein-protein interaction networks, with Z-test calculations affirming superior outcomes. Overall, the proposed algorithm proved effective in predicting network structures, showing improved performance compared with other methods.}, archive = {J_ACCESS}, author = {P. Lakshmi and P. Manikandan and D. Ramyachitra}, doi = {10.1109/ACCESS.2025.3549880}, journal = {IEEE Access}, pages = {45077-45088}, shortjournal = {IEEE Access}, title = {An improved bagging of machine learning algorithms to predict motif structures from protein-protein interaction networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing IoT intrusion detection systems through horizontal federated learning and optimized WGAN-GP. ACCESS, 13, 45059-45076. (www)

  @article{ , abstract = {The Internet of Things (IoT) ecosystem is fraught with substantial vulnerabilities, particularly in the realm of cybersecurity attacks. Network Intrusion Detection Systems (NIDS) stand as a pivotal element in mitigating these cybersecurity risks. This paper introduces an innovative approach to fortifying IoT security by effectively addressing the data limitations inherent in AI-based NIDS. We present a data generation model that harnesses Generative Adversarial Networks (GANs). Specifically, the GAN variant we employ is Wasserstein GAN with Gradient Penalty (WGAN-GP), which combines the Wasserstein loss formulation with a gradient norm penalty to stabilize training and improve the quality of generated data. The performance is optimized with Genetic Algorithms, focusing on hyper-parameter selection and federated learning for shared model weights. The model’s training is conducted on four well-established benchmark datasets: UNSW-NB15, IoT-23, CSE-CIC-IDS2018, and MQTT-IoT-IDS2020. We conduct a comprehensive comparative analysis between the generated synthetic data and real-world datasets, rigorously assessing their impact on training Machine Learning (ML) models. The findings underscore the efficacy of our approach, demonstrating a significant improvement in detection accuracy, achieving a 99% accuracy rate when combining the generated data with real datasets. This study highlights the paramount significance of innovative techniques in enhancing the security of IoT systems. Furthermore, it presents a promising avenue for generating high-quality synthetic tabular data, despite its complexity and time-consuming implementation. Such data can be leveraged across a large spectrum of applications, including training ML models, data augmentation, and privacy-preserving data sharing.}, archive = {J_ACCESS}, author = {Wayoud Bouzeraib and Afifa Ghenai and Nadia Zeghib}, doi = {10.1109/ACCESS.2025.3547255}, journal = {IEEE Access}, pages = {45059-45076}, shortjournal = {IEEE Access}, title = {Enhancing IoT intrusion detection systems through horizontal federated learning and optimized WGAN-GP}, volume = {13}, year = {2025}, }
• (2025). Dynamic line drawing: A motion semantics-based photo-to-sketch rendering algorithm. ACCESS, 13, 45048-45058. (www)

  @article{ , abstract = {Generating line drawings from single photorealistic images featuring dynamic scenes or characters poses significant challenges. Existing algorithms predominantly focus on static scenes or objects, often producing uninspiring and monotonous line drawings when processing images with motion elements. This limitation primarily arises because these traditional methods emphasize static inherent features such as shape and three-dimensional geometry, while neglecting the motion semantics present in the original images, which are crucial for accurately depicting dynamics. To address these shortcomings, we introduce a novel algorithm for generating dynamic line drawings based on motion semantics. This algorithm incorporates a motion estimation network, a line drawing optimization algorithm, and a motion line generation algorithm. Through motion object mask estimation and motion compensation, our motion estimation network precisely predicts areas of motion and effectively removes motion blur from the previous frame. This ensures that subsequent line drawings accurately reflect the real motion of dynamic objects (foreground) in the scene and maintain rich line detail. Our line drawing optimization algorithm uses the motion mask to adjust the complexity and thickness of lines for static objects (background), thereby enhancing the depiction of dynamic foreground objects. Additionally, our motion line generation algorithm leverages a single static image and the estimated previous frame to calculate the direction of motion and interactively generate motion lines, enriching the dynamic texture of the line drawings. Experiments demonstrate that our method not only enhances the expressiveness of dynamic foreground objects in line drawings but also simplifies the background, directing viewers’ attention to the depicted motion.}, archive = {J_ACCESS}, author = {Qianwen Lu and Ye Tian and Shunsuke Kamijo}, doi = {10.1109/ACCESS.2025.3549870}, journal = {IEEE Access}, pages = {45048-45058}, shortjournal = {IEEE Access}, title = {Dynamic line drawing: A motion semantics-based photo-to-sketch rendering algorithm}, volume = {13}, year = {2025}, }
• (2025). Design and characterization of an atmospheric pressure dielectric barrier discharge reactor for potential air disinfection applications. ACCESS, 13, 45037-45047. (www)

  @article{ , abstract = {Dielectric Barrier Discharge (DBD) technology is increasingly applied for disinfection purposes, primarily for surfaces and materials. However, its use in air disinfection is still relatively underexplored, with limited studies focusing on reactor optimization. This paper presents the design and characterization of a laboratory-scale DBD reactor, named Grid-like Air Plasma Sanitizer (GAPS), optimized for air treatment. The reactor features two grid-shaped electrodes positioned within a sealed container designed to create a serpentine channel for the airflow, enhancing the contact surface between treated air and produced discharge. The reactor is powered by a custom-designed power supply, based on a multilevel inverter architecture, for a total bipolar output of ±1.2kV. The power supply generates a high-voltage square waveform with a peak of ±1.2kV, a period of $8 \mu \text {s}$ and a rise time of $6\text {kV}/\mu \text {s}$ , promoting the formation of a strong non-equilibrium plasma for microbial inactivation. Electrical performance is characterized using both the Lissajous diagram and the equivalent circuit method, giving results in good agreement with eachothers. Furthermore, electrostatic FEMM simulations show details on the electric field intensity distribution and reactor capacitance. The GAPS produces a uniform and homogeneous plasma discharge under an air flow rate of 12.5LPM, without a significant increase in the reactor temperature, and a low specific power consumption of $0.014\text {W}/cm^{2}$ . These results show promising potential for the GAPS and lay the groundwork for future research, including biological experiments to assess its efficacy in air disinfection applications.}, archive = {J_ACCESS}, author = {Silvia G. Scaltriti and Andrea Cristofolini and Gabriele Neretti}, doi = {10.1109/ACCESS.2025.3548933}, journal = {IEEE Access}, pages = {45037-45047}, shortjournal = {IEEE Access}, title = {Design and characterization of an atmospheric pressure dielectric barrier discharge reactor for potential air disinfection applications}, volume = {13}, year = {2025}, }
• (2025). Self-isolation in MIMO array using mode diversity. ACCESS, 13, 45023-45036. (www)

  @article{ , abstract = {A new isolation technique is proposed for multiple-input multiple-output (MIMO) microstrip patch antenna (MPA) arrays utilizing mode diversity. Typically, similar modes are excited in each element of the MIMO array. However, the proposed MIMO array uses two elements of different sizes excited in distinct modes at the same frequency. It is found that the TM01 mode excited in one MPA, a hybrid mode (TM $_{03} +$ TM21) is induced in the coupled MPA, while exciting the TM03 mode in another patch induces a quasi-TM11 mode in the coupled MPA. Both induced modes exhibit weak fields at the feeding points, reducing the mutual coupling between the two patch antennas without requiring extra decoupling structure. Additionally, the induced modes create a null in the broadside direction, reducing cross-polarization (x-pol) levels. An isolation level over 50 dB is achieved in $1\times 2$ MIMO array at 5.8 GHz. The design is adaptable to larger array. Therefore, to validate the design technique, a $1\times 4$ array is simulated, fabricated and measured, showing 30–45 dB isolation among the ports in the passband. Moreover, the design features an x-pol around -40 dB in broadside direction in both E- and H-planes, ensuring effective polarization diversity. The technique is applied to a MIMO dielectric resonator antenna (DRA) array, demonstrating excellent isolation and radiation pattern performance. Furthermore, the technique is versatile and can be applied to various MIMO configurations. Therefore, simulations have been performed for $2\times 4$ MPA array, validating the effectiveness of isolation in both the E- and H-planes. This approach also optimizes space utilization, allowing for the inclusion of additional patch elements to form hybrid-coupled MIMO MPAs within confined spaces.}, archive = {J_ACCESS}, author = {Manzoor Elahi and Slawomir Koziel and Leifur Leifsson}, doi = {10.1109/ACCESS.2025.3549619}, journal = {IEEE Access}, pages = {45023-45036}, shortjournal = {IEEE Access}, title = {Self-isolation in MIMO array using mode diversity}, volume = {13}, year = {2025}, }
• (2025). An iterative data-driven intelligent multiple-attribute decision making for football teaching quality evaluation in higher education. ACCESS, 13, 45010-45022. (www)

  @article{ , abstract = {In higher education, football plays a crucial role as it aids students in building up their mental, physical, and collaborative skills. Determining the quality of football teaching involves unique challenges due to the complex nature of teaching methodologies, diverse outcomes of students and resource variance. This study provides the interactive, information-based, intelligent multiple-attribute decision-making (MADM) framework to analyze football teaching quality comprehensively. Educators and administrators can use interactive tools to investigate the relationship between numerous attributes, enabling immediate examination and refining the decision-making procedure. The assessment involves critical attributes that include teamwork, physical fitness development, strategic thinking, instructional competence, student engagement, psychological resilience, coaching, ethics, sportsmanship, and assessment and feedback. Among the five methods, the research validates method 5 as the most effective teaching method because it yields superior football training outcomes. The proposed framework shows evidence of reliability through its comparison with standard models. The study generates substantial information that helps to enhance football coaching methods in higher education institutions to develop data-driven educational instruction.}, archive = {J_ACCESS}, author = {Tongtong Cai}, doi = {10.1109/ACCESS.2025.3548889}, journal = {IEEE Access}, pages = {45010-45022}, shortjournal = {IEEE Access}, title = {An iterative data-driven intelligent multiple-attribute decision making for football teaching quality evaluation in higher education}, volume = {13}, year = {2025}, }
• (2025). DiffSmoke: Two-stage sketch-based smoke illustration design using diffusion models. ACCESS, 13, 44997-45009. (www)

  @article{ , abstract = {Due to the rapid explosion of generative artificial intelligence (AI) techniques, generative models are revolutionizing content design applications. However, it is still a challenging task to generate smoke design illustrations due to the complex dynamics and intricate constraints. Previous studies for deep-learning-based fluid design commonly adopted conditional generative adversarial networks (cGAN) for target velocity field generation under the constraint of input sketches, which may behave in unstable training and miss the hidden geometric structure and flow properties inherent in the complex smoke dynamics. To solve these issues, we propose DiffSmoke, a smoke illustration generation method using the two-stage latent diffusion model. In the first stage, sketch inputs serve as the generator condition for generating the Lagrangian Coherent Structure (LCS) region, which correlates with the hyper finite time Lyapunov exponent (hyper-FTLE) field to depict the flow properties. In the second stage, we use LCS data in the generator for the velocity field generation. The evaluation results show that DiffSmoke can generate velocity fields matching the shapes of given sketches. In addition, DiffSmoke enables users to conduct smoke design from hand-drawn sketches and achieve more robust and stable generation results in contrast to cGAN and one-stage strategy.}, archive = {J_ACCESS}, author = {Hengyuan Chang and Tianyu Zhang and Syuhei Sato and Haoran Xie}, doi = {10.1109/ACCESS.2025.3548433}, journal = {IEEE Access}, pages = {44997-45009}, shortjournal = {IEEE Access}, title = {DiffSmoke: Two-stage sketch-based smoke illustration design using diffusion models}, volume = {13}, year = {2025}, }
• (2025). Digital twins for cyber-physical healthcare systems: Architecture, requirements, systematic analysis, and future prospects. ACCESS, 13, 44963-44996. (www)

  @article{ , abstract = {Over the past few years, the demand for personalized and accurate healthcare solutions has significantly increased, aiming to enhance medical effectiveness. Digital Twins (DTs), a transformative technology in the evolution of Industry 4.0, have expanded their applications from traditional fields like manufacturing and automotive industries to revolutionizing healthcare. DTs offer a more advanced and precise method of healthcare delivery, enabling better patient-specific care by creating real-time digital replicas of patients’ health conditions. Despite these advancements, healthcare professionals still heavily rely on their expertise to make critical decisions, highlighting the importance of continuous refinement of knowledge and skills to navigate complex healthcare challenges. Personalized treatments and comprehensive healthcare support remain essential for optimizing patient outcomes, though they are still in the early phases of development. This paper introduces a reference architecture specifically designed for Digital Twins in cyber-physical healthcare systems, outlining the key requirements and challenges in developing digital frameworks for such systems. It explores the fundamental principles of Digital Twins in healthcare, focusing on their applications in product design, production processes, and system performance. It also provides a detailed review of current research on healthcare data management, personalized healthcare, and operational efficiency, along with the associated requirements, challenges, and application scenarios. Furthermore, the paper discusses major obstacles and outlines potential future research directions, offering a roadmap to advance healthcare data management, personalized medicine, and operational control within Digital Twin frameworks.}, archive = {J_ACCESS}, author = {M. S. Roopa and K. R. Venugopal}, doi = {10.1109/ACCESS.2025.3547991}, journal = {IEEE Access}, pages = {44963-44996}, shortjournal = {IEEE Access}, title = {Digital twins for cyber-physical healthcare systems: Architecture, requirements, systematic analysis, and future prospects}, volume = {13}, year = {2025}, }
• (2025). ReAHGN: Adaptive heterogeneous graph neural network with relation-aware embedding. ACCESS, 13, 44951-44962. (www)

  @article{ , abstract = {Heterogeneous graph neural networks (HGNs) have attracted more and more attention recently due to their wide applications such as node classification, community detection, and recommendation. Significant progress has been made by graph neural networks. However, we argue that attention mechanisms that focus on computing higher ranking scores for specific types of nodes cannot select the most relevant neighbors for any target node. To enhance the expressiveness of the HGNs, we propose ReAHGN, a Relation-aware embedding for Adaptive Heterogeneous Graph Neural Network. Specifically, we present an adaptive attention mechanism that assigns different weights for each type of target node automatically. In addition, a relation-aware embedding method is adopted to fuse the edge type information effectively. ReAHGN is evaluated for three downstream tasks and on ten public datasets. Experiment results demonstrate that ReAHGN can outperform the state-of-the-art HGNs.}, archive = {J_ACCESS}, author = {Xiaoyu Zhu and Xinzhe Yu and Enze Zha and Shiyang Lin}, doi = {10.1109/ACCESS.2025.3549928}, journal = {IEEE Access}, pages = {44951-44962}, shortjournal = {IEEE Access}, title = {ReAHGN: Adaptive heterogeneous graph neural network with relation-aware embedding}, volume = {13}, year = {2025}, }
• (2025). Empowering individual preferences in mobile notifications: A balanced approach to cognitive load and information needs. ACCESS, 13, 44936-44950. (www)

  @article{ , abstract = {Excessive smartphone notifications can disrupt workflows and intensify fatigue. Although existing approaches have made significant progress in reducing interruptions using sensor- or context-based timing, less attention has been paid to personal preferences and mental fatigue. To address these limitations, we propose a user-driven notification-reception mode configuration system with three customizable modes (Immediate, While in Use, and On Demand), enabling users to determine how and when they receive notifications at both the app and keyword levels. In a two-week field study with 26 participants, our system effectively reduced notification volume, lowered habitual phone-checking, and maintained stable usage times. Additionally, the participants reported decreases in smartphone addiction scores and notification fatigue. Finally, we discuss the broader significance of personalized notification management in promoting healthier device engagement and mental well-being, emphasizing how fine-grained customization can mitigate overstimulation, support digital wellness, and provide design implications for future notification systems.}, archive = {J_ACCESS}, author = {Jaehwan Kim and Sangkeun Park}, doi = {10.1109/ACCESS.2025.3549033}, journal = {IEEE Access}, pages = {44936-44950}, shortjournal = {IEEE Access}, title = {Empowering individual preferences in mobile notifications: A balanced approach to cognitive load and information needs}, volume = {13}, year = {2025}, }
• (2025). Deep learning based intelligent voiceprint recognition, positioning, and perception in cable monitoring. ACCESS, 13, 44928-44935. (www)

  @article{ , abstract = {This article explores the application of deep learning based intelligent voiceprint recognition for cable monitoring, positioning and perception. In response to the monitoring needs of external force damage in cable trenches, the study combines deep learning technology and voiceprint recognition methods, aiming to achieve high-precision cable condition monitoring and damage localization. Utilizing convolutional neural networks (CNN) in deep learning models to automatically extract higher-level voiceprint features. Input the cable vibration signal to be recognized into the trained deep learning model, and the model will output the damage type corresponding to the signal. By comparing the model output with the preset damage type labels, automatic identification of cable external force damage can be achieved. The experimental results show that the application of deep learning based intelligent voiceprint recognition and positioning perception in cable monitoring has significant potential in improving the safety protection ability of cable trenches, providing a new technological approach for the intelligent monitoring and maintenance of cable systems.}, archive = {J_ACCESS}, author = {Yajun Huo and Kai Sun and Juan Du and Jun Liu and Yong Wang and Chun Wang and Liang Guo and Xu Cheng and Shangxiang Duan}, doi = {10.1109/ACCESS.2025.3531882}, journal = {IEEE Access}, pages = {44928-44935}, shortjournal = {IEEE Access}, title = {Deep learning based intelligent voiceprint recognition, positioning, and perception in cable monitoring}, volume = {13}, year = {2025}, }
• (2025). DTH-YOLO: Enhanced YOLOv8n with dynamic task-aligned head for mousehole detection. ACCESS, 13, 44912-44927. (www)

  @article{ , abstract = {In recent years, the increasing number of mouseholes in grassland areas has accelerated desertification, leading to a decline in grassland productivity and severely impacting the economic benefits of herders. Therefore, accurately and efficiently detecting and locating mouseholes has become an urgent research problem. UAV photography, with its advantages of wide coverage and flexibility, has gradually become an effective tool for mousehole detection. However, due to varying flight altitudes causing significant changes in target scale, severe occlusion of targets, and the small size of burrows, aerial detection faces high rates of missed and false detections, posing a significant challenge to burrow identification. To address these issues, we propose the DTH-YOLO, based on YOLOv8n. First, we replace the original detection head with the Dynamic Task-Aligned Head (DTH) to enhance feature alignment across different tasks and improve detection accuracy. Secondly, we design the C2f-RVB to optimize the feature extraction process, significantly reducing model parameters and computational costs. Finally, we introduce the Context Guided Block (CGB) for downsampling, effectively capturing contextual information in the target area, thereby improving detection performance for small objects. Experimental results show that, on a custom aerial mousehole dataset, the DTH-YOLO achieves an 8.5% increase in mAP@0.5, a 5.6% improvement in precision, a 42% reduction in parameters, and a 22% decrease in GFLOPs compared to the YOLOv8n. Furthermore, ablation studies demonstrate the effectiveness of each proposed module. The source code for the complete system can be found at https://github.com/xvzhe834/DTH-YOLO.}, archive = {J_ACCESS}, author = {Zhe Xu and Xiaoling Luo and Xiaojing Gao and Xin Pan}, doi = {10.1109/ACCESS.2025.3546946}, journal = {IEEE Access}, pages = {44912-44927}, shortjournal = {IEEE Access}, title = {DTH-YOLO: Enhanced YOLOv8n with dynamic task-aligned head for mousehole detection}, volume = {13}, year = {2025}, }
• (2025). Ensuring satellite operational integrity: A power budget analysis for next generation satellites. ACCESS, 13, 44901-44911. (www)

  @article{ , abstract = {As satellite missions become more complex, reliable power management is crucial for success. Traditional power budgeting approaches often operate at the system level, leading to inefficiencies in power allocation, particularly during critical phases such as eclipse mode. This study presents a component-level power budgeting framework to enhance operational integrity for next-generation satellites, specifically focusing on power load calculation, power generation, and battery capacity requirements. Key challenges such as power shortages, thermal fluctuations, and battery management require efficient power allocation and thermal control to ensure system reliability. Additionally, laser communication, while a critical capability for advanced satellites, becomes more complex during eclipse mode as in this period the mission execution will totally depends on the onboard available power. This analysis is based on detailed component-level power load calculations for eclipse mode. The total power load in this scenario is 38.922 W, with an operational mode of 27.5179 W, accounting for the power consumption of other subsystems. These calculations incorporate each module’s power load, weighted by its duty cycle per orbit. The solar panels generate 65 W of power, with an effective power output of 45.599 W, as simulated in STK software. The eclipse mode, at an altitude of 525 km and an inclination angle of 83 degrees, lasts for 37.7% of the orbit, resulting in a power consumption of 27.343 W during this time. The calculated power consumption in eclipse mode is 27.343 W, with a minor error of 0.175 W. To meet battery requirements, the battery must provide at least 48.34 W, considering an 80% Depth of Discharge (DOD), for a 10-second high-power communication window. The battery capacity should exceed 48.34 W to ensure safe operation in eclipse mode. Based on these calculations, the satellite’s power generation and storage system are sufficient to support its operations during eclipse mode, ensuring reliable and uninterrupted performance.}, archive = {J_ACCESS}, author = {Atif Mahmood and Ayman Muhammad Abdallah and Bazilah Binti Baharom and Asia Saeed Kajo Habilah}, doi = {10.1109/ACCESS.2025.3547826}, journal = {IEEE Access}, pages = {44901-44911}, shortjournal = {IEEE Access}, title = {Ensuring satellite operational integrity: A power budget analysis for next generation satellites}, volume = {13}, year = {2025}, }
• (2025). Evolutionary game-based battery scheduling: A comparative study for prosumers in smart grids. ACCESS, 13, 44884-44900. (www)

  @article{ , abstract = {Integrating prosumers equipped with renewable energy resources (RES) and battery energy storage systems (BESS) in local energy markets (LEMs) offers economic benefits, in addition to significant environmental advantages. In this paper, a comprehensive energy management system (EMS) framework for battery scheduling is proposed to optimize the use of distributed energy resources (DERs) among prosumers in smart grid communities. A novel decentralized evolutionary game theory (EGT) algorithm is introduced to enhance energy management while preserving privacy and scalability. The primary objective is to develop an efficient, reliable, private, and scalable algorithm for battery scheduling that ensures economic efficiency and system stability, even under dynamic market conditions. The results show that, with different feed-in tariffs (FIT), the EGT algorithm reduces the standard deviation of hourly prices by 51% and 57% when having 20% and 80% FIT rates, respectively, in prosumer-based smart grid community (SGC), demonstrating its effectiveness in stabilizing market conditions. A comprehensive comparative analysis is conducted between the proposed EGT algorithm and established methods such as centralized optimization (CO), game theory (GT), and auction-based approaches. The comparison focuses on key performance metrics, including peak-to-average ratio (PAR), price volatility, economic efficiency, and computational performance. The results highlight the computational capabilities of the decentralized EGT algorithm in optimizing battery scheduling and effectively mitigating price fluctuations for SGCs with increasing prosumer participation.}, archive = {J_ACCESS}, author = {Anas Karaki and Khaled Abedrabboh and Luluwah Al-Fagih}, doi = {10.1109/ACCESS.2025.3549053}, journal = {IEEE Access}, pages = {44884-44900}, shortjournal = {IEEE Access}, title = {Evolutionary game-based battery scheduling: A comparative study for prosumers in smart grids}, volume = {13}, year = {2025}, }
• (2025). An effective adaptive downsampling method for high-resolution multi-panel image segmentation. ACCESS, 13, 44872-44883. (www)

  @article{ , abstract = {In modern healthcare, multi-panel images have become increasingly prevalent in medical diagnosis and treatment, representing about 50% of the medical literature. These images integrate diverse imaging modalities, such as X-rays, CT scans, and MRIs, into a unified composite image, facilitating physicians to examine all modalities simultaneously. However, retrieving sub-images from multi-panel images poses a challenge for existing medical image retrieval systems, as they often treat these multi-panel images as single-panel images, thereby restricting access to their constituent sub-images. Consequently, precise segmentation of multi-panel images into sub-images is imperative for effective retrieval. Current segmentation methods are computationally expensive when applied to large-scale, high-resolution multi-panel images. To address this challenge, we propose an adaptive downsampling-based segmentation method that identifies the inter-panel borders separating the sub-images of a multi-panel image by selectively scanning alternate rows and columns, rather than every row and column as done in state-of-the-art approaches. We evaluated the method on a subset of the ImageCLEFmed 2016 dataset, which includes both single-panel and multi-panel images. The experimental results demonstrate that the proposed method significantly reduces computational time while effectively segmenting multi-panel images.}, archive = {J_ACCESS}, author = {Faqir Gul and Mohsin Shah and Mushtaq Ali and Tanzeela Qazi and Muneer Ahmad and Abid Mehmood}, doi = {10.1109/ACCESS.2025.3548431}, journal = {IEEE Access}, pages = {44872-44883}, shortjournal = {IEEE Access}, title = {An effective adaptive downsampling method for high-resolution multi-panel image segmentation}, volume = {13}, year = {2025}, }
• (2025). Af-CAN: Multimodal emotion recognition method based on situational attention mechanism. ACCESS, 13, 44858-44871. (www)

  @article{ , abstract = {In the pursuit of developing an efficient and harmonious human-computer interaction interface, Emotion Recognition in Conversations (ERC) is particularly important. It requires the system to delicately capture and understand the nuances of human emotional fluctuations during the communication process. Currently, although emotional signals are prevalent in various modalities of conversation such as audio, video, and text, multimodal Emotion Recognition in Conversations (ERC) still remains a challenging problem to tackle due to its inherent complexity. Previous research has tended to rely on a single modality, particularly text information, while neglecting the rich emotional cues present in audio and video modalities. Based on the current research status and challenges such as inadequate extraction of contextual emotional dynamic features and data scarcity, a multimodal emotion recognition method called Attention-based Fusion Contextual Attention Network (Af-CAN) has been proposed to break through these limitations. Af-CAN is meticulously designed with a multimodal feature fusion mechanism that can extract emotion-relevant features from different sources of information and uses advanced attention mechanisms to integrate these features, ensuring the comprehensiveness and accuracy of emotion recognition. Furthermore, in response to the characteristics of emotional dynamics and context dependency in conversations, this framework introduces a special context modeling unit capable of tracking the evolution of emotional states in the conversation and the mutual influence of emotions between speakers. Experimental evaluations carried out on multiple standard datasets have shown that Af-CAN outperforms existing ERC systems on various evaluation metrics, particularly showing significant advantages in handling complex emotional changes in conversations, laying a solid foundation for advancing the application of emotional intelligence in human-computer interaction.}, archive = {J_ACCESS}, author = {Xue Zhang and Mingjiang Wang and Xiao Zeng and Xuyi Zhuang}, doi = {10.1109/ACCESS.2024.3471613}, journal = {IEEE Access}, pages = {44858-44871}, shortjournal = {IEEE Access}, title = {Af-CAN: Multimodal emotion recognition method based on situational attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Ultra wideband, low frequency, and low profile combined metamaterial-ferrite absorber. ACCESS, 13, 44849-44857. (www)

  @article{ , abstract = {This paper presents a novel absorber topology that combines an absorbing metamaterial with a ferrite tile, resulting in an ultra-wideband absorber with a bandwidth of 196%, covering the frequency range of 10 MHz to 3500 MHz (1:350). The proposed absorber exhibits exceptional performance while maintaining compact dimensions, with a unit cell measuring only $20\times 20\times 38$ mm, making it one of the electrically thinnest absorbers reported in the literature. To validate the proposed structure, a prototype was fabricated, and experimental measurements were conducted. The measured results align closely with the simulations, confirming the absorber’s ability to achieve over 90% absorption within the targeted frequency range. This innovative design is well-suited for applications requiring compact, lightweight, and high-performance broadband absorption solutions, such as anechoic chambers.}, archive = {J_ACCESS}, author = {Amir Sahraei and Ramezan Ali Sadeghzadeh and Hadi Aliakbarian and Guy A. E. Vandenbosch}, doi = {10.1109/ACCESS.2025.3547873}, journal = {IEEE Access}, pages = {44849-44857}, shortjournal = {IEEE Access}, title = {Ultra wideband, low frequency, and low profile combined metamaterial-ferrite absorber}, volume = {13}, year = {2025}, }
• (2025). Interference analysis of LoRaWAN and sigfox in large-scale urban IoT networks. ACCESS, 13, 44836-44848. (www)

  @article{ , abstract = {As reported by IoT Analytics, LoRaWAN, NB-IoT, and Sigfox dominate the low-power wide-area network market, covering 86% of it, considering both user adoption and ecosystem backing. NB-IoT operates on licensed frequencies, in contrast to Sigfox and LoRaWAN, which share sub-GHz ISM bands, possibly leading to mutual interference. In this paper, an analytical model is proposed to predict interference in ISM bands, focusing on the coexistence between Sigfox and LoRaWAN technologies, in realistic urban environments and under different traffic conditions and duty cycles. The choice of such environments and simulation parameters is supported by a thorough literature review, and the theoretical model is validated using the SEAMCAT software tool. The findings provide new perspectives on the coexistence of Sigfox and LoRaWAN for evolving Internet of things solutions. Moreover, we evaluate the effectiveness of employing protection distance mechanisms as a strategy for mitigating interference.}, archive = {J_ACCESS}, author = {Domenico Garlisi and Antonino Pagano and Fabrizio Giuliano and Daniele Croce and Ilenia Tinnirello}, doi = {10.1109/ACCESS.2025.3550014}, journal = {IEEE Access}, pages = {44836-44848}, shortjournal = {IEEE Access}, title = {Interference analysis of LoRaWAN and sigfox in large-scale urban IoT networks}, volume = {13}, year = {2025}, }
• (2025). Enhanced nighttime vehicle detection for on-board processing. ACCESS, 13, 44817-44835. (www)

  @article{ , abstract = {Nighttime vehicle detection poses significant challenges, particularly in scenarios with limited lighting, where visibility is often compromised. To address this problem, this paper proposes a novel nighttime vehicle detection system that dynamically adapts to extreme lighting conditions, ranging from bright daytime scenarios to challenging nighttime conditions where the vehicle’s appearance may be entirely lost. For this purpose, a multi-granularity detection approach is adopted, automatically combining bounding-box and point-based representations depending on the vehicle’s visibility. Bounding-box detections, reporting location and size information, are selected when the vehicle appearance is mostly visible, such as in daytime or urban nighttime scenarios with sufficient artificial street illumination. Point-based detections, indicating only location information, are used when the vehicle’s appearance is not discernible, such as in rural nighttime scenarios with little or no street illumination. The system is designed as a multi-head neural network built on a shared Hourglass backbone that accepts bounding-box and point-based annotations for training and can automatically predict, depending on the scenario, vehicle bounding boxes or point-based predictions. Extensive evaluations on a combined dataset of BDD100K and PVDN demonstrate that the proposed system achieves higher detection accuracy and robustness compared to existing methods, with mean Average Precision (mAP) scores of 0.7134 on BDD100K, 0.6621 on PVDN, and 0.6814 on the combined dataset. Additionally, a self-acquired dataset, FNTVD, further enhances the evaluation by providing real-world driving conditions. The system also achieves real-time performance at 45.45 FPS, making it suitable for practical applications.}, archive = {J_ACCESS}, author = {Leyre Encío and Daniel Fuertes and Carlos R. del-Blanco and Iu Aguilar and Cristina Pérez-Benito and Aleksandar Jevtić and Fernando Jaureguizar and Narciso García}, doi = {10.1109/ACCESS.2025.3548837}, journal = {IEEE Access}, pages = {44817-44835}, shortjournal = {IEEE Access}, title = {Enhanced nighttime vehicle detection for on-board processing}, volume = {13}, year = {2025}, }
• (2025). A survey on statistical and ML-based demand forecasting methods for spare parts in aviation. ACCESS, 13, 44800-44816. (www)

  @article{ , abstract = {In the aviation industry, inventory management based on non-smooth demand forecasting is an ongoing challenge. Generally, aircraft parts lack easily observable demand patterns. Through twenty-six articles published from 2012 to 2023, this literature review discusses the performance of several forecasting techniques using time series data. These methodologies include statistical forecasting methods, such as Croston’s method and single exponential smoothing (SES), and machine learning (ML) models built using ensembles of decision trees or neural networks. Research into demand forecasting does not only focus on achieving and maintaining accurate predictions, but also on lowering costs associated with over- or under-forecasting. While classical methods remain popular due to their ease of use, explainability, and cost-effectiveness, ML models demonstrate potential for higher accuracy under optimal conditions. With a focus on the aviation domain, this survey provides an extensive overview of demand forecasting methods that can assist businesses with improving inventory control of aircraft stock.}, archive = {J_ACCESS}, author = {Chelsea M. Zuvieta and Joffrey L. Leevy and Taghi M. Khoshgoftaar}, doi = {10.1109/ACCESS.2025.3550091}, journal = {IEEE Access}, pages = {44800-44816}, shortjournal = {IEEE Access}, title = {A survey on statistical and ML-based demand forecasting methods for spare parts in aviation}, volume = {13}, year = {2025}, }
• (2025). Advancing EMC analysis with GAN-driven signal classification and waveform modulation. ACCESS, 13, 44789-44799. (www)

  @article{ , abstract = {This study advances Electromagnetic Compatibility (EMC) by investigating how electromagnetic interference (EMI) from Radio Frequency (RF) sources affects digital interconnects. Unlike traditional analyses centered on Continuous Wave (CW) signals, we adopt an RF-focused approach using S-parameter data and consistent RF power to emphasize steady-state responses. This method eliminates the need for time-domain conversions, allowing for more accurate analysis. Our research introduces a novel image-based classification system that accurately assesses signal safety based on steady-state responses. By leveraging a Generative Adversarial Network (GAN) trained on ‘safe’ and ‘unsafe’ signal images, our system can effectively recognize and distinguish between these two states. The GAN’s ability to generate realistic signal patterns enhances classification accuracy, especially when empirical data is limited. This approach has been validated through multiple transformations to ensure robustness and reliability. The findings offer significant improvements in EMC analysis and provide practical guidelines for designing robust digital interconnects. These advancements contribute to enhancing the reliability and security of electronic devices in environments with high RF interference, making them better suited for real-world commercial applications where signal integrity is critical.}, archive = {J_ACCESS}, author = {Mona Esmaeili and Sameer D. Hemmady and Oameed Noakoasteen and Edl Schamiloglu and Christos Christodoulou and Payman Zarkesh-Ha}, doi = {10.1109/ACCESS.2025.3548033}, journal = {IEEE Access}, pages = {44789-44799}, shortjournal = {IEEE Access}, title = {Advancing EMC analysis with GAN-driven signal classification and waveform modulation}, volume = {13}, year = {2025}, }
• (2025). Transformer abnormal state identification based on TCN-transformer model in multiphysics. ACCESS, 13, 44775-44788. (www)

  @article{ , abstract = {Transformers are critical components in power systems, and their operational stability plays a decisive role in ensuring the safety and reliability of the power grid. To address the challenges of accurately assessing transformer health due to the influence of load and environmental factors during actual operation, this paper analyzes the electrical, thermal, and vibrational characteristics of transformers. A k-means++ algorithm is employed to classify operating conditions based on three key parameters: transformer load current, ambient temperature, and operating voltage. A fusion model based on the Temporal Convolutional Network-Transformer (TCN-Transformer) is proposed to identify abnormal operating states of transformers. Experiments were conducted using a 500 kV transformer as an example. The results demonstrate that the proposed TCN-Transformer model significantly outperforms comparative algorithms in terms of prediction accuracy. The model effectively captures critical information within the data, achieving superior multivariate feature sequence prediction for transformers. These findings validate the reasonableness and accuracy of the proposed method for identifying abnormal transformer operating states.}, archive = {J_ACCESS}, author = {Junjie Feng and Ruosong Shang and Ming Zhang and Guojun Jiang and Qiong Wang and Guangyong Zhang and Wei Jin}, doi = {10.1109/ACCESS.2025.3546341}, journal = {IEEE Access}, pages = {44775-44788}, shortjournal = {IEEE Access}, title = {Transformer abnormal state identification based on TCN-transformer model in multiphysics}, volume = {13}, year = {2025}, }
• (2025). Optimizing blockchain network performance using blake3 hash function in POS consensus algorithm. ACCESS, 13, 44760-44774. (www)

  @article{ , abstract = {Recent years have seen extensive adoption of blockchain technology across a variety of application domains, all with the goal of enhancing data privacy, system trustworthiness, and security. One of the biggest problems with blockchain is its inability to scale; other problems include energy consumption, latency, throughput, and the ever-increasing volume of daily transactions. The consensus technique relies on hash functions, which are important to highlight. Thus, such development is fundamental to blockchain advances in terms of structure. This study introduces a revolutionary change to the Proof-of-Stake (POS) consensus methods by suggesting the replacement of the commonly used SHA256 hash function with the extremely efficient Blake3. Many blockchain-based systems, including POS algorithms, still employ the widely used SHA256 algorithm for cryptographic hashing. Nevertheless, fresh research has shown that SHA256 has performance and security flaws. We show that the Blake3 hash function, is better than the SHA256 hash in many respects, including latency, throughput, and energy, via rigorous testing and functional analysis. Diverse parameters were utilized, including the quantity of blocks and validators. Seen cases are taken into account for performance evaluation. In the initial scenario, utilizing 500 blocks and 4 validators, our proposed methodology has surpassed the benchmark by achieving a 66% reduction in latency, over 50% in throughput, and a 55% decrease in energy consumption. The rate of enhancement is nearly uniform across all other instances, indicating that the implementation of Blake3 within the conventional POS consensus mechanism has demonstrated its advantages.}, archive = {J_ACCESS}, author = {Zainab Abdullah Jasim and Ameer Kadhim Hadi}, doi = {10.1109/ACCESS.2025.3546723}, journal = {IEEE Access}, pages = {44760-44774}, shortjournal = {IEEE Access}, title = {Optimizing blockchain network performance using blake3 hash function in POS consensus algorithm}, volume = {13}, year = {2025}, }
• (2025). Multiplier operated controller for CCM boost PFC converter with regulated input impedance and improved power factor. ACCESS, 13, 44750-44759. (www)

  @article{ , abstract = {Power factor correction (PFC) plays a crucial role in power electronics, particularly in enhancing the efficiency and stability of power systems. As continuous conduction mode (CCM) boost PFC converters become increasingly prevalent, research into their control strategies has deepened. While average current mode (ACM) control is commonly used, it faces several challenges in practical applications. Traditional ACM control methods struggle with input impedance regulation, especially within certain frequency ranges, where maintaining constant input impedance is difficult. This leads to issues such as current distortion and reduced efficiency. Additionally, bandwidth adjustment and ripple disturbance suppression with standard control procedures remain significant challenges. To address these issues, this paper proposes a novel multiplier-operated controller specifically designed for CCM boost PFC converters. The controller employs an innovative methodology to effectively modify and maintain input impedance at a defined constant within a specific frequency range. Furthermore, a specialized compensator is integrated to optimize bandwidth and minimize ripple disturbances. Experimental results from a 600W prototype demonstrate that the proposed controller achieves a power factor of up to 0.98 and significantly enhances efficiency and other performance metrics. This study provides new insights and technical approaches for improving the control strategies of CCM boost PFC converters.}, archive = {J_ACCESS}, author = {Jianbin Chen and Chengyu Yang and Jianjun Zou and Kai Chen}, doi = {10.1109/ACCESS.2025.3548096}, journal = {IEEE Access}, pages = {44750-44759}, shortjournal = {IEEE Access}, title = {Multiplier operated controller for CCM boost PFC converter with regulated input impedance and improved power factor}, volume = {13}, year = {2025}, }
• (2025). Performance improvement of brushless wound rotor vernier motor for washing machine applications. ACCESS, 13, 44739-44749. (www)

  @article{ , abstract = {This paper presents the design and performance analysis of a brushless wound rotor vernier machine (BLWRVM) for variable speed applications, particularly in the context of domestic appliances like automatic washing machines which have two cycles of operation: the wash cycle at low-speed and dry-cycle at high-speed. The proposed BLWRVM utilizes a different winding configuration that enhances the performance compared to the existing reference machine. The machine topology uses a dual winding setup on the stator, a 3-phase main ABC1 winding and a 1-phase additional A2 winding on the stator, specifically engineered to generate both fundamental and subharmonic components of MMF in the airgap of the machine. In order to achieve the brushless operation, the additional excitation winding is placed on the rotor which is connected through a rectifier to the field winding. The voltages are induced in this excitation winding using the MMF’s sub-harmonic component. These AC-induced voltages are rectified by a rotating rectifier placed on the rotor periphery and are then supplied to the main field winding. A 4-pole, 24-slot stator and 44-pole 44-slot outer rotor machine is designed. The fill factor of the machine’s stator slot was then increased to 35% and the rotor’s slot fill factor was increased to 30%. Furthermore, different skew angles of the rotor were analyzed to find the optimal skew angle that yields minimum torque ripple. The performance characteristics were compared with a reference BLWRVM. To validate the design a 2D-FEA was carried out to find out the performing characteristics of the proposed machine and reference machines using ANSYS Electromagnetic suite version 2022 R1.}, archive = {J_ACCESS}, author = {Kapeel Kumar and Qasim Ali and Sheeraz Ahmed and Muhammad Humza and Abdul Aziz Memon and Mudassir Raza Siddiqi}, doi = {10.1109/ACCESS.2025.3548864}, journal = {IEEE Access}, pages = {44739-44749}, shortjournal = {IEEE Access}, title = {Performance improvement of brushless wound rotor vernier motor for washing machine applications}, volume = {13}, year = {2025}, }
• (2025). Ancient wall painting restoration based on improved adaptive sample block criminisi algorithm and CDD algorithm. ACCESS, 13, 44722-44738. (www)

  @article{ , abstract = {To better protect and restore ancient mural art, this study proposes an improved adaptive sample block Criminisi algorithm to address the shortcomings of traditional manual restoration methods. To enhance the efficiency and quality of image restoration, this study constructs a curvature driven diffusion model based on curvature adaptive parameters and smoothing functions. The experiment showed that compared with the Criminisi and the enhanced Criminisi algorithms, the average PSNR of the research algorithm increased by 12.47% and 7.17%, and the structural similarity index increased by 5.54% and 2.82%, respectively. The subjective average score of this algorithm for image sample restoration was 9.1 points, and the average time for image restoration was 23.25 seconds. Compared with deep residual network and generative adversarial network, the proposed algorithm increases the peak signal-to-noise ratio by 36.59% and 20.42%, respectively, and reduces the image restoration time by 31.92% and 24.80%, respectively. The results indicate that the proposed image restoration method can improve the quality and efficiency of image restoration. This study can provide certain reference value for artificial restoration and has important research significance for the continuation of world history and culture.}, archive = {J_ACCESS}, author = {Xiujie Wu}, doi = {10.1109/ACCESS.2025.3548323}, journal = {IEEE Access}, pages = {44722-44738}, shortjournal = {IEEE Access}, title = {Ancient wall painting restoration based on improved adaptive sample block criminisi algorithm and CDD algorithm}, volume = {13}, year = {2025}, }
• (2025). LLM-based text style transfer: Have we taken a step forward?. ACCESS, 13, 44707-44721. (www)

  @article{ , abstract = {Text style transfer is the task of altering the stylistic way in which a given sentence is written while maintaining its original meaning. The task requires models to identify and modify various stylistic properties, such as politeness, formality, and sentiment. With the advent of Large Language Models (LLMs) and their remarkable performances for a variety of tasks, numerous LLMs have emerged in the past few years. This paper provides an overview of recent advancements in text style transfer using LLMs. The discussion is focused on LLM-based approaches commonly used for text generation and their adoption for text style transfer. The paper is organized around three main groups of methods: prompting techniques for LLMs, fine-tuning techniques for LLMs, and memory-augmented LLMs. The discussion emphasizes the similarities and differences among the discussed methods and groups, along with the challenges and opportunities that are expected to direct and foster further research in the field.}, archive = {J_ACCESS}, author = {Martina Toshevska and Sonja Gievska}, doi = {10.1109/ACCESS.2025.3548967}, journal = {IEEE Access}, pages = {44707-44721}, shortjournal = {IEEE Access}, title = {LLM-based text style transfer: Have we taken a step forward?}, volume = {13}, year = {2025}, }
• (2025). Cyber attack prediction: From traditional machine learning to generative artificial intelligence. ACCESS, 13, 44662-44706. (www)

  @article{ , abstract = {The escalating sophistication of cyber threats poses significant risks to individuals, organizations, and nations. Cybercrime, encompassing activities like hacking and data breaches, has severe economic and societal consequences. In today’s interconnected world, robust cybersecurity measures are paramount to mitigate these risks and protect sensitive information. However, traditional security solutions struggle to keep pace with the evolving threat landscape. Artificial Intelligence (AI) offers a powerful arsenal of techniques to address these challenges. This paper explores the application of AI methods, including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Explainable AI (XAI), and Generative AI, in solving various cybersecurity problems. This paper presents a comprehensive analysis of AI techniques for enhancing cybersecurity. Key contributions include: 1) comparative study of ML and DL methods: Evaluating their accuracy, applicability, and suitability for various cybersecurity challenges; 2) investigation into XAI approaches: Enhancing the transparency and interpretability of AI-powered security solutions, particularly in anomaly detection; 3) exploration of emerging trends in Generative AI (Gen-AI) and NLP: Examining their potential to simulate and mitigate cyber threats through advanced techniques like threat intelligence generation and attack simulations; 4) application of GenAI in cybersecurity and real-world products of GenAI for cyber security. This research aims to advance the state-of-the-art in AI-driven cybersecurity by providing insights into effective and reliable solutions for mitigating cyber risks and improving the overall security posture.}, archive = {J_ACCESS}, author = {Shilpa Ankalaki and Aparna Rajesh Atmakuri and M. Pallavi and Geetabai S Hukkeri and Tony Jan and Ganesh R. Naik}, doi = {10.1109/ACCESS.2025.3547433}, journal = {IEEE Access}, pages = {44662-44706}, shortjournal = {IEEE Access}, title = {Cyber attack prediction: From traditional machine learning to generative artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). LLM-enhanced Human–Machine interaction for adaptive decision-making in dynamic manufacturing process environments. ACCESS, 13, 44650-44661. (www)

  @article{ , abstract = {Modern production systems generate vast amounts of process data that hold valuable insights for optimizing manufacturing processes. However, production personnel often face the challenge of interpreting this information, especially when dealing with unexpected anomalies or when insights beyond standard reports are required. This challenge arises both from the complex data structures in which the data is provided, and the lack of analytical expertise. This research introduces an approach that leverages Large Language Models (LLMs) to facilitate natural language queries and flexible data visualization, allowing production personnel to interact effortlessly with complex datasets. Tested on process data from an industrial extrusion process that has been enhanced using data augmentation techniques, the proposed concept demonstrates the capability to retrieve relevant data and present tailored visualizations based on simple user prompts. The results demonstrate that LLM-driven data exploration can support production personnel and help overcome the challenges described, which arise from the complex nature of manufacturing data and the specialized domain knowledge required. Future work will concentrate on improving accuracy, robustness, and further integration of domain-specific knowledge, aiming to provide a more reliable and accessible tool for various industrial environments.}, archive = {J_ACCESS}, author = {Zozan Keskin and Dominik Joosten and Nils Klasen and Meike Huber and Chang Liu and Benny Drescher and Robert H. Schmitt}, doi = {10.1109/ACCESS.2025.3549529}, journal = {IEEE Access}, pages = {44650-44661}, shortjournal = {IEEE Access}, title = {LLM-enhanced Human–Machine interaction for adaptive decision-making in dynamic manufacturing process environments}, volume = {13}, year = {2025}, }
• (2025). Accelerating euclidean distance transforms: A fast and flexible approach with multi-vendor GPU, multi-threading, and multi-language support. ACCESS, 13, 44636-44649. (www)

  @article{ , abstract = {Euclidean distance transforms are fundamental in image processing and computer vision, with critical applications in medical image analysis and computer graphics. However, existing implementations often lack performance, flexibility, or cross-platform compatibility. This paper introduces a novel approach to accelerating Euclidean distance transforms using hardware-agnostic GPU acceleration, multi-threading, and cross-language support. Our method, implemented in Julia with Python bindings, supports multiple GPU platforms including NVIDIA CUDA, AMD ROCm, Apple Metal, and Intel oneAPI. Benchmarks demonstrate substantial performance improvements, achieving speedups by a factor of 250 for 2D and a factor of 400 for 3D transforms compared to optimized CPU implementations. We showcase the impact of our approach through two real-world applications: accelerating the Hausdorff distance loss function for medical image segmentation, achieving a 7.4-fold improvement in processing speed with enhanced accuracy, and enhancing a GPU-optimized distance transform-based skeletonization algorithm with performance gains up to a factor of 88. Our open-source implementation provides a flexible, high-performance solution for exact Euclidean distance transforms, advancing the state-of-the-art in medical image analysis and computer vision.}, archive = {J_ACCESS}, author = {Dale Black and Wenbo Li and Qiyu Zhang and Sabee Molloi}, doi = {10.1109/ACCESS.2025.3548563}, journal = {IEEE Access}, pages = {44636-44649}, shortjournal = {IEEE Access}, title = {Accelerating euclidean distance transforms: A fast and flexible approach with multi-vendor GPU, multi-threading, and multi-language support}, volume = {13}, year = {2025}, }
• (2025). Multi-target point path planning and tracking of unmanned surface vehicle using SFLOS algorithm and incremental PID control with crane-assisted. ACCESS, 13, 44620-44635. (www)

  @article{ , abstract = {To enhance the multi-target path planning and tracking performance of Unmanned Surface Vehicles (USVs), a combined control strategy integrating the Serret-Frenet Line-of-Sight (SFLOS) algorithm and an incremental PID controller is proposed to achieve precise path tracking in complex environments. Additionally, a novel finite-time observer is presented to compensate for the sideslip angle caused by environmental disturbances in real time. The heading controller of the USV is designed using incremental PID control, and an improved Particle Swarm Optimization (PSO) algorithm is proposed to optimize the PID parameters. The stability and effectiveness of the proposed method are validated through simulations. Furthermore, a USV equipped with a crane-assisted experimental platform is constructed to conduct verification experiments for course control, guidance law, and planned path tracking control. Both simulation and real-world tests demonstrate the effectiveness and feasibility of the proposed approach.}, archive = {J_ACCESS}, author = {Xiangyang Cao and Yaojie Zheng and Hanbin Xiao and Min Xiao}, doi = {10.1109/ACCESS.2025.3549377}, journal = {IEEE Access}, pages = {44620-44635}, shortjournal = {IEEE Access}, title = {Multi-target point path planning and tracking of unmanned surface vehicle using SFLOS algorithm and incremental PID control with crane-assisted}, volume = {13}, year = {2025}, }
• (2025). A novel approach to the prediction of alzheimer’s disease progression by leveraging neural processes and a transformer encoder model. ACCESS, 13, 44607-44619. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) presents a significant global health challenge, necessitating accurate and early prediction methods for effective intervention and treatment planning. In this work, a novel approach to meta-learning for the prediction of AD is proposed, which leverages the combined power of neural processes (NPs) and transformer architectures. We introduce a framework that integrates NPs with a transformer encoder to model the complex temporal dependencies inherent in longitudinal health data, where our model learns to capture subtle patterns and variations indicative of disease progression. The novelty of our approach lies in the fusion of NPs, renowned for their ability to model stochastic processes, with transformer architectures, known for their ability to capture long-range dependencies. This combination enables our model to effectively adapt to individual patient trajectories and generalize across diverse populations, enhancing its predictive performance and robustness. We trained our proposed model with the Alzheimer’s Disease Prediction Of Longitudinal Evolution dataset (TADPOLE), which contains three classes: Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD. The experimental results demonstrate that the proposed model enhances the prediction of these models in terms of mAUC, Recall, and Precision by $0.937\pm 0.014$ , $0.920\pm 0.010$ , and $0.923\pm 0.009$ , respectively. These findings prove the efficacy of the proposed framework in accurately predicting the progression of AD, highlighting its potential for early detection and personalized treatment strategies.}, archive = {J_ACCESS}, author = {Emad Al-Anbari and Hossein Karshenas and Bijan Shoushtarian}, doi = {10.1109/ACCESS.2025.3548173}, journal = {IEEE Access}, pages = {44607-44619}, shortjournal = {IEEE Access}, title = {A novel approach to the prediction of alzheimer’s disease progression by leveraging neural processes and a transformer encoder model}, volume = {13}, year = {2025}, }
• (2025). Improving cardiovascular disease prediction with deep learning and correlation-aware SMOTE. ACCESS, 13, 44590-44606. (www)

  @article{ , abstract = {Cardiovascular disease (CVD) ranks among the top causes of mortality globally, underscoring the urgent necessity for advanced predictive models to enhance early detection and preventative measures. In this direction, this study investigates the performance of five well-established deep learning (DL) models, namely Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Autoencoder in predicting CVD using a diverse patient dataset. To tackle the prevalent class imbalance issue in medical datasets, we introduce an enhanced Synthetic Minority Over-sampling Technique (SMOTE). This innovative technique enhances traditional SMOTE by incorporating feature correlations to produce more realistic synthetic samples. We compare model performance across three scenarios: without SMOTE, with traditional SMOTE, and with enhanced SMOTE, using metrics such as Accuracy, Precision, Recall, F1-Score, and Area Under the Curve (AUC). Our results show that the enhanced SMOTE significantly improves model performance, especially in recall and AUC-ROC. Notably, the CNN model with enhanced SMOTE prevailed, achieving the highest overall performance with an AUC of 0.90, an Accuracy of 0.91, a Precision of 0.89, a Recall of 0.86, and an F1-Score equal to 0.87, making it the most effective model in this study. This research highlights the potential of the enhanced SMOTE in developing robust predictive models for CVD, with broader implications for healthcare analytics.}, archive = {J_ACCESS}, author = {Maria Trigka and Elias Dritsas}, doi = {10.1109/ACCESS.2025.3549417}, journal = {IEEE Access}, pages = {44590-44606}, shortjournal = {IEEE Access}, title = {Improving cardiovascular disease prediction with deep learning and correlation-aware SMOTE}, volume = {13}, year = {2025}, }
• (2025). Cross-modal hashing retrieval based on density clustering. ACCESS, 13, 44577-44589. (www)

  @article{ , abstract = {Cross-modal hashing retrieval methods have attracted much attention for their effectiveness and efficiency. However, most of the existing hashing methods have the problem of how to precisely learn potential correlations between different modalities from binary codes with minimal loss. In addition, solving binary codes in different modalities is an NP-hard problem. To overcome these challenges, we initially propose a novel adaptive fast cross-modal hashing retrieval method under the inspiration of DBSCAN clustering algorithm, named Cross-modal Hashing Retrieval Based on Density Clustering (DCCH). DCCH utilizes the global density correlation between different modalities to select representative instances to replace the entire data precisely. Furthermore, DCCH excludes the adverse effects of noise points and leverages the discrete optimization process to obtain hash functions. The extensive experiments show that DCCH is superior to other state-of-the-art cross-modal methods on three benchmark bimodal datasets, i.e., Wiki, MIRFlickr and NUS-WIDE. Therefore, the experimental results also prove that our method DCCH is comparatively usable and efficient.}, archive = {J_ACCESS}, author = {Xiaojun Qi and Xianhua Zeng and Hongmei Tang}, doi = {10.1109/ACCESS.2020.2978876}, journal = {IEEE Access}, pages = {44577-44589}, shortjournal = {IEEE Access}, title = {Cross-modal hashing retrieval based on density clustering}, volume = {13}, year = {2025}, }
• (2025). Vision transformer-based anomaly detection in smart grid phasor measurement units using deep learning models. ACCESS, 13, 44565-44576. (www)

  @article{ , abstract = {Modern smart grids become increasingly complex and thus require advanced anomaly detection mechanisms to guarantee system stability and reliability. New real-time measurement devices (Phasor Measurement Units (PMUs) provide high-resolution, time-synchronized voltage, frequency, and phase angle measurements. However, this is insufficient in complex and subtle anomalies; traditional threshold-based and statistical approaches fail to detect such anomalies and instead require deep learning techniques for better effectiveness. On the other hand, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have proven to be deep learning models that can be used for PMU anomaly detection. Through hierarchical convolutional filters and self-attention mechanisms, CNNs exploit localized feature extraction counterparts to ViTs, which employ self-attention mechanisms to capture long-range dependencies of PMU data and a global contextual relationship. Although ViTs have shown capabilities for computer vision tasks, their efficacy in PMU-based anomaly detection remains under-researched, which motivates this comparative study. Based on such capability, we hypothesize that CNNs will be outperformed by ViTs in their capability to differentiate similar anomalies, especially since they can better model global spatial-temporal dependencies over CNNs’ localized feature extraction. Specifically, in this study, four CNN architectures (DenseNet201, InceptionV3, ResNet152, MobileNetV2) and four ViT models (ViT-Base-16, ViT-Base-32, ViT-Large-16, ViT-Large-32) are evaluated on a PMU anomaly dataset. Accuracy, precision, recall, F1-score, and computational efficiency are evaluated to assess these models as to whether they can perform real-time smart grid monitoring. Compared to CNN models, ViT-Large-32 achieved the highest accuracy of 98.91%, far exceeding them. ViT were more precise and recalled generation loss and motor switching anomaly numbers, while CNNs were faster to infer but more inaccurate in classification. The results confirm that ViTs achieve state-of-the-art performance in detecting PMU anomalies and are a promising grid monitoring and predictive maintenance solution. Real-time deployment of such systems remains a challenge that should be explored using computational optimizations, and hybrid CNN-ViT models for balancing sufficient performance with acceptable efficiency should be studied in large-scale innovative grid applications.}, archive = {J_ACCESS}, author = {Zhibin Liu and Yibo Wang and Qingwei Wang and Man Hu}, doi = {10.1109/ACCESS.2025.3549679}, journal = {IEEE Access}, pages = {44565-44576}, shortjournal = {IEEE Access}, title = {Vision transformer-based anomaly detection in smart grid phasor measurement units using deep learning models}, volume = {13}, year = {2025}, }
• (2025). Effect of programming schemes on short-term instability in 1T1R configuration. ACCESS, 13, 44555-44564. (www)

  @article{ , abstract = {Short-term instability is one of the key challenges for redox-based resistive random-access memory (ReRAM) reaching practical applications in the field of neuromorphic computing. Developing programming schemes that balance resistance fine-tuning with short-term stability is crucial for improving the reliability of ReRAM devices. In this work, we explore the impact of programming schemes on the short-term variability of valence change mechanism (VCM)-based filamentary ReRAM devices. We design three programming schemes: a baseline multi-pulse scheme, a single short-pulse scheme, and a single long-pulse scheme. By comparing the programming statistics of the one-transistor-one-resistor (1T1R) structures under different schemes and analyzing their evolution over time, we propose the hypothesis that the number and width of pulses influence the filament size and internal distribution of oxygen vacancies, and thus the short-term stability of ReRAM devices. We employ three-dimensional Kinetic Monte Carlo simulation, constructing filaments of varying sizes and adjusting the oxygen vacancy distribution to simulate the outcomes of different programming schemes. The simulation results align with experimental findings. Based on existing experimental data and modeling studies, we propose programming strategies that balance programmability and reliability for VCM-based filamentary ReRAM devices.}, archive = {J_ACCESS}, author = {Xiaohua Liu and Yang Chen and Nils Kopperberg and Oliver Solfronk and Susanne Hoffmann-Eifert and Stephan Menzel and Rainer Waser and Stefan Wiefels}, doi = {10.1109/ACCESS.2025.3547818}, journal = {IEEE Access}, pages = {44555-44564}, shortjournal = {IEEE Access}, title = {Effect of programming schemes on short-term instability in 1T1R configuration}, volume = {13}, year = {2025}, }
• (2025). PC-match: Semi-supervised learning with progressive contrastive and consistency regularization. ACCESS, 13, 44543-44554. (www)

  @article{ , abstract = {As artificial intelligence developed rapidly, deep learning models have been applied in various domains. While labeling is crucial to training models in fields that demand specific knowledge, producing such labeled datasets is expensive. Semi-supervised learning (SSL) is becoming a potential solution to this problem, utilizing data without label information. However, conventional SSL methods focus solely on predicting data classes while minimizing classification loss, which can lead to ambiguous decision boundaries, especially for classes with similar characteristics. In this study, we propose enhancing the baseline SSL method by incorporating a contrastive loss. We emphasize that samples near the decision boundaries have a negative impact on the model’s performance. Our method addresses the issue of uncertain boundaries in the representation space by focusing on the unique characteristics of each class. We conducted experiments to validate the effectiveness of our proposed method using a limited number of labeled samples. The results demonstrate that our method effectively enhances performance, particularly in environments with limited labeled data, as evidenced by visual analysis.}, archive = {J_ACCESS}, author = {Mikyung Kang and Sooyon Seo and Moohong Min}, doi = {10.1109/ACCESS.2025.3549465}, journal = {IEEE Access}, pages = {44543-44554}, shortjournal = {IEEE Access}, title = {PC-match: Semi-supervised learning with progressive contrastive and consistency regularization}, volume = {13}, year = {2025}, }
• (2025). DT-GA-FLANN: Decision tree-based genetically optimized FLANN for missing value imputation and classification. ACCESS, 13, 44526-44542. (www)

  @article{ , abstract = {In the current landscape, addressing missing values poses a significant challenge when dealing with real-world problems. In this work, a hybrid model, DT-GA-FLANN, is proposed to address the missing value problem and enhance classifier performance. The missing values are imputed using Classification and Regression Trees (CART), ensuring robust data preprocessing. For classification, a genetically optimized Functional Link Artificial Neural Network (FLANN) is used, chosen for its faster convergence and lower computational complexity compared to multi-layer neural networks. However, functional expansion in FLANN can increase architectural complexity, which is effectively mitigated using Genetic Algorithm (GA). GA is selected for its adaptability, parallel processing capabilities, and efficiency in complex optimization tasks. The FLANN parameters are iteratively fine-tuned to maximize performance. The performance of DT-GA-FLANN is validated through extensive experiments on four real-world and two synthetic datasets. It is compared against ten hybrid models, comprising four statistical and six machine learning-based hybrid models. Experimental analysis reveals that DT-GA-FLANN achieves an average improvement in accuracy, precision, recall, and F-measure by 3.7%, 4.3%, 3.1%, and 3.8%, respectively over machine learning models, and by 10.2%, 12.4%, 10.8%, and 11.6%, respectively over statistical models on real-world datasets. In case of synthetic datasets, it further improves accuracy, precision, recall, and F-measure by 4.2%, 5.1%, 4.7%, and 4.3%, respectively over machine learning models, and by 9.1%, 10.8%, 9.4%, and 9.9%, respectively over statistical models. These results confirm DT-GA-FLANN’s superior classification performance and generalization ability across diverse datasets. The experimental results show that the genetically optimized FLANN significantly outperforms other hybrid models across multiple performance metrics.}, archive = {J_ACCESS}, author = {Monalisa Jena and Satchidananda Dehuri and Sung-Bae Cho}, doi = {10.1109/ACCESS.2025.3548315}, journal = {IEEE Access}, pages = {44526-44542}, shortjournal = {IEEE Access}, title = {DT-GA-FLANN: Decision tree-based genetically optimized FLANN for missing value imputation and classification}, volume = {13}, year = {2025}, }
• (2025). Linear programming-based decision support system of solar power reserve management in pulsation free current circulation (PFCC) for agricultural systems. ACCESS, 13, 44510-44525. (www)

  @article{ , abstract = {Sustainably powering agricultural systems including greenhouses, irrigation, and farm equipment relies on the Pulsation Free Current Circulation System’s (PFCC) efficient use of solar energy reserves. Improving the distribution of solar power reserves in light of the fact that solar energy output varies over time is the primary goal of this research. Energy reserve expenses are reduced and storage battery operating life is extended using an interval linear programming technique. Power distribution that is both dependable and efficient is achieved by use of the system’s models, which take into consideration factors like allocation costs, power reserve and network losses. Through the use of a two-tiered structural approach, the optimization procedure guarantees balanced dispatching and rapid responsiveness to fluctuating energy needs by managing distribution and reserve capacity in tandem. Enhancing the sustainability of solar-powered agricultural systems, the suggested technology minimizes battery deterioration and ensures energy availability during important agricultural activities. Multi-power reserve systems are also used to reduce distributed energy swings, which makes the system even more reliable and stable. According to the findings of the tests, the technology has the power to greatly improve the efficiency and longevity of solar energy in farming, with an output efficiency of 0.99(in decimal). This strategy tackles the problem of energy management and sets the stage for renewable energy solutions that can be easily scaled and adjusted for use in agriculture. This will guarantee the sustainability and effectiveness of these solutions in the long run.}, archive = {J_ACCESS}, author = {M. Saivineeth and Venkateswarlu. B}, doi = {10.1109/ACCESS.2025.3549464}, journal = {IEEE Access}, pages = {44510-44525}, shortjournal = {IEEE Access}, title = {Linear programming-based decision support system of solar power reserve management in pulsation free current circulation (PFCC) for agricultural systems}, volume = {13}, year = {2025}, }
• (2025). Utility meets privacy: A critical evaluation of tabular data synthesizers. ACCESS, 13, 44497-44509. (www)

  @article{ , abstract = {Evaluating synthetic data requires careful consideration of both utility and privacy. This study analyzes 12 synthesizers across 17 tabular health datasets, providing large-scale, comparable evaluation results. A novel utility-privacy score integrates a privacy measure into the evaluation, quantifying the trade-off between the two. Membership inference analysis is extended for robust privacy assessment, with reusable code provided for further research. Key findings include: 1) despite its simplicity, SMOTE achieves the best results of all synthesizers, but no single synthesizer consistently outperforms all others across all datasets; 2) utility and privacy are inherently correlated, with improvements in one compromising the other; 3) some synthesizers exhibit greater robustness to variations in data properties, such as sample and feature size, and some properties have a stronger impact than others. These findings underscore how closely the utility of a synthesizer is tied to individual datasets and privacy considerations, and highlight the importance of incorporating these aspects into future research and adopting broader, more diverse evaluation frameworks.}, archive = {J_ACCESS}, author = {Julian Höllig and Michaela Geierhos}, doi = {10.1109/ACCESS.2025.3549680}, journal = {IEEE Access}, pages = {44497-44509}, shortjournal = {IEEE Access}, title = {Utility meets privacy: A critical evaluation of tabular data synthesizers}, volume = {13}, year = {2025}, }
• (2025). Cross layer power allocation by graph neural networks in heterogeneous D2D video communications. ACCESS, 13, 44484-44496. (www)

  @article{ , abstract = {The massive connectivity trend was set to shape B5G/6G networks. Device-to-device (D2D) communications play a crucial role in massive connectivity in the context of Internet of Things (IoT) applications. Recently, a heterogeneous interference graph neural network (HIGNN) was proposed for resource allocation in heterogeneous networks. The HIGNN captured the spatial information hidden in heterogeneous network topology and was scalable. However, existing methods primarily focused on resource allocation at the physical layer only and did not adequately address the cross-layer issues involved in video transmission. Therefore, in this paper, we propose the video-optimized heterogeneous interference graph neural network (VD-HIGNN) as a cross-layer D2D resource allocation method for video transmission, which introduces the following contributions: 1) joint source encoder rate and beamforming/power control, 2) incorporating video rate distortion function parameters from the application layer into the node features, and 3) changing the loss function from data rate to Peak-Signal-to-Noise-Ratio (PSNR), a function of video rate distortion and a metric of video quality. Simulation results demonstrate that our proposed VD-HIGNN outperforms two physical layer baseline schemes: the iterative fractional programming method by 0.53 dB and HIGNN by approximately 2 dB for video transmission. Moreover, when scaled to larger problems with 2-12 times the number of nodes within a fixed area size, the VD-HIGNN achieves 94% or more of the performance of a retrained model, showcasing its scalability and generalization ability.}, archive = {J_ACCESS}, author = {Shu-Ming Tseng and Sz-Tze Wen and Chao Fang and Mehdi Norouzi}, doi = {10.1109/ACCESS.2025.3548854}, journal = {IEEE Access}, pages = {44484-44496}, shortjournal = {IEEE Access}, title = {Cross layer power allocation by graph neural networks in heterogeneous D2D video communications}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid model for brain ischemic stroke detection using feature fusion and convolutional block attention module. ACCESS, 13, 44466-44483. (www)

  @article{ , abstract = {Brain stroke is the second leading cause of death worldwide, following ischemic heart disease. Ischemic stroke occurs when blood vessels are obstructed by a thrombus or other blockages. Prompt and accurate diagnosis of ischemic stroke is critical for patient survival. This study proposes a novel approach for ischemic stroke detection from computed tomography (CT) images, utilizing a hybrid feature extraction technique combined with a convolutional block attention module (CBAM). The hybrid feature extraction leverages the strengths of two pre-trained models, DenseNet121 and MobileNetV3, through feature fusion to provide a comprehensive representation of brain CT images. The CBAM module is integrated to enhance the most relevant features by focusing on both channel and spatial attention mechanisms, significantly improving the model’s ability to detect ischemic strokes with high accuracy. The proposed approach was developed and evaluated on a unique first-hand dataset (Dataset 1) collected from the Specialized private Hospital in Palestine. To further demonstrate the robustness and generalizability of the method, it was also tested on a public dataset (Dataset 2). The results show that the proposed model achieved outstanding performance across all metrics: on Dataset 1, it reached an accuracy of 99.21%, precision of 99.17%, recall of 99.32%, and an F1-score of 99.24%. On Dataset 2, it achieved an accuracy of 98.73%, precision of 98.71%, recall of 98.74%, and an F1-score of 98.88%. The exceptional results are attributed to the power of feature fusion, which creates a robust and comprehensive feature representation. In addition to the CBAM, which refines the feature maps by selectively focusing on the most important channels and spatial regions in brain CT images. This has led to substantial improvement in the detection of ischemic stroke areas.}, archive = {J_ACCESS}, author = {Ahmad Abumihsan and Amani Yousef Owda and Majdi Owda and Mobarak Abumohsen and Lampros Stergioulas and Mohammad Ahmad Abu Amer}, doi = {10.1109/ACCESS.2025.3549269}, journal = {IEEE Access}, pages = {44466-44483}, shortjournal = {IEEE Access}, title = {A novel hybrid model for brain ischemic stroke detection using feature fusion and convolutional block attention module}, volume = {13}, year = {2025}, }
• (2025). Research on bearing fault diagnosis methods based on various convolutional neural network architectures. ACCESS, 13, 44445-44465. (www)

  @article{ , abstract = {Bearing fault diagnosis is essential for ensuring the reliable and stable operation of mechanical equipment. While Convolutional Neural Networks (CNNs) have demonstrated significant advancements in fault diagnosis, challenges such as varying operational conditions and feature extraction complexity persist. This paper introduces an innovative method that synergistically combines multiple CNN architectures to enhance the accuracy and robustness of bearing fault diagnosis. Specifically, we design five distinct convolutional network structures: SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and Multi-Domain Dual-Aggregation Convolution (MD-DAConv). These architectures integrate multi-directional, multi-scale, and residual connections to optimize feature extraction depth and breadth. Furthermore, we implement advanced feature fusion and information transmission mechanisms to bolster the model’s generalization and resilience against data variability. Experimental results on the Case Western Reserve University (CWRU) bearing dataset and SJTU-SY bearing fault diagnosis dataset demonstrate that the proposed method significantly outperforms existing approaches, achieving higher diagnostic accuracy and enhanced robustness. The findings highlight the critical role of architectural diversity and effective feature integration in advancing bearing fault diagnosis, offering a comprehensive solution to current limitations in the field.}, archive = {J_ACCESS}, author = {Mingshen Xu and Po Guan and Xinyu Shi and Runji Jiang and Jingjia Tian and Jianghai Geng and Gaoxian Xiong}, doi = {10.1109/ACCESS.2025.3548693}, journal = {IEEE Access}, pages = {44445-44465}, shortjournal = {IEEE Access}, title = {Research on bearing fault diagnosis methods based on various convolutional neural network architectures}, volume = {13}, year = {2025}, }
• (2025). Physical layer evaluation on IEEE 802.11p with different configurations in NLOS scenarios for V2V communications. ACCESS, 13, 44428-44444. (www)

  @article{ , abstract = {Motivated by the evolution of vehicular technologies and applications, the vehicle-to-everything (V2X) communications can be realized by the dedicated short-range communications (DSRC) and cellular V2X (C-V2X) which are undergoing continuous and widespread development. As the first standardized DSRC technology, IEEE 802.11p, that has been studied with large-scale field trials performed worldwide, is more mature and robust than the other V2X technologies. The main contributions of the proposed work over previous work are listed as follows. Firstly, to break the limitation of partial physical layer (PHY) evaluation, extensive PHY metrics, which include the packet error rate (PER), packet reception ratio (PRR), output packet inter-arrival time (IAT), and output effective data rate, are adequately employed to fulfill complete PHY evaluation. Secondly, to avoid incomplete analysis on antenna configurations, various multi-antenna configurations, containing the multiple-input multiple-output (MIMO), single-input multiple-output (SIMO), and multiple-input single-output (MISO) systems, are involved together with the single-input single-output (SISO) configuration to realize comprehensive analysis on diverse antenna configurations. Finally, to overcome unobvious exhibition on effect of parameters on PHY performance, considerably different packet sizes and modulation and coding schemes (MCSs) are investigated under the urban non-line-of-sight (NLOS) and highway NLOS scenarios to disclose the deep impact of each parameter. Important conclusions from a thorough MATLAB-based PHY simulation are summarized as follows. Firstly, in comparison with the SISO system, the multi-antenna systems are more favorable in reducing the PER, increasing the PRR and transmission coverage, decreasing the output packet IAT, and elevating the output effective data rate, below the signal-to-noise ratio (SNR) threshold and above the distance threshold. Secondly, the packet size and the MCS should be determined suitably to adapt to the high-reliability, low-latency, or high-throughput requirement in different applications. Finally, compared to the highway NLOS scenario with higher Doppler effect, the urban NLOS scenario is more tolerant to the larger packet and higher MCS in the vehicle-to-vehicle (V2V) communications with its lower PER, larger PRR and transmission coverage, smaller output packet IAT, and higher output effective data rate.}, archive = {J_ACCESS}, author = {Shuting Guo and Daniel N. Aloi and Jia Li and Hongmei Zhao}, doi = {10.1109/ACCESS.2025.3548881}, journal = {IEEE Access}, pages = {44428-44444}, shortjournal = {IEEE Access}, title = {Physical layer evaluation on IEEE 802.11p with different configurations in NLOS scenarios for V2V communications}, volume = {13}, year = {2025}, }
• (2025). Channel-time-frequency attention module for improved multi-channel speech enhancement. ACCESS, 13, 44418-44427. (www)

  @article{ , abstract = {Both spatial and tempo-spectral information are essential for multi-channel speech enhancement, a field that has gained significant popularity in recent years. While many studies focus on improving feature extraction capabilities through unique network architectures, these approaches often prioritize raw feature learning without fully addressing how to effectively utilize the extracted features for enhanced performance. In this work, we focus on the post-extracted features and introduce a Channel-Time-Frequency Attention (CTFA) module, which allocates weights to the extracted features, aiming to enhance feature utilization and enabling the model to focus more effectively on informative features. The CTFA module is structured with three parallel attention branches—channel, time, and frequency branches—to effectively refine both spatial and tempo-spectral features. It facilitates better feature reuse by assigning greater weight to effective features, thereby improving the model’s robustness. We incorporate the CTFA module into our previously proposed model and conduct an ablation study to evaluate its effectiveness. Extensive experimental results confirm the efficacy of the CTFA module, with our proposed method outperforming state-of-the-art baselines.}, archive = {J_ACCESS}, author = {Xiao Zeng and Mingjiang Wang}, doi = {10.1109/ACCESS.2025.3546703}, journal = {IEEE Access}, pages = {44418-44427}, shortjournal = {IEEE Access}, title = {Channel-time-frequency attention module for improved multi-channel speech enhancement}, volume = {13}, year = {2025}, }
• (2025). Maximum flow model with multiple origin and destination and its application in designing urban drainage systems. ACCESS, 13, 44403-44417. (www)

  @article{ , abstract = {In this paper, a multi-origin and multi-destination maximum flow model is proposed by integrating the objective programming method with graph theory. Through Model 1, low-cost design of drainage pipeline networks can be achieved. For instance, in the experiments, certain pipelines such as Pipeline 9, Pipeline 20, and Pipeline 27 can be excluded during the initial construction phase while still maintaining effective drainage performance and reducing costs. Model 2 enables the monitoring of drainage capacity at various nodes within the existing drainage system. For example, under a specific rainfall intensity, it was observed that certain nodes, such as Nodes 9~16, Node 45, and Node 48, exhibit poor drainage capacity, necessitating precautionary measures. Model 3 focuses on the expansion planning of the drainage system, ensuring its normal operation under certain rainfall intensities. The results based on a specific area in Changchun City demonstrate that the proposed models not only possess strong practical application value in the design of urban drainage systems but also have broader applicability in other fields such as traffic flow and logistics supply chain planning. This study opens up new avenues for model construction and enriches the practical application effects of mixed-integer programming models.}, archive = {J_ACCESS}, author = {Mu Ren and Shiliang Wang and Bohan Liang and Cui Wei}, doi = {10.1109/ACCESS.2025.3549101}, journal = {IEEE Access}, pages = {44403-44417}, shortjournal = {IEEE Access}, title = {Maximum flow model with multiple origin and destination and its application in designing urban drainage systems}, volume = {13}, year = {2025}, }
• (2025). Sensory substitution device for time presentation: Spatial–Temporal vibrotactile encoding for presenting time on the human wrist. ACCESS, 13, 44385-44402. (www)

  @article{ , abstract = {Presenting information privately such as alertness levels and time on the wrist via vibrotactile feedback proves invaluable for visually impaired individuals. Additionally, in situations where the visual channel is occupied, this serves as a discreet solution for sighted users, allowing them to stay informed during meetings or tasks without the need to overtly check their watches, thus minimizing potential distractions. However, it is a challenging task to present time accurately and efficiently to the users using vibrotactile modality due to the perceptual limits of human’s haptic channel. Inspired by the metaphors of mechanical and digital watches that have been widely used in our daily lives, we proposed two novel spatial-temporal vibrotactile encoding strategies. By varying the location, number, and duration of the vibrotactile stimuli, these strategies are capable of presenting the exact information about the current time through a series of encoded tactile cues. A physical prototype was developed and fifteen participants were recruited to evaluate the two solutions. Two experiments were performed to evaluate the two encoding strategies. The results showed that the mechanical and digital encoding strategies achieved an average correct rate of $90.55 \pm 5.2\%$ and $95.22 \pm 4.1\%$ during the focused state, and $95.28 \pm 3.3\%$ and $97.78 \pm 3.8\%$ during the distracted state, respectively $(mean \pm SD)$ . Experimental results provide deep insights into utilizing the spatial-temporal patterns of vibrotactile stimuli for developing industrial-scale wearable haptic devices to present time and quantitative information efficiently and privately to the users.}, archive = {J_ACCESS}, author = {Naqash Afzal and Irfan Hussain and Zejian Zhou and Domenico Prattichizzo and Lakmal Seneviratne and Yuru Zhang and Dangxiao Wang}, doi = {10.1109/ACCESS.2025.3548552}, journal = {IEEE Access}, pages = {44385-44402}, shortjournal = {IEEE Access}, title = {Sensory substitution device for time presentation: Spatial–Temporal vibrotactile encoding for presenting time on the human wrist}, volume = {13}, year = {2025}, }
• (2025). SynMorph: Generating synthetic face morphing dataset with mated samples. ACCESS, 13, 44366-44384. (www)

  @article{ , abstract = {Face morphing attack detection (MAD) algorithms have become essential to overcome the vulnerability of face recognition systems. To solve the lack of large-scale and public-available datasets due to privacy concerns and restrictions, in this work we propose a new method to generate a synthetic face morphing dataset with 2450 identities and more than 100k morphs. The proposed synthetic face morphing dataset is unique for its high-quality samples, different types of morphing algorithms, and the generalization for both single and differential morphing attack detection scenarios. For experiments, we apply face image quality assessment and vulnerability analysis to evaluate the proposed synthetic face morphing dataset from the perspective of biometric sample quality and morphing attack potential on face recognition systems. The results are benchmarked with an existing SOTA synthetic dataset and a representative non-synthetic dataset and indicate improvement compared with the SOTA. Additionally, we design different protocols and study the applicability of using the proposed synthetic dataset on training morphing attack detection algorithms.}, archive = {J_ACCESS}, author = {Haoyu Zhang and Raghavendra Ramachandra and Kiran Raja and Christoph Busch}, doi = {10.1109/ACCESS.2025.3548957}, journal = {IEEE Access}, pages = {44366-44384}, shortjournal = {IEEE Access}, title = {SynMorph: Generating synthetic face morphing dataset with mated samples}, volume = {13}, year = {2025}, }
• (2025). Soccer-CLIP: Vision language model for soccer action spotting. ACCESS, 13, 44354-44365. (www)

  @article{ , abstract = {In the rapidly advancing field of computer vision, the application of multimodal models—specifically, vision-language frameworks—has shown substantial promise for complex tasks such as video-based action spotting. This paper introduces Soccer-CLIP, a vision-language model specially designed for soccer action spotting. Soccer-CLIP incorporates an innovative domain-specific prompt engineering strategy, leveraging large language models (LLMs) to refine textual representations for precise alignment with soccer-specific actions. Our model integrates both visual and textual features to enhance recognition accuracy of critical soccer events. With the temporal augmentation techniques devised for input videos, Soccer-CLIP builds upon existing methodologies to address the inherent challenges of temporally sparse event annotations within video sequences. Evaluations on the SoccerNet Action Spotting benchmark demonstrate that Soccer-CLIP outperforms previous state-of-the-art models, exploring the effectiveness of our model’s capacity to capture domain-specific contextual nuances. This work represents a significant advancement in automated sports analysis, providing a robust and adaptable framework for broader applications in video recognition and temporal action localization tasks.}, archive = {J_ACCESS}, author = {Yoonho Shin and Sanghoon Park and Youngsub Han and Byoung-Ki Jeon and Soonyoung Lee and Byung Jun Kang}, doi = {10.1109/ACCESS.2025.3549293}, journal = {IEEE Access}, pages = {44354-44365}, shortjournal = {IEEE Access}, title = {Soccer-CLIP: Vision language model for soccer action spotting}, volume = {13}, year = {2025}, }
• (2025). Recovering 3D basin basement relief using high-precision magnetic data through particle swarm optimization and back propagation algorithm. ACCESS, 13, 44343-44353. (www)

  @article{ , abstract = {The Inversion of magnetic basement interfaces in basins is critical for interpreting potential field data and studying geothermal resource distribution as well as basin formation and evolution. This paper proposes a new method for the inversion of magnetic basement interfaces using a particle swarm optimization algorithm that combines potential field processing and machine learning techniques. This method generates magnetic base interface models and the corresponding magnetic anomaly data through the random midpoint displacement method and magnetic interface finite element forward simulation. These anomalies are then handled using techniques, such as directional transformations, analytical continuation, spatial derivatives, and fractional transformations. Feature attributes were extracted, and the Gini importance was used to quantify feature factor contributions, screen out effective features, and improve algorithm efficiency. Validity and practicality were verified through an analysis of the theoretical and noise models. The proposed machine learning-based method is more intelligent, efficient, and accurately reflects the undulations of magnetic-based interfaces. Application to magnetic survey data in the Datong Basin resulted in a reliable basin-based model that matched known structural information, thereby opening a new direction for magnetic interface inversion research.}, archive = {J_ACCESS}, author = {Shen Yan and Xinjun Zhang and Zhongda Shang and Kai Wang and Yixin Ma}, doi = {10.1109/ACCESS.2025.3546636}, journal = {IEEE Access}, pages = {44343-44353}, shortjournal = {IEEE Access}, title = {Recovering 3D basin basement relief using high-precision magnetic data through particle swarm optimization and back propagation algorithm}, volume = {13}, year = {2025}, }
• (2025). LLM-driven social influence for cooperative behavior in multi-agent systems. ACCESS, 13, 44330-44342. (www)

  @article{ , abstract = {This paper presents a novel approach to fostering cooperative behavior in multi-agent systems (MAS) through Large Language Model (LLM)-driven social influence. We propose a theoretical framework where agents’ decision-making processes are influenced not through direct action but by subtle, narrative-driven influences disseminated by LLMs. These influences guide agents toward cooperative behaviors, such as rural repopulation, without requiring explicit policy interventions. We introduce a formal model grounded in game theory and social network dynamics, where agents balance the direct benefits of action with the indirect payoffs of LLM-guided influence. Using NASH equilibrium and Evolutionarily Stable Strategies (ESS), we demonstrate how cooperative behaviors emerge even when agents remain inactive but are subtly influenced by LLMs. Our experimental simulations validate the model, showing a strong positive correlation between network centrality and influence propagation ( $r = 0.969,\; p \lt 0.006$ ). Furthermore, temporal analysis reveals that the average influence increases from approximately 0.05–0.06 in the initial steps to 0.08–0.09 in later stages, indicating a cumulative and self-sustaining trend. In addition, the influence values exhibit a near-normal distribution (Shapiro-Wilk test, $p = 0.285$ ) and yield a large effect size (Cohen’s $d = 4.530$ ) when comparing agents with high versus low network centrality. Through visualization techniques and statistical metrics, we demonstrate the effectiveness of the proposed framework and identify promising directions for future research in AI-driven social influence. This study highlights the potential of LLM-driven narratives as a cost-effective, scalable alternative to traditional policy interventions, offering a new paradigm for promoting societal cooperation in areas such as rural repopulation, sustainability, and community development.}, archive = {J_ACCESS}, author = {J. de Curtò and I. de Zarzà}, doi = {10.1109/ACCESS.2025.3548451}, journal = {IEEE Access}, pages = {44330-44342}, shortjournal = {IEEE Access}, title = {LLM-driven social influence for cooperative behavior in multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). BMEA-ViT: Breast cancer classification using lightweight customized vision transformer architecture with multi-head external attention. ACCESS, 13, 44317-44329. (www)

  @article{ , abstract = {Breast cancer is one of the primary concerns for women’s mortality across the world. Breast cancer can be diagnosed by an expert pathologist through histopathological examination of the breast lesions. Manual analysis of histopathology images by an expert pathologist is time-consuming. Therefore, automated computer-aided diagnosis plays a significant role in breast cancer prognosis. State-of-the-art (SOTA) convolution neural networks (CNNs) have earlier been utilized in the development of such systems. However, CNNs have large inductive biases and cannot model long-term dependencies in terms of global features. In this proposed work, a customised Vision Transformer (ViT) architecture with multi-head external attention, termed as BMEA-ViT, has been developed for breast cancer diagnosis. The Transformer has much lesser inductive bias and can capture long-term dependencies using an attention mechanism. However, the multi-headed self-attention (MSA) unit used in the originally proposed ViT quadratically increases its computational complexity. Therefore, in the proposed customised ViT, the MSA unit has been replaced with the multi-head external attention (MEA) unit with linear complexity. In contrast to MSA, MEA is simply implemented using a sequence of two linear layers whose weights are shared across the whole dataset. This replacement of MSA with MEA results in better generalizability with fewer inductive biases. The proposed method achieves 95.74%, 96.96%, 98.18%, and 97.25% classification accuracy on the publicly available ‘BreakHis’ dataset at four different magnification levels: $\times 40$ , $\times 100$ , $\times 200$ , and $\times 400$ , respectively. The experimental results suggest that the proposed method outperforms other recent architectures.}, archive = {J_ACCESS}, author = {Ritesh Maurya and Nageshwar Nath Pandey and Satyajit Mahapatra}, doi = {10.1109/ACCESS.2025.3547862}, journal = {IEEE Access}, pages = {44317-44329}, shortjournal = {IEEE Access}, title = {BMEA-ViT: Breast cancer classification using lightweight customized vision transformer architecture with multi-head external attention}, volume = {13}, year = {2025}, }
• (2025). A performance evaluation of open source autonomous driving frameworks: Case studies of apollo and autoware. ACCESS, 13, 44294-44316. (www)

  @article{ , abstract = {Autonomous vehicles (AVs) are revolutionizing transportation by enhancing safety and efficiency. Despite significant advancements, the literature lacks a systematic benchmarking methodology for open-source autonomous driving stacks (ADS) in realistic simulation environments. This paper presents a methodology for comparing the performance of different autonomous driving (AD) frameworks. It has been applied to compare two leading open-source frameworks, Autoware.AI and Apollo.Auto, focusing on their trajectory planning modules in simulated scenarios. We conducted comprehensive simulations using the LGSVL simulator, integrating both stacks with high-fidelity sensor models and realistic environments. Using a diverse set of performance metrics, including trajectory deviation, computational effort, safety distance, energy consumption, and trajectory smoothness, we could quantitatively assess any planner. Our assessment revealed distinct trade-offs between the planners. Autoware.AI’s Open Planner provided higher safety margins by maintaining larger clearance distances from obstacles, resulting in increased path lengths and energy consumption. Conversely, Apollo.Auto’s Public Road planner offered smoother trajectories and enhanced passenger comfort but required higher computational resources. This work bridges the gap in benchmarking open-source ADS, providing valuable insights into their performance. Our findings contribute to the development of safer and more efficient autonomous vehicles by offering a systematic evaluation of key open-source platforms and highlighting the trade-offs inherent in trajectory planning.}, archive = {J_ACCESS}, author = {Marcus Vinícius Leal De Carvalho and Roberto Simoni and Leopoldo Rideki Yoshioka and João Francisco Justo and Bruno Mamoru Kato Kawakami}, doi = {10.1109/ACCESS.2025.3549649}, journal = {IEEE Access}, pages = {44294-44316}, shortjournal = {IEEE Access}, title = {A performance evaluation of open source autonomous driving frameworks: Case studies of apollo and autoware}, volume = {13}, year = {2025}, }
• (2025). Analysis and design of mmWave passive CMOS circulators with unequal port impedances. ACCESS, 13, 44288-44293. (www)

  @article{ , abstract = {This paper presents a new theoretical framework for integrated linearly periodically time-varying circulators, with a focus on non-50 $\Omega $ port impedance scenarios. The current literature predominantly addresses 50 $\Omega $ port impedance cases, thereby missing opportunities to enhance system features with favorable impedance levels on each port. In particular, at microwave frequencies, the proper choice of a low impedance for the transmitter output and an intermediate-to-high impedance for the receiver input is beneficial. Furthermore, our method boosts antenna performance since an optimum feed impedance can be chosen. We present a theoretical analysis to establish the general conditions for non-reciprocal operation in these unconventional impedance scenarios, and derive a formula that can be used to design the optimal operation. The analysis is validated through simulations of three circulator designs: an ideal S-parameter-based circulator, a switched transmission line circulator, and a switched all-pass network circulator. These designs incorporate non-50 $\Omega $ port impedances for demonstration purposes and are designed using the proposed methodology for 25GHz operation. The simulations demonstrate that the sample circulators achieve competitive performance in terms of both loss and isolation.}, archive = {J_ACCESS}, author = {Saeed Naghavi and Kari Stadius and Anu Lehtovuori and Ville Viikari and Jussi Ryynänen}, doi = {10.1109/ACCESS.2025.3549719}, journal = {IEEE Access}, pages = {44288-44293}, shortjournal = {IEEE Access}, title = {Analysis and design of mmWave passive CMOS circulators with unequal port impedances}, volume = {13}, year = {2025}, }
• (2025). Multimodal sentiment analysis based on expert mixing of subtask representations. ACCESS, 13, 44278-44287. (www)

  @article{ , abstract = {Multimodal sentiment analysis has garnered significant attention for capturing and comprehending human emotions more authentically and comprehensively, multimodal sentiment analysis has garnered significant attention. However, existing multi-task methods under multimodal sentiment analysis tasks are primarily text-driven, fail to fully consider non-linguistic modality tasks and lack the ability to extract subtask information from fused representations. To maximize the utilization of non-linguistic modality information in multimodal data, this paper proposes a subtask representation selection module based on an expert mixture to enhance the feature representation capabilities of speech and image subtasks. The module consisted of two parts: an expert network and a gating network. The expert network contains multiple identical experts, each trained to process a specific feature subspace and extract specific subtask representations from the multimodal fused representation to enhance the subtask performance. The gating network consists of speech and image parts, which are responsible for learning the contributions of the corresponding experts to their respective tasks and performing weighted fusion to strengthen the emotional expressions of the speech and image modality subtasks. The experimental results on the CH-SIMS and CH-SIMSv2s datasets demonstrate that the proposed subtask module based on an expert mixture can effectively improve the performance of multimodal sentiment analysis.}, archive = {J_ACCESS}, author = {Ling Lei and Wangjun He and Qiuyan Zheng and Bing Zhu}, doi = {10.1109/ACCESS.2025.3549280}, journal = {IEEE Access}, pages = {44278-44287}, shortjournal = {IEEE Access}, title = {Multimodal sentiment analysis based on expert mixing of subtask representations}, volume = {13}, year = {2025}, }
• (2025). Multiobjective time-space grid optimization of lithium-ion electrochemical model based on stability analysis and limit region decoupling strategy. ACCESS, 13, 44260-44277. (www)

  @article{ , abstract = {Lithium-ion batteries have been widely adopted as power sources in automotive and other fields. Balancing voltage prediction accuracy and simulation time is critical for the performance of battery management systems (BMS). This study proposes a time space grid optimization method based on the single particle model with electrolyte dynamics (SPMe) to enhance the computational efficiency and accuracy of electrochemical models while achieving a trade-off between voltage estimation precision and computational time. A second-order finite difference algorithm and Fourier stability analysis were used to determine the convergence constraints for SPMe model concentration calculations. A multi-objective time space grid optimization model was developed and solved using a multi-objective artificial bee colony algorithm. The effectiveness of the proposed method was validated by comparing simulation results under different scenarios. The model provides optimal time space grid configurations for various current profiles, including 1C-rate constant-current charging, pulse current (PC), hybrid current (HC), and urban dynamometer driving schedule (UDDS) current profiles. The SPMe model achieved a voltage estimation root mean square error (RMSE) of less than 5 mV, while maintaining a simulation time of less than 10 ms per second. This work provides a new idea for voltage estimation accuracy and calculation time balance of existing battery management systems under complex operating conditions.}, archive = {J_ACCESS}, author = {Libin Zhang and Minghang Zhang and Hongying Shan and Guan Xu and Jingsheng Dong and Xuemeng Bai}, doi = {10.1109/ACCESS.2025.3546617}, journal = {IEEE Access}, pages = {44260-44277}, shortjournal = {IEEE Access}, title = {Multiobjective time-space grid optimization of lithium-ion electrochemical model based on stability analysis and limit region decoupling strategy}, volume = {13}, year = {2025}, }
• (2025). ADR-SALD: Attention-based deep residual sign agnostic learning with derivatives for implicit surface reconstruction. ACCESS, 13, 44243-44259. (www)

  @article{ , abstract = {Learning 3D shape directly from raw data (i.e., un-oriented meshes, raw point clouds or triangle soups) and reconstructing high fidelity surfaces are still a difficult problem in computer vision and graphics. Several approaches have been proposed to learn from raw data, however, their reconstruction quality is somewhat limited in capturing small detail. Moreover, they introduce surface sheet in case of big gaps and empty spaces, and struggle in reconstructing small openings and thin structure. In this study, we address these problems by proposing a novel attention-based variational autoencoder architecture, ADR-SALD where the encoder and decoder are constructed based on the idea of residual feature learning and inception-like neural structure. We have adopted two different self attention mechanisms for sign agnostic learning in the encoder, which allow the proposed approach to learn the global spatial contextual dependencies and local features simultaneously for the 3D shape. This novel architecture solves the surface sheet problem of previous approaches such as SALD. Moreover, our experimental results show that ADR-SALD is more successful in reconstructing thin structure than the state-of-the-art approaches SALD and DC-DFFN, and has significant performance in separating small gaps. The proposed approach outperforms the baseline state-of-the-art approaches by reconstruction quality and quantitative measures.}, archive = {J_ACCESS}, author = {Abol Basher and Jani Boutellier}, doi = {10.1109/ACCESS.2025.3549279}, journal = {IEEE Access}, pages = {44243-44259}, shortjournal = {IEEE Access}, title = {ADR-SALD: Attention-based deep residual sign agnostic learning with derivatives for implicit surface reconstruction}, volume = {13}, year = {2025}, }
• (2025). Data-aided channel-gain calibration algorithm for recovering channel amplitude reciprocity in SFLC-OFDM systems. ACCESS, 13, 44231-44242. (www)

  @article{ , abstract = {This paper proposes a practical gain calibration algorithm that estimates and compensates for channel amplitude asymmetry in a space-frequency line code (SFLC) orthogonal frequency division multiplexing (OFDM) system. During uplink (UL) communications from the base station (BS) to user equipment (UE), the SFLC BS can acquire channel state information (CSI) for downlink (DL) communications from the BS to the UE by utilizing UL-DL channel reciprocity. However, the channel amplitudes between UL and DL are generally observed to be asymmetric in practical channels. This asymmetry results in an unexpected phase rotation of the decoded SFLC symbols at the UE, as the CSI used for SFLC encoding at the BS does not align with the actual DL channels. To address this issue, we have designed a data-aided channel-gain calibration algorithm (CCA). Based on the mathematical derivation of the phase rotation of received SFLC data symbols at the UE, we developed the CCA to adjust the received gain and restore channel amplitude symmetry. To practically validate the designed CCA, we implemented an off-the-shelf SFLC-OFDM communication system on a software modem testbed using Universal Software Radio Peripherals (USRPs). Experiments conducted with real-time SFLC-OFDM signals demonstrate that the proposed CCA significantly enhances SFLC decoding performance.}, archive = {J_ACCESS}, author = {Songmin Lee and Jingon Joung and Jihoon Choi and Juyeop Kim}, doi = {10.1109/ACCESS.2025.3549046}, journal = {IEEE Access}, pages = {44231-44242}, shortjournal = {IEEE Access}, title = {Data-aided channel-gain calibration algorithm for recovering channel amplitude reciprocity in SFLC-OFDM systems}, volume = {13}, year = {2025}, }
• (2025). Parameter-based transfer learning for bearing fault diagnosis using small samples under variable working conditions. ACCESS, 13, 44214-44230. (www)

  @article{ , abstract = {While measured vibration signals are widely accepted as an indicator of machine health conditions, whether debilitating or ideal, it remains challenging to effectively use them for real-time condition monitoring. The difficulty associated with data acquisition from faulty equipment due to hazard-related concerns further exacerbates the problem. Massive simulation data generated from a rotor-bearing dynamic model is utilized to build a machine learning model using a convolutional neural network (CNN). Several transfer learning strategies are utilized in conjunction with the small samples to facilitate real-world diagnosis. Two case studies are used to substantiate the model’s reliability. In one case, three out of four strategies show a perfect diagnosis, as opposed to the other case, where two out of four strategies achieve the same level of accuracy. The study reveals that positive transfer strategies are superior when developing a diagnosis framework using relatively small samples in both cross-domain and cross-working conditions.}, archive = {J_ACCESS}, author = {Presley Aduwenye and Charles Nutakor and Lassi Roininen and Jussi Sopanen}, doi = {10.1109/ACCESS.2025.3548549}, journal = {IEEE Access}, pages = {44214-44230}, shortjournal = {IEEE Access}, title = {Parameter-based transfer learning for bearing fault diagnosis using small samples under variable working conditions}, volume = {13}, year = {2025}, }
• (2025). Adaptive frequency control to overcome packet dropouts and uncertainties in hybrid power systems. ACCESS, 13, 44196-44213. (www)

  @article{ , abstract = {The increasing integration of renewable energy into hybrid power systems (HPS) introduces significant uncertainties, posing challenges to frequency control. Additionally, packet dropouts in measurement and control signals further complicate frequency regulation under these uncertain conditions. Existing frequency regulation techniques often neglect real-world data on load and source uncertainties, packet dropouts in measurement data, and lack comprehensive control. This paper presents a novel adaptive control architecture designed to maintain network frequency stability in the presence of packet dropouts and system uncertainties. The proposed methodology consists of offline uncertainty characterization and online adaptive control. A deep learning model predicts load uncertainties, while supervised machine learning models characterize renewable fluctuations, informing model-based control. Data-driven predictive control (DDPC) strategy has been implemented to compensate for packet dropouts. The DDPC features a triggering mechanism that activates adaptive control, combining uncertainty prediction with auxiliary control for dynamic frequency regulation. The proposed approach is validated through time-domain simulations on an isolated HPS with integrated renewable and distributed energy resources (DERs). Despite significant uncertainties and packet dropouts, the DDPC framework successfully limits frequency deviations to within 100 mHz. Furthermore, hardware-in-the-loop (HIL) simulations confirm the practical feasibility and robustness of the methodology. Integrating offline uncertainty characterization with online adaptive control offers an effective solution for robust frequency control in renewable integrated HPS.}, archive = {J_ACCESS}, author = {Renuka Loka and Alivelu M. Parimi and Srinivasa Rao Gampa and Nallapaneni Manoj Kumar}, doi = {10.1109/ACCESS.2025.3549462}, journal = {IEEE Access}, pages = {44196-44213}, shortjournal = {IEEE Access}, title = {Adaptive frequency control to overcome packet dropouts and uncertainties in hybrid power systems}, volume = {13}, year = {2025}, }
• (2025). Development of an improved KOA algorithm for solving task allocation in hilly orchards with weeding robots. ACCESS, 13, 44184-44195. (www)

  @article{ , abstract = {Multi-machine collaboration in agricultural machinery is a key focus in current research, with task allocation being an indispensable component. However, the current optimization objectives for task allocation in agricultural machinery are mostly confined to travel distance or time, aiming to balance task distribution. These methods are not suitable for emerging electric agricultural machinery, especially when operating in hilly areas. To address these limitations, this study proposed a task allocation method optimized for energy consumption, specifically for weeding robots in hilly orchards. Initially, drones were employed to obtain the Digital Surface Model (DSM) and orthophotos of the orchard test area. After processing the data through vegetation filtering, DEM construction, and slope analysis, slope information of the surface was derived. An electronic map of the orchard reflecting this slope information was then generated. Subsequently, the task allocation problem for weeding robots in hilly orchards was defined. A mathematical model was then established with energy consumption as the optimization objective. Finally, a Golden Kepler Optimization Algorithm (GKOA) was developed and tested through simulations using real data from the test area. The results indicated that, compared to Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Kepler Optimization Algorithm (KOA), GKOA reduced the optimal solution cost by 10.3%, 8.2%, 7.0%, and 4.5%, respectively. This task allocation method was able to achieve the optimal task allocation plan with lower travel energy consumption costs and a higher balance in task distribution, whether for all plots in the orchard or nested plots.}, archive = {J_ACCESS}, author = {Xiaolin Xie and Hang Jin and Heng Wang and Man Xu and Cheng Zhang and Xin Jin and Zhihong Zhang}, doi = {10.1109/ACCESS.2025.3548162}, journal = {IEEE Access}, pages = {44184-44195}, shortjournal = {IEEE Access}, title = {Development of an improved KOA algorithm for solving task allocation in hilly orchards with weeding robots}, volume = {13}, year = {2025}, }
• (2025). Dynamic window approach using GOA search algorithm for collision avoidance based on obstacle trajectory prediction. ACCESS, 13, 44169-44183. (www)

  @article{ , abstract = {Mobile robots not only need to navigate autonomously in complex environments, but also need to avoid collisions with dynamic obstacles, especially pedestrians. This paper realizes dynamic obstacles detection and state estimation algorithm with LiDAR data, and proposes the path planning and obstacle avoidance algorithm based on dynamic obstacle trajectory prediction. The current planning methods of mobile robots are mostly applicable to static scenes and lack adaptability to pedestrian dynamics. The core idea of the proposed algorithm is to guide the mobile robot towards the back of the pedestrian to reduce the psychological pressure on the pedestrian as well as to avoid interfering with the pedestrian’s movement and to achieve dynamic obstacle avoidance, which is in line with the idea of going around from the back in the Convention on the International Regulations for the Avoidance of Collisions at Sea (COLREGS). Firstly, the pedestrian detection method is studied on the basis of Lidar data, and the pedestrian motion state is estimated based on Kalman filter to avoid error detection. Secondly, the dynamic window approach (DWA) method is improved and pedestrian dynamic is added with carefully designed algorithm for path planning and obstacle avoidance. Furthermore, to improve the computational efficiency and flexibility, the GOA(Grasshopper Optimization Algorithm) is used to improve the DWA search schema. Finally, the effectiveness of the proposed method is proved by comparing the existing methods with simulation and experimental tests. The research of this paper can improve the safety of the robot in the human-machine crowd scene.}, archive = {J_ACCESS}, author = {Yuan Liu and Lingyu Zhang and Jiasheng Liu and YingYing She and Birui Zhang and Feiyan Min}, doi = {10.1109/ACCESS.2025.3546748}, journal = {IEEE Access}, pages = {44169-44183}, shortjournal = {IEEE Access}, title = {Dynamic window approach using GOA search algorithm for collision avoidance based on obstacle trajectory prediction}, volume = {13}, year = {2025}, }
• (2025). Improved path planning and controller design based on PRM. ACCESS, 13, 44156-44168. (www)

  @article{ , abstract = {This paper introduces the application of the Probabilistic Road Map (PRM) method in path planning and designs a path tracking controller based on the Lyapunov function. To enhance the smoothness of the paths generated by the PRM, Bézier curves are employed. Additionally, to address the path detection challenges encountered by the PRM algorithm in complex environments, this paper introduces normal distribution sampling and adaptive cost factors. By dynamically adjusting the PRM sampling points, the success rate of path detection is improved. The Improved PRM (IPRM) algorithm demonstrates better performance in terms of shorter path generation compared to the original PRM, RRT* and Bi-RRT algorithms. A path tracking controller is designed by integrating the curvature polynomial of the exploration path with the Lyapunov function. In complex mapping scenarios, dynamic obstacle avoidance strategies are incorporated to prevent collisions between the robot and obstacles. Experimental results indicate that the proposed controller achieves faster convergence, smaller tracking errors, and greater stability compared to traditional PD and PI controllers.}, archive = {J_ACCESS}, author = {Shengjin Chen and Guangyong Yang and Guanghai Cui and Shang Yi and Lihuang Wu}, doi = {10.1109/ACCESS.2025.3548326}, journal = {IEEE Access}, pages = {44156-44168}, shortjournal = {IEEE Access}, title = {Improved path planning and controller design based on PRM}, volume = {13}, year = {2025}, }
• (2025). Implementation and evaluation of a person identification system considering face image recognition accuracy and privacy protection. ACCESS, 13, 44135-44155. (www)

  @article{ , abstract = {In recent years, the use of facial recognition in personal authentication systems has been increasing, raising growing concerns about privacy violations due to the misuse of detailed facial images and feature data. Especially, it has become possible to reconstruct clear facial images from feature data using Generative Adversarial Network (GAN), making concealment of feature data a major issue. Facial recognition can be performed by means of encrypted feature data using quasi-homomorphic encryption, but this has the problem of being very computationally expensive. In this paper, we propose a system that enables personal identification in public spaces while simultaneously achieving privacy protection. To address the above problem, the proposed system adds epsilon noise ( $\epsilon $ -noise) to facial features, limiting the noise to a specific intensity, thus reducing computational load and allowing personal identification without transmitting detailed facial features over the network. By varying the intensity of the added noise, we measure the accuracy and risk of facial image reconstruction and determine the optimal intensity of noise needed to mitigate this threat. Our comprehensive evaluations demonstrate that real-time personal identification in public spaces is achievable while maintaining a balance between privacy protection and identification accuracy. We also showed that the proposed method is much faster than quasi-homomorphic encryption and thus can be used for real-time use cases such as video blogging.}, archive = {J_ACCESS}, author = {Satoru Matsumoto and Tomoki Yoshihisa and Hideyuki Shimonishi and Tomoya Kawakami and Yuuichi Teranishi}, doi = {10.1109/ACCESS.2025.3547758}, journal = {IEEE Access}, pages = {44135-44155}, shortjournal = {IEEE Access}, title = {Implementation and evaluation of a person identification system considering face image recognition accuracy and privacy protection}, volume = {13}, year = {2025}, }
• (2025). RLSTSVM: A robust prediction method for pancreatic postoperative complications. ACCESS, 13, 44123-44134. (www)

  @article{ , abstract = {Predicting pancreatic postoperative complications accurately is critical for assisting clinicians in performing risk assessments and tailoring personalized treatment and care plans. However, existing methods struggle with noisy and incomplete data from measurement errors and patient-specific variability, highlighting the urgent need for improved predictive approaches. This study alleviates the problem by introducing a novel predictive approach to improve our understanding and management of pancreatic postoperative complications. The research centers on the least squares twin support vector machine, enhanced with an L1-norm distance mechanism, referred to as RLSTSVM, to improve its prediction accuracy and robustness across both normal and noisy datasets. Further, we developed an iterative optimization framework to effectively deal with the nonconvex $L_{1}$ -norm term in objective functions. This framework not only enhances the performance of models but also demonstrates significant potential for addressing other related optimization problems. The effectiveness of our RLSTSVM is validated through experiments conducted on real-world pancreatic postoperative data. Our RLSTSVM improves the prediction accuracy of the baseline of 0.65 and 3.98 on normal and contaminated data, respectively. These findings highlight the potential of RLSTSVM to reduce postoperative complications and mortality rates, providing a valuable tool for healthcare system improvement and contributing to the development of evidence-based public health policies.}, archive = {J_ACCESS}, author = {He Yan and Xiang Zhu and Yan Liu and Jing Qiu and Xiaohua Hu}, doi = {10.1109/ACCESS.2025.3530468}, journal = {IEEE Access}, pages = {44123-44134}, shortjournal = {IEEE Access}, title = {RLSTSVM: A robust prediction method for pancreatic postoperative complications}, volume = {13}, year = {2025}, }
• (2025). AutoEffFusionNet: A new approach for cervical cancer diagnosis using ResNet-based autoencoder with attention mechanism and genetic feature selection. ACCESS, 13, 44107-44122. (www)

  @article{ , abstract = {Cervical cancer poses a significant global health challenge, necessitating accurate and efficient diagnostic solutions. This study introduces a novel hybrid framework, AutoEffFusionNet, that integrates unsupervised feature learning through ResNet-based autoencoders with attention mechanisms and supervised learning via transfer learning models. By leveraging the complementary strengths of these approaches, the proposed method achieves enhanced diagnostic accuracy in cervical cancer classification. Genetic algorithms optimize the feature selection process, retaining only the most relevant attributes, thereby addressing feature redundancy and improving computational efficiency. The selected features are then classified using a Support Vector Machine, effectively combining deep learning’s feature extraction capabilities with machine learning’s robust classification strengths. Additionally, Grad-CAM visualizations are incorporated to highlight critical regions influencing the classification decisions, enhancing interpretability and transparency. The framework was rigorously evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, achieving remarkable accuracies of 99.26% and 100%, respectively. These results demonstrate the effectiveness of the proposed model in addressing key challenges in cervical cancer diagnosis and its potential for deployment in clinical applications.}, archive = {J_ACCESS}, author = {Yahya Dogan}, doi = {10.1109/ACCESS.2025.3543850}, journal = {IEEE Access}, pages = {44107-44122}, shortjournal = {IEEE Access}, title = {AutoEffFusionNet: A new approach for cervical cancer diagnosis using ResNet-based autoencoder with attention mechanism and genetic feature selection}, volume = {13}, year = {2025}, }
• (2025). Actuator fault tolerant control in switched systems: A comprehensive approach integrating clustering, classification, and LMI-based compensation. ACCESS, 13, 44090-44106. (www)

  @article{ , abstract = {This paper presents a new strategy to address the challenges associated with the design of fault tolerant tolerant control (FTC) in stochastic switched systems. In fact, the problem of predicting the switched mode in discrete behavior is based on a set of input-output database. To address this, the proposed methodologies incorporate a combination of clustering and classification techniques to determine the number of sub-models. The estimated actuator fault is then compensated by controller generated for each sub model. The controller gains are given by solving the Linear Matrix Inequality (LMIs), which is derived based on the general Lyapunov function. The fast convergence of the actuator fault, which is estimated using the proposed data-driven method, minimizes the instability time of the switched system after fault occurrence and attenuates the disturbance. Finally, the effectiveness of the proposed method against actuator faults is validated through two numerical analyses. The first example shows a faulty stochastic switched system with two modes and the second example shows a practical application of a double cart system with elastic coupling.}, archive = {J_ACCESS}, author = {Salwa Yahia and Saida Bedoui and Kamel Abderrahim}, doi = {10.1109/ACCESS.2025.3545556}, journal = {IEEE Access}, pages = {44090-44106}, shortjournal = {IEEE Access}, title = {Actuator fault tolerant control in switched systems: A comprehensive approach integrating clustering, classification, and LMI-based compensation}, volume = {13}, year = {2025}, }
• (2025). Enhancing hand interactions and accessibility in virtual reality environments for users with motor disabilities: A practical case study on VR-shopping. ACCESS, 13, 44062-44089. (www)

  @article{ , abstract = {Over the past decade, Virtual Reality (VR) has achieved significant advancements in both quality and accessibility of its devices, particularly with VR headsets that offer an enhanced immersive experience at a reduced cost. This improvement is not only in graphical fidelity but also in interactivity within virtual environments, highlighted by advancements in tracking systems that allow direct manipulation with hands-free of external devices. However, these technologies are not fully adapted for use by individuals with motor disabilities in their arms and hands. This study addresses the ethical and moral obligation to make VR accessible to all users by proposing specific adaptations to the manual interaction mechanisms. Focusing on VR e-commerce as a use case, which is anticipated to revolutionize the shopping experience in the coming years, this study explores a scenario where users need to navigate and manipulate virtual products to examine and make purchase decisions. The experimental phase was conducted in a real-world setting at the Hospital Nacional de Parapléjicos in Toledo (HNPT), involving patients with spinal cord injuries and motor limitations. The results demonstrate that the adapted interactions not only enable users to perform tasks that are impossible with conventional mechanisms but also reduce the time and effort required to complete these tasks. Specifically, completion times achieved by users increased up to 91.6%. Also, the number of tasks completed highly increased in comparison with unadapted interactions. We elaborate an Effort Degree (ED) formula with various data items based on hand movements, that demonstrated that adapted interactions}, archive = {J_ACCESS}, author = {Rubén Grande and Javier Albusac and Vanesa Herrera and Dorothy Monekosso and Ana De Los Reyes-Guzman and David Vallejo and J. J. Castro-Schez}, doi = {10.1109/ACCESS.2025.3549527}, journal = {IEEE Access}, pages = {44062-44089}, shortjournal = {IEEE Access}, title = {Enhancing hand interactions and accessibility in virtual reality environments for users with motor disabilities: A practical case study on VR-shopping}, volume = {13}, year = {2025}, }
• (2025). The effect of asymmetric transistor aging on systolic arrays for mission critical machine learning applications. ACCESS, 13, 44041-44061. (www)

  @article{ , abstract = {Deep neural networks (DNNs) excel in various applications, such as computer vision, natural language processing, and other mission-critical systems. As the computational complexity of these models grows, there is an increasing need for specialized accelerators to handle the demanding workloads. In response, advancements in Very Large Scale Integration (VLSI) process nodes have significantly intensified the development of machine learning (ML) accelerators, offering enhanced transistor miniaturization and power efficiency. However, the susceptibility of these advanced nodes to transistor aging poses risks to ML accelerator performance, prediction accuracy, and reliability, which can impact the functional safety of mission-critical systems. This study focuses on the impact of asymmetric transistor aging, induced by Bias Temperature Instability (BTI), on systolic arrays (SAs), which are integral to many ML accelerators in mission-critical systems. Our aging-aware analysis indicates that SAs experience asymmetric aging, causing logical elements to age at varying rates. In addition, our simulations show that asymmetric transistor aging introduces persistent and transient faults in the SA’s datapath, compromising the overall resiliency of the ML model. Our simulation results show that even with less than 1% of transient failure events, the top-1 prediction accuracy of ResNet-18 ML model drops significantly by 32–50% and with approximately 0.8% of transient failure events PTQ4ViT drops by almost 90%. To address this issue, we propose new hardware mechanisms and design flow solutions that can successfully mitigate the impact of asymmetric transistor aging on ML accelerator reliability with minimal power and area overhead.}, archive = {J_ACCESS}, author = {Firas Ramadan and Gil Shomron and Freddy Gabbay}, doi = {10.1109/ACCESS.2025.3548966}, journal = {IEEE Access}, pages = {44041-44061}, shortjournal = {IEEE Access}, title = {The effect of asymmetric transistor aging on systolic arrays for mission critical machine learning applications}, volume = {13}, year = {2025}, }
• (2025). Application of the deeplabcut for the analysis and study on the crawling behavior of henosepilachna vigintioctopunctata (Fabricius). ACCESS, 13, 44024-44040. (www)

  @article{ , abstract = {Studying insect crawling behavior parameters can provide key data for pest control. This research focuses on the intelligent analysis of crawling behavior parameters using the Epilachna vigintioctopunctata as an example. Due to the small size and fast crawling speed of insects, existing methods struggle to achieve precise tracking of their leg movements. By integrating the pose estimation and motion analysis framework DeepLabCut with markerless pose estimation techniques for keypoint tracking, we designed and implemented an intelligent analysis algorithm based on pixel coordinate values to calculate crawling parameters. Data with a confidence level exceeding 0.9 accounted for 85.57% of the total, while data below 0.7 accounted for 11.49%. Compared to manually labeled coordinates, the mean absolute error (MAE) ranged from 1.0407 to 6.8053, with a maximum deviation distance of 0.1381 mm. Experimental results indicate that our method significantly reduces manual observation time while balancing accuracy and efficiency. It can also be applied to the crawling behavior of other small insects, analyzing parameters such as stride speed, stride frequency, and stride length, offering a new approach to pest control.}, archive = {J_ACCESS}, author = {Wei Zhan and Zhou Ke and Chenglong Wang and Yinglong Pan and Yong Sun and Xingyu Yin}, doi = {10.1109/ACCESS.2025.3543311}, journal = {IEEE Access}, pages = {44024-44040}, shortjournal = {IEEE Access}, title = {Application of the deeplabcut for the analysis and study on the crawling behavior of henosepilachna vigintioctopunctata (Fabricius)}, volume = {13}, year = {2025}, }
• (2025). A data-driven recommendation system for enhancing non-functional requirements elicitation in scrum-based projects. ACCESS, 13, 44000-44023. (www)

  @article{ , abstract = {Context: Agile software development, particularly Scrum, enables teams to manage evolving requirements by emphasizing face-to-face communication and incremental deliveries. Although effective in addressing functional requirements, agile methods often overlook non-functional requirements during the initial stages of software projects, potentially leading to cost overruns on software and hardware and project failures exceeding 60%. Objective: In this article, we introduce a data-driven recommendation system to assist Scrum teams in eliciting NFRs effectively and early in the development lifecycle. Method: Our proposed solution applies the k-nearest neighbors algorithm to recommend non-functional requirements by leveraging historical project data structured through a taxonomy of user stories. We evaluated the system through offline experiments under the cross-validation protocol, utilizing datasets from 13 real-world projects. Results: Our recommendation system achieved an F-measure of up to 79%, demonstrating its ability to provide accurate and context-aware non-functional requirements suggestions. Conclusion: These findings suggest that our solution supports agile teams by automating non-functional requirement elicitation and enhancing decision-making processes, thereby addressing critical gaps in non-functional requirement integration within Scrum-based projects.}, archive = {J_ACCESS}, author = {Felipe Ramos and Alexandre Costa and Mirko Perkusich and Luiz Silva and Dalton Valadares and Ademar de Sousa Neto and Felipe Cunha and Hyggo Almeida and Angelo Perkusich}, doi = {10.1109/ACCESS.2025.3548631}, journal = {IEEE Access}, pages = {44000-44023}, shortjournal = {IEEE Access}, title = {A data-driven recommendation system for enhancing non-functional requirements elicitation in scrum-based projects}, volume = {13}, year = {2025}, }
• (2025). Minimizing delay at closely spaced signalized intersections through green time ratio optimization: A hybrid approach with K-means clustering and genetic algorithms. ACCESS, 13, 43981-43999. (www)

  @article{ , abstract = {Closely spaced intersections play a key role in traffic flow management. This study aims to model different traffic related parameters to minimize the delay of a closely spaced intersection by optimizing the green time ratio with the help of the genetic algorithm. The factors influencing optimization were selected as the distance between two adjacent intersections, cycle time, degree of saturation, green time ratio, volume, and the queue length-to-distance ratio, which is considered the Index parameter. The dataset was calibrated, validated, and used to simulate the analysis of traffic scenarios using SIDRA Intersection. To provide a clearer analysis, the k-means clustering algorithm was applied to divide the distances into three clusters. Among these clusters, the distance range between 110 and 160 meters is identified as the transition zone. The optimal green time ratio to minimize delay value for closely spaced intersection clusters was determined within a range of 0.58 to 0.69. To ensure a more comprehensive analysis, these values are used to examine their impact on delays. For this reason, the scenarios were restructured again with SIDRA using the newly optimized values to evaluate whether there is any reduction in the traffic-related parameters. The delay values and their temporal fluctuations showed significant improvements with this hybrid approach. The optimized green time ratios reduced delay, degree of saturation, and CO2 emissions by 8.95%, 8%, and 4.72% at the downstream intersection, and by 6.86%, 6.16%, and 7.09% at the upstream intersection, respectively.}, archive = {J_ACCESS}, author = {Ruti R. Politi and Serhan Tanyel}, doi = {10.1109/ACCESS.2025.3549970}, journal = {IEEE Access}, pages = {43981-43999}, shortjournal = {IEEE Access}, title = {Minimizing delay at closely spaced signalized intersections through green time ratio optimization: A hybrid approach with K-means clustering and genetic algorithms}, volume = {13}, year = {2025}, }
• (2025). A model-free deep reinforcement learning algorithm for solving multi-agent nash equilibrium with unstable communication. ACCESS, 13, 43973-43980. (www)

  @article{ , abstract = {Most reinforcement learning (RL) algorithms proposed to solve Nash equilibrium in multi-agent systems assume stable communication conditions or rely on accurate models of the environment. However, these assumptions are often unrealistic in practical applications since communication is not always stable, and obtaining precise models for dynamic environments in real-time is challenging. To address these issues, this paper proposes a model-free RL algorithm based on the deep deterministic policy gradient (DDPG) algorithm. The proposed method is designed to handle communication instability and the resulting variability in information exchange between agents. Analytical and numerical results demonstrate that the proposed algorithm does not require storing data from neighboring agents. It also achieves better adaptability and convergence under unstable communication conditions compared to existing methods.}, archive = {J_ACCESS}, author = {Yuannan Jiang and Shengming Jiang and Xiaofeng Wang}, doi = {10.1109/ACCESS.2025.3549276}, journal = {IEEE Access}, pages = {43973-43980}, shortjournal = {IEEE Access}, title = {A model-free deep reinforcement learning algorithm for solving multi-agent nash equilibrium with unstable communication}, volume = {13}, year = {2025}, }
• (2025). Enhancing reinforcement learning-based energy management through transfer learning with load and PV forecasting. ACCESS, 13, 43956-43972. (www)

  @article{ , abstract = {Effective energy management in microgrids with renewable energy sources is crucial for maintaining system stability while minimizing operational costs. However, traditional Reinforcement Learning (RL) controllers often encounter challenges, including long training time and instability during the training process. This study introduces a novel approach that integrates Transfer Learning (TL) techniques with RL controllers to address these issues. By using synthetic datasets generated by advanced forecasting models, such as ResNet18+BiLSTM, the proposed method pre-trains RL agents, embedding domain knowledge to enhance performance. The results, based on one year of operational data, show that TL-enhanced RL controllers significantly reduce cumulative operation costs and system imbalance, achieving up to a 62.63% reduction in costs and an 80% improvement in balance compared to baseline models. Furthermore, the proposed method improves initial performance and shortens the training duration needed to reach operational thresholds. This approach demonstrates the potential of combining TL with RL to develop efficient, cost-effective solutions for real-time energy management in complex power systems.}, archive = {J_ACCESS}, author = {Chang Xu and Masahiro Inuiguchi and Naoki Hayashi and Wong Jee Keen Raymond and Hazlie Mokhlis and Hazlee Azil Illias}, doi = {10.1109/ACCESS.2025.3548990}, journal = {IEEE Access}, pages = {43956-43972}, shortjournal = {IEEE Access}, title = {Enhancing reinforcement learning-based energy management through transfer learning with load and PV forecasting}, volume = {13}, year = {2025}, }
• (2025). Hyperdimensional intelligent sensing for efficient real-time audio processing on extreme edge. ACCESS, 13, 43947-43955. (www)

  @article{ , abstract = {The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications like gunshot detection systems (GSDS), and the proliferation of edge sensors exacerbates these issues. This paper proposes a groundbreaking approach with a near-sensor model tailored for intelligent audio-sensing frameworks. Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. It is highly adaptable for efficient ASIC design implementation, offering superior energy efficiency compared to conventional embedded CPUs or GPUs, and is compatible with the trend of shrinking microphone sensor sizes. Comprehensive evaluations at both software and hardware levels underscore the model’s efficacy. Software assessments through detailed ROC curve analysis revealed a delicate balance between energy conservation and quality loss, achieving up to 82.1% energy savings with only 1.39% quality loss. Hardware evaluations highlight the model’s commendable energy efficiency when implemented via ASIC design, especially with the Google Edge TPU, showcasing its superiority over prevalent embedded CPUs and GPUs.}, archive = {J_ACCESS}, author = {Sanggeon Yun and Ryozo Masukawa and Hanning Chen and Sungheon Jeong and Wenjun Huang and Arghavan Rezvani and Minhyoung Na and Yoshiki Yamaguchi and Mohsen Imani}, doi = {10.1109/ACCESS.2025.3543232}, journal = {IEEE Access}, pages = {43947-43955}, shortjournal = {IEEE Access}, title = {Hyperdimensional intelligent sensing for efficient real-time audio processing on extreme edge}, volume = {13}, year = {2025}, }
• (2025). Memcapacitive spiking neurons and associative memory application. ACCESS, 13, 43933-43946. (www)

  @article{ , abstract = {The Hodgkin and Huxley neuron model describes the complex behavior of biological neurons. However, due to the complexity of these computations, the Hodgkin and Huxley models are impractical for use in large-scale networks. In contrast, Izhikevich introduced a simpler model capable of producing various firing patterns typical of cortical neurons. This study proposes a novel model of memcapacitive-based neurons that offers a potential implementation of spiking neurons with energy efficiency due to the inherent storage nature of memcapacitive devices. The findings demonstrate that memcapacitive neurons can produce 23 firing patterns similar to Izhikevich neurons but at significantly higher firing rates. Memcapacitive neurons exhibit firing patterns associated with excitatory, inhibitory, and thalamocortical neurons. Similar to Izhikevich neurons, pulse-coupled neural networks of memcapacitive neurons display collective behaviors, such as synchronous and asynchronous responses, which are common in the biological brain. Compared to Hopfield and Izhikevich networks in content-addressable memory applications, memcapacitive networks successfully retrieved correct memory patterns with high accuracy, even for distorted inputs of up to 40%. The simulation results illustrate that the novel model of the memcapacitive spiking neuron offers a potential advancement in implementing artificial spiking neurons with high energy efficiency, bringing a step closer to mimicking biological neurons.}, archive = {J_ACCESS}, author = {S. J. Dat Tran}, doi = {10.1109/ACCESS.2025.3549357}, journal = {IEEE Access}, pages = {43933-43946}, shortjournal = {IEEE Access}, title = {Memcapacitive spiking neurons and associative memory application}, volume = {13}, year = {2025}, }
• (2025). An investigation into the impact of typical defects on transformer oil pressure: Unveiling the potential for fault localization. ACCESS, 13, 43919-43932. (www)

  @article{ , abstract = {Large oil-filled transformers are integral components of power systems, but internal arc faults during operation can lead to catastrophic failures, including explosions, which have occurred globally in recent years. These incidents pose significant risks to the stable operation of power systems and, in some cases, endanger the safety of operational and maintenance personnel. This study focuses on a 220 kV transformer, and develops a multiphysics model to investigate the propagation of pressure waves generated by internal arcs in transformer oil. The model analyzes the influence of several factors on pressure wave characteristics, including the iron core, fault point location, arc energy, and the blockage of the oil flow at the lower end of the oil storage tank. Notably, the iron core is a critical factor that must not be overlooked in the modeling process. When compared to models that ignore the iron core, the oil pressure in the upper housing is found to decrease by nearly 40%. Furthermore, the study suggests that deploying more than three distributed sensors within the transformer could enable effective fault source localization. To ensure a sensitive and reliable response to faults, key recommendations for sensor selection are provided: the response threshold should not be less than 0.1 MPa, and the response time should be under 20 ms. The findings of this paper offer essential data and insights for advancing distributed oil pressure sensing technologies in transformers and contribute to enhancing the safe operation of transformer equipment.}, archive = {J_ACCESS}, author = {Jinghui Ruan and Lu Tian and Kaibin Liang and Lezhi Ou and Puming Xu and Ping Peng and Yun Liu and Lipeng Zhong}, doi = {10.1109/ACCESS.2025.3544801}, journal = {IEEE Access}, pages = {43919-43932}, shortjournal = {IEEE Access}, title = {An investigation into the impact of typical defects on transformer oil pressure: Unveiling the potential for fault localization}, volume = {13}, year = {2025}, }
• (2025). SURE: Session-based uninteresting item removal for enhanced recommendations. ACCESS, 13, 43904-43918. (www)

  @article{ , abstract = {Sequential recommendation systems are designed to predict user’ s subsequent actions and recommend relevant items, particularly within session-based interactions where user preferences can change rapidly. A significant challenge in these systems is dealing with short sequences of user interactions, which often lack sufficient data to capture user preferences, leading to imprecise recommendations. Although various research efforts have aimed to improve the handling of short sequences, the presence of uninteresting items within these sequences continues to hinder the accuracy of the recommendation system. To address this challenge, we propose a novel approach, Session-based Uninteresting Item Removal for Enhanced Recommendations (SURE), which integrates Short-Sequence Enhancement with the innovative removal of uninteresting items using association rule mining. Our method incorporates a backward prediction model that generates pseudo-historical items, enriching short sequences while preserving relevant information. The core novelty of our approach lies in the targeted removal of uninteresting items through association rule mining. By systematically identifying and filtering out items deemed uninteresting based on user preferences, we refine the sequence data to ensure that only relevant and engaging items remain. This refinement process enhances the overall quality and accuracy of the recommendations. Our approach significantly improves recommendation performance, achieving an average gain of 7.31% in mean reciprocal rank (MRR) and higher Normalised Discounted Cumulative Gain (nDCG) and accuracy scores across multiple real-world datasets compared to state-of-the-art techniques. These advancements highlight the model’ s ability to deliver more accurate, relevant, and interpretable recommendations, demonstrating its potential for real-world applications.}, archive = {J_ACCESS}, author = {Navaneeth Sivakumar and Ankith Motha and G. Suganeshwari and S. P. Syed Ibrahim and Vijayan Sugumaran}, doi = {10.1109/ACCESS.2025.3549133}, journal = {IEEE Access}, pages = {43904-43918}, shortjournal = {IEEE Access}, title = {SURE: Session-based uninteresting item removal for enhanced recommendations}, volume = {13}, year = {2025}, }
• (2025). Efficient large graph partitioning scheme using incremental processing in GPU. ACCESS, 13, 43889-43903. (www)

  @article{ , abstract = {As the processing of large-scale graphs on a single device is infeasible without partitioning, graph partitioning algorithms are essential for various algorithms and distributed computing tasks utilizing graph data. However, graph partitioning is a nondeterministic polynomial time NP-Complete problem, which is characterized by high computational complexity. To address this complexity, previous studies have proposed processing graphs in parallel using GPUs. Nonetheless, due to the limited memory space of GPUs compared to CPUs, they are susceptible to out-of-memory (OOM) issues. This research proposes a GPU-accelerated graph partitioning technique that employs dynamic memory management and incremental processing. The proposed method incrementally processes large graphs and reduces the overall size of the graph through streaming clustering on the CPU. The reduced graph is sufficiently small to be processed on the GPU. The method combines an initial partitioning based on the label propagation algorithm with the high-degree replicated first algorithm to leverage the high parallel processing capabilities of the GPU and manage the computational load of graph partitioning. Experiments on various large-scale real-world graph datasets demonstrate the efficiency, scalability, and superior partitioning quality of the proposed method. Specifically, the method achieves execution speeds up to 9 times faster than CPU-based streaming techniques on large graphs and improves the replication factor by over 20% compared to existing methods. Furthermore, it demonstrates stable processing of large-scale graphs that previous GPU-based methods such as GPU-P could not handle owing to memory limitations.}, archive = {J_ACCESS}, author = {Hyeonbyeong Lee and Jeonghyun Baek and Sangho Song and Yuna Kim and Hyunjung Hwang and Jongtae Lim and Dojin Choi and Kyoungsoo Bok and Jaesoo Yoo}, doi = {10.1109/ACCESS.2025.3547976}, journal = {IEEE Access}, pages = {43889-43903}, shortjournal = {IEEE Access}, title = {Efficient large graph partitioning scheme using incremental processing in GPU}, volume = {13}, year = {2025}, }
• (2025). FLaNS: Feature-label negative sampling for out-of-distribution detection. ACCESS, 13, 43878-43888. (www)

  @article{ , abstract = {Many existing deep learning models suffer from a fundamental limitation, where they often misclassify Out-of-Distribution (OOD) data as In-Distribution (ID) data. OOD data represent data patterns that differ from the training distribution, such as images of unseen classes or different domains. This misclassification occurs because deep learning models are inherently designed to classify inputs into one of their known categories. To address this limitation, we propose Feature-Label Negative Sampling (FLaNS), which exploits the observation that OOD data inherently exhibit mismatches between features and their assigned labels. Our method constructs negative data by deliberately creating feature-label mismatches from ID data, which naturally deviate from the learned ID distribution. These synthetic data enable the Support Vector Machine (SVM) to learn decision boundaries that discriminate between ID and OOD data without requiring actual OOD data during training. We evaluated our method on the NCT-CRC-HE and CIFAR-100 datasets using two backbone models. Our approach demonstrates stable and reliable performance in both Receiver Operating Characteristics (ROC) Curve Area (AUROC) and False Positive Rate at 95% True Positive Rate (FPR95) metrics compared to existing methods. This research enhances OOD detection by introducing a method that leverages feature-label mismatches in ID data, improving detection performance without needing diverse OOD data, which are often difficult to collect.}, archive = {J_ACCESS}, author = {Chaejin Lim and Junhee Hyeon and Kiseong Lee and Dongil Han}, doi = {10.1109/ACCESS.2025.3548702}, journal = {IEEE Access}, pages = {43878-43888}, shortjournal = {IEEE Access}, title = {FLaNS: Feature-label negative sampling for out-of-distribution detection}, volume = {13}, year = {2025}, }
• (2025). Variable action set-based monte carlo tree search algorithm for AAV autonomous collision avoidance. ACCESS, 13, 43861-43877. (www)

  @article{ , abstract = {Autonomous collision avoidance of Autonomous Aerial Vehicles (AAVs) is becoming a hot research topic in Urban Air Mobility (UAM) operations. Many studies modeled this problem as a Markov decision process (MDP) and adopted intelligent methods such as reinforcement learning and Monte Carlo Tree Search (MCTS) to design collision avoidance algorithms. Among these studies, some algorithms with discrete action sets have achieved good results in collision avoidance. However, the fixed discrete action set often leads to low flight quality, as the rigid control makes the AAV unable to stably fly straight in the desired direction but can only fly wobbly. To address this problem, a Variable Action Set-based MCTS algorithm (VASM) for autonomous collision avoidance is proposed in this paper. By introducing an additional action option that is carefully designed to improve the stability and efficiency of flight along any desired heading, a variable action set is constructed for VASM. In addition, a reward function that takes into account more rational evaluation factors is also proposed to facilitate better decision-making. We conducted numerical experiments to verify the effectiveness of VASM, and results show that compared with the conventional MCTS algorithm, VASM can better improve flight control and flight quality with only a small increase in computational cost, achieving more efficient and energy-saving flight guidance. Specifically, in high-density airspace, VASM reduces average flight distance by 15.34%, average flight time by 15.08%, average flight turns by 36.64%, conflict probability by 18.56%, while increasing target arrival probability to 98.3%.}, archive = {J_ACCESS}, author = {Fulong Xu and Bo Li}, doi = {10.1109/ACCESS.2025.3547885}, journal = {IEEE Access}, pages = {43861-43877}, shortjournal = {IEEE Access}, title = {Variable action set-based monte carlo tree search algorithm for AAV autonomous collision avoidance}, volume = {13}, year = {2025}, }
• (2025). CCT: A cyclic co-teaching approach to train deep neural networks with noisy labels. ACCESS, 13, 43843-43860. (www)

  @article{ , abstract = {The detrimental impact of noisy labels on the generalization performance of deep neural networks has sparked research interest in learning with noisy labels (LNL). Among the various methods proposed to mitigate this effect, the Co-Teaching method, characterized by co-training with the small-loss criterion, is one of the most established approaches and is widely employed as a key component in recent LNL methods. Although Co-Teaching can mitigate the overfitting effect, it still remains, especially in scenarios with high rates of label noise in datasets. Strategies from the LNL literature to address this typically include the use of disagreement techniques and alternative loss functions. In this paper, we propose the Cyclic Co-Teaching (CCT) method, which employs cyclic variations in the learning rate and sample retention rate at the mini-batch level, along with a checkpoint mechanism that ensures that training in subsequent cycles always resumes from the best models obtained so far. For optimizing the method, we developed a framework that incorporates a pre-training phase to obtain an optimized vanilla model used to initialize CCT model weights, and a transparent univariate optimization strategy for hyperparameters that does not necessarily require a clean validation set. Experimental results on synthetic and real-world datasets, under different types and levels of noise and employing various neural network architectures, demonstrate that CCT outperforms several state-of-the-art LNL methods in most evaluated scenarios.}, archive = {J_ACCESS}, author = {Jorge K. S. Kamassury and Henrique Pickler and Filipe R. Cordeiro and Danilo Silva}, doi = {10.1109/ACCESS.2025.3548510}, journal = {IEEE Access}, pages = {43843-43860}, shortjournal = {IEEE Access}, title = {CCT: A cyclic co-teaching approach to train deep neural networks with noisy labels}, volume = {13}, year = {2025}, }
• (2025). SPARQ: A cyber-resilient voltage regulation using soft Q-learning approach for autonomous grid operations. ACCESS, 13, 43830-43842. (www)

  @article{ , abstract = {The growing integration of distributed energy resources and increased interconnectivity in cyber-physical power systems (CPPSs) have heightened their complexity. This complexity has made voltage stability control more vulnerable, especially under cybersecurity threats. Cybersecurity threats enable the manipulation of critical system states, potentially causing blackouts and cascading failures. This highlights the need for adaptive, efficient, and resilient control mechanisms to ensure CPPS stability. This paper presents a novel Stability and voltage Protection Achieved with Resilient Soft Q-learning (SPARQ). The proposed approach leverages a Soft Q-Learning (SQL) framework to autonomously regulate voltage stability while addressing the impact of cyber attacks. The proposed SQL-based control system incorporates adaptive preprocessing mechanisms to normalize observations and enhance policy robustness. The study evaluates the performance of the SQL agent under both normal and cyber-attacked scenarios, with simulated disturbances such as voltage variability, stochastic load dynamics, and deliberate data injections. Comprehensive experiments on the IEEE 14-bus, reduced IEEE 118-bus, and IEEE 118-bus systems demonstrate the effectiveness of the SQL framework in achieving improved voltage regulation. Additionally, the SQL framework exhibits faster convergence and higher rewards compared to baseline reinforcement learning methods. Moreover, the framework’s effectiveness under cyber attack highlights its potential for resilient voltage stability control in modern CPPSs.}, archive = {J_ACCESS}, author = {Mohamed Massaoudi}, doi = {10.1109/ACCESS.2025.3548517}, journal = {IEEE Access}, pages = {43830-43842}, shortjournal = {IEEE Access}, title = {SPARQ: A cyber-resilient voltage regulation using soft Q-learning approach for autonomous grid operations}, volume = {13}, year = {2025}, }
• (2025). Comparison of two-level and three-level graded collision warning systems under distracted driving conditions. ACCESS, 13, 43818-43829. (www)

  @article{ , abstract = {Advancements in vehicle technology such as collision warning systems are essential for improving driver alertness and decision-making to enhance road safety. This study evaluates the effectiveness of 2-level and 3-level graded collision warning systems on driver performance under various driving conditions. Forty participants participated in a controlled driving simulator study using a within-between-participant design to examine the impact of these warning systems on response time, collision frequency, physiological responses, and visual attention dynamics. The 3-level system providing graduated alerts through visual, haptic, and auditory cues, showed a significant reduction in response times and collision frequencies compared to the 2-level system. This improvement likely results from the multi-sensory approach that supports cognitive load theory and facilitates hazard detection. Although physiological measures such as Electrodermal Activity and Heart Rate did not show significant differences between the systems, the 3-level system produced more consistent responses suggesting a stable emotional state and reduced stress. Eye-tracking data indicated that the 3-level system improved sustained visual attention and reduced distraction. Subjective evaluations favored auditory warnings emphasizing the importance of user-friendly and intuitive systems. The findings demonstrate the potential of multi-level warning systems to enhance driver safety and performance in high-risk scenarios and suggest the need for customizable systems to accommodate individual differences in cognitive load management. These insights can inform developing advanced collision warning systems that mitigate risks associated with distracted driving and promote a safer driving environment.}, archive = {J_ACCESS}, author = {Khatereh Shariatmadari and Siby Samuel and Shi Cao and Amandeep Singh}, doi = {10.1109/ACCESS.2025.3549290}, journal = {IEEE Access}, pages = {43818-43829}, shortjournal = {IEEE Access}, title = {Comparison of two-level and three-level graded collision warning systems under distracted driving conditions}, volume = {13}, year = {2025}, }
• (2025). Predicting user purchases from clickstream data: A comparative analysis of clickstream data representations and machine learning models. ACCESS, 13, 43796-43817. (www)

  @article{ , abstract = {Predicting purchase events from e-commerce clickstream data is a critical challenge with significant implications for optimizing marketing strategies and enhancing customer experience. This study addresses this challenge by systematically evaluating and comparing multiple data representations – aggregated session attributes, recent user actions, and hybrid combinations – which bridges gaps in the existing literature and demonstrates the superiority of hybrid approaches. Unlike prior research, which typically focuses on single representations, our approach combines aggregated session-level summaries with granular, sequential user actions to capture both long-term and short-term behavioral patterns. Through comprehensive experimentation, we compared multiple machine learning models, including LightGBM, decision trees, gradient boosting, SVC, and logistic regression, using real-world e-commerce clickstream data. Notably, the hybrid representation with LightGBM achieved superior predictive performance, significantly outperforming alternative methods. Feature importance analysis revealed key factors influencing purchase likelihood, such as time since the last event, session duration, and product interactions. This study provides actionable insights into real-time marketing interventions by demonstrating the practical utility of hybrid data representations and efficient tree-based models. Our findings offer a scalable and interpretable framework for e-commerce platforms to enhance purchase predictions and optimize marketing strategies.}, archive = {J_ACCESS}, author = {A. Aylin Tokuç and Tamer Dag}, doi = {10.1109/ACCESS.2025.3548267}, journal = {IEEE Access}, pages = {43796-43817}, shortjournal = {IEEE Access}, title = {Predicting user purchases from clickstream data: A comparative analysis of clickstream data representations and machine learning models}, volume = {13}, year = {2025}, }
• (2025). Integrating a spore germination sensor with continuous wavelet transform for detecting orchid diseases in greenhouses. ACCESS, 13, 43781-43795. (www)

  @article{ , abstract = {Traditional methods of orchid disease detection rely on manual inspection, which is both labor-intensive and inefficient. Modern smart solutions integrate Internet of Things (IoT) and Artificial Intelligence (AI) technologies to address these limitations in commercial greenhouses. Our approach leverages IoT for real-time monitoring of critical environmental factors, such as temperature and humidity, to enhance disease prediction accuracy, with a specific focus on Phalaenopsis orchids. We propose OrchidTalk-v2, a novel and dynamically adaptable system designed to improve disease risk monitoring. OrchidTalk-v2 integrates an intelligent spore germination sensor with Continuous Wavelet Transform (CWT) for feature extraction, feeding the data into a three-dimensional Convolution LSTM network model. The system achieves a precision rate exceeding 93% and provides early alerts for disease outbreaks with a recall rate above 92.75%. This marks a significant advancement in prediction accuracy for orchid disease detection within controlled greenhouse environments.}, archive = {J_ACCESS}, author = {Yi-Bing Lin and Claire Yi-Ting Chen and Wan-Jung Hsieh and Wen-Liang Chen and Edward W. Sun and Yun-Wei Lin}, doi = {10.1109/ACCESS.2025.3547930}, journal = {IEEE Access}, pages = {43781-43795}, shortjournal = {IEEE Access}, title = {Integrating a spore germination sensor with continuous wavelet transform for detecting orchid diseases in greenhouses}, volume = {13}, year = {2025}, }
• (2025). Integrated risk grid map for collision avoidance and mitigation maneuvers of autonomous vehicle. ACCESS, 13, 43767-43780. (www)

  @article{ , abstract = {As autonomous driving algorithms evolve, algorithms for collision avoidance are being developed for situations that primary algorithms cannot resolve. Generally, the positions of all surrounding vehicles are predicted, and the risk is assessed to create a risk map, which is then used to generate a safe path. Most algorithms focus on collision avoidance without considering scenarios in which avoidance fails and a collision occurs. However, collision avoidance algorithms need to consider imminent collision scenarios and provide information about potential injuries or damage. Therefore, this paper proposes an integrated risk map capable of identifying the safest space, utilizing data on the risk prediction of surrounding vehicles, the severity of collisions in autonomous vehicles, and human injury data. The integrated risk map comprises two layers: a risk prediction grid map based on relative information with surrounding vehicles and a severity grid map based on collision severity and human injury data in collision zones. These two layers are combined to calculate an integrated risk value, enhancing passenger safety by considering potential damage when generating a collision avoidance path. The proposed integrated risk map was evaluated using the same path generation algorithm and scenarios as those used for risk maps based solely on relative information. The evaluation demonstrated that the integrated risk map selects safer avoidance paths by considering the severity of potential injuries, compared to risk maps that only consider relative information.}, archive = {J_ACCESS}, author = {Junseok Shim and Jaeseung Yu and Kibeom Lee}, doi = {10.1109/ACCESS.2025.3539400}, journal = {IEEE Access}, pages = {43767-43780}, shortjournal = {IEEE Access}, title = {Integrated risk grid map for collision avoidance and mitigation maneuvers of autonomous vehicle}, volume = {13}, year = {2025}, }
• (2025). Optimized sliding mode frequency controller for power systems integrated energy storage system with droop control. ACCESS, 13, 43749-43766. (www)

  @article{ , abstract = {This paper proposes an adaptive-based single-phase higher-order sliding mode controller (SMC) optimized by the honey badger algorithm (HBA). The developed control method is employed for load frequency control (LFC) in electrical power systems (EPSs) that incorporates a battery energy storage system (BESS) with droop control. Unlike conventional SMCs, the proposed higher-order SMC eliminates the reaching phase by utilizing the second derivative of the sliding motion and embedding the sliding surface directly within the original system, significantly reducing oscillations and chattering effects. Furthermore, an adaptive mechanism dynamically estimates the unknown bound of system uncertainties, enabling real-time compensation and minimizing steady-state errors. The system’s global stability is rigorously analyzed through linear matrix inequality (LMI) and Lyapunov stability theory, highlighting the robustness and practicality of the proposed method. To further enhance the system performance, the HBA is employed to optimize controller parameters, providing a balance between fast response and low overshoot. Additionally, a coordinated BESS control strategy integrating droop control and state-of-charge management is introduced, enabling seamless interaction between the energy storage system and generators to mitigate large disturbances in uncertain EPSs. Finally, extensive case studies under various configurations and conditions demonstrate the superior performance of the proposed controller compared to other related methods. Simulation results consistently confirm that the proposed solution outperforms alternative approaches across all scenarios. This is demonstrated by the minimal over/undershoot value in frequency deviations: $1.913\times 10^{-3}$ for the isolated EPS, $3.3134\times 10^{-4}$ and $0.6476\times 10^{-4}$ for two-area EPS equipped with ten BESS units. In the final case, the presence of BESS results in a significant reduction in peak value by approximately 22.9%, 16.5%, and 22.3% of each respective area of EPS.}, archive = {J_ACCESS}, author = {Anh-Tuan Tran and Van Van Huynh and Jae Woong Shim and Chee Peng Lim}, doi = {10.1109/ACCESS.2025.3548875}, journal = {IEEE Access}, pages = {43749-43766}, shortjournal = {IEEE Access}, title = {Optimized sliding mode frequency controller for power systems integrated energy storage system with droop control}, volume = {13}, year = {2025}, }
• (2025). Bias-compensated PNLMS algorithm with multi-segment function for noisy input. ACCESS, 13, 43741-43748. (www)

  @article{ , abstract = {To address the estimation bias caused by ignoring input noise in existing adaptive filtering algorithms, a new proportionate-type algorithm is proposed in this paper. First, a bias-compensation term is derived based on an unbiased criterion when constructing the cost function of the algorithm to achieve unbiased estimation. Next, this bias-compensation term is integrated into the mu-law PNLMS (MPNLMS) algorithm to design the bias-compensated PNLMS combined with the multi-segment function (BC-MS-PNLMS) algorithm. Simulation results for echo paths and underwater channels demonstrate that the BC-MS-PNLMS algorithm outperforms other sparse-type algorithms in sparse experimental environments.}, archive = {J_ACCESS}, author = {Zhan Jin and Zhonghao Yang and Qianjin Li and Linfeng Ma}, doi = {10.1109/ACCESS.2025.3546064}, journal = {IEEE Access}, pages = {43741-43748}, shortjournal = {IEEE Access}, title = {Bias-compensated PNLMS algorithm with multi-segment function for noisy input}, volume = {13}, year = {2025}, }
• (2025). Time-frame integrate-and-fire neuron circuit for low energy inference hardware spiking neural networks. ACCESS, 13, 43728-43740. (www)

  @article{ , abstract = {Hardware spiking neural networks (SNNs) are gaining increasing attention as promising next-generation computing approaches owing to their parallel computing and energy-efficient nature. Recent SNNs have achieved excellent performance by introducing artificial neural networks (ANN) to the SNN conversion training method. However, SNNs face significant challenges when implemented on hardware platforms. These challenges include achieving low energy consumption, difficulties in minimizing conversion error, and representing ANN functions other than the rectified linear unit activation function. This paper proposes mixed-signal complementary metal-oxide-semiconductor time-frame integrate-and-fire (TIF) neuron circuits and corresponding SNNs to achieve high accuracy with low energy consumption. TIF neurons integrate synaptic inputs during a time-frame and generate output spikes under synchronization signals based on the integrated input. When building SNNs with TIF neurons, bias and max-pooling can be effectively implemented, which is difficult to achieve using conventional SNNs with integrate-and-fire (IF) neuron circuits. In addition, TIF neurons support pipelining operations, further enhancing the inference throughput of the system. Simulation results with the CIFAR-10 dataset show that SNNs with TIF neurons achieve 0.48 %p higher classification accuracy while consuming 92 % lower neuron energy than conventional IF-based SNNs.}, archive = {J_ACCESS}, author = {Yeonwoo Kim and Bosung Jeon and Jonghyuk Park and Woo Young Choi}, doi = {10.1109/ACCESS.2025.3548318}, journal = {IEEE Access}, pages = {43728-43740}, shortjournal = {IEEE Access}, title = {Time-frame integrate-and-fire neuron circuit for low energy inference hardware spiking neural networks}, volume = {13}, year = {2025}, }
• (2025). A hybrid prediction model integrating artificial intelligence and geospatial analysis for disaster management. ACCESS, 13, 43716-43727. (www)

  @article{ , abstract = {A natural or man-made disaster must be predicted accurately and in time. Advanced modelling is essential to predict increasingly frequent and intense natural events, enabling better response and recovery strategies. Hybrid machine learning is used to predict floods, assess earthquake damage, and control wildfires. Convolutional Neural Networks (CNNs), Gradient Boosting Machines (GBMs) and Support Vector Machines (SVMs) are used in the proposed model. We evaluate the proposed machine learning model in comparison to conventional statistical analysis and single machine learning techniques. The proposed hybrid model provided 90% correct predictions for flood events in Bangladesh with precision and recall values of 88% and 85%, respectively. In the assessment of earthquake damage in Japan, an accuracy of 92% was achieved with a precision of 90% and a recall of 89% with an F1 score of 89%. In the management of wildfires in California, an accuracy of 88% with a precision of 85% were achieved. The proposed hybrid model outperforms conventional techniques due to its higher reliability in predicting floods.}, archive = {J_ACCESS}, author = {Muhammad Asim Saleem and Watit Benjapolakul and Wattanasak Srisiri and Surachai Chaitusaney and Pasu Kaewplung}, doi = {10.1109/ACCESS.2025.3545449}, journal = {IEEE Access}, pages = {43716-43727}, shortjournal = {IEEE Access}, title = {A hybrid prediction model integrating artificial intelligence and geospatial analysis for disaster management}, volume = {13}, year = {2025}, }
• (2025). Empirical investigation of key enablers for secure DevOps practices. ACCESS, 13, 43698-43715. (www)

  @article{ , abstract = {DevSecOps integrates security practices from development to operations in the search for faster and more secure responses to business needs. DevSecOps is an approach based on the 4 pillars of the CAMS model (culture, measurement, automation, and sharing). On the other hand, there is an increasing interest in ensuring sustainability in software work making it more optimal and viable overtime. Given that sustainability has not received much attention in the DevSecOps arena, this study aims to identify the enablers that are important for addressing CAMS by means of a multivocal literature review and to check their practical implacability in real-world practices. To do so, firstly, 35 enablers related to CAMS principles were identified as a result of the multivocal literature review conducted. Based on the literature findings, a hypothetical model was developed based on the DevSecOps enablers. Next, a quantitative survey was executed, and 214 responses were collected from experts. Finally, authors used Smart-PLS (3.0) and analyzed collected data to form the final set of enablers. Results show that these enablers could positively impact the execution of DevSecOps practices in a sustainable way. Additionally, results also reveal that when deploying DevSecOps, practitioners should consider the enablers that were explored and mapped against culture, automation, measurement, and sharing. The findings of this study will help practitioners and academics to understand DevSecOps principles and the areas that need to be considered by industry professionals to make DevSecOps sustainable.}, archive = {J_ACCESS}, author = {Muhammad Azeem Akbar and Abeer Abdulaziz AlSanad}, doi = {10.1109/ACCESS.2025.3549183}, journal = {IEEE Access}, pages = {43698-43715}, shortjournal = {IEEE Access}, title = {Empirical investigation of key enablers for secure DevOps practices}, volume = {13}, year = {2025}, }
• (2025). Exploring the context of industry 4.0, continuous processes, and mining industry: A systematic literature review and research directions. ACCESS, 13, 43666-43697. (www)

  @article{ , abstract = {Industry 4.0 offers significant potential to enhance efficiency and competitiveness in continuous processes, such as those in the mining sector. However, the implementation of its principles in continuous production sectors presents specific challenges, mainly due to the unique characteristics of the processed materials and the demand for continuous production. This study conducted a systematic literature review to investigate the application of Industry 4.0 in continuous processes within the mining sector, focusing on the relationship between Industry 4.0 design principles and their integration into these processes. The objectives were to: (i) identify and describe existing evidence on the integration of Industry 4.0 in continuous processes in mining, (ii) synthesize the opportunities Industry 4.0 offers for continuous production processes in mining, and (iii) foster discussions on future research directions. The results highlighted critical gaps in the application of Industry 4.0 principles in the mining industry and proposed a comprehensive theoretical framework that maps opportunities and provides practical implementation guidelines. This framework emphasizes the need for customized approaches to improve operational efficiency, reduce costs, and enhance global competitiveness. The conclusions offer a foundation for future empirical research and promote innovation and digital transformation in the mining industry, focusing on adapting optimization and process control techniques for continuous processes.}, archive = {J_ACCESS}, author = {Aline Menardi Culchesk and Vagner Luiz Gava and Ana Lucia Figueiredo Facin and Rodrigo Franco Gonçalves and Jocieli Francisco da Silva and Mauro de Mesquita Spinola}, doi = {10.1109/ACCESS.2025.3546242}, journal = {IEEE Access}, pages = {43666-43697}, shortjournal = {IEEE Access}, title = {Exploring the context of industry 4.0, continuous processes, and mining industry: A systematic literature review and research directions}, volume = {13}, year = {2025}, }
• (2025). Vehicular-computational resource geofencing: Efficient spatiotemporal uncertainty estimation. ACCESS, 13, 43653-43665. (www)

  @article{ , abstract = {Future vehicles are envisioned as smart objects equipped with sensors, actuators, storage capacity and computing resources. Intelligent transportation systems (ITS) have the potential to enhance transportation efficiency, road safety, and sustainability. Vehicular cloud computing (VCC) has emerged to facilitate intervehicle communication and data exchange enabling ITS services. However, existing VCC solutions are unable to satisfy the stringent quality of experience (QoE) requirements for running vehicular applications, especially delay- and throughput-sensitive applications, given the high communication delay and latency with the central cloud. To encounter these challenges, vehicular edge computing (VEC) solutions have emerged which rely on infrastructure or roadside units (RSUs) to bring cloud computing services closer to the user. Our objective is to enable the utilization of computational resources embedded in moving service vehicles to further enhance the QoE of vehicular users requesting computational tasks in urban environments. This methodology has the potential to extend the computation coverage to areas with limited RSU infrastructure as well as reduce the computational latency as services are provided closer to the user. To enable this approach, the service vehicles need to develop a spatiotemporal awareness of other computational resources within range and predict their future availability. The objective of this research is to propose an efficient computational resource geofencing solution to maximize the QoE of vehicular users requiring computational tasks. We evaluate the efficiency of the proposed resource geofencing algorithm under different realistic operating conditions. According to our evaluation, the service vehicles estimate the spatiotemporal uncertainty and predict the availability of computational resources with up to 99% confidence.}, archive = {J_ACCESS}, author = {Ghada Afifi and Bassem Mokhtar}, doi = {10.1109/ACCESS.2025.3547358}, journal = {IEEE Access}, pages = {43653-43665}, shortjournal = {IEEE Access}, title = {Vehicular-computational resource geofencing: Efficient spatiotemporal uncertainty estimation}, volume = {13}, year = {2025}, }
• (2025). Photo absorption noise for molecular information exchange. ACCESS, 13, 43642-43652. (www)

  @article{ , abstract = {Versatile Nanoengineered Molecular Communication (MC) systems applications include Healthcare, Environment, Security and Telecommunication (HEST). In the MC system, the challenging issues are modeling, mitigating, and analyzing different impairments, such as noise and interference. Recently, literature presents Radiation Absorption Noise (RAN), which is the rise in temperature due to the radiation absorption of incident Electromagnetic (EM) waves. However, a complementary noise, a rise in temperature due to the absorption of Photons by Nano-Particles (NPs) at Plasmonic resonance in the THz or optical range, is missing from open literature. Therefore, this manuscript presents the rise in temperature due to Photothermal heating, either due to Photo Thermal Therapy (PTT) or due to the coexistence of MC and THz Communication (TC), which can be termed Photo Absorption Noise (PAN). First, a closed-form expression for the Probability Density Function (PDF) of the proposed PAN is presented in terms of several physical parameters such as polarization factors, Radius of NP, conductivity of metal NPs, permittivity of metal NP, thermal conductivity of medium and subtract. Further, a closed-form expression for Error Probability (EP) under PAN is also derived to quantify the effect of different physical parameters on an exchange of molecular information. Finally, numerical quantification through simulated results is presented for each closed-form expression. It is essential to mention that each simulated result perfectly agrees with the theoretical background.}, archive = {J_ACCESS}, author = {S. Pratap Singh and Amit Kumar and Raghevendra Jaiswal and Kanwarpreet Kaur and Rajkumar Singh Rathore and Tiansheng Yang}, doi = {10.1109/ACCESS.2025.3544910}, journal = {IEEE Access}, pages = {43642-43652}, shortjournal = {IEEE Access}, title = {Photo absorption noise for molecular information exchange}, volume = {13}, year = {2025}, }
• (2025). Image description compression in classification structural methods. ACCESS, 13, 43631-43641. (www)

  @article{ , abstract = {The problem solved in the article is reduction of computational costs for the image classification process when applying structural methods. The main focus is implementing tools for granulation, screening, and clustering processing a set of elements of etalon descriptions. As a result of compression, each etalon is transformed into a reduced set of descriptors or data centroids, ensuring high speed and performance of image classification. Several variants of simple data compression schemes are assessed and compared to the traditional linear search method, along with two variants of etalon clustering. The comparison includes results achieved for the entire data set and for each of the images separately. The paper presents the results of software modeling of the proposed approaches for two experimental sets containing images of football club logos and artistic paintings. The test sample includes a set of images from the etalon database along with other images that do not belong to the database and with a set of geometric transformations of shift, scale, and rotation in the field of view applied to them. The research covers practical issues of choosing threshold parameters to set the equivalence of descriptors and minimizing the number of class votes to ensure the required level of classification accuracy. Testing has confirmed a significant processing acceleration and a sufficiently increasing level of classification accuracy due to employing compression. Particularly the conducted modeling revealed a tenfold increase in speed. It has been experimentally confirmed that using a clustering apparatus has a much higher potential in terms of classification accuracy and speed than simple sifting or granulation schemes based on close description components.}, archive = {J_ACCESS}, author = {Volodymyr Gorokhovatskyi and Iryna Tvoroshenko and Olena Yakovleva and Monika Hudáková}, doi = {10.1109/ACCESS.2025.3548910}, journal = {IEEE Access}, pages = {43631-43641}, shortjournal = {IEEE Access}, title = {Image description compression in classification structural methods}, volume = {13}, year = {2025}, }
• (2025). Tunable energy-efficient approximate circuits for self-powered AI and autonomous edge computing systems. ACCESS, 13, 43607-43630. (www)

  @article{ , abstract = {Artificial Intelligence is applied in various domains of compute-intensive applications ranging from image recognition healthcare to statistical analysis. Additionally, recent advancements in Deep Neural Network (DNN) running on millions of devices for various AI tasks deliver an accuracy comparable to human levels. However, the accuracy in computations came with an additional cost of increased computational resources and power consumption in traditional computing units. Moreover, this problem becomes more complex while deploying computationally intensive heavy machine learning (ML) models on energy-constrained edge devices. Approximate computing has emerged as a promising paradigm for error-tolerant AI/ML applications deployed on energy-constrained edge devices where the complexity of hardware computing units can be reduced by optimizing circuit logic while slightly trading off computational accuracy. Therefore, we propose novel approximate compressors to design multiply and accumulate (MAC) hardware unit of Deep Neural Network and Convolutional Neural Network (DNN/CNN) that achieve energy-efficient and faster computations with slightly reduced precision. We also propose tunable compressors and MAC unit that support switching between two approximation modes to enable runtime adjustment of energy efficiency and accuracy for energy-autonomous edge devices. We validated and verified the design of the proposed approximate circuit at 7nm and 55nm technology nodes. The simulation result for the tunable compressor shows an average reduction of 49 % in energy consumption, and 30 % in delay compared to the state-of-the-art compressor. In addition, an average reduction of 36 % in energy consumption and 18 % in delay was observed for the MAC unit compared with the conventional MAC. To analyze the impact on the accuracy of the final output, we evaluated the error matrices for the approximate MAC unit and observed a low MRED (mean relative error distance) of 0.19. Furthermore, we implemented a simple CNN for pattern detection and integrated our proposed solution to validate its benefits, achieving 15% lower power consumption and 7% lower area overhead without functionality loss.}, archive = {J_ACCESS}, author = {Shubham Garg and Kanika Monga and Nitin Chaturvedi and S. Gurunarayanan}, doi = {10.1109/ACCESS.2025.3548458}, journal = {IEEE Access}, pages = {43607-43630}, shortjournal = {IEEE Access}, title = {Tunable energy-efficient approximate circuits for self-powered AI and autonomous edge computing systems}, volume = {13}, year = {2025}, }
• (2025). SMFA: Strengthening multi-factor authentication with steganography for enhanced security. ACCESS, 13, 43593-43606. (www)

  @article{ , abstract = {Traditional password-based authentication mechanisms exhibit significant security vulnerabilities, prompting the adoption of Multi-Factor Authentication (MFA) to enhance user verification. However, current MFA methods, such as OffPAD, One-Time Password Tokens (OTT), and smartcard-based systems, remain vulnerable to attacks like Man-in-the-Middle (MITM), session hijacking, replay, phishing, and denial of service (DoS). Additionally, the conventional single-server authentication approach is inefficient and inadequate for modern security needs. This study introduces Secure Multi-Factor Authentication (SMFA), a novel framework designed to address these challenges by leveraging steganography for secure credential transmission, reducing susceptibility to MITM and session hijacking attacks. SMFA also incorporates an additional Universal Serial Bus (USB) device as a supplementary authentication factor and employs a multi-server architecture to overcome the limitations of single-server models. A comprehensive analysis comparing SMFA to existing MFA protocols reveals its superior ability to counter MITM, replay, DoS, user impersonation, and password-guessing attacks. The results demonstrate that SMFA offers a robust and effective solution, addressing the weaknesses of both traditional password-based systems and current MFA methods.}, archive = {J_ACCESS}, author = {Afjal H. Sarower and Touhid Bhuiyan and Maruf Hassan and Mohammad Shamsul Arefin and Gahangir Hossain}, doi = {10.1109/ACCESS.2025.3545769}, journal = {IEEE Access}, pages = {43593-43606}, shortjournal = {IEEE Access}, title = {SMFA: Strengthening multi-factor authentication with steganography for enhanced security}, volume = {13}, year = {2025}, }
• (2025). GRL-ITransformer: An intelligent method for multi-wind-turbine wake analysis based on graph representation learning with improved transformer. ACCESS, 13, 43572-43592. (www)

  @article{ , abstract = {The importance of examining the wake effect of wind farms for optimizing their layout and augmenting their power generation efficiency is immense. Considering that the establishment of extensive wind farms often leads to a significant number of turbines being positioned downstream of preceding ones, it significantly diminishes their power generation efficiency. In our study, we propose a graph representation learning model with improved Transformer (GRL-ITransformer) to better integrate feature information, so that the model can capture the dynamic time relationship of different variables and establish its spatial relationship, striving to enhance the precision in predicting wind turbine wake field. Different from the previous way involving handling reduced-order and separating prediction process, we combine the reduced-order technique with the proposed model to make the model more efficiently and intelligently determine the number of modes required for model prediction. After that, the data driven method is employed to update the parameters, and the superiority of GRL-ITransformer is highlighted by analyzing and comparing with the existing five classical intelligent algorithms (belongs to four categories). The comprehensive results show that GRL-ITransformer has excellent performance in wind turbine wake field prediction and reconstruction, and always possesses the lowest error for a series of error evaluation indexes among all models.}, archive = {J_ACCESS}, author = {Kang Han and Li Xu}, doi = {10.1109/ACCESS.2025.3549035}, journal = {IEEE Access}, pages = {43572-43592}, shortjournal = {IEEE Access}, title = {GRL-ITransformer: An intelligent method for multi-wind-turbine wake analysis based on graph representation learning with improved transformer}, volume = {13}, year = {2025}, }
• (2025). Short-term vehicle speed prediction based on spatio-temporal traffic graph networks. ACCESS, 13, 43557-43571. (www)

  @article{ , abstract = {Predictive information is an important research direction in vehicle energy management. As the most intuitive item among numerous predictive information, the accurate and real-time prediction of vehicle speed is significant for vehicle motion control and energy management optimization. However, traditional vehicle speed prediction methods generally predict future speed changes based on the experience of historical vehicle speed changes and current vehicle speed information, which belongs to prediction in the pure time dimension. The spatial information of traffic scenes that directly affects vehicle behaviors has not been widely applied. Aiming at the complex spatio-temporal correlation in the vehicle motion process, this paper proposes a spatio-temporal graph convolutional network (GCN-LSTM). This network combines graph convolutional network (GCN) and long short-term memory network (LSTM) and is used for vehicle speed prediction. Among them, the GCN can handle the complex topological structures in graph data to extract the spatial features of traffic scenes, and the LSTM can handle the time features of the dynamic changes in traffic scenes. Training and testing are carried out on two datasets of traffic congestion and smooth traffic. The simulation results show that compared with other benchmark models, the proposed model has the smallest prediction error. Meanwhile, based on the datasets, the impact of the expansion of the amount of spatial information on the prediction accuracy of the model in this paper is verified. The results indicate that abundant spatial dimension information can further improve the prediction accuracy.}, archive = {J_ACCESS}, author = {Yifang Feng and Tao Chen and Yue Li and Yonghan Gao and Guoli Zhang and Guowei Cao}, doi = {10.1109/ACCESS.2025.3548130}, journal = {IEEE Access}, pages = {43557-43571}, shortjournal = {IEEE Access}, title = {Short-term vehicle speed prediction based on spatio-temporal traffic graph networks}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based lesion detection in endoscopy: A systematic literature review. ACCESS, 13, 43532-43556. (www)

  @article{ , abstract = {A lesion is an abnormal change in body tissue or organ caused by various factors such as inflammation, infection, or abnormal cell growth. The presence of lesions often indicates a serious condition requiring medical attention. Early detection of lesions is crucial for effective treatment and preventing disease progression. Endoscopy is a key tool for lesion identification and evaluation, and the integration of deep learning techniques has further enhanced its potential. This systematic literature review (SLR) examines 35 studies to provide a comprehensive overview of lesion detection using deep learning on endoscopic images. The review explores deep learning techniques, modifications to baseline models, datasets, preprocessing and augmentation methods, and evaluation metrics. A meta-analysis of average performance metrics—accuracy, precision, recall, and specificity—was also conducted to identify trends across datasets and lesion types. Additionally, emerging trends such as federated learning, lightweight models, and ethical considerations are briefly discussed, addressing critical aspects for clinical translation. While significant advancements have been made, challenges remain, including dataset biases, scalability in resource-limited settings, and ensuring generalizability. Insights gained from this review are expected to guide future developments in lesion detection models, contributing to improved diagnostic accuracy, scalability, and patient outcomes.}, archive = {J_ACCESS}, author = {Naim Rochmawati and Chastine Fatichah and Bilqis Amaliah and Agus Budi Raharjo and Frédéric Dumont and Emilie Thibaudeau and Cédric Dumas}, doi = {10.1109/ACCESS.2025.3548167}, journal = {IEEE Access}, pages = {43532-43556}, shortjournal = {IEEE Access}, title = {Deep learning-based lesion detection in endoscopy: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Application of enhanced self-adaptive virtual inertia control for efficient frequency control of renewable energy-based microgrid system integrated with electric vehicles. ACCESS, 13, 43520-43531. (www)

  @article{ , abstract = {The frequency control of an islanded microgrid (MG) is a challenging task due to the lack of system inertia as it is highly penetrated with renewable energy sources (RESs). Current work suggests overcoming this issue with an energy storage system (ESS)-based virtual inertia (VI) approach by providing appropriate proportional damping instead of a fixed value. In this study to overcome the frequency control issue, a fuzzy-based self-adaptive enhanced VI controller (SAEVIC) coordinated with electric vehicles (EV) is proposed. The controller is proposed to stabilize the system frequency and balance state of charge (SOC) of plugged-in electric vehicles (EVs). The performance of the proposed controller is justified in terms of frequency control over with/without conventional VI control, conventional enhanced VI control, and self-adaptive VI control. The system frequency and SOC signal are considered for the control action of the proposed controller. The impact of EV integration on the system frequency dynamics is tested. The validation of the proposed controller is carried out with a system injected with stochastic disturbances, high and low levels of renewable energies, denial of service attacks on renewable energy, and disturbed operating conditions with varied internal parameters. It is noticed that with the SAEVIC approach, the overshoot (OS)-11.40%, undershoot (US)- 46.46%, settling time (ST)-98.6% and fitness value-10.27% are decreased as compared to conventional enhanced VI approach under Stochastic variations of wind, PV, and multi-step load disturbance of MG system.}, archive = {J_ACCESS}, author = {Sonalika Mishra and Preeti Ranjan Sahu and Ramesh Chandra Prusty and Sidhartha Panda and Taha Selim Ustun and Ahmet Onen}, doi = {10.1109/ACCESS.2025.3548649}, journal = {IEEE Access}, pages = {43520-43531}, shortjournal = {IEEE Access}, title = {Application of enhanced self-adaptive virtual inertia control for efficient frequency control of renewable energy-based microgrid system integrated with electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Enhancing facial recognition and expression analysis with unified zero-shot and deep learning techniques. ACCESS, 13, 43508-43519. (www)

  @article{ , abstract = {This paper presents a new model that enhances face recognition while addressing challenges such as illumination variation and noise in facial images. The model integrates state-of-the-art illumination normalization based on Retinex theory with median filtering to improve noise reduction and recognition accuracy. Utilizing pre-trained VGG-16 weights, it incorporates aspects of the Boltzmann machine to extract intricate facial features while reducing overfitting. Additionally, this paper merges zero-shot facial expression recognition methodologies with illumination-robust face recognition within a unified facial analysis framework. Integrating pre-processing models with large language models and advanced feature extraction techniques enables the model to excel across various benchmarks, including FERET, LFW, CelebA, and VGGFace2 datasets. These findings indicate that combining superior normalization techniques with zero-shot learning significantly enhances face recognition and expression analysis under challenging real-world conditions. This solution is practical and robust against variations in illumination and noise encountered in everyday situations.}, archive = {J_ACCESS}, author = {Sasan Karamizadeh and Saman Shojae Chaeikar and Maryam Khanian Najafabadi}, doi = {10.1109/ACCESS.2025.3546061}, journal = {IEEE Access}, pages = {43508-43519}, shortjournal = {IEEE Access}, title = {Enhancing facial recognition and expression analysis with unified zero-shot and deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Online estimation of the state of health for the lithium-ion battery based on open circuit model. ACCESS, 13, 43490-43507. (www)

  @article{ , abstract = {The accurate prediction of the state of health (SOH) for lithium-ion batteries is of significant importance in proactively avoiding unsafe behavior. The commonly used indicator of the SOH is the ratio of available capacity to rated capacity based on its equivalent circuit model (ECM) where the open circuit voltage (OCV) is obtained through large numbers of offline battery experiments. In this research, a method based on the ECM is proposed to estimate available capacity and OCV online. Firstly, the Thevenin model of the lithium-ion battery is established, of which the parameters are identified online by the recursive least squares with forgetting factor algorithm. Then, the OCV model is set up based on the first-order Taylor’s formula where the first-order partial derivation of OCV against state of charge (SOC) is modeled as unmodeled noise. The adaptive unscented Kalman filter is leveraged to estimate the available capacity and OCV online, both of which are adopted to estimate SOC reconstructing the relationships between the SOC and OCV in the whole lifespan. The results indicate that the available capacity estimated with the proposed method converges quickly to the vicinity of the true value under accelerated aging and normal aging conditions. The relative errors under these two aging conditions are respectively confined to below 3% and 2.3%. This demonstrates good estimation accuracy and robustness against wrong initial values. The proposed method provides valuable guidance for online state estimation throughout the entire lifecycle of the lithium-ion battery.}, archive = {J_ACCESS}, author = {Pan Yajia and Junda Li and Wang Tian'An and Yang Shengxun and Zhang Yunxuan and Li Zigang and Hu Yanwen}, doi = {10.1109/ACCESS.2025.3536166}, journal = {IEEE Access}, pages = {43490-43507}, shortjournal = {IEEE Access}, title = {Online estimation of the state of health for the lithium-ion battery based on open circuit model}, volume = {13}, year = {2025}, }
• (2025). Automatic brain tumor segmentation: Advancing U-net with ResNet50 encoder for precise medical image analysis. ACCESS, 13, 43473-43489. (www)

  @article{ , abstract = {Automated brain tumor segmentation from MRI images is critical for accurate diagnosis and treatment planning. This study presents a novel ResUNet50-based approach, integrating ResNet50 as an encoder within the U-Net framework to achieve robust and precise segmentation. The proposed model was evaluated on two datasets: a Kaggle-based T1-CE MRI dataset and BraTS 2018, ensuring comprehensive assessment across different imaging conditions. ResUNet50 outperformed state-of-the-art models, achieving Dice coefficients exceeding 0.95 and Jaccard indices above 0.91 on the Kaggle dataset. Additionally, experiments on BraTS 2018 Whole Tumor segmentation across multiple MRI modalities (FLAIR, T1, T1-CE, and T2) demonstrated high accuracy on both High-Grade Gliomas (HGG) and Low-Grade Gliomas (LGG), confirming model generalization. Statistical significance tests (Paired t-tests and Wilcoxon Signed-Rank Tests, p < 0.05) validated the improvements over existing approaches. Furthermore, ResUNet50 reduced parameters by over 60% and accelerated inference time by $4.8\times $ compared to U-Net, enhancing its potential for clinical deployment. Future work will focus on ensemble learning and bio-inspired optimization for improved robustness across multi-center MRI datasets. Explainable AI techniques such as Grad-CAM and saliency maps will be incorporated to enhance interpretability, improving clinical applicability.}, archive = {J_ACCESS}, author = {Shoffan Saifullah and Rafał dreżewski and Anton Yudhana and Andiko Putro Suryotomo}, doi = {10.1109/ACCESS.2025.3547430}, journal = {IEEE Access}, pages = {43473-43489}, shortjournal = {IEEE Access}, title = {Automatic brain tumor segmentation: Advancing U-net with ResNet50 encoder for precise medical image analysis}, volume = {13}, year = {2025}, }
• (2025). A hybrid approach to traffic incident management: Machine learning-based prediction and patrol optimization. ACCESS, 13, 43455-43472. (www)

  @article{ , abstract = {The objective of Traffic Incident Management (TIM) is to respond to traffic accidents and other traffic incidents as immediately as possible, and to eliminate the consequences of such incidents with the utmost expediency. One of the most critical issues in traffic incident management is the problem of assigning police patrols. Despite the existence of numerous defined patrol areas within urban contexts, police teams are typically deployed to locations where accidents and traffic incidents are most likely to occur. The deployment of police patrols to the closest locations to potential accident sites in a dynamic traffic system is an effective strategy for reducing response times, minimizing carbon emissions, and lowering costs. This study presents a novel five-stage hybrid approach to the patrol assignment problem, which estimates the traffic accident-incident location, event time, and traffic incident duration based on machine learning algorithms. The successful prediction results are then used as inputs in the patrol assignment optimization model. The presented model was tested in a pilot region defined in Istanbul, and the results demonstrated that the machine learning-based model reduced the response time to traffic incidents, transportation costs, and was effective in reducing carbon emissions in the police assignment problem.}, archive = {J_ACCESS}, author = {Yusuf Sait Türkan and Mesut Ulu}, doi = {10.1109/ACCESS.2025.3544765}, journal = {IEEE Access}, pages = {43455-43472}, shortjournal = {IEEE Access}, title = {A hybrid approach to traffic incident management: Machine learning-based prediction and patrol optimization}, volume = {13}, year = {2025}, }
• (2025). Federated explainable AI-based alzheimer’s disease prediction with multimodal data. ACCESS, 13, 43435-43454. (www)

  @article{ , abstract = {Alzheimer’s Disease (AD) is a progressive neurological disease that severely impairs cognitive function. Early detection is critical for effective treatment and management. Machine Learning (ML) methods are often used to ensure early detection and prediction. However, ML has various issues, including the data island problem. The fragmentation that results from the data island problem makes building reliable, effective ML models more complex, and it is particularly problematic in industries where privacy is a concern, like healthcare. Federated Learning (FL) can help tackle the data island problem by keeping sensitive patient data decentralized and enabling many institutions to work together on model training without exchanging raw data, all while maintaining privacy compliance. As Random Forest (RF) is proven to be the best-performing classifier in this research, an RF classifier is used to create FL. The model incorporates multiple data modalities, such as Magnetic Resonance Imaging (MRI) segmentation and clinical and psychological data, to capture the variety of characteristics influencing the progress of AD. Another concerning issue with ML is its uninterpretable character. We use SHapley Additive exPlanations (SHAP) Explainable Artificial Intelligence (XAI) techniques that emphasize important factors impacting model decisions in order to improve predictability and transparency. This explainability promotes confidence in AI-based diagnoses by enabling researchers and physicians to comprehend the underlying mechanisms guiding the predictions. The combination of XAI, FL, and Open Access Series of Imaging Studies (OASIS-3) Multimodal data offers an interpretable, scalable, reliable, and privacy-centered solution for multiple complex issues, such as predicting AD. This approach results in better diagnosis precision, greater security, and increased confidence in AI technologies, making it a novel methodology in medical sciences. With data privacy maintained, our method produces 98.93% accurate predictions, providing a solid detection strategy for AD. The suggested approach’s F1-score, Precision, Recall, and AUC are 98.93%, 98.94%, 98.93%, and 99.97%, respectively. This work also shows that a multimodal dataset performs better than a single modal dataset.}, archive = {J_ACCESS}, author = {Sobhana Jahan and Md. Rawnak Saif Adib and Syed Mahmudul Huda and Md. Sazzadur Rahman and M. Shamim Kaiser and A. S. M. Sanwar Hosen and Deepak Ghimire and Mi Jin Park}, doi = {10.1109/ACCESS.2025.3547343}, journal = {IEEE Access}, pages = {43435-43454}, shortjournal = {IEEE Access}, title = {Federated explainable AI-based alzheimer’s disease prediction with multimodal data}, volume = {13}, year = {2025}, }
• (2025). Optimizing inertia estimation in virtual synchronous generators with MMC-based proportional integral and model predictive control strategies. ACCESS, 13, 43424-43434. (www)

  @article{ , abstract = {The increasing integration of renewable energy sources influences the power grid, resulting in a reduction in the traditional inertia supplied by synchronous generators. Significant frequency deviations are caused by power fluctuations, which are affected by the grid’s stability due to the reduction of inertia and damping. To address this problem, the idea of a Virtual Synchronous Generator (VSG) was created to simulate the fundamental features of a synchronous generator, including damping and inertia. This study proposes the use of a Proportional-Integral (PI) controller and Model Predictive Control (MPC) for the computation of the inertia in a Virtual Synchronous Generator (VSG) controller. Power and frequency responses are balanced by the PI controller, which employs a control law that is derived from the cross-error between power variations and system frequency droop. The MPC method establishes an optimal output trajectory that minimizes input costs associated with inertia and damping factors, effectively balancing power and frequency responses. Fluctuations in power output result in frequency variations between 49.754 Hz and 50.0267 Hz. In steady-state conditions, the frequency oscillates around the nominal value with an amplitude of 0.042 Hz and stabilizes within 150 milliseconds. When reactive power output changes, these oscillations increase to 0.069 Hz after half a second VSG-PI reducing the peak frequency to 50.180 Hz and VSG-MPC to 50.177 Hz. The minimum frequency is reduced to 49.833 Hz for VSG-PI and 49.842 Hz and 49.844 Hz for VSG-MPC. During steady-state, VSG-PI and VSG-MPC exhibit frequency oscillations of 0.035 Hz and 0.024 Hz, respectively, with stabilization times of 150 ms and 80 ms. Reactive power output changes cause these oscillations to increase to 0.060 Hz for VSG-PI and 0.023 Hz for VSG-MPC. VSG-MPC shows similar peak frequency and stabilization time to VSG-PI but outperforms other methods by further reducing the minimum frequency. The rate of change of frequency (RoCoF) is 2.096 Hz/s for VSG-PI and 2.692 Hz/s for VSG-MPC.}, archive = {J_ACCESS}, author = {M. Chethan and Ravi Kuppan}, doi = {10.1109/ACCESS.2025.3547367}, journal = {IEEE Access}, pages = {43424-43434}, shortjournal = {IEEE Access}, title = {Optimizing inertia estimation in virtual synchronous generators with MMC-based proportional integral and model predictive control strategies}, volume = {13}, year = {2025}, }
• (2025). Design of battery management system for grid energy storage based on FreeRTOS and RTDS testing system. ACCESS, 13, 43414-43423. (www)

  @article{ , abstract = {A battery management system design and test scheme are proposed to meet the test requirements for high-precision state-of-energy (SOE) calculation in energy storage systems and enable joint debugging with the power system. The proposed system’s hardware and software design, along with the testing scheme using RTDS as the main control logic, are presented. In this design, the SOE value of the single unit can be calculated using the extended Kalman algorithm and other correction methods in the energy storage system with multiple strings. The experimental results show that the SOE error is 1.5% under the condition of constant power test at room temperature. Additionally, the RTDS battery model simulates battery data, and the high-pressure box control logic is integrated into RTDS to enable direct control of the RTDS battery cluster output voltage via the battery management system.}, archive = {J_ACCESS}, author = {Huan Li and Hongyuan Yuan and Shi He and Yu Zhou}, doi = {10.1109/ACCESS.2025.3548785}, journal = {IEEE Access}, pages = {43414-43423}, shortjournal = {IEEE Access}, title = {Design of battery management system for grid energy storage based on FreeRTOS and RTDS testing system}, volume = {13}, year = {2025}, }
• (2025). Application of semantic information module in LiDAR-based simultaneous-localization-and- mapping algorithm. ACCESS, 13, 43398-43413. (www)

  @article{ , abstract = {Autonomous driving and robotics require precise perception of the surrounding environment. Therefore, the simultaneous-localization-and-mapping algorithm, which is used for accurate pose estimation and environmental perception, is of great importance. LiDAR, with its high measurement accuracy and ability to work around the clock, is widely used as the main sensor for simultaneous-localization-and-mapping algorithms. As the mass production technology of LiDAR becomes increasingly mature, various LiDAR based simultaneous-localization-and-mapping algorithms have been proposed one after another. In this paper, comparative experiments are conducted on the latest excellent algorithms, on this basis, a high-precision and robust LiDAR-based simultaneous-localization-and-mapping algorithm suitable for different scenarios is proposed. This algorithm combines the simultaneous-localization-and-mapping algorithm with the Scan Context semantic descriptor. Firstly, in the front end of the algorithm, the iterative Kalman filter is used for point cloud scan registration. In the back end, factor graph optimization is adopted to reduce global trajectory drift. Secondly, the algorithm performs loop closure detection from coarse to fine through Scan Context and the iterative closest point algorithm. Finally, the environmental map generated by the algorithm is managed by an incremental kd-tree. The evaluation experiments show that the algorithm proposed in this paper has significantly improved accuracy and robustness, which promotes the wide application of algorithms in different scenarios.}, archive = {J_ACCESS}, author = {Shihong Huang and Dalin Ma and Jiayang Wu and Caihou Lin and Jinhui Liu and Cheng Hu}, doi = {10.1109/ACCESS.2025.3532478}, journal = {IEEE Access}, pages = {43398-43413}, shortjournal = {IEEE Access}, title = {Application of semantic information module in LiDAR-based simultaneous-localization-and- mapping algorithm}, volume = {13}, year = {2025}, }
• (2025). Research on point cloud registration and stitching fusion algorithm based on GCN-PRFNet. ACCESS, 13, 43384-43397. (www)

  @article{ , abstract = {Point-cloud registration and stitching are important topics in the field of robot navigation and 3D reconstruction, e.g., the accuracy of point cloud registration and stitching in robot navigation directly affects the accuracy of map construction. Many researchers have proposed various algorithms for deep learning-based point cloud registration and stitching methods with good performance, and although there are end-to-end methods that have made progress, they still have limitations in local feature fusion efficiency and geometric detail retention. To address this issue, a fusion algorithm for registration and stitching based on a GCN-PRFNet point cloud is proposed. The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. GCN-PRFNet can efficiently handle the task of point cloud registration and splicing and fusion in partially overlapping regions and is robust to noise. The model is trained on the ModelNet40 dataset, and its registration and splicing accuracies are improved by 53.9%, 20.1%, 8.3%, 12.2%, 6.1% and 1.8% when compared with the traditional iterative closest point and learning-based PointNetLK, DGCNN, RPM-Net, DCP, and PointViG methods. This indicates that the constructed model is effective in point cloud registration and splicing. Meanwhile, point-cloud registration and splicing tests were performed on five self-constructed artefact datasets, and their registration and splicing accuracies were over 90%, indicating that the constructed end-to-end point-cloud registration and splicing model is considerably effective in real-world application scenarios.}, archive = {J_ACCESS}, author = {Wenhao Zeng and Gongbing Su and Zixuan Su and Rui Li and Jun Chen}, doi = {10.1109/ACCESS.2025.3548170}, journal = {IEEE Access}, pages = {43384-43397}, shortjournal = {IEEE Access}, title = {Research on point cloud registration and stitching fusion algorithm based on GCN-PRFNet}, volume = {13}, year = {2025}, }
• (2025). Construction and performance evaluation of grain porosity prediction models based on metaheuristic algorithms and machine learning. ACCESS, 13, 43360-43383. (www)

  @article{ , abstract = {Grain porosity is a key parameter affecting the heat and moisture transfer in grain piles, and is related to the accuracy of heat and moisture transfer calculations and simulation studies in grain piles. To effectively measure the porosity of grains under different pressures, this study used a powerful machine learning algorithm to set six input parameters affecting porosity and experimentally acquired the data required for machine learning modeling. Eight machine learning-based models for grain porosity prediction were developed. Four meta-heuristic algorithms (i.e., Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO), and Multi-Verse Optimizer (MVO)) were combined with Support Vector Regression (SVR) to obtain four hybrid models (i.e., PSO-SVR, SCA-SVR, HHO-SVR, and MVO-SVR), respectively, in order to improve the accuracy and methodology of prediction. In addition, four other intelligent models, namely SVR, least-squares support vector regression, random forest, and partial least-squares regression, were constructed and compared with the four hybrid models. Finally, the accuracies of the eight models were evaluated and compared using several metrics: coefficient of determination (R2), variance accounted for (VAF), Nash-Sutcliffe efficiency (NSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and scatter index (SCI). The results indicate that the HHO-SVR model achieved the highest accuracy during both the training and testing phases, with R2 values of 0.97660 and 0.95613, respectively. The HHO-SVR model predicts grain porosity with reasonable accuracy and exhibits superior performance compared to other models.}, archive = {J_ACCESS}, author = {Jiahao Chen and Jiaxin Li and Deqian Zheng and Chaosai Liu and Runmei Zhao and Jiayi Zhang}, doi = {10.1109/ACCESS.2025.3532980}, journal = {IEEE Access}, pages = {43360-43383}, shortjournal = {IEEE Access}, title = {Construction and performance evaluation of grain porosity prediction models based on metaheuristic algorithms and machine learning}, volume = {13}, year = {2025}, }
• (2025). Simulation-based logical scenario generation and analysis methodology for evaluation of autonomous driving systems. ACCESS, 13, 43338-43359. (www)

  @article{ , abstract = {The safety of autonomous driving systems (ADSs) must be assessed through different scenarios before ADSs can be deployed. Scenario representation is a method to condense the infinite number of scenarios that can occur in real-world traffic into a finite set to facilitate testing. The scenarios are categorized into four levels: functional, abstract, logical, and concrete. A logical scenario is one in which the parameters representing the scenario are defined in the form of ranges. Research has been conducted on real-world traffic to derive reasonable ranges for logical scenario parameters. However, such studies incur elevated costs for scenario detection, lack consideration of hazardous situations, and suffer from driving style–related and regional biases in data. Therefore, this study introduces a simulation software–based method to overcome the cost and data bias problems while generating logical scenarios that account for both normal and hazardous situations. The proposed method involves analyzing the results for three of the scenarios specified in the ISO 34502 standard and qualitatively validating the findings against results from studies using real-world vehicle data.}, archive = {J_ACCESS}, author = {Jongwon Jeon and Jaeyeon Yoo and Taeyoung Oh and Jinwoo Yoo}, doi = {10.1109/ACCESS.2025.3548906}, journal = {IEEE Access}, pages = {43338-43359}, shortjournal = {IEEE Access}, title = {Simulation-based logical scenario generation and analysis methodology for evaluation of autonomous driving systems}, volume = {13}, year = {2025}, }
• (2025). A novel multi-image encryption scheme using generalized rectangular transform and advanced 5-D hyperchaotic map. ACCESS, 13, 43316-43337. (www)

  @article{ , abstract = {This paper presents GRTPHM (Generalized Rectangular Transform and Penta-Hyperchaotic Map), a novel multi-image encryption scheme that enhances security, efficiency, and versatility. The encryption process consists of two key phases: pixel permutation and pixel diffusion. The pixel permutation phase leverages the Generalized Rectangular Transform (GRT) to rearrange pixel positions using a flexible transformation matrix, effectively disrupting strong correlations between neighboring pixels and improving resistance to statistical analysis. In the pixel diffusion phase, a bit-level random permutation (BLRP) technique is employed alongside a Penta-Hyperchaotic Map (PHM), an advanced 5-D discrete memristor and meminductor-based hyperchaotic map, integrated with the SHA-512 hashing function. Unlike traditional pixel-based diffusion methods, BLRP shuffles bits, achieving finer confusion and making structural inference significantly more difficult for attackers. The PHM generates a highly unpredictable chaotic sequence, ensuring substantial changes in ciphertext even for minor modifications in plaintext or encryption parameters. This enhances the avalanche effect, strengthening resistance to differential and statistical attacks. GRTPHM introduces a novel reverse rectangular transform (IGRT) that overcomes the limitations of existing approaches, enabling efficient and accurate image reconstruction. Notably, decryption does not require prior knowledge of the transformation matrix’s periodicity and achieves a speedup of up to 299 times compared to state-of-the-art methods. Extensive robustness testing against cropping, salt & pepper noise, and hybrid attacks confirms that GRTPHM can maintain satisfactory decryption accuracy and visual quality, even under severe image distortions. The results indicate that cropping attacks cause greater degradation than noise-based attacks due to the irreversible loss of contiguous pixel regions. The scheme supports a wide range of image sizes and formats, including rectangular and equilateral images, as well as single and multi-image encryption scenarios. Performance evaluations demonstrate that GRTPHM outperforms existing encryption techniques in terms of accuracy, computational efficiency, security, and adaptability. Comparative analyses confirm its robust encryption and decryption capabilities, effectively protecting against chosen-plaintext and differential attacks. These results establish GRTPHM as a comprehensive and reliable solution for modern image encryption applications.}, archive = {J_ACCESS}, author = {Yu-Chi Lan and Chung-Ming Wang}, doi = {10.1109/ACCESS.2025.3547891}, journal = {IEEE Access}, pages = {43316-43337}, shortjournal = {IEEE Access}, title = {A novel multi-image encryption scheme using generalized rectangular transform and advanced 5-D hyperchaotic map}, volume = {13}, year = {2025}, }
• (2025). The JPEG pleno learning-based point cloud coding standard: Serving man and machine. ACCESS, 13, 43289-43315. (www)

  @article{ , abstract = {Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing.}, archive = {J_ACCESS}, author = {André F. R. Guarda and Nuno M. M. Rodrigues and Fernando Pereira}, doi = {10.1109/ACCESS.2025.3549316}, journal = {IEEE Access}, pages = {43289-43315}, shortjournal = {IEEE Access}, title = {The JPEG pleno learning-based point cloud coding standard: Serving man and machine}, volume = {13}, year = {2025}, }
• (2025). Continuum robotic catheter systems for transcatheter mitral valve procedures: A technical review. ACCESS, 13, 43275-43288. (www)

  @article{ , abstract = {Transcatheter mitral valve procedures have transformed the treatment of mitral regurgitation and stenosis by providing less invasive alternatives to conventional open-heart surgery. However, they introduce stringent requirements for catheter navigation, force modulation, and real-time imaging in a highly dynamic cardiac environment. In this article, a comprehensive technical overview of continuum robotic catheter systems developed specifically for transcatheter mitral valve interventions is presented. Fundamental design principles of flexible, tendon-driven architectures are examined, highlighting their capacity to navigate tortuous vascular pathways and offer multi-degree-of-freedom control. The integration of advanced sensing technologies, real-time imaging methods, and intelligent control strategies is discussed. Clinical studies and in vivo validations are reviewed, underscoring critical performance metrics such as positional accuracy, procedural safety, and device miniaturization. Persistent challenges are also addressed, including limited high-fidelity data for machine learning, a lack of robust haptic feedback in delicate cardiac tissue manipulation, and regulatory hurdles for complex robotic platforms. Furthermore, emerging innovations in materials science, three-dimensional printing, and sensor fusion are explored, illustrating the potential for next-generation systems that enhance precision while reducing operator workload. Finally, key opportunities for future research are outlined, with an emphasis on personalized navigation algorithms, standardized evaluation protocols, and broader applicability in cardiovascular and endovascular procedures.}, archive = {J_ACCESS}, author = {Di Ding and Tianliang Yao and Haoyu Wang and Xusen Sun and Rong Luo}, doi = {10.1109/ACCESS.2025.3548273}, journal = {IEEE Access}, pages = {43275-43288}, shortjournal = {IEEE Access}, title = {Continuum robotic catheter systems for transcatheter mitral valve procedures: A technical review}, volume = {13}, year = {2025}, }
• (2025). Distributed practical bipartite output consensus for heterogeneous multi-agent systems with actuator attacks and external disturbances under edge-based event-triggered mechanism. ACCESS, 13, 43259-43274. (www)

  @article{ , abstract = {This study presents an edge-based event-triggered control method to address the practical bipartite output consensus problem for heterogeneous multi-agent systems (MASs) under actuator attacks and external disturbances. Initially, a fully distributed edge-triggered compensator is introduced to efficiently observe the bipartite state of the leader. Zeno behavior is then eliminated for each agent. To address the negative consequences caused by actuator attacks, a distributed state predictor is designed. And a distributed resilient controller is proposed to mitigate the effects of actuator attacks and external disturbances, ensuring practical bipartite output consensus. Finally, the effectiveness of the approach is validated through a numerical example and a comparative example. Compared to existing methods, the proposed approach offers four key advantages: it removes dependence on global topology, eliminates continuous communication, and supports signed communication topologies; it reduces unnecessary communication through an edge-based event-triggered mechanism; it introduces a distributed state predictor to handle actuator attacks and complex external disturbances, improving robustness; and the resilient control strategy ensures practical consensus, providing a more practical and robust solution.}, archive = {J_ACCESS}, author = {Chunhui Lv and Yuliang Cai and Hanguang Su and Qiang He}, doi = {10.1109/ACCESS.2025.3548580}, journal = {IEEE Access}, pages = {43259-43274}, shortjournal = {IEEE Access}, title = {Distributed practical bipartite output consensus for heterogeneous multi-agent systems with actuator attacks and external disturbances under edge-based event-triggered mechanism}, volume = {13}, year = {2025}, }
• (2025). Densely placed rectenna array with decoupling slots for enhanced received power in far-field wireless power transfer. ACCESS, 13, 43249-43258. (www)

  @article{ , abstract = {In this paper, a rectenna array system using decoupling slots with a defected ground structure (DGS) is proposed to improve received power in wireless power transfer (WPT) applications. The proposed rectenna array features a $4\times 4$ configuration with an antenna spacing of $\lambda _{0}/3$ and introduces decoupling slots based on a meander line DGS structure between the H-plane arrays. The proposed rectenna array operates at 5.8 GHz and has dimensions of $78\times 78\times 0.76$ mm3. The rectenna array achieves miniaturization, enhanced gain, and the ability to receive power regardless of the antenna polarization, through the high isolation formed by the decoupling slots. For performance validation, the operating principle of the proposed rectenna array system was theoretically analyzed and simulated. Additionally, the received power was measured based on the polarization of the transmitting antenna, and the results were compared with a conventional rectenna array and a proposed rectenna array without decoupling slots, both with the same area. The measurements show that the rectenna array with decoupling slots improves the power reception in environments with polarization mismatch, compared to the conventional rectenna arrays.}, archive = {J_ACCESS}, author = {Min-Seong Kim and Dong-Hwan Park and Mohamed Elhefnawy and Wang-Sang Lee}, doi = {10.1109/ACCESS.2025.3548974}, journal = {IEEE Access}, pages = {43249-43258}, shortjournal = {IEEE Access}, title = {Densely placed rectenna array with decoupling slots for enhanced received power in far-field wireless power transfer}, volume = {13}, year = {2025}, }
• (2025). Blueberry remaining shelf-life prediction based on the PSO-CNN-BiLSTM-MHA model. ACCESS, 13, 43238-43248. (www)

  @article{ , abstract = {Remaining shelf-life prediction of blueberries is crucial in reducing economic losses and enhancing market competitiveness. To meet the demand for high accuracy in predicting the remaining shelf-life of blueberries, this paper proposes a fusion model (PSO-CNN-BiLSTM-MHA) that combines Particle Swarm Optimization (PSO), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM), and Multi-Head Attention (MHA) mechanisms for predicting the remaining shelf-life of ‘Emerald’ blueberries under two temperature conditions, $4^{\circ } \text {C}$ and $25^{\circ } \text {C}$ . In this study, seven key features were selected from fifteen quality indicators of blueberries using the embedded method, and the PSO algorithm was used to determine the optimal hyperparameter combination of the model, which effectively improved its prediction performance. The experimental results show that our model outperforms other models in all evaluation metrics under both temperature conditions and demonstrates excellent prediction ability and stability. This study provides an effective technical approach for the accurate prediction of the remaining shelf-life of blueberries and other fruits.}, archive = {J_ACCESS}, author = {Mengya Liu and Xu Cheng and Yu Cao and Qian Zhou}, doi = {10.1109/ACCESS.2025.3548720}, journal = {IEEE Access}, pages = {43238-43248}, shortjournal = {IEEE Access}, title = {Blueberry remaining shelf-life prediction based on the PSO-CNN-BiLSTM-MHA model}, volume = {13}, year = {2025}, }
• (2025). UAV signal modulation recognition algorithm based on joint features. ACCESS, 13, 43224-43237. (www)

  @article{ , abstract = {The precise identification of drone modulation schemes serves as a fundamental basis for achieving intelligent drone recognition. To address the limitations of existing algorithms that overly rely on single features, resulting in low recognition rates and excessive model complexity, this paper proposes a drone signal modulation recognition algorithm based on joint features. The algorithm begins by employing Maximum Likelihood Estimation (MLE) to compensate for phase noise, mitigating its adverse effects on subsequent modulation recognition. Next, it combines the signal’s time-frequency representation with IQ data derived from higher-order cumulants as joint features, which are then input into a recognition network composed of 2D convolutional layers (Conv2D) and Long Short-Term Memory (LSTM) networks. Additionally, parameter dynamic fixed-point quantization is applied to optimize the weights and biases of the model, reducing resource consumption during practical deployment. Experimental results demonstrate that when the Signal-to-Noise Ratio (SNR) exceeds 2 dB, the proposed algorithm achieves a recognition accuracy of up to 90% for nine common drone modulation schemes, substantially outperforming comparable models. After quantization, the recognition performance remains nearly unaffected, while computational resource requirements are greatly reduced, making the algorithm highly suitable for deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Yang Zheng and Zhihai Zhuo}, doi = {10.1109/ACCESS.2025.3526194}, journal = {IEEE Access}, pages = {43224-43237}, shortjournal = {IEEE Access}, title = {UAV signal modulation recognition algorithm based on joint features}, volume = {13}, year = {2025}, }
• (2025). Dual-band rectifier design with high efficiency using simple dual band-stop network for wireless power transfer. ACCESS, 13, 43218-43223. (www)

  @article{ , abstract = {This paper presents a novel structure for designing a dual-band (DB) rectifier with high efficiency and compact size for wireless power transfer. The proposed circuit employs a simple dual band-stop network cascaded by only two transmission lines (TLINs) which can yield open-circuit termination simultaneously for the second harmonics of both two separate frequency bands, enabling an efficient DB power recycling mechanism with minimum insertion loss for power conversion efficiency (PCE) enhancement. The closed-form formulas are derived to extract the TLIN parameters with a low complexity. A rectifier based on a Schottky diode model BAT15-03W is simulated and measured for verification at two target operating frequencies of 0.915 GHz and 2.45 GHz. The measured results show peak PCE of 79.2% and 77.8% observed at 0.9 GHz and 2.36 GHz, respectively. Moreover, wide dynamic range for PCE>50% is equally recorded at both two frequencies with 17.5 dB. Also, the proposed rectifier exhibits a small electrical size of $0.102\lambda \times 0.105\lambda $ , indicating advantages in terms of high PCE and compactness, as compared to the other similar designs.}, archive = {J_ACCESS}, author = {Dang-An Nguyen and Yujin Choi and Chulhun Seo}, doi = {10.1109/ACCESS.2024.3382718}, journal = {IEEE Access}, pages = {43218-43223}, shortjournal = {IEEE Access}, title = {Dual-band rectifier design with high efficiency using simple dual band-stop network for wireless power transfer}, volume = {13}, year = {2025}, }
• (2025). Improvement of deep learning models using retinal filter: A systematic evaluation of the effect of gaussian filtering with a focus on industrial inspection data. ACCESS, 13, 43201-43217. (www)

  @article{ , abstract = {The range of products manufactured in the industry is vast, as are the anomalies that may occur during the manufacturing process. Some companies have initiated the automation of the quality control process and the implementation of modern deep learning methodologies to promptly identify defective objects. In addition to architectural optimizations within the deep learning model, the utilization of pre-processing filters can also be employed to help improve recognition performance. The Gaussian filter serves as an illustrative example, exhibiting functionality that parallels aspects of the human visual processing system. Although some authors have successfully applied this filter, a comprehensive study assessing its actual impact on a large number of objects and the extent to which various performance indicators are affected remains pending, which is the primary objective of this study. To this end, a variety of architectural approaches are employed, including Xception, InceptionV3, ResNet50V2, VGG19, and VGG16. In addition to hyperparameter tuning, all architectures utilize transfer learning. The results demonstrate that the implementation of an innovative Gaussian filter approach enhances the balanced accuracy in at least one architecture for 13 out of 15 product categories. Furthermore, the filtering approach positively influences various performance indicators across the majority of categories. It can be concluded that the Gaussian filter is often an effective technique for enhancing model performance across various product categories, making it a valuable and efficient tool for industrial defect detection in quality control applications. This study provides an overview of the results achieved and other key performance indicators for all the models used.}, archive = {J_ACCESS}, author = {Ricardo Buettner and Christopher Mai and Pascal Penava}, doi = {10.1109/ACCESS.2025.3549271}, journal = {IEEE Access}, pages = {43201-43217}, shortjournal = {IEEE Access}, title = {Improvement of deep learning models using retinal filter: A systematic evaluation of the effect of gaussian filtering with a focus on industrial inspection data}, volume = {13}, year = {2025}, }
• (2025). Improved method of driving safety field modeling by considering the abnormal transfer of energy between vehicles. ACCESS, 13, 43190-43200. (www)

  @article{ , abstract = {This study presents a Driving Safety Field (DSF) modeling method for quantifying the risks faced by autonomous vehicles, aiming to address the real-time and precise quantification of risks posed by other road participants during the driving process. The proposed modeling method is based on the concept that accidents involve abnormal energy transfer. By considering the interactions between the ego vehicle and other road participants, the model introduces reduced mass and vector velocity and utilizes coordinate transformations to ensure that risks are distributed along the direction of relative velocity, thereby satisfying Newton’s third law of motion regarding interactions between vehicles. Compared to previous methods, the proposed DSF modeling method offers higher interpretability and more direct quantification of instantaneous risks. Specifically, any change in the speed or position of any road participant in the environment leads to significant changes in the DSF force map. By simulation analyzing three risk-avoidance paths—driving straight, turning right, and making a U-turn—and comparing the distribution of driving risk field forces across 18 different scenarios, the results show that the proposed model can effectively identify driving risks. The U-turn strategy to the opposite lane is the most optimal risk-avoidance solution, reducing overall risk by 73.93% when the vehicle speed is 6 m/s. This method provides an intuitive and comprehensive safety situational awareness capability for autonomous vehicles. This ability is of great significance for improving the safety of autonomous driving and reducing the occurrence of traffic accidents.}, archive = {J_ACCESS}, author = {Huilan Li and Xunjia Zheng and Xiangyang Xu}, doi = {10.1109/ACCESS.2025.3549081}, journal = {IEEE Access}, pages = {43190-43200}, shortjournal = {IEEE Access}, title = {Improved method of driving safety field modeling by considering the abnormal transfer of energy between vehicles}, volume = {13}, year = {2025}, }
• (2025). Privacy-first ML for chronic kidney disease prediction: Exploring a decentralized approach using blockchain and IPFS. ACCESS, 13, 43178-43189. (www)

  @article{ , abstract = {Chronic Kidney Disease (CKD) pose a global health challenge due to their increasing incidence and delayed detection, compounded by the burden they place on healthcare systems worldwide. This surge is particularly pronounced in both industrialized and emerging nations, fueled by the rising prevalence of conditions such as diabetes, hypertension, and obesity. While Artificial Intelligence (AI) shows promise for early CKD detection, issues with data security, model transparency, and decision-making trust have created uncertainty among both patients and healthcare providers. To tackle these issues, a decentralized collaborative learning framework utilizing explainable AI to detect CKD is proposed. This approach combines the privacy-preserving nature of blockchain technology with the interpretability of explainable AI models. A real-time environment was set up to conduct these experiments, simulating a network of healthcare providers collaborating on CKD detection while maintaining patient privacy.}, archive = {J_ACCESS}, author = {Vikram Puri and Ishaani Priyadarshini and Aman Kataria and Sita Rani and Hong Min}, doi = {10.1109/ACCESS.2025.3548645}, journal = {IEEE Access}, pages = {43178-43189}, shortjournal = {IEEE Access}, title = {Privacy-first ML for chronic kidney disease prediction: Exploring a decentralized approach using blockchain and IPFS}, volume = {13}, year = {2025}, }
• (2025). CAPRI: A context-aware privacy framework for multi-agent generative AI applications. ACCESS, 13, 43168-43177. (www)

  @article{ , abstract = {While the swift advancement of cloud-based Large Language Models (LLMs) has significantly increased the efficiency and automation in business processes, it has also introduced considerable privacy concerns regarding Personally Identifiable Information (PII) and other protected data in multimodal forms, such as text, video, or images, being exported, potentially insecurely, outside the corporate environments. Although traditional anonymization-based techniques can alleviate these risks in offline applications, such as summarization or classification, incorporating it into online LLM workflows poses substantial challenges, particularly when these workflows encompass real-time transactions involving multiple stakeholders, as commonly observed in multi-agent generative AI applications. This study explores these challenges and proposes novel context-aware privacy frameworks and methods to address these issues. We employ a local privacy-focused gatekeeper LLM to contextually pseudonymize PII and assign unique identifiers as part of a new mapping process, thereby facilitating re-identification in real-time operations while safeguarding privacy when interacting with cloud-based LLMs. Our proposed methodologies and frameworks adeptly integrate privacy considerations into LLM and LLM Agent workflows, preserving both privacy and data utility while maintaining operational efficiency and utility comparable to non-anonymized generative AI processes.}, archive = {J_ACCESS}, author = {Jae H. Park and Vijay K. Madisetti}, doi = {10.1109/ACCESS.2025.3549312}, journal = {IEEE Access}, pages = {43168-43177}, shortjournal = {IEEE Access}, title = {CAPRI: A context-aware privacy framework for multi-agent generative AI applications}, volume = {13}, year = {2025}, }
• (2025). 3D face anti-spoofing with dense squeeze and excitation network and neighborhood-aware kernel adaptation scheme. ACCESS, 13, 43145-43167. (www)

  @article{ , abstract = {Face anti-spoofing is a critical challenge in biometric security systems, where sophisticated spoofing techniques pose significant threats. To enhance the effectiveness and efficiency of the methods employed for face anti-spoofing, this paper presents a new lightweight 3D face anti-spoofing framework characterized by several advanced mechanisms. To this purpose, the proposed architecture introduces DenseNet, a Squeeze and Excitation mechanism, and a new computational component called Neighborhood-Aware Kernel Adaptation (NAKA) that adaptively modifies 3D convolution kernels according to spatial proximity. Initially, an adaptive thresholding-based wavelet decomposition is employed for image denoising, followed by cross-channel attention to improve feature learning. Finally, Multiple Instance Learning (MIL) is used to address face anti-spoofing for the first time by modeling the spatial and temporal variations across facial areas. We validate our framework on two publicly available datasets: CelebA-Spoof and CASIA-SURF. We compared the performance of our proposed framework with several state-of-the-art methods using Classification accuracy, Precision, Recall, F1-score, ACER, APCER, and BPCER. Our model realizes 99.62% on the CelebA-Spoof and 99.86% on CASIA-SURF datasets. The proposed approach realized superior results in terms of high classification accuracy (99.62% and 99.86%), precision (99.85% and 99.83%), recall (99.39% and 99.84%), F-score (99.62% and 99.84%), ACER (0.0038/ 0.0014), FPR (0.0015/ 0.0013), APCER (0.0015/0.0016), and BPCER (0.0061/0.0013). These results are compared with 10 state-of-the-art methods to show the effectiveness of our approach in outperforming existing methods. The global comparative results reveal that the proposed approach is relatively effective in determining masked and true face scans.}, archive = {J_ACCESS}, author = {Mohammed Kareem Hussein Hussein and Osman Nuri Ucan}, doi = {10.1109/ACCESS.2025.3548418}, journal = {IEEE Access}, pages = {43145-43167}, shortjournal = {IEEE Access}, title = {3D face anti-spoofing with dense squeeze and excitation network and neighborhood-aware kernel adaptation scheme}, volume = {13}, year = {2025}, }
• (2025). A novel intensity-corrected blue channel compensation and edge-preserving contrast enhancement using laplace filter and sigmoid function for sand-dust image enhancement. ACCESS, 13, 43127-43144. (www)

  @article{ , abstract = {Outdoor computer vision systems face significant challenges due to reduced visibility and severe color distortion in the images captured in sand-dust-affected environments. This study aims to improve the visibility of sand-dust-degraded images. To achieve this goal, a novel and effective method is proposed to remove the sand-dust color cast and enhance image visibility. The proposed method combines two essential color model methods to remove the sand-dust color cast and enhance image clarity. In the initial phase, sand-dust removal is achieved using a novel Intensity-corrected blue channel compensation along with white balancing for color adjustment based on the Red-Green-Blue (RGB) color model. In the next phase, a novel Edge-preserving contrast enhancement method is applied to improve the visibility under sand-dust conditions. This method consists of CLAHE, a Gaussian blur filter, a Laplace filter, and the sigmoid function. Using the Hue-Saturation-Value (HSV) color model, CLAHE is applied for contrast enhancement; the Gaussian blur filter removes high-frequency noise, and the Laplace filter enhances edge detection, all targeting the V (Value) channel to refine image details, while the sigmoid function adjusts saturation in the Saturation (S) channel, ensuring natural color balance and improved feature visibility. In-depth qualitative and quantitative evaluations are conducted on images with varying levels of sand-dust intensity (weak, moderate, strong, extreme). The proposed method shows superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and processing speed, while significantly reducing computational complexity. Compared to the state-of-the-art CNN and all previous methods, our proposed method is efficient for real-time applications with minimal hardware requirements, making it ideal for embedded vision systems. Furthermore, a novel Energy Efficiency Index (EEI) is used to assess computational cost-effectiveness. The evaluation results confirm that the proposed method outperforms all previous and advanced deep learning methods in terms of visual quality, metrics, time complexity, and energy efficiency, making it a promising solution for sand-dust image enhancement in real-world applications.}, archive = {J_ACCESS}, author = {Muhammad Khawaja Kashif Masood and Enrique Nava Baro and Pablo Otero Roth}, doi = {10.1109/ACCESS.2025.3548972}, journal = {IEEE Access}, pages = {43127-43144}, shortjournal = {IEEE Access}, title = {A novel intensity-corrected blue channel compensation and edge-preserving contrast enhancement using laplace filter and sigmoid function for sand-dust image enhancement}, volume = {13}, year = {2025}, }
• (2025). A joint adaptive neuro-fuzzy inference system and binary quantum-based avian navigation algorithm for optimal resource monitoring, task scheduling and migration in cloud-based system. ACCESS, 13, 43109-43126. (www)

  @article{ , abstract = {A vast amount of data is generated daily from all aspects of individual lives due to the increasing proliferation of devices that are connected to the internet. These internet-connected gadgets lack the necessary capacity, resources, and storage to effectively process and retain large volumes of data with accuracy and reliability. Therefore, it is vital to regulate and process the unpredictable data created by a computing paradigm with robust resource specifications. Cloud computing has been considered an appealing solution for effectively analyzing and storing this data within a specific timeframe. Furthermore, the existence of multiple conflicting factors and the classification of the problem as NP-hard make resource management and task consolidation major obstacles in Cloud-based systems. This research suggests a hybrid Resource Monitoring, Task Scheduling and Migration technique that combines an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Quantum-based Avian Navigation Optimizer technique (BQANA) to tackle these challenges. The BQANA algorithm is utilized to enhance the control parameters of the ANFIS system. Additionally, a load balancing technique is proposed to provide an even distribution of workloads among Cloud Virtual Machines (VMs) to optimize resource management. Furthermore, a task migration strategy has also been adopted to offload the overloaded tasks to under-utilized VMs. The proposed approach presented in this study has been thoroughly validated using extensive simulations on real-world benchmark datasets, specifically for Quality of Service (QoS) characteristics. The simulation results demonstrate that the proposed methodology outperforms previous methods with regard to makespan, resource utilization, response time, energy consumption, and load balancing, with respective enhancements of 24.7%, 15.4%, 16.9%, 4.51%, and 23.4%.}, archive = {J_ACCESS}, author = {Aliva Priyadarshini and Sateesh Kumar Pradhan and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3547057}, journal = {IEEE Access}, pages = {43109-43126}, shortjournal = {IEEE Access}, title = {A joint adaptive neuro-fuzzy inference system and binary quantum-based avian navigation algorithm for optimal resource monitoring, task scheduling and migration in cloud-based system}, volume = {13}, year = {2025}, }
• (2025). A 500MS/s 14-bit pipelined ADC with startup protection circuit in 40 nm CMOS. ACCESS, 13, 43097-43108. (www)

  @article{ , abstract = {This paper presents a 500 MS/s 14-bit pipelined analog-to-digital converter (ADC) implemented in a 40 nm CMOS process. During power-up, the system is initially unstable, and the terminal voltage of certain transistors may exceed their specified breakdown voltage, putting them at risk of damage. To address this issue, we propose an input buffer and an operational amplifier, each equipped with a startup protection circuit, effectively mitigating these risks without significantly impacting system performance. To improve front-end nonlinearity and inter-stage gain errors caused by the finite gain of the operational amplifier and capacitance mismatches, we implement calibration dither and linearization dither techniques. The measurement results show that injecting dither improves the signal-to-noise distortion ratio (SNDR), increasing it from 64.3 dB to 70 dB, while the spurious-free dynamic range (SFDR) rises from 68.5 dB to 88.5 dB. Notably, the SFDR remains as high as 85.5 dB for input frequencies up to the Nyquist rate.}, archive = {J_ACCESS}, author = {Ben He and Xuan Guo and Hanbo Jia and Kai Sun and Xinyu Liu}, doi = {10.1109/ACCESS.2025.3548843}, journal = {IEEE Access}, pages = {43097-43108}, shortjournal = {IEEE Access}, title = {A 500MS/s 14-bit pipelined ADC with startup protection circuit in 40 nm CMOS}, volume = {13}, year = {2025}, }
• (2025). Co-pilot for project managers: Developing a PDF-driven AI chatbot for facilitating project management. ACCESS, 13, 43079-43096. (www)

  @article{ , abstract = {Our AI-driven PDF Chatbot is specialized for Project Management (PM) Automation and acts as a virtual Project Manager that offers continuous support to global teams. It interprets PDF data like SRS reports and interview transcripts by utilizing Open-Assistant’s SFT-1 12B Model. Insights from interviews of 15 project managers have enriched the knowledge base of our chatbot and ultimately enabled informative responses to the stakeholders of the project. Advanced AI techniques ensure efficient text preprocessing, including tokenization, numerical normalization, lowercasing, removing punctuation, removing extra spaces, recursive character text splitter, and lemmatization. It is primarily tailored for e-commerce project and provides precise guidance based on e-commerce data and risk management factors. With an average cosine similarity of 80.80% and semantic similarity score of 85.21%, it consistently aligns with PDF Contents and optimize the project management phases & methodologies. This innovation enhances Human-Robot Interaction, PM Automation, facilitates decision-making, and enables uninterrupted communication. While AI-driven PDF chatbots like ChatPDF and SciSummary exist, our chatbot is uniquely focused on automating project management tasks, providing tailored insights for e-commerce projects and decision-making, thus offering a breakthrough in PM automation. To ensure the chatbot’s robustness in context-aware responds, we compare our chatbot with ChatPDF and Sci-summary which are some PDF driven chatbots. Making our work available open-source on https://github.com/codewithkhurshed/SPM-project-repo can enhance its accessibility and promote future research opportunities in PDF driven chatbot development.}, archive = {J_ACCESS}, author = {Khorshed Alam and Mahbubul Haq Bhuiyan and Mohammad Shafiqul Islam and Abul Hossain Chowdhury and Zaheed Ahmed Bhuiyan and Suman Ahmmed}, doi = {10.1109/ACCESS.2025.3548519}, journal = {IEEE Access}, pages = {43079-43096}, shortjournal = {IEEE Access}, title = {Co-pilot for project managers: Developing a PDF-driven AI chatbot for facilitating project management}, volume = {13}, year = {2025}, }
• (2025). Explainable predictive factors for inter-agency partnership success. ACCESS, 13, 43056-43078. (www)

  @article{ , abstract = {Development partnerships that overcome the intrinsic limitations of individual organizations are essential to achieve the goals of the 2030 Agenda for Sustainable Development. However, one in five strategic partnerships fails, incurring higher costs for lesser impact. Assuming that partnership performance is proportionate to co-financed project ratings, we used the corresponding evaluation reports and project ratings from multiple multilateral organizations to automate the identification and analysis of critical predictive factors for partnership success. This approach ultimately helps form more selective partnerships. Our automation engine integrates text mining, sentiment and semantic analysis, and supervised Machine Learning with explainable and opaque algorithms. To achieve this goal, we compared four alternative Machine Learning models. By applying Shapley’s additive explanation values to trained models, we ranked factors by criticality, identifying their Top-10 rank. With bootstrap sampling, instead, we computed confidence intervals. Our rating predictions were rather good. Explainable AI models achieved a Matthews Correlation Coefficient of 0.749. Opaque algorithms reached 0.780. Both compare well with the current success rates of 81% for project co-financing partnerships and 75% for knowledge partnerships. Our prototype engine may help organizations improve partnership appraisal efficiency and improve collaboration. Future work will include refining factor formulations, deploying models with incremental learning, and introducing causality analysis.}, archive = {J_ACCESS}, author = {Nicola Drago and Gonçalo Afonso Braz and Tullio Vardanega}, doi = {10.1109/ACCESS.2025.3547439}, journal = {IEEE Access}, pages = {43056-43078}, shortjournal = {IEEE Access}, title = {Explainable predictive factors for inter-agency partnership success}, volume = {13}, year = {2025}, }
• (2025). A review of discount policies in supplier selection: Trends, opportunities, and challenges. ACCESS, 13, 43028-43055. (www)

  @article{ , abstract = {With global supply chains becoming increasingly intricate due to factors like globalization, technological advancements, and unforeseen disruptions, supplier selection emerges as a pivotal aspect of sustainable supply chain management. This paper presents a systematic literature review focusing on the role of discount policies in supplier selection. Employing a detailed bibliometric analysis using the extensive Scopus database, this study meticulously examines the scholarly discourse on supplier selection strategies, particularly those influenced by discounting. In this analysis, we analyze 197 papers from 2002 to 2023 to identify and discuss key themes, influential research works, and the dominant methodological trends in supplier selection involving discount policies. Findings unveil the strategic significance of incorporating discount policies, emphasizing their impact on an organization’s cost structure, profitability, and supply chain efficiency. Moreover, this study illuminates the intricacies and challenges organizations face in implementing these policies while identifying unexplored avenues and potential opportunities for future research. By offering a holistic view of the impact of discount policies on supplier selection, this paper aims to be an indispensable resource for scholars, practitioners, and decision-makers in the evolving realm of supply chain management.}, archive = {J_ACCESS}, author = {Nima Pourmohammadreza and Zahra Sadat Hasanpour Jesri and Mehdi A. Kamran}, doi = {10.1109/ACCESS.2025.3547051}, journal = {IEEE Access}, pages = {43028-43055}, shortjournal = {IEEE Access}, title = {A review of discount policies in supplier selection: Trends, opportunities, and challenges}, volume = {13}, year = {2025}, }
• (2025). HSDetect-net: A fuzzy-based deep learning steganalysis framework to detect possible hidden data in digital images. ACCESS, 13, 43013-43027. (www)

  @article{ , abstract = {Recent progress in digital image steganalysis has been heavily influenced by advancements in Deep Learning (DL), particularly using Convolutional Neural Networks (CNNs). These networks have become a preferred choice for addressing complex classification challenges. Despite their success, current CNN-based approaches for steganalysis often encounter difficulties in accurately identifying concealed information, especially when dealing with complex textures generated by steganographic techniques. To overcome these issues, this study introduces HSDetect-Net, a novel CNN framework that integrates fuzzy logic to enhance detection capabilities. The proposed HSDetect-Net’s architecture employs specialized small-sized convolutional kernels to extract complex details and incorporates a fuzzy layer to refine classification accuracy. Evaluations conducted on the Break Our Steganographic System Base (BOSSBase) and Break Our Watermarking System (BOWS) datasets reveal that HSDetect-Net achieves remarkable performance, with an accuracy of 99.07% and an F1 score of 99.68%. Compared to existing methods, HSDetect-Net improves the accuracy of detecting hidden data in images, making it a promising solution to the steganalysis of digital images.}, archive = {J_ACCESS}, author = {Ntivuguruzwa Jean de la Croix and Tohari Ahmad and Fengling Han and Royyana Muslim Ijtihadie}, doi = {10.1109/ACCESS.2025.3546510}, journal = {IEEE Access}, pages = {43013-43027}, shortjournal = {IEEE Access}, title = {HSDetect-net: A fuzzy-based deep learning steganalysis framework to detect possible hidden data in digital images}, volume = {13}, year = {2025}, }
• (2025). An ensemble-based auto insurance fraud detection using BQANA hyperparameter tuning. ACCESS, 13, 42997-43012. (www)

  @article{ , abstract = {The prevalence of insurance fraud in the auto industry poses significant financial challenges and undermines customer trust. Despite the application of machine learning methods to reduce these losses, current literature lacks effective tuned algorithms for detecting fraud in insurance claims. To address this gap, this study proposes an ensemble-based method with a weighted voting strategy for auto insurance fraud detection. The study uses the Binary Quantum-Based Avian Navigation Optimizer Algorithm (BQANA) to optimize the hyperparameters of Support Vector Machines (SVM), Random Forest (RF), and XGBoost classifiers, which are combined into an ensemble. To address the dataset’s imbalance, random undersampling was applied to create five legitimate-to-fraudulent claim ratios: A:A, 1:1, 2:1, 4:1, and 8:1. The performance of BQANA was compared with Genetic Algorithms and Simulated Annealing for hyperparameter tuning. The results indicate that the ensemble model with BQANA-optimized hyperparameters outperforms other methods, particularly at a 1:1 ratio, achieving 99.94% Accuracy, 98.93% Precision, 100% Recall, and a 99.46% F1-score. These metrics surpass those obtained without optimization or with traditional tuning methods. This research highlights the efficacy of the BQANA algorithm in optimizing hyperparameters for classification models. By combining these optimized classifiers into an ensemble, the study significantly enhances predictive accuracy in car insurance fraud detection, offering notable improvements over conventional methods.}, archive = {J_ACCESS}, author = {Afsaneh Gheysarbeigi and Morteza Rakhshaninejad and Mohammad Fathian and Farnaz Barzinpour}, doi = {10.1109/ACCESS.2025.3548529}, journal = {IEEE Access}, pages = {42997-43012}, shortjournal = {IEEE Access}, title = {An ensemble-based auto insurance fraud detection using BQANA hyperparameter tuning}, volume = {13}, year = {2025}, }
• (2025). A seq-to-seq temporal convolutional network for volleyball jump monitoring using a waist-mounted IMU. ACCESS, 13, 42986-42996. (www)

  @article{ , abstract = {Jump monitoring for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sequence-to-sequence (seq-to-seq) classification without using the sliding window technique. The model was evaluated on volleyball players during a lab session with a fixed protocol of jumping and landing tasks, and during four volleyball training sessions, respectively. The MS-TCN model achieved better performance than a state-of-the-art deep learning model but with lower computational cost. In the lab sessions, most jump counts showed small differences between the predicted jumps and video-annotated jumps, with an overall count showing a Limit of Agreement (LoA) of $0.1\pm 3.40$ (r =0.884). For comparison, the proposed algorithm showed slightly worse results than VERT (a commercial jumping assessment device) with a LoA of $0.1\pm 2.08$ (r =0.955) but the differences were still within a comparable range. In the training sessions, the recognition of three types of jumps exhibited a mean difference from observation of less than 10 jumps: block, smash, and overhead serve. These results showed the potential of using a single IMU to recognize the types of volleyball jumps. The proposed architecture provided high resolution of recognition and required fewer parameters compared with state-of-the-art models.}, archive = {J_ACCESS}, author = {Meng Shang and Camilla de Bleecker and Jos Vanrenterghem and Roel de Ridder and Sabine Verschueren and Carolina Varon and Walter de Raedt and Bart Vanrumste}, doi = {10.1109/ACCESS.2025.3545560}, journal = {IEEE Access}, pages = {42986-42996}, shortjournal = {IEEE Access}, title = {A seq-to-seq temporal convolutional network for volleyball jump monitoring using a waist-mounted IMU}, volume = {13}, year = {2025}, }
• (2025). Direction finding using a four-ray street canyon propagation model in presence of periodic lamp-posts for use in microcellular wireless communication and angular estimation. ACCESS, 13, 42977-42985. (www)

  @article{ , abstract = {A four-ray street canyon model, including the polarization effect, is considered as the basic propagation model and an array of the periodic lampposts is added within the scenario. Using the MUISC (Multiple Signal Classification) direction-finding algorithm the angle of arrival to the access point is computed in both azimuth and elevation. Through this, the root mean square errors of DOA are estimated in both azimuth and elevation. It is demonstrated that the presence of the lampposts increases significantly the angle of arrival estimation errors. The evolution of the errors is computed by moving the mobile set along and across the street. The channel angular spread is computed and through Monte-Carlo simulations the mean and the standard deviation of the angular spread are computed both azimuth and the elevation domains. Monte-Carlo simulations were used to evaluate the root mean square errors. The resulting values are compared to the 3GPP TR 38.901 measurement model and a relatively good agreement is observed. The method could be used in microcellular wireless communication systems for angular estimation. The main point of this work is the combination of the lampposts with a four-ray street canyon model which gives in results which are near to a real propagation scenario.}, archive = {J_ACCESS}, author = {Sarah Poormohammad and Forouhar Farzaneh}, doi = {10.1109/ACCESS.2025.3546768}, journal = {IEEE Access}, pages = {42977-42985}, shortjournal = {IEEE Access}, title = {Direction finding using a four-ray street canyon propagation model in presence of periodic lamp-posts for use in microcellular wireless communication and angular estimation}, volume = {13}, year = {2025}, }
• (2025). Toward user-guided seismic facies interpretation with a pre-trained large vision model. ACCESS, 13, 42965-42976. (www)

  @article{ , abstract = {Recent advancements in computer vision and deep learning have made a paradigm shift in seismic facies interpretation. Various supervised and unsupervised deep learning-based seismic facies research using Convolutional Neural Network (CNN) as the baseline architecture have been proposed and achieved promising results. However, these approaches are often limited by the lack of available high-quality labelled dataset and minimum explainability of the model. This is further compounded by the fact that these models do not allow user-guided interaction, which limits the ability of seismic interpreters to perform localized seismic facies segmentation through prompts. To optimize the already existing approach, we present a time-efficient and high precision alternative by reformulating seismic facies segmentation as a Segment All or Segment One (SASO) task. In this paper, we propose FaciesSAM, based on Fast Segment Anything Model, for seismic facies segmentation. For the first time, our paper highlights that FaciesSAM can help to decouple seismic facies identification into all-instance segmentation (segment all) and prompt-guided selection (segment one) for broad and localized facies interpretation, respectively. The effectiveness of our proposed method was evaluated on the benchmark F3 geological dataset. Experimental results shows that our method is effective, even when trained on fewer seismic images, achieving a mAP0.5 of 83.3% with 3.1% increase in pixel accuracy and 4.6% increase in mean class accuracy when compared to the CNN benchmark results. Furthermore, our method meets real-time processing speed of 1.14ms per seismic section. These results underscore the capacity of prompt-based CNN detectors to solving specific geological challenges such as seismic interpretation accuracy and processing speed.}, archive = {J_ACCESS}, author = {Joshua Atolagbe and Ardiansyah Koeshidayatullah}, doi = {10.1109/ACCESS.2025.3547931}, journal = {IEEE Access}, pages = {42965-42976}, shortjournal = {IEEE Access}, title = {Toward user-guided seismic facies interpretation with a pre-trained large vision model}, volume = {13}, year = {2025}, }
• (2025). Knowledge distillation and transformer-based framework for automatic spine CT report generation. ACCESS, 13, 42949-42964. (www)

  @article{ , abstract = {Spine Computed Tomography (SCT) is essential for identifying fractures, tumors and degenerative spine diseases, assisting medical practitioners in formulating an accurate diagnosis and treatment. One of the core element of SCT is reporting. The effectiveness of spine reporting is often limited by challenges such as an inadequate infrastructure and lack of experts. Automated SCT analysis has the potential to revolutionize spinal healthcare and improve patient outcomes. To achieve this objective, we proposed a framework for spine report generation that utilizes transformer architecture, trained on textual reports alongside the visual features extracted from the sagittal slices of the SCT volume. A foundation model is used to perform Knowledge Distillation (KD) alongside an encoder to ensure an optimal performance. The proposed framework is evaluated on the public dataset (VerSe20). The incorporation of KD results improved both the BERT and BLEU1 score on the dataset, from 0.7486 to 0.7522 and 0.6361 to 0.7291. Additionally, the proposed framework is evaluated using four different types of reports: original radiologist reports, reports without spine-level annotations, rephrased reports, and reports generated by ChatGPT-4o (ChatGPT). The evaluation without spine-level annotations demonstrates superior performance across most metrics, achieving the highest BLEU-1 and ROUGE-L scores, with a BLEU-1 of 0.9293 and a ROUGE-L score of 0.9297. In contrast, the other techniques achieved moderate scores across all metrics. Finally, experienced radiologists assessed the spine report and have given high rating to the original reports across all three criteria (completeness, conciseness and correctness), in comparison to the generated reports. This study’s findings suggest that omitting spine-level annotations can improve the quality of text generation.}, archive = {J_ACCESS}, author = {Humaira Batool and Asmat Mukhtar and Sajid Gul Khawaja and Norah Saleh Alghamdi and Asad Mansoor Khan and Adil Qayyum and Ruqqayia Adil and Zawar Khan and Muhammad Usman Akram and Muhammad Usman Akbar and Anders Eklund}, doi = {10.1109/ACCESS.2025.3546131}, journal = {IEEE Access}, pages = {42949-42964}, shortjournal = {IEEE Access}, title = {Knowledge distillation and transformer-based framework for automatic spine CT report generation}, volume = {13}, year = {2025}, }
• (2025). FiSC: A novel approach for fitzpatrick scale-based skin analyzer’s image classification. ACCESS, 13, 42934-42948. (www)

  @article{ , abstract = {The Fitzpatrick scale is a widely used tool in dermatology for categorizing skin types based on melanin levels and sensitivity to ultraviolet light. The primary objective of this study is to enhance the accuracy of Fitzpatrick scale classification by addressing limitations in existing methodologies. Current approaches either rely on custom-designed hardware or utilize the Individual Typology Angle (ITA) for image classification; however, these methods typically allow for a one-tone difference in classification and achieve a maximum accuracy of approximately 75%. A primary task for skin tone classification in images, is to apply filters to detect skin regions in an image. However, the filters proposed for detecting skin do not apply to general datasets. In this paper, we propose a novel classification method that employs specialized filters to accurately detect and remove skin surface attributes, such as wrinkles and pores, using a controlled environment dataset obtained from a professional skin analyzer device. Our method involves modeling image features as a nine-dimensional feature vector, followed by a dimensionality reduction process to identify the most influential features and dominant areas within the feature space, enabling deployment on low-power devices. We conducted extensive classification experiments using various Machine Learning algorithms. The results of our cross-validation tests demonstrate a significant improvement in classification accuracy, reaching up to 97%, thereby outperforming state-of-the-art methods without relaxing the accuracy criteria.}, archive = {J_ACCESS}, author = {Guillermo Crocker Garcia and Muhammad Numan Khan and Aftab Alam and Josue Obregon and Tamer Abuhmed and Eui-Nam Huh}, doi = {10.1109/ACCESS.2025.3547573}, journal = {IEEE Access}, pages = {42934-42948}, shortjournal = {IEEE Access}, title = {FiSC: A novel approach for fitzpatrick scale-based skin analyzer’s image classification}, volume = {13}, year = {2025}, }
• (2025). Multimodal emotional detection system for virtual educational environments: Integration into microsoft teams to improve student engagement. ACCESS, 13, 42910-42933. (www)

  @article{ , abstract = {In modern educational settings, especially virtual environments, understanding students’ emotional states has become crucial in improving participation, engagement, and academic performance. Current emotional detection systems, such as Affectiva and Emotient, offer facial and vocal emotional recognition but present limitations regarding integration, scalability, and adaptability to widely used platforms such as Microsoft Teams. These systems often require complex integrations and are not readily adaptable to the dynamic nature of the educational environment. This study proposes an innovative solution for real-time emotional detection, using a multimodal approach that combines speech and facial expression analysis within the Microsoft Teams platform. By integrating TensorFlow, PyTorch, OpenCV, and Dlib, we developed a system capable of accurately detecting emotions such as happiness, stress, and calm, with up to 95% detection precision for positive emotions. The system was tested in an educational environment, demonstrating its ability to process multiple interactions simultaneously without compromising performance. Results include high precision in essential emotion detection and significant improvements in student engagement. However, the system showed limitations in detecting more complex emotions such as stress and frustration, suggesting further refining the model.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Rommel Gutierrez and Aracely Mera-Navarrete}, doi = {10.1109/ACCESS.2025.3546772}, journal = {IEEE Access}, pages = {42910-42933}, shortjournal = {IEEE Access}, title = {Multimodal emotional detection system for virtual educational environments: Integration into microsoft teams to improve student engagement}, volume = {13}, year = {2025}, }
• (2025). Autonomous aerial vehicle object detection based on spatial perception and multiscale semantic and detail feature fusion. ACCESS, 13, 42897-42909. (www)

  @article{ , abstract = {Owing to changes in the spatial position of Autonomous Aerial Vehicle (AAV) aerial images and limited platform resources, most existing AAV aerial image detection models have low accuracy, and it is difficult to achieve a good balance between detection performance and lightweight. To solve the above problems, an object detection model of AAV aerial images based on YOLOv8s, called BSDS-YOLOv8s, is proposed. In the proposed model the Dynamic Head (DyHead) is used to replace the detection head of YOLOv8s firstly, which can improve the spatial perception ability of the detection head of the model. Second, to improve the detection performance of DyHead and make the model lightweight, a new feature pyramid network (SDI-MBiFPN) in the Neck of YOLOv8s is proposed, which contains a Multiscale Bidirectional Feature Pyramid Network (BiFPN), called MBiFPN, and a feature fusion method based on the redesigned Semantic and Detail Fusion (SDI). Finally, a method that integrates Soft Non-Maximum Suppression (Soft-NMS) with Generalized Intersection over Union (GIoU), called GIoU-Soft-NMS, is proposed to enhance the model’s post-processing capability and reduce the missed and false detection rates, thereby further improving the detection accuracy of the model. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of BSDS-YOLOv8s reached 49.6% and 34.1%, respectively, on the VisDrone2019 dataset. This is an improvement of 9.2 and 9.8 percentage points over YOLOv8s, respectively. The number of parameters and the floating point operations (FLOPs) were 8.03M and 26.6G, 27.8% and 7.6% less than those of YOLOv8s, respectively.}, archive = {J_ACCESS}, author = {Wei Rao and Siyuan Chen and Dan Li}, doi = {10.1109/ACCESS.2025.3547825}, journal = {IEEE Access}, pages = {42897-42909}, shortjournal = {IEEE Access}, title = {Autonomous aerial vehicle object detection based on spatial perception and multiscale semantic and detail feature fusion}, volume = {13}, year = {2025}, }
• (2025). Model predictive control with reinforcement learning-based speed profile generation in racing simulator. ACCESS, 13, 42887-42896. (www)

  @article{ , abstract = {Model Predictive Control (MPC) is a widely used optimal control strategy, particularly effective in managing complex constraints. It excels at optimizing performance within feasible limits, such as minimizing lap times in vehicle racing. However, its effectiveness can be hindered by computational burdens and model inaccuracies. To address these challenges, MPC problems are often simplified, and parameters are manually tuned through iterative adjustments—a process that is both time-consuming and labor-intensive. This paper introduces a Reinforcement Learning-based Speed Profile Model Predictive Control (RLSP-MPC) approach, which leverages deep reinforcement learning (DRL) to approximate global optima via an exploration-exploitation framework, eliminating the need for manual tuning. The RLSP-MPC method reformulates the traditional MPC speed optimization task into a DRL-enhanced speed profile tracking problem, reducing parameter dependency and improving lap times. The DRL network provides boundary constraints and a reference velocity profile to MPC, which then reconstructs the trajectory while adhering to these constraints. Polynomial functions are employed to reduce the dimensionality of the network output in velocity profile design. Extensive experiments conducted using the F1Tenth Simulator confirm that the proposed method effectively integrates the strengths of MPC and DRL, achieving superior lap times and enhanced trajectory tracking performance while maintaining real-time computational feasibility. Specifically, the proposed method reduces lap times by an average of approximately 22.07% compared to conventional MPC and 14.52% compared to LMPC across multiple racing tracks, demonstrating consistent and statistically significant performance improvements.}, archive = {J_ACCESS}, author = {Min-Seong Kim and Tae-Hyoung Park}, doi = {10.1109/ACCESS.2025.3547820}, journal = {IEEE Access}, pages = {42887-42896}, shortjournal = {IEEE Access}, title = {Model predictive control with reinforcement learning-based speed profile generation in racing simulator}, volume = {13}, year = {2025}, }
• (2025). Auxiliary particle filtering with multitudinous lookahead sampling for accurate target tracking. ACCESS, 13, 42874-42886. (www)

  @article{ , abstract = {The auxiliary particle filter, which is the popular extension of the standard bootstrap particle filter, is known to assist in drawing particles from regions of high probability mass of the posterior density by leveraging the incoming measurement information in the sampling process. The filter accomplishes this by looking ahead in time to determine those particles that become important when propagated forward, retract, and then propagate those particles forward in time. The key problem with this approach is that a particle determined to be important may not fall in regions of importance when actually propagated forward, either because of a large diffusion of the state transition kernel and/or a highly informative measurement, thus defeating the entire purpose of the filter. This problem leads to degeneracy. This paper proposes a method of sampling a multitude of particles for each particle to make such a decision. The key idea here is to use multiple disturbances, instead of one as does the auxiliary particle filter, as lookahead means to guide particles to regions of high probability in the posterior probability density. Through evaluation, we show that the proposed idea overcomes the said problem and exhibits less degeneracy and high tracking accuracy.}, archive = {J_ACCESS}, author = {Praveen B. Choppala and Ramoni Adeogun}, doi = {10.1109/ACCESS.2025.3548424}, journal = {IEEE Access}, pages = {42874-42886}, shortjournal = {IEEE Access}, title = {Auxiliary particle filtering with multitudinous lookahead sampling for accurate target tracking}, volume = {13}, year = {2025}, }
• (2025). Multi-architecture halide template-driven automatic library function generation for simulink models. ACCESS, 13, 42866-42873. (www)

  @article{ , abstract = {This paper presents a templated approach for automatic generation of optimized library functions using Halide as a replacement for the functions generated by Simulink. Although Simulink is widely used for the development of embedded systems, the automatically generated code may have limitations in compute-intensive models. Therefore, we propose library function generation using Halide from prewritten templates, extracting the Simulink model parameters using a model-based parallelizer. Experiments were conducted on central processing units (CPUs) and graphical processing units (GPUs) to evaluate performance. On CPUs, compared with compiler vectorization and the basic linear algebra subprograms (BLAS) library, Halide achieved up to 65.77 times speedup in multi-core and up to 36.20 times speedup in single-core scenarios. Although Halide did not always outperform BLAS for larger matrix sizes, it still showed considerable improvements. On GPUs, experiments on MX450 and Jetson platforms demonstrated that Halide’s performance was comparable to that of cuBLAS and even surpassed it for larger matrices.}, archive = {J_ACCESS}, author = {Qi Li and Shanwen Wu and Masato Edahiro}, doi = {10.1109/ACCESS.2025.3548105}, journal = {IEEE Access}, pages = {42866-42873}, shortjournal = {IEEE Access}, title = {Multi-architecture halide template-driven automatic library function generation for simulink models}, volume = {13}, year = {2025}, }
• (2025). SECNN: Squeeze-and-excitation convolutional neural network for sentence classification. ACCESS, 13, 42858-42865. (www)

  @article{ , abstract = {Sentence classification constitutes a fundamental task in natural language processing. Convolutional Neural Networks (CNNs) have gained prominence in this domain due to their capacity to extract n-gram features through parallel convolutional filters, effectively capturing local lexical correlations. However, due to the constrained receptive field of convolutional operations, conventional CNNs exhibit limitations in modeling long-range contextual dependencies. To address this, attention mechanisms âĂŞ which enable global contextual modeling and keyword saliency detection âĂŞ have been integrated with CNN architectures to enhance classification performance. Diverging from conventional approaches that emphasize lexical-level attention, this study introduces a novel Squeeze-and-Excitation Convolutional Neural Network (SECNN) that implements channel-wise attention on CNN feature maps. Specifically, SECNN aggregates multi-scale convolutional features as distinct semantic channels and employs Squeeze-and-Excitation (SE) blocks to learn channel-wise attention weights, thereby enabling dynamic feature recalibration based on inter-channel dependencies. Across MR, IMDb, AGNews and DBpedia benchmark datasets, the proposed model achieves marginal yet consistent improvements (0.2% F1 on MR; 0.1% on DBpedia) over baseline methods, suggesting statistically advantages in two of four evaluated tasks.}, archive = {J_ACCESS}, author = {Shandong Yuan and Zili Zou and Han Zhou and Yun Ren and Jianping Wu and Kai Yan}, doi = {10.1109/ACCESS.2025.3548111}, journal = {IEEE Access}, pages = {42858-42865}, shortjournal = {IEEE Access}, title = {SECNN: Squeeze-and-excitation convolutional neural network for sentence classification}, volume = {13}, year = {2025}, }
• (2025). High-performance NTRU accelerator using a direct memory access controller. ACCESS, 13, 42850-42857. (www)

  @article{ , abstract = {NTRU is a well-established and widely used public-key cryptography. It has been standardized in IEEE Std1363.1 and X9.98, and its variant was submitted to the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization contest. In this study, we proposed a high-performance accelerator implementation of the recent NTRU version submitted to the NIST PQC contest. In NTRU, there are two types of polynomials: ternary polynomial (TP) that has coefficients in $\{-1,0,1\}$ , and non-TP. In contrast to previous methods that support both multiplication of a TP and a non-TP and multiplication of non-TPs, the proposed accelerator performs only multiplication of non-TPs by converting a TP to a non-TP in advance. We also leveraged a direct memory access controller and simplified the definitions of the polynomial registers. Consequently, we reduced the latency, data transfer time, and resources, such as the need for a large number of registers and multiplexers. We synthesized the accelerator using 28-nm CMOS process technology. The results showed that the accelerator can perform encryption and decryption within 0.8 and $1.8~\mu $ s, respectively, at the maximum clock frequency of 1.67 GHz, requiring 494.9 k gate counts. Furthermore, the proposed accelerator demonstrated a higher performance with fewer resources than the existing accelerators.}, archive = {J_ACCESS}, author = {Seon Bhin Kim and Piljoo Choi and Dong Kyue Kim}, doi = {10.1109/ACCESS.2024.3505293}, journal = {IEEE Access}, pages = {42850-42857}, shortjournal = {IEEE Access}, title = {High-performance NTRU accelerator using a direct memory access controller}, volume = {13}, year = {2025}, }
• (2025). Enhancing lidar-based 3D classification through an improved deep learning framework with residual connections. ACCESS, 13, 42836-42849. (www)

  @article{ , abstract = {The rapid advancement of autonomous driving and autonomous vehicle technology has intensified the need for robust environment perception. In this work, we leverage automotive LiDAR point cloud data, which are inherently sparse, noisy, and unstructured, posing challenges to conventional classification methods. To address these difficulties, we adopt deep learning-based 3D neural networks and specifically enhance the VoxNet architecture. Through systematic adjustments—such as increasing convolutional kernel size and quantity, incorporating dropout and Batch Normalization layers, and integrating residual connections—we achieve substantial accuracy gains. In particular, our improved VoxNetVR5 model attains 97.92% accuracy, outperforming the baseline VoxNet by around 4–5 percentage points (from ~93% to ~98%). This enhancement is critical for safety-sensitive autonomous driving applications, as even modest accuracy improvements significantly reduce classification errors. Although the increased network complexity and added layers lead to longer training times, the robust performance justifies the computational cost. The proposed VoxNetVR5 architecture and its associated hyperparameter optimizations offer a powerful and efficient solution for processing large-scale, high-dimensional LiDAR data, paving the way for safer and more reliable vehicle perception systems.}, archive = {J_ACCESS}, author = {Shih-Lin Lin and Jun-Yi Wu}, doi = {10.1109/ACCESS.2025.3547942}, journal = {IEEE Access}, pages = {42836-42849}, shortjournal = {IEEE Access}, title = {Enhancing lidar-based 3D classification through an improved deep learning framework with residual connections}, volume = {13}, year = {2025}, }
• (2025). A novel RMS-driven deep reinforcement learning for optimized portfolio management in stock trading. ACCESS, 13, 42813-42835. (www)

  @article{ , abstract = {Algorithmic stock trading has improved tremendously, with Reinforcement Learning (RL) algorithms being more adaptable than classic approaches like mean reversion and momentum. However, challenges remain in adequately depicting market events and generating suitable rewards to influence the trading decisions of an agent in a dynamic environment. This study proposed an improved stock market trading framework termed the RMS-Driven Deep Reinforcement Learning (DRL) model for optimal portfolio management. The research attempts to give a more comprehensive view of the market and, as a result, enhance trading decisions by including consumer information and incorporating news sentiments into the model, in addition to data from typical earnings reports. More specifically, three kinds of DRL models are presented, combined with data from stock earnings reports, Max Drawdown rewards, and sentiment indicators (RMS), known as PPO_RMS, A2C_RMS, and DDPG_RMS, respectively. The findings of this research indicate that the integrated model, mainly the DDPG_RMS effectively outperforms the baseline ^DJI index in many risk-return analyses in ratios showing better risk management, and profitability. The proposed stock trading model generates a maximum cumulative return of 27% with a Sharpe ratio of 0.66, showing an appropriate trade-off between risk and return. This approach, which incorporates sentiment analysis and Max Drawdown rewards, significantly enhances the model’s performance in adapting to changing market conditions. Therefore, the results emphasize the appropriateness of integrating sentiment indices with traditional financial data to enhance a trader’s performance while also offering essential information to aid in the development of improved trading tactics in continuously changing financial markets.}, archive = {J_ACCESS}, author = {Asma Sattar and Amna Sarwar and Saira Gillani and Maryam Bukhari and Seungmin Rho and Muhammad Faseeh}, doi = {10.1109/ACCESS.2025.3546099}, journal = {IEEE Access}, pages = {42813-42835}, shortjournal = {IEEE Access}, title = {A novel RMS-driven deep reinforcement learning for optimized portfolio management in stock trading}, volume = {13}, year = {2025}, }
• (2025). SwinVision: Detecting small objects in low-light environments. ACCESS, 13, 42797-42812. (www)

  @article{ , abstract = {Neural networks have been widely employed in the field of object detection. Transformers enable effective object detection through global context awareness, modular design, scalability, and adaptability to diverse target scales. However, small object detection requires careful consideration due to its comprehensive computations, data requirements, and real-time performance challenges. To address these issues, we present SwinVision, an innovative framework for small object detection in low-light environments. This research shows a balanced approach between computational efficiency and detection accuracy for advancing object detection in low-light scenarios. Firstly, a Swin Transformer-based computing network is introduced and optimized for object detection in large-scale areas. The framework balances computational power and resource efficiency, surpassing conventional transformers. Secondly, we present the STLE module, which enhances the features of low-light images for beneficial object detection. The last building block is a specialized Swin-based detection block for accurate detection of small, detailed objects in resource-constrained scenarios. Experiments conducted on the VisDrone dataset significantly ameliorated existing methods such as YOLOv8x, with a 6.31% increase in mAP and 12.55% in AP50. SwinVision’s effectiveness in low-light environments, especially with small objects, establishes a foundation for robust detection systems adapting to various environmental challenges.}, archive = {J_ACCESS}, author = {Tao Dai and Qi Wang and Yuancheng Shen and Shang Gao}, doi = {10.1109/ACCESS.2025.3548151}, journal = {IEEE Access}, pages = {42797-42812}, shortjournal = {IEEE Access}, title = {SwinVision: Detecting small objects in low-light environments}, volume = {13}, year = {2025}, }
• (2025). Enhancing anomaly detection in attributed networks using proximity preservation and advanced embedding techniques. ACCESS, 13, 42777-42796. (www)

  @article{ , abstract = {The increasing relevance of anomaly detection in attributed networks is gaining traction in fields such as cybersecurity, finance, and healthcare. However, large-scale attributed networks often exhibit noisy and inconsistent node properties, which negatively affect anomaly detection accuracy and disrupt the network’s structure. A key challenge is maintaining the integrity of both network and node feature structures during the embedding process. To address this, we propose a novel approach that combines a Graph Convolution Auto encoder (GCAE) with self-supervised learning, proximity preservation, and adversarial training using Generative Adversarial Networks (GAN). First, Laplacian smoothing is applied to reduce noise in node properties, followed by Laplacian sharpening to highlight important features. These enhanced features are then fed into the GCAE, which encodes node attributes into a latent space using graph convolutional layers. Self-supervised tasks like attribute masking and edge prediction further enhance the GCAE’s ability to capture the graph’s structure. Additionally, proximity preservation ensures that the latent space reflects both first order and high-order proximity. The inclusion of GAN refines the embeddings, aligning them closer to the true distribution of the graph data. This method effectively preserves both node features and network structure, making the embedding robust and distinguishable. Empirical evaluations on four real-world datasets demonstrate that our approach surpasses state-of-the-art methods, setting a new benchmark for anomaly detection in attributed networks. Our framework has significant potential to advance both research and practical applications in anomaly detection.}, archive = {J_ACCESS}, author = {Wasim Khan and Mohammad Ishrat and Mohammad Nadeem Ahmed and Shafiqul Abidin and Mohammad Husain and Mohd Izhar and Abu Taha Zamani and Mohammad Rashid Hussain and Arshad Ali}, doi = {10.1109/ACCESS.2025.3544260}, journal = {IEEE Access}, pages = {42777-42796}, shortjournal = {IEEE Access}, title = {Enhancing anomaly detection in attributed networks using proximity preservation and advanced embedding techniques}, volume = {13}, year = {2025}, }
• (2025). RRT-based optimizer: A novel metaheuristic algorithm based on rapidly-exploring random trees algorithm. ACCESS, 13, 42744-42776. (www)

  @article{ , abstract = {Real-world optimization problems are becoming increasingly complex and require effective and versatile algorithms to provide reliable solutions. However, the no-free-lunch theorem indicates that no single optimization algorithm can solve all optimization problems accurately. Consequently, new optimization methods are required. Inspired by the search mechanism of the Rapidly-exploring Random Trees (RRT) algorithm commonly used in robot path planning, we propose a novel metaheuristic algorithm called RRT-based Optimizer (RRTO). This is the first time that the concept of the RRT algorithm has been integrated with metaheuristic algorithms. The key innovation of RRTO is its three position update strategies: adaptive step size wandering, absolute difference-based adaptive step size, and boundary-based adaptive step size. These strategies enable RRTO to efficiently explore the search space while guiding the population toward high-quality solutions. To evaluate its effectiveness, the RRTO is tested on 23 standard benchmark functions, the CEC2017 test suite, and six constrained optimization problems. In comparison with more than eight peer metaheuristic algorithms, RRTO achieves competitive results across diverse problems. Specifically, among the 35 metrics across the six constrained optimization problems, RRTO ranks first in 26 and second in 4.}, archive = {J_ACCESS}, author = {Guang-Jin Lai and Tao Li and Bao-Jun Shi}, doi = {10.1109/ACCESS.2025.3547537}, journal = {IEEE Access}, pages = {42744-42776}, shortjournal = {IEEE Access}, title = {RRT-based optimizer: A novel metaheuristic algorithm based on rapidly-exploring random trees algorithm}, volume = {13}, year = {2025}, }
• (2025). Study on the fourier defects of cone-beam computed laminography. ACCESS, 13, 42733-42743. (www)

  @article{ , abstract = {Cone-beam computed laminography (CBCL) is widely used in non-destructive testing of plate-like samples due to flexible scanning geometry and fine image details. However, the Fourier domain defects lead to superimposed artifacts, which significantly degrade the image quality. This paper aims to assess the data distribution of CBCL projections in the Fourier domain, and to precisely delineate the Fourier defective volume. We derive the mapping scheme from CBCL projections to the Fourier domain using the Generalized Fourier Slice Theorem. Then, practical defect delineation methods are proposed for the cases of CBCL global scanning (i.e., non-truncated projections) and local scanning (i.e., truncated projections). Simulation experiments have been conducted to validate the proposed methods, indicating that the data incompleteness of CBCL is related not only to the scanning parameters but also to the shape and orientation of the object. Moreover, a de-artifacting algorithm based on Fourier priori information has been preliminarily implemented, which shows huge potential in efficiency and image quality over conventional SART-based algorithms.}, archive = {J_ACCESS}, author = {Ji Pengxiang and Hu Xiaodong and Zou Jing}, doi = {10.1109/ACCESS.2025.3547727}, journal = {IEEE Access}, pages = {42733-42743}, shortjournal = {IEEE Access}, title = {Study on the fourier defects of cone-beam computed laminography}, volume = {13}, year = {2025}, }
• (2025). An approach to truck driving risk identification: A machine learning method based on optuna optimization. ACCESS, 13, 42723-42732. (www)

  @article{ , abstract = {In order to provide safe development of road freight traffic, this paper proposes a truck driving risk identification method based on Optuna optimization of machine learning model. First, the risk characterization indicators were extracted from the natural driving data of trucks, and the threshold value of each indicator was determined using a box plot-based method. Second, the truck driving risk was quantified into three categories of low level, medium level, and high level risk, and the unbalanced data were processed using a hybrid sampling algorithm. Finally, the tree-based decision tree (DT) model, random forest (RF) model, Light Gradient Boosting Machine (LightGBM) model and eXtreme Gradient Boosting (XGBoost) model were selected for training and Optuna was used for hyperparameter optimization of the model. The results are shown to indicate that the machine learning model based on Optuna optimization can effectively identify truck driving risks. Combining the running time, precision, recall, and F1-Score, the LightGBM model optimized based on the Tree-structured Parzen Estimator (TPE) algorithm has the best performance with a precision of 0.98. In addition, the speed mean has the highest feature importance of 14%, which needs to be focused on when preventing truck driving risks. The research results can provide policy support for transportation management departments to formulate risk control measures for trucks.}, archive = {J_ACCESS}, author = {Zhaofei Wang and Hao Li and Qiuping Wang}, doi = {10.1109/ACCESS.2025.3547445}, journal = {IEEE Access}, pages = {42723-42732}, shortjournal = {IEEE Access}, title = {An approach to truck driving risk identification: A machine learning method based on optuna optimization}, volume = {13}, year = {2025}, }
• (2025). Why AI: A comparative study for detection methods in DC series arc fault. ACCESS, 13, 42703-42722. (www)

  @article{ , abstract = {With the advancement of direct current (DC) power grids, ensuring reliable fault detection for safety has become increasingly critical. DC series arc faults, in particular, remain challenging to detect accurately. Although artificial intelligence (AI)-based methods have shown promising performance in recent years, questions have arisen within the industry regarding the necessity of AI algorithms, especially when rule-based methods have also demonstrated strong results in previous studies. This study aims to identify a highly generalizable solution for industrial applications, especially for resource-constrained devices that operate with low sampling rates and limited computational power. Through a comparative analysis of AI-based and rule-based approaches, supported by a comprehensive review of the literature and experimental validation, this research provides statistical evidence highlighting the strengths and weaknesses of both methods. It is demonstrated that AI-based methods are more adaptable to varying conditions, and that integrating pre-feature extraction with domain knowledge can enhance general performance, particularly in such physical problems with considerable prior research.}, archive = {J_ACCESS}, author = {Yufei Mao and Sadaf Safa and Gianni Smith and Leon Wurth and Roland Weiss and Jens Hagemeyer}, doi = {10.1109/ACCESS.2025.3548309}, journal = {IEEE Access}, pages = {42703-42722}, shortjournal = {IEEE Access}, title = {Why AI: A comparative study for detection methods in DC series arc fault}, volume = {13}, year = {2025}, }
• (2025). Multiscale feature fusion for salient object detection of strip steel surface defects. ACCESS, 13, 42689-42702. (www)

  @article{ , abstract = {Strip steel is an indispensable and important raw material for production in many industries, including the aerospace, automobile and bridge industries, and surface defects of strip steel may lead to huge economic losses. However, accurately and quickly detecting strip steel defects remains challenging due to variations in flaws, complex backgrounds, and noise interference. This paper proposes a novel multiscale feature fusion (MFF) method for salient object detection of strip steel surface defects, fusing multiscale features through the following three steps: 1) generating rough multiscale features using upsampling/downsampling or convolution operations with sampling techniques, 2) applying self-attention operations to each feature to generate a refined representation, and 3) fusing the multiscale features from the previous two steps for salient object detection. For the first step, MFF uses ResNet (convolutions with downsampling operations) instead of sampling techniques to generate multiscale features because convolution excels at extracting local regional features (e.g., edge and contour information). For the second step, a novel self-attention block with fast computation is proposed for extracting the global dependencies of foreground local features obtained in the first step. For the last step, MFF uses a novel cross-attention block, comprising a channel attention block and a spatial attention block, to effectively combine convolution and self-attention, generating features with enhanced expressive power. Additionally, a threshold-attention block is employed to fuse multiscale features, improving semantic and spatial representation, thereby enabling the detector to adapt to strip steel defects of varying sizes. Experimental results show that, compared with state-of-the-art methods, the proposed method achieves the best performance in terms of mean absolute error, structural similarity, weighted F-measure and Pratt’s figure of merit. These results demonstrate that the proposed approach not only enhances detection accuracy but also significantly improves the adaptability of the model to various defect patterns. By addressing the challenges of complex backgrounds and noise interference, this study provides a practical solution for industrial defect detection.}, archive = {J_ACCESS}, author = {Li Zhang and Xirui Li and Yange Sun and Yan Feng and Huaping Guo}, doi = {10.1109/ACCESS.2025.3532303}, journal = {IEEE Access}, pages = {42689-42702}, shortjournal = {IEEE Access}, title = {Multiscale feature fusion for salient object detection of strip steel surface defects}, volume = {13}, year = {2025}, }
• (2025). Impact of phase noise on range estimation accuracy in harmonic FMCW radar systems. ACCESS, 13, 42669-42688. (www)

  @article{ , abstract = {This paper investigates the impact of phase noise on range estimation accuracy in harmonic Frequency-Modulated Continuous-Wave (FMCW) radar systems. Harmonic FMCW radars offer advantages in many applications due to their ability to suppress clutter. However, phase noise, particularly in harmonic systems, presents a significant challenge by degrading range accuracy and increasing frequency measurement errors. In this study, a comprehensive theoretical model is developed to quantify the effects of phase noise on range estimation errors, providing a foundation for understanding its implications on system performance. This model is rigorously validated through both extensive simulations and real-world measurements, offering a holistic assessment of phase noise behavior under practical operating conditions. The results demonstrate that phase noise severely impacts range estimation accuracy, with its effects becoming more pronounced at greater target distances. These findings are further substantiated by experimental evaluations using a practical harmonic radar system, where the system’s range accuracy is analyzed under realistic conditions. This study provides valuable insights and design guidelines for mitigating its impact in harmonic FMCW radar architectures. The results highlight the necessity of advanced phase noise suppression techniques, including optimized hardware configurations and adaptive signal processing methods, to enhance performance in high-precision applications such as industrial positioning and biomedical sensing.}, archive = {J_ACCESS}, author = {Ahmed El-Awamry and Feng Zheng and Thomas Kaiser and Maher Khaliel}, doi = {10.1109/ACCESS.2025.3548903}, journal = {IEEE Access}, pages = {42669-42688}, shortjournal = {IEEE Access}, title = {Impact of phase noise on range estimation accuracy in harmonic FMCW radar systems}, volume = {13}, year = {2025}, }
• (2025). Analysis and quantification of demand flexibility for resilient distribution networks: A systematic review. ACCESS, 13, 42650-42668. (www)

  @article{ , abstract = {Modern power systems face increasing operational challenges due to the integration of renewable energy sources (RESs) and evolving demand patterns. Demand flexibility (DF) has emerged as a transformative solution by dynamically adjusting electricity consumption to align with grid conditions. This review systematically investigates DF in distribution networks through three critical dimensions: quantification methodologies, regulatory frameworks, and techno-economic impacts. Advanced quantification methods, including time-varying elasticity models, alternating current (AC) multi-temporal optimal power flow simulations, and region-based flexibility quantification techniques, are examined to establish accurate measurement frameworks. The regulatory and market mechanisms promoting DF adoption are analyzed, emphasizing dynamic pricing schemes and performance-based incentives. The role of policy in overcoming barriers such as consumer resistance, data privacy concerns, and limited market access for aggregators is critically evaluated. The techno-economic impacts of DF integration reveal significant benefits in grid reliability and infrastructure investment deferral, while highlighting implementation challenges. Through a synthesis of cutting-edge methodologies, this review provides actionable insights for researchers, policymakers, and industry stakeholders advancing resilient, flexible distribution networks in the evolving energy landscape.}, archive = {J_ACCESS}, author = {Mohamed Massaoudi and Katherine R. Davis and Khandaker Akramul Haque}, doi = {10.1109/ACCESS.2025.3548526}, journal = {IEEE Access}, pages = {42650-42668}, shortjournal = {IEEE Access}, title = {Analysis and quantification of demand flexibility for resilient distribution networks: A systematic review}, volume = {13}, year = {2025}, }
• (2025). A dual mode detection method for unexploded ordnance based on YOLOv5 for low altitude unmanned aerial vehicle. ACCESS, 13, 42634-42649. (www)

  @article{ , abstract = {Unexploded ordnance (UXO) presents a significant risk to both the natural environment and human safety. Current deep learning detection mechanisms are characterized by limited interpretability, resulting in a persistent absence of detection methods that are simultaneously efficient, secure, and precise. To address these challenges, a dual-mode detection method based on interpretation-promoted YOLOv5 is proposed in this paper. A comprehensive dataset has been constructed to focus on the scarcity of low-altitude UXO target datasets, incorporating both visible light and infrared imagery. Dataset augmentation has been implemented through the application of generative adversarial networks for image super-resolution reconstruction, thereby enhancing the robustness of the dataset. To tackle issues of low detection accuracy and inadequate background discrimination associated with a single information source, an integration of visible light and infrared data has been proposed to enhance the interpretability of the YOLOv5 algorithm, leading to improved detection performance. Extensive low-altitude detection experiments were conducted in field environments using unmanned aerial vehicles (UAVs). Experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 97.1% and an impressive detection speed of up to 60.3 frames per second.}, archive = {J_ACCESS}, author = {Zhongao Ling and Hui Zhao and Xu Zhao and Ziyu Liu and Wenbin Chen}, doi = {10.1109/ACCESS.2025.3537058}, journal = {IEEE Access}, pages = {42634-42649}, shortjournal = {IEEE Access}, title = {A dual mode detection method for unexploded ordnance based on YOLOv5 for low altitude unmanned aerial vehicle}, volume = {13}, year = {2025}, }
• (2025). App design method based on A-KANO and SOC-TM models: A case study of a healthy diet app for the elderly. ACCESS, 13, 42622-42633. (www)

  @article{ , abstract = {This study addresses the research gap in designing health diet apps for elderly individuals with chronic diseases, as most existing apps target younger users and fail to meet the specific needs of older adults. Utilizing the A-KANO model for design optimization and the SOC-TM model for usability evaluation, we developed a “Diet and Medicine Health” app tailored for elderly users. The findings indicate that design improvements based on the A-KANO model significantly enhance operational efficiency, task execution effectiveness, and user satisfaction. The app outperforms the “Mint Health” app in terms of efficiency and usability, particularly in reducing cognitive load and operational errors. However, this study has limitations, including a relatively small sample size and a focus on a specific demographic. Future research should expand the sample range and explore the integration of emerging technologies to further enhance app functionality. This research provides valuable theoretical insights and practical guidance for designing age-friendly health apps.}, archive = {J_ACCESS}, author = {Wei Liu and Xing-Yuan Li and Wei-Jie Liu and Yi-Nan Fei}, doi = {10.1109/ACCESS.2025.3543432}, journal = {IEEE Access}, pages = {42622-42633}, shortjournal = {IEEE Access}, title = {App design method based on A-KANO and SOC-TM models: A case study of a healthy diet app for the elderly}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning approach for skin lesion segmentation with dual encoders and channel-wise attention. ACCESS, 13, 42608-42621. (www)

  @article{ , abstract = {Skin cancer poses a significant global health challenge due to its increasing incidence rates. Accurate segmentation of skin lesions is essential for early detection and successful treatment, yet many current techniques struggle to balance computational efficiency with the ability to capture complex lesion features. This paper aims to develop an advanced deep learning model that improves segmentation accuracy while maintaining computational efficiency, offering a solution to the limitations of existing methods. We propose a novel dual-encoder deep learning architecture incorporating Squeeze-and-Excitation (SE) attention blocks. The model integrates two encoders: a pre-trained ResNet-50 for extracting local features efficiently and a Vision Transformer (ViT) to capture high-level features and long-range dependencies. The fusion of these features, enhanced by SE attention blocks, is processed through a CNN decoder, ensuring the model captures both local and global contextual information. The proposed model was evaluated on three benchmark datasets, ISIC 2016, ISIC 2017, and ISIC 2018, achieving Intersection over Union (IoU) scores of 89.53%, 87.02%, and 84.56%, respectively. These results highlight the model’s ability to outperform current methods in balancing segmentation accuracy and computational efficiency. The findings demonstrate that the proposed model enhances medical image analysis in dermatology, providing a promising tool for improving the early detection of skin cancer.}, archive = {J_ACCESS}, author = {Asaad Ahmed and Guangmin Sun and Anas Bilal and Yu Li and Shouki A. Ebad}, doi = {10.1109/ACCESS.2025.3548135}, journal = {IEEE Access}, pages = {42608-42621}, shortjournal = {IEEE Access}, title = {A hybrid deep learning approach for skin lesion segmentation with dual encoders and channel-wise attention}, volume = {13}, year = {2025}, }
• (2025). An efficient large-scale 3D map stitching algorithm using automatic overlapping area identification. ACCESS, 13, 42587-42607. (www)

  @article{ , abstract = {Quality of 3D point cloud maps is essential for navigation and localization in Autonomous Mobile Robots, yet creating these maps for large-scale areas presents challenges, stemming from the processing of numerous points. In such situations, constructing a 3D map can be accomplished by dividing it into smaller regions and then merging them to generate a complete map by performing a 3D map stitching algorithm. Currently, these overlapping areas are manually selected, which leads to potential errors. In response, a novel method to automatically identify overlapping areas is proposed to perform map stitching based on the overlapping areas only instead of the entire maps. Utilizing the proposed method results in a significant reduction in time consumption. The proposed automatic method incorporates the DBSCAN algorithm for clustering, template matching for identifying corresponding clusters, and a binary-search algorithm for parameter optimization. The proposed method was evaluated on several large-scale 3D maps, including the KITTI dataset, and compared against manual selection and the use of entire maps in the map-merge-3D algorithm. The method achieves a significant reduction in the time required for the 3D map stitching process, amounting to a 38.64% decrease compared to using the entire maps. In terms of accuracy, the proposed method reduces translation error to 0.1723m and rotation error to 0.1763°, representing decreases of 5.28% and 16.16%, respectively, while manual selection results in a translation error of 0.4278m and rotation error of 0.7123°, increases of 135.25% and 238.75% respectively, compared to the entire maps 0.1819m and 0.2103°.}, archive = {J_ACCESS}, author = {Hsien-I Lin and Muhammad Ahsan Fatwaddin Shodiq and An-Kai Jeng and Chun-Wei Chang}, doi = {10.1109/ACCESS.2025.3548859}, journal = {IEEE Access}, pages = {42587-42607}, shortjournal = {IEEE Access}, title = {An efficient large-scale 3D map stitching algorithm using automatic overlapping area identification}, volume = {13}, year = {2025}, }
• (2025). Interval secure event-triggered control of hybrid power system under DoS attack. ACCESS, 13, 42574-42586. (www)

  @article{ , abstract = {This note considers the active secure event-triggered control(ETC) problem of hybrid power system under DoS attack. A combination of load frequency control (LFC) and virtual inertia control (VIC) is adopted to deal with the influence of uncertainty and lower inertia induced by renewable energy like wind and solar power. To active defend DoS attack interrupting communication of measurement and control, an interval secure event-triggered mechanism (ISETM) is proposed under software defined network (SDN). Both a triggering transmission and a secure triggering interval are generated where the triggering packet is transmitted over SDN data plane and the secure triggering interval is sent to SDN control plane regulating SDN cybersecurity mechanism. Under ISETM, multi-area hybrid power system is modeled by a delay system with two triggering conditions. Furthermore, interval secure event-triggered LFC-VIC of hybrid power system is formulated by a $H_{\infty } $ control problem. A sufficient criterion of hybrid power system with the prescribed $H_{\infty } $ performance level is derived by using Lyapunov-Krasovskii functional method. A co-designed method of ISETM and LFC-VIC gains is given by linear matrix inequalities (LMIs). Finally, a two-area hybrid power system is simulated to verify the validness of the proposed interval secure event-triggered control (ISETC) method.}, archive = {J_ACCESS}, author = {Dashuang Chong and Tongshu Si and Zihao Cheng and Feng Yang and Jigang Liu and Zongwang Lv}, doi = {10.1109/ACCESS.2025.3548455}, journal = {IEEE Access}, pages = {42574-42586}, shortjournal = {IEEE Access}, title = {Interval secure event-triggered control of hybrid power system under DoS attack}, volume = {13}, year = {2025}, }
• (2025). Energy market manipulation via false-data injection attacks: A review. ACCESS, 13, 42559-42573. (www)

  @article{ , abstract = {Locational Marginal Prices (LMPs) are critical indicators in modern energy markets, representing the cost of delivering electricity at specific locations while considering the generation and transmission constraints. LMPs facilitate the transition to dynamic energy markets by providing real-time pricing signals that reflect supply and demand conditions, thereby incentivizing efficient resource allocation and encouraging investments in renewable energy sources. However, determining LMPs requires the processing of vast amounts of data, including real-time electricity demand, generation capacities, transmission line statuses, and market bids. Owing to vulnerabilities in the underlying sensors and communication infrastructure, adversaries can launch profit-driven stealthy False Data Injection Attacks (FDIAs) to manipulate LMPs. Such manipulations can have severe consequences, including inflated electricity prices, reduced market efficiency, distorted competition, and hindered integration of renewable energy sources. Although several studies have examined the operational consequences of FDIAs, their financial impact on energy market outcomes remains insufficiently explored. This work presents a comprehensive review of FDIAs aimed at manipulating LMPs, a key pricing mechanism in modern energy markets. A detailed analysis was conducted to identify vulnerabilities arising from both the energy system infrastructure and market operations. In addition, existing energy market threat models and defense mechanisms are systematically reviewed. Finally, key research gaps are identified, and future research directions are outlined to enhance the resilience of energy markets against FDIA threats.}, archive = {J_ACCESS}, author = {Ghadeer O. Alsharif and Christos Anagnostopoulos and Angelos K. Marnerides}, doi = {10.1109/ACCESS.2025.3548914}, journal = {IEEE Access}, pages = {42559-42573}, shortjournal = {IEEE Access}, title = {Energy market manipulation via false-data injection attacks: A review}, volume = {13}, year = {2025}, }
• (2025). Energy consumption analysis of 5G C-V2X sensor sharing for tele-operated driving. ACCESS, 13, 42547-42558. (www)

  @article{ , abstract = {As autonomous driving technology advances, the demand for unmanned mobility applications continues to grow. However, due to the imperfections in current autonomous driving systems, incidents still occur, highlighting the challenges of full driverless services. Moreover, the computation of complex autonomous driving algorithms requires an on-board computing unit, which consumes a large amount of energy. To address these limitations, tele-operated driving (ToD) has emerged as a promising solution for enhancing autonomous intelligent transportation systems (ITS). By enabling remote entities, such as remote users or servers, to control vehicles and manage edge cases in autonomous driving, ToD combines the benefits of both unmanned mobility and human oversight. To support ToD service, a real-time sensor sharing system for vehicles is essential, and cellular vehicle-to-everything (C-V2X) communication is suitable for the required network connectivity. However, most research has not focused on high-volume data transmission, which is required for sensor sharing systems. Additionally, the energy consumption of C-V2X, which directly impacts the battery efficiency of electric vehicles (EVs) as an example, has not been thoroughly examined. In this paper, we propose an evaluation framework for energy consumption analysis of ToD. Based on this framework, we analyze the energy consumption of vehicle for sensor data transmission over 5G C-V2X under varying channel conditions and multi-user scenarios. We also investigate the extent to which using ToD is energy-saving compared to the energy consumption of an on-board high-performance computing unit. Our findings indicate that the uplink-based sensor sharing system is more energy-efficient than its sidelink-based counterpart. Additionally, sensor sharing for ToD can save more energy of the battery in the vehicle compared to relying on the high-performance on-board computing unit.}, archive = {J_ACCESS}, author = {Hanyoung Park and Yongjae Jang and Kanghyun Ko and Ji-Woong Choi}, doi = {10.1109/ACCESS.2025.3548116}, journal = {IEEE Access}, pages = {42547-42558}, shortjournal = {IEEE Access}, title = {Energy consumption analysis of 5G C-V2X sensor sharing for tele-operated driving}, volume = {13}, year = {2025}, }
• (2025). CAMFusion: Context-aware multi-modal fusion framework for detecting sarcasm and humor integrating video and textual cues. ACCESS, 13, 42530-42546. (www)

  @article{ , abstract = {Detecting sarcasm and humor in Bangla videos requires a deep understanding of both linguistic and cultural contexts. Existing methodologies often fall short due to their reliance on single-modal data, which limits their ability to capture the intricate interplay between visual and textual cues. To address these challenges, we propose a new multi-modal framework designed explicitly for Bangla sarcasm and humor detection. This framework includes developing a novel dataset comprising diverse video clips annotated across humor, sarcasm, and normal categories. This dataset is vital for training and evaluating models tailored to the Bangla language. Our approach introduces the Context-Aware Multi-modal Fusion Framework, which effectively integrates visual features extracted using Efficient Fusion Time-Distributed MobileNetV2 using 0.985M parameters with textual features processed through Bidirectional LSTM and GRU layers, achieving an accuracy of 90%. Additionally, we developed the Bangla Text Extraction Algorithm, which enhances text extraction from complex video frames, ensuring that critical contextual information is captured. Generability tests conducted on multiple Bangla multi-modal datasets demonstrate substantial performance improvements, underscoring the robustness and adaptability of our model. These findings highlight the effectiveness of our system in detecting sarcasm and humor in Bangla videos, paving the way for advancements in computational linguistics and cultural understanding.}, archive = {J_ACCESS}, author = {Musfequa Rahman and Md. Al-Mamun Provath and Kaushik Deb and Pranab Kumar Dhar and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3535694}, journal = {IEEE Access}, pages = {42530-42546}, shortjournal = {IEEE Access}, title = {CAMFusion: Context-aware multi-modal fusion framework for detecting sarcasm and humor integrating video and textual cues}, volume = {13}, year = {2025}, }
• (2025). Deep temporal LSTM regression network (DTLR-net) model for optimizing quality of video streaming quality in CDN-P2P model. ACCESS, 13, 42521-42529. (www)

  @article{ , abstract = {Due to the increasing popularity of live streaming, the need for more efficient and effective content delivery servers has become more prevalent. With the help of the latest generation of streaming models, such as the Content Delivery Network (CDN), it can reduce the burden on the servers. However, the quality of the video streaming process is still not always guaranteed due to the varying peer chunks and the capacity of the hosts. This paper proposes a hybrid approach that places the peers in a sub-layer, which takes into account the serviceability of the hosts. The members of the mesh topology are chosen based on the various factors that affect the quality of the stream. This study presents a novel approach that takes advantage of the potential of a Deep Temporal LSTM Regression Network (DTLR-Net) Model to predict the trajectories of mobile peers within network. Using deep learning tools such as Long Short-Term Memory (LSTM) networks, this novel technique addresses the intricate challenges posed by the dynamic movement patterns of mobile peers. This paper shows that the proposed hybrid structure is more effective than the traditional methods. It has a 25% increase in the peers’ upload capacity and 19% decrease in the start-up delay.}, archive = {J_ACCESS}, author = {Satyanarayanareddy Marri and P. Chenna Reddy}, doi = {10.1109/ACCESS.2025.3547777}, journal = {IEEE Access}, pages = {42521-42529}, shortjournal = {IEEE Access}, title = {Deep temporal LSTM regression network (DTLR-net) model for optimizing quality of video streaming quality in CDN-P2P model}, volume = {13}, year = {2025}, }
• (2025). Security enhanced CLS and CL-AS scheme without pairings for VANETs. ACCESS, 13, 42508-42520. (www)

  @article{ , abstract = {While vehicle ad-hoc networks (VANETs) have many advantages, they also present privacy and security concerns. Certificate management issues have been seen in traditional public key infrastructure-based privacy-preserving authentication schemes, while key escrow problem exists in identity-based privacy-preserving authentication techniques. Also, existing cryptographic techniques rely heavily on assumptions about tamper-proof equipment to ensure their security. A proposal has been made for a certificateless aggregate signature system for VANETs addressing these issues and was proved provably unforgeable against collusion attack. However, we found that the proposed technique was insecure and could not withstand collusion assault. Therefore, this paper presents an improved and secure certificateless aggregate signature technique for VANETs. We also illustrate the security & performance evaluation of our presented technique, and based upon the hardness assumption of the elliptic curve discrete logarithm problem, we have shown that the technique is safe against existential forgery on adaptive chosen message attack in the random oracle model. Also, the presented technique has better efficiency compared to some recent existing authentication techniques.}, archive = {J_ACCESS}, author = {Anjali Bansal and Saru Kumari and Nishant Doshi and Mohammed Amoon and Marko Hölbl}, doi = {10.1109/ACCESS.2025.3546699}, journal = {IEEE Access}, pages = {42508-42520}, shortjournal = {IEEE Access}, title = {Security enhanced CLS and CL-AS scheme without pairings for VANETs}, volume = {13}, year = {2025}, }
• (2025). Feedback adaptive IMM filter for jump markov systems with heavy-tailed process and measurement noises. ACCESS, 13, 42494-42507. (www)

  @article{ , abstract = {The interacting multiple model (IMM) filter algorithm is highly effective in addressing the issue of rapid target maneuvers within tracking scenarios. Nonetheless, owing to the unpredictable nature of target movements and the intricate nature of the measurement system, substantial levels of heavy-tailed process and measurement noises are induced. In the context of the jump Markov system (JMS), we introduce a two-layer feedback adaptive IMM filter (TFAIMM) approach to tackle the instability issues in IMM algorithms, which are influenced by heavy-tailed process and measurement noises. Firstly, the initial fused states are pre-filtered with the involvement of maneuverability. Secondly, the system’s state, degrees of freedom, scale matrix, and target maneuverability parameter are deduced from the pre-filtering outcomes, employing Student’s t-distribution traits and the variational Bayesian (VB) approach. The target maneuverability parameter feedback acts on models probability calculation and pre-filtering. To validate the effectiveness of the TFAIMM in tracking multi-model maneuvering targets, experiments are carried out under various heavy-tailed noise conditions and UWB dataset. Our method is significantly effective in tracking complex targets with uncertain noise.}, archive = {J_ACCESS}, author = {Zedong Liang and Yingzhi Wang and Liang Wang and Fanjun Meng and Guangpeng Hu and Lifu Zhang and Wenying Dou}, doi = {10.1109/ACCESS.2025.3545620}, journal = {IEEE Access}, pages = {42494-42507}, shortjournal = {IEEE Access}, title = {Feedback adaptive IMM filter for jump markov systems with heavy-tailed process and measurement noises}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on light field occlusion removal: Trends, challenges, and future directions. ACCESS, 13, 42472-42493. (www)

  @article{ , abstract = {Overcoming occlusions in light field (LF) imaging is a challenging yet complex task crucial for scene understanding, image quality enhancement, and restoring visual details in obstructed scenes. This review examines contemporary occlusion removal methods, spanning from classical techniques to advanced deep learning approaches that leverage LF data’s spatial and angular dimensions. We categorize these methods into two domains: (1) single-view inpainting methods often adapted for LF contexts, and (2) specialized LF occlusion removal techniques that exploit multi-view data. The review explores how these methods mitigate occlusion artifacts and also investigates LF acquisition technologies, representations, and the role of loss functions in optimizing model performance. A discussion of publicly available datasets and performance evaluation metrics addresses the challenges of handling large occlusions. The review concludes with future research directions, emphasizing hybrid approaches, refined loss functions, and scalable solutions for LF occlusion removal.}, archive = {J_ACCESS}, author = {Mostafa Farouk Senussi and Mahmoud Abdalla and Mahmoud Salaheldin Kasem and Mohamed Mahmoud and Bilel Yagoub and Hyun-Soo Kang}, doi = {10.1109/ACCESS.2025.3548133}, journal = {IEEE Access}, pages = {42472-42493}, shortjournal = {IEEE Access}, title = {A comprehensive review on light field occlusion removal: Trends, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). EGRN-YOLO: An enhanced multi-view remote sensing detection algorithm for onshore wind turbines based on YOLOv7. ACCESS, 13, 42457-42471. (www)

  @article{ , abstract = {Wind turbines, as the core components of wind power generation systems, play a crucial role in determining the overall generation efficiency and operational safety. However, the challenges posed by complex backgrounds, significant variations in the scale of wind turbine targets, and arbitrary orientations in unmanned aerial vehicle (UAV) remote sensing images have significantly increased the difficulty of real-time wind turbine detection. To address these challenges, this paper introduces an enhanced multi-view onshore wind turbine remote sensing detection algorithm for UAVs based on YOLOv7, termed EGRN-YOLO. Firstly, the lightweight network EfficientNetV2 is utilized as the feature extraction backbone to reduce the number of model parameters and computational load. Secondly, to further minimize the model parameters, the GhostSPPCSPC module is introduced to replace the original pyramid module SPPCSPC. To maintain a balance between accuracy and parameter efficiency, the CAM attention mechanism is integrated to create the GhostSPPCSPC_CAM module, which effectively expands the network’s receptive field and enhances its deep learning capabilities. Subsequently, to improve the network’s focus on small targets and reduce the redundancy of gradient information during neural network inference, the RepNCSPELAN4_KAN module is proposed and integrated into the neck network. Finally, the NWD loss function is introduced to replace the CIoU loss function, thereby improving the accuracy of positive and negative sample allocation for small targets. Experimental results on the self-constructed VOC_WIND_TURBINE dataset demonstrate that the improved EGRN-YOLO model, compared to the benchmark YOLOv7 model, achieves significant improvements of 1.7% in mean average precision (mAP), 3.3% in precision, 1.8% in recall, and 18.1% in inference time. These results indicate a well-balanced trade-off between high accuracy and model efficiency, effectively meeting the demands for identifying wind turbines in complex scenes within remote sensing images.}, archive = {J_ACCESS}, author = {Renzheng Xue and Haiqiang Xu and Qianlong Wu}, doi = {10.1109/ACCESS.2025.3548117}, journal = {IEEE Access}, pages = {42457-42471}, shortjournal = {IEEE Access}, title = {EGRN-YOLO: An enhanced multi-view remote sensing detection algorithm for onshore wind turbines based on YOLOv7}, volume = {13}, year = {2025}, }
• (2025). BiPruneFL: Computation and communication efficient federated learning with binary quantization and pruning. ACCESS, 13, 42441-42456. (www)

  @article{ , abstract = {Federated learning (FL) is a decentralized learning framework that allows a central server and multiple devices, referred to as clients, to collaboratively train a shared model without transmitting their private data to a central server. This approach helps to preserve data privacy and reduce the risk of information leakage. However, FL systems often face significant communication and computational overhead due to frequent exchanges of model parameters and the intensive local training required on resource-constrained clients. Existing solutions typically apply compression techniques such as quantization or pruning but only to a limited extent, constrained by the trade-off between model accuracy and compression efficiency. To address these challenges, we propose BiPruneFL, a communication- and computation-efficient FL framework that combines quantization and pruning while maintaining competitive accuracy. By leveraging recent advances in neural network pruning, BiPruneFL identifies subnetworks within binary neural networks without significantly compromising accuracy. Additionally, we employ communication compression strategies to enable efficient model updates and computationally lightweight local training. Through experiments, we demonstrate that BiPruneFL significantly outperforms other baselines, achieving up to $88.1\times $ and $80.8\times $ more efficient communication costs during upstream and downstream phases, respectively, and reducing computation costs by $3.9\times $ to $34.9\times $ depending on the degree of quantization. Despite these efficiency gains, BiPruneFL achieves accuracy comparable to, and in some cases surpassing, that of uncompressed federated learning models.}, archive = {J_ACCESS}, author = {Sangmin Lee and Hyeryung Jang}, doi = {10.1109/ACCESS.2025.3547627}, journal = {IEEE Access}, pages = {42441-42456}, shortjournal = {IEEE Access}, title = {BiPruneFL: Computation and communication efficient federated learning with binary quantization and pruning}, volume = {13}, year = {2025}, }
• (2025). A 7T MRI-guided learning method for automatic hippocampal subfield segmentation on routine 3T MRI. ACCESS, 13, 42428-42440. (www)

  @article{ , abstract = {Accurate segmentation of hippocampal subfields in MRI scans is crucial for aiding in the diagnosis of various neurological diseases and for monitoring brain states. However, due to limitations of imaging systems and the inherent complexity of hippocampal subfield delineation, achieving accurate hippocampal subfield delineation on routine 3T MRI is highly challenging. In this paper, we propose a novel Guided Learning Network (GLNet) that leverages 7T MRI to enhance the accuracy of hippocampal subfield segmentation on routine 3T MRI. GLNet aligns and learns shared features between 3T MRI and 7T MRI through a modeling approach based on domain-specific and domain-shared feature learning, leveraging the features of 7T MRI to guide learning for 3T MRI features. In this process, we also introduce a Multi-Feature Attention Fusion (MFAF) block to integrate both specific and shared features from each modality. By leveraging an attention mechanism, MFAF adaptively focuses on relevant information between the specific and shared features within the same modality, thereby reducing the impact of irrelevant information. Additionally, we further proposed an Online Knowledge Distillation (OLKD) method to distill detailed knowledge from 7T MRI into 3T MRI, enhancing the feature representation capability and robustness of the 3T MRI segmentation model. Our method was validated on PAIRED 3T-7T HIPPOCAMPAL SUBFIELD DATASET, and the experimental results demonstrate that GLNet outperforms other competitive methods.}, archive = {J_ACCESS}, author = {Linjin Wang and Jiangtao He and Guohong Geng and Lisha Zhong and Xinwei Li}, doi = {10.1109/ACCESS.2025.3548726}, journal = {IEEE Access}, pages = {42428-42440}, shortjournal = {IEEE Access}, title = {A 7T MRI-guided learning method for automatic hippocampal subfield segmentation on routine 3T MRI}, volume = {13}, year = {2025}, }
• (2025). A three-level inverter-based model predictive control design for optimal wind energy systems. ACCESS, 13, 42414-42427. (www)

  @article{ , abstract = {This paper introduces an innovative model predictive control strategy for a grid-connected wind energy system using a three-level inverter. The method features a command structure with a single DC/AC multilevel three-phase inverter, which significantly decreases the number of converters required, thereby reducing the system design costs in comparison to the traditional back-to-back configuration that requires both DC/AC and AC/DC converters. The inverter is modelled discretely at the synchronous reference frame with a discrete-time prediction for future direct and quadrature components of the grid current and DC-link capacitor voltages. The controller evaluates each possible switching state of the inverter with a cost function and chooses the state that minimizes this cost for realization during the next sampling period. The proposed approach is verified using a processor-in-the-loop methodology along with a software simulation. The results show that this approach optimizes wind power generation and enhances the energy quality injected into the grid, as indicated by the lower total harmonic distortion compared with previous methods.}, archive = {J_ACCESS}, author = {Hassan Abouobaida and Safeer Ullah and Ahmed S. Alsafran and Ambe Harrison and Ghulam Hafeez and Baheej Alghamdi and Habib Kraiem}, doi = {10.1109/ACCESS.2025.3547996}, journal = {IEEE Access}, pages = {42414-42427}, shortjournal = {IEEE Access}, title = {A three-level inverter-based model predictive control design for optimal wind energy systems}, volume = {13}, year = {2025}, }
• (2025). AI-assisted educational framework for floodplain manager certification: Enhancing vocational education and training through personalized learning. ACCESS, 13, 42401-42413. (www)

  @article{ , abstract = {Floodplain management is critical for mitigating flood risks and safeguarding communities. The FloodPlain Manager (FPM) certification is essential for professionals in this field, but current preparation methods often fall short in providing comprehensive, accessible, and engaging study resources. This research introduces a novel AI-assisted educational tool designed specifically for FPM certification preparation and training process. Leveraging advanced natural language processing and machine learning techniques, this tool offers personalized learning experiences, interactive question-and-answer sessions, and real-time feedback to aspiring floodplain managers. The system architecture integrates certification-specific content through a sophisticated document parsing process, ensuring relevance and accuracy. Evaluation of the tool, conducted through text similarity analysis, demonstrates its effectiveness in preparing candidates for the FPM certification exam. With 91.7% accuracy for open-ended questions and 95.12% for multiple-choice questions, the tool offers a personalized learning experience through dynamic flashcards and adaptive quizzes, highlighting its potential to enhance vocational training and exam readiness. This study underscores the transformative role of AI in professional education and suggests future directions for expanding the tool’s capabilities and application to other certifications.}, archive = {J_ACCESS}, author = {Ramteja Sajja and Vinay Pursnani and Yusuf Sermet and Ibrahim Demir}, doi = {10.1109/ACCESS.2025.3548591}, journal = {IEEE Access}, pages = {42401-42413}, shortjournal = {IEEE Access}, title = {AI-assisted educational framework for floodplain manager certification: Enhancing vocational education and training through personalized learning}, volume = {13}, year = {2025}, }
• (2025). Sensorless control of bilateral LSRM with eccentricities based on coupled voltage. ACCESS, 13, 42388-42400. (www)

  @article{ , abstract = {Based on the structural characteristics and magnetic flux distribution of the segmented bilateral LSRM (Linear Switched Reluctance Motor), a magnetic network model of the motor under single-side excitation is constructed using the magnetic circuit segmentation method in this paper. The influence of the lateral displacement of the motor mover on the winding inductance is analyzed. On the basis of the coupled voltage method for estimating mover position, the relationships between the coupled voltages and the positions of the motor in both directions under asymmetric air gaps are further studied, and a position detection index that can avoid the influence of uneven air gaps is proposed. Finally, on the laboratory prototype test platform, sensorless control experiments are completed for the motor without lateral displacement of the mover and under different lateral displacement conditions. The experimental results show that the LSRM position sensorless control method based on the coupled voltages of both coils proposed in this paper can achieve accurate estimation of the starting position, smooth starting operation, and chopping control velocity regulation operation under different lateral offsets of the motor.}, archive = {J_ACCESS}, author = {Fang Liu and Xinliang Li and Wei Jiang and Feng Zhou}, doi = {10.1109/ACCESS.2025.3548039}, journal = {IEEE Access}, pages = {42388-42400}, shortjournal = {IEEE Access}, title = {Sensorless control of bilateral LSRM with eccentricities based on coupled voltage}, volume = {13}, year = {2025}, }
• (2025). Intelligent archive management based on deep learning technology driven by artificial intelligence. ACCESS, 13, 42377-42387. (www)

  @article{ , abstract = {To improve the efficiency and intelligence level of an archive management system (AMS) in multimodal data processing, this study proposes and designs an intelligent AMS based on deep learning (DL). However, the traditional AMS faces problems, such as inaccurate data classification, low query efficiency, long response delay, and insufficient system expansibility, when dealing with massive multimodal data. To address these issues, mainstream DL models such as Decoding-Enhanced Bidirectional Encoder Representation from Transformers with Disentangled Attention (DeBERTa), Contrastive Language-Image Pretraining (CLIP), and Swin Transformer are compared with the proposed optimized models. The optimized model’s performance improvement in multimodal archive management tasks has been validated through multidimensional experimental assessments. In comprehensive performance comparison experiments, the optimized model demonstrates excellent performance across several key metrics, including resource consumption, response time, data processing throughput, query efficiency, and fault recovery capability. For instance, the optimized model’s response time in text processing tasks is 98.367 milliseconds (ms), significantly lower than the Swin Transformer’s 156.234 ms. Regarding audio processing tasks, the optimized model’s resource consumption is only 4.387 GigaByte (GB), markedly lower than DeBERTa’s 6.823 GB. Furthermore, in terms of user satisfaction, the proposed model scores as high as 9.238 in text processing, indicating an enhancement in the user experience. Through effectiveness evaluation experiments, this study further confirms the superiority of the optimized model in terms of accuracy, processing delay, self-learning ability, error rate, security assessment, and system scalability. Moreover, the optimized model achieves an accuracy of 94.23% in text processing, nearly 4% higher than DeBERTa, and reduces the error rate in audio processing to 3.78%, showing greater stability and reliability. Therefore, this study provides new solutions for AMS in the fields of multimodal data processing and intelligent management, especially in enhancing system performance, optimizing user experience, and strengthening system security and scalability.}, archive = {J_ACCESS}, author = {Jiahang Li and Jiashu Wang}, doi = {10.1109/ACCESS.2025.3546499}, journal = {IEEE Access}, pages = {42377-42387}, shortjournal = {IEEE Access}, title = {Intelligent archive management based on deep learning technology driven by artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Contactless infant height measurement for enhanced early detection of stunting using computer vision techniques. ACCESS, 13, 42364-42376. (www)

  @article{ , abstract = {Stunting, a critical health issue affecting child growth and development, is prevalent in developing countries and is characterized by significantly reduced height for age. Traditional height measurement methods often require physical contact, which can lead to measurement inaccuracies and potential discomfort for infants. This study introduces a contactless method for measuring infant height using advanced computer vision techniques and the MediaPipe Pose library. By detecting key body points and applying Euclidean distance calculations, the proposed approach offers precise height estimation. Validation uses baby dolls (38 cm and 49 cm) and real infants (n =12) under varying body postures and lighting conditions. A fixed-size green mat (100 cm) was used as a reference for converting pixel distances into actual measurements. The method achieved an average accuracy of 99.76% for the 38 cm doll and 99.67% for the 49 cm doll. For real infants, the system demonstrated an average accuracy of 98.48%. This confirms that the system performs effectively in measuring infant height, even under conditions of non-ideal body posture. Furthermore, these results suggest that the proposed system is an effective and practical alternative for infant height measurements, supporting the early detection of stunting in diverse real-world settings.}, archive = {J_ACCESS}, author = {Risfendra and Aripriharta and Suherman and Gheri Febri Ananda and Dwi Sudarno Putra and Fahmi}, doi = {10.1109/ACCESS.2025.3548159}, journal = {IEEE Access}, pages = {42364-42376}, shortjournal = {IEEE Access}, title = {Contactless infant height measurement for enhanced early detection of stunting using computer vision techniques}, volume = {13}, year = {2025}, }
• (2025). Thiruvananthapuram aerial LiDAR dataset (TALD): A benchmark for complex urban point cloud. ACCESS, 13, 42350-42363. (www)

  @article{ , abstract = {Urban heterogeneity is influenced by population density, cultural factors, and historical background. Three-dimensional mapping using LiDAR is essential for capturing structural and spatial changes in complex urban areas. Machine learning-based algorithms for processing point clouds play vital role in transforming unprocessed LiDAR data into relevant information suitable for urban applications such as object recognition, and 3D mapping. This underscores the importance of having range of benchmark LiDAR datasets to enhance development, testing, and validation of algorithms tailored to various urban contexts. However, existing Airborne LiDAR Scanned (ALS) datasets represent a limited range of global land cover diversity. To address this gap, we introduce Thiruvananthapuram Aerial LiDAR Dataset (TALD), a benchmark dataset covering 9 square kilometers from Thiruvananthapuram, Kerala, India. This South Indian region exhibits high-density mixed urban development integrating both built and vegetative elements. TALD, derived from ALS point clouds, has an average point density of 12 points/m2 and includes colored LiDAR points classified into buildings, trees, shrubs, and ground. The dataset is created through systematic pre-processing, classification using automated algorithms and manual corrections, and instance segmentation for noise removal. It includes X, Y, Z coordinates (UTM 43N), RGB values, return number, number of returns, scan angle rank, and class designation. The Land-cover Diversity Index (LDI) is 1.49 for TALD, significantly higher than DALES (0.23) and ISPRS Vaihingen 3D (0.37), highlighting its focus on tropical urban environments with dense vegetation and complex infrastructure. TALD serves as a valuable benchmark for advancing point cloud processing, supporting urban mapping in challenging landscapes.}, archive = {J_ACCESS}, author = {Jayati Vijaywargiya and Anandakumar M. Ramiya}, doi = {10.1109/ACCESS.2025.3546628}, journal = {IEEE Access}, pages = {42350-42363}, shortjournal = {IEEE Access}, title = {Thiruvananthapuram aerial LiDAR dataset (TALD): A benchmark for complex urban point cloud}, volume = {13}, year = {2025}, }
• (2025). Dynamic characteristics-based capacity optimization strategy for hybrid AA-CAES and battery storage systems in source-grid-load-storage integrated base. ACCESS, 13, 42336-42349. (www)

  @article{ , abstract = {Advanced adiabatic compressed air energy storage (AA-CAES) is a promising large-scale energy storage technology, offering a long lifespan, low maintenance, and high safety. However, its slower response speed limits its ability to handle the rapid fluctuations of wind and solar power. Combining AA-CAES with battery storage in a hybrid system provides an optimal solution for integrated energy bases, prompting the need for robust capacity planning. Existing AA-CAES planning strategies, developed primarily for grid-connected applications, often neglect AA-CAES’s dynamic characteristics, making them unsuitable for hybrid contexts. To address this issue, this paper proposes a capacity optimization strategy that incorporates AA-CAES’s dynamic behavior into a cost-minimization model with operational constraints. Using historical wind, solar, and load data, the proposed approach is compared with conventional battery-only configurations. The case study demonstrates that the proposed CAES-Li hybrid energy storage system achieves 30-45% annualized cost reductions compared to traditional Li-ES configurations, with sensitivity analyses revealing critical optimization pathways through efficiency enhancements and technology cost reductions.}, archive = {J_ACCESS}, author = {Jiahua Ni and Yuwei Chen and Arman Goudarzi and Tong Wang and Lingang Yang and Shengwei Mei}, doi = {10.1109/ACCESS.2025.3548044}, journal = {IEEE Access}, pages = {42336-42349}, shortjournal = {IEEE Access}, title = {Dynamic characteristics-based capacity optimization strategy for hybrid AA-CAES and battery storage systems in source-grid-load-storage integrated base}, volume = {13}, year = {2025}, }
• (2025). Self-DSNet: A novel self-ONNs based deep learning framework for multimodal driving distraction detection. ACCESS, 13, 42322-42335. (www)

  @article{ , abstract = {Distraction can cause delayed decision-making and slower awareness, posing significant risks in driving. For reliable driving systems, continuous monitoring of driver behavior is essential to mitigate the impact of distractions. Current strategies for distraction detection widely rely on machine learning models, but the non-linear relationships among various data modalities complicate the identification of optimal combinations. To address these challenges, we propose a novel model, Self-DSNet, for efficient driving distraction detection. The proposed Self-DSNet model utilizes Self-Organizing Neural Network (Self-ONN) layers to enhance complex pattern learning within the data. We employed a publicly available multimodal dataset encompassing three data categories: physiological, vehicle dynamics, and vision-based data. Our approach aims to identify the normal state and three types of distractions: cognitive, emotional, and sensorimotor. The model was evaluated using both single-modality and combined-modality data, focusing on binary classification to distinguish between distracted and non-distracted driving states. The Self-DSNet model demonstrated an impressive accuracy of 94.23% when using vision-based data alone. Incorporating additional physiological data, such as heart rate and breathing rate, alongside vehicle dynamics data, such as steering behavior, further enhanced the model’s performance. The combined data approach achieved a 95.13% accuracy in detecting driving distractions. Specifically, the binary classification yielded a 96.58% accuracy with vision-based data, which increased to 97.31% when steering, breathing rate, and heart rate data were included. Our approach significantly outperformed state-of-the-art methods in terms of classification accuracy. The proposed Self-DSNet model offers a robust solution for driving distraction detection by effectively leveraging multimodal data and enhancing complex pattern recognition through the Self-ONN layers. The model’s high accuracy rates underscore its potential for improving driving safety by providing reliable and continuous monitoring of driver behavior. Future research may focus on real-time implementation and the integration of additional data sources to further refine and validate the model’s effectiveness in diverse driving scenarios.}, archive = {J_ACCESS}, author = {Mamun Or Rashid and Md. Mosarrof Hossen and Mohammad Nashbat and Mazhar Hasan-Zia and Ali K. Ansaruddin Kunju and Amith Khandakar and Azad Ashraf and Molla Ehsanul Majid and Saad Bin Abul Kashem and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3545359}, journal = {IEEE Access}, pages = {42322-42335}, shortjournal = {IEEE Access}, title = {Self-DSNet: A novel self-ONNs based deep learning framework for multimodal driving distraction detection}, volume = {13}, year = {2025}, }
• (2025). Neural fault observer-based on-board prognostics for automotive electric power steering systems. ACCESS, 13, 42312-42321. (www)

  @article{ , abstract = {Electric power steering (EPS) systems form a fundamental unit in modern automotive vehicles, providing motor assistance to aid the driver’s manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and reduce maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe mechanisms and provide value-added maintenance strategies. While related works in the field have shown promising results, they are limited to proof-of-concept studies validated under simulation and test-bed environments. Here, we present a novel deep learning (DL)-based method to detect EPS performance degradation using experimental data acquired from a commercial vehicle’ s controller area network (CAN) bus. Our approach initially proposes a neural fault observer model and its adversarial learning scheme to represent the EPS system’s normal operating dynamics. We demonstrate that our proposed model can detect degradation levels down to ten percent from normal conditions under various driving scenarios based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures. Furthermore, we provide physically relevant intuitions of our closed-box model’s inference mechanism based on its attention-based saliency map to strengthen the reliability aspect of our data-driven approach. Lastly, we demonstrate that a quantized model can operate in real-time on an automotive electronic control unit (ECU) device.}, archive = {J_ACCESS}, author = {Gyuwon Kim and Jongick Won and Sangjin Ko and Jinhwan Lee}, doi = {10.1109/ACCESS.2025.3546926}, journal = {IEEE Access}, pages = {42312-42321}, shortjournal = {IEEE Access}, title = {Neural fault observer-based on-board prognostics for automotive electric power steering systems}, volume = {13}, year = {2025}, }
• (2025). Iron ore information extraction based on CNN-LSTM composite deep learning model. ACCESS, 13, 42296-42311. (www)

  @article{ , abstract = {In the mining, processing, and use of minerals, iron ore information identification is crucial. Traditional determination techniques are always accompanied by problems including lengthy experiment cycles, poor accuracy, exorbitant expenses, and significant workloads. In contrast, with the use of data-driven and sophisticated algorithms, modern hyperspectral technology can quickly deliver high-precision iron ore information, increasing efficiency. Magnetite collected from an iron ore mine in the Tangshan area is used as a pilot study, and its spectral data are used as the data source. The raw spectra are preprocessed Savitzky-Golay smoothing, jump point correction, and envelope removal. The bands are subsequently screened by correlation analysis, successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), down to 50 dimensions using principal component analysis (PCA). A convolutional neural network (CNN)-long short-term memory (LSTM) composite model is suggested to concurrently forecast the particle size and water content of magnetite based on its spectral characteristics. According to the model’s results, the particle size classification accuracy is 91.67%, the F1 score is 0.92, the coefficient of determination (R2) for the water content regression is 0.89023, the mean squared error (MSE) is 0.00082, the root mean square error (RMSE) is 0.02872, and the mean absolute error (MAE) is 0.01558. The composite model performs best with superior predictive performance compared to CNN, LSTM, decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) models. The findings will push the mining sector toward more intelligence and efficiency, especially in the areas of smart mining and quick mineral appraisal.}, archive = {J_ACCESS}, author = {Haili Chen and Mengxiang Xia and Yaping Zhang and Ruonan Zhao and Bingran Song and Yang Bai}, doi = {10.1109/ACCESS.2025.3545647}, journal = {IEEE Access}, pages = {42296-42311}, shortjournal = {IEEE Access}, title = {Iron ore information extraction based on CNN-LSTM composite deep learning model}, volume = {13}, year = {2025}, }
• (2025). Equilibrium optimizer for supply chain design under demand uncertainty. ACCESS, 13, 42285-42295. (www)

  @article{ , abstract = {Supply chain design (SCD) is a complex optimization challenge that involves coordinating various elements of a supply chain to ensure efficient production, distribution, and fulfillment of customer demands. This paper proposes an improved equilibrium optimizer (IEO) algorithm to develop a supply chain network. The first novelty lies in considering the uncertainty of customer demands, the upper and lower product limits of manufacturers, and product discounts to minimize the total economic cost. The second novelty concerns the improvements to the EO algorithm in the equilibrium pool, control parameters, and position correction. Position correction ensures that solutions meet the various constraints of SCD, and improves the feasibility of the algorithm. For small-, medium-, and large-scale test cases, the proposed algorithm has been observed to outperform the original EO algorithm and four well-known algorithms, the imperialist competitive algorithm (ICA), a hybrid algorithm of grey wolf optimizer and particle swarm optimization (GWOPSO), the whale optimization algorithm (WOA), and the teaching-learning based optimization algorithm (TLBO), in terms of optimal solutions and operational efficiency. IEO demonstrates outstanding performance in solving SCD problems.}, archive = {J_ACCESS}, author = {Xiaofang Li and Pei Hu and Jiulong Zhu}, doi = {10.1109/ACCESS.2025.3548146}, journal = {IEEE Access}, pages = {42285-42295}, shortjournal = {IEEE Access}, title = {Equilibrium optimizer for supply chain design under demand uncertainty}, volume = {13}, year = {2025}, }
• (2025). Keypoint detection based on curvature grouping and adaptive sampling. ACCESS, 13, 42272-42284. (www)

  @article{ , abstract = {In the keypoint detection algorithm, the farthest point sampling methods and random sampling methods are usually used to select candidate points, then keypoints are screened out from the neighborhood of the candidate points. In the methods, reproducibility and interpretability of keypoints are relatively poor. To address the problems, this paper introduces point cloud geometric features to build a keypoint detection and description network that makes the keypoints more stable and their geometric properties easier to distinguish. The details of point cloud geometric features include: 1) Grouping the point cloud using curvature, Candidate points and their dilation clusters are found through adaptive random sampling and neighborhood expansion; 2) Aggregating the dilation cluster features by using the channel-position attention mechanism to generate keypoints based on the scores; 3) Jointly generating the descriptors using the dilation cluster features and the attention feature maps. Experiments on ModelNet40 and KITTI datasets show that the method in this paper improves the Repeatability by more than 8% compared to the existing methods, and the improvement is more obvious when the number of keypoints is reduced. Additionally, the inlier rate is enhanced by 8% and 3% on the two datasets, respectively.}, archive = {J_ACCESS}, author = {Bifu Li and Yu Cheng and Weitong Li}, doi = {10.1109/ACCESS.2024.3439613}, journal = {IEEE Access}, pages = {42272-42284}, shortjournal = {IEEE Access}, title = {Keypoint detection based on curvature grouping and adaptive sampling}, volume = {13}, year = {2025}, }
• (2025). Prediction of myocardial infarction based on non-ECG sleep data combined with domain knowledge. ACCESS, 13, 42262-42271. (www)

  @article{ , abstract = {Prediction of myocardial infarction (MI) is crucial for early intervention and treatment. Machine learning has increasingly been applied in the realm of disease prediction. This study explores the feasibility of utilizing easily obtainable heart rate (HR) and respiratory rate (RR) data collected during nocturnal sleep, in conjunction with clinical characteristics and medical domain knowledge, to predict MI. Data for this investigation were sourced from the Sleep Heart Health Study (SHHS) program in the United States, which was categorized into MI and non-MI groups based on the occurrence or absence of MI during follow-up, involving a total of 488 participants. Multiple features related to HR and RR were extracted and integrated with clinical features; four algorithms—MLP, SVM, XGBoost, and CNN—were employed for model construction. The findings indicated that the MLP model exhibited superior performance, achieving an accuracy rate 71.1%. Furthermore, three medical rules age, HR, and RR were incorporated into the MLP model to mitigate the limitations of small sample sizes. The experiments demonstrate that the model’s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. The findings indicate that leveraging non-cardiac electrophysiological data obtained during sleep alongside medical domain knowledge can significantly enhance the accuracy of early predictions regarding cardiac MI while offering novel insights for its prevention and diagnosis.}, archive = {J_ACCESS}, author = {Changyun Li and Yonghan Zhao and Qihui Mo and Zhibing Wang and Xi Xu}, doi = {10.1109/ACCESS.2025.3548118}, journal = {IEEE Access}, pages = {42262-42271}, shortjournal = {IEEE Access}, title = {Prediction of myocardial infarction based on non-ECG sleep data combined with domain knowledge}, volume = {13}, year = {2025}, }
• (2025). NOMA and millimeter-wave-assisted SWIPT for full-duplex cooperative D2D communications. ACCESS, 13, 42254-42261. (www)

  @article{ , abstract = {This paper presents a novel approach by combining simultaneous wireless information and power transfer (SWIPT) with non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave)-assisted full-duplex (FD) device-to-device (D2D) communications, a configuration not extensively explored in existing literature. Specifically, the proposed system utilizes a SWIPT-enabled D2D user that harvests energy from the base station, thereby facilitating cellular downlink transmission. In addition, directional antennas at both transmitting and receiving nodes are employed to effectively counteract frequency-dependent path loss, significantly enhancing system performance in mmWave frequencies. The novelty of this work lies in its comprehensive analysis, where analytical expressions of outage probabilities are derived, and these are validated through Monte Carlo simulations. Further, the impact of key system parameters, including signal-to-noise ratio, energy harvesting coefficient, and power allocation, on outage performance is thoroughly examined.}, archive = {J_ACCESS}, author = {Rahul Bajpai and Sukriti Mishra and Naveen Gupta}, doi = {10.1109/ACCESS.2025.3546104}, journal = {IEEE Access}, pages = {42254-42261}, shortjournal = {IEEE Access}, title = {NOMA and millimeter-wave-assisted SWIPT for full-duplex cooperative D2D communications}, volume = {13}, year = {2025}, }
• (2025). Human following and guidance by autonomous mobile robots: A comprehensive review. ACCESS, 13, 42214-42253. (www)

  @article{ , abstract = {Collaborative and companion robots are at the forefront of technological innovation, transforming human-robot interaction to address a wide range of tasks and activities. This review provides a comprehensive examination of the current state of research on human following and guidance with autonomous mobile robots. Covering the evolution of research from the inception to the latest advancements in this area, we categorize existing literature based on various attributes including fields of application, technologies employed, and social acceptability. We critically analyze and compare state-of-the-art approaches in perception, tracking, planning, control, and human-robot interaction, highlighting their effectiveness and feasibility. We further classify studies based on application domains where person following and guiding tasks are particularly impactful, such as healthcare, personal assistance, logistics, and tour guiding. We identify persistent challenges and outline open problems, offering recommendations for future research directions. Our review aims to serve as a foundational reference for researchers and practitioners, fostering continued innovation and development in the deployment of autonomous robots for human following and guidance.}, archive = {J_ACCESS}, author = {Andrea Eirale and Mauro Martini and Marcello Chiaberge}, doi = {10.1109/ACCESS.2025.3548134}, journal = {IEEE Access}, pages = {42214-42253}, shortjournal = {IEEE Access}, title = {Human following and guidance by autonomous mobile robots: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Harmonic path integral diffusion. ACCESS, 13, 42196-42213. (www)

  @article{ , abstract = {Harmonic Path Integral Diffusion (H-PID) introduces a novel approach to sampling from complex, continuous probability distributions by creating a time-dependent “bridge” from an initial point to the target distribution. Formulated as a Stochastic Optimal Control problem, H-PID balances control effort and accuracy through a unique three-level integrable structure: Top Level: Potential, force, and gauge terms combine to form a linearly solvable Path Integral Control system based on Green functions. Mid Level: With quadratic potentials and affine force/gauge terms, the Green functions reduce to Gaussian forms, mirroring quantum harmonic oscillators in imaginary time. Bottom Level: For a uniform quadratic case, the optimal drift/control reduces to a convolution of the target distribution with a Gaussian kernel, enabling efficient sampling. Implementation-wise the low-level H-PID operates without neural networks, allowing it to run efficiently on standard CPUs while achieving high precision. Validated on Gaussian mixtures and CIFAR-10 images, H-PID reveals a “weighted state” parameter as an order parameter in a dynamic phase transition, signaling early completion of the sampling process. This feature positions H-PID as a strong alternative to traditional methods sampling, such as simulated annealing, particularly for applications that demand analytical control, computational efficiency, and scalability.}, archive = {J_ACCESS}, author = {Hamidreza Behjoo and Michael Chertkov}, doi = {10.1109/ACCESS.2025.3548396}, journal = {IEEE Access}, pages = {42196-42213}, shortjournal = {IEEE Access}, title = {Harmonic path integral diffusion}, volume = {13}, year = {2025}, }
• (2025). A robust game approach for on spot price cloud markets in microservice-based applications. ACCESS, 13, 42178-42195. (www)

  @article{ , abstract = {The evolution and widespread adoption of virtualization, service-oriented architectures, autonomic computing, and utility computing have converged, giving rise to Cloud Computing, which enables the on-demand delivery of software, hardware, and data as services. As Cloud-based services become more numerous and dynamic, developing efficient service provisioning policies is increasingly challenging due to the impact of estimation errors in the operating parameters of applications such as execution time or request rate for each application and for each service. In this paper, we take the perspective of Software as a Service (SaaS) providers which host their applications at an Infrastructure as a Service (IaaS) provider. Each SaaS needs to comply with quality of service requirements, specified in Service Level Agreement (SLA) contracts with the end-users, which determine the revenues and penalties based on the achieved performance level. SaaS providers should maximize their revenue and minimize their cost by deciding how many VMs for each class (reserved, on-demand or spot) are to be used, while IaaS must decide the price of VMs in order to maximize his revenue. The problem is modeled using a Generalized Nash Equilibrium Problem (GNEP). The uncertainty in the application parameters, that are not known until run time, is specifically addressed in this paper using a cardinality-constrained robust optimization approach to formulate a robust version of the SaaS problem. Furthermore, we propose a best-reply algorithm to identify a robust GNE, ensuring that the solution remains feasible even under the worst-case scenarios of uncertain parameters. An analytical model is developed to derive the equilibrium. For validation, the analytical model results are compared with simulations that reflect real situations, characterized by more realistic parameter distributions and limited buffer capacities.}, archive = {J_ACCESS}, author = {Hamta Sedghani and Mauro Passacantando and Riccardo Lancellotti and Mina Zolfy Lighvan and Danilo Ardagna}, doi = {10.1109/ACCESS.2025.3547659}, journal = {IEEE Access}, pages = {42178-42195}, shortjournal = {IEEE Access}, title = {A robust game approach for on spot price cloud markets in microservice-based applications}, volume = {13}, year = {2025}, }
• (2025). SynGauss: Real-time 3D gaussian splatting for audio-driven talking head synthesis. ACCESS, 13, 42167-42177. (www)

  @article{ , abstract = {In the field of virtual human generation, Neural Radiance Fields (NeRF) have made significant strides in precise geometric modeling and color accuracy, establishing new benchmarks for complex viewpoint synthesis and 3D reconstruction. Despite these advancements, existing methods face substantial limitations in real-time dynamic facial expression capture and managing high-frequency details, particularly in rapid facial movements and accurate lip synchronization. These constraints are largely due to the high computational load and the dense data requirements hamper real-time rendering. Additionally, traditional radiance fields struggle to capture subtle facial changes driven by audio, often resulting in animations that lack expressiveness and naturalness. Building upon the foundation laid by TalkingGaussian,this paper introduces an advanced framework named SynGauss that employs 3D Gaussian Splatting to precisely decouple facial and lip movements. We have enhanced this approach by incorporating lip expression coefficients and a regional multi-head attention mechanism, which allow for detailed and controlled animation of complex facial dynamics. Our modifications provide a more refined control over lip movements and facial expressions, significantly improving the realism and expressiveness of the animations while maintaining the efficiency required for real-time applications. This approach holds great promise for real-time applications such as virtual assistants and immersive entertainment experiences, offering more realistic and controllable animation generation.(Project address https://github.com/zzyfight0703/SynGauss/tree/main)}, archive = {J_ACCESS}, author = {Zhanyi Zhou and Quandong Feng and Hongjun Li}, doi = {10.1109/ACCESS.2025.3548015}, journal = {IEEE Access}, pages = {42167-42177}, shortjournal = {IEEE Access}, title = {SynGauss: Real-time 3D gaussian splatting for audio-driven talking head synthesis}, volume = {13}, year = {2025}, }
• (2025). User trajectory prediction in cellular networks using multi-step LSTM approaches: Case study and performance evaluation. ACCESS, 13, 42143-42166. (www)

  @article{ , abstract = {User trajectory prediction is a critical component in enhancing network resource management and improving Quality of Service (QoS) in 5G cellular networks. This research addresses the challenge of accurately predicting user trajectories by leveraging two approach that integrates Long Short-Term Memory (LSTM) and Transformer models. While LSTM excels in capturing sequential temporal patterns, Transformer introduces multi-head attention mechanisms to model complex spatial and temporal dependencies, filling a significant research gap in trajectory prediction. The study employs a real-world user trajectory dataset collected from 5G cellular networks, encompassing diverse scenarios with high temporal variability. Comprehensive data preprocessing steps, such as filtering, padding, and normalization, were applied to ensure data consistency and suitability for deep learning. Performance evaluation is conducted using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Average Distance Error to compare the models. Results reveal that the Transformer model outperforms LSTM in both accuracy and efficiency, achieving an Average Distance Error of 0.818 meters under the optimal configuration, compared to 11.54 meters for the best LSTM configuration. However, the Transformer model exhibits susceptibility to overfitting at higher epochs, emphasizing the need for regularization techniques. These findings demonstrate the potential of Transformer-based approaches in improving prediction accuracy and enabling advanced network management strategies, such as optimized handovers and latency reduction.}, archive = {J_ACCESS}, author = {Iskandar and Hajiar Yuliana and Hendrawan and Adriel Timoteo and Fabian Rafinanda Benyamin and Naufal Bhanu Anargyarahman}, doi = {10.1109/ACCESS.2025.3547811}, journal = {IEEE Access}, pages = {42143-42166}, shortjournal = {IEEE Access}, title = {User trajectory prediction in cellular networks using multi-step LSTM approaches: Case study and performance evaluation}, volume = {13}, year = {2025}, }
• (2025). Econometric modeling combining MCMC algorithm and random coefficient quantile AR model. ACCESS, 13, 42129-42142. (www)

  @article{ , abstract = {The current econometric models have the disadvantages of low prediction accuracy and poor model fitting effect. To solve these problems, this study combines Markov chain Monte Carlo algorithm with random coefficient quantile auto-regression model, and optimizes the econometric model based on the fusion algorithm. The results showed that compared with financial time series prediction algorithms, the fusion algorithm improved the prediction accuracy by 4.8% and the computation speed by 6.5 bps. The econometric model based on the fusion algorithm was compared with other models. The results showed that the optimized model improved the prediction accuracy by 10.7% compared to the AdaBoost econometric model. In summary, the fusion algorithm proposed in the study can improve the prediction efficiency of econometric models, accelerate the prediction process, optimize economic benefits, and reduce investment risks for enterprises and individuals.}, archive = {J_ACCESS}, author = {Ting Wang}, doi = {10.1109/ACCESS.2025.3547348}, journal = {IEEE Access}, pages = {42129-42142}, shortjournal = {IEEE Access}, title = {Econometric modeling combining MCMC algorithm and random coefficient quantile AR model}, volume = {13}, year = {2025}, }
• (2025). Prediction of promotors in agrobacterium and klebsiella using novel feature engineering and ensemble learning approach. ACCESS, 13, 42116-42128. (www)

  @article{ , abstract = {This paper presents an efficient machine learning-based model for the prediction of promoters in bacterial genomes, specifically targeting Agrobacterium tumefaciens, Klebsiella aerogenes, and Xanthomonas campestris. Agrobacterium tumefaciens is a well-known bacterium that induces plant tumors, such as crown gall disease, negatively impacting crop health and productivity. In contrast, Klebsiella aerogenes is an opportunistic pathogen responsible for various human infections, including healthcare-associated infections that often exhibit antibiotic resistance. Additionally, Xanthomonas campestris is a significant plant pathogen that causes various diseases in crops, making its study important for agricultural sustainability. Accurate promoter prediction in these bacterial species is crucial for understanding gene regulation mechanisms, with potential applications in agricultural biotechnology and medical research. In this study, a novel feature engineering approach was employed to extract significant features from the dataset. A Random Forest classifier was utilized to generate probabilistic features based on the probability estimates for each sequence, providing deeper insights into the data. These features were then used to train an ensemble learning model, combining the strengths of multiple classifiers to enhance prediction accuracy. Our method achieved remarkable accuracy, precision, and recall rates of 99%, 98%, and 99%, respectively, which not only demonstrate the effectiveness of our model but also exceed the current performance of the state of the art in this area. By leveraging advanced classification techniques, we provide a robust framework for promoter prediction that can significantly enhance genetic engineering and biotechnological applications involving these organisms.}, archive = {J_ACCESS}, author = {Nagwan Abdel Samee and Rawan Talaat and Ali Raza and Hadil Shaiba and Souham Meshoul}, doi = {10.1109/ACCESS.2025.3544520}, journal = {IEEE Access}, pages = {42116-42128}, shortjournal = {IEEE Access}, title = {Prediction of promotors in agrobacterium and klebsiella using novel feature engineering and ensemble learning approach}, volume = {13}, year = {2025}, }
• (2025). Indoor air wellness: A predictive model for pollution control using advanced AI techniques. ACCESS, 13, 42099-42115. (www)

  @article{ , abstract = {Indoor air quality (IAQ) has a crucial impact on health, yet many spaces suffer from unnoticed pollution. This study introduces “Indoor Air Wellness”, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering and artificial neural networks, to enhance indoor air pollution prediction and management. The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy of up to 96. 55%, a root mean square error, a Mean Absolute Error, and a Mean Squared Prediction Error as low as 9.85, 6.12, and $3.15~g/m$ . “Indoor Air Wellness” operates in three phases: IAQ prediction, control, and energy-efficient green building design. IAQ prediction consists of Learning and Analysis subphases, continuously monitoring and forecasting indoor parameters such as temperature, humidity, and CO levels while considering external factors. The Analysis phase implements control measures based on these predictions, leading to a 60% improvement in energy utilization in green buildings compared to traditional methods. Simulations and real-world applications demonstrate its effectiveness in reducing pollutants by up to 23% and minimizing energy consumption by up to 48%. The “Indoor Air Wellness” model enhances air quality and promotes energy efficiency, positioning it as a valuable tool for residential, commercial, and industrial applications. This study contributes to advancing smart environmental management, advocating for healthier indoor environments through AI-driven solutions.}, archive = {J_ACCESS}, author = {Kalyan Chatterjee and Muntha Raju and Bhoomeshwar Bala and U. Nikitha and Gampala Prabhas and Naim Ahmad and Ayman Qahmash and Wade Ghribi and Bernardo Lemos and Saurav Mallik}, doi = {10.1109/ACCESS.2025.3547808}, journal = {IEEE Access}, pages = {42099-42115}, shortjournal = {IEEE Access}, title = {Indoor air wellness: A predictive model for pollution control using advanced AI techniques}, volume = {13}, year = {2025}, }
• (2025). Challenges in implementing cross-border digital identity systems for global public infrastructure: A comprehensive analysis. ACCESS, 13, 42083-42098. (www)

  @article{ , abstract = {The increasing demand for secure and interoperable cross-border digital identity systems has led to challenges in standardization, cybersecurity, and regulatory alignment. Despite advancements in frameworks like eIDAS (EU) and Aadhaar (India), the lack of globally unified standards remains a significant barrier to adoption. This study conducts a systematic literature review (SLR) following the PRISMA framework, analyzing 45 peer-reviewed studies (2018–2024) from Scopus, Web of Science, IEEE Xplore, and Springer. The analysis categorizes challenges into four key domains: technical (interoperability and cybersecurity issues), regulatory (data protection inconsistencies), organizational (financial and governance constraints), and societal (digital inclusion and trust concerns). Findings reveal that 45% of reported challenges stem from interoperability gaps, while 35% result from regulatory fragmentation. Economic disparities further limit digital identity adoption, particularly in developing regions. PPPs are being investigated as a strategic approach to advance blockchain technology and AI-driven identity verification, harmonize regulations, and promote global cooperation to address these issues. This research proposes a scalable and secure digital identity framework, offering actionable recommendations for policymakers, industry stakeholders, and researchers to build inclusive and interoperable global identity ecosystems.}, archive = {J_ACCESS}, author = {Suhono Harso Supangkat and Hendra Sandhi Firmansyah and Irma Rizkia and Rezky Kinanda}, doi = {10.1109/ACCESS.2025.3547373}, journal = {IEEE Access}, pages = {42083-42098}, shortjournal = {IEEE Access}, title = {Challenges in implementing cross-border digital identity systems for global public infrastructure: A comprehensive analysis}, volume = {13}, year = {2025}, }
• (2025). Nomenclature overview and beat frequency in the family of AC/AC modular multilevel cascade converters (MMCC). ACCESS, 13, 42064-42082. (www)

  @article{ , abstract = {The family of modular multilevel cascade converter (MMCC) has gained significant attention owing to its modular configuration, scalability, and superior harmonic performance. nearest-level control (NLC) not only leverages MMCC modularity but also minimize switching losses, making it highly effective for high-frequency operation. Nevertheless, applying NLC in the AC/AC MMCC configurations results in beat frequency oscillations, evident as coupled-frequency sidebands in the output voltage spectrum. This paper extends MMCC family nomenclature to include the emerging modular and multilevel topologies. Using the MMCC-triple star bridge cell (TSBC) topology, this study systematically investigates the beat frequency oscillations, introduces a double coordinate analysis and implements decoupled control loops. Our research presents a theoretical understanding and practical mitigation of beat frequency distortion. Simulation results validate the effectiveness of the proposed decoupled NLC, demonstrating its potential to enhance power quality in applications such as solid-state transformers (SSTs).}, archive = {J_ACCESS}, author = {Mohammed Azharuddin Shamshuddin and Ralph M. Kennel and Diego Verdugo}, doi = {10.1109/ACCESS.2025.3546880}, journal = {IEEE Access}, pages = {42064-42082}, shortjournal = {IEEE Access}, title = {Nomenclature overview and beat frequency in the family of AC/AC modular multilevel cascade converters (MMCC)}, volume = {13}, year = {2025}, }
• (2025). Evaluating critical success factors in AI-driven drug discovery using AHP: A strategic framework for optimization. ACCESS, 13, 42045-42063. (www)

  @article{ , abstract = {Artificial Intelligence (AI) is reshaping drug discovery by accelerating the identification of therapeutic candidates and reducing development timelines and costs. However, its effectiveness depends on addressing key success factors that influence AI integration. This study presents a novel framework using the Analytic Hierarchy Process (AHP) to systematically evaluate and rank these factors, addressing a crucial gap in strategic planning for AI adoption in pharmaceutical research. The framework comprises six key criteria: Data Quality and Management (DQM), Algorithm Performance and Optimization (APO), Interpretability and Explainability (IE), Regulatory Compliance and Ethical Considerations (RCEC), Computational Efficiency and Scalability (CES), and Validation and Experimental Confirmation (VEC). Expert-driven pairwise comparisons identified Accuracy (ACC), Generalizability (GEN), and Experimental Validation (EV) as top priorities, highlighting the importance of reliable data, robust algorithms, and rigorous validation processes to ensure trustworthy AI outputs. This research contributes to strategic AI adoption by addressing data inconsistencies, algorithmic bias, and scalability limitations. The proposed framework enhances AI applications’ efficiency, scalability, and ethical alignment, promoting the development of transparent and reliable drug discovery systems. This comprehensive evaluation is a valuable resource for researchers and industry professionals, facilitating the strategic adoption of AI and bridging the gap between computational predictions and real-world therapeutic outcomes.}, archive = {J_ACCESS}, author = {Amir Mohamed Talib and Areen Metib Al-Hgaish and Rodziah Binti Atan and Abdulaziz Alshammari and Fahad Omar Alomary and Razali Yaakob and Abdulaziz Alsahli and Mohd Hafeez Osman}, doi = {10.1109/ACCESS.2025.3546925}, journal = {IEEE Access}, pages = {42045-42063}, shortjournal = {IEEE Access}, title = {Evaluating critical success factors in AI-driven drug discovery using AHP: A strategic framework for optimization}, volume = {13}, year = {2025}, }
• (2025). Magnetic resonance-based electric properties tomography via green’s integral identity. ACCESS, 13, 42029-42044. (www)

  @article{ , abstract = {A new approach to Magnetic Resonance-based Electric Properties Tomography (EPT) is presented. The method applies Green’s integral identity to the equation that regulates the EPT problem. The resultant integral equations are used to impose the consistency of the measured values of the radiofrequency field. This is achieved by seeking dielectric properties values that allow satisfying the identity within suitable kernels of voxels. In each kernel, an overdetermined system of equations is written, and the corresponding problem is solved in the least squares sense, providing an index of trustworthiness of the solution. Both the complete formulation and its phase-based approximation are presented. The application of a filter, which post-processes the raw results based on the index of trustworthiness, is also discussed. The performance of the method is evaluated on synthetic data and experimental measurements acquired on a heterogeneous brain phantom and on four human volunteers. The reconstructions are compared to those produced through a Helmholtz-EPT with adaptive kernel. The new EPT method performs well in all tests.}, archive = {J_ACCESS}, author = {Luca Zilberti and Alessandro Arduino and Umberto Zanovello and Jessica A. Martinez and Kevin Moulin and Adriano Troia and Oriano Bottauscio}, doi = {10.1109/ACCESS.2025.3546036}, journal = {IEEE Access}, pages = {42029-42044}, shortjournal = {IEEE Access}, title = {Magnetic resonance-based electric properties tomography via green’s integral identity}, volume = {13}, year = {2025}, }
• (2025). Anomaly-based intrusion detection for IoMT networks: Design, implementation, dataset generation, and ML algorithms evaluation. ACCESS, 13, 41994-42028. (www)

  @article{ , abstract = {The Internet of Things has transformed the healthcare sector through the introduction of the Internet of Medical Things (IoMT) technology. However, IoMT networks remain vulnerable to a wide range of threats due to their resource-constrained characteristics and heterogeneity. Therefore, novel security mechanisms such as accurate and efficient Anomaly-based Intrusion Detection Systems (AIDSs), taking into consideration the inherent limitations of the IoMT networks, are necessary to be developed before IoMT networks reach their full potential in the market. This paper is an extension of our previous works and presents a new and refined design of a hybrid AIDS for IoMT networks. Furthermore, we provide implementation details on Raspberry Pi devices and performance evaluation results that demonstrate the efficacy of our approach. For its detection purposes, the AIDS employs Novelty detection and Outlier detection algorithms as these types of ML algorithms can detect both known and unknown types of attacks. Then, we tuned the hyperparameters of various Novelty Detection and Outlier Detection ML algorithms and evaluated their performance. Afterwards, the best performing ML algorithms (i.e., OCSVM, LOF, G_KDE, PW_KDE, B_GMM, MCD and IsoForest) are selected to be integrated into the AIDS deployed on an IoT/IoMT testbed. In addition, we evaluated the performance of the deployed AIDS during runtime, and the runtime evaluation results indicate: (i) a strong detection performance for some of the integrated ML algorithms, and (ii) a low computational cost (i.e., less than 1 % cpu usage) of the AIDS for all integrated ML algorithms.}, archive = {J_ACCESS}, author = {Georgios Zachos and Georgios Mantas and Kyriakos Porfyrakis and Joaquim Manuel Camões Sobral de Bastos and Jonathan Rodriguez}, doi = {10.1109/ACCESS.2025.3547572}, journal = {IEEE Access}, pages = {41994-42028}, shortjournal = {IEEE Access}, title = {Anomaly-based intrusion detection for IoMT networks: Design, implementation, dataset generation, and ML algorithms evaluation}, volume = {13}, year = {2025}, }
• (2025). DP-FedCMRS: Privacy-preserving federated learning algorithm to solve heterogeneous data. ACCESS, 13, 41984-41993. (www)

  @article{ , abstract = {In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and gradients can be used to infer original data, posing a threat to user privacy. To address these issues, this paper proposes a Differential Privacy based Federated Learning algorithm with clustered model random selection (DPFedCMRS), which first clusters clients with similar data distributions into the same cluster, and then each cluster randomly selects a model from other clusters in iteration to learn the characteristics of different data distributions to solve the problem of data heterogeneity. Differential privacy is applied to the algorithm to achieve sample-level differential privacy and protect the client’s data privacy. An adaptive clustering algorithm is also proposed, which combines gradient quantile sparsification to amplify data characteristics to Ensure high-accuracy clustering results under the condition of high privacy guarantee. Many experiments are carried out on three classical datasets, MNIST, FMNIST and CIFAR10, the accuracy of the model trained by DPFedCMRS is increased by 5.98%, 5.16% and 2.05% compared with the existing methods when the privacy budget is 2, indicating that DPFedCMRS improves the performance of federated learning in the case of highly heterogeneous data distribution and high privacy guarantee.}, archive = {J_ACCESS}, author = {Yang Zhang and Shigong Long and Guangyuan Liu and Junming Zhang}, doi = {10.1109/ACCESS.2025.3546473}, journal = {IEEE Access}, pages = {41984-41993}, shortjournal = {IEEE Access}, title = {DP-FedCMRS: Privacy-preserving federated learning algorithm to solve heterogeneous data}, volume = {13}, year = {2025}, }
• (2025). Ranking assisted unsupervised morphological disambiguation of turkish. ACCESS, 13, 41974-41983. (www)

  @article{ , abstract = {In comparison to English, Turkish is an agglutinative language with fewer resources. The agglutinative properties of words result in a significant number of morphological analyses, creating uncertainty in morphological disambiguation and syntactic parsing. Traditional approaches typically rely on supervised learning models based on the correct morphological analysis of a given phrase. In this study, we propose a ranking method to limit and filter out irrelevant morphological tags from all possible combinations of morphological analyses of a given sentence without supervision. The suggested method selects less ambiguous analyses for statistical aggregation and applies inference through the PageRank algorithm on a densely connected graph. Subsequently, this graph is utilized to develop a voting schema for each test word based on the connections in the test sentence. Experimental evaluations of the proposed methods on three independently and manually annotated test datasets indicate a token accuracy of approximately 80% and an accuracy of around 61% for ambiguous tokens. In all ranking evaluations, the best scores from the PageRank variations significantly outperform those of Self-Attention LSTM and ELMO deep learning models. The training process of PageRank is notably straightforward and efficient, requiring $O(n^{2})$ parameter adjustments, which is considerably fewer than those required by the backpropagation method used in neural network training. Furthermore, to reduce ambiguity in sentences from different genres with scarce samples, the proposed method is easily adaptable.}, archive = {J_ACCESS}, author = {Hayri Volkan Agun and Özkan Aslan}, doi = {10.1109/ACCESS.2025.3547303}, journal = {IEEE Access}, pages = {41974-41983}, shortjournal = {IEEE Access}, title = {Ranking assisted unsupervised morphological disambiguation of turkish}, volume = {13}, year = {2025}, }
• (2025). A novel multi-objective fuzzy deep learning framework for predictive maintenance in industrial internet of things. ACCESS, 13, 41955-41973. (www)

  @article{ , abstract = {As industrial systems advance within the framework of Industry 4.0, predictive maintenance (PdM) has become essential to minimize downtime and improve operational efficiency in industrial Internet of Things (IIoT) environments. However, traditional PdM methods often struggle with handling complex high-dimensional data and managing uncertainty, leading to limitations in predictive accuracy and adaptability. This paper introduces MO-FCNN, a novel PdM model that integrates fuzzy logic with deep convolutional neural networks (DCNN) to manage data uncertainty and utilizes non-dominated sorting biogeography-based optimization (NSBBO) to improve the overall maintenance decision-making process. Building upon the MO-FCNN-based PdM framework, this approach targets the unique challenges of high-dimensionality and dynamic data in IIoT, enhancing both the interpretability and performance of PdM systems. The proposed method leverages NSBBO in two critical areas: backpropagation optimization within CNN’s fully connected neural network layers and membership function optimization to refine fuzzy logic handling of uncertain or noisy information. This combined framework is designed to maximize equipment uptime and optimize resource allocation by dynamically adapting to varying maintenance demands and operating conditions. Extensive experimental evaluations, including performance metrics on resource allocation, predictive accuracy, and maintenance scheduling efficiency, demonstrate that MO-FCNN outperforms traditional PdM approaches. The findings underscore the ability of this model to serve as a reliable tool for advanced asset management, offering significant improvements in predictive accuracy, computational efficiency, and adaptability to various IIoT conditions.}, archive = {J_ACCESS}, author = {Jiangang Feng and Jicheng Kan}, doi = {10.1109/ACCESS.2025.3547863}, journal = {IEEE Access}, pages = {41955-41973}, shortjournal = {IEEE Access}, title = {A novel multi-objective fuzzy deep learning framework for predictive maintenance in industrial internet of things}, volume = {13}, year = {2025}, }
• (2025). Joint estimation of CFO and sparse channel for high-mobility RIS-assisted MIMO-OFDMA uplink system. ACCESS, 13, 41943-41954. (www)

  @article{ , abstract = {Reconfigurable intelligent surface (RIS) is envisioned to enhance next-generation wireless networks by dynamically controlling signal reflections to improve coverage and spectral efficiency. However, in high-mobility RIS-assisted systems, the doubly selective nature of the wireless channel and carrier frequency offset (CFO) introduces severe interference, leading to significant performance degradation. Accurate joint estimation is required to achieve reliable communication in RIS-assisted MIMO-OFDMA uplink systems in a high-mobility environment. In this paper, we investigate the joint estimation of CFO and doubly selective channel (DSC) in a RIS assisted multiple input multiple output orthogonal frequency division multiple access (MIMO-OFDMA) uplink system. To obtain joint estimates of CFO and channel with low complexity, we use the basis expansion model (BEM) to capture the time variations of the sparse wireless channel. The variations of BEM coefficients and CFO over subsequent OFDMA symbols for the entire transmission are modeled using the first-order autoregressive (AR) model. We propose a novel sparse Bayesian Schmidt-extended Kalman filter (sBSEKF) based method to estimate the BEM coefficients and CFO of each user separately during the pilot transmission stage. The Bayesian Cramer-Rao bound (BCRB) of the proposed joint estimation problem is also derived by exploiting the statistical properties of the unknown parameters. Furthermore, we derive the optimum reflection coefficients for RIS for the data transmission stage to improve the system performance. This optimization is carried out by considering the dominant tap of the channel, ensuring enhanced signal quality and improved achievable rate. Performance of the system under high mobility scenarios is quantified in terms of normalized mean square error (NMSE) and bit error rate (BER) through extensive Monte Carlo simulation studies. Furthermore, the BCRB is also demonstrated and compared with the NMSE to validate the effectiveness and accuracy of the proposed method.}, archive = {J_ACCESS}, author = {A. G. Murali Krishna and P. S. Sanoopkumar and S. M. Sameer}, doi = {10.1109/ACCESS.2025.3544895}, journal = {IEEE Access}, pages = {41943-41954}, shortjournal = {IEEE Access}, title = {Joint estimation of CFO and sparse channel for high-mobility RIS-assisted MIMO-OFDMA uplink system}, volume = {13}, year = {2025}, }
• (2025). P-BERT: Toward long sequence modeling by enabling language representation with prefix sequence compression, soft position embedding, and data augmentation for patent relevance assessment. ACCESS, 13, 41928-41942. (www)

  @article{ , abstract = {Recent works have increasingly adopted pre-trained language models, such as BERT, to model technical semantics for patent relevance assessment. However, existing truncation and divide-merge strategies, used to handle input length constraints, results in feature loss and semantic isolation. This limits the effectiveness of pre-trained language models in capturing long sequence semantics for patent relevance assessment. Because patent relevance is evaluated based on local sequence semantics, this paper proposes a patent-oriented bidirectional encoder representation from transformers (P-BERT) to enable the semantic modeling ability of long sequences. The characteristics of P-BERT lie in a prefix sequence compression method, a soft position embedding method and a data augmentation method. The first one can retrain more technical features within the input constraints via transforming full sequences into partial sequences while the second one reconstructs local sequence semantics for partial sequences. As for the last one, it contains sample and feature augmentation to improve the generalization and robustness. Due to the scarcity of training corpus for patent relevance assessment, a dual constraint strategy is proposed to generate a supervised corpus, DualCons. Experimental results demonstrate that DualCons can help BERT obtain the best ranking performance compared with other strategies. Model evaluations show the effectiveness of P-BERT. It reaches an improvement of 2%-6% in recall-oriented metrics and an improvement of 1%-5% in accuracy-oriented indicators over the state-of-the-art baselines. All those would promote the engineering applications of pre-trained language models in patent relevance assessment.}, archive = {J_ACCESS}, author = {Fei Wang and Xingchen Shi and Dongsheng Wang and Yinxia Lou}, doi = {10.1109/ACCESS.2025.3545430}, journal = {IEEE Access}, pages = {41928-41942}, shortjournal = {IEEE Access}, title = {P-BERT: Toward long sequence modeling by enabling language representation with prefix sequence compression, soft position embedding, and data augmentation for patent relevance assessment}, volume = {13}, year = {2025}, }
• (2025). Regional sentimental flows and hierarchical drivers of urban waste sorting: An integrated OCC-LSTM and LDA-DEMATEL-ISM approach using social media data. ACCESS, 13, 41909-41927. (www)

  @article{ , abstract = {Effective waste sorting policies are essential for promoting sustainable waste management, yet their impact on regional public sentiments and underlying drivers has received limited attention. This study utilizes a comprehensive framework to analyze how policies shape the regional flow of public sentiments and the hierarchical relationships among influencing factors, using social media data as a lens. This study collected garbage classification data from Weibo, using deep learning model to analyze policy’s impact on regional attention flow under different sentiments. It also employed the Latent Dirichlet Allocation (LDA), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) methods to classify and integrate influencing factors under different sentiment themes. The results indicate that: (1) Positive sentiments spread from the eastern regions driven by policies to other areas, while negative sentiments, though still concentrated in the eastern regions, gradually see increasing influence from policies in central and western regions. (2) Policy support is a fundamental factor in promoting waste sorting. (3) The hierarchical network of influencing factors reveals that policies interact with driving factors at the societal, technological, and individual levels. This study offers comprehensive analysis and support for waste classification policy implementation and public acceptance enhancement.}, archive = {J_ACCESS}, author = {Feixue Sui and Hengxu Zhang}, doi = {10.1109/ACCESS.2025.3547766}, journal = {IEEE Access}, pages = {41909-41927}, shortjournal = {IEEE Access}, title = {Regional sentimental flows and hierarchical drivers of urban waste sorting: An integrated OCC-LSTM and LDA-DEMATEL-ISM approach using social media data}, volume = {13}, year = {2025}, }
• (2025). Enhancing cybersecurity of a hydroelectric power plant through digital twin modeling and explainable AI. ACCESS, 13, 41887-41908. (www)

  @article{ , abstract = {Hydroelectric power plants (HEPPs) are vital components of the renewable energy infrastructure, making their operational security and efficiency critical. HEPPs face increasing vulnerability to cyber threats, which can disrupt operations and compromise energy production. This study investigates the integration of Digital Twin (DT) technology with Explainable Artificial Intelligence (XAI) to improve cybersecurity and anomaly detection in a HEPP located in Türkiye. The DT model simulates key operational parameters, such as water flow, mechanical power, and turbine speed, enabling real-time monitoring, optimization, and secure cybersecurity testing. Simulated cyberattacks on the DT have revealed vulnerabilities in the Modbus protocol, while SHapley Additive exPlanations (SHAP) analysis, an XAI technique, clarifies the influence of operational parameters on anomaly detection outcomes. Gravity with a SHAP value of 0.0001 and water density with a SHAP value of 0.0018 have been identified as the least influential parameters, suggesting limited variability or impact on the detected anomalies. These findings enable the prioritization of critical variables while reducing unnecessary monitoring efforts. The proposed integration improves the accuracy of anomaly detection, enables precise vulnerability identification, and mitigates operational risks without impacting real-world systems. The results demonstrate the effectiveness of the model in detecting anomalies and strengthening the resilience of the system against cyber threats. This approach also provides actionable insights to optimize operational processes, ensuring secure and efficient energy production. The study highlights the transformative potential of DT-XAI integration in advancing the sustainability, security, and resilience of critical energy infrastructures.}, archive = {J_ACCESS}, author = {Ismail Erkek and Erdal Irmak}, doi = {10.1109/ACCESS.2025.3547672}, journal = {IEEE Access}, pages = {41887-41908}, shortjournal = {IEEE Access}, title = {Enhancing cybersecurity of a hydroelectric power plant through digital twin modeling and explainable AI}, volume = {13}, year = {2025}, }
• (2025). SmartData: Toward the data-driven design of critical systems. ACCESS, 13, 41865-41886. (www)

  @article{ , abstract = {Machine Learning algorithms and safety models are enabling higher levels of autonomy in modern Cyber-Physical Systems (CPS). Ensuring safe autonomous operation requires strict adherence to timing and security constraints, best expressed in terms of the data consumed rather than tasks executed. This paper introduces a Data-Centric design for Data-Driven Systems using SmartData, a data construct enriched with metadata to encapsulate origin, semantics, and relationships. SmartData interact via Interest relationships, inheriting requirements such as freshness, periodicity, and security. We extend SmartData with six novel stereotypes: Sensor, Storage, Transformer, Secure, Persistent, and Actuator. To facilitate system design, we propose a method to algorithmically build a SmartData Graph (SDG), a directed graph representing the relationships between SmartData elements. The SDG construction algorithm dynamically updates demands for timing, security, and persistence, ensuring data production satisfies all data requirements. Therefore, a Data-Driven design that can be built directly from the system’s data requirements at early states. With the notion of how actuation is expected, we comprise the dataflows necessary to perform this actuation. This approach allows system designers to estimate latency, bandwidth, and data generation periodicity while identifying critical paths requiring reliable communication and processing technologies. The SmartData API bridges design and implementation, enabling seamless integration. We demonstrate the proposed method through a use case of an imitation-learning-based autonomous driving system implemented on a Linux platform and integrated with the CARLA simulator.}, archive = {J_ACCESS}, author = {José L. Conradi Hoffmann and Antônio A. Fröhlich}, doi = {10.1109/ACCESS.2025.3548542}, journal = {IEEE Access}, pages = {41865-41886}, shortjournal = {IEEE Access}, title = {SmartData: Toward the data-driven design of critical systems}, volume = {13}, year = {2025}, }
• (2025). SLIPT-assisted dual-hop VLC/RF with relay selection: Outage probability analysis and optimization. ACCESS, 13, 41854-41864. (www)

  @article{ , abstract = {This paper proposes a hybrid visible light communication/radio frequency (VLC/RF) system with simultaneous lightwave information and power transfer (SLIPT) utilizing relay selection to take the advantages of both VLC and RF systems in terms of spectrum efficiency and coverage. The relay is selected from multiple devices in the VLC coverage and equipped with both solar cell and RF antenna for energy harvesting, receiving signal and transmitting message to the end user. Especially, the devices in optical communication range are randomly located with the uniform distribution, and the light intensity from the light emitting diode (LED) contains alternating current (AC) and direct current (DC) elements for the signal detection and energy harvesting. The relay decodes the signal to retransmit to the end user by harvested power via RF channels in the second hop. Then, we derive the closed-form of the outage probability (OP) of the proposed system. The analytical expression of the OP performance is validated by simulation results, and the impacts of parameters of the system are examined. The results show that the increase in the number of the relay can reduce the OP and transmitted power. The high value of threshold of transmission rate at the relay can increase the error in the performance. In addition, decreasing AC value and boosting DC value can enhance OP performance of the system, and vice versa. Moreover, the optimization problem is formulated to minimize the OP performance subject to AC peak value and DC bias to satisfy linear characteristics of the LED operation. To solve the optimization problem, the particle swarm optimization (PSO) algorithm is deployed and modified to attain the optimal solution. The PSO algorithm greatly improves the OP performance in comparison to the numerical method.}, archive = {J_ACCESS}, author = {van Khoe Ta and Ic-Pyo Hong and Giang N. Tran}, doi = {10.1109/ACCESS.2025.3547893}, journal = {IEEE Access}, pages = {41854-41864}, shortjournal = {IEEE Access}, title = {SLIPT-assisted dual-hop VLC/RF with relay selection: Outage probability analysis and optimization}, volume = {13}, year = {2025}, }
• (2025). Joint content and shape adaptive transforms based on a GMRF model for coding arbitrarily shaped image segments. ACCESS, 13, 41839-41853. (www)

  @article{ , abstract = {It is known that shape-adaptive (SA) transform coding can outperform block-based transform coding for images. However, there is no simple way to construct optimal transforms for arbitrarily shaped image segments. We present a novel method for constructing SA transforms using a non-causal finite lattice homogeneous Gauss-Markov random field (GMRF) model. Karhunen-Loéve transform (KLT) of the GMRF model serves as a compact parameterized model for the KLT of an arbitrarily shaped segment, where the model parameters represent the image texture. In contrast to other SA transforms considered in the literature, our approach makes it possible to optimize the transform to an image segment in the sense that if the GMRF model is exact for the texture, the resulting KLT is the optimal transform for the image segment. An important part of the proposed GMRF model is a modified version of asymmetric Neumann boundary conditions designed to ensure a high transform coding gain for arbitrarily shaped image segments. By using a candidate set of GMRF parameters estimated from a training set of images, our KLT model can be used for joint content and shape adaptive transform (JCSAT) coding. Experimental results show that JCSAT coding outperforms other known SA transforms, as well as the standard 2D-DCT.}, archive = {J_ACCESS}, author = {Shehan Senarath and Pradeepa Yahampath}, doi = {10.1109/ACCESS.2025.3546822}, journal = {IEEE Access}, pages = {41839-41853}, shortjournal = {IEEE Access}, title = {Joint content and shape adaptive transforms based on a GMRF model for coding arbitrarily shaped image segments}, volume = {13}, year = {2025}, }
• (2025). Transformer-based sensor fusion for autonomous vehicles: A comprehensive review. ACCESS, 13, 41822-41838. (www)

  @article{ , abstract = {Sensor fusion is vital for many critical applications, including robotics, autonomous driving, aerospace, and beyond. Integrating data streams from different sensors enables us to overcome the intrinsic limitations of each sensor, providing more reliable measurements and reducing uncertainty. Moreover, deep learning-based sensor fusion unlocked the possibility of multimodal learning, which utilizes different sensor modalities to boost object detection. Yet, adverse weather conditions remain a significant challenge to the reliability of sensor fusion. However, introducing the Transformer deep learning model in sensor fusion presents a promising avenue for advancing its sensing capabilities, potentially overcoming that challenge. Transformer models proved powerful in modeling vision, language, and numerous other domains. However, these models suffer from high latency and heavy computation requirements. This paper aims to provide: 1) an extensive overview of sensor fusion and transformer models; 2) an in-depth survey of the state-of-the-art (SoTA) methods for Transformer-based sensor fusion, focusing on camera-LiDAR and camera-radar methods; and 3) a quantitative analysis of the SoTA methods, uncovering research gaps and stimulating future work.}, archive = {J_ACCESS}, author = {Ahmed Abdulmaksoud and Ryan Ahmed}, doi = {10.1109/ACCESS.2025.3545032}, journal = {IEEE Access}, pages = {41822-41838}, shortjournal = {IEEE Access}, title = {Transformer-based sensor fusion for autonomous vehicles: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). The influence of blockchain on intellectual property pledge financing decisions in 3D printing technology. ACCESS, 13, 41797-41821. (www)

  @article{ , abstract = {3D printing technology is an innovative manufacturing method that has garnered widespread attention and application across various fields, including medical, automotive, and aerospace industries. Despite its vast potential, 3D printing technology faces challenges such as high financing risk and constrained technological development. This paper, from a supply chain perspective, constructs a three-level supply chain comprising a 3D printing technology vendor, a manufacturer, and a retailer. First, we construct a foundational model that incorporates the advantages of technology vendors with patented innovations and adopts the intellectual property rights (IPR) pledge financing model. Subsequently, we introduce blockchain technology to optimize this financing approach, resulting in three distinct models. Through numerical simulations, we focus on analyzing the impact of several factors—namely, the technology vendor’s own capital, the level of customization in 3D printing, the customization cost coefficient, blockchain penetration, and the initial investment cost of blockchain—on the decision variables and profits of various stakeholders in the supply chain. The findings indicate that patent pledge financing and blockchain technology affect technology vendors and their supply chains differently based on the amount of their own capital. To achieve higher levels of customization and reduce customization costs, the integration of blockchain can be beneficial. Additionally, when the initial investment cost of blockchain is relatively low, it can further enhance the overall profit of the supply chain, building upon the existing patent pledge financing model.}, archive = {J_ACCESS}, author = {Liurui Deng and Shaodan Li and Yuntao Zhu}, doi = {10.1109/ACCESS.2025.3541119}, journal = {IEEE Access}, pages = {41797-41821}, shortjournal = {IEEE Access}, title = {The influence of blockchain on intellectual property pledge financing decisions in 3D printing technology}, volume = {13}, year = {2025}, }
• (2025). Adaptive contrast enhancement with lesion focusing (ACELF). ACCESS, 13, 41785-41796. (www)

  @article{ , abstract = {This study introduces an innovative image processing algorithm, namely Adaptive Contrast Enhancement with Lesion Focusing (ACELF), which is aimed at enhancing the visualization of brain lesions images. Despite the advancements in medical imaging technologies, the early detection and accurate visualization of brain anomalies remain challenging due to limitations in contrast and detail resolution of current imaging techniques. ACELF addresses these challenges by employing advanced processing strategies to improve the clarity and contrast of brain lesions while preserving the overall integrity of the image. This technique combines intensity threshold-based segmentation, a custom histogram matching algorithm for contrast enhancement, and gamma correction for brightness adjustment in non-lesioned areas. Lesion identification is refined through pixel intensity comparison against standard brain tissue norms, while lesion visibility is further enhanced by modulating intensity based on calculated scaling factors. Additionally, entropy scaling is applied to local regions of the image, ensuring complexity is appropriately managed to optimize contrast. The algorithm demonstrated superior performance in Entropy, Enhancement Measure Estimation (EME), and Contrast Improvement Index (CII), validating its effectiveness in enhancing medical image quality and image contrast.}, archive = {J_ACCESS}, author = {Jinger Ang and Kok Swee Sim and Shing Chiang Tan and Chee Peng Lim}, doi = {10.1109/ACCESS.2025.3546913}, journal = {IEEE Access}, pages = {41785-41796}, shortjournal = {IEEE Access}, title = {Adaptive contrast enhancement with lesion focusing (ACELF)}, volume = {13}, year = {2025}, }
• (2025). Investigating the potential of an ANFIS-based maximum power point tracking controller for solar photovoltaic systems. ACCESS, 13, 41768-41784. (www)

  @article{ , abstract = {The efficacy of Maximum Power Point Trackers (MPPT) in enhancing the efficiency of photovoltaic (PV) modules has been well documented. They facilitate the maximization of power output from the modules by ensuring impedance matching between the PV modules and the connected load. A range of MPPT techniques has been developed, varying in terms of complexity, tracking speed, cost, accuracy, sensor requirements, and hardware demands. The present paper is concerned with the design and modelling of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller. The proposed system is designed to maximize the efficiency of PV modules under varying environmental conditions and offers a dynamic and adaptive control strategy to accommodate changes in temperature, irradiance, and load. The implementation of this strategy entails the definition of fuzzy rules, which are delineated in accordance with the operating conditions of the PV modules. These rules provide a logical structure that enables the system to make accurate and rapid decisions by using the input parameters of temperature, irradiance, and load to reach the maximum power point. The employment of fuzzy logic is instrumental in accommodating the intricate and non-linear characteristics inherent in the system, thereby facilitating the dynamic attainment of the optimal operating point in accordance with the prevailing environmental conditions. The efficacy of the proposed ANFIS-based MPPT controller is evaluated through extensive simulations conducted in the MATLAB/SIMULINK environment. The simulation results demonstrate the effectiveness of the system in operating under various load, temperature, and irradiance conditions. In addition, comparisons with the conventional Incremental Conductance (INC) MPPT technique indicate that the ANFIS-based controller provides a more stable and faster dynamic response, reducing oscillations around the maximum power point (MPP).}, archive = {J_ACCESS}, author = {Yavuz Türkay and Ahmet Gürkan Yüksek}, doi = {10.1109/ACCESS.2025.3547954}, journal = {IEEE Access}, pages = {41768-41784}, shortjournal = {IEEE Access}, title = {Investigating the potential of an ANFIS-based maximum power point tracking controller for solar photovoltaic systems}, volume = {13}, year = {2025}, }
• (2025). Output current ripple in electric vehicles with asymmetrical dual three-phase arrangement applying interleaved carrier-based PWM techniques. ACCESS, 13, 41743-41767. (www)

  @article{ , abstract = {Asymmetrical dual three-phase (ADTP) multiphase arrangements are becoming increasingly important for powertrain applications within the automotive sector. However, further research and development should be carried out in order to improve these arrangements. Lately, there has been a growing interest in exploring enhancements for the pulse-width modulation (PWM) techniques aimed at reducing the current stress of the propulsion system’s DC-Link. In electric vehicle (EV) applications, the voltage-source inverter (VSI) must deliver high power density while meeting stringent reliability requirements. A critical component within the VSI is the DC-Link capacitor, which occupies up to 40% of the inverter volume and contributes to approximately 30% of power converter failures. Reducing the DC-Link ripple current can significantly enhance reliability and reduce system size. However, improvements on the input side often come at the expense of the output side, where excessive current ripple increases Joule losses and induces torque ripple, leading to mechanical vibrations and reduced motor efficiency. Addressing this trade-off is particularly important in ADTP systems, as these configurations are increasingly adopted for high-density, fault-tolerant propulsion systems. In this context, this work evaluates the most relevant carrier-based (CB) PWM techniques regarding the output current ripple quality of an ADTP. For this purpose, a novel method is proposed to calculate the harmonic distortion factor (HDF) of an ADTP arrangement. It is based on the double Fourier integral formalism, instead of the conventional time-domain analysis. The method developed in this work provides accurate harmonic profiles for a thorough comparison among CB-PWMs and enhances the theoretical understanding of how harmonic cancellation works. This innovative approach not only makes the results insensitive to simulation time resolution but also effectively mitigates errors and offsets that arise over the time-domain integration of the voltage measurement. This type of analysis is particularly meaningful when examining the interleaved variations of these CB-PWMs, as they focus on harmonic reduction or cancellation. The experimental validation of this method demonstrates that the results are consistent, providing confidence in the theoretical analysis. In this sense, this work determines how the interleaving schemes, typically used to benefit the DC-Link capacitor, affect the HDF of the ADTP arrangements. Furthermore, this study points out which PWM technique is most promising for ADTP-based propulsion systems, for both improving the performance of the powertrain input (DC side) and its output (AC side, i.e. VSI + electric machine), thus helping engineers and researchers in this field.}, archive = {J_ACCESS}, author = {Ander Demarcos and Endika Robles and Unai Ugalde and Ibon Elosegui and Jon Andreu}, doi = {10.1109/ACCESS.2025.3545147}, journal = {IEEE Access}, pages = {41743-41767}, shortjournal = {IEEE Access}, title = {Output current ripple in electric vehicles with asymmetrical dual three-phase arrangement applying interleaved carrier-based PWM techniques}, volume = {13}, year = {2025}, }
• (2025). Optimizing urban intersection management in mixed traffic using deep reinforcement learning and genetic algorithms. ACCESS, 13, 41723-41742. (www)

  @article{ , abstract = {This study aims to optimize lane configurations at urban intersections within mixed traffic environments, integrating both Connected Autonomous Vehicles (CAVs) and Human-driven Vehicles (HVs). By employing genetic algorithm and deep reinforcement learning (DRL), the research seeks to dynamically adjust lane configurations to improve intersection efficiency under varying traffic conditions and CAV penetration rates. This study utilizes a genetic algorithm to solve the dynamic lane configuration problem in a mixed traffic environment and concludes that higher traffic volumes require dedicated CAV lanes to significantly reduce vehicle delays. Additionally, the effectiveness of dynamic lane configuration is further validated through the DRL model, which shows significant improvements in average speed and waiting time as the CAV penetration rate increases. The study highlights the importance of adaptive strategies for managing complex urban traffic, providing valuable insights for future urban traffic management and planning. The integration of genetic algorithm with DRL underscores the potential for developing flexible and efficient solutions to optimize urban intersection management in mixed traffic environments. These findings suggest that adaptive lane configuration strategies can support the broader adoption of autonomous driving technologies and contribute to the development of smarter and more efficient urban transportation systems.}, archive = {J_ACCESS}, author = {Jiajun Shen and Yu Wang and Haoyu Wang and Guanyu Fu and Zhipeng Zhou and Jingxin Dong}, doi = {10.1109/ACCESS.2025.3547849}, journal = {IEEE Access}, pages = {41723-41742}, shortjournal = {IEEE Access}, title = {Optimizing urban intersection management in mixed traffic using deep reinforcement learning and genetic algorithms}, volume = {13}, year = {2025}, }
• (2025). Seasonal analysis of polarimetric responses utilizing ku-band GB-SAR time series data. ACCESS, 13, 41708-41722. (www)

  @article{ , abstract = {This study evaluated the seasonal dynamics of $H/\bar {\alpha }$ target decomposition parameters derived from the spatially and temporally averaged coherence matrix of fully polarimetric Ku-band GB-SAR time series data over a one-year analysis period across three distinct land cover types in a residential area in Hokkaido, Japan. Overall, mean values of $H_{spatial}/\bar {\alpha }_{spatial}$ and $H_{temp}/\bar {\alpha }_{temp} $ parameters were identified across different $H/\bar {\alpha }~2$ D space zones for each land cover type, indicating that they exhibited a specific characteristic associated with their scattering mechanism. Subsequently, seasonal changes were observed to shift their seasonal mean values, with the transitions occurring within the same $H/\bar {\alpha }~2$ D space zone for the built-up land cover type. As for tree land cover type, zonal alterations were more prevalent in $H_{spatial}/\bar {\alpha }_{spatial}$ than in $H_{temp}/\bar {\alpha }_{temp}$ for each seasonal transition. However, only the $H_{spatial}/\bar {\alpha }_{spatial}$ seasonal mean values for the shrub land cover type demonstrated zone changes. Afterward, correlations between precipitation and $H_{spatial}/\bar {\alpha }_{spatial}$ parameters were more pronounced in vegetated-related land cover types than in man-made ones. Meanwhile, snowfall had stronger relationships with built-up and tree areas, whereas shrub areas showed weaker correlations. Concurrently, statistically significant positive correlations were identified between $H_{temp}$ and precipitation across all land cover types. Nevertheless, the $\bar {\alpha }_{temp}$ yielded mixed results. For snowfall, built-up areas demonstrated more pronounced responses than trees and shrubs. Finally, outputs of this work could enhance knowledge of the underexplored yet compelling implementation of Ku-band GB-SAR time series data for continuous land cover monitoring activities.}, archive = {J_ACCESS}, author = {Dandy Aditya Novresiandi and Yuta Izumi and Motoyuki Sato and Fathin Nurzaman and Koki Urano}, doi = {10.1109/ACCESS.2025.3548139}, journal = {IEEE Access}, pages = {41708-41722}, shortjournal = {IEEE Access}, title = {Seasonal analysis of polarimetric responses utilizing ku-band GB-SAR time series data}, volume = {13}, year = {2025}, }
• (2025). Enhancing breast cancer diagnosis with bidirectional recurrent neural networks: A novel approach for histopathological image multi-classification. ACCESS, 13, 41682-41707. (www)

  @article{ , abstract = {In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. These challenges highlighted the necessity for more powerful and flexible models. In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). The BRNN structure consists of four unique elements: the backbone branch for transfer learning, the Gated Recurrent Unit (GRU), the residual collaborative branch, and the feature fusion module. Specifically, the transfer learning aspect exploits a Convolutional Neural Network (CNN) based on the ResNet50 architecture to draw image features from the ImageNet dataset, enhanced with an attention mechanism for improved feature representation. Additionally, the residual branch identifies specific pathological features using Floating Point Operations (FLOP). This cooperative method ensures thorough extraction of breast cancer classification features. The BRNN model was tested on the BreaKHis breast cancer dataset, comprising 7,909 microscopic images across 8 various classes from 82 patients. Our model achieved an average classification accuracy of 97.25 percent, exceeding current leading techniques. The BRNN model, refined using the Adagrad optimization algorithm, efficiently integrates the learned features from both branches. This tool provides oncologists with a substantial enhancement in diagnosing and planning treatment for breast cancer.}, archive = {J_ACCESS}, author = {Rajendra Babu Chikkala and Chinta Anuradha and Patnala S. R. Chandra Murty and S. Rajeswari and N. Rajeswaran and M. Murugappan and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3542989}, journal = {IEEE Access}, pages = {41682-41707}, shortjournal = {IEEE Access}, title = {Enhancing breast cancer diagnosis with bidirectional recurrent neural networks: A novel approach for histopathological image multi-classification}, volume = {13}, year = {2025}, }
• (2025). A few-shot collapsed transmission tower detection method combining large and small models in remote sensing image. ACCESS, 13, 41670-41681. (www)

  @article{ , abstract = {Detecting collapsed transmission line towers in remote sensing images is of great importance for post-disaster reconstruction of transmission lines. However, the scarcity of such fault samples makes it challenging for mainstream deep learning-based object detection algorithms to perform effectively, leading to unsatisfactory detection results. Recently, large language model (LLM) and multi-modal large models (MMLM) have demonstrated strong zero-shot and few-shot detection capabilities. However, due to their reliance on text-based understanding of image content, they struggle with fine-grained image details, making it difficult to accurately detect the targets and locations of collapsed towers in remote sensing images. This paper proposes a hybrid method combining large vision model and our improved small model for few-shot detection of collapsed transmission towers. We firstly improve YOLO V10 method by replace its large kernel structure with our new structure called IKL. And then we propose a collapsed tower detection method which leverages the strong zero-shot and few-shot learning capabilities of MMLM, while utilizing the fine-grained localization and detailed description capabilities of our improved small model. This synergy overcomes the limitations of each model type while retaining their advantages. Experimental results demonstrate that the proposed method effectively distinguishes between collapsed and intact towers and achieves superior few-shot learning performance compared to using MMLM or small models alone, significantly improving the accuracy of collapse detection.}, archive = {J_ACCESS}, author = {Cheng Huanyu and Gu Yingcheng and Xi Mengting and Zhong Qiuyuan and Wei Liu}, doi = {10.1109/ACCESS.2025.3547831}, journal = {IEEE Access}, pages = {41670-41681}, shortjournal = {IEEE Access}, title = {A few-shot collapsed transmission tower detection method combining large and small models in remote sensing image}, volume = {13}, year = {2025}, }
• (2025). Synchrony vision: Capturing body motion synchrony through phase difference using the kinect. ACCESS, 13, 41658-41669. (www)

  @article{ , abstract = {This study introduces a Kinect-based system for real-time detection of body motion synchrony, addressing limitations of traditional methods such as high costs, intrusiveness, subjectivity, and lack of real-time analysis. Using a phase difference detection algorithm, the system analyzes synchrony offering a comprehensive framework for quantifying interpersonal coordination. The system captures acceleration data from up to six individuals, calculates phase differences, and provides immediate feedback. The original algorithm was modified to address challenges such as gravitational acceleration, inverted axes, and peak detection thresholds, improving the accuracy of Kinect data analysis. Experimental results demonstrate the system’s overall detection accuracy of 89.2% under controlled conditions. Additionally, a comparison with an accelerometer-based method revealed a strong correlation (r = 0.73, p = 0.002), indicating alignment in detecting synchrony. While the Kinect-based system offers advantages in scalability and usability, it exhibits limitations in detecting high-speed movements. To enhance its accuracy, potential improvements include algorithmic refinements, hardware upgrades, and AI-driven models to adaptively refine motion detection.}, archive = {J_ACCESS}, author = {Jinhwan Kwon}, doi = {10.1109/ACCESS.2025.3548142}, journal = {IEEE Access}, pages = {41658-41669}, shortjournal = {IEEE Access}, title = {Synchrony vision: Capturing body motion synchrony through phase difference using the kinect}, volume = {13}, year = {2025}, }
• (2025). HorGait: Advancing gait recognition with efficient high-order spatial interactions in LiDAR point clouds. ACCESS, 13, 41644-41657. (www)

  @article{ , abstract = {Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. Due to the limitation in spatial perception capability inherent in 2D gait representations, LiDAR can directly capture 3D gait features and represent them as point clouds, reducing environmental and lighting interference in recognition while significantly advancing privacy protection. For complex 3D representations, shallow networks fail to achieve accurate recognition, making vision Transformers the foremost prevalent method. However, the prevalence of dumb patches has limited the widespread use of Transformer architecture in gait recognition. This paper proposes a method named HorGait, which utilizes a hybrid model with a Transformer architecture for gait recognition on the planar projection of 3D point clouds from LiDAR. Specifically, it employs a hybrid model structure called LHM Block to achieve input adaptation, long-range, and high-order spatial interaction of the Transformer architecture. Additionally, it uses large convolutional kernel CNNs to segment the input representation, replacing attention windows to reduce dumb patches. Extensive experiments demonstrated that HorGait achieved a Rank-1 accuracy of 82.54% on the SUSTech1K dataset, surpassing the state-of-the-art Transformer architecture method by 8.33%. This confirms the hybrid model’s ability to execute the complete Transformer process and excel in point cloud planar projection. The outstanding performance of HorGait offers new insights for the future application of the Transformer architecture in gait recognition.}, archive = {J_ACCESS}, author = {Jiaxing Hao and Yanxi Wang and Zhigang Chang and Hongmin Gao and Zihao Cheng and Chen Wu and Xin Zhao and Peiye Fang and Rachmat Muwardi}, doi = {10.1109/ACCESS.2025.3547759}, journal = {IEEE Access}, pages = {41644-41657}, shortjournal = {IEEE Access}, title = {HorGait: Advancing gait recognition with efficient high-order spatial interactions in LiDAR point clouds}, volume = {13}, year = {2025}, }
• (2025). Energy routing protocols for energy internet: A review on multi-agent systems, metaheuristics, and artificial intelligence approaches. ACCESS, 13, 41625-41643. (www)

  @article{ , abstract = {The Energy Internet (EI) is transforming power networks by integrating Smart Grids (SGs), Distributed Energy Sources (DESs), and advanced communication and data technologies. This transformation increases complexity, as energy transmission evolves into a multi-source, multi-path, and multi-load system, with Peer-to-Peer (P2P) energy trading markets and Energy Routers as central drivers. As power networks grow and become more decentralized, the need for efficient and adaptive power routing protocols has become crucial to ensure their reliable and scalable management. This review focuses on energy routing strategies using multi-Agent architectures, Artificial Intelligence, and Metaheuristic optimization techniques. These approaches are well-suited to support the transformation of power networks into more distributed, dynamic, and complex systems. Spanning research from 2018 to 2024, this paper consolidates diverse studies, filling a critical gap by providing a comprehensive overview of power routing solutions for the evolving EI. It highlights key methodologies, limitations, and future research directions, offering a valuable reference for researchers.}, archive = {J_ACCESS}, author = {Amani Fawaz and Imad Mougharbel and Kamal Al-Haddad and Hadi Y. Kanaan}, doi = {10.1109/ACCESS.2025.3546620}, journal = {IEEE Access}, pages = {41625-41643}, shortjournal = {IEEE Access}, title = {Energy routing protocols for energy internet: A review on multi-agent systems, metaheuristics, and artificial intelligence approaches}, volume = {13}, year = {2025}, }
• (2025). Assessing the impact of supplier group stability on public procurement performance in slovakia: A causal forest analysis of collusion signals. ACCESS, 13, 41607-41624. (www)

  @article{ , abstract = {Public procurement plays a crucial role in economic systems, accounting for approximately 15% of global GDP. While electronic reverse auctions (eRAs) enhance transparency and efficiency, repeated supplier interactions can lead to reduced competition and potential collusion. This study investigates the relationship between supplier group stability in historical savings and its impact on procurement outcomes, leveraging an extensive dataset of 30,349 public contracts from Slovakia’s national e-procurement platform, EKS. Using advanced causal machine learning methods, specifically causal forests, the study estimates heterogeneous treatment effects to uncover the nuances of supplier behavior. Results indicate that contracts involving supplier portfolios with stable tender performance experience, on average, 71% lower savings compared to those with volatile outcomes. However, the magnitude of this negative impact varies significantly across supplier group dynamics, with some situations exhibiting noticeable collusion signals. By estimating individual treatment effects and aggregating them at the supplier portfolio level, the model identifies groups showing potential competition suppression. The study’s findings highlight the presence of collusion signals even in highly transparent procurement systems, emphasizing the importance of targeted policy interventions.}, archive = {J_ACCESS}, author = {Gabriel Demeter and Radoslav Delina and Jaroslav Urminsky and Marketa Zajarosova}, doi = {10.1109/ACCESS.2025.3547688}, journal = {IEEE Access}, pages = {41607-41624}, shortjournal = {IEEE Access}, title = {Assessing the impact of supplier group stability on public procurement performance in slovakia: A causal forest analysis of collusion signals}, volume = {13}, year = {2025}, }
• (2025). A novel high-sensitivity time-domain fault classifier applied to inverter-based resource interconnection lines. ACCESS, 13, 41590-41606. (www)

  @article{ , abstract = {This paper presents a performance review of existing fault classifiers when applied to Inverter-Based Resource (IBR) interconnection lines and proposes a new high-sensitivity time-domain fault classification methodology. The proposed method is based on self-adjusting thresholds, and it is investigated regarding its performance when applied to IBR or conventional generator terminals, being validated for different grid short-circuit levels, various IBR topologies/controls, and considering several fault types, inception angles, resistances, and locations. A typical IBR interconnection system topology is modeled using the PSCAD software for such studies. Comparisons with the main state-of-the-art phase-selection and fault classification methods highlight the superiority of the proposed one that, besides overcoming the challenges presented by IBRs for this task, provides shorter operating times by not relying on phasor estimation techniques.}, archive = {J_ACCESS}, author = {Moisés J. B. B. Davi and Mario Oleskovicz and Felipe V. Lopes}, doi = {10.1109/ACCESS.2025.3548539}, journal = {IEEE Access}, pages = {41590-41606}, shortjournal = {IEEE Access}, title = {A novel high-sensitivity time-domain fault classifier applied to inverter-based resource interconnection lines}, volume = {13}, year = {2025}, }
• (2025). A mobile AR computer-aided diagnosis: 6 DoF brain tumor pose estimator using a fine-tuned EfficientPose-based model. ACCESS, 13, 41571-41589. (www)

  @article{ , abstract = {Augmented reality (AR) technology is rapidly advancing, enabling interactive 3D virtual environments and fostering human interaction and participation. AR has made significant strides in medicine, particularly in medical imaging, leading to improved diagnostic capabilities. To contribute to this transformative landscape, we have developed an accessible and cost-effective AR application that can be used with affordable smartphones. Our application offers automatic brain tumour segmentation, 6 degrees of freedom (6 DoF) phantom head pose estimation, AR visualisation, and interaction, eliminating the need for expensive head-mounted devices (HMD). This paper introduces a novel approach that utilises transfer learning and fine-tuning techniques to train a deep learning model capable of accurately estimating the 6 DoF object pose using RGB images alone. We also present a brain tumour segmentation method based on a specific technique of Gradient Vector Flow (GVF)-based active contour models and demonstrate 3D reconstruction and printing of a phantom head. Furthermore, we propose a comprehensive mobile AR platform, ARBrain, specifically designed for localisation and visualisation of brain tumours. This platform incorporates the deployment of the 6 DoF pose estimator into the AR mobile platform, enabling 3D AR visualisation of the brain and tumour, as well as AR interaction through voice and hand-tactile gesture commands. To evaluate the effectiveness of our approach, we employ a layering procedure and compare it with other recent virtual overlay techniques. Through automatic tumour segmentation, precise pose estimation, and advanced AR interaction capabilities, our ARBrain platform offers an accessible and cost-effective solution for brain tumour localisation and visualisation, empowering medical professionals to make accurate diagnoses and treatment plans.}, archive = {J_ACCESS}, author = {Kahina Amara and Mohamed Amine Guerroudji and Oussama Kerdjidj and Nadia Zenati and Shadi Atalla and Wathiq Mansoor and Naeem Ramzan}, doi = {10.1109/ACCESS.2025.3547951}, journal = {IEEE Access}, pages = {41571-41589}, shortjournal = {IEEE Access}, title = {A mobile AR computer-aided diagnosis: 6 DoF brain tumor pose estimator using a fine-tuned EfficientPose-based model}, volume = {13}, year = {2025}, }
• (2025). Prediction of ball position using CNN methods with zed camera on goalkeeper robot application. ACCESS, 13, 41559-41570. (www)

  @article{ , abstract = {EEPIS Robot Soccer On Wheeled (ERSOW) is a wheeled soccer robot team competing in the Indonesian Wheeled Soccer Robot Contest. The ERSOW goalkeeper robot currently faces a challenge in responding to incoming balls due to delays in image processing, leading to slow movement responses. This delay can be mitigated by implementing a prediction system. By predicting the ball’s trajectory, the robot can make timely movements to block shots effectively. This research designs a prediction system using the ZED stereo camera to enhance the goalkeeper robot’s responsiveness in preventing goals. The research identifies the ball’s position during airborne and ground states using the ZED camera. RGB-D readings are processed with Deep Learning methods to train on ball data and extract 3D object information from the dataset. The ball detection system is tested in three scenarios: right, center, and left positions relative to the robot. Accuracy evaluation is conducted three times for ball coordinates on the X, Y, and Z axes, resulting in X-, Y-, and Z-axis accuracy values of 90%, 80.77%, and 100%, respectively, with a $\le 5$ cm error tolerance. These results allow accurate determination of the ball’s X, Y, and Z positions. The ball position data is then trained using Convolutional Neural Networks (CNN) to predict the ball’s arrival position for the goalkeeper robot. Evaluation results show an overall accuracy of 70%, with an F1-score of 76% for the left position, 39% for the center position, and 84% for the right position.}, archive = {J_ACCESS}, author = {Setiawardhana and Iwan Kurnianto Wibowo and Nizhar Achmad Husein Bernardt}, doi = {10.1109/ACCESS.2025.3546346}, journal = {IEEE Access}, pages = {41559-41570}, shortjournal = {IEEE Access}, title = {Prediction of ball position using CNN methods with zed camera on goalkeeper robot application}, volume = {13}, year = {2025}, }
• (2025). Defect detection in industrial soldering processes using machine learning: A critical literature review. ACCESS, 13, 41533-41558. (www)

  @article{ , abstract = {As electrical devices take on more life-critical roles, such as in autonomous driving, ensuring the quality of solder joints during production becomes increasingly important. Recently, there has been a growing interest in using machine learning techniques for this purpose. However, current research lacks a comprehensive overview that categorizes and analyzes relevant studies based on their specific intervention points within the production process. This literature review aims to examine and evaluate research coverage along three dimensions: intervention points in the process, non-destructive testing methods, and machine learning techniques employed. For this review, 112 conference papers and journal articles published since 2010 were selected from three databases using the PRISMA methodology. These publications were classified into the three dimensions previously mentioned, summarized, and analyzed. Furthermore, the literature core is critically evaluated to identify research gaps and limitations. The analysis shows that most studies focus on solder joint control, with few addressing intervention points in solder paste and component placement. Visual imaging and neural networks are the dominant techniques for non-destructive testing and machine learning, respectively. Despite a variety of literature that uses high-performance neural networks, meeting industrial detection standards often requires tolerating high false alarm rates. The findings contribute to structuring existing research and identifying research needs, particularly in validating these systems and integrating data from various testing methods and intervention points.}, archive = {J_ACCESS}, author = {Luca Eisentraut and Johanna Hosch and Maksym Roytenberg and Amélie Benecke and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3547847}, journal = {IEEE Access}, pages = {41533-41558}, shortjournal = {IEEE Access}, title = {Defect detection in industrial soldering processes using machine learning: A critical literature review}, volume = {13}, year = {2025}, }
• (2025). Personality trait prediction from facial sketch leveraged by expression muscles. ACCESS, 13, 41524-41532. (www)

  @article{ , abstract = {This work explores whether facial sketches can be used to predict personality traits, representing, to our knowledge, the first systematic investigation of this approach in the literature. Unlike traditional RGB facial images that capture detailed features, sketch-based images emphasize the structure and movement of facial expression muscles, thereby providing a novel perspective for personality prediction. Our approach introduces three key innovations: expression muscle-guided feature weighting to improve prediction accuracy by prioritizing biologically relevant patterns; data augmentation through intermediate sketches generated via the 25-Step Sketching Approach to mitigate data scarcity; and comprehensive validation on a dataset of 12,320 individuals. Experimental results demonstrate that our sketch-based model achieves comparable accuracy to image-based models for specific personality traits, while ablation studies confirm the complementary benefits of both expression muscle weighting and sketch augmentation strategies. These findings, coupled with the newly constructed sketch datasets, offer valuable multimodal resources and methodological insights for researchers in affective computing and behavioral science.}, archive = {J_ACCESS}, author = {Lifen Weng and Jiangbin Guo and Qibing Zhu}, doi = {10.1109/ACCESS.2025.3547959}, journal = {IEEE Access}, pages = {41524-41532}, shortjournal = {IEEE Access}, title = {Personality trait prediction from facial sketch leveraged by expression muscles}, volume = {13}, year = {2025}, }
• (2025). PSG-pair: A psychological snapshot guided model for improved query-response pairing in psychological wellness communities for university students. ACCESS, 13, 41505-41523. (www)

  @article{ , abstract = {Psychological wellness, especially among university students, has become an increasingly significant global health issue, with millions affected worldwide. In response to the growing demand for accessible psychological support, we propose a novel model, Psychological Snapshot Guided Pairing (PSG-Pair), designed to enhance query-response pairing in Community Question Answering for Psychological Wellness (CQA-PW). Unlike traditional methods that focus solely on conceptual-level matching, PSG-Pair integrates role-based psychological snapshots derived from the historical posts of help-seekers and supporters. The model operates in two phases: the initial screening phase, which utilizes a BERT-based retrieval model to filter relevant supportive posts, and the pairing phase, which incorporates psychological snapshots using a stacked attention mechanism to refine conceptual pairings based on the psychological characteristics of users. Extensive experiments conducted on the CLPsych 2022 Shared Task dataset demonstrate that PSG-Pair significantly outperforms traditional single-phase models, enhancing both precision and recall in pairing processes. The inclusion of psychological snapshots allows the model to better handle the complexities of psychological wellness scenarios, especially for university students, thereby improving the overall effectiveness of automated psychological support systems. However, this study has several limitations. Firstly, the dataset used for experiments, although rich, still suffers from data imbalance and noise due to the high proportion of irrelevant negative samples, which could potentially impact the model’s performance. Secondly, while the approach demonstrates promising results in the context of psychological wellness, the generalizability of the model to other domains or applications remains uncertain. Further exploration into the adaptability of PSG-Pair to diverse scenarios is required. Additionally, while the current evaluation metrics adequately reflect the retrieval and pairing capabilities, there is a need for the development of more tailored evaluation systems to assess models within the unique context of psychological wellness support for university students. Future work should also investigate how to mitigate biases in user-generated content, as the quality and authenticity of answers in non-factual Q&A platforms can vary significantly, potentially affecting the accuracy of the pairing.}, archive = {J_ACCESS}, author = {Yongsheng Ma and Xianhui Sun and Aiqun Ma}, doi = {10.1109/ACCESS.2025.3545902}, journal = {IEEE Access}, pages = {41505-41523}, shortjournal = {IEEE Access}, title = {PSG-pair: A psychological snapshot guided model for improved query-response pairing in psychological wellness communities for university students}, volume = {13}, year = {2025}, }
• (2025). Pseudo-labeling domain adaptation using multi-model learning. ACCESS, 13, 41486-41504. (www)

  @article{ , abstract = {With the constant growth of state-of-the-art models, obtaining sufficient labeled data to train these models for specific domains has become increasingly costly. Domain adaptation methods offer a potential solution to enhance model performance in new, unseen domains while minimizing the need for manual annotation of target domain. Despite recent advances in using pseudo-labeling for domain adaptation, significant challenges remain in maximizing the effectiveness of pseudo-labeling, particularly when aiming to create informative and interpretable representations from pseudo-labels. To address these challenges, we introduce the method Pseudo-labeling Domain Adaptation (PDA), which leverages pseudo-labels generated by multiple models to create a robust cross-domain representation. Additionally, to further mitigate the domain-shift problem, we propose a novel method called UMAP Domain Adaptation (UMAP DA), a UMAP-based technique that allows for connections only between nodes from different domains. We use these representations to construct a heterogeneous bipartite graph, where a neural network is employed for final classification. Experiments on six different datasets show an average F1-score improvement of 8 points, measuring the harmonic mean of precision and recall, compared to existing methods in the literature. The proposed method enhances both performance and interpretability, offering a new direction for cross-domain learning with pseudo-labels.}, archive = {J_ACCESS}, author = {Victor Akihito Kamada Tomita and Ricardo Marcondes Marcacini}, doi = {10.1109/ACCESS.2025.3547813}, journal = {IEEE Access}, pages = {41486-41504}, shortjournal = {IEEE Access}, title = {Pseudo-labeling domain adaptation using multi-model learning}, volume = {13}, year = {2025}, }
• (2025). A reduced DC-link voltage-based NSC topology for an on-grid PEMFC microgrid capable of handling unbalanced load conditions and grid disturbances. ACCESS, 13, 41471-41485. (www)

  @article{ , abstract = {This article presents a nine-switch converter-based on-grid proton exchange membrane fuel cell microgrid system and its control with a reduced DC-link voltage requirement for the NSC. With the proposed topology, the do link voltage requirement of the NSC could be reduced by nearly 33%. Apart from the DC link voltage reduction, this work proposes a minimum voltage injection-based control scheme, which helps achieve the claimed voltage reduction. This work has also proposed a self-tuned filter-based modified indirect current control scheme, which ensures a balanced grid current during unbalanced load conditions. The proposed system is evaluated under various grid and load-side disturbances. The performance of the proposed system, along with its associated control structures, is studied under nearly 28% grid voltage sag and 10% swell. The control structure delivers a constant load voltage and a steady grid power flow during grid voltage sag and swell. The proposed system also performed efficiently during load and generation side disturbances Such as load change, change in active power, and reactive power references. During unbalanced load conditions, the proposed modified indirect current control scheme effectively compensated for the load imbalance and resulted in balanced grid currents. The whole system is mathematically modeled in the Matlab/Simulink platform, and the system’s performance is validated using an OPAL-RT 4510 real-time platform.}, archive = {J_ACCESS}, author = {Swagat Pati and Amar Bijay Nanda and Kalpana Bijayeeni Samal and Futa Osumanu and Abhijeet Choudhury}, doi = {10.1109/ACCESS.2025.3546975}, journal = {IEEE Access}, pages = {41471-41485}, shortjournal = {IEEE Access}, title = {A reduced DC-link voltage-based NSC topology for an on-grid PEMFC microgrid capable of handling unbalanced load conditions and grid disturbances}, volume = {13}, year = {2025}, }
• (2025). Regression model employing spiking neural network for bio-signal analysis with hardware integration. ACCESS, 13, 41456-41470. (www)

  @article{ , abstract = {Spiking neural networks, known for mimicking the brain’s functionality resulting in efficient algorithms, are gaining attention across various problems and applications. However, their potential in regression tasks remains relatively unexplored. This study focuses on leveraging the spiking neural architecture in conjunction with Fourier analysis and support vector regression to estimate heart rates from electrocardiogram signal. We evaluated the regression errors of our model using three distinct elctrocardiogram datasets and assessed its performance on neuromorphic hardware by embedding spike-based layers. Our findings reveal that, compared to the conventional deep learning models, the proposed spiking neural system achieves a computational efficiency improvement while maintaining the competitive regression accuracy. Finally, we discuss the regression performance, energy efficiency, biological plausibility, and potential applications of the proposed neuromorphic system.}, archive = {J_ACCESS}, author = {Choongseop Lee and Geunbo Yang and Jaewoo Baek and Yuntae Park and Mingyu Cheon and Jongkil Park and Cheolsoo Park}, doi = {10.1109/ACCESS.2025.3544379}, journal = {IEEE Access}, pages = {41456-41470}, shortjournal = {IEEE Access}, title = {Regression model employing spiking neural network for bio-signal analysis with hardware integration}, volume = {13}, year = {2025}, }
• (2025). Simple yet powerful: Machine learning-based IoT intrusion system with smart preprocessing and feature generation rivals deep learning. ACCESS, 13, 41435-41455. (www)

  @article{ , abstract = {With the rapid advancements in deep learning, IoT intrusion detection systems have increasingly adopted deep learning models as the state-of-the-art solution due to their ability to handle complex data patterns. However, these solutions introduce the risk of overengineering, in which the complexity of the model outweighs its practical benefits. In contrast, classical machine learning techniques offer a more efficient alternative but are often overlooked due to a lack of focus on data pre-processing, which is critical for achieving optimal performance. Here we propose a classical machine learning system, built around a Random Forest classifier paired with a novel feature extraction algorithm adapted from Explainable Boosted Linear Regression (EBLR). Our workflow emphasizes the importance of well-structured preprocessing pipelines missing data handling, categorical feature encoding, and multicollinearity reduction, paired with classical machine learning models. We evaluated our method on the ToN-IoT dataset, which contains various network traffic data sets and various types of attacks. Experimental results show that our model achieves an area under the curve (AUC) score of 0.99 on both training and test sets with high performance in a variety of attack categories. Finally, we show that our method outperforms existing deep learning models, thus providing a novel and effective solution for intrusion detection in IoT environments. Experimental results show that our model achieves an area under the curve (AUC) score of 0.99 in both the training and test sets. Furthermore, the classifier achieves precision, recall and F1 score values of 0.999, 0.988, and 0.994, respectively, for normal traffic detection, while maintaining strong performance for other attack categories, such as denial of service (precision: 0.985, recall: 0.977, F1 score: 0.981) and scanning (precision: 0.984, recall: 0.992, F1 score: 0.988). Injection and ransomware attack types also demonstrate precision and recall scores above 0.90. These results highlight that, when paired with appropriate preprocessing and feature engineering, classical machine learning models still can provide an effective solution for intrusion detection in IoT environments.}, archive = {J_ACCESS}, author = {Kazim Kivanç Eren and Kerem Küçük and Fatih Özyurt and Omar H. Alhazmi}, doi = {10.1109/ACCESS.2025.3547642}, journal = {IEEE Access}, pages = {41435-41455}, shortjournal = {IEEE Access}, title = {Simple yet powerful: Machine learning-based IoT intrusion system with smart preprocessing and feature generation rivals deep learning}, volume = {13}, year = {2025}, }
• (2025). Water meter reading based on text recognition techniques and deep learning. ACCESS, 13, 41422-41434. (www)

  @article{ , abstract = {In an increasingly technology-driven and automated world, optimizing energy management efficiency is crucial. A key component of this optimization is the precise measurement of water consumption, which plays a vital role in the allocation of resources for both households and businesses. While automated water meters are available, their adoption remains limited, particularly in less developed regions where manual meter reading is still common. This manual process often leads to inaccuracies and inefficiencies. This research aims to automate water meter readings, particularly in challenging and diverse environmental conditions. We propose a structured two-step approach to Automated Meter Reading, involving the identification of the counter region followed by digit segmentation and recognition. Using Convolutional Neural Networks, particularly YOLOv8 for detecting the counter region, we evaluate six CNN-based techniques for digit segmentation and recognition: PP-OCRv3, Structure-Preserving Inner Offset Network, Transformer-based Optical Character Recognition, RobustScanner, Show-Attend-and-Read, and Convolutional Recurrent Neural Network.}, archive = {J_ACCESS}, author = {Bay Nguyen van and Anh Nguyen and Kiet Tran-Trung and Thien Ho Huong and Ha Duong Thi Hong and Hau Nguyen Trung and Vinh Truong Hoang}, doi = {10.1109/ACCESS.2025.3547225}, journal = {IEEE Access}, pages = {41422-41434}, shortjournal = {IEEE Access}, title = {Water meter reading based on text recognition techniques and deep learning}, volume = {13}, year = {2025}, }
• (2025). Fault detection in photovoltaic systems using a machine learning approach. ACCESS, 13, 41406-41421. (www)

  @article{ , abstract = {Research and development of intelligent fault monitoring in photovoltaic systems are crucial for efficient energy generation. In response to the industry’s demand for innovative solutions to enhance energy output and reduce maintenance costs, this study explores machine-learning approaches for the autonomous detection and classification of faults caused by partial shading and dirt accumulation in photovoltaic modules. The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. The research explored data collected from two real photovoltaic systems, each with distinct module characteristics and power ratings. Data were gathered for systems without faults, with faults simulated by partial shading, and faults simulated by dirt accumulation. Crucial information, including voltage, current, ambient temperature, and irradiance, was recorded to assess and classify these kinds of faults. This study presents three main contributions: the implementation and comparison of multiple machine learning models for fault detection, an investigation into the feasibility of identifying these faults using only electrical and environmental data, and an analysis of model performance in a photovoltaic system different from the one used for training. The results indicate that models trained on a specific system achieve high accuracy but face challenges when applied to systems with different characteristics, suggesting that each new photovoltaic system to be monitored should be included in the training phase to enhance classification performance. Noteworthy results were obtained with the Artificial Neural Network model, achieving precision values exceeding 98%.}, archive = {J_ACCESS}, author = {Jossias Zwirtes and Fausto Bastos Líbano and Luís Alvaro de Lima Silva and and Edison Pignaton de Freitas}, doi = {10.1109/ACCESS.2025.3547838}, journal = {IEEE Access}, pages = {41406-41421}, shortjournal = {IEEE Access}, title = {Fault detection in photovoltaic systems using a machine learning approach}, volume = {13}, year = {2025}, }
• (2025). PP-PQB: Privacy-preserving in post-quantum blockchain-based systems: A systematization of knowledge. ACCESS, 13, 41382-41405. (www)

  @article{ , abstract = {Blockchain technology has produced effective solutions and provides security by using cryptographic tools for various applications, attracting attention from the academic community. Therefore, researchers have taken advantage of the features of blockchain technology to increase the security of the ecosystem. Recently, as the existence of quantum computers has been felt, researchers have started to benefit from post-quantum cryptography to increase privacy and security. There has been an increase in data and asset protection in post-quantum blockchain-based solutions. To the best of our knowledge, there is no comprehensive review or taxonomy that provides a complete picture of post-quantum secure structures with privacy-preserving techniques that have the potential to be used in blockchain. This paper aims to close this gap by systematically examining these approaches and revealing the deficiencies in the existing literature and the development potential in these areas. The taxonomy examines the role of blockchain technology in post-quantum cryptography and emphasizes the potential of technologies such as zero-knowledge proof to ensure privacy in post-quantum blockchain-based systems. We also review the existing literature on addressing the performance overhead, interoperability, scalability, and security challenges in implementing post-quantum cryptography in zero-knowledge proof-enabled blockchain architectures that protect against quantum computing threats. The studies are collected from journal papers in widely used academic databases between 2018 and 2024. The studies are subjected to certain elimination criteria, and 13 studies are reviewed in detail. Our approach will facilitate discussions on future research directions by proposing the accessibility of post-quantum cryptography against quantum threats to blockchain systems and solutions to the challenges that arise in the integration phase.}, archive = {J_ACCESS}, author = {Bora Bugra Sezer and Sedat Akleylek and Urfat Nuriyev}, doi = {10.1109/ACCESS.2025.3545943}, journal = {IEEE Access}, pages = {41382-41405}, shortjournal = {IEEE Access}, title = {PP-PQB: Privacy-preserving in post-quantum blockchain-based systems: A systematization of knowledge}, volume = {13}, year = {2025}, }
• (2025). C-HIDE: A steganographic framework for robust data hiding and advanced security using coverless hybrid image encryption with AES and ECC. ACCESS, 13, 41367-41381. (www)

  @article{ , abstract = {Coverless image steganography conceals information without modifying the carrier image, addressing vulnerabilities in traditional methods. However, existing approaches often require transmitting metadata, raising suspicion and security risks. To overcome these limitations, we propose Coverless Hybrid Image Data Encryption (C-HIDE), a robust steganographic method integrating Advanced Encryption Standard (AES) for data confidentiality and Elliptic Curve Cryptography (ECC) for secure key exchange. The system ensures secure transmission without altering cover images, making embedded data harder to detect. C-HIDE eliminates metadata transmission by enabling both sender and receiver to independently generate synchronized coverless image datasets (CIDs) using random seeds. Encrypted secret data is mapped to images whose hash sequences correspond to segments of the message, with Speeded-Up Robust Features (SURF) ensuring reliable image matching. At the receiver’s end, ECC-decrypted AES keys recover the original message while SURF retrieves relevant images. Experimental results demonstrate that C-HIDE achieves an embedding capacity of 574 bits per image, significantly surpassing DCT (256 bits) and DWT (128 bits) techniques. The system maintains 98.5% accuracy under attacks such as noise addition, cropping, and geometric transformations. Furthermore, it enhances security by eliminating metadata transmission, achieving a zero additional information ratio, unlike conventional methods requiring up to 25% extra data. By integrating encryption, minimizing detection, and removing metadata transmission, C-HIDE provides a secure, efficient, and scalable solution for covert communication in real-world applications.}, archive = {J_ACCESS}, author = {Sahar A. El-Rahman and Ahmed E. Mansour and Leila Jamel and Manal Abdullah Alohali and Mohamed Seifeldin and Yasmin Alkady}, doi = {10.1109/ACCESS.2025.3546255}, journal = {IEEE Access}, pages = {41367-41381}, shortjournal = {IEEE Access}, title = {C-HIDE: A steganographic framework for robust data hiding and advanced security using coverless hybrid image encryption with AES and ECC}, volume = {13}, year = {2025}, }
• (2025). Hybrid deep learning for human fall detection: A synergistic approach using YOLOv8 and time-space transformers. ACCESS, 13, 41336-41366. (www)

  @article{ , abstract = {Falls are a major concern, particularly for elderly individuals and vulnerable populations, often leading to severe injuries if not detected promptly. Traditional sensor-based fall detection methods suffer from limitations such as discomfort, maintenance challenges, and susceptibility to false readings. To address these issues, we propose a hybrid deep learning-based system that leverages video-based techniques for accurate and efficient fall detection. Our approach integrates YOLOv8 for real-time human detection with Time-Space Transformers for temporal motion analysis, ensuring robust recognition of fall incidents while minimizing false positives and negatives. The proposed system is evaluated on two benchmark datasets, CUCAFall and DiverseFALL10500, which contain diverse and challenging environmental scenarios. Experimental results demonstrate that our model outperforms existing state-of-the-art methods, achieving a mean Average Precision (mAP) of 0.9955 on CUCAFall and 0.950 on DiverseFALL10500, along with F1-scores exceeding 0.998 for all classes in certain configurations. These results confirm the model’s ability to distinguish fall events from normal activities accurately. Furthermore, the system maintains computational efficiency, making it suitable for real-time deployment in healthcare and smart home environments. By enhancing spatial and temporal awareness, our hybrid approach significantly improves fall detection reliability, ensuring timely intervention and enhancing safety for at-risk individuals.}, archive = {J_ACCESS}, author = {Yousef Sanjalawe and Salam Fraihat and Mosleh Abualhaj and Salam R. Al-E’Mari and Emran Alzubi}, doi = {10.1109/ACCESS.2025.3547914}, journal = {IEEE Access}, pages = {41336-41366}, shortjournal = {IEEE Access}, title = {Hybrid deep learning for human fall detection: A synergistic approach using YOLOv8 and time-space transformers}, volume = {13}, year = {2025}, }
• (2025). Ultra-low-cost radar cross section measurement and validation method using a 3D-printed scale model in a non-anechoic chamber environment. ACCESS, 13, 41324-41335. (www)

  @article{ , abstract = {This paper validates the accuracy of radar cross section (RCS) measurements conducted in an environment that significantly reduces the three main costs associated with RCS measurements: spatial costs, target under test (TUT) production costs, and the costs of constructing and maintaining an anechoic chamber. Measurement results for simple shapes that satisfy far-field conditions, such as metal spheres and metal plates, are presented. Additionally, results are provided for a geometrically complex 1/15 aircraft scale model that does not satisfy far-field conditions. The noise equivalent RCS (NERCS) of the proposed non-anechoic chamber setup is determined by measuring various sizes of metal spheres, and the RCS error over time is evaluated by measuring a metal plate continuously for 12 h in a non-temperature and humidity-controlled environment. The NERCS is found to be -43.2 dBsm. The mean RCS error for the metal plate, measured immediately after background measurement and again after 12 h, is found to be 0.005 dB and 0.220 dB, respectively. For RCS measurements of the 1/15 scale model, an image-based near-field to far-field transformation (NFFFT) algorithm is employed. The NFFFT results, including image filtering, show a mean RCS error of 5.6 dB. This error is 1.9 dB higher than previously reported results obtained in an anechoic chamber environment using an amplifier to enhance the signal-to-noise ratio (SNR). These findings can serve as guidelines for considering measurement results in a highly simplified RCS measurement setup, offering rapid feedback crucial for stealth platform design.}, archive = {J_ACCESS}, author = {Cheongmin Lee and Youngjae Ryu and Yeong-Hoon Noh and Ilyoung Oh and Jungje Ha and Jong-Gwan Yook}, doi = {10.1109/ACCESS.2025.3544629}, journal = {IEEE Access}, pages = {41324-41335}, shortjournal = {IEEE Access}, title = {Ultra-low-cost radar cross section measurement and validation method using a 3D-printed scale model in a non-anechoic chamber environment}, volume = {13}, year = {2025}, }
• (2025). Performance parameters consideration for cellular system upgrades in developing countries. ACCESS, 13, 41309-41323. (www)

  @article{ , abstract = {This paper focuses on the 5G backhaul network and the impacts of backhaul technologies on the Quality of Service (QoS) and user experiences, in particular, end to end delay (E2E) and capacity planning requirements. In particular, the aim is to facilitate the work of providers, developers, and investors when planning to introduce 5G technology to developing countries. This work looks into employing a simulation-based approach to consider bandwidth aspects when designing/ upgrading current/ future cellular systems in developing countries. It presents a scheme to maximize the use of bandwidth considering both capacity and delay aspects and helps to identify major parameters that influence system design for different 5G use cases and scenarios. Simulation proves that the method to determine the required link capacity is by observing the traffic delay and users access statistics as well as by increasing the capacity incrementally by changing the factor for each link in the network, until optimal capacity is achieved. The results indicate that within the “broadband in the crowd” scenario for 5G services and applications, the necessary bandwidth for last-mile network connections can vary depending on the service type. Specifically, bandwidth requirements can be lessened for ultra-low latency services and applications, with even greater reductions possible for those that do not require such low latency. These adjustments are observed when the backbone link is operating at its full capacity. For developing countries, a hybrid topology based on the existing networks is utilized, where financial considerations will play an important role in determining the backhaul network topology with optimization for the specific requirements.}, archive = {J_ACCESS}, author = {Inas Al-Zubaidi and Rajagopal Nilavalan and Hamed H. S. Al-Raweshidy}, doi = {10.1109/ACCESS.2025.3543854}, journal = {IEEE Access}, pages = {41309-41323}, shortjournal = {IEEE Access}, title = {Performance parameters consideration for cellular system upgrades in developing countries}, volume = {13}, year = {2025}, }
• (2025). Altitude stabilization of VTOL system with nonlinear control strategy. ACCESS, 13, 41300-41308. (www)

  @article{ , abstract = {The Vertical Take-Off and Landing (VTOL) system is a highly nonlinear system operating in severe ambient conditions, reflects the attention of the control engineering community. Robust control strategies are necessary to handle various perturbations while maintaining the altitude of the VTOL system. The objective of this research work is to develop and implement a nonlinear control strategy aimed at accurately tracking the pitch of the VTOL system using the Backstepping Control Strategy (BCS) and Super-Twisting Sliding Mode Control (STSMC) law, while effectively compensating for air resistances. Transient and steady-state analysis demonstrates that the pitch of the VTOL system controlled by the BCS, accurately tracks with a steady-state error of less than 5% as compared to STSMC. The controller input denoted dc current provided to the motor of the VTOL body shows minimal fluctuations increasing its reliability and life span based on nonlinear control strategy. The vital significance of this research article is to validate the robustness of BCS under external disturbances and parametric variations. Stability analysis of the BCS is performed using Lyapunov theory, a crucial step in validating the control law for flight dynamical systems prior to AAV takeoff.}, archive = {J_ACCESS}, author = {Muhammad Ilyas and Awais Khan and Zahoor Ahmed and Dong-Won Jung and Rayees Khan}, doi = {10.1109/ACCESS.2025.3544133}, journal = {IEEE Access}, pages = {41300-41308}, shortjournal = {IEEE Access}, title = {Altitude stabilization of VTOL system with nonlinear control strategy}, volume = {13}, year = {2025}, }
• (2025). MPD: A meteorological and pollution dataset: A comprehensive study of machine and deep learning methods for air pollution forecasting. ACCESS, 13, 41282-41299. (www)

  @article{ , abstract = {Air pollution is a significant global issue, being one of the leading causes of chronic diseases affecting the respiratory and neurological systems, and resulting in millions of deaths each year. Additionally, the scarcity of air quality sensors, due to their high cost, limits the availability of accurate data. In this study, we present a dataset that combines air quality and meteorological variables, with data sourced from the historical records of the Community of Madrid. Furthermore, we propose several baseline methods for this dataset. We then validate these baseline methods using another reference dataset, outperforming previous state-of-the-art methods. All the code and data is available in https://github.com/capo-urjc/MPD.git.}, archive = {J_ACCESS}, author = {Alejandra Abalo-García and Sergio Hernández-García and Iván Ramírez and Emanuele Schiavi}, doi = {10.1109/ACCESS.2025.3547038}, journal = {IEEE Access}, pages = {41282-41299}, shortjournal = {IEEE Access}, title = {MPD: A meteorological and pollution dataset: A comprehensive study of machine and deep learning methods for air pollution forecasting}, volume = {13}, year = {2025}, }
• (2025). Exploring PID and FOPID control methods for thrust vectoring in aerospace systems-A beginner’s guide. ACCESS, 13, 41270-41281. (www)

  @article{ , abstract = {This research serves as an extensive resource for undergraduate, postgraduate students and early-career researchers to provide them with detailed insight into the design, modeling, and implementation of control strategies for thrust vectoring rocket systems. The study presents a dynamic model that incorporates real-world complications, including nozzle oscillations, thrust variations, and fluctuating mass moments of inertia, based on a simplified flat-Earth premise. The performance of two control schemes—Proportional-Integral-Derivative (PID) control and Fractional-Order PID (FOPID) control—is assessed and compared using a systematic manner. The tutorial methodically illustrates how FOPID management enhances system stability, adaptability to disturbances, and overall performance, positioning it as a viable alternative for aerospace applications. The study instructs readers on constructing a physical prototype with SolidWorks software and experimentally evaluating the control algorithms. This text offers a fundamental comprehension of robust control approaches, enabling students and researchers with practical abilities to tackle high precision thrust vectoring issues in aerospace systems and space missions.}, archive = {J_ACCESS}, author = {Menhal Abdulmohsen Alkodur and Ali Hussain Almusharraf and Ghulam E. Mustafa Abro and Eman Mahmoud and Ayman M. Abdallah}, doi = {10.1109/ACCESS.2025.3545944}, journal = {IEEE Access}, pages = {41270-41281}, shortjournal = {IEEE Access}, title = {Exploring PID and FOPID control methods for thrust vectoring in aerospace systems-A beginner’s guide}, volume = {13}, year = {2025}, }
• (2025). Text analysis of digital commentary on ice and snow tourism based on artificial intelligence and long short-term memory neural network. ACCESS, 13, 41259-41269. (www)

  @article{ , abstract = {The comments on ice and snow tourism are characterized by high levels of noise, unstructured content, and complex information. However, existing sentiment analysis methods exhibit significant limitations in terms of accuracy and the depth of feature extraction. To address these challenges, this study proposes an intelligent sentiment analysis algorithm based on a multi-model fusion approach: the Improved Dynamic Convolutional and Attention-based Bidirectional Long Short-Term Memory Model (IDCAN-BiLSTM). The aim is to enhance the effectiveness of sentiment analysis for ice and snow tourism reviews. Firstly, the review data is cleaned, denoised, and segmented. High-quality text vector embeddings are then generated using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to capture the deep semantic features of the review text. Subsequently, the IDCAN-BiLSTM model employs a Dynamic Convolutional Neural Network (DCNN) to extract the local features of reviews, thereby increasing sensitivity to specific sentiment words. Following this, a Multi-Head Attention (MHA) mechanism is utilized to focus on key sentiment information within the reviews, effectively addressing the challenges posed by complex and lengthy texts. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) module comprehensively captures the global contextual information in the reviews, improving both the sentiment classification accuracy and the contextual recognition capabilities of the model. Experimental results demonstrate that the IDCAN-BiLSTM model achieves outstanding performance in the sentiment classification of ice and snow tourism reviews, with an accuracy of 92.17% and an F1 score of 0.93. These results significantly surpass those of traditional sentiment analysis methods. In particular, the model shows superior performance in the sentiment classification of long review texts, effectively enhancing the accuracy and granularity of sentiment recognition through dynamic convolution and the self-attention mechanism. Moreover, the model distinguishes sentiment tendencies across different user groups regarding their experiences in ice and snow tourism. This capability provides valuable data support for optimizing services and enabling precision marketing strategies in the ice and snow tourism sector.}, archive = {J_ACCESS}, author = {Qi Zhuang and Zhengjie Chu and Jun Li}, doi = {10.1109/ACCESS.2025.3548125}, journal = {IEEE Access}, pages = {41259-41269}, shortjournal = {IEEE Access}, title = {Text analysis of digital commentary on ice and snow tourism based on artificial intelligence and long short-term memory neural network}, volume = {13}, year = {2025}, }
• (2025). HFD-YOLO: Improved YOLO network using efficient attention modules for real-time one-stage human fall detection. ACCESS, 13, 41248-41258. (www)

  @article{ , abstract = {Detecting human falls has become a crucial technology for intelligent surveillance systems, particularly in smart factories. A one-stage fall detection model based on the YOLO network provides a solution for resource-limited environments, offering real-time detection at high speeds. This work proposes HFD-YOLO, an improved version of YOLOv8n, for one-stage human fall detection. It introduces a Sequential Efficient Attention Module (SEAM) and Residual Bottleneck Self-Attention (RBSA) for feature enhancement, leading to improved detection precision. The SEAM includes efficient channel and spatial attention and applies a large separable kernel to consider a wider receptive field in the spatial attention module. The RBSA uses a bottleneck mechanism to mitigate issues related to excessive parameters and computations. The proposed HFD-YOLO outperforms other methods on two benchmark datasets (Fall Detection and Human Fall Detection) based on mean average precision (mAP). With a low computation load and relatively few parameters, HFD-YOLO achieves an average of 23.54, 18.71, and 29.18 frames per second on an Intel Core i7-9750H CPU, Intel Core i5-6600 CPU, and NVIDIA Jetson Orin Nano GPU devices, respectively, making it suitable for real-time human fall detection to support intelligent surveillance systems in resource-constrained environments. The demo video is presented at https://bit.ly/HFD-YOLO-AP.}, archive = {J_ACCESS}, author = {Adri Priadana and Duy-Linh Nguyen and Xuan-Thuy Vo and Jehwan Choi and Russo Ashraf and Kanghyun Jo}, doi = {10.1109/ACCESS.2025.3547360}, journal = {IEEE Access}, pages = {41248-41258}, shortjournal = {IEEE Access}, title = {HFD-YOLO: Improved YOLO network using efficient attention modules for real-time one-stage human fall detection}, volume = {13}, year = {2025}, }
• (2025). Interpretable machine learning models for predicting cesarean delivery in class III obese cohorts. ACCESS, 13, 41230-41247. (www)

  @article{ , abstract = {Obese women who undergo unplanned cesarean sections face a higher risk of complications, and these unplanned cesareans often occur after an unsuccessful labor induction. Each year, more American women are at a high risk of such complications, especially in the presence of increased rate of pre-pregnancy obesity. Although previous research has explored machine learning models to predict cesarean likelihood, there has been limited focus on class III obese women (BMI $\geq 40$ ) who have undergone labor induction. In this paper, we study a cohort of class III obese women at our institution who were induced for labor, aiming to identify those at a higher risk for cesarean delivery. We propose a machine learning framework that incorporates a variety of models, including Support Vector Machine, Random Forest, eXtreme Gradient Boosting (XGBoost), K-nearest neighbors, Naïve Bayes, and logistic regression, for predictive analysis. Further, we investigate the significance of individual predictors using SHapley Additive exPlanations (SHAP) method to enhance the interpretability of our models. Moreover, we conduct a comparative analysis of the cohort of nulliparous with combined cohort of nulliparous and multiparous patients. Our comparative analysis shows logistic regression to be the most accurate in predicting the need for cesareans in the nulliparous cohort, while random forest outperformed other models in the combined dataset.}, archive = {J_ACCESS}, author = {Rachel Bennett and Stephanie L. Pierce and Talayeh Razzaghi}, doi = {10.1109/ACCESS.2025.3547992}, journal = {IEEE Access}, pages = {41230-41247}, shortjournal = {IEEE Access}, title = {Interpretable machine learning models for predicting cesarean delivery in class III obese cohorts}, volume = {13}, year = {2025}, }
• (2025). Essential frequency analysis for stacked cu-CNT composite cells of TSVs. ACCESS, 13, 41221-41229. (www)

  @article{ , abstract = {This paper presents a comprehensive frequency analysis of copper-carbon nanotube (Cu-CNT) composite cells in stacked through-silicon vias (TSVs), focusing on a novel approach in which Cu-CNT composite cells are distributed normally within the TSVs. This distribution is designed to simulate real-world conditions and enhance the reliability of the findings. The key contributions of this work encompass a comprehensive assessment of insertion and return losses related to electrical characteristics of stacked Cu-CNT TSVs, varying critical factors such as frequency and material composition to evaluate the system performance. Also the eye diagrams are investigated at various data rates, which shed light on the signal quality and integrity for high-speed applications. The findings show that Cu-CNT composites outperform conventional copper-based TSVs in terms of performance metrics, drawing attention to their promise as next-generation, high-density interconnects for 3D integrated circuits.}, archive = {J_ACCESS}, author = {Mekala Girish Kumar and Yash Agrawal and Harish Pulluri and Rohit Sharma}, doi = {10.1109/ACCESS.2025.3547963}, journal = {IEEE Access}, pages = {41221-41229}, shortjournal = {IEEE Access}, title = {Essential frequency analysis for stacked cu-CNT composite cells of TSVs}, volume = {13}, year = {2025}, }
• (2025). Efficient computation of collatz sequence stopping times: A novel algorithmic approach. ACCESS, 13, 41210-41220. (www)

  @article{ , abstract = {The Collatz conjecture, which posits that any positive integer will eventually reach 1 through a specific iterative process, is a classic unsolved problem in mathematics. This research focuses on designing an efficient algorithm to compute the stopping time of numbers in the Collatz sequence, achieving significant computational improvements. By leveraging structural patterns in the Collatz tree, the proposed algorithm minimizes redundant operations and optimizes computational steps. Unlike prior methods, it efficiently handles extremely large numbers without requiring advanced techniques such as memoization or parallelization. Experimental evaluations confirm computational efficiency improvements of approximately 28% over state-of-the-art methods. These findings underscore the algorithm’s scalability and robustness, providing a foundation for future large-scale verification of the conjecture and potential applications in computational mathematics.}, archive = {J_ACCESS}, author = {Eyob Solomon Getachew and Beakal Gizachew Assefa}, doi = {10.1109/ACCESS.2025.3548031}, journal = {IEEE Access}, pages = {41210-41220}, shortjournal = {IEEE Access}, title = {Efficient computation of collatz sequence stopping times: A novel algorithmic approach}, volume = {13}, year = {2025}, }
• (2025). D-DDPM: Deep denoising diffusion probabilistic models for lesion segmentation and data generation in ultrasound imaging. ACCESS, 13, 41194-41209. (www)

  @article{ , abstract = {The Denoising Diffusion Probabilistic Model (DDPM) has gained significant attention for its powerful image generation and segmentation capabilities, particularly in biomedical applications where accuracy is critical. In breast cancer detection, ultrasound imaging is widely used due to its safety, affordability, and non-ionizing nature. However, the inherent challenges of ultrasound data, such as noise and artifacts, make accurate tumor segmentation difficult, often leading to misdiagnosis. We propose a novel Deep Denoising Probabilistic Diffusion Model (D-DDPM) designed to enhance tumor segmentation in breast ultrasound images to address these limitations. Our model incorporates a nested U-Net architecture with Residual U-blocks (RSU), significantly improving feature learning and segmentation precision. In addition to performing segmentation, D-DDPM generates synthetic data, augmenting existing real datasets to improve data size with a diverse range of high-quality samples. We validated D-DDPM on several breast ultrasound datasets, comparing its performance to state-of-the-art methods. The proposed D-DDPM achieves a Dice score improvement of 2.26%, 4.24%, and 5% over the runner-up model, demonstrating superior performance on all BUS datasets. Both qualitative and quantitative results demonstrate the ability of D-DDPM to deliver more accurate and reliable segmentation results, offering promising potential to improve clinical decision-making in cancer diagnosis.}, archive = {J_ACCESS}, author = {Abdalrahman Alblwi and Saleh Makkawy and Kenneth E. Barner}, doi = {10.1109/ACCESS.2025.3548128}, journal = {IEEE Access}, pages = {41194-41209}, shortjournal = {IEEE Access}, title = {D-DDPM: Deep denoising diffusion probabilistic models for lesion segmentation and data generation in ultrasound imaging}, volume = {13}, year = {2025}, }
• (2025). Modeling and solution for course of action planning driven by operational task network. ACCESS, 13, 41169-41193. (www)

  @article{ , abstract = {The success or failure of a war is intricately linked to the quality of the operational plan, which relies on key elements such as decomposing operational tasks and a planning Course of Action (COA). To address the issue of current operational task decomposition and COA planning being separate without a clear expression of the task-action traction support relationship, we propose an operational task network-based model for COA planning. This model integrates classical Hierarchical Task Network (HTN) and Influence Net (IN) reasoning methods, utilizing HTN for operational task analysis and using IN for COA planning. Through ontology modeling, correlation mapping between operational tasks and actions is achieved. This paper clarifies distinctions and connections between core concepts such as operational task and operational action while also realizing integrated planning from task analysis to COA development. Furthermore, it elucidates the internal mechanism by which operational tasks drive the design of COA. Simulation experiments are conducted to verify the feasibility and effectiveness of this proposed model.}, archive = {J_ACCESS}, author = {Jianping Wu and Yunjun Lu and Dezhi Li and Yun Ren and Wenlu Zhou and Shandong Yuan}, doi = {10.1109/ACCESS.2025.3532979}, journal = {IEEE Access}, pages = {41169-41193}, shortjournal = {IEEE Access}, title = {Modeling and solution for course of action planning driven by operational task network}, volume = {13}, year = {2025}, }
• (2025). Cost-effective single-layer metallic lens antenna with high aperture efficiency. ACCESS, 13, 41159-41168. (www)

  @article{ , abstract = {This paper presents a novel millimeter-wave high-gain single-layer lens antenna design that features a metallic ring as its foundational element. Leveraging characteristic modes analysis, the shape of the structure is systematically optimized. The resulting planar antenna exhibits outstanding radiation performance alongside its simplicity, achieving a gain exceeding 20 dBi at 20.50 GHz, and an aperture efficiency of 63 %). These exceptional performance characteristics make it ideal for applications in 5G and 6G systems. Additionally, the use of a cost-effective manufacturing process increases its potential for widespread adoption in future communication technologies.}, archive = {J_ACCESS}, author = {Daniel Santillán-Haro and Eva Antonino-Daviu and Felipe Vico-Bondia and Miguel Ferrando-Bataller}, doi = {10.1109/ACCESS.2025.3536083}, journal = {IEEE Access}, pages = {41159-41168}, shortjournal = {IEEE Access}, title = {Cost-effective single-layer metallic lens antenna with high aperture efficiency}, volume = {13}, year = {2025}, }
• (2025). Next-generation automation in neuro-oncology: Advanced neural networks for MRI-based brain tumor segmentation and classification. ACCESS, 13, 41141-41158. (www)

  @article{ , abstract = {Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology.}, archive = {J_ACCESS}, author = {Syed Sajid Hussain and Niyaz Ahmad Wani and Jasleen Kaur and Naveed Ahmad and Sadique Ahmad}, doi = {10.1109/ACCESS.2025.3547796}, journal = {IEEE Access}, pages = {41141-41158}, shortjournal = {IEEE Access}, title = {Next-generation automation in neuro-oncology: Advanced neural networks for MRI-based brain tumor segmentation and classification}, volume = {13}, year = {2025}, }
• (2025). A literature review on applications of explainable artificial intelligence (XAI). ACCESS, 13, 41111-41140. (www)

  @article{ , abstract = {As AI technologies, particularly deep learning models, have advanced, their inherent “black box” nature has raised significant concerns regarding accountability, fairness, and trust, especially in critical domains such as healthcare, finance, and criminal justice. We present a detailed exploration of XAI, emphasizing its essential role in improving the interpretability and transparency of complex AI systems in various application domains. Health-related applications were notably using XAI, emphasizing diagnostics, and medical imaging. Other notable domains of use of XAI is encompassed environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and social media. Furthermore, nascent applications in law, education, and social care underscore the growing influence of XAI. The analysis indicates a prevalent application of local explanation techniques, especially SHAP and LIME, with a preference for SHAP due to its stability and mathematical assurances. Each technique is analysed for its strengths and limitations in providing clear, actionable insights into model decision-making processes, thereby aiding stakeholders in understanding AI behaviour. Ultimately, this document underscores the critical challenges for XAI in fostering user trust, enhancing decision-making processes, and ensuring that AI technologies are utilized responsibly and ethically across various applications, paving the way for a more transparent and accountable AI landscape. We believe that by serving as a guide for future studies in the area, our systematic review contributes to the body of literature on XAI.}, archive = {J_ACCESS}, author = {Khushi Kalasampath and K. N. Spoorthi and Sreeparvathy Sajeev and Sahil Sarma Kuppa and Kavya Ajay and Angulakshmi Maruthamuthu}, doi = {10.1109/ACCESS.2025.3546681}, journal = {IEEE Access}, pages = {41111-41140}, shortjournal = {IEEE Access}, title = {A literature review on applications of explainable artificial intelligence (XAI)}, volume = {13}, year = {2025}, }
• (2025). CHRExpert: An AI-driven court of human rights expert assistant for legal practitioners utilizing transformer models. ACCESS, 13, 41097-41110. (www)

  @article{ , abstract = {The growing complexity of human rights litigation, combined with the increasing volume of legal documents, presents significant challenges for legal professionals in efficiently managing case preparation and decision-making. This paper introduces CHRExpert, an Artificial Intelligence (AI)-driven legal assistant designed to support law practitioners in analyzing judicial decisions and predicting case outcomes. Utilizing a Pretrained Generative Transformer (GPT) with 6 billion parameters, fine-tuned on the European Court of Human Rights (ECHR) dataset, CHRExpert automates legal document analysis by identifying key legal provisions, performing statutory interpretation, and applying analogical reasoning to suggest legal strategies. The system is designed to support practitioners in aligning their legal arguments with judicial reasoning, particularly in post-litigation analysis. Evaluations based on final judgments demonstrated an accuracy of 83% in predicting outcomes for cases involving Articles 3, 6, and 8 of the European Convention on Human Rights, with an average AUC of 0.93. Additionally, CHRExpert reduced the time required for case preparation by 40%, streamlining legal workflows and improving efficiency. This paper demonstrates the potential practical application of AI to enhance legal processes by offering accurate predictions and experiential insights, making it a valuable tool for human rights litigation.}, archive = {J_ACCESS}, author = {Yahya Al-Shareef}, doi = {10.1109/ACCESS.2025.3547763}, journal = {IEEE Access}, pages = {41097-41110}, shortjournal = {IEEE Access}, title = {CHRExpert: An AI-driven court of human rights expert assistant for legal practitioners utilizing transformer models}, volume = {13}, year = {2025}, }
• (2025). Accelerating distributed repartition joins on skewed datasets via patch-based shuffling. ACCESS, 13, 41068-41096. (www)

  @article{ , abstract = {In distributed workloads involving joins and aggregations, skewed attribute values often cause load balancing issues, leading to stragglers and increased execution times. Existing solutions often rely on cost-based models, require extensive parameter tuning, or necessitate modifications to distributed execution engines, limiting their usability and generality. To address this challenge, we present a novel patch-based repartitioning algorithm that eliminates load imbalances while minimizing network overhead. Our approach extends the subset-replicate technique by leveraging data distribution and location statistics to optimize data locality and reduce unnecessary data movement. Unlike traditional hash-based methods, our technique is skew-insensitive, requires no parameter tuning, and integrates seamlessly into existing distributed execution engines as a drop-in replacement for the shuffle mechanism. The proposed method operates in three distinct stages: (1) statistics collection and load threshold computation, (2) patch-based subgroup assignment to ensure optimal load balancing with minimal replication, and (3) informed data shuffling and join execution. This structured process ensures even workload distribution across workers while reducing network I/O. Theoretical analysis proves the scalability, skew robustness, and load-balancing guarantees of our approach, establishing bounds on maximum worker load and network data movement. Experimental evaluations demonstrate that our method achieves perfectly balanced workloads and reduces execution time by up to 81% compared to conventional hash-based joins under moderate to high skew, while introducing negligible overhead at low skew levels. These improvements are attributed to reduced data movement, optimized worker utilization, and the algorithm’s robust theoretical foundation. Our research provides a versatile and practical solution for skewed data processing, significantly advancing the efficiency of distributed data management systems.}, archive = {J_ACCESS}, author = {Evdokia Kassela and Ioannis Konstantinou and Nectarios Koziris}, doi = {10.1109/ACCESS.2025.3547623}, journal = {IEEE Access}, pages = {41068-41096}, shortjournal = {IEEE Access}, title = {Accelerating distributed repartition joins on skewed datasets via patch-based shuffling}, volume = {13}, year = {2025}, }
• (2025). Hard-positive prototypical networks for few-shot classification. ACCESS, 13, 41054-41067. (www)

  @article{ , abstract = {Prominent prototype-based classification (PbC) approaches, such as Prototypical Networks (ProtoNet), use the average of samples within a class as the class prototype. In these methods which we call Mean-PbC, a discriminant classifier is defined based on the minimum Mahalanobis distance from class prototypes. It is well known that if the data of each class is normally distributed, then the use of Mahalanobis distance leads to an optimal discriminant classifier. We propose the Hard-Positive Prototypical Networks (HPP-Net), which also employs the Mahalanobis distance, despite assuming the class distribution may be unnormalized. HPP-Net learns class prototypes from hard (near-boundary) samples that are less similar to the class center and have a higher misclassification probability. It also employs a learnable parameter to capture the covariance of samples around the new prototypes. The valuable finding of this paper is that a more accurate discriminant classifier can be attained by applying the Mahalanobis distance in which the mean is a “hard-positive prototype”, and the covariance is learned via the model. The experimental results on Omniglot, CUB, miniImagenet and CIFAR-100 datasets demonstrate that HPP-Net achieves competitive performance compared to ProtoNet and several other prototype-based few-shot learning (FSL) methods.}, archive = {J_ACCESS}, author = {Mahsa Fazaeli-Javan and Reza Monsefi and Kamaledin Ghiasi-Shirazi}, doi = {10.1109/ACCESS.2025.3544638}, journal = {IEEE Access}, pages = {41054-41067}, shortjournal = {IEEE Access}, title = {Hard-positive prototypical networks for few-shot classification}, volume = {13}, year = {2025}, }
• (2025). Knowledge sharing among hospitals of different levels: A complex network evolutionary game approach. ACCESS, 13, 41040-41053. (www)

  @article{ , abstract = {Knowledge sharing among hospitals of different levels is essential for the effective utilization of medical resources. This paper develops a complex network evolutionary game model to examine the knowledge sharing behaviors between general hospitals and community health service centers, considering the influences of government reward and punishment mechanisms, as well as patient evaluations. Numerical simulations are employed to explore and analyze the effects of key factors on the evolution of strategies among game players. The results show that synergy benefits, government reward and punishment benefits, patient evaluation reward and punishment benefits, and sharing costs have significant impacts on the evolutionary outcomes of knowledge sharing among hospitals. Compared with general hospitals, community health service centers exhibit a stronger willingness to share knowledge. Enhancing the knowledge sharing capabilities of general hospitals and the learning capacities of community health service centers, fostering knowledge synergy, reducing sharing costs, while encouraging active patient participation, and implementing effective government reward and punishment mechanisms, can further promote knowledge sharing among these institutions and improve the overall quality of healthcare services. The findings also suggest that the proposed model has broader applications, not only in healthcare system but also in sectors where resource sharing and collaboration are critical. Policymakers can leverage these insights to design more effective strategies that promote cooperation and efficient resource allocation across different institutions.}, archive = {J_ACCESS}, author = {Nan Chen and Na Lv and Zhiwei Chen}, doi = {10.1109/ACCESS.2025.3546632}, journal = {IEEE Access}, pages = {41040-41053}, shortjournal = {IEEE Access}, title = {Knowledge sharing among hospitals of different levels: A complex network evolutionary game approach}, volume = {13}, year = {2025}, }
• (2025). Improving LVRT capability in grid connected PV system using DRL-based controller. ACCESS, 13, 41025-41039. (www)

  @article{ , abstract = {As the grid integration of renewable energy sources increases, improving the Low Voltage Ride-Through (LVRT) capability of these systems has become critical for grid stability and energy security. This paper focuses on achieving LVRT capability in a two-stage three-phase grid-connected photovoltaic (PV) system. Deep Reinforcement Learning (DRL) techniques address this challenge through their ability to adapt to dynamic and uncertain grid conditions. DRL stands out from traditional methods due to its capacity to generalize across varying fault scenarios while minimizing system errors and ensuring grid code (GC) compliance. A grid-connected PV system is modeled in MATLAB/Simulink and controlled using two DRL algorithms: Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3). The performances of these DRL methods are evaluated against a conventional Proportional-Integral (PI) controller optimized with the Symbiotic Organisms Search (SOS) algorithm under three-phase balanced faults. Key performance metrics include t-test results, GC compliance, AC current and DC voltage limits, and error values under three-phase balanced faults conditions. The analysis shows that TD3 achieved 4.31% lower error than DDPG and 12.85% lower error than SOS-PI. The t-test analysis revealed a significant difference in ITAEav performance between TD3 and SOS-PI (p =0.0089), indicating TD3’s superior performance. DRL-based controllers effectively ensure GC compliance across all tested faults, while the SOS-PI controller struggles to generalize to different fault conditions. This highlights the necessity of DRL techniques for achieving reliable and robust LVRT capability in grid-connected PV systems.}, archive = {J_ACCESS}, author = {Büşra Özgenç and Yesim Aysel Baysal Aslanhan and İsmail Hakki Altaş}, doi = {10.1109/ACCESS.2025.3547014}, journal = {IEEE Access}, pages = {41025-41039}, shortjournal = {IEEE Access}, title = {Improving LVRT capability in grid connected PV system using DRL-based controller}, volume = {13}, year = {2025}, }
• (2025). IACGAN-ST: A fault diagnosis method for scraper conveyor bearings under imbalanced data conditions. ACCESS, 13, 41011-41024. (www)

  @article{ , abstract = {Bearings in mining scraper conveyors, essential transmission components, are frequently subjected to harsh environments, including high temperatures, heavy impact loads, dust contamination, and prolonged operational durations. Vibration signals from these bearings face acquisition challenges, compounded by a significant imbalance between normal and fault data, which complicates fault diagnosis. Conventional fault diagnosis methods often fail to adequately resolve these issues. To address these challenges, this study introduces a novel bearing fault diagnosis approach that combines an Improved Auxiliary Classifier Generative Adversarial Network (IACGAN) with a Swin Transformer (ST). Initially, continuous wavelet transform (CWT) is employed to convert vibration signals into two-dimensional time-frequency representations. Subsequently, a Convolution and Attention Fusion Module (CAFM) is incorporated into the ACGAN generator to enhance the quality and diversity of generated samples. Moreover, a gradient penalty term is added to the discriminator’s loss function to improve training stability and mitigate mode collapse. Finally, the Swin Transformer is trained on a hybrid dataset comprising high-quality time-frequency images produced by IACGAN and original samples. Experimental results show that the Swin Transformer achieves superior diagnostic accuracy under various imbalance conditions, reaching 99.1% when the data is balanced at a 1:1 ratio. Additionally, during a field application at the E2312 mining face of Gaohe Energy Coal Mine, the IACGAN-ST model attained a diagnostic accuracy of 96.06%, significantly outperforming benchmark models such as SVM, CNN, and ResNet. This approach effectively mitigates data imbalance and significantly enhances the fault diagnosis accuracy for scraper conveyor bearings.}, archive = {J_ACCESS}, author = {Hongwei Wang and Shengyu Guan and Xuesong Wang and Lijie Wang and Qun Zhang and Kaiman Liu and Zhiwen Fang and Hongli Wang}, doi = {10.1109/ACCESS.2025.3547737}, journal = {IEEE Access}, pages = {41011-41024}, shortjournal = {IEEE Access}, title = {IACGAN-ST: A fault diagnosis method for scraper conveyor bearings under imbalanced data conditions}, volume = {13}, year = {2025}, }
• (2025). Resource allocation on low-earth orbit edge infrastructure: Taxonomy, survey, and research challenges. ACCESS, 13, 40992-41010. (www)

  @article{ , abstract = {Low Earth Orbit (LEO) Edge Computing integrates space technology with Edge Computing to enhance connectivity and reduce latency, addressing critical limitations of terrestrial infrastructures. By leveraging LEO satellites, this paradigm enables global, low-latency data processing for applications such as remote sensing, Earth observation, and worldwide communications. However, it also presents challenges in resource allocation, thermal management, and space debris mitigation. This paper provides a comprehensive taxonomy and in-depth survey on resource allocation strategies in LEO Edge environments, identifying key challenges and future research directions.}, archive = {J_ACCESS}, author = {Fábio Diniz Rossi and Paulo Silas Severo de Souza and Marcelo Caggiani Luizelli}, doi = {10.1109/ACCESS.2025.3547571}, journal = {IEEE Access}, pages = {40992-41010}, shortjournal = {IEEE Access}, title = {Resource allocation on low-earth orbit edge infrastructure: Taxonomy, survey, and research challenges}, volume = {13}, year = {2025}, }
• (2025). Design and fabrication of textile-based electrodes for biomedical applications in wearable electronics devices: Electrocardiography, electromyography, and tactile sensing. ACCESS, 13, 40977-40991. (www)

  @article{ , abstract = {The increasing prevalence of wearable electronic devices for healthcare monitoring has marked notable advancement. Typical medical parameters include tracking physiological signals such as electrocardiography (ECG), photoplethysmography (PPG), and electromyography (EMG), which derive vital signs, including heart rate, SpO2 level, blood pressure, activity tracking, and related metrics. Traditional methods employing wet electrodes present limitations, such as skin irritation and signal degradation over long-term monitoring, are not easy to integrate with wearable devices and are not reusable. The proposed work in this paper introduces a textile-based electrode (TBE) as a solution to these challenges. TBEs offer advantages, such as long-term monitoring capability, skin conformability, and easy integration with wearable devices. This paper presents the design, fabrication, and characterization of TBE for different biomedical applications, including ECG, EMG, and tactile sensing. The fabrication of TBE involves the embroidery technique of conductive yarns with different combinations of design parameters. The fabricated TBEs are validated on multiple human subjects, and their performance are compared with wet electrodes, also statistical analysis is carried out. The obtained results are evident that optimized TBE designs can effectively acquire ECG and EMG that is comparable to the signal acquired using wet electrodes. Additionally, textile-based interdigitated electrodes (IDEs) are designed and fabricated for tactile sensing applications. The fabricated IDEs can detect touch and tap inputs based on changes in resistance, showing potential for use in wearable security armbands and wearable rehabilitation assistance devices. The study concludes that textile-based wearable electronic devices are feasible for various healthcare monitoring applications.}, archive = {J_ACCESS}, author = {Aarthy Prabakaran and Elizabeth Rufus}, doi = {10.1109/ACCESS.2025.3547147}, journal = {IEEE Access}, pages = {40977-40991}, shortjournal = {IEEE Access}, title = {Design and fabrication of textile-based electrodes for biomedical applications in wearable electronics devices: Electrocardiography, electromyography, and tactile sensing}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection in 5G and wi-fi networks: A survey of current methods, challenges, and perspectives. ACCESS, 13, 40950-40976. (www)

  @article{ , abstract = {The convergence of 5G and Wi-Fi networks has become a cornerstone of next-generation communication systems, offering seamless connectivity, improved performance, and diversified use cases. However, this integration introduces unique security challenges, particularly in the context of intrusion detection systems. This survey explores the threats and vulnerabilities inherent in 5G and Wi-Fi access networks, highlighting the need for customized intrusion detection solutions in these environments. In addition, we present the various types and architectures of intrusion detection systems, the existing datasets used to evaluate their performance, and the recent detection methods that accurately identify the presence or absence of an attack. Furthermore, the paper provides a comprehensive overview of state-of-the-art studies, positioning them within current 5G/Wi-Fi architectures. Finally, we highlight the key architectural differences and conclude by discussing the implications of 5G/Wi-Fi architectural convergence in the development of future intrusion detection systems. This convergence raises crucial concerns about the integration of security mechanisms in a unified ecosystem. Hence, this work aims to serve as a foundation for future research on robust and scalable detection mechanisms in converged 5G/Wi-Fi networks.}, archive = {J_ACCESS}, author = {Cherifa Hamroun and Anne Fladenmuller and Michel Pariente and Guy Pujolle}, doi = {10.1109/ACCESS.2025.3546338}, journal = {IEEE Access}, pages = {40950-40976}, shortjournal = {IEEE Access}, title = {Intrusion detection in 5G and wi-fi networks: A survey of current methods, challenges, and perspectives}, volume = {13}, year = {2025}, }
• (2025). Cross-medium transmission method based on 8B/10B special position coding. ACCESS, 13, 40943-40949. (www)

  @article{ , abstract = {To address limited transmission rates resulting from the laser’s low repetition frequency in cross-medium communication, this study proposed a specialized position coding cross-medium transmission method based on 8B/10B to enhance the transmission efficiency. Strategies such as odd-even alternating coding, position coding, and setting empty codes were designed to address the impact of pulse position modulation on transmission rates caused by the “back-to-back” phenomenon. This method ensures that when a pulse occurs in the last four positions of the preceding transmission cycle, it will not occur in the first four positions of the subsequent cycle. A corresponding transmission system was constructed to validate the specialized position coding cross-medium transmission method based on 8B/10B. A laser with a repetition frequency of 60 Hz was used to conduct experiments in a water pool and successfully achieved cross-medium communication at a transmission rate of 181.8 bps.}, archive = {J_ACCESS}, author = {Miaoyi Hu and Qi Tan and Zhengyi Zhao and Shuai Zhong and Hang Xu and Yiguang Yang and Jianquan Yao}, doi = {10.1109/ACCESS.2025.3546289}, journal = {IEEE Access}, pages = {40943-40949}, shortjournal = {IEEE Access}, title = {Cross-medium transmission method based on 8B/10B special position coding}, volume = {13}, year = {2025}, }
• (2025). A deep learning model for psychological support in student entrepreneurship. ACCESS, 13, 40931-40942. (www)

  @article{ , abstract = {The rise in entrepreneurial endeavours has brought attention to college students’ mental health issues, and developing an accurate assessment tool for them has become a key area of study. The paper proposes the fully embedded bi-order network (FE-BiON) model, a deep learning-based mental health evaluation tool that may be used to evaluate the mental health of college students launching their own businesses. The model innovatively combines fully embedded feature engineering and high-low order parallel network structure, thus improving the recognition ability and evaluation accuracy of psychological characteristics. In order to verify the validity of the model, this paper uses three datasets: The national health and nutrition inspection survey in the United States, the national health and nutrition inspection survey in South Korea, and the behavioral risk factor monitoring system for testing and comparative experiments. The findings demonstrate that in three public datasets, the accuracy and F1 value of the FE-BiON model reach as high as 0.91 and 0.88, respectively, outperforming other comparator models and demonstrating greater prediction performance. The results of ablation experiment further prove that the synergistic effect of high-low order networks and the fully embedded feature processing method have played a significant role in capturing the complex relationship between features and improving the prediction performance of the model. The suggested FE-BiON model performs better in mental health prediction, as evidenced by its accuracy of over 90% in all mental health levels on the assessment of college students’ mental health. The research results provide scientific basis for the development of entrepreneurship education in colleges and universities, theoretical support for the formulation of mental health intervention measures, and practical guidance for improving the success rate of college students’ entrepreneurship, which has important educational and policy influence.}, archive = {J_ACCESS}, author = {Tao Yang and Wendong Xu and Lingbo Wang and Morozova Olga Petrovna}, doi = {10.1109/ACCESS.2025.3547833}, journal = {IEEE Access}, pages = {40931-40942}, shortjournal = {IEEE Access}, title = {A deep learning model for psychological support in student entrepreneurship}, volume = {13}, year = {2025}, }
• (2025). Sampled-data-based local stability of neutral time-delay systems with actuator saturation. ACCESS, 13, 40923-40930. (www)

  @article{ , abstract = {In this work, the local stability issue of neutral delay system with actuator saturation under sampled-data controller is studied. Firstly, considering the influence of actuator saturation on the system, the sampled-data controller with variable sampling period is designed by using the input delay method, which can ensure the closed-loop neutral system to achieve local stability. Then, we combine polytopic method to conduct saturated nonlinear part, which is characterized by a convex combustion of state feedback and auxiliary delay feedback. By making full use of sampling information, the less conservative stability condition is given in the form of linear matrix inequalities (LMIs). In addition, the attraction of domain (DA) is also optimized. The proficiency and merits of the suggested method are demonstrated via a numerical example in the end.}, archive = {J_ACCESS}, author = {Dapeng Li and Shuiqing Ye and Chao Ge}, doi = {10.1109/ACCESS.2025.3547496}, journal = {IEEE Access}, pages = {40923-40930}, shortjournal = {IEEE Access}, title = {Sampled-data-based local stability of neutral time-delay systems with actuator saturation}, volume = {13}, year = {2025}, }
• (2025). Unison duty-phase peak current control for pole point inductor-based dual active bridge converter with zero DC bias current. ACCESS, 13, 40899-40922. (www)

  @article{ , abstract = {The DC-DC converters capable of high gain with bidirectional capability are used in the grid in conjunction with energy storage systems consisting of fuel cells, batteries, and supercapacitors to support the peak load times. This paper proposes a DC-DC converter having features like high gain, single-stage, bidirectional, and is derived from the reference topology of a dual active bridge (DAB) converter. The proposed converter is called the pole point inductor-based dual active bridge (PPI-DAB) converter. The primary bridge is a boost full bridge (BFB), which is current-fed, and the secondary bridge is a typical full bridge (FB). The gain of the PPI-DAB is twice of a DAB. The converter dynamics are studied by modeling using the generalized averaged modeling (GAM) method. The averaged equivalent circuit, as well as the small-signal model, are derived from the GAM. The converter is designed for an input voltage of 80 V DC and an output voltage of 800 V DC with a power of 1.5 kW. Alongside this, due to different average currents flowing through each of the switches, different drain-to-source resistance of the switches, changes in phase-shift leading to transients, etc., there is an unbalanced volt-second balance equation, which results in a DC bias current in the auxiliary inductor, saturating the high-frequency transformer (HFT). The paper presents a current mode control method to address this issue. The HFT is protected during transients due to the direct current control method. This control strategy is applicable for normal DAB converters where the current DC bias issues are observed. The topology along with the control is simulated in PLECS and the control as well as the functioning of the topology is verified. The control method is then implemented on an experimental setup of a PPI-DAB converter to validate the controller performance. The simulation and experimental results are presented to validate the efficacy of the proposed method for the PPI-DAB converter.}, archive = {J_ACCESS}, author = {R. Nitheesh and N. Lakshminarasamma and B. Arun Karuppaswamy}, doi = {10.1109/ACCESS.2025.3547845}, journal = {IEEE Access}, pages = {40899-40922}, shortjournal = {IEEE Access}, title = {Unison duty-phase peak current control for pole point inductor-based dual active bridge converter with zero DC bias current}, volume = {13}, year = {2025}, }
• (2025). Multi-objective approach for optimal sizing and placement of EVCS in distribution networks with distributed rooftop PV in metropolitan city. ACCESS, 13, 40883-40898. (www)

  @article{ , abstract = {The optimal sizing and location of electric vehicle charging station (EVCS) integrated with distributed generation (DG) in the distribution network are the subject of this study. In modern electric power systems, the integration of EVCS and DG must be carefully planned. Inappropriate sizing and location of EVCS can cause network overload, increase power losses, and allow for voltage variations outside the standard. This study adopts a reference distribution network that is representative of a typical business distribution network in a metropolitan city in Indonesia. To resolve the issue of determining the size and location of EVCS, the hybrid genetic algorithm-modified salp swarm algorithm (HGAMSSA) optimization method with multi-objective function is implemented. There are three scenarios used when determining the size and placement of EVCS. In the first scenario, the EVCS comprises chargers at levels 1, 2, and 3, with DG integration. In the second scenario, EVCS is comprised of level 2 and level 3, with DG integration. While in the third scenario, EVCS only consists of level 3 chargers with DG integration. The simulation results indicate that network utilization can be optimized at 79.00%, 75.53%, and 76.37% when electric cars (EV) operate in grid to vehicle (G2V) mode, compared to a base load of 29.46%. In vehicle to grid (V2G) mode, the energy supplied by the electric vehicle (EV) via the EVCS is 0.91 MWh. Additionally, the distribution network receives 10.036 MWh of electricity from rooftop photovoltaic (PV).}, archive = {J_ACCESS}, author = {Syechu Dwitya Nugraha and Mochamad Ashari and Dedet Candra Riawan}, doi = {10.1109/ACCESS.2025.3547327}, journal = {IEEE Access}, pages = {40883-40898}, shortjournal = {IEEE Access}, title = {Multi-objective approach for optimal sizing and placement of EVCS in distribution networks with distributed rooftop PV in metropolitan city}, volume = {13}, year = {2025}, }
• (2025). Novel dynamic chord reference method in railway track irregularity measurement based on vehicle. ACCESS, 13, 40875-40882. (www)

  @article{ , abstract = {The chord reference method (CRM) is a well-established approach employed in railway maintenance for identifying and quantifying track irregularity. However, the static CRM is characterized by a substantial amount of labor involvement and a correspondingly low level of efficiency. The process of employing dynamic inertial reference method often presents challenges, particularly when it comes to identifying the appropriate reference base in an intuitive manner. If dynamic irregularity can be transformed into dynamic CRM value, the advantages of static CRM in maintenance can be effectively exerted, and the shortcomings of low efficiency and heavy workload can be overcome. This paper presents an innovative methodology for dynamic CRM utilizing vehicle as platform. The proposed method aims to improve the accuracy and efficiency of CRM assessments, particularly those requiring real-time or continuous inspection. Firstly, the pitch angular velocity of the vehicle is measured by incorporating gyroscope-based pitch angular velocity sensors into vehicle. Secondly, the dynamic CRM value is computed by employing the basic chord length of 0.5 m, derived from the pitch angular velocity of the vehicle. Subsequently, we developed a unique filter that generates dynamic CRM values in response to diverse chord lengths, including 10 m, 20 m, and so on. Finally, an experimental comparison is conducted to discern the variations between the dynamic CRM value and spatial curve amplitude value of track irregularity on a specific railway line. A statistical approach is employed to derive a correction coefficient, which aids in mitigating discrepancies and enhancing the accuracy of measurements. The results from real-world tests are provided, which demonstrate that the proposed methodology outperforms static CRM and dynamic inertial reference method by noticeable.}, archive = {J_ACCESS}, author = {Yan Wang and Wenbo Zhao and Shibin Wei and Jiyou Fei and Fei Yang and Peng Dai}, doi = {10.1109/ACCESS.2025.3544254}, journal = {IEEE Access}, pages = {40875-40882}, shortjournal = {IEEE Access}, title = {Novel dynamic chord reference method in railway track irregularity measurement based on vehicle}, volume = {13}, year = {2025}, }
• (2025). Enhanced gain and isolation dual-band dual-port MIMO antenna with integrated lens for millimeter-wave 5G internet-of-things applications. ACCESS, 13, 40859-40874. (www)

  @article{ , abstract = {This paper proposes a dual-band dual-port Multiple Input Multiple Output (MIMO) antenna with enhanced gain and port isolation for 5G millimeter-wave (mm-wave) Internet of Things (IoT) applications. The gain enhancement is achieved using a 3D-printed dielectric lens integrated with the antenna, whereas port isolation is improved using a novel 2D compact metamaterial incorporated between the MIMO elements. The proposed integrated lens MIMO antenna (ILMA) design exhibits lower mutual coupling, low fabrication cost, and higher gain. At the same time, it uses a simple planar antenna feeding technique, which features a small footprint compared to larger antenna arrays typically employed for gain enhancement. The overall size of the two-port integrated lens MIMO antenna is $19\times 29\times 13$ mm3. The peak gain without dielectric lens is 6.5 dBi and 7.9 dBi at the two operating bands, which is further enhanced with a 3D-printed dielectric lens to 9.5 and 11.4 dBi at 26 GHz and 38 GHz, respectively. The proposed ILMA exhibits high isolation exceeding 30 dB and 22 dB at 26 GHz and 38 GHz. Experimental validation of the ILMA prototype corroborates the relevance of the assumed design approach. Compared to the MIMO antennas reported in the recent literature, the suggested integrated lens MIMO antenna not only offers a higher gain while maintaining a small footprint but also improves the isolation between the radiating elements. These features make the proposed design an attractive choice for mm-wave 5G internet-of-things (IoT) applications.}, archive = {J_ACCESS}, author = {Bilal Tariq Malik and Shahid Khan and Jamal Nasir and Slawomir Koziel}, doi = {10.1109/ACCESS.2025.3547085}, journal = {IEEE Access}, pages = {40859-40874}, shortjournal = {IEEE Access}, title = {Enhanced gain and isolation dual-band dual-port MIMO antenna with integrated lens for millimeter-wave 5G internet-of-things applications}, volume = {13}, year = {2025}, }
• (2025). Multi-modal federated learning over cell-free massive MIMO systems for activity recognition. ACCESS, 13, 40844-40858. (www)

  @article{ , abstract = {This paper addresses the problem of Multi-modal Federated Learning (MFL) over resource-limited Cell-Free massive MIMO (CF-mMIMO) networks for the application of Human Activity Recognition (HAR). MFL leverages diverse data modalities across various clients, while the CF-mMIMO network ensures consistent service quality, crucial for collaborative training. The primary challenges of MFL are data heterogeneity, which includes statistical and modality heterogeneity that complicate data fusion, client collaboration, and inference with missing data, and system heterogeneity, where devices with dissimilar modalities experience varied processing and communication delays, increasing overall training latency. To tackle these issues, we propose a late-fusion model architecture that allows flexible client participation with any combination of data modalities, and formulate an optimization problem to jointly minimize latency and global loss in MFL. We propose a prioritized device-modality selection scheme that allows flexible participation of devices. Additionally, we employ a modified Particle Swarm Optimization (PSO) algorithm for efficient resource allocation. Extensive experiments validate our framework, demonstrating substantial reductions in training time and significant improvements in model performance, particularly an average improvement of 15% and 23% in test accuracy compared to the other fusion models when missing one and two modalities in the inference phase.}, archive = {J_ACCESS}, author = {Seyed Mohammad Sheikholeslami and Pai Chet Ng and Jamshid Abouei and Konstantinos N. Plataniotis}, doi = {10.1109/ACCESS.2025.3548001}, journal = {IEEE Access}, pages = {40844-40858}, shortjournal = {IEEE Access}, title = {Multi-modal federated learning over cell-free massive MIMO systems for activity recognition}, volume = {13}, year = {2025}, }
• (2025). The assessment of terminals for containers and service quality evaluation using a circular intuitionistic fuzzy intelligent algorithm. ACCESS, 13, 40824-40843. (www)

  @article{ , abstract = {To optimize operational efficiency and raise customer satisfaction, it is essential to evaluate container terminals and their services effectively. To rank container terminals according to a variety of criteria, this study provides a novel method that combines the Circular Intuitionistic Fuzzy Hamy Mean (CIFHM) models with an Intelligent Decision Support System (IDSS) to overcome hesitations. The CIFHM model has been established to overcome the complications of decision-making processes in situations where conventional approaches might not be enough. The CIFHM operator is utilized to combine various aspects of service quality, such as infrastructure, cost-effectiveness, environmental sustainability, operational efficiency, and customer satisfaction. This model provides a more sophisticated demonstration of ambiguity by applying the intuitionistic fuzzy set (IFS) theory, which considers membership (MD) and non-membership degree (NMD). The CIFHM model further expands decision-making by including circular intuitionistic fuzzy sets (CIFSs), allowing more precise and flexible information aggregation. A case study on many container terminals is offered to show how well the suggested approach works to assess service quality. The findings provide stakeholders, politicians, and terminal managers with useful information to raise standards of service and optimize operations.}, archive = {J_ACCESS}, author = {Shan Qian Lin and Jing Zhao and Wei Yang and Xin Cheng Wu and Xiao Hong Zhuang}, doi = {10.1109/ACCESS.2025.3546264}, journal = {IEEE Access}, pages = {40824-40843}, shortjournal = {IEEE Access}, title = {The assessment of terminals for containers and service quality evaluation using a circular intuitionistic fuzzy intelligent algorithm}, volume = {13}, year = {2025}, }
• (2025). Viewpoint generation algorithm for gimbal-mounted robots for visual inspection of civil engineering structures. ACCESS, 13, 40813-40823. (www)

  @article{ , abstract = {We propose a viewpoint generation algorithm for the visual inspection of civil engineering structures using robots equipped with a gimbal-mounted camera. This algorithm enables robots to completely inspect target surfaces at the required spatial resolution while reducing movement by effectively utilizing the gimbal. Previous studies generally assume that the camera is always oriented orthogonally to the target surface, and they focus on the inspection position optimization problem, resulting in the underutilization of gimbals and heavy movement of robots. In this study, firstly we have formulated the conditions under which the collected data’s quality does not deteriorate in terms of damage detection, and we have relaxed the orthogonal measuring restrictions. Based on the conditions, we propose a viewpoint generation algorithm that can carry out inspection work by changing measurement orientations instead of moving the robot. In simulation experiments, our method reduces inspection time by 47.7% compared to the benchmark method for the same tasks of inspecting a pier surface with the required spatial resolution. In addition, the viewpoint generation time was reduced by 96.8% compared to the benchmark method. We also conducted experiments in real environments using pan-tilt heads and confirmed that our algorithm can collect data qualified for the damage detection of visual inspections.}, archive = {J_ACCESS}, author = {Masaya Haneda and Weitong Wu and Yuki Funabora and Shinji Doki and Kae Doki}, doi = {10.1109/ACCESS.2025.3546992}, journal = {IEEE Access}, pages = {40813-40823}, shortjournal = {IEEE Access}, title = {Viewpoint generation algorithm for gimbal-mounted robots for visual inspection of civil engineering structures}, volume = {13}, year = {2025}, }
• (2025). Evaluation of blockchain-based tracking and tracing system with uncertain information: A multi-criteria decision-making approach. ACCESS, 13, 40795-40812. (www)

  @article{ , abstract = {Blockchain technology has transformed data management by providing a decentralized, secure, and transparent way to record transactions. These blockchain-based tracking and tracing system features are critical for enhancing supply chain visibility, security, authenticity, and reliability. As industries increasingly adopt these systems, selecting the optimal architecture and configuration becomes a complex decision-making challenge. Moreover, the diverse range of criteria such as scalability, security, and cost affect in making decisions. By considering it, this study evaluates various blockchain-based tracking systems within the fuzzy framework. The paper introduced the COCOSO model within the Picture Fuzzy (PF) framework. This decision-making tool considers multiple conflicting criteria and then utilizes it to evaluate the real-world scenario of a blockchain-based tracking and tracing system. Moreover, the importance of decision-making in blockchain-based systems, their societal impacts, risk mitigation, and their ethical and environmental impacts have been defined. Moreover, to verify the validity and reliability of the proposed approach, a comparison analysis was performed to show the efficacy of the proposed model. Finally, concluding remarks are discussed, which conclude the discussion by defining its advantages, limitations, and future direction.}, archive = {J_ACCESS}, author = {Geng Niu}, doi = {10.1109/ACCESS.2025.3546275}, journal = {IEEE Access}, pages = {40795-40812}, shortjournal = {IEEE Access}, title = {Evaluation of blockchain-based tracking and tracing system with uncertain information: A multi-criteria decision-making approach}, volume = {13}, year = {2025}, }
• (2025). Design of a horizontal and vertical polarized slotted waveguide antenna array for an airborne ka-band polarimetric interferometric SAR system. ACCESS, 13, 40774-40794. (www)

  @article{ , abstract = {This paper presents the design of a fully polarimetric low-profile antenna array intended for use in a side-looking airborne synthetic aperture radar (SAR) sensor operating at Ka-band. The proposed antenna provides maximum gain of up to 23 dB, a low side lobe level and high cross-polarization suppression values exceeding 38 dB. Furthermore, the proposed antenna demonstrates robust behavior and stable electrical performance in a variety of environmental conditions encountered during the measurement campaigns. The antenna system was designed using slotted waveguide technology, comprising separate antenna arrays for each polarization, which were mounted side by side on the aircraft platform. An appropriate design was developed based on theoretical analysis, considering the specifications of the electrical and mechanical systems. The horizontally polarized antenna consists of four subarrays, each comprising 22 transverse slots cut into the narrow waveguide wall. The alternately placed iris pairs enable radiation. The antenna with vertically polarized radiation is realized through the implementation of four subarrays, comprising 18 longitudinal slots, situated within the broad waveguide wall. Appropriate feeding networks with unbalanced amplitude distributions were designed to feed both arrays. For the horizontally polarized antenna, a conventional H-plane T-junction power divider and a Bethe-hole directional waveguide coupler were used. In contrast, a two-stage H-plane T-junction with a non-uniform amplitude taper was applied for vertical polarization. The proposed design was validated by compact test range measurements, which demonstrated a very good agreement with the simulation results.}, archive = {J_ACCESS}, author = {Alicja Schreiber and Markus Limbach and Bernd Gabler}, doi = {10.1109/ACCESS.2025.3545917}, journal = {IEEE Access}, pages = {40774-40794}, shortjournal = {IEEE Access}, title = {Design of a horizontal and vertical polarized slotted waveguide antenna array for an airborne ka-band polarimetric interferometric SAR system}, volume = {13}, year = {2025}, }
• (2025). CRS-NET: A CNN network for traffic sign detection. ACCESS, 13, 40760-40773. (www)

  @article{ , abstract = {Accurate traffic sign detection is a challenging task while very important for the safety of autonomous driving. Current learning-based traffic sign detection models generally can achieve the amenable detection results while enduring the large model size and heavy computation cost. Thus, these huge models may be unsuitable for mobile applications. This paper proposes an improved Yolov8s-based lightweight traffic sign detection model named as CRS-NET. In the proposed model, the Coordinate Attention (CA) module is added to the neck network of original Yolov8s to enhance the fusion of spatial features and channel features. Also, we introduce depthwise separable convolution to separate spatial and channel information, the Representation learning with visual tokens (RepViT) Block integrates a Light RepViT Block (LRB) module. It is used in the neck network to reduce the number of parameters and computational complexity. In the head, attention module Spatially Enhanced Attention Module (SEAM) is leveraged to reduce model size and locates the accurate position of the traffic sign. Last, the Wise- Intersection over Union (WIoU) loss function is employed to improve the performance of the detector. The CRS-NET model has been evaluated using the CCTSDB2021 dataset, achieving a 3.9% improvement in accuracy, a 1.1% increase in mAP, with a 1.8M reduction in Parameters and a 5.5G decrease in FLOPs.}, archive = {J_ACCESS}, author = {Wangyang Cai and Xiaoyan Yu and Xianwei Rong}, doi = {10.1109/ACCESS.2025.3545149}, journal = {IEEE Access}, pages = {40760-40773}, shortjournal = {IEEE Access}, title = {CRS-NET: A CNN network for traffic sign detection}, volume = {13}, year = {2025}, }
• (2025). Performance optimization in lead-free perovskite solar cell: A comparative approach. ACCESS, 13, 40747-40759. (www)

  @article{ , abstract = {Perovskite solar cells (PSCs) technology posses potential among numerous solar cells due to their promising sustainable energy solutions. However, commercial viability of lead-based perovskites is challenging due to its toxicity and stability challenges. Tin-based perovskites have shown up as a lead-free, comparatively excellent alternative, but the efficiency and stability of tin perovskites needs further optimization. In this study, formamidinium tin-based PSC was simulated using SCAPS-1D and optimized key architectural characteristics such as thickness, doping concentration, bandgap, and electron affinity of the absorber and charge transport layers. The effect of these changes on efficiency was examined, and the experimental outcomes were compared with existing literature to determine optimal parameters. These optimal variations in the architecture depicts optimal parameters such as open-circuit voltage of 0.764 V, a short-circuit current density of 25.28 mA/cm2, a fill factor of 73.26% and a PCE of 14.17%. A thicker absorber layer combined with optimization of the charge transport layers resulted in reduced charge recombination losses. Further improvements in the bandgap and electron affinity led to better energy-level alignment and improved performance. This paper illustrates that careful optimization of the architectural parameters can lead to a significant enhancement in the efficiency of PSC for tin. These studies are essential for developing efficient, non-toxic and potentially scalable lead-free solar cells.}, archive = {J_ACCESS}, author = {Aisha Shaikh and Pervez Hameed Shaikh and Shoaib Ahmed Khatri and Muhammad Saeed and Hamza Shah and Laveet Kumar}, doi = {10.1109/ACCESS.2025.3543244}, journal = {IEEE Access}, pages = {40747-40759}, shortjournal = {IEEE Access}, title = {Performance optimization in lead-free perovskite solar cell: A comparative approach}, volume = {13}, year = {2025}, }
• (2025). An absolute capacitive encoder using sample-and-hold demodulator to reduce interference between sine and cosine channels. ACCESS, 13, 40730-40746. (www)

  @article{ , abstract = {This paper presents an absolute capacitive rotary encoder using a sample-and-hold demodulator (SHD) to reduce interference between sine and cosine channels. The capacitive encoder measures the rotation angle of a shaft and is widely used in industrial fields, such as servo motors, robot joint and gimbal positioning. The encoder converts the capacitive changes of sine and cosine channel electrodes, which vary with the rotation of the sinusoidal-patterned rotor, into voltage signals through a charge amplifier. Since the charge amplifier receives signals from each channel simultaneously, interference can occur between the sine and cosine channels. To reduce the interference, this paper proposes SHD. The SHD is a simple circuit composed of an operational amplifier and switches, which effectively separates multi-channel signals by adjusting the sampling timing. The signals from the sine and cosine channels separated by the SHD are smoothed by low pass filters to become analog signals. These analog signals are converted into digital signals and processed by a field programmable gate array to calculate the rotation angle. A MATLAB simulation was conducted to demonstrate that SHD can ideally reduce channel interference by 94.9%. The proposed encoder was designed and tested. It achieved a signal-to-noise ratio of 68.7 dB, accuracy of ±0.022°, resolution of 0.0055°, and nonlinearity of 0.27%.}, archive = {J_ACCESS}, author = {Junghyun Park and Hyoungho Ko and Taeyup Kim and Sanghoon Lee and Jong-Mo Seo and Dong-Il Cho}, doi = {10.1109/ACCESS.2025.3547334}, journal = {IEEE Access}, pages = {40730-40746}, shortjournal = {IEEE Access}, title = {An absolute capacitive encoder using sample-and-hold demodulator to reduce interference between sine and cosine channels}, volume = {13}, year = {2025}, }
• (2025). Evaluating generative AI models for image-text modification. ACCESS, 13, 40703-40729. (www)

  @article{ , abstract = {Diffusion-based Image Editing models that utilize text prompts and reference images were developed to mitigate the limitations of the text-based image generation models in retaining the generated image’s structure. The domain oversaw the development of several successful image editing models and benchmark datasets that evaluate these models using auto-rating metrics. One critical limitation is that the metrics faced hurdles aligning with human assessments due to the models’ multi-modal nature and unique unexplored challenges. Human alignment is essential to identify bias, measure user satisfaction, and understand context. The prominence of human alignment grows if these models are used in real-world applications and scenarios where ground truths are absent. Through this research, we are introducing a diverse dataset, with the most common image domains and text prompts with varied challenges, and developing a human-evaluation-based framework that accurately analyzes the performance of these models. Additionally, these human-evaluation ratings validate the relevance of the auto-rating metrics, providing insights for future research. Thus, the research qualitatively evaluates several cutting-edge generative AI-based image editing models and auto rating metrics using a newly introduced diversely challenged dataset and human-evaluation framework. The detailed experimental materials is available at https://github.com/princepatrick/image-edit-evaluation}, archive = {J_ACCESS}, author = {Jayesh Soni and Himanshu Upadhyay and Prince Patrick Anand Victor and Sarvapriya Tripathi}, doi = {10.1109/ACCESS.2025.3547677}, journal = {IEEE Access}, pages = {40703-40729}, shortjournal = {IEEE Access}, title = {Evaluating generative AI models for image-text modification}, volume = {13}, year = {2025}, }
• (2025). Enhancing voice spoofing detection: A hybrid approach with VGGish-LSTM model for improved security in automatic speaker verification systems. ACCESS, 13, 40682-40702. (www)

  @article{ , abstract = {Cybercrimes frequently manifest through the utilization of the internet. Spoofing attacks epitomize an advanced facet of contemporary cybercrime. Considerable research has been conducted in the domain of these diverse spoofing attack techniques. Recent spoofing attacks have predominantly concentrate on voice spoofing, driven by the vulnerabilities of Automatic Speaker Verification systems. Such attacks contains significant threats, resulting in fraudulent activities including imitation of someone’s voice for financial scam, bypassing security locks, or getting sensitive information and data. To address these concerns, this research focuses on the detection of voice spoofing attacks by utilizing a hybrid VGGish-LSTM model. While previous transfer learning methodologies have essentially been applied to image classification tasks, our methodology focuses on voice classification. In comparison to VGGish, another transfer learning model i.e. YAMNet is also utilized. These models support the extraction of significant features i.e. embeddings, capable of mitigating bias and facilitating analysis with limited datasets. For classification, LSTM and 1D CNN models are applied on the extracted embeddings. The evaluation uncovers that the hybrid VGGish-LSTM model exhibits better performance than alternative configurations. We achieved a remarkable accuracy of 99.11% with an equal error rate of 1.18% on ASV Spoof 2019 LA dataset. This research helps the general public to recognize spoof attacks, thereby helping in the prevention of frauds by the detection of synthesized speech.}, archive = {J_ACCESS}, author = {Komal Shahzad and Saima Farhan and Yasin-Ul-Haq and Rabia Sana and Salman Pathan}, doi = {10.1109/ACCESS.2025.3544639}, journal = {IEEE Access}, pages = {40682-40702}, shortjournal = {IEEE Access}, title = {Enhancing voice spoofing detection: A hybrid approach with VGGish-LSTM model for improved security in automatic speaker verification systems}, volume = {13}, year = {2025}, }
• (2025). Single-phase multilevel inverter with a coupled inductor. ACCESS, 13, 40671-40681. (www)

  @article{ , abstract = {Multilevel inverters have become indispensable components in various power electronic applications, particularly in medium-and high-voltage scenarios. This paper introduces a novel single-phase seventeen-level inverter design that utilizes three DC sources and a coupled inductor, resulting in a reduced size and cost. The incorporation of coupled inductors empowers multilevel inverters to enhance performance, yielding high-quality output voltages while minimizing current stress on power devices. A topology-specific level shifted pulse-width modulation (LS-PWM) technique is presented and thoroughly discussed. The proposed topology is simulated and a hardware prototype was constructed to verify the performance of the proposed seventeen-level topology. The outcomes indicate that the implemented topology successfully attains the anticipated performance. Furthermore, a comparative analysis with other existing topologies has been conducted, unequivocally confirming the superior performance, efficiency, and cost of the proposed topology, setting it apart as an outstanding solution in the field of multilevel inverters.}, archive = {J_ACCESS}, author = {Noor Jaafar Jawad Alradhawi and Sirin Koc and Khaled Heshamt Barakat Mohamed Ghazy and Abdolhamid Moallemi Khiavi}, doi = {10.1109/ACCESS.2025.3547835}, journal = {IEEE Access}, pages = {40671-40681}, shortjournal = {IEEE Access}, title = {Single-phase multilevel inverter with a coupled inductor}, volume = {13}, year = {2025}, }
• (2025). Robot-based assessment of sitting balance: Muscular responses to self- and externally-controlled perturbations in seated position. ACCESS, 13, 40658-40670. (www)

  @article{ , abstract = {In the field of rehabilitation, traditional methods for assessing and training core stability and trunk control involve exercises targeting breathing, muscle strengthening, and both static and dynamic balance. Recently, a novel approach using robotic platforms has emerged. These systems provide comprehensive quantitative balance assessment, covering static, voluntary, and reactive components. By offering precise measurements of voluntary movement control and automatic postural adjustments, these platforms offer valuable insights into the complex mechanisms of postural control, both in standing and sitting. In this study, we present a comprehensive characterization of two different sitting balance tasks performed with hunova, a medical robotic device equipped with two electromechanical platforms: one beneath the feet and one beneath the seat. Sixteen unimpaired subjects participated in the study, divided into two groups based on the assessment protocol: Group1 underwent an assessment testing performance when the sitting platform moved in response to the users’ weight shift; Group2 underwent an assessment testing postural responses to preprogrammed predictable continuous perturbations imposed by the device. We analyzed upper trunk motion, weight shift, and muscular activations, also in terms of spinal maps and muscle synergies, to investigate muscle recruitment mechanisms and motor control strategies. We found that subjects adopted different muscular strategies in the two assessments, and that arm position, visual feedback, and adjustable task parameters impacted on the stability as measured by the upper trunk motion. Through this comprehensive evaluation, we not only characterized sitting balance in response to self-induced and externally imposed perturbations but also gained insights into the potential of these tasks for assessment and rehabilitation applications.}, archive = {J_ACCESS}, author = {G. Marchesi and V. Squeri and A. Bellitto and G. Pinna and C. Pierella and A. Canessa and A. de Luca and M. Casadio}, doi = {10.1109/ACCESS.2025.3540654}, journal = {IEEE Access}, pages = {40658-40670}, shortjournal = {IEEE Access}, title = {Robot-based assessment of sitting balance: Muscular responses to self- and externally-controlled perturbations in seated position}, volume = {13}, year = {2025}, }
• (2025). A building block to the design of partial discharge free HV and MV cables: Modeling defects and discharge inception voltage. ACCESS, 13, 40649-40657. (www)

  @article{ , abstract = {It is well assessed that partial discharges, PD, are one of the major causes of MV and HV cable system breakdown, thus investigating partial discharge phenomenology and harmfulness is a key topic in relation to electric asset component reliability. Defects, which are able to generate partial discharges can be ineludible in cable systems, from manufacturing to on-field assembly, but they can also be structural, as in impregnated EHV cables. Associating defect type and size with partial discharge inception voltage at the design stage could allow cable systems free of partial discharges to be designed, for given type and size of defects, at nominal voltage. This paper shows how to model successfully internal and surface defects in cables providing an accurate estimate of the PD inception voltage. Such an estimate is validated through innovative and automatic PD measurement software which can be able, automatically, to acquire and identify the type of partial discharges generated by defects, rejecting efficiently noise.}, archive = {J_ACCESS}, author = {Gian Carlo Montanari and Muhammad Shafiq and Sukesh Babu Myneni}, doi = {10.1109/ACCESS.2025.3547333}, journal = {IEEE Access}, pages = {40649-40657}, shortjournal = {IEEE Access}, title = {A building block to the design of partial discharge free HV and MV cables: Modeling defects and discharge inception voltage}, volume = {13}, year = {2025}, }
• (2025). An optimized deep learning framework for energy-efficient resource allocation in UAV-assisted wireless networks. ACCESS, 13, 40632-40648. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) play a crucial role in resource management and offer advantages in fifth-generation (5G) and beyond networks, such as enhanced coverage, on-demand deployment, and flexible connectivity for managing data traffic spikes. However, limited power supply restricts operational time and demands efficient energy management. Real-time coordination of UAVs with ground networks and efficient data transmission further complicate resource management. Traditional methods often fail to provide the control and processing needed to optimize energy, resulting in higher operational costs, as UAVs require frequent recharging due to limited onboard energy. To address these limitations, this paper proposes a solution that combines the Double Exponential Crayfish Optimization Algorithm (DECOA) with Deep Belief Networks (DBN). DECOA merges the Crayfish Optimization Algorithm (COA) with Double Exponential Smoothing (DES) to enhance optimization for efficient resource allocation in UAV-assisted networks. DBN is applied to reduce overall network energy consumption while ensuring reliable connectivity by predicting energy consumption, enabling more accurate and optimized decision-making. The simulation results show that the proposed DECOA-based DBN model achieved an average energy efficiency of 0.80, a throughput of 0.75 Mb/s, and a computational time of 1434.5 seconds using 10 UAVs, thereby outperforming traditional methods by 26.5% in energy efficiency, 35.99% in computational time, and 35.08% in throughput. These metrics reflect a substantial reduction in energy consumption, quicker processing times, and higher data transmission rates, proving the effectiveness of the proposed DECOA-based DBN approach in UAV-assisted wireless networks.}, archive = {J_ACCESS}, author = {Yanan Tian and Adil Khan and Shabeer Ahmad and Syed Agha Hassnain Mohsan and Faten Khalid Karim and Babar Hayat and Samih M. Mostafa}, doi = {10.1109/ACCESS.2025.3546225}, journal = {IEEE Access}, pages = {40632-40648}, shortjournal = {IEEE Access}, title = {An optimized deep learning framework for energy-efficient resource allocation in UAV-assisted wireless networks}, volume = {13}, year = {2025}, }
• (2025). Real-time image processing applications in automatic BGA inspection system. ACCESS, 13, 40621-40631. (www)

  @article{ , abstract = {With the rapid advancement in wafer packaging technology, especially the surging demand for chips, enhancing product quality and process efficiency has become increasingly crucial. This article delves into the automatic detection of pins on Ball Grid Array (BGA) within wafer packaging processes. This system is engineered with a flexible software and hardware architecture to address evolving industrial requirements, facilitating swift adaptation to new processing standards and technological demands. By utilizing Programmable Logic Controller (PLC) to control a three-axis gantry slide combined with industrial camera imaging technology, this system achieves high efficiency and precise positioning, thereby delivering high-quality image. This article utilizes YOLOv10 image processing technology and machine learning algorithms to effectively achieve accurate identification and classification of BGA defects. The YOLOv10 is chosen for its outstanding recognition capabilities and swift processing speed, enabling the rapid and accurate identification of minor defects, such as bent pins, missing pins, and solder ball defects. Through large image analysis, this system has been proven to enhance detection accuracy and reduce errors of manual detection. This article primarily addresses issues in semiconductor manufacturing processes and improves the product yield rate in current production lines. By effectively integrating AI-based detection technology into semiconductor manufacturing, it replaces labor-intensive tasks, enhancing efficiency and precision.}, archive = {J_ACCESS}, author = {Chiung-Hsing Chen and Cheng-Chang Chiu and Shao-En Kao and Hsiang Li}, doi = {10.1109/ACCESS.2025.3546056}, journal = {IEEE Access}, pages = {40621-40631}, shortjournal = {IEEE Access}, title = {Real-time image processing applications in automatic BGA inspection system}, volume = {13}, year = {2025}, }
• (2025). A virtual infrastructure model based on data reuse to support intelligent transportation system applications. ACCESS, 13, 40607-40620. (www)

  @article{ , abstract = {Intelligent Transportation Systems (ITS) have significantly improved transportation quality by using applications capable of monitoring, managing, and improving the transportation system. However, the large number of devices required to provide data to ITS applications has become a challenge in recent years, particularly the high installation and maintenance costs made broad deployment impracticable. Despite several advances in smart city research and the internet of things (IoT), research on ITS is still in the early stages. In this sense, to improve data collection and maintenance strategies for ITS systems, this article proposes a virtual infrastructure model based on data reuse, mainly autonomous vehicle (AV) data, to support ITS applications. It presents design choices and challenges for deploying a virtual infrastructure based on Beyond 5G (B5G) communication and data reuse, followed by developing a proof of concept of an AV data acquisition system evaluated through simulation. The results show that the extra data collection module results in a 1.1% increase in total memory usage with direct sensor collection and a 2.6% increase with application performance management (APM) data collection on the reference hardware. This data reuse setup can significantly improve ITS data challenges with minimal impact on current technology stack on the Autonomous vehicles currently in circulation.}, archive = {J_ACCESS}, author = {Christian Miranda and Antonio Santos da Silva and João Paulo Javidi da Costa and Giovanni Almeida Santos and Daniel Alves da Silva and Edison Pignaton de Freitas and Alexey Vinel}, doi = {10.1109/ACCESS.2025.3547160}, journal = {IEEE Access}, pages = {40607-40620}, shortjournal = {IEEE Access}, title = {A virtual infrastructure model based on data reuse to support intelligent transportation system applications}, volume = {13}, year = {2025}, }
• (2025). Pulmonary acoustic sensor telemetry array: A prototype for remote monitoring, diagnosis, and prognostication of pulmonary sounds. ACCESS, 13, 40594-40606. (www)

  @article{ , abstract = {This paper presents the pulmonary acoustic sensor telemetry array, a novel hardware prototype developed for remote pulmonary monitoring, diagnosis, and prognostication of pulmonary sounds in clinical settings. The proposed hardware prototype employs an array of acoustic sensors strategically positioned at standard auscultation sites on the patient’s chest and back, enabling real-time acquisition and analysis of pulmonary sounds with an edge computing device. This task is beyond the capabilities of existing commercially available digital stethoscopes. The hardware prototype presented herein addresses critical challenges in pulmonary care by providing continuous, high-fidelity, multi-site acoustic data collection. This functionality is pivotal for enhancing diagnostic accuracy and enabling early detection of respiratory abnormalities, potentially reducing treatment delays in acute respiratory conditions. The pulmonary acoustic sensor telemetry array (PASTA) integrates a compact design, multi-channel monitoring, and real-time telemetry, distinguishing itself from existing commercial medical devices. Performance evaluations against established digital stethoscopes, conducted using a high-fidelity medical simulation training manikin, demonstrate adequate sensitivity and signal quality. The potential applications of the pulmonary acoustic sensor telemetry array extend beyond inpatient care, making it suitable for remote monitoring in outpatient environments such as hospital-at-home and outpatient hospice programs. By bridging a significant gap in current medical device offerings, the pulmonary acoustic sensor telemetry array sets a new standard in pulmonary auscultation and serves as a foundational platform for future advancements in Internet-of-Things enabled healthcare solutions.}, archive = {J_ACCESS}, author = {Md Suruz Miah and Zuguang Liu and Colin Diercks and Connor R. Davey and Reid D. Jockisch and Adam R. Cross}, doi = {10.1109/ACCESS.2025.3546967}, journal = {IEEE Access}, pages = {40594-40606}, shortjournal = {IEEE Access}, title = {Pulmonary acoustic sensor telemetry array: A prototype for remote monitoring, diagnosis, and prognostication of pulmonary sounds}, volume = {13}, year = {2025}, }
• (2025). Digital twin with soft actor-critic reinforcement learning for transitioning from industry 4.0 to 5.0. ACCESS, 13, 40577-40593. (www)

  @article{ , abstract = {Industry 5.0 builds on the foundations of Industry 4.0 and ensures the transition to a sustainable, flexible, and human-centric workplace. The transition from Industry 4.0 to Industry 5.0 addresses important limitations of traditional automation, such as limited flexibility, human-machine collaboration. Industry 5.0 seeks to overcome these issues through evolution, with an emphasis on sustainability, resilience, and human-centric interactions. However, these changes come with challenges related to real-time adaptation and optimal resource allocation in dynamic industrial environments. To facilitate this transition, we propose a Digital Twin (DT) system that employs a Soft Actor-Critic (SAC), which is a reinforcement learning algorithm. This integration enhances adaptability in real-time, efficient resource allocation, and flexibility in dynamic industrial environments. The SAC-DT algorithm is evaluated using three main scenarios: demand change management, real-time control under uncertain conditions, and optimization of performance. These scenarios demonstrate how the SAC-DT increases productivity, adaptability, reliability, energy efficiency, and reduced downtime. The proposed scheme is compared with baseline models which include DRL-DRP and GCNN-DRL. The analyses show that the SAC-DT scheme yields better productivity, flexibility and, resource utilization efficiency, thus highlighting the applicability of this proposed scheme as valuable to move closer to Industry 5.0 that will guarantee solid, reliable, pragmatic, sustainable technological implementations that meet industrial goals and requirements in future.}, archive = {J_ACCESS}, author = {Hamid Asmat and Ikram Ud Din and Ahmad Almogren and Muhammad Yasar Khan}, doi = {10.1109/ACCESS.2025.3546085}, journal = {IEEE Access}, pages = {40577-40593}, shortjournal = {IEEE Access}, title = {Digital twin with soft actor-critic reinforcement learning for transitioning from industry 4.0 to 5.0}, volume = {13}, year = {2025}, }
• (2025). Multi-objective approach for distribution system planning considering stochastic customer-owned distributed energy resources. ACCESS, 13, 40561-40576. (www)

  @article{ , abstract = {The installation of distributed energy resources (DERs) by end-customers has increased significantly in recent years. While customer-owned DERs offer various benefits, their integration adds complexity to distribution system planning due to the unpredictability of their quantity, sizes, and locations. Consequently, they are referred to as stochastic customer-owned DERs in this study. In the specialized literature, optimization-based DER allocation is often explored; however, these allocation problems typically assume that the distribution system operator has control over the allocation of DERs within the grid, whereas real-world investments are predominantly driven by end-customers. Therefore, in this article, a multi-objective planning approach is proposed considering stochastic customer-owned DERs. This approach simultaneously addresses dynamic network reconfiguration, capacitors allocation, and dynamic adjustment of on-load tap changer transformer. The main objective is to minimize power loss costs and improve the system’s voltage profile by minimizing a novel voltage consistency indicator (VCI) introduced in this article. The optimization problem is solved using the Multi-objective Cuckoo Search, with the final solution selected through the Fuzzy Decision-making Method. Additionally, the Monte Carlo Method is employed to address uncertainties related to load, DER generation, and the quantity, sizes, and locations of customer-owned DERs. Finally, the results highlighted the importance of conducting a multi-period analysis, as it led to improvements compared to the static analysis. The proposed approach effectively reduced the VCI by 72.41% and minimized the power loss costs by 68.56% compared to the original system.}, archive = {J_ACCESS}, author = {Renato S. F. Ferraz and Rafael S. F. Ferraz and Vanio F. S. Júnior and Augusto C. Rueda-Medina}, doi = {10.1109/ACCESS.2025.3547277}, journal = {IEEE Access}, pages = {40561-40576}, shortjournal = {IEEE Access}, title = {Multi-objective approach for distribution system planning considering stochastic customer-owned distributed energy resources}, volume = {13}, year = {2025}, }
• (2025). A comprehensive evaluation and assessment practices of electromagnetic interferences in electric vehicle. ACCESS, 13, 40520-40560. (www)

  @article{ , abstract = {In this work, electromagnetic interference (EMI) poses significant challenges in a variety of industries, with the automotive sector being notably impacted by its stringent electromagnetic compatibility (EMC) standards and regulations. This article investigates the primary factors that motivate EMI research and evaluates the most recent advancements in EMI management. Examining the driving forces behind EMI research and navigating through the current state-of-the-art knowledge surrounding EMI. This includes characterization in both time and frequency domains, with conducted EMI generally ranging from 150 kHz to 30 MHz and radiated EMI from 30 MHz to 1 GHz, all of which have substantial consequences for electric vehicles (EVs). The investigation also explores the regulations that govern EMC in the automotive sector, providing a comprehensive understanding of significant standards including the United Nations Economic Commission for Europe (UNECE), the International Special Committee on Radio Interference (CISPR), the International Standard Organization (ISO), and the Society of Automotive Engineers (SAE). Additionally, it examines EMI measurement techniques for emission testing, emphasizing the use of EMI filters like FMLF, AEF, and HEF, achieving suppression from <40 dB $\mu $ V to 65 dB. Soft-switching techniques such as ZVS and ZCS improve efficiency (up to 97.4%) and reduce EMI from 7 dB to 35 dB. Random modulation and chaotic PWM techniques show suppression levels ranging from 12 dB to 40 dB. Additionally, the paper outlines potential future challenges and opportunities in managing EMI, highlighting the importance of continuous research to overcome these obstacles and improve the overall reliability and performance of EV systems.}, archive = {J_ACCESS}, author = {K. Ramya and Janaki Gopalakrishnan and Bharatiraja Chokkalingam and Rajesh Verma and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3534017}, journal = {IEEE Access}, pages = {40520-40560}, shortjournal = {IEEE Access}, title = {A comprehensive evaluation and assessment practices of electromagnetic interferences in electric vehicle}, volume = {13}, year = {2025}, }
• (2025). A novel privacy framework for satellite images based on theory of chaos and chess pieces castle and king. ACCESS, 13, 40503-40519. (www)

  @article{ , abstract = {Satellite images are normally captured through Unmanned Aerial Vehicles (UAVs), drones, different aircraft, and other flying objects. These images are being used in many areas to get their utility. Occasionally, these images are very sensitive and confidential. Hence, they must be protected through some state-of-the-art cryptosystem. In this work, a novel image cryptographic work has been carried out by combining chaotic systems and the chess pieces of Castle and King. Apart from that, a 5D multi-wing hyperchaotic system has been employed to spawn the streams of random numbers. These streams of random numbers have been further customized to make them a good fit for the proposed algorithm. Two pairs of random numbers from the first & second streams and third & fourth streams control the addresses of the chess pieces, Castle and King, on the big chessboard. As these chess pieces maneuver on the chessboard, the pixels of the satellite images are swapped with each other. In particular, the movement of the chess pieces has been ingeniously linked with the swapping of the pixels of the given satellite images. These operations have been repeated at certain times to inject confusion effects in the suggested image encryption algorithm. The fifth and last stream of random numbers has been employed to introduce the requisite diffusion effects. Plaintext sensitivity –a necessary feature to foil the potential differential attacks, has been achieved by taking the SHA-256 hash codes of given images. Simulation results validate proof of concept. Moreover, the cipher has been subjected to comprehensive performance analyses using the security parameters of key space, histogram, correlation, information entropy, local Shannon entropy, irregular and maximum deviation, etc. We got an information entropy of 7.9981 and a computational time of 1.2445 seconds. These results suggest that the proposed satellite image cipher can be installed in some real-world settings to reap its benefits.}, archive = {J_ACCESS}, author = {Nayef Alqahtani}, doi = {10.1109/ACCESS.2025.3541833}, journal = {IEEE Access}, pages = {40503-40519}, shortjournal = {IEEE Access}, title = {A novel privacy framework for satellite images based on theory of chaos and chess pieces castle and king}, volume = {13}, year = {2025}, }
• (2025). DOA estimation by feature extraction based on parallel deep neural networks and MRMR feature selection algorithm. ACCESS, 13, 40480-40502. (www)

  @article{ , abstract = {An active research topic in the array signal processing field is the direction of arrival (DOA) estimation, where establishing an efficient estimation of radio waves received from an antenna array is of great interest. Recent studies mostly focus on deep neural network (DNN) algorithms to improve the performance of DOA estimation in mmWave communication systems. These studies did not give importance to the diversity of features extracted from the data and the relationship between them, which affects the efficiency of DOA estimation. To improve the performance, we propose a new model for DOA estimation based on a parallel deep neural network (PDNN). It efficiently extracts spatial and temporal features, then fuses them and selects the distinct ones while removing the redundant ones. In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). Combining spatial and temporal features increases efficiency due to feature diversity. Then, a minimum redundancy and maximum relevance (MRMR) algorithm is proposed to select the distinct features. The performance of the proposed model is evaluated by conducting a wide range of experiments on uncorrelated narrowband data with different scenarios and based on the root mean square error (RMSE) coefficient. The results by computer simulations on a uniform linear array show that the proposed structure significantly increases the accuracy of DOA estimation and is more robust to changes in input parameters than traditional algorithms, such as estimation of signal parameters via invariant rotation (ESPRIT), classification of multiple signals (MUSIC), and DNN method. These results demonstrate the superiority of the proposed PDNN model over other techniques, especially in terms of accuracy.}, archive = {J_ACCESS}, author = {Ashwaq Neaman Hassan Al-Tameemi and Mahmood Mohassel Feghhi and Behzad Mozaffari Tazehkand}, doi = {10.1109/ACCESS.2025.3547146}, journal = {IEEE Access}, pages = {40480-40502}, shortjournal = {IEEE Access}, title = {DOA estimation by feature extraction based on parallel deep neural networks and MRMR feature selection algorithm}, volume = {13}, year = {2025}, }
• (2025). Bridging research gaps in industry 5.0: Synergizing federated learning, collaborative robotics, and autonomous systems for enhanced operational efficiency and sustainability. ACCESS, 13, 40456-40479. (www)

  @article{ , abstract = {Industry 5.0 marks a transformative shift in industrial operations, emphasizing human-machine collaboration, sustainability, and intelligent automation. This study conducts a Systematic Literature Review (SLR) to analyze research contributions from leading digital libraries, including IEEE, ACM, Springer, and MDPI, focusing on publications between 1992 and 2024. The objective is to identify existing research gaps, challenges, and synergies across Federated Learning (FL), Collaborative Robotics (Cobots), and Autonomous Systems (AS). From an initial collection of 12,079 papers, a core set of 92 relevant publications was selected based on predefined research questions. This review provides a structured analysis to guide scholars and practitioners in optimizing the interplay between FL, cobots, and autonomous systems, aiming to enhance adaptability, operational resilience, and environmental sustainability. By addressing unresolved challenges and highlighting emerging opportunities, this SLR contributes to bridging research gaps and supports the development of strategic frameworks that balance efficiency with sustainability, advancing the goals of Industry 5.0.}, archive = {J_ACCESS}, author = {Ashraf Zia and Muhammad Haleem}, doi = {10.1109/ACCESS.2025.3541822}, journal = {IEEE Access}, pages = {40456-40479}, shortjournal = {IEEE Access}, title = {Bridging research gaps in industry 5.0: Synergizing federated learning, collaborative robotics, and autonomous systems for enhanced operational efficiency and sustainability}, volume = {13}, year = {2025}, }
• (2025). Generative artificial intelligence in computer programming: Does it enhance learning, motivation, and the learning environment?. ACCESS, 13, 40438-40455. (www)

  @article{ , abstract = {Generative artificial intelligence (GenAI) is emerging as a transformative technology in higher education, particularly in programming instruction. However, its impact on learning, motivation, and the educational environment must still be fully understood. This study aims to determine the capacity of GenAI to generate effective computer programming learning in STEM university students, comparing it with active learning methods based on video. An experiment was conducted with 40 computer engineering students divided into two groups: one using GenAI (Google Gemini 1.5) and another employing educational videos. Pre- and post-tests of knowledge and the Intrinsic Motivation Inventory (IMI) were applied to evaluate learning, intrinsic motivation, and the learning environment. No significant differences in learning were found between the groups. However, GenAI significantly increased perceived autonomy and reduced perceived effort and pressure, while video-based learning significantly improved perceived competence. These findings suggest that both methods seem to motivate in diverse ways and that they could complement each other in an integrated teaching approach, offering new perspectives for designing programming learning environments in higher education.}, archive = {J_ACCESS}, author = {Claudio Cubillos and Rafael Mellado and Daniel Cabrera-Paniagua and Enrique Urra}, doi = {10.1109/ACCESS.2025.3532883}, journal = {IEEE Access}, pages = {40438-40455}, shortjournal = {IEEE Access}, title = {Generative artificial intelligence in computer programming: Does it enhance learning, motivation, and the learning environment?}, volume = {13}, year = {2025}, }
• (2025). DVL2024: A larger ultra-high-definition video dataset for perceptual quality study. ACCESS, 13, 40429-40437. (www)

  @article{ , abstract = {This paper introduces DVL2024, an expansive ultra-high definition (UHD) video dataset meticulously curated for advancing perceptual video quality assessment (VQA) research. DVL2024 stands as the largest available UHD video quality assessment dataset in stunning 4K resolution ( $3840\times 2160$ ), making it a highly valuable resource for the research community. The dataset comprises a diverse collection of 643 4K UHD video sequences, each spanning 10 seconds and captured at a high frame rate of 50 frames per second (fps). The videos are stored in raw 10-bit 4:2:0 YUV format, ensuring their fidelity and authenticity. To ensure the reliability of subjective evaluations, our annotation process adheres to the rigorous guidelines set forth by ITU-R BT.500-13, involving the participation of 32 carefully selected individuals with a wide age range, spanning from teenagers to sixties individuals, with an average age of 27.2 years. DVL2024 exhibits several notable advantages that contribute to its importance in research. The dataset encompasses a wide range of video content, capturing diverse scenes and subjects while incorporating recordings from various camera types. It also incorporates a rich variety of complex and realistic distortions, providing a comprehensive evaluation of perceptual quality. The subjective scores provided by the participants are meticulously balanced, resulting in a robust and reliable Mean Opinion Score (MOS) distribution. Furthermore, we conducted benchmark experiments using DVL2024 to assess the performance of widely recognized VQA methods. The meticulously crafted dataset we have developed has demonstrated exceptional performance in rigorous benchmark evaluations, consistently exceeding the SROCC threshold of 0.7 and even achieving remarkable levels of 0.8 across all tested networks. These findings validate the significance of DVL2024 and pave the way for the advancement of UHD VQA research. We are committed to making the DVL2024 dataset widely accessible, fostering research and development in video quality assessment. DVL2024 is publicly available at https://github.com/GZHU-DVL/DVL2021.}, archive = {J_ACCESS}, author = {Jiefeng He and Jinchun Yuan and Fengchuang Xing}, doi = {10.1109/ACCESS.2025.3541021}, journal = {IEEE Access}, pages = {40429-40437}, shortjournal = {IEEE Access}, title = {DVL2024: A larger ultra-high-definition video dataset for perceptual quality study}, volume = {13}, year = {2025}, }
• (2025). An artificial intelligence-based approach with photoplethysmogram and heart rate variability for sleep bruxism diagnosis. ACCESS, 13, 40413-40428. (www)

  @article{ , abstract = {Bruxism is jaw muscle activity that can cause functional and aesthetic changes in the jaw and dental structures of individuals. It can be observed during sleep or while awake. Owing to the disadvantages of using polysomnography (PSG) for the definitive diagnosis of bruxism, it is important to develop alternative and reliable diagnostic systems. In this study, we propose a noninvasive and practical solution for diagnosing sleep bruxism using photoplethysmogram (PPG) and heart rate variability (HRV). We created a database by extracting features from PPG and HRV. We used Principal Component Analysis (PCA) to reduce the data size and Fisher’s feature selection algorithm to identify the most important features. Using four artificial intelligence (AI) algorithms, we built classification models that distinguished bruxism labels from control labels. The models were optimized and tested using unseen data. We evaluated the performance of the models using six criteria and validated them using leave-one-out (LOO). Singular Value Decomposition (SVD) of HRV is the most important biomarker for separating bruxism from control data. In addition, the durations of the falling and rising edges of the PPG and the amplitude values of these durations in some percentiles are also important for increasing classification success. The results, methodology used, and easy acquisition of PPG and HRV make it possible to apply the proposed model to embedded systems. The use of the proposed model in clinical evaluation provides significant advantages. This study is innovative in the literature and sheds light on future studies.}, archive = {J_ACCESS}, author = {Omer Eris and Mehmet Recep Bozkurt and Seval Bulut Eris and Cahit Bilgin}, doi = {10.1109/ACCESS.2025.3546720}, journal = {IEEE Access}, pages = {40413-40428}, shortjournal = {IEEE Access}, title = {An artificial intelligence-based approach with photoplethysmogram and heart rate variability for sleep bruxism diagnosis}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection on smart grids with optimized convolutional long short-term memory model. ACCESS, 13, 40399-40412. (www)

  @article{ , abstract = {The integration of digital technologies into traditional power systems has increased the efficiency and sustainability of power grids, transforming traditional grids into smart grids. However, this transformation has also introduced new vulnerabilities, such as susceptibility to false data injection (FDI) attacks, which can lead to significant energy theft. Recent reports estimate that these attacks cost utility providers approximately 101 billion dollars annually. This study presents an approach for anomaly detection in smart grids through energy consumption readings from smart meters on the customer side using an optimized lightweight convolutional long short-term memory (ConvLSTM) model. This study benchmarks and evaluates different machine learning models against seven FDI attacks, which are multi-class labeled. The evaluated machine learning models include traditional shallow detectors, deep learning-based detectors, and hybrid models that employ both horizontal and vertical detection strategies. Through extensive experimentation, the optimized ConvLSTM model is shown to demonstrate superior performance in detecting attacks; it achieves a high accuracy of 91.3% compared with other models in classifying these attacks. The results indicate that the proposed model provides a robust solution for improving the security and reliability of smart grids, and it offers significant benefits to utility providers who seek to mitigate energy theft and enhance grid resilience.}, archive = {J_ACCESS}, author = {Ahmad N. Alkuwari and Saif Al-Kuwari and Abdullatif Albaseer and Marwa Qaraqe}, doi = {10.1109/ACCESS.2025.3547037}, journal = {IEEE Access}, pages = {40399-40412}, shortjournal = {IEEE Access}, title = {Anomaly detection on smart grids with optimized convolutional long short-term memory model}, volume = {13}, year = {2025}, }
• (2025). Research on dynamic optimization control strategy with the synergistic participation of wind power and energy storage system for power grid. ACCESS, 13, 40386-40398. (www)

  @article{ , abstract = {The uncertainty of the sustainable energy such as wind power has serious adverse impact on the stability of power grid with the penetration of it increasing. The utilization of the sustainable energy in automatic generation control of power grid was an effective method to solve the problem. Therefore, an optimal strategy of frequency regulation with the participation of wind power and battery energy storage system was proposed in this paper. Firstly, the automatic generation control (AGC) model of multi-area interconnected power grid with high penetration of wind power was constructed. Fast frequency response optimized power point tracking (FFR-OPPT) method was designed aiming at the participation of wind turbine generator (WTG) in AGC. Due to the different positions of the WTG and the influence of its wake effect, the bearable wind speeds were different. According to that wind speed, the WTGs were classified. Secondly, considering that WTG controlled by FFR-OPPT was constrained by the wind speed, a comprehensive control method of battery energy storage system (BESS) was applied to assist WTG for the frequency regulation. The change of BESS participation states was determined not only by the wind speed but also by its state of charge (SOC). Considering the complexity of the control system and the requirement of frequency regulation in which WTG and BESS participate, the model predictive control (MPC) controller was used for dynamic optimal control of the system. The simulation results show that the control method proposed in this paper was feasible and efficient.}, archive = {J_ACCESS}, author = {Xi-Lin Zhao and Ze-Lin Gui and Chu-Feng Gong and Xiao-Yu Ye}, doi = {10.1109/ACCESS.2025.3546652}, journal = {IEEE Access}, pages = {40386-40398}, shortjournal = {IEEE Access}, title = {Research on dynamic optimization control strategy with the synergistic participation of wind power and energy storage system for power grid}, volume = {13}, year = {2025}, }
• (2025). Enhancing data privacy: A comprehensive survey of privacy-enabling technologies. ACCESS, 13, 40354-40385. (www)

  @article{ , abstract = {Privacy is a fundamental human right, especially crucial in our modern digital age. With the rapid advancement of technology, ensuring individuals’ privacy has become increasingly complex. Our survey paper aims to shed light on various privacy engineering technologies that play a crucial role in protecting personal data. We delve into four key areas: data anonymization, data encryption, synthetic data generation, and differential privacy. These technologies serve as essential tools in safeguarding online privacy. Data anonymization, for instance, includes removing or modifying identifiable information from datasets to protect individuals’ identities. Encryption secures data by converting it into a code that can only be decoded by authorized parties. Synthetic data generation creates artificial data that closely resembles real data but doesn’t contain any identifiable information. Differential privacy adds a small amount of controlled noise to protect sensitive information. Throughout our exploration, we not only explain the principles and techniques behind these technologies but also the tools used for each of these techniques and evaluation criteria and also examine their practical applications. By understanding their strengths, limitations, and real-world implementations, we gain valuable insights into how they contribute to the broader goal of ensuring privacy in our digital world.}, archive = {J_ACCESS}, author = {Qaiser Razi and Raja Piyush and Arjab Chakrabarti and Anushka Singh and Vikas Hassija and G. S. S. Chalapathi}, doi = {10.1109/ACCESS.2025.3546618}, journal = {IEEE Access}, pages = {40354-40385}, shortjournal = {IEEE Access}, title = {Enhancing data privacy: A comprehensive survey of privacy-enabling technologies}, volume = {13}, year = {2025}, }
• (2025). The neuromarketing: Bridging neuroscience and marketing for enhanced consumer engagement. ACCESS, 13, 40331-40353. (www)

  @article{ , abstract = {This review paper examines the influence of neuromarketing on consumer behavior research, emphasizing its origins, methodologies, impacts, and implications for customer engagement. This paper conducts a thorough narrative literature review on neuromarketing, analyzing the application of diverse neuroscientific techniques, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), positron emission tomography (PET), magnetoencephalography (MEG), and facial coding/eye tracking, to assess cerebral responses and forecast consumer behavior. Recent scholarly investigations underscore how neuromarketing utilizes contemporary breakthroughs to transform consumer behavior research by offering enhanced insights into cognition and emotion. These sophisticated tools have contested conventional study methodologies, facilitating a more comprehensive comprehension of the interaction between marketing stimuli and the brain. The theoretical and practical aspects of the findings are that advanced neuroimaging techniques like fMRI, EEG, and eye-tracking have revealed the complex interactions between emotions, attention, and memory that traditional methods and neuromarketing transform marketing by giving marketers new tools and approaches to boost results. This review synthesizes previous research on the topic, highlighting the necessity for equilibrium between scientific advancement and ethical accountability in neuromarketing, thereby benefiting both academic and commercial sectors.}, archive = {J_ACCESS}, author = {Guo Song and Md. Abu Issa Gazi and Abdul Waaje and Md. Mustaqim Roshid and Rejaul Karim and Md. Atikur Rahaman and Zhang Min and Abdul Rahman Bin S. Senathirajah}, doi = {10.1109/ACCESS.2025.3545742}, journal = {IEEE Access}, pages = {40331-40353}, shortjournal = {IEEE Access}, title = {The neuromarketing: Bridging neuroscience and marketing for enhanced consumer engagement}, volume = {13}, year = {2025}, }
• (2025). Enhancing physical education analytics: An analytic hierarchy process-based approach for college sport. ACCESS, 13, 40317-40330. (www)

  @article{ , abstract = {Assessment of the sports skills of college students is a very hectic and uncertain process due to the inclusion of various ambiguous and uncertain factors. To rank and evaluate performance in sports organizations, this study suggests a hybrid model combining the multi-criteria decision-making (MCDM) model with the Fuzzy Analytical Hierarchy Process (F-AHP). Sports organizations can evaluate performance, provide rankings, and increase management effectiveness with the help of this approach. Additionally, the paper presents sophisticated aggregation operators to deal with ambiguous and uncertain data, including power operators and Aczel-Alsina expressions. Picture Fuzzy Aczel Alsina Power Weighted Average (PFAAPWA) and Ordered Weighted Average (PFAAPOWA) are two new tools that result from the expansion of these operators with innovative techniques in picture fuzzy environments. These approaches facilitate the improvement of data aggregation and the elucidation of intricate criteria linkages. An algorithm for multi-attribute group decision-making is also created, improving choice accuracy when information is unclear.}, archive = {J_ACCESS}, author = {Yuanxin Gui}, doi = {10.1109/ACCESS.2025.3542557}, journal = {IEEE Access}, pages = {40317-40330}, shortjournal = {IEEE Access}, title = {Enhancing physical education analytics: An analytic hierarchy process-based approach for college sport}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A visual prompt-based mobile learning system for improved algebraic understanding in students with learning disabilities”. ACCESS, 13, 40316. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “A Visual Prompt-Based Mobile Learning System for Improved Algebraic Understanding in Students With Learning Disabilities”}, archive = {J_ACCESS}, author = {Peng-Chan Chang and Rong-Ho Lin}, doi = {10.1109/ACCESS.2025.3539110}, journal = {IEEE Access}, pages = {40316}, shortjournal = {IEEE Access}, title = {Corrections to “A visual prompt-based mobile learning system for improved algebraic understanding in students with learning disabilities”}, volume = {13}, year = {2025}, }
• (2025). Towards a context awareness system using IoT, AI, and big data technologies. ACCESS, 13, 40302-40315. (www)

  @article{ , abstract = {The intensive use of connected objects in smart homes has led to a growing demand for intelligent systems for the automatic and simplified control of daily life. These systems seek to simplify the control of functions such as lighting, heating, security, and appliance operation, all of which contribute to household comfort and energy efficiency. In this view, context, which encompasses information about the physical environment, user preferences, ongoing activities, and other relevant variables, can greatly enhance the ability of smart home systems to anticipate and respond to the needs of occupants. However, integrating context in smart home systems presents significant technical and conceptual challenges. One key challenge is the development of accurate and dynamic context models that can consider diverse and changing variables. Additionally, the need to process large volumes of data from multiple IoT devices in real-time requires robust and scalable architectures. This study focuses on creating a context-aware system that leverages AI, IoT, and big data technologies to simplify home function control, anticipate user needs, and address the challenges of context modeling and real-time data processing, ultimately contributing to smarter, more responsive interactions between users and their home environment. We start our study by exploring the domain of context-awareness and architectures related to IoT, Big Data, and AI. We then propose a design of an innovative architecture for a context-aware application for smart homes. We finally test the feasibility and performance of the proposed architecture in a real environment.}, archive = {J_ACCESS}, author = {Sanaa Mouhim and Fadwa Lachhab}, doi = {10.1109/ACCESS.2025.3546865}, journal = {IEEE Access}, pages = {40302-40315}, shortjournal = {IEEE Access}, title = {Towards a context awareness system using IoT, AI, and big data technologies}, volume = {13}, year = {2025}, }
• (2025). Revealing the influence of semantic similarity on survey responses: A synthetic data generation approach. ACCESS, 13, 40285-40301. (www)

  @article{ , abstract = {Questionnaires are essential for measuring self-reported attitudes, beliefs, and behaviour in many research fields. Semantic similarity of the questions is recognized as a source of covariance in the human data, implying that response patterns partly arise from the questionnaire itself. A practical method to assess the influence of semantic similarity could significantly facilitate the design of questionnaires and the interpretation of their results. The current study presents a novel method for estimating the influence of semantic similarity for questionnaires with Likert-scale responses. The method transforms Likert-scale responses into natural language sentences and applies the Sentence-BERT algorithm to compute a semantic similarity matrix. Synthetic response data are generated using the semantic similarity matrix and a noise parameter as input. Synthetic data can then be analysed using the same tools as human survey data, making the comparison straightforward. The method was tested with a questionnaire measuring the acceptance of automated driving. Synthetic data explained 40% of the observed correlations in the human response data. This means that semantic similarity substantially influenced responses. Using synthetic data, it was possible to identify the same factor structure as in the human data and to identify relationships between factors that might have been inflated by semantic similarity. This demonstrates that semantically generated synthetic data could help in designing multi-factor questionnaires and correctly interpreting the found relationships between factors.}, archive = {J_ACCESS}, author = {Esko Lehtonen and Tommi Buder-Gröndahl and Sina Nordhoff}, doi = {10.1109/ACCESS.2025.3546565}, journal = {IEEE Access}, pages = {40285-40301}, shortjournal = {IEEE Access}, title = {Revealing the influence of semantic similarity on survey responses: A synthetic data generation approach}, volume = {13}, year = {2025}, }
• (2025). An on-orbit data marketplace for distributed space domain awareness. ACCESS, 13, 40275-40284. (www)

  @article{ , abstract = {Modern space vehicles capture a wealth of sensor data that is in high-demand by a wide range of commercial and nation-state beneficiaries. This data can be used to monitor our planet, monitor space traffic and manage collision avoidance, among other use cases. While there is ongoing investment into cross-linking satellite constellations and building an ‘on-orbit internet’, it remains unclear how the integral requesting and delivery of this data will be facilitated. We propose a distributed marketplace for space domain awareness that engages smart contracts and a distributed ledger to manage the engagement of satellites across an untrusted ecosystem. Furthermore, this work implements a simulated on-orbit marketplace consisting of independent nodes able to efficiently and securely exchange data and services under a series of representative scenarios. The marketplace software payload was designed to conform to the power and compute constraints of a modern space vehicle while implementing secure smart contracting and format-preserving validation techniques, demonstrating the practical viability of the on-orbit marketplace as a concept.}, archive = {J_ACCESS}, author = {Nathaniel G. Gordon and Gregory Falco}, doi = {10.1109/ACCESS.2025.3547271}, journal = {IEEE Access}, pages = {40275-40284}, shortjournal = {IEEE Access}, title = {An on-orbit data marketplace for distributed space domain awareness}, volume = {13}, year = {2025}, }
• (2025). Privacy and accuracy implications of model complexity and integration in heterogeneous federated learning. ACCESS, 13, 40258-40274. (www)

  @article{ , abstract = {Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying computational capabilities and thus train models with different complexities compared to the server’s model. However, FL is not without vulnerabilities: recent studies have shown that it is susceptible to membership inference attacks (MIA), which can compromise the privacy of client data. In this paper, we examine the intersection of these two aspects, heterogeneous FL and its privacy vulnerabilities, by focusing on the role of client model integration, the process through which the server integrates parameters from clients’ smaller models into its larger model. To better understand this process, we first propose a taxonomy that categorizes existing heterogeneous FL methods and enables the design of seven novel heterogeneous FL model integration strategies. Using CIFAR-10, CIFAR-100, and FEMNIST vision datasets, we evaluate the privacy and accuracy trade-offs of these approaches under three types of MIAs. Our findings reveal significant differences in privacy leakage and performance depending on the integration method. Notably, introducing randomness in the model integration process enhances client privacy while maintaining competitive accuracy for both the clients and the server. This work provides quantitative light on the privacy-accuracy implications client model integration in heterogeneous FL settings, paving the way towards more secure and efficient FL systems.}, archive = {J_ACCESS}, author = {Gergely D. Németh and Miguel Ángel Lozano and Novi Quadrianto and Nuria Oliver}, doi = {10.1109/ACCESS.2025.3546478}, journal = {IEEE Access}, pages = {40258-40274}, shortjournal = {IEEE Access}, title = {Privacy and accuracy implications of model complexity and integration in heterogeneous federated learning}, volume = {13}, year = {2025}, }
• (2025). Optimizing air pollution forecasting models through knowledge distillation: A novel GCN and TRANS_GRU methodology for indian cities. ACCESS, 13, 40237-40257. (www)

  @article{ , abstract = {As air pollution remains a pressing issue affecting environmental and public health, precise forecasting and monitoring are essential. Machine learning and deep learning models are increasingly applied in air pollution studies, using their advanced capabilities to decode intricate pollutant data. A review of the existing literature on air quality forecasting reveals significant gaps: first, a lack of focus on identifying dominant pollutants for specific dates and cities, and second, the widespread use of models with excessively high computational demands. To address these challenges, we introduced the Graph Convolutional Networks (GCNs) for finding single and multi-dominant pollutants, and Transformer_Gated Recurrent Unit (TRANS_GRU) hybrid deep learning model for improved accuracy in the prediction of short and long-term pollutants levels subsequently applied Knowledge distillation (KD) for efficient, lightweight modelling. This approach demonstrates superior performance over traditional models such as long-short-term memory (LSTM) and LSTM_BiGRU (LSTM_Bidirectional Gated Recurrent Unit) in various metrics. The TRANS_GRU model achieves improvements of 2.36% in the R2 score, 15.23% in the mean squared error (MSE), 19.12% in the mean absolute error (MAE), and 7.93% in the root mean squared error (RMSE) compared to LSTM. Against LSTM_BiGRU, shows enhancements of 4.71% in the R2 score, 27.64% in the MSE, 33.19% in the MAE, and 14.86% in the RMSE. These advances not only address existing research gaps, but also equip environmental scientists and public health professionals with more effective tools to manage air quality every day.}, archive = {J_ACCESS}, author = {Sudhir Kumar and Vaneet Kour and Ankit Raj and Tagru Tapung and Shivendu Mishra and Rajiv Misra and T. N. Singh}, doi = {10.1109/ACCESS.2025.3546504}, journal = {IEEE Access}, pages = {40237-40257}, shortjournal = {IEEE Access}, title = {Optimizing air pollution forecasting models through knowledge distillation: A novel GCN and TRANS_GRU methodology for indian cities}, volume = {13}, year = {2025}, }
• (2025). Enhanced intention recognition model based on text classification. ACCESS, 13, 40226-40236. (www)

  @article{ , abstract = {Internet technology has been deeply integrated into human daily life, with a wide array of life-oriented apps significantly enhancing the convenience of daily activities. The customer service system, acting as the central communication hub between enterprises and customers, is under substantial business pressure. Intelligent customer service systems can efficiently assist human customer service representatives in addressing relatively simple and common issues based on user descriptions. As a vital branch of artificial intelligence, intention recognition can give all kinds of intelligent systems the ability to understand natural language. To address the limitation that traditional pre-trained model-based intent classification fails to introduce key text information in target intention classification, this paper proposes a novel model incorporating multi-granularity matching and inconsistent regularization. This model utilizes encoders to model key text information within the target intent and introduces multi-granularity matching to ensure enhanced model performance. Various experiments were conducted on a public dataset called KUAKE_QIC, and the results show that the proposed model has a good performance in terms of accuracy, in addition to the effectiveness of tail category recognition and the performance of small sample scenarios. Specifically, compared with the existing models, the accuracy rate of our model increased by 10.1%–0.96%, the recall rate increased by 4.85%–0.81%, the F1 score increased by 7.46%–0.88%, and the recall-3 metric improved by 7.14%–1.63%. The model proposed in this paper represents a significant advancement over existing models without pre-trained on dialogue corpora.}, archive = {J_ACCESS}, author = {Wenyu Shi and Vladimir Y. Mariano}, doi = {10.1109/ACCESS.2025.3547017}, journal = {IEEE Access}, pages = {40226-40236}, shortjournal = {IEEE Access}, title = {Enhanced intention recognition model based on text classification}, volume = {13}, year = {2025}, }
• (2025). A 0.8–1.4-GHz synthesizable DPLL using a background gain mismatch calibrated feedforward phase noise cancellation. ACCESS, 13, 40210-40225. (www)

  @article{ , abstract = {This article presents a fully synthesizable digital PLL supported by a feedforward phase noise cancellation (FPNC) path. With the FPNC path, the rms jitter of the DPLL was enhanced (reduced) by around 25% at different output frequencies. A gain mismatch calibration circuit was proposed to maintain this jitter enhancement over different process, voltage, and temperature (PVT) variations. A proof-of-concept chip was built in 65 nm CMOS technology with an area of 0.105 mm2. At an output frequency of 1.0 GHz, the rms jitter of the DPLL was decreased from 1.0 ps to 753 fs with the cost of around 0.7 mW power consumption due to the FPNC path and the calibration circuit. This jitter reduction improves FoM from -234.4 to -236.0 dB. To confirm the effectiveness of the FPNC path and its background calibration circuit, different measurements were performed under different output frequencies, supply voltages, bandwidths, and chips, revealing a performance enhancement under all those conditions.}, archive = {J_ACCESS}, author = {Waleed Madany and Yuncheng Zhang and Ashbir Aviat Fadila and Hongye Huang and Junjun Qiu and Jill Mayeda and Atsushi Shirane and Kenichi Okada}, doi = {10.1109/ACCESS.2025.3547366}, journal = {IEEE Access}, pages = {40210-40225}, shortjournal = {IEEE Access}, title = {A 0.8–1.4-GHz synthesizable DPLL using a background gain mismatch calibrated feedforward phase noise cancellation}, volume = {13}, year = {2025}, }
• (2025). Consensus partition guided incomplete multi-view clustering. ACCESS, 13, 40198-40209. (www)

  @article{ , abstract = {Incomplete multi-view clustering aims to derive a comprehensive consensus partition that is shared across all views, a topic that has attracted significant attention in recent years. Most existing methods for incomplete multi-view clustering attempt to directly learn the consensus partition matrix from noisy, complete or incomplete graphs. However, these approaches have limitations: they often overlook the discriminative information within view-specific partition matrices and fail to fully consider the relationships between the partition and the graph. To address these issues, we introduce two new regularization strategies in this paper. One regularity employs a consensus partition matrix to guide the learning process for multiple complete graphs. The other regularity combines multiple view-specific partition matrices to ensure consistency across different views at the discriminative partition level. Building on these concepts, we propose a novel approach for incomplete multi-view clustering, called Consensus Partition-guided Incomplete Multi-view Clustering (CPIMC). This method leverages consensus information at the partition level to complete multiple incomplete graphs and enforce consistency across views. Extensive experiments conducted on several commonly used incomplete multi-view datasets show that CPIMC outperforms state-of-the-art methods in incomplete multi-view clustering.}, archive = {J_ACCESS}, author = {Chunyu Yang and Hongyun Yue}, doi = {10.1109/ACCESS.2025.3543099}, journal = {IEEE Access}, pages = {40198-40209}, shortjournal = {IEEE Access}, title = {Consensus partition guided incomplete multi-view clustering}, volume = {13}, year = {2025}, }
• (2025). NR-QNN: Noise-resilient quantum neural network. ACCESS, 13, 40185-40197. (www)

  @article{ , abstract = {Quantum Neural Networks (QNNs) based on parameterized quantum circuits (PQCs) are gaining significant research attention due to their potential to achieve quantum advantages on Near-Term Noisy Intermediate-Scale Quantum (NISQ) computers. However, executing QNNs on NISQ devices is challenging due to quantum noise. To address this, we propose a noise-resilient QNN (NR-QNN) that leverages the unique characteristics of PQCs to perform noise-aware optimizations during the inference stage of QNNs. Specifically, NR-QNN employs two optimization techniques to mitigate the impact of noise on QNNs: quantum pruning and sensitivity-aware qubit mapping. The first technique is quantum pruning which identifies gates with small angle of rotations and removes them to simplify circuit of QNNs. The second optimization technique is sensitivity-aware qubit mapping which maps more important logical qubits to more reliable physical qubits. This technique is based on the observation that there can be variation in the sensitivity of an output to input qubits in a QNN. NR-QNN exploits this variability and guides qubit allocation to use reliable physical qubits for sensitive logical qubits. Our evaluation on a real quantum computer demonstrates that NR-QNN enhances the robustness of QNNs, enabling them to operate effectively on NISQ devices.}, archive = {J_ACCESS}, author = {Sohrab Sajadimanesh and Hanieh Aghaee Rad and Jean Paul Latyr Faye and Ehsan Atoofian}, doi = {10.1109/ACCESS.2025.3546956}, journal = {IEEE Access}, pages = {40185-40197}, shortjournal = {IEEE Access}, title = {NR-QNN: Noise-resilient quantum neural network}, volume = {13}, year = {2025}, }
• (2025). Event type and relationship extraction based on dependent syntactic semantic augmented graph networks. ACCESS, 13, 40169-40184. (www)

  @article{ , abstract = {In natural language processing, tasks like trigger word extraction, event type recognition, and event relation extraction are essential. These tasks facilitate the extraction of salient information through detailed textual analysis, enhancing the semantic understanding of texts. The extraction of trigger words and event types often faces challenges due to polysemous words and complex sentence structures, which can impair semantic representation. To address this, this paper introduces a Dependency Syntactic Analysis model and proposes the Event Type Extraction Model based on Gravitational Network with Enhanced Dependency Semantics (GNEDS). This model clarifies complex relationships and structures among words with in sentences, significantly improving contextual information comprehension. This enables more accurate identification of trigger words and their contextual links, thereby enhancing the text’s semantic representation. Furthermore, traditional research in event relation recognition has primarily focused on intrasentential relations, but real-world texts often display multisentence event relations that involve complex contextual and implicit reasoning. To overcome the limitations of existing models in cross-sentence event relation extraction, this study introduces a Graph Convolutional Neural Network (GCN) and an innovative concept of document nodes. A new model, Document Event Relationship Extraction based on Graph Convolutional Network with Enhanced Dependency Semantics (GCNEDS) is proposed. It captures long distance dependencies between sentences within a document with greater accuracy, marking a significant advancement in the field of event type and relationship extraction based on dependent syntactic-semantic augmented graph networks.}, archive = {J_ACCESS}, author = {Min Zuo and Zexi Song and Qingchuan Zhang and Yueheng Liu and Di Wu and Yuanyuan Cai}, doi = {10.1109/ACCESS.2025.3546963}, journal = {IEEE Access}, pages = {40169-40184}, shortjournal = {IEEE Access}, title = {Event type and relationship extraction based on dependent syntactic semantic augmented graph networks}, volume = {13}, year = {2025}, }
• (2025). MAD-CTI: Cyber threat intelligence analysis of the dark web using a multi-agent framework. ACCESS, 13, 40158-40168. (www)

  @article{ , abstract = {The dark web is a host to illicit activities where hacker forums, blogs, and articles provide significant insights into Cyber Threat Intelligence (CTI) that are frequently unavailable on the surface web. The increasing incidence of security breaches underscores the necessity for advanced CTI solutions to defend against emerging threats. This paper introduces MAD-CTI, a novel multi-agent framework based on Large Language Models (LLM) designed to extract insights from dark web sources. It independently scrapes, analyzes, and classifies content related to vulnerabilities, malware, and hacking, by leveraging a multi-agent architecture to improve efficiency, scalability, and consistency. By utilizing state-of-the-art LLM models and agents, we demonstrate how organizations can adopt this methodology to enhance the accuracy and efficiency of CTI.}, archive = {J_ACCESS}, author = {Sayuj Shah and Vijay K. Madisetti}, doi = {10.1109/ACCESS.2025.3547172}, journal = {IEEE Access}, pages = {40158-40168}, shortjournal = {IEEE Access}, title = {MAD-CTI: Cyber threat intelligence analysis of the dark web using a multi-agent framework}, volume = {13}, year = {2025}, }
• (2025). Improvement of a stiffness tunable mechanism by design of horizontally asymmetrical beam structure. ACCESS, 13, 40149-40157. (www)

  @article{ , abstract = {Stiffness tunable mechanisms are crucial for enhancing the noninvasiveness and stability of organ manipulation during laparoscopic surgeries. They allow for flexible adaptation to organ shapes in a low stiffness state, providing a larger grasping area, while maintaining high bending rigidity in a high stiffness state for stable force transmission. Our group developed a rapid pneumatical stiffness switching mechanism using identical two-beam structure, but the stiffness gain was insufficient for miniaturization. In this study, we improved the identical two-beam structure by proposing a horizontally asymmetrical beam structure, which is composed of differently shaped upper and lower beams. In the low stiffness state, the horizontally asymmetrical beam structure can satisfy the requirement of liver shape followability. In the high stiffness state, the horizontally asymmetrical beam structure achieves greater bending stiffness because the contact surface between the beams is shifted from the center—where shear forces are maximal—to the upper and lower ends, which reduces the slippery at the contact surface. Moreover, the horizontally asymmetrical beam structure maintains bending stiffness at any angle of bend. The new design improved the stiffness tunable mechanism by providing a higher stiffness gain and greater potential for miniaturization, which is necessary for being used by minimal invasive surgery assistant robot.}, archive = {J_ACCESS}, author = {Dongbo Zhou and Kotaro Tadano and Takahiro Kanno and Yoshikazu Nakajima}, doi = {10.1109/ACCESS.2025.3546327}, journal = {IEEE Access}, pages = {40149-40157}, shortjournal = {IEEE Access}, title = {Improvement of a stiffness tunable mechanism by design of horizontally asymmetrical beam structure}, volume = {13}, year = {2025}, }
• (2025). Fraud detection on crowdfunding platforms using multiple feature selection methods. ACCESS, 13, 40133-40148. (www)

  @article{ , abstract = {In recent years, crowdfunding has emerged as an alternative funding source for startups and emerging businesses, experiencing significant growth. However, this growth has also led to an increase in fraudulent activities. Despite the potential for fraud in the realm of crowdfunding, there is limited knowledge of the phenomenon due to a lack of data on actual instances of fraudulent campaigns. In this paper, we aim to address this deficiency by collecting and analyzing publicly accessible web and social media data from a hundred fraudulent crowdfunding projects. In order to identify and comprehend the distinguishing characteristics of fraudulent campaigns, we first propose 1) using a wide variety of characteristics of campaign projects and project creators, including their profiles, behavior, social traits, and language; then, 2) we propose to use and combine three well-known multiple feature selection methods, which are based on Correlation-based Feature Selection (CFS), Pearson Correlation Coefficient (PCC), and Information Gain (IG), to identify representative features of fraudulent campaigns. Our approach identifies 10 commonly selected key features of fraudulent crowdfunding campaigns, three of which are new, original findings. We provide and discuss our findings and interpretations on the 10 commonly selected key features in relation to previous studies, based on which we construct a fraud detection model with 82.04% accuracy. We also employ Shapley Additive ExPlanations (SHAP) to interpret the fraud detection model, explaining the importance of each feature.}, archive = {J_ACCESS}, author = {Seunghun Lee and Heemin Park and Hyun-Chul Kim}, doi = {10.1109/ACCESS.2025.3547396}, journal = {IEEE Access}, pages = {40133-40148}, shortjournal = {IEEE Access}, title = {Fraud detection on crowdfunding platforms using multiple feature selection methods}, volume = {13}, year = {2025}, }
• (2025). Predictive time-series analysis of single-qubit quantum circuit outcomes for a superconducting quantum computer: Forecasting error patterns. ACCESS, 13, 40115-40132. (www)

  @article{ , abstract = {Quantum computing promises a paradigm shift in computational power. However, a major challenge is mitigating the inherent noise and errors in quantum circuits. As quantum computers operate, their fragile qubits are highly susceptible to environmental disturbances, leading to errors that affect the outcomes of repeated circuit executions. Understanding and predicting these errors is crucial for improving the accuracy and reliability of quantum computing systems. In this work, we analyze and predict the output patterns of a single qubit quantum circuit by treating the results of repeated executions as a time series. Specifically, we collect measurement data from multiple runs of a quantum circuit and construct a time series from these observations. By training a predictive model, we aim to forecast future outcomes, providing insights into the error behavior of the quantum circuit. Additionally, we analyze time series data from two different circuits executed on the same qubit to investigate potential relationships and assess whether one dataset can be used to predict the other. Our findings reveal key characteristics of quantum circuit outputs, including stationarity, autocorrelation, seasonality, trends, linearity, and causality. The analysis highlights intriguing behaviors within the dataset. Furthermore, we evaluate multiple time series prediction methods and determine that XGBoost (Extreme Gradient Boosting) outperforms other approaches, demonstrating its effectiveness in accurately predicting quantum computing outputs in subsequent runs.}, archive = {J_ACCESS}, author = {Mohammadreza Saghafi and Lamine Mili}, doi = {10.1109/ACCESS.2025.3546259}, journal = {IEEE Access}, pages = {40115-40132}, shortjournal = {IEEE Access}, title = {Predictive time-series analysis of single-qubit quantum circuit outcomes for a superconducting quantum computer: Forecasting error patterns}, volume = {13}, year = {2025}, }
• (2025). A systematic review on automated warehouse with multiple-deep rack. ACCESS, 13, 40103-40114. (www)

  @article{ , abstract = {The adoption of multiple-deep rack configurations, particularly double-deep racks, in automated warehouses has gained considerable research due to their efficiency in maximizing storage density and floor space utilization. Automated warehouses employ advanced technologies such as automated guided vehicles (AGVs) and stacker cranes, minimizing human intervention. While existing reviews have explored emerging trends in automated warehouses, there remains a gap in literature specifically addressing research needs for warehouses utilizing multiple-deep rack configurations. This systematic literature review analyses fifty English-language research publications spanning from 2014 to 2024, focusing exclusively on the application of multiple-deep racks in automated warehousing systems. The review underscores the critical importance of integrating multiple-deep rack systems to enhance warehouse efficiency and productivity. The discussion section provides a comprehensive examination of optimization strategies and storage allocation methods employed in these systems. Furthermore, the review identifies key success factors essential for the effective implementation and operation of multiple-deep rack configurations in automated warehouses. These insights not only inform future research directions but also offer practical implications for enhancing performance and scalability in automated warehouse settings.}, archive = {J_ACCESS}, author = {Szu-Jean Tan and Lee-Yeng Ong and Meng-Chew Leow}, doi = {10.1109/ACCESS.2025.3547173}, journal = {IEEE Access}, pages = {40103-40114}, shortjournal = {IEEE Access}, title = {A systematic review on automated warehouse with multiple-deep rack}, volume = {13}, year = {2025}, }
• (2025). Enhancing design stability and flexibility in partial isolation type LDMOS. ACCESS, 13, 40090-40102. (www)

  @article{ , abstract = {This study comprehensively analyzes the performance variation mechanisms in partial isolation type laterally double-diffused metal-oxide-semiconductor (Pi-LDMOS) field-effect transistors. The concept of the ’silicon gap’ in this study refers to the space between the drift region and the partial insulator (Pi). For the Pi-LDMOS structure requiring fabrication verification, with p-substrate and n-drift doping concentrations of $3\times 10^{16}$ cm−3 and $5\times 10^{16}$ cm−3, respectively, high-precision software SENTAURUS TCAD simulations are conducted to analyze breakdown voltage (BV), specific on-resistance (R $_{\mathrm {on,sp}}$ ), and figure of merit (FOM) for parameter ranges of 0- $10~\mu $ m for Pi length (L $_{\mathrm {pi}}$ ), 0- $20~\mu $ m for Pi thickness (t $_{\mathrm {pi}}$ ), and 0- $1~\mu $ m for silicon gap thickness (g $_{\mathrm {pi}}$ ). The maximum BV (BV $_{\max }$ ) and maximum FOM (FOM $_{\max }$ ) in this device can be achieved by optimizing the balance between the vertex point electric field (E $_{\mathrm {T}}$ ) and the vertical point electric field (E $_{\mathrm {C}}$ ), which are modulated by Lpi and tpi variations. Acceptable range area (A $_{\mathrm {acceptable}}$ ) is defined as the rectangular area bounded by the permissible ranges of Lpi and tpi that achieve target values for both BV and FOM. Compared to g $_{\mathrm {pi}}=0.0~\mu $ m, when g $_{\mathrm {pi}}=1.0~\mu $ m, Aacceptable increases by 37.3 and 39.2 times for BV $\ge 120$ V and BV $\ge 100$ V respectively, and by 4 and 18.7 times for FOM $\ge 4.5$ MW/cm2 and FOM $\ge 4.0$ MW/cm2 respectively. Moreover, variation rate of Ron,sp ( $\Delta $ R $_{\mathrm {on,sp}}$ ) decreases from 7% to 0%. These results demonstrate that considering the silicon gap in Pi-LDMOS design can enhance design stability and flexibility through expanded design margins and consistent performance distributions in achieving the target performance.}, archive = {J_ACCESS}, author = {Shinwook Kim and Dongyeong Kim and Suyeon Kim and Je Won Park and Hyerin Lee and Hyeona Seo and Sowon Kim and Chaehyuk Lim and Jeonghyeon Yun and Juwon Lee and Sokhee P. Jung and Myoung Jin Lee}, doi = {10.1109/ACCESS.2025.3547251}, journal = {IEEE Access}, pages = {40090-40102}, shortjournal = {IEEE Access}, title = {Enhancing design stability and flexibility in partial isolation type LDMOS}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based MPPT approach to enhance CubeSat power generation. ACCESS, 13, 40076-40089. (www)

  @article{ , abstract = {The Electrical Power System (EPS) is a vital subsystem in CubeSats, responsible for powering all onboard components. Due to standardized size and weight constraints, the limited surface area of solar panels restricts power generation. To address this, Maximum Power Point Tracking (MPPT) is crucial. However, traditional MPPT techniques struggle in CubeSats’ dynamic orbital environments, where solar irradiance varies across different facets. This paper presents a novel MPPT method that combines a Deep Feedforward Neural Network (DFFNN) with a Proportional-Integral (PI) controller to adapt to these rapidly changing conditions. A year-long simulation of a 3U CubeSat in Systems Tool Kit (STK) generated real-time orbital temperature(T) and irradiance(G) data, producing 70,000 data points. The dataset was divided into 70% for training, 15% for testing, and 15% for validation to develop the DFFNN model. The trained DFFNN was integrated into a Simulink model and tested under variable illumination conditions. The results showed that the DFFNN-based MPPT achieved a 97.4% efficiency, outperforming Perturb and Observe (P&O) at 88.9%, Incremental Conductance (InC) at 89.7%, and Particle Swarm Optimization (PSO) at 94.08%. Additionally, the proposed method achieved a system efficiency of 88.3%, reduced power ripple to less than 2.5%, and significantly improved transient performance. These findings confirm the effectiveness of the DFFNN-PI approach in optimizing power extraction for CubeSat missions under dynamic space conditions.}, archive = {J_ACCESS}, author = {Abdulazez Abagero and Yoseph Abebe and Abera Tullu and Young Seok Jung and Sunghun Jung}, doi = {10.1109/ACCESS.2025.3546066}, journal = {IEEE Access}, pages = {40076-40089}, shortjournal = {IEEE Access}, title = {Deep learning-based MPPT approach to enhance CubeSat power generation}, volume = {13}, year = {2025}, }
• (2025). Intersection-specific trajectory prediction for road users: A review. ACCESS, 13, 40054-40075. (www)

  @article{ , abstract = {Intersections are critical points in urban traffic networks, accounting for over 50% of traffic accidents and nearly 30% of fatalities, highlighting the need for enhanced safety measures. Accurate trajectory prediction at intersections is essential for advanced driver assistance systems and autonomous vehicles to predict the future states of the traffic agents, enabling safer and more efficient navigation. This review examines methodologies for predicting road user trajectories at intersections, categorizing them into traditional models, machine learning techniques, and hybrid approaches. We conduct a comparative analysis of benchmark datasets and evaluation metrics. Key challenges such as sensor fusion, adaptive modeling of dynamic traffic scenarios, and enhancing computational efficiency are suggested for future research. By addressing these challenges and emphasizing the importance of benchmarking and real-world validation, this review aims to drive advancements in trajectory prediction models, ultimately contributing to safer and more efficient urban traffic management.}, archive = {J_ACCESS}, author = {Xiyuan Guo and Morteza Adl and Behzad Abdi and Ali Emadi}, doi = {10.1109/ACCESS.2025.3546325}, journal = {IEEE Access}, pages = {40054-40075}, shortjournal = {IEEE Access}, title = {Intersection-specific trajectory prediction for road users: A review}, volume = {13}, year = {2025}, }
• (2025). Simulation of hip abduction-adduction exoskeletons for assisting frontal-plane stability in elderly individuals. ACCESS, 13, 40044-40053. (www)

  @article{ , abstract = {Frontal-plane instability is positively correlated with higher incidence of severe injuries in elderly individuals. The effect of hip abduction-adduction assistance on stability in elderly individuals is not fully understood. This study investigates how the magnitude, timing, and location of hip abduction-adduction assistance affects the margin of stability. Methods: The OpenSim biomechanics software was used to generate simulations of eighteen elderly individuals while they stood with both feet on the floor and a lateral perturbation force of magnitude 5%, 10%, or 15% of bodyweight was applied to the pelvis. Contralateral, ipsilateral, or bilateral hip abduction-adduction assistance was applied following the perturbation. The change in margin of stability was used to predict the effectiveness of each assistance strategy and for comparison across subjects. Results: All assistance strategies improved the margin of stability; the greatest mean improvement was provided by the contralateral assistance strategy. For the 5%, 10%, and 15% bodyweight perturbations, contralateral assistance of 0.75 N $\cdot $ m/kg improved margin of stability by $11.9~\pm ~1.41$ mm, $11.9~\pm ~1.39$ mm, and $12.0~\pm ~1.39$ mm, respectively. Conclusion: The margin of stability can be improved by applying hip abduction-adduction assistance to the contralateral hip with fast actuation strategies. Significance: The results of this study can be used by exoskeleton designers to improve the stability of elderly individuals by adopting a hip abduction-adduction assistance strategy.}, archive = {J_ACCESS}, author = {Thomas Burton and Thomas K. Uchida and Marc Doumit}, doi = {10.1109/ACCESS.2025.3546257}, journal = {IEEE Access}, pages = {40044-40053}, shortjournal = {IEEE Access}, title = {Simulation of hip abduction-adduction exoskeletons for assisting frontal-plane stability in elderly individuals}, volume = {13}, year = {2025}, }
• (2025). KITE-DDI: A knowledge graph integrated transformer model for accurately predicting drug-drug interaction events from drug SMILES and biomedical knowledge graph. ACCESS, 13, 40028-40043. (www)

  @article{ , abstract = {It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the potential to cause significant bodily injury and could even be fatal. Hence, it is essential to identify all the DDI events before prescribing multiple drugs to a patient. Most contemporary research for predicting DDI events relies on either information from Biomedical Knowledge graphs (KG) or drug SMILES, with very few managing to merge data from both to make predictions, while others use heuristic algorithms to extract features from SMILES and KGs, which are then fed into a Deep Learning framework to generate output. In this study, we propose a KG-integrated Transformer architecture to generate an end-to-end fully automated Machine Learning pipeline for predicting DDI events with high accuracy. The algorithm takes full-scale molecular SMILES sequences of a pair of drugs and a biomedical KG as input and predicts the interaction between the two drugs with high precision. The results show superior performance in two different benchmark datasets compared to existing state-of-the-art models especially when the test and training sets contain distinct sets of drug molecules. This demonstrates the strong generalization of the proposed model, indicating its potential for DDI event prediction for newly developed drugs. The model does not depend on heuristic models for generating embeddings and has a minimal number of hyperparameters, making it easy to use while demonstrating outstanding performance in low-data scenarios.}, archive = {J_ACCESS}, author = {Azwad Tamir and Jiann-Shiun Yuan}, doi = {10.1109/ACCESS.2025.3547594}, journal = {IEEE Access}, pages = {40028-40043}, shortjournal = {IEEE Access}, title = {KITE-DDI: A knowledge graph integrated transformer model for accurately predicting drug-drug interaction events from drug SMILES and biomedical knowledge graph}, volume = {13}, year = {2025}, }
• (2025). Detection of broken rotor bars in presence of load oscillations. ACCESS, 13, 40012-40027. (www)

  @article{ , abstract = {In industrial applications, the reliable performance of induction motors (IM) is essential. This paper proposes an improved method to distinguish between broken rotor bar (BRB) faults and load oscillations (LO) by analyzing stator current components in the rotor-flux reference frame. The approach employs a diagnostic fault indicator based on the amplitude ratio of current oscillations in the direct and quadrature axes, with a threshold established through simulation data and a support vector classifier (SVC). If the detected spectral peak pair corresponds to a BRB, the fault indicator exceeds the threshold. Conversely, peaks caused by LO result in an indicator value significantly below this threshold. The explicit and unambiguous separation between BRB and LO effects allows for the practical recommendation of a unity threshold. Simulations and experimental results validate the method’s robustness, demonstrating accurate fault identification even under challenging conditions, such as lightly loaded motors with a single BRB. The method relies solely on current and voltage measurements, avoiding the need for speed sensors. With low computational demands, it integrates seamlessly into existing motor drive systems. These features make it a practical and cost-effective tool for real-time fault detection and predictive maintenance in industrial settings.}, archive = {J_ACCESS}, author = {Klemen Drobnič and Mitja Nemec and Henrik Lavrič and Vanja Ambrožič and Rastko Fišer}, doi = {10.1109/ACCESS.2025.3546705}, journal = {IEEE Access}, pages = {40012-40027}, shortjournal = {IEEE Access}, title = {Detection of broken rotor bars in presence of load oscillations}, volume = {13}, year = {2025}, }
• (2025). Real-time gait phase detection based on LSTM-ResMLP-LightGBM approach for exoskeleton in outdoor activity. ACCESS, 13, 39993-40011. (www)

  @article{ , abstract = {Gait phase detection plays a significant role in human motion intention recognition in lower limb exoskeleton. This study proposes a LSTM-ResMLP-LightGBM method for real-time gait phase detection in multigrade loads in outdoor activity. LSTM is exploited to extract the underlying meaning and construct a lengthy time relationship in multiple features. ResMLP effectively extracts key information to minimize multi-feature redundancy. LightGBM is an external framework based on gradient boosting decision tree with leaf-wise growth strategy. There are five locomotion modes in diverse terrains, such as LW, SA, SD, RA, and RD. In every locomotion mode, a gait cycle is divided into five subphases, such as HS, MS, HO, PW, and AS. The time delay for actual gait phase detected by the hybrid method is distributed in multigrade loads in five locomotion modes. The proposed method has a prominent effect on gait phase detection in terms of assessment criterion of MSE, MAE, time delay, accuracy, precision, recall, F1 score, and confusion matrix. What’s more, compared with the general approaches of TST, TFT, TCL, and NGBoost, the proposed method makes great influence on gait phase detection in five locomotion modes. In addition, the mean early detection time is 63.79 ms that contributes to enhance the response effect of lower limb exoskeleton. Finally, this study supplies a feasible and stable method of gait phase detection for human-robot cooperative walking.}, archive = {J_ACCESS}, author = {Yifan Gao and Jianbin Zheng and Chunbo Yang and Ruoxi Yan and Chaojie Wang and Jing Tang and Zequan Jiang}, doi = {10.1109/ACCESS.2025.3544758}, journal = {IEEE Access}, pages = {39993-40011}, shortjournal = {IEEE Access}, title = {Real-time gait phase detection based on LSTM-ResMLP-LightGBM approach for exoskeleton in outdoor activity}, volume = {13}, year = {2025}, }
• (2025). Automatic classification of speech dysarthric intelligibility levels using textual feature. ACCESS, 13, 39982-39992. (www)

  @article{ , abstract = {The comprehension of human language is fundamentally important in modern intelligent systems. Automatic Speech Intelligibility assessment involves determining the efficiency with which another speaker comprehends the same speech. Changes in the acoustic perception of speech may be due to neurological disorders or accidental ingestion of toxic substances. Dysarthria is one such disorder that affects the pronunciation of speech due to deterioration in the neuromuscular control of speech organs. Dysarthric speech is characterized by impairments that hinder clear pronunciation and understanding, presenting significant challenges for accurate transcription and classification. To the best of our knowledge, no previous studies have focused exclusively on using textual features for this purpose, with most existing research relying primarily on speech data. This study introduces a novel Hybrid CNN-BLSTM model that utilizes textual features extracted through Automatic Speech Recognition (ASR) to classify intelligibility levels in dysarthric speech using the TORGO dataset. Our approach addresses transcription inconsistencies by employing ASR to convert speech to text. We compare the proposed Hybrid CNN-BLSTM model with state-of-the-art deep learning models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN). Experimentally, the Hybrid CNN-BLSTM model, optimized with the Stochastic Gradient Descent (SGD), achieved the highest accuracy, reaching 90 % in classifying intelligibility levels.}, archive = {J_ACCESS}, author = {Ghadeer F. Alharbi and Najwa K. Alamri and Sahar F. Sabbeh}, doi = {10.1109/ACCESS.2025.3547041}, journal = {IEEE Access}, pages = {39982-39992}, shortjournal = {IEEE Access}, title = {Automatic classification of speech dysarthric intelligibility levels using textual feature}, volume = {13}, year = {2025}, }
• (2025). A review of evolving challenges in transmission expansion planning problems. ACCESS, 13, 39964-39981. (www)

  @article{ , abstract = {Transmission Expansion Planning (TEP) is a well-established field in power systems, focused on identifying the best timing and location for new transmission lines and related infrastructure. Its main goals are to meet growing electricity demand, ensure system reliability, and maintain economic efficiency. However, recent changes in the energy sector-such as the rapid growth of renewable energy, the push for decarbonisation, and the rise of electric vehicles-have introduced new challenges and uncertainties for TEP. This paper reviews more than 150 research articles to explore how these trends are reshaping TEP. We identify key insights, emerging challenges, and research gaps, emphasizing the need for improved tools and approaches to address the complexities of modern power systems. Finally, we discuss the need for research in TEP to incorporate uncertainties like energy storage systems (ESS), electric vehicle adoption, and high renewable energy integration, using advanced algorithms and real-world data to enhance accuracy and relevance.}, archive = {J_ACCESS}, author = {Shengming Cao and Waqquas Bukhsh}, doi = {10.1109/ACCESS.2025.3547115}, journal = {IEEE Access}, pages = {39964-39981}, shortjournal = {IEEE Access}, title = {A review of evolving challenges in transmission expansion planning problems}, volume = {13}, year = {2025}, }
• (2025). Rep-MobileViT: Texture and color classification of solid wood floors based on a re-parameterized CNN-transformer hybrid model. ACCESS, 13, 39950-39963. (www)

  @article{ , abstract = {In the context of intelligent manufacturing for furniture, the classification of solid wood floors is critical for automating quality control and enhancing production efficiency. The final aesthetic of installed solid wood floors largely depends on the uniformity of color and texture. However, most current research does not address the simultaneous classification of both texture and color in solid wood floors, and there is a notable gap in studies focusing on the classification of color-difference wood floors and white-edge wood floors. This paper proposes a re-parameterized CNN-Transformer hybrid architecture based on the MobileViT model, named Rep-MobileViT. Specifically, the RepAIRB module is introduced, incorporating an asymmetric convolutional block (ACB) and a re-parameterized structure within the inverted residual block (IRB) module to enhance the network’s receptive field without increasing computational costs. Additionally, the RepMFF-MobileViT modu le is designed, utilizing a multi-feature fusion strategy and the RepTransformer structure to reduce the number of parameters while improving the network’s expressiveness and stability. In the preprocessing stage, a contrast-limited adaptive histogram equalization and brightness blending (CLAHE-LB) technique is employed to enhance image clarity, highlighting texture details while preserving original color information. Experimental results indicate that the proposed method achieves an overall classification accuracy of 94.98%, with a processing time of only 20.94 ms. Comparative analyses demonstrate that this approach outperforms existing machine learning methods by over 12% in classification accuracy.}, archive = {J_ACCESS}, author = {Anning Duanmu and Sheng Xue and Zhenye Li and Yajun Zhang and Chao Ni}, doi = {10.1109/ACCESS.2025.3545645}, journal = {IEEE Access}, pages = {39950-39963}, shortjournal = {IEEE Access}, title = {Rep-MobileViT: Texture and color classification of solid wood floors based on a re-parameterized CNN-transformer hybrid model}, volume = {13}, year = {2025}, }
• (2025). A comprehensive deep learning framework for microwave stroke classification: Combining signal analysis, clinical variables, and antenna system measurements. ACCESS, 13, 39935-39949. (www)

  @article{ , abstract = {Microwave signal-based binary classification for detecting the presence of stroke presents a promising avenue for cost-effective and portable diagnosis. However, implementing this technology in real-world settings remains challenging due to difficulties in accurately modeling wave scattering, which often leads to suboptimal performance of classification systems. Our proposed deep learning framework addresses this issue by bypassing complex modeling tasks and directly extracting stroke signatures from wavelet-filtered microwave signals, combined with clinical variables and antenna measurements. Experiments conducted with total of 431 real-world stroke patients demonstrate that our system significantly outperforms recent classification approaches achieving 76.19% accuracy. Furthermore, our approach shows comparable performance to magnetic resonance imaging (MRI) based deep learning classification, in scenarios with limited imaging data. These results not only highlight the potential of microwave imaging (MWI) as a critical diagnostic tool but also suggest that MWI devices could become a viable option for clinical stroke diagnosis in the near future.}, archive = {J_ACCESS}, author = {Wei Yuan and Phawis Thammasorn and Lingxiao Wang and Shih Mo}, doi = {10.1109/ACCESS.2025.3546558}, journal = {IEEE Access}, pages = {39935-39949}, shortjournal = {IEEE Access}, title = {A comprehensive deep learning framework for microwave stroke classification: Combining signal analysis, clinical variables, and antenna system measurements}, volume = {13}, year = {2025}, }
• (2025). Power system stability with high penetration of renewable energy sources: Challenges, assessment, and mitigation strategies. ACCESS, 13, 39912-39934. (www)

  @article{ , abstract = {The incorporation of renewable energy sources (RESs) into power systems has significantly increased in recent years due to growing environmental, economic, and energy security concerns, posing new challenges for power system stability. This paper first presents a modified stability categories classification for power systems with increased RES penetration, distinguishing between fundamental and non-fundamental stability. Moreover, the paper comprehensively surveys the existing techniques for evaluating power system stability (PSS) with high RES penetration. It highlights key factors, such as the level of RES penetration, network topology, assessment and mitigation strategies, and the simulation tools used. The PSS methodologies in the literature are categorized into three main categories: model-based, optimization-based, and artificial intelligence (AI)-based approaches. This survey aims to equip researchers and practitioners with a coherent framework for navigating PSS assessment techniques. It offers promising avenues and insights into future research directions and related challenges, particularly addressing PSS with increased RESs penetration.}, archive = {J_ACCESS}, author = {Jana Sheikh Ali and Yazan Qiblawey and Abdulrahman Alassi and Ahmed M. Massoud and S. M. Muyeen and Haitham Abu-Rub}, doi = {10.1109/ACCESS.2025.3546491}, journal = {IEEE Access}, pages = {39912-39934}, shortjournal = {IEEE Access}, title = {Power system stability with high penetration of renewable energy sources: Challenges, assessment, and mitigation strategies}, volume = {13}, year = {2025}, }
• (2025). Efficient real-time sports action pose estimation via EfficientPose and temporal graph convolution. ACCESS, 13, 39901-39911. (www)

  @article{ , abstract = {Accurate and efficient human pose estimation is crucial for precise motion tracking and performance feedback in real-time sports analysis. This paper presents a real-time pose estimation framework that integrates EfficientPose and T-GCN (Temporal Graph Convolutional Networks) to address the challenges of dynamic and complex sports scenarios. EfficientPose utilizes a highly efficient network architecture to achieve accurate 3D pose estimation from single-frame images, providing a robust foundation for subsequent temporal modeling. T-GCN further refines the motion trajectories by modeling temporal dependencies and spatial relationships across frames, ensuring temporal continuity and spatial consistency. Experimental results demonstrate the superior performance of the proposed framework, achieving the lowest Mean Absolute Error (MAE: 30.5 mm), the highest Multiple Object Tracking Accuracy (MOTA: 80.2%), and maintaining a real-time frame rate (45 FPS) across multiple benchmarks. Compared to traditional methods, the proposed approach exhibits significant advantages in handling high-speed motions, occlusions, and complex multi-agent interactions, enabling high-precision and temporally stable pose estimation. This framework provides an efficient and robust solution for real-time sports performance analysis, offering valuable scientific support for performance feedback and tactical decision-making.}, archive = {J_ACCESS}, author = {Yuanzhe Ma and Hui Li and Hongqiao Yan}, doi = {10.1109/ACCESS.2025.3542240}, journal = {IEEE Access}, pages = {39901-39911}, shortjournal = {IEEE Access}, title = {Efficient real-time sports action pose estimation via EfficientPose and temporal graph convolution}, volume = {13}, year = {2025}, }
• (2025). Robust UAV tracking via information synergy fusion and multi-dimensional spatial perception. ACCESS, 13, 39886-39900. (www)

  @article{ , abstract = {In the field of UAV tracking, existing trackers based on Siamese networks fails to fully exploit the context of the target and capture the correlations between the template and search images. These issues reduce their effectiveness in UAV tracking, especially in complex environments with challenges such as occlusion, similar object, scale variations and so on. To this end, we propose a novel robust UAV tracking method based on information synergy fusion and multi-dimensional spatial perception, named SiamFM. First, coordinate attention (CA) is introduced to embed position-aware information into the channel dimension, effectively enhancing the feature extraction capability of backbone network. Second, an information synergy fusion (ISF) module is designed to execute multiple depth-wise cross-correlation operations and accumulate the results, which gradually refines and extracts deeper feature information to accurately capture the tracking target. Then, a multi-dimensional spatial perception (MSP) module is constructed to compute global dependencies and spatial dependency information during the tracking phase, which aggregates target feature information from different scales. This new operation allows the tracker to robustly adapt to changes in target scale and proportion. Finally, comprehensive experiments conducted on traditional tracking benchmarks such as DTB70, UAV123, UAV20L, Visdrone2018, and UAVDT demonstrate that SiamFM achieves real-time performance while effectively addressing various challenging scenarios, maintaining high robustness and accuracy. For the average running speed, our tracker can reach 40 FPS, meeting the needs of UAV real-time tracking.}, archive = {J_ACCESS}, author = {Yong Lin and Mingfeng Yin and Yingjie Zhang and Xiaoteng Guo}, doi = {10.1109/ACCESS.2025.3546939}, journal = {IEEE Access}, pages = {39886-39900}, shortjournal = {IEEE Access}, title = {Robust UAV tracking via information synergy fusion and multi-dimensional spatial perception}, volume = {13}, year = {2025}, }
• (2025). Electrolysers: A review on trends, electrical modeling, and their dynamic responses. ACCESS, 13, 39870-39885. (www)

  @article{ , abstract = {The global movement towards high integration of renewable energy sources into power systems to combat climate change underscores the importance of clean energy sources in sustainable development of future energy systems. Simultaneously, hydrogen is gaining prominence as a clean fuel, particularly in sectors like industry and transportation. However, the intermittent nature of renewable energies poses challenges for demand-supply balance and grid stability, necessitating efficient storage solutions. Additionally, the hydrogen economy introduces a new dimension to the challenge of power system balance management. In terms of electrical modeling, particularly for power system stability analysis, dynamic models of electrolysers are essential. Water electrolysis, powered by surplus renewable energy, has emerged as a promising method for hydrogen production, fostering advancements in sustainable energy practices. This paper provides an overview of electrolysis, exploring the electrical behavior of Alkaline, proton exchange membrane, and solid oxide electrolysers, while also categorizing them from a modelling perspective. Respectively, it examines the electrical modeling of electrolysers, encompassing three main formats: electrical equivalent circuit (EEC), mathematical formulation (MF), and block diagram (BD) presentation. Additionally, this paper investigates the dynamic responses of Alkaline and proton exchange membrane electrolysers, identified as the most suitable types for integration into power system dynamic studies. Through a review of existing literature and categorization of models, this paper aims to offer a comprehensive understanding of electrolyser behavior and dynamics in operation of power grids.}, archive = {J_ACCESS}, author = {Mahyar Tofighi-Milani and Sajjad Fattaheian-Dehkordi and Matti Lehtonen}, doi = {10.1109/ACCESS.2025.3546546}, journal = {IEEE Access}, pages = {39870-39885}, shortjournal = {IEEE Access}, title = {Electrolysers: A review on trends, electrical modeling, and their dynamic responses}, volume = {13}, year = {2025}, }
• (2025). A deep-layered water flow optimizer for global continuous optimization problems and parameter estimation of solar photovoltaic models. ACCESS, 13, 39840-39869. (www)

  @article{ , abstract = {The laminar operator in water flow optimizer (WFO) determines the direction of the population based on randomly selected individual pointing towards the current best individual. This random flow direction introduces uncertainty and may lead to local optima. In this paper, we introduce a deep-layered structure that guides the population towards global optima by exchanging information at each layer, proposing the deep-layered water flow optimizer (DWFO). This deep-layered structure not only determines the flow direction but also enriches population diversity to avoid local optima. In the experimental section, we compare DWFO with nine state-of-the-art algorithms on the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark functions and the CEC2011 real-world optimization problems. DWFO achieved average win rates of 83.62% and 78.28%, respectively, which thoroughly validates its superior performance and practicality. In the discussion section, we demonstrate the significance of hierarchical interactions between layers and the multi-layered structure in enhancing population diversity and balancing exploration and exploitation. We also analyze the impact of the deep-layered structure on algorithm complexity. Finally, we apply DWFO to solve the parameter estimation problem in solar photovoltaic models, providing a detailed introduction to four different types of photovoltaic models. We then conduct a comparative analysis of the parameter optimization results of DWFO and seven other state-of-the-art algorithms across six different photovoltaic models. DWFO achieved an average success rate of 83.33%, confirming its effectiveness in the photovoltaic field. This provides an advanced optimization method and practical application reference for research in the solar energy domain.}, archive = {J_ACCESS}, author = {Zhentao Tang and Kaiyu Wang and Mingxin Zhu and Yongxuan Yao and Lan Zhuang and Huiqin Chen and Jing Li and Li Yan and Shangce Gao}, doi = {10.1109/ACCESS.2025.3546986}, journal = {IEEE Access}, pages = {39840-39869}, shortjournal = {IEEE Access}, title = {A deep-layered water flow optimizer for global continuous optimization problems and parameter estimation of solar photovoltaic models}, volume = {13}, year = {2025}, }
• (2025). Performance of STAR-RIS-aided RSMA networks: Impact of correlated fading channels and SIC imperfections. ACCESS, 13, 39826-39839. (www)

  @article{ , abstract = {This paper investigates the outage performance of the downlink of a wireless network, where a base station communicates with two users using the Rate-Splitting Multiple Access (RSMA) protocol. To overcome obstacles that block direct transmission, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is used to enable communication. The users are positioned on opposite sides of the STAR-RIS, which operates under a mode-switching protocol, assigning elements for either transmission or reflection. The channels are assumed to undergo Rician fading, and the system performance is analyzed considering the correlation effects caused by closely spaced STAR-RIS elements, for which the model employed to generate correlated channels is introduced. Additionally, at the users’ receiver side, we consider that the decoding of users’ messages is subject to imperfect successive interference cancellation (SIC). An approximate analytical expression is also derived for the scenario with uncorrelated STAR-RIS elements and imperfect SIC. Monte-Carlo simulations further explore the system performance, focusing on key parameters such as the number of STAR-RIS elements, channel-correlation factor, power allocation factors of common and private streams relative to the RSMA protocol, and error factor of imperfect SIC. This work’s contributions provide a deeper understanding of the impact of correlated channels and imperfect SIC on STAR-RIS-aided networks, offering insights for optimizing performance under realistic deployment conditions.}, archive = {J_ACCESS}, author = {Eduardo Goncalves Gomes and Henry Ramiro Carvajal Mora and Edgar Eduardo Benitez Olivo}, doi = {10.1109/ACCESS.2025.3546553}, journal = {IEEE Access}, pages = {39826-39839}, shortjournal = {IEEE Access}, title = {Performance of STAR-RIS-aided RSMA networks: Impact of correlated fading channels and SIC imperfections}, volume = {13}, year = {2025}, }
• (2025). Research on dynamic collision avoidance for UGVs using the fusion of VO and APF algorithms. ACCESS, 13, 39813-39825. (www)

  @article{ , abstract = {Unmanned Ground Vehicles (UGVs), with their excellent maneuverability, have been widely utilized in various areas, including ground search and rescue, urban logistics, and public transportation. However, the limitations of the vehicle’s body structure pose challenges in sharp turning situations during the collision avoidance process of UGVs. The Artificial Potential Field (APF), as an obstacle avoidance algorithm featuring a smooth steering path, can effectively solve the problem of static collision avoidance. However, in dynamic scenarios, the APF algorithm may still produce paths that require sharp turns due to the lack of predictive ability. Therefore, this paper studies a novel algorithm (VO-APF) combining the Velocity Obstacle (VO) algorithm and the APF algorithm. Firstly, the Quaternion Vehicle Domain (QVD), considering relative position, is introduced into the VO algorithm to enhance its applicability in collision avoidance judgments within the vehicle domain. The reasonable point of collision avoidance is determined by predicting the collision avoidance time. Secondly, building upon the APF, the attraction function associated with collision avoidance targets and the repulsion function concerning obstacle vehicle areas are redefined. In the APF framework, UGVs are concurrently subjected to both attractive and repulsive forces. Finally, under the framework of modern traffic regulations governing collision avoidance, dynamic obstacle avoidance is achieved while minimizing intrusion into other vehicle domains, thereby enhancing overall safety during collision avoidance. The simulation results of three common dynamic collision avoidance scenarios show that the VO algorithm can fulfill the collision avoidance requirements, but its own vehicle intrudes into the obstacle vehicle domain for an extended period; The APF algorithm cannot meet the collision avoidance requirements in the crossing scenario, and it will also invade the vehicle domain of the obstacle vehicle; Compared to the APF and VO algorithms, the VO-APF algorithm reduces the vehicle domain overlap time by up to 47.53% and by at least 25.73%. It achieves shorter vehicle domain overlap times and improved safety performance, which effectively enhances the autonomous obstacle avoidance abilities of UGVs, providing a reliable guarantee for their autonomous navigation.}, archive = {J_ACCESS}, author = {Feng Xiong and Youbing Feng and Litao Cai}, doi = {10.1109/ACCESS.2025.3540509}, journal = {IEEE Access}, pages = {39813-39825}, shortjournal = {IEEE Access}, title = {Research on dynamic collision avoidance for UGVs using the fusion of VO and APF algorithms}, volume = {13}, year = {2025}, }
• (2025). Person re-identification with self-supervised teacher for in-box noise. ACCESS, 13, 39800-39812. (www)

  @article{ , abstract = {Person Re-Identification, which has been extensively researched for its wide applicability, concentrates solely on the entity of “person” within image retrieval cases. This presents a significant challenge. The person re-identification task, which follows the detector algorithm step, is limited to information within the detected box. This limitation leads to what we refer to as “in-box noise,”, a type of noise that is detrimental to the predefined notion of the box-identity pair, caused by any object, other identity, or environment that obscures or interferes with the recognition of the target identity within the box. To address this in-box noise, we propose a methodology that involves training with a self-supervised teacher model. This approach exploits the relevant identity information within the box-identity pair, enabling parallel learning with the main re-identification task and interpreting the critical identity areas in the image as guided by the teacher. This methodology demonstrates impressive performance on benchmark datasets, achieving a mean average precision (mAP) of 73.4 and a rank-1 score of 88.8 on MSMT17, and an mAP of 89.7 and a rank-1 score of 95.2 on Market1501.}, archive = {J_ACCESS}, author = {Yonghyeok Seo and Seung-Hun Kim}, doi = {10.1109/ACCESS.2025.3546683}, journal = {IEEE Access}, pages = {39800-39812}, shortjournal = {IEEE Access}, title = {Person re-identification with self-supervised teacher for in-box noise}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning solutions for microgrid control and management: A survey. ACCESS, 13, 39782-39799. (www)

  @article{ , abstract = {A microgrid (MG) is part of a distribution system that comprises loads and distributed energy resources, capable of operating either connected to or islanded from the primary grid. Having an appropriate design, MG controllers improve energy efficiency, playing a vital role in the modern distribution system. Thus, MG management and control has become a broad area of research due to its complex operation. Reinforcement learning (RL) offers adaptive solutions for handling MG complex dynamics and nonlinearity. It is an alternative to traditional algorithms and control methods in tasks, such as load frequency control, resource allocation, and energy management. Due to the relevance of the topic, this survey examined the role of RL in MG control and management, offering a comprehensive update on previous reviews, categorising articles by RL type, control objectives, and MG operational modes. Additionally, hardware implementations and performance assessments across RL-based solutions were evaluated. The present survey identified key research trends and gaps, contributing to understanding the role of RL in MG management and control and guiding future solutions in the field.}, archive = {J_ACCESS}, author = {Pedro I. N. Barbalho and Anderson L. Moraes and Vinicius A. Lacerda and Pedro H. A. Barra and Ricardo A. S. Fernandes and Denis V. Coury}, doi = {10.1109/ACCESS.2025.3546578}, journal = {IEEE Access}, pages = {39782-39799}, shortjournal = {IEEE Access}, title = {Reinforcement learning solutions for microgrid control and management: A survey}, volume = {13}, year = {2025}, }
• (2025). A hybrid speech enhancement technique based on discrete wavelet transform and spectral subtraction. ACCESS, 13, 39765-39781. (www)

  @article{ , abstract = {Speech quality and intelligibility are often severely degraded by background noise in communication systems such as hearing aid (HA) and speech recognition technologies, compromising their effective use. In low Signal-to-Noise Ratio (SNR) conditions, various approaches and algorithms are applied to improve speech quality and intelligibility. This study introduces a novel hybrid speech enhancement framework that synergistically integrates Spectral Subtraction (SS) and Discrete Wavelet Transform (DWT) to address limitations of traditional noise reduction techniques. Traditional SS methods generate musical noise artifacts due to static noise estimation, while standard DWT approaches struggle with selective thresholding and static coefficient processing. To overcome these challenges, the proposed SS method incorporates iterative noise estimation, Voice Activity Detection (VAD), minimum statistics for dynamic noise adaptation, Spectral Smoothing and phase-aware spectral reconstruction. Concurrently, in the enhanced DWT method adaptive noise refinement with phase-aware soft thresholding is employed to detail coefficients, and the Spatial and Intensity filter is adapted to the approximation coefficients to improve low-frequency features and retain structural integrity while reducing distortion. The integrated SS-DWT framework significantly improves noise suppression, reduces musical noise artifacts, and enhances signal clarity as it leverages the strengths of both phase-aware spectral reconstruction in improved SS and phase-aware soft thresholding in DWT, particularly in adaptive noise refinement and thresholding. Proposed speech enhancement network evaluated and experimental results show that the hybrid SS-DWT method outperforms existing systems, achieving up to 34.15 dB in SDR, 0.98 in STOI, and 3.84 in PESQ, demonstrating significant improvements in speech quality under various noisy conditions.}, archive = {J_ACCESS}, author = {Yasir Iqbal and Tao Zhang and Teddy Surya Gunawan and Agus Pratondo and Xin Zhao and Yanzhang Geng and Mira Kartiwi and Nasir Saleem and Sami Bourouis}, doi = {10.1109/ACCESS.2025.3546434}, journal = {IEEE Access}, pages = {39765-39781}, shortjournal = {IEEE Access}, title = {A hybrid speech enhancement technique based on discrete wavelet transform and spectral subtraction}, volume = {13}, year = {2025}, }
• (2025). Adversarial contrastive autoencoder with shared attention for audio-visual correlation learning. ACCESS, 13, 39753-39764. (www)

  @article{ , abstract = {Cross-modal audio-visual correlation learning has been an active research topic, which aims to embed audio and visual feature sequences into a common subspace where their correlation is maximized. The challenge of audio-visual correlation learning lies in two major aspects: 1) audio and visual feature sequences respectively contain different patterns belonging to different feature spaces, and 2) semantic mismatch between audio and visual sequences inevitably happens during cross-modal matching. Most existing methods only take the first aspect into account, therefore facing the difficulty in distinguishing matched and mismatched semantic correlations between audio and visual sequences. In this work, an adversarial contrastive autoencoder with a shared attention network (ACASA) is proposed for correlation learning in audio-visual retrieval. In particular, the proposed shared attention mechanism is parameterized, in which local salient information is enhanced to contribute to the final feature representation. Simultaneously, adversarial contrastive learning is exploited to maximize semantic feature consistency and improve the ability to distinguish matched and mismatched samples. Both inter-modal and intra-modal semantic information are utilized to supervise the model to learn more discriminative feature representation. Extensive experiments on the VEGAS and AVE datasets demonstrate that the proposed ACASA method outperforms state-of-the-art approaches in cross-modal audio-visual retrieval.}, archive = {J_ACCESS}, author = {Jiwei Zhang and Yi Yu and Suhua Tang and Wei Li}, doi = {10.1109/ACCESS.2025.3546610}, journal = {IEEE Access}, pages = {39753-39764}, shortjournal = {IEEE Access}, title = {Adversarial contrastive autoencoder with shared attention for audio-visual correlation learning}, volume = {13}, year = {2025}, }
• (2025). Conformal anomaly detection for predictive maintenance in thermal power plants. ACCESS, 13, 39738-39752. (www)

  @article{ , abstract = {Thermal power plants are essential for maintaining a stable electricity supply but face high operational costs and potential economic losses due to unexpected equipment failures. Predictive maintenance (PM) aims to reduce these risks by detecting early anomalies, but existing anomaly detection methods lack statistical control over the false positive rate (FPR), leading to frequent false alarms and inefficient maintenance interventions. We hypothesize that a conformal anomaly detection (CAD)-based approach, incorporating calibration-conditional p-value adjustments, can effectively mitigate the issue of uncontrolled FPR by ensuring statistically valid anomaly detection for power plant PM, even when multiple dependent hypothesis tests are performed on operational data. We introduce a CAD framework for power plant PM, which integrates calibration-conditional validity adjustments to systematically control FPR while maintaining sensitivity to operational faults. The evaluation results of the proposed CAD framework for PM in thermal power plants show that it ensures statistically valid FPR control, significantly reducing false alarms while maintaining high sensitivity to faults and strong discriminative ability between normal and faulty operations. By providing automatic and statistically valid FPR control without manual threshold tuning, the proposed framework offers a robust and scalable solution for anomaly detection in thermal power plant PM, improving maintenance efficiency and reducing unnecessary interventions.}, archive = {J_ACCESS}, author = {Ognjen Kundacina and Vladimir Vincan and Gorana Gojic and Vukan Ninkovic and Dragisa Miskovic}, doi = {10.1109/ACCESS.2025.3546451}, journal = {IEEE Access}, pages = {39738-39752}, shortjournal = {IEEE Access}, title = {Conformal anomaly detection for predictive maintenance in thermal power plants}, volume = {13}, year = {2025}, }
• (2025). Optimization and validation of wafer surface defect detection algorithm based on RT-DETR. ACCESS, 13, 39727-39737. (www)

  @article{ , abstract = {In response to the issue of poor detection performance on wafer surface defect spots and elongated scratches, an improved RT-DETR method for wafer surface defect detection is proposed. Firstly, a dynamic snake convolutional layer is introduced to detect elongated scratches where conventional convolutional kernels fail to extract features effectively. Secondly, to address the problem of information loss in small targets, an attention-based Transformer encoder module and a feature fusion network based on residual thinking are proposed. Finally, verification is conducted using a wafer test dataset. Experimental results demonstrate that compared to the original RT-DETR method, the model exhibits a 4.1% improvement in detecting small particles and a 5.4% improvement in scratch detection performance. Fully meeting the requirements of intelligent manufacturing and high detection accuracy.}, archive = {J_ACCESS}, author = {Ao Xu and Yanwei Li and Hongbo Xie and Rui Yang and Jianjie Li and Jiaying Wang}, doi = {10.1109/ACCESS.2025.3543525}, journal = {IEEE Access}, pages = {39727-39737}, shortjournal = {IEEE Access}, title = {Optimization and validation of wafer surface defect detection algorithm based on RT-DETR}, volume = {13}, year = {2025}, }
• (2025). ICCA-RAG: Intelligent customs clearance assistant using retrieval-augmented generation (RAG). ACCESS, 13, 39711-39726. (www)

  @article{ , abstract = {Document processing and query generation tasks in customs declaration scenarios face key challenges such as the complexity of multimodal data, adaptability to dynamic regulations, and ambiguity in query semantics. This study proposes a Retrieval-Augmented Generation system (ICCA-RAG) that addresses the core issues of processing complex customs documents and dynamically generating queries through multimodal document parsing, sparse-dense hybrid storage, and context-driven large language model generation. In terms of multimodal document parsing, the system supports comprehensive parsing of PDFs, images, tables, and text, which are uniformly transformed into semantic vectors and keyword indices for hybrid storage. By combining the retrieval and generation modules, the ICCA-RAG system achieves significant improvements in contextual relevance and generation accuracy. Compared to traditional methods, the ICCA-RAG system demonstrates a 20.1% increase in answer correctness, a 15.3% increase in answer relevancy, and an 18.7% increase in the faithfulness of generated content, with outstanding performance in noisy query scenarios. The research findings validate the ICCA-RAG system’s advancement and applicability in handling complex document processing and professional domain question-answering tasks, while also providing a transferable technical framework for other fields, such as law and healthcare.}, archive = {J_ACCESS}, author = {Rong Hu and Sen Liu and Panpan Qi and Jingyi Liu and Fengyuan Li}, doi = {10.1109/ACCESS.2025.3544408}, journal = {IEEE Access}, pages = {39711-39726}, shortjournal = {IEEE Access}, title = {ICCA-RAG: Intelligent customs clearance assistant using retrieval-augmented generation (RAG)}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of dust storm detection and prediction techniques: Leveraging satellite data, ground observations, and machine learning. ACCESS, 13, 39694-39710. (www)

  @article{ , abstract = {Dust storms and sandstorms are meteorological phenomena that threaten human health, agriculture, transportation, and the environment. These storms, capable of transporting fine particulate matter over long distances, contribute to soil erosion, reduced air quality, and respiratory illnesses. Despite their widespread impact, accurately detecting and predicting dust storms remains a complex task due to the variability of environmental conditions and the limitations of existing data and models. This survey paper provides a comprehensive review of current dust storm detection and prediction research, focusing on numerical and machine learning approaches such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). By analyzing various published techniques, this paper identifies gaps in existing methods and emphasizes the need for improved detection accuracy and early prediction systems. Key datasets such as MODIS, GOES-16, and ground-based measurements are examined, alongside the integration of hybrid data sources, to enhance detection capabilities. The survey also highlights the importance of developing robust, real-time prediction models to mitigate dust storms’ environmental and public health risks. In synthesizing the existing literature, this paper offers insights into the strengths and limitations of current methodologies while proposing future research directions aimed at improving machine learning applications for dust storm detection and prediction.}, archive = {J_ACCESS}, author = {A. M. Mutawa and Abdullah Alshaibani and Laalea Anwer Almatar}, doi = {10.1109/ACCESS.2025.3541075}, journal = {IEEE Access}, pages = {39694-39710}, shortjournal = {IEEE Access}, title = {A comprehensive review of dust storm detection and prediction techniques: Leveraging satellite data, ground observations, and machine learning}, volume = {13}, year = {2025}, }
• (2025). Intelligent damage detection for high strength bolts on wind turbines based on deep cognitive method. ACCESS, 13, 39680-39693. (www)

  @article{ , abstract = {In numerous wind power companies, the quality of high-strength bolts plays a pivotal role in ensuring the safety of wind turbines. However, existing damage detection methods tend to be cumbersome, and detection can sometimes be challenging due to environmental and other factors. This paper introduces TriNet, a novel acoustic-based damage detection approach tailored for assessing high-strength bolts assembled in wind turbines. By leveraging tapping acoustic data, TriNet evaluates bolt damage and assigns a confidence level to its assessments. The model integrates a Convolutional Neural Network (CNN), an attention mechanism, and a routing algorithm to process voiceprint images obtained from professional audio signal acquisition equipment at four standard damage depths. Additionally, a confidence-based sample weighting technique is proposed to enhance the performance of the deep learning model. Experimental data, incorporating various noise intensities, demonstrates the effectiveness of TriNet in industrial settings. This method significantly improves upon traditional ultrasonic damage detection techniques, addressing the escalating demand for efficient and precise bolt quality assessment in the wind power industry.}, archive = {J_ACCESS}, author = {Lüeshi Li and Beiming Yuan and Lu Liu and Ruizhuo Song}, doi = {10.1109/ACCESS.2025.3535791}, journal = {IEEE Access}, pages = {39680-39693}, shortjournal = {IEEE Access}, title = {Intelligent damage detection for high strength bolts on wind turbines based on deep cognitive method}, volume = {13}, year = {2025}, }
• (2025). Automated detection of chilean mine waste storage facilities using advanced deep learning models and sentinel-2 satellite imagery. ACCESS, 13, 39666-39679. (www)

  @article{ , abstract = {Mine Waste Storage Facilities (MWSFs) in Chile present substantial environmental and safety risks due to their extensive scale and the hazardous nature of their contents. This study proposes an automated detection approach that integrates Sentinel-2 satellite imagery with advanced deep learning models to address these critical issues. A central contribution of this research is the development of MineWasteCL_DB, a comprehensive public dataset comprising over 30,000 annotated images and 320,093 labels for diverse MWSF types, including Tailings Storage Facilities (TSFs), Waste Rock Dumps (WRDs), and Leaching Waste Dumps (LWDs). The study employs the YOLOv8x-seg model, selected for its high precision, to validate the presence of 96.15% of officially registered TSFs. Furthermore, it identified 141 WRDs and 112 LWDs in the Antofagasta Region, facilities absent from any official national registry. These findings underscore the methodology’s potential for widespread application and the necessity for routine monitoring across additional regions. The results provide a robust framework for advancing the understanding and management of MWSFs, thereby improving regulatory oversight and promoting environmental safety. The methodology supports not only the efficient monitoring of registered facilities but also the preliminary identification and prospective registration of unregistered sites. This capability enhances the oversight capacities of regulatory authorities while fostering the protection of environmental and public safety.}, archive = {J_ACCESS}, author = {Manuel Silva and Gabriel Hermosilla and Gabriel Villavicencio and Giovanni Cocca-Guardia and Pierre Breul and Vicente Aprigliano}, doi = {10.1109/ACCESS.2025.3546150}, journal = {IEEE Access}, pages = {39666-39679}, shortjournal = {IEEE Access}, title = {Automated detection of chilean mine waste storage facilities using advanced deep learning models and sentinel-2 satellite imagery}, volume = {13}, year = {2025}, }
• (2025). Self-supervised learning for temporal action segmentation in industrial and manufacturing videos. ACCESS, 13, 39650-39665. (www)

  @article{ , abstract = {Reliable methods for process control are required in maintenance to prevent emergencies, maintain high quality of work, and minimize potential risks to workers. Strict adherence to technological processes is also an essential requirement for compliance with production standards. The development of computer vision technologies opens new prospects in the field of video analytics for manufacturing and technological processes. However, effective implementation of AI-based solutions in monitoring systems requires overcoming a number of existing challenges. Among these limitations are difficulties in collecting annotated datasets necessary for training neural network models to solve specific tasks. In this work, we propose a methodology for creating a foundation industrial model (FIM) based on deep learning technology, that allows easy adaptation of algorithms for specific tasks with minimal requirements for annotated datasets. The solution for temporal action segmentation involves the use of self-supervised learning concepts and visual transformers. In order to validate the proposed approach, a dataset was prepared from several technological processes and a part of the data was annotated to enable model training for up to 14 event categories. The tested approaches include the X3D-M architecture and ViT-L. The pre-training is performed using the V-JEPA approach. On the test subset, the proposed approach leads to the Mean over Frame (MoF) of 0.817 and 0.653 for oil change and tire replacement process recognition, respectively. The experiments carried out confirm the feasibility of the proposed approach.}, archive = {J_ACCESS}, author = {Yuliya Vybornova and Maxim Aleshin and Svetlana Illarionova and Ilya Novikov and Dmitrii Shadrin and Artem Nikonorov and Evgeny Burnaev}, doi = {10.1109/ACCESS.2025.3545768}, journal = {IEEE Access}, pages = {39650-39665}, shortjournal = {IEEE Access}, title = {Self-supervised learning for temporal action segmentation in industrial and manufacturing videos}, volume = {13}, year = {2025}, }
• (2025). Adaptive and interpretable speed control rules for en route arrival management using extended learning classifier system. ACCESS, 13, 39631-39649. (www)

  @article{ , abstract = {Air-transport infrastructure is facing increasing congestion, necessitating innovative approaches to optimize arrival management and minimize delays near airports. To address this issue, this study developed an en route arrival management framework that employs a learning classifier system (LCS) to reduce arrival delays and fuel consumption by generating adaptive and interpretable speed-control rules. A virtual design database, created from multiobjective optimization and integrated with a cellular automaton (CA)-based air-traffic model, facilitates the development of these rules. Within this database, a binary target variable represents speed-control decisions, whereas explanatory variables encompass factors such as aircraft proximity and congestion levels. The LCS is trained on this dataset to generate speed-control rules, and their effectiveness is evaluated through CA-based traffic simulations. The results indicate that the LCS achieved 96.8% accuracy in predicting appropriate speed-control actions, significantly outperforming traditional decision-tree methods (70.5% accuracy). Furthermore, experimental findings demonstrate reductions in average flight times by 10–20 s and up to 5 fewer spacing adjustments for traffic within 600 s before and after the arrival of speed-controlled flights. Overall, the LCS reduced total flight time by 1,801 s and total fuel consumption by 1,650.1 kg per operational cycle, resulting in an estimated annual cost savings of 466,000. Furthermore, the LCS approach successfully extracts both sector-specific and common rules, offering enhanced adaptability to various traffic scenarios. These results suggest that the ability to generalize rules across different airspace sectors can improve the safety and efficiency of air-traffic management.}, archive = {J_ACCESS}, author = {Katsuhiro Sekine and Tomoaki Tatsukawa and Takeru Aoki and Kozo Fujii and Eri Itoh}, doi = {10.1109/ACCESS.2025.3546640}, journal = {IEEE Access}, pages = {39631-39649}, shortjournal = {IEEE Access}, title = {Adaptive and interpretable speed control rules for en route arrival management using extended learning classifier system}, volume = {13}, year = {2025}, }
• (2025). High precision infant facial expression recognition by improved YOLOv8. ACCESS, 13, 39621-39630. (www)

  @article{ , abstract = {Infant facial expression recognition is of great importance for early care, which can help parents and caregivers to identify the emotional state of infants. In this study, we propose an improved YOLOv8 model for infant facial expression recognition. Compared with traditional YOLOv8 model, following improvements have been proposed. First, the Swin Transformer V2 structure is introduced in the backbone network part to improve the model’s ability to model global features. Second, the AKConv and RepViTBlock modules are added to the head structure to optimize the feature extraction and fusion process. Then, the BiFPN is treated as the feature pyramid network (FPN) module to enhance the multi-scale feature fusion capability. Based on the above improvements, the proposed YOLOv8 model can effectively handle the subtle differences and multi-scale changes in infant facial expression, resulting in high-Precision and high-Recall facial expression recognition, which is suitable to recognize the complex and fine-grained facial expression. The applied dataset is composed of more than 9000 images, which are labeled as cry, happy, neutral and back of head. The experimental results show that the improved YOLOv8 model outperforms the original YOLOv8 and other mainstream facial expression recognition models.}, archive = {J_ACCESS}, author = {Shiwei Ren and Meng Sun and Bin Wang and Manyi Liu and Shaoyang Men}, doi = {10.1109/ACCESS.2025.3543950}, journal = {IEEE Access}, pages = {39621-39630}, shortjournal = {IEEE Access}, title = {High precision infant facial expression recognition by improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). A novel local binary patterns-based approach and proposed CNN model to diagnose breast cancer by analyzing histopathology images. ACCESS, 13, 39610-39620. (www)

  @article{ , abstract = {Breast cancer, which is among the most common types of cancer in women, is a serious disease that requires attention due to its high risk of death. In this respect, every effort made to help early diagnosis is remarkable. This article proposed two methods, one CNN-based and the other local binary pattern (LBP)-based, to perform the preliminary diagnosis process on breast cancer histopathology images with high performance. Within the scope of the study, the high performance of the two proposed methods was applied to two separate comprehensive datasets most preferred in breast cancer studies. The proposed CNN model has 20 layers and is called quad star LBP (QS-LBP) due to its star-like structure based on the proposed LBP. The histopathology images improved with the QS-LBP method were then analyzed with the most commonly used Random Forest and Optimized Forest algorithms among machine learning algorithms. The BreaKHis dataset contains images with 40X, 100X, 200X, and 400X magnification resolutions and contains approximately 7924 images. The other comprehensive dataset containing analyzed histopathology images contains approximately 278 thousand images. Both datasets examined are extremely important in terms of the significant number of breast cancer histopathology images they contain. The results obtained with the QS-LBP method developed within the scope of the study are 94.58% accuracy, 92.3% F1 score, and 97.9% AUC/ROC, respectively. The results obtained with the proposed CNN model are 98.27% accuracy, 98% F1 score, and 97% AUC/ROC, respectively. The QS-LBP method and CNN model developed within the scope of the study outperform many existing methodologies in classifying breast cancer histopathology images as benign and malignant. In addition to all these, the accuracy of both methods proposed within the scope of the article can be compared with some state-of-the-art methods.}, archive = {J_ACCESS}, author = {Mehmet Gül}, doi = {10.1109/ACCESS.2025.3545052}, journal = {IEEE Access}, pages = {39610-39620}, shortjournal = {IEEE Access}, title = {A novel local binary patterns-based approach and proposed CNN model to diagnose breast cancer by analyzing histopathology images}, volume = {13}, year = {2025}, }
• (2025). Active to reactive power attack in interconnected electric power systems and electric vehicle charging stations. ACCESS, 13, 39600-39609. (www)

  @article{ , abstract = {Vehicle-to-grid is a promising technology that can improve the power system’s stability and controllability. Despite these advantages, it could also create new threats to the power system’s security. A novel switching attack initiated from electric vehicle charging stations against the power system was investigated. In this attack, a portion of electric vehicles at each bus were compromised to switch from charging active power to discharging reactive power during peak hours. The results demonstrate that the switching attack disrupts the power system and causes significant over-voltages. The highest levels of over-voltage were observed when the attacker identified the most vulnerable buses and altered the charging and discharging pattern of electric vehicles. It was shown that a Jacobian-based method can be used to identify the most sensitive buses.}, archive = {J_ACCESS}, author = {Beheshteh Raouf and Seyedamirabbas Mousavian}, doi = {10.1109/ACCESS.2025.3546201}, journal = {IEEE Access}, pages = {39600-39609}, shortjournal = {IEEE Access}, title = {Active to reactive power attack in interconnected electric power systems and electric vehicle charging stations}, volume = {13}, year = {2025}, }
• (2025). Rateless deep joint source channel coding for 3D point cloud. ACCESS, 13, 39585-39599. (www)

  @article{ , abstract = {To enable untethered volumetric services, one of the primary challenges lies in reconstructing a high-quality 3D point cloud for each user, despite variations in channel conditions and bandwidth availability. Conventional digital point cloud coding approaches, such as tree-based or graph-based methods, reduce transmission data effectively; however, they are prone to quality degradation due to lossy compression and entropy coding, especially under fluctuating channels and limited bandwidth. To address these limitations, we propose a novel scheme inspired by deep joint source-channel coding (DJSCC). DJSCC compresses the 3D point cloud into coded symbols, which are then decoded via a graph auto-encoder (GAE) architecture. Additionally, the coded symbols are directly mapped to transmission symbols through analog modulation, allowing the point cloud quality to adapt dynamically to each user’s channel conditions. Unlike existing DJSCC schemes, our proposed scheme includes a non-uniform dropout mechanism that provides a rateless feature, enabling the point cloud quality to enhance based on the available bandwidth progressively. Experimental results with a point cloud dataset demonstrate that the proposed scheme mitigates quality degradation due to variations in channel quality and bandwidth better than the existing DJSCC scheme. Moreover, the proposed scheme shows improved generalization performance, leading to superior point cloud quality in narrow-band scenarios.}, archive = {J_ACCESS}, author = {Shoichi Ibuki and Tsubasa Okamoto and Takuya Fujihashi and Toshiaki Koike-Akino and Takashi Watanabe}, doi = {10.1109/ACCESS.2025.3546514}, journal = {IEEE Access}, pages = {39585-39599}, shortjournal = {IEEE Access}, title = {Rateless deep joint source channel coding for 3D point cloud}, volume = {13}, year = {2025}, }
• (2025). An integrated prediction-decision unit commitment model based on deep incremental quantile neural networks. ACCESS, 13, 39572-39584. (www)

  @article{ , abstract = {Uncertainties and variabilities stemming from renewable energy make accurate prediction challenging, and thus pose significant difficulties for power system operation. This paper proposes an integrated prediction-decision unit commitment (IPD-UC) framework based on deep incremental quantile neural networks. First, the quantile distribution of the forecasting errors is obtained using deep incremental quantile neural networks. Error intervals subject to the pre-defined confidence requirements are selected using winkle coefficients. Then, a cost-oriented interval selection strategy is developed to obtain the economically optimal interval based on up-to-date information. Finally, an IPD-UC model is proposed to make optimal and robust decisions in response to uncertainties originating from renewable energy and the prediction algorithms. Case studies on the IEEE RTS 24-bus system and the IEEE 118-bus system demonstrate that the proposed integrated prediction-decision model reduces the total annual system cost by 4.2% and improves accuracy by 0.31%, showcasing its ability to enhance economic benefits and resilience.}, archive = {J_ACCESS}, author = {Han Zhang and Tao Wu and Bo Hu and Changzheng Shao and Yifan SU and Gang Ruan}, doi = {10.1109/ACCESS.2025.3545969}, journal = {IEEE Access}, pages = {39572-39584}, shortjournal = {IEEE Access}, title = {An integrated prediction-decision unit commitment model based on deep incremental quantile neural networks}, volume = {13}, year = {2025}, }
• (2025). Real-time posture correction of squat exercise: A deep learning approach for performance analysis and error correction. ACCESS, 13, 39557-39571. (www)

  @article{ , abstract = {The squat is a widely performed exercise essential for maintaining physical fitness and overall well-being. It involves a series of movements in which an individual lowers their torso by bending the knees, and then returns to the starting position. Incorrect squat execution can lead to physical injuries, highlighting the importance of expert guidance. However, many individuals cannot afford professional training due to high costs, necessitating an automated workout analysis system. Existing vision-based squat analysis models face challenges related to occlusion and environmental factors. To address these limitations, we propose a method that utilizes squat videos captured by a stereo camera. The MediaPipe algorithm extracts and tracks keypoints from video frames. Detected keypoints are then used to classify squat types and evaluate squat performance using a Bi-CGRU model. Additionally, we introduce a regression-based approach to predict the correct squat posture. A deep learning-based regressor, trained on a dataset of squat videos, estimates the correct joint angles. When an improper squat is detected, the system provides feedback by presenting the correct squat form to the participant. Experimental results demonstrate that the Bi-CGRU classification model achieves 96.1% accuracy, while the pose correction regression model attains 99.0% accuracy using mean squared error loss. Both qualitative and quantitative evaluations indicate that our approach outperforms state-of-the-art methods utilizing stereo video input. The system helps users to refine their squat technique and reduce the risk of injury by offering real-time posture correction based on biomechanics guidance.}, archive = {J_ACCESS}, author = {Prashant Rao and C. S. Asha and P. Raghavendra Rao}, doi = {10.1109/ACCESS.2025.3545207}, journal = {IEEE Access}, pages = {39557-39571}, shortjournal = {IEEE Access}, title = {Real-time posture correction of squat exercise: A deep learning approach for performance analysis and error correction}, volume = {13}, year = {2025}, }
• (2025). Leveraging clean power from base transceiver stations for hybrid and fast electric vehicle charging stations system with energy storage devices. ACCESS, 13, 39523-39556. (www)

  @article{ , abstract = {Numerous emerging country mobile telecommunications industries are expanding quickly, which poses challenges for network operators include network congestion and subpar service delivery. Primarily due to absence of affordable and dependable electricity solution in such areas. With the aim of delivering a dependable and affordable energy solution in rising globe, this study gives a thorough analysis of fundamental concepts of renewable energy technology (RET). Based on region’s energy resources’ availability, dynamism, and techno economic viability, a grid-connected hybrid renewable energy (HRE) system with a power conversion and battery storage unit has been developed for Base Transceiver Stations (BTS) telecom industry. A simulation model created in HOMER Grid has been implemented for evaluating proposed system’s performance using actual load data from 15 distinct BTS locations all across Pakistan. These sites are also connected to environment friendly electric vehicle (EV) charging stations, with addition of incentives. Optimization results indicates that after integrating on-grid HRE resources, there is significant decrease in objective parameters i.e., 87.97% decrease in LCOE and 95.26% decrease in NPC, varies from 0.03384 ${\$}$ /kWh to 0.00407 ${\$}$ /kWh and 0.09173 ${\$}$ M to 0.00434 ${\$}$ M, respectively. By making BTS operations more cost-efficient, environment friendly, and dependably effective, the recommended approach will improve both living circumstances of host communities and quality of mobile services.}, archive = {J_ACCESS}, author = {Muhammad Bilal Ali and Abdullah Altamimi and Syed Ali Abbas Kazmi and Zafar A. Khan}, doi = {10.1109/ACCESS.2025.3546848}, journal = {IEEE Access}, pages = {39523-39556}, shortjournal = {IEEE Access}, title = {Leveraging clean power from base transceiver stations for hybrid and fast electric vehicle charging stations system with energy storage devices}, volume = {13}, year = {2025}, }
• (2025). RAG4DS: Retrieval-augmented generation for data Spaces—A unified lifecycle, challenges, and opportunities. ACCESS, 13, 39510-39522. (www)

  @article{ , abstract = {Retrieval-Augmented Generation (RAG) has gained significant attention from many researchers as an effective solution to address the hallucination issue of Foundational Models (FMs), particularly Large Language Models (LLMs). Although the RAG framework is considered a successful approach for enhancing LLMs by providing a suitable retrieval mechanism to obtain appropriate external knowledge, it still has limitations in acquiring high-quality knowledge from diverse data sources. The complementary integration of RAG and data spaces is proposed to exploit RAG’s capabilities within data spaces. Data spaces provide RAG with the ability to obtain diverse and high quality data sources from several data providers under secure data-sharing mechanisms and direct data exchange negotiations. At the same time, RAG enhances the support services of data spaces. In this paper, we present a high-level architecture for RAG data space models (RAG-DSMs) with a unified lifecycle for RAG and data spaces, highlight the possible challenges of the proposed integration while presenting potential opportunities. Moreover, we present two use cases for leveraging RAG-DSMs in the mobility and health domains.}, archive = {J_ACCESS}, author = {Majjed Al-Qatf and Rafiqul Haque and Saeed Hamood Alsamhi and Samuele Buosi and Muhammad Asif Razzaq and Mohan Timilsina and Ammar Hawbani and Edward Curry}, doi = {10.1109/ACCESS.2025.3545387}, journal = {IEEE Access}, pages = {39510-39522}, shortjournal = {IEEE Access}, title = {RAG4DS: Retrieval-augmented generation for data Spaces—A unified lifecycle, challenges, and opportunities}, volume = {13}, year = {2025}, }
• (2025). Toward interpretable hybrid AI: Integrating knowledge graphs and symbolic reasoning in medicine. ACCESS, 13, 39489-39509. (www)

  @article{ , abstract = {Knowledge Graphs (KGs) are data structures that enable the integration of heterogeneous data sources and supporting both knowledge representation and formal reasoning. This paper introduces TrustKG, a KG-based framework designed to enhance the interpretability and reliability of hybrid AI systems in healthcare. Positioned within the context of lung cancer, TrustKG supports link prediction, which uncovers hidden relationships within medical data, and counterfactual prediction, which explores alternative scenarios to understand causal factors. These tasks are addressed through two specialized hybrid AI systems, VISE and HealthCareAI, which combine symbolic reasoning with inductive learning over KGs to provide interpretable AI solutions for clinical decision-making. Leveraging KGs to represent biomedical properties and relationships, and augmenting them with learned patterns through symbolic reasoning, our hybrid approach produces models that are both accurate and transparent. This interpretability is particularly important in medical applications, where trust and reliability in AI-driven predictions are paramount. The empirical analysis demonstrates the effectiveness of VISE and HealthCareAI in improving the predictive accuracy and clarity of model outputs. By addressing challenges in link prediction—such as discovering previously unknown connections between medical entities—and in counterfactual prediction, TrustKG, with VISE and HealthCareAI, underscores the potential of integrating KGs with symbolic AI to create trustworthy, interpretable AI systems in healthcare. This paper contributes to the advancement of semantic AI, offering a pathway for robust and reliable AI solutions in clinical settings.}, archive = {J_ACCESS}, author = {Yashrajsinh Chudasama and Hao Huang and Disha Purohit and Maria-Esther Vidal}, doi = {10.1109/ACCESS.2025.3529133}, journal = {IEEE Access}, pages = {39489-39509}, shortjournal = {IEEE Access}, title = {Toward interpretable hybrid AI: Integrating knowledge graphs and symbolic reasoning in medicine}, volume = {13}, year = {2025}, }
• (2025). Advanced step density modulation technique to reduce current ripples in high-frequency inverter for WPT EV charging systems. ACCESS, 13, 39473-39488. (www)

  @article{ , abstract = {This paper analyzes Step Density Modulation (SDM) technique for high-frequency inverters in Wireless Power Transfer (WPT) systems for Electric Vehicle (EV) charging. While Pulse Density Modulation (PDM) is commonly used to control high-frequency inverters, it faces limitations in managing current ripple, leading to electromagnetic interference (EMI), reduced power transfer efficiency, and system performance degradation. Step Density Modulation is proposed as an advanced alternative to PDM, offering dynamic pulse density adjustments in discrete stages to align with inverter switching characteristics. This approach minimizes the effects of dead time, significantly reducing current ripple and total harmonic distortion (THD), thereby enhancing waveform quality and system efficiency. A comprehensive mathematical model compares current ripple magnitude, harmonic spectrum, and inverter delay time for both modulation techniques. MATLAB simulations, validated through hardware prototype implementation on a 5 kW WPT EV charger, demonstrate that SDM reduces current ripple by up to 30% and THD of 20% compared to PDM. The hardware prototype confirms smoother inverter output, improved power stability, and increased efficiency, establishing SDM as a reliable and practical solution for enhancing WPT systems in EV charging applications.}, archive = {J_ACCESS}, author = {J. Franklin and R. K. Pongiannan and Roobaea Alroobaea and Ahmed Emara and R. Narayanamoorthi}, doi = {10.1109/ACCESS.2025.3545846}, journal = {IEEE Access}, pages = {39473-39488}, shortjournal = {IEEE Access}, title = {Advanced step density modulation technique to reduce current ripples in high-frequency inverter for WPT EV charging systems}, volume = {13}, year = {2025}, }
• (2025). Empirical modeling and performance evaluation of monolithic-3D dynamic random-access memory. ACCESS, 13, 39466-39472. (www)

  @article{ , abstract = {Among the various three-dimensional (3D) stacking techniques essential for scaling electronic devices, monolithic-3D (M3D) integration technology offers the highest integration density. However, this technology faces challenges such as sensitivity to high temperatures and electrical interference owing to the thin interlayer dielectric (ILD). In this study, we realized a 2-tier M3D structure using an MoS2 field-effect transistor (FET) to minimize the process temperature. This device demonstrated favorable electrical properties, such as an on-off ratio of $10^{4}$ , subthreshold swing of 0.286 V/dec, and on-current of $0.1~\mu $ A. In addition, ILD showed a low current of $10^{-11}$ A to isolate the device of each tier. The electrical interferences on 2-tier M3D transistor were measured by focusing on the back-gate effect when varying the ILD thickness. We used TCAD modeling to quantitatively verify the impact of the back-gate effect on the operating characteristics of the DRAM cell. The errors in the DRAM cell data were evaluated based on the swing in the bitline voltage caused by electrical interference during the read operations. We also analyzed the variation in the bitline voltage swing under the back-gate effect. The bitline voltage swing exhibited a significant change during the read operation of Data 1, whereas no such change was observed for Data 0. These results demonstrate that the accuracy of the readout data can be improved by attenuating signal interference, including crosstalk and back-gate effects. This paper quantitatively elucidates the influence of electrical interference on the operation of M3D DRAM and presents it as a key indicator for 3D stacked DRAM design.}, archive = {J_ACCESS}, author = {Sangjun Hwang and Jo-Hak Jeong and Hee-Tae Kim and Jungwoo Bong and Ho-Sung Lee and Bhishma Pandit and Gyeore Lee and Donggu Im and Sung Haeng Cho and Hyeon-Sik Jang and Byung Jin Cho and Keun Heo}, doi = {10.1109/ACCESS.2025.3545903}, journal = {IEEE Access}, pages = {39466-39472}, shortjournal = {IEEE Access}, title = {Empirical modeling and performance evaluation of monolithic-3D dynamic random-access memory}, volume = {13}, year = {2025}, }
• (2025). Generative adversarial networks for stochastic grid planning considering individual distributed energy resource. ACCESS, 13, 39455-39465. (www)

  @article{ , abstract = {To consider the future impact of distributed energy resources (DERs) on grid planning, certain transmission and distribution system operators assume future profiles of conventional loads (CLs) and DERs and calculate the net load by adding them together. As certain DERs such as energy storage (ES) are operated according to grid conditions and renewable energy generation, the profiles must be assumed considering the correlation between CLs and DERs for calculating the net load. In addition, a spatial correlation is vital to properly understanding grid constraints on lines and transformers by load flow analysis. In this study, we proposed a method using a generative adversarial network to generate various profiles that simulate three-dimensional correlations such as time, space, and type of DERs. The objective is to show that the proposed method can adequately quantify the uncertainty of the future load assumption for grid planning, which is becoming increasingly difficult with the expansion of DERs. It was shown that the proposed method can account for the peak and off-peak deviations of individual DERs and appropriately assume the probability distribution of future aggregated loads. In fact, we evaluated the validity of the probability distribution using a reliability diagram and found that the deviation from the perfect reliability line was reduced by 33% on average against the compared method. Furthermore, we performed load flow analysis using the load profiles generated by the proposed method and confirmed that the load flows on lines and transformers can also be assumed with appropriate probability distributions.}, archive = {J_ACCESS}, author = {Hiroki Ichinomiya and Kenichi Kawabe and Surachai Chaitusaney}, doi = {10.1109/ACCESS.2025.3546468}, journal = {IEEE Access}, pages = {39455-39465}, shortjournal = {IEEE Access}, title = {Generative adversarial networks for stochastic grid planning considering individual distributed energy resource}, volume = {13}, year = {2025}, }
• (2025). Enhanced stability and power quality control of LCL-GCI under weak grid conditions using THQVC. ACCESS, 13, 39442-39454. (www)

  @article{ , abstract = {Grid disturbances and weak grid conditions often lead to synchronization instability in LCL- filtered grid-connected inverters (LCL-GCI), causing challenges in maintaining system stability and power quality. This paper presents a comprehensive stability analysis and introduces a novel third harmonic quadrature voltage control (THQVC) strategy to address these problems. The instability in conventional controllers, caused by the non-linear behavior of the phase-locked loop (PLL) under weak grid conditions, leads to distorted voltage at the point of common coupling (PCC) and affects the overall system performance. To mitigate these challenges, the THQVC controller was designed to regulate the q-axis voltage at the PCC, ensuring that the voltage remains sinusoidal. The stability of the system is evaluated through single-input single-output (SISO) small-signal modeling and derivation of the return ratio, analyzed using the Nyquist criterion (NC). Simulation results show that the conventional controller becomes unstable as the grid inductance and PLL bandwidth increase, particularly during the transition from strong to weak grid conditions. In contrast, the proposed THQVC controller maintains stability across grid inductances up to 11 mH, even with PLL bandwidths as high as 250 Hz. The experimental results further validate the effectiveness of the controller in significantly reducing the total harmonic distortion in voltage (THDv) and current (THDi), thereby enhancing power quality under weak grid conditions. This study highlights the robust performance of the THQVC controller, providing an advanced control solution that ensures stability and improves the power quality in LCL-GCI systems operating under weak grid scenarios.}, archive = {J_ACCESS}, author = {Pratchaya Pathomchaiwat and Bunlung Neammanee and Jirawut Benjanarasut}, doi = {10.1109/ACCESS.2025.3546517}, journal = {IEEE Access}, pages = {39442-39454}, shortjournal = {IEEE Access}, title = {Enhanced stability and power quality control of LCL-GCI under weak grid conditions using THQVC}, volume = {13}, year = {2025}, }
• (2025). Nonlinear robust techniques applied to control the stability of an electric tricycle. ACCESS, 13, 39428-39441. (www)

  @article{ , abstract = {This paper presents an original application of Sliding Mode (SM) control to stabilize an electric tricycle prototype using an on-board servomotor. SM controllers are known for their robustness against noise, disturbances, and model uncertainties. First, we design an inverted pendulum and cart controller to develop the methodology to be applied to the vehicle. A simplified mathematical model is adopted to design an SM controller for the inverted pendulum and cart and subsequently tested in the experimental setup. The approach is then extended to design an SM controller based on a simplified mathematical model for the tricycle prototype, for which an experimental parameter identification was conducted. This controller is then tested on a complex multibody structure representing the tricycle. The effectiveness of the proposed methodology is shown through successful experimental validation for the pendulum control, together with simulated control of the multibody tricycle model. These results highlight the achieved performance and robustness of both controllers. The next future step is to apply the methodology to the prototype vehicle moving on a real street.}, archive = {J_ACCESS}, author = {Lucas S. Olinda and Diego M. Bravo and Janito V. Ferreira and Pablo S. Meirelles and Rodrigo M. Bacurau and Eurípedes G. O. Nóbrega}, doi = {10.1109/ACCESS.2025.3546551}, journal = {IEEE Access}, pages = {39428-39441}, shortjournal = {IEEE Access}, title = {Nonlinear robust techniques applied to control the stability of an electric tricycle}, volume = {13}, year = {2025}, }
• (2025). Toward privacy-preserving data Sharing—An australian healthcare perspective. ACCESS, 13, 39405-39427. (www)

  @article{ , abstract = {The rise of big data has brought increased urgency to the importance of privacy-preserving data sharing in healthcare. In Australia, health records exist in various databases; however data sharing is limited. While many consumers and healthcare professionals recognise the advantages of sharing data for research and health care services, misgivings about privacy and security persist. This study examined current perspectives on data sharing, investigating the trust level in privacy preserving data sharing tools and techniques among healthcare professionals and organisations, and their openness to adopting technology for secure data sharing. We incorporated participants from various healthcare professions across Australia. We aimed to uncover prevalent attitudes toward data sharing in the healthcare sector by employing a mix of descriptive statistics and knowledge discovery methods. Findings revealed that despite significant concerns regarding data breaches and sharing, Australian healthcare professionals have a notable willingness to embrace technological solutions for safer data sharing. Interestingly, healthcare professionals’ motivations for data sharing differed from those observed in consumer-focused studies, with a stronger emphasis on enhancing patient outcomes rather than solely supporting research. This study offers valuable insights into formulating effective policies and strategies to foster privacy-preserving data sharing within the Australian healthcare system. We surveyed 82 healthcare professionals, including both clinical and non-clinical staff from the Australian Healthcare sector. The dataset containing the survey results discussed in this paper is available for download here: https://doi.org/10.26189/2be780bd-c782-460e-8fd2-1b29efc3f51a.}, archive = {J_ACCESS}, author = {Kimley Foster and Nectarios Costadopoulos and Arash Mahboubi and Sabih Ur Rehman and Md Zahidul Islam}, doi = {10.1109/ACCESS.2025.3545778}, journal = {IEEE Access}, pages = {39405-39427}, shortjournal = {IEEE Access}, title = {Toward privacy-preserving data Sharing—An australian healthcare perspective}, volume = {13}, year = {2025}, }
• (2025). LN-SCNet: A lightweight convolutional neural network for SAR ship classification. ACCESS, 13, 39394-39404. (www)

  @article{ , abstract = {Ship classification is a critical challenge in the domain of synthetic aperture radar (SAR) automatic target recognition, primarily due to the large number of pixels occupied by ships in high-resolution SAR images. This results in substantial input sizes, which lead to increased computational demands and a large number of parameters in the classification network. In this paper, a new SAR ship classification method based on a lightweight convolutional neural network (CNN) is proposed. The method aims to achieve competitive accuracy and efficiency in classifying ships within SAR images. To evaluate the effectiveness of the proposed method, extensive experiments were conducted on the publicly available FUSAR-Ship dataset. The experimental results demonstrate that the proposed method achieves a classification accuracy of 76.12%and an F1 score of 56.67%. Furthermore, comparisons with other state-of-the-art lightweight CNN methods indicate that the proposed method outperforms existing methods in terms of classification performance.}, archive = {J_ACCESS}, author = {Shuang Zhao and Weihang Li and Fangyu Shen and Ming You}, doi = {10.1109/ACCESS.2025.3546764}, journal = {IEEE Access}, pages = {39394-39404}, shortjournal = {IEEE Access}, title = {LN-SCNet: A lightweight convolutional neural network for SAR ship classification}, volume = {13}, year = {2025}, }
• (2025). Estimating remaining usable time of batteries with uncertainty quantification: A case study on base transceiver station application using real-life data. ACCESS, 13, 39380-39393. (www)

  @article{ , abstract = {Systems consisting of multiple components can experience downtime during their operational lifetime due to issues with one or more of these components. For instance, accurately predicting the remaining time before a battery depletes its total energy is crucial for systems like a Base Transceiver Station. This paper addresses how long the battery will sustain a Base Transceiver Station under varying load conditions with the associated uncertainty when the external power source is interrupted. A customized approach using battery modeling and load forecasting is designed to predict the remaining usable time of the battery. In total, 7 Base Transceiver Stations are considered and clustered into 3 groups based on their installed battery capacity. First, a battery model is developed employing the modified Shepard model using real-life measured battery discharge data. Since the load currents are unknown beforehand, they are forecasted using a time series modeling approach. The resulting uncertainty from both models is then quantified using a prediction interval. Using our approach, the averaged Mean Absolute Error of the remaining usable time estimates is 12.61 minutes for all discharges. The associated uncertainty with these estimation using the 95% prediction interval is 26.43 minutes. The estimation errors are relatively small compared to true discharge times, as some batteries can provide energy close to 15 hours. The overall estimation results closely matched the measured values, providing valuable insights for proactive planning and management of potential failures.}, archive = {J_ACCESS}, author = {Yonas Tefera and Stijn Luca and Dereje Hailemariam Woldegebreal and Peter Karsmakers}, doi = {10.1109/ACCESS.2025.3546456}, journal = {IEEE Access}, pages = {39380-39393}, shortjournal = {IEEE Access}, title = {Estimating remaining usable time of batteries with uncertainty quantification: A case study on base transceiver station application using real-life data}, volume = {13}, year = {2025}, }
• (2025). TrapMI: A data protection method to resist model inversion attacks in split learning. ACCESS, 13, 39364-39379. (www)

  @article{ , abstract = {Split learning is a neural network training approach that can overcome the limitations of traditional deep neural networks in edge artificial intelligence environments. It offers the advantage of privacy protection because it transmits intermediate features that are calculated via the client-side model and the client does not need to send the original input data to the server. However, concerns remain regarding data privacy leakage because an attacker can still attempt model inversion attacks based on the intermediate features. We introduce several shortcomings of existing defense techniques for such attacks and present a new defense approach called TrapMI. The proposed method can induce an attacker to generate a class-specific target image that appears different from the original image when inverting the input image. We analyze the performance through quantitative and qualitative evaluations. Furthermore, the AutoGenerator is proposed to overcome the problem whereby the client cannot perform modulation that requires the target image because the class of the input image is unknown during this phase. De-identified images are automatically modulated in the inference phase using this approach. The proposed method was evaluated on two datasets, three classification models, and three split points. Its resistance was measured using a deeper and stronger inverse model than those in previous studies. Overall, the proposed method ensures data privacy protection at a significantly higher level while maintaining a similar task performance to that of existing defense technologies.}, archive = {J_ACCESS}, author = {Hyunsik Na and Daeseon Choi}, doi = {10.1109/ACCESS.2025.3545597}, journal = {IEEE Access}, pages = {39364-39379}, shortjournal = {IEEE Access}, title = {TrapMI: A data protection method to resist model inversion attacks in split learning}, volume = {13}, year = {2025}, }
• (2025). A deep learning approach for image analysis and reading body weight from digital scales in pigs farms. ACCESS, 13, 39353-39363. (www)

  @article{ , abstract = {Body weight is an important measure in fattening farms that allows pig farmers to monitor weight gain, manage feeding, and care for animal health. Traditionally, pig weighing is done directly, i.e. placing one or more pigs at a time on a scale while the total body weight is displayed and recorded. However, this process is labor-intensive, causes stress to the animals, and is highly prone to manual recording errors. Recently, several deep learning-based image analysis methods have emerged to estimate pig body weight, but these only consider the animal’s body characteristics. For this reason, an automatic deep learning-based approach is introduced for reading pig body weight from digital scale images. This reading is done by recognizing the body weight values indicated on the scale screen during the weighing process. For this purpose, convolutional neural network models are developed from scale screen segmentation to scale digit classification used to build the body weight value. The proposed approach is applied and validated in a real case study of fattening pigs from a Spanish company. Computational results showed that our approach read body weight with an average error of 20.2 g for a group of pigs with an average weight of 44 kg, taking less than 50 milliseconds to individually recognize the weight value. Therefore, our approach is reliable to support decisions in pig fattening management and suitable to be embedded into real-time weighing systems and useful too for image annotation purposes.}, archive = {J_ACCESS}, author = {Nicolás A. Reyes-Reyes and Mihai Catalin Doja and Pol Llagostera-Blasco and Lluís M. Plà-Aragonés and Marcela C. González-Araya}, doi = {10.1109/ACCESS.2025.3543027}, journal = {IEEE Access}, pages = {39353-39363}, shortjournal = {IEEE Access}, title = {A deep learning approach for image analysis and reading body weight from digital scales in pigs farms}, volume = {13}, year = {2025}, }
• (2025). DC series arc fault detection capability using auxiliary filter for module-level PV systems. ACCESS, 13, 39343-39352. (www)

  @article{ , abstract = {A module-level power electronics for photovoltaic (PV) systems can achieve the maximum power generation for each PV panel, which can overcome the partial shading of PV systems. However, it increases the possibility of series arc fault condition between the PV panel and module-level power electronics. The damaged and loose connector can induce the DC series arc fault condition. This paper investigates DC series arc fault detection capability based on the non-inverting buck-boost converter using the resonant filter for the PV applications. From the arc fault characteristics, the designed impedance model can clarify the DC series arc fault condition. The operational principle and design methodology of proposed buck-boost converter are analyzed with considerations of power conversion and series arc fault detection capability. The experimental results using the 800-W prototype converter can verify the series arc fault detection capability with proposed topology using the resonant filter.}, archive = {J_ACCESS}, author = {Chun-Sung Kim and Wan Kim and Hwa-Pyeong Park}, doi = {10.1109/ACCESS.2025.3545457}, journal = {IEEE Access}, pages = {39343-39352}, shortjournal = {IEEE Access}, title = {DC series arc fault detection capability using auxiliary filter for module-level PV systems}, volume = {13}, year = {2025}, }
• (2025). Detecting adversarial examples using cross-modal semantic embeddings from images and text. ACCESS, 13, 39330-39342. (www)

  @article{ , abstract = {Deep learning models are highly susceptible to adversarial attacks, where subtle perturbations in the input images lead to misclassifications. Adversarial examples typically distort specific features that are critical for accurate image classification. Therefore, distorting the visual semantic characteristics via adversarial attacks is challenging. Building on this insight, we propose a method that leverages a multimodal space for detection of adversarial examples. Our approach utilizes cross-modal semantic embeddings derived from the images and labels. We employed image and text encoders trained to maximize the similarity between the embeddings extracted from images and labels, optimized these cross-modal semantic embeddings and mapped them to a common semantic space to extract detailed semantic features. When the embeddings extracted from an image and its label are semantically similar, they are closely positioned in this space. Based on these observations, we compared the similarities between the embeddings of the input images with those of their corresponding labels to detect adversarial examples. We evaluated our proposed method on classification benchmark datasets and demonstrated detection rates of over 91.75% for various adversarial attacks. Moreover, we determined that the ground truth had higher similarity than the other label to the detected adversarial sample, enabling robust predictions with the most similar label.}, archive = {J_ACCESS}, author = {Sohee Park and Gwonsang Ryu and Daeseon Choi}, doi = {10.1109/ACCESS.2025.3542825}, journal = {IEEE Access}, pages = {39330-39342}, shortjournal = {IEEE Access}, title = {Detecting adversarial examples using cross-modal semantic embeddings from images and text}, volume = {13}, year = {2025}, }
• (2025). LEST: Large-scale LiDAR semantic segmentation with deployment-friendly transformer architecture. ACCESS, 13, 39318-39329. (www)

  @article{ , abstract = {Large-scale LiDAR-based point cloud semantic segmentation is a critical challenge for autonomous driving perception. Most state-of-the-art LiDAR semantic segmentation methods rely on complex operators, such as sparse 3D convolutions or KdTree structures, which hinder their deployment on modern embedded devices. While transformer architectures have gained prominence in natural language processing (NLP) and 2D computer vision, their application to large-scale point cloud semantic segmentation remains limited. In this paper, we introduce LEST (LiDAR sEmantic Segmentation architecture with Transformer), a novel framework built entirely on simple operators. LEST incorporates two key innovations: a Space Filling Curve (SFC) grouping strategy and a DIStance-based COsine (DISCO) linear transformer. Experimental results demonstrate that our model achieves competitive performance on the nuScenes semantic segmentation validation set and the SemanticKITTI test set, while maintaining a deployment-friendly design.}, archive = {J_ACCESS}, author = {Chuanyu Luo and Nuo Cheng and Sikun Ma and Han Li and Xiaohan Li and Shengguang Lei and Pu Li}, doi = {10.1109/ACCESS.2025.3545957}, journal = {IEEE Access}, pages = {39318-39329}, shortjournal = {IEEE Access}, title = {LEST: Large-scale LiDAR semantic segmentation with deployment-friendly transformer architecture}, volume = {13}, year = {2025}, }
• (2025). Reducing uncertainty of weak supervised signals via mutual information techniques. ACCESS, 13, 39306-39317. (www)

  @article{ , abstract = {Weakly supervised learning (WSL) refers to training models using imperfect or noisy labels, which can significantly reduce the costs associated with manual labeling. However, the model performance may be affected by the uncertainty of weakly supervised signals (WSS). To address this problem, we propose a novel Mutual Information-based Network (MINet), which reduces the reliance on weak labels by maximizing the mutual information (MI) and minimizing the conditional mutual information (CMI) between representations and labels. MINet differs from existing methods by using a dual-branch model structure that follows a mutual information-based objective function. The objective of one branch is to maximize MI, which helps to extract valuable information from weak labels. The second branch reduces the dependence of data representations on their corresponding weak labels, which lessens the negative effects of WSS. By applying variational approximation and reparameterization techniques, we optimize the proposed model and achieve significant improvements in robustness and generalization on four image classification datasets. Moreover, the experiments show that the model can effectively utilize the effective information from WSS and reduce their uncertainty.}, archive = {J_ACCESS}, author = {Yichen Liu and Hanlin Feng and Xin Zhang}, doi = {10.1109/ACCESS.2025.3546605}, journal = {IEEE Access}, pages = {39306-39317}, shortjournal = {IEEE Access}, title = {Reducing uncertainty of weak supervised signals via mutual information techniques}, volume = {13}, year = {2025}, }
• (2025). SmoothDectector: A smoothed dirichlet multimodal approach for combating fake news on social media. ACCESS, 13, 39289-39305. (www)

  @article{ , abstract = {The rapid dissemination of fake news in the digital era has become a pressing concern. The ease of generating and manipulating fake content, including images, text, audio, and videos, has significantly fueled the spread of misinformation on social media platforms. These platforms often lack rigorous editorial scrutiny, exacerbating this problem. Although recent studies have explored multimodal fake news detection to learn shared representations of textual and visual information, they often learn discrete latent representations, merely concatenations of multimodal features. Simple concatenation or summation operations hinder the dynamic interaction of multimodal features. Furthermore, most models rely on additional subtasks, such as reconstruction and event discrimination. The performance of these models depends heavily on subtasks, which can be mathematically complex and time-consuming. This reliance limits the ability of researchers to explore different modeling assumptions freely. This study introduces a novel approach that integrates a probabilistic algorithm with a deep neural network to effectively capture the uncertainties and diversities in the shared latent representation of multimodal data. Specifically, our model utilizes continuous latent representations by leveraging a smoothed Dirichlet distribution, facilitating the identification of shared hidden patterns across textual and visual modalities. In addition, our model demonstrates the powerful properties of generative models when integrated with neural network models. Our results underscore the potential of integrating a probabilistic algorithm with a deep neural network to address the challenges of fake news detection in a multimodal setting. To support further research and reproducibility, we made the code related to this work publicly accessible.}, archive = {J_ACCESS}, author = {Akinlolu Oluwabusayo Ojo and Fatma Najar and Nuha Zamzami and Hanen T. Himdi and Nizar Bouguila}, doi = {10.1109/ACCESS.2025.3546876}, journal = {IEEE Access}, pages = {39289-39305}, shortjournal = {IEEE Access}, title = {SmoothDectector: A smoothed dirichlet multimodal approach for combating fake news on social media}, volume = {13}, year = {2025}, }
• (2025). Research on detection and counting method of green walnut based on YOLOv8n-RBP. ACCESS, 13, 39275-39288. (www)

  @article{ , abstract = {In natural environments, green walnuts often experience occlusion by branches and leaves, fruit overlap, and varying lighting conditions. To address the issues of low detection accuracy, missed detections, and false positives in the YOLO model, this study proposes an improved YOLOv8n-based detection and counting model for green walnuts, named YOLOv8n-RBP. First, a receptive field-concentrated attention module (RFCBAM) is integrated into the backbone network to enhance feature extraction capabilities. Second, a BiFPN-GLSA module is introduced to replace the Path Aggregation Network (PANet) in the neck, improving the fusion of feature layers from the backbone and Neck networks and enhancing the model’s ability to capture both global and local spatial features. Lastly, to address the weak generalization and slow convergence issues of the CIoU loss function in detection tasks, the PIoUv2 loss function is employed to accelerate bounding box regression and improve detection performance. Experimental results demonstrate that the YOLOv8n-RBP model excels across multiple evaluation metrics. Specifically, the model achieves a mean average precision (mAP@0.5) of 82.2% and a recall rate of 72.4%, with a model size of only 4.65 MB, 2.2 million parameters, and 8.3 GFLOPs. Compared to the original YOLOv8n model, the recall rate and mAP@0.5 improve by 2.7% and 2.5%, respectively, while the number of parameters, FLOPs, and model size decrease by 26.7%, 0.6%, and 22.0%, respectively. Further deployment on the NVIDIA Jetson Xavier NX demonstrates the model’s robust performance under natural conditions, indicating its suitability for orchard operations.}, archive = {J_ACCESS}, author = {Bangbang Chen and Keke Tan and Kun Li and Baojian Ma and Xiangdong Liu}, doi = {10.1109/ACCESS.2025.3546313}, journal = {IEEE Access}, pages = {39275-39288}, shortjournal = {IEEE Access}, title = {Research on detection and counting method of green walnut based on YOLOv8n-RBP}, volume = {13}, year = {2025}, }
• (2025). Mobility control for optimal position of body area network user for high communication quality in BAN mesh networks. ACCESS, 13, 39263-39274. (www)

  @article{ , abstract = {In this paper, we propose a user mobility control method that improves data application throughput by relocating the Body Area Network (BAN) node to an optimal position where the throughput of intra-BAN (On-Body) and inter-BAN (Inter-Sink) communication topologies are compatible to the BAN mesh network. The effectiveness of the proposed user mobility control method was validated through evaluation, showing significant performance improvements. The technology that connects multiple BAN nodes to Wi-Fi mesh networks has been proposed to provide excellent communication quality at a low cost and in real-time. The user mobility control method can improve communication quality by leveraging users’ mobility, which holds promise for enhancing communication quality. Various heuristic methods for user mobility control have been proposed, but their throughput performance is likely to be poor because these methods cannot decide where to move considering both On-body and Inter-Sink communications in a BAN mesh network. In BAN mesh networks, On-Body communication and Inter-Sink communication have a trade-off relationship depending on distance. Therefore, mobility control must consider both types of communication simultaneously. Our proposed method considers On-Body communication and Inter-Sink communication independently and moves them to where the amount of overflow between their optimal positions is minimized. Using a spring model, the movement destination is whereby the springs are balanced based on the amount of traffic overflow optimization. The proposed method was evaluated for the initial position of the BAN nodes in terms of three variables: the volume of traffic, the branching angle $\theta $ with the root node and the distance d from the root node. In our evaluation, there was an improvement of 15.7% when d and $\theta $ were small. With small d and larger $\theta $ , there was an improvement of 54.8%. Furthermore, when d is large, the method showed a 12.3% improvement for small $\theta $ , and a 39.2% improvement for large $\theta $ .}, archive = {J_ACCESS}, author = {Jihyeok Yang and Takumi Kobayashi and Daisuke Anzai and Eng Keong Lua and Tutomu Murase}, doi = {10.1109/ACCESS.2025.3545890}, journal = {IEEE Access}, pages = {39263-39274}, shortjournal = {IEEE Access}, title = {Mobility control for optimal position of body area network user for high communication quality in BAN mesh networks}, volume = {13}, year = {2025}, }
• (2025). Deterministic compression of word embeddings. ACCESS, 13, 39248-39262. (www)

  @article{ , abstract = {Word embeddings are an indispensable technology in the field of artificial intelligence, particularly when working with natural language processing models. To further enhance their usability, several studies have tackled the compression of word embeddings while maintaining task performance. In the same vein, this study proposes a word embedding compression method that vastly differs from recent neural compression methods. A notable characteristic of the proposed method is that it guarantees a stable and reproducible compressed representation under identical configurations. The central idea of the proposed method is utilizing a deterministic procedure and a convex optimization. More specifically, we leverage the property of obtaining identical solutions for identical convex optimization problems to construct a deterministic procedure throughout the entire compression process. We conduct experiments on both intrinsic and extrinsic evaluation with various word embeddings. Our experimental results show the effectiveness of the proposed method in terms of the performance vs. compression ratio trade-off and the ability to offer reproducible results. Notably, the results of the machine translation experiment on the extrinsic evaluation show that the proposed method compresses the word embedding layer of the Long Short-Term Memory (LSTM) Decoder by a factor of 258 with a 0.2 point decrease in BLEU Score.}, archive = {J_ACCESS}, author = {Yuki Nakamura and Jun Suzuki and Takumi Ito and Kentaro Inui}, doi = {10.1109/ACCESS.2025.3546226}, journal = {IEEE Access}, pages = {39248-39262}, shortjournal = {IEEE Access}, title = {Deterministic compression of word embeddings}, volume = {13}, year = {2025}, }
• (2025). A hybrid single-phase transformerless solar photovoltaic grid-connected inverter with reactive power capability and reduced leakage current. ACCESS, 13, 39235-39247. (www)

  @article{ , abstract = {Among the renewable energy sources, photovoltaic (PV) solar power represents one of the most potential. The use of grid-integrated solar power is much more popular than off-grid systems. The total system cost and weight can be reduced by eliminating transformers for grid-tied applications. However, a leakage current with a high peak value flows across the system in the absence of galvanic isolation between an ac grid and PV. In addition, reactive power injection must be ensured in future grid-tied systems. In this paper, the authors have proposed a new hybrid topology using both decoupling and mid-point clamping techniques to reduce the root mean square (RMS) and peak value of leakage current. In addition, the proposed topology can provide both active and reactive power to the grid. The topology is simulated in the Matlab/Simulink environment for both the open loop and closed loop conditions and then validated through experiments. In the open loop conditions, leakage current issue has been addressed. In the closed loop conditions, active power (P) and reactive power (Q) are injected into the grid using model predictive control (MPC). Finally, a comparative analysis with other similar topologies has been performed.}, archive = {J_ACCESS}, author = {Md Faruk Kibria and Saad Mekhilef and Marizan Mubin and Kok Soon Tey and Ahmed Elsanabary and Mehdi Seyedmahmoudian and Houssem Jerbi}, doi = {10.1109/ACCESS.2025.3546670}, journal = {IEEE Access}, pages = {39235-39247}, shortjournal = {IEEE Access}, title = {A hybrid single-phase transformerless solar photovoltaic grid-connected inverter with reactive power capability and reduced leakage current}, volume = {13}, year = {2025}, }
• (2025). Finite-time pure pursuit guidance control of a four mecanum wheeled mobile robot with active disturbance rejection. ACCESS, 13, 39214-39234. (www)

  @article{ , abstract = {This paper addresses the challenge of precise control and robust trajectory tracking for Four Mecanum Wheeled Mobile Robots (FMWMR) operating in environments where time-sensitive responses are critical and external conditions may vary. Traditional control approaches often fall short due to slow convergence rates and limited resilience to disturbances. To tackle these issues, we propose a novel control framework that integrates Prescribed-Time (PT) control, Cascaded Extended State Observers (CESO), and Pure Pursuit Guidance (PPG). The PT control component ensures that the system converges to the desired state within a user-defined prescribed time, regardless of initial conditions or disturbances, while CESO enhances disturbance estimation through a multi-layered observer structure, ensuring rapid and reliable adaptation under varying conditions. PPG is incorporated to improve trajectory tracking by guiding the robot toward a designated lookahead point, aligning the robot’s velocity with the target’s direction to enable smooth and computationally efficient path following that leverages FMWMR’s omnidirectional capabilities. We provide theoretical guarantees of stability and convergence, demonstrating that the proposed control architecture outperforms traditional finite-time and ESO-based control methods in terms of tracking accuracy, disturbance rejection, and adherence to temporal constraints. Numerical simulations validate the effectiveness and robustness of the framework, highlighting its potential for real-time applications in environments that demand high precision and adaptability.}, archive = {J_ACCESS}, author = {Nayan Banik and Jawhar Ghommam and Mohammad H. Rahman}, doi = {10.1109/ACCESS.2025.3546864}, journal = {IEEE Access}, pages = {39214-39234}, shortjournal = {IEEE Access}, title = {Finite-time pure pursuit guidance control of a four mecanum wheeled mobile robot with active disturbance rejection}, volume = {13}, year = {2025}, }
• (2025). Computerized diagnosis methods for pediatric cardiology: A review. ACCESS, 13, 39192-39213. (www)

  @article{ , abstract = {Pediatric cardiac disorders include an extensive range of heart conditions in infants, children and carried over to adolescents in certain cases. These disorders may be congenital or acquired and can vary in impacting the life of the pediatric subject which requires complex surgical procedures and certain cases that do not require medical intervention. Congenital heart disease (CHD) is present during birth and acquired cardiac diseases are developed after birth predominantly due to autoimmune responses or infections. Various acquisition techniques help in visualizing the heart, identify disorders and help the physicians to plan for operative procedures. Pediatric Cardiac Screening is one of the crucial techniques to record cardiac activity which has difficulties in acquisition as children tend to move during the procedure. The data obtained from these modalities may suffer from various artifacts which makes the diagnosis difficult for the clinicians. To make the diagnosis easier and artifact free, artificial intelligence plays a vital role. Artificial Intelligence (AI)-based techniques from traditional machine learning (ML) to deep learning (DL) techniques for classification and segmentation of pediatric cardiac signals and images are systematically reviewed. DL based studies have become a choice of research in pediatric healthcare. Support Vector Machines with linear kernels are the most commonly used ML based classifiers in the reviewed papers. DL methods use Convolutional Neural Networks (CNN) as the primary classifier and U-Net architectures are preferred for segmentation studies in the reviewed papers. There are a very few surveys available to present the diagnostics related to pediatric cardiac disorders and this paper tries to bring out the challenges along with the traditional machine learning and emerging deep learning techniques implemented in classification and segmentation of the pediatric cardiac disorders.}, archive = {J_ACCESS}, author = {R. Shantha Selvakumari and Kumaradevan Punithakumar and D. Selvathi and S. Chrisilla and R. Sri Abinaya and Nilanjan Ray}, doi = {10.1109/ACCESS.2025.3545829}, journal = {IEEE Access}, pages = {39192-39213}, shortjournal = {IEEE Access}, title = {Computerized diagnosis methods for pediatric cardiology: A review}, volume = {13}, year = {2025}, }
• (2025). A dual convolution model for grating lobe prediction in directivity of parametric array loudspeakers. ACCESS, 13, 39183-39191. (www)

  @article{ , abstract = {There has been a growing interest in the evolution of steerable parametric array loudspeakers for manipulating highly directional audio beams without mechanical steering. Given this potential, understanding sound directivity is the basis for designing a successful steerable parametric array loudspeaker. Current directivity models face challenges in predicting grating lobes due to the neglect of key factors such as transducer spacing. To address this, we propose a sound field modeling framework tailored for parametric array loudspeakers using Westervelt’s formula. Within this framework, a pioneering Dual Convolutional Model is introduced to account for critical parameters of the phased array. The efficacy of the proposed model, compared with traditional convolutional approaches, has been validated through benchmark studies and experimental measurements demonstrating high accuracy in predicting grating lobes.}, archive = {J_ACCESS}, author = {Shangming Mei and Yihua Hu and Mohammed Alkahtani and Mohammad Nasr Esfahani}, doi = {10.1109/ACCESS.2025.3546882}, journal = {IEEE Access}, pages = {39183-39191}, shortjournal = {IEEE Access}, title = {A dual convolution model for grating lobe prediction in directivity of parametric array loudspeakers}, volume = {13}, year = {2025}, }
• (2025). A low profile wide beam metasurface antenna via complementary sources concept. ACCESS, 13, 39176-39182. (www)

  @article{ , abstract = {A metasurface antenna with wide beam and low profile is designed and proposed in this paper. Characteristic mode analysis is carried out to elaborate the operating principle of the proposed wide beam metasurface antenna. The wide beam characteristic is obtained based on complementary sources theory. A qusi-TM01 mode and a qusi-TM03 mode are selected to candidate modes. The appropriate modal modifying strategy is implemented under the guidance of characteristic mode analysis. The slots are etched on the side patches to modify the modal characteristic of the two candidate modes independently. A prototype is analyzed, fabricated and measured to validate the principle. The results demonstrate that a wide beam of 220° is obtained.}, archive = {J_ACCESS}, author = {Quanguo Sun and Xu Zhou and Deqiang Yang}, doi = {10.1109/ACCESS.2025.3545664}, journal = {IEEE Access}, pages = {39176-39182}, shortjournal = {IEEE Access}, title = {A low profile wide beam metasurface antenna via complementary sources concept}, volume = {13}, year = {2025}, }
• (2025). BlockMEDC: Blockchain smart contracts system for securing moroccan higher education digital certificates. ACCESS, 13, 39152-39175. (www)

  @article{ , abstract = {Morocco’s Vision 2030, known as Maroc Digital 2030, aims to position the kingdom as a regional leader in digital technology by boosting digital infrastructure, fostering innovation, and advancing digital skills. Complementing this initiative, the Pacte ESRI 2030 strategy, launched in 2023, seeks to transform the higher education, research, and innovation sectors by integrating state-of-the-art digital technologies. In alignment with these national strategies, this paper introduces BlockMEDC, a blockchain-based system for securing and managing Moroccan educational digital certificates. Leveraging Ethereum Layer 2 (zk-Rollups) smart contracts and the InterPlanetary File System, BlockMEDC automates the issuance, management, and verification of academic credentials across Moroccan universities. The proposed system addresses key issues such as document authenticity, manual verification, and lack of interoperability, delivering a secure, transparent, and significantly low-cost solution that aligns with Morocco’s digital transformation goals for the education sector.}, archive = {J_ACCESS}, author = {Mohamed Fartitchou and Ismail Lamaakal and Khalid El Makkaoui and Zakaria El Allali and Yassine Maleh}, doi = {10.1109/ACCESS.2025.3546177}, journal = {IEEE Access}, pages = {39152-39175}, shortjournal = {IEEE Access}, title = {BlockMEDC: Blockchain smart contracts system for securing moroccan higher education digital certificates}, volume = {13}, year = {2025}, }
• (2025). A hybrid fault detection method of independent component analysis and auto-associative kernel regression for process monitoring in power plant. ACCESS, 13, 39135-39151. (www)

  @article{ , abstract = {In complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. However, DCSs not only have the advantages of collecting large amounts of operational history data, but they also have the shortcoming of limited monitoring capabilities, such as early detection, due to their reliance on generating fault alarms based on simple threshold values. To improve the stability and reliability of industrial processes, it is essential to operate DCS in conjunction with data-driven process monitoring technologies. In this paper, we propose a novel hybrid model combining independent component analysis (ICA) and auto-associative kernel regression (AAKR) to address the limitations of both models. The proposed model (ICA+AAKR) introduces a new method, cumulative percentage distance (CPD), which can determine the appropriate number of independent components (ICs) for dimensionality reduction in ICA. By inputting the dimension-reduced IC matrix into AAKR, the issue of excessive computation time caused by lazy learning in AAKR is effectively mitigated. We applied the proposed fault detection method to two well-known benchmarks (multivariate dynamic process and Tennessee Eastman process) and a real-world application (actual tube leakage in power plant) to verify its monitoring performance. The experimental results validated superior detection performance compared to existing methods for the two benchmark problems. In addition, the method demonstrated the potential to enhance process stability and reliability by enabling remarkable early detection of tube leakage in a circulating fluidized bed boiler at the power plant.}, archive = {J_ACCESS}, author = {Seunghwan Jung and Jonggeun Kim and Sungshin Kim}, doi = {10.1109/ACCESS.2025.3546450}, journal = {IEEE Access}, pages = {39135-39151}, shortjournal = {IEEE Access}, title = {A hybrid fault detection method of independent component analysis and auto-associative kernel regression for process monitoring in power plant}, volume = {13}, year = {2025}, }
• (2025). Accelerated medical microwave tomography via heterogeneous computing. ACCESS, 13, 39117-39134. (www)

  @article{ , abstract = {In practical applications, such as medical microwave imaging, the implementation of three-dimensional (3D) numerical electromagnetic (EM) inverse scattering algorithms has been limited for over two decades owing to their high computational cost. The emergence of graphics processing unit (GPU) computing has offered new possibilities to alleviate the computational burden related to EM calculations; the optimized deployment of EM inverse scattering algorithms on GPUs or other accelerators, however, has not been explored. To this end, we present the first complete GPU implementation of an iterative EM inverse scattering algorithm, which employs a suite of optimization tools in both the forward and inverse problems solved at each iteration. Importantly, we study the impact of various implementation alternatives on computational savings in different computing platforms. Our results demonstrate the significance and benefits of jointly optimizing the forward and inverse problems in implementations in heterogeneous systems. Importantly, our optimization achieves the fastest fully numerical implementation of nonlinear, iterative, EM inverse scattering algorithms that has ever been reported in the literature, resulting in real-time, 3D MWT-based reconstructions. This can pave the way for the development of clinical systems for microwave-based applications such as breast cancer or stroke detection, which are designed to take full advantage of 3D MWT scanning and expand their imaging capabilities.}, archive = {J_ACCESS}, author = {Thomas Vasileiou and Panagiotis Kosmas}, doi = {10.1109/ACCESS.2025.3546635}, journal = {IEEE Access}, pages = {39117-39134}, shortjournal = {IEEE Access}, title = {Accelerated medical microwave tomography via heterogeneous computing}, volume = {13}, year = {2025}, }
• (2025). Research on multi-objective pedestrian tracking algorithm based on full-size feature fusion. ACCESS, 13, 39107-39116. (www)

  @article{ , abstract = {In the field of Multi-Object Tracking (MOT), the current mainstream approach is the tracking by detection paradigm, which heavily relies on the accuracy of the detector, the comprehensiveness of feature extraction, and the superiority of the data association matching algorithm. Most existing pedestrian re-identification methods are based on convolutional neural networks (CNNs), which struggle to balance both local and global features of pedestrians. Given that current detectors are already highly advanced, this paper proposes a full-scale feature fusion-based multi-object pedestrian tracking algorithm named BOS-SORT. The algorithm utilizes the proposed feature extraction network, Better Omni-Scale Net (BOSNet), to captures both global and local appearance information, effectively reducing appearance information loss. Furthermore, it employs an improved association matching algorithm, AveSort, to combines IoU and appearance features for initial data association while smoothing target motion states to minimize matching errors in high-similarity scenarios. The BOS-SORT system integrates these methods and demonstrates exceptional capability in aligning global trajectories with real trajectories. Experimental results show that it achieves state-of-the-art Higher Order Tracking Accuracy (HOTA) scores of 66.2 and 65.3 on the MOT17 and MOT20 datasets, respectively.}, archive = {J_ACCESS}, author = {Yihuai Zhu and Zhandong Liu and Ke Li and Yong Li and Xiangwei Qi and Nan Ding}, doi = {10.1109/ACCESS.2025.3546818}, journal = {IEEE Access}, pages = {39107-39116}, shortjournal = {IEEE Access}, title = {Research on multi-objective pedestrian tracking algorithm based on full-size feature fusion}, volume = {13}, year = {2025}, }
• (2025). Stepper motor position control using PD and MPC algorithms embedded in programmable logic controller. ACCESS, 13, 39096-39106. (www)

  @article{ , abstract = {This research studies the implementation of Proportional-Derivative (PD) and Model Predictive Control (MPC) approaches embedded in an industrial Programmable Logic Controller (PLC) to achieve the precise position control of a stepper motor. The MPC algorithm is widely used in industrial plants, particularly in slower processes, such as press machines and heat treatment. However, their application in faster processes, such as servomotors and robotics, is often require faster optimization algorithms or more powerful processors. In this project, MATLAB Simulink® was used to model the motor and test the controller in advance to reduce the field commissioning time. Once validated, the model was exported to PLC-compatible code for real-time system integration. In addition, this study compares the result of MPC algorithm with traditional control strategies, such as PD. To generate pulses and read the motor encoder signal, the project used special function modules and implemented IEC 61131 programming code in ladder logic, function blocks, and structured text. The integration of Simulink with PLCs proved successful, demonstrating the adaptability and responsiveness of the system under varying conditions. Detailed analyses of motor position and speed further highlight the performance of the system. The PD controller exhibited more effective results in controlling position than the PI and MPC controllers. Implementing MPC on PLCs present challenges such as limited computational power, memory, and programming flexibility. PLCs often lack the processing capacity for real-time optimization and fast sampling rates required for accurate predictions.}, archive = {J_ACCESS}, author = {Anshul Jaswal and Ma’moun Abu-Ayyad and Yash Lad and Anilchandra Attaluri}, doi = {10.1109/ACCESS.2025.3544509}, journal = {IEEE Access}, pages = {39096-39106}, shortjournal = {IEEE Access}, title = {Stepper motor position control using PD and MPC algorithms embedded in programmable logic controller}, volume = {13}, year = {2025}, }
• (2025). Design and evaluation of a sector beam antenna for 79-GHz short-range radar sensors. ACCESS, 13, 39087-39095. (www)

  @article{ , abstract = {This paper presents the design and implementation of sector beam transmit (Tx) antennas for achieving wide detection coverage in a 79-GHz short-range radar (SRR) sensor for automotive or construction heavy equipment applications. The Tx antennas generate three sector beams in the azimuth plane, enabling broad radiating coverage. These antennas are realized using series-fed microstrip patch arrays with phase-adjusting power dividers. Prototypes of the designed Tx antennas were fabricated and measured, confirming the overall wide field of view and validating the performance against simulation results. Additionally, an 18-element linear patch array was designed for the four receive (Rx) channels to enhance detection accuracy with low sidelobe level in the elevation plane. The radar sensor module was developed and tested, demonstrating wide detection capabilities, and the proposed design can be applied to the actual environment.}, archive = {J_ACCESS}, author = {Jae-Ho Lee and Jong Min Lee and Dong-Wook Seo}, doi = {10.1109/ACCESS.2025.3546240}, journal = {IEEE Access}, pages = {39087-39095}, shortjournal = {IEEE Access}, title = {Design and evaluation of a sector beam antenna for 79-GHz short-range radar sensors}, volume = {13}, year = {2025}, }
• (2025). UPN-GCN: Update positive-negative graph convolution neural network in non-euclidean structured data. ACCESS, 13, 39076-39086. (www)

  @article{ , abstract = {Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures. such as image data and language data, and achieved good results, expanding the application scope of GCN. Although there are numerous methods for handling non-Euclidean structured data, there are few studies that approach non-Euclidean structured data from a graph perspective. Constructing non-Euclidean data structures using classical measurement methods results in significant information loss. Therefore, it hinders the generalization of GCN in non-Euclidean structure data. Some researchers have proposed to construct an intuitive fuzzy relationship, namely the positive-negative relationship, in non-Euclidean data, and explained the physical meaning of this relationship. Based on the positive-negative relationship, a graph convolutional neural network model PN-GCN was constructed. However, this positive-negative relationship does not accurately reflect the differences and similarities between data, thereby reducing the accuracy of node classification. Therefore, we have designed a new convolutional neural network model UPN-GCN based on updating positive and negative graphs. When using the adjacency matrix obtained from positive and negative relationships to perform GCN operations on data, the feature representations obtained from each iteration are continuously updated to optimize the adjacency matrix of positive and negative relationships, making the representation of data differences and similarities more accurate. Finally, we validated the accuracy and robustness of this adjacency matrix update operation based on positive and negative relationships through experimental comparisons.}, archive = {J_ACCESS}, author = {Lijun Fan and Shichao Yi and Wenrui Guan and Pingxin Wang}, doi = {10.1109/ACCESS.2025.3539664}, journal = {IEEE Access}, pages = {39076-39086}, shortjournal = {IEEE Access}, title = {UPN-GCN: Update positive-negative graph convolution neural network in non-euclidean structured data}, volume = {13}, year = {2025}, }
• (2025). Remote real-time testing of physical components using communication setup automation. ACCESS, 13, 39066-39075. (www)

  @article{ , abstract = {This paper addresses the challenge of testing physical components in remote grids. It is a critical area often constrained by limitations of existing real-time simulation-based methods. Current literature emphasizes real-time simulators using analog input and output interfaces for safe testing. However, this approach is hindered by high investment cost and necessary operational knowledge. In contrast, our method focuses solely on a component-specific expertise. This offers a more accessible and feasible testing environment. Despite its potential, the pure physical component approach is discussed barely in current literature. Particularly, when it comes to testing complex grid systems. To overcome this obstacles, we introduce a innovative solution: remote component testing distributed across several facilities. This paper outlines a robust architecture. It integrates power converters and the virtual gateway VILLASnode. This enables seamless connectivity between remote grids via the internet and addresses the shortcomings of traditional single facility testing environments. Furthermore, we include an automated communication setup along with a comprehensive delay analysis to ensure that real-time requirements are met effectively. A comparative study of common communication protocols is included to guide future implementations. To validate our architecture’s functionality, we conduct a case study that emulates unavailable remote physical components in a microgrid using two different control strategies. This successfully demonstrates the exchange and emulation of power setpoints. Our findings reveal facility-specific limitations - such as resolution constraints - that persists regardless of architectural advancements. This paper lays a solid foundation for remote physical component testing. It paves the way for replication and further exploration through more sophisticated case studies in the future.}, archive = {J_ACCESS}, author = {Alexandra Bach and Antonello Monti}, doi = {10.1109/ACCESS.2025.3546311}, journal = {IEEE Access}, pages = {39066-39075}, shortjournal = {IEEE Access}, title = {Remote real-time testing of physical components using communication setup automation}, volume = {13}, year = {2025}, }
• (2025). CAEB7-UNet: An attention-based deep learning framework for automated segmentation of C-spine vertebrae in CT images. ACCESS, 13, 39051-39065. (www)

  @article{ , abstract = {Accurate segmentation of vertebrae in computed tomography (CT) images possess serious challenges due to the irregular vertebral boundaries, low contrast and brightness, and noise in CT scans. This study presents a novel channel attention-based EfficientNetB7-UNet (CAEB7-UNet) method to address this complex task effectively. The proposed model introduces an upgraded ReLU-based channel attention module (CAM) in the skip connection which restrains the nonessential attributes by suppressing them and accentuates the relevant features by emphasizing them to boost the overall segmentation performance. In this work, an improved EfficientNetB7 is employed as the encoder for feature extraction, the fusion of local and global features is enhanced through the upgraded CAM in skip connection, and the up-sampling is performed in the decoder. Further, the model is optimized by incorporating hyperparameter optimization, specifically, hybrid learning rate scheduler strategies, along with the AdamW optimizer and custom data augmentation. A total of 34,782 CT images obtained from the RSNA-2022 cervical spine fracture detection challenge is utilized in this study. The proposed model achieves outstanding performance, yielding a dice score index (DSI) of 96.14% and mean intersection over union (mIoU) of 91.46%. Moreover, a comparative performance analysis of CAEB7-UNet with two state-of-the-art models is carried out on the same dataset. Our approach outperforms both the models, with the best one by 8.1%, 6.73%, 12.7%, and 11.98% in terms of DSI, mIoU, precision, and F1-score respectively. Additionally, it requires merely 0.38 seconds to generate the segmentation mask of a single slice of a CT scan.}, archive = {J_ACCESS}, author = {Abhishek Kumar Pandey and Kedarnath Senapati and G. P. Pateel}, doi = {10.1109/ACCESS.2025.3546324}, journal = {IEEE Access}, pages = {39051-39065}, shortjournal = {IEEE Access}, title = {CAEB7-UNet: An attention-based deep learning framework for automated segmentation of C-spine vertebrae in CT images}, volume = {13}, year = {2025}, }
• (2025). Measurement of teacher’s orchestration load: A framework and a case study on tool flexibility. ACCESS, 13, 39035-39050. (www)

  @article{ , abstract = {Teacher orchestration in Computer-Supported Collaborative Learning (CSCL) environments demands managing multiple tasks across different social levels, often under tight constraints, leading to an increased orchestration load. This load represents the cognitive and physical effort teachers invest in real-time coordination of learning activities, which remains underexplored, particularly how the flexibility of orchestration tools influences this burden. In response to this gap, we propose a comprehensive framework for tracking and characterizing orchestration load, focusing on the intensity and dynamics of teachers’ actions while implementing CSCL scripts. The framework integrates multimodal data sources, including observable orchestration actions, physiological metrics, and self-reported insights, to comprehensively analyze the orchestration load. We illustrate the applicability of the framework through a case study comparing two CSCL orchestration tools: PyramidApp, which offers pre-configured, structured enactment of the Pyramid collaborative learning flow pattern, and EthicApp, a more flexible tool allowing real-time design adjustments for a variety of instructional designs. Our findings reveal that PyramidApp facilitates a streamlined orchestration process with lower intensity and reduced teacher workload. At the same time, EthicApp’s high flexibility increases orchestration intensity, requiring more cognitive effort from teachers during real-time phase configuration. These results underscore the importance of balancing flexibility with usability in orchestration tool design, as overly flexible environments may overwhelm teachers, especially during high cognitive load scenarios. This study contributes a methodological framework for evaluating orchestration tools and highlights key design considerations for reducing orchestration load in CSCL environments.}, archive = {J_ACCESS}, author = {Claudio Alvarez and Ishari Amarasinghe and Gustavo Zurita and Davinia Hernandez-Leo and Lubna Hakami and Luis Rojas}, doi = {10.1109/ACCESS.2025.3531241}, journal = {IEEE Access}, pages = {39035-39050}, shortjournal = {IEEE Access}, title = {Measurement of teacher’s orchestration load: A framework and a case study on tool flexibility}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Design of compact dual-band eighth-mode SIW antenna for on-body ISM band application”. ACCESS, 13, 39033-39034. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Design of Compact Dual-Band Eighth-Mode SIW Antenna for On-Body ISM Band Application”.}, archive = {J_ACCESS}, author = {Muthukumara Rajaguru Kattiakara Muni Samy and Abhishek Gudipalli}, doi = {10.1109/ACCESS.2025.3546694}, journal = {IEEE Access}, pages = {39033-39034}, shortjournal = {IEEE Access}, title = {Corrections to “Design of compact dual-band eighth-mode SIW antenna for on-body ISM band application”}, volume = {13}, year = {2025}, }
• (2025). Prediction of high-risk cardiac arrhythmia based on optimized deep active learning. ACCESS, 13, 39006-39032. (www)

  @article{ , abstract = {In contemporary society, numerous challenges are effectively addressed by applying computer science and artificial intelligence. Each year, a substantial number of fatalities occur due to various illnesses; however, many of these can be anticipated and managed through the utilization of AI technologies. Among the significant health concerns, cardiac arrhythmia is particularly noteworthy within the domain of cardiovascular diseases, which is the primary focus of this study. Cardiac arrhythmias are classified into distinct categories, with some being benign while others present considerable risks. This article proposes a novel methodology that employs advanced AI techniques for predicting high-risk cases of cardiac arrhythmia in individuals. The study delineates a new framework that integrates deep learning, fuzzy logic, and active learning, aiming to provide an innovative approach for the early detection of cardiac arrhythmias through Optimized Deep Active Learning (ODAL). The proposed ODAL framework achieved superior performance, with an F1 score of 90% for non-sinus rhythm classification and a precision of 92%. Additionally, the ROC analysis of the framework demonstrated satisfactory performance, achieving an accuracy of 86%, sensitivity of 86%, and specificity of 82%. These results underscore the model’s effectiveness in accurately identifying high-risk arrhythmia cases compared to other classifiers.}, archive = {J_ACCESS}, author = {Homeyra Amiri and Javad Mohammadzadeh and Seyed Mohsen Mirhosseini and Alireza Nikravanshalmani}, doi = {10.1109/ACCESS.2025.3544904}, journal = {IEEE Access}, pages = {39006-39032}, shortjournal = {IEEE Access}, title = {Prediction of high-risk cardiac arrhythmia based on optimized deep active learning}, volume = {13}, year = {2025}, }
• (2025). Embedded artificial intelligence for IoT applications using the MAX78000. ACCESS, 13, 38979-39005. (www)

  @article{ , abstract = {Recent advances in embedded AI and IoT have revolutionized the development of intelligent edge devices. This work provides a tutorial on developing and deploying AI models on the MAX78000, a low-power microcontroller designed specifically for AI applications. Starting with the foundational understanding of neural networks and machine learning, this tutorial explores the architecture and capabilities of the MAX78000, which integrates a CNN accelerator with an ARM Cortex-M4 core. We give practical guidance on creating, training, and quantizing AI models, detailing essential tools, frameworks, and the deployment process. Real-world examples illustrate the versatility of AI microcontrollers and their performance in various IoT applications. Emphasizing the importance of accessible development tools, this tutorial aims to increase awareness within the IoT community about low-power accelerators. This enables developers to create efficient, real-time AI solutions, highlighting the transformative potential of embedded AI in IoT.}, archive = {J_ACCESS}, author = {Martina Balbi and Lance Doherty and Thomas Watteyne}, doi = {10.1109/ACCESS.2025.3546557}, journal = {IEEE Access}, pages = {38979-39005}, shortjournal = {IEEE Access}, title = {Embedded artificial intelligence for IoT applications using the MAX78000}, volume = {13}, year = {2025}, }
• (2025). Data processing integrating singular value decomposition algorithm and tensor chain decomposition algorithm. ACCESS, 13, 38964-38978. (www)

  @article{ , abstract = {In the era of big data, processing information in multi-linear arrays is a challenge. In this paper, a parallel singular value decomposition algorithm based on unilateral Jacobi is proposed, and a data processing model of long and short memory network combined with the parallel tensor chain decomposition algorithm is constructed. The results show that the parallel efficiency of the algorithm reaches 0.95 with 30 cores, the compression ratio is 10, the accuracy and recall rate are 0.98 and 0.96, respectively. On the ImageNet dataset, model indicators are all over 0.9, showing excellent performance. The research not only improves the efficiency of data processing, but also provides new solutions for high-dimensional data analysis, especially in the aspects of feature extraction and dimensionality reduction. Combined with the advantages of LSTM in processing time series data, the overall performance of the model is improved.}, archive = {J_ACCESS}, author = {Hao Zhang}, doi = {10.1109/ACCESS.2025.3546029}, journal = {IEEE Access}, pages = {38964-38978}, shortjournal = {IEEE Access}, title = {Data processing integrating singular value decomposition algorithm and tensor chain decomposition algorithm}, volume = {13}, year = {2025}, }
• (2025). Incorporating AI literacy and AI anxiety into TAM: Unraveling chinese scholars’ behavioral intentions toward adopting AI-assisted literature reading. ACCESS, 13, 38952-38963. (www)

  @article{ , abstract = {The proliferation of Artificial Intelligence has brought about novel approaches and opportunities for scholars, especially those non-native English speakers, to read and process the enormous amount of literature. However, research on the utilization of AI in assisted literature reading is limited and requires further academic focus. This study incorporates AI literacy and AI anxiety into the technology acceptance model to explore the factors influencing Chinese scholars’ behavioral intention regarding AI-assisted literature reading. 368 graduate students and 259 faculty members from Hubei, China, participated in this study. Partial-least squares structural equation modeling was applied for hypothesis testing and multi-group analyses to explore in depth the differences in the relationships of the variables across groups. This study reveals that AI literacy and AI anxiety are important players in influencing Chinese scholars’ perceived ease of use, perceived usefulness, and behavioral intention in using AI-assisted literature reading. Further, this study found significant differences in these relationships across disciplines and social roles of scholars, which provides valuable insights into the application and development of AI in academic literature reading.}, archive = {J_ACCESS}, author = {Li Pan and Huiqin Luo and Qizhen Gu}, doi = {10.1109/ACCESS.2025.3546572}, journal = {IEEE Access}, pages = {38952-38963}, shortjournal = {IEEE Access}, title = {Incorporating AI literacy and AI anxiety into TAM: Unraveling chinese scholars’ behavioral intentions toward adopting AI-assisted literature reading}, volume = {13}, year = {2025}, }
• (2025). Delay-controlled frequency tuning of a MEMS sensor for neuromorphic acoustic sensing. ACCESS, 13, 38940-38951. (www)

  @article{ , abstract = {Frequency tunability marks the ability to change the characteristic frequency of an oscillator. For MEMS sensors, this is usually achieved by exploiting a sophisticated geometry with a nonlinear stress-strain relationship. This is known as active frequency tuning. However, MEMS sensors with such geometries are difficult to manufacture and the characteristic frequency can have a lower limit, which is constrained by the material constants and geometry of the MEMS sensor. To address this issue, we propose a different approach to enable active frequency tunability, which is based on designing a feedback loop with a controllable time delay. We show by analyzing the necessary condition of the Hopf theorem that the characteristic frequency of this system can be increased indefinitely or decreased to 90% of its original value by appropriately adjusting the delay and the feedback strengths. These observations can be explained by combining with the undelayed and delayed feedback loop, which implies that the phase and the amplitude of the feedback signal can be controlled. In addition, the gain of the sensor becomes tunable, since the Andronov-Hopf bifurcation can be controlled with this feedback loop. These results are particularly interesting for mimicking the cochlea functionality, as the cochlear is assumed to exhibit an Andronov-Hopf bifurcation. Hence, this approach can be, e.g., used for neuromorphic acoustic sensing, while keeping the geometry of this MEMS sensor as simple as possible.}, archive = {J_ACCESS}, author = {Hermann Folke Johann Rolf and Petro Feketa and Thomas Meurer}, doi = {10.1109/ACCESS.2025.3546435}, journal = {IEEE Access}, pages = {38940-38951}, shortjournal = {IEEE Access}, title = {Delay-controlled frequency tuning of a MEMS sensor for neuromorphic acoustic sensing}, volume = {13}, year = {2025}, }
• (2025). Dynamical modeling and distributed vibration control for the multi-plate jointed by hinges. ACCESS, 13, 38925-38939. (www)

  @article{ , abstract = {The dynamical characteristics and vibration control for the multi-plate jointed by hinges are investigated in this paper. The connection for the multi-plate fixed on the external component is the discontinuous interval constraint, which is different the traditional point constraint. A novel power series multiplier method is proposed to describe the local fixed support constraint boundary. This method not only overcomes the dependency of the deformation function’s leading term but also addresses the limitation of traditional Lagrange multiplier methods, which are confined to point constraints. Subsequently, the Lagrange multiplier method is utilized to establish the constraints imposed by the hinges on the inter-plate connections. The natural frequencies and modes of the structure are obtained by using the Rayleigh-Ritz method. A comparison with finite element results validates the effectiveness of the proposed method. The impact of hinge stiffness on the structure’s natural characteristics is also examined. Furthermore, the electro-mechanical coupling dynamic equations for hinged multi-plate structure with piezoelectric actuators are established, and the control effects on the structural response under nonlinear excitation are examined. A proportional-derivative control law is adopted to adjust the voltages of piezoelectric sensors and actuators, optimize control parameters, and achieve optimal control effects. The results indicate that the power series multiplier constraint method can accurately characterize interval constraints and is well-suited for constructing dynamic models of plates with non-classical boundary conditions. The distributed control method based on piezoelectric actuators enables targeted vibration suppression for specific modal shapes.}, archive = {J_ACCESS}, author = {Tianxiang Zhang and Yuteng Cao and Mei Liu}, doi = {10.1109/ACCESS.2025.3544829}, journal = {IEEE Access}, pages = {38925-38939}, shortjournal = {IEEE Access}, title = {Dynamical modeling and distributed vibration control for the multi-plate jointed by hinges}, volume = {13}, year = {2025}, }
• (2025). Integrating deep metric learning, semi supervised learning, and domain adaptation for cross-dataset EEG-based emotion recognition. ACCESS, 13, 38914-38924. (www)

  @article{ , abstract = {The variability of EEG signals produced by different trial conditions and different devices presents significant challenges in developing practical EEG-based emotion recognition systems. Much of the research on developing a generalizable EEG emotion recognition approach focuses on cross-subject and cross-session contexts. Although current cross-subject methods yield satisfactory outcomes on certain EEG emotion datasets, their effectiveness diminishes in cross-dataset scenarios. Additionally, the performance of existing cross-dataset methods remains inferior compared to methods that are trained and evaluated within the same dataset. To address these challenges, inspired by the effective application of deep metric learning (DML) in zero-shot and few-shot learning tasks, this paper introduces a cross-dataset emotion recognition method. The proposed approach integrates DML, domain-specific batch normalization (DSBN), shared batch normalization statistics, and adversarial learning. Specifically, our method extracts cross-domain features from the input signals using DSBN and shared batch normalization statistics. Then, the proposed DML loss minimizes intra-class variations of EEG features across different subjects and domains, while maximizing differences between different classes. Moreover, it captures the semantic order of emotions in the learned embedding. To further improve the generalization of the feature encoder, we employ adversarial learning with domain and subject discriminators. We evaluate our method on six cross-dataset scenarios. The results show that it consistently outperforms peer methods across the scenarios. For example, our method achieves an accuracy of 63.49% on SEED $\to $ SEED-IV, improving the state-of-the-art result by 2.25%.}, archive = {J_ACCESS}, author = {Hawraa Razzaq Abed Alameer and Pedram Salehpour and Hadi S. Aghdasi and Mohammad-Reza Feizi-Derakhshi}, doi = {10.1109/ACCESS.2025.3536549}, journal = {IEEE Access}, pages = {38914-38924}, shortjournal = {IEEE Access}, title = {Integrating deep metric learning, semi supervised learning, and domain adaptation for cross-dataset EEG-based emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Randomization-driven hybrid deep learning for diabetic retinopathy detection. ACCESS, 13, 38901-38913. (www)

  @article{ , abstract = {Diabetic retinopathy (DR), a severe consequence of diabetes, dramatically enhances the likelihood of experiencing vision impairment. Timely identification is crucial for efficient intervention, as untreated diabetic retinopathy can progress to irreversible vision loss. Despite advances, existing diagnostic methods face challenges such as resource dependency, variability in accuracy, and limited accessibility, especially in underserved regions. This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. We enhance the model’s diagnostic capability through complex image preprocessing techniques, such as improved noise reduction and morphological approaches. Additionally, we use Otsu’s thresholding method to segment blood vessels accurately. Our methodology demonstrates superior performance in DR screening, significantly exceeding traditional diagnostic methods. Specifically, our precision reached 96.10%, sensitivity was 95.35%, specificity achieved 97.06%, and accuracy was 96.10%. This research enhances the precision of DR diagnosis by applying it to different publicly accessible datasets. It contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.}, archive = {J_ACCESS}, author = {A. M. Mutawa and G. R. Hemalakshmi and N. B. Prakash and M. Murugappan}, doi = {10.1109/ACCESS.2025.3546359}, journal = {IEEE Access}, pages = {38901-38913}, shortjournal = {IEEE Access}, title = {Randomization-driven hybrid deep learning for diabetic retinopathy detection}, volume = {13}, year = {2025}, }
• (2025). Finetuning large language models for vulnerability detection. ACCESS, 13, 38889-38900. (www)

  @article{ , abstract = {This paper presents the results of finetuning large language models (LLMs) for the task of detecting vulnerabilities in Java source code. We leverage WizardCoder, a recent improvement of the state-of-the-art LLM StarCoder, and adapt it for vulnerability detection through further finetuning. To accelerate training, we modify WizardCoder’s training procedure, also we investigate optimal training regimes. For the imbalanced dataset with many more negative examples than positive, we also explore different techniques to improve classification performance. The finetuned WizardCoder model achieves improvement in ROC AUC and F1 measures on balanced and imbalanced vulnerability datasets over CodeBERT-like model, demonstrating the effectiveness of adapting pretrained LLMs for vulnerability detection in source code. The key contributions are finetuning the state-of-the-art code LLM, WizardCoder, increasing its training speed without the performance harm, optimizing the training procedure and regimes, handling class imbalance, and improving performance on difficult vulnerability detection datasets. This demonstrates the potential for transfer learning by finetuning large pretrained language models for specialized source code analysis tasks.}, archive = {J_ACCESS}, author = {Aleksei Shestov and Rodion Levichev and Ravil Mussabayev and Evgeny Maslov and Pavel Zadorozhny and Anton Cheshkov and Rustam Mussabayev and Alymzhan Toleu and Gulmira Tolegen and Alexander Krassovitskiy}, doi = {10.1109/ACCESS.2025.3546700}, journal = {IEEE Access}, pages = {38889-38900}, shortjournal = {IEEE Access}, title = {Finetuning large language models for vulnerability detection}, volume = {13}, year = {2025}, }
• (2025). A novel CNN-based framework for detection and classification of power quality disturbances: Exploring multi-class versus multi-label classification. ACCESS, 13, 38878-38888. (www)

  @article{ , abstract = {The detection and classification of power quality (PQ) disturbances remains a significant challenge because of the rapid integration of renewable energy sources (RES), widespread use of power electronics, and increasing prevalence of sensitive microcontrollers. These evolving PQ issues necessitate the development of accurate and reliable methods for identifying and classifying PQ disturbances. In this paper, we propose a novel model based on a deep convolutional neural network (DCNN) for the feature extraction and classification of PQ disturbances. The architecture of the model was inspired by the visual geometry group (VGG), which is known for its effectiveness in image processing. The extracted features are highly suitable for both multi−class (MC) and multi−label (ML) classification tasks, effectively addressing the complexity of PQ disturbance signals. The ML approach proved its excellence in the classification of complex PQ disturbances. The performance of the model was rigorously evaluated using various metrics across different scenarios, which demonstrated exceptional accuracy and robustness. The model was trained, validated, and tested using synthetically generated data under different signal−to−noise ratio (SNR) scenarios ensuring its effectiveness in practical applications.}, archive = {J_ACCESS}, author = {Aleksandra Zlatkova and Dimitar Taskovski}, doi = {10.1109/ACCESS.2025.3546520}, journal = {IEEE Access}, pages = {38878-38888}, shortjournal = {IEEE Access}, title = {A novel CNN-based framework for detection and classification of power quality disturbances: Exploring multi-class versus multi-label classification}, volume = {13}, year = {2025}, }
• (2025). Automatic adjustment method of operating parameters in laser cleaning process based on image processing. ACCESS, 13, 38865-38877. (www)

  @article{ , abstract = {When using laser cleaning on metals with different levels of corrosion, it is necessary to adjust the operating parameters of the laser to achieve the cleaning standards without damaging the metal itself. Traditional laser cleaning methods rely on experience, which are subjective and poorly adaptable. This paper proposes an automatic parameter adjustment method based on image processing. The method first extracts the corrosion image features of the metal and then establishes a parameter prediction model based on the Particle Swarm Optimization-Back Propagation(PSO-BP) neural network. The model takes the color and texture features of the metal to be cleaned as input and the operating parameters of laser cleaning as output. Comparative experiments show that the PSO-BP neural network prediction model has significant advantages over the traditional Back Propagation(BP) neural network model. It was also found that when using the combination of color and texture features of the corrosion image as the input to the model, the prediction accuracy was improved. Tests on 100 sets of experimental data show that the average prediction accuracy of the proposed method can reach over 85%, meeting the requirements of practical laser cleaning applications.}, archive = {J_ACCESS}, author = {Anjun Yu and Hao Cao and Yonghua Xiong and Jinhua She}, doi = {10.1109/ACCESS.2025.3545618}, journal = {IEEE Access}, pages = {38865-38877}, shortjournal = {IEEE Access}, title = {Automatic adjustment method of operating parameters in laser cleaning process based on image processing}, volume = {13}, year = {2025}, }
• (2025). Linear programming of the loopwise route representation for the shortest path problems by introducing the reference link flow. ACCESS, 13, 38856-38864. (www)

  @article{ , abstract = {Loopwise route representation (LRR), which has been recently proposed as an alternative network representation, can determine the optimal path for the vehicle routing problems with a simpler optimization formulation. However, it requires a significant computing burden due to nonlinear optimization. This study proposes a novel linearization approach for the LRR by introducing the reference link flow. The proposed method consists of two stages: selecting the reference link flow in Stage 1 and formulating a linearized optimization problem in Stage 2. Through the above two stages, the proposed method does not require nonlinear functions while maintaining the unique advantages of the LRR. To validate the performance of the proposed method, numerical experiments are designed with ideal grid-type networks of various sizes. For comparison, integer linear programming (ILP) is conducted in the corresponding link-wise route representation. Numerical results demonstrate that the proposed linearization outperforms conventional ILP in terms of computing time and solution accuracy. For a $100\times 100$ grid-type network, the proposed method takes 0.15 seconds, whereas conventional ILP takes 1.39 seconds. Furthermore, the proposed method provides a 0-to-1% relative error (i.e., optimal and near-optimal solutions) in 802 cases out of total 1000 cases, whereas conventional ILP provides the same level of errors only in 406 cases. With further work, the proposed linearization scheme could be applied to other vehicle routing problems such as a traveling salesman problem in order to enhance a computational efficiency.}, archive = {J_ACCESS}, author = {Geunu Kim and Changyeong Kim and Sanghoon Park and Jeonghun Kim and In Gwun Jang}, doi = {10.1109/ACCESS.2025.3546320}, journal = {IEEE Access}, pages = {38856-38864}, shortjournal = {IEEE Access}, title = {Linear programming of the loopwise route representation for the shortest path problems by introducing the reference link flow}, volume = {13}, year = {2025}, }
• (2025). The summarization of italian tax-law decisions: The case of the PRODIGIT project. ACCESS, 13, 38833-38855. (www)

  @article{ , abstract = {This paper presents an innovative approach to the automated summarization of tax-law decisions developed within the PRODIGIT project. The work addresses the growing challenge of managing large volumes of judicial rulings, which often hinder access to relevant legal precedents. We introduce a hybrid methodology that combines extractive and abstractive summarization techniques, with a particular emphasis on the application of large language models (LLMs) for abstractive summarization. Our approach includes designing and evaluating various prompt-based configurations, leading to the development of a “combined summarization” method, where distinct summary components are generated and integrated into a cohesive text. Experimental results, validated by tax law experts, demonstrate that this method yields the most comprehensive and accurate summaries. Additionally, we explore the integration of these summaries into semantic search functions, enabling users to retrieve and comprehend relevant case law efficiently. Our findings highlight the potential of AI-driven summarization tools to enhance legal transparency, promote judicial consistency, and support the work of judges, lawyers, and legal scholars.}, archive = {J_ACCESS}, author = {Alessia Fidelangeli and Federico Galli and Andrea Loreggia and Giuseppe Pisano and Riccardo Rovatti and Piera Santin and Giovanni Sartor}, doi = {10.1109/ACCESS.2025.3545419}, journal = {IEEE Access}, pages = {38833-38855}, shortjournal = {IEEE Access}, title = {The summarization of italian tax-law decisions: The case of the PRODIGIT project}, volume = {13}, year = {2025}, }
• (2025). Distributed asymptotic tracking for higher-order multi-agent systems with unknown control directions under directed graphs. ACCESS, 13, 38825-38832. (www)

  @article{ , abstract = {This paper addresses the distributed tracking problem for higher-order nonlinear multi-agent systems characterized by unknown control directions and uncertain dynamics under directed graphs. A novel adaptive control algorithm is developed using Nussbaum-type functions, nonlinear transformations, and compensating functions to ensure the asymptotic convergence of all agents to the leader while maintaining the boundedness of all closed-loop signals. Unlike previous results, the presented approach does not rely on function approximators, guarantees asymptotic convergence rather than bounded residual errors, and applies to general directed graphs containing a spanning tree. Theoretical analysis, supported by Lyapunov stability theory, establishes the effectiveness of the proposed controller in handling unknown control directions and system uncertainties. Comparative simulation studies involving a network of single-link robots demonstrate the superior performance of the proposed method in achieving precise distributed tracking. The results show improved convergence accuracy and computational efficiency over existing techniques, making this approach a promising solution for distributed control applications in multi-agent systems.}, archive = {J_ACCESS}, author = {Zhihua Zhang and Chaoli Wang and Xuan Cai}, doi = {10.1109/ACCESS.2025.3546282}, journal = {IEEE Access}, pages = {38825-38832}, shortjournal = {IEEE Access}, title = {Distributed asymptotic tracking for higher-order multi-agent systems with unknown control directions under directed graphs}, volume = {13}, year = {2025}, }
• (2025). A 3M-hybrid model for the restoration of unique giant murals: A case study on the murals of yongle palace. ACCESS, 13, 38809-38824. (www)

  @article{ , abstract = {The unique giant murals of Yongle Palace, as valuable cultural heritage, have suffered damage and urgently require restoration. Deep learning inpainting algorithm offers promising solutions for image restoration. However, the restoration of unique giant murals presents two challenges: 1) The unique and scarce data of mural poses significant training difficulties, and 2) the giant size leads to a wider range of defect types and sizes, increasing the complexity of restoration. To address these challenges, a 3M-Hybrid model is proposed. Firstly, on the data level, based on the frequency characteristics of mural data, multi-frequency complementary learning is employed to enhance the model’s restoration capability. Secondly, on the model structure level, a pre-trained Visual Transformer (VIT) is integrated into the Convolutional Neural Networks (CNN) module to alleviate data scarcity and reduce domain bias. Finally, seam and structural distortion problems arising from repairing oversized defects are mitigated by multi-scale and multi-perspective strategies, including data segmentation and fusion. Experimental results demonstrate the efficacy of our proposed model. In regular-sized mural restoration, it improves Structural Similarity Image Measurement (SSIM) and Peak Signal-to-Noise Ratio (PSNR) by 14.61% and 4.73%, respectively, compared to the best model among four representative CNN models. Additionally, it achieves favorable results in the final restoration of giant murals.}, archive = {J_ACCESS}, author = {Jing Yang and Nur Intan Raihana Ruhaiyem and Chichun Zhou}, doi = {10.1109/ACCESS.2025.3542320}, journal = {IEEE Access}, pages = {38809-38824}, shortjournal = {IEEE Access}, title = {A 3M-hybrid model for the restoration of unique giant murals: A case study on the murals of yongle palace}, volume = {13}, year = {2025}, }
• (2025). Efficient optical line terminal placement for passive optical network deployment. ACCESS, 13, 38793-38808. (www)

  @article{ , abstract = {A passive optical network (PON) is applied to the last mile network to connect end users to the core network, though its cost-effectiveness and resource-efficiency may not be optimized. There are multiple optical modulation types, each of which provides different transmission distance and speed. Nevertheless, the simultaneous use of miltiple modulation types within the same network has not been considered. In this paper, we consider a PON deployment problem in which the number of optical line terminal (OLT) placement is minimized to cover the users in a given service area. We propose an OLT placement problem, in which different OLTs can select different modulation types whilst covering other vertices. We then propose a heuristic placement determination algorithm that, with the fewest number of OLT placements, provides network coverage for all users. The results show that our model can achieve fewer OLT placements than that disallowing multiple type selections. The solutions by the algorithm are consistent with that by optimization calculation in smaller topology. And the algorithm is able to provide OLT placement solutions in reasonable time.}, archive = {J_ACCESS}, author = {Cheng Jin and Yu Zheng and Mingcong Yang and Yongbing Zhang}, doi = {10.1109/ACCESS.2025.3546666}, journal = {IEEE Access}, pages = {38793-38808}, shortjournal = {IEEE Access}, title = {Efficient optical line terminal placement for passive optical network deployment}, volume = {13}, year = {2025}, }
• (2025). Enhancing mobile app recommendations with crowdsourced educational data using machine learning and deep learning. ACCESS, 13, 38778-38792. (www)

  @article{ , abstract = {In the rapidly evolving digital landscape, personalized recommendations have become essential for enhancing user experience. Machine learning models analyze user behavior patterns to suggest relevant entertainment, education, or e-commerce content. Mobile devices make it easier to gather educational data through crowdsourcing, which opens new possibilities for improving app recommendation algorithms. This paper provides valuable methodologies for scalable student recommendation and educational systems, highlighting DL’s advantages over CF in handling sparse, time-sensitive datasets. The objective of this study is to recommend apps to university students by category based on app usage patterns. Data was used to evaluate these 806 university students to train the Collaborative Filtering (CF) and Contemporary Deep Learning (DL) models. The results demonstrate that Gated Recurrent Units (GRU) are the best option for real-time, customized suggestions because of their capacity to simulate successive interactions and adjust to changing user behavior. The GRU yields the lowest mean errors MAE of 0.2246, RMSE=0.2516, and superior short-term predictions k=4 MAE of 0.1319 and RMSE of 0.1319. Other techniques, i.e., Stacked Auto-Encoder, exhibit the sign of overfitting with an MAE of 0.0001, whereas the LSTM and Graph Auto-Encoder perform below GRU with an MAE of 0.3453 and 0.8992. Although the CF techniques suffer from temporal dynamics and data sparsity, even the KNNBasic stands out among all CF algorithms with the lowest MAE of 0.548 and RMSE of 0.754, demonstrating the highest predictive accuracy.}, archive = {J_ACCESS}, author = {Naadiya Mirbahar and Kamlesh Kumar and Asif Ali Laghari}, doi = {10.1109/ACCESS.2025.3541116}, journal = {IEEE Access}, pages = {38778-38792}, shortjournal = {IEEE Access}, title = {Enhancing mobile app recommendations with crowdsourced educational data using machine learning and deep learning}, volume = {13}, year = {2025}, }
• (2025). A two-phase drone-truck optimization framework for patrolling and thermal comfort: Sustainable hajj safety. ACCESS, 13, 38745-38777. (www)

  @article{ , abstract = {The annual Hajj pilgrimage, attended by millions in the holy city of Mecca, Saudi Arabia, is characterized by extreme weather conditions and dense crowds, posing significant challenges in crowd management and thermal comfort. This study introduces a novel optimization framework for a drone-truck cooperative system designed to enhance thermal comfort during Hajj. The framework focuses on deploying drones to continuously monitor real-time data on air quality, temperature, humidity, and crowd conditions, followed by optimized responses to provide timely interventions. A key feature of the proposed framework is its integration of energy consumption and carbon emission considerations, aligning with sustainability and humanitarian objectives. The framework aims to minimize travel time and the total costs associated with truck and drone deployment, spraying material distribution, energy consumption, and carbon emissions. By simulating various scenarios in key Hajj locations-Arafat, Muzdalifah, and Mina Tent City-the study identifies optimal drone routing and deployment strategies to mitigate health risks associated with thermal stress and overcrowding. This research represents the first integration of drones, lab-on-drones, and spraying drones for thermal comfort enhancement during Hajj, promoting a safer and more environmentally sustainable pilgrimage experience. It underscores the transformative potential of technology in addressing critical public health challenges at large-scale, short-term, high-density events.}, archive = {J_ACCESS}, author = {Shrouq Gamal and Mujahid N. Syed}, doi = {10.1109/ACCESS.2025.3545793}, journal = {IEEE Access}, pages = {38745-38777}, shortjournal = {IEEE Access}, title = {A two-phase drone-truck optimization framework for patrolling and thermal comfort: Sustainable hajj safety}, volume = {13}, year = {2025}, }
• (2025). Bi-directional hybrid attention feature pyramid network for detecting diabetic macular edema in retinal fundus images. ACCESS, 13, 38726-38744. (www)

  @article{ , abstract = {Diabetic macular edema (DME) is an advanced stage of Diabetic Retinopathy (DR), caused when exudates encroach on the macula region of the retina. Early detection of DME symptoms can avert vision impairment in most of the cases. A comprehensive and automated detection framework will greatly facilitate early detection of DME in diabetic patients. In this work, we propose a convolutional neural network based model, entitled as Bi-directional Hybrid Attention based Feature Pyramid Network (BhAFPN), to grade diabetic macular edema stages. The proposed model uses a bi-directional fusion of multi-scale features, augmented through hybrid-attention mechanisms implanted in the bottom-up and top-down feedback paths. A multi-level global attention block (GAB) mechanism weighs the augmented feature maps obtained from different scales according to their contribution to the final DME grade prediction. The model includes SVM classifier to perform DME screening from the extracted features of the fully connected layer (combinedly referred as BhAFPN+SVM). In addition, the network is also able to localize suspicious DME anomaly regions in fundus images in a weakly-supervised manner. The proposed model is significantly more light-weighted than the state-of-the-art (SOTA) models in DME grading task. The generalization of the proposed model is tested on multiple public retinal datasets. The proposed model outperformed all the reviewed SOTA methods on IDRiD and MESSIDOR-1 datasets in DME severity grading task by achieving the highest AUC-ROC scores of 0.9901 and 0.9946, respectively. Likewise, the BhAFPN+SVM model outperformed the SOTA models in DME screening task on both MESSIDOR-1 and MESSIDOR-2 datasets by achieving the highest accuracy of 95.92% and 90.83%, respectively. The interpretability of the model is analyzed using Grad-CAM-based heatmap visualization method and t-SNE plots. The lightweight nature of the proposed model along with its effective learning capabilities and robust performance in DME grading task makes it a viable and optimal candidate to be integrated with a real-time multi-disease detection systems.}, archive = {J_ACCESS}, author = {Nilarun Mukherjee and Souvik Sengupta and Mohammad Nadeem Ahmed and Syed Irfan Yaqoob and Mohammad Rashid Hussain and Abu Taha Zamani}, doi = {10.1109/ACCESS.2025.3545873}, journal = {IEEE Access}, pages = {38726-38744}, shortjournal = {IEEE Access}, title = {Bi-directional hybrid attention feature pyramid network for detecting diabetic macular edema in retinal fundus images}, volume = {13}, year = {2025}, }
• (2025). Distributed resilient clustering algorithm for virtual power plants under cyber attacks. ACCESS, 13, 38714-38725. (www)

  @article{ , abstract = {In this paper, the resilient control issue of virtual power plants (VPPs) under hybrid cyber attacks, specifically focusing on denial-of-service (DoS) and false data injection (FDI) attacks is first addressed. To solve this problem, a novel distributed resilient clustering secondary control technique is developed to ensure the normal operation of the VPP under cyber attacks. Specifically, an FDI attack estimation technique is first introduced to precisely estimate unknown FDI attacks affecting the bulk power supply. Based on the estimator, distributed resilient secondary controllers are designed to effectively combat the effects of FDI attacks and help achieve the frequency and voltage restoration as well as the proportional allocation of power requirement and state-of-charge (SoC) balancing of energy storage systems (ESSs). Under the proposed FDI attack estimation-based cooperative resilient secondary controller, the stability of the closed-loop systems subjected to hybrid FDI and DoS attacks is rigorously analyzed using Lyapunov theory. Finally, several simulation examples are introduced to verify the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Jia Wu and Yifan Sun and Jinyue Qian and Yin Cui and Qian Wang and Li Zhuo}, doi = {10.1109/ACCESS.2025.3546103}, journal = {IEEE Access}, pages = {38714-38725}, shortjournal = {IEEE Access}, title = {Distributed resilient clustering algorithm for virtual power plants under cyber attacks}, volume = {13}, year = {2025}, }
• (2025). HiFiSeg: High-frequency information enhanced polyp segmentation with global-local vision transformer. ACCESS, 13, 38704-38713. (www)

  @article{ , abstract = {Numerous studies have demonstrated the strong performance of Vision Transformer (ViT)-based methods across various computer vision tasks. However, ViT models often struggle to effectively capture high-frequency components in images, which are crucial for detecting small targets and preserving edge details, especially in complex scenarios. This limitation is particularly challenging in colon polyp segmentation, where polyps exhibit significant variability in structure, texture, and shape. High-frequency information, such as boundary details, is essential for achieving precise semantic segmentation in this context. To address these challenges, we propose HiFiSeg, a novel network for colon polyp segmentation that enhances high-frequency information processing through a global-local vision transformer framework. HiFiSeg leverages the pyramid vision transformer (PVT) as its encoder and introduces two key modules: the global-local interaction module (GLIM) and the selective aggregation module (SAM). GLIM employs a parallel structure to fuse global and local information at multiple scales, effectively capturing fine-grained features. SAM selectively integrates boundary details from low-level features with semantic information from high-level features, significantly improving the model’s ability to accurately detect and segment polyps. Extensive experiments on five widely recognized benchmark datasets demonstrate the effectiveness of HiFiSeg for polyp segmentation. Notably, the mDice scores on the challenging CVC-ColonDB and ETIS datasets reached 0.826 and 0.822, respectively, underscoring the superior performance of HiFiSeg in handling the specific complexities of this task.}, archive = {J_ACCESS}, author = {Jingjing Ren and Xiaoyong Zhang and Lina Zhang}, doi = {10.1109/ACCESS.2025.3544187}, journal = {IEEE Access}, pages = {38704-38713}, shortjournal = {IEEE Access}, title = {HiFiSeg: High-frequency information enhanced polyp segmentation with global-local vision transformer}, volume = {13}, year = {2025}, }
• (2025). Nonlinearity analysis of down-sampled paths in high-speed ADC-based SerDes receivers. ACCESS, 13, 38698-38703. (www)

  @article{ , abstract = {Analog-to-Digital Converter (ADC) based Serializer- Deserializer (SerDes) receivers perform down-sampling in analog domain to address the speed limitation of the ADCs. This paper presents an analysis of the impact of down-sampling on nonlinearity of the analog data path. The nonlinearity is derived to quantify the contribution of down-sampled and non-down-sampled paths. The effect of bandwidth of down-sampled path on nonlinearity is analyzed. Nonlinearity estimators are proposed to distinguish between down-sampled and non-down-sampled path nonlinearity in real time receiver operation. The proposed analysis is applied to a 50 Gbps ADC based receiver.}, archive = {J_ACCESS}, author = {Archit Joshi}, doi = {10.1109/ACCESS.2025.3545668}, journal = {IEEE Access}, pages = {38698-38703}, shortjournal = {IEEE Access}, title = {Nonlinearity analysis of down-sampled paths in high-speed ADC-based SerDes receivers}, volume = {13}, year = {2025}, }
• (2025). Contactless surface following via teleoperated manipulator based on point cloud. ACCESS, 13, 38685-38697. (www)

  @article{ , abstract = {This study focused on security robots as a substitute for security guards. For patrol tasks, the operation of body scans using teleoperated manipulators equipped with metal detectors has been considered to determine whether individuals are carrying hazardous items. When a handheld metal detector is waved over a person to be inspected, it must follow the subject’s clothing from a certain distance. In this study, a method for acquiring an object’s shape in-situ and following its surface without contact is proposed. The resulting scheme captures the surface information of an object using Light Detection and Ranging (LiDAR) immediately prior to operation. The distance between the hand and the object is then controlled using a point cloud and the operator’s control input. To confirm the effectiveness of the proposed method, subject experiments were conducted with three operators. As a result, the percentage of distance errors significantly increases within ±10 mm in the proposed method. The percentage of the distance error within ±10 mm in the experiment without control was 78.7%, and the percentage in the experiment with control was 99.8%. In addition, the time in which the proposed method completes the surface-following task is significantly decreased: The median time in the experiment without control was 63.1 s, while that in the experiment with control was 24.6 s. These results indicate that the distance can be easily maintained when an input device is pressed forward and swung left and right, reducing the operation time.}, archive = {J_ACCESS}, author = {Teppei Tsujita and Shunsuke Matsushima and Tadamasa Kitahara and Satoko Abiko}, doi = {10.1109/ACCESS.2025.3546027}, journal = {IEEE Access}, pages = {38685-38697}, shortjournal = {IEEE Access}, title = {Contactless surface following via teleoperated manipulator based on point cloud}, volume = {13}, year = {2025}, }
• (2025). Deep adaptive chaos synchronization based on optimization algorithm. ACCESS, 13, 38671-38684. (www)

  @article{ , abstract = {In this study, we propose a novel Deeply Optimized Adaptive chaotic synchronization algorithm system (DOA), which adopts the ideas of genetic algorithm, Deep Image Prior (DIP) network, Deep Convolutional Generative Adversarial Network (DCGAN) network, and slide mode control algorithm. Traditional Deep Learning (DL)-based methods perform well in complex multi-parameter operations, but training on large datasets is typically a complicated, time-consuming, and high-cost process. Such methods are also difficult to adapt to dynamic parameter changes. The algorithmic network model in the proposed DOA reduces reliance on large datasets by learning the deep mining methods of the data characteristics in DIP, and can adjust system parameters adaptively, accurately, and quickly, providing high synchronization efficiency and excellent stability over various chaotic signals. By applying Lyapunov stability theory, the robustness and global stability of the model in dynamic systems are proven. This paper also uses an advanced Recurrent Neural Network (RNN)-based chaotic synchronization system as a benchmark. The simulation results show that, when compared to the Recurrent Neural Network based synchronization system, the DOA architecture has significant advantages in robustness, convergence, and training over noisy channels. Experiments show that under strong noise (AWGN variance = 2) and parameter mismatch (±20 percent drift), the synchronization error of DOA (<0.3) is significantly lower than that of the RNN benchmark system (>1.5), and the training data volume is reduced by more than 30%. Simulation results show that, the DOA architecture has significant advantages in robustness, convergence, and training over noisy channels. The proposed DOA scheme improves the effect of chaotic synchronization and paves the way for the development of a new class of modulator schemes that meet the robustness, convergence, and training requirements for encrypted communication.}, archive = {J_ACCESS}, author = {Jinzhi Liu and Tianhao Zuo}, doi = {10.1109/ACCESS.2025.3545441}, journal = {IEEE Access}, pages = {38671-38684}, shortjournal = {IEEE Access}, title = {Deep adaptive chaos synchronization based on optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Smart packet delivery in mobile underwater sensors networks (M-CTSP). ACCESS, 13, 38655-38670. (www)

  @article{ , abstract = {This research is focused on leveraging the Travelling Salesman Problem (TSP) to establish the optimal route of getting from a source node to a destination node in an underwater dynamic location. In this context, all nodes are mobile and slightly adjust their positions due to water currents. Clustering is introduced (CTSP) to identify the best possible route, where cluster heads are chosen using the known criterium of center of mass of the cluster as well as residual energy. Packets travel from source to destination (nodes) in a multiloop process, visiting cluster heads nodes, with minimum energy consumption. The intention behind using TSP in this approach is thus to efficiently handle the dynamic movement of underwater nodes by conserving energy and ensuring delivery of packets can be relied upon. Additionally, to the movement-related challenges, this method also enhances network lifetime by providing a systematic and energy-efficient way of transmitting information through cluster heads. Simulations using the novel M-CTSP into the routing algorithm yields to promising results the M-CTSP protocol enhances routing execution with the use of dynamic clustering and traveling salesman optimization. This results in a 40% rise in energy use reduction, 15% improvement in packet delivery with a 30% increase in network lifetime when compared to normal clustering-based protocols: LEACH and EECBP-FOA. Experimental results prove that M-CTSP contributes extensively in terms of node mobility packet delivery and energy conservation in dynamic UWSN environments.}, archive = {J_ACCESS}, author = {Abdul Moid Khan and Miguel-Ángel Luque-Nieto and Ali Akbar Siddique}, doi = {10.1109/ACCESS.2025.3542302}, journal = {IEEE Access}, pages = {38655-38670}, shortjournal = {IEEE Access}, title = {Smart packet delivery in mobile underwater sensors networks (M-CTSP)}, volume = {13}, year = {2025}, }
• (2025). Modal gain and loss in three-layer optical waveguides. ACCESS, 13, 38628-38654. (www)

  @article{ , abstract = {A perturbation analysis based on the imaginary part of the complex permittivity rather than on the electric fields associated with stimulated emission shows that the formula for TM gain applies to any increase or decrease in the mode amplitude, whether due to boundary irregularities, material inhomogeneities, doping levels, or stimulated emissions. The resulting derived equations for the TE and TM modal gain confinement factors are superior to previously published approximate equations and provide accurate results for practical photonic waveguides. The modal gain can be larger than the gain of a plane wave traveling in a medium with the same gain if the gain is only in the middle layer (for both TE and TM modes), and in some TM cases, with gain only in the evanescent layers. In some cases, the TM modal gain or loss can be a factor of approximately 10 larger than the TE modal gain or loss. The derived analytical formulas showed excellent agreement with the results obtained from an accurate numerical approach for the gain and modal gain confinement factors for waveguides with small, medium, and large index differences. The formulae derived in this study can be useful for periodic optical waveguides, semiconductor lasers, optical amplifiers, distributed reflector lasers, optical waveguide isolators, and silicon photonic waveguides. The results of these analytical formulae can be used to verify numerical methods applied to more complex structures.}, archive = {J_ACCESS}, author = {Nikhil Polley and Tso-Min Chou and Weida Zhang and Ethan Chou and Jerome K. Butler and Gary A. Evans}, doi = {10.1109/ACCESS.2025.3543486}, journal = {IEEE Access}, pages = {38628-38654}, shortjournal = {IEEE Access}, title = {Modal gain and loss in three-layer optical waveguides}, volume = {13}, year = {2025}, }
• (2025). Design of enhanced license plate information recognition algorithm based on environment perception. ACCESS, 13, 38609-38627. (www)

  @article{ , abstract = {Aiming at the problem of difficulty in completely and accurately identifying number plate information in bad weather, this study proposes an algorithm based on environmental perception and the enhanced recognition of number plate information. Based on the YOLOv11 framework, we developed a histogram YOLO (H-YOLO) detector and designed an Enhanced Transformer Block (ETB) to improve the extraction of regional features of the number plates in complex environments. Meanwhile, a Real-Time Weather-Aware Image Enhancement Module is designed, which integrates real-time environment awareness technology to dynamically adjust the image enhancement strategy according to the changing weather conditions and improve the image quality. Optimization of the detection architecture was achieved through thresholding and independent image enhancement of the plate region. Comparing the H-YOLO detector with the baseline YOLOv11 model, we found that the training precision and recall of our model were improved by 2.62% and 1.8%, respectively. Furthermore, in real-world detection experiments, the H-YOLO detector was improved by 12 percentage points. The effectiveness of our proposed perceptual enhancement algorithm is further confirmed by the fact that when validating the China City Parking Dataset 2019 (CCPD 2019), it improves the detection rate by 24.53% compared to traditional image enhancement methods. In terms of computational efficiency, the new detection architecture reduced the load by approximately 9% and improved the inference speed by 15%. This study effectively improves the success rate of number plate recognition under real-time changing and complex weather conditions, and provides reliable technical support for practical applications.}, archive = {J_ACCESS}, author = {Zilu Wang and Limin Zheng and Gang Li}, doi = {10.1109/ACCESS.2025.3544634}, journal = {IEEE Access}, pages = {38609-38627}, shortjournal = {IEEE Access}, title = {Design of enhanced license plate information recognition algorithm based on environment perception}, volume = {13}, year = {2025}, }
• (2025). The TMAP framework: Describing the design space of technology-mediated audience participation in live music. ACCESS, 13, 38593-38608. (www)

  @article{ , abstract = {Technology-mediated audience participation (TMAP) offers a wide variety of ways to enhance the involvement of spectators during a music performance. Technological change has created rich new opportunities for such interactive performance experiences. Effective design of TMAP requires balancing knowledge from diverse human and technological perspectives and taking into account very different characteristics and requirements in live music performance. However, research in this distinctive area of interaction design is limited, and the provision of practical guidance for designers has been scarcely addressed thus far. To bridge this gap, we propose the TMAP Framework, which describes the design space of audience participation in a practice-oriented way to support design-related processes. This descriptive framework is grounded on existing literature from the last 25 years, and has been iteratively improved and peer reviewed with different experts. It was validated using actual TMAP performances not included in the framework development. The TMAP Framework contains 180 entities in a tree-like sorted structure on four levels. Alongside the contribution to knowledge of extensively describing the design space of TMAP in live music, with the validation of the framework, we demonstrate its completeness, stability and generalisability. In this regard, the framework may also serve as a common language between different perspectives and practitioners, thus addressing the highly interdisciplinary nature of the TMAP field.}, archive = {J_ACCESS}, author = {Oliver Hödl and Luca Turchet}, doi = {10.1109/ACCESS.2025.3543064}, journal = {IEEE Access}, pages = {38593-38608}, shortjournal = {IEEE Access}, title = {The TMAP framework: Describing the design space of technology-mediated audience participation in live music}, volume = {13}, year = {2025}, }
• (2025). Research on flexible material identification and positioning based on machine vision. ACCESS, 13, 38584-38592. (www)

  @article{ , abstract = {The cutting of flexible materials (such as garment fabrics, leather, and packaging) is a preliminary process in product manufacturing. The accuracy of cutting determines the product production quality. The core challenge is the recognition and positioning accuracy of the marker information. Based on the positioning information, the cutter head position is initialized, establishing the correspondence between the physical coordinates of the robot and the pixel coordinates of the data image. Traditional shape-based template matching algorithms are prone to local optima and overall shifts when dealing with size scaling and deformation of flexible materials, resulting in decreased positioning accuracy. This study proposes an optimization positioning algorithm based on a standard deviation assessment to reduce the overall shift in shape matching, further improving the positioning accuracy on the original basis. Without the need to establish a scaling matrix for the template, marker information can still be located on the scaled and deformed fabrics, significantly reducing the algorithm complexity. Finally, this machine vision system is applied to equipment at a CNC cutting device manufacturing enterprise in Ningbo. Using a resolution of $640\times 480$ pixels with a black-and-white industrial camera and a lens at a distance of 9 cm from the material, the positioning accuracy was controlled within three pixels. The accuracy of the equipment was close to that of its well-known domestic and international counterparts.}, archive = {J_ACCESS}, author = {Xiaowei Zhou and Qingmei Pan and Dingyuan Jiang and Jiazang Zhang}, doi = {10.1109/ACCESS.2025.3540883}, journal = {IEEE Access}, pages = {38584-38592}, shortjournal = {IEEE Access}, title = {Research on flexible material identification and positioning based on machine vision}, volume = {13}, year = {2025}, }
• (2025). Moment-aware video retrieval for video corpus moment retrieval. ACCESS, 13, 38574-38583. (www)

  @article{ , abstract = {Video corpus moment retrieval (VCMR) task aims to retrieve a specific moment from a large corpus of untrimmed videos. This task has been addressed by decomposing it into video retrieval and moment retrieval subtasks each with specialized heads (i.e., democratic decomposition), due to the computational complexity. However, this approach overlooks the interdependency between the subtasks, which is crucial for temporally fine-grained query-video alignment. To address this suboptimality, we propose an integrated learning framework that explicitly establishes a connection between the two subtasks, namely moment-aware video retrieval, allowing them to benefit from each other’s learning process. Furthermore, we employ a curriculum-based negative sampling strategy that gradually provides harder negative samples to enhance the discriminative ability between semantically similar negative videos. We empirically show that the proposed approach outperforms state-of-the-art methods on three benchmarks – TVR, ActivityNet, and DiDeMo. Notably, on TVR, our method achieves 10.04% in VCMR R@1 with tIoU=0.7, representing a 1.68% absolute improvement over prior work, and similarly demonstrates consistent gains on ActivityNet (4.98% in VCMR R@1 with tIoU=0.5) and DiDeMo, validating the effectiveness of our integrated approach.}, archive = {J_ACCESS}, author = {Yura Choi and Daechul Ahn and Jonghyun Choi}, doi = {10.1109/ACCESS.2025.3542720}, journal = {IEEE Access}, pages = {38574-38583}, shortjournal = {IEEE Access}, title = {Moment-aware video retrieval for video corpus moment retrieval}, volume = {13}, year = {2025}, }
• (2025). An explainable deep learning network with transformer and custom CNN for bean leaf disease classification. ACCESS, 13, 38562-38573. (www)

  @article{ , abstract = {Bean rust and angular leaf spot pose significant challenges to bean cultivation, impacting yields. Prompt disease identification maximizes productivity, but traditional methods need specialized expertise. This research presents an explainable deep learning model that combines the Pyramid Vision Transformer (PVT) and Group Context Aware Depthwise Shuffle Network (GCADSN). The PVT effectively models long-range dependencies, identifying disease patterns across larger leaf areas, while the GCADSN focuses on capturing nuanced, context-specific features. This combined approach leads to a richer representation of the input image, resulting in improved disease classification. Model explainability is provided through GradCAM visualizations, highlighting the image regions crucial for the model’s predictions and enabling transparent, class-specific insights. The model’s performance was rigorously tested using the IBean dataset, a collection of images depicting various bean leaf diseases, including common rust, angular leaf spot, and healthy leaves. Our proposed network achieved a high accuracy of 97.66%, outperforming many current state-of-the-art deep learning models. Furthermore, it demonstrated strong performance across other key metrics, with an F1 score of 97.67%, precision of 97.83%, and recall of 97.67%. Importantly, the model’s computational efficiency makes it well-suited for practical application in real-world agricultural scenarios.}, archive = {J_ACCESS}, author = {R. Karthik and R. Aswin and K. S. Geetha and K. Suganthi}, doi = {10.1109/ACCESS.2025.3546017}, journal = {IEEE Access}, pages = {38562-38573}, shortjournal = {IEEE Access}, title = {An explainable deep learning network with transformer and custom CNN for bean leaf disease classification}, volume = {13}, year = {2025}, }
• (2025). Serious games supported by virtual Reality—Literature review. ACCESS, 13, 38548-38561. (www)

  @article{ , abstract = {Serious games supported by virtual reality (VR) offer innovative solutions across diverse fields, such as education, therapy, and professional training, leveraging immersive and interactive environments to enhance engagement and learning outcomes. This paper provides a comprehensive literature review of VR-based serious games, focusing on their applications, benefits, and challenges. Key themes include the transformative impact of VR on education through improved motivation and experiential learning, therapeutic applications addressing physical and mental health, and professional training in hazardous and high-skill environments. Despite their potential, adoption is hindered by challenges such as high costs, limited accessibility, ethical concerns, and technical limitations, including cybersickness and scalability issues. This review also highlights emerging trends, including AI-driven adaptive learning, advancements in haptic feedback, and the integration of multisensory experiences, offering opportunities to address these barriers. By synthesizing current research and identifying critical gaps, this paper provides actionable recommendations for researchers and developers to maximize the impact of serious VR games in creating inclusive, effective, and scalable solutions for diverse user needs.}, archive = {J_ACCESS}, author = {Nadia Damianova and Santiago Berrezueta-Guzman}, doi = {10.1109/ACCESS.2025.3544022}, journal = {IEEE Access}, pages = {38548-38561}, shortjournal = {IEEE Access}, title = {Serious games supported by virtual Reality—Literature review}, volume = {13}, year = {2025}, }
• (2025). Efficient and asymptotically optimal vehicle motion planning with stochastic template-based RRT*. ACCESS, 13, 38533-38547. (www)

  @article{ , abstract = {Kinodynamic motion planning plays a vital role in robotics, particularly in autonomous driving, where planned trajectories must satisfy kinematic and dynamic constraints while ensuring both safety and efficiency. Although existing kinodynamic RRT* algorithms achieve asymptotic optimality, their high computational cost often limits their practicality in high-dimensional or complex environments, such as autonomous driving scenarios. To enhance efficiency in such scenarios, motion templates with predefined action sequences have been proposed as a guiding strategy for planners. However, traditional fixed templates lack the flexibility and adaptability required to handle dynamic and diverse driving conditions, reducing their effectiveness in real-world applications. To overcome these limitations, we propose Stochastic Template-Based RRT* (ST-RRT*), a novel approach that introduces stochasticity into the template generation process. By dynamically generating templates guided by probabilistic models, ST-RRT* achieves efficient exploration, improves adaptability to complex constraints, and retains the asymptotic optimality guarantees of RRT*. We demonstrate the effectiveness of ST-RRT* through experiments in automotive environments, showcasing its ability to generate high-quality trajectories under stringent motion constraints. Additionally, we validate its generalizability by applying it to other kinodynamic planning scenarios, highlighting its efficiency, robustness, and versatility compared to state-of-the-art methods.}, archive = {J_ACCESS}, author = {Shaoyu Yang and Masamichi Shimosaka}, doi = {10.1109/ACCESS.2025.3546158}, journal = {IEEE Access}, pages = {38533-38547}, shortjournal = {IEEE Access}, title = {Efficient and asymptotically optimal vehicle motion planning with stochastic template-based RRT*}, volume = {13}, year = {2025}, }
• (2025). Scalable 5G NR rate-matcher and rate-dematcher for efficient use in FPGA accelerators. ACCESS, 13, 38515-38532. (www)

  @article{ , abstract = {5G new radio supports a variety of new technologies and services, demanding significant improvements in radio access network (RAN) latency, throughput, and flexibility. Disaggregation addresses these challenges by splitting the RAN into network units-central (CU), distributing (DU), and radio (RU), enabling data processing virtualization and implementation on off-the-shelf hardware. However, implementing upper physical layer (PHY) processing on off-the-shelf hardware alone might cause inefficient usage of the server processors. Therefore, acceleration is often needed to offload heavy processing, focusing on low-density parity-check (LDPC) codec as the most compute-intensive task in the PHY. Additionally, LDPC coding is tightly coupled with rate-matching. This paper presents novel hardware architectures of rate-matcher and rate-dematcher, targeting field programmable gate array (FPGA) RAN accelerators. The presented solution’s approach to memory organization allows highly parallel operation with efficient hardware resource usage. The architecture is flexible, enabling a selection of various parallelism levels for instant integration with other PHY components, and achieves a throughput of up to 150 Gbps for rate-matching, and up to 35 Gbps for rate-dematching. Both components have been integrated into a Peripheral Component Interconnect Express (PCIe) FPGA acceleration card with an LDPC encoder and decoder. The accelerator performance has been evaluated against OpenAirInterface PHY software. By measuring the acceleration impact on the processor load, it has been shown that with the proposed components, the acceleration efficiency can be increased by an order of magnitude compared to the LDPC-only solution.}, archive = {J_ACCESS}, author = {Nemanja Filipović and Dragomir El Mezeni and Vladimir L. Petrović}, doi = {10.1109/ACCESS.2025.3546301}, journal = {IEEE Access}, pages = {38515-38532}, shortjournal = {IEEE Access}, title = {Scalable 5G NR rate-matcher and rate-dematcher for efficient use in FPGA accelerators}, volume = {13}, year = {2025}, }
• (2025). Cooperative control of power grid frequency based on expert-guided deep deterministic policy gradient algorithm. ACCESS, 13, 38502-38514. (www)

  @article{ , abstract = {In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency based on the Expert-Guided Deep Deterministic Policy Gradient (EGDDPG) algorithm. This method is data-driven and continuously interacts with the environment to adaptively optimize the control strategy. On the one hand, EGDDPG incorporates expert data during training to guide the agent, enabling faster learning of effective strategies and reducing training convergence time. On the other hand, random disturbances are introduced to simulate the uncertainties in the power system, encouraging the agent to learn control strategies across different environmental states. This approach avoids the overfitting issues associated with fixed disturbance training, enhancing the adaptability and robustness of the algorithm. Simulation results show that the proposed EGDDPG algorithm converges faster and demonstrates stronger control capability and adaptability across various scenarios. It effectively reduces frequency deviation fluctuations and overshoot.}, archive = {J_ACCESS}, author = {Tao Shen and Jing Zhang and Yu He and Shengsun Yang and Demu Zhang and Zhaorui Yang}, doi = {10.1109/ACCESS.2025.3546492}, journal = {IEEE Access}, pages = {38502-38514}, shortjournal = {IEEE Access}, title = {Cooperative control of power grid frequency based on expert-guided deep deterministic policy gradient algorithm}, volume = {13}, year = {2025}, }
• (2025). A privacy-preserving federated learning with a feature of detecting forged and duplicated gradient model in autonomous vehicle. ACCESS, 13, 38484-38501. (www)

  @article{ , abstract = {Recently, autonomous vehicular (AV) applications have garnered significant attention regarding the privacy of collected sensing data. Consequently, federated learning (FL) has emerged as a prominent solution to address the privacy concerns associated with sharing original data. This work introduces FL into the AV domain, enabling vehicles to train models locally and provide local updates to a server instead of transmitting raw datasets, thereby preserving data privacy from potential exposure by a malicious centralized cloud server (CC-server). However, a malicious CC-server may still infer the content of the AV’s dataset by analyzing the uploaded gradient models. To counter this, various privacy-preserving federated learning (PPFL) approaches have been proposed to prevent a malicious CC-server from executing revealing or forgery attacks on the uploaded gradient models. Nonetheless, the PPFL framework presents an opportunity for a malicious AV to conduct poisoning attacks on the global model by uploading forged or duplicated trained models to the CC-server. This paper proposes an Efficient Privacy-Preserving Federated Learning framework with the capability of detecting forged and duplicated models (PPFL-DP), which identifies forged uploaded gradient models. Specifically, we design a threshold $(\varepsilon)$ algorithm based on Euclidean distance to serve as an effective method for detecting poisoning attacks. Furthermore, to ensure the privacy preservation of the $(\varepsilon)$ method, we leverage group-oblivious signcryption (GOSC) combined with homomorphic cryptography to develop a sign-homomorphic encryption (SHE) protocol. This protocol effectively preserves privacy in FL while being compatible with the threshold $(\varepsilon)$ design. The proposed SHE protocol achieves both privacy preservation and the detection of poisoning attacks in FL simultaneously. Through experimental results and theoretical analysis, the proposed SHE protocol demonstrates high efficiency in protecting client privacy while maintaining a high accuracy in detecting forged uploaded models, without adversely affecting the quality of the global model.}, archive = {J_ACCESS}, author = {Abdulrahman Alamer and Sultan Basudan}, doi = {10.1109/ACCESS.2025.3545786}, journal = {IEEE Access}, pages = {38484-38501}, shortjournal = {IEEE Access}, title = {A privacy-preserving federated learning with a feature of detecting forged and duplicated gradient model in autonomous vehicle}, volume = {13}, year = {2025}, }
• (2025). Machine learning based channel estimation for low-power internet of things (LP-IoT) devices: A probabilistic analysis. ACCESS, 13, 38470-38483. (www)

  @article{ , abstract = {The swift expansion of Low-Power Internet of Things (LP-IoT) devices has significantly impacted industries such as smart homes, healthcare, agriculture, and industrial automation. In these interconnected environments, reliable wireless communication is essential for efficient data transmission. Channel estimation techniques play a crucial role in assessing the state of the wireless channel before transmitting data. While conventional techniques have been developed, they often struggle in dynamic environments due to substantial computational demands and limited adaptability. Recent advancements in Machine Learning (ML) offer promising improvements in wireless channel estimation for LP-IoT by capturing complex relationships and adapting to changing conditions of wireless channel. Considering the ML models as the potential substitute in the field of wireless channel estimation, this paper builds on our previous work by providing a detailed analysis of two advanced ML-based models, which demonstrates their applicability and reliability in practical indoor environments. While these models have shown potential, a comprehensive analysis of their accuracy in expanded environments has been lacking. To address this gap, we conduct a probability analysis to evaluate the models’ estimation accuracy and confidence levels, alongside a scalability analysis to assess performance as network size and complexity grow. The results confirm the effectiveness of these ML-based models and provide valuable insights into their suitability for large-scale LP-IoT applications. Ultimately, this study contributes to the advancement of intelligent LP-IoT communication systems by bridging the gap between theoretical research and real-world deployment.}, archive = {J_ACCESS}, author = {Samrah Arif and M. Arif Khan and Sabih Ur Rehman}, doi = {10.1109/ACCESS.2025.3543341}, journal = {IEEE Access}, pages = {38470-38483}, shortjournal = {IEEE Access}, title = {Machine learning based channel estimation for low-power internet of things (LP-IoT) devices: A probabilistic analysis}, volume = {13}, year = {2025}, }
• (2025). Low-light image enhancement via new intuitionistic fuzzy generator-based retinex approach. ACCESS, 13, 38454-38469. (www)

  @article{ , abstract = {Image enhancement refers to the technique of improving an image by modifying certain aspects of its spectrum. Circumstances such as low-light and extreme darkness can lead to the loss of information, color, and quality in images. In order to address this type of situation, the current research offers an innovative approach to enhancing image quality. It employs the intuitionistic fuzzy generator (IFG) and the histogram equalization Retinex (HER) to enhance contrast in low-light and dark images, thereby maintaining color constancy. Initially, the image is fuzzified and subsequently transformed into an intuitionistic fuzzy image (IFI) by incorporating the new IFG. IFI enhances the precision of gray levels effectively by handling the uncertainty between different regions of pixels within the image. HER maintains the natural color balance in an image where fine details are more visible even in challenging lighting conditions. Experimental results demonstrate that the proposed algorithm consistently outperforms existing image enhancement methods, yielding images that maintain optimal balance in contrast level and detail using standard full referral metrics such as absolute mean brightness error, mutual information, peak signal to noise ratio, and non-referral metrics like entropy, contrast per pixel, spatial frequency, blind reference less image spatial quality evaluator, and mean intensity gradient.}, archive = {J_ACCESS}, author = {M. S. Ragavendirane and S. Dhanasekar}, doi = {10.1109/ACCESS.2025.3545258}, journal = {IEEE Access}, pages = {38454-38469}, shortjournal = {IEEE Access}, title = {Low-light image enhancement via new intuitionistic fuzzy generator-based retinex approach}, volume = {13}, year = {2025}, }
• (2025). Scenario-based optimal power flow evaluation for wind power allocation capacity in modern power systems. ACCESS, 13, 38443-38453. (www)

  @article{ , abstract = {Integrating wind power into modern energy grids presents significant challenges due to its unpredictable nature. This study introduces a scenario-based optimal power flow model with Wind Farms Optimal Capacity in Grid (SOPF-WOCG) to enhance system flexibility and minimize curtailment. By leveraging the Generalized Dynamic Factor Model (GDFM), we improve wind power and load forecasts, filtering out noise and capturing key dependencies. Our findings demonstrate that this approach leads to better reserve management, fewer constraint violations, and improved system performance. Case studies show tangible benefits, including a 2.9% reduction in operational costs, a 2.7% decrease in ancillary costs, a 5.8% drop in curtailment expenses, and a 2.1% increase in the grid’s capacity to accommodate wind power. These results highlight the effectiveness of the proposed method in optimizing renewable energy integration while ensuring grid stability. Future work will explore real-time applications and integration with advanced forecasting and optimization techniques to further enhance grid reliability and efficiency.}, archive = {J_ACCESS}, author = {Aamer A. Shah and Shuchen Wang and Gai Liu and Rizwan Ul Hassan and Aamir Nawaz}, doi = {10.1109/ACCESS.2025.3542360}, journal = {IEEE Access}, pages = {38443-38453}, shortjournal = {IEEE Access}, title = {Scenario-based optimal power flow evaluation for wind power allocation capacity in modern power systems}, volume = {13}, year = {2025}, }
• (2025). Efficiency-sensitive feature selection and probabilistic feature transfer for scenic image recomposition. ACCESS, 13, 38430-38442. (www)

  @article{ , abstract = {In this research, we address the sophisticated task of reconstructing the semantic elements within complex scenes, a critical endeavor for a broad range of artificial intelligence (AI) applications. Our objective is to effortlessly blend multi-channel perceptual visual features for accurately adjusting to scenic images with detailed spatial layouts. Central to our approach is the development of a deep hierarchical model, carefully crafted to replicate human gaze movements with high accuracy. Utilizing the BING objectness metric, our model excels at rapidly and accurately identifying semantically and visually important scenic patches by detecting objects or their parts at various scales in diverse settings. Following this, we formulate a time-sensitive feature selector to obtain high-quality visual features from different scenic patches. To emulate the human ability to pinpoint essential scene segments, we implement a technique termed locality-preserved learning (LRAL). This method effectively generates gaze shift paths (GSP) for each scene by 1) preserving the local coherence of varied scenes, and 2) intelligently selecting scene segments that match human visual attention. With LRAL, we systematically create a GSP for each scene and derive its deep feature set through a deep aggregation model. These deep GSP features are then incorporated into a probabilistic transfer model for retargeting a variety of sceneries. Our methodology’s efficacy is confirmed through comprehensive empirical studies, highlighting its substantial advantages.}, archive = {J_ACCESS}, author = {Zhendong Du and Yu Yu}, doi = {10.1109/ACCESS.2025.3546310}, journal = {IEEE Access}, pages = {38430-38442}, shortjournal = {IEEE Access}, title = {Efficiency-sensitive feature selection and probabilistic feature transfer for scenic image recomposition}, volume = {13}, year = {2025}, }
• (2025). HIDS-RPL: A hybrid deep learning-based intrusion detection system for RPL in internet of medical things network. ACCESS, 13, 38404-38429. (www)

  @article{ , abstract = {The proliferation of the Internet of Medical Things (IoMT) has improved medical examination behaviors in their accuracy, real-time capability, interconnection, and intelligence level. However, the increasing connectivity of medical devices has also made IoMT networks vulnerable to various cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Existing intrusion detection systems (IDS) are not adequately equipped to handle and mitigate these sophisticated cyberattacks. Moreover, given the dynamic nature of IoMT traffic, where the Routing Protocol for Low-Power and Lossy Networks (RPL) is often deployed to adapt changes in network topology, the intrusion detection system faces significant challenges in maintaining up-to-date threat detection capabilities. This paper proposes a hybrid Deep Learning-Based Intrusion Detection System for the RPL protocol in IoMT networks. The suggested model, designated HIDS-RPL, results from the hybridization of the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM), typically employed for sequence data prediction. We evaluate the proposed algorithm to detect intrusions using the benchmark CIC-DDoS2019 dataset. This dataset contains a large and diverse collection of traffic flows from real-world DDoS attacks, including SYN floods, UDP floods, HTTP floods, etc. We evaluated our novel methodology against five DDoS attacks: DNS, UDP, UDP-Lag, NTP, and SYN. In comparison to the most recent methods, our suggested model achieves an accuracy of 99.87%, a precision of 98.5%, a recall rate of 98.64%, and an F1-score of 98.54%.}, archive = {J_ACCESS}, author = {Abdelwahed Berguiga and Ahlem Harchay and Ayman Massaoudi}, doi = {10.1109/ACCESS.2025.3545918}, journal = {IEEE Access}, pages = {38404-38429}, shortjournal = {IEEE Access}, title = {HIDS-RPL: A hybrid deep learning-based intrusion detection system for RPL in internet of medical things network}, volume = {13}, year = {2025}, }
• (2025). A comprehensive systematic review of AI-driven approaches to self-directed learning. ACCESS, 13, 38387-38403. (www)

  @article{ , abstract = {Artificial intelligence (AI)-enhanced technologies are increasingly being researched and applied in learning and instruction. Previous studies have demonstrated the growing influence of AI tools in improving learning outcomes and transforming the educational landscape. However, limited research has explored learners’ engagement with AI resources to support self-directed learning, and no comprehensive review exists in this context. This study conducts a systematic review to examine the potential directions underlying the use of AI-enabled resources to assist learners in self-directed learning. The review targeted articles from high-profile databases such as Web of Science and ScienceDirect. A range of AI resources, including intelligent tutoring systems and conversational agents, were identified in the reviewed articles for their roles in providing personalized learning support, question-answering assistance, timely feedback, action recommendations, and interactive learning experiences that foster self-directed learning aspirations. The review also demonstrated that AI-supported self-directed learning can have a positive effect on the satisfaction of learners’ autonomy and competence needs. Besides that, prior knowledge of the learners acts as a facilitator for the continued use of AI resources, thereby positively affecting the outcomes of self-directed learning. Although AI in education is still a rare research field, future studies should focus, for example, on the use of concrete AI tools, such as intelligent tutoring systems or dialog systems supporting other forms of self-directed learning.}, archive = {J_ACCESS}, author = {Muhammad Younas and Dina Abdel Salam El-Dakhs and Yicun Jiang}, doi = {10.1109/ACCESS.2025.3546319}, journal = {IEEE Access}, pages = {38387-38403}, shortjournal = {IEEE Access}, title = {A comprehensive systematic review of AI-driven approaches to self-directed learning}, volume = {13}, year = {2025}, }
• (2025). Prosumer cost efficiency and ensuring grid stability through a hierarchical PPO framework in decentralized community energy management. ACCESS, 13, 38368-38386. (www)

  @article{ , abstract = {This research introduces a methodology based on reinforcement learning to address the cost reduction challenges faced by prosumers in a smart home-based community. The proposed method tackles the unpredictability of distributed energy resources (DERs) and the heterogeneity of prosumers by introducing Hierarchical Home Energy Management with a Noise-Adaptive Proximal Policy Optimization (H2EN-PPO) framework. This multi-agent problem is addressed using deep reinforcement learning using decentralized critic and decentralized value neural networks. The highest-tier node focuses on resolving the internal energy pricing predicament, while the lower nodes manage the scheduling of household appliances. To emulate human behavior accurately, distributed actual power transition data obtained from various domestic devices is incorporated, accounting for aspects such as environmental circumstances and and dynamic pricing in real-time. Real-world datasets are employed in simulations to evaluate the proposed approach. To maintain grid stability, a suitable reward function is implemented to prevent overloading of the transformer connected to a particular community. This reward function is also designed to address the variation in human behavior when it comes to electric vehicle (EV) charging, taking into account factors like range anxiety and time anxiety. Additionally, this function is capable of optimizing the utilization of the photovoltaic (PV) generation. The findings indicate that the proposed approach substantially minimizes prosumers’ daily costs, outperforming existing methods. The H2EN-PPO framework offers advantages in terms of cost reduction and efficient resource utilization in community energy trading scenarios. The contributions of this research lie in addressing the challenges of DER uncertainty and prosumer heterogeneity, as well as incorporating realistic human behavior models into the optimization framework.}, archive = {J_ACCESS}, author = {Ajay Singh and Bijaya Ketan Panigrahi}, doi = {10.1109/ACCESS.2025.3542873}, journal = {IEEE Access}, pages = {38368-38386}, shortjournal = {IEEE Access}, title = {Prosumer cost efficiency and ensuring grid stability through a hierarchical PPO framework in decentralized community energy management}, volume = {13}, year = {2025}, }
• (2025). Embedded machine learning-based voltage fingerprinting for automotive cybersecurity. ACCESS, 13, 38342-38367. (www)

  @article{ , abstract = {This research presents the development and validation of an embedded system for real-time intrusion detection in automotive environments, leveraging voltage-based ECU fingerprinting. The proposed system integrates advanced machine learning algorithms, including SVM, ANN, and DT, optimized for anomaly detection, with linear SVM achieving the highest accuracy. To demonstrate feasibility, the system was implemented on automotive-grade microcontrollers, AURIX Tricore platform TC375. A robust test environment replicating real CAN traffic was employed to validate the approach, based on low-cost embedded devices (Arduino R3/R4 and MCP2515 module). To address synchronization challenges between the CAN transceiver and ADC channels, a circular buffer strategy was introduced, significantly reducing false positives and enhancing system stability. Performance evaluations under nominal and attack scenarios confirm the effectiveness of the solution, offering a practical and reliable method to enhance in-vehicle cybersecurity and mitigate risks for drivers and passengers.}, archive = {J_ACCESS}, author = {Pierpaolo Dini and Michele Zappavigna and Ettore Soldaini and Sergio Saponara}, doi = {10.1109/ACCESS.2025.3545245}, journal = {IEEE Access}, pages = {38342-38367}, shortjournal = {IEEE Access}, title = {Embedded machine learning-based voltage fingerprinting for automotive cybersecurity}, volume = {13}, year = {2025}, }
• (2025). DSNI-net: Forensic network for detecting splicing in salt-and-pepper noisy images. ACCESS, 13, 38325-38341. (www)

  @article{ , abstract = {Image splicing forensics technology is an important way to detect and locate spliced areas in naturally tampered images. However, common noise, especially salt-and-pepper (s&p) noise, can change pixel values to 0 or 255, significantly reducing the accuracy of splicing forensic methods. As the noise intensity increases, the impact on forensic methods gradually increases. This paper proposes a forensic method to enhance the robustness of the forensic method in scenarios with high-intensity s&p noise called forensic network for detecting splicing in s&p noisy images (DSNI-Net). The image is first denoised using a combination of residual network and median layer (ML) before forensics. The CNN network extracts shallow and deep feature channels in the image, uses ML to remove s&p noise, and reconstructs the image content through the convolution structure to restore image details. Secondly, the feature comparison structure is used to extract tampering traces during the forensic process. This ensures that the final extracted features are uniquely related to the image itself and are used to reveal areas of image tampering. Experimental results on noisy datasets demonstrate the robustness and effectiveness of DSNI-Net under various intensities of s&p noise. Notably, at a noise intensity level of 5%, DSNI-Net improves forensic performance by 37%. Generalization experiments further confirm that DSNI-Net is robust against other types of noise and JPEG compression.}, archive = {J_ACCESS}, author = {Wuyang Shan and Yijie Xie and Pengbo Wang and Deng Zou and Junying Qiu and Jun Li}, doi = {10.1109/ACCESS.2025.3545830}, journal = {IEEE Access}, pages = {38325-38341}, shortjournal = {IEEE Access}, title = {DSNI-net: Forensic network for detecting splicing in salt-and-pepper noisy images}, volume = {13}, year = {2025}, }
• (2025). Vision-based UAV localization on various viewpoints. ACCESS, 13, 38317-38324. (www)

  @article{ , abstract = {Given that the Global Positioning System (GPS) may not always be precise or available, it would be advantageous for drone positioning to utilize a vision-based approach. It analyzes geometric features to determine the drone’s position and identify its flight path. In the literature, an existing vision-based study is proposed to position the drone by a shallow Convolutional Neural Network (CNN). However, they only consider a constant viewpoint (towards the north). This is not practical when implemented in the real world. Hence, we start to examine the impact of viewpoint variations. While the drone’s cameras capture images, these changing viewpoints will affect the accuracy and stability of models. In this paper, MobileNetV2 is the base for creating a classification mechanism for accurate and stable position estimation. Our findings illustrate the importance and relationship of various training data among the different performances of the model. The model achieves only 17% accuracy without applying rotational augmentation in the most practical scenario. However, the accuracy significantly improves by incorporating rotational augmentation with 15-degree increments to 95%.}, archive = {J_ACCESS}, author = {Yee-Ming Ooi and Che-Cheng Chang and Yu-Min Su and Chiao-Ming Chang}, doi = {10.1109/ACCESS.2025.3545581}, journal = {IEEE Access}, pages = {38317-38324}, shortjournal = {IEEE Access}, title = {Vision-based UAV localization on various viewpoints}, volume = {13}, year = {2025}, }
• (2025). Joint learning of underwater terrain matching and suitability analysis via SO(2) elevation transformer. ACCESS, 13, 38301-38316. (www)

  @article{ , abstract = {Underwater navigation reliability heavily depends on accurate terrain evaluation. While conventional approaches treat terrain matching and suitability assessment as separate processes, this separation often leads to inconsistent and imprecise terrain evaluations. To address these limitations, we propose an integrated framework that employs a unified terrain map encoder to simultaneously handle both matching and suitability analysis tasks. At the core of our framework lies the SO(2) Elevation Embedding Transformer (SEET), which combines rotation-equivariant CNN with elevation embeddings. The SEET encoder is pre-trained through self-supervised contrastive learning on underwater elevation data, eliminating the need for manual labeling. Our extensive experimental validation demonstrates the framework’s effectiveness, showing superior matching accuracy with minimal navigation deviation. The distinct performance gap observed between suitable and unsuitable regions further validates the effectiveness of our suitability analysis approach.}, archive = {J_ACCESS}, author = {Yan Han and Gang Fan and Qichen Yan and Pengyun Chen and Xiaolong Yan and Tinghai Yan and Guoguang Chen}, doi = {10.1109/ACCESS.2025.3545563}, journal = {IEEE Access}, pages = {38301-38316}, shortjournal = {IEEE Access}, title = {Joint learning of underwater terrain matching and suitability analysis via SO(2) elevation transformer}, volume = {13}, year = {2025}, }
• (2025). Remote sensing surveillance using multilevel feature fusion and deep neural network. ACCESS, 13, 38282-38300. (www)

  @article{ , abstract = {Human action recognition from aerial imagery poses significant challenges due to the dynamic nature of the scenes and the complexity of human movements. In this paper, we present an enhanced system that combines YOLO for human detection with a complete multilevel feature fusion approach to improve recognition of human actions in drone-captured photos. Our system presents a reliable drone-based human action system through the integration of state-of-the-art methods for multilevel feature extraction and object detection. Initially, frames are extracted individually from drone footage sequences. Preprocessing techniques, which include Gaussian blur, grayscale conversion, and background removal, are used for every frame to improve image quality and feature reliability. For object detection, we effectively locate and recognize human subjects in these aerial frames using YOLO approach. Afterward, the framework extracts 14 body landmarks that represent the shape of the human body by keypoint extraction. Four significant features are employed to capture the complexity of human movement effectively: the incorporation of 3D point cloud data adds depth to the image and makes it feasible to construct a more detailed three-dimensional representation; measuring the angles between keypoints provides significant details on joint orientations which are essential for posture analysis; and geodesic distance measure the shortest paths along the surface of the body to provide useful insight into the spatial relationships between keypoints. The extracted features are optimized by using quadratic discriminant analysis. In the end, a deep neural network was trained to perform the action classification. Three benchmark datasets, the UAV Gesture, UAV Human, and UCF-ARG datasets, were used for our experiments and system testing. Our model achieved corresponding action recognition accuracy values of 90.15%, 72.37%, and 76.50% on each of these datasets.}, archive = {J_ACCESS}, author = {Laiba Zahoor and Haifa F. Alhasson and Mohammed Alnusayri and Mohammed Alatiyyah and Dina Abdulaziz Alhammadi and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3542435}, journal = {IEEE Access}, pages = {38282-38300}, shortjournal = {IEEE Access}, title = {Remote sensing surveillance using multilevel feature fusion and deep neural network}, volume = {13}, year = {2025}, }
• (2025). Polynomial phase signal denoising via sparse representations over a SMAF-based dictionary learning algorithm. ACCESS, 13, 38268-38281. (www)

  @article{ , abstract = {In this paper, we address the problem of denoising polynomial phase signals (PPS) by removing additive white Gaussian noise. Our approach is based on sparse representation using a trained dictionary, which is obtained through the secondary moving average filtering (SMAF) dictionary learning algorithm. The proposed algorithm consists of two main steps: first, we utilize K-SVD or other dictionary learning algorithms to obtain the initial trained dictionary; second, we apply the SMAF method to modify the dictionary atoms, enabling them to behave more smoothly and effectively reducing the noise present in the atomic structures. Consequently, the signal-to-noise ratio (SNR) of the reconstructed signal using sparse representation over the refined dictionary is significantly improved compared to K-SVD and RLS-DLA. To achieve optimal denoising effectiveness, we first estimate the SNR of the PPS using our proposed SNR estimation algorithm. Based on the estimated SNR, we then determine the number of samples for the moving average filter impulse response.}, archive = {J_ACCESS}, author = {Guojian Ou and Chenping Zeng and Jiaqiang Dong and Die Han}, doi = {10.1109/ACCESS.2025.3545558}, journal = {IEEE Access}, pages = {38268-38281}, shortjournal = {IEEE Access}, title = {Polynomial phase signal denoising via sparse representations over a SMAF-based dictionary learning algorithm}, volume = {13}, year = {2025}, }
• (2025). On the synergy of IoMT devices and ceiling-mounted systems for advanced medical data analytics. ACCESS, 13, 38255-38267. (www)

  @article{ , abstract = {The article explores a novel framework that combines Ceiling-Mounted Systems (CMS) and Internet of Medical Things (IoMT) devices to address critical challenges in healthcare data management. By leveraging the capabilities of IoMT devices for real-time data collection, the proposed CMS’s robust sensing, storage, and processing features support efficient resource allocation in hospital environments. The proposed approach achieves approximately 35% reduction in latency, a 25% improvement in energy efficiency, and a 40% decrease in Age of Information (AoI) compared to traditional frameworks. The multi-objective optimization problem minimizes energy consumption and latency while ensuring fairness and timely data collection, which is particularly critical in patient monitoring and time-sensitive diagnostics scenarios. Deep Reinforcement Learning (DRL) methods solve the resource allocation challenge under realistic constraints. Specifically, Twin-Delayed Deep Deterministic Policy Gradients (TD3) and Soft Actor-Critic (SAC) algorithms are adopted to optimize task scheduling and system decisions in dynamic, resource-constrained settings. Simulation results demonstrate that SAC achieves approximately 20% faster convergence and 15% better adaptability in dynamic hospital environments compared to TD3, making it more suitable for real-time healthcare applications. These findings underscore the benefits of integrating IoMT devices with the proposed CMS infrastructures to meet healthcare requirements, such as robust security, high reliability, and real-time responsiveness.}, archive = {J_ACCESS}, author = {Andreas Andreou and Constandinos X. Mavromoustakis and Evangelos K. Markakis and Athina Bourdena and George Mastorakis}, doi = {10.1109/ACCESS.2025.3546495}, journal = {IEEE Access}, pages = {38255-38267}, shortjournal = {IEEE Access}, title = {On the synergy of IoMT devices and ceiling-mounted systems for advanced medical data analytics}, volume = {13}, year = {2025}, }
• (2025). Optimal DC-link voltage mapping for SiC-based EV drives: Considering the impact of a synchronous boost converter. ACCESS, 13, 38239-38254. (www)

  @article{ , abstract = {This paper seeks to identify an optimal DC-link voltage across the complete range of drive operating conditions utilizing a loss minimization approach by integrating a DC-DC converter into the powertrain, thereby enhancing powertrain efficiency. This involves a comprehensive analysis of power losses in a connected silicon carbide (SiC)-based converter-inverter system, incorporating temperature variations, alongside a finite element method (FEM) analysis of losses in an Interior Permanent Magnet (IPM) synchronous machine, accounting for variable DC-link voltages. The results are then compared with those of traditional silicon-insulated-gate bipolar transistor (Si-IGBT) systems. The findings reveal that including a DC-DC converter into a powertrain, and adjusting the optimum DC-link voltages, is advantageous, particularly for low battery terminal voltages. Consequently, the powertrain system, incorporating a DC-DC boost converter, exhibits lower total loss values, with a difference of up to 5 kW loss difference for high-speed, low-torque regions compared to the case when not incorporating the DC-DC converter. Furthermore, applying the proposed optimal DC-link profile in the Worldwide harmonized Light vehicle Test Cycle (WLTC) leads to a reduction of up to 16% in accumulated energy losses in the SiC driveline compared to its IGBT counterpart. In addition, applying the optimal DC-link profile reduces energy losses by 58% in the SiC-based system and by 54% in the IGBT-based system, compared to operating with a fixed 300 V DC-bus voltage.}, archive = {J_ACCESS}, author = {Sepideh Amirpour and Torbjörn Thiringer and Sima Soltanipour and Yu Xu}, doi = {10.1109/ACCESS.2025.3546025}, journal = {IEEE Access}, pages = {38239-38254}, shortjournal = {IEEE Access}, title = {Optimal DC-link voltage mapping for SiC-based EV drives: Considering the impact of a synchronous boost converter}, volume = {13}, year = {2025}, }
• (2025). A survey on real-time object detection on FPGAs. ACCESS, 13, 38195-38238. (www)

  @article{ , abstract = {This paper focuses on real-time object detection systems, analyzing existing Field-Programmable Gate Arrays (FPGAs) implementations that aim to achieve the best efficiency, performance, and accuracy at the same time. These three metrics are typically crucial for domains such as autonomous driving, and robotics. Fortunately, recent advancements in object detection models, particularly based on Convolutional Neural Networks (CNNs), have significantly improved object detection accuracy and speed. When these models are combined with FPGAs, it is possible to achieve even more power efficiency and more easily satisfy real-time constraints. FPGAs can deliver low latency and high throughput by leveraging true parallelism making them suitable platforms for developing real-time object detection systems. This paper reviews existing literature on FPGA-based real-time object detection, discussing commonly used algorithms, acceleration techniques, and optimization strategies. Evaluation metrics and typical datasets for assessing real-time systems are also examined. We have compared the performance of these implementations by using pixel throughput as a fair metric across different systems while processing video streams or images. Insights into state-of-the-art works, comparative analysis, challenges, and future research directions are provided to guide researchers interested in leveraging FPGA devices for real-time object detection applications.}, archive = {J_ACCESS}, author = {Seyed Hani Hozhabr and Roberto Giorgi}, doi = {10.1109/ACCESS.2025.3544515}, journal = {IEEE Access}, pages = {38195-38238}, shortjournal = {IEEE Access}, title = {A survey on real-time object detection on FPGAs}, volume = {13}, year = {2025}, }
• (2025). Lightweight deep learning for sEMG-based fingers position classification and embedded system deployment. ACCESS, 13, 38181-38194. (www)

  @article{ , abstract = {This paper presents a lightweight deep learning approach for classifying and tracking finger positions based on surface electromyography (sEMG) signals, designed with a perspective of its implementation in embedded systems. Unlike traditional studies that focus on static hand gestures, this research highlights the continuous tracking of finger positions during dynamic hand movements, such as the transition between open and closed states. A 1D Convolutional Neural Network was developed and validated using experimental data, achieving a classification accuracy of 97% in controlled scenarios. The architecture of the model balances computational efficiency and classification performance, making it deployable on resource-constrained embedded technology. This feature highlights its potential for real-time applications in prosthetics, robotics, and human-computer interaction. Although further optimization is needed for better generalization to unseen data, this study emphasizes the significance of developing deployable algorithms that excel beyond simulation environments focusing on enhancing model robustness and validating its real-time performance through hardware-based implementations. The findings indicate notable advancements in connecting sophisticated machine-learning techniques with effective embedded solutions for complex, dynamic tasks.}, archive = {J_ACCESS}, author = {Victor H. Benitez and Jesus Pacheco and Guillermo Cuamea-Cruz}, doi = {10.1109/ACCESS.2025.3545827}, journal = {IEEE Access}, pages = {38181-38194}, shortjournal = {IEEE Access}, title = {Lightweight deep learning for sEMG-based fingers position classification and embedded system deployment}, volume = {13}, year = {2025}, }
• (2025). One-hot multi-level leaky integrate-and-fire spiking neural networks for enhanced accuracy-latency tradeoff. ACCESS, 13, 38163-38180. (www)

  @article{ , abstract = {Spiking neural networks (SNNs) hold significant promise as energy-efficient alternatives to conventional artificial neural networks (ANNs). However, SNNs require computations across multiple timesteps, resulting in increased latency, heightened energy consumption, and additional memory access overhead. Techniques to reduce SNN latency down to a unit timestep have emerged to realize true superior energy efficiency over ANNs. Nonetheless, this latency reduction often comes at the expense of noticeable accuracy degradation. Therefore, achieving an optimal balance in the tradeoff between accuracy and energy consumption by adjusting the latency of multiple timesteps remains a significant challenge. This work leverages an additional dimension to enhance the accuracy-energy tradeoff space using a novel one-hot multi-level leaky integrate-and-fire (M-LIF) neuron model. The proposed one-hot M-LIF model represents the inputs and outputs of hidden layers as a set of one-hot binary-weighted spike lanes to find better tradeoff points while still being able to model conventional SNNs. For image classification on static datasets, we demonstrate one-hot M-LIF SNNs outperform iso-architecture conventional LIF SNNs in terms of accuracy (2% higher than VGG16 SNN on ImageNet) while still being energy-efficient ( $20\times $ lower energy than VGG16 ANN on ImageNet). For dynamic vision datasets, we demonstrate the ability of M-LIF SNNs to reduce latency by $3\times $ compared to conventional LIF SNNs while limiting accuracy degradation (<1%).}, archive = {J_ACCESS}, author = {Pierre Abillama and Changwoo Lee and Andrea Bejarano-Carbo and Qirui Zhang and Dennis Sylvester and David Blaauw and Hun-Seok Kim}, doi = {10.1109/ACCESS.2025.3546508}, journal = {IEEE Access}, pages = {38163-38180}, shortjournal = {IEEE Access}, title = {One-hot multi-level leaky integrate-and-fire spiking neural networks for enhanced accuracy-latency tradeoff}, volume = {13}, year = {2025}, }
• (2025). Integrate the temporal scheme for unsupervised video summarization via attention mechanism. ACCESS, 13, 38147-38162. (www)

  @article{ , abstract = {In this work, we present a novel unsupervised scheme named SegSum, designed for video summarization through the creation of video skims. Most contemporary methods involve training a summarizer to assign importance scores to individual video frames, which are then aggregated to calculate scores for video segments produced by methods like Kernel Temporal Segmentation(KTS). Nonetheless, this methodology restricts the summarizer’s access to vital information essential for generating the summary—specifically, spatial-temporal relationships in video segments. Our proposed method incorporates the segment information obtained from KTS into the learning process of the summarizer based on concentrated attention architecture in deep learning models. In our experiment, we extensively evaluated our method across several datasets and many architectural frameworks for unsupervised video summarization. By incorporating a concentrated attention module, we managed to secure top F1-scores on established benchmarks, recording 54% on the SumMe dataset and 62% on the TVSum dataset. Furthermore, even with a straightforward Regressor network, SegSum demonstrates competitive performance, producing summaries that closely align with human annotations.}, archive = {J_ACCESS}, author = {Vo Quoc Bang and Vo Hoai Viet}, doi = {10.1109/ACCESS.2025.3546149}, journal = {IEEE Access}, pages = {38147-38162}, shortjournal = {IEEE Access}, title = {Integrate the temporal scheme for unsupervised video summarization via attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Sports event video sequence action recognition based on LDCT networks and MTSM networks. ACCESS, 13, 38133-38146. (www)

  @article{ , abstract = {Due to the complexity of sports events scenes and the variability of dynamic features, the overall effect of sports video action recognition is poor. To solve this problem, the study proposes a sports video action recognition technique based on local difference feature and channel temporal. The study uses local frame extraction mode to extract spatial features and channel temporal excitation mode to extract local motion features. Considering the difficulty of hands-on recognition in complex race scenes, a motion recognition model combining 3D convolutional networks is introduced, and the extraction of motion features is realized by motion enhancement module and spatiotemporal feature aggregation module. In the LDCT network performance test, the research model outperformed similar models with 98.2% accuracy in Top-5 and 0.315 training loss in the home-made dataset. In addition, in the more complex competition scenario test, the average recognition accuracy of the research model in fencing, volleyball, equestrian, and basketball was 95.6%, which outperformed similar models. In the comparison of model recognition time consumed, the average time consumed by the research model was 0.34sm, which resulted in the shortest time consumed for sport recognition. Finally, in the comprehensive performance comparison, the research model outperformed in terms of accuracy and resource overhead. It indicated that the research technique satisfied the requirements of video action recognition for competitions. The research will provide technical support for the recognition of sports and the improvement of related techniques.}, archive = {J_ACCESS}, author = {Chunhua Xue and Jianjian Lin}, doi = {10.1109/ACCESS.2025.3546266}, journal = {IEEE Access}, pages = {38133-38146}, shortjournal = {IEEE Access}, title = {Sports event video sequence action recognition based on LDCT networks and MTSM networks}, volume = {13}, year = {2025}, }
• (2025). A comprehensive system to support decision making in highly complex project portfolio situations. ACCESS, 13, 38115-38132. (www)

  @article{ , abstract = {This paper describes a new approach in project portfolio selection (PPS) problems, emphasizing the need to overcome traditional deficiencies with respect to multicriteria decision-making and multiobjective optimization. While existing methods typically allow the solving of partial aspects of the PPS problem, the proposed approach seeks to provide a holistic framework dealing with aspects like interdependence between projects, interaction among criteria, the incorporation of both cardinal and ordinal information, and a hierarchical multiobjective optimization. Unlike approaches that optimize portfolios neglecting superiority of some projects, or those that only assess individual projects without considering overall portfolio performance, the proposal allows for a compromise between both objectives. A case study is given, proving the application of the proposal for developing well-balanced portfolios aligned with strategic organizational goals and stakeholder preferences. The results point to significant improvements in the efficiency and effectiveness of decision-making, especially in complex project environments. This research contributes not only to the advancement of the theoretical framework of PPS but also to practical implications for portfolio management in a wide variety of organizational contexts.}, archive = {J_ACCESS}, author = {Efrain Solares and Eduardo Fernández and Carlos A. Coello Coello and Xochitl Segura Lozano and Reimundo Moreno-Cepeda and Raymundo Diaz}, doi = {10.1109/ACCESS.2025.3545302}, journal = {IEEE Access}, pages = {38115-38132}, shortjournal = {IEEE Access}, title = {A comprehensive system to support decision making in highly complex project portfolio situations}, volume = {13}, year = {2025}, }
• (2025). Design of an intermittent-computing-oriented nonvolatile register with a switching-probability-aware store-and-verify scheme. ACCESS, 13, 38104-38114. (www)

  @article{ , abstract = {This paper describes the configuration of a magnetic tunnel junction (MTJ)-based nonvolatile register designed for the greedy utilization of supplied energy in intermittent computing. The MTJ device is a type of spintronic device that exhibits two resistance states in a nonvolatile manner, and switching between these states is probabilistic. To maintain data consistency in intermittent computing, achieving a high probability of success for MTJ device switching is essential. Conventional MTJ-based nonvolatile registers guarantee a specific success rate through data backup processes with large currents flowing for long periods. In contrast, the proposed register performs data store operations with short pulses repeated as needed with verification of information stored in the nonvolatile memory section, enabling energy-efficient data backup while ensuring the equivalent success rate. Performance evaluation using a 55 nm CMOS/MTJ-hybrid process technology demonstrates that the proposed nonvolatile register reduces the energy required for data backup by 65% compared to conventional configurations. Additionally, it has been shown that the proposed method can improve the active time ratio of intermittent computing using an IoT system based on a RISC-V CPU by approximately 70%.}, archive = {J_ACCESS}, author = {Masanori Natsui and Takahiro Hanyu}, doi = {10.1109/ACCESS.2025.3546590}, journal = {IEEE Access}, pages = {38104-38114}, shortjournal = {IEEE Access}, title = {Design of an intermittent-computing-oriented nonvolatile register with a switching-probability-aware store-and-verify scheme}, volume = {13}, year = {2025}, }
• (2025). MDAC-net: A multi-scale dense attention cascade network for retinal vessel segmentation from fundus images. ACCESS, 13, 38091-38103. (www)

  @article{ , abstract = {The precise segmentation of retinal vessel can facilitate more accurate evaluation of the health of subject by medical professionals. The raid advancement of deep learning technology has led to the remarkable performance of U-Net and its variant networks in retinal vessel segmentation tasks. However, the existing algorithms still face challenges in obtaining high-precision segmentation results, manifested in the complex and variable structure of the retinal vessel itself; the existing U-Net series networks have problems such as a lack of linguistic information, limited receptive field and insufficient learning capacity for small vessel. We propose a novel retinal vessel segmentation network, named multi-scale dense attention cascade network (MDAC-Net), which incorporates multi-scale feature fusion and co-optimization of multiple attention mechanisms to deal with the above challenges. 1) We propose a new inter-stage attention module (NIAM) to realize the information exchange between the subnetworks, which promote the fine particles segment subnetwork (FPSS) to pay more attention to small vessel region. 2) Considering the context information and vessel details, we adopt dilated convolution module (DCM) to replace the traditional convolution operation. 3) We integrate a multi-scale convolutional attention module (MBAM) in the subnetwork, which suppress noise and irrelevant information. 4) We use Grad-CAM to visualize the segmentation results, which can guide doctors to analyze the pathogenic factors of different subjects more accurately. The experimental results demonstrate that MDAC-Net, while remaining relatively lightweight, achieves accuracy of more than 97% on three public datasets, especially remarkable segmentation effects for small vessel.}, archive = {J_ACCESS}, author = {Jia Li and Li Huang and Hao Ouyang and Dingjian Cai and Shuoqi Zheng and Min Yang}, doi = {10.1109/ACCESS.2025.3543046}, journal = {IEEE Access}, pages = {38091-38103}, shortjournal = {IEEE Access}, title = {MDAC-net: A multi-scale dense attention cascade network for retinal vessel segmentation from fundus images}, volume = {13}, year = {2025}, }
• (2025). MFF: A deep learning model for multi-modal image fusion based on multiple filters. ACCESS, 13, 38076-38090. (www)

  @article{ , abstract = {Multi-modal image fusion mainly refers to the feature fusion of two or more different images taken from the same perspective range to increase the amount of information contained in an image. This study proposes a multi-modal image fusion deep network called the MFF network. Compared with traditional image fusion models, the MFF network decomposes high-frequency features more finely. In contrast to popular transformer networks, the MFF network utilizes multiple filter networks for the corresponding high and low-frequency feature extraction, thereby improving the model training and inference time. First, GaborNet filtering modules were used by the MFF network to extract high-frequency texture features and invertible neural networks (INN) modules are employed for extracting high-frequency edge features. These two sets of features constitute the high-frequency characteristics of an image. The LEF module is utilized as a low-pass filter to acquire the low-frequency characteristics of an image. The method involving low-frequency feature correlation and high-frequency feature non-correlation was used for image training and fusion purposes. By systematically comparing the TNO, MSRS, and RoadScene datasets with other state-of-the-art image fusion models, the experimental results indicate that the MFF model achieves superior performance in visible-infrared image fusion. Furthermore, evaluations on the LLVIP dataset confirm the model’s effectiveness in downstream machine vision tasks. Additionally, comparisons using the MRI_CT, MRI_PET, and MRI_SPECT datasets demonstrate that the MFF model exhibits exceptional performance in medical image fusion.}, archive = {J_ACCESS}, author = {Yuequn Wang and Zhengwei Li and Jianli Wang and Leqiang Yang and Bo Dong and Hanfu Zhang and Jie Liu}, doi = {10.1109/ACCESS.2025.3540007}, journal = {IEEE Access}, pages = {38076-38090}, shortjournal = {IEEE Access}, title = {MFF: A deep learning model for multi-modal image fusion based on multiple filters}, volume = {13}, year = {2025}, }
• (2025). Coloring dynamic graphs with a similarity and pool-based evolutionary algorithm. ACCESS, 13, 38054-38075. (www)

  @article{ , abstract = {The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. Evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based Evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. The algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.}, archive = {J_ACCESS}, author = {Gizem Sungu Terci and Betül Boz}, doi = {10.1109/ACCESS.2025.3546108}, journal = {IEEE Access}, pages = {38054-38075}, shortjournal = {IEEE Access}, title = {Coloring dynamic graphs with a similarity and pool-based evolutionary algorithm}, volume = {13}, year = {2025}, }
• (2025). Performance analysis: Discovering semi-markov models from event logs. ACCESS, 13, 38035-38053. (www)

  @article{ , abstract = {Process mining is a well-established discipline of data analysis focused on the discovery of process models from information systems’ event logs. Recently, an emerging subarea of process mining, known as stochastic process discovery, has started to evolve. Stochastic process discovery considers frequencies of events in the event data and allows for a more comprehensive analysis. In particular, when the durations of activities are presented in the event log, performance characteristics of the discovered stochastic models can be analyzed, e.g., the overall process execution time can be estimated. Existing performance analysis techniques usually discover stochastic process models from event data, and then simulate these models to evaluate their execution times. These methods rely on empirical approaches. This paper proposes analytical techniques for performance analysis that allow for the derivation of statistical characteristics of the overall processes’ execution times in the presence of arbitrary time distributions of events modeled by semi-Markov processes. The proposed methods include express analysis, focused on the mean execution time estimation, and full analysis techniques that build probability density functions (PDFs) of process execution times in both continuous and discrete forms. These methods are implemented and tested on real-world event data, demonstrating their potential for what-if analysis by providing solutions without resorting to simulation. Specifically, we demonstrated that the discrete approach is more time-efficient for small duration support sizes compared to the simulation technique. Furthermore, we showed that the continuous approach, with PDFs represented as Mixtures of Gaussian Models (GMMs), facilitates the discovery of more compact and interpretable models.}, archive = {J_ACCESS}, author = {Anna Kalenkova and Lewis Mitchell and Matthew Roughan}, doi = {10.1109/ACCESS.2025.3546033}, journal = {IEEE Access}, pages = {38035-38053}, shortjournal = {IEEE Access}, title = {Performance analysis: Discovering semi-markov models from event logs}, volume = {13}, year = {2025}, }
• (2025). Morphological and topological analysis of the human renal arterial tree using μ-CT scans of corrosion cast specimens. ACCESS, 13, 38014-38034. (www)

  @article{ , abstract = {This study addresses the analysis of morphological and topological relationships within the human renal arterial tree, which have significant implications for improving clinical practices, particularly in minimally invasive renal surgeries, where detailed information on vascular supply is necessary to preserve healthy parenchyma. Recent advances in imaging technologies, coupled with increased computational power, provide unprecedented detail in anatomical assessment and enable a comprehensive analysis. The presented investigation employed $33~\mu $ -CT scans of corrosion cast speciments of renal arterial trees, which underwent systematic processing, including binary reconstruction, artifact removal, cavity filling, skeletonization, and conversion into directed graph representations. This approach facilitated the computation of geometrical parameters and enabled morphological and topological analyses. The results confirm the feasibility of using $\mu $ -CT imaging to investigate both global population-level trends and intra-renal tree variations. Key findings reveal no strong individual predisposition for specific branching degrees, with trifurcations and quadfurcations being notable exceptions. Additionally, a significant inverse correlation between vessel length and diameter across generations was observed. The analysis of subtrees indicated which vessels supply specific renal segments, offering valuable information for preoperative planning, particularly in tumor surgeries. Key insights into vessel branching patterns highlight their relevance for renal surgeries. These findings have direct applications in enhancing algorithms for the reconstruction, segmentation, and visualization of preoperative CT scans by leveraging insights from $\mu $ -CT based vascular analysis. Despite limitations such as datasize constraints and artifacts inherent to historical corrosion cast specimens, this study provides a proof-of-concept framework. The proposed methodology lays the foundation for large-scale investigations and the integration of $\mu $ -CT-derived vascular patterns into patient-specific preoperative models, ultimately improving surgical navigation and patients’ outcomes. The suite of algorithms developed by us is easily scalable as more data becomes available and can be adapted for other tree-like structures, further enhancing their applicability.}, archive = {J_ACCESS}, author = {Katarzyna Heryan and Janusz Skrzat}, doi = {10.1109/ACCESS.2025.3545807}, journal = {IEEE Access}, pages = {38014-38034}, shortjournal = {IEEE Access}, title = {Morphological and topological analysis of the human renal arterial tree using μ-CT scans of corrosion cast specimens}, volume = {13}, year = {2025}, }
• (2025). Research on collaborative optimization method of CCHP regional integrated energy system based on improved multivariate universe algorithm. ACCESS, 13, 38002-38013. (www)

  @article{ , abstract = {Aiming to address the prevailing issue where Cold Heat and Power (CCHP)-type integrated energy systems are primarily optimized for either economy or environmental friendliness, this paper conducts an exhaustive synergistic optimization analysis of the CCHP system, focusing on the dual objectives of economy and environmental friendliness. In this study, an optimization model of the CCHP system encompassing units such as gas turbines, gas boilers, and electric chillers is formulated. By integrating Pareto theory, an adaptive grid method, and a roulette strategy into the multiverse algorithm, an enhanced multi-objective multiverse algorithm is developed, which notably enhances the convergence accuracy, convergence speed, and stability of the solutions. A case study conducted in a representative northern region yielded the following experimental results: When compared with both the traditional particle swarm algorithm and an improved version of it, the CCHP-type integrated energy system optimized using the enhanced multi-objective multiverse algorithm reduced operating costs by 7.98% and carbon dioxide emissions by 12%, relative to the original system. This outcome underscores the remarkable capability of the improved multiverse algorithm in balancing the economic and environmental aspects of the system, thereby providing a robust foundation and valuable reference for the planning and design of subsequent energy supply systems. Through this synergistic optimization analysis, a win-win scenario is achieved, balancing both economic and environmental benefits, which lays a solid groundwork for the sustainable development of future energy systems.}, archive = {J_ACCESS}, author = {Dahai Xu and Changle Yu and Wenwen Li and Su Zhang and Zhengda Li and Zhihui Qu and Pengtao Li and Xingfan Han}, doi = {10.1109/ACCESS.2024.3469229}, journal = {IEEE Access}, pages = {38002-38013}, shortjournal = {IEEE Access}, title = {Research on collaborative optimization method of CCHP regional integrated energy system based on improved multivariate universe algorithm}, volume = {13}, year = {2025}, }
• (2025). Ensemble learning for precise state-of-charge estimation in electric vehicles lithium-ion batteries considering uncertainty. ACCESS, 13, 37990-38001. (www)

  @article{ , abstract = {Accurate state-of-charge (SoC) estimation is crucial for enhancing the performance, longevity, safety, and reliability of lithium-ion batteries (LiBs) in electric vehicles (EVs). This study presents a comprehensive machine learning (ML)-based approach for SoC estimation of EV LiBs, addressing the challenges of model reliability, uncertainty, and real-world data variability. To ensure the model’s robustness and generalizability, preprocessing techniques, including normalization and scaling, were employed alongside rigorous cross-validation methods. A well-structured ML pipeline was developed to integrate these processes, optimizing the entire model development cycle for efficiency and practical implementation. In the ML pipeline, we utilized Extra Trees Regressor (ETR) and Light Gradient Boosting Machine (LightGBM) and proposed an ensemble model, combining the strengths of ETR and LightGBM, namely ETR-GBM. We benchmarked the model’s performance against other ML models, such as CatBoost and Random Forest (RF). Under uncertain conditions, the proposed model emphasized its reliability and robustness, and its conclusions underscored the efficacy of the SoC estimation approach. The ETR-GBM consistently outperforms the individual models (ETR, LightGBM, XGBoost, CatBoost, Support Vector Regression (SVR), Random Forest (RF), and Bayesian) when noise is added to the training dataset. With a noise standard deviation of 0.1, the ETR-GBM demonstrated superior performance, achieving a Root Mean Square Error (RMSE) of 0.41%, surpassing the individual models, which exhibited RMSE values ranging from 0.85% to 0.91%.}, archive = {J_ACCESS}, author = {Aya Haraz and Khalid Abualsaud and Ahmed M. Massoud}, doi = {10.1109/ACCESS.2025.3539792}, journal = {IEEE Access}, pages = {37990-38001}, shortjournal = {IEEE Access}, title = {Ensemble learning for precise state-of-charge estimation in electric vehicles lithium-ion batteries considering uncertainty}, volume = {13}, year = {2025}, }
• (2025). Around2center: Self-supervised denoising from the around to the center using masks. ACCESS, 13, 37979-37989. (www)

  @article{ , abstract = {For image denoising tasks, it is often fraught with many challenges to obtain paired noisy images and clean images. While self-supervised learning promises to mitigate reliance on manual labels, generating meaningful supervisory signals remains a challenge. Existing masking strategies, while augmenting training data diversity, can alter noise structure and lead to edge effects. To address these challenges, we proposed an framework named Around2center for denoising using only a single noisy image. Firstly, to enhance local feature extraction and accentuate noise-signal boundaries, we divided the input image into multiple scales. Then, we employed a novel edge corner masker to strategically mask the image, enabling targeted processing of specific regions while mitigating identity mapping and preserving relevant information. A mask integrator innovatively fused the denoised image and masked plates, organically combining local and global information to effectively address denoising tasks in diverse scenarios. Notably, the generated integrated image served as a robust self-supervised label, eliminating the need for paired clean-noisy datasets. Finally, we provided a theoretical analysis of the loss function’s convergence properties, ensuring the framework’s soundness and efficacy. Extensive quantitative and qualitative evaluation results fully confirmed the superior denoising performance of our proposed method when dealing with both synthetic noise and real noise datasets. The code is available at https://github.com/eternity-city/Around2center.}, archive = {J_ACCESS}, author = {Yaru Zhang and Jiantao Liu and Guoqing Xu and Yuxi Han}, doi = {10.1109/ACCESS.2025.3545287}, journal = {IEEE Access}, pages = {37979-37989}, shortjournal = {IEEE Access}, title = {Around2center: Self-supervised denoising from the around to the center using masks}, volume = {13}, year = {2025}, }
• (2025). Sub-synchronous oscillation phenomenon analysis of grid-connected direct drive-doubly fed hybrid wind farms via VSC-HVdc system. ACCESS, 13, 37966-37978. (www)

  @article{ , abstract = {When integrating offshore wind farms, comprising either direct-drive or doubly fed induction generators, into onshore power grids via voltage source converter-based high-voltage direct current (VSC-HVDC) transmission, subsynchronous oscillation (SSO) may occur, compromising system stability. Existing research predominantly focuses on single-type wind turbine systems, with limited attention paid to hybrid systems composed of both turbine types. To address this gap, this paper first develops three mathematical models corresponding to direct-drive, doubly fed, and hybrid wind farms connected through VSC-HVDC. A method for establishing the equivalent small-signal model of a hybrid system, comprising multiple turbines of both types, is then proposed. Using modal analysis, small-signal models for the three systems are derived, and various oscillatory modes are identified via eigenvalue analysis. The results show that systems containing doubly fed turbines exhibit SSO, whereas systems with only direct-drive turbines experience oscillations in different frequency ranges. Participation factor analysis further reveals that the doubly fed turbine is the primary contributor to SSO in the hybrid wind farm. Subsequently, the impacts of key parameters, such as the wind turbine’s DC-link capacitance, AC-side reactance, the VSC-HVDC AC-side reactor, and the PI controller settings of the doubly fed turbine’s rotor-side converter, on SSO frequency and damping ratio are examined. These findings are expected to provide theoretical support for the development of hybrid direct-drive and doubly fed wind farms.}, archive = {J_ACCESS}, author = {Fusen Xiang and Si Liao and Hao Zhang and Longfu Luo}, doi = {10.1109/ACCESS.2025.3544685}, journal = {IEEE Access}, pages = {37966-37978}, shortjournal = {IEEE Access}, title = {Sub-synchronous oscillation phenomenon analysis of grid-connected direct drive-doubly fed hybrid wind farms via VSC-HVdc system}, volume = {13}, year = {2025}, }
• (2025). Enhancing voice phishing detection using multilingual back-translation and SMOTE: An empirical study. ACCESS, 13, 37946-37965. (www)

  @article{ , abstract = {With the widespread global trend of voice phishing or vishing attacks, the development of effective detection models using artificial intelligence (AI) has been hindered by the lack of high-quality and large volumes of data. This lack of data reflecting a real vishing scenario often leads to imbalanced datasets and biased detection models. Therefore, we present in this paper a data augmentation (DA) method for expanding the imbalanced Korean call content vishing (KorCCVi) dataset to address the existing data asymmetry problem and enhance the performance of Korean vishing detection. The proposed approach for DA involves using the back-translation (BT) method with three different intermediate languages: English, Chinese, and Japanese. The proposed method offers several advantages over the traditional synthetic minority oversampling technique (SMOTE), which is the main technique used to compare with our multilingual BT approach. Using these two DA techniques, several machine learning (ML) and deep learning (DL) models were trained on the original imbalanced dataset, the dataset balanced with SMOTE and its variants, and the dataset augmented with our method. We analyzed the impact of these DA methods on the performance of the models, demonstrated the benefits of each approach, and suggested the most suitable approach. The performance of the trained models was evaluated using the accuracy, precision, recall, and F1-score metrics. The experimental results demonstrated that the proposed multilingual BT method effectively expands the dataset while preserving its contextual and linguistic characteristics. The average performance of the models revealed that those trained on the augmented dataset outperformed the other models. They achieved F1-scores of 98.91% for the back-translated data, 98.14% for the original data, and 97.23% for SMOTE.}, archive = {J_ACCESS}, author = {Milandu Keith Moussavou Boussougou and Prince Hamandawana and Dong-Joo Park}, doi = {10.1109/ACCESS.2025.3545250}, journal = {IEEE Access}, pages = {37946-37965}, shortjournal = {IEEE Access}, title = {Enhancing voice phishing detection using multilingual back-translation and SMOTE: An empirical study}, volume = {13}, year = {2025}, }
• (2025). EfficientTransformer: A dynamic anomaly detection model for industrial control networks. ACCESS, 13, 37931-37945. (www)

  @article{ , abstract = {As industrial control network threats become increasingly complex, traditional intrusion detection systems (IDS) struggle to capture implicit relationships due to feature redundancy and intricate feature interactions. This leads to increased computational complexity and higher detection latency, making it difficult for industrial control systems(ICS) to meet the fast and accurate response requirements for security events. In this study, we propose a dynamic anomaly detection model for industrial control networks, named EfficientTransformer. The model uses a 1D Convolutional Neural Network (1D-CNN) to extract local features from the data, while a linear multi-head self-attention mechanism, replacing the traditional Transformer’s multi-head attention mechanism, provides global learning capabilities. This reduces computational complexity and enables efficient parallel learning. Additionally, to address the issue of class imbalance, the model incorporates a weighted cross-entropy loss function that assigns higher weights to the minority class of abnormal traffic, thereby improving the model’s anomaly detection ability. This innovation further mitigates issues of feature redundancy and complex feature interactions, enhancing the model’s dynamic processing capability and accuracy. The method was validated on the Oil and Gas Gathering and Transportation Full-Process Industrial Platform Attack-Defense Field, and the Catalytic Reforming Unit Process Platform at the Key Laboratory of Information Security for the Petrochemical Industry in Liaoning Province. Experimental results show that the proposed EfficientTransformer improves accuracy by 1.01% and 2.26% compared to the standard Transformer on the two datasets and significantly reduces testing time, demonstrating its applicability in the field of industrial information security.}, archive = {J_ACCESS}, author = {Jinyang Liu and Guogang Wang and Xuejun Zong and Bowei Ning and Kan He}, doi = {10.1109/ACCESS.2025.3545659}, journal = {IEEE Access}, pages = {37931-37945}, shortjournal = {IEEE Access}, title = {EfficientTransformer: A dynamic anomaly detection model for industrial control networks}, volume = {13}, year = {2025}, }
• (2025). Reed-muller code-based index codebook design for dual-mode OFDM-IM. ACCESS, 13, 37924-37930. (www)

  @article{ , abstract = {The orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a research topic attracting attention due to its ability to enhance efficiency by carrying additional bits. Recently, dual mode OFDM-IM (DM-OFDM-IM), which leverages inactive subcarriers to enhance spectral efficiency beyond OFDM-IM, has been extensively studied. Research efforts have focused on various aspects of DM-OFDM-IM, such as design of index codebooks, constellation design, symbol detection, and channel coding. However, the effect of index codebook design has not been widely considered in existing research. In this paper, we propose a construction method of index codebooks for DM-OFDM-IM based on the Reed-Muller codes. This approach aims to maximize the distances between index codewords so the index bit error rates are lowered and this also influences the demodulation of the symbol bits. It is shown via simulations that the proposed index codebook achieves lower overall bit error rates than conventional ones as well as index bit error rates for various modulation parameters.}, archive = {J_ACCESS}, author = {Seunggi Choi and Hosung Park}, doi = {10.1109/ACCESS.2025.3545181}, journal = {IEEE Access}, pages = {37924-37930}, shortjournal = {IEEE Access}, title = {Reed-muller code-based index codebook design for dual-mode OFDM-IM}, volume = {13}, year = {2025}, }
• (2025). Multi-modal fused-attention network for depression level recognition based on enhanced audiovisual cues. ACCESS, 13, 37913-37923. (www)

  @article{ , abstract = {In recent years, substantial research has focused on automated systems for assessing depression levels using different types of data, such as audio and visual inputs. However, signals recorded from individuals with depression can be influenced by external factors, such as the recording equipment and environment, making it essential to create a system that is resilient to these interferences to maintain accuracy. This study introduces a fused-attention model for evaluating depression severity using enhanced multi-modal data inputs. Applying several pre-trained advanced models, this article incorporates audiovisual sequences with augmentation. The framework includes two novel components, which we term as the FIE and VIE blocks, for extracting detailed facial and vocal features. The FIE block utilizes ResNet-18 to enhance the feature representation of video frames and integrates two types of attention mechanisms to capture spatial-temporal patterns. Meanwhile, the VIE block processes the Mel spectrogram of the audio signal, followed by an optimized Swin transformer block to extract auditory features. The model demonstrates strong performance, accurately identifying depression severity in 3-second audiovisual sequences with an 81.4% accuracy rate on the AVEC2014 dataset, and achieves a Kappa score of 0.731 and an MF1 index of 0.798. Furthermore, it shows high resilience to noise, underscoring its ability to mitigate the effects of recording equipment and environmental conditions in depression level estimation.}, archive = {J_ACCESS}, author = {Yihan Zhou and Xiaokang Yu and Zixi Huang and Feierdun Palati and Zeyu Zhao and Zihan He and Yuan Feng and Yuxi Luo}, doi = {10.1109/ACCESS.2025.3545587}, journal = {IEEE Access}, pages = {37913-37923}, shortjournal = {IEEE Access}, title = {Multi-modal fused-attention network for depression level recognition based on enhanced audiovisual cues}, volume = {13}, year = {2025}, }
• (2025). Nonlinear sampled-data control systems: Stability theorem via commutativity of linearization and discretization. ACCESS, 13, 37904-37912. (www)

  @article{ , abstract = {In this study, we propose a local stability theory for sampled-data systems employing Lyapunov’s indirect method. Our proposed method focuses on the relationship between exact discretization and linear approximation, demonstrating the feasibility of deriving an approximate model of the sampled-data system without directly solving the differential equations. We demonstrate that the local stability of the approximate model coincides with that of the sampled-data system. Consequently, by designing a controller that stabilizes this model, we can effectively stabilize the sampled-data system. The proposed theory can be utilized in the same manner as Lyapunov’s indirect method in continuous-time systems, making it an easily manageable approach.}, archive = {J_ACCESS}, author = {Tatsuya Oshima and Shin Kawai and Triet Nguyen-van}, doi = {10.1109/ACCESS.2025.3543653}, journal = {IEEE Access}, pages = {37904-37912}, shortjournal = {IEEE Access}, title = {Nonlinear sampled-data control systems: Stability theorem via commutativity of linearization and discretization}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-based voting for feature drift-aware intrusion detection: An incremental learning framework. ACCESS, 13, 37872-37903. (www)

  @article{ , abstract = {In Intrusion Detection Systems (IDS), stream data classification faces significant challenges due to concept drifts and feature evolution, where traditional methods struggle to maintain accuracy over time. One critical challenge is feature drift, which refers to changes in the relevance of features over time, directly impacting the model’s classification accuracy. This paper introduces the Incremental Feature Drift-Aware Genetic Programming Combiner (IFDA-GPC), which integrates a Voting Enhanced Deep Q-Network Multi-Agent Feature Selection (VE-DQN-MAFS) mechanism to address these challenges. The framework extends the existing IGPC architecture by incorporating dynamic feature selection and employing a multi-agent system with voting-based aggregation. This approach enhances feature selection decisions, especially in cases where agents provide conflicting assessments of feature relevance. By reconciling these variations, the framework ensures consistency and reliability in real-time classification tasks. The framework was evaluated using benchmark datasets, including KDD Cup ’99, CICIDS-2017, HIKARI-2021, and ISCX2012, under both evolving and non-evolving scenarios. Results demonstrate that GPC-KOS-DFS, derived from IFDA-GPC, significantly outperformed existing methods in accuracy, F1-score, recall, and AUC metrics. Notably, it achieved an accuracy of 93% on the CICIDS-2017 dataset, showcasing its effectiveness in handling feature drifts while maintaining high classification performance. These findings establish IFDA-GPC as a robust solution for managing evolving data streams in intrusion detection systems.}, archive = {J_ACCESS}, author = {Methaq A. Shyaa and Noor Farizah Ibrahim and Zurinahni Binti Zainol and Rosni Abdullah and Mohammed Anbar}, doi = {10.1109/ACCESS.2025.3544221}, journal = {IEEE Access}, pages = {37872-37903}, shortjournal = {IEEE Access}, title = {Reinforcement learning-based voting for feature drift-aware intrusion detection: An incremental learning framework}, volume = {13}, year = {2025}, }
• (2025). Semi circular SIW cavity coupled patch filtenna. ACCESS, 13, 37865-37871. (www)

  @article{ , abstract = {Here, a planar design of a filtering antenna is presented. The design mainly comprises a semi-circular substrate integrated waveguide (SIW) cavity as a driven element and a rectangular parasitic patch with loaded metallic vias. The coax feed is used to excite the SIW cavity and the cavity excites the rectangular patch by a coupling mechanism. The loaded four metallic vias help realize the gain characteristic’s sharp selectivity with radiation null at both edges of the operating band. The simulated investigation shows that broadband response is achieved by using such a topology. The measured results show a broad band response of 7.90 % impedance bandwidth with a flat realized gain performance of 7.34 dBi in the entire operating band. The proposed design offers attractive features such as small foot prints, high gain, small cross polarization, high selectivity, and a high front-to-back ratio.}, archive = {J_ACCESS}, author = {Ashish Chandelkar and Arvind Kumar and Prakash Jadhav and Ayman A. Althuwayb and Khaled Aliqab}, doi = {10.1109/ACCESS.2025.3543629}, journal = {IEEE Access}, pages = {37865-37871}, shortjournal = {IEEE Access}, title = {Semi circular SIW cavity coupled patch filtenna}, volume = {13}, year = {2025}, }
• (2025). Design of wearable intelligent pulse detection system based on flexible sensors. ACCESS, 13, 37850-37864. (www)

  @article{ , abstract = {The pulse serves as an early indicator of metabolic disorders and provides a measure of cardiovascular and pulmonary function. Monitoring pulse activity can help detect and prevent cardiovascular diseases and related conditions. Advances in sensor technology have enabled the integration of wearable devices into various research fields, offering an objective method for assessing both physical and mental health. This paper introduces an intelligent analysis system for wearable pulse detection using flexible piezoelectric sensors. The system’s hardware includes piezoelectric sensors, a signal amplifier, and a controller, which together ensure the reliable acquisition of radial pulse wave signals. To process the collected data, the system applies ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-WPD (Wavelet packet decomposition) and cubic spline interpolation algorithms. These techniques help eliminate noise interference and correct baseline drift. An adaptive threshold detection algorithm is also employed to extract key time-frequency features from the pulse signals. The experimental outcomes demonstrate that the system is capable of effectively monitoring the human radial pulse wave, achieving noise reduction and single-period segmentation of the acquired signals, extracting time-domain characteristics of the pulse, and analyzing the frequency distribution of the pulse. These results indicate that the system’s ability to conduct continuous monitoring and real-time pulse wave analysis may significantly contribute to the enhancement of physiological health monitoring and facilitate the early detection and prevention of diseases.}, archive = {J_ACCESS}, author = {Can Liang and Yifeng Xu and Wenhao You and Chengxu Huang and Qi Gui and Zhan Qin and Zongming Tan and Baohua Tan}, doi = {10.1109/ACCESS.2025.3532492}, journal = {IEEE Access}, pages = {37850-37864}, shortjournal = {IEEE Access}, title = {Design of wearable intelligent pulse detection system based on flexible sensors}, volume = {13}, year = {2025}, }
• (2025). Adaptive fixed-time command filtered tracking control for parametric nonlinear systems with unknown nonlinear control coefficient and unknown disturbances. ACCESS, 13, 37839-37849. (www)

  @article{ , abstract = {This paper addresses the issue of adaptive fixed-time tracking control (FTTC) for a category of parametric nonlinear systems characterized by unknown nonlinear control coefficient (UNCC) and unknown external disturbances (UEDs). To effectively handle unknown parameters and unknown disturbances, the adaptive parameter estimation method is introduced into the control law design. Meanwhile, some parameter adaptive laws are presented to estimate these unknown parameters. To eliminate repeated differentiation of virtual control laws, a class of nonlinear command filters is introduced. Furthermore, the filtering errors are compensated through the designed adaptive compensation control laws. Subsequently, the Nussbaum gain function technique is further used to deal with the UNCC in the system. Leveraging command-filtering backstepping control (BC) and fixed-time control method, an adaptive command filtered FTTC law is final presented to achieve the fixed time tracking. The theoretical analysis results indicate that the designed control law guarantees the convergence of tracking error to a small neighborhood of zero within a fixed time, while ensuring that all signals within the closed-loop system remain bounded. The proposed control law’s effectiveness is demonstrated through three simulation examples.}, archive = {J_ACCESS}, author = {Xiyu Zhang and Youjun Zhou and Chun Feng and Xiongfeng Deng}, doi = {10.1109/ACCESS.2025.3544655}, journal = {IEEE Access}, pages = {37839-37849}, shortjournal = {IEEE Access}, title = {Adaptive fixed-time command filtered tracking control for parametric nonlinear systems with unknown nonlinear control coefficient and unknown disturbances}, volume = {13}, year = {2025}, }
• (2025). Single-phase dual-pole line start permanent magnet synchronous motor. ACCESS, 13, 37828-37838. (www)

  @article{ , abstract = {Household applications such as refrigerator compressors, washing machines, evaporative air conditioners, etc. are often run by Induction Motors (IMs) at two operating speeds. The transition between the high-speed and low-speed is typically realized either with a variable frequency drive solution or a structural pole-changing solution. Although the latter solution (i.e., pole-changing) eliminates the cost and losses associated with the drive unit, it requires a larger slot area to accommodate two sets of stator windings. As a result, the efficiency of dual-pole IMs available in the market is less than their single-pole counterpart. Since the efficiency of permanent magnet synchronous motors is generally higher than that of IMs, and to improve the operational efficiency in household applications this paper aims to convert the two-speed single-phase IM with the two-speed single-phase Line Start Permanent Magnet (LSPM) synchronous motor. For this purpose, the first generation of dual-pole (4p/6p), single phase LSPM motor is designed and presented. The performance characteristic of the proposed design is analyzed in finite element (FE) simulation and validated via a series of experimental tests on the motor prototype.}, archive = {J_ACCESS}, author = {Fakhrossadat Ghoroghchian and Yi Du and Ebrahim Amiri and Zhuofan He and Aliakbar Damaki Aliabad and Akbar Rahideh}, doi = {10.1109/ACCESS.2025.3544933}, journal = {IEEE Access}, pages = {37828-37838}, shortjournal = {IEEE Access}, title = {Single-phase dual-pole line start permanent magnet synchronous motor}, volume = {13}, year = {2025}, }
• (2025). Power transformer vibration analysis model based on ensemble learning algorithm. ACCESS, 13, 37812-37827. (www)

  @article{ , abstract = {The inflow of DC magnetic flux into the core of power transformers under DC bias conditions leads to a significant increase in operational losses, noise levels, and vibration amplitudes, posing substantial threats to the stability and reliability of the power system. To comprehensively examine the effects of DC bias on the vibration characteristics of power transformers, a fully coupled 3D finite element model of the transformer’s electromagnetic-structural physical fields was developed. This model was validated through experimental comparison, confirming its accuracy in representing physical behaviors. In the second phase, a parametric finite element model, combined with the Sobol method, was utilized to design a set of samples for sensitivity analysis, enabling a quantitative study of the interactions among key characteristic parameters. Finally, a prediction model based on ensemble learning was proposed, using the Stacking method to integrate decision tree regression, support vector machine, and extreme learning machine algorithms. Three mainstream heuristic algorithms were applied for hyperparameter optimization, with the genetic algorithm (GA) proving to best meet the requirements, limiting the maximum prediction error to 1.2 dB. The model was validated on two in-service transformers operating under extreme conditions—DC bias and significant load fluctuations, the maximum error is less than 3 dB, demonstrating certain generalizability.}, archive = {J_ACCESS}, author = {Baidi Shi and Yongfeng Jiang and Wei Xiao and Jingyu Shang and Meng Li and Zixing Li and Xinfu Chen}, doi = {10.1109/ACCESS.2025.3542355}, journal = {IEEE Access}, pages = {37812-37827}, shortjournal = {IEEE Access}, title = {Power transformer vibration analysis model based on ensemble learning algorithm}, volume = {13}, year = {2025}, }
• (2025). The confluence of big data and cloud computing in SME adoption strategies. ACCESS, 13, 37789-37811. (www)

  @article{ , abstract = {This paper explores the adoption of cloud computing and big data technologies in small and medium enterprises (SMEs), focusing on synthesizing key factors influencing adoption. By conducting a comprehensive literature review and analyzing theoretical frameworks such as the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, this study proposes a novel conceptual model tailored to the unique socio-economic context of SMEs in the MENA region. The methodology includes an analysis of 30 research articles, highlighting enablers and barriers to adoption. Key contributions include a taxonomy of factors and actionable insights for policymakers and practitioners. This research addresses gaps in existing studies by providing a region-specific perspective on cloud computing and big data technologies adoption for SMEs. This strong tendency towards services provided by cloud computing is very clear, and to shape their future IT, it’s worth highlighting the existence of such technology. A synthesis of the literature on cloud computing adoption was presented in this paper classifying reviews based on factors that play an important role in taking the adoption decision incorporating the theoretical frameworks. Moreover, the relationship between big data and cloud computing has been explored. In addition to proposing a synthesized findings model, this paper includes various factors derived from the literature and related theoretical frameworks. In this paper we select research articles from broadly recognized research databases, as well as undertaking a comprehensive review of these selected articles focusing on the adoption of cloud computing and big data. It also includes a bibliography containing the most relevant publications in these domains, from 2011 to 2024. By examining 30 articles, the objectives of the paper are to analyze data derived from articles as well as to study the academic frameworks used in both developing and developed nations. Inferring this knowledge is important to the growing cloud computing market and accelerating its utilization among SMEs in the Middle East, and North Africa (MENA) region was the intent behind working on this paper.}, archive = {J_ACCESS}, author = {Mahmoud Ahmad El Skafi and Manal M. Yunis and Ahmed Zekri and Julie Bu Daher}, doi = {10.1109/ACCESS.2025.3543265}, journal = {IEEE Access}, pages = {37789-37811}, shortjournal = {IEEE Access}, title = {The confluence of big data and cloud computing in SME adoption strategies}, volume = {13}, year = {2025}, }
• (2025). Hardware trojan detection in open-source hardware designs using machine learning. ACCESS, 13, 37771-37788. (www)

  @article{ , abstract = {The globalization of the hardware supply chain reduces costs but increases security challenges with the potential insertion of hardware trojans by third parties. Traditional detection methods face scalability limitations by relying solely on simple examples (e.g., AES). Although open-source hardware promotes transparency, it does not guarantee security. In this research, Natural Language Processing (NLP) and Machine Learning (ML) techniques were applied to identify hardware trojans in complex open hardware designs (e.g., RISC-V, MIPS). Using data from existing benchmarks (ISCAS85-89, TrustHub) and synthetic data generated with Large Language Models (LLM), a dataset of 3,808 instances was used in this research. The approach using TF-IDF and Decision Tree (DT) achieved 97.26%, surpassing the state of the art. The use of LLMs with prompt optimization achieved a recall of 99%, minimizing false negatives. A novel framework integrating NLP, ML, and LLMs was developed to enhance the security of open-source hardware.}, archive = {J_ACCESS}, author = {Victor Takashi Hayashi and Wilson Vicente Ruggiero}, doi = {10.1109/ACCESS.2025.3546156}, journal = {IEEE Access}, pages = {37771-37788}, shortjournal = {IEEE Access}, title = {Hardware trojan detection in open-source hardware designs using machine learning}, volume = {13}, year = {2025}, }
• (2025). Harmonic current suppression for open-ending winding PMSM based on voltage injection. ACCESS, 13, 37762-37770. (www)

  @article{ , abstract = {Since the advantages of multilevel output, voltage utilization rate and enhanced tolerant capacity, the open-end winding permanent magnet synchronous motor (OEW-PMSM) with common DC bus has received increasing research focus. Due to the distortion of air-gap magnetic field and nonlinear factors, different order harmonic currents are introduced to distort the current. Besides, due to the existence of the common DC bus, the 3rd order harmonic voltage would distort the fundamental currents more obviously. In the OEW-PMSM drive system, the disturbances caused by the harmonic currents would reduce the output performance. In this paper, by establishing harmonic mathematical model, a harmonic voltage injection method is proposed. The variables of the corresponding d-q coordinate system are decoupled. And the injection voltages are calculated by the reference currents, which are generated by simple closed-loop controllers. Simulation and experimental results are expressed to verify the proposed method.}, archive = {J_ACCESS}, author = {Chunyang Jiang and Hongbin Zhang and Hongchen Liu and Dan Li and Desong Chen}, doi = {10.1109/ACCESS.2025.3545663}, journal = {IEEE Access}, pages = {37762-37770}, shortjournal = {IEEE Access}, title = {Harmonic current suppression for open-ending winding PMSM based on voltage injection}, volume = {13}, year = {2025}, }
• (2025). Enhancing crowd safety at hajj: Real-time detection of abnormal behavior using YOLOv9. ACCESS, 13, 37748-37761. (www)

  @article{ , abstract = {This study introduces an innovative approach for detecting abnormal behavior in real-time during the Hajj pilgrimage, utilizing the advanced capabilities of the YOLOv9 algorithm. Faced with the challenges of monitoring dense crowds, existing methodologies often fall short in accuracy, efficiency, and cost-effectiveness. Leveraging deep learning, this research accurately identifies features of abnormal behavior from the HAJJv2 dataset, specifically curated and annotated for the Hajj context. Optimization of the YOLOv9 algorithm for this scenario demonstrated superior performance metrics (mean Average Precision (mAP@0.5), Recall, and Precision) when compared with its predecessors (YOLOv4, YOLOv5, YOLOv7, and YOLOv8), highlighting significant improvements in detection accuracy and real-time applicability. A pivotal aspect of this research was the implementation of critical data pre-processing steps to enhance the object detection model’s performance. These included a few-shot data sampling framework and comprehensive data augmentation techniques (flipping, rotation, scaling, and blurring) to increase the training dataset’s variability and representativeness. Such pre-processing measures are instrumental in addressing occlusions and variations in viewpoints, thereby ensuring the model’s robust generalization capabilities in real-world scenarios. The application of these methodologies not only improves safety and security measures during the Hajj but also offers a versatile framework that can be adapted for abnormal behavior detection in various crowded environments. This paper contributes to the field of crowd management and safety by providing a scalable, accurate, and efficient solution for the real-time detection of potential risks, ensuring a safer environment for one of the largest human assemblies worldwide.}, archive = {J_ACCESS}, author = {Amani A. Alsabei and Tahani M. Alsubait and Hosam H. Alhakami}, doi = {10.1109/ACCESS.2025.3545256}, journal = {IEEE Access}, pages = {37748-37761}, shortjournal = {IEEE Access}, title = {Enhancing crowd safety at hajj: Real-time detection of abnormal behavior using YOLOv9}, volume = {13}, year = {2025}, }
• (2025). A novel decision making approach to analyze optimization of key factors in sports culture dissemination using T-spherical fuzzy information. ACCESS, 13, 37737-37747. (www)

  @article{ , abstract = {In this everchanging realm of sports culture, the authenticity, accessibility, and participation of the viewers are the key factors required to distribute information effectively. We can enhance and modify these factors by customizing the information to a specific range of people and scenarios (circumstances) and using them in an innovative framework that can use fuzzy logic and human opinion. This research introduced a sophisticated framework for developing sports culture that used advanced methods and algorithms to optimize and adapt the information. The developed framework can adjust communication in an authentic and captivating way by integrating humanized computing, and the presence of fuzzy logic also assists in dealing with complex and uncertain elements such as emotions and cultural sensitivity. Through this framework, our goal is to expand the impact and accessibility of sports culture and ensure that the information is authentic and accurate according to cultural values and integrates with the opinions of people around the world. The fuzzy logic is incorporated to enhance the accuracy of the information obtained from different sources. Different sports cultures are analyzed based on common key factors using advanced operational rules to compute fuzzy details.}, archive = {J_ACCESS}, author = {Peng Wang}, doi = {10.1109/ACCESS.2025.3543979}, journal = {IEEE Access}, pages = {37737-37747}, shortjournal = {IEEE Access}, title = {A novel decision making approach to analyze optimization of key factors in sports culture dissemination using T-spherical fuzzy information}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of multi-omics integration using graph neural networks for cancer classification. ACCESS, 13, 37724-37736. (www)

  @article{ , abstract = {Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection; hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. Experimental validation is performed with a dataset of 8,464 samples from 31 cancer types and normal tissue, comprising messenger-RNA, micro-RNA, and DNA methylation data. The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. The findings also suggest that correlation-based graph structures enhance the models’ ability to identify shared cancer-specific signatures across patients in comparison to protein-protein interaction networks-based graph structures. The code and data used in this study are available in the link (https://github.com/FadiAlharbi2024/Graph_Based_Architecture.git).}, archive = {J_ACCESS}, author = {Fadi Alharbi and Aleksandar Vakanski and Boyu Zhang and Murtada K. Elbashir and Mohanad Mohammed}, doi = {10.1109/ACCESS.2025.3540769}, journal = {IEEE Access}, pages = {37724-37736}, shortjournal = {IEEE Access}, title = {Comparative analysis of multi-omics integration using graph neural networks for cancer classification}, volume = {13}, year = {2025}, }
• (2025). A review of lightweight IoT authentication protocols from the perspective of security requirements, computation, communication, and hardware costs. ACCESS, 13, 37703-37723. (www)

  @article{ , abstract = {The Internet of Things (IoT) is a broad area that encompasses various use cases. Numerous use cases include various IoT devices, which necessitate various cryptographic methods to provide distinct security requirements. Due to their limited resources, most IoT devices require lightweight cryptographic implementations. The requirements of use cases must be revealed to use cryptography effectively. Therefore, classifying the security requirements for these scenarios is beneficial when blueprinting a new system design. Authentication is the first level of security and is crucial for ensuring the secure exchange of information. Designing an IoT authentication protocol requires addressing both security requirements and physical constraints. This paper reviews and classifies the studies in IoT authentication published in the last decade. The studies are categorized into two main categories: the authentication factors and commonly preferred cryptographic primitives. Authentication factors include passwords, RFID, smart cards, and OTPs, while cryptographic primitives focus on techniques such as ECC and PUFs. Following the study selection, this review evaluates the provided security requirements alongside computational, communication, and hardware costs, while also considering application domains. It concludes by highlighting the most common approaches and identifying existing gaps in the literature. Additionally, it examines the trade-offs between attack resistance and protocol costs.}, archive = {J_ACCESS}, author = {Isil Cetintav and Mehmet Tahir Sandikkaya}, doi = {10.1109/ACCESS.2025.3546147}, journal = {IEEE Access}, pages = {37703-37723}, shortjournal = {IEEE Access}, title = {A review of lightweight IoT authentication protocols from the perspective of security requirements, computation, communication, and hardware costs}, volume = {13}, year = {2025}, }
• (2025). AlexCapsNet: An integrated architecture for image classification with background noise. ACCESS, 13, 37690-37702. (www)

  @article{ , abstract = {Capsule networks (CapsNet) are a pioneering architecture that can encode image features into vectors rather than scalars, addressing the limitations of traditional Convolutional Neural Networks (CNNs). This process is achieved by the dynamic routing algorithm and can maintain the image’s spatial hierarchies. CapsNet has demonstrated the state-of-the-art performance in simple datasets such as MNIST, but its performance degrades in more complex datasets. To solve this problem, AlexCapsNet architecture is proposed in this paper, in which the classic classification model AlexNet is used as the feature extraction layer. This allows CapsNet to capture deeper and more semantic features. The comprehensive evaluation with four datasets shows AlexCapsNet has improved performance when compared with the baseline and other CapsNet variants. Besides, our experiments on seven datasets show the reconstruction module in the CapsNet degrades the performance in the datasets with background noise. AlexCapsNet removes the reconstruction module and therefore can adapt to these complicated datasets. Our code is available at https://github.com/BaoBao0926/AlexCapsNet.}, archive = {J_ACCESS}, author = {Muyi Bao and Nanlin Jin and Ming Xu}, doi = {10.1109/ACCESS.2025.3544661}, journal = {IEEE Access}, pages = {37690-37702}, shortjournal = {IEEE Access}, title = {AlexCapsNet: An integrated architecture for image classification with background noise}, volume = {13}, year = {2025}, }
• (2025). Analyzing the impact of transmission strategies on localization performance in wireless sensor networks. ACCESS, 13, 37673-37689. (www)

  @article{ , abstract = {Localization, essential in WSN applications, enables sensor nodes to determine their physical positions by referencing anchor nodes. We evaluate broadcast and unicast packet transmissions at the data-link layer for their impact on localization performance. Implemented on the Contiki-NG operating system, the study examines how anchor node density and antenna range affect localization success and the number of required anchor nodes between broadcast-based and unicast-based localization propagation in protocol stack. Results using Cooja simulator, demonstrate the trade-offs between unicast and broadcast transmission approaches, particularly in terms of network overhead, energy consumption and localization performance. For instance, with an antenna range of 20 meters, achieving a localization ratio of over 90% requires only 20% anchor density with broadcast transmission, whereas unicast transmission requires a 60% anchor density to achieve the same ratio. This demonstrates that broadcast localization can lead to approximately a 33% reduction in hardware costs, offering significant efficiency gains. These findings provide insights into optimal propagation techniques and highlight the advantages of broadcasting in resource-constrained WSN deployments.}, archive = {J_ACCESS}, author = {İsmail Hakki Turan and Doğan Yildiz and Sercan Demirci and Müge Sayit}, doi = {10.1109/ACCESS.2025.3545263}, journal = {IEEE Access}, pages = {37673-37689}, shortjournal = {IEEE Access}, title = {Analyzing the impact of transmission strategies on localization performance in wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). YOLO-RDM: Innovative detection methods for eggplants and stems in complex natural environment. ACCESS, 13, 37656-37672. (www)

  @article{ , abstract = {Accurate identification of eggplants and their stems in complex environments is crucial for the research on intelligent harvesting equipment for eggplants. Current object detection methods have limited and traditional approaches to eggplant research. To achieve rapid identification of eggplants and stems in complex environments and enhance overall accuracy, this study establishes a database for eggplants and stems and proposes an efficient detection model based on the YOLOv8n network. First, a lightweight Receptive-Field Attention Convolution (RFAConv) and Mixed Local Channel Attention (MLCA) attention mechanism are used to design the C2f_RM module, replacing the C2f module in YOLOv8n to create a lightweight yet effective model. Next, the DD-Head structure reparameterization detection head module is integrated, effectively merging deep and shallow information while retaining small-scale target feature information to improve detection accuracy. Finally, the loss function is substituted with the MPDIoU loss, optimizing the bounding box regression algorithm and enhancing convergence speed and regression accuracy. The proposed YOLO-RDM model (C2f_RM, DD-Head, MPDIoU) attains an average accuracy of 93.6% on the dataset, representing a 3.4% improvement over the original YOLOv8n model. Detection accuracies for eggplants and stems increased by 1.1% and 5.8%, respectively. Additionally, the F1 score improved by 4.96%, reaching 89.09%. This model demonstrates superior detection performance and robustness for both eggplants and their stems, providing valuable technical support for the automation of eggplant harvesting.}, archive = {J_ACCESS}, author = {Qin Liu and Zhibin Zhou and Lu Xiong and Meilian Lu and Jingwen Ouyang}, doi = {10.1109/ACCESS.2025.3545670}, journal = {IEEE Access}, pages = {37656-37672}, shortjournal = {IEEE Access}, title = {YOLO-RDM: Innovative detection methods for eggplants and stems in complex natural environment}, volume = {13}, year = {2025}, }
• (2025). Study of magnetic shielding of ferromagnetic cores, with solenoidal HTS tape screens, for SFCL applications. ACCESS, 13, 37649-37655. (www)

  @article{ , abstract = {Superconducting Fault Current Limiters (SFCL) are reliable and low environmental impact protection devices, easily adaptable within the distribution networks of distributed generation systems. An Inductive-resistive (IR) SFCL, previously presented by authors, is currently under development. The inductive stage of this IR-SFCL has a magnetic screen to prevent the magnetic field from affecting the resistive stage during normal operation, among other functions. Usually, the magnetic screens are made of superconducting bulk, and have low reliability under thermal cycles, since they are brittle and expensive. To solve this problem, screens built from commercial high temperature superconducting (HTS) tape were studied and the best configuration for use in the IR-SFCL, chosen. This was the superconducting multilayer solenoidal screen. This work studies the behaviour of the basic IR-SFCL ferromagnetic core, when it is shielded by the chosen superconducting multilayer solenoidal screen. This paper shows the magnetic conditions to prevent or permit the magnetization of the core, which is necessary information to size the inductive stage of the new IR-SFCL prototype. Finally, a brief study of the shielding mechanisms of the new screen was carried out. The result of the tests proves that the diamagnetism of the tape superconducting materials is almost totally responsible for the magnetic shielding.}, archive = {J_ACCESS}, author = {Alfredo Álvarez and Belén Rivera and Belén Pérez and Pilar Suárez}, doi = {10.1109/ACCESS.2025.3545575}, journal = {IEEE Access}, pages = {37649-37655}, shortjournal = {IEEE Access}, title = {Study of magnetic shielding of ferromagnetic cores, with solenoidal HTS tape screens, for SFCL applications}, volume = {13}, year = {2025}, }
• (2025). Exploring misconceptions and challenges of electric vehicle adoption in india. ACCESS, 13, 37632-37648. (www)

  @article{ , abstract = {As the global challenge for sustainable transportation rises, electric vehicles (EVs) have emerged as a promising solution to combat the adverse effects of conventional Internal Combustion (IC) engine vehicles on the environment. In India, the acceptance of EVs has been expanding momentum, driven by government plans, technological advancements, and raising environmental awareness. However, misconceptions persist regarding the complete ecological footprint of EVs. This paper aims to explore the prevalent misconceptions related to electric vehicles in India, focusing on two primary aspects: emissions and challenges. The study examines the lifecycle emissions, encompassing battery production, electricity generation, and disposal, to assess their environmental benefits comprehensively. Additionally, the research identifies challenges hindering EV adoption in India, including charging infrastructure, upfront costs, and range anxiety. The research draws on a combination of literature review, data analysis, and case studies to fully understand the current electric vehicle misconceptions and challenges in India. The study finds that with only 23% of India’s electricity from renewables, the rapid growth of EVs could increase emissions and up to 90% improper battery recycling and disposal further exacerbating environmental harm. By shedding light on misconceptions and challenges, this study aims to inform policymakers and stakeholders, facilitating a faster transition to cleaner and more sustainable transportation in India.}, archive = {J_ACCESS}, author = {Sitharthan Ramachandran and S. Saravanan and S. Julius Fusic and Arul Rajagopalan}, doi = {10.1109/ACCESS.2025.3545749}, journal = {IEEE Access}, pages = {37632-37648}, shortjournal = {IEEE Access}, title = {Exploring misconceptions and challenges of electric vehicle adoption in india}, volume = {13}, year = {2025}, }
• (2025). Pallet pose estimation based on front face shot. ACCESS, 13, 37624-37631. (www)

  @article{ , abstract = {Autonomous forklifts are attracting much attention to address the serious shortage of forklift operators in the logistics industry. For autonomous forklifts to load pallets, they need to accurately estimate the 3D position and 3D orientation, i.e., the 6D pose, of the pallet. Therefore, many 6D pose estimation methods have been developed, and the latest ones utilize deep learning with a large number of pallet images for training. However, conventional methods have a problem in that estimation accuracy significantly decreases when the pallets in the captured images have appearances different from those used in training. To address this problem, the authors propose Front Face Shot (FFS), a novel 6D pose estimation method based on only the front face shot of the pallet. FFS robustly and highly accurately estimates 6D pose even in cases of unlearned pallet appearances by analyzing the 3D structure of the pallet from the front face shot by utilizing a convolutional neural network and kernel ridge regression. Experiments showed that FFS achieved the same level of estimation accuracy from untrained images as from trained ones, whereas the accuracy of conventional methods halved.}, archive = {J_ACCESS}, author = {Natsuki Kai and Hiroshi Yoshida and Takashi Shibata}, doi = {10.1109/ACCESS.2025.3538045}, journal = {IEEE Access}, pages = {37624-37631}, shortjournal = {IEEE Access}, title = {Pallet pose estimation based on front face shot}, volume = {13}, year = {2025}, }
• (2025). 5G communications in nuclear: Potential use cases and security considerations. ACCESS, 13, 37611-37623. (www)

  @article{ , abstract = {As fifth-generation (5G) communications continues to revolutionize the future of wireless technology, there is growing demand to utilize its benefits for critical infrastructures such as nuclear power plants (NPPs). In regard to achieving full automation and control in the operation of existing and future nuclear reactors, the unique capabilities of 5G can bring several potential advantages over other wireless technologies. However, a deep investigation is needed for the availability and security of 5G communications under various NPP operational scenarios. This article examines how 5G security capabilities can be architecturally deployed in nuclear applications so as to replace existing communication infrastructures. We discuss the current use of all wireless technologies in NPPs with their key features. Consequently, we investigated several NPP use cases in which 5G offers potential advantages but entails specific security considerations. The present article covers the characteristics of 5G communications, general challenges to its application in nuclear, and the security gaps that need to be addressed. We also highlight certain 5G security-by-design features that can help addressing current stringent NPP requirements. In addition, we discuss some future research direction that can facilitate the implementation of 5G in a nuclear facility. The findings presented herein can help foster 5G deployment in NPPs, thus enabling secured data transmission, cost savings, and increased operational efficiency with enhanced reliability.}, archive = {J_ACCESS}, author = {Imtiaz Nasim and Amitabh Mishra and Vivek Agarwal and Arupjyoti Bhuyan}, doi = {10.1109/ACCESS.2025.3544962}, journal = {IEEE Access}, pages = {37611-37623}, shortjournal = {IEEE Access}, title = {5G communications in nuclear: Potential use cases and security considerations}, volume = {13}, year = {2025}, }
• (2025). EmoNet: Deep attentional recurrent CNN for x (Formerly twitter) emotion classification. ACCESS, 13, 37591-37610. (www)

  @article{ , abstract = {Emotion classification from social media data is critical for market research, sentiment analysis, and understanding human behavior, yet the unstructured nature of Twitter data poses significant challenges for conventional models. To overcome these challenges, we propose EmoNet, a novel Deep Attentional Recurrent Convolutional Neural Network (DAR-CNN) that integrates an advanced attention mechanism to capture intricate contextual dependencies in textual data. EmoNet utilizes an AttentionEmbedder module to generate query, key, and value vectors, which are then processed through an attention layer to compute weighted representations. These representations are concurrently fed into LSTM, CNN, and GRU layers to extract diverse sequential and spatial features, with global max pooling applied to each pathway before concatenation and final classification via fully connected layers. We employ K-fold cross-validation to rigorously evaluate EmoNet against baseline models, including RoBERTa, BERT, and DistilBERT. Our DAR-CNN model achieves a remarkable accuracy of 94.51% and an F1 score of 94.55%, outperforming existing state-of-the-art methods. These results demonstrate our effective combination of attentional mechanisms with recurrent and convolutional architectures, establishing EmoNet as a robust solution for emotion detection on social media platforms.}, archive = {J_ACCESS}, author = {Md. Shakil Hossain and Md. Mithun Hossain and Md. Shakhawat Hossain and M. F. Mridha and Mejdl Safran and Sultan Alfarhood}, doi = {10.1109/ACCESS.2025.3545863}, journal = {IEEE Access}, pages = {37591-37610}, shortjournal = {IEEE Access}, title = {EmoNet: Deep attentional recurrent CNN for x (Formerly twitter) emotion classification}, volume = {13}, year = {2025}, }
• (2025). Syntactic-aware text classification method embedding the weight vectors of feature words. ACCESS, 13, 37572-37590. (www)

  @article{ , abstract = {Text classification is the process of categorizing sentences into predefined classes on the basis of their meanings or functions. In this context, feature words play a crucial role in representing the core meaning and key information of sentences, reflecting the main content and deep semantics of scientific literature. Optimizing text classification models using feature words is an important task in the information mining of scientific literature. To address text classification considering feature words, this study constructs a set of feature words from a linguistic perspective and quantitatively expresses the weight differences of various feature words on the basis of syntactic structure features. By embedding the weight information, absolute position, relative position, and contextual information of feature words into pre-trained models, the universal text classification models considering feature word vector weights is established. The analysis of hyperparameter space relationships reveals that the hyperparameter settings are not simple linear relationships and that their interactions are often nonlinear and complex. Employing the Bayesian optimization algorithm facilitates the model in finding the optimal hyperparameters while reducing computational costs. Based on the public dataset RCMR 280K, the experimental results demonstrate that considering feature words enables the model to capture sentence structure and features in text classification task, improving the recognition accuracy of the model. Compared with base models (BERT, SciBERT, RoBERTa and ModernBERT) and their regularized models which consider feature words, the improved ModernBERT, which is based on weighted feature word vectors, achieves an average F1 of 95.8% in the recognition tasks of research objective, method, result and conclusion sentences, an improvement of 1.2%, 0.8%, 1.1%, and 0.9%, respectively. In addition, the results show that large language models (LLMs) exhibit robust performance in text classification tasks, with the fine-tuned GLM4-9B achieving superior performance by reaching an average F1 of 91.9%. This model can be applied to sentence recognition and classification tasks in various domains. By updating or replacing the feature word table, the model can be adapted to recognize and classify different types of sentences, providing a theoretical basis or model foundation for other downstream tasks. More details and model weights are public at https://huggingface.co/wmsr22/Classification/tree/main}, archive = {J_ACCESS}, author = {Meng Wang and Jisu Kim and Yuting Yan}, doi = {10.1109/ACCESS.2025.3545877}, journal = {IEEE Access}, pages = {37572-37590}, shortjournal = {IEEE Access}, title = {Syntactic-aware text classification method embedding the weight vectors of feature words}, volume = {13}, year = {2025}, }
• (2025). Next-generation text summarization: A t5-LSTM FusionNet hybrid approach for psychological data. ACCESS, 13, 37557-37571. (www)

  @article{ , abstract = {Automatic text summarization (ATS) has developed as a vital method for compressing massive amounts of textual content into concise and useful summaries, to retrieve more effective and useful information. ATS reduces textual statistics into coherent and shorter versions especially focusing on psychological text summarization to extract insights and emotional states, assisting in better analysis and understanding of psychological contents. In this context, this study proposes a new hybrid model T5-LSTM FusionNet, to enhance textual content summarization in the field of psychology. The motivation derives from the developing extent and accessibility of psychological literature online, which necessitates exceptional techniques for extracting significant findings quickly and reliably. The recommended T5-LSTM FusionNet model combines the benefits of Text-to-Text Transfer Transformer (T5) and Long Short-Term Memory (LSTM). The dataset with 5480 records, accumulated from numerous psychology-associated websites, has been used to verify its performance. T5-LSTM FusionNet’s overall performance is evaluated against several latest models along with T5, LSTM, BERT, and DistilBERT. Measures such as accuracy, precision, recall, F1-score, and ROUGE rankings are used to evaluate the model’s exceptional summarization. With T5-LSTM FusionNet accomplishing a precision of 0.72, recall of 0.72, F1-score of 0.71, and accuracy of 0.74, the effects show significant improvement over individual models like T5 and LSTM, as well as competitive models like BERT and DistilBERT, in terms of summarization effectiveness and accuracy. Furthermore, T5-LSTM FusionNet plays thoroughly in catching both unigram and bigram overlaps concerning summaries, as proven through a comparison study using ROUGE metrics. This suggests that T5-LSTM FusionNet can retain sequence integrity and relevance in summarizing tasks. This work advances ATS techniques in psychology by presenting a hybrid model that combines sequential and transformer-based learning strategies.}, archive = {J_ACCESS}, author = {Bilal Khan and Muhammad Usman and Inayat Khan and Jawad Khan and Dildar Hussain and Yeong Hyeon Gu}, doi = {10.1109/ACCESS.2025.3540590}, journal = {IEEE Access}, pages = {37557-37571}, shortjournal = {IEEE Access}, title = {Next-generation text summarization: A t5-LSTM FusionNet hybrid approach for psychological data}, volume = {13}, year = {2025}, }
• (2025). Pattern reconfigurable antenna using circular grooved SIW for 5G millimeter-wave communications. ACCESS, 13, 37545-37556. (www)

  @article{ , abstract = {This article presents a novel design of a 360° azimuthal plane pattern reconfigurable antenna (PRA) based on a substrate integrated waveguide (SIW) technique for 5G NR (n258) millimeter-wave (mmWave) applications. The proposed antenna consists of a compact and planar structure, exhibits wider bandwidth, better beam-shaping capability and smaller beam scanning resolution (while covering the whole azimuth plane) than contemporary designs. The design includes 20 PIN diodes for 360° pattern reconfiguration. These were simulated using the impedances given from the PIN diode’s datasheet. In the measurements, static conductive patches were used for ON states PIN diodes. The main challenge in the design of the planar CSIW at mmWave frequency bands is maintaining impedance matching when different combinations of PIN diodes are activated. Novel optimized grooves in the SIW geometry and parasitic elements created from the 2D high-impedance surfaces (HISs) have significantly improved the performance of the proposed antenna. The antenna operates from 23.5 to 27.5 GHz (covering the n258 band) having a peak gain of 11.2 dBi at 26.5 GHz and >8.5 dBi over the whole bandwidth. Moreover, it has the capability to generate dual beams from 23.3 to 26 GHz making it a promising low-cost beamforming candidate for future wireless communication systems.}, archive = {J_ACCESS}, author = {Amitkumar Patel and William G. Whittow and Chinthana J. Panagamuwa}, doi = {10.1109/ACCESS.2025.3546145}, journal = {IEEE Access}, pages = {37545-37556}, shortjournal = {IEEE Access}, title = {Pattern reconfigurable antenna using circular grooved SIW for 5G millimeter-wave communications}, volume = {13}, year = {2025}, }
• (2025). Modeling and analysis of stacker terminal picking accuracy based on the jacobian-torsor theory. ACCESS, 13, 37534-37544. (www)

  @article{ , abstract = {Stacker cranes play a significant role in modern logistics and warehousing systems, with their terminal pickup accuracy directly impacting the safety and reliability of equipment automation systems. This article introduces a method for predicting the accuracy of the stacker crane fork’s pickup position in the assembly process. Based on the concept of small displacement torsors, we conduct a structural analysis of the stacker crane to identify various error sources that affect the terminal accuracy of the stacker crane and establish corresponding small displacement torsors. Then, the assembly error transfer and accumulation of principal components are expressed through the Jacobian matrix, and the contribution of different tolerance values, such as the precision of the crane’s transmission components or the geometric tolerances of the separation surface, to cumulative errors, is determined. The method’s comprehensive approach provides clear and practical guidance for predicting the assembly accuracy of a stacker crane, instilling confidence in its applicability and effectiveness.}, archive = {J_ACCESS}, author = {Guowei Li and Yong Yang and Qun Sun and Kaihua Qian}, doi = {10.1109/ACCESS.2025.3545089}, journal = {IEEE Access}, pages = {37534-37544}, shortjournal = {IEEE Access}, title = {Modeling and analysis of stacker terminal picking accuracy based on the jacobian-torsor theory}, volume = {13}, year = {2025}, }
• (2025). Small language model agent for the operations of continuously updating ICT systems. ACCESS, 13, 37522-37533. (www)

  @article{ , abstract = {The management of ICT systems requires the implementation of appropriate operational procedures that are aligned with the specific system state and adhere to the latest updates and revisions of manuals and policies. The implementation of autonomous system operation has the potential to reduce the time and burden of human endeavors. Large Language Model (LLM) agents are expected to interact with ICT systems and operate them autonomously. However, existing LLM agents presume a sophisticated reasoning capability of API-based proprietary LLMs, which gives rise to concerns regarding operational costs and confidentiality. Therefore, we propose a novel framework for the Small Language Model (SLM) agent that is designed to adapt to a continuously updating environment. The proposed method addresses the limitation of the reasoning performance of the SLM agent using nested thoughts and prompt reconfiguration. In particular, we empirically identify the shortcut reasoning of the SLM agent and put forward an exemplar selection method to address this issue. We extensively evaluate the proposed method using both synthetic and real-world benchmarks and demonstrate that it significantly enhances the performance of the SLM agent with some computational overhead in comparison with the baselines.}, archive = {J_ACCESS}, author = {Nobukazu Fukuda and Haruhisa Nozue and Haruo Oishi}, doi = {10.1109/ACCESS.2025.3544637}, journal = {IEEE Access}, pages = {37522-37533}, shortjournal = {IEEE Access}, title = {Small language model agent for the operations of continuously updating ICT systems}, volume = {13}, year = {2025}, }
• (2025). StriPre: A VM disk migration prefetching algorithm. ACCESS, 13, 37514-37521. (www)

  @article{ , abstract = {Virtual machine (VM) cross-cloud migration involves relocating virtual machine instances between different cloud environments, offering advantages such as improved scalability, disaster recovery capabilities, and efficient resource allocation optimization. Two strategies exist for live migration: “pre-copy” and “post-copy”. These methods are initially applied to memory migration, but their drawbacks, such as data redundancy and poor virtual machine IO performance are amplified in cross-cloud disk migration due to high network latency, limited transmission bandwidth, and large data volumes, thereby constraining the performance of cross-cloud virtual machine migration. To reduce the performance impact caused by the latency of cross-cloud migration, we present StriPre in this paper. StriPre is a prefetching algorithm based on disk access and striped feature design, leveraging the alternating characteristics of disk accesses and performing striping recognition on IO requests. Compared to the commonly used sequential prefetching in memory, StriPre’s prefetch hit rate has increased by $1.2\times $ to $2.2\times $ . Our experiments have demonstrated that StriPre can effectively reduce the virtual machine startup time during cross-cloud disk migration. Under extreme cross-cloud latency of 160ms, StriPre can shorten the startup time by $3.48\times $ compared to sequential prefetching.}, archive = {J_ACCESS}, author = {Yuchi Chen and Tianlei Xiong}, doi = {10.1109/ACCESS.2025.3545836}, journal = {IEEE Access}, pages = {37514-37521}, shortjournal = {IEEE Access}, title = {StriPre: A VM disk migration prefetching algorithm}, volume = {13}, year = {2025}, }
• (2025). MIN: Moiré inpainting network with position adaptive mask for 3-D height reconstruction. ACCESS, 13, 37501-37513. (www)

  @article{ , abstract = {In the AI-driven computer vision industry, height measurement of Printed Circuit Board images typically relies on laser or Moiré methods. In this paper, we focus on the Moiré method, known for its high accuracy and fast measurement speed. However, when using Moiré method, shadows and light reflections are generated on Printed Circuit Board surface that cause significant errors in height measurement. To address this problem, we propose a Moiré Inpainting Network, which integrates the Moiré method with an image inpainting model architecture. Our approach leverages a Generative Adversarial Network to accurately identify and reconstruct shadow and reflection regions. The network takes 2D Printed Circuit Board Moiré images as input and outputs heights of Printed Circuit Board. We evaluate performance using Height Reconstruction Rate, Shadow Reconstruction Rate, and Reflection Reconstruction Rate, metrics we define in this paper. Comparative experiments show that our method outperforms state-of-the-art inpainting models for Moiré images, proving its effectiveness in computer vision applications. Moreover, we achieve a reasonable inference time, enabling real-time deployment in Printed Circuit Board manufacturing.}, archive = {J_ACCESS}, author = {Tae-Jung Kim and Min-Ho Ha and Saba Arshad and Tae-Hyoung Park}, doi = {10.1109/ACCESS.2025.3545748}, journal = {IEEE Access}, pages = {37501-37513}, shortjournal = {IEEE Access}, title = {MIN: Moiré inpainting network with position adaptive mask for 3-D height reconstruction}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of test infrastructure for higher parallel wafer level testing of system-on-chip. ACCESS, 13, 37493-37500. (www)

  @article{ , abstract = {Semiconductor companies have been striving to reduce their manufacturing costs. High parallelism is a key factor in reducing costs during wafer-level testing. Wafer testing is conducted using Automatic Test Equipment (ATE) along with test infrastructures such as the Prober Interface Board (PIB), signal tower, and probe card. In this paper, we present the mechanical and electrical details of a wafer-level test infrastructure designed for higher parallel testing of system-on-chips (SoCs). In previous test infrastructures, a high site count was constrained by the available probe pins on the signal tower for wafer testing. However, the proposed signal tower addresses this bottleneck by utilizing a combination of 620 pin and 731 pin modules. This allows more probe pins to transmit signals from the ATE instrument, enabling multi-site testing. The number of available probe pins increased by 68.96% compared to the state-of-the-art infrastructure, without compromising the application area. The experimental results demonstrate that our proposed test infrastructure is suitable for mass production at the wafer-level, considering factors such as deflection, total compression forces, and electrical specifications.}, archive = {J_ACCESS}, author = {Seung-Han Lee and Jin Hwan Park and Young-Woo Lee}, doi = {10.1109/ACCESS.2025.3544246}, journal = {IEEE Access}, pages = {37493-37500}, shortjournal = {IEEE Access}, title = {Design and implementation of test infrastructure for higher parallel wafer level testing of system-on-chip}, volume = {13}, year = {2025}, }
• (2025). Mitigating cyber risks in smart cyber-physical power systems through deep learning and hybrid security models. ACCESS, 13, 37474-37492. (www)

  @article{ , abstract = {The rise of renewable energy integration in smart grids brings new cybersecurity challenges, prompting this study to examine vulnerabilities in Smart Cyber-Physical Power Systems (CPPS). The integration of renewable energy sources, such as wind and solar, into smart grids poses operational risks due to their decentralized and variable characteristics, particularly within the communication layers essential for real-time monitoring and control. While increasing integration of renewable energy sources does not directly impact cybersecurity vulnerabilities, the primary challenge arises from their decentralization. Addressing this decentralization requires the use of cyber layers between supply and demand, introducing vulnerabilities of cyber threats to the control and communication systems of the power system. These layers, vulnerable to diverse cyber-attacks like false data injection (FDI), denial of service (DoS), and replay assaults, might compromise grid stability and security. To address these risks, the research proposes a hybrid approach that integrates conventional cybersecurity strategies with machine learning (ML) approaches to improve cyber-attack detection. The research highlights the use of deep learning models, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, for real-time anomaly identification in grid data. These models, developed using a PSCAD-simulated dataset augmented with synthetic cyber-attacks, exhibit considerable advancements in threat identification and mitigation. The study emphasizes the difficulties in identifying cyber risks in grids with significant renewable integration, such as frequency instability and diminished system inertia, and suggests energy storage alternatives and sophisticated forecasting models to mitigate these issues. By incorporating a novel pre-processing method that leverages feature derivatives, the proposed models achieve over 98% accuracy in detecting cyber threats, providing a robust framework for protecting smart power grids from evolving cyber risks.}, archive = {J_ACCESS}, author = {M. A. S. P. Dayarathne and M. S. M. Jayathilaka and R. M. V. A. Bandara and V. Logeeshan and S. Kumarawadu and Chathura Wanigasekara}, doi = {10.1109/ACCESS.2025.3545637}, journal = {IEEE Access}, pages = {37474-37492}, shortjournal = {IEEE Access}, title = {Mitigating cyber risks in smart cyber-physical power systems through deep learning and hybrid security models}, volume = {13}, year = {2025}, }
• (2025). DOA estimation based on CNNs embedded with mamba. ACCESS, 13, 37467-37473. (www)

  @article{ , abstract = {The convolutional neural networks (CNN) have been proved to be more efficient in Direction of Arrival (DOA) estimation of underwater acoustic array signals. Most of CNNs use the covariance matrix of the array signal or the spectrum as features to estimate the angle of incidence directly or to remove the noise. However, feature processing of the original signal inevitably loses part of the information, and using the original signal directly makes the number of parameters and computation huge. We propose a DOA estimation method that does not require feature processing. Mamba’s ability to handle very long sequences is utilized instead of feature extraction. Upon completion of the modeling and simulation processes, the model that has been put forward demonstrates a superior level of precision across a spectrum of scenarios characterized by differing snapshot counts and signal-to-noise ratios when juxtaposed with the CNN. Notably, in conditions where the signal-to-noise ratio is diminished, the proposed model exhibits a generalization accuracy that surpasses that of CNN by a margin of 3%. The approach delineated in this research paper is capable of reliably discerning the direction of arrival of multiple signals whose characteristics are not previously known.}, archive = {J_ACCESS}, author = {Zhang Ziyan and Yi Shichao and Wang Chengyi}, doi = {10.1109/ACCESS.2025.3544803}, journal = {IEEE Access}, pages = {37467-37473}, shortjournal = {IEEE Access}, title = {DOA estimation based on CNNs embedded with mamba}, volume = {13}, year = {2025}, }
• (2025). A 3D most vulnerable path search method for physical protection systems based on the EASI model. ACCESS, 13, 37457-37466. (www)

  @article{ , abstract = {Physical Protection System (PPS) is a crucial system for critical infrastructure to protect its vital information, and it is essential to analyze the vulnerability of PPS. Established methods for PPS vulnerability analysis mainly focus on one- and two-dimensional environments. However, the air and underwater threats are increasing, and more effective methods are needed to deal with these new threats than the existing methods. The security of the physical protection system in three-dimensional space needs to be analyzed. The most vulnerable path search is a critical link in the security assessment of the physical protection system, so this paper proposes a three-dimensional physical protection system based on the Estimate of Adversary Sequence Interruption (EASI) model of the most vulnerable path search method. A three-dimensional facility model is established, and the algorithm’s search points are reduced based on existing methods. The search efficiency is improved by restricting the intermediate points and the angle between the start and target points and improving the heuristic function. The improved algorithms are simulated and compared, and the results show that the paths generated by the improved algorithms are more reasonable, and the algorithms consume less time and have shorter path lengths.}, archive = {J_ACCESS}, author = {Aiguo Li and Yanfeng Deng}, doi = {10.1109/ACCESS.2025.3545264}, journal = {IEEE Access}, pages = {37457-37466}, shortjournal = {IEEE Access}, title = {A 3D most vulnerable path search method for physical protection systems based on the EASI model}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving machine learning (PPML) inference for clinically actionable models. ACCESS, 13, 37431-37456. (www)

  @article{ , abstract = {Machine learning (ML) refers to algorithms (often models) that are learned directly from data, germane to past experience. As algorithms have constantly been evolving with the exponential increase of computing power and vastly generated data, privacy of algorithms as well as of data becomes extremely important due to regulations and IP rights. Therefore, it is vital to address privacy and security concerns of both data and model together with other performance metrics when commercializing machine learning models. Our aim is to show that privacy-preserving machine learning inference methods can safeguard the intellectual property of models and prevent plaintext models from disclosing information about the sensitive data employed in training these ML models. Additionally, these methods protect the confidentiality of model users’ sensitive patient data. We accomplish this by performing a security analysis to determine an appropriate query limit for each user, using the European Spine Study Group’s (ESSG) adult spinal deformity dataset. We implement a privacy-preserving tree-based machine learning inference and run two security scenarios (scenario A and scenario B) containing four parts with progressively increasing the number of synthetic data points, which are used to enhance the accuracy of the attacker’s substitute model. A target model is generated with particular operation site(s) in each scenario, and substitute models are built with nine-time threefold cross-validation using the XGBoost algorithm with the remaining sites’ data to assess the security of the target model. First, we create box plots of the test sets’ accuracy, sensitivity, precision, and F-score metrics to compare the substitute models’ performance with the target model. Second, we compare the gain values of the target and substitute models’ features. Third, we provide an in-depth analysis to check the inclusion of target model split points in substitute models with a heatmap. Finally, we compare the outputs of public and privacy-preserving models and report intermediate timing results. The privacy-preserving XGBoost model results are identical to the original plaintext model in the aforementioned two scenarios in terms of prediction accuracy. The differences between performance metrics of best-performing substitute models and target models are 0.27, 0.18, 0.25, 0.26 for scenario A, and 0.04, 0, 0.04, and 0.03 for scenario B for accuracy, sensitivity, precision, and F-score, respectively. The differences between target model accuracy and the mean accuracy values of models in each scenario on the substitute models’ test dataset are 0.38 for scenario A and 0.14 for scenario B. Based on our findings, we conclude that machine learning models (i.e., our target models) may contribute to the advancement in the field of application where they are deployed. Ensuring the security of both the model and the user data enables the protection of the intellectual property of ML models, preventing the leakage of sensitive information used in training and model users’ data.INDEX TERMS Homomorphic encryption, privacy-preserving machine learning, XGBoost.}, archive = {J_ACCESS}, author = {Baris Balaban and Seyma Selcan Magara and Caglar Yilgor and Altug Yucekul and Ibrahim Obeid and Javier Pizones and Frank Kleinstueck and Francisco Javier Sanchez Perez-Grueso and Ferran Pellisé and Ahmet Alanay and Erkay Savas and Çetin Bağcı and Osman Ugur Sezerman and European Spine Study Group}, doi = {10.1109/ACCESS.2025.3540261}, journal = {IEEE Access}, pages = {37431-37456}, shortjournal = {IEEE Access}, title = {Privacy-preserving machine learning (PPML) inference for clinically actionable models}, volume = {13}, year = {2025}, }
• (2025). Codebook-based trellis-coded quantization scheme using K-means clustering for massive MIMO systems. ACCESS, 13, 37423-37430. (www)

  @article{ , abstract = {This paper introduces a codebook-based trellis-coded quantization (TCQ) approach utilizing K-means clustering, designed specifically for massive multiple-input multiple-output systems. The proposed TCQ scheme follows the structure of conventional TCQ, quantizing channels through a trellis and source constellation to minimize latency. The performance of channel quantization significantly depends on the chosen source constellation. The conventional TCQ scheme, using a fixed set of constellations as source constellations, imposes limitations on beamforming gain performance. In our proposed TCQ scheme, we employ a codebook to utilize a set of constellations tailored to the channel environments as the source constellation, aiming for enhanced beamforming gain performance. This approach leads to a slight increase in feedback overhead. The codebook’s constellation sets are generated by determining centroids through K-means clustering of channel elements and mapping them to high-order quadrature amplitude modulation constellations. Simulation results demonstrate that our proposed TCQ scheme, with a comparable computational complexity, shows the improved average beamforming gains compared to the conventional TCQ scheme.}, archive = {J_ACCESS}, author = {Sungsoo Park and Gyuyeol Kong}, doi = {10.1109/ACCESS.2025.3545673}, journal = {IEEE Access}, pages = {37423-37430}, shortjournal = {IEEE Access}, title = {Codebook-based trellis-coded quantization scheme using K-means clustering for massive MIMO systems}, volume = {13}, year = {2025}, }
• (2025). Techno-commercial evaluation of renewable energy-based power-to-X plants: A comparative analysis of grid-connected versus islanded facilities. ACCESS, 13, 37408-37422. (www)

  @article{ , abstract = {Global warming has accelerated the transition towards clean and renewable energy (RE) resources. However, the intermittent nature of these resources presents challenges to their effective integration into power systems. A practical and sustainable solution involves storing the generated energy in energy storage systems (ESS) or converting it into renewable fuels and chemicals through power-to-x (PtX) processes. The latter has gained increased attention because of its ability to stabilize energy systems over extended periods. The application of PtX enables decarbonization of sectors in which direct electrification is challenging. PtX microgrids (MG) can operate in either grid-connected/on-grid or islanded/off-grid modes. Each operation mode requires specific equipment and strategies to ensure the power balance, stability, and resilience within the power system. Choosing the optimal operational mode for a PtX MG can be complex, because the on-grid and off-grid configurations differ significantly in terms of both technical performance and financial viability. Therefore, this paper presents a techno-commercial comparative assessment of a real industrial RE-based PtX MG, focusing on hydrogen (H2) production plants, under both operational modes. First, the essential components of the PtX MG and additional equipment required for each mode are introduced. Second, financial analysis evaluates the contribution of various equipment costs to the total equipment cost. Finally, the steady-state and transient performances of the on-grid and off-grid PtX MG are compared. These include load flow analysis (LFA), short circuit analysis (SCA), harmonic flow analysis (HFA), and low-voltage ride-through (LVRT) simulations. The PtX MG power system is modeled using PowerFactory software.}, archive = {J_ACCESS}, author = {Seyedamin Valedsaravi}, doi = {10.1109/ACCESS.2025.3542195}, journal = {IEEE Access}, pages = {37408-37422}, shortjournal = {IEEE Access}, title = {Techno-commercial evaluation of renewable energy-based power-to-X plants: A comparative analysis of grid-connected versus islanded facilities}, volume = {13}, year = {2025}, }
• (2025). AI techniques and applications for online social networks and media: Insights from BERTopic modeling. ACCESS, 13, 37389-37407. (www)

  @article{ , abstract = {This study examines the role of Artificial Intelligence (AI) in enhancing personalization, analyzing information dynamics, and developing scalable methodologies within Online Social Networks and Media (OSNEM), with a focus on user protection. Through a systematic review using the PRISMA framework and BERTopic modeling, key AI applications in OSNEM were identified, including fake news detection, sentiment analysis, hate speech detection, big data analysis, bot detection, and insights into public health, disaster relief, and mental health. Although AI techniques and multimodal frameworks have significantly improved content personalization, challenges like algorithmic bias and echo chambers remain. To address these, the implementation of fairness-aware learning models is recommended to ensure personalization stays ethical. Advanced AI techniques, such as Dynamic Memory Networks and Temporal Convolutional Networks, have shown strong capabilities in tracking opinion dynamics and combating misinformation. Additionally, Generative AI offers opportunities for content creation but also raises concerns about misinformation, requiring robust moderation frameworks. Emerging technologies like Artificial Real Intelligence (ARI), which simulate human reasoning and decision-making, could further improve the management of complex online interactions. The study highlights the need for scalable AI methodologies, such as multitask learning frameworks, to efficiently handle the vast amounts of real-time data generated by social media while addressing cross-platform adaptability and computational efficiency.}, archive = {J_ACCESS}, author = {Prema Nedungadi and G. Veena and Kai-Yu Tang and Remya R. K. Menon and Raghu Raman}, doi = {10.1109/ACCESS.2025.3543795}, journal = {IEEE Access}, pages = {37389-37407}, shortjournal = {IEEE Access}, title = {AI techniques and applications for online social networks and media: Insights from BERTopic modeling}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of LIME and SHAP interpreters with explainable ML-based diabetes predictions. ACCESS, 13, 37370-37388. (www)

  @article{ , abstract = {Explainable artificial intelligence is beneficial in converting opaque machine learning models into transparent ones and outlining how each one makes decisions in the healthcare industry. To comprehend the variables that affect decision-making regarding diabetes prediction that can be accounted for by model-agnostic techniques. In this project, we investigate how to generate local and global explanations for a machine-learning model built on a logistic regression architecture. We trained on 253,680 survey responses from diabetes patients using the explainable AI techniques LIME and SHAP. LIME and SHAP were then used to explain the predictions produced by the logistic regression and Random forest-based model on the validation and test sets. With a discussion of future work, the comparative analysis and discussion of various experimental findings between LIME and SHAP are provided, along with their strengths and weaknesses in terms of interpretation. With a high accuracy of 86% on the test set, we used LR architecture with a spatial attention mechanism, demonstrating the possibility of merging machine learning and explainable AI to improve diabetes prediction, diagnosis, and treatment. We also focus on various applications, difficulties, and probable future directions of machine learning models for LIME and SHAP interpreters.}, archive = {J_ACCESS}, author = {Shamim Ahmed and M. Shamim Kaiser and Mohammad Shahadat Hossain and Karl Andersson}, doi = {10.1109/ACCESS.2024.3422319}, journal = {IEEE Access}, pages = {37370-37388}, shortjournal = {IEEE Access}, title = {A comparative analysis of LIME and SHAP interpreters with explainable ML-based diabetes predictions}, volume = {13}, year = {2025}, }
• (2025). Trajectory planning of autonomous vehicles to ensure target QoS requirements in 6G mobile networks. ACCESS, 13, 37361-37369. (www)

  @article{ , abstract = {The rollout of future 6G mobile networks is expected to deliver cutting-edge services with ultra-fast speeds, minimal latency and widespread connectivity. To support these target Quality-of-Service (QoS) requirements, Intelligent Transportation Systems involving self-driving vehicles should be designed to facilitate the autonomous driving operations to be decided in the 6G edge/core network, while providing seamless and immersive high bandwidth services to the humans that are travelling inside the vehicles. This task becomes particularly challenging in complex urban environments, especially when communication occurs over millimeter wave (mmWave) frequency bands, due to the fragmented coverage of cells in such situations. This paper studies the performance gains that is feasible when the trajectory of the Autonomous Vehicle (AV) is selected according to the QoS that the 6G network can provide in different parts of a Manhattan grid layout. More precisely, a graph is constructed in which the nodes are the crossroads and the weight of the edges connecting nodes is proportional to the minimum Spectral Efficiency (SE) across the streets that connect the given intersections. Then, the end-to-end trajectory of the vehicle can be selected based on the Maximizing the Minimum (Max-Min) scheme, which maximizes the minimum SE, encountered by the AV. The simulation results show that the proposed Max-Min scheme achieves significant performance improvements, delivering notable gains in the order of 40%-60% (20%-30%) are observed for 10th (50th) percentile SE, when the trajectory from the starting point to the destination is optimized based on the coverage of the mobile network.}, archive = {J_ACCESS}, author = {Inam Ullah and Hesham El Sayed and Alexis A. Dowhuszko and Manzoor Ahmed Khan and Jyri Hämäläinen}, doi = {10.1109/ACCESS.2025.3543204}, journal = {IEEE Access}, pages = {37361-37369}, shortjournal = {IEEE Access}, title = {Trajectory planning of autonomous vehicles to ensure target QoS requirements in 6G mobile networks}, volume = {13}, year = {2025}, }
• (2025). A tale of side friction elements in dhaka city: From modeling real cases to revealing impacts through event-based simulation. ACCESS, 13, 37344-37360. (www)

  @article{ , abstract = {A common problem on the streets of Dhaka, the capital of a developing country Bangladesh, is the reduction in the effective road width and blockage of roads due to various side friction elements. Despite significant efforts to study the effects of side friction in different contexts, a comprehensive study that efficiently models side friction elements and then analyzes their impacts using a contextually appropriate simulator for heterogeneous non-lane-based traffic in cities like Dhaka is yet to be conducted in the literature. Therefore, in this study, we attempt to fill this gap. Here, based on real data analysis, we list side friction elements that exhibit substantial impacts on the regular traffic flow on the streets of Dhaka, which includes different categories of roadside objects, pedestrians, and non-motorized vehicles. Subsequently, we model the side friction elements using parameters that effectively capture their characteristics. Leveraging the formulated models, we extend our microscopic road traffic simulator, DhakaSim, which has been proven to appropriately simulate the heterogeneous non-lane-based traffic in cities like Dhaka. To the best of our knowledge, the updated simulator is the only simulator capable of simulating heterogeneous traffic of cities like Dhaka while capturing its real inherent complexities, such as a large number of parked vehicles and irregular pedestrian movement. We rigorously validate the updated simulator using Google Maps data and utilize it to conduct an extensive simulation study analyzing the impacts of side friction elements. Our study reveals that the average speed of Dhaka can be reduced by up to 36.55% and the average waiting time can be increased by up to 161.3% due to side friction. Here, pedestrians exhibit a significantly more negative impact on traffic compared to other side friction elements, making them one of the leading causes of congestion on the streets of Dhaka. Moreover, while non-motorized traffic reduces the network’s average speed, it plays a crucial role in maintaining a good flow rate on Dhaka’s low-capacity roads.}, archive = {J_ACCESS}, author = {Ahmed Mahir Sultan Rumi and Hasan Nahiyan Nobel and A. H. M. Osama Haque and Abu Humayed Azim Fahmid and Tarik Reza Toha and Nouf Alaloula and Najla Abdulrahman Al-Nabhan and Abdulaziz Abdulrahman S. Alnamlah and A. B. M. Alim Al Islam}, doi = {10.1109/ACCESS.2025.3544511}, journal = {IEEE Access}, pages = {37344-37360}, shortjournal = {IEEE Access}, title = {A tale of side friction elements in dhaka city: From modeling real cases to revealing impacts through event-based simulation}, volume = {13}, year = {2025}, }
• (2025). Human pose estimation and event recognition via feature extraction and neuro-fuzzy classifier. ACCESS, 13, 37328-37343. (www)

  @article{ , abstract = {This paper introduces an advanced framework for Human Pose Estimation (HPE) and Semantic Event Classification (SEC), addressing the growing demand for sophisticated human skeleton modeling, context-aware feature extraction, and machine learning techniques for accurate event recognition in real-world scenarios. HPE, a fundamental task in applications such as sports analytics and surveillance systems, involves predicting human joint locations from visual data. Despite substantial progress driven by deep learning advancements, particularly in handling occlusions and crowded environments, a comprehensive integration of these innovations remains limited in current literature. To bridge this gap, we propose a novel HPE and SEC system featuring a robust six-stage pipeline. The workflow begins with preprocessing steps, including video-to-image sequence conversion, floor removal to reduce noise, grayscale transformation, and human silhouette extraction via binary masks. Human detection is performed using the GrabCut algorithm. Full-body feature extraction incorporates 1-D Fourier Transform, 2-D energy-based motion analysis, 0–180° intensity mapping via the Radon Transform algorithm, and movable body part detection using Fourier-Based Correlation. Key point features are characterized through the degree of freedom, human landmark detection via the HSV algorithm, and angular point analysis using the media pipe algorithm. Features are then optimized using the Ray Optimizer and classified through a Neuro-Fuzzy classifier. The proposed system achieves classification accuracies of 94.82%, 92.33%, and 93.63% on the UCF Sports Actions, Human Action Recognition (HAR), and MPII Human Pose datasets, respectively, outperforming state-of-the-art methods and demonstrating its effectiveness in tackling real-world challenges.}, archive = {J_ACCESS}, author = {Muhammad Hanzla and Naif S. Alshammari and Shuaa S. Alharbi and Wasim Wahid and Nouf Abdullah Almujally and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3538691}, journal = {IEEE Access}, pages = {37328-37343}, shortjournal = {IEEE Access}, title = {Human pose estimation and event recognition via feature extraction and neuro-fuzzy classifier}, volume = {13}, year = {2025}, }
• (2025). Overcoming fairness and latency challenges in BBR with an adaptive delay detection. ACCESS, 13, 37318-37327. (www)

  @article{ , abstract = {In 2016, Google introduced a new congestion control algorithm called Bottleneck Bandwidth and Round-trip propagation time (BBR). BBR aims to set the data-sending rate of flows to operate at an optimal operating point—maximizing throughput, minimizing delay, and reducing packet loss. However, when multiple BBR flows share a bottleneck link with a deep buffer, they often deviate from the optimal point, resulting in significant queuing delays, and serious fairness issues especially when flows with different RTTs are present. Existing methods to enhance BBR’s performance typically cap the sending rates of longer RTT flows to improve fairness and reduce latency, but these do not tackle the fundamental problem. In this paper, we introduce a modification to BBR, termed BBR Refined (BBR-R), which enhances throughput fairness among diverse RTT flows and lowers queuing delays. By targeting the underlying causes of unfairness, BBR-R adaptively adjusts delay detection intervals to clear queues in the bottleneck link buffer while preserving link utilization. Our experimental results show that BBR-R enhances RTT fairness by 30% over BBR and other variants, and reduces latency by 30% compared to the original BBR.}, archive = {J_ACCESS}, author = {Zewei Han and Go Hasegawa}, doi = {10.1109/ACCESS.2025.3545088}, journal = {IEEE Access}, pages = {37318-37327}, shortjournal = {IEEE Access}, title = {Overcoming fairness and latency challenges in BBR with an adaptive delay detection}, volume = {13}, year = {2025}, }
• (2025). BFER-net: Babies facial expression recognition model using ResNet12 enabled few-shot embedding adaptation and convolutional block attention modules. ACCESS, 13, 37302-37317. (www)

  @article{ , abstract = {The recognition of babies’ facial expressions is challenging due to the limited availability of annotated data and the complex nature of their emotions.To address this problem, this work introduces a novel dataset, FER-BYC (Facial Expression Recognition for Bangladeshi Young Children), comprising 1,425 annotated images of babies’ facial expressions across seven emotional categories: disgust, fear, anger, happiness, neutral, sadness, and surprise. This dataset fills an enormous void in the domain, as there exist limited prior studies on babies’ facial expression detection. We’ve proposed a fusion model named BFER-Net. Here, we have deployed the feature extraction process. A Convolutional Block Attention Module (CBAM) was integrated into the Modified ResNet12 architecture. It allows the model to focus on the most relevant facial features. The implementation of few-shot learning techniques, like FEAT (Few-shot Embedding Adaptation with Transformer), Modified ResNet12, and Prototypical Networks, are especially appropriate for the small dataset size. Additionally, the proposed approach has been evaluated on the FER-BYC dataset, exhibiting 94.06% validation accuracy, which is better than the performance of traditional methods, and gaining higher classification accuracy. This research not only introduces new dataset but also gives a robust technique for baby facial expression recognition.}, archive = {J_ACCESS}, author = {Sumiya Arafin and Adnan Ferdous Ashrafi and Md. Golam Rabiul Alam and Ashis Talukder}, doi = {10.1109/ACCESS.2025.3545759}, journal = {IEEE Access}, pages = {37302-37317}, shortjournal = {IEEE Access}, title = {BFER-net: Babies facial expression recognition model using ResNet12 enabled few-shot embedding adaptation and convolutional block attention modules}, volume = {13}, year = {2025}, }
• (2025). A dual-perspective self-supervised IoT intrusion detection method based on topology reconstruction and feature perturbation. ACCESS, 13, 37290-37301. (www)

  @article{ , abstract = {The rapid growth of the Internet of Things has driven intelligent advancements across various fields. However, the proliferation of IoT devices and diverse network interactions also introduces significant security challenges. As a critical technology for securing IoT, intrusion detection systems aim to identify potential threats by analyzing network traffic features. Yet, traditional models struggle to capture the complex topological structures in IoT environments, and their training often relies heavily on large amounts of labeled data, making them unsuitable for IoT settings where massive data is continually generated. This paper presents a graph-based framework leveraging self-supervised learning to address these challenges. This framework employs a graph encoder to capture topological information and generate graph embeddings, utilizing topology reconstruction and feature perturbation to create diverse loss graphs that enhance feature learning. Additionally, the custom adaptive cosine loss function dynamically adjusts the loss weights of different samples and utilizes cosine similarity to align the embeddings of real and predicted graphs, thereby maximizing mutual information between local embeddings of real graphs and global summaries. The model was evaluated on four public datasets, and the experimental results demonstrate that the proposed method significantly improves detection performance. It outperforms existing self-supervised graph neural network models, validating the applicability of this approach in settings with unlabeled data.}, archive = {J_ACCESS}, author = {Ruisheng Li and Huimin Shen and Qilong Zhang}, doi = {10.1109/ACCESS.2025.3545278}, journal = {IEEE Access}, pages = {37290-37301}, shortjournal = {IEEE Access}, title = {A dual-perspective self-supervised IoT intrusion detection method based on topology reconstruction and feature perturbation}, volume = {13}, year = {2025}, }
• (2025). Collaborative fault tolerance for cyber-physical systems: The diagnosis stage. ACCESS, 13, 37278-37289. (www)

  @article{ , abstract = {The reliability and robustness of cyber-physical systems (CPS) are critical aspects of the current industrial landscape. The high level of autonomous and distributed components associated with a large number of devices makes CPS prone to faults. Despite their importance and benefits, traditional fault tolerance methodologies, namely local and/or centralized, often overlook the potential benefits of collaboration between cyber-physical components. This paper introduces a collaborative fault diagnosis methodology for CPS, integrating self-fault diagnosis capabilities in agents and leveraging collaborative behavior to enhance fault diagnosis. The contribution of this paper relay in propose a methodology for fault diagnosis for CPS, based on multi-agent system (MAS) technology as a backbone of infra-structure, highlighting the components, agent behavior, functionalities, and interaction protocols, to explore the benefits of communication and collaboration between agents. The proposed methodology enhance the accuracy of fault diagnosis when compared with local approach. A case study was conducted in a laboratory-scale warehouse, focusing on diagnosing drift, bias, and precision faults in temperature and humidity sensors. Experimental results reveal that the collaborative methodology significantly outperforms the local approach in fault diagnosis, as evidenced by performance improvements in diagnosis classification. The statistical significance of these results was validated using the Wilcoxon signed-ranks test for paired samples.}, archive = {J_ACCESS}, author = {Luis Piardi and Pedro Costa and André Schneider de Oliveira and Paulo Leitão}, doi = {10.1109/ACCESS.2025.3543081}, journal = {IEEE Access}, pages = {37278-37289}, shortjournal = {IEEE Access}, title = {Collaborative fault tolerance for cyber-physical systems: The diagnosis stage}, volume = {13}, year = {2025}, }
• (2025). Multilevel inverter based on a dual two-level nine-switch converter for a dual three-phase or six-phase motor drive system. ACCESS, 13, 37263-37277. (www)

  @article{ , abstract = {The nine-switch inverter is considered as a very interesting solution for three-phase dual output applications, such as dual three-phase or six-phase motor drive systems. The main advantage of this approach is a converter topology with a reduced number of power semiconductors. However, the AC output voltages are limited to only two levels. This work proposes a new inverter topology for dual three-phase or six-phase motor drive systems with multilevel output characteristics. The proposed topology is based on two isolated inverters, but the number of legs is reduced from six to three. This structure creates a modular multilevel inverter with a high number of voltage levels while using a comparatively smaller number of power semiconductors. A modulation strategy, associated with the proposed topology, is presented to ensure multilevel operation. The proposed topology and modulation strategy will be tested first through a simulation in Matlab/Simulink and then by laboratory tests using an experimental prototype. The obtained results show a good agreement between simulations and experiments, both confirming the theoretical studies.}, archive = {J_ACCESS}, author = {V. Fernão Pires and Armando Cordeiro and Daniel Foito and A. J. Pires and J. Fernando A. Silva and Hao Chen}, doi = {10.1109/ACCESS.2025.3543703}, journal = {IEEE Access}, pages = {37263-37277}, shortjournal = {IEEE Access}, title = {Multilevel inverter based on a dual two-level nine-switch converter for a dual three-phase or six-phase motor drive system}, volume = {13}, year = {2025}, }
• (2025). Review of visual defect detection technology for micro coaxial cable harnesses. ACCESS, 13, 37243-37262. (www)

  @article{ , abstract = {The identification of defects in micro coaxial cable harnesses is of paramount importance in ensuring the quality of the production process. However, the prevailing methodology for detecting such defects in micro coaxial cable harnesses is predominantly manual, which has resulted in poor detection accuracy and precision. Furthermore, this approach had a deleterious effect on both the efficiency and quality of the production process. In order to achieve a rapid and precise defect detection technology, this paper provided a comprehensive analysis of the production line status and the various defect types associated with micro coaxial cable harnesses. Subsequently, we discussed the application of traditional image processing methods, machine learning methods, and deep learning methods to defect detection technology for high-precision components, and summarized the algorithmic performance, advantages and limitations of the various methods. The findings of this paper demonstrated that traditional image processing methods and machine learning methods exhibited inadequate robustness, were susceptible to illumination-related variations, and had a restricted scope of applicability. In contrast, deep learning methods have shown improved detection precision and efficiency, nevertheless, remaining constrained by certain limitations. This paper presents a discussion and proposes the corresponding suggestions from three perspectives: the impact of illumination environment, defect dataset and defect detectability. The objective was to enhance the precision and robustness of visual defect detection, thus establishing a foundation for the subsequent design of visual inspection system for defects in micro coaxial cable harnesses. Furthermore, it furnished pertinent relevant references for the application of high-precision component defect visual inspection.}, archive = {J_ACCESS}, author = {Jin Li and Zhipeng Guo and Lei Ye and Hailin Wang}, doi = {10.1109/ACCESS.2025.3545057}, journal = {IEEE Access}, pages = {37243-37262}, shortjournal = {IEEE Access}, title = {Review of visual defect detection technology for micro coaxial cable harnesses}, volume = {13}, year = {2025}, }
• (2025). Plasma cleaning technology: Mechanisms, influencing factors, and applications. ACCESS, 13, 37221-37242. (www)

  @article{ , abstract = {Plasma cleaning technology has emerged as a revolutionary approach in surface treatment, offering a non-contact, non-destructive, and environmentally friendly alternative to traditional cleaning methods. This paper provides a comprehensive review of the mechanisms, influencing factors and applications of plasma cleaning technologies. The characteristics of plasma cleaning technologies with different excitation methods, such as direct current discharge, low-frequency discharge, radio frequency discharge, and microwave plasma cleaning, are analyzed. The key factors that affect the cleaning effectiveness, such as the type of working gas, pressure, discharge power, cleaning duration and working distance, have been summarized. The applications of plasma cleaning are explored extensively, highlighting its significance in the maintenance of optical components, semiconductor industry, surface treatment of metals and metal oxides, and conservation of cultural heritage. Finally, the paper discusses the current challenges faced by plasma cleaning technology and offers perspectives on future trends.}, archive = {J_ACCESS}, author = {Jianan Sun and Yanmei Yu and Jianming Tang and Yan Zeng and Jiaxuan Chen}, doi = {10.1109/ACCESS.2025.3532662}, journal = {IEEE Access}, pages = {37221-37242}, shortjournal = {IEEE Access}, title = {Plasma cleaning technology: Mechanisms, influencing factors, and applications}, volume = {13}, year = {2025}, }
• (2025). Battery management system: Threat modeling, vulnerability analysis, and cybersecurity strategy. ACCESS, 13, 37198-37220. (www)

  @article{ , abstract = {The Battery Management System (BMS) plays a crucial role in modern energy storage technologies, ensuring battery safety, performance, and longevity. However, as the BMS becomes more sophisticated and interconnected, it faces increasing cybersecurity challenges that can lead to catastrophic failures and safety hazards. This paper provides a comprehensive overview of cyberattacks targeting both traditional and wireless BMS. It explores various attack vectors, including malware injection, electromagnetic interference (EMI), temperature sensing manipulation, sensor malfunctioning and fault injection, and jamming attacks on modern BMS. Through threat modeling and vulnerability analysis, this paper examines the potential impacts on BMS functionality, safety, and performance. We highlight vulnerabilities associated with different BMS architectures and components, emphasizing the need for robust cybersecurity measures to protect against emerging threats. Cybersecurity measures are essential to protect the system from potential threats that could trigger false alarms, cause malfunctions, or lead to dangerous failures. Unauthorized access or tampering with the BMS can disrupt its fault response mechanisms, jeopardizing system performance and associated resources. Key cybersecurity strategies include intrusion detection systems (IDS), crypto-based authentication, secure firmware updates, and hardware-based security mechanisms such as trusted platform modules (TPMs). These measures strengthen BMS resilience by preventing unauthorized access and ensuring data integrity. Our findings are essential for mitigating risks in various sectors, including electric vehicles (EVs), renewable energy, and grid storage. They underscore the importance of ongoing research and development of adaptive security strategies to safeguard BMS against evolving cyber threats. Additionally, we propose a trust mechanism that secures the connection between input sensors and the BMS, ensuring the reliability and safety of battery-powered systems across various industries.}, archive = {J_ACCESS}, author = {Shravan Murlidharan and Varsha Ravulakole and Jyothi Karnati and Hafiz Malik}, doi = {10.1109/ACCESS.2025.3543249}, journal = {IEEE Access}, pages = {37198-37220}, shortjournal = {IEEE Access}, title = {Battery management system: Threat modeling, vulnerability analysis, and cybersecurity strategy}, volume = {13}, year = {2025}, }
• (2025). Analysis and design of a 50-kW two stage converters for EV fast charger with high efficiency and wide output voltage of 150–1000 v. ACCESS, 13, 37182-37197. (www)

  @article{ , abstract = {Generally, the DC-DC converters used in DC fast chargers must cover a wide output voltage with a fixed input voltage, and a single module needs to supply 30-50 kW of power. When designing an LLC resonant converter to cover a wide output voltage with a narrow frequency at high power, a small magnetizing inductance is required, resulting in increased switching turn-off losses. Additionally, a large air gap in the transformer is needed, which increases AC losses in the transformer windings owing to the fringing effect, degrading efficiency. Therefore, to cover a wide output voltage with a fixed input voltage while achieving high power and efficiency, the LLC resonant converter followed by the buck converter is proposed in this paper. Because the LLC resonant converter operates at a fixed frequency, a large magnetizing inductance can be used. This results in lower switching turn-off losses and a smaller air gap in the transformer, which decreases AC losses in the transformer windings. Additionally, by optimally selecting the frequencies of each converter and output voltage of the LLC resonant converter, which functions as the input voltage of the buck converter, high efficiency can be achieved. To confirm the validity of the proposed structure of the DC-DC converter, an 820 V input and 150-1,000 V/50 kW output prototype is built and tested.}, archive = {J_ACCESS}, author = {Ha-Min Park and Hyung-Woo Lee and Chan-Bae Park and Jae-Bum Lee and Jin-Chul Kim and Ju Lee}, doi = {10.1109/ACCESS.2025.3544277}, journal = {IEEE Access}, pages = {37182-37197}, shortjournal = {IEEE Access}, title = {Analysis and design of a 50-kW two stage converters for EV fast charger with high efficiency and wide output voltage of 150–1000 v}, volume = {13}, year = {2025}, }
• (2025). A hybrid transformer-based framework for multi-document summarization of turkish legal documents. ACCESS, 13, 37165-37181. (www)

  @article{ , abstract = {The rapid growth in the volume and complexity of legal documents has created an urgent need for automated tools to help legal professionals efficiently extract and summarize critical information. While numerous studies have addressed this issue across various languages, Turkish legal document summarization presents unique challenges due to the language’s agglutinative structure and domain-specific terminology. This gap is particularly significant, given Turkey’s extensive legal system and the increasing volume of digital legal documents. This study represents the first comprehensive effort to address this gap by introducing a hybrid approach that combines traditional extractive techniques, including TF-IDF and TextRank, with advanced transformer-based models such as LED, Long-T5, BART-large, and GPT-3.5 Turbo. A new dataset of 2,000 Turkish civil cases, carefully curated and validated from publicly accessible legal platforms, was developed to support this research. Extractive summarization methods were evaluated based on cosine similarity, content precision, recall, and F1-score, with TF-IDF achieving a content F1-score of 0.47 using keyword-based summaries. Transformer-based models demonstrated significant improvements, with GPT-3.5 Turbo achieving the highest ROUGE scores (ROUGE-1: 55%, ROUGE-2: 35%, ROUGE-L: 42%, ROUGE-Sum: 44%), showcasing its superior ability to generate accurate and coherent summaries. By tackling the unique linguistic challenges of Turkish agglutinative morphology, this research provides the first foundation for scalable, automated tools to enhance legal professionals’ efficiency and decision-making within Turkey’s legal system.}, archive = {J_ACCESS}, author = {Maha Ahmed Abdullah Albayati and Oguz Findik}, doi = {10.1109/ACCESS.2025.3545750}, journal = {IEEE Access}, pages = {37165-37181}, shortjournal = {IEEE Access}, title = {A hybrid transformer-based framework for multi-document summarization of turkish legal documents}, volume = {13}, year = {2025}, }
• (2025). Procurement strategy for electric power companies with demand signal updating under supply-demand uncertainty. ACCESS, 13, 37147-37164. (www)

  @article{ , abstract = {Electric power companies face uncertainties in both supply and demand during the procurement of electrical materials. Supply uncertainty is derived from unpredictable delivery quantities from suppliers and material quality inspection failures, and demand uncertainty is derived from the lead time, as the exact demand is not known at the time of placing the order, but the demand information is updated over time. Based on two ordering opportunities provided by the supplier, the decision on whether to place a second order is made based on the judgement of the acquired demand signals between the two ordering periods. This paper constructs a demand information updating model based on Bayesian theory to mathematically represent the demand information update and determine the threshold for demand signal updates, categorizing demand signals into high and low demand signals. Based on the judgment of demand information updating, a two-stage ordering model for the electric power company is constructed, providing the optimal first ordering strategy and the optimal second ordering strategy after assessing the demand signals, along with a sensitivity analysis of the demand signal threshold judgment and the system parameters for the two orderings. Numerical and case analysis indicates that the demand signal threshold decreases with increasing inventory costs and material ordering prices, and increases with rising shortage costs and coefficient of variation. Furthermore, conducting a second order after assessing the demand signal can reduce costs and enhance service levels.}, archive = {J_ACCESS}, author = {Jinxia Li and Qi Chen and Fuguo Wen and Chengyong Wu and Qian Feng and Haiyan Wang}, doi = {10.1109/ACCESS.2025.3545260}, journal = {IEEE Access}, pages = {37147-37164}, shortjournal = {IEEE Access}, title = {Procurement strategy for electric power companies with demand signal updating under supply-demand uncertainty}, volume = {13}, year = {2025}, }
• (2025). Intelligent motion control to enhance the swimming performance of a biomimetic underwater vehicle using reinforcement learning approach. ACCESS, 13, 37128-37146. (www)

  @article{ , abstract = {This article develops an intelligent motion control strategy using reinforcement learning to regulate a biomimetic autonomous underwater vehicle (BAUV) swimming performance. The BAUV is driven by the oscillatory motion of a lunate-shaped caudal fin. To navigate effectively, the vehicle must regulate its propeller’s motion to swim toward goals. The caudal fin’s oscillatory motion is defined by sine functions, where the amplitude, frequency, bias, and time shifting are the main flapping parameters that must be regulated. The developed algorithm, based on the n-step state-action-reward-state-action on-policy learning, fine-tunes these parameters. Further, deep Q learning network speed adjusters were designed to enhance BAUV guidance. By integrating waypoint guidance systems and intelligent path trackers, the vehicle reduces its heading deviation and distance to goals. The effectiveness of these methods was validated through simulations featuring various disturbances like longitudinal, lateral, and swirl currents. The proposed scheme allowed the vehicle to reach desired targets efficiently, even in the presence of strong currents.}, archive = {J_ACCESS}, author = {Juan A. Algarín-Pinto and Luis E. Garza-Castañón and Adriana Vargas-Martínez and Luis I. Minchala-Ávila and Pierre Payeur}, doi = {10.1109/ACCESS.2025.3544482}, journal = {IEEE Access}, pages = {37128-37146}, shortjournal = {IEEE Access}, title = {Intelligent motion control to enhance the swimming performance of a biomimetic underwater vehicle using reinforcement learning approach}, volume = {13}, year = {2025}, }
• (2025). A new model of trapped charge sources in switching transient studies in the presence of shunt reactors. ACCESS, 13, 37118-37127. (www)

  @article{ , abstract = {Insulation coordination studies are of great importance in power grid reliability. In this paper, a new method is proposed for modeling trapped charge sources (TCS) in switching transient studies. The TCS is used to take into account the voltage stored in line capacitors during reclosing operation after fault occurrence. The proposed model is designed based on the active filter concept, thus overcoming the limitations of conventional TCS for simulating transient states in EMTP/ATPDraw. Given the natural frequencies of a transmission line to which the proposed TCS (PTCS) is connected, it injects the appropriate frequencies and eliminates voltage oscillations which limit the use of TCS. To verify the efficiency of the PTCS, it is implemented in a real system with a shunt reactor, and the results are then compared with field measurements. A comparison of the results shows that the PTCS eliminates the voltage oscillations in the simulation before closing and provides a smooth voltage with the desired amplitude. Using the proposed model, the maximum line switching overvoltage is correctly calculated; this, in turn, results in a more accurate transmission line insulation design, which is technically and economically beneficial.}, archive = {J_ACCESS}, author = {Mohsen Akafi-Mobarakeh and Reza Shariatinasab and Pierluigi Siano and Behrooz Vahidi and Hamid Reza Najafi}, doi = {10.1109/ACCESS.2025.3544109}, journal = {IEEE Access}, pages = {37118-37127}, shortjournal = {IEEE Access}, title = {A new model of trapped charge sources in switching transient studies in the presence of shunt reactors}, volume = {13}, year = {2025}, }
• (2025). An improved convolutional neural networks: Quantum pseudo-transposed convolutional neural networks. ACCESS, 13, 37108-37117. (www)

  @article{ , abstract = {Recent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks. Building on the operational principles of classical transposed convolutional neural networks (CNNs), we introduce a novel quantum variant: the Quantum Pseudo-Transposed Convolutional Neural Network (QPTCNN). The QPTCNN adapts the concept of classical transposed CNNs to the quantum domain, leveraging a hybrid quantum-classical framework that combines a quantum convolutional layer with a classical fully connected layer. In the QPTCNN, the quantum convolutional layer emulates a transposed convolution operation, ensuring that the output feature map retains the same dimensions as the input image. This is accomplished using rotational angle encoding and a ring-structured quantum circuit, interconnected by two-qubit control gates such as CNOT and CRY gates, facilitating efficient quantum convolution. We evaluated the performance of the QPTCNN on the MNIST and Fashion-MNIST datasets, with two distinct versions of the model: Model A, which utilizes a CNOT-gate entanglement circuit, and Model B, which employs a CRY-gate entanglement circuit. The results demonstrate that both Model A and Model B achieve strong performance across the datasets. However, Model A outperforms Model B, achieving higher classification accuracy and lower loss compared to earlier models. These findings suggest that the QPTCNN is highly capable of learning and extracting relevant feature information from input images, making it well-suited for high-performance image classification tasks. This work represents a significant advancement in quantum-enhanced image classification.}, archive = {J_ACCESS}, author = {Li Hai and Chen Liang and Hao Yaming and Yu Wenli and Shi Fengquan}, doi = {10.1109/ACCESS.2025.3543164}, journal = {IEEE Access}, pages = {37108-37117}, shortjournal = {IEEE Access}, title = {An improved convolutional neural networks: Quantum pseudo-transposed convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). A relationship-aware feature update method for enhanced graph-based neural networks. ACCESS, 13, 37096-37107. (www)

  @article{ , abstract = {This paper presents a novel feature update method that leverages the relationships among batch elements, addressing scenarios both with and without an external graph. In the absence of an external graph, a relationship learning algorithm is developed, and the relationship network is further utilized to update the features. When an external graph is available, this information is integrated by deriving a graph-based loss using a Bayesian network. The proposed method is applied to both Graph Convolutional Networks (GCNs) and Convolutional Neural Networks (CNNs) for person re-identification. Experimental results demonstrate that, with an external graph, the method achieves superior accuracy and faster learning rates on the Cora and Citeseer datasets. Additionally, it outperforms state-of-the-art baseline methods in mean average precision, rank-1 accuracy, and learning rate on the Market-1501, DukeMTMC, VeRi-776, and MSMT17 datasets. Furthermore, similar to batch normalization, this method can be incorporated as a module within a neural network.}, archive = {J_ACCESS}, author = {Conggui Huang}, doi = {10.1109/ACCESS.2025.3545586}, journal = {IEEE Access}, pages = {37096-37107}, shortjournal = {IEEE Access}, title = {A relationship-aware feature update method for enhanced graph-based neural networks}, volume = {13}, year = {2025}, }
• (2025). A video summary generation model based on hybrid attention using multimodal features. ACCESS, 13, 37078-37095. (www)

  @article{ , abstract = {In the process of producing digital media art works, it is usually necessary to select suitable parts from a lot of materials. Video summarization can classify and extract key segments from material videos based on image content, audio features, and other factors. By analyzing the video content, the redundant information is eliminated to form a short video summary. Video summary is the key technology of Internet video distributed storage and search in we-media era. The encoder of the video summary model often ignores the modal information except the video frame, and its decoder has structural defects in the processing of long video sequences, which easily leads to excessive self-attention weight variance and low accuracy. Based on this, we propose a Video Summary Generation Model Based on Multimodal Feature Fusion and Hybrid Attention (abbr. VSMHA), which contains a multimodal feature fusion encoder and a hybrid attention decoder. For the encoder, the image features, audio features and optical flow features are effectively fused to construct a multimodal feature fusion encoder. For the decoder, the hybrid attention mechanism (i.e., local attention modules, global attention modules and residual inputs) is used to reduce the variance of attention weights on long videos, and the decoder is constructed by weighted fusion of context-related temporal information output by Bidirectional Gated Recurrent Unit (BiGRU) network. We conduct the experiment on four different datasets (TvSum, SumMe, OVP and YouTube), compared with other baseline models, the proposed VSMHA achieved the best results. Specifically, when evaluated on the augmented datasets of TvSum and SumMe, the VSMHA model exhibited remarkable improvements, its performance surpassed the previous models’ highest scores by 2.18% and 0.27% respectively. Through these in-depth analysis, we found that the VSMHA model outperforms other two-modal fusion models in terms of robustness. This indicates that our model can maintain stable performance and better adapt to various data characteristics and application scenarios, further highlighting its superiority and practical value in video summarization tasks.}, archive = {J_ACCESS}, author = {Xiaofei Pang and Lu Gao and Liming Zhang}, doi = {10.1109/ACCESS.2025.3545113}, journal = {IEEE Access}, pages = {37078-37095}, shortjournal = {IEEE Access}, title = {A video summary generation model based on hybrid attention using multimodal features}, volume = {13}, year = {2025}, }
• (2025). Fault type classification in the presence of inverter-based resources: Review, challenges, and future works. ACCESS, 13, 37051-37077. (www)

  @article{ , abstract = {Fault type classification (FTC) is a major unit of industrial distance relays that affects some other protective schemes such as single-pole tripping and reclosing, fault location and etc. Traditional FTC techniques make use of either phase changes between current symmetrical components or magnitude differences between superimposed currents. Since inverter-based resources (IBRs) have different current signatures than those of synchronous generators, conventional FTC approaches suffer from misrecognition when applied to a transmission line emanating from an IBR. The fault current contribution of IBRs is determined by the host grid code (GC) and the internal controller. Therefore, this paper demonstrates the limitations of the conventional FTC methods employed in industrial applications for protection of lines that originate from IBRs under modern host GCs. First, the influence of various GCs on the classic FTC techniques is analyzed based on the negative-sequence control strategies. Then, some experimental tests are executed to evaluate the accuracy of industrial FTC methods in the attendance of IBRs. After that, various newly proposed FTC methods in the presence of IBRs are studied, and their advantages and disadvantages are discussed. Lastly, potential research gaps in the area of FTC with IBRs are noted for further study and investigation in academic circles.}, archive = {J_ACCESS}, author = {Mohammad Mehdi Mobashsher and Seyyed Mehdi Hosseini and Ali Akbar Abdoos and Sayyed Mohammad Hashemi and Majid Sanaye-Pasand and Maher Azzouz}, doi = {10.1109/ACCESS.2025.3545765}, journal = {IEEE Access}, pages = {37051-37077}, shortjournal = {IEEE Access}, title = {Fault type classification in the presence of inverter-based resources: Review, challenges, and future works}, volume = {13}, year = {2025}, }
• (2025). An internet-of-things based on ILPP, petri nets, and artificial neural networks for controlling tool failures in flexible manufacturing systems under complex operational conditions. ACCESS, 13, 37035-37050. (www)

  @article{ , abstract = {Petri nets are utilized for modeling and evaluating tool failures in flexible manufacturing systems (FMSs) characterized by “concurrency,” “conflicts,” “resource sharing,” and “sequential operations.” The effectiveness of the development controller for complicated FMSs depends on the quality of tool maintenance techniques that can minimize costs, increase productivity, reduce unscheduled downtime, and minimize disturbances to other FMS processes. This paper develops a new online controller that combines “colored Petri nets” (CPNs), the “internet of things” (IoT), and “artificial neural networks” (ANNs) to prevent early tool failures in FMSs. First, a structurally minimal approach-based PN, “vector covering approach” (VCA), and “place invariants” (PIs) are employed to generate the “integer linear programming problem” (ILPP); solving this ILPP enables the obtaining of an optimal set of monitors to ensure the FMS’s liveness. Second, in the first stage, the algorithm achieves a model with maximal permissiveness; nonetheless, its structure is complex. Consequently, CPNs are used to achieve minimal structural complexity by merging all control places into one controller. Next, a method that integrates the resultant network from the second step with IoT and ANN is developed for “fault detection and treatment,” ensuring the online control of tool failures in the FMS. Furthermore, a PN simulator is employed for verification, validation, and assessment of the liveness and reliability of the proposed model. Comparative analysis with existing literature indicates that the models are more effective in preventing tool failures in FMSs.}, archive = {J_ACCESS}, author = {Husam Kaid and Abdulrahman Al-Ahmari and Khaled N. Alqahtani and Abdulmajeed Dabwan and Mustafa M. Nasr and Mohammed H. Alhaag}, doi = {10.1109/ACCESS.2025.3544772}, journal = {IEEE Access}, pages = {37035-37050}, shortjournal = {IEEE Access}, title = {An internet-of-things based on ILPP, petri nets, and artificial neural networks for controlling tool failures in flexible manufacturing systems under complex operational conditions}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based approaches and comparisons for estimating missing meteorological data and determining the optimum data set in nuclear energy applications. ACCESS, 13, 37019-37034. (www)

  @article{ , abstract = {Good data analysis is required for the optimal design of nuclear energy projects. However, due to financial or technical reasons, data cannot be collected regularly, which leads to missing data problems. Missing values in data sets can seriously affect research results. There are two main motivations for the study. The first motivation of the study was to define the estimation of missing data in the meteorological data set and its usability in the nuclear energy industry by using Machine Learning (ML)-based Linear Regression (LR), Decision Trees (DT) and Random Forest (RF) algorithms. Its second motivation is to determine the optimum set/number of meteorological data required for nuclear energy projects using the best-performing ML algorithm. For this purpose, 31 years of meteorological data regarding the wind speed, rainfall amount, snowpack and air temperature required for nuclear energy projects by the nuclear policy board in Turkey were analyzed. In this way, some difficulties such as processing and organizing the data created by unnecessary and large data due to its volume and speed have been prevented. In this study, which is based on incomplete meteorological measurement data, the mechanism belongs to the MCAR type. Linear Regression method reached the highest performance with 91.6%. Additionally, by normalizing the data set using Standardization and Normalization scaling techniques, this performance increased to 93.3% and 98.9%, respectively. On the other hand, it has been observed that a 14-year training set is sufficient as a data set in nuclear energy applications.}, archive = {J_ACCESS}, author = {Fatih Topaloğlu}, doi = {10.1109/ACCESS.2025.3545361}, journal = {IEEE Access}, pages = {37019-37034}, shortjournal = {IEEE Access}, title = {Machine learning-based approaches and comparisons for estimating missing meteorological data and determining the optimum data set in nuclear energy applications}, volume = {13}, year = {2025}, }
• (2025). POLIPHONE: A dataset for smartphone model identification from audio recordings. ACCESS, 13, 37006-37018. (www)

  @article{ , abstract = {When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuable insights for various applications, including legal proceedings and integrity investigations. The source attribution problem has been addressed in different domains, from identifying the camera model used to capture specific photographs to detecting the synthetic speech generator or microphone model used to create or record given audio tracks. Recent advancements in this area rely heavily on machine learning and data-driven techniques, which often outperform traditional signal processing-based methods. However, a drawback of these systems is their need for large volumes of training data, which must reflect the latest technological trends to produce accurate and reliable predictions. This presents a significant challenge, as the rapid pace of technological progress makes it difficult to maintain datasets that are up-to-date with real-world conditions. For instance, in the task of smartphone model identification from audio recordings, the available datasets are often outdated or acquired inconsistently, making it difficult to develop solutions that are valid beyond a research environment. In this paper we present POLIPHONE, a dataset for smartphone model identification from audio recordings. It includes data from 20 recent smartphones recorded in a controlled environment to ensure reproducibility and scalability for future research. The released tracks contain audio data from various domains (i.e., speech, music, environmental sounds), making the corpus versatile and applicable to a wide range of use cases. We also present numerous experiments to benchmark the proposed dataset using a state-of-the-art classifier for smartphone model identification from audio recordings.}, archive = {J_ACCESS}, author = {Davide Salvi and Daniele Ugo Leonzio and Antonio Giganti and Claudio Eutizi and Sara Mandelli and Paolo Bestagini and Stefano Tubaro}, doi = {10.1109/ACCESS.2025.3545152}, journal = {IEEE Access}, pages = {37006-37018}, shortjournal = {IEEE Access}, title = {POLIPHONE: A dataset for smartphone model identification from audio recordings}, volume = {13}, year = {2025}, }
• (2025). Robust classification of encrypted network services using convolutional neural networks optimized by information bottleneck method. ACCESS, 13, 36995-37005. (www)

  @article{ , abstract = {The rapid growth of encrypted network traffic poses significant challenges for network monitoring and security systems, as traditional methods often fail to accurately classify encrypted services. This paper proposes a novel approach to achieve robust classification of encrypted network services using Convolutional Neural Networks (CNNs) optimized by the Information Bottleneck (IB) method (IB-CNN). The IB method is employed to reduce the complexity of the model while retaining the most relevant information for classification, thereby improving its performance and generalization capabilities. We design and implement a CNN architecture tailored to the characteristics of encrypted network traffic, and apply the IB method to optimize the intermediate representations. Extensive experiments on a diverse dataset of encrypted network services demonstrate that our approach significantly outperforms existing methods in terms of accuracy and robustness, even in the presence of varying network conditions and adversarial attacks. Additionally, we analyze the impact of different IB parameters on the classification performance and provide insights into the optimal configuration for practical deployment. This study contributes to advancing the state-of-the-art in encrypted network service classification and offers a promising direction for enhancing the security and reliability of network monitoring systems.}, archive = {J_ACCESS}, author = {Zhijiong Wang and Wei Lin and Yu Chen and Mang I. Vai}, doi = {10.1109/ACCESS.2025.3545476}, journal = {IEEE Access}, pages = {36995-37005}, shortjournal = {IEEE Access}, title = {Robust classification of encrypted network services using convolutional neural networks optimized by information bottleneck method}, volume = {13}, year = {2025}, }
• (2025). Legal query RAG. ACCESS, 13, 36978-36994. (www)

  @article{ , abstract = {Recently, legal practice has seen a significant rise in the adoption of Artificial Intelligence (AI) for various core tasks. However, these technologies remain in their early stages and face challenges such as understanding complex legal reasoning, managing biased data, ensuring transparency, and avoiding misleading responses, commonly referred to as hallucinations. To address these limitations, this paper introduces Legal Query RAG (LQ-RAG), a novel Retrieval-Augmented Generation framework with a recursive feedback mechanism specifically designed to overcome the critical shortcomings of standard RAG implementations in legal applications. The proposed framework incorporates four key components: a custom evaluation agent, a specialized response generation model, a prompt engineering agent, and a fine-tuned legal embedding LLM. Together, these components effectively minimize hallucinations, improve domain-specific accuracy, and deliver precise, high-quality responses for complex queries. Experimental results demonstrate that the fine-tuned embedding LLM achieves a 13% improvement in Hit Rate and a 15% improvement in Mean Reciprocal Rank (MRR). Comparisons with general LLMs reveal a 24% performance gain when using the Hybrid Fine-Tuned Generative LLM (HFM), the specialized response generation model integrated into the LQ-RAG framework. Furthermore, LQ-RAG achieves a 23% improvement in relevance score over naive configurations and a 14% improvement over RAG with Fine-Tuned LLMs (FTM). These findings underscore the potential of domain-specific fine-tuned LLMs, combined with advanced RAG modules and feedback mechanisms, to significantly enhance the reliability and performance of AI in legal practice. The reliance of this study on a proprietary model as the evaluation agent, combined with the lack of feedback from human experts, highlights the need for improvement. Future efforts should focus on developing a specialized legal evaluation agent and enhancing its performance by incorporating feedback from domain experts.}, archive = {J_ACCESS}, author = {Rahman S. M. Wahidur and Sumin Kim and Haeung Choi and David S. Bhatti and Heung-No Lee}, doi = {10.1109/ACCESS.2025.3542125}, journal = {IEEE Access}, pages = {36978-36994}, shortjournal = {IEEE Access}, title = {Legal query RAG}, volume = {13}, year = {2025}, }
• (2025). Class concept representation from contextual texts for training-free multi-label recognition. ACCESS, 13, 36965-36977. (www)

  @article{ , abstract = {The power of large vision-language models (VLMs) has been demonstrated for downstream vision tasks, including multi-label recognition (MLR) with a training-free approach or prompt tuning by measuring the cosine similarity between the text features related to class names and the visual features of the images. Previous studies typically represent class-related text features by averaging simple handcrafted prompts with class names (e.g., “a photo of class name”), which lack the contextual information required to effectively handle diverse content and capture the co-occurrence of multiple semantic classes within an image. In addition, prompt tuning inherently requires labeled data or additional training, making it susceptible to overfitting for context tokens and hindering generalization. To address these limitations, we propose a training-free and label-free MLR framework that leverages the abundant text descriptions of novel classes to narrow the gap between the visual and text features at inference. Inspired by how humans form the concepts on words using contexts or the patterns of co-occurrence with other words, we propose a class concept representation for zero-shot MLR for large VLMs, which the rich contextual information embedded in large-scale image descriptions (e.g., “A person holding a large pair of scissors”). To further improve the alignment of the visual features of VLMs to the class concept representation, we also present a context-guided visual representation based on an attention process that operates in the same linear space as the class concept representation. Experimental results obtained on diverse benchmarks demonstrate that the proposed methods substantially improve the performance of existing zero-shot MLR methods, achieving an average improvement of 9.4% in mean average precision (mAP) over the zero-shot contrastive language-image pretraining (CLIP) method and 2.2% in mAP over the TaI-DPT zero-shot prompt-tuning method. In addition, our proposed method can synergistically work with existing prompt tuning methods, consistently improving the performance of the DualCoOp and TaI-DPT in a training-free manner with a negligible increase in inference time.}, archive = {J_ACCESS}, author = {Dong Un Kang and Hyunwoo Lee and Se Young Chun}, doi = {10.1109/ACCESS.2025.3545772}, journal = {IEEE Access}, pages = {36965-36977}, shortjournal = {IEEE Access}, title = {Class concept representation from contextual texts for training-free multi-label recognition}, volume = {13}, year = {2025}, }
• (2025). A framework for institutional privacy considered full DNS over HTTPS architecture. ACCESS, 13, 36951-36964. (www)

  @article{ , abstract = {Domain Name System (DNS) has become an indispensable infrastructure for domain name resolution, which is essential for using Internet services today. Until now, privacy protection in DNS has mainly focused on the end user side, encrypting traffic between end terminals and DNS full-service resolvers, and the communication between DNS full-service resolvers and authoritative DNS servers still occurs in clear text. A DNS query from a DNS full-service resolver to an authoritative DNS server does not typically pose a privacy issue because the source IP address originates from the DNS full-service resolver. However, several recent reports have indicated that privacy leaks can occur at the institutional level by wiretapping the DNS communication or analyzing the domain name resolution logs of authoritative DNS servers. In order to further strengthen privacy in DNS, we propose a framework for domain name resolution that considers institutional privacy: full-DoH DNS architecture. The evaluation results on the prototype system confirmed that the end user privacy leakage risk on DNS communication between the end terminal and authoritative DNS servers could be significantly mitigated, and the overhead was acceptable for using Internet services. The main contributions of this paper can be summarized as follows: 1) we proposed a novel domain name resolution framework enabling end user preferences considering institutional privacy; 2) the evaluation results confirmed deployment in a local network environment and real-world Internet; and 3) we discussed the related issues of the proposed framework with respect to operations in a real network environment.}, archive = {J_ACCESS}, author = {Satoru Sunahara and Yong Jin and Katsuyoshi Iida and Yoshiaki Takai}, doi = {10.1109/ACCESS.2025.3542633}, journal = {IEEE Access}, pages = {36951-36964}, shortjournal = {IEEE Access}, title = {A framework for institutional privacy considered full DNS over HTTPS architecture}, volume = {13}, year = {2025}, }
• (2025). NeuSD: Surface completion with multi-view text-to-image diffusion. ACCESS, 13, 36939-36950. (www)

  @article{ , abstract = {We present a new method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured. Our approach builds on two recent developments: surface reconstruction with neural radiance fields for reconstruction of the visible parts of the surface, and the guidance of pre-trained 2D diffusion models in the form of Score Distillation Sampling (SDS) for plausible synthesis of the unobserved parts. We obtain geometric details on the synthesized part by employing normal maps as a pure geometric representation for SDS instead of color renderings which are entangled with the appearance information. We propose a way to reduce the stochasticity of the SDS loss which results in an increased level of detail of the surface. Finally, we propose a tuning-free mechanism to achieve multi-view consistency in SDS, which increases the consistency between the synthesized and visible parts of the surface. We evaluate our approach on the BlendedMVS dataset and demonstrate significant qualitative and quantitative improvements over competing methods.}, archive = {J_ACCESS}, author = {Savva Ignatyev and Daniil Selikhanovych and Oleg Voynov and Yiqun Wang and Peter Wonka and Stamatios Lefkimmiatis and Evgeny Burnaev}, doi = {10.1109/ACCESS.2025.3536004}, journal = {IEEE Access}, pages = {36939-36950}, shortjournal = {IEEE Access}, title = {NeuSD: Surface completion with multi-view text-to-image diffusion}, volume = {13}, year = {2025}, }
• (2025). LOOC: Localizing organs using occupancy networks and body surface depth images. ACCESS, 13, 36930-36938. (www)

  @article{ , abstract = {We introduce a novel approach for the precise localization of 67 anatomical structures from single depth images captured from the exterior of the human body. Our method uses a multi-class occupancy network, trained using segmented CT scans augmented with body-pose changes, and incorporates a specialized sampling strategy to handle densely packed internal organs. Our contributions include the application of occupancy networks for occluded structure localization, a robust method for estimating anatomical positions from depth images, and the creation of detailed, individualized 3D anatomical atlases. We outperform localization using template matching and provide qualitative real-world reconstructions. This method promises improvements in automated medical imaging and diagnostic procedures by offering accurate, non-invasive localization of critical anatomical structures.}, archive = {J_ACCESS}, author = {Pit Henrich and Franziska Mathis-Ullrich}, doi = {10.1109/ACCESS.2025.3543736}, journal = {IEEE Access}, pages = {36930-36938}, shortjournal = {IEEE Access}, title = {LOOC: Localizing organs using occupancy networks and body surface depth images}, volume = {13}, year = {2025}, }
• (2025). Spatio-temporal and semantic enhanced context-aware navigation for indoor robots. ACCESS, 13, 36909-36929. (www)

  @article{ , abstract = {Indoor robot navigation demands that robots not only map their environment, but also understand the context within it. Traditional geometric, topological, and semantic models offer specialized capabilities, but often lack the integration of contextual knowledge critical for navigating unfamiliar spaces. To address this limitation, we introduce the Context-Aware Rapidly-exploring Random Tree (CA-RRT) algorithm, which integrates semantic awareness into the exploration process. The CA-RRT algorithm improves navigational efficiency by combining metric, topological, and semantic models to construct a complete representation of contextual knowledge. This hybrid approach, augmented by spatio-temporal data, allows robots to interpret their environment and infer the relations more effectively. The proposed CA-RRT algorithm, implemented within the robot operating system, leverages ontology design patterns, such as n-ary semantic relations, to encode context-aware constraints directly into the exploration process. Experimental results indicate that CA-RRT substantially improves search efficiency and semantic accuracy compared to the baseline A ROS Multi Ontology References (ARMOR) framework and traditional Rapidly-exploring Random Tree (RRT) algorithm, especially when dealing with complex environments where semantic constraints are vital. This novel approach holds promise for the advancement of the capabilities of autonomous robots, enabling them to navigate more intelligently and efficiently in dynamically changing indoor spaces. The proposed approach achieved minimum distance, execution time, and higher performance in terms of accuracy, precision, and coverage area compared to the baseline ARMOR framework and other exploration strategies used in the existing work.}, archive = {J_ACCESS}, author = {R. Gayathri and V. Uma and R. Rajakumar and R. Vishnu Priya and R. Vedhapriyavadhana}, doi = {10.1109/ACCESS.2025.3545074}, journal = {IEEE Access}, pages = {36909-36929}, shortjournal = {IEEE Access}, title = {Spatio-temporal and semantic enhanced context-aware navigation for indoor robots}, volume = {13}, year = {2025}, }
• (2025). Gravitational search algorithm swarm-based UAV reconnaissance for multiple targets detection in unknown environment. ACCESS, 13, 36897-36908. (www)

  @article{ , abstract = {Target detection in an unknown environment is a crucial aspect of reconnaissance using a swarm of unmanned aerial vehicles (UAVs). An efficient target detection technique is required to minimize the number of iterations for searching and maximize the coverage area with respect to the number of iterations and detected targets. This paper proposes a gravitational search algorithm (GSA) swarm-based UAV reconnaissance scheme to detect targets in an unknown environment. Additionally, different GSA-based searching methods are analyzed to identify the most efficient one with the minimum number of iterations and maximum coverage. Extensive simulations are performed, and the results of the proposed scheme are compared with existing search schemes. The results demonstrate that the proposed GSA swarm-based detection scheme requires fewer iterations and provides greater area coverage than existing UAV reconnaissance schemes for target detection in an unknown environment.}, archive = {J_ACCESS}, author = {Ahmed Al Amin and Irfan Azam and Md Masuduzzaman and Abdullah Qayyum and Muhammad Sajid Sarwar and Saud Khan and Soo Young Shin}, doi = {10.1109/ACCESS.2025.3545733}, journal = {IEEE Access}, pages = {36897-36908}, shortjournal = {IEEE Access}, title = {Gravitational search algorithm swarm-based UAV reconnaissance for multiple targets detection in unknown environment}, volume = {13}, year = {2025}, }
• (2025). Enhancing road safety: Detection of animals on highways during night. ACCESS, 13, 36877-36896. (www)

  @article{ , abstract = {Animals on highways at night can create serious accidents that put both human and wildlife lives in danger. Conventional monitoring systems are rendered useless due to poor visibility and inadequate lighting, emphasizing the necessity for a sophisticated detecting system. Using YOLOv5, a cutting- edge object detection algorithm, this study presents a way to identify animals on highways at night and reduce collisions. To improve image quality in low-light circumstances, the model uses pre-processing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and a robust Retinex model. This enhancement enables YOLOv5 to achieve improved detection accuracy on enhanced photos, increasing its effectiveness in spotting animals in low-light situations. YOLOv5 offers quick, highly accurate detection in contrast to conventional techniques like infrared and thermal imaging, which frequently lack the resolution for accurate species identification. The model, trained on a broad dataset, performed well, with a precision of 0.923, recall of 0.773, mean average precision (mAP50) of 0.802, and mean average precision (mAP50-95) of 0.567, demonstrating its competence in spotting animals in normal nighttime circumstances. Despite the model’s good performance in normal low-light situations, problems still arise in unfavorable conditions including rain, fog, and dense foliage, which can lower detection accuracy. This study aims to improve road safety and aid to wildlife conservation by minimizing highway collisions. Subsequent endeavors will center on enhancing the model’s resilience to withstand harsh circumstances and further diminish the likelihood of accidents.}, archive = {J_ACCESS}, author = {K. Parkavi and Agniv Ganguly and Shivansh Sharma and Ashesh Banerjee and Keshav Kejriwal}, doi = {10.1109/ACCESS.2025.3545490}, journal = {IEEE Access}, pages = {36877-36896}, shortjournal = {IEEE Access}, title = {Enhancing road safety: Detection of animals on highways during night}, volume = {13}, year = {2025}, }
• (2025). Transfer learning and custom loss applied to transformer-based text translation for sign language animated subtitles. ACCESS, 13, 36858-36876. (www)

  @article{ , abstract = {Online learning’s rise presents unique challenges for the deaf community, particularly in understanding educational videos. This research addresses the problem by proposing a solution to generate animated subtitles in the Indonesian Sign Language System, namely SIBI (Sistem Isyarat Bahasa Indonesia). The existing method produces word-by-word animations from directly recognized spoken text that are too fast and difficult to follow. We developed a text translation model for the Moodle application to shorten the original spoken text of the educational videos into SIBI text without losing the crucial information. We propose transfer learning to train our transformer-based models and use a custom loss function to ensure alignment with the SIBI dictionary. Pre-trained models mBART50 and NLLB200 were fine-tuned on the SIBIVID-MP12 dataset, which was created in collaboration with Special Education teachers. Experiment results show that the proposed method improves translation metrics significantly, with the best performance observed for the NLLB200 model fine-tuned with our proposed custom loss, achieving sacreBLEU, chrF++, METEOR, and ROUGE-L improvements of 71%, 9.79%, 22.92%, and 14.55%, respectively. This research demonstrates the potential for enhanced inclusivity in online learning for the deaf community.}, archive = {J_ACCESS}, author = {Evan Aurelrius and Andhira Henrisen Sikoko and Erdefi Rakun and Kurniawati Azizah}, doi = {10.1109/ACCESS.2025.3542785}, journal = {IEEE Access}, pages = {36858-36876}, shortjournal = {IEEE Access}, title = {Transfer learning and custom loss applied to transformer-based text translation for sign language animated subtitles}, volume = {13}, year = {2025}, }
• (2025). A comparative study of image processing techniques for javanese ancient manuscripts enhancement. ACCESS, 13, 36845-36857. (www)

  @article{ , abstract = {Digital preservation to preserve information in ancient manuscripts is often not optimal because of the object’s condition, which has degraded over time. Therefore, quality enhancement is required through several image processing techniques to optimize the readability of the information within them. This study explores five types of filters, Gaussian Blur, Adaptive Thresholding, Canny Edge Detection, Morphology, and Sobel Algorithm, to address the readability challenges of three types of ancient Javanese manuscripts, Serat Piwulang Cablaka, Serat Pakem Gancaripun Lelampahan, and Serat Piwulang Nulis. This study evaluates how each filter enhances visual clarity while maintaining structural integrity using evaluation metrics such as the Structural Similarity Index Measure (SSIM) and Normalized Cross-Correlation (NCC). Based on the evaluation of the result, this study revealed that Gaussian Blur performed well, attaining an average SSIM value of 0.91, signifying its proficiency in preserving the original visual structure and texture. Furthermore, this study emphasizes the potential of combining Gaussian Blur with techniques like Adaptive Thresholding and Morphologyological operations to achieve an optimal balance between improving legibility and maintaining the authenticity of manuscripts.}, archive = {J_ACCESS}, author = {Imam Yuadi and Novanto Yudistira and Habiddin Habiddin and Khoirun Nisa'}, doi = {10.1109/ACCESS.2025.3545455}, journal = {IEEE Access}, pages = {36845-36857}, shortjournal = {IEEE Access}, title = {A comparative study of image processing techniques for javanese ancient manuscripts enhancement}, volume = {13}, year = {2025}, }
• (2025). Semi-supervised learning for estimation of a speaker’s intimacy enhanced with classification models of auxiliary tasks. ACCESS, 13, 36832-36844. (www)

  @article{ , abstract = {A dialog system is required to take a user’s intimacy into account to converse comfortably, since the appropriate style and content of a conversation between familiar people can be different from those between strangers. This paper aims to estimate the level of intimacy of a speaker during a dialog, and mainly focuses on tackling the problem of the sparseness of the labeled datasets for this task. Since there is no publicly available dataset in Japanese, we construct a new dialog corpus annotated with speakers’ intimacy. In addition, we propose a novel semi-supervised learning method that uses both labeled and unlabeled data for the intimacy estimation task. The existing method, Simple instance-Adaptive self-Training (SAT), which iterates the assignment of pseudo-labels to unlabeled data and training a model, is extended so that external knowledge can be incorporated to enhance the quality of the pseudo-labels. Specifically, classification models of two other tasks, Dialog Act Classification and Emotion Recognition in Conversation, are used as external knowledge, since relatively good models of these tasks can be trained using existing large-scale datasets. The results of experiments show that the proposed method significantly outperforms baselines including SAT, especially when the amount of labeled data is small.}, archive = {J_ACCESS}, author = {Takuto Miura and Hideaki Kanai and Kiyoaki Shirai and Natthawut Kertkeidkachorn}, doi = {10.1109/ACCESS.2025.3544600}, journal = {IEEE Access}, pages = {36832-36844}, shortjournal = {IEEE Access}, title = {Semi-supervised learning for estimation of a speaker’s intimacy enhanced with classification models of auxiliary tasks}, volume = {13}, year = {2025}, }
• (2025). A compact quasi-static model for magnetic dipole resonance in metallic nanoparticles with large radii. ACCESS, 13, 36824-36831. (www)

  @article{ , abstract = {Magnetic resonance in metallic nanoparticles has garnered significant attention from scientists for its promising applications in medical diagnostics, data storage technologies, and the development of advanced metamaterials. The behavior of these applications is driven by the interaction of metallic nanoparticles with electromagnetic fields, thus creating electric and magnetic dipole resonances. In nanoparticles with small radii ( $radius \lt 40$ nm), the response is primarily governed by the electric dipole resonance and can be described by the simplified quasi-static approximation model. However, the magnetic resonance in metallic nanoparticles occurs at significantly larger radii, and its current understanding is limited to the Mie solution and numerical field solvers. These methods provide little insight into the underlying mechanisms of the magnetic dipole resonance. In this paper, we developed a compact series resonance model for magnetic dipole resonance using the quasi-static approximation ( $radius \ll wavelength$ ) and linear circuit theory. The scattering cross-section efficiency, along with the spherical and fringe impedance, are calculated using current and voltage expressions. The simulation results closely align with those of the Mie solution. The quasi-static model now enables the voltage and current approach to effectively model larger nanoparticles (40 nm $\lt ~radius~\lt ~90$ nm), whereas it was initially restricted to nanoparticles less than 40 nm radius. The proposed work enhances understanding and is valuable for large-scale all-photonic applications involving magnetic resonance in metallic nanoparticles.}, archive = {J_ACCESS}, author = {Ahsan Irshad and Qamrosh Sajjad and Sohaib Manzoor and Ghulam Asghar and Mehboob Alam}, doi = {10.1109/ACCESS.2025.3539078}, journal = {IEEE Access}, pages = {36824-36831}, shortjournal = {IEEE Access}, title = {A compact quasi-static model for magnetic dipole resonance in metallic nanoparticles with large radii}, volume = {13}, year = {2025}, }
• (2025). A synergy between machine learning and formal concept analysis for crowd detection. ACCESS, 13, 36804-36823. (www)

  @article{ , abstract = {To enhance public safety, crowd detection and prevention systems have essentially become a natural means to manage diverse crowded areas, such as urban settings, transportation hubs, and event venues. Recent systems take advantage of the synergy between machine learning, data mining, and image processing to extract/analyze features from crowded zones and recognize patterns and anomalies from the crowd behavior. Additionally, image processing tools play a key role in real-time monitoring by analyzing video feeds to detect crowd density, flow direction, and identify potential risks like overcrowding or emergencies. However, most existing solutions focus on the detection phase and often overlook integrated error handling and robust decision-making frameworks to ensure accurate and actionable crowd prevention. Aiming to solve these issues, we take advantage of the prediction capabilities of machine learning models and the analysis and clustering strengths of Formal Concept Analysis (FCA) chosen for its strong mathematical foundation and superior clustering capabilities compared to traditional methods, as highlighted in recent works such as K-means or hierarchical clustering. We used the first technique to extract useful knowledge from areas’ produced images while mitigating potential error accumulation through modular error-checking mechanisms. A neural network is used to mark human bodies, determine the position of walking individuals, and predict crowd levels. Such information is, thereafter, inputted to the FCA-based decision system to ensure an explicit representation and modelling of crowd data, thanks to lattice structures. These latter’s hierarchical view helped us identify the crowded areas and manage them as clustered zones, based on their common crowd information. We also define bottom-up parsing algorithms to recommend the suitable crowd prevention plan w.r.t. the crowd level. Experiments have successfully proved the ability of FCA to exclude low-crowd zones, locate crowded areas, and provide actionable crowd management insights, which may complement crowd counting techniques.}, archive = {J_ACCESS}, author = {Anas M. Al-Oraiqat and Oleksandr Drieiev and Sattam Almatarneh and Mohammadnoor Injadat and Karim A. Al-Oraiqat and Hanna Drieieva and Yassin M. Y. Hasan}, doi = {10.1109/ACCESS.2025.3545559}, journal = {IEEE Access}, pages = {36804-36823}, shortjournal = {IEEE Access}, title = {A synergy between machine learning and formal concept analysis for crowd detection}, volume = {13}, year = {2025}, }
• (2025). Multi-functional homomorphic encryption method based on crowd sensing networks. ACCESS, 13, 36795-36803. (www)

  @article{ , abstract = {In response to the security and privacy issues associated with sensing devices in contemporary crowd sensing systems, the paper proposes a crowd sensing networks method based on the MFHE (the Multi-Functional Homomorphic Encryption) architecture. This method leverages fully homomorphic encryption technology, thereby eliminating the use of plaintext throughout the sensing process and enhancing security compared to traditional encryption methods. Initially, a sensing model is constructed based on the classic crowd sensing architecture. Subsequently, fully homomorphic encryption technology is employed for plaintext-free data transmission. The encryption scheme is developed on the MFHE architecture, with additional improvements introduced through indistinguishability obfuscation and puncturable pseudorandom function methods, which serve to shorten the ciphertext and reduce complexity, rendering it more suitable for crowd sensing environments. Finally, experiments conducted on real-world datasets demonstrate that the proposed method is effective in terms of both privacy protection and time efficiency.}, archive = {J_ACCESS}, author = {Hui Chen and Xingyu Mou and Zixuan Wang and Tianting Wu and Xin Wang and Chengcheng Wang and Ruixiang Song and Lifang Song and Xiaoling Jiang and Xinjing Zhang and Yunfeng Li}, doi = {10.1109/ACCESS.2025.3544763}, journal = {IEEE Access}, pages = {36795-36803}, shortjournal = {IEEE Access}, title = {Multi-functional homomorphic encryption method based on crowd sensing networks}, volume = {13}, year = {2025}, }
• (2025). Bounded Human–Robot interaction control using joint torque estimation from electromyographic signals. ACCESS, 13, 36775-36794. (www)

  @article{ , abstract = {Many of the most sophisticated human-robot interaction control schemes fall short when it comes to ensuring safe interactions, primarily because they do not generate bounded control actions. Recent advancements have introduced saturating control schemes to tackle this problem, but these approaches are heavily reliant on precise dynamics of the robotic system. In this context, the paper proposes an innovative adaptive impedance control scheme that utilizes electromyographic (EMG) signals to estimate joint torque, thereby ensuring the generation of bounded control actions to regulate the behavior of exoskeleton-type robotic systems. This scheme is classified as a hybrid controller, which sets it apart from the majority of kinematic controllers that typically use proportional-derivative (PD) or proportional-integral-derivative (PID) control schemes. Unlike these conventional approaches, which depend on accurate mathematical models for effective tracking and stability, the proposed scheme integrates adaptive mechanisms to enhance performance. Validation through numerical simulations and experimental tests using the Hill muscle model confirms the scheme’s effectiveness and reliability in managing both isometric and continuous joint movements, underscoring its significant potential for ensuring safe and adaptive human-exoskeleton interactions.}, archive = {J_ACCESS}, author = {Marco O. Mendoza-Gutiérrez and Víctor I. Ramírez-Vera and Isela Bonilla-Gutiérrez and Emilio J. González-Galván and Ambrocio Loredo-Flores}, doi = {10.1109/ACCESS.2025.3544626}, journal = {IEEE Access}, pages = {36775-36794}, shortjournal = {IEEE Access}, title = {Bounded Human–Robot interaction control using joint torque estimation from electromyographic signals}, volume = {13}, year = {2025}, }
• (2025). Harnessing the power of attention for patch-based biomedical image classification. ACCESS, 13, 36760-36774. (www)

  @article{ , abstract = {The analysis of biomedical images, particularly brain MRI scans, is critical in healthcare and medical research. However, conventional approaches such as convolutional neural networks (CNNs) often struggle to capture complex spatial and contextual relationships within medical imaging data, limiting their generalization capabilities. To address these limitations, we propose a novel framework for brain MRI analysis using Vision Transformers (ViTs) augmented with a Shifted Patching Technique (S.P.T.). This approach combines conventional non-overlapping patching with shifted patching to better model intricate spatial dependencies and contextual associations. The proposed method is evaluated on the BRAIN MRI dataset, with standardized image resolution achieved through Lanczos5 interpolation. Further validation is performed on the ChestX-ray14 and Camelyon17 datasets to assess its generalizability across medical imaging modalities. Performance metrics such as accuracy, F1-score, and precision demonstrate that the proposed ViT model consistently outperforms traditional CNN-based methods. Ablation studies further confirm the significant contribution of the S.P.T. and other architectural components to overall performance. These findings highlight the potential of ViT-based architectures with innovative patching techniques as powerful tools for biomedical image analysis. This work offers a robust and scalable solution for enhancing diagnostic accuracy and advancing medical research applications.}, archive = {J_ACCESS}, author = {Gousia Habib and Ishfaq Ahmad Malik and Shaima Qureshi and In-Ho Ra and Saurabh Singh and Surbhi Sharma}, doi = {10.1109/ACCESS.2024.3524403}, journal = {IEEE Access}, pages = {36760-36774}, shortjournal = {IEEE Access}, title = {Harnessing the power of attention for patch-based biomedical image classification}, volume = {13}, year = {2025}, }
• (2025). A lightweight, intelligent autonomous exploration system for indoor robots using improved random walk with back-off strategies. ACCESS, 13, 36723-36759. (www)

  @article{ , abstract = {Autonomous exploration in unstructured indoor settings requires effective navigation approaches to manage obstacles, dead ends, and resource constraints. Conventional algorithms, like the TurtleBot3 Drive (TB3), frequently struggle with inadequate area coverage, inefficient obstacle management, and substantial computational demands. This study presents the Intelligent Exploration (IE) algorithm, an innovative method that integrates an improved random walk technique, real-time LiDAR-based obstacle avoidance, and a strategic back-off mechanism to optimize exploration efficiency. Detailed simulations using the TurtleBot3 Burger in the Gazebo simulator under the ROS Noetic framework, performed from various starting positions and over different time intervals (ranging from 2 to 15 minutes), demonstrated the efficiency of the proposed method when integrated with SLAM Gmapping. The IE algorithm consistently surpassed the baseline TurtleBot3 Drive (TB3) algorithm across critical metrics, attaining a 35% increment in coverage area (16.25 m2 vs 11.99 m2), traversing 35% longer distances (113.95 m vs 84.07 m), and identifying 17% more obstacles (137.82 vs 118.11). The algorithm maintained a reliable average localization error of 0.005 m while exhibiting memory efficiency by the reduced memory usage by up to 13.7% relative to TB3. By addressing limitations in coverage, obstacle handling, and resource efficiency, the IE algorithm provides a scalable and lightweight solution for autonomous indoor exploration, particularly on resource-constrained robotic systems.}, archive = {J_ACCESS}, author = {Blesson Mathews Luke and S. Abraham Sampson}, doi = {10.1109/ACCESS.2025.3545452}, journal = {IEEE Access}, pages = {36723-36759}, shortjournal = {IEEE Access}, title = {A lightweight, intelligent autonomous exploration system for indoor robots using improved random walk with back-off strategies}, volume = {13}, year = {2025}, }
• (2025). DRANet: Deep learning-based automatic white balancing approach to CVCC. ACCESS, 13, 36714-36722. (www)

  @article{ , abstract = {As a core component of the image processing pipeline, an image signal processor plays a critical role in automatic white balancing (AWB) for computer vision color constancy (CVCC). With the recent advent of deep convolutional neural network (DCNN), image signal processors have experienced enormous progress in CVCC. A myriad of deep learning-based CVCC models have emerged and outperformed their statistics-based, shallow counterparts. In this context, this article presents a novel deep learning-based AWB approach: the Dual Residual Aggregated Network (DRANet). Homogeneous Dual Residual Blocks (DRBs) are a key component of the proposed DRANet, intended to design the architecture with high cardinality. The homogeneous DRBs perform transformations simultaneously and their outputs are processed in a concatenating manner. This characterizes the proposed DRANet as a simple and wide but still deep structure which contributes to advancing illumination estimation accuracy to an innovative level and addressing the overfitting and long-term dependency issues that conventional approaches have long been struggling with. The proposed DRANet offers another advantage that comes with high cardinality, that is, a reduction in the number of parameters necessary to run the architecture. The experimental results provide compelling evidence that the proposed AWB approach has achieved an innovative progress in illumination estimation accuracy, as well as validating the invariance of both illumination and image device.}, archive = {J_ACCESS}, author = {Ho-Hyoung Choi}, doi = {10.1109/ACCESS.2025.3545633}, journal = {IEEE Access}, pages = {36714-36722}, shortjournal = {IEEE Access}, title = {DRANet: Deep learning-based automatic white balancing approach to CVCC}, volume = {13}, year = {2025}, }
• (2025). Hybrid convolutional neural network-transformer model for end-to-end binaural sound source localization in reverberant environments. ACCESS, 13, 36701-36713. (www)

  @article{ , abstract = {The end-to-end binaural sound source localization model can implicitly extract features from the original signal waveforms and take full advantage of neural networks. In this study, we propose a new deep learning-based end-to-end binaural sound source localization model called WavLocT, which employs a gammatone filter bank to simulate the frequency properties of the human ear and decomposes the signals received by microphones into subband signals. The WavLocT model has a unique feature extraction block that incorporates a convolutional neural network (CNN) and transformer structure, in which binaural localization features are extracted from different subband signal waveforms via the CNN structure. Additionally, we use a selective attention mechanism for different frequency subbands via the transformer encoder. In the training phase, we simulated three reverberant rooms of different sizes and trained two models separately for diffuse and directional noise environments. In the testing phase, we applied the trained models to both binaural room impulse response (BRIR)-matched and BRIR-mismatched environments. We selected the root mean square error (RMSE) and accuracy (Acc) as evaluation metrics to evaluate model performance and compared the results with those of two recent CNN-based end-to-end binaural sound source localization models. The results of the simulation experiments demonstrated that the proposed WavLocT model effectively estimated the azimuth of the desired speech signal in both diffuse and directional noise environments. Specifically, in the diffuse noise environment, WavLocT achieved an average RMSE of 6.45° and Acc of 70.02% across all mismatched rooms, outperforming WavLoc (6.72° RMSE, 56.10% Acc) and WavLocEC (7.46° RMSE, 65.86% Acc). In the directional noise environment, WavLocT achieved an average RMSE of 7.61° and Acc of 63.38% across all mismatched rooms, outperforming WavLoc (8.34° RMSE, 52.38% Acc) and WavLocEC (8.19° RMSE, 58.05% Acc).}, archive = {J_ACCESS}, author = {Xinyi Chen and Lijia Zhao and Jie Cui and Hua Li and Xiaodong Wang}, doi = {10.1109/ACCESS.2025.3545065}, journal = {IEEE Access}, pages = {36701-36713}, shortjournal = {IEEE Access}, title = {Hybrid convolutional neural network-transformer model for end-to-end binaural sound source localization in reverberant environments}, volume = {13}, year = {2025}, }
• (2025). Real-time moving vehicle counting and speed estimation toward efficient traffic surveillance. ACCESS, 13, 36687-36700. (www)

  @article{ , abstract = {This paper presents a Spatial-Temporal Diagram (STD) algorithm for real-time vehicle counting and speed estimation in camera-based traffic surveillance. The algorithm consists of four main steps: STD graph generation highlighting vehicles as peaks, graph refinement using Gaussian Mixture Model likelihood optimization, peak detection through RANdom SAmple Consensus model fitting, and traffic parameter computation. Testing on over 11 million video frames from diverse sources, including 511 highway cameras, NVIDIA AI City Challenge, and Next Generation Simulation datasets, demonstrated the algorithm’s robustness across varying illumination, weather conditions, and road infrastructures. The algorithm achieved average accuracies of 95.4%, 96.9%, and 96.1% for Precision, Recall, and F1-Score, respectively, outperforming traditional deep learning methods while requiring less computational resources.}, archive = {J_ACCESS}, author = {Danesh Shokri and Christian Larouche and Saeid Homayouni}, doi = {10.1109/ACCESS.2025.3540950}, journal = {IEEE Access}, pages = {36687-36700}, shortjournal = {IEEE Access}, title = {Real-time moving vehicle counting and speed estimation toward efficient traffic surveillance}, volume = {13}, year = {2025}, }
• (2025). Hardware implementation of improved banker’s fixed-point rounding algorithm. ACCESS, 13, 36679-36686. (www)

  @article{ , abstract = {In recent years, FPGA-based convolutional neural networks (CNNs) accelerator has received tremendous research interest, especially in fields such as autonomous driving and robotics. For the purpose of accelerating convolution computations, Winograd fast convolution algorithm is frequently employed. However, during implementation of the Winograd algorithm on FPGA, multiple rounding operations occur, and the accuracy of these operations substantially impacts the convolution results. The banker’s rounding algorithm, compared to other rounding algorithms, has advantages such as a more symmetric error distribution and smaller errors, making it suitable for Winograd convolution computation. However, the conventional banker’s rounding algorithm is proposed for floating-point calculations, yet FPGA implements fixed-point arithmetic. Moreover, it frequently rounds 0.5 to 0, leading to the issue of convolution weight invalidation and introducing significant errors. To overcome these challenges, an improved hardware circuit designed for implementing the fixed-point banker’s rounding algorithm is proposed. Experimental results show that compared with common rounding up and rounding down methods, the proposed algorithm exhibits smaller errors and effectively resolves the issue of weight invalidation in conventional banker’s rounding, leading to a significant 55.6% improvement in computational accuracy.}, archive = {J_ACCESS}, author = {Peng Yun and Wang Yubing and Zhang Yingxu and Liang Lei and Zhang Jun and Zhang Jianwei and Qin Li and Wang Lijun}, doi = {10.1109/ACCESS.2025.3543540}, journal = {IEEE Access}, pages = {36679-36686}, shortjournal = {IEEE Access}, title = {Hardware implementation of improved banker’s fixed-point rounding algorithm}, volume = {13}, year = {2025}, }
• (2025). Robust heterogeneous feature discriminant embedding used for remote sensing feature-level domain adaptation. ACCESS, 13, 36665-36678. (www)

  @article{ , abstract = {The development of sensor technology provides multisource remote sensing images (RSIs) observed from different resolutions and angles for object recognition applications. To process multisource data presenting diverse feature distributions, it is proper to exploit domain adaptation methods to reduce the distribution differences across sources. However, most domain-adaptive methods can only deal with homogeneous RSIs and rely on standard discriminative criteria, making it challenging to ensure effective subsequent recognition results. To attack these obstacles, the Robust heterogeneous feature discriminant embedding (RHFDE) method is proposed to learn domain adaptation discriminant features using cross-resolution and cross-angle RSIs. To deal with heterogeneous input, the specific-size projection matrices are constructed to map multisource RSIs. Then, unlike the current methods relying on common discriminant criteria, the support vector machine discriminant term is established and forms the optimization model. To improve robustness and adapt to unlabeled samples, the adaptive weight factors and semi-supervised learning strategy are presented to further improve RHFDE’s performance. Results from experiments using datasets with multi-resolution and multi-angles show that RHFDE obtains better recognition accuracy than typical feature-level domain adaptation methods.}, archive = {J_ACCESS}, author = {Shuang Lyu and Peng Liu and Yang Yang}, doi = {10.1109/ACCESS.2025.3542593}, journal = {IEEE Access}, pages = {36665-36678}, shortjournal = {IEEE Access}, title = {Robust heterogeneous feature discriminant embedding used for remote sensing feature-level domain adaptation}, volume = {13}, year = {2025}, }
• (2025). Coordinated traffic-signal control of wide area network via hierarchical reinforcement learning. ACCESS, 13, 36658-36664. (www)

  @article{ , abstract = {Traffic-signal control is key to ensuring smooth traffic flows in urban areas. However, controlling traffic by considering various traffic characteristics is a complex and challenging task. Although rule-based methods are typically employed, they have limitations. In this context, deep reinforcement learning-based methods have attracted attention because they do not require environmental models and can generate control policies without human intervention. Previous studies on traffic signal control have primarily employed autonomous decentralized control using the information from each intersection and implementing coordination among intersections. However, optimizing traffic control for an entire city using only data from near the intersections is challenging. In addition, learning the primitive control rules of traffic signals using all the information across a wide area significantly increases the action dimension and further complicates learning. However, this is determined ad-hoc and does not guarantee rationality. Therefore, this paper presents a hierarchical architecture that divides global and local controls. By introducing hierarchy and applying multiobjective reinforcement learning, this study proposes switching multiple intersection-control policies corresponding to the traffic-flow ratio, which guarantees Pareto optimization of traffic signal control among intersections.}, archive = {J_ACCESS}, author = {Takumi Saiki and Sachiyo Arai}, doi = {10.1109/ACCESS.2025.3544800}, journal = {IEEE Access}, pages = {36658-36664}, shortjournal = {IEEE Access}, title = {Coordinated traffic-signal control of wide area network via hierarchical reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). High-precision and robust DNN model for predicting quality factor of WPT-oriented slotted ground resonators. ACCESS, 13, 36647-36657. (www)

  @article{ , abstract = {Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (Q) enhance power transfer efficiency. However, it is pertinent to note that the resonator size, slot shape, and location result in distinct Q outcomes. Therefore, this work delves into the use of ML for predicting Q of SGP resonators. It can be predicted through a deep learning approach, owing to its capacity to learn from the implicit associations between input and output data. Hence, a deep neural network (DNN) model was designed using 20006 data files generated by electromagnetic (EM) simulations. DNN demonstrated its effectiveness, achieving an accuracy of 99.26%, thereby outperforming other benchmark ML models. Furthermore, the model proved its robustness in predicting Q of variously sized resonators and showed 98.3% accuracy. Subsequently, this enables anticipating the Q metric of scaled resonators without the need for exhaustive EM simulations. The predicted Q values were supported through experimental measurements. Finally, the SGP resonators were aptly employed to exhibit the near-field WPT system.}, archive = {J_ACCESS}, author = {K. Dautov and G. Tolebi and M. S. Hashmi and A. Jarndal and E. Almajali and G. Nauryzbayev}, doi = {10.1109/ACCESS.2025.3545141}, journal = {IEEE Access}, pages = {36647-36657}, shortjournal = {IEEE Access}, title = {High-precision and robust DNN model for predicting quality factor of WPT-oriented slotted ground resonators}, volume = {13}, year = {2025}, }
• (2025). 3D flight path planning for UAV based on improved particle swarm optimization algorithm. ACCESS, 13, 36637-36646. (www)

  @article{ , abstract = {Unmanned aerial vehicle (UAV) has been widely used in various fields such as agriculture, petroleum, military, meteorology, and geographic surveying. In actual flight, unmanned aerial vehicle needs to find the shortest path and avoid all threats. An improved particle swarm optimization algorithm combined with genetic algorithm method is presented in this paper to solve the path planning problem of UAV which easily falls into local optimization. The algorithm enhances early stage global search capability and later period local optimization capability compared to traditional particle swarm algorithm. To ensure that particles are distributed in key search areas of the environment, Gaussian distribution is used to initialize the particle distribution. Optimization ability can be further improved by the linear transformation operation on the well performing particles. Logistic function is employed to set the dynamic mutation probability. To improve the global search capability, random initialization operations are performed on particles with poor performance. Simulations in simple terrain and complex terrain environment are carried out to testify the feasibility and efficiency of the proposed improved algorithm.}, archive = {J_ACCESS}, author = {Cunjie Li and Qingli Zhao and Canyi Che}, doi = {10.1109/ACCESS.2025.3543175}, journal = {IEEE Access}, pages = {36637-36646}, shortjournal = {IEEE Access}, title = {3D flight path planning for UAV based on improved particle swarm optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Double adjacent error correction codes for ultra-fast cache memories. ACCESS, 13, 36626-36636. (www)

  @article{ , abstract = {Error correction codes are commonly used to protect cache memories from soft errors. As technology feature size scales deeper into sub-nanometer regime, radiation-induced soft error can causes double adjacent error (DAE). Several double adjacent error correction (DAEC) codes have been introduced to address DAEs, however, they miscorrect some nonadjacent double errors. In progress, a class of DAEC orthogonal Latin squares (OLS) codes is introduced to eliminates all miscorrections, using the orthogonality property of OLS codes, and also reduces the decoding delay time. The main drawback comes from the large number of check bits, imposed by the conventional OLS codes. In this paper, two coding approaches are developed based on a modified SEC OLS coding scheme that requires less number of check bits. The first approach is a class of SEC-DED-DAEC codes proposed to reduce the number of check bits compared to the existing SEC-DED-DAEC OLS codes. The second approach is a class of SEC-DAEC codes with a very high speed decoding process. This approach is designed as SEC OLS scheme and integrated with new modules for detecting and correcting the DAE error. The evaluation of the proposed SEC-DAEC codes in 45nm ASIC technology shows promising results. The decoding delay for protecting 16, 64, and 256 bit data words is less by at least 20% over existing SEC-DED and SEC-DAEC codes.}, archive = {J_ACCESS}, author = {Rabah Abood Ahmed and Khairulmizam Samsudin}, doi = {10.1109/ACCESS.2024.3522023}, journal = {IEEE Access}, pages = {36626-36636}, shortjournal = {IEEE Access}, title = {Double adjacent error correction codes for ultra-fast cache memories}, volume = {13}, year = {2025}, }
• (2025). Tuberculosis lesion segmentation improvement in X-ray images using contextual background label. ACCESS, 13, 36611-36625. (www)

  @article{ , abstract = {Pulmonary tuberculosis (PTB) is a serious, potentially fatal, infectious disease that primarily affects the lungs, and poses a significant threat to public health. To detect PTB at an early stage by screening chest X-Ray (CXR) images for tuberculosis (TB) lesions, we propose a semantic segmentation scheme that uses a deep learning algorithm. However, this scheme requires high-quality training data. To improve the TB-lesion segmentation model performance, a contextual background label process was designed for decomposing the heterogeneous CXR image background. From the designed process, five background subclasses consisting of: lung, mediastinum, body, doc, and background provided to modify the ground truth data for use in training the segmentation models, which was designed in four models to assess the performance improvement of the proposed scheme. The experimental results confirmed the applicability of the designed schemes. The TB-lesion segmentation models demonstrated improvements in terms of reduced false positives and better visualizations of the shape and location of TB lesions than the visualized approximation from the classification methods. The proposed model demonstrated highest scores of 88.68% on Dice, 83.55% on Intersection over Union, and 98.64% on precision for TB-lesion detection. The proposed models were externally validated to demonstrate their generalizability. They returned sensitivity, specificity and accuracy scores of 89.00%, 95.00% and 90.00%.}, archive = {J_ACCESS}, author = {Sahasat Khumang and Supaporn Kansomkeat and Wiwatana Tanomkiat and Sathit Intajag}, doi = {10.1109/ACCESS.2025.3532631}, journal = {IEEE Access}, pages = {36611-36625}, shortjournal = {IEEE Access}, title = {Tuberculosis lesion segmentation improvement in X-ray images using contextual background label}, volume = {13}, year = {2025}, }
• (2025). Light attack: A physical world real-time attack against object classifiers. ACCESS, 13, 36601-36610. (www)

  @article{ , abstract = {It is well known that deep neural networks (DNNs) are vulnerable to adversarial examples. In the digital world, most of the existing work makes classifiers or detectors fail by adding perturbations that are imperceptible to humans. In the physical world, existing work mostly invalidates classifiers or detectors by adding large unrealistic perturbations. In this paper, we attack the target classifiers from a new perspective of light attack. First, we define three lighting modes (Line, Point, Area), then we control the wavelength, intensity and position of light to accomplish adversarial attack, and finally we conduct ablation experiments and compare our method with existing mainstream methods. Experiments demonstrate that our proposed method is effective in both digital and physical worlds, and in darker environments, our attack method is much better than existing ones. Light attack enriches the current family of the adversarial attack while paving the way for future defense against light attacks as well as the study of light attack laws in nature.}, archive = {J_ACCESS}, author = {Ruizhe Hu and Ting Rui and Yan Ouyang and Jinkang Wang and Qunyan Jiang and Yinan Du}, doi = {10.1109/ACCESS.2022.3181197}, journal = {IEEE Access}, pages = {36601-36610}, shortjournal = {IEEE Access}, title = {Light attack: A physical world real-time attack against object classifiers}, volume = {13}, year = {2025}, }
• (2025). Optimized flexible microheater with integrated electrodes and microchannel for on-chip bacterial incubation and electrochemical detection. ACCESS, 13, 36590-36600. (www)

  @article{ , abstract = {The exponential rise in antimicrobial resistance (AMR) has gained growing interest in developing point-of-care devices capable of conducting inexpensive on-site bacterial infection testing. While these systems aim to incorporate more complex diagnostic methods into simple portable chips, they remain tethered to laboratory incubators requiring high-power inputs. Even though bacterial incubators play a vital role in providing optimal growth conditions for bacterial culture, they lack portability, which restricts their utility in real-time applications. A simple, flexible, portable incubation system holds significant potential for developing point-of-care kits. Herein, a flexible silver ink-based resistive microheater has been fabricated using inkjet printing. The fabricated microheater serves a multifaceted purpose, allowing for efficient growth conditions for culturing bacteria, their on-site quantification, and antibiotic susceptibility testing. The in-house fabricated microheater has been successfully employed for bacteria culturing at $37~^{\circ }$ C and developing a bacteria-on-chip platform for AMR detection. The microheater performance has been parametrically optimized using Multiphysics simulations and the design of experiments. The results obtained have been highlighted, achieving an appropriate incubation temperature of 37°C at an electric potential as low as 1.5 V with minimal thermal loss, ensuring superior temperature uniformity for 72-hour incubation and costing less than US 0.25 (₹ 20). Further, the fabricated microheater is reusable, stable, and water-resistant and has significant potential for developing affordable and turnkey point-of-care diagnostic kits for real-time applications.}, archive = {J_ACCESS}, author = {Sonal Fande and Khairunnisa Amreen and D. Sriram and Sanket Goel}, doi = {10.1109/ACCESS.2025.3544132}, journal = {IEEE Access}, pages = {36590-36600}, shortjournal = {IEEE Access}, title = {Optimized flexible microheater with integrated electrodes and microchannel for on-chip bacterial incubation and electrochemical detection}, volume = {13}, year = {2025}, }
• (2025). Automotive DNN-based object detection in the presence of lens obstruction and video compression. ACCESS, 13, 36575-36589. (www)

  @article{ , abstract = {Recent advances in sensing, processing, machine learning, and communication technologies are accelerating assisted and automated functions development for commercial vehicles. Environmental perception sensor data streams are processed to generate a correct and complete situational awareness. It is of utmost importance to assess the robustness of the sensor data pipeline, particularly in the case of data degradation in a noisy and variable environment. Sensor data reduction and compression techniques are key for higher levels of driving automation, as there is an expectation that traditional automotive vehicle wired networks will not be able to support the needed sensor datarates (i.e. more than 10 perception sensors, including cameras, LiDARs, and RADARs, generating tens of Gb/s of data). This work proposes for the first time to consider video compression for camera data transmission on vehicle wired networks in the presence of highly noisy data, e.g. partially obstructed camera field of view. The effects are discussed in terms of machine learning vehicle detection accuracy drop, and also visualising how detection performance spatially varies on the frames using the recently introduced metric, the Spatial Recall Index (SRI). The presented parametric obstruction noise model is generated to emulate real-world patterns, whereas compression is based on the well-established AVC/H.264. While Deep Neural Networks’ (DNNs’) performance is stable with lossy compression (up to 70:1) of ‘ideal’ data, when noise is added there is a significant accuracy degradation, in the range of a 7%-90% decrease. The proposed compression and noise tuning of the DNN training improves the performance up to 35%, enhancing the noise and compression robustness of the system. However, in the presence of compression combined with extreme levels of noise (i.e. more than 80% of pixels affected), DNN performance significantly degrades, up to a 90% decrease, even with re-training. This issue needs to be carefully considered in the design phase of perception and communication networks used to transmit sensor data.}, archive = {J_ACCESS}, author = {Gabriele Baris and Boda Li and Pak Hung Chan and Carlo Alberto Avizzano and Valentina Donzella}, doi = {10.1109/ACCESS.2025.3544773}, journal = {IEEE Access}, pages = {36575-36589}, shortjournal = {IEEE Access}, title = {Automotive DNN-based object detection in the presence of lens obstruction and video compression}, volume = {13}, year = {2025}, }
• (2025). Meta-RHDC: Meta reinforcement learning driven hybrid lyrebird falcon optimization for dynamic load balancing in cloud computing. ACCESS, 13, 36550-36574. (www)

  @article{ , abstract = {Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. Efficient load balancing is essential to maintain optimal performance and maximize resource utilization, ensuring an even distribution of network traffic across servers, preventing overload, enhancing response times, and improving system reliability. This paper proposes a Meta Reinforcement Learning Driven Hybrid Lyrebird Falcon Optimization for Dynamic Load Balancing in Cloud Computing (Meta-RHDC), a novel approach for dynamic load balancing in cloud environments. The Meta-RHDC model leverages convolutional and recurrent neural networks to predict virtual machine loads and dynamically classify them into overloaded and underloaded categories. By integrating reinforcement learning with advanced optimization techniques, Meta-RHDC significantly improves task scheduling and load balancing compared to existing methods such as Load Optimization Algorithm (LOA), Reinforcement Learning (RL), and Falcon Optimization Algorithm (FOA). Extensive experiments conducted on the CloudSim platform demonstrate that Meta-RHDC achieves substantial improvements in key performance metrics. For task counts, makespan is reduced by 19.51%, energy consumption by 22.75%, and balanced CPU utilization by 21.98%. Resource utilization increases by 32.52%, scalability efficiency improves by 49.03%, and the failure rate decreases by 19.72%. For virtual machine (VM) counts, makespan improves by 30.57%, energy consumption by 42.59%, and balanced CPU utilization by 36.85%. Resource utilization rises by 31.75%, scalability efficiency increases by 36.61%, and the failure rate drops by 38.19%. These results confirm the robustness and efficiency of Meta-RHDC in optimizing execution, resource management, and scalability across diverse workloads, making it a superior solution for dynamic load balancing in cloud computing.}, archive = {J_ACCESS}, author = {Mallu Shiva Rama Krishna and D. Khasim Vali}, doi = {10.1109/ACCESS.2025.3544775}, journal = {IEEE Access}, pages = {36550-36574}, shortjournal = {IEEE Access}, title = {Meta-RHDC: Meta reinforcement learning driven hybrid lyrebird falcon optimization for dynamic load balancing in cloud computing}, volume = {13}, year = {2025}, }
• (2025). Bus signal priority solution for non-uniform vehicle arrival conditions. ACCESS, 13, 36537-36549. (www)

  @article{ , abstract = {Bus signal priority (BSP) is an affordable and less infrastructure-demanding operational strategy that is used to improve the efficiency and reliability of transit vehicles. Majority of the studies on BSP are designed with the assumption of uniform vehicle arrivals and departures, ignoring the intra-cycle and inter-cycle traffic variations. However, real-world situations frequently involve fluctuations in the number of vehicle arrivals from one signal cycle to the next or within a cycle, which the available solutions cannot handle easily. This study addresses this using an analytical solution based on the incremental queue accumulation (IQA) method for BSP with two signal controller actions, i.e. red truncation (RT) and green extension (GE). The basic objective of the proposed solution is to minimize the total person delay at the intersection, considering all vehicles in all approaches. The solution was evaluated by integrating it with VISSIM and compared for both intra-cycle and inter-cycle flow variations for three scenarios: using basic Webster equation, using Webster’s random delay term for non-uniform arrivals, and using the IQA method for non-uniform arrivals. The results showed that the proposed solution using the IQA method outperformed both in terms of overall person delay reduction and increase in the percentage of priority provided.}, archive = {J_ACCESS}, author = {Nazia Tazin Imran and Lelitha Devi Vanajakshi}, doi = {10.1109/ACCESS.2025.3544873}, journal = {IEEE Access}, pages = {36537-36549}, shortjournal = {IEEE Access}, title = {Bus signal priority solution for non-uniform vehicle arrival conditions}, volume = {13}, year = {2025}, }
• (2025). Measuring online battery AC resistance using a DC-DC converter with time and frequency hybrid method. ACCESS, 13, 36527-36536. (www)

  @article{ , abstract = {The estimation of battery state is important for ensuring the stability of batteries. The AC resistance of a battery is one of the factors used to estimate the battery state, and is measured using a Fast Fourier Transform (FFT), a technique that analyzes the frequency components. The power converter can implement the dual functionality of power conversion and online AC resistance measurements and achieve high cost-effectiveness with the addition of only a control algorithm. However, the high FFT resolution in a Digital Signal Processor (DSP) requires a long computation time and Random-Access Memory (RAM) to process large amounts of data. Therefore, it is difficult to measure low-frequency ranges using AC resistance calculations based on the FFT. In this study, a time- and frequency-domain hybrid measurement method to enhance low-frequency AC resistance measurements with the desired frequency resolution is proposed. This study investigates frequency resolution in the low-frequency range using time- and frequency-domain measurement methods. The operational principle of the proposed method was analyzed to design a measurement algorithm. The experimental results verified the performance of the proposed method by measuring the AC resistance of a battery within the desired frequency range.}, archive = {J_ACCESS}, author = {Seong-Jong Kim and Hwa-Pyeong Park and Tae-Hwan Jin and In-Jun Lee}, doi = {10.1109/ACCESS.2025.3544808}, journal = {IEEE Access}, pages = {36527-36536}, shortjournal = {IEEE Access}, title = {Measuring online battery AC resistance using a DC-DC converter with time and frequency hybrid method}, volume = {13}, year = {2025}, }
• (2025). Bridging remote operations and augmented reality: An analysis of current trends. ACCESS, 13, 36502-36526. (www)

  @article{ , abstract = {Teleoperation provides an innovative approach for completing tasks in environments where humans cannot be physically present, such as hazardous or difficult-to-reach areas. Augmented Reality (AR) enhances this process by overlaying critical information directly into the user’s field of view, streamlining task execution and spatial awareness. This paper presents a comprehensive topical review of recent trends in both teleoperation and AR, highlighting their integration to improve remote human-machine interaction. We analyze advancements in both technologies, identify practical applications, and offer insights into their convergence in fields such as aerospace, medical robotics, and industrial automation. By examining academic publications and statistical trends from major databases, we project the future trajectory of AR-teleoperation and discuss potential challenges in implementing these systems, particularly regarding latency, cognitive overload, and security. This topical review aims to provide a detailed analysis of the state of the art and offers insights for future research in this rapidly evolving field.}, archive = {J_ACCESS}, author = {Andres de la Rosa-Garcia and Ángel Israel Soto Marrufo and David Luviano-Cruz and Alma Rodriguez-Ramirez and Francesco Garcia-Luna}, doi = {10.1109/ACCESS.2025.3544633}, journal = {IEEE Access}, pages = {36502-36526}, shortjournal = {IEEE Access}, title = {Bridging remote operations and augmented reality: An analysis of current trends}, volume = {13}, year = {2025}, }
• (2025). VASIR: Open-source android application for visual analog scale intelligibility rating of dysarthric speech. ACCESS, 13, 36492-36501. (www)

  @article{ , abstract = {We present an open-source application for mobile devices called VASIR that provides a reliable and sensitive tool for accessible intelligibility rating using visual analog scales. VASIR is validated by examining the intelligibility in 23 Parkinson’s disease (PD) patients treated with subthalamic nucleus deep brain stimulation (DBS) in on and off stimulation states, and 23 healthy controls. Two rater groups of naive and expert listeners performed intelligibility ratings using the provided application. An excellent intraclass correlation was found in both the naive (ICC = 0.97) and expert (ICC = 0.95) groups. Both listener groups were able to confidently differentiate between the controls and DBS groups (p < 0.001) whereas only the expert group was able to find statistically significant differences in intelligibility between on and off stimulation states (p = 0.031).}, archive = {J_ACCESS}, author = {Petr Krýže and Vojtěch Illner and Tomáš Kouba and Michal Novotný and Michal Šimek and Jan Švihlík and Tereza Tykalová and Jan Rusz}, doi = {10.1109/ACCESS.2025.3544740}, journal = {IEEE Access}, pages = {36492-36501}, shortjournal = {IEEE Access}, title = {VASIR: Open-source android application for visual analog scale intelligibility rating of dysarthric speech}, volume = {13}, year = {2025}, }
• (2025). Improving data cleaning by learning from unstructured textual data. ACCESS, 13, 36470-36491. (www)

  @article{ , abstract = {In data analysis, a significant amount of erroneous or incomplete data can hinder informed organizational decisions prompting the need for automated data cleaning. Leveraging successful artificial intelligence techniques across various domains, several initiatives have introduced machine learning models to tackle these data-quality related issues. In this paper, we study a novel strategy to enhance data cleaning using a machine learning-based model trained on unstructured data. The study involves training a machine learning model with instances that satisfy the dataset constraints (such as functional dependencies), and examining its impact on efficiency in repairing dirty data. We also suggest improving data repair by comparing the probabilities of machine learning model predictions against a threshold, replacing the actual data with higher certainty predictions. Moreover, a hybrid data cleaning approach can enhance the effectiveness of our proposed method, resulting in higher repair quality. Experiments demonstrate promising results in terms of precision (0.88) and recall (0.94), particularly when the training data are clean from constraint violations and sufficiently large.}, archive = {J_ACCESS}, author = {Rihem Nasfi and Guy de Tré and Antoon Bronselaer}, doi = {10.1109/ACCESS.2025.3543953}, journal = {IEEE Access}, pages = {36470-36491}, shortjournal = {IEEE Access}, title = {Improving data cleaning by learning from unstructured textual data}, volume = {13}, year = {2025}, }
• (2025). RepVGG-MEM: A lightweight model for garbage classification achieving a balance between accuracy and speed. ACCESS, 13, 36451-36469. (www)

  @article{ , abstract = {Currently, existing garbage image classification models predominantly operate on low-end devices and encounter significant challenges, including limitations in computing resources, storage capacity, and classification accuracy. This paper proposes an improved lightweight model, RepVGG-MEM, specifically designed to address the resource constraints of low-end devices. The backbone of this model is derived from the lightweight RepVGG architecture, augmented by the integration of a multi-scale convolutional attention module to enhance high-quality feature extraction. Experimental results demonstrate that the RepVGG-MEM model outperforms its counterparts, achieving an accuracy of 93.26%, with a parameter count of 7.2 million and a floating-point operations (FLOPs) of 1.41 billion. This performance reflects a commendable balance between accuracy and processing speed. Furthermore, the model’s redundancy is minimized through pruning techniques, which significantly reduce both complexity and computational overhead. The optimal pruned version of the model is designated as RepVGG-MEM5. In this iteration, the parameter count is further reduced to 1.2 million and the FLOPs decrease to 0.55 billion, with a minor accuracy decline of only 1.17%. These findings indicate that it is possible to significantly reduce the model’s parameters and FLOPs without a substantial loss in accuracy, thereby enhancing the overall performance of the model while achieving an optimal balance of accuracy and speed. As a result, this research contributes to the development of an efficient and lightweight convolutional neural network model for garbage classification.}, archive = {J_ACCESS}, author = {Qiuxin Si and Sang Ik Han}, doi = {10.1109/ACCESS.2025.3544631}, journal = {IEEE Access}, pages = {36451-36469}, shortjournal = {IEEE Access}, title = {RepVGG-MEM: A lightweight model for garbage classification achieving a balance between accuracy and speed}, volume = {13}, year = {2025}, }
• (2025). Improving power distribution resilience through optimal PV and BES allocation with a cost-based optimization framework for normal and emergency conditions. ACCESS, 13, 36436-36450. (www)

  @article{ , abstract = {Recent natural disasters and man-made attacks have imposed substantial challenges on power distribution companies and consumers. The integration of photovoltaic (PV) systems into power distribution networks has risen due to environmental, technical, and economic factors. Additionally, technological advancements have made it possible to provide reactive power using PV systems and battery energy storage (BES) systems. This article proposes a comprehensive framework for the optimal allocation of PV and BES systems within the power distribution system to minimize energy losses and energy not served (ENS) during normal conditions, as well as load interruption under emergency conditions. The framework models the formation of small microgrids, accounting for operational and physical limitations, coordinating them with the network recovery process, and considering various production and load scenarios to maximize the restoration of interrupted loads during emergency conditions. An analysis has been conducted to determine the penetration levels of BES in power distribution systems under these conditions. A Mixed-Integer Quadratic Programming (MIQP) formulation is employed for cost optimization, with the model coded in MATLAB and implemented on a modified IEEE 33-bus network. Results demonstrate that the proposed method significantly enhances the distribution network’s resilience during emergencies, achieving a 22.3% reduction in load interruptions and a 26.5% decrease in associated costs. Additionally, energy losses are reduced by 6.7%, while ENS improves by 7.2% compared to configurations optimized solely for normal conditions. This research underscores the importance of strategically integrating PV and BES systems to improve performance metrics in normal and emergency scenarios within power distribution networks.}, archive = {J_ACCESS}, author = {Mohammad Taghi Ettehad and Reza Mohammadi Chabanloo and Mohammad Taghi Ameli}, doi = {10.1109/ACCESS.2025.3544662}, journal = {IEEE Access}, pages = {36436-36450}, shortjournal = {IEEE Access}, title = {Improving power distribution resilience through optimal PV and BES allocation with a cost-based optimization framework for normal and emergency conditions}, volume = {13}, year = {2025}, }
• (2025). Using permutation-based feature importance for improved machine learning model performance at reduced costs. ACCESS, 13, 36421-36435. (www)

  @article{ , abstract = {In Software Quality Assurance (SQA), predicting defect-prone software modules is essential for ensuring software reliability and consistency. This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. These high computational costs and uncertain payoffs make most Software engineering researchers reluctant to optimize ML models. This creates a need for novel techniques that can achieve near-optimal performance of hyperparameter settings while maintaining the computational efficiency of default settings. To address this, we employed five ML models, Decision Tree, Ranger, Random Forest, Support Vector Machine, and k-nearest Neighbors, and optimized their parameters using the random search technique. Our experiments covered six diverse Software Fault Prediction (SFP) datasets, encompassing various software features, application domains, and defect patterns, to evaluate the approach’s generalizability and effectiveness. Moreover, the Permutation Feature Importance (PFI)-based model-agnostic method was employed to identify the top ten features most critical for model accuracy and efficiency. These selected features were used to retrain the ML models without hyperparameters (default settings) to determine whether similar performance could be achieved at low computational cost. The results show an average accuracy improvement of 77.39% and a 92.02% reduction in computational cost. The most important case attained a 99.25% accuracy improvement and a 96.77% cost reduction. Such results clearly show that PFI-based feature selection is capable of high performance at a fraction of computational cost, offering an efficient solution for software engineers to optimize ML models.}, archive = {J_ACCESS}, author = {Adam Khan and Asad Ali and Jahangir Khan and Fasee Ullah and Muhammad Faheem}, doi = {10.1109/ACCESS.2025.3544625}, journal = {IEEE Access}, pages = {36421-36435}, shortjournal = {IEEE Access}, title = {Using permutation-based feature importance for improved machine learning model performance at reduced costs}, volume = {13}, year = {2025}, }
• (2025). Pro-CHASER: A prototype testbed for car hacking attack simulation and evaluation using raspberry pi to investigate threat landscape. ACCESS, 13, 36401-36420. (www)

  @article{ , abstract = {Recent advancements in transportation technology have necessitated a seamless integration of sensors, cameras, and communication protocols for autonomous vehicles. Ensuring cybersecurity for these vehicles has become critical, as they are vulnerable to attacks that could jeopardize safety and performance. While machine learning and deep learning models could be developed to address cybersecurity issues in autonomous vehicles, developing these models requires large datasets for training. Obtaining real-world data from actual vehicles also presents significant challenges. To address these limitations, a low-cost Pro-CHASER testbed— Prototype testbed for Car Hacking Attack Simulation and Evaluation using Raspberry Pi—was developed using a Controller Area Network (CAN) bus network with CAN-HAT (Hardware Attached on Top). This testbed utilized CAN to simulate autonomous vehicle functions and explore attack vectors. Microsoft Threat Modelling Tool, and MITRE ATT&CK framework were used to identify the vulnerabilities in the context of autonomous vehicles. The identified vulnerabilities were exploited using the designed testbed by employing many security testing tools to simulate CAN message injection attacks, USB exploits, and ROS-based fake image publisher attack. Through these attack simulations, the testbed functionalities were successfully compromised. The results demonstrated that message injection into the CAN bus manipulated sensor data that led to erroneous decision-making. Unauthorized access to the physical access points led to the injection of a malicious payload via a USB port. The computer vision system’s response to the false data ultimately compromised the vehicle’s perception and control. The findings highlight the capability of Pro-CHASER in identifying cyber security risks in autonomous vehicles, which offers a practical and low-cost platform for threat simulation and evaluation.}, archive = {J_ACCESS}, author = {J. Vanajakshi and A. Renuka and Vidya Kamath and N. D. Adesh}, doi = {10.1109/ACCESS.2025.3544776}, journal = {IEEE Access}, pages = {36401-36420}, shortjournal = {IEEE Access}, title = {Pro-CHASER: A prototype testbed for car hacking attack simulation and evaluation using raspberry pi to investigate threat landscape}, volume = {13}, year = {2025}, }
• (2025). Complex permittivity measurements of film-type materials: Effect of the waveguide flange. ACCESS, 13, 36388-36400. (www)

  @article{ , abstract = {The flexibility and thinness of most film-type materials make it difficult to use conventional measurement methods such as those in international or organizational standards to accurately measure the complex permittivity of these materials. This led us to develop a novel approach—based on the tip waveguide method and equivalent circuit algorithm—to accurately measure the complex permittivity of film-type materials in the X-band. The tip waveguide method, newly proposed in this paper, allows for a practical and relatively accurate measurement of the s-parameters of film-type materials. The equivalent circuit algorithm is an inversion algorithm that calculates the complex permittivity from the measured s-parameters. To verify the accuracy and validity of this measurement approach, we conducted 3D electromagnetic simulations and various experiments. The 3D electromagnetic simulation tool (CST) was used to analyze the effect of the flange of conventional commercial waveguides. This led to the proposal of the tip waveguide method with a minimized flange size and the validation of its accuracy is presented. A comparison of the results of the complex permittivity measurements from various commercial measurement systems with those of the proposed approach enabled us to confirm that the proposed measurement approach can measure the complex permittivity of film-type materials in a practical and relatively accurate manner.}, archive = {J_ACCESS}, author = {Han-Hee Lee and Sang Wook Yoon and Jae Wook Lee}, doi = {10.1109/ACCESS.2025.3541923}, journal = {IEEE Access}, pages = {36388-36400}, shortjournal = {IEEE Access}, title = {Complex permittivity measurements of film-type materials: Effect of the waveguide flange}, volume = {13}, year = {2025}, }
• (2025). A lightweight fault diagnosis method based on knowledge distillation under time-varying rotational speeds. ACCESS, 13, 36374-36387. (www)

  @article{ , abstract = {Intelligent fault diagnosis models for engineering must meet real-time requirements while satisfying high diagnosis rates. However, collecting a large amount of fault information of different working conditions in the engineering environment is difficult, and the lack of fault data is a problem that has been difficult to solve. This paper presents a lightweight fault diagnosis model for wind turbine gearboxes based on transfer learning and knowledge distillation. The model is designed to be lightweight and can be used under time-varying rotational speeds. First, TL-ResPConv based on transfer learning and partial convolution was designed as a teacher network model to train a lightweight student network model for fault diagnosis based on TL-ResPConv-KD. Then, the student model obtained through knowledge distillation training is used as the distilled fault diagnosis model. Finally, the performance of the different models is validated and compared on a laboratory simulated planetary gearbox dataset. The experiments prove that the proposed model outperforms other models in fault recognition, achieving a balance between lightweight and high diagnostic accuracy. This makes it a practical network with higher diagnostic accuracy for fault diagnosis under time-varying rotational speeds, meeting the demand for lightweight deployment of edge equipment.}, archive = {J_ACCESS}, author = {Xiwang Yang and Yarong Wang and Lele Gao and Jia Luo and Licheng Jing and Jinying Huang and Guangpu Liu and Chenfeng Yang}, doi = {10.1109/ACCESS.2024.3431228}, journal = {IEEE Access}, pages = {36374-36387}, shortjournal = {IEEE Access}, title = {A lightweight fault diagnosis method based on knowledge distillation under time-varying rotational speeds}, volume = {13}, year = {2025}, }
• (2025). Analyzing the first-order statistical properties of vehicle-to-vehicle rician fading channel. ACCESS, 13, 36359-36373. (www)

  @article{ , abstract = {Vehicle-to-vehicle (V2V) communication channels have distinct characteristics compared to fixed-to-mobile channels (F2M). Both the transmitter and receiver in V2V systems use low-elevation antennas and are in motion, and the surrounding environment and traffic patterns influence the channel characteristics. To effectively understand and model V2V communication systems, it is essential to characterize the channel using first-order statistical properties, precisely the space time frequency correlation function (STF-CF) and space Doppler frequency power spectral density (SDF-PSD). These metrics enable a comprehensive channel characterization, dynamic environmental adaptation, understanding of mobility effects, spatial variations, and detailed insights into signal power distribution across different frequencies. This paper presents a simplified geometric two-ring (SGTR) model of a V2V channel with demystified and concise mathematical analysis for STF-CFs and SDF-PSD characteristics. The analysis yields closed-form solutions consistent with established models. Additionally, the numerical results demonstrate excellent agreement with theoretical predictions, offering valuable insights into the statistical properties of V2V Rician fading channels.}, archive = {J_ACCESS}, author = {Sylvester T. Akiishi and Ebenezer Esenogho and Ahmed Ali and Modisane Cameron}, doi = {10.1109/ACCESS.2025.3544436}, journal = {IEEE Access}, pages = {36359-36373}, shortjournal = {IEEE Access}, title = {Analyzing the first-order statistical properties of vehicle-to-vehicle rician fading channel}, volume = {13}, year = {2025}, }
• (2025). A heuristic approach for the nationwide charging station location problem for intercity trips. ACCESS, 13, 36348-36358. (www)

  @article{ , abstract = {Electric vehicles (EVs) are vital in achieving a sustainable and eco-friendly transportation system. However, the transition to EVs faces significant challenges, mainly range anxiety, which is the fear of running out of battery power without access to charging facilities. Identifying locations for Electric Vehicle Charging Stations (EVCS) to address this issue and promote EV adoption is crucial, especially for intercity travel. The study proposes a three-stage solution methodology utilizing the Variable Transportation Demand Model (VTDM) established for nationwide transportation planning. The proposed approach includes identifying EVCS locations, with path-based traffic demands derived from the VTDM’s generalized cost-based shortest path methodology for different trip purposes. The proposed approach was validated using real-world big data from the Türkiye National Transportation Master Plan (TNTMP). The results revealed that 2,161 charging stations are required to ensure uninterrupted travel throughout the network. Furthermore, the study identifies a discrepancy in the availability of charging station infrastructure across different regions, with the majority of new facilities concentrated in the eastern region of Turkey. Addressing this infrastructure gap is crucial for accelerating the adoption of electric vehicles (EVs), reducing carbon emissions, and promoting sustainable transportation systems at the national level.}, archive = {J_ACCESS}, author = {Yavuz Delice and Ayyuce Aydemir-Karadag and Halit Ozen}, doi = {10.1109/ACCESS.2025.3544976}, journal = {IEEE Access}, pages = {36348-36358}, shortjournal = {IEEE Access}, title = {A heuristic approach for the nationwide charging station location problem for intercity trips}, volume = {13}, year = {2025}, }
• (2025). Optimizing power quality signal compression: Harnessing compressed sensing and reconstruction techniques for big data measurement. ACCESS, 13, 36339-36347. (www)

  @article{ , abstract = {The following research proposes a compression technique that combines traditional lossy compression methods with newer ones to identify properties of power quality signals. The data collected undergoes biorthogonal wavelet transformation and filter integration to remove the ripple added to the signal. The system utilizes Matching Pursuit to create an orthogonal dictionary, achieving compression ratios of 846:1. The quality indicators achieved are Percentage of Retained Energy (RTE) =0.9969, Normalized Mean Squared Error NMSE =0.0030, and Correlation (COR) =0.9969, demonstrating the technique’s efficiency. This research’s results surpass the most relevant papers in Q1 journals.}, archive = {J_ACCESS}, author = {Milton Ruiz and Edwin Garcia}, doi = {10.1109/ACCESS.2025.3544147}, journal = {IEEE Access}, pages = {36339-36347}, shortjournal = {IEEE Access}, title = {Optimizing power quality signal compression: Harnessing compressed sensing and reconstruction techniques for big data measurement}, volume = {13}, year = {2025}, }
• (2025). Comprehensive comparison of machine learning and kalman filter battery state of charge estimators. ACCESS, 13, 36321-36338. (www)

  @article{ , abstract = {This paper provides a comprehensive review of five different methods for estimating state of charge (SoC): feedforward neural network (FNN), nonlinear autoregressive with exogenous inputs (NARX) neural network, long short-term memory (LSTM) neural network, extended Kalman filter (EKF), and unscented Kalman filter (UKF). The analysis was conducted using a comprehensive experimental dataset for 21700 cylindrical lithium ion cells from an electric vehicle. The dataset covers a wide range of operational conditions, including three vehicle payloads (80 kg to 1000 kg), six temperatures (−20°C to 40°C), and different drive cycles. Ten different measurement offset error values are added to the data, capturing sensor error that may be present in the vehicle, and incorrect initial SoC estimates are also considered. The computational efficiency and memory requirements of each algorithm when deployed on an NXPő S32K146 Evaluation Board and NXPő GreenBox 3 Real-Time development platform were also explored, which is critical for real-time applications in vehicle control units. The results show that the NARX model consistently outperformed the other models, with an RMSE of 1.70%, while other models ranged between 2% and 3%. All algorithms demonstrated robustness to sensor errors, with current, voltage, and temperature offsets increasing estimation errors by no more than 0.5%. The NARX and LSTM models showed over 90% faster convergence times compared to other methods. Computational efficiency was similar across all models, with execution times ranging from 1 to 2 ms for estimating SoC of 10 cells at 1 Hz on the GreenBox platform. Overall, the findings suggest that the NARX model offers the best trade-off considering memory usage, execution time, and accuracy among the analyzed models, being a reliable choice for SoC estimation in electric vehicles.}, archive = {J_ACCESS}, author = {Romulo Vieira and Phil Kollmeyer and Lea Pitault and Ali Emadi}, doi = {10.1109/ACCESS.2025.3545380}, journal = {IEEE Access}, pages = {36321-36338}, shortjournal = {IEEE Access}, title = {Comprehensive comparison of machine learning and kalman filter battery state of charge estimators}, volume = {13}, year = {2025}, }
• (2025). Novel data-driven geolocation approach for detecting smuggled internet traffic. ACCESS, 13, 36306-36320. (www)

  @article{ , abstract = {Enforcing Internet censorship in decentralised networks, such as those in India, Iraq, and Russia, poses significant challenges due to the intricate nature of their interconnected subnetworks. This paper presents a novel approach that combines Internet routing traffic analysis with IP geolocation data to identify smuggled prefixes and their associated autonomous system numbers, using Iraq as a case study. Our methodology integrates diverse datasets, including integrated autonomous system number data from IPinfo and Cloudflare Radar, Internet routing traffic from the RouteViews project, and patterns observed during periodic Internet shutdowns for national exams. By cross-referencing these data sources, we enhance the detection of smuggled autonomous system numbers and provide insights into the geographical distribution of unauthorised Internet traffic. Furthermore, the study addresses evasion techniques, such as AS-PATH prepending, and proposes collaborative strategies to improve detection accuracy. These contributions provide a scalable and robust framework for strengthening Internet governance and enhancing security in fragmented network infrastructures.}, archive = {J_ACCESS}, author = {Bahaa Al-Musawi and Dina Shehada and Abir Jaafar Hussain}, doi = {10.1109/ACCESS.2025.3543876}, journal = {IEEE Access}, pages = {36306-36320}, shortjournal = {IEEE Access}, title = {Novel data-driven geolocation approach for detecting smuggled internet traffic}, volume = {13}, year = {2025}, }
• (2025). Interpretable prediction of a decentralized smart grid based on machine learning and explainable artificial intelligence. ACCESS, 13, 36285-36305. (www)

  @article{ , abstract = {The concept of a decentralized smart grid has emerged as a viable approach for efficiently managing and distributing electrical energy. Ensuring the stability and reliability of the grid, particularly with the integration of renewable energy sources and the increase in the number of prosumers, is a primary challenge in this domain. This study addresses this challenge by leveraging machine learning (ML) models and explainable artificial intelligence (XAI) techniques to predict the stability of a decentralized smart grid. A four-node star network implementing the decentralized smart grid control (DSGC) concept was investigated, and a dataset based on simulations of this network was used. Ten ML models, including Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), Gradient Boosting (GBoost), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were compared for their performance in predicting the grid stability. Models were evaluated using various metrics, and XAI methods, specifically SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots, were employed to enhance the interpretability of the models. The results demonstrate that the ANN model outperformed the other ML algorithms, achieving an area under the curve (AUC) of 99.4% and showing commendable performance in terms of cumulative accuracy (CA), precision, recall, and F1-score, all of which reached 96.2%. The SHAP and ICE analyses provided a comprehensive understanding of the model’s behavior, highlighting the critical influence of parameters such as reaction time, nominal power, and price elasticity on the stability of the decentralized smart grid. The findings contribute to the development of more reliable and interpretable predictive models for decentralized smart grid stability, which can aid in the design and optimization of effective control mechanisms.}, archive = {J_ACCESS}, author = {Ahmet Cifci}, doi = {10.1109/ACCESS.2025.3543759}, journal = {IEEE Access}, pages = {36285-36305}, shortjournal = {IEEE Access}, title = {Interpretable prediction of a decentralized smart grid based on machine learning and explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Cluster optimization for exponential, right-triangular, and uniformly distributed data. ACCESS, 13, 36273-36284. (www)

  @article{ , abstract = {Cluster analysis aims to categorize data objects into cohesive groups based on their intrinsic characteristics, often modeled by probability distributions. This paper presents a novel Mathematical Programming-Dynamic Programming (MP-DP) clustering method developed by the authors, applied to datasets characterized by exponential, right-triangular, and uniform distributions. The MP-DP technique optimizes cluster partitions by leveraging the probability distributions inherent in the data. We conducted a comparative evaluation to assess the performance of MP-DP against four established clustering methodologies: K-Means, Fuzzy C-Means, expectation-maximization, and Genie++ hierarchical clustering. Results from extensive simulations and real-world datasets consistently demonstrate the superior efficacy of MP-DP in achieving optimal clustering outcomes. Specifically, MP-DP excels in handling diverse data distributions and effectively mitigating the effects of noise and uncertainty, thereby enhancing clustering accuracy and reliability. This study highlights the significant advancement offered by MP-DP in clustering research. It underscores the method’s potential for applications across various domains, such as healthcare, environmental monitoring, and manufacturing, where robust and efficient data clustering is essential for insightful data analysis and decision-making.}, archive = {J_ACCESS}, author = {Gabiriele Bulivou and Karuna G. Reddy and M. G. M. Khan}, doi = {10.1109/ACCESS.2025.3544830}, journal = {IEEE Access}, pages = {36273-36284}, shortjournal = {IEEE Access}, title = {Cluster optimization for exponential, right-triangular, and uniformly distributed data}, volume = {13}, year = {2025}, }
• (2025). Assessing the suitability of distributed energy resources in distribution systems based on the voltage margin: A case study of jeju, south korea. ACCESS, 13, 36263-36272. (www)

  @article{ , abstract = {With the growing need for transitioning to clean energy, increasing the share of renewable energy sources has become a global priority. The integration of distributed energy resources (DERs), such as photovoltaic (PV) and wind turbine (WT) systems, is crucial for optimizing the current distribution systems. Despite its benefits, DER integration presents technical challenges. DERs can cause rapid power output changes, leading to voltage instability and overvoltage, thereby impacting the power quality and equipment reliability. Mitigating this instability often requires costly grid infrastructure upgrades. Therefore, optimizing existing networks is essential to reduce these costs. To achieve this, the voltage profile of the target area must be thoroughly assessed and locations suitable for DER integration must be identified to ensure stable operation and voltage stability. This study proposes a new evaluation index, the Voltage Margin Evaluation Index (VMEI), to address the limitations of the existing indices. This index evaluates voltage stability and promotes DER integration in a region. Using the VMEI, we assessed the suitability of DER integration in Jeju, South Korea. Results demonstrate that integrating a 0.5 MW DER at Node 37 in Feeder 5, the optimal point with a VMEI of 1.00066, maintained the maximum voltage at 22.97 kV within the regulated range and improved voltage stability. Conversely, Node 29 in Feeder 4, with the lowest VMEI of 0.97262, resulted in a maximum voltage of 23.84 kV, exceeding the voltage regulation range, demonstrating that VMEI effectively identifies suitable DER integration points. This study provides valuable insights for improving DER integration into distribution systems, aiding the global transition to sustainable energy.}, archive = {J_ACCESS}, author = {Jun-Hyuk Nam and Seong-Jun Park and Dong-Il Cho and Yun-Jin Cho and Won-Sik Moon}, doi = {10.1109/ACCESS.2025.3544744}, journal = {IEEE Access}, pages = {36263-36272}, shortjournal = {IEEE Access}, title = {Assessing the suitability of distributed energy resources in distribution systems based on the voltage margin: A case study of jeju, south korea}, volume = {13}, year = {2025}, }
• (2025). Research on vertical shaft detection system based on CMOS-camera and lidar. ACCESS, 13, 36247-36262. (www)

  @article{ , abstract = {This research focuses on the pivotal role of coal mining shaft safety. Deformations and fractures in the shaft wall can cause significant risks to mine operations, which require regular safety inspection to prevent hazards. Existing shaft detection systems cannot provide any prior notifications before damage occurs, with complex systems and low intelligence. To solve these problems, our proposed system has three key indicators: shaft deformation, shaft perpendicularity, and horizontal vibration of the lifting container. This intelligent system reduces the likelihood of significant shaft damage by enhancing automation, intelligence, and efficiency. The research starts by defining the essential features required for the shaft detection system, like data acquisition system, data transmission, and data processing. Hardware includes LiDAR sensors, CMOS cameras, laser collimators, control board, batteries, and software implemented on ROS. This system collects data, simulates shaft conditions, and conducts horizontal vibration displacement detection experiments. Moreover, we validated shaft deformation and perpendicularity detection under static conditions. This research concludes by analyzing the accuracy of the detection system in actual working conditions, confirming its practicality and reliability. Overall, this research solves critical issues in shaft safety and introduces an intelligent, efficient, and smart solution.}, archive = {J_ACCESS}, author = {Chi Ma and Samsil Arefin Mozumder and Md Mahbub Hossen and Manman Shao and Yuqiang Jiang}, doi = {10.1109/ACCESS.2024.3420888}, journal = {IEEE Access}, pages = {36247-36262}, shortjournal = {IEEE Access}, title = {Research on vertical shaft detection system based on CMOS-camera and lidar}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection with infinite set dictionary learning and atom dimension adaptation. ACCESS, 13, 36233-36246. (www)

  @article{ , abstract = {Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse representations with atoms taken from a set around a central vector; the set can be a cone or may have a probability distribution associated to it. We propose a method for adaptively adjusting the size of set-atoms in Gaussian and cone dictionary learning. The purpose of the algorithm is to match the atom sizes with their contribution in representing the signals. The proposed algorithm not only decreases the representation error, but also improves anomaly detection, for a class of anomalies called ‘dependency’. We obtain better detection performance than state-of-the-art methods.}, archive = {J_ACCESS}, author = {Andra Băltoiu and Denis C. Ilie-Ablachim and Bogdan Dumitrescu}, doi = {10.1109/ACCESS.2025.3544748}, journal = {IEEE Access}, pages = {36233-36246}, shortjournal = {IEEE Access}, title = {Anomaly detection with infinite set dictionary learning and atom dimension adaptation}, volume = {13}, year = {2025}, }
• (2025). A 450 mV tunable low-frequency OTA-C third-order elliptic low-pass filter for bio-signal applications. ACCESS, 13, 36224-36232. (www)

  @article{ , abstract = {This paper introduces a tunable third-order elliptic low-pass OTA-C filter specifically designed for biological signal applications. The proposed filter leverages a multiple-input operational transconductance amplifier (MI-OTA) that operates at low voltage and low power consumption. This innovative approach simplifies the filter topology and significantly reduces the number of active and passive components required. By utilizing the multiple-input capability of the OTA, the voltage gains of the low-pass transfer function can be easily increased. The filter operates with a supply voltage of 450 mV and consumes only 40.5 nW of power while achieving a cutoff frequency of 112.9 Hz and a dynamic range (DR) of 66.7 dB at 1% total harmonic distortion (THD). The filter’s performance was validated through the effective filtering of interfering ECG signals. The design and simulations were carried out using the Cadence environment and TSMC’s $0.18~\mu $ m CMOS technology. Monte Carlo (MC) analysis and process, voltage, and temperature (PVT) corner analysis were conducted to ensure the robustness of the design.}, archive = {J_ACCESS}, author = {Montree Kumngern and Tomasz Kulej and Fabian Khateb}, doi = {10.1109/ACCESS.2025.3544735}, journal = {IEEE Access}, pages = {36224-36232}, shortjournal = {IEEE Access}, title = {A 450 mV tunable low-frequency OTA-C third-order elliptic low-pass filter for bio-signal applications}, volume = {13}, year = {2025}, }
• (2025). Controlling cable driven parallel robots Operations—Deep reinforcement learning approach. ACCESS, 13, 36212-36223. (www)

  @article{ , abstract = {Deep Reinforcement Learning (DRL) is a powerful approach for generating control strategies for a variety of complex systems, representing an emerging paradigm in control applications. An important feature of Deep RL is that it does not explicitly model the process, but instead it relies on optimization-driven techniques to devise effective control policies. Despite its remarkable success in simulated environments, RL holds great potential in real-world applications. This article explores the complex challenges involved in implementing Deep Reinforcement Learning (DRL) algorithms on a cable-driven parallel robot. A key contribution of this work as specific advancement is the integration of a Proportional-Integral-Derivative (PID) controller within the RL framework, establishing a unique approach to CDPR control that leverages adaptive learning capabilities. A Reinforcement Learning (RL) agent for reference tracking is trained using the novel application of the adaptive-featured Twin Delayed Deep Deterministic (TD3) policy gradient algorithm, tailored to enhance CDPR adaptability and precision in dynamic environments. The first step is to test the performance of the trained agent on point-to-point robotic application tasks. As a result of such tasks, it is possible to evaluate the level of adaptability and performance of the RL agent. Multiple experiments are conducted to assess the versatility of the RL agent involving linear and circular scenarios. This research significantly advances the field by demonstrating the applicability of RL for complex robotic structures like CDPRs, showcasing promising results that underline the robustness and adaptability of the proposed approach. As a result of the TD3 adaptive learning process, the trained agent is able to perform the designated action in order to determine which policy stands out as the most rewarding.}, archive = {J_ACCESS}, author = {Muhammad Kamran Joyo and Abdulmajeed M. Alenezi and Wenfu Xu and Mohamad A. Alawad and Muhmmad Tayyab Yaqoob and Noor Maricar and Sheroz Khan}, doi = {10.1109/ACCESS.2025.3539702}, journal = {IEEE Access}, pages = {36212-36223}, shortjournal = {IEEE Access}, title = {Controlling cable driven parallel robots Operations—Deep reinforcement learning approach}, volume = {13}, year = {2025}, }
• (2025). Barrier function and terminal synergetic-based controllers for the chemotherapy of brain tumor. ACCESS, 13, 36201-36211. (www)

  @article{ , abstract = {Lumps formed by abnormal cell growth in the brain constitute brain tumors. Treatments differ depending on the specificity of the tumor and the condition of the patient; surgery, radiation therapy and targeted therapy are suggested for less severe tumors, while for severe tumors; chemotherapy is recommended. The last one, while being suggested and effective also in conjunction with surgery, has side effects that could be fatal. Therefore, it is mandatory to determine the appropriate dosage to obtain the desired effects without affecting healthy and immune cells. In this research work, advanced barrier function-based sliding mode and terminal synergetic-based controllers are designed to determine the amount of chemotherapy to eliminate tumor cells and maintain the right amount of healthy and immune cells. In particular, a barrier-based sliding mode controller is designed for dynamically controlling the chemotherapy drug for the tumor system, whereas the stability and convergence of the system are checked using the Lyapunov theory. The performance of the controller has been verified using MATLAB software based on different control parameters. The controller has remained successful in reducing the tumor cells and maintaining safe number of healthy and immune cells, showing favorable results in terms of steady-state error, over-under shoots, and rate of convergence.}, archive = {J_ACCESS}, author = {Muhammad Zubair and Daniela Iacoviello and Sheraz Ahmad Babar}, doi = {10.1109/ACCESS.2025.3544757}, journal = {IEEE Access}, pages = {36201-36211}, shortjournal = {IEEE Access}, title = {Barrier function and terminal synergetic-based controllers for the chemotherapy of brain tumor}, volume = {13}, year = {2025}, }
• (2025). TDA SegUNet: Topological data analysis-based shape-aware brain tumor segmentation. ACCESS, 13, 36190-36200. (www)

  @article{ , abstract = {Brain tumors are a pressing concern for the medical community. Advanced MRI techniques are essential for the accurate detection and segmentation of tumors and their sub-regions, especially the enhancing tumor. However, due to the complex structure of the human brain, brain tumor segmentation remains a challenging task. This study introduces a novel model, 2D Topological Data Analysis based segmentation model backed by UNet referred to as TDA-SegUNet, designed for the precise segmentation of brain tumors and their sub-regions. TDA-SegUNet is a U-Net-based segmentation model that integrates topological data analysis (TDA) to extract shape-based local and global features from MRI scans. For this purpose, we constructed 0 and 1-dimensional homology-based persistence images (PIs) that capture local and global features, respectively. The BraTS20 MRI CCA-Patch dataset, along with corresponding persistence images (PIs) constructed using TDA, was utilized for model evaluation. The proposed model was trained for 2D segmentation on the BraTS20 CCA-Patch dataset and demonstrated improved performance compared to state-of-the-art techniques in segmenting the whole tumor, core tumor, and enhancing tumor regions, achieving validation Dice scores of 90.37%, 89.56%, and 81.33%, respectively.}, archive = {J_ACCESS}, author = {Ansar Rahman and Ayesha Satti and Ahmad Raza Shahid and Qaisar M. Shafi and Khadija Farooq and Asad Ali Safi}, doi = {10.1109/ACCESS.2025.3543071}, journal = {IEEE Access}, pages = {36190-36200}, shortjournal = {IEEE Access}, title = {TDA SegUNet: Topological data analysis-based shape-aware brain tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). ATT-BLKAN: A hybrid deep learning model combining attention is used to enhance business process prediction. ACCESS, 13, 36175-36189. (www)

  @article{ , abstract = {The role of predictive business process tasks in business process management is significant, as they are capable of anticipating potential process events and implementing timely interventions to address discrepancies between the anticipated and actual workflow. Nevertheless, existing deep learning-based predictive methods are unable to adequately address the current problem due to shortcomings in the training data, the model itself, or the architectures employed. In this paper, we propose a novel training framework for business process prediction based on improved BiLSTM-KAN, which addresses the issue of adaptability to continuous time data. This is achieved by enhancing the BiLSTM model’s ability to capture long-term dependencies through the addition of Agent Attention, while utilising KAN in place of the traditional Multi-Layer Perceptron (MLP) to improve prediction performance and mechanism interpretability. The results demonstrate that the proposed method outperforms all baseline methods in terms of prediction accuracy. This is evidenced by experiments conducted on five real publicly available event logs, which yielded improvements in accuracy of 12.4%, 7.16%, 9.77%, 12.27%, and 5.98%, respectively. The proposed method offers novel insights into the domain of predictive business processes and demonstrates the considerable potential of KAN in the field of predictive analytics.}, archive = {J_ACCESS}, author = {Junyi Xu and Xianwen Fang}, doi = {10.1109/ACCESS.2025.3545071}, journal = {IEEE Access}, pages = {36175-36189}, shortjournal = {IEEE Access}, title = {ATT-BLKAN: A hybrid deep learning model combining attention is used to enhance business process prediction}, volume = {13}, year = {2025}, }
• (2025). Multi-agent-based cognitive intelligence in non-linear mental healthcare-based situations. ACCESS, 13, 36162-36174. (www)

  @article{ , abstract = {This study introduces a novel framework for the early detection of anxiety and depression symptoms through the integration of Ambient Intelligence (AmI) and Multi-Agent Systems (MAS). Leveraging a Belief-Desire-Intention (BDI) reasoning mechanism, our system enables real-time monitoring and intervention with high precision. Compared to existing methods such as PMMHA, DWDM, MHL, and SMAD, the proposed methodology demonstrates significant improvements in multiple performance metrics. The system achieves an accuracy of 95%, surpassing competing approaches, and reduces latency to under 6 milliseconds for emergent decision-making. It maintains a success rate above 95% while effectively managing energy consumption, which increases non-linearly from 1.0 Joules at 100 KB to 6.1 Joules at 1000 KB of data. This scalable and adaptive approach addresses critical limitations in mental health detection, offering a reliable solution for improving mental healthcare. Future work will focus on testing the framework with publicly available mental health datasets and conducting clinical trials to further validate its efficacy.}, archive = {J_ACCESS}, author = {Kiran Saleem and Misbah Saleem and Ahmad Almogren and Alanod Almogren and Upinder Kaur and Salil Bharany and Ateeq Ur Rehman}, doi = {10.1109/ACCESS.2025.3544096}, journal = {IEEE Access}, pages = {36162-36174}, shortjournal = {IEEE Access}, title = {Multi-agent-based cognitive intelligence in non-linear mental healthcare-based situations}, volume = {13}, year = {2025}, }
• (2025). Complete object-compositional neural implicit surfaces with 3D pseudo supervision. ACCESS, 13, 36151-36161. (www)

  @article{ , abstract = {Neural implicit surface reconstruction has recently emerged as a prominent paradigm in multi-view 3D reconstruction using deep learning. In contrast to traditional multi-view stereo methods, signed distance function (SDF)-based approaches leverage neural networks to effectively represent 3D scenes. Furthermore, to reconstruct scenes and individual objects separately, some studies have extended the framework for object-compositional neural implicit surface reconstruction by utilizing 2D instance masks to supervise the SDF of each object. Nonetheless, these methods often reconstruct objects as partial shapes in scenes captured from sparse viewpoints or in complex scenes containing multiple objects. This issue primarily stems from the absence of a 3D prior, which fails to provide sufficient geometry for partially observed and occluded regions. We propose a framework for completing the partial object shapes of object-compositional neural implicit representation utilizing a diffusion-based 3D mesh generation model. The existing diffusion model, trained only on large-scale 3D object datasets, generates complete shapes from partial shapes; however, their results differ significantly from the objects in the scene. To complete the representation of partial shapes while ensuring shape consistency across multi-view images, we combine the SDF values, output by the diffusion model, with the object-compositional neural implicit representation. The combined representation is then volume-rendered to fine-tune the diffusion model utilizing a 2D prior. Furthermore, the complete shape generated by our method can serve as pseudo 3D priors to provide the geometry for the unobserved regions in object-compositional representation. Extensive experiments demonstrate that our novel framework significantly improves the reconstruction quality of unobserved regions.}, archive = {J_ACCESS}, author = {Wongyeom Kim and Jisun Park and Kyungeun Cho}, doi = {10.1109/ACCESS.2025.3544705}, journal = {IEEE Access}, pages = {36151-36161}, shortjournal = {IEEE Access}, title = {Complete object-compositional neural implicit surfaces with 3D pseudo supervision}, volume = {13}, year = {2025}, }
• (2025). A study on the lightweight and fast response GRU techniques for indoor continuous motion recognition based on wi-fi CSI. ACCESS, 13, 36135-36150. (www)

  @article{ , abstract = {The field of human activity recognition (HAR) using channel state information (CSI) is advancing rapidly through recent research. In this study, we propose and investigate a novel lightweight and fast-response gated recurrent unit (LFR-GRU) model that can distinguish continuous dynamic and static postures over time of a person without electronic devices in an indoor environment by utilizing the propagation characteristics of Wi-Fi signals. The CSI time-series amplitude data used in this study are collected from the characteristics of continuous movements and postures in two directions (horizontal and vertical) using an 802.11n Intel network interface card and a Wi-Fi access point. The proposed LFR-GRU model is evaluated using criteria such as F1-score, total number of parameters, learning time, processing speed, and memory usage, and its performance is compared with conventionally available models like 1D convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and Bi-GRU. The experiment results show that the proposed LFR-GRU model achieved an F1-score accuracy of 94.16% when trained with one person’s data and 96% accuracy when trained with two people. Additionally, it demonstrates comprehensively improved performance compared to other time-series models in terms of weight reduction, computational cost, and fast response (the model’s ability to respond quickly to input) speed. Based on these results, it can be confirmed that the proposed LFR-GRU model has sufficient potential to real-time HAR applications in indoor environments.}, archive = {J_ACCESS}, author = {Kyongseok Jang and Chao Sun and Junhao Zhou and Yongbin Seo and Youngok Kim and Seyeong Choi}, doi = {10.1109/ACCESS.2025.3544627}, journal = {IEEE Access}, pages = {36135-36150}, shortjournal = {IEEE Access}, title = {A study on the lightweight and fast response GRU techniques for indoor continuous motion recognition based on wi-fi CSI}, volume = {13}, year = {2025}, }
• (2025). Dynamic topology evolution and multi-objective routing optimization for efficient VANET communication. ACCESS, 13, 36124-36134. (www)

  @article{ , abstract = {This paper presents a dynamic topology evolution model and a multi-objective routing optimization algorithm tailored for Vehicular Ad Hoc Networks (VANETs) to address the challenges of efficient routing in highly dynamic topologies. The proposed algorithm utilizes a Dynamic Topology Evolution (DTE) model to evaluate routing stability by capturing continuous changes in network topology. By integrating a cooperative relay mechanism and a multi-objective relay node selection method, the algorithm significantly enhances data transmission success rates and reduces end-to-end latency. Furthermore, the implementation of Dijkstra’s algorithm enables efficient path selection with low computational complexity. Simulations using real urban traffic data demonstrate that the proposed algorithm outperforms existing protocols in terms of packet delivery rates and transmission delays, particularly in complex urban environments.}, archive = {J_ACCESS}, author = {Hao Wang}, doi = {10.1109/ACCESS.2025.3541003}, journal = {IEEE Access}, pages = {36124-36134}, shortjournal = {IEEE Access}, title = {Dynamic topology evolution and multi-objective routing optimization for efficient VANET communication}, volume = {13}, year = {2025}, }
• (2025). Design of a convolutional neural network for the synthesis of a spherical crank-rocker mechanism. ACCESS, 13, 36112-36123. (www)

  @article{ , abstract = {The use of CNN has proven to be a crucial tool in solving complex problems, particularly since they utilize images as inputs. Given that the synthesis of mechanisms is a complex task, it has been approached in various ways, ranging from solving the equations that describe kinematic motion to methods that employ heuristic algorithms to solve the problem, including numerical methods among others. This work presents an innovative method for the dimensional synthesis of spherical mechanisms, which is based on the use of CNNs. In the proposed method, the trajectory that must be followed by the coupler point of a spherical four-bar mechanism is converted into images so they can be used as inputs for the CNN. The CNN is trained through a database generated with already known spherical mechanisms that met constraints such as the Grashof criterion and transmission angle constraints, among others, since these ensured the mechanism would function properly. In addition, the kinematic study of the spherical mechanism is presented through relative angles, as this allows for the generation of databases and the extraction of kinematic parameters to label the input images used in CNN training. Once the CNN was trained, the neural network was capable of performing the dimensional synthesis of the mechanism. Moreover, it was tested with three different case studies, resulting in three distinct mechanisms. The obtained results were evaluated using the metr2ev function, which allows for the comparison of two trajectories and provides information on their similarity in both time and frequency. The best result obtained was a similarity of 0.9953, indicating that the trajectory generated by the mechanism is reliable concerning the required precision.}, archive = {J_ACCESS}, author = {Luis Ernesto Valencia-Segura and María Aurora Diozcora Vargas-Treviño and Sergio Vergara-Limon and Carlos Leopoldo Carreón-Díaz de León and Daniel Marcelo González-Arriaga and Marciano Vargas-Treviño and Jaime Gutiérrez-Gutiérrez and Michelle Guerra-Marín}, doi = {10.1109/ACCESS.2025.3544450}, journal = {IEEE Access}, pages = {36112-36123}, shortjournal = {IEEE Access}, title = {Design of a convolutional neural network for the synthesis of a spherical crank-rocker mechanism}, volume = {13}, year = {2025}, }
• (2025). Detecting all-to-one backdoor attacks in black-box DNNs via differential robustness to noise. ACCESS, 13, 36099-36111. (www)

  @article{ , abstract = {The all-to-one (A2O) backdoor attack is one of the major adversarial threats against neural networks. Most existing A2O backdoor defenses operate in a white-box context, necessitating access to the backdoored model’s architecture, hidden layer outputs, or internal parameters. The necessity for black-box A2O backdoor defenses arises, particularly in scenarios where only the network’s input and output are accessible. However, prevalent black-box A2O backdoor defenses often mandate assumptions regarding the locations of triggers, as they leverage hand-crafted features for detection. In instances where triggers deviate from these assumptions, the resultant hand-crafted features diminish in quality, rendering these methods ineffective. To address this issue, this work proposes a post-training black-box A2O backdoor defense that maintains consistent efficacy regardless of the triggers’ locations. Our method hinges on the empirical observation that, in the context of A2O backdoor attacks, poisoned samples are more resilient to uniform noise than clean samples in terms of the network output. Specifically, our approach uses a metric to quantify the resiliency of the given input to the uniform noise. A novelty detector, trained utilizing the quantified resiliency of available clean samples, is deployed to discern whether the given input is poisoned. The novelty detector is evaluated across various triggers. Our approach is effective on all utilized triggers. Lastly, an explanation is provided for our observation.}, archive = {J_ACCESS}, author = {Hao Fu and Prashanth Krishnamurthy and Siddharth Garg and Farshad Khorrami}, doi = {10.1109/ACCESS.2025.3543333}, journal = {IEEE Access}, pages = {36099-36111}, shortjournal = {IEEE Access}, title = {Detecting all-to-one backdoor attacks in black-box DNNs via differential robustness to noise}, volume = {13}, year = {2025}, }
• (2025). A method of the vibration information detection for rotating machinery based on the rolling-shutter CMOS and digital image processing. ACCESS, 13, 36082-36098. (www)

  @article{ , abstract = {It is crucial to develop precise, fast, cost-effective, and reliable technology for monitoring the condition of rotating machinery and diagnosing faults to ensure industrial production efficiency and safety. The study investigates non-contact vibration information acquisition and fault diagnosis methods. In response to the current reliance on high-speed camera hardware for visual vibration measurement, a method for visual vibration-based information acquisition and fault diagnosis is proposed. This method integrates the imaging characteristics of rolling shutter CMOS with digital image processing technology to significantly increase the sampling frequency of one-dimensional horizontal vibration information for low-cost cameras. A vibration information acquisition system is established to conduct multiple experiments on 42 vibration states. The results were consistent with theoretical values. The maximum relative error did not exceed 1.36%, and the relative standard deviation ranged from 0% to 0.6%, demonstrating the system’s accurate and stable acquisition results. Comparative analysis of the diagnostic results using the K-Nearest Neighbor, AdaBoost, CatBoost, and Random Forest algorithms revealed that the Random Forest algorithm achieved the highest diagnostic accuracy, exceeding 98%. The results identified the Random Forest algorithm as the most suitable for fault diagnosis based on vibration information in this work.}, archive = {J_ACCESS}, author = {Yonggong Yuan and Ji Zhang and Nanwangdi Li and Haifeng Wang and Yuxin Hu and Yilong Wang and Ning Mei and Han Yuan}, doi = {10.1109/ACCESS.2025.3542187}, journal = {IEEE Access}, pages = {36082-36098}, shortjournal = {IEEE Access}, title = {A method of the vibration information detection for rotating machinery based on the rolling-shutter CMOS and digital image processing}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based detection of tuberculosis using a gaussian chest X-ray image filter as a software lens. ACCESS, 13, 36065-36081. (www)

  @article{ , abstract = {Tuberculosis remains one of the most prevalent and lethal infectious diseases, with millions of cases reported each year. Convolutional neural networks have proven effective in detecting such diseases from medical images, achieving high accuracy in identifying tuberculosis from chest X-rays. However, many models are limited by small datasets, lack of cross-validation or have not achieved an optimal level of detection performance. In the context of diagnosing diseases, it is crucial to continually strive for increasingly accurate and robust solutions. This study focuses on the distinct characteristics of tuberculosis lesions, such as their large structures and gradual transitions between healthy and infected tissue. We propose that optimal detection performance may not rely on more complex architectures but instead on optimizing preprocessing techniques to highlight these features. Specifically, a ResNet50-based architecture with Gaussian filtering was evaluated on a dataset of 7,000 images using stratified 5-fold cross-validation. The results show an average accuracy of 99.2%, outperforming the unfiltered model (97.7%) and literature (99.05%), thus setting a new benchmark. The findings demonstrate that leveraging tuberculosis-specific features through Gaussian filtering provides an effective approach to enhance diagnostic performance.}, archive = {J_ACCESS}, author = {Luca Eisentraut and Christopher Mai and Johanna Hosch and Amélie Benecke and Pascal Penava and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3544923}, journal = {IEEE Access}, pages = {36065-36081}, shortjournal = {IEEE Access}, title = {Deep learning-based detection of tuberculosis using a gaussian chest X-ray image filter as a software lens}, volume = {13}, year = {2025}, }
• (2025). AS-faster-RCNN: An improved object detection algorithm for airport scene based on faster R-CNN. ACCESS, 13, 36050-36064. (www)

  @article{ , abstract = {Currently, the rapid development of the aviation industry has made the safety of the airport becomes more and more important. The most important part of this is the capability of discriminate the different type of objects correctly. However, the existing detection models have the problems of degradation, lacking of detection capability for deformed and small objects and single feature extraction, causing low detection accuracy. To overcome these problems, we design an object detection method for airport scene named AS-Faster-RCNN. Firstly the ResNet-101 substitute for VGG-16 as the backbone network to improve the ability of detecting small objects, prevent the degradation and enhance the ability of detecting the small objects. Secondly, The DCN (Deformable Convolution Network) is employed in the backbone to strengthen the ability of extracting features for deformed objects. Finally, the CBAM (Convolutional Block Attention Module) is added to the backbone to extract multidimensional features to enhance performance of the model. We design some experiemnts to prove the feasibility of the method and the results demonstrate the mAP(mean Average Precision) has increased by 5.3% comapred to the basline model, and compared with other object detection models, its mAP also increased to a certain extent.}, archive = {J_ACCESS}, author = {Zhige He and Yuanqing He}, doi = {10.1109/ACCESS.2025.3539930}, journal = {IEEE Access}, pages = {36050-36064}, shortjournal = {IEEE Access}, title = {AS-faster-RCNN: An improved object detection algorithm for airport scene based on faster R-CNN}, volume = {13}, year = {2025}, }
• (2025). Residential building energy usage prediction using bayesian-based optimized XGBoost algorithm. ACCESS, 13, 36036-36049. (www)

  @article{ , abstract = {With the growing energy demand in residential buildings, selecting energy-efficient building designs is crucial for sustainable development. Therefore, engineers and designers try to find a reliable tool to help them analyze and predict the energy consumption of buildings in the early design stages before construction. The state-of-the-art eXtreme Gradient Boosting (XGB) algorithm was successfully used to estimate the Heating Load (HL) and Cooling Load (CL) energy usage based on building design characteristics. To enhance the performance of XGB and improve prediction reliability, we propose a novel Bayesian optimization approach using the Tree-Structured Parzen Estimator (TPE), leading to the development of the TPE-XGB model for HL and CL estimation. Also, a new fair evaluation method was proposed to have a fair evaluation. The evaluation results based on the proposed fair evaluation method showed that the proposed model has outperformed other state-of-the-art models and improved the prediction accuracy of HL and CL by 3.4% (from 0.175 to 0.167) and 10.4% (from 0.307 to 0.275), respectively. As a result, the new proposed model provides the opportunity to be used as a strong and reliable tool in the early stages of building construction and to evaluate different designs/plans regarding energy consumption efficiency to select the best design with high reliability.}, archive = {J_ACCESS}, author = {Nabaa Riyadh Baqer and Parviz Rashidi-Khazaee}, doi = {10.1109/ACCESS.2025.3544699}, journal = {IEEE Access}, pages = {36036-36049}, shortjournal = {IEEE Access}, title = {Residential building energy usage prediction using bayesian-based optimized XGBoost algorithm}, volume = {13}, year = {2025}, }
• (2025). Third ventricle width measurements based on YOLO and localized intensity features. ACCESS, 13, 36024-36035. (www)

  @article{ , abstract = {Transcranial ultrasound (TCS) imaging can quantitatively measure third ventricle width (TVW) in Parkinson’s (PD) patients, helping physicians detect cognitive dysfunction in PD patients as early as possible. However, the transmitted unified TVW measurement has problems such as low efficiency and unstable measurement. For this reason, this paper proposes an automatic TVW measurement for TCS images. Firstly, to solve the problem of third ventricle detection in the complex background of TCS images, the YOLO-TV improvement model is proposed to improve the detection accuracy. The YOLO-TV model introduces a multi-head attention mechanism in the feature extraction network to enhance the network to extract feature at different scales. Meanwhile, a depth separable path aggregation module is used to improve the channel sensitivity of the feature fusion. Secondly, to solve the problem of third ventricle segmentation measurement, a third ventricle segmentation algorithm based on local intensity features is proposed, which realizes automatic segmentation measurement of third ventricle by calculating the local intensity features of the region and localization of the multi-angle projection method. The detection accuracy of the YOLO-TV model in the validation set reaches 98.50%, and the average measurement deviation between the automatic measurement results of the method proposed in this paper and the manual measurement results of the doctors is 0.089 mm, and the correlation coefficient between the two is 0.9643, which indicates that the method can be used to accurately measure the width of third ventricle.}, archive = {J_ACCESS}, author = {Xiao Zhou and Ao Wan and Xingang Mou and Hongling Gao and Zheng Xue}, doi = {10.1109/ACCESS.2024.3438551}, journal = {IEEE Access}, pages = {36024-36035}, shortjournal = {IEEE Access}, title = {Third ventricle width measurements based on YOLO and localized intensity features}, volume = {13}, year = {2025}, }
• (2025). WirelessNet: An efficient radio access network model based on heterogeneous graph neural networks. ACCESS, 13, 36006-36023. (www)

  @article{ , abstract = {A network digital twin can enable the ability to safely and rapidly recreate what-if scenarios of mobile networks for more cost effective and intelligent network optimization. Towards enabling a network digital twin of mobile networks, accurate and efficient radio access network models are needed. In this work, we present WirelessNet, a novel radio access network model based on Heterogeneous Message Passing Graph Neural Networks (HMPGNNs). WirelessNet represents network nodes and the underlying wireless phenomena between them as nodes and edges of different type in a heterogeneous graph. Heterogeneous graphs are fed as samples into the HMPGNN model to simulate the wireless phenomena within WirelessNet’s model architecture. Model parameters associated to the same underlying wireless phenomena are shared across network nodes. Results using system-level simulations to train and evaluate our proposal, show that WirelessNet efficiently outputs accurate downlink rates and vector representations of users, even for network deployments unseen during training, with significantly less computational runtime than a cellular network simulator and more accuracy than typical neural network architectures. With ablation experiments, we validate the downlink signal-to-interference-and-noise ratio (SINR) user equipment (UE) node feature as the most significant contributor to reconstruct downlink rates. In a more practical setting without SINR and with reference signal received power (RSRP) from serving base station (BS), WirelessNet generalizes to unseen network deployments and significantly outperforms homogeneous graph neural networks (GNNs). We further show the benefits of our proposal by implementing two network applications served by WirelessNet, namely: radio access network deployment planning and artificial intelligence/machine learning (AI/ML) model training for quality of service (QoS) prediction.}, archive = {J_ACCESS}, author = {José Perdomo and M. A. Gutierrez-Estevez and Chan Zhou and Jose F. Monserrat}, doi = {10.1109/ACCESS.2025.3545120}, journal = {IEEE Access}, pages = {36006-36023}, shortjournal = {IEEE Access}, title = {WirelessNet: An efficient radio access network model based on heterogeneous graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Optimization of the backstepping control parameters of an active electrohydraulic suspension to improve passenger comfort and road handling. ACCESS, 13, 35985-36005. (www)

  @article{ , abstract = {This study introduces an innovative optimization strategy for Electro-Hydraulic Active Suspension Systems (EHASS), combining game theory with Particle Swarm Optimization (PSO) to tune backstepping control parameters. Unlike conventional approaches relying on manual tuning or trial-and-error, our method systematically optimizes these parameters, ensuring a well-balanced trade-off between ride comfort and road handling. The optimization process considers worst-case road disturbances, leading to a 79.5% reduction in tracking error, a 44.7% decrease in VDV, and a 51.2% improvement in Crest Factor, complying with ISO 2631 standards. Comprehensive validation across ten road profiles, including highly irregular terrains, confirms the robustness of the proposed method. Additionally, a comparison with Genetic Algorithm (GA)-based optimization highlights that PSO achieves superior convergence and performance. These findings establish a new benchmark for intelligent suspension control, making our approach a strong candidate for real-world automotive applications.}, archive = {J_ACCESS}, author = {Rachid Fattah and Jean-Pierre Kenne and Khalid Benjelloun and Ahmed Chebak}, doi = {10.1109/ACCESS.2025.3544867}, journal = {IEEE Access}, pages = {35985-36005}, shortjournal = {IEEE Access}, title = {Optimization of the backstepping control parameters of an active electrohydraulic suspension to improve passenger comfort and road handling}, volume = {13}, year = {2025}, }
• (2025). Aerial image classification in post flood scenarios using robust deep learning and explainable artificial intelligence. ACCESS, 13, 35973-35984. (www)

  @article{ , abstract = {Efficiently delivering timely assistance to flooded regions is a critical imperative, and leveraging deep-learning methodologies has demonstrated significant efficacy in addressing environmental challenges. Several authors have collected data from specific regions and presented the methodologies for classifying flooded images. However, there are two main limitations of existing methodologies and benchmark datasets. Firstly, the models are trained on the images collected from specific geographical regions, which limits their ability to generalize when encountering images with diverse features or from varied regions. Secondly, the models are trained on high-resolution images and lack classification for the low-resolution images. In this study, we curated a dataset by merging benchmark datasets and acquiring images from web repositories. The main objective of this study is to address resolution issues and enhance model accuracy across diverse regions. We conducted a comparative analysis of the curated dataset using various deep-learning models based on CNN architecture. The experimental findings revealed that MobileNet and Xception outperformed ResNet-50, VGG-16, and Inception(v3) models, achieving an impressive accuracy rate of approximately 98% and an f1-score of 92% for flood class. Additionally, we have also used XAI Lime to interpret model results.}, archive = {J_ACCESS}, author = {Abdul Manaf and Nimra Mughal and Kazim Raza Talpur and Bandeh Ali Talpur and Ghulam Mujtaba and Samar Raza Talpur}, doi = {10.1109/ACCESS.2025.3543078}, journal = {IEEE Access}, pages = {35973-35984}, shortjournal = {IEEE Access}, title = {Aerial image classification in post flood scenarios using robust deep learning and explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). DPF-bi-RRT*: An improved path planning algorithm for complex 3D environments with adaptive sampling and dual potential field strategy. ACCESS, 13, 35958-35972. (www)

  @article{ , abstract = {This research work, we introduce a path planning algorithm called DPF-Bi-RRT* which integrates a Dual Potential Field mechanism with a Targeted Sampling Strategy to address path planning issues in complex 3D spaces. The algorithm achieves a good trade off between the global path optimization and precise local obstacle avoidance by combining dual-attraction and dual-repulsion mechanisms. The algorithm achieves effective exploration of path regions of high quality by dynamically adjusting the sampling distribution. Additionally, a biased random sampling strategy improves computational efficiency by directing sampling resources toward sections with higher promise of optimal paths, dramatically reducing computational cost. The dual potential field model lead to more flexible method in collision avoidance and can improve the precision of collision avoidance, especially in cluttered dynamical spaces. We perform comparative simulations of DPF-Bi-RRT* versus RRT*, Bi-RRT*, and APF-Bi-RRT* across three defined environments to demonstrate that DPF-Bi-RRT* results in lower average node counts, less computational time, and longer path lengths than all three. Results validate its capability of generating smooth, collision free and globally optimized paths making it especially suitable for autonomous aerial vehicle (AAV) navigation in complex 3D environments.}, archive = {J_ACCESS}, author = {Lin Ge and Swee King Phang and Nohaidda Sariff}, doi = {10.1109/ACCESS.2025.3544546}, journal = {IEEE Access}, pages = {35958-35972}, shortjournal = {IEEE Access}, title = {DPF-bi-RRT*: An improved path planning algorithm for complex 3D environments with adaptive sampling and dual potential field strategy}, volume = {13}, year = {2025}, }
• (2025). Induction motor under unbalanced voltage subharmonics. ACCESS, 13, 35946-35957. (www)

  @article{ , abstract = {In addition to fundamental harmonics, the voltage waveform may contain various undesirable components, including subharmonics (subsynchronous interharmonics) and interharmonics (SaIs), which are components with frequencies less than or not an integer multiple of the fundamental frequency. Voltage SaIs are considered especially detrimental power quality disturbances, exerting negative impacts on rotating machinery. They are generated by various electrical equipment, including single-phase equipment. This work addresses the effects of the occurrence of subharmonics in one phase of the supply voltage on three-phase cage induction motors. The results of 2D finite element computations and experimental investigations show that subharmonics occurring in one phase cause high torque pulsations and unacceptable vibrations. For the investigated motor, the minimal value of the voltage subharmonic causing excessive vibration is more than twice that in the case of balanced three-phase subharmonics. It is concluded that power quality standards should contain separate limits for SaIs averaged for three line voltages and for SaIs occurring in each phase voltage.}, archive = {J_ACCESS}, author = {Piotr Gnaciński and Marcin Pepliński and Adam Muc and Damian Hallmann}, doi = {10.1109/ACCESS.2025.3544458}, journal = {IEEE Access}, pages = {35946-35957}, shortjournal = {IEEE Access}, title = {Induction motor under unbalanced voltage subharmonics}, volume = {13}, year = {2025}, }
• (2025). Use of survival analysis for shell companies analyses in a brazilian state. ACCESS, 13, 35933-35945. (www)

  @article{ , abstract = {One of the current problems in tax evasion is the creation of shell companies. Such companies operate in the provision of spurious credits, harming the market and the State. The Tax Authority needs to act as soon as possible to prevent such companies from generating the credit, as well as within the constitutional deadline for recovery of the amounts. The present work aims to analyze how the SEFAZ of the State of São Paulo has acted on this issue. Through the use of survival analysis in Python, it is sought to verify how the performance was in the period from 2015 to 2023. The time to detect shell companies as well as the time required for administrative processes are analyzed. First, an analysis is made through Kaplan Meier. Subsequently, using computational tools, the possibility of parameterizing the survival curve is verified. Through Cox regression, it is verified if there are relevant covariates that can be studied. The results are expected to provide subsidies for the development of lines of action within the SEFAZ to minimize the survival time of such companies.}, archive = {J_ACCESS}, author = {Jônatas Dell Ducas and Rodrigo Colnago Contreras}, doi = {10.1109/ACCESS.2025.3543714}, journal = {IEEE Access}, pages = {35933-35945}, shortjournal = {IEEE Access}, title = {Use of survival analysis for shell companies analyses in a brazilian state}, volume = {13}, year = {2025}, }
• (2025). A design of high-efficiency RF/Solar/ Triboelectric/Electromagnetic energy harvesting system. ACCESS, 13, 35922-35932. (www)

  @article{ , abstract = {This paper presents a comprehensive exploration of a Hybrid Energy Harvesting System designed to harvest energy from diverse sources, including 2.4 GHz and 5.8 GHz RF energy, electromagnetic (EM) energy, solar energy, and triboelectric energy. The proposed converter operates within a wide input voltage range, maximizing efficiency and stability. A reconfigurable RF-DC converter is used for both 2.4 GHz and 5.8 GHz frequencies to achieve high power conversion efficiency at low input power level. A maximum of 52 % and 44.8 % PCE is achieved at −14 dBm and −2 dBm respectively. Electromagnetic rectifier produces an output voltage of 1 V with a PCE of 95.6 % which operates with a 15 Hz input frequency. The voltage of the tribo rectifier is limited by the limiter circuit to 2.5 V with a PCE of 96 % concerning load current. A voltage combiner is used to combine all these harvested voltages and pass them to the buckboost DC-DC converter. This hybrid energy harvesting system is designed and fabricated in 180 nm bipolar-CMOSDMOS (BCD) technology with an active area of 2.8mm $\times 2.5$ mm. The buck-boost DC-DC converter incorporated into our hybrid energy harvesting system achieves an impressive peak Power Conversion Efficiency (PCE) of 95 %. This innovative system integrates EM, solar, triboelectric, and RF energy harvesters, collectively ensuring a reliable power source. The ultimate goal is to deliver a steady 5 V DC voltage, skillfully regulated by the buck-boost DC-DC converter.}, archive = {J_ACCESS}, author = {Muhammad Basim and Nabeel Ahmad and Syed Adil Ali Shah and Ghayur Hussain and Chang Heyon Kim and Jong Wan Jo and Young Gun Pu and Keum Cheol Hwang and Youngoo Yang and Kang-Yoon Lee}, doi = {10.1109/ACCESS.2025.3542366}, journal = {IEEE Access}, pages = {35922-35932}, shortjournal = {IEEE Access}, title = {A design of high-efficiency RF/Solar/ Triboelectric/Electromagnetic energy harvesting system}, volume = {13}, year = {2025}, }
• (2025). Trade-space exploration with data preprocessing and machine learning for satellite anomalies reliability classification. ACCESS, 13, 35903-35921. (www)

  @article{ , abstract = {Satellite reliability is critical to ensuring uninterrupted operations in aerospace systems, where anomalies can lead to mission failures and significant economic losses. Existing anomaly classification methods often lack scalability, interpretability, and adaptability to diverse datasets. This study introduces the Trade-Space Exploration Machine Learning (TSE-ML) framework, a comprehensive pipeline for satellite anomaly classification that optimizes preprocessing, transformation, normalization, and machine learning stages. Leveraging a Seradata dataset spanning 66 years and 4,455 satellite records, the framework systematically evaluates four data cleaning methods, four data transformation techniques, five normalization strategies, and seven machine learning algorithms across 480 configurations. The optimal configuration, comprising Iterative Imputation, FastText, Robust Scaling, and Decision Tree, achieved the highest testing accuracy of 95.74% with competitive computational efficiency. The Decision Tree model delivered superior accuracy and provided interpretability, revealing critical factors influencing satellite anomalies, such as Age Since Launch, Design Life, and Orbit Category. Stratified 5-fold cross-validation ensured robustness and generalizability of the results. The TSE-ML framework’s transparency and high performance enable actionable insights for improving satellite design, operational planning, and anomaly mitigation. Future research will focus on real-time anomaly detection, integrating satellite telemetry data, and extending the framework to other space applications. This study establishes a robust, interpretable foundation for advancing anomaly classification in aerospace engineering, addressing the dual challenges of reliability and operational efficiency.}, archive = {J_ACCESS}, author = {Abdul Mutholib and Nadirah Abdul Rahim and Teddy Surya Gunawan and Mira Kartiwi}, doi = {10.1109/ACCESS.2025.3543813}, journal = {IEEE Access}, pages = {35903-35921}, shortjournal = {IEEE Access}, title = {Trade-space exploration with data preprocessing and machine learning for satellite anomalies reliability classification}, volume = {13}, year = {2025}, }
• (2025). Advances in spectral imaging: A review of techniques and technologies. ACCESS, 13, 35848-35902. (www)

  @article{ , abstract = {Spectral imaging systems enable a significant leap forward in identifying and characterizing materials and objects through their unique spectral signatures. This review explores the advances in spectral imaging systems (SIS) designs, analyzing both the techniques and technologies. It provides an overview of the fundamental principles of spectral imaging, including different panchromatic, multispectral, and hyperspectral imaging systems. Progress in the design of multispectral imaging (MSI) systems are comprehensively surveyed with focus on the optical technologies employed. Advances in hyperspectral imaging (HSI) system are discussed, covering various acquisition techniques such as staring, snapshot, whiskbroom, and pushbroom techniques. In the case of staring technique, an in-depth examination is provided on different filtering technologies, including dispersive and diffractive optics, acousto-optic tunable filters (AOTFs), Fabry-Perot interferometers (FPI), liquid crystal tunable filters (LCTFs), and linear variable filters (LVFs). A detailed overview and progress in snapshot techniques is provided, followed by a thorough examination of whiskbroom and pushbroom techniques. Finally, it offers insights into the potential future directions in SIS research.}, archive = {J_ACCESS}, author = {Sani Mukhtar and Amir Arbabi and Jaime Viegas}, doi = {10.1109/ACCESS.2025.3544476}, journal = {IEEE Access}, pages = {35848-35902}, shortjournal = {IEEE Access}, title = {Advances in spectral imaging: A review of techniques and technologies}, volume = {13}, year = {2025}, }
• (2025). A robust framework for comprehensive container image vulnerability assessment. ACCESS, 13, 35837-35847. (www)

  @article{ , abstract = {Container technologies have become integral to modern cloud-native application development, offering flexibility, scalability, and ease of deployment. Although containers offer efficiency and scalability in deploying and managing applications, they face security concerns such as malware and privilege escalation due to their weaker isolation. To address these issues, various security solutions have been developed, among which container image scanning technology is regarded as a fundamental security measure. However, existing approaches mainly focus on OS packages, neglecting manually installed or migrated packages, which limits their effectiveness in detecting real-world threats. In this paper, we propose a novel and rigorous container image vulnerability assessment system designed to ensure the secure deployment of applications across both standard and non-standard container images. Unlike existing methods, the proposed system focuses on direct file system inspection rather than relying on package manager analysis or Dockerfile inspection. In addition, the propose approach enables the system to detect vulnerabilities that may be introduced during container execution or through runtime-installed packages. To evaluate the effectiveness of the proposed system, we conducted extensive experiments on datasets of container images collected from various scenarios, and the experimental results demonstrate that our system significantly outperforms existing solutions in detecting vulnerabilities.}, archive = {J_ACCESS}, author = {Youngsoo Kim and Cheolhee Park and Dowon Hong}, doi = {10.1109/ACCESS.2025.3545124}, journal = {IEEE Access}, pages = {35837-35847}, shortjournal = {IEEE Access}, title = {A robust framework for comprehensive container image vulnerability assessment}, volume = {13}, year = {2025}, }
• (2025). Experimental study on the change rule of typical defective discharge characteristics of transformer oil paper under the action of oil flow fiber impurity particles. ACCESS, 13, 35829-35836. (www)

  @article{ , abstract = {This paper addresses the issue of fiber impurity particle movement and accumulation on the surface of the electrode at the outlet of the converter transformer during long-term operation, which leads to changes in various local discharge characteristics and mechanisms in the insulating oil. An oil flow device experimental platform was constructed to study the discharge characteristics of fiber impurity particles under different particle concentrations, electrode spacings, and insulating medium conditions. The study found that: 1) The higher the concentration of fiber impurities in the contaminated insulating oil, the easier it is for a complete “impurity bridge” to form between the electrodes. At the same time, the discharge quantity associated with the accumulation of impurities also increases. 2) When the concentration of fiber impurities in the contaminated insulating oil is fixed, as the electrode spacing increases, the number of continuous “fiber impurity bridges” formed between the electrodes decreases sharply, while the discharge quantity increases dramatically. As the thickness of the insulating paperboard increases, the accumulation of fiber impurity particles is reduced, and the discharge signal gradually strengthens.}, archive = {J_ACCESS}, author = {Jing Du and Lingyi Wang and Xinrong Chen and Kun Chen and Xu Chen and Guozhi Zhang}, doi = {10.1109/ACCESS.2025.3541775}, journal = {IEEE Access}, pages = {35829-35836}, shortjournal = {IEEE Access}, title = {Experimental study on the change rule of typical defective discharge characteristics of transformer oil paper under the action of oil flow fiber impurity particles}, volume = {13}, year = {2025}, }
• (2025). Robust extrinsic calibration for LiDAR-camera systems via depth and height complementary supervision network. ACCESS, 13, 35818-35828. (www)

  @article{ , abstract = {Extrinsic calibration between cameras and LiDAR (Light Detection and Ranging) is a fundamental step for numerous autonomous driving applications, such as 3D object detection, lane detection, and trajectory planning. However, conventional calibration methods are laborious and require dedicated data collection. Although recent research has shown the potential of learning-based solutions for end-to-end use, existing methods tend to focus on feature matching rather than geometric constraints, leading to inefficiency and instability in traffic scenarios with various disturbances. In this paper, we introduce an improved deep-learning-based joint calibration framework for LiDAR-camera systems, termed the Complementary Calibration Network (Co-CalibNet). The main contribution of our work lies in proposing a novel dual-channel geometric supervision calibration framework that integrates both depth and height supervision to achieve robust extrinsic calibration. Additionally, we introduce an Attention-based Fusion Module (AFM) for efficient feature fusion. Furthermore, we incorporate time compensation and iterative calibration techniques to further enhance the robustness of the algorithm in handling initial alignment errors. Evaluations on the DAIR-V2X and KITTI datasets demonstrate that Co-CalibNet achieves state-of-the-art calibration performance while exhibiting greater robustness to initial misalignment. Since it requires no targets or human effort, Co-CalibNet can be seamlessly integrated into any LiDAR-camera architecture, suggesting significant value and broad application prospects in autonomous driving systems.}, archive = {J_ACCESS}, author = {Chen Yaqing and Wang Huaming}, doi = {10.1109/ACCESS.2025.3542279}, journal = {IEEE Access}, pages = {35818-35828}, shortjournal = {IEEE Access}, title = {Robust extrinsic calibration for LiDAR-camera systems via depth and height complementary supervision network}, volume = {13}, year = {2025}, }
• (2025). SLEEP-SAFE: Self-supervised learning for estimating electroencephalogram patterns with structural analysis of fatigue evidence. ACCESS, 13, 35805-35817. (www)

  @article{ , abstract = {Recently, deep learning frameworks have gained increasing attentions from electroencephalogram (EEG)-based driver’s fatigue estimation, thanks to their unprecedented feature extraction calibre. However, it is still challenging to develop session- and/or subject-independent system, because of the complex structural characteristics of EEG signals. In this regard, this work proposes a novel deep convolutional neural network architecture that can learn spectro-spatio-temporal representation of the vigilance EEG signals, thereby achieving a powerful mental status recognition ability. Specifically, the proposed network pretrained via two novel self-supervision pretext tasks. Further, both differential entropy and EEG signal itself are exploited to acquire rich features. To demonstrate the validity of the proposed methods, this work conduct intra- and inter-subject classification experiments using a publicly available dataset. In the exhaustive experiments, we observed that our proposed framework has practical availability. Specifically, our proposed multiple path structure improved the model’s performance by $3\sim 7$ percentage points (%p) in the session-independent setting and by $6\sim 9$ %p in the subject-independent setting. Besides, the novel self-supervised learning strategy enhanced the performance by $10\sim 17$ and $12\sim 16$ %p in the session- and subject-independent case, respectively. Furthermore, this work also investigate strengths and society-friendliness of our proposed framework.}, archive = {J_ACCESS}, author = {Wonjun Ko and Jeongwon Choe and Jonggu Kang}, doi = {10.1109/ACCESS.2025.3545094}, journal = {IEEE Access}, pages = {35805-35817}, shortjournal = {IEEE Access}, title = {SLEEP-SAFE: Self-supervised learning for estimating electroencephalogram patterns with structural analysis of fatigue evidence}, volume = {13}, year = {2025}, }
• (2025). Blockchain and NFT-based solution for genomic data management, sharing, and monetization. ACCESS, 13, 35780-35804. (www)

  @article{ , abstract = {Non-fungible tokens (NFTs) are unique digital assets stored on blockchains. NFTs are ideally suited for tokenizing genomic data, as they empower individuals with complete control over them. Next-generation sequencing (NGS) technology creates repositories of sequenced data from individuals’ raw genomic data, which raises challenges related to data ownership, management, and secure sharing. In this paper, we propose a blockchain and NFT-based solution that addresses the challenges of managing, sharing, and monetizing genomic data while preserving privacy using Threshold Cryptography and Fully Homomorphic Encryption (FHE). We integrate the proposed solution with the Interplanetary File System (IPFS), a decentralized storage system, to handle the substantial amount of genomic data off-chain. We develop three smart contracts to facilitate genomic data management, sharing, and monetization. We introduce composable NFTs to ensure that sequenced genomic data (SGD) NFTs are always linked to the parent raw genomic data (RGD) NFTs to maintain traceability. We present various diagrams and algorithms to illustrate the functionality of our solution. Our testing and validation results demonstrate that smart contracts function as intended. The cost evaluation shows that implementing the solution on a private blockchain is more feasible and user-friendly. Our solution provides a comprehensive framework for genomic data management, sharing, and monetization, with privacy-preserving mechanisms and traceability. We provide guidelines for the generalizability of our solution beyond genomics and outline the challenges and limitations of the proposed solution. We make the source code of the smart contracts publicly available on GitHub.}, archive = {J_ACCESS}, author = {Ahmad Musamih and Khaled Salah and Raja Jayaraman and Samer Ellaham and Mohammed Omar and Ibrar Yaqoob}, doi = {10.1109/ACCESS.2025.3544643}, journal = {IEEE Access}, pages = {35780-35804}, shortjournal = {IEEE Access}, title = {Blockchain and NFT-based solution for genomic data management, sharing, and monetization}, volume = {13}, year = {2025}, }
• (2025). A computational intelligence framework integrating data augmentation and meta-heuristic optimization algorithms for enhanced hybrid nanofluid density prediction through machine and deep learning paradigms. ACCESS, 13, 35750-35779. (www)

  @article{ , abstract = {This research presents a robust and comprehensive framework for predicting the density of hybrid nanofluids using state-of-the-art machine learning and deep learning techniques. Addressing the limitations of conventional empirical approaches, the study used a curated dataset of 436 samples from the peer-reviewed literature, which includes nine input parameters such as the nanoparticle, base fluid, temperature (°C), volume concentration ( $\phi $ ), base fluid density ( $\rho _{\text {bf}}$ ), density of primary and secondary nanoparticles ( $\rho _{\text {np1}}$ and $\rho _{\text {np2}}$ ), and volume mixture ratios of primary and secondary nanoparticles. Data preprocessing involved outlier removal via the Interquartile Range (IQR) method, followed by augmentation using either autoencoder-based or Gaussian noise injection, which preserved statistical integrity and enhanced dataset diversity. The research analyzed fourteen predictive models, employing advanced hyperparameter optimization methods facilitated by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). In particular, autoencoder-based augmentation combined with hyperparameter optimization consistently improved predictive accuracy across all models. For machine learning models, Gradient Boosting achieved the most remarkable performance, with R2 scores of 0.99999 and minimal MSE values of 0.00091. Among deep learning models, Recurrent Neural Networks (RNN) stacked with Linear Regression achieved superior performance with an R2 of 0.9999, MSE of 0.0014, and MAE of 0.012. The findings underscore the synergy of advanced data augmentation, meta-heuristic optimization, and modern predictive algorithms in modelling hybrid nanofluid density with unprecedented precision. This framework offers a scalable and reliable tool for advancing nanofluid-based applications in thermal engineering and related domains.}, archive = {J_ACCESS}, author = {Priya Mathur and Hammad Shaikh and Farhan Sheth and Dheeraj Kumar and Amit Kumar Gupta}, doi = {10.1109/ACCESS.2025.3543475}, journal = {IEEE Access}, pages = {35750-35779}, shortjournal = {IEEE Access}, title = {A computational intelligence framework integrating data augmentation and meta-heuristic optimization algorithms for enhanced hybrid nanofluid density prediction through machine and deep learning paradigms}, volume = {13}, year = {2025}, }
• (2025). Visibility aware in-hand object pose tracking in videos with transformers. ACCESS, 13, 35733-35749. (www)

  @article{ , abstract = {In-hand object pose estimation is essential in various engineering applications, such as quality inspection, reverse engineering, and automated manufacturing processes. However, achieving accurate pose estimation becomes difficult when objects are heavily occluded by the hand or blurred due to motion. To address these challenges, we propose a novel framework that leverages the power of transformers for spatial-temporal reasoning across video sequences. Our approach utilizes transformers to capture both spatial relationships within each frame and temporal dependencies across consecutive frames, allowing the model to aggregate information over time and improve pose predictions. A key innovation of our framework is the introduction of a visibility-aware module, which dynamically adjusts pose estimates based on the object’s visibility. This module utilizes temporally-aware features extracted by the transformers, allowing the model to aggregate pose information across multiple frames. By integrating this aggregated information, the model can maintain high accuracy even when portions of the object are not visible in certain frames. This capability is particularly crucial in dynamic environments where the object’s appearance can change rapidly due to hand movements or interactions with other objects. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques, achieving a 6% improvement in overall accuracy and over 11% better performance in handling occlusions.}, archive = {J_ACCESS}, author = {Phan Xuan Tan and Dinh-Cuong Hoang and Eiji Kamioka and Anh-Nhat Nguyen and Duc-Thanh Tran and Van-Hiep Duong and Anh-Truong Mai and Duc-Long Pham and Khanh-Toan Phan and Xuan-Tung Dinh and Tran Thi Thuy Trang and Xuan-Duong Pham and Nhat-Linh Nguyen and Thu-Uyen Nguyen and Viet-Anh Trinh and Khanh-Duong Tran and Son-Anh Bui}, doi = {10.1109/ACCESS.2025.3545049}, journal = {IEEE Access}, pages = {35733-35749}, shortjournal = {IEEE Access}, title = {Visibility aware in-hand object pose tracking in videos with transformers}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based coding strategy for improved cochlear implant speech perception in noisy environments. ACCESS, 13, 35707-35732. (www)

  @article{ , abstract = {Automatic speech recognition (ASR) and speech enhancement are essential tools in modern life, aiding not only in machine interaction but also in supporting individuals with hearing impairments. These processes begin with capturing speech in analog form and applying signal processing algorithms to ensure compatibility with devices like cochlear implants (CIs). However, CIs, with their limited number of electrodes, often cause speech distortion, and despite advancements in state-of-the-art signal processing techniques, challenges persist, particularly in noisy environments with multiple speech sources. The rise of artificial intelligence (AI) has introduced innovative strategies to address these limitations. This paper presents a novel deep learning (DL)-based technique that leverages attention mechanisms to improve speech intelligibility through noise suppression. The proposed approach includes two strategies: the first integrates temporal convolutional networks (TCNs) and multi-head attention (MHA) layers to capture both local and global dependencies within the speech signal, enabling precise noise filtering and improved clarity. The second strategy builds on this framework by additionally incorporating bidirectional gated recurrent units (Bi-GRU) alongside TCN and MHA layers, further refining sequence modeling and enhancing noise reduction. The optimal model configuration, using TCN-MHA-Bi-GRU with a kernel size of 16, achieved a compact model size of 788K parameters and recorded training, and validation losses of 0.0350 and 0.0446, respectively. Experimental results on the TIMIT and Harvard Sentences datasets, enriched with diverse noise sources from the DEMAND database, yielded high intelligibility scores with a short-time objective intelligibility (STOI) of 0.8345, word recognition score (WRS) of 99.2636, and an near correlation coefficient (LCC) of 0.9607, underscoring the model’s capability to enhance speech perception in noisy CI environments, ensuring a balance between model size and speech quality, and surpassing the existing state-of-the-art techniques.}, archive = {J_ACCESS}, author = {Billel Essaid and Hamza Kheddar and Noureddine Batel and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3542953}, journal = {IEEE Access}, pages = {35707-35732}, shortjournal = {IEEE Access}, title = {Deep learning-based coding strategy for improved cochlear implant speech perception in noisy environments}, volume = {13}, year = {2025}, }
• (2025). Optimizing traffic speed prediction using a multi-objective genetic algorithm-enhanced RNN for intelligent transportation systems. ACCESS, 13, 35688-35706. (www)

  @article{ , abstract = {Over the past decade, major cities have faced significant traffic congestion, accidents, and pollution due to increased vehicle usage, urbanization, and migration. An Intelligent Transportation System (ITS) can enhance transportation planning and alleviate congestion. ITS utilizes traffic prediction models to help prevent traffic bottlenecks, improve mobility and safety, and reduce environmental impacts. However, developing these models involves several challenges, including understanding spatiotemporal nonlinearities, making accurate predictions, minimizing prediction time, and reducing model complexity. Many existing approaches integrate Convolutional Neural Networks (CNNs) and variants of Recurrent Neural Networks (RNNs) to analyze spatially correlated traffic data over time. Nevertheless, these hybrid models often require significant storage space, contain numerous learnable parameters, and involve extensive training, validation, and testing times. To address these challenges, we propose a novel methodology that combines a genetic algorithm (GA) with Random Forest Cross-Validation (RF-CV) to evaluate input features and select the most relevant subset. Additionally, we developed a Multi-Objective Genetic Algorithm (MOGA)-enhanced RNN model to optimize hyperparameters and achieve accurate traffic speed predictions. Our proposed methodology balances the trade-offs between prediction accuracy, model size, and computational efficiency by identifying an optimal set of relevant features and hyperparameters. We evaluated our model using the Performance Measurement System (PeMS)-10 dataset and compared its performance against baseline and advanced models from existing literature. Our model achieved a Mean Absolute Error (MAE) of 0.028993, an $R^{2}$ score of 0.999490, and training, validation, and testing times of 81.64 seconds, 0.15 seconds, and 0.18 seconds, respectively. Additionally, the model size was 203,118 bytes, with 14,617 parameters. A comprehensive comparative study demonstrates that our approach outperforms state-of-the-art models in both prediction accuracy and computational efficiency.}, archive = {J_ACCESS}, author = {C. Swetha Priya and F. Sagayaraj Francis}, doi = {10.1109/ACCESS.2025.3544864}, journal = {IEEE Access}, pages = {35688-35706}, shortjournal = {IEEE Access}, title = {Optimizing traffic speed prediction using a multi-objective genetic algorithm-enhanced RNN for intelligent transportation systems}, volume = {13}, year = {2025}, }
• (2025). SA-MARL: Novel self-attention-based multi-agent reinforcement learning with stochastic gradient descent. ACCESS, 13, 35674-35687. (www)

  @article{ , abstract = {In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal development in this realm is the introduction of the mixing network, representing a significant leap forward in the capabilities of multi-agent systems. Drawing inspiration from COMA and VDN methodologies, the mixing network overcomes limitations in extracting combined Q-values from joint state-action interactions. Previous approaches like COMA and VDN faced constraints in fully utilizing the state-provided information during training, limiting their effectiveness. QMIX and QVMinMax addressed this issue by employing neural networks to convert centralized states into weights for a second neural network, akin to hyper- networks. However, these solutions presented challenges such as computational intensity and susceptibility to local minima. To overcome these hurdles, our proposed methodology introduces three key contributions. First, we introduce the state- fusion network, an innovative alternative to traditional mixing, with a self-attention mechanism. Second, to address the local optima problem in MARL algorithms, we leverage the Grey Wolf Optimizer for weight and bias selection, adding a stochastic element for improved optimization. Finally, we comprehensively compare with QMIX, evaluating performance under two optimization methods: Gradient Descent and Stochastic Optimizer. Using the StarCraft II Learning Environment (SC2LE) as our experimental platform, our results demonstrate the superiority of our methodology over QMIX, QVMinMax, and QSOD in absolute performance, particularly when operating under resource constraints. Our proposed methodology contributes to the ongoing evolution of MARL techniques, showcasing advancements in attention mechanisms and optimization strategies for enhanced multi-agent system capabilities.}, archive = {J_ACCESS}, author = {Rabbiya Younas and Hafiz Muhammad Raza Ur Rehman and Ingyu Lee and Byung-Won On and Sungwon Yi and Gyu Sang Choi}, doi = {10.1109/ACCESS.2025.3544961}, journal = {IEEE Access}, pages = {35674-35687}, shortjournal = {IEEE Access}, title = {SA-MARL: Novel self-attention-based multi-agent reinforcement learning with stochastic gradient descent}, volume = {13}, year = {2025}, }
• (2025). An adaptive fault feature extraction method for aeroengine intershaft bearing based on casing vibration. ACCESS, 13, 35662-35673. (www)

  @article{ , abstract = {Fault diagnosis of the intershaft bearing of aeroengine is quite challenging because vibrations can only be measured on the distant external casing, resulting in weak fault features and complex interference. To address this issue, this paper proposes an adaptive multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) method to extract fault impulses of intershaft bearings based on casing signals. First, envelope harmonic product spectrum (EHPS) is used to estimate the bearing fault frequency and determine the parameter search range. Additionally, an evolved firefly algorithm (EFA) is introduced, incorporating mutation and step-size factors to enhance the convergence speed and global search capability of the algorithm. Furthermore, a new objective function EP is proposed that considers both the time-domain complexity and characteristics of periodic pulses, which is designed to enhance the capability to localize fault periodic impulses. Simulation results show that the proposed EPEFA-MOMEDA can accurately extract the fault period from simulated signals containing high-energy harmonics and random impulse noise without prior knowledge of the fault period. Finally, verification was conducted using real aero-engine casing vibration data. Comparative analysis demonstrates that this method is practical for engineering applications in extracting intershaft bearing faults from casing vibrations.}, archive = {J_ACCESS}, author = {Chen Liang and Haijun Xuan and Guoqiang Li and Jiashuo Zhang and Dahai Zhang}, doi = {10.1109/ACCESS.2025.3543375}, journal = {IEEE Access}, pages = {35662-35673}, shortjournal = {IEEE Access}, title = {An adaptive fault feature extraction method for aeroengine intershaft bearing based on casing vibration}, volume = {13}, year = {2025}, }
• (2025). Optimized breast cancer classification using PCA-LASSO feature selection and ensemble learning strategies with optuna optimization. ACCESS, 13, 35645-35661. (www)

  @article{ , abstract = {Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide, making early and accurate detection crucial for effective treatment. This study presents a novel and optimized breast cancer classification system using machine learning models enhanced through advanced hyperparameter tuning techniques and statistical validation methods. The publicly available Breast Cancer Wisconsin (Diagnostic) dataset was utilized and underwent preprocessing steps, including dimensionality reduction and feature selection methods such as Principal Component Analysis (PCA) combined with Least Absolute Shrinkage and Selection Operator (LASSO). The classifiers employed include Random Forest, Support Vector Machine (SVM), Gradient Boosting, and Logistic Regression, which were further refined using GridSearchCV, RandomizedSearchCV, and Optuna, with 3-fold cross-validation implemented to ensure robust evaluation of model performance. The novelty of this work lies in integrating advanced feature selection methods with Optuna-driven optimization and ensemble learning strategies to achieve superior accuracy. A comprehensive evaluation of the proposed models was performed using key classification metrics such as precision, recall, F1-score, and accuracy, with the Optuna-optimized ensemble model achieving the highest accuracy of 99.423%. Statistical validation of the results was conducted using ANOVA tests, confirming the significance of the improvements achieved through hyperparameter optimization. Additionally, feature importance scores for Random Forest and Gradient Boosting provide insights into the most influential factors in the classification process. This work demonstrates the efficacy of combining dimensionality reduction, ensemble learning, and Optuna optimization, validated through 3-fold cross-validation, offering a novel and reliable approach to interpretable breast cancer detection systems.}, archive = {J_ACCESS}, author = {Prabhat Kumar Sahu and Taiyaba Fatma}, doi = {10.1109/ACCESS.2025.3539746}, journal = {IEEE Access}, pages = {35645-35661}, shortjournal = {IEEE Access}, title = {Optimized breast cancer classification using PCA-LASSO feature selection and ensemble learning strategies with optuna optimization}, volume = {13}, year = {2025}, }
• (2025). RUL prediction of DC contactor using CNN-LSTM with channel attention and fusion of dual aggregated features. ACCESS, 13, 35634-35644. (www)

  @article{ , abstract = {Predicting the remaining useful life (RUL) of DC contactors is crucial for the reliability of aerospace, rail transit, and electric vehicle systems. Challenges arise due to high-dimensional operational data, difficulty fusing spatial-temporal features, and noisy environments. This paper proposes a novel deep learning model called DAF-CA-CNN-LSTM, which integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), a Channel Attention (CA) mechanism, and a Dual Aggregated Features (DAF) strategy. The CNN extracts local spatial features, while the LSTM captures long-term temporal dependencies. The CA mechanism enhances focus on critical degradation features by adaptively weighting feature channels. The DAF strategy enriches feature representation by combining aggregated encoded features with original inputs. An experimental system was developed to collect operational data under specific conditions. Key features were extracted, preprocessed, and used to train and evaluate the model. Results show that the DAF-CA-CNN-LSTM model significantly outperforms traditional LSTM and CNN-LSTM models in RUL prediction, achieving higher accuracy and robustness in complex, noisy environments. This model effectively addresses DC contactor life prediction challenges, offering a promising tool for improving maintenance strategies and operational reliability.}, archive = {J_ACCESS}, author = {Sai Wang and Yuanfeng Zhang and Hao Huang and Yun Shi and Jianfei Si}, doi = {10.1109/ACCESS.2024.3516200}, journal = {IEEE Access}, pages = {35634-35644}, shortjournal = {IEEE Access}, title = {RUL prediction of DC contactor using CNN-LSTM with channel attention and fusion of dual aggregated features}, volume = {13}, year = {2025}, }
• (2025). LLM teacher-student framework for text classification with no manually annotated data: A case study in IPTC news topic classification. ACCESS, 13, 35621-35633. (www)

  @article{ , abstract = {With the ever-increasing number of news stories available online, classifying them by topic, regardless of the language they are written in, has become crucial for enhancing readers’ access to relevant content. To address this challenge, we propose a teacher-student framework based on large language models (LLMs) for developing multilingual news topic classification models of reasonable size with no need for manual data annotation. The framework employs a Generative Pretrained Transformer (GPT) model as the teacher model to develop a news topic training dataset through automatic annotation of 20,000 news articles in Slovenian, Croatian, Greek, and Catalan. Articles are classified into 17 main categories from the Media Topic schema, developed by the International Press Telecommunications Council (IPTC). The teacher model exhibits high zero-shot performance in all four languages. Its agreement with human annotators is comparable to that between the human annotators themselves. To mitigate the computational limitations associated with the requirement of processing millions of texts daily, smaller BERT-like student models are fine-tuned on the GPT-annotated dataset. These student models achieve high performance comparable to the teacher model. Furthermore, we explore the impact of the training data size on the performance of the student models and investigate their monolingual, multilingual, and zero-shot cross-lingual capabilities. The findings indicate that student models can achieve high performance with a relatively small number of training instances, and demonstrate strong zero-shot cross-lingual abilities. Finally, we publish the best-performing news topic classifier, enabling multilingual classification with the top-level categories of the IPTC Media Topic schema.}, archive = {J_ACCESS}, author = {Taja Kuzman and Nikola Ljubešić}, doi = {10.1109/ACCESS.2025.3544814}, journal = {IEEE Access}, pages = {35621-35633}, shortjournal = {IEEE Access}, title = {LLM teacher-student framework for text classification with no manually annotated data: A case study in IPTC news topic classification}, volume = {13}, year = {2025}, }
• (2025). Physical layer cyber resilience oriented dynamic secure polar code. ACCESS, 13, 35609-35620. (www)

  @article{ , abstract = {Cyber resilience has attracted worldwide attention. Security and reliability of information transmission are considered to be two key characteristics from the perspective of physical layer cyber resilience. Consequently, a dynamic secure polar code is proposed in this paper to improve the physical layer resilience of the non-degraded channels, in which a shared key is firstly used as a seed to produce a pseudo-random sequence that is further used to generate a dynamic generator matrix to obfuscate the coding structure. Then, a small amount of random column vectors are inserted into the generator matrix to destroy the systematicness of the code. Finally, the hash value of the key is used as frozen bits to avoid the leaking of the key. Confidentiality analysis and computer simulations were carried out for the proposed scheme. The results show that the proposed scheme has high security against cryptanalysis attacks. Meanwhile the eavesdropper’s bit error rate is kept above 0.435 in non-degraded channel scenarios, and the reliability is almost the same with that of the original polar code. Furthermore, the frame error rate of the proposed scheme is about 1/10 of the existing schemes in low signal-to-noise ratio regimes.}, archive = {J_ACCESS}, author = {Sifan Yao and Yu Dai and Gangtao Han and Zhiqing Tang and Dalong Zhang}, doi = {10.1109/ACCESS.2025.3545029}, journal = {IEEE Access}, pages = {35609-35620}, shortjournal = {IEEE Access}, title = {Physical layer cyber resilience oriented dynamic secure polar code}, volume = {13}, year = {2025}, }
• (2025). A techno-economic modeling approach to 6G network deployment: Exploring costs and use case feasibility. ACCESS, 13, 35597-35608. (www)

  @article{ , abstract = {In recent years, significant research has explored 6G vision, enabling technologies, business models, and potential applications. However, the economic feasibility of these advanced and intelligent services must be addressed before standardization. To support the successful realization of 6G promises, we quantified the technical requirements and assessed the economic viability of the proposed solutions using current 5G data and appropriate multipliers. An example application of our analysis shows that the required performance improvements and network densification lead to significantly higher infrastructure costs, with 6G investments estimated to be 200%–840% higher than those of 5G, depending on the use case. In addition, both revenue and Average Revenue Per User (ARPU) are projected to increase considerably. For example, implementing 6G in the case of Pervasive Connectivity & Smart Cities requires an ARPU growth of 10% and a 479% increase in revenue compared with today’s 5G, making it highly feasible. However, use cases such as AR/VR applications present challenges, with ARPU requiring a 582% increase to achieve 407% revenue growth. The proposed approach represents a significant contribution that offers economic insights to stakeholders by quantifying and assessing various 6G deployment scenarios. It does so in a structured and replicable manner, fostering an open dialogue on the economic potential of 6G within a well-defined framework.}, archive = {J_ACCESS}, author = {Dimitris Kokkinis and Nikolaos Ioannou and Dimitrios Katsianis and Dimitris Varoutas}, doi = {10.1109/ACCESS.2025.3544890}, journal = {IEEE Access}, pages = {35597-35608}, shortjournal = {IEEE Access}, title = {A techno-economic modeling approach to 6G network deployment: Exploring costs and use case feasibility}, volume = {13}, year = {2025}, }
• (2025). Sidelobe reduction for window functions by multiplication of cosine-sum windows. ACCESS, 13, 35588-35596. (www)

  @article{ , abstract = {Various studies have considered the reduction in sidelobes when using window functions, and further sidelobe reduction using existing design results is an important perspective. In this study, we propose reducing side lobes by multiplying cosine-sum windows with existing window functions. This process shifts and synthesizes the frequency profile of the window function and may effectively reduce sidelobes. This is advantageous in that unlike the convolution between window functions, the data length is short. The proposed method was first applied to Gaussian windows, and it was found that they exhibit better performance than that with Gaussian windows alone and approximate confined Gaussian windows. We also showed that the peak and integrated sidelobe ratios of the proposed method are superior to those of cosine-sum windows designed with a single parameter. Moreover, we demonstrated that the proposed method can control the sidelobe decay of a Gaussian window. The second application of the proposed method was to the Nuttall, Blackman-Harris, and Blackman-Nuttall windows. We clarified the derived properties, showing that their lower sidelobes could be reduced further.}, archive = {J_ACCESS}, author = {Tomoya Yamaoka and Tadashi Oshima}, doi = {10.1109/ACCESS.2025.3544540}, journal = {IEEE Access}, pages = {35588-35596}, shortjournal = {IEEE Access}, title = {Sidelobe reduction for window functions by multiplication of cosine-sum windows}, volume = {13}, year = {2025}, }
• (2025). STE-NTP: A long-short period aware network traffic prediction model. ACCESS, 13, 35574-35587. (www)

  @article{ , abstract = {As the scale of network rapidly expands, the density and complexity of network connections have reached unprecedented levels, increasing the complexity of network management. Software-Defined Networking (SDN) enables efficient network modeling techniques by providing a controller interface, thereby implementing Network Traffic Prediction (NTP) and directly controlling underlying network hardware. However, existing NTP methods face challenges in handling the highly nonlinear and frequently bursty characteristics of network traffic, particularly in capturing and analyzing the spatiotemporal features of the traffic. To address this issue, this paper proposes an innovative NTP model, STE-NTP:Time-Space Encoding Based Network Traffic Prediction Model. This model utilizes advanced spatial and temporal encoding techniques to comprehensively process both spatial and temporal information, thereby improving prediction accuracy and efficiency. Additionally, an LTST-Extraction Block(long-term and short-term Extraction Block) is designed to enhance the model’s ability to predict long-term and short-term events in network traffic data through Long-term and Short-term feature extraction techniques.To further validate the model’s performance, 50,000 time units covering 200 routing schemes were simulated on the NSFNET and Geant2 network topologies using OMNeT++. The proposed STE-NTP model was then compared against other advanced prediction models in both short-term and long-term forecasting tasks.The results demonstrate that proposed STE-NTP exhibits significant advantages across multiple key performance metrics. These experiments not only validate the effectiveness of the STE-NTP model in predicting complex network traffic but also highlight its potential value in practical applications.}, archive = {J_ACCESS}, author = {Longfei Li and Kyungbaek Kim}, doi = {10.1109/ACCESS.2025.3545117}, journal = {IEEE Access}, pages = {35574-35587}, shortjournal = {IEEE Access}, title = {STE-NTP: A long-short period aware network traffic prediction model}, volume = {13}, year = {2025}, }
• (2025). Prediction of atmospheric refractivity from clutter power images using a convolutional neural network and a trilinear atmospheric model. ACCESS, 13, 35567-35573. (www)

  @article{ , abstract = {In this paper, we propose a method for predicting atmospheric conditions from clutter power images using a convolutional neural network (CNN). The proposed refractivity from clutter (RFC) method employs a CNN model that utilizes clutter power images as input. These input data, generated by the Advanced Refractive Prediction System (AREPS) EM simulator, using four variables of a theoretical trilinear atmospheric model. The proposed RFC method outputs values for these four variables, which can be used to predict the current atmospheric conditions in a short time. This CNN model is trained on 50,000 data samples, with a training-to-validation data ratio of 4:1. Furthermore, as cross-validation, the trained CNN model is utilized to predict atmospheric conditions using actual data measured in Baengnyeongdo, and the findings show that increasing the number of data samples improves performance. These results demonstrate the capability of the proposed RFC method to predict various atmospheric conditions in real time.}, archive = {J_ACCESS}, author = {Taekyeong Jin and Doyoung Jang and Hosung Choo}, doi = {10.1109/ACCESS.2025.3543847}, journal = {IEEE Access}, pages = {35567-35573}, shortjournal = {IEEE Access}, title = {Prediction of atmospheric refractivity from clutter power images using a convolutional neural network and a trilinear atmospheric model}, volume = {13}, year = {2025}, }
• (2025). Parameter-efficiently leveraging session information in deep learning-based session-aware sequential recommendation. ACCESS, 13, 35555-35566. (www)

  @article{ , abstract = {In recommender systems, leveraging user interaction history as sequential information has recently led to significant performance improvements. However, in many online services, user interactions are often grouped into sessions that inherently share user preferences, requiring a distinct approach from conventional sequence representation techniques. Existing studies have introduced various methods to integrate session information into sequential recommendation models, but most rely on complex network structures, such as hierarchical networks, or introduce substantial additional parameters. In this paper, we revisit the importance of incorporating session information in sequential recommendation models. We propose three methods to enhance recommendation performance by effectively utilizing session information while minimizing additional parameter overhead in deep learning-based sequential recommendation models: session token, session segment embeddings, and temporal self-attention. The proposing methods are designed to be easily integrated into both RNN-based and attention-based models. We demonstrate the effectiveness of the proposed methods through extensive experiments on real-world recommendation datasets, achieving up to a 10% performance improvement with only 1% additional parameters.}, archive = {J_ACCESS}, author = {Jinseok Seol and Youngrok Ko and Sang-Goo Lee}, doi = {10.1109/ACCESS.2025.3545243}, journal = {IEEE Access}, pages = {35555-35566}, shortjournal = {IEEE Access}, title = {Parameter-efficiently leveraging session information in deep learning-based session-aware sequential recommendation}, volume = {13}, year = {2025}, }
• (2025). Contextual sequence-based user behavior anomaly detection. ACCESS, 13, 35539-35554. (www)

  @article{ , abstract = {Given a series of user action sequences, Contextual Sequence-Based User Behavior Anomaly Detection (CS-UBAD) identifies anomalous sequences that deviate from normal behavior patterns. The CS-UBAD problem is important for detecting insider threats, such as unauthorized access, intellectual property theft, or other malicious activities within an organization’s systems. In this paper, we propose a novel approach called Contiguous, Contextual, and Classifying Pipeline (C3P), which integrates pattern mining and the ABC (Antecedent-Behavior-Consequence) model to calculate anomaly scores without requiring human intervention. Our method reduces the computational complexity while accurately detecting anomalous sequences.}, archive = {J_ACCESS}, author = {Omar Gonzales and Kwangsoo Yang and Shihong Huang}, doi = {10.1109/ACCESS.2025.3543500}, journal = {IEEE Access}, pages = {35539-35554}, shortjournal = {IEEE Access}, title = {Contextual sequence-based user behavior anomaly detection}, volume = {13}, year = {2025}, }
• (2025). CQS-attention: Scaling up the standard attention computation for infinitely long sequences. ACCESS, 13, 35527-35538. (www)

  @article{ , abstract = {Transformer models suffer from unaffordable high memory consumption when the sequence is long and standard self-attention is utilized. We developed a sequence parallelism scheme called CQS-Attention that can break the limit of sequence length. A long sequence is divided into multiple overlapping subsequences. The attention of each subsequence is independently computed and gathered as the final exact attention of the original long sequence. CQS-Attention is a fork-join parallel model comprising three components: Scheduler, Workers, and Tiler. The Scheduler equally partitions computation responsibility in a completely mutually exclusive manner and ensures the local subsequence length is minimum. Each worker independently computes the standard attention of the assigned subsequence and transfers local results to the Tiler, which produces the final attention. CQS-Attention makes attention computation embarrassingly parallel. Hence, it enjoys great performance regarding single-device memory and computation time consumption, mathematical stability and scalability. More importantly, it is fully compatible with all state-of-the-art attention optimizations. Our code and supplementary information (SI) are available at https://github.com/CQS-Attention/CQS_Attention.}, archive = {J_ACCESS}, author = {Yiming Bian and Arun K. Somani}, doi = {10.1109/ACCESS.2025.3544550}, journal = {IEEE Access}, pages = {35527-35538}, shortjournal = {IEEE Access}, title = {CQS-attention: Scaling up the standard attention computation for infinitely long sequences}, volume = {13}, year = {2025}, }
• (2025). Research on personalized recommendation algorithm integrating cross-grained sentiment and rating interaction features. ACCESS, 13, 35516-35526. (www)

  @article{ , abstract = {To investigate the impact of cross-grained sentiments on user feature representation and address the issue of data sparsity, this paper proposes a Personalized Recommendation Algorithm Integrating Cross-Grained Sentiment and Rating Interaction Features (ICSR). The algorithm begins by employing a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model and a Bi-GRU (Bidirectional Gated Recurrent Units) network to derive feature vectors from user and item reviews. Sentiment dictionaries and attention mechanisms are then applied to assign appropriate weights to the review features of users and items, respectively. To capture a richer set of sentiment features, a cross-grained sentiment feature fusion module is introduced. This module leverages an LDA (Latent Dirichlet Allocation) model and dependency syntactic analysis techniques to extract fine-grained sentiment features, while a word2vec pre-trained model and sentiment dictionaries are used to capture coarse-grained sentiment features. These features are then fused to form comprehensive cross-grained sentiment representations. Finally, rating interaction features are extracted using matrix factorization techniques, and all features are integrated and fed into a DeepFM model for rating prediction. Experimental results on Amazon datasets demonstrate that the proposed ICSR algorithm significantly outperforms baseline algorithms in terms of recommendation performance.}, archive = {J_ACCESS}, author = {Bing Li and Jiangtao Dong and Xile Wang and Yuqi Hou and Biao Yang}, doi = {10.1109/ACCESS.2025.3530431}, journal = {IEEE Access}, pages = {35516-35526}, shortjournal = {IEEE Access}, title = {Research on personalized recommendation algorithm integrating cross-grained sentiment and rating interaction features}, volume = {13}, year = {2025}, }
• (2025). ADWEH: A dynamic prioritized workflow task scheduling approach based on the enhanced harris hawk optimization algorithm. ACCESS, 13, 35490-35515. (www)

  @article{ , abstract = {Cloud Computing has transformed IT service delivery by offering scalable, on-demand resources essential for managing diverse and complex workloads. However, efficient task scheduling remains a critical challenge due to the NP-hard nature of mapping workflows to virtualized resources, impacting performance, resource utilization, energy efficiency, and reliability. This highlights the urgent need for advanced scheduling strategies. This paper presents ADWEH (A Dynamic Prioritized Workflow Task Scheduling approach based on the Enhanced Harris Hawk Optimization), a novel framework designed to overcome these challenges. ADWEH combines deep reinforcement learning with dynamic prioritization strategies to assign task priorities based on dependencies and runtime requirements. This integrated approach ensures optimal resource allocation, enhances scheduling efficiency, and improves adaptability to varying workloads. The proposed approach has been implemented and thoroughly tested using WorkflowSim, a simulation toolkit for modeling and evaluating workflow scheduling strategies. ADWEH’s performance is benchmarked against state-of-the-art algorithms, including Deep Q-Network(DQN), Advantage Actor-Critic(AC2), and PWSA3C, using scientific workflows including Montage, Cybershake, SIPHT, LIGO. These Four workflows are tested on datasets like HPC2N, NASA, Uniform, and Random, ensuring robust evaluation under diverse scenarios. Simulation results demonstrate that ADWEH significantly outperforms DQN, A2C, and PWSA3C approaches, achieving up to 31.9% shorter makespan, a 24% reduced in energy consumption, a 29% Enhancement in scalability efficiency, and a 52% increase in resource utilization, while reducing failure rates by over 47%. The results position ADWEH as a robust, efficient solution for improving the performance, reliability of cloud-based workflows, addressing the evolving demands of modern cloud environments.}, archive = {J_ACCESS}, author = {Mallu Shiva Rama Krishna and D. Khasim Vali}, doi = {10.1109/ACCESS.2025.3543880}, journal = {IEEE Access}, pages = {35490-35515}, shortjournal = {IEEE Access}, title = {ADWEH: A dynamic prioritized workflow task scheduling approach based on the enhanced harris hawk optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Bitcoin ordinals: Bitcoin price and transaction fee rate predictions. ACCESS, 13, 35478-35489. (www)

  @article{ , abstract = {Ordinals, a method for creating unique digital assets on the Bitcoin blockchain, has significantly impacted the blockchain over the past year, yet there is a notable lack of research on it. This study is the first to demonstrate that Bitcoin Ordinals-related data are crucial features for predicting Bitcoin transaction fee rates and prices. Our main contributions are threefold. 1) Dataset Construction: We construct a dataset that includes Bitcoin chain data, Ordinals index data, and Ordinals market data, as well as a dataset excluding Ordinals-related data. Our findings reveal that the fluctuation in the number of Ordinals inscriptions tends to correlate with market activity. When the Ordinals market is active, the share of Ordinals inscribed fees and the average Bitcoin transaction fee rate remain high. We argue that the upgrades of SegWit and Taproot drove the creation and development of Bitcoin Ordinals. Combined with users’ interest in Ordinals, this in turn affected the Bitcoin blockchain and its price; 2) Prediction: Using three metrics (MAE, RMSE, and MAPE) and the TemporalFusionTransformer model as a baseline, our comparative experiments show that Bitcoin Ordinals-related data is essential for predicting Bitcoin transaction fee rates and prices. This finding aids investors and participants in the Bitcoin Ordinals market in avoiding losses and leveraging congestion-related arbitrage opportunities, thus enabling more accurate decision-making in the cryptocurrency market; 3) Chronos Model: Additionally, the fine-tuned Chronos model achieves metrics comparable to or better than those of the TemporalFusionTransformer for shorter time intervals, especially in low-noise environments. With its outstanding zero-shot prediction performance, fast execution, and easy cloud deployment, the Chronos model allows investors and market participants to quickly obtain high-quality predictions without requiring complex data features.}, archive = {J_ACCESS}, author = {Minxing Wang and Pavel Braslavski and Vyacheslav Manevich and Dmitry I. Ignatov}, doi = {10.1109/ACCESS.2025.3541302}, journal = {IEEE Access}, pages = {35478-35489}, shortjournal = {IEEE Access}, title = {Bitcoin ordinals: Bitcoin price and transaction fee rate predictions}, volume = {13}, year = {2025}, }
• (2025). Enhancing malaria detection through deep learning: A comparative study of convolutional neural networks. ACCESS, 13, 35452-35477. (www)

  @article{ , abstract = {The need for accurate and efficient malaria diagnosis has driven research into automated solutions using deep learning. This study presents a comparative analysis of five convolutional neural network (CNN) models for malaria detection in microscopic blood cell images. We evaluated models ranging from a basic architecture to advanced iterations employing data augmentation and VGG16 transfer learning. Using a dataset of 24,958 training and 2,600 test images, we assessed performance based on accuracy, precision, recall, F1-score, and computational efficiency. Our results demonstrate that a simpler CNN model (Model_1) achieved greater than 99% accuracy, surpassing the performance of more complex models while requiring significantly less computational resources. This finding underscores the potential of efficient deep learning strategies for developing scalable and cost-effective malaria diagnostic tools.}, archive = {J_ACCESS}, author = {Yassine Benachour and Farid Flitti and Haris M. Khalid}, doi = {10.1109/ACCESS.2025.3542160}, journal = {IEEE Access}, pages = {35452-35477}, shortjournal = {IEEE Access}, title = {Enhancing malaria detection through deep learning: A comparative study of convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Mental health assessment model for college students using circular q-rung orthopair fuzzy muirhead means and MULTIMOORA method. ACCESS, 13, 35433-35451. (www)

  @article{ , abstract = {This work investigates the enhanced MULTIMOORA technique using the circular q-rung orthopair fuzzy set (Cq-ROFS) for mental health assessment among college students. Recently, Cq-ROFS was proposed as the development of circular Pythagorean FSs (C-PFSs), circular intuitionistic FSs (C-IFSs), and circular rung orthopair FSs (q-ROFSs). However, they are only considered for a few essential algebraic characteristics. In the present paper, we extend our investigation on circular q-ROFSs (Cq-ROFSs) within Muirhead mean aggregation operators (AOs) by incorporating additional algebraic law mathematical features. These include the circular q-rung orthopair fuzzy (Cq-ROF) weighted Muirhead mean (Cq-ROFWMM) and Cq-ROF dual weighted Muirhead mean (T-SFDWMM) aggregation operators (AOs). To compute the essential aspects, we apply the MULTIMOORA technique, incorporating the proposed operators for Cq-ROF numbers (Cq-ROFNs), enhancing the value and effectiveness of the evaluation process. Employing the proposed Cq-ROF MULTIMOORA model to select the best mental health treatment options based on different attributes such as student engagement, evidence-based effectiveness, and accessibility and inclusivity. The ranking results indicate that $~{{\vert\!\!\!\mathrm {G}}}{_{1}}$ is the most effective treatment model, outperforming all other alternatives across the evaluation criteria. A real-world case study demonstrates the proposed method’s practical applicability and reliability in selecting optimal mental health strategies. Additionally, sensitivity analysis confirms the model’s stability, making it a valuable tool for evidence-driven mental health policy and intervention planning in educational settings.}, archive = {J_ACCESS}, author = {Gou Ying}, doi = {10.1109/ACCESS.2025.3542804}, journal = {IEEE Access}, pages = {35433-35451}, shortjournal = {IEEE Access}, title = {Mental health assessment model for college students using circular q-rung orthopair fuzzy muirhead means and MULTIMOORA method}, volume = {13}, year = {2025}, }
• (2025). Simultaneous optimization of hyperparameters and physical parameters in 1D electromagnetic inversion using modified optimized particle swarm optimization. ACCESS, 13, 35417-35432. (www)

  @article{ , abstract = {Electromagnetic (EM) methods are widely used for mapping subsurface electrical resistivity structures. Interpreting geophysical EM data often involves solving inversion problems with subjective modeling assumptions, known as hyperparameters. The inherently non-unique nature of EM data inversion, combined with these subjective assumptions, can lead to significantly different solutions among practitioners, complicating the interpretation process. This study introduces a novel modification of the Optimized Particle Swarm Optimization (OPSO) method to simultaneously optimize hyperparameters and physical parameters in 1D EM inversion. A robust objective function based on the L1 norm, designed to prioritize model simplicity, was developed to guide the optimization process. Minimizing this function eliminates the need to manually define the values and dimensions of the model parameters and Lagrange multipliers, enhancing objectivity while evading needlessly complex solutions. Synthetic experiments with transient electromagnetic (TEM) and magnetotelluric (MT) datasets, incorporating 10% and 5% Gaussian noise respectively, achieved best-case weighted mean absolute errors (WMAE) of 0.73 for TEM and 0.84 for MT. The modified OPSO effectively retrieved layer thickness, electrical resistivity, optimal number of layers, and Lagrange multiplier, closely matching the true model for both datasets. The practical applicability of the modified OPSO was validated using real-field data, including the TEM-2-1 dataset acquired from Santolo, Indonesia, and the PCS008 and PC5000 datasets from the COPROD2 MT collection. Best inversion results achieved WMAE values of 0.95 (four-layer model, TEM-2-1), 5.59 (five-layer model, PCS008), and 2.55 (four-layer model, PC5000), demonstrating the method’s effectiveness in real-world EM data inversion while avoiding overly complex models.}, archive = {J_ACCESS}, author = {I Gede Putu Fadjar Soerya Djaja and Elfitra Desifatma and Vitasha Adha Chaerul and Enjang Jaenal Mustopa and Wahyu Srigutomo}, doi = {10.1109/ACCESS.2025.3543922}, journal = {IEEE Access}, pages = {35417-35432}, shortjournal = {IEEE Access}, title = {Simultaneous optimization of hyperparameters and physical parameters in 1D electromagnetic inversion using modified optimized particle swarm optimization}, volume = {13}, year = {2025}, }
• (2025). Power regulation strategy of grid-forming bidirectional interlinking converters in hybrid AC-DC microgrid with energy storage management. ACCESS, 13, 35401-35416. (www)

  @article{ , abstract = {This study proposes a power regulation strategy for a bidirectional interlinking converter (BIC) in a hybrid AC/DC microgrid. The proposed control strategy utilizes grid forming virtual synchronous generator (VSG) control of BIC to achieve global power sharing (GPS) between two sub-grids, AC and DC sub-grids, in islanded mode. Both grid-connected mode and islanded mode are incorporated in the system. This approach allows BIC to operate under a voltage source fault in the AC subgrid. The control strategy includes both communication-based and communication-less approaches to handle communication equipment failures. In the DC-side, a droop shift control strategy is proposed, taking into account the energy storage system (ESS) and its management with the virtual output power method. Simultaneously, the droop shift control can increase the load response in the DC side of BIC. During periods of low load demand, the ESS can be charged, and the overall power sharing on the AC and DC sides can be achieved. In addition, this study combines virtual impedance and frequency restoration control of BIC to enhance active and reactive power sharing. Furthermore, the system stability is analyzed. Finally, the feasibility of the proposed method is verified through simulations and prototype experimental results.}, archive = {J_ACCESS}, author = {Yi-Hung Liao and Wei-Rong Lai and Meng-Hsuan Yu and Ting-Hsien Lin}, doi = {10.1109/ACCESS.2025.3543888}, journal = {IEEE Access}, pages = {35401-35416}, shortjournal = {IEEE Access}, title = {Power regulation strategy of grid-forming bidirectional interlinking converters in hybrid AC-DC microgrid with energy storage management}, volume = {13}, year = {2025}, }
• (2025). Fuzzy masks for correlation matrix pruning. ACCESS, 13, 35387-35400. (www)

  @article{ , abstract = {Among statistical methods used in data analysis processes, correlation analysis holds one of the most significant places. Using correlation, analysts measure prediction potential between values of a pair of attributes of a dataset which can be summarized into a correlation matrix for any multidimensional dataset. Such matrices are usually sizable, and hard to read. Pruning of the correlation matrix for the precise selection of attribute pairs of a considered dataset which bear strong prediction potential is conventionally conducted via correlation matrix masks. Since these masks are commonly designed as crisp borders for the acceptability of values in the matrix, there is a strong lack of nuance in this approach. The work presented in the scope of this study focuses on the design and implementation of fuzzy masks for correlation matrices. This objective is divided into three main tasks - firstly, the visualization of correlation coefficient value frequency and its relationship to fuzzy membership function is designed and implemented, then visualisation of basic regression analysis and correlation context of attribute pairs in the fuzzy area of a studied dataset is designed and implemented, and lastly, the proposed approach is evaluated via case studies on three benchmarking datasets. The results obtained with the fuzzy approach to correlation matrix masks show a qualitative improvement in the matrix pruning task and a more appropriate identification of the relevant parts of the dataset compared to the conventional, crips approach.}, archive = {J_ACCESS}, author = {Adam Dudáš and Alžbeta Michalíková and Roman Jašek}, doi = {10.1109/ACCESS.2025.3544027}, journal = {IEEE Access}, pages = {35387-35400}, shortjournal = {IEEE Access}, title = {Fuzzy masks for correlation matrix pruning}, volume = {13}, year = {2025}, }
• (2025). GNSS simulation for automotive: Introducing 3D scene-dependent multipath with CARLA. ACCESS, 13, 35376-35386. (www)

  @article{ , abstract = {Realistic Global Navigation Satellite System (GNSS) synthetic data is essential for the research and development of vehicular applications, such as Advanced Driver Assistance Systems (ADAS), autonomous driving, and solutions or scenarios that are difficult and expensive to test in the real world, such as vehicular cooperative positioning. However, generating GNSS synthetic data is complex due to satellite dynamics, signal characteristics, and various noise and error sources. This complexity increases in automotive contexts by vehicle movement and environmental factors influencing signal propagation, with multipath effects being particularly challenging to simulate accurately. This paper introduces a novel pipeline that leverages a 3D virtual environment to produce more realistic GNSS synthetic data for automotive applications. The pipeline integrates the CARLA Simulator and GPSoft’s SatNav Toolbox for Matlab, with custom-developed modules that generate raw GNSS measurements incorporating environment- and location-specific multipath effects. Our contributions include a tailored simulation pipeline for automotive applications, with integration of GNSS satellite orbits within CARLA, a dynamic multipath model reflecting obstacles in the simulated environment, and a synthetic dataset generated by this approach available to the community. Evaluation on CARLA’s Town03 map showed that while standard multipath models result in unrealistic uniform effects, our dynamic model produces effects that correlate with the vehicle’s surroundings, accurately reflecting real-world conditions such as increased errors in urban areas and lack of signals in tunnels. This approach can support the research, development, and validation of GNSS positioning algorithms and Artificial Intelligence (AI) model training, with potential applications extending also beyond the automotive context.}, archive = {J_ACCESS}, author = {Cristiano Pendão and Ivo Silva and Fabricio Botelho and Hélder Silva}, doi = {10.1109/ACCESS.2025.3543752}, journal = {IEEE Access}, pages = {35376-35386}, shortjournal = {IEEE Access}, title = {GNSS simulation for automotive: Introducing 3D scene-dependent multipath with CARLA}, volume = {13}, year = {2025}, }
• (2025). IntPort: An intelligent portfolio construction technique based on financial forecasting by statistical average method. ACCESS, 13, 35355-35375. (www)

  @article{ , abstract = {Investors are inclined to predict stock values, and conventional studies indicate that using machine learning (ML) for stock price prediction is the most effective method. This study focuses on ML-based stock price forecasting that aims to assist people with limited investment capabilities by providing a high-quality approach to price prediction. The research utilizes various characteristics of the current stock price, such as ‘Open,’ ‘Low,’ ‘High, ‘Adj Close’, and ‘Close’. In addition, it incorporates principal component analysis (PCA), linear discriminant analysis (LDA), and a statistical average method (SAM) to create additional features. These features and current stock price attributes are combined to form a feature vector for ML methods. The study also emphasizes building a portfolio that reduces investment risk while capitalizing on the expected upward movement of stock prices. Experimental results indicate that incorporating the SAM-price feature associated with the ML technique, bidirectional long short-term memory (Bi-LSTM), leads to superior forecasting accuracy compared to existing approaches. It is evident from previous literary works that various supervised learning methods have successfully predicted stock prices. Our research is dedicated to producing a novel approach to generate better price accuracy by forecasting and an effective portfolio that can be beneficial to the general public. The investigation reveals an accuracy rate of 98.14% when applied with the SAM-price feature defined by the additional feature dataset (AFD), surpassing the accuracy achieved by traditional pricing features alone. Additionally, as a novel price feature, AFD generates no over-fitting. Furthermore, the portfolio generated using the SAM-price function demonstrates a return on investment of 145.475% and a Sharpe ratio value of 77.62, indicating a favorable risk-return trade-off.}, archive = {J_ACCESS}, author = {Hasan M. Sami and Md Shah Jalal and Md Faisal Kabir and Mohammad Nurul Huda}, doi = {10.1109/ACCESS.2025.3543741}, journal = {IEEE Access}, pages = {35355-35375}, shortjournal = {IEEE Access}, title = {IntPort: An intelligent portfolio construction technique based on financial forecasting by statistical average method}, volume = {13}, year = {2025}, }
• (2025). Event-triggered control for fuzzy-based networked wind-turbines-integrated power systems with distributed communication delays. ACCESS, 13, 35339-35354. (www)

  @article{ , abstract = {This paper proposes an event-triggered control for tie-line networked power systems (NPSs) equipped with wind turbines involving distributed communication delays. Distinct to the previous studies, this paper extends an existing model by considering the effect of wind turbine generator (WTG) output, pitch angle, power coefficient, and tip-speed ratio concerning the power system. Further, a linear form of complex nolinear model is expressed as Takagi-Sugeno fuzzy-based models. In addition, communication delays during network transmission are modeled in distributed form through probability density function. Theoretically, this paper derives a sufficient stability conditions to guarantee the global asymptotical stability of NPSs via event-triggered load frequency control algorithm. To validate the proposed conditions, the considered model is numerically simulated and the outcomes are compared with traditional control schemes.}, archive = {J_ACCESS}, author = {Girija Panneerselvam and Prakash Mani}, doi = {10.1109/ACCESS.2025.3543720}, journal = {IEEE Access}, pages = {35339-35354}, shortjournal = {IEEE Access}, title = {Event-triggered control for fuzzy-based networked wind-turbines-integrated power systems with distributed communication delays}, volume = {13}, year = {2025}, }
• (2025). Exploring security enhancements in kubernetes CNI: A deep dive into network policies. ACCESS, 13, 35322-35338. (www)

  @article{ , abstract = {With the explosive growth of Kubernetes adoption, Container Network Interfaces (CNIs) have become critical components for configuring and securing container networks, but a comprehensive analysis of their security capabilities and performance impact is noticeably lacking. Our study conducts a comprehensive security analysis of the major CNI plugins (Cilium, Calico, WeaveNet, Kube-router, and Antrea) in cloud-native environments with Kubernetes through extensive evaluation of Layer 3/4 policy processing, policy complexity scaling, pod scalability, and Layer 7 policy processing. The experimental results show that eBPF-based Cilium maintains 8.9K Mbps throughput under complex L3/4 policies, but drops to 94 Mbps with L7 processing, while Antrea achieves 6.6K Mbps at L7 through HTTP filtering, with performance degrading as policy complexity increases. Under high concurrent pod loads, iptables-based CNIs show a 60-70% reduction in throughput, while Cilium maintains performance within 10% of baseline. These results reveal critical trade-offs between architectural choices and security capabilities, and provide practical guidelines for CNI selection based on specific operational and security requirements in cloud-native environments.}, archive = {J_ACCESS}, author = {Bom Kim and Jinwoo Kim and Seungsoo Lee}, doi = {10.1109/ACCESS.2025.3543841}, journal = {IEEE Access}, pages = {35322-35338}, shortjournal = {IEEE Access}, title = {Exploring security enhancements in kubernetes CNI: A deep dive into network policies}, volume = {13}, year = {2025}, }
• (2025). Obstacle detection and warning system for visually impaired using IoT sensors. ACCESS, 13, 35309-35321. (www)

  @article{ , abstract = {Ensuring safe and independent mobility for visually impaired individuals requires efficient obstacle detection systems. This study introduces an innovative smart knee glove, integrating machine learning technologies for real-time obstacle detection and alerting. The system is equipped with ultrasonic sensor, PIR sensor and a buzzer, with data processing managed by an Arduino Uno microcontroller. To enhance detection accuracy, multiple machine learning algorithms including Decision Tree (DT), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Gaussian Naïve Bayes (GNB) are utilized. A novel Voting Classifier ensemble method is proposed, effectively combining the strengths of these classifiers to maximize performance. Rigorous cross-fold validation ensures robust evaluation under varying conditions. Experimental results demonstrates that the system achieves an impressive 98.34% detection accuracy within a 4-meter range, with high precision, recall and F1 scores. These findings underscore the system’s reliability and potential to empower visually impaired users with safer, more autonomous navigation, marking a significant advancement in obstacle detection technologies.}, archive = {J_ACCESS}, author = {Sunnia Ikram and Imran Sarwar Bajwa and Amna Ikram and Isabel de la Torre Díez and Carlos Eduardo Uc Ríos and Ángel Kuc Castilla}, doi = {10.1109/ACCESS.2025.3543299}, journal = {IEEE Access}, pages = {35309-35321}, shortjournal = {IEEE Access}, title = {Obstacle detection and warning system for visually impaired using IoT sensors}, volume = {13}, year = {2025}, }
• (2025). Prediction of wafer performance: Use of functional outlier detection and regression. ACCESS, 13, 35298-35308. (www)

  @article{ , abstract = {Optical emission spectroscopy (OES) data is essential for virtual metrology, enabling accurate predictions of wafer performance in plasma etching processes. This approach not only reduces the need for physical measurements of product quality, leading to significant resource savings, but also supports improved decision-making, particularly in process control and quality assurance. To exploit the consecutive nature of OES data, we propose a prediction method based on a functional approach using multivariate functional partial least squares regression, coupled with dimension reduction and a novel outlier detection technique via functional independent component analysis. The proposed approach improves prediction performance by capturing the continuous nature of OES data and effectively extracting the components that describe the data structure. Numerical experiments, including simulation studies and real-world applications of OES data, demonstrate the effectiveness of the proposed method through superior prediction performance, as evidenced by low RMSE and MAE values, particularly in the presence of outliers.}, archive = {J_ACCESS}, author = {Kyusoon Kim and Seunghee Oh and Kiwook Bae and Hee-Seok Oh}, doi = {10.1109/ACCESS.2025.3544244}, journal = {IEEE Access}, pages = {35298-35308}, shortjournal = {IEEE Access}, title = {Prediction of wafer performance: Use of functional outlier detection and regression}, volume = {13}, year = {2025}, }
• (2025). Decoding the factors of electric vehicle supply chain resilience: A structured categorization for enhancing resilience through factor synergies. ACCESS, 13, 35281-35297. (www)

  @article{ , abstract = {This study focuses on building resilience in electric vehicle (EV) supply chain to address the growing challenges of global market uncertainties. Grounded in dynamic capabilities theory, the study identifies and categorizes 16 key factors influencing Supply Chain Resilience (SCR) through a review of 117 academic papers and data analysis from 8 supply chain experts and 374 EV supply chain enterprises. These factors are grouped into four dynamic capability sets: sensing capability, external resource integration capability, internal resource integration capability, and transforming capability. The Structural Equation Modeling (SEM) was used to validate the impact paths of these four dynamic capabilities on SCR, revealing that external resource integration capability has the most significant positive effect on SCR, followed by internal resource integration and sensing capabilities, while the impact of transforming capability is relatively weaker. The findings suggest that EV companies should prioritize strengthening both external and internal resource integration to enhance SCR. This research provides a theoretical foundations and practical guidance for EV companies to improve SCR in a complex global environment, offering new perspectives and empirical support for future studies in supply chain management.}, archive = {J_ACCESS}, author = {Yanxuan Li and Vatcharapol Sukhotu}, doi = {10.1109/ACCESS.2025.3543910}, journal = {IEEE Access}, pages = {35281-35297}, shortjournal = {IEEE Access}, title = {Decoding the factors of electric vehicle supply chain resilience: A structured categorization for enhancing resilience through factor synergies}, volume = {13}, year = {2025}, }
• (2025). Safe reinforcement learning via episodic control. ACCESS, 13, 35270-35280. (www)

  @article{ , abstract = {Safe reinforcement learning (Safe RL) aims to learn policies capable of learning and adapting within complex environments while ensuring actions remain free from catastrophic consequences. This is a critical consideration in domains such as robotics, autonomous vehicles, and healthcare. Unlike traditional RL, which focuses mainly on maximizing episodic rewards, Safe RL integrates safety constraints to balance rewards and safety. Current Safe RL algorithms, while promising, lack sample efficiency as they require extensive environmental interactions to perform multi-objective optimization. This paper introduces an episodic-control-based method to enhance sample efficiency in safe policy optimization. In RL, methods based on episodic control involve the storing and replaying of past experiences or episodes, aiding in more efficient and precise policy optimization. Our proposed method involves clustering, measuring, and storing previous states based on a joint metric of returns and safety. Subsequently, we retrieve these state measurements and incorporate them into the policy optimization process through reward shaping. This approach effectively guides the policy towards high-return and safe decisions. We evaluate the performance of our method on established Safe RL benchmarks, including six safety-critical agent control tasks. The results demonstrate that our method can concurrently achieve higher episodic returns and fewer violations of safety constraints compared to the baseline methods, suggesting an effective balance between earning rewards and safety.}, archive = {J_ACCESS}, author = {Zhuo Li and Derui Zhu and Jens Grossklags}, doi = {10.1109/ACCESS.2025.3535679}, journal = {IEEE Access}, pages = {35270-35280}, shortjournal = {IEEE Access}, title = {Safe reinforcement learning via episodic control}, volume = {13}, year = {2025}, }
• (2025). Multiuser FDSS-based DCT-spread massive MIMO OFDM system for secure RIS-assisted UAV-enabled networks. ACCESS, 13, 35254-35269. (www)

  @article{ , abstract = {Enhancing spectral efficiency (SE), reducing out-of-band (OOB) power emission, minimizing multiuser interference (MUI), and ensuring secure data communication are key challenges in the development of future-generation unmanned aerial vehicle (UAV)-enabled wireless communication systems. To address these challenges and emphasize effective solutions, we propose a novel and secure reconfigurable intelligent surface (RIS)-assisted UAV-enabled multiuser frequency-domain spectrum shaping (FDSS)-based discrete cosine transform (DCT)-Spread massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) system. The system incorporates physical layer security (PLS) based on two-dimensional cosine-sine interleaved chaotic map (2D-CSIM), block diagonalization (BD) precoding, FDSS with null carriers, turbo and repeat-and-accumulate (RA) channel coding, Cholesky decomposition-based zero-forcing (CD-ZF), and lattice reduction-based minimum mean square error (LR-MMSE) signal detection schemes. The 2D-CSIM enhances confidentiality in audio signal transmission, while the BD precoding reduces MUI. In addition, the FDSS with null carriers reduces OOB emissions and enhances the system’s SE. The turbo and RA channel coding with CD-ZF and LR-MMSE signal detection schemes improve the bit error rate (BER) performance. Simulation results show the effectiveness of the proposed system. It achieves significant PLS for audio transmission, with low correlation coefficients of <2% for four users between the original and encrypted signals. We also quantify the effectiveness of the 2D-CSIM-based encryption technique using the National Institute of Standards and Technology (NIST) Statistical Test Suite. The system achieves an OOB reduction of 334 dB, SE of 33.30 bps/Hz, and peak-to-average power ratio (PAPR) of 8.89 dB at a complementary cumulative distribution function (CCDF) of $1 \times 10^{-3}$ , which are comparable to state-of-the-art systems. At signal-to-noise ratios (SNRs) of 27–28.5 dB, the four users achieved a BER of $1\times 10^{-4}$ with RA coding, LR-MMSE detection, and lower-order quadrature amplitude modulation (QAM) constellation.}, archive = {J_ACCESS}, author = {Md. Najmul Hossain and Atia Kaniz and Sk. Tamanna Kamal and Shaikh Enayet Ullah and Tetsuya Shimamura}, doi = {10.1109/ACCESS.2025.3544477}, journal = {IEEE Access}, pages = {35254-35269}, shortjournal = {IEEE Access}, title = {Multiuser FDSS-based DCT-spread massive MIMO OFDM system for secure RIS-assisted UAV-enabled networks}, volume = {13}, year = {2025}, }
• (2025). An intelligent digital twin streamlines production planning in the context of custom 3D workpieces for powder coating. ACCESS, 13, 35243-35253. (www)

  @article{ , abstract = {An Intelligent Digital Twin (IDT) was created to analyze 3D-CAD models for optimizing individual production plans in the context of powder coating. The IDT performs a kinematic simulation of the workpieces in preparation for the subsequent real production. We utilized the novel concept of analyzing CAD files directly in a game engine after an automatic runtime import. This runtime import of 3D-CAD models into game engines is a unique feature that addresses a research gap with significant reuse potential in other applications. This approach is necessary because of the custom shapes of the workpieces, which undergo feature detection, virtual assembly, and optimization by simulation. This leads to a fast and early calculation of production costs, thereby enhancing the competitiveness of the company. The IDT is fully automated and is available 24/7 to plan production immediately after receiving a 3D-CAD file, thereby boosting production planning.}, archive = {J_ACCESS}, author = {Cedric Kiener and Steffen Schwarzer and Simon Zimmermann}, doi = {10.1109/ACCESS.2025.3544140}, journal = {IEEE Access}, pages = {35243-35253}, shortjournal = {IEEE Access}, title = {An intelligent digital twin streamlines production planning in the context of custom 3D workpieces for powder coating}, volume = {13}, year = {2025}, }
• (2025). Evaluating pretrained deep learning models for image classification against individual and ensemble adversarial attacks. ACCESS, 13, 35230-35242. (www)

  @article{ , abstract = {The robustness of Deep Neural Networks (DNNs) against adversarial attacks is an important topic in the area of deep learning. To fully investigate the robustness of DNNs, this study examines four frequently used white box adversarial attack techniques, namely, the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), DeepFool, and their effects on DNN models for the image classification task. The results show that ResNet152 and DenseNet201 are less vulnerable comparing to other DNN models to a variety of individual attacks, highlighting their intrinsic strength even in the lack of specific adversarial training. Further, we propose two ensemble adversarial attacks combining three individual attacks for generating adversarial examples from the tiny ImageNet, CIFAR-10, CIFAR-100, and SVHN datasets for DNN model evaluation. It is observed that the performance of the DNNs deteriorate significantly under the proposed ensemble adversarial attacks even after defensive measures have been applied. For instance, the accuracy of the most robust DNN that we tested, namely the defense distillation enhanced DenseNet201, dropped more than 59% under the proposed ensemble adversarial attacks, comparing to only 34% decrease under the individual attacks.}, archive = {J_ACCESS}, author = {Mafizur Rahman and Prosenjit Roy and Sherri S. Frizell and Lijun Qian}, doi = {10.1109/ACCESS.2025.3544107}, journal = {IEEE Access}, pages = {35230-35242}, shortjournal = {IEEE Access}, title = {Evaluating pretrained deep learning models for image classification against individual and ensemble adversarial attacks}, volume = {13}, year = {2025}, }
• (2025). Laser guard: Efficiently detecting laser-based physical adversarial attacks in autonomous driving. ACCESS, 13, 35219-35229. (www)

  @article{ , abstract = {The fast development of deep learning (DL) enables even resource-constrained devices to tackle complex artificial intelligence (AI) tasks, especially those related to environment perception in autonomous driving systems (ADS). However, AI models deployed in the real world are exposed to the threats of adversarial examples (AE). One specific type of physical attack utilizes laser beams or spots planted on images rather than crafted pixel-level perturbations to manipulate the victim deep neural networks (DNN) prediction. These attacks easily mislead traffic sign recognition and object detection in ADS. Laser-based adversarial attacks are cognitively stealthy but visually conspicuous, invalidating the previous defenses designed for digital attacks. This study considers two state-of-the-art (SOTA) laser-based attacks and establishes a benchmark comprising thousands of AEs. Such AEs have distinct pattern features, significant occupation, high contrast, and low variance. Based on the observation, a lightweight detection framework, Laser Guard, is proposed. Specifically, preprocessing methods are used to approximate the laser-perturbed areas, followed by a statistics-based strategy to determine abnormalities in the given samples. This framework can be applied in a plug-and-play manner with DNNs in intelligent vehicles. Extensive experimental results show that the framework can effectively filter out about 70-75% of laser-based street sign AEs, and extends well to other objects, successfully filtering out 80%. The detection latency of objects AEs is marginal, with the average detection time for laser spots being approximately 24 ms, and for laser beams, it is around 57 ms.}, archive = {J_ACCESS}, author = {Lijun Chi and Mounira Msahli}, doi = {10.1109/ACCESS.2025.3540653}, journal = {IEEE Access}, pages = {35219-35229}, shortjournal = {IEEE Access}, title = {Laser guard: Efficiently detecting laser-based physical adversarial attacks in autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Leveraging ResNet50 with swin attention for accurate detection of OCT biomarkers using fundus images. ACCESS, 13, 35203-35218. (www)

  @article{ , abstract = {Diabetes can impact the retina and cause a decline in vision for patients as a result of Diabetic Retinopathy (DR). Diabetic Macular Edema (DME) is a complication that results from the chronic damage to the tiny blood vessels of the retina that arises in the non-proliferative stage of DR (NPDR) but can also be present in proliferative DR (PDR) and potentially leading to vision loss. The duration of diabetes in patients affects both the prevalence and incidence of macular edema, as well as the progression of retinopathy. Therefore, regular screening of diabetes patients for the early detection of retinal abnormalities is essential to prevent the development and progression of DR and DME. The proposed model predicts DME-associated biomarkers, typically identified in Optical Coherence Tomography (OCT), using 2D fundus images. These biomarkers include center-involved diabetic macular edema (ci-DME), neurosensory detachment (NSD), Intraretinal fluid (IRF), disorganization of the retinal inner layers (DRIL), hyperreflective foci (HRF), and disruptions in the inner segment/outer segment (IS/OS) junction, utilizing 2D fundus images. The model integrates the feature extraction capability of ResNet50 with the spatial structural domain knowledge provided by the Swin attention augmentation layer. 2D fundus image datasets were collected to train and evaluate the model. In two distinct datasets, the model achieved a validation accuracy of 85.7% (95% CI: 81.6–90.6%) and 89.5% (95% CI: 85.6–93.4%), Cohen’s Kappa of 0.68 (95% CI: 0.61–0.77) and 0.75 (95% CI: 0.67–0.82), sensitivity of 88.6% (95% CI: 85.6–92.1%) and 79.6% (95% CI: 70.6–85.4%), specificity of 79.6% (95% CI: 70.3–88.9%) and 93.7% (95% CI: 90.7–96.8%), respectively, with an overall validation accuracy of 87%. The proposed model helps in identifying the DME-associated biomarkers, using 2D fundus images making it a promising tool for detecting and assessing DME-related features.}, archive = {J_ACCESS}, author = {S. Tamilselvi and M. Suchetha and Rajiv Raman}, doi = {10.1109/ACCESS.2025.3544332}, journal = {IEEE Access}, pages = {35203-35218}, shortjournal = {IEEE Access}, title = {Leveraging ResNet50 with swin attention for accurate detection of OCT biomarkers using fundus images}, volume = {13}, year = {2025}, }
• (2025). Microwave technique for brain monitoring: State of the art, promising applications, challenges, and future directions. ACCESS, 13, 35183-35202. (www)

  @article{ , abstract = {The brain is considered one of the central organs constituting complex biological phenomena that reflect upon the health and well-being of a person. Wearable brain monitoring systems have witnessed increased research and technological achievements in the last two decades. Microwave-based techniques have demonstrated high potential for brain monitoring and have technological benefits, such as non-ionizing radiation, small size, cost-effectiveness, portability, non-invasive operation, and good penetration depth. This review aims to present a critical analysis of research on the utilization of microwaves for brain monitoring. Followed by a comprehensive literature search and thematic analysis, four Research Questions (RQs) related to microwave systems in brain monitoring are formulated and studied. In response to these RQs, the key approaches for microwave modeling of the human brain are explored. Further, a brief outline of different applications focusing on developments in microwave systems for brain temperature measurement, stroke detection, intracranial pressure (ICP) monitoring, and skull fracture detection are elaborated. Additionally, thematic analysis is presented in each application area on inter-technology comparison of the microwave technique with other technologies. Further, the challenges in the development of microwave systems and their clinical usage in different brain monitoring setups are explored along with future directions. This review can serve as a platform for brain researchers to understand the potential of the emerging microwave technology for brain monitoring.}, archive = {J_ACCESS}, author = {Daljeet Singh and Mariella Särestöniemi and Teemu Myllylä}, doi = {10.1109/ACCESS.2025.3544032}, journal = {IEEE Access}, pages = {35183-35202}, shortjournal = {IEEE Access}, title = {Microwave technique for brain monitoring: State of the art, promising applications, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). A study on the SLAM of automotive vehicles using bumper-mounted dual LiDAR. ACCESS, 13, 35174-35182. (www)

  @article{ , abstract = {This study proposes a localization and three-dimensional (3D) mapping algorithm for autonomous vehicles using bumper-mounted light detection and ranging (LiDAR) sensors. Traditional roof-mounted LiDAR systems provide a wide field of view (FOV) but have drawbacks such as increased vehicle height, reduced aerodynamics, and greater exposure to environmental factors. The proposed approach addresses these challenges by installing two LiDAR sensors at the ends of the vehicle bumper, optimizing the bumper-level FOV while minimizing interference from ground-level obstacles. The algorithm integrates point cloud data from both sensors using iterative closest point (ICP) matching and feature extraction techniques, enabling accurate vehicle odometry and 3D mapping. Real-world experiments were conducted using a test vehicle equipped with the proposed system. The results demonstrate that the bumper-mounted configuration provides localization accuracy comparable to roof-mounted systems, especially in the XY plane, despite minor Z-axis discrepancies. Additionally, the proposed system expands the horizontal FOV through dual LiDAR placement, addressing the limitation of rear-view coverage. These findings suggest that the bumper-mounted LiDAR system offers a practical and reliable solution for autonomous driving, enhancing robustness against environmental challenges while maintaining mapping accuracy. This study confirms the potential of bumper-mounted LiDAR configurations for real-world applications in autonomous vehicle technology.}, archive = {J_ACCESS}, author = {Jae-Hun Jang and Hyeong-Jun Kim and Kyung-Chang Lee}, doi = {10.1109/ACCESS.2025.3544350}, journal = {IEEE Access}, pages = {35174-35182}, shortjournal = {IEEE Access}, title = {A study on the SLAM of automotive vehicles using bumper-mounted dual LiDAR}, volume = {13}, year = {2025}, }
• (2025). Multi-sonar fusion-based precision underwater 3D reconstruction for optimal scan path planning of AUV. ACCESS, 13, 35157-35173. (www)

  @article{ , abstract = {This paper proposes a multi-sonar fusion-based underwater three-dimensional (3D) point cloud generation method for optimal scan path planning of an autonomous underwater vehicle (AUV). 3D reconstruction based on forward-looking sonar (FLS), a crucial sensor due to its robustness in underwater environments, is essential for underwater mapping and scan path planning of AUVs. A multi-sonar fusion method using FLS and profiling sonar (PS) in a previous study can eliminate the false slope points generated due to noise and the ambiguity in the vertical field-of-view (FoV) of the FLS. However, it shows limitations depending on “the encountered yaw angle”, defined as the angle between AUV’s scan path direction and the front edge line of object’s upper area, which cannot be easily accounted for using a two-dimensional (2D) PS map. The proposed method detects the encountered yaw angle in the current scan path and estimates the next optimal scan path where the encountered yaw angle is suitable for fusion. Subsequently, using the fused 3D reconstruction result from the optimal scan path, we propose a 3D reconstruction of PS data for fusion with the FLS. We evaluated our method in the simulator and water tank experiments. The results show that the proposed method surpasses other methods, FLS only and the conventional fusion method with the FLS and 2D PS map in both environments. Finally, the proposed method will contribute to applications such as underwater exploration and AUV navigation, which require accurate mapping and scan path planning.}, archive = {J_ACCESS}, author = {Sehwan Rho and Hangil Joe and Minsung Sung and Jason Kim and Seungmin Kim and Son-Cheol Yu}, doi = {10.1109/ACCESS.2025.3542084}, journal = {IEEE Access}, pages = {35157-35173}, shortjournal = {IEEE Access}, title = {Multi-sonar fusion-based precision underwater 3D reconstruction for optimal scan path planning of AUV}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review on the charging technologies of electric vehicles (EV) and their impact on power grid. ACCESS, 13, 35124-35156. (www)

  @article{ , abstract = {In the near future the conventional fuel-based transportation system is expected to be entirely replaced by Electric Vehicles (EVs) due to their significant environmental benefits. While contemporary charging technologies demonstrate great potential to accelerate the growth of the EV market, several challenges remain to be addressed for their widespread advancement. The primary concerns of EV charging technology include charging time, speed, cost, reliability, power converter performance, and its impact on the utility grid. With this background, this study provides a detailed overview of various charging topologies used in EVs, which encompass the charging methods, power levels, as well as unidirectional and bidirectional AC-DC and DC-DC converters. Each analysis is carried out according to the viewpoints, effects, efficiency, power density, transfer speed, reliability, and limitations of charging converters, for both slow and fast-charging phenomena. It explores diverse EV charging methods, including CC, WC, and swapping stations, focusing on on-board and off-board charging, as well as advanced wireless technologies like inductive, capacitive, and optical charging. Moreover, EV charging impacts on the electrical grid are discussed, focusing on waveform distortion, voltage fluctuations, grid stability, frequency regulation, reactive power compensation, and load management. It emphasizes how EV helps balance out grid oscillations brought on by the unpredictability of the power grid. To explore the current charging techniques and suggest modern solutions, a deep literature assessment is undertaken to identify exciting research gaps. This research bridges gaps in the current model and opens possibilities for future solutions by thoroughly examining EV interactions with the smart grid. Ultimately, this review will aid academics in driving further research and development while offering industry professionals valuable guidance for optimizing and implementing reliable and efficient EV charging solutions.}, archive = {J_ACCESS}, author = {Mohammed Masud Rana and S. M. Mahfuz Alam and Faiaz Allahma Rafi and Swarup Bashu Deb and Boker Agili and Miao He and Mohd. Hasan Ali}, doi = {10.1109/ACCESS.2025.3538663}, journal = {IEEE Access}, pages = {35124-35156}, shortjournal = {IEEE Access}, title = {Comprehensive review on the charging technologies of electric vehicles (EV) and their impact on power grid}, volume = {13}, year = {2025}, }
• (2025). Algorithm for mining maximal balanced bicliques using formal concept analysis. ACCESS, 13, 35113-35123. (www)

  @article{ , abstract = {One of the most fundamental models for cohesive subgraph mining in network analysis is that which involves the use of cliques. In bipartite graph analysis, the detection of maximal balanced bicliques (MBB) is an important problem with numerous applications, including VLSI design, protein interactions, and social networks. However, MBB detection is difficult, complex, and time-consuming. In the current paper, to address these disadvantages, we propose a new algorithm for detecting MBB using formal concept analysis (FCA) on bipartite graphs. We applied an algorithm to compute formal concepts from the formal context, which is an alternative way of representing a bipartite graph. We proved that the MBB problem is equivalent to the semi-equiconcept enumeration problem in the formal context. Therefore, the semi-equiconcept mining algorithm was applied to the MBB enumeration problem. However, since the existing FCA algorithm cannot be directly applied to mine all MBBs, the FCA algorithm was modified in its entirety. Thorough asymptotic analysis was performed on the proposed algorithm. Experiments were also conducted on various real-world bipartite graphs to which the proposed algorithm was applied, and our results were found to be significantly better than those obtained by the preexisting algorithm.}, archive = {J_ACCESS}, author = {Ilkhomjon Sadriddinov and Sony Peng and Sophort Siet and Dae-Young Kim and Doo-Soon Park and Gangman Yi}, doi = {10.1109/ACCESS.2024.3419838}, journal = {IEEE Access}, pages = {35113-35123}, shortjournal = {IEEE Access}, title = {Algorithm for mining maximal balanced bicliques using formal concept analysis}, volume = {13}, year = {2025}, }
• (2025). Analysis of the influence of CT imaging conditions on the CT image features of pulmonary nodule phantoms. ACCESS, 13, 35101-35112. (www)

  @article{ , abstract = {By studying the impact of CT imaging conditions on image features of pulmonary nodule phantoms, this study explores possibility to optimize the diversity of imaging conditions when building a test set for AI-enabled medical device software for pulmonary nodule analysis. In this study, 15 phantoms mimicking three types of pulmonary nodules were placed in different positions in a whole lung phantom. CT images were collected under combination of different parameters, including tube voltage, tube current-time product, collimator width and reconstruction operator (14 sets in total). The texture features (GLCM, GLRLM, GLSZM, GLDM, NGTDM) of CT images of pulmonary nodules were extracted using the PyRadiomics package in Python, and the five main texture features were selected using the LASSO method. To analyze the data, correlation analysis and non-parametric tests were applied, with a correction method used to control errors in multiple comparisons. The results showed that changes in the reconstruction kernel affected the stability of texture features in CT images of pulmonary nodules more significantly than other imaging parameters in this study. Different reconstruction kernels influenced the representation of image details, leading to variations in image sharpness and noise levels, which in turn affected the stability of the extracted feature values. Meanwhile, no significant changes were induced by variation of tube voltage, tube current-time product and collimator width during image acquisition. These findings suggest that when building the test set for AI-enabled medical device software for pulmonary nodule analysis, it is essential to carefully consider the diversity of reconstruction kernels and their impact on data quality, thereby enhancing the representativeness of the test set.}, archive = {J_ACCESS}, author = {Jing Li and Daidi Zhong and Shuo Wang and Chunjing Tao and Hao Wang}, doi = {10.1109/ACCESS.2025.3544439}, journal = {IEEE Access}, pages = {35101-35112}, shortjournal = {IEEE Access}, title = {Analysis of the influence of CT imaging conditions on the CT image features of pulmonary nodule phantoms}, volume = {13}, year = {2025}, }
• (2025). Enhanced coati optimization algorithm for static and dynamic transmission network expansion planning problems. ACCESS, 13, 35068-35100. (www)

  @article{ , abstract = {The power systems are becoming more and more complex due to the inclusion of new components and increasing load demand. Consequently, it is imperative to incorporate additional generation units and transmission links into the system. Transmission Network Expansion Planning (TNEP) seeks to include generation units and transmission lines into the system at optimal locations and minimal costs. Mathematical techniques are extensively employed to address the problem. Nonetheless, mathematical methods necessitate extensive computation durations. Consequently, novel solution strategies are under investigation. The TNEP problem is characterized by an innovative and effective metaheuristic optimization techniques. This study presents a novel Opposition Based Learning and Fitness Distance Balance based Coati Optimization Algorithm (FDBCOA-OBL) designed to address Static and Dynamic TNEP problems. An extensive experimental investigation was undertaken to evaluate the efficacy of the suggested method in addressing the benchmark test suites and the TNEP problem. The FDBCOA-OBL algorithm, utilizing the Elite OBL approach, surpassed all other comparative versions in addressing the benchmark test problems. The Wilcoxon analysis indicates that it lost 6 problems, tied in 110, and won 166 problems. The proposed approach resolved the TNEP problem in 6, 25, and 93-bus test systems. The Static TNEP solution was applied to the 6 and 25 bus test systems, while the Dynamic Multistage TNEP method was utilized in the 93-bus test system. The acquired investment expenses were compared to the research already documented in the literature. The findings indicate that the suggested method demonstrates robust performance.}, archive = {J_ACCESS}, author = {Muhammet Demirbas and M. Kenan Dosoglu and Serhat Duman}, doi = {10.1109/ACCESS.2025.3544523}, journal = {IEEE Access}, pages = {35068-35100}, shortjournal = {IEEE Access}, title = {Enhanced coati optimization algorithm for static and dynamic transmission network expansion planning problems}, volume = {13}, year = {2025}, }
• (2025). Data-driven approaches for estimation of EV battery SoC and SoH: A review. ACCESS, 13, 35048-35067. (www)

  @article{ , abstract = {Electric vehicle (EV) technologies have marked a staunch foundation in the transportation industry. The precise assessment of State of Charge (SoC) as well as State of Health (SoH) is essential for problems like range anxiety and unanticipated breakdown in EVs. In that regard, we have examined various methodologies, including traditional methods like Coulomb Counting (CC) and Open Circuit Voltage (OCV), advanced filter-based approaches, and contemporary data-driven methods. An extensive evaluation of different methods, along with the identification of strengths and weaknesses, is discussed. Data-driven estimation using Machine learning algorithms demonstrates superior accuracy and adaptability in sophisticated battery management systems. External battery parameters such as voltage, current, time, and temperature (V.C.T.T) and internal battery parameters such as impedance and ultrasonic data are the principal constituents of the Data-driven approaches. In this study, machine learning algorithms exhibited substantial enhancements in predicting and maintaining the lifespan of electric vehicle batteries. Nevertheless, there remains a requirement for ongoing advancement in battery systems to up-hold environmentally friendly transportation and incorporate pioneering estimation techniques to improve the reliability and lifespan of batteries.}, archive = {J_ACCESS}, author = {Shahid Gulzar Padder and Jayesh Ambulkar and Atul Banotra and Sudhakar Modem and Sidharth Maheshwari and Kolleboyina Jayaramulu and Chinmoy Kundu}, doi = {10.1109/ACCESS.2025.3539528}, journal = {IEEE Access}, pages = {35048-35067}, shortjournal = {IEEE Access}, title = {Data-driven approaches for estimation of EV battery SoC and SoH: A review}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of deep learning methods for classification of ablated regions in hyperspectral images of atrial tissue. ACCESS, 13, 35029-35047. (www)

  @article{ , abstract = {Radiofrequency ablation (RFA) is used to treat atrial fibrillation (AF). Viability gaps between ablated regions can lead to AF recurrence; thus, correctly detecting RFA lesions is important for successful treatment. Hyperspectral imaging (HSI) has been previously shown to aid in visualizing RFA-affected areas. However, automated classification of ablated/unabled tissue in HSI remains open. We comparatively analyze state-of-the-art deep learning models for HSI classification from the field of remote sensing. We group them based on model architecture and utilization of spectral and spatial contexts. We deploy a pre-clinical HSI dataset obtained from excised left atrial tissue of four porcine and five bovine samples. The 45 classification algorithms are benchmarked through two types of experiments: objective and subjective. For objective evaluation, per HSI cube, we limit the training and testing to rectangular areas of known classes (ablated/unablated), as reliable classification masks for whole HSI cubes are not available. For both porcine and bovine data, models incorporating graph neural networks demonstrate superior performance. The best eight algorithms are further compared in a subjective experiment. We ask seventeen participants to rate RGB visualizations of model outputs. In this experiment, the performance of an algorithm is assessed on the whole HSI cube, and is subject to participants’ interpretation of the extent of RFA-affected tissue. Our comparative analysis allows us to observe the suitability of different deep learning architectures for HSI classification of affected tissue. These efforts contribute towards the potential future utilization of HSI for surgical guidance in RFA or similar procedures.}, archive = {J_ACCESS}, author = {Yeva Gabrielyan and Arpi Hunanyan and Sona Bezirganyan and Lusine Davtyan and Ani Avetisyan and Narine Sarvazyan and Aram Butavyan and Varduhi Yeghiazaryan}, doi = {10.1109/ACCESS.2025.3543122}, journal = {IEEE Access}, pages = {35029-35047}, shortjournal = {IEEE Access}, title = {Comparative analysis of deep learning methods for classification of ablated regions in hyperspectral images of atrial tissue}, volume = {13}, year = {2025}, }
• (2025). Suppression of capacitor leakage through thermal budget control in DRAM with ZrO2-based dielectrics. ACCESS, 13, 35019-35028. (www)

  @article{ , abstract = {As the DRAM devices continue to scale down, the leakage current of the capacitors is having a significant impact on DRAM operation. We have analyzed the factors that significantly affect the leakage current of DRAM capacitors and improved the leakage current accordingly. In this research, we modeled the degradation of the ZrO2-based dielectric layer due to subsequent high-temperature processes in back-end-of-line (BEOL) processes after forming the Metal-Insulator–Metal (MIM) capacitor in DRAM from two perspectives, and we developed a low-temperature process and verified the validity of these models. We introduced two models for the degradation of the dielectric film caused by high-temperature processes. First, a band gap decreases due to the loss of oxygen in the dielectric material ZrO2. The interface between the ZrO2 dielectric layer and the TiN top electrode was simulated, and through XPS analysis of the ZrO2 layer, it was confirmed that the oxygen defects induced in ZrO2 by the high-temperature process reduce the Schottky barrier, resulting in an increase in leakage current. Second, another cause was the degradation of surface roughness due to the crystallization of the dielectric film. The XRD analysis results show that ZrO2 is crystallized at a processing temperature of 500°C, and the deterioration of surface roughness in the crystallized region can be confirmed through AFM analysis. In addition, partial crystallization patterns occurring during high-temperature processing can also be seen in TEM images. Ultimately, based on these two models, we confirmed that capacitor leakage in actual devices was improved by the BEOL low-temperature process, and this approach provides crucial insight for the continued scaling of DRAM capacitors.}, archive = {J_ACCESS}, author = {Dong-Sik Park and Eun-Ok Lee and Jong-Min Lee and Eunae Cho and Byongdeog Choi}, doi = {10.1109/ACCESS.2024.3502036}, journal = {IEEE Access}, pages = {35019-35028}, shortjournal = {IEEE Access}, title = {Suppression of capacitor leakage through thermal budget control in DRAM with ZrO2-based dielectrics}, volume = {13}, year = {2025}, }
• (2025). Infrared small target detection based on double gray-values descend angle contrast measure. ACCESS, 13, 35004-35018. (www)

  @article{ , abstract = {Infrared small target detection remains a subject of significant theoretical research value and practical application potential. However, the complexity of the detection environment and the interference from salt noise considerably increase the difficulty of the detection task. In scenarios involving small infrared target detection against complex backgrounds, existing methods face substantial challenges in achieving an optimal balance between superior detection capability and real-time performance. Furthermore, the influence of noise on detection efficacy is an unavoidable issue. To address these challenges, this paper proposes an anti-noise detection method that excels in both real-time performance and detection ability. Initially, energy residuals are computed to pre-assess potential target areas. Subsequently, the minimum difference between the central pixel and its local neighborhood is calculated to position the target region positioning twice, thereby enhancing targets while suppressing background noise. Finally, by defining a double gray-values descend angle and calculating its tangent quotient, further enhancement of targets is achieved alongside suppression of additional background interference and salt noise. Experimental evaluations were conducted on four publicly available datasets, comparing our proposed approach with seven existing algorithms. The results demonstrate that our method facilitates rapid detection of small targets affected by salt noise within complex backgrounds while exhibiting exceptional detection capabilities and real-time performance. Notably, DGDACM achieves its fastest single-frame detection time at just 0.0719 seconds—5.1% faster than the second-best average single-frame time.}, archive = {J_ACCESS}, author = {Song Guan and Dali Zhou and Xiaodong Wang and Pengji Zhou}, doi = {10.1109/ACCESS.2025.3540406}, journal = {IEEE Access}, pages = {35004-35018}, shortjournal = {IEEE Access}, title = {Infrared small target detection based on double gray-values descend angle contrast measure}, volume = {13}, year = {2025}, }
• (2025). A physics-coupled deep learning framework for hydrodynamic diffusion modeling in watershed systems: Integrating spatiotemporal networks and environmental constraints. ACCESS, 13, 34985-35003. (www)

  @article{ , abstract = {The modeling and simulation of complex spatiotemporal systems are crucial for understanding and solving multidimensional dynamical systems, particularly in earth and environmental sciences. Accurate comprehension and computational modeling of fluid transport, environmental processes, and substance diffusion depend heavily on solving governing equations. Despite significant advancements in artificial intelligence techniques, such as deep learning and neural operator methods, challenges persist regarding robustness, scalability, and adherence to physical laws in hydrodynamic systems. This paper introduces a multi-scale interdisciplinary hybrid learning framework that integrates physics-informed neural networks with neural operator-based deep learning techniques to model hydrodynamic transport processes. By incorporating convolutional neural networks for multi-scale feature extraction and implementing hard constraints to enforce physical boundary conditions, the proposed framework enhances the stability and accuracy of predictions in dynamic fluid systems. The approach facilitates efficient reconstruction of spatiotemporal characteristics and parameterized dynamics while ensuring physical consistency. Through case studies of two-dimensional solute diffusion equations, the framework demonstrates superior generalizability and robustness in addressing high-dimensional and nonlinear fluid systems. Comparative experiments with multiple baseline models highlight significant improvements in prediction accuracy, noise resistance, and adaptability to sparse datasets. The proposed method effectively addresses limitations of traditional data-driven approaches by integrating domain-specific physical principles, providing a reliable and high-fidelity solution for hydrodynamic modeling and environmental system monitoring. This research offers a transformative approach to feature extraction, dynamic representation, and physically consistent modeling of complex spatiotemporal systems.}, archive = {J_ACCESS}, author = {L. Jia and N. Yen and Y. Pei}, doi = {10.1109/ACCESS.2025.3542173}, journal = {IEEE Access}, pages = {34985-35003}, shortjournal = {IEEE Access}, title = {A physics-coupled deep learning framework for hydrodynamic diffusion modeling in watershed systems: Integrating spatiotemporal networks and environmental constraints}, volume = {13}, year = {2025}, }
• (2025). Multi-graph anomaly detection in business processes with scalable neural architectures. ACCESS, 13, 34969-34984. (www)

  @article{ , abstract = {Business Process Management (BPM) solutions are critical for organizational efficiency, but their potential remains limited by inadequate effectiveness in anomaly detection capabilities for real-world deployment. This study addresses key challenges in developing production-ready anomaly detection systems that are scalable, efficient, and adaptable across diverse business domains. We propose several enhancements to a state-of-the-art graph-based autoencoder model to overcome these barriers. This includes improved artificial anomaly injection methods that more accurately reflect real-world scenarios to overcome the scarcity of annotated datasets in real-world environments. A comprehensive study of multiple model architectures is conducted, incorporating Graph Attention v2 in the encoder and replacing Gated Recurrent Unit (GRU) decoders with Transformers, thereby achieving comparable or superior performance with half the computational cost. Introducing a denoising objective alongside reconstruction, we lay the foundation for targeted training on domain-specific anomalies without compromising general detection capabilities. We demonstrate the solution’s reliability and generalizability in varied business domains by conducting comprehensive evaluations on diverse public and private datasets. The results indicate significant improvements in scalability and real-world applicability while maintaining and enhancing detection accuracy, with results showing up to 22% increase in anomaly detection performance.}, archive = {J_ACCESS}, author = {Stanley Hsu and Ege Gülce and Teoman Berkay Ayaz and Alper Ozcan and Akhan Akbulut}, doi = {10.1109/ACCESS.2025.3544268}, journal = {IEEE Access}, pages = {34969-34984}, shortjournal = {IEEE Access}, title = {Multi-graph anomaly detection in business processes with scalable neural architectures}, volume = {13}, year = {2025}, }
• (2025). Positive anchor area merge algorithm: A knowledge distillation algorithm for fruit detection tasks based on yolov8. ACCESS, 13, 34954-34968. (www)

  @article{ , abstract = {In the agricultural sector, employing machine vision technology for fruit target detection holds significant research importance and broad application prospects, such as enabling fruit growth monitoring, yield prediction, and fruit sorting. The Yolov8 model, as the latest model in the field of object detection, boasts advantages including high execution efficiency and detection accuracy. However, when it comes to fruit object detection, which means counting and locating target fruits in an image, the performance of the Yolov8 model shows a noticeable decline compared to its performance on the standard COCO dataset. To address this issue, knowledge distillation is a highly versatile method that uses a large teacher model to guide the training of a smaller student model, thereby improving the detection accuracy of the student model. This thesis proposes a Yolov8 knowledge distillation method tailored for fruit recognition tasks, which improves the network through knowledge distillation and implements a knowledge distillation method based on positive anchor area merging to enhance detection accuracy for fruit recognition tasks. On our self-constructed fruit dataset, which contains over 3,000 images for each category, we compared our model with other similar state-of-the-art models in terms of resource consumption and detection accuracy. While maintaining a low resource overhead, our model achieved an mAP(50) of 99.47%, which is higher than other models that range from 99.1% to 99.3%. In the ablation experiments, we also demonstrated the practical significance of dividing the positive sample area. Finally, we deployed the model on an embedded system for real-time detection of on-site images. These experiments illustrate the practicality of our method for recognizing fruits in real-world scenarios.}, archive = {J_ACCESS}, author = {Xiangqun Shi and Xian Zhang and Yifan Su and Xun Zhang}, doi = {10.1109/ACCESS.2025.3544361}, journal = {IEEE Access}, pages = {34954-34968}, shortjournal = {IEEE Access}, title = {Positive anchor area merge algorithm: A knowledge distillation algorithm for fruit detection tasks based on yolov8}, volume = {13}, year = {2025}, }
• (2025). PLFF-SLAM: A point and line feature fused visual SLAM algorithm for dynamic illumination environments. ACCESS, 13, 34946-34953. (www)

  @article{ , abstract = {Mobile robots rely on Visual Simultaneous Localization and Mapping (SLAM) as their primary technology. However, in environments with dynamic lighting changes, current state-of-the-art visual SLAM techniques are vulnerable to trajectory drift, which leads to poor robustness and low positioning accuracy. This work proposes PLFF-SLAM, a point and line feature fused visual SLAM method, as a solution to these issues. Firstly, we designed a point feature extraction method based on the GCNV2 network, which improves the extraction performance of the point feature network by modifying the encoding layer structure and reconstructing the loss function. Then, to extract and match line features, we design an enhanced LSD algorithm. To address trajectory drift, we finally combine the point and line characteristics, which significantly increases the visual SLAM system’s robustness and positioning accuracy. We conduct validation on public datasets UMA-VI and EuRoC. The results show the proposed PLFF-SLAM algorithm, compared with existing methods has a smaller absolute trajectory error and higher pose estimation accuracy, which can effectively improve the accuracy and robustness of the visual SLAM algorithm in dynamic illumination environments.}, archive = {J_ACCESS}, author = {Shucheng Huang and Wenhan Ren and Mingxing Li}, doi = {10.1109/ACCESS.2025.3540563}, journal = {IEEE Access}, pages = {34946-34953}, shortjournal = {IEEE Access}, title = {PLFF-SLAM: A point and line feature fused visual SLAM algorithm for dynamic illumination environments}, volume = {13}, year = {2025}, }
• (2025). Advanced techniques for designing wideband and highly selective bandpass filters through cascaded modules. ACCESS, 13, 34936-34945. (www)

  @article{ , abstract = {This paper presents a novel design approach for the realization of highly-selective wideband bandpass filters (BPFs). The technique builds on a conventional filter topology, featuring shunt short-circuited stubs connected by transmission lines, in which the traditional $\lambda _{g}$ /4 interconnecting lines and $\lambda _{g}$ /4 short-circuited stubs are replaced by a building block comprising $\lambda _{g}$ /8 lines separated by $\lambda _{g}$ /8 short-circuited coupled-line sections. To enhance selectivity, $\lambda _{g}$ /2 open- and short-circuited coupled-line segments are used at the filter’s input and output. The paper provides design guidelines and formulas through ABCD analysis for practical filter implementations. Additionally, the design examples are demonstrated, along with their practical validation through the manufacturing and testing of a fourth, and a fifth-order microstrip-type BPFs as well as a fifth-order BPF with improved selectivity.}, archive = {J_ACCESS}, author = {Adnan Nadeem and Nosherwan Shoaib and Antonis Papadakis and Symeon Nikolaou and Dimitra Psychogiou and Photos Vryonides}, doi = {10.1109/ACCESS.2025.3543597}, journal = {IEEE Access}, pages = {34936-34945}, shortjournal = {IEEE Access}, title = {Advanced techniques for designing wideband and highly selective bandpass filters through cascaded modules}, volume = {13}, year = {2025}, }
• (2025). Advanced brain tumor segmentation with a multiscale CNN and conditional random fields. ACCESS, 13, 34925-34935. (www)

  @article{ , abstract = {The use of high-precision automatic algorithms for segmenting brain tumors has the potential to improve disease diagnosis, treatment monitoring, and large-scale pathological studies. In this study, we present a novel 9-layer multiscale architecture designed specifically for the semantic segmentation of 3D medical images, with a particular focus on brain tumor images, using convolutional neural networks. Our innovative solution incorporates several significant enhancements, including the use of variable-sized filters between layers and the early incorporation of residual connections from the very first layer. These modifications allow us to reduce the number of layers in our network while significantly improving the accuracy of 3D medical image segmentation. For post-processing the network’s soft segmentation, we employ a 3D fully connected Conditional Random Field, which effectively removes false positives. This approach enables our model to achieve an impressive accuracy of 99.88%, a sensitivity of 99.86%, and a specificity of 99.96%, outperforming existing models. This combination of innovations positions our solution as a major advancement in the field of 3D medical image segmentation, offering an optimal balance between accuracy and efficiency for clinical applications.}, archive = {J_ACCESS}, author = {Ala Guennich and Mohamed Othmani and Hela Ltifi}, doi = {10.1109/ACCESS.2025.3541183}, journal = {IEEE Access}, pages = {34925-34935}, shortjournal = {IEEE Access}, title = {Advanced brain tumor segmentation with a multiscale CNN and conditional random fields}, volume = {13}, year = {2025}, }
• (2025). Yolov5s-PSG: Improved yolov5s-based helmet recognition in complex scenes. ACCESS, 13, 34915-34924. (www)

  @article{ , abstract = {In the field of industrial safety, due to the existence of color, distance and other reasons in complex industrial environments caused by the helmet small target detection methods have the problem of misdetection and omission, and the Yolov5s model for real-time detection of helmets is not ideal. for the purpose of Improvement of extraction capabilities multi-scale features, improve the network’s focus on key features, and achieve an excellent balanced effect for accuracy and speed, the model Yolov5s-PSG based on Yolov5s improvement is proposed. First, the C3_PPA module improves the detection accuracy through a multi-branch feature extraction strategy and an attention mechanism. Second, the SimAM attention mechanism enhances the inspection accuracy by adaptively weighting the feature maps and thus improving the detection efficiency. Finally, the GSconv convolutional layer combines the SC module and the DSC module in a channel shuffling manner to infiltrate the information of the SC module within some of the information of the DSC module, which improves the detection speed without changing the accuracy.Yolov5s-PSG model outperforms the conventional target inspection in terms of accuracy, recall, map value, and loss function, where the accuracy is improved to 93.1%, recall to 91.2%, and map value to 94.6%. These findings are important for helmet inspection to find the optimal solution in terms of accuracy and speed for better helmet inspection in shop-floor factory environments.}, archive = {J_ACCESS}, author = {Yi Li and Xiaolin Shi and Xiaolong Xu and Han Zhang and Fan Yang}, doi = {10.1109/ACCESS.2025.3544280}, journal = {IEEE Access}, pages = {34915-34924}, shortjournal = {IEEE Access}, title = {Yolov5s-PSG: Improved yolov5s-based helmet recognition in complex scenes}, volume = {13}, year = {2025}, }
• (2025). Correlating without confidence: The overlooked role of uncertainty when ranking objective measures. ACCESS, 13, 34903-34914. (www)

  @article{ , abstract = {In the quality assessment of multimedia content, correlating objective measures with subjective assessments is standard practice. However, unlike fields such as biology and psychology, multimedia quality research often overlooks the crucial aspect of confidence intervals in these correlations. This oversight leads to an underestimation of uncertainty and hinders effective comparison of objective measures. We propose a novel approach that addresses this gap by adapting established statistical methods to the unique challenges of multimedia quality assessment. Our method integrates bootstrapping and Monte Carlo simulations to estimate confidence intervals for correlation coefficients, even when only summary statistics are available. The ranking of measures is done using Cliff’s delta. Our approach enhances the precision of measure comparisons and reveals the inherent variability in subjective ratings across diverse datasets. We demonstrate significant improvements in discriminative power between objective measures by applying our method to common databases for image and light field quality assessment. Our results show that previously assumed differences between measures can be statistically insignificant, challenging long-held assumptions in the field. An implementation of the proposed method to promote reproducible research can be found at https://osf.io/2x4g8/.}, archive = {J_ACCESS}, author = {Adam Zizien and Karel Fliegel}, doi = {10.1109/ACCESS.2025.3544307}, journal = {IEEE Access}, pages = {34903-34914}, shortjournal = {IEEE Access}, title = {Correlating without confidence: The overlooked role of uncertainty when ranking objective measures}, volume = {13}, year = {2025}, }
• (2025). Distribution and moments of the idle period and interarrival time in the G/M/1 queueing system. ACCESS, 13, 34887-34902. (www)

  @article{ , abstract = {The Idle period is relevant in a wide variety of systems and has been extensively studied in the literature. This paper derives the distribution and moments of the idle period for different interrarrival time distributions in the G/M/1 queueing system. Specifically, Log-Normal (LN), Weibull, arbitrary-order Hyper-Exponential (HE), arbitrary order Erlang, and Deterministic distribution are considered. It is demonstrated that when the interarrival time follows HE (Erlang) distribution, the idle period follows a corresponding HE (Coxian) distribution. The effects of the distribution and coefficient of variation of the interarrival time, and the traffic load on the distribution and first four standardized moments of the idle period are numerically evaluated. Additionally, the accuracy of the derived distribution and moments of the idle period when the LN interarrival time is approximated by HE distributions of different orders (using the Expectation-Maximization algorithm) is investigated. Numerical results show a good fit accuracy between idle period distributions obtained under the LN and the m-th order HE interarrival time models. It is observed that the fitting accuracy (in terms of the Kolmogorov-Smirnov distance) improves as the order of the HE distribution increases. Finally, the standardized moments of the idle period are compared when the interarrival time follows either LN or Weibull distributions with identical first two moments. Numerical results indicate that the values of the standardized moments of the idle period are higher when the interarrival time follows a LN distribution, due to its heavier tail compared to the Weibull distribution.}, archive = {J_ACCESS}, author = {Felipe A. Cruz-Pérez and Sandra Lirio Castellanos-López and Genaro Hernández-Valdez and Mario Eduardo Rivero-Ángeles}, doi = {10.1109/ACCESS.2025.3544101}, journal = {IEEE Access}, pages = {34887-34902}, shortjournal = {IEEE Access}, title = {Distribution and moments of the idle period and interarrival time in the G/M/1 queueing system}, volume = {13}, year = {2025}, }
• (2025). What motivates second language majors to use generative AI for informal learning? insights from the theory of planned behavior. ACCESS, 13, 34877-34886. (www)

  @article{ , abstract = {Generative artificial intelligence (GenAI) holds great promise for enhancing informal second language (L2) learning, but its impact largely depends on how it is used. This study explores the factors influencing L2 majors’ intention to use GenAI for learning, based on the Theory of Planned Behavior (TPB). Data were collected from 668 L2 majors at various Chinese universities through an online questionnaire with six validated scales. Structural equation modeling (SEM) via AMOS 24 revealed several key findings. First, demographic factors (gender and age) did not significantly affect the TPB constructs. Second, subjective norm and attitude positively influenced behavioral intention, while perceived behavioral control did not have a significant effect, contrary to TPB predictions. Additionally, GenAI literacy was found to be a positive predictor of behavioral intention, both directly and indirectly through attitude. External beliefs positively influenced subjective norm, perceived behavioral control, and attitude, which in turn impacted behavioral intention, with mediation effects observed through subjective norm and attitude. However, perceived behavioral control did not mediate the relationship between external beliefs and behavioral intention. This study concludes by discussing implications and future directions.}, archive = {J_ACCESS}, author = {Hanwei Wu and Zhangkun Dong}, doi = {10.1109/ACCESS.2025.3544489}, journal = {IEEE Access}, pages = {34877-34886}, shortjournal = {IEEE Access}, title = {What motivates second language majors to use generative AI for informal learning? insights from the theory of planned behavior}, volume = {13}, year = {2025}, }
• (2025). Low-power intelligent wireless sensor network for precision agriculture oriented agricultural greenhouse management system. ACCESS, 13, 34865-34876. (www)

  @article{ , abstract = {Wireless sensor networks are able to monitor environmental parameters in agricultural greenhouses in real time, thus realizing precise control of the growing environment of crops. However, wireless sensor networks usually need to run for a long time, which requires high energy consumption. To reduce the energy consumption of wireless sensor networks in agricultural greenhouses, this study proposes a research on low-power intelligent wireless sensor networks for agricultural greenhouse management system for precision agriculture. The results showed that when the number of iterations reached 100, the clustering accuracy was as high as 94%, and the mean square error was the lowest 0.018, while when the number of iterations was 20, the mean square error was also maintained at 0.018. This showed that the method used performed well in optimizing quality of service routing in wireless sensor networks. When the number of sensor nodes was 100, the improved whale optimization algorithm accounted for only 15% of the routing energy consumption, while the simulated annealing algorithm accounted for 53% of the routing energy consumption. This further verified the effectiveness of the proposed method in improving the clustering accuracy and reducing the energy consumption of wireless sensor networks, especially in agricultural greenhouse management system. In conclusion, the research method not only effectively improves the clustering accuracy, but also significantly reduces the energy consumption, providing an efficient wireless sensor network optimization scheme for agricultural greenhouse management.}, archive = {J_ACCESS}, author = {Hongguo Jin}, doi = {10.1109/ACCESS.2025.3542866}, journal = {IEEE Access}, pages = {34865-34876}, shortjournal = {IEEE Access}, title = {Low-power intelligent wireless sensor network for precision agriculture oriented agricultural greenhouse management system}, volume = {13}, year = {2025}, }
• (2025). Job demand estimation using text embeddings of patent classification codes and occupational data. ACCESS, 13, 34854-34864. (www)

  @article{ , abstract = {This study presents a novel method for estimating job demand by analyzing the relationship between technological advancements and occupational requirements through patent data. By integrating textual data from the United States Patent and Trademark Office (USPTO) and the Occupational Information Network (O*NET), the research develops a framework using advanced text embedding techniques. Semantic similarity measures are applied to match Cooperative Patent Classification (CPC) code descriptions with job task descriptions, constructing a similarity matrix that quantifies the technological relevance of various occupations. The approach estimates annual job task demand based on patent filing trends, providing a data-driven perspective on labor market evolution. To enhance reliability, multiple text embedding models are assessed to determine the most effective one for measuring semantic similarities. Aggregated job task demand estimates are used to project overall job demand trends systematically. Validation is conducted by comparing predicted job demand metrics with empirical employment data, demonstrating a strong correlation between estimated and actual trends. The findings highlight the impact of technological progress on job market dynamics, identifying occupations with increasing or declining demand. The study also acknowledges limitations, such as the need for improved job-task matching and additional data integration, while discussing broader implications for labor market forecasting.}, archive = {J_ACCESS}, author = {Taehyun Ha and Ahram Moon}, doi = {10.1109/ACCESS.2025.3544161}, journal = {IEEE Access}, pages = {34854-34864}, shortjournal = {IEEE Access}, title = {Job demand estimation using text embeddings of patent classification codes and occupational data}, volume = {13}, year = {2025}, }
• (2025). SERN-AwGOP: Squeeze-and-excitation residual network with an attention-weighted generalized operational perceptron for atrial fibrillation detection. ACCESS, 13, 34844-34853. (www)

  @article{ , abstract = {Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with increased stroke risk. Early and accurate detection of AF is crucial for effective management and improved patient outcomes. This paper presents SERN-AwGOP, a deep learning model designed for robust and efficient AF detection using 12-lead electrocardiogram (ECG) signals. The model employs branched nodal operators with dilated and separable convolutions, capturing multi-scale features across the 12-lead input. Channel-wise recalibration and an attention mechanism dynamically weight features within each residual block. For AF detection, SERN-AwGOP achieves a 5-fold mean accuracy of 99.11% on the CUSPH database and 93.90% on the CPSC 2018 database. An occlusion study highlights the P-wave and QRS complex as critical ECG segments for AF identification. The model demonstrates robustness against common ECG signal distortions, maintaining 93.03% accuracy on data affected by Gaussian noise, time warping, and random baseline wander. It achieves 97.78% accuracy on hardware-corrupted data with electrode disconnections and amplifier saturation. SERN-AwGOP exhibits high data efficiency, achieving 99.31% accuracy with only 25% of the training data, and shows potential for cross-database generalization with 73.14% accuracy when trained on CPSC 2018 and tested on CUSPH and even more improvements are seen through finetuning based transfer learning. These results indicate SERN-AwGOP’s resilience to real-world ECG signal processing challenges and its potential for practical clinical applications in AF detection.}, archive = {J_ACCESS}, author = {Soumil Hooda and Rajesh Kumar Tripathy}, doi = {10.1109/ACCESS.2025.3544493}, journal = {IEEE Access}, pages = {34844-34853}, shortjournal = {IEEE Access}, title = {SERN-AwGOP: Squeeze-and-excitation residual network with an attention-weighted generalized operational perceptron for atrial fibrillation detection}, volume = {13}, year = {2025}, }
• (2025). Target detection and image enhancement for underwater environment: Research on improving YOLOv7. ACCESS, 13, 34831-34843. (www)

  @article{ , abstract = {Aiming at the common low accuracy and efficiency problems in underwater target detection, this paper designs an innovative algorithm based on the YOLOv7 framework. The core of the research lies in enhancing the feature extraction capability of the model, optimizing the feature expression of the model by introducing the CBAM attention mechanism, and improving the original structure of the neck component. To further enhance the computational efficiency, a deep decomposition feature expression module is designed, which significantly reduces the computational complexity and the number of parameters of the model. In addition, by introducing the Focal-EIoU loss function, combined with a more accurate penalty mechanism, the matching between the predicted frame and the actual labelled frame is made more accurate, which effectively improves the accuracy and reliability of detection. Meanwhile, the image enhancement algorithm designed in this paper plays an important role in improving the quality of the input data, which effectively enhances the performance of the target detection model in the complex underwater environment. Experimental results show that on the URPC2020 dataset, the method in this paper improves mAP@0.5 by 2% compared to YOLOv7 while reducing the computational complexity by 14.4%.}, archive = {J_ACCESS}, author = {Yang Luo and Wen Feng}, doi = {10.1109/ACCESS.2025.3544061}, journal = {IEEE Access}, pages = {34831-34843}, shortjournal = {IEEE Access}, title = {Target detection and image enhancement for underwater environment: Research on improving YOLOv7}, volume = {13}, year = {2025}, }
• (2025). Cognate production using character-based neural machine translation without segmentation. ACCESS, 13, 34824-34830. (www)

  @article{ , abstract = {Cognates are words that share a common origin or have been borrowed across languages, often exhibiting similarities in both sound and meaning. In this work, we introduce a fully character-level neural sequence-to-sequence model for cognate production that does not require any segmentation. Our model operates at the character-level to transform a source word into its corresponding cognate in the target language, thereby obviating out-of-vocabulary issues and alleviating the need for subword segmentation. We evaluated our approach on a novel dataset and found that it outperforms both statistical machine translation baselines and prior neural methods on the same training dataset, as measured by standard coverage and mean reciprocal rank metrics. These results underscore the effectiveness of character-level sequence-to-sequence architectures for cognate generation in diverse language settings, including cross-alphabetic transformations.}, archive = {J_ACCESS}, author = {Tsolmon Zundui and Khuyagbaatar Batsuren and Tsendsuren Munkhdalai and Amarsanaa Ganbold}, doi = {10.1109/ACCESS.2025.3543652}, journal = {IEEE Access}, pages = {34824-34830}, shortjournal = {IEEE Access}, title = {Cognate production using character-based neural machine translation without segmentation}, volume = {13}, year = {2025}, }
• (2025). Remote sensing image dehazing via progressive frequency filtering and adaptive self-attention. ACCESS, 13, 34812-34823. (www)

  @article{ , abstract = {To meet the requirements of forest fire detection, a remote sensing (RS) image dehazing algorithm has been proposed, which always addresses the challenges posed by haze and smoke in real-world forest fire detection tasks. Moreover, RS image dehazing is a very difficult task due to the complex image degradation and haze distribution. Current learning-based methods have great performance for RS image dehazing while ignoring the frequency characteristics and treating various features equally. In this study, a stagewise PADehazeNet using progressive frequency filtering and the adaptive self-attention mechanism is proposed. Specifically, a progressive frequency filtering structure is presented to decompose high-frequency features from low-rank features. Progressive feature sharing tensors are used to concatenate and share the multiscale high-frequency features. In addition, an adaptive self-attention mechanism is used to select effective features from low-frequency components so that the global tone can be reconstructed in an efficient way. Extensive experimental results state that the proposed PADehazeNet achieves an outstanding performance compared to recent comparative models on both synthetic and real-world datasets. In summary, this algorithm should be used in the forest fire detection issue via RS.}, archive = {J_ACCESS}, author = {Lei Xu and Yijia Xu and Xuejian Gong and Yipeng Mao}, doi = {10.1109/ACCESS.2025.3543397}, journal = {IEEE Access}, pages = {34812-34823}, shortjournal = {IEEE Access}, title = {Remote sensing image dehazing via progressive frequency filtering and adaptive self-attention}, volume = {13}, year = {2025}, }
• (2025). Design of a beamforming tri-band rectifier for RF energy harvesting. ACCESS, 13, 34802-34811. (www)

  @article{ , abstract = {Ambient RF energy is considered as one of the most promising resources for powering low-power electronics, especially in IoT and wireless sensor network applications. One of the most significant challenges in RF energy harvesting is the low ambient RF power density which decreases the conversion efficiency of the rectifiers. Another problem is that RF power is spread over different frequency bands in our surroundings. In response to these challenges, this paper focuses on designing a beamforming tri-band harvester capable of capturing RF energy from GSM-900, DCS-1800, and WiFi-2400 frequency bands. This rectifier consists of a novel tri-band Butler matrix with asymmetric hybrid couplers as the beamforming network to maintain high gain harvesting while ensuring proper spatial coverage. Additionally, a tri-band rectifying circuit utilizing a new single branch impedance matching network has been designed to convert RF power to DC at three frequency bands. The measured power conversion efficiency of this beamforming rectifier is found to be 53.4%, 44%, and 16.8%, respectively at the three frequency bands for an ambient RF power level of -15 dBm and also the maximum PCE of 67% for -5 dBm input power. The proposed tri-band Butler matrix along with the tri-band rectifier circuit is then fabricated on RT-duroid 5880 and RO4003C substrates. Comparison done between measured and simulated results validates that the proposed network is able to harvest incoming RF energy efficiently from different directions at three desired frequency bands.}, archive = {J_ACCESS}, author = {Saeed Abdollahi and Ahmad Bakhtafrouz and Mohsen Maddahali}, doi = {10.1109/ACCESS.2025.3544146}, journal = {IEEE Access}, pages = {34802-34811}, shortjournal = {IEEE Access}, title = {Design of a beamforming tri-band rectifier for RF energy harvesting}, volume = {13}, year = {2025}, }
• (2025). Automatic segmentation and characterization of structure planes from borehole images based on deep learning. ACCESS, 13, 34789-34801. (www)

  @article{ , abstract = {Structural characteristics of rock masses are crucial in geotechnical engineering, yet manual identification of structural planes from borehole images is limited by efficiency and reliability. To address this, we developed an improved U-Net-based segmentation network, specifically tailored for structural planes in borehole images, enabling automatic identification and characterization. The model integrates deformable convolutions and channel attention mechanisms to improve structure plane detection, allowing for precise segmentation of geological structures under various challenging conditions. Furthermore, we introduce an automated workflow that couples segmentation results with curve fitting and parameter estimation techniques to precisely quantify critical structural attributes, including dip direction, dip angle, and aperture. The proposed method was evaluated on a borehole image dataset, achieving a mean IoU of 69.53%, with 93.59% for the background class and 45.47% for the structure plane class, demonstrating its effectiveness in segmenting both dominant and complex regions. The dataset is available upon request and will be made publicly available in future studies. Results also validates the effectiveness of estimating structural plane parameters. Compared to exist methods, our approach specifically addresses the unique challenges of borehole images. By providing a reliable and efficient tool for structure plane segmentation and parameter characterization, this study enhances the accuracy and efficiency of rock mass structure detection and analysis.}, archive = {J_ACCESS}, author = {Shuangyuan Chen and Zengqiang Han and Yi Cheng and Chao Wang}, doi = {10.1109/ACCESS.2025.3534269}, journal = {IEEE Access}, pages = {34789-34801}, shortjournal = {IEEE Access}, title = {Automatic segmentation and characterization of structure planes from borehole images based on deep learning}, volume = {13}, year = {2025}, }
• (2025). Practical evaluation framework for real-time multi-object tracking: Achieving optimal and realistic performance. ACCESS, 13, 34768-34788. (www)

  @article{ , abstract = {This paper introduces an enhanced evaluation framework to assess the real-world efficacy of multi-object tracking (MOT) systems, focusing on holistic assessment encompassing detection, ReID (Re-Identification), and tracking components. The Lightweight Integrated Tracking-Feature Extraction (LITE) paradigm is proposed as a novel method that seamlessly integrates ReID features within the tracking pipeline, minimizing computational overhead. Unlike conventional frameworks, which often overlook real-world constraints, our approach benchmarks tracker performance in practical scenarios using off-the-shelf detectors. A significant insight derived from our framework indicates that practitioners can attain a HOTA (Higher Order Tracking Accuracy) score of up to 30% by customizing input resolutions and confidence thresholds. In contrast, those who are unaware of these optimizations may only achieve a HOTA score of 10%. This finding underscores the critical advantage offered by our evaluation method. Comprehensive experiments reveal that LITE enables ReID-based trackers to operate with similar speeds to motion-only systems (uses only motion cues, such as object trajectory and velocity, to detect and track objects over time without incorporating appearance features), without compromising accuracy. Our findings underscore the LITE paradigm’s potential to shift the dynamics of MOT, offering a balanced solution between computational efficiency and high-performance tracking. The evaluation framework not only standardizes tracker assessment but also highlights the versatility of LITE across diverse datasets and edge devices. The source code for this research is publicly available at https://github.com/Jumabek/LITE.}, archive = {J_ACCESS}, author = {Jumabek Alikhanov and Dilshod Obidov and Mirsaid Abdurasulov and Hakil Kim}, doi = {10.1109/ACCESS.2025.3541177}, journal = {IEEE Access}, pages = {34768-34788}, shortjournal = {IEEE Access}, title = {Practical evaluation framework for real-time multi-object tracking: Achieving optimal and realistic performance}, volume = {13}, year = {2025}, }
• (2025). Rethinking programming education: A lecture-free approach. ACCESS, 13, 34758-34767. (www)

  @article{ , abstract = {Just-in-time hands-on experience is essential for learning programming. It not only helps reinforce learned concepts immediately, but also encourages the exploration of new concepts and language constructs. Integrating hands-on activities into traditional programming classrooms, however, presents a fundamental challenge. Coordinating instructor explanations with student coding activities and providing timely feedback to all students before moving forward is impractical for large or medium-sized classes. This paper demonstrates how cognitive and constructive learning principles can help transform a classroom into an active learning environment that blends conceptual learning and hands-on experience for an object-oriented programming course. Instead of attending traditional lectures, our students use the class time to participate in Moodle-based activities that feature diverse question types and immediate feedback. They work at their own pace, individually or in small teams, while the instructor guides them and provides support as needed. In a section of 41 students, 88% preferred this approach over lectures. These students scored 14.2% higher on the same exam compared to 111 peers enrolled in other sections that used traditional teaching in the same semester.}, archive = {J_ACCESS}, author = {Abdulhadi Shoufan}, doi = {10.1109/ACCESS.2025.3542389}, journal = {IEEE Access}, pages = {34758-34767}, shortjournal = {IEEE Access}, title = {Rethinking programming education: A lecture-free approach}, volume = {13}, year = {2025}, }
• (2025). A strategy for enhanced post-FRT active power recovery in expandable point-to-point VSC-HVdc links. ACCESS, 13, 34747-34757. (www)

  @article{ , abstract = {This paper proposes a non-linear DC power modulation strategy for expandable point-to-point (PtP) high-voltage DC (HVDC) systems. The goal is to enhance the active power management during post-fault conditions of the interconnected AC networks. The proposed strategy is developed by defining exponentially decaying functions, which, depending on the HVDC network configuration of the expandable HVDC system, alter the active current reference in a voltage source converter (VSC) affected by an AC network’s disturbance, without utilizing proportional-integral (PI) controllers. Furthermore, it is investigated whether the produced alteration can fulfill the post-fault active power recovery (PFAPR) requirements of VSC-HVDC systems, even in situations when no communication protocols between the VSC units are used. Lastly, it is demonstrated, through simulation experiments, that the expandable HVDC system (working in a point-to-point (PtP) or a multi-terminal (MT) network configuration), shows a better performance (in terms of the PFAPR profile and the DC voltage response) when the proposed strategy is utilized instead of conventional main-supplementary or droop control strategies.}, archive = {J_ACCESS}, author = {Arcadio Perilla and José Luis Rueda Torres}, doi = {10.1109/ACCESS.2025.3543604}, journal = {IEEE Access}, pages = {34747-34757}, shortjournal = {IEEE Access}, title = {A strategy for enhanced post-FRT active power recovery in expandable point-to-point VSC-HVdc links}, volume = {13}, year = {2025}, }
• (2025). Advanced agricultural query resolution using ensemble-based large language models. ACCESS, 13, 34732-34746. (www)

  @article{ , abstract = {Effective knowledge retrieval is crucial for addressing challenges related to optimization, such as pest management, soil health and crop productivity. Current single-model approaches struggle with limited accuracy, inconsistent responses, and inability to handle the increasing complexity of agricultural data, leading to unreliable recommendations for farmers. This study demonstrates an innovative weighted voting ensemble method that improves agricultural knowledge retrieval by combining Meta-LLaMA 3.1, Agricultural-BERT, and BERT-based-uncased. The ensemble model optimizes the prediction process by utilizing domain-specific data and a weighted voting mechanism to improve query performance and answer production. Our study outperformed individual models in providing accurate and contextually relevant responses, with an accuracy of 93%. We evaluated the system using both BLEU and ROUGE metrics to assess the quality of the generated text. Our ensemble model achieved a BLEU score 53.8 and demonstrated superior performance in the ROUGE evaluation, with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.70, 0.55 and 0.61. These results highlight the method’s ability to generate contextually suitable, domain-specific responses that address the practical needs of agricultural experts. By integrating advanced LLMs with domain-specific knowledge, the proposed ensemble system significantly improves agricultural knowledge retrieval and provides more accurate and practical responses in the field. The findings suggest that the ensemble approach can effectively support decision-making in agricultural practices, particularly in management agricultural optimization.}, archive = {J_ACCESS}, author = {Cyreneo Dofitas and Yong-Woon Kim and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3541602}, journal = {IEEE Access}, pages = {34732-34746}, shortjournal = {IEEE Access}, title = {Advanced agricultural query resolution using ensemble-based large language models}, volume = {13}, year = {2025}, }
• (2025). Demand response program toward sustainable power supply: Current status, challenges, and prospects in malaysia. ACCESS, 13, 34706-34731. (www)

  @article{ , abstract = {Rising carbon (CO2) emissions, driven by increasing energy demand across residential, commercial, and industrial sectors, present a critical environmental challenge, exacerbating climate change. While numerous studies explore the potential of Demand Response (DR) strategies to regulate energy consumption and mitigate environmental impact, significant gaps remain in their global standardization and inclusivity across diverse electricity markets. This paper addresses these gaps by providing a comprehensive review of existing DR frameworks within renewable-driven distribution systems, with a focus on identifying barriers to universal DR adoption. Unlike prior studies, this review introduces a novel framework that systematically evaluates demand-side flexibility using coordinated and uncoordinated DR initiatives, highlighting their comparative impacts on grid stability and renewable energy integration. Special emphasis is placed on the critical role of DR in maintaining voltage stability through innovative optimization techniques and dynamic pricing mechanisms tailored to market-specific conditions. A case study of Peninsular Malaysia is presented to illustrate actionable reforms and strategies, including Incentive-Based Regulation (IBR) and regionally optimized dynamic pricing schemes, that can enhance DR adoption. By addressing geographical and market-specific variations, this study bridges the gap between existing DR models and their scalability, advocating for inclusive, robust, and adaptive DR strategies. This work proposes an incentive-based DR framework specifically designed for the Malaysian electricity market, aimed at encouraging customer participation through tax incentives and flexible demand-side initiatives. The proposed framework for Malaysian Electricity consumers achieves a 6.3–9.2% reduction in peak load and a 5–10% reduction in electricity costs. The findings position DR as a cornerstone of sustainable power systems, harmonizing renewable energy integration with enhanced system reliability and inclusivity. This paper’s novelty lies in proposing a tailored DR program for the Malaysian market that aligns technical optimization with policy reforms, offering a clear pathway toward a resilient and environmentally sustainable energy future.}, archive = {J_ACCESS}, author = {Mohammad Sameer Ahmad and Nurulafiqah Nadzirah Mansor and Hazlie Mokhlis and Kanendra Naidu and Hasmaini Mohamad and Farah Ramadhani}, doi = {10.1109/ACCESS.2025.3541841}, journal = {IEEE Access}, pages = {34706-34731}, shortjournal = {IEEE Access}, title = {Demand response program toward sustainable power supply: Current status, challenges, and prospects in malaysia}, volume = {13}, year = {2025}, }
• (2025). OCADN: Improving accuracy in multi-class arrhythmia detection from ECG signals with a hyperparameter-optimized CNN. ACCESS, 13, 34687-34705. (www)

  @article{ , abstract = {Arrhythmia, a heart rhythm disorder, remains a serious global health problem due to its potential to cause complications such as stroke and heart failure. Early detection and accurate classification of arrhythmia are crucial for appropriate medical intervention. Although deep learning holds promise for automatic arrhythmia detection using ECG signals, several challenges need to be addressed. Previous studies often utilize limited and imbalanced datasets and lack exploration of optimal pre-processing and feature extraction methods. To overcome these limitations, this study proposes the Optimized Cardiac Arrhythmia Detection Network (OCADN), a CNN-based model with hyperparameter optimization and advanced pre-processing techniques such as Discrete Wavelet Transform (DWT) and Z-score normalization. As a comparison to OCADN, this research also develops an arrhythmia detection model using the LSTM algorithm. Experimental results demonstrate that OCADN outperforms LSTM, achieving high accuracy, precision, sensitivity, specificity, and F1-score on both training and test data. The consistent performance of OCADN on both datasets indicates its robustness and potential for clinical implementation. OCADN with hyperparameter tuning exhibits accuracy, precision, sensitivity, specificity, and F1-score of 99.97%, 99.97%, 99.97%, 99.99%, and 99.97%, respectively, on the training data. Meanwhile, the performance on the testing data for accuracy, precision, sensitivity, specificity, and F1-score is 98.87%, 95.23%, 98.09%, 99.65%, and 96.59%, respectively.}, archive = {J_ACCESS}, author = {Satria Mandala and Wisnu Jatmiko and Siti Nurmaini and Ardian Rizal and Adiwijaya}, doi = {10.1109/ACCESS.2025.3544273}, journal = {IEEE Access}, pages = {34687-34705}, shortjournal = {IEEE Access}, title = {OCADN: Improving accuracy in multi-class arrhythmia detection from ECG signals with a hyperparameter-optimized CNN}, volume = {13}, year = {2025}, }
• (2025). SOT-MTJ-based non-volatile flip-flop with in-memory randomness for application in grain stream ciphers. ACCESS, 13, 34677-34686. (www)

  @article{ , abstract = {This paper proposes a method for in-memory true random number generation (TRNG) by leveraging the dual functionality of spin-orbit torque based magnetic tunnel junction (SOT-MTJ) while showcasing its efficacy in hardware-efficient Grain stream ciphers for lightweight cryptographic applications. Depending upon its mode of operation, SOT-MTJ acts as both a memory element and a true random number generator. To demonstrate its practical application, SOT-MTJ based non-volatile flip flop (NVFF) is designed which is further utilized to implement Grain-128 stream cipher, as a case study. The SOT-MTJ based NVFF not only carries out the standard shift operation for cipher implementation but also functions as an in-situ initial vector generator for generating key stream, eliminating the need for an additional TRNG circuit. The results show that the proposed Grain-128 cipher design is $5.6\times $ and $2.5\times $ more energy efficient and $5\times $ and $2\times $ faster as compared to STT and SOT-MTJ based designs. Furthermore, in comparison to CMOS based cipher design, the proposed technique shows nearly $\sim 34\times $ more efficiency in terms of area overhead. The proposed approach holds huge promise for resource-constrained cryptographic applications in edge devices.}, archive = {J_ACCESS}, author = {Arshid Nisar and Furqan Zahoor and Sidhaant Sachin Thakker and Kunal Kranti Das and Subhamoy Maitra and Brajesh Kumar Kaushik and Anupam Chattopadhyay}, doi = {10.1109/ACCESS.2025.3543733}, journal = {IEEE Access}, pages = {34677-34686}, shortjournal = {IEEE Access}, title = {SOT-MTJ-based non-volatile flip-flop with in-memory randomness for application in grain stream ciphers}, volume = {13}, year = {2025}, }
• (2025). Beyond adaptive control: A control method for nonlinear systems with uncertainties, applied to COVID-19. ACCESS, 13, 34667-34676. (www)

  @article{ , abstract = {When the outcome of an action cannot be precisely known, it is difficult to select actions to get a desired result. This problem can be caused by uncertain parameters, such as not knowing how slippery a road is when driving in icy conditions. Adaptive control techniques can estimate uncertainties using past measurements, but the confidence in these estimates is not used to inform future control actions. Dual control, an improvement on adaptive control, can estimate the reductions in uncertainty that will result from control actions and probes the system to identify the uncertain parameters to a sufficient level to optimize the desired goal. However, existing dual control approaches have been computationally intractable for all but the simplest of control problems. Here we show that our novel and computationally efficient dual iterative linear quadratic Gaussian controller outcompetes an adaptive iterative linear quadratic Gaussian controller, using the control of COVID-19 as an example application. The dual controller performed 6.4% better than the adaptive controller in selecting policies to minimize the social and economic costs associated with both the policies and case counts using an established model of COVID-19 with sixteen uncertain parameters. Our results demonstrate that dual control is a powerful control tool that can handle complex, nonlinear, and stochastic systems in a robust and actively adaptive way while improving their performance.}, archive = {J_ACCESS}, author = {Andrew C. Mathis and Juan A. Carretero and Jonathon W. Sensinger}, doi = {10.1109/ACCESS.2025.3541536}, journal = {IEEE Access}, pages = {34667-34676}, shortjournal = {IEEE Access}, title = {Beyond adaptive control: A control method for nonlinear systems with uncertainties, applied to COVID-19}, volume = {13}, year = {2025}, }
• (2025). Markov chain-based resource-efficient and QoS-aware scheduling for latency-critical and best-effort tasks. ACCESS, 13, 34649-34666. (www)

  @article{ , abstract = {The importance of data centers in modern computing environments is continuously increasing, and ensuring the Quality of Service (QoS) of Latency-Critical (LC) tasks is essential to prevent system failures and performance degradation. Resource isolation techniques, widely used to guarantee the QoS of LC tasks, allocate additional resources until the QoS requirements are satisfied. However, traditional methods do not consider the performance saturation point of Best-Effort (BE) tasks and allocate all remaining resources to BE tasks after meeting LC task requirements, resulting in resource wastage. Furthermore, the high overhead caused by real-time resource adjustments can lead to system performance degradation. To address these issues, this paper proposes a weight-based Markov Chain model for resource optimization. The proposed model evaluates resource efficiency through offline profiling of balanced resource combinations and determines the optimal resource allocation strategy in advance. By accurately identifying the minimum resources required to ensure LC task QoS and predicting the performance saturation point of BE tasks, the model prevents unnecessary resource wastage. Unlike traditional methods, the proposed model leverages weight-based profiling to significantly reduce real-time scheduling overhead and achieve balanced resource allocation. Experimental results demonstrate that the proposed model maintains LC task QoS while guaranteeing BE task performance at a level comparable to existing resource isolation scheduling techniques. Additionally, the model successfully optimizes resource utilization and effectively reduces profiling overhead compared to traditional methods. The proposed approach optimizes both resource efficiency and performance in multi-task server environments.}, archive = {J_ACCESS}, author = {Seokwon Choi and Hyeonsang Eom}, doi = {10.1109/ACCESS.2025.3543964}, journal = {IEEE Access}, pages = {34649-34666}, shortjournal = {IEEE Access}, title = {Markov chain-based resource-efficient and QoS-aware scheduling for latency-critical and best-effort tasks}, volume = {13}, year = {2025}, }
• (2025). Detection of faulty vibration in oil-immersed OLTC by fiber bragg grating based on double cantilever beam type sensitization. ACCESS, 13, 34639-34648. (www)

  @article{ , abstract = {Fiber Bragg grating sensing offers advantages, such as resistance to electromagnetic interference, small size, strong multiplexing and wide detection bandwidth, which can be used to detect on-load tap changer (OLTC) fault vibration detection. This paper designs a fiber Bragg grating (FBG) vibration sensor based on a double cantilever beam amplification structure. The designed sensor adopts a two-point adhesive packaging structure, and theoretical analysis shows that the resonant frequency of the Bragg grating vibration sensor is 3200 Hz. The goodness of fit ( $R^{2}$ ) of the two designed sensors are 0.9993 and 0.9996, respectively, and the sensors can effectively detect vibration when the acceleration varies from 2 m/s2 to 20 m/s2. A real oil-immersed OLTC fault simulation platform was built at Shandong Taikai Company, and vibration detection was carried out for four types of faults. A comparison of internal and external fiber Bragg grating sensors for detecting OLTC vibration signals revealed that the signal-to-noise ratio of the fiber Bragg grating sensor when embedded in the inner cylinder was significantly higher than when placed in the outer cylinder, and the signal amplitude was also higher in the inner cylinder. This is because during the propagation of the vibration signal, the signal traveling from the inner cylinder to the outer cylinder leads to significant attenuation of higher-frequency vibration signals, causing signal distortion. When the drive shaft bolt is loose or the arc-shaped plate is loose, the signal frequency mainly ranges from 500 Hz to 750 Hz, and the energy in this frequency band exceeds 40%. When contact wear or spring fatigue occurs, the energy mainly concentrates in the 0 Hz to 250 Hz range, with energy exceeding 35% in this frequency band. The spectrum allows different fault signals to be distinguished, laying a solid foundation for subsequent fault mode recognition and fault severity classification of OLTC.}, archive = {J_ACCESS}, author = {Junda He and Bingxin Chen and Yajie Zhang and Zhaoyi Liao and Lirong Liu}, doi = {10.1109/ACCESS.2025.3542075}, journal = {IEEE Access}, pages = {34639-34648}, shortjournal = {IEEE Access}, title = {Detection of faulty vibration in oil-immersed OLTC by fiber bragg grating based on double cantilever beam type sensitization}, volume = {13}, year = {2025}, }
• (2025). GATEKEEPER platform: Secure processing environment for european health data space. ACCESS, 13, 34627-34638. (www)

  @article{ , abstract = {The European Health Data Space (EHDS) is a significant initiative within the European Union aimed at improving healthcare through better use of health data and collaboration. This involves sharing and utilization of health data in a secure and interoperable manner across EU member states. With the advent of modern technology, different types of health data are being acquired ranging from traditional electronic health records to real-time wearable signals for improved healthcare delivery and management. However, there are certain challenges associated with compliance to EHDS, such as secure continuous integration (CI) and continuous delivery (CD), scalability and adaptability towards new technologies and applications, tolerance against IoT cyber-attacks, requirement of advanced data storage methods, processing power, interoperability solutions for data analysis and connectivity. In order to address these, a flagship project of the European Commission, GATEKEEPER, represents a pioneering effort designed to transform healthcare delivery through the utilization of sophisticated digital technologies. The architecture of the GATEKEEPER platform has been meticulously crafted, in compliance with the EHDS, to integrate a multitude of data sources, deploying advanced analytical tools to enable the development of novel solutions for the effective prediction, detection, and management of chronic health conditions. GATEKEEPER addresses the aforementioned challenges by integrating collaborative tools for data management and consistency, machine learning modelling and management tool, activity feeds and issue tracking tools with developers authentication, CI/CD tools allowing, among others scalability in multiple formats, tools for assessing security, vulnerability and dependency for third party packages.}, archive = {J_ACCESS}, author = {Eugenio Gaeta and Muhammad Salman Haleem and Laura Lopez-Perez and Mirko Manea and Maria Fernanda Cabrera Umpierrez and Maria Teresa Arredondo Waldmeyer and Leandro Pecchia and Giuseppe Fico}, doi = {10.1109/ACCESS.2025.3539559}, journal = {IEEE Access}, pages = {34627-34638}, shortjournal = {IEEE Access}, title = {GATEKEEPER platform: Secure processing environment for european health data space}, volume = {13}, year = {2025}, }
• (2025). Toward efficient cancer detection on mobile devices. ACCESS, 13, 34613-34626. (www)

  @article{ , abstract = {Recent advancements in deep learning for cancer detection have achieved impressive accuracy, yet high computational costs and latency remain significant barriers for practical deployment on resource-constrained devices, such as smartphones and IoT platforms. This study focuses on optimizing MobileNetV1 and MobileNetV2 models to achieve more efficient, real-time cancer type identification. Through optimization strategies including selective layer unfreezing, pruning, and quantization, we demonstrate significant improvements in model size, inference time, and efficiency. For MobileNetV1, model size was reduced from 13.1 MB to 3.23 MB, and inference time was cut from 23 ms to 14 ms, with an F1 score above 0.99. For MobileNetV2, the model size was reduced from 9.41 MB to 2.82 MB, with inference times reduced from 24 ms to 13 ms, while maintaining a high F1 score of 0.98. The efficiency scores for MobileNetV1 and MobileNetV2 were 0.984 and 0.994, respectively, significantly outperforming other state-of-the-art neural networks such as VGG16 (efficiency score: 0.458), ResNet50 (0.418), and DenseNet121 (0.731). These findings demonstrate that with appropriate optimizations, MobileNet models can be deployed on edge devices, achieving high accuracy (above 95%), fast inference times (under one second), and superior efficiency, making them ideal candidates for real-time cancer detection in resource-constrained environments like mobile and IoT devices.}, archive = {J_ACCESS}, author = {Janghyeon Lee and Jongyoul Park and Yongkeun Lee}, doi = {10.1109/ACCESS.2025.3543838}, journal = {IEEE Access}, pages = {34613-34626}, shortjournal = {IEEE Access}, title = {Toward efficient cancer detection on mobile devices}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of mobile experiment algorithm designer interface for network control system laboratory. ACCESS, 13, 34598-34612. (www)

  @article{ , abstract = {This paper introduces the Mobile Client Interface (MCI), an Android application, that grants remote access and experimentation with the Networked Control System Laboratory (NCSLab) resources. The MCI serves as a cost-effective alternative to traditional laboratories by facilitating remote interaction with real hardware platforms. Unlike the web interface, MCI provides a native, touch-optimized user experience, fostering user engagement. In realizing this, a Mobile Experiment Algorithm Designer (MEAD) is developed for NCSLab that empowers users to visually design control algorithms via library of functional blocks, providing easiness in performing experimentation remotely from hand-held devices. Hence eliminating the need for intense coding knowledge and expertise. The application integrates with NCSLab’s back-end services for physical experiments with their output response readily available on mobile devices. Thus, it may serve as an excellent educational tool for students and researchers providing on-the-go accessibility, flexibility and customization of experiment algorithm design for NCSLab resources. The working and functionality of MCI and MEAD is validated by conducting an experiment on the Air Bearing Spacecraft (ABS) platform where the algorithm design, simulation validation and experiment are performed through the mobile device. Hence, MCI signifies the potential of mobile client interfaces to facilitate user work experience with remote laboratories and paves the way for further advancements in mobile interfaces for remote laboratory applications.}, archive = {J_ACCESS}, author = {Jahan Zaib Bhatti and Guo-Ping Liu and Shafqat Ali and Nasim Ullah and Ivo Pergl and Lukas Prokop and Vojtech Blazek}, doi = {10.1109/ACCESS.2025.3544097}, journal = {IEEE Access}, pages = {34598-34612}, shortjournal = {IEEE Access}, title = {Design and implementation of mobile experiment algorithm designer interface for network control system laboratory}, volume = {13}, year = {2025}, }
• (2025). Enhancing robustness against adversarial attacks in multimodal emotion recognition with spiking transformers. ACCESS, 13, 34584-34597. (www)

  @article{ , abstract = {Deep neural networks have demonstrated significant potential in applications such as human-computer interaction and emotion analysis, particularly in multimodal emotion recognition. However, they remain vulnerable to adversarial examples, where subtle perturbations can severely degrade classifier performance. Inspired by the sparse, asynchronous spiking activity and inherent nonlinearity of spiking neural networks (SNNs), we propose a novel framework, the Sliding Parallel Spiking Convolutional Vision Transformer (SPSNCVT), designed for robust and efficient multimodal emotion recognition. Our framework processes multiple signals, including facial expressions, voice, and text, using aligned heatmap features and multiscale wavelet transforms for precise feature extraction. Experimental results indicate that the SPSNCVT framework significantly improves classification accuracy when confronted with adversarial attacks such as fast gradient sign method (FGSM), basic iterative method (BIM), and projected gradient descent (PGD), achieving a performance gain of 3.60%-4.01% and 7.03%-13.73% compared to baseline models. Furthermore, SPSNCVT demonstrates excellent performance in terms of energy efficiency and computational speed, highlighting its practical deployment potential in real-time application scenarios.}, archive = {J_ACCESS}, author = {Guoming Chen and Zhuoxian Qian and Dong Zhang and Shuang Qiu and Ruqi Zhou}, doi = {10.1109/ACCESS.2025.3544086}, journal = {IEEE Access}, pages = {34584-34597}, shortjournal = {IEEE Access}, title = {Enhancing robustness against adversarial attacks in multimodal emotion recognition with spiking transformers}, volume = {13}, year = {2025}, }
• (2025). Enhancing user experience in free and open-source software: An integrated maturity model framework. ACCESS, 13, 34563-34583. (www)

  @article{ , abstract = {Free and open-source software has rapidly grown and garnered considerable attention in recent years. However, the design complexities and guidelines inherent to Free and Open Source Software (FOSS) pose a challenging task in enhancing the User Experience (UX) of FOSS. Several pre-existing models have significantly contributed to the existing body of knowledge, yet they often overlook FOSS-specific attributes that establish a direct correlation with the efficiency of the UX in FOSS. To address this gap, the study proposes a FOSS UX enhancement model (FUEM) designed to align with current FOSS practices and guidelines, aiming to improve user experience in free and open-source software. The proposed model consists of six levels, with each level encompassing UX maturity influencing factors (UMXIF) identified in prior research. These factors are re-evaluated through a Likert scale questionnaire to gather feedback from developers, project managers, and UI/UX designers, as the previous study solely included the perspectives of UX experts. Our findings reveal that these factors positively impact FOSS projects, and integrating them into FUEM will enable the FOSS community to ascertain their level of UX maturity and identify which missing UMXIF they must adopt to progress to the next stage of UX maturity in FUEM. Furthermore, the tailored model developed for FOSS is validated through feedback analysis of expert reviews and quantitative analysis employing a one-way ANOVA test. The ANOVA test indicates that the p-value exceeds the significant threshold of. 05, suggesting that the FOSS UX model can considerably enhance UX in FOSS projects by providing a structured pathway for improving UX maturity, serving as a valuable resource for the FOSS community, guiding the development of more user-centric open-source software, and fostering a deeper understanding of UX maturity practices explicitly tailored for FOSS environments.}, archive = {J_ACCESS}, author = {Elif Zamir and Abdul Rehman and Sara Zamir and Fuad A. M. Al-Yarimi and Assad Abbas}, doi = {10.1109/ACCESS.2025.3543304}, journal = {IEEE Access}, pages = {34563-34583}, shortjournal = {IEEE Access}, title = {Enhancing user experience in free and open-source software: An integrated maturity model framework}, volume = {13}, year = {2025}, }
• (2025). Conjecture interaction optimization model for intelligent transportation systems in smart cities using reciprocated multi-instance learning for road traffic management. ACCESS, 13, 34539-34562. (www)

  @article{ , abstract = {Intelligent Transportation Systems (ITS) routing in smart cities is planned and maintained based on vehicle communication, demands, and terminal interaction. The influencing factors, such as road and traffic conditions, demand a prior conjecture to improve transportation, navigation comfort, and efficiency. Therefore, a Conjecture Interaction Optimization Model using terminal-communication assistance is introduced in this article. This model accounts for the road’s physical condition and traffic density for setting up routing efficiency. The priorities are dynamic and based on the nearest assisting/ communicating terminal to improve the forecast on dynamic vehicle routing. The routing and traffic avoidance decisions are pursued using reciprocated multi-instance convergence learning (RMICL). RMICL algorithm optimizes real-time vehicle communication to analyze the vehicles’ active and discarded interactions for minimum delay and route refinement. The MIL algorithm integrates multi-instance learning into the interaction labelling process, enhancing the routing prediction by differentiating between reliable and erroneous data. It considers improved traffic avoidance and ensuring convergence robustness in dynamic traffic management for smart cities. The convergence optimization is used to identify the low latency route decision outcomes from the communicating terminal. The routing identifies the possible combination of available interacting terminals with precision traffic forecast; the convergence must be slight between the conjecture and the actual routing traffic. Contrarily, reverse convergence instances identify traffic locations based on discarded interactions. The process is iterated from low traffic to highly interactive route searches based on location. Therefore, the characteristics of conjectures are updated with the forward and reverse reciprocation instances. This model leverages the ITS decision convergence over traffic and routing efficiencies. The results of the proposed model are identified as follows: this model improves traffic detection by 10.76% through 11.67% high interaction throughput to reduce 12.66% travel time for the distance-covered variant.}, archive = {J_ACCESS}, author = {Abdullah Faiz Al Asmari and Ahmed Almutairi and Fayez Alanazi and Tariq Alqubaysi and Ammar Armghan}, doi = {10.1109/ACCESS.2025.3542847}, journal = {IEEE Access}, pages = {34539-34562}, shortjournal = {IEEE Access}, title = {Conjecture interaction optimization model for intelligent transportation systems in smart cities using reciprocated multi-instance learning for road traffic management}, volume = {13}, year = {2025}, }
• (2025). What gets measured gets improved: Monitoring machine learning applications in their production environments. ACCESS, 13, 34518-34538. (www)

  @article{ , abstract = {Machine learning (ML) applications face many new, hardly predictable aspects in their production environments. Detecting new aspects in an ML production environment and understanding their impacts on the ML application is crucial if organizations are to ensure ML applications functionality. A monitoring entity is essential if one is to monitor ML applications in their production environments, to both continually minimize risks and improve ML application’s performance. But existing monitoring approaches are struggling to deal with specifics that arise from ML applications. We aim at deriving monitoring practices and providing a holistic view over required steps in successful ML applications monitoring. Since there has been little research on this topic, we followed a qualitative research approach, i.e., we conducted an interview study combined with a multivocal literature review. Thus, we provide a theoretical framework of an ML-enabled agent in its production environment, five characteristics of ML applications’ production environments and 17 monitoring practices – 14 practices arranged sequentially on a typical quality management cycle and three cross-sectional practices. To outline the ML specifics that arise in monitoring ML applications, we investigate the five ML production environment characteristics’ influences on the ML monitoring practices.}, archive = {J_ACCESS}, author = {Dominik Protschky and Luis Lämmermann and Peter Hofmann and Nils Urbach}, doi = {10.1109/ACCESS.2025.3534628}, journal = {IEEE Access}, pages = {34518-34538}, shortjournal = {IEEE Access}, title = {What gets measured gets improved: Monitoring machine learning applications in their production environments}, volume = {13}, year = {2025}, }
• (2025). Dense segmentation techniques using deep learning for urban scene parsing: A review. ACCESS, 13, 34496-34517. (www)

  @article{ , abstract = {Dense segmentation tasks, including semantic, instance, and panoptic segmentation, are essential for improving our comprehension of urban landscapes. This paper examines various deep learning methodologies to enhance dense segmentation in urban scene parsing. Additionally, it delves into the challenges associated with adapting these deep learning models for practical, real-world applications and explores enhancements to address these challenges. Multiple datasets for urban scene parsing were analyzed, with the Cityscapes and Mapillary Vistas datasets identified as more suitable for urban scene parsing. Among them, the Mapillary Vistas dataset has greater diversity and complexity. Although Vision Transformers are computationally demanding, they exhibit superior performance in dense segmentation tasks. Prominent techniques that enhance the performance of Vision Transformers (ViTs), such as Encoder-Decoder architectures, Atrous convolutions, Atrous Spatial Pyramid Pooling (ASPP), dual decoders, and mask transformers are reviewed. Ensuring the performance of Vision Transformer models across various domains and fine-tuning them for practical, real-world applications present critical challenges in dense segmentation tasks for urban scene parsing. Among the various thematic attention approaches explored to ensure models concentrate on regions of interest, Masked Attention, and Visual Prompt Tuning mechanisms have shown superior results. Nonetheless, considerable potential remains for enhancing generalization across diverse urban datasets and improving overall efficiency.}, archive = {J_ACCESS}, author = {Rajesh Ankareddy and Radhakrishnan Delhibabu}, doi = {10.1109/ACCESS.2025.3543944}, journal = {IEEE Access}, pages = {34496-34517}, shortjournal = {IEEE Access}, title = {Dense segmentation techniques using deep learning for urban scene parsing: A review}, volume = {13}, year = {2025}, }
• (2025). Adaptive pricing-based optimized resource utilization in networked microgrids. ACCESS, 13, 34483-34495. (www)

  @article{ , abstract = {The performance of networked microgrids primarily depends on the design of internal market structure for maximum resource utilization, optimized power sharing, and enhanced economic efficiency. This article presents a novel framework for resource optimization within the networked microgrids. First, each microgrid is optimized using a local energy management system to compute power shortage/surplus depending on various parameters including local generation (solar photovoltaic), battery energy storage system, and load profile. The total shortage/surplus is obtained by aggregating the shortage/surplus of each microgrid, facilitating the calculation of adaptive internal trade price. The internal trade prices are responsive to load variations and time-of-use prices, thereby encouraging internal trading within the networked microgrids. The internal trading price is strategically set to be lower than buying price from the grid and higher than selling price to the grid to maximize overall revenue of the entire network. Subsequently, a central energy management system is formulated, determining optimized power sharing between microgrids based on the adaptive internal trading. The proposed strategy is validated on the IEEE-33 bus test feeder using improved accelerated particle swarm optimization, achieving an annual power loss reduction of 1795.8 kW and an overall cost reduction of 7.2%. These results confirm the practicality and effectiveness of the proposed framework in real-world scenarios.}, archive = {J_ACCESS}, author = {Syed Muhammad Ahsan and Muhammad Ahmad Iqbal and Akhtar Hussain and Petr Musilek}, doi = {10.1109/ACCESS.2025.3543760}, journal = {IEEE Access}, pages = {34483-34495}, shortjournal = {IEEE Access}, title = {Adaptive pricing-based optimized resource utilization in networked microgrids}, volume = {13}, year = {2025}, }
• (2025). Dissection of an approval-based trusted-token fraud scheme used across multiple blockchain systems. ACCESS, 13, 34467-34482. (www)

  @article{ , abstract = {This article examines a type of token high-yield investment fraud based on a technique known as approval phishing. The scheme is further exacerbated by deficiencies in the presentation and validation of token approvals by wallet software. The study presents the findings of an almost year-long investigation into the internal operations of several groups of scammers targeting users worldwide and stealing millions of dollars each month. Over the course of the investigation, the author acquired detailed information about the scammers, their victims, and, most importantly, the fraud scenarios and operational details. Furthermore, the article highlights the critical role of wallet software in accurately presenting and validating smart contract calls. A significant contribution of this work is a comprehensive security analysis and comparison of eighteen popular blockchain wallets from the perspective of approval scams. These results can assist users in selecting the right wallet and, at the same time, aid wallet providers in improving their products. Finally, the article proposes potential security improvements to empower users with the necessary information to identify fraudulent activities.}, archive = {J_ACCESS}, author = {Blazej Adamczyk}, doi = {10.1109/ACCESS.2025.3543651}, journal = {IEEE Access}, pages = {34467-34482}, shortjournal = {IEEE Access}, title = {Dissection of an approval-based trusted-token fraud scheme used across multiple blockchain systems}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of V2V communication by QC-LDPC code and NOMA-UM-MIMO techniques. ACCESS, 13, 34449-34466. (www)

  @article{ , abstract = {This study addresses the critical challenges in Vehicle-to-Vehicle (V2V) communications, particularly the issues of high mobility, dynamic channel conditions, and interference that adversely affect Bit Error Rate (BER) performance and overall system capacity. We propose an innovative integration of NOMA with a specific form of Quasi Cyclic-Low Density Parity Check (QC-LDPC) coding to enhance communication reliability and efficiency in vehicular environments, ensuring effective system operation even without clustering. Analytical and numerical simulations based on the NOMA-MIMO system are conducted to derive BER formulas for both near $\text {(}{Bob}_{1})$ and far $\text {(}{Bob}_{2})$ users. The effectiveness of Successive Interference Cancellation (SIC) techniques in mitigating interference in vehicular environments is also explored. Additionally, mathematical formulations for outage probability in NOMA-MIMO systems indicate that outage probability decreases with increased transmit power as users get closer to the base station. Our research demonstrates that QC-LDPC codes without $Girth_{4}$ cycles significantly enhance error correction capabilities and achieve higher spectral efficiency over Rician fading channels. OFDMA-LDPC and SM-NOMA (Spatial Multiplexing-Non Orthogonal Multiple Access) based vehicular communication systems are also compared, emphasizing the impact of $2\times 2$ and $4\times 4$ MIMO configurations on BER and capacity. The results highlight NOMA’s superiority and the simplified structure of QC-LDPC, resulting in enhanced system performance in dynamic vehicular communication scenarios. Moreover, increasing the antennas at both ends leads to further performance improvements, with results achieved at a code rate 1/3 among the benchmarked schemes. The Sum-Product algorithm is often employed as a preferred decoding method in high-mobility scenarios due to its efficiency and simplicity, which enhance communication reliability and system capacity. This research also addresses the challenges associated with high vehicle velocities and the resulting Doppler shifts, which are significant in highway and urban scenarios. The mentioned effects were particularly pronounced at a frequency of 0.2 THz and a speed of 120 km/h, impacting signal quality and reliability.}, archive = {J_ACCESS}, author = {Maryam Mohammed F. Abdullah and Osama Alluhaibi and Asim Egemen Yilmaz and Aykut Kalaycioglu}, doi = {10.1109/ACCESS.2025.3531511}, journal = {IEEE Access}, pages = {34449-34466}, shortjournal = {IEEE Access}, title = {Performance enhancement of V2V communication by QC-LDPC code and NOMA-UM-MIMO techniques}, volume = {13}, year = {2025}, }
• (2025). Leveraging personalized customer experiences in mobile edge computing through split learning using smart data-driven modeling. ACCESS, 13, 34435-34448. (www)

  @article{ , abstract = {The rapid growth and opportunities of Mobile Edge Computing (MEC) technology have transformed smart consumer electronics like smartphones and wearable devices that produce massive and complicated data sets. Analyzing consumer electronics data to understand customers is difficult because of its vast scale and detailed content. To solve these issues, we present Enhanced Smart Data-Driven Modeling (ESDDM), which combines Smart Data-Driven Modeling (SDDM) with modern Deep Learning (DL). ESDDM combines multiple data streams to better understand consumer electronics systems beyond the normal Machine Learning (ML) capabilities. ESDDM’s integration with Split Learning (SL) protects data by lessening transmission risks and avoiding central cloud storage while keeping sensitive information secure on user devices and delivering better system performance. The prediction results from ESDDM show its strength with a low Mean Squared Error (MSE) of 0.267, which reveals its capability to reshape the MEC domain while creating business possibilities and better customer outcomes.}, archive = {J_ACCESS}, author = {V. Vinoth Kumar and K. M. Karthick Raghunath and Iyappan Perumal and K. Manikandan}, doi = {10.1109/ACCESS.2025.3543577}, journal = {IEEE Access}, pages = {34435-34448}, shortjournal = {IEEE Access}, title = {Leveraging personalized customer experiences in mobile edge computing through split learning using smart data-driven modeling}, volume = {13}, year = {2025}, }
• (2025). Modeling and control of AC microgrids under unbalanced utility grid voltage using the invariant-ellipsoid method. ACCESS, 13, 34409-34434. (www)

  @article{ , abstract = {The increasing penetration of renewable microgrids (MGs) into utility grids (UGs) has introduced new stability and power quality challenges, especially under the unbalanced voltage of the utility grid. This paper presents a novel approach for modelling and controlling AC microgrids (MGs) influenced by unbalanced utility grid voltages, emphasizing inverter-based resources (IBRs). The proposed control design is based on the application of the invariant-ellipsoid method for disturbance rejection. In the presence of asymmetrical power exchange between the microgrid and utility grid, a negative-sequence rejection strategy is applied to ensure that the power transactions remain balanced. This study investigates two operational modes: 1) the MG feeds power to the UG (grid forming), and 2) the opposite mode (grid feeding). A simulation verification was conducted to assess the performance of the invariant-ellipsoid disturbance rejection method. Additionally, a comparison between the proposed invariant-ellipsoid tracker and a traditional H $\infty $ control strategy highlights the efficacy of the former in mitigating UG unbalanced voltage issues while reducing computational complexity. The results demonstrate that the invariant-ellipsoid approach provides robust performance under various unbalanced scenarios with improved dynamic response and reduced oscillations in the delivered power.}, archive = {J_ACCESS}, author = {Hilmy Awad and Ehab H. E. Bayoumi and Hisham M. Soliman and Michele de Santis and Paola Verde}, doi = {10.1109/ACCESS.2025.3544136}, journal = {IEEE Access}, pages = {34409-34434}, shortjournal = {IEEE Access}, title = {Modeling and control of AC microgrids under unbalanced utility grid voltage using the invariant-ellipsoid method}, volume = {13}, year = {2025}, }
• (2025). Distortion measurement metric for human image refinement and evaluation using distorted image datasets. ACCESS, 13, 34390-34408. (www)

  @article{ , abstract = {This paper introduces novel evaluation metrics for quantifying distortion in human image retouching and presents a COCO-based retouched image dataset to validate their effectiveness. The dataset is constructed using human images from the COCO dataset, which provides a large-scale and diverse collection of images, including a variety of facial structures, poses, and lighting conditions, making it well-suited for human-centered retouching research. Despite extensive research into the perceptual assessment of visual discomfort induced by distorted images, a significant gap exists in the availability of quantifiable evaluation metrics and dedicated distortion datasets. To address this issue, we developed two distinct evaluation metrics and generated an optimal dataset tailored for this purpose. The two-evaluation metrics proposed in this study are the Distorted Line Similarity (DLS) metric, which uses edge detection, and the Point-to-Point (P2P) metric, which leverages corner detection. In contrast to traditional image quality assessment metrics such as PSNR and SSIM, these new metrics quantify the changes in lines, curves, and feature points within an image, providing meaningful results when assessing image distortion. Furthermore, the proposed framework produces results with reduced distortion compared to existing retouching applications. Using the proposed metrics, we demonstrate that the images generated by the proposed framework exhibit significantly less distortion than the original distorted images. The demand for retouching images in which the main object is a person increases. Excessive distortions can be quantified and detected in contexts such as immigration procedures using the proposed metrics. By publicly releasing the COCO-based retouched image dataset, which includes the original images, distorted images, and images with minimized distortions used in our experiments, we aim to demonstrate the quality of our dataset and contribute to the field of image distortion minimization research.}, archive = {J_ACCESS}, author = {Gwangyeol Yu and Kyungseo Yoon and Sangwoo Lee and Yeju Shin and Jonghyuk Park}, doi = {10.1109/ACCESS.2025.3543542}, journal = {IEEE Access}, pages = {34390-34408}, shortjournal = {IEEE Access}, title = {Distortion measurement metric for human image refinement and evaluation using distorted image datasets}, volume = {13}, year = {2025}, }
• (2025). Feedforward and feedback-based retarded proportional controller of a servomechanism. ACCESS, 13, 34378-34389. (www)

  @article{ , abstract = {In this paper is introduced a methodology for solving the regulation and tracking control problem in servomechanisms through a Proportional Retarded controller with a Feedforward and Feedback (PR+FF) compensation scheme. The key advantage of this approach is that it enables regulation and tracking control without requiring velocity measurements, which are typically needed in most control schemes, such as Proportional Derivative controllers. The design of the PR+FF control is grounded in $\sigma $ -stability analysis and the D-partitions method. Its performance is evaluated on an experimental platform consisting of a servomechanism. The proposed controller is also compared with a Proportional Derivative, Proportional Integral and Proportional Integral Derivative schemes using filters compensation. The results are analyzed from a frequency perspective, using Bode plots, and by examining error and control signals in real-time.}, archive = {J_ACCESS}, author = {Manuel Alejandro Ojeda-Misses and Hilario Martines-Arano and Anilu Franco-Arcega and Virgilio López-Morales and Aldo Márquez-Grajales}, doi = {10.1109/ACCESS.2025.3533955}, journal = {IEEE Access}, pages = {34378-34389}, shortjournal = {IEEE Access}, title = {Feedforward and feedback-based retarded proportional controller of a servomechanism}, volume = {13}, year = {2025}, }
• (2025). Optimized flare performance analysis through multi-modal machine learning and temporal standard deviation enhancements. ACCESS, 13, 34362-34377. (www)

  @article{ , abstract = {Flaring is a routine practice in the upstream gas industry to dispose of waste gases, but its efficiency can drop significantly under non-ideal conditions such as crosswinds, over-aeration, or over-steaming. These inefficiencies lead to incomplete combustion, producing harmful substances like carbon monoxide and unburned methane, which contribute significantly to global warming. Current solutions for monitoring flare efficiency are often complex or expensive, limiting their widespread adoption. This work introduces a novel framework for estimating flare combustion efficiency (CE) using a multi-modal machine learning architecture enhanced by a Temporal Standard Deviation (TSD) preprocessing technique. Our approach combines synchronized visual data with minimal field measurements (FM) for accurate efficiency estimation. We first extract sequential frames from an RGB video stream of flares and process them to extract TSD images, which essentially highlight the variability and dynamic changes in the combustion process. Next, we extract image feature representations from TSD images using the state-of-the-art Vision Transformer (ViT) and fuse them with experimentally selected FM data to create a comprehensive combustion dataset. A CatBoost regression model is then trained on this dataset to estimate the final CE. Our proposed framework is validated using real-world data from industrial flare upstream operations. The results demonstrate significant improvements in estimation accuracy and reliability compared to traditional methods, achieving an R-squared score of 94.77% with minimal FM data. This approach not only enhances the understanding of combustion dynamics but also offers a scalable, cost-effective solution for continuous flare monitoring and optimization.}, archive = {J_ACCESS}, author = {Said Boumaraf and Pengfei Li and Muaz Al Radi and Fares Oussama Abdelhafez and Abderaouf Behouch and Khalid Yousef Al Awadhi and Hamad Karki and Sajid Javed and Jorge Dias and Naoufel Werghi}, doi = {10.1109/ACCESS.2025.3540558}, journal = {IEEE Access}, pages = {34362-34377}, shortjournal = {IEEE Access}, title = {Optimized flare performance analysis through multi-modal machine learning and temporal standard deviation enhancements}, volume = {13}, year = {2025}, }
• (2025). An intelligent regional integrated energy dispatching approach considering source-load uncertainty. ACCESS, 13, 34350-34361. (www)

  @article{ , abstract = {The integrated energy framework (IES) source-load uncertainty has been the focus of previous analyses, which have ignored the impact of the uncertainty in the integrated interest reaction (IDR). To address the problem of insufficient consideration of source load uncertainty in traditional scheduling models, a two-layer optimization model is proposed that combines non-parametric density estimation and copula theory to improve the traditional source load uncertainty modeling method. Based on this, the economic and reliability balance of the integrated energy system was achieved through the design of energy coupling devices and optimized scheduling strategies for multiple scenarios. A multi-micro-grid integrated energy system (MMIES) model was constructed in the study. In order to avoid the generation and errors of short-term speculative information and to obtain an ideal scheduling plan, an ideal transition scheduling was proposed in the second stage. The experimental results showed that the total cost of the system was inversely proportional to the reserve capacity during the change of confidence level from 0.65 to 1.00. When the confidence level was low ( $\alpha =0.65$ ), the total cost was $6.3825\times 10^{4}$ yuan, and the reserve capacity was low. When the confidence level was high ( $\alpha =1.00$ ), the total cost increased to $6.7642\times 10^{4}$ yuan, and the reserve capacity significantly increased. Further comparison between the unified scheduling model and the decentralized collaborative scheduling model shows that the decentralized model can better balance the interests of multiple parties and has higher applicability. The replay results show that the research method recognizes the reliability and economically optimal activity of MMIES, taking into account the ambiguity of the source load and the interests of each partner.}, archive = {J_ACCESS}, author = {Tianmeng Yuan and Yong Mu and Weibo Yin}, doi = {10.1109/ACCESS.2025.3541259}, journal = {IEEE Access}, pages = {34350-34361}, shortjournal = {IEEE Access}, title = {An intelligent regional integrated energy dispatching approach considering source-load uncertainty}, volume = {13}, year = {2025}, }
• (2025). Two-layer intelligent learning control using output recurrent fuzzy neural long short-term memory broad learning system with RMSprop. ACCESS, 13, 34334-34349. (www)

  @article{ , abstract = {Achieving precise and robust control in dynamic environments remains a challenge, particularly in nonlinear and uncertain systems. This paper proposes a novel two-layer intelligent learning control framework to address these issues, integrating an output recurrent fuzzy neural long short-term memory broad learning system (ORFNLSTM-BLS) with a root mean squared propagation (RMSprop) optimization algorithm. The first layer focuses on real-time system identification, leveraging ORFNLSTM-BLS to learn complex nonlinear system dynamics and effectively capture long-term dependencies in sequential data. The second layer implements an intelligent adaptive controller based on the ORFNLSTM-BLS, which dynamically adjusts control actions for time-varying setpoint tracking and disturbance rejection. The RMSprop optimization algorithm is applied across both layers to adaptively regulate learning rates, ensuring faster convergence and mitigating gradient issues. The convergence of the proposed method is provided through five theorems, ensuring gradient boundedness and learning rate conditions. Simulation and experimental results validate that, compared to conventional PID, the proposed method achieves superior tracking accuracy, with the best case showing an 82% reduction in RMSE, and enhanced control efficiency, with an 85% reduction in ITAE. Additionally, computational analysis shows a 10% increase in processing time for SISO tasks and 84% for MIMO cases. Despite the higher complexity, the method remains feasible for real-time applications. This work advances intelligent control systems, offering a robust solution for environments with sequential data and uncertainties.}, archive = {J_ACCESS}, author = {Ali Rospawan and Ching-Chih Tsai and Chi-Chih Hung}, doi = {10.1109/ACCESS.2025.3543867}, journal = {IEEE Access}, pages = {34334-34349}, shortjournal = {IEEE Access}, title = {Two-layer intelligent learning control using output recurrent fuzzy neural long short-term memory broad learning system with RMSprop}, volume = {13}, year = {2025}, }
• (2025). Early detection of lung cancer using predictive modeling incorporating CTGAN features and tree-based learning. ACCESS, 13, 34321-34333. (www)

  @article{ , abstract = {Artificial intelligence (AI) has revolutionized several domains and medical science has significantly benefited from AI-based approaches. Machine learning models enable the robust and accurate analysis of large streams of medical data, assisting medical experts in creating specialized and targeted treatment plans. Lung cancer also requires further automated detection tools as it remains one of the most lethal cancers worldwide, necessitating advancements in early and accurate detection methods. This study presents a novel approach to lung cancer classification by leveraging synthetic data generated using conditional tabular generative adversarial networks (CTGAN) and applying a random forest (RF) classifier. The proposed model has shown exceptional performance with an accuracy of 98.93%, and precision, F1 score, and recall of 99%. To validate the efficacy of the approach, we conducted extensive experiments using nine other classifiers, including support vector machines, k-nearest neighbors, decision trees, and others. These classifiers were evaluated with different data balancing techniques such as synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and SMOTE-ENN, alongside original, unbalanced dataset. Comparative analysis revealed that the CTGAN-RF model outperforms these traditional classifiers, particularly in handling class imbalance and improving predictive accuracy. Additionally, the robustness of the proposed model was confirmed through 5-fold cross-validation, further emphasizing its reliability. The approach was benchmarked against state-of-the-art (SOTA) methods in lung cancer detection, highlighting significant improvements in classification metrics. This comprehensive evaluation highlights the potential of synthetic data augmentation combined with machine learning techniques in enhancing the early detection and diagnosis of lung cancer, opening the way for improved patient outcomes and personalized treatment strategies.}, archive = {J_ACCESS}, author = {Abdulrahman Alzahrani}, doi = {10.1109/ACCESS.2025.3543215}, journal = {IEEE Access}, pages = {34321-34333}, shortjournal = {IEEE Access}, title = {Early detection of lung cancer using predictive modeling incorporating CTGAN features and tree-based learning}, volume = {13}, year = {2025}, }
• (2025). Downtilt optimization of array antennas based on envelope radiation patterns for HAPS service links with massive MIMO. ACCESS, 13, 34307-34320. (www)

  @article{ , abstract = {High-altitude platform stations (HAPSs) are gaining traction as a promising technology for significantly expanding coverage to aerial, maritime, and mountainous areas in 6G communication. Service links with a single HAPS are expected to provide extensive coverage ranging from 50 km to 100 km, making the design of suitable array antenna configurations for different coverage sizes a critical challenge. In multi-user multiple-input multiple-output (MU-MIMO) systems, communication capacity depends not only on the array antenna configuration but also on user distribution and precoding schemes. Consequently, optimizing antenna configurations while considering all these factors is practically complex. This paper proposes antenna configuration optimization method capable of maximizing any given percentile value of signal-to-noise ratio (SNR) to suit the coverage size of HAPS by isolating the geometric configuration of the antennas from other variables. Specifically, by utilizing the rotational symmetry of the HAPS service area, we model arbitrary array antennas as multi-tier circular arrays. We introduce an evaluation metric based on the beam envelope and formulate the antenna configuration optimization problem as a downtilt optimization problem. The joint optimization problem of downtilt angles can be reduced to individual optimization problems for each circular array, and we demonstrate the downtilt optimization using the gradient descent method. Based on the optimization results, we propose a tilted cylindrical array, comprising side elements of a traditional cylindrical array antenna tilted at optimal angles, and compare its performance with conventional planar, hemispherical, and cylindrical arrays. Consequently, the proposed tilted cylindrical array demonstrates the highest performance, improving the 50-th percentile and 5-th percentile SNR values by approximately 3.5 dB in a coverage radius of 50 km compared to its non-tilted counterpart. Improvement effects were observed in both single-user MIMO and MU-MIMO scenarios, confirming the effectiveness of the proposed antenna configuration optimization method.}, archive = {J_ACCESS}, author = {Koichi Maki and Koji Tashiro and Tsutomu Ishikawa and Kenji Hoshino}, doi = {10.1109/ACCESS.2025.3543862}, journal = {IEEE Access}, pages = {34307-34320}, shortjournal = {IEEE Access}, title = {Downtilt optimization of array antennas based on envelope radiation patterns for HAPS service links with massive MIMO}, volume = {13}, year = {2025}, }
• (2025). A non-PCM-based 2 × 2 MIMO antenna array with low radar cross-section using characteristic mode analysis. ACCESS, 13, 34296-34306. (www)

  @article{ , abstract = {In this paper, we introduced a $2 \times 2$ multiple-input-multiple-output (MIMO) antenna array using a non-polarization conversion metasurface (NPCM) with reduced radar cross section (RCS). To achieve a low RCS, a chess-board configuration is typically adopted. However, in conventional NPCM designs, subarrays with 180° phase shifts apart exhibit radiating modes at different resonance frequencies due to varied unit cell sizes and spacing. To address this, a characteristic mode analysis (CMA) has been performed to investigate the radiation mechanism of each subarray, ensuring they resonate within the same frequency band. Through this analysis, the feeding structure was optimized based on the slot-coupled patch antenna theory to align the radiating modes of the subarrays within the desired bandwidth. Specifically, the fundamental mode TM01 of one subarray and higher order mode TM03 of the other were successfully excited to operate within the same frequency range. This configuration enables the low-RCS NPCM surface to implement a MIMO system in which each subarray radiates independently within the same frequency band. Full-wave simulations and measurements have been carried out to verify the performance of the proposed antenna array. The results indicate that the MIMO antenna maintains good isolation, ranging from 22 to 50 dB among ports within the operating band from 9.6 to 10.7 GHz, and exhibits RCS reduction under different polarization ranging from 9 to 19 dB across the band from 8.5 to 12 GHz. Moreover, the RCS under oblique incidence also demonstrate good performance, making it suitable for radar applications.}, archive = {J_ACCESS}, author = {Manzoor Elahi and Slawomir Koziel and Leifur Leifsson}, doi = {10.1109/ACCESS.2025.3544115}, journal = {IEEE Access}, pages = {34296-34306}, shortjournal = {IEEE Access}, title = {A non-PCM-based 2 × 2 MIMO antenna array with low radar cross-section using characteristic mode analysis}, volume = {13}, year = {2025}, }
• (2025). GA-VAE: Enhancing local feature representation in VQ-VAE through genetic algorithm-based token optimization. ACCESS, 13, 34286-34295. (www)

  @article{ , abstract = {This paper introduces GA-VAE, a fine-tuning framework that enhances local feature representation in pre-trained Vector Quantized-VAE (VQ-VAE) models through genetic algorithm-based optimization. While VQ-VAE models have shown promise in learning discrete latent representations for complex data distributions, their performance is often limited by underutilized tokens in the codebook, particularly those representing local features, resulting in incomplete feature capture. Our proposed Codebook Genetic Algorithm (CGA) selectively optimizes these underutilized tokens by applying genetic operations to enhance their representation capability while maintaining the well-learned features of frequently used tokens. The framework identifies tokens with low utilization rates and systematically evolves them through carefully designed genetic operations, promoting the emergence of meaningful latent vectors that better capture local features. Experimental results demonstrate that our method effectively improves the representation of local features while maintaining computational efficiency, leading to more balanced codebook utilization and enhanced reconstruction quality across various datasets.}, archive = {J_ACCESS}, author = {Jinghui Jiang and Dongjoon Kim and Bohyoung Kim and Yeong-Gil Shin}, doi = {10.1109/ACCESS.2025.3542384}, journal = {IEEE Access}, pages = {34286-34295}, shortjournal = {IEEE Access}, title = {GA-VAE: Enhancing local feature representation in VQ-VAE through genetic algorithm-based token optimization}, volume = {13}, year = {2025}, }
• (2025). Enhancing vertical application and network co-design: A solution for multipath channel switching in ROS2 with 3GPP integration. ACCESS, 13, 34269-34285. (www)

  @article{ , abstract = {In this paper, we address the challenging issue of co-designing the Vertical Application Layer (VAL) with Third Generation Partnership Project (3GPP) and non-3GPP networks. This complex problem involves the cooperation of at least the above three distinct domains. To illustrate the problem, we present a design for multipath channel switching in Robot Operating System 2 (ROS 2), a feature not natively supported. We propose a solution within ROS 2 and then extend this solution to enable 3GPP cooperation via Common API Framework (CAPIF), finally integrating with Vertical Application Enablers (VAEs). These steps significantly enhance the co-design process, as demonstrated by our novel quantitative evaluation method. We also evaluate the contribution of Key Performance Indicators (KPIs) from the VAL layer, analyzing how network performance impacts safety zones in robotics.}, archive = {J_ACCESS}, author = {Géza Szabó}, doi = {10.1109/ACCESS.2025.3543905}, journal = {IEEE Access}, pages = {34269-34285}, shortjournal = {IEEE Access}, title = {Enhancing vertical application and network co-design: A solution for multipath channel switching in ROS2 with 3GPP integration}, volume = {13}, year = {2025}, }
• (2025). Internet of things-based smart precision farming in soilless agriculture: Opportunities and challenges for global food security. ACCESS, 13, 34238-34268. (www)

  @article{ , abstract = {The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper aims to explore the opportunities and challenges of IoT-based soilless farming. It also highlights its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, these methods also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By highlighting the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations.}, archive = {J_ACCESS}, author = {Monica Dutta and Deepali Gupta and Sumegh Tharewal and Deepam Goyal and Jasminder Kaur Sandhu and Manjit Kaur and Ahmad Ali Alzubi and Jazem Mutared Alanazi}, doi = {10.1109/ACCESS.2025.3540317}, journal = {IEEE Access}, pages = {34238-34268}, shortjournal = {IEEE Access}, title = {Internet of things-based smart precision farming in soilless agriculture: Opportunities and challenges for global food security}, volume = {13}, year = {2025}, }
• (2025). Evaluation of the high-frequency transmission characteristics of coplanar transmission lines fabricated on a copper-plated cycloolefin polymer using a direct sputtering copper seed layer. ACCESS, 13, 34227-34237. (www)

  @article{ , abstract = {This study evaluated and compared the high-frequency transmission characteristics of coplanar transmission lines fabricated on copper-plated cycloolefin polymer (COP) films using two different seed layer formation methods—our proposed direct copper sputtering and electroless copper plating— at frequencies up to 100 GHz. The direct copper sputtering method achieved slightly lower transmission loss than that of the electroless copper plating method below 60 GHz. However, above 60 GHz, the transmission loss was significantly lower in the direct copper sputtering method. Specifically, at 100 GHz, the transmission loss in the direct copper sputtering and electroless copper plating methods was −0.817 dB/cm and −0.977 dB/cm, respectively. This difference is primarily attributed to the skin effect at high frequencies. In the direct copper sputtering method, the current predominantly flows through the copper plating layer, which has high conductivity. In contrast, in the electroless copper plating method, the current mainly flows through the electroless copper plating seed layer, which has significantly lower conductivity than the copper plating layer. Overall, these results indicate that the type of seed layer used significantly affects the high-frequency transmission characteristics. In particular, the direct copper sputtering method is well-suited for next-generation 6G wireless communication systems, where minimizing transmission loss is critical for energy efficiency and signal quality.}, archive = {J_ACCESS}, author = {Akihiro Shimizu and Kazuhiro Fukada and Mitsunori Abe and Akiko Matsui and Shinichi Endo}, doi = {10.1109/ACCESS.2025.3543494}, journal = {IEEE Access}, pages = {34227-34237}, shortjournal = {IEEE Access}, title = {Evaluation of the high-frequency transmission characteristics of coplanar transmission lines fabricated on a copper-plated cycloolefin polymer using a direct sputtering copper seed layer}, volume = {13}, year = {2025}, }
• (2025). Work place safety: Machine learning techniques for assessing workplace incident severity. ACCESS, 13, 34211-34226. (www)

  @article{ , abstract = {Occupational injury severity prediction is paramount across all industries, necessitating the adoption of advanced methodologies to enhance predictive analysis. This study leverages Machine Learning (ML) techniques to build a robust and effective predictive model for predicting the severity of occupational injuries. Analyzing a comprehensive dataset of occupational injury records from Occupational Safety and Health Administration (OSHA), this research explores the efficacy of seven distinct ML algorithms AdaBoostClassifier (ABC), K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Extreme Learning Machine (ELM), Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), and Stacked Model (SM). Among these, ABC emerges as the standout performer, demonstrating superior accuracy and F1-score, Specifically 92.5% and 99.3% accuracy in predicting hospitalization and amputation severity, respectively, thus underscoring the potential of ensemble learning in refining occupational injury prediction models. Permutation feature importance analysis reveals that the nature of the injury, the body part affected, and the source are pivotal factors in the dataset shaping injury severity predictions. This study empowers stakeholders to devise informed corrective and preventive strategies for mitigating workplace injuries. ABC has shown robust performance in developing injury prevention frameworks.}, archive = {J_ACCESS}, author = {Munayr Hasan Khalleefah Hassan and Wagdi M. S. Khalifa}, doi = {10.1109/ACCESS.2025.3543136}, journal = {IEEE Access}, pages = {34211-34226}, shortjournal = {IEEE Access}, title = {Work place safety: Machine learning techniques for assessing workplace incident severity}, volume = {13}, year = {2025}, }
• (2025). An environment-adaptive Position/Force control based on physical property estimation. ACCESS, 13, 34200-34210. (www)

  @article{ , abstract = {The current methods to generate robot actions for automation in significantly different environments have limitations. This paper proposes a new method that matches the impedance of two prerecorded action data with the current environmental impedance to generate highly adaptable actions. This method recalculates the command values for the position and force based on the current impedance to improve reproducibility in different environments. Experiments conducted under conditions of extreme action impedance, such as position and force control, confirmed the superiority of the proposed method over existing motion reproduction system. The advantages of this method include the use of only two sets of motion data, significantly reducing the burden of data acquisition compared with machine-learning-based methods, and eliminating concerns about stability by using existing stable control systems. This study contributes to improving the environmental adaptability of robots while simplifying the action generation method.}, archive = {J_ACCESS}, author = {Tomoya Kitamura and Yuki Saito and Hiroshi Asai and Kouhei Ohnishi}, doi = {10.1109/ACCESS.2025.3543618}, journal = {IEEE Access}, pages = {34200-34210}, shortjournal = {IEEE Access}, title = {An environment-adaptive Position/Force control based on physical property estimation}, volume = {13}, year = {2025}, }
• (2025). Review of virtual reality integration for safer and efficient mining operations. ACCESS, 13, 34177-34199. (www)

  @article{ , abstract = {Mining is a complex, multifaceted industry that requires ongoing adaptation. The integration of Virtual Reality (VR) offers promising solutions to some of the industry’s key challenges, such as the need for safety simulations and task training for harsh working conditions, data analysis, remote operations, and many more. VR, as part of the broader Extended Reality (XR) framework, provides immersive environments that enable users to experience scenarios that may be costly and dangerous to replicate or that are not possible to replicate in the real world. However, despite the potential of VR, there is a limited comprehensive review of its applications within the mining sector, particularly in recent years. This paper presents a literature review focused on VR’s use in mining, aiming to provide answers to the extent of VR-related publications over the years, current use areas of VR in the industry, existing research gaps, and potential future applications. By examining the state of VR in mining, this review seeks to offer a roadmap for future research and development, highlighting areas where VR can significantly enhance education, safety, operational processes, and more.}, archive = {J_ACCESS}, author = {Nilufer K. B. Akbulut and Angelina Anani and Sefiu O. Adewuyi}, doi = {10.1109/ACCESS.2025.3541724}, journal = {IEEE Access}, pages = {34177-34199}, shortjournal = {IEEE Access}, title = {Review of virtual reality integration for safer and efficient mining operations}, volume = {13}, year = {2025}, }
• (2025). Optimization model for electric vehicle (EV) fleet charging location assignment. ACCESS, 13, 34160-34176. (www)

  @article{ , abstract = {This paper addresses a critical challenge that utility providers face as commercial electric vehicle (EV) fleets rapidly expand. Specifically, it focuses on optimizing charging infrastructure for medium- and heavy-duty electric vehicles while managing constrained grid capacity. Businesses’ increasing adoption of mid-size to heavy EV fleets has created a significant surge in electricity demand, often exceeding the local grid’s ability to support charging at vehicles’ base locations. Supply chain constraints that hinder timely infrastructure upgrades exacerbate this mismatch between demand and capacity. We present an optimization model for EV fleet charging location assignment that tackles this issue. Our approach considers multiple commercial fleet operators, each with a set of base locations for their vehicles. The model accounts for limited charging capacity at these bases and proposes strategically placing charging hubs in areas with excess grid capacity. We incorporate a flexible incentive framework into our model to encourage the use of these hubs and other non-base charging locations. The primary objective of this study is to optimize the allocation of charging resources for commercial EV fleets and to maintain grid stability in the face of rapidly growing demand. Our model integrates fleet operational constraints, grid limitations, and incentive structures to provide a comprehensive solution that benefits fleet operators and utility providers. To validate our approach, we perform a series of computational experiments based on realistic data from the city of San Antonio, TX, a major urban center in Texas. These simulations demonstrate the model’s effectiveness in managing peak demand, optimizing resource utilization, and providing actionable insights for infrastructure planning.}, archive = {J_ACCESS}, author = {Gonzalo Martinez Medina and Krystel K. Castillo-Villar and Omar Abbaas}, doi = {10.1109/ACCESS.2025.3543748}, journal = {IEEE Access}, pages = {34160-34176}, shortjournal = {IEEE Access}, title = {Optimization model for electric vehicle (EV) fleet charging location assignment}, volume = {13}, year = {2025}, }
• (2025). A pseudo variance algorithm for semi-supervised semantic segmentation. ACCESS, 13, 34149-34159. (www)

  @article{ , abstract = {The key aspect of semi-supervised semantic segmentation lies in the construction of pseudo-labels. The conventional approach involves selecting the prediction with the highest confidence as the pseudo-label. However, this method can introduce an issue where the class with the highest confidence may be an incorrect prediction, especially for categories with ambiguous features, while another high-confidence prediction may be correct. To address this concern, we propose an effective method for constructing pseudo-labels that integrates two high-confidence sources of information to mitigate the impact of erroneous predictions. Specifically, we devise a pseudo-variance function that combines the two highest confidences to better evaluate the quality of prediction results. Additionally, an adaptive coefficient is set to enable the method to adapt to various datasets. The pseudo variance algorithm is integrated into the training process by incorporating a novel loss function, which maps the prediction results to pseudo-labels associated with their confidences and subsequently re-adjusts the network parameters. Experimental results on different datasets demonstrate that our method outperforms previous approaches under similar conditions, showcasing superior performance.}, archive = {J_ACCESS}, author = {Bin Li and Mengting Ye and Xiangyuan Jiang and Xiaojing Ma and Wenxu Sun and Jiyang Chen and Sile Ma}, doi = {10.1109/ACCESS.2025.3542178}, journal = {IEEE Access}, pages = {34149-34159}, shortjournal = {IEEE Access}, title = {A pseudo variance algorithm for semi-supervised semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). ContrastLOS: A graph-based deep learning model with contrastive pre-training for improved ICU length-of-stay prediction. ACCESS, 13, 34132-34148. (www)

  @article{ , abstract = {Accurate prediction of intensive care unit (ICU) length of stay (LOS) is crucial for optimizing resource allocation and improving patient outcomes. We propose ContrastLOS, a novel graph-based deep learning model that integrates graph transformer networks with contrastive pre-training to enhance ICU LOS prediction. By constructing dynamic patient similarity graphs, ContrastLOS captures intricate inter-patient relationships and temporal dependencies. Through contrastive learning on unlabeled data, the model develops robust patient embeddings suitable for both classification and regression tasks. Experiments on three comprehensive ICU datasets—MIMIC-III, MIMIC-IV v3.0, and eICU—show that ContrastLOS consistently outperforms state-of-the-art baselines. For classification (predicting whether LOS exceeds 3 or 7 days), it achieves AUROC scores of up to 82.1%, improving performance by up to 5.2% compared to competitive methods. In regression tasks, ContrastLOS attains the lowest RMSE (2.41 days) and MAE (1.48 days) on the multi-center eICU dataset. Notably, it maintains an AUROC of 76.8% with only 10% labeled data, highlighting its effectiveness in low-resource settings. These findings suggest that ContrastLOS can serve as a robust clinical decision support tool for critical care management.}, archive = {J_ACCESS}, author = {Guangrui Fan and Aixiang Liu and Chao Zhang}, doi = {10.1109/ACCESS.2025.3540896}, journal = {IEEE Access}, pages = {34132-34148}, shortjournal = {IEEE Access}, title = {ContrastLOS: A graph-based deep learning model with contrastive pre-training for improved ICU length-of-stay prediction}, volume = {13}, year = {2025}, }
• (2025). An enhanced fractional open circuit voltage MPPT method for rapid and precise MPP tracking in standalone photovoltaic systems. ACCESS, 13, 34115-34131. (www)

  @article{ , abstract = {In this research it is essential to use a maximum power point tracking (MPPT) method to capture the maximum power from solar panels in varying weather conditions. Instead of focusing solely on individual optimization MPPT algorithms, current studies now explore combining different methods for better results. This study introduces a hybrid MPPT approach that combines the Musical Chairs Algorithm (MCA) and Fractional Open Circuit Voltage (FOCV). By using one decision variable, this technique simplifies the process and reduces the time needed to reach optimal performance. Unlike FOCV’s limitations in power output and tracking efficiency, this new approach offers improved outcomes. The effectiveness of this method is tested under irradiance conditions like gradual changes in irradiance condition, sinusoidal patterns, and real irradiance profile using MATLAB/SIMULINK. The simulation findings reveal that this new method surpasses studied methods by achieving faster convergence within 0.08 s and slightly higher efficiency rates of up to 99.8% under different irradiance conditions. The study demonstrates that integrating MCA with FOCV improves efficiency by nearly 7% while reducing oscillations during steady-state operation. Moreover, this paper provides a comprehensive assessment of various MPPT methods from the literature, examining factors such as controlled variable, complexity, tracking speed, and efficiency. Furthermore, this innovative method promises response times, better efficiency levels and reduced oscillations compared to other MPPT approaches.}, archive = {J_ACCESS}, author = {Muhannad J. Alshareef}, doi = {10.1109/ACCESS.2025.3543327}, journal = {IEEE Access}, pages = {34115-34131}, shortjournal = {IEEE Access}, title = {An enhanced fractional open circuit voltage MPPT method for rapid and precise MPP tracking in standalone photovoltaic systems}, volume = {13}, year = {2025}, }
• (2025). Robust pareto transistor sizing of GaN HEMTs for millimeter-wave applications. ACCESS, 13, 34105-34114. (www)

  @article{ , abstract = {This paper introduces a robust Pareto design approach for transistor sizing of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs), particularly for power amplifier (PA) and low-noise amplifier (LNA) designs in 5G applications. We consider five key design variables and two settings (PAs and LNAs) where we have multiple objectives. We assess designs based on three critical objectives, evaluating each by its worst-case performance across a range of Gate-Source Voltages ( $V_{\text {GS}}$ ). We conduct simulations across a range of $V_{\text {GS}}$ values to ensure a thorough and robust analysis. For PAs, the optimization goals are to maximize the worst-case modulated average output power ( $P_{\text {out,avg}}$ ) and power-added efficiency ( $\text {PAE}_{\text {avg}}$ ) while minimizing the worst-case average junction temperature ( $T_{\text {j,avg}}$ ) under a modulated 64-QAM signal stimulus. In contrast, for LNAs, the focus is on maximizing the worst-case maximum oscillation frequency ( $f_{\max }$ ) and Gain, and minimizing the worst-case minimum noise figure ( $\text {NF}_{\min }$ ). We utilize a derivative-free optimization method to effectively identify robust Pareto optimal device designs. This approach enhances our comprehension of the trade-off space, facilitating more informed decision-making. Furthermore, this method is general across different applications. Although it does not guarantee a globally optimal design, we demonstrate its effectiveness in GaN transistor sizing. The primary advantage of this method is that it enables the attainment of near-optimal or even optimal designs with just a fraction of the simulations required for an exhaustive full-grid search.}, archive = {J_ACCESS}, author = {Rafael Perez Martinez and Stephen Boyd and Srabanti Chowdhury}, doi = {10.1109/ACCESS.2025.3543787}, journal = {IEEE Access}, pages = {34105-34114}, shortjournal = {IEEE Access}, title = {Robust pareto transistor sizing of GaN HEMTs for millimeter-wave applications}, volume = {13}, year = {2025}, }
• (2025). Transformer-based multi-scale feature remote sensing image classification model. ACCESS, 13, 34095-34104. (www)

  @article{ , abstract = {To address the challenge of poor classification accuracy due to complex backgrounds, large intra-scale variations, and high inter-scale similarity in remote sensing scene classification (RSSC), we propose a new remote sensing scene classification model called multi-scale dual-branch classification network (MDBC-Net). The model is composed of a Trans-branch and CNN-branch in parallel, which can fully utilize the local attention of the CNN-branch structure and the global attention mechanism of the Trans-branch structure, thereby improving the model’s ability to focus on features of different scales. Due to the complexity of backgrounds in RSSC, we require features at different scales to obtain richer scene information. Thus we design a down-sampling module in the model to obtain multi-scale features. Finally, we adopt the polynomial form of cross entropy for the trained loss function to improve the generalization performance and robustness of the model. Experiments have shown that the model achieves advanced performance on three datasets: NWPU-RESISC45, AID, and UC Served.}, archive = {J_ACCESS}, author = {Ting Sun and Jun Li and Xiangrui Zhou and Zan Chen}, doi = {10.1109/ACCESS.2024.3520253}, journal = {IEEE Access}, pages = {34095-34104}, shortjournal = {IEEE Access}, title = {Transformer-based multi-scale feature remote sensing image classification model}, volume = {13}, year = {2025}, }
• (2025). Presentation of body sway sensation considering the distinction between self-motion and environmental motion induced by alternating vibrations to antagonistic tendons in the ankles. ACCESS, 13, 34081-34094. (www)

  @article{ , abstract = {In virtual reality (VR) environments and immersive content, the use of kinesthetic illusions has garnered attention as a simple yet effective method to present whole-body motion sensations, such as body sway and tilt. This study proposes a technique to present sensations of whole-body sway by alternately vibrating the antagonistic muscles of the ankle and manipulating the switching speed (frequency) of these vibrations to induce the interpretation of the sway as either “the body is moving” or “the environment (ground) is moving.” To validate the effectiveness of this method, three experiments were conducted. In Experiment 1, we investigated the effect of the switching frequency on the interpretation of sway. The results revealed a tendency for low frequencies to be interpreted as “self-sway” and high frequencies as “environmental sway.” In Experiment 2, we examined the influence of shifting the center of pressure (CoP) in the anterior-posterior direction on sway interpretation. While the CoP position had minimal impact on sway interpretation, the results suggested that the strength of the illusion slightly decreased under high-frequency conditions when the CoP was biased towards the toes or heels. In Experiment 3, we presented visual stimuli of sway with various phase delays and assessed whether the match between the visual stimuli and the vibration-induced sway illusion changed. Although phase delays did not strongly affect the matching scores, the results indicated that visual stimuli might contribute to the interpretation of the illusion. These findings demonstrate the potential of a technique that adjusts the switching frequency of vibrations to differentiate between “self-sway” and “environmental sway” in VR and immersive content. Furthermore, the limited impact of integrating visual stimuli on the interpretation and strength of the illusion suggests that highly immersive and convincing experiences can be designed even without strict synchronization between visual and tactile stimuli.}, archive = {J_ACCESS}, author = {Eifu Narita and Keigo Ushiyama and Izumi Mizoguchi and Hiroyuki Kajimoto}, doi = {10.1109/ACCESS.2025.3544102}, journal = {IEEE Access}, pages = {34081-34094}, shortjournal = {IEEE Access}, title = {Presentation of body sway sensation considering the distinction between self-motion and environmental motion induced by alternating vibrations to antagonistic tendons in the ankles}, volume = {13}, year = {2025}, }
• (2025). Dual circularly polarized bow-tie-shaped stacked yagi-uda antenna arrays for internet of things applications. ACCESS, 13, 34070-34080. (www)

  @article{ , abstract = {This paper presents the design and analysis of two dual circularly polarized stacked Yagi-Uda antenna arrays, referred to as Type A and Type B. The proposed Type A Yagi-Uda antenna array consists of four printed bow-tie-shaped elements arranged at different angles: a director, a reflector, and two orthogonal driven elements. The driven elements are connected to coaxial cables via LC baluns. The Type B configuration introduces a complementary slotted bow-tie director element in place of the printed one, while the driven and reflector elements remain unchanged. Exciting the upper driven element generates right-hand circular polarization (RHCP), whereas left-hand circular polarization (LHCP) is achieved when the lower driven element is excited. Both proposed antenna arrays share the same dimensions of $0.39\lambda _{0} \times 0.39\lambda _{0} \times 0.27\lambda _{0}$ , where $\lambda _{0}$ represents the free-space wavelength at the operating frequency of 2.45 GHz. Measured results confirm that Type B outperforms Type A in several aspects. The Type B antenna array achieves a fractional impedance bandwidth of 8%, a 3-dB axial ratio (AR) bandwidth of 19.6%, and a peak gain of 4.49 dBic. In comparison, Type A exhibits a fractional impedance bandwidth of 2.45%, a 3-dB axial ratio bandwidth of 2.45%, and a peak gain of 3.5 dBic. These findings highlight the effectiveness of the complementary slotted bow-tie director in enhancing antenna performance, making Type B a promising candidate for Internet of Things (IoT) applications.}, archive = {J_ACCESS}, author = {Tae-Hyeon Kim and Mohamed Elhefnawy and Kyoung-Hun Kim and Wang-Sang Lee}, doi = {10.1109/ACCESS.2025.3543909}, journal = {IEEE Access}, pages = {34070-34080}, shortjournal = {IEEE Access}, title = {Dual circularly polarized bow-tie-shaped stacked yagi-uda antenna arrays for internet of things applications}, volume = {13}, year = {2025}, }
• (2025). URcon: Unified and reconfigurable control and verification platform for multi-mode customized eDRAM and SRAM macros. ACCESS, 13, 34060-34069. (www)

  @article{ , abstract = {With the rapid development of various technologies, processing large amounts of data has become essential. To address this trend, various high-density and high-performance memories have emerged. However, they employ different operation modes and architectures, necessitating the redesign of the control and verification platforms each time a new memory type is supported. To solve this problem, we propose a unified and reconfigurable platform to support the control and verification of various memories, each with different structures and operational modes. Some of the control and verification functions are integrated into the chip as a unified memory command controller (UMCC) to alleviate the limitations posed by the finite number of I/O pads. Other control and verification functions are implemented on a field-programmable gate array (FPGA) to support various memory operations and testing. Additionally, a software application has been used to manage the FPGA controller, enabling the flexible modification of input variables and enhancing the reconfigurability of the proposed platform. We designed prototype chips for customized 16-kb multi-mode embedded dynamic random-access memory (eDRAM) and static RAM (SRAM) macros using a 28-nm CMOS process. Their various operations and performances, such as data write, single- and multi-column data read, refresh, data retention time analysis, and power measurement, have been validated through the proposed platform. This platform can be extended and applied to verify other memories with minimal modifications.}, archive = {J_ACCESS}, author = {Ho-Sung Lee and Ik-Hyeon Jeon and Jiwon Lee and Hyeonjun Cheon and Joo-Hyung Chae}, doi = {10.1109/ACCESS.2025.3544157}, journal = {IEEE Access}, pages = {34060-34069}, shortjournal = {IEEE Access}, title = {URcon: Unified and reconfigurable control and verification platform for multi-mode customized eDRAM and SRAM macros}, volume = {13}, year = {2025}, }
• (2025). Compact 16-port MIMO antenna for sub-6 GHz communications. ACCESS, 13, 34051-34059. (www)

  @article{ , abstract = {A 16-port multiple-input-multiple-output (MIMO) antenna for sub-6 GHz communication systems is presented in this paper. It is designed by arranging the eight dual polarized antenna elements in $2 \times 4$ array pattern. Each dual polarized antenna element is incorporated inside a shielded quarter-mode substrate integrated waveguide (S-QMSIW) cavity, which is orthogonally fed by a pair of $50 \; \Omega $ coaxial lines. Two U-shaped slots are etched on the top-surface of S-QMSIW resonator to obtain two radiating patches in each of the dual-polarized unit element. The designed antenna radiates over the frequency band of 5.32-6.35 GHz (FBW = 17.54 %), which covers n46, n47, n96 and n102 bands of sub-6 GHz communications. Due to the strategic placement of dual polarized antenna elements, high inter-port isolation (>45 dB) over the entire operating bandwidth is attained. The overall dimensions of the proposed MIMO antenna are 88 mm $ \times 44$ mm $ \times 0.508$ mm. The designed antenna also exhibits high gain and excellent MIMO performance.}, archive = {J_ACCESS}, author = {Gunjan Srivastava and Akhilesh Mohan and Sachin Kumar and Hyun Chul Choi and Kang Wook Kim}, doi = {10.1109/ACCESS.2025.3541738}, journal = {IEEE Access}, pages = {34051-34059}, shortjournal = {IEEE Access}, title = {Compact 16-port MIMO antenna for sub-6 GHz communications}, volume = {13}, year = {2025}, }
• (2025). Asymmetric and harmonic current suppression of dual three-phase PMSM based on double-integral sliding mode control. ACCESS, 13, 34038-34050. (www)

  @article{ , abstract = {Asymmetric and harmonic current components, primarily the fundamental, $5^{\mathrm {th}}$ -, and $7^{\mathrm {th}}$ -order harmonics, are inherent in asymmetric dual three-phase permanent magnet synchronous motors (DTP-PMSMs). These components reduce power efficiency and may cause system instability. To cope with these issues, in this study, a novel control scheme based on double-integral sliding mode control (DISMC) is proposed to suppress the asymmetric and harmonic current components. The proposed control scheme operates by managing the currents in the x-y subspace of vector space decomposition (VSD) stationary reference frame to zero. Therefore, the proposed control scheme significantly reduces the number of required controllers and eliminates the need for coordinate transformation. In addition, owing to its extra integral term, which offers superior performance in suppressing steady-state error, the proposed method delivers enhanced performance across the entire operating range compared to the widely used quasi-proportional-integral-resonance (Q-PIR) control. Furthermore, unlike resonant controllers that require variable gains, this method employs a fixed gain, resulting in reduced current oscillations during transient conditions. Detailed simulation and experimental results have confirmed the validity and effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Jae-Ho Hyun and Syed Mohammad Maaz and Dong-Choon Lee and Dong-Hun Kim}, doi = {10.1109/ACCESS.2025.3542459}, journal = {IEEE Access}, pages = {34038-34050}, shortjournal = {IEEE Access}, title = {Asymmetric and harmonic current suppression of dual three-phase PMSM based on double-integral sliding mode control}, volume = {13}, year = {2025}, }
• (2025). Incremental closed frequent itemsets mining-based approach using maximal candidates. ACCESS, 13, 34023-34037. (www)

  @article{ , abstract = {Incremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics. In the incremental frequent itemset mining approach, approximate methods are popular approaches that attempt to improve the accuracy of global frequent itemsets and minimize computational overhead by removing candidates at an early stage of the mining process. However, these methods are hampered by the generation of a large number of candidates, imprecise support count estimation, and the lack of utilization of optimal methods in the data-splitting process. To address these challenges, a Maximal Candidates-based Incremental Closed Frequent Itemsets Mining approach called MC-ICFIM is proposed. This approach minimizes computational costs by reducing the generation of candidate itemsets through the discovery of closed itemsets using maximal candidates, rather than generating all frequent itemsets. The proposed approach uses the train_test_split method to randomly divide a dataset into partitions based on a specified ratio. This aids in the effective identification of closed frequent itemsets that are consistent across different partitions. This approach reduces the search space and computational costs for incremental updates by applying similarity measurements to local maximal candidates instead of to local closed candidates, thereby facilitating the pruning of numerous uninteresting candidates. The experimental findings revealed that this approach is more accurate and efficient than existing methods for large and dynamic datasets.}, archive = {J_ACCESS}, author = {Mohammed A. Al-Zeiadi and Basheer M. Al-Maqaleh}, doi = {10.1109/ACCESS.2025.3543755}, journal = {IEEE Access}, pages = {34023-34037}, shortjournal = {IEEE Access}, title = {Incremental closed frequent itemsets mining-based approach using maximal candidates}, volume = {13}, year = {2025}, }
• (2025). The impact of energy storage system control parameters on frequency regulation strategies. ACCESS, 13, 34010-34022. (www)

  @article{ , abstract = {The large-scale development of battery energy storage systems (BESS) has enhanced grid flexibility in power systems. From the perspective of power system planners, it is essential to consider the reliability of BESS to ensure stable grid operation amid a high reliance on renewable energy. Therefore, this paper investigates BESS models and dynamic parameters used in planning future grids from the viewpoint of power planners. By measuring output response data from BESS units of three companies, dynamic responses are converted into WECC second generic model parameters using optimization algorithms, allowing their use in power system analysis software. Subsequently, using Taiwan’s actual power system as the simulation background, N-1 simulations are conducted to explore the impact and benefits of BESS parameters when implementing frequency regulation strategies under two different BESS capacity specifications: 2 MW and 10 MW. In the 2 MW scenario, a comparison of the parameters from the three BESS units under frequency regulation strategies shows slight differences in the rise times of their output responses. However, for a 2 MW capacity, the frequency nadir in the power system remains consistently at 58.692 Hz. The simulation results based on the parameters defined in this research demonstrate the importance of using accurately measured and solved parameters in simulations. In the 10 MW scenario, the BESS capacity can offset the generator trip capacity, thereby revealing differences in response time between different parameters, with the frequency nadirs for the solved parameters being 59.755 Hz, 59.773 Hz, and 59.762 Hz, respectively.}, archive = {J_ACCESS}, author = {Chia-Ming Chang and Hong-Chan Chang and Chun-Chun Hung and Cheng-Chien Kuo}, doi = {10.1109/ACCESS.2024.3522657}, journal = {IEEE Access}, pages = {34010-34022}, shortjournal = {IEEE Access}, title = {The impact of energy storage system control parameters on frequency regulation strategies}, volume = {13}, year = {2025}, }
• (2025). Improved particle swarm optimization with redundant solution constraints for robust data reconciliation. ACCESS, 13, 33995-34009. (www)

  @article{ , abstract = {In the alumina evaporation production process, accurate and reliable process data is crucial for the effectiveness of the soft measurement models used to estimate the concentration of the outlet mother liquor. However, process data is often compromised by harsh industrial conditions such as high temperature, pressure, and alkalinity, leading to measurement errors, missing data, and anomalies. To address these issues, we propose a robust data reconciliation method that utilizes redundant solution constraints and an improved particle swarm optimization (PSO) algorithm with chaotic mutation to correct erroneous measurements and estimate unmeasured variables. This method leverages redundant process data and integrates statistical analysis with process mechanisms to enhance data accuracy. The proposed method was tested on actual industrial data from a multi-effect evaporation system. Results demonstrate that our approach significantly improves the reliability of the reconciled data, providing a robust foundation for soft measurement models. This work presents a novel solution to the challenges of data reconciliation in complex industrial processes, ensuring better accuracy and reliability in the presence of harsh operational conditions.}, archive = {J_ACCESS}, author = {Xiaoshan Qian and Lisha Xu and Xingli Cui}, doi = {10.1109/ACCESS.2025.3539922}, journal = {IEEE Access}, pages = {33995-34009}, shortjournal = {IEEE Access}, title = {Improved particle swarm optimization with redundant solution constraints for robust data reconciliation}, volume = {13}, year = {2025}, }
• (2025). Evaluating the financial dynamics of electric vehicle charging stations in thailand: Implications of energy cost variability. ACCESS, 13, 33985-33994. (www)

  @article{ , abstract = {This study examines the financial impact of energy costs on electric vehicle charging stations by comparing financial models with fixed and variable energy pricing structures. Through an in-depth financial simulation, key metrics such as Internal Rate of Return, Net Present Value, and payback period were calculated to evaluate the profitability and risk associated with each model. Findings reveal that charging stations with fixed energy costs generally experience a higher Internal Rate of Return at 14.55% and a Net Present Value of 1,711,781, with a payback period of four years. In comparison, stations with variable energy costs exhibit a lower Internal Rate of Return at 7.28% and a Net Present Value of 1,347,781, although the payback period remains consistent at four years. These results demonstrate that fixed energy costs enhance investment predictability and profitability, while variable costs increase exposure to energy price fluctuations, which raises financial risk but may capture greater returns under favorable market conditions. The analysis highlights the critical importance of accounting for energy cost variability in financial planning to maintain sustainable, profitable operations. This research provides essential insights for investors and policymakers to optimize electric vehicle charging infrastructure investments, supporting effective and profitable operations.}, archive = {J_ACCESS}, author = {Pathomthat Chiradeja and Chayanut Sottiyaphai and Santipont Ananwattanaporn and and Atthapol Ngaopitakkul}, doi = {10.1109/ACCESS.2025.3544079}, journal = {IEEE Access}, pages = {33985-33994}, shortjournal = {IEEE Access}, title = {Evaluating the financial dynamics of electric vehicle charging stations in thailand: Implications of energy cost variability}, volume = {13}, year = {2025}, }
• (2025). Automated on-demand manufacturing: Laser fabrication concept for soft mechanisms. ACCESS, 13, 33976-33984. (www)

  @article{ , abstract = {This study proposes a concept for on-demand manufacturing of soft mechanisms using laser technology for welding and cutting plastic sheets. Soft mechanisms, which are known for their flexibility, compliance, and lightweight properties, have been explored in various fields. Despite advancements in soft robotics, challenges such as lengthy production times, complex fabrication processes, and reliance on specialized expertise persist. Laser processing is a prominent technique for the fabrication of soft mechanisms, particularly film-based mechanisms, in terms of production time and manufacturability. Such methodologies are considered innovative; however, they remain underexplored. This study advances laser methodology by introducing a novel on-demand production approach that leverages laser-based techniques to fabricate multilayered soft mechanisms from plastic sheet materials. The feasibility of this concept, applicable to both small and large mechanisms, was validated using a commercial laser-cutting machine. We successfully fabricated a range of soft mechanisms, including a peristaltic robot capable of moving at 3.3 mm/s while carrying a load of 1.2 kg, and a stackable valve mechanism that switches flow paths using a plastic leaf spring and an inflatable airbag, both constructed from multiple plastic sheets. Key contributions of this study include the development of innovative fabrication protocols for soft mechanisms and multiple examples demonstrating the versatility of the technique. This study advances the field of soft robotics by providing a practical and efficient solution for manufacturing complex, large-scale soft robots with potential applications in space exploration, industrial automation, and other unstructured environments.}, archive = {J_ACCESS}, author = {Tomonari Yamamoto and Akiya Kamimura}, doi = {10.1109/ACCESS.2025.3542854}, journal = {IEEE Access}, pages = {33976-33984}, shortjournal = {IEEE Access}, title = {Automated on-demand manufacturing: Laser fabrication concept for soft mechanisms}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven prediction of wear rate and phase formation in high entropy alloy coatings for enhanced durability and performance. ACCESS, 13, 33956-33975. (www)

  @article{ , abstract = {High Entropy Alloys (HEAs) are widely recognized for their excellent microstructure and properties, enhancing their effectiveness in surface modification through coatings techniques. These HEA coatings exhibit superior wear and corrosive resistance, making them suitable for various industries. However, accessing the wear behaviour and phase evolution of HEA coatings is complex and time-consuming due to their multiple element’s nature. To address this, Machine Learning (ML) techniques were integrated to predict the wear rate and phase formation in HEA coatings processed through thermal spray methods. Ten ML models such as AdaBoost, XGBoost, CatBoost, GBRT, DT, SVM-RBF, MLP, BNN, MLR and HR were utilized to predict wear rate, Feature engineering was conducted using Mutual Information (MI) and Pearson Correlation Coefficient (PCC) to access feature significance, Variance Inflation Factors (VIFs) analyzed multicollinearity, identified influential elements for wear rate prediction and aiding in the development of novel Lightweight High Entropy Alloys (LHEAs) coating compositions. For phase prediction, four ML models including RF, GNB, ANN and Logistic regression were evaluated. Results demonstrated that XGBoost achieved the highest predictive effectiveness with an R2 of 0.98 and the lowest error values, validated against experimental data. In phase prediction, the RF model exhibited the best accuracy of 98.5% for novel LHEA coatings. These findings highlight the potential of ML techniques in facilitating material design and coating optimization.}, archive = {J_ACCESS}, author = {S. Sivaraman and N. Radhika and Muhammad Abubaker Khan}, doi = {10.1109/ACCESS.2025.3542507}, journal = {IEEE Access}, pages = {33956-33975}, shortjournal = {IEEE Access}, title = {Machine learning-driven prediction of wear rate and phase formation in high entropy alloy coatings for enhanced durability and performance}, volume = {13}, year = {2025}, }
• (2025). A deep learning model using transformer network and expert optimizer for an hour ahead wind power forecasting. ACCESS, 13, 33935-33955. (www)

  @article{ , abstract = {The renewable energy platform cannot operate without an accurate wind power forecast. The power system can better manage its supply and guarantee grid reliability with an accurate wind power forecast. Accurate forecasting is difficult to achieve, though, because wind power generation is inherently unpredictable and intermittent. Predicting wind time series is challenging due to its nonlinearity, intermittent nature, and variability. Deep learning (DL) methods can be useful in situations when the data lacks a defined structure. With some accuracy and consistency, these methods are able to forecast wind power. To achieve this, a novel modified Siamese transformer-network (MST-Net) model with a multi-attention mechanism that enhances their ability to pay more attention to various input sequences, better capturing long-term dependency for wind forecasting. Optimizing system efficiency is also crucial thus fine-tuning of PID controller parameters using a self-adaptive mountain gazelle optimizer (SA-MGO) is employed to achieve minimal Mean Squared Error (MSE) and THD. The proposed model efficiency has been confirmed by contrasting its prediction outcomes with those of other deep learning models, including Siamese Network, LSTM, and Transformer. The proposed system is simulated in MATLAB. The proposed method is tested by comparing its efficacy with other accepted approaches using error measures such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Square Root Error (MSRE), and Absolute Error (AE). The model’s performance is tested using real-time datasets with a cap value of 1500 kW. MST-Net closely tracks actual power trends, while other models either underpredict or smooth the data excessively.}, archive = {J_ACCESS}, author = {Anushalini Thiyagarajan and B. Sri Revathi and Vishnu Suresh}, doi = {10.1109/ACCESS.2025.3543594}, journal = {IEEE Access}, pages = {33935-33955}, shortjournal = {IEEE Access}, title = {A deep learning model using transformer network and expert optimizer for an hour ahead wind power forecasting}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving tools and technologies: Government adoption and challenges. ACCESS, 13, 33904-33934. (www)

  @article{ , abstract = {Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens’ sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments’ challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government’s diverse roles in privacy preservation—regulator, enforcer, user, and service provider—and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.}, archive = {J_ACCESS}, author = {Sidik Prabowo and Aji Gautama Putrada and Ikke Dian Oktaviani and Maman Abdurohman and Marijn Janssen and Hilal Hudan Nuha and Sarwono Sutikno}, doi = {10.1109/ACCESS.2025.3540878}, journal = {IEEE Access}, pages = {33904-33934}, shortjournal = {IEEE Access}, title = {Privacy-preserving tools and technologies: Government adoption and challenges}, volume = {13}, year = {2025}, }
• (2025). Computerized adaptive testing to balance exposure bias and measurement accuracy using zero-suppressed binary decision diagrams. ACCESS, 13, 33883-33903. (www)

  @article{ , abstract = {Computerized adaptive testing (CAT) presents a tradeoff dilemma involving item exposure bias and measurement accuracy. To resolve this dilemma, we propose a new two-step CAT mechanism to balance exposure bias and measurement accuracy. Using zero-suppressed binary decision diagram (ZDD), the proposed method first selects and presents an optimal item from an equivalent item pool, which uniformly divides the whole item pool. The first step rapidly provides a roughly approximated ability estimate of an examinee. The second step produces a more accurate ability estimate of the examinee. Specifically, the second step selects an optimal item from the whole item pool and presents an item with a difficulty parameter value that is approximately equal to the examinee ability estimate. Numerical experiment results underscore the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Maomi Ueno and Kazuma Fuchimoto and Wakaba Kishida and Yoshimitsu Miyazawa}, doi = {10.1109/ACCESS.2025.3543554}, journal = {IEEE Access}, pages = {33883-33903}, shortjournal = {IEEE Access}, title = {Computerized adaptive testing to balance exposure bias and measurement accuracy using zero-suppressed binary decision diagrams}, volume = {13}, year = {2025}, }
• (2025). Toward universal multiplexer multiply-accumulate architecture in stochastic computing. ACCESS, 13, 33874-33882. (www)

  @article{ , abstract = {Stochastic Computing (SC) has recently gained attraction due to its inherent error tolerance and extremely simple arithmetic hardware, making it particularly effective for accelerating modern applications such as neural networks on resource-constrained devices. Traditional SC architectures often adopt binary computing principles, relying on dedicated hardware, i.e. AND gates for multiplication and multiplexers (MUX) for addition. However, SC’s mathematical foundation enables the fusion of complex operations into remarkably simple hardware. Several SC studies demonstrated the potential of MUX-based architectures to perform multiply-and-accumulate (MAC) operations, but existing designs face correlation complication, scaling problems, and limited application scope. This paper introduces an auxiliary logic block to address the complexity of MUX select inputs, significantly enhancing the scalability of MUX-based MAC operations. The proposed approach has been validated through SC image processing tasks, including grayscale conversion and Sobel edge detection, achieving up to 75% reduction in hardware resource utilization on field-programmable gate arrays (FPGAs) and up to 96% improvement in computational accuracy compared to traditional AND/XNOR-based SC multipliers.}, archive = {J_ACCESS}, author = {Yang Yang Lee and Zaini Abdul Halim and Mohd Nadhir Ab Wahab and Tarik Adnan Almohamad}, doi = {10.1109/ACCESS.2025.3539986}, journal = {IEEE Access}, pages = {33874-33882}, shortjournal = {IEEE Access}, title = {Toward universal multiplexer multiply-accumulate architecture in stochastic computing}, volume = {13}, year = {2025}, }
• (2025). Challenges and opportunities in D-band transmitter architectures and antenna design, integration, and scalability for 6G communications: A brief review. ACCESS, 13, 33849-33873. (www)

  @article{ , abstract = {This article reviews the current state-of-the-art (SOTA) key enabling technologies that pave the way for the advancement of scalable 6G antenna array systems for future communications. The focus is on the development of transmitter building blocks, considering the implementation of size, weight, area, and power (SWaP) constrained nodes. First, an overview of communication technologies from 1G to 6G is presented, followed by a discussion of key performance indicators and link budget analysis. To provide further insights into D-band (110-170 GHz) system design challenges, this review highlights the significance of 1) high-gain and high-linearity power amplifiers, 2) spectrally pure local oscillator (LO) signal sources, 3) high data rate modulator architectures, 4) scalable antenna array development, and 5) heterogeneous integration approaches. Furthermore, this article presents ongoing research and development on transmitter building blocks across various device fabrication technologies such as CMOS, BiCMOS, and III-V semiconductors. Special attention is given to the high data rate modulator architectures, which comprise 1) heterodyne, 2) homodyne, and 3) direct-digital modulators, followed by addressing the implementation challenges and opportunities in coherent and noncoherent modulation schemes within the context of SWaP-constrained scalable arrays for D-band. Additionally, this article reviews heterogeneous integration (HI) while focusing on chiplet designs for multifunctionality, low-loss packaging trends with thermal management solutions, chip-antenna interconnects featuring novel antenna technologies, and bottlenecks in mmWave/sub-THz measurements.}, archive = {J_ACCESS}, author = {Shah Zaib Aslam and Alexander Wilcher and Baibhab Chatterjee and Yong Kyu Yoon and David P. Arnold and Siddhartha Sinha}, doi = {10.1109/ACCESS.2025.3542771}, journal = {IEEE Access}, pages = {33849-33873}, shortjournal = {IEEE Access}, title = {Challenges and opportunities in D-band transmitter architectures and antenna design, integration, and scalability for 6G communications: A brief review}, volume = {13}, year = {2025}, }
• (2025). CorNet: Enhancing motion deblurring in challenging scenarios using correlation image sensor. ACCESS, 13, 33834-33848. (www)

  @article{ , abstract = {Motion deblurring in scenes involving small moving objects or low-illumination conditions is challenging. This paper presents an effective deep-learning solution that utilizes correlation images as key auxiliaries to address the problem. The correlation image, produced by a three-phase correlation image sensor (3PCIS), is a temporal correlation between incident light and reference signals within a frame time, which encodes intensity changes of incident light over the exposure time. Since correlation images explicitly record motion information lost during the blurring process during exposure, they can be used for accurately identifying the location and degree of blur, especially in low-illumination conditions and scenarios with small moving objects. Therefore, we combine correlation images and motion-blurred images as inputs and build a two-stream network for motion deblurring. Two key designs in our model are 1) Shared-gated Block (SGB), which enables information exchange between the two encoders and selectively allows useful information to pass through the network to obtain high-quality output; 2) a Motion-guided Block (MGB), decoding process that can draw more attention to the blurred areas in the image, thereby achieving clearer textures and details restoration in the blurred areas. The experimental results show that our model not only can successfully eliminate the motion blur in the above challenging scenarios, but also achieves a state-of-the-art 36.02dB in Peak Signal-to-Noise Ratio (PSNR) on the GoPro dataset with simulated correlation images.}, archive = {J_ACCESS}, author = {Pan Wang and Toru Kurihara and Jun Yu}, doi = {10.1109/ACCESS.2025.3543599}, journal = {IEEE Access}, pages = {33834-33848}, shortjournal = {IEEE Access}, title = {CorNet: Enhancing motion deblurring in challenging scenarios using correlation image sensor}, volume = {13}, year = {2025}, }
• (2025). Dynamic fuzzy cognitive maps-based crowd analysis using time series obtained from video processing. ACCESS, 13, 33813-33833. (www)

  @article{ , abstract = {Fuzzy cognitive maps treat the components of a problem or system expressed as fuzzy concepts and model the system with the relationships between these concepts. We predicted that FCM’ s approach to calculating with these relationships could perform multivariate time-series forecasting with high performance. However, especially in real-world systems and related data sets, numerical values that clearly express the relationships between time series elements are not included, and this is a challenge. Another challenge that FCMs have for these problems is that real-world systems are highly dynamic structures and these relationships have variable properties in different situations. We evaluated these challenges as the main motivation factor and developed a GA-based method to determine system relationships for different states of the system. Then, in order to dynamically handle these relationships determined for different states on FCM, we took advantage of neural architectures that take the initial concept vectors as input and calculate the relationships between these concepts. In order to evaluate the performance of the time-series forecasting approach we developed, we performed time-series forecasting on two different scenarios using an artificially generated data set and a benchmark data set containing real-world data. In this way, we saw that the time series modeling performance of our proposed system is over 95%. FCMs perform the calculations they have made until the system becomes stable. This allows time series analyses to be performed not only depending on time but also depending on the steady state. Therefore, using this capability of the approach we developed to model time series formed from crowd analysis data obtained with video analytics is quite suitable in terms of providing the contribution points we present in this article. In this context, we integrated our FCM-based time series forecasting approach to two different video analytics scenarios by applying two different crowd analysis approaches we developed within the scope of this study. The success of our proposed method is over 95% both for performing for forecasting the time series obtained as a result of crowd analysis. Beside, crowd analysis approaches developed in this study have similar performance to state of the art approaches in the literature.}, archive = {J_ACCESS}, author = {Turan Goktug Altundogan and Mehmet Karaköse and Orhan Yaman and Senem Tanberk and Fatih Mert and Asım Egemen Yılmaz}, doi = {10.1109/ACCESS.2025.3542190}, journal = {IEEE Access}, pages = {33813-33833}, shortjournal = {IEEE Access}, title = {Dynamic fuzzy cognitive maps-based crowd analysis using time series obtained from video processing}, volume = {13}, year = {2025}, }
• (2025). Methodology for commercial vehicle mechanical systems maintenance: Data-driven and deep-learning-based prediction. ACCESS, 13, 33799-33812. (www)

  @article{ , abstract = {This paper presents a predictive maintenance (PdM) strategy for commercial vehicles, focusing on the turbocharger—a critical yet often under-monitored component. By combining sensor signals, workshop maintenance logs, and technical specifications, the study demonstrates how data-driven deep-learning techniques can robustly identify pending failures. Specifically, Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) architectures were employed to capture temporal dependencies and detect patterns that conventional approaches and purely onboard monitoring might overlook. Results on real-world fleet data indicate that BiLSTM achieved higher recall (98.65%) and a lower cost-score than standard LSTM, highlighting its effectiveness in minimizing missed failures. Although BiLSTM incurred slightly higher computational overhead, its superior performance underscores the value of integrating multi-sourced data and advanced sequence models for reliable, actionable PdM in heavy-duty fleets.}, archive = {J_ACCESS}, author = {Romulo Gonçalves Lins and Tiago Nascimento de Freitas and Ricardo Gaspar}, doi = {10.1109/ACCESS.2025.3543600}, journal = {IEEE Access}, pages = {33799-33812}, shortjournal = {IEEE Access}, title = {Methodology for commercial vehicle mechanical systems maintenance: Data-driven and deep-learning-based prediction}, volume = {13}, year = {2025}, }
• (2025). A hybrid machine learning-based framework for data injection attack detection in smart grids using PCA and stacked autoencoders. ACCESS, 13, 33783-33798. (www)

  @article{ , abstract = {Cyberattacks, especially data injection attacks, are becoming more common as smart grids are increasingly interconnected. In addition, accurate and unbiased high-quality data is required for model training. Most of the data we collect from the real world is sparse, incomplete, inconsistent, and skewed. To address these issues, we have proposed a framework to detect such attacks in this study. Using a stacked autoencoder architecture, synthetic instances of minority class data were generated. The generated classes address the imbalances in the data to enhance the generalizability of the model and address diverse attack scenarios. Various machine learning algorithms were evaluated, and the Random Forest (RF) model consistently achieved superior accuracy, ranging from 99.32% to 95.89%. In particular, traditional algorithms such as Logistic Regression (LR) exhibited sensitivity to dimensionality reductions, experiencing a 16.96% accuracy drop when the principal components were reduced from all to 10. In contrast, RF demonstrated resilience, with only a 1.67% mean accuracy drop under similar conditions. Both RF and XGBoost (XGB) emerged as standout models, showcasing high accuracy and robust performance even with dimensionality reduction via principal component analysis (PCA). However, reducing PCA components from 10 to 5 led to performance decreases in all models. The Support Vector Machine (SVM) Classifier shows the highest accuracy drop of 14.21%. This study shows the importance of understanding algorithmic behavior and data features and how it can impact the performance of ML models. This analysis will strengthen cybersecurity in smart grids and focusing on the critical need for careful feature selection and tuning, particularly for models sensitive to dimensionality reduction.}, archive = {J_ACCESS}, author = {Shahid Tufail and Hasan Iqbal and Mohd Tariq and Arif I. Sarwat}, doi = {10.1109/ACCESS.2025.3543751}, journal = {IEEE Access}, pages = {33783-33798}, shortjournal = {IEEE Access}, title = {A hybrid machine learning-based framework for data injection attack detection in smart grids using PCA and stacked autoencoders}, volume = {13}, year = {2025}, }
• (2025). HeteroAvatar: Generation of gaussian head avatars with correct geometry using hetero rendering. ACCESS, 13, 33768-33782. (www)

  @article{ , abstract = {Head avatar reconstruction is a crucial research topic with wide-ranging applications. For this purpose, utilizing the promising technique of 3D Gaussian splatting significantly improves rendering quality. However, this method encounters challenges, such as imprecise geometry and noisy surfaces. To address these issues, we propose HeteroAvatar, a novel method for generating a Gaussian head avatar with correct geometry from a monocular video of a dynamic head. To achieve this, we introduce the hetero rendering method, which concurrently employs both 3D Gaussian rendering and mesh rendering techniques during training. This approach enables HeteroAvatar to accurately capture the head geometry with smooth surfaces while maintaining photorealistic rendering quality. Additionally, our approach is capable of generating a mesh-based head avatar, which ensures compatibility with a wide range of traditional graphics applications. Experimental results demonstrate that HeteroAvatar outperforms state-of-the-art methods in terms of reconstructed geometry quality and photorealistic rendering with fewer 3D Gaussians. Project page: https://felizang.github.io/HeteroAvatar.}, archive = {J_ACCESS}, author = {Ahmet Cagatay Seker and Jun Seok Kang and Sang Chul Ahn}, doi = {10.1109/ACCESS.2025.3543163}, journal = {IEEE Access}, pages = {33768-33782}, shortjournal = {IEEE Access}, title = {HeteroAvatar: Generation of gaussian head avatars with correct geometry using hetero rendering}, volume = {13}, year = {2025}, }
• (2025). Congestion control in constrained application protocol for the internet of things: State-of-the-art, challenges, and future directions. ACCESS, 13, 33733-33767. (www)

  @article{ , abstract = {The rapid development of smart sensors has enabled the integration of wireless sensor networks (WSNs) with the Internet. The emergence of the Internet of Things (IoT) represents a significant paradigm shift, with WSNs remaining essential to its framework. Most smart sensor nodes will serve as foundational components, which can cause network congestion. Congestion is a critical factor affecting the performance of IoT networks. The Constrained Application Protocol (CoAP) has been developed by the Internet Engineering Task Force (IETF) for IoT communications. CoAP is a lightweight, request/response-based RESTful protocol designed for resource-constrained nodes. Resource-constrained networks often face limitations in hardware and link capabilities, leading to challenges such as packet loss and increased delay. This review examines CoAP, highlighting its distinct fundamental features that facilitate deployment in constrained IoT environments. This paper provides an overview of WSNs as foundational elements for IoT applications, followed by a discussion of low-power wide area networks (LPWANs) and their standardisation as promising technologies for these applications. This paper discusses prominent state-of-the-art mechanisms for congestion control (CC) in CoAP, highlighting several research challenges and future directions. For controlling congestion in IoT resource-constrained networks, it suggests a three-state Markov model and machine learning-based strategies.}, archive = {J_ACCESS}, author = {Godfrey A. Akpakwu and Topside E. Mathonsi and Tshimangadzo M. Tshilongamulenzhe and Solly P. Maswikaneng and Tonderai Muchenje}, doi = {10.1109/ACCESS.2025.3543415}, journal = {IEEE Access}, pages = {33733-33767}, shortjournal = {IEEE Access}, title = {Congestion control in constrained application protocol for the internet of things: State-of-the-art, challenges, and future directions}, volume = {13}, year = {2025}, }
• (2025). Adaptive sliding mode control of a boost converter with unknown constant power load. ACCESS, 13, 33714-33732. (www)

  @article{ , abstract = {In this paper, output voltage regulation in a boost converter with constant power load (CPL) is carried out by means of sliding-mode control (SMC) with an estimation loop of the output power. The estimation procedure is based on the integral of an odd-symmetric function of the output voltage error, which confers an adaptive nature to the switching regulator. Rational, trigonometric, sigmoid and $\mathop {\mathrm {sign}}\nolimits $ -type odd-symmetric functions are analyzed to select the best candidate for output power estimation. In addition, a two-degree polynomial surface is considered to induce the sliding motions. Subsequently, the corresponding conditions for the existence of the sliding mode and for the stability of the equilibrium point including the estimation dynamics are derived. One of the main features of this proposal is that the resulting controller can be implemented analogically, requiring operational amplifier-based circuits plus a divider. PSIM $^{\unicode {0x00A9}} $ and MATLAB $^{\unicode {0x00A9}} $ simulations show a fast recovery in response to large-signal disturbances in the load power and zero steady-state output voltage error. Experimental results obtained from a 500 W prototype are in perfect agreement with both theoretical predictions and numerical simulations.}, archive = {J_ACCESS}, author = {David A. Zambrano-Prada and Abdelali El Aroudi and Oswaldo López-Santos and Luis Vázquez-Seisdedos and Luis Martí-Nez-Salamero}, doi = {10.1109/ACCESS.2025.3543659}, journal = {IEEE Access}, pages = {33714-33732}, shortjournal = {IEEE Access}, title = {Adaptive sliding mode control of a boost converter with unknown constant power load}, volume = {13}, year = {2025}, }
• (2025). Client selection for generalization in accelerated federated learning: A multi-armed bandit approach. ACCESS, 13, 33697-33713. (www)

  @article{ , abstract = {Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. It has attracted a growing interest in recent years due to its advantages in terms of privacy considerations, and communication resources. In FL, selected clients train their local models and send a function of the models to the server, which consumes a random processing and transmission time. The server updates the global model and broadcasts it back to the clients. The client selection problem in FL is to schedule a subset of the clients for training and transmission at each given time so as to optimize the learning performance. In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). We analyze BSFL theoretically, and show that it achieves a logarithmic regret, defined as the loss of BSFL as compared to a genie that has complete knowledge about the latency means of all clients. We conducted evaluations under both i.i.d. and non-i.i.d. scenarios using a synthetic dataset with a linear regression model and two well-known datasets, Fashion-MNIST and CIFAR-10 with CNN-based classification models. The results demonstrate that BSFL outperforms existing methods.}, archive = {J_ACCESS}, author = {Dan Ben Ami and Kobi Cohen and Qing Zhao}, doi = {10.1109/ACCESS.2025.3543441}, journal = {IEEE Access}, pages = {33697-33713}, shortjournal = {IEEE Access}, title = {Client selection for generalization in accelerated federated learning: A multi-armed bandit approach}, volume = {13}, year = {2025}, }
• (2025). Design choices for an integrated optical electric field sensor in lithium niobate on insulator. ACCESS, 13, 33683-33696. (www)

  @article{ , abstract = {Electric field sensors have demanding requirements regarding their measurable field strength and frequency range. The research community and industry are applying new approaches from related fields to meet these requirements. One approach is to exploit the electro-optical properties and fabrication methods of lithium niobate known from optical communication. This path is pursued in this work, showing the influence of several parameters, including device orientation relative to the crystal axis, on the performance of the chip as an electric field sensor. For this, several microresonators and Bragg gratings were fabricated on a lithium niobate chip and characterized optically and electro-optically on chip with electrodes, as well as in a free space arrangement, where an external electric field is applied to validate the approach for industry applications. A normalized modulation shift of ~110 nm2/V for a microresonator aligned along the extraordinary crystal axis and a shift of ~60 nm2/V for the Bragg grating was measured on chip. Varying the resonator size, a slope of ~(10 mV)/(1 kV/cm) to ~(20 mV)/(1 kV/cm) could be achieved in a free space arrangement at a fixed laser wavelength and with ac electric fields. Depending on the applied field strength, the resonator size is identified as the main factor in achieving the desired measurement range. Including a drop waveguide to the layout of the resonator achieves additional power tunability. The Bragg grating shows to be a valid alternative if an even smaller footprint of below $1~\mu $ m $\times 100~\mu $ m is favorable. A strong linear relation of the modulation slope of ~ (1.5 mV)/(1 kV/cm) was achieved for a locally applied ac field on-chip.}, archive = {J_ACCESS}, author = {Daniel Wackernagel and Fabian Kaufmann and Andreas Maeder and Rachel Grange and Christian M. Franck}, doi = {10.1109/ACCESS.2025.3538846}, journal = {IEEE Access}, pages = {33683-33696}, shortjournal = {IEEE Access}, title = {Design choices for an integrated optical electric field sensor in lithium niobate on insulator}, volume = {13}, year = {2025}, }
• (2025). Message from the new editor-in-chief of IEEE access. ACCESS, 13, 33682. (www)

  @article{ , archive = {J_ACCESS}, author = {Mehrdad Saif}, doi = {10.1109/ACCESS.2025.3540535}, journal = {IEEE Access}, pages = {33682}, shortjournal = {IEEE Access}, title = {Message from the new editor-in-chief of IEEE access}, volume = {13}, year = {2025}, }
• (2025). Semi-discretization of a euler-bernoulli beam and its application to motion planning. ACCESS, 13, 33665-33681. (www)

  @article{ , abstract = {We consider a Euler-Bernoulli beam with sliding cantilever boundary conditions at both ends. The control input to the beam is the force acting on one of the cantilevers. We derive an $n^{\mathrm {th}}$ -order semi-discrete approximation of the beam PDE and prove that the solution to the $n^{\mathrm {th}}$ -order semi-discrete system converges to the solution of the PDE as n tends to infinity. The motion planning problem addressed in this paper is to find a control input which will transfer the beam PDE from one steady state to another over a prescribed time interval. To address this problem, we design control inputs for transferring the semi-discrete systems from one steady state to another using the flatness technique. We show that a control input which solves the motion planning problem for the beam PDE can be obtained as a limit of a sequence of control inputs which solve certain motion planning problems for a sequence of semi-discrete systems of increasing order. We illustrate our theoretical results in simulations.}, archive = {J_ACCESS}, author = {Soham Chatterjee and Aman Batra and Vivek Natarajan}, doi = {10.1109/ACCESS.2025.3537830}, journal = {IEEE Access}, pages = {33665-33681}, shortjournal = {IEEE Access}, title = {Semi-discretization of a euler-bernoulli beam and its application to motion planning}, volume = {13}, year = {2025}, }
• (2025). An improved triangulation oversampling method for processing unbalanced data. ACCESS, 13, 33655-33664. (www)

  @article{ , abstract = {In classification tasks, the algorithms heavily depend on big data, yet data category imbalance hinders the model’s ability to adequately learn from scarce class samples, impacting its overall learning capacity. This paper addresses the challenge of data imbalance in data-driven classification problems, which introduce an Improved Finite Elements-Synthetic Minority Oversampling Technique (IFE-SMOTE) for data balancing. This method triangulates the sample space leveraging FE-SMOTE, designates three strategic points within each split element based on defined rules, and synthetically generates samples within the linear vicinity of these points. The mathematical expectation of generated samples aligns with the triangle center of the original minority samples, while their variance closely mirrors that of the triangle center, ensuring statistical consistency with the original data. The corresponding theorem is provided and its validity is proved. Numerical experiments confirm the effectiveness of the proposed IFE-SMOTE method. The IFE-SMOTE algorithm classified eight datasets and compared them with other oversampling algorithms using G-mean, F-measure, and AUC. IFE-SMOTE’s average scores were 0.9273, 0.9754, and 0.9309, respectively, outperforming FE-SMOTE by 0.0268, 0.0059, and 0.0253, and other algorithms’ averages by 0.0493, 0.0169, and 0.0402.}, archive = {J_ACCESS}, author = {Jingjing Liu and Yefeng Liu and Yanwei Ma and Qichun Zhang}, doi = {10.1109/ACCESS.2025.3538867}, journal = {IEEE Access}, pages = {33655-33664}, shortjournal = {IEEE Access}, title = {An improved triangulation oversampling method for processing unbalanced data}, volume = {13}, year = {2025}, }
• (2025). LED-based optical wireless power transmission for automatic tracking and powering mobile object in real time. ACCESS, 13, 33643-33654. (www)

  @article{ , abstract = {This research proposes an optical wireless power transmission (OWPT) system utilizing light-emitting diodes (LED) to enable real-time automatic charging for mobile devices. The system features a high-power, high-efficiency optical transmission design, incorporating a dual-axis beam-steering mechanism to maintain precise alignment with moving receivers in free space. By integrating machine vision and predictive control algorithms, the system ensures accurate tracking and stable power delivery under dynamic operating conditions. Simulation results show a large-scale power charging range. Experimental results demonstrate an output of up to 0.8 W from a 40 cm2 GaAs photovoltaic receiver at a 1 m distance. Additionally, the system supports the uninterrupted operation of low-power devices moving at speeds of up to 30 cm/s within a 4 m range. Validated through simulations and experiments, the system showcases its potential of automatic OWPT technology. It offers a reliable and efficient power solution for application scenarios such as smart homes and factories, eliminating the constraints associated with batteries or physical cables.}, archive = {J_ACCESS}, author = {Mingzhi Zhao and Tomoyuki Miyamoto}, doi = {10.1109/ACCESS.2025.3542769}, journal = {IEEE Access}, pages = {33643-33654}, shortjournal = {IEEE Access}, title = {LED-based optical wireless power transmission for automatic tracking and powering mobile object in real time}, volume = {13}, year = {2025}, }
• (2025). SoGAR: Self-supervised spatiotemporal attention-based social group activity recognition. ACCESS, 13, 33631-33642. (www)

  @article{ , abstract = {Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across spatio-temporal domains. Our proposed approach efficiently uses transformer-based encoders to alleviate the weakly supervised setting of group activity recognition. By leveraging the benefits of transformer models, our approach can model long-term relationships along spatio-temporal dimensions. Our proposed SoGAR method achieves state-of-the-art results on three group activity recognition benchmarks, namely JRDB-PAR, NBA, and Volleyball datasets, surpassing the current state-of-the-art in terms of F1-score, MCA, and MPCA metrics.}, archive = {J_ACCESS}, author = {Naga VS Raviteja Chappa and Pha Nguyen and Alexander H. Nelson and Han-Seok Seo and Xin Li and Page Daniel Dobbs and Khoa Luu}, doi = {10.1109/ACCESS.2025.3541986}, journal = {IEEE Access}, pages = {33631-33642}, shortjournal = {IEEE Access}, title = {SoGAR: Self-supervised spatiotemporal attention-based social group activity recognition}, volume = {13}, year = {2025}, }
• (2025). Obstacle avoidance in a crowd with a plastic recurrent mushroom body model. ACCESS, 13, 33619-33630. (www)

  @article{ , abstract = {In safety-critical environments, effective obstacle avoidance requires robots to navigate safely while minimizing power consumption to sustain endurance. Continuous memory plays a crucial role in predicting pedestrian intentions, allowing for more accurate and proactive navigation. While conventional spiking neural networks (SNNs) provide low power consumption, they often lack of sufficient long-term memory capabilities. To address this, we propose the Plastic Recurrent Mushroom Body (PRMB) model, a novel bio-inspired SNN architecture integrates by dendritic structures, restructured connections, and a stable equation update to enhance spatiotemporal memory. Experimented in crowded settings across varying human densities, the PRMB model demonstrated exceptional obstacle avoidance performance, effectively balancing memory retention with computational efficiency. This work advances safe and effective obstacle avoidance, facilitating the deployment of intelligent robotic systems in challenging, real-world scenarios.}, archive = {J_ACCESS}, author = {Liying Tao and Zonglin Yang and Gaoming Li and Delong Shang}, doi = {10.1109/ACCESS.2025.3533590}, journal = {IEEE Access}, pages = {33619-33630}, shortjournal = {IEEE Access}, title = {Obstacle avoidance in a crowd with a plastic recurrent mushroom body model}, volume = {13}, year = {2025}, }
• (2025). Predicting quality of multimedia experience using electrocardiogram and respiration signals. ACCESS, 13, 33600-33618. (www)

  @article{ , abstract = {Quality of experience (QoE) has become a crucial field of study in recent years as multimedia technology continues to grow in popularity. Accurate and reliable methods of evaluating QoE are necessary, but they can be challenging due to their multidimensional nature. QoE, a comprehensive measure of user satisfaction with multimedia services, is influenced by human, system, and contextual dimensions. However, the human factor is often overlooked in QoE assessment studies, which plays a pivotal role in accurately representing end-users perceptions of quality. This article proposes using physiological signals to assess the QoE of immersive multimedia applications, which provides insights into users’ emotional and cognitive states. Specifically, we present an implicit evaluation of user QoE in terms of perceived quality, sense of presence, and user content preference using a publicly accessible database. We compare the performances of several machine learning (ML) and deep learning (DL) models in predicting QoE subjective scores from electrocardiograms and respiration signals. The ML models considered are support vector machines, k-nearest neighbor, and random forest algorithms; the DL models considered are bidirectional long-short-term memory (BLSTM), convolutional neural networks (CNN), and a hybrid model of CNN and BLSTM (CNN-BLSTM). We evaluate the performances of these models based on individual and fusion modalities. Additionally, we present a comparison of 2-class and 3-class classifications of QoE scores based on the fusion data. The results demonstrate that the BLSTM network, based on fusion data, has the best performance for classifying QoE subjective scores into two classes, with an F1-score of 87.55% and 81.22% for perceived audio quality and overall quality, respectively.}, archive = {J_ACCESS}, author = {Sowmya Vijayakumar and Ronan Flynn and Peter Corcoran and Niall Murray}, doi = {10.1109/ACCESS.2024.3420103}, journal = {IEEE Access}, pages = {33600-33618}, shortjournal = {IEEE Access}, title = {Predicting quality of multimedia experience using electrocardiogram and respiration signals}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey of fire weather index (FWI) systems and IoT applications in peatland fire management. ACCESS, 13, 33579-33599. (www)

  @article{ , abstract = {This paper presents a theoretical overview of peatland forest fire management, focusing on the integration of IoT-based monitoring systems and Fire Weather Index (FWI) for improved fire prediction and mitigation. The paper examines the impact of wildfires on carbon loss in Southeast Asian peatland forests and reviews existing IoT solutions for monitoring environmental conditions, such as temperature, humidity, and soil moisture. Furthermore, the paper surveys the current advancements in FWI systems, highlighting the role of machine learning in improving the accuracy of fire risk predictions by analyzing large environmental datasets. The research discusses challenges related to integrating IoT with FWI systems and considers critical factors such as topography, altitude, and human activities. By synthesizing existing literature, this paper aims to outline the current state of research and propose directions for future work to enhance fire risk prediction and management in peatland ecosystems.}, archive = {J_ACCESS}, author = {Lu Li}, doi = {10.1109/ACCESS.2025.3543570}, journal = {IEEE Access}, pages = {33579-33599}, shortjournal = {IEEE Access}, title = {A comprehensive survey of fire weather index (FWI) systems and IoT applications in peatland fire management}, volume = {13}, year = {2025}, }
• (2025). Development and evaluation of enhanced navigation filters for underwater glider. ACCESS, 13, 33571-33578. (www)

  @article{ , abstract = {In this research, we introduce advanced navigation filters designed for underwater gliders, which play a crucial role in collecting oceanic data such as temperature and salinity. Due to their reliance on low-cost, low-precision MEMS (Microelectromechanical Systems)-based IMUs (Inertial Measurement Units) and unique operational constraints, these gliders experience significant navigational inaccuracies. To address this, we proposed two filters, each optimized for minimal sensor use: the first integrates an accelerometer, magnetometer, and depth gauge, and the second adds a gyroscope. These filters estimate the glider’s velocity and position by converting sensor data into the navigation coordinate system. We not only conducted rigorous evaluations of our proposed filters through comprehensive simulations and Monte Carlo numerical analyses, but also fabricated an actual glider to apply these filters in real sea trials. The results of these sea trials were consistent with our simulation predictions, confirming the effectiveness of our navigation filters in practical scenarios.}, archive = {J_ACCESS}, author = {Tae Suk Yoo and San Hee Kang and Junghan Lee and Daehyeong Ji and Moon Hwan Kim}, doi = {10.1109/ACCESS.2025.3539247}, journal = {IEEE Access}, pages = {33571-33578}, shortjournal = {IEEE Access}, title = {Development and evaluation of enhanced navigation filters for underwater glider}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning-based dynamic scheduling for real-time applications in LTE and RAN slicing for eMBB in 5G. ACCESS, 13, 33555-33570. (www)

  @article{ , abstract = {While LTE (4G) networks offer high-speed data transmission, 5G is necessary to meet the growing demands of modern communication. The main goals of 5G are to make communications more agile by significantly increasing network capacity, lowering latency, and speeding up the networks to handle a massive number of connected devices. To materialize this vision, a new concept has emerged in 5G called network slicing; hence, network providers can offer tailored quality of service based on every type of user according to their specific requirements. However, efficient resource allocation in LTE and across slices or even within a slice in 5G systems to meet QoS is still one of the most critical issues for network designers while resources change over the complexity, dynamic nature, and resource constraints of the network. Classic resource allocation methods cannot cope with the dynamics of the changing network environment; hence, there is an increasing need for faster and smarter resource allocation schemes. This paper proposes an efficient dynamic scheduling algorithm based on machine learning methods for real-time applications in LTE networks, extending to 5G using Radio Access Network (RAN) slicing with a focus on enhanced Mobile Broadband (eMBB) slices. Carrying out extensive simulations and evaluations by using the ns-3 simulator, we compare and analyze various downlink scheduling rules in terms of system throughput, spectral efficiency, average delay, and packet loss ratio. The simulation results show that the proposed algorithm always outperforms the QoS performance, especially for real-time applications.}, archive = {J_ACCESS}, author = {Houssem Eddine Benmadani and Mohamed Azni and Turki Essa Alharbi and Mohammed S. Alzaidi and Mohamed Tounsi}, doi = {10.1109/ACCESS.2025.3541531}, journal = {IEEE Access}, pages = {33555-33570}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning-based dynamic scheduling for real-time applications in LTE and RAN slicing for eMBB in 5G}, volume = {13}, year = {2025}, }
• (2025). Research on seismic noise attenuation method based on wavelet transform and f-x-VMD. ACCESS, 13, 33544-33554. (www)

  @article{ , abstract = {Random noise in seismic data seriously affects velocity analysis and migration imaging quality. Effective attenuation of random noise is an important link in seismic data processing. The traditional random noise attenuation method needs to select appropriate processing parameters, which has poor adaptability and requires a lot of calculation, resulting in the random noise can not be effectively attenuated. This paper combines f-x Variational Mode Decomposition (f-x-VMD) with adaptive wavelet threshold noise attenuation method and proposes a seismic noise attenuation method based on wavelet transform and f-x-VMD. In order to better attenuate the random noise, the signal is first converted to the frequency domain and is decomposed into intrinsic mode functions (IMFs) by VMD, then the IMFs are attenuated by the wavelet threshold noise attenuation method in the frequency domain and the time domain respectively, and finally the IMFs are reconstructed as signal through the inverse wavelet transform. The experimental results of simulated seismic data and real seismic data show that the proposed method has better random noise attenuation effect than the traditional method.}, archive = {J_ACCESS}, author = {Chenhao Lian and Wei Chen}, doi = {10.1109/ACCESS.2025.3543607}, journal = {IEEE Access}, pages = {33544-33554}, shortjournal = {IEEE Access}, title = {Research on seismic noise attenuation method based on wavelet transform and f-x-VMD}, volume = {13}, year = {2025}, }
• (2025). Integrate question information with learning behavior for knowledge tracing. ACCESS, 13, 33532-33543. (www)

  @article{ , abstract = {This paper explores Knowledge Tracing (KT) and its role in modeling students’ knowledge progression over time. While traditional approaches such as Bayesian Knowledge Tracing and Deep Knowledge Tracing have established a solid foundation in this field, previous research often overlooked the complex interactions between problem features and learner behaviors. To address this gap, this paper introduces the Knowledge Tracking Model for Integrating Question Information and Learning Behaviour (IQILB-KT), a novel framework that enhances the precision of knowledge tracing in online educational systems. By integrating the effects of question characteristics and student behaviour via a channel attention mechanism, the model captures deeper insights into both question information and the learning process. Furthermore, the IQILB-KT model employs a Bottleneck Attention Module (BAM) to calculate learning behaviour synergies, replacing the previous reliance on covariance statistics. This enhances the stability and accuracy of the model when dealing with learning behaviour synergy data. Finally, the efficacy of the IQILB-KT model was validated through rigorous testing on two real-world datasets from the ASSISTments educational platform. The results demonstrate that the model outperforms all existing methods in predicting students’ future performance and tracking their knowledge states in real time.}, archive = {J_ACCESS}, author = {Sheng Su and Pingfei Zeng and Chunhua Kang and Tao Xin}, doi = {10.1109/ACCESS.2025.3543536}, journal = {IEEE Access}, pages = {33532-33543}, shortjournal = {IEEE Access}, title = {Integrate question information with learning behavior for knowledge tracing}, volume = {13}, year = {2025}, }
• (2025). Low-noise modified-RGC transimpedance amplifier with bandwidth enhancement using an intrinsic negative-RC network. ACCESS, 13, 33521-33531. (www)

  @article{ , abstract = {This paper presents a dual feedback transimpedance amplifier (TIA) with a modified regulated-cascode (RGC) topology that employs a negative resistance-capacitance (NRC) network to enhance both bandwidth and noise performance. By combining a negative resistance-capacitance network with transconductance boosting (gm-boosting), the proposed topology provides greater flexibility in circuit design, improving the performance of the circuit, in relation to both input and output nodes, compared to conventional RGC topologies. This increased design flexibility enables optimal input impedance with lower power consumption, particularly in the presence of large photodiode (PD) capacitance, while avoiding output node headroom limitations. Additionally, the use of a negative capacitance at the output node allows for increment of the output resistance (gain) without concerns about headroom limitations or load capacitance constraints, resulting in a significant noise reduction and bandwidth enhancement. To demonstrate the effectiveness of the proposed circuit techniques, the TIA topology is fabricated in $0.35~\mu $ m CMOS technology. Measurement results show 69 dB $\Omega $ transimpedance gain, 1.7 GHz 3-dB bandwidth, and 6 pA/ $\surd $ Hz input referred noise current spectral density. The circuit power consumption and area are 7.4 mW and of 0.01 mm2, respectively.}, archive = {J_ACCESS}, author = {Behnam Abdollahi and Baset Mesgari and Saeed Saeedi and Zahra Sohrabi and Horst Zimmermann}, doi = {10.1109/ACCESS.2025.3543679}, journal = {IEEE Access}, pages = {33521-33531}, shortjournal = {IEEE Access}, title = {Low-noise modified-RGC transimpedance amplifier with bandwidth enhancement using an intrinsic negative-RC network}, volume = {13}, year = {2025}, }
• (2025). Measuring catastrophic forgetting in cross-lingual classification: Transfer paradigms and tuning strategies. ACCESS, 13, 33509-33520. (www)

  @article{ , abstract = {Cross-lingual transfer leverages knowledge from a resource-rich source language, commonly English, to enhance performance in less-resourced target languages. Two widely used strategies are: Cross-Lingual Validation (CLV), which involves training on the source language and validating on the target language, and Intermediate Training (IT), where models are first fine-tuned on the source language and then further trained on the target language. While both strategies have been studied, their effects on encoder-based models for classification tasks remain underexplored. In this paper, we systematically compare these strategies across six multilingual classification tasks, evaluating downstream performance, catastrophic forgetting, and both zero-shot and full-shot scenarios. Additionally, we contrast parameter-efficient adapter methods with full-parameter fine-tuning. Our results show that IT generally performs better in the target language, whereas CLV more effectively preserves source-language knowledge across multiple cross-lingual transfers. These findings underscore the trade-offs between optimizing target performance and mitigating catastrophic forgetting.}, archive = {J_ACCESS}, author = {Boshko Koloski and Blaž Škrlj and Marko Robnik-Šikonja and Senja Pollak}, doi = {10.1109/ACCESS.2025.3543608}, journal = {IEEE Access}, pages = {33509-33520}, shortjournal = {IEEE Access}, title = {Measuring catastrophic forgetting in cross-lingual classification: Transfer paradigms and tuning strategies}, volume = {13}, year = {2025}, }
• (2025). High-speed islanding detection in smart grids using a state observer and IoT infrastructure. ACCESS, 13, 33496-33508. (www)

  @article{ , abstract = {In this study, we introduce an innovative islanding detection scheme for grid-connected distribution networks utilizing a Discrete Ensemble Kalman Filter (DEKF) in conjunction with Internet of Things (IoT) devices for real-time monitoring and communication. Voltage signals from the point of common coupling (PCC) are acquired via IoT devices using the Message Queuing Telemetry Transport (MQTT) protocol. The DEKF accurately estimates PCC voltage states under various conditions, while IoT integration ensures continuous data acquisition and swift communication with the monitoring system. Residuals, computed as the difference between measured and estimated signals, serve as reliable indicators for islanding detection. Simulations in MATLAB Simulink demonstrate a detection accuracy of 99.5% with a response time under 1.5 milliseconds. The scheme proves robust against non-islanding events, offering a dependable and efficient solution for smart grid protection with minimal computational overhead.}, archive = {J_ACCESS}, author = {Shahid Karim and Prajoona Valsalan and Faisal Alsaif and Haseeb Tahir and Faisal Mumtaz}, doi = {10.1109/ACCESS.2025.3541983}, journal = {IEEE Access}, pages = {33496-33508}, shortjournal = {IEEE Access}, title = {High-speed islanding detection in smart grids using a state observer and IoT infrastructure}, volume = {13}, year = {2025}, }
• (2025). Cooperative control of intersection traffic signals based on multi-agent reinforcement learning for carbon dioxide emission reduction. ACCESS, 13, 33485-33495. (www)

  @article{ , abstract = {Abnormal weather is occurring around the world, including the hottest weather in 174 years of observation records, the largest fire in Europe’s observation records, and approximately twice the average annual rainfall recorded in one day. This abnormal climate is highly related to greenhouse gases, and efforts to reduce emissions are required in various fields. This study aims to reduce carbon dioxide emissions in the transportation sector, which accounts for a high proportion of emissions. A multi-agent reinforcement learning technique is used for adaptive traffic signal control, and especially a novel cooperative approach is introduced, when considering neighboring intersections. We consider not only the adjacent intersection’s last reward as a Q-function but also its state and action as state. This method has the advantage of considering only vehicles from adjacent intersections that enter an intersection. The proposed method was evaluated on roads in Icheon City, and the results show that it reduces waiting time and carbon dioxide emissions.}, archive = {J_ACCESS}, author = {Hyemin Kim and Jinhyuk Park and Dongbeom Kim and Chulmin Jun}, doi = {10.1109/ACCESS.2025.3539685}, journal = {IEEE Access}, pages = {33485-33495}, shortjournal = {IEEE Access}, title = {Cooperative control of intersection traffic signals based on multi-agent reinforcement learning for carbon dioxide emission reduction}, volume = {13}, year = {2025}, }
• (2025). AETUnet: Enhancing retinal segmentation with parameter-efficient UNet architecture and lightweight attention mechanism. ACCESS, 13, 33471-33484. (www)

  @article{ , abstract = {Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults, making early and accurate detection crucial. This paper presents AETUnet (Attention Enhanced Transformer UNet), a new lightweight architecture to improve on-response retinal lesion segmentation. By combining expanded convolutions with a lightweight attension mechanism, AETUnet improved segmentation accuracy while remaining computationally efficient. Evaluated on the DRIVE and IDRiD datasets, AETUnet has outperformed existing models like Unet++ and ResUnet. On the DRIVE dataset, it achieved a mean Intersection over Union (MIoU) of 86.5, a Dice Score of 0.884, and an Accuracy of 97.64%. For haemorrhage segmentation on the IDRiD dataset, AETUnet recorded an MIoU of 89.33, a Dice Score of 0.882, and Accuracy of 98.46%, showcasing strong performance across all lesion types. AETUnet, with its low number of model parameters and excellent results is clinically practical for real-time applications in resource-constrained settings and provides important foundational steps towards more wide-ranging imaging tasks in other domains.}, archive = {J_ACCESS}, author = {Kinza Nazir and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2025.3539372}, journal = {IEEE Access}, pages = {33471-33484}, shortjournal = {IEEE Access}, title = {AETUnet: Enhancing retinal segmentation with parameter-efficient UNet architecture and lightweight attention mechanism}, volume = {13}, year = {2025}, }
• (2025). A comprehensive analysis on enhancing multi-objective evolutionary algorithms using chaotic dynamics and dominant relationship-based search strategies. ACCESS, 13, 33455-33470. (www)

  @article{ , abstract = {In optimization and decision-making, multi-objective optimization has emerged as a pivotal challenge. Over the past three decades, the concerted efforts of scholars and practitioners across various disciplines have significantly advanced the study and implementation of Multi-Objective Evolutionary Algorithms (MOEAs). MOEAs stand at the forefront of multi-objective decision-making methodologies, marking a vibrant area of inquiry within evolutionary computation. This body of work categorizes MOEAs into three distinct streams: Decomposition-based MOEA algorithms, Dominant relationship-based MOEA algorithms, and Evaluation index-based MOEA algorithms. Focusing specifically on dominance-based MOEAs, this study integrates them with chaotic evolution (CE) strategies to enhance the efficacy of multi-objective optimization processes. Through comparative analysis against traditional algorithms, the newly proposed chaotic MOEA demonstrates superior optimization performance, thereby setting a robust groundwork for the continuous evolution and application of MOEAs.}, archive = {J_ACCESS}, author = {Zitong Wang and Yan Pei and Jianqiang Li}, doi = {10.1109/ACCESS.2024.3430970}, journal = {IEEE Access}, pages = {33455-33470}, shortjournal = {IEEE Access}, title = {A comprehensive analysis on enhancing multi-objective evolutionary algorithms using chaotic dynamics and dominant relationship-based search strategies}, volume = {13}, year = {2025}, }
• (2025). Remote sensing image scene classification based on mutual learning with complementary multi-features. ACCESS, 13, 33436-33454. (www)

  @article{ , abstract = {A novel neural network model based on mutual learning, with complementary multi-features (MLCMFNet), is proposed for scene classification, addressing common issues with insufficient extraction to more effectively learn target features from remote sensing images. First, a DenseNet-67 framework is adopted as the backbone network for local feature extraction, to identify multi-level image features. Second, a multi-feature fusion module (MFFM) is designed to extract and fuse feature information from different images by effectively locating, normalizing, fusing, and integrating significant regional features. Third, a multi-attention fusion module (MAFM) is introduced to extract and fuse feature information from different depths by focusing on specific features used to extract more representative local image details. In addition, a Swin transformer module (STM) is employed to extract global image features. Finally, unlike conventional knowledge distillation in which a teacher network guides a student network, the two networks constructed in this study establish a complementary relationship between local and global features. As such, these networks learn from each other and share experiences to more effectively aggregate extracted details and identify target features. The proposed MLCMFNet method was evaluated using four popular open-source datasets (RSSCN7, AID, UC-Merced, and NWPU-RESISC45). Experimental results demonstrated that the proposed MLCMF-Net framework effectively improved the accuracy of scene classification compared to existing models.}, archive = {J_ACCESS}, author = {Anzhi Chen and Mengyang Xu}, doi = {10.1109/ACCESS.2025.3543459}, journal = {IEEE Access}, pages = {33436-33454}, shortjournal = {IEEE Access}, title = {Remote sensing image scene classification based on mutual learning with complementary multi-features}, volume = {13}, year = {2025}, }
• (2025). Employing hybrid pointer networks with deep reinforcement learning for drone routing in delivery using public transportation as carriers. ACCESS, 13, 33424-33435. (www)

  @article{ , abstract = {Since the inception of Autonomous Aerial Vehicles (AAVs), also known as drones, and the rapid growth of the AAV industry, significant steps have been taken to design drones for diverse applications. Particularly in the last decade, the industry’s progress has been so swift that novel uses for drones continue to emerge regularly. One field that has demonstrated promising potential for future applications is drone-based delivery services. However, a notable challenge in utilizing drones for deliveries is their limited operational range. To address this challenge, we propose a machine learning model that leverages public transportation vehicles as carriers to extend the range of drones, integrating Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL) to solve the complex routing problem. The primary objective is to minimize the total flying distance covered by drones while adhering to the capacity limitations and scheduled routes of public transportation buses. Notably, our model ensures that no drone is serviced if the bus roof capacity is fully utilized. By employing HPNs, we effectively manage complicated interactions between drones, buses, and delivery points, simplifying the optimization process to achieve cutting-edge results. The model was carefully optimized and fine-tuned after being trained in a simulated environment to maximize its performance. Results show that combining drone delivery services with bus service significantly lowers energy usage and improves the system’s capacity to fulfill delivery obligations.}, archive = {J_ACCESS}, author = {Mahmoud Masoud and Omar Ashraf Ibrahim and Mohammed Elhenawy}, doi = {10.1109/ACCESS.2025.3543007}, journal = {IEEE Access}, pages = {33424-33435}, shortjournal = {IEEE Access}, title = {Employing hybrid pointer networks with deep reinforcement learning for drone routing in delivery using public transportation as carriers}, volume = {13}, year = {2025}, }
• (2025). Grasping deformable objects in industry application: A comprehensive review of robotic manipulation. ACCESS, 13, 33403-33423. (www)

  @article{ , abstract = {Grasping deformable objects remains a challenging operational task for robots in diverse industrial applications. Different characteristics of deformable objects to be gripped need to be considered in the mechanical design of the gripper. Mechanical grippers often rely on sensors and appropriate control strategies to grasp deformable objects. This study classifies deformable objects, grippers and gripper manufacturers, and their corresponding gripping strategies. In the study of control strategies, model-based algorithm control strategies are often ineffective as often the objects to be gripped are unknown in terms of its rigidity and other morphological characteristics. In contrast, model-free algorithms do not need parametric information of the objects as only input-output signal is required. This allows the model-free controlled grippers adapt to diverse and unstructured environments. Finally, the advantages and disadvantages of current deformable object-grasping techniques are discussed and summarized. The challenges and future directions of robots grasping deformable objects are pointed out.}, archive = {J_ACCESS}, author = {Wang Yuanyang and Muhammad Nasiruddin Mahyuddin}, doi = {10.1109/ACCESS.2025.3543737}, journal = {IEEE Access}, pages = {33403-33423}, shortjournal = {IEEE Access}, title = {Grasping deformable objects in industry application: A comprehensive review of robotic manipulation}, volume = {13}, year = {2025}, }
• (2025). A game-based charging station selection for UAVs with cost-awareness. ACCESS, 13, 33387-33402. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) are massively utilized as airborne base stations for the growing demand of communication. The fixed battery capacity prevents UAV applications from further development. The existing four types of UAV power supply technologies have their pros and cons with lacking of operation universality. Therefore, a charging station selection framework that combines battery hotswapping and inductive coupling (IC) techniques is developed in this article. The IC strategy for charging station selection is explored and discussed in detail. First, the objective function and constraints of IC strategy among UAV interactions with cost-awareness are established considering the congestion. Second, the non-cooperative game with pure strategy is employed to describe the interaction among UAVs. The proof of existence for Nash equilibrium shows that each UAV will obtain its minimum cost at the Nash equilibrium point. Then, a two-layer iterative Nash equilibrium solving algorithm is designed as the optimization algorithm. The validation and complexity analysis for optimization algorithm are performed. Finally, a case study is simulated and compared with benchmarks. The optimal preference ratio of weighting factors for the effect of flight distance and charging price on charging cost is deduced through introducing the evaluation cost metric. The simulation results demonstrate that the proposed strategy has lower cost than proximity selection strategy. The time consumption of strategy operation is lower than genetic algorithm strategy, which is only on the order of 10-2 seconds. The charging stations are utilized evenly to avoid congestion by mean and variance analysis. And the case study also proves the proposed system strategy is robust and high operation universal.}, archive = {J_ACCESS}, author = {Jiahao He and Bo Li and Xiaomin He and Tao Wang and Yi Zheng and Zhilong Zhao and Tong Ling}, doi = {10.1109/ACCESS.2025.3543410}, journal = {IEEE Access}, pages = {33387-33402}, shortjournal = {IEEE Access}, title = {A game-based charging station selection for UAVs with cost-awareness}, volume = {13}, year = {2025}, }
• (2025). Fast detection and classification of ink by ion mobility spectrometry and artificial intelligence. ACCESS, 13, 33379-33386. (www)

  @article{ , abstract = {The ink can be a significant piece of evidence in the evaluation of written documents. In this work we present a new way of detection and classification of inks from ballpoint pens of different producers, using the ion mobility spectrometry (IMS) technique and artificial intelligence methods. IMS enables fast, sensitive, on-site collection of spectrometric data, which can be immediately analyzed using machine learning methods. In this study, an Advanced IMS (AIMS) instrument was employed to gather ink data, and several machine learning classifiers (including Extra Trees, Gradient Boosting, Random Forest, and Support Vector Classifier) were applied, some of which were integrated into an ensemble voting classifier for enhanced performance. The method demonstrated a high accuracy of 95,23% in classifying inks and provided promising results in estimating the age of written text without compromising the integrity of the sample. These findings highlight the potential applications of the proposed method in forensic investigations, offering a portable, efficient, and non-destructive solution for ink analysis and ageing.}, archive = {J_ACCESS}, author = {Matej Petrík and Emanuel Mataš and Martin Sabo and Michal Ries and Štefan Matejčík}, doi = {10.1109/ACCESS.2025.3543776}, journal = {IEEE Access}, pages = {33379-33386}, shortjournal = {IEEE Access}, title = {Fast detection and classification of ink by ion mobility spectrometry and artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection and performance analysis with exponential smoothing model powered by genetic algorithms and meta optimization. ACCESS, 13, 33361-33378. (www)

  @article{ , abstract = {This study employs a genetic algorithm to optimize the parameters of the Third Order Exponential Smoothing model for predicting on the real-time traffic datasets of the Numenta Anomaly Benchmark (NAB). The genetic algorithm process was executed with different population sizes and gene sets. In addition, a parameter sensitivity analysis was conducted, through which the ideal number of genes and population size providing the best results within the specified range were determined. Moreover, a novel approach incorporating meta-optimization techniques is proposed to enhance the efficiency of the genetic algorithm optimization process, aiming to achieve improved accuracy in anomaly detection. The proposed methodology has been tested on various traffic data scenarios across different datasets to detect deviations critical to traffic management systems. Performance comparisons using the NAB scoring system demonstrate that the method developed in this study outperforms the majority of existing NAB algorithms, as well as the contemporary approaches of Isolation Forest, Multi-Layer Perceptron Regressor (MLPRegressor), and hybrid K-Nearest Neighbors - Gaussian Mixture Models (KNN + GMM), and is competitive with leading algorithms. The proposed approach, which achieved scores of 54.41 for ‘Standard’, 53.95 for ‘reward_low_FP_rate’, and 69.61 for ‘reward_low_FN_rate’, indicates improvements of 3.67%, 4.45%, and 2.63%, respectively, compared to the average scores of the NAB algorithms. The findings indicate that the proposed approach not only detects anomalies with high precision but also dynamically adapts to changing data characteristics without requiring manual recalibration. This study proposes a robust traffic anomaly detection method that ensures reliable monitoring and potentially facilitates effective traffic management and planning.The results of the study can be extended to other areas requiring real-time data monitoring and anomaly detection, offering a scalable solution adaptable to different contexts and requirements.}, archive = {J_ACCESS}, author = {Ali Kerem Güler and Hüseyin Fuat Alsan and Taner Arsan}, doi = {10.1109/ACCESS.2025.3543030}, journal = {IEEE Access}, pages = {33361-33378}, shortjournal = {IEEE Access}, title = {Anomaly detection and performance analysis with exponential smoothing model powered by genetic algorithms and meta optimization}, volume = {13}, year = {2025}, }
• (2025). Enhancing gait analysis for parkinson’s disease detection and severity staging with a parallel Conv1D-efficient transformer and bidirectional GRU hybrid architecture. ACCESS, 13, 33351-33360. (www)

  @article{ , abstract = {Gait analysis is a critical tool for diagnosing and assessing the severity of Parkinson’s disease (PD). This study introduces a novel parallel hybrid architecture combining Conv1D, Efficient Transformers, and Bidirectional GRU layers to analyze gait data for both PD detection and severity staging. Conv1D layers extract local spatial features, Efficient Transformers capture contextual dependencies, and Bidirectional GRUs model temporal patterns in VGRF signals. Designed to balance computational efficiency and scalability, the model demonstrates state-of-the-art performance, achieving 95.7% accuracy in PD detection and 87.3% accuracy in severity staging on the PhysioNet gait dataset. Additionally, the architecture is highly versatile, offering potential for application in other 1D signal analysis tasks.}, archive = {J_ACCESS}, author = {Xiaoli Huan and Hong Zhou and Byungkwan Jung and Long Ma}, doi = {10.1109/ACCESS.2025.3543749}, journal = {IEEE Access}, pages = {33351-33360}, shortjournal = {IEEE Access}, title = {Enhancing gait analysis for parkinson’s disease detection and severity staging with a parallel Conv1D-efficient transformer and bidirectional GRU hybrid architecture}, volume = {13}, year = {2025}, }
• (2025). Research on pre-acceleration protection of dual-end DC distribution systems based on the energy characteristics of transient current signals. ACCESS, 13, 33341-33350. (www)

  @article{ , abstract = {The protection technology for direct current (DC) distribution lines is crucial for the safe and stable operation of the entire system. Currently, there is extensive research on the protection mechanisms based on voltage, current, and other transient quantities during faults in DC distribution systems, which can meet the rapid action requirements. However, there is a lack of consideration for real-world equipment, particularly regarding disturbances and uncontrollable errors. On the other hand, protection schemes based on active protection technologies or devices often overlook the fault characteristics inherent in electrical quantities, indicating that there is room for improvement in rapid response capabilities. Considering this, this paper employs wavelet transform technology to extract the energy characteristics of current signals at the moment of a fault. By integrating the operational characteristics of circuit breakers, the author proposes a pre-acceleration protection scheme for a dual-end DC distribution system based on the energy characteristics of transient current signals. The entire protection mechanism is initiated by the rate of change of fault current, while the circuit breakers in the system are configured with reasonable delays based on a stepped timing approach. The energy characteristics of the capacitor discharge current signal, extracted using Symlets wavelets, serve as the acceleration criterion for the pre-acceleration action, enabling the circuit breaker to swiftly isolate the fault. If the calculated conditions for pre-acceleration are met, the circuit breaker will commence the pre-acceleration action to eliminate the fault, ensuring rapid fault clearance. If the conditions are not satisfied, the circuit breaker will operate according to the predetermined stepped timing. Finally, the protection scheme is validated through the Shenzhen Baolong Industrial City DC demonstration project, assessing its performance under both single-pole and double-pole fault conditions. Simulation results indicate that this protection scheme effectively operates under various scenarios, including single-pole faults, inter-pole faults, and faults occurring at different locations with varying grounding resistances, successfully achieving fault isolation.}, archive = {J_ACCESS}, author = {Zhaoyang Wei}, doi = {10.1109/ACCESS.2025.3543699}, journal = {IEEE Access}, pages = {33341-33350}, shortjournal = {IEEE Access}, title = {Research on pre-acceleration protection of dual-end DC distribution systems based on the energy characteristics of transient current signals}, volume = {13}, year = {2025}, }
• (2025). High-speed and enhanced motion control for a wheeled-legged humanoid robot using a two-wheeled inverted pendulum with roll joint. ACCESS, 13, 33330-33340. (www)

  @article{ , abstract = {Wheeled-legged humanoid robots combine the mobility of wheels with the versatility of legs, offering significant advantages for locomotion. This paper proposes a hierarchical control framework for such robots, using a two-wheeled inverted pendulum with a roll joint (TWIP-R) as a template model. The framework integrates a motion planner and a whole-body controller. The motion planner utilizes a linear quadratic regulator (LQR) to dynamically adjust the zero moment point (ZMP), counteracting centrifugal forces and enabling stable, dynamic movements. Meanwhile, the whole-body controller (WBC), based on centroidal momentum, solves an optimization problem via quadratic programming (QP) while incorporating constraints from the rolling contact condition of the wheels and the dynamics of the humanoid robot. This framework generates optimized torque commands, enabling feasible and stable motion even in dynamic scenarios. Simulations featuring challenging maneuvers, such as slalom, demonstrate its ability to enhance stability and dynamic performance compared to traditional two-wheeled inverted pendulum (TWIP)-based methods by leveraging a dynamics model with roll motion. This framework demonstrates the potential of wheeled-legged humanoid robots to achieve dynamic, stable, and efficient locomotion in a variety of scenarios.}, archive = {J_ACCESS}, author = {Jaewoo An and Jun Young Kim and Myo-Taeg Lim and Yonghwan Oh}, doi = {10.1109/ACCESS.2025.3540107}, journal = {IEEE Access}, pages = {33330-33340}, shortjournal = {IEEE Access}, title = {High-speed and enhanced motion control for a wheeled-legged humanoid robot using a two-wheeled inverted pendulum with roll joint}, volume = {13}, year = {2025}, }
• (2025). A metamaterial loaded high gain low profile RHCP microstrip antenna for X-band applications. ACCESS, 13, 33321-33329. (www)

  @article{ , abstract = {This paper presents the design of a right-hand circularly polarized (RHCP) microstrip antenna loaded with metamaterial to achieve high-gain performance. The metamaterial (MTM), a $5 \times 5~$ array of unit cells with a negative refractive index, is designed on one-sided FR4 dielectric material with dimensions $0.79\lambda _{0} \times 0.79\lambda _{0} \times 0.023\lambda _{0}$ , where $\lambda _{0}$ is the free-space wavelength at 8.76 GHz. Both the antenna and the unit cells are designed on an FR4 substrate with a loss tangent of 0.02 and relative permittivity of 4.4. The antenna and MTM maintain the same area, with an air gap of 1 mm between them. The overall structure achieves a gain of 8.1 dBic at 9.5 GHz with stacking, compared to 4.4 dBic without the MTM stacking. The measured bandwidth of the proposed antenna is 1.98 GHz (8.76-10.74 GHz), which falls within the lower X-band, making it suitable for both military and maritime radar applications.}, archive = {J_ACCESS}, author = {Manoj Kumar Shrivastava and Ripudaman Singh and Diptiranjan Samantaray and Azharuddin Khan and Anil Kumar Gautam and Amit Kumar Singh}, doi = {10.1109/ACCESS.2025.3542460}, journal = {IEEE Access}, pages = {33321-33329}, shortjournal = {IEEE Access}, title = {A metamaterial loaded high gain low profile RHCP microstrip antenna for X-band applications}, volume = {13}, year = {2025}, }
• (2025). Enhancing phishing detection: A machine learning approach with feature selection and deep learning models. ACCESS, 13, 33308-33320. (www)

  @article{ , abstract = {With the rise in cybercrime, phishing remains a significant concern as it targets individuals with fake websites, causing victims to disclose their private information. The effective implementation of phishing detection relies on cost efficiency, with the increased feature extraction factor contributing to these costs. This research analyzes a dataset containing 58,645 URLs, examining 111 features of the latest phishing websites dataset to identify the differences between phishing sites and legitimate sites. Astonishingly, using only 14 characteristics, the feedforward model achieved a remarkable accuracy of 94.46%, confirming the efficiency of Machine Learning in phishing detection. Through the exploitation of a multiple classifier collection, including Deep Neural Network (DNN), Wide and Deep, and TabNet, this research advances ongoing efforts to improve the accuracy and efficiency of phishing detection mechanisms and enhance cybersecurity defenses against malicious activities. The methodology introduces a new metric called the ‘anti-phishing score,’ which evaluates performance based on false positives and negatives, beyond traditional model accuracy. The model was trained through a robust design of extensive experimentation and hyperparameter-sensitive grid search, ensuring an optimized configuration for phishing detection. Furthermore, the trained model was validated on a new dataset to evaluate its generalizability, enhancing its practical applicability. Through the integration of feature selection principles, advanced algorithmic techniques, and comprehensive evaluation approaches, this research offers a robust approach to phishing detection, considering the evolving nature of cyber threats. The findings provide a beneficial framework for cybersecurity specialists and researchers, enabling more effective preventive measures against phishing attacks.}, archive = {J_ACCESS}, author = {Ganesh S. Nayak and Balachandra Muniyal and Manjula C. Belavagi}, doi = {10.1109/ACCESS.2025.3543738}, journal = {IEEE Access}, pages = {33308-33320}, shortjournal = {IEEE Access}, title = {Enhancing phishing detection: A machine learning approach with feature selection and deep learning models}, volume = {13}, year = {2025}, }
• (2025). Enhancing accessibility through machine learning: A review on visual and hearing impairment technologies. ACCESS, 13, 33286-33307. (www)

  @article{ , abstract = {Assistive technologies powered by machine learning are transforming the way sensory impairments are addressed, offering innovative solutions for individuals with hearing and visual disabilities. This paper provides a comprehensive review of machine learning algorithms designed to enhance accessibility for these groups. For hearing impairments, the analysis focuses on advanced models such as Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP), examining their effectiveness in auditory assistive applications. In the context of visual impairments, state-of-the-art object detection frameworks like You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and RetinaNet are evaluated for their capability to enable real-time object recognition and navigation aids. The study also reviews the Generative Artificial Intelligence based applications for visual and hearing impaired use cases. The study addresses the unique challenges and requirements associated with each type of sensory impairment, with particular emphasis on the customization and fine-tuning of machine learning models for personalized, effective solutions. Additionally, it highlights the transformative potential of deep learning models in advancing assistive technologies, ultimately aiming to enhance the quality of life for individuals with sensory disabilities. By advocating for the development and integration of such technologies, this paper underscores the importance of inclusivity and empowerment in creating a more equitable society.}, archive = {J_ACCESS}, author = {Pal Patel and Shreyansh Pampaniya and Ananya Ghosh and Ritu Raj and Deepa Karuppaih and Saravanakumar Kandasamy}, doi = {10.1109/ACCESS.2025.3539081}, journal = {IEEE Access}, pages = {33286-33307}, shortjournal = {IEEE Access}, title = {Enhancing accessibility through machine learning: A review on visual and hearing impairment technologies}, volume = {13}, year = {2025}, }
• (2025). Decoupled feature and self-knowledge distillation for speech emotion recognition. ACCESS, 13, 33275-33285. (www)

  @article{ , abstract = {Speech Emotion Recognition seeks to capture emotional information from speech signals. While prior research has predominantly focused on the extraction and fusion of acoustic and end-to-end features, redundancy within these features often hinders model performance. In this work, we introduce DFSD-EMO, a novel model that decouples each feature type into shared and private components through a common encoder and private encoders. The shared features capture commonalities across feature types, whereas the private features retain each feature type’s unique information. Building on this structure, we propose a decoder and a decoupling loss function to guide parameter optimization. Additionally, an adaptive cross-attention fusion module is designed to enhance the representation capacity of decoupled features by learning adaptive weights. The model is trained via self-distillation. Experimental results show that DFSD-EMO surpasses baseline models on the IEMOCAP, SAVEE, EmoDB and MELD datasets. Ablation studies further confirm the effectiveness of feature decoupling and the adaptive cross-attention fusion module.}, archive = {J_ACCESS}, author = {Haixiang Yu and Yuan Ning}, doi = {10.1109/ACCESS.2025.3542948}, journal = {IEEE Access}, pages = {33275-33285}, shortjournal = {IEEE Access}, title = {Decoupled feature and self-knowledge distillation for speech emotion recognition}, volume = {13}, year = {2025}, }
• (2025). Geodesic dominated coloring of graphs. ACCESS, 13, 33259-33274. (www)

  @article{ , abstract = {In this paper, we introduce geodesic dominated coloring, a novel variant of dominated coloring in graphs, where each color class is dominated by at least one geodesic. This concept builds upon the classical dominated coloring framework, offering fresh perspectives on vertex domination and graph coloring. We begin by examining the intricate relationships between domination parameters closely linked to geodesic dominated coloring, emphasizing their connections, contrasts, and the transfer of hereditary properties. To motivate this study, we highlight the significance of geodesic dominated coloring and its potential applications across diverse domains. We establish key foundational results, including lower bounds for the geodesic dominated chromatic number, to provide deeper insights into this innovative graph coloring paradigm. Furthermore, we analyze the computational complexity of the problem and prove its NP-completeness for arbitrary graphs. As part of our contributions, we determine the exact values of the geodesic dominated chromatic number for various graph structures, such as cycle, hypermesh, complete binary tree, sibling tree, and hypertree.}, archive = {J_ACCESS}, author = {M. Paruvatha Vathana and R. Jayagopal}, doi = {10.1109/ACCESS.2025.3542427}, journal = {IEEE Access}, pages = {33259-33274}, shortjournal = {IEEE Access}, title = {Geodesic dominated coloring of graphs}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of single motor-driven deployable grasping mechanism for non-cooperative space debris. ACCESS, 13, 33246-33258. (www)

  @article{ , abstract = {In this paper, a new deployable grasping mechanism for non-cooperative space debris is proposed and developed. This grasping mechanism consists of three robotic fingers connected to a platform. Each finger is developed by combining a series of scissors mechanisms, in such a way that one mechanism drives the next. A half scissors mechanism is used at the end of finger as a its tip. These fingers are deployable and their length increases and decreases with the closing and opening of the scissors mechanism. Each deployable modules is equipped with a grasp driver mechanism, which can gradually bend the finger during the process of increase in its length, in order to accomplish the grasping of the non-cooperative space debris. Each finger is designed as an under-actuated mechanism, to save the development cost and make the finger lightweight. A special mechanism is developed in the platform of the grasping mechanism, such that single motor can be used to deploy and bend all the fingers, simultaneously. In the end, the validation of the working and effectiveness of the proposed deployable grasping mechanism is given through simulations and experimental work. It can be observed through the results that the proposed mechanism is able to grasp large objects with simultaneous deployment and bending of all fingers by using single motor.}, archive = {J_ACCESS}, author = {Sajjad Manzoor and Yibo Wang and Kyungtae Kim and Qiang Lu and Youngjin Choi}, doi = {10.1109/ACCESS.2025.3543731}, journal = {IEEE Access}, pages = {33246-33258}, shortjournal = {IEEE Access}, title = {Design and analysis of single motor-driven deployable grasping mechanism for non-cooperative space debris}, volume = {13}, year = {2025}, }
• (2025). A survey and framework for education in the metaverse. ACCESS, 13, 33231-33245. (www)

  @article{ , abstract = {The rapid advancement of technology is reshaping education, and the metaverse stands at the forefront of this transformation. Beyond its roots in gaming and entertainment, the metaverse is poised to revolutionize how we learn by creating immersive, interactive, and personalized educational environments. However, despite its potential, a comprehensive framework for integrating the metaverse into education remains absent. This study conducts a systematic literature review to analyze and categorize existing research on education in the metaverse. The review follows a PRISMA-based methodology, utilizing Scopus, Web of Science, and PubMed databases for data collection. Studies were analyzed to identify trends, goals, and types of metaverse technologies explored. Building on this review, this paper proposes a Meta-education framework that addresses the interconnected roles of learners, instructors, and institutions in metaverse-driven education systems. The framework highlights how the metaverse enhances learner engagement, supports innovative teaching strategies, and enables institutional scalability. This study concludes that while the metaverse holds great potential to revolutionize education, significant challenges remain, including content development, instructor readiness, and integration of AI technologies. The proposed framework provides practical guidelines for future research and offers implications for educators, policymakers, and researchers aiming to create inclusive, sustainable, and transformative learning environments.}, archive = {J_ACCESS}, author = {Jalal Safari Bazargani and Abolghasem Sadeghi-Niaraki and Soo-Mi Choi}, doi = {10.1109/ACCESS.2025.3543496}, journal = {IEEE Access}, pages = {33231-33245}, shortjournal = {IEEE Access}, title = {A survey and framework for education in the metaverse}, volume = {13}, year = {2025}, }
• (2025). Realization of proper nonlinear systems and its application to the disturbance observer for van der pol oscillator. ACCESS, 13, 33220-33230. (www)

  @article{ , abstract = {Control systems can be represented using either higher-order input-output differential equations or state-space representations, depending on the control problem being addressed. While a state-space realization can always be obtained for linear systems described by proper higher-order input-output differential equations, the same does not hold for nonlinear systems. This paper focuses on finding the conditions under which a state-space realization can be derived for proper nonlinear systems. Two necessary and sufficient conditions for this realization problem are presented: the first concerns the existence of a realization in observer canonical form, and the second addresses general realizability. To address these problems, the paper employs the polynomial/transfer function formalism for nonlinear systems, which is analogous to the linear case but incorporates differential forms and skew polynomial rings. The applicability of the proposed approach is demonstrated through an academic example and a practical case study. Specifically, the paper introduces a disturbance observer for the Van der Pol oscillator. Unlike traditional nonlinear disturbance observers, the proposed observer relies solely on the system output, although it requires adherence to restrictive integrability conditions.}, archive = {J_ACCESS}, author = {Miroslav Halás and Martin Dodek}, doi = {10.1109/ACCESS.2025.3543655}, journal = {IEEE Access}, pages = {33220-33230}, shortjournal = {IEEE Access}, title = {Realization of proper nonlinear systems and its application to the disturbance observer for van der pol oscillator}, volume = {13}, year = {2025}, }
• (2025). A multi-phase DRL-driven SDN migration framework addressing budget, legacy service compatibility, and dynamic traffic. ACCESS, 13, 33202-33219. (www)

  @article{ , abstract = {Software-Defined Networking (SDN) is a network architecture that offers enhanced flexibility, programmability, and more efficient traffic load management by decoupling the control plane from the data plane. However, complete migration to SDN is challenging for most organizations due to costs and operational complexities. Hybrid SDN has emerged as a practical incremental path where legacy and SDN-enabled nodes coexist, yet existing migration strategies typically address only individual challenges such as dynamic traffic patterns, legacy service compatibility, and budget constraints. This paper introduces SMART (SDN Migration Assisted by a Deep Reinforcement Learning (DRL) Technique), a comprehensive framework that simultaneously addresses dynamic traffic patterns, legacy service compatibility, and phased migration under budget constraints. By integrating a DRL model with a clustering algorithm, SMART determines the migration sequence to minimize link utilization and reduce the number of SDN-enabled nodes required for effective traffic load distribution under dynamic traffic patterns. Extensive evaluations on the Abilene and GEANT network topologies demonstrate that SMART outperforms three existing approaches, achieving most of the SDN benefits by migrating only 36% and 52% of legacy nodes, respectively. This approach can potentially lower migration costs by up to 64% while achieving network optimization objectives. These insights provide both a foundation for future research in network migration strategies and practical guidance for organizations planning cost-effective transitions from legacy to SDN-based architectures.}, archive = {J_ACCESS}, author = {Kai Yuan Tan and Saw Chin Tan and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3543236}, journal = {IEEE Access}, pages = {33202-33219}, shortjournal = {IEEE Access}, title = {A multi-phase DRL-driven SDN migration framework addressing budget, legacy service compatibility, and dynamic traffic}, volume = {13}, year = {2025}, }
• (2025). Reliability assessment of optical physical unclonable functions based on the spatial distribution of catastrophic failure sites in MIM structures. ACCESS, 13, 33189-33201. (www)

  @article{ , abstract = {Catastrophic failure sites, also referred to as breakdown spots, in Metal-Insulator-Semiconductor (MIS) and Metal-Insulator-Metal (MIM) structures are the consequence of the formation of conducting paths across the thin oxide film that separates the contact electrodes. When the energy released by the sudden occurrence of this kind of shorts is high enough, the events are clearly detected in the top area of the structure as a random spatial point pattern. As it was demonstrated in previous works, the distribution of failure sites obtained this way can be used to generate optically detectable cryptographic keys in the context of Physically Unclonable Functions (PUFs). In this paper, we pay special attention to the reliability of the associated fingerprints. Reliability is evaluated in terms of a number of features of the binarized images such as the rotation and translation of the observation window, resolution, illumination, noise conditions, and particularities of the used optical system. The obtained results demonstrate that the generated fingerprints meet the essential requirements of reliability, reaching values between ~90 and 99% in all the considered scenarios. By means of a simulated experiment, which closely resembles the practical application of the proposed method, we are able to assess how good the identification of the registered images is and therefore the feasibility of the considered approach. To complete the picture, the investigated PUFs are shown to be resilient to temperature and electrical stress attacks which makes them highly suitable for security applications.}, archive = {J_ACCESS}, author = {Marc Porti and Alvaro Solis and Alex Calatayud and Montserrat Nafría and Enrique Miranda}, doi = {10.1109/ACCESS.2025.3543000}, journal = {IEEE Access}, pages = {33189-33201}, shortjournal = {IEEE Access}, title = {Reliability assessment of optical physical unclonable functions based on the spatial distribution of catastrophic failure sites in MIM structures}, volume = {13}, year = {2025}, }
• (2025). Integrating data privacy and energy management in smart cities with partial sustainable IoT networks. ACCESS, 13, 33176-33188. (www)

  @article{ , abstract = {IoT networks are transitioning towards greener underlying technologies. In smart cities environments, while some devices are already sustainable with rechargeable batteries and energy harvesting hardware, many still rely on traditional non-sustainable components. This partial sustainable Internet of Things (IoT) network poses a research challenge, since the profile of sustainable nodes aims for neutral or self-powered operation, which may conflict with the energy-saving goals of traditional non-sustainable nodes. In this challenging scenario, it is essential to share Energy Information (EI), i.e. battery state-of-charge, among the nodes to efficiently manage energy resources. However, EI sharing is valuable data for attackers to perform energy-based attacks, exploiting the most vulnerable nodes in smart cities. To tackle these problems, this article presents the Dual Energy PROfile for interNet-of-thiNgs with Enhanced Security (DEPRONN-ES), which is a solution designed to support partially sustainable networks, i.e. traditional and sustainable nodes, and applies differential privacy to enhance data privacy levels of the EI sharing. The performed evaluation shows the proposed solution is able to reduce the success rate of attacks in 87% and its effectiveness in terms of consumed resources of the attacked node. Besides, DEPRONN-ES is able to support conflicting profiles to operate in distinct energy consumption levels. This framework utilized emulation of IoT devices and real data traces of indoor light intensity measurements provided by Columbia University as a harvesting source of the rechargeable batteries of sustainable nodes.}, archive = {J_ACCESS}, author = {Yuri Santo and Antônio Abelém and André Riker and Eirini Eleni Tsiropoulou}, doi = {10.1109/ACCESS.2025.3541213}, journal = {IEEE Access}, pages = {33176-33188}, shortjournal = {IEEE Access}, title = {Integrating data privacy and energy management in smart cities with partial sustainable IoT networks}, volume = {13}, year = {2025}, }
• (2025). ReTMiC: Reliability-aware thermal management in multicore mixed-criticality embedded systems. ACCESS, 13, 33157-33175. (www)

  @article{ , abstract = {As the number of cores in multicore platforms increases, temperature constraints may prevent powering all cores simultaneously at maximum voltage and frequency level. Thermal hot spots and unbalanced temperatures between the processing cores may degrade the reliability. This paper introduces a reliability-aware thermal management scheduling (ReTMiC) method for mixed-criticality embedded systems. In this regard, ReTMiC meets Thermal Design Power as the chip-level power constraint at design time. In order to balance the temperature of the processing cores, our proposed method determines balancing points on each frame of the scheduling, and at run time, our proposed lightweight online re-mapping technique is activated at each determined balancing point for balancing the temperature of the processing cores. The online mechanism exploits the proposed temperature-aware factor to reduce the system’s temperature based on the current temperature of processing cores and the behavior of their corresponding running tasks. Our experimental results show that the ReTMiC method achieves up to 12.8°C reduction in the chip temperature and 3.5°C reduction in spatial thermal variation in comparison to the state-of-the-art techniques while keeping the system reliability at a required level.}, archive = {J_ACCESS}, author = {Sepideh Safari and Mohsen Ansari and Shaahin Hessabi and Jörg Henkel}, doi = {10.1109/ACCESS.2025.3542472}, journal = {IEEE Access}, pages = {33157-33175}, shortjournal = {IEEE Access}, title = {ReTMiC: Reliability-aware thermal management in multicore mixed-criticality embedded systems}, volume = {13}, year = {2025}, }
• (2025). Probabilistic SIL verification of a synthesized fault-tolerant model for reliable safety assessment. ACCESS, 13, 33148-33156. (www)

  @article{ , abstract = {In safety-critical domains such as industrial processes and automotive systems, accidents can have serious consequences, including the potential loss of human life. To mitigate risks associated with hazards, these systems must incorporate robust safety capabilities. International standards, such as IEC 61508, mandate the implementation of safety functions and require their verification through SIL (Safety Integrity Level) assessments. However, performing accurate SIL verification remains challenging due to the uncertainty of real-world data, which can be influenced by factors such as temperature variations (i.e., heating effects), environmental stressors, and component aging. In particular, failure rates, which serve as fundamental data for SIL verification, are typically assumed to be constant values derived under ideal conditions. But in practice, these rates vary over time due to the progressive degradation of system components, leading to potential discrepancies between theoretical safety assessments and real-world reliability. To address this issue, researchers have explored Markov models as a dynamic approach for improving SIL verification. In this paper, we propose a novel approach based on Markov; constructing one of the fault-tolerant architecture, 2oo3 (2-out-of-3), as a model and verifying SIL using probabilistic model checking. Our approach synthesizes the model to prevent systematic faults, such as human errors, and enhances verification accuracy. To validate our approach, we compare the results of the probabilistic model checking performed on our synthesized model with those obtained from a model without synthesis. The results demonstrate that the synthesized model exhibits the smallest difference rate and a more predictable variation from the target values specified in the IEC 61508 standard for SIL verification. Experimental results confirm that the proposed method enhances the reliability of SIL verification, increasing confidence in safety assessments.}, archive = {J_ACCESS}, author = {Daehui Jeong and Gihwon Kwon and Ryeonggu Kwon}, doi = {10.1109/ACCESS.2025.3543227}, journal = {IEEE Access}, pages = {33148-33156}, shortjournal = {IEEE Access}, title = {Probabilistic SIL verification of a synthesized fault-tolerant model for reliable safety assessment}, volume = {13}, year = {2025}, }
• (2025). Deep learning for ore haulage monitoring: Vibrational analysis using a VGG16 network. ACCESS, 13, 33138-33147. (www)

  @article{ , abstract = {Automated detection of work cycles in underground ore haulage is crucial for optimizing operational efficiency and minimizing costs, yet existing methods often rely on manual logging or basic telematics, which can be inefficient and prone to errors. This study addresses this gap by presenting a novel approach that utilizes IMU sensor mounted on haul trucks to accurately estimate ore haulage cycles. By collecting data from various work shifts, we analyzed 1473 unique ore haulage cycles across various machines and environments, demonstrating the robustness of our method. The proposed solution incorporates advanced deep learning models, specifically VGG16 and autoencoders, to process the sensor data and detect cycles effectively. Our approach not only enhances real-time tracking of truck operations but also enables better route planning and load management, thereby reducing idle time. The model achieved an accuracy of approximately 87%, validating its effectiveness as an easy to implement solution for work cycle detection in the challenging conditions of underground mines. This automation facilitates continuous optimization by providing valuable insights into operational performance, contributing to improved long-term productivity. The findings indicate that leveraging IMU sensor data through machine learning techniques presents a significant advancement in the field of ore haulage cycle detection, ultimately benefiting the mining industry by enhancing efficiency and reducing operational costs.}, archive = {J_ACCESS}, author = {Artur Skoczylas and Paweł Stefaniak and Sergii Anufriiev and Wioletta Koperska}, doi = {10.1109/ACCESS.2025.3542192}, journal = {IEEE Access}, pages = {33138-33147}, shortjournal = {IEEE Access}, title = {Deep learning for ore haulage monitoring: Vibrational analysis using a VGG16 network}, volume = {13}, year = {2025}, }
• (2025). Magneto inductive (MI) channel variables prediction through machine learning linear regression method, for underwater and underground WSNs. ACCESS, 13, 33124-33137. (www)

  @article{ , abstract = {In today’s digital era, where information flows seamlessly and is readily available and accessible. However, these information and communication systems are highly dependent on the robustness and adaptability of communication links, which must endure the dynamic and often unpredictable changes occurring in the communication environment, especially in underwater and underground communication. As technology advances and demands for reliable data transfer grows, the resilience of these links becomes increasingly critical to ensure an uninterrupted access to information. For this we will be analyzing a new paradigm in Wireless Sensor Networks (WSNs), i.e. Magneto Inductive (MI) communication. Which has the capability to provide robustness to the communication link as well as the security of the information can also be controlled, by utilizing the directionality factor of the created magnetic flux around the MI tri-directional (MI-TD) coils. In this study we will examine MI communication variable factors, which have effects on the output of the MI channel’s response, such as frequency of applied excitation current, number of turns, radius of the communicating coils, and changing communication distance between nodes. Than we will use a linear regression machine learning (ML) model to predict the values of the aforementioned variables, which will help to predict the MI channel’s response in the form of Path loss/Bit Error Rate (BER). The model that we have created for the prediction purpose, archives an estimation accuracy of 92%.}, archive = {J_ACCESS}, author = {Aman Muhammad and Fuzhong Li and Syed Agha Hassnain Mohsan and Zahid Ullah Khan and Waqar Khan and Sajid Ullah Khan and Ziqi Han and Faheem Khan}, doi = {10.1109/ACCESS.2025.3541921}, journal = {IEEE Access}, pages = {33124-33137}, shortjournal = {IEEE Access}, title = {Magneto inductive (MI) channel variables prediction through machine learning linear regression method, for underwater and underground WSNs}, volume = {13}, year = {2025}, }
• (2025). A hybrid decision support framework using MEREC-RAFSI with spherical fuzzy numbers for selecting banking financial aid recipients. ACCESS, 13, 33101-33123. (www)

  @article{ , abstract = {Decades ago, a bank thoroughly utilized experts to determine financial decisions based on their assessments by evaluating risks qualitatively against the company’s financial statements, business scenarios, and individual or conventional client interview results. However, this provoked a relatively subjective evaluation process and faced more complex criteria conditions with specific requirements. In addition, policymakers continued to use uncertain information in the evaluation process despite the increasing concerns over inconsistency, indeterminacy, and uncertainty in data that have emerged and become significant challenges in decision-making. Therefore, this paper introduces an integrated decision support framework based on spherical fuzzy numbers (SFNs) for overcoming a banking financial aid recipient evaluation. The proposed framework combines two developed algorithms, which are a spherical fuzzy number method based on the removal effects of criteria (SFN-MEREC) and a spherical fuzzy number ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (SFN-RAFSI) algorithm. The SFN-MEREC is used to determine objective criteria weight coefficients, whereas the SFN-RAFSI is used to select the appropriate banking financial aid recipient. The proposed framework exhibits the results in line and a positive correlation with other spherical fuzzy decision-making methods authenticated through comparative analysis and weighted Spearman’s rank correlation coefficient evaluations. Finally, a rank reversal simulation analysis demonstrates the framework’s ability and stability in managing inconsistent, indeterminate, and uncertain data, ensuring its robustness throughout the evaluation process.}, archive = {J_ACCESS}, author = {Irvanizam Irvanizam and Mahyuddin K. M. Nasution and Tulus Tulus and Erna B. Nababan}, doi = {10.1109/ACCESS.2025.3539200}, journal = {IEEE Access}, pages = {33101-33123}, shortjournal = {IEEE Access}, title = {A hybrid decision support framework using MEREC-RAFSI with spherical fuzzy numbers for selecting banking financial aid recipients}, volume = {13}, year = {2025}, }
• (2025). Brain network dynamics and multiscale modeling of neurodegenerative disorders: A review. ACCESS, 13, 33074-33100. (www)

  @article{ , abstract = {It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer’s disease (AD), Parkinson’s disease (PD), and cortical spreading depression (CSD). We investigate how the brain’s biological processes and associated multiphysics interact and how this influences the structure and functionality of the brain. We review the literature on brain network models and dynamic processes, highlighting the need for sophisticated mathematical and statistical modelling techniques. Specifically, we go through large-scale brain network models relevant to AD and PD, highlighting the pathological mechanisms and potential therapeutic strategies investigated in the literature. Additionally, we investigate the propagation of CSD in the brain and its implications for neurological disorders. Furthermore, we discuss how data-driven approaches and artificial neural networks refine and validate models related to NDDs. Overall, this review underscores the significance of coupled multiscale models in deciphering disease mechanisms, offering potential avenues for therapeutic development and advancing our understanding of pathological brain dynamics.}, archive = {J_ACCESS}, author = {Hina Shaheen and Roderick Melnik}, doi = {10.1109/ACCESS.2025.3542634}, journal = {IEEE Access}, pages = {33074-33100}, shortjournal = {IEEE Access}, title = {Brain network dynamics and multiscale modeling of neurodegenerative disorders: A review}, volume = {13}, year = {2025}, }
• (2025). Research on the route planning problem of drone and truck collaborative delivery in restricted areas. ACCESS, 13, 33062-33073. (www)

  @article{ , abstract = {Aiming at the low efficiency of logistics distribution and the problem of restricted areas, we develop a collaborative distribution model that integrates trucks and drones, allowing for single takeoff and multiple drone deliveries. By considering the restricted areas for trucks and drones, a mathematical model has been constructed and a multi-stage heuristic optimization algorithm is proposed. This algorithm combines ant colony algorithm and simulated annealing algorithm, and a variety of neighborhood generation operators are introduced to improve the optimization effect. The model and algorithm are verified by different examples. Results show that the average solutions of algorithm is efficiency higher than Guorbi in small-scale instance, compared with the traditional truck distribution, the collaborative distribution shrink the 22.3% distribution time on average. Additionally, the increase in the restricted area customers and truck numbers has a significant impact on the complexity of route planning and distribution efficiency. Our research results can provide valuable guidance for future drone participation in complex urban and rural logistics distribution scenarios.}, archive = {J_ACCESS}, author = {Jun Zhou and Qiang Yu and Ziming Xue and Leibo Yang}, doi = {10.1109/ACCESS.2025.3540880}, journal = {IEEE Access}, pages = {33062-33073}, shortjournal = {IEEE Access}, title = {Research on the route planning problem of drone and truck collaborative delivery in restricted areas}, volume = {13}, year = {2025}, }
• (2025). Hybrid model flux observer for PMSM sensorless control system considering parameter disturbances and measurement noise. ACCESS, 13, 33049-33061. (www)

  @article{ , abstract = {The hybrid model flux observer integrates the advantages of the voltage and current models to improve its control performance. However, at low speeds, the observer is more sensitive to the noise present in the measurement signals, causing the observation results to deviate from the actual values, and the situation of parameter mismatch will pose a serious challenge to the stability of the control system. Through mathematical derivation in this paper, the influence degree of resistance and inductance parameters on the observation angle error at different rotational speeds is obtained, and it is proposed to introduce an improved extended state observer to compensate for the system parameter disturbances to enhance the system robustness. By constructing a state equation considering measurement noise and analyzing the Bode diagram in this paper, it is concluded that the improved extended state observer, compared to the traditional observer, can reduce the sensitivity to high-frequency measurement noise and improve the estimation performance of low-frequency disturbances, and its stability conditions are derived. Finally, comparison experiments of the starting process, load mutation, parameter mutation and different flux observer methods under different load conditions are designed. The experimental results show that this control strategy, in the low-speed domain, can enhance the system’s resistance to parameter disturbances, improve the measurement accuracy of the system, and effectively suppress the measurement noise existing in the system, verifying the effectiveness and feasibility of this control strategy.}, archive = {J_ACCESS}, author = {An-Fei Xu and Xuesong Zhu and Lei Yuan}, doi = {10.1109/ACCESS.2025.3535713}, journal = {IEEE Access}, pages = {33049-33061}, shortjournal = {IEEE Access}, title = {Hybrid model flux observer for PMSM sensorless control system considering parameter disturbances and measurement noise}, volume = {13}, year = {2025}, }
• (2025). A transformer-based model for state of charge estimation of electric vehicle batteries. ACCESS, 13, 33035-33048. (www)

  @article{ , abstract = {Batteries play a critical role in Electric Vehicle (EV) systems devices. The safety and performance of these applications rely on accurate Battery Management Systems (BMS) to monitor and optimize battery performance. Traditional BMS systems face challenges in charge prediction processes due to complex chemical processes and battery aging, leading to faults. The absence of a perfect sensor highlights limitations in measurement issues arising from external factors, especially sensor noise. Therefore, there is a need for algorithms that can solve the real-world battery charge prediction problem. This study compares an innovative solution, the Transformer model, with traditional Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) models, and Support Vector Regression (SVR). This research aims to provide new perspectives on Battery State of Charge (SoC) predictions using NASA, BMW i3, Stanford University Battery Datasets, and real-world battery data obtained from L5 EV of the Musoshi brand collected for this work. This research’s primary objective is to apply the Transformer model to real-world battery data, evaluating it as a significant step in EV range optimization and battery management. The Transformer model in the study achieved the best result with Root Mean Square Error (RMSE) value closest to 1 (~0.99).}, archive = {J_ACCESS}, author = {Metin Yılmaz and Eyüp Çinar and Ahmet Yazıcı}, doi = {10.1109/ACCESS.2025.3542961}, journal = {IEEE Access}, pages = {33035-33048}, shortjournal = {IEEE Access}, title = {A transformer-based model for state of charge estimation of electric vehicle batteries}, volume = {13}, year = {2025}, }
• (2025). Enhancing agile software development: A systematic literature review of requirement prioritization and reprioritization techniques. ACCESS, 13, 32993-33034. (www)

  @article{ , abstract = {Agile software development places great importance on requirement prioritization and reprioritization, which allow teams to concentrate on providing the most beneficial features to satisfy stakeholders. Most systematic review articles on prioritization techniques focus on traditional methods and ignore recent approaches that use artificial intelligence (AI). Additionally, there is a notable scarcity of literature addressing the neglected domain of reprioritisation and a lack of review papers analyzing semi-automated approaches. To fill this gap, this systematic literature review includes an in-depth review of newly developed AI-based and semi-automated techniques, in addition to widely used traditional prioritization methods. This study analyzed 76 primary studies from five credible electronic databases (Springer Link, IEEE Xplore, Scopus, Science Direct, and ACM Digital Library) to address six selected research questions. This literature review paper is unique in that it covers conventional approaches, reprioritization techniques, and AI-based and semi-automated techniques in a single review, which has not been done in previous papers. The findings highlight the strength and weakness of each technique. This review also identifies the most commonly used prioritization techniques in agile software development and the key challenges in requirement prioritization. Future research opportunities in the field of reprioritization are revealed by the gaps identified in the literature. This research contributes to enhancing the agility and effectiveness of agile software development (ASD).}, archive = {J_ACCESS}, author = {Noshin Tasneem and Hazura Binti Zulzalil and Sa’Adah Hassan}, doi = {10.1109/ACCESS.2025.3539357}, journal = {IEEE Access}, pages = {32993-33034}, shortjournal = {IEEE Access}, title = {Enhancing agile software development: A systematic literature review of requirement prioritization and reprioritization techniques}, volume = {13}, year = {2025}, }
• (2025). 3D reconstruction and deformation detection of rescue shaft based on RGB-D camera. ACCESS, 13, 32981-32992. (www)

  @article{ , abstract = {Efficient and accurate 3D reconstruction of rescue shafts in mining accidents is a critical and challenging task, particularly in low-texture environments. This paper proposes a novel method for real-time 3D model reconstruction, deformation detection, and accessibility analysis of rescue shafts using an RGB-D camera. The approach captures depth and color data from the shaft’s low-texture walls and employs advanced feature extraction and matching algorithms to generate a high-precision 3D point cloud. A hybrid Iterative Closest Point-Perspective n Point (ICP-PNP) algorithm ensures precise camera pose estimation, and motion errors between adjacent frames are minimized to optimize the 3D point cloud. The reconstructed model is refined using Poisson surface reconstruction, achieving millimeter-level pose estimation accuracy and a global trajectory consistency error within 2%. Experimental results demonstrate the superiority of the Speeded Up Robust Features (SURF) algorithm in feature extraction and the effectiveness of the Random Sample Consensus (RANSAC) algorithm in filtering mismatched points. The method also provides deformation profiles and accessibility predictions, with diameter estimates ranging from 510 mm to 540 mm, enabling accurate assessments of shaft usability and deformation trends. This framework enhances the precision and efficiency of rescue operations, offering a robust tool for real-time decision-making in mining emergencies.}, archive = {J_ACCESS}, author = {Hairong Gu and Bokai Liu and Lishun Sun and Mostak Ahamed and Jia Luo}, doi = {10.1109/ACCESS.2025.3543179}, journal = {IEEE Access}, pages = {32981-32992}, shortjournal = {IEEE Access}, title = {3D reconstruction and deformation detection of rescue shaft based on RGB-D camera}, volume = {13}, year = {2025}, }
• (2025). Method for acquiring passenger standing positions on subway platforms based on binocular vision. ACCESS, 13, 32971-32980. (www)

  @article{ , abstract = {Obtaining the standing positions of passengers on the subway platform can provide data support for more accurate passenger flow guidance measures. This paper utilizes binocular vision technology to acquire a depth matrix of the platform scene and employs the YOLOX object detection algorithm to identify the positions of passengers’ heads within the depth matrix. The depth values of the center points of passengers’ heads are extracted, and then, through coordinate mapping, these depth values are converted into a platform coordinate system, allowing us to determine the positions of passengers on the platform and subsequently obtain the distribution data of passengers at the station. The distribution data of passengers can provide more accurate information for passenger flow guidance. Due to the lack of binocular image data for underground platform layers in existing public datasets, this study constructs an underground island platform model using Blender software and generates a dataset containing 200 test scenarios. Validation of the proposed model using this dataset shows that 91.9% of the predicted standing positions of passengers exhibit an error of less than 41.8 centimeters, with an average prediction error of 18.1 centimeters and an average computation time of 860 milliseconds. Furthermore, the model’s performance in real subway scenarios has been verified, demonstrating its effective performance in real-world environments.}, archive = {J_ACCESS}, author = {Jing Zuo and Yanchong Wang and Runqi Wang}, doi = {10.1109/ACCESS.2025.3543130}, journal = {IEEE Access}, pages = {32971-32980}, shortjournal = {IEEE Access}, title = {Method for acquiring passenger standing positions on subway platforms based on binocular vision}, volume = {13}, year = {2025}, }
• (2025). Efficient LLMs training and inference: An introduction. ACCESS, 13, 32944-32970. (www)

  @article{ , abstract = {ChatGPT was released in late November 2022, making a significant impact globally. Following this release, numerous domestic and international open-source projects for large model training emerged, including Alpaca, BOOLM, LLaMA, ChatGLM, DeepSpeedChat, and ColossalChat. Both academia and industry have a growing need to train large models for optimizing downstream tasks. Research has demonstrated that instruction tuning using high-quality training data can enable models with over 10 billion parameters to exhibit emergent abilities, particularly in complex reasoning tasks. Training a model with tens of billions of parameters is often necessary to improve business metrics using large model technology. However, most research teams lack the extensive computing resources required for such large-scale models, often possessing only a few V100 (32G) GPUs and occasionally a few A100s. Consequently, training or inferring a large model of this magnitude is impractical under limited computational resources. Therefore, adopting optimization strategies during the training and inference stages is essential to address these challenges. This survey summarizes a series of optimization techniques for large models during the training and inference phases, enabling the training of large models even with constrained computational resources.}, archive = {J_ACCESS}, author = {Rui Li and Deji Fu and Chunyu Shi and Zhilan Huang and Gang Lu}, doi = {10.1109/ACCESS.2024.3501358}, journal = {IEEE Access}, pages = {32944-32970}, shortjournal = {IEEE Access}, title = {Efficient LLMs training and inference: An introduction}, volume = {13}, year = {2025}, }
• (2025). Identification of anomalous behavior through the observation of an individual’s emotional variation: A systematic review. ACCESS, 13, 32927-32943. (www)

  @article{ , abstract = {Emotion recognition plays a critical role in understanding how individuals perceive and interact with the world by accurately inferring emotions from various sources, such as physical signals and physiological responses. Anomalous emotion recognition, in particular, focuses on identifying emotions that deviate significantly from expected patterns in specific contexts or individuals, including abrupt emotional shifts, inconsistencies across different emotional signals, or persistent, atypical emotional patterns. The importance of recognizing anomalous emotions has grown, particularly in intelligent systems designed for mental health monitoring and crime prevention, where early detection can enhance context-sensitive interventions. This study presents a systematic review that investigates the link between emotional variations and anomalous behaviors across diverse settings. By examining the relationship between emotional shifts and deviant behaviors, the study sheds light on the challenges of detecting emotional anomalies and assesses the efficacy of various recognition methods. A thorough analysis of 102 relevant studies highlights the potential and limitations of current approaches, emphasizing the need for more diverse datasets, improved algorithm robustness, and broader applications to enhance performance in real-world scenarios.}, archive = {J_ACCESS}, author = {Monique S. L. S. Tomaz and Bruno J. T. Fernandes and Alessandra Sciutti}, doi = {10.1109/ACCESS.2025.3540034}, journal = {IEEE Access}, pages = {32927-32943}, shortjournal = {IEEE Access}, title = {Identification of anomalous behavior through the observation of an individual’s emotional variation: A systematic review}, volume = {13}, year = {2025}, }
• (2025). A multi-frame super-resolution reconstruction method based on optical flow for space noncooperative objects. ACCESS, 13, 32918-32926. (www)

  @article{ , abstract = {With the continuous advancement of space technology, the number of defunct spacecraft, abandoned rocket bodies, and debris in space is increasing. These non-cooperative objects occupy a significant amount of orbital resources and pose a substantial threat to the safety of on-orbit spacecraft. This paper focuses on close-proximity operations in space and aims to address the limitation of camera resolution by proposing an optical flow-based multi-frame super-resolution reconstruction algorithm. This algorithm employs a multi-level wavelet convolutional network (MWCNN) for feature extraction and uses SpyNet to obtain multi-level optical flow between different frames. The multi-level optical flow pyramid alignment network is used to align features, and a recurrent network is utilized for frame-by-frame feature fusion. Finally, a reconstruction network generates high-resolution images. Extensive experiments have demonstrated that our proposed method effectively enhances the perception capabilities of space non-cooperative objects.}, archive = {J_ACCESS}, author = {Heng Deng and Zhao Zhang and Dong Zhou and Lingyu Ma}, doi = {10.1109/ACCESS.2025.3541297}, journal = {IEEE Access}, pages = {32918-32926}, shortjournal = {IEEE Access}, title = {A multi-frame super-resolution reconstruction method based on optical flow for space noncooperative objects}, volume = {13}, year = {2025}, }
• (2025). AFDR-det: Adaptive feature dual-refinement oriented detector for remote sensing object detection. ACCESS, 13, 32901-32917. (www)

  @article{ , abstract = {Arbitrary-oriented object detection is a significant and challenging remote sensing image detection task. However, due to two types of feature misalignment in one-stage oriented detectors, their performance has yet to meet expectations. In remote sensing images, objects are distributed in arbitrary orientations, while convolutional features are inherently axis-aligned. This inevitably leads to spatial misalignment between the heuristically defined anchors and the convolutional features. Additionally, most head networks use two branch networks to implement object localization and classification tasks separately, resulting in a lack of information interaction between the classification and localization tasks, leading to spatial feature misalignment issues. In this paper, we propose an Adaptive Feature Dual-Refinement Detector (AFDR-Det) to address the above challenges. First, we propose a novel neck network that integrates gated fusion modules and transformer blocks into the feature pyramid architecture, thereby enhancing the neck’s ability to capture both local details and long-range contextual information. Secondly, we propose a Feature Dual Refinement Head, which consists of two sub-modules: a Modulated Anchor Refinement Module (MARM) and a Task-aware Feature Alignment Module (TFAM). MARM aims to generate high-quality anchors and adaptively align features with the corresponding anchors. TFAM further enforces feature consistency between the two tasks from the spatial dimension by enhancing information interaction between the classification and regression branches, thereby achieving feature alignment between the tasks of classification and localization. We evaluated the proposed AFDR-Det on three challenging public remote sensing image datasets: DOTA, DIOR-R, and HRSC2016. The experimental results demonstrate that our method achieved mean average precision (mAP) scores of 80.54%, 66.90%, and 98.51% on the DOTA, DIOR-R, and HRSC2016 datasets, respectively, indicating significant improvements over the baseline methods.}, archive = {J_ACCESS}, author = {Jie Yang and Li Zhou and Yongfeng Ju}, doi = {10.1109/ACCESS.2025.3543440}, journal = {IEEE Access}, pages = {32901-32917}, shortjournal = {IEEE Access}, title = {AFDR-det: Adaptive feature dual-refinement oriented detector for remote sensing object detection}, volume = {13}, year = {2025}, }
• (2025). Characterizing the oscillatory properties of bulk electric systems. ACCESS, 13, 32883-32900. (www)

  @article{ , abstract = {This paper presents a process for characterizing the oscillatory dynamics of a large bulk power system. As a demonstration, the process is applied to the Western Interconnection of North America. Several complementary analysis approaches, both new and existing, are employed to provide a comprehensive understanding of the oscillatory properties of the system. Established modal analysis techniques based on ringdown and mode-meter algorithms are utilized. In addition, we derive and apply methods based on spectral correlation analysis to identify modal frequencies, distinguish between modes that are closely spaced in frequency, and determine locations at which the modes are observable. Critical interarea modes are identified and characterized using actual-system synchrophasor measurements taken over several years of operation in concert with industry-standard simulation models. This includes 145 hours of PMU data and two planning base cases.}, archive = {J_ACCESS}, author = {Daniel J. Trudnowski and Jim Follum and Ryan T. Elliott and David A. Schoenwald and John W. Pierre}, doi = {10.1109/ACCESS.2025.3543457}, journal = {IEEE Access}, pages = {32883-32900}, shortjournal = {IEEE Access}, title = {Characterizing the oscillatory properties of bulk electric systems}, volume = {13}, year = {2025}, }
• (2025). HIDS-IoMT: A deep learning-based intelligent intrusion detection system for the internet of medical things. ACCESS, 13, 32863-32882. (www)

  @article{ , abstract = {The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.}, archive = {J_ACCESS}, author = {Abdelwahed Berguiga and Ahlem Harchay and Ayman Massaoudi}, doi = {10.1109/ACCESS.2025.3543127}, journal = {IEEE Access}, pages = {32863-32882}, shortjournal = {IEEE Access}, title = {HIDS-IoMT: A deep learning-based intelligent intrusion detection system for the internet of medical things}, volume = {13}, year = {2025}, }
• (2025). Strategic decision support system with probabilistic linguistic term sets: Extended CRADIS approach for supply chain risk management in sports industry. ACCESS, 13, 32853-32862. (www)

  @article{ , abstract = {The global sports supplies industry, characterized by its complex and geographically dispersed supply chains, presents companies with a multitude of risk factors. These range from disruptions in raw material sourcing to geopolitical instability. This paper explores the challenges and opportunities associated with implementing effective supply chain risk management (SCRM) strategies within this industry. The study delve into the various types of risks faced by companies and propose strategies for mitigating them. One such strategy is Multi-Criteria Decision Making (MCDM). MCDM empowers companies to objectively evaluate and prioritize risks based on a range of factors and facilitates data-driven decision-making regarding supplier selection and risk mitigation approaches, ultimately leading to a more resilient and competitive supply chain. The paper highlights that the sports supplies industry can significantly benefit from the application of Probabilistic Linguistic Term Sets (PLTS) for enhanced risk assessment and management. By integrating PLTS into the decision-making process, companies gain a more nuanced understanding of supplier reliability and can implement proactive mitigation strategies. This translates to an overall increase in supply chain resilience and competitiveness. This paper proposes an advanced approach that leverages PLTS to address uncertainties inherent in decision-making. The study introduces innovative PLTS meticulously designed using the CRADIS method. CRADIS is a robust tool that aids in selecting optimal goals within the decision-making process. It combines various MCDM strategies, offering a powerful approach to navigate complex decision scenarios with multiple criteria. To demonstrate the adaptability and effectiveness of this proposed methodology, a case study is presented, showcasing its practical application and potential impact. To validate the effectiveness and real-world applicability of our improved MCDM CRADIS method, a comparative analysis with existing decision-making methods is conducted. This comparison highlights the strengths and advancements offered by the proposed approach.}, archive = {J_ACCESS}, author = {Kai Qiu and Jingjing Chen and Shahzaib Ashraf and Tooba Shahid}, doi = {10.1109/ACCESS.2024.3416391}, journal = {IEEE Access}, pages = {32853-32862}, shortjournal = {IEEE Access}, title = {Strategic decision support system with probabilistic linguistic term sets: Extended CRADIS approach for supply chain risk management in sports industry}, volume = {13}, year = {2025}, }
• (2025). Unleashing the power of NVMe in cloud: A complete software-defined emulation study. ACCESS, 13, 32834-32852. (www)

  @article{ , abstract = {Non-Volatile Memory Express (NVMe) is revolutionizing cloud storage by offering high throughput, low latency, and efficient resource utilization. Understanding its performance characteristics under diverse workload conditions is crucial for optimizing storage configurations in cloud environments. Accordingly, this study leverages NVMeVirt, a powerful software-defined NVMe emulator, to investigate the impact of various workload characteristics on critical performance metrics. We analyze the influence of I/O distribution patterns (sequential, random, and Zipfian), queue depths, and the number of concurrent jobs on throughput, latency, and IOPS. We also analyze the energy consumption of both NVMe and SATA SSDs. Results demonstrate that NVMeVirt effectively emulates NVMe SSDs, exhibiting high throughput and low latency across diverse workloads. Sequential workloads consistently achieve higher throughput and significantly lower latency compared to random and Zipfian workloads, particularly at higher queue depths. Specifically, NVMe achieves up to 8.72% higher throughput and 34.89% lower latency than SATA for sequential workloads. This highlights the importance of aligning storage configurations with applications’ anticipated data access patterns. Our analysis also reveals the potential for optimizing energy efficiency by carefully adjusting workload parameters and storage configurations. Random and Zipfian workloads, due to increased seek operations and internal SSD activity, demonstrate higher energy consumption, underscoring the need for efficient data management strategies in cloud environments. This study provides valuable insights into the performance characteristics of NVMe SSDs and their advantages over SATA SSDs under various workload conditions. It highlights the importance of considering data access patterns and storage configurations for optimizing performance and energy efficiency in cloud-like environments. The findings of this paper can provide researchers with an optimized design and deployment of NVMe-based storage systems in cloud data centers to meet the demands of modern data-intensive applications effectively.}, archive = {J_ACCESS}, author = {Mahdi Siamaki and Bardia Safaei}, doi = {10.1109/ACCESS.2025.3544432}, journal = {IEEE Access}, pages = {32834-32852}, shortjournal = {IEEE Access}, title = {Unleashing the power of NVMe in cloud: A complete software-defined emulation study}, volume = {13}, year = {2025}, }
• (2025). A novel approach based on quantum key distribution using BB84 and e91 protocol for resilient encryption and eavesdropper detection. ACCESS, 13, 32819-32833. (www)

  @article{ , abstract = {Quantum cryptography is anticipated to drive substantial advancements in cybersecurity. The impending arrival of quantum cryptography compromises current encryption methods, possibly compromising the effectiveness of traditional key management-based security protocols. One fundamental Quantum Key Distribution (QKD) protocol, BB84, encounters challenges when operating with fewer Quantum bits (Qubits) and bases that only support up to 8 Qubits. This limitation weakens the system’s security, making brute force, intercept, and resend attacks less challenging. Consequently, this study proposes a method to enhance the security of the BB84 protocol, to reduce susceptibility to attacks and eavesdropping. The improved BB84 protocol utilizes 9, 12, and 16 quantum bits along with two, and three bases to significantly bolster security. This allows authorized parties to eliminate the use of compromised keys. Additionally, the study implements the E91 QKD protocol utilizing the Entanglement Pair Generation (EPR) method to produce secure keys. While the existing E91 protocol ensures security through Bell’s theorem and Bell’s inequality, it overlooks the impact of noise, leading to inaccuracies in eavesdropper detection. To address this, the study introduces an additional security measure. Whenever an eavesdropper attempts to measure the quantum state, the proposed E91 protocol collapses its state from $\vert 10\rangle $ to $\vert 11\rangle $ , setting the first Qubit to $\vert 1\rangle $ and the other Qubit to $\vert 0\rangle $ , thus providing the eavesdropper with incorrect information, accompanied by a phase angle of $15\pi $ /8. This leads to a misconception, preventing eavesdroppers from obtaining useful details about transferred quantum states. Additionally, considering that the proposed E91 protocol relies on entangled particles and utilizes double Qubit gates, which are inherently noisier than single Qubit gates and more susceptible to quantum decoherence, this study employs error mitigation techniques during the final measurement to predict outcomes more efficiently.}, archive = {J_ACCESS}, author = {Noor Ul Ain and Muhammad Waqar and Anas Bilal and Ajung Kim and Haider Ali and Umair Ullah Tariq and Muhammad Shahroz Nadeem}, doi = {10.1109/ACCESS.2025.3539178}, journal = {IEEE Access}, pages = {32819-32833}, shortjournal = {IEEE Access}, title = {A novel approach based on quantum key distribution using BB84 and e91 protocol for resilient encryption and eavesdropper detection}, volume = {13}, year = {2025}, }
• (2025). Analysis of low area digital Up/Down clipping counter for digital in-memory computing. ACCESS, 13, 32808-32818. (www)

  @article{ , abstract = {As deep learning becomes more prevalent in state-of-the-art research, its energy overhead caused by the extensive data transfer has become a significant challenge. To address this issue, in-memory computing (IMC) has been actively explored as a solution, enabling computations to be performed directly within memory. Among various IMC architectures, magnetic random access memory-based digital IMC architecture for ternary output binary neural networks has garnered attention due to its ability to reduce area by utilizing a single bit-cell per synapse and guarantee accuracy through digital operation using XNOR-like operations on ternary outputs. Because a counter must be attached to each column to accumulate sum values in digital IMC macro, minimizing the area overhead of the counter is important. This paper discusses and compares the design of Up/Down Clipping Counters based on toggle flip-flop (TFF) and ripple carry adder (RCA). Finally, it proposes a novel RCA-based counter. The proposed counter achieves a 45.32% reduction in layout area compared to the TFF(J)-based AND-OR Counter which had the least modified structure from the conventional counter, resulting in a significant area reduction. Additionally, it achieves a 56.94% reduction in energy consumption.}, archive = {J_ACCESS}, author = {Siyeol Lee and Geonwoo Lee and Seongmin Ahn and Taehui Na}, doi = {10.1109/ACCESS.2025.3531982}, journal = {IEEE Access}, pages = {32808-32818}, shortjournal = {IEEE Access}, title = {Analysis of low area digital Up/Down clipping counter for digital in-memory computing}, volume = {13}, year = {2025}, }
• (2025). Research on container multimodal transportation multi-objective path optimization from hinterlands to shanghai port. ACCESS, 13, 32794-32807. (www)

  @article{ , abstract = {Given Shanghai Port’s proximity to the sea and the Yangtze River, this article investigated the optimization of Shanghai Port’s foreign trade container multimodal transport path. First and foremost, because the range of the port hinterland generating container is large, Sichuan Province, Chongqing Municipality, Hubei Province, Hunan Province, Jiangxi Province, Anhui Province, and Jiangsu Province are chosen as the major container generating hinterland. And the container intermodal transportation network is centered on these cities and provinces. Then, using minimizing total transportation cost and total transportation time as objective functions, a multi-objective container multimodal transportation path optimization model is built. The improved multi-objective particle swarm approach is used to solve the mathematical mode. Finally, the efficient multimodal container transportation routes from Yibin, Luzhou, Chongqing, Yi-chang, Wuhan, Jiujiang, and Yueyang to Shanghai Port are identified. The acquired results are used to discuss the port competitiveness and freight organization games. It closes by verifying the usefulness of the approaches and models presented in this study. It is intended that the study presented in this paper will serve as a model and method reference for optimizing container multimodal transportation patterns.}, archive = {J_ACCESS}, author = {Chuan Huang and Hanbing Sun and Changjian Liu and Xiaoqing Zhang and Tianhang Gao and Jia Tian}, doi = {10.1109/ACCESS.2025.3532387}, journal = {IEEE Access}, pages = {32794-32807}, shortjournal = {IEEE Access}, title = {Research on container multimodal transportation multi-objective path optimization from hinterlands to shanghai port}, volume = {13}, year = {2025}, }
• (2025). A fusion modeling approach for six-phase hybrid excitation synchronous motor, leveraging finite-element analysis, and experimental data-driven. ACCESS, 13, 32781-32793. (www)

  @article{ , abstract = {In the rapidly evolving automotive electrification landscape, Belt-driven Starter and Generator (BSG) systems demand advanced motor modeling techniques. This research addresses critical challenges in six-phase hybrid excitation synchronous motors (6P-HESM) by developing a comprehensive framework that bridges theoretical modeling and practical automotive applications. A fusion modeling approach for 6P-HESM, leveraging finite-element analysis (FEA) and experimental data-driven is proposed in this paper to serve the application of 6P-HESM in BSG application. The proposed approach focuses on the fusion model construction, including analytical model derivation, model parameters acquisition with FEA and experimental data-driven and model fusion. The first one is carried out by vector space decomposition method with consideration of delta connection in stator windings. The second one is realized by experimental measurements, FEA and their combination with data-driven means, such as artificial neural networks. The last one is performed in MATLAB/Simulink platform based on the vehicle BSG scenario, and torque estimation is used as the target of model validation. Finally, the validation experiment is performed on test benches. The proposed model demonstrates superior torque estimation performance in BSG application without phase current. The proposed approach is also suitable for six-phase or three-phase permanent magnet synchronous motor, three-phase HESM and so on.}, archive = {J_ACCESS}, author = {Xiao Zeng and Yunhao Ma and Jiali Wan and Yiyu Liu}, doi = {10.1109/ACCESS.2025.3538055}, journal = {IEEE Access}, pages = {32781-32793}, shortjournal = {IEEE Access}, title = {A fusion modeling approach for six-phase hybrid excitation synchronous motor, leveraging finite-element analysis, and experimental data-driven}, volume = {13}, year = {2025}, }
• (2025). Observer-based deep reinforcement learning for robust missile guidance and control. ACCESS, 13, 32769-32780. (www)

  @article{ , abstract = {Conventional missile guidance and control systems are difficult to adapt to complex environments, and new missile guidance systems with fast and accurate decision-making capabilities and high robustness are urgently needed to meet the needs of current and future military and aerospace missions. This study presents a deep reinforcement learning (DRL)-based missile guidance system that integrates an extended state observer (ESO) using a leaky proximal policy optimization algorithm. The ESO estimates the state and disturbance information for the missile attitude control system, which is then used as an input for the DRL control. This information helps the agent gain an enhanced understanding of the system and environmental states, leading to more accurate decision making. This approach improves the convergence speed and robustness in controlling the missile attitudes during the interception of maneuvering targets. Furthermore, the system demonstrates robust performance with up to a 30% variation in aerodynamic parameters, maintaining stable control at all times. This approach performs well in uncertain and high-speed environments and is highly relevant to modern aerospace applications. This study lays the foundation for further research on integrating intelligent control algorithms with advanced learning techniques, as it presents a novel and promising missile guidance control strategy based on integrating ESO and deep reinforcement learning.}, archive = {J_ACCESS}, author = {Wenwen Wang and Zhihua Chen}, doi = {10.1109/ACCESS.2025.3542129}, journal = {IEEE Access}, pages = {32769-32780}, shortjournal = {IEEE Access}, title = {Observer-based deep reinforcement learning for robust missile guidance and control}, volume = {13}, year = {2025}, }
• (2025). A novel reconfigurable closed-chain leg mechanism based on phase difference adjustment for multiple walking motion of quadruped robot. ACCESS, 13, 32756-32768. (www)

  @article{ , abstract = {The utilization of single degree-of-freedom (DOF) closed-chain mechanisms as leg mechanisms of quadruped robots has been studied because of its advantages, such as a lightweight structure and simplified control resulting from the decreased number of actuators needed for operation. Furthermore, to improve the scalability of these approaches, researchers have investigated the use of reconfigurable structures. Nevertheless, the traditional method that relies on adjusting the length of links presents a substantial challenge in achieving precise production of various trajectories while maintaining the centroid positions of those trajectories. We propose a novel reconfigurable structure that utilizes the phase difference between the crank gears to generate desired trajectories accurately. The proposed mechanism guarantees that the centroid positions of multiple trajectories remain confined inside the specified border. Comparison with conventional reconfigurable mechanisms demonstrates the ability of the proposed mechanism to achieve high trajectory accuracy over multiple trajectories, even in environments with limited centroid position changes. Additionally, a gait stability analysis of a quadruped robot with the proposed leg mechanism in a simulation environment was conducted to verify its suitability.}, archive = {J_ACCESS}, author = {Hyeonbeom Shin and Jiho Park and Hosu Lee and Jungwon Yoon}, doi = {10.1109/ACCESS.2025.3542080}, journal = {IEEE Access}, pages = {32756-32768}, shortjournal = {IEEE Access}, title = {A novel reconfigurable closed-chain leg mechanism based on phase difference adjustment for multiple walking motion of quadruped robot}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of decentralized federated learning: Impact of fully and K-connected topologies, heterogeneous computing resources, and communication bandwidth. ACCESS, 13, 32741-32755. (www)

  @article{ , abstract = {Decentralized federated learning (DFL) enables collaborative model training across distributed devices while preserving data privacy. Despite this, the performance of DFL in real-world scenarios, characterized by varying network topologies, heterogeneous client resources, and communication bandwidth, remains underexplored. This study analyzes the impact of network topologies, focusing on fully connected and k-connected structures, while examining how computing resource and communication bandwidth heterogeneity affect system efficiency using diverse datasets such as Fashion MNIST, CIFAR-10, and CIFAR-100. The results show that fully connected topologies offer robust communication but suffer from significant scalability issues. By contrast, k-connected networks provide a more scalable solution with reduced communication overhead. Furthermore, clients with limited CPU resources increase the convergence times by as much as 30%, while those with low communication bandwidth variations exacerbate the convergence times by up to 50%. These findings provide practical insights and guidelines for optimizing and designing scalable DFL systems suitable for real-world deployments.}, archive = {J_ACCESS}, author = {Vo van Truong and Pham Khanh Quan and Dong-Hwan Park and Taehong Kim}, doi = {10.1109/ACCESS.2025.3542772}, journal = {IEEE Access}, pages = {32741-32755}, shortjournal = {IEEE Access}, title = {Performance evaluation of decentralized federated learning: Impact of fully and K-connected topologies, heterogeneous computing resources, and communication bandwidth}, volume = {13}, year = {2025}, }
• (2025). Automatic landmark detection in severe craniomaxillofacial deformities via three-dimensional (3D) point cloud deformation model and deep learning networks. ACCESS, 13, 32724-32740. (www)

  @article{ , abstract = {The accurate and efficient detection of landmarks is crucial for diagnosing and treating CMF deformities, which often involve various structural abnormalities in bone structure displacement, defects, and facial asymmetry. The automated detection of CMF malformation landmarks has been extensively studied; however, these studies have been hindered by significant computational burdens and limited patient populations. Furthermore, the automated detection of landmarks in patients with bone defects and facial asymmetry has been overlooked. This research proposes a new landmark detection framework based on point cloud deformation model and deep learning networks. Firstly, to address the issue of insufficient patient data, we employ the point cloud deformation model for data augmentation. By means of deformation, severely deformative data can be transformed from normal human data for model training. The next step involves applying a 3D point cloud CNN model to all landmarks, enabling coarse localization. Subsequently, an assessment is made on whether there are any bone defects within the region where these landmarks are located. The accurate localization is achieved using two different models: 1) For landmarks in areas without bony structural defects (normal landmarks), the CNN semantic segmentation model for 3D images is used; 2) For landmarks in defect areas (defect landmarks), the 3D point cloud CNN semantic segmentation model is applied. The effectiveness of our proposed framework is demonstrated through the presentation of experimental results. Collectively, our work establishes a novel pathway for surface-based morphometry and medical shape analysis. Through two stages of localization, the accuracy rate is finally $1.19~\pm ~0.71$ mm (CT), $0.91~\pm ~0.42$ mm (CBCT) for normal landmark and $1.13\pm 0.75$ mm (CT), $0.94~\pm ~0.51$ mm (CBCT) for defect landmark, which is superior to the most advanced related methods.}, archive = {J_ACCESS}, author = {Meng Xu and Zhaoyang Luo and Bingyang Liu and Zhiyan Wang and Xiaojun Tang and Tao Song}, doi = {10.1109/ACCESS.2025.3541601}, journal = {IEEE Access}, pages = {32724-32740}, shortjournal = {IEEE Access}, title = {Automatic landmark detection in severe craniomaxillofacial deformities via three-dimensional (3D) point cloud deformation model and deep learning networks}, volume = {13}, year = {2025}, }
• (2025). Adversarial domain adaptation-based EEG emotion transfer recognition. ACCESS, 13, 32706-32723. (www)

  @article{ , abstract = {This paper introduces an Emotion Domain Adversarial Neural Network (EDANN) model for Electroencephalogram (EEG) emotion recognition, designed to accomplish EEG emotion classification across various periods and subjects. The model is composed of three essential components: an encoder, a label classifier, and a domain discriminator. Utilizing adversarial training, EDANN can extract features that are discriminative among different categories and invariant across domains. The study employed two public datasets: the DEAP dataset, which includes EEG signals from 32 participants for emotion analysis; and the SEED dataset, comprising emotional responses from 15 Chinese participants to 15 Chinese film clips. Both datasets utilized specific emotion models for labeling emotions, albeit with varying levels of precision. Extensive experiments on both Session-to-Session and Subject-to-Subject transfer tasks have demonstrated the proposed model’s superior performance in terms of accuracy, precision, recall, and F1 score for emotion recognition. The findings of this study not only illustrate the efficacy of EDANN in EEG-based emotion recognition but also underscore the importance of considering significant inter-individual differences when designing and evaluating machine learning models. These methodologies enable researchers to utilize limited data resources more efficiently, thus propelling the advancement of emotion recognition technology.}, archive = {J_ACCESS}, author = {Ting Li and Zhan Wang and Huijing Liu}, doi = {10.1109/ACCESS.2025.3540436}, journal = {IEEE Access}, pages = {32706-32723}, shortjournal = {IEEE Access}, title = {Adversarial domain adaptation-based EEG emotion transfer recognition}, volume = {13}, year = {2025}, }
• (2025). An optimized cascaded CNN approach for feature extraction from brain MRIs for tumor classification. ACCESS, 13, 32681-32705. (www)

  @article{ , abstract = {This study enhances brain tumor classification by leveraging pre-trained models and attention mechanisms, ultimately improving accuracy and reliability in medical imaging diagnostics through feature extraction. A generative adversarial network (GAN) is employed for artifact removal, and images are resized to $224\times 224$ pixels. Four pre-trained models—VGG16, ResNet50, NASNet, and DenseNet121—capture distinct features, and a Custom-CNN integrates these models with the convolutional block attention module (CBAM). The bald eagle optimization (BEO) algorithm is used to optimize the hyperparameters while balancing exploration and exploitation. The proposed Custom-CNN outperformed all pre-trained models in classifying brain tumor data across three datasets (Br35H, FIGSHARE, and SARTAJ), achieving a highest accuracy of 0.9722, sensitivity of 0.9689, precision of 0.9721, and an F-score of 0.9708 for the Br35H dataset. The incorporation of CBAM (Custom-CNN-CBAM) resulted in consistent performance improvements, with accuracy increasing from 0.9433 to 0.9572 for the Br35H dataset and from 0.9336 to 0.9501 for the FIGSHARE dataset, achieving the highest F-score of 0.9598. The enhanced version of the Custom-CNN-CBAM model utilizing the BEO, referred to as Custom-CNN-CBAM-BEO, demonstrated superior convergence speed and accuracy compared to models optimized with genetic algorithm (GA) and particle swarm optimization (PSO), highlighting the effectiveness of the BEO optimization approach. The evaluation of various Custom-CNN models incorporating attention mechanisms and optimization techniques revealed that the Custom-CNN-CBAM-BEO model achieved the best average rank of 5.68 in the Friedman test, with the Bonferroni-Dunn test confirming its superiority.}, archive = {J_ACCESS}, author = {Santosh Kumar Chhotray and Debahuti Mishra and Sarada Prasanna Pati and Sashikala Mishra}, doi = {10.1109/ACCESS.2025.3543214}, journal = {IEEE Access}, pages = {32681-32705}, shortjournal = {IEEE Access}, title = {An optimized cascaded CNN approach for feature extraction from brain MRIs for tumor classification}, volume = {13}, year = {2025}, }
• (2025). A systematic review of open datasets used in text-to-image (T2I) gen AI model safety. ACCESS, 13, 32661-32680. (www)

  @article{ , abstract = {Novel research aimed at text-to-image (T2I) generative AI safety often relies on publicly available datasets for training and evaluation, making the quality and composition of these datasets crucial. This paper presents a comprehensive review of the key datasets used in the T2I research, detailing their collection methods, compositions, semantic and syntactic diversity of prompts and the quality, coverage, and distribution of harm types in the datasets. By highlighting the strengths and limitations of the datasets, this study enables researchers to find the most relevant datasets for a use case, critically assess the downstream impacts of their work given the dataset distribution, particularly regarding model safety and ethical considerations, and also identify the gaps in dataset coverage and quality that future research may address.}, archive = {J_ACCESS}, author = {Trupti Bavalatti and Osama Ahmed and Dhaval Potdar and Rakeen Rouf and Faraz Jawed and Manish Kumar Govind and Siddharth Krishnan}, doi = {10.1109/ACCESS.2025.3539933}, journal = {IEEE Access}, pages = {32661-32680}, shortjournal = {IEEE Access}, title = {A systematic review of open datasets used in text-to-image (T2I) gen AI model safety}, volume = {13}, year = {2025}, }
• (2025). Optimizing stroke recognition with MediaPipe and machine learning: An explainable AI approach for facial landmark analysis. ACCESS, 13, 32636-32660. (www)

  @article{ , abstract = {Early detection of stroke is critical for improving survival rates and recovery. This study presents an innovative approach to stroke diagnosis through the analysis of facial landmarks combining MediaPipe’s facial landmark detection with advanced machine learning models’ Random Forest (RF), Extreme Gradient Boosting (XGB), and Categorical Boosting (CB). A comprehensive dataset of stroke and non-stroke facial images are curated, with MediaPipe extracting 228 facial landmarks from key regions affected by stroke-related asymmetry. Explainable AI (XAI) techniques are applied to enhance model interpretability, allowing for a deeper understanding of the most significant facial regions contributing to stroke prediction. The machine learning models are individually optimized through hyperparameter tuning, and further we propose a Multimodal Voting Classifier (MVC). By aggregating the predictions through majority voting or weighted averaging, the system reduces the likelihood of incorrect classifications that might arise from a single model’s limitations. This results in a more stable and generalized model with improved diagnostic accuracy, as demonstrated by its 94.75% accuracy, than any individual model’s performance. Feature importance analysis, facilitated by XAI, identified key facial regions—such as the eyes, cheeks, and lips—as critical indicators of stroke, significantly improving the model’s diagnostic precision. Additionally, t-SNE visualization is employed to provide insight into classifying the stroke and non-stroke cases, reinforcing the model’s robustness. Real-time metrics show the model’s efficiency and adaptability across diverse hardware, enabling practical clinical integration. The proposed system offers a cost-effective, non-invasive diagnostic tool that can be implemented in real-time, potentially improving accessibility to stroke diagnosis in remote and resource-limited areas.}, archive = {J_ACCESS}, author = {Reshad Ul Karim and Sammam Mahdi and Abrar Samin and Aniqua Nusrat Zereen and M. Abdullah-Al-Wadud and Jia Uddin}, doi = {10.1109/ACCESS.2025.3550577}, journal = {IEEE Access}, pages = {32636-32660}, shortjournal = {IEEE Access}, title = {Optimizing stroke recognition with MediaPipe and machine learning: An explainable AI approach for facial landmark analysis}, volume = {13}, year = {2025}, }
• (2025). Predicting future intrablock links in directed networks using triadic patterns. ACCESS, 13, 32617-32635. (www)

  @article{ , abstract = {Complex networks model real-world relationships between entities where any two entities share more than one kind of relationship. Directed multilayer networks are used to represent such networks effectively, capturing the heterogeneity exhibited by the nodes and the directionality of relationships. The link prediction problem refers to predicting relationships (links) between the entities (nodes) that may arise in the future or identifying missing links to reconstruct the network. Our previous work proposed a novel block formation algorithm to determine future links between the blocks. This work proposes an intrablock link prediction technique within the blocks’ nodes exhibiting strong internal connectivity. The novelty of the proposed approach lies in extracting triads from a block using G-Trie by formulating a probabilistic model to predict the links between triads based on isomorphic properties, activity of nodes, and presence of common and influencer nodes. The experimental results on social, biological, and ecological datasets demonstrate that our hybrid framework achieved higher link prediction accuracy for both directed dense networks and sparse networks with lesser computational complexity.}, archive = {J_ACCESS}, author = {Lekshmi S. Nair and J. Swaminathan}, doi = {10.1109/ACCESS.2025.3542904}, journal = {IEEE Access}, pages = {32617-32635}, shortjournal = {IEEE Access}, title = {Predicting future intrablock links in directed networks using triadic patterns}, volume = {13}, year = {2025}, }
• (2025). Privacy-preserving techniques in biometric systems: Approaches and challenges. ACCESS, 13, 32584-32616. (www)

  @article{ , abstract = {The widespread adoption of smart technologies and digital services has intensified the demand for reliable and secure identity authentication systems. Biometric credentials, offering significant advantages over traditional methods, have emerged as a preferred solution. However, preserving the privacy of biometric data remains a critical challenge. This paper presents a comprehensive study of privacy-preserving techniques and potential risks associated with biometric systems. A novel taxonomy and structured framework are introduced to classify existing research in this domain. The study evaluates state-of-the-art approaches across a wide range of biometric modalities, including facial recognition, fingerprint analysis, eye-based identification, Electroencephalogram (EEG), Electrocardiogram (ECG), voice recognition, and hand geometry, highlighting their respective benefits, limitations, and application areas. Additionally, the paper examines the balance between privacy, security, and recognition performance, addressing current challenges and proposing future research directions. This work serves as a valuable resource for researchers and industry professionals, supporting the development of privacy-centric and secure biometric authentication technologies in an evolving digital landscape.}, archive = {J_ACCESS}, author = {K. Krishna Prakasha and U. Sumalatha}, doi = {10.1109/ACCESS.2025.3541649}, journal = {IEEE Access}, pages = {32584-32616}, shortjournal = {IEEE Access}, title = {Privacy-preserving techniques in biometric systems: Approaches and challenges}, volume = {13}, year = {2025}, }
• (2025). Mathematical model for prediction of pressure fluctuations in common rail. ACCESS, 13, 32574-32583. (www)

  @article{ , abstract = {This paper is based on fluid dynamics principle and analyzes the generation mechanism of pressure fluctuations in common rail (CR). We have derived a hyperbolic partial differential equation (HPDE) to describe pressure fluctuations in CR based on mass continuity equation and momentum conservation equation of unsteady one-dimensional flow. By using numerical difference method we have developed first-order differential equations representing the state matrix of the system. Time derivatives of pump pressure and injection pressure are taken as system inputs and pressure fluctuations at different locations along CR are chosen as outputs. Pressure fluctuation characteristics in CR have been predicted based on the state space method. Simulation results of the model have been compared with experimental data to assess the capability of the model for predicting rail pressure dynamic fluctuation characteristics at different operating conditions. The comparisons show that the proposed mathematical model can predict pressure fluctuation characteristics in CR precisely.}, archive = {J_ACCESS}, author = {Yun Bai and Chengda Du and Qiang Sun and Chao Yang and Simin Lan}, doi = {10.1109/ACCESS.2025.3541261}, journal = {IEEE Access}, pages = {32574-32583}, shortjournal = {IEEE Access}, title = {Mathematical model for prediction of pressure fluctuations in common rail}, volume = {13}, year = {2025}, }
• (2025). Inferring urban air temperatures from land surface temperatures with the E3SM land model (uELM), satellite observations, and measurement campaign. ACCESS, 13, 32564-32573. (www)

  @article{ , abstract = {This study investigates how urban land-cover variability—from dense built areas to predominantly vegetated neighborhoods—affects the relationship between land surface temperature (LST) and near-surface air temperature (T2M) using an ultrahigh-resolution land model (uELM). 125 simulations adjusting fractions of urban vs. vegetation, mid-rise vs. low-rise buildings and tree vs. lawn cover reveal strong LST-T2M coupling, as well as its dependency on land cover and diurnal variations. These physics-based findings support a machine-learning, inverse-modeling approach, training a XGBoost algorithm on simulated LST-T2M pairs to estimate T2M from satellite-based LST. Applied to the Chicago region with GOES-16 data and compared against vehicle-based measurements, the model and measurements agrees relatively well midday but shows evening mismatches tied to uneven cooling and hyperlocal factors during observation. Despite these discrepancies, blending mechanistic modeling with data-driven inversion has potential to refine urban T2M estimates, informing heat mitigation strategies and advancing urban climate research.}, archive = {J_ACCESS}, author = {Jangho Lee}, doi = {10.1109/ACCESS.2025.3543323}, journal = {IEEE Access}, pages = {32564-32573}, shortjournal = {IEEE Access}, title = {Inferring urban air temperatures from land surface temperatures with the E3SM land model (uELM), satellite observations, and measurement campaign}, volume = {13}, year = {2025}, }
• (2025). Decoding on-field roles in football: A sociological analysis using intelligent MCDM algorithm with fuzzy information for player positioning and technical-tactical behavior. ACCESS, 13, 32540-32563. (www)

  @article{ , abstract = {Football is a challenging sport that demands an understanding of on-field roles, the nuances of player positioning, and clear player roles crucial for strategic advantage and achieving optimal team performance. The football strategy involves the careful planning and execution of player roles and actions to outmaneuver opponents and achieve team goals. However, effective strategies in football are challenging due to the unpredictable nature of the game, as it involves factors like real-time decision-making, varying player skills, and the fluid dynamics of play, which add to the game’s complexity. The traditional approaches often fail to capture these nuanced details. So, football strategy demands a decision-making model that considers these details and assists in achieving optimal team performance. This study introduces a new approach to analyzing player roles and technical-tactical behaviour by proposing a sociological framework based on the multicriteria decision-making (MCDM) algorithm. The proposed study defines the complex circular intuitionistic fuzzy Bonferroni mean (Comp-CIFBM) aggregation operator (AO), its properties, and then the decision algorithm which utilizes the proposed operator and accommodates the uncertainty and imprecision inherent in data of sociological and game performance analysis by defining a multi-faceted view of each player’s contribution to team dynamics. Moreover, to demonstrate the reliability of the proposed approach in refining the strategic insights (plans to improve team performance based on analyzing players performances, team dynamics, and opposition strategies) and supporting the decisions for on-field role allocation and tactical planning in football, a comprehensive comparison has been made with the prior approaches that highlight the proposed approach’s efficacy. Overall, the findings highlight the value of a decision-making framework for improving strategic planning, allowing for a more precise and adaptable approach to football management.}, archive = {J_ACCESS}, author = {Lu Yan and Nannan Wang and Rui Chen}, doi = {10.1109/ACCESS.2025.3540952}, journal = {IEEE Access}, pages = {32540-32563}, shortjournal = {IEEE Access}, title = {Decoding on-field roles in football: A sociological analysis using intelligent MCDM algorithm with fuzzy information for player positioning and technical-tactical behavior}, volume = {13}, year = {2025}, }
• (2025). Incipient fault localization using sheath and impedance-based method for single-bonded underground cables. ACCESS, 13, 32529-32539. (www)

  @article{ , abstract = {Electric energy plays a fundamental role in driving global productivity and is essential for industrial, commercial, and residential activities. As energy demand continues to rise, insulated power cables have become increasingly critical in modern power grids, ensuring reliable transmission and distribution. However, insulation aging can progressively lead to faults and result in system outages. Given that over 70% of these interruptions in isolated cables are caused by phase-to-ground faults, this presents a significant challenge to the stability and reliability of power systems. Current fault detection strategies aim to identify incipient faults–small, transient electrical discharges within the insulation–before they escalate into permanent faults. Despite this, traditional methods face limitations in terms of precision and applications, especially for single-bonded cables with short lengths and low impedance values. To overcome these limitations, this paper proposes an enhanced impedance-based method for locating incipient faults using the metallic sheath of single-bonded cables. This method integrates metallic sheath parameters to improve the accuracy of incipient fault localization while avoiding the uncertainty associated with estimating unknown parameters. Unlike previous approaches that rely on voltage and current measurements at both ends of the cable, the proposed method simplifies data collection by reducing the number of monitored parameters. Simulations validate the proposed approach, demonstrating its ability to reduce errors in locating fault points in underground cables. The primary contribution of this study is improving the accuracy of incipient fault localization, offering a more efficient and effective solution for identifying and locating faults in insulated cables at their earliest stages.}, archive = {J_ACCESS}, author = {Elaine C. Carmo and Leonardo A. R. Silva and Thales A. C. Maia}, doi = {10.1109/ACCESS.2025.3538535}, journal = {IEEE Access}, pages = {32529-32539}, shortjournal = {IEEE Access}, title = {Incipient fault localization using sheath and impedance-based method for single-bonded underground cables}, volume = {13}, year = {2025}, }
• (2025). Inferences for skew-wiener degradation model: Approximate bayesian computation via gibbs-step and model selection. ACCESS, 13, 32511-32528. (www)

  @article{ , abstract = {The Skew-Wiener Degradation (SKWD) model presents a novel approach for addressing unit-to-unit variability in degradation processes by integrating the skew-normal distribution into the Wiener process. This study introduces a robust parameter estimation method for the Skew-Wiener model using Approximate Bayesian Computation with Gibbs sampling (ABC-Gibbs), providing an alternative to the Expectation-Maximization (EM) algorithm. ABC-Gibbs offers a computationally efficient, likelihood-free inference approach, particularly well-suited for high-dimensional parameter spaces where direct likelihood calculations are computationally expensive. Through extensive simulations and real-world applications, including laser and LED degradation datasets, the efficiency and practical utility of the proposed method were validated. Additionally, an ABC model choice (ABC-MC) procedure is implemented to compare linear and nonlinear SKWD models, identifying the most suitable model for specific degradation datasets. The findings demonstrate that ABC significantly enhances parameter estimation accuracy, and ABC-MC effectively distinguishes between competing models, making the approach highly valuable for reliability analysis.}, archive = {J_ACCESS}, author = {Isyaku Muhammad and Mustapha Muhammad and Ren Aihua and Aiqiang Zhang}, doi = {10.1109/ACCESS.2025.3543391}, journal = {IEEE Access}, pages = {32511-32528}, shortjournal = {IEEE Access}, title = {Inferences for skew-wiener degradation model: Approximate bayesian computation via gibbs-step and model selection}, volume = {13}, year = {2025}, }
• (2025). UWB sensors optimization deployment for narrow and long space environments. ACCESS, 13, 32497-32510. (www)

  @article{ , abstract = {In this paper, a detailed study on Ultra-wideband (UWB) sensor deployment was carried out to satisfy requirements of achieving higher positioning accuracy for the narrow and long underground roadway. We derived the dilution of precision (DOP) relationship between time of arrival (TOA) and time difference of arrival (TDOA) positioning model, and the particle swarm optimization deployment (PSOD) strategy was applied to optimize the anchor nodes (ANs) arrangement to achieve a better configuration under the guarantee of complete coverage. Simulation and experiments verification were conducted via the UWB positioning system. The experimental results demonstrate that the PSOD scheme exhibits the best performance possessing the lowest DOP as compared with equidistance optimization deployment (EOD) and random deployment sensor (RDS) scheme in both positioning models. The proposed PSOD scheme is able to achieve best positioning accuracy with the theoretical mean estimation accuracy of 0.119 m. The localization accuracy of PSOD scheme on the three axes orientation is improved by 39.1%, 49.6%, and 43.2% as compared with the RSD scheme, which will provide meaningful guidance in terms of UWB arrangement in narrow and long underground space.}, archive = {J_ACCESS}, author = {Bo Cao and Liyong Zhang and Menghui Han and Menglan Li and Biyong Xu and Qiliang Zeng and Guanpei Bao and Zhihua Yao}, doi = {10.1109/ACCESS.2025.3542806}, journal = {IEEE Access}, pages = {32497-32510}, shortjournal = {IEEE Access}, title = {UWB sensors optimization deployment for narrow and long space environments}, volume = {13}, year = {2025}, }
• (2025). Collaborative filtering recommendation-based random negative sampling and graph attention. ACCESS, 13, 32486-32496. (www)

  @article{ , abstract = {In this era of information overload, recommendation systems significantly enhance the efficiency of information delivery, better meeting the needs of users. Currently, GCN-based recommendation systems typically use degree normalization or mean pooling to aggregate neighbor messages. These methods learn embedding representations for users and items. However, both message-passing mechanisms overlook the varying importance of different neighbor nodes to the target node. As a result, the learned representations of users and items are not sufficiently accurate. Furthermore, they do not take into account the importance of data augmentation of metadata, which limits the recommendation performance. On the other hand, during the model’s loss optimization process, sample imbalance is prone to occur. Negative samples greatly outnumber positive samples, which leads to a certain degree of overfitting in the model. To address this issue, this paper proposes a collaborative filtering recommendation based on random negative sampling and graph attention (NGACF). First, perform data augmentation on the initial embeddings of users and items. Then, before the propagation of each layer in the embedding propagation layers, graph attention networks (GAT) are used to aggregate information from the target node’s neighbors. This approach captures the importance of different neighboring nodes, thereby enriching the target node representations. In the loss optimization module, a random negative sampling strategy is incorporated as an auxiliary loss to mitigate the problem of imbalanced sample classes during training. This approach reduces model overfitting, facilitating better optimization and improving the model’s generalization ability. Finally, experiments were conducted on three public datasets. In particular, on the Amazon-Book dataset, the results show that the proposed method outperforms the baseline model. Recall@20 improved by 3.65%, and NDCG@20 increased by 2.86%. These results further validate the effectiveness of the proposed model.}, archive = {J_ACCESS}, author = {Weiqiang Li and Xianghui Li and Xiaowen Liu and Xinhuan Chen and Ming Ma}, doi = {10.1109/ACCESS.2025.3541126}, journal = {IEEE Access}, pages = {32486-32496}, shortjournal = {IEEE Access}, title = {Collaborative filtering recommendation-based random negative sampling and graph attention}, volume = {13}, year = {2025}, }
• (2025). The power-oriented graphs modeling technique: From the fundamental principles to the systematic, step-by-step modeling of complex physical systems. ACCESS, 13, 32470-32485. (www)

  @article{ , abstract = {Modeling physical systems is an essential skill for engineers, since it enables to achieve a deep understanding of their dynamic behavior and, consequently, to develop effective control strategies. The first part of this paper provides a recap of the fundamental principles and properties of the Power-Oriented Graphs (POG) modeling technique, by also referring to a case study to consolidate them. The POG technique is then compared with the other two main graphical modeling techniques available in the literature, namely Bond Graph (BG) and Energetic Macroscopic Representation (EMR). The second part of this paper introduces the new Fast Modeling POG (FMPOG) procedure. The FMPOG, which operates in the POG framework, is a methodical step-by-step procedure that enables the readers to quickly derive the power-oriented graphical model of physical systems starting from their schematics. From the power-oriented graphical model, the state-space model can then be directly determined. To ensure the FMPOG procedure is easily usable by the entire community, we apply it to three examples in different energetic domains in this paper, guiding the reader step-by-step through the derivation of the physical systems models. A freely available Matlab/Simulink program is provided in a repository, allowing the users to automatically apply the FMPOG procedure to various classes of physical systems. This program allows to convert the physical systems schematics into the corresponding POG block schemes and, ultimately, into the state-space mathematical models.}, archive = {J_ACCESS}, author = {Davide Tebaldi and Roberto Zanasi}, doi = {10.1109/ACCESS.2025.3537862}, journal = {IEEE Access}, pages = {32470-32485}, shortjournal = {IEEE Access}, title = {The power-oriented graphs modeling technique: From the fundamental principles to the systematic, step-by-step modeling of complex physical systems}, volume = {13}, year = {2025}, }
• (2025). Trajectory classification through topological data analysis perspectives. ACCESS, 13, 32458-32469. (www)

  @article{ , abstract = {This paper examines the application of Topological Data Analysis (TDA) for trajectory classification, aiming to improve the interpretation of complex spatial movement patterns. By utilizing TDA, we explore the hidden structures in trajectory datasets, offering a fresh perspective on classification methods. Our study integrates TDA into trajectory analysis, highlighting its ability to capture spatial features that conventional methods may miss. We assess TDA’s effectiveness using both simulated and real-world trajectory data from a survey comparing existing classifiers. TDA demonstrated significant performance improvements, with accuracy gains of up to 42.95% in certain scenarios. Notably, in real-world datasets, TDA increased accuracy by 38.49% for hurricane trajectory classification and improved precision by 39.24%. Simulated trajectories provided a controlled environment to further test TDA’s robustness. The results underscore the potential of TDA to enhance trajectory analysis, uncovering complex spatial patterns and relationships that traditional methods may overlook.}, archive = {J_ACCESS}, author = {Miriam Esteve and Antonio Falcó}, doi = {10.1109/ACCESS.2025.3543111}, journal = {IEEE Access}, pages = {32458-32469}, shortjournal = {IEEE Access}, title = {Trajectory classification through topological data analysis perspectives}, volume = {13}, year = {2025}, }
• (2025). Establishing pattern sequences using stochastic trees and bayesian belief networks with an application to security patterns. ACCESS, 13, 32443-32457. (www)

  @article{ , abstract = {Organizational patterns can be applied to correct problems or build a new organization from scratch. Security patterns can help organizations develop secure software. Security in organization can be established using sequences of organizational and security patterns. Security patterns can help organizations develop secure software. Although isolated applications of one or more security patterns are typical in practice, an effective response to security threats requires the utilization of an entire pattern catalog. Composing software patterns in a particular sequence order makes them more efficient. Solutions for establishing sequences of security and organizational patterns are often documented in plain text using explicit pattern relationships. Identifying the most probably used security and organizational patterns from their text descriptions allows us to establish meaningful sequences from these patterns. In this work, we show how we established 14 sequences of security patterns using the strongest symmetries of the relationships. We calculated the strongest symmetries of relationships between patterns identified in the Bayesian belief network as conditionally dependent patterns. We calculated the strongest symmetries of relationships using conditional probabilities of security pattern use with the stochastic tree built on deliberately chosen sequences of security patterns. Sequences of security patterns established in this way can be used after meaningful sequences of organizational patterns to implement security in the working organization. The applicability of belief networks and stochastic trees is not limited to security or organizational patterns. We consider applying them to patterns engineering software for cloud.}, archive = {J_ACCESS}, author = {Viktor Matovič and Valentino Vranić}, doi = {10.1109/ACCESS.2025.3543319}, journal = {IEEE Access}, pages = {32443-32457}, shortjournal = {IEEE Access}, title = {Establishing pattern sequences using stochastic trees and bayesian belief networks with an application to security patterns}, volume = {13}, year = {2025}, }
• (2025). Simulation analysis of near and remote field pulse eddy current composite detection method for single-metal pipeline. ACCESS, 13, 32434-32442. (www)

  @article{ , abstract = {Pipeline integrity is crucial for safely transporting energy resources. Traditional electromagnetic non-destructive testing (NDT) methods like eddy current testing (ECT) face limitations in detecting deep defects due to the skin effect. This study proposes a novel near and remote field pulse eddy current composite detection method that integrates pulsed eddy current testing and pulsed remote field eddy current testing to enhance defect detection and localization. A simulation model was established, showing the feasibility of the composite probe. The study demonstrated that the proposed method can distinguish between defects on the inner and outer walls of the pipeline. Parameter optimization identified 25 Hz as the optimal excitation frequency and a 50% duty cycle for the excitation signal. A three-layer shielding disc of copper, silicon steel, and copper (1: 1: 4) provided the best shielding effectiveness. The near and remote field pulse eddy current composite detection method improves detection capabilities and accuracy, enabling precise identification of inner and outer wall defects.}, archive = {J_ACCESS}, author = {Xiaomin Shen and Xiaoting Guo and Zihan Wang and Mingda Song and Wenguang Hu}, doi = {10.1109/ACCESS.2025.3532540}, journal = {IEEE Access}, pages = {32434-32442}, shortjournal = {IEEE Access}, title = {Simulation analysis of near and remote field pulse eddy current composite detection method for single-metal pipeline}, volume = {13}, year = {2025}, }
• (2025). ENDNet: Extra-node decision network for subgraph matching. ACCESS, 13, 32424-32433. (www)

  @article{ , abstract = {Subgraph matching, which involves identifying a query graph within a larger data graph, is a fundamental problem that has applications in various fields. Although graph neural networks (GNNs) are commonly used in learning-based subgraph matching, they face challenges due to their convolution process. Specifically, GNNs generate new node features by aggregating information from adjacent nodes, meaning that even if nodes in the query and data graphs start with identical features, the presence of extraneous nodes or edges in the data graph can distort feature representations and hinder accurate matching. Ensuring feature similarity between the query and data graphs despite these extraneous elements is a key challenge. To address this, we propose ENDNet, a model that improves subgraph matching by detecting and mitigating the influence of extraneous nodes. ENDNet uses a denormalized matching matrix to identify extra nodes and neutralizes their impact by setting their feature values to zero before aggregation and convolution. Additionally, ENDNet incorporates a shared-graph convolutional network, leveraging the sigmoid function to refine feature extraction. Experiments on four open datasets demonstrate that ENDNet outperforms existing trainable subgraph matching models, improving accuracy from 91.6% to 99.1% on the COX2 dataset. Ablation studies further confirm the effectiveness of extra-node determination and the shared-graph convolutional network, highlighting their importance in subgraph matching. The code for ENDNet is available on GitHub.}, archive = {J_ACCESS}, author = {Masaki Shirotani and Motoki Amagasaki and Masato Kiyama}, doi = {10.1109/ACCESS.2025.3543206}, journal = {IEEE Access}, pages = {32424-32433}, shortjournal = {IEEE Access}, title = {ENDNet: Extra-node decision network for subgraph matching}, volume = {13}, year = {2025}, }
• (2025). Machine learning aided tapered four-port MIMO antenna for V2X communications with enhanced gain and isolation. ACCESS, 13, 32411-32423. (www)

  @article{ , abstract = {In this communication, a 4-port Multiple-Input-Multiple-Output (MIMO) antenna is analyzed and investigated for vehicle-to-everything (V2X) communication centered at 5.9 GHz. The proposed optimized single antenna consists of a tapered radiating antenna with a defective ground structure fed with stepped impedance transmission line feed. The proposed 4-port MIMO antenna has a dimension of $96\times 64\times 0.8$ mm3 printed on the FR4 substrate with a relative permittivity of 4.4 and loss tangent of 0.02. To obtain the proposed single antenna unit, parametric analysis, and evolution stages have been investigated and discussed. The impedance bandwidth of the proposed antenna is 5.66 - 6.00 GHz with a peak gain of 7.85 dB and radiation efficiency of 99%. In addition, machine learning techniques such as XG (Extreme Gradient) Boost, Random Forest, and Deep Neural Networks (DNN) were employed in the optimization process to predict and fine-tune the antenna’s design parameters. The stacking ensemble method, combining these models, was used to improve the accuracy of the antenna performance prediction. By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. Further, to validate the MIMO antenna characteristics, different diversity parameters have been calculated such as ECC (Envelope Correlation Coefficient), DG (Diversity Gain), CCL (Channel Capacity Loss), and TARC (Total Active Reflection Coefficient). The fabricated antenna is modeled, and measured findings are found to be in coherence with simulated findings.}, archive = {J_ACCESS}, author = {Nagesh Kallollu Narayanaswamy and Yazeed Alzahrani and Krishna Kanth Varma Penmatsa and Ashish Pandey and Ajay Kumar Dwivedi and Vivek Singh and Manoj Tolani}, doi = {10.1109/ACCESS.2025.3543183}, journal = {IEEE Access}, pages = {32411-32423}, shortjournal = {IEEE Access}, title = {Machine learning aided tapered four-port MIMO antenna for V2X communications with enhanced gain and isolation}, volume = {13}, year = {2025}, }
• (2025). Simulation tools for cognitive radio network: A survey. ACCESS, 13, 32384-32410. (www)

  @article{ , abstract = {Due to recent expansion of wireless communications, it has become impossible to cope with the allotment of the precious spectrum while resources for wireless communication are bounded and finite. Hence, the cognitive radio concept is introduced to ensure optimal utilization of these invaluable resources. By emerging research in the field of cognitive radio network, fresh researchers are looking for a suitable simulator for the cognitive radio network since the implementation and testbed is not practical and feasible invariably. Moreover, there is no simulator that has been designed with cognitive radio networks in mind. Furthermore, this research area is relatively new. Therefore, often it becomes very hard to decide which network simulator to work with, as the availability of reference is significantly low. As a result, much effort is required to make a proper decision. In this paper, we take a detailed look at current simulators used by researchers in the field of cognitive radio networks to help researchers identify appropriate tools for simulation. To further aid researchers, we classify the simulation tools, demonstrate their architectures, describe features, elucidate advantages and disadvantages of these tools, and report some open issues and challenges for future research.}, archive = {J_ACCESS}, author = {A. K. M. Muzahidul Islam and Rakin Bin Rabbani and Md Shadman Aadeeb and Mahdi Zareei and Nafees Mansoor and Molla Md Zubaer}, doi = {10.1109/ACCESS.2025.3537498}, journal = {IEEE Access}, pages = {32384-32410}, shortjournal = {IEEE Access}, title = {Simulation tools for cognitive radio network: A survey}, volume = {13}, year = {2025}, }
• (2025). The digital twin city in enhancing flood evacuation systems: Future opportunities and challenges. ACCESS, 13, 32371-32383. (www)

  @article{ , abstract = {In recent decades, the increasing frequency and severity of natural disasters have necessitated the development of more effective disaster management systems. This paper explores the potential of digital twin technology to enhance flood evacuation systems. A digital twin-city model can simulate urban environments, predict flood inundation, and identify optimal evacuation routes in real-time. The study conducts a systematic literature review to assess current advancements, evaluating the integration of 3D city models, artificial intelligence, and Internet of things sensors in flood evacuation planning. Results indicate that digital twin technology significantly improves predictive capabilities and evacuation efficiency, although data integration, model accuracy, and computational demands remain. Future research should focus on enhancing data integration, refining simulation models, improving predictive analytics, developing advanced visualization tools, and validating models across diverse environments to ensure reliability and effectiveness.}, archive = {J_ACCESS}, author = {Ary Setijadi Prihatmanto and Abdurrakhman Prasetyadi and Ambar Yoganingrum and Ridwan Sutriadi and Ana Hadiana}, doi = {10.1109/ACCESS.2025.3539346}, journal = {IEEE Access}, pages = {32371-32383}, shortjournal = {IEEE Access}, title = {The digital twin city in enhancing flood evacuation systems: Future opportunities and challenges}, volume = {13}, year = {2025}, }
• (2025). A single phase frequency-variable cuk-type AC-AC converter with natural commutation and continuous current. ACCESS, 13, 32357-32370. (www)

  @article{ , abstract = {Single-phase compound AC-AC converters are increasingly being recognized for their capability to perform voltage and frequency conversion without the necessity of DC-bus energy storage components. However, existing research still faces challenges such as excessive high-frequency switches and current discontinuity. A Cuk-type single-phase AC-AC converter is proposed in this article. It allows for both bucking and boosting of the input voltage with the ability to operate at a step-changed frequency, offering a broad range of voltage and frequency regulation. Besides, it is featured with low power loss and high efficiency for there are only six switches used and only one switch works at high frequency. Additionally, it acquires natural commutation capability without additional snubber circuits or complex commutation control strategies. Furthermore, the input and output currents are continuous, obviating the need for additional input or output filters and thus enhancing power density. The operation principle and circuit analysis are detailed within. To validate the converter’s feasibility, a 200W laboratory prototype has been built and experiments have been conducted in both buck and boost modes at output frequencies of 25Hz, 50Hz, and 100Hz, demonstrating the converter’s effectiveness.}, archive = {J_ACCESS}, author = {Zhenkun Xiong and Liangzong He}, doi = {10.1109/ACCESS.2025.3543024}, journal = {IEEE Access}, pages = {32357-32370}, shortjournal = {IEEE Access}, title = {A single phase frequency-variable cuk-type AC-AC converter with natural commutation and continuous current}, volume = {13}, year = {2025}, }
• (2025). Improving low-resource kazakh-english and turkish-english neural machine translation using transfer learning and part of speech tags. ACCESS, 13, 32341-32356. (www)

  @article{ , abstract = {This study presents a novel translation framework by combining transfer learning and part-of-speech (POS) tagging methods to improve the performance of low-resource neural machine translation models using Kazakh-English and Turkish-English language pairs. It is aimed to maximize the effectiveness of transfer learning by taking advantage of the structural similarities of Turkish and Kazakh languages and to obtain more accurate and consistent translation results by integrating grammatical and syntactic information into the model with POS tags. For Kazakh, POS tags were generated using the RoBERTa model, while for Turkish, the Zemberek library was employed, and these tags were used as an additional feature in Transformer-based models. The findings show that using transfer learning and POS tags alone increases the performance, but when these two methods are used together, more meaningful and consistent results are obtained. The results obtained in the study are examined with BLEU, chrF, and METEOR metrics, and detailed analyses are made. The models created for the Kazakh-English translation direction are compared with different models, and it is seen that much better results are obtained with the methods used. For the Turkish-English translation direction, the results were examined using Tatoeba and TED2020 corpora of different sizes. In particular, significant improvements were observed in the experiments conducted on the Tatoeba corpus, and significant increases were obtained in the examined metrics. In this context, the methods applied in the study achieved successful results for low-resource languages, and the generalizability of the proposed approach was demonstrated with the use of different corpora and language pairs.}, archive = {J_ACCESS}, author = {Bilge Kağan Yazar and Erdal Kiliç}, doi = {10.1109/ACCESS.2025.3542491}, journal = {IEEE Access}, pages = {32341-32356}, shortjournal = {IEEE Access}, title = {Improving low-resource kazakh-english and turkish-english neural machine translation using transfer learning and part of speech tags}, volume = {13}, year = {2025}, }
• (2025). Online hand gesture recognition using semantically interpretable attention mechanism. ACCESS, 13, 32329-32340. (www)

  @article{ , abstract = {Hand gesture recognition (HGR) is a field of action recognition widely used in various domains such as robotics, virtual reality (VR), and augmented reality (AR). In this paper, we propose a semantically interpretable attention technique based on the compression and exchange of local and global information for real-time dynamic hand gesture recognition. In this research, we focus on data comprising hand landmark coordinates and online recognition of multiple gestures within a single sequence. Specifically, our approach has two paths to learn intraframe and interframe information separately. The learned information is compressed in the local and global perspectives, and the compressed information is exchanged through cross-attention. By using this approach, the importance of each hand landmark and frame, which can be interpreted semantically, can be extracted, and this information is used in the attention process on the intraframe and interframe information. Finally, the intraframe and interframe information to which attention is applied is integrated, which effectively enables comprehensive feature extraction of both local and global information. Experimental results demonstrated that the proposed method enabled concise and rapid hand-gesture recognition. It provided 95% accuracy in real-time hand-gesture recognition on a SHREC’22 dataset and accurately estimated the conclusion of a given gesture. Additionally, with a speed of approximately 294 frames per second (FPS), our model is well-suited for real-time systems, offering users immersive experience. This demonstrates its potential for effective application in real-world environments.}, archive = {J_ACCESS}, author = {Moon Ju Chae and Sang Hoon Han and Hyeok Nam and Jae Hyeon Park and Min Hee Cha and Sung In Cho}, doi = {10.1109/ACCESS.2025.3540721}, journal = {IEEE Access}, pages = {32329-32340}, shortjournal = {IEEE Access}, title = {Online hand gesture recognition using semantically interpretable attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Intelligent match counter in a video game arena. ACCESS, 13, 32320-32328. (www)

  @article{ , abstract = {This paper presents the development of an intelligent match counter for video game arenas, a rapidly expanding sector where manual match counting is pivotal but prone to errors, often resulting in financial losses. The proposed solution introduces an automated system that leverages cutting-edge advancements in computer vision, including convolutional neural networks (CNNs) for scoreboard detection and optical character recognition (OCR) for text extraction. The system accurately extracts pertinent data, such as team names, scores, and game time, and performs match counting based on data variations. A comprehensive methodology was adopted, combining functional and structural modeling, hardware integration utilizing a Raspberry Pi nano-computer, and the design of an ergonomic, 3D-printed enclosure. Real-world testing confirms the system’s efficacy, meeting the precision and real-time processing demands of the arena environment. This work represents a significant advancement in the modernization of video game arena management, improving transparency, profitability, and operational efficiency. Moreover, it offers a scalable solution that can be adapted to other automated management applications.}, archive = {J_ACCESS}, author = {Brummel D. Mayano and Langwana L. Yan-Mol and Jordan F. Masakuna}, doi = {10.1109/ACCESS.2025.3542425}, journal = {IEEE Access}, pages = {32320-32328}, shortjournal = {IEEE Access}, title = {Intelligent match counter in a video game arena}, volume = {13}, year = {2025}, }
• (2025). Type-3 fuzzy data-driven control of heterogeneous multi-agent systems. ACCESS, 13, 32306-32319. (www)

  @article{ , abstract = {This study investigates the data-based consensus problem of discrete-time heterogeneous multi-agent systems (MASs) through the development of distributed protocols. A type-3 (T3) fuzzy logic system (FLS) is presented to effectively model the inherent nonlinearities present in MASs, allowing for more accurate representation of agent dynamics. Utilizing a Lyapunov stability approach alongside a linear matrix inequalities (LMIs) framework, the sufficient conditions are derived for designing the data-driven distributed control protocols. The results ensure that the consensus error dynamics are asymptotically stable, thereby guaranteeing the achievement of consensus among agents. Furthermore, the optimal linear quadratic regulator (LQR) problem is addressed within the context of heterogeneous MASs, providing an additional layer of control performance optimization. Also, the design procedure is analyzed under conditions of noisy data, which is critical for real-world applications where sensor inaccuracies are prevalent. To validate the theoretical findings, extensive simulations are conduced that showcase the effectiveness and robustness of the proposed methods. The implementation is demonstrated on a network comprising four multiple autonomous underwater vehicles (AUVs), hyper-chaotic systems, and various configurations of discrete-time MASs. The results highlight the advantages of suggested data-driven control approach, emphasizing its practicality and efficiency in achieving consensus in complex multi-agent environments. This work paves the way for future research in robust control strategies for heterogeneous physical and financial systems operating under uncertainty. For example, by the use of the suggested data-driven approach to achieve consensus among heterogeneous agents, the financial institutions can enhance their decision-making processes, and optimize performance under uncertainty.}, archive = {J_ACCESS}, author = {Yaqing Liang and Man-Wen Tian and Du Changdong and Khalid A. Alattas and Afef Fekih and Ardashir Mohammadzadeh}, doi = {10.1109/ACCESS.2025.3541345}, journal = {IEEE Access}, pages = {32306-32319}, shortjournal = {IEEE Access}, title = {Type-3 fuzzy data-driven control of heterogeneous multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). Implementation and performance evaluation of machine learning-based apriori algorithm to detect non-technical losses in distribution systems. ACCESS, 13, 32289-32305. (www)

  @article{ , abstract = {The emergence and augmentation of nontechnical losses (NTLs) in a power distribution system has always been considered a critical issue in the global electricity market. Being considered as uncharged, unlawfully or unfairly charged consumed electricity, NTLs due to fraudulent activities can impose a serious burden on the grid and revenue loss in the state’s budget. The detection of NTLs based on the analysis of huge consumer dataset using machine learning is an acknowledged approach due to its expedient performance compared to manual inspection. This paper addresses a comprehensive investigation of monthly electricity consumption data of ~15000 consumers over three years using machine learning for NTL detection. The data is acquired by meters having automatic meter reading (AMR) capability and is processed using support vector machine (SVM) classifier, deep neural network (DNN), gradient boosted reinforcement learning (GBRL) and apriori algorithm. The results of ML algorithms are assessed by various performance metrics based on the confusion matrix and compared among themselves as well as with the findings from the published works. The least recall score of 0.8926 is exhibited by SVM classifier and poorest scores of accuracy (0.9376), specificity (0.9451), precision (0.7521), F1 (0.8183) and false positive rate (0.0549) are given by DNN program. With apriori algorithm, the scores of accuracy, recall, specificity, precision, F1 and false positive rate are observed as 0.9964, 0.9927, 0.9971, 0.9843, 0.9885, 0.0029 respectively. In all the performance based six domains, a significant improvement of 6%, 10%, 5%, 23%, 17% and 5% respectively compared to the least scores is demonstrated by the apriori algorithm. Therefore, in all these domains, the apriori algorithm being reported for the first time in this research work outperforms all other methods of this paper as well as the similar published works.}, archive = {J_ACCESS}, author = {Muhammad Faheem Akhtar and Haroon Farooq and Muhammad Naveed Akhtar and Ghulam Amjad Hussain and Syed Abdul Rahman Kashif and Zeeshan Rashid and Madia Safdar}, doi = {10.1109/ACCESS.2025.3541722}, journal = {IEEE Access}, pages = {32289-32305}, shortjournal = {IEEE Access}, title = {Implementation and performance evaluation of machine learning-based apriori algorithm to detect non-technical losses in distribution systems}, volume = {13}, year = {2025}, }
• (2025). Dual-channel knowledge tracing with self-supervised contrastive and directed interaction learning. ACCESS, 13, 32276-32288. (www)

  @article{ , abstract = {With the widespread adoption of the internet, intelligent tutoring systems have experienced rapid advancements in education. Knowledge tracing (KT) aims to predict students’ future performance by modeling their behavioral sequences. While existing models have made notable progress in some areas, challenges remain in fully uncovering the higher-order relationships among exercises, concepts, and students. To address these limitations and effectively capture the intricate dependencies between learning behaviors and exercises, this paper introduces a novel approach: Dual-Channel Knowledge Tracing with Self-Supervised Contrastive and Directed Interaction Learning (SCDI). This method enhances predictive accuracy by leveraging graph-structured embeddings of interaction data, offering a more comprehensive analysis of the learning process. SCDI consists of two distinct channels. The first channel constructs a heterogeneous graph with student, exercise, and concept nodes, using a Meta-path Aggregated Graph Neural Network (MAGNN) to extract high-order information from exercise and concept nodes. It also incorporates a self-supervised graph contrastive learning mechanism to improve the quality of node embeddings. The second channel builds a directed graph from interaction sequences, using Graph Attention Convolution (GATC) to capture dynamic relationships and model how interactions evolve over time. Features from both channels are integrated and passed to a downstream KT model to predict students’ future performance. Experimental evaluations on four datasets demonstrate that SCDI significantly enhances the accuracy of downstream KT models, establishing its effectiveness and robustness.}, archive = {J_ACCESS}, author = {Zuowei Zhang}, doi = {10.1109/ACCESS.2025.3542870}, journal = {IEEE Access}, pages = {32276-32288}, shortjournal = {IEEE Access}, title = {Dual-channel knowledge tracing with self-supervised contrastive and directed interaction learning}, volume = {13}, year = {2025}, }
• (2025). Feasibility of battery-powered all-electric propulsion for short-haul commuter aircraft. ACCESS, 13, 32260-32275. (www)

  @article{ , abstract = {Until recently, all-electric battery-powered aircraft seemed to commercialize this decade, but several projects have stalled owing to technical limitations. To examine these challenges, this paper developed a detailed equivalent circuit model (ECM) for sizing all-electric propulsion systems. We apply the ECM to 9- and 19-seat commuter aircraft proposed for Norway’s short-haul route network, using mission profile data obtained from 1500 under 200 nautical miles. Regression analysis of these data reveals that the cruising power needed is roughly 43 % of the power needed for takeoff and climbing. However, our findings indicate that given the current technical performance, the overall weight exceeds the allowable maximum takeoff weight, driven primarily by the battery and thermal management system. Notably, the shortest missions nearly meet the weight requirements but are actually constrained by power output rather than energy storage, introducing an additional criterion for battery sizing. Nevertheless, this power constraint for shorter routes ensures surplus energy storage, which diminishes the need for additional battery reserves for aircraft diversions. In contrast, diversion energy reserve needs dominate the energy requirements for longer routes. Finally, it is concluded that, in the short term, the only viable means of achieving all-electric, battery-powered aviation in Norway’s short-haul network is to reduce either seat capacity or total payload.}, archive = {J_ACCESS}, author = {Markus Aasen Anker and Christian Hartmann and Jonas Kristiansen Nøland}, doi = {10.1109/ACCESS.2025.3542157}, journal = {IEEE Access}, pages = {32260-32275}, shortjournal = {IEEE Access}, title = {Feasibility of battery-powered all-electric propulsion for short-haul commuter aircraft}, volume = {13}, year = {2025}, }
• (2025). Asymptotic tracking stability control method for grid-connected wind turbine systems based on quantitative trajectory constraints. ACCESS, 13, 32248-32259. (www)

  @article{ , abstract = {To address the issue of instability induced by direct-drive wind turbines connected to weak AC grids, a grid-connected asymptotic tracking stabilization control strategy for direct-drive wind turbines based on quantitative trajectory constraints is proposed. First, an equivalent mathematical model of the direct-drive wind turbine unit is established, with the dq-axis coupling caused by unmodeled dynamics, power fluctuations, and operational mode switching regarded as inherent disturbances of the system, thereby enhancing modeling accuracy. Secondly, a grid-connected asymptotic tracking stabilization control method for wind turbines based on quantitative trajectory constraints is proposed. This method ensures transient response trajectory constraints throughout the process while guaranteeing steady-state tracking ability and robust operation of the overall system, ensuring that the entire transient response trajectory remains within the specified constraint range, thereby improving the stable operation of the wind turbine grid-connected system. Finally, a simulation model is built in PSCAD for comparative validation. The simulation results demonstrate that the proposed control strategy enhances the dynamic performance of the full transient response trajectory, effectively improving the stable operation capability of the overall system.}, archive = {J_ACCESS}, author = {Lv Zhenhua and Ge Yaming and Li Qiang}, doi = {10.1109/ACCESS.2025.3542774}, journal = {IEEE Access}, pages = {32248-32259}, shortjournal = {IEEE Access}, title = {Asymptotic tracking stability control method for grid-connected wind turbine systems based on quantitative trajectory constraints}, volume = {13}, year = {2025}, }
• (2025). Improvement of erase performance using p-type gate in IGZO channel-based 3D NAND flash. ACCESS, 13, 32237-32247. (www)

  @article{ , abstract = {A novel structure using p-doped polysilicon with a new program/erase scheme is proposed for the first time in this paper to enhance erase performance in amorphous indium gallium zinc oxide (IGZO) channel 3D-NAND Flash. In the proposed structure, the tunneling of both electrons and holes occurs simultaneously during program and erase operation. Electron tunneling is dominant at the center of the cell, while hole tunneling is dominant at the cell corners in the erase operation. By adjusting the pass voltage of adjacent cells, the programming and erasing of the cell were switched. To evaluate the program and erase performance of the proposed structure, the incremental step pulse program (ISPP) and the incremental step pulse erase (ISPE) characteristics were compared with basic IGZO channel structures and polysilicon channel structures. The proposed structure overcomes the poor erase performance of the conventional IGZO channel structure. Also, it showed better erase performance compared to the IGZO channel structure and conventional polysilicon channel structure. The program and erase performances were optimized through engineering the O/N/O layer.}, archive = {J_ACCESS}, author = {Minkyo Suh and Youngho Jung and Daewoong Kang}, doi = {10.1109/ACCESS.2025.3537994}, journal = {IEEE Access}, pages = {32237-32247}, shortjournal = {IEEE Access}, title = {Improvement of erase performance using p-type gate in IGZO channel-based 3D NAND flash}, volume = {13}, year = {2025}, }
• (2025). Discrete event simulation-driven method solving permutation flowshop scheduling problem in digital twins. ACCESS, 13, 32225-32236. (www)

  @article{ , abstract = {Digital twin technology is becoming increasingly vital in intelligent production and Industry 4.0. Nevertheless, the simulation system of the digital twin frequently serves merely as a virtual representation of the actual system, neglecting its significant analytical and decision-making capabilities. This research introduces a discrete-event simulation-based digital twin optimization methodology to determine the optimal job sequence for the permutation flowshop scheduling problem (PFSP). The concept of sequence-dependent recovery time (SDRT) is brought into the PFSP for the first time, and the four subproblems of PFSP with SDRT was proposed. The computational simulation findings on a benchmark dataset indicate that the SDRT substantially influences optimization, enhancing average scheduling performance by around 14% relative to integrating recovery time into processing time. The empirical case study findings indicate that the suggested simulation-optimization strategy decreases completion time by 0.95% relative to the conventional mathematical approach while addressing the actual PFSP using SDRT. These findings validate the effectiveness of discrete event simulation-based digital twin technology for addressing PFSP with SDRT.}, archive = {J_ACCESS}, author = {Guangzhen Li and Lei Zhang and Xiangyu Zhao}, doi = {10.1109/ACCESS.2025.3541223}, journal = {IEEE Access}, pages = {32225-32236}, shortjournal = {IEEE Access}, title = {Discrete event simulation-driven method solving permutation flowshop scheduling problem in digital twins}, volume = {13}, year = {2025}, }
• (2025). Short-term power load forecasting based on DPSO-LSSVM model. ACCESS, 13, 32211-32224. (www)

  @article{ , abstract = {The accurate prediction of short-term power load is a critical element for maintaining the normal and stable operation of the power system. For short-term power load forecasting, the collected power load data is preprocessed to quantify temperature, weather, and date types. A short-term load forecasting model based on least squares support vector machine is constructed, and the optimal parameters of the model are established. The dynamic particle swarm optimization algorithm is utilized to dynamically adjust the parameters to achieve higher accuracy in load forecasting. The findings denoted that the average absolute percentage error of the least squares support vector machine model using linear kernel function is only 3.75%, the average absolute error is only 256.38MW, and the root mean square error is only 311.20MW. The mean absolute percentage error of the proposed model is only 1.91%, significantly lower than other advanced models. The developed model has stronger adaptability and higher prediction accuracy in dealing with the complexity and dynamic changes of power load data, providing effective technical support for the operation optimization and decision-making of the power system.}, archive = {J_ACCESS}, author = {Shujun Ji and Linhao Zhang and Jinteng Wang and Tao Wei and Jiadong Li and Bu Ling and Jinglong Xu and Zuoping Wu}, doi = {10.1109/ACCESS.2025.3540434}, journal = {IEEE Access}, pages = {32211-32224}, shortjournal = {IEEE Access}, title = {Short-term power load forecasting based on DPSO-LSSVM model}, volume = {13}, year = {2025}, }
• (2025). A comparative study of GNNs and rule-based methods for synthetic social network generation. ACCESS, 13, 32198-32210. (www)

  @article{ , abstract = {Social network data, and web-graph data in general, sees great amounts of research and is widely used commercially. Due to privacy or other concerns it often cannot be shared verbatim. Synthetic counterparts to such data allow easier and safer sharing. Models currently applied to producing such synthetic data are typically rule-based, and though efficient, struggle to express complex topological characteristics. Graph Neural Network (GNN) models, in other domains such as molecules and proteins, trade-off efficiency for expressive abilities in generating graph structures. We compare GNNs and rule-based models for synthetic social networks and web-graphs from Facebook, Twitch, GitHub and Deezer. As a necessary data-processing step we propose and demonstrate a sampling procedure to construct network datasets from using datasets from single large networks. With structural and social network specific measurements, we evaluate how realistically synthetic networks behave in typical social network analysis applications. We find that the Gated Recurrent Attention Network (GRAN), an archetypal GNN model, extends well to social networks. In comparison to popular rule-based methods, R-MAT (Recursive-MATrix) and BTER (Block Two-level Erdos-Renyi), GRAN is better able to produce a variety of social networks and web-graphs.}, archive = {J_ACCESS}, author = {Alex O. Davies and Nirav S. Ajmeri and Telmo De Menezes E. Silva Filho}, doi = {10.1109/ACCESS.2025.3541091}, journal = {IEEE Access}, pages = {32198-32210}, shortjournal = {IEEE Access}, title = {A comparative study of GNNs and rule-based methods for synthetic social network generation}, volume = {13}, year = {2025}, }
• (2025). Corrections to “CardioGPT: An ECG interpretation generation model”. ACCESS, 13, 32197. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “CardioGPT: An ECG Interpretation Generation Model”.}, archive = {J_ACCESS}, author = {Guohua Fu and Jianwei Zheng and Islam Abudayyeh and Chizobam Ani and Cyril Rakovski and Louis Ehwerhemuepha and Hongxia Lu and Yongjuan Guo and Shenglin Liu and Huimin Chu and Bing Yang}, doi = {10.1109/ACCESS.2025.3542260}, journal = {IEEE Access}, pages = {32197}, shortjournal = {IEEE Access}, title = {Corrections to “CardioGPT: An ECG interpretation generation model”}, volume = {13}, year = {2025}, }
• (2025). A reliability evaluation flow for assessing the impact of permanent hardware faults on integer arithmetic circuits. ACCESS, 13, 32177-32196. (www)

  @article{ , abstract = {Arithmetic circuits are fundamental building blocks in modern digital computers, allowing for precise mathematical operations and driving the digital age. They are essential components in almost every digital device, from basic CPUs to advanced accelerators in AI applications. In particular, in safety-critical fields like automotive, avionics, and medical equipment, the flawless operation of these circuits is paramount to ensure the correct system operation. Unfortunately, silicon devices manufactured with cutting-edge technologies are more likely to be affected by faults. Their effects can eventually produce computational errors and lead to catastrophic consequences. Since arithmetic modules play an important role in AI-oriented circuits, it is crucial to study the degree of severity of the fault-induced errors affecting them, both to estimate the achieved level of safety and to support the development of effective fault mitigation mechanisms. The analysis can also be used to guide designers in the selection of the most suitable arithmetic module for a given application. Also, the fault evaluation process is an essential part of safety methodologies like failure mode effects and criticality analysis (FMECA). This work proposes a method to analyze the inherent reliability of arithmetic modules, and uses as case studies four multiplier circuits: two variants of 32-bit Dadda multipliers (Circuit A and Circuit B) and two variants of 8-bit Booth multipliers (Circuit C and Circuit D). The proposed flow allows us to comprehensively assess their reliability and evaluate the impact and severity of permanent faults affecting the selected circuits. The analysis is based on extensive fault injection campaigns using both random values and traces from DNN workloads as input operands. We evaluated the impact of faults on every circuit by calculating and comparing different metrics, such as the mean absolute error (MAE), the mean relative error (MRE), the mean square error (MSE), the mean operations between errors (MOBE), the bit error rate (BER), and the fault activation and propagation rate (FAPR). The results allow for assessing the reliability of each multiplier and its suitability for a given application scenario. We also show that the considered workload significantly impacts the fault severity for all multipliers. As a summary, the combination of fault injection campaigns with application-specific benchmarks proposed in this work plays a vital role in accurately forming a reliability verdict for a circuit design when adopting different evaluation metrics in terms of error analysis, error rate, and fault severity evaluations.}, archive = {J_ACCESS}, author = {Nikolaos I. Deligiannis and Juan-David Guerrero-Balaguera and Riccardo Cantoro and Serag E. D. Habib and Matteo Sonza Reorda}, doi = {10.1109/ACCESS.2025.3534274}, journal = {IEEE Access}, pages = {32177-32196}, shortjournal = {IEEE Access}, title = {A reliability evaluation flow for assessing the impact of permanent hardware faults on integer arithmetic circuits}, volume = {13}, year = {2025}, }
• (2025). Denoising diffusion probabilistic models for cloud removal and land surface temperature retrieval from a single sample. ACCESS, 13, 32161-32176. (www)

  @article{ , abstract = {Land Surface Temperature (LST) monitoring is a key factor in demonstrating global climate change. Considering that cloud cover often obscures accurate remotely sensed LST data, it is important to develop approaches to reconstruct occluded areas. This paper presents an inpainting technique, designated RePaint, which utilizes Denoising Diffusion Probabilistic Models (DDPM) for the removal of clouds and the retrieval of LST data from Landsat-8 remote sensing imagery. The DDPM was trained with cloud-free LST image patches, and binary masks were formed by the segmentation process using the Ukis Cloud Shadow Mask to condition the region to be reconstructed. To simulate cloud occlusion in the test data, three cloud cover scenarios with 15%, 22% and 50% missing rate data were investigated. The performance of the approach was evaluated through a combination of quantitative and qualitative assessments. The quantitative analysis showed that the model exhibited consistent performance during the reconstruction process, achieving a mean squared error 0.0001, a peak signal to noise ratio 41.2888, and a structural similarity index 0.9826. In order to provide a more comprehensive visual representation of the results, boxplots and the Gaussian distribution of the temperature error in the LST reconstruction were included. Images of the absolute error of the reconstruction and scatterplots were used for qualitative analysis of randomly selected LST patches. The results demonstrate that the characteristics of the land surface have been preserved, exhibiting a minimum incidence of temperature reconstruction error and spatial coherence across all scenarios.}, archive = {J_ACCESS}, author = {Quefren O. Leher and Emili S. Bezerra and Thuanne Paixão and Facundo Palomino-Quispe and Ana Beatriz Alvarez}, doi = {10.1109/ACCESS.2025.3542014}, journal = {IEEE Access}, pages = {32161-32176}, shortjournal = {IEEE Access}, title = {Denoising diffusion probabilistic models for cloud removal and land surface temperature retrieval from a single sample}, volume = {13}, year = {2025}, }
• (2025). Qualitative analysis of factors influencing the adoption of china’s AVS technology standard over US’s MPEG: Evidence from the asian region. ACCESS, 13, 32147-32160. (www)

  @article{ , abstract = {China’s Audio Video Coding Standard (AVS) has gained traction in different parts of the Asian region to reduce dependence on United States (US) Moving Picture Experts Group (MPEG). However, a significant research gap remains unexamined regarding the decision to adopt AVS over MPEG. This study delves into the factors influencing the preference for China’s AVS technology over the US’s MPEG standard in Cambodia, Myanmar, Cuba, and Uzbekistan. Through a qualitative analysis, the motivations and decision-making processes behind selection of AVS over MPEG are explored. This study involved 30 participants each from the chosen countries and 7 participants from the US. The study utilized in-depth interviews, document analysis, comparative case studies, and expert panels as research methods. The findings show that considerations such as costs, technological capabilities, licensing issues, and geopolitical factors significantly impact adoption of AVS over MPEG. The study enhances the drivers behind China’s AVS technology standard adoption over the US’s MPEG and the intricate relationship between technology, geopolitics and national interests. This study also highlights the interplay between technological standards, and the country’s effort to reduce dependence on a single dominant power by embracing alternative collaborations through AVS. Our study suggests the need for careful evaluation and strategic decision-making. The implications outlined in this study guide navigation of the complexities of technology standards adoption and contribute to a broader understanding between AVS and MPEG. The implications of the findings are crucial for policymakers and industry stakeholders involved in video compression and technology standards.}, archive = {J_ACCESS}, author = {Zhang Yunsheng and Oluwole Nurudeen Omonijo}, doi = {10.1109/ACCESS.2025.3542824}, journal = {IEEE Access}, pages = {32147-32160}, shortjournal = {IEEE Access}, title = {Qualitative analysis of factors influencing the adoption of china’s AVS technology standard over US’s MPEG: Evidence from the asian region}, volume = {13}, year = {2025}, }
• (2025). A detailed review for predicting the quantity of sugar from sugarcane using various models. ACCESS, 13, 32122-32146. (www)

  @article{ , abstract = {The production of jaggery and white sugar is an essential aspect of the sugarcane industry. This review aims to assess a variety of techniques for estimating sugar content in sugarcane, including laboratory methods, formula calculations, device-based measurements, and machine-learning algorithms. This review aims to analyze various aspects of sugar production, including sugar prediction, processing techniques, and sugarcane quality parameters, and focuses on the use of sugarcane juice parameters to construct predictive models. Predicting the sugar content in sugarcane is crucial for optimizing agricultural practices, harvesting schedules, and processing operations in the sugar industry, including jaggery and white sugar production. Researchers have explored several methodologies and techniques for accurately predicting the sugar content of sugarcane prior to harvesting. The present analysis highlights that conventional regression algorithms can predict sugar content;however, multicollinearity restricts their effectiveness. To improve prediction accuracy and overcome existing limitations, future research should concentrate on developing advanced models that mitigate multicollinearity. In the existing research, it is difficult for users to comprehend the output of sugar prediction values. In the sugar industry, buyers and sellers typically measure sugarcane in metric tons, whereas sugar manufacturers typically measure sugar in kilograms. Therefore, it is necessary to analyze sugarcane juice parameters and develop a machine-learning model based on these data. Enhanced machine learning techniques and comprehensive quality parameters have the potential to significantly enhance the precision of sugar content predictions.}, archive = {J_ACCESS}, author = {Kathirvel Narayanasamy and Ilayaraja Venkatachalam}, doi = {10.1109/ACCESS.2024.3522495}, journal = {IEEE Access}, pages = {32122-32146}, shortjournal = {IEEE Access}, title = {A detailed review for predicting the quantity of sugar from sugarcane using various models}, volume = {13}, year = {2025}, }
• (2025). Variable length multi-objective whale optimization for trust aware data gathering in wireless sensor network. ACCESS, 13, 32100-32121. (www)

  @article{ , abstract = {Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance.}, archive = {J_ACCESS}, author = {Mohammed Ayad Saad and Rosmina Jaafar and Kalaivani Chellappan}, doi = {10.1109/ACCESS.2025.3538276}, journal = {IEEE Access}, pages = {32100-32121}, shortjournal = {IEEE Access}, title = {Variable length multi-objective whale optimization for trust aware data gathering in wireless sensor network}, volume = {13}, year = {2025}, }
• (2025). SAU-net: A deep learning approach for glacier mapping based on multisource remote sensing data. ACCESS, 13, 32087-32099. (www)

  @article{ , abstract = {Glaciers are vital indicators of climate change, particularly in the Tibetan Plateau, which is experiencing unprecedented glacial retreat due to rising temperatures. This study presents a novel deep learning model, SAU-Net, for automatic glacier segmentation using multisource remote sensing data. SAU-Net, built upon the U2-Net architecture, incorporates a two-layer nested U-shaped structure and a Simple Attention Module (SimAM) to enhance feature extraction and segmentation accuracy. The model is applied to the Himalayas and Karakoram regions, capturing diverse glacier types and environmental conditions. Results demonstrate that SAU-Net achieved an impressive accuracy of 94.6% and $F_{\beta }$ score of 0.835, significantly outperforming the conventional U-Net model in glacier identification tasks. The successful application of SAU-Net not only addresses challenges associated with glacier mapping in complex terrains but also provides a scalable method for generating regional glacier inventories, contributing to improved monitoring and understanding of glacial dynamics under climate change. This research underscores the transformative potential of deep learning in environmental monitoring and provides a framework for future studies in cryospheric science.}, archive = {J_ACCESS}, author = {Yang Xiang and Longfei Zhao and Jingxiang Li and Fanfan Gao and Siyuan Bian and Man Hou and Xin Luo and Chen Guo}, doi = {10.1109/ACCESS.2025.3542834}, journal = {IEEE Access}, pages = {32087-32099}, shortjournal = {IEEE Access}, title = {SAU-net: A deep learning approach for glacier mapping based on multisource remote sensing data}, volume = {13}, year = {2025}, }
• (2025). Design process of high-power density hybrid type motor using 3D printing. ACCESS, 13, 32072-32086. (www)

  @article{ , abstract = {In this paper, a high-power hybrid electric motor is proposed in which 3DP technique is applied to the fabrication process to improve the power density of the motor. energy saving and environmental issues have become a global issue, research on increasing power density to simplify the system while reducing the resources consumed in manufacturing has become important in recent years. To improve power density, research is being conducted on optimizing various design parameters. However, in the case of motors manufactured by the general mold pressing process, a pressing process is used to press metal sheets into a mold of the desired shape with a strong force, but due to the limitations of the process itself, the shape design is limited and the design parameters for improving power density are also limited. Therefore, this paper applies 3DP technology to secure the shape freedom of the motor and proposes a hybrid motor design process to improve the output density. Among the 3DP production methods, the SLM method is adopted, and the SLM technique has the advantage of securing high strength and mechanical precision, and it is advantageous for design because it can freely manufacture objects with complex shapes. The hybrid motor is a structure that combines a radial type of permanent magnet motor and an axial type of permanent magnet motor based on the design freedom secured through the 3D printing process, and the shape was optimized to satisfy a higher output density than the general basic model.}, archive = {J_ACCESS}, author = {Hyo-Gu Kim and Ye-Seo Lee and Seung-Hoon Ko and Na-Rim Jo and Won-Ho Kim}, doi = {10.1109/ACCESS.2025.3542789}, journal = {IEEE Access}, pages = {32072-32086}, shortjournal = {IEEE Access}, title = {Design process of high-power density hybrid type motor using 3D printing}, volume = {13}, year = {2025}, }
• (2025). Spectrum shaping error correction codes for high-density data storage systems. ACCESS, 13, 32063-32071. (www)

  @article{ , abstract = {The study provides a novel and systematic method for designing high-efficiency spectral shaping error-correction codes (ECCs) for high-density data storage systems. The design includes the application of a high-efficiency spectral shaping k constrained code to the input user data, followed by an ECC for error correction, and the application of another spectral shaping code k constrained to the parity part of the ECC. The designed spectral shaping ECC can effectively mitigate the noise and interference affecting the data signal of high-density data storage systems and directly correct the system errors. The proposed code design method facilitates the application of spectrum shaping codes with long codeword lengths, which can achieve a high coding efficiency for the system. Furthermore, it also enables the soft-decision decoding (SDD) of ECCs, which has a much stronger error correction capability than the conventional hard-decision decoding (HDD). The proposed code design method is general and systematic; hence, it can be applied to any efficient spectral shaping codes as well as systematic ECCs.}, archive = {J_ACCESS}, author = {Chi Dinh Nguyen}, doi = {10.1109/ACCESS.2025.3542290}, journal = {IEEE Access}, pages = {32063-32071}, shortjournal = {IEEE Access}, title = {Spectrum shaping error correction codes for high-density data storage systems}, volume = {13}, year = {2025}, }
• (2025). Phase noise influence on the transfer function estimation of analog to digital converters using the histogram method. ACCESS, 13, 32051-32062. (www)

  @article{ , abstract = {The presence of phase noise in the signal generator used to characterize analog-to-digital converters (ADCs) affects the precision of the estimates made. This work focuses specifically on estimating the transition voltages of ADCs made with the Histogram Test Method. A mathematical relationship between phase noise standard deviation and the standard deviation of the transition voltage estimates made is derived. This empowers test engineers to choose the proper test parameters to achieve a desired bound on the precision of the estimations thus guaranteeing a certain confidence interval width and optimizing the test duration. The results obtained are verified through numerical simulations using a Monte Carlo type procedure.}, archive = {J_ACCESS}, author = {Francisco A. C. Alegria}, doi = {10.1109/ACCESS.2025.3541559}, journal = {IEEE Access}, pages = {32051-32062}, shortjournal = {IEEE Access}, title = {Phase noise influence on the transfer function estimation of analog to digital converters using the histogram method}, volume = {13}, year = {2025}, }
• (2025). Implementing altruistic collaborative healing in smart manufacturing: Context, simulation, and analysis. ACCESS, 13, 32031-32050. (www)

  @article{ , abstract = {The rapid transformation of manufacturing companies and frequent market disruptions have significantly increased the complexity of industrial engineering process design. To address these challenges, innovative approaches such as biologicalisation—the integration of biological principles into manufacturing processes—are emerging. Building on this research direction, our research group has been developing the concept of “altruistic collaborative healing.” This concept envisions manufacturing elements capable of sharing resources or capabilities (“Donors”) with elements in need (“Recipients”), ensuring uninterrupted task performance. While the foundation of this concept is established, several critical elements remain unexplored. This paper contributes in three key areas. First, it expands the theoretical framework by examining altruism in animal species and comparing altruistic collaborative healing to traditional self-healing maintenance approaches. Second, it provides a systematic guideline for implementing the altruistic concept through robotic simulation tools (Robotic Operating System2). Finally, the concept is validated in an energy-sharing scenario, showing advantages over manual methods. The framework was tested by simulating a mobile robot’s movement between recipient and altruistic positions using mobile robot simulation packages and altruistic algorithms. These simulations were compared to a scenario involving manual battery exchange, demonstrating the model’s efficiency and important downtime reduction.}, archive = {J_ACCESS}, author = {Luis A. Estrada-Jimenez and Joaquim S. Macuacua and Sanaz Nikghadam-Hojjati and Jose Barata}, doi = {10.1109/ACCESS.2025.3542572}, journal = {IEEE Access}, pages = {32031-32050}, shortjournal = {IEEE Access}, title = {Implementing altruistic collaborative healing in smart manufacturing: Context, simulation, and analysis}, volume = {13}, year = {2025}, }
• (2025). An explainable machine learning network for classification of autism spectrum disorder using optimal frequency band identification from brain EEG. ACCESS, 13, 32016-32030. (www)

  @article{ , abstract = {Autism Spectrum Disorder (ASD) is characterized by repetitive and restricted behavior, involving challenges for subjective detection methods. There is a need for an objective diagnostic technique that maximizes accuracy while utilizing a minimum of Electroencephalography (EEG) channels. This research aims to enhance ASD classification by utilizing spatial patterns in EEG signals. The primary objective is to identify the optimal frequency band for classification. This work analyzes EEG data from ten ASD children and ten Typically Developed (TD) children. A novel spatial filtering method is proposed using wavelet transform, filterbank, tikhonov regularization, and common spatial pattern algorithms to design the spatial filters for EEG signals from central lobe regions (C3, C4, and Cz). In order to determine optimal frequency bands, peak-to-peak amplitudes are extracted from these spatially filtered signals, and the most informative features are selected using correlation coefficients. The proposed method identifies the optimal frequency band in the alpha and beta ranges, specifically 12-16 Hz within 8-32 Hz, using Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Additional features, including fuzzy dispersion entropy, fuzzy entropy, fractional fuzzy dispersion entropy, Detrended Fluctuation Analysis (DFA) exponent, and multifractal DFA-related features, are incorporated for the optimal frequency band and features selected using Shapley additive explanations (SHAP). Results from the experiments demonstrate the efficiency of this approach with selected features, and the SVM with RBF achieved an accuracy of 93.59%, precision of 96.97%, recall of 94.12%, specificity of 92.19%, and F1-score of 95.52% on the optimal frequency band.}, archive = {J_ACCESS}, author = {S. Saranya and R. Menaka}, doi = {10.1109/ACCESS.2025.3542390}, journal = {IEEE Access}, pages = {32016-32030}, shortjournal = {IEEE Access}, title = {An explainable machine learning network for classification of autism spectrum disorder using optimal frequency band identification from brain EEG}, volume = {13}, year = {2025}, }
• (2025). Nonsingular sliding mode guidance law for impact time control with field-of-view constraint. ACCESS, 13, 32004-32015. (www)

  @article{ , abstract = {In this paper, a new missile impact time control guidance law using sliding mode control technology is proposed, aiming at intercepting stationary targets. The research focuses on improving the time-to-go estimation method, abandoning the small-angle assumption to achieve a more accurate estimation, and innovatively incorporating the field-of-view (FOV) constraint into the design of the guidance law. The guidance law ensures the missile’s seeker remains within the specified FOV even with large initial lead angles, improving stability and enhancing launch flexibility in complex environments. By selecting reasonable control parameters, the impact time error and lead angle of the missile converge rapidly, boosting guidance efficiency. Lyapunov stability theory verifies its reliability and safety, and the absence of singularities in the guidance command significantly enhances the system’s stability and robustness. The simulation results show that the proposed guidance law is adaptable to varieties of working conditions and exhibits excellent performance. Compared with the existing method, the guidance law satisfies the impact time control and FOV constraint even when the missile is launched at a large initial lead angle. This feature makes the guidance law show significant potential in the field of salvo attack.}, archive = {J_ACCESS}, author = {Lei Zhang and Wenjun Yi and Jun Guan}, doi = {10.1109/ACCESS.2025.3531042}, journal = {IEEE Access}, pages = {32004-32015}, shortjournal = {IEEE Access}, title = {Nonsingular sliding mode guidance law for impact time control with field-of-view constraint}, volume = {13}, year = {2025}, }
• (2025). New tools for tuning PID controllers using orthogonal polynomials. ACCESS, 13, 31992-32003. (www)

  @article{ , abstract = {This paper presents an algorithm to determine PID controller tuning regions that guarantee the stability of closed-loop control systems using the pole placement method, assigning a known robustly stable polynomial that depends on a vector of uncertain parameters as the desired closed-loop characteristic polynomial. The algorithm generates PID gains contingent on uncertain parameters. This robustly stable polynomial is constructed using modified classical weights and relies on well-known properties of the theory of orthogonal polynomials, including the recurrence relation. In addition, it considers linear combinations of two orthogonal polynomials with consecutive degrees. An advantage of the proposed approach is the considerable flexibility in selecting both the closed-loop characteristic polynomial and number of uncertain parameters. As this problem arises from the challenge of assigning n closed-loop poles with only three free gains, the methodology uses the Moore-Penrose generalized inverse of a certain matrix to obtain expressions of the PID gains. Therefore, the proposed methodology does not work in certain cases of linear time-invariant systems. Three examples were provided to illustrate the design algorithm.}, archive = {J_ACCESS}, author = {Alejandro Arceo and Luis E. Garza and Gerardo Romero and José Luis Valdéz}, doi = {10.1109/ACCESS.2025.3542358}, journal = {IEEE Access}, pages = {31992-32003}, shortjournal = {IEEE Access}, title = {New tools for tuning PID controllers using orthogonal polynomials}, volume = {13}, year = {2025}, }
• (2025). Dynamic text augmentation for robust sentiment analysis: Enhancing model performance with EDA and multi-channel CNN. ACCESS, 13, 31978-31991. (www)

  @article{ , abstract = {The rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. However, the limited availability of labeled datasets and challenges related to data imbalance often hinder the development of robust sentiment analysis models. In this study, we propose a novel framework dynamic Easy Data Augmentation (EDA) technique with a Multi-Channel Convolutional Neural Network (MCNN) to address these issues. The EDA techniques including synonym replacement (SR), random insertion (RI), random deletion (RD), and random swap (RS), enrich the training data by introducing diversity, thereby enhancing model robustness. The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. Furthermore, the integration of Word2Vec embeddings ensures meaningful text representation, leveraging context-independent word relationships for improved sentiment analysis. Experimental evaluations demonstrate the effectiveness of the proposed approach, highlighting significant improvements in F1-score compared to baseline models. The findings underscore the potential of dynamic text augmentation and efficient deep learning architectures to overcome the challenges of limited training data, offering a scalable solution for sentiment analysis in real-world applications. Additionally, the proposed model achieves an average performance improvement of 11.34% over previous models.}, archive = {J_ACCESS}, author = {Komang Wahyu Trisna and Jinjie Huang and Yuanjian Chen and I Gede Juliana Eka Putra}, doi = {10.1109/ACCESS.2025.3538621}, journal = {IEEE Access}, pages = {31978-31991}, shortjournal = {IEEE Access}, title = {Dynamic text augmentation for robust sentiment analysis: Enhancing model performance with EDA and multi-channel CNN}, volume = {13}, year = {2025}, }
• (2025). A robust multi-scale depthwise separable with dual-reservoir bi-LSTM model for gait phase recognition across complex terrains. ACCESS, 13, 31963-31977. (www)

  @article{ , abstract = {Gait phase recognition plays a pivotal role in improving wearable exoskeletons by providing phase-specific assistance to enhance human movement. This study proposes a novel model, the Multi-Scale Depthwise Separable Convolution with Dual-Reservoir Bi-directional Long Short-Term Memory (MSDSC-DR-BiLSTM), which recognizes four key gait sub-phases: heel strike, foot-flat, heel-off, and swing. The model employs a multi-scale depthwise separable convolution layer to capture both local and global gait features, while a dual-reservoir BiLSTM network effectively models temporal dependencies for precise phase transitions. To tackle class imbalance, a Focal Loss function is employed. We validated the model on a dataset of gait data from 25 healthy subjects across five terrains: level ground walking, stair ascent, stair descent, ramp ascent, and ramp descent. Experimental results show that the MSDSC-DR-BiLSTM model achieves over 95% accuracy in subject-independent 5-fold cross-validation. In leave-one-out cross-validation, the accuracy for each terrain is as follows: level ground walking (95.45%), stair ascent (98.69%), stair descent (96.94%), ramp ascent (99.27%), and ramp descent (97.3%). These results demonstrate the model’s robustness and adaptability, establishing a reliable foundation for phase-specific assistance in wearable lower-limb exoskeletons.}, archive = {J_ACCESS}, author = {Jing Tang and Zequan Jiang and Chen Yao and Minghu Wu}, doi = {10.1109/ACCESS.2025.3541585}, journal = {IEEE Access}, pages = {31963-31977}, shortjournal = {IEEE Access}, title = {A robust multi-scale depthwise separable with dual-reservoir bi-LSTM model for gait phase recognition across complex terrains}, volume = {13}, year = {2025}, }
• (2025). Three-dimensional human model-guided view morphing for clothed gait silhouettes. ACCESS, 13, 31949-31962. (www)

  @article{ , abstract = {Silhouette-based gait recognition is a popular biometric modality because of its non-intrusive nature and its ability to capture identity information from a distance and at low resolution. However, cross-view gait recognition presents significant challenges because the appearance changes drastically across different viewpoints. Existing gait datasets are sparse in view coverage, limiting the generalization of models to novel views. The evaluation of current state-of-the-art gait recognition models is confined to the views present in the training dataset, posing challenges for practical application scenarios. In this paper, we propose a three-dimensional (3D) human-model-guided view-morphing framework to generate high-quality gait silhouettes for arbitrary views, enhancing gait recognition performance for novel views unseen during training. By leveraging the body topology of the 3D human model, unlike existing GAN-based or deformation-based methods that rely only on two-dimensional images, the proposed method enables fine control over the output views and achieves plausible deformations, even for self-occluded body parts, through the supervision of the pseudo ground-truth deformation field computed from the 3D model. To enhance the realism of silhouette generation, we use clothed silhouettes alongside skin silhouettes rendered from the 3D model (e.g., the SMPL) to train the deformation estimation network to account for clothing and accessories in the final output. We evaluated our approach on the OU-MVLP, CASIA-B, and CASIA-E datasets, demonstrating significant improvements in gait recognition performance, particularly in view-limited scenarios. For example, recovering 6.47% rank-1 recognition rate on OU-MVLP using three close views only and an average of 24.36% and 8.34% on CASIA-B and CASIA-E normal clothing, respectively, using a single view. Our framework increases the robustness of the cross-view problem, enhancing the applicability of gait recognition in real-world settings such as surveillance and security.}, archive = {J_ACCESS}, author = {Mohamad Ammar Alsherfawi Aljazaerly and Chi Xu and Shuqiong Wu and Xiang Li and Yasushi Yagi}, doi = {10.1109/ACCESS.2025.3542837}, journal = {IEEE Access}, pages = {31949-31962}, shortjournal = {IEEE Access}, title = {Three-dimensional human model-guided view morphing for clothed gait silhouettes}, volume = {13}, year = {2025}, }
• (2025). DTS: A new deterministic approach to derive the optimal synchronization conditions of a synchronous condenser. ACCESS, 13, 31936-31948. (www)

  @article{ , abstract = {This paper presents an original deterministic approach to improve the success rate of the synchronization process of a synchronous condenser. In fact, the start-up process of a synchronous condenser cannot rely on the presence of a supply system to regulate its speed during the synchronization process, as happens with traditional synchronous generators. A static frequency converter is used to start the machine, but it should be disconnected before the grid synchronization to avoid damages, making the process uncontrollable. The proposed method aims to analytically determine the optimal frequency and phase angle to disconnect the converter in order to ensure synchronization with the grid within proper thresholds. The accuracy of the method is confirmed by a comparison with on-field recording of real synchronous condenser start-up processes. This procedure enables significantly reducing the frequency threshold for the synchronization so to limit the thermal and mechanical stresses of the machine and to increase its lifecycle.}, archive = {J_ACCESS}, author = {Francesco Sanniti and Domenico Tedeschi and Luca Rusalen and Roberto Benato}, doi = {10.1109/ACCESS.2025.3542264}, journal = {IEEE Access}, pages = {31936-31948}, shortjournal = {IEEE Access}, title = {DTS: A new deterministic approach to derive the optimal synchronization conditions of a synchronous condenser}, volume = {13}, year = {2025}, }
• (2025). Scalable hybrid deep models for individual pharmacy cost prediction. ACCESS, 13, 31912-31935. (www)

  @article{ , abstract = {In this study, we introduce two innovative hybrid models designed for predicting individual pharmacy costs: the Autoencoder-Gated Recurrent Unit (Auto-GRU) and the GoogLeNet-Residual Network (GR-Net). Utilizing data from high utilizers obtained through the Medicaid rebate program, these models aim to provide accurate predictions of total pharmacy costs for individual patients. Our approach involves rigorous data preprocessing, including the removal of missing values, and the fine-tuning of hyperparameters using the Adam optimizer. We systematically evaluate and compare the performance of these hybrid models with that of four individual models, Autoencoder (AE), Gated Recurrent Unit (GRU), GoogLeNet, and Residual Network (ResNet), using performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), training time, inference time and memory usage. Without correlation, Auto-GRU outperformed its individual models with an MSE value of 0.1675 versus GRU’s MSE of 0.2806 and AE’s MSE of 0.4086. GR-Net, without correlation, also beats the individual models with an MSE value of 0.0027 versus GoogLeNet’s MSE value of 0.0106 and ResNet’s MSE value of 0.0201. Similarly, with correlation, Auto-GRU outperformed its individual models with an MSE value of 0.2655 versus GRU’s MSE of 0.4089 and AE’s MSE of 0.4243. GR-Net, with correlation, also beats the individual models with an MSE value of 0.0097 versus GoogLeNet’s MSE value of 0.0882 and ResNet’s MSE value of 0.0106. Both the hybrid models outperformed the individual models in terms of MAE, MAE and RMSE both with and without correlation. Also, to interpret models prediction, we have implemented Local Interpretable Model-agnostic Explanations, an eXplainable Artificial Intelligence (XAI) technique. These findings highlight the robustness and effectiveness of the hybrid models in predicting pharmacy costs, underscoring their potential for integration into healthcare expense management systems.}, archive = {J_ACCESS}, author = {Muhammad Talha Ashfaq and Nadeem Javaid and Nabil Alrajeh and Yousra Asim and Syed Muhammad Abrar Akber}, doi = {10.1109/ACCESS.2025.3541750}, journal = {IEEE Access}, pages = {31912-31935}, shortjournal = {IEEE Access}, title = {Scalable hybrid deep models for individual pharmacy cost prediction}, volume = {13}, year = {2025}, }
• (2025). IMAX: A power-efficient multilevel pipelined CGLA and applications. ACCESS, 13, 31899-31911. (www)

  @article{ , abstract = {The rapid advancement of artificial intelligence (AI) applications has driven a growing need for flexible and highly efficient hardware architectures. To address these demands, we propose IMAX, a novel coarse-grained linear array (CGLA) architecture that alternates cache memory and processing units in a linear structure to absorb irregular memory access latencies. This design achieves exceptional performance and energy efficiency. IMAX3 further enhances the architecture by introducing optimized communication, double buffering, and advanced sparse matrix multiplication (SpGEMM) techniques, delivering significant performance improvements. Real-time evaluations on the Xilinx VPK180 SoC demonstrate IMAX3’s remarkable capabilities: up to 503 times faster execution than GTX 1080Ti in SpGEMM and 10 times the energy efficiency of Jetson AGX Orin in FFT. Additionally, IMAX3 outperforms related architectures in matrix multiplication, achieving speeds approximately 23 times faster than STRELA and 61 times faster than RipTide, with substantial gains in energy efficiency. These results confirm IMAX3 as a cutting-edge, energy-efficient, and flexible hardware platform that surpasses traditional GPUs and other architectures, particularly in real-time processing and low-power environments. IMAX3 sets a new standard in hardware acceleration, making it an ideal solution for the evolving computational needs of AI-driven applications.}, archive = {J_ACCESS}, author = {Tomoya Akabe and Vu Trung Duong LE and Yasuhiko Nakashima}, doi = {10.1109/ACCESS.2024.3524415}, journal = {IEEE Access}, pages = {31899-31911}, shortjournal = {IEEE Access}, title = {IMAX: A power-efficient multilevel pipelined CGLA and applications}, volume = {13}, year = {2025}, }
• (2025). Natural fault-tolerant model-free predictive flux control in five-phase PMSM drives under any-phase open-circuit fault. ACCESS, 13, 31889-31898. (www)

  @article{ , abstract = {The fault-tolerant control strategies always need to not only diagnose open-circuit fault phase but also reconfigure transformation matrix and control structure according to specific fault phase, thus increasing complexity of control algorithm. Furthermore, the parameters of five-phase permanent magnet synchronous motor (PMSM) are susceptible to perturbations under open-circuit fault condition, thus deteriorating operational performance. To address these issues, a natural fault-tolerant model-free predictive flux control (NFT-MFPFC) was proposed. The novelty of proposed strategy lies in the development of an ultra-local model with constant regulation coefficient, and a new extended state observer (ESO) which employed to estimate the stator inductance and the disturbances caused by any-phase open-circuit fault. Based on these, the faulty motor with model-free predictive control can not only operate smoothly under any-phase open-circuit fault condition without fault diagnosis and reconfiguration of control system and transformation matrix, but also have none adjustment of ultra-local model parameter and strong robustness even in the case of parameter mismatch. In addition, the virtual voltage vectors were adopted to restrain the third harmonic currents under both healthy and open-circuit fault conditions. The experimental results were presented to verify the feasibility of proposed strategy.}, archive = {J_ACCESS}, author = {Cheng Chen and Huawei Zhou and Chen Ye and Tao Tao}, doi = {10.1109/ACCESS.2025.3542591}, journal = {IEEE Access}, pages = {31889-31898}, shortjournal = {IEEE Access}, title = {Natural fault-tolerant model-free predictive flux control in five-phase PMSM drives under any-phase open-circuit fault}, volume = {13}, year = {2025}, }
• (2025). A comparative study of network slicing techniques for effective utilization of channel for 5G and beyond 5G networks. ACCESS, 13, 31870-31888. (www)

  @article{ , abstract = {Radio Resource is a finite and crucial resource for any wireless communication. We have huge expectations from the 5G and beyond 5G wireless communication networks. If we want to meet this with huge expectations of Next Generation Wireless Network (NGWN) without compromising the Quality of Service (QoS), then we must have the Radio Resource Management (RRM) techniques for effective utilization of available and finite Radio Resource. The NGWN will be experiencing large number of connected devices which needs huge and on-demand bandwidth allocation without compromising the QoS. This on-demand bandwidth allocation is implemented using Network Slicing (NS) techniques to meet the requirements of the device-level service and application-level service. The objective of this work is to investigate various NS techniques for enhanced radio resource utilization in NGWN. It focuses on addressing the challenges of dynamic bandwidth allocation by employing advanced decision-making mechanisms to fulfill the ever-changing demands of next-generation applications. The study explores and compares static and dynamic NS techniques for application-level service provisioning. Dynamic NS is analyzed with an emphasis on integrating soft computing methods to handle decision-making processes for on-demand bandwidth allocation. Comparative simulations and evaluations of these techniques are conducted to assess their efficiency and adaptability. The major challenge in dynamic NS is making critical and right decisions to meet the dynamic requirements of NGWN applications. This challenge is addressed using the soft computing techniques in making decisions to cater to the dynamic on demand requirements of the NGWN applications. The key findings of this research work are to analyze the dynamic NS, when integrated with soft computing techniques and offers superior adaptability and decision-making capabilities, enabling effective bandwidth allocation for NGWN applications. The results demonstrate that these methods enhance channel utilization and cater to diverse service requirements, making them critical for the successful deployment of NGWN. This study provides insights into embedding robust NS techniques into NGWN to achieve optimal radio resource utilization while addressing the dynamic needs of next-generation wireless communication systems.}, archive = {J_ACCESS}, author = {Prakash B Metre and Gauri Kalnoor and Mahesh G and Gowrishankar S}, doi = {10.1109/ACCESS.2025.3542569}, journal = {IEEE Access}, pages = {31870-31888}, shortjournal = {IEEE Access}, title = {A comparative study of network slicing techniques for effective utilization of channel for 5G and beyond 5G networks}, volume = {13}, year = {2025}, }
• (2025). Fresh or rotten? enhancing rotten fruit detection with deep learning and gaussian filtering. ACCESS, 13, 31857-31869. (www)

  @article{ , abstract = {We address the pressing issue of food waste by proposing a robust image classification model that can reliably detect rotten fruits. More than half of the fruit yield is lost along the supply chain, with post-harvest losses due to rottenness playing a pivotal role, as even a single decomposing piece can cause huge damage to nearby produce. Our transfer learning-based model uses the ResNet50 convolutional neural network architecture as a binary classification model to distinguish between fresh and rotten fruits. The model performance is enhanced with a Gauss filter and a dropout layer to ensure robustness and prevent overfitting. We achieve high accuracies beyond 99% on unseen test data, setting a new benchmark and outperforming previous efforts. Our work has theoretical and practical implications. To the best of our knowledge, we are the first to explore the use of Gauss filters to preprocess input images in fruit classification. We find that Gauss filters with small kernel sizes improve the performance of our model. Our research can improve post-harvest applications through automation. It can thus help reduce food waste, improve food safety, and reduce costs for growers, distributors, and retailers, thereby improving the overall efficiency of the supply chain.}, archive = {J_ACCESS}, author = {Leopold Fischer-Brandies and Lucas Müller and Justus Johannes Riegger and Ricardo Buettner}, doi = {10.1109/ACCESS.2025.3542612}, journal = {IEEE Access}, pages = {31857-31869}, shortjournal = {IEEE Access}, title = {Fresh or rotten? enhancing rotten fruit detection with deep learning and gaussian filtering}, volume = {13}, year = {2025}, }
• (2025). Adaptive focal loss for keypoint-based deep learning detectors addressing class imbalance. ACCESS, 13, 31842-31856. (www)

  @article{ , abstract = {Keypoint-based deep learning detectors have proven highly effective in object detection tasks by predicting specific keypoints to determine object classification and location. Examples include CornerNet, CenterNet, ExtremeNet, RetinaNet, FCOS, and ObjectBox. Despite their strengths, these methods are particularly susceptible to class imbalance, which can result in poor detection performance for less frequent classes. Popular solutions such as hard sampling, soft sampling, and sampling-free methods have been proposed to tackle this issue. However, these approaches often have inherent limitations, including sensitivity to hyperparameters and neglecting the gradient dynamics of the loss function. To address these challenges, this paper proposes Adaptive Focal Loss (AFL), which combines the strengths of Focal Loss (FL) and Class-Balanced Loss (CBL) while introducing an Adaptive Gradient Function. This function is specifically designed to mitigate the impact of large gradients during the early stages of training. Extensive experiments demonstrate that AFL significantly outperforms classical sampling methods across key metrics on the MS COCO, LVIS, and PASCAL VOC datasets. Notably, on the LVIS dataset, AFL achieves an average improvement of over 3% $AP_{r}$ compared to the baseline Focal Loss across multiple keypoint-based detectors, showcasing its effectiveness in enhancing rare class accuracy. Furthermore, AFL delivers substantial improvements in the $AP_{0.5\sim 0.95}$ metric, surpassing the baseline Focal Loss by an average of over 2%, 1%, and 0.2% on the LVIS, MS COCO, and PASCAL VOC datasets, respectively. This highlights AFL’s capability to enhance the overall accuracy of foreground objects. By providing a more balanced and robust solution to class imbalance, AFL demonstrates superior performance in challenging scenarios, making it a valuable advancement in keypoint-based detection.}, archive = {J_ACCESS}, author = {Zhihao Su and Afzan Adam and Mohammad Faidzul Nasrudin}, doi = {10.1109/ACCESS.2025.3538917}, journal = {IEEE Access}, pages = {31842-31856}, shortjournal = {IEEE Access}, title = {Adaptive focal loss for keypoint-based deep learning detectors addressing class imbalance}, volume = {13}, year = {2025}, }
• (2025). DevOps integration with capability model maturity integration: A systematic mapping review. ACCESS, 13, 31829-31841. (www)

  @article{ , abstract = {The Capability Maturity Model Integration (CMMI) is a framework for improving and optimizing organizational processes. DevOps is a revolutionary method that bridges the gap between software development and IT operations by encouraging cooperation and continuous improvement. The combination of CMMI with DevOps is a potential technique for improving software development processes. This systematic mapping review looks at the existing literature on the integration of CMMI and DevOps, identifying how they have been combined, the common aspects between them, the possibility and feasibility of their integration, and whether this integration leads to an improvement in the software development process. A thorough search of numerous databases was done, followed by the application of strict inclusion and exclusion criteria to select relevant studies. A final pool was obtained containing 24 relevant papers. The findings show the benefits of combining CMMI and DevOps to reach a higher level of process improvement, in software development. However, a study about the integration between CMMI and DevOps was not found. Several frameworks have been developed to enhance the combination between them; most of them are models for measuring DevOps maturity based on CMMI. This review emphasizes the need for additional research to develop a model for integrating CMMI with DevOps, which will be the next step in the context of an ongoing study. By giving a comprehensive review of the present state of research, this study adds to a better understanding of how CMMI and DevOps can be effectively coupled to promote software industry advancements.}, archive = {J_ACCESS}, author = {Rania Rajab and Mouhib Alnoukari}, doi = {10.1109/ACCESS.2025.3542630}, journal = {IEEE Access}, pages = {31829-31841}, shortjournal = {IEEE Access}, title = {DevOps integration with capability model maturity integration: A systematic mapping review}, volume = {13}, year = {2025}, }
• (2025). A quantum-enhanced artificial neural network model for efficient medical image compression. ACCESS, 13, 31809-31828. (www)

  @article{ , abstract = {The ability to effectively store and transmit high-resolution images such as MRI and CT scans without losing quality is critical to modernizing medical imaging. Traditional compression methods risk losing essential medical image data, which requires perfect detail for diagnosis. Quantum algorithms use superposition and entanglement to compress faster while preserving important information. This research presents a Quantum-enhanced Artificial Neural Network (QANN) model that combines quantum feature extraction with classical neural network topologies to improve image compression. Our approach consists of converting standardized classical data into quantum states, controlling these states using parameterized quantum circuits, and measuring the resulting states to produce enhanced feature vectors. The quantum-enhanced features are fed into a traditional neural network for image compression. The experimental results clearly show that our QANN framework outperforms standard models in terms of accurate reconstructed images, reduced size, and increased space-saving percentage, especially when dealing with large and complicated datasets. The QANN model demonstrates how quantum computing can significantly enhance the effectiveness of medical image processing solutions. Kaggle brain CT and MRI datasets and COVID-CXNet chest x-ray images are used. The proposed QANN model improves peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Using quantum technology, the image size is reduced for MRI (73.3 %), X-ray (74.1%), and CT-SCAN (71.8%) to save space.}, archive = {J_ACCESS}, author = {Balasubramani Subbiyan and Renjith Prabhavathi Neelakandan and Kavisankar Leelasankar and Rajkumar Rajavel and Muthukumaran Malarvel and Achyut Shankar}, doi = {10.1109/ACCESS.2025.3542807}, journal = {IEEE Access}, pages = {31809-31828}, shortjournal = {IEEE Access}, title = {A quantum-enhanced artificial neural network model for efficient medical image compression}, volume = {13}, year = {2025}, }
• (2025). Multi-classification refactoring framework using hopfield neural network for sustainable software development. ACCESS, 13, 31785-31808. (www)

  @article{ , abstract = {Adopting high-quality source code is the ultimate way through which software evolution can be ensured as sustainable. Continuous refactoring in complex software systems ensures longevity and increases architecture knowledge sustainability. However, decision-making about refactoring is a challenge because the benefits of refactoring are vague and very difficult for the developers to quantify, as different refactoring strategies have different effects on quality attributes. No research has developed a multi-classification refactoring framework using artificial neural networks (ANN), specifically hopfield neural networks (HNN), to classify refactoring strategies and improve external software quality and sustainability. Therefore, this study proposes a multi-classification refactoring framework using HNN that classifies refactoring strategies by their impact on external quality attributes. Five stages have been conducted to perform this study, including selecting case studies, identifying the external quality attributes, identifying the most commonly used refactoring strategies in practice, conducting the experiments, and conducting the classification process using HNN. The proposed framework categorizes the refactoring strategies into three categories (positive, negative, and ineffective). By providing clear classifications and descriptions of each strategy, the proposed framework helps developers make informed decisions about how to improve the design and structure of their code. It helps developers mitigate risks associated with code changes by providing guidance on which strategies are likely to yield positive results for specific quality attributes. The proposed multi-classification refactoring framework enhances software sustainability by enhancing critical quality attributes. It supports maintainability, adaptability, and long-term viability, helping to ensure that the software systems remain relevant, efficient, and valuable over time.}, archive = {J_ACCESS}, author = {Abdullah Almogahed and Hairulnizam Mahdin and Yeong Hyeon Gu and Mohammed A. Al-Masni and Shehab Abdulhabib Alzaeemi and Mazni Omar and Abdulwadood Alawadhi and Samera Obaid Barraood and Abdul Rehman Gilal and Adnan Ameen Bakather}, doi = {10.1109/ACCESS.2025.3542087}, journal = {IEEE Access}, pages = {31785-31808}, shortjournal = {IEEE Access}, title = {Multi-classification refactoring framework using hopfield neural network for sustainable software development}, volume = {13}, year = {2025}, }
• (2025). Task-agnostic adaptive activation scaling network for LLMs. ACCESS, 13, 31774-31784. (www)

  @article{ , abstract = {The advent of Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), offering unprecedented capabilities for understanding and generating human-like texts. However, the effectiveness of these models in specific domains often requires fine-tuning, which can be computationally expensive and increases the risk of overfitting. To address these challenges, we introduce Task-Agnostic Adaptive Activation Scaling Network (TAAS-Net), a novel multi-task learning model architecture designed to adapt pre-trained LLMs to new tasks with minimal computational and storage overhead. TAAS-Net leverages an adaptive activation scaling network to enhance parameter efficiency and prevent overfitting, thereby improving model generalization. It incorporates a task-agnostic adaptive activation scaling layer that introduces task-specific, learnable activation scaling vectors, enhancing the model’s adaptability across various tasks. In addition, Low-Rank Adaptation within the attention modules facilitates the transfer of knowledge across different tasks. TAAS-Net’s integration of multi-task learning and parameter-efficient fine-tuning schemes enables efficient fine-tuning in multi-task scenarios, with a reduced parameter count compared to existing methods such as MoE-LoRA and X-LoRA. Our model also handles mixed-task batches during inference, thereby enhancing versatility and operational efficiency. Through a series of experiments on benchmark datasets, including the General Language Understanding Evaluation (GLUE), IMDB, and Agnews, TAAS-Net demonstrated superior performance over existing baselines, underscoring its potential for applications requiring rapid deployment and high accuracy. The architectural design of TAAS-Net streamlines the model adaptation process and reduces resource demands, offering a scalable and resource-efficient solution for adapting LLMs to new tasks.}, archive = {J_ACCESS}, author = {Ni Jia and Tong Liu and Jiadi Chen and Ying Zhang and Song Han}, doi = {10.1109/ACCESS.2025.3542415}, journal = {IEEE Access}, pages = {31774-31784}, shortjournal = {IEEE Access}, title = {Task-agnostic adaptive activation scaling network for LLMs}, volume = {13}, year = {2025}, }
• (2025). Optimizing crop recommendations with improved deep belief networks: A multimodal approach. ACCESS, 13, 31762-31773. (www)

  @article{ , abstract = {With the increase in world population, agricultural planning is significant to ensure food security. Timely recommendations for crops could be valuable for planning food production and maintaining food sustainability. This proposed work suggests a crop recommendation model considering physical soil characteristics, chemical soil characteristics, climate, and crop characteristics, using Improved Deep Belief Networks (IDBN). For this study, four important Indian crops—rice, maize, finger millet and sugarcane —were taken into account. The soil, weather, and crop datasets were gathered from different sources and subjected to feature selections method to extract optimal features. The crop recommendation model was developed using the proposed IDBN with Gaussian Restricted Boltzmann Machines and Ranger Optimizer with selected optimal features as input. Gaussian Restricted Boltzmann Machines enable the use of continuous values, and Ranger Optimizer ensures rapid convergence with selected optimal features as input. The findings from the investigation confirm that the suggested IDBN outperforms conventional Deep Belief Networks (DBN). The effectiveness of the crop recommendation model was evaluated using IDBN, and its performance was compared with several other machine learning algorithms.}, archive = {J_ACCESS}, author = {J. Madhuri and B. Ramana Reddy and Gauri Kalnoor and M. Indiramma and N. Nagarathna}, doi = {10.1109/ACCESS.2025.3542284}, journal = {IEEE Access}, pages = {31762-31773}, shortjournal = {IEEE Access}, title = {Optimizing crop recommendations with improved deep belief networks: A multimodal approach}, volume = {13}, year = {2025}, }
• (2025). Transformative transfer learning for MRI brain tumor precision: Innovative insights. ACCESS, 13, 31749-31761. (www)

  @article{ , abstract = {Accurate brain tumor detection is essential for effective treatment and improved patient outcomes, yet conventional MRI classification methods often struggle with complex and variable imaging data. This study introduces the Transformative Transfer Learning (TTL) model, leveraging pre-trained deep neural networks fine-tuned for MRI brain tumor classification, using datasets from Nickparvar and Cheng. High-quality inputs were achieved through preprocessing, including resizing, normalization, noise reduction, and data augmentation. The TTL model, employing architectures such as VGG16, ResNet-50, InceptionV3, and DenseNet-121, demonstrated a substantial improvement over traditional methods, achieving an accuracy of 94.5%, sensitivity of 92.8%, and specificity of 93.1%. Additionally, the model yielded a precision of 93.2%, recall of 92.8%, F1-score of 93.0%, and true positive and true negative rates of 92.8% and 93.1%, respectively. Training and validation loss values of 0.15 and 0.18 confirm effective model convergence. These metrics underscore the model’s potential as a robust tool in clinical diagnostics, providing a pathway to more accurate and efficient MRI-based brain tumor detection.}, archive = {J_ACCESS}, author = {Raja Waseem Anwar and Mohammad Abrar and Faizan Ullah}, doi = {10.1109/ACCESS.2025.3542451}, journal = {IEEE Access}, pages = {31749-31761}, shortjournal = {IEEE Access}, title = {Transformative transfer learning for MRI brain tumor precision: Innovative insights}, volume = {13}, year = {2025}, }
• (2025). Study of subsurface structures northwest of ascraeus mons, mars, based on variational bayesian monte carlo. ACCESS, 13, 31737-31748. (www)

  @article{ , abstract = {Tharsis Montes is the largest volcanic province on Mars, and Ascraeus Mons in its northeastern part is the largest volcano of the province. The material composition of the lava flows and lava plains to the northwest of Ascraeus Mons is important for the understanding of the volcanic activity and geological history of Mars. SHARAD (SHAllow RADar), an orbital radar onboard MRO (Mars Reconnaissance Orbiter), can be used to study the electrical properties of subsurface materials and subsurface geological structures. In this paper, Variational Bayesian Monte Carlo (VBMC) is used to calculate the dielectric constant and loss tangent of lava flows and lava plains material to the northwest of Ascraeus Mons, determining the electrical parameters without knowing the depth of the subsurface reflector. The range of dielectric constants and loss tangents of these lava flows and plains is consistent with basaltic lava flows, with dielectric constants of the lava flows in the range of 7.7-14.4, and loss tangent in the range of 0.025-0.037. The differences in the electrical parameters of the lava flows may be caused by spatial and temporal differences in volcanic activities and the different sources of the lava flows. The permittivity of the surface layer of the lava plain ranges from 8.8 to 14.4, and the loss tangent ranges from 0.015 to 0.037. If the lava flows are directly overlying the lava plains, the plains underneath the lava flows would have a larger permittivity, but our calculations of the permittivity of the lava flow and the lava plain are similar, and therefore, the reason for the subsurface reflectors may not be the difference in dielectric constants of the materials in the different geological units, but rather the presence of a thin weathered layer with a low dielectric constant between the two layers of lava material.}, archive = {J_ACCESS}, author = {Qian Wang and Sixin Liu and Jianfu Ni and Qi Lu}, doi = {10.1109/ACCESS.2025.3540824}, journal = {IEEE Access}, pages = {31737-31748}, shortjournal = {IEEE Access}, title = {Study of subsurface structures northwest of ascraeus mons, mars, based on variational bayesian monte carlo}, volume = {13}, year = {2025}, }
• (2025). Using synthetic gaussian noise to explore stochastic resonance in cancer subtype classification. ACCESS, 13, 31729-31736. (www)

  @article{ , abstract = {Stochastic resonance (SR) allows models to detect weak signals or features that might otherwise be lost in noise, thereby improving the classification performance in Artificial Intelligence (AI) models. This study represents one of the first applications of SR in enhancing AI-driven cancer diagnostics. We compared eight common AI classification models (random forest, logistic regression, Naive Bayes, XGBoost, support vector machine, gradient boost, AdaBoost, and K-Nearest Neighbor) using performance metrics to evaluate their effectiveness in distinguishing cancer subtypes at varying noise levels. We trained our machine learning models using a dataset published by the University of California, Irvine’s Machine Learning Repository, which consists of 20,531 Ribonucleic Acid sequence gene expression features and 801 samples representing the five cancer subtypes. While previous studies on this dataset focused on feature selection, our study explores the controlled addition of synthetic Gaussian noise at five different levels through SR to uncover weak signals in the data. Furthermore, we selected 80 significant genes by evaluating the Analysis of Variance f-values and removing constant features. Our results indicate that models enhanced with SR can classify gene expression with high accuracy (93-98%) when noise levels are increased to $\sigma =4$ . Logistic regression and support vector machine models performed the best in noise-free conditions. However, model accuracy decreased when noise levels exceeded $\sigma =4$ , declining to 83-93% when $\sigma =8$ across all models, showing machine learning vulnerability to unrestrained noise. The results of this study suggest that controlled noise implementation can enhance cancer diagnostic accuracy through SR.}, archive = {J_ACCESS}, author = {William Liang}, doi = {10.1109/ACCESS.2025.3539309}, journal = {IEEE Access}, pages = {31729-31736}, shortjournal = {IEEE Access}, title = {Using synthetic gaussian noise to explore stochastic resonance in cancer subtype classification}, volume = {13}, year = {2025}, }
• (2025). A silicon photonic 32-input coherent combiner for turbulence mitigation in free space optics links. ACCESS, 13, 31718-31728. (www)

  @article{ , abstract = {Free space optical links promise high data rates for future wireless communications systems, but performance is degraded by turbulence, limiting the potential use cases. A photonic integrated circuit (PIC) for turbulence mitigation is reported and characterized, which coherently combines 32 input optical signals into a single output fiber. The PIC was fabricated using a low-loss, and high integration density, thick silicon-on-insulator (SOI) process and packaged with 32 input fibers and 1 output fiber. The basic building block is a $2\times 2$ Mach-Zehnder interferometer (MZI) with an external (to the MZI branches) and an internal thermal phase shifter, and a bandwidth in excess of 80 kHz. The PIC monolithically integrates 31 MZIs and 31 germanium photodetectors and is suitable in principle for turbulence mitigation in LEO-ground and horizontal free space optics links. Improvements to the device for the coherent combination of 64 inputs and for the reduction of insertion losses are also discussed.}, archive = {J_ACCESS}, author = {Lorenzo de Marinis and Peter Seigo Kincaid and Yann Lucas and Lea Krafft and Vincent Michau and Matteo Cherchi and Mikko Karppinen and Giampiero Contestabile}, doi = {10.1109/ACCESS.2025.3541870}, journal = {IEEE Access}, pages = {31718-31728}, shortjournal = {IEEE Access}, title = {A silicon photonic 32-input coherent combiner for turbulence mitigation in free space optics links}, volume = {13}, year = {2025}, }
• (2025). Gradual variation-based dual-stream deep learning for spatial feature enhancement with dimensionality reduction in early alzheimer’s disease detection. ACCESS, 13, 31701-31717. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is one of the most common neurological brain disorders, developing gradually and primarily causing the deterioration of brain function, leading to memory loss and behavioral changes. Early detection through computer-aided systems is crucial for effective intervention and treatment, providing automated and highly accurate diagnoses of Alzheimer’s by analyzing brain scans, such as MRI, to assess atrophy in key regions like the hippocampus and entorhinal cortex. Recently, many researchers have been developing computer-aided AD recognition systems using various technologies. However, existing approaches face challenges in achieving good performance due to difficulties in capturing complex spatial features and handling high-dimensional data. This study proposes a Gradual Variation-Based Dual-Stream Deep Learning (DL) framework for Spatial Feature Enhancement with Dimensionality Reduction to address these challenges. The method begins by converting MRI voxel data into slice images, followed by the extraction of gradual spatial variations based on the Haar Wavelet Transform (WT) to capture large-scale structural changes. These features are processed through a dual-stream DL model, where each stream captures complementary spatial features. The concatenated features are refined using a channel attention (CA) module, which selects the most discriminative features while performing dimensionality reduction to retain only the most important information, eliminating redundant or less informative features. Finally, a DL-based classification module is applied to detect early signs of Alzheimer’s disease. We evaluated the model on two datasets, Complete 1Yr 1.5T and Complete 3Yr 3T, from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), using MRI scans from three planes (axial, sagittal, and coronal). The proposed method achieved 99.05% accuracy, 99.05% precision, 99.05% recall, 99.73% ROC-AUC, and 99.33% F1-score, significantly outperforming state-of-the-art methods. These results demonstrate the robustness and computational efficiency of our approach for early Alzheimer’s diagnosis, offering a valuable tool for clinical applications.}, archive = {J_ACCESS}, author = {Najmul Hassan and Abu Saleh Musa Miah and Taro Suzuki and Jungpil Shin}, doi = {10.1109/ACCESS.2025.3542458}, journal = {IEEE Access}, pages = {31701-31717}, shortjournal = {IEEE Access}, title = {Gradual variation-based dual-stream deep learning for spatial feature enhancement with dimensionality reduction in early alzheimer’s disease detection}, volume = {13}, year = {2025}, }
• (2025). UFOIL: An unsupervised fusion of image processing and language understanding. ACCESS, 13, 31683-31700. (www)

  @article{ , abstract = {In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively; and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively.}, archive = {J_ACCESS}, author = {Md. Abdur Rahman and Md. Tanzimul Hasan and Umar Farooq Howlader and Md. Abdul Kader and Md. Motaharul Islam and Phuoc Hung Pham and Mohammad Mehedi Hassan}, doi = {10.1109/ACCESS.2025.3542417}, journal = {IEEE Access}, pages = {31683-31700}, shortjournal = {IEEE Access}, title = {UFOIL: An unsupervised fusion of image processing and language understanding}, volume = {13}, year = {2025}, }
• (2025). Asymmetric multi-layer compression: Decoupling inter-layer coding dependencies with a learned model. ACCESS, 13, 31671-31682. (www)

  @article{ , abstract = {Multi-layer compression is employed to achieve scalability by providing a base bitstream and additional enhancement layers to be used when extra bandwidth is available. In this work, we combine handcrafted and learned solutions, isolating the former in the base layer and using the latter in the enhancement layer. Moreover, the proposed Asymmetric Multi-layer Compression (AMLC) model decouples the base and enhancement layers at the encoding side, leveraging the end-to-end enhancement layer model to simplify the coding process. AMLC encodes an image $1.28\times $ faster than a fully learned multi-layer codec while maintaining similar quality and coding efficiency despite the lower quality of the handcrafted base layer used in AMLC. While the proposed asymmetric encoder together with the handcrafted base layer reduce the overall complexity, the learned enhancement layer brings benefits in terms of coding efficiency. AMLC outperforms the coding efficiency of the Scalable HEVC (SHVC) reference software, a handcrafted multi-layer codec, achieving BD-Rate gains of -27.86%, -34.19%, and -26.57% for PSNR-YUV, PSNR-RGB, and MS-SSIM quality metrics, respectively. Finally, an equivalent implementation of the proposed model adding the inter-layer dependency on the encoding side produces results that are similar to the ones from AMLC’s, confirming its potential to simplify multi-layer compression without significant losses in coding efficiency.}, archive = {J_ACCESS}, author = {Luiz Henrique Cancellier and André Beims Bräscher and Ismael Seidel and Mateus Grellert and Luís A. da Silva Cruz and José Luís Güntzel}, doi = {10.1109/ACCESS.2025.3542979}, journal = {IEEE Access}, pages = {31671-31682}, shortjournal = {IEEE Access}, title = {Asymmetric multi-layer compression: Decoupling inter-layer coding dependencies with a learned model}, volume = {13}, year = {2025}, }
• (2025). Data-driven dynamic state estimation framework using a koopman operator-based linear predictor. ACCESS, 13, 31660-31670. (www)

  @article{ , abstract = {Dynamic state estimation (DSE) is a fundamental task in many fields, including control systems, robotics, and signal processing. Traditional DSE methods, which rely on mathematical models to describe system dynamics, are often limited in their applicability to real-world scenarios due to inaccuracies and assumptions. In this paper, we propose a purely data-driven DSE framework based on a Koopman operator-based linear predictor. The Koopman operator is a powerful tool in dynamical systems theory that allows us to analyze and predict the behavior of nonlinear systems. By leveraging the Koopman operator extracted solely from measured input-output data through the extended dynamic mode decomposition with control (EDMDc) method, a linear predictor that can accurately estimate the state variables of a dynamic system is developed. Introducing the extended Kalman filter (EKF) as an estimation method, the learned Koopman operator-based linear predictor is then used to estimate the current state of the system given only the past and present input-output measurements. To evaluate the effectiveness of the proposed framework, we conduct experiments on both simulated and real-world datasets of complex power systems. The results demonstrate that the proposed data-driven approach outperforms traditional model-based approaches in terms of accuracy and robustness. Moreover, the proposed framework is capable of handling nonlinear and time-varying systems, making it applicable to a wide range of practical cases.}, archive = {J_ACCESS}, author = {Deyou Yang and Han Gao and Zhe Chen and Yanling Lv and Lixin Wang}, doi = {10.1109/ACCESS.2025.3535521}, journal = {IEEE Access}, pages = {31660-31670}, shortjournal = {IEEE Access}, title = {Data-driven dynamic state estimation framework using a koopman operator-based linear predictor}, volume = {13}, year = {2025}, }
• (2025). Corrections to “An ensemble hybrid framework: A comparative analysis of metaheuristic algorithms for ensemble hybrid CNN features for plants disease classification”. ACCESS, 13, 31659. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification”.}, archive = {J_ACCESS}, author = {Khaoula Taji and Ali Sohail and Tariq Shahzad and Bilal Shoaib Khan and Muhammad Adnan Khan and Khmaies Ouahada}, doi = {10.1109/ACCESS.2025.3542202}, journal = {IEEE Access}, pages = {31659}, shortjournal = {IEEE Access}, title = {Corrections to “An ensemble hybrid framework: A comparative analysis of metaheuristic algorithms for ensemble hybrid CNN features for plants disease classification”}, volume = {13}, year = {2025}, }
• (2025). Retraction notice: A novel radio frequency identification collision resolution method based on statistical learning. ACCESS, 13, 31658. (www)

  @article{ , archive = {J_ACCESS}, author = {Zhong Dongbo}, doi = {10.1109/ACCESS.2025.3542937}, journal = {IEEE Access}, pages = {31658}, shortjournal = {IEEE Access}, title = {Retraction notice: A novel radio frequency identification collision resolution method based on statistical learning}, volume = {13}, year = {2025}, }
• (2025). Construction of an intelligent analysis system for crop health status based on drone remote sensing data and CNN. ACCESS, 13, 31643-31657. (www)

  @article{ , abstract = {The health of crops is of great consequence to the security of the global food supply and the sustainability of agricultural economies. To address the shortcomings of traditional monitoring methods, which are characterized by high labor intensity, low efficiency, and insufficient timeliness, this paper proposes an innovative intelligent analysis system. The system uses remote sensing data from drones and convolutional neural network technology to achieve efficient crop classification and accurate identification of pests and diseases. Specifically, the system adopts a multi-scale attention convolutional network to optimize crop classification, improves the cyclic consistent adversarial network for remote sensing image translation to enhance the dataset, and improves the lightweight MobileNet V2 for disease and pest recognition. The proposed method achieved an average F1 and intersection to union ratio of 94.67% and 89.14% for crop classification and recognition, respectively. When translating crop remote sensing images, the Frechette distance and kernel distance were 98.73 and 3.08, respectively. The translated image enhanced the dataset and improved recognition accuracy and convergence speed. When identifying crop pests and diseases, the accuracy and recall were 97.14% and 97.18%, respectively. The parameter count was reduced to 2.01MB, indicating superiority. This study solves the uncertainty in remote sensing data and the complexity and limited number of training samples for convolutional neural networks. This provides technical support for the transformation of agriculture towards intelligence and sustainability.}, archive = {J_ACCESS}, author = {Haolin Yang and Peilong Xu and Shengtian Zhang and Hyeonseok Kim and Incheol Shin}, doi = {10.1109/ACCESS.2025.3540279}, journal = {IEEE Access}, pages = {31643-31657}, shortjournal = {IEEE Access}, title = {Construction of an intelligent analysis system for crop health status based on drone remote sensing data and CNN}, volume = {13}, year = {2025}, }
• (2025). LEU3: An attention augmented-based model for acute lymphoblastic leukemia classification. ACCESS, 13, 31630-31642. (www)

  @article{ , abstract = {Acute Lymphoblastic Leukemia (ALL), a cancer affecting the blood and bone marrow, requires precise classification for accurate diagnosis, personalized treatment plans, and improved predictive assessments to enhance patient survival and quality of life. This study presents LEU3, a novel classification model designed to improve the accuracy of leukemia detection from peripheral blood smear (PBS) images. LEU3 leverages an attention-based convolutional neural network (CNN) architecture, incorporating pooling layers, a global average pooling layer, and dense layers with dropout for regularization. The model is trained with an Adam optimizer comprising with four classes: Benign, early malignant pre-B, malignant pre-B, and malignant pro-B. Data augmentation techniques were employed to increase training set diversity. Additionally, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are used to enhance interpretability and transparency in the model’s decision-making process. LEU3 achieved a test accuracy of 99% and a validation accuracy of 99% on 484 PBS images, demonstrating a 3% improvement over the baseline model. These results underline the potential of LEU3 in supporting medical professionals by reducing diagnostic workload and improving the accuracy of leukemia classification.}, archive = {J_ACCESS}, author = {Monoronjon Dutta and Mayen Uddin Mojumdar and Md Alamgir Kabir and Narayan Ranjan Chakraborty and Shah Md Tanvir Siddiquee and Saad Abdullah}, doi = {10.1109/ACCESS.2025.3542609}, journal = {IEEE Access}, pages = {31630-31642}, shortjournal = {IEEE Access}, title = {LEU3: An attention augmented-based model for acute lymphoblastic leukemia classification}, volume = {13}, year = {2025}, }
• (2025). MDE-based approach for accelerating the development of recommender systems in smart tourism. ACCESS, 13, 31615-31629. (www)

  @article{ , abstract = {Recommender systems (RSs) have become fundamental computational tools deployed across diverse domains, including e-commerce, tourism, and streaming platforms to facilitate personalized content delivery through the analysis of user preferences, behavioral patterns, and interaction data. With the use of Machine Learning (ML) techniques, these RSs can be implemented either through established frameworks such as NReco and Apache Mahout, or through custom development. Despite the availability of multiple implementation approaches, RS development remains computationally complex, necessitating substantial expertise in both software engineering principles and artificial intelligence methodologies. To address these technical challenges, this paper presents a Domain-Specific Language (DSL) framework that streamlines and accelerates RS development for smart tourism applications through a Model-Driven Engineering (MDE) methodology. The proposed framework minimizes the requirement for extensive programming or ML expertise and enables developers to efficiently generate customized RS implementations while maintaining system quality. The DSL framework automates the generation of code that leverages the services provided by the Apache Mahout framework for implementing recommendation algorithms, abstracting the underlying complexity and enabling developers to focus on high-level system design rather than the technical details of algorithm implementation. In addition, the framework integrates a code generator and a modeling tool, allowing developers to design and implement recommendation systems with reduced complexity and enhanced productivity. Unlike generic or low-code platforms, this approach combines domain-specific abstractions with automation tools to address the unique challenges of the smart tourism sector, ensuring adaptability, efficiency, and high-quality results.}, archive = {J_ACCESS}, author = {Adnane Souha and Charaf Ouaddi and Lamya Benaddi and Lahbib Naimi and El Mahi Bouziane and Abdeslam Jakimi}, doi = {10.1109/ACCESS.2025.3543058}, journal = {IEEE Access}, pages = {31615-31629}, shortjournal = {IEEE Access}, title = {MDE-based approach for accelerating the development of recommender systems in smart tourism}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Precision agriculture through deep learning: Tomato plant multiple diseases recognition with CNN and improved YOLOv7”. ACCESS, 13, 31614. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Precision Agriculture Through Deep Learning: Tomato Plant Multiple Diseases Recognition With CNN and Improved YOLOv7”).}, archive = {J_ACCESS}, author = {Muhammad Umar and Saud Altaf and Shafiq Ahmad and Haitham Mahmoud and Adamali Shah Noor Mohamed and Rashid Ayub}, doi = {10.1109/ACCESS.2025.3542259}, journal = {IEEE Access}, pages = {31614}, shortjournal = {IEEE Access}, title = {Corrections to “Precision agriculture through deep learning: Tomato plant multiple diseases recognition with CNN and improved YOLOv7”}, volume = {13}, year = {2025}, }
• (2025). Model of a novel PCB coil for high-sensitivity metal detector. ACCESS, 13, 31603-31613. (www)

  @article{ , abstract = {Metal detectors detect hidden metal objects through electromagnetic induction. Significant research has focused on improving the detector sensitivity by redesigning the structure of differential receiving coils. Higher sensitivity allows the detector to identify smaller metal objects or detect objects at greater depths. This is significant in applications such as security screening and industrial quality control, where precision is essential. This paper introduces a novel receiving PCB coil prototype for a high-sensitivity metal detector and presents a numerical modeling method to optimize the coil size. The strip coils distributed around the emitting coil are employed in the differential receiving coils, thereby strengthening the induced voltages caused by the electromagnetic field excited from the detected metal objects. The balance state of the differential receiving coils is analyzed using a numerical model based on the Biot-Savart law, solving the total magnetic flux from the emitting windings. An optimization problem is constructed from the numerical model, and the optimal design parameters of the receiving coil are determined via a heuristic algorithm. Experimental results indicate that the newly designed prototype exhibits superior sensitivity compared to commercial differential detectors of comparable size. Specifically, the sensitivity of the novel detector is twice that of the advanced commercial counterpart.}, archive = {J_ACCESS}, author = {Han Zhang and Mingxing Song and Yuejiu Zhu and Xianze Xu and Fengqiu Xu}, doi = {10.1109/ACCESS.2025.3541930}, journal = {IEEE Access}, pages = {31603-31613}, shortjournal = {IEEE Access}, title = {Model of a novel PCB coil for high-sensitivity metal detector}, volume = {13}, year = {2025}, }
• (2025). Proposal of emergency operation and active power dispatch control scheme for bipolar VSC-MTDC system. ACCESS, 13, 31590-31602. (www)

  @article{ , abstract = {In a bipolar voltage source converter-based multi-terminal DC (VSC-MTDC) system, the power flow of the positive and negative pole networks can be controlled independently. This advantage allows bipolar systems to maintain operation with only the healthy pole even in the event of DC faults. However, existing power flow control methods for bipolar systems do not include the optimal mode-switching of each converter’s operation and may not be sufficiently reliable during fault conditions. Thus, this paper proposes a new control scheme considering coordination between the poles and flexibility in power shifting. The proposed method consists of two steps: (a) a mode-switching method of converters to maintain DC voltage stability during converter trips and (b) an active power dispatch control method to enable power shifting from the faulty pole to the healthy pole, mitigating the risk of power surplus and ensuring a robust response to AC grid stability issues. The power assurance coefficient $\omega _{ass,i}^{m}$ is defined as critical factor for determining the interpolar power dispatch. The simulation results show that an average transmission power outage of approximately 220 MW can be prevented during a single-pole trip while maintaining the operation within the rated VSC capacity when $\omega _{ass,i}^{m}$ is set to 0.5. This highlights the effectiveness of the proposed method in maintaining the system reliability and stability. Overall, the proposed method enhances the transmission capability by maintaining the operation within the rated capacity, enabling interpolar power shifting, and mitigating the AC grid instability during fault conditions regardless of the bipolar configuration.}, archive = {J_ACCESS}, author = {Ayaka Nakamura and Mikimasa Iwata and Yasunobu Yokomizu and Naoto Kodama}, doi = {10.1109/ACCESS.2025.3542740}, journal = {IEEE Access}, pages = {31590-31602}, shortjournal = {IEEE Access}, title = {Proposal of emergency operation and active power dispatch control scheme for bipolar VSC-MTDC system}, volume = {13}, year = {2025}, }
• (2025). A comprehensive blockchain-based system for educational qualifications management and verification to counter forgery. ACCESS, 13, 31562-31589. (www)

  @article{ , abstract = {The prevalence of fake educational credentials poses a threat to the meritocratic nature of the education system and job markets. Verification of certificates to combat forgery has been a challenging endeavor due to the weaknesses of the current methods. Blockchain, capitalizing on its unique attributes, can provide an optimal solution to certification and verification problems by ensuring disintermediation, immutability, tamper-proof, efficiency, and security. Efforts to explore its potential in addressing these problems continue to gain momentum. However, the existing blockchain-based initiatives do not offer a holistic solution to the forgery problem, as they solely focus on a single education level or institution. Furthermore, these initiatives lack the essential features required to fully address this problem. This paper proposes a comprehensive blockchain-enabled system for issuing certificates from different educational levels and institutions in the country, providing a one-stop center for verifiers, such as employers, to verify all certificates a candidate possesses. As a proof of concept, a decentralized application (DApp), ElimuChain, has been developed, utilizing smart contracts and the InterPlanetary File System (IPFS). The system is deployed on the Binance Smart Chain (BSC) blockchain to evaluate its applicability in addressing the problem in the Tanzanian context. The results demonstrate that the proposed solution successfully manages the certification and verification process, and it is cost-effective, scalable, and efficient. Moreover, its performance was compared with the previous solutions in terms of latency and throughput. The comparison results show that it performs better than the counterpart for transactional operations.}, archive = {J_ACCESS}, author = {Said H. Said and Ramadhani S. Sinde and Efraim M. Kosia and Mussa A. Dida}, doi = {10.1109/ACCESS.2025.3542545}, journal = {IEEE Access}, pages = {31562-31589}, shortjournal = {IEEE Access}, title = {A comprehensive blockchain-based system for educational qualifications management and verification to counter forgery}, volume = {13}, year = {2025}, }
• (2025). Can synthetic data protect privacy?. ACCESS, 13, 31544-31561. (www)

  @article{ , abstract = {To systematically evaluate the privacy protection performance of synthetic data generation algorithms (Synthpop, CTGAN, RTVAE, TVAE, DataSynthesizer), this study applied various safety metrics. Synthetic data is designed to protect sensitive information while maintaining statistical similarities to the original data, but a high degree of similarity can increase the risk of re-identification. Therefore, privacy protection was measured using metrics such as DCR, NNDR, Identification Risk Indicator, Inference Risk Indicator, CM3, DUPI, and pMSE. The results showed that Synthpop provided high data utility, but its high similarity to the original data posed significant privacy risks. Conversely, DataSynthesizer and CTGAN demonstrated superior privacy protection by balancing utility and privacy effectively. RTVAE and TVAE maintained a clear distinction from the original data, enhancing privacy protection, though some cases showed decreased data utility. These findings suggest the importance of selecting algorithms based on specific privacy and utility requirements, emphasizing the need to consider the trade-off between data utility and privacy protection depending on dataset characteristics.}, archive = {J_ACCESS}, author = {Gidan Min and Junhyoung Oh}, doi = {10.1109/ACCESS.2025.3542266}, journal = {IEEE Access}, pages = {31544-31561}, shortjournal = {IEEE Access}, title = {Can synthetic data protect privacy?}, volume = {13}, year = {2025}, }
• (2025). MPC-based walking stability control for bipedal robots on uneven terrain. ACCESS, 13, 31532-31543. (www)

  @article{ , abstract = {This paper proposes an algorithm capable of regenerating gait patterns online. The algorithm is designed using the Model Predictive Control (MPC) framework, with the Linear Inverted Pendulum Model as the basis of the predictive model. For bipedal robot walking, it divides ZMP (Zero Moment Point) constraints into single-leg support, double-leg support, and start-end phases, reformulating them into a complete quadratic programming problem. This approach allows for adjustments to both the foot placement and the timing of steps to maintain dynamic stability during walking. To address the limited contact force sensing capability between the robot and the ground in the gait generator, pressure sensors on both feet of the robot are used to monitor pressure distribution, adjusting the allocation of pressure between the feet accordingly. Finally, simulation and experimental results demonstrate the performance of the proposed method. The controller maintains dynamic stability for the bipedal robot when walking on either soft artificial grass or hard wooden boards.}, archive = {J_ACCESS}, author = {Chih-Cheng Liu and Yi-Chung Lin and Chia-Sheng Li}, doi = {10.1109/ACCESS.2025.3541745}, journal = {IEEE Access}, pages = {31532-31543}, shortjournal = {IEEE Access}, title = {MPC-based walking stability control for bipedal robots on uneven terrain}, volume = {13}, year = {2025}, }
• (2025). GlassGaussian: Extending 3D gaussian splatting for realistic imperfections and glass materials. ACCESS, 13, 31517-31531. (www)

  @article{ , abstract = {3D scene reconstruction methods have made significant advancements in handling reflections. However, these methods are primarily effective on ideal mirror surfaces, which limits their applicability in real-world scenarios where imperfections or materials such as glass. The inability of current state-of-the-art (SOTA) methods to accurately model these complex reflective surfaces often results in artifacts and a significant drop in rendering quality. This paper introduces a novel extension to 3D Gaussian Splatting techniques by modifying the image fusion equation and incorporating a single-channel Spherical Harmonics representation to measure the reflectance of each Gaussian kernel from different directions. For glass scene renderings, our method shows an approximate 1dB PSNR improvement compared to current SOTA techniques. Comprehensive experiments demonstrate the effectiveness of our approach in various challenging scenarios.}, archive = {J_ACCESS}, author = {Junming Cao and Jiadi Cui and Sören Schwertfeger}, doi = {10.1109/ACCESS.2025.3541856}, journal = {IEEE Access}, pages = {31517-31531}, shortjournal = {IEEE Access}, title = {GlassGaussian: Extending 3D gaussian splatting for realistic imperfections and glass materials}, volume = {13}, year = {2025}, }
• (2025). Ontological framework for the analysis of outcome-based curriculum in higher education. ACCESS, 13, 31497-31516. (www)

  @article{ , abstract = {Outcome-Based Education (OBE) is a globally recognized approach for enhancing educational quality and aligning academic programs with industry needs. Despite its advantages, implementing OBE poses significant challenges, particularly in maintaining alignment across program-level outcomes, course-specific learning outcomes, and other curriculum components. This paper introduces OBC-ONTO, an ontology developed to streamline curriculum design within the OBE framework in higher education. The proposed ontology addresses curriculum coherence issues by applying core OBE principles, such as clarity of focus, backward design, expanded opportunities, and high expectations. OBC-ONTO was developed based on in-depth domain analysis and the formulation of competency questions derived from scenarios where a reviewer assesses whether a curriculum meets OBE requirements. The uniqueness of this framework lies in its application of OBE principles, its support for curriculum reviewers’ tasks, and its ability to accommodate various educational levels, including diploma, vocational, bachelor’s, and master’s programs. The framework’s effectiveness is demonstrated through automated consistency checks, competency questions answering, and expert evaluations, proving its versatility across various academic settings. This study highlights OBC-ONTO as a valuable tool for curriculum designers and reviewers, offering a systematic approach to ensure alignment with OBE standards and enhance curriculum quality assurance.}, archive = {J_ACCESS}, author = {Siti Aminah and Adila Alfa Krisnadhi and Achmad Nizar Hidayanto}, doi = {10.1109/ACCESS.2025.3542881}, journal = {IEEE Access}, pages = {31497-31516}, shortjournal = {IEEE Access}, title = {Ontological framework for the analysis of outcome-based curriculum in higher education}, volume = {13}, year = {2025}, }
• (2025). Golden chip-free hardware trojan detection using attention-based non-local convolution with simple recurrent unit. ACCESS, 13, 31485-31496. (www)

  @article{ , abstract = {The globalization of the semiconductor industry introduces vulnerabilities like hardware Trojans that compromise hardware security and pose serious risks to critical applications such as military and medical systems. Traditional hardware Trojan detection methods including reverse engineering (destructive) and golden chip comparison (non-destructive) are often associated with high costs and limited practicality. The emergence of machine learning and deep learning models has enhanced the feasibility of hardware Trojan detection, as these models can learn complex patterns and representations from extensive datasets. This research presents an attention-based non-local convolution model integrated with a simple recurrent unit (Att_NLC-SRU) for hardware Trojan classification effectively eliminating the need for a golden chip. In this approach, a non-local convolutional neural network embedded with an attention module plays a crucial role in efficiently extracting global spatial features from relevant regions. Furthermore, temporal features are extracted using recurrent neural networks such as the simple recurrent unit. The integration of these methodologies has proven to be significantly effective. The proposed model was validated using the advanced encryption standard (AES) benchmarks, achieving the highest accuracy of 99% on the AES-T1600 benchmark.}, archive = {J_ACCESS}, author = {Rama Devi Maddineni and Deepak Ch}, doi = {10.1109/ACCESS.2025.3542431}, journal = {IEEE Access}, pages = {31485-31496}, shortjournal = {IEEE Access}, title = {Golden chip-free hardware trojan detection using attention-based non-local convolution with simple recurrent unit}, volume = {13}, year = {2025}, }
• (2025). Optimizing secure multi-user ISAC systems with STAR-RIS: A deep reinforcement learning approach for 6G networks. ACCESS, 13, 31472-31484. (www)

  @article{ , abstract = {The rapid evolution of wireless communication technologies and the increasing demand for multi-functional systems have led to the emergence of integrated sensing and communication (ISAC) as a key enabler for future 6G networks. Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently garnered significant attention for their ability to enhance signal coverage and improve system efficiency. This paper investigates a STAR-RIS-assisted ISAC system designed to secure communication for multiple legitimate users (LUs) while safeguarding against multiple eavesdroppers (Eves). By jointly optimizing the base station (BS) transmit beamforming, STAR-RIS transmission and reflection coefficients, and receive filters, the proposed framework aims to maximize the long-term average secrecy rate for all LUs. Constraints are imposed to ensure minimum echo signal-to-noise ratios (SNRs) for sensing and meet the achievable rate requirements of LUs. To address the inherent complexity of this non-convex problem, two deep reinforcement learning (DRL) algorithms are proposed. Numerical results demonstrate that the proposed system achieves significant improvements in secrecy rate compared to conventional RIS setups. This work provides a scalable and efficient approach for secure multi-user ISAC systems, making it highly relevant for future 6G networks, smart cities, and IoT applications.}, archive = {J_ACCESS}, author = {Mian Muhammad Kamal and Syed Zain Ul Abideen and M. A. Al-Khasawneh and Amerah Alabrah and Raja Sohail Ahmed Larik and Muhammad Irfan Marwat}, doi = {10.1109/ACCESS.2025.3542607}, journal = {IEEE Access}, pages = {31472-31484}, shortjournal = {IEEE Access}, title = {Optimizing secure multi-user ISAC systems with STAR-RIS: A deep reinforcement learning approach for 6G networks}, volume = {13}, year = {2025}, }
• (2025). Advancing sustainable energy transition through green hydrogen valleys. ACCESS, 13, 31442-31471. (www)

  @article{ , abstract = {The development of Green Hydrogen Valleys (GHVs) is pivotal in the global shift toward sustainable energy and climate change mitigation. However, GHVs face challenges such as safe hydrogen injection, scalable electrolyzer deployment, and effective liquid hydrogen (LH2) containment, necessitating innovative solutions. This review offers a comprehensive analysis of current scientific literature and recent advancements in GHV implementation, focusing on critical areas including electrolysis technology, material compatibility, hydrogen transportation, and economic feasibility. It underscores the necessity of establishing a clean hydrogen value chain to achieve climate neutrality objectives. Additionally, the study outlines a three-phase roadmap—activation, scaling, and market integration—to facilitate the seamless incorporation of GHVs into the energy sector. Through this analysis, the review provides valuable insights for researchers, industry stakeholders, and policymakers, guiding strategic decisions and investments toward a sustainable energy future powered by green hydrogen. The findings underscore the significant role of GHVs in achieving climate and sustainability goals by reducing emissions and meeting global energy demands. Notably, scaling green hydrogen production could reduce carbon dioxide emissions of global energy demands by 2050. This review emphasizes the importance of international collaboration and ongoing research efforts to expedite GHV deployment and maximize their climate benefits.}, archive = {J_ACCESS}, author = {Aymen Flah and Habib Kraiem and M. Jayachandran and Claude Ziad El-Bayeh and B. Srikanth Goud and Vojtech Blazek and Mohammed Mujahid Irfan}, doi = {10.1109/ACCESS.2025.3542554}, journal = {IEEE Access}, pages = {31442-31471}, shortjournal = {IEEE Access}, title = {Advancing sustainable energy transition through green hydrogen valleys}, volume = {13}, year = {2025}, }
• (2025). EffTEE: Efficient image classification and object detection on mobile devices using trusted execution environments. ACCESS, 13, 31423-31441. (www)

  @article{ , abstract = {Deep neural networks (DNNs) play a crucial role in image classification and object detection, with applications in autonomous driving, security, Unmanned Aerial Vehicle (UAV) navigation, and robotics. Ensuring secure execution on mobile devices is challenging due to the sensitivity of input data and DNN architectures. Hardware-based Trusted Execution Environments (TEEs), such as ARM TrustZone, offer security but face resource and performance limitations when handling full-scale DNNs. This work introduces EffTEE, a security framework that enables efficient DNN execution within mobile TEEs. EffTEE employs dynamic suppression to prune unimportant neurons, foundational neuron restructuring to optimize memory usage, and dynamic slicing for effective model partitioning. Experimental results show that EffTEE reduces inference time by $2 - 60 \times $ while maintaining accuracy comparable to existing secure DNN methods. These findings demonstrate EffTEE’s potential for secure and efficient DNN deployment in resource-constrained environments.}, archive = {J_ACCESS}, author = {Bin Hu and Junyong You and Kuan Huang and Meng Xu and Dan Liu and Sugang Ma}, doi = {10.1109/ACCESS.2025.3538712}, journal = {IEEE Access}, pages = {31423-31441}, shortjournal = {IEEE Access}, title = {EffTEE: Efficient image classification and object detection on mobile devices using trusted execution environments}, volume = {13}, year = {2025}, }
• (2025). Low-cost real-time non-invasive milk quality monitoring using 60 GHz FMCW radars. ACCESS, 13, 31411-31422. (www)

  @article{ , abstract = {Combining the vast growing field of Internet of Things (IoT) technologies and a low-cost electromagnetic radar solution for the real-time monitoring of liquid products during production may enable companies to address quality issues quickly and efficiently. This paper demonstrates a proof-of-concept that an electromagnetic radar can be implemented for milk monitoring. The Dielectric Assessment Kit (DAK) is used for the initial dielectric characterization of different milk fat concentrations. A 60 GHz radar sensor is used for the characterization of milk fat in cartons. AI models can be integrated into the data processing to correlate the radar signals to the milk fat concentration of a product. The real-time processing allows products to be flagged if they deviate too much from their reference sample. This work demonstrates a low-cost, non-invasive 60 GHz FMCW radar system for real-time milk quality monitoring. A classification accuracy of 87.5% was achieved for moving cartons, and 100% for stationary cartons. The results confirm the potential for scaling this approach in industrial production lines.}, archive = {J_ACCESS}, author = {Jessica Chong and Ala Eldin Omer and Stefan Idziak and Lan Wei and George Shaker}, doi = {10.1109/ACCESS.2025.3541596}, journal = {IEEE Access}, pages = {31411-31422}, shortjournal = {IEEE Access}, title = {Low-cost real-time non-invasive milk quality monitoring using 60 GHz FMCW radars}, volume = {13}, year = {2025}, }
• (2025). Hybrid PWM technique and capacitor voltage balancing control for four-level asymmetrical flying capacitor inverter. ACCESS, 13, 31401-31410. (www)

  @article{ , abstract = {This paper proposes a novel hybrid PWM technique for the four-level (4L) asymmetrical hybrid flying capacitor (AHFC) inverter, a variant of the HFC topology. This technique combines level-shifted and phase-shifted PWM methods to achieve natural voltage balancing for flying capacitors under ideal and steady-state operating conditions. To maintain balanced DC-link capacitor voltages, a control strategy based on zero-sequence-voltage injection is developed. Moreover, a comprehensive analysis of the 4L-AHFC inverter is performed, comparing it to other 4L inverter topologies in terms of component count, cost, total harmonic distortion (THD), and power losses. A 2-kV/1-MVA 4L-AHFC inverter system offers a 10% lower in total standing voltage (TSV) and a 20% cost reduction compared to the conventional HFC circuit. Furthermore, the 4L-AHFC inverter with the proposed PWM method exhibits lower THD at medium-to-high modulation indices and reduced power losses compared to the HFC inverter. Finally, the effectiveness of the proposed PWM scheme and capacitor voltage balancing control has been validated through experimental results for a 3-kVA hardware prototype.}, archive = {J_ACCESS}, author = {Dinh Du To and Dong-Choon Lee}, doi = {10.1109/ACCESS.2025.3541270}, journal = {IEEE Access}, pages = {31401-31410}, shortjournal = {IEEE Access}, title = {Hybrid PWM technique and capacitor voltage balancing control for four-level asymmetrical flying capacitor inverter}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning task scheduling method for real-time performance awareness. ACCESS, 13, 31385-31400. (www)

  @article{ , abstract = {Load balancing is essential for the efficient delivery of cloud computing services, ensuring stable operation and robust performance under high load conditions. However, existing load-balancing task scheduling algorithms struggle to adapt to load performance fluctuations in real-time, leading to inaccuracies in evaluating task execution efficiency and consequently impacting the quality of service in actual cloud task scheduling. To address this issue, we propose a real-time performance-aware task scheduling method based on the Soft Actor-Critic (RTPA-SAC) algorithm. This method dynamically detects server load performance changes in real-time, enhancing environmental consistency and adaptability in stochastic, dynamic task scheduling, thereby improving load balancing. First, we construct a bounded load performance loss function to evaluate task execution efficiency, considering the impact of parallel task interference. Next, a reward mechanism is introduced, which takes into account both load fluctuations and response times, optimizing task load variance within quality of service constraints to minimize response time. Finally, By leveraging the Soft Actor-Critic algorithm, the proposed scheduling strategy enhances exploratory and stable decision-making in task scheduling. Experimental results show that RTPA-SAC outperforms baseline methods in load balancing, evidenced by improvements in task response time, average task load variance, and task success rate.}, archive = {J_ACCESS}, author = {Jinming Wang and Shaobo Li and Xingxing Zhang and Keyu Zhu and Cankun Xie and Fengbin Wu}, doi = {10.1109/ACCESS.2025.3534980}, journal = {IEEE Access}, pages = {31385-31400}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning task scheduling method for real-time performance awareness}, volume = {13}, year = {2025}, }
• (2025). A boat type fault tolerant multilevel inverter topology resilient to switch fault. ACCESS, 13, 31368-31384. (www)

  @article{ , abstract = {Ensuring the reliability of multilevel inverters (MLIs) is a significant focal point in numerous sensitive industrial applications. In a conventional MLI, an abnormal situation arises when a fault occurs within any of the switches. Consequently, the other components may suffer severe damage. In such a situation, the faulty inverter must be removed. Thus, this work presents a new fault tolerant MLI (FT MLI) topology, which incorporates more redundant switching states in comparison to normal MLIs. As a result, the proposed FT MLI topology is resilient to open-circuit faults in the switches. An explanation of the operation of the proposed topology is provided, accompanied by a description of the FT capabilities for symmetric mode as well as asymmetric mode. The proposed topology has per unit total standing voltage of 6.33 and 6.5 for symmetric and asymmetric modes of operation, respectively. Simulation results using MATLAB/Simulink and real-time results using Typhoon hardware-in-the-loop (HIL)-402 device are presented to demonstrate the FT MLI topology’s effectiveness. This paper compares the proposed topology with other FT MLI topologies, emphasizing the advantages of the proposed topology.}, archive = {J_ACCESS}, author = {Haroon Rehman and Mohd Tariq and Chun-Hsin Chang and Adil Sarwar and Shafiq Ahmad and and Hwa-Dong Liu}, doi = {10.1109/ACCESS.2025.3541172}, journal = {IEEE Access}, pages = {31368-31384}, shortjournal = {IEEE Access}, title = {A boat type fault tolerant multilevel inverter topology resilient to switch fault}, volume = {13}, year = {2025}, }
• (2025). ELTrack: Events-language description for visual object tracking. ACCESS, 13, 31351-31367. (www)

  @article{ , abstract = {The integration of Natural Language (NL) descriptions into Visual Object Tracking (VOT) has shown promise in enhancing the performance of RGB-based tracking by providing richer, contextually aware information that helps to address issues like appearance variations, model drift, and ambiguous target representation. However, the growing complexity of VOT tasks, particularly in scenarios involving fast-moving objects and challenging lighting conditions, necessitates the development of more robust and adaptable tracking frameworks. Traditional visual trackers, which rely solely on RGB data, often struggle with these challenges. Event cameras offer promising solutions that capture changes in a scene as they happen because of their reduced latency, making them highly effective in scenarios where traditional visible cameras often struggle, such as in low-light environments or when tracking rapid motion. Despite the process in events and NL tracking, the fusion of events and NL remains underexplored due to the lack of large-scale NL-described datasets and event-based benchmarks. To address these gaps, we present ELTrack, a novel multi-modal NL-based VOT framework that, to the best of our knowledge, is the first to integrate event data with NL descriptions in VOT. ELTrack synthesizes event data, filters out noise, and applies imprinting and a step decay function to introduce a novel event image representation called Pseudo-Frames. Additionally, we generate NL descriptions using a Visual-Language (VL) image-captioning module featuring BLIP-2 and GPT-4. These modalities are seamlessly integrated using a superimpose fusion module to enhance tracking performance. Our ELTrack is a generic pipeline and can be integrated with any of the existing SoTA trackers. Extensive experiments demonstrate that ELTrack achieves significantly better performance across a variety of publicly available VOT datasets. The source code of the ELTrack is publicly available at: https://github.com/HamadYA/ELTrack-Correlating-Events-and-Language-for-Visual-Tracking.}, archive = {J_ACCESS}, author = {Mohamad Alansari and Khaled Alnuaimi and Sara Alansari and Sajid Javed}, doi = {10.1109/ACCESS.2025.3540445}, journal = {IEEE Access}, pages = {31351-31367}, shortjournal = {IEEE Access}, title = {ELTrack: Events-language description for visual object tracking}, volume = {13}, year = {2025}, }
• (2025). Optimizing pre-trained code embeddings with triplet loss for code smell detection. ACCESS, 13, 31335-31350. (www)

  @article{ , abstract = {Code embedding represents code semantics in vector form. Although code embedding-based systems have been successfully applied to various source code analysis tasks, further research is required to enhance code embedding for better code analysis capabilities, aiming to surpass the performance and functionality of static code analysis tools. In addition, standard methods for improving code embedding are essential to develop more effective embedding-based systems, similar to augmentation techniques in the image processing domain. This study aims to create a contrastive learning-based system to explore the potential of a generic method for enhancing code embedding for code classification tasks. A triplet loss-based deep learning network is designed to optimize in-class similarity and increase the distance between classes. An experimental dataset that contains code from Java, Python, and PHP programming languages and 4 different code smells is created by collecting code from open-source repositories on GitHub. We evaluate the proposed system’s effectiveness with widely used BERT, CodeBERT, and GraphCodeBERT pretrained models to create code embedding for the code classification task of code smell detection. Our findings indicate that the proposed system may offer improvements in accuracy, an average of 8% and a maximum of 13% for models. These results suggest that incorporating contrastive learning techniques into the generation process of code representation as a preprocessing step can enhance performance in code analysis.}, archive = {J_ACCESS}, author = {Alı Nızam and Ertuğrul İslamoğlu and Ömer Kerem Adali and Musa Aydin}, doi = {10.1109/ACCESS.2025.3542566}, journal = {IEEE Access}, pages = {31335-31350}, shortjournal = {IEEE Access}, title = {Optimizing pre-trained code embeddings with triplet loss for code smell detection}, volume = {13}, year = {2025}, }
• (2025). Self-supervised electrocardiograph de-noising. ACCESS, 13, 31326-31334. (www)

  @article{ , abstract = {The electrocardiogram (ECG) records heartbeats and is potentially life-saving. However, the ECG signals (e.g., recorded by the standard ECG monitoring system or the Holter ECG monitoring system) heavily suffered from the noises. Thus, the recorded signals involve meaningful cardiac deflections, other biological waves (e.g., caused by the muscle or electrode movements), and even some noises from the monitoring devices (e.g., the power cables). These noise sources would result in inaccurate analyses of cardiac diseases, thus requiring the ECG de-noising methods in data pre-processing phase before the diagnoses. Previous work provided various ECG de-noising approaches, typically based on some filter algorithms or some wave decomposition algorithms. Most of these approaches did not profoundly consider the ECG signals’ specific data structure, and were not adaptive to the signals recorded by various devices or different skin electrodes. Inspired by the ECG recording theory, we find it available to extract noise information from noisy ECG signals directly. We propose a new ECG de-noising method implemented by the neural network, which de-noise the ECG signals without the supervision of the clean signals. The procedure in the self-supervision is straightforward: we first estimate and simulate the noise signals according to the given noisy ECG signals, and then “subtract” the simulated noises to obtain the de-noised ECG signals by the neural network. Experiments on a public dataset verify that our approach is adaptive to ECG signals from different patients and devices. Also, it is proven that the classification on the ECG signals de-noised by the proposed de-noising methods outperforms those with the traditional de-noising methods.}, archive = {J_ACCESS}, author = {Xiaoqiang Liu and Yisen Huang and Yubin Wang and Chanchan Lin and Yingxuan Huang and Xiaobo Liu}, doi = {10.1109/ACCESS.2025.3539094}, journal = {IEEE Access}, pages = {31326-31334}, shortjournal = {IEEE Access}, title = {Self-supervised electrocardiograph de-noising}, volume = {13}, year = {2025}, }
• (2025). Dynamic ReLab: A binary path-based labeling scheme for dynamic XML data. ACCESS, 13, 31318-31325. (www)

  @article{ , abstract = {Dynamic updates in XML data present significant challenges for maintaining efficient query performance, particularly in large-scale and dynamic environments. Existing labeling schemes, such as ReLab, Dietz encoding, and region numbering, fail to address these challenges effectively due to their reliance on re-labeling entire subtrees during updates, leading to significant computational and memory overhead. These limitations hinder their applicability in dynamic scenarios where frequent updates are required. This study introduces Dynamic ReLab, a novel binary path-based labeling scheme explicitly designed to overcome the inefficiencies of traditional approaches in handling dynamic XML data. By integrating binary path encoding with subtree-based labeling, Dynamic ReLab enables efficient label generation and maintenance, ensuring the quick determination of structural relationships, such as ancestor-descendant and parent-child, without extensive re-labeling. The proposed scheme is particularly advantageous in scenarios where XML data undergo frequent updates, as it significantly reduces the time and memory required for label maintenance, thereby improving overall performance. Experimental results on real-world XML datasets demonstrate that although Dynamic ReLab incurs higher overhead during initial label generation, it substantially outperforms traditional schemes in update processing efficiency. This improvement is achieved through innovative techniques, such as hierarchical bit-masking and binary path concatenation, which streamline the update process and ensure the integrity of the XML structure. These results highlight Dynamic ReLab’s relevance for modern applications requiring real-time adaptability and high-performance query processing in dynamic XML data environments.}, archive = {J_ACCESS}, author = {Yanche Ari Kustiawan and Khairil Imran Ghauth}, doi = {10.1109/ACCESS.2025.3541398}, journal = {IEEE Access}, pages = {31318-31325}, shortjournal = {IEEE Access}, title = {Dynamic ReLab: A binary path-based labeling scheme for dynamic XML data}, volume = {13}, year = {2025}, }
• (2025). Waveshift augmentation: A physics-driven strategy in fine-grained plant disease classification. ACCESS, 13, 31303-31317. (www)

  @article{ , abstract = {Recent advancements in computer vision and machine learning enable automatic and precise plant disease diagnosis. However, the necessity for meticulous data segmentation by origin and the demand for extensive data collection lead to performance degradation in even state-of-the-art models. These issues arise because symptom-identifying features are often weak and diverse, and domain differences can overshadow these distinctions. To address these challenges, this study introduces Waveshift augmentation, a novel online augmentation technique inspired by the natural phenomenon of light propagation. The method aims to enhance model generalization by simulating light dynamics to improve adaptability to varying image conditions. Our methodology involves applying Waveshift augmentation to a large-scale fine-grained private plant dataset (tomato, strawberry, cucumber, and eggplant) and several public datasets, complemented by geometric transformations and contemporary techniques such as CLAHE, AugMix, and RandAugment. The results demonstrate that Waveshift augmentation achieves significant improvements in diagnostic performance, with notable macro F1 score gains across different backbone architectures including EfficientNetV2 (1.37 points), Swin Transformer (1.35 points), and ConvNeXt (0.80 points). Moreover, our method maintains competitive computational efficiency and supports real-time applications. This study highlights the effectiveness of Waveshift augmentation in overcoming generalization challenges and opens avenues for its application in various image-processing disciplines. Future work will focus on refining the hyperparameter optimization and exploring the impact of varying image shapes and asymmetric propagators.}, archive = {J_ACCESS}, author = {Gent Imeraj and Hitoshi Iyatomi}, doi = {10.1109/ACCESS.2025.3541780}, journal = {IEEE Access}, pages = {31303-31317}, shortjournal = {IEEE Access}, title = {Waveshift augmentation: A physics-driven strategy in fine-grained plant disease classification}, volume = {13}, year = {2025}, }
• (2025). ANFIS parallelization control on triple-axis sun tracker to minimize solar rays incidence angle on photovoltaic. ACCESS, 13, 31286-31302. (www)

  @article{ , abstract = {This study proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) based on model with Parallelization Control Technique (PCT) implemented in a triple-axis sun tracker system. The system aims to maximize solar energy absorption by minimizing the angle of incidence of sunlight on Photovoltaic (PV) panels. It is designed with three manipulator arms, each controlled in parallel by a distinct ANFIS model. The primary goal is to adjust the PV panel orientation to optimal azimuth and altitude angles. The tetrahedron sensor is employed to detect the direction of sunlight. This study tests various types of consequent rules to find the optimal configuration for each ANFIS model, including first-order, second-order, and third-order polynomial functions. The analysis results show that the second-order consequent rule provides the highest accuracy across the three ANFIS models. The proposed system can cover azimuth range from 0° to 360° and altitude angle from 60° to 90°. The experiment also demonstrated that the system consistently moves toward a stable condition, even in the presence of minor disturbances. These findings indicate that ANFIS with PCT can minimize the angle of sunlight incidence, enhance system flexibility, and accelerate response time, making it a viable solution for increasing energy harvesting by PV panels.}, archive = {J_ACCESS}, author = {Hari Anna Lastya and Yuwaldi Away and Tarmizi and Ira Devi Sara and Roslidar and Andri Novandri}, doi = {10.1109/ACCESS.2025.3542337}, journal = {IEEE Access}, pages = {31286-31302}, shortjournal = {IEEE Access}, title = {ANFIS parallelization control on triple-axis sun tracker to minimize solar rays incidence angle on photovoltaic}, volume = {13}, year = {2025}, }
• (2025). Automatic seed word selection for topic modeling. ACCESS, 13, 31269-31285. (www)

  @article{ , abstract = {Topic modeling is widely used to uncover latent semantic topics from a corpus. However, topic models often struggle to identify minor topics due to their tendency to prioritize dominant patterns in the data. They are also hindered by polysemous words and general terms, which frequently appear in multiple contexts, making topic assignment difficult. Seed-guided topic modeling addresses these issues by incorporating prior knowledge through “seed words”. Existing approaches, however, primarily rely on supervised selection using label-dependent metrics or manual selection. Both are limited by scalability and susceptible to human bias, particularly when dealing with unstructured real-world data. As a result, the selection of seed words in unsupervised settings remains underexplored. To address these challenges, we propose an automated seed word selection process that identifies diverse and cohesive word sets based on inter-word relationships. We instantiate this process with $\textsf {SeedCapture}$ , an algorithm that utilizes co-occurrence to capture meaningful word associations. Unlike prior methods, $\textsf {SeedCapture}$ operates in a fully unsupervised manner, requiring no predefined labels or human intervention. $\textsf {SeedCapture}$ requires minimal parameter tuning and is highly adaptable, enabling seamless integration into existing seed-guided topic models. Through extensive quantitative and qualitative evaluations across multiple datasets and topic models, we demonstrate that $\textsf {SeedCapture}$ achieves results comparable to those obtained through supervised seed word selection.}, archive = {J_ACCESS}, author = {Dahyun Jeong and Jeongin Hwang and Yunjin Choi and Yoon-Yeong Kim}, doi = {10.1109/ACCESS.2025.3540410}, journal = {IEEE Access}, pages = {31269-31285}, shortjournal = {IEEE Access}, title = {Automatic seed word selection for topic modeling}, volume = {13}, year = {2025}, }
• (2025). WideConvNet: A novel wide convolutional neural network applied to ERP-based classification of alcoholics. ACCESS, 13, 31257-31268. (www)

  @article{ , abstract = {This paper presents WideConvNet, a novel convolutional neural network (ConvNet or CNN) that outperforms three established end-to-end models – EEGNet, Shallow Convolutional Neural Network (ShallowConvNet), and Deep Convolutional Neural Network (DeepConvNet) – in classifying event-related potentials (ERPs) from electroencephalogram (EEG) signals. At the core of WideConvNet is an adapted inception module for 1D data, utilizing varying size 1D filters inspired by the wavelet scattering transform, which are learned during the training phase to capture temporal patterns across different scales within the EEG signals. The performance of WideConvNet was evaluated on multichannel EEG data from 20 male subjects with use disorder (AUD) and 20 healthy male controls, each providing 16 1.0-s ERPs. Using twice repeated 10-fold leave-subjects-out stratified cross-validation, WideConvNet largely outperformed both EEGNet and ShallowConvNet in terms of area under the curve of the receiver operating characteristic (AUC-ROC, $p=0.00003, 0.0001$ ). Whereas, in terms of accuracy it largely outperformed EEGNet and moderately outperformed ShallowConvNet ( $p=0.00029, 0.00024$ ). Despite comparable performances to DeepConvNet, WideConvNet required 28% fewer parameters and was more interpretable. The Shapley additive explanation (SHAP) values revealed parietal and occipital regions as the key contributors to both WideConvNet and DeepConvNet predictions. The findings suggest that WideConvNet, specifically on short-duration EEG signals, offers a balanced solution, combining robust classification performance with improved computational efficiency, making it suitable for clinical diagnosis and real-time brain-computer interface (BCI) applications.}, archive = {J_ACCESS}, author = {Abdul Baseer Buriro and Attaullah Buriro and Aziz Altaf Khuwaja and Abdul Aziz Memon and Nabeel Siddiqui and Stephen J. Weddell and Richard D. Jones}, doi = {10.1109/ACCESS.2025.3541985}, journal = {IEEE Access}, pages = {31257-31268}, shortjournal = {IEEE Access}, title = {WideConvNet: A novel wide convolutional neural network applied to ERP-based classification of alcoholics}, volume = {13}, year = {2025}, }
• (2025). Real-time transient stability prediction in power systems using ensemble learning and dynamic state estimation. ACCESS, 13, 31239-31256. (www)

  @article{ , abstract = {This work presents a novel approach for real-time transient stability prediction in power systems, combining synchrophasor data with model-driven dynamic state estimations. The proposed method integrates dynamic state information, obtained through the Square Root Unscented Kalman Filter (SR-UKF), with an ensemble of machine learning (ML) algorithms in a Voting Classifier framework. The ensemble leverages the strengths of Multilayer Perceptron, Random Forest, Support Vector Machine, Convolutional Neural Network, and LightGBM to enhance predictive accuracy. By combining Phasor Measurement Unit (PMU) measurements with dynamic state estimations, the framework provides a more comprehensive view of transient stability, leading to improved prediction performance. Evaluated using a dataset from the NPCC 48-machine 140-bus system, the proposed method demonstrated accuracy gains ranging from 0.06% to 8.30%, with peak performance reaching 99.96% when dynamic states were included. Furthermore, the method exhibited strong generalization across varying system topologies and renewable penetration levels, maintaining high accuracy even under line outage conditions. This approach not only enhances prediction accuracy but also proves robust in challenging scenarios, such as noisy environments and short post-fault observation windows, making it well-suited for real-time applications in modern power systems.}, archive = {J_ACCESS}, author = {Kemal Aygul and Anurag K. Srivastava and Istemihan Genc}, doi = {10.1109/ACCESS.2025.3542535}, journal = {IEEE Access}, pages = {31239-31256}, shortjournal = {IEEE Access}, title = {Real-time transient stability prediction in power systems using ensemble learning and dynamic state estimation}, volume = {13}, year = {2025}, }
• (2025). Improving face presentation attack detection through deformable convolution and transfer learning. ACCESS, 13, 31228-31238. (www)

  @article{ , abstract = {Face presentation attack detection (PAD) is essential for ensuring the security and reliability of face recognition systems by preventing unauthorized access through spoofing attempts. Attackers can exploit various methods, such as printed photos, video replays, paper masks, 3D masks, or makeup, to imitate a legitimate user’s biometric traits. In this paper, we propose an enhanced face PAD solution that leverages the deformable convolutional layer within the MobileNetV2 architecture to improve detection accuracy. By replacing the standard convolution layer with a Deformable ConvNets V2, the proposed model adapts dynamically to spatial distortions, capturing more detailed and robust features for effective face PAD. Extensive experiments on the Replay-Attack, Replay-Mobile, ROSE-Youtu, OULU-NPU, and SiW-Mv2 datasets validate the superiority of the proposed approach. The method achieves a half total error rate (HTER) of 0.0% on both the Replay-Attack and Replay-Mobile datasets, 1.26% on ROSE-Youtu, 4.88% on SiW-Mv2, and an ACER of 0.208% on OULU-NPU, outperforming several existing methods. These results highlight the robustness and effectiveness of our approach in safeguarding face recognition systems against presentation attacks.}, archive = {J_ACCESS}, author = {Shakeel Muhammad Ibrahim and Muhammad Sohail Ibrahim and Shujaat Khan and Young-Woong Ko and Jeong-Gun Lee}, doi = {10.1109/ACCESS.2025.3541546}, journal = {IEEE Access}, pages = {31228-31238}, shortjournal = {IEEE Access}, title = {Improving face presentation attack detection through deformable convolution and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Two-stage residual projection network for image bit-depth enhancement. ACCESS, 13, 31215-31227. (www)

  @article{ , abstract = {Low bit-depth (LBD) images produce stubborn false contour artifacts and make detailed information disappear, making bit-depth enhancement (BDE) a challenging task. Considering the mixture of structural distortions and real edges in LBD images, multi-scale features are crucial for the BDE tasks. However, existing CNN-based methods suffer from structural bottlenecks, which make it difficult to capture sufficient LBD features in a single-stage network. To overcome this issue, this paper proposes a two-stage residual projection network (TRPN) to explore the multi-scale features of BDE. An encoder-decoder structure based on alternating up and down sampling is proposed to learn wide context information in stage 1. In stage 2, a residual projection module based on dense connection is proposed to preserve the detailed texture as much as possible and avoid over-smoothing in non-flat regions, which is caused by alternating up and down sampling. To efficiently utilize multi-scale features, we introduce a supervised attention module that improves network ability by dynamically adjusting the attention weights within the model. Finally, extensive experiments demonstrate that our method achieves outstanding performance improvements both quantitatively and qualitatively, which illustrates its effectiveness.}, archive = {J_ACCESS}, author = {Zepeng Liu and Yizong Wang and Yayuan Sang and Chao Liu and Jiya Tian}, doi = {10.1109/ACCESS.2025.3536302}, journal = {IEEE Access}, pages = {31215-31227}, shortjournal = {IEEE Access}, title = {Two-stage residual projection network for image bit-depth enhancement}, volume = {13}, year = {2025}, }
• (2025). Multi-robot collaborative exploration on communication-constrained environments. ACCESS, 13, 31205-31214. (www)

  @article{ , abstract = {Multi-robot cooperative exploration systems can gather environmental data in harsh and unknown scenarios, reducing the risk of human exposure and enhancing exploration capabilities. However, some communication-constrained (e.g., post-disaster) scenarios hinder the ability of robots to exchange map information. To resolve this issue, existing approaches utilize control strategies or deep reinforcement learning to enhance the map exchange process and the associated decision-making algorithm. However, the performance of these methods is limited in such scenarios due to the following challenges: 1) the extensive exchange of redundant information increases the burden on the robots’ communication bandwidth; 2) exploration capabilities are limited when map information transmission is blocked. To overcome these challenges, we propose a novel framework entitled CMICE (Communication-constrained Multi-Robot Intelligent Collaborative Exploration). Specifically, the framework contains the following two components: 1) a Non-Essential Non-Communication (NENC) component is introduced to reduce bandwidth requirements significantly; 2) a CNN-based Multi-agent TD3 (CMATD3) algorithm is designed to enhance exploration capabilities in the above scenarios. Experimental results demonstrate that CMICE outperforms the state-of-the-art models in both ideal and communication-constrained environments.}, archive = {J_ACCESS}, author = {Guangdong Lu and Hantao Ding and Jian Hou and Shangfei Zheng}, doi = {10.1109/ACCESS.2025.3539518}, journal = {IEEE Access}, pages = {31205-31214}, shortjournal = {IEEE Access}, title = {Multi-robot collaborative exploration on communication-constrained environments}, volume = {13}, year = {2025}, }
• (2025). Ensemble network graph-based classification for botnet detection using adaptive weighting and feature extraction. ACCESS, 13, 31183-31204. (www)

  @article{ , abstract = {The number of cybersecurity threats increases every year due to the rapid improvement of methods and tools used by hackers to infect devices. These threats form a network, which is called a botnet, to send and receive commands. Botnets can launch malicious attacks using malware to infect targets in the network and then control them to do illegal things. Previous research has demonstrated that security systems can identify attacks by analyzing communication among bots in a network using a graphing approach. While this analytical method demonstrates satisfactory accuracy, it still faces challenges related to low recall, precision, and F1-score, due to issues such as imbalanced data and the complexity of botnet behavior. This research addresses these challenges by analyzing network flow using adaptive weighting and feature extraction. Network flows are represented in a graph with IP addresses as vertices and communication links between IP addresses as edges. Since botnet attack activity forms a relatively small percentage compared with millions of recorded network flow data, the data is grouped using time gap analysis to handle the imbalance problem. Furthermore, network flows are represented in two graphs, and each edge is weighted based on the 16 types of weighting. The graph representation and weighting output are stored in out-degree and in-degree graph metadata for classification. The analysis is carried out in an ensemble manner with weighting and threshold values to determine whether an IP address is a botnet or a normal host. The experimental results obtained using CTU-13, NCC, and NCC-2 datasets produce reliable performance with an average accuracy of 99.99%, along with 80.91% precision, 93.10% recall, 82.15% f1-score and 39.55 second execution time. The proposed model can function as an effective tool for the forensic analysis of botnet attacks, allowing network administrators to analyze the characteristics of botnet activities and anticipate potential future threats.}, archive = {J_ACCESS}, author = {Muhammad Aidiel Rachman Putra and Tohari Ahmad and Dandy Pramana Hostiadi and Royyana Muslim Ijtihadie}, doi = {10.1109/ACCESS.2025.3541125}, journal = {IEEE Access}, pages = {31183-31204}, shortjournal = {IEEE Access}, title = {Ensemble network graph-based classification for botnet detection using adaptive weighting and feature extraction}, volume = {13}, year = {2025}, }
• (2025). On calibration of prompt learning using temperature scaling. ACCESS, 13, 31171-31182. (www)

  @article{ , abstract = {Prompt learning has emerged as a valuable technique for enhancing vision-language models (VLMs) for downstream tasks in specific domains, resulting in high performance on such tasks. However, existing prompt learning methods place too much focus on increasing complexity, overlooking the natural issue of learning with limited data, which leads to overfitting. In this study, we address this issue by applying temperature scaling (TS) during training to improve confidence calibration. By sharpening predictions, TS reduces overfitting and enhances generalization across diverse datasets. Extensive experiments demonstrate that our approach improves the calibration of CLIP-based methods without compromising accuracy. Our method is model-agnostic and applicable to broader tasks, offering a simple yet effective solution for zero-shot and few-shot learning.}, archive = {J_ACCESS}, author = {Khanh-Binh Nguyen and Chae Jung Park}, doi = {10.1109/ACCESS.2025.3538617}, journal = {IEEE Access}, pages = {31171-31182}, shortjournal = {IEEE Access}, title = {On calibration of prompt learning using temperature scaling}, volume = {13}, year = {2025}, }
• (2025). Investigation of magnetic gear system based on the use of superconductor and permanent magnet. ACCESS, 13, 31159-31170. (www)

  @article{ , abstract = {Magnetic gears claim to represent the latest technology in power transmission, offering many advantages over mechanical gear systems. Combined with superconductivity, these innovative structures have the potential to provide efficiency, low energy loss and good performance for industrial applications. This article examines the principles and applications of magnetic gears associated with the superconductivity. Magnetic gear works on the principle of utilizing magnetic fields between rotating elements to transmit mechanical force. Unlike traditional gear systems that rely on physical contact between gears, magnetic gears transmit torque through the interaction of magnetic forces. With the presence of superconductivity, stronger magnetic fields and lower energy losses can be achieved in magnetic gears. In this context, a superconducting magnetic gear system was examined for various configurations based on the finite element method using Comsol Multiphysics software. First of all, a standard magnetic gear was presented to establish benchmarking for the use of bulk superconductor in light of the experimental and theoretical analysis. Accordingly, the superconductor was incorporated into the stator layer to observe the Meissner effect in the gear mechanism. In addition, the superconducting model in the rotor layer of the magnetic gear was also introduced based on the magnetized bulk superconductor approach. Both results associated with the use of superconductor in the rotors and stator indicate that quantitative improvement can be achieved for the torque transfer performance in comparison with standard magnetic gear.}, archive = {J_ACCESS}, author = {Emre Akyerden and Ahmet Cansiz}, doi = {10.1109/ACCESS.2025.3541736}, journal = {IEEE Access}, pages = {31159-31170}, shortjournal = {IEEE Access}, title = {Investigation of magnetic gear system based on the use of superconductor and permanent magnet}, volume = {13}, year = {2025}, }
• (2025). Machine learning techniques for enhanced intrusion detection in IoT security. ACCESS, 13, 31140-31158. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDSs) are fundamental to safeguarding computer networks. Intrusion detection systems must become more effective as new attacks are developed and networks grow. Anomaly-based automated detection stands out due to its superior performance among the various detection techniques. However, with the increasing complexity and frequency of cyberattacks, managing vast amounts of data remains challenging for anomaly-based NIDS. Therefore, it is necessary to find an efficient method for solving the problem by using classification with an intrusion detection system which analyzes enormous amounts of traffic data. This research introduces a new model that leverages machine learning (ML) and deep learning (DL) to enhance detection effectiveness and ensure reliability. The approach optimizes data preprocessing by integrating SMOTE for effective data balancing and Pearson’s Correlation Coefficient (PCC) for feature selection. We compared several ML and DL techniques to detect and address the most efficient one for our pipeline. Compared with other approaches, LSTM and RF show superior results when tested on the WSN-DS, UNSW-NB15, and CIC-IDS 2017 datasets. Additionally, the proposed solution prevents biases from arising by addressing imbalanced datasets.}, archive = {J_ACCESS}, author = {Hanadi Hakami and Muhammad Faheem and Majid Bashir Ahmad}, doi = {10.1109/ACCESS.2025.3542227}, journal = {IEEE Access}, pages = {31140-31158}, shortjournal = {IEEE Access}, title = {Machine learning techniques for enhanced intrusion detection in IoT security}, volume = {13}, year = {2025}, }
• (2025). Leveraging cloud resources for distributed training of residual CNNs in aerial image classification. ACCESS, 13, 31131-31139. (www)

  @article{ , abstract = {Aerial image classification is crucial across multiple sectors, including environmental monitoring, agriculture, and urban planning. However, processing large-scale aerial imagery efficiently poses challenges in model performance, computational efficiency, and scalability. This research introduces a novel convolutional neural network (CNN) architecture tailored for cactus identification from aerial photographs. The proposed cloud-based pipeline enhances training efficiency through scalable data storage, preprocessing, and distributed training across platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This comparative analysis demonstrates the model’s computational efficiency, shorter training durations, and cost-effectiveness. The model integrates residual connections and depthwise separable convolutions, achieving 96.7% accuracy on the aerial cactus identification dataset. The results highlight the model’s high performance, cost-efficiency, and scalability, making it suitable for real-world aerial image classification tasks. Future work aims to further optimize the model using advanced techniques and extend its application to multi-class classification challenges.}, archive = {J_ACCESS}, author = {Shantanu Kumar and Shruti Singh and Narendra Kumar Dewangan}, doi = {10.1109/ACCESS.2025.3542093}, journal = {IEEE Access}, pages = {31131-31139}, shortjournal = {IEEE Access}, title = {Leveraging cloud resources for distributed training of residual CNNs in aerial image classification}, volume = {13}, year = {2025}, }
• (2025). Forest fuel load estimation using combined sentinel-1 and sentinel-2 data: A case study of the greater khingan mountains in china. ACCESS, 13, 31121-31130. (www)

  @article{ , abstract = {Accurate estimation of forest fuel load (FFL) is essential for effective forest management and fire risk assessment. This study presents an approach for estimating FFL in the Greater Khingan Mountains by integrating Sentinel-1 SAR and Sentinel-2 optical data. The combined data provided complementary structural and spectral information, allowing for a more comprehensive analysis of forest characteristics. We evaluated multiple models, including simple linear regression (SLR), multiple linear regression (MLR), Random Forest (RF), XGBoost, and multilayer perceptron (MLP). Results showed that ensemble models, particularly XGBoost and RF, achieved the highest accuracy, with XGBoost attaining the lowest MAE (18.43 t/hm2), lowest RMSE (25.50 t/hm2), and highest R2 (0.86) when using combined data sources. Sentinel-2 data consistently outperformed single-source models, highlighting the value of multi-source data fusion in forest fuel estimation. This methodology is scalable to other regions, offering a robust approach for forest fuel load assessment and enhancing fire risk management. However, the transferability of our approach to different ecological settings requires further consideration. Regional variations in vegetation types, climate conditions, and topography may significantly influence the model’s performance. Additionally, seasonal changes in fuel load distribution could affect prediction accuracy. To address these scalability challenges, future work should explore domain adaptation techniques and model fine-tuning strategies to enhance the generalizability of the proposed method across diverse geographic regions.}, archive = {J_ACCESS}, author = {Pei Zhou and Lizhi Liu and Mei Zhou}, doi = {10.1109/ACCESS.2025.3542128}, journal = {IEEE Access}, pages = {31121-31130}, shortjournal = {IEEE Access}, title = {Forest fuel load estimation using combined sentinel-1 and sentinel-2 data: A case study of the greater khingan mountains in china}, volume = {13}, year = {2025}, }
• (2025). Innovative defense: Deep learning-powered intrusion detection for IoT networks. ACCESS, 13, 31105-31120. (www)

  @article{ , abstract = {Security is considered as one of the primary characteristics in technical setup, as rise of IoT devices can be prone to various challenges, thus is extremely important to detect the attacks in order to prevent horrendous consequences, hence IDS (Intrusion Detection System) is utilized. Typically, different tactics are used for identification of attacks, however, manual approaches are considered to be time consuming and prone to error. Thus, AI based approaches are utilized for effective detection of attacks, however, prevailing works often lack in terms of accuracy and primarily focuses on binary classification of attacks. Hence, in order to overcome these pitfalls proposed framework utilizes proposed UN (Unify Net) and proposed NSSN (Neutro Sequential Sense Net) functions. Proposed UN model enhances the spatial features learning the property of LSTM model which amplifies the efficacy of the classifiers for intrusion detection. After employing proposed UN, proposed NSSN is primarily used for handling the issues associated to ambiguity information in intrusion data by extracting various deep features and helps in improving the performance and precision of model for multiclass classification and aids in preventing overfitting of the model. Dataset used in the model is IoT device network logs dataset, where five attacks and non-attack is specified. Eventually, the performance of the proposed mechanism is gauged by means of evaluation metrics. Besides, comparative analysis is carried out to compare the performance of the model and its ability to identify multiple attacks present in IoT network.}, archive = {J_ACCESS}, author = {Adel Binbusayyis}, doi = {10.1109/ACCESS.2025.3542275}, journal = {IEEE Access}, pages = {31105-31120}, shortjournal = {IEEE Access}, title = {Innovative defense: Deep learning-powered intrusion detection for IoT networks}, volume = {13}, year = {2025}, }
• (2025). DSNET: A lightweight segmentation model for segmentation of skin cancer lesion regions. ACCESS, 13, 31095-31104. (www)

  @article{ , abstract = {Currently, most skin disease segmentation tasks tend to use large models to achieve better segmentation performance. However, in real medical application scenarios, due to the limited computing and storage resources of hardware devices, it is difficult to deploy large models in business systems for applications. To address this problem, we propose a lightweight segmentation architecture that concentrates more on details. This model achieves optimal segmentation performance while maintaining low model parameters and computational complexity. To reduce the model size and guarantee model segmentation performance, we proposed a detail-enhanced separable difference convolution as a base module in the model. Second, we designed a dynamic gate attention module as a bridge between the encoding and decoding layers to further improve the expressiveness of flat texture information. Furthermore, we propose a boundary-interior correction loss function that focuses on both the boundary and the interior of the region, which effectively solves the dermatosis segmentation task for problems such as boundary blurring and interior missing. We conducted extensive experiments on two publicly available skin lesion segmentation datasets (ISIC2017 and ISIC2018). Our method achieved state-of-the-art results compared to previous studies. DSNET has only 0.291M parameters and 0.287 GFLops, and it achieves 81.30% mIoU and 89.81% DSC metrics, respectively. DSNET has two main features: lightweight and high efficiency, which not only highlights dermatological detail feature extraction but also supports deployment on medical mobile devices.}, archive = {J_ACCESS}, author = {Yucong Chen and Guang Yang and Xiaohua Dong and Junying Zeng and Chuanbo Qin}, doi = {10.1109/ACCESS.2025.3539521}, journal = {IEEE Access}, pages = {31095-31104}, shortjournal = {IEEE Access}, title = {DSNET: A lightweight segmentation model for segmentation of skin cancer lesion regions}, volume = {13}, year = {2025}, }
• (2025). A review on security challenges in V2X communications technology for VANETs. ACCESS, 13, 31069-31094. (www)

  @article{ , abstract = {The Vehicular Ad-hoc Network (VANET) is emerging as a new networking/communication model that offers remarkable approaches for controlling and managing vehicular traffic. In this scenario, Virtual traffic light (VTL) techniques, advanced navigation support, and fleet management facilities attempt to handle traffic management and control concerns by leveraging vehicular network communication paradigms. The most important communication models in vehicular networks are characterized as vehicle-to-everything (V2X) communications technology. In V2X communications, security issues, and related countermeasures are crucial, as they are in other wireless technologies. Our survey provides a systematic overview of various research directions by highlighting and investigating the most critical V2X security issues and related countermeasures. First, we discuss the points mentioned in the previous papers, including the architecture, main characteristics, and communication models of the V2X networks. Furthermore, some of the most significant applications of these mission-critical networks reported in the published papers and the main security challenges and requirements will be highlighted. We also arrange corresponding (existing) resources, to investigate different solutions for securing V2X communications. Finally, a few suggestions for future research related to V2X security challenges are presented. In general, this article reviews and summarizes the studies of the last ten years. This survey will help make it easier for scholars studying V2X communication security and related fields to keep track of the academic frontier, by offering a sufficiently clear landscape and a roadmap.}, archive = {J_ACCESS}, author = {Eslam Farsimadan and Leila Moradi and Francesco Palmieri}, doi = {10.1109/ACCESS.2025.3541035}, journal = {IEEE Access}, pages = {31069-31094}, shortjournal = {IEEE Access}, title = {A review on security challenges in V2X communications technology for VANETs}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable and scalable artificial intelligence acceleration hardware architecture with RISC-V CNN coprocessor for real-time seizure detection. ACCESS, 13, 31057-31068. (www)

  @article{ , abstract = {Epilepsy is a neurological disorder characterized by recurrent seizures. These seizures are caused by abnormal electrical activity in the brain. Seizures are often accompanied by involuntary partial or whole-body convulsions, frothing at the mouth, and possible loss of consciousness, putting a patient at high risk. Electroencephalogram (EEG) can be used to diagnose epilepsy. This study proposes a seizure detection algorithm to identify a seizure attack with EEG. This algorithm includes a simplified signal preprocessor and a nearly optimized convolutional neural network (CNN). This study also proposes an artificial intelligence acceleration (AIA) hardware architecture, including a deep learning accelerator (DLA) and a two-stage reduced instruction set computer-V (RISC-V) central control unit (CPU), to implement the detection algorithm in real-time operation. The accelerator is implemented in System-Verilog and validated on the Xilinx PYNQ-Z2 Field Programmable Gate Array (FPGA) board. The implementation consumes 3535 lookup tables, 2283 flip-flops, 28 KB of block random-access memory, six digital signal processors, and seven input/output (I/O). The total power consumption is 0.108 W in 1-MHz operation frequency. The detection algorithm provides 99.06% accuracy on fixed-point operations with a detection latency of 128 ms/class. The application-specific integrated circuit (ASIC) performance of the AIA hardware architecture is also tested with a 180 nm 1P6M process. The total power of the AIA is 1.29 mW. The core circuit of the RISC-V CPU and DLA consumes $80~\mu $ W and $84.5~\mu $ W, respectively. Moreover, the AIA can be reconfigurable. Thus, the accelerator can execute different deep-learning models to fit various wearable applications for biomedical acquisition systems.}, archive = {J_ACCESS}, author = {Shuenn-Yuh Lee and Ming-Yueh Ku and Sing-Yu Pan and Chou-Ching Lin}, doi = {10.1109/ACCESS.2025.3538781}, journal = {IEEE Access}, pages = {31057-31068}, shortjournal = {IEEE Access}, title = {Reconfigurable and scalable artificial intelligence acceleration hardware architecture with RISC-V CNN coprocessor for real-time seizure detection}, volume = {13}, year = {2025}, }
• (2025). A novel weighted loss TabTransformer integrating explainable AI for imbalanced credit risk datasets. ACCESS, 13, 31045-31056. (www)

  @article{ , abstract = {Credit risk assessment often faces significant challenges due to class imbalance and the opaque nature of machine learning models, which can result in biased predictions and hinder trust among stakeholders. To address these issues, this study proposes a framework combining the TabTransformer model with weighted loss techniques to balance class distributions and improve predictive accuracy. Applied to the BISAID and German Credit datasets, the method demonstrated notable improvements in accuracy, from 86.35% to 89.27% and 93% to 95%, respectively, along with improved minority class AUC and precision-recall metrics. To ensure transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed, highlighting critical predictors such as “Financing Needs” and “Credit Amount.” By integrating fairness mechanisms through weighted loss and explainability via XAI, the proposed framework and weighted loss TabTransformer mitigate bias, enhance model performance, and provide actionable insights for borrowers and stakeholders. These findings establish a reliable, equitable, and transparent approach to credit evaluation.}, archive = {J_ACCESS}, author = {Kristoko Dwi Hartomo and Christian Arthur and Yessica Nataliani}, doi = {10.1109/ACCESS.2025.3541878}, journal = {IEEE Access}, pages = {31045-31056}, shortjournal = {IEEE Access}, title = {A novel weighted loss TabTransformer integrating explainable AI for imbalanced credit risk datasets}, volume = {13}, year = {2025}, }
• (2025). LENet: A lightweight and efficient high-resolution network for human pose estimation. ACCESS, 13, 31032-31044. (www)

  @article{ , abstract = {Due to the huge requirements of performing human pose estimation tasks on edge devices with limited resources, more and more researchers have turned to work on the design of lightweight human pose estimation networks. As a typical lightweight network, Lite-HRNet achieves high performance in human pose estimation with a relatively low model complexity, but its inference speed is not ideal in practical applications. In order to address this issue, we propose a lightweight and efficient network (LENet), which is not only capable of learning comprehensive information under a lightweight architecture for real-time human pose estimation, but also has a faster inference speed than other common lightweight networks, so that the decision-making efficiency and user experience of real-time systems would be improved. We design two blocks, Recursive Fusion Block (RFB) and Deep Shuffle Block (DSB), to construct the model architecture. The RFB implements multi-scale feature fusion in a more lightweight way, it only carries out scale transformation between adjacent branches. The DSB fully utilizes computational resources to extract expressive information during the whole processing. Experimental results demonstrate that with the same complexity as of Lite-HRNet, our LENet yields inference speeds of 40.8 FPS and 150.4 FPS on CPU and GPU respectively, obtaining a great improvement of 83% over that of Lite-HRNet. Furthermore, LENet is more competitive than Lite-HRNet in terms of achieving a superior tradeoff among inference speed, overall performance and complexity of the model.}, archive = {J_ACCESS}, author = {Ming Zhang and Xiandong Yu and Wenqiang Li and Xin Shu and Lei Pan and Zhongwei Shen}, doi = {10.1109/ACCESS.2025.3538507}, journal = {IEEE Access}, pages = {31032-31044}, shortjournal = {IEEE Access}, title = {LENet: A lightweight and efficient high-resolution network for human pose estimation}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of cross M-QAM modulation over standardized RF2 5G frequency bands using the η-μ fading model. ACCESS, 13, 31024-31031. (www)

  @article{ , abstract = {This paper delves into the study of the generalized short-term fading model $\eta $ - $\mu $ . It introduces, for the first time in the literature, closed-form expressions for the Symbol Error Probability (SEP) of cross M-QAM modulation operating under the $\eta $ - $\mu $ fading channel. Furthermore, the SEP of the aforementioned modulation is evaluated across selected frequency bands within the standardized Range Frequency 2 (RF2) millimeter-wave (mm-wave) for 5G systems, specifically at 26, 28, and 39 GHz. The assessment is based on channel modeling results available in the literature, derived from an extensive measurement campaign conducted under both line-of-sight (LoS) and non-line-of-sight (nLoS) conditions within an indoor environment, and considering different Tx-Rx distances. The findings provide valuable information on the performance of cross M-QAM modulation across different modulation orders, frequencies, line-of-sight conditions, and Tx-Rx distances in realistic mm-wave 5G scenarios, enhancing our understanding of its suitability in various real deployment conditions.}, archive = {J_ACCESS}, author = {Wilian Eurípedes Vieira and Pedro Luiz Lima Bertarini and Karine Barbosa Carbonaro and Gilberto Arantes Carrijo and Edson Agustini and André Antônio Dos Anjos}, doi = {10.1109/ACCESS.2025.3540713}, journal = {IEEE Access}, pages = {31024-31031}, shortjournal = {IEEE Access}, title = {Performance evaluation of cross M-QAM modulation over standardized RF2 5G frequency bands using the η-μ fading model}, volume = {13}, year = {2025}, }
• (2025). Internet of paint (IoP): Design, challenges, applications, and future directions. ACCESS, 13, 31016-31023. (www)

  @article{ , abstract = {The proliferation of nano-technology has enabled novel applications in various fields, including the potential for miniaturized terahertz (THz)-enabled devices. The extra-large bandwidth available in the THz spectrum can facilitate high-speed communication, even for transmission through lossy media. Culminating these capabilities, this paper introduces a new paradigm: Internet of Paint (IoP), transforming the 40,000-year-old concept of paint with extensive connectivity and sensing capabilities. IoP will enable seamless, massively parallel, and high-capacity communication and sensing capabilities, enabling innovative applications. IoP is expected to offer communication and sensing using nano-devices embedded in paint. In this paper, challenges towards the realization of IoP are discussed. Simulations indicate that THz signals in this stratified medium can form lateral waves that propagate along the Air-Paint interface and that relatively high channel capacity can be achieved by optimizing the transceiver locations, the paint thickness, and color. Addressing the challenges regarding nano-devices, nano-transceivers, materials, antennas, and power for IoP holds the potential to transform communication technologies and their seamless integration with living spaces.}, archive = {J_ACCESS}, author = {Lasantha Thakshila Wedage and Mehmet Can Vuran and Bernard Butler and Christos Argyropoulos and Sasitharan Balasubramaniam}, doi = {10.1109/ACCESS.2025.3539121}, journal = {IEEE Access}, pages = {31016-31023}, shortjournal = {IEEE Access}, title = {Internet of paint (IoP): Design, challenges, applications, and future directions}, volume = {13}, year = {2025}, }
• (2025). A survey on parameter server architecture: Approaches for optimizing distributed centralized learning. ACCESS, 13, 30993-31015. (www)

  @article{ , abstract = {Deep learning has emerged as a cornerstone technology across various domains, from image classification to natural language processing. However, the computational and data demands of training large-scale neural networks pose significant challenges. Distributed learning approaches, particularly those leveraging data parallelism, have become critical to addressing these challenges. Among these, the parameter server architecture stands out as a widely adopted and scalable solution, enabling efficient training of large models across distributed systems. This survey provides a comprehensive exploration of the parameter server architecture, detailing its design principles and operation. It categorizes and critically analyzes research advancements across five key aspects: consistency control, network optimization, parameter management, straggler handling, and fault tolerance. By synthesizing insights from a wide range of studies, this work highlights the trade-offs and practical effectiveness of various approaches while identifying open challenges and future research directions. The survey aims to serve as a foundational resource for researchers and practitioners striving to enhance the performance and scalability of distributed deep learning systems.}, archive = {J_ACCESS}, author = {Nikodimos Provatas and Ioannis Konstantinou and Nectarios Koziris}, doi = {10.1109/ACCESS.2025.3535085}, journal = {IEEE Access}, pages = {30993-31015}, shortjournal = {IEEE Access}, title = {A survey on parameter server architecture: Approaches for optimizing distributed centralized learning}, volume = {13}, year = {2025}, }
• (2025). Learning-driven algorithm with dual evolution patterns for solving large-scale multiobjective optimization problems. ACCESS, 13, 30976-30992. (www)

  @article{ , abstract = {Recently, a series of evolutionary algorithms have been proposed to enhance the search efficiency when handling large-scale multiobjective optimization problems (LSMOPs). Among them, learning-driven evolutionary algorithms have shown to be promising by learning the related evolutionary pattern with a specific neural network. However, the learned evolutionary patterns primarily focus on convergence while ignoring diversity, which may potentially cause an inadequate search. In this paper, we propose a learning-driven algorithm with dual evolution patterns (DEPLA) for solving LSMOPs. The main idea is to learn two evolution patterns in the evolutionary process, one focusing on convergence and the other on diversity, which would be helpful to produce promising solutions efficiently. To be specific, DEPLA first divides the population into two parts to be updated with different strategies. A half of the solutions with good convergence and distribution will be updated with the learned evolution pattern focusing on convergence, while the rest of the solutions are repositioned by another learned evolution pattern towards diversity. Through learning the dual evolution patterns in both convergence and diversity, DEPLA is capable of efficiently tackling LSMOPs. Abundant experiments are conducted on the well-known LSMOPs test suites with up to 10,000 decision variables. The experimental results demonstrate that our DEPLA outperforms six state-of-the-art large-scale multiobjective evolutionary algorithms in handling LSMOPs.}, archive = {J_ACCESS}, author = {Mingshuo Song and Wei Song and Khin Wee Lai}, doi = {10.1109/ACCESS.2025.3541271}, journal = {IEEE Access}, pages = {30976-30992}, shortjournal = {IEEE Access}, title = {Learning-driven algorithm with dual evolution patterns for solving large-scale multiobjective optimization problems}, volume = {13}, year = {2025}, }
• (2025). Cluster-HGNN: Deep local features clustering for few-shot image classification with hybrid graph neural networks. ACCESS, 13, 30965-30975. (www)

  @article{ , abstract = {Graph neural networks (GNNs) have shown great promise in few-shot learning, where they typically represent the entire feature of a sample as a node. However, this approach can overlook finer details within the sample, as GNNs usually measure the distance between nodes to determine overall differences, making them prone to background noise interference. To alleviate this limitation, we propose a novel framework based on a hybrid graph neural network for few-shot image classification, incorporating deep local features clustering (Cluster-HGNN). This framework comprises two types of GNNs: a global feature GNN and a local feature GNN. The former applies traditional label propagation to classify query nodes, while the latter treats feature embeddings as multiple deep local features, aggregating these by analyzing the categories of each query node’s K nearest neighbours to infer the sample’s category. The proposed design incorporates local features to rectify the final classification outcome, even in scenarios where the global features may inaccurately assess the current sample. Furthermore, the framework integrates multi-scale techniques and clustering to expand the feature space and reduce the influence of background noise on classification performance. As a result, Cluster-HGNN achieves state-of-the-art results on standard few-shot image classification benchmarks.}, archive = {J_ACCESS}, author = {Hongxuan Wu and Like Xin}, doi = {10.1109/ACCESS.2025.3538610}, journal = {IEEE Access}, pages = {30965-30975}, shortjournal = {IEEE Access}, title = {Cluster-HGNN: Deep local features clustering for few-shot image classification with hybrid graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Introducing geo-glocal explainable artificial intelligence. ACCESS, 13, 30952-30964. (www)

  @article{ , abstract = {Geospatial use cases involve data with a geospatial and a temporal dimension. Machine learning is applied to such use cases for tasks such as prediction and classification. However, machine learning models are often perceived as opaque and incomprehensible. Explainable artificial intelligence can be used for either global explanations that provide an overview of the model behavior, or local explanations that explain individual predictions in detail. These approaches may be either too general or too detailed for analysis in geospatial contexts. This study introduces a novel concept ‘Geo-Glocal Explainable Artificial Intelligence’ that combines the strengths of global and local explanations to provide a more balanced explanatory power. The concept uses the geospatial and temporal dimension in the data to aggregate local explanations into geo-glocal slots, to focus on the geospatial context. These slots are scalable and can be applied according to the different dimensions in the data. The concept is applied to a real-world use case-predicting car park occupancy. The results of the application are evaluated against the traditional global and local explanations. The study shows that bridging the gap between local detail and global overview can be of great benefit to geospatial machine learning, improving understandability of the model’s behavior in a geospatial context.}, archive = {J_ACCESS}, author = {Cédric Roussel and Klaus Böhm}, doi = {10.1109/ACCESS.2025.3541781}, journal = {IEEE Access}, pages = {30952-30964}, shortjournal = {IEEE Access}, title = {Introducing geo-glocal explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Unveiling the power of large language models: A comparative study of retrieval-augmented generation, fine-tuning, and their synergistic fusion for enhanced performance. ACCESS, 13, 30936-30951. (www)

  @article{ , abstract = {Large-language model optimization for a particular application is crucial and challenging in natural language processing. This study compares two salient techniques for retrieve-augmented generation (RAG) and fine-tuning along with a new hybrid method that combines both. In this study, we investigate the effectiveness of various methods using the Stanford Question Answering Dataset (SQuAD), Microsoft Machine Reading Comprehension (MS MARCO) and SQL CREATE TABLE statements. RAG is used because it enriches the model responses with external data without much computational load during the inference. Fine-tuning updates the model parameters to improve the contextual accuracy. Our hybrid model balances the accuracy and efficiency of the two techniques. While fine-tuning entails semantic precision, RAG is more resource efficient. The hybrid approach while it may not offer surpassing results over fine-tuning-offers a balanced solution in scenarios where the application demands both efficiency and accuracy. These findings represent the trade-off involved in LLM optimization and offers a scope for further studies and practical applications.}, archive = {J_ACCESS}, author = {Gülsüm Budakoglu and Hakan Emekci}, doi = {10.1109/ACCESS.2025.3542334}, journal = {IEEE Access}, pages = {30936-30951}, shortjournal = {IEEE Access}, title = {Unveiling the power of large language models: A comparative study of retrieval-augmented generation, fine-tuning, and their synergistic fusion for enhanced performance}, volume = {13}, year = {2025}, }
• (2025). The impact of IT capabilities on organizational agility with the moderating effect of organizational learning. ACCESS, 13, 30915-30935. (www)

  @article{ , abstract = {This study aims to explore the theoretical foundations of our model regarding the relationships among IT capabilities, organizational learning, and organizational agility in SMEs in China, and to examine the direct impact of IT capabilities on agility and the moderating effect of organizational learning. Questionnaires were developed and distributed to senior and middle managers of SMEs in the Chinese manufacturing sector, resulting in 307 valid samples. The study employed structural equation modeling (SEM) using SPSS and AMOS to analyze the collected data. The results indicate a positive relationship between IT capabilities and organizational agility. Organizational learning positively moderates the relationship between IT capabilities and market agility. Additionally, organizational learning positively moderates the influence of IT knowledge and IT operation on technology agility. However, organizational learning does not significantly moderate the impact of IT infrastructure on technology agility.}, archive = {J_ACCESS}, author = {Wenxue Yi and Seung Woon Kim}, doi = {10.1109/ACCESS.2025.3542575}, journal = {IEEE Access}, pages = {30915-30935}, shortjournal = {IEEE Access}, title = {The impact of IT capabilities on organizational agility with the moderating effect of organizational learning}, volume = {13}, year = {2025}, }
• (2025). BH curve tracing method based on magnetic contact force. ACCESS, 13, 30907-30914. (www)

  @article{ , abstract = {This paper proposes a novel method for tracing the BH curve of electrical steel based on magnetic contact force. Generally, the BH curve of electrical steel is measured using methods such as the Epstein frame or the vibrating sample magnetometer (VSM). However, these conventional methods have inherent limitations. The Epstein frame struggles with accurate measurements at high magnetic fields, while the VSM is costly and difficult to miniaturize. The proposed method addresses these challenges by precisely measuring the magnetic contact force between a sensor and a core to trace the magnetic flux density and field strength within the sample. By employing a DC magnetic field, this approach eliminates errors caused by harmonics. Additionally, the system utilizes a simple closed-loop magnetic circuit without air gaps, significantly enhancing measurement accuracy. This design enables the generation of high magnetic fields with minimal energy, allowing for a cost-effective and compact implementation that differentiates it from existing methods. To validate the proposed method, a simple electromagnet model was developed, and its performance was assessed by comparing numerical calculations with experimental measurements. The results demonstrate the effectiveness and feasibility of this innovative approach for BH curve measurement.}, archive = {J_ACCESS}, author = {Chang-Hoon Seok and Jangho Seo and Yang-Hyun Kim and Gui-Hwan Kim and Hong-Soon Choi}, doi = {10.1109/ACCESS.2025.3539373}, journal = {IEEE Access}, pages = {30907-30914}, shortjournal = {IEEE Access}, title = {BH curve tracing method based on magnetic contact force}, volume = {13}, year = {2025}, }
• (2025). City-scale spatio-temporal modeling of 5G downlink exposure of users and non-users by ray-tracing in a real urban environment. ACCESS, 13, 30894-30906. (www)

  @article{ , abstract = {In 5G networks, base stations dynamically form directional beams toward users, coupling the spatial and temporal variations of electromagnetic field exposure. This interdependence introduces significant challenges to exposure modelling, as spatial and temporal components are often evaluated separately. Therefore, we propose a novel spatio-temporal method that incorporates both active users and non-users in realistic 5G exposure simulations. Pedestrian movement is modelled using an agent-based model, and ray-tracing techniques are employed to simulate electric field strengths. Unlike prior studies that focus mainly on static scenarios, or dynamic settings without accounting for precoding effects, our work integrates precoding techniques with dynamic users. In addition, this work also provides a comprehensive comparison of exposure levels for users and non-users. The proposed method is validated with increasing complexity: single-user, two-user, and multi-user scenarios ( $\mathrm {10~}~\text {to}~\mathrm {50~}$ users). In addition, different precoding techniques and antenna configurations are investigated. The results show that users experience 5.2 dB to 3.7 dB higher field strengths for $8\times 8$ antenna arrays compared to $4\times 4$ arrays, highlighting the increased directionality of larger arrays. Non-users also experience increased exposure, with median differences up to 2.4 dB. Zero-forcing precoding reduces median exposure for users by up to 9.6 dB and for non-users by 1.1 dB compared to maximum ratio transmission precoding in multi-user settings. Importantly, all exposure levels remain well below 4 % of the ICNIRP guidelines, even under maximum antenna power. These findings provide critical insights into the interaction between antenna configuration, precoding, and user dynamics, offering a novel perspective on exposure modelling in realistic 5G environments.}, archive = {J_ACCESS}, author = {Matthias Leeman and Robin Wydaeghe and Jeroen van der Straeten and Samuel Goegebeur and Günter Vermeeren and Wout Joseph}, doi = {10.1109/ACCESS.2025.3541352}, journal = {IEEE Access}, pages = {30894-30906}, shortjournal = {IEEE Access}, title = {City-scale spatio-temporal modeling of 5G downlink exposure of users and non-users by ray-tracing in a real urban environment}, volume = {13}, year = {2025}, }
• (2025). The optimal scheduling of integrated energy system considering the incentive and punishment mechanism of electric and thermal carbon emission factors. ACCESS, 13, 30874-30893. (www)

  @article{ , abstract = {In the context of global industrialization, integrated energy systems (IES) have emerged as a crucial means of facilitating green and low-carbon transformation. To comprehensively explore the potential for carbon reduction through source-load synergy and achieve low-carbon and high-efficiency operation of the system, an IES optimal scheduling method that incorporates a reward and punishment mechanism for dynamic electricity and thermal carbon emission factors is proposed. Firstly, the concepts of energy coupling and thermal energy cascade utilization are considered, and an IES-coupled equipment cascade utilization model with carbon capture is established with the objective of realizing high-grade use of thermal energy in combination with thermal load demand. Secondly, an improved IES dynamic electricity and thermal carbon emission factors model is proposed to characterize the carbon emission intensity and renewable energy consumption capacity. Based on this, an integrated demand response (IDR) mechanism driven by electricity, thermal reward, and carbon price is established to guide users toward low-carbon behaviors and renewable energy consumption. Finally, a two-stage optimal scheduling model is constructed. In the pre-scheduling phase, the initial scheduling plan is solved, and the load distribution is adjusted by the integrated demand response. In the re-scheduling phase, the load curve is updated to obtain the final scheduling plan. Through simulation verification, the proposed scheduling model reduces carbon emissions by 7.7 %, improves the renewable energy consumption rate by 4 %, and reduces the comprehensive operating cost of the system by 4.3 %, which indicates that the method proposed in this paper can effectively promote the emission reduction and consumption and reduce the operating cost of the system.}, archive = {J_ACCESS}, author = {Kaiyan Wang and Hengxiang He and Ningning Yang and Xiaowei Wang and Rong Jia}, doi = {10.1109/ACCESS.2025.3542018}, journal = {IEEE Access}, pages = {30874-30893}, shortjournal = {IEEE Access}, title = {The optimal scheduling of integrated energy system considering the incentive and punishment mechanism of electric and thermal carbon emission factors}, volume = {13}, year = {2025}, }
• (2025). Personalized learning through MBTI prediction: A deep learning approach integrated with learner profile ontology. ACCESS, 13, 30861-30873. (www)

  @article{ , abstract = {Personalized e-learning systems have transformed the delivery of educational content, aligning it more closely with individual learners’ needs. Among the various approaches to achieving this personalization, the Myers-Briggs Type Indicator (MBTI) personality prediction emerged as a valuable tool for adapting learning content to match individual preferences and learning styles. Despite these advancements, personalized e-learning systems quite struggle with handling large volumes of information, often facing difficulties in balancing prediction accuracy and learning efficiency. To ensure personalization, integrating advanced technologies such as machine learning and deep learning is crucial for creating more sustainable and effective learning environments. This paper proposes a novel framework that integrates MBTI personality prediction with a deep learning approach and a Learner Profile ontology (LPO) to enhance the personalization and recommendation process. The proposed method employs the BERT model, a Transformer-based architecture, for personality prediction, combined with an oversampling technique to handle data imbalance. The predicted personality type is incorporated into a Semantic Web Rule Language (SWRL)-based ontology enriched with WordNet, enabling better alignment of learning resources with individual learner traits. Experimental results validate the efficacy of this integrated approach, demonstrating significant improvements in learner satisfaction and educational outcomes.}, archive = {J_ACCESS}, author = {Samia Bousalem and Fouzia Benchikha and Naila Marir}, doi = {10.1109/ACCESS.2025.3542701}, journal = {IEEE Access}, pages = {30861-30873}, shortjournal = {IEEE Access}, title = {Personalized learning through MBTI prediction: A deep learning approach integrated with learner profile ontology}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence for control in laser-based additive manufacturing: A systematic review. ACCESS, 13, 30845-30860. (www)

  @article{ , abstract = {Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape metal parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite advantages, these processes are inherently, as they are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 16 relevant articles sourced from Scopus, IEEE Xplore, and Web of Science databases. The primary objective of this work is to contribute to the advancement of autonomous manufacturing systems capable of self-monitoring and self-correction, ensuring optimal part quality, enhanced efficiency, and reduced human intervention. Our findings indicate that 62.5 % of the 16 analyzed studies have deployed AI-driven controllers in real-world scenarios, with over 56 % using AI for control strategies, such as Reinforcement Learning. Furthermore, 62.5 % of the studies employed AI for process modeling or monitoring, which was integral to the development or data pipelines of the controllers. By defining a groundwork for future developments, this review not only highlights current advancements but also hints future innovations that will likely include AI-based controllers.}, archive = {J_ACCESS}, author = {João Sousa and Benedikt Brandau and Roya Darabi and Armando Sousa and Frank Brueckner and Ana Reis and Luís Paulo Reis}, doi = {10.1109/ACCESS.2025.3537859}, journal = {IEEE Access}, pages = {30845-30860}, shortjournal = {IEEE Access}, title = {Artificial intelligence for control in laser-based additive manufacturing: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Interval-stochastic programming for integrated generation, transmission, and energy storage system (ESS) planning considering uncertainty in renewable energy sources. ACCESS, 13, 30834-30844. (www)

  @article{ , abstract = {In recent years, renewable energy sources have been expanding worldwide to create sustainable power systems. While the transition to sustainable system provides benefits such as reducing greenhouse gas emissions, it creates many problems within the system. For example, due to the volatility and uncertainty of renewable energy, problems such as an imbalance in supply and demand and a lack of flexibility are appearing. This paper focuses on these problems encountered or to be encountered globally. This paper divides the uncertainty of renewables into uncertainty due to natural variability and uncertainty due to lack of information. To address these challenges, this paper presents a new integrated planning method for generators, transmission lines, and ESS, considering uncertainties of renewable energy. An interval-stochastic programming method is introduced, using Benders decomposition method for 2-stage optimization. This paper applies a bi-objective optimization technique for decision making, and a minimum regret cost criterion. Furthermore, this paper demonstrates the effectiveness of the proposed method through numerical results based on a model of simplified Korean power system. The conclusion emphasizes the importance of ESS in integrated expansion planning to effectively manage the uncertainties of renewable energy and ensure a sustainable power system.}, archive = {J_ACCESS}, author = {Deukyoung Lee and Dosung Kim and Sung-Kwan Joo}, doi = {10.1109/ACCESS.2025.3538649}, journal = {IEEE Access}, pages = {30834-30844}, shortjournal = {IEEE Access}, title = {Interval-stochastic programming for integrated generation, transmission, and energy storage system (ESS) planning considering uncertainty in renewable energy sources}, volume = {13}, year = {2025}, }
• (2025). Corrections to “OntoCommerce: Incorporating ontology and sequential pattern mining for personalized E-commerce recommendations”. ACCESS, 13, 30833. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations”.}, archive = {J_ACCESS}, author = {Ghulam Mustafa and Naveed Ahmad Jhamat and Zeeshan Arshad and Nadia Yousaf and Md. Nazmul Abdal and Mohammed Maray and Dokhyl Alqahtani and Mohamad Amir Merhabi and Muhammad Abdul Aziz and Touseef Ahmad}, doi = {10.1109/ACCESS.2025.3541914}, journal = {IEEE Access}, pages = {30833}, shortjournal = {IEEE Access}, title = {Corrections to “OntoCommerce: Incorporating ontology and sequential pattern mining for personalized E-commerce recommendations”}, volume = {13}, year = {2025}, }
• (2025). Enhancing short-term load forecasting through K-shape clustering and deep learning integration. ACCESS, 13, 30817-30832. (www)

  @article{ , abstract = {Short-term load forecasting (STLF) is essential for the efficient operation and management of modern power grids, impacting dispatch and trading strategies in electricity markets. However, accurately forecasting short-term loads remains challenging due to the difficulty in categorizing diverse operational modes and the limited availability of exogenous variables such as temperature and economic indicators. To address these challenges, this study introduces K-NBEATSx, a novel model that integrates clustering and deep learning methodologies. The methodology begins by using K-Shape clustering to categorize electric load data based on shape similarity, effectively distinguishing different operational modes. Subsequently, the Neural Basis Expansion Analysis With Exogenous Variables (NBEATSx) method is applied by incorporating trend and seasonality modules to enhance forecasting accuracy. Case studies using load datasets from 3 different countries demonstrate that the proposed model outperforms traditional deep learning models across various operational scenarios. Additionally, the integration of clustering algorithms is validated as an effective strategy for improving prediction performance. This research offers an effective new methodology for deep-learning-based STLF, contributing to enhancing the reliability and efficiency of power system operation.}, archive = {J_ACCESS}, author = {Wentao Zhang and Meng Cheng and Qianliang Xiang and Qinmiao Li}, doi = {10.1109/ACCESS.2025.3541574}, journal = {IEEE Access}, pages = {30817-30832}, shortjournal = {IEEE Access}, title = {Enhancing short-term load forecasting through K-shape clustering and deep learning integration}, volume = {13}, year = {2025}, }
• (2025). High performance square patch power dividers. ACCESS, 13, 30811-30816. (www)

  @article{ , abstract = {This letter presents a high-performance, low-cost, and miniaturised Square Patch Power Divider (SPPD) with a defected ground plane, specifically designed for broadband applications. The innovative design integrates square ring-shaped slots in both the signal and ground planes of the square power divider, effectively extending multiple surface current paths to accommodate longer wavelengths. This approach not only achieves significant miniaturisation but also enhances bandwidth, ensuring superior performance. Additionally, the slots reduce the radiative characteristics of the patch, thereby minimizing insertion loss and improving overall device efficiency. A systematic design methodology is introduced, supported by a detailed parametric study for performance optimisation. The proposed SPPD demonstrates a remarkable 35% reduction in size (Patch area/ $\lambda ^{2}$ ) and a 30% improvement in fractional bandwidth compared to similar recent reported works. Furthermore, the device maintains minimal insertion loss and excellent port isolation, making it an ideal solution for modern wireless communication systems that demand compact, high-performance components.}, archive = {J_ACCESS}, author = {Ajay Kumar Singh and Kamla Prasan Ray and Bibhuti Bhusan Padhy}, doi = {10.1109/ACCESS.2025.3541405}, journal = {IEEE Access}, pages = {30811-30816}, shortjournal = {IEEE Access}, title = {High performance square patch power dividers}, volume = {13}, year = {2025}, }
• (2025). Generative AI in engineering and computing education: A scoping review of empirical studies and educational practices. ACCESS, 13, 30789-30810. (www)

  @article{ , abstract = {Since the release of diverse generative AI (GenAI) tools such as ChatGPT, Google Gemini, DALL $\cdot $ E, and GitHub Copilot, there has been much debate around the impacts and implications of these tools on education. Currently, extant literature remarks on the affordances, challenges, and opportunities of GenAI, but few studies report and analyze empirical studies and educational practices coming up by GenAI usage in learning settings. Then, in this Scoping Review (ScR) based on 146 studies retrieved from the databases SCOPUS, Web of Science (WoS), and ERIC, we analyzed the implications of integrating GenAI in engineering and computing education from K-12 to tertiary levels. We adopted an approach starting from the bibliometric features of the studies in terms of authors, cites, years, or cluster topics, and navigating to the identification of methodologies, strategies, AI literacy instruments and guidelines, learning outcomes, and students’ and teachers’ perceptions, among other features. We advocate that current educational practices in engineering and computing with GenAI can indicate to us a roadmap of its potentialities, uses, and risks from the standpoint of both teachers and students, and this could help us to create more reflexive methodologies that enhance the teaching-learning process based on the evidence. Our purpose with the outcomes and conclusions of this scoping review is to support educators, faculty members, and other stakeholders in engineering and computing education to co-create educational methodologies that articulate GenAI with curricula, AI literacy, and prompt engineering encompassing students’ learning domains such as cognitive, affective, or behavioral.}, archive = {J_ACCESS}, author = {Jonathan Álvarez Ariza and Milena Benitez Restrepo and Carola Hernández Hernández}, doi = {10.1109/ACCESS.2025.3541424}, journal = {IEEE Access}, pages = {30789-30810}, shortjournal = {IEEE Access}, title = {Generative AI in engineering and computing education: A scoping review of empirical studies and educational practices}, volume = {13}, year = {2025}, }
• (2025). Optically transparent coding metasurface with dual-polarization conversion and RCS reduction characteristics. ACCESS, 13, 30779-30788. (www)

  @article{ , abstract = {The basic stealth technology techniques are often limited to single frequency bands or single functionality, presenting a challenge for achieving superior stealth performance. In this study, an optically transparent coding metasurface (OTCMS) is designed based on glass and indium tin oxide (ITO) to realize dual-polarization conversion along with radar cross section (RCS) reduction at different frequencies. The fundamental unit, “meta-0,” consists of a diagonal I-shaped ITO resistive pattern that converts linearly polarized incident wave into its orthogonal component in two frequency bands (9.46-11.63 GHz and 15.86-17.28 GHz) with a fractional bandwidths of 21% and 9% respectively, whereas linear polarized incident wave is converted to circular polarized reflected wave in the 12.50-15.26 GHz band, with a fractional bandwidth of 20%. Next, the Pancharatnam-Berry (PB) phase is utilized to generate “meta-1” by simply rotating the “meta-0” with 90° to achieve a 180° phase difference between them. The “meta-0” and “meta-1” are arranged in an array using an optimized coding sequence to form OTCMS for RCS reduction. A reduction of more than 10 dB in RCS has been achieved across dual frequency bands for linearly polarized incident wave from 8.9-11.6 and 15.9-17.7 GHz. A prototype has been fabricated, and experimental outcomes align well with simulated results. This work opens avenues for the development of multifunctional and low-profile devices for applications in stealth jet windows, optical communication, microwave screening and beyond.}, archive = {J_ACCESS}, author = {Saima Hafeez and Jianguo Yu and Abdul Majeed and Fahim Aziz Umrani and Yibo Huang}, doi = {10.1109/ACCESS.2024.3498955}, journal = {IEEE Access}, pages = {30779-30788}, shortjournal = {IEEE Access}, title = {Optically transparent coding metasurface with dual-polarization conversion and RCS reduction characteristics}, volume = {13}, year = {2025}, }
• (2025). PolAttNet: A deep learning framework for polarized image reflection removal with multi-attention mechanisms. ACCESS, 13, 30762-30778. (www)

  @article{ , abstract = {Reflection removal is a crucial yet challenging task in computer vision, essential for enhancing image clarity and visibility in applications such as autonomous navigation, medical imaging diagnostics, and digital photography. However, existing methods often struggle with complex reflections and underutilize the rich information embedded in polarized images. To address these challenges, we propose PolAttNet, an innovative deep learning framework specifically designed for effective reflection removal in polarized images. PolAttNet integrates a novel combination of standard attention mechanisms and linear attention mechanisms to capture both global and local features. This dual-attention strategy enables the model to dynamically focus on the most relevant features, significantly enhancing its ability to suppress reflections without compromising computational efficiency. Furthermore, PolAttNet employs ConvNeXt blocks, advanced convolutional units designed to enhance the extraction of fine-grained features from polarized data. These blocks, coupled with Group Normalization, ensure stable and efficient training even with small batches of complex polarized data. We conduct extensive experiments on a public polarized dataset to evaluate the performance of PolAttNet. Our results demonstrate that PolAttNet outperforms state-of-the-art methods across various metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The model excels in separating reflection and transmission layers, providing clear and high-quality images crucial for downstream tasks. Additionally, our ablation studies highlight the significant contributions of each component within PolAttNet, validating the effectiveness of our architectural choices. In summary, PolAttNet significantly advances polarized image processing, offering an efficient and robust solution for reflection removal.}, archive = {J_ACCESS}, author = {Chao Wang and Daisuke Miyazaki}, doi = {10.1109/ACCESS.2025.3541640}, journal = {IEEE Access}, pages = {30762-30778}, shortjournal = {IEEE Access}, title = {PolAttNet: A deep learning framework for polarized image reflection removal with multi-attention mechanisms}, volume = {13}, year = {2025}, }
• (2025). Automated seeding in hydroponic greenhouse with a dual-arm robotic system. ACCESS, 13, 30745-30761. (www)

  @article{ , abstract = {Automation in agriculture, often referred to as Agriculture 4.0, has attracted significant interest in recent decades. The integration of technologies like artificial intelligence, robotic manipulation, and autonomous navigation, commonly used in industrial automation, also shows great potential in the agricultural sector. While these technologies are being extensively applied to automate tasks such as fruit harvesting, there is a notable lack of research focused on their application to other essential tasks, such as seeding. In this context, this article introduces an autonomous bi-manipulator robot designed to automate the seeding process in a hydroponic greenhouse. The dual-arm robotic system is capable of perceiving the environment to accurately identify the location of growing cubes within planters, securely position the cubes without causing collisions within the hydroponic system, collect seeds, and deposit them directly into the small holes of the growing cubes. Several perception and manipulation strategies are proposed and experimentally evaluated to enhance the overall process. The results demonstrate that automating this task with a dual-arm mobile robot is both feasible and efficient, achieving an average seeding time of 75 s per rock-wool cube with the most efficient strategy, including cube positioning and verification of proper seed placement.}, archive = {J_ACCESS}, author = {Daniel Rodríguez-Nieto and Eduardo Navas and Roemi Fernández}, doi = {10.1109/ACCESS.2025.3541954}, journal = {IEEE Access}, pages = {30745-30761}, shortjournal = {IEEE Access}, title = {Automated seeding in hydroponic greenhouse with a dual-arm robotic system}, volume = {13}, year = {2025}, }
• (2025). Atmospheric scattering model combined with image segmentation in foggy image enhancement. ACCESS, 13, 30732-30744. (www)

  @article{ , abstract = {In order to solve the problem of obtaining clear images in foggy weather, this study conducted research on combining physical imaging models with Ostu threshold image segmentation algorithms, aiming to optimize computer defogging algorithms, improve the defogging processing performance and detail restoration ability of foggy images. The study analyzed the changes in transmittance of the research scene based on the atmospheric scattering model, and achieved image restoration of different fog concentration areas through image segmentation, thereby improving the defogging efficiency. The comparative test results show that the algorithm performs excellently in terms of convergence speed, fitness value, accuracy, and recall rate. In addition, the average errors of the model in the training and test sets were 1.35 and 0.98, respectively, which were smaller than those of other comparison algorithms, demonstrating superior analytical capabilities. The inference time of the proposed algorithm on the training and test sets was 0.97 s and 0.95 s, respectively, which was the fastest among all algorithms. At the same time, the algorithm processed 2.5 million samples on the training and test sets, using 124MB and 120MB of memory respectively, with floating-point operations of 8.4 GFLOP and 8.5 GFLOP, respectively. It demonstrated a good balance between efficiency and resource utilization. The experimental results of the defogging effect showed that the proposed algorithm outperformed other compared algorithms in terms of mean square error, peak signal-to-noise ratio, and structural similarity when processing foggy images in different scenarios, indicating that the algorithm could make the restored images closer to fog free images in different scenarios. Moreover, the image visibility and visual contrast of this algorithm were 87.83% and 86.52%, respectively, which were 37.48% and 46.19% higher than traditional algorithms. The experimental results have verified the effectiveness and feasibility of this method in practical applications, especially in autonomous driving, video surveillance, and remote sensing image processing, which have broad application potential and significant importance.}, archive = {J_ACCESS}, author = {Qiaoling Liu and Na Yu and Jinfu Cheng}, doi = {10.1109/ACCESS.2025.3541216}, journal = {IEEE Access}, pages = {30732-30744}, shortjournal = {IEEE Access}, title = {Atmospheric scattering model combined with image segmentation in foggy image enhancement}, volume = {13}, year = {2025}, }
• (2025). Low cost TSV repair architecture using switch-based matrix for highly clustered faults. ACCESS, 13, 30722-30731. (www)

  @article{ , abstract = {Through-silicon via (TSV), responsible for inter-layer communication in high-bandwidth memory (HBM), plays a critical role in HBM operation. Therefore, faults occur in TSVs can critically impact the entire chips. However, due to their structural characteristics, TSVs are prone to faults occurring both during the manufacturing process and after sale. Accordingly, numerous studies have been conducted on TSV repair methods to ensure TSV reliability. These studies have focused on improving repair rates for the faults occurring within a single TSV repair group. Recent studies have achieved an optimal repair rate by repairing faulty TSVs (FTSVs) up to the number of redundant TSVs (RSTVs) inserted. However, actual faults tend to cluster within TSV repair groups rather than being evenly distributed. In response, this paper proposes a low cost TSV repair architecture (LCTRA) that, while slightly reducing the repair rate for evenly distributed faults, is optimized for repairing highly clustered faults. The proposed method achieves high repair performance for the highly clustered faults with lower area and delay overhead compared to conventional methods, and the repair performance is quantitatively demonstrated through experimental results in various environments.}, archive = {J_ACCESS}, author = {Seokjun Jang and Donghyun Han and Sunghoon Kim and Dayoung Kim and Sungho Kang}, doi = {10.1109/ACCESS.2025.3541678}, journal = {IEEE Access}, pages = {30722-30731}, shortjournal = {IEEE Access}, title = {Low cost TSV repair architecture using switch-based matrix for highly clustered faults}, volume = {13}, year = {2025}, }
• (2025). A hybrid deep learning model for network intrusion detection system using Seq2Seq and ConvLSTM-subnets. ACCESS, 13, 30705-30721. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDS) are essential for identifying and mitigating malicious activities in network environments. As cyber threats evolve in complexity, traditional NIDSs often struggle to detect sophisticated attacks effectively, especially those involving intricate temporal and spatial dependencies within network traffic. The ability to capture these dependencies is crucial for reducing false positive rates and improving detection accuracy. However, existing models face significant challenges, including handling varying sequence lengths and capturing long-range dependencies, which are essential for accurate anomaly detection. This paper proposes a hybrid model that combines Sequence to Sequence (Seq2Seq) architecture with Convolutional Long Short-Term Memory (ConvLSTM) units to address these challenges. This hybrid model handles spatial and temporal dependencies by incorporating convolutional layers within LSTM cells. This enables the model to leverage the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) alongside the sequential learning strengths of LSTM networks. In addition, to enhance the interpretability of the model, the proposed architecture integrates Explainable Artificial Intelligence (XAI) through Local Interpretable Model-agnostic Explanations (LIME). This approach provides insights into the model’s decision-making process, highlighting the temporal and spatial features that influence predictions, and improving transparency in detecting anomalies. Experimental evaluations on benchmark datasets, including CIC-IDS 2017, CIC- ToN-IoT, and UNSW-NB15, demonstrate that the proposed hybrid ConvLSTM-Seq2Seq model outperforms existing methods in reducing false positives and achieving higher accuracy. This model offers a promising solution for NIDSs by improving detection capabilities and better handling complex temporal and spatial relationships in network data.}, archive = {J_ACCESS}, author = {S. Hariharan and Y. Annie Jerusha and G. Suganeshwari and S. P. Syed Ibrahim and Uday Tupakula and Vijay Varadharajan}, doi = {10.1109/ACCESS.2025.3541399}, journal = {IEEE Access}, pages = {30705-30721}, shortjournal = {IEEE Access}, title = {A hybrid deep learning model for network intrusion detection system using Seq2Seq and ConvLSTM-subnets}, volume = {13}, year = {2025}, }
• (2025). RPZ-based suspicious direct outbound DNS traffic detection mechanism with adaptive policy updates. ACCESS, 13, 30694-30704. (www)

  @article{ , abstract = {Domain Name System (DNS) based C&C communication has gotten much attention over the past decade due to the increase of malware using DNS protocol. A legitimate usage of DNS protocol in domain name resolution requires Name Server (NS) records and the corresponding glue A records achievements before sending DNS queries to the authoritative DNS servers. The achievements generally are conducted on DNS full-service resolvers. However, malware-infected computers tend to communicate with the C&C servers directly using DNS protocol, which is called direct outbound DNS traffic in this research, without via any DNS full-service resolvers on purpose to conceal their malicious behavior. In this research, we present a suspicious direct outbound DNS traffic detection mechanism using DNS Response Policy Zone (RPZ) in order to detect and block the malicious behavior of malware-infected computers during the C&C communication stage. All NS records and the corresponding glue A records achieved in an organization network via DNS full-service resolvers will be registered in the RPZ and the DNS packets whose destination IP addresses have no match in the RPZ will be identified as suspicious direct outbound DNS traffic. We implemented a prototype system and evaluated the features of the proposed mechanism in a local Software Defined Network based (SDN-based) experimental environment. The evaluation results confirmed that the prototype system correctly detected and blocked the target DNS traffic based on the policies of RPZ. Moreover, we confirmed that adaptive policy update is applicable to the mechanism and the performance of the DNS traffic process is acceptable for deployment in the real network.}, archive = {J_ACCESS}, author = {Hikaru Ichise and Yong Jin and Katsuyoshi Iida}, doi = {10.1109/ACCESS.2025.3542234}, journal = {IEEE Access}, pages = {30694-30704}, shortjournal = {IEEE Access}, title = {RPZ-based suspicious direct outbound DNS traffic detection mechanism with adaptive policy updates}, volume = {13}, year = {2025}, }
• (2025). Quantum hall resistance effect based on maximum entropy transfer rate spectrum. ACCESS, 13, 30678-30693. (www)

  @article{ , abstract = {With the continuous development of topological quantum materials, traditional mobility spectrum analysis methods can no longer meet the practical measurement needs. Based on this, starting from solving the Boltzmann formula and combining the principle of maximum entropy, a method of maximum entropy transfer rate spectrum is proposed by using numerical integration instead of analytical integration. The results show that there are five different electronic energy levels in the conduction process of PtBi2, a large magnetoresistance material with hexagonal structure, which is consistent with theoretical simulations. Among these materials, there is a hole energy spectrum with 0.964m2/Vs, which contributes 15% to the conductivity, largely due to Dirac electrons. At the same time, the study observes the existence of Dirac electrons and discovers the large magnetoresistance effect, which is not related to the compensation mechanism between their charge holes. In addition, the mobility of each band decreases with increasing temperature, while the morphology of the charge density and mobility spectra remains unchanged. The research approach proposed in the study can avoid conventional research methods and can compare and analyze the ratio of high mobility and low mobility, majority and minority carriers, to obtain the concentration and mobility of various types of carriers, providing a new approach for studying the electronic transport characteristics of multi-carrier systems.}, archive = {J_ACCESS}, author = {Siyun Wang and Zhixin Li and Mingming Chen and Shufeng Lu and Qing Xu and Zhongdong Wang}, doi = {10.1109/ACCESS.2025.3541825}, journal = {IEEE Access}, pages = {30678-30693}, shortjournal = {IEEE Access}, title = {Quantum hall resistance effect based on maximum entropy transfer rate spectrum}, volume = {13}, year = {2025}, }
• (2025). Variable sampling area RRT algorithm based on limiting joint angle for robotic arm path planning. ACCESS, 13, 30665-30677. (www)

  @article{ , abstract = {It is an important capacity to plan an optimal working path without collision for robotic arms. Rapid exploration random tree (RRT) is a popular algorithm in the field of path planning. However, the high computational cost caused by directly using RRT to plan the path of robotic arms hinders the practical application of RRT. In this paper, a variable sampling area RRT (VSA-RRT) algorithm is proposed to improve the computational efficiency of robotic arm path planning. An obstacle avoidance model based on limiting joint angle is first developed to judge the collision. Through this obstacle avoidance model, the collision judgment of robotic arm is converted into the joint angle judgment problem, and thus, the collision situation can be directly recognized with the given joint angles. Then, a sampling area dividing strategy is proposed on the basis of the limiting joint angles, where the motion space of robotic arm is divided into several subareas. Subsequently, VSA-RRT is proposed by integrating both the obstacle avoidance model and the sampling area dividing strategy. In VSA-RRT, the sampling area is automatically selected from the divided subareas, leading the sampling process to continuously move towards the target point, and the new sampling point is generated by using the obstacle avoidance model, ensuring that any new sampling point is collision-free. Finally, the effectiveness of the proposed VSA-RRT is demonstrated with three examples. The example results show that VSA-RRT decreases the sampling number by 44.98%, 19.95% and 15.00% compared with the basic RRT, RRT with SAD and APF-RRT respectively.}, archive = {J_ACCESS}, author = {Zhi-Jie Ding and Xin-Jia Meng and Li-Xiang Zhang and Yi-Bin Guo and Meng-Qiu Chen and Shi-Hang Zhen}, doi = {10.1109/ACCESS.2025.3541645}, journal = {IEEE Access}, pages = {30665-30677}, shortjournal = {IEEE Access}, title = {Variable sampling area RRT algorithm based on limiting joint angle for robotic arm path planning}, volume = {13}, year = {2025}, }
• (2025). Review of cryogenic power electronics for all-electric aircraft. ACCESS, 13, 30646-30664. (www)

  @article{ , abstract = {To tackle emissions from the aviation industry and make it sustainable, research has been targeting electrifying aircraft. For this aim, articles have been focusing on how to replace regular jet engines with liquid hydrogen as fuel. This would be beneficial as hydrogen is expected to play a dual role, providing fuel for fuel cells to propel the aircraft electrically and to supply cooling to enable high-power density technologies such as superconducting machines and cryogenic power electronics circuits. However, using higher power density, especially cryogenic power electronics, can be challenging. This article investigates cryogenic power electronics for use on all-electric aircraft across different aspects, where it lays out how to integrate power electronics at cryogenic temperature into the architecture of the all-electric aircraft, how to use them with hydrogen fuel cells, and the voltage levels of the system. It reviews different semiconductor device technologies, circuit topologies, and passive devices to be used in power electronics circuitry, as well as logic circuits that are suitable for use at cryogenic temperatures. In addition, the article discusses the challenges of using semiconductor devices at such low temperatures, including the reliability, overall cooling, and recycling of hydrogen.}, archive = {J_ACCESS}, author = {Abdelrahman Elwakeel and Yuchuan Liao and Yudi Xiao and Rafael Peña Alzola and Min Zhang and Weijia Yuan}, doi = {10.1109/ACCESS.2025.3539572}, journal = {IEEE Access}, pages = {30646-30664}, shortjournal = {IEEE Access}, title = {Review of cryogenic power electronics for all-electric aircraft}, volume = {13}, year = {2025}, }
• (2025). An iterative novel co-simulation method for electro-thermal multiphysics analysis of RF power amplifier. ACCESS, 13, 30636-30645. (www)

  @article{ , abstract = {This paper proposes a novel co-simulation method for analyzing the interaction between circuit performance and electro-thermal multiphysics effects in a radio frequency (RF) power amplifier at the PCB board level, particularly under the influence of an external electromagnetic pulse (EMP). First, a SPICE equivalent model of the power amplifier circuit is developed, and the RF output node voltage is calculated. These results are subsequently used as boundary conditions in the electro-thermal analysis. The temperature distribution is obtained using electro-thermal multiphysics coupling theory. Additionally, since both circuit components and the PCB board are typically temperature-sensitive, a reverse analysis is conducted to study the effect of temperature variations on circuit node voltage. Numerical examples demonstrate the accuracy and efficiency of the proposed method, with a comparison to COMSOL Multiphysics® validating its performance. Furthermore, the method is successfully applied to analyze the electromagnetic interference (EMI) in an RF power amplifier. The results indicate that when the amplitude of the external pulse signal exceeds 1 V, the amplifier temperature can increase to 555 K, leading to high-temperature distortion in the power amplifier.}, archive = {J_ACCESS}, author = {Xu-Feng Lou and Kang-Min Sun and Jian Wang and Yin-Shui Xia and Wei-Jun Wu and Wen-Yan Yin}, doi = {10.1109/ACCESS.2025.3528038}, journal = {IEEE Access}, pages = {30636-30645}, shortjournal = {IEEE Access}, title = {An iterative novel co-simulation method for electro-thermal multiphysics analysis of RF power amplifier}, volume = {13}, year = {2025}, }
• (2025). Enhancing security and privacy in 5G device-to-device communication: A secure gale-shapley algorithm approach. ACCESS, 13, 30623-30635. (www)

  @article{ , abstract = {Device-to-Device (D2D) communication is pivotal in enhancing the performance of 5G networks by improving spectral efficiency, reducing latency, and supporting applications such as the Internet of Things (IoT). Despite these benefits, direct communication pathways in D2D pose significant security and privacy challenges, such as unauthorized access, data eavesdropping, and privacy breaches. To address these issues, we propose a robust and adaptive security framework that integrates AI-enhanced physical layer key generation, full-duplex adaptive jamming, and differential privacy. Our approach employs secure multi-party computation (MPC) and lightweight encryption to protect user preferences and communication data during resource allocation while ensuring optimal system performance. AI-driven key generation dynamically adapts to changing network conditions, whereas the full-duplex adaptive jamming mechanism effectively counteracts eavesdropping threats. We validated the framework through extensive MATLAB simulations, demonstrating its ability to achieve high throughput and low latency, even in the presence of various security threats. The results confirm the efficacy of the framework in safeguarding D2D communications, making it well suited for mission-critical 5G applications, where both performance and security are paramount.}, archive = {J_ACCESS}, author = {Musaad Alruwaili and Junghwan Kim and Jared Oluoch}, doi = {10.1109/ACCESS.2025.3540745}, journal = {IEEE Access}, pages = {30623-30635}, shortjournal = {IEEE Access}, title = {Enhancing security and privacy in 5G device-to-device communication: A secure gale-shapley algorithm approach}, volume = {13}, year = {2025}, }
• (2025). A novel model for predicting PM2.5 concentrations utilizing graph convolutional networks and transformer. ACCESS, 13, 30613-30622. (www)

  @article{ , abstract = {With the development of the global economy, PM2.5 fine particulate matter concentration has emerged as a major environmental issue worldwide, significantly impacting human health. However, most existing research methods largely ignore the spatial characteristics of PM2.5 concentrations. In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. Initially, adjacency matrices are constructed using geographic latitude, longitude, and altitude information to represent the spatial relationships among monitoring stations. These spatial relationships are then extracted using GCN. The extracted spatial features are transformed into value, position, and temporal representations via embedding blocks. The encoded temporal information is converted into frequency domain representations through the encoding layer, then reconverted into temporal information after attention calculations, and input into the decoding layer to produce the prediction results. Finally, a Huber loss is used to optimize the neural network parameters and enhance the robustness of the model. The GCN-FFPformer model has been compared with traditional time series models and advanced Transformer models using real-world datasets. The results indicate that on the Beijing-Tianjin-Hebei dataset, MAE, RMSE, and TIC were reduced by 9.65%, 11.67%, and 12.51% on average compared to other models, demonstrating that GCN-FFPformer is a novel method for accurately predicting PM2.5 concentrations in urban areas.}, archive = {J_ACCESS}, author = {Yuan Huang and Feilong Han and Qimeng Feng}, doi = {10.1109/ACCESS.2025.3541774}, journal = {IEEE Access}, pages = {30613-30622}, shortjournal = {IEEE Access}, title = {A novel model for predicting PM2.5 concentrations utilizing graph convolutional networks and transformer}, volume = {13}, year = {2025}, }
• (2025). Explainable video topics for content taxonomy: A multimodal retrieval approach to industry-compliant contextual advertising. ACCESS, 13, 30597-30612. (www)

  @article{ , abstract = {Owing to the increased video content consumption in recent years, the need for advanced contextual advertising methods that leverage increasing user engagement and relevance on advertisement-based video-on-demand platforms has increased. Traditional behavior-based advertisement targeting is waning, particularly owing to the recent strict privacy policies that favor user consent and privacy. This study proposes an innovative approach for integrating advanced natural language processing with multimodal analysis for video contextual advertising. To this end, transformer-based architectures, specifically BERTopic, computer vision techniques, and large language models were used to extract sets of topics from visual and textual video data automatically and systematically. The proposed framework decodes the taxonomy of content efficiently through videos in different levels of noise and languages. Empirical analysis of the YouTube-8M dataset shows the potential for the approach to change the paradigm in video advertising. Built to be scalable and easily adaptable, this solution can handle multifarious and complex user-generated content well, suited for a wide range of applications across various media platforms.}, archive = {J_ACCESS}, author = {Waruna de Silva and Anil Fernando}, doi = {10.1109/ACCESS.2025.3542562}, journal = {IEEE Access}, pages = {30597-30612}, shortjournal = {IEEE Access}, title = {Explainable video topics for content taxonomy: A multimodal retrieval approach to industry-compliant contextual advertising}, volume = {13}, year = {2025}, }
• (2025). Validation of smartwatch-based blood pressure monitoring in young adults: Multi-perspective analysis by dual comparison with cuff-based devices. ACCESS, 13, 30587-30596. (www)

  @article{ , abstract = {Smartwatches have become popular for blood pressure estimation, offering continuous monitoring with advanced convenience. However, clinical guidelines have not fully endorsed their use due to validation concerns. Many studies have compared watch-based BP measurements with cuff-based devices, but there is no clear consensus. To overcome previous methodological limitations, we collected systolic blood pressure measurements from both watch and cuff devices simultaneously, taking two readings at the same time of measurement (six participants over four days). Simply averaging the difference between watch and cuff measurements was slightly above the clinical threshold. Average error depended on individuals and calibration. Proper calibration may achieve clinically acceptable accuracy. The data showed that cuff BP measurements also vary, affecting comparative accuracy. After adjusting the inconsistency of cuff-based devices, watch-based blood pressure errors were within the clinical threshold. Daily variation analysis indicated watches being more conservative. The watch was able to distinguish participants with higher or lower daily standard deviations. The circadian cycle detected by the watch showed some relation to the cuff, but it is too early to confirm statistically significant similarity. This study evaluated the validity of watch-based BP monitoring from various perspectives, concluding that the watch might be valid for clinical use in young consumers for preventive or pre-diagnosis purpose. We also provided methodological guidelines for validating watch devices by cuff-based devices.}, archive = {J_ACCESS}, author = {Youngro Lee and Taekyung Lee and Joowon Lee and Hae-Young Lee and Jong-Mo Seo}, doi = {10.1109/ACCESS.2025.3533585}, journal = {IEEE Access}, pages = {30587-30596}, shortjournal = {IEEE Access}, title = {Validation of smartwatch-based blood pressure monitoring in young adults: Multi-perspective analysis by dual comparison with cuff-based devices}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Advancements in polyp detection: A developed single shot multibox detector approach”. ACCESS, 13, 30586. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Advancements in Polyp Detection: A Developed Single Shot Multibox Detector Approach”}, archive = {J_ACCESS}, author = {Mohamed Achraf Belabbes and Yassine Oukdach and Meryem Souaidi and Lahcen Koutti and Said Charfi}, doi = {10.1109/ACCESS.2025.3540622}, journal = {IEEE Access}, pages = {30586}, shortjournal = {IEEE Access}, title = {Corrections to “Advancements in polyp detection: A developed single shot multibox detector approach”}, volume = {13}, year = {2025}, }
• (2025). The effects of servitization, digitalization, and digital learning orientation on sustainable performance: A hybrid PLS-SEM-ANN model. ACCESS, 13, 30570-30585. (www)

  @article{ , abstract = {Sustainability is a global concern. Industry 4.0 is driving the manufacturing sector toward sustainability within the Triple Bottom Line (TBL) framework, emphasizing economic, environmental, and social performance. However, limited empirical research has examined how servitization, digitalization, and digital learning orientation (DLO) interact to influence all three dimensions of sustainable performance. This study addresses this gap by proposing a tri-theoretical framework based on TBL, Resource-based View (RBV), and Dynamic Capability View (DCV). A hybrid partial least squares structural equation modeling-artificial neural network (PLS-SEM-ANN) model was developed to examine the direct effects of servitization and digitalization, the mediating effect of digitalization, and the moderating effect of DLO. An empirical analysis of 519 Chinese manufacturing employees revealed that servitization and digitalization positively impact sustainable performance, with digitalization acting as a mediating factor. Servitization also positively impacts digitalization. However, DLO negatively moderates the relationship between servitization and sustainable performance. A case study of Amazon.com, Inc. validates our quantitative findings by demonstrating the impact of servitization, digitalization, and DLO strategies on sustainable performance in real-world applications. These findings reveal theoretical and practical implications for effectively leveraging servitization, digitalization, and DLO to achieve practical sustainability.}, archive = {J_ACCESS}, author = {Xiaoxia Wu and Yunkai Tang and Xin Fang and Yueling Xu and Sijia Qiao}, doi = {10.1109/ACCESS.2025.3538635}, journal = {IEEE Access}, pages = {30570-30585}, shortjournal = {IEEE Access}, title = {The effects of servitization, digitalization, and digital learning orientation on sustainable performance: A hybrid PLS-SEM-ANN model}, volume = {13}, year = {2025}, }
• (2025). Predicting the classification of heart failure patients using optimized machine learning algorithms. ACCESS, 13, 30555-30569. (www)

  @article{ , abstract = {Heart failure is a critical condition with a high mortality rate, making accurate survival prediction essential for timely interventions. This study proposes an optimized machine learning approach using Gradient Boosting Machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) to predict heart failure survival. The dataset, sourced from Kaggle, includes clinical features such as age, ejection fraction, and serum creatinine levels for 299 heart failure patients. To address the imbalance in survival outcomes, Synthetic Minority Over-sampling Technique (SMOTE) was employed to balance the dataset, followed by SelectKBest and Chi-square feature selection methods to retain the most significant predictors. The optimized hyperparameters for the GBM model were identified using the AIW-PSO algorithm, which effectively balanced exploration and exploitation by adaptively adjusting inertia weights. Model selection was further refined using information criteria, including Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), ensuring that the best-performing model was chosen based on both predictive accuracy and model complexity. The optimized GBM model achieved a test accuracy of 94%, demonstrating superior performance compared to traditional machine learning models. The study underscores the importance of hyperparameter tuning through metaheuristic algorithms and highlights the potential of AIW-PSO in enhancing model performance for clinical prediction tasks. These findings have significant implications for clinical decision-making, offering a reliable and interpretable tool for predicting patient outcomes in heart failure management.}, archive = {J_ACCESS}, author = {Marzia Ahmed and Mohd Herwan Sulaiman and Md Maruf Hassan and Touhid Bhuiyan}, doi = {10.1109/ACCESS.2025.3541069}, journal = {IEEE Access}, pages = {30555-30569}, shortjournal = {IEEE Access}, title = {Predicting the classification of heart failure patients using optimized machine learning algorithms}, volume = {13}, year = {2025}, }
• (2025). MCC: Multi-cluster contrastive semi-supervised segmentation framework for echocardiogram videos. ACCESS, 13, 30543-30554. (www)

  @article{ , abstract = {Variability in sonographer expertise often leads to low-quality ultrasound imaging, presenting significant challenges for accurate echocardiogram video segmentation. Current methods require extensive annotations, which are impractical given the large number of frames and artifacts in videos. To address this, we propose a Multi-Cluster Contrastive (MCC) learning framework, a semi-supervised approach that minimizes annotation requirements while maintaining high segmentation performance. Leveraging contrastive loss to enhance foreground feature extraction, our method incorporates multi-cluster contrastive loss to utilize multiple annotated ground-truths per batch and an anchor frame selection algorithm to improve segmentation performance. Experimental results on two public echocardiography datasets (MCE and EchoNet-Dynamic) demonstrate the effectiveness of our method, achieving state-of-the-art performance. The MCC framework enhances segmentation practicality by reducing annotation requirements, particularly for developing new datasets, and facilitates efficient segmentation of low-quality echocardiogram videos. Our implementation is available at https://github.com/windstormer/MCC.}, archive = {J_ACCESS}, author = {Yu-Jen Chen and Shr-Shiun Lin and Yiyu Shi and Tsung-Yi Ho and Xiaowei Xu}, doi = {10.1109/ACCESS.2025.3541173}, journal = {IEEE Access}, pages = {30543-30554}, shortjournal = {IEEE Access}, title = {MCC: Multi-cluster contrastive semi-supervised segmentation framework for echocardiogram videos}, volume = {13}, year = {2025}, }
• (2025). Weakly supervised deep learning for arabic tweet sentiment analysis on education reforms: Leveraging pre-trained models and LLMs with snorkel. ACCESS, 13, 30523-30542. (www)

  @article{ , abstract = {This study introduces a novel approach to sentiment classification of Arabic tweets regarding educational reforms in Saudi Arabia. The complexity of the Arabic language, with its numerous dialects, poses challenges for natural language processing tasks, particularly when large volumes of data require manual annotation. To overcome the limitations of traditional labeling methods, we developed a weakly supervised learning framework that combines LLMs (GPT-3.5) and pre-trained language models (MarBERT and XLM-RoBERTa) to generate high-quality weakly labeled training data using the Snorkel framework. We fine-tuned the AraBERT model with this weakly labeled data for sentiment classification. Our experimental results demonstrated the effectiveness of the proposed approach, achieving 83% precision, 76% recall, and an 85% F1 score in classifying tweets as positive, negative, or neutral. Comparative analysis showed that GPT-3.5 outperformed Llama 2 in prompting tasks, and our weakly supervised model surpassed baseline machine learning methods. These findings highlight the potential of weakly supervised learning in analyzing public opinion on Arabic social media platforms without relying on large, labeled datasets.}, archive = {J_ACCESS}, author = {Alanoud Alotaibi and Farrukh Nadeem and Mohamed Hamdy}, doi = {10.1109/ACCESS.2025.3541154}, journal = {IEEE Access}, pages = {30523-30542}, shortjournal = {IEEE Access}, title = {Weakly supervised deep learning for arabic tweet sentiment analysis on education reforms: Leveraging pre-trained models and LLMs with snorkel}, volume = {13}, year = {2025}, }
• (2025). A novel nonlinear time-dependent hazard extended intelligent reliability prediction approach for electric vehicle motor controller. ACCESS, 13, 30505-30522. (www)

  @article{ , abstract = {The reliability of Electric Vehicle Motor Controllers (EVMCs) is critical for the safety and performance of electric vehicles (EVs). Traditional reliability prediction models often fall short due to their reliance on linear assumptions and time-invariant factors, failing to capture the complex, nonlinear, and time-dependent nature of real-world operational conditions. The lower Concordance index (C-index) scores limit the time-series intelligent methods that address the nonlinear time-dependent characteristic. To address this gap, this paper proposes an Extended Hazard Nonlinear and Time-Dependent (EHNTD) strategy based on the Lifelines Python library, specifically targeting the reliability of EVMC. The prediction methodology adheres to Automotive Electronics Council (AEC) standards, using estimated failure data under desired operating conditions. The EHNTD reliability prediction technique demonstrates superior accuracy for EVMC, achieving a C-index of 0.95 and outperforming other intelligent methods for nonlinear hazard data. The insights and proposed EHNTD approach presented in this paper are intended to advance EVMC reliability studies and can be applied in the broader field of data-extended reliability engineering and research.}, archive = {J_ACCESS}, author = {N. S. Raghavendra Rao and A. Chitra and Daki Krishnachaitanya and Maher Al-Greer}, doi = {10.1109/ACCESS.2025.3540458}, journal = {IEEE Access}, pages = {30505-30522}, shortjournal = {IEEE Access}, title = {A novel nonlinear time-dependent hazard extended intelligent reliability prediction approach for electric vehicle motor controller}, volume = {13}, year = {2025}, }
• (2025). Leveraging large language model for enhanced text-to-SQL parsing. ACCESS, 13, 30497-30504. (www)

  @article{ , abstract = {Text-to-SQL conversion, the process of generating SQL queries from natural language input, has gained significant attention due to its potential to simplify database interaction. Although benchmarks in this task have driven advancements in the field, the challenges posed by complex join logic and the rich diversity of natural language expressions remain significant obstacles. These complexities underscore the ongoing difficulty of accurately bridging the gap between natural language and structured query representations, particularly in cross-domain and real-world scenarios. Recent research, including intermediate representations, relation-aware transformers, and large language models such as T5 and LLaMA, has improved performance by addressing the semantic gap between natural language and SQL. In this work, we propose SLENet, a novel approach that uses state-of-the-art large language models (LLMs) to enhance semantic understanding and SQL generation. Our method integrates three core innovations: (1) the use of advanced LLMs for context-aware representations, (2) syntax-constrained SQL decoder to ensure grammatical correctness, and (3) search-based prompt optimization utilizing external knowledge sources like WikiSQL. These innovations collectively address schema comprehension and SQL generation complexities. Evaluations on the Spider benchmark demonstrate that SLENet significantly outperforms existing methods, achieving higher exact matching accuracy and effectively handling complex SQL components. Our contributions highlight the importance of combining LLMs with syntax constraints and external data for advancing cross-domain semantic parsing.}, archive = {J_ACCESS}, author = {Zecheng Zhan and E. Haihong and Meina Song}, doi = {10.1109/ACCESS.2025.3540072}, journal = {IEEE Access}, pages = {30497-30504}, shortjournal = {IEEE Access}, title = {Leveraging large language model for enhanced text-to-SQL parsing}, volume = {13}, year = {2025}, }
• (2025). Trans-border trusted data spaces: A general framework supporting trustworthy international data circulation. ACCESS, 13, 30481-30496. (www)

  @article{ , abstract = {In today’s interconnected global landscape, the seamless circulation of data across borders has emerged as a pivotal factor underpinning the global economy, scientific research, and technological advancements. It is imperative to enable a secure data-sharing ecosystem that ensures trustworthiness, which, in the cross-border context, refers to traceability, accountability, and multiparty compliance. However, a significant challenge remains: the lack of a universally applicable framework tailored to these scenarios, one could provide a trust foundation and corresponding assurances. To address this, we have taken “traceable, accountable, and multiparty compliant data-sharing” as design principles and propose a Trans-border Trusted Data Spaces (TTDS) framework. TTDS adopts the International Data Spaces (IDS) information model used in EU data-sharing projects to ensure interoperability and leverage the existing tools and standards to establish trustworthiness of participant identities. Building upon this, the framework provides traceability and accountability for data usage through security features including IP layer traffic monitoring, zero-trust identity management, smart contracts, etc. It also offers multi-party compliance support through a natural language processing (NLP) based rule interpretation framework. Compared to existing data-sharing frameworks, TTDS provides a more reliable and general “infrastructure” tailored to the specific needs of cross-border scenarios. In this paper, we present the functional and technical architecture as well as the core components of TTDS. And then propose a system implementation based on IPv6 for the more urgently needed aspects of data traceability, empirically demonstrating its effectiveness in achieving robust traceability and provided effective evidence to support accountability.}, archive = {J_ACCESS}, author = {Chi Zhang and Yan Liu and Mudi Xu and Xingxing Yang and Peng Li and Changshui Yang and Qiang Liu and Xinning Xiong and Pan Chen and Wenyong Wang}, doi = {10.1109/ACCESS.2025.3541295}, journal = {IEEE Access}, pages = {30481-30496}, shortjournal = {IEEE Access}, title = {Trans-border trusted data spaces: A general framework supporting trustworthy international data circulation}, volume = {13}, year = {2025}, }
• (2025). Mobile action modeling: Efficient and effective scenery classification with ball applications. ACCESS, 13, 30469-30480. (www)

  @article{ , abstract = {Human action recognition in ball sports presents significant challenges in computer vision. In this article, a new pipeline is designed utilizing mobile depth cameras, such as the iPhone’s LiDAR, to improve action recognition accuracy by integrating depth data into gesture analysis. Our methodology encodes human joint information captured by depth cameras, employing an intelligent accumulation of joint point data to classify various actions. The proposed pipeline offers two flexible techniques: shallow and deep joint point accumulation. Shallow accumulation prioritizes rapid detection for time-sensitive applications, while deep accumulation ensures higher accuracy for detailed 3D action reconstruction. By leveraging depth data to model regions of human activity, our system effectively distinguishes between multiple ball sport actions. It further calculates joint angles and repairs occluded points, enhancing recognition reliability. Experiments indicate the robustness of the proposed pipeline in complex scenarios. However, challenges remain, particularly in joint point detection within intricate backgrounds and under varying environmental conditions. These challenges underscore the importance of improving foreground extraction and tracking mechanisms. This work demonstrates the potential of integrating depth information with intelligent joint analysis to overcome traditional limitations in human action recognition, advancing its application in ball sports.}, archive = {J_ACCESS}, author = {Dacheng Ma}, doi = {10.1109/ACCESS.2025.3541866}, journal = {IEEE Access}, pages = {30469-30480}, shortjournal = {IEEE Access}, title = {Mobile action modeling: Efficient and effective scenery classification with ball applications}, volume = {13}, year = {2025}, }
• (2025). Sentiment analysis of twitter data using NLP models: A comprehensive review. ACCESS, 13, 30444-30468. (www)

  @article{ , abstract = {Social media platforms, particularly Twitter, have become vital sources for understanding public sentiment due to the rapid, large-scale generation of user opinions. Sentiment analysis of Twitter data has gained significant attention as a method for comprehending public attitudes, emotional responses, and trends which proves valuable in sectors such as marketing, politics, public health, and customer services. In this paper, we present a systematic review of research conducted on sentiment analysis using natural language processing (NLP) models, with a specific focus on Twitter data. We discuss various approaches and methodologies, including machine learning, deep learning, and hybrid models with their advantages, challenges, and performance metrics. The review identifies key NLP models commonly employed, such as transformer-based architectures like BERT, GPT, etc. Additionally, this study assesses the impact of pre-processing techniques, feature extraction methods, and sentiment lexicons on the effectiveness of sentiment analysis. The findings aim to provide researchers and practitioners with a comprehensive overview of current methodologies, insights into emerging trends, and guidance for future developments in the field of sentiment analysis on Twitter data.}, archive = {J_ACCESS}, author = {Aish Albladi and Minarul Islam and Cheryl Seals}, doi = {10.1109/ACCESS.2025.3541494}, journal = {IEEE Access}, pages = {30444-30468}, shortjournal = {IEEE Access}, title = {Sentiment analysis of twitter data using NLP models: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Improving patch optimization with buggy block for complex program repair. ACCESS, 13, 30420-30443. (www)

  @article{ , abstract = {Automated program repair (APR) identifies and repairs bugs automatically. However, previous studies have faced some limitations in terms of counting the number of repaired bug locations, generating and evaluating patches, etc. Therefore, to overcome these limitations, we propose the “MCRepair++” technique for APR that applies minimized repaired locations, revised patch filtering and ranking, and a proportional patch combination. First, the minimized repaired locations merge the divided cases and exclude overlapping cases to improve preprocessing for buggy blocks. Next, the revised patch filtering and ranking filter out unnecessary patches and favor patches, which have fewer expected actions, with a high score. Finally, the proportional patch combination yields high top k values to reasonably select patches while considering the size of each buggy chunk. We conducted several experiments and evaluated the proposed approach using six project modules of Defects4J. On Defects4J, MCRepair++ correctly fixed 79 bugs, including 31 multi-chunk bugs. It also showed better performance than the baselines. Moreover, it found many unique bugs, including multi-chunk bugs, compared to the baselines.}, archive = {J_ACCESS}, author = {Jisung Kim and Aslan Safarovich Abdinabiev and Byungjeong Lee}, doi = {10.1109/ACCESS.2025.3541336}, journal = {IEEE Access}, pages = {30420-30443}, shortjournal = {IEEE Access}, title = {Improving patch optimization with buggy block for complex program repair}, volume = {13}, year = {2025}, }
• (2025). Long-range LiDAR vehicle detection through clustering and classification for autonomous racing. ACCESS, 13, 30406-30419. (www)

  @article{ , abstract = {With the expansion of autonomous driving technology, autonomous racing has been actively studied in recent years. For safe autonomous racing, fast computation speed and wide detection range are essential. However, existing methods can perform detection only in short range due to the sparsity of LiDAR data. While some methods have been proposed to address this limitation, they often neglect real-time processing, making them unsuitable for racing environments. To address this, we propose a novel clustering and classification-based method specifically designed to enhance long-range vehicle detection while maintaining computational efficiency. First, our method employs a lightweight road segmentation and ground removal module to eliminate irrelevant data, significantly reducing computational overhead without down-sampling. Second, we introduce a 2D Bird’s Eye View (BEV)-based clustering approach, which is faster and more robust than traditional 3D Euclidean clustering, to generate object candidates efficiently. Additionally, a machine learning classifier trained on long-range vehicle features is incorporated to enhance detection accuracy at extended distances. Finally, range-view-based verification is performed to refine and finalize detection results, ensuring high reliability. The proposed method is evaluated on the aiMotive dataset, long-range dataset. Experimental results demonstrate that our approach achieves over 80% detection accuracy at distances exceeding 50 meters, with a computation time of only 25 ms per frame. These results underscore the novelty and effectiveness of the proposed method as an optimized solution for long-range vehicle detection in real-time autonomous racing environments.}, archive = {J_ACCESS}, author = {Na-Young Lim and Tae-Hyoung Park}, doi = {10.1109/ACCESS.2025.3541267}, journal = {IEEE Access}, pages = {30406-30419}, shortjournal = {IEEE Access}, title = {Long-range LiDAR vehicle detection through clustering and classification for autonomous racing}, volume = {13}, year = {2025}, }
• (2025). UAV-based remote sensing monitoring of maize growth using comprehensive indices. ACCESS, 13, 30395-30405. (www)

  @article{ , abstract = {Timely, efficient, and accurate acquisition of crop growth data is crucial for agricultural decision-making and management. This research focuses on maize, utilizing unmanned aerial vehicle (UAV) technology to monitor its growth and gather data on plant height, chlorophyll content, and leaf area index. Comprehensive growth monitoring indices, CGMICV and CGMICT, were developed using the coefficient of variation method (CV) and the technique for order preference by similarity to an ideal solution (TOPSIS) based on the coefficient of variation method of empowerment (Coefficient of variation-TOPSIS, CT) respectively. A correlation analysis between twelve vegetation indices and the comprehensive growth monitoring indices was conducted to select the optimal vegetation index for model input. Maize growth inversion models were established using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF), with model efficacy compared using performance metrics. The models were further interpreted using Shapley additive explanations (SHAP) values. The results indicate that the correlation between the comprehensive growth monitoring indices and vegetation indices was generally higher than that observed with individual indices. Among the three machine learning methods, the RF model demonstrated superior performance, showing the highest accuracy in the growth monitoring model established with CGMICT, with a coefficient of determination ( $R^{2}$ ) of 0.724, a root mean square error (RMSE) of 0.117, and mean absolute error (MAE) of 0.097. SHAP analysis revealed that GNDVI contributed most significantly to the predictive accuracy of the growth model. Spatial imaging distribution of maize inversion using the optimal CGMICT-RF model revealed discernible differences in overall crop growth. This study confirms the precision and reliability of this approach, providing a valuable reference for maize growth monitoring and regional crop production surveillance.}, archive = {J_ACCESS}, author = {Tingrui Yang and Jinghua Zhao and Ming Hong and Mingjie Ma and Shijiao Ma and Yingying Yuan}, doi = {10.1109/ACCESS.2025.3541098}, journal = {IEEE Access}, pages = {30395-30405}, shortjournal = {IEEE Access}, title = {UAV-based remote sensing monitoring of maize growth using comprehensive indices}, volume = {13}, year = {2025}, }
• (2025). Securing linux cloud environments: Privacy-aware federated learning framework for advanced malware detection in linux clouds. ACCESS, 13, 30377-30394. (www)

  @article{ , abstract = {Cloud computing is integral to modern IT infrastructure, with Linux-based virtual machines (VMs) comprising 95% of public cloud environments. This widespread use makes Linux VMs a prime target for cyberattacks, particularly advanced malware designed for financial gain, data theft, or operational sabotage. Traditional malware detection methods, despite their sophistication, often operate directly on the VMs they protect, making them susceptible to evasion by advanced malware-based threats. Furthermore, these methods are limited by their reliance on data confined to individual VMs, hindering their ability to generalize across different environments. While machine learning (ML) algorithms are frequently used to enhance malware detection, they typically require extensive data sharing, which poses significant risks to data confidentiality and user privacy. To overcome these challenges, we propose a federated learning-based framework for detecting unknown malware in Linux cloud environments. This framework allows a community of VMs, each equipped with a trusted local malware detection mechanism, to collaborate and enhance detection capabilities without sharing the underlying data, thus preserving privacy. The approach involves continuously capturing and converting volatile memory dumps into images, which are then used to train a federated convolutional neural network (CNN) in a decentralized manner. This eliminates the need for manual feature extraction and mitigates the risk of a single point of failure. Experimental results on widely-used Linux VMs demonstrate the framework’s effectiveness, achieving an AUC of up to 98.3% in detecting unknown malware, providing a robust and privacy-preserving solution for cloud security.}, archive = {J_ACCESS}, author = {Tom Landman and Nir Nissim}, doi = {10.1109/ACCESS.2025.3540955}, journal = {IEEE Access}, pages = {30377-30394}, shortjournal = {IEEE Access}, title = {Securing linux cloud environments: Privacy-aware federated learning framework for advanced malware detection in linux clouds}, volume = {13}, year = {2025}, }
• (2025). AMD GPU-based selected acceleration of PRACH and PUSCH in 5G NR. ACCESS, 13, 30365-30376. (www)

  @article{ , abstract = {The software-defined physical layer is a recent trend in the 5G/B5G open radio access network. Most implementations are based on a central processing unit, i.e., Intel XEON and ARM. This work proposes an uplink acceleration on an AMD graphics processor unit and OpenCL to show a new opportunity in 5th Generation New Radio HIGH-PHY. The key accelerators are the physical random access channel and the physical uplink shared channel under a real-time test environment. Referring to 5G NR, we balance and match slot formats and GPU resources to ensure the PHY works properly on the AMD MI.50 with fine-tuned parallelism and memory mapping. This work suggests that the AMD GPU is suitable for 5G ORAN.}, archive = {J_ACCESS}, author = {Nung-Yi Kuo and Terng-Yin Hsu}, doi = {10.1109/ACCESS.2025.3533547}, journal = {IEEE Access}, pages = {30365-30376}, shortjournal = {IEEE Access}, title = {AMD GPU-based selected acceleration of PRACH and PUSCH in 5G NR}, volume = {13}, year = {2025}, }
• (2025). FastADnet: Fast anomaly detection via core-feature centered cluster reconstruction network. ACCESS, 13, 30350-30364. (www)

  @article{ , abstract = {Industrial anomaly detection plays a critical role in computer vision, driving advancements across various sectors. In recent works in this field, there are trade-offs between computational complexity and anomaly detection accuracy for effective deployment. To achieve both reduced latency and improved accuracy, we propose a simple reconstruction network that learns local clusters centered at core-features derived from PatchCore. By leveraging these features that represent local data distributions and their adjacent patch-features, our network effectively enhances anomaly detection along with reduced inference time. Specifically, the proposed reconstruction network is designed to reconstruct core-features from their neighbors, effectively acting as a denoiser for Neighbor Noise. So neighbor noise is defined as the distance between a core-feature and its adjacent patch-features, which can be regarded as a noise added to core-faeture. By reducing this noise, our network creates tightly clustered reconstruction of core-features and their neighbors, excluding reconstructions of anomaly features. Evaluated on MVTecAD and MPDD datasets, our approach outperforms existing methods in both inference efficiency and accuracy. The code is available at https://github.com/Nadaeyeob/FastADnet.}, archive = {J_ACCESS}, author = {Daeyeob Na and Youngjoon Yoo}, doi = {10.1109/ACCESS.2025.3540435}, journal = {IEEE Access}, pages = {30350-30364}, shortjournal = {IEEE Access}, title = {FastADnet: Fast anomaly detection via core-feature centered cluster reconstruction network}, volume = {13}, year = {2025}, }
• (2025). Analyzing activity of daily living data utilizing motor activity log toward quantitative scoring system. ACCESS, 13, 30334-30349. (www)

  @article{ , abstract = {Assessment of stroke severity and recovery progress relies on a therapist’s rating or score. It is typically administered manually with subjective input from therapists. This method is exposed to inconsistency, particularly when involving different therapists which depends on their own experiences and expertise. This paper presents a study on one-way ANOVA analysis to investigate the impact of force, forearm and elbow movement, Activity of Daily Living (ADL) equipment motion, and time duration on the MAL score during the execution of ADLs. A Motor Activity Log (MAL) is employed as the standard clinical assessment benchmark, where ten ADLs have been selected from the MAL standard for data collection purposes involving 30 healthy individuals and 56 stroke patients. The analyses are divided into two which are Analysis 1) focuses on the data with therapist rating 5, while Analysis 2) considers the data with therapist ratings ranging from 1 to 5. Data inputs including force, forearm and elbow movement, ADLs equipment motion, and activity time duration have been collected using sensors of force, distance, Inertial Measurement Unit (IMU), and encoders. Output data in MAL scores are obtained manually from therapists using the current methodology. The results indicate significant differences in 19 out of 40 cases for Analysis 1) and 85 out of 100 cases for Analysis 2). This paper contributes towards an objective and accurate automatic scoring system for a more consistent and efficient assessment of stroke patients’ performance and recovery progress.}, archive = {J_ACCESS}, author = {Mohd Azri Bin Abd Mutalib and Norsinnira Zainul Azlan and Nor Mohd Haziq Norsahperi and Ibrahim Hafizu Hassan}, doi = {10.1109/ACCESS.2025.3537619}, journal = {IEEE Access}, pages = {30334-30349}, shortjournal = {IEEE Access}, title = {Analyzing activity of daily living data utilizing motor activity log toward quantitative scoring system}, volume = {13}, year = {2025}, }
• (2025). An ensemble bayesian dynamic linear model for human activity recognition. ACCESS, 13, 30316-30333. (www)

  @article{ , abstract = {Human activity recognition (HAR) has been gaining attention in recent years as a result of its many applications in health, sports, entertainment, and surveillance. Due to the current ubiquity of inertial measurement units (IMUs) in consumer electronics, including accelerometers and gyroscopes, HAR applications have increasingly used signals produced by these sensors. However, HAR from IMU data is challenging, since time-series data generated from human activity are typically multivariate, non-stationary, and noisy. In this work, we investigate the application of Bayesian dynamic linear models (BDLMs) to the online classification of time-series data of human activity acquired from IMUs. BDLMs are promising in HAR from IMU signals because they seamlessly handle temporal dependencies and uncertainty inherent in sensor data. Unlike static classifiers, BDLMs account for the sequential nature of IMU signals, enabling more accurate tracking of transitions between activities. In particular, we propose a method based on an ensemble BDLM for online HAR that is fully transparent and requires little preprocessing of data. We test the proposed method in two tasks, activity classification and intensity classification, and use two real datasets with diverse activities and subjects. The experimental results indicate that the proposed ensemble BDLM is competitive with consolidated benchmark methods and can be an effective method in real applications of online HAR.}, archive = {J_ACCESS}, author = {Anselmo R. Pitombeira-Neto and Diego S. de França and Lívia A. Cruz and Ticiana L. C. da Silva and José F. Antonio de Macedo}, doi = {10.1109/ACCESS.2025.3541385}, journal = {IEEE Access}, pages = {30316-30333}, shortjournal = {IEEE Access}, title = {An ensemble bayesian dynamic linear model for human activity recognition}, volume = {13}, year = {2025}, }
• (2025). Evaluation of a monopole and dipole hybrid antenna array with individual floating shields for 10.5 tesla human head MRI applications. ACCESS, 13, 30306-30315. (www)

  @article{ , abstract = {The frequencies required for ultra-high field (UHF) magnetic resonance imaging (MRI) applications dictate the necessity for higher channel count of transmit arrays to improve the uniformity and efficiency of the transmit magnetic (B $_{1}^{+}$ ) field using a parallel transmit system (pTx). Based on the comparison of 8-channel arrays, our previous study suggested the concept of a monopole and dipole hybrid antenna (MDH) array for human head imaging at 10.5 Tesla (T) MRI. Here we expanded it to a 16-channel MDH array to obtain more uniform B $_{1}^{+}$ field distribution. Then, we further expanded it to a MDH array with individual floating shields (MDHFS) to extend coverage through the z-direction of MRI. To evaluate these arrays, we compared the B $_{1}^{+}$ efficiencies of the 16-channel MDH and MDHFS arrays with the same dimensions both in electromagnetic field simulations and experiments at 10.5 T MRI. Both results revealed that the MDHFS array (0.52/ $0.36~\mu $ T/ $\surd $ W for the simulation and experiment, respectively) showed ~35% lower peak B $_{1}^{+}$ efficiency compared to the MDH array (0.76/ $0.59~\mu $ T/ $\surd $ W for the simulation and experiment, respectively)). However, compared to the MDH array (~10 cm), the MDHFS array (~20 cm) had longer coverage in the entire phantom. Especially, for human head imaging, simulations indicated a 43.5% lower specific absorption rate (SAR) and 22.1% higher SAR efficiency at 10.5 T with the MDHFS array (0.26 W/kg and $1.04~\mu $ T/ $\surd $ (W/kg), respectively) compared to the MDH array (0.46 W/kg and $0.81~\mu $ T/ $\surd $ (W/kg), respectively).}, archive = {J_ACCESS}, author = {Myung Kyun Woo}, doi = {10.1109/ACCESS.2025.3540885}, journal = {IEEE Access}, pages = {30306-30315}, shortjournal = {IEEE Access}, title = {Evaluation of a monopole and dipole hybrid antenna array with individual floating shields for 10.5 tesla human head MRI applications}, volume = {13}, year = {2025}, }
• (2025). Anomaly identification method for the detection process of determining formaldehyde emission from wood-based panels by the chamber method using GCN-BiLSTM model. ACCESS, 13, 30287-30305. (www)

  @article{ , abstract = {The chamber method is a standardized testing methods for determining formaldehyde emission from wood-based panels. Due to the complexity and multiple stages involved in the overall detection process, abnormalities are often difficult to perceive when they occur, which may compromise the accuracy of formaldehyde concentration measurements. Therefore, this paper proposes an anomaly identification method for the formaldehyde detection process of wood-based panels, combining Graph Convolutional Neural Network and Bidirectional Long and Short-Term Memory network (GCN-BiLSTM) models. The proposed method first establishes a graph relationship model between the detection time and the operational information of the detection equipment based on the detection standard requirements. Subsequently, the GCN aggregates neighborhood information from the detection equipment nodes to extract graph structural features. The BiLSTM is then used to capture the temporal dependencies of the equipment’s operational features. Finally, the Softmax function is used to obtain the classification results of anomalies. Experimental results demonstrate that the GCN-BiLSTM model effectively identifies anomalies in the formaldehyde detection process of wood-based panels. It exhibits higher detection accuracy compared to other methods. The research presented in this paper provides significant theoretical support and practical implications for the intelligent management of formaldehyde detection in wood-based panels.}, archive = {J_ACCESS}, author = {Qingchun Jiao and Kuokuo Wang and Bin Lin and Zili Chen and Gaoqing Xu and Jinfei Ye and Min Zhu and Yifan Zhang}, doi = {10.1109/ACCESS.2025.3540907}, journal = {IEEE Access}, pages = {30287-30305}, shortjournal = {IEEE Access}, title = {Anomaly identification method for the detection process of determining formaldehyde emission from wood-based panels by the chamber method using GCN-BiLSTM model}, volume = {13}, year = {2025}, }
• (2025). Enhancing indoor localization with temporally-aware separable group shuffled CNNs and skip connections. ACCESS, 13, 30274-30286. (www)

  @article{ , abstract = {Fingerprint-based indoor localization is the predominant localization approach for GPS-restricted environments due to its minimal hardware requirements. However, its performance is affected by signal fluctuations caused by shadowing, fading, and multipath effect, which necessitates models capable of capturing temporal variations. Numerous machine learning and deep learning algorithms have been introduced to surmount this limitation, within which the convolutional neural network (CNN) stands out as the most prominent. The 1D and 2D CNN models developed for indoor localization have the ability to extract spatial features but not temporal, leading to degradation in online localization accuracy. In contrast to 2D CNNs, 3D CNNs possess the ability to extract spatio-temporal information, but their computational complexity precludes real-time implementation. In this paper, a novel 3D-separable CNN for indoor localization is designed using skip connections and group shuffling. By employing depth-wise and point-wise convolutions, separable convolutions significantly reduce computational complexity, achieving a 10-fold improvement over conventional convolution, which facilitating real-time applications at the cost of reduced accuracy. Incorporating skip connections and group shuffling can help offset this accuracy decline. The 2D CNN, 2D separable CNN, and 3D CNN are used as benchmarks and evaluated on the UJILIB public dataset. Numerical results reveal that the proposed model is able to attain a positioning accuracy of 66.28% with an average positioning error of 1.04 meters in distance. Compared to the 2D separable CNN, the proposed 3D separable CNN outperforms it by achieving a 24.03% lower average positioning error.}, archive = {J_ACCESS}, author = {Muhammad Rizwan and Yin Hoe Ng and Hin-Yong Wong and Chee Keong Tan}, doi = {10.1109/ACCESS.2025.3535948}, journal = {IEEE Access}, pages = {30274-30286}, shortjournal = {IEEE Access}, title = {Enhancing indoor localization with temporally-aware separable group shuffled CNNs and skip connections}, volume = {13}, year = {2025}, }
• (2025). Tree variational autoencoder for code. ACCESS, 13, 30262-30273. (www)

  @article{ , abstract = {Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder-decoder architectures are suboptimal for source code because they ignore the grammatical structure that can be derived with an Abstract Syntax Tree parser. We propose a structured Variational Auto-Encoder based on TreeLSTM that operates directly on the AST. We train it along with a baseline sequence VAE on a dataset of competitive programming submissions We find the structured model to perform better in most tests, with some notable exceptions. These findings suggest structured autoencoder models could enable more effective generation and manipulation of source code for tasks like automated bug fixing and generative programming.}, archive = {J_ACCESS}, author = {Vadim Liventsev and Sander de Bruin and Aki Härmä and Milan Petković}, doi = {10.1109/ACCESS.2025.3532522}, journal = {IEEE Access}, pages = {30262-30273}, shortjournal = {IEEE Access}, title = {Tree variational autoencoder for code}, volume = {13}, year = {2025}, }
• (2025). Time-series large language models: A systematic review of state-of-the-art. ACCESS, 13, 30235-30261. (www)

  @article{ , abstract = {Large Language Models (LLMs) have transformed Natural Language Processing (NLP) and Software Engineering by fostering innovation, streamlining processes, and enabling data-driven decision-making. Recently, the adoption of LLMs in time-series analysis has catalyzed the emergence of time-series LLMs, a rapidly evolving research area. Existing reviews provide foundational insights into time-series LLMs but lack a comprehensive examination of recent advancements and do not adequately address critical challenges in this domain. This Systematic Literature Review (SLR) bridges these gaps by analysing state-of-the-art contributions in time-series LLMs, focusing on architectural innovations, tokenisation strategies, tasks, datasets, evaluation metrics, and unresolved challenges. Using a rigorous methodology based on PRISMA guidelines, over 700 studies from 2020 to 2024 were reviewed, with 59 relevant studies selected from journals, conferences, and workshops. Key findings reveal advancements in architectures and novel tokenization strategies tailored for temporal data. Forecasting dominates the identified tasks with 79.66% of the selected studies, while classification and anomaly detection remain underexplored. Furthermore, the analysis reveals a strong reliance on datasets from the energy and transportation domains, highlighting the need for more diverse datasets. Despite these advancements, significant challenges persist, including tokenization inefficiencies, prediction hallucinations, and difficulties in modelling long-term dependencies. These issues hinder the robustness, scalability, and adaptability of time-series LLMs across diverse applications. To address these challenges, this SLR outlines a research roadmap emphasizing the improvement of tokenization methods, the development of mechanisms for capturing long-term dependencies, the mitigation of hallucination effects, and the design of scalable, interpretable models for diverse time-series tasks.}, archive = {J_ACCESS}, author = {Shamsu Abdullahi and Kamaluddeen Usman Danyaro and Abubakar Zakari and Izzatdin Abdul Aziz and Noor Amila Wan Abdullah Zawawi and Shamsuddeen Adamu}, doi = {10.1109/ACCESS.2025.3535782}, journal = {IEEE Access}, pages = {30235-30261}, shortjournal = {IEEE Access}, title = {Time-series large language models: A systematic review of state-of-the-art}, volume = {13}, year = {2025}, }
• (2025). Integrating decision tools for environmental impact reduction in sustainable urban planning. ACCESS, 13, 30212-30234. (www)

  @article{ , abstract = {Environmental impact reduction is crucial for sustainable urban planning, as it mitigates the harmful effects of urbanization on ecosystems, climate, and public health. As cities expand, minimizing environmental degradation through well-planned strategies becomes essential to ensuring long-term sustainability. This paper integrates T-Spherical Fuzzy Sets (T-SFS) to address the inherent uncertainty in evaluating urban development alternatives. T-SFS enables more accurate assessments by capturing expert judgment variability, particularly for complex criteria such as energy efficiency, biodiversity, and land use optimization. In addition, the study employs the CRADIS method alongside the Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) technique to assign precise weights to evaluation criteria. This integrated approach ensures that each criterion’s significance is accurately represented, supporting robust decision-making. The results provide a comparative analysis of five urban development alternatives, helping urban planners prioritize sustainable options that balance environmental, social, and economic factors. By incorporating fuzzy logic and advanced decision-making techniques, this study offers a comprehensive tool for policymakers to make informed decisions that contribute to sustainable urban growth, ultimately fostering cities that are more resilient, eco-friendly, and beneficial to society.}, archive = {J_ACCESS}, author = {Wei Zhu and Yuhui Ding}, doi = {10.1109/ACCESS.2025.3540140}, journal = {IEEE Access}, pages = {30212-30234}, shortjournal = {IEEE Access}, title = {Integrating decision tools for environmental impact reduction in sustainable urban planning}, volume = {13}, year = {2025}, }
• (2025). Edu-metaverse: A comprehensive review of virtual learning environments. ACCESS, 13, 30186-30211. (www)

  @article{ , abstract = {In the era of the maturity of various cutting-edge technologies, the concept of Metaverse has emerged as a revolutionary force reshaping how we engage with the digital world. Metaverse, expected to be the next iteration of the Internet, represents a large, interconnected digital environment where users, in the form of avatars, engage with one another, by offering a glimpse into a future where no boundaries exist between the physical and virtual worlds. As we navigate this innovative world, it becomes clear that the Metaverse can fundamentally alter various fields, mainly education, by creating new opportunities for engagement and immersive learning. In doing so, it not only facilitates collaborative learning experiences but also addresses the broader global challenges encapsulated in Sustainable Development Goal 4 - Quality Education. This paper contributes to the discourse by proposing a practical architecture for real-world Edu-Metaverse, suggesting a comprehensive taxonomy for the Edu-Metaverse, defining its essential components, and outlining innovative pedagogical approaches. Furthermore, the paper introduces an ecosystem model that illustrates the intricate interplay between the various components within the Edu-Metaverse. Addressing its key measures, the paper delves into strategies to ensure that the Edu-Metaverse is a space where learners of diverse backgrounds can participate meaningfully. By aligning these efforts with Sustainable Development Goal 4 - Quality Education, the Edu-Metaverse has the potential to become a powerful tool in promoting inclusive and equitable learning experiences. However, it is crucial to acknowledge the inherent limitations and challenges associated with the integration of the Edu-Metaverse into educational practices. The paper examines these obstacles, offering an in-depth understanding of the challenges that must be overcome to fully realize the potential of this innovative approach to education.}, archive = {J_ACCESS}, author = {Chaimae Illi and Azeddine Elhassouny}, doi = {10.1109/ACCESS.2025.3540944}, journal = {IEEE Access}, pages = {30186-30211}, shortjournal = {IEEE Access}, title = {Edu-metaverse: A comprehensive review of virtual learning environments}, volume = {13}, year = {2025}, }
• (2025). Open-domain factoid question-answering in urdu: Data and methods. ACCESS, 13, 30167-30185. (www)

  @article{ , abstract = {Open-domain factoid question-answering (ODFQA) aims to answer questions posed in natural language by retrieving and extracting relevant information from large, unstructured text sources. A range of applications have benefited from ODFQA including improved search relevancy in search engines and information retrieval, enable semantic searches, interactive and personalized learning systems, and the creation of large language models. Although significant research on ODFQA for English and other languages has been done, Urdu-specific research in this area remains limited. This is due to the lack of high-quality datasets and the challenges associated with processing Urdu text. To address the unavailability of Urdu-specific resources for the ODFQA task, we developed a benchmark corpus, comprising $3,985$ Urdu questions and corresponding Urdu Wikipedia articles, with $1,006$ answerable and $2,979$ unanswerable questions. Each question in our proposed corpus was manually annotated by three independent annotators. As a secondary contribution, we carried out extensive experimentation using a range of state- of-the-art models, including retrievers (BM25 and Sentence-BERT), multilingual transformers (mBERT, XLM-RoBERTa-Large, XLM-RoBERTa-Large-Squad2), and large language models (GPT-3.5-Turbo-0125, GPT-4o-mini-2024-07-18) on our proposed corpus. Best results were obtained using the XLM-RoBERTa-Large-Squad2 model with $F_{1} = 0.61$ and $EM = 0.41 \text{@}k = 20$ . While the finetuned GPT-4o-mini model was the best model, with $F_{1} = 0.81$ and $EM = 0.81 \text{@}k=1$ . To foster research in the Urdu ODFQA, our proposed corpus is freely available under the Creative Commons license.}, archive = {J_ACCESS}, author = {Muhammad Shakeel and Rao Muhammad Adeel Nawab}, doi = {10.1109/ACCESS.2025.3540939}, journal = {IEEE Access}, pages = {30167-30185}, shortjournal = {IEEE Access}, title = {Open-domain factoid question-answering in urdu: Data and methods}, volume = {13}, year = {2025}, }
• (2025). Adaptive circular receptive field for remote sensing image target detection. ACCESS, 13, 30155-30166. (www)

  @article{ , abstract = {Remote sensing images contain a variety of targets. The circular and square-like targets are very common. However, general methods are prone to incorporating background information and have problems with rotation angles when detecting circular and square-like targets. The Adaptive Circular Receptive Field Network (ACRFNet) is proposed to address these problems. Firstly, a radius detection head based on the center point is designed, which predicts the center point and radius of the target to obtain a bounding circle. Compared with the rectangular bounding box, the bounding circle contains less background information and has rotation invariance, achieving in more accurate localization. Secondly, in order to better extract the features of circular targets and cope with scale changes, a multi-scale feature fusion method based on adaptive circular convolution is proposed. Adaptive circular receptive fields replace the ordinary convolution of square receptive fields, and a feature pyramid structure is introduced to enrich the semantic information of the feature map. Finally, in order to reasonably evaluate the performance of the model, Circle IOU is used to calculate mAP to fit the bounding circle. The experimental results on the NWPU VHR-10 and DIOR datasets demonstrate that the model has significantly improved detection accuracy for circular and square-like targets and has better generalization for other types of targets.}, archive = {J_ACCESS}, author = {Ning Wen and Yun Ge and Xiaoyu Yang and Meigen Yu and Ting Wang}, doi = {10.1109/ACCESS.2025.3538575}, journal = {IEEE Access}, pages = {30155-30166}, shortjournal = {IEEE Access}, title = {Adaptive circular receptive field for remote sensing image target detection}, volume = {13}, year = {2025}, }
• (2025). Program-analysis-based race digger for improving the reliability of high-performance applications. ACCESS, 13, 30137-30154. (www)

  @article{ , abstract = {High-performance applications commonly utilize OpenMP, which supports shared-memory-based multi-processing programming. Unlike sequential programs, they are exposed to various programming errors owing to the different paradigms in executing parallel programs. One of these errors, the data race, is dependent on the sequence (timing) of other events or the execution flow of programs; therefore, it is not easy to detect. Although ample efforts have been made to build data-race detection techniques and tools, even state-of-the-art tools such as Intel Inspector still have functionality and performance issues. In this paper, we devise PCSP and AMSP, self-developed synthetic programs written in OpenMP directives to thoroughly examine these issues and have refined the problems of Intel Inspector through these programs. To resolve these problems, we propose a program analysis-based race digger (PARD). The race digger consists of a program analyzer, a source instrumentor, an adaptive on-the-fly race detector, and a race analyzer. It passes through three phases to detect data races and finally provides the root causes of detected races using the race analyzer. We demonstrate the functionality and performance advantages of PARD over Intel Inspector. Regarding functionality, experimental results show that while Intel Inspector can only verify data races in the simple model, PARD verifies data races in both simple and complex models; The results show that PARD’s data-race detection time is better than that of Intel Inspector by MIN 99.04% ~ MAX 99.89% in single-process systems and MIN 98.77% ~ MAX 99.68% in multi-process systems.}, archive = {J_ACCESS}, author = {Young-Joo Kim}, doi = {10.1109/ACCESS.2025.3527567}, journal = {IEEE Access}, pages = {30137-30154}, shortjournal = {IEEE Access}, title = {Program-analysis-based race digger for improving the reliability of high-performance applications}, volume = {13}, year = {2025}, }
• (2025). Design and performance analysis of a single-port bidirectional soft actuator and its integration into a versatile 3-finger robot gripper. ACCESS, 13, 30127-30136. (www)

  @article{ , abstract = {This paper presents the design, fabrication, and characterization of a new soft robotic finger capable of bidirectional bending using single-port pneumatic actuation. Unlike the conventional soft pneumatic finger designs that require multiple input ports and/or actuation methods (i.e., positive and negative pressure), the proposed soft actuator uses a pre-curved body design to achieve bidirectional bending with a single port and a single actuation method (i.e., positive pneumatic pressure only), simplifying both fabrication and control. Finite Element Analysis is used to refine the finger design to achieve the desired bending capabilities and tip force. The finger prototype is fabricated using silicone casting, and its performance is characterized over a pressure range of 0-100 kPa. Experimental results demonstrate a bending range of 68° [ $- 34^{\circ } ~34^{\circ } $ ] for the applied pressure range and a maximum tip force of 2.72 N at 100 kPa. A three-finger robot gripper is developed, and grasping experiments are conducted to showcase the gripper’s ability to successfully grasp objects of various sizes, shapes, and textures. The bidirectional bending allows the gripper to grasp objects exceeding its normal grasp area three times (18 cm).}, archive = {J_ACCESS}, author = {Rene M. Suarez Flores and Sajid Nisar}, doi = {10.1109/ACCESS.2024.3522076}, journal = {IEEE Access}, pages = {30127-30136}, shortjournal = {IEEE Access}, title = {Design and performance analysis of a single-port bidirectional soft actuator and its integration into a versatile 3-finger robot gripper}, volume = {13}, year = {2025}, }
• (2025). Nonlinear value function approximation method with easy hyperparameter tuning and convergence guarantee. ACCESS, 13, 30117-30126. (www)

  @article{ , abstract = {Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-and-error in the environment. Because this can be applicable even when the state transition function of the control object is unknown or it is difficult to create its model properly, this can reduce the designer’s burden. Reinforcement learning repeats evaluating a policy and improving it based on that evaluation. An algorithm called value function approximation is used in this evaluation. Value function approximation is a general term for algorithms of approximating a function called value function, which maps an arbitrary state to how much reward can be obtained in the future when acting according to a policy from that state. Value function approximation should have high approximation accuracy and hyperparameter easy to tune. Although various value function approximation methods have been proposed so far, some of them does not have convergent guarantee and the others’ hyperparameter tuning is difficult. The purpose of this paper is to propose a value function approximation method that have convergence guarantee even with nonlinear function approximator and whose hyperparameter tuning is easy and to verify its performance through numerical experiments. To this end, we focused on GTD2 method, which is one of the value function approximation methods that has convergence guarantee even with a nonlinear function approximator. GTD2 method has the drawback that its hyperparameter is difficult to tune appropriately so that the approximation does not diverge during learning. Therefore, we firstly clarified the cause of the divergence of GTD2 method. Secondly, based on this cause, we proposed Normalized and Regularized GTD2 (NRGTD2) method, that incorporates a method for suppressing divergence into GTD2 method. Finally, through numerical experiments, it was clarified that the proposed method suppresses the divergence and can achieve better approximation accuracy than conventional methods by easy hyperparameter tuning.}, archive = {J_ACCESS}, author = {Yuuya Watabe and Takeshi Shibuya}, doi = {10.1109/ACCESS.2025.3540900}, journal = {IEEE Access}, pages = {30117-30126}, shortjournal = {IEEE Access}, title = {Nonlinear value function approximation method with easy hyperparameter tuning and convergence guarantee}, volume = {13}, year = {2025}, }
• (2025). Multi-stage neural network-based ensemble learning approach for wheat leaf disease classification. ACCESS, 13, 30101-30116. (www)

  @article{ , abstract = {Prompt and accurate classification of wheat leaf diseases and their severity is essential for precise diagnosis, effective pesticide application, efficient disease control, and enhanced wheat production and quality. Nevertheless, the wide range of wheat diseases poses a significant challenge in terms of their identification, especially in intricate agricultural landscapes. The utilization of conventional models has major limitations in wheat disease detection, including dataset-specific performance, overfitting due to limited data, and high computational needs, making deployment in resource-constrained situations difficult. To address these challenges, this research proposes a multi-stage Convolutional Neural Network (CNN) based Ensemble Learning (EL) approach, which utilizes several concurrent CNN models with a bagged EL method to classify wheat leaf diseases. The various stages in the EL approach employ a multi-level framework to enhance feature extraction and capture complex data patterns. This improves the model’s emphasis on diseases while reducing the influence of complex backgrounds on disease identification. The proposed method, integrating pretrained CNNs and a bagging ensemble technique, achieved an accuracy of 86.78%, 98.28%, and 99.16% on the three publicly available datasets, outperforming the state-of-the-art models. These results demonstrate the potential of the proposed model for real-time disease diagnosis.}, archive = {J_ACCESS}, author = {Samia Nawaz Yousafzai and Inzamam Mashood Nasir and Sara Tehsin and Dania Saleem Malik and Ismail Keshta and Norma Latif Fitriyani and Yeonghyeon Gu and Muhammad Syafrudin}, doi = {10.1109/ACCESS.2025.3541347}, journal = {IEEE Access}, pages = {30101-30116}, shortjournal = {IEEE Access}, title = {Multi-stage neural network-based ensemble learning approach for wheat leaf disease classification}, volume = {13}, year = {2025}, }
• (2025). QDLTrans: Enhancing english neural machine translation with quantized attention block and tunable dual learning. ACCESS, 13, 30088-30100. (www)

  @article{ , abstract = {Neural Machine Translation (NMT) is a fundamental task in natural language processing, typically relying on large-scale parallel corpora to achieve high translation quality. However, resource-scarce language pairs, such as English-to-Vietnamese, suffer from a lack of high-quality bilingual data, which limits the effectiveness of NMT models. This paper proposes QDLTrans, a framework designed to enhance translation performance under resource-scarce conditions by integrating the multilingual pre-trained model ML-BERT into the Transformer architecture. To address the complexity of incorporating pre-trained language models, we introduce a Quantized Attention Block (QAB), which effectively distills semantic information from ML-BERT and integrates it with the Transformer. Additionally, we propose a Tunable Dual Learning (TDL) strategy that enriches the training process by leveraging both forward and reverse samples, significantly improving bilingual representation. Through a two-stage optimization process, we mitigate knowledge interference during fine-tuning using DoRA (Weight-Decomposed Low-Rank Adaptation), further enhancing model performance. Experimental results on resource-scarce translation datasets (i.e., IWSLT’14 En-De, IWSLT’15 En-Vi) demonstrate that QDLTrans achieves substantial improvements over baseline models with only a slight increase in computational costs. Specifically, our method outperforms existing approaches in both translation quality and efficiency, offering a balanced solution for enhancing NMT in low-resource settings.}, archive = {J_ACCESS}, author = {Xing Liu}, doi = {10.1109/ACCESS.2025.3540934}, journal = {IEEE Access}, pages = {30088-30100}, shortjournal = {IEEE Access}, title = {QDLTrans: Enhancing english neural machine translation with quantized attention block and tunable dual learning}, volume = {13}, year = {2025}, }
• (2025). MusicReco: Interactive interface modeling with user-centered design in a music recommendation system. ACCESS, 13, 30058-30087. (www)

  @article{ , abstract = {Recommendation technologies are widespread in streaming services, e-commerce, social media, news, and content management. Besides recommendation generation, its presentation is also important. Most research and development focus on the technical aspects of recommendation generation; therefore, a gap exists between recommendation generation and its effective presentation and user interaction. This study focuses on how personalized recommendations can be presented and interacted with in a music recommendation system using interactive visual interfaces. Interactive interface modeling with User-Centered Design (UCD) in a recommendation system is essential for creating a user-friendly, engaging, and personalized experience. By involving users in the recommendation process and considering their feedback, the system can deliver more relevant content, foster user trust, and improve overall user satisfaction and engagement. In this study, the visual interface design and development of a personalized music recommendation prototype (MusicReco) are presented using an iterative UCD approach, involving twenty end-users, one researcher, three academic professionals, and four experts. As the study is more inclined toward the recommendation presentation and visual modeling, we used a standard content-based filtering algorithm on the publicly available Spotify dataset for music recommendation generation. End-users helped to mature the MusicReco prototype to a basic working version through continuous feedback and design inputs on their needs, context, preferences, personalization, and effective visualization. Moreover, MusicReco captures the idea of mood-based tailored recommendations to encourage end-users. Overall, this study demonstrates how UCD can enhance the presentation and interaction of mood-based music recommendations, effectively engaging users with advancements in recommendation algorithms as a future focus.}, archive = {J_ACCESS}, author = {Mats Ottem Frantzvaag and Ayan Chatterjee and Debasish Ghose and Soumya P. Dash}, doi = {10.1109/ACCESS.2025.3540201}, journal = {IEEE Access}, pages = {30058-30087}, shortjournal = {IEEE Access}, title = {MusicReco: Interactive interface modeling with user-centered design in a music recommendation system}, volume = {13}, year = {2025}, }
• (2025). An enhanced zebra optimization algorithm with multiple strategies for global optimization and feature selection problems: A hepatocellular carcinoma case study. ACCESS, 13, 30036-30057. (www)

  @article{ , abstract = {The Zebra Optimization Algorithm (ZOA) is a newly proposed biology-based metaheuristic method drawn from zebras in nature, designed to tackle optimization problems. As with other metaheuristic algorithms, ZOA has limitations, including a decrease in population diversity, an imbalance between exploration and exploitation, restricted exploration abilities, and the inclination to get stuck in local optima. To tackle them, this paper proposes an enhanced version of ZOA with multiple strategies called CLESQ-ZOA, which combines three strategies: chaotic mapping, Logarithmic Spiral Strategy (LSS), and Enhanced Search Quality (ESQ). In CLESQ-ZOA, the initial population is produced utilizing chaotic mapping to increase population diversity and provide a more uniform and diverse spread of the initial solutions. The integration of LSS with CLESQ-ZOA allows for greater search capabilities and more efficient exploration of the search space by each individual. Moreover, the ESQ is integrated into the algorithm to minimize the likelihood of getting trapped in local optima by maintaining an equilibrium between exploration and exploitation. These multiple strategies are implemented to enhance population diversity, boost the search capability of CLESQ-ZOA, and strike a delicate balance between exploration and exploitation. The effectiveness of CLESQ-ZOA has been evaluated using standard benchmark functions. Additionally, a binary version of CLESQ-ZOA is also proposed in this paper. The binary version of CLESQ-ZOA is applied to twelve feature selection benchmark datasets to demonstrate its effectiveness in real-world applications. Besides, CLESQ-ZOA has been applied to Hepatocellular Carcinoma to further evaluate its effectiveness. The findings obtained from the CLESQ-ZOA are compared with various metaheuristic methods and the performance is analyzed with Wilcoxon rank-sum and Friedman tests. Experimental and statistical results confirm the effectiveness and reliability of CLESQ-ZOA. Evaluations of feature selection datasets show that CLESQ-ZOA exhibits effective performance in determining the optimal feature subset.}, archive = {J_ACCESS}, author = {Feyza Altunbey Özbay}, doi = {10.1109/ACCESS.2025.3541975}, journal = {IEEE Access}, pages = {30036-30057}, shortjournal = {IEEE Access}, title = {An enhanced zebra optimization algorithm with multiple strategies for global optimization and feature selection problems: A hepatocellular carcinoma case study}, volume = {13}, year = {2025}, }
• (2025). Two-round optimization algorithm based on quadric error metrics. ACCESS, 13, 30021-30035. (www)

  @article{ , abstract = {Triangular mesh models are widely used for 3D model displays, but large models can cause slow loading and transmission, especially on mobile or web platforms. The quadric error metrics (QEM) algorithm is known for its efficient simplification and effectiveness. Nevertheless, it lacks clear prioritization for folding flat areas within the model, leaving room for improvement. This paper reproduces the work of Zhang Yun et al., finding that while their method reduces simplification errors, it increases the folding time of flat areas. The study improves the QEM algorithm by combining the angle and area of the first-order neighboring triangles of the edge, resulting in faster simplification but higher errors, making it less versatile. To address these issues, a two-round simplification method is proposed. The first round simplifies planar areas, and the second focuses on the entire model. Compared to the recursive algorithm, the two-round method showcases shorter simplification times, particularly evident when the wall.obj exhibits a lower simplification ratio. For simplification rates below 40%, it reduces the average simplification time by 22.77% and yields better model accuracy than the QEM algorithm. On bunny_norm.obj, the simplification error is smaller than with the QEM algorithm, showcasing an average error reduction of 21.82%. Visually, the two-round proposed algorithm retains more detailed features and has better visual effects. However, because of the lower computational efficiency of the two-round simplification, future research directions may involve parallel computing and GPU utilization for acceleration.}, archive = {J_ACCESS}, author = {Han Chang and Di Zhang and Yanan Dong and Yijun Yang and Hafiz Shafiq Ur Rehman Khalil and Xueting Wang}, doi = {10.1109/ACCESS.2025.3541436}, journal = {IEEE Access}, pages = {30021-30035}, shortjournal = {IEEE Access}, title = {Two-round optimization algorithm based on quadric error metrics}, volume = {13}, year = {2025}, }
• (2025). Probabilistic allocation of payload code rate and header copies in LR-FHSS networks. ACCESS, 13, 30010-30020. (www)

  @article{ , abstract = {We evaluate the performance of the LoRaWAN Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) technique using a device-level probabilistic strategy for code rate and header replica allocation. Specifically, we investigate the effects of different header replica and code rate allocations at each end-device, guided by a probability distribution provided by the network server. As a benchmark, we compare the proposed strategy with the standardized LR-FHSS data rates DR8 and DR9. Our numerical results demonstrate that the proposed strategy consistently outperforms the DR8 and DR9 standard data rates across all considered scenarios. Notably, our findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.}, archive = {J_ACCESS}, author = {Jamil de Araujo Farhat and Jean Michel de Souza Sant’Ana and João Luiz Rebelatto and Nurul Huda Mahmood and Gianni Pasolini and Richard Demo Souza}, doi = {10.1109/ACCESS.2025.3541407}, journal = {IEEE Access}, pages = {30010-30020}, shortjournal = {IEEE Access}, title = {Probabilistic allocation of payload code rate and header copies in LR-FHSS networks}, volume = {13}, year = {2025}, }
• (2025). The role of multiple data characteristics in EEG-based biometric recognition: The impact of states, channels, and frequencies. ACCESS, 13, 29994-30009. (www)

  @article{ , abstract = {Electroencephalogram (EEG)-based biometric recognition systems have attracted substantial interest in recent years because of their promising applications in various fields, notably in biometric security. This study examines the influence of multiple attributes of data, such as channels, frequencies, and data states, on the accuracy of person recognition using EEG data. We analyzed EEG recordings from 20 participants, captured with affective and non-affective, that contain resting, visually evoked potential, and motor activity states, during a single session. Utilizing a data set comprising both affective and non-affective states, we developed a person recognition system that is resistant to affective fluctuations and the challenges posed by non-affective conditions. This hybrid data set (combining data on affective and non-affective states) introduces a novel approach that extends beyond what has been addressed in previous studies. Furthermore, our research establishes a comparative framework for evaluating the effects of alpha, beta, and gamma frequency ranges, as well as frontal, central, and occipital channel regions, on the biometric system. This framework offers valuable insights into which factors perform better for recognition throughout affective and non-affective data sets. The results obtained by employing a surrogate model demonstrated accuracy rates of 95.34% for affective, 84.86% for non-affective, and 93.92% for hybrid data sets. Notably, the affective data demonstrated higher classification success across all EEG frequencies and channel regions compared to the non-affective data.}, archive = {J_ACCESS}, author = {Tuba Salturk and Nihan Kahraman}, doi = {10.1109/ACCESS.2025.3541176}, journal = {IEEE Access}, pages = {29994-30009}, shortjournal = {IEEE Access}, title = {The role of multiple data characteristics in EEG-based biometric recognition: The impact of states, channels, and frequencies}, volume = {13}, year = {2025}, }
• (2025). A compact vivaldi-shaped array using antipodal vivaldi antennas to stabilize the high-frequency radiation pattern. ACCESS, 13, 29983-29993. (www)

  @article{ , abstract = {In this study, a novel ultra-wideband antipodal Vivaldi antenna with a Chebyshev tapering profile and exponential slots is designed and measured. The dimensions of the proposed antenna are 95mm $ \times 100$ mm $ \times 0.127$ mm and the relative dimensions are $0.31\lambda _{0} \times 0.327\lambda _{0} \times 0.0004\lambda _{0}$ where $\lambda _{0}$ is the free space wavelength at the lowest operating frequency at 0.98 GHz for a $VSWR \leq 1.9$ . Several array configurations are proposed and all elementary array sources are combined in a mirror configuration, including the feeding part, to drastically reduce cross-polarization. First, a planar array is created at a distance $d=150mm$ between elementary sources. Then, from this primary source, an oblique array is created by inclining the two antennas at an angle $\theta $ , which cancels the ambiguity lobes in the upper frequency band. Finally, the single antenna is bent along a Vivaldi curvature, creating a mechanical tilt depending on the curvature parameters in H-plane. An array is formed by two curved Vivaldi antennas and exhibits a tilting effect with better compactness than the oblique array. For measurements, several foams with Vivaldi curvatures were created, allowing us to shape the Vivaldi antennas along various array distances, resulting in various beam shapes depending on the configuration used. The optimal distance $d=50mm$ provides a more stable radiation pattern across frequencies and cancels out ambiguity lobes for distances > $2\lambda _{0}$ . These Vivaldi-shaped arrays are measured and show good agreement with simulations.}, archive = {J_ACCESS}, author = {Julien Harel and Mohamed Himdi and Olivier Lafond and Olivier Clauzier and Olivier Vivares}, doi = {10.1109/ACCESS.2025.3541348}, journal = {IEEE Access}, pages = {29983-29993}, shortjournal = {IEEE Access}, title = {A compact vivaldi-shaped array using antipodal vivaldi antennas to stabilize the high-frequency radiation pattern}, volume = {13}, year = {2025}, }
• (2025). A hyperspectral classification method based on deep learning and dimension reduction for ground environmental monitoring. ACCESS, 13, 29969-29982. (www)

  @article{ , abstract = {Hyperspectral remote sensing images exhibit high dimensionality, a large volume of data, and significant redundant information. Before using deep learning methods for ground monitoring and classification, dimension reduction is often necessary. In response to the limitations of traditional principal component analysis in achieving comprehensive feature extraction, which may impact classification accuracy and computational efficiency, a combined classification method for hyperspectral remote sensing data is proposed, which uses t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction algorithm instead of traditional Principal Component Analysis (PCA) algorithm, and combines it with a deep learning network model Hybrid Spectral Network (HybridSN), to achieve accurate classification of ground cover and roof materials. Simultaneously, to further validate the dimensionality reduction effect of the t-SNE algorithm and its impact on the deep network model, for the same dataset, another deep learning network Deep Feature Fusion Network (DFFN) was set as the experimental control group. The dataset used in this article is the publicly available aerial hyperspectral remote sensing dataset University of Pavia dataset (UP), and the main process is as follows: Firstly, for the UP dataset, PCA and t-SNE algorithms are used for data dimension reduction. Subsequently, these two datasets after dimension reduction are input into HybridSN and DFFN deep learning models for classification, respectively. Finally, the accuracy of the classification results is assessed, and based on this evaluation, the effectiveness of different dimensionality reduction algorithms is compared and analyzed. The experimental results demonstrate that the HybridSN and DFFN models combined with t-SNE dimension reduction, are capable of effectively extracting hybrid species of ground objects, while pre-serving clear edge information in hyperspectral remote sensing image classification, achieving ground environment monitoring. They also exhibit superior performance in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.}, archive = {J_ACCESS}, author = {Qiao Zhe and Wei Gao and Chen Zhang and Gang Du and Yan Li and Desheng Chen}, doi = {10.1109/ACCESS.2025.3542038}, journal = {IEEE Access}, pages = {29969-29982}, shortjournal = {IEEE Access}, title = {A hyperspectral classification method based on deep learning and dimension reduction for ground environmental monitoring}, volume = {13}, year = {2025}, }
• (2025). A patent infringement analysis approach based on patent knowledge graph driven fusion of graph and image similarity. ACCESS, 13, 29944-29968. (www)

  @article{ , abstract = {Patent infringement analysis (PIA) represents a critical task in patent circumvention design. It aims to identify the likelihood of infringement for target technologies to enhance product innovation, serving as a crucial measure for technical protection. Traditional PIA processes rely on examiners’ subjective judgments on the patent’s technical advantages and textual similarity, leading to the infringement results heavily dependent on personal experience. Pervious similarity calculation models based on keywords or textual content lack the utilization of knowledge of functions, structures, and features within patents and their interrelations, resulting in inaccurate assessments of technological infringement for similar patents. Furthermore, the potential of patent images in infringement analysis remains underutilized. To overcome the issues, a patent knowledge graph (PKG) driven patent similarity calculation model fusing graph similarity (GS) and image similarity (IS) is proposed to achieve the prediction of the probability of patent infringement using patent text and structure images as multimodal data. First, an ontology model based on requirement-function-structure-location features (RFSL) is constructed. Four entity types are extracted from patent texts and images using a fine-tuned named entity recognition (NER) model combined with semantic relation analysis. Second, syntactic matching rules for eight types of entity relationships are constructed to extract triples, mapping patent texts into graph networks via the PKG. Finally, the integrated Graphical Neural Network (GNN) and Convolutional Neural Network (CNN) are integrated to calculate the overall similarity between the newly filed patent and the comparison patent to output the infringement probability. A case study of steel pipe welding device design is used to validate the proposed approach, then the comparison results confirm the potential of fusion GS and IS in the application in PIA.}, archive = {J_ACCESS}, author = {Liting Jing and Chenlong Zhou and Di Feng and Yubo Dou and Xiaoyan Fan and Shaofei Jiang}, doi = {10.1109/ACCESS.2025.3541874}, journal = {IEEE Access}, pages = {29944-29968}, shortjournal = {IEEE Access}, title = {A patent infringement analysis approach based on patent knowledge graph driven fusion of graph and image similarity}, volume = {13}, year = {2025}, }
• (2025). Improving accuracy of IC surface defects detection via enhanced-CycleGAN data augmentation. ACCESS, 13, 29932-29943. (www)

  @article{ , abstract = {An important step in integrated circuit (IC) manufacturing is inspection of the chip surface for defects. In practice, there exists a data imbalance problem associated with IC surface defect images which affects the detection performance of a deep learning-based detection model. In this paper, this data imbalance problem is addressed via generating synthetic IC surface defect images by the generative network of Enhanced-CycleGAN. The main contribution of this work involves exhibiting that such a data augmentation enables defective IC surfaces to be detected at higher detection accuracies compared with the situation when no data augmentation is used. First, the generated synthetic images by the Enhanced-CycleGAN generative network are compared with the synthetic generative networks of GAN, VAE, Diffusion, and CycleGAN as well as with the traditional image processing data augmentation. The closeness of synthetic images to real images is assessed based on five similarity metrics of Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity (VIF), Fréchet Inception Distance (FID), and Kernel Inception Distance (KID). The assessment conducted based on these metrics indicates that the synthetic IC surface defect images generated by Enhanced-CycleGAN resemble closer to the real IC surface defect images compared to the other generative networks. Second, the impact of the data augmentation by the Enhanced-CycleGAN generative network on the detection of IC surface defects is examined by considering three typical and commonly used detection models of VGG16, ResNet50, and YOLO11. It is shown that our Enhanced-CycleGAN data augmentation improves the detection performance across all the three detection models.}, archive = {J_ACCESS}, author = {Lamia Alam and Nasser Kehtarnavaz}, doi = {10.1109/ACCESS.2025.3541614}, journal = {IEEE Access}, pages = {29932-29943}, shortjournal = {IEEE Access}, title = {Improving accuracy of IC surface defects detection via enhanced-CycleGAN data augmentation}, volume = {13}, year = {2025}, }
• (2025). DomainLynx: Advancing LLM techniques for robust domain squatting detection. ACCESS, 13, 29914-29931. (www)

  @article{ , abstract = {Domain squatting, the malicious registration of deceptive domain names, poses a significant threat to Internet security. This study presents an enhanced version of DomainLynx, a system leveraging Large Language Models (LLMs) for detecting domain squatting. We introduce novel techniques that combine LLMs with domain-specific knowledge to identify a wide range of squatting tactics, including complex hybrid methods that merge multiple deception techniques. Our improved system features refined detection algorithms and an advanced validation process that significantly reduces false positives while maintaining high accuracy. Comprehensive evaluations using various LLM configurations demonstrate DomainLynx’s superior performance. Using a state-of-the-art LLM, the system achieved 94.7% accuracy on a diverse dataset of 1,649 squatting domains. In a month-long real-world test, DomainLynx detected 34,359 potential squatting domains from 2.09 million new registrations, outperforming existing methods by 2.5 times. Further analysis confirmed the system’s effectiveness across different squatting types. We also present case studies of hybrid-squatting domains, such as those combining typos with brand impersonation, offering insights into emerging threats. This research advances Internet security by providing a more accurate, adaptable, and thoroughly evaluated LLM-based tool for combating evolving domain squatting threats, contributing to safer online environments for users and organizations worldwide.}, archive = {J_ACCESS}, author = {Daiki Chiba and Hiroki Nakano and Takashi Koide}, doi = {10.1109/ACCESS.2025.3542036}, journal = {IEEE Access}, pages = {29914-29931}, shortjournal = {IEEE Access}, title = {DomainLynx: Advancing LLM techniques for robust domain squatting detection}, volume = {13}, year = {2025}, }
• (2025). MKD-net: A novel neuro evolutionary approach for blockchain-based secure medical image classification using multi-kernel DLM. ACCESS, 13, 29900-29913. (www)

  @article{ , abstract = {In the era of digital healthcare, secure and accurate classification of medical images is of utmost importance, particularly in situations of data imbalance. Owing to advancements in the domain of nature-inspired optimization and human brain simulations, this paper presents a novel approach to a neuro-evolutionary algorithm for medical image classification. In the proposed methodology, we develop a non-iterative learning-based neural network that can handle class imbalance with the help of class-specific regularizations, named multi-kernel deep neural networks (MKD-Net). Multiple kernels are employed in the proposed MKD-Net for different data representations in the classification of heterogeneous data. The MKD-Net parameters are optimized using the Enhanced-JAYA algorithm. ResNet-50, known for its incomparable feature drawing ability, has been used in MKD-Net as an input image feature extractor. Experimental evaluations conducted on benchmark medical image datasets demonstrate that MKD-Net achieves superior classification performance, particularly in imbalanced datasets. Key metrics such as classification accuracy, sensitivity, specificity, precision, and F1-score consistently improve over traditional models. Specifically, the proposed MKD-Net obtained accurate classification on ATT and ALL-IDB1 datasets; 97.44% and 97.46% accuracy on ALL-IDB2 and CoVid datasets respectively. Moreover, the integration with blockchain provide secure feature storage and also ensures data integrity, traceability, and enhanced privacy. By combining advanced feature extraction, optimization, and security mechanisms, MKD-Net sets a new benchmark in medical image classification, showcasing its potential for scalable and secure healthcare applications.}, archive = {J_ACCESS}, author = {Virendra P. Vishwakarma and Abhay Kumar Yadav}, doi = {10.1109/ACCESS.2025.3541096}, journal = {IEEE Access}, pages = {29900-29913}, shortjournal = {IEEE Access}, title = {MKD-net: A novel neuro evolutionary approach for blockchain-based secure medical image classification using multi-kernel DLM}, volume = {13}, year = {2025}, }
• (2025). ALPHA- α and bi-ACT are all you need: Importance of position and force information/ control for imitation learning of unimanual and bimanual robotic manipulation with low-cost system. ACCESS, 13, 29886-29899. (www)

  @article{ , abstract = {Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. As the need for diverse controls increases, there are demand for low-cost bimanual robots that consider various motor inputs. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and“A” “L”ow-cost “P”hysical “Ha”rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA- $\alpha $ ). Bi-ACT leverages bilateral control to utilize both position and force information, enhancing the robot’s adaptability to object characteristics such as hardness and shape. The concept of ALPHA- $\alpha $ is affordability, ease of use, repairability, ease of assembly, and diverse control modes such as position, velocity, and torque mode. In our experiments, we conducted detailed analysis of Bi-ACT in unimanual tasks involving objects with varying hardness, shape, and weight, confirming its superior performance and adaptability. We also applied Bi-ACT to bimanual tasks such as “Egg Handling” and “Open Cap” using ALPHA- $\alpha $ . The experimental outcomes demonstrated a high success rate in bimanual operations, validating the effectiveness of our approach in real-world scenarios. These results suggest that Bi-ACT and ALPHA- $\alpha $ can advance automation in daily life and industrial settings. Video available at: https://mertcookimg.github.io/alpha-biact/}, archive = {J_ACCESS}, author = {Masato Kobayashi and Thanpimon Buamanee and Takumi Kobayashi}, doi = {10.1109/ACCESS.2025.3541200}, journal = {IEEE Access}, pages = {29886-29899}, shortjournal = {IEEE Access}, title = {ALPHA- α and bi-ACT are all you need: Importance of position and force information/ control for imitation learning of unimanual and bimanual robotic manipulation with low-cost system}, volume = {13}, year = {2025}, }
• (2025). Review of the intelligent frameworks for pitch angle control in wind turbines. ACCESS, 13, 29864-29885. (www)

  @article{ , abstract = {Among the renewable energy sources, wind energy plays a crucial role in meeting the increasing global demand for clean energy. The variability in energy production makes wind turbines produce intermittent and varying power resources, variable mechanical loads, and a non-linear dynamic. The primary parameters to be controlled in wind turbines are the blade pitch angle and the generator torque. The challenges in designing the pitch controller include various non-linearities of wind turbines, constraints on pitch angle and its rate, and the stochastic nature of wind speed and unstructured model dynamics. Therefore, effectively addressing these complexities is important to ensure the stability and safety of a wind turbine. Existing literature extensively covered various control technologies applied to wind turbine systems, highlighting the diverse control methodologies and their applicability. However, there is limited evidence of the existing review papers focusing on applying intelligent control approaches to the collective pitch regulation of wind turbines. A comprehensive survey of the intelligent control approaches in collective pitch regulation was presented in this paper. It highlights the advantages of intelligent control strategies over traditional control methods in mitigating the challenges posed by system non-linearities. These approaches significantly enhance the performance of the wind energy generation system. Moreover, this paper presents the critical assessment areas for effective wind farm development and corresponding grid integration. This, in turn, will facilitate the growth of wind power utilization and assist researchers and engineers in integrating wind energy into the grid.}, archive = {J_ACCESS}, author = {Abdulbasit Hassan and Ghali Ahmad and Md Shafiullah and Asif Islam and Md Shafiul Alam}, doi = {10.1109/ACCESS.2025.3540367}, journal = {IEEE Access}, pages = {29864-29885}, shortjournal = {IEEE Access}, title = {Review of the intelligent frameworks for pitch angle control in wind turbines}, volume = {13}, year = {2025}, }
• (2025). Multi-task spiking neural network for simultaneous vapor recognition and concentration estimation. ACCESS, 13, 29847-29863. (www)

  @article{ , abstract = {Neural networks have been instrumental in advancing machine olfaction systems, greatly enhancing their ability to process olfactory information. As the drive to integrate sensing and signal processing on chip continues, recent advancements emphasize the need for simple yet efficient pattern recognition systems. Spiking Neural Networks (SNNs) have gained significant attention in the realm of machine olfaction for their high computational efficiency and biological realism, closely mirroring the human olfactory system’s approach to processing odors. Many studies have successfully applied SNN models for vapor classification; however, limited research exists on their use for concentration estimation. This article not only addresses this gap but also introduces a simple three-layer, multi-task shared SNN that simultaneously performs vapor recognition and concentration estimation using carbon black-polymer composite sensor arrays. Multi-task learning using SNNs proves particularly beneficial in this context by utilizing shared computations across tasks to optimize resource usage. Experimental results demonstrate that integrating both tasks into a single network enhances overall computational efficiency and reduces model complexity without sacrificing performance. Remarkably, this proof-of-concept design achieves a 33% reduction in the total number of neurons while performing 48% fewer synaptic operations per forward pass compared to two separate single-task SNNs. Logic synthesis further reveals that the shared approach consumes approximately 37% less power and occupies 37% less silicon area, showcasing the potential of multi-task learning with SNNs to propel artificial olfaction systems.}, archive = {J_ACCESS}, author = {Pedro Sartori Locatelli and Salma Ait Fares and Dalton Martini Colombo and Kamal El-Sankary and Michael S. Freund}, doi = {10.1109/ACCESS.2025.3541408}, journal = {IEEE Access}, pages = {29847-29863}, shortjournal = {IEEE Access}, title = {Multi-task spiking neural network for simultaneous vapor recognition and concentration estimation}, volume = {13}, year = {2025}, }
• (2025). Integrating explainable artificial intelligence in anomaly detection for threat management in E-commerce platforms. ACCESS, 13, 29830-29846. (www)

  @article{ , abstract = {In modern cybersecurity, detecting sophisticated threats in high-volume and dynamic network environments is a persistent challenge. Current advanced techniques, such as recurrent neural networks and transformers, achieve remarkable precision but lack interpretability, limiting their adoption in operational environments where analysts must validate predictions and make informed decisions. This gap between precision and explainability creates significant barriers in critical environments such as e-commerce platforms, were real-time traffic and evolving threats demand speed and transparency. This study proposes an explainable anomaly detection model that integrates SHAP, LIME, and Grad-CAM to address these challenges and provide interpretable predictions. The model was evaluated in a representative e-commerce environment, targeting sophisticated threats such as distributed denial of service attacks and unauthorized access. The proposed system enables analysts to understand, validate, and refine model predictions efficiently by combining advanced detection capabilities with real-time explanation generation. The results demonstrate the model’s effectiveness, achieving 95% precision and generating explanations in less than 500 milliseconds, outperforming traditional methods such as clustering and achieving precision comparable to transformers with significantly lower inference times. In addition, the system improved the efficiency of analysts’ decision-making, reducing validation times by 33% and increasing confidence by 20%.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Angel Jaramillo-Alcázar and Alexandra Maldonado Navarro and Aracely Mera-Navarrete}, doi = {10.1109/ACCESS.2025.3541979}, journal = {IEEE Access}, pages = {29830-29846}, shortjournal = {IEEE Access}, title = {Integrating explainable artificial intelligence in anomaly detection for threat management in E-commerce platforms}, volume = {13}, year = {2025}, }
• (2025). Plug-and-play framework for assessment of renewable energy community strategies. ACCESS, 13, 29811-29829. (www)

  @article{ , abstract = {Recent regulatory advancements by the European Union have positioned Renewable Energy Communities (RECs) at the forefront of energy policy discussions, reigniting significant interest and debate within the scientific community. However, despite all the developments, RECs still face several challenges that need to be addressed and researched to ensure their development and economic sustainability. One key challenge is the creation of effective and fair energy-sharing mechanisms. Thus, this study proposes a Home Energy Management System (HEMS) and a Two-Stage Collaborative Energy Management Strategy (TCEMS) to manage a REC based on both technical and economic aspects, promoting an efficient operation that meets both individual and collective requirements. This is a cooperative and collaborative strategy in which renewable energy production resources and storage systems are shared in an integrated and equitable manner, ensuring a fair and balanced distribution of renewable energy within the REC. In the strategy’s first stage, a dynamic Key of Repartition (KoR) is employed to adaptively allocate renewable energy production among REC participants. In the second stage, Mixed Binary Integer Programming (MBIP) is used to efficiently manage energy surpluses and deficits, ensuring their optimal allocation across participants storage systems. To evaluate the proposed strategy’s performance, a one-year computational simulation was conducted, followed by an experimental validation in a pilot REC over a 24-hour period. The results confirmed the effectiveness and robustness of the strategy, indicating reduced dependence on the utility power grid and optimized utilization of local energy resources.}, archive = {J_ACCESS}, author = {J. P. D. Faria and J. A. N. Pombo and S. J. P. S. Mariano and M. R. A. Calado}, doi = {10.1109/ACCESS.2025.3539533}, journal = {IEEE Access}, pages = {29811-29829}, shortjournal = {IEEE Access}, title = {Plug-and-play framework for assessment of renewable energy community strategies}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A hybrid approach for forecasting occupancy of building’s multiple space type”. ACCESS, 13, 29810. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “A Hybrid Approach for Forecasting Occupancy of Building’s Multiple Space Type”}, archive = {J_ACCESS}, author = {Iqra Rafiq and Anzar Mahmood and Ubaid Ahmed and Ahsan Raza Khan and Kamran Arshad and Khaled Assaleh and Naeem Iqbal Ratyal and Ahmed Zoha}, doi = {10.1109/ACCESS.2025.3541481}, journal = {IEEE Access}, pages = {29810}, shortjournal = {IEEE Access}, title = {Corrections to “A hybrid approach for forecasting occupancy of building’s multiple space type”}, volume = {13}, year = {2025}, }
• (2025). Functionally-possible subcircuit-based tests for path delay faults. ACCESS, 13, 29801-29809. (www)

  @article{ , abstract = {Advanced chip fabrication technologies are susceptible to defects of various types, including small delay defects that are modeled by path delay faults. Structural (scan-based) tests targeting the detection of hardware defects use scan chains to control and observe the circuit state. Between scan operations, a small number of functional capture cycles (typically one or two) is used for activating faults and capturing the circuit response in the scan chains for observation. Functional tests complement scan-based tests by exercising the circuit at-speed for a large number of clock cycles without using the scan chains. Functional tests are important for the detection of defects that can only be detected under functional operation conditions, or after a large number of clock cycles of functional operation. Multicycle scan-based tests offer some of the benefits of functional tests with the significantly lower costs of scan-based tests. Motivated by these benefits, this article suggests a special type of multicycle scan-based tests with functional properties for the detection of path delay faults. While ensuring functional operation conditions, a test propagates path delay faults through the same subcircuit in consecutive clock cycles to exercise the subcircuit, and enhance the ability to detect small delay defects in it. Experimental results are presented for benchmark circuits in an academic simulation environment to demonstrate the existence of tests with these properties.}, archive = {J_ACCESS}, author = {Irith Pomeranz}, doi = {10.1109/ACCESS.2025.3539981}, journal = {IEEE Access}, pages = {29801-29809}, shortjournal = {IEEE Access}, title = {Functionally-possible subcircuit-based tests for path delay faults}, volume = {13}, year = {2025}, }
• (2025). A multi-functional grid-tied PV system using a split source inverter with energy management and power quality improvement features. ACCESS, 13, 29789-29800. (www)

  @article{ , abstract = {In this paper, split-source inverter (SSI) is proposed for multi-functional grid-connected (MFGC) application because it offers the better boosting capability with fewer components. Furthermore, the SSI could operate as a full active power filter (APF), unlike the impedance source inverter (ZSIs) topologies. This paper proposes a sliding mode control (SMC) scheme for controlling both DC and AC sides of the MFGC system based SSI, the MFGC system aims to compensate the reactive power at grid side, satisfy the non-linear load power demand using the extracted PV power, and inject the remaining PV power into the grid. On DC-side, the SMC scheme is designed to control the DC input current while estimating the duty cycle that ensures the boosting capability. Then, a decoupled control scheme based on the SMC is designed to control the AC-side of the system and estimate the reference voltages that are fed to a modified space vector modulation (MSVM) stage. The entire system is simulated using the MATLAB/Simulink environment and validated experimentally through real-time hardware in the loop setup. The obtained results from simulation and HIL setup demonstrate the effectiveness and robustness of the proposed control scheme in terms of power management and power quality enhancement.}, archive = {J_ACCESS}, author = {Abbes Kihal and Billel Talbi and Abdelbasset Krama and Abdelbaset Laib and Abdeslem Sahli}, doi = {10.1109/ACCESS.2025.3538069}, journal = {IEEE Access}, pages = {29789-29800}, shortjournal = {IEEE Access}, title = {A multi-functional grid-tied PV system using a split source inverter with energy management and power quality improvement features}, volume = {13}, year = {2025}, }
• (2025). Achievable sum rate analysis for moving relay assisted cooperative NOMA system over rician fading channels. ACCESS, 13, 29778-29788. (www)

  @article{ , abstract = {The number of mobile users on onboard vehicles is increasing continuously. Vehicular users suffer from vehicle penetration loss, fast variation of channel conditions, and signal strengths. These challenges are made even worse by a growing need for high data rates and high quality of service requirements in vehicular scenarios, which cause frequent handovers, call drops, and complete outages. The achievable rate supports adapting techniques for communication to dynamically changing conditions, ensuring reliable and efficient transmission of information despite variations. Moving relay (MR) has been proven to be one of the most promising techniques for handling such scenarios over Rayleigh fading channels. However, the performance of MR for non-vehicular users needs to be analyzed over Rician fading channel in terms of achievable rate. This paper proposes a cooperative non-orthogonal multiple access (NOMA) based system over Rician fading for non-vehicular users with half duplex decode and forward protocol-based MRs in downlink communication. We derive the achievable sum rate of the proposed system for non-vehicular users and analyze its performance in terms of power allocation coefficient, transmit signal-to-noise ratio (SNR), and Rician factor. Also, a comparison of the proposed system with a conventional orthogonal multiple access (OMA) system is provided. The simulation results show that the proposed cooperative NOMA system achieves 11.96 % higher achievable sum rate at 5 dB SNR and 14.88% higher achievable sum rate at 25 dB SNR compared to conventional OMA system. The analytical results are verified through Monte Carlo simulations. The proposed system may be useful for designing high-speed vehicular scenarios in 5G and beyond communication.}, archive = {J_ACCESS}, author = {Qazi Saeed Ahmad and Mohd Javed Khan and Imran Ullah Khan and Indrasen Singh and Saif Ahmad and Ghanshyam Singh}, doi = {10.1109/ACCESS.2025.3540552}, journal = {IEEE Access}, pages = {29778-29788}, shortjournal = {IEEE Access}, title = {Achievable sum rate analysis for moving relay assisted cooperative NOMA system over rician fading channels}, volume = {13}, year = {2025}, }
• (2025). Innovative study on volatility prediction model for new energy stock indices. ACCESS, 13, 29754-29777. (www)

  @article{ , abstract = {Stock market volatility is a pivotal research area in finance, and accurately forecasting stock market volatility has long been a challenge for both academia and practice. The emergence of the new energy industry has drawn widespread attention to new energy stock indices; however, research on forecasting their volatility remains limited. To address this, this paper proposes a deep learning ensemble model based on decomposition optimization for predicting the volatility of new energy stock indices. The model comprises three components: variational mode decomposition (VMD), sparrow search algorithm (SSA), and echo state network (ESN). Initially, this paper employs VMD to decompose the original volatility series of new energy stock indices into multiple subsequences. Subsequently, SSA is utilized to optimize ESN. Finally, the constructed VMD-SSA-ESN model is employed to forecast the volatility of new energy stock indices. Through comparative analysis with other forecasting models, this paper finds that the VMD-SSA-ESN model exhibits significantly better forecasting performance across all selected new energy stock index volatility predictions. The research results indicate that the model constructed in this paper can adequately capture the characteristics of the volatility series, and both VMD and SSA effectively enhance the model’s forecasting accuracy and stability. This study can provide robust support for investment decision-making and risk management in the new energy stock market.}, archive = {J_ACCESS}, author = {Yanguo Li and Chao Long}, doi = {10.1109/ACCESS.2025.3535584}, journal = {IEEE Access}, pages = {29754-29777}, shortjournal = {IEEE Access}, title = {Innovative study on volatility prediction model for new energy stock indices}, volume = {13}, year = {2025}, }
• (2025). An auto-adjusting algorithm to enhance indoor localization accuracy: A real-time experimental analysis. ACCESS, 13, 29739-29753. (www)

  @article{ , abstract = {Indoor localization systems (ILS) have become essential tools to address the challenge of locating and tracking items and individuals, such as children, the elderly, and patients with Alzheimer’s or dementia. In this study, our aim is to develop an auto-adjusting algorithm to select coefficients based on the current automatically Received Signal Strength Indicator (RSSI) class. The method adopted by the Internet of Things (IoT) model integrated with Bluetooth Low Energy (BLE) to achieve this objective. The system is designed to track lost items and individuals using a wearable central unit (mini-Raspberry Pi) as a controller and BLE nodes as peripheral devices. The developed system includes Bluetooth beacons, data aggregation, storage, and a web interface for real-time tracking and visualization. The RSSI foot printing method is adopted to detect a specific zone within indoor environments. A web-based application has also been developed to enable monitoring and management of the designed system. The study was evaluated in a real-time experimental environment (with fixed and auto-adjust coefficients) to explore the challenges of accurately determining indoor locations in five rooms. The proposed method initially succeeded in reducing the error caused by fixed coefficients and RSSI by 28.03%. The results demonstrated that the auto-tuning algorithm with dynamic coefficients was able to improve the accuracy of positioning by dynamically adjusting RSSI coefficients; this study successfully reduced the average absolute percentage error of indoor localization by 8% and decreased the maximum localization error to 2.01 meters.}, archive = {J_ACCESS}, author = {Othman A. Alfahad and Husham Saied and Emad Malaekah and Ahmed Al Rashdi and Mohammed Emam and Mohsen Bakouri}, doi = {10.1109/ACCESS.2025.3541796}, journal = {IEEE Access}, pages = {29739-29753}, shortjournal = {IEEE Access}, title = {An auto-adjusting algorithm to enhance indoor localization accuracy: A real-time experimental analysis}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A systematic literature review: Are automated essay scoring systems competent in real-life education scenarios?”. ACCESS, 13, 29738. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Systematic Literature Review: Are Automated Essay Scoring Systems Competent in Real-Life Education Scenarios?”).}, archive = {J_ACCESS}, author = {Wenbo Xu and Rohana Mahmud and Wai Lam Hoo}, doi = {10.1109/ACCESS.2025.3541480}, journal = {IEEE Access}, pages = {29738}, shortjournal = {IEEE Access}, title = {Corrections to “A systematic literature review: Are automated essay scoring systems competent in real-life education scenarios?”}, volume = {13}, year = {2025}, }
• (2025). A chronic kidney disease diagnostic model based on an interpretable deep belief rule base. ACCESS, 13, 29718-29737. (www)

  @article{ , abstract = {Chronic Kidney Disease (CKD) has become a serious public health problem because of its characteristic ’three highs and one low’: high prevalence, high disability rate, high medical costs, and low awareness. Therefore, an accurate diagnosis of CKD is crucial. Given the unique nature of the medical field, it is essential to ensure that the results of the CKD diagnostic model are trustworthy to doctors and patients and are practically applicable. Belief Rule Base (BRB) models explain their results through transparent reasoning processes and belief distributions. However, existing BRB models are typically constructed as one-time setups that require reconstruction to include new patient indicators. Furthermore, redundant rules within BRB models increase the difficulty of optimization, and randomness in the optimization process can affect the interpretability of the model. To address these challenges, this paper presents a CKD diagnostic model based on an interpretable Deep Belief Rule Base (DBRB-I). The model leverages a deep BRB structure to enhance scalability and prevent rule explosions. Additionally, it introduces a novel rule reduction method that incorporates expert knowledge to simplify redundant rules in an interpretable way, thus easing the optimization process. To preserve the model’s interpretability, the optimization algorithm includes constraints grounded in expert knowledge. Case studies and comparisons with seven other methods show that the CKD diagnostic model based on DBRB-I outperforms the others in a practical diagnostic setting.}, archive = {J_ACCESS}, author = {Yawen Zhao and Kexin Zhang and Xiping Duan and Shuping Che and Ning Ma}, doi = {10.1109/ACCESS.2025.3541391}, journal = {IEEE Access}, pages = {29718-29737}, shortjournal = {IEEE Access}, title = {A chronic kidney disease diagnostic model based on an interpretable deep belief rule base}, volume = {13}, year = {2025}, }
• (2025). Harnessing large language models for software vulnerability detection: A comprehensive benchmarking study. ACCESS, 13, 29698-29717. (www)

  @article{ , abstract = {Despite various approaches being employed to detect software vulnerabilities, the number of reported software vulnerabilities shows an upward trend over the years. This suggests the problems are not caught before the code is released, which could be caused by many factors, like lack of awareness, limited efficacy of the existing vulnerability detection tools or the tools not being user-friendly. To help combat some issues with traditional vulnerability detection tools, we propose using large language models (LLMs) to assist in finding vulnerabilities in source code. LLMs have shown a remarkable ability to understand and generate code, underlining their potential in code-related tasks. The aim is to test multiple state-of-the-art LLMs and identify the best prompting strategies, allowing extraction of the best value from the LLMs. We leverage findings from prompting-focused research, benchmarking approaches like chain of thought, tree of thought and self-consistency for vulnerability detection use-cases. We provide an overview of the strengths and weaknesses of the LLM-based approach and compare the results to those of traditional static analysis tools. We find that LLMs can pinpoint more issues than traditional static analysis tools, outperforming traditional tools in terms of recall and F1 scores. However, LLMs are more prone to generate false positive classifications than traditional tools. The experiments are conducted using the Java programming language and the results should benefit software developers and security analysts responsible for ensuring that the code is free of vulnerabilities.}, archive = {J_ACCESS}, author = {Karl Tamberg and Hayretdin Bahsi}, doi = {10.1109/ACCESS.2025.3541146}, journal = {IEEE Access}, pages = {29698-29717}, shortjournal = {IEEE Access}, title = {Harnessing large language models for software vulnerability detection: A comprehensive benchmarking study}, volume = {13}, year = {2025}, }
• (2025). A measurement error model based on finite mixture of the skew-normal distributions for brain MR image segmentation. ACCESS, 13, 29684-29697. (www)

  @article{ , abstract = {The accuracy of medical image segmentation plays a crucial role in assisting with diagnosis. One commonly used method is the Gaussian mixture model (GMM) due to its high accuracy and low complexity. However, GMM faces challenges when dealing with images that follow asymmetrical distributions, as it is primarily designed for symmetrical distributions. Additionally, GMM does not account for measurement errors, which can hinder accurate distribution fitting when images contain various measurement errors. Furthermore, GMM does not incorporate spatial information, making it susceptible to noise during image segmentation. To address these issues, we propose a finite mixture measurement error model based on skew-normal distributions. This model allows for varying variances of measurement errors among different subjects. Furthermore, we introduce a novel anisotropic spatial information constraint term to preserve object details while reducing the impact of noise. Finally, we propose an EM type algorithm to estimate the parameters and maximize the likelihood. Experimental results using brain MR images demonstrate that our proposed method outperforms existing techniques in terms of segmentation accuracy.}, archive = {J_ACCESS}, author = {Kaili Zhang and Xiaoxu Zhu}, doi = {10.1109/ACCESS.2025.3540971}, journal = {IEEE Access}, pages = {29684-29697}, shortjournal = {IEEE Access}, title = {A measurement error model based on finite mixture of the skew-normal distributions for brain MR image segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhancing domain generalization performance in low-resource setting via external dataset and pseudo labeling with sentence-BERT. ACCESS, 13, 29673-29683. (www)

  @article{ , abstract = {Recent studies on data augmentation have focused on improving model performance with limited training data within a specific dataset. While the goal is to enhance performance on the dataset itself, this approach also addresses broader challenges, such as enhancing domain generalization. Building on this, we propose the Out-of-Domain Pseudo Labeling (OOD-PL) method, a data augmentation technique designed to ensure data diversity and enhance domain generalization of model in low-resource settings. Our approach introduces external data and assigns pseudo labels based on semantic vicinal interpolation with the intended training data. We observed significant improvements in domain generalization across three datasets from different domains. Unlike traditional methods, this approach utilizes other samples as a form of augmentation for the training data. Our method can be flexibly integrated with existing augmentation techniques, and we demonstrated that it performs well even when the available training data is extremely limited. Furthermore, we conducted various in-depth analysis experiments to strengthen the validity of our proposed method and demonstrate its robustness in effectively enhancing domain generalization. As a result, we were able to propose a methodology that overcomes the limitations of using specific datasets, even in situations where their availability is restricted, by leveraging out-of-domain samples.}, archive = {J_ACCESS}, author = {Junho Lee and Seunguk Yu and Jinhee Jang and Keunhyeung Park and Youngbin Kim}, doi = {10.1109/ACCESS.2025.3540090}, journal = {IEEE Access}, pages = {29673-29683}, shortjournal = {IEEE Access}, title = {Enhancing domain generalization performance in low-resource setting via external dataset and pseudo labeling with sentence-BERT}, volume = {13}, year = {2025}, }
• (2025). AI-synthesized image detection: Source camera fingerprinting to discern the authenticity of digital images. ACCESS, 13, 29660-29672. (www)

  @article{ , abstract = {Detecting AI-synthesized images remains a challenge due to their increasing realism. Traditional methods often fall short in addressing this evolving landscape where testing images can be produced by more powerful generative models or combined with various post-processing operations. To address this, we propose a robust method capitalizing on the distinctive global fingerprints present in real images from physical cameras and fixed patterns in synthesized images generated by generative models. Our approach builds on adapting camera fingerprinting initially designed for source camera identification, leveraging global fingerprints from the mid-frequency band. We rigorously evaluated our methodology through cross-model testing, demonstrating its exceptional generalization capability across different generative models and datasets. Our results, validated on the $DF^{3}$ and CNN-aug datasets with a broader range of generative models, highlight the method’s effectiveness in detecting AI-synthesized images and distinguishing between generative models. Comparisons with state-of-the-art methods show that our approach achieves superior performance, particularly in scenarios involving post-processing operations. This camera fingerprinting approach stands out for its resilience and accuracy, offering a promising solution for image forensics in addressing the challenges posed by sophisticated AI-generated content.}, archive = {J_ACCESS}, author = {Manisha and Chang-Tsun Li and Karunakar A. Kotegar}, doi = {10.1109/ACCESS.2025.3537558}, journal = {IEEE Access}, pages = {29660-29672}, shortjournal = {IEEE Access}, title = {AI-synthesized image detection: Source camera fingerprinting to discern the authenticity of digital images}, volume = {13}, year = {2025}, }
• (2025). CAPTAIN: Capsule network integrated with multilingual transformer for textual abusive content detection in online social networks. ACCESS, 13, 29644-29659. (www)

  @article{ , abstract = {As online discussion and dialogue evolve, the proliferation of abusive content presents a substantial challenge to preserving a respectful and inclusive digital environment. Researchers are tackling the rise of abusive content on online social networks by developing more effective detection methods. Fine-tuning language models to identify derogatory and disturbing content has produced positive results. However, most efforts concentrate on high-resource languages, overlooking low-resource languages. This paper addresses this gap by proposing a method called CAPTAIN, which integrates a capsule network (CapsNet) with the multilingual Bidirectional encoder representations from transformers (mBERT). We select each component of CAPTAIN to enhance the understanding and classification of Urdu language text. CAPTAIN utilizes a publicly available multi-class dataset and outperforms both traditional machine learning and state-of-the-art methods on this low-resource Urdu dataset. This work also emphasizes the importance of balanced datasets for effectively detecting abusive content, especially in low-resource languages like Urdu, where class imbalance can significantly hinder model performance. The CAPTAIN achieves an F1-Score of 87% surpassing the state-of-the-art baselines by over 5% and it also achieves an average 96% ROC-AUC score for all classes. This study also examined how different hyperparameters affect performance, finding that more complex architectures and higher routing iteration rates decrease performance, while a less complex structure and higher capsule dimension rate improve classification performance.}, archive = {J_ACCESS}, author = {Ovais Bashir Gashroo and Monica Mehrotra and Mudasir Ahmad Wani and Mohammed A. ElAffendi}, doi = {10.1109/ACCESS.2025.3533257}, journal = {IEEE Access}, pages = {29644-29659}, shortjournal = {IEEE Access}, title = {CAPTAIN: Capsule network integrated with multilingual transformer for textual abusive content detection in online social networks}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Design and implementation of active antennas for IoT-based healthcare monitoring system”. ACCESS, 13, 29643. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Design and Implementation of Active Antennas for IoT-Based Healthcare Monitoring System”.}, archive = {J_ACCESS}, author = {Umar Musa and Shaharil Mohd Shah and Huda A. Majid and Ismail Ahmad Mahadi and Mohamad Kamal A. Rahim and Muhammad Sani Yahya and Zuhairiah Zainal Abidin}, doi = {10.1109/ACCESS.2025.3540534}, journal = {IEEE Access}, pages = {29643}, shortjournal = {IEEE Access}, title = {Corrections to “Design and implementation of active antennas for IoT-based healthcare monitoring system”}, volume = {13}, year = {2025}, }
• (2025). Research on EMI noise suppression of zeta converter based on chaotic spread spectrum. ACCESS, 13, 29633-29642. (www)

  @article{ , abstract = {High-frequency switching in power converters, characterized by rapid voltage and current fluctuations, is primary source of Electromagnetic interference (EMI) noise. This study proposes a novel four-dimensional chaotic circuit as a modulation signal for spread spectrum circuit in the control stage of a Zeta converter to mitigate EMI. The research methodology involves modeling the Zeta converter and analyzing system stability, followed by the design of a new four-dimensional chaotic circuit. The chaotic signal generated by this circuit serves as the modulation signal, implementing chaotic spread spectrum modulation technology to suppress EMI noise in the Zeta converter. To validate the proposed theoretical analysis, the converter is studied using a time-domain circuit simulation software, it shows that this method achieves EMI noise suppression exceeding 10 dB compared to a Zeta converter without chaotic spread spectrum technology. Experiments on a prototype circuit confirm theoretical results.}, archive = {J_ACCESS}, author = {Yifei Sun and Tengfei Liu and Xuejun Zong and Kan He and Lian Lian and Feng Jiang}, doi = {10.1109/ACCESS.2025.3541100}, journal = {IEEE Access}, pages = {29633-29642}, shortjournal = {IEEE Access}, title = {Research on EMI noise suppression of zeta converter based on chaotic spread spectrum}, volume = {13}, year = {2025}, }
• (2025). A survey on computer vision-based automatic assessment of stroke and facial palsy. ACCESS, 13, 29613-29632. (www)

  @article{ , abstract = {Timely and accurate diagnosis is a critical component of stroke treatment and management. Recent advancements in computer vision technologies have shown the potential to aid with the process before the patient has access to radiology. This survey curates current research on computer vision-based approaches for stroke and other facial pathology screening based on the patient’s physical symptoms such as facial asymmetry and eye movements. Our work aims to identify gaps in the current state of research and put forward a critical review that could be linked to limited data availability, reproducibility of published methods, and robustness of proposed methods on unseen data. A catalogue of methods utilising machine learning, deep neural networks, and classical computer vision techniques are compiled together with the descriptions of datasets applicable for research in computer vision-based stroke and facial palsy screening. Based on the prevalent state of research where novel techniques are trained and evaluated on datasets that are not released to the public we provide an independent analysis of the state-of-the-art methods based on experimental implementations. The analysis demonstrates a fair comparison between methods through evaluations on identical settings, with the results suggesting a general theme of problems with generalization.}, archive = {J_ACCESS}, author = {Zolbayar Shagdar and Anette Huuse Farmen and Seyed Ali Amirshahi and Kiran Raja}, doi = {10.1109/ACCESS.2025.3540658}, journal = {IEEE Access}, pages = {29613-29632}, shortjournal = {IEEE Access}, title = {A survey on computer vision-based automatic assessment of stroke and facial palsy}, volume = {13}, year = {2025}, }
• (2025). Rapid colorectal tissue classification using data-driven raman techniques. ACCESS, 13, 29601-29612. (www)

  @article{ , abstract = {Colorectal cancer is among the most widespread cancers globally, and the risk of developing this disease increases with age. This has led to the recommendation that screening should begin in middle-aged patients. Consequently, the implementation of prevention programs has resulted in a greater number of samples being available for histological analysis. Raman spectroscopy offers a diagnostic solution to this challenge. In this study, we present a rapid procedure for Raman tissue analysis and classification using machine learning methods, including both supervised and hybrid supervised-unsupervised approaches, to distinguish between healthy and pathological cases based on Raman spectroscopy data. Raman spectra were acquired using a handheld portable spectrometer with an automatic BubbleFill preprocessing algorithm, covering at spectral range of 500 to 1800 cm-1. Various machine-learning algorithms have been evaluated for classification, including Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost), and hybrid supervised-unsupervised methods involving dimensionality reduction, clustering, and classification. The experimental results indicated promising classification accuracy, achieving up to 82% accuracy using the MLP algorithm. This suggests the potential effectiveness of neural network methodologies for the classification of Raman spectroscopy data for the diagnosis of colorectal cancer.}, archive = {J_ACCESS}, author = {Jakub Tomeš and Daniela Janstová and Shayestegan Mohsen and Alla Sinica and Zuzana Kováčová and Jaromír Petrtýl and Jan Mareš}, doi = {10.1109/ACCESS.2025.3539181}, journal = {IEEE Access}, pages = {29601-29612}, shortjournal = {IEEE Access}, title = {Rapid colorectal tissue classification using data-driven raman techniques}, volume = {13}, year = {2025}, }
• (2025). Integrating advanced techniques: RFE-SVM feature engineering and nelder-mead optimized XGBoost for accurate lung cancer prediction. ACCESS, 13, 29589-29600. (www)

  @article{ , abstract = {Early detection of lung cancer is crucial for improving patient survival and reducing mortality. However, medical datasets often face challenges like irrelevant features and class imbalance, complicating accurate predictions. This study presents a comprehensive AI-powered lung cancer classification approach that enhances predictive accuracy and treatment planning. Our methodology combines Recursive Feature Elimination with Support Vector Machines (RFE-SVM) for effective feature selection and employs the XGBoost ensemble learning algorithm for classification, optimized using the Nelder-Mead algorithm. Evaluating the model’s generalizability on two distinct lung cancer datasets, results show that our approach outperforms traditional machine learning models, achieving 100% accuracy. This research highlights the importance of advanced computational techniques in healthcare, paving the way for more personalized and effective patient care.}, archive = {J_ACCESS}, author = {Sarah Ayad and Hamdi A. Al-Jamimi and Ammar El Kheir}, doi = {10.1109/ACCESS.2025.3536034}, journal = {IEEE Access}, pages = {29589-29600}, shortjournal = {IEEE Access}, title = {Integrating advanced techniques: RFE-SVM feature engineering and nelder-mead optimized XGBoost for accurate lung cancer prediction}, volume = {13}, year = {2025}, }
• (2025). Hybrid CNN-LSTM and proximal policy optimization model for traffic light control in a multi-agent environment. ACCESS, 13, 29577-29588. (www)

  @article{ , abstract = {Conventional traffic light control systems often exhibit rigid timing patterns, limited flexibility, and insufficient adaptability to changing traffic conditions. This paper addresses urban traffic management challenges, addressing researchers and professionals in traffic engineering and intelligent transportation systems. To overcome these challenges, this paper presents an innovative traffic light control method, integrating a CNN-LSTM model for traffic state prediction, combined with a Proximal Policy Optimization (PPO) algorithm for traffic light control decision-making. The model is based on a representation of intersection states through key indicators (such as active green phase, congestion levels, congestion variations, and vehicle speeds) and employs deep reinforcement learning to optimize traffic light control strategies. The adopted method is compared with fixed traffic light control approaches and a reinforcement learning (Q-learning) approach in a simulated environment using SUMO. The simulations consider diverse traffic scenarios and realistic urban conditions to ensure robust evaluation. The experimental results indicate that traffic efficiency is significantly improved by up to 92% in scenarios managing medium traffic demand, with up to 2000 vehicles per hour in the North-South scenario, while congestion indicators are substantially reduced. These improvements are achieved under conditions where traffic remains below the saturation threshold, ensuring stable flow management.The proposed method demonstrates its effectiveness in optimizing signalized intersections, significantly enhancing traffic flow in pre-saturation conditions, while opening perspectives for further research in oversaturated networks. These results illustrate the power of integrating spatiotemporal prediction and PPO-based control for dynamic and adaptive traffic management in urban networks.}, archive = {J_ACCESS}, author = {Nada Faqir and Yassine Ennaji and Loqman Chakir and Jaouad Boumhidi}, doi = {10.1109/ACCESS.2025.3541042}, journal = {IEEE Access}, pages = {29577-29588}, shortjournal = {IEEE Access}, title = {Hybrid CNN-LSTM and proximal policy optimization model for traffic light control in a multi-agent environment}, volume = {13}, year = {2025}, }
• (2025). CrackVision: Effective concrete crack detection with deep learning and transfer learning. ACCESS, 13, 29554-29576. (www)

  @article{ , abstract = {Crack evaluation is critical for assessing the structural integrity of concrete, significantly impacting safety, functionality, and durability. In response to the growing demand for more advanced crack detection techniques, this paper presents CrackVision, an advanced framework that leverages Transfer Learning (TL) to enhance traditional methods of concrete crack detection. The framework utilizes pre-trained Convolutional Neural Networks (CNNs), such as ResNet50, Xception, and InceptionV3, which are fine-tuned to address the unique challenges of concrete crack detection. Initially trained on extensive datasets, these models have been further enhanced to improve their accuracy and reliability. The effectiveness of these models is rigorously evaluated using two key datasets: METU and SDNET2018. Strategic resampling techniques were employed to address the data imbalance in the SDNET2018 dataset, thereby improving the effectiveness of CrackVision for both binary and multi-class classification tasks. The findings reveal that the ResNet50 model achieves remarkable accuracy, reaching 99.95% on the METU dataset and consistently exceeding 97% on the balanced SDNET2018 dataset. These results demonstrate the effectiveness of the CrackVision framework, particularly emphasizing the exceptional performance of the ResNet50 model through Transfer Learning in concrete crack detection.}, archive = {J_ACCESS}, author = {Abdulrahman A. Alkannad and Ahmad Al Smadi and Shuyuan Yang and Mutasem K. Al-Smadi and Moeen Al-Makhlafi and Zhixi Feng and Zhenlong Yin}, doi = {10.1109/ACCESS.2025.3540841}, journal = {IEEE Access}, pages = {29554-29576}, shortjournal = {IEEE Access}, title = {CrackVision: Effective concrete crack detection with deep learning and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Screening dyslexia using visual auditory computer games and machine learning. ACCESS, 13, 29541-29553. (www)

  @article{ , abstract = {Reading acquisition is one the main keys for school success and a crucial component for empowering individuals to participate meaningfully in society. Yet, it is still a challenging skill to acquire for around 10% of children that have dyslexia, a type of neuro-developmental disorder that affects the ability to learn how to read and write. Dyslexia is often under-diagnosed, and normally children with dyslexia are only detected once they fail in school, even though dyslexia is not related to general intelligence. In this work, we present an approach for screening dyslexia using language-independent games in combination with machine learning models. To reach this goal, we designed the content of a computer game, collected data from 137 children playing this game (51 with dyslexia) in different languages -German, Spanish and English- and created a prediction model using different machine learning classifiers. Our method provides a precision of 0.78 and recall of 0.79 for German and a precision of 0.83 and recall of 0.80 for all languages when Extra Trees are used, with an accuracy of 0.67 and 0.75, respectively. Our results open the possibility of inexpensive online early screening of dyslexia for young children using non-linguistic elements.}, archive = {J_ACCESS}, author = {Maria Rauschenberger and Ricardo Baeza-Yates and Luz Rello}, doi = {10.1109/ACCESS.2025.3539719}, journal = {IEEE Access}, pages = {29541-29553}, shortjournal = {IEEE Access}, title = {Screening dyslexia using visual auditory computer games and machine learning}, volume = {13}, year = {2025}, }
• (2025). YOLO-SAIL: Attention-enhanced YOLOv5 with optimized bi-FPN for ship target detection in SAR images. ACCESS, 13, 29523-29540. (www)

  @article{ , abstract = {Synthetic Aperture Radar (SAR) is useful for monitoring sea surfaces and detecting targets on ships. However, interpreting SAR images can be challenging due to the high density of ships, an imbalanced foreground-to-background ratio, and the small size of targets. It has recently become increasingly popular to apply deep learning algorithms to the identification of ships in SAR images. Nevertheless, the presence of intricate backgrounds and multi-scale vessels makes it difficult for deep networks to detect distinctive targets, in part due to the presence of intricate backgrounds and multi-scale vessels. As a solution to these challenges, this research article introduces a new ship identification model known as YOLO-SAIL. The improved Bi-directional Feature Pyramid Network (Bi-FPN) has been replaced by a conventional Path Aggregation Network (PAN) to extract more powerful semantic features and sharpen the distinction between multi-scale targets. Secondly, a Normalized Attention Mechanism (NAM) is introduced to optimize attention toward dense ship targets by merging contextual cues with global interdependencies. Finally, a new layer of feature fusion is introduced to locate smaller ships. We evaluated YOLO-SAIL against two public benchmark datasets, the SAR Ship Detection Dataset (SSDD) and the High Resolution SAR Images Dataset (HRSID). The proposed model, YOLO-SAIL, achieves an F1-score of 98.22% in SSDD datasets and 94.68% in HRSID datasets, outperforming most existing models.}, archive = {J_ACCESS}, author = {Prabu Selvam and P. Shanmuga Sundari and M. Tamilselvi and T. Suresh and M. Murugappan and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3536621}, journal = {IEEE Access}, pages = {29523-29540}, shortjournal = {IEEE Access}, title = {YOLO-SAIL: Attention-enhanced YOLOv5 with optimized bi-FPN for ship target detection in SAR images}, volume = {13}, year = {2025}, }
• (2025). A comparative evaluation of recommender systems tools. ACCESS, 13, 29493-29522. (www)

  @article{ , abstract = {Due to the vast flow of information on the Internet, easy and effective access to information has become crucial. Recommender systems are important in information filtering, as they significantly impact large-scale internet web services such as YouTube, Netflix, and Amazon. As the demand for personalized recommendations continues to grow, researchers and practitioners alike strive to develop tools specifically designed for this purpose to meet the increasing need. In this work, we address the challenges associated with selecting software frameworks and Machine Learning (ML) algorithms for Recommender Systems (RSs), thus, we offer a detailed comparison of 42 open-source RS software to provide insights into their different features and capabilities. Furthermore, the paper presents a concise overview of various ML algorithms to generate recommendations, reviews the most used performance metrics to evaluate RS, and then compares several ML algorithms provided by four popular recommendation tools: Microsoft Recommenders, Lenskit, Turi Create, and Cornac.}, archive = {J_ACCESS}, author = {Ayoub Akhadam and Oumayma Kbibchi and Loubna Mekouar and Youssef Iraqi}, doi = {10.1109/ACCESS.2025.3541014}, journal = {IEEE Access}, pages = {29493-29522}, shortjournal = {IEEE Access}, title = {A comparative evaluation of recommender systems tools}, volume = {13}, year = {2025}, }
• (2025). Energy consumption optimization of multi-dimensional U-nets on CGLA. ACCESS, 13, 29476-29492. (www)

  @article{ , abstract = {The increasing demand for energy-efficient medical image processing has highlighted the limitations of traditional GPU-based solutions, particularly in mobile or remote scenarios where power consumption and computational efficiency are critical. Two-dimensional (2D) and three-dimensional (3D) convolutional neural networks (CNNs), such as U-Net (2D U-Net), have become essential in computer vision and medical diagnosis tasks. However, while 2D and 3D CNNs, widely used for medical image segmentation, offer superior performance in capturing spatial context and suffer from performance bottlenecks and high energy consumption on traditional GPUs, they significantly increase computational and memory requirements, posing challenges for conventional hardware. To address these challenges, we propose the Interposed Memory Accelerator eXtension series 3 (IMAX3), a novel chiplet-based, non-von Neumann architecture with coarse-grained linear accelerator (CGLA) to efficiently support both 2D and 3D U-Net models, delivering superior energy efficiency and computational performance. IMAX3 optimizes data processing by employing advanced data preloading mechanisms and double-buffered cache memory, reducing redundant memory retrievals and minimizing inter-chip communication. Our experimental results demonstrate that IMAX3, implemented on 28 nm technology, occupies only $12.5~mm^{2}$ , consumes just 2.61–2.67 W, and significantly outperforms General-Purpose Computing on Graphics Processing Units (GPGPUs), Application-Specific Integrated Circuit (ASIC), Coarse-Grained Reconfigurable Architecture (CGRA), and Field-Programmable Gate Array (FPGA)-based designs. For 2D U-Net, IMAX3 achieves an energy efficiency of 18.68 GOPs/W, surpassing FPGA-based designs by $1.68\times $ to $5.6\times $ and reducing energy usage by $1.8\times $ . In the case of ICA U-Net (3D U-Net), IMAX3 demonstrates $1.47\times $ to $3.56\times $ higher energy efficiency compared to ASIC and CGRA-based designs. These results establish IMAX3 as a scalable and energy-efficient solution for next-generation medical image processing applications.}, archive = {J_ACCESS}, author = {Duong Thi Sang and Ren Imamura and Tomoya Akabe and Yasuhiko Nakashima}, doi = {10.1109/ACCESS.2025.3539417}, journal = {IEEE Access}, pages = {29476-29492}, shortjournal = {IEEE Access}, title = {Energy consumption optimization of multi-dimensional U-nets on CGLA}, volume = {13}, year = {2025}, }
• (2025). Mosaic reduction model using local thermal diffusion equation. ACCESS, 13, 29463-29475. (www)

  @article{ , abstract = {In this paper, we propose Mosaic reduction model using local thermal diffusion equation(MRLTDE) based on local heat diffusion equation and the U-net architecture. Due to the inherent problems such as unsmooth mapping, the traditional GAN-based method cannot achieve the best image restoration effect, which is reflected in the high differences in structure, brightness, contrast, and other aspects of the restored image, as well as high noise, blur distortion, color deviation and other issues. Therefore, we use the diffusion model for image demosaicing tasks, we improve the thermal diffusion model, and create a new local diffusion method to create image sequences with small changes between adjacent frames, so as to go further than previous schemes in image generation effect. Experiments of our scheme on CelebA datasets show that our model outperforms the existing GAN-based methods in terms of detail recovery, evaluated by two indicators: PSNR(Focus on pixel-level accuracy) and SSIM (focuses on the structural similarity and visual perception of images).In our experimental results, this model outperforms the base model on the above two indexes, in addition to well-defined metrics, even by ordinary people’s visual standards, our generation results are better than base. Detailed codes and experimental results are available at https://github.com/lhyf10561/MRLTDE.}, archive = {J_ACCESS}, author = {Huayifu Lv}, doi = {10.1109/ACCESS.2025.3531005}, journal = {IEEE Access}, pages = {29463-29475}, shortjournal = {IEEE Access}, title = {Mosaic reduction model using local thermal diffusion equation}, volume = {13}, year = {2025}, }
• (2025). A global irradiance prediction model using convolutional neural networks, wavelet neural networks, and masked multi-head attention mechanism. ACCESS, 13, 29445-29462. (www)

  @article{ , abstract = {Accurate prediction of global irradiance is critical for optimizing energy management in photovoltaic (PV) systems, particularly in solar-powered electric vehicles (ESVs). However, traditional models struggle to capture the complex spatial and temporal dependencies in irradiance data, limiting prediction accuracy under varying weather conditions. Existing approaches, including statistical methods, conventional machine learning models, and standalone deep learning techniques like LSTM, fail to integrate local features and long-term dependencies simultaneously, creating a need for more robust solutions. This paper introduces a novel hybrid framework, CNN-WNN-MMHA, that combines Convolutional Neural Networks (CNN), Wavelet Neural Networks (WNN), and a Masked Multi-Head Attention (MMHA) mechanism. The CNN extracts spatial and local features, WNN performs frequency decomposition to capture multi-scale variations, and MMHA models temporal dependencies while encoding positional information.The model is trained and evaluated on a real-world climatic dataset from Tunisia, collected over eight years. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art methods such as LSTM, BiLSTM, and CNN-LSTM, achieving a 79% reduction in MAPE and superior generalization performance across diverse weather scenarios.This advancement enhances energy forecasting reliability, supporting smarter route planning and energy optimization for solar-powered vehicles, with potential extensions to other renewable energy systems.}, archive = {J_ACCESS}, author = {Walid Mchara and Lazhar Manai and Mohamed Abdellatif Khalfa and Monia Raissi and Salah Hannechi}, doi = {10.1109/ACCESS.2025.3539463}, journal = {IEEE Access}, pages = {29445-29462}, shortjournal = {IEEE Access}, title = {A global irradiance prediction model using convolutional neural networks, wavelet neural networks, and masked multi-head attention mechanism}, volume = {13}, year = {2025}, }
• (2025). DMN-SLAM: Multi-MLPs neural implicit representation SLAM for dynamic environments. ACCESS, 13, 29432-29444. (www)

  @article{ , abstract = {Addressing the limitations of traditional V-SLAM in large-scale and dynamic environments, this paper proposes a multi-MLPs neural implicit representation SLAM in Dynamic Environments(DMN-SLAM), which leverages neural radiance fields to facilitate local mapping in dynamic scenes. This system aims to achieve high-fidelity 3D scene reconstruction, making it suitable for real-world autonomous driving applications. Specifically, the proposed DMN-SLAM system includes a pre-processing semantic segmentation module that processes the scene to eliminate dynamic objects, thereby reducing their impact on SLAM localization and mapping. In the tracking module, a traditional feature-based visual odometry module is employed to estimate poses, enhancing tracking accuracy. In the mapping module, five different MLP neural implicit encoding structures are implemented to efficiently extract and represent detailed features of the scene, enabling local updates and optimization. Finally, a dynamic and efficient octree structure is introduced to store voxel features. This structure not only significantly reduces storage requirements but also allows for rapid querying and updating of map points, further enhancing the performance and practicality of system. Experimental results demonstrate that the proposed method achieves high reconstruction accuracy and quality without the need for pretraining. DMN-SLAM outperforms other neural radiance field SLAM methods in terms of tracking accuracy and mapping quality in dynamic environments.}, archive = {J_ACCESS}, author = {Xiaofeng Lian and Maomao Kang and Xiao Sun and Li Tan and He Liu}, doi = {10.1109/ACCESS.2025.3540585}, journal = {IEEE Access}, pages = {29432-29444}, shortjournal = {IEEE Access}, title = {DMN-SLAM: Multi-MLPs neural implicit representation SLAM for dynamic environments}, volume = {13}, year = {2025}, }
• (2025). Offender characterization and prediction: A case study of the kingdom of bahrain. ACCESS, 13, 29406-29431. (www)

  @article{ , abstract = {Crime analysis using data mining techniques can uncover valuable information to assist law enforcement investigations. This study analyses a crime data set from the Kingdom of Bahrain to demonstrate an integrated framework using exploratory analysis and machine learning for predictive modelling. The study data set, comprising 720 cases, was first explored to reveal patterns, including prevalent theft and assault crimes concentrated in four areas. Data sensitivity measures were implemented by anonymising specific geographic areas, represented by characters A to T. Supervised learning algorithms such as Additive Regression, Random Forest, Naïve Bayes, and Decision Tree are then applied to predict unknown offender characteristics such as age, sex, and occupation. The algorithms achieve strong predictive performance. Additive regression achieved a high correlation coefficient of 0.889 for the prediction of the age of the offender, and Random Forest achieved 83% precision in predicting sex through 10-fold cross-validation. However, challenges arise with unbalanced nationality data. A survey of 22 police officers was conducted, discussing the study results and the feasibility of the predictive modelling approach to enhance investigative efficiency. The responses received were mostly positive, reflecting a strong endorsement of the potential of the proposed method to enhance law enforcement practices. This research highlights a promising methodology that combines exploratory and machine learning techniques to extract actionable insights from crime data. With appropriate caution, it can improve investigative efficiency by making data-driven predictions of the attributes of the offender. Further work should expand data sets and explore advanced algorithms while addressing ethical concerns regarding predictive modelling in criminal justice.}, archive = {J_ACCESS}, author = {Ebrahim Jaffar Alasfoor and Omar Alshaikh and Isa Inuwa-Dutse and Saad Khan and Simon Parkinson}, doi = {10.1109/ACCESS.2025.3531655}, journal = {IEEE Access}, pages = {29406-29431}, shortjournal = {IEEE Access}, title = {Offender characterization and prediction: A case study of the kingdom of bahrain}, volume = {13}, year = {2025}, }
• (2025). A multifaceted vision of the human-AI collaboration: A comprehensive review. ACCESS, 13, 29375-29405. (www)

  @article{ , abstract = {Human-AI collaboration has evolved into a complex, multidimensional paradigm shaped by research in various domains. Key areas such as human-in-the-loop systems, Interactive Machine Learning (IML), Hybrid Intelligence, and Human-Agent Interaction have significantly contributed to this development. However, these fields often lack cohesion, underscoring the need for a cohesive perspective to advance. This work addresses this gap by integrating insights from diverse aspects of collaboration to present a holistic approach to fostering effective and adaptive interactions between humans and artificial agents. It emphasizes empowering end-users with greater control and involvement in decision-making processes, thereby enhancing both the levels of interactivity and adaptability within intelligent systems. Moving beyond a focus on AI training techniques, this paper presents a broader perspective on incorporating human input into AI decision-making and learning processes, highlighting the importance of flexibility in systems and user engagement. The manuscript proposes a framework encompassing five levels of human integration and examines their relationship with core collaboration aspects, including the system purpose, participant expertise, and system proactivity. By synthesizing current knowledge on human-AI collaboration and outlining essential design principles, this work aims to advance the field and foster interdisciplinary collaboration among researchers, practitioners, and designers.}, archive = {J_ACCESS}, author = {Maite Puerta-Beldarrain and Oihane Gómez-Carmona and Rubén Sánchez-Corcuera and Diego Casado-Mansilla and Diego López-de-Ipiña and Liming Chen}, doi = {10.1109/ACCESS.2025.3536095}, journal = {IEEE Access}, pages = {29375-29405}, shortjournal = {IEEE Access}, title = {A multifaceted vision of the human-AI collaboration: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of high-speed carry look-ahead decimal adder (CLDA) using CMOS technology. ACCESS, 13, 29361-29374. (www)

  @article{ , abstract = {Decimal arithmetic is gaining more attention by researchers due to their importance in many human-centric applications. Hardware implementations of decimal arithmetic are preferable over their software counterparts as the former leads in performance especially when it comes to real-time applications. Decimal addition is a main decimal operation as most of the other decimal operation can be implemented using decimal addition. Complementary Metal Oxide Semiconductor (CMOS) technology has the advantage of reducing the average power consumption and the propagation delay in digital systems. Decimal adders can be designed as a ripple carry addition (RCA) structure or as a carry look-ahead addition (CLA) structure with the latter being faster with extra hardware cost. This work proposes two CMOS-based carry look-ahead decimal adders (CLDAs) that can be used within any decimal arithmetic unit. Operands of 1-digit size, 2-digit size, 3-digit size, and 4-digit size are investigated. The Boolean logic for each of the proposed CLDAs is developed and the CMOS realization is provided. The circuit of each of the proposed CLDAs is simulated with $45~nm$ technology library using the LT-SPICE spice simulation tool. The simulation shows promising results in terms of speed, power, and power delay product (PDP).}, archive = {J_ACCESS}, author = {Abdelsalam Al Share and Osama Al-Khaleel and Fadi Nessir Zghoul and Mohammad Al-Khaleel and Chris Papachristou}, doi = {10.1109/ACCESS.2025.3540836}, journal = {IEEE Access}, pages = {29361-29374}, shortjournal = {IEEE Access}, title = {Design and implementation of high-speed carry look-ahead decimal adder (CLDA) using CMOS technology}, volume = {13}, year = {2025}, }
• (2025). Microcontroller-driven battery management in hybrid energy systems: A systematic review of applications, control strategies, and emerging trends. ACCESS, 13, 29341-29360. (www)

  @article{ , abstract = {Microcontroller-driven battery management systems (BMS) are crucial for various applications, including electric vehicles, portable electronics, and renewable energy storage. These systems monitor and control critical parameters such as voltage, current, temperature, and state of charge to optimize battery performance and lifespan. Hence, this study aims to conduct a systematic literature review on microcontroller-driven battery management in hybrid energy systems by focusing on applications, control strategies, and emerging trends, among others. To collect documents, a systematic procedure was employed using targeted keywords to search within the Scopus database, while focusing on relevant, recent engineering papers. Eligible documents were refined, exported, and carefully assessed for relevancy, ensuring a comprehensive dataset for analysis. Among others, review findings highlighted that microcontroller-based systems are making big strides in renewable energy and battery management. Tools like Maximum Power Point Tracking (MPPT) and Pulse Width Modulation (PWM) help improve efficiency, while microcontrollers like Arduino and TMS320F28379D balance cost and performance depending on the project. Lithium-ion batteries are favored for their long life, but lead-acid batteries still have their place because they are cheaper. This study aims to contribute by conducting a systematic literature review using tailored keywords to identify and analyze advancements in microcontroller-driven battery management systems. Key contributions include reviewing innovations in battery charging systems, exploring hybrid systems integrating multiple power sources, examining control strategies and algorithms, and assessing renewable energy-based charging systems. The study further identifies patterns, themes, and emerging trends in the literature, highlights research gaps, and proposes directions for future work in this field.}, archive = {J_ACCESS}, author = {Ripfumelo Humphrey Malele and Bongumsa Mendu and Bessie Baakanyang Monchusi}, doi = {10.1109/ACCESS.2025.3539929}, journal = {IEEE Access}, pages = {29341-29360}, shortjournal = {IEEE Access}, title = {Microcontroller-driven battery management in hybrid energy systems: A systematic review of applications, control strategies, and emerging trends}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Electrifying urban transportation: A comparative study of battery swap stations and charging infrastructure for taxis in chicago”. ACCESS, 13, 29340. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Electrifying Urban Transportation: A Comparative Study of Battery Swap Stations and Charging Infrastructure for Taxis in Chicago”.}, archive = {J_ACCESS}, author = {Sofia Borgosano and Daniele Martini and Michela Longo and Federica Foiadelli}, doi = {10.1109/ACCESS.2025.3540528}, journal = {IEEE Access}, pages = {29340}, shortjournal = {IEEE Access}, title = {Corrections to “Electrifying urban transportation: A comparative study of battery swap stations and charging infrastructure for taxis in chicago”}, volume = {13}, year = {2025}, }
• (2025). Early breast cancer prediction using thermal images and hybrid feature extraction-based system. ACCESS, 13, 29327-29339. (www)

  @article{ , abstract = {The aggressive nature of breast cancer makes it the leading cause of cancer-related death in women. Infrared thermal imaging has great potential for early prediction of breast cancer, offering non-invasive, cost-effective, non-radiation exposure and applicability to be used regularly for both young and old women. The infrared energy emitted by the breast surface is highly related to its physiological characteristics. This valuable information is encoded in the spatial and local intensity variations of acquired breast images, revealing hidden patterns. This article introduces a novel hybrid feature extraction approach that combines traditional and deep learning techniques to improve the prediction of breast cancer. The proposed approach makes use of two main methods for feature extraction. The first method generates texture and edge maps based on the Gabor filter bank, Canny edge detector, and holistically nested edge detector to inherit the distinctive information and patterns associated with breast functionality. Then, an image fusion process is provided to construct one composite image retaining all the map’s information from which a highly valuable feature vector is extracted using the histogram of oriented gradients (HOG). The second method extracts deep features from the breast images capturing high-level predictive information using ResNet-50 and MobileNet pre-trained convolution neural networks (CNNs). The principal component analysis (PCA) is utilized for dimensionality reduction. The back end of the methodology uses support vector machine (SVM) and extreme gradient boosting (XGB) classification algorithms to establish the relationship between the retrieved feature vector and breast functionality. The results are impressive with 95.51% and 96.22% of accuracy, 96.62% and 97.19% of sensitivity, and 94.76% and 95.23% of specificity using SVM and XGB, respectively, on the test set. These findings show the significant advantages of the proposed framework for early prediction of breast cancer using thermal images.}, archive = {J_ACCESS}, author = {Doaa Youssef and Hanan Atef and Shaimaa Gamal and Jala El-Azab and Tawfik Ismail}, doi = {10.1109/ACCESS.2025.3541051}, journal = {IEEE Access}, pages = {29327-29339}, shortjournal = {IEEE Access}, title = {Early breast cancer prediction using thermal images and hybrid feature extraction-based system}, volume = {13}, year = {2025}, }
• (2025). Medium and long term energy forecasting methods: A literature review. ACCESS, 13, 29305-29326. (www)

  @article{ , abstract = {Estimating utility demand remains a significant challenge worldwide, being accuracy often compromised by numerous variables involved and limited relevant data available; this compromises models and impacts resource planning, infrastructure, and energy purchases. Energy systems must prioritize efficient resource utilization to address these challenges in the context of technological advances, economic changes, and environmental concerns. This paper conducts a bibliometric and systematic review of energy forecasting methods; the literature review covers the main studies conducted, including the most commonly used variables in forecasting studies, the techniques used, and the forecasting time horizon. As a result, the review presented here will facilitate the selection of the best models, variables, and time horizons for different forecasting applications.}, archive = {J_ACCESS}, author = {Josivan Rodrigues Dos Reis and Jonathan Muñoz Tabora and Matheus Carvalho de Lima and Flávia Pessoa Monteiro and Suzane Cruz de Aquino Monteiro and Ubiratan Holanda Bezerra and Maria Emília de Lima Tostes}, doi = {10.1109/ACCESS.2025.3540999}, journal = {IEEE Access}, pages = {29305-29326}, shortjournal = {IEEE Access}, title = {Medium and long term energy forecasting methods: A literature review}, volume = {13}, year = {2025}, }
• (2025). PointNet++SAKS: A point cloud model based on KANs and attention mechanism for objects classification and semantic segmentation. ACCESS, 13, 29292-29304. (www)

  @article{ , abstract = {Traditional point clouds models based on multilayer perceptrons (MLPs) lack inherent support for spatial data structures and fail to effectively process spatial relationships. To address this limitation, we propose PointNet++ SAKS, a deep learning network for point clouds processing. The network incorporates a local feature aggregation module that replaces the structure of MLPs with Kolmogorov-Arnold networks (KANs), and is able to capture complex higher-order spatial dependencies in point clouds through its recursive and adaptive network structure. Unlike MLPs, which rely only on layer-by-layer weighted linear transformations, KANs introduce more nonlinear and hierarchical interactions in modeling the local structure, making it better able to understand and model the complex relationships between points in a local region. The introduced SegNext combines Transformer and Convolutional Neural Network (CNN) to extract features from different scales using convolutional kernels of different sizes or convolutional operations with different step sizes. On the Sydney Urban Objects Dataset classification, overall accuracy (OA) and mean accuracy (mAcc) metrics improved by 4.89% and 9.47%, respectively. On ModelNet40, OA and mAcc are improved by 4.9% and 5.2%, respectively, and training time is reduced. For the semantic segmentation task, the OA, mAcc, and intersection over union (IoU) metrics are improved by 1.1%, 4.6%, and 3.7%, respectively. We show that PointNet++SAKS outperforms the baseline model with high accuracy and speed.}, archive = {J_ACCESS}, author = {Xiaofeng Lu and Zhiwei Guan and Dangfeng Pang and Rupeng Dou and Xiaolong Zheng}, doi = {10.1109/ACCESS.2025.3541023}, journal = {IEEE Access}, pages = {29292-29304}, shortjournal = {IEEE Access}, title = {PointNet++SAKS: A point cloud model based on KANs and attention mechanism for objects classification and semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). The role of accuracy and validation effectiveness in conversational business analytics. ACCESS, 13, 29279-29291. (www)

  @article{ , abstract = {This study examines how conversational business analytics can bridge the skill gap of end users that hinders traditional self-service analytics. By leveraging generative AI, conversational business analytics enables end users to independently retrieve data, process it, and generate information. Using Text-to-SQL as an example, this study proposes theoretical models grounded in expected utility theory to examine two levels of AI support: partial support, where AI translates natural language requests into SQL and the generated information serves directly as the basis for decision-making, and full support, which includes an additional validation step. The models define conditions where AI-driven information generation surpasses human delegation. These conditions underscore the critical interplay between AI accuracy and validation effectiveness as pivotal factors for the successful integration of AI. The findings suggest that partial support is viable when the AI accuracy is sufficiently high. In contrast, full support necessitates both adequate accuracy and robust validation. Insufficient validation impairs decisions, highlighting the need for effective validation techniques to fully leverage conversational business analytics. Moreover, the dependence on user-driven validation introduces additional risks, as its effectiveness is contingent on the user’s experience or familiarity with SQL and underlying data structures. This insight challenges conventional validation techniques for AI-generated information and highlights the need to use techniques that reduce the reliance on the technical expertise of end users.}, archive = {J_ACCESS}, author = {Adem Alparslan}, doi = {10.1109/ACCESS.2025.3540975}, journal = {IEEE Access}, pages = {29279-29291}, shortjournal = {IEEE Access}, title = {The role of accuracy and validation effectiveness in conversational business analytics}, volume = {13}, year = {2025}, }
• (2025). Application of soft computing for prediction of peripheral neuropathy in T2DM-A real-time data analysis. ACCESS, 13, 29264-29278. (www)

  @article{ , abstract = {Type 2 Diabetes mellitus (T2DM) patients are experiencing diabetic foot problems that heavily burden healthcare systems around the globe. Although there have been challenges with diagnosing and treating these problems using traditional approaches, the advent of machine learning technology signals the beginning of a new age in diabetic foot care, with the promise of improved precision and customized treatment plans. Machine learning is a beneficial tool for extracting essential insights from large, complicated datasets to improve the accuracy of diabetic foot diagnosis and therapeutic planning. This research aims to employ artificial intelligence to build a decision support system that will use clinical and demographic variables to predict the likelihood that individuals with mild, moderate, or severe peripheral neuropathy may develop diabetic foot syndrome in T2DM. Real-time processing of clinical information is made possible by a customized stacked ensemble model, which offers immediate peripheral neuropathy risk prediction with low computing latency. The system’s capacity to transform raw patients’ data into valuable insights in milliseconds supports quick clinical decision-making. Additionally, comparison and testing have been conducted on four deep learning algorithms: ResMLP, LSTM, DNN, and 1D-CNN. The predictions produced by the classifiers have been interpreted using three explainers: LIME, Eli5, and SHAP. Using the mutual information feature selection technique, the final stack reached a maximum accuracy of 99%. The three most significant markers that helped predict the onset of diabetic foot syndrome were area under pressure, vpt right, and vpt left; the encouraging findings point to the possibility of predicting diabetic foot condition with a decision system.}, archive = {J_ACCESS}, author = {Mehewish Musheer Sheikh and Mamatha Balachandra and V. G. Narendra and Arun G. Maiya}, doi = {10.1109/ACCESS.2025.3535590}, journal = {IEEE Access}, pages = {29264-29278}, shortjournal = {IEEE Access}, title = {Application of soft computing for prediction of peripheral neuropathy in T2DM-A real-time data analysis}, volume = {13}, year = {2025}, }
• (2025). A compact cardiopulmonary resuscitation chest compression device using a torsion-based series elastic actuator. ACCESS, 13, 29252-29263. (www)

  @article{ , abstract = {Cardiopulmonary Resuscitation (CPR) is crucial in emergency cardiac arrest situations. To reduce paramedic fatigue and ensure steady CPR performance, automated chest compression devices have been developed. However, current devices do not consider the patient’s chest condition, potentially increasing the risk of rib fractures and reducing cardiac output. This paper proposes a chest compression device using a linear series elastic actuator (SEA) for CPR. The SEA can compress and measure force simultaneously, adjusting compliance based on the spring’s stiffness. Unlike other SEAs that use compression springs, this design employs a torsion spring. The mechanism and the working principle of the linear SEA are presented, along with an analysis of the custom torsion spring. Experiments on a test bench validate the spring’s stiffness and assess the device’s performance. Additional experiments with a CPR training manikin demonstrate the device’s suitability for compression depth tracking and force control. The proposed design is space-efficient and offers accurate compression force sensing, providing a promising solution for effective CPR in emergencies.}, archive = {J_ACCESS}, author = {Sungmoon Hur and Hyunbum Cho and Joowan Kim and Yonghwan Oh and Jaeheung Park}, doi = {10.1109/ACCESS.2025.3537717}, journal = {IEEE Access}, pages = {29252-29263}, shortjournal = {IEEE Access}, title = {A compact cardiopulmonary resuscitation chest compression device using a torsion-based series elastic actuator}, volume = {13}, year = {2025}, }
• (2025). Data communication challenges of connected and automated vehicles in rural areas. ACCESS, 13, 29220-29251. (www)

  @article{ , abstract = {The integration of connected and automated vehicles (CAVs) into rural areas offers a promising solution to the unique challenges posed by limited infrastructure. This paper provides an in-depth review of the key data communication challenges that impede the seamless deployment of CAVs in these environments. We examine the development of vehicular communication technologies, evaluating their performance and the limitations of current systems in rural settings. The study explores advanced network architectures, protocols, and technologies such as vehicle-to-everything (V2X) communications, with a focus on enhancing network coverage, improving data transmission reliability, and ensuring robust security. Additionally, we analyze the specific characteristics of rural transportation networks, highlighting the need for adaptable technologies and improved infrastructure to support CAV deployment. The paper also discusses the integration of intelligent transportation systems (ITS) with CAVs, emphasizing their potential to improve safety, efficiency, and quality of life in rural communities. Through detailed text mining and topic modeling analysis, we identify current trends, challenges, and research gaps in communication networks for CAVs in rural areas, offering a comprehensive understanding of the landscape and guiding future research efforts. Our findings stress the importance of enhancing network connectivity, developing resilient communication systems, and addressing cybersecurity concerns to realize the benefits of CAVs in rural areas fully. This work contributes to the existing body of knowledge by proposing innovative solutions to overcome the infrastructural, technological, and policy-related barriers that currently prevent the effective deployment of CAVs in rural transportation systems.}, archive = {J_ACCESS}, author = {Mahsa Tavasoli and Abdolhossein Sarrafzadeh and Milad Khaleghi and Mohammad Zakaria and Hannaneh Barahouei Pasandi and Ali Karimoddini}, doi = {10.1109/ACCESS.2025.3539598}, journal = {IEEE Access}, pages = {29220-29251}, shortjournal = {IEEE Access}, title = {Data communication challenges of connected and automated vehicles in rural areas}, volume = {13}, year = {2025}, }
• (2025). Performance and energy analysis of a fuel cell electric vehicle. ACCESS, 13, 29206-29219. (www)

  @article{ , abstract = {In the last century, individual transportation vehicles become much more common and they cause great environmental problems. Renewable and sustainable energy solutions will help reduce these environmental problems. Thus, there is a trend towards electric vehicle from internal combustion engine vehicle. Fuel cell electric vehicles, with their competitive driving range, short refueling time and zero emissions, are considered the most likely solution among the vehicle technologies of the future. Since the fuel cell is late to meet the sudden power requirements, it must be hybridized with a second energy device having high power dynamics. In this study, a mid-size passenger vehicle, having 80 kW fuel cell stack supported by a 1.97 kWh battery pack, is modeled with Matlab. The vehicle is simulated on Worldwide Harmonized Light Vehicle Test Procedure (WLTP) Class 3 driving cycle and fuel consumption and range values are 2.15 L/100 km as gasoline equivalent and 859.2 km, respectively. The average efficiency of the internal permanent magnet synchronous motor providing vehicle propulsion is 93.4% throughout the cycle. The overall efficiency of the vehicle is found to be 56.2%, based on the higher heating value of the consumed hydrogen. The emission value of the vehicle is 75.1 gCO2e/km according to the average hydrogen production emission intensity.}, archive = {J_ACCESS}, author = {Habip Sahin and Hikmet Esen}, doi = {10.1109/ACCESS.2025.3540715}, journal = {IEEE Access}, pages = {29206-29219}, shortjournal = {IEEE Access}, title = {Performance and energy analysis of a fuel cell electric vehicle}, volume = {13}, year = {2025}, }
• (2025). A survey on spoken italian datasets and corpora. ACCESS, 13, 29190-29205. (www)

  @article{ , abstract = {Spoken Italian datasets are curated collections of audio recordings featuring native Italian speech in various contexts (e.g., spontaneous dialogues, read text, telephone conversations), often accompanied by transcriptions or linguistic annotations. They serve as foundational resources for a wide range of applications, including Automatic Speech Recognition (ASR), Text-To-Speech (TTS) synthesis, emotion detection, and broader linguistic research. Despite Italian’s status as a richly diverse Romance language—marked by significant dialectal variation—publicly available large-scale corpora have remained comparatively underrepresented when contrasted with those of major world languages such as English or Mandarin. In this survey, we present a comprehensive examination of 66 spoken Italian datasets, highlighting their key characteristics, data collection methodologies, and annotation frameworks. We categorize the datasets by speech type (e.g., conversational, monologic, spontaneous), by source (e.g., broadcast media, telephone calls, field recordings), and by demographic or linguistic attributes (including dialects and sociolinguistic features). Our analysis uncovers critical issues around dataset scarcity, demographic underrepresentation, and restricted accessibility, limiting broader research and development efforts. To address these gaps, we propose best practices and future directions—such as expanding demographic coverage, promoting open-access models, and standardizing annotation protocols—to enrich Italian speech data resources. The complete dataset inventory is publicly available on GitHub and archived on Zenodo, offering researchers and developers a valuable reference. By highlighting both the achievements and the shortcomings in existing resources, this work ultimately aims to foster collaboration and to spur further advancements in Italian speech technologies and linguistic research.}, archive = {J_ACCESS}, author = {Marco Giordano and Claudia Rinaldi}, doi = {10.1109/ACCESS.2025.3538952}, journal = {IEEE Access}, pages = {29190-29205}, shortjournal = {IEEE Access}, title = {A survey on spoken italian datasets and corpora}, volume = {13}, year = {2025}, }
• (2025). Efficient prefetch and issue scheduling approaches for simultaneous multithreading applied to superscalar RISC-V processor. ACCESS, 13, 29177-29189. (www)

  @article{ , abstract = {Thread level parallelism (TLP) is a common approach to achieve parallelism where Instruction level parallelism (ILP) is insufficient. Hardware multithreading is a prevalent approach in the micro-architecture layer for tolerating long events such as memory access, mispredictions, and accelerators latencies by utilizing idle cycles and avoiding CPU stalling. Multithreading architectures are commonly used across many processors and various embedded edge devices to improve performance. This work suggests a new 2-way superscalar in-order Multithreading (MT) micro-architecture applied to a superscalar RISC-V processor. This work also proposes efficient prefetch-scheduling and Issue-scheduling algorithms for Simultaneous Multithreading (SMT) processors. The suggested micro-architecture was implemented on an FPGA chip. The results show that the proposed prefetch-scheduling and issue-scheduling algorithms outperform the 2-way superscalar Fine-Grained Multi-Threaded (FGMT) and the Coarse-Grained Multi-Threaded (CGMT) approaches. The proposed architecture is evaluated using the standard MiBench and RISCV benchmarks, demonstrating an average processor core utilization improvement of up to 52% in terms of IPC using four threads.}, archive = {J_ACCESS}, author = {Hananya Ribo and Shlomo Greenberg}, doi = {10.1109/ACCESS.2025.3540311}, journal = {IEEE Access}, pages = {29177-29189}, shortjournal = {IEEE Access}, title = {Efficient prefetch and issue scheduling approaches for simultaneous multithreading applied to superscalar RISC-V processor}, volume = {13}, year = {2025}, }
• (2025). A transformer-based multimodal object detection system for real-world applications. ACCESS, 13, 29162-29176. (www)

  @article{ , abstract = {Obstacle detection is a critical task for visually impaired individuals to ensure safe navigation and hazard avoidance. This study presents FusionSight, an innovative multimodal fusion model that integrates radar and image data to address challenges in real-time object classification for dynamic environments. The system leverages an Arduino Uni microcontroller for data acquisition and transmission, enabling seamless communication between radar and image datasets and the cloud environment. For image data, the Vision Transformer (ViT) was employed to extract high-level features, capturing fine details and long-range dependencies essential for accurate object recognition. Concurrently. Radar data was processed using a Convolutional Neural Network (CNN) to extract spatial and temporal features such as distance, speed and velocity critically for understanding object dynamics. To unify these diverse modalities, a Feature Fusion Multimodal Transformer (FFMA) was utilized, facilitating the integration of complementary features into a comprehensive representation. This fusion mechanism enables the model to effectively handle challenges such as occlusion, overlapping objects and varying lighting conditions. The unified features were classified into four categories “close, far, moving and fast-moving” using a Feed-Forward Neural Network (FFN). The classification results were then converted into actionable audible feedback, providing real-time navigation assistance to visually impaired users. The FusionSight model set a benchmark in multimodal data fusion, achieving an impressive classification accuracy of 99% when using static dataset and 98% accuracy with real-time dataset. This study demonstrates the practical implementation navigation for visually impaired individuals and other use cases involving dynamic and complex environments.}, archive = {J_ACCESS}, author = {Sunnia Ikram and Imran Sarwar Bajwa and Amna Ikram and M. Abdullah-Al-Wadud and Haleema Pk}, doi = {10.1109/ACCESS.2025.3539569}, journal = {IEEE Access}, pages = {29162-29176}, shortjournal = {IEEE Access}, title = {A transformer-based multimodal object detection system for real-world applications}, volume = {13}, year = {2025}, }
• (2025). Interpretable parkinson’s disease detection using group-wise scaling. ACCESS, 13, 29147-29161. (www)

  @article{ , abstract = {This study is aimed at detecting Parkinson’s disease by analyzing voice measurements made using a mobile phone. The key objectives include creating a model that ensures accurate predictions while maintaining interpretability, consistent with the existing literature on Parkinson’s disease. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the datasets, demonstrating 9.5% improvement over conventional scaling for three publicly available data sets. We also show the importance of evaluating the developed model on unseen individuals to achieve reliable classification results. The developed model is shown to offer an accuracy of 82% for unseen individuals, surpassing current state-of-the-art approaches. Furthermore, we offer insights into the decision-making of the model using Shapley additive explanation values. Our analysis reveals that shorter and less variable voiced segments and more variable unvoiced segments, suggesting a monotone voice pattern with frequent pauses, increase the likelihood of classifying the voice as a Parkinson’s disease voice. Additionally, greater variability and rate of voiced segments, low variability of unvoiced segments, higher pitch variation, and spectral flux, suggesting continuous phonation and dynamic modulation, correlate with healthy voices. These features align well with the relevant medical literature, confirming our results. The significance of our proposed model lies in its generalizability and reliability for Parkinson’s disease detection, potentially decelerating disease progression, reducing healthcare costs, and improving quality of life for patients.}, archive = {J_ACCESS}, author = {Niloofar Momeni and Susanna Whitling and Andreas Jakobsson}, doi = {10.1109/ACCESS.2025.3540600}, journal = {IEEE Access}, pages = {29147-29161}, shortjournal = {IEEE Access}, title = {Interpretable parkinson’s disease detection using group-wise scaling}, volume = {13}, year = {2025}, }
• (2025). Minima possible weights: A homogenous deep ensemble method for cross-subject motor imagery classification. ACCESS, 13, 29134-29146. (www)

  @article{ , abstract = {Motor Imagery (MI) systems in Brain-Computer Interface (BCI) research provide communication and control solutions for individuals with motor impairments, yet cross-subject classification remains challenging due to substantial inter-subject variability. In this study, we propose the Minima Possible Weights (MPW) method, an unsupervised learning approach designed to enhance MI classification through ensemble deep learning. MPW aggregates predicted probabilities from multiple models and selects the class with the lowest associated weight for the final prediction. We evaluated MPW against various ensemble learning and test-time adaptation methods using two benchmark datasets: BCI Competition IV Dataset 2a and PhysionetMI. Our results indicate that MPW achieves cross-subject classification accuracy of up to 64.75% on BCI Competition IV Dataset 2a and 66.92% on PhysionetMI. Although the current performance is not yet sufficient for practical BCI applications, MPW shows potential in reducing calibration time and easing the burden of adapting models to new subjects.}, archive = {J_ACCESS}, author = {Quang Pham Lam Dinh and Isao Nambu}, doi = {10.1109/ACCESS.2025.3540164}, journal = {IEEE Access}, pages = {29134-29146}, shortjournal = {IEEE Access}, title = {Minima possible weights: A homogenous deep ensemble method for cross-subject motor imagery classification}, volume = {13}, year = {2025}, }
• (2025). Decision algorithm for physical education in higher education and circular fermatean fuzzy framework. ACCESS, 13, 29122-29133. (www)

  @article{ , abstract = {This paper seeks to establish the significance of efficient evaluation of physical education programs in colleges and universities in the development of human personality, emotional health and fitness and the sociable demeanor of the learners. Nevertheless, the critical assessment methods present strong problems due to the inherent randomness and individuality. To overcome these complexities, an enhanced method of Circular Interval-Valued Fermatean Fuzzy Dombi Mean (CrIVF-FDM) operators is proposed in this research work. Through the application of Fermatean fuzzy sets, the degrees of importance and interdependence of criteria are considered in the model, together with the aggregation flexibility that the Dombi mean operator provides. Thus, the algorithm contains circular interval values to capture source uncertainty and ambiguity clearly while providing a sound assessment basis. The study also shows the effectiveness of the algorithm using physical education programs of higher learning institutions, and therefore, the Algorithm can provide summarized, accurate, and flexible quality assurance assessment. The findings suggest that it may help decision-makers crack this particular nut in determining the best courses of action toward increasing the quality of physical education, cultivating leadership, and enhancing students’ all-around growth. The decision maker in this work collects human opinion-based data to rank alternatives through CrIVF-FDM operators that employ on defined criteria and find out a best alternative.}, archive = {J_ACCESS}, author = {Hui Jian Luo}, doi = {10.1109/ACCESS.2025.3539730}, journal = {IEEE Access}, pages = {29122-29133}, shortjournal = {IEEE Access}, title = {Decision algorithm for physical education in higher education and circular fermatean fuzzy framework}, volume = {13}, year = {2025}, }
• (2025). Analyzing the economic viability of microgrid solutions in the south african market. ACCESS, 13, 29091-29121. (www)

  @article{ , abstract = {Amidst South Africa’s persistent energy crisis, this study explores the viability of microgrid systems as a pivotal solution to enhance energy reliability and sustainability. By scrutinizing factors like resource availability, grid connectivity, demand dynamics, regulatory frameworks, and financial implications, a comprehensive techno-economic analysis reveals the substantial potential of microgrids. These systems not only augment energy access and fortify grid resilience but also facilitate the seamless integration of renewable energy sources, aligning with global sustainability goals. Economic evaluations underscore favorable returns on investment, emphasizing the long-term cost efficiencies and broader societal benefits. The microgrid showcases strong financial performance with an Internal Rate of Return (IRR) of 23.1%, competitive Blended Cost of Energy (BCoE) at ${\$}$ 0.073/kWh, Levelized Cost of Energy (LCoE) at ${\$}$ 0.186/kWh lower than Eskom grid tariffs, and a rapid payback time of 5.5 years. The proposed microgrid has the potential to save 1,135,949.737 tons of carbon dioxide (CO2) over its 30-year lifecycle.}, archive = {J_ACCESS}, author = {Mpho Sam Nkambule and Ali Nabil Hasan and Thokozani Shongwe}, doi = {10.1109/ACCESS.2025.3539691}, journal = {IEEE Access}, pages = {29091-29121}, shortjournal = {IEEE Access}, title = {Analyzing the economic viability of microgrid solutions in the south african market}, volume = {13}, year = {2025}, }
• (2025). Enhancing thyroid nodule assessment with UTV-ST swin kansformer: A multimodal approach to predict invasiveness. ACCESS, 13, 29081-29090. (www)

  @article{ , abstract = {Assessing the invasiveness of thyroid nodules, particularly whether they have metastasized to lymph nodes, is crucial for guiding treatment decisions. Current diagnostic methods, including ultrasound imaging, are limited by operator dependence and interpretative variability, complicating accurate evaluation of nodule invasiveness. To address these limitations, this study introduces the UTV-ST Swin Transformer, a deep learning model that combines ultrasound video data with standardized clinical information to predict the invasiveness of thyroid nodules. The model classifies nodules into three categories: non-invasive, central lymph node metastasis (CLNM), and central plus lateral lymph node metastasis (CLNM+LLNM). By analyzing ultrasound video features using the Video Swin Transformer and clinical data using a text analysis module based on the KAN network, and then fusing these features, the model achieves a classification accuracy of 82.1% and an average AUC of 94.2%. These results surpass the performance of traditional methods, particularly in distinguishing different degrees of invasiveness, even under noisy conditions. This study highlights the potential of the UTV-ST Swin Transformer model in improving the accuracy of thyroid nodule assessment, reducing reliance on operator expertise, and providing a more consistent and automated method for evaluating nodule invasiveness.}, archive = {J_ACCESS}, author = {Yufang Zhao and Yue Li and Yanjing Zhang and Xiaohui Yan and Guolin Yin and Liping Liu}, doi = {10.1109/ACCESS.2025.3531705}, journal = {IEEE Access}, pages = {29081-29090}, shortjournal = {IEEE Access}, title = {Enhancing thyroid nodule assessment with UTV-ST swin kansformer: A multimodal approach to predict invasiveness}, volume = {13}, year = {2025}, }
• (2025). Multi-class diagnosis of neurodegenerative diseases using effective deep learning models with modified DenseNet-169 and enhanced DeepLabV 3+. ACCESS, 13, 29060-29080. (www)

  @article{ , abstract = {Early detection lowers the death rate and allows for prompt treatment of the exhausted individual with neurodegenerative diseases. Most existing classification studies can either not generate probabilistic predictions or consider uncertainty, or do not reflect medical practice, where patients may have unusual variations, comorbidities, or early stages of the disease. This research proposes a novel method for classifying patients and healthy individuals from other distinctive neurodegenerative diseases that consider ambiguity and spatial information. Based on improved deep learning models, we presented a multi-class neurodegenerative disease classification in this research that accurately conducts multi-class or three-class classifications. First, we use various pre-processing methods to make the data suitable for analysis, such as noise reduction, contrast improvement, and data augmentation. The CapsNet model is then used to extract the images’ valuable features. Then, the multi-class classifications of neurodegenerative diseases are classified using the Modified DenseNet-169 model. After classification, the accurate delineation of disease regions is effectively segmented using the Enhanced DeepLabV3+ model. Aided by the PPMI and ADNI datasets, this Multi-class Neurodegenerative Disease Dataset (MNDD) was generated. The outcomes of our experiments illustrate that the proposed model attains an exceptional accuracy of 99.27% on Dataset 1 and 99.14% on Dataset 2, surpassing the performance of several well-known existing deep learning models. Through advanced deep-learning techniques and image segmentation, this research improves early diagnosis and treatment outcomes in neurodegenerative disease classifications.}, archive = {J_ACCESS}, author = {Srinivas Katkam and V. Prema Tulasi and B. Dhanalaxmi and J. Harikiran}, doi = {10.1109/ACCESS.2025.3529914}, journal = {IEEE Access}, pages = {29060-29080}, shortjournal = {IEEE Access}, title = {Multi-class diagnosis of neurodegenerative diseases using effective deep learning models with modified DenseNet-169 and enhanced DeepLabV 3+}, volume = {13}, year = {2025}, }
• (2025). A plug-in curriculum scheduler for improved deformable medical image registration. ACCESS, 13, 29046-29059. (www)

  @article{ , abstract = {Deformable image registration is a crucial task in medical image analysis, and its complexity has spurred significant research and ongoing progress. Much of the work in this area has concentrated on achieving incremental performance gains by adjusting network architectures or introducing new loss functions. However, these modifications are often tailored to specific tasks or datasets, which limits their general applicability. To address this limitation, we propose an innovative solution: a plug-in curriculum scheduler that can be seamlessly integrated into existing methods without changing their core architecture. Our scheduler, inspired by curriculum learning, progressively increases task difficulty to enhance performance, incorporating sample difficulty and matching accuracy as key criteria. Sample difficulty is assessed at voxel and volume levels, using Variance of Gradients (VoG) for voxel complexity and Gaussian blurring for volume evaluation, while matching accuracy involves gradually increasing supervision for improved alignment and accuracy. We empirically demonstrate that this scheduler achieves superior accuracy and visual quality in various tasks and datasets. Our code is available at https://github.com/JYeShin/Plug-InCurriculumScheduler.}, archive = {J_ACCESS}, author = {Jiyoon Shin and Youngwook Kim and Jungwoo Lee}, doi = {10.1109/ACCESS.2025.3539890}, journal = {IEEE Access}, pages = {29046-29059}, shortjournal = {IEEE Access}, title = {A plug-in curriculum scheduler for improved deformable medical image registration}, volume = {13}, year = {2025}, }
• (2025). Optimized temporal denoised convolutional autoencoder for enhanced ADHD classification using fMRI data. ACCESS, 13, 29031-29045. (www)

  @article{ , abstract = {Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects individuals across all age groups, from childhood through adulthood, and can significantly impact daily functioning. Early and accurate diagnosis of ADHD is crucial in clinical practice to ensure effective intervention and management, which can improve patient outcomes. However, traditional classification methods, while effective in many classification tasks, often struggle to achieve high accuracy in ADHD diagnosis. These limitations highlight the need for more sophisticated approaches. In response to this challenge, this paper introduces two innovative deep learning models specifically designed for ADHD classification using functional magnetic resonance imaging (fMRI). We propose an Optimized Temporal Denoised Convolutional Autoencoder (OTDCAE) framework, which utilizes resting-state fMRI (rs-fMRI) data to enhance diagnostic accuracy. The model combines a Denoising Autoencoder (DAE) for spatial feature extraction with an Optimal Temporal Convolutional Network (OTCN) for temporal sequence classification, resulting in robust performance even with a limited dataset. Here, for optimizing the hyper-parameters of the Temporal Convolutional Network (TCN), the optimizer Adaptive Osprey Algorithm (AOA) is presented and it is named as OTCN. To evaluate the effectiveness of our approach, we applied it to the widely-used ADHD-200 dataset. The results demonstrate the model’s strong performance, achieving an impressive accuracy of 98.8% and an F-score of 98.5%, with 90% of the dataset used for training. These findings underscore the potential of deep learning techniques, particularly when applied to neuroimaging data, in advancing the accuracy and efficiency of ADHD diagnosis. Moreover, this research highlights the broader implications of integrating artificial intelligence (AI) into mental health assessments, offering promising avenues for future exploration and development in this field.}, archive = {J_ACCESS}, author = {Zarina Begum and Kareemulla Shaik}, doi = {10.1109/ACCESS.2025.3539706}, journal = {IEEE Access}, pages = {29031-29045}, shortjournal = {IEEE Access}, title = {Optimized temporal denoised convolutional autoencoder for enhanced ADHD classification using fMRI data}, volume = {13}, year = {2025}, }
• (2025). Adapting the IEEE c57.91 standard for underground transformers with natural ester insulating: Analysis, thermal modeling, and life expectancy estimation. ACCESS, 13, 29018-29030. (www)

  @article{ , abstract = {Transformers are among the most expensive components of the distribution networks in the Electric Power System, and one of the challenges for energy utilities is to assess the real operating conditions of these devices. Various mathematical models exist in the literature to represent the thermal behavior of transformers. Utilities typically use the IEEE STD C57.91 standard, which was developed for overhead transformers that use insulating mineral oil. This paper proposes an adaptation of the IEEE STD C57.91 standard for underground transformers utilizing insulating natural ester. Initially, the standard is evaluated through a case study involving transformers in the underground distribution system of the State Electric Power Company in Porto Alegre, Brazil. The preliminary results reveal limitations in the standard. Consequently, a thermal model is developed and validated for underground transformers with insulating natural ester. The IEEE STD C57.91 standard, for calculating the top-oil temperature, is then compared with the model, revealing an error of 56.9 %. An adaptation of the standard is proposed, which significantly reduces the error to 1.98 %. Finally, the impact of this adaptation on calculating the transformers’ life expectancy is assessed, showing an increase in the device’s lifespan by approximately 2.2 years.}, archive = {J_ACCESS}, author = {Taciana Paula Enderle and Airam Sausen and Paulo Sérgio Sausen and Maurício de Campos}, doi = {10.1109/ACCESS.2025.3540592}, journal = {IEEE Access}, pages = {29018-29030}, shortjournal = {IEEE Access}, title = {Adapting the IEEE c57.91 standard for underground transformers with natural ester insulating: Analysis, thermal modeling, and life expectancy estimation}, volume = {13}, year = {2025}, }
• (2025). Multi-scale spatial-temporal graph attention network for charging station load prediction. ACCESS, 13, 29000-29017. (www)

  @article{ , abstract = {The precision of electric vehicle (EV) charging station load forecasting is paramount for the efficient management of smart grids, fulfilling the charging demands of EV users and enhancing power system operations and planning. While existing forecasting methods have made strides, they often fall short in addressing the challenges of low spatial resolution and spatial heterogeneity. Low spatial resolution, characterized by sparse data distribution and a dearth of information between geographically diverse charging stations, coupled with spatial heterogeneity, which is evident in the fluctuating energy demand across different regions, requires a sophisticated approach. In this study, we introduce the Multi-Scale Spatial-Temporal Graph Attention Network (MSSTGAN), a pioneering framework that adeptly navigates these complexities. MSSTGAN employs an encoder-decoder architecture underpinned by split-pyramid attention to resolve issues of low spatial resolution, enabling the model to process multi-scale data and capture temporal dynamics effectively. Moreover, it incorporates graph attention mechanisms to manage spatial heterogeneity in energy demand, ensuring a nuanced understanding of regional variations. Our framework’s efficacy is underscored by its superior performance over existing baselines on four real-world EV charging station datasets, showcasing its prowess in tackling the intricacies of low spatial resolution and spatial heterogeneity.}, archive = {J_ACCESS}, author = {Ran Tian and Jinshi Wang and Zhihui Sun and Jiarui Wu and Xin Lu and Longlong Chang}, doi = {10.1109/ACCESS.2025.3541118}, journal = {IEEE Access}, pages = {29000-29017}, shortjournal = {IEEE Access}, title = {Multi-scale spatial-temporal graph attention network for charging station load prediction}, volume = {13}, year = {2025}, }
• (2025). A detailed analytical modeling of eddy current losses in an underwater medium for wireless power transfer. ACCESS, 13, 28988-28999. (www)

  @article{ , abstract = {Eddy current loss (ECL) in underwater wireless power transfer (UWPT) systems can significantly affect the efficiency of underwater vehicles. This study provides comprehensive modeling of the eddy current effects in circular planar coils. The analytical model has been extended to any number of transmitter and receiver coils’ turns exposed to different conductive regions, providing a general formulation applicable to various scenarios. From the circuit perspective, ECLs appear as the additional resistances in both primary and secondary coils, as well as the newly introduced mutual resistance that in turn contributes to power loss. To derive the general formulas for additional resistances, the electric field intensities are calculated by solving the Helmholtz equation within different surrounding environments, considering geometrical parameters such as the number of turns, coil radii, and spacing between turns. Three frequently occurring scenarios in underwater applications are selected to provide a comprehensive understanding of ECLs. The general formulas are solved by properly defining the boundary condition, and then the additional resistances are calculated specifically for each scenario. Finite Element Method (FEM) simulations and measurements are conducted to validate the analytical formulation. The results confirm the high accuracy and reliability of the model. It effectively captures the impact of changes in the geometrical parameters of the coils on additional resistances, well predicts the effect of increasing the input source frequency, and demonstrates how the effects of eddy currents change with the volume of the surrounding conductive medium.}, archive = {J_ACCESS}, author = {Sadjad Shafiei and Seyed Saeid Heidari Yazdi and Nurtas Adaikhan and Mehdi Bagheri}, doi = {10.1109/ACCESS.2025.3539925}, journal = {IEEE Access}, pages = {28988-28999}, shortjournal = {IEEE Access}, title = {A detailed analytical modeling of eddy current losses in an underwater medium for wireless power transfer}, volume = {13}, year = {2025}, }
• (2025). Verification of variable magnetic flux in motor with magnetic composite materials inserted into the rotor. ACCESS, 13, 28979-28987. (www)

  @article{ , abstract = {Variable magnetic flux motors that utilize the leakage flux in the rotor are attracting increasing attention because they can achieve high efficiency and operate under a wide range of conditions with a simple configuration. Motors with magnetic composite material inserted into the rotor can achieve the variable magnetic flux effect. This paper reports the effect of the magnetic properties of the magnetic composite material on the characteristics of a variable magnetic flux. The magnetic properties of the magnetic composite material can be adjusted by varying the filling rate of the soft magnetic powder. The results of an analysis demonstrated that the filling rate of the magnetic composite material affects the characteristics of the variable magnetic flux. A prototype motor was fabricated in which magnetic composite materials were inserted between the permanent magnets of the rotor, and its torque characteristics were measured. The results demonstrated that the torque constant increased depending on the armature current, which differs from conventional interior permanent motors. The findings of this study indicate that motors with a magnetic composite material inserted into the rotor can achieve a variable magnetic flux effect, and this variable magnetic flux effect can expand the motor output range to the high-speed rotation region.}, archive = {J_ACCESS}, author = {Mitsuhide Sato and Ryoto Takagi and Ren Washioka and Seunghwan Lee and Jun Kitajima and Keigo Takazawa and Tsutomu Mizuno and Masami Nirei}, doi = {10.1109/ACCESS.2025.3539096}, journal = {IEEE Access}, pages = {28979-28987}, shortjournal = {IEEE Access}, title = {Verification of variable magnetic flux in motor with magnetic composite materials inserted into the rotor}, volume = {13}, year = {2025}, }
• (2025). Efficient hierarchical hash-based multi-field packet classification with fast update for software switches. ACCESS, 13, 28962-28978. (www)

  @article{ , abstract = {Packet classification that attracts wide interests in router designs is one of the most important functionalities to provide services such as QoS, packet forwarding, firewall, and VPN. With the expanding network and the rise of software-defined networks, software routers/switches are needed to enable a wide variety of network applications in physical or virtual networks. Existing packet classification schemes used in software switches had become difficult to support a fast classification process for large multi-field rulesets. In addition, it is necessary to have fast updates that are frequently performed. In this paper, we propose a hash-based partitioning scheme for a multi-field packet classification problem. The proposed design is a 4-level data structure based on hashing. It selects the suitable fields to construct the data structures so that we can quickly narrow down the search space to find matched rules. The hash table used in the proposed design is more effective than the existing search data structures. Our experimental results show that the proposed scheme performs best in all four-performance metrics of memory consumption, construction time, classification time, and update time compared with the six most recently proposed schemes, CutTSS, TabTree, TupleMerge, CutSplit, PartitionSort, and PSTSS. The average number of bytes required per rule is only 27-99% of that needed in these schemes. The construction time of our scheme is 2.06 to 327.82 times faster than these schemes. Most importantly, the proposed scheme is 1.92-16.65 and 2.22-4.54 times faster than these six schemes in throughput and update time, respectively.}, archive = {J_ACCESS}, author = {Yeim-Kuan Chang and Yu-Hung Lin and Jia-Chang Chang and Wenjun Li and Sun-Yuan Hsieh}, doi = {10.1109/ACCESS.2025.3540411}, journal = {IEEE Access}, pages = {28962-28978}, shortjournal = {IEEE Access}, title = {Efficient hierarchical hash-based multi-field packet classification with fast update for software switches}, volume = {13}, year = {2025}, }
• (2025). Deep learning approaches in the effects of recession and FOMC minutes on oil prices. ACCESS, 13, 28946-28961. (www)

  @article{ , abstract = {The financial industry is increasingly interested in predictive analysis and forecasting using time series data. Understanding the relationship between recessions and oil markets is crucial for developing financial forecasts and strategic decisions. This study uses advanced deep learning models to examine the interaction between recession signals and crude oil prices. Data covering recession periods include key economic indicators such as Gross Domestic Product (GDP) fluctuations, unemployment rates, consumer spending trends, business investments, and housing market dynamics. Additionally, Federal Open Market Committee (FOMC) minutes are used to capture economic assessments and monetary policy decisions by the Federal Reserve during recessions, providing insights into policymakers’ expectations and responses. Data is augmented using Time-series Generative Adversarial Networks (TimeGAN) to capture intricate patterns in oil prices. By focusing on feature selection, this study aims to identify patterns from historical data and relationships between recession signals and oil price movements. Long Short-Term Memory networks (LSTMs), Gated Recurrent Units (GRUs), Transformer, and ensemble learning techniques are used to predict crude oil prices during recessions. This research provides insights into how recession signals and Federal Reserve policy decisions influence crude oil prices, offering a comprehensive view of the dynamics between economic downturns and the energy market.}, archive = {J_ACCESS}, author = {Ahmet Karabas and Nizamettin Aydin}, doi = {10.1109/ACCESS.2025.3537822}, journal = {IEEE Access}, pages = {28946-28961}, shortjournal = {IEEE Access}, title = {Deep learning approaches in the effects of recession and FOMC minutes on oil prices}, volume = {13}, year = {2025}, }
• (2025). Estimating near-surface air temperature from satellite-derived land surface temperature using temporal deep learning: A comparative analysis. ACCESS, 13, 28935-28945. (www)

  @article{ , abstract = {This study develops and compares three deep learning methods—LSTM, TCN, and N-BEATS—for estimating near-surface air temperature (T2M) from satellite-derived land surface temperature (LST) and land cover metrics such as NDVI and NDBI. By incorporating temporal context through varying look-back windows, these models substantially outperform non-temporal baselines, reducing root-mean-square error (RMSE) from around 2.6–2.8°C to below 1.8°C, and underscoring the value of historical LST observations for capturing the evolving surface-air temperature relationship. Longer lags generally improve accuracy, although N-BEATS performance plateaus beyond a certain window, reflecting both diminishing returns and practical limitations linked to missing cloud-free satellite data. Seasonal and diurnal evaluations show higher errors in spring and midday hours, likely due to rapid vegetation changes and stronger physical and dynamical processes that make T2M less predictable. Spatially, stations with denser vegetation exhibit elevated errors, suggesting that transpiration and canopy effects complicate the LST-T2M linkage. For extreme-event detection, LSTM provides the fewest false alarms (highest precision), N-BEATS captures the most extremes (highest recall), and TCN offers the best overall balance in precision and recall (highest F1). While cloud-free satellite coverage remains a limitation, future work could explore adaptive lag strategies, additional data sources, and more advanced data-fusion techniques. These results highlight that satellite-based temperature monitoring, when combined with suitable deep learning architectures, can reliably estimate T2M based on LST, further addressing gaps in near-surface observations and facilitating the detection of critical T2M extremes. This framework has direct applications in heat-warning systems, resource management, precision agriculture, and urban climate adaptation, and stands to benefit further from ongoing advancements in satellite sensing technology.}, archive = {J_ACCESS}, author = {Jangho Lee}, doi = {10.1109/ACCESS.2025.3539581}, journal = {IEEE Access}, pages = {28935-28945}, shortjournal = {IEEE Access}, title = {Estimating near-surface air temperature from satellite-derived land surface temperature using temporal deep learning: A comparative analysis}, volume = {13}, year = {2025}, }
• (2025). Design of a machine learning approach to anomaly detection in tyre-road interaction. ACCESS, 13, 28920-28934. (www)

  @article{ , abstract = {Tyres show a strong non-linear dependence on vertical force, road roughness, wear level, temperature gradient, and slip resulting in an additional challenge in calibration, whose parameters may vary significantly with the tyre’s condition. An additional challenge to identifying and modeling the multi-dimensional tyre variability lies in the low accuracy level of tyre-road interaction data presenting physical inconsistencies and outliers, thus affecting outdoor testing scenarios. Indeed, outliers, gaps, or errors in the data can compromise calibration performance, potentially leading to incorrect model identification and rendering it unsuitable for further offline and online applications. In this paper, the authors aim to optimize the process of identifying tyre parameters by applying machine learning techniques to the dataset’s pre-processing with particular attention to clustering and anomaly detection algorithms. The process is split into two phases: first, different clustering algorithms are applied to the tyre data to group similar operating conditions; then, anomaly detection algorithms are applied to clustered data to recognize and remove inconsistencies. Additionally, to objectively compare the proposed data processing results, the preprocessed specifically acquired experimental data have been employed for the calibration of the reference mathematical tyre formulation, comparing the deviations of the fundamental tyre-related quantities to the previously identified tyre model, already validated in both offline and online scenarios. For the grip coefficient evaluation versus both lateral and longitudinal slip variables, the Elliptic Envelope algorithm shows to be the best anomaly detection algorithm while the One-Class Support Vector Machine technique demonstrates lower deviations for the stiffness evaluation in both longitudinal and lateral directions.}, archive = {J_ACCESS}, author = {Aleksandr Sakhnevych and Nicola Pasquino and Giancarlo Sperlì}, doi = {10.1109/ACCESS.2025.3539923}, journal = {IEEE Access}, pages = {28920-28934}, shortjournal = {IEEE Access}, title = {Design of a machine learning approach to anomaly detection in tyre-road interaction}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based vulnerability detection solutions in smart contracts: A comparative and meta-analysis of existing approaches. ACCESS, 13, 28894-28919. (www)

  @article{ , abstract = {In the short history of smart contracts, substantial losses have occurred due to unaccounted vulnerabilities in the smart contracts loaded onto the blockchain. Vulnerabilities in smart contracts threaten the viability and confidence of blockchain technology. Machine and deep learning architectures have been increasingly proposed to assist with writing smart contracts and detecting vulnerabilities to minimize the risks of successful attacks. This paper presents the findings of deep learning vulnerability detection in smart contracts collated from selected research papers and provides an overview of the vulnerability detection architecture. This paper assessed each component of the overall architecture, individually and in combinations, to infer potential relationships in increasing detection rates in smart contract vulnerability detection tools. This study found that the area of detecting smart contract vulnerabilities is gravitating towards deep learning models, with a particular focus on combining neural networks in serial or parallel to achieve high detection results. Feature extraction of syntax and semantic information greatly determines the detection results of a model, which suggests a strong relationship between the use of source code and neural networks. Despite this relationship, given the inaccessibility and unavailability of smart contract source code, future work should focus on efficiently extracting features and context from opcodes, which are more readily available on the blockchain. Based on these findings, the study offers an in-depth discussion of the matters arising, open issues, and key aspects such as bytecode and opcode in smart contracts, failure of attention mechanisms, the rationale of hybrid models, dataset challenges, computational complexity in a large-scale blockchain environment, optimization strategies, Expunge and interdisciplinary approaches to smart contract vulnerability detection. These discussions enhance the practical relevance of the paper, highlighting the opportunities for scaling deep learning models for real-world blockchain applications.}, archive = {J_ACCESS}, author = {Michael Bresil and Pwc Prasad and Md Shohel Sayeed and Umar Ali Bukar}, doi = {10.1109/ACCESS.2025.3532326}, journal = {IEEE Access}, pages = {28894-28919}, shortjournal = {IEEE Access}, title = {Deep learning-based vulnerability detection solutions in smart contracts: A comparative and meta-analysis of existing approaches}, volume = {13}, year = {2025}, }
• (2025). Distance relaying for the protection of modern power system networks. ACCESS, 13, 28861-28893. (www)

  @article{ , abstract = {Distance relays (DRs) have long been considered one of the most reliable protection schemes for transmission lines (TLs), providing primary and backup protection. However, the increasing complexity of transmission networks, the integration of large-scale renewable energy and dynamic operating conditions present significant challenges to their effectiveness. Traditional DRs protection logic often struggles when networks deviate from predefined configurations, potentially leading to protection failures. Numerous solutions have been proposed in the literature to address individual challenges in distance relaying for the protection of TLs. However, there are only a few review papers which comprehensively cover these issues across multiple domains of problems encountered in modern power system networks. This paper thoroughly reviews the technical, topological, and operational challenges that impact DRs performance, as reported in the literature. It systematically categorizes protection issues and their corresponding solutions available in the literature into twelve key areas: power swings, voltage instability, load encroachment, parallel lines, multi-terminal lines, flexible alternating current transmission systems (FACTS) devices, series compensation, high voltage direct current (HVDC) lines, renewable energy integration, microgrids, high-impedance faults, and cyberattacks. Additionally, the paper identifies emerging research opportunities to tackle the increasing complexity of modern power systems, aiming to foster the development of more robust and adaptive distance protection schemes for TLs.}, archive = {J_ACCESS}, author = {Rahul Pujari and Mahamad Nabab Alam}, doi = {10.1109/ACCESS.2025.3539919}, journal = {IEEE Access}, pages = {28861-28893}, shortjournal = {IEEE Access}, title = {Distance relaying for the protection of modern power system networks}, volume = {13}, year = {2025}, }
• (2025). A survey of oscillation localization techniques in power systems. ACCESS, 13, 28836-28860. (www)

  @article{ , abstract = {With the increasingly high penetration of renewable energy sources and the more integration of flexible loads, the power system is currently experiencing significant changes. While these changes aim to meet the growing needs of modern society, they also lead to the occurrence of more and more power oscillation events. These oscillations could be harmful to the operation of the power system, even causing power outages in poorly damped power systems. Hence, it is important to locate the source of these oscillations in the complicated power system, as control strategies or actions can be taken to mitigate them. To locate the source of oscillations accurately, many efficient techniques for oscillation source localization have been developed in the past decade using different physical properties of the power system and different methods. This survey focuses on these techniques to localize the source of oscillation in power systems. They can be categorized according to the physical metrics used, such as energy, mode shape, traveling wave, and damping torque. They can also be classified according to the specific methods used, such as data-driven, machine/deep learning, and transforms. The principles of these techniques, as well as their advantages and disadvantages, are discussed. Based on this survey, future research challenges on oscillation localization are outlined. The survey aims to provide a comprehensive review of the most existing techniques for oscillation source localization in the literature so that researchers and engineers can identify the most appropriate technique for their respective domains and applications.}, archive = {J_ACCESS}, author = {Yunfei Chen and Ziyu Fan and David Gregory and Xiaoyao Zhou and Ronak Rabbani}, doi = {10.1109/ACCESS.2025.3540318}, journal = {IEEE Access}, pages = {28836-28860}, shortjournal = {IEEE Access}, title = {A survey of oscillation localization techniques in power systems}, volume = {13}, year = {2025}, }
• (2025). Crowd anomaly detection from drone and ground. ACCESS, 13, 28824-28835. (www)

  @article{ , abstract = {Video anomaly detection (VAD) is crucial for intelligent video surveillance systems. Many VAD methods for crowds in public spaces have been evaluated using videos captured by fixed cameras. Recently, drones have also been used for video surveillance, but the datasets of human activity captured by drones are relatively small or include actions by only a small number of people. Therefore, we created a large-scale dataset called Crowd Anomaly detection from Drone and Ground (CADG). A moving drone and two fixed ground cameras captured $448\times 3$ clips, comprising 236K $\times 3$ frames, of five types of crowd activities crucial to video surveillance. We also propose a unified learning framework for simultaneous anomaly detection and classification using a simple yet strong and computationally inexpensive temporal encoder. Experiments on the dataset show that the method detects crowd anomalies more accurately than conventional methods, but still highlight the difficulties of stable VAD from a moving drone and bridging the domain gap between views. The dataset is available on the project page at https://cadg24.github.io/home.}, archive = {J_ACCESS}, author = {Yoshiki Ito and Tatsuya Sasaki and Satoshi Kondo}, doi = {10.1109/ACCESS.2025.3540438}, journal = {IEEE Access}, pages = {28824-28835}, shortjournal = {IEEE Access}, title = {Crowd anomaly detection from drone and ground}, volume = {13}, year = {2025}, }
• (2025). RADSAFiE: A netlist-level fault injection user interface application for FPGA-based digital systems. ACCESS, 13, 28809-28823. (www)

  @article{ , abstract = {Field Programmable Gate Arrays are extensively utilized across numerous domains, including telecommunications, cryptography, Machine Learning, and safety-critical applications. In critical applications, FPGAs are often exposed to environmental challenges, such as Single Event Upsets, which can affect the reliability of the design. Therefore, assessing the robustness of FPGA-based systems becomes crucial to ensure their correct functionality under normal operation and mitigate potential risks. This can be done by developing a fault injection environment to simulate the effect of Single Event Effects over the specified Device Under Test. Firstly, this paper briefly reviews other related works, highlighting the differences between the various approaches. Then, our novel netlist-level fault injection tool called RADSAFiE is introduced. The tool is characterized by a Python-based User Interface that allows users to modify an input netlist by inserting fault injection logic primitives. The user can select between two interfaces: a Graphical User Interface or a Command Line Interface, both taking an HDL netlist as input. We discuss the supported fault types and highlight the compatibility with different FPGA vendors. We also conduct code profiling on the application and analyze, for specific operations, both execution time and memory usage. Specifically, we focus on a use case that features the Soft Graphic Processing Unit IP provided by IngeniArs S.r.l., namely GPU@SAT. We present performance results across netlists of different sizes and offer a concise overview of the simulation environment. Finally, in the last section of the paper, we hint at possible future development and conclude the work.}, archive = {J_ACCESS}, author = {Matteo Monopoli and Matteo Biondi and Pietro Nannipieri and Silvia Moranti and Luca Fanucci}, doi = {10.1109/ACCESS.2025.3539932}, journal = {IEEE Access}, pages = {28809-28823}, shortjournal = {IEEE Access}, title = {RADSAFiE: A netlist-level fault injection user interface application for FPGA-based digital systems}, volume = {13}, year = {2025}, }
• (2025). Multimodal music genre classification of sotho-tswana musical videos. ACCESS, 13, 28799-28808. (www)

  @article{ , abstract = {Music genre classification is a fundamental task in music information retrieval, aimed at discerning the categorical placement, or genre, of a given musical piece. Such classification holds significant utility in facilitating music recommendations tailored to individual preferences. Traditionally, efforts in music genre classification have primarily concentrated on unimodal analyses, focusing either on textual (lyrics) or audio components of music. However, given that music communicates through multiple modalities, including lyrics, audio, and visuals, there exists a pressing need to integrate these diverse sources of information. Multimodal music genre classification thus emerges as a solution, leveraging the amalgamation of two or more modalities present in music production to discern genre categorizations effectively. Moreover, existing endeavors in music genre classification have predominantly centered on compositions in Western languages, with comparatively limited attention devoted to non-Western languages. This study addresses these gaps by employing deep learning and language models to undertake multimodal music genre classification specifically within the context of Sotho-Tswana musical videos. The findings of this study demonstrate the efficacy of multimodal music genre classification, showcasing superior performance compared to unimodal approaches relying solely on lyrics for genre classification.}, archive = {J_ACCESS}, author = {Osondu E. Oguike and Mpho Primus}, doi = {10.1109/ACCESS.2025.3536026}, journal = {IEEE Access}, pages = {28799-28808}, shortjournal = {IEEE Access}, title = {Multimodal music genre classification of sotho-tswana musical videos}, volume = {13}, year = {2025}, }
• (2025). Classification of petrographic thin section images with depthwise separable convolution and dilated convolution. ACCESS, 13, 28789-28798. (www)

  @article{ , abstract = {To enhance the precision and efficiency of petrographic thin section image classification and reduce the subjectivity resulting from manual classification methods, a new classification model (DC-PC-Dilated-IR-V2) in term of the deep convolutional network is constructed in this study. In the DC-PC-Dilated-IR-V2 model, the depthwise separable convolution (DSC) is applied to avoid the increase problem of parameters and calculations due to too many convolutional layers; the attention module is introduced to obtain a better representation of characteristics through information fusion and weighting, which allows the model to focus on the target area of thin section images; in addition, the dilated convolution is taken in, which enables the model to extract more global characteristics and higher-level semantic characteristics without increasing the convolution parameters, thereby improving the classification precision of the model. The model was evaluated using a dataset comprising 4000 thin section images, classified into categories such as small pores with fine throats, medium pores, feldspar dissolution pores, dissolution-enlarged pores, and microfractures. Our extensive experiments demonstrate the superior performance of DC-PC-Dilated-IR-V2 compared to other classifiers, including back propagation (BP) neural networks, support vector machines (SVM), AlexNet, VGG-16, ResNet-101, ResNeXt-101, and DenseNet. Specifically, DC-PC-Dilated-IR-V2 achieves the highest accuracy (99.84%), Macro_F1 score (0.9787), and Kappa coefficient (0.9763) among all tested methods. Additionally, it significantly reduces the parameter count and computational load with only 15.9 million parameters and 1259 billion FLOPs, ensuring efficiency without sacrificing accuracy. The average training time is also significantly lower than that of its counterparts. These improvements highlight the potential of DC-PC-Dilated-IR-V2 for more efficient and accurate petrographic image classification.}, archive = {J_ACCESS}, author = {Shaowei Pan and Xingxing Cheng and Wenjing Fan}, doi = {10.1109/ACCESS.2025.3540477}, journal = {IEEE Access}, pages = {28789-28798}, shortjournal = {IEEE Access}, title = {Classification of petrographic thin section images with depthwise separable convolution and dilated convolution}, volume = {13}, year = {2025}, }
• (2025). Deep pruner and adaptive cost volume multiview stereo network for 3D reconstruction. ACCESS, 13, 28777-28788. (www)

  @article{ , abstract = {Reconstructing three-dimensional (3D) images is imperative in computer vision because it assists in restoring the 3D structure of a scene. However, challenges like accurate matching in low-texture and reflective areas, along with inefficient feature extraction, degrade 3D reconstruction quality and increase computational complexity. To address these challenges, we propose a robust multi-view stereo network, DPrun-RMVSNet, designed to enhance matching in occluded regions and improve feature extraction for texture-less and reflective surfaces. Our model incorporates a recurrent neural network (RNN) with long-short-term memory (LSTM) to handle depth interference. The feature network captures essential information about the image content, such as edges, textures, and corners. To reduce computational costs, we introduce a novel deep pruner feature network (DPF) with an adaptive cost volume, enabling efficient and accurate 3D model creation. The proposed model was trained using the public DTU dataset and evaluated on two benchmark datasets including DTU, and Tank and Temple. Additionally, we conduct an ablation study to assess the impact of the proposed methods, offering both quantitative and qualitative evaluations to validate the model’s effectiveness. Experimental results show that our model improves state-of-the-art (SOTA) approaches, achieving better reconstruction accuracy while using less execution time and memory.}, archive = {J_ACCESS}, author = {Junaid Jamshid and Wan Wanggen and Khurram Shahzad and A. A. M. Muzahid and Yuan Kang}, doi = {10.1109/ACCESS.2025.3535616}, journal = {IEEE Access}, pages = {28777-28788}, shortjournal = {IEEE Access}, title = {Deep pruner and adaptive cost volume multiview stereo network for 3D reconstruction}, volume = {13}, year = {2025}, }
• (2025). Toward the development of a novel flexible sensor for simultaneous measurement of acupoint resistance and temperature in human subjects. ACCESS, 13, 28766-28776. (www)

  @article{ , abstract = {Acupoint sensitization theory guides clinical diagnosis and treatment, positing that disease factors alter the state of acupoints, typically manifesting as changes in temperature, resistance, and pain threshold. Flexible sensors can rapidly detect sensitized acupoints and evaluate efficacy by measuring the resistance and temperature values of acupoints. Consequently, utilizing ultra-thin polyimide as a substrate, the research group developed a flexible sensor capable of simultaneously detecting acupoint temperature and resistance while providing therapeutic functions. the designed sensor features a compact size, low mass, high flexibility, and high measurement accuracy. It achieves a temperature measurement accuracy of 0.002-0.02°C within the range of 0-40°C. The detection error falls within the range of 10.5 to $17.5\Omega $ within the range of 2.1K $\Omega $ to 100K $\Omega $ . Furthermore, the designed sensor has been applied to human sensitized acupoint monitoring and intervention experiments, demonstrating favorable performance, which offers potential for future acupoint sensitization research.}, archive = {J_ACCESS}, author = {Zheng Yu and Yusheng Tang and Linshuai Zhang and Ziqian Wang and Xuanhao Shu and Hang Li and Yafei Zhang and Xuerong Luo and Xihong Yang and Xianfeng Cao and Shuguang Yu and Chuanbiao Wen}, doi = {10.1109/ACCESS.2025.3539368}, journal = {IEEE Access}, pages = {28766-28776}, shortjournal = {IEEE Access}, title = {Toward the development of a novel flexible sensor for simultaneous measurement of acupoint resistance and temperature in human subjects}, volume = {13}, year = {2025}, }
• (2025). Low-complexity architecture for high-speed 50G-PON LDPC decoder. ACCESS, 13, 28751-28765. (www)

  @article{ , abstract = {We propose a hardware architecture for 50G-PON LDPC decoder achieving high throughput and high error correcting capability while maintaining low level of resource utilization and implementation complexity. Our approach employs phased decoding as a key algorithm which effectively balances the competing goals of high throughput and low amount of resource utilization. We also propose a fixed-structured wiring network between variable nodes and check nodes, leveraging the quasi-cyclic property of parity-check matrix to significantly reduce implementation complexity. To further simplify the decoder structure and enhance the throughput, we propose a blockwise column cyclic shift in the parity-check matrix, along with rules for normalizing and quantizing messages generated in decoder. Our design also incorporates a pipelined structure of computation units. By integrating the proposed design schemes, we draw a sophisticated architecture for high-speed and low-complexity LDPC decoder that achieves the seamless decoding throughput of 49.7664 Gbps, even on a single FPGA board.}, archive = {J_ACCESS}, author = {Kon-Woo Kwon and Kwangok Kim and Kyeonghwan Doo and Hwanseok Chung and Jeong Woo Lee}, doi = {10.1109/ACCESS.2025.3540450}, journal = {IEEE Access}, pages = {28751-28765}, shortjournal = {IEEE Access}, title = {Low-complexity architecture for high-speed 50G-PON LDPC decoder}, volume = {13}, year = {2025}, }
• (2025). Power loss observations in algorithmically designed PMSM stator windings utilizing the freedoms afforded by additive manufacturing. ACCESS, 13, 28733-28750. (www)

  @article{ , abstract = {Permanent magnet synchronous machines (PMSM) are an increasingly popular motor topology for many industries such as automotive, aerospace, manufacturing, energy and premium consumer goods. PMSM offer many advantages compared to other topologies such as induction machines due to their higher power density, greater operational efficiency and high torque, whilst remaining robust and fault tolerant. One specific area of interest is the stator windings. With increasing operational speed, the geometric configuration of the winding becomes ever increasingly important in order to mitigate increasing AC losses. The windings conductor lay in slot, position, shape and orientation become critical in combating loss mechanisms such as skin effect, proximity effect and armature reaction. Additive manufacturing offers a novel geometric design freedom that has previously been unattainable using traditional manufacturing methods and enables the ability to create highly novel winding design methodologies that could potentially revolutionise winding design and performance. This work investigates the losses observed from an algorithmically designed stator winding, additively manufactured in AlSi10Mg using the laser powder bed fusion process. A motorette sub-assembly was constructed and the DC and AC power losses were measured and compared against a theoretical 3D FEA based simulation model. The outcome observed a 9.4% difference in losses comparing theoretical and measured winding values utilising the RAC/RDC measured. Samples of the additively manufactured conductor parts have been tested for resistivity and conductivity.}, archive = {J_ACCESS}, author = {John McKay and Jill Miscandlon and Tatyana Konkova}, doi = {10.1109/ACCESS.2025.3538813}, journal = {IEEE Access}, pages = {28733-28750}, shortjournal = {IEEE Access}, title = {Power loss observations in algorithmically designed PMSM stator windings utilizing the freedoms afforded by additive manufacturing}, volume = {13}, year = {2025}, }
• (2025). A metrology-driven approach to distilling live wireless signals into immunity test signals. ACCESS, 13, 28711-28732. (www)

  @article{ , abstract = {In the era of ubiquitous electronic devices, immunity testing is essential to ensure these devices’ operational efficacy and safety. This study focuses on the growing challenge of assessing immunity in the context of modern wireless communication technologies, such as 5G and WLAN, which are not yet fully addressed by current international standards. We propose a metrology-driven methodology to develop immunity test signals by distilling live wireless signals into simplified; representative test signals that capture key electromagnetic characteristics. The methodology involves simulating various configurations of the wireless technologies, analyzing duty cycle and pulse repetition rate distributions, and identifying common modes. These common modes guide the design of pulse-modulated immunity test signals, which are validated experimentally by testing their impact on a device under test. Our results demonstrate that these test signals effectively replicate the electromagnetic disturbances caused by live 5G and WLAN signals, enabling more realistic immunity testing for electronic devices, including medical equipment. This comprehensive approach supports future research and standardization efforts in developing more representative immunity tests of live wireless signals. We demonstrate its application to 5G cellular and WLAN protocols, showcasing its potential to address the unique challenges posed by these emerging technologies.}, archive = {J_ACCESS}, author = {Susanna Mosleh and Jason B. Coder and John M. Ladbury and Mohamad Omar Al Kalaa and Jeffrey L. Silberberg and Yasaman Ardeshirpour and Joshua W. Guag and Yongkang Liu and Seth J. Seidman}, doi = {10.1109/ACCESS.2025.3540754}, journal = {IEEE Access}, pages = {28711-28732}, shortjournal = {IEEE Access}, title = {A metrology-driven approach to distilling live wireless signals into immunity test signals}, volume = {13}, year = {2025}, }
• (2025). A 10 × 10 multi-band MIMO antenna system for LTE, 5G, wi-fi 7, and X-band communication applications. ACCESS, 13, 28693-28710. (www)

  @article{ , abstract = {To attend with the needs of modern wireless communication equipment, this paper proposes a multi-band planar antenna MIMO system. The antenna system operates in the frequency range of 0.8-1.04 GHz, 1.6-2.71 GHz, and 4.69-11.5 GHz, supporting LTE, 5G NR, Wi-Fi 7, and X bands. The antenna proposed in this paper is 60 mm $\times 10$ mm. A feed-through coupling structure reduces its size and achieves broadband coverage to adapt to increasingly compact electronic communication. The design uses multi-branch microstrip lines and long slot structures to ensure high gain and high efficiency over a wide spectrum and achieve omnidirectional radiation of the antenna. In the proposed MIMO antenna system, the envelope correlation coefficient (ECC) remains below 0.5 and even approaches zero at high frequencies. Additionally, the system maintains overall isolation below -10 dB, demonstrating excellent signal isolation and anti-interference capabilities. In order to confirm the impact of antenna radiation on the human body, the analysis results of the specific absorption rate (SAR) and power density (PD) confirmed that the radiation impact of the antenna system on the human body is lower than the international standard value. The MIMO antenna system proposed in this paper is simple in design and doesn’t require additional passive components in the circuit, which greatly reduces the manufacturing cost. When integrated into laptops, routers, and smartphones, it exhibits stable antenna performance, making it efficient and flexible for application in a variety of wireless communication environments.}, archive = {J_ACCESS}, author = {Ming-An Chung and Kai-Xiang Chen and Chia-Chun Hsu and Chia-Wei Lin}, doi = {10.1109/ACCESS.2025.3540637}, journal = {IEEE Access}, pages = {28693-28710}, shortjournal = {IEEE Access}, title = {A 10 × 10 multi-band MIMO antenna system for LTE, 5G, wi-fi 7, and X-band communication applications}, volume = {13}, year = {2025}, }
• (2025). Leveraging bi-focal perspectives and granular feature integration for accurate reliable early alzheimer’s detection. ACCESS, 13, 28678-28692. (www)

  @article{ , abstract = {Being the most commonly known neurodegeneration, Alzheimer’s Disease (AD) is annually diagnosed in millions of patients. The present medical scenario still finds the exact diagnosis and classification of AD through neuroimaging data as a challenging task. Traditional CNNs can extract a good amount of low-level information in an image while failing to extract high-level minuscule particles, which is a significant challenge in detecting AD from MRI scans. To overcome this, we propose a novel Granular Feature Integration method to combine information extraction at different scales along with an efficient information flow, enabling the model to capture both broad and fine-grained features simultaneously. We also propose a Bi-Focal Perspective mechanism to highlight the subtle neurofibrillary tangles and amyloid plaques in the MRI scans, ensuring that critical pathological markers are accurately identified. Our model achieved an F1-Score of 99.31%, precision of 99.24%, and recall of 99.51%. These scores prove that our model is significantly better than the state-of-the-art (SOTA) CNNs in existence.}, archive = {J_ACCESS}, author = {Shravan Venkatraman and V. Pandiyaraju and A. Abeshek and S. Pavan Kumar and S. A. Aravintakshan}, doi = {10.1109/ACCESS.2025.3540567}, journal = {IEEE Access}, pages = {28678-28692}, shortjournal = {IEEE Access}, title = {Leveraging bi-focal perspectives and granular feature integration for accurate reliable early alzheimer’s detection}, volume = {13}, year = {2025}, }
• (2025). Joint topic-emotion modeling in financial texts: A novel approach to investor sentiment and market trends. ACCESS, 13, 28664-28677. (www)

  @article{ , abstract = {This paper presents a novel approach to financial text analysis by jointly modeling topics and emotions within financial news and social media discussions, thereby advancing market trend understanding and investor sentiment analysis. Leveraging the Neural Variational Document Model (NVDM) for topic modeling, we effectively capture the thematic structure across global financial texts, achieving a coherence score of 0.68 and a perplexity of −8.5. For emotion classification, a fine-tuned FinBERT model yields an accuracy of 96.3%, F1 Score of 97.8%, precision of 98.3%, and recall of 97.5%. Our integrated framework not only uncovers the latent topics driving market discourse but also associates these topics with nuanced emotional states, providing a comprehensive perspective on financial sentiment. This joint modeling approach enhances the interpretability and reliability of financial market predictions, offering a scalable solution for understanding investor emotions with significant potential for applications in market analysis, trading strategies, and economic forecasting.}, archive = {J_ACCESS}, author = {Md Nazmul Hossain Mir and Md Shahriar Mahmud Bhuiyan and Md Al Rafi and Gourab Nicholas Rodrigues and M. F. Mridha and Md. Abdul Hamid and Muhammad Mostafa Monowar and Md Zia Uddin}, doi = {10.1109/ACCESS.2025.3538760}, journal = {IEEE Access}, pages = {28664-28677}, shortjournal = {IEEE Access}, title = {Joint topic-emotion modeling in financial texts: A novel approach to investor sentiment and market trends}, volume = {13}, year = {2025}, }
• (2025). Multiple experts-based decision algorithm for the analysis of consumer preferences in the transformation of creative product value. ACCESS, 13, 28645-28663. (www)

  @article{ , abstract = {Consumer preferences play a significant role in transforming products. The manufacturer selects product designs, styles, colors, and layouts to attract consumers. However, it isn’t easy to analyze the customer’s preferences due to various factors. This article aims to develop an approach to examine consumer preferences when ranking products. This study analyzes the customer’s preferences by allowing them to express their opinions about the objects. The interval-valued Pythagorean fuzzy rough set (IVPYFRS) allows the consumers to give their views independently using the membership function. Integrating the rough and interval-valued Pythagorean fuzzy sets reduces uncertainty and ambiguity in modeling consumer preferences. Consequently, the approach developed in this study enhances the accuracy of analyzing consumer preferences regarding products. In this study, the interval-valued Pythagorean fuzzy rough Aczel-Alsina weighted geometric, interval-valued Pythagorean fuzzy rough Aczel-Alsina ordered weighted geometric, and interval-valued Pythagorean fuzzy rough Aczel-Alsina hybrid weighted geometric (IVPYFRAAHWG) operators are developed to tank some specific products are ranked by analyzing the consumer preferences based on some particular factors.}, archive = {J_ACCESS}, author = {Jing Wang}, doi = {10.1109/ACCESS.2025.3538863}, journal = {IEEE Access}, pages = {28645-28663}, shortjournal = {IEEE Access}, title = {Multiple experts-based decision algorithm for the analysis of consumer preferences in the transformation of creative product value}, volume = {13}, year = {2025}, }
• (2025). A meta-reinforcement learning-based poisoning attack framework against federated learning. ACCESS, 13, 28628-28644. (www)

  @article{ , abstract = {As a distributed machine learning paradigm, federated learning enables clients to collaboratively train a global model without sharing their raw data, thus preserving data privacy while still utilizing the data. However, the distributed nature of federated learning makes it vulnerable to poisoning attacks, which undermine the integrity and availability of the model by injecting carefully crafted perturbations into the data or model. Most existing poisoning attacks rely on heuristic approaches, which are significantly mitigated by robust aggregation strategies during long-term federated learning training. To overcome this limitation, this work proposes a novel poisoning attack framework based on meta-reinforcement learning. The global data distribution of the clients is first inferred from the global gradient using a conditional generative adversarial network. The inferred distribution is then used to simulate the federated learning environment locally for reinforcement learning training. A novel scaling and noise injection attack is introduced by designing unique scaling coefficients and noise values for the gradient of each layer’s parameters using reinforcement learning. Furthermore, meta-reinforcement learning is leveraged to enhance the generalization capability of the attack, ensuring effectiveness across various robust aggregation strategies. Experimental results demonstrate that our approach significantly reduces model accuracy to around 10% across three datasets under various aggregation strategies, outperforming existing methods and exhibiting superior generalization ability and attack performance.}, archive = {J_ACCESS}, author = {Wei Zhou and Donglai Zhang and Hongjie Wang and Jinliang Li and Mingjian Jiang}, doi = {10.1109/ACCESS.2025.3538891}, journal = {IEEE Access}, pages = {28628-28644}, shortjournal = {IEEE Access}, title = {A meta-reinforcement learning-based poisoning attack framework against federated learning}, volume = {13}, year = {2025}, }
• (2025). A novel modular data acquisition system for distributed monitoring of partial discharge in industrial drives. ACCESS, 13, 28617-28627. (www)

  @article{ , abstract = {Insulation fault detection in rotating machinery using Partial Discharge (PD) has been a hot research topic over the last decades. In recent years, attention has also been paid to low voltage machines, following the advent of wide bandgap semiconductor devices. However, most of the solutions currently available are based on analog technology, which entails high costs, bulky wiring, and long installation times, making them unattractive for the market. This paper presents a low-cost, modular, and full-digital PD monitoring device, which implements a Capacitive Coupling (CC) front end for PD conditioning and an innovative, digital monitoring network, based on the Automotive Audio Bus (A2B), particularly suitable for deployment in large industrial plants, ensuring complete coverage and continuous monitoring of critical machinery without suffering of electromagnetic disturbances. The proposed system has been simulated in PSpice, then built and tested against a reference, consisting of a laboratory grade oscilloscope, using two different PD interfaces: CC and High Frequency Current transformer (HF-C). The comparison was carried out by subjecting a low voltage induction motor to a standard PD test to evaluate PD inception voltage detection with the different methods. The digital oscilloscope demonstrated a slightly better sensitivity concerning the onset PD voltage threshold, while the proposed monitoring device exhibits a greater robustness at transition voltages, being less prone to false positives. The two systems are equivalent at 1.4 kV while the proposed solution performs significantly better at higher voltages and the HF-C transducer demonstrated even greater sensitivity at these voltage levels. The results show that the proposed PD CC sensor has satisfactory sensitivity towards detecting onset of PD activity and appears less susceptible to noise than the digital oscilloscope CC sensor.}, archive = {J_ACCESS}, author = {A. Toscani and D. Pinardi and D. Barater and F. Immovilli}, doi = {10.1109/ACCESS.2025.3540219}, journal = {IEEE Access}, pages = {28617-28627}, shortjournal = {IEEE Access}, title = {A novel modular data acquisition system for distributed monitoring of partial discharge in industrial drives}, volume = {13}, year = {2025}, }
• (2025). MPFNet: Multiscale prediction network with cross fusion for real-time semantic segmentation. ACCESS, 13, 28605-28616. (www)

  @article{ , abstract = {Semantic segmentation currently plays an important role in computer vision and is widely applied in both industry and human life. The self-driving car is one of the most trending applications, which assists humans in making informed decisions. The self-driving application has to interpret visual information from street scenes. However, how to effectively segment a long range of objective sizes is still a challenging problem. A feature pyramid network (FPN) builds up an architecture by processing four different features to contribute contextual and spatial information to the final map. Each feature can suitably process a specific range of objective sizes. Nevertheless, the final feature combination is not optimal when they raise the computation cost and reduce the semantic weights. We propose a multi-scale prediction network with cross-fusion in order to address the aforementioned drawbacks. The prediction module consists of three different predictions that allow the architecture to efficiently extract information of various sizes. Each prediction is generated from a pair of feature pyramids used to predict object classes. Furthermore, the cross-scale fusion is designed to enhance the weight aggregation of the final score map. The core component of the cross-fusion is the selective attention mechanism that determines uncertain weights of the lower prediction and then selects the complement from the adjacent prediction. By implementing this proposed scheme, we have achieved good results 78.3% mIoU and 45 FPS on Cityscapes and 45.9% mIoU on Mapillary Vistas datasets. Our method outperforms the baseline method with 7.0 mIoU improvement and a 27 FPS speedup on Cityscapes dataset. The experiment results demonstrate that the proposed model achieves a reasonable balance between performance and efficiency.}, archive = {J_ACCESS}, author = {van Toan Quyen and Min Young Kim}, doi = {10.1109/ACCESS.2025.3540454}, journal = {IEEE Access}, pages = {28605-28616}, shortjournal = {IEEE Access}, title = {MPFNet: Multiscale prediction network with cross fusion for real-time semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Design and evaluation of a spherical multi-layered unit for string jamming mechanisms. ACCESS, 13, 28594-28604. (www)

  @article{ , abstract = {In soft robotics, it is desirable to be able to switch between two states, namely a soft state for adaptation to the environment and a rigid state for the transmission of forces. String jamming mechanisms, composed of bead-like connected units, have gained attention for their ability to switch between these states. This paper presents the design and evaluation of a new multi-layered unit for string jamming mechanisms aimed at enhancing holding torque while maintaining high flexibility. The proposed spherical-layered unit addresses the dual-state challenge by introducing a spherical multi-layer structure that enhances frictional forces and improves holding torque in the rigid state without compromising flexibility in the soft state. Through theoretical analysis and experimental validation, we demonstrate that the proposed unit achieves an improvement in each fitting performance and holding torque compared to existing designs. This study provides guidelines for future applications of variable stiffness technologies, particularly in soft robotics, manipulators, and haptic devices.}, archive = {J_ACCESS}, author = {Ryohei Michikawa and Fumitoshi Matsuno}, doi = {10.1109/ACCESS.2025.3529628}, journal = {IEEE Access}, pages = {28594-28604}, shortjournal = {IEEE Access}, title = {Design and evaluation of a spherical multi-layered unit for string jamming mechanisms}, volume = {13}, year = {2025}, }
• (2025). High-level synthesis for FPGAs—A hardware engineer’s perspective. ACCESS, 13, 28574-28593. (www)

  @article{ , abstract = {The recent decades have witnessed unprecedented advances in the complexity of digital hardware systems, yet their design methods are still mostly based on manual register-transfer level (mRTL) languages such as VHDL and Verilog, introduced in the 1980s. While allowing exact system description, these languages have low productivity and require special expertise. High-level synthesis (HLS) promises to increase the productivity of hardware design by allowing system description from abstract, timeless source code, which is synthesized into optimized RTL code by an HLS tool according to technological constraints. However, HLS is still seen as somewhat immature technology with a non-consolidated offering of tools with varying features. Furthermore, the quality of results (QoR) of HLS is seen to be worse than with mRTL methods. This study sheds light on the status of HLS today. The emphasis is on field-programmable gate arrays (FPGAs) that allow fast development cycles. The study briefly covers the history of HLS, describes the HLS design flow, and lists the benefits and remaining challenges. The offering of current commercial and academic HLS tools is surveyed along with their features. A literature survey covering academic articles published between 2017 and 2024 on the QoR and productivity of HLS is presented. The results show that a gap of some margin still exists between the QoR of the HLS and mRTL methods. However, in productivity, HLS clearly outcompetes mRTL. Based on the study, several recommendations are made for HLS tool developers to close the QoR gap and accelerate the adoption of the method.}, archive = {J_ACCESS}, author = {Sakari Lahti and Timo D. Hämäläinen}, doi = {10.1109/ACCESS.2025.3540320}, journal = {IEEE Access}, pages = {28574-28593}, shortjournal = {IEEE Access}, title = {High-level synthesis for FPGAs—A hardware engineer’s perspective}, volume = {13}, year = {2025}, }
• (2025). Practical prescribed-time consensus tracking control for nonlinear heterogeneous MASs with bounded time-varying gain under mismatching and non-vanishing uncertainties. ACCESS, 13, 28557-28573. (www)

  @article{ , abstract = {This paper investigates the practical prescribed-time trajectory tracking consensus control problem for nonlinear heterogeneous multi-agent systems subject to mismatched uncertainties and non-vanishing disturbances, which cannot be directly compensated through control inputs. A time-varying scaling function defined by piecewise power functions is adopted, and a distributed controller is designed using the adaptive backstepping method. The study proposes a practical prescribed-time trajectory tracking consensus algorithm for heterogeneous multi-agent systems that avoids the use of infinite control gains. Unlike prescribed-time consensus tracking methods for heterogeneous multi-agent systems based on control switching, which involve discontinuities at switching points due to infinite control gains, the proposed control scheme incorporates softening units to ensure continuous control actions at segment points. Furthermore, the proposed control approach is effective over the entire time interval without requiring prior knowledge of the upper or lower bounds of $g_{i,m}$ , thereby broadening its applicability. Finally, theoretical analysis and simulation experiments validate the rationality and effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Yandong Li and Hui Cai and Ling Zhu and Yuyi Huang and Zehua Zhang and Yuan Guo}, doi = {10.1109/ACCESS.2025.3540077}, journal = {IEEE Access}, pages = {28557-28573}, shortjournal = {IEEE Access}, title = {Practical prescribed-time consensus tracking control for nonlinear heterogeneous MASs with bounded time-varying gain under mismatching and non-vanishing uncertainties}, volume = {13}, year = {2025}, }
• (2025). Near-infrared electronically-controlled variable attenuator for W-band frequency range. ACCESS, 13, 28550-28556. (www)

  @article{ , abstract = {This article presents the design, fabrication, and performance evaluation of a novel high-performance millimeter-wave (mm-wave) variable attenuator operating across the 75–110 GHz frequency band, controlled via near-infrared (NIR) illumination. The device achieves a maximum attenuation range of 50 dB with precise control steps as small as 0.1 dB. A high-resistivity (HR) silicon-based image waveguide, with a resistivity of $\rho \gt 10$ k $\Omega \cdot $ cm, serves as the transmission line, operating within the WR10 rectangular waveguide standard. The silicon image waveguide is seamlessly integrated with WR10 metallic rectangular waveguides at both input and output through optimized metallic fixtures. Its dimensions are tailored to support single-mode $\textrm {E}_{11}\textrm {y}$ operation. Attenuation control is achieved using three NIR light-emitting diodes (LEDs) with a total power consumption of 21 mW at a wavelength of 870 nm. The NIR illumination generates charge carriers in the silicon waveguide, increasing its conductivity and thereby attenuating the mm-wave signal with minimal phase distortion. The generated carrier mechanism in silicon is based on the absorption of near-infrared (NIR) photons. As a result, charge carriers are excited from the valence band to the conduction band, leading to a change in conductivity. This attenuation mechanism provides levels up to 50 dB and ensures precise, energy-efficient operation. The measured return loss (S11) is higher than 17 dB throughout the frequency range of 75 to 110 GHz. The compact and robust structure of the attenuator makes it ideal for emerging millimeter-wave (mm-wave) and low-terahertz (THz) wireless communication systems and phased array applications. It is worth mentioning that the designed attenuator can also function as an mm-THz modulator. By carefully selecting a short carrier lifetime in doped silicon and choosing suitable LEDs, it is indeed possible to significantly increase the modulation rate.}, archive = {J_ACCESS}, author = {Suren Gigoyan and Mohammad-Reza Nezhad-Ahmadi}, doi = {10.1109/ACCESS.2025.3540365}, journal = {IEEE Access}, pages = {28550-28556}, shortjournal = {IEEE Access}, title = {Near-infrared electronically-controlled variable attenuator for W-band frequency range}, volume = {13}, year = {2025}, }
• (2025). Memory robot design: A new perspective from human brain model and large language model. ACCESS, 13, 28539-28549. (www)

  @article{ , abstract = {With the spread of generative AI, the study proposed a memory-based cognitive robot architecture by using a Large Language Model (LLM), inspired by the working memory of the human brain model. A card-pairing task is designed to perform visual working memory with 60 participants with the measurement of electroencephalography (EEG). The proposed human brain model is a muti-featured EEG model, represented by the concept of brain energy, connectivity, and complexity. Band power ratios show that the anti-correlation of alpha and gamma waveforms can be observed in the occipital lobe. Brain connectivity is represented through magnitude squared coherence and phase locking value, and brain complexity is calculated by the Katz fractal dimension. Three pathways—attention, short-term memory, and distraction resistance are revealed. The changes in Katz fractal dimension are discovered in the frontal and occipital lobes. A novel memory architecture of robot cognition designed from human brain model includes three types of memory—short-term memory, working memory, and long-term memory, which is driven by GPT-4o. Two memory tests, a recalling test, and a working memory test, are conducted to validate the memory ability of the robot. The precision / recall / F1-score of working memory performance is 1.0 / 0.35 / 0.519, and the average accuracy of recalling test is 0.7. It is demonstrated that the different types of human memory functions can be implemented in the cognitive robot architecture driven by LLM. The research not only provides insight into the working memory of the human brain model but also realizes the robot architecture with the application of generative AI.}, archive = {J_ACCESS}, author = {Jia-Hsun Lo and Han-Pang Huang and Yen-Ching Chen and Jen-Hau Chen}, doi = {10.1109/ACCESS.2025.3538889}, journal = {IEEE Access}, pages = {28539-28549}, shortjournal = {IEEE Access}, title = {Memory robot design: A new perspective from human brain model and large language model}, volume = {13}, year = {2025}, }
• (2025). A modified parallel NET (MPNET)-based deep learning technique for the segmentation and quantification of visceral and superficial adipose tissues of CT scans. ACCESS, 13, 28522-28538. (www)

  @article{ , abstract = {This study introduces a modified parallel net (MPNET), a novel deep learning model designed for accurate segmentation and quantification of visceral and superficial adipose tissues. This was used to quantify the visceral and superficial adipose tissues found at the L3 levels of vertebra in CT scans. This will be used to predict the likelihood of the patient developing diabetes or cardiovascular diseases from existing CT scan data. MPNET was compared with state-of-the-art models like UNET, R2UNET, UNET++, and nnUNET. This approach advances the accuracy and efficiency of image segmentation demonstrating a faster learning curve and lower losses at early epochs than traditional models., We developed and validated using a limited dataset of 14 single-slice DICOM files for each patient extracted from the National Health Service UK. The outputs from MPNET not only matched but often exceeded traditional metrics such as the Dice coefficient and IoU in nuanced anatomical delineation, providing greater clinical realism and applicability in segmentation results. As a pilot study, this research paves the way for a forthcoming validation study on a larger and more ethnically diverse dataset.}, archive = {J_ACCESS}, author = {Josteve Adekanbi and Debashish Das and Nouh Elmitwally and Aliyuda Ali and Vince I. Madai and Bahadar Bhatia and John Morlese and Muhammed Afsal and Tanay Shah}, doi = {10.1109/ACCESS.2025.3538626}, journal = {IEEE Access}, pages = {28522-28538}, shortjournal = {IEEE Access}, title = {A modified parallel NET (MPNET)-based deep learning technique for the segmentation and quantification of visceral and superficial adipose tissues of CT scans}, volume = {13}, year = {2025}, }
• (2025). Parameter determination in basis pursuit denoising via stein’s unbiased risk estimate for underwater acoustic communication with orthogonal signal division multiplexing. ACCESS, 13, 28505-28521. (www)

  @article{ , abstract = {This paper proposes a novel method for determining parameters in underwater acoustic (UWA) communication using orthogonal signal division multiplexing (OSDM) combined with basis pursuit denoising (BPDN). Recent studies revealed that BPDN can improve channel estimation accuracy and communication quality in OSDM by deriving the channel impulse response from the received signal along with the regularization parameter value. The existing method determines the regularization parameter value through a linear relationship between the noise standard deviation and the optimal parameter value. This linear relationship is tuned by a UWA communication simulation based on the Monte Carlo method, which entails a high computational cost. To avoid the linear tuning procedure, ideally, the regularization parameter should be determined by minimizing the channel estimation error. However, given that the original channel impulse response is unknown for both the transmitter and receiver, we are unable to compute the actual estimation error. Therefore, we introduce the parameter determination method rooted in Stein’s unbiased risk estimate and propose a novel approximate soft thresholding function for computing an unbiased estimate of the channel estimation error. Our simulation results revealed that the unbiased estimate calculated using the proposed method could approximate the channel estimation error even in circumstances where the original channel impulse response is unknown. In addition, our sea trial demonstrated that the proposed parameter determination method achieved almost the same demodulation performance as the existing system.}, archive = {J_ACCESS}, author = {Ryoichi Ishijima and Tadashi Ebihara and Naoto Wakatsuki and Yuka Maeda and Koichi Mizutani}, doi = {10.1109/ACCESS.2024.3516316}, journal = {IEEE Access}, pages = {28505-28521}, shortjournal = {IEEE Access}, title = {Parameter determination in basis pursuit denoising via stein’s unbiased risk estimate for underwater acoustic communication with orthogonal signal division multiplexing}, volume = {13}, year = {2025}, }
• (2025). Radio frequency fingerprint acquisition and identification for small sample DMR signals under blind synchronization. ACCESS, 13, 28492-28504. (www)

  @article{ , abstract = {Radio frequency Fingerprint (RFF) is an emerging security technology that identifies devices by utilizing the RF impairments of hardware devices, with the acquisition of fingerprint signals serving as the foundation of fingerprint identification. Under the condition of blind synchronization, this paper proposes a fingerprint signal acquisition method based on time-frequency energy spectrum detection and a fingerprint identification method based on wavelet transform for small sample signals. The datasets of transient fingerprints and steady-state fingerprints are constructed through data preprocessing operations such as frequency offset compensation and power normalization. Utilizing signals collected from Digital Mobile Radio (DMR) terminals, this paper investigates the influence of sampling rate, classification model and other factors on the identification performance under the Over-the-Air (OTA) acquisition and direct RF acquisition modes. The results indicate that under the OTA acquisition with a sampling rate of 25 kHz, the random forest classification algorithm can achieve 99% identification accuracy for transient fingerprints, whereas further increasing the sampling rate offers no substantial improvement in RFF identification accuracy.}, archive = {J_ACCESS}, author = {Wenlong Gou and Bing Han and Pengfei Sun and Yang Yu and Gang Wu}, doi = {10.1109/ACCESS.2025.3539669}, journal = {IEEE Access}, pages = {28492-28504}, shortjournal = {IEEE Access}, title = {Radio frequency fingerprint acquisition and identification for small sample DMR signals under blind synchronization}, volume = {13}, year = {2025}, }
• (2025). Data augmentation framework for improved classification in object detectors. ACCESS, 13, 28476-28491. (www)

  @article{ , abstract = {Deep Learning techniques have been classified as a significant advancement for data-driven industries such as electrical machine manufacturing. It has been used successfully for quality inspection in various manufacturing cases, enabling automated product inspection. However, a massive bottleneck in deploying deep learning for quality inspection in manufacturing operations is the unavailability of training data required to develop an effective model. Data augmentation techniques offer a solution by increasing the size and diversity of the training dataset. In recent years, studies have shown an improvement in the performance of object detectors through image augmentation. However, there is a gap in knowledge regarding the choice of an image augmentation technique and the reasoning behind the choice. This study proposes a framework for choosing an appropriate augmentation method in various scenarios. It was achieved by investigating pixel-level transformations in the context of data augmentation to enhance the training performance of Quality Inspection (QI) models in scenarios with extremely limited data. Firstly, the effects of four pixel-level transformations on YOLOv7 and SSD real-time detectors are analysed. Secondly, the augmentation effect transferability across dissimilar datasets is discussed. Lastly, a framework is proposed for choosing the appropriate augmentation method in various scenarios. The results show that the proposed framework provides consistent results with an average improvement of up to 35% in meanAP and a reduction of 50% in the classification loss for the electrical wires dataset.}, archive = {J_ACCESS}, author = {Ioan-Alexandru Herdea and Divya Tiwari and John Oyekan and Ashutosh Tiwari}, doi = {10.1109/ACCESS.2025.3539455}, journal = {IEEE Access}, pages = {28476-28491}, shortjournal = {IEEE Access}, title = {Data augmentation framework for improved classification in object detectors}, volume = {13}, year = {2025}, }
• (2025). COSMIC-based early software size estimation using deep learning and domain-specific BERT. ACCESS, 13, 28463-28475. (www)

  @article{ , abstract = {Although the Common Software Measurement International Consortium (COSMIC) has a substantial role in accurate and early size measure, it needs structured processes and strict specification of requirements. So, using COSMIC for software size measurement is challenging and demands automation for effective and efficient measurements. In this study, we proposed an automated COSMIC-based early software size estimation approach using a domain-specific BERT called RE-BERT and deep learning algorithms. Two major experiments were employed in the research: the first was further pretraining of the generic BERT model over requirement engineering domain texts to produce a domain-specific pre-trained model called RE-BERT, while the second experiment was developing a deep learning regressor model for COSMIC size estimations using RE-BERT. Multi-Layer Perceptron (MLP) and the BERT Regressors are used to train the size estimation models. The experimental results showed that RE-BERT MLP provides 0.691 MAE and 0.988 MSE, which is better among other regression models (BASE-BERT MLP, RE-BERT regressor, and BASE-BERT regressor). On average, RE-BERT-based regressor models provided 1.23% to 3.19% improvement over BASE-BERT regressor models. This shows that domain-specific pre-trained models have a good effect on the performance of deep learning models. This research also ensures that BERT is good in regressions tasks like its counterpart classification.}, archive = {J_ACCESS}, author = {Yohannes Sefane Molla and Esubalew Alemneh and Samuel Temesgen Yimer}, doi = {10.1109/ACCESS.2025.3540548}, journal = {IEEE Access}, pages = {28463-28475}, shortjournal = {IEEE Access}, title = {COSMIC-based early software size estimation using deep learning and domain-specific BERT}, volume = {13}, year = {2025}, }
• (2025). DentC_BSFLNet: Boundary refined and spatial adaptive feature learning network for enhanced dental caries segmentation. ACCESS, 13, 28444-28462. (www)

  @article{ , abstract = {The accurate segmentation of dental caries from orthopantomograms (OPGs) is significant for early diagnosis and efficient treatment planning. However, the challenges such as noise artifacts, variations in image quality, subtle boundary transitions remain major issues in automatic caries detection from dental radiographs. An advanced segmentation framework titled “ ${DentC\_BSFL}_{Net}^{\prime\prime}$ , is proposed to tackle these challenges. The proposed model integrates $Swin-UNETI$ blocks to enhance the extraction of global context and local features for caries detection by leveraging Involution layers for spatially adaptive feature learning. A Multi-Path Scharr-Enhanced Boundary Detection $({MPSBD}_{E})$ encoder ensures accurate edge detection and boundary representation of complex and noisy image regions. The Scharr edge detection operator, optimized for subtle intensity gradient detection with enhanced gradient computation and rotational symmetry, is integrated for the exact delineation of carious regions. The Caries Boundary Feature Refinement $({CBFR}_{M})$ module efficiently refines caries segmentation by focusing on fine-grained caries boundary regions. Furthermore, the proposed ${DentC\_BSFL}_{Net}$ model employed a boundary-focused loss function to prioritize regions of interest while suppressing the irrelevant features in OPGs. The proposed model obtained 98.53% accuracy with a Dice score of 95.93% and an IoU score of 92.19%. The performance of the ${DentC\_BSFL}_{Net}$ framework is evaluated using an annotated dental caries dataset with 153 private OPGs from a Dental College and Research Centre in Maharashtra. The obtained results illustrate the efficiency of the ${DentC\_BSFL}_{Net}$ network in minimizing false positives for accurately segmenting caries from OPGs, hence improving diagnostic precision and adaptability in clinical applications.}, archive = {J_ACCESS}, author = {R. Neeraja and L. Jani Anbarasi}, doi = {10.1109/ACCESS.2025.3539844}, journal = {IEEE Access}, pages = {28444-28462}, shortjournal = {IEEE Access}, title = {DentC_BSFLNet: Boundary refined and spatial adaptive feature learning network for enhanced dental caries segmentation}, volume = {13}, year = {2025}, }
• (2025). A sensitivity analysis on design of the EU-DEMO steady-state electrical power distribution grid via monte carlo-based probabilistic power flow models. ACCESS, 13, 28433-28443. (www)

  @article{ , abstract = {The EU-DEMO (EUropean DEMOnstration Power Plant) fusion reactor is one of the most ambitious energy projects aiming at demonstrating the feasibility of nuclear fusion to be a clean, safe, and sustainable source of energy. The distribution grid of the DEMO facility plays a crucial role in delivering the net generated power to the European transmission grid, so it is imperative for its operation to be reliable. The proper design of the electrical subsystems within the fusion power plant is a fundamental factor that must be considered to guarantee reliable and secure operations. This paper presents the models and the results of a sensitivity analysis on the design and sizing of the components of DEMO electrical power system, under different operating conditions, exploiting a Monte Carlo-based Probabilistic Power Flow methodology. This approach was selected to assess the impact of uncertainties on DEMO steady-state power distribution grid and to identify the critical components, which need special requests in their design. The results of the sensitivity analysis can provide valuable insights for the design and sizing of the distribution system of the facility.}, archive = {J_ACCESS}, author = {M. Caldora and S. Panella and M. C. Falvo and S. Minucci and A. Ferro and T. Franke}, doi = {10.1109/ACCESS.2025.3537577}, journal = {IEEE Access}, pages = {28433-28443}, shortjournal = {IEEE Access}, title = {A sensitivity analysis on design of the EU-DEMO steady-state electrical power distribution grid via monte carlo-based probabilistic power flow models}, volume = {13}, year = {2025}, }
• (2025). The effect of input length on prediction accuracy in short-term multi-step electricity load forecasting: A CNN-LSTM approach. ACCESS, 13, 28419-28432. (www)

  @article{ , abstract = {Accurate load forecasting is crucial for effective power system management and planning in the context of growing electricity demand triggered by the proliferation of technological devices and rapid digitalization. Since electrical energy is largely non-storable, short-term electrical load forecasting plays a critical role for system operators. This paper presents an innovative hybrid deep learning model that combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for short-term multi-step load forecasting using real-time hourly data from a residential customer. The model is tested on 12 different configurations with symmetrically increasing input lengths, including weather data. The results show that increasing the input length improves the learning performance of the model for all conditions. In addition, selecting an input length greater than the output length has been shown to improve prediction accuracy, with an improvement of 67% in Mean Absolute Percentage Error (MAPE) and 70% in Root Mean Square Error (RMSE). Moreover, it was observed that the multi-step forecasting performance with increased input length is more successful than the single-step forecasting performance.}, archive = {J_ACCESS}, author = {Şeyda Özdemır and Yakup Demır and Özal Yildirim}, doi = {10.1109/ACCESS.2025.3540636}, journal = {IEEE Access}, pages = {28419-28432}, shortjournal = {IEEE Access}, title = {The effect of input length on prediction accuracy in short-term multi-step electricity load forecasting: A CNN-LSTM approach}, volume = {13}, year = {2025}, }
• (2025). A sensory glove with a limited number of sensors for recognition of the finger alphabet of polish sign language. ACCESS, 13, 28408-28418. (www)

  @article{ , abstract = {Existing data glove designs are fitted with multiple sensors. This is because existing designs often prioritize accuracy at the cost of ergonomics, accessibility, and affordability. This paper addresses this gap by building a model of a novel sensory glove with a reduced number of sensors and using machine learning paradigms in experiments to maximize gesture recognition performance. Usability tests of the data glove were carried out with 15 participants performing static and dynamic gestures in the alphabet of Polish Sign Language (APJM). The influence hierarchy of individual piezoelectric sensors was determined using a decision tree algorithm during previous stage of research, which achieved 94% accuracy with data from only three sensors. For dynamic gesture recognition, a neural network with convolutional layers and GRU recursive units achieved 99% accuracy using data from all sensors. The main contribution of the work is the identification of a universal set of three piezoelectric sensors and six inertial sensor axes for classification accuracy exceeding 98% for APJM letters. The results support the idea of simplified sensory glove designs with fewer than five sensors, promising improved ergonomics, reliability, and accessibility for people with disabilities.}, archive = {J_ACCESS}, author = {Jakub Piskozub and Pawel Strumillo}, doi = {10.1109/ACCESS.2025.3540578}, journal = {IEEE Access}, pages = {28408-28418}, shortjournal = {IEEE Access}, title = {A sensory glove with a limited number of sensors for recognition of the finger alphabet of polish sign language}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning for rechargeable AAV-assisted data collection from dense mobile sensor nodes. ACCESS, 13, 28398-28407. (www)

  @article{ , abstract = {In the realm of the Internet of Things (IoT), Autonomous Aerial Vehicles (AAVs) have garnered significant attention due to their high mobility and cost-effectiveness. However, the limited onboard energy, kinematic constraints, and highly dynamic environments present significant challenges for AAVs in the context of continuous real-time data collection scenarios. To address this issue, we investigate the utilization of a rechargeable AAV for data collection tasks in scenarios with densely mobile sensor nodes. This study formulates the problem as a Markov decision process and designs a reinforcement learning approach called guided search twin-dueling-double deep Q-Network (GS-TD3QN). Within this framework, the goal is to optimize the flight path, charging strategy, and data upload intervals to collectively maximize the total number of uploaded data packets, improve energy efficiency, and minimize the average age of information. Additionally, we propose an action filter to mitigate collision risks and explore various scheduling strategies. Ultimately, by evaluating the performance with simulation results, we confirm the effectiveness of the proposed algorithm and validate its applicability across varying quantities of nodes.}, archive = {J_ACCESS}, author = {Shanshan Bai and Xueyuan Wang and M. Cenk Gursoy and Guangqi Jiang and Shoukun Xu}, doi = {10.1109/ACCESS.2025.3539888}, journal = {IEEE Access}, pages = {28398-28407}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning for rechargeable AAV-assisted data collection from dense mobile sensor nodes}, volume = {13}, year = {2025}, }
• (2025). ARKAIV: Predicting data exfiltration using supervised machine learning based on tactics mapping from threat reports and event logs. ACCESS, 13, 28381-28397. (www)

  @article{ , abstract = {Data breach attacks are unique, particularly when attackers exfiltrate data from their target’s systems. As data breaches continue to increase in both frequency and severity, they pose escalating risks to organizations and society. Despite this, no prior research has focused on predicting exfiltration occurrences based on sequences of tactics identified from low-level logs. Additionally, integrating low-level logs with high-level conceptual frameworks remains a critical challenge. The urgency of automating the mapping process and developing advanced methods to assist defenders in analyzing exfiltration occurrences within their systems is evident. This paper addresses these gaps by developing a machine learning (ML) model to predict the occurrence of data exfiltration by analyzing the sequence of tactics employed by an attacker. We propose ARKAIV, which provides two main contributions: bridging the gap level between low-level logs and high-level data breach conceptual frameworks and integrating collected event logs and ML models to predict exfiltration tactics. To create our dataset, we extracted tactics from threat reports, refined the data to include ten features, and balanced using the Synthetic Minority Oversampling Technique with Edited Nearest Neighbor (SMOTE+ENN) technique. The ML model predicts exfiltration occurrences based on tactics identified from low-level logs as input. To optimize model performance, we benchmarked three resampling methods, five feature selection techniques, and five ML algorithms. Our key contributions include the creation of a novel dataset, the comprehensive techniques used to develop the ML model, and the proposed prediction method, which advances existing research. Additionally, we validate ARKAIV with case studies using event logs from real-world incidents. Our findings demonstrate that ARKAIV effectively predicts exfiltration occurrences with higher accuracy than existing approaches, providing a valuable tool for enhancing organizational cybersecurity.}, archive = {J_ACCESS}, author = {Arif Rahman Hakim and Kalamullah Ramli and Muhammad Salman and Bernardi Pranggono and Esti Rahmawati Agustina}, doi = {10.1109/ACCESS.2024.3524502}, journal = {IEEE Access}, pages = {28381-28397}, shortjournal = {IEEE Access}, title = {ARKAIV: Predicting data exfiltration using supervised machine learning based on tactics mapping from threat reports and event logs}, volume = {13}, year = {2025}, }
• (2025). Learning ordinality in semantic segmentation. ACCESS, 13, 28369-28380. (www)

  @article{ , abstract = {Semantic segmentation consists of predicting a semantic label for each image pixel. While existing deep learning approaches achieve high accuracy, they often overlook the ordinal relationships between classes, which can provide critical domain knowledge (e.g., the pupil lies within the iris, and lane markings are part of the road). This paper introduces novel methods for spatial ordinal segmentation that explicitly incorporate these inter-class dependencies. By treating each pixel as part of a structured image space rather than as an independent observation, we propose two regularization terms and a new metric to enforce ordinal consistency between neighboring pixels. Two loss regularization terms and one metric are proposed for structural ordinal segmentation, which penalizes predictions of non-ordinal adjacent classes. Five biomedical datasets and multiple configurations of autonomous driving datasets demonstrate the efficacy of the proposed methods. Our approach achieves improvements in ordinal metrics and enhances generalization, with up to a 15.7% relative increase in the Dice coefficient. Importantly, these benefits come without additional inference time costs. This work highlights the significance of spatial ordinal relationships in semantic segmentation and provides a foundation for further exploration in structured image representations.}, archive = {J_ACCESS}, author = {Ricardo P. M. Cruz and Rafael Cristino and Jaime S. Cardoso}, doi = {10.1109/ACCESS.2025.3537601}, journal = {IEEE Access}, pages = {28369-28380}, shortjournal = {IEEE Access}, title = {Learning ordinality in semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Integrating physical and virtual experiences in cultural tourism: An adaptive multimodal recommender system. ACCESS, 13, 28353-28368. (www)

  @article{ , abstract = {After a period of closure, there has been a renaissance in the world of tourism, particularly cultural tourism, where it is necessary to guide the visitor to discover the arts and cultural heritage. This renewed interest has highlighted historical challenges and new issues, suggesting the need for innovative technologies to enhance the cultural experience. Technologies such as the Internet of Things (IoT) and Context and Situation Awareness make it possible to collect and analyze environmental data in real time, enabling dynamic adaptation of tourist routes to specific conditions. This study proposes a recommendation system based on semantic approaches and probabilistic graphs designed to offer hybrid paths, both real and virtual, aimed at enhancing the user experience based on visitor preferences and environmental conditions. Through a multimodal approach, the system enhances engagement through dynamic multimedia content, combining physical and virtual experiences. It allows users to overcome the physical limitations of specific points of interest while providing an immersive and personalized experience. The proposed approach was validated through two strategies. The first evaluates the accuracy of the suggestions made through the proposed approach to test its reliability. The second consists of an experimental campaign involving the implementation of cultural routes within the Archaeological Park of Pompeii, where satisfactory results were obtained.}, archive = {J_ACCESS}, author = {Mario Casillo and Francesco Colace and Angelo Lorusso and Domenico Santaniello and Carmine Valentino}, doi = {10.1109/ACCESS.2025.3539205}, journal = {IEEE Access}, pages = {28353-28368}, shortjournal = {IEEE Access}, title = {Integrating physical and virtual experiences in cultural tourism: An adaptive multimodal recommender system}, volume = {13}, year = {2025}, }
• (2025). Benchmarking and evaluating large language models in phishing detection for small and midsize enterprises: A comprehensive analysis. ACCESS, 13, 28335-28352. (www)

  @article{ , abstract = {The proliferation of Generative Artificial Intelligence (GenAI) has driven significant innovation but also introduced new security risks, particularly in social engineering attacks such as phishing. Despite the potential for misuse of GenAI in such attacks, research on its use for defense against these threats is limited. To address this issue, this study addresses the unique cybersecurity needs of small and midsize enterprises (SMEs) by utilizing high-quality email datasets, categorized into human and AI-generated phishing and legitimate emails, to evaluate the effectiveness of different GenAI models. The results demonstrated that the open-source Llama-3-8b-instruct model outperformed other alternatives, achieving the highest accuracy and F1-score, while offering a cost-effective and flexible solution for SMEs. This approach lies in its focus on base models with default parameters, emphasizing ease of implementation without the need for additional training or fine-tuning. Compared to existing methods, this approach simplifies adoption while maintaining robust detection capabilities. The proposed prompt template enables LLMs to provide explanations and suggestions, assisting users in making informed decisions. Practical recommendations for SMEs include deployment strategies and cost-effective management of human factors in cybersecurity. However, the study acknowledges limitations in its reliance on base models and emphasizes the need for further research on fine-tuning and parameter optimization. These findings suggest the feasibility of using LLMs for real-world cybersecurity applications against phishing attacks in the context of SMEs, although certain risks and challenges remain.}, archive = {J_ACCESS}, author = {Jun Zhang and Peiqiao Wu and Jeffrey London and Dan Tenney}, doi = {10.1109/ACCESS.2025.3540075}, journal = {IEEE Access}, pages = {28335-28352}, shortjournal = {IEEE Access}, title = {Benchmarking and evaluating large language models in phishing detection for small and midsize enterprises: A comprehensive analysis}, volume = {13}, year = {2025}, }
• (2025). Factor graphs in optimization-based robotic Control—A tutorial and review. ACCESS, 13, 28315-28334. (www)

  @article{ , abstract = {Factor graphs, initially developed as probabilistic graphical models, have been widely employed for solving large-scale inference problems in robotics, particularly in tasks such as pose estimation, Structure from Motion (SfM), or Simultaneous Localization and Mapping (SLAM). Their capability to efficiently model uncertainty and the locality of sensor data has made them crucial for robotic perception and situational awareness. Recently, factor graphs have evolved beyond their probabilistic origins and are also being applied to deterministic optimization problems, such as robotic planning and control. This paper first aims to provide a comprehensive tutorial on factor graphs and the formulation and solution of the related optimization problems within the context of robotics perception. In addition, we undertake a thorough review of approaches that extend factor graphs—traditionally solved by unconstrained optimization—to optimal control tasks, emphasizing how they handle the constraints intrinsic to control problems. Finally, we analyze the potential of factor graphs for the seamless integration of robotic situational awareness, planning, and control, which remains one of the most critical challenges in achieving fully autonomous robot operations in complex environments.}, archive = {J_ACCESS}, author = {Anas Abdelkarim and Holger Voos and Daniel Görges}, doi = {10.1109/ACCESS.2025.3534993}, journal = {IEEE Access}, pages = {28315-28334}, shortjournal = {IEEE Access}, title = {Factor graphs in optimization-based robotic Control—A tutorial and review}, volume = {13}, year = {2025}, }
• (2025). Semantic communication empowered 6G networks: Techniques, applications, and challenges. ACCESS, 13, 28293-28314. (www)

  @article{ , abstract = {With the explosion of intelligent network applications and the prosperity of artificial intelligence (AI) technologies, the sixth generation (6G) wireless networks are not only expected to further improve network capacity, but also anticipated to establish a new architecture of “Intelligent Connectivity of Everything”. Semantic communication (SC) is a promising solution for future 6G networks due to its natural capability of integrating application requirements and information meaning into data transmission processes. In this paper, a comprehensive survey that overviews how SC can be applied for 6G networks and the key technologies of SC is presented. For this purpose, we first provide a detailed overview of the concepts of semantic information (SI) and SC, as well as the classifications of SC. Then, we present the vision of mutual support between SC technologies and 6G networks, as well as the potential benefits using SC for 6G applications. To achieve the benefits of SC, the fundamental theories and four important technologies in SC are thoroughly investigated, which are SC system architecture design, SI extraction, SI transmission, and SC performance evaluation. Then, we introduce open problems and potential research directions pertaining to SC. In a nutshell, this paper provides a holistic review of concepts, applications, fundamentals, key technologies, existing challenges, and open issues of SC tailored to the requirements of 6G networks.}, archive = {J_ACCESS}, author = {Yining Wang and Han Han and Yixiao Feng and Jianchao Zheng and Bo Zhang}, doi = {10.1109/ACCESS.2025.3532797}, journal = {IEEE Access}, pages = {28293-28314}, shortjournal = {IEEE Access}, title = {Semantic communication empowered 6G networks: Techniques, applications, and challenges}, volume = {13}, year = {2025}, }
• (2025). Orchard navigation method based on RS-SC loop frame search method and SLAM technology. ACCESS, 13, 28280-28292. (www)

  @article{ , abstract = {With the rapid development of agricultural intelligence, the application of intelligent agricultural robots in orchard management has been widely concerned. However, the complex environment in apple orchards, such as the occlusion of branches and leaves, dynamic changes and differences in lighting conditions, poses great challenges to the positioning accuracy and map construction ability of synchronous positioning and map construction technology. In order to solve the problem that existing synchronous localization and map construction techniques are not accurate enough in this kind of environment, a loopback detection algorithm based on neighborhood search and scanning context fusion is proposed. The method realizes rough loop frame search through neighborhood search to improve speed, and global precise search combined with scanning context to improve accuracy. In the loop frame matching, an optimization algorithm combining normal distribution transformation and iterative nearest point is used to reduce the cumulative error significantly. According to the environmental characteristics of apple orchard, the research preprocessed the point cloud data collected by LiDAR, and combined with the LiDAR odometer method based on line and plane feature point matching, the pose estimation and map construction were carried out. The experimental results show that compared to the independent use of Radius Search and Scan Context, the proposed algorithm increases the number of loopback frames by 50.19% and 7.23%, respectively, and reduces the root mean square error by 28.31% and 45.60%, respectively. The research results significantly improve the positioning and mapping performance of synchronous positioning and map construction technology in the complex environment of apple orchards, and provide technical support for the application of intelligent agricultural robots in orchard management.}, archive = {J_ACCESS}, author = {Ning Xu and Qingshan Meng and Fengping Liu and Zhihe Li and Guangming Wang and Na Guo and Wenxuan Wu}, doi = {10.1109/ACCESS.2025.3540487}, journal = {IEEE Access}, pages = {28280-28292}, shortjournal = {IEEE Access}, title = {Orchard navigation method based on RS-SC loop frame search method and SLAM technology}, volume = {13}, year = {2025}, }
• (2025). FD-GRNet: A dendritic-driven GRU framework for advanced stock market prediction. ACCESS, 13, 28265-28279. (www)

  @article{ , abstract = {Time series forecasting in financial markets presents significant challenges due to the inherent nonlinearity, volatility, and dynamic nature of market data. The unique architecture of the flexible dendritic-driven gated recurrent network (FD-GRNet) enables it to effectively manage both long-term dependencies and nonlinear patterns in financial time series. In pursuit of this capability, FD-GRNet integrates two novel components: the flexible dendritic neuron model (FDNM), enhancing the model’s capacity to capture complex nonlinearities, and the dendritic gated recurrent network (DGRNet), improving its ability to handle temporal dependencies. Comprehensive experiments on major global stock market indices demonstrate that FD-GRNet consistently outperforms several comparative algorithms across multiple evaluation metrics. Ablation studies further highlight the essential roles of FDNM and DGRNet in improving the model’s accuracy and robustness. Future research will focus on optimizing the model for broader time series forecasting applications.}, archive = {J_ACCESS}, author = {Tongyan Liu and Jiayi Li and Zihang Zhang and Hang Yu and Shangce Gao}, doi = {10.1109/ACCESS.2025.3541074}, journal = {IEEE Access}, pages = {28265-28279}, shortjournal = {IEEE Access}, title = {FD-GRNet: A dendritic-driven GRU framework for advanced stock market prediction}, volume = {13}, year = {2025}, }
• (2025). Enhanced visible light communication for real-time audio with interference-resilient protocols. ACCESS, 13, 28248-28264. (www)

  @article{ , abstract = {Despite its potential for high-speed data transmission, visible light communication (VLC) technology faces significant challenges in achieving reliable real-time audio streaming due to interference, signal degradation, and synchronization issues. These limitations hinder the widespread adoption of VLC technology, especially in high-quality audio transmission. This paper introduces a novel VLC transceiver design incorporating advanced interference-resilient protocols, dynamic frame structuring, and robust error correction algorithms to enhance audio streaming performance. The proposed system achieves a signal-to-noise ratio (SNR) of 45 dB, reduces the bit error rate (BER) by up to four orders of magnitude to $1 \times 10^{-6}$ , and lowers the total latency by 50%. Furthermore, synchronization errors are reduced by 66.7%, while the mean opinion score (MOS) reaches 4.8, and the objective difference grade (ODG) improves to -0.5. The comparative analysis demonstrates significant advantages over Wi-Fi (802.11ac), VLC IM/DD, and state-of-the-art VLC systems. These findings establish the proposed system as a reliable solution for real-time, high-fidelity audio applications and a foundation for extending VLC technology to broader domains.}, archive = {J_ACCESS}, author = {Kanchana Chathurangi Ahangama and Hemali Pasqual and Chinthaka Premachandra and H. Waruna Haripriya Premachandra}, doi = {10.1109/ACCESS.2025.3538588}, journal = {IEEE Access}, pages = {28248-28264}, shortjournal = {IEEE Access}, title = {Enhanced visible light communication for real-time audio with interference-resilient protocols}, volume = {13}, year = {2025}, }
• (2025). Investigations on creepage distances in power electronic systems for electrified aircraft. ACCESS, 13, 28204-28247. (www)

  @article{ , abstract = {Power electronics design offers many options to achieve the high required power density to replace conventional aircraft components. Two promising approaches are to increase the power supply voltage and use wide-bandgap semiconductors. However, there are many concerns about the high-voltage approach, particularly regarding reliability and fault tolerance. Improving the power density with high voltages requires wide-bandgap semiconductors, which further stresses the insulation. Furthermore, the environmental stressors are partly unknown because of the need for a design basis. Existing industrial standards and other investigations address this issue only partly. This publication uses a mission profile and a design of experiment-based reliability assessment approach for creepage distances on printed circuit boards to overcome the concerns against the promising high voltage approach in aviation applications from the perspective of power electronics. The investigation delivers experimentally and mathematically described wet and dry surface flashover results. Furthermore, the influence of degradation of creepage distances caused by wide-bandgap semiconductors is experimentally investigated. The publication also gives design recommendations for gaining reliability and fault tolerance of creepage distances on printed circuit boards for operating power-dense power electronics in integrated electrified propulsion systems.}, archive = {J_ACCESS}, author = {Hendrik Schefer and Maximilian Bien and Jan Gülink and Dirk Bösche and Jan Göing and Regine Mallwitz and Jens Friedrichs}, doi = {10.1109/ACCESS.2025.3537165}, journal = {IEEE Access}, pages = {28204-28247}, shortjournal = {IEEE Access}, title = {Investigations on creepage distances in power electronic systems for electrified aircraft}, volume = {13}, year = {2025}, }
• (2025). Selecting images with entropy for frugal knowledge distillation. ACCESS, 13, 28189-28203. (www)

  @article{ , abstract = {Frugal knowledge distillation is becoming increasingly important as it enables the distillation process to function effectively in resource-constrained environments. A key aspect of achieving this efficiency lies in minimizing the amount of training data required. To address this, we propose an entropy-based data selection method that identifies smaller subsets from the original dataset, focusing on images that retain the highest informational content. We explore the effectiveness of entropy-based method in combination with five different image representations to determine the subsets most effective for transferring knowledge to the student model. Our experimental evaluation on benchmark datasets, including CIFAR-10, MNIST, and FashionMNIST, shows that our approach outperforms other state-of-the-art image selection methods in most scenarios. It achieves over 3% higher accuracy compared to random selection methods while maintaining similar knowledge distillation time and energy efficiency.}, archive = {J_ACCESS}, author = {Michail Kinnas and John Violos and Nikolaos Ioannis Karapiperis and Ioannis Kompatsiaris}, doi = {10.1109/ACCESS.2025.3540384}, journal = {IEEE Access}, pages = {28189-28203}, shortjournal = {IEEE Access}, title = {Selecting images with entropy for frugal knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). DomainHarvester: Uncovering trustworthy domains beyond popularity rankings. ACCESS, 13, 28167-28188. (www)

  @article{ , abstract = {Allow lists are crucial in cybersecurity for distinguishing safe websites from potential threats. Traditional approaches relying on website popularity often fail to capture trustworthy but less-visited domains, leading to increased false positives and overlooked niche websites. This paper presents DomainHarvester, an innovative bottom-up system that leverages the web’s hyperlink structure and a Transformer-based machine learning approach to systematically identify and include these underrepresented yet legitimate domains. Results demonstrate how DomainHarvester dynamically curates an expanded allow list (DHList), substantially reducing the risk of false positives while retaining high precision in excluding malicious sites. Comprehensive evaluations and a real-world case study with a managed security services provider illustrate the efficacy and practicality of this approach. By integrating DomainHarvester, organizations and researchers can benefit from a more inclusive and globally representative cybersecurity allow list, addressing limitations in existing top-list-based solutions.}, archive = {J_ACCESS}, author = {Daiki Chiba and Hiroki Nakano and Takashi Koide}, doi = {10.1109/ACCESS.2025.3539882}, journal = {IEEE Access}, pages = {28167-28188}, shortjournal = {IEEE Access}, title = {DomainHarvester: Uncovering trustworthy domains beyond popularity rankings}, volume = {13}, year = {2025}, }
• (2025). Current trends and advances in extractive text summarization: A comprehensive review. ACCESS, 13, 28150-28166. (www)

  @article{ , abstract = {Given the rapid increase of textual data in various fields, text summarization has become essential for efficient information handling. Over recent decades, numerous methods have been proposed to enhance summarization processes, and various review papers and books have been published to encapsulate these methodologies and discuss their implications. However, existing reviews often fail to provide a comprehensive retrospective of recent advancements, particularly concerning detailed architectural frameworks, the field’s current state, evaluation methodologies, and unresolved challenges. This paper addresses this gap by presenting a detailed analysis of the extractive approaches, encompassing their inherent strengths, limitations, and underlying mechanisms. We present a detailed, multi-layered architectural framework designed to advance and develop summarization models, thereby supporting researchers in their endeavors. The text summarization framework consists mainly of text preprocessing, feature extraction, sentence scoring, use of a base model, sentence selection and output summary, and post-processing. Furthermore, this review of 145 research articles categorizes domain-specific summarization techniques, focusing on unique challenges and tailored strategies for news, scientific articles, and social media. These techniques include statistical, fuzzy logic, rule, optimization, graph, clustering-based, machine learning, and deep learning. We emphasize the impact of evaluation metrics and benchmark datasets in performance assessment, providing a detailed analysis of the commonly utilized datasets and metrics (mainly ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S) in the current literature. This review article is a valuable resource for advancing text summarization techniques in natural language processing and machine learning by identifying future research directions and open challenges. Notable challenges include expanding summarization for complex tasks, multiple documents, multimodal user input, multi-format and multilingual data, refining the stopping criteria, and improving the evaluation metrics.}, archive = {J_ACCESS}, author = {Maryam Azam and Shah Khalid and Sulaiman Almutairi and Hasan Ali Khattak and Abdallah Namoun and Amjad Ali and Hafiz Syed Muhammad Bilal}, doi = {10.1109/ACCESS.2025.3538886}, journal = {IEEE Access}, pages = {28150-28166}, shortjournal = {IEEE Access}, title = {Current trends and advances in extractive text summarization: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Assistive technology classification for students with disabilities in higher education: A systematic literature review. ACCESS, 13, 28135-28149. (www)

  @article{ , abstract = {Students with disabilities (SWDs) in higher education often encounter obstacles that hinder their learning and participation in classroom activities. These challenges include difficulties accessing learning materials, lack of information, and a limited number of lecturers with adequate knowledge on handling SWDs. Therefore, the use of assistive technology (AT) can help mitigate these issues by providing accessible learning materials and supporting tools to accommodate the needs of SWDs. The purpose of this research review is to synthesize the existing literature on AT in higher education that supports students with disabilities (SWDs). This systematic review examined peer-reviewed articles published between 2013 and 2023. A total of 546 records were identified through database searches, and after screening, 133 studies were included in the review. The studies were sourced from Scopus and IEEE Xplore databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The detailed classification of the reviewed articles provides insight for educators, researchers, and policymakers seeking to enhance accessibility and inclusiveness in higher education institutions. AT has emerged as a significant tool in solving the challenges faced by SWDs and promoting their inclusion and accessibility in higher education. This review highlights the significance of AT in creating an inclusive educational environment and offers insights into further advancements in this area. The classification developed in this review can serve as a resource for identifying effective AT interventions and guiding future advancements in this area.}, archive = {J_ACCESS}, author = {Nuril Kusumawardani Soeprapto Putri and Umi Laili Yuhana and Daniel Oranova Siahaan and Wenny Rahayu and Eric Pardede}, doi = {10.1109/ACCESS.2025.3538322}, journal = {IEEE Access}, pages = {28135-28149}, shortjournal = {IEEE Access}, title = {Assistive technology classification for students with disabilities in higher education: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). A power-efficient 0.5668 TOPS/W digital logic accelerator implemented using 40-nm CMOS process for underwater object recognition usage. ACCESS, 13, 28123-28134. (www)

  @article{ , abstract = {DNN (deep neural network) and CNN (convolution neural network) have been widely used in real-time artificial intelligent (AI) applications, particularly image or video recognitions, because they have been proved physically in many occasions. However, most prior AI hardware works either suffered from high on-silicon area cost or low usage thereof. This investigation presents a power efficient and high performance implementation of a digital logic accelerator (DLA) for the real-time underwater object recognition. The proposed DLA is also featured with 2-dimensional PE (processing element) array to increase the processing throughput by the enhancement of parallelism. The DLA design was realized and fabricated using TSMC 40-nm CMOS process. Not only the post-layout simulation results are shown, the on-silicon measurement outcome as well as the system validation in water are also demonstrated to prove the function correctness and the performance. The area efficiency (GOPS/mm2) is 4.562, and the power efficiency (TOPS/W) is 0.5668 on silicon, which both are the best to date.}, archive = {J_ACCESS}, author = {Chua-Chin Wang and Shih-Heng Luo and Hsin-Che Wu and Ralph Gerard B. Sangalang and Chewn-Pu Jou and Harry Hsia and Lan-Chou Cho}, doi = {10.1109/ACCESS.2025.3539760}, journal = {IEEE Access}, pages = {28123-28134}, shortjournal = {IEEE Access}, title = {A power-efficient 0.5668 TOPS/W digital logic accelerator implemented using 40-nm CMOS process for underwater object recognition usage}, volume = {13}, year = {2025}, }
• (2025). Advancements and challenges in video-based deception detection: A systematic literature review of datasets, modalities, and methods. ACCESS, 13, 28098-28122. (www)

  @article{ , abstract = {Video-based deception detection has emerged as a promising field that leverages advances in computer vision, machine learning, and multimodal analysis to capture a wealth of nonverbal cues for identifying deceptive behavior. However, the field faces significant challenges related to dataset development, methodological approaches, and ethical considerations. This systematic literature review (SLR) aims to provide a comprehensive analysis of video-based deception detection research, with five distinct contributions: 1) an unprecedented analysis of 21 datasets, revealing critical gaps and opportunities in data resources; 2) a novel evaluation framework for assessing dataset quality and ecological validity; 3) a systematic comparison of multimodal integration approaches, identifying optimal strategies for combining visual, audio, and textual cues; 4) a critical examination of temporal modeling techniques for capturing the dynamic nature of deceptive behavior; and 5) a roadmap for addressing ethical challenges in deployment. Following the PRISMA guidelines, we reviewed studies published between 2019 and 2024 in major databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, and Springer Link. The review process involved a rigorous two-stage screening, which resulted in the inclusion of 42 primary research papers. Our analysis revealed several key findings: 1) only 52.4% of identified datasets are publicly accessible, highlighting a critical gap in research reproducibility; 2) multimodal approaches consistently outperform unimodal methods, with accuracy improvements of 10-15%; 3) deep learning architectures, particularly LSTM variants and attention mechanisms, demonstrate superior performance in capturing temporal aspects of deception; 4) the Real-Life Trial Dataset emerged as the most frequently used dataset (65% of studies), indicating a preference for high-stakes ecologically valid data; and 5) significant ethical challenges remain unaddressed, particularly regarding privacy, bias, and cross-cultural validity. This review makes several novel contributions to advance the field: 1) provides a comprehensive framework for dataset evaluation and development; 2) identifies optimal strategies for multimodal integration and temporal modeling; 3) presents a structured approach to addressing ethical considerations; and 4) offers a detailed roadmap for future research priorities. These contributions will guide researchers in developing more robust, ethical, and generalizable deception detection systems, while addressing critical gaps in current methodologies and datasets.}, archive = {J_ACCESS}, author = {Yeni Dwi Rahayu and Chastine Fatichah and Anny Yuniarti and Yusti Probowati Rahayu}, doi = {10.1109/ACCESS.2025.3533545}, journal = {IEEE Access}, pages = {28098-28122}, shortjournal = {IEEE Access}, title = {Advancements and challenges in video-based deception detection: A systematic literature review of datasets, modalities, and methods}, volume = {13}, year = {2025}, }
• (2025). Optimization of state clustering and safety verification in deep reinforcement learning using KMeans++ and probabilistic model checking. ACCESS, 13, 28085-28097. (www)

  @article{ , abstract = {Ensuring the safety of Deep Reinforcement Learning (DRL) systems remains a significant challenge, particularly in real-time applications such as autonomous driving and robotics, where incorrect decisions can lead to catastrophic failures. This study proposes a novel safety verification framework that combines state abstraction with probabilistic model checking to quantitatively analyze failure risks. The continuous state space is clustered using the KMeans++ algorithm, enabling efficient state space reduction. A Discrete-Time Markov Chain (DTMC) model is then constructed for each cluster, capturing probabilistic transitions between abstracted states. The PRISM model checker is employed to verify failure probabilities and invariance properties, providing a rigorous quantitative evaluation of system safety. Counterexample analysis identifies critical failure paths, offering actionable insights for policy improvement. Experimental results demonstrate that the optimal number of clusters balances state space reduction with accurate failure analysis, enabling scalable verification. By leveraging model checking within an abstracted state space, this approach enhances the reliability and safety of DRL systems and establishes a pathway for their deployment in safety-critical domains.}, archive = {J_ACCESS}, author = {Ryeonggu Kwon and Gihwon Kwon}, doi = {10.1109/ACCESS.2025.3540428}, journal = {IEEE Access}, pages = {28085-28097}, shortjournal = {IEEE Access}, title = {Optimization of state clustering and safety verification in deep reinforcement learning using KMeans++ and probabilistic model checking}, volume = {13}, year = {2025}, }
• (2025). A deep neural network-based high performance robust position controller for servomechanisms. ACCESS, 13, 28071-28084. (www)

  @article{ , abstract = {A new high-performance deep neural network-based robust quasi-time optimal servomechanism (DNN-RQTOS) method for position control is proposed in this study. The controller is trained by a dataset produced by using Robust Quasi-Time-Optimal Servomechanism (RQTOS) method. RQTOS approach is founded on discrete-time sliding mode position control (DSMC) method. This approach is implemented to a vector-controlled induction motor servo-system, utilizing quasi-optimal time-varying sliding surfaces to provide both robust and high performance control. The dataset used to train the new DNN-based controller is created in a wide range of uncertainty and position references. The DNN-based controller successfully learns the behavior of the fixed structure RQTOS controller and reveals superior performance. Specifically, the new DNN-based controller offers better transient response and load torque rejection under parameter uncertainty, compared to the sliding mode controller from which the training data were produced. Error and performance metrics of DNN-RQTOS are seen better than those of RQTOS. In addition, the chattering behaviors, which is the major drawback of sliding mode control, has been effectively eliminated.}, archive = {J_ACCESS}, author = {Mehmet Ílyas Bayindir}, doi = {10.1109/ACCESS.2025.3540628}, journal = {IEEE Access}, pages = {28071-28084}, shortjournal = {IEEE Access}, title = {A deep neural network-based high performance robust position controller for servomechanisms}, volume = {13}, year = {2025}, }
• (2025). Probe-assisted fine-grained control for non-differentiable features in symbolic music generation. ACCESS, 13, 28059-28070. (www)

  @article{ , abstract = {As symbolic music generation evolves, research interest is shifting toward more controlled and steerable generative processes to support creative decisions. Previous methods focus on global conditioning or fine-grained control through input sequences but often limit flexibility for real-time interventions and require modifications to the model’s architecture. We introduce a novel symbolic music generation framework by combining a Transformer encoder-decoder with probe models, which enable us to interpret the encoder hidden state using pre-defined non-differentiable musical features, and subsequently manipulate the hidden state to achieve a set of desired attributes in the generated music. This method allows fine-grained control over specific musical features without altering the underlying model architecture. Probes can be trained jointly with the generative model or applied post-training, enabling adaptable control without retraining the model. Our experiments demonstrate that this intervention effectively influences the model output without hindering the music quality. This approach enhances both the flexibility and interpretability of symbolic music generation, enabling better real-world applicability for music generation models.}, archive = {J_ACCESS}, author = {Rafik Hachana and Bader Rasheed}, doi = {10.1109/ACCESS.2025.3540543}, journal = {IEEE Access}, pages = {28059-28070}, shortjournal = {IEEE Access}, title = {Probe-assisted fine-grained control for non-differentiable features in symbolic music generation}, volume = {13}, year = {2025}, }
• (2025). Dual-knowledge-driven interpretable decision support system for stroke critical-care rehabilitation: Design and multi-center study. ACCESS, 13, 28044-28058. (www)

  @article{ , abstract = {The study aimed to develop a clinical decision support system for stroke critical-care rehabilitation in medical intensive care units (ICUs) to address the challenges of complex patient conditions, diverse rehabilitation needs, and subjectivity in physician prescriptions. This study proposed a dual-knowledge-driven intelligent rehabilitation decision support system (RDSS) for personalized intensive-care rehabilitation of severe-stroke patients. The system uses proposed collaborative reasoning that integrates semantic reasoning and knowledge graph-based reasoning to recommend rehabilitation plan, other relevant treatment options, potential complications and comorbidities, and risk factors to consider based on the patient’s clinical context information. In order to validate whether this system can enhance doctors’ capability in formulating rehabilitation prescriptions, a comparative evaluation study and a retrospective study were conducted respectively. The results of the comparative study showed that the overlap rate (73.3%) between the RDSS and the gold standard was higher than the average overlap rate (59.4%) between most (96.6%) rehabilitation professionals and the gold standard. In the retrospective study, 92 patients from two hospitals were enrolled, the overlap rate between the rehabilitation plans provided by the RDSS and those provided by the gold standard was over 50% for all cases, with nearly one-fifth achieving a 100% match. Moreover, 46.7% of rehabilitation plans had an overlap rate of 80% or higher, and the overall average overlap rate reached 78.10%. These findings suggest that the proposed RDSS demonstrated strong performance in recommending rehabilitation plans and providing risk reminders, indicating its potential feasibility for use in clinical practice.}, archive = {J_ACCESS}, author = {Ziming Yin and Yanhao Gong and Jing He and Ling Ren and Xin Li and Xianrui Hu and Yu Pan and Hongliu Yu}, doi = {10.1109/ACCESS.2025.3538280}, journal = {IEEE Access}, pages = {28044-28058}, shortjournal = {IEEE Access}, title = {Dual-knowledge-driven interpretable decision support system for stroke critical-care rehabilitation: Design and multi-center study}, volume = {13}, year = {2025}, }
• (2025). Corrections to “GrapeLeafNet: A dual-track feature fusion network with inception-ResNet and shuffle-transformer for accurate grape leaf disease identification”. ACCESS, 13, 28043. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “GrapeLeafNet: A Dual-Track Feature Fusion Network With Inception-ResNet and Shuffle-Transformer for Accurate Grape Leaf Disease Identification”)}, archive = {J_ACCESS}, author = {R. Karthik and R. Menaka and S. Ompirakash and P. Bala Murugan and M. Meenakashi and Sindhia Lingaswamy and Daehan Won}, doi = {10.1109/ACCESS.2025.3540361}, journal = {IEEE Access}, pages = {28043}, shortjournal = {IEEE Access}, title = {Corrections to “GrapeLeafNet: A dual-track feature fusion network with inception-ResNet and shuffle-transformer for accurate grape leaf disease identification”}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A very large-scale integration (VLSI) chip design for abnormal heartbeat detection using a data-shifting neural network (DSNN)”. ACCESS, 13, 28042. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Very Large-Scale Integration (VLSI) Chip Design for Abnormal Heartbeat Detection Using a Data-Shifting Neural Network (DSNN)”.}, archive = {J_ACCESS}, author = {Yuan-Ho Chen and Szi-Wen Chen and Hong-Wen Jian and Shinn-Yn Lin and Rou-Shayn Chen}, doi = {10.1109/ACCESS.2025.3540185}, journal = {IEEE Access}, pages = {28042}, shortjournal = {IEEE Access}, title = {Corrections to “A very large-scale integration (VLSI) chip design for abnormal heartbeat detection using a data-shifting neural network (DSNN)”}, volume = {13}, year = {2025}, }
• (2025). Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security. ACCESS, 13, 28029-28041. (www)

  @article{ , abstract = {In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our model architecture uniquely incorporates dual shared layers alongside modality-specific enhancements for comprehensive feature extraction. The system undergoes rigorous training with a joint loss function, optimizing for accuracy across diverse biometric inputs. Feature-level fusion via Principal Component Analysis (PCA) and classification through Gradient Boosting Machines (GBM) further refine the authentication process. Our approach demonstrates significant improvements in authentication accuracy and robustness, paving the way for advanced secure identity verification solutions.}, archive = {J_ACCESS}, author = {S. Vatchala and C. Yogesh and Yeshwanth Govindarajan and M. Krithik Raja and Vishal Pranav Amirtha Ganesan and Aashish Vinod Arul and Dharun Ramesh}, doi = {10.1109/ACCESS.2025.3534223}, journal = {IEEE Access}, pages = {28029-28041}, shortjournal = {IEEE Access}, title = {Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security}, volume = {13}, year = {2025}, }
• (2025). An overview and classification of machine learning approaches for radar signal deinterleaving. ACCESS, 13, 28008-28028. (www)

  @article{ , abstract = {Electronic Warfare (EW) receivers are passive systems that are designed to detect and identify active radar emitters in the environment. The radar pulses emitted by multiple sources are received and must be deinterleaved, in other words, sorted according to the waveform to which they belong, i.e. according to their emitter. As radar signals are more complex and observations are denser, new Machine Learning (ML) approaches appear in the literature to enhance traditional Radar Signal Deinterleaving (RSD). In this paper, we propose an overview of the ML approaches to RSD. To this end, we identify some criteria to characterize a method: its used technique, underlying assumptions, exploited parameters, input characterization, and architectural pattern. First, the problem of RSD is detailed with its challenges and operational requirements. We then outline the methods of the literature inside a taxonomy based on the technique criterion: the first category includes PRI estimation methods including histogram-based methods, and ML techniques constitute three other categories: clustering, RNN, CNN. Finally, the other identified criteria are explained and discussed.}, archive = {J_ACCESS}, author = {Louis Lesieur and Jean-Marc Le Caillec and Ali Khenchaf and Vincent Guardia and Abdelmalek Toumi}, doi = {10.1109/ACCESS.2025.3539589}, journal = {IEEE Access}, pages = {28008-28028}, shortjournal = {IEEE Access}, title = {An overview and classification of machine learning approaches for radar signal deinterleaving}, volume = {13}, year = {2025}, }
• (2025). Combining endpoint detection and one-dimensional CNN-based classifier for non-technical loss screening in smart grids. ACCESS, 13, 27994-28007. (www)

  @article{ , abstract = {Automated meter reading systems (AMRS) are employed to measure and record electricity consumption data in smart grids, facilitating the construction of various consumption models for residential, commercial, and industrial electricity users. These standard electricity-consumption models (SECM), along with their associated consumption patterns, can be further used for applications in load forecasting, technical loss (TL) analysis, and non-technical loss (NTL) detection. Therefore, this study applies the SECM to NTL detection by proposed novel screening model that combines the endpoint detection (EPD) and short-time Fourier transform (STFT) with a one-dimensional (1D) convolutional neural network (1D CNN) based classifier. The EPD method is employed to preliminarily detect discrepancies between metering data and historical records, focusing on the time-domain variations in electricity consumption. Subsequently, the STFT is applied to analyze the frequency contains in the drastically changing time-domain data and then generates the visualization color feature patterns. With theses feature patterns, the 1D-CNN based classifier is used to identify the data into normal (Nor), suspected incidents (SI), fraud incidents (FI), and fault or power outage (OUT) events. In experimental tests, this study simulates the electricity consumption data involving Nor and NTL events by using SECM for training and validating the “STFT +1D-CNN-based classifier” model. Through ten-fold cross-validation, the feasibility of the proposed method is evaluated, and the experimental results demonstrate the promising performances, achieving average Recall (%) of 99.12%, average Precision (%) of 87.50%, and average Accuracy (%) of 90.48%, and average F1 Score of 0.9333 for NTL (SI and FI) events detection, and also show average Recall (%) of 99.35%, average Precision (%) of 86.36%, and average Accuracy (%) of 88.89%, and average F1 Score of 0.9268 for NTL and OUT events detection, respectively. These findings confirm the effectiveness and robustness of the proposed classifier model.}, archive = {J_ACCESS}, author = {Ping-Tzan Huang and Feng-Chang Gu and Chia-Hung Lin and Chao-Lin Kuo and Neng-Sheng Pai and Yung-Chang Luo and Wen-Cheng Pu}, doi = {10.1109/ACCESS.2025.3539763}, journal = {IEEE Access}, pages = {27994-28007}, shortjournal = {IEEE Access}, title = {Combining endpoint detection and one-dimensional CNN-based classifier for non-technical loss screening in smart grids}, volume = {13}, year = {2025}, }
• (2025). DCKD: Distribution-corrected knowledge distillation for enhanced industrial defect detection. ACCESS, 13, 27984-27993. (www)

  @article{ , abstract = {Deploying deep learning models for industrial defect detection is challenging, as smaller models lack precision and larger models require significant computational resources. To address this, knowledge distillation has been used to transfer knowledge from a larger teacher model to a smaller student model, though its effectiveness is limited by inaccurate teacher predictions and inadequate handling of difficult samples. In this paper, a novel method, Distribution-Corrected Knowledge Distillation (DCKD), is proposed to overcome these issues. A Logits Distribution Correction (LDC) Module is introduced to correct inaccuracies in the teacher model’s predictions, and an Iterative Hard Sample Aggregation (IHA) Module is used to enhance the student model’s ability to handle challenging cases. This approach significantly improves detection accuracy across multiple industrial datasets, surpassing state-of-the-art distillation techniques. Notably, the student model outperforms the teacher model on the NEU (The Northeastern University surface defect dataset) and CG-DF (The Cover Glass-Dark Field dataset) datasets, with mAP scores improving from 0.766 and 0.716 to 0.794 and 0.747, respectively.}, archive = {J_ACCESS}, author = {Weidong Du and Hao Zhou}, doi = {10.1109/ACCESS.2025.3533479}, journal = {IEEE Access}, pages = {27984-27993}, shortjournal = {IEEE Access}, title = {DCKD: Distribution-corrected knowledge distillation for enhanced industrial defect detection}, volume = {13}, year = {2025}, }
• (2025). Timing planning knowledge representation for micro assembly based on ontology. ACCESS, 13, 27961-27983. (www)

  @article{ , abstract = {Micro assembly is a focal point in assembly research because of its high assembly accuracy requirements and high assembly difficulty. Current micro assembly techniques focus on precision and consistency, yet face a significant efficiency obstacle due to the conflict between precision and efficiency. Therefore, recent micro assembly challenges mainly include prolonged planning cycles, complex assembly processes, and suboptimal efficiency. An effective method to solve the efficiency problem is assembly timing intelligent decision-making, which can realize assembly efficiency optimization intelligently while ensuring assembly feasibility. As knowledge modeling is essential to intelligent decision-making, this paper proposes a micro assembly timing planning knowledge representation method. Firstly, micro assembly process knowledge is analyzed from two aspects: microdevices assembly process knowledge and micro assembly system process knowledge. Then, ontology-based micro assembly timing knowledge modeling method is proposed to realize the systematic knowledge representation for micro assembly timing planning, which is elaborated by the top-level micro assembly process ontology and micro assembly timing planning ontology. In conclusion, the proposed knowledge modeling theory and method present practical and implementable technical approaches for intelligent decision-making in timing planning. In addition, the knowledge modeling method from top-level ontology to application ontology provides the knowledge modeling logic and knowledge modeling process respectively.}, archive = {J_ACCESS}, author = {Jiahui Qian and Yigang Wang and Zhijing Zhang}, doi = {10.1109/ACCESS.2025.3540087}, journal = {IEEE Access}, pages = {27961-27983}, shortjournal = {IEEE Access}, title = {Timing planning knowledge representation for micro assembly based on ontology}, volume = {13}, year = {2025}, }
• (2025). Time-modulated array-based rotating electric-field vector method for extracting angle information. ACCESS, 13, 27952-27960. (www)

  @article{ , abstract = {This study proposes the time-modulated array (TMA) based rotating electric-field vector (REV) method as a means of extracting the angle information of the incoming fully correlated signals. By varying the delay parameter of the TMA signal, the REV signal can be formed, and its maximum or minimum can be used to extract the target angle. Furthermore, the target angle can be extracted by applying the inverse discrete Fourier transform to successive uniform samples of the first harmonic of the REV signal. Compared to the method that employs the magnitude ratios of the fundamental and harmonic components, the proposed method requires only the magnitude of the first harmonic component. Moreover, the angle information extracted is unambiguous. Additionally, this method can be extended to multiple target cases with the aid of a multi-element TMA and a complex REV method. In this case, inverse filtering is adopted to provide a higher angular resolution with a small loss in signal-to-noise ratio. An error analysis for the proposed method was performed by numerical simulation, and both the simulated and experimental results of the TMA-REV system fully validated the proposed method.}, archive = {J_ACCESS}, author = {Geon U Kim and Sang-Hwa Yi and Jeong Phill Kim}, doi = {10.1109/ACCESS.2025.3540052}, journal = {IEEE Access}, pages = {27952-27960}, shortjournal = {IEEE Access}, title = {Time-modulated array-based rotating electric-field vector method for extracting angle information}, volume = {13}, year = {2025}, }
• (2025). A novel pairing free revocable certificateless encryption with ciphertext evolution for healthcare system. ACCESS, 13, 27940-27951. (www)

  @article{ , abstract = {With the advancement of modern healthcare systems, patients now utilize medical sensor nodes to generate health data. These data are then transmitted to healthcare providers for analysis and diagnosis of the patient. These data are sensitive in nature and, therefore, need to preserve their privacy, security, and confidentiality. Another crucial aspect of a healthcare system is the effective revocation of malicious users. Revocable encryption schemes, particularly those with ciphertext evolution, have emerged as promising solutions. However, all the existing revocable schemes with ciphertext evolution have expensive bilinear pairing, map-to-point hash, modular exponentiation operations, rendering them unsuitable for resource constrained medical sensor nodes. In this paper, we propose a novel pairing free revocable certificateless encryption scheme with ciphertext evolution. Also, we prove that our scheme is secure against adaptive chosen ciphertext attacks. Our comparative analysis demonstrates that our scheme achieves substantial reductions in total computational cost by 74.9% to 86.4% and reduces ciphertext communication cost by 45.9% to 72.9%, making it the most effective among related schemes.}, archive = {J_ACCESS}, author = {Moirangthem Rabindra Singh and Rabindra K. Barik and Shahazad Niwazi Qurashi and Surmila Thokchom and Diptendu Sinha Roy}, doi = {10.1109/ACCESS.2025.3533367}, journal = {IEEE Access}, pages = {27940-27951}, shortjournal = {IEEE Access}, title = {A novel pairing free revocable certificateless encryption with ciphertext evolution for healthcare system}, volume = {13}, year = {2025}, }
• (2025). Nonharmonic analysis-based detection-performance improvement technique for distance estimation of 24 GHz narrowband FMCW radar. ACCESS, 13, 27928-27939. (www)

  @article{ , abstract = {The 24 GHz narrowband frequency-modulated continuous-wave (FMCW) radar has been widely applied in automotive and other vital sensors. However, its range resolution depends on the bandwidth, which is limited to 60 cm in its narrowband of 24.0–24.25 GHz. Furthermore, at low-range resolutions, the power spectral density (PSD) of the fast Fourier transform and multiple signal classification algorithms typically used for distance measurements often suffer from sidelobe interference between the signals. In particular, under low signal-to-noise ratios (SNRs), the distance-estimation errors significantly increases. This is a major obstacle in the development of applications that requiring high accuracy, such as autonomous driving and patient-health monitoring. Therefore, we propose a technique to enhance the range resolution and noise resistance of the 24 GHz FMCW radar using nonharmonic analysis (NHA), a high-resolution frequency-analysis method. In the PSD of NHA, a sinusoidal signal is represented as a single-line spectrum. Experimental results using a metal reflector demonstrate that the proposed method significantly suppresses sidelobes and achieves a high range resolution, as evidenced by the sparse PSDs obtained. Furthermore, simulations and a pedestrian-tracking experiment show that the proposed method achieves lower RMSE and higher peak SNR than conventional FFT and MUSIC methods, even at low input SNRs. This method is expected to enhance the performance of various radar applications, such as vehicle-perimeter monitoring, robot navigation and indoor monitoring.}, archive = {J_ACCESS}, author = {Shotaro Koyama and Tsubasa Suzuki and Junpei Tsutsumi and Taisei Matsuyama and Takumu Ikenaga and Fumiya Shinohara and Masaya Hasegawa and Shigeki Hirobayashi and Kazuo Yoshida}, doi = {10.1109/ACCESS.2025.3540397}, journal = {IEEE Access}, pages = {27928-27939}, shortjournal = {IEEE Access}, title = {Nonharmonic analysis-based detection-performance improvement technique for distance estimation of 24 GHz narrowband FMCW radar}, volume = {13}, year = {2025}, }
• (2025). Optimizing vision transformers: Unveiling ‘Focus and forget’ for enhanced computational efficiency. ACCESS, 13, 27908-27927. (www)

  @article{ , abstract = {Vision Transformers are renowned for their accuracy in computer vision tasks but are computationally and memory expensive, making them challenging to deploy on resource-constrained edge devices. In our research paper, we introduce a revolutionary approach to designing energy-aware dynamically prunable Vision Transformers for use in edge applications. Our solution denoted as Incremental Resolution Enhancing Transformer (IRET), works by the sequential sampling of the input image. However, in our case, the embedding size of input tokens is considerably smaller than prior-art solutions. This embedding is used in the first few layers of the IRET vision transformer until a reliable attention matrix is formed. Then the attention matrix is used to sample additional information using a learnable 2D lifting scheme only for important tokens and IRET drops the tokens receiving low attention scores. Hence, as the model pays more attention to a subset of tokens for its task, its focus and resolution also increase. This incremental attention-guided sampling of input and dropping of unattended tokens allow IRET to significantly prune its computation tree on demand. By controlling the threshold for dropping unattended tokens and increasing the focus of attended ones, we can train a model that dynamically trades off complexity for accuracy. Moreover, using early exiting our model is capable of doing anytime prediction. This is especially useful for real-word energy-sensitive edge devices, where accuracy and complexity could be dynamically traded based on factors such as battery life, reliability, etc.}, archive = {J_ACCESS}, author = {Banafsheh Saber Latibari and Houman Homayoun and Avesta Sasan}, doi = {10.1109/ACCESS.2025.3540399}, journal = {IEEE Access}, pages = {27908-27927}, shortjournal = {IEEE Access}, title = {Optimizing vision transformers: Unveiling ‘Focus and forget’ for enhanced computational efficiency}, volume = {13}, year = {2025}, }
• (2025). Design consideration and experimental validation of a common grounded high step-up DC-DC converter with high voltage gain in both high and low duty cycles. ACCESS, 13, 27893-27907. (www)

  @article{ , abstract = {In this paper, a new non-isolated high step-up DC-DC converter is proposed, which can achieve high voltage gains in both low and high duty cycles. This allows different high voltage gains to be achieved in either low and high duty cycles. Therefore, by changing the duty cycle in proper time periods, the voltage stress on switches could be modified in a way that they can achieve a better life span, without having any noticeable change in the output voltage. The structure of the converter reduces the voltage stress on switches with small number of elements and the output voltage ripple is lower than 1% of the output voltage using a small capacitor. The input current remains continuous, and the converter maintains a common ground between input and output terminals, making it ideal for battery storage systems and renewable energy applications such as photovoltaic (PV) panels. The paper thoroughly examines the operation in Continuous Conduction Mode (CCM), including an efficiency analysis, the derivation of the small-signal model, and closed-loop analysis. Additionally, the Discontinuous Conduction Mode (DCM) operation is explored in detail. The proposed converter is benchmarked against other boost converters, and a laboratory-scale prototype is constructed to validate the theoretical findings through experimental results.}, archive = {J_ACCESS}, author = {Seyed Hossein Aleyasin and Amir Safaeinasab and Mohsen Taghizadeh Kejani and Karim Abbaszadeh and Hadi Tarzamni}, doi = {10.1109/ACCESS.2025.3540381}, journal = {IEEE Access}, pages = {27893-27907}, shortjournal = {IEEE Access}, title = {Design consideration and experimental validation of a common grounded high step-up DC-DC converter with high voltage gain in both high and low duty cycles}, volume = {13}, year = {2025}, }
• (2025). A deterministic method for contactless monitoring of vital signs using multiple FMCW radars in the 24 GHz band. ACCESS, 13, 27868-27892. (www)

  @article{ , abstract = {In this manuscript, the problem of estimating human vital signs through a plurality of single-input multiple-output frequency modulated continuous wave radars is investigated. A novel algorithm for real-time estimation of heart rate and respiration rate, and for apnea detection is developed. It employs a selection combining technique and a filtering method inspired by Bayesian filtering principles. Our experimental results evidence that the proposed algorithm, tested on commercial radar devices, outperforms well known vital signs estimation methods available in the technical literature at the price of a limited increase in computational complexity.}, archive = {J_ACCESS}, author = {Elia Vignoli and Pasquale Di Viesti and Giorgio Matteo Vitetta}, doi = {10.1109/ACCESS.2025.3540316}, journal = {IEEE Access}, pages = {27868-27892}, shortjournal = {IEEE Access}, title = {A deterministic method for contactless monitoring of vital signs using multiple FMCW radars in the 24 GHz band}, volume = {13}, year = {2025}, }
• (2025). Integrating travel survey and amap API data into travel mode choice analysis with interpretable machine learning models: A case study in china. ACCESS, 13, 27852-27867. (www)

  @article{ , abstract = {Travel survey data has long been one of the fundamental sources for travel mode choice behavior analysis. It can provide a wealth of travel-related information and detailed household attributes of individuals. However, precise travel path data, especially data relating to choices is usually lacking. Online Amap API service is promising to compensate for this lack by providing tools to calculate routes, and display key information such as travel time and cost. This study integrated revealed choice survey data with path data from an online Amap API, creating a multi-source combined dataset. Furthermore, two machine learning models (extreme gradient boosting and random forest) were applied for travel mode predictive analysis based on the original travel survey data and the integrated data. Results exhibit that the overall prediction accuracy of the XGB model improves from 0.82 to 0.84, the RF model from 0.78 to 0.82, demonstrating that integrating Amap API data can improve the performance of models is improved after Amap API data is integrated. Additionally, the Shapley additive explanations analysis (SHAP) method was applied for model interpretation. The results identify travel cost, travel time, age, and monthly income as the key factors. These findings highlight the importance of fusing big data techniques with small data in enhancing predictive accuracy and provide valuable insights into travel behavior studies.}, archive = {J_ACCESS}, author = {Li Tang and Xuan Lin and Jingcai Yu and Chuanli Tang}, doi = {10.1109/ACCESS.2025.3540082}, journal = {IEEE Access}, pages = {27852-27867}, shortjournal = {IEEE Access}, title = {Integrating travel survey and amap API data into travel mode choice analysis with interpretable machine learning models: A case study in china}, volume = {13}, year = {2025}, }
• (2025). Advances in non-contact human vital sign detection: A detailed survey of radar and wireless solutions. ACCESS, 13, 27833-27851. (www)

  @article{ , abstract = {Radar-based systems for non-contact vital sign (VS) detection have seen significant advances in recent years, with innovations in portable radar and antenna designs along with signal processing techniques. These systems are crucial for applications ranging from healthcare to emergency response, providing continuous and accurate monitoring of VSs without the need for direct contact with the subject. In other words, important physiological indicators such as respiratory and heart rates can be measured without electrodes. Currently, the utilization of various technologies in clinical settings is reported which demonstrates the advances achieved in the accurate extraction of VSs. Significant attempts have been made by the researchers leading to an increase in the overall performance of the designed healthcare systems. This study covers recently presented advances across different radar and wireless technologies, focusing on their unique contributions and comparative performances in various application scenarios. The recent studies published in the domain of VSs are summarized in detail, and afterward the authors’ outlook regarding the future of this concept is presented. This review would pave the way for designers to develop a suitable and high-performance apparatus applicable to health monitoring.}, archive = {J_ACCESS}, author = {Lida Kouhalvandi and Saeid Karamzadeh}, doi = {10.1109/ACCESS.2025.3540716}, journal = {IEEE Access}, pages = {27833-27851}, shortjournal = {IEEE Access}, title = {Advances in non-contact human vital sign detection: A detailed survey of radar and wireless solutions}, volume = {13}, year = {2025}, }
• (2025). Sidelobe level suppression in linear and planar phased arrays using grey wolf optimization with local search refinements. ACCESS, 13, 27818-27832. (www)

  @article{ , abstract = {Phased array antennas play a critical role in satellite communication systems, where minimizing peak sidelobe levels (PSLL) is essential to prevent interference and enhance signal quality. This paper presents a novel hybrid optimization approach, grey wolf optimizer with Nelder-Mead local search (GWO-NM), to suppress PSLL effectively in both linear antenna arrays (LAA) and planar antenna arrays (PAA). The proposed method combines the global search capability of grey wolf optimizer (GWO) with the Nelder-Mead local refinement to address the PSLL suppression challenge. The optimization process was applied to both 32-element and 100-element LAA, achieving PSLL reductions to −52.63 dB and −56.39 dB, respectively, and these results are also extended to the corresponding PAA configuration. The results were further validated through full-wave electromagnetic simulations in CST Microwave Studio 2024, considering practical effects such as mutual coupling and material losses. Also, this suppression comes with trade-offs, including a widened first null beamwidth from 18.34° to 19.78° for the 32-element arrays and from 6.47° to 9.71° for the 100-element arrays in both LAA and their corresponding PAA compared to the conventional algorithm, along with a marginal decrease of 0.5-1 dB in directivity observed in electromagnetic simulations. The GWO-NM algorithm outperformed other metaheuristic optimization techniques, including PSO, ACO, CSO, ABCO, and conventional GWO, demonstrating superior sidelobe suppression across different array configurations while maintaining an acceptable trade-off with other key radiation parameters.}, archive = {J_ACCESS}, author = {Muhammad Athallah Adriansyah and Aditya Inzani Wahdiyat and Ismi Rosyiana Fitri and Noor Asniza Murad and Catur Apriono}, doi = {10.1109/ACCESS.2025.3540456}, journal = {IEEE Access}, pages = {27818-27832}, shortjournal = {IEEE Access}, title = {Sidelobe level suppression in linear and planar phased arrays using grey wolf optimization with local search refinements}, volume = {13}, year = {2025}, }
• (2025). A single-stage photovoltaic module defect detection method based on optimized YOLOv8. ACCESS, 13, 27805-27817. (www)

  @article{ , abstract = {Defect detection in photovoltaic (PV) modules presents significant challenges. In the presence of inconspicuous or small-scale defects, downsampling operations can cause features to vanish, and computational complexity increases as the model deepens, resulting in reduced detection accuracy, higher latency, and challenges in deploying the model on edge devices with limited computational resources. In response, this article proposes a single-stage object detection model based on YOLOv8, the PSA-PVdetector (PSA-det). The core innovation of PSA-det is the novel Partial Spatial Attention (PSA) mechanism, which integrates Partial Convolution (PConv) with Spatial Attention (SA) to optimize feature extraction and reduce computational overhead. For small-scale defect detection, a Multi-Channel Feature Fusion (MCFF) detection head is designed to enable finer-grained center prediction. Additionally, ShapeIoU is employed as the bounding box regression metric, enhancing the classical IoU by incorporating bounding box shape, thereby improving defect localization accuracy. Experimental results demonstrate that PSA-det achieves an mAP50 of 87.2% on the Panel-2 dataset and 72.0% on the Solar dataset. On the Nvidia Jetson Xavier NX DK platform, PSA-det achieves an inference latency as low as 2.6 ms, effectively balancing accuracy and real-time performance.}, archive = {J_ACCESS}, author = {Yihong Gao and Chengxin Pang and Xinhua Zeng and Pengyi Jiang}, doi = {10.1109/ACCESS.2025.3539872}, journal = {IEEE Access}, pages = {27805-27817}, shortjournal = {IEEE Access}, title = {A single-stage photovoltaic module defect detection method based on optimized YOLOv8}, volume = {13}, year = {2025}, }
• (2025). The butterfly effect: Color-guided image generation from unconditional diffusion models. ACCESS, 13, 27794-27804. (www)

  @article{ , abstract = {Diffusion models, which generate images by denoising initial noise through iterative steps, have emerged as a new state-of-the-art family of deep generative models. Several methods have been proposed to guide user requirements to unconditional diffusion models without an additional training process. However, these methods generate images using complex functions, necessitating the calculation of gradients. To address this issue, we propose a new approach called Color-Guided Diffusion (CGDiff) that guides the color of images generated by unconditional diffusion models, without requiring any structural modification or additional training. Specifically, CGDiff adds color-specific noise to the generating image according to a guidance color during the early denoising steps. Despite its simplicity, CGDiff effectively offers color guidance to diffusion models, resulting in generated images imbued with the guidance color. Furthermore, since diverse diffusion models commonly share the denoising process for image generation, CGDiff can be easily applied to any pre-trained diffusion model. Extensive experimental results with various off-the-shelf diffusion models demonstrate the effectiveness and broad applicability of our CGDiff.}, archive = {J_ACCESS}, author = {Yunha Yeo and Daeho Um}, doi = {10.1109/ACCESS.2025.3539101}, journal = {IEEE Access}, pages = {27794-27804}, shortjournal = {IEEE Access}, title = {The butterfly effect: Color-guided image generation from unconditional diffusion models}, volume = {13}, year = {2025}, }
• (2025). The impact of green finance on carbon emissions based on fixed effects model. ACCESS, 13, 27783-27793. (www)

  @article{ , abstract = {During the 14th Five Year Plan period, China will focus on carbon reduction. Green finance has become the main driving force for energy conservation. To explore how green finance affects carbon emissions, a fixed effects model is used for empirical analysis, with the Green Finance Development (GFD) as the explanatory variable, total carbon emissions as the dependent variable, and per capita GDP, technological innovation, industrial structure, environmental protection intensity, and foreign investment level as control variables. Two hypotheses are verified. Firstly, industrial carbon emissions can be significantly declined by improving the GFD. Secondly, the heterogeneity existed in the impact of GFD on industrial carbon emissions across various regions and industries. In the bidirectional fixed effects model regression analysis, the GFD was -0.923, the coefficient of industrial structure optimization was -0.563, and the coefficient of environmental protection was -0.896, all of which were adversely associated with total industrial carbon emissions. In heterogeneity analysis, the GFD had the greatest impact on the eastern region, succeeded by the eastern region, and finally the western region. The GFD is adversely associated with the total industrial carbon emissions, and there is heterogeneity between these two in different regions and industries, providing important empirical evidence for policy makers.}, archive = {J_ACCESS}, author = {Wen Tu and Qing Ma and Xin Zhao and Wen Liu}, doi = {10.1109/ACCESS.2025.3534240}, journal = {IEEE Access}, pages = {27783-27793}, shortjournal = {IEEE Access}, title = {The impact of green finance on carbon emissions based on fixed effects model}, volume = {13}, year = {2025}, }
• (2025). Advancing interoperable IoT-based access control systems: A unified security approach in diverse environments. ACCESS, 13, 27767-27782. (www)

  @article{ , abstract = {The convergence of the Internet of Things (IoT) and the construction industry represents a potent opportunity to fundamentally redefine the processes of constructing and maintaining buildings. The integration of IoT technology has the potential to revolutionize building safety, efficiency, and sustainability, offering a promising avenue for innovation and advancement within the industry. In this work, we propose an innovative design and implementation of an interoperable physical access control IoT-based system that can be easily integrated with existing access control security systems and used in various environments such as offices, homes, and public buildings. Based on our developed and implemented physical access control IoT-based system, we identified several challenges in the current state of access control systems. These challenges include difficulties in integrating different security systems, the absence of interoperable standards, and the need for further research to enhance the scalability and performance of such systems.}, archive = {J_ACCESS}, author = {Mihai Penica and Mangolika Bhattacharya and William O’Brien and Sean McGrath and Martin Hayes and Eoin O’Connell}, doi = {10.1109/ACCESS.2025.3538748}, journal = {IEEE Access}, pages = {27767-27782}, shortjournal = {IEEE Access}, title = {Advancing interoperable IoT-based access control systems: A unified security approach in diverse environments}, volume = {13}, year = {2025}, }
• (2025). Evaluation system of blind spot warning algorithm based on vehicle to everything. ACCESS, 13, 27754-27766. (www)

  @article{ , abstract = {The vehicle anti-collision system stands as a pivotal innovation, markedly reducing the frequency of vehicular accidents by issuing timely alerts to drivers about imminent collision risks. This preemptive safety feature is now a standard component within the safety packages provided by many automotive manufacturers. While current anti-collision technologies, predominantly advanced driver assistance systems that synthesize diverse sensor inputs, are sophisticated, their performance can be impeded by the operational constraints of the sensors themselves. This limits their capacity to ensure traffic safety comprehensively and efficiently. To overcome these constraints, there has been a significant shift towards anti-collision systems employing short-range wireless communication technologies. This paper introduces the design and development of a blind spot warning system based on cellular vehicle-to-everything technology. It enables the fluid communication of vehicle and road segment data among the host vehicle, remote vehicles, and roadside units through cellular vehicle-to-everything protocols. The system evaluates spatial relationships of surrounding RVs to the HV and the associated collision risks by analyzing the consolidated vehicle and environmental data, subsequently delivering early warnings to the driver. Utilizing simulation software named Virtual Test Drive, this research constructs detailed collision scenarios and performs exhaustive Hardware-in-the-Loop testing. A comparative analysis of various experimental data sets confirms the algorithm’s proficiency in delivering precise and prompt blind spot collision alerts to the HV, validating its effectiveness in bolstering vehicular safety.}, archive = {J_ACCESS}, author = {Yanzhao Yang and Jian Wang and Xinyu Yang and Tingyu Zhang and Xinyu Guo}, doi = {10.1109/ACCESS.2025.3531942}, journal = {IEEE Access}, pages = {27754-27766}, shortjournal = {IEEE Access}, title = {Evaluation system of blind spot warning algorithm based on vehicle to everything}, volume = {13}, year = {2025}, }
• (2025). Development of vehicle motion control logic for ride comfort optimization based on passenger seating position. ACCESS, 13, 27743-27753. (www)

  @article{ , abstract = {This paper proposes a vehicle motion control method to enhance ride comfort at the passenger’s position, aiming to provide optimal ride comfort to passengers during Advanced Driver Assistance System(ADAS)/Autonomous Driving(AD) driving. The control logic is designed to prevent collisions with the preceding vehicle and maintain the road curvature, as in conventional ADAS/AD systems, while also minimizing the longitudinal and lateral accelerations, as well as the lateral velocity at the passenger’s position. The proposed control logic consists of a target vehicle motion module and a controller module. In the target vehicle motion module, the target longitudinal velocity, target lateral velocity, and target yaw rate at the vehicle’s center of gravity are determined. The target longitudinal velocity is determined to prevent collisions with the preceding vehicle and minimize both longitudinal and lateral accelerations, while the target lateral velocity is determined to minimize the lateral velocity at the passenger’s position. The target yaw rate is determined to track the road’s curvature. To track the determined target motions, the controller calculates control inputs for the drive motor, hydraulic braking, front-wheel steering, and torque vectoring. The controller consists of a longitudinal control module and a lateral control module. The longitudinal control module determines the control inputs for the drive motor and hydraulic braking needed to track the target longitudinal velocity, while the lateral control module determines the control inputs for front-wheel steering and torque vectoring needed to track the target lateral velocity and target yaw rate. The proposed control logic was validated in a simulation environment using MATLAB/Simulink and CarSim. The simulation results confirmed that the proposed approach improves the ride comfort at the passenger’s location compared to the conventional ADAS/AD systems.}, archive = {J_ACCESS}, author = {Beomhyuk Park and Sang-Ho Lee and Jee-Yoon Suh and Wanki Cho and Seung-Han You}, doi = {10.1109/ACCESS.2025.3538923}, journal = {IEEE Access}, pages = {27743-27753}, shortjournal = {IEEE Access}, title = {Development of vehicle motion control logic for ride comfort optimization based on passenger seating position}, volume = {13}, year = {2025}, }
• (2025). Investigating the effectiveness of H-bridge in mitigating switch faults of a single-phase five-level inverter. ACCESS, 13, 27729-27742. (www)

  @article{ , abstract = {The use of multilevel inverters (MLIs) is increasingly popular due to their ability to operate at high power and reduce voltage stress on semiconductor switches compared to conventional inverters. However, the greater number of conducting semiconductor switches in MLIs, coupled with their higher failure rate, can weaken the inverter system. Five-level inverters provide an optimal balance by leveraging MLI benefits without significantly increasing semiconductor devices. This paper defines parameters to evaluate both quantitative and qualitative aspects of three recently proposed fault-tolerant MLI topologies. By scrutinizing these topologies, a generalized redundant leg architecture is proposed. This architecture is adaptable to any five-level MLI topology, whether it requires fault tolerance or already possesses it. The proposed solution, validated through comparative analysis and experiments, demonstrates superior fault tolerance with only one switch conducting from the redundant leg under faulty conditions, outperforming related works.}, archive = {J_ACCESS}, author = {Shivam Prakash Gautam and Satyajit Mohanty and Santanu Kumar Dash and Manik Jalhotra and Lalit Kumar Sahu and Michele Roccotelli and Kashem M. Muttaqi}, doi = {10.1109/ACCESS.2025.3537896}, journal = {IEEE Access}, pages = {27729-27742}, shortjournal = {IEEE Access}, title = {Investigating the effectiveness of H-bridge in mitigating switch faults of a single-phase five-level inverter}, volume = {13}, year = {2025}, }
• (2025). IoT-enabled adaptive watering system with ARIMA-based soil moisture prediction for smart agriculture. ACCESS, 13, 27714-27728. (www)

  @article{ , abstract = {Agriculture is the backbone of every country as it fulfills the necessities of human beings as well as animals and also supports the country’s economy in terms of revenue by exporting food products to the international market. But due to rapid growth of industries and population food and water shortage is becoming a very critical issue. Especially in Pakistan shortage of water and unpredicted weather conditions are affecting agricultural areas, and as a result, food shortage is increasing. A solution to handle the water shortage is a controlled watering system for crops. In this paper, we describe two different IoT-based systems for crop soil moisture, soil temperature, and outdoor temperature and humidity monitoring, one is Wi-Fi based while the other is a GSM-based system. This helps us monitor the soil moisture content and other parameters in real-time which will help agriculture researchers and farmers to use water according to system reports. The system uses an ARIMA (autoregressive integrated moving average) machine learning model to predict the soil moisture for the next ten days which will help the farmers to arrange the water in Hyetal areas where canal water is not reachable. The predictions are compared with real-time data and the success rate of the ARIMA ML model is from $80-90\%$ . The proposed research will open the door for agriculture researchers to gather new datasets and predictions by adopting this proposed research which will be beneficial to farmers to cultivate the crops and fruits in hyetal lands according to data recommendation.}, archive = {J_ACCESS}, author = {Muhammad Afzal and Iftikhar Ahmed Saeed and Muhammad Noman Sohail and Mohamad Hanif Md Saad and Mahidur R. Sarker}, doi = {10.1109/ACCESS.2025.3532447}, journal = {IEEE Access}, pages = {27714-27728}, shortjournal = {IEEE Access}, title = {IoT-enabled adaptive watering system with ARIMA-based soil moisture prediction for smart agriculture}, volume = {13}, year = {2025}, }
• (2025). Small-object semantic segmentation of satellite ship images using modified U-net with morphological loss. ACCESS, 13, 27700-27713. (www)

  @article{ , abstract = {Satellite aerial images have been successfully used for many applications. However, it is still challenging to detect small objects due to low-resolution and environmental conditions. To overcome this challenge, we propose segmentation methods using atrous spatial pyramid pooling (ASPP) and modified residual U-Nets with weighted loss functions. ASPP is a spatial pyramid pooling method with different atrous convolutions that can enlarge receptive fields without reducing the feature map resolution. A residual block enables the network to optimize more easily during training with skip connections. We adapted this residual block to the U-Net and modified it with atrous convolutions to enlarge the receptive field. We also applied a loss function computed by combining a focal loss function and a new loss function for small objects. Furthermore, the loss function for small objects is computed only for small objects by applying white top-hat processing. We tested the proposed method using the Airbus ship segmentation dataset. The experimental results show that the proposed methods produced improved Dice and mIoU performance by 2 to 8 percent compared to existing segmentation models. The proposed methods successfully detected some small objects missed by the existing segmentation models.}, archive = {J_ACCESS}, author = {D. Shim and C. Lee}, doi = {10.1109/ACCESS.2025.3538876}, journal = {IEEE Access}, pages = {27700-27713}, shortjournal = {IEEE Access}, title = {Small-object semantic segmentation of satellite ship images using modified U-net with morphological loss}, volume = {13}, year = {2025}, }
• (2025). Corrections to “EEG datasets for healthcare: A scoping review”. ACCESS, 13, 27699. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “EEG Datasets for Healthcare: A Scoping Review”.}, archive = {J_ACCESS}, author = {Caroline Peres da Silva and Salvatore Tedesco and Brendan O’Flynn}, doi = {10.1109/ACCESS.2025.3540186}, journal = {IEEE Access}, pages = {27699}, shortjournal = {IEEE Access}, title = {Corrections to “EEG datasets for healthcare: A scoping review”}, volume = {13}, year = {2025}, }
• (2025). Distributed adaptive time-varying mean square formation tracking control for linear multi-agent systems with external random noise disturbance based on joint connectivity topology. ACCESS, 13, 27684-27698. (www)

  @article{ , abstract = {This work investigates the issue of controlling formation tracking in linear multi-agent systems with time variations under external stochastic disturbances. In addition to accomplishing the intended time-varying formation, the followers must follow the leader’s state trajectory, as they are also impacted by outside disruptions. First, the external disturbance of the leader and followers is described using Brownian motion, and the formation problem of the system is a global optimization problem. Furthermore, for both fixed and jointly connected topologies of the multi-agent system, a fully distributed time-varying formation tracking controller is designed using the Riccati inequality. An adaptive coupling weight suppression term is introduced to ensure the convergence of the system’s adaptive coupling weights under random noise interference. This is achieved without requiring global topology information, relying solely on the relative error between adjacent agents to adaptively adjust and update the weights. Using Lyapunov’s stability theory, the stability of the time-varying formation system of multi-agents with external random disturbances is studied under both fixed and jointly connected topologies. It is proven that under external random disturbances, the system is able to achieve time-varying formation, and the time-varying formation tracking error asymptotically converges to zero. Lastly, the strategy works of the approach is demonstrated through digital simulations.}, archive = {J_ACCESS}, author = {Yuyi Huang and Yandong Li and Ling Zhu and Hui Cai and Yuan Guo}, doi = {10.1109/ACCESS.2025.3539995}, journal = {IEEE Access}, pages = {27684-27698}, shortjournal = {IEEE Access}, title = {Distributed adaptive time-varying mean square formation tracking control for linear multi-agent systems with external random noise disturbance based on joint connectivity topology}, volume = {13}, year = {2025}, }
• (2025). TLEABLCNN: Brain and alzheimer’s disease detection using attention-based explainable deep learning and SMOTE using imbalanced brain MRI. ACCESS, 13, 27670-27683. (www)

  @article{ , abstract = {Alzheimer’s disease (AD) is one of the primary causes of dementia. It degenerates the brain and reduces the activity of individuals by disrupting their memory and physiological functions. A comprehensive examination of specific brain tissue is required in order to accurately diagnose a brain condition using magnetic resonance imaging. The main aim of this research was to develop a rapid and effective technique for detecting healthy persons before the onset of brain tumours, including AD, pituitary tumours, gliomas, and meningiomas. This work presents a lightweight convolutional architecture based on EfficientNet with a Squeeze Attention Block using transfer learning. The proposed approach used lightweight layers with an L2 regularizer, global pooling (2D), and batch normalisation to construct the model, including two dropout layers. This paper utilises the synthetic minority oversampling approach to address the issues of overfitting and imbalanced samples by balancing two significantly imbalanced MRI datasets. We evaluated the efficacy of the minority strategy by doing experiments on both the training set and the entire dataset. The proposed model demonstrates high efficiency, with the risk of overfitting mitigated by applying SMOTE solely to the training data, while the test and validation datasets remain unaffected. The effectiveness of the proposed approach was evaluated using several performance measures and compared with previous published research. We evaluated the proposed approach for explainability via gradient-weighted class activation mapping to comprehend the model’s behaviour and its predictions. The proposed framework offers advantages over existing models in terms of computing efficiency, explainability, generalization, and clinical significance.}, archive = {J_ACCESS}, author = {Erol Kina}, doi = {10.1109/ACCESS.2025.3539550}, journal = {IEEE Access}, pages = {27670-27683}, shortjournal = {IEEE Access}, title = {TLEABLCNN: Brain and alzheimer’s disease detection using attention-based explainable deep learning and SMOTE using imbalanced brain MRI}, volume = {13}, year = {2025}, }
• (2025). High accuracy data classification and feature selection for incomplete information systems using extended limited tolerance relation and conditional entropy approach. ACCESS, 13, 27657-27669. (www)

  @article{ , abstract = {Data classification and feature/attribute selection approaches play important role in enabling organizations to extract meaningful insights from vast and complex datasets. Besides, the accuracy and processing time are two parameters of interest to determine which approach is favourable or suitable for enormous data. Moreover, the presence of redundant, incomplete, noisy and inconsistent data made more concern to accuracy and computational resources. The issue of incomplete data is addressed in limited studies due to its complexities, particularly on data classification and accuracy as well as attribute selection. The limited tolerance relation between objects is the favourable approach used in this scenario. However, the accuracy and the data classification rate need to be improved. In this paper, a new approach called extended limited tolerance relation with the similarity precision among objects to improve the data classification with high accuracy will be presented and the feature/attribute selection is performed using conditional entropy. Comparative analysis and experiment result between the proposed approach with limited tolerance relation approach in terms of data classification and accuracy are presented. The proposed approach comparatively improved the accuracy with better data classification rate and feature selection while preserving the consistency of the information in incomplete information systems that is worthy of attention.}, archive = {J_ACCESS}, author = {Mustafa Mat Deris and Jemal H. Abawajy and Iwan Tri Riyadi Yanto and Adiwijaya Adiwijaya and Tutut Herawan and Ainur Rofiq and Riswan Efendi and Mohamad Jazli Shafizan Jaafar}, doi = {10.1109/ACCESS.2025.3538278}, journal = {IEEE Access}, pages = {27657-27669}, shortjournal = {IEEE Access}, title = {High accuracy data classification and feature selection for incomplete information systems using extended limited tolerance relation and conditional entropy approach}, volume = {13}, year = {2025}, }
• (2025). Multi-view 3D scene abstraction from drone-captured RGB images. ACCESS, 13, 27641-27656. (www)

  @article{ , abstract = {3D map reconstruction plays a critical role in fields such as autonomous driving, urban planning, and virtual reality. Using 2D images from real-world settings, multi-view image-based 3D map reconstruction offers a practical solution across these sectors. Specifically, generating 3D maps from realistic drone-view images has broad applicability. However, direct reconstruction from drone-view images is challenging due to extreme viewing angles and unstable image conditions like motion blur or varying light. Additionally, simultaneously generating and utilizing 3D maps from drone-captured images presents significant challenges, including fragmented sub-tasks such as 3D point cloud reconstruction, surface reconstruction, and shape fitting. To address these challenges, we introduce the 3D Scene Abstraction, which seamlessly integrates image matching, 3D reconstruction, 3D semantic segmentation, clustering, and shape fitting to produce 3D maps from RGB images. Our approach enhances the robustness of 3D map generation by merging 2D semantic information with 3D point clouds, ensuring reliable results under diverse drone-captured image conditions. For efficient data representation, we employ multiple primitives based on prior knowledge of various building shapes, improving both map formation performance and computational efficiency. Our approach not only enhances the effectiveness of 3D map reconstruction but also reduces the amount of data required to represent the scene, thereby improving overall efficiency and practical usability. These contributions address practical challenges in the field and lay the foundation for future advancements in 3D map reconstruction technologies.}, archive = {J_ACCESS}, author = {Wooseong Jeong and Jihun Kim and Hyeokjun Kweon and Kuk-Jin Yoon}, doi = {10.1109/ACCESS.2025.3538906}, journal = {IEEE Access}, pages = {27641-27656}, shortjournal = {IEEE Access}, title = {Multi-view 3D scene abstraction from drone-captured RGB images}, volume = {13}, year = {2025}, }
• (2025). CroMatcher 2.0: A comprehensive analysis of the improved ontology matching system. ACCESS, 13, 27610-27640. (www)

  @article{ , abstract = {One of the main challenges in ontology matching is to match ontologies with high accuracy. Therefore, ontology matching systems typically use multiple basic matchers, each targeting a specific ontology component for the matching process. However, optimizing the combination of these matchers remains an open problem. In this paper, we present CroMatcher 2.0, an improved ontology matching system that aims to overcome these challenges. We introduce two new basic matchers. The first matcher determines correspondence between entities by comparing strings obtained from entity IDs and annotations using the English lexical database to find similarities between tokens of the compared strings, considering their mutual relations (synonyms, hypernyms, etc.). The second matcher determines the correspondence between entities using the special mediator ontology, which is very valuable for ontology matching as it can contain additional information about the compared ontologies. In this paper, we tested this matcher on the Ontology Alignment Evaluation Initiative Anatomy track by using the Uberon mediator ontology, which contains a lot of information about anatomical structures. We also introduce a new weighted aggregation method (Autoweight 3.0) that automatically determines the weighting factors of the basic matchers in the parallel composition. CroMatcher 2.0 was evaluated in three test cases of the Ontology Alignment Evaluation Initiative (Benchmark, Anatomy, and Conference) and showed competitive performance compared to other state-of-the-art systems. The results position CroMatcher 2.0 among the best ontology matching systems for these datasets and confirm the effectiveness of the newly introduced methods.}, archive = {J_ACCESS}, author = {Marko Gulić and Boris Vrdoljak and Marina Ptiček}, doi = {10.1109/ACCESS.2025.3538655}, journal = {IEEE Access}, pages = {27610-27640}, shortjournal = {IEEE Access}, title = {CroMatcher 2.0: A comprehensive analysis of the improved ontology matching system}, volume = {13}, year = {2025}, }
• (2025). Design and HDL implementation of pulse-arrival-time estimation using XGBoost regression with tree-recycling architecture. ACCESS, 13, 27596-27609. (www)

  @article{ , abstract = {This paper presents a pulse-arrival-time (PAT) estimation scheme using Extreme Gradient Boosting (XGBoost) regression and its implementation with hardware description language (HDL). PAT is a significant metric for the estimation of pulse wave velocity (PWV) and blood pressure. We leverage morphological features of single photoplethysmogram (PPG) waveforms and subject-specific parameters to estimate PAT values through a decision tree ensemble regression model. XGBoost regression is adopted for PAT estimation through a comparative study conducted in software. To enable the integration of the regression model into a single chip, we propose a tree-recycling architecture for an area-efficient implementation of the decision tree ensemble model. The proposed PAT estimation has been implemented on a field-programmable gate array (FPGA, Artix7, Xilinx) for real-time verification using a dataset. Additionally, the active area of the proposed scheme in an integrated circuit was estimated after the place-and-route (PnR) procedure, resulting in an area of 1.44 mm2. We utilized the Vital Signs DataBase (VitalDB) for training and testing the proposed PAT estimation in a software-hardware co-design framework. Validation on 80 subjects yielded a determination coefficient (R2) of 0.983 in the linear regression analysis after implementing the HDL on an FPGA.}, archive = {J_ACCESS}, author = {Hyo-Jeong Choi and Ji-Yong Um}, doi = {10.1109/ACCESS.2025.3540440}, journal = {IEEE Access}, pages = {27596-27609}, shortjournal = {IEEE Access}, title = {Design and HDL implementation of pulse-arrival-time estimation using XGBoost regression with tree-recycling architecture}, volume = {13}, year = {2025}, }
• (2025). Research on pedestrian re-identification based on enhanced residual attention network. ACCESS, 13, 27587-27595. (www)

  @article{ , abstract = {Pedestrian re-identification is influenced by various complex factors such as camera angles, posture changes, and object occlusion. How to effectively extract discriminative feature information remains a key challenge. To address this issue, this paper proposes an enhanced neural network based on residual attention mechanism and random erasure. Firstly, by introducing attention mechanism in the residual network, the response of key features is effectively strengthened and redundant features are suppressed, thereby improving the discriminative performance of the network. Secondly, using random erasure as a data augmentation method effectively alleviates the impact of occlusion on the model’s recognition ability, reduces the risk of overfitting, and improves the network’s generalization performance. In addition, using triplet loss for supervised training of the network promotes better clustering performance of samples in the feature space, thereby significantly improving recognition accuracy. The experimental results show that the proposed method achieves a Rank-1 accuracy of 88.55% and an average accuracy (mAP) of 79.45% on the Market-1501 dataset, which is superior to existing methods. Verified that the proposed method can better extract discriminative pedestrian features and improve the performance of pedestrian re-identification models.}, archive = {J_ACCESS}, author = {Liqin Liu and Eric Blancaflor and Mideth Abisado}, doi = {10.1109/ACCESS.2025.3535702}, journal = {IEEE Access}, pages = {27587-27595}, shortjournal = {IEEE Access}, title = {Research on pedestrian re-identification based on enhanced residual attention network}, volume = {13}, year = {2025}, }
• (2025). BVQA: Connecting language and vision through multimodal attention for open-ended question answering. ACCESS, 13, 27570-27586. (www)

  @article{ , abstract = {Visual Question Answering (VQA) is a challenging problem of Artificial Intelligence (AI) that requires an understanding of natural language and computer vision to respond to inquiries based on visual content within images. Research on VQA has gained immense traction due to its wide range of applications in aiding visually impaired individuals, enhancing human-computer interaction, facilitating content-based image retrieval systems, etc. While there has been extensive research on VQA, most were predominantly focused on English, often overlooking the complexity associated with low-resource languages, especially in Bengali. To facilitate research in this arena, we have developed a large scale Bengali Visual Question Answering (BVQA) dataset by harnessing the in-context learning abilities of the Large Language Model (LLM). Our BVQA dataset encompasses around 17,800 diverse open-ended QA Pairs generated from the human-annotated captions of ≈3,500 images. Replicating existing VQA systems for a low-resource language poses significant challenges due to the complex nature of their architectures and adaptations for particular languages. To overcome this challenge, we proposed Multimodal CRoss-Attention Network (MCRAN), a novel framework that leverages pretrained transformer architectures to encode the visual and textual information. Furthermore, our method utilizes a multi-head attention mechanism to generate three distinct vision-language representations and fuses them using a sophisticated gating mechanism to answer the query regarding an image. Extensive experiments on BVQA dataset show that the proposed method outperformed the existing baseline across various answer categories. The benchmark and source code is available at https://github.com/eftekhar-hossain/Bengali-VQA.}, archive = {J_ACCESS}, author = {Md. Shalha Mucha Bhuyan and Eftekhar Hossain and Khaleda Akhter Sathi and Md. Azad Hossain and M. Ali Akber Dewan}, doi = {10.1109/ACCESS.2025.3540388}, journal = {IEEE Access}, pages = {27570-27586}, shortjournal = {IEEE Access}, title = {BVQA: Connecting language and vision through multimodal attention for open-ended question answering}, volume = {13}, year = {2025}, }
• (2025). MQ-GNN: A multi-queue pipelined architecture for scalable and efficient GNN training. ACCESS, 13, 27550-27569. (www)

  @article{ , abstract = {Graph Neural Networks (GNNs) are powerful tools for learning graph-structured data, but their scalability is hindered by inefficient mini-batch generation, data transfer bottlenecks, and costly inter-GPU synchronization. Existing training frameworks fail to overlap these stages, leading to suboptimal resource utilization. This paper proposes MQ-GNN, a multi-queue pipelined framework that maximizes training efficiency by interleaving GNN training stages and optimizing resource utilization. MQ-GNN introduces Ready-to-Update Asynchronous Consistent Model (RaCoM), which enables asynchronous gradient sharing and model updates while ensuring global consistency through adaptive periodic synchronization. Additionally, it employs global neighbor sampling with caching to reduce data transfer overhead and an adaptive queue-sizing strategy to balance computation and memory efficiency. Experiments on four large-scale datasets and ten baseline models demonstrate that MQ-GNN achieves up to ${4.6\,\times }$ faster training time and 30% improved GPU utilization while maintaining competitive accuracy. These results establish MQ-GNN as a scalable and efficient solution for multi-GPU GNN training. The code is available at MQ-GNN.}, archive = {J_ACCESS}, author = {Irfan Ullah and Young-Koo Lee}, doi = {10.1109/ACCESS.2025.3539976}, journal = {IEEE Access}, pages = {27550-27569}, shortjournal = {IEEE Access}, title = {MQ-GNN: A multi-queue pipelined architecture for scalable and efficient GNN training}, volume = {13}, year = {2025}, }
• (2025). Fine-tuning EEG channel utilization for emotionally stimulated biometric authentication. ACCESS, 13, 27537-27549. (www)

  @article{ , abstract = {Biometric authentication relies on distinct biological traits of individuals to validate their identity, enhancing security measures. However, variations in an individual’s emotional state can impact the reliability of the biometric system. In this study, we propose a novel pipeline to evaluate electroencephalography (EEG)-based biometric system across different emotional states and optimize critical brain regions using machine learning algorithms. EEG signals from the DEAP dataset were classified into four emotional states: HAHV, HALV, LALV, and LAHV. We extracted a comprehensive set of statistical, time, frequency, entropy, fractal, spectral, and shape features from each channel. Machine learning classifiers, including Random Forest, Gradient Boosting, Extreme Gradient Boosting, LightGBM, CatBoost, and Bagging, were used for participant authentication. Our results revealed that the CatBoost classifier performed well across all stimuli with average accuracies of 84%, 85%, 86%, and 83% for HAHV, HALV, LALV, and LAHV, respectively. We found that features from channels FC1, Fz, C4 & Pz, and FC1 significantly contributed to EEG authentication on stimuli such as HAHV, HALV, LALV, and LAHV, respectively. Features such as skewness and the theta-to-alpha frequency band ratio consistently performed well across stimuli, demonstrating EEG signals’ potential for robust biometric authentication by addressing emotional variations.}, archive = {J_ACCESS}, author = {Chetan Rakshe and Christy Bobby Thomas and Mohanavelu Kalathe and Vanteemar S. Sreeraj and Ganesan Venkatasubramanian and Deepesh Kumar and A. Amalin Prince and Jac Fredo Agastinose Ronickom}, doi = {10.1109/ACCESS.2025.3539502}, journal = {IEEE Access}, pages = {27537-27549}, shortjournal = {IEEE Access}, title = {Fine-tuning EEG channel utilization for emotionally stimulated biometric authentication}, volume = {13}, year = {2025}, }
• (2025). Cloth-changing person re-identification based on the backtracking mechanism. ACCESS, 13, 27527-27536. (www)

  @article{ , abstract = {Person re-identification has been extensively studied and has made significant progress in recent years. However, traditional person re-identification methods mainly rely on clothing-related features, which become unreliable in real-world scenarios involving frequent clothing changes. To address this issue, we propose a novel cloth-changing person re-identification method based on a backtracking mechanism, which extracts identity features independent of clothing and mitigates the impact of clothing changes on recognition performance. Unlike traditional methods, our approach simulates the brain’s cognitive mechanism, effectively combining appearance features and body shape information to achieve a deeper understanding of identity. Additionally, we introduce a feature infiltration module that integrates body shape features with appearance features extracted by the backbone network, enabling the model to extract clothing-independent features. During training, we use a clothes suppression loss function to reduce the model’s reliance on clothing features, further improving robustness. In the inference stage, the backtracking branch is removed, allowing the main network to focus on extracting clothing-independent features, thereby improving recognition accuracy. Experiments on two benchmark datasets demonstrate that our approach has better robustness and accuracy, thereby confirming its superiority.}, archive = {J_ACCESS}, author = {Xuan Liu and Hua Han and Kaiyu Xu and Li Huang}, doi = {10.1109/ACCESS.2025.3538976}, journal = {IEEE Access}, pages = {27527-27536}, shortjournal = {IEEE Access}, title = {Cloth-changing person re-identification based on the backtracking mechanism}, volume = {13}, year = {2025}, }
• (2025). A lightweight direction-aware network for vehicle detection. ACCESS, 13, 27516-27526. (www)

  @article{ , abstract = {Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. However, most high-precision vehicle detection algorithms suffer from high computational effort and slow detection speeds, resulting in the challenging task of deploying these algorithms on mobile devices. In this paper, we propose a lightweight direction-aware network (LDAN) based on the YOLOv8 for vehicle detection on mobile devices. First, a lightweight C2f-GSP module is proposed to optimize the backbone network, which enhances the interaction of local features and fully extracts vehicle information. Then, a triple efficient coordinate attention mechanism (TECA) is designed. The mechanism can fully perceive the details and salient information of input features in multiple directions, thus improving the ability of the model to capture critical features. Moreover, to further reduce model parameters and computational requirements, a lightweight shared convolutional detection head (SCL-Head) is devised using a parameter-sharing mechanism. Finally, experimental results on the KITTI dataset show that the proposed method not only reduces resource consumption but also improves the accuracy of vehicle detection, which provides a novel technical path to realize real-time and accurate vehicle detection on mobile devices.}, archive = {J_ACCESS}, author = {Luxia Yang and Yilin Hou and Hongrui Zhang and Chuanghui Zhang}, doi = {10.1109/ACCESS.2025.3539692}, journal = {IEEE Access}, pages = {27516-27526}, shortjournal = {IEEE Access}, title = {A lightweight direction-aware network for vehicle detection}, volume = {13}, year = {2025}, }
• (2025). Generating high-quality datasets with critical state samples for data-driven power system applications. ACCESS, 13, 27507-27515. (www)

  @article{ , abstract = {Data-driven approaches effectively solve power system problems but rely significantly on data. However, the critical state samples (CSS) in historical datasets are underrepresented, hindering accurate modeling of power systems. The use of unbalanced datasets limits the performance of data-driven techniques. This paper proposed the WGANTL method, which uses Wasserstein generative adversarial network (WGAN) and transfer learning (TL) to obtain high-quality datasets with CSS for data-driven power systems applications. Voltage and phase angle are selected as sample features, and the WGANTL model is built to learn CSS constraints and distributions. Tests on IEEE-30 and IEEE-300 systems show that WGANTL generates high-quality datasets with excellent variety and coverage. The variety of the datasets is improved by $9.13\times $ and $8.6\times $ than the baseline method. The coverage is improved by 14.8% and 14.4%. Using the generated datasets as training sets, the trained machine learning models have high accuracy in solving the AC optimal power flow problem, improving by 18% and 19% over the baseline performance. In addition, WGANTL provides $211\times $ and $238\times $ generation speedup.}, archive = {J_ACCESS}, author = {Zixuan Zhang and Xiaoqing Bai and Zonglong Weng and Jiawen Xiao and Jiale Zhang}, doi = {10.1109/ACCESS.2025.3539787}, journal = {IEEE Access}, pages = {27507-27515}, shortjournal = {IEEE Access}, title = {Generating high-quality datasets with critical state samples for data-driven power system applications}, volume = {13}, year = {2025}, }
• (2025). Research and application of digital twin and simulation teaching methodology in DVOR. ACCESS, 13, 27487-27506. (www)

  @article{ , abstract = {Doppler VHF Omnidirectional Radio Range (DVOR) plays a critical role as a key component of Ground Equipment for Aircraft Navigation (GEAN). So far, little research has addressed the question of how to use Digital Twin (DT) for GEAN. Given that there is a notable paucity of empirical research specifically focusing on the DVOR data acquisition method and Virtual Reality (VR) teaching technology. The specific objective of this study is to explore the application of DT and VR teaching in DVOR. Additionally, this research specifically focuses on the VRB-52D type of the DVOR system. In this study, a novel real-time data acquisition algorithm is proposed to implement The Real-Time Data-Driven Equipment Health Monitoring Subsystem (RTDEHMS). Furthermore, fault logic rules for DVOR are proposed for fault simulation to implement The Virtual Simulation Teaching Subsystem (VSTS). This study not only addresses how to remotely monitor the operational status of DVOR but also resolves the limited opportunities for Communication, Navigation, and Surveillance Equipment Maintenance Personnel (CNS Personnel) to engage in the practical operation of DVOR during their training process. With this system, CNS Personnel can efficiently manage DVOR, and it can also help students learn about DVOR quickly. Finally, an offline survey provided quantitative data from 40 participants. The results of the survey show that almost all participants are satisfied with the system. This research provides a timely and necessary study of the DT application on the DVOR. This study not only confirms the possibility of DT system for GEAN, but also the proposed fault logic construction method is applicable to other GEAN. This study also provides practical experience in modeling, data collection, and interface standards for GEAN, facilitating the standardization and collaboration of GEAN. It provides essential technical support for modern aviation ground support.}, archive = {J_ACCESS}, author = {Zhuowen Xiang and Caiyan Tian and Yunpeng Dai and Hongyu Xiang and Lili Huang and Xinqiao Liu and Youhui Zhang}, doi = {10.1109/ACCESS.2025.3539709}, journal = {IEEE Access}, pages = {27487-27506}, shortjournal = {IEEE Access}, title = {Research and application of digital twin and simulation teaching methodology in DVOR}, volume = {13}, year = {2025}, }
• (2025). Study on rapid calculation and graded early warning system for conductor temperature of 10 kV XLPE cables. ACCESS, 13, 27477-27486. (www)

  @article{ , abstract = {Accurate and rapid calculation for cable’s conductor temperature, coupled with timely alerts, is crucial for the safe and stable operation of cables. In this paper, we present a study on a rapid calculation and graded early warning system for conductor temperatures of 10 kV cross-linked polyethylene (XLPE) cables, detailing the system architecture and graded warning strategy. Firstly, a coupled electric-thermal-fluid finite element model is established, which is used to determine the warning temperatures for the cable outer sheath. Secondly, a rapid calculation method for the conductor transient temperature is proposed, which is based on the superposition principle. Finally, the accuracy and practicality of this system are verified by actual monitoring data and finite element simulation. The results demonstrate that the system can accurately calculate transient conductor temperatures with error less than 1 °C and computation time less than 1 second, indicating significantly applicable value in engineering. Based on the system, accurate conductor temperature calculations and alerts can be made, providing a theoretical basis for operation and maintenance of urban distribution cable lines.}, archive = {J_ACCESS}, author = {Qishun Li and Sichao Chen and Haifeng Qiu and Guohua Zhou and Bin Lv}, doi = {10.1109/ACCESS.2025.3538714}, journal = {IEEE Access}, pages = {27477-27486}, shortjournal = {IEEE Access}, title = {Study on rapid calculation and graded early warning system for conductor temperature of 10 kV XLPE cables}, volume = {13}, year = {2025}, }
• (2025). DuCo-net: Dual-contrastive learning network for medical report retrieval leveraging enhanced encoders and augmentations. ACCESS, 13, 27462-27476. (www)

  @article{ , abstract = {The conventional process of generating medical radiology reports is labor-intensive and time-consuming, requiring radiologists to describe findings meticulously from imaging studies. This manual approach often causes undesirable delays in patient care. Despite advancements in computer vision and deep learning, developing an effective computer-aided solution to generate automated medical reports remains challenging. The recent advancements in deep learning technology, especially with the advent of contrastive learning, have shown significant performance in natural language supervision. However, their application to medical report generation, particularly in the domain of chest x-rays (CXR), has been limited due to the lack of large annotated datasets. Many studies have proposed multimodal contrastive learning schemes to address the data scarcity problem for natural images. However, none of these techniques have been efficiently explored in terms of medical report generation. This study addresses these challenges by proposing a dual contrastive learning network (DuCo-Net) containing backbone and augmented networks. The backbone network is trained on the original data, while the augmented network emphasizes cross-model augmentation learning in a unified framework. DuCo-Net enables two complementary learning mechanisms: intra-modal learning, where each network learns specialized features within its modality (either image or text), and inter-modal learning, which captures relationships between image and text modalities through a combined loss function. This dual learning approach leverages modified DenseNet121 and BioBERT models with advanced pooling techniques specifically tailored for handling medical data. Comprehensive evaluations on two publicly available datasets demonstrate that DuCo-Net significantly outperforms current benchmarks. On the Indiana University Chest X-rays dataset, our proposed methodology demonstrates significant improvements across standard metrics (BLEU-1: 0.50, ROUGE: 0.40, METEOR: 0.24, F1: 0.40). For the MIMIC-CXR dataset, the framework maintains robust performance (BLEU-1: 0.42, ROUGE: 0.34, METEOR: 0.20, F1: 0.34), representing substantial improvements over existing state-of-the-art approaches in medical report generation.}, archive = {J_ACCESS}, author = {Zahid Ur Rahman and Ju-Hwan Lee and Dang Thanh Vu and Iqbal Murtza and Jin-Young Kim}, doi = {10.1109/ACCESS.2025.3538325}, journal = {IEEE Access}, pages = {27462-27476}, shortjournal = {IEEE Access}, title = {DuCo-net: Dual-contrastive learning network for medical report retrieval leveraging enhanced encoders and augmentations}, volume = {13}, year = {2025}, }
• (2025). Prototype-based explanation for semantic gap reduction with distributional embedding. ACCESS, 13, 27449-27461. (www)

  @article{ , abstract = {The demand for interpretable models has driven the exploration of explainable approaches grounded in human-friendly case-based reasoning. Among these approaches, prototype-based methods have proven effective in performing case-based reasoning by utilizing prototypes and similarity scores. However, their interpretability is affected by degraded similarity in the input space and latent space. This semantic gap leads to inconsistent explanation for images that are perceived to be similar, which undermines the reliability of the explanation. In this paper, we propose a distributional embedding framework in which the embedding is randomly sampled from a parameterized distribution in a regularized latent space. With a simple modification, our method significantly improves the reliability of the model’s explanation by bridging the gap between similarity in human perception and explanation. To demonstrate this, we conduct experiments ranging from small-scale scenarios to direct explanation regarding similarity. Extensive comparisons with a real-world dataset and multiple backbone networks showcase the usability and efficacy of the proposed framework.}, archive = {J_ACCESS}, author = {Hyungjun Joo and Sangwoo Hong and Hyeonggeun Han and Youngseok Yoon and Jungwoo Lee}, doi = {10.1109/ACCESS.2025.3535884}, journal = {IEEE Access}, pages = {27449-27461}, shortjournal = {IEEE Access}, title = {Prototype-based explanation for semantic gap reduction with distributional embedding}, volume = {13}, year = {2025}, }
• (2025). Characterization of human gait by means of the trajectory of the instantaneous centers of rotation of lower limb segments in the sagittal plane. ACCESS, 13, 27438-27448. (www)

  @article{ , abstract = {Gait analysis in the clinical context is essential for identifying structural and functional limitations in individuals. By examining the spatial and temporal characteristics of a subject’s gait and comparing them to reference values, clinicians can gather valuable insights that aid in diagnosing diseases, developing treatment plans, and evaluating their efficacy. While much of the existing research on kinematic gait-related data has focused primarily on joint motion, other measures, such as the trajectories of the instantaneous center of rotation (ICR), remain underexplored. This paper investigates the potential of ICR trajectories of the lower limb segments as a biomechanical measure for assessing human gait. To achieve this, gait trials were conducted with subjects walking both overground and on a treadmill using a wearable inertial motion capture system, while sports cameras recorded motion in the sagittal plane. Kinematic data were acquired through video processing with the open-source tool Kinovea and directly via Xsens Analyze software. The positions of the ICRs of the lower limb segments in the sagittal plane were calculated for each time sample, and the determined trajectories were analyzed in time, frequency, and state space. The results indicate that the ICR trajectories of the shank exhibit a characteristic pattern, suggesting their potential use in gait analysis. Despite challenges posed by the asymptotic behavior of the studied signals and numerical errors related to the methods used, this study highlights the considerable potential of using ICR trajectories to enhance our understanding of human gait.}, archive = {J_ACCESS}, author = {Marek Iwaniec and Ludwin Molina Arias and Joanna Iwaniec}, doi = {10.1109/ACCESS.2025.3539452}, journal = {IEEE Access}, pages = {27438-27448}, shortjournal = {IEEE Access}, title = {Characterization of human gait by means of the trajectory of the instantaneous centers of rotation of lower limb segments in the sagittal plane}, volume = {13}, year = {2025}, }
• (2025). Graph multi-resolution transformer for road traffic anomaly detection. ACCESS, 13, 27428-27437. (www)

  @article{ , abstract = {Traffic anomaly detection has become increasingly vital for ensuring the safety, efficiency, and resilience of urban transportation systems. The emergence of megacities and the integration of autonomous vehicles (AVs) introduce new challenges for anomaly detection in mixed-traffic environments. AVs exhibit distinct driving behaviors compared to human drivers, resulting in more complex traffic scenarios that necessitate advanced detection methods. This paper presents a novel Graph Multi-Resolution Transformer (GMRT) model designed for traffic anomaly detection in environments where autonomous and human-driven vehicles coexist. Utilizing simulation data from the autonomous driving demonstration zone in South Korea, the GMRT effectively captures temporal patterns at multiple resolutions and leverages graph-based spatial relationships to enhance anomaly detection performance. Unlike traditional models that focus on macro-level traffic flow, our approach operates at a micro-level (lane-based), enabling precise identification of anomalies on a per-lane basis. Comparative experiments against established time-series models demonstrate the superior accuracy and robustness of GMRT, particularly in early anomaly detection across varied temporal horizons. The results underscore the model’s potential as a robust solution for contemporary and future urban traffic management challenges, especially in scenarios involving the increasing penetration of AVs.}, archive = {J_ACCESS}, author = {Donghyun Park and Sung-Soo Choi and Donghyun Lim and Yong-Shin Kang}, doi = {10.1109/ACCESS.2025.3538836}, journal = {IEEE Access}, pages = {27428-27437}, shortjournal = {IEEE Access}, title = {Graph multi-resolution transformer for road traffic anomaly detection}, volume = {13}, year = {2025}, }
• (2025). Modeling interference for diffusive molecular communication under different biological deployment. ACCESS, 13, 27416-27427. (www)

  @article{ , abstract = {In Molecular Communication (MC) systems, with multiple transmitting Nano Nodes, interference is one of the most challenging impairments. On the other hand, MC system highly depends on the deployment scenarios such as, Rectangular Biological Deployment (RBD), Cylindrical Biological Deployment (CBD) and Spherical Biological Deployment (SBD). Though, statistical physical models of interference under RBD are proposed in literature but it is in implicit forms containing complex integrals. However, interference under CBD and SBD deployments are missing from open literatures. Whereas, this research presents interference model for Diffusive Molecular Communication (DMC) under CBD and SBD scenarios. In particular, analytical expressions for Probability Density Function (PDF) of the received power spectral density (PSD) under CBD and SBD are derived. Derived PDF of PSD is further employed to present the Probability of Interference (POI) under both the deployments. It is noteworthy to mention that all the derived expressions are novel and in their explicit forms. Further, presented analysis includes quantification of PDF of PSD and POI for both the deployments. Furthermore, comparative analysis under different domains such as, in frequency domain ( $\omega $ ) varying from 0 to 2 Hz and in ‘s’ domain varying from 0 to $5\times 10^{4}$ [ ${\mathrm {molecules}}^{2}\mathrm {m}^{-6}{\mathrm {sec~Hz}}^{-1}$ ] are also presented for both the deployments. Observations reveal that the PSD of the received signal or POI both increases rapidly with an increase in the value of frequency ‘ $\omega $ ’. In particular, for a given value of ‘s’ as value of ‘ $\omega $ ’ is increased from 0.99 Hz to 1.79 Hz, the value of PDF of the received PSD increases. Also, for given transmitter distance ( $r_{k}$ ) as the value of ‘ $\omega $ ’ is increased from 0.39 Hz to 1.89 Hz, the value of POI increases. Moreover, the derived expressions are validated by providing the results of the Monte Carlo simulation. Finally, the proposed analysis may be used in understanding the effect of interference in various real scenarios of human organs or tissues such as kidney, liver or stomach which are characterized by cylindrical or spherical deployments.}, archive = {J_ACCESS}, author = {Suman Yadav and S. Pratap Singh and Bharat Gupta}, doi = {10.1109/ACCESS.2025.3539875}, journal = {IEEE Access}, pages = {27416-27427}, shortjournal = {IEEE Access}, title = {Modeling interference for diffusive molecular communication under different biological deployment}, volume = {13}, year = {2025}, }
• (2025). Intelligence evaluating computational power: A multi-factor method. ACCESS, 13, 27398-27415. (www)

  @article{ , abstract = {With the rapid development of hardware technologies and the continuous optimization of deep learning tasks, accurately evaluating the computational power of artificial intelligence (AI) systems has become a critical requirement. However, the diversity of hardware architectures and the complexity of deep learning tasks have hindered the establishment of a widely accepted unified evaluation standard. To address this issue, we propose a novel evaluation method that integrates key metrics such as model performance, computational throughput, and energy efficiency, providing a more comprehensive and reliable assessment of AI hardware computational capabilities. Through benchmark tests on servers, statistical analysis of the method, and case studies in cloud, edge, and terminal environments, this approach not only ensures the accuracy of existing evaluation frameworks but also reveals the key characteristics and potential influencing factors of hardware computational power from multiple dimensions.}, archive = {J_ACCESS}, author = {Yuhuan Huang and Hang Li and Dongdong Lu and Zhe Zhang and Wenjie Tie}, doi = {10.1109/ACCESS.2025.3538977}, journal = {IEEE Access}, pages = {27398-27415}, shortjournal = {IEEE Access}, title = {Intelligence evaluating computational power: A multi-factor method}, volume = {13}, year = {2025}, }
• (2025). Interpretable machine learning models for PISA results in mathematics. ACCESS, 13, 27371-27397. (www)

  @article{ , abstract = {The Program for International Student Assessment (PISA) 2022 provides a global framework for assessing educational performance worldwide. We addressed a principled observation of characteristics and disparities in Mathematics performance among Spanish adolescents while showing the factors influencing these results. To address this question, we proposed using advanced Machine Learning techniques through possibly non-linear predictive models that identify key drivers of Mathematics performance to inform data-driven educational policies and interventions that improve learning outcomes. By preprocessing the PISA dataset, we categorized students into Low, Medium, and High proficiency levels and employed various binary classification models to discern predictive patterns. In addition, a stacking meta-model integrating the strengths of eight distinct predictive models was developed to enhance prediction accuracy. Our results demonstrated that the meta-model outperforms individual models in predicting student performance across various proficiency levels, consistently showing superior metrics in Precision, Recall, and Area Under Curve (AUC) scores. Specifically, the meta-model achieved an AUC score of 0.9766 when classifying students in the Low and High proficiency categories. We adopted the Shapley Additive exPlanations method to demystify the model decisions, highlighting significant predictors such as grade repetition, access to digital devices, and extracurricular Mathematics classes. We also introduced an interactive dashboard, harnessing Uniform Manifold Approximation and Projection for dimensionality reduction and enabling a granular view of the educational landscape. The intention of all this is to contribute to an education system that is well-informed and effectively adapts to the diverse needs of students.}, archive = {J_ACCESS}, author = {Ismael Gómez-Talal and Luis Bote-Curiel and José Luis Rojo-Álvarez}, doi = {10.1109/ACCESS.2025.3538585}, journal = {IEEE Access}, pages = {27371-27397}, shortjournal = {IEEE Access}, title = {Interpretable machine learning models for PISA results in mathematics}, volume = {13}, year = {2025}, }
• (2025). Lifelong person search. ACCESS, 13, 27359-27370. (www)

  @article{ , abstract = {Person search is the task to localize a query person in gallery datasets of scene images. Existing methods have been mainly developed to handle a single target dataset only, however diverse datasets are continuously given in practical applications of person search. In such cases, they suffer from the catastrophic knowledge forgetting in the old datasets when trained on new datasets. In this paper, we first introduce a novel problem of lifelong person search (LPS) where the model is incrementally trained on the new datasets while preserving the knowledge learned in the old datasets. We propose an end-to-end LPS framework that facilitates the knowledge distillation to enforce the consistency learning between the old and new models by utilizing the prototype features of the foreground persons as well as the hard background proposals in the old domains. Moreover, we also devise the rehearsal-based instance matching to further improve the discrimination ability in the old domains by using the unlabeled person instances additionally. Experimental results demonstrate that the proposed method achieves significantly superior performance of both the detection and re-identification to preserve the knowledge learned in the old domains compared with the existing methods.}, archive = {J_ACCESS}, author = {Jae-Won Yang and Seungbin Hong and Jae-Young Sim}, doi = {10.1109/ACCESS.2025.3539927}, journal = {IEEE Access}, pages = {27359-27370}, shortjournal = {IEEE Access}, title = {Lifelong person search}, volume = {13}, year = {2025}, }
• (2025). Fully automated software product line evolution with diverse artifacts. ACCESS, 13, 27325-27358. (www)

  @article{ , abstract = {Existing approaches in software product lines usually neglect knowledge modeling and simulation of the interaction between features capable of bringing dynamism and automation. Consequently, these solutions miss opportunities to resolve associated and emerging problems, including defect detection or quality assurance, which can be solved by effectively extracting and utilizing knowledge from data based on the differences between variants. We bring capabilities to seize them in introducing a fully automated and minimalistic approach to software product line evolution that strictly focuses on handling variability at low-level code fragments. It incorporates the autonomous modeling of emerging knowledge across preconfigured simulations. Specifically, fully automated knowledge-driven software product line evolution provides various views on an existing software product line, its variants, and their evolution through semantic and structural information accompanied by the time and order of the performed changes. We initially developed our approach for software product line evolution, followed by its successful application to the evolution of fractal scripts. We present it accordingly. Fractal products are much simpler because an application state does not span out of recursive behavior, making it manageable within the drawing. Each change is propagated into repetitive phases, causing the visual performance of the implemented feature to be infinitely detailed. Even minor changes in low code fragments tend to manifest as user-visible features owing to recursive behavior, allowing one to massively introduce new features and/or configure existing features and manage variability observable as infinitely detailed shapes. Future applications propose automated observation of more comprehensive in-code representation of feature trees.}, archive = {J_ACCESS}, author = {Jakub Perdek and Valentino Vranić}, doi = {10.1109/ACCESS.2025.3539868}, journal = {IEEE Access}, pages = {27325-27358}, shortjournal = {IEEE Access}, title = {Fully automated software product line evolution with diverse artifacts}, volume = {13}, year = {2025}, }
• (2025). Hybrid model for network traffic anomaly detection based on parallel two-stage feature fusion. ACCESS, 13, 27310-27324. (www)

  @article{ , abstract = {The increasing frequency and complexity of network attacks underscore the need for robust detection models capable of accurately identifying malicious activities in network traffic. In this paper, we propose GRU-ResCBANet, a novel deep learning-based hybrid model for network traffic anomaly detection. The proposed model utilizes a parallel two-stage feature extraction and fusion technique. In the first stage, we enhance the convolutional neural network by introducing residual units and convolutional block attention module to improve spatial and channel feature extraction. In the second stage, gated recurrent units are used to capture temporal dependencies in the network traffic data. The features extracted from both stages are then fused to form a comprehensive representation of the data. Finally, abnormal traffic detection is performed through a fully connected layer, which classifies the network traffic. We evaluate GRU-ResCBANet’s performance on the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets, achieving binary classification accuracies of 99.70%, 99.16%, and 99.83%, and multi-class classification accuracies of 99.69%, 98.23%, and 99.79%, respectively. Comparative analysis with eight other models, as well as models reported in the literature, demonstrates that GRU-ResCBANet offers superior detection performance in both binary and multi-class classification tasks.}, archive = {J_ACCESS}, author = {Changpeng Ji and Huan Liu and Wei Dai}, doi = {10.1109/ACCESS.2025.3538319}, journal = {IEEE Access}, pages = {27310-27324}, shortjournal = {IEEE Access}, title = {Hybrid model for network traffic anomaly detection based on parallel two-stage feature fusion}, volume = {13}, year = {2025}, }
• (2025). Design of an improved model for anomaly detection in CCTV systems using multimodal fusion and attention-based networks. ACCESS, 13, 27287-27309. (www)

  @article{ , abstract = {Traditional approaches for video analysis often misdefine anomalies; they usually rely on single-modality input and have inadequate management of complex temporal patterns. This paper resolves these limitations by proposing a comprehensive scheme for multimodal Closed-Circuit Television (CCTV) video analysis. The utilized techniques in this paper comprise the Multimodal Deep Boltzmann Machine (MDBM), Multimodal Variational Autoencoder (MVAE) and Attention-based Fusion Networks, all of which fully utilize the learned representations. MDBM learns shared representations out of heterogeneous data sources, MVAE captures the inherent distribution of multi-modalities, while the mechanism of attention in fusion networks is done to stress important features. Finally, temporal context is modeled using long short-term memory and transformer networks, temporal convolutional networks and transformer networks with temporal encoding. Long Short-Term Memory (LSTM) can capture long-range dependencies in sequential data, while Temporal Convolutional Network (TCN) efficiently models temporal patterns using convolutional layers and Transformer Networks fathom the relative importance of temporal features against one another through self-attention, thus improving their detection accuracy for anomalies that happen over a long duration. The proposed models also offer good improvements in the performance of anomaly detection. In particular, accuracy improved by 5% using MDBM, the false positive rate reduced by 15% with MVAE, a more than 10% improvement in the F1-score with the attentive fusion network, a 20% reduction in reconstruction error with Deep Convolutional Autoencoder (DCA), detection precision improved by 12% using Adversarially Learned Inference (ALI) and a gain of 8% in Area Under the Curve (AUC) using Deep InfoMax (DIM) operations.}, archive = {J_ACCESS}, author = {V. Srilakshmi and Sai Babu Veesam and Mallu Shiva Rama Krishna and Ravi Kumar Munaganuri and Dulam Devee Sivaprasad}, doi = {10.1109/ACCESS.2025.3536501}, journal = {IEEE Access}, pages = {27287-27309}, shortjournal = {IEEE Access}, title = {Design of an improved model for anomaly detection in CCTV systems using multimodal fusion and attention-based networks}, volume = {13}, year = {2025}, }
• (2025). Impact of driving cycles and terrain on the performance and cost of EV battery chemistries: A comparative analysis and evaluation. ACCESS, 13, 27268-27286. (www)

  @article{ , abstract = {The widespread adoption of electric vehicles (EVs) faces significant cost challenges, primarily driven by battery pack expenses. Key attributes such as energy density, longevity, power density, safety, and cost can vary greatly depending on battery chemistry, including variations in anode, cathode, and electrolyte compositions within the same chemistry, which directly affect vehicle performance, range, and pricing. This study offers a comprehensive comparison of the cost and performance of various battery chemistries, including lithium-ion (Li-ion), sodium-ion (Na-ion), and solid-state batteries (SSBs), in the context of EV applications. The comparison involved configuring entry-level and premium EV batteries with capacities of 42 kWh and 85 kWh, respectively, using standardized cell sizes for each chemistry. Powertrain consumption was simulated under different driving conditions such as highway, urban dynamic, and urban aggressive across various road slopes using MATLAB/Simulink and AVL CRUISETM M software. The results highlight the influence of gravimetric energy density on energy consumption rates, vehicle purchasing cost per mileage considering cycle life, and the impact of terrain types on vehicle range. Notably, our findings indicate that battery chemistries with lower energy density can be a cost-effective alternative, especially in regions with flat terrain.}, archive = {J_ACCESS}, author = {Eymen Ipek and Cagatay Bilgin and Melih Yordem and Yigit Iscanoglu and Murat Yilmaz}, doi = {10.1109/ACCESS.2025.3539877}, journal = {IEEE Access}, pages = {27268-27286}, shortjournal = {IEEE Access}, title = {Impact of driving cycles and terrain on the performance and cost of EV battery chemistries: A comparative analysis and evaluation}, volume = {13}, year = {2025}, }
• (2025). Accurately inferring physical activity levels and sleep from wrist-worn actigraphy recordings with sample rates as low as 10 hz. ACCESS, 13, 27257-27267. (www)

  @article{ , abstract = {Inferring longitudinal Physical Activity (PA) levels and sleep timings from wrist-worn sensors may facilitate personalized insights into day-to-day profile assessments and can be used to monitor a range of physical- and mental-health outcomes, including towards symptom monitoring and rehabilitation. We used the publicly available CAPTURE-24 dataset, comprising 148 participants with ~24-hour concurrent three-dimensional wrist-worn accelerometer data and minute-by-minute labels used as ground truth: sleep, sedentary, light, moderate-vigorous PA. First, we down-sampled the raw accelerometry data to 10 Hz to ensure the generalizability of our methodology across longitudinal studies which typically use similarly low sample rates for actigraphy. Subsequently, we computed four complementary acceleration summary measures and 10 additional smoothened outputs for each acceleration summary measure to derive 44 features characterizing minute-by-minute PA. These features were presented into different classifiers casted as a 4-class classification problem. We trained the model using the first 98 participants and assessed model performance and generalization on the remaining 50 participants. Using a random forest classifier, we demonstrated accurately estimating PA levels and sleep with 87% overall accuracy (F1-score=0.80) including 98.6% correct sleep detection. These findings processing the down-sampled actigraphy data to 10 Hz match or exceed state-of-art results recently reported in the literature achieved using considerably more sophisticated and time-consuming methods (including deep learning) which required actigraphy data sampled at 100 Hz. Collectively, these findings support the deployment of longitudinal, large-scale actigraphy data with sample rates as low as 10 Hz, towards accurately estimating personalized day-to-day PA and sleep profiles in healthcare community studies.}, archive = {J_ACCESS}, author = {Athanasios Tsanas}, doi = {10.1109/ACCESS.2025.3539278}, journal = {IEEE Access}, pages = {27257-27267}, shortjournal = {IEEE Access}, title = {Accurately inferring physical activity levels and sleep from wrist-worn actigraphy recordings with sample rates as low as 10 hz}, volume = {13}, year = {2025}, }
• (2025). Dilated convolution and YOLOv8 feature extraction network: An improved method for MRI-based brain tumor detection. ACCESS, 13, 27238-27256. (www)

  @article{ , abstract = {The extremely low tumor prevalence in the general population makes accurate image-based tumor identification and classification a significant obstacle for medical researchers. Timely therapies and better patient outcomes depend on the capacity to identify cancers in anomalous circumstances. This research seeks to solve the problem by identifying and categorizing brain tumors in difficult cases using deep learning (DL) methods. Hence this paper, Dilated Convolution and YOLOv8 Feature Extraction Network (DC-YOLOv8FEN) is proposed to improve tumor detection accuracy. To begin, the feature extraction network (FEN) is improved with the help of the self shuffle attention (SSA), and the feature map is maximized with the help of vision transformer block. Secondly, a dual feature pyramid network (DFPN) is built to provide more discriminative data for dynamic sparse attention mechanism to extract features from the shallow network and top-down routes to direct the following network modules to fuse features. In addition, the DC-YOLOv8FEN uses a position regression loss function and adds Wise-IoU’s dynamic non-monotonic focus mechanism to make it even better. Based on the experimental results, the proposed method achieved precision of 95.2%, recall rate of 90.5%, accuracy of 99.5% and F1-score of 96.5% which evinces the outstanding performance compared with the existing methods. Radiologists may use this technology for better identification and diagnose brain tumors in real-time medical imaging systems, which improves patient outcomes and reduces diagnostic delays.}, archive = {J_ACCESS}, author = {Lincy Annet Abraham and Gopinath Palanisamy and Veerapu Goutham}, doi = {10.1109/ACCESS.2025.3539924}, journal = {IEEE Access}, pages = {27238-27256}, shortjournal = {IEEE Access}, title = {Dilated convolution and YOLOv8 feature extraction network: An improved method for MRI-based brain tumor detection}, volume = {13}, year = {2025}, }
• (2025). The challenges of driverless mobile vehicles in shared workspace in the industry 5.0 era. ACCESS, 13, 27228-27237. (www)

  @article{ , abstract = {In the era of the Fourth Industrial Revolution and Industry 5.0, driverless material handling technologies are becoming a mainstay of production and distribution facilities and have never been so close to human workers. Laser Guided Vehicles (LGVs) and Autonomous Mobile Robots (AMRs), with open navigation routes, are increasingly being used in different sectors, sharing the same spaces with the workforce. However, this coexistence poses different challenges when autonomous systems must share workspaces and walkways with human workers. The main concerns revolve around the potential safety risks to workers and disruption to operations. In particular, the performance of the driverless mobile system inevitably suffers from sharing the same environment with unpredictable human activities and movements, causing delays, slowdowns, and inefficiencies in the transport system. Although human-robot interaction can be seen as an advantage, if not properly designed and managed, it can lead to several inefficiencies such as production delays due to speed loss, backlogs, and workstation downtime. A new discrete event simulation model is developed here to support the efficient design of an LGV fleet when human-robot coexistence and interaction need to be considered. This model can combine different design parameters such as the number of vehicles and their performances, safe distances, reaction times, pedestrian paths, and the relative frequency of worker crossings. The aim of this work is to provide a graphical tool that can assist the designer of the LGV fleet in the correct definition of both the number of vehicles and the paths, by supporting the correct assessment of the vehicles’ efficiency and their real performance.}, archive = {J_ACCESS}, author = {Nicola Berti and Niloofar Katiraee and Silvia Zuin and Riccardo Aldrighetti and Alessandro Persona and Daria Battini}, doi = {10.1109/ACCESS.2025.3539928}, journal = {IEEE Access}, pages = {27228-27237}, shortjournal = {IEEE Access}, title = {The challenges of driverless mobile vehicles in shared workspace in the industry 5.0 era}, volume = {13}, year = {2025}, }
• (2025). Neural dynamics for computing and automation: A survey. ACCESS, 13, 27214-27227. (www)

  @article{ , abstract = {Neural dynamics have achieved great advancements in the past decade, and find their applications in computing and automation. Although there are some existing survey papers on neural dynamics, to our knowledge, there is no survey covering its applications on computing and automation in the last decade at the same time. Having a clear picture of this aspect would help researchers identify the research gaps in the area of neural dynamics. In this paper, we provide a review of the development of neural dynamics for computing and automation. For each application area, we classify the detailed problems and provide an analysis of the current neural dynamic methods or models. We also identify some interesting research topics and directions for further investigation.}, archive = {J_ACCESS}, author = {Ameer Tamoor Khan and Yufei Wang and Tinglei Wang and Cheng Hua}, doi = {10.1109/ACCESS.2025.3539374}, journal = {IEEE Access}, pages = {27214-27227}, shortjournal = {IEEE Access}, title = {Neural dynamics for computing and automation: A survey}, volume = {13}, year = {2025}, }
• (2025). Bidirectional dielectric barrier discharge (DBD) plasma generator for radar cross section (RCS) reduction of dihedral corner reflector in X-band. ACCESS, 13, 27204-27213. (www)

  @article{ , abstract = {This study proposes a bidirectional dielectric barrier discharge (DBD) plasma generator, based on coplanar electrodes, for radar cross-section (RCS) reduction of a dihedral corner reflector. The bidirectional discharging mode, achieved through a simple modification of the bias circuit, increases the plasma layer thickness from less than 1 mm in the unidirectional mode—confined between the coplanar electrodes—to 4.572 mm in this work, corresponding to the distance between the coplanar electrodes and the bottom plate, which serves as an additional electrode in the bidirectional mode. The increased plasma thickness significantly enhances the interaction between the plasma and incident electromagnetic waves, overcoming the limited RCS reduction performance typically observed with coplanar electrode DBD plasmas, while retaining their simplicity and versatility. Moreover, while previous plasma-based RCS reduction approaches have primarily focused on planar targets, this study applies the method to a three-dimensional dihedral corner reflector with a 90° angle, modeling structures that often dominate the RCS of real-world targets. Experimental results for normal incidence and linear polarization, measured under far-field conditions in the X-band, reveal that the bidirectional discharging mode achieves an additional RCS reduction of up to 9.0 dB compared to the unidirectional mode, resulting in a maximum RCS reduction of 20.9 dB at 9.8 GHz. Additionally, a $46.3\%~10$ -dB RCS reduction bandwidth was achieved, maintaining an RCS reduction above 10 dB across the entire X-band and exceeding 15 dB over the majority of the band. Within the X-band, an average RCS reduction of 16.3 dB was observed. These results demonstrate that the proposed plasma-based technology can not only effectively reduce the RCS of practical geometries but also deliver bandwidth performance that surpasses conventional counterparts.}, archive = {J_ACCESS}, author = {Changseok Cho and Jinwoo Jung and Yongshik Lee and Minsu Choi and Shinjae You and Hyunsoo Lee and Jungje Ha and Cheonyeong Kim}, doi = {10.1109/ACCESS.2025.3539767}, journal = {IEEE Access}, pages = {27204-27213}, shortjournal = {IEEE Access}, title = {Bidirectional dielectric barrier discharge (DBD) plasma generator for radar cross section (RCS) reduction of dihedral corner reflector in X-band}, volume = {13}, year = {2025}, }
• (2025). Biomolecular detection using AlN/β-Ga2O3 MOSHEMT: A machine learning-assisted analytical and simulation framework. ACCESS, 13, 27191-27203. (www)

  @article{ , abstract = {This study explored the potential of an AlN/ $\beta -Ga_{2}O_{3}$ MOSHEMT (Metal Oxide Semiconductor High Electron Mobility Transistor) as a biosensor. An analytical framework was developed to effectively detect biomolecules, with the biosensor operating by modifying electrical parameters in response to changes in the dielectric constant of these biomolecules. The findings demonstrate significant sensitivity to various biomolecules, with Uricase displaying the highest sensitivity. In comparison to the AlGaN/GaN MOSHEMT, the AlN/ $\beta -Ga_{2}O_{3}$ MOSHEMT showed improved sensitivity in terms of drain current. Additionally, the machine learning (ML) model created for this investigation correlates strongly with the simulation results. It can accurately predict outcomes within its trained range. Implementing the ML model leads to considerable reductions in both computational costs and time for similar simulations. Furthermore, it eliminates empirical adjustments typically required in traditional physics-based models.}, archive = {J_ACCESS}, author = {Aishwarya Tomar and Apurba Chakraborty and Rahul Kumar}, doi = {10.1109/ACCESS.2025.3538041}, journal = {IEEE Access}, pages = {27191-27203}, shortjournal = {IEEE Access}, title = {Biomolecular detection using AlN/β-Ga2O3 MOSHEMT: A machine learning-assisted analytical and simulation framework}, volume = {13}, year = {2025}, }
• (2025). Broadband multilayer absorber design using double-stage cognitive seeker optimization algorithm. ACCESS, 13, 27178-27190. (www)

  @article{ , abstract = {In this paper, a double-stage cognitive seeker optimization (DSC-SO) algorithm is developed for evaluating optimum design parameters of broadband microwave absorbers. The algorithm not only gives freedom of selecting different number of layers and optimizing the order of layers and their thickness in different stages (double-stage) but also utilizes a cognitive search to improve the optimization. Contributions of the cognitive search and the two stages of our algorithm are first examined to show its efficiency. Then, its performance has been evaluated for two different optimization problems (a broadband absorber for one incidence angle and a broadband absorber with multiple incidence angles) in comparison with similar algorithms in the literature. It has been shown that our algorithm determines either fewer number of layers or smaller overall absorber thickness or both when compared with the results of other algortihms. Finally, the effect of fabrication tolerance is conducted to examine the importance of fewer number of layers.}, archive = {J_ACCESS}, author = {Ugur Cem Hasar and Yunus Kaya and Huseyin Korkmaz and Teodor Iliev}, doi = {10.1109/ACCESS.2025.3537661}, journal = {IEEE Access}, pages = {27178-27190}, shortjournal = {IEEE Access}, title = {Broadband multilayer absorber design using double-stage cognitive seeker optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). CutMAA: Motion-aware data augmentation for light field super-resolution. ACCESS, 13, 27167-27177. (www)

  @article{ , abstract = {Light field super-resolution (SR) is a task that aims to enhance the spatial resolution of light field images by utilizing information from multiple sub-aperture images (SAIs). While deep learning-based methods have demonstrated impressive performance, their application is often hindered by the lack of sufficient training data. To address this challenge, we propose CutMAA, a motion-aware data augmentation (DA) specialized for light field SR. Existing DA methods for light field SR do not consider the spatial-angular correlation inherent in light fields. By contrast, CutMAA leverages motion information to effectively incorporate such correlation. CutMAA calculates the motion difference between the central SAI and others, performing a warping process to align the pixel positions of each SAI accordingly, resulting in warped SAIs. From these warped SAIs, patches are extracted, blended, and then pasted into the light field at other resolutions. Compared to the previous DAs, our method significantly enhances the light field SR performance. Since CutMAA can be seamlessly integrated into existing frameworks, this ensures broad applicability across various light field SR scenarios.}, archive = {J_ACCESS}, author = {Sojin Yun and Namhyuk Ahn and In Kyu Park}, doi = {10.1109/ACCESS.2025.3539920}, journal = {IEEE Access}, pages = {27167-27177}, shortjournal = {IEEE Access}, title = {CutMAA: Motion-aware data augmentation for light field super-resolution}, volume = {13}, year = {2025}, }
• (2025). Precise assimilation prediction of short-term and long-term maize irrigation water based on EnKF-DSSAT and fuzzy optimization-DSSAT models. ACCESS, 13, 27150-27166. (www)

  @article{ , abstract = {With the progress of information technology, precision irrigation technology has become the core of modern agriculture. In particular, technologies such as Internet of Things (IoT), Big Data and Artificial Intelligence (AI) have provided strong support for the intelligence of agricultural production. This paper focuses on the precise prediction of irrigation water use in the maize industry through Ensemble Kalman Filter (EnKF) and fuzzy optimization methods combined with the DSSAT (Decision Support System for Agrotechnology Transfer) model. We use the remote sensing data of land moisture and leaf area in the Yellow Huaihai Plain provided by Google Earth Engine (GEE), as well as the maize market data released by the Ministry of Agriculture (MOA), to make predictions through the EnKF-DSSAT and fuzzy optimization-DSSAT models. The results showed that these models achieved high accuracy of 98.11% and 97.78% in short-term and long-term forecasts, respectively, which were significantly better than the traditional models. We also introduce a Boltzmann machine-based fusion algorithm to improve the model convergence speed and prediction accuracy. Ultimately, this paper verifies the important influence of policy factors on long-term irrigation prediction and proposes an adaptive prediction model and policy recommendations, which provide innovative methods and technical support for the implementation of precision irrigation technology.}, archive = {J_ACCESS}, author = {Yanshu Yu and Youxi Luo and Xinhang Wang and Xinran Wang and Chaozhu Hu}, doi = {10.1109/ACCESS.2025.3539775}, journal = {IEEE Access}, pages = {27150-27166}, shortjournal = {IEEE Access}, title = {Precise assimilation prediction of short-term and long-term maize irrigation water based on EnKF-DSSAT and fuzzy optimization-DSSAT models}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable multibeam holographic antenna based on liquid-crystal technology for ka-band LEO satellite application. ACCESS, 13, 27141-27149. (www)

  @article{ , abstract = {The requirements of low Earth orbit (LEO) satellite systems for high-precision and selective multiple coverage, have driven much interest and demand for millimeter-wave multibeam antennas. However, the key challenge is to provide fully independent control of each beam with a single source, and metasurfaces have paved the way to solve this problem. Here, we present a reconfigurable multibeam holographic antenna (HA) at 36 GHz. In particular, we used liquid crystals (LC) to implement a low power consumption and lightweight antenna. Each unit cell is selectively driven by connected bias lines according to the calculated holographic pattern. Consequently, the proposed multibeam HA can radiate in the desired direction and control each beam independently. Furthermore, the gain difference in each beam can be corrected by the amplitude coefficient to radiate with nearly equal gain in each direction. The proposed HA demonstrated the scanning range of 60° at 36 GHz, with a peak gain of 8.7 dBi. Additionally, the peak directivity is 12.7 dBi, the 3-dB bandwidth is about 1 GHz at the center frequency of 36 GHz, and the operating bandwidth is 2.8 GHz ( $34.8-37.6$ GHz).}, archive = {J_ACCESS}, author = {Youngin Yoo and Jinki Park and Changjae Lee and Dong Ki Yoon and Seong-Ook Park}, doi = {10.1109/ACCESS.2025.3538044}, journal = {IEEE Access}, pages = {27141-27149}, shortjournal = {IEEE Access}, title = {Reconfigurable multibeam holographic antenna based on liquid-crystal technology for ka-band LEO satellite application}, volume = {13}, year = {2025}, }
• (2025). On monitoring of the shape parameter of the inverse gaussian distribution via memoryless chart under bayesian setup. ACCESS, 13, 27126-27140. (www)

  @article{ , abstract = {The Inverse Gaussian distribution (IGD) is a probability distribution with several applications in various fields. The proposed study is prone to consider the problem of monitoring the shape parameter of IGD using the Bayesian method. The Bayesian setup effectively handles the situation in the manufacturing industry where parametric uncertainty is unavoidable. In this study, we focus on determining the monitoring threshold for the shape parameter of the IGD in phase-II and designing Shewhart control charts based on classical and Bayesian methods under different loss functions (LFs). The influence of hyperparameters on upper control limits (UCLs) has been explored. The evaluation is based on different performance measures such as average run length (ARL), the standard deviation of run length (SDRL), and the median of run length (MDRL). The simulation study illustrates the effectiveness of the proposed Bayesian Shewhart charts compared to the classical Shewhart chart. The suggested approach for Shewhart charts proves highly effective in promptly identifying shape parameter shifts and outperforming their classical counterpart in detecting faults quickly. Using a real dataset, the proposed technique is also exemplified, and the findings validate the conclusions based on simulative results.}, archive = {J_ACCESS}, author = {Tahir Abbas and Amara Javed and Nasir Abbas and Muhammad Abid}, doi = {10.1109/ACCESS.2025.3538905}, journal = {IEEE Access}, pages = {27126-27140}, shortjournal = {IEEE Access}, title = {On monitoring of the shape parameter of the inverse gaussian distribution via memoryless chart under bayesian setup}, volume = {13}, year = {2025}, }
• (2025). FastGEMF: Scalable high-speed simulation of stochastic spreading processes over complex multilayer networks. ACCESS, 13, 27112-27125. (www)

  @article{ , abstract = {Predicting stochastic spreading processes across large-scale multi-layered networks remains a significant computational challenge due to the intricate interplay between network structure and spread dynamics. This study introduces FastGEMF, a novel, scalable simulation framework for exact, high-speed modeling of Markov chain processes on complex multi-layer networks. Inspired by the Gillespie algorithm and optimized for efficiency, FastGEMF achieves logarithmic time complexity per event, enabling simulations on networks with millions of nodes and edges without sacrificing accuracy. It introduces an event-driven algorithm with cautious update strategies, supporting diverse multi-compartment spreading processes. FastGEMF is implemented in Python programming language as an open-source package, providing accessibility to researchers and practitioners across domains such as epidemiology, cybersecurity, and information propagation, establishing an exact baseline for model validation and comparative analysis.}, archive = {J_ACCESS}, author = {Mohammad Hossein Samaei and Faryad Darabi Sahneh and Caterina Scoglio}, doi = {10.1109/ACCESS.2025.3539345}, journal = {IEEE Access}, pages = {27112-27125}, shortjournal = {IEEE Access}, title = {FastGEMF: Scalable high-speed simulation of stochastic spreading processes over complex multilayer networks}, volume = {13}, year = {2025}, }
• (2025). Catalyzing sustainable energy transition in oman: A peer-to-peer energy trading model with integrated GHG emission mitigation strategies. ACCESS, 13, 27098-27111. (www)

  @article{ , abstract = {Exploring the transformative potential of a peer-to-peer (P2P) energy trading model, this study examines how integrating greenhouse gas (GHG) emission mitigation strategies can facilitate a sustainable energy transition in Oman. This paper proposes a new P2P energy trading framework incorporating GHG credits to enable direct energy trading between prosumers and consumers in a decentralized network without intermediaries. The proposed framework prioritizes energy trading between prosumers based on their GHG credits. It employs the non-dominated sorting genetic algorithm-II (NSGA-II) to determine the energy and GHG selling prices by optimizing three objective functions: maximizing prosumers’ profits, minimizing consumers’ costs, and reducing the penalty associated with prosumers’ power loss factor (PLF) due to their use of the grid network. The NSGA-II algorithm constraints are investment-driven, with energy cost limits based on PV investment payback periods and GHG allowance cost limits derived from international GHG trading data. This approach ensures the social welfare of all participants while accounting for system losses incurred by transacting peers. The proposed framework was tested in different case studies on the IEEE 14-bus system with three prosumers and eight consumers by using data in Oman grid. The proposed framework concept offers a practical approach to energy trading that considers various operating conditions and potential losses, ensuring more efficient and resilient transactions within diverse market environments.}, archive = {J_ACCESS}, author = {Tariq Al-Abri and Ahmet Onen and Muhammad Waseem Khan and Rashid Al-Abri and Ahmed Salim Alrawahi and Saleh Al-Saadi}, doi = {10.1109/ACCESS.2025.3538943}, journal = {IEEE Access}, pages = {27098-27111}, shortjournal = {IEEE Access}, title = {Catalyzing sustainable energy transition in oman: A peer-to-peer energy trading model with integrated GHG emission mitigation strategies}, volume = {13}, year = {2025}, }
• (2025). Residual energy maximization in RIS aided cooperative spectrum sensing with PUEA: Relative performance in PS and TS mode. ACCESS, 13, 27081-27097. (www)

  @article{ , abstract = {In this work, a performance comparison on radio frequency (RF) energy harvesting (EH) in power splitting (PS) and time switching (TS) modes in reconfigurable intelligent surfaces (RIS) aided cooperative spectrum sensing (CSS) is made. CSS model considers multiple primary users (PUs), however, one of them seems to be operative or not at a time along with a single PU emulation attacker (PUEA) node. Time frame comprises of sensing-reporting slots in CSS accompanied by EH. A distant dependent model of channel gain in RIS reflector is developed for calculating the harvested residual energy (RE). The primary objective is to maximize the total RE while meeting a predefined detection and false alarm probabilities of PU along with the individual secondary user’s (SU’s) energy causality constraint. The optimal values of sensing duration, power splitting factor (in PS) and amplifying gain are calculated through Lagrange method. Simulation results show the efficacy of the proposed work due to RIS on total RE highlighting a gain of 45% and 38.97% for PS and TS mode compared to the existing work while maintaining the above mentioned constraints. Performances of RE with the change in the placement of RIS near/far to PU (in presence or absence of PUEA) and the same near/far to PUEA are analyzed for both PS and TS mode. If PU transmit power is increased, the maximum value of RE for PS mode (both in presence and absence of PUEA) is achieved when RIS is placed near to the SU transmitter. The maximum value of RE for both PS and TS modes are found to be at the middle when RIS is moved from the latter position to nearer to the PUEA.}, archive = {J_ACCESS}, author = {Avik Banerjee and Santi P. Maity and Veerapu Goutham}, doi = {10.1109/ACCESS.2025.3539766}, journal = {IEEE Access}, pages = {27081-27097}, shortjournal = {IEEE Access}, title = {Residual energy maximization in RIS aided cooperative spectrum sensing with PUEA: Relative performance in PS and TS mode}, volume = {13}, year = {2025}, }
• (2025). Voltage compensation method for long-distance distribution networks with scattered loads based on the cooperative operation of DVR and distributed power sources. ACCESS, 13, 27070-27080. (www)

  @article{ , abstract = {The Dynamic Voltage Restorer (DVR) is an energy storage-based series compensation device that can provide voltage compensation for loads when the voltage in the power grid is out of limits. To address temporary voltage deviations in the distribution network caused by long-distance lines, fluctuating renewable energy sources, or sudden load increases, a two-layer voltage compensation model for the distribution network is established. This model combines the DVR with distributed power sources within the network. The upper layer aims to minimize the output real power of the DVR, utilizing semi-definite programming. Meanwhile, the lower layer aims to minimize network losses in the distribution network. The lower optimization model is converted into a convex form using second-order cone programming for branch flow equations and is solved by partitioning. Each partition only obtains boundary information from adjacent partitions and performs distributed solving using the alternating direction method of multipliers (ADMM). Finally, the effectiveness of this method is verified on a real distribution system in China and the IEEE 33-bus system.}, archive = {J_ACCESS}, author = {Yukun Qiu and Jianyong Zhao and Heng Nian}, doi = {10.1109/ACCESS.2025.3534844}, journal = {IEEE Access}, pages = {27070-27080}, shortjournal = {IEEE Access}, title = {Voltage compensation method for long-distance distribution networks with scattered loads based on the cooperative operation of DVR and distributed power sources}, volume = {13}, year = {2025}, }
• (2025). Attention enhanced InceptionNeXt-based hybrid deep learning model for lung cancer detection. ACCESS, 13, 27050-27069. (www)

  @article{ , abstract = {Lung cancer is the most common cause of cancer-related mortality globally. Early diagnosis of this highly fatal and prevalent disease can significantly improve survival rates and prevent its progression. Computed tomography (CT) is the gold standard imaging modality for lung cancer diagnosis, offering critical insights into the assessment of lung nodules. We present a hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs). By optimizing and integrating grid and block attention mechanisms with InceptionNeXt blocks, the proposed model effectively captures both fine-grained and large-scale features in CT images. This comprehensive approach enables the model not only to differentiate between malignant and benign nodules but also to identify specific cancer subtypes such as adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. The use of InceptionNeXt blocks facilitates multi-scale feature processing, making the model particularly effective for complex and diverse lung nodule patterns. Similarly, including grid attention improves the model’s capacity to identify spatial relationships across different sections of the picture, whereas block attention focuses on capturing hierarchical and contextual information, allowing for precise identification and categorization of lung nodules. To ensure robustness and generalizability, the model was trained and validated using two public datasets, Chest CT and IQ-OTH/NCCD, employing transfer learning and pre-processing techniques to improve detection accuracy. The proposed model achieved an impressive accuracy of 99.54% on the IQ-OTH/NCCD dataset and 98.41% on the Chest CT dataset, outperforming state-of-the-art CNN-based and ViT-based methods. With only 18.1 million parameters, the model provides a lightweight yet powerful solution for early lung cancer detection, potentially improving clinical outcomes and increasing patient survival rates.}, archive = {J_ACCESS}, author = {Burhanettin Ozdemir and Emrah Aslan and Ishak Pacal}, doi = {10.1109/ACCESS.2025.3539122}, journal = {IEEE Access}, pages = {27050-27069}, shortjournal = {IEEE Access}, title = {Attention enhanced InceptionNeXt-based hybrid deep learning model for lung cancer detection}, volume = {13}, year = {2025}, }
• (2025). A faster, integrated, and trusted certificate authentication and issuer validation system based on blockchain. ACCESS, 13, 27037-27049. (www)

  @article{ , abstract = {Verifying the legitimacy of original documents such as educational degree certificates is crucial. If these are found to be fraudulent, it can cause significant disruptions in the hiring process, resulting in substantial productivity losses. The researchers suggested several proposals to preserve these certificates. However, the challenge is still to have an integrated, tamper-proof and low-cost solution where the certificate issuer and the certificate itself are validated in a single platform. This paper proposes an integrated solution that uses a decentralized blockchain-based certificate verification and issuer validation system. In addition to this, it will protect the certificates from being tampered with. To search faster, hash function mapping has been employed. The proposed solution is experimentally validated by creating a blockchain network using Ethereum where each peer node represents an entity of a certificate verification system such as a validator, certificate issuer, certificate holder and the end-user of the client. The performance of the designed solution is measured by the execution and transaction cost in terms of gas consumption. A comparative analysis has been performed on similar types of tasks reported in the existing work performed on the same platform. It has been observed that the cost incurred for adding a certificate is minimal for the proposed approach. Furthermore, the searching time for the certificates is minimized by using a hash-based searching methodology. The results show that the search time has drastically gone down when certificates are not available.}, archive = {J_ACCESS}, author = {Rojalina Priyadarshini and Rhishav Pandey and K. C. Ankit and Deepesh Bhandari and Birendra Khadka and Rabindra Kumar Barik and Manob Jyoti Saikia}, doi = {10.1109/ACCESS.2025.3539180}, journal = {IEEE Access}, pages = {27037-27049}, shortjournal = {IEEE Access}, title = {A faster, integrated, and trusted certificate authentication and issuer validation system based on blockchain}, volume = {13}, year = {2025}, }
• (2025). Reconstruction and classification of brain strokes using deep learning-based microwave imaging. ACCESS, 13, 27024-27036. (www)

  @article{ , abstract = {Prompt identification of the type of brain stroke is a pivotal measure for medical practitioners in commencing therapeutic interventions for patients afflicted with stroke. In this paper, our purpose is to diagnose the type of stroke using high-quality images. This paper presents a novel methodology for image reconstruction using U-Net, followed by the classification of brain stroke type through a deep convolutional neural network. To achieve the intended objective, the initial step involves the utilization of the distorted Born iterative technique, employing a suitable initial guess, and a proper generalized Tikhonov regularization approach. The quality of the reconstructed images is high but not enough to correctly classify the type of brain stroke. To further enhance their quality, a U-Net is employed, which is trained using the reconstructed images obtained in the first step. The quality of the images results from U-Net is very high, but in a few situations, the brain stroke cannot be identified in the reconstructed images. So we propose a new deep convolutional neural network to classify the type of brain stroke, utilizing images reconstructed by U-Net. The simulations are conducted in two dimensions and across three levels of signal-to-noise ratio for the scattered field, specifically at 30, 20, and 10 dB. The results obtained indicate a significantly high rate of detection in identifying the type of strokes in all signal-to-noise ratios.}, archive = {J_ACCESS}, author = {Sayyed Saleh Sayyed Mousavi and Mohammad Saeed Majedi}, doi = {10.1109/ACCESS.2025.3538773}, journal = {IEEE Access}, pages = {27024-27036}, shortjournal = {IEEE Access}, title = {Reconstruction and classification of brain strokes using deep learning-based microwave imaging}, volume = {13}, year = {2025}, }
• (2025). A scalable 16-element and 45.5 dBm-EIRP D-band CMOS phased-array transceiver integrated with antenna-in-package for 6G communications. ACCESS, 13, 27007-27023. (www)

  @article{ , abstract = {This paper presents a D-band 16-element phased-array transceiver IC with integrated dual-polarized antenna for 6G wireless communications. Fabricated in 28-nm CMOS, the transceiver operates with five component carriers (5CCs) supporting carrier aggregation in the 138–150 GHz range with 12 GHz bandwidth. The transceiver implements various circuit techniques to overcome the $f_{T}$ limitations of 28-nm CMOS, including differential amplifiers with cross-coupled capacitors and transformer-based inter-stage matching networks. The TX achieves a maximum EIRP of 45.5 dBm with saturated output power and demonstrates EVMs of 5.34-7.2% for 64-QAM signals at 30 Gbps. The RX exhibits less than 8 dB noise figure and achieves EVMs of 5.47-7.21% for 64-QAM signals at 24 Gbps. In over-the-air link testing at 2 m distance, the system achieves less than 4% EVM with both 64- and 256-QAM OFDM signals, representing the best performance reported to date for D-band CMOS implementations. The transceiver IC occupies 29.7 mm2 die area and consumes 3,681 mW and 747 mW for TX and RX operation, respectively.}, archive = {J_ACCESS}, author = {Hyohyun Nam and Hyeongjin Kim and Sungjae Oh and Jungsik Kim and Chae Jun Lee and Meeran Kim and Hee Sung Lee and Seung Hun Kim and Jaekwang Kwon and Kyeongho Yeom and Dongjin Jung and Dongki Kim and Seong-Kyun Kim and Dae Young Lee}, doi = {10.1109/ACCESS.2025.3539380}, journal = {IEEE Access}, pages = {27007-27023}, shortjournal = {IEEE Access}, title = {A scalable 16-element and 45.5 dBm-EIRP D-band CMOS phased-array transceiver integrated with antenna-in-package for 6G communications}, volume = {13}, year = {2025}, }
• (2025). Improved efficiency and reliability of a single-stage solar water pumping system. ACCESS, 13, 26991-27006. (www)

  @article{ , abstract = {The solar water pumping systems (SWPS) are the most promising solutions for water problems as they can make agriculture economical and sustainable. This paper introduces an efficient, cost-effective, and simple structure single-stage standalone SWPS that incorporates the perovskite solar-cells (PSCs) and interior permanent magnet synchronous motor (IPMSM). First, the proposed SWPS is selected and designed step by step. The degradation of solar cells (about 1% per year) and the temperature effects are considered within design stage. Second, an improved control strategy is introduced to achieve the maximum power point tracking (MPPT) form PV panels in addition to the best motor efficiency using field-oriented control (FOC) and maximum torque per ampere (MTPA) control strategies. The degradation effects are considered within the proposed control strategy. The proposed system is built and investigated using MATLAB/Simulink environment. The results revealed the superiority of the proposed system technically and economically. The results showed that the performance and dynamics of MPPT algorithm depends mainly on dc capacitance. The proper gain design of voltage controller can minimize the variations of PV power in steady state conditions. The proposed control strategy can reach maximum power point (MPP) within a time of 1.4 sec including the starting time of motor that is totally suits SWPS.}, archive = {J_ACCESS}, author = {Mahmoud Hamouda and Hanaa Elsherbiny and Alaa A. Zaky and Ismoil Odinaev and Vladimír Bureš and Amir Abdel Menaem}, doi = {10.1109/ACCESS.2025.3538333}, journal = {IEEE Access}, pages = {26991-27006}, shortjournal = {IEEE Access}, title = {Improved efficiency and reliability of a single-stage solar water pumping system}, volume = {13}, year = {2025}, }
• (2025). An explainable deep learning framework for sorghum weed classification using multi-scale feature enhanced DenseNet. ACCESS, 13, 26973-26990. (www)

  @article{ , abstract = {Sorghum is a vital crop known for its resilience in harsh environmental conditions, playing a key role in food security and bioenergy production across the globe. Accurate sorghum weed classification is crucial for efficient agricultural management, yet traditional methods are labour-intensive and resource-heavy. This research presents the first work on the sorghum weed classification dataset, introducing a novel deep-learning model using a customised DenseNet-169, enhanced with two innovative feature extraction blocks: Dimensional Convolutional Feature Fusion (DCFF) and Deformable Multi-Kernel Channel Fusion (DMKCF). These blocks improve multi-scale feature extraction and adaptively capture complex patterns from the weed images. The model also integrates Triplet Attention (TA) to refine feature representations and employs Grad-CAM and LIME for visual interpretability, offering insights into the classification process. The proposed network, when tested on the SorghumWeedDataset_Classification, achieved a testing accuracy of 99.07%, surpassing several state-of-the-art CNN models. The proposed model provides a robust solution for sorghum weed classification, advancing agricultural monitoring and management practices. Additionally, the model achieved 100% accuracy for the broad leaf weed class, further underscoring its effectiveness in identifying and classifying sorghum weeds with absolute precision.}, archive = {J_ACCESS}, author = {Armaano Ajay and S. Sandosh and Aryan Saji and Harsh Agarwal}, doi = {10.1109/ACCESS.2025.3538937}, journal = {IEEE Access}, pages = {26973-26990}, shortjournal = {IEEE Access}, title = {An explainable deep learning framework for sorghum weed classification using multi-scale feature enhanced DenseNet}, volume = {13}, year = {2025}, }
• (2025). Application of temporal action detection technology in abnormal event detection of surveillance video. ACCESS, 13, 26958-26972. (www)

  @article{ , abstract = {By detecting abnormal violation event in surveillance videos, the safety management capabilities in high-risk power operations can be improved. This research constructs an intelligent abnormal event detection technology using deep learning algorithms, aiming to improve the detection accuracy of anomaly event. This research improves the parameter setting method and fully connected layer of three-dimensional convolutional networks to enhance their ability to recognize three-dimensional features. An improved algorithm is adopted as the basic structure of temporal action detection technology. Frame interpolation is applied to improve the accuracy of temporal action detection. A monitoring video anomaly event detection model based on the improved temporal action detection technology is established. The experiment outcomes show that the improved three-dimensional convolutional network achieves convergence after 32 iterations, with an accuracy of 99.15% and a recall rate of 98.3%. The average accuracy of the three datasets tested is better than other algorithms. The average precision of the research model for detecting throwing objects from high altitude, crossing fences, smoking, and checking electricity without gloves are 89.1%, 88.9%, 96.6%, and 96.2%, respectively. The accuracy for abnormal event detection of different time periods is superior to other models. The average recall value of the research model is 94.3%, which is higher than other models. The results indicate that the research model has the capacity to accurately recognize abnormal events in massive, diverse, and complex surveillance videos. The abnormal event detection model proposed in the study can be applied to the intelligent management platform of the power industry, thereby improving the safety management capability in power operations.}, archive = {J_ACCESS}, author = {Chenxiang Lin and Teng Ma and Fei Wu and Jian Qian and Feilong Liao and Jinaye Huang}, doi = {10.1109/ACCESS.2025.3533552}, journal = {IEEE Access}, pages = {26958-26972}, shortjournal = {IEEE Access}, title = {Application of temporal action detection technology in abnormal event detection of surveillance video}, volume = {13}, year = {2025}, }
• (2025). Trajectory prediction and intention recognition based on CNN-GRU. ACCESS, 13, 26945-26957. (www)

  @article{ , abstract = {Accurate trajectory prediction and intention recognition are essential for effective decision-making, as different intents manifest in distinct trajectory patterns. This study focuses on trajectory prediction and intention recognition for airborne moving targets by proposing an integrated approach that leverages trajectory data for both tasks. Separate models were developed for trajectory prediction and intention recognition, with the trajectory prediction outcomes subsequently applied to enhance the accuracy of intention recognition. The proposed multi-channel parallel GRU-CNN neural network combines the temporal analysis capabilities of GRU with the spatial feature extraction strengths of CNN through a weight allocation strategy. This model achieved a notable performance, with a Mean Absolute Error (MAE) not exceeding 6.877. Additionally, a trajectory sequence similarity assessment system was constructed by incorporating Dynamic Time Warping (DTW), Hausdorff Distance (HD), and rate of altitude change, facilitating the classification of trajectories with similar intents. The integration of trajectory prediction into intention recognition significantly improved accuracy, as demonstrated by experimental results showing an accuracy rate exceeding 82%. These findings validate the effectiveness of the proposed method and offer an innovative technical solution for intention recognition of airborne moving targets.}, archive = {J_ACCESS}, author = {Jinghao Du and Dongdong Lu and Fei Li and Ke Liu and Xiaolan Qiu}, doi = {10.1109/ACCESS.2025.3539931}, journal = {IEEE Access}, pages = {26945-26957}, shortjournal = {IEEE Access}, title = {Trajectory prediction and intention recognition based on CNN-GRU}, volume = {13}, year = {2025}, }
• (2025). A formal verification library design for behavioral refinement of CompCert clight. ACCESS, 13, 26927-26944. (www)

  @article{ , abstract = {As computer systems become increasingly ubiquitous in our daily lives, ensuring the safety and reliability of system software has become a major concern across various fields of computer science. Among the various methodologies for ensuring software correctness, formal verification using interactive theorem provers is recognized as the method providing the strongest assurance of formal guarantees with a minimal trusted computing base. While many verification frameworks in interactive theorem provers adopt separation logic, there is another paradigm that has not been as extensively studied in this context: behavioral refinement, despite its effectiveness being demonstrated in several large-scale software verification projects. In this work, we develop a concrete theory of behavioral refinement for verifying C programs against abstract specifications. For the formal C semantics, we employ the Clight language provided by the CompCert verified compiler project. We use interaction trees (ITrees) as the representation framework for abstract specifications. We have implemented our approach as a C-program verification library, C2IRef, in the Coq theorem prover, and demonstrated its effectiveness by verifying several practical C programs.}, archive = {J_ACCESS}, author = {Yoonseung Kim}, doi = {10.1109/ACCESS.2025.3539584}, journal = {IEEE Access}, pages = {26927-26944}, shortjournal = {IEEE Access}, title = {A formal verification library design for behavioral refinement of CompCert clight}, volume = {13}, year = {2025}, }
• (2025). ROI-based multimodal neuroimaging feature fusion method and its graph neural network diagnostic model. ACCESS, 13, 26915-26926. (www)

  @article{ , abstract = {Single-modality neuroimaging data often provide limited information and are constrained by technical issues such as signal-to-noise ratio, and resolution limitations, potentially leading to biases and an incomplete understanding of brain complexities. This can hinder the development of diagnostic and therapeutic strategies for brain disorders. To address these challenges, this paper presents the Multimodal Graph Neural Network Model based on Feature Fusion (MMP-DGNN), which leverages sMRI and PET data. The model employs an algorithm to extract and accurately describe sample features using an autoencoder. During feature fusion, a shared adjacency matrix based on feature similarity and phenotypic data is constructed for graph representation. A dual-layer graph neural network then classifies the features, with the results fused at the decision layer for final classification. Experimental results show that MMP-DGNN achieves superior classification performance of 98.17%, outperforming other methods in multimodal neuroimaging data classification.}, archive = {J_ACCESS}, author = {Xuan Wang and Xiaopeng Yang and Xiaotong Zhang and Yang Chen}, doi = {10.1109/ACCESS.2024.3435433}, journal = {IEEE Access}, pages = {26915-26926}, shortjournal = {IEEE Access}, title = {ROI-based multimodal neuroimaging feature fusion method and its graph neural network diagnostic model}, volume = {13}, year = {2025}, }
• (2025). Unified security framework using device-specific fingerprint: Mitigating hardware trojans and authenticating firmware updates. ACCESS, 13, 26897-26914. (www)

  @article{ , abstract = {System-on-Chips (SoCs) are ubiquitously used as smart devices with heterogeneous components embedded within them. The effective functioning of a system depends on Intellectual Property (IP) and firmware updates, which enhance performance and reliability and rectify vulnerabilities throughout the lifespan of any SoC. However, malicious logic intrusion by intruders can compromise the system’s efficacy at the design phase of Internet of Things (IoT) applications. The proposed robust unified security framework efficiently streamlines the identification of hardware Trojans and secure firmware updates of the IPs in all intricate and heterogeneous SoCs without the need for the golden reference model. In the post-silicon validation technique, the authentication protocol utilizes the device-specific fingerprint of a Physical Unclonable Function (PUF) to authenticate the IPs. An enhanced Balanced Anderson Arbiter PUF (BAA-PUF) integrating two sources of randomness is physically implemented in ZCU104 MPSoC to generate 256 bits. The parameters of BAA-PUF are determined by majority voting and are close to the ideal value. The average execution time for identifying the Trojan with the proposed framework is found to be 40.184 ms. Two metrics have been introduced to demonstrate the efficacy of the proposed framework, namely, utilization scale, which illustrates the impact of Trojans on the resource utilized and digest uniqueness, which demonstrates the presence of Trojans and ranges from 45.3% to 55%. The performance of the authentication protocol is also validated based on computation and communication costs. The authentication protocol is analyzed using the formal technique, employing Burrows–Abadi–Needham (BAN) logic and informal methods to confirm its robustness against hardware-based attacks.}, archive = {J_ACCESS}, author = {S Hemavathy and Kokila Jagadeesh and V. S. Kanchana Bhaaskaran}, doi = {10.1109/ACCESS.2025.3538936}, journal = {IEEE Access}, pages = {26897-26914}, shortjournal = {IEEE Access}, title = {Unified security framework using device-specific fingerprint: Mitigating hardware trojans and authenticating firmware updates}, volume = {13}, year = {2025}, }
• (2025). Boosting cervical cancer prediction leveraging a hybrid FT-transformer model. ACCESS, 13, 26876-26896. (www)

  @article{ , abstract = {Cervical Cancer (CC) remains a significant threat to women’s health, despite being largely preventable, and is a leading cause of mortality worldwide. Early and accurate prediction is crucial for timely treatment and improved survival rates, particularly given the diverse risk factors contributing to CC development. To address this critical clinical need, we propose an innovative Hybrid FT-Transformer model that synergistically integrates a Feature Tokenization (FT) Transformer with Depthwise convolutional neural networks and Long Short-Term Memory (LSTM) networks for precise CC prediction. Explainable Artificial Intelligence (XAI) tools, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), provide insights into the model’s decision-making process, aiding in validation by clinicians. Trained on a UCI repository dataset focusing on Hinselmann, Biopsy, and Schiller outcomes, our proposed model achieved outstanding accuracies: 99.09% for Hinselmann, 98.90% for Biopsy, and 98.75% for Schiller, using 10-fold cross-validation. These results highlight the model’s superiority over current state-of-the-art approaches, with significant potential to enhance CC screening and early diagnosis, ultimately improving patient outcomes.}, archive = {J_ACCESS}, author = {Md Emamul Hossen and S. M. Mahim and Shakib Al Hasan and Md Khairul Islam and Md Shohidul Islam and Salahuddin Khan and Mohammad Alibakhshikenari and Peiman Parand and Md Sipon Miah}, doi = {10.1109/ACCESS.2025.3538566}, journal = {IEEE Access}, pages = {26876-26896}, shortjournal = {IEEE Access}, title = {Boosting cervical cancer prediction leveraging a hybrid FT-transformer model}, volume = {13}, year = {2025}, }
• (2025). Real-time performance evaluation for 5G multi-link communication in industrial application. ACCESS, 13, 26864-26875. (www)

  @article{ , abstract = {The rapid advancement of network communication technologies has unlocked significant potential for advanced manufacturing industries. This new wave of industrial transformation, known as the Industry 4.0 era, is characterized by the integration of cutting-edge technologies such as Fifth Generation mobile communication technology (5G). Experimental commercial 5G deployments in factories have been reported, but these applications are generally limited to specific use cases. At present, there are no foundational standards for 5G in production lines, and its industrial adoption remains in the exploratory phase. This motivates an in-depth investigation into the potential of 5G for practical applications in production workshops, with a particular focus on evaluating its real-time performance in the presence of concurrent operations. This research focuses on the latency and packet loss performance of multi-link communication between multiple programmable logic controllers to evaluate the system’s real-time performance and reliability. By using different scenarios and parameter settings, and introducing other concurrently running services as background interference, it conducts extensive measurements and comprehensively analyzes the behavior of the resulting data. The empirical results indicate that the latency of the 5G system increases with higher load, particularly when concurrent services are running. However, adding more links does not lead to a significant increase in system latency, indicating the system’s robustness. Measured results also demonstrate the system’s high reliability in multi-link setups. The abnormal latency behavior can be caused by increased load and background interference, emphasizing the need for enhancing 5G performance to meet the high real-time requirements of industrial applications.}, archive = {J_ACCESS}, author = {Da Liu and Yanling Zhao and Gongdai Liu and Congbo Wang and Lingyun Zhou and Ying Qian}, doi = {10.1109/ACCESS.2025.3539679}, journal = {IEEE Access}, pages = {26864-26875}, shortjournal = {IEEE Access}, title = {Real-time performance evaluation for 5G multi-link communication in industrial application}, volume = {13}, year = {2025}, }
• (2025). MBHGA: A matrix-based hybrid genetic algorithm for solving an agent-based model of controlled trade interactions. ACCESS, 13, 26843-26863. (www)

  @article{ , abstract = {The article discusses the development and study of a new matrix-based hybrid genetic algorithm (MBHGA) for solving an agent-based model of firms’ behavior with controlled trade interactions. The proposed model employs symmetric strategies optimized using the MBHGA algorithm. This algorithm combines evolutionary search with real-coded crossover and matrix binary-coded crossover as genetic operators, creating a hybrid approach. The aim of the designed system is to assist decision-makers in selecting optimal strategies for their firms in situations where some companies impose restrictions on interactions. As demonstrated by the results of optimization experiments, the MBHGA algorithm significantly outperformed other methods, such as RCGA, PSO, GWO, SPEA2, and NSGA-II, in terms of both accuracy (measured by deviations from reference values) and the quality of the Pareto front approximation (measured by LHV, IGD, and CPF) when solving an agent-based model of firms’ behavior. As demonstrated in this study, the performance of MBHGA significantly depends on the size of the model (i.e., the number of agents), and it can be enhanced by parallelizing the evolutionary search process, including matrix crossover operations for decision variables. Optimization experiments using a genetic algorithm were conducted to maximize utility functions for agent companies under trade restrictions. The results showed that trade restrictions have a negative impact on the utility function values in both single- and multi-objective optimization, for selected countries and for all countries’ firms. However, an optimization-based approach using the MBHGA algorithm can help minimize the negative consequences of trade restrictions by providing the ability to find nondominated solutions and the best possible trade-offs.}, archive = {J_ACCESS}, author = {Andranik S. Akopov}, doi = {10.1109/ACCESS.2025.3539460}, journal = {IEEE Access}, pages = {26843-26863}, shortjournal = {IEEE Access}, title = {MBHGA: A matrix-based hybrid genetic algorithm for solving an agent-based model of controlled trade interactions}, volume = {13}, year = {2025}, }
• (2025). Machine-learning-based real-time, high-fidelity neutron source identification via cavitation acoustic shock spectra in centrifugally tensioned metastable fluid detectors. ACCESS, 13, 26818-26842. (www)

  @article{ , abstract = {Neutron source spectrometry and energy discrimination represent a time-consuming, laborious endeavor requiring complex instrumentation across the nuclear science and technology sector affecting diverse fields such as nuclear power, radiation safety, safeguards, security, isotope production, and nuclear medicine. This paper demonstrates the development of a neural-network-based model for discriminating between diverse neutron energy spectrum sources via acoustic analysis of shock signals resulting from cavitation detection events (CDEs) within centrifugal tensioned metastable fluid detectors (CTMFDs). Acoustic shock traces from CDEs were recorded using the Cf-252 (Cf) fission spectrum source, the plutonium-beryllium (PuBe) hard spectrum ( $\alpha $ ,n) source, and the deuterium-tritium (DT) accelerator-driven monoenergetic 14 MeV neutron source which provided neutrons of diverse energy spectrums spanning 0.02 eV to 14 MeV. The resultant time-series voltage signals were then transformed into spectrograms to reveal the distribution of acoustic power across the frequency and time domains. More than 42,000 spectrograms were produced. A convolutional neural network (CNN) was trained to classify the spectrograms according to the neutron source they originated from. This model separated the Cf-252 and DT neutron sources with ~96% accuracy, the Cf-252 and PuBe sources with ~98% accuracy, and the PuBe and DT sources with ~90% accuracy.}, archive = {J_ACCESS}, author = {Bailey Christensen and Rusi Taleyarkhan}, doi = {10.1109/ACCESS.2025.3539174}, journal = {IEEE Access}, pages = {26818-26842}, shortjournal = {IEEE Access}, title = {Machine-learning-based real-time, high-fidelity neutron source identification via cavitation acoustic shock spectra in centrifugally tensioned metastable fluid detectors}, volume = {13}, year = {2025}, }
• (2025). Validation framework for analyzing complex anatomical structures: Application of L-system models. ACCESS, 13, 26798-26817. (www)

  @article{ , abstract = {Accurate morphological and topological analysis of renal vascular trees is crucial for understanding vascular architecture, detecting patterns, and identifying pathological deviations. However, the complexity of renal vascular trees, particularly in corrosive endocasts, requires custom-designed algorithms to address unique challenges in reconstruction, segmentation, skeletonization, and graph-based representation. A key issue is the lack of a direct method to validate the correctness of these parameters in the absence of a ground truth prior to pattern analysis, with manual validation being infeasible due to the intricate nature of the renal vasculature. To address this challenge, we introduce a systematic validation framework utilizing artificially generated renal vascular models. We developed computational models using L-systems and fabricated them via 3D printing. These models were scanned using microcomputed tomography ( $\mu $ -CT), simulating the process for in vivo endocasts. We then applied the same computational pipeline to analyze the synthetic models, comparing the derived vascular parameters with the known geometries. Our results demonstrate that the computational methods can accurately replicate the morphology and topology of artificial models, with quantified validation showing high precision in the analysis. This approach provides a reliable methodology for validating vascular analysis algorithms, setting a benchmark for accurate morphological and topological assessments in complex vascular networks. It also highlights the value of synthetic models in verifying algorithm integrity, offering a solid foundation for future research on the analysis of complex anatomical structures.}, archive = {J_ACCESS}, author = {Katarzyna Heryan and Jacek Tarasiuk and Janusz Skrzat}, doi = {10.1109/ACCESS.2025.3539837}, journal = {IEEE Access}, pages = {26798-26817}, shortjournal = {IEEE Access}, title = {Validation framework for analyzing complex anatomical structures: Application of L-system models}, volume = {13}, year = {2025}, }
• (2025). Bi-modal multiperspective percussive (BiMP) dataset for visual and audio human fall detection. ACCESS, 13, 26782-26797. (www)

  @article{ , abstract = {Falls are the leading cause of injuries and fatalities among older individuals propagating concerns for health and safety. Mitigating these concerns requires timely intervention and response to minimize health complications. Autonomous fall detection systems are often offered as a means to alleviate these concerns. Typical fall detection systems classify a fall event using either inertial- or vision-based data. Aside from these two modes of input for fall detection, sparse human fall audio data is available. Audio datasets associated with the dynamic impact of human falls from the perspective of a spatial environment are both unavailable publicly and focused on niche applications. This dataset provides multimodal composition of visual and audio data to aid in fall detection technique development. Though this dataset emphasizes audio novelty, a synchronized visual component is included offering a multifaceted collection of data. Audio input for fall detection systems presents an opportunity for new approaches towards autonomous fall detection. This dataset comprises human movement representing activities of daily living and falls of 25 participants in various residential settings, yielding 1,300 instances of unique visual and audio samples. Promising utility of the bi-modal multiperspective percussive (BiMP) dataset is demonstrated through experimental data evaluations using techniques including: GoogLeNet, Long Short Term Memory, Continuous Wavelet Transforms, and Short-time Fourier Transforms for human fall detection achieving accuracies up to 96%. The concept of audio-based fall detection has the potential to mitigate concerns regarding privacy and invasiveness, in addition to broadening the scope of fall detection mechanisms.}, archive = {J_ACCESS}, author = {Joe Dibble and Michael C. F. Bazzocchi}, doi = {10.1109/ACCESS.2025.3531324}, journal = {IEEE Access}, pages = {26782-26797}, shortjournal = {IEEE Access}, title = {Bi-modal multiperspective percussive (BiMP) dataset for visual and audio human fall detection}, volume = {13}, year = {2025}, }
• (2025). Enhancing content caching in D2D networks: A stackelberg game approach with wireless energy transfer and social awareness. ACCESS, 13, 26765-26781. (www)

  @article{ , abstract = {In a cache-enabled device-to-device (D2D) network, cooperative D2D caching holds promise for reducing data traffic and overall waiting time. However, the act of content caching and transmission can impose a drain on battery power, thereby diminishing users’ enthusiasm. In this study, we examine a D2D group wherein all D2D transmitters (D2D-TXs) collaborate to serve users alongside the utilization of wireless power transfer for charging D2D-TXs. We consolidate the cache capacities of all D2D-TXs into a single entity and formulate a unified caching policy. Additionally, we design an incentive power mechanism aimed at users with social consciousness to incentivize D2D-TXs. To meet users’ quality of service (QoS) requirements, we employ the Poisson point process and a hexagonal grid model to assess the likelihood of successful content delivery from base stations (BSs) and D2D-TXs to a user situated at the center of the D2D group. Addressing users’ waiting time constraints, we evaluate the cumulative waiting time using inter-contact and G/G/1 queueing models. A Stackelberg game framework is employed to resolve conflict between D2D-TXs and users. The objective is to achieve a Stackelberg equilibrium, wherein users minimize costs by establishing an incentive power benchmark based on their QoS, while D2D-TXs maximize utility through optimized content caching strategies. Simulation results demonstrate that cooperative caching, with equitable distribution of cached contents among D2D-TXs, yields lower total waiting time than non-cooperative caching. Furthermore, as user density, total cache size, and data skewness increase, our proposed caching scheme exhibits minimal total waiting time for D2D-TXs and users.}, archive = {J_ACCESS}, author = {Yu-Wei Chan and Chung-Ming Shih and Feng-Tsun Chien}, doi = {10.1109/ACCESS.2025.3539106}, journal = {IEEE Access}, pages = {26765-26781}, shortjournal = {IEEE Access}, title = {Enhancing content caching in D2D networks: A stackelberg game approach with wireless energy transfer and social awareness}, volume = {13}, year = {2025}, }
• (2025). Conceptualization and mechanical design of a cam-follower-based chewing robot with three oral chambers. ACCESS, 13, 26752-26764. (www)

  @article{ , abstract = {Understanding the changes in food texture during mastication and the interaction between food and the masticatory system is critical for the development of food products. In vivo studies are often constrained by ethical considerations, limited access, and challenges in ensuring repetitive and objective observations. As a result, in vitro studies using chewing robots have gained prominence. However, most existing chewing robots can only replicate limited aspects of oral functions. This study aims to develop a new chewing robot to mimic the human food masticating process. By analyzing the pros and cons of existing chewing robots, the technical requirements for the new design were identified. A chewing robot with three chambers and chewing trajectories to be achieved by a camshaft is proposed to improve the efficiency of the in vitro experiments. Additionally, a V-shaped food repositioning mechanism, requiring no additional actuation, is integrated into the chambers to facilitate food manipulation, which is validated by experiments. Finally, the robot is built and demonstrated to successfully chew food samples via automatic operation.}, archive = {J_ACCESS}, author = {Xudong Wang and Bangxiang Chen and Jaspreet S. Dhupia and John E. Bronlund and Jianda Han and Weiliang Xu}, doi = {10.1109/ACCESS.2025.3539623}, journal = {IEEE Access}, pages = {26752-26764}, shortjournal = {IEEE Access}, title = {Conceptualization and mechanical design of a cam-follower-based chewing robot with three oral chambers}, volume = {13}, year = {2025}, }
• (2025). Deep stereo network with cross-correlation volume construction and least square aggregation. ACCESS, 13, 26739-26751. (www)

  @article{ , abstract = {Stereo matching is of great importance in robot operation, autonomous driving and virtual reality. Large textureless regions and depth discontinuity regions are still the error-prone regions of stereo matching tasks. Traditional correlation-based volumes only measure the feature similarity within the same channel of the feature maps, resulting in insufficient feature similarity learning between different channels, which leads to poor performance of stereo networks in large textureless regions with high feature similarity requirements. To address the problems in large textureless regions, we propose the cross-correlation based cost volume construction which adequately learn the feature similarity in different channels of the feature maps. To address the problems in depth discontinuity regions and other gradient sensitive regions, we propose the differentiable least square aggregation module which can sufficiently utilize the gradient information and enhance the aggregation ability of the cost aggregation network for gradient features. Extensive experiments show that the proposed method solves the problems effectively in the above difficult regions and achieves competitive performance on Scene Flow dataset, KITTI 2012 dataset and KITTI 2015 dataset.}, archive = {J_ACCESS}, author = {Guowei An and Yaonan Wang and Yang Mo and Kai Zeng and Qing Zhu and Xiaofang Yuan}, doi = {10.1109/ACCESS.2025.3538762}, journal = {IEEE Access}, pages = {26739-26751}, shortjournal = {IEEE Access}, title = {Deep stereo network with cross-correlation volume construction and least square aggregation}, volume = {13}, year = {2025}, }
• (2025). PFRNet: A small object detection method based on parallel feature extraction and attention mechanism. ACCESS, 13, 26727-26738. (www)

  @article{ , abstract = {To address the challenge of detecting small objects in aerial and satellite remote sensing images with low-resolution, we propose a high-precision object detection method based on PFRNet. PFRNet incorporates parallel feature extraction branches and a progressive feature refinement mechanism, significantly enhancing the model’s ability to perceive detailed features. In addition, PFRNet introduces the spatial pyramid pooling fusion with spatial attention (SPPFSPA) module, which integrates multi-scale features with an attention mechanism, enabling the model to better focus on areas of interest, thereby improving detection performance. Results demonstrate that PFRNet achieves outstanding detection accuracy, markedly outperforming other algorithms, particularly in small object detection. Visualization analysis reveals that the PFR module effectively captures richer and more comprehensive visual features in images, providing robust input for subsequent detection tasks, which is crucial for PFRNet’s superior performance. Overall, the proposed PFRNet model makes significant strides in small object detection in UAV aerial and satellite remote sensing images, offering strong support for applications such as intelligent transportation and precision agriculture.}, archive = {J_ACCESS}, author = {Hai Lin and Ji Wang and Jingguo Li}, doi = {10.1109/ACCESS.2025.3538608}, journal = {IEEE Access}, pages = {26727-26738}, shortjournal = {IEEE Access}, title = {PFRNet: A small object detection method based on parallel feature extraction and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Time-optimal motion planning and anti-sloshing control for a container under disturbances. ACCESS, 13, 26707-26726. (www)

  @article{ , abstract = {Sloshing is the term for the liquid’s internal movement, which is often undesirable in technological processes. This paper aims to provide a solution for suppressing sloshing liquid in a moving cylindrical container. To prevent the negative impact of the sloshing problem, our approach is a combination of generating a desired path, robust control, and output constraints. To begin with, this research introduces a discrete model derived from a nonlinear equivalent mass-spring-damper system. Subsequently, after establishing the flatness of the approximate nonlinear model, we introduce rest-to-rest and time-optimal trajectory planning. We overcome the unmeasurable state variable and disturbance with the Linear Extended State Observer (LESO) algorithm. In addition, to address the challenge of tracking the orbital path, we suggest a control technique Terminal Sliding Mode Control (TSMC), which is well-known for its fast convergence while ensuring global stability. Moreover, to meet the constraints of a practical system, the control barrier function is applied. Lastly, the effectiveness of this control methodology is validated by comprehensive numerical simulations.}, archive = {J_ACCESS}, author = {Khanh Nguyen Viet and Hue Luu Thi and Thanh Cao Duc and Huy Nguyen Danh and Minh Nhat Vu and Tung Lam Nguyen}, doi = {10.1109/ACCESS.2025.3533541}, journal = {IEEE Access}, pages = {26707-26726}, shortjournal = {IEEE Access}, title = {Time-optimal motion planning and anti-sloshing control for a container under disturbances}, volume = {13}, year = {2025}, }
• (2025). Covariance matrix reconstruction to improve DoA estimation using subspace method in low SNR regime. ACCESS, 13, 26695-26706. (www)

  @article{ , abstract = {Traditional Direction of Arrival (DoA) estimation methods, such as Multiple Signal Classification Algorithm (MUSIC), Root MUSIC (R-MUSIC), and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), often suffer significant performance degradation in low Signal-to-Noise Ratio (SNR) environments. To address this challenge, this paper introduces a novel approach utilizing a Residual Network (ResNet) based Convolutional Neural Network (CNN) to enhance DoA estimation performance. Our method involves reconstructing the covariance matrix using the proposed CNN model, which is trained with covariance matrices from the actual received signals to their corresponding covariance matrices from the ideal signals. This reconstruction improves the accuracy of the covariance matrix, leading to enhanced DoA estimation and approaching the Cramer Rao Lower Bound (CRLB), especially at very low SNR levels. Simulation results demonstrate the effectiveness of our approach. At an SNR of −20dB, the root mean square error (RMSE) for MUSIC improved from 25.16° to 19.67°, for ESPRIT from 29.31° to 17.77°, and for R-MUSIC from 26.28° to 19.81°. The proposed method also shows substantial improvements in performance with varying numbers of snapshots and under different angle separation conditions. These results highlight the enhanced noise resilience and accuracy of our method.}, archive = {J_ACCESS}, author = {Sunita Khichar and Wiroonsak Santipach and Lunchakorn Wuttisittikulkij}, doi = {10.1109/ACCESS.2025.3528328}, journal = {IEEE Access}, pages = {26695-26706}, shortjournal = {IEEE Access}, title = {Covariance matrix reconstruction to improve DoA estimation using subspace method in low SNR regime}, volume = {13}, year = {2025}, }
• (2025). Two-tiered privacy preserving framework for software-defined networking driven defence mechanism for consumer platforms. ACCESS, 13, 26684-26694. (www)

  @article{ , abstract = {Software-defined networking (SDN) is a novel network theory that divides the controller from the network devices such as switches and routers. The integrated SDN structure enables the global network organization and tackles the necessity of present data centers. There are great advantages presented by the architecture of SDN, the hazard of novel assaults is a vital issue and can avert the widespread acceptance of SDNs. The controller of SDN is an essential part, and it is a tempting aim for the invaders. If the attacker effectively acquires the SDN controller, it can transmit the traffic depending upon its desires, causing severe loss to the complete system. Mobile users can access crucial actual services over wireless models such as software-defined networks (SDNs) topologies and the Internet of Things (IoTs). Thus, managing power consumption and system and device congestion turns into a main problem for SDN-based IoT applications. Network intrusion detection systems (NIDSs) are significant devices for identifying and protecting the network landscape from anomalous attacks and malicious activity. Currently, deep Learning (DL) has revealed desired outcomes in a diversity of problems like speech, image, text applications, etc. Whereas numerous works used DL for NIDSs, almost all these methods neglect the outcome of the overfitting issue throughout the execution of DL techniques. This study presents a novel Enhancing Software-Defined Networking Security with Deep Learning and Hybrid Feature Selection (ESDNS-DLHFS) technique for consumer platforms. The proposed ESDNS-DLHFS system primarily focuses on protecting data privacy in SDN-assisted IoT platforms. In the ESDNS-DLHFS method, the initial phase of min-max normalization is executed to scale the input data. For the feature selection process, the hybrid crow search arithmetic optimization algorithm (HCSAOA) is utilized to optimally select feature subsets. Next, the deep bidirection- al long short-term memory (Deep BiLSTM) technique is applied to detect intrusions. Finally, the enhanced artificial orca’s algorithm (EAOA) based hyperparameter tuning process is executed to increase the overall classification outcomes. To certify the improved predictive outcomes of the ESDNS-DLHFS technique, an extensive range of experiments are implemented on the benchmark dataset. The comparison outcome study shows the promising performance of the ESDNS-DLHFS technique compared to the recent approaches.}, archive = {J_ACCESS}, author = {Sultan Refa Alotaibi and Hessa Alfraihi and Nuha Alruwais and Mohammed Maray and Achraf Ben Miled and Ali M. Al-Sharafi and Moneerah Alotaibi and Samah Hazzaa Alajmani}, doi = {10.1109/ACCESS.2025.3538331}, journal = {IEEE Access}, pages = {26684-26694}, shortjournal = {IEEE Access}, title = {Two-tiered privacy preserving framework for software-defined networking driven defence mechanism for consumer platforms}, volume = {13}, year = {2025}, }
• (2025). Design and process analysis of a split-gate trench power MOSFET with bottom-trench high-k pillar superjunction for enhanced performance. ACCESS, 13, 26676-26683. (www)

  @article{ , abstract = {In this paper, we propose a simulation-based novel Split-Gate Trench MOSFET structure with an optimized fabrication process to enhance power efficiency, switching speed, and thermal stability for high-performance semiconductor applications. Integrating High-k pillars Superjunction beneath the Split-Gate enhancing breakdown performance by reducing critical field intensity by up to 35%, the device achieves a 15% improvement in Figures of Merit (FOMs) for $\mathrm {BV}^{2}/R_{\mathrm {on,sp}}$ . Dynamic testing reveals approximately a 25% reduction in both input and output capacitance, as well as gate-drain charge ( $Q_{\text {GD}}$ ). This reduction, coupled with an approximately 40% improvement in Baliga’s High-Frequency Figure of Merit (BHFFOM) and over 20% increase in the New High-Frequency Figure of Merit (NHFFOM), underscores the design’s suitability for high-speed, high-efficiency power electronics. Simulations examining the effects of High-k pillar depth indicate that an optimal depth of $3.5 \; \mu $ m achieves a balanced performance between BV and $R_{\text {on,sp}}$ . The influence of High-k materials on BT-Hk-SJ MOSFET performance was investigated by comparing hafnium dioxide (HfO2), nitride, and oxynitride. Among these, HfO2 demonstrated optimal performance across static, dynamic, and diode characteristics due to its high dielectric constant, while material choice had minimal impact, with variations kept within 5%.}, archive = {J_ACCESS}, author = {Yunteng Jiang and Zhentao Xiao and Zonghao Zhang and Junchen Zhang and Chenxing Wang and Wenjun Li and Haimeng Huang and Aynul Islam and Hongqiang Yang}, doi = {10.1109/ACCESS.2025.3533212}, journal = {IEEE Access}, pages = {26676-26683}, shortjournal = {IEEE Access}, title = {Design and process analysis of a split-gate trench power MOSFET with bottom-trench high-k pillar superjunction for enhanced performance}, volume = {13}, year = {2025}, }
• (2025). Extended state observer-based estimation for nonlinear markov jump systems with dynamic event-triggered communication. ACCESS, 13, 26667-26675. (www)

  @article{ , abstract = {This paper primarily investigates the extended state observer design issue for nonlinear Markov jump systems under limited communication bandwidth. In order to achieve reasonable network resource configurations while estimating the system states and total disturbances, a continuous-time dynamic event-triggered strategy is incorporated into the observer design. Accordingly, the transmission of the measured output occurs only when the predefined conditions are satisfied. In contrast to the conventional static triggering strategy, the dynamic event-triggered mechanism enables adaptive threshold adjustment by introducing an internal dynamic variable. In addition, a carefully constructed intermediate variable is considered to indirectly obtain the estimate of the extended state. Then, combining Lyapunov method with stochastic analysis technique, it is proved that the estimation error is exponentially ultimately bounded in mean square under the proposal. At last, a case study of the mass-spring system with four operating modes is presented to illustrate the feasibility of the proposed design approach.}, archive = {J_ACCESS}, author = {Pengcheng Zhang and Shiyu Jiao and Jun Chen and Sangmoon Lee}, doi = {10.1109/ACCESS.2025.3539586}, journal = {IEEE Access}, pages = {26667-26675}, shortjournal = {IEEE Access}, title = {Extended state observer-based estimation for nonlinear markov jump systems with dynamic event-triggered communication}, volume = {13}, year = {2025}, }
• (2025). Design of a novel end-effector for robotic bag removal of shiitake mushroom logs. ACCESS, 13, 26651-26666. (www)

  @article{ , abstract = {According to agronomic requirements, the bag removal of shiitake mushroom logs should be carried out promptly during factory production. However, the current manual bag removal process is time-consuming and labor-intensive, limiting the development of the shiitake mushroom industry. Robotic bag removal is the best solution to this problem. In this paper, firstly, a novel robotic bag removal method was proposed, measuring and analyzing the biomechanical parameters of shiitake mushroom logs. It was determined that the maximum normal stress that does not cause damage when gripping the logs is 455.56 N. Secondly, a novel bag removal end-effector was designed, including a gripping mechanism and a vacuum sponge sucker mechanism. Thirdly, a multi-objective evolutionary algorithm combined with numerical simulation was used to optimize the suction hole layout and vacuum degree of the vacuum sponge sucker, reducing the maximum deformation and overall deformation of the bag during the bag removal process by 36.11 % and 56.23 %, respectively. Furthermore, a robotic bag removal test bench was built using the designed bag removal end-effector. The test results showed a bag removal success rate of 87.04 % and a gripping damage rate of 0 %, proving the effectiveness and feasibility of this method. The designed end-effector has high potential for integration into a robot to support the bag removal process of shiitake mushroom logs in factory-based production.}, archive = {J_ACCESS}, author = {Shixin Ma and Tianhang Ding and Fan Zhou and Jiaoling Wang and Mingyou Wang and Weidong Song}, doi = {10.1109/ACCESS.2025.3532893}, journal = {IEEE Access}, pages = {26651-26666}, shortjournal = {IEEE Access}, title = {Design of a novel end-effector for robotic bag removal of shiitake mushroom logs}, volume = {13}, year = {2025}, }
• (2025). Option-based deep reinforcement learning for topology control of power systems. ACCESS, 13, 26639-26650. (www)

  @article{ , abstract = {In this paper, we propose an option-based deep reinforcement learning (DRL) algorithm called option-critic with long short-term memory (OC-LSTM), which combines the option-critic (OC) framework containing a hierarchical policy structure with a long short-term memory (LSTM) network, which makes full use of the powerful time-series feature extraction capability of LSTM networks and uses the OC framework to learn power system topology control policy of the power system. Specifically, in a complex and variable power system, the OC-LSTM extracts key power system state information through the LSTM network and uses the OC framework to define and optimize the high-level options and low-level action policy, which effectively reduces the dimensionality of the agent’s topology control action space in the decision-making process. This combination improves the accuracy of topology control policies and effectively maintains the stability of the power system. The experimental results show that the OC-LSTM algorithm outperforms the benchmark DRL algorithm during training, with the ablation experiment further highlighting the effectiveness of LSTM in power system feature extraction. Additionally, the OC-LSTM algorithm enables stable operation of the IEEE 5-Bus, IEEE 14-Bus, and L2RPN WCCI 2020 power systems for 60 hours, all without human expert intervention.}, archive = {J_ACCESS}, author = {Haotian Zhang and Chen Wang and Myoung Hoon Lee and Jun Moon}, doi = {10.1109/ACCESS.2025.3539770}, journal = {IEEE Access}, pages = {26639-26650}, shortjournal = {IEEE Access}, title = {Option-based deep reinforcement learning for topology control of power systems}, volume = {13}, year = {2025}, }
• (2025). Machine learning in hospitality: Interpretable forecasting of booking cancellations. ACCESS, 13, 26622-26638. (www)

  @article{ , abstract = {The phenomenon of cancellations in hotel bookings is one of the main pain points in the hospitality sector as it skews demand signals and can result in revenue losses estimated at about 20 %. Yet, forecasting booking cancellations remains an underresearched area, particularly in the understanding of the behavioral drivers of cancellations. This paper addresses this gap by proposing a new approach to predicting hotel booking cancellations rooted in stacked generalization and Explainable Artificial Intelligence (XAI). Specifically, the combination of linear, tree-based, non-linear and deep learning models into a single meta-model resulted in an increased accuracy rate to 96 %. In addition, this work focuses on interpretability, identifying the driving behavioral factors of cancellation as location, type of room, and customer segments. This approach can provide hoteliers with both highly accurate predictions as well as marketing intelligence that would allow them to drive strategy to minimize loss resulting from cancellations. The results of the research provide an effective solution to the challenges involved in forecasting booking cancellations, balancing forecast prediction accuracy with the ability to provide actionable insights.}, archive = {J_ACCESS}, author = {Ismael Gómez-Talal and Mana Azizsoltani and Pilar Talón-Ballestero and Ashok Singh}, doi = {10.1109/ACCESS.2025.3536094}, journal = {IEEE Access}, pages = {26622-26638}, shortjournal = {IEEE Access}, title = {Machine learning in hospitality: Interpretable forecasting of booking cancellations}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection and prevention system (IDPS) model for IIoT environments using hybridized framework. ACCESS, 13, 26608-26621. (www)

  @article{ , abstract = {As the Industrial Internet of Things (IIoT) becomes more popular, cyber threats have more places to attack. This is why intrusion detection and prevention systems (IDPS) are so important for keeping industrial environments safe and secure. The main goal of the proposed research is to create a complete Intrusion Detection and Prevention System (IDPS) for IIoT. This system will include detection and protection security models to keep the network safe from cyberattacks and other strange things happening. Convolutional Neural Networks (CNNs) are used in pattern recognition for the detection and protection models in this research. This helps find IIoT networks with strange traffic patterns. Additionally, blockchain-assisted reinforcement learning (RL) uses real-time learning and decision-making to stop or lessen threats on its own. The novelty of this research lies in the combination of deep learning and blockchain-based security for intrusion detection and prevention. While there are already models for finding intrusions, this is the first time that reinforcement learning has been used for dynamic threat prevention along with blockchain to ensure secure communication and data integrity in the IIoT domain. This hybrid approach ensures a higher level of security by continuously learning and adapting to new types of attacks. This approach utilizes a novel Intrusion Detection and Prevention System (IDPS) designed for IIoT environments, which is capable of real-time detection and response to cyber threats. In the simulation parameters, this research shows higher detection accuracy and lower false positive rates using the proposed hybrid model. The integration of deep learning and blockchain technology enhances security for industrial applications.}, archive = {J_ACCESS}, author = {Manohar Srinivasan and N. C. Senthilkumar}, doi = {10.1109/ACCESS.2025.3538461}, journal = {IEEE Access}, pages = {26608-26621}, shortjournal = {IEEE Access}, title = {Intrusion detection and prevention system (IDPS) model for IIoT environments using hybridized framework}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based real-time driver cognitive distraction detection. ACCESS, 13, 26589-26607. (www)

  @article{ , abstract = {Driver distraction is one of the main causes of traffic accidents. While there are different types of distraction (manual, visual, cognitive), cognitive distraction is particularly challenging, being only partially related to visual features detectable through cameras or an eye tracker system. Moreover, since cognitive distraction is not a point in time phenomenon, spotting this kind of distraction requires the processing of a certain time interval, which poses a further challenge for real-time performance. After a data collection campaign with N =42 subjects undertaking a twenty-question task (TQT) in a driving simulator, we developed a driver cognitive distraction detection system, with the goal of filling in some key gaps we identified in the literature towards real world deployment. First, we assessed the effectiveness of state-of-the-art time series-oriented deep learning models in learning features from 60 Hz raw input signals, thus implementing an end-to-end machine learning approach, without manual feature engineering. We demonstrated that such models are able to classify time-windows as small as 0.5 seconds, and are also more robust to sensor failures. Second, also with the support of AI explainability, we showed that processing vehicular data is fundamental to ensure performance, while physiological signals provide a less important, but still useful, contribution. Third, through a between- and within-subject design comparison, we showed that eye-tracker and, particularly, physiological signals are much more prone to inter-individual variability, thus overfitting. This is fundamental to consider for commercial deployment, as it would require fine-tuning the system with data from the actual end-user. Fourth, we quantitatively measured the effect of such variability for all types of signals, demonstrating its huge relevance, and shown that deep learning models dedicated to time series processing are better able to generalize across users than the more commonly employed shallow machine learning models. Finally, with a focus on in-vehicle deployability, which is of significant industrial interest, we measured also such metrics as model size, inference time, and energy consumption, showing feasibility on two embedded platforms, which is a key advancement towards on-board deployment of robust, real-time cognitive distraction detection systems.}, archive = {J_ACCESS}, author = {Matteo Fresta and Francesco Bellotti and Igor Bochenko and Luca Lazzaroni and Gaëtan Merlhiot and Fabio Tango and Riccardo Berta}, doi = {10.1109/ACCESS.2025.3539392}, journal = {IEEE Access}, pages = {26589-26607}, shortjournal = {IEEE Access}, title = {Deep learning-based real-time driver cognitive distraction detection}, volume = {13}, year = {2025}, }
• (2025). Predicting ultra-short-term wind power combinations under extreme weather conditions. ACCESS, 13, 26575-26588. (www)

  @article{ , abstract = {The prediction of wind power generation is an important basis for the rational scheduling of new energy sources in wind power. However, the severe fluctuations in wind power output under extreme weather conditions pose a serious challenge for ultra-short-term wind power output prediction. A combination forecasting method for ultra-short-term wind power that comprehensively considers extreme weather and normal weather is proposed to address the above issues. To explore the differences in power time series characteristics under different scenarios in terms of the relationships between wind power output prediction errors and different weather types, time series generative adversarial networks are used to expand small sample datasets under extreme weather conditions, and a bidirectional long short-term memory (BiLSTM) deterministic prediction model based on a time attention mechanism is established. On this basis, a kernel density estimation probability prediction model is proposed for different extreme scenarios. A case study was conducted to verify the data of a wind farm in China. At the 90% confidence level, the proposed method improved the PICP and PINAW of various extreme weather conditions by averages of 10.04% and 20.47%, respectively. The results showed that the proposed method has good adaptability for wind power prediction under extreme weather conditions.}, archive = {J_ACCESS}, author = {Wanting Li and Tongguang Yang and Jingyu Yang and Li Peng}, doi = {10.1109/ACCESS.2025.3539718}, journal = {IEEE Access}, pages = {26575-26588}, shortjournal = {IEEE Access}, title = {Predicting ultra-short-term wind power combinations under extreme weather conditions}, volume = {13}, year = {2025}, }
• (2025). The obfuscated network steganography. ACCESS, 13, 26564-26574. (www)

  @article{ , abstract = {Network header steganography is a specialized field of steganography that focuses on embedding hidden data within network headers rather than the payload of a communication. Traditional methods typically utilize a single header field as a covert channel to conceal secret messages. However, these approaches are susceptible to detection using statistical analysis techniques, such as the chi-square test, which can identify anomalies in the data distribution. In this paper, we present an enhanced algorithm to overcome the limitations of single-channel steganography. Our method manipulates the data with redundant bits generated by BCH code. By carefully adjusting these bits, the algorithm effectively evades detection by the chi-square test, making the concealed data virtually undetectable to standard statistical scrutiny. On the receiver side, we employ peterson-gorenstein-zierler decoder to accurately recover the adjusted bits, ensuring reliable communication without data loss. This innovative approach significantly improves the security of network header steganography. Experimental results demonstrate that our algorithm offers at least 80% higher security compared to previous techniques, marking a substantial advancement in the field. This improvement enhances the practicality and robustness of covert communication in network systems, addressing a critical challenge faced by earlier methods.}, archive = {J_ACCESS}, author = {Da-Chih Lin and Shang-Yu Chou}, doi = {10.1109/ACCESS.2025.3538633}, journal = {IEEE Access}, pages = {26564-26574}, shortjournal = {IEEE Access}, title = {The obfuscated network steganography}, volume = {13}, year = {2025}, }
• (2025). Efficient task scheduling and load balancing in fog computing for crucial healthcare through deep reinforcement learning. ACCESS, 13, 26542-26563. (www)

  @article{ , abstract = {In healthcare, real-time decision making is crucial for ensuring timely and accurate patient care. However, traditional computing infrastructures, with their wide ranging capabilities, suffer from inherent latency, which compromises the efficiency of time-sensitive medical applications. This paper explores the potential of fog computing to better address this challenge, proposing a new framework that uses deep reinforcement learning (DRL) to advance task scheduling in crucial healthcare. The paper addresses the limitations of cloud computing systems. It proposes and replaces a fog computing architecture in supporting low latency for healthcare applications. This architecture reduces transmission latency by placing processing nodes close to the source of data generation, namely IoT-enabled healthcare devices. The foundation of this approach is the DRL model, which is designed to dynamically optimize the partition of computational tasks across fog nodes to improve both data throughput and operational response times. The effectiveness of the proposed DRL based fog computing model is validated with a series of simulations performed with the SimPy simulation environment. In such simulations, diverse healthcare scenarios, ranging from continuous patient monitoring systems to crucial emergency response applications, are recreated, providing a rich framework for testing the real-time processing capabilities of the model. This algorithm, DRL, has been fine-tuned and extensively implemented in these scenarios to show how the algorithm controls and optimizes tasks and their urgency in accordance with resource demand. By dynamically learning from real-time system states and optimizing task allocation to minimize delays, the DRL model reduces the makespan by up to 30% compared to traditional scheduling approaches. Comparative performance analysis indicated a 30% reduction in task completion times, a 40% reduction in operational latency, and a 25% improvement in fault tolerance relative to traditional scheduling approaches. The flexibility of the DRL model is further considered through its application to diverse real-time data processing contexts in industrial automation and smart traffic systems.}, archive = {J_ACCESS}, author = {Prashanth Choppara and Bommareddy Lokesh}, doi = {10.1109/ACCESS.2025.3539336}, journal = {IEEE Access}, pages = {26542-26563}, shortjournal = {IEEE Access}, title = {Efficient task scheduling and load balancing in fog computing for crucial healthcare through deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). A comprehensive assessment of EEG entropy measures for bipolar disorder diagnosis using machine learning. ACCESS, 13, 26527-26541. (www)

  @article{ , abstract = {Bipolar disorder (BD) is a common psychiatric condition with varying severity, characterized by alternating episodes of mania or hypomania and depression, making accurate and timely diagnosis challenging. Electroencephalography (EEG) nonlinear features may serve as valuable biomarkers for psychological disorders, reflecting the brain’s nonlinear dynamics. Although changes in EEG power spectral density are well-documented in BD patients, entropy measures offer a complementary nonlinear approach that is less explored. This study provides a comprehensive evaluation of various entropy measures, including approximate entropy, sample entropy, permutation entropy (PermEn), fuzzy entropy (FuzzyEn), and dispersion entropy (DispEn). Additionally, to capture the complexity of signal fluctuations at different scales, four multiscale entropies are evaluated to assess their effectiveness in distinguishing individuals with BD from healthy controls (HCs). We used resting-state EEG data from both eyes-closed and eyes-open (EO) conditions, collected from 60 individuals: 30 patients with BD (15 females, mean age =37.33, standard deviation (SD) =8.83) and 30 HC participants (13 females, mean age =35.73, SD =9.01). After preprocessing the raw EEG data, five basic and four multiscale entropy measures were calculated separately for the five cortical brain regions. Statistical analyses and machine learning approaches were then applied to assess the differences in entropy measures between the two groups. The results showed that BD patients exhibited higher irregularity in the frontal and central brain regions compared to HC participants across all basic entropy measures. The most significant changes were observed for DispEn and FuzzyEn measures. When considering multiscale entropies, the most significant results were found in the beta frequency band (approximately scale factor 4) in anterior brain regions, particularly for multiscale dispersion entropy (MDE). Furthermore, the best classification performance was achieved during the EO condition for the MDE measure, with an accuracy of 95%, specificity of 93.33%, and sensitivity of 96.66%. The study highlights alterations in entropy measures, particularly MDE in the frontal brain region, suggesting its potential as a sensitive biomarker for bipolar and related disorders.}, archive = {J_ACCESS}, author = {Hamed Bagheri and Seyed Rabi Mahdavi and Nazila Eyvazzadeh and Ali Neshaseh-Riz and Masoumeh Sajadi Rad and Shahryar Sadeghi and Reza Gerami and Mohammadreza Azimi Aval}, doi = {10.1109/ACCESS.2025.3539323}, journal = {IEEE Access}, pages = {26527-26541}, shortjournal = {IEEE Access}, title = {A comprehensive assessment of EEG entropy measures for bipolar disorder diagnosis using machine learning}, volume = {13}, year = {2025}, }
• (2025). A survey on optimal transport for machine learning: Theory and applications. ACCESS, 13, 26506-26526. (www)

  @article{ , abstract = {Optimal Transport (OT) theory has seen increasing attention from the computer science community due to its potency and relevance in modeling and machine learning (ML). OT provides powerful tools for comparing probability distributions and producing optimal mappings that minimize cost functions. Consequently, OT has been widely implemented in computer vision tasks such as image retrieval, image interpolation, and semantic correspondence, as well as in broader applications spanning domain adaptation, natural language processing, and variational inference. In this survey, we aim to convey the emerging prominence and widespread applications of OT methods across various ML areas and outline future research directions. We first provide a history of OT. We then introduce a mathematical formulation and the prerequisites to understand OT, including Kantorovich duality, entropic regularization, KL Divergence, and Wasserstein barycenters. Given the computational complexity of OT, we discuss entropy-regularized version of computing optimal mappings that facilitate practical applications of OT across diverse ML domains. Further, we review prior studies on OT applications in ML. To this end, we cover the following: computer vision, graph learning, neural architecture search, document representation, domain adaptation, model fusion, medicine, natural language processing, and reinforcement learning. Finally, we outline future research directions and key challenges that could drive the broader integration of OT in ML.}, archive = {J_ACCESS}, author = {Luiz Manella Pereira and M. Hadi Amini}, doi = {10.1109/ACCESS.2025.3539926}, journal = {IEEE Access}, pages = {26506-26526}, shortjournal = {IEEE Access}, title = {A survey on optimal transport for machine learning: Theory and applications}, volume = {13}, year = {2025}, }
• (2025). AEFFNet: Attention enhanced feature fusion network for small object detection in UAV imagery. ACCESS, 13, 26494-26505. (www)

  @article{ , abstract = {The rapid advancement of unmanned aerial vehicle (UAV) technology has markedly increased the use of drone-captured imagery across various applications, necessitating enhanced accuracy and real-time performance in UAV image detection. Addressing the specific challenges posed by small and densely distributed objects in such images, we introduce an attention enhanced feature fusion network (AEFFNet) designed specifically for small object detection in UAV imagery. Firstly, a hybrid attention module with associated multi-axis frequency and spatial attention was designed to enhance the feature extraction of small objects. Secondly, an adjacent layer feature fusion module is innovatively proposed in order to boost the detection capabilities for small and occluded objects. Finally, a series experiments are conducted on the VisDrone2023 dataset, which involves a large number of small objects photographed by drones. Our evaluations, conducted on the VisDrone2023 dataset, demonstrate substantial improvements over the YOLOv8m baseline model, with a 3.0% increase in mean Average Precision (mAP) and a 4.4% rise in AP50.}, archive = {J_ACCESS}, author = {Zhaoyu Nian and Wenzhu Yang and Hao Chen}, doi = {10.1109/ACCESS.2025.3538873}, journal = {IEEE Access}, pages = {26494-26505}, shortjournal = {IEEE Access}, title = {AEFFNet: Attention enhanced feature fusion network for small object detection in UAV imagery}, volume = {13}, year = {2025}, }
• (2025). Cyclic reformulation-based system identification for periodically time-varying systems. ACCESS, 13, 26483-26493. (www)

  @article{ , abstract = {This paper presents a novel system identification algorithm for linear periodically time-varying (LPTV) plants within a discrete-time framework. The algorithm integrates a cyclic reformulation with a state coordinate transformation of the cycled system to enable precise identification of LPTV dynamics. Specifically, subspace identification methods are applied to the cycled signals, allowing the extraction of Markov parameters, which form the basis for constructing the state-space model. Furthermore, the sparsity properties of the matrices derived from these Markov parameters are analyzed and utilized to design the coordinate transformation matrix, enhancing the representation and interpretability of the identified LPTV model. Unlike conventional methods that rely on specific periodic input signals, the proposed approach leverages the inherent periodic structure of the system, eliminating such requirements and improving flexibility in practical applications. Numerical simulations are conducted to demonstrate the algorithm’s effectiveness and accuracy in capturing the dynamic behavior of LPTV plants under realistic conditions, highlighting its potential for broad applicability.}, archive = {J_ACCESS}, author = {Hiroshi Okajima and Yusuke Fujimoto and Hiroshi Oku and Haruto Kondo}, doi = {10.1109/ACCESS.2025.3537086}, journal = {IEEE Access}, pages = {26483-26493}, shortjournal = {IEEE Access}, title = {Cyclic reformulation-based system identification for periodically time-varying systems}, volume = {13}, year = {2025}, }
• (2025). Improved sliding mode control of wheel PMSM under bounded disturbance rate of change conditions. ACCESS, 13, 26471-26482. (www)

  @article{ , abstract = {Traditional electric vehicle hub permanent magnet synchronous motor (PMSM) uses vector control based on PI controllers. Model parameter mismatches and load disturbances can cause the dq-axis control current to deviate from the desired value, thereby reducing the speed and torque response performance. This paper proposes an improved sliding mode control strategy (ISMC) under bounded disturbance variation rate conditions. Regarding the speed loop design, a new sliding mode surface is introduced to accelerate the tracking speed of the speed commands and achieve finite-time convergence of the speed loop under load disturbances. Simultaneously, an improved exponential reaching law is designed to effectively reduce the high-frequency chatter in the sliding and reaching phases. For the current loop design, a control method based on the per-unit model is proposed to improve the robustness of the system’s model parameters and decoupling performance. Simulation and experimental results show that, compared with PI control, the speed output regulation time is reduced by more than 30 ms and the overshoot is reduced by more than 20% in the no-load startup phase of the motor under ISMC control. The speed response time is reduced by more than 50 ms in the load phase, and the speed and dq-axis current tracking are free of steady-state errors, which realizes better dynamic and steady-state control of the motor.}, archive = {J_ACCESS}, author = {Deng Pan and Yicai Liu}, doi = {10.1109/ACCESS.2025.3538482}, journal = {IEEE Access}, pages = {26471-26482}, shortjournal = {IEEE Access}, title = {Improved sliding mode control of wheel PMSM under bounded disturbance rate of change conditions}, volume = {13}, year = {2025}, }
• (2025). A rapid aphasia assessment battery for non-written chinese dialect. ACCESS, 13, 26456-26470. (www)

  @article{ , abstract = {Stroke, a leading contributor to mortality and disability in China, significantly impacts language skills such as speaking, listening, reading, writing, and communication in a considerable proportion of patients who experience aphasia. However, there is a lack of aphasia assessment batteries tailored to non-written Chinese dialect, the Teochew dialect, leading to challenges in timely diagnosis and treatment. Current assessment batteries, both domestically and internationally, are often time-consuming and not adapted to local dialects, making evaluation and treatment of Teochew dialect-speaking aphasia patients difficult. Additionally, limited healthcare resources in aphasia treatment result in missed opportunities for recovery. To address these issues, this paper presents a rapid assessment battery designed specifically for Teochew dialect-speaking aphasia patients. The primary objective is to provide a reliable multidimensional assessment of language abilities within approximately 15 minutes. The battery’s content, including assessment materials, procedures, and scoring criteria, adheres to scientific principles. Furthermore, rigorous psychometric validation ensures its clinical applicability and widespread adoption. Experimental results demonstrate the Method’s excellent psychometric reliability, alignment with clinical diagnoses, and the ability to complete assessments within 15 minutes. In conclusion, this assessment battery efficiently generates reliable multidimensional aphasia functional reports, enhancing diagnostic efficiency and facilitating patient rehabilitation.}, archive = {J_ACCESS}, author = {Seedahmed S. Mahmoud and Weibin Cheng and Yin Wang and Jiawei Chen and Yuming Zhong and Qiang Fang}, doi = {10.1109/ACCESS.2025.3539613}, journal = {IEEE Access}, pages = {26456-26470}, shortjournal = {IEEE Access}, title = {A rapid aphasia assessment battery for non-written chinese dialect}, volume = {13}, year = {2025}, }
• (2025). Bangladeshi vehicle classification and detection using deep convolutional neural networks with transfer learning. ACCESS, 13, 26429-26455. (www)

  @article{ , abstract = {Vehicle classification and detection has been a field of application for deep learning and image processing which play a very important role in intelligent transport management and AI-assisted driving systems. In this paper, we have presented a vehicle classification and detection system to detect and classify low-speed and high-speed Bangladeshi vehicles. To begin, we have implemented and tested the performance of the 11 pre-trained deep convolutional neural network (CNN) models: YOLOv8 Classify, MobileNetV2, GoogLeNet, AlexNet, ResNet-50, SqueezeNet, VGG19, DenseNet-121, Xception, InceptionV3, and NASNetMobile on the six vehicle classification and detection datasets: BIT-Vehicle, IDD, DhakaAI, Poribohon-BD, Sorokh-Poth, and VTID2. We have found that YOLOv8 Classify, MobileNetV2, and GoogLeNet models outperform other models in categorising vehicle types (e.g., car, truck, bus) in images where the vehicle is already the main subject. Next, we have customised the LabelImg image annotation tool to improve the following features: (a) Changing Label Font & Border, (b) Detecting Incorrect Labels, (c) Abbreviating Label Names, (d) Improving Crosshair & Bounding Box Guide, (e) Adding Progress Information, and (f) Improving File List Panel. We have collected data from real-world highway conditions in Dhaka city and labelled 54,556 objects from 5,460 images based on 16 Bangladeshi on-road vehicle classes. Furthermore, we have built a Bangladeshi native vehicle detection classifier for locating and identifying vehicles within larger scenes, often with multiple objects using YOLOv8 Detect and SSD-Mobilenet V2 models and later deploying this classifier into NVIDIA Jetson Nano Developer Kit (a small and powerful computer). Finally, we have tested the proposed Bangladeshi vehicle detection system with different timing, lighting, and weather conditions in several areas of Dhaka city. The proposed system can detect and classify low-speed and high-speed vehicles with an average 93% detection rate and 98% accuracy, while facing challenges that include issues with image annotation tools like poor label visibility, lack of error checking, and limited guidance, as well as difficulties in setting up the NVIDIA Jetson Nano embedded device for efficient model deployment.}, archive = {J_ACCESS}, author = {Farid and Proshanta Kumer Das and Monirul Islam and Ebna Sina}, doi = {10.1109/ACCESS.2025.3539713}, journal = {IEEE Access}, pages = {26429-26455}, shortjournal = {IEEE Access}, title = {Bangladeshi vehicle classification and detection using deep convolutional neural networks with transfer learning}, volume = {13}, year = {2025}, }
• (2025). Analysis and optimal design of hybrid magnetic circuit reluctance direct drive motor. ACCESS, 13, 26420-26428. (www)

  @article{ , abstract = {Reluctance direct-drive motors have become a hot research topic in recent years due to their high torque density, therefore, this paper proposes a novel hybrid magnetic circuit reluctance direct drive motor (HMRM) to meet the requirements of high torque density in low-speed and high-torque application. The stator and rotor of the motor are all convex pole structure. The stator teeth are grooved, in which permanent magnets with tangential magnetization are placed. The rotor is a simple reluctance structure. With this cleverly designed stator and rotor structure, the air gap magnetic field is modulated to improve the torque density of the motor. Firstly, the structure and working principle of the motor are introduced, and the expression between air gap density and torque is derived based on the magnetomotive force permeance model, which is utilized to analyze its working harmonics to enhance the torque density. Secondly, a multi-objective optimization model is proposed to the proposed HMRM, where the design objective of maximum torque and minimized torque fluctuation selected and optimized with multi-objective genetic algorithm method. Finally, a finite element model of the HMRM is developed and its performance is compared with that of the conventional permanent magnet motor. The results show that the proposed HMRM has greater torque density, higher permanent magnets utilization, and lower motor cost. The research theory and design methodology in this paper are significant in promoting the application of hybrid magnetic circuit reluctance motors in direct drive systems.}, archive = {J_ACCESS}, author = {Fuqiang Wang and Baoquan Kou and Lu Zhang and Changchuang Huang}, doi = {10.1109/ACCESS.2025.3539673}, journal = {IEEE Access}, pages = {26420-26428}, shortjournal = {IEEE Access}, title = {Analysis and optimal design of hybrid magnetic circuit reluctance direct drive motor}, volume = {13}, year = {2025}, }
• (2025). Reducing influence of noise in regression problems on memristor computing-in-memory chips. ACCESS, 13, 26411-26419. (www)

  @article{ , abstract = {Memristor computing-in-memory (CIM) technology has shown great promise for creating energy-efficient computing chips. However, noise issues on the CIM chip remain problematic due to non-ideal factors such as process variations in device manufacturing and environmental interferences, especially in regression problems. This work delves into the effects of noise on various regression models, encompassing linear regression and vector regression, as well as on the confidence value prediction in object detection models. We introduce novel calibration algorithms both average error and dynamic error as well as confidence threshold calibration techniques, specifically designed to mitigate noise induced inaccuracies. Comprehensive experimentation across diverse datasets validates the effectiveness of our proposed algorithms, highlighting their potential for enhancing CIM chip performance for regression tasks in the presence of noise.}, archive = {J_ACCESS}, author = {Hao Tang and Lu Liu and Zhouzheng Li and Zhiping Jia and Qingtian Zhang}, doi = {10.1109/ACCESS.2025.3540002}, journal = {IEEE Access}, pages = {26411-26419}, shortjournal = {IEEE Access}, title = {Reducing influence of noise in regression problems on memristor computing-in-memory chips}, volume = {13}, year = {2025}, }
• (2025). Analysis of near-fall detection method utilizing dynamic motion images and transfer learning. ACCESS, 13, 26398-26410. (www)

  @article{ , abstract = {This study explores a model for detecting fall, non-fall and near-fall events as frequent experiences of near-falls are closely associated with a heightened risk of falls. Detecting near-falls can lead to more accurate predictions of falls. However, near-falls exhibit certain movement patterns similar to actual falls, making it challenging to distinguish between near-fall events and falls. We investigated the detection of fall-related activities, including falls, near-falls, and non-falls, by utilizing dynamic motion images derived from video clips. There were two primary classification approaches: a vanilla convolutional neural network (CNN) model and a transfer learning approach that utilizes InceptionV3 and DenseNet201 models as feature extractors and train conventional machine learning classifiers, such as support vector machine (SVM), K-nearest neighborhood, decision tree, and random forest, and adaptive boosting models. The vanilla CNN model achieved a high accuracy of 97.89% compared to the transfer learning approach, which reached a maximum accuracy of 95.54% for binary classification of fall and non-fall events. On the other hand, the transfer learning approach, which integrated feature from InceptionV3 and DenseNet201 into machine learning classifiers, achieved an accuracy of up to 90.14% for the three-class classification of fall, non-fall, and near-fall events. The findings of this study underscores the modelÃs robustness in detecting various fall-related activities, highlighting its potential for improving safety in at-risk populations.}, archive = {J_ACCESS}, author = {Jung-Yeon Kim and Nab Mat and Chomyong Kim and Awais Khan and Hyo-Wook Gil and Jiwon Lyu and Euyhyun Chung and Kwang Seock Kim and Seob Jeon and Yunyoung Nam}, doi = {10.1109/ACCESS.2025.3539449}, journal = {IEEE Access}, pages = {26398-26410}, shortjournal = {IEEE Access}, title = {Analysis of near-fall detection method utilizing dynamic motion images and transfer learning}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A crowd counting and localization network based on adaptive feature fusion and multi-scale global attention up sampling”. ACCESS, 13, 26397. (www)

  @article{ , archive = {J_ACCESS}, author = {Min Wang and Li Huang and Jingke Yan and Jin Huang and Tao Yang}, doi = {10.1109/ACCESS.2025.3539363}, journal = {IEEE Access}, pages = {26397}, shortjournal = {IEEE Access}, title = {Corrections to “A crowd counting and localization network based on adaptive feature fusion and multi-scale global attention up sampling”}, volume = {13}, year = {2025}, }
• (2025). 6D pose estimation algorithm based on improved YOLOv5 with asymptotic feature pyramid network and attention mechanism. ACCESS, 13, 26384-26396. (www)

  @article{ , abstract = {Accurate 6D pose estimation is crucial for applications such as augmented reality and robotic arm manipulation. Inaccurate pose estimation can lead to augmented reality failures or robotic grasping errors, directly affecting system performance and safety. However, real-world 6D pose estimation presents significant challenges, including cluttered backgrounds, lighting variations, and the absence of surface textures. These issues make it difficult for existing methods to achieve both high accuracy and real-time performance. To address these challenges, we improve the YOLOv5-based 6D pose estimation algorithm. We enhance the original convolutional neural network model by introducing position-aware channel attention into the backbone network, increasing flexibility and precision in feature processing across different positions. Furthermore, the C3 module is replaced with the lightweight C2F module to improve feature extraction capabilities, maintaining accuracy while reducing model complexity. Additionally, the neck component is enhanced by replacing the BiFPN (Bi-directional Feature Pyramid Network) with the AFPN (Asymptotic Feature Pyramid Network), and the EMA module is integrated into the three C2F modules to boost multi-scale feature fusion and model stability. Finally, the PnP algorithm is applied to calculate the 6D pose. Experiments conducted on the public LINEMOD dataset show that our method significantly outperforms the original YOLOv5-6D method in terms of the 2D reprojection, 5cm5degree, and ADD metrics. The proposed method enhances accuracy and accelerates inference, meeting the requirements for robust pose estimation.}, archive = {J_ACCESS}, author = {Yan Zhang and Hanyu Ye}, doi = {10.1109/ACCESS.2025.3539743}, journal = {IEEE Access}, pages = {26384-26396}, shortjournal = {IEEE Access}, title = {6D pose estimation algorithm based on improved YOLOv5 with asymptotic feature pyramid network and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Fault detection and localization in an MT-VSC-HVDC network via a hybrid CNN-transformer model with a multi-head attention mechanism. ACCESS, 13, 26365-26383. (www)

  @article{ , abstract = {In this paper, a non-unit protection approach is suggested for recognizing fault events (internal and external) and their location in a High Voltage Direct Current (HVDC) system employing a deep learning (DL) model. This work utilizes a small sample size of DC current signals from one end of an HVDC-fed system, achieving a balance between data manageability and preserving vital information related to faults. The simulation of fault samples is carried out with the maximum possible operating scenarios, such as different fault resistances, locations, types of faults, and noisy conditions. Afterward, combined data with varying noise values is trained by the proposed DL model. The testing is performed under individual noise levels to evaluate model robustness and accuracy in real-time scenarios. In the DL framework, the work focuses on two core architectures, Convolutional Neural Networks (CNN) and Transformer models, and assesses their performance individually. Furthermore, it explores the hybrid model CNN-Transformer with a multi-head attention mechanism to capitalize on their combined strengths. Afterward, a detailed comparative result evaluation with other DL models is carried out to gauge robustness and accuracy in fault detection and localization. Comparative outcomes reveal that the Transformer models performed more remarkably in varying noise conditions, while recurrent models excelled in sequential data processing. The hybrid DL methods provided a balanced trade-off, improving fault detection and localization. The study utilizes several performance metrics such as accuracy, precision, recall, F1 score, MSE, RMSE, MAPE, and R2 errors to provide a comprehensive assessment. The fault detection without noise conditions attained an accuracy of 100%; however, there was a slight decrease (1.5%) in 20db noisy conditions. The proposed approach archives excellent performance in fault location tasks, such as MAPE:0.0308, MSE:1.6415, RMSE:1.2814, and R2:0.9982. The performance of the proposed approach is also validated with varying system operating conditions and comparative performance analysis with other state-of-the-art techniques. The findings offer valuable insights into the optimal model configurations and their practical applicability for reliable HVDC system protection, contributing to more effective and real-time fault diagnosis solutions.}, archive = {J_ACCESS}, author = {Manohar Mishra and Jai Govind Singh}, doi = {10.1109/ACCESS.2025.3534247}, journal = {IEEE Access}, pages = {26365-26383}, shortjournal = {IEEE Access}, title = {Fault detection and localization in an MT-VSC-HVDC network via a hybrid CNN-transformer model with a multi-head attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Topic modeling and sentiment analysis of arabic news headlines for a societal well-being scoring and monitoring system: Moroccan use case. ACCESS, 13, 26345-26364. (www)

  @article{ , abstract = {In today’s information-rich society, the rapid dissemination of news content across digital platforms plays a pivotal role in shaping public perception and discourse, yet they remain underutilized for sentiment analysis compared to other social media data. This study addresses this gap by using advanced Natural Language Processing (NLP) techniques to analyze Modern Standard Arabic (MSA) news content, categorizing headlines into themes like politics, business, education, weather, and sport, and applying sentiment analysis to classify them as positive or negative. These models form the foundation of our Societal Wellbeing Scoring and Monitoring System, which quantifies and monitors societal sentiment across key Quality of Life (QoL) aspects in Moroccan news. Our approach utilizes an LDA topic modeling approach in the preprocessing stage on the Moroccan News Arabic Dataset (MNAD) to enhance the quality of selected data sample before building classification models. Following this, we compare deep learning models with AraBERT and asafaya/bert-base-arabic (ASAFAYA) word embeddings to traditional machine learning algorithms using term frequency-inverse document frequency (TF-IDF). Notably, the GRU model with ASAFAYA embeddings excelled in the topic modeling task, achieving a 94.74% accuracy. We further analyzed public sentiment using data from Hibapress, a Moroccan news website, by scraping titles, likes, and dislikes to assign positive and negative sentiment scores. Leveraging a Bi-GRU model with ASAFAYA embeddings, we achieved an accuracy of 90.23%. We propose the Societal Wellbeing Scoring and Monitoring System, an aspect-based sentiment analysis framework designed to assign a wellbeing score over a specified sample or time period. Our findings highlight the effectiveness of combining topic modeling with deep learning-driven sentiment analysis to derive actionable insights from Arabic news, thereby enhancing the understanding of societal dynamics in Morocco.}, archive = {J_ACCESS}, author = {Ayoub Jannani and Soukaina Bouhsissin and Nawal Sael and Faouzia Benabbou}, doi = {10.1109/ACCESS.2025.3538888}, journal = {IEEE Access}, pages = {26345-26364}, shortjournal = {IEEE Access}, title = {Topic modeling and sentiment analysis of arabic news headlines for a societal well-being scoring and monitoring system: Moroccan use case}, volume = {13}, year = {2025}, }
• (2025). DRR-YOLO: A study of small target multi-modal defect detection for multiple types of insulators based on large convolution kernel. ACCESS, 13, 26331-26344. (www)

  @article{ , abstract = {The existing insulator defect detection algorithms are mainly characterized by their ability to identify only a single type of defect, accompanied by relatively low accuracy, a Dilated Re-parameterized Residual-YOLO (DRR-YOLO) algorithm is proposed, which is capable of identifying four defects of each of the four insulator types. Firstly, a new module DRR is proposed based on Dilated Re-param Block (DRB) and Dilation-wise Residual (DWR), which is combined with the C2f module of the YOLOv8 model to enhance the model’s detection effect for targets at different scales; Secondly, the Large Separable Kernel Attention SPPF (LSPPF) module is proposed to replace the SPPF in the original model, which enables the model to better preserve the feature information of the target while streamlining the structure; In addition, the advantages of MPDIoU and Inner-IoU are combined, and the loss function of the original model is replaced with Inner-MPDIoU. Finally, the DRR-YOLO model was compared with other existing models. Experiments demonstrate that the accuracy of the proposed model is superior to that of other models. Compared with the best - performing YOLOv5s and YOLOv8s, the mAP value on the validation set has increased by 1.9 percentage points and 2.2 percentage points respectively, reaching 94.7%. At the same time, it can be seen from the detection results that the proposed model has the highest confidence in detecting small targets.}, archive = {J_ACCESS}, author = {Mingming Hu and Jun Liu and Junfu Liu}, doi = {10.1109/ACCESS.2025.3539831}, journal = {IEEE Access}, pages = {26331-26344}, shortjournal = {IEEE Access}, title = {DRR-YOLO: A study of small target multi-modal defect detection for multiple types of insulators based on large convolution kernel}, volume = {13}, year = {2025}, }
• (2025). Aircraft trajectory prediction with inverted transformer. ACCESS, 13, 26318-26330. (www)

  @article{ , abstract = {Aircraft trajectory prediction plays a crucial role in air traffic management and significantly enhances operational safety and efficiency. In this work, we propose an aircraft trajectory prediction model that utilizes an Inverted Transformer, which is a state-of-the-art model for time-series prediction. In a vanilla Transformer, temporal tokens containing the multivariate features are embedded for each time step, but in the Inverted Transformer, each series is embedded independently into variate tokens. The Inverted Transformer captures multivariate correlations in a trajectory sequence, resulting in improved prediction accuracy. The proposed model was validated with actual air traffic surveillance data from the terminal airspace of Incheon International Airport, South Korea. The experimental results demonstrate that the prediction accuracy from the proposed model is superior to those of other deep learning-based prediction models.}, archive = {J_ACCESS}, author = {Seokbin Yoon and Keumjin Lee}, doi = {10.1109/ACCESS.2025.3539742}, journal = {IEEE Access}, pages = {26318-26330}, shortjournal = {IEEE Access}, title = {Aircraft trajectory prediction with inverted transformer}, volume = {13}, year = {2025}, }
• (2025). MulGCN: MultiGraph convolutional network for aspect-level sentiment analysis. ACCESS, 13, 26304-26317. (www)

  @article{ , abstract = {Aspect-level sentiment analysis (ALSA) is used to identify the sentiment polarities of the given aspects in a sentence. Various approaches have been proposed to improve the performance of ALSA, most recently graph convolutional networks (GCNs). Although GCN-based ALSA methods have obtained the promised results, how to effectively and simultaneously harness the semantic, syntactic structure information from the dependency tree and the contextual affective knowledge regarding the specific aspect remains a challenging research question. This research proposes a novel sentiment analysis method applied at the aspect level, called multigraph convolutional network (MulGCN), by integrating three GCNs. Unlike previous GCNs, the MulGCN model can simultaneously capture features related to three knowledge: syntax, semantics, and context by combining the dependency parser tree, affective information in SenticNet, and inter-aspect-aware technique. The research starts with a comprehensive survey of articles related to ALSA methods based on GCN to evaluate and unify the approach, thereby identifying the point where GCN has not been adequately used in ALSA methods to have a basis for proposing appropriate improvements to improve performance. Next, three knowledge-based GCNs are built to represent and extract high-level features related to syntax, semantics, and context. Then, the fusion mechanism is used to integrate the extracted features. Finally, these features are fed into a classifier consisting of convolutional layers to determine the sentiment polarity of the aspects. The MulGCN model will be experimented on three benchmark datasets. The experimental results prove the effectiveness of MulGCN model for improving the performance of ALSA including the accuracy and the $F_{1}$ score.}, archive = {J_ACCESS}, author = {Huyen Trang Phan and Van Du Nguyen and Ngoc Thanh Nguyen}, doi = {10.1109/ACCESS.2025.3537340}, journal = {IEEE Access}, pages = {26304-26317}, shortjournal = {IEEE Access}, title = {MulGCN: MultiGraph convolutional network for aspect-level sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). Fast and accurate non-linear time-domain modeling of synchronous machines. ACCESS, 13, 26289-26303. (www)

  @article{ , abstract = {The paper presents a novel method for non-linear 2D magnetic analyses of synchronous electric machines, with the emphasis on permanent magnet and synchronous reluctance AC machines with distributed windings. The foundation of the methodology lies in the air gap magnetic flux being an integral quantity, connecting the two magnetic structures – stator and rotor. The implementation was done through the fixed magnetic permeance configuration. In the method, the rotor and stator sub-models of different complexity were developed, that iteratively exchange the results between each other via the frozen permeability method, until a sufficient accuracy is achieved. Each of the sub-models can have an arbitrary number of nodes for scalar magnetic potentials, in order to accurately describe the magnetic conditions. However, we showed in the paper that only a small number of nodes in both sub-models is sufficient to achieve the accuracy of the results, comparable with the 2D finite element method results. The small number of nodes reflects in the reduced computational efforts of the presented method, giving it a promising forecast for numerous analyses, e.g. for calculations of voltage forms, inductances, armature reaction effects, iron loss estimations, etc. and further, for various optimization procedures.}, archive = {J_ACCESS}, author = {Mario Vukotić and Franz Diwoky and Martin Mohr and Damijan Miljavec}, doi = {10.1109/ACCESS.2025.3539780}, journal = {IEEE Access}, pages = {26289-26303}, shortjournal = {IEEE Access}, title = {Fast and accurate non-linear time-domain modeling of synchronous machines}, volume = {13}, year = {2025}, }
• (2025). Federated conditional variational auto encoders for cyber threat intelligence: Tackling non-IID data in SDN environments. ACCESS, 13, 26273-26288. (www)

  @article{ , abstract = {Federated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN). However, with the heterogenous nature of future sixth generation (6G) systems and continuously evolving threats spectrum, the training data at silos of individual SDN domains is expected to be non-independent and identically distributed (non-IID), which leads to substantial degradation in the performance of Federated Learning. To solve this problem, a novel framework, named, Federated Conditional Variational Auto Encoders (FCVAE) is proposed for Federated Learning based enhanced CTI with non-IID data. Firstly, based on the mathematical relation between expected weight divergence and the extent of non-IID statistics in data, a regularization expression is derived for mitigation of Non-IID effects. Secondly, threat-specific data is generated with CVAEs at each silo based on global parameters of latent space, to mitigate the performance degradation of CTI due to Non-IID data. Finally, the performance of the proposed scheme is evaluated with simulation results using the InSDN dataset, which depicts significant enhancement from 92% to 97% as potential solution to mitigate impact of Non-IID data on CTI in heterogenous networks.}, archive = {J_ACCESS}, author = {Syed Hussain Ali Kazmi and Rosilah Hassan and Faizan Qamar and Kashif Nisar and Mohammed Azmi Al-Betar}, doi = {10.1109/ACCESS.2025.3529894}, journal = {IEEE Access}, pages = {26273-26288}, shortjournal = {IEEE Access}, title = {Federated conditional variational auto encoders for cyber threat intelligence: Tackling non-IID data in SDN environments}, volume = {13}, year = {2025}, }
• (2025). WasDom: An efficient write protection for wasm JITed code with ARM domain. ACCESS, 13, 26260-26272. (www)

  @article{ , abstract = {WebAssembly (Wasm) is a binary instruction format designed to run web applications efficiently and securely across different browsers, including Chrome’s V8, Firefox’s SpiderMonkey, and Safari’s JavaScriptCore. While Wasm’s Just-In-Time (JIT) compilation offers significant performance benefits by converting wasm code into machine code (JITed code), it introduces security vulnerabilities by violating the W^X (Write XOR Execute) policy. Conventional methodologies, such as Intel MPK for safeguarding JITed code, are constrained to specific hardware and are ineffectual in mobile environments. Consequently, there is a necessity for the development of a ARM-compatible solution. This paper proposes WasDom, an efficacious write protection mechanism for wasm JITed code on ARM architecture. Leveraging ARM’s domain-based memory management, WasDom employs a randomized domain allocation strategy to permit multiple cores to access JITed code securely. The prototype of WasDom was implemented in the V8 runtime and demonstrated a minimal performance overhead of less than 11% while providing strong write protection. The system manages memory permissions dynamically through the Domain Access Control Register (DACR), ensuring that memory regions are writable during JIT compilation and executable during runtime, thus enforcing a strong W^X policy.}, archive = {J_ACCESS}, author = {Suhyeon Song and Chaewon Shin and Donghyun Kwon}, doi = {10.1109/ACCESS.2025.3537756}, journal = {IEEE Access}, pages = {26260-26272}, shortjournal = {IEEE Access}, title = {WasDom: An efficient write protection for wasm JITed code with ARM domain}, volume = {13}, year = {2025}, }
• (2025). Design and experimental study of a composite piezoelectric stick-slip actuator based on elliptical trajectory motion. ACCESS, 13, 26248-26259. (www)

  @article{ , abstract = {A composite piezoelectric stick-slip actuator (CPSSA) is proposed to address the issue of low output efficiency of piezoelectric stick-slip actuators during operation caused by the displacement backward phenomenon. CPSSA uses piezoelectric stack as the excitation source comprises, combining with a flexible lever and a bridge displacement amplification mechanism to create an approximate elliptical trajectory motion of the driving foot. This trajectory adjusting the normal pressure between the driving foot and the moving guideway to minimize backward movement. Firstly, the structure is designed and analyzed theoretically, elucidating the operational principle. Secondly, the transient dynamics of CPSSA are analyzed using COMSOL simulation software to optimize key parameters. Finally, a prototype is fabricated, and CPSSA characterization tests are conducted. Experimental results reveal the driving foot of CPSSA realizes elliptical trajectory motion and a maximum single-step output displacement of $11.6\mu $ m, a minimum backward displacement of 5.67% and displacement resolution of $0.075\mu $ m. At driving frequency of 500 Hz and an input voltage of 150 V, the maximum output speed is 5.45 mm/s, and the maximum load is 60 g. The structure not only effectively suppresses backward displacement but also demonstrates good motion stability and high motion accuracy. These findings offer valuable insights for future research on suppressing backward displacement in piezoelectric stick-slip actuators.}, archive = {J_ACCESS}, author = {Xiaochao Tian and Zhenming Wang and Yingyu Dai and Jinlong Liu}, doi = {10.1109/ACCESS.2025.3538940}, journal = {IEEE Access}, pages = {26248-26259}, shortjournal = {IEEE Access}, title = {Design and experimental study of a composite piezoelectric stick-slip actuator based on elliptical trajectory motion}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of UAV-based FSO communication system for real time traffic monitoring and management in smart cities. ACCESS, 13, 26236-26247. (www)

  @article{ , abstract = {This paper presents an unmanned aerial vehicle (UAV) based FSO communication system with Alamouti scheme for traffic monitoring and management in smart cities to avoid traffic congestion during peak hours. Efficient traffic monitoring and management systems are pivotal for refining traffic flow, reducing traffic congestion, and improving overall urban mobility. By integrating UAV (as relay) and Alamouti scheme in FSO communication system, it is possible to significantly improve the system’s overall performance. Since UAV is used for coverage area improvement, the proposed system model contributes for dual hop FSO communication system. In order to enhance the system’s overall performance and to mitigate the effects of atmospheric turbulence, the Alamouti scheme is employed in the proposed system model. The system’s performance has been investigated by taking factors such as atmospheric loss, pointing error, atmospheric turbulence and angle-of-arrival fluctuation caused due to the hovering state of UAV. The symbol error rate (SER) of the proposed system is determined here using the moment generating function (MGF) technique. Further, the obtained theoretical equation for SER is simulated in MATLAB and the Monte-Carlo simulation is employed to validate the results.}, archive = {J_ACCESS}, author = {D Komathi and G Aarthi}, doi = {10.1109/ACCESS.2025.3538935}, journal = {IEEE Access}, pages = {26236-26247}, shortjournal = {IEEE Access}, title = {Performance analysis of UAV-based FSO communication system for real time traffic monitoring and management in smart cities}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Development of a hybrid AHP-TOPSIS decision-making framework for technology selection in hospital medication dispensing processes”. ACCESS, 13, 26235. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Development of a Hybrid AHP-TOPSIS Decision-Making Framework for Technology Selection in Hospital Medication Dispensing Processes”).}, archive = {J_ACCESS}, author = {Ganda Boonsothonsatit and Supachai Vongbunyong and Nilubon Chonsawat and Wirachchaya Chanpuypetch}, doi = {10.1109/ACCESS.2025.3539109}, journal = {IEEE Access}, pages = {26235}, shortjournal = {IEEE Access}, title = {Corrections to “Development of a hybrid AHP-TOPSIS decision-making framework for technology selection in hospital medication dispensing processes”}, volume = {13}, year = {2025}, }
• (2025). ADS-YOLO: A multi-scale feature extraction remote sensing image object detection algorithm based on dilated residuals. ACCESS, 13, 26225-26234. (www)

  @article{ , abstract = {Object detection in remote sensing images is of significant research value in fields such as environmental monitoring and urban planning. However, the large variation in object sizes, along with challenges such as small and densely packed objects, makes this task particularly challenging. To address these issues, we propose an algorithm for multi-scale feature extraction in remote sensing image detection using dilated residuals (ADS-YOLO). Firstly, to address the challenges of scale variation and small target size, the Dilation-wise Residual (DWR) design is employed to form the C2f_DWR module, which restructures the bottleneck structure within the C2f segment to facilitate the extraction and fusion of multi-scale contextual information, thus reducing the difficulty associated with target scale variation. Secondly, inspired by the Adown subsampling convolution module from YOLOv9, we use it to replace the convolutions in the Backbone, enabling the model to capture finer image details at higher levels, while maintaining accuracy and reducing computational load. Lastly, to address the issue of dense targets, we design the Soft-NMS-ShapeIoU module to improve the consistency of bounding boxes and target shapes, while also suppressing adjacent boxes. Experimental results demonstrate that, on the publicly available remote sensing image datasets DIOR, RSOD, and NWPU VHR-10, the proposed ADS-YOLO model outperforms other state-of-the-art methods by a significant margin.}, archive = {J_ACCESS}, author = {Jianying Li and Yajun Chen and Meiqi Niu and Wenhao Cai and Xiaoyang Qiu}, doi = {10.1109/ACCESS.2025.3538548}, journal = {IEEE Access}, pages = {26225-26234}, shortjournal = {IEEE Access}, title = {ADS-YOLO: A multi-scale feature extraction remote sensing image object detection algorithm based on dilated residuals}, volume = {13}, year = {2025}, }
• (2025). EVI-GPBO: Estimated variance integration-based gaussian process bayesian optimization. ACCESS, 13, 26208-26224. (www)

  @article{ , abstract = {Gaussian process Bayesian optimization (GPBO) based on the upper confidence bound is a method for finding the approximate maximum solution using observations maximizing $E + \beta V^{1/2}$ consisting of the estimated average E and variance V obtained by Gaussian process regression. The exploration weight $\beta $ is the coefficient for adjusting the exploration-exploitation trade-off, which affects search performance. In previous studies, constant value, logarithmic decay, and probabilistic value as the exploration weight have been proposed, and they exhibit good search performance. For better search performance, we propose a method for setting the exploration weight using estimated variance integration (EVI) as a novel meta-heuristics algorithm. Because the integration of the estimated variance V represents the total amount of uncertainty in an entire search space, using it as the exploration weight, we can expect the search performance to be higher than that of previous methods. In this study, an exact analytical solution for integrating the estimated variance in the Gaussian kernel case was provided to realize EVI-GPBO. Experiments on searching for the global optimum using benchmark functions for evaluating approximate search algorithms were conducted to evaluate EVI-GPBO. The results indicate that EVI-GPBO can find optimal solutions with a high percentage compared to the existing methods. In a more realistic setting, an experiment on hyperparameter tuning for a support vector machine was conducted. The results indicate that EVI-GPBO obtained a higher score than the other existing methods. In summary, we conclude that EVI-GPBO is an effective search algorithm.}, archive = {J_ACCESS}, author = {Yuto Omae and Yohei Kakimoto and Makoto Sasaki and Masaya Mori}, doi = {10.1109/ACCESS.2025.3539209}, journal = {IEEE Access}, pages = {26208-26224}, shortjournal = {IEEE Access}, title = {EVI-GPBO: Estimated variance integration-based gaussian process bayesian optimization}, volume = {13}, year = {2025}, }
• (2025). Optimized task offloading in multi-domain IoT networks using distributed deep reinforcement learning in edge computing environments. ACCESS, 13, 26193-26207. (www)

  @article{ , abstract = {In the Internet of Things (IoT) networks, sensors, gateways, and services interoperate at different levels to provide services to the end users. IoT networks are deployed in different domains for specific tasks that can be monitored from remote locations. The increase in the number of IoT-connected devices and their notable limited computational power calls for resource-efficient and in-between layers of task processing on the network. In this study, we utilized deep reinforcement learning to intelligently model the offloading policies as Markov Decision Process (MDP) for IoT devices in a distributed manner by considering IoT devices as agents that make offloading decisions taking into account the environmental dynamics. To attain optimal policy in the learning process that caters to high dimensionality, deep Q-network was employed to model the agents’ interaction in a dynamic and environment-sensitive manner. The architecture allows local decision-making by IoT edge nodes for tasks offloading to edge servers based on connectivity, resource availability, and proximity. Extensive simulation under different learning rates, batch sizes, and memory sizes shows that the proposed scheme with the utilization of a CNN approximator generates optimal policy and higher convergence performance with lower latency than the conventional Q-learning model and several other existing algorithms.}, archive = {J_ACCESS}, author = {Ojonukpe Sylvester Egwuche and Japie Greeff and Absalom El-Shamir Ezugwu}, doi = {10.1109/ACCESS.2025.3535870}, journal = {IEEE Access}, pages = {26193-26207}, shortjournal = {IEEE Access}, title = {Optimized task offloading in multi-domain IoT networks using distributed deep reinforcement learning in edge computing environments}, volume = {13}, year = {2025}, }
• (2025). Class imbalance in network traffic classification: An adaptive weight ensemble-of-ensemble learning method. ACCESS, 13, 26171-26192. (www)

  @article{ , abstract = {Network Traffic Classification (NTC) serves as a crucial element in network management, and the rapid progress in machine learning has inspired the utilization of learning methods to discern network traffic. The inherent characteristics of network traffics result in uneven class distributions when datasets are shaped, creating a phenomenon known as class imbalance. This phenomenon has garnered growing attention across various research fields. Despite encountering performance setbacks attributed to class imbalance, this challenge remains inadequately examined in the realm of network traffic classification. This paper introduces the Adaptive Weight Ensemble-of-Ensemble Learning (AWEE) method as an innovative solution to this challenge. The AWEE integrates multiple ensemble layers with a dynamic weight adjustment mechanism, showcasing the collaborative intelligence of diverse modeling strategies. Using a sliding window-based validation approach, the model enhances adaptability and robustness to address concept drift in dynamic data streams. Experimental studies on benchmark datasets demonstrate the superior performance of AWEE (achieving an outstanding accuracy rate of over 98%), highlighting its effectiveness in handling class imbalance challenges across diverse network traffic scenarios. AWEE, outperforms competitive methods, including algorithmic-level, cost-sensitive, and data-level techniques, showcasing its robustness and superior performance in addressing class imbalance challenges across a wide range of network traffic scenarios.}, archive = {J_ACCESS}, author = {Mahmoud Abbasi and Sebastián López Flórez and Amin Shahraki and Amir Taherkordi and Javier Prieto and Juan M. Corchado}, doi = {10.1109/ACCESS.2025.3538170}, journal = {IEEE Access}, pages = {26171-26192}, shortjournal = {IEEE Access}, title = {Class imbalance in network traffic classification: An adaptive weight ensemble-of-ensemble learning method}, volume = {13}, year = {2025}, }
• (2025). Non-invasive quantification of corrosion in cu interconnects using regression models with S-parameters. ACCESS, 13, 26157-26170. (www)

  @article{ , abstract = {Copper electrodes are crucial in electronic systems but are prone to corrosion, which can degrade performance and lead to system failures and safety risks. Existing fault diagnosis methods are limited in their ability to effectively and non-destructively evaluate corrosion due to constraints in size resolution and the lack of effective indicators for corrosion evolution in electronic packages. This study presents a non-destructive quantification method using regression models with scattering parameters, offering early detection and high diagnostic accuracy. We gathered a comprehensive database of scattering parameter patterns reflecting various levels of corrosion to enhance the reliability of fault detection and diagnosis in electronic devices. To evaluate the feasibility of scattering parameters for non-destructive and quantitative corrosion assessment in interconnects, we conducted a comparative study across ten categories of models: Basic Linear Regression, Regularized Linear Models, Advanced Linear Models, Bayesian Models, Robust Models, Kernel-based Models, Neighbor-based Models, Tree-based Models, Gradient Boosting Models, and Neural Networks. Despite testing various advanced models, the Basic Linear Regression model outperformed all of them, achieving a Mean Absolute Error as low as 0.214% (Evaluated corrosion range: 0 - 100%). This superior performance is attributed to the close and linear relationship between scattering parameters and the degree of corrosion, demonstrating that scattering parameters are effective indicators for corrosion. Additionally, integrating independent scattering parameter channels with a data augmentation method using interpolation further enhanced the diagnostic accuracy of the regression models.}, archive = {J_ACCESS}, author = {Tae Yeob Kang and Seunghyeok Hong}, doi = {10.1109/ACCESS.2025.3536287}, journal = {IEEE Access}, pages = {26157-26170}, shortjournal = {IEEE Access}, title = {Non-invasive quantification of corrosion in cu interconnects using regression models with S-parameters}, volume = {13}, year = {2025}, }
• (2025). FLSH: A framework leveraging similarity hashing for android malware and variant detection. ACCESS, 13, 26142-26156. (www)

  @article{ , abstract = {As technology advances, the frequency and complexity of cyberattacks continue to rise, with Android OS, given its significant market share, becoming a prime target for sophisticated malware attacks. While the core categories of malware remain consistent, minor modifications often allow these variants to evade detection, posing significant challenges for security systems. To address this, various techniques and algorithms have been employed to improve malware detection and classification. In this paper, we focus on leveraging fuzzy hashes to calculate the similarity index between files, aiding in the identification of malicious content within seemingly legitimate APK files. Our research enhances the accuracy and reliability of fuzzy hashes, particularly for static features, in detecting Android malware and its variants. Unlike traditional approaches, our method employs a distinctive static feature-based fuzzy hashing technique. We conducted experiments on a dataset of 2000 APK files, including both benign and malicious samples, and classified malware into six categories trojan, adware, spyware, virus, downloader, and hacktool. The results showed a significant improvement in precision, recall, and F-measure, achieving an overall accuracy of 96.67%, without relying on complex machine learning or deep learning methods.}, archive = {J_ACCESS}, author = {Hassan Jalil Hadi and Alina Khalid and Faisal Bashir Hussain and Naveed Ahmad and Mohammed Ali Alshara}, doi = {10.1109/ACCESS.2025.3537110}, journal = {IEEE Access}, pages = {26142-26156}, shortjournal = {IEEE Access}, title = {FLSH: A framework leveraging similarity hashing for android malware and variant detection}, volume = {13}, year = {2025}, }
• (2025). Toward variational structural learning of bayesian networks. ACCESS, 13, 26130-26141. (www)

  @article{ , abstract = {This study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables. Traditional approaches, such as MCMC and stochastic search, often encounter computational barriers due to the super-exponential growth of the Directed Acyclic Graph (DAG) space. Our method introduces a scalable alternative by leveraging a factorized variational family to approximate the posterior distribution over DAG structures, enabling efficient computation of Bayesian scores and predictive posterior inference. Unlike previous methods, which are constrained by high computational costs or domain-specific limitations, this approach achieves tractability through mean-field variational inference and tractable updating equations, allowing application to significantly larger datasets. Empirical results on benchmark datasets demonstrate that the proposed framework consistently outperforms state-of-the-art methods in terms of scalability and predictive accuracy while maintaining robustness across diverse scenarios. This work represents a key step towards scalable Bayesian structural learning and opens avenues for future research to refine the variational approximation and incorporate advanced parallelization techniques.}, archive = {J_ACCESS}, author = {Andrés R. Masegosa and Manuel Gómez-Olmedo}, doi = {10.1109/ACCESS.2025.3533878}, journal = {IEEE Access}, pages = {26130-26141}, shortjournal = {IEEE Access}, title = {Toward variational structural learning of bayesian networks}, volume = {13}, year = {2025}, }
• (2025). Enhanced network bootstrapping technique using multi-channel in 6TiSCH minimal cells. ACCESS, 13, 26116-26129. (www)

  @article{ , abstract = {As the Internet of Things (IoT) continues to expand, the importance of reliable wireless networks grows significantly. In response, IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) was introduced to provide reliable communication by combining Time-Slotted Channel Hopping (TSCH) with IPv6. However, despite these advantages, wireless collisions in the 6TiSCH minimal cell arise as the network’s scale grows, which causes problems in network bootstrapping. So we propose the Enhanced Network Bootstrapping technique using Multi-Channel in 6TiSCH minimal cells (EBMC), which enhances bootstrapping by utilizing multi-channel in minimal cells. EBMC reduces wireless collisions, accelerates synchronization, and shortens Routing Protocol for Low-Power and Lossy Networks (RPL) joining times. EBMC comprises three techniques: transmission channel offset selection by packet type, selective control packet reception to maximize resource utilization, and avoiding Enhanced Becone (EB) transmission collisions with the clock source. Experimental results using the 6TiSCH simulator show that EBMC outperforms conventional 6TiSCH, Pairing strategy, and Time-Variant RGB (TRGB) in terms of synchronization time, topology formation, energy consumption, and end-to-end latency.}, archive = {J_ACCESS}, author = {Jeongbae Park and Sang-Hwa Chung}, doi = {10.1109/ACCESS.2025.3539276}, journal = {IEEE Access}, pages = {26116-26129}, shortjournal = {IEEE Access}, title = {Enhanced network bootstrapping technique using multi-channel in 6TiSCH minimal cells}, volume = {13}, year = {2025}, }
• (2025). Predicting student performance based on knowledge characteristics and learning ability. ACCESS, 13, 26105-26115. (www)

  @article{ , abstract = {Knowledge tracking is a significant area of inquiry within contemporary educational research. Recent advancements in deep learning have led to the development of knowledge tracking models that demonstrate superior predictive capabilities compared to traditional approaches. In response to the challenges of limited interpretability and the complexities associated with long sequence dependencies in existing deep knowledge tracking frameworks, this study introduces a novel deep knowledge tracking model that incorporates an attention mechanism. This model utilizes the knowledge features of exercises alongside students’ learning abilities as input layer feature information, enabling it to more effectively identify critical features at various temporal points throughout the learning process, ultimately enhancing predictive performance. Extensive experimentation was conducted using five authentic datasets, with results indicating that the proposed model offers a more precise evaluation of students’ knowledge acquisition. Additionally, findings from ablation studies underscore the importance of incorporating practice difficulty and learning ability, which together enrich the input layer’s feature information and yield a synergistic effect that positively influences the model’s predictive accuracy. The outcomes of this research have the potential to facilitate personalized learning by monitoring the learning trajectories and knowledge states of online learners.}, archive = {J_ACCESS}, author = {Zhonghua Zhan and Tongping Shen and Jiaming Wang}, doi = {10.1109/ACCESS.2025.3538700}, journal = {IEEE Access}, pages = {26105-26115}, shortjournal = {IEEE Access}, title = {Predicting student performance based on knowledge characteristics and learning ability}, volume = {13}, year = {2025}, }
• (2025). Compounded real-time operating systems for rich real-time applications. ACCESS, 13, 26079-26104. (www)

  @article{ , abstract = {In embedded systems, the emergence of rich applications requires real-time operating systems (RTOSs) that provide rich features. This paper describes compounded RTOSs (cRTOSs) that provide the rich features of a general-purpose operating system (GPOS) in a traditional RTOS (tRTOS). A cRTOS partitions a physical machine with a hypervisor and executes the GPOS and tRTOS in parallel. In the cRTOS, a rich RT application executes in the tRTOS, and accesses not only the RT features of the tRTOS via local system calls but also the rich features of the GPOS via remote system calls and signals. The remote system calls and signals are built on top of remote procedure calls to a service program in the GPOS. Contrary to existing solutions such as PREEMPT_RT or Xenomai, the cRTOS does not require modification of the GPOS kernel and allows the use of a wide range of tRTOSs. This paper evaluates the proposed method by implementing three cRTOSs on Raspberry Pi 4B. They use Jailhouse as the hypervisor, Linux as the GPOS, and NuttX or FreeRTOS as the tRTOS. This paper also evaluates their usability by implementing three rich RT applications on the three cRTOSs. This paper compared the RT performance of the implemented cRTOSs to PREEMPT_RT and Xenomai using timing accuracy and interrupt latency benchmarks. The results show that the cRTOSs delivered a better RT performance with a minimum of 5 microsecond jitter and a well-bounded lower maximum latency in these two benchmarks than the other RTOSs.}, archive = {J_ACCESS}, author = {Chung-Fan Yang and Yasushi Shinjo}, doi = {10.1109/ACCESS.2025.3538561}, journal = {IEEE Access}, pages = {26079-26104}, shortjournal = {IEEE Access}, title = {Compounded real-time operating systems for rich real-time applications}, volume = {13}, year = {2025}, }
• (2025). GLF-NET: Global and local dynamic feature fusion network for real-time steel strip surface defect detection. ACCESS, 13, 26063-26078. (www)

  @article{ , abstract = {Surface defect detection plays a crucial role in ensuring the quality standards of hot-rolled steel strips. To meet the demands for high precision and real-time performance in industrial defect detection, this paper introduces an improved one-stage detector based on YOLOv5s, named GLF-NET, that focuses on a good balance between speed and precision. Firstly, an Attention Augmented Module (AAM) is proposed and used in the backbone, with the aim of minimizing the loss of semantic and location information of defects during the process of feature extraction. Secondly, to enrich the model’s capacity of representing multi-scale features of defects, an innovative Global and Local Dynamic Feature Fusion (GLF) module is designed and plugged into the top-down FPN part of the neck, bridging the semantic gap between different feature layers and enabling the model to adaptively select features for fusion. Additionally, a novel Receptive Field Augmented Module (RFA) is proposed and integrated into the bottom-up PAN structure of the neck, enhancing the detector’s ability of perceiving defects with irregular shapes and large aspect ratios. Extensive experimental results on the NEU-DET steel strip surface defect dataset demonstrate that GLF-NET obtains an impressive mAP value of 79.2%, exceeding YOLOv5s by 4.2%. Furthermore, with an impressive detection speed of 95 Frames Per Second (FPS), GLF-NET not only meets the real-time demands of industrial defect detection but also demonstrates exceptional capabilities in defect detection. Code is available at https://github.com/MYF1124/GLF-NET.}, archive = {J_ACCESS}, author = {Yunfei Ma and Zhaohui Zhang and Shaocheng Ma and Kailun Shi and Chenglong Fan}, doi = {10.1109/ACCESS.2025.3539350}, journal = {IEEE Access}, pages = {26063-26078}, shortjournal = {IEEE Access}, title = {GLF-NET: Global and local dynamic feature fusion network for real-time steel strip surface defect detection}, volume = {13}, year = {2025}, }
• (2025). On privacy, accuracy, and fairness trade-offs in facial recognition. ACCESS, 13, 26050-26062. (www)

  @article{ , abstract = {Face recognition (FR) technology has a concurrent interaction between accuracy, privacy, and fairness. To investigate this, we present a deep learning FR model trained on the BUPT-Balancedface dataset, which is racially balanced, and incorporate differential privacy (DP) into the private variant of the model to ensure data confidentiality, retaining the focus on fairness aspects. We analyze the verification accuracy of private (with different privacy budgets) and non-private models using a variety of benchmark FR datasets. Our results show that the non-private model achieves reasonably high accuracy comparable to the current state-of-the-art models. The private model shows a trade-off between accuracy and privacy, as well as between fairness and privacy, meaning that enhancing privacy tends to reduce both accuracy and fairness. Our findings indicate that DP unevenly reduces accuracy across demographics and suggest that adjusting the privacy budget allows for better balancing of privacy, accuracy, and fairness. Furthermore, we extend our experiments to consider real-world bias by training our private model on the imbalanced CASIA-WebFace dataset, where variability in accuracy and fairness disparities are amplified, showing the impact of dataset composition on the interplay between privacy, accuracy, and fairness. Additionally, we show that traditional membership inference attacks (MIAs) can compromise privacy in FR systems. We further introduce a more realistic, identity-based MIA (I-MIA) tailored specifically for FR. Our analysis demonstrates that DP significantly mitigates privacy risks from both traditional MIAs and the proposed I-MIA.}, archive = {J_ACCESS}, author = {Amir Zarei and Ahmad Hassanpour and Kiran Raja}, doi = {10.1109/ACCESS.2025.3536784}, journal = {IEEE Access}, pages = {26050-26062}, shortjournal = {IEEE Access}, title = {On privacy, accuracy, and fairness trade-offs in facial recognition}, volume = {13}, year = {2025}, }
• (2025). Artistic intelligence: A diffusion-based framework for high-fidelity landscape painting synthesis. ACCESS, 13, 26037-26049. (www)

  @article{ , abstract = {Generating high-fidelity landscape paintings presents significant challenges, necessitating precise control over both structure and style. This paper introduces LPGen, a novel diffusion-based model specifically designed for landscape painting generation. LPGen incorporates a decoupled cross-attention mechanism that serves as a style controller, independently processing stylistic features to manage the style of the generated images. Furthermore, LPGen incorporates a structural controller—a multi-scale encoder that governs the layout of landscape paintings, achieving a balance between aesthetics and composition. The model is pre-trained on a curated dataset of high-resolution landscape images categorized by distinct artistic styles, followed by fine-tuning to ensure detailed and consistent outputs. Extensive evaluations demonstrate that LPGen outperforms current state-of-the-art models in producing structurally accurate and stylistically coherent paintings. This work advances AI-generated art and opens new avenues for exploring the intersection of technology and traditional artistic practices. Our code, dataset, and model weights will be made publicly available.}, archive = {J_ACCESS}, author = {Wanggong Yang and Yifei Zhao}, doi = {10.1109/ACCESS.2024.3518532}, journal = {IEEE Access}, pages = {26037-26049}, shortjournal = {IEEE Access}, title = {Artistic intelligence: A diffusion-based framework for high-fidelity landscape painting synthesis}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based detection of one and two-column textual blocks in camera-captured pashto documents images. ACCESS, 13, 26027-26036. (www)

  @article{ , abstract = {The paper explores the layout analysis and classification task of Pashto document images, a field with limited research due to the language’s low-resource status. It uses Document Image Analysis (DIA) to detect one-column and two-column text blocks from Pashto documents captured using handheld cameras. A novel dataset containing real-world documents is annotated using bounding boxes to distinguish between one-column and two-column layouts. Layout analysis plays a vital role in Document Image Analysis (DIA) and answers questions about the nature of Document Images integral parts (components). These components are mainly text, graphics, and tabular data. The detection/identification of such elements is initially dependent on the general layout of a document. Textual columns are one of the basic layout structures present in document images. Such textual columns need identification as one-column or two-column before feeding the textual blocks into an Optical Character Recognition (OCR) system. Also, it becomes more crucial if the document images belong to a low-resource language like Pashto. Thus, this work contributes mainly in two aspects. First, it contributes to creating a dataset containing camera-captured document images with one-column and two-column patterns. Second, it proposes a deep learning model to identify/detect one-column and two-column textual blocks in the Pashto text images. The proposed model utilizes a reputed variant of a Faster Region-based Convolutional Neural Network (R-CNN) called single shot detector (SSD). The evaluation was done by examining the test set and a mean average precision (mAP) of 89% is achieved as a baseline.}, archive = {J_ACCESS}, author = {Badshah Saeed and Siraj Muhammad and Khalil Ullah and Abdallah Namoun and Ahmad Khan and Ikram Syed and Syed Sajid Ullah and Ibrar Hussain}, doi = {10.1109/ACCESS.2025.3531713}, journal = {IEEE Access}, pages = {26027-26036}, shortjournal = {IEEE Access}, title = {Deep learning-based detection of one and two-column textual blocks in camera-captured pashto documents images}, volume = {13}, year = {2025}, }
• (2025). Compressive signal processing for estimating range-velocity-AoA in FMCW radar applications. ACCESS, 13, 26008-26026. (www)

  @article{ , abstract = {Frequency-modulated continuous wave (FMCW) radars are known to accurately estimate the parameters of targets with low-cost and low-power transceiver systems. This work shows that some features associated with the spectrum of estimated parameters allow one to integrate the compressive sampling (CS) theory into the FMCW signal processing. To this end, we establish a new analytical framework through a tensor format to facilitate a systematic and convenient FMCW signal processing model. By observing the sparsity feature in the tensor of FMCW spectrum, we develop an extensive theoretical analysis to justify the use of CS theory in the FMCW radars, and it enabled us to propose a novel scheme, namely compressive FMCW signal processing, for estimating range, velocity, and angle-of-arrival (AoA) of the targets. The addition of CS theory allows our proposed scheme to use sampling-rates below Nyquist criterion, thus minimizing the number of sampled data and mitigating the issues related to high-rate and power-hungry analog-to-digital converter (ADC) in extremely high-frequency radar applications. Furthermore, the proposed compressive FMCW signal processing also significantly reduces the number of radio-frequency (RF) front-end elements necessary for estimating AoA, leading to a further saving in cost and power consumption of the FMCW radars. Despite having sub-Nyquist sampling-rates and reduced RF front-end elements, the performance evaluations show that our CS-based approach maintains the estimation capability and accuracy of conventional FMCW signal processing.}, archive = {J_ACCESS}, author = {Eny Sukani Rahayu and Dyonisius Dony Ariananda and Risanuri Hidayat and Gabriel Daniel}, doi = {10.1109/ACCESS.2025.3536614}, journal = {IEEE Access}, pages = {26008-26026}, shortjournal = {IEEE Access}, title = {Compressive signal processing for estimating range-velocity-AoA in FMCW radar applications}, volume = {13}, year = {2025}, }
• (2025). The spatial layout and block development correlation analysis of tropical island towns based on GIS approaches: A case study of hainan province. ACCESS, 13, 25995-26007. (www)

  @article{ , abstract = {As critical nodes in rural and urban development, small towns play a significant role in promoting regional economic growth. However, challenges such as unreasonable spatial layouts and unclear industrial positioning have emerged, hindering sustainable development for both the towns and their surrounding areas. To elucidate the relationship between tropical island towns and regional development, this study examines 100 representative towns in Hainan Province, China. Utilizing ArcGIS software and spatial analysis methods—including kernel density estimation, nearest neighbor index, and spatial autocorrelation analysis—the spatial distribution patterns of these towns and their correlations with regional development factors are explored. The findings reveal significant clustering in the spatial distribution of towns, particularly tourist towns, which tend to concentrate in areas rich in tourism resources. Key factors influencing town development are identified, such as resource endowment, market accessibility, transportation infrastructure, and population distribution. These insights provide theoretical guidance for optimizing the spatial layout of small towns by leveraging geographical and resource advantages, thereby enhancing sustainable development and regional competitiveness.}, archive = {J_ACCESS}, author = {Hao Bai and Jingzhi Liu and Xinying Cao}, doi = {10.1109/ACCESS.2025.3537666}, journal = {IEEE Access}, pages = {25995-26007}, shortjournal = {IEEE Access}, title = {The spatial layout and block development correlation analysis of tropical island towns based on GIS approaches: A case study of hainan province}, volume = {13}, year = {2025}, }
• (2025). Resource adaptive automated task scheduling using deep deterministic policy gradient in fog computing. ACCESS, 13, 25969-25994. (www)

  @article{ , abstract = {The rapidly increasing complexity of Internet of Things applications and the exponential growth in data generation pose significant challenges in terms of latency and network capacity constraints, especially in cloud computing. Fog computing carries have emerged as an effective solution by decentralizing data processing, reducing latency, and bringing computation closer to the data sources. This paper presents a novel adaptive scheduling framework based on the DDPG algorithm for task scheduling optimization in fog computing environments. Our framework is based on DDPG, a reinforcement learning algorithm well suited for continuous action spaces, adapting scheduling strategies to real-time changes in task demands and resource availability. Such capability allows for highly accurate decisions to be made in real time, which is of particular importance for latency-sensitive applications such as autonomous vehicles, remote healthcare, and industrial automation. The contributions of this paper include the development of an adaptive scheduling framework that can support sequential, parallel, and dependency-based scheduling. This framework improves several critical performance metrics by 30%, reduces makespan, reduces fault tolerance by 25%, and improves system scalability and reliability by 20%. Data processing localization through our approach reduces bandwidth and latency usage by up to 40% and improves data privacy and efficiency in data management. Simpy simulated this work using Google Cloud Jobs datasets, and the results support the promising combination of advanced machine learning and fog computing. This research demonstrates the transformative impact of deep reinforcement learning in improving task scheduling in distributed computing environments, laying a strong foundation for further research aimed at harnessing the full capabilities of fog computing for the ongoing demands of IoT applications.}, archive = {J_ACCESS}, author = {Prashanth Choppara and S. Sudheer Mangalampalli}, doi = {10.1109/ACCESS.2025.3539606}, journal = {IEEE Access}, pages = {25969-25994}, shortjournal = {IEEE Access}, title = {Resource adaptive automated task scheduling using deep deterministic policy gradient in fog computing}, volume = {13}, year = {2025}, }
• (2025). Lower energy storage-based 9L- switched capacitor ANPC inverter topology with voltage boosting features. ACCESS, 13, 25958-25968. (www)

  @article{ , abstract = {This article proposes a novel 9L-switched capacitor inverter circuit with a voltage-boosting feature. The presented circuit uses fewer energy-stored capacitors, which reduces the size and cost. The conventional Active Neutral Point Clamped (ANPC) inverter output voltage is half of the input voltage, i.e. dc-link voltage requirement is double the output voltage which leads to several challenges such as high dc-link capacitor size and voltage ratings, front-end DC-DC boost converter, etc. These issues are eliminated in the proposed topology due to boosting features, but a soft-charging method is required to suppress the inrush current. A detailed comparison between the proposed and existing SC-based 9L- inverter is presented. To validate the proposed circuit, the simulation is carried out using MATLAB/Simulink tool and the same is verified in a prototype setup for 0.8 kW and its efficiency is 97.02% which is superior than the other topologies relating to this research work.}, archive = {J_ACCESS}, author = {R. Sekar and M. Jagabar Sathik and Mamdouh L. Alghaythi and Meshari S. Alshammari and K. Vijayakumar}, doi = {10.1109/ACCESS.2025.3535941}, journal = {IEEE Access}, pages = {25958-25968}, shortjournal = {IEEE Access}, title = {Lower energy storage-based 9L- switched capacitor ANPC inverter topology with voltage boosting features}, volume = {13}, year = {2025}, }
• (2025). Facial feature extraction algorithm based on improved YOLOv7-tiny. ACCESS, 13, 25946-25957. (www)

  @article{ , abstract = {Facial feature extraction is a critical step in driver fatigue detection, essential for improving driving safety. This paper proposes a novel driver facial feature extraction algorithm based on YOLOv7-Tiny to address the challenges of low precision and practical deployment in existing fatigue detection systems. The algorithm employs depthwise-separable convolution combined with spatial depth convolution to effectively extract key facial features. An attention module is integrated between the backbone and neck networks to enhance contextual understanding and filter out irrelevant information, enabling precise extraction of detailed and distinguishable features, even under varying lighting conditions and driver poses. Additionally, a feature fusion module is introduced to merge features from different receptive fields, improving multi-scale feature extraction and reducing the miss rate for small objects. The proposed algorithm achieves a detection accuracy of 59.8% mAP on the Drowsy-Driving-Det dataset, marking an 8.5% improvement over the original method, alongside a 60.9% reduction in model parameters. This improved algorithm not only meets real-time deployment requirements but also maintains high detection accuracy, making it well-suited for facial fatigue feature extraction in complex driving environments and edge deployment scenarios.}, archive = {J_ACCESS}, author = {Yining Yao and Yawen Wang and Changyuan Wang and Yibo Zhang and Tingting Liu and Gaofeng Wang}, doi = {10.1109/ACCESS.2025.3534235}, journal = {IEEE Access}, pages = {25946-25957}, shortjournal = {IEEE Access}, title = {Facial feature extraction algorithm based on improved YOLOv7-tiny}, volume = {13}, year = {2025}, }
• (2025). Transient plane wave transmission through an N-layer multilayer structure with bianisotropic behavior. ACCESS, 13, 25934-25945. (www)

  @article{ , abstract = {An analysis based on electromagnetic reflection and transmission properties of multilayer structures are in general needed to get more information about their electromagnetic responses before their implementation for some specific applications involving absorption, shielding, and diagnosis. Such an analysis could be exercised by either frequency-domain or time-domain. While the former one is based on the steady-state response, the latter one is based on the transient response which thus provides more information about electromagnetic properties of multilayer structures. A procedure relying on a direct theoretical time-domain analysis is preferable to a time-domain analysis based on computational procedures such as the finite-time-difference-domain or the fast Fourier transform. In this study, a series presentation of transient plane wave transmission coefficient of N-layer composite structures involving bianisotropic behavior is derived for the first time in the literature in terms of reduced transmission coefficients (the concept of $ \it {subregion}$ ). The wave matrix approach is applied for the theoretical analysis of the reduced transmission coefficient after application of the concept of $ \it {subregion}$ . Implementation of this concept to our problem is presented step-by-step to make a clear understanding of its application to bianisotropic multilayer structures. The derived reduced transmission coefficient is validated by different multilayer structures (different layer numbers, different layer lengths, and different polarizations) composed of individual layers whose electromagnetic responses are synthesized by the Lorentz dispersion model. Advantages and limitations of the derived formalism are also presented.}, archive = {J_ACCESS}, author = {Ugur Cem Hasar and Yunus Kaya and Huseyin Korkmaz and Teodor Iliev}, doi = {10.1109/ACCESS.2025.3539558}, journal = {IEEE Access}, pages = {25934-25945}, shortjournal = {IEEE Access}, title = {Transient plane wave transmission through an N-layer multilayer structure with bianisotropic behavior}, volume = {13}, year = {2025}, }
• (2025). Eclipse attacks in blockchain networks: Detection, prevention, and future directions. ACCESS, 13, 25918-25933. (www)

  @article{ , abstract = {This paper presents a comprehensive study on eclipse attacks in blockchain networks by describing how eclipse attacks work, their effects, detection, and prevention. In this context, understanding and controlling network-level attacks, such as eclipse attacks, is an essential task in relation to assurance and reliability for decentralized systems that utilize blockchain technology. An eclipse attack is a sequence of network-layer attacks that monopolize the connections to a target node to isolate it from the rest of the network. Eclipse attacks that focus on node discovery manipulation, can have a substantial impact on a blockchain network, by increasing transaction computation cost, transaction censorship, and consensus disruption. We studied eclipse attacks on a blockchain network. The attack vectors were associated with node discovery manipulation, network partitioning, and information flow exploitation. This paper also reviews state-of-the-art detection methods and prevention strategies, shedding light on their effectiveness and limitations. Awareness of eclipse attacks and their effect provides the motivation for further research in developing practical and resilient security measures for blockchain networks.}, archive = {J_ACCESS}, author = {Zubaida Rehman and Mark A. Gregory and Iqbal Gondal and Hai Dong and Mengmeng Ge}, doi = {10.1109/ACCESS.2025.3538837}, journal = {IEEE Access}, pages = {25918-25933}, shortjournal = {IEEE Access}, title = {Eclipse attacks in blockchain networks: Detection, prevention, and future directions}, volume = {13}, year = {2025}, }
• (2025). A novel method for short-circuit current suppression based on instant system reconstruction. ACCESS, 13, 25908-25917. (www)

  @article{ , abstract = {With the continuous development of the smart energy systems for carbon reduction and energy saving, the problem of short-circuit current exceeding the standard has become increasingly prominent. While adjusting the system operating mode and installing current-limiting equipment can suppress short-circuit current effectively, these measures may influence the system stability and increase costs. Therefore, a method for short-circuit current suppression based on instant system reconstruction is proposed in this paper. The breaking current of circuit breaker for typical substations is firstly analyzed. Then three control strategies for transient topology reconstruction are proposed, and their applicability is analyzed to provide diverse options for practical engineering applications. Finally, considering the requirement for the system topology reconstruction, the hardware and software architecture for high-speed protection control is constructed. The fast estimation of current state is realized using the Kalman estimator to meet the requirements for short-circuit current suppression. The simulation result shows the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Gang Xu and Xin Qie and Bin Han and Hua Xie and Wenjie Wang and Yue Yu and Jie Hu}, doi = {10.1109/ACCESS.2025.3535751}, journal = {IEEE Access}, pages = {25908-25917}, shortjournal = {IEEE Access}, title = {A novel method for short-circuit current suppression based on instant system reconstruction}, volume = {13}, year = {2025}, }
• (2025). An analytical system design for forensic fingerprint examination. ACCESS, 13, 25893-25907. (www)

  @article{ , abstract = {Contactless visualization of latent fingerprints has gained considerable attention for crime scene investigations as it can offer an option to collect traces using non-invasive and non-destructive treatment. Considering the complex structure of fingerprint development methods and the tedious process steps performed in these methods, new approaches to acquire, store, transfer, and analyze latent fingerprints practically without requiring any deep knowledge and expertise are needed. This paper introduces a novel contactless latent fingerprint development method that uses a camera embedded in a mobile device and the apparatus used to increase the latent fingerprint’s visibility. The enhancement approach is presented to make the fingerprint collected with the designed apparatus usable and valid. As a result, an alternative system design for latent fingerprint development and enhancement is described in detail. Two state-of-the-art fingerprint matchers are used to measure the verification performance of the acquired latent fingerprint. It has been seen that latent fingerprints can be developed with contactless methods using optical treatments alone from reflective and non-porous surfaces. Furthermore, the stages of detecting, developing, and acquiring latent fingerprints from reflective and non-porous surfaces are carried out using the suggested development method without the requirement for forensic professionals. The proposed enhancement approach allows for the direct use of the enhanced fingerprint in the biometric identification/ verification system by extracting distinctive properties from the latent fingerprint.}, archive = {J_ACCESS}, author = {Bilgehan Arslan and Seref Sagiroglu}, doi = {10.1109/ACCESS.2025.3535581}, journal = {IEEE Access}, pages = {25893-25907}, shortjournal = {IEEE Access}, title = {An analytical system design for forensic fingerprint examination}, volume = {13}, year = {2025}, }
• (2025). Empowering sustainability: The crucial role of IoT-enabled distributed learning systems in reducing carbon footprints. ACCESS, 13, 25872-25892. (www)

  @article{ , abstract = {Electrical energy plays a pivotal role in modern society by powering homes, industries, and transportation systems. However, the production of electricity is associated with significant carbon emissions, primarily from fossil fuel-based power generation, and there is 1.1% rise in carbon emissions by 2023 compared to 2022. Mitigating carbon emissions from electrical energy is a critical global challenge that requires a multifaceted approach. Transitioning to cleaner energy sources and improving energy efficiency are essential steps to reduce the environmental impact of electricity generation. Energy management is crucial to reduce energy consumption effectively. So this study proposes a Multi-Model Energy Management System (MEnMS) integrated with a Fractal Internet of Things (IoT) architecture to address enhanced energy management by reducing energy usage, and carbon footprint. The study conducts a detailed energy consumption analysis across distinct cases. From the analysis, it can be seen that an average of 25% of energy can be saved with MEnMS without IoT energy overhead. Key observations include, EnMS with IoT devices and automation offers smartness, they do not lead to a significant reduction in energy consumption. Moreover, these IoT devices and centralized learning consume more energy. However, integrating IoT devices with distributed learning and multiple models significantly reduces energy consumption as well as the carbon footprint. The analysis reveals that the MEnMS system outperforms alternative approaches, particularly at higher occupancy levels, establishing itself as the most efficient energy management solution. At an occupancy level of 25 users, it achieves an impressive 8% reduction in energy consumption compared to the Traditional System, showcasing its unique capability to scale energy savings as occupancy increases. This innovative system combines advanced local processing with EQC optimization, providing a cutting-edge approach to sustainable energy management in high-occupancy scenarios. Furthermore, the algorithms driving occupant-centric automation and the indoor localization method demonstrate remarkable performance, achieving an efficiency of 92% and an accuracy of 90%, respectively. Therefore, the MEnMs framework can be used to monitor energy usage thereby reducing energy consumption, which results in a low-carbon footprint. By tracking the activity, the occupants get a clear understanding of their carbon footprint and they can make adjustment to reduce carbon emissions.}, archive = {J_ACCESS}, author = {Anjana M S and Aryadevi Remanidevi Devidas and Maneesha Vinodini Ramesh}, doi = {10.1109/ACCESS.2025.3539333}, journal = {IEEE Access}, pages = {25872-25892}, shortjournal = {IEEE Access}, title = {Empowering sustainability: The crucial role of IoT-enabled distributed learning systems in reducing carbon footprints}, volume = {13}, year = {2025}, }
• (2025). Fusion of hand biometrics for border control involving fingerprint and finger vein. ACCESS, 13, 25858-25871. (www)

  @article{ , abstract = {In this paper, we proposed an advanced multimodal fusion technique for fingerprint and finger vein recognition algorithms, incorporating novel improvements to established methods. Leveraging Scale-Invariant Feature Transform (SIFT) and Fast Library for Approximate Nearest Neighbors (FLANN), our approach integrates preprocessing enhancements such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and a robust descriptor alignment mechanism to optimize feature extraction and matching. This innovation enhances security, robustness, accuracy, and privacy in biometric systems. Additionally, the fusion of fingerprint and finger vein modalities is implemented at both score-level and feature-level, where feature-level fusion demonstrates superior performance by effectively addressing compatibility issues between modalities and reducing information leakage. Extensive evaluations using databases such as SOCOFing, FVC, CASIA, FV-USM, PLUSVein-FV3, and UTFVP validate the effectiveness of the proposed system. Our results show that feature-level fusion outperforms traditional approaches, achieving higher accuracy and resilience against environmental factors. This study provides a scalable and practical solution for contemporary biometric verification needs, particularly in border control and security applications.}, archive = {J_ACCESS}, author = {George Kumi Kyeremeh and Mohamed Abdul-Al and Rami Qahwaji and Nazar T. Ali and Raed A. Abd-Alhameed}, doi = {10.1109/ACCESS.2025.3538591}, journal = {IEEE Access}, pages = {25858-25871}, shortjournal = {IEEE Access}, title = {Fusion of hand biometrics for border control involving fingerprint and finger vein}, volume = {13}, year = {2025}, }
• (2025). Hierarchical clustering-based algorithm for deployment planning of LoRa gateway. ACCESS, 13, 25837-25857. (www)

  @article{ , abstract = {Long-Range Wide Area Network (LoRaWAN) has become a promising communication method for the Internet of Things (IoT) system since it is capable of long-range communication with low power usage. Presently, LoRa gateway (GW) deployment in Indonesia relies on conventional methods, i.e., predicting coverage without incorporating formulas specific to LoRa GW performance or accounting for the area type. This research aims to deploy the LoRa GW using a machine-learning algorithm to cover every sensor demand. In this paper, we simulate, deploy, and measure the signal strength transmission between recommended LoRa GWs and end device (ED) demands to ensure the algorithm works and is usable on each different environment characteristic. The results of these experiments show that the algorithm can generate coordinate point recommendations for the LoRa GW to cover all ED demands in three different characteristic areas.}, archive = {J_ACCESS}, author = {Kalam Adhiansyah Lutfie and Prima Dewi Purnamasari and Dadang Gunawan and I. Ketut Agung Enriko and Filbert H. Juwono}, doi = {10.1109/ACCESS.2025.3539369}, journal = {IEEE Access}, pages = {25837-25857}, shortjournal = {IEEE Access}, title = {Hierarchical clustering-based algorithm for deployment planning of LoRa gateway}, volume = {13}, year = {2025}, }
• (2025). Speech intelligibility prediction using binaural processing for hearing loss. ACCESS, 13, 25817-25836. (www)

  @article{ , abstract = {As the global issue of hearing loss becomes increasingly severe, developing effective speech intelligibility prediction methods is crucial for improving the performance of hearing aids. However, current methods struggle in noisy environments and overlook individual differences in hearing loss between ears, which impacts prediction accuracy. Therefore, this study proposes a non-intrusive speech intelligibility prediction method that incorporates the binaural processing for hearing loss. The proposed method simulates the multi-stage binaural processing of the outer, middle, and inner ear and integrates binaural cues through an equalization-cancellation model to mitigate masking effects in noisy environments. Key features extracted from speech signals serve as inputs for a hybrid speech intelligibility model combining long short-term memory (LSTM) and light gradient boosting machine (LightGBM) models. The proposed method captures the critical features of speech signals, especially in challenging environments and for different types of hearing loss. Experimental results show that, compared to the baseline system of the second Clarity Prediction Challenge (CPC2) dataset, the proposed method achieves an 8.3% reduction in root mean squared error (RMSE). Notably, the proposed method reduces RMSE by 12.8% when predicting inconsistent hearing loss compared to listeners with consistent hearing levels, confirming the potential of combining hearing loss modeling with binaural processing.}, archive = {J_ACCESS}, author = {Xiajie Zhou and Candy Olivia Mawalim and Masashi Unoki}, doi = {10.1109/ACCESS.2025.3538708}, journal = {IEEE Access}, pages = {25817-25836}, shortjournal = {IEEE Access}, title = {Speech intelligibility prediction using binaural processing for hearing loss}, volume = {13}, year = {2025}, }
• (2025). PortableNet: An automatical diagnosis method for esophageal motility disorders. ACCESS, 13, 25806-25816. (www)

  @article{ , abstract = {The detection of esophageal motility disorders serves as a crucial diagnostic indicator for preventing esophageal diseases. Currently, the swallowing cloud image obtained by high-resolution esophageal manometry is mainly used for esophageal motility analysis; however, manual analysis of swallowing cloud images presents numerous drawbacks, including time-intensive processes, labor-intensive procedures, and subjectivity in diagnosis. In response to these challenges, we propose an esophageal motility analysis model based on deep learning techniques to facilitate preliminary diagnosis of swallowing cloud images, thereby augmenting diagnostic efficiency for medical practitioners. Initially, we construct an esophageal motility dataset. Subsequently, we introduce a light weight model, termed PortableNet, derived from MobileNet architecture. The research findings demonstrate that the PortableNet model achieves a recognition accuracy of 93.99% with only 4.3 million parameters. In terms of classification performance, the model also achieves a precision of 94.73%, a recall of 93.39%, and an F1-score of 94.06%. Compared to the MobileNet architecture model, there is a 5.05% increase in recognition accuracy and a 58.65% reduction in size. These results collectively indicate that the PortableNet model delivers superior overall performance, offering both high accuracy and efficiency in recognizing esophageal motility swallowing cloud images. Such advancements can significantly enhance diagnostic efficiency among medical professionals, thereby offering valuable insights for future research endeavors.}, archive = {J_ACCESS}, author = {Junwu Lin and Xiaofang Wu and Qun Chen and Cunhan Guo and Liyi Ma and Shaoye Luo and Jiayan Huang}, doi = {10.1109/ACCESS.2025.3537641}, journal = {IEEE Access}, pages = {25806-25816}, shortjournal = {IEEE Access}, title = {PortableNet: An automatical diagnosis method for esophageal motility disorders}, volume = {13}, year = {2025}, }
• (2025). Potentiality of self sovereign identities in smart grid. ACCESS, 13, 25794-25805. (www)

  @article{ , abstract = {By applying the building theory from case study research by Kathleen M. Eisenhardt, this article describes a parsimonious and coherent process that puts into light as Self Sovereign Identity technology integrated into smart grids allows to give individuals back control of their energy data. First three research questions were defined. Next, significant cases studies were selected. Then the process moves forward by iteratively cycling through the case studies, developing theory, and cycling through white papers and existing literature articles until reaching the closure hence theoretical saturation. Through this process, focus of the article is delineating the potentialities and the challenges to face to integrate this cutting-edge technology in smart grid, highlighting as this technology allows to create a more reliable and transparent energy system. Results highlight for example as SSI paradigm ensures a high level of identity assurance for IoT devices within a grid, and allows users to maintain ultimate control over devices, hence over their energy data.}, archive = {J_ACCESS}, author = {Luisanna Cocco and Roberto Tonelli}, doi = {10.1109/ACCESS.2025.3539447}, journal = {IEEE Access}, pages = {25794-25805}, shortjournal = {IEEE Access}, title = {Potentiality of self sovereign identities in smart grid}, volume = {13}, year = {2025}, }
• (2025). Application of multi-objective control in power and load optimization of wind turbines. ACCESS, 13, 25783-25793. (www)

  @article{ , abstract = {During operation, wind turbine systems are affected by external wind speed disturbances and the dynamic coupling of the system, which can lead to significant resonance phenomena within certain frequency ranges. Resonance causes a dramatic increase in the system’s dynamic response at specific frequencies, resulting in unstable power output and exacerbating the accumulation of fatigue loads, posing a threat to the safety and operational reliability of the wind turbine. To address this issue, this study proposes a multi-objective H $_{\mathrm {\infty }}$ /generalized H2 additional damping control method, which improves control precision by minimizing the integral of time-weighted absolute error of state variables, effectively suppressing the resonance peaks at critical frequencies and reducing the resulting power and load fluctuations. Experimental results show that, compared to reference open-source controller tuning, the proposed strategy reduces the standard deviations of output power, tower-top fore-aft displacement, and tower base roll moment by 19.97%, 18.90%, and 30.13%, respectively. This approach significantly enhances the power stability of the wind turbine system and effectively reduces load fluctuations, providing an efficient solution for improving the operational reliability and lifespan of wind turbines.}, archive = {J_ACCESS}, author = {Jiawang Yuan and Shan He and Jing Cheng and Weiqing Wang}, doi = {10.1109/ACCESS.2025.3539575}, journal = {IEEE Access}, pages = {25783-25793}, shortjournal = {IEEE Access}, title = {Application of multi-objective control in power and load optimization of wind turbines}, volume = {13}, year = {2025}, }
• (2025). Island partition strategy based on entropy method-set pair analysis for microgrids. ACCESS, 13, 25765-25782. (www)

  @article{ , abstract = {The scientific and reasonable island partition of microgrids containing DG is of great practical significance for ensuring the safe switching of microgrid island operation and improving the reliability of microgrid power supply. The existing island partitioning strategies focus on power balance and basic load distribution but often neglect multiple load attributes, resulting in failure to guarantee the reliability of the load power supply with a relatively high comprehensive evaluation level. Therefore, to comprehensively consider the influence of multiple load attributes on islanding, an island partitioning strategy based on entropy method-set pair analysis is proposed for microgrids in this paper. First, the entropy method is used to objectively assign the weights of three indexes of the level of the load, the economic loss of the load power outage and the coefficient of change per unit power. Next, the set pair analysis (SPA) principle is combined with the comprehensive connection degree of the index to determine the comprehensive connection degree of different loads while considering each index, and the connection degree is used as the final island partition power supply recovery coefficient of the load. Finally, the dynamic programming algorithm is used to solve the objective function of island partitioning. The IEEE-69 node system verifies the proposed strategy. The verification results show that compared with the existing methods, the proposed strategy can restore the power supply of the important load more effectively and reduce the economic loss when the load is cut off. The proposed island partitioning strategy shows significant potential in improving the reliability and economy of the power supply for microgrids with important loads, especially for microgrids with complex loads.}, archive = {J_ACCESS}, author = {Zhaoyang Zhang and Hong Zhang and Li Zhou and Zhiwei Li and Wei Wang and Sihu Chu and Wenhang Chang and Can Wang and Zhuoli Zhao and Chun Sing Lai and Loi Lei Lai}, doi = {10.1109/ACCESS.2025.3538628}, journal = {IEEE Access}, pages = {25765-25782}, shortjournal = {IEEE Access}, title = {Island partition strategy based on entropy method-set pair analysis for microgrids}, volume = {13}, year = {2025}, }
• (2025). A blockchain-based E-participation framework utilizing zero-knowledge proofs with guaranteed sampling and differential reward mechanisms. ACCESS, 13, 25752-25764. (www)

  @article{ , abstract = {Blockchain-based E-participation systems significantly enhance transparency, data integrity, and security compared to traditional E-participation methods. However, existing systems often face challenges, such as inefficient attribute sampling in Zero-Knowledge Proof (ZKP)-based systems and the absence of effective differential reward mechanisms to distinguish between sincere and insincere participants. This paper introduces a blockchain-based E-participation framework designed to address these challenges. The proposed approach improves attribute sampling in ZKP-based systems by incorporating attribute keys, enabling efficient and secure sampling of participants without compromising privacy. This ensures that only eligible participants are selected while maintaining the integrity of the sampling process. Furthermore, the framework uses Shapley Values to implement a robust differential reward system that fairly compensates participants based on their sincerity, encouraging genuine contributions while penalizing insincere behavior. The security of the proposed framework is rigorously validated through a comprehensive security analysis, and its performance is thoroughly evaluated to demonstrate its effectiveness. Additionally, the feasibility of this approach is demonstrated through a prototype with real-world participants, highlighting its practicality and potential for deployment in E-participation systems.}, archive = {J_ACCESS}, author = {Jungwon Seo and Juhui Lee and Yunjae Joo and Kangho Lee and Vijayan Sugumaran and Sooyong Park}, doi = {10.1109/ACCESS.2025.3538006}, journal = {IEEE Access}, pages = {25752-25764}, shortjournal = {IEEE Access}, title = {A blockchain-based E-participation framework utilizing zero-knowledge proofs with guaranteed sampling and differential reward mechanisms}, volume = {13}, year = {2025}, }
• (2025). EILEEN: A multi-modal framework for extracting alcohol consumption patterns from bilingual clinical notes. ACCESS, 13, 25741-25751. (www)

  @article{ , abstract = {In this work, we introduce EILEEN (Efficient Inference for Language-based Extraction of EHR Notes), a novel multi-modal natural language processing (NLP) framework designed to extract various alcohol consumption patterns from unstructured clinical notes, particularly in bilingual and non-English contexts. Recent advances in NLP have significantly improved information extraction capability across various domains. However, identifying patterns of alcohol consumption in medical documents remains underexplored, with existing approaches heavily relying on traditional NLP methods such as bag-of-words models that require extensive text preprocessing. These methods are often limited to English-language clinical settings, where robust medical ontologies and NLP toolkits are available to support preprocessing tasks. Therefore, this limitation hinders their use in multilingual healthcare settings and in environments lacking robust NLP toolkits to facilitate preprocessing. Motivated by the need for a more generalizable and accurate approach, this paper investigates the impact of large language models (LLMs) in advancing alcohol consumption pattern extraction from clinical notes. By reducing the need for manual preprocessing and improving adaptability to multilingual clinical notes, this work aims to enable broader, more practical applications of NLP models in extracting alcohol consumption patterns from clinical notes. By fine-tuning multilingual language models along with additional data sources, EILEEN effectively analyzes unstructured electronic health records (EHR) without relying on traditional concept normalization or extensive text preprocessing resources. Furthermore, the multi-modal component of EILEEN enables it to integrate and leverage diverse types of alcohol-related information, such as various types and amounts of alcohol consumed by a patient, thereby improving its pattern extraction accuracy. Our experiments, conducted in two different medical institutions in Korea, demonstrate that EILEEN significantly outperforms existing NLP methods in accurately identifying clinically relevant alcohol consumption patterns. By providing accurate, detailed, and clinically useful alcohol consumption patterns from unstructured clinical notes, EILEEN empowers healthcare practitioners with actionable insights essential for informed clinical decision-making.}, archive = {J_ACCESS}, author = {Han Kyul Kim and Yujin Park and Yoon Ji Kim and Seungah Yi and Yeju Park and Sujin So and Hyeon-Ji Lee and Ye Seul Bae}, doi = {10.1109/ACCESS.2025.3538803}, journal = {IEEE Access}, pages = {25741-25751}, shortjournal = {IEEE Access}, title = {EILEEN: A multi-modal framework for extracting alcohol consumption patterns from bilingual clinical notes}, volume = {13}, year = {2025}, }
• (2025). Leveraging synthetic data for improved manipuri-english code-switched ASR. ACCESS, 13, 25723-25740. (www)

  @article{ , abstract = {Accurately recognizing code-switched speech presents a significant challenge in the field of Automatic speech recognition (ASR), particularly for low-resource regional languages. In this work, we investigate various approaches to enhance ASR performance for code-switched Manipuri-English speech by generating synthetic code-switched sentences from parallel language-pair datasets and employing audio augmentation techniques. We propose a hybrid model of transformer and pointer generation network to generate high quality code-switched sentences, by leveraging the parallel monolingual texts as input. Transformer helps in attending the long-term dependencies of the input and the pointer generator in copying from source while still maintaining the code-switching constraints. We test our data augmentation method by using two parallel text corpora that we have developed. Using text generated by our proposed model, we achieve significant reductions in perplexity (PPL) for a language modeling task, surpassing the performance of text generated by other baseline models. We also explore different strategy of training the language model (LM) to improve ASR performance. Moreover, we observed that combining audio augmentation techniques outperform each individual method.}, archive = {J_ACCESS}, author = {Naorem Karline Singh and Wangkheimayum Madal and Chingakham Neeta Devi and Hoomexsun Pangsatabam and Yambem Jina Chanu}, doi = {10.1109/ACCESS.2025.3538664}, journal = {IEEE Access}, pages = {25723-25740}, shortjournal = {IEEE Access}, title = {Leveraging synthetic data for improved manipuri-english code-switched ASR}, volume = {13}, year = {2025}, }
• (2025). Patch and model size characterization for on-device efficient-ViTs on small datasets using 12 quantitative metrics. ACCESS, 13, 25704-25722. (www)

  @article{ , abstract = {Vision transformers (ViTs) have emerged as a successful alternative to convolutional neural networks (CNNs) in deep learning (DL) applications for computer vision (CV), particularly excelling in accuracy on large-scale datasets within high-performance computing (HPC) or cloud domains. However, in the context of resource-constrained mobile and edge AI devices, there is a lack of systematic and comprehensive investigations into the challenging optimizations for both device-agnostic (e.g., accuracy and model size) and device-related (e.g., latency, memory usage, and power/energy consumption) multi-objectives. To resolve this problem, we first 1) introduce five device-agnostic (DA) and seven device-related (DR) quantitative metrics, 2) using which we thoroughly characterize the effects of ViT hyper-parameters on small datasets in terms of patch size and model size, and then 3) propose a simple yet effective optimization technique called the hierarchical and local (HelLo) tuning method for efficient ViTs. The results show that our method achieves significant improvements of up to 85% in MACs, 67.2% in inference latency, 77.7% in train latency/time, 63.3% in GPU memory, 73.8% in energy consumption, and 263.0% in FoM, with minimal accuracy degradation (up to 2%).}, archive = {J_ACCESS}, author = {Jurn-Gyu Park and Aidar Amangeldi and Nail Fakhrutdinov and Meruyert Karzhaubayeva and Dimitrios Zorbas}, doi = {10.1109/ACCESS.2025.3536471}, journal = {IEEE Access}, pages = {25704-25722}, shortjournal = {IEEE Access}, title = {Patch and model size characterization for on-device efficient-ViTs on small datasets using 12 quantitative metrics}, volume = {13}, year = {2025}, }
• (2025). Design of an iterative method for time series forecasting using temporal attention and hybrid deep learning architectures. ACCESS, 13, 25683-25703. (www)

  @article{ , abstract = {In the realm of time series forecasting, traditional models often struggle to effectively manage the intricate dependencies and high dimensionality inherent in multivariate data samples. This limitation becomes increasingly problematic in dynamic environments where temporal relevance and variable interdependencies fluctuate significantly. Addressing these challenges, this paper introduces the Temporal Graph Attention Model for Time Series Analysis (TGAMTSA), a novel deep learning framework designed to enhance prediction accuracy and model adaptability in complex time series contexts and scenarios. TGAMTSA incorporates a suite of advanced techniques to overcome the deficiencies of conventional forecasting models. Initially, it employs Temporal Attention Mechanisms to dynamically allocate attention across different timestamps and variables, significantly enhancing the model’s ability to capture critical temporal dependencies. This method improves the focus on relevant information and achieves a 10% average reduction in mean absolute error (MAE) compared to models devoid of such mechanisms. Further augmenting its capability, TGAMTSA utilizes a Hybrid Architecture that synergistically combines 1D Convolutional Neural Networks (CNNs) and a dual arrangement of Quad Long Short-Term Memory (LSTM) and Quad Gated Recurrent Units (GRU) networks. This configuration adeptly extracts both spatial and temporal features, yielding a 15% reduction in prediction error across various datasets. Additionally, by representing the multivariate time series data as a graph in which variables are nodes connected by edges denoting temporal relationships, TGAMTSA leverages Graph Neural Networks (GNNs) to decode complex inter-variable dependencies, resulting in a 20% improvement in prediction accuracy over traditional methods. Transfer Learning and Domain Adaptation strategies further enhance the framework’s adaptability. By harnessing pre-trained models on analogous tasks, TGAMTSA swiftly adapts to new domains, particularly under conditions of scarce labeled data, leading to a 25% increase in prediction accuracy on unseen datasets and samples. TGAMTSA’s comprehensive approach not only addresses the limitations of existing forecasting models by providing a robust method to handle multivariate time series data with varying temporal dynamics but also sets a new benchmark in predictive accuracy and model flexibility. The implications of this work are profound, potentially revolutionizing forecasting methodologies in numerous real-world applications from finance to climate modeling, where accurate and adaptable time series analysis is crucial for real-time use case scenarios.}, archive = {J_ACCESS}, author = {Yuvaraja Boddu and A. Manimaran}, doi = {10.1109/ACCESS.2025.3538577}, journal = {IEEE Access}, pages = {25683-25703}, shortjournal = {IEEE Access}, title = {Design of an iterative method for time series forecasting using temporal attention and hybrid deep learning architectures}, volume = {13}, year = {2025}, }
• (2025). Enhancing expressway ramp merge safety and efficiency via spatiotemporal cooperative control. ACCESS, 13, 25664-25682. (www)

  @article{ , abstract = {In the context of autonomous driving on expressways, the issue of ensuring safe and efficient ramp merging remains a significant challenge. Existing systems often struggle to accurately assess the status and intentions of other vehicles, leading to a persistent occurrence of accidents despite efforts to maintain safe distances. This study proposes a novel spatiotemporal cooperative control approach integrating vehicle-road coordination to address this critical issue. A comprehensive methodology is developed, beginning with the calculation of safe distances under varying spatiotemporal conditions. This involves considering multiple factors, including vehicle speed differentials, positioning errors, and clock synchronization errors. Subsequently, an advanced vehicle conflict risk evaluation model is constructed. By incorporating collision acceleration and emergency acceleration as key parameters, this model offers a more accurate and detailed evaluation of potential risks during the ramp merging process. Based on the calculated safe distances and conflict risk evaluations, a mainline priority coordinated control method is formulated. This method enables the pre-planning of vehicle trajectories, effectively reducing conflicts among vehicles. Through rigorous simulations using diverse traffic volume and speed scenarios, the efficacy of the proposed strategy is validated. The results demonstrate remarkable improvements, with the average delay time reduced by an impressive 97.96% and fuel consumption decreased by 6.01%. These outcomes indicate that the proposed approach not only enhances the speed of vehicle merging but also significantly reduces latency and fuel consumption, thereby enhancing the overall performance of ramp merging operations.}, archive = {J_ACCESS}, author = {Ting Peng and Xiaoxue Xu and Yuan Li and Jie Wu and Tao Li and Xiang Dong and Yincai Cai and Peng Wu and Sana Ullah}, doi = {10.1109/ACCESS.2025.3539370}, journal = {IEEE Access}, pages = {25664-25682}, shortjournal = {IEEE Access}, title = {Enhancing expressway ramp merge safety and efficiency via spatiotemporal cooperative control}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A modified singular value decomposition (MSVD) approach for the enhancement of CCTV low-quality images”. ACCESS, 13, 25663. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “A Modified Singular Value Decomposition (MSVD) Approach for the Enhancement of CCTV Low-Quality Images”.}, archive = {J_ACCESS}, author = {Shahzada Fahad and Sami Ur Rahman and Fakhre Alam and Muhammad Yousaf and Faten S. Alamri and Naveed Abbas and Saeed Ali Bahaj and Amjad R. Khan}, doi = {10.1109/ACCESS.2025.3538396}, journal = {IEEE Access}, pages = {25663}, shortjournal = {IEEE Access}, title = {Corrections to “A modified singular value decomposition (MSVD) approach for the enhancement of CCTV low-quality images”}, volume = {13}, year = {2025}, }
• (2025). U2-net-combined end-to-end unsupervised learning method for implementing accurate computer-generated phase-only hologram. ACCESS, 13, 25650-25662. (www)

  @article{ , abstract = {Holography is an emerging technology where phase-only spatial light modulator (SLM) is better than amplitude-only SLM because of its enhanced diffraction efficiency and rendering quality. However, phase-only interference pattern reconstruction is an ill-posed inverse problem, difficult to solve by standard supervised neural network approaches. Accurately representing the target input during reconstruction of phase-only hologram (POH) can become challenging given the inherent high spatial frequency of phase data and the complexity of its fringe patterns, expressing the inverse nature of the problem. Therefore, the model may be trained improperly, inducing major compromise in the predicted hologram’s quality. As a result, an unsupervised approach can be effective to resolve such a problem. This paper presents a novel combined U2-net-based computer-generated hologram (CUSNet-CGH) architecture, an end-to-end deep learning model comprising two distinctively designed combined convolution networks that accurately generate 2D POH. The proposed framework contains U2-net which is known for performing unsupervised learning tasks without relying on pre-trained backbones from labeled dataset, making it an ideal approach for conducting unsupervised training. The numerical reconstructions are compared with several other state-of-the-art methods such as holoencoder, holonet and CCNN-CGH to verify the enhanced quality. The average PSNR of the proposed end-to-end approach is as high as $\sim ~34$ dB with an average SSIM of 0.91, featured by DIV2K dataset, outperforming the state-of-the-art methods analyzed in the paper. Additionally, the time required to reconstruct the predicted output of the proposed model is verified as competitive, challenging the latest benchmarks, thanks to its minimized number of trainable parameters, and ensuring that the model is computationally lightweight. Finally, experiments also demonstrate that the proposed model yields high-quality optical reconstruction.}, archive = {J_ACCESS}, author = {F. M. Fahmid Hossain and Hui-Ying Wu and Shariar Md Imtiaz and Md Biddut Hossain and Tuvshinjargal Amgalan and Rupali Kiran Shinde and Ki-Chul Kwon and Hyun-Eui Kim and Kwan-Hee Yoo and Nam Kim}, doi = {10.1109/ACCESS.2025.3538978}, journal = {IEEE Access}, pages = {25650-25662}, shortjournal = {IEEE Access}, title = {U2-net-combined end-to-end unsupervised learning method for implementing accurate computer-generated phase-only hologram}, volume = {13}, year = {2025}, }
• (2025). Quaternary neural belief propagation decoding of quantum LDPC codes with overcomplete check matrices. ACCESS, 13, 25637-25649. (www)

  @article{ , abstract = {Quantum low-density parity-check (QLDPC) codes are promising candidates for error correction in quantum computers. One of the major challenges in implementing QLDPC codes in quantum computers is the lack of a universal decoder. In this work, we first propose to decode QLDPC codes with a belief propagation (BP) decoder operating on overcomplete check matrices. Then, we extend the neural BP (NBP) decoder, which was originally studied for suboptimal binary BP decoding of QLPDC codes, to quaternary BP decoders. Numerical simulation results demonstrate that both approaches as well as their combination yield a low-latency, high-performance decoder for several short to moderate length QLDPC codes.}, archive = {J_ACCESS}, author = {Sisi Miao and Alexander Schnerring and Haizheng Li and Laurent Schmalen}, doi = {10.1109/ACCESS.2025.3539475}, journal = {IEEE Access}, pages = {25637-25649}, shortjournal = {IEEE Access}, title = {Quaternary neural belief propagation decoding of quantum LDPC codes with overcomplete check matrices}, volume = {13}, year = {2025}, }
• (2025). Study of GaN hall effect magnetic sensors. ACCESS, 13, 25622-25636. (www)

  @article{ , abstract = {The GaN Hall sensors are the latest advancements in non-intrusive active monitoring, designed to be integrated on GaN power transistors to enable in-situ condition monitoring, hence boosting device reliability and life. This paper describes step-by-step the basic procedures required for the fabrication of a three-terminal Hall-effect sensor on GaN, challenges and a robust testing method that can deliver consistent results when testing under diverse laboratory external hazards. This work is presenting the first three-terminal current sensing GaN Hall sensor reported in literature, which have been tested at room temperature under various magnetic field intensities and polarities in order to determine its sensitivity. Since even when properly using the most suitable scientific equipment, errors may occur from insufficient information regarding the relative angle between the magnetic field and the active area of the sensor or, data records affected by imperceptible equipment vibrations, this work provides a practical solution on how to detect and mitigate these shortcomings: magnetic quasi-static sources alternatives are discussed and transient recordings of different intensities and polarities of the magnetic field are separated and interpreted similar with the spectrometry diagrams. This comprehensive presented study delivers a consistent testing framework for the new emerging generation of magnetic sensors.}, archive = {J_ACCESS}, author = {Vlad Marsic and Soroush Faramehr and Isha Maini and David A. J. Moran and Petar Igic}, doi = {10.1109/ACCESS.2025.3539435}, journal = {IEEE Access}, pages = {25622-25636}, shortjournal = {IEEE Access}, title = {Study of GaN hall effect magnetic sensors}, volume = {13}, year = {2025}, }
• (2025). Characterization of the collagen extraction manufacturing process using markov chains and artificial neural networks. ACCESS, 13, 25612-25621. (www)

  @article{ , abstract = {Modelling a process requires a priori knowledge of the possible causal relationships between the study variables and the response, especially when the raw material is waste. A new approach to characterizing a manufacturing process is presented to extend data that are difficult to obtain by direct experimentation. This research aims to identify industrial applications for derivatives from animal by-products, such as tilapia skin, to develop an alternative methodology for issues related to collagen. Empirical data from a design of experiments (DoE) fed a Markov chain Monte Carlo algorithm in an artificial neural network (MCMC-ANN) for data imputation, numerical simulation, and training. The DoE model presented a coefficient of 86.92% in the observed data in the first stage of the methodology. In the second stage, the predictive model MCMC-ANN presented a mean square error (MSE) of 0.00171, and a standard deviation (SD) of 0.00133. The model was tested using validation data obtained outside the training process. The SD of empirical validation of MCMC-ANN were 1.7890, 0.2051 and 0.0919 for three random points. The product of this research is a four-dimensional predictive model that estimates the yield with decreasing material, energy resources, and the time required for the collagen extraction process from the skin waste of tilapia. The proposed tool obtains a minimum adjustment with a deviation whose tolerance is valid for predicting the collagen extraction yield. MCMC-ANN can be applied to any case that requires a deeper analysis with more data or experiments.}, archive = {J_ACCESS}, author = {Rosa Trasviña-Osorio and Sergio Alonso-Romero and Juan de Anda-Suárez and Valentín Calzada-Ledesma and Javier Yañez-Mendiola and Luis Fernando Villanueva-Jiménez and Erick Rojas-Mancera}, doi = {10.1109/ACCESS.2025.3536327}, journal = {IEEE Access}, pages = {25612-25621}, shortjournal = {IEEE Access}, title = {Characterization of the collagen extraction manufacturing process using markov chains and artificial neural networks}, volume = {13}, year = {2025}, }
• (2025). EMFF-net: Edge-enhancement multi-scale feature fusion network. ACCESS, 13, 25598-25611. (www)

  @article{ , abstract = {According to medical research, colorectal polyps are considered typical precancerous lesions, making colonoscopic polyp images crucial for the early diagnosis of rectal cancer. However, variations in polyp size and shape, texture inconsistencies, and boundary ambiguities often pose significant challenges for polyp segmentation. To address these issues, we propose a multi-scale feature fusion network based on edge enhancement. Specifically, we utilize multi-scale feature fusion in each feature layer, where the extracted features are fused. From these extracted features, we generate a global mapping graph as a bootstrap region. Additionally, we introduce Spatial Channel Convolution (SCEConv) and Reverse Gated Channel Transformer (RGCT) to incorporate boundary information into the segmentation network. This approach enhances the layered features and produces a more refined segmentation map. Extensive qualitative and quantitative experiments on five benchmark datasets and two private datasets demonstrate that the EMFF-Net proposed in this paper significantly improves segmentation accuracy across six metrics. This represents a clear advantage over traditional CNNs and existing SOTA techniques, Especially, we achieved 81% mDice and 74% mIoU on the CVC-ColonDB dataset.}, archive = {J_ACCESS}, author = {Xuhui Guan and Jiwang Zhou and Jian Chen and Xiaodan Xu and Yizhang Jiang and Kaijian Xia}, doi = {10.1109/ACCESS.2025.3538504}, journal = {IEEE Access}, pages = {25598-25611}, shortjournal = {IEEE Access}, title = {EMFF-net: Edge-enhancement multi-scale feature fusion network}, volume = {13}, year = {2025}, }
• (2025). A novel ensemble classifier selection method for software defect prediction. ACCESS, 13, 25578-25597. (www)

  @article{ , abstract = {The presence of software defects significantly impacts the quality of software systems and increases development and maintenance costs. To improve system quality and reduce costs, it is necessary to predict software defects in the early stages of the software development lifecycle. This paper proposes Double Fault Disagreement (DFD), a novel diversity metric and method for selecting competent base classifiers for ensemble learning-based software defect prediction. To consider the diversity features of the base learners, several base learners with strong diversity are chosen to build ensemble learning. This method makes full use of the diversity characteristics of base learners, leverages their classification ability, optimizes the selection method for ensemble learning, and enhances the predictive performance of the ensemble model. The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). The DFD model achieves superior performance on eight public NASA and PROMISE datasets (six of which are imbalanced) across five performance indicators, including area under the curve (AUC), geometric mean (G-Mean), F1 score, Matthews correlation coefficient (MCC), and Balance. Furthermore, the DFD method is not only highly performant but also requires a small number of base learners to converge rapidly, thereby reducing the amount of data and computation time required.}, archive = {J_ACCESS}, author = {Xin Dong and Jie Wang and Yan Liang}, doi = {10.1109/ACCESS.2025.3537658}, journal = {IEEE Access}, pages = {25578-25597}, shortjournal = {IEEE Access}, title = {A novel ensemble classifier selection method for software defect prediction}, volume = {13}, year = {2025}, }
• (2025). VGG-16, VGG-16 with random forest, resnet50 with SVM, and EfficientNetB0 with XGBoost-enhancing bone fracture classification in X-ray using deep learning models. ACCESS, 13, 25568-25577. (www)

  @article{ , abstract = {Millions of cases of bone fractures are reported every year, and accuracy in classification is crucial to help with proper management and treatment. The recently developed techniques of Machine Learning, particularly Deep Learning, have been effective in increasing diagnosis precision and efficiency. We utilized a diverse dataset comprising 10 different classes of fracture types captured in X-Ray images. This paper makes a comparison of different machine learning models on classifying bone fractures: VGG-16, VGG-16 with Random Forest, ResNet-50 with Support Vector Machine, and EfficientNetB0 with XGBoost. Model performances were evaluated with respect to parameters of precision, recalls, and F1-scores. According to results, VGG-16 and its variant ensemble with Random Forest outperformed with an accuracy of 0.95 when compared to others on every parameter for different classes of fractures. Results indicate that models based on VGG16 are quite effective for bone fracture classification.}, archive = {J_ACCESS}, author = {Spoorthy Torne and Dasharathraj K. Shetty and Krishnamoorthi Makkithaya and Prasiddh Hegde and Manu Sudhi and Phani Kumar Pullela and T Tamil Eniyan and Ritesh Kamath and Staissy Salu and Pranav Bhat and S. Girisha and P. S. Priya}, doi = {10.1109/ACCESS.2025.3534818}, journal = {IEEE Access}, pages = {25568-25577}, shortjournal = {IEEE Access}, title = {VGG-16, VGG-16 with random forest, resnet50 with SVM, and EfficientNetB0 with XGBoost-enhancing bone fracture classification in X-ray using deep learning models}, volume = {13}, year = {2025}, }
• (2025). Real-time quality monitoring and anomaly detection for vision sensors in connected and autonomous vehicles. ACCESS, 13, 25557-25567. (www)

  @article{ , abstract = {The emergence of Connected and Autonomous Vehicles (CAVs) has revolutionized the transportation landscape paving the way for enhanced traffic mobility and innovative and sustainable transportation. Autonomous vehicles rely on sensor data to obtain information of the internal state of the system and the impact of the external environment to achieve self-driving autonomy. In that sense, anomalies in sensor readings may reflect faults in sensor components, which in turn can compromise the decision-making functionality of the autonomous system. Video Anomaly Detection (VAD) serves as a pivotal technology in intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within video frames. This paper proposes a supervised video anomaly assessment framework for autonomous vehicles leveraging Image Quality Assessment (IQA) and Drift Detection techniques. On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. Our experimental results demonstrate high accuracy and robustness for anomaly detection in comparison to existing supervised techniques, which is crucial for real-time and autonomous operations of CAVs.}, archive = {J_ACCESS}, author = {Elena Politi and Charalampos Davalas and Christos Chronis and George Dimitrakopoulos and Dimitrios Michail and Iraklis Varlamis}, doi = {10.1109/ACCESS.2025.3536524}, journal = {IEEE Access}, pages = {25557-25567}, shortjournal = {IEEE Access}, title = {Real-time quality monitoring and anomaly detection for vision sensors in connected and autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). BEVFusion with dual hard instance probing for multimodal 3D object detection. ACCESS, 13, 25546-25556. (www)

  @article{ , abstract = {False negatives (FN) in 3D object detection, which occur when small, distant, or hidden objects are missed, pose significant safety risks in autonomous driving systems. Recent multi-modal fusion methods have been proposed to enhance 3D object detection by combining the geometric accuracy of LiDAR point clouds with the rich semantic features of camera images. However, few methods explicitly address false negatives, and many fail to effectively align and interact multimodal features during the fusion process. To address these challenges, we propose BEVFusion with Dual Hard Instance Probing (BEVFusion-DHIP), a novel 3D object detection framework designed to systematically reduce false negatives. BEVFusion-DHIP incorporates Hard Instance Probing (HIP) into both LiDAR BEV features and 3D position-aware image features, progressively refining the detection of challenging objects across multiple stages. Furthermore, we introduce a Deformable Attention Fusion Network (DAFusionNet) to dynamically align and fuse LiDAR and camera BEV features during the fusion process, effectively mitigating spatial misalignment and enhancing inter-modal feature interaction. Experimental results on the nuScenes dataset show that the proposed BEVFusion-DHIP outperforms state-of-the-art lidar and camera+lidar based 3D object detection models. For example, BEVFusion-DHIP achieves improvements of 3.0 and 3.2 in mAP and NDS, respectively, compared to the baseline model BEVFusion.}, archive = {J_ACCESS}, author = {Taeho Kim and Joohee Kim}, doi = {10.1109/ACCESS.2025.3538866}, journal = {IEEE Access}, pages = {25546-25556}, shortjournal = {IEEE Access}, title = {BEVFusion with dual hard instance probing for multimodal 3D object detection}, volume = {13}, year = {2025}, }
• (2025). Convolutional neural networks for direction of arrival estimation compared to classical estimators and bounds. ACCESS, 13, 25533-25545. (www)

  @article{ , abstract = {Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. With the increasing amount of research in this area, it is important to balance the performance and computational costs of CNNs with classical methods of DoA estimation such as Multiple Signal Classification (MUSIC). We outline the performance of both methods of DoA estimation for single-source and two-source cases for multiple array conditions. The results are also compared to the Cramer-Rao lower bound (CRLB) and conventional beamforming. For each source case, CNNs were trained for a perfect uniform line array (ULA) and tested against data from a perfect ULA, perturbed ULAs, ULAs with missing sensors, and ULAs with muffled sensors. We show that for the single-source case, the CNNs do not offer any performance improvement relative to MUSIC at low signal-to-noise ratio (SNR). For the two-source cases, the CNNs perform better than MUSIC but only at low SNR. For the remaining array cases, the CNNs outperform MUSIC. These results indicate that the performance improvements from CNNs are highest for situations where there is signal model to data mismatch (imperfect information). This work also illustrates that the CNN estimators developed in this work exceed the CRLB and are biased estimators caused by the lack of unbiased constraint in the loss function during training of the CNNs.}, archive = {J_ACCESS}, author = {Christopher J. Bell and Kaushallya Adhikari and Andrew Brown}, doi = {10.1109/ACCESS.2025.3538997}, journal = {IEEE Access}, pages = {25533-25545}, shortjournal = {IEEE Access}, title = {Convolutional neural networks for direction of arrival estimation compared to classical estimators and bounds}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A systematic literature review of digital twin research for healthcare systems: Research trends, gaps, and realization challenges”. ACCESS, 13, 25532. (www)

  @article{ , abstract = {Presents corrections to the paper, “A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges.”}, archive = {J_ACCESS}, author = {Md. Doulotuzzaman Xames and Taylan G. Topcu}, doi = {10.1109/ACCESS.2025.3538272}, journal = {IEEE Access}, pages = {25532}, shortjournal = {IEEE Access}, title = {Corrections to “A systematic literature review of digital twin research for healthcare systems: Research trends, gaps, and realization challenges”}, volume = {13}, year = {2025}, }
• (2025). Enhancing preschool language acquisition through robotic assistants: An evaluation of effectiveness, engagement, and acceptance. ACCESS, 13, 25520-25531. (www)

  @article{ , abstract = {Effective early language learning is crucial in education, and finding innovative ways to support it can make a significant difference. This study examines how a robotic assistant can enhance English vocabulary learning for preschool children aged 4 to 6. The study was conducted in two preschools and involved 40 participants split into two groups. Performance assessments were conducted at three stages to evaluate vocabulary acquisition and sentence construction abilities. Results revealed that children interacting with the robot learned 23% more words than those taught through traditional methods, particularly in the early months of exposure. The data analysis, comprising quantitative evaluations of vocabulary learning and qualitative surveys of parents and teachers, highlighted increased engagement, ease of classroom integration, and strong acceptance of the robot. Despite challenges such as student distraction and the need for consistent teacher supervision, the findings demonstrate that robotic assistants complement traditional teaching methods, offering enhanced engagement and improved learning outcomes.}, archive = {J_ACCESS}, author = {Santiago Berrezueta-Guzman and María Dolón-Poza}, doi = {10.1109/ACCESS.2025.3538323}, journal = {IEEE Access}, pages = {25520-25531}, shortjournal = {IEEE Access}, title = {Enhancing preschool language acquisition through robotic assistants: An evaluation of effectiveness, engagement, and acceptance}, volume = {13}, year = {2025}, }
• (2025). A practical cache partitioning method for multi-core processor on a commercial safety-critical partitioned RTOS. ACCESS, 13, 25505-25519. (www)

  @article{ , abstract = {In modern airborne systems, software plays a crucial role for meeting functional, safety, and performance requirements. Integrated Modular Avionics (IMA) and multi-core processors (MCPs) are also adopted for hardware to enhance performance and Space, Weight, and Power (SWaP). While MCPs improve efficiency, they introduce nondeterministic behaviors due to resource contention and challenges for the safety of avionics systems. This research proposes a practical cache partitioning method for commercial Real-Time Operating Systems (RTOS) on MCPs to reduce cache contention and enhance the safety and performance of avionics systems. A systematic procedure was developed based on DO-178 and CAST 32A to identify shared resources and prevent interference. Additionally, we developed a cache partitioning guide tool to decide the optimal cache partition for each application. This research demonstrated that shared L2 caches could cause severe performance degradation up to 88% using the WindRiver VxWorks 653 on the PowerPC T2080 processor. By applying the proposed partitioning technique, the system achieved 0% performance degradation, eliminating cache contention and ensuring consistent application performance. The partitioning approach also ensured deterministic application behavior, a critical requirement for safety-critical systems. Validation in a flight simulation environment confirmed the practicality of the method in real-world scenarios.}, archive = {J_ACCESS}, author = {Taeho Kim}, doi = {10.1109/ACCESS.2025.3538540}, journal = {IEEE Access}, pages = {25505-25519}, shortjournal = {IEEE Access}, title = {A practical cache partitioning method for multi-core processor on a commercial safety-critical partitioned RTOS}, volume = {13}, year = {2025}, }
• (2025). Interacting large language model agents. bayesian social learning based interpretable models. ACCESS, 13, 25465-25504. (www)

  @article{ , abstract = {This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. While both fields are mature, their application to decision-making involving interacting LLMAs remains unexplored. Motivated by Bayesian sentiment analysis on online platforms, we construct interpretable models and stochastic control algorithms that enable LLMAs to interact and perform Bayesian inference. Because interacting LLMAs learn from both prior decisions and external inputs, they can exhibit bias and herding behavior. Thus, developing interpretable models and stochastic control algorithms is essential to understand and mitigate these behaviors. This paper has three main results. First, we show using Bayesian revealed preferences from microeconomics that an individual LLMA satisfies the necessary and sufficient conditions for rationally inattentive (bounded rationality) Bayesian utility maximization and, given an observation, the LLMA chooses an action that maximizes a regularized utility. Second, we utilize Bayesian social learning to construct interpretable models for LLMAs that interact sequentially with each other and the environment while performing Bayesian inference. Our proposed models capture the herding behavior exhibited by interacting LLMAs. Third, we propose a stochastic control framework to delay herding and improve state estimation accuracy under two settings: 1) centrally controlled LLMAs and 2) autonomous LLMAs with incentives. Throughout the paper, we numerically demonstrate the effectiveness of our methods on real datasets for hate speech classification and product quality assessment, using open-source models like LLaMA and Mistral and closed-source models like ChatGPT. The main takeaway of this paper, based on substantial empirical analysis and mathematical formalism, is that LLMAs act as rationally bounded Bayesian agents that exhibit social learning when interacting. Traditionally, such models are used in economics to study interacting human decision-makers.}, archive = {J_ACCESS}, author = {Adit Jain and Vikram Krishnamurthy}, doi = {10.1109/ACCESS.2025.3538599}, journal = {IEEE Access}, pages = {25465-25504}, shortjournal = {IEEE Access}, title = {Interacting large language model agents. bayesian social learning based interpretable models}, volume = {13}, year = {2025}, }
• (2025). Dynamic modeling of needle-tissue interaction applied to soft tissue damage during needle extraction. ACCESS, 13, 25457-25464. (www)

  @article{ , abstract = {The interaction between the needle and the soft tissue during medical procedures can damage tissue, hindering performance. Reducing tissue damage can improve target accuracy in some procedures, such as biopsies, electrode placement, or drug delivery. This research aims to study the effect of needle speed on tissue damage. For this purpose, we propose a damage model that incorporates the dynamic needle-tissue interaction and the plane strain problem related to the hole left in the tissue after needle extraction. The optimization of the model provides a speed which minimizes tissue damage. A hypodermic needle was inserted into ex vivo turkey breast tissue at different insertion speeds. The size of the tissue hole and the friction stress from the needle-tissue interaction were used to measure damage and develop the damage model. The results revealed significant differences in the hole sizes and the friction stress measurements when different insertion speeds were used. Finally, the damage model was fitted to the experimental data and a speed value that minimizes tissue damage could be obtained.}, archive = {J_ACCESS}, author = {Erick D. Chávez Pereda and Hernán A. González Rojas and Antonio J. Sánchez Egea}, doi = {10.1109/ACCESS.2025.3539102}, journal = {IEEE Access}, pages = {25457-25464}, shortjournal = {IEEE Access}, title = {Dynamic modeling of needle-tissue interaction applied to soft tissue damage during needle extraction}, volume = {13}, year = {2025}, }
• (2025). Analysis of arc re-ignition in vacuum interrupter-based HVdc circuit breakers. ACCESS, 13, 25446-25456. (www)

  @article{ , abstract = {The protection of HVdc systems has long been challenged by the absence of natural current zero-crossing, typically addressed using L-C branches. However, the phenomenon of multiple arc reignitions in vacuum interrupter (VI) based HVdc circuit breakers (CBs) has been insufficiently investigated in existing literature. This paper addresses this gap by systematically analyzing the arc-reignition phenomenon in VI-HVdc-CBs, focusing on the impact of inductance (L), capacitance (C), and resistance (R) on arc reignitions, arcing time, and transient recovery voltage (TRV). A seven-phase testing methodology is used to explore these parameters. The results show that increasing L reduces arc reignitions but increases arcing time, with 0.5 mH identified as the optimal value for L. In contrast, increasing C significantly reduces both TRV and arcing time, with $200~\mu $ F selected as the optimal capacitance. Resistance (R) had minimal effect on TRV and arcing time once optimal L-C values were set. The paper further extends the investigation by evaluating active L-C branches and multi-injection methods. The results indicate that active L-C branches improve performance, but the multi-injection method outperforms both passive and active designs, achieving the lowest TRV and arcing time. This work provides novel insights into the arc-reignition behavior of VI-HVdc-CBs and offers practical guidelines for optimizing HVdc circuit breaker performance, aiming to minimize arc reignitions, TRV, and arcing time for more reliable HVdc system protection.}, archive = {J_ACCESS}, author = {Tamer Eliyan and Ali Saeed Almuflih and Z. M. S. Elbarbary and Fady Wadie}, doi = {10.1109/ACCESS.2025.3538465}, journal = {IEEE Access}, pages = {25446-25456}, shortjournal = {IEEE Access}, title = {Analysis of arc re-ignition in vacuum interrupter-based HVdc circuit breakers}, volume = {13}, year = {2025}, }
• (2025). Performance benchmarking of psychomotor skills using wearable devices: An application in sport. ACCESS, 13, 25432-25445. (www)

  @article{ , abstract = {Mastering psychomotor skills, such as those essential in sports, rehabilitation, and professional training, often requires a precise understanding of motion patterns and performance metrics. This study proposes a versatile framework for optimizing psychomotor learning through human motion analysis. Utilizing a wearable IMU sensor system, the motion trajectories of a given psychomotor task are acquired and then linked to points in a performance space using a predefined set of quality metrics specific to the psychomotor skill. This enables the identification of a benchmark cluster in the performance space, which represents a group of reference points that define optimal performance across multiple criteria, allowing correspondences to be established between the performance clusters and sets of trajectories in the motion space. As a result, common or specific deviations in the performance space can be identified, enabling remedial actions in the motion space to optimize performance. A thorough validation of the proposed framework is done in this paper using a Table Tennis forehand stroke as a case study. The resulting quantitative and visual representation of performance empowers individuals to optimize their skills and achieve peak performance.}, archive = {J_ACCESS}, author = {Mahela Pandukabhaya and Tharaka Fonseka and Madhumini Kulathunge and Roshan Godaliyadda and Parakrama Ekanayake and Chanaka Senanayake and Vijitha Herath}, doi = {10.1109/ACCESS.2025.3536837}, journal = {IEEE Access}, pages = {25432-25445}, shortjournal = {IEEE Access}, title = {Performance benchmarking of psychomotor skills using wearable devices: An application in sport}, volume = {13}, year = {2025}, }
• (2025). Effects of operation prediction sharing for collaborative avatar robot. ACCESS, 13, 25419-25431. (www)

  @article{ , abstract = {A collaborative avatar robot, in which multiple users cooperate with a single-robot avatar, improves operability by leveraging the judgment and skills of both operators. However, collaboration among users is essential for a smooth operation. In this paper, we propose a system that provides a visual display of operational predictions to facilitate mutual understanding of actions among users in a single robot arm as a collaborative avatar robot. The robotic arm was controlled by two operators, while the system predicts operations 0.3 seconds ahead using machine learning based on one operator’s actions. These predictions were visually presented to the other operator. Pick-and-place experiments were conducted, where two operators were assigned roles as leader and follower, with only the leader knowing the correct block to manipulate. The task performance and operability were evaluated with and without prediction system. Additionally, since operability and workability are affected by the control ratio of the two operators, three control ratio conditions were evaluated for comparison purposes. The results suggest that the followers’ subjective ratings in the sense of agency, smoothness, and workload significantly improved and that the operation effort of the leader significantly decreased while maintaining the sense of agency, reducing timing discrepancies between the operators.}, archive = {J_ACCESS}, author = {Hyuga Suzuki and Hikari Yukawa and Kouta Minamizawa and Yoshihiro Tanaka}, doi = {10.1109/ACCESS.2025.3538277}, journal = {IEEE Access}, pages = {25419-25431}, shortjournal = {IEEE Access}, title = {Effects of operation prediction sharing for collaborative avatar robot}, volume = {13}, year = {2025}, }
• (2025). Precise recognition and feature depth analysis of tennis training actions based on multimodal data integration and key action classification. ACCESS, 13, 25409-25418. (www)

  @article{ , abstract = {To address the issues of accuracy and generalization in action recognition within complex tennis training scenarios, this study proposes an Adaptive Semantic-Enhanced Convolutional Neural Network (ASE-CNN) model. The model optimizes multimodal data integration and complex action classification performance, enabling precise analysis of key action features in tennis training. Experimental results demonstrate that ASE-CNN excels in multimodal feature extraction, achieving a Receiver Operating Characteristic-Area Under Curve (ROC-AUC) value of 0.85–0.95 and a Feature Distribution Uniformity of 0.75–0.85, validating its accuracy and stability in feature classification. The fused weighted Precision-Recall Area Under Curve (PR-AUC) value ranges from 0.80 to 0.92, highlighting its capability for synergistic optimization of multimodal data. For key action recognition, the classification accuracy reaches up to 0.98, with Euclidean distances between key actions and ideal action templates as low as 0.05–0.15, demonstrating exceptional fine-detail discrimination. Compared to state-of-the-art models, ASE-CNN exhibits significant advantages in per-frame processing time and resource utilization efficiency, offering potential for efficient real-time feedback in resource-constrained environments. This study provides scientific evidence and technical support for optimizing actions and improving strategies in professional training.}, archive = {J_ACCESS}, author = {Weichao Yang}, doi = {10.1109/ACCESS.2025.3538780}, journal = {IEEE Access}, pages = {25409-25418}, shortjournal = {IEEE Access}, title = {Precise recognition and feature depth analysis of tennis training actions based on multimodal data integration and key action classification}, volume = {13}, year = {2025}, }
• (2025). An intelligent approach for the challenges in the evaluation of public accountant firms under uncertainties. ACCESS, 13, 25387-25408. (www)

  @article{ , abstract = {For certified public accountant (CPA) firms to maintain high levels of competent assistance, achievement evaluations are essential. However, because aspects like customer fulfilment, productivity, economic success, and legality include personal and interconnected assessments, standard approaches to assessment frequently fall short of addressing the inherent complexity and uncertainty in decision-making. To overcome these issues, this paper suggests a unique multiple-criteria decision-making (MCDM) model based on complex T-Spherical fuzzy Frank (CTSFF) operations. The approach uses complex T-spherical fuzzy Frank Weighted Averaging (CTSFFWA) and complex T-spherical fuzzy Frank Weighted Geometric (CTSFFWG) operators to manage multi-attribute assessment under unclear and uncertain circumstances. The ability to handle subjective and unclear data while integrating interconnections between several assessment requirements is lacking in the practical assessment methods currently used by CPA companies. This restriction makes obtaining trustworthy and useful information for enhancing company operations more difficult. The intricacy of contemporary CPA operations, such as the increasing requirement to consider qualitative assessments and a range of client viewpoints, calls for creating a strong and flexible structure to support decision-making. The inability of conventional fuzzy frameworks to accurately represent these subtleties necessitates using sophisticated techniques. To retain mathematical accuracy while accounting for ambiguity via membership, non-membership, and abstention degrees, the suggested model uses CTSFF logic. A thorough assessment of CPA company achievement through various factors is made possible by the improved aggregation possibilities introduced using CTSFFWA and CTSFFWG operators. Flexibility evaluation is additionally built into the framework to guarantee the findings’ reliability and pinpoint ideal factor setups. This research gives decision-makers a versatile instrument to modify the framework for various expert business sectors. It offers a methodical and comprehensive way to assess the efficiency of CPA firms. This investigation makes an essential contribution to the discipline of decision-making under uncertainty by filling in the deficiencies in the assessment methods that are currently in use.}, archive = {J_ACCESS}, author = {Zijie Yu}, doi = {10.1109/ACCESS.2025.3536498}, journal = {IEEE Access}, pages = {25387-25408}, shortjournal = {IEEE Access}, title = {An intelligent approach for the challenges in the evaluation of public accountant firms under uncertainties}, volume = {13}, year = {2025}, }
• (2025). MILP modeling and optimization of multi-objective three-stage flexible job shop scheduling problem with assembly and AGV transportation. ACCESS, 13, 25369-25386. (www)

  @article{ , abstract = {In the real manufacturing environment, the machining stage of the jobs and the assembly stage of the products are often completed in different workshops. In addition, automatic guided vehicle (AGV) plays an indispensable role in the transportation of jobs from machining workshop to assembly workshop. This paper studies multi-objective three-stage flexible job shop scheduling problem (FJSP-T-A) with minimizing both the makespan and the total energy consumption. In FJSP-T-A, jobs are first machined in flexible job shop, then are transported to assembly workshop by AGVs, and finally are assembled in assembly workshop. To solve this problem, a mixed-integer linear programming model (MILP) is developed and the optimal Pareto front for small-scale instances are solved by using the $\varepsilon $ -method. FJSP-T-A is NP-hard, and an efficient multi-population co-evolutionary algorithm (MPCEA) is proposed to efficiently solve large-scale instances. In the MPCEA, we design a strategy to select relatively high-quality individuals to enhance the algorithm’s convergence speed, and design a multi-objective variable-neighborhood search (MOVNS) method to improve the local search ability. Experiments are conducted to prove the effectiveness of the MILP model and the MPCEA.}, archive = {J_ACCESS}, author = {Shiming Yang and Leilei Meng and Saif Ullah and Biao Zhang and Hongyan Sang and Peng Duan}, doi = {10.1109/ACCESS.2025.3535825}, journal = {IEEE Access}, pages = {25369-25386}, shortjournal = {IEEE Access}, title = {MILP modeling and optimization of multi-objective three-stage flexible job shop scheduling problem with assembly and AGV transportation}, volume = {13}, year = {2025}, }
• (2025). A survey of deep learning techniques for arabic aspect-based sentiment analysis. ACCESS, 13, 25350-25368. (www)

  @article{ , abstract = {As a valuable tool for comprehending the emotions and perspectives of individuals, the significance of sentiment analysis has risen as a result of the growing of user-generated Arabic content online. Aspect-based sentiment analysis (ABSA) has recently gained significant attention and has become one of the most popular research points. The main objective of ABSA is to extract the aspects and identify their corresponding sentiment polarity from a provided review or text. Compared to generic sentiment analysis, the outcome provides more in-depth information. This paper aims to explore the deep learning (DL) methods employed in Arabic ABSA, and provides a new taxonomy that organizes various ABSA studies depending on the number of tasks processed. Additionally, the models proposed for Arabic ABSA and their contributions and limitations are discussed and summarized to identify gaps in the field. Furthermore, Arabic datasets for ABSA are reviewed as well. Specifically, this article analyzes studies published between 2019 and April 2024. In addition to ascertaining potential future directions that would encourage researchers to contribute to Arabic ABSA studies and generate more effective algorithms.}, archive = {J_ACCESS}, author = {Dalal Alqusair and Mounira Taileb and and Hassanin Al-Barhamtoshy}, doi = {10.1109/ACCESS.2025.3539263}, journal = {IEEE Access}, pages = {25350-25368}, shortjournal = {IEEE Access}, title = {A survey of deep learning techniques for arabic aspect-based sentiment analysis}, volume = {13}, year = {2025}, }
• (2025). A method for measuring strawberry leaf area based on three-dimensional point cloud instance segmentation. ACCESS, 13, 25339-25349. (www)

  @article{ , abstract = {With effective protective covering and microclimate control, greenhouse crops offer significant advantages, such as high yield and quality, remaining unaffected by seasonal variations and meeting the demand for diverse agricultural products. Efficient production relies on precise automatic control of the environment and nutrients, and the leaf area index is a crucial growth parameter that affects indoor microclimate and nutrient transport within plants. Therefore, real-time monitoring of leaf area is essential for adjusting control strategies. This study introduces a strawberry three-dimensional point cloud instance segmentation method to address the challenge of stem and leaf instance segmentation in calculating plant leaf area using three-dimensional point cloud data. High-quality point cloud data were obtained using a three-dimensional scanner, and feature enhancement was achieved through the Leaf Vein and Boundary Preserving Sampling method. The network achieved an average precision of 90.41% for instance segmentation, with the precision of leaf segmentation reaching 93.63%. The Mean Absolute Error of the reconstructed leaf area, calculated using the Poisson surface reconstruction method with boundary processing, was 5.51 cm2, with a Root Mean Square Error of 6.91 cm2 and a Coefficient of Determination of 0.867. These findings provide valuable technical support and references for greenhouse cultivation and smart agriculture applications. The source code and dataset can be accessed at https://github.com/suyangsuluo/SGC.}, archive = {J_ACCESS}, author = {Zhipeng Li and Shusheng Wang and Yuanping Su and Dongyun Yu}, doi = {10.1109/ACCESS.2025.3539083}, journal = {IEEE Access}, pages = {25339-25349}, shortjournal = {IEEE Access}, title = {A method for measuring strawberry leaf area based on three-dimensional point cloud instance segmentation}, volume = {13}, year = {2025}, }
• (2025). Defect detection in concrete structures based on characteristics of hammer reaction force and apparent stiffness of concrete. ACCESS, 13, 25325-25338. (www)

  @article{ , abstract = {Concrete structures play a vital role in civil engineering. However, their deterioration poses a significant safety risk, making regular inspection essential. The impact method detects defects by analyzing characteristic reaction forces generated when striking areas with internal cavities, offering the advantage of being unaffected by acoustic noise and not requiring direct sensor contact. Traditional approaches, relying solely on reaction force signals, have struggled with low defect detection accuracy due to the similarity of signals between healthy and defective concrete and limited data quantity. To address this challenge, we propose a novel method that enhances defect discrimination accuracy by integrating statistical processing and physical property analysis within a machine learning framework designed for force signal characteristics. Our method employs wavelet transformation to convert short-duration force signals into high-resolution time-frequency features, capturing their non-stationary behavior in detail. For dimensionality reduction, we use Uniform Manifold Approximation and Projection to accurately embed data clusters near decision boundaries in a low-dimensional space. The embedded data is then clustered using Fuzzy c-means, and defect cluster identification is automated based on the apparent stiffness of the concrete. Experimental validation through field and laboratory tests confirmed the effectiveness of our method, demonstrating a significant improvement in defect detection accuracy. By advancing the precision and automation of the impact method, this study contributes a valuable tool for enhancing the safety and maintenance of concrete structures.}, archive = {J_ACCESS}, author = {Koki Shoda and Jun Younes Louhi Kasahara and Qi An and Atsushi Yamashita}, doi = {10.1109/ACCESS.2025.3535927}, journal = {IEEE Access}, pages = {25325-25338}, shortjournal = {IEEE Access}, title = {Defect detection in concrete structures based on characteristics of hammer reaction force and apparent stiffness of concrete}, volume = {13}, year = {2025}, }
• (2025). A comprehensive survey of fake text detection on misinformation and LM-generated texts. ACCESS, 13, 25301-25324. (www)

  @article{ , abstract = {This paper presents a pioneering and comprehensive analysis of fake text, a pressing issue in the digital age, by categorizing it into two main types: Misinformation and LM-generated texts. It is the first study to systematically dissect and examine the intricate challenges and nuances in distinguishing between genuine and artificial text. Through a meticulous review of various methodologies and technologies in fake text detection, the paper provides an in-depth evaluation of their effectiveness across diverse scenarios. Furthermore, this research delves into the significant societal impacts of both misinformation and LM-generated texts, underlining the urgent need for precise and effective detection mechanisms in our increasingly information-saturated world. This extensive survey not only offers a unique perspective on the current landscape of fake text detection, but also paves the way for future research, highlighting critical areas where further innovation and exploration are essential.}, archive = {J_ACCESS}, author = {Soonchan Kwon and Beakcheol Jang}, doi = {10.1109/ACCESS.2025.3538805}, journal = {IEEE Access}, pages = {25301-25324}, shortjournal = {IEEE Access}, title = {A comprehensive survey of fake text detection on misinformation and LM-generated texts}, volume = {13}, year = {2025}, }
• (2025). Leveraging quantum LSTM for high-accuracy prediction of viral mutations. ACCESS, 13, 25282-25300. (www)

  @article{ , abstract = {The rapid mutations of viruses such as COVID-19 pose significant challenges for vaccine development and effective disease management. In response to these challenges, this study introduces a novel quantum-enhanced LSTM (QLSTM) model designed to predict genetic mutations, specifically focusing on viral protein sequences. The QLSTM model leverages quantum computing techniques, including superposition and entanglement, to improve the model’s ability to handle the high-dimensional, nonlinear structure inherent in viral genomic datasets. These quantum enhancements allow the model to capture complex relationships in the data, improving its accuracy and performance in mutation detection compared to traditional methods. To improve model predictions, we use two key preprocessing techniques TF-IDF for efficient feature extraction and PCA for dimensionality reduction of genomic sequences. TF-IDF helps the model focus on the most informative nucleotide features, while PCA reduces the size of the data, making the model computationally efficient without sacrificing important information. The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). These results indicate that QLSTM not only provides high accuracy in mutation predictions in SARS-CoV-2 protein sequences but also provides deep insights into the functional implications of such mutations. The model identifies mutation hotspots that affect virus spread, immune evasion, and protein structure, providing key biological insights for future vaccine development and therapeutic strategies.}, archive = {J_ACCESS}, author = {Prashanth Choppara and Bommareddy Lokesh}, doi = {10.1109/ACCESS.2025.3539337}, journal = {IEEE Access}, pages = {25282-25300}, shortjournal = {IEEE Access}, title = {Leveraging quantum LSTM for high-accuracy prediction of viral mutations}, volume = {13}, year = {2025}, }
• (2025). New energy vehicles’ technological innovation strategy under dual credit policy: The role of blockchain adoption and demand information sharing. ACCESS, 13, 25256-25281. (www)

  @article{ , abstract = {This paper explores the impact of market uncertainty and risk attitudes on technological innovation in new energy vehicles (NEVs) under the dual credit policy, focusing on the roles of manufacturers’ demand information sharing and blockchain adoption. We develop game-theoretic models based on mean-variance theory to derive optimal strategies across four scenarios. Our analysis reveals that the dual credit policy can effectively incentivize NEV technological innovation, with the incentive effect becoming more pronounced as the technological innovation credit coefficient or credit price increases. However, manufacturers’ demand information sharing does not always amplify the positive impact of the dual credit policy on technological innovation; under certain conditions, it may even diminish the level of innovation. This effect is contingent upon consumer demand uncertainty and the risk sensitivity of supply chain members. Moreover, the adoption of blockchain technology is subject to a cost threshold, which can be raised by the dual credit policy. As the technological innovation credit coefficient or credit price rises, the conditions for blockchain adoption decreases, thereby facilitating the digital transformation of the NEV industry.}, archive = {J_ACCESS}, author = {Miaomiao Ma and Yuyu Li and Bo Huang}, doi = {10.1109/ACCESS.2025.3537686}, journal = {IEEE Access}, pages = {25256-25281}, shortjournal = {IEEE Access}, title = {New energy vehicles’ technological innovation strategy under dual credit policy: The role of blockchain adoption and demand information sharing}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis of word embedding models and design of effective feature vector for classification of amazon product reviews. ACCESS, 13, 25239-25255. (www)

  @article{ , abstract = {Sentiment Analysis (SA) is a well-known and emerging research field in the area of Natural Language Processing (NLP) and text classification. Feature engineering is considered to be one of the major steps in the Machine Learning (ML) pipeline with effective feature extraction playing a vital role in improving the performance of the SA tasks. Choosing an appropriate feature from the text is considered to be the most challenging task in text classification. This study examines the implementation of different traditional feature extraction models such as Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), N-grams, and word embeddings like Word2Vec and Bidirectional Encoder Representations from Transformers (BERT) on an Amazon review dataset. Furthermore, the hyperparameters of the BERT model were fine-tuned, optimizing its performance on the SA task. Additionally, this research explores the effectiveness of combining the word vectors generated using TF-IDF and BERT for SA. The proposed hybrid model implements an effective negation handling approach and combines TF-IDF with BERT to improve performance for Amazon product review classification.The hybrid model was evaluated using several performance metrics, including accuracy, recall, precision, and F1-score. The proposed hybrid model shows promising results achieving an accuracy of 88%. The integration of BERT and TF-IDF not only enhances the model’s ability to understand and interpret text but also demonstrates the potential of combining advanced and traditional NLP techniques.}, archive = {J_ACCESS}, author = {B. Priya Kamath and M. Geetha and U. Dinesh Acharya and Dipesh Singh and Ayush Rao and Shwetha Rai and Roopashri Shetty}, doi = {10.1109/ACCESS.2025.3536631}, journal = {IEEE Access}, pages = {25239-25255}, shortjournal = {IEEE Access}, title = {Comprehensive analysis of word embedding models and design of effective feature vector for classification of amazon product reviews}, volume = {13}, year = {2025}, }
• (2025). NAVA: A network-adaptive view-aware volumetric video streaming framework. ACCESS, 13, 25223-25238. (www)

  @article{ , abstract = {Volumetric video is the emerging format for representing real-world dynamic objects such as humans in Extended Reality (XR) applications. However, real-time streaming of volumetric video to user devices is challenging due to the extremely high data rate and low latency requirements. This paper introduces NAVA, a novel network-adaptive view-aware volumetric video streaming framework for XR scenes consisting of multiple volumetric sequences. The proposed framework dynamically adapts the quality of individual volumetric sequences based on network conditions and the user’s viewpoint to optimize streaming performance under network constraints. In our framework, multiple versions with different quality of individual volumetric video are prepared and stored on the server in advance. The rate allocation problem is formulated as a optimization problem by taking into account the visible area of individual sequences as well as the network constraint. We then present two solutions to decide the quality of each volumetric video in real-time. Extensive evaluation shows that the proposed framework can increase the viewport quality by $0.5\sim 1.1$ dB compared to existing methods. The outcome of this study is expected to accelerate the adoption of real-time interactive XR applications, enabling users to experience and interact with dynamic virtual environments seamlessly.}, archive = {J_ACCESS}, author = {Nguyen Long Quang and Truong Thu Huong and Duc Nguyen}, doi = {10.1109/ACCESS.2025.3538802}, journal = {IEEE Access}, pages = {25223-25238}, shortjournal = {IEEE Access}, title = {NAVA: A network-adaptive view-aware volumetric video streaming framework}, volume = {13}, year = {2025}, }
• (2025). Photovoltaic farm production forecasting: Modified metaheuristic optimized long short-term memory-based networks approach. ACCESS, 13, 25198-25222. (www)

  @article{ , abstract = {The finite availability and unsustainable nature of fossil fuel sources have spurred growing interest in renewable energy sources. Nevertheless, substantial efforts are still required to fully incorporate energy coming from renewable sources into current power distribution networks. Although reliability plays a crucial role in enhancing the sustainability of energy production, the dependence of solar power plants on weather conditions poses challenges to maintaining consistent output without significant storage expenses. Consequently, precise forecasting of photovoltaic power generation is essential for effective grid management and energy trade market. Machine learning models have proven to be a prospective resolution due to their ability to process large datasets and capture intricate patterns within the data. This study investigates the application of metaheuristics optimization techniques to enhance light-weighted long short-term memory (LSTM) based models with and without attention for predicting power generation from photovoltaic plants. Furthermore, a modified metaheuristics optimization method based on the renowned particle swarm optimization algorithm is proposed to address the rigorous demands of hyperparameters’ optimization. Rigorous simulations on a real-world data were carried out, along with strict comparative analysis with other potent metaheuristics algorithms. A publicly available photovoltaic dataset consisting of the measurements from two plants in India was used. Additionally, this study utilized a supplementary dataset collected from a photovoltaic power plant located on the roof of Institute Mihailo Pupin (IMP) in Belgrade, Serbia. Proposed research tries to fill-in the gap in this research domain, since light-weighted LSTM models were not examined enough for this specific challenge according to the literature survey. The best produced models attained mean squared error (MSE) scores of only 0.007297 for Indian Plant 1, 0.007662 for Indian Plant 2, and 0.001812 for Institute Mihailo Pupin dataset, emphasizing considerable potential of the suggested approach for real-world applications. Finally, the applicability of the top-performance models was validated with tiny machine learning (TinyML).}, archive = {J_ACCESS}, author = {Aleksandar Stojkovic and Bosko Nikolic and Miodrag Zivkovic and Nebojsa Bacanin}, doi = {10.1109/ACCESS.2025.3537407}, journal = {IEEE Access}, pages = {25198-25222}, shortjournal = {IEEE Access}, title = {Photovoltaic farm production forecasting: Modified metaheuristic optimized long short-term memory-based networks approach}, volume = {13}, year = {2025}, }
• (2025). SensorDBSCAN: Semi-supervised active learning powered method for anomaly detection and diagnosis. ACCESS, 13, 25186-25197. (www)

  @article{ , abstract = {Fault detection and diagnosis (FDD) is a critical challenge in industrial processes aimed at minimizing risks such as safety hazards, costly downtime, and suboptimal production. Traditional supervised FDD methods offer great performance while heavily relying on large volumes of labeled data, whereas unsupervised methods do not depend on labeled data, though are inferior in performance compared to supervised ones. In this paper, we propose SensorDBSCAN, a novel semi-supervised method for anomaly detection and diagnosis. The key innovation lies in achieving good performance with minimal labeled data - less than 1% of the dataset - by leveraging active and contrastive learning techniques. The proposed approach combines a transformer-based encoder trained with a triplet-based contrastive learning objective and the classical density-based clustering algorithm DBSCAN, enabling strong feature extraction, efficient and interpretable feature space organization and simple clustering algorithm. Unlike existing methods, SensorDBSCAN eliminates the need for manual labeling large amounts of data, cluster analysis, and pre-defining cluster numbers, providing greater usability in real-world cases. We validate the effectiveness of our method on the Tennessee Eastman Process (TEP) and its advanced simulations (TEP Rieth and TEP Rieker). SensorDBSCAN demonstrates better performance on well-known and realistic datasets, reducing labeling requirements while maintaining high accuracy of fault detection and diagnostics. The code is available at https://github.com/K0mp0t/sensordbscan.}, archive = {J_ACCESS}, author = {Petr Ivanov and Maria Shtark and Alexander Kozhevnikov and Maksim Golyadkin and Dmitry Botov and Ilya Makarov}, doi = {10.1109/ACCESS.2025.3537649}, journal = {IEEE Access}, pages = {25186-25197}, shortjournal = {IEEE Access}, title = {SensorDBSCAN: Semi-supervised active learning powered method for anomaly detection and diagnosis}, volume = {13}, year = {2025}, }
• (2025). OsteoNet—A framework for identifying osteoporosis in bone radiograph images using attention-based VGG network. ACCESS, 13, 25175-25185. (www)

  @article{ , abstract = {Diagnosing osteoporosis from X-ray images poses a significant challenge due to the visual similarities between images from healthy subjects and patients. In this paper, we present a novel method for detecting osteoporosis. Our approach utilizes local phase quantization (LPQ) to identify fine texture variations that indicate changes in bone density, which are characteristic of osteoporosis. Furthermore, we augment the VGG network with an attention mechanism that incorporates both channel and spatial attentions. Channel attention is applied by calculating the mean and variance across channels, followed by a softmax operation to highlight essential features in the channel. Spatial attention is achieved through average pooling followed by exponential weighting to focus on relevant spatial regions within the images. A comparative analysis of the proposed method on ISBI 2014 challenge dataset against existing methods revealed that hand-crafted features mimicking gradients outperform those based on statistics, and CNN-based methods generally exhibit better performance. Our proposed method achieved an accuracy of 74.1%. These findings elucidate on the efficacy of integrating differential image features with attention mechanisms to improve the accuracy of osteoporosis identification from X-ray images.}, archive = {J_ACCESS}, author = {Abdul Wahab Muzaffar and Farhan Riaz and Muhammad Tahir}, doi = {10.1109/ACCESS.2025.3538828}, journal = {IEEE Access}, pages = {25175-25185}, shortjournal = {IEEE Access}, title = {OsteoNet—A framework for identifying osteoporosis in bone radiograph images using attention-based VGG network}, volume = {13}, year = {2025}, }
• (2025). A new diagnosing method for psoriasis from exhaled breath. ACCESS, 13, 25163-25174. (www)

  @article{ , abstract = {Psoriasis is a chronic inflammatory skin disease with a high global prevalence. A skin biopsy is still required to diagnose the disease; no non-invasive diagnosis method has been found. It has become a popular approach for physicians as a support system, as it classifies biological data collected without human intervention in various ways with machine learning methods. Numerous studies have been conducted using machine learning methods to increase the accuracy, performance, speed, and reliability of diagnosing various diseases. This study aims to predict whether a group of patients admitted to Hitit University Erol Olçok Training and Research Hospital have psoriasis based on exhaled breath measurements using an electronic nose system which was produced for this study by the authors. In total, 263 clinical records were examined; 120 (45.6%) were obtained from healthy individuals, while 143 (54.4%) belonged to psoriasis patients. In order to distinguish data from those of psoriasis patients and those of healthy individuals, six different machine learning algorithms were used on the breath data set. The best classification result was provided by the ExtraTreesClassifier algorithm, with an accuracy rate of 96.1%, while other algorithms have rates between 66.6% and 94.2%. The most important outcome of this study is that the model determined to distinguish psoriasis patients from healthy ones can also help in the early diagnosis of psoriasis.}, archive = {J_ACCESS}, author = {Bilge Han Tozlu and Ömer Faruk Akmeşe and Cemaleddin Şimşek and Engin Şenel}, doi = {10.1109/ACCESS.2025.3536308}, journal = {IEEE Access}, pages = {25163-25174}, shortjournal = {IEEE Access}, title = {A new diagnosing method for psoriasis from exhaled breath}, volume = {13}, year = {2025}, }
• (2025). Digital ektypon: Using an RTI dome for digitizing squeezes and epigraphic research. ACCESS, 13, 25154-25162. (www)

  @article{ , abstract = {This paper unravels the transformative potential of Reflectance Transformation Imaging (RTI) in the context of epigraphy, shedding light on previously concealed dimensions of ancient artifacts, while introducing this technology to the digitization of epigraphic squeezes. Facing challenges related to the delicate nature of these paper-based copies and their preservation needs, this work developed a novel workflow incorporating RTI by a specially crafted Dome engineered for this purpose. The construction, calibration, and operational aspects of this dome are presented in this paper using as a case study the “Palaikastron Hymn” squeeze. The resulting high-resolution Polynomial Texture Maps enable the detailed exploration of inscriptions, enhancing the visibility of faded and subtle relief previously unseen. This research not only pioneers the application of RTI in the realm of epigraphy but also underscores its potential benefits in preserving and comprehensively studying fragile cultural artifacts, while enhancing scholarly analysis and understanding. In a world where the past meets cutting-edge technology, this research augments the arsenal of tools for digital humanities, ensuring that the legacy of ancient inscriptions is studied, preserved, and appreciated by scholars and enthusiasts alike.}, archive = {J_ACCESS}, author = {Ilias N. Gotsikas and Yannis Z. Tzifopoulos and Pericles A. Mitkas}, doi = {10.1109/ACCESS.2025.3531991}, journal = {IEEE Access}, pages = {25154-25162}, shortjournal = {IEEE Access}, title = {Digital ektypon: Using an RTI dome for digitizing squeezes and epigraphic research}, volume = {13}, year = {2025}, }
• (2025). Assessing driving risk level: Harnessing deep learning hybrid model with intercity bus naturalistic driving data. ACCESS, 13, 25141-25153. (www)

  @article{ , abstract = {Driving risk assessment is crucial for enhancing traffic safety, especially given the severe consequences of highway accidents. This study advances the field by developing a deep learning hybrid model for time series analysis to categorize driving risks into low, moderate, and high levels. By collecting naturalistic driving data from intercity buses, the model is trained on an extensive dataset featuring 27,057 journey-based instances, incorporating dynamic GPS data and static journey background information from over 300 drivers. The model’s effectiveness is highlighted by its outstanding weighted average F1-score of 0.932, indicating exceptional robustness and predictive accuracy. Through comprehensive feature engineering and examinations of three temporal neural models, this research identifies the best parameter configurations. The key finding is that including static journey background information leads to improvements by 8.8% on average in model performance. Additionally, the high-risk level prediction F1-score reaches 0.728 for the proposed model, which is up to 9.3 times better than the performance of the machine learning baseline model. This breakthrough in driving risk prediction not only represents a major advancement in traffic safety management but also has practical implications for fleet scheduling management among transportation companies in the future. By applying this model, companies can enhance passenger safety and comfort, showcasing the significant potential of deep learning in real-world applications.}, archive = {J_ACCESS}, author = {Wei-Hsun Lee and Che-Yu Chang}, doi = {10.1109/ACCESS.2025.3538693}, journal = {IEEE Access}, pages = {25141-25153}, shortjournal = {IEEE Access}, title = {Assessing driving risk level: Harnessing deep learning hybrid model with intercity bus naturalistic driving data}, volume = {13}, year = {2025}, }
• (2025). Vision-based pick and place control system for industrial robots using an eye-in-hand camera. ACCESS, 13, 25127-25140. (www)

  @article{ , abstract = {In this paper, we present a vision-based pick-and-place control system for industrial robots using an eye-in-hand camera. In industry, using robots with cameras greatly improves efficiency and performance. Previous studies have focused on utilizing robotic arms for the pick-and-place process in simulated environments. The challenge when experimenting with real systems lies in aligning the coordinate systems between the robot and the camera, as well as ensuring high data accuracy during experimentation. To address this issue, our research focuses on utilizing a low-cost 2D camera combined with deep learning algorithms mounted on the end-effector of the robotic arm. This study is evaluated in both simulation and real-world experiments. We propose a novel approach that combines the YOLOv7 (You Only Look Once V7) deep learning network with GAN (Generative Adversarial Networks) to achieve fast and accurate object recognition. This system uses deep learning to process camera data to extract object positions for the robot in real-time. Due to its advantages of fast inference and high accuracy, YOLO is applied as the baseline for research. By training the deep learning model on diverse objects, it effectively recognizes and detects any object in the robot’s workspace. Through experimental results, we demonstrate the feasibility and effectiveness of our vision-based pick-and-place system. Our research contributes an important advancement in the field of industrial robots by showcasing the potential of using a 2D camera and an integrated deep learning system for object manipulation.}, archive = {J_ACCESS}, author = {van-Truong Nguyen and Phu-Tuan Nguyen and Shun-Feng Su and Phan Xuan Tan and Thanh-Lam Bui}, doi = {10.1109/ACCESS.2025.3536496}, journal = {IEEE Access}, pages = {25127-25140}, shortjournal = {IEEE Access}, title = {Vision-based pick and place control system for industrial robots using an eye-in-hand camera}, volume = {13}, year = {2025}, }
• (2025). Multi-task nonparametric regression under joint sparsity. ACCESS, 13, 25115-25126. (www)

  @article{ , abstract = {This study investigates a multi-task estimation under joint sparsity. We consider estimating multiple functions when functions of interest share common sparsity patterns. An $\ell _{2}$ penalty is imposed to enforce common sparsity patterns across component functions. A non-asymptotic oracle inequality is established to illustrate a possible improvement of the estimation error bound achieved by the proposed pooled estimator in comparison with the usual projection estimator. The proposed method is implemented with the alternating direction method of multipliers algorithm. Numerical studies are conducted to complement the theoretical results. We apply the proposed method to the ozone data to illustrate a practical applicability. The numerical results show that the proposed method detects the underlying sparsity patterns, thereby providing a desirable estimator that significantly outperforms the projection estimator.}, archive = {J_ACCESS}, author = {Jae-Hwan Jhong and Gyeongmin Kim and Kwan-Young Bak}, doi = {10.1109/ACCESS.2025.3538481}, journal = {IEEE Access}, pages = {25115-25126}, shortjournal = {IEEE Access}, title = {Multi-task nonparametric regression under joint sparsity}, volume = {13}, year = {2025}, }
• (2025). Conservation voltage reduction in distribution networks: A comprehensive review. ACCESS, 13, 25097-25114. (www)

  @article{ , abstract = {Conservation voltage reduction (CVR) is an efficient technique that lowers voltage levels to reduce aggregated energy consumption and achieve peak demand shaving in distribution networks. Utilities have considered CVR as a cost-effective technique to improve energy efficiency and reduce customers’ bills. This paper bridges the gap between theory and practice by providing a comprehensive and systematic review of CVR research progress from various aspects, including the effects of CVR on the operation and efficiency of both individual loads, such as residential appliances, and composite commercial and industrial loads. Engineers need to know the detailed CVR assessment methodologies in the literature, including (i) comparison-based, (ii) regression-based, (iii) load modeling-based, and (iv) simulation-based methods. Existing techniques of CVR implementation, including voltage/VAR control (VVC) and voltage/VAR optimization (VVO), are detailed to clarify practical applications working in the field, allowing researchers to develop and improve. Afterward, a complete review of the CVR utilization in active distribution networks with the presence of distributed generation (DG) and integrating CVR in microgrid applications is carried out. In addition, a survey about the economic benefits of CVR for both utilities and customers is analyzed. This paper gives engineers and researchers a handy tool to evaluate the state-of-the-art critically, clarify the latest and advanced technologies, and highlight challenges, limitations, and further developments in CVR applications.}, archive = {J_ACCESS}, author = {Ibrahim Alzubi and Saher Albatran and Issam A. Smadi and Salman Harasis}, doi = {10.1109/ACCESS.2025.3538526}, journal = {IEEE Access}, pages = {25097-25114}, shortjournal = {IEEE Access}, title = {Conservation voltage reduction in distribution networks: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). EMIKNet: Expanding multiple-instance inverse kinematics network for multiple end-effector and multiple solutions. ACCESS, 13, 25087-25096. (www)

  @article{ , abstract = {Inverse Kinematics (IK) is essential for robot control but suffers from computational complexity as the number of links and end-effectors increases, thereby affecting real-time control accuracy and performance. In this letter, we propose a neural network-based IK approach to efficiently address the IK challenges in complex robotic systems with multiple end-effectors and methods for obtaining multiple solutions. The proposed method is constructed via interconnected Gaussian Mixture Models (GMM), which consist of two main components: (i) a hierarchical computation framework for calculating joint angles while considering joint limits, and (ii) a ‘sub-link’ concept to reduce the hierarchical computation. The experimental results of 7-Degrees of Freedom (DOF) robot arm manipulator with a single end-effector and 22-DOF robot arm-hand manipulator with multiple end-effectors show that the proposed method reduces both Euclidean Distance and Angular Distance as well as the Run-time, compared with the model-based numerical and earlier neural network methods. We also verify multiple solutions obtained through hierarchical computation of GMM.}, archive = {J_ACCESS}, author = {Youn-Jae Go and Jun Moon}, doi = {10.1109/ACCESS.2025.3539022}, journal = {IEEE Access}, pages = {25087-25096}, shortjournal = {IEEE Access}, title = {EMIKNet: Expanding multiple-instance inverse kinematics network for multiple end-effector and multiple solutions}, volume = {13}, year = {2025}, }
• (2025). Federated reinforcement learning in stock trading execution: The FPPO algorithm for information security. ACCESS, 13, 25074-25086. (www)

  @article{ , abstract = {Stock trading execution is a critical component in the complex financial market landscape, and the development of a robust trade execution framework is essential for financial institutions pursuing profitability. This paper presents the Federated Proximal Policy Optimization (FPPO) algorithm, an adaptive trade execution framework that leverages joint reinforcement learning. The FPPO algorithm demonstrates significant improvements in model performance across various stocks, with average returns enhanced by 3% to 15%. It also exhibits superior performance in key metrics such as the reward function value, showcasing its effectiveness in different financial contexts. The paper further explores the model’s performance under the FPPO algorithm with varying numbers of client nodes and different risk preferences, underscoring the importance of these factors in model construction. The results substantiate the FPPO algorithm’s capability to safeguard privacy, ensure high performance, and enable the creation of personalized trading models in the optimal trade execution problem. This positions investors to gain a competitive edge in the dynamic and complex financial markets. Although the FPPO algorithm demonstrates significant potential in trade execution optimization, it may need to integrate a broader range of real-world variables and develop advanced privacy-preserving mechanisms to enhance its applicability in diverse financial contexts.}, archive = {J_ACCESS}, author = {Haogang Feng and Yue Wang and Shida Zhong and Tao Yuan and Zhi Quan}, doi = {10.1109/ACCESS.2025.3538859}, journal = {IEEE Access}, pages = {25074-25086}, shortjournal = {IEEE Access}, title = {Federated reinforcement learning in stock trading execution: The FPPO algorithm for information security}, volume = {13}, year = {2025}, }
• (2025). Experimental analysis of fuzzy gain-scheduled PID controller with a feedforward compensator for an ultra-precise piezoactuated micropositioning stage. ACCESS, 13, 25059-25073. (www)

  @article{ , abstract = {The piezoactuated micropositioning stage discussed in this study employs an all-ceramic insulation and a flexure-guide mechanism. It demonstrates remarkable capabilities, including achieving subnanometer resolution, high positioning accuracy, rapid response, and a high load capacity. The successful operation of this stage relies on the crucial role played by piezoelectric actuators. However, the inherent nonlinear hysteresis characteristics of these actuators, if not compensated for, can significantly compromise the precision of the positioning. This paper presents an experimental analysis of the piezoactuated micropositioning stage studied. Initially, a conventional proportional-integral-derivative (PID) feedback controller is utilized. Subsequently, intelligent fuzzy and fuzzy gain-scheduled PID feedback controllers are implemented, along with a hysteresis feedforward compensator. The main objective is to achieve ultra-precise positioning while avoiding the complexities associated with inverse hysteresis modeling. The experimental results from the piezoactuated micropositioning stage demonstrate the effectiveness of the intelligent fuzzy gain-scheduled PID controller with a feedforward compensator in achieving precise positioning. The peak-to-peak tracking error is 0.5% of the $12\mu $ m, 10Hz reference trajectory, marking a 54% and 88% performance improvement over the intelligent fuzzy controller, and classical PID controller, respectively.}, archive = {J_ACCESS}, author = {Hussain Kahil and Irfan Ahmad and Yasser Bin Salamah}, doi = {10.1109/ACCESS.2025.3538260}, journal = {IEEE Access}, pages = {25059-25073}, shortjournal = {IEEE Access}, title = {Experimental analysis of fuzzy gain-scheduled PID controller with a feedforward compensator for an ultra-precise piezoactuated micropositioning stage}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning-based resource allocation for QoE enhancement in wireless VR communications. ACCESS, 13, 25045-25058. (www)

  @article{ , abstract = {Wireless virtual reality (VR) communication applications have emerged as a transformative technology, offering innovative solutions in various areas of everyday life. However, the successful deployment of these applications faces challenges in ensuring high quality of experience (QoE), especially in environments with limited network resources. This research paper presents a novel approach to address the challenge of enhancing QoE by incorporating deep reinforcement learning (DRL) techniques in the resource allocation process. The proposed model takes into account the quality of service (QoS) parameters of the 5G new radio (NR) network to optimize its operation, ensuring a seamless and immersive VR experience. Specifically, the resource allocation strategy adopts a policy that maximizes the transmission-related QoE value based on the evolving characteristics of the communication channel and user interactions. To evaluate the effectiveness of the proposed approach, extensive simulations and comparative analyses against traditional resource allocation methods are performed. The results demonstrate significant improvements in the transmission-related QoE values and highlight the superiority of the DRL-based resource allocation approach in the dynamic and unpredictable wireless environments.}, archive = {J_ACCESS}, author = {Georgios Kougioumtzidis and Vladimir K. Poulkov and Pavlos I. Lazaridis and Zaharias D. Zaharis}, doi = {10.1109/ACCESS.2025.3538546}, journal = {IEEE Access}, pages = {25045-25058}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning-based resource allocation for QoE enhancement in wireless VR communications}, volume = {13}, year = {2025}, }
• (2025). Fibromyalgia detection and diagnosis: A systematic review of data-driven approaches and clinical implications. ACCESS, 13, 25026-25044. (www)

  @article{ , abstract = {Fibromyalgia syndrome (FMS) is a long-lasting medical condition that poses significant challenges for diagnosis and management because of its complex and poorly understood nature. It affects millions of people around the globe, predominantly women, causing widespread pain, fatigue, cognitive impairments, and mood disturbances. The lack of objective measures to address FMS complicates its assessment, often leading to delayed or misdiagnosed cases. By hindering daily activities and productivity, FMS negatively impacts the quality of the patient’s life. Innovative approaches that use medical data, such as bio-signals and bioimaging, combined with machine learning techniques, hold the promise of deepening our knowledge of FMS, which might in turn lead to systems that offer efficient, precise, and personalized physician support. Furthermore, artificial intelligence-driven identification of biomarkers and patient subgroups could improve FMS management. In this systematic review, we explore the role of artificial intelligence in understanding FMS pathophysiology, discuss the present limitations, and shed light on future research avenues, aiming to translate findings into improved clinical outcomes.}, archive = {J_ACCESS}, author = {Anirudh Atmakuru and Subrata Chakraborty and Massimo Salvi and Oliver Faust and Prabal Datta Barua and Makiko Kobayashi and Ru San Tan and Filippo Molinari and Abdul Hafeez-Baig and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2025.3539196}, journal = {IEEE Access}, pages = {25026-25044}, shortjournal = {IEEE Access}, title = {Fibromyalgia detection and diagnosis: A systematic review of data-driven approaches and clinical implications}, volume = {13}, year = {2025}, }
• (2025). Design of an integrated model for video summarization using multimodal fusion and YOLO for crime scene analysis. ACCESS, 13, 25008-25025. (www)

  @article{ , abstract = {The scenario of crime scene analysis in video summarization is more demanding in that it involves the accurate and efficient extraction of critical key events from multi-camera footage, which may include the identification of persons of interest, weapons and complicated environments. The current approaches suffer from occlusions, cross-camera person re ID and small-scale weapon detection, leading to the lack of a complete and inaccurate summary. Moreover, these methods are not very robust against changing environments and do not incorporate much feedback for continuous improvement operations. To overcome these limitations, this paper presents a comprehensive system for video summarization through multimodal fusion and spatiotemporal analysis across multiple camera streams. The system integrates the following advanced technologies: AGMFN for person detection and identity matching which employs multi-head attention to fuse RGB frames, motion data and optical flow. YOLOv8 with Feature Pyramid Networks is used for multiple Scale weapon detection in order to capture smaller, partially occluded objects within cluttered scenes. Spatio-temporal action localization is achieved with the help of 3D Convolutional Neural Networks, along with Temporal Attention Networks that capture all weapon-related actions with the best set of critical frames. Finally, a feedback-driven reinforcement learning framework named RL-HITL allows continuous improvement based on human input, which enhances the adaptability of the system over temporal instance sets. This integrated system has good accuracy in person detection ranging from 95-98%, weapon detection at 92-95% and even action localization ranging from 88-91%. At the same time, it reduces the length of video by 70-80%. Real-time learning through RL-HITL ensures model refinement and hereby gives long-term benefits in security and surveillance application, hence analysis at a crime scene for different scenarios.}, archive = {J_ACCESS}, author = {Sai Babu Veesam and Aravapalli Rama Satish}, doi = {10.1109/ACCESS.2025.3538282}, journal = {IEEE Access}, pages = {25008-25025}, shortjournal = {IEEE Access}, title = {Design of an integrated model for video summarization using multimodal fusion and YOLO for crime scene analysis}, volume = {13}, year = {2025}, }
• (2025). CPW-fed dual band flexible antenna for energy IoT applications at 380/940 MHz. ACCESS, 13, 24997-25007. (www)

  @article{ , abstract = {In this paper, we present the design and fabrication of a dual-band flexible antenna for energy Internet of Things (IoT) applications operating at 380 MHz and 940 MHz. The antenna features a coplanar waveguide (CPW) feed structure and a T-shaped monopole design on a polyimide substrate, with tapered stepped ground plane and square ring-shaped parasitic elements to minimize performance degradation due to bending. The antenna size is $210\times 236\times 0.2$ mm3, achieving bandwidths of 25.2% ( $344\sim 440$ MHz) and 15.2% ( $863\sim 1006$ MHz) when flat, with peak gains of 2.52 dBi and 4.90 dBi, respectively. We evaluate the antenna’s performance under various bending conditions using cylindrical styrofoam structures with radii of 25, 35, 45 cm, and the bandwidth degradation is within 4%. To verify the realistic applicability in energy IoT applications, we perform performance analysis considering a transmission tower environment with a cylindrical metal structure having a diameter of 60 cm, ensuring that performance degradation is minimized at a distance of at least 5 cm from the metal structure. Link margin calculations for Korea Electric Power Corporation (KEPCO)’s IoT specifications demonstrate values of 34.08 dB at 380 MHz and 31.94 dB at 940 MHz, confirming the antenna’s suitability for KEPCO’s energy IoT applications. These results indicate that the proposed antenna meets the requirements for efficient operation in KEPCO’s IoT infrastructure.}, archive = {J_ACCESS}, author = {Soyeong Lee and Sangjoon do and Hoosung Lee and Daeyeong Yoon and Yong Bae Park}, doi = {10.1109/ACCESS.2025.3538011}, journal = {IEEE Access}, pages = {24997-25007}, shortjournal = {IEEE Access}, title = {CPW-fed dual band flexible antenna for energy IoT applications at 380/940 MHz}, volume = {13}, year = {2025}, }
• (2025). Frontal face generation based on attitude point guidance and attention mechanism improvement. ACCESS, 13, 24988-24996. (www)

  @article{ , abstract = {The deflection of face angle is the most important factor affecting the accuracy in face recognition, non-frontal faces make some face recognition systems lose their due functions, the existing frontal face conversion methods often have the phenomenon of distortion and lack of identity. Aiming at the above problems, this paper proposes an face-frontal network which combines heat map of key points of face and improved attention mechanism. The network consists of a generator network and two discriminators networks, and the thermal map of the key points of the frontal face is used as a priori condition to guide the generation of the frontal face. In the generator part, the self-attention mechanism is introduced to obtain the dependence between feature points and other position features, which enhances the illumination perception ability of the network layer. At the same time, local attention is used in a discriminator to improve the local detail generation ability of the network in the face. Compared with other advanced frontal face generation methods, the proposed method has improved the accuracy of Rank-1 face recognition compared with other methods. The recognition rate of Rank-1 on Multi-PIE data set with small angle deflection is higher than other methods, the average recognition rate of Rank-1 on Multi-PIE setting2 is 97.41%, which is higher than the advanced method. Experimental results show that the proposed method can generate positive faces with corresponding identities from non-positive faces, which can be directly used in recognition tasks and has high recognition accuracy.}, archive = {J_ACCESS}, author = {Jihui Zhao and Zhengyi Yuan and Junhu Zhang and Haitao Li and Hui Li}, doi = {10.1109/ACCESS.2024.3520618}, journal = {IEEE Access}, pages = {24988-24996}, shortjournal = {IEEE Access}, title = {Frontal face generation based on attitude point guidance and attention mechanism improvement}, volume = {13}, year = {2025}, }
• (2025). A comprehensive approach to intrusion detection in IoT environments using hybrid feature selection and multi-stage classification techniques. ACCESS, 13, 24970-24987. (www)

  @article{ , abstract = {The rapid expansion of Internet of Things (IoT) devices has led to an increasingly complex threat landscape, challenging traditional Intrusion Detection Systems (IDS) to effectively handle the vast and diverse data generated by IoT networks. This paper presents a novel IDS that integrates Quantum-Inspired Particle Swarm Optimization (QIPSO) with Adaptive Neuro-Fuzzy Inference System (ANFIS) for optimized feature selection, followed by a multi-stage classification pipeline using Capsule Networks (CapsNets) and Attention Augmented-Recurrent Neural Networks (RNNs). The proposed approach addresses the limitations of existing methods by capturing both hierarchical and temporal dependencies in network traffic, improving the detection of subtle and advanced attacks. Evaluation on the TON-IoT and BOT-IoT datasets demonstrates that the proposed method significantly outperforms state-of-the-art techniques. Specifically, it achieves 98.83% accuracy, 98.56% precision, and 98.65% F-Measure on the TON-IoT dataset, and 98.6% accuracy, 98.5% precision, and 98.94% F-Measure on the BOT-IoT dataset, showcasing its superior performance over existing IDS models. This paper’s key contribution lies in the integration of feature selection and classification techniques tailored for IoT environments, filling a critical gap in the state of the art and offering a more adaptive and efficient solution for real-time intrusion detection.}, archive = {J_ACCESS}, author = {G. Logeswari and J. Deepika Roselind and K. Tamilarasi and V. Nivethitha}, doi = {10.1109/ACCESS.2025.3532895}, journal = {IEEE Access}, pages = {24970-24987}, shortjournal = {IEEE Access}, title = {A comprehensive approach to intrusion detection in IoT environments using hybrid feature selection and multi-stage classification techniques}, volume = {13}, year = {2025}, }
• (2025). A real-time human pose measurement system for human-in-the-loop dynamic simulators. ACCESS, 13, 24954-24969. (www)

  @article{ , abstract = {The possibility of achieving real-time evaluation of human pose enables the ability to control robotic platforms based on user’s pose (or even on user’s inertial properties) in Human-In-The-Loop simulators, for sports and rehabilitation as an example. This study presents a vision-based, marker-less measurement system for real-time 3D human pose estimation. The system exploits pre-trained 2D human pose detection models and integrates an $\alpha \text {-}\beta \text {-}\gamma $ filter to reduce fluctuations in detected key points. It also introduces a novel Weighted Direct Linear Triangulation method, enhancing 3D reconstruction accuracy by assigning higher weights to key points consistent across current and previous frames. The method’s accuracy and execution time are assessed using the public Human3.6M dataset, evaluating different model configurations, formats, camera setups, and acquisition modes for real-time applications. The YOLOv8x-pose model with four cameras achieves the highest accuracy, with a Mean-Per-Joint Position Error of 18.2 mm and an execution time of 15 ms, outperforming state-of-the-art methods. Converting models to the TensorRT framework reduces execution time by 4.2 ms without significant accuracy loss. The system is integrated into a clinical rehabilitation device, a three-degree-of-freedom parallel kinematic machine, to facilitate patient participation in exergames. The proposed human-pose estimation method achieves real-time performance, enabling the motion platform to be controlled dynamically based on the patient’s actual standing pose.}, archive = {J_ACCESS}, author = {Nicola Giulietti and Davide Todesca and Marco Carnevale and Hermes Giberti}, doi = {10.1109/ACCESS.2025.3538332}, journal = {IEEE Access}, pages = {24954-24969}, shortjournal = {IEEE Access}, title = {A real-time human pose measurement system for human-in-the-loop dynamic simulators}, volume = {13}, year = {2025}, }
• (2025). Neural network-based lower limb prostheses control using super twisting sliding mode control. ACCESS, 13, 24929-24953. (www)

  @article{ , abstract = {This paper presents a method for controlling the prosthetic leg using surface Electromyography (sEMG) signals, Artificial Neural Network (ANN), and Super Twisting Sliding Mode Control (ST-SMC). The triggering signal is extracted from the user’s muscles and intense signal preprocessing that includes filtering, rectification, linearization, and Mean Average Value (MAV) feature extraction. The ANN predicts joint angles for walking, upstairs, and downstairs using the processed sEMG signals of the muscles and measured and filtered target joint angles. The neural network structure is built using Feed-forward Neural Network (FFNN) architecture and Levenberg-Marquardt (LM) back-propagation training algorithm for accuracy, fast convergence, and reliable optimization of nonlinear relationships. The ST-SMC controller regulates the motion of the prosthetic joints according to specified reference trajectories. MATLAB signal analyzers, neural network fitting packages, and Simulink are used to preprocess signals, train the FFNN for dynamic modeling of the system, and design controllers. The proposed ST-SMC is compared with conventional SMC. Simulation results show that training the neural network with processed data increases regression value and decreases trajectory tracking mean squared error (MSE). The controller’s robustness against internal parameter change and external environmental changes is demonstrated through parameter variation and disturbance analysis.}, archive = {J_ACCESS}, author = {Adisu Tadese Demora and Chala Merga Abdissa}, doi = {10.1109/ACCESS.2025.3538689}, journal = {IEEE Access}, pages = {24929-24953}, shortjournal = {IEEE Access}, title = {Neural network-based lower limb prostheses control using super twisting sliding mode control}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A comprehensive review of direction-of-arrival estimation and localization approaches in mixed-field sources scenario”. ACCESS, 13, 24928. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Comprehensive Review of Direction-of-Arrival Estimation and Localization Approaches in Mixed-Field Sources Scenario”).}, archive = {J_ACCESS}, author = {Amir Masoud Molaei and Bijan Zakeri and Seyed Mehdi Hosseini Andargoli and Muhammad Ali Babar Abbasi and Vincent Fusco and Okan Yurduseven}, doi = {10.1109/ACCESS.2025.3538358}, journal = {IEEE Access}, pages = {24928}, shortjournal = {IEEE Access}, title = {Corrections to “A comprehensive review of direction-of-arrival estimation and localization approaches in mixed-field sources scenario”}, volume = {13}, year = {2025}, }
• (2025). Urdu lip reading systems for digits in controlled and uncontrolled environment. ACCESS, 13, 24906-24927. (www)

  @article{ , abstract = {Lip reading technology can significantly benefit various domains, such as enhancing communication for the hearing impaired person, assisting in noisy environments, and improving security with silent password inputs. Despite advancements in lip reading for several languages, there has been limited success in developing an effective model for Urdu lip reading due to the lack of an appropriate dataset and the challenges faced by earlier models, such as the unsuccessful adaptation of the LipNet model to Urdu. To address these issues, we contribute by introducing the ULRD dataset, employing diverse data augmentation techniques, and comparing three DNN models: a Hybrid 2D-3D CNN-LSTM model, a LipNet-based 2D CNN-LSTM model, and a baseline 3D CNN-GRU model. Each model is evaluated in both controlled and uncontrolled environments, using both seen and unseen data. Results indicate that the LipNet-based 2D CNN-LSTM model achieves overall 92.15 % high accuracy in all conditions, but the Hybrid model demonstrates impressive generalization with an overall 90.00 % accuracy on unseen data due to its enhanced spatiotemporal feature extraction capability. Additionally, the precision: 0.91, recall: 0.91, and F1-Score: 0.91 results of LipNet-based 2D CNN-LSTM model are also high, then its other competitors models. The other various findings highlight the effectiveness of different DNN architectures and the potential improvements offered by the ULRD dataset for Urdu lip reading research.}, archive = {J_ACCESS}, author = {Amanullah Baloch and Mushtaq Ali and Lal Hussain and Touseef Sadiq and Badr S. Alkahtani}, doi = {10.1109/ACCESS.2025.3531640}, journal = {IEEE Access}, pages = {24906-24927}, shortjournal = {IEEE Access}, title = {Urdu lip reading systems for digits in controlled and uncontrolled environment}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Development p, q, r−Spherical fuzzy einstein aggregation operators: Application in decision-making in logo design”. ACCESS, 13, 24905. (www)

  @article{ , abstract = {In the above article [1], reference 48 was retracted. As the work in this reference is no longer reliable, we are removing it from the reference list AND replacing it with [2]. The corresponding discussion in Section V-F. Table 10 is changed to the following:TABLE 10Comparison MethodsScore valuesOption $\Delta _{1}$ $\Delta _{2}$ $\Delta _{3}$ $\Delta _{4}$ $\Delta _{5}$ Garg et al. [47]0.23490.18640.17900.15480.1945 $\Delta _{1}$ Mahmood et al. [2]0.15170.12090.10510.09450.1384 $\Delta _{1}$ Mahnaz et al. [49]0.27630.23550.22780.20310.2534 $\Delta _{1}$ Rahim et al. [25]0.54820.51470.50380.49700.5326 $\Delta _{1}$ Akhtar et al. [50]0.43550.38480.37160.34180.4093 $\Delta _{1}$ Ashraf et al. [51]0.31470.25600.24810.23570.2839 $\Delta _{1}$ Ashraf et al. [52]0.49120.45440.43560.42650.4873 $\Delta _{1}$}, archive = {J_ACCESS}, author = {Lin Kang and Salma Khan and Muhammad Rahim and Kamal Shah and Thabet Abdeljawad}, doi = {10.1109/ACCESS.2025.3538377}, journal = {IEEE Access}, pages = {24905}, shortjournal = {IEEE Access}, title = {Corrections to “Development p, q, r−Spherical fuzzy einstein aggregation operators: Application in decision-making in logo design”}, volume = {13}, year = {2025}, }
• (2025). LLM-guided crowdsourced test report clustering. ACCESS, 13, 24894-24904. (www)

  @article{ , abstract = {This paper proposes a clustering method for crowdsourced test reports based on a large language model to solve the limitations of existing methods in processing repeated reports and utilizing multi-modal information. Existing crowdsourced test report clustering methods have significant shortcomings in handling duplicate reports, ignoring the semantic information of screenshots, and underutilizing the relationship between text and images. The emergence of LLM provides a new way to solve these problems. By integrating the semantic understanding ability of LLM, key information can be extracted from the test report more accurately, and the semantic relationship between screenshots and text descriptions can be used to guide the clustering process, thus improving the accuracy and effectiveness of clustering. The method in this paper uses a pre-trained LLM (such as GPT-4) to encode the text in the test report, and uses a visual model such as CLIP to encode the application screenshots, converting the text descriptions and images into high-dimensional semantic vectors. The cosine similarity is then used to calculate the similarity between the vectors, and semantic binding rules are constructed to guide the clustering process, ensuring that semantically related reports are assigned to the same cluster and semantically different reports are assigned to different clusters. Through experimental verification, this method is significantly superior to traditional methods in several evaluation indicators, demonstrating its great potential in improving the efficiency and quality of crowdsourced test report processing. In the future, this method is expected to be widely used in the process of software testing and maintenance, and further promote technological progress.}, archive = {J_ACCESS}, author = {Ying Li and Ye Zhong and Lijuan Yang and Yanbo Wang and Penghua Zhu}, doi = {10.1109/ACCESS.2025.3530960}, journal = {IEEE Access}, pages = {24894-24904}, shortjournal = {IEEE Access}, title = {LLM-guided crowdsourced test report clustering}, volume = {13}, year = {2025}, }
• (2025). Expectation-maximization aided modified weighted sequential energy detector for distributed cooperative spectrum sensing. ACCESS, 13, 24880-24893. (www)

  @article{ , abstract = {Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detection capability. This is due to the fact that it uses the energy only from the present sensing interval for the PU detection. Previous studies have shown that even with fewer observed samples per energy statistics, improved detection capabilities can be achieved by aggregating both present and past ED samples in a test statistic. Thus, a weighted sequential energy detector (WSED) has been proposed, but it is based on aggregating all the collected ED samples over an observation window. For a highly dynamic PU over the consecutive sensing intervals, that involves also combining the outdated samples in the test statistic that do not correspond to the present state of the PU. In this paper, we propose a modified WSED (mWSED) that uses the primary user states information over the window to aggregate only the highly correlated ED samples in its test statistic. In practice, since the PU states are a priori unknown, we also develop a joint expectation-maximization and Viterbi (EM-Viterbi) algorithm based scheme to iteratively estimate the states by using the ED samples collected over the window. The estimated states are then used in mWSED to compute its test statistics, and the algorithm is referred to here as the EM-mWSED algorithm. Simulation results show that EM-mWSED outperforms other schemes and its performance improves by increasing the average number of neighbors per SU in the network, and by increasing the SNR or the number of samples per energy statistic.}, archive = {J_ACCESS}, author = {Mohammed Rashid and Jeffrey A. Nanzer}, doi = {10.1109/ACCESS.2025.3537611}, journal = {IEEE Access}, pages = {24880-24893}, shortjournal = {IEEE Access}, title = {Expectation-maximization aided modified weighted sequential energy detector for distributed cooperative spectrum sensing}, volume = {13}, year = {2025}, }
• (2025). Deep unfolding-aided parameter tuning for plug-and-play-based video snapshot compressive imaging. ACCESS, 13, 24867-24879. (www)

  @article{ , abstract = {Snapshot compressive imaging (SCI) captures high-dimensional data efficiently by compressing it into two-dimensional observations and reconstructing high-dimensional data from two-dimensional observations with various algorithms. The plug-and-play (PnP) method is a promising approach for the video SCI reconstruction because it can leverage both observation models and denoising methods for videos. Since the reconstruction accuracy significantly depends on the choice of noise level parameters, this paper proposes a deep unfolding-based method for tuning these parameters in PnP-based video SCI. For the training of the parameters, we prepare training data from the densely annotated video segmentation dataset, reparametrize the noise level parameters, and apply the checkpointing technique to reduce the required memory. Simulation results show that the trained noise level parameters via the proposed approach exhibit a non-monotonic pattern, which is different from the assumptions in the conventional convergence analyses of PnP-based algorithms. These findings provide new insights into both the application of deep unfolding and the theoretical basis of PnP algorithms.}, archive = {J_ACCESS}, author = {Takashi Matsuda and Ryo Hayakawa and Youji Iiguni}, doi = {10.1109/ACCESS.2025.3538499}, journal = {IEEE Access}, pages = {24867-24879}, shortjournal = {IEEE Access}, title = {Deep unfolding-aided parameter tuning for plug-and-play-based video snapshot compressive imaging}, volume = {13}, year = {2025}, }
• (2025). Squeeze-and-excitation vision transformer for lung nodule classification. ACCESS, 13, 24852-24866. (www)

  @article{ , abstract = {Lung cancer is one of the deadliest cancers. Early diagnosis of lung cancer can increase the 5-year survival rate to 70%, and the lung nodule classification is the basis for early diagnosis. However, due to the small scales and variations in shape and texture of lung nodules, accurate classification of lung nodules is a challenging task. Deep learning models have been widely used in lung nodule classification. But due to the lack of interpretability for these models, it has not been widely applied in clinical. Therefore, a model that can be interpretable and focus on key features such as shape and texture is necessary. Moreover, comparing the performance of different models is challenging due to the different training and testing sets used. This study combines 2 attention mechanisms and proposes a novel deep learning model called squeeze-and-excitation vision transformer (SE-ViT). Self-attention in SE-ViT can extract the features of relationship between different patches, which can be used to determine the heterogeneity and shape of lung nodules. The SE mechanism assigns higher weights to patches containing crucial information. Finally, attention maps generated by Grad-CAM demonstrate that 2 attention mechanisms enable the model to focus on key areas for classifying benign and malignant lung nodules. In this study, the SE-ViT achieves an average accuracy of 0.863, precision of 0.872, recall of 0.876, F1-score of 0.872 and AUC of 0.862 on public dataset LUNA16. This result shows a significant improvement compared to ViT and SE-CNN.}, archive = {J_ACCESS}, author = {Xiaozhong Xue and Yanhe Ma and Weiwei Du and Yahui Peng}, doi = {10.1109/ACCESS.2025.3529127}, journal = {IEEE Access}, pages = {24852-24866}, shortjournal = {IEEE Access}, title = {Squeeze-and-excitation vision transformer for lung nodule classification}, volume = {13}, year = {2025}, }
• (2025). Security and market liquidity enhanced transactive energy system for residential end users. ACCESS, 13, 24833-24851. (www)

  @article{ , abstract = {The swift progress of economically viable sustainable energy resources, coupled with advancements in communication and computational technologies, has facilitated the introduction of the Transactive Energy System (TES) within the smart grid. However, TES is facing critical challenges in security and economics. Security concerns include data integrity, authentication, confidentiality, scalability, and privacy, while economic challenges involve penalty reduction and market liquidity. In order to address these issues, this study proposes an integrated approach combining elliptic curve digital signatures with hash functions for data integrity and authentication, Paillier cryptography for confidentiality, and calibrated noise for privacy preservation. Further, scalability is achieved through an intelligent grouping mechanism, while market liquidity is enhanced using a demand-supply weighting strategy and a demand response approach based on locality net demand. Moreover, case studies were conducted to assess the effectiveness of the proposed approach, resulting in average electricity bill savings of 12.29%, improvements in privacy protection of 41.07%, and an enhancement in computing scalability of 44.79%. These results validate the proposed framework as a secure, scalable, and economically viable solution for TES.}, archive = {J_ACCESS}, author = {Vanga Anil and S. L. Arun}, doi = {10.1109/ACCESS.2025.3538330}, journal = {IEEE Access}, pages = {24833-24851}, shortjournal = {IEEE Access}, title = {Security and market liquidity enhanced transactive energy system for residential end users}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid GCN-LSTM algorithm for energy stock price prediction: Leveraging temporal dynamics and inter-stock relationships. ACCESS, 13, 24815-24832. (www)

  @article{ , abstract = {Energy stock price prediction is a pivotal challenge in financial forecasting, characterized by high volatility and complexity influenced by geopolitical factors, regulatory shifts, and sector-specific issues. Traditional methods often struggle to account for the intricate dependencies and temporal patterns present in energy stock data. To address these limitations, this study introduces a hybrid model that integrates a Graph Convolutional Network (GCN) with an attention-enhanced Long Short-Term Memory (LSTM) architecture. By employing a graph structure derived from Dynamic Time Warping (DTW), the GCN captures inter-stock relationships, while the attention mechanism within the LSTM component refines the modelling of temporal dynamics, allowing the model to focus on the most relevant historical information. Experimental evaluations across multiple energy stocks show that this combined LSTMGC model significantly outperforms conventional approaches- including Linear Regression, GRU, MLP, and standalone LSTMs- when assessed using Mean Squared Error (MSE) and R-squared (R2) metrics. By jointly leveraging spatial and temporal dependencies, as well as the selective attention mechanism, the proposed framework enhances predictive accuracy and reliability, offering valuable insights for investors and policymakers navigating the evolving energy market.}, archive = {J_ACCESS}, author = {Babak Amiri and Amirali Haddadi and Kosar Farajpour Mojdehi}, doi = {10.1109/ACCESS.2025.3536889}, journal = {IEEE Access}, pages = {24815-24832}, shortjournal = {IEEE Access}, title = {A novel hybrid GCN-LSTM algorithm for energy stock price prediction: Leveraging temporal dynamics and inter-stock relationships}, volume = {13}, year = {2025}, }
• (2025). A finite element analysis-based modeling and evaluation method for sternum support brace. ACCESS, 13, 24804-24814. (www)

  @article{ , abstract = {Sternum support braces are commonly used in thoracic surgery to secure the chest after open chest procedures. Despite their widespread use in postoperative recovery, current modeling and performance evaluation methods have significant limitations that hinder the optimization and improvement of sternum support braces. To address these challenges, this study proposes a finite element analysis-based method for modeling and evaluating sternum support braces. First, a 3D model of the brace was constructed using 3D scanning technology, followed by mathematical modeling using the finite element method. Subsequently, the factors affecting users’ experience with sternum support braces were analyzed. Three quantitative indicators were designed to scientifically assess brace performance. Finally, four commonly used sternum support braces were selected as examples for simulation modeling, performance evaluation, experimentation, and surveys to validate the effectiveness of the proposed method. The findings demonstrate that this method effectively addresses the difficulties in measuring the pressure exerted by sternum support braces and in quantifying their performance. It offers a simple and accurate means of evaluating the performance of sternum support braces, and the evaluation results align closely with patients’ actual experiences and needs. This method provides a scientific basis for further optimization of design and clinical applications.}, archive = {J_ACCESS}, author = {Linhua Lv and Junyi Hu and Wanquan Liu and Jianqing Peng}, doi = {10.1109/ACCESS.2025.3536472}, journal = {IEEE Access}, pages = {24804-24814}, shortjournal = {IEEE Access}, title = {A finite element analysis-based modeling and evaluation method for sternum support brace}, volume = {13}, year = {2025}, }
• (2025). Vehicle detection and tracking based on improved YOLOv8. ACCESS, 13, 24793-24803. (www)

  @article{ , abstract = {With the rapid increase in transportation pressure, the demand for efficient traffic recognition and tracking systems is growing. Traditional methods have certain limitations when dealing with complex situations in traffic scenes, such as large weights and insufficient detection accuracies, et al. Therefore, we propose a novel method based on YOLOv8n. Firstly we introduced SCC_Detect based on SCConv on the detection head to reduce the computation of redundant features. Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. The BotSORT tracker is embedded in the period of inference, which achieves more accurate and stable recognition and tracking results in the traffic scene. The experimental results show that the proposed model reduces parameters and weight by approximately 36.5% and 25% respectively at the expense of only 0.2% mAP@0.5 compared with YOLOv8n on the UA-DETRAC dataset. In terms of tracking, MOTA, IDF1 and MOTP of the BoTSORT algorithm on the test video were superior to those of DeepSORT and ByteTrack. The accuracy was improved, and the number of lost tracks was reduced. It has a high practical value and application prospects in traffic detection and deployment.}, archive = {J_ACCESS}, author = {Yunxiang Liu and Shujun Shen}, doi = {10.1109/ACCESS.2025.3538556}, journal = {IEEE Access}, pages = {24793-24803}, shortjournal = {IEEE Access}, title = {Vehicle detection and tracking based on improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). A special points and neural network-based dynamic multi-objective optimization algorithm. ACCESS, 13, 24765-24792. (www)

  @article{ , abstract = {This paper introduces a special points and neural network- based dynamic multi-objective optimization algorithm (SPNN-DMOA) for solving dynamic multi-objective optimization problems (DMOPs) with an irregularly changing pareto set. In the stage of population initialization, the algorithm employs a feedforward neural network (FNN) along with special points to generate an initial population. The FNN is trained with historical special points (knee point, boundary point, center point), and the current special points are generated by the FNN when an environmental change is detected. Then the decision variables are classified into convergence variables and diversity variables. The convergence variables of special points are locally searched to form a new population and the best individuals of this population are selected. Finally, a portion of the initial population is generated by conducting a local search on the diversity variables of best individuals, while the remaining portion is produced using random strategies. SPNN-DMOA only needs to maintain non-dominated solutions in proximity to special points, which reduces the computational complexity in the dynamic evolution process. The proposed algorithm has been compared with other state-of-the-art algorithms on a series of benchmark problems, demonstrating its superior performance in optimizing DMOPs.}, archive = {J_ACCESS}, author = {Sanyi Li and Wenjie Hou and Peng Liu and Weichao Yue and Qian Wang}, doi = {10.1109/ACCESS.2025.3538537}, journal = {IEEE Access}, pages = {24765-24792}, shortjournal = {IEEE Access}, title = {A special points and neural network-based dynamic multi-objective optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Log-driven behavior discrimination of process robots using entropy similarity. ACCESS, 13, 24749-24764. (www)

  @article{ , abstract = {In the context of Robotic Process Automation, process robot refers to robot software corresponding to a specific process, with the ability to capture and interpret process management. It is a fact that many process robots are derived from the same base model in practical applications, and the main reasons include the inevitable and flexible adaptability of process models. This fact raises the question of many process robots having a high degree of similarity, and it is hard to differentiate them from each other under the state-of-the-art similarity measurements. In order to differentiate similar but different process robots, in this paper we propose an approach to process robot behavior discrimination, from the perspective that only event logs are given as known, that is, the reference models of process models are kept unknown. The biggest innovation lies in that the proposed behavior discrimination method is based on log-entropy similarity measurement using probability distributions and statistical distance theory. Specifically, using the concept of log entropy, event logs are transformed to probability distributions of trace multisets and activity pairs multisets, respectively. Then, JS and KL divergence is used to measure the statistical distance between the two probability distributions of processes, and these statistical distances are the foundations of log similarity measurements. Finally, a series of experiments are conducted on a set of publicly available datasets, validating the feasibility, generality, and effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Lu Li and Huan Fang}, doi = {10.1109/ACCESS.2025.3538749}, journal = {IEEE Access}, pages = {24749-24764}, shortjournal = {IEEE Access}, title = {Log-driven behavior discrimination of process robots using entropy similarity}, volume = {13}, year = {2025}, }
• (2025). Blockchain-powered secure and scalable threat intelligence system with graph convolutional autoencoder and reinforcement learning feedback loop. ACCESS, 13, 24736-24748. (www)

  @article{ , abstract = {As the field of cybersecurity has experienced continual changes, up-to-date techniques have become increasingly necessary to analyze and defend against threats. Furthermore, the current methods consistently produce false alarms and sometimes completely miss real threats. This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. To secure the exchange of threat intelligence information, a safe blockchain network is used, which comprises Byzantine Fault Tolerance for high data integrity and Zero-Knowledge Proofs for access control. All relevant information is cleaned and standardized prior to analysis. Subsequently, graph convolutional neural networks with autoencoders are trained on large unlabeled sets of threat data to automatically label various types of threats, with the system employing fuzzy logic to rank and score possible threats. Furthermore, we implemented a feedback loop that incorporates reinforcement learning, thereby improving model performance over time according to guidance provided by cybersecurity specialists. The proposed system achieved high accuracy, precision, negative predictive value, and MCC, as well as notably low FPR and FNR values. The results establish that the proposed system is a reliable and effective measure for detecting cyberthreats.}, archive = {J_ACCESS}, author = {Mohamad Khayat and Ezedin Barka and Mohamed Adel Serhani and Farag Sallabi and Khaled Shuaib and Heba M. Khater}, doi = {10.1109/ACCESS.2025.3538160}, journal = {IEEE Access}, pages = {24736-24748}, shortjournal = {IEEE Access}, title = {Blockchain-powered secure and scalable threat intelligence system with graph convolutional autoencoder and reinforcement learning feedback loop}, volume = {13}, year = {2025}, }
• (2025). Energy-optimized monitoring system for underground cave environments based on long preamble LoRa. ACCESS, 13, 24717-24735. (www)

  @article{ , abstract = {Underground environments represent a hostile environment. The observation of underground cave systems is fundamental to protecting and understanding the dynamics of these delicate environments. Wireless Sensor Networks (WSNs) have been demonstrated to be a useful solution for data gathering and communication in harsh environments. This research paper deals with an experimental activity for the monitoring of the speleological karst caves in Stiffe, Italy. The presented monitoring system is based on an asynchronous LoRa network that leverages Channel Activity Detection (CAD) for in-cave communication. This study is part of a pre-commercial survey in sight of a future fixed installation in the aforementioned location. The proposed network is composed of sensor nodes that are placed along a portion of the cave to form a linear structure. The low-power energetic performances are achieved using long preamble-based communication and sleep routines that allow the nodes to have a power dissipation in the order of hundreds of microwatts for the majority of a reading cycle. The cave retrieved data are gathered remotely and accessed by the management personnel for data analysis. The experimental system reports promising results for a commercial implementation, demonstrating satisfactory energetic performances allowing the management personnel to charge the devices with intervals of multiple months. The radio performances of the devices inside the cave has shown to be difficult, enabling a communication range inferior to one hundred of meters for each hop, mostly due to the irregular morphology of the tunnels. This paper reports the implementation of the whole system, starting from the architectural details to the experimental results obtained in the field.}, archive = {J_ACCESS}, author = {Mattia Ragnoli and Gianluca Barile and Alfiero Leoni and Stefano Ricci and Vincenzo Stornelli}, doi = {10.1109/ACCESS.2025.3538544}, journal = {IEEE Access}, pages = {24717-24735}, shortjournal = {IEEE Access}, title = {Energy-optimized monitoring system for underground cave environments based on long preamble LoRa}, volume = {13}, year = {2025}, }
• (2025). Automated internal defect identification and localization based on a near-field SAR millimeter-wave imaging system. ACCESS, 13, 24698-24716. (www)

  @article{ , abstract = {Fast and cost-effective detection of internal defects is essential for structural integrity inspection in various applications such as manufacturing, construction, and aerospace. Current internal non-destructive testing (NDT) methods, such as computed tomography, can be costly, time-consuming, and constrained by object size. The lightweight and affordable millimeter-wave (mmWave) radar has demonstrated the capability of detecting objects beneath surfaces or obstacles through generated near-field synthetic aperture radar (SAR) images. However, its use in precise internal inspection has not been fully explored due to the significant noise in the SAR images and high rate of false identification. To enable accurate and fast inspections using the mmWave radar, this work establishes a robust and automated internal defect detection system. It employs a compact mmWave radar system mounted on a stable rail-based scanning mechanism, generating high-resolution near-field SAR images with an enhanced signal-to-noise ratio through denoising. For fast and accurate detection, an automated defect localization algorithm is developed. The accuracy of detection is ensured by modeling and separating internal defects from disturbances introduced by the scanning mechanism and noise. Experiments were conducted using 3D-printed blocks with synthetic defects to demonstrate the detection capability of the proposed system. Internal defects were accurately detected across variations in shape, material permittivity, depth, and size. The proposed method achieved an average accuracy of 91.7%, outperforming existing methods. The compact design of the radar system enables seamless integration with larger scanning systems, while the automated detection algorithm can be readily implemented as a software module within existing sensing systems. This integration of hardware and software components yields a versatile, low-cost framework for rapid internal health inspection that adapts to various industrial applications and object sizes.}, archive = {J_ACCESS}, author = {Quoc Cuong Bui and Weizhi Lin and Qiang Huang and Gyung-Su Byun}, doi = {10.1109/ACCESS.2025.3531913}, journal = {IEEE Access}, pages = {24698-24716}, shortjournal = {IEEE Access}, title = {Automated internal defect identification and localization based on a near-field SAR millimeter-wave imaging system}, volume = {13}, year = {2025}, }
• (2025). Enhancing semi-supervised learning with concept drift detection and self-training: A study on classifier diversity and performance. ACCESS, 13, 24681-24697. (www)

  @article{ , abstract = {Machine learning algorithms that assist in decision-making are becoming crucial in several areas, such as healthcare, finance, marketing, etc. Algorithms exposed to a larger and more relevant amount of training data tend to perform better. However, the availability of labeled data without human expert intervention is a challenging task, especially in data stream learning with concept drifts, where data is generated rapidly, in real-time, with the possibility of changes in the data distribution. Concept drift occurs in supervised, semi-supervised and unsupervised learning environments, and is addressed through different approaches such as statistics, machine learning, among others. Currently, the use of drift detectors with base classifiers in semi-supervised learning is uncommon. Semi-supervised classifiers often consume a lot of memory and run-time, and the addition of a detection mechanism increases the computational cost. Furthermore, classification in semi-supervised environments can lead to problems related to labeling data to training: an error in this process can negatively impact model performance. This article investigates the use of supervised concept drift detectors in semi-supervised learning problems, highlighting how the detectors can improve the performance of classification. It also explores the influence of diversity in classifier ensembles, showing that increased diversity contributes to enhanced accuracy and robustness of models in concept drift scenarios. Additionally, it introduces a self-training approach to provide more labels and optimize model learning and adaptation. This research also details updates in the Massive Online Analysis (MOA) framework, that supports the simulation of semi-supervised scenarios. The experiments conducted to test the proposed approach used Hoeffding Tree (HT) and Naive Bayes (NB) as base classifiers, which were also employed as members of the ensembles used in this research. These classifiers were combined with several detectors and tested on a total of 84 artificial and five real-world datasets. The experiments were conducted with 15% and 30% of labeled data, the main percentages addressed in this research, while 100% was used to provide additional grounding in some cases. The results indicate that detectors created for supervised learning can be effectively used in semi-supervised environments. Furthermore, the tests using the introduced self-training approach demonstrates that the inclusion of additional labels significantly improves the performance of the classifiers. These findings may lead to a paradigm shift for future research, as many researchers have not considered concept drift detectors as a viable alternative due to the limited presence of labels in most real-world data streams.}, archive = {J_ACCESS}, author = {José L. M. Pérez and Roberto S. M. Barros and Silas G. T. C. Santos}, doi = {10.1109/ACCESS.2025.3538710}, journal = {IEEE Access}, pages = {24681-24697}, shortjournal = {IEEE Access}, title = {Enhancing semi-supervised learning with concept drift detection and self-training: A study on classifier diversity and performance}, volume = {13}, year = {2025}, }
• (2025). DFTD-YOLO: Lightweight multi-target detection from unmanned aerial vehicle viewpoints. ACCESS, 13, 24672-24680. (www)

  @article{ , abstract = {Due to the low detection accuracy of small and dense target objects in multi-target detection tasks from the unmanned aerial vehicle (UAV) perspective and the deployment of deep learning models for UAVs as embedded devices, these models must be lightweight. In this study, we propose an improved algorithm, DFTD-YOLO, based on YOLOv8n. We designed a new neck feature fusion network. The network better balances information transfer between shallow and deep layers through a detailed information extraction module and an abstract feature information aggregation module, effectively reducing the loss of detail information with gradient flow and improving detection performance. In addition, we designed a new detection head called the TDD-Head. This module enhances the feature interaction between the classification and regression tasks through the task alignment mechanism and shared convolution, which reduces model parameters and computation and improves model performance. To validate the model, we conducted validation experiments on the VisDrone2021 dataset. The experimental results showed a 33.67% reduction in the number of parameters, 17.3% reduction in the amount of computation, 10.74% improvement in mAP@0.5, and 13.2% improvement in mAP@0.5:0.95 compared with the existing YOLOv8n. The results demonstrate the considerable potential of the model for multitarget detection tasks from the UAV perspective.}, archive = {J_ACCESS}, author = {Yuteng Chen and Zhaoguang Liu}, doi = {10.1109/ACCESS.2025.3535624}, journal = {IEEE Access}, pages = {24672-24680}, shortjournal = {IEEE Access}, title = {DFTD-YOLO: Lightweight multi-target detection from unmanned aerial vehicle viewpoints}, volume = {13}, year = {2025}, }
• (2025). Arithmetically fast position transformation for view synthesis and depth estimation. ACCESS, 13, 24661-24671. (www)

  @article{ , abstract = {In this paper we present a method of fast computation of matrix transformation in the process of position transformation of objects of the scene between different, virtual or real, camera positions. The process finds extensive use in virtual view generation in Free Viewpoint Video (FVV) and virtual reality applications as well as in depth estimation algorithms. The proposed method relies on the reformulation of the matrix equation used in the process. As a result, the number of necessary arithmetic operations is reduced and some of the calculations can be reused for consecutive pixel position transformations. The presented algorithm produces identical output as an unoptimized algorithm with approximately 22% reduction of the processing time averaged over the examined representative test sequences.}, archive = {J_ACCESS}, author = {Krzysztof Wegner and Tomasz Grajek and Krzysztof Klimaszewski}, doi = {10.1109/ACCESS.2025.3537923}, journal = {IEEE Access}, pages = {24661-24671}, shortjournal = {IEEE Access}, title = {Arithmetically fast position transformation for view synthesis and depth estimation}, volume = {13}, year = {2025}, }
• (2025). Detection schemes with low-resolution ADCs and spatial oversampling for transmission with higher-order constellations in terahertz band. ACCESS, 13, 24643-24660. (www)

  @article{ , abstract = {In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side, corresponding to a single-input multiple-output (SIMO) transmission. We fairly compare different analog-to-digital converter (ADC) parametrizations by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-bit quantization is investigated with different oversampling factors. We analytically compute the statistics of the detection variable, and we propose the optimal and several suboptimal detection schemes for arbitrary quantization resolutions. Then, we evaluate the symbol error rate (SER) of the different detectors for 16- and 64-ary quadrature amplitude modulation (QAM). The results indicate that there is a noticeable performance degradation of the suboptimal detectors compared to the optimal detector when the constellation size is larger than the number of quantization levels. Furthermore, at low signal-to-noise ratios (SNRs), 1-bit quantization outperforms 2- and 3-bit quantization, respectively, even when employing higher-order constellations. We confirm our analytical results by Monte Carlo simulations. Both a pure line-of-sight (LoS) and a more realistically modeled indoor THz channel are considered. Then, we optimize the input signal constellation with respect to SER for 1- and 2-bit quantization. The results give insights for optimizing higher-order constellations for arbitrary quantization resolutions and show that the minimum SER can be lowered significantly by appropriately placing the constellation points.}, archive = {J_ACCESS}, author = {Christian Forsch and Peter Zillmann and Osama Alrabadi and Stefan Brueck and Wolfgang Gerstacker}, doi = {10.1109/ACCESS.2025.3537583}, journal = {IEEE Access}, pages = {24643-24660}, shortjournal = {IEEE Access}, title = {Detection schemes with low-resolution ADCs and spatial oversampling for transmission with higher-order constellations in terahertz band}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Artificial hummingbird optimization algorithm with hierarchical deep learning for traffic management in intelligent transportation systems”. ACCESS, 13, 24642. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems”.}, archive = {J_ACCESS}, author = {Abdulrahman Alruban and Hanan Abdullah Mengash and Majdy M. Eltahir and Nabil Sharaf Almalki and Ahmed Mahmud and Mohammed Assiri}, doi = {10.1109/ACCESS.2025.3536576}, journal = {IEEE Access}, pages = {24642}, shortjournal = {IEEE Access}, title = {Corrections to “Artificial hummingbird optimization algorithm with hierarchical deep learning for traffic management in intelligent transportation systems”}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of meta-learning methods for cold-start issue in recommendation systems. ACCESS, 13, 24622-24641. (www)

  @article{ , abstract = {The cold-start issue in recommendation systems refers to the challenge of recommending items or users when minimal or no prior data is available. Meta-learning methods have emerged as a response to this challenge due to their ability to transfer prior knowledge to recommendation tasks. However, meta-learning techniques are still new, and a general literature review is missing. This paper reviews the existing literature on meta-learning techniques specifically designed to solve the cold-start issue in recommender systems. A systematic review of the literature published between 2018 and June 2024 was conducted, identifying only experimental papers that use meta-learning methods to solve the cold-start issue. Advances, strengths, and weaknesses of such methods were analyzed, and possible research directions for the future were identified. The results demonstrate the application of model-independent meta-learning (MAML) and other techniques such as optimization-based methods, few-shot learning frameworks, and gradient-based meta-learning methods to solve the cold start problem. It also shows how meta-learning improvement can be achieved by combining different strategies, using transfer learning, and effectively implementing the strategy. Some of the areas for further research are also listed. In summary, this work contributes to verifying the central mechanisms of the recently proposed meta-learning models for further research in dealing with the cold-start issue. Insights into user-item interactions, critical applications, and evaluation standards are provided.}, archive = {J_ACCESS}, author = {Jamallah M. Zawia and Maizatul Akmar Binti Ismail and Mohammad Imran and Buce Trias Hanggara and Diva Kurnianingtyas and Silvi Asna and Quang Tran Minh}, doi = {10.1109/ACCESS.2025.3536025}, journal = {IEEE Access}, pages = {24622-24641}, shortjournal = {IEEE Access}, title = {Comprehensive review of meta-learning methods for cold-start issue in recommendation systems}, volume = {13}, year = {2025}, }
• (2025). Simulation of software development team productivity incorporating social and human factors: A system dynamics model. ACCESS, 13, 24604-24621. (www)

  @article{ , abstract = {Managing software development work teams requires planning resources and activities to complete projects and deliver products satisfactorily and successfully. Estimating project time is part of the planning stage and is mainly conducted using methods based on technical factors. However, since software development is a process involving people with high levels of interaction, it is necessary to consider non-technical factors in project management. This paper presents a simulation model to support informed decision-making during planning, considering that non-technical factors, specifically social and human factors (SHF), can affect product delivery time. From a systems perspective, software development is a complex system. Therefore, System Dynamics (SD) modeling based on the rework cycle archetype is used. The resulting model allows for analyzing the productivity of software development teams, integrating three key SHF: communication, leadership, and teamwork. The generated burndown charts are used to demonstrate that the model constitutes a basal structure oriented to understand the productivity behavior of work teams. By taking a systemic approach, the model introduces new ways to identify dynamic behaviors and facilitates the prediction of possible scenarios in the evolution of tasks, which helps work teams manage their risks. Additionally, leadership strategies in accordance with the team’s status and a good perception of communication can reduce rework and improve the ability to deliver software products on time. To the best of our knowledge, the literature reported on approaches that holistically integrate these elements is limited, which makes this proposal a significant contribution to the discipline.}, archive = {J_ACCESS}, author = {Luz Marcela Restrepo-Tamayo and Gloria Piedad Gasca-Hurtado and Johnny Valencia-Calvo}, doi = {10.1109/ACCESS.2025.3527330}, journal = {IEEE Access}, pages = {24604-24621}, shortjournal = {IEEE Access}, title = {Simulation of software development team productivity incorporating social and human factors: A system dynamics model}, volume = {13}, year = {2025}, }
• (2025). Multimodal emotion recognition: Emotion classification through the integration of EEG and facial expressions. ACCESS, 13, 24587-24603. (www)

  @article{ , abstract = {Despite advances in the field of emotion recognition, the research field still faces two main limitations: the use of deep models for increasingly complex calculations and the identification of emotions through various data types. This study aims to advance the knowledge on multimodal emotion recognition by combining electroencephalography (EEG) signals with facial expressions, using advanced models such as Transformer, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results validate the effectiveness of this approach, demonstrating the high accuracy of the Gated Recurrent Unit (GRU) model, which achieved an average of 91.8% classification accuracy on unimodal (EEG-only) data and an average of 97.8% classification accuracy on multimodal (EEG and facial expressions) datasets in the multi-class emotion categories. The findings emphasize that by applying a multi-class classification framework, multimodal approaches offer significant improvements over traditional unimodal techniques. This work presents a framework that captures complex neural dynamics and visible emotional cues, enhancing the robustness and accuracy of emotion recognition systems. These results have important practical implications, showing how integrating various data sources with advanced models can overcome the limitations of single-modality systems.}, archive = {J_ACCESS}, author = {Songül Erdem Güler and Fatma Patlar Akbulut}, doi = {10.1109/ACCESS.2025.3538642}, journal = {IEEE Access}, pages = {24587-24603}, shortjournal = {IEEE Access}, title = {Multimodal emotion recognition: Emotion classification through the integration of EEG and facial expressions}, volume = {13}, year = {2025}, }
• (2025). High stability single-port dual band microwave sensor based on interdigital capacitor structure with asymmetry branch feedline. ACCESS, 13, 24576-24586. (www)

  @article{ , abstract = {This paper proposes a single-port interdigital capacitor (IDC) resonator based on asymmetric branch feed line with high stability performance for permittivity detection of solid materials with a permittivity range of 1 - 6.15. The microwave sensor is designed using a single-port resonator operating at two different resonant frequencies $f_{r1} = 1.61$ GHz and $f_{r2} = 2.52$ GHz. Dual band frequency was proposed using asymmetric branch feed line. In addition, to confine the electric field concentration of the resonator, an interdigital capacitor (IDC) structure is proposed as a solution. Furthermore, a copper shield was proposed as conducting material to evaluate performance stability of the sensor from disturbance effect with range of $d = 1$ cm – 2.5 cm. Based on the measurement results, the sensor has high stability both without and with disturbance with an a Frequency Detection Resolution (FDR) of 0.009 - 0.4 GHz/ $\Delta \varepsilon _{\mathrm {r}}$ , a Normalized Sensitivity (NS) of 0.4% - 4.4%, and an average accuracy of 90% - 95% for both resonance frequencies, respectively. Therefore, this sensor can be recommended for several applications such as biomedical industry, pharmaceuticals and material quality control especially for outdoor measurements that are potentially affected by electromagnetic interference and disturbance.}, archive = {J_ACCESS}, author = {Syah Alam and Indra Surjati and Lydia Sari and Raden Deiny Mardian and Teguh Firmansyah and Muhammad Iqbal and Slamet Widodo and Mudrik Alaydrus and Zahriladha Zakaria}, doi = {10.1109/ACCESS.2025.3538042}, journal = {IEEE Access}, pages = {24576-24586}, shortjournal = {IEEE Access}, title = {High stability single-port dual band microwave sensor based on interdigital capacitor structure with asymmetry branch feedline}, volume = {13}, year = {2025}, }
• (2025). Automatic classification of difficulty of texts from eye gaze and physiological measures of l2 english speakers. ACCESS, 13, 24555-24575. (www)

  @article{ , abstract = {Reading is an essential method for adults to learn new languages, but difficulty reading texts in a foreign language can increase learners’ anxiety. Identifying text difficulty from the reader’s perspective can aid language learning by tailoring texts to readers’ needs. There is little research focusing on L2 speakers or using a multimodal approach, i.e., using multiple sensors, to detect subjective difficulty. In this study ( $N=30$ ) we determined L2 speakers’ subjective difficulty while reading using language proficiency and objective text difficulty, combined with physiological data. We compared machine learning classifiers combining eye, skin and heart sensor data against models using each modality separately. Additionally, we assessed the effect on model performance of shifting the data to account for delayed physiological responses. The models detected 3 levels of subjective difficulty (low, medium, high) and were evaluated using leave-one-participant-out (LoPo) and leave-one-document-out (LoDo) cross-validation. The results showed acceptable levels of generalization to new participants ( $Acc_{LoPo} = 0.434$ ) and documents ( $Acc_{LoDo} = 0.521$ ). Combining sensor data from all modalities improved predictions in both LoDo and LoPo cross-validation, compared to each modality in isolation. Shifting the data to account for physiological response delay did not improve model performance compared to not shifting the data. These findings support refining subjective difficulty detection models and their implementation in adaptive language learning systems. Finally, this work contributes to the field of cognitive science and technology by laying the foundation for innovative approaches to cognitive state detection.}, archive = {J_ACCESS}, author = {Javier Melo and Leigh Fernandez and Shoya Ishimaru}, doi = {10.1109/ACCESS.2025.3537156}, journal = {IEEE Access}, pages = {24555-24575}, shortjournal = {IEEE Access}, title = {Automatic classification of difficulty of texts from eye gaze and physiological measures of l2 english speakers}, volume = {13}, year = {2025}, }
• (2025). Automatic segmentation of abdominal aortic aneurysm from computed tomography angiography using a patch-based dilated UNet model. ACCESS, 13, 24544-24554. (www)

  @article{ , abstract = {Abdominal Aortic Aneurysm (AAA) is still a socially relevant public health challenge, evidenced by an 82.1% increase in associated fatalities from 1990 to 2019 (with 172,427 deaths in 2019 alone). In a clinical setting, computed tomography angiography (CTA) is the imaging modality of choice for monitoring and/or presurgical planning of AAA patients. However, manual segmentation of CTA images is labor intensive and time consuming. Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. The deep-learning pipeline proposed in this work is designed to automatically segment AAA CTA images. The framework adapted a fully developed patch-based dilated modified U-Net model, which shows remarkable efficiency in accurately delineating AAA regions within the CTA scans. During the prediction phase, the deep learning architecture demonstrates exceptional speed, requiring $17~\pm ~0.02$ milliseconds per frame to generate the final segmented output. Building upon this work, we included the application of Non-Uniform Rational B-Splines (NURBS) to enhance the segmentation process. This advancement is essential in addressing the critical need for clinical accuracy in medical image segmentation. NURBS enables the creation of continuous curves that seamlessly conform to the intricate contours of anatomical structures, offering a significant improvement in segmentation accuracy. Through the integration of advanced deep learning architectures and the precision of NURBS for segmentation refinement, coupled with the fast processing time and accurate segmentation, the the proposed model represents a promising clinical tool that can be used in the clinical management of AAAs.}, archive = {J_ACCESS}, author = {Merjulah Roby and Juan C. Restrepo and Haehwan Park and Satish C. Muluk and Mark K. Eskandari and Seungik Baek and Ender A. Finol}, doi = {10.1109/ACCESS.2025.3533417}, journal = {IEEE Access}, pages = {24544-24554}, shortjournal = {IEEE Access}, title = {Automatic segmentation of abdominal aortic aneurysm from computed tomography angiography using a patch-based dilated UNet model}, volume = {13}, year = {2025}, }
• (2025). Dual-polarization self-coherent transceivers for free space optical communications in the presence of atmospheric turbulence. ACCESS, 13, 24530-24543. (www)

  @article{ , abstract = {The self-coherent scheme offers significant advantages for free-space optical (FSO) communications, particularly due to its higher oscillator-signal mixing efficiency compared to conventional coherent schemes. This paper presents an analysis and simulation results of an FSO system utilizing the dual-polarization (DP) self-coherent scheme, where independent quadrature amplitude modulation (QAM) data is transmitted for each polarization. Atmospheric turbulence, however, introduces random polarization rotation and polarization-dependent phase shifts, which cause the effective channel matrix to become singular at certain angles. This leads to an increased bit error rate (BER) in conventional systems that use two photodetectors (PDs). To address this issue, we propose a novel detection scheme using three PDs. This study evaluates the performance of long-haul FSO transceivers, comparing the DP self-coherent scheme with the conventional coherent scheme under a variety of conditions, including satellite-to-ground, ground-to-satellite, and satellite-to-satellite links. The analysis spans different levels of atmospheric turbulence, ranging from weak to moderate to strong, providing a comprehensive assessment of transmission efficacy.}, archive = {J_ACCESS}, author = {Yeganeh Nasr and Shiva Kumar and Jiaoyang Bi and Mahdi Naghshvarianjahromi and M. Jamal Deen}, doi = {10.1109/ACCESS.2025.3535789}, journal = {IEEE Access}, pages = {24530-24543}, shortjournal = {IEEE Access}, title = {Dual-polarization self-coherent transceivers for free space optical communications in the presence of atmospheric turbulence}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based feature extraction technique for single document summarization using hybrid optimization technique. ACCESS, 13, 24515-24529. (www)

  @article{ , abstract = {Presently, the exponential growth of unstructured data on the web and social networks has made it increasingly challenging for individuals to retrieve relevant information efficiently. Over the years, various text summarization techniques have been developed to address this issue. However, traditional approaches that rely on directly extracting words often lead to redundancies and fail to establish a strong connection between the summary and the original document. This paper presents a novel Deep Learning (DL)-based text summarization approach incorporating the following phases: pre-processing, feature extraction, vectorization, and summarization using a hybrid Cat Swarm Optimization (CSO) and Harris Hawk Optimization (HHO) algorithm. Initially, input documents undergo pre-processing steps, including sentence segmentation, word tokenization, stop word removal, and lemmatization, to enhance text quality. Features are then extracted using a Restricted Boltzmann Machine (RBM) to obtain nine key attributes. Vectorization is performed using Term Frequency-Inverse Document Frequency (TF-IDF) to represent sentences in vector form. The hybrid CSO-HHO algorithm is subsequently applied to generate summaries. The proposed method’s efficiency was evaluated using datasets from the Document Understanding Conference (DUC), specifically DUC-2002, DUC-2003, and DUC-2005. Metrics such as sensitivity, readability, coherence, precision, BLEU score, ROUGE score, and F-score were analyzed to assess performance. The proposed approach’s results were compared with existing methods, including CSO, QABC, PSO, GJO, FF, and machine learning techniques like SVM and RF. The hybrid CSO-HHO algorithm achieved an accuracy of 99.56%, demonstrating its superiority in text summarization tasks.}, archive = {J_ACCESS}, author = {Jyotirmayee Rautaray and Sangram Panigrahi and Ajit Kumar Nayak and Premananda Sahu and Kaushik Mishra}, doi = {10.1109/ACCESS.2025.3538169}, journal = {IEEE Access}, pages = {24515-24529}, shortjournal = {IEEE Access}, title = {Deep learning-based feature extraction technique for single document summarization using hybrid optimization technique}, volume = {13}, year = {2025}, }
• (2025). A new pipeline for snooping keystroke based on deep learning algorithm. ACCESS, 13, 24498-24514. (www)

  @article{ , abstract = {This research focuses on the vulnerabilities of keystroke by logging on a physical computer keyboard, known as Snooping Keystroke. This category of attacks occurs recording an audio track with a smartphone while typing on the keyboard, and processing the audio to detect individual pressed keys. To address this issue, mathematical wavelet transforms have been tested, while key recognition has been implemented using the inference test of a deep learning model based on a Temporal Convolutional Network (TCN). The novelty of the proposed pipeline lies in its dynamic audio analysis and keystroke recognition, which splits the wave based on audio signal peaks generated by key presses. This approach enables an attack in real-world conditions without knowing the exact number of keystrokes typed by the user. Experimental results show a peak accuracy of 98.3%.}, archive = {J_ACCESS}, author = {Massimo Orazio Spata and Valerio Maria Russo and Alessandro Ortis and Sebastiano Battiato}, doi = {10.1109/ACCESS.2025.3536877}, journal = {IEEE Access}, pages = {24498-24514}, shortjournal = {IEEE Access}, title = {A new pipeline for snooping keystroke based on deep learning algorithm}, volume = {13}, year = {2025}, }
• (2025). Predicting postoperative re-tear of arthroscopic rotator cuff repair using artificial intelligence on imbalanced data. ACCESS, 13, 24487-24497. (www)

  @article{ , abstract = {Retears after rotator cuff surgery are a common complication. Accurate prediction of retear is essential to minimise the risk of retear. Most of the existing big methods for assisted diagnosis are tested on balanced datasets, whereas in the medical field there is data imbalance due to the rare nature of a particular disease or case. Therefore, this paper proposes a weight-based logits cross-entropy loss function. The model in this paper is mainly divided into two parts. This paper employs a pairwise associative encoder (PAE) to construct edges, calculating similarity scores between different nodes to effectively handle the complementary relationship between phenotypic and non-phenotypic data. The other part is the weight network, which calculates the weights of different nodes of different classes to assign weights to the objective function through the GCN model. The main body of the final objective function is the weighted cross-entropy loss function, while the logits of the model output are adjusted and a regularisation term is added to the objective function in order to deal with the discrete points in the data. The classification performance of the proposed method is tested on the dataset of whether rotator cuff is re-torn after surgery, and the results show that our method achieves state-of-the-art performance in a five-fold cross-validation setting.The code of this work is available at https://github.com/nishuihan111/WL-GCN.git.}, archive = {J_ACCESS}, author = {Zhibin Zhang and Zhewei Zhang and Zhaoxiang Peng and Yihong Dong}, doi = {10.1109/ACCESS.2025.3538595}, journal = {IEEE Access}, pages = {24487-24497}, shortjournal = {IEEE Access}, title = {Predicting postoperative re-tear of arthroscopic rotator cuff repair using artificial intelligence on imbalanced data}, volume = {13}, year = {2025}, }
• (2025). Adaptive neural network control for industrial optical tweezers with uncertain closed architecture. ACCESS, 13, 24478-24486. (www)

  @article{ , abstract = {The control of closed architecture industrial optical tweezers systems presents substantial challenges due to the limited knowledge of inner controller configurations and control gain structures. Conventional methods, such as the computed-torque approach, often prove inadequate for closed architecture robots because the torque terminal is inaccessible to end-users. This creates a disconnect between advanced control algorithms and practical industrial needs. To tackle these challenges, we propose a novel approach utilizing adaptive neural network-based control methodology. Our method employs neural networks to approximate the low-level controller, thereby bypassing the constraints of traditional closed-loop architectures. By integrating an external feedback loop that operates independently of the inner control architecture, our approach offers flexibility for design adjustments and facilitates advanced control actions. Furthermore, we conduct a comprehensive stability analysis of external feedback control systems interacting with a fixed inner loop. In summary, this study represents a pioneering effort in addressing the complexities of industrial optical tweezers systems with closed uncertain architectures, providing an innovative solution aimed at enhancing control capabilities for applications in industrial settings.}, archive = {J_ACCESS}, author = {Gulam Dastagir Khan and Ibrahim Al-Naimi}, doi = {10.1109/ACCESS.2025.3538809}, journal = {IEEE Access}, pages = {24478-24486}, shortjournal = {IEEE Access}, title = {Adaptive neural network control for industrial optical tweezers with uncertain closed architecture}, volume = {13}, year = {2025}, }
• (2025). Early diagnosis and severity assessment of weligama coconut leaf wilt disease and coconut caterpillar infestation using deep learning-based image processing techniques. ACCESS, 13, 24463-24477. (www)

  @article{ , abstract = {Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particular, Weligama Coconut Leaf Wilt Disease (WCWLD) and Coconut Caterpillar Infestation (CCI) damage coconut trees, causing severe coconut production loss in Sri Lanka and nearby coconut-producing countries. Currently, both WCWLD and CCI are detected through on-field human observations, a process that is not only time-consuming but also limits the early detection of infections. This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. Further, this paper presents the use of the You Only Look Once (YOLO) object detection model to count the number of caterpillars distributed on leaves with CCI. The introduced methods were tested and validated using datasets collected from Matara, Puttalam, and Makandura, Sri Lanka. The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively. In addition, the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%. Furthermore, the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.}, archive = {J_ACCESS}, author = {Samitha Vidhanaarachchi and Janaka L. Wijekoon and W. A. Shanaka P. Abeysiriwardhana and Malitha Wijesundara}, doi = {10.1109/ACCESS.2025.3537664}, journal = {IEEE Access}, pages = {24463-24477}, shortjournal = {IEEE Access}, title = {Early diagnosis and severity assessment of weligama coconut leaf wilt disease and coconut caterpillar infestation using deep learning-based image processing techniques}, volume = {13}, year = {2025}, }
• (2025). Analysis of melanin concentration on reflective pulse oximetry using monte carlo simulations. ACCESS, 13, 24454-24462. (www)

  @article{ , abstract = {We investigate the impact of melanin concentration on the accuracy of oxygen saturation SpO2 estimation using reflective photoplethysmography (PPG) measurements. Monte Carlo (MC) simulations were used to analyse photon-tissue interactions, and detection of reflected light during systole and diastole. We model a reflective pulse oximeter and analyse the perfusion index (PI), to represent the signal change due varying blood flow during systole and diastole. Moreover, we derive the ratio-of-ratios (RoR), to represent the absorption difference between PIs of different light wavelengths, and SpO2 depending on skin tone. We derive calibration models for SpO2 estimation based on MC simulations tailored to specific melanin concentrations ranging from 2.55% to 30.5%, as well as a generalised population model, to study reflective pulse oximeter performance. While current pulse oximeters are often calibrated for low melanin concentrations, our results show that calibrating for an appropriate skin tone range substantially increases the pulse oximeter performance. Skin tone-adapted calibration reduced the root mean square error $A_{\mathrm {rms}}$ of SpO2 estimation by 44% compared to a generalised population model. SpO2 estimation error was minimal at a distance of 4 mm to 5 mm between light source and photodetector of a reflective pulse oximeter. We conclude that skin tone-adapted calibration should be applied to make reflective pulse oximeter applicable to any human, independent of skin pigmentation.}, archive = {J_ACCESS}, author = {Maximilian Reiser and Oliver Amft and Andreas Breidenassel}, doi = {10.1109/ACCESS.2025.3538281}, journal = {IEEE Access}, pages = {24454-24462}, shortjournal = {IEEE Access}, title = {Analysis of melanin concentration on reflective pulse oximetry using monte carlo simulations}, volume = {13}, year = {2025}, }
• (2025). Towards a standard benchmarking framework for domain adaptation in intelligent fault diagnosis. ACCESS, 13, 24426-24453. (www)

  @article{ , abstract = {Domain shift is a major problem facing contemporary data-based intelligent fault diagnosis (IFD) solutions. While domain adaptation (DA) methods have been proposed to address this issue, standardizing DA benchmarks has not received much attention. Existing studies often use ad-hoc evaluation methods with inconsistent dataset partitioning, labeling, and evaluation criteria. We propose an integrated benchmarking framework to bridge the gap between DA development and evaluation research. Our framework incorporates domain shift key factors such as operating conditions (OCs) and fault levels (FLs) into dataset partitioning, labeling, and evaluation phases. The fault dataset is split into distinct subsets using FLs and OCs as partitioning parameters, while maintaining the original diagnostic classification labels. The DA method is comprehensively evaluated under controlled conditions using permuted subset pairs of the partitioned dataset. The framework is applied to two popular datasets – the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing fault datasets. We demonstrate the framework’s capabilities and application mechanisms using a CNN classifier and an adversarial DA algorithm. Benchmarking results reveal significant accuracy drops from 99% to 36% and 45% with variances of 15% and 20% for the CWRU and PU datasets, respectively, under controlled domain shift conditions. The proposed framework establishes a foundation for standardizing IFD DA benchmarks, bridging the gap between development and evaluation research.}, archive = {J_ACCESS}, author = {Mohammed M. Farag}, doi = {10.1109/ACCESS.2025.3537817}, journal = {IEEE Access}, pages = {24426-24453}, shortjournal = {IEEE Access}, title = {Towards a standard benchmarking framework for domain adaptation in intelligent fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Frozen weights as prior for parameter-efficient fine-tuning. ACCESS, 13, 24411-24425. (www)

  @article{ , abstract = {In the fields of natural language processing and computer vision, the emergence of large pre-trained models has led to the adoption of fine-tuning them for downstream tasks as an important paradigm. However, the full fine-tuning approach often comes with a hefty cost, which is not feasible for many researchers. Therefore, in recent years, numerous fine-tuning methods have been proposed to efficiently learn incremental updates of pre-trained weights in a more parameter-efficient way (e.g., employing low-rank increments or introducing adapters to modify the network architecture). However, most of these methods involve adding a set of incrementally learned parameters from scratch. From the perspective of full fine-tuning, these approaches often fail to fully exploit the connection between incremental changes during fine-tuning and the frozen weights of the pre-trained model. In order to delve into how to more effectively harness the weights of pre-trained models during the fine-tuning process to fully acquire new knowledge, we propose a novel parameter-efficient approach, which reuses the Frozen Weights as a prior (FoWA). We adapt the incremental matrix to the unitary matrix obtained from the singular value decomposition of the frozen weights, and further fine-tune the model by incorporating prior information. Through the frozen weight prior, FoWA can automatically select the appropriate rank and decouple the number of trainable parameters from the rank. We have conducted extensive experiments on various tasks, including natural language processing, question answering, natural language generation, and visual classification, demonstrating the effectiveness of FoWA.}, archive = {J_ACCESS}, author = {Xiaolong Ma and Peishun Liu and Haojie Gao and Zikang Yan and Ningning Ma and Wenqiang Liu and Xuefang Wang and Ruichun Tang}, doi = {10.1109/ACCESS.2025.3529807}, journal = {IEEE Access}, pages = {24411-24425}, shortjournal = {IEEE Access}, title = {Frozen weights as prior for parameter-efficient fine-tuning}, volume = {13}, year = {2025}, }
• (2025). A network traffic prediction model based on layered training graph convolutional network. ACCESS, 13, 24398-24410. (www)

  @article{ , abstract = {Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. The conventional model that extracts space-correlated features of traffic flow have the problem of high computational complexity and long training time which limits the model’s application on rapid routing deployment. This paper therefore proposes a layered training graph convolutional network (LT-GCN) to decrease the training time greatly with the nearly same prediction accuracy as graph convolutional network (GCN). Instead of training on parameters in all hidden layers simultaneously, LT-GCN develops a new layer-by-layer training pattern for multiple hidden layers to degrade the computational complexity in training process. LT-GCN is then further integrated with gated recurrent unit (GRU) that is called LTGG model to achieve the joint extraction of time-correlated and space-correlated features of traffic flow for more accurate prediction. Experimental results demonstrate that LT-GCN outperforms the classical GCN model on training time and LTGG exhibits greater performance than other benchmark models on prediction accuracy.}, archive = {J_ACCESS}, author = {Yulian Li and Yang Su}, doi = {10.1109/ACCESS.2025.3538265}, journal = {IEEE Access}, pages = {24398-24410}, shortjournal = {IEEE Access}, title = {A network traffic prediction model based on layered training graph convolutional network}, volume = {13}, year = {2025}, }
• (2025). Leveraging an enhanced CodeBERT-based model for multiclass software defect prediction via defect classification. ACCESS, 13, 24383-24397. (www)

  @article{ , abstract = {Ensuring software reliability through early-stage defect prevention and prediction is crucial, particularly as software systems become increasingly complex. Automated testing has emerged as the most practical approach to achieving bug-free and efficient code. In this context, machine learning-driven methods, especially those leveraging natural language models, have gained significant traction for developing effective techniques. This paper introduces a novel framework for automating software defect prediction, focusing on eight specific defects: SIGFPE, NZEC, LOGICAL, SYNTAX, SIGSEGV, SIGABRT, SEMANTIC, and LINKER. Our research involves a specialized dataset comprising nine classes, including eight common programming errors and one error-free class. The goal is to enhance software testing and development processes by identifying defects within code snippets. The proposed framework utilizes a CodeBERT-based algorithm for defect prediction, optimizing model hyperparameters to achieve superior accuracy. Comparative analysis against established models such as RoBERTa, Microsoft CodeBERT, and GPT-2 demonstrates that our approach yields significant improvements in prediction performance, with accuracy gains of up to 20% and 7% respectively in binary and multi class experimentation. Empirical studies validate the effectiveness of neural language models like CodeBERT for software defect prediction, highlighting substantial advancements in software testing and development techniques. These findings underscore the potential benefits of incorporating advanced machine learning models into the software development lifecycle.}, archive = {J_ACCESS}, author = {Rida Ghafoor Hussain and Kin-Choong Yow and Marco Gori}, doi = {10.1109/ACCESS.2024.3525069}, journal = {IEEE Access}, pages = {24383-24397}, shortjournal = {IEEE Access}, title = {Leveraging an enhanced CodeBERT-based model for multiclass software defect prediction via defect classification}, volume = {13}, year = {2025}, }
• (2025). Headline-guided extractive summarization for thai news articles. ACCESS, 13, 24368-24382. (www)

  @article{ , abstract = {Text summarization is a process of condensing lengthy texts while preserving their essential information. Previous studies have predominantly focused on high-resource languages, while low-resource languages like Thai have received less attention. Furthermore, earlier extractive summarization models for Thai texts have primarily relied on the article’s body, without considering the headline. This omission can result in the exclusion of key sentences from the summary. To address these limitations, we propose CHIMA, an extractive summarization model that incorporates the contextual information of the headline for Thai news articles. Our model utilizes a pre-trained language model to capture complex language semantics and assigns a probability to each sentence to be included in the summary. By leveraging the headline to guide sentence selection, CHIMA enhances the model’s ability to recover important sentences and discount irrelevant ones. Additionally, we introduce two strategies for aggregating headline-body similarities, simple average and harmonic mean, providing flexibility in sentence selection to accommodate varying writing styles. Experiments on publicly available Thai news datasets demonstrate that CHIMA outperforms baseline models across ROUGE, BLEU, and F1 scores. These results highlight the effectiveness of incorporating the headline-body similarities as model guidance. The results also indicate an enhancement in the model’s ability to recall critical sentences, even those scattered throughout the middle or end of the article. With this potential, headline-guided extractive summarization offers a promising approach to improve the quality and relevance of summaries for Thai news articles.}, archive = {J_ACCESS}, author = {Pimpitchaya Kositcharoensuk and Nakarin Sritrakool and Ploy N. Pratanwanich}, doi = {10.1109/ACCESS.2025.3538329}, journal = {IEEE Access}, pages = {24368-24382}, shortjournal = {IEEE Access}, title = {Headline-guided extractive summarization for thai news articles}, volume = {13}, year = {2025}, }
• (2025). Research on YOLOv5 oracle recognition algorithm based on multi-module fusion. ACCESS, 13, 24358-24367. (www)

  @article{ , abstract = {The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. Additionally, models trained on such data tend to struggle with recognizing rare or unseen characters, often leading to recognition errors. Therefore, improving the robustness of these models is essential. This paper presents a novel recognition algorithm based on YOLOv5, incorporating BiFPN-SDI, C3-DAttention, and Detect_Efficient to significantly enhance detection performance. BiFPN-SDI enables more precise feature fusion and attention mechanisms, improving the detection of small targets. C3-DAttention combines channel and spatial attention mechanisms to enhance feature extraction in deep convolutional neural networks. Detect_Efficient further improves the model’s detection and recognition capabilities. Experimental results show that the proposed improvements lead to a 0.7% increase in precision, a 1.1% increase in recall, and a 0.3% improvement in MAP@50. Furthermore, the model’s parameter count is reduced to 1,009,668, and its processing speed is increased to 90 fps, significantly improving the ability to extract and recognize features in oracle bone script.}, archive = {J_ACCESS}, author = {Xinhang Zhang and Zhenhua Ma and Yaru Zhang and Huiying Ru}, doi = {10.1109/ACCESS.2025.3536553}, journal = {IEEE Access}, pages = {24358-24367}, shortjournal = {IEEE Access}, title = {Research on YOLOv5 oracle recognition algorithm based on multi-module fusion}, volume = {13}, year = {2025}, }
• (2025). Optimal asynchronous guaranteed cost control of uncertain markov jump systems with actuator saturation and nonideal transition probabilities. ACCESS, 13, 24345-24357. (www)

  @article{ , abstract = {This paper investigates the problem of dynamic event-triggered asynchronous guaranteed cost control for uncertain Markov jump systems with actuator saturation and nonideal transition probabilities. First, a hidden Markov model is employed to estimate system modes that cannot be accurately detected in practical scenarios. Second, a dynamic event-triggered mechanism is established based on the hidden Markov model to conserve limited network resources and an asynchronous event-triggered state feedback control law is designed. Third, a convex hull representation is adopted to deal with saturated inputs and parameter uncertainties are incorporated in the system model. Moreover, since it is complex to obtain accurate transition probabilities for the Markov chain of the original system and the hidden Markov model in the observation process, unknown and uncertain transition probabilities are simultaneously considered in both mode transitions and mode observation processes, and a separation strategy is implemented to handle the nonideal probabilities in each process independently. Through the application of Lyapunov equations and linear matrix inequality techniques, sufficient conditions are derived that ensure the resulting system is stochastically stable with/without nonideal transition probabilities and satisfies specific cost bounds under three different initial boundary conditions. Finally, a convex optimization algorithm is proposed to design the optimal asynchronous event-triggered guaranteed cost control law, and a DC motor model is provided to validate its effectiveness.}, archive = {J_ACCESS}, author = {Jiazheng Liu and Yang Yang and Yaru Song}, doi = {10.1109/ACCESS.2025.3538593}, journal = {IEEE Access}, pages = {24345-24357}, shortjournal = {IEEE Access}, title = {Optimal asynchronous guaranteed cost control of uncertain markov jump systems with actuator saturation and nonideal transition probabilities}, volume = {13}, year = {2025}, }
• (2025). A large-scale snow depth retrieval method for alaska based on point-surface fusion and random forest model. ACCESS, 13, 24336-24344. (www)

  @article{ , abstract = {Accurate snow depth (SD) monitoring is crucial for understanding climate change and managing water resources. However, due to the sparse distribution of meteorological stations and the limited accuracy of passive microwave remote sensing data, the retrieval accuracy of large-scale snow depth in regions with complex terrain and variable climate conditions has faced significant challenges. To address this issue, this paper proposes a large-scale snow depth retrieval method based on point-surface fusion technology with the Random Forest (RF) model. The method integrates ground-based snow depth measurements with passive microwave brightness temperature data using the RF algorithm and incorporates geographic coordinates, elevation, brightness temperature, brightness temperature gradient differences, and time variables for each grid cell in Alaska, which significantly improves the accuracy and spatial resolution of the large-scale snow depth retrieval. Five-fold cross-validation results show the model exhibits excellent fitting performance (R $^{2} =0.9627$ , MAE =4.6 cm, RMSE =10.08 cm), particularly demonstrating strong robustness in sparse meteorological stations. The results indicate that the proposed method effectively captures the spatiotemporal variations in snow depth across Alaska from 2008 to 2016, providing valuable technical support for snow depth monitoring and climate change research in cold regions.}, archive = {J_ACCESS}, author = {Ningjun Wang and Tiantian Liu}, doi = {10.1109/ACCESS.2025.3538988}, journal = {IEEE Access}, pages = {24336-24344}, shortjournal = {IEEE Access}, title = {A large-scale snow depth retrieval method for alaska based on point-surface fusion and random forest model}, volume = {13}, year = {2025}, }
• (2025). Optimizing subchannel assignment and power allocation for network slicing in high-density NOMA networks: A Q-learning approach. ACCESS, 13, 24323-24335. (www)

  @article{ , abstract = {The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every service has distinct quality of service (QoS) requirements, it is essential to ensure the seamless coexistence of these devices. The main difficulty is allocating network resources to maximize spectrum utilization while meeting mMTC’s massive connectivity demands and offering URLLC’s demand for ultra-reliable, low-latency communication. In this study, non-orthogonal multiple access (NOMA) network slicing is utilized to share radio resources among various services, thereby improving connectivity for large-scale device deployments. When these services exist in high-density NOMA environments characterized by high network congestion with radio resource sharing, the level of difficulty increases significantly. To address these issues, an optimization algorithm is proposed for subchannel assignment and power allocation in NOMA high-density networks for URLLC and mMTC devices. The solution adopts a Q-learning algorithm to optimize decision-making processes and ensure efficient resource sharing between URLLC and mMTC devices, while satisfying their distinct QoS requirements. Extensive simulations demonstrate that the proposed algorithm is flexible and scalable in dynamic scenarios, outperforming random and exhaustive search algorithms in high-density NOMA networks in terms of sum rates. The sum rate of the proposed algorithm increased by approximately 23.44% compared to that of the exhaustive search algorithm.}, archive = {J_ACCESS}, author = {Suhare Solaiman}, doi = {10.1109/ACCESS.2025.3538654}, journal = {IEEE Access}, pages = {24323-24335}, shortjournal = {IEEE Access}, title = {Optimizing subchannel assignment and power allocation for network slicing in high-density NOMA networks: A Q-learning approach}, volume = {13}, year = {2025}, }
• (2025). Siamese neural networks in unmanned aerial vehicle target tracking process. ACCESS, 13, 24309-24322. (www)

  @article{ , abstract = {With the continuous maturity of unmanned aerial vehicle (UAV) technology, its application is more and more extensive. At the same time, the problem of UAV target tracking has also been widely concerned. Aiming at the problem of low recognition accuracy of small target, a target tracking model of UAV based on siamese neural network (SNN) is studied. Firstly, based on the YOLOv5 recognition model, convolutional attention module and multi-scale feature fusion network are introduced. On the basis of the intersection over union loss, the effective intersection over union loss is proposed to improve the loss function, and an improved YOLOv5 target recognition model is established. Then, a fine-grained classification regression network is proposed, which uses per-pixel classification regression to train the tracker. A target tracking model based on SNN is established by adjusting the results with a fine-tuning module. The results showed that the improved YOLOv5 model combined with the optimized loss function had the highest average accuracy of 47.84% and a frame rate of 28.34fps, which was better than the traditional YOLOv5 model. The recognition accuracy in the fused dataset is 93.12%, with a loss value of less than 0.01, which is superior to YOLOv3, YOLOv4, and traditional YOLOv5 models. The method has strong anti-jamming ability in the acceptable range. The target tracking model based on SNN has the highest tracking accuracy and still has good tracking performance in color image environment, which shows certain feasibility and superiority. To sum up, the model built in this study has good application effects and plays a certain role in promoting the development of the UAV industry.}, archive = {J_ACCESS}, author = {Athraa Sabeeh Hasan Allak and Jianjun Yi and Haider M. Al-Sabbagh and Liwei Chen}, doi = {10.1109/ACCESS.2025.3536461}, journal = {IEEE Access}, pages = {24309-24322}, shortjournal = {IEEE Access}, title = {Siamese neural networks in unmanned aerial vehicle target tracking process}, volume = {13}, year = {2025}, }
• (2025). Enhancing digital identity and access control in event management systems using sui blockchain. ACCESS, 13, 24295-24308. (www)

  @article{ , abstract = {The evolution of blockchain technology presents transformative potential for event management, particularly in enhancing security, transparency, and efficiency across the industry. This paper introduces a blockchain-based event management system that leverages Sui blockchain technology to address critical challenges such as ticket fraud, data security, and high transaction fees, which are prevalent in traditional event management systems. The proposed system incorporates several innovative features: Tickets to the System: Utilizing the concept of NFT, the system guarantees that each ticket is unique, is tamper proof and cannot be forged. It also ensures the validity of tickets and at the same time ensures that the ticket is transferable back and forth while the ownership of the ticket is wholly and clearly disclosed. zkLogin for Seamless User Authentication: zkLogin is then implemented in the platform to ensure users can be authenticated in a Privacy-Preserving manner while at the same time having High Security. This mechanism can be highly beneficial for the users because they can log in as quickly and discreetly as possible without any threat to their accounts’ safety. Suiet & Sui Wallet for Cost-Effective Transactions: Merging of Suiet and Sui Wallet enables cheap transactions and something that is always financially draining when using blockchain systems is deposits costly transactions fees. This makes the platform to be open to a wider market especially to those who are organizing small scale events. Due to decentralizing the event management process the platform reduces all possible risks that can occur in condition of centralized systems like for example single points of failure and also creates higher transparency of ticket sales and distribution. Furthermore, this greatly decentralizes the system which drastically decreases the transaction costs making the solution fiscally sustainable for events of various scales. This paper also presents the design and implementation of the platform as well as the flexibility of the platform depending on the kind and size of the event. The research indicates that the use of this blockchain technology increases the security and the verifiability of the event tickets in addition to providing a solution to the whole flow of the event beginning from ticket distribution to ticket authentication and transaction management.}, archive = {J_ACCESS}, author = {Suthir Sriram and P. R. Tharaniesh and Priyanka Saraf and Nivethitha Vijayaraj and Thangavel Murugan}, doi = {10.1109/ACCESS.2025.3539107}, journal = {IEEE Access}, pages = {24295-24308}, shortjournal = {IEEE Access}, title = {Enhancing digital identity and access control in event management systems using sui blockchain}, volume = {13}, year = {2025}, }
• (2025). Mechanical wear characteristics of 170 kV gas-insulated switchgear circuit-breaker contacts before arc initiation: An accelerated lifetime test approach. ACCESS, 13, 24287-24294. (www)

  @article{ , abstract = {This study investigates the mechanical wear characteristics of 170 kV gas-insulated switchgear (GIS) circuit breaker contacts by conducting accelerated life testing. Samples identical to those used in 170 kV GIS were subjected to up to 20,000 cycles, which is the maximum guaranteed lifespan. Visual inspections, contact resistance measurements, mass change assessments, and operational characteristic evaluations, including trip and close coil excitation currents, were performed. Considerable surface damage and increased contact resistance were observed after approximately 12,000 operations, indicating that mechanical wear before arc formation had a substantial impact on the electrical performance and lifespan of circuit breaker contacts. The results reveal that the lifetime and reliability of GIS circuit breakers can be improved by addressing the pure mechanical wear before arc occurrence during the maintenance of GIS circuit breakers.}, archive = {J_ACCESS}, author = {Jong-Geon Lee and Jae-Hong Koo}, doi = {10.1109/ACCESS.2025.3532805}, journal = {IEEE Access}, pages = {24287-24294}, shortjournal = {IEEE Access}, title = {Mechanical wear characteristics of 170 kV gas-insulated switchgear circuit-breaker contacts before arc initiation: An accelerated lifetime test approach}, volume = {13}, year = {2025}, }
• (2025). Image quality assessment based on multi-scale representation and shifting transformer. ACCESS, 13, 24276-24286. (www)

  @article{ , abstract = {In automatic control systems, sensors and cameras are often used to capture images of the environment or processes being monitored. The quality of these images is paramount as it directly affects the system’s ability to accurately interpret and respond to the visual information. Image Quality Assessment (IQA) is a crucial metric for intelligent control systems and computer vision tasks, such as surveillance, restoration, and fingerprint identification, significantly advancing algorithm development in these areas. Recently, transformer-based algorithms have excelled in computer vision, particularly in image classification, surpassing convolutional neural network (CNN) methods. To enhance IQA using transformers, we propose Swin-MIQT, a multi-scale spatial pooling transformer with shifted windows. As a no-reference (NR) IQA method, Swin-MIQT processes images at their original resolution without resizing or cropping, unlike standard vision transformers. By using shifted windows, we reduce computational load through efficient self-attention processing. Additionally, a spatial pyramid pooling layer captures diverse image quality information, improving IQA accuracy for distorted images. Comprehensive experiments show that Swin-MIQT achieves state-of-the-art performance on three synthetic distortion databases (LIVE, LIVE MD, TID2013) and competitive results on three authentic distortion databases (LIVE Challenge, KonIQ-10K, SPAQ). The outstanding performance demonstrates that Swin-MIQ possesses robust learning and generalization capabilities across all referenced distorted databases.}, archive = {J_ACCESS}, author = {Geng Fu and Ziyu Wang and Cuijuan Zhang and Zerong Qi and Mingzheng Hu and Shujun Fu and Yunfeng Zhang}, doi = {10.1109/ACCESS.2025.3531416}, journal = {IEEE Access}, pages = {24276-24286}, shortjournal = {IEEE Access}, title = {Image quality assessment based on multi-scale representation and shifting transformer}, volume = {13}, year = {2025}, }
• (2025). Multi-objective simulated annealing for efficient task allocation in UAV-assisted edge computing for smart city traffic management. ACCESS, 13, 24251-24275. (www)

  @article{ , abstract = {Smart city traffic management relies increasingly on UAV-assisted edge computing systems to process real-time data and make informed decisions. A critical challenge in these systems is the efficient allocation of computational tasks across available edge computing resources. While existing technologies provide solutions for data collection (UAVs), processing (computer vision), and control (reinforcement learning), the integration and resource optimization of these components remains a significant challenge. We propose a multi-objective simulated annealing (MOSA) algorithm for optimizing task allocation in edge computing systems, focusing on three key objectives: minimizing active computational nodes, optimizing energy distribution, and reducing execution time. We compared the MOSA algorithm with uniform random allocation, greedy algorithm, and single-objective simulated annealing (SOSA) methods under both standard and peak load conditions. The peak load scenario tested system performance under significantly increased computational demands and reduced resource availability. Our evaluation focused on three key metrics: the number of active nodes, energy distribution efficiency, and task execution time. The proposed MOSA algorithm demonstrated superior resource utilization under standard conditions and maintained robust performance during peak loads, showing significant improvements over baseline methods in all metrics. Results show that MOSA effectively balances multiple objectives while adapting to varying operational demands. It consistently outperformed comparison methods in minimizing active nodes while maintaining competitive performance in energy distribution and execution time. The framework demonstrated particular strength in maintaining efficiency under significantly increased computational loads, offering a robust solution for task allocation in edge computing systems. While some limitations exist in real-world applications, this work provides a strong foundation for optimizing resource utilization in smart city systems that integrate multiple computational tasks.}, archive = {J_ACCESS}, author = {Ahmed Shamil Mustafa and Salman Yussof and Nurul Asyikin Mohamed Radzi}, doi = {10.1109/ACCESS.2025.3538676}, journal = {IEEE Access}, pages = {24251-24275}, shortjournal = {IEEE Access}, title = {Multi-objective simulated annealing for efficient task allocation in UAV-assisted edge computing for smart city traffic management}, volume = {13}, year = {2025}, }
• (2025). Investigating low latency communications with link-layer rate of NOMA under nakagami-m fading channel in finite blocklength regime. ACCESS, 13, 24237-24250. (www)

  @article{ , abstract = {This paper investigates link-layer rate of two-user downlink non-orthogonal multiple access (NOMA) networks under Nakagami-m fading channel with short packet communications. The overall reliability of the NOMA users is based on transmission error probability and queuing delay violation probability. We use an effective capacity tool to find the reliability and latency of the two-user with short packet communications. We derive closed-form expressions for the achievable effective capacity of both near user and far user of downlink NOMA networks with short packet communications under Nakagami-m fading channel conditions. We investigate the impact of transmit signal-to-noise ratio, delay exponent, transmission error probability, and various blocklength on the achievable effective capacity of each user. The impact of varying packet sizes (blocklength) and transmit SNR on the total achievable EC of NOMA users are investigated in detail. We also perform Monte-Carlo simulations to validate our proposed closed-form expressions of two-user downlink NOMA.}, archive = {J_ACCESS}, author = {Muhammad Imran and Muhammad Amjad and Muhammad Khalil Afzal and Muhammad Usman and Byung-Seo Kim}, doi = {10.1109/ACCESS.2025.3537464}, journal = {IEEE Access}, pages = {24237-24250}, shortjournal = {IEEE Access}, title = {Investigating low latency communications with link-layer rate of NOMA under nakagami-m fading channel in finite blocklength regime}, volume = {13}, year = {2025}, }
• (2025). Face recognition method for underground engineering based on dual-target domain adaptation and discriminative feature learning. ACCESS, 13, 24223-24236. (www)

  @article{ , abstract = {The poor lighting and dust in underground engineering environments often induce serious noise and motion blur to face recognition cameras, decreasing the recognition accuracy even under Near-InfraRed (NIR) mode. Although various methods to improve algorithm robustness can enhance the overall model performance, the face recognition accuracy remains insufficient for the large number of blurred and NIR images generated during underground engineering. Accordingly, this paper proposes a novel face recognition method for underground engineering environments based on dual-target domain adaptation (DTDA) and discriminative feature learning (DFL). The targeting of the algorithm to blurred and NIR images is enhanced through DTDA, while the inter-class feature variability and intra-class feature compactness for domain adaptation face recognition are ensured by DFL. Extensive experiments are conducted on public and self-built datasets to verify the proposed method. The results show that (1) dual domain adversarial loss, dual CORAL loss, Blur-to-Sharp and NIR-to-VIS image reconstruction, and face keypoints intermediate domain can improve the DTDA performance of the model. (2) Dedicated encoders, center loss, and triplet loss can improve the DFL ability of the model. (3) The high Rank-1 accuracies and area under curve (AUC) values of the Receiver Operating Characteristic (ROC) curves on the three domains indicate that the proposed face recognition method can adapt to the complex working conditions in underground engineering environments.}, archive = {J_ACCESS}, author = {Yongqiang Yu and Cong Guo and Lidan Fan and Jiyun Zhang and Liwei Yu and Peitao Li}, doi = {10.1109/ACCESS.2025.3529698}, journal = {IEEE Access}, pages = {24223-24236}, shortjournal = {IEEE Access}, title = {Face recognition method for underground engineering based on dual-target domain adaptation and discriminative feature learning}, volume = {13}, year = {2025}, }
• (2025). Navigating the challenges and opportunities of securing internet of autonomous vehicles with lightweight authentication. ACCESS, 13, 24207-24222. (www)

  @article{ , abstract = {The Internet of Things (IoT) can be defined as the network of physical objects, or “things,” embedded with sensors and software for processing and exchanging data with other devices and ecosystems using the Internet as a medium. With its rapid growth over the past decade, it has permeated several application domains, including intelligent vehicular systems. The Internet of Autonomous Vehicles (IoAV) is a subset of IoT that envisions dynamic autonomous driving without human intervention. The dynamic nature of the environment in which autonomous vehicles operate introduces significant challenges, such as real-time communication and security vulnerabilities. These challenges cannot be directly addressed by standard cybersecurity solutions designed primarily for static IoT environments. In this work, we outline the various vulnerabilities of the IoAV systems, and we delve into the critical importance of adopting lightweight security protocols. These protocols are crucial to ensure robust protection while at the same time not jeopardizing the performance of the IoAV system. We also highlight the fast lightweight security protocols implemented on heterogeneous embedded, low-power, high-performance computing platforms as a viable solution to address these challenges.}, archive = {J_ACCESS}, author = {Hazem Ismail Ali and Harrison Kurunathan and Mohamed Hamdy Eldefrawy and Flavius Gruian and Magnus Jonsson}, doi = {10.1109/ACCESS.2025.3537800}, journal = {IEEE Access}, pages = {24207-24222}, shortjournal = {IEEE Access}, title = {Navigating the challenges and opportunities of securing internet of autonomous vehicles with lightweight authentication}, volume = {13}, year = {2025}, }
• (2025). Path planning for coal mining masonry robots combined with trajectory optimization. ACCESS, 13, 24197-24206. (www)

  @article{ , abstract = {In underground coal mining, the efficiency of masonry robots is hindered by complex environmental conditions and pose constraints. This study proposes a novel path planning algorithm combining an improved Rapidly-exploring Random Tree (RRT) with Particle Swarm Optimization (PSO), followed by trajectory optimization under mechanical constraints to identify the time-optimal path. The improved RRT incorporates dynamic sampling regions and tree reorganization to reduce redundancy and enhance efficiency. A dynamic step length strategy is also introduced to address obstacle avoidance in complex underground environments, ensuring robotic arm safety. The modified PSO algorithm is then used for path planning and trajectory optimization, incorporating obstacle avoidance and pose constraints. Simulation results show that the integrated algorithm significantly reduces path length, sampling points, and search time compared to traditional RRT, RRT*, and informed RRT*. Additionally, trajectory optimization with PSO, considering joint posture constraints, reduces operation time by approximately 13% compared to ant colony optimization. This research provides key technical insights for improving the efficiency and safety of masonry robots in coal mining.}, archive = {J_ACCESS}, author = {Xingyi Qian and Yan Wang}, doi = {10.1109/ACCESS.2025.3539023}, journal = {IEEE Access}, pages = {24197-24206}, shortjournal = {IEEE Access}, title = {Path planning for coal mining masonry robots combined with trajectory optimization}, volume = {13}, year = {2025}, }
• (2025). Analysis of gas pipeline failure factors based on the novel bayesian network by machine learning optimization. ACCESS, 13, 24181-24196. (www)

  @article{ , abstract = {Assessing the failure of urban gas pipelines is crucial for identifying risk factors and preventing gas accidents that result in economic losses and casualties. Most previous studies on gas pipeline failure assessment are based on the basic Bayesian network (BN). However, the conditional probability table (CPT) determined by expert experience is often affected by subjective factors, leading to inaccurate evaluation results. This paper proposes a novel gas pipeline failure risk assessment model based on Bayesian network optimized by machine learning. Firstly, the pipeline fault tree model is constructed according to the accident data that leads to pipeline failure, and the fault tree model is mapped to the Bayesian network. Secondly, by establishing a neural network model, the value of the CPT of the Bayesian network is determined. Aiming at the problem that the true probability of a Bayesian network child node cannot be obtained, the Noisy-OR gate model is introduced to determine the CPT of the node. The advantage of the novel Bayesian network model is that the value of CPT is determined based on data-driven, which effectively reduces the subjective impact of expert experience. Subsequently, the pipeline risk analysis is conducted based on statistical data from the Pipeline and Hazardous Materials Safety Administration (PHMSA). The two-way inference function of Bayesian network is used to obtain the failure probability of gas pipeline and the posterior probability of root node. By comparing with the results of traditional methods, the rationality of the results obtained by the model is verified. Finally, the sensitivity analysis identified Other (Other external damage), third-party excavation damage, incorrect operation and fire or explosion as key influences on gas pipeline failure. The Bayesian network model established in this study is based on data-driven. It effectively reduces the effect of subjectivity brought by expert experience and helps to improve the accuracy of failure assessment. This study reflects that machine learning technology will be an important research direction in the field of gas pipeline risk assessment in the future.}, archive = {J_ACCESS}, author = {Shuangqing Chen and Shun Zhou and Zhe Xu and Yongbin Liu and Bing Guan and Xiaoyu Jiang and Wencheng Li}, doi = {10.1109/ACCESS.2025.3537678}, journal = {IEEE Access}, pages = {24181-24196}, shortjournal = {IEEE Access}, title = {Analysis of gas pipeline failure factors based on the novel bayesian network by machine learning optimization}, volume = {13}, year = {2025}, }
• (2025). Crafting the path: Robust query rewriting for information retrieval. ACCESS, 13, 24171-24180. (www)

  @article{ , abstract = {Query rewriting aims to generate a new query that can complement the original query to improve the information retrieval system. Recent studies on query rewriting, such as query2doc, query2expand and querey2cot, rely on the internal knowledge of Large Language Models (LLMs) to generate a relevant passage to add information to the query. Nevertheless, the efficacy of these methodologies may markedly decline in instances where the requisite knowledge is not encapsulated within the model’s intrinsic parameters. In this paper, we propose a novel structured query rewriting method called Crafting The Path tailored for retrieval systems. Crafting The Path involves a three-step process that crafts query-related information necessary for finding the passages to be searched in each step. Specifically, the Crafting The Path begins with Query Concept Comprehension, proceeds to Query Type Identification, and finally conducts Expected Answer Extraction. Experimental results show that our method outperforms previous rewriting methods, especially in less familiar domains for LLMs. We demonstrate that our method is less dependent on the internal parameter knowledge of the model and generates queries with fewer factual inaccuracies. Furthermore, we observe that Crafting The Path demonstrates superior performance in the retrieval-augmented generation scenarios.}, archive = {J_ACCESS}, author = {Ingeol Baek and Jimin Lee and Joonho Yang and Hwanhee Lee}, doi = {10.1109/ACCESS.2025.3538665}, journal = {IEEE Access}, pages = {24171-24180}, shortjournal = {IEEE Access}, title = {Crafting the path: Robust query rewriting for information retrieval}, volume = {13}, year = {2025}, }
• (2025). Meta learning strategies for comparative and efficient adaptation to financial datasets. ACCESS, 13, 24158-24170. (www)

  @article{ , abstract = {This research proposes a Meta learning framework for financial time series forecasting, designed to rapidly adapt to novel market conditions with minimal retraining. The framework operates in two stages: 1) pretraining on a diverse set of financial datasets, including stocks (e.g., MSFT, AAPL) and cryptocurrencies (e.g., BTC, ETH), and 2) fine-tuning on recent data to adapt to new markets. The model utilizes XGBoost with dynamic feature engineering, which adjusts technical indicators (e.g., Relative Strength Index, Bollinger Bands) to account for evolving market conditions. Experimental results demonstrate that the proposed framework achieves significant improvements in Root Mean Squared Error (15%) and Mean Absolute Percentage Error (10%) compared to traditional methods, such as simple moving averages and exponential smoothing. These findings highlight the framework’s robustness, scalability, and ability to manage dynamic market behaviors, making it an effective tool for both short-term traders and long-term investors. Compared to LSTM-GARCH, the proposed Meta learning model achieves an RMSE of 0.82 (versus up to 10.11), an MAE of 0.61 (versus up to 8.39), and a DA of 67.33% (versus up to 50.44%).}, archive = {J_ACCESS}, author = {Kubra Noor and Ubaida Fatima}, doi = {10.1109/ACCESS.2024.3516490}, journal = {IEEE Access}, pages = {24158-24170}, shortjournal = {IEEE Access}, title = {Meta learning strategies for comparative and efficient adaptation to financial datasets}, volume = {13}, year = {2025}, }
• (2025). SurvBeNIM: The beran-based neural importance model for explaining survival models. ACCESS, 13, 24137-24157. (www)

  @article{ , abstract = {A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind SurvBeNIM is to extend the Beran estimator by incorporating the importance functions into its kernels and by implementing these importance functions as a set of neural networks which are jointly trained in an end-to-end manner. Two strategies of using and training the whole neural network implementing SurvBeNIM are proposed. The first one explains a single instance, and the neural network is trained for each explained instance. According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. Various numerical experiments compare the method with different existing explanation methods developed for survival analysis, in particular, for models SurvBex, SurvNAM, and SurvLIME. Results of experiments with synthetic and real data show that the proposed method outperforms existing explanation methods. A code implementing the proposed method is publicly available.}, archive = {J_ACCESS}, author = {Lev V. Utkin and Danila Y. Eremenko and Andrei V. Konstantinov and Vladimir A. Muliukha}, doi = {10.1109/ACCESS.2025.3537459}, journal = {IEEE Access}, pages = {24137-24157}, shortjournal = {IEEE Access}, title = {SurvBeNIM: The beran-based neural importance model for explaining survival models}, volume = {13}, year = {2025}, }
• (2025). Optimal WSN deployment to measure air quality in dynamic multi-source dispersion scenarios: A case study in arequipa. ACCESS, 13, 24124-24136. (www)

  @article{ , abstract = {Air quality is a crucial determinant of public health, significantly impacting global populations. According to the World Health Organization, air pollution causes approximately 7 million premature deaths each year, with 99% of the global population exposed to air that fails to meet recommended quality standards. Poor air quality leads to severe health issues, including respiratory and cardiovascular diseases. Given these alarming statistic, it is imperative for governments, organizations, and communities to prioritize air quality management. Effective measurement strategies are the first step in reducing health risks associated with pollution. Thus, to address this public health challenge, this article introduces a novel dynamic multi-source dispersion model that accounts for variations in wind velocity and direction over time, recognizing that air pollution is influenced by rapidly changing environmental factors. Additionally, three optimization problems are presented to identify the optimal locations for sensor nodes. The first problem enhances pollution sensing capabilities, the second maximizes the population served, aiming to ensure that air quality information is accessible to as many residents as possible, and the third one prioritizes the needs of vulnerable groups. Finally, we present results from a case study conducted on the campus of the National University of San Agustín in Arequipa, Peru, demonstrating the practical application of our proposed methodology in real-world scenarios.}, archive = {J_ACCESS}, author = {Alexander Hilario-Tacuri and Alberth Tamo and Marco Pinares-Mamani and Walter Butron and Romel Jimenez}, doi = {10.1109/ACCESS.2025.3534097}, journal = {IEEE Access}, pages = {24124-24136}, shortjournal = {IEEE Access}, title = {Optimal WSN deployment to measure air quality in dynamic multi-source dispersion scenarios: A case study in arequipa}, volume = {13}, year = {2025}, }
• (2025). MASC: Wearable design for infectious disease detection through machine learning. ACCESS, 13, 24108-24123. (www)

  @article{ , abstract = {We present an innovative approach for designing a wearable solution that utilizes machine learning to systematically optimize the monitoring of vital signs for early detection of COVID-19 infections in symptomatic patients. This approach correlates sensor data trends with disease predictions, utilizing existing hospital patient data to enhance diagnosis accuracy. Our methodology offers a scalable, cost-effective solution to manage and prevent infectious diseases beyond COVID-19, addressing the limitations of traditional diagnostic methods. A functional prototype has been developed, supporting the effectiveness of continuous health monitoring in infection detection. The wearable continuously monitors key vitals such as body temperature, heart rate, respiratory rate, and oxygen saturation levels, providing an early warning system for timely medical intervention. This wearable device holds promise for transforming infectious disease detection and management, benefiting healthcare professionals and individuals alike.}, archive = {J_ACCESS}, author = {Sumaiya Afroz Mila and Bhagawat Baanav Yedla Ravi and Md Rafiul Kabir and Sandip Ray}, doi = {10.1109/ACCESS.2025.3538518}, journal = {IEEE Access}, pages = {24108-24123}, shortjournal = {IEEE Access}, title = {MASC: Wearable design for infectious disease detection through machine learning}, volume = {13}, year = {2025}, }
• (2025). Copula-driven learning techniques for physical layer authentication using multimodal data. ACCESS, 13, 24091-24107. (www)

  @article{ , abstract = {In this paper, we present a study on copula-driven learning techniques for physical layer authentication (PLA) in wireless communication, using data from multiple modalities. The collective multimodal data is considered as an attribute vector, which is used as a test statistic for the underlying multi-level hypothesis testing problem of PLA. We consider regular-vine copula-based information fusion approaches across various combinations of these attributes resulting in different architectures, which are tested across different datasets. In particular, we consider three datasets which include a Monte Carlo simulations-based synthetic dataset, and publicly available automotive factory (AF) and open area test site (OATS) datasets from National institute of standards and technology (NIST). A comparative performance study of the proposed architectures across the datasets is carried out in terms of detection accuracy. For the classification task, we consider some of the well-known learning algorithms including long short-term memory (LSTM), random forest, K-nearest neighbor, support vector machine and bagging tree techniques. Moreover, we study the effect of correlation introduced across the attributes, and compare it with the case with no correlation among the attributes. Our extensive results provide intra-algorithm, intra-architecture and inter-architecture insights, and show that LSTM offers the best performance, across the datasets and proposed architectures.}, archive = {J_ACCESS}, author = {Sahana Srikanth and Sanjeev Gurugopinath and Sami Muhaidat}, doi = {10.1109/ACCESS.2025.3532996}, journal = {IEEE Access}, pages = {24091-24107}, shortjournal = {IEEE Access}, title = {Copula-driven learning techniques for physical layer authentication using multimodal data}, volume = {13}, year = {2025}, }
• (2025). Target detection using fused unidentical photonics-based LFM sub-band radar signals with an adaptive feed forward network equalizer. ACCESS, 13, 24080-24090. (www)

  @article{ , abstract = {To address the challenges of costly frontend high frequency opto-electronics devices and the requirement of high bandwidth for improved range, resolution several band fusion techniques are proposed in this work. However, conventional band fusion techniques fuse the bands with identical bandwidth and same chirp rates. This paper proposes and experimentally demonstrates sub-bands fusion of photonically generated linear frequency modulated (Ph-LFM) radar signals with unidentical bandwidth and chirp rates using an adaptive, delay-less feed-forward network equalizer (FFNE). We demonstrate this using optical injection in a semiconductor laser to generate Ph-LFM signals at different IEEE X-KA radar sub-bands: 19.25–23.94 GHz and 24.06–28.31 GHz (bandgap 0.12 GHz), 19.69–23.06 GHz and 23.625–27 GHz (bandgap 0.56 GHz), and 8–11.5 GHz and 12.75–17 GHz (bandgap 1.25 GHz). Time-frequency analysis (TFA) was used to obtain sub-bandgap signals of 0.12 GHz, 0.56 GHz, and 1.25 GHz which are coherently fused using the FFNE with a particle swarm optimization (PSO) algorithm to optimize complex-valued weights. The method is evaluated by measuring the range resolution and peak-to-sidelobe level (PSL) in detecting two objects separated by 2 cm and 3 cm. The FFNE achieves significant improvement over 10 dB in PSL and resolves previously unresolvable distances, with maximum range resolutions of 1.8 cm, 2.2 cm, and 2 cm, closely matching theoretical values for full-band LFM signals with bandwidth of 9.06 GHz, 7.31 GHz, and 9 GHz, respectively. Experimental results demonstrate the FFNE’s superior performance in enhancing range resolution and PSL in multi-sub-band radar systems compared to scenarios without FFNE.}, archive = {J_ACCESS}, author = {Bikash Nakarmi and S. M. Rezwanul Islam and Hum Nath Parajuli and Ikechi Augustine Ukaegbu and Aigerim Ashimbayeva and Carlo Molardi and T. D. Subash and Xiangchuan Wang and Shilong Pan}, doi = {10.1109/ACCESS.2025.3535472}, journal = {IEEE Access}, pages = {24080-24090}, shortjournal = {IEEE Access}, title = {Target detection using fused unidentical photonics-based LFM sub-band radar signals with an adaptive feed forward network equalizer}, volume = {13}, year = {2025}, }
• (2025). Continuum and soft robots in minimally invasive surgery: A systematic review. ACCESS, 13, 24053-24079. (www)

  @article{ , abstract = {Faster recovery, reduced trauma, and improved patient outcomes drive innovations in minimally invasive surgery (MIS). Notwithstanding significant advancements, traditional MIS tools have been limited in navigating deep anatomical pathways and offering precise control at target sites. Continuum robotics has emerged as a solution, with recent developments enabling greater flexibility and maneuverability in surgical interventions. In this review, we first highlight recent developments in mechanical-continuum robots for traditional minimally invasive surgery and then summarize the current state-of-the-art in steerable catheter-based interventions. We discuss limitations to current approaches and explore the emerging potential of soft robots as a novel strategy to address the challenge of developing versatile, highly articulated flexible tools for minimally invasive surgical interventions. We hope that this review will, on the one hand, provide an introduction and resource for students and researchers alike, and on the other hand, will stimulate discussion vis-à-vis future directions in minimally invasive surgery.}, archive = {J_ACCESS}, author = {Fahad Iqbal and Mojtaba Esfandiari and Golchehr Amirkhani and Hamidreza Hoshyarmanesh and Sanju Lama and Mahdi Tavakoli and Garnette R. Sutherland}, doi = {10.1109/ACCESS.2025.3535677}, journal = {IEEE Access}, pages = {24053-24079}, shortjournal = {IEEE Access}, title = {Continuum and soft robots in minimally invasive surgery: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Estimating complex signals with a fuzzy-enhanced kalman filter: A note on “the output regulation and the kalman filter as the signal generator”. ACCESS, 13, 24041-24052. (www)

  @article{ , abstract = {The authors recently introduced a Kalman filter-based approach for estimating noise-contaminated unmodeled signals. However, in that work, the dimension of the Kalman filter depends on the complexity of the signals being estimated, which are not known a priori. This typically requires a trial-and-error process, often resulting in an excessively large filter dimension that complicates the method’s practical application. To overcome this problem, this paper proposes a solution for estimating complex, unmodeled, and noise-contaminated signals while simultaneously obtaining a low-dimensional dynamical model whose output approximates the actual noise-free signal. This is achieved by incorporating a fuzzy error covariance matrix into the standard linear Kalman filter algorithm. The proposed modification prevents the Kalman filter estimate from drifting away from the true signal and provides an alternative to the traditional abrupt resetting of the error covariance matrix. Notably, this modification can be seen as a soft reset of certain initial conditions and, as demonstrated, does not compromise the Kalman filter’s stability. Furthermore, the result shows that incorporating simple fuzzy logic concepts may substantially enhance the performance of some established algorithms.}, archive = {J_ACCESS}, author = {Jesús Alberto Meda-Campaña and Juan Carlos García-Hernández and Rodolfo Daniel Velázquez-Sánchez and Luis Alberto Páramo-Carranza and Tonatiuh Hernández-Cortés and Ricardo Tapia-Herrera}, doi = {10.1109/ACCESS.2025.3537497}, journal = {IEEE Access}, pages = {24041-24052}, shortjournal = {IEEE Access}, title = {Estimating complex signals with a fuzzy-enhanced kalman filter: A note on “the output regulation and the kalman filter as the signal generator”}, volume = {13}, year = {2025}, }
• (2025). User model for virtual learning based on adaptive gamification. ACCESS, 13, 24028-24040. (www)

  @article{ , abstract = {This study presents the development and evaluation of a hybrid user model designed for adaptive gamification within a virtual learning environment based on the Felder-Silverman Learning Style model. The research aims to enhance student engagement and learning outcomes by personalizing gamification elements according to individual learning styles. The methodology consists of four phases: 1) analyzing learning styles among STEM students, 2) mapping gamification elements to their learning styles, 3) designing and developing a user model, and 4) evaluating its usability. The user model integrates both stereotypical and dynamic modeling approaches, allowing for initial categorization and continuous adaptation based on learner interactions. A cognitive walkthrough evaluation conducted with experts and end-users demonstrated high task completion rates, indicating the model’s potential effectiveness. Usability test results identified areas for improvement, specifically in visual design, navigation, and user feedback. The study contributes to the field of adaptive e-learning by providing a flexible framework that can potentially improve learning outcomes and sustain student engagement in virtual learning environments. Future work should focus on implementing the recommended enhancements and conducting empirical studies to validate the model’s effectiveness in real-world educational settings.}, archive = {J_ACCESS}, author = {Intan Yusrina Zairon and Tengku Siti Meriam Tengku Wook and Syahanim Mohd Salleh and Hadi Affendy Dahlan}, doi = {10.1109/ACCESS.2025.3537599}, journal = {IEEE Access}, pages = {24028-24040}, shortjournal = {IEEE Access}, title = {User model for virtual learning based on adaptive gamification}, volume = {13}, year = {2025}, }
• (2025). Terahertz-based IRS-assisted secure symbiotic radio communication: A DRL approach. ACCESS, 13, 24014-24027. (www)

  @article{ , abstract = {Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance.}, archive = {J_ACCESS}, author = {Muhammad Shahwar and Manzoor Ahmed and Touseef Hussain and Sajed Ahmad and Wali Ullah Khan and Muhammad Sheraz and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3539088}, journal = {IEEE Access}, pages = {24014-24027}, shortjournal = {IEEE Access}, title = {Terahertz-based IRS-assisted secure symbiotic radio communication: A DRL approach}, volume = {13}, year = {2025}, }
• (2025). A real-time safe navigation proposal for mobile robots in unknown environments using meta-heuristics. ACCESS, 13, 23987-24013. (www)

  @article{ , abstract = {Autonomous navigation in mobile robots represents a significant challenge in unknown and dynamic environments, where the need to avoid obstacles and find safe trajectories in real-time requires efficient solutions. This work presents the MetaHeuristic Real-Time Safe Navigation (MHRTSN) strategy, which combines potential fields with population-based meta-heuristics to optimize trajectory planning and navigation in such environments. The MHRTSN strategy was tested through a series of simulations using different static and dynamic scenarios, comparing the performance of MHRTSN-GA and MHRTSN-PSO versions in terms of displacement, distance traveled, and CPU and clock times. The results demonstrate that both versions provide sub-optimal solutions, with MHRTSN-PSO showing superior performance in terms of required processing time and better convergence compared to MHRTSN-GA when using a small number of individuals. Comparisons with other approaches in the literature showed that MHRTSN generated paths of equivalent length to those of the literature, but in a safer manner. Thus, the proposed approach offers an efficient and safe solution for autonomous navigation in mobile robots, contributing to the advancement of this field in real-world applications. Future work may explore the application of the strategy in physical robotic systems and more complex environments.}, archive = {J_ACCESS}, author = {Micael Balza and Mateus A. Goldbarg and Sérgio N. Silva and Lucileide M. D. Silva and Marcelo A. C. Fernandes}, doi = {10.1109/ACCESS.2025.3536081}, journal = {IEEE Access}, pages = {23987-24013}, shortjournal = {IEEE Access}, title = {A real-time safe navigation proposal for mobile robots in unknown environments using meta-heuristics}, volume = {13}, year = {2025}, }
• (2025). A low complexity dual-phase alternating scheme is used with the PTS method to reduce PAPR for B5G systems. ACCESS, 13, 23971-23986. (www)

  @article{ , abstract = {In this paper, we investigate the peak-to-average power ratio (PAPR) issue in orthogonal frequency division multiplexing (OFDM) and beyond fifth-generation (B5G) systems. To deal with this problem, we propose an efficient two-stage scheme to alleviate PAPR obsession in OFDM, F-OFDM, and UFMC waveforms, named dual-phase alternating partial transmission sequence (DPA-PTS). In the first stage, we use the partial transmission sequence (PTS) scheme to produce an acceptable PAPR threshold, which can be used as the initial upper bound level for the second stage. To reduce computational complexity, before entering the second stage, we employ the pre-process by Monte Carlo method to estimate the amount of subcarriers worth altering, denoted as $\beta $ . In the second stage, we use the dual-phase alternation of 0 and $\pi $ radian to wimple the effect of the peaking power of each subcarrier on PAPR one by one until the estimated amount $\beta $ . It can significantly reduce the computational complexity due to only being made in the simple amplitude phase-reverse operation for maximum $\beta $ critical amended subcarriers. Simulation results show that the proposed scheme achieves better PAPR performance and computational complexity regardless of the waveform than previous well-known techniques, such as PTS, selective mapping (SLM), etc. In the UFMC example, when the complementary cumulative distribution function (CCDF) is $10^{-4}$ , compared with the original waveform, the PTS scheme, and the SLM scheme, the PAPR performance of the proposed scheme is improved by about 2.74, 1.87, and 0.35 dB respectively. Of course, our proposed method’s bit error rate (BER) consistently outperforms the original waveform due to a valid PAPR reduction that weakens nonlinear distortion from the power amplifier.}, archive = {J_ACCESS}, author = {Yung-Ping Tu and Chen-Wei Hsu}, doi = {10.1109/ACCESS.2025.3538778}, journal = {IEEE Access}, pages = {23971-23986}, shortjournal = {IEEE Access}, title = {A low complexity dual-phase alternating scheme is used with the PTS method to reduce PAPR for B5G systems}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of RIS-assisted smart grid wide area network with RF energy harvesting. ACCESS, 13, 23959-23970. (www)

  @article{ , abstract = {This paper presents a comprehensive performance analysis of a reconfigurable intelligent surface (RIS)-assisted smart grid (SG) communication network with radio-frequency (RF) energy harvesting (EH). Here, we have considered a wide area network (WAN) where the dynamic behaviour of home appliances (HAs) switches between active and doze states. The switching action is modelled using a Markov chain coupled with a Saleh-Valenzuela (S-V) channel. These assumptions enable a detailed evaluation of key system parameters, including the number of active appliances, traffic intensity, power splitting ratio, RIS reflectors, and distances between HAs and the control unit (CU). Our study reveals that the proposed system significantly enhances communication performance compared to conventional SG WANs without RIS or RF EH. Specifically, a notable improvement in average bit error probability (ABEP) is demonstrated, achieving up to 7 dB signal-to-noise (SNR) enhancement over benchmarks under identical conditions. We have derived the closed-form average bit error probability (ABEP) expressions for both RIS with dual-hop (RIS-DH) and RIS as transmitter (RIS-T) cases. The results demonstrate that increasing the number of active appliances, traffic intensity, power splitting ratio, and fading severity impacts overall system performance. The derived analytical results are validated through Monte Carlo simulations. This work highlights the potential of RIS and EH technologies to improve energy efficiency and reliability in future SG networks.}, archive = {J_ACCESS}, author = {Kandi Veera Venkata Ramana and Hemanta Kumar Sahu}, doi = {10.1109/ACCESS.2025.3536890}, journal = {IEEE Access}, pages = {23959-23970}, shortjournal = {IEEE Access}, title = {Performance analysis of RIS-assisted smart grid wide area network with RF energy harvesting}, volume = {13}, year = {2025}, }
• (2025). Homomorphic signature with designated evaluator. ACCESS, 13, 23950-23958. (www)

  @article{ , abstract = {Homomorphic signature scheme with a designated evaluator extends traditional signature schemes by allowing only a specified entity to perform homomorphic operations on the signatures. However, existing schemes of this type are restricted to linear computations in their homomorphic evaluations, limiting their applicability in broader contexts. In this paper, we propose the first generic construction of a homomorphic signature scheme with a designated evaluator that supports arbitrary polynomial computations in its homomorphic evaluations. Technically, our construction leverages functional commitment schemes capable of arbitrary (polynomial) computations, combined with linear homomorphic signature schemes. Additionally, we achieve a post-quantum variant of this homomorphic signature scheme with a designated evaluator, provided that the underlying building blocks are post-quantum secure. This represents the first post-quantum realization of such a scheme.}, archive = {J_ACCESS}, author = {Kazuma Shiraishi and Keisuke Hara and Toi Tomita and Kyosuke Yamashita and Junji Shikata}, doi = {10.1109/ACCESS.2025.3534409}, journal = {IEEE Access}, pages = {23950-23958}, shortjournal = {IEEE Access}, title = {Homomorphic signature with designated evaluator}, volume = {13}, year = {2025}, }
• (2025). EEG transformer for classifying students’ epistemic cognition states in educational contexts. ACCESS, 13, 23935-23949. (www)

  @article{ , abstract = {Epistemic cognition encompasses the processes by which individuals achieve knowledge and understanding and is pivotal in human learning, typically in educational settings. Despite its importance, epistemic cognition is often studied using traditional educational research methods such as questionnaires and interviews. However, these methods primarily rely on self-reporting and retrospective accounts, which may not effectively capture the dynamic, situational, and often implicit nature of epistemic cognition as it unfolds in real-time learning environments. This study addresses this limitation by employing transformers and publicly available electroencephalography (EEG) datasets to classify students’ epistemic cognition states. For the first time, we demonstrate that transformers, enhanced with educational domain-specific preprocessing and categorical embeddings, achieve state-of-the-art performance in classifying epistemic cognition across diverse contexts. Our model achieved an accuracy of 99.63% and 99.65% for distance learning and online understanding tasks, respectively, outperforming existing transformers in cognition and metacognition studies. Grounded in the Learning Sciences, which emphasize an interdisciplinary and scientific approach to understanding human learning, this applied research demonstrates the transformative potential of integrating artificial intelligence with education and neuroscience to generate actionable insights into educational practices.}, archive = {J_ACCESS}, author = {Rosanna Yuen-Yan Chan}, doi = {10.1109/ACCESS.2025.3538036}, journal = {IEEE Access}, pages = {23935-23949}, shortjournal = {IEEE Access}, title = {EEG transformer for classifying students’ epistemic cognition states in educational contexts}, volume = {13}, year = {2025}, }
• (2025). Impact of loads and photovoltaic uncertainties on cascaded failure in transmission networks of future power grids. ACCESS, 13, 23924-23934. (www)

  @article{ , abstract = {The increase investment of renewable energy resources (RESs) into power systems, such as solar photovoltaics (PVs), introduces additional uncertainty in transmission line loading. This uncertainty adds challenges to cascading failure analyses of power systems especially in future power scenarios of high penetration of RESs. In this paper, cascaded failures of power systems caused by the sequence tripping of transmission lines in the presence of RESs (mainly PV systems) is analyzed. By studying the lost power and the line failure probability, the potential impact of integrating more RESs on cascaded failures is investigated. In this regard, the uncertainties of RESs are examined by adding PV systems probability distribution function that reflects the solar irradiance for a typical day.A transmission-boosting approach is proposed in this paper to minimize the impact of failure risk to mitigate the possibility of the cascaded failure caused by the increased penetration of RESs. This paper presents a systematic approach to mitigate the risk of cascading failures via reconducting of transmission lines. Simulation studies for different penetration scenarios of PV systems have been carried out to test the impact on the cascaded failure and to validate the proposed transmission-boosting approach. The results in this paper imply that the increase in penetration of PV systems in the power grid would increase the potential of both cascaded failure risk and occurrence. In addition, the results have shown the efficacy of the proposed transmission-boosting approach in minimizing the cascaded failure risk when implemented. The findings have been validated using the modified version of the IEEE 39-bus test system modeled and simulated in Matlab.}, archive = {J_ACCESS}, author = {Wasseem Al-Rousan and Rafat Aljarrah and Mazaher Karimi and Firas Obeidat and Qusay Salem}, doi = {10.1109/ACCESS.2025.3537720}, journal = {IEEE Access}, pages = {23924-23934}, shortjournal = {IEEE Access}, title = {Impact of loads and photovoltaic uncertainties on cascaded failure in transmission networks of future power grids}, volume = {13}, year = {2025}, }
• (2025). Motor-less robotic gripper: Driving mechanism by robotic manipulator movement. ACCESS, 13, 23913-23923. (www)

  @article{ , abstract = {This paper proposes a novel robotic gripper capable of antipodal grasping without requiring motors in its structure. The developed gripper generates grasping motion through the rotation of the manipulator’s terminal joint while maintaining its rotational capability for posturing. The key feature is a self-motion switching mechanism that automatically switches between finger motion and gripper’s orientation based on the tip force applied to the fingers. When the tip force is below a threshold value, the mechanism generates finger opening/closing motion; when the force exceeds the threshold, it activates the gripper’s orientation. This enables the robotic system to perform standard object manipulation tasks, including grasping, transferring with posture adjustment, and placing, without the installation of a motor. A special control strategy is developed to determine the manipulator motion for achieving the desired gripper motion during object manipulation. The developed gripper is evaluated experimentally, demonstrating that the gripper achieves a maximum closing speed of 165.9 mm/s and a grasping force of 28.5 N with 3D-printed components. The motor-less structure eliminates the need for cable installation and electrical settings in the gripper, contributing to system simplification. The effectiveness of the gripper is experimentally validated through various pick-and-place operation tests.}, archive = {J_ACCESS}, author = {Toshihiro Nishimura and Kosei Ueno and Tetsuyou Watanabe}, doi = {10.1109/ACCESS.2025.3538001}, journal = {IEEE Access}, pages = {23913-23923}, shortjournal = {IEEE Access}, title = {Motor-less robotic gripper: Driving mechanism by robotic manipulator movement}, volume = {13}, year = {2025}, }
• (2025). Network-scale impact of vegetation loss on coverage and exposure for 5G networks. ACCESS, 13, 23902-23912. (www)

  @article{ , abstract = {This study investigates the effects of vegetation on 5G network performance, with a particular focus on coverage, user exposure, and base station deployment strategies in an urban environment (Utrecht, The Netherlands). This is the first study to perform network planning simulations that account for vegetation and building-induced propagation challenges on a city-wide scale, providing understanding of their effects on 5G network performance and exposure. The study also explores the influence of user height, examining how vegetation’s blocking and shielding effects vary with user height. By evaluating both sub-6 GHz and mmWave networks under various simulated scenarios, the research qualifies the dual role of vegetation as both a coverage barrier and a mitigator of user exposure. Key findings include a significant 14.71% reduction in coverage for sub-6 GHz networks in the presence of vegetation and a 42.98% decrease in downlink whole-body SAR in mmWave networks due to vegetation’s shielding effects. Flexible base station placement is shown to effectively counteract coverage losses while maintaining stable exposure metrics, but mmWave networks remain highly sensitive to environmental obstructions. These findings emphasize the importance of incorporating vegetation and other environmental factors into network planning, especially for high-frequency 5G networks, to ensure optimal performance and limit user exposure.}, archive = {J_ACCESS}, author = {Jorn Schampheleer and Anke Huss and Margot Deruyck}, doi = {10.1109/ACCESS.2025.3538054}, journal = {IEEE Access}, pages = {23902-23912}, shortjournal = {IEEE Access}, title = {Network-scale impact of vegetation loss on coverage and exposure for 5G networks}, volume = {13}, year = {2025}, }
• (2025). Design optimization of a 3-PUU parallel machine for friction stir welding robots. ACCESS, 13, 23888-23901. (www)

  @article{ , abstract = {Friction Stir Welding (FSW) has recently emerged as an effective solid-state joining technique for welding high-strength aluminum alloys and light metals, particularly in the railway industry. However, a limitation of using FSW in this industry is the restricted workspace available. To address this issue, a 3-PUU (Prismatic and Universal Joints) parallel machine with 3-DoF (Degrees of Freedom) translational movement is utilized to support the FSW robot’s tool head, allowing the robot to operate over a larger horizontal workspace. This paper focuses on the design and optimization of a 3-PUU parallel machine to achieve an optimal configuration based on performance indices, specifically the Local Conditioning Index (LCI) and the dynamic isotropy index ( $d_{1}$ ). Initially, a design space atlas was created, mapping both LCI and $d_{1}$ were identified. A Genetic Algorithm (GA) was then used to determine a single optimal design within this region. A cuboid workspace was defined for this optimal design, and two test trajectories were generated for the robot to follow. A prototype of the optimal design was built, and a series of experiments were conducted to verify and validate the kinematic and dynamic models within the defined cuboid workspace, which provides foundations for further industrial applications of the proposed machine in FSW.}, archive = {J_ACCESS}, author = {Eka Marliana and Latifah Nurahmi and Arif Wahjudi and I. Made Londen Batan and Guowu Wei}, doi = {10.1109/ACCESS.2025.3536482}, journal = {IEEE Access}, pages = {23888-23901}, shortjournal = {IEEE Access}, title = {Design optimization of a 3-PUU parallel machine for friction stir welding robots}, volume = {13}, year = {2025}, }
• (2025). A novel computational framework for visual snow syndrome. ACCESS, 13, 23877-23887. (www)

  @article{ , abstract = {Visual Snow Syndrome (VSS) is a neurological condition that impairs the perceived image by causing the appearance of numerous constantly flickering dots, similar to those seen on a television screen that does not receive the antenna signal properly. This article presents two innovative applications using emerging technologies to help people suffering from VSS. One is based on Augmented and Virtual Reality to simulate the visual perception experienced by people affected by such syndrome in real time on the image sequence acquired by a smartphone’s camera. The other is based on a web platform based on a Virtual Reality engine, designed to create exercises parametrically to improve the UX and the usability of an experimental therapy that aims to alleviate the syndrome’s symptoms. We present the architecture of the two systems, which is characterised by a very simple yet very effective approach and use, as documented by the comments made by users. The results of an evaluation questionnaire submitted to volunteers who tested and scored the mobile App and eventually expressed opinions and suggestions are reported. The questionnaire results show users’ positive appreciation, as they outline in their comments how important it is for patients to have simulation tools that allow them to better explain their pathologies.}, archive = {J_ACCESS}, author = {Damiano Perri and Osvaldo Gervasi}, doi = {10.1109/ACCESS.2025.3536923}, journal = {IEEE Access}, pages = {23877-23887}, shortjournal = {IEEE Access}, title = {A novel computational framework for visual snow syndrome}, volume = {13}, year = {2025}, }
• (2025). Improvement of assemble-to-order model processes with process mining: Dynamic analysis and hybrid approaches. ACCESS, 13, 23866-23876. (www)

  @article{ , abstract = {This article explores the application of process mining techniques to dynamically observe operations and enhance production planning efficiency in assemble-to-order (ATO) manufacturing systems using a hybrid approach. The study addresses critical challenges in managing the repetitive and complex workflows characteristic of ATO models. It highlights inefficiencies that impact productivity, lead times, and process flow. By combining direct flow graphs, inductive visual miner, and state chart workflows with a recursive algorithm, the proposed hybrid methodology identifies bottlenecks with repetitive works, analyzes variant impacts, and uncovers non-value-added activities. As a case study, the manufacturing processes of an automotive company employing an ATO strategy were analyzed. The hybrid approach achieved a fitness value of 92.5% with 77% precision, significantly improving upon the initial life cycle miner algorithm results (fitness value: 79.5%). Insights derived from the analysis informed actionable recommendations to optimize production workflows. This study demonstrates the potential of hybrid process mining techniques for understanding and improving ATO systems, offering a dynamic and scalable framework for addressing inefficiencies in similar production models.}, archive = {J_ACCESS}, author = {Ufuk Celik and Yuksel Yurtay}, doi = {10.1109/ACCESS.2025.3538171}, journal = {IEEE Access}, pages = {23866-23876}, shortjournal = {IEEE Access}, title = {Improvement of assemble-to-order model processes with process mining: Dynamic analysis and hybrid approaches}, volume = {13}, year = {2025}, }
• (2025). Improved performance and voltage stability of islanded inverters using single-loop PI-lead controller. ACCESS, 13, 23851-23865. (www)

  @article{ , abstract = {This paper introduces a novel single-loop control scheme for voltage regulation in islanded inverters, using a proportional-integral-lead (PI-Lead) controller designed within the synchronous reference frame (SRF) through a loop-shaping approach. Commonly, dual-loop controllers have been employed for this purpose owing to several limitations, such as insufficient stability with a narrow gain margin, a trade-off between stability and bandwidth, and constrained bandwidth due to the need for a significantly lower outer voltage loop bandwidth compared to the inner current one. The proposed method overcomes these challenges by integrating a Lead compensator, which enhances voltage regulation by eliminating steady-state error, improving stability margins, and providing a fast transient response while maintaining robustness against model parameter variations. Additionally, the control strategy reduces dependence on current measurements, except when dealing with inductive loads where virtual resistor-based active damping is necessary. Despite the challenges posed by multi-resonance phenomena and coupling effects inherent in single-loop SRF-based modeling, a comprehensive frequency-domain analysis is performed, with systematic controller parameter design guidelines to mitigate multi-gain crossover issues. Rigorous experimental results validate the theoretical findings and simulations, demonstrating the superior performance and practical effectiveness of the proposed control strategy compared to existing methods.}, archive = {J_ACCESS}, author = {Mohammed E. Benzoubir and Abderezak Lashab and Khaled Rayane and Mohammed Benmiloud and Mohamed Bougrine and Atallah Benalia and Mohamed Trabelsi and Hani Vahedi}, doi = {10.1109/ACCESS.2025.3537716}, journal = {IEEE Access}, pages = {23851-23865}, shortjournal = {IEEE Access}, title = {Improved performance and voltage stability of islanded inverters using single-loop PI-lead controller}, volume = {13}, year = {2025}, }
• (2025). Research on wood defects feature imbalance optimization and recognition. ACCESS, 13, 23841-23850. (www)

  @article{ , abstract = {Deep learning is a promising method to achieve automatic wood defects detection which is indispensable for wood production; however, such a technique faces challenges caused by poor generalization ability and low recognition accuracy on light defects. In this study, the problems are attributed to imbalanced feature distribution of wood defects which are rich in diversity. To this end, two improvements on CNN classifier loss function are proposed to enhance the performance on wood defects recognition. Firstly, Cross Entropy loss is modified to allow the model to pay more attention to individual sample discrepancy during training. Secondly, a new loss, Var Loss is proposed to add to main loss function in order to decrease the variance of classification results, thus reducing the impact of sample diversity on model performance. Classical CNN classifiers are employed to distinguish three kinds of wood images: dead knot, live knot and ordinary grain. Results show that Modified Cross Entropy makes model more sensitive to hard samples regardless of the batchsize. Var Loss tends to decrease the fluctuation of prediction confidences, making model more robust in practical use. An overall accuracy increase of 6.6% is achieved, the accuracy on live knot has an increase of 17%, and the missing rate of defects is decreased by 15%. Besides, generalization ability test indicates that new methods allow the classifier to have comparable accuracies on five additional datasets. The proposed loss functions improve the model performance through optimizing the model training process, providing a new idea for deep learning application in wood defects detection.}, archive = {J_ACCESS}, author = {Xiao Wang}, doi = {10.1109/ACCESS.2025.3538285}, journal = {IEEE Access}, pages = {23841-23850}, shortjournal = {IEEE Access}, title = {Research on wood defects feature imbalance optimization and recognition}, volume = {13}, year = {2025}, }
• (2025). Non-linear control of interleaved boost converter using disturbance observer-based approach. ACCESS, 13, 23833-23840. (www)

  @article{ , abstract = {Applications such as fuel cell-based electric cars can benefit from the inherent characteristics of interleaved boost converters (IBCs), which include high voltage gain and minimal output voltage and input current ripples. To achieve effective regulation in the face of parameter changes, its critical to maintain the globally stable regulated IBC, especially as the use of highly variable loads increases. An innovative Integral sliding-mode controller along with a disturbance observer for the two-phase IBC (2PIBC) is suggested to ensure such large-signal stability. The time-domain approach is employed to verify the averaged model of the system, which forms basis of designing the controlled system. Several aspects of SMC design are discussed in detail, such as global stability, control signal creation, and sliding surface type selection. The proposed controller is shown to have excellent tracking capabilities with low computations. The use of the recommended control algorithm is demonstrated through MATLAB results and also using experiments based on prototype and using dSPACE 1104.}, archive = {J_ACCESS}, author = {Avinash Mishra and Sanjoy Mandal and Jean-Yves Dieulot and Mrinal R. Bachute and Mohd Faizan and Anna Pinnarelli and Mehrdad Heidari and Alireza Soleimani}, doi = {10.1109/ACCESS.2025.3537826}, journal = {IEEE Access}, pages = {23833-23840}, shortjournal = {IEEE Access}, title = {Non-linear control of interleaved boost converter using disturbance observer-based approach}, volume = {13}, year = {2025}, }
• (2025). TepiSense: A social computing-based real-time epidemic surveillance system using artificial intelligence. ACCESS, 13, 23816-23832. (www)

  @article{ , abstract = {Artificial Intelligence (AI) technologies have enabled researchers to develop tools to monitor real-world events and user behavior using social media platforms. Twitter is particularly useful for gathering invaluable information related to diseases and public health to build real-time disease surveillance systems. Such systems offer a cost-effective and efficient alternative to the passive, expensive, and time-consuming process of using data from healthcare organizations and hospitals. In this paper, we propose a novel system of TepiSense to automatically perform disease surveillance of epidemic-prone diseases. Our system classifies tweets related to diseases and further identifies ‘indication’ tweets that highlight the presence of patients. Our system consists of four distinct modules of pre-processor, feature extractor, classifier, and evaluator. TepiSense compares the performance of 3 feature extraction techniques, 9 machine/deep learning models, and 3 Large Language Models (LLMs). To test the performance of our system, we build a dataset of Twitter Epidemic Surveillance Corpus (TESC) containing 23.9K English and 13K labelled Urdu tweets related to six diseases: COVID19, hepatitis, malaria, flu, dengue, and HIV/AIDS. Our results show that mBERT LLM achieves the highest F-measure values of 0.96 and 0.83 for topic and indication tweets classification, respectively. Furthermore, we compute the correlation of signals generated by our system with real-world cases to test the efficacy on COVID19 disease. We notice that real-world cases have a correlation of 0.58-0.63 with the indication category tweets. Finally, we develop an interactive and user-friendly dashboard to disseminate the analytics of our system. Overall, our system offers a powerful tool for real-time disease surveillance using social media with potential implications for public health policy and decision-making.}, archive = {J_ACCESS}, author = {Bilal Tahir and Muhammad Amir Mehmood}, doi = {10.1109/ACCESS.2025.3537168}, journal = {IEEE Access}, pages = {23816-23832}, shortjournal = {IEEE Access}, title = {TepiSense: A social computing-based real-time epidemic surveillance system using artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Optimizing photovoltaic array performance under partial shading using a golden ratio-based configuration: A comparative analysis of 16 configurational variants. ACCESS, 13, 23795-23815. (www)

  @article{ , abstract = {The growing need for generation of electric power because of increasing energy consumption requires the need of renewable energy sources. Solar photovoltaic (PV) systems are suitable solution to this issue, however its efficiency might decrease under partial shade conditions. In this work, we propose a new array configuration for a $4\times 4$ solar PV array under different partial shading environments. We validate the proposed method by testing 18 different shading patterns that our configuration greatly increases power output, lowers mismatch losses, and improves the fill factor relative to traditional techniques. Comparatively to the conventional Total cross-Tied (TCT) method’s 68 W and fill factor of 0.29, proposed method achieves a power output of 76 W, a reduction in mismatch losses to 15%, and a fill factor of 0.32 under diagonal and long partial shading. The proposed produces a maximum power output of 88 W for inverse diagonal conditions, effectively lowering power losses by 20%. With up to a 12% increase in power output and a 15% decrease in mismatch losses under several shading conditions, the proposed configuration consistently outperforms conventional techniques. This proposed method offers an effective solution for improving power generation under abnormal conditions and shows significant improvement in PV array optimization.}, archive = {J_ACCESS}, author = {Sakthivel Ganesan and Prince Winston David and Hariharasudhan Thangaraj and Devakirubakaran Samithas and Praveen Kumar Balachandran and Muhammad Ammirrul Atiqi Mohd Zainuri}, doi = {10.1109/ACCESS.2025.3538129}, journal = {IEEE Access}, pages = {23795-23815}, shortjournal = {IEEE Access}, title = {Optimizing photovoltaic array performance under partial shading using a golden ratio-based configuration: A comparative analysis of 16 configurational variants}, volume = {13}, year = {2025}, }
• (2025). Do assistive technologies moderate the effect of transformational leadership on the workplace well-being of disabled employees?. ACCESS, 13, 23782-23794. (www)

  @article{ , abstract = {Exploring the transformation leadership dimensions and workplace well-being from the perspective of disabled employees is an important research problem in recent times. However, the association between transformational leadership dimensions and the workplace well-being of disabled employees has not received due attention in the available empirical literature. In this state, the current paper investigates the association between transformational leadership dimensions and workplace well-being by focusing on disabled employees in organizations. In addition, it aims to examine the moderating effect of assistive technologies between these variables. To this end, this research adopted a quantitative approach based on the SPSS macro-PROCESS to interpret data collected from 319 disabled employees using a survey. The findings confirmed the limited positive relationships between transformational leadership and the well-being of disabled employees at work. This impact is still significant for three components of well-being at work, including job satisfaction in the first level, perceived discrimination, and the need for recovery. Further, this research supports that the moderating effect of assistive technology exists and is still positive for satisfaction with the use of assistive technology and the frequency of its use. Findings offer a new use of transformational leadership theory in disability management through the lenses of The Technology Acceptance Model.}, archive = {J_ACCESS}, author = {Wided Ragmoun and Abdulaziz Alfalih}, doi = {10.1109/ACCESS.2025.3537828}, journal = {IEEE Access}, pages = {23782-23794}, shortjournal = {IEEE Access}, title = {Do assistive technologies moderate the effect of transformational leadership on the workplace well-being of disabled employees?}, volume = {13}, year = {2025}, }
• (2025). Analytical analysis of the mechanism and mitigation methods of transient overvoltage in direct-drive wind turbine generators connected to weak power system. ACCESS, 13, 23768-23781. (www)

  @article{ , abstract = {In regions with extensive wind power collection, the grid’s robustness diminishes, and transient overvoltage phenomena during the restoration phase following an AC short-circuit fault pose significant challenges to secure and stable operation. Addressing this issue, this paper establishes a mathematical model for the grid-connected system of direct-drive wind turbine generators. The expression of the terminal current is derived in detail, the impact of the inner current control loop is considered, leading to a specific expression for transient overvoltage. A comprehensive analysis is then conducted to identify various influencing factors of transient overvoltage, including grid short-circuit ratio, active power recovery speed, reactive current increment, and the response speed of the inner current loop. The conclusion is that a smaller short-circuit ratio results in a larger voltage drop and reactive current increment, which in turn leads to a higher transient overvoltage. Additionally, a slower active power recovery rate corresponds to a slower overvoltage recovery speed. Based on this analysis, an analytical control parameter optimization method is proposed to mitigate the amplitude and recovery speed of fault recovery overvoltage under various short-circuit ratios. Ultimately, a simulation model was developed within the PSCAD/EMTDC platform, and the accuracy of the transient overvoltage formulation was substantiated via analytical case studies. Furthermore, the effectiveness of the proposed method for mitigating fault recovery overvoltage was confirmed through rigorous simulation.}, archive = {J_ACCESS}, author = {Xu Tian and Shengtao Wu and Luran Wei and Lixin Zhang and Nan Wang and Quan Zheng}, doi = {10.1109/ACCESS.2025.3537639}, journal = {IEEE Access}, pages = {23768-23781}, shortjournal = {IEEE Access}, title = {Analytical analysis of the mechanism and mitigation methods of transient overvoltage in direct-drive wind turbine generators connected to weak power system}, volume = {13}, year = {2025}, }
• (2025). Adaptive bi-directional RRT algorithm for three-dimensional path planning of unmanned aerial vehicles in complex environments. ACCESS, 13, 23748-23767. (www)

  @article{ , abstract = {This paper proposes an Adaptive Bi-directional Rapidly-exploring Random Tree (ABi-RRT) algorithm with the objective of addressing challenges in three-dimensional path planning of unmanned aerial vehicle (UAV) operating in complex environments. The algorithm utilizes an adaptive sampling strategy and an adjustable step size to enhance performance relative to the Bi-RRT algorithm. In contrast to the Bi-RRT algorithm, which utilizes a random sampling strategy and a fixed step size, potentially leading to inefficiencies and limited search effectiveness, the ABi-RRT algorithm incorporates a dynamic mechanism. This mechanism adjusts the sampling strategy and step size based on the complexity of the search environment. This adaptive approach reduces the randomness in sampling, improves the target-oriented nature of the search, and consequently enhances overall search efficiency. Additionally, a greedy path pruning algorithm is integrated to minimize the path length and reduce the number of points along the path. The utilization of cubic B-spline curves further improves the coherence and smoothness of the generated paths. Simulation results demonstrate that the ABi-RRT algorithm can rapidly and effectively produce paths that are smoother, shorter, and of superior quality compared to existing RRT algorithms. Notably, the ABi-RRT algorithm achieves a 100% success rate in path planning. Furthermore, its average running time is significantly reduced, outperforming the ABA* and AAE-RRT* algorithms by approximately a factor of fifteen and three, respectively. This performance is particularly valuable for UAVs operating under stringent performance constraints in complex three-dimensional environments.}, archive = {J_ACCESS}, author = {Nan Li and Sang Ik Han}, doi = {10.1109/ACCESS.2025.3537697}, journal = {IEEE Access}, pages = {23748-23767}, shortjournal = {IEEE Access}, title = {Adaptive bi-directional RRT algorithm for three-dimensional path planning of unmanned aerial vehicles in complex environments}, volume = {13}, year = {2025}, }
• (2025). Convolutional attention based mechanism for facial microexpression recognition. ACCESS, 13, 23732-23747. (www)

  @article{ , abstract = {Unanticipated and rapid change in facial expression are micro-expression (ME) that are hard to hide after an emotionally charged event. Facial microexpressions are transient and subtle, making identification challenging. Recognition of MEs are very crucial in the light of personal intention phase identification. Previous studies had challenges recognizing ME due to complicated spatiotemporal linkage in video data. Using the ConvMixer architecture, we Proposed a novel technique for facial microexpression identification based on convolutional attention mechanism. The research uses SAMM, SMIC, and CASME-II are benchmark datasets used to perform experiments. ConvMixer deployed to analyze the SAMM dataset where ConvMixer achieved an amazing 99.73% accuracy, 97.3% precision, 96.5% recall, and 99% F1-Score while 10-fold cross-validation. In addition, we extended our analysis to the CASME-II dataset, where ConvMixer attained an F1-Score of 99.4%, an accuracy of 99.12%, a precision of 98.3%, and a recall of 98.7%. These findings indicate that ConvMixer regularly outperforms other MER architectures, while capturing video specific and dynamic characteristics. ConvMixer architecture are good in capturing both spatial and temporal correlations and extracts spatial information using depthwise convolutions and channel mixing processes. High F1-Score, recall, precision, and accuracy across several datasets demonstrate the robustness and adaptability of the ConvMixer architecture. Finally, our findings show that the Convolutional Attention-Based Mechanism for facial microexpression recognition (CABM-FMER) works effectively for identifying facial MEs.}, archive = {J_ACCESS}, author = {Hafiz Khizer Bin Talib and Kaiwei Xu and Yanlong Cao and Yuan-Ping Xu and Zhijie Xu and Muhammad Zaman and Adnan Akhunzada}, doi = {10.1109/ACCESS.2024.3525151}, journal = {IEEE Access}, pages = {23732-23747}, shortjournal = {IEEE Access}, title = {Convolutional attention based mechanism for facial microexpression recognition}, volume = {13}, year = {2025}, }
• (2025). Real-time pose prediction and dynamic anchor selection for ultra-wideband tagless gate. ACCESS, 13, 23717-23731. (www)

  @article{ , abstract = {Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. In addition, the location of the user and MD might be different because of the pose of MD. Therefore, the knowledge about exact location and the pose of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. Based on the channel impulse response of UWB, DynaPose classifies the signal condition, such as line-of-sight (LOS) and non-LOS (NLOS), using deep learning models. To calculate the exact location of MD, DynaPose selects LOS signals to minimize the localization error from NLOS signals. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, and ultimately detects four different poses with an accuracy of 0.961 in real-time. Finally, the localization accuracy is achieved by 2.23 cm and 17.86 cm in static and dynamic scenarios, respectively.}, archive = {J_ACCESS}, author = {Junyoung Choi and Joohyun Lee and Sagnik Bhattacharya}, doi = {10.1109/ACCESS.2025.3537976}, journal = {IEEE Access}, pages = {23717-23731}, shortjournal = {IEEE Access}, title = {Real-time pose prediction and dynamic anchor selection for ultra-wideband tagless gate}, volume = {13}, year = {2025}, }
• (2025). Finite-time output robust control for restricted joint flight emulator robotic arm with adaptive tangent barrier gains. ACCESS, 13, 23700-23716. (www)

  @article{ , abstract = {This research presents a novel, robust controller for acceleration tracking in robotic manipulators with time-varying joint limitations, accounting for articulation constraints. The proposed robotic system comprises an arm with limited joint mobility, simulating the dynamic movements required for effective motion cueing in flight simulators. The proposed robust control approach utilizes adaptive state-dependent gains to achieve finite-time convergence of the acceleration tracking error. Controller gains are determined using a class of controlled tangent barrier Lyapunov functions, ensuring that state constraints are satisfied. A stability analysis of the tracking error provides an explicit design for the state-dependent gains. A convex optimization technique based on matrix inequalities enhances the controller’s convergence rate. Given the complete forward characteristic of the recommended manipulation device, a finite-time convergent super-twisting-based differentiator may be employed to conduct the output feedback of the proposed controller specifically. The robust controller is tested on a reliable platform, a digital replica of the robotic manipulator. Indirect validation of tracking error convergence is provided through numerical evaluations, which also comply with articulation constraints and demonstrate the impact of gain optimization design. A standard state feedback control architecture serves as a benchmark for comparison. The origin is shown to be a fixed-time stable equilibrium point for the tracking error space, provided state space constraints are met, as evidenced by the faster convergence of the mean square estimation of the tracking error. Experimental assessments demonstrate that the barrier controller effectively tracks reference trajectories despite modeling uncertainties and implementation challenges, thereby validating the proposed controller.}, archive = {J_ACCESS}, author = {Isaac Chairez and Alejandra Hernandez-Sanchez and Caridad Mireles and Arthur Mukhamedov and Grigory Bugriy and Stepan Lemak and Viktor Chertopolokhov}, doi = {10.1109/ACCESS.2025.3535683}, journal = {IEEE Access}, pages = {23700-23716}, shortjournal = {IEEE Access}, title = {Finite-time output robust control for restricted joint flight emulator robotic arm with adaptive tangent barrier gains}, volume = {13}, year = {2025}, }
• (2025). AppCraft: Model-driven development framework for mobile applications. ACCESS, 13, 23658-23699. (www)

  @article{ , abstract = {Mobile app developers often encounter a significant challenge in developing well-structured mobile apps capable of supporting multiple platforms and diverse functional requirements. The main current practice involves coding versions for different platforms separately using traditional software development methods. Implementing any changes across these variants requires manual effort, demanding a significant amount of time and resources. In response, software engineering has focused on improving the development process to create high-quality mobile applications. One promising approach is Model-Driven Development (MDD), which generates low-level code from high-level models, enabling developers to “write once, run anywhere”. This paper proposes AppCraft, an MDD-based framework designed for developing cross-platform mobile apps. AppCraft facilitates the generation of complex, intelligent, and well-structured apps by addresses three types of variations in mobile apps: platform-related variations, built-in capabilities, and app functionalities. Additionally, this paper describes the use of AppCraft for supporting the integration of machine learning models in mobile apps. The framework comprises a domain-specific language, a text-based modelling editor, and a set of model-to-code transformations. The framework’s applicability was assessed by automatically generating the implementation of eight different case studies in the healthcare domain. Additionally, the productivity was evaluated by comparing the time and effort required using AppCraft versus a manual coding process. As part of the evaluation, a usability study was conducted to assess the usability of AppCraft-generated apps. The results demonstrate that AppCraft is applicable and beneficial for the automated generation of usable mobile apps, highlighting significant reductions in development time and effort.}, archive = {J_ACCESS}, author = {Lyan Alwakeel and Kevin Lano and Hessa Alfraihi}, doi = {10.1109/ACCESS.2025.3536321}, journal = {IEEE Access}, pages = {23658-23699}, shortjournal = {IEEE Access}, title = {AppCraft: Model-driven development framework for mobile applications}, volume = {13}, year = {2025}, }
• (2025). Testing the strength of chaotic systems as seeds in a pseudo random bit generator. ACCESS, 13, 23645-23657. (www)

  @article{ , abstract = {Chaotic random bit generators have become an integral part of chaotic cryptography and other data protection applications, where they are used as sources of deterministic randomness, to secure an information signal. The performance of the generator though will be heavily affected by the chaotic system that is chosen. The statistical performance, key space size, operations per bit ratio, and speed, are all aspects affected by the chosen system. Thus, the same bit generator will vary in efficiency under different chaotic maps. Driven by these facts, this work studies the performance of a common modulo-based bit generator under different chaotic maps. First, the structure of the bit generator’s nonlinear hash is studied. Then, using a collection of different maps, the generator is evaluated with respect to randomness, key space size, operations per bit ratio, and execution speed. The comparison results are evaluated, to provide a series of observations regarding the use of chaotic systems in bit generators. These observations can serve as guidelines for all future chaotic encryption algorithms, and chaos-based security-related applications in general.}, archive = {J_ACCESS}, author = {Lazaros Moysis and Marcin Lawnik and Christos Volos and Murilo S. Baptista and Sotirios K. Goudos and George F. Fragulis}, doi = {10.1109/ACCESS.2025.3537182}, journal = {IEEE Access}, pages = {23645-23657}, shortjournal = {IEEE Access}, title = {Testing the strength of chaotic systems as seeds in a pseudo random bit generator}, volume = {13}, year = {2025}, }
• (2025). Microgrid islanding detection using D-PMU and phase angle analysis of negative sequence impedance. ACCESS, 13, 23629-23644. (www)

  @article{ , abstract = {Unintentional islanding detection is a major challenge during the operation of a microgrid. When islanding occurs, distributed energy resources (DERs) need to be disconnected quickly, under 2 seconds, which makes fast islanding detection crucial. This paper describes a novel method for microgrid islanding detection utilizing distribution phasor measurement unit (D-PMU). The method involves examining the changes in the negative sequence impedance angle over time. Unlike past literature that uses only the phase angles of voltage and current sequence components for islanding detection, this method is more effective, as the phase angle of impedance captures the overall effects of resistance and reactance, which offers a clearer understanding of electrical behavior during disturbances. The study models a six-bus microgrid test case and a three-phase distribution phasor measurement unit (D-PMU) in PSCAD/EMTDC. Different non-islanding and islanding cases are analyzed through simulation, and the proposed method’s performance is evaluated in both the six-bus microgrid model and the standard IEEE-34 node system. The technique’s online performance is assessed using the industry-standard PhasorSmart software. The suggested detection technique can effectively distinguish non-islanding events while achieving islanding detection in under 50 ms, significantly faster than current passive techniques.}, archive = {J_ACCESS}, author = {Asim Chaulagain and Ramakrishna Gokaraju and Nurul Chowdhury and Krish Narendra}, doi = {10.1109/ACCESS.2025.3537458}, journal = {IEEE Access}, pages = {23629-23644}, shortjournal = {IEEE Access}, title = {Microgrid islanding detection using D-PMU and phase angle analysis of negative sequence impedance}, volume = {13}, year = {2025}, }
• (2025). RNA-BioLens: A novel raspberry pi-based digital microscope with image processing for acute lymphoblastic leukemia detection. ACCESS, 13, 23618-23628. (www)

  @article{ , abstract = {Blood analysis plays a critical role in understanding an individual’s health, particularly by examining the morphology and concentration of blood cells. Accurate blood cell analysis is essential for precise diagnosis, especially in identifying cancerous cells, such as Acute Lymphoblastic Leukemia (ALL) — Indonesia’s most common childhood cancer, according to the Indonesian Pediatric Society (IDAI). Morphological examination relies on each cell type’s consistent and defining traits, traditionally performed through blood smears and bone marrow aspiration, where cell morphology is assessed under light or digital microscopes. However, manual examination methods can be prone to bias and require extended processing times. While digital microscopes offer a more advanced alternative, they often come with high implementation costs and complexity. To address these challenges, this study presents a simplified digital microscope system integrated with the YOLOv4 (You Only Look Once) algorithm combined with additional segmentation and feature extraction methods designed explicitly for ALL cell detection. This system aims to enhance detection accuracy and speed while balancing complexity and procurement costs. The microscope, equipped with a 10X eyepiece lens, a 100X objective lens, and a Raspberry Pi camera module, successfully achieved cellular-level magnification and was further developed into the “RNA BioLens” application. This application achieved a detection accuracy of 97.67% for the overall detection system, demonstrating its potential as a reliable tool for efficient and accurate diagnosis.}, archive = {J_ACCESS}, author = {Angeline Dwi Sanjaya and Rachmad Setiawan and Nada Fitrieyatul Hikmah}, doi = {10.1109/ACCESS.2025.3532305}, journal = {IEEE Access}, pages = {23618-23628}, shortjournal = {IEEE Access}, title = {RNA-BioLens: A novel raspberry pi-based digital microscope with image processing for acute lymphoblastic leukemia detection}, volume = {13}, year = {2025}, }
• (2025). Student engagement dataset (SED): An online learning activity dataset. ACCESS, 13, 23607-23617. (www)

  @article{ , abstract = {Distance learning has become a popular educational medium, and the Internet has spread since the early 2000s. To leverage this phenomenon, learning analytics and data mining can provide insights into improving pedagogy and assessing student engagement. To this end, a student-centric dataset was constructed by extracting data from Universiti Malaya’s Moodle-based Virtual Learning Environment (VLE), which serves approximately 25,000 students annually. In this paper, we present the Student Engagement Dataset (SED). The dataset consists of 16,609 students and 2,407 courses. It contains information such as grades and daily logged online activities (approximately 12 million data points), including temporal data across four tables. The tables include student engagement features created by aggregating raw activity data. Here, we present the dataset’s properties and describe the data collection, selection, and processing steps. Correlation analysis of student engagement features showed a statistically significant but weak negative correlation between the number of courses, early morning logins, assignments, and top students’ performance. SED is expected to present new opportunities for researchers in the learning analytics domain.}, archive = {J_ACCESS}, author = {M. S. S. Kassim and Z. H. Azizul and A. A. H. Ahmad Fuaad}, doi = {10.1109/ACCESS.2025.3531102}, journal = {IEEE Access}, pages = {23607-23617}, shortjournal = {IEEE Access}, title = {Student engagement dataset (SED): An online learning activity dataset}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence and physics-based anomaly detection in the smart grid: A survey. ACCESS, 13, 23597-23606. (www)

  @article{ , abstract = {The integration of advanced communication systems and distributed resources has transformed power systems, enhancing control and efficiency in the Smart Grid. However, this increased complexity introduces new vulnerabilities, heightening risks of cyber-attacks, equipment failures, and other anomalies. Anomaly detection methods increasingly rely on Machine Learning techniques, that represent a game-changer tool for data analysis. The aim of this survey is to review anomaly detection techniques in the Smart Grid, focusing on methods that combine Artificial Intelligence and physics-based modeling. This work systematically examines the current state of research, evaluating the investigated use cases, the algorithms, the performances and the validation of the papers, identifying key gaps, and offering insights for advancing in this research field.}, archive = {J_ACCESS}, author = {Giovanni Battista Gaggero and Paola Girdinio and Mario Marchese}, doi = {10.1109/ACCESS.2025.3537410}, journal = {IEEE Access}, pages = {23597-23606}, shortjournal = {IEEE Access}, title = {Artificial intelligence and physics-based anomaly detection in the smart grid: A survey}, volume = {13}, year = {2025}, }
• (2025). Analysis of ChatGPT-generated codes across multiple programming languages. ACCESS, 13, 23580-23596. (www)

  @article{ , abstract = {Our research focuses on the intersection of artificial intelligence (AI) and software development, particularly the role of AI models in automating code generation. With advancements in large language models like ChatGPT, developers can now generate code from natural language prompts, a task that traditionally required significant manual input and expertise. AI-generated code promises to boost productivity by enabling faster prototyping and automating repetitive coding tasks. However, as these models are increasingly adopted in real-world applications, questions surrounding their efficiency and code quality become critical. This research investigates ChatGPT-4o, a state-of-the-art language model, and its ability to generate functional, high-quality code in different programming languages. By comparing performance between Python and Java, the study seeks to shed light on AI’s capabilities and limitations in code generation, addressing not only functional correctness but also broader software engineering concerns such as memory usage, runtime efficiency, and maintainability. The study addresses key questions related to the performance, code quality, and error management of AI-generated code by analyzing solutions for 300 data structure problems and 300 problems from the LeetCode platform. The findings reveal notable performance differences between the two languages: Java demonstrated superior runtime performance, particularly for medium and hard problems, while Python exhibited better memory efficiency across all complexity levels. The research also highlighted significant gaps in code quality, with both languages showing deficiencies in documentation and exception management. This study contributes to the literature by offering a comprehensive cross-language analysis of ChatGPT-4o’s programming capabilities, addressing a gap in the evaluation of AI-generated code performance.}, archive = {J_ACCESS}, author = {Sally Almanasra and Khaled Suwais}, doi = {10.1109/ACCESS.2025.3538050}, journal = {IEEE Access}, pages = {23580-23596}, shortjournal = {IEEE Access}, title = {Analysis of ChatGPT-generated codes across multiple programming languages}, volume = {13}, year = {2025}, }
• (2025). Exploring the landscape of programming language identification with machine learning approaches. ACCESS, 13, 23556-23579. (www)

  @article{ , abstract = {The increasing complexity of modern software development necessitates tools and methodologies for code analysis, maintenance, and migration in multi-language Integrated Development Environments (IDEs). The security needs of the software development process also recently led to the introduction of the Software Bill of Materials (SBOM), which vendors must include in their products. Automated tools become imperative to speed up software production and distribution. In the present code development landscape, automated Programming Language Identification (PLI) helps to develop secure software development with its in-depth insights into programming consequences for code maintenance, legacy system management, code analysis, quality assurance, software modernization, migration, and code search. With the increasing success of Machine Learning (ML) models in detection methods, researchers use them for PLI showing superior performance of detection compared to the basic method of identification based on the source file extension. In this paper, we present the first survey for ML-based PLI methods providing insights into the domain’s present status and guiding towards a futuristic tool development for PLI. We evaluate various ML techniques that recognize programming languages. These techniques include conventional, operation-based, and data source-based techniques. Our study examines the advantages and limitations of identifying the programming language in text, images, and videos. Our investigation also emphasizes the capabilities of the existing solutions that improve software development practices. Furthermore, our survey analyzes the strengths and limitations of existing code detection tools such as GitHub’s Linguist, Pygments, highlight.js, Ace, Google’s Code Prettify, SourcererCC, Guesslang, and SonarLint. We also provide some open research problems for future researchers.}, archive = {J_ACCESS}, author = {Amandeep Verma and Rahul Saha and Gulshan Kumar and Alessandro Brighente and Mauro Conti and Tai-Hoon Kim}, doi = {10.1109/ACCESS.2025.3538108}, journal = {IEEE Access}, pages = {23556-23579}, shortjournal = {IEEE Access}, title = {Exploring the landscape of programming language identification with machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). Real-time analysis of event response based on RT-thread. ACCESS, 13, 23547-23555. (www)

  @article{ , abstract = {The event response is a crucial feature in the real-time operating system RT-Thread, used for synchronizing threads. This paper aims to analyze the real-time performance of the event response and provide practical insights for its application. To achieve this, a low-latency piling timing analysis approach is proposed. The precise measurement of thread scheduling time is achieved by accurately recording timestamp of critical moments without external interruptions. Moreover, the theoretical time of thread scheduling under different compilation optimization levels is analyzed. And, a thread synchronization experiment based on event response is designed to measure the actual time of thread scheduling on Arm Cortex-M4 microcontroller STM32L431RCT6. The experimental results show that the low-latency piling timing analysis approach proposed in this paper has an error of about 1.5 microseconds compared to the measurement time of the logic analyzer and the theoretical analysis time. Finally, by comparing and analyzing theoretical time and actual measurement time of thread scheduling, it is evident that the compilation optimization level and PendSV exception are the key factors influencing the real-time performance of RT-Thread event response.}, archive = {J_ACCESS}, author = {Hui Liu and Wenyin Tao and Zhixin Hong and Lianmin Shi}, doi = {10.1109/ACCESS.2025.3537660}, journal = {IEEE Access}, pages = {23547-23555}, shortjournal = {IEEE Access}, title = {Real-time analysis of event response based on RT-thread}, volume = {13}, year = {2025}, }
• (2025). Comprehensive study on zero-shot text classification using category mapping. ACCESS, 13, 23526-23546. (www)

  @article{ , abstract = {Existing zero-shot text classification methods based on large pre-trained models with added prompts exhibit strong representational capacity and scalability but have relatively poor commercial applicability. Approaches that fine-tune smaller models using label mappings and existing datasets for zero-shot classification are simpler but suffer from weaker generalization capabilities. This paper employs three strategies to improve the accuracy and generalization of pre-trained models in zero-shot text classification tasks: 1) Utilizing a pre-trained model that transforms inputs into a standardized multiple-choice format. 2) Constructing a text classification training set using Wikipedia text data to fine-tune the pre-trained model; 3) Proposing a zero-shot category mapping method based on GloVe text similarity, using Wikipedia categories as substitutes for text labels. Without fine-tuning on the target labels, this method achieves performance comparable to the best models fine-tuned with target labels.}, archive = {J_ACCESS}, author = {Kai Zhang and Qiuxia Zhang and Chung-Che Wang and Jyh-Shing Roger Jang}, doi = {10.1109/ACCESS.2025.3538103}, journal = {IEEE Access}, pages = {23526-23546}, shortjournal = {IEEE Access}, title = {Comprehensive study on zero-shot text classification using category mapping}, volume = {13}, year = {2025}, }
• (2025). Fault and severity diagnosis using deep learning for self-organizing networks with imbalanced and small datasets. ACCESS, 13, 23508-23525. (www)

  @article{ , abstract = {With the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks. Developing effective self-organizing approaches often depends on data-driven or learning-based methods, which require well-structured and balanced datasets. However, in practical scenarios, datasets are often imbalanced or even very small. To address this issue from the fault diagnosis aspect of SONs, this paper investigates the learning-based fault and severity diagnosis approaches under imbalanced and small datasets for wireless networks. We first propose a deep learning-based diagnosis framework, in which the diagnosis problem can be cast as a regression problem. Then, several approaches, including re-weighting, distribution smoothing, and balanced MSE, that can be used to resolve the imbalanced issue for regression problem are examined under the diagnosis purpose. Subsequently, to resolve the issue that the amount of data samples for diagnosis could be few, model pre-training and meta-learning-based approaches are used, aiming to quickly adapt the pre-trained diagnosis model to the targeting scenarios for diagnosis. Extensive simulation results based on realistic setups are conducted to evaluate our proposed approaches. Results show that our approaches can effectively diagnose the faults and their severity and outperform the baseline approaches under imbalanced and small datasets.}, archive = {J_ACCESS}, author = {Hsin-Chang Tsai and Ming-Chun Lee and Chao-Hao Hsu}, doi = {10.1109/ACCESS.2025.3537659}, journal = {IEEE Access}, pages = {23508-23525}, shortjournal = {IEEE Access}, title = {Fault and severity diagnosis using deep learning for self-organizing networks with imbalanced and small datasets}, volume = {13}, year = {2025}, }
• (2025). DLiGRU-X: Efficient X-vector-based embeddings for small-footprint keyword spotting system. ACCESS, 13, 23498-23507. (www)

  @article{ , abstract = {Deployment of deep learning-based speech processing models for real-world applications on devices with limited processing capacity and memory constraints poses significant challenges. This paper introduces an enhanced deep learning model based on the X-vector architecture, named dilated light-gated recurrent unit in X-vector (DLiGRU-X) to address these challenges specifically for small-footprint keyword spotting (KWS) tasks. The DLiGRU-X model enhances temporal feature extraction with a LiGRU and reduces computational complexity through dilated convolution techniques. The proposed model efficiently learns speech signal characteristics, making it suitable for scenarios with limited hardware resources and can handle an expanded vocabulary size of keyword identification. The proposed model is validated on the Google Speech Command public dataset, and its performance is compared with other recently proposed deep learning models for KWS. The proposed model achieves an excellent trade-off between recognition accuracy and computational complexity, outperforming various advanced keyword spotting models. Notably, despite a reduction in model parameters, DLiGRU-X maintains an accuracy of 97% without significant decline. This model offers greater flexibility compared to previous models, allowing users to adjust and expand the set of targeted vocabulary according to their needs and deploy the model in resource-constrained environments.}, archive = {J_ACCESS}, author = {Zong-En Wu and Shao-Jung Chan and Yeshanew Ale Wubet and Kuang-Yow Lian}, doi = {10.1109/ACCESS.2025.3536470}, journal = {IEEE Access}, pages = {23498-23507}, shortjournal = {IEEE Access}, title = {DLiGRU-X: Efficient X-vector-based embeddings for small-footprint keyword spotting system}, volume = {13}, year = {2025}, }
• (2025). Efficient handling of data imbalance in health insurance fraud detection using meta-reinforcement learning. ACCESS, 13, 23482-23497. (www)

  @article{ , abstract = {Data imbalance is one of the major challenges in health insurance fraud detection where the distribution of classes within the dataset is significantly skewed, leading statistical models to be biased toward the dominant class. The algorithmic approaches to handling imbalance involve modification to the loss functions to sensitize them to the minor class. Our research focuses on the efficiency of meta-models in addressing imbalance and introduces Meta-reinforcement learning (Meta-RL) as a novel solution for learning under imbalance. Meta-RL leverages meta-learning principles to learn the characteristics of fraudulent instances dynamically. This adaptability comes from its ability to learn shared representations and optimal task-specific strategies using limited samples. By using task distribution and reward shaping, our experiments on Meta-RL algorithms, RL2, and VariBAD achieve superior sample efficiency and adaptability for varying degrees of imbalance. The efficiency of the models is measured using imbalance-safe metrics like Geometric mean, Harmonic mean, and Mathew’s Correlation Coefficient (MCC), and metrics like Cohen’s Kappa score are used to gauge the consistency of the results. This research is the first to apply Meta-RL to the problem of data imbalance in fraud detection, contributing to a generalizable and efficient framework for imbalanced learning. The findings of this research show that Meta-RL algorithms can be effectively tuned to handle data imbalance without modification to their objective functions and hence, can be considered an appropriate option for health insurance fraud detection solutions.}, archive = {J_ACCESS}, author = {Supriya Seshagiri and K. V. Prema}, doi = {10.1109/ACCESS.2025.3536479}, journal = {IEEE Access}, pages = {23482-23497}, shortjournal = {IEEE Access}, title = {Efficient handling of data imbalance in health insurance fraud detection using meta-reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Unfixed bias iterator: A new iterative format. ACCESS, 13, 23472-23481. (www)

  @article{ , abstract = {Partial differential equations (PDEs) have a wide range of applications in physics and computational science. Solving PDEs numerically is usually done by first meshing the solution region with finite difference method (FDM) and then using iterative methods to obtain an approximation of the exact solution on these meshes, hence decades of research to design iterators with fast convergence properties. With the renaissance of neural networks, many scholars have considered using deep learning to speed up solving PDEs, however, these methods leave poor theoretical guarantees or sub-convergence. We build our iterator on top of the existing standard hand-crafted iterative solvers. At the operational level, for each iteration, we use a deep convolutional network to modify the current iterative result based on the historical iterative results as a way to achieve faster convergence. At the theoretical level, due to the introduced historical iterative results, our iterator is a new iterative format: Unfixed Bias Iterator. We provide sufficient theoretical guarantees and theoretically prove that our iterator can obtain correct results with convergence, as well as a better generalization. Finally, experimental results show that our iterator has a convergence speed far beyond that of other iterators and exhibits strong generalization ability.}, archive = {J_ACCESS}, author = {Zeqing Zhang and Xue Wang and Jiamin Shen and Man Zhang and Sen Yang and Fanchang Yang and Wei Zhao and Jia Wang}, doi = {10.1109/ACCESS.2025.3534257}, journal = {IEEE Access}, pages = {23472-23481}, shortjournal = {IEEE Access}, title = {Unfixed bias iterator: A new iterative format}, volume = {13}, year = {2025}, }
• (2025). Prediction of multidimensional poverty status with machine learning classification at household level: Empirical evidence from tanzania. ACCESS, 13, 23461-23471. (www)

  @article{ , abstract = {Over fifty percent of the population in Tanzania suffers from multidimensional poverty. Because of the high poverty rate and slow improvement, ending poverty by the year 2030 remains challenging and empirically testable proposition and part of a shared challenge. The main purpose of this study is to predict multidimensional poverty status with the help of best performance-supervised machine-learning algorithms. To achieve this objective, longitudinal data from the 2014/15 and 2020/21 surveys, sourced from the Tanzania National Bureau of Statistics (NBS) were analyzed. A variety of supervised machine-learning algorithms such as RBF Kernel in SVM, Linear Kernel in SVM, Polynomial Kernel in SVM, Random Forest, Logistic regression classifier, Decision tree, Gradient Boosting, K-Nearest Neighbours Classifier, Naïve Bayes Classifier, Artificial Neuron Network and Ensemble Learning Model were implemented to predict multidimensional poverty status for each dataset. This captured the dynamic changes from 2014 to 2021 at the national level. The study employed data pre-processing techniques and adjusted imbalance through weighted categorical cross entropy and 5-Fold Cross Validation to mitigate the inefficiencies due to overfitting and under-fitting of the algorithms. Additionally, dimensionality data reduction was performed using Principal Component Analysis (PCA). With regard to the evaluation metrics, we show that the Ensemble Learning Model has achieved the best performance modelling both balanced and unbalanced datasets. Our policy recommendations draw on the results of the algorithmic predictions.}, archive = {J_ACCESS}, author = {Ngong’Ho Bujiku Sende and Snehanshu Saha and Leon Ruganzu and Saibal Kar}, doi = {10.1109/ACCESS.2025.3537807}, journal = {IEEE Access}, pages = {23461-23471}, shortjournal = {IEEE Access}, title = {Prediction of multidimensional poverty status with machine learning classification at household level: Empirical evidence from tanzania}, volume = {13}, year = {2025}, }
• (2025). Recommender systems for unified modeling language and vice Versa—A systematic literature review. ACCESS, 13, 23426-23460. (www)

  @article{ , abstract = {Recommender systems (RSs) are fundamental tools that address data redundancy and serve as intelligent supplements for tasks such as data retrieval and refinement by analyzing user behavior. Nowadays, RSs are utilized in various domains, ranging from filtering web news based on user preferences to recommending movies, music, books, and articles in e-commerce. Additionally, these systems are extensively employed to facilitate software engineering activities, including modeling. Modeling environments are enriched with RSs that assist in building models by providing recommendations based on previous solutions to similar problems within the same domain. Consequently, there is growing research interest in approaches that aid the modeling process. This paper presents a systematic literature review (SLR) that analyzes how recommender systems techniques are used to suggest UML diagrams, as well as the role of UML diagrams in describing recommender systems. In addition, it discusses methods for evaluating primary studies, the challenges that primary studies have addressed, and the domains of study that primary studies have targeted (based on an analysis of 4789 papers). We believe this study will guide researchers and professionals in identifying recommender system techniques for generating UML diagram suggestions and understanding the overall purpose of using UML diagrams. Furthermore, it may contribute to a broader understanding of the research process and inspire future research on recommender system techniques within other modeling languages. The results show that 45% of the studies use content-based techniques to suggest UML diagrams, with 77% of the recommendations being structural diagrams (such as class diagrams). On the other hand, to design the components of the proposed approaches (recommender systems), behavioral diagrams are generally used (53% on average), focusing on knowledge-based techniques (28% on average). Finally, the study shows that researchers use content-based (38%) and knowledge-based (41%) techniques to recommend design models. The analysis revealed that the following challenges were identified: 19 studies dealt with the cold start problem, 20 studies with sparsity issues, 11 studies with scalability concerns, 3 studies with diversity challenges, and 12 studies with other types of challenges.}, archive = {J_ACCESS}, author = {Elaheh Azadi Marand and Amir Sheikhahmadi and Moharram Challenger and Parham Moradi and Alireza Khalilipour}, doi = {10.1109/ACCESS.2025.3535527}, journal = {IEEE Access}, pages = {23426-23460}, shortjournal = {IEEE Access}, title = {Recommender systems for unified modeling language and vice Versa—A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). SVFRH: A growth stage-based compartmental model for predicting the disease incident in tomato (Solanum lycopersicum). ACCESS, 13, 23412-23425. (www)

  @article{ , abstract = {The tomato is one of the most widely cultivated crops globally, yet it faces susceptibility to various diseases, resulting in substantial yield losses. Over decades, research has primarily focused on addressing these challenges through computer vision and deep learning techniques. In this work, we employ a comprehensive modelling approach that combines compartmental and logistic regression models to thoroughly address disease dynamics in tomato crops, with a particular focus on tomato early blight diseases. Our methodology employs the SVFRH (Susceptible, Vegetative, Flowering, Ripening and Harvesting) model to analyze disease dynamics across distinct plant growth stages. This stage-specific approach enables us to capture disease progression while considering the unique interactions between environmental factors, disease transmission, and plant development at each stage. By incorporating temporal and stage-specific modeling, we focus on understanding how disease spreads and evolves over time, with the aim of improving the precision of disease detection and developing effective control strategies. The accuracy of these models is ensured through rigorous assessments of key mathematical properties, such as positivity (to guarantee non-negative state variables) and boundedness (to ensure realistic limits on disease progression). The next-generation matrix method is then applied to calculate the basic reproduction number $\left ({{ R_{0} }}\right)$ . Numerical simulations validate the theoretical findings, and the outcomes indicate an increase in the number of vectors around the field with a decrease in tomato yield, emphasizing the epidemiological significance of these findings. The results demonstrate the model’s capacity to accurately predict disease incidents at each growth stage. An overall probability of disease incidents is calculated by combining the probabilities from each stage-specific model. The proposed model is capable of providing a dependable and efficient means of disease detection and prevention in tomato crops, enabling timely actions by farmers. Furthermore, this approach has the potential for generalization to other crops, promising improvements in crop health and yield within the realm of agriculture.}, archive = {J_ACCESS}, author = {Athira P. Shaji and S. Hemalatha}, doi = {10.1109/ACCESS.2025.3537953}, journal = {IEEE Access}, pages = {23412-23425}, shortjournal = {IEEE Access}, title = {SVFRH: A growth stage-based compartmental model for predicting the disease incident in tomato (Solanum lycopersicum)}, volume = {13}, year = {2025}, }
• (2025). A new attack recovery approach and h∞ performance analysis for LFC systems with FDI attacks and uncertainties. ACCESS, 13, 23399-23411. (www)

  @article{ , abstract = {This paper focuses on the attack recovery and $H_{\infty } $ performance analysis for load frequency control (LFC) systems with false data injection (FDI) attacks and uncertainties. Firstly, considering the impact of FDI attacks, a new Informer-based attack recovery (IAR) model is proposed for recovering FDI attacks. The IAR model is capable of extracting rich attack signal features from historical measurement data by leveraging multi-task learning. Compared with previous models, including LSTM and LSTM-AE, the IAR model improves the efficiency of recovering attacks for a future period and improves the recovery accuracy. Then, the recovered attacks can be used to regulate the control input signals of LFC systems to mitigate the impact of FDI attacks. Secondly, the uncertainties are considered in the LFC systems, which mainly consists of process and measurement noise. The $H_{\infty } $ performance of LFC systems with uncertainties and FDI attacks is analyzed. Finally, two datasets are generated for validating the effectiveness of the proposed attack recovery model and the analysis of $H_{\infty } $ performance.}, archive = {J_ACCESS}, author = {Xuxia He and Ruimei Zhang and Puying Wang}, doi = {10.1109/ACCESS.2025.3534798}, journal = {IEEE Access}, pages = {23399-23411}, shortjournal = {IEEE Access}, title = {A new attack recovery approach and h∞ performance analysis for LFC systems with FDI attacks and uncertainties}, volume = {13}, year = {2025}, }
• (2025). Throughout maximization for IRS-assisted WPCN with hybrid TDMA-NOMA scheme. ACCESS, 13, 23384-23398. (www)

  @article{ , abstract = {This work leverages an Intelligent Reflecting Surface (IRS) to improve throughput performance of a wireless powered communication network (WPCN). A novel hybrid multiple access strategy is conceived, where the IRS set the time-division multiple access (TDMA) to allocate its phase shifts, each of which performs the non-orthogonal multiple access for multiple Internet of Things (IoT) devices. These devices firstly collect energy in a non-linear manner disseminated by a power station (PS) at downlink wireless energy transfer (WET) stage. The gathered energy is then utilized by these devices to convey data information to an access point (AP) by employing the TDMA protocol with flexible time slots at the uplink wireless information transfer (WIT) stage. During each time slot, the NOMA protocol is utilized for all IoT devices’ data transmission, thus creating a hybrid TDMA-NOMA framework. To assess the overall performance of the system model under investigation, we aim to maximize the network throughput, subject to the constraints of the downlink WET and uplink WIT time scheduling, the power allocation for each time slots, and unit-modulus IRS phase shifts of downlink WET and uplink WIT, thereby highlighting energy harvesting and information transmission capabilities. The formulated problem is inherently not jointly convex due to the coupled nature of the variables, making it difficult to resolve directly. To deal with this challenge, a power allocation factor is first introduced for each IoT device, and then an alternating optimization (AO) algorithm is proposed to jointly design the power allocation, IRS phase shifts, and time scheduling in an alternated fashion. To be specified, the optimal power allocation factor can be obtained using interior-point methods for given time scheduling and IRS phase shifts. Also, the time slots of downlink WET and uplink WIT stages are theoretically derived in closed-form expressions using the Lagrange dual method and the Karush-Kuhn–Tucker (KKT) conditions. Additionally, the semi-closed-form expressions of IRS phase shifts during WET and WIT stages are iteratively achieved through Riemannian Manifold Optimization (RMO) and quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) algorithms. Finally, the numerical results are demonstrated to highlight the effectiveness of the proposed algorithm, the optimal IRS phase shift design, and the optimal WET time scheduling design in comparison to the benchmarks.}, archive = {J_ACCESS}, author = {Yizheng Ma and Ruoyi Wu and Yi Zhang and Yupeng Shang and Linzhen Zhu}, doi = {10.1109/ACCESS.2025.3537988}, journal = {IEEE Access}, pages = {23384-23398}, shortjournal = {IEEE Access}, title = {Throughout maximization for IRS-assisted WPCN with hybrid TDMA-NOMA scheme}, volume = {13}, year = {2025}, }
• (2025). Analysis of surprisingly popular voting for opinion aggregation on social networks. ACCESS, 13, 23371-23383. (www)

  @article{ , abstract = {This paper investigates how the structure of social networks affects an opinion aggregation method, surprisingly popular (SP) voting. SP leverages respondents’ metacognition: estimation of the rarity of their opinions among those of all respondents. Since a respondent’s metacognition is considered to be shaped by people around them, we can better understand metacognition and SP voting performance by focusing on the structure of social networks. We analyzed the effect of respondents’ referring to connected individuals on SP voting performance when they predict other respondents’ opinions. We also analyzed the effect of the structure of social networks on respondents’ metacognition and on SP voting performance. Simulation experiments using various social networks revealed that respondents’ referring to connected individuals is important in SP voting performance and that the structure of social networks plays an important role in respondents’ metacognition and SP voting performance. Even if respondents refer to connected individuals, their metacognition can differ from the actual distribution of opinions across all respondents depending on the structure of the social networks, which greatly affects SP voting performance.}, archive = {J_ACCESS}, author = {Yu Yamashita and Yuko Sakurai and Satoshi Oyama and Masaki Onishi and Atsuyuki Morishima}, doi = {10.1109/ACCESS.2025.3532754}, journal = {IEEE Access}, pages = {23371-23383}, shortjournal = {IEEE Access}, title = {Analysis of surprisingly popular voting for opinion aggregation on social networks}, volume = {13}, year = {2025}, }
• (2025). Design of low power and low noise instrumentation amplifier for biopotential acquisition: A review. ACCESS, 13, 23359-23370. (www)

  @article{ , abstract = {This paper presents a comprehensive review of amplifier design techniques for biomedical sensor implementation focusing on Capacitively Coupled Instrumentation Amplifier (CCIA). The study covers the definition of the biosignal measurement environment, examining factors that affect intrinsic noise and low noise amplifier design techniques. It analyzes the causes of input impedance reduction and explores associated boosting techniques. The sources of half-cell potential are investigated, along with DC servo loops (DSL) and methods for improving their settling time and noise performance. Through an analysis of various issues related to biopotential measurement and a state-of-the-art survey, this paper aims to provide valuable insights for the design and advancement of Instrumentation Amplifiers (IA) for IoT and biopotential measurement.}, archive = {J_ACCESS}, author = {Nahmil Koo}, doi = {10.1109/ACCESS.2025.3537998}, journal = {IEEE Access}, pages = {23359-23370}, shortjournal = {IEEE Access}, title = {Design of low power and low noise instrumentation amplifier for biopotential acquisition: A review}, volume = {13}, year = {2025}, }
• (2025). SkelETT—Skeleton-to-emotion transfer transformer. ACCESS, 13, 23344-23358. (www)

  @article{ , abstract = {Emotion recognition plays an essential role in human-computer interaction, spanning diverse domains from human-robot communication and virtual reality to mental health assessment and affective computing. Traditionally, this field has heavily relied on visual and auditory cues, such as facial expressions and speech analysis. However, these modalities alone may not comprehensively capture the full spectrum of human emotion and suffer limitations due to noise or occlusion. Human skeletons, derived from depth sensors or pose estimation algorithms, offer an alternative for facial expression, including valuable spatial and temporal cues. In this paper, we introduce a novel approach to emotion recognition by pre-training a transformer model on a large dataset of unsupervised human skeleton representations and subsequently fine-tuning it for emotion classification. By exposing the model to an extensive corpus of unlabeled human skeleton data, we can effectively learn to represent complex spatial and temporal dependencies inherent in body movements. Following this foundational knowledge acquisition, the model undergoes fine-tuning on a smaller, labeled dataset tailored for emotion classification tasks. We introduce SkelETT, an encoder-only transformer architecture for body emotion recognition. Comprising a series of encoder layers, SkelETT patches 2D body pose representations, it also includes multi-head self-attention mechanisms and position-wise feed-forward networks, providing a powerful framework for extracting hierarchical features from sequential body pose data. We propose and evaluate the impact of different fine-tuning strategies on pose data using the MPOSE action recognition dataset as a pre-training source. Transfer performance is measured on the BoLD body emotion recognition dataset. Compared to the state-of-the-art, we report significant gains in accuracy ( $\approx ~34$ % higher), training time ( $\approx ~50$ % less), and model complexity reduction ( $\approx ~80$ % less trainable parameters).}, archive = {J_ACCESS}, author = {Pedro Victor Vieira Paiva and Josué Junior Guimarães Ramos and Marina Gavrilova and Marco Antônio Garcia de Carvalho}, doi = {10.1109/ACCESS.2025.3534145}, journal = {IEEE Access}, pages = {23344-23358}, shortjournal = {IEEE Access}, title = {SkelETT—Skeleton-to-emotion transfer transformer}, volume = {13}, year = {2025}, }
• (2025). VANETs routing optimization: Combining message priority, free nodes, and mobility direction. ACCESS, 13, 23331-23343. (www)

  @article{ , abstract = {Vehicular Ad Hoc Networks (VANETs) face challenges due to high vehicle mobility, which leads to frequent changes in network topology and unstable links. To tackle this, we propose a novel routing strategy called MPFD, which enhances message delivery rates and reduces End-to-End (E2E) delay. MPFD operates in two phases. The first phase, the message management phase, prioritizes messages based on urgency and controls message propagation by considering hop count (H) and Time-To-Live (TTL), ensuring only critical messages are forwarded. The second phase, the relay node selection phase, uses free nodes to identify the best relay for forwarding messages. Candidate nodes are evaluated based on community association, movement direction, and proximity to the destination. Simulations show that the MPFD strategy significantly improves message delivery speed, reduces overhead and E2E delay, and enhances the overall message delivery ratio in VANETs.}, archive = {J_ACCESS}, author = {Fang Xu and Chunmeng Yang and Hao Chen and Manzoor Ahmed and Min Deng and Yuanlin Lyu}, doi = {10.1109/ACCESS.2025.3537920}, journal = {IEEE Access}, pages = {23331-23343}, shortjournal = {IEEE Access}, title = {VANETs routing optimization: Combining message priority, free nodes, and mobility direction}, volume = {13}, year = {2025}, }
• (2025). A study on the application of explainable AI on ensemble models for predictive analysis of chronic kidney disease. ACCESS, 13, 23312-23330. (www)

  @article{ , abstract = {Chronic Kidney Disease (CKD) is one of the widespread Chronic diseases with no known ultimo cure and high morbidity. Research demonstrates that progressive CKD is a heterogeneous disorder that significantly impacts kidney structure and functions, eventually leading to kidney failure. The goal of this research is to first develop an accurate ensemble model for prediction of unseen cases of CKD given the biomarkers. Also, we have implemented the Explainable AI (XAI) algorithms to interpret the decision-making process of the ensemble models in terms of dominating features and the feature values. The takeaway from our research is to aid the physicians make an informed decision about the disease and provide a case by case explanation behind their decisions. Also, XAI algorithms would allow the patients or subjects understand the causes behind their disease at early stages so that they can be cautious about the progression of the disease to later stages.}, archive = {J_ACCESS}, author = {K. M. Tawsik Jawad and Anusha Verma and Fathi Amsaad and Lamia Ashraf}, doi = {10.1109/ACCESS.2025.3535692}, journal = {IEEE Access}, pages = {23312-23330}, shortjournal = {IEEE Access}, title = {A study on the application of explainable AI on ensemble models for predictive analysis of chronic kidney disease}, volume = {13}, year = {2025}, }
• (2025). A pre-emptive scheduling mechanism for service assurance of network slicing in next generation cellular networks. ACCESS, 13, 23297-23311. (www)

  @article{ , abstract = {As the number of services and applications with diverse requirements keeps increasing, it is therefore of paramount importance to develop more efficient resource allocation techniques. In this paper, we propose a pre-emptive scheduling mechanism for managing network slices, specifically enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC), in 5G and beyond (5GB) networks, leveraging network slicing to optimize resource management. The proposed mechanism effectively balances the trade-off among throughput, latency, and reliability by dynamically reallocating limited radio resources between two slices. Through system-level simulations, we demonstrate that the stringent latency requirements of the URLLC slice are satisfied while minimizing the eMBB throughput degradation. The simulation results indicate that, compared to traditional resource allocation techniques, the puncturing mechanism enhances resource utilization, offering a scalable solution for the latency and throughput requirements of 5GB networks.}, archive = {J_ACCESS}, author = {Ege Engin and Ibrahim Hokelek and Ali Gorcin and Hakan Ali Cirpan}, doi = {10.1109/ACCESS.2025.3536997}, journal = {IEEE Access}, pages = {23297-23311}, shortjournal = {IEEE Access}, title = {A pre-emptive scheduling mechanism for service assurance of network slicing in next generation cellular networks}, volume = {13}, year = {2025}, }
• (2025). Analysis of a flux-concentrated V-type biased-flux slot motor. ACCESS, 13, 23285-23296. (www)

  @article{ , abstract = {Biased flux motors are a configuration of electrical machine where the rotor is pure iron and magnets are buried in the stator yoke iron. In this paper, a biased-flux motor is proposed and validated in which the stator coreback permanent magnets are configured to a flux concentrating V-type arrangement. By switching from the more usual circumferential magnets, it has been shown that torque production per unit magnet mass can increase by over 20% for a moderate 0.5% increase in torque ripple. This is by virtue of the significant increase in flux concentration in the air gap, which is proven mathematically, via simulation and validated experimentally. It is also shown that due to the new permanent magnet shape, demagnetization is limited. A comprehensive comparison is done to show the superiority of the proposed structure. Finally, validation of the theoretical and FEM analyses has been done experimentally by a prototyped 24/25 V-type motor.}, archive = {J_ACCESS}, author = {Mohammad Amirkhani and Ehsan Farmahini Farahani and Mohammad Afrank and Mojtaba Mirsalim and Nick J. Baker}, doi = {10.1109/ACCESS.2025.3532828}, journal = {IEEE Access}, pages = {23285-23296}, shortjournal = {IEEE Access}, title = {Analysis of a flux-concentrated V-type biased-flux slot motor}, volume = {13}, year = {2025}, }
• (2025). Fatigue state evaluation of urban railway transit drivers using psychological, biological, and physical response signals. ACCESS, 13, 23270-23284. (www)

  @article{ , abstract = {Understanding the driver state (e.g., fatigue state) is critical in improving the safety of urban rail transit operations. This study reports a study that used the simulation experiment to evaluate the effects of fatigue on professional drivers’ psychological, biological, and physical responses and thus develop a fatigue detection model, enabling early warning and ensuring operational safety. Specifically, we applied statistical analytics on self-reported fatigue feelings, the Mackworth Clock Task, the Stroop Test, and the Iowa Gambling Task to evaluate the impact of fatigue on driver performance. The results show that fatigue reduced the participants’ reaction time and cognitive ability and led professional drivers to make more conservative decisions. Moreover, we utilized heart rate signals, electrodermal activity, and eye movements from wearable devices and cameras to build a driver fatigue detection model with machine-learning methods. We analyzed the impact of different time window lengths on fatigue detection. The results indicate that as the length of the time window increases, the data captures more comprehensive information, leading to improved fatigue detection accuracy. Furthermore, multi-feature fusion significantly enhanced model performance. With feature fusion, even short-term time windows can encapsulate sufficient information for effective fatigue detection. These findings not only contribute to a deeper understanding of the risks posed by fatigue in professional driving, but also pave the way to developing a real-time, non-invasive fatigue detection system, enhancing traffic safety.}, archive = {J_ACCESS}, author = {Hao Wu and Yubo Jiao and Chaozhe Jiang and Tong Wang and Jiangbo Yu}, doi = {10.1109/ACCESS.2025.3533487}, journal = {IEEE Access}, pages = {23270-23284}, shortjournal = {IEEE Access}, title = {Fatigue state evaluation of urban railway transit drivers using psychological, biological, and physical response signals}, volume = {13}, year = {2025}, }
• (2025). A fourfold bi-filter and permuted bi-quad-wrapper feature selection method for finding optimal moments of multi-trajectory transient records in transient analysis. ACCESS, 13, 23247-23269. (www)

  @article{ , abstract = {Fast and accurate transient analysis (TA) requires a meticulously explored nonlinear transient excursion space (NTES). Hence, designing a polyhedral feature selection scheme (FSS) to fix the TA bottleneck raised by feeding non-discriminative transient records into predictive models is a significant task to avoid discordant TA indices results. Hence, we offer a fourfold bi-filter and permuted bi-quad-wrapper (F2FP2QW) FSS to find optimal transient moments (OTMs) from multi-trajectory transient records (MTTRs). The F2FP2QW model exerts varied fourfold repetition of the linked four-stage, including the bi-filter (relevancy rate (RR) and conditional-based RR (CRR) calculations) and bi-wrapper (incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr)). The RR and CRR mounted on mutual information (MI) and conditional MI (CMI) in the filter stages. In the case of wrapper methods, hyperplane-based classifiers (HCs), namely support vector machine (SVM) and twin SVM (TWSVM), play the main role in IWSS and IWSSr tree formation. Due to kernel-based learning necessity in NTES, embedding the radial basis function (RBF), dynamic time warping (DTW), and polynomial (POL) kernels into dual HCs is on the agenda. Generally, wrapper stages include ${ }_{TWSVM}^{SVM} \textrm {IWSS}_{RBF/\,POL}^{RBF/\,DTW\,} $ and ${ }_{TWSVM}^{SVM} \textrm {IWSSr}_{RBF/\,POL}^{RBF/\,DTW\,}$ in a 24-way permutation. By applying the multifaced filter-wrapper scenario of F2FP2QW on 28-trajectory transient data, the OTMs are obtained. Finally, the efficacy of OTMs survived by the F2FP2QW model in TA is evaluated via a cross-validation scenario. The results show that F2FP2QW has a prediction accuracy of 98.87 % and a processing time of 102.587 milliseconds for transient stability prediction.}, archive = {J_ACCESS}, author = {Seyed Alireza Bashiri Mosavi and Omid Khalaf Beigi}, doi = {10.1109/ACCESS.2025.3537917}, journal = {IEEE Access}, pages = {23247-23269}, shortjournal = {IEEE Access}, title = {A fourfold bi-filter and permuted bi-quad-wrapper feature selection method for finding optimal moments of multi-trajectory transient records in transient analysis}, volume = {13}, year = {2025}, }
• (2025). A CNN-RF hybrid approach for rice paddy fields mapping in indramayu using sentinel-1 and sentinel-2 data. ACCESS, 13, 23234-23246. (www)

  @article{ , abstract = {Rice, cultivated in paddy fields, is one of the staple foods in the world, especially in Asia. In Indonesia, a substantial number of rice paddy fields have been converted into residential or industrial areas, threatening food security. Therefore, it is necessary to monitor the adequacy of rice paddy field areas. Recently, remote sensing has become the most widely used method for mapping rice paddy fields. This research focuses on developing a classification model for rice paddy field mapping using remote sensing with radar and optical data fusion, including input variations in polarization, texture, and optical derivative indices. This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. The experiment used combinations of input data, including variations of single and multisource data, to achieve optimal results. Research findings in some districts of Indramayu show that the scheme combining Sentinel-1 features with GLCM (gray-level co-occurrence matrix) and Sentinel-2 features with selected bands provides the best results, with an overall accuracy of 98.23% and a Kappa coefficient of 0.96, using the CNN-RF method. CNN-RF outperforms other classifiers owing to the hybrid learning combination, which improves the accuracy through feature extraction by CNN and handles complex relationships between features while reducing overfitting by RF. This study contributes to the development of accurate and efficient rice paddy field mapping techniques using remote sensing.}, archive = {J_ACCESS}, author = {Dodi Sudiana and Mia Rizkinia and Rahmat Arief and Tiara De Arifani and Anugrah Indah Lestari and Dony Kushardono and Anton Satria Prabuwono and Josaphat Tetuko Sri Sumantyo}, doi = {10.1109/ACCESS.2025.3537818}, journal = {IEEE Access}, pages = {23234-23246}, shortjournal = {IEEE Access}, title = {A CNN-RF hybrid approach for rice paddy fields mapping in indramayu using sentinel-1 and sentinel-2 data}, volume = {13}, year = {2025}, }
• (2025). DC-DC boost converter design with analog feedback control for cryogenic applications. ACCESS, 13, 23220-23233. (www)

  @article{ , abstract = {To bias Silicon PhotoMultiplier (SiPM) sensors, which are used for the readout of scintillation light at cryogenic temperatures, it is essential to develop power supply units that provide stable voltages and low noise. In modern particle physics experiments, these systems may be placed outside the time projection chambers, which are filled with liquid argon (LAr) due to its scintillation properties. However, in the Deep Underground Neutrino Experiment (DUNE) Vertical Drift design, the Photon Detection System (PDS) electronic readout and bias circuits and sensors must be installed on the cathode plane, which is biased at a high DC voltage (-300 kV). Therefore, it is not possible to make any electrical connections between the SiPM and the external interface. Instead, the power is provided through optical fibers using Power over Fiber (PoF) technology. However, the output voltage of these systems is only a few volts and varies in relation to the required current. This paper proposes a 1-to-10 step-up converter solution that operates at cryogenic temperatures to obtain a stable and very low noise 48 V output supply voltage for the SiPM. The design of the converter is based on a boost DC-DC topology, where the components have been selected and characterized to function at cryogenic temperatures. The converter control relies on a type III compensator analog feedback control loop. Simulations and experimental results of a converter prototype, along with the sizing of the control circuit, are presented in this paper.}, archive = {J_ACCESS}, author = {Danilo Santoro and Niccolò Gallice and Marco Bassani and Paolo Cova and Nicola Delmonte and Massimo Lazzaroni and Valeria Trabattoni and Andrea Zani}, doi = {10.1109/ACCESS.2025.3536887}, journal = {IEEE Access}, pages = {23220-23233}, shortjournal = {IEEE Access}, title = {DC-DC boost converter design with analog feedback control for cryogenic applications}, volume = {13}, year = {2025}, }
• (2025). Six sigma approach to developing a microgrid station for research and teaching. ACCESS, 13, 23209-23219. (www)

  @article{ , abstract = {Design for Six Sigma (DFSS) is a statistical methodology for improving the quality of processes and their organizational areas worldwide used since the 80’s. However, most of the existing laboratories for obtaining experimental results, which validate theoretical proposals that are being studied in the microgrids research field, have been designed without following any roadmap. The purpose of this study was to develop a microgrid station for research and teaching at the Power Electronic Laboratory of Escola Politècnica Superior d Enginyeria de Vilanova i la Geltrú (EPSEVG) based on a DFSS tool called IDDOV, which stands for Identify, Define, Develop, Optimize, and Verify. An IDDOV roadmap to conduct a laboratory design development is provided and applied to design a laboratory at EPSEVG in this study. The results show that a well-structured roadmap, such as IDDOV, promotes a culture of continuous improvement for the microgrid research bed, encouraging feedback from users and stakeholders to identify opportunities for improvement. The conclusion of this paper highlights the benefits of using the DFSS conceptual methodology to develop a microgrid station for research and teaching, such as adaptation to educational needs; enhancement of the learning experience; refutation of experimental errors; cost-effective, flexible, and modular design; collaborative learning environment; data-driven teaching; and continuous improvement. As electrical grids are dynamic systems in constant evolution, a new IDDOV roadmap should be carried out in future studies, to enhance the test bed recommended here.}, archive = {J_ACCESS}, author = {Gabriel Nasser Doyle de Doile and Miguel Castilla and Pedro Paulo Balestrassi and Antonio Carlos Zambroni de Souza and Jaume Miret}, doi = {10.1109/ACCESS.2025.3537501}, journal = {IEEE Access}, pages = {23209-23219}, shortjournal = {IEEE Access}, title = {Six sigma approach to developing a microgrid station for research and teaching}, volume = {13}, year = {2025}, }
• (2025). Explanatory LSTM-AE-based anomaly detection for time series data in marine transportation. ACCESS, 13, 23195-23208. (www)

  @article{ , abstract = {Ensuring the normal operation of mechanical equipment is crucial in marine transportation, as data anomalies in these systems can lead to serious safety incidents, environmental pollution, and economic losses. To improve the accuracy and efficiency of anomaly detection in ship equipment data, a long-short-term memory auto-encoder model (LSTM-AE) has been developed, tailored for time-series anomaly detection. The normal behavior patterns of key metrics, such as temperature, pressure, and rotational speed, are learned and captured by this model, enabling abnormal states in these metrics to be accurately identified. The approach is based on the encoder in the long-short-term memory (LSTM) network, where input time-series data is converted into a lower-dimensional, implicit representation, and an attempt is made to reconstruct the original input data via a decoder. Trained exclusively on anomaly-free data, the model ensures a low reconstruction error on normal data. However, when input data that significantly deviates from the training set is encountered, a high reconstruction error is produced, thereby allowing potential anomalies to be flagged. To enhance the interpretability of the results, explainable artificial intelligence (XAI) techniques are incorporated, specifically shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), to identify which features have the most impact on detected anomalies. The LSTM-AE model shows superior performance compared to other data generation models such as GAN and diffusion models, which have accuracy issues and require high computational cost. In addition, the integration of XAI methods has advantages in the interpretation of the results, solving the problem that these existing methods often lack transparency.}, archive = {J_ACCESS}, author = {Zhan Wang and Mwamba Kasongo Dahouda and Hyoseong Hwang and Inwhee Joe}, doi = {10.1109/ACCESS.2025.3535695}, journal = {IEEE Access}, pages = {23195-23208}, shortjournal = {IEEE Access}, title = {Explanatory LSTM-AE-based anomaly detection for time series data in marine transportation}, volume = {13}, year = {2025}, }
• (2025). Solving truthfulness-privacy trade-off in mixed data outsourcing by using data balancing and attribute correlation-aware differential privacy. ACCESS, 13, 23171-23194. (www)

  @article{ , abstract = {In the modern era, data of diverse types (medical, financial, etc.) are outsourced from data owner environments to the public domains for data mining and knowledge discovery purposes. However, data often encompass sensitive information about individuals, and outsourcing the data without sufficient protection may endanger privacy. Anonymization methods are mostly used in data outsourcing to protect privacy; however, it is very hard to apply anonymity to datasets of poor quality while maintaining an equilibrium between privacy, utility, and truthfulness (i.e., ensuring the values in anonymized data are consistent with the real data). To address these technical problems, we propose and implement a data balancing and attribute correlation-aware differential privacy (DP) method for mixed data outsourcing while accomplishing the three crucial objectives of privacy, truthfulness, and utility. Our method first identifies quality-related issues in the data and solves them in an automated manner by adding the fewest possible good-quality synthetic records. We propose a data partitioning method that exploits correlations between attributes to create blocks of data to lessen the amount of noise added by the DP model. To preserve higher truthfulness while guaranteeing privacy, categorical attributes are considered as one unit, and an exponential mechanism is applied to them. The numerical attributes are transformed using the Laplace mechanism with a relatively higher $\epsilon $ . The joint application of these mechanisms to data blocks enables effective resolution of the truthfulness-privacy trade-off, and data usability is extremely high. Extensive experiments are performed on three benchmark datasets to demonstrate the effectiveness of our method in real scenarios. The experiment results and analysis indicate significantly better performance on four different evaluation metrics compared to the recent state-of-the-art (SOTA) DP-based methods. Furthermore, our method has better efficiency than its counterparts.}, archive = {J_ACCESS}, author = {Abdul Majeed and Seong Oun Hwang}, doi = {10.1109/ACCESS.2025.3537109}, journal = {IEEE Access}, pages = {23171-23194}, shortjournal = {IEEE Access}, title = {Solving truthfulness-privacy trade-off in mixed data outsourcing by using data balancing and attribute correlation-aware differential privacy}, volume = {13}, year = {2025}, }
• (2025). Detection and classification of teacher-rated children’s activity levels using millimeter-wave radar and machine learning: A pilot study in a real primary school environment. ACCESS, 13, 23156-23170. (www)

  @article{ , abstract = {Traditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machine learning for the objective and semi-automatic detection and classification of children’s activity levels, defined as restlessness, within a real classroom environment. Two objectives are pursued: confirming the feasibility of restlessness detection using millimeter-wave radar and applying standard machine learning method for restlessness classification. The experiment involves a nine-day observational study, using two radar systems to monitor the activities of 14 children in a primary school. Radar data analysis involves the extraction of distinctive features for restlessness detection and classification. Results indicate the successful detection of restlessness using millimeter-wave radar, demonstrating its potential to capture nuanced body movements in a privacy-protected manner. Machine learning models trained on radar data achieve a classification accuracy of 100%, outperforming other methods in terms of non-invasiveness, lack of body restraint, multi-target applications, and privacy protection. The study’s contributions extend to children, parents, and educational practitioners, emphasizing non-invasiveness, privacy protection, and evidence-based support. Despite limitations such as a short monitoring duration and a small sample size, this pilot study lays the foundation for future research in non-invasive restlessness detection using non-contact monitoring technologies. The integration of millimeter-wave radar and machine learning offers a promising avenue for efficient and ethical trait assessments in real-world educational environments, contributing to the advancement of child psychology and education. This work supports efforts for non-contact monitoring of children’s activity holding promise such as non-invasive, privacy protection, multi-targets, objective evaluation, and computer-aided screening.}, archive = {J_ACCESS}, author = {Tianyi Wang and Takuya Sakamoto and Yu Oshima and Itsuki Iwata and Masaya Kato and Haruto Kobayashi and Manabu Wakuta and Masako Myowa and Tokomo Nishimura and Atsushi Senju}, doi = {10.1109/ACCESS.2025.3527037}, journal = {IEEE Access}, pages = {23156-23170}, shortjournal = {IEEE Access}, title = {Detection and classification of teacher-rated children’s activity levels using millimeter-wave radar and machine learning: A pilot study in a real primary school environment}, volume = {13}, year = {2025}, }
• (2025). Letter and person recognition in freeform air-writing using machine learning algorithms. ACCESS, 13, 23142-23155. (www)

  @article{ , abstract = {Air-writing is an emerging form of human-computer interaction that enables text entry through hand movements in the air. This paper explores air-writing-based person recognition alongside letter recognition to analyze the correlation between subjects and their writing styles. This analysis has potential applications in early childhood education for assessing cognitive and psychomotor skills, as well as in user authentication. A comprehensive framework for freeform air-writing analysis using a wearable glove is introduced, encompassing person and letter recognition, letter segmentation, feature extraction, and dataset generation. Unlike user authentication where person recognition depends on predefined content, such as a passcode or signature, person recognition here depends on the underlying writing style of the writer in free-form. The free-form air-writing indicates the user freedom to select any letter with any size (varying from a quarter to the full size of an A4 page) to write on the air. In the study, a dataset is also developed, containing the air-writing signals from the wearable glove, integrated with IMU sensors (gyroscope and accelerometer). Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. To the best of the authors’ knowledge, there is no study for person recognition from freeform air-writing letters through IMU sensors, and also there is no study using the Fourier and Wavelet features in this context. Furthermore, the study is also original due to its publicly available air-writing dataset on the Turkish alphabet and also applying various machine learning algorithms. The experimental results show that SubSpace KNN is superior to the others under the suggested parameter settings.}, archive = {J_ACCESS}, author = {Huseyin Kunt and Zeki Yetgin and Furkan Gözükara and Turgay Çelík}, doi = {10.1109/ACCESS.2025.3536609}, journal = {IEEE Access}, pages = {23142-23155}, shortjournal = {IEEE Access}, title = {Letter and person recognition in freeform air-writing using machine learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Fusion features-based entity recognition method for safety knowledge of non-coal open-pit mine. ACCESS, 13, 23131-23141. (www)

  @article{ , abstract = {Knowledge graph technology that provides important information and data support for improving the level of safety production, brings together related laws, regulations and construction methods of non-coal open-pit mining production. However, as a key step in the construction of knowledge graph, it is a major challenge to recognize and extract entities from the complex field of safety production in non-coal open-pit mines. In this paper, a new entity recognition method based on fusion features, MSAL (Multilayer Self-attention Lexicon), is proposed, which shows better performance of entity recognition in this special field. A word-level enhancement feature SoftLexicon is adopted to solve the problem of flat entity boundary generated by character sequence model in Chinese named entity recognition. Then, the SoftLexicon feature information is dynamically weighted and fused using a self-attention mechanism. In order to solve the problem of text information not being fully utilized by the pre-trained model, a multi-layer fusion method combining hidden state and transformer layers is proposed. Comparison and ablation experiments were carried out to demonstrate the proposed method’s effects. The experimental results show that the recall rate of relevant indicators under the test set is 67.54% in contrast to other models, and the training speed and portability performance are obviously better.}, archive = {J_ACCESS}, author = {Ziji Ma and Rui Zhao and Jianhua Huang and Shicheng Liu and Feilong Wang and Zhikang Shuai}, doi = {10.1109/ACCESS.2025.3528419}, journal = {IEEE Access}, pages = {23131-23141}, shortjournal = {IEEE Access}, title = {Fusion features-based entity recognition method for safety knowledge of non-coal open-pit mine}, volume = {13}, year = {2025}, }
• (2025). A bore-integrated patch antenna array for whole-body excitation in ultra-high-field magnetic resonance imaging. ACCESS, 13, 23120-23130. (www)

  @article{ , abstract = {The human body’s ultra-high field magnetic resonance imaging suffers from the inhomogeneity of the radio frequency magnetic field $B_{1}^{+}$ and the high-peak levels of SAR created in body tissues during transmission. Both issues are typically addressed in the research domain using a tight-fit local array of antenna elements attached to the body and driven with customized phases by multiple transmit channels. The same approach faces difficulties in fast and reliable SAR evaluation, precise positioning, and patient comfort in the clinical environment. Recently, a novel bore-integrated array of stripline antenna elements with substantial free space was introduced for whole-body imaging at 7 T, showing, however, insufficient transmission efficiency (the level of $B_{1}^{+}$ at the unit input power). This work aims to improve the efficiency of the bore-integrated array configuration, which, unlike the tight-fit configuration, may be beneficial for prospective clinical workflow. After analyzing the proposed solutions, particularly the stripline antenna arrays, we found that one of the main limitations is the high level of losses within the array element. We propose using patch antenna elements separated by compact dual-loop decoupling resonators to overcome this limitation. Comparing the proposed elements with striplines in the same bore-integrated eight-channel configuration, we demonstrate numerically and experimentally on the bench a transmit efficiency improvement at least 3 times depending on the excitation mode. Furthermore, the proposed array is shown to overcome both the stripline array and volumetric TEM coil in terms of SAR efficiency approaching the level of the state-of-the-art tight-fit array of fractionated dipole antennas. Our results support the feasibility of integrating multichannel transmit array coils into the bore liner of a 7 T system while compromising transmit efficiency and providing competitive SAR efficiency.}, archive = {J_ACCESS}, author = {Svetlana S. Egorova and Nikolai A. Lisachenko and Egor I. Kretov and Yang Gao and Xiaotong Zhang and Stanislav B. Glybovski and Georgiy A. Solomakha}, doi = {10.1109/ACCESS.2025.3536361}, journal = {IEEE Access}, pages = {23120-23130}, shortjournal = {IEEE Access}, title = {A bore-integrated patch antenna array for whole-body excitation in ultra-high-field magnetic resonance imaging}, volume = {13}, year = {2025}, }
• (2025). Dynamic car-following model with jerk suppression for highway autonomous driving. ACCESS, 13, 23111-23119. (www)

  @article{ , abstract = {This study proposes a dynamic safe car-following strategy that is based on dynamic adjustment of headway time with jerk suppression. Reinforcement learning models trained with this strategy result in enhanced safety and driving comfort, validated using real driving data from the Next Generation Simulation (NGSIM) I-80 and HighD datasets. Simulation results demonstrate significant reduction in the risk of collisions. More importantly, low collision rates are maintained with driving speed profiles that are different from the training data, exhibiting cross-dataset generalizability. It also significantly improves driving comfort, with a 10% jerk reduction compared to existing models.}, archive = {J_ACCESS}, author = {Ke Liu and Jing Ma and Edmund M.-K. Lai}, doi = {10.1109/ACCESS.2025.3535596}, journal = {IEEE Access}, pages = {23111-23119}, shortjournal = {IEEE Access}, title = {Dynamic car-following model with jerk suppression for highway autonomous driving}, volume = {13}, year = {2025}, }
• (2025). Modeling multimedia ad exposure: The role of low-level audiovisual features. ACCESS, 13, 23091-23110. (www)

  @article{ , abstract = {This study investigates whether low-level audio and video features can explain the variance in multimedia exposure as measured by the Multimedia Advertising Exposure Scale (MMAES). The goal is to understand the role of these features in predicting exposure and explore whether incorporating nonlinear relationships based on interactions can improve modeling. An observational study with young participants (N=287) evaluated exposure to eight video advertisements. Linear and polynomial regression models were used to predict MMAES scores using low-level features. Results indicated that polynomial models outperformed linear models, capturing complex interactions and providing better predictive accuracy. The best polynomial model explained 34.6% of the variance in MMAES scores, with an MAE of 0.207 and an RMSE of 0.269. Bootstrap analysis confirmed model robustness, showing lower error rates and stable coefficients for polynomial models. SHapley Additive exPlanations (SHAP) analysis highlighted key features and their interactions, improving interpretability. These findings underscore the importance of nonlinear relationships in modeling multimedia exposure, with implications for optimizing multimedia advertising strategies.}, archive = {J_ACCESS}, author = {Urban Burnik and Andrej Košir and Gregor Strle}, doi = {10.1109/ACCESS.2025.3536633}, journal = {IEEE Access}, pages = {23091-23110}, shortjournal = {IEEE Access}, title = {Modeling multimedia ad exposure: The role of low-level audiovisual features}, volume = {13}, year = {2025}, }
• (2025). Sealless production ultracentrifuge and its magnetically self-bearing openable motors for purification in viral nanotechnology. ACCESS, 13, 23072-23090. (www)

  @article{ , abstract = {Viral nanotechnology enables new possibilities for gene therapies and vaccines. However, the manufacturing of viral vectors lacks a satisfactory production capable method for purification of full capsids. Empty or partially empty capsids need to be removed to avoid immunotoxicity. The state of the art production ultracentrifuges (PUCFs) are limited, especially in rotational speed to approximately 40 krpm by rotary seals, by complexity and virus containment. This prevents PUCFs from widespread use in industry for viral vector production. This article proposes a novel PUCF type with rotational speed potential towards 100 krpm with a hermetically enclosed process chamber without any rotary seals, i.e. a sealless production ultracentrifuge (SL-PUCF) omitting contamination risk. The openable vertical axis self-bearing motors, the novel sealless flow path design and the needed ultra high-speed potential towards 100 krpm pose new technical problems, for which this article proposes and experimentally validates solutions. A SL-PUCF prototype with its vertical axis magnetically self-bearing openable motors (O-SBMs) and openable burst armor (O-BA) is realized, validated and operated as a system. The general working principle of the SL-PUCF system is experimentally validated by whey protein sedimentation. Furthermore, new future smart capability potential of the proposed new SL-PUCF technology suspended and driven by O-SBMs is unveiled as an outlook and examples shown in case studies.}, archive = {J_ACCESS}, author = {Emanuel J. Hubmann and Daniel Steinert and Thomas Nussbaumer and Johann W. Kolar}, doi = {10.1109/ACCESS.2025.3537559}, journal = {IEEE Access}, pages = {23072-23090}, shortjournal = {IEEE Access}, title = {Sealless production ultracentrifuge and its magnetically self-bearing openable motors for purification in viral nanotechnology}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis of DDoS anomaly detection in software-defined networks. ACCESS, 13, 23013-23071. (www)

  @article{ , abstract = {Software-Defined Networking (SDN) offers significant advantages for modern networks, including flexibility, centralized control, and reduced dependency on vendor-specific hardware. However, these benefits introduce security vulnerabilities, particularly from Distributed Denial-of-Service (DDoS) attacks, which represent some of the most disruptive threats to SDN environments. A review of the literature shows that while various techniques have been proposed to counteract DDoS threats, many studies have focused on single detection methods, with only a few utilizing multiple approaches. This fragmented focus limits a comprehensive approach to addressing DDoS threats across the SDN layers. To bridge this gap, this paper presents the first comprehensive review of DDoS anomaly detection in SDN, examining over 165 primary research articles published between 2020 and 2024. A novel taxonomy of DDoS attacks is introduced, categorizing them by distinct characteristics, and mapping each attack type to relevant detection methods within specific SDN layers. The survey provides a layer-by-layer analysis of DDoS detection techniques, covering the application, control, and infrastructure layers, and offers a structured overview that clarifies the applicability and effectiveness of each method. The paper concludes by synthesizing key findings, identifying unresolved challenges, and outlining future research directions to advance DDoS detection mechanisms in SDN. This roadmap is designed to guide researchers in addressing security vulnerabilities and enhancing SDN resilience against evolving DDoS threats.}, archive = {J_ACCESS}, author = {Abdinasir Hirsi and Mohammed A. Alhartomi and Lukman Audah and Adeb Salh and Nan Mad Sahar and Salman Ahmed and Godwin Okon Ansa and Abdullahi Farah}, doi = {10.1109/ACCESS.2025.3535943}, journal = {IEEE Access}, pages = {23013-23071}, shortjournal = {IEEE Access}, title = {Comprehensive analysis of DDoS anomaly detection in software-defined networks}, volume = {13}, year = {2025}, }
• (2025). Research on parameter-efficient knowledge graph completion methods and their performance in the cybersecurity field. ACCESS, 13, 22998-23012. (www)

  @article{ , abstract = {As an advanced knowledge management and reasoning tool, knowledge graph technology can significantly enhance the efficiency of managing and analyzing cyberthreat intelligence, providing strong technical support for cybersecurity threat identification and situational awareness. This paper proposes a multi-level graph clustering-based model for completing cyberthreat intelligence knowledge graphs (GCCKG). The model utilizes a multi-level graph clustering approach to divide the knowledge graph into communities and selects a portion of high-degree entities within each community as the reserved entities. Additionally, we propose a path-enhanced cosine similarity measurement method to measure the similarity between entities, and a graph attention network is employed to iteratively update the embeddings of entities and relations, capturing key information in the graph-structured data. Experimental results demonstrate that the proposed GCCKG model significantly improves evaluation values such as MRR and Hits@K in knowledge graph completion tasks on several general domain knowledge graphs, including FB15K, WN18, FB15K-237, as well as the cyberthreat intelligence knowledge graph CS13K, while also significantly reducing the model’s parameter size. This provides a novel solution for knowledge graph completion in the field of cyberthreat intelligence.}, archive = {J_ACCESS}, author = {Bin Chen and Hongyi Li and Ze Shi}, doi = {10.1109/ACCESS.2025.3535143}, journal = {IEEE Access}, pages = {22998-23012}, shortjournal = {IEEE Access}, title = {Research on parameter-efficient knowledge graph completion methods and their performance in the cybersecurity field}, volume = {13}, year = {2025}, }
• (2025). Self-capacitance sensor for smart diaper. ACCESS, 13, 22989-22997. (www)

  @article{ , abstract = {The significant socio-economic impacts of low birth rates and aging populations have increasingly strained healthcare resources. Enhancing the capacity of limited caregiving personnel is now essential, and Information communication technology (ICT) offers a promising solution. This study demonstrates the application of ICT in healthcare by developing a smart diaper sensor. A uni-electrode self-capacitance sensor was implemented to measure moisture levels in diapers by detecting changes in capacitance in response to urine. Integrated with an IoT system, this setup enables real-time monitoring, remote alerts, and data analysis. A prototype sensor was externally attached to standard diapers, ensuring easy use and maintaining hygiene without direct contact. Experimental tests confirmed the high sensitivity and accuracy of the sensor, with a significant increase in the moisture indicator value even with as little as 1 ml of urine. The device demonstrated stable, reproducible results, with a coefficient variation of 0.059%, validating its effectiveness for detecting diaper wetness. The IoT integration allowed seamless data transmission and analysis, providing caregivers with timely alerts and care tracking. The proposed solution represents a significant advancement in sustainable diaper technology, leveraging IoT and capacitive sensing to enhance healthcare management for both elderly and infant care. This technology addresses essential needs in healthcare settings by improving caregiving efficiency and reducing workload.}, archive = {J_ACCESS}, author = {Jiun-Hung Lin and Chun-Hao Lu and Shih-Tsang Tang}, doi = {10.1109/ACCESS.2025.3535144}, journal = {IEEE Access}, pages = {22989-22997}, shortjournal = {IEEE Access}, title = {Self-capacitance sensor for smart diaper}, volume = {13}, year = {2025}, }
• (2025). A novel MoCo-based self-supervised learning framework for solar panel defect detection. ACCESS, 13, 22977-22988. (www)

  @article{ , abstract = {Defect detection in solar panels remains constrained by the limitations of manual labeling and the inefficiency of traditional inspection methods, which often struggle with large, high-resolution imagery. This study presents a novel self-supervised learning approach using the Momentum Contrast (MoCo) framework to address these challenges without relying on annotated datasets. Leveraging MoCo’s robust feature extraction and K-Nearest Neighbors (KNN) clustering, our method achieves accurate defect identification, bypassing the dependency on labeled data. Evaluated on the ELPV dataset, our approach attained a notable 96.95% accuracy, demonstrating significant improvement over existing unsupervised methods like KDAD, SAOE, DRA, and BGAD-FAS, and even outperforming some supervised models such as Adapted VGG19, Adapted VGG16, and ShuffleNet. Additionally, our model achieved 99.44% accuracy on the EL dataset, underscoring its adaptability and robustness across different environments. This framework offers a scalable, automated solution for image-level defect detection, poised to enhance efficiency and reduce manual intervention in industrial applications.}, archive = {J_ACCESS}, author = {Jun Huang and Shamsul Arrieya Ariffin and Yongqiang Chen and Jinghui Lin and Wanting Xu}, doi = {10.1109/ACCESS.2025.3529701}, journal = {IEEE Access}, pages = {22977-22988}, shortjournal = {IEEE Access}, title = {A novel MoCo-based self-supervised learning framework for solar panel defect detection}, volume = {13}, year = {2025}, }
• (2025). Repairing errors of inconsistent data based on WFT-nets. ACCESS, 13, 22964-22976. (www)

  @article{ , abstract = {Data consistency is crucial for ensuring the reliability and accuracy of workflow systems. Inconsistent data caused by improper data operations, if not promptly repaired, can compromise the correctness of workflow systems. Existing methods for repairing inconsistent data mostly focus on either the process data or the persistent data. However, it is difficult for such methods to comprehensively deal with the errors of data inconsistency in workflow systems. Because workflow systems contain not only the process data, but also the persistent data. Based on Workflow net with tables (WFT-nets), this paper fully considers the above two types of data and their interaction to repair inconsistent data. The causes of inconsistent data are first analyzed in depth based on WFT-nets, with a specific focus on improper data element/table operations. In addition, these operations are further classified, and corresponding repair strategies are proposed. And then, in the WFT-net, transitions attached with such improper operations are called abnormal ones. To repair the errors of data inconsistency, the reachable configuration graph (RC-Graph) of the WFT-net is traversed to locate all possible abnormal transitions, and a repair algorithm is presented according to the sequence of “process data first, persistent data later”. Finally, a set of comparative experiments with WFD-nets is done to demonstrate the effectiveness of the proposed repairing method.}, archive = {J_ACCESS}, author = {Xiaoyan Tao and Yang Li and Xianwen Fang}, doi = {10.1109/ACCESS.2025.3529016}, journal = {IEEE Access}, pages = {22964-22976}, shortjournal = {IEEE Access}, title = {Repairing errors of inconsistent data based on WFT-nets}, volume = {13}, year = {2025}, }
• (2025). Efficiency improvement in dynamic wireless power transfer by placing a magnetic sheet under the power transmitting coil. ACCESS, 13, 22956-22963. (www)

  @article{ , abstract = {Electric vehicles have a short driving range and require long charging times. Wireless power transfer (WPT) during vehicle moving can extend the driving range of such vehicles. In WPT for stationary vehicles, ferrite cores are used in the back yoke to improve power transfer efficiency. However, issues associated with the high cost, durability, and excessive weight can emerge when installing ferrite coils on roads. As a result, ferrite is not used in the back yoke of the transmitting side during WPT for moving vehicles. In this study, we propose a lightweight and flexible magnetic sheet made of a magnetic composite material. This sheet can be placed under the transmitting coil. In this way, the coil Q factor and coupling coefficient can be improved. In this study, we report the improvement in transmission efficiency when the receiving coil moves and consequently, the improvement in the overall transfer efficiency of the power supply section.}, archive = {J_ACCESS}, author = {Yu Katagiri and Yinggang Bu and Tsutomu Mizuno and Mitsuhide Sato}, doi = {10.1109/ACCESS.2025.3536029}, journal = {IEEE Access}, pages = {22956-22963}, shortjournal = {IEEE Access}, title = {Efficiency improvement in dynamic wireless power transfer by placing a magnetic sheet under the power transmitting coil}, volume = {13}, year = {2025}, }
• (2025). Optimal speech signal processing based on dynamic multi-scale analysis. ACCESS, 13, 22946-22955. (www)

  @article{ , abstract = {To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal’s time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).}, archive = {J_ACCESS}, author = {Zefei Guo and Xiao Li and Yuchen Zhang}, doi = {10.1109/ACCESS.2025.3535950}, journal = {IEEE Access}, pages = {22946-22955}, shortjournal = {IEEE Access}, title = {Optimal speech signal processing based on dynamic multi-scale analysis}, volume = {13}, year = {2025}, }
• (2025). An efficient and hybrid deep learning-driven model to enhance security and performance of healthcare internet of things. ACCESS, 13, 22931-22945. (www)

  @article{ , abstract = {The development of wireless communication technology has led to an exponential growth in the Healthcare Internet of Things (H-IoT). Sensors and actuators are used in smart medical devices to collect data about the human body, which is then sent to the fog layer for analysis. However, H-IoT devices present security and Quality of Service (QoS) concerns because of their critical nature, complexity, and dynamic features, which make them incompatible with conventional network topologies. Furthermore, reducing superfluous data and identifying effective fog nodes are difficult tasks. We provide a novel Software-Defined Networking (SDN)- driven Deep Learning (DL) approach to develop a secure, intelligent, and efficient framework for smart H-CIoT networks in order to address the aforementioned challenges. In this method, we have first considered SDN architecture as a promising solution since it allows for reconfiguration over static network infrastructure and manages the distributed architecture of intelligent H-CIoT networks by keeping the data and control planes apart. Secondly, a security module based on Bidirectional Long Short-Term Memory (BiLSTM) is implemented to recognize various forms of attacks within the H-CIoT network. Third, the past medical records of the patients are used to train the DL model. It then makes an informed decision about whether to send the data to the fog layer. The CNN approach is also included in the suggested framework to choose the best fog node. The simulation results indicate that the proposed framework achieved an accuracy of 99.59%, an F1-score of 99.53%, a latency of 3 ms, energy consumption of 55 W, and a probability of 0.92%. It outperforms the baseline and current methods with improvements of 5% in accuracy, 4% in F1-score, 10 ms in latency, 25 W in energy consumption, and 0.66% in probability, respectively.}, archive = {J_ACCESS}, author = {Muhammad Babar and Muhammad Usman Tariq and Basit Qureshi and Zabeeh Ullah and Fahim Arif and Zahid Khan}, doi = {10.1109/ACCESS.2025.3536638}, journal = {IEEE Access}, pages = {22931-22945}, shortjournal = {IEEE Access}, title = {An efficient and hybrid deep learning-driven model to enhance security and performance of healthcare internet of things}, volume = {13}, year = {2025}, }
• (2025). Ultra-short-term wind power forecasting based on DT-DSCTransformer model. ACCESS, 13, 22919-22930. (www)

  @article{ , abstract = {When using the Transformer model for wind power prediction, the accuracy of the model predictions tends to be reduced due to the shift in the wind power data distribution, channel mixing, and the inability of the model to establish strong correlations. To address these challenges, this paper proposes an ultra-short-term wind power prediction model based on the DT-DSCTransformer. First, the model applies DT’s self-learning standardization and de-standardization parameters to standardize the input and de-standardize the output, mitigating the impact forecasting of data distribution shifts on prediction accuracy. Second, the proposed De-Stationary Channel Attention (DSCAttention) mechanism is introduced. By incorporating De-Stationary Attention (DSAttention) into the channel attention mechanism while maintaining channel independence, the model establishes stronger inter-channel correlations, addressing the performance degradation caused by channel mixing and weak correlations. Finally, experimental analysis demonstrates that the proposed model achieves the highest prediction accuracy compared to commonly used time series forecasting models.}, archive = {J_ACCESS}, author = {Yanlong Gao and Feng Xing and Lipeng Kang and Mingming Zhang and Caiyan Qin}, doi = {10.1109/ACCESS.2025.3537158}, journal = {IEEE Access}, pages = {22919-22930}, shortjournal = {IEEE Access}, title = {Ultra-short-term wind power forecasting based on DT-DSCTransformer model}, volume = {13}, year = {2025}, }
• (2025). AI innovations in rPPG systems for driver monitoring: Comprehensive systematic review and future prospects. ACCESS, 13, 22893-22918. (www)

  @article{ , abstract = {Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive load and distraction; and 4) emotional state monitoring and dataset development, indicating a trend toward holistic driver assessment. While deep learning has improved rPPG signal extraction, challenges remain in consistent physiological metric detection under dynamic conditions. There is also a lack of diverse population representation, especially female drivers, in datasets. The review underscores the potential of AI-enhanced camera systems to improve road safety, emphasizing the need for diverse, multimodal data integration for comprehensive monitoring.}, archive = {J_ACCESS}, author = {Soha G. Ahmed and Katrien Verbert and Nazar Zaki and Ashraf Khalil and Hamad Aljassmi and Fady Alnajjar}, doi = {10.1109/ACCESS.2025.3535540}, journal = {IEEE Access}, pages = {22893-22918}, shortjournal = {IEEE Access}, title = {AI innovations in rPPG systems for driver monitoring: Comprehensive systematic review and future prospects}, volume = {13}, year = {2025}, }
• (2025). Weakly-supervised deep shape-from-template. ACCESS, 13, 22868-22892. (www)

  @article{ , abstract = {We propose WS-DeepSfT, a novel deep learning-based approach to the Shape-from-Template (SfT) problem, which aims at reconstructing the 3D shape of a deformable object from a single RGB image and a template. WS-DeepSfT addresses the limitations of existing SfT techniques by combining a weakly-supervised deep neural network (DNN) for registration and a classical As-Rigid-As-Possible (ARAP) algorithm for 3D reconstruction. Unlike previous deep learning-based SfT methods, which require extensive synthetic data and depth sensors for training, WS-DeepSfT only requires regular RGB video of the deforming object and a segmentation mask to discriminate the object from the background. The registration model is trained without synthetic data, using videos where the object undergoes deformations, while ARAP does not require training and infers the 3D shape in real-time with minimal overhead. We show that WS-DeepSfT outperforms the state-of-the-art, in both accuracy and robustness, without requiring depth sensors or synthetic data generation. WS-DeepSfT thus offers a robust, efficient, and scalable approach to SfT, bringing it closer to applications such as augmented reality.}, archive = {J_ACCESS}, author = {Sara Luengo-Sanchez and David Fuentes-Jimenez and Cristina Losada-Gutierrez and Daniel Pizarro and Adrien Bartoli}, doi = {10.1109/ACCESS.2025.3534271}, journal = {IEEE Access}, pages = {22868-22892}, shortjournal = {IEEE Access}, title = {Weakly-supervised deep shape-from-template}, volume = {13}, year = {2025}, }
• (2025). Dual-path adaptive channel attention network based on feature constraints for face anti-spoofing. ACCESS, 13, 22855-22867. (www)

  @article{ , abstract = {Interference factors in visible light image data, such as backgrounds and lighting, often lead to poor performance of RGB-based single-modality face anti-spoofing methods. To address these limitations, we propose an innovative face anti-spoofing framework. Within this framework, we design a convolutional neural network (CNN) based on the Dual-path Adaptive Channel Attention (DACA) module, aiming to filter the features of the input facial images to extract key information. In addition, we develop feature constraints method based on Inner Similarity Estimation (ISE), which effectively enhances intra-class consistency by reducing the distance between samples and their class center. This method narrows the intra-class sample distribution and improves class separability, preventing the model from learning excessive irrelevant information and enhancing the robustness and generalization of face anti-spoofing. We test our method on the CASIA SURF dataset, CASIA SURF-CeFA dataset, and CASIA FASD dataset, which shows that our method has significant advantages in distinguishing between live and spoofed faces.}, archive = {J_ACCESS}, author = {Nana Li and Zhipeng Weng and Fangmei Liu and Zuhe Li and Wei Wang}, doi = {10.1109/ACCESS.2025.3534906}, journal = {IEEE Access}, pages = {22855-22867}, shortjournal = {IEEE Access}, title = {Dual-path adaptive channel attention network based on feature constraints for face anti-spoofing}, volume = {13}, year = {2025}, }
• (2025). Observer-based aperiodic time-triggered intermittent control for exponential consensus of multi-agent systems. ACCESS, 13, 22842-22854. (www)

  @article{ , abstract = {In this paper, an observer-based aperiodic time-triggered intermittent control protocol (OATICP) is explored for achieving exponential consensus of multi-agent systems (MASs). The mathematical expression of OATICP is initially put forward. OATICP only relies on the output information at sampling instants to estimate the state of the agent itself, and the state estimation information of the neighbor agents at sampling instants to generate the control signal. Then, for the exponential consensus of MASs under OATICP, the mixed Lyapunov functional (MLF) is constructed to establish sufficient conditions. Thirdly, by resorting to linear matrix inequality (LMI) and singular value decomposition (SVD) techniques, OATICP can be designed to realize the exponential consensus of MASs. As a result, a corollary is obtained. Finally, two simulation examples are provided for verification.}, archive = {J_ACCESS}, author = {Xueming Xu and Qingzhi Wang and Baozeng Fu and Lijie Wang}, doi = {10.1109/ACCESS.2025.3535735}, journal = {IEEE Access}, pages = {22842-22854}, shortjournal = {IEEE Access}, title = {Observer-based aperiodic time-triggered intermittent control for exponential consensus of multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). Equivalent circuit parameter measurement of multiphase induction machine by exploitation of space harmonic relations. ACCESS, 13, 22831-22841. (www)

  @article{ , abstract = {A multiphase induction machine model using vector space decomposition provides insights into many space harmonics through decoupled reference frames. In order to utilize this potential of the multiphase machine, the parameters of each vector space must be identified. These parameters are usually identified using standard no-load and locked rotor tests of each torque-producing vector space. However, these tests do not provide a method for identifying stator leakage inductances, which have comparable magnitudes to the magnetizing inductances for higher-order vector spaces. Thus, the magnetizing current cannot be neglected during the locked-rotor test. This paper proposes an accurate method for identifying the T-model parameters of a multiphase induction machine, which can be conveniently translated into the model of choice. The proposed method use harmonic relations to segregate the stator leakage inductance from the total stator inductance, enabling accurate locked-rotor and partial-load tests for the measurement of rotor side parameters. Lastly, a 9-phase induction machine is used for the evaluation of the proposed parameter measurement method. These measured parameters are used for the field-oriented control of each torque-producing vector space, whose performances are analyzed by quantifying the measured output torque error and linearity.}, archive = {J_ACCESS}, author = {Omer Ikram Ul Haq and R. S. Kanchan and Sjoerd G. Bosga and Luca Peretti}, doi = {10.1109/ACCESS.2025.3537335}, journal = {IEEE Access}, pages = {22831-22841}, shortjournal = {IEEE Access}, title = {Equivalent circuit parameter measurement of multiphase induction machine by exploitation of space harmonic relations}, volume = {13}, year = {2025}, }
• (2025). Automatic segmentation of asphalt cracks on highways after large-scale and severe earthquakes using deep learning-based approaches. ACCESS, 13, 22820-22830. (www)

  @article{ , abstract = {This study develops a deep learning-based automated system for detecting and segmenting earthquake-induced asphalt cracks, offering a rapid and reliable solution for post-disaster road condition assessments. Unlike traditional manual inspections, which are time-consuming and error-prone, our approach leverages advanced segmentation techniques to ensure accurate, pixel-level classification of various crack types. The main challenge of this study was determining the damage caused to highways by earthquakes with magnitudes greater than 7.0, which occur approximately once every 200 years. The most crucial step in the automatic detection of these damages is the reliable preparation of a high-accuracy dataset. To achieve this, pixel-based labels were created by experts in the construction field by analyzing each pixel value. Following two major earthquakes, a unique dataset for segmenting roadway deterioration was created through intensive and detailed studies. This study aims to present the performance results of popular deep learning-based segmentation models in an unbiased manner, providing a feasible infrastructure for future real-time applications. The innovative aspect of this research lies in the creation of a unique post-earthquake dataset, collected and labeled from highways affected by the February 6, 2023 earthquakes in Turkey (Mw = 7.7 and Mw = 7.6). Deep learning models, including SegNet, Attention SegNet, U-Net, FCN (8s), and DeepLab, were trained and tested on this dataset. Among these, the SegNet model achieved the best performance with an average accuracy of 86.72%, precision of 92.99%, and sensitivity of 78.45%. By demonstrating superior performance metrics compared to existing methods, this study provides a robust framework for future infrastructure monitoring and maintenance strategies, ensuring safer and more resilient transportation networks in disaster-prone regions.}, archive = {J_ACCESS}, author = {Mehmet Yilmaz and Erkut Yalcin and Fatih Demir and Ahmet Munir Ozdemir and Muhammed Atar and Aysegul Gunes and Beyza Furtana Yalcin and Ertugrul Cambay}, doi = {10.1109/ACCESS.2025.3536554}, journal = {IEEE Access}, pages = {22820-22830}, shortjournal = {IEEE Access}, title = {Automatic segmentation of asphalt cracks on highways after large-scale and severe earthquakes using deep learning-based approaches}, volume = {13}, year = {2025}, }
• (2025). Maximally PCB-space-saving hybrid integration of millimeter-wave and microwave antennas for 5G and B5G smartphones. ACCESS, 13, 22812-22819. (www)

  @article{ , abstract = {This paper proposes a simple, low-cost, and practical hybrid integration design of a 5G FR2 millimeter-wave antenna module based on a thin flexible printed circuit (FPC) and a 5G FR1 microwave antenna based on a metal frame to maximally save precious printed circuit board (PCB) space for more competitive 5G and beyond 5G (B5G) smartphones. The thin FPC-based millimeter-wave antenna module consists of a $1\times 4$ millimeter-wave antenna array (embracing the 3GPP 5G band n261 and maximally steering to 45°), an IC shielding case, and a connector. The whole millimeter-wave antenna module is integrated to a metal cantilever extending from the metal frame (as a microwave antenna) serving as the carrier for the millimeter-wave antenna module. Due to the cantilever carrier, no PCB space will be occupied so that the PCB space utilization can be maximized. The integrated microwave antenna can well embrace plenty of 3GPP 5G bands, e.g., n7, n12, n14, n18, n20, n28, n29, n38, n41, n83, n89, n90, and the 2.4 GHz WLAN band. As a result, this presented hybrid antenna integration scheme is a compelling total solution of millimeter-wave and microwave antennas for more advanced 5G and B5G handsets. Also, simulated and measured performance of the integrated millimeter-wave and microwave antennas shows good agreement.}, archive = {J_ACCESS}, author = {Huan-Chu Huang and Jie Wu and Shuang Cui}, doi = {10.1109/ACCESS.2025.3536468}, journal = {IEEE Access}, pages = {22812-22819}, shortjournal = {IEEE Access}, title = {Maximally PCB-space-saving hybrid integration of millimeter-wave and microwave antennas for 5G and B5G smartphones}, volume = {13}, year = {2025}, }
• (2025). Mobility aware spectrum handoff in 6G-enabled cognitive radio vehicular centralized and ad-hoc networks. ACCESS, 13, 22800-22811. (www)

  @article{ , abstract = {In this paper, we propose an integrated model of the cell-based and pool-based spectrum handoff (SH) process of a non-stationary cognitive user (CU) under a heterogeneous spectrum environment (HetSE) in a 6G-enabled cognitive radio cellular network (CRCN). We model the Link Maintenance Probability (LMP) and Link Failure Probability (LFP) of the CUs under a Heterogeneous Spectrum Environment (HSE) with licenced and unlicensed spectrum pools. These performance measuring metrics are derived for various SH schemes: intracell/intrapool SH, intracell/interpool SH, intercell/intrapool SH, and intercell/interpool SH, considering the primary user (PU) activity model and CU mobility. Considering $D_{th}$ as the waiting threshold period for CU, we analyze the effect PU’s arrival rate and $D_{th}$ on the performance measuring metrics in two different network architectures: Cognitive Radio-Vehicular Ad-Hoc Network (CR-VANET) and Cognitive Radio-Vehicular Centralized Network (CR-VCNET). In addition, we derive the LMP and LFP of a non-stationary CU in $k^{th}$ cell and compare the results among various cells. Further, we realize the effect of various service time distributions of PUs and CUs on the performance metrics, and lognormal service time distribution offers better SH performance as compared to the exponential and Erlangen distribution models. We also perform Monte-Carlo simulation for the performance measuring metrics to validate the proposed integrated SH model in CRCNs.}, archive = {J_ACCESS}, author = {Shanidul Hoque and Hemanta Kumar Sahu and Mohammed Nazibul Hasan and Rajesh Saha}, doi = {10.1109/ACCESS.2025.3537096}, journal = {IEEE Access}, pages = {22800-22811}, shortjournal = {IEEE Access}, title = {Mobility aware spectrum handoff in 6G-enabled cognitive radio vehicular centralized and ad-hoc networks}, volume = {13}, year = {2025}, }
• (2025). Refined urban functional zones identification via empirical bayesian kriging: A POI-weighted scoring innovation. ACCESS, 13, 22784-22799. (www)

  @article{ , abstract = {The refined identification of Urban Functional Zones (UFZs) is crucial for effective urban planning, resource allocation, and environmental monitoring. However, achieving high precision and comprehensive identification of UFZs has been challenging. The rapid proliferation and open accessibility of multi-source data, such as remote sensing imagery and socio-economic datasets, have introduced new opportunities for the dynamic identification of UFZs. This paper proposes a Point of Interest (POI)-weighted scoring method based on Empirical Bayesian Kriging (EBK), which is empirically demonstrated in Xuzhou, China. We integrate multi-source data, including remote sensing-based land use/cover information, road networks, POIs, and building geometry data, to classify UFZs based on their dependence on buildings. Results indicate that: 1) The EBK interpolation method not only accounts for the spatial and directional distance relationships between known and unknown sample points, but also produces more accurate raster images and provides more precise estimates in unmeasured ranges. 2) The method successfully identifies UFZs for 99.5% of the buildings in the central urban area of Xuzhou. Additionally, the incorporation of multi-source data enables the extraction of traffic and ecological zones, facilitating the identification of non-building-reliant zones within the city, thereby enhancing the completeness and comprehensiveness of urban spatial recognition. 3) The overall accuracy achieved is 84.4%, with a Kappa coefficient of 0.804 for the classification results, which represents a significant improvement in UFZs identification accuracy compared to traditional methods and offers a robust scientific basis for urban planning and resource optimization.}, archive = {J_ACCESS}, author = {Xuan Du and Yisha Pan and Xiaoyan Yang and Longgao Chen and Liangchen Liu and Ying Lin}, doi = {10.1109/ACCESS.2025.3536000}, journal = {IEEE Access}, pages = {22784-22799}, shortjournal = {IEEE Access}, title = {Refined urban functional zones identification via empirical bayesian kriging: A POI-weighted scoring innovation}, volume = {13}, year = {2025}, }
• (2025). Early diagnosis of alzheimer’s disease using adaptive neuro K-means clustering technique. ACCESS, 13, 22774-22783. (www)

  @article{ , abstract = {Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, behavioral changes, and impaired self-care, often preceded by Mild Cognitive Impairment (MCI). Not all MCI cases progress to AD, creating a diagnostic challenge. This study proposes a novel framework for early AD diagnosis using T1-weighted Magnetic Resonance Imaging (MRI). The approach integrates the Adaptive Moving Self-Organizing Map (AMSOM), a neural network technique for unsupervised training and tissue segmentation, with K-means clustering and Principal Component Analysis (PCA) for feature selection. AMSOM dynamically updates neuron weights to improve segmentation accuracy. Classification is performed using various algorithms, evaluated on sensitivity, accuracy, precision, and similarity metrics. Compared to existing techniques such as Fuzzy C-means (FCM) and hybrid Self-Organizing Mapping-K-means (SOM-FKM), the proposed method demonstrates statistically significant improvements in tissue segmentation and classification. It achieved a mean accuracy of 99.8%, reducing the Mean Squared Error (MSE) from 2.3 to 0.44 and improving the Discriminative Overlap Index (DOI) and Tissue Clarity (TC) values to 0.435105 and 0.282381, respectively. Implemented in MATLAB, this method provides a robust, efficient framework for early AD detection, surpassing existing approaches in precision and reliability.}, archive = {J_ACCESS}, author = {Karan Kumar and Shweta Agrawal and Isha Suwalka and Celestine Iwendi and Cresantus N. Biamba}, doi = {10.1109/ACCESS.2025.3533638}, journal = {IEEE Access}, pages = {22774-22783}, shortjournal = {IEEE Access}, title = {Early diagnosis of alzheimer’s disease using adaptive neuro K-means clustering technique}, volume = {13}, year = {2025}, }
• (2025). An improved yolov10n for detection of bronchoalveolar lavage cells. ACCESS, 13, 22764-22773. (www)

  @article{ , abstract = {Bronchoalveolar lavage fluid (BALF) is a liquid sample that reflects the biological status of lung tissues, containing a wealth of components such as cells and proteins. These components provide a non-invasive method to obtain pathological information about the lungs, serving as a powerful complement to traditional lung biopsies. However, the similarity in morphology and function of cells in BALF, combined with the diversity of sample processing and analysis methods, can lead to confusion in recognizing and distinguishing these cellular features. This study presents an improved Yolov10 method for the detection and classification of BALF cells, specifically targeting macrophages, lymphocytes, neutrophils, and eosinophils. The backbone network incorporates the PLWA module in place of the PSA module to enhance the acquisition of useful information, and the C2f-DC module replaces the C2f module to improve image feature extraction capabilities. Furthermore, the head network employs the Cross-Attention Fusion module (CAP) to enhance the retrieval of image information. Experimental results demonstrate that the model achieves a mean Average Precision (mAP) of 86.5% and a recall rate of 79.1%, confirming the model’s effectiveness.}, archive = {J_ACCESS}, author = {Peihe Jiang and Shaoqi Li and Yanfen Lu and Xiaogang Song}, doi = {10.1109/ACCESS.2025.3532493}, journal = {IEEE Access}, pages = {22764-22773}, shortjournal = {IEEE Access}, title = {An improved yolov10n for detection of bronchoalveolar lavage cells}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of federated and centralized learning systems in predicting cellular downlink throughput using CNN. ACCESS, 13, 22745-22763. (www)

  @article{ , abstract = {Data traffic in cellular networks has surged due to the growing number of users and high-bandwidth applications. The quality of service (QoS) for users will degrade if the network resources cannot handle the increasing traffic volume. A user’s application requires a minimum throughput to maintain QoS levels above the Service Level Agreement (SLA) promised by the operator. The network’s ability to handle traffic growth will depend on well-prepared network resource management. KPI traffic prediction is one solution to anticipate traffic surges. This study utilized Federated Learning (FL) as a machine learning paradigm and employed CNN as a model to predict throughput in the cellular network. The efficacy of the model is compared with Centralized Learning (CL) and other deep learning models, including MLP, RNN, LSTM, and GRU. The experimental results indicate that the CNN model implemented in FL outperforms both CL and the other models. The number of rounds used in the FL system is held in two rounds, and the model’s performance remains steady with an increasing number of clients, showing superior performance compared to CL.}, archive = {J_ACCESS}, author = {Kukuh Nugroho and Hendrawan and Iskandar}, doi = {10.1109/ACCESS.2025.3528527}, journal = {IEEE Access}, pages = {22745-22763}, shortjournal = {IEEE Access}, title = {Comparative analysis of federated and centralized learning systems in predicting cellular downlink throughput using CNN}, volume = {13}, year = {2025}, }
• (2025). New evaluation method for fuzzy cluster validity indices. ACCESS, 13, 22728-22744. (www)

  @article{ , abstract = {Cluster analysis is the process of associating data objects with classes of similar objects. One key aspect of the clustering problem is to determine the optimal number of groups on a data set to effectively partition it. The process of establishing accurate indicators is called cluster validity, and it evaluates the quality of a clustering, including the optimal number of clusters. A cluster validity index (CVI) is a function that carries out that evaluation based on a series of features like intra-class compactness, inter-class separation, object density, and membership degree of objects to each cluster, among others. Best-c validation protocol has been used to evaluate CVIs previously, but it makes a strong assumption about the correctness of the clustering performed. We propose a new protocol based on mapping the internal CVI values to an external CVI and performing statistical tests to analyze the results. The experiments are conducted evaluating the partitions generated by the fuzzy c-means clustering algorithm over 84 UCI datasets with 31 fuzzy CVIs. Experimental results evidenced the need for bigger experimental setups compared to previous studies. Also, they show that among the CVIs involved, there is none with a significant statistical difference over the other given by a Friedman test. However, the Wilcoxon signed-rank test demonstrates that in 1 vs. 1, the CVI KPBM outperforms the others in most of the fuzzifiers (m). Just except for $m=2.0$ which consolidates the results in previous studies where none of the CVIs were able to improve consistently among the selected group of CVIs.}, archive = {J_ACCESS}, author = {Ismay Pérez-Sánchez and Miguel Angel Medina-Pérez and Raúl Monroy and Octavio Loyola-González and Andres Eduardo Gutierrez-Rodríguez}, doi = {10.1109/ACCESS.2025.3535417}, journal = {IEEE Access}, pages = {22728-22744}, shortjournal = {IEEE Access}, title = {New evaluation method for fuzzy cluster validity indices}, volume = {13}, year = {2025}, }
• (2025). 5G fronthaul in modular p4: ECPRI protocol processing and precise BMv2 timestamps for PTP-1588. ACCESS, 13, 22710-22727. (www)

  @article{ , abstract = {P4, a domain-specific language (DSL) for programming network devices, offers flexibility in defining packet processing behaviors. This paper demonstrates the use of P4 to achieve modular eCPRI protocol processing and enhanced PTP-1588 synchronization, both critical for 5G fronthaul applications in Open Radio Access Network (O-RAN) environments. By implementing an eCPRI packet processing unit based on the eCPRI Specification Version 2 and inspired by Intel’s FPGA-based IP, we enable modular addition of new message types and custom packet processing functionality in P4. Our approach reduced lines of code per type by 85% and decreased configuration time by up to 5x compared to traditional methods, significantly simplifying complexity. Additionally, we introduce precise ingress and automatic egress timestamps for the BMv2 software switch to improve PTP-1588 accuracy, reducing error margins from 24,000 microseconds to 60 microseconds (99.75% improvement) and achieving sub-microsecond precision. Extensive testing in a Mininet environment validates these improvements, demonstrating enhanced precision and flexibility in handling time-sensitive protocols. While this paper focuses on 5G fronthaul applications in O-RAN networks, the techniques and results presented are equally applicable to other use cases across end-to-end 5G networks and beyond, paving the way for modular, high-precision, and programmable solutions in future open and interoperable network architectures.}, archive = {J_ACCESS}, author = {Atabak Nojavan and Bill Pontikakis and François-Raymond Boyer and Yvon Savaria}, doi = {10.1109/ACCESS.2025.3536362}, journal = {IEEE Access}, pages = {22710-22727}, shortjournal = {IEEE Access}, title = {5G fronthaul in modular p4: ECPRI protocol processing and precise BMv2 timestamps for PTP-1588}, volume = {13}, year = {2025}, }
• (2025). A design procedure and control method for improving efficiency of a family of resonant converters over wide operating conditions. ACCESS, 13, 22694-22709. (www)

  @article{ , abstract = {Resonant converters are widely recognized as highly efficient converters. They find numerous applications in various industries such as electric vehicles, battery chargers, and photovoltaic cells. However, low efficiency under light-load or no-load conditions is one of their main challenges, which arises when their switching frequencies deviate significantly from their resonance frequencies during output voltage regulation, resulting in high circulating currents and switching harmonics values. Here, a novel design procedure and control method are proposed for the resonant converters, which have parallel components in their resonant tank circuits to address their light-load or no-load low-efficiency issues. This control algorithm is based on the frequency modulation method and utilizes a variable capacitor or inductor in the resonant tank circuit, simultaneously. By designing each converter to operate between its two open and short-circuit resonance frequencies, and regulating its output voltage by changing the voltage gain-frequency characteristics to operate near a resonance frequency, consistently circulating currents are well limited. To validate the given method, a 100-500 W LLC resonant converter with 180-220 V input voltage and 48 V output voltage has been designed, implemented, and tested under different conditions to demonstrate its good performance. Experimental results clearly show that its efficiency exceeds 96% under all operating conditions.}, archive = {J_ACCESS}, author = {Jasem Shahsevani and Reza Beiranvand}, doi = {10.1109/ACCESS.2025.3537326}, journal = {IEEE Access}, pages = {22694-22709}, shortjournal = {IEEE Access}, title = {A design procedure and control method for improving efficiency of a family of resonant converters over wide operating conditions}, volume = {13}, year = {2025}, }
• (2025). Real-world parkinson’s hand tremor detection using ensemble learning techniques. ACCESS, 13, 22678-22693. (www)

  @article{ , abstract = {Hand tremor is a major motor symptom of Parkinson’s disease (PD) that seriously affects the life quality of patients. Conventional tremor detection methods have mainly focused on resting state. However, resting tremor is not always sufficient to accurately distinguish PD tremor from other tremor disorders such as essential tremor. Examining tremor during movement provides additional diagnostic value, but also has challenges. In this paper, we propose a novel approach to detect Parkinsonian hand tremor at walking state by exploiting the periodic synchronization between arm swings and leg steps that is observed in natural walking, specifically by using a wrist-worn sensor to capture the characteristic hand tremor that is induced during walking in Parkinson’s patients. Our method first applies a dynamic scanning mechanism to screen out valid walking fragments from whole walking sequence. We then exploit walking periodicity to combine time and frequency domain features, and design two new accelerometer-based features, Average Peak Interval Time (APIT) and Average Peak Frequency (APF), which capture the characteristic amplitude and frequency patterns of PD tremors during natural walking movements. Finally, unlike traditional approaches that use uniform feature weighting, our proposed variance adaptive AdaBoost (VA-AdaBoost) algorithm uniquely incorporates accelerometer signal variance to adaptively weight samples for feature training. The results of our five-fold cross-validation experimental evaluation using the PD-BioStampRC21 dataset show that our method achieved an accuracy of 86.67% and an AUC score of 95.56%, representing a significant improvement over the baseline model’s accuracy of 72.67% and surpassing other existing methods. Our method enables more accurate detection of subtle tremor patterns in real-world conditions compared to conventional methods. The proposed method has shown potential in the integration of wearable sensors and ensemble learning techniques for the early and accessible diagnosis of Parkinsonian hand tremor, ultimately contributing to improved patient care and treatment.}, archive = {J_ACCESS}, author = {Sungwook Hur and Jieming Zhang and Moon-Hyun Kim and Tai-Myoung Chung}, doi = {10.1109/ACCESS.2025.3534293}, journal = {IEEE Access}, pages = {22678-22693}, shortjournal = {IEEE Access}, title = {Real-world parkinson’s hand tremor detection using ensemble learning techniques}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable ultra-miniaturized MIMO antenna for tissue-independent communication in injectable medical implants. ACCESS, 13, 22663-22677. (www)

  @article{ , abstract = {This article introduces a compact, reconfigurable multiple-input-multiple-output (MIMO) antenna tailored for injectable in-body implants, designed to operate efficiently in the 2.45 GHz industrial, scientific, and medical (ISM) band, ensuring consistent performance regardless of the host tissue type, such as muscle or fat. The antenna design achieves miniaturization using meandered lines and shorting pins, while reconfigurability is obtained by adjusting the meandered length with an ON-OFF switch (diode). When the diode is ON, the antenna’s meandered length is increased, thereby extending its effective length and enabling resonance in the 2.42–2.48 GHz range for fat tissues. Conversely, when the diode is OFF, the effective length is reduced, allowing the same antenna to operate within the 2.38–2.48 GHz range in muscle tissues. The proposed antenna, fabricated on a flexible polyimide substrate ( $\varepsilon _{r} =3.5$ ), with overall dimensions of 5 mm $\times 2$ mm $\times 0.05$ mm, is enclosed within a biocompatible polyimide casing. The antenna’s performance is evaluated through simulations in homogeneous single-layer (fat/muscle) and three-layer (skin, fat, and muscle) phantoms, along with a realistic heterogeneous hand model, and validated through an ex-vivo setup, which includes fabricating the antenna prototype and measuring its performance in pork meat and a phantom solution. This innovative, miniaturized, tissue-independent MIMO antenna achieves isolation greater than 15 dB via orthogonal arrangement and 1 mm element spacing, with impedance bandwidths of 60 MHz in fat and 100 MHz in muscle. Furthermore, MIMO characteristics are evaluated and antenna robustness and safety are verified through specific absorption rate measurements using voxel models, highlighting the antenna’s suitability for injectable applications.}, archive = {J_ACCESS}, author = {L. Harlan and M. Susila and Sachin Kumar and Hyun Chul Choi and Kang Wook Kim}, doi = {10.1109/ACCESS.2025.3535780}, journal = {IEEE Access}, pages = {22663-22677}, shortjournal = {IEEE Access}, title = {Reconfigurable ultra-miniaturized MIMO antenna for tissue-independent communication in injectable medical implants}, volume = {13}, year = {2025}, }
• (2025). Compact ku-band orthomode transducer using plus-shaped waveguide. ACCESS, 13, 22656-22662. (www)

  @article{ , abstract = {This paper presents a compact Ku-band orthomode transducer (OMT) utilizing a plus-shaped waveguide (P-WG), which allows for the effective separation of vertical and horizontal polarization fields in a compact form factor. The proposed P-WG, with a characteristic impedance intermediate between that of the input square waveguide (S-WG) and the output rectangular waveguide (R-WG), enables the use of an iris for impedance matching instead of a bulky multi-step impedance transformer, thereby facilitating miniaturization. Additionally, small-square waveguides (SS-WGs) are employed to effectively suppress in-band spurious resonances, further broadening the bandwidth. Measurements of the fabricated OMT demonstrate notable characteristics: return loss exceeding 10 dB for vertical polarization from 13.5 to 17.5 GHz and for horizontal polarization from 13.6 to 16.7 GHz, insertion loss below 0.8 dB, isolation over 46 dB, and cross-polarization levels above 41 dB, confirming its suitability for Ku-band applications.}, archive = {J_ACCESS}, author = {Jongmin Lee and Sungmin Noh and Sangpil Lee and Jinho Jeong}, doi = {10.1109/ACCESS.2025.3535503}, journal = {IEEE Access}, pages = {22656-22662}, shortjournal = {IEEE Access}, title = {Compact ku-band orthomode transducer using plus-shaped waveguide}, volume = {13}, year = {2025}, }
• (2025). Six-phase fully coreless axial-flux permanent-magnet generator: Design, fabrication, and performance analysis. ACCESS, 13, 22647-22655. (www)

  @article{ , abstract = {Coreless axial-flux permanent-magnet (AFPM) machines have recently gained prominence as efficient power generation solutions. Eliminating the core resolves challenges such as eddy current losses and cogging torque, enhancing overall efficiency. This topology enables high power density, further amplified in fully coreless configurations. Nevertheless, the proliferation of coreless AFPM machines faces hurdles as to high leakage flux and weaker structural integrity. Additionally, adopting multi-phase setups in AFPM introduces challenges, requiring additional winding coils and enlarging the stator diameter, leading to bulkier machine designs. This article underscores the significance of a lightweight, fully coreless six-phase machine and proposes a modular, multi-stage, multi-phase design tailored for fault-tolerant small-scale power generation applications. Furthermore, it explores the impact of employing double-layer winding coils on air-gap-flux density and power output. To realize this aim, a six-phase multi-stage AFPM generator was designed and simulated using ANSYS Maxwell. Subsequently, a laboratory-scale prototype was fabricated accordingly. The simulation findings closely align with the experimental results obtained from the prototype, demonstrating the achievement of 1.3 kW power output. Notably, the six-phase machine with fully coreless concept exhibits a substantial power density of 205 W/kg. Multi-stage design boost efficiency and power density, presenting potential for small-scale applications.}, archive = {J_ACCESS}, author = {Turki M. Alsuwian and Asiful Habib and Muhammad Ammirrul Atiqi Mohd Zainuri and Ahmad Asrul Ibrahim and Nasrudin Abd Rahim and Adam R. H. Alhawari and Abdulkarem H. M. Almawgani and Saleh Almasabi and A. T. Hindi and Mohamed Fauzi Packeer Mohamed}, doi = {10.1109/ACCESS.2025.3526165}, journal = {IEEE Access}, pages = {22647-22655}, shortjournal = {IEEE Access}, title = {Six-phase fully coreless axial-flux permanent-magnet generator: Design, fabrication, and performance analysis}, volume = {13}, year = {2025}, }
• (2025). Low complexity unquantized forward stack decoding algorithm for spinal codes in measurement while drilling communication. ACCESS, 13, 22637-22646. (www)

  @article{ , abstract = {The complex environments of Measurement-While-Drilling (MWD), characterized by intense vibrations and high temperatures, can lead to random attenuation of optical intensity, phase mismatch, and signal distortion, thereby reducing the signal-to-noise ratio (SNR) of optical signals. Under severe channel fluctuations, forward stack decoding (FSD) tends to search more invalid nodes, increasing decoding complexity. This paper proposes an unquantized forward stack decoding algorithm that dynamically adjusts the number of retained candidate nodes based on channel conditions. With the ability to evaluate channel status dynamically, the proposed method achieves low-complexity decoding and enhances transmission performance. In simulations under channel conditions with SNR ranging from -14 to 5 dB, the average decoding complexity was reduced by 58% compared to conventional FSD. In communication experiments using a dynamically variable optical attenuator to generate random attenuation between 0 and -30 dB, the average decoding complexity was reduced by 23.6% compared to FSD. Furthermore, this work integrates the non-quantized forward stack decoding with the superposition UEP-Spinal code to construct a novel MWD communication system. In simulated MWD environment, the proposed system achieved an average bit error rate (BER) of $5.58\times 10^{-5}$ , with BER consistently below 0.1% across all intervals. Compared to the traditional Spinal code system, the decoding complexity was reduced by 31.5%. The results demonstrate that the newly developed communication system exhibits high reliability, low decoding latency, and high effective transmission rates in simulated drilling platform scenarios, making it well-suited for MWD communication applications.}, archive = {J_ACCESS}, author = {Xiaoyang Yu and Lei Liang and Ke Jiang and Tianwei Chen}, doi = {10.1109/ACCESS.2025.3531649}, journal = {IEEE Access}, pages = {22637-22646}, shortjournal = {IEEE Access}, title = {Low complexity unquantized forward stack decoding algorithm for spinal codes in measurement while drilling communication}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of wearable antennas with flexible periodic structures for body-effect mitigation. ACCESS, 13, 22590-22636. (www)

  @article{ , abstract = {Recent advancements in wireless technology have prompted the development of wearable antennas, particularly for Wireless Body Area Networks (WBANs) used in sports, military activities, healthcare, and identification systems. Unlike traditional antennas, wearable antennas operate very close to the curved human body. Consequently, their performance metrics such as reflection coefficient, bandwidth, radiation characteristics, efficiency, gain, directivity, and Specific Absorption Rate (SAR) can be significantly affected by interactions with human body tissues. To address these challenges, periodic structures are integrated into wearable antenna designs to mitigate the effects of coupling and absorption by the human body. To date, a limited number of works have surveyed, compared, and categorized the proposed wearable antenna designs integrated with periodic structure based on their operating frequency bands and their interactions with the human body. To this end, this paper contributes to the literature by detailing the selection of materials, highlighting the limitations of wearable antennas, characterizing the periodic structures, elaborate on dielectric properties data of human body models, discussing the influence of periodic structures on antenna performance and their significance in isolating them from the human body. Furthermore, this study provides an in-depth review of 81 papers evaluating the performance of antennas integrated with periodic structures, both in free space and on the human body, across single-band, dual-band, and triple-band designs. The selected papers were chosen based on their publication in reputable journals, the quality of their research, and the comprehensiveness of their data analysis. In addition, it provides a summary of several common flexible materials used in wearable antennas with their electrical properties as well as the dielectric properties of the human body for different frequencies. Moreover, it discusses the challenges and considerations involved in designing appropriate wearable antennas.}, archive = {J_ACCESS}, author = {Adel Ashyap and Raad Raad and Faisel Tubbal and Wajid Ali Khan and Suhila Abulgasem}, doi = {10.1109/ACCESS.2025.3536525}, journal = {IEEE Access}, pages = {22590-22636}, shortjournal = {IEEE Access}, title = {Comprehensive review of wearable antennas with flexible periodic structures for body-effect mitigation}, volume = {13}, year = {2025}, }
• (2025). Cloud platform selection model in the framework of differential quality game with fuzzy information. ACCESS, 13, 22578-22589. (www)

  @article{ , abstract = {The paper presents a mathematical model integrated into the computational core of a decision support system (DSS) for the selection of cloud platforms under conditions of fuzzy information. The model facilitates the development of rational strategies for players’ interaction with cloud platforms, allowing to balance investment, security and quality of services provided. This study proposes a methodology for determining optimal strategies in cloud platform selection using a differential quality game under fuzzy conditions, taking into account their preferences and opportunities to use technologies related to the analysis of user activities, i.e., digital footprints. The performed computational experiments confirmed the effectiveness of the model and allowed us to visualize the results of the interaction based on synthetic data. The results of the study may be useful to analysts dealing with cloud solution selection problems under uncertainty and cybersecurity needs. The relevance of the study is due to the global trend of business processes moving to the cloud and the increasing requirements for data security in the digitalization of the economy, which makes the choice of an optimal cloud platform strategically significant. Employing a differential quality game with fuzzy information, this study introduces a reliable DSS that incorporates cost, security and QoS to select the most appropriate cloud platform. The computational experiments validate the model in reproducing plausible interactions yielding relevant insights for decision-makers.}, archive = {J_ACCESS}, author = {Kaiyrbek Makulov and Arkadii A. Chikrii and Valery Lakhno and Bagdat Yagaliyeva and Volodimir Malyukov and Inna Malyukova and Miroslav Lakhno}, doi = {10.1109/ACCESS.2025.3535814}, journal = {IEEE Access}, pages = {22578-22589}, shortjournal = {IEEE Access}, title = {Cloud platform selection model in the framework of differential quality game with fuzzy information}, volume = {13}, year = {2025}, }
• (2025). DataLab as a service: Distributed computing framework for multi-interactive analysis environments. ACCESS, 13, 22566-22577. (www)

  @article{ , abstract = {During the current data era, data analysis across multiple disciplines has become a critical task for researchers to obtain meaningful insights and solve complex problems that are immeasurable using traditional technologies. Big Data has led to the development of state-of-the-art technologies that have revolutionized the process of experimentation. These innovations span from automating the setup of the infrastructure required for data analysis to providing user-friendly interfaces that simplify coding and result visualization. However, managing and scaling these resources for large-scale data processing remains a challenge. In this work, we introduce a novel framework called Datalab as a Service which integrates cutting-edge and open-source technologies to offer an online platform designed for both resource providers and researchers. The platform enables users to easily and automatically deploy interactive environments tailored for data analysis, thereby streamlining the process of managing computational resources. Through DLaaS, users gain access to cloud-based infrastructures and distributed computing resources, which are essential for performing compute-intensive tasks on massive datasets. The framework ensures scalability, resource management and optimization, and high availability, all within an accessible and user-friendly platform. Furthermore, this paper presents several use cases where researchers have successfully utilized DLaaS resources, demonstrating its practical applications in real-world scenarios.}, archive = {J_ACCESS}, author = {Aida Palacio Hoz and Andrés Heredia Canales and Ezequiel Cimadevilla Álvarez and Marta Obregón Ruiz and Álvaro López García}, doi = {10.1109/ACCESS.2025.3536637}, journal = {IEEE Access}, pages = {22566-22577}, shortjournal = {IEEE Access}, title = {DataLab as a service: Distributed computing framework for multi-interactive analysis environments}, volume = {13}, year = {2025}, }
• (2025). Effective lock detectors based on costas loop output for SBPSK mobile communications. ACCESS, 13, 22551-22565. (www)

  @article{ , abstract = {In this study, we address the problem of lock detection at the output of a Costas loop when robust modulations for bandwidth-constrained military communications are used. In this respect, we propose two novel detectors that share the important constant false alarm rate property with respect to the noise power. The first decision scheme is an improvement of the detector proposed in (Nan et al., 2021), whereas the second architecture is grounded on the likelihood ratio test where in place of the unknown parameters suitable estimates are used. Such estimates are obtained through an iterative strategy relying on the Expectation Maximization algorithm. Moreover, when possible, we provide exact statistical characterizations of the considered decision schemes (including the competitors) or, in the other case, suitable approximations that allow for a straightforward performance evaluation. The numerical examples are obtained by means of simulated data and in comparison with the most common lock detectors for this kind of communications. The numerical results highlight that the proposed detectors represent a viable means to manage real-time lock detection.}, archive = {J_ACCESS}, author = {D. Ferramola and T. Casilli and S. Pradella and G. Giunta and D. Orlando}, doi = {10.1109/ACCESS.2025.3536360}, journal = {IEEE Access}, pages = {22551-22565}, shortjournal = {IEEE Access}, title = {Effective lock detectors based on costas loop output for SBPSK mobile communications}, volume = {13}, year = {2025}, }
• (2025). Determinants of ICT adoption and performance in healthcare: The role of individual attributes and organizational contextual factors. ACCESS, 13, 22535-22550. (www)

  @article{ , abstract = {Implementing Information and Communication Technologies (ICTs) in the healthcare sector faces several barriers, and the pace of adoption may vary depending on several factors, including individual attributes such as competence and experience, along with contextual factors such as size, type, and location of the organization. We conducted a global survey involving healthcare professionals (n =204) to understand the role of various determinants in adopting ICTs and their implications for organizational performance. Data was analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) and multiple regression for micro-level constructs. Findings indicate that while individual attributes positively influence organizational performance, the direct impact of ICT adoption on performance is limited. While front-end technologies enhance performance, base technologies may occasionally hinder it. This stresses the necessity of thoroughly assessing specific technology applications to ensure they operate effectively. Furthermore, the relationships between competence and experience in ICT adoption were not statistically significant, indicating that individual attributes may have less influence on ICT adoption; this implies that technological advancements typically occur through management decisions in collaboration with the relevant IT department, emphasizing the importance of increased user participation and the process of adoption and effective use, which are distinct aspects while evaluating individual aspects. Future research should focus on detailed and specific applications of technology and nuanced, long-term performance outcomes.}, archive = {J_ACCESS}, author = {Abey Jose and Alejandro F. Mac Cawley and Luis Enberg Gaete and Guilherme Luz Tortorella and Roberto Vassolo and Manisha Kumar and Nick Rich and Maneesh Kumar}, doi = {10.1109/ACCESS.2025.3536089}, journal = {IEEE Access}, pages = {22535-22550}, shortjournal = {IEEE Access}, title = {Determinants of ICT adoption and performance in healthcare: The role of individual attributes and organizational contextual factors}, volume = {13}, year = {2025}, }
• (2025). Tropospheric ducting: A comprehensive review and machine learning-based classification advancements. ACCESS, 13, 22510-22534. (www)

  @article{ , abstract = {Tropospheric ducts, characterized by anomalous positive vertical refractive index gradients, significantly influence radio wave propagation by enhancing signal strength and extending communication ranges. While beneficial in some contexts, these phenomena can disrupt communication systems, particularly in maritime and coastal regions where ducting conditions are prevalent. This paper addresses the challenge of accurately predicting and mitigating the effects of tropospheric ducts on radio propagation, which is critical for the reliability of next-generation communication systems. We present a comprehensive review of radio propagation in tropospheric ducts, encompassing fundamental concepts, theoretical models, and estimation techniques. To enhance the classification of anomalous propagation in the troposphere, including ducting, we develop machine learning models based on real-world data obtained from King Fahd University of Petroleum and Minerals (KFUPM). Our findings demonstrate that machine learning models, particularly support vector machines, can effectively classify ducting conditions, offering superior predictive performance compared to other models. The advancements presented in this paper address the challenges faced by next-generation radio systems and contribute to the development of effective mitigation strategies. The insights gained pave the way for further research and optimization of communication system performance in duct-prone environments.}, archive = {J_ACCESS}, author = {Mohammed Banafaa and Ali Hussein Muqaibel}, doi = {10.1109/ACCESS.2025.3537160}, journal = {IEEE Access}, pages = {22510-22534}, shortjournal = {IEEE Access}, title = {Tropospheric ducting: A comprehensive review and machine learning-based classification advancements}, volume = {13}, year = {2025}, }
• (2025). Lightweight AES design for IoT applications: Optimizations in FPGA and ASIC with DFA countermeasure strategies. ACCESS, 13, 22489-22509. (www)

  @article{ , abstract = {With the increasing interconnectivity of devices, the Internet of Things (IoT) has revolutionized the industry and daily life. However, the proliferation of IoT devices has also increased security risks, which requires robust protection mechanisms for sensitive data and critical infrastructure. The Advanced Encryption Standard (AES) remains the benchmark for securing IoT systems while balancing low power consumption, minimal area usage, and moderate throughput with high security. This paper offers a comprehensive review of the latest lightweight AES architectural designs, including optimizations to the Substitution Box (S-Box), Sub-Bytes, Shift Rows, Mix Columns, and Add Round Key steps, assessing their impact on gate count, area, maximum frequency, power consumption, and throughput in field programable gate arrays (FPGA) and Application-specific integrated circuit (ASIC) implementations. In addition, this study addresses vulnerabilities in lightweight AES cryptographic hardware to side-channel attacks (SCA), specifically focusing on Differential Fault Analysis (DFA). In addition, the analysis explores fault scenarios, rounds, and injection positions to assess the severity of the fault. In addition, the study reviews DFA countermeasures that highlight fault detection methods, error detection levels, protection positions, and associated design overheads such as area, frequency, and throughput penalties, with special consideration for resource-constrained IoT devices. This study identifies critical gaps in lightweight AES and security challenges while discussing countermeasures that balance security with design efficiency. Finally, this study provides valuable insights for finding research directions to strengthen the robustness of AES in lightweight IoT environments.}, archive = {J_ACCESS}, author = {Salman Ahmed and Nabihah Ahmad and Nasir Ali Shah and Ghulam E. Mustafa Abro and Ardhi Wijayanto and Abdinasir Hirsi and Abdul Rehman Altaf}, doi = {10.1109/ACCESS.2025.3533611}, journal = {IEEE Access}, pages = {22489-22509}, shortjournal = {IEEE Access}, title = {Lightweight AES design for IoT applications: Optimizations in FPGA and ASIC with DFA countermeasure strategies}, volume = {13}, year = {2025}, }
• (2025). Eliminating network depth: Genetic algorithm for parameter optimization in CNNs. ACCESS, 13, 22473-22488. (www)

  @article{ , abstract = {Recent advances in Convolutional Neural Networks (CNNs) have significantly enhanced image classification performance. However, CNNs often require large numbers of parameters, leading to increased computational complexity, prolonged training times, and substantial resource demands. Achieving higher classification accuracy typically involves deepening network architectures, which further exacerbates these challenges. This paper proposes a novel method based on a genetic algorithm to optimize parameter selection, enabling the construction of CNNs that achieve superior accuracy with fewer parameters. By focusing on parameters with the most significant impact on performance, the method reduces the need for deeper networks, thereby minimizing computational costs. Experimental results demonstrate that the proposed algorithm outperforms its counterparts. For instance, the generated CNN achieves an accuracy improvement of 0.75 percentage points over ResNet-110 while using 84% fewer parameters. These findings highlight the method’s potential to balance efficiency and accuracy, making it a promising solution for resource-constrained applications.}, archive = {J_ACCESS}, author = {M. Askari and S. Soleimani and M. H. Shakoor and M. Momeni}, doi = {10.1109/ACCESS.2025.3533976}, journal = {IEEE Access}, pages = {22473-22488}, shortjournal = {IEEE Access}, title = {Eliminating network depth: Genetic algorithm for parameter optimization in CNNs}, volume = {13}, year = {2025}, }
• (2025). Graph coarsening approach to the vehicle routing problem: An approximation strategy. ACCESS, 13, 22459-22472. (www)

  @article{ , abstract = {In the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, solving complex optimization problems such as the Vehicle Routing Problem (VRP) remains a formidable challenge. To overcome this obstacle, we introduce a novel method in this paper that focuses on reducing the number of edges in a graph based on Euclidean distances between vertices while preserving the crucial characteristics needed for solving the VRP. Our graph coarsening approach combined with the inflation method enables the reduced graph to be processed efficiently by classical, quantum or hybrid (quantum-classical) solvers, resulting in a faster computation time. When using the proposed method with hybrid algorithms, we have demonstrated an improvement of approximately 50% in the solution found by the hybrid solver. The practicality of our method on quantum hardware highlights its potential to contribute to the advancement of quantum and hybrid algorithms and applications during the NISQ era.}, archive = {J_ACCESS}, author = {Katarzyna Nałęcz-Charkiewicz and Arnav Das and Turbasu Chatterjee and Joshua Keene and Paweł Gora and Carlos C. N. Kuhn}, doi = {10.1109/ACCESS.2025.3534677}, journal = {IEEE Access}, pages = {22459-22472}, shortjournal = {IEEE Access}, title = {Graph coarsening approach to the vehicle routing problem: An approximation strategy}, volume = {13}, year = {2025}, }
• (2025). A cross-regional load forecasting method based on a pseudo-distributed federated learning strategy. ACCESS, 13, 22446-22458. (www)

  @article{ , abstract = {Accurate load forecasting serves as the core foundation for grid planning and operations. Traditional load forecasting methods often rely solely on historical load data from a single region for training, making the models region-specific and leading to significant accuracy degradation when applied to other regions. This limits the generalization ability of these models to cross-regional load forecasting tasks. To address this issue, this study proposed a collaborative training strategy based on pseudo-distributed federated learning. Inspired by the pseudo-distributed concept, this strategy builds multiple sub-models by serially training load datasets from different regions on the same server. After a certain number of local epochs for each sub-model, parameter aggregation was performed. The aggregated parameters are then updated into each sub-model, and this process is repeated during each global epoch until the model converges, ultimately forming a global model capable of forecasting loads across multiple regions. Experiments demonstrated that this strategy exhibited exceptional generalization ability across various deep learning models, federated learning methods, and datasets.}, archive = {J_ACCESS}, author = {Jinsong Deng and Shaotang Cai and Weinong Wu and Rong Jiang and Hongyu Deng and Jinhua Ma and Yonghang Luo}, doi = {10.1109/ACCESS.2025.3536097}, journal = {IEEE Access}, pages = {22446-22458}, shortjournal = {IEEE Access}, title = {A cross-regional load forecasting method based on a pseudo-distributed federated learning strategy}, volume = {13}, year = {2025}, }
• (2025). Novel fusion technique for high-performance automated crop edge detection in smart agriculture. ACCESS, 13, 22429-22445. (www)

  @article{ , abstract = {Optimising vegetable production systems is crucial for maintaining and enhancing agricultural productivity, particularly for crops like lettuce. Separating the crop from the background poses a significant challenge when using automated tools. To address this, a novel technique has been developed to automatically detect the vegetative area of lettuces, optimising time and eliminating subjectivity during crop inspections. The proposed deep learning model integrates the YOLOv10 object detector, the K-means classifier, and a segmentation method known as superpixel. This combination enables lettuce area identification using bounding box labels instead of contour labels during training, improving efficiency compared to other methods like YOLOv8 and Detectron2. Additionally, the combination of the YKMS method with YOLOv8 (YKMSV8) is evaluated, where YKMS serves as a label assistant. These methods are also used as benchmarks to compare the proposed approach. For the training of each methods, a custom database has been created using a low-cost, low-power custom IoT node deployed on a real farm to provide the most accurate data. Throughout the comparison, a custom metric is used to evaluate performance both in training and inference, balancing computational cost and area error, making it applicable in agriculture. Performance metric is associated with computational cost factor and accuracy factor whose value are respectively 65% and 35%, ensuring applicability for autonomous agricultural devices. Computational cost is prioritised to maintain battery life during extended campaigns. The results of the custom metric during inference indicated that the YKMSV8 method achieved the highest performance, followed by Detectron2, YOLOv8, and, lastly, YKMS. Regarding area error, YOLOv8 exhibited the lowest mean error, followed by Detectron2, while YKMSV8 and YKMS produced similar values. In terms of inference time, YKMSV8 was the most computationally efficient, followed by YOLOv8, YKMS, and, finally, Detectron2.}, archive = {J_ACCESS}, author = {F. Martínez and James B. Romaine and P. Johnson and A. Cardona Ruiz and Pablo Millán Gata}, doi = {10.1109/ACCESS.2025.3536701}, journal = {IEEE Access}, pages = {22429-22445}, shortjournal = {IEEE Access}, title = {Novel fusion technique for high-performance automated crop edge detection in smart agriculture}, volume = {13}, year = {2025}, }
• (2025). AI-driven meat food drying time prediction for resource optimization and production planning in smart manufacturing. ACCESS, 13, 22420-22428. (www)

  @article{ , abstract = {The meat food manufacturing industries play a crucial role in delivering various meat products to global consumers. However, one of the significant challenges within this industry is optimizing food processing efficiency across various stages, as it directly affects both product quality and production costs. Drying is one of the crucial stages, wherein moisture is extracted from the meat to reach the desired moisture levels. This prevents spoilage and influences product quality, safety, and overall production efficiency. The drying time is variable, contingent on factors such as the type of meat, quantity, environmental factors, and the desired product characteristics. This variability contributes to the complexity and multifaceted nature of the issue. Conventional approaches for estimating drying times often depend on empirical rules or manual observations, which can be time-consuming, subjective, and susceptible to human error. Therefore, implementing an automation solution by developing a predictive model for drying times in meat manufacturing is essential for optimizing the production lifecycle. Recognizing the potential of advanced computational techniques, machine learning algorithms have demonstrated promising results across various predictive tasks in recent years. Building on this, this research paper aims to explore the utilization of machine learning methods in predicting the drying time of meat-based food products incorporating multiple factors including structure and properties of food, environmental factors, food mass, and physical parameters of food containers. Furthermore, the paper explores correlations, performs feature importance analysis, and addresses the challenges and limitations within this context.}, archive = {J_ACCESS}, author = {Rajnish Rakholia and Andrés L. Suárez-Cetrulo and Manokamna Singh and Ricardo Simón Carbajo}, doi = {10.1109/ACCESS.2025.3534918}, journal = {IEEE Access}, pages = {22420-22428}, shortjournal = {IEEE Access}, title = {AI-driven meat food drying time prediction for resource optimization and production planning in smart manufacturing}, volume = {13}, year = {2025}, }
• (2025). A dynamic landslide warning model based on grey system theory. ACCESS, 13, 22407-22419. (www)

  @article{ , abstract = {Landslides are among the most frequent and destructive geological disasters, causing significant loss of life and property. Research on landslide prediction is crucial for minimizing disaster-related losses, ensuring public safety, and developing reliable early warning systems and preparedness strategies. Existing prediction technologies are broadly classified into mechanical modeling methods and mathematical modeling methods. Compared to mechanical modeling, mathematical modeling relies on data analysis, enabling the use of existing monitoring data without requiring detailed geological information. Among these, the grey model stands out for its high accuracy with minimal information requirements, as well as its simplicity and ease of implementation. Based on these advantages, grey model-based landslide prediction has emerged as a key research focus. This paper proposes a dynamic landslide warning model that integrates the concept of risk with grey system theory, using a small amount of real-time monitoring data. Experimental results demonstrate the model’s effectiveness in accurately predicting landslides and providing timely warnings for unstable slopes.}, archive = {J_ACCESS}, author = {Ke Ma and He Weng and Yumin Chen and Saeed Sarajpoor and Yang Shen}, doi = {10.1109/ACCESS.2025.3536888}, journal = {IEEE Access}, pages = {22407-22419}, shortjournal = {IEEE Access}, title = {A dynamic landslide warning model based on grey system theory}, volume = {13}, year = {2025}, }
• (2025). Drone video anomaly detection by future segmentation prediction and spatio- temporal relational modeling. ACCESS, 13, 22395-22406. (www)

  @article{ , abstract = {In traffic surveillance, accurate video anomaly detection is vital for public safety, yet environmental changes, occlusions, and visual obstructions pose significant challenges. In this research, we introduce DAD-FSM, an innovative drone-based video anomaly detection system that leverages a spatio-temporal relational cross-transformer to enhance the encoding of visual and temporal features for future segmentation. Additionally, we propose the motion-aware frame prediction loss function (MAFL) to improve the model’s representation and the background and foreground separation of moving objects. Our method achieves state-of-the-art (SOTA) AUC scores of 68.13% on the UIT-ADrone dataset and 73.5% mAUC on the Drone-Anomaly dataset, surpassing previous methods by 2.68% and 5.71% respectively. The approach is further validated on the CUHK Avenue dataset, underscoring its global applicability and effectiveness in diverse traffic scenarios. These results demonstrate the potential of our model for broad use in traffic surveillance applications.}, archive = {J_ACCESS}, author = {Ahmed Fakhry and Janghoon Lee and Jong Taek Lee}, doi = {10.1109/ACCESS.2025.3536623}, journal = {IEEE Access}, pages = {22395-22406}, shortjournal = {IEEE Access}, title = {Drone video anomaly detection by future segmentation prediction and spatio- temporal relational modeling}, volume = {13}, year = {2025}, }
• (2025). Developing data-driven QFD: A systemic approach to employing product manuals and customer reviews. ACCESS, 13, 22380-22394. (www)

  @article{ , abstract = {Despite the importance of data analytics, quality function deployment (QFD) development remains both qualitative and expert-driven. Although some studies have been conducted on data-driven QFD, most have relied solely on quantifying customer requirements, neglecting the quantification of engineering characteristics from a data-driven approach. This study aims to develop a new approach to data-driven QFD using customer reviews and product manuals. Through an in-depth investigation of the product manual structure, this study suggests a systematic method for extracting engineering characteristics and interpreting data-driven QFD. The results are expected to provide practical guidelines for the QFD literature as well as product planning practice by suggesting a systematic framework for developing a data-driven approach and holistic approach. Furthermore, this study aims to ensure a more comprehensive understanding of customer needs and engineering capabilities, thereby enhancing the overall effectiveness of QFD in product development.}, archive = {J_ACCESS}, author = {Gamunnarbi Park and Shinho Kim and Youngjung Geum}, doi = {10.1109/ACCESS.2025.3532658}, journal = {IEEE Access}, pages = {22380-22394}, shortjournal = {IEEE Access}, title = {Developing data-driven QFD: A systemic approach to employing product manuals and customer reviews}, volume = {13}, year = {2025}, }
• (2025). Assessment of teaching quality of traditional decorative patterns in colleges and universities using an MCDM approach and circular intuitionistic information. ACCESS, 13, 22365-22379. (www)

  @article{ , abstract = {Evaluating the teaching quality of traditional decorative patterns in colleges and universities is critical, especially when human judgment and subjective opinions are involved. This paper aims to analyze this assessment process using an enhanced framework of circular intuitionistic fuzzy sets (C-IFS) and the Dombi triangular norm. The aim of this research is to apply the Dombi t-norm (DTN) and t-conorm (DTCN) for aggregating the information within the circular intuitionistic fuzzy (C-IF) framework to evaluate the teaching quality of traditional decorative patterns. Another important feature of the proposed study is that prioritization is used in decision-making preferences by using a priority-based weight vector. To serve this purpose, we mainly proposed the Circular Intuitionistic Fuzzy Dombi Prioritized weighted averaging (C-IFDPWA) and Circular Intuitionistic Fuzzy Dombi Prioritized geometric (C-IFDPWG) operators. Moreover, the special cases of proposed aggregation operators (AOs) and their validity are discussed. An intelligent algorithm is provided involving prioritization of attributes and experts based on proposed AOs to evaluate the multi-criteria group decision-making (MCGDM) problem, particularly in assessing teaching quality. A numerical example is included to demonstrate the effectiveness and flexibility of the proposed work, and to validate its application, a comparison analysis has been conducted. The superiority of this model lies in the fact that general structure, less time complexity, and prioritization of factors make it real. The suggested framework prioritizes decision criteria and manages ambiguity well, making it a reliable tool for assessing the quality of instruction. A case study demonstrates its practical relevance and highlights the advantages of applying fuzzy logic to assess the teaching quality of traditional decorative patterns in colleges and universities.}, archive = {J_ACCESS}, author = {Zi Wang and Liping Pan}, doi = {10.1109/ACCESS.2025.3534843}, journal = {IEEE Access}, pages = {22365-22379}, shortjournal = {IEEE Access}, title = {Assessment of teaching quality of traditional decorative patterns in colleges and universities using an MCDM approach and circular intuitionistic information}, volume = {13}, year = {2025}, }
• (2025). Optimizing energy and spectral efficiency in mobile networks: A comprehensive energy sustainability framework for network operators. ACCESS, 13, 22342-22364. (www)

  @article{ , abstract = {The urgent need to meet Environmental, Social, and Governance net-zero commitments and the financial risks posed by rising energy costs, are placing increasing pressure on Mobile Network Operators (MNOs) to optimise energy use. Given the significant energy consumption from Base Stations (BSs) and the fact that Fifth Generation (5G) deployments are not meeting expectations for reduced energy consumption when compared to Fourth Generation (4G), MNOs are shifting from a load-centric optimisation approach to a service-centric paradigm that balances spectral efficiency and energy efficiency. This paper introduces the Energy Sustainability Framework, which provides a composite index to evaluate the spectral and energy efficiencies of BSs, summarised across four classes: A, B, C, and D. This framework supports network auditing, benchmarking, monitoring, and energy efficiency optimization while ensuring the desired Quality of Service (QoS). By leveraging Machine Learning (ML) and Explainable Artificial Intelligence (XAI) techniques applied to live data - including Performance Management (PM) and Energy Management (EM) metrics - integrated with domain expertise, the framework goes beyond deterministic computation to identify the root-cause factors influencing the BSs’ energy class, thereby enabling targeted optimisation. The framework can be flexibly applied at the location (site), BS (cell), or equipment type (hardware) levels. Notably, the implemented ML and XAI model achieved an F1-Score of 0.78 for 5G scenarios and 0.83 for 4G scenarios in predicting the Energy Sustainability Class, relying on a reduced set of indicators encompassing the most influential factors. An additional analysis, based on domain expertise, provided evidence of potential causal relationships between the class and observed variables involving channel quality, Multiple Input Multiple Output (MIMO) utilisation, network topology and energy-saving functionalities. For example, the results show that using higher order modulation coding schemes reduces the probability of a BS falling into class D by 50%. These findings provide practical insights for developing more sustainable network operations.}, archive = {J_ACCESS}, author = {Luís Mata and Marco Sousa and Pedro Vieira and Maria Paula Queluz and António Rodrigues}, doi = {10.1109/ACCESS.2025.3536281}, journal = {IEEE Access}, pages = {22342-22364}, shortjournal = {IEEE Access}, title = {Optimizing energy and spectral efficiency in mobile networks: A comprehensive energy sustainability framework for network operators}, volume = {13}, year = {2025}, }
• (2025). Real-time overshoot and undershoot detection in cellular networks. ACCESS, 13, 22325-22341. (www)

  @article{ , abstract = {One of the most crucial aspects of cellular networks is coverage, as it determines the areas where users can connect to the network and utilize its services. In the past, the use of planning tools was common practice for the establishment of coverage areas and network capacity prior to the deployment of a network. However, issues with coverage, such as interference or coverage gaps, may arise due to equipment malfunctions, suboptimal configurations, or alterations in the propagation environment. In particular, an inadequate antenna tilt configuration can result in overshoot or undershoot situations on the network, which in turn can give rise to the aforementioned problems. This paper proposes a methodology for the real-time detection of overshoot and undershoot situations. To achieve this goal, KPI (Key Performance Indicators) are analyzed using machine learning techniques. Given the difficulty of detecting coverage problems in mobile networks, the results obtained suggest that the methodology provides a consistent knowledge base for optimizing the antenna tilt, thereby improving network performance.}, archive = {J_ACCESS}, author = {José Antonio Trujillo and Rasmus Lykke and Isabel de-la-Bandera and Søren Søndergaard and Troels B. Sønrensen and Raquel Barco and Preben E. Mogensen}, doi = {10.1109/ACCESS.2025.3537327}, journal = {IEEE Access}, pages = {22325-22341}, shortjournal = {IEEE Access}, title = {Real-time overshoot and undershoot detection in cellular networks}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning for reconfigurable robotic soccer. ACCESS, 13, 22314-22324. (www)

  @article{ , abstract = {Robots are showing great impact and, in recent trends, appearing in areas such as education and entertainment. Robotic soccer is becoming more prevalent in competitions, furthering research in robotics and artificial intelligence. Reconfigurable robotics is used in application domains such as cleaning, multi-terrain locomotion, and logistical support, but reconfigurability has yet to be introduced in robotic soccer. Using reconfigurable robots provides increased flexibility and adaptability in the game of soccer. This paper proposes Reinforcement Learning (RL) to train an agent to kick a ball toward a goal using reconfiguration. RL was used with the Proximal Policy Optimisation (PPO) algorithm to train and optimise goal scoring. The environment was developed and trained in Unity. Training included the agent learning to approach the ball in an optimal position to hit the ball into a goal using reconfiguration. Two use cases of penalty and free kicks were used to validate the accuracy of the proposed model, which resulted in goal conversion of 81% and 67%, respectively. Moreover, the results confirm that this method allows a reconfigurable robot to adapt to the soccer field and perform the best move out of the myriad possibilities in this complex yet competitive game.}, archive = {J_ACCESS}, author = {M. Shameer Ahamed and J. J. J. Pey and S. M. Bhagya P. Samarakoon and M. A. Viraj J. Muthugala and Mohan Rajesh Elara}, doi = {10.1109/ACCESS.2025.3536497}, journal = {IEEE Access}, pages = {22314-22324}, shortjournal = {IEEE Access}, title = {Reinforcement learning for reconfigurable robotic soccer}, volume = {13}, year = {2025}, }
• (2025). Methods for user-controlled synthesis of blood vessel trees in medical applications: A survey. ACCESS, 13, 22300-22313. (www)

  @article{ , abstract = {Various applications in medicine require geometric models of the underlying blood vessel networks. This ranges from anatomical visualizations, via surgical training systems, to machine learning-based anatomical segmentation frameworks. Especially the latter require large amounts of data. However, both manual segmentation of vascular imaging data, as well as hand-crafting of vessel trees by 3D modellers is tedious and time-consuming. As a possible remedy, approaches for automated synthesis of vessel trees have been proposed in the past. These differ in controllabity by a user, the parameters affecting the visual results, and the computational performance. Therefore, in this paper we provide a survey of state-of-the-art methods for blood vessel synthesis, with a focus on user-control. We introduce several selected key approaches, survey options for steering the outcome, and discuss advantages and disadvantages for achieving certain characteristics of the synthesized trees. In the end, we aim to provide insights into optimizing and selecting vascular synthesis models for specific medical applications.}, archive = {J_ACCESS}, author = {Nikolaus Rauch and Matthias Harders}, doi = {10.1109/ACCESS.2025.3536998}, journal = {IEEE Access}, pages = {22300-22313}, shortjournal = {IEEE Access}, title = {Methods for user-controlled synthesis of blood vessel trees in medical applications: A survey}, volume = {13}, year = {2025}, }
• (2025). Robot dynamic path planning based on prioritized experience replay and LSTM network. ACCESS, 13, 22283-22299. (www)

  @article{ , abstract = {To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. First, Prioritized Experience Replay (PER) is utilized to prioritize experience data and optimize storage and sampling operations through the SumTree structure, rather than the conventional experience queue. Second, the LSTM network is introduced to enhance the dynamic adaptability of the DDQN algorithm. Owing to the introduction of the LSTM model, the experience samples must be sliced and populated. The performance of the proposed LPDDQN algorithm is compared with five other path planning algorithms in both static and dynamic environments. Simulation analysis shows that in a static environment, LPDDQN demonstrates significant improvements over traditional DDQN in terms of convergence, number of moving steps, success rate, and number of turns, with respective improvements of 24.07%, 17.49%, 37.73%, and 61.54%. In dynamic and complex environments, the success rates of all algorithms, except TLD3 and the LPDDQN, decreased significantly. Further analysis reveals that the LPDDQN outperforms the TLD3 by 18.87%, 2.41%, and 39.02% in terms of moving steps, success rate, and number of turns, respectively.}, archive = {J_ACCESS}, author = {Hongqi Li and Peisi Zhong and Li Liu and Xiao Wang and Mei Liu and Jie Yuan}, doi = {10.1109/ACCESS.2025.3532449}, journal = {IEEE Access}, pages = {22283-22299}, shortjournal = {IEEE Access}, title = {Robot dynamic path planning based on prioritized experience replay and LSTM network}, volume = {13}, year = {2025}, }
• (2025). Physical implications and physics-based design conditions of nominal inertia matrix for operational space perturbation observer. ACCESS, 13, 22270-22282. (www)

  @article{ , abstract = {This study investigated the physical implications and design conditions for a nominal inertia matrix for operational space perturbation observer-based control, including operational space disturbance observer-based control and operational space time delay control. Previous reports indicated that using a constant diagonal nominal inertia matrix deteriorated control performance and led to instability. However, the underlying physical implications of the constant diagonal nominal inertia matrix and its effect on control performance remained unclear for decades. We identified that the main problem arises from the loss of information on the robot’s kinematics. The loss resulted in a joint space representation of the operational space nominal inertia matrix that is neither positive-definite nor uniformly bounded, with its principal inertia becoming zero at singular configurations. Moreover, uncompensated residual coupled dynamics of the robot are significantly amplified by the inverse of a principal inertia approaching zero, disturbing the closed-loop dynamics and causing dynamic coupling and even instability. In light of these findings, physics-based conditions for selecting the operational space nominal inertia matrix were proposed: operational space nominal inertia must be positive-definite, and its joint space representation must be positive-definite and uniformly bounded from below. Extensive experiments using a 2 degree-of-freedom robot verified these physical implications and physics-based conditions. Our findings are expected to provide insights into the role and selection criteria of the operational space nominal inertia matrix, thereby promoting the practical application of the operational space perturbation observer in various robot applications requiring robust, efficient, and straightforward control.}, archive = {J_ACCESS}, author = {Sang Hyun Park and Seongil Hwang and Murim Kim and Sang Hoon Kang}, doi = {10.1109/ACCESS.2025.3536799}, journal = {IEEE Access}, pages = {22270-22282}, shortjournal = {IEEE Access}, title = {Physical implications and physics-based design conditions of nominal inertia matrix for operational space perturbation observer}, volume = {13}, year = {2025}, }
• (2025). Research progress in computational methods for system-level coupling of electromagnetic pulse. ACCESS, 13, 22259-22269. (www)

  @article{ , abstract = {This paper reviews recent system-level analysis methods for electromagnetic pulse coupling with local and network systems. For local systems, starting from the electromagnetic topology (EMT) method which utilizes the Baum-Liu-Tesche (BLT) equation, the time-domain BLT equation and the transient EMT method are reviewed. Their abilities to analyze nonlinear systems are examined. The transient EMT method is not only applicable to nonlinear cases but can also be easily integrated with circuit or device analysis. For large network systems, direct analysis presents significant challenges. A simplified method based on the effective coupling length approach and its application in high-altitude electromagnetic pulse coupling with power grids are discussed. This simplified method reduces the complexity of analysis from $N^{2}$ to N, where N represents the number of lines in the network. Additionally, a prediction model that utilizes artificial neural networks (ANN) for HEMP coupling with overhead lines is presented. Finally, the challenges faced in system-level coupling analysis are outlined and suggestions for future research are offered.}, archive = {J_ACCESS}, author = {Haiyan Xie and Jianguo Wang}, doi = {10.1109/ACCESS.2025.3536787}, journal = {IEEE Access}, pages = {22259-22269}, shortjournal = {IEEE Access}, title = {Research progress in computational methods for system-level coupling of electromagnetic pulse}, volume = {13}, year = {2025}, }
• (2025). A near-zero energy smart greenhouse integrated into a microgrid for sustainable energy and microclimate management. ACCESS, 13, 22243-22258. (www)

  @article{ , abstract = {This paper presents a novel smart greenhouse integrated into a microgrid (SGIM) designed to optimize energy and microclimate management for sustainable agriculture. The SGIM integrates photovoltaic (PV) panels, a micro-combined heat and power (micro-CHP) unit, and an energy storage system to deliver efficient, localized energy generation and management. Within this framework, a Nonlinear Model Predictive Control (NMPC) and an Extended Kalman Filter (EKF) are employed to regulate critical microclimate parameters such as temperature, relative humidity, CO2 concentration, and lighting intensity, while optimally managing energy storage to reduce grid power imports. The NMPC minimizes a cost function encompassing multiple objectives and constraints, whereas the EKF enhances control precision by addressing measurement errors and model noise. Simulations revealed that the SGIM met over 83% of its energy needs through local generation, with only 3.8% sourced from the external grid. This approach offers an effective solution for achieving near-zero energy consumption in sustainable agriculture, with scalability for various greenhouse types and sizes.}, archive = {J_ACCESS}, author = {Tuan Minh Tran and Ahmed Ouammi and Louis A-Dessaint}, doi = {10.1109/ACCESS.2025.3537536}, journal = {IEEE Access}, pages = {22243-22258}, shortjournal = {IEEE Access}, title = {A near-zero energy smart greenhouse integrated into a microgrid for sustainable energy and microclimate management}, volume = {13}, year = {2025}, }
• (2025). Outdoor massive MIMO testbed with directional beams: Design, implementation, and validation. ACCESS, 13, 22230-22242. (www)

  @article{ , abstract = {Massive MIMO has emerged as a key technology for the deployment of 5G networks, significantly enhancing network capacities. By simultaneously scheduling multiple users on the same time and frequency resources, substantial improvements in network capacity and spectral efficiency can be achieved. However, various bottlenecks in deployments, such as limited antenna directivity, interference from neighboring sectors, and power division among scheduled layers, restrict the achievable number of paired users, ultimately limiting network capacity. Current deployments demonstrate around $8-16$ user pairings in downlink using 64 antenna ports (128 antenna elements) in a 120° sector by utilizing multiuser MIMO techniques. In this work, we study the achievable multiuser MIMO gains in an outdoor environment by deploying narrow directional beams using a rectangular antenna structure having 48 antenna ports (192 antenna elements), each port with a beamwidth of 30° in azimuth and 90° in elevation. By leveraging this two-dimensional structure, we explore the two dimensions to identify spatially uncorrelated users for pairing. Through extensive outdoor evaluations using the testbed, we demonstrate the capability of pairing up to 10 users simultaneously within the 30° sector using QPSK modulation scheme. Furthermore, our analysis of normalized pairing opportunities for all users revealed an unequal distribution of pairing opportunities among different users. To address this, we introduced a fair scheduling algorithm alongside user pairing, which reduces the simultaneous number of paired users to 7 with QPSK modulation within the 30° sector, ensuring equal pairing opportunities for all users. Additionally, to support higher modulation scheme we modified the correlation threshold and achieved a pairing of 4 users with 16QAM modulation, and 3 users with 64QAM modulation scheme. Our experimental results indicate that the use of fixed directional beams to offer 360° coverage using a set of antenna panels results in substantial multiuser MIMO gains compared to existing 5G multiuser MIMO methods. All our findings are supported by comprehensive experimental analyses detailed in this paper.}, archive = {J_ACCESS}, author = {Spandan Bisoyi and Pasupuleti Muralimohan and Harish Kumar Dureppagari and Pavan Reddy Manne and Kiran Kuchi}, doi = {10.1109/ACCESS.2025.3536157}, journal = {IEEE Access}, pages = {22230-22242}, shortjournal = {IEEE Access}, title = {Outdoor massive MIMO testbed with directional beams: Design, implementation, and validation}, volume = {13}, year = {2025}, }
• (2025). Functionally-equivalent formalization and automated model checking of function block diagrams. ACCESS, 13, 22197-22229. (www)

  @article{ , abstract = {In the development and verification of safety-critical and safety-related Instrumentation and Control (I&C) systems, it is essential to ensure there is no deviation from the requirements of the assignment during development. Model checking is a method of formal verification which can be used to prove whether a formal model satisfies its formal requirement. Since algorithms of I&C systems are generally informal, they can not be verified by model checking directly, but they must be carefully translated. This article presents a new method based on functionally-equivalent formalisation and model checking. This method can be used for automatic verification of I&C algorithms by model checking while preserving obtained proofs from a formalised model in the original algorithm. There are several problems associated with the verification of PLCs by model checking: 1) State space explosion, 2) Model consistency, 3) Specifying Properties to be Checked, 4) Representing PLC execution cycle, 5) TONs timers representation. This aims of this article is to address points 1), 2), 4) and 5). The article also presents conditions for implementing these algorithms in a target I&C system under which the obtained proofs can also be expected in the physical I&C system. This article primarily focuses on formalisation and model checking of Function Block Diagram (FBD) algorithms. However, the presented methods can also be extended to other programming languages.}, archive = {J_ACCESS}, author = {Tomas Ausberger and Karel Kubicek and Pavla Medvedcova}, doi = {10.1109/ACCESS.2025.3535890}, journal = {IEEE Access}, pages = {22197-22229}, shortjournal = {IEEE Access}, title = {Functionally-equivalent formalization and automated model checking of function block diagrams}, volume = {13}, year = {2025}, }
• (2025). Design of an improved method for visual rendering in the metaverse using CIEM and MSRANet. ACCESS, 13, 22176-22196. (www)

  @article{ , abstract = {The metaverse is a fast-growing frontier in virtual reality that requires future visual rendering techniques to realize better user experience. Most of the existing approaches are normally challenged by wide-angle views and computational efficiency, with personalization at low energy consumption for the best possible user experience and engagement. This paper alleviates these challenges by proposing a set of innovative models tailored to optimize visual rendering in deployments of the metaverse. The Cooperative Insect Eye model can then bio-inspire compound insect eyes for wide-angle and high-resolution panoramas with low distortion to increase field-of-view coverage by 20% and reduce rendering timestamp by 15%. Multi-Scale Residual Attention Network combines residual learning with the attention mechanism at multiple scales, achieving a latency reduction of 25% and an image quality improvement of 10% to balance high visual fidelity with computational efficiency. Adaptive User Profiling and Vision Enhancement (AUPVE), allows dynamic changes of the visual settings based on real-time user data, raising the level of satisfaction by 30% and session time—by 20%. Anticipatory Scene Rendering (ASR) utilizes predictive modeling in order to allow for the pre-rendering of scenes based on user behavior, in this way significantly reducing latency by 40% with an accuracy of 85% in seamless navigation. Finally, BEER, standing for Bioinspired Energy-Efficient Rendering, borrows from the energy-efficient way of visual processing in the human brain through a spiking neural network that reduces energy consumption by 35% without image quality degradation. On the whole, these models have substantially improved the state of the art of metaverse rendering, with far-reaching ramifications for future virtual reality environments by improving the user experience to become more immersive, personalize and efficient.}, archive = {J_ACCESS}, author = {Janapati Venkata Krishna and Priyanka Singh and Regonda Nagaraju and Setti Vidya Sagar Appaji and Attuluri Uday Kiran and K. Spandana}, doi = {10.1109/ACCESS.2025.3532634}, journal = {IEEE Access}, pages = {22176-22196}, shortjournal = {IEEE Access}, title = {Design of an improved method for visual rendering in the metaverse using CIEM and MSRANet}, volume = {13}, year = {2025}, }
• (2025). MLOps components, tools, process, and metrics: A systematic literature review. ACCESS, 13, 22166-22175. (www)

  @article{ , abstract = {With the growing popularity of machine learning, implementations of the environment for developing and maintaining these models, called MLOps, are becoming more common. The number of publications in this area is relatively small, although growing rapidly. Our goal was to review the current state of the literature in the MLOps area and answer the following research questions: What classes of tools are used in MLOps environments? Which tool implementations are the most popular? What processes are implemented within MLOps? What metrics are used to measure the effectiveness of MLOps implementation? Based on this review, we identified classes of tools included in the MLOps architecture, along with their most popular implementations. While some tools originate from DevOps practices, others, such as Model Orchestrators, Feature Stores, and Model Repositories, are unique to MLOps. We propose a reference MLOps architecture based on these findings and outline the stages of the model production process. We also sought metrics that would allow us to assess and compare the effectiveness of MLOps practices, but unfortunately, we were unable to find a satisfactory answer in this area.}, archive = {J_ACCESS}, author = {Adrian P. Woźniak and Mateusz Milczarek and Joanna Woźniak}, doi = {10.1109/ACCESS.2025.3534990}, journal = {IEEE Access}, pages = {22166-22175}, shortjournal = {IEEE Access}, title = {MLOps components, tools, process, and metrics: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). 2.5D inductive intertwined frequency selective surface for band-pass and high miniaturization applications. ACCESS, 13, 22156-22165. (www)

  @article{ , abstract = {This paper presents a detailed design, simulation, measurement, and validation of an Inductive Intertwined Frequency Selective Surface (IIFSS) employing a 2.5D configuration (2.5DIIFSS). The proposed unit cell achieves high miniaturization, with dimensions as small as $0.0096\lambda _{0} \times 0.0096\lambda _{0}$ at 0.268 GHz, using 4 vias per unit cell. The compact design delivers a fractional bandwidth of 87% for the passband associated with the fundamental harmonic. Additionally, the unique 2.5D configuration introduces extra inductance and capacitance, effectively shifting higher harmonics to higher frequencies, thus maintaining a stable stop band beyond the fundamental harmonic. The angular stability analysis reveals minimal variation up to an incidence angle of 60° for both TE and TM modes across the fundamental harmonic. To elucidate the underlying physics, an equivalent circuit model was developed, accurately capturing the fundamental harmonic behavior of the structure. To further validate the design and demonstrate its scalability, a prototype was designed and fabricated for operation at 2.4 GHz, addressing the measurement challenges associated with the original 0.268 GHz design. This prototype was rigorously tested in a transmission regime, with measurement results showing good agreement with simulation data, thereby confirming the efficacy and practicality of the proposed design.}, archive = {J_ACCESS}, author = {Juan Andrés Vásquez-Peralvo and Rocio Chueca Lasheras and Juan Carlos Merlano Duncan and Shuai Zhang and Pavel Pechac and Václav Kabourek and Symeon Chatzinotas}, doi = {10.1109/ACCESS.2025.3535565}, journal = {IEEE Access}, pages = {22156-22165}, shortjournal = {IEEE Access}, title = {2.5D inductive intertwined frequency selective surface for band-pass and high miniaturization applications}, volume = {13}, year = {2025}, }
• (2025). O2O-PLB: A one-to-one-based optimizer with priority and load balancing mechanism for resource allocation in fog-cloud environments. ACCESS, 13, 22146-22155. (www)

  @article{ , abstract = {In the era of the Internet of Things (IoT), Fog and Cloud computing have become critical frameworks for managing large-scale, distributed systems. However, the challenge of optimizing resource allocation remains significant, especially in dynamic and diverse environments. This paper presents O2O-PLB, a new One-to-One-Based Optimizer with Priority and Load Balancing mechanism aimed at improving resource allocation in Fog-Cloud settings. O2O-PLB adopts a priority-based approach, assigning tasks according to urgency, system limitations, and available resources, while its load balancing feature ensures an even distribution of tasks to prevent congestion and inefficiency. The method integrates Fog and Cloud resources effectively, boosting system performance and reducing latency. Simulation results show that O2O-PLB outperforms traditional resource allocation methods in resource usage, response times, and latency reduction. Based on the experimental results, the O2O-PLB algorithm significantly outperforms the benchmark algorithms across essential performance metrics at varying task loads. In terms of response time, O2O-PLB achieves an average reduction of 30% over Greedy-LC, 40% over GA, 45% over MOPSO, and 55% compared to EALB. For latency, O2O-PLB achieves an average decrease of 25% relative to Greedy-LC, 35% over GA, 40% compared to MOPSO, and 50% over EALB. When it comes to load imbalance, O2O-PLB consistently improves by approximately 60% over both MOPSO and EALB, 50% over GA, and 40% over Greedy-LC, indicating strong task distribution capabilities. In terms of task failure rate, O2O-PLB reduces failures by 65% compared to EALB, 50% over GA, 40% over MOPSO, and 35% over Greedy-LC. The findings suggest that O2O-PLB provides an effective solution for optimizing Fog-Cloud resource management, making it a promising tool for future IoT applications.}, archive = {J_ACCESS}, author = {V. C. Bharathi and S. Syed Abuthahir and Monelli Ayyavaraiah and G. Arunkumar and Usama Abdurrahman and Sardar Asad Ali Biabani}, doi = {10.1109/ACCESS.2025.3536210}, journal = {IEEE Access}, pages = {22146-22155}, shortjournal = {IEEE Access}, title = {O2O-PLB: A one-to-one-based optimizer with priority and load balancing mechanism for resource allocation in fog-cloud environments}, volume = {13}, year = {2025}, }
• (2025). A review on person re-identification techniques and its analysis. ACCESS, 13, 22133-22145. (www)

  @article{ , abstract = {Person re-identification (Re-ID) emerges as a captivating realm within computer vision, dedicated to the task of recognizing the same individual across diverse camera angles or locations. The realm of video-based person re-identification (video re-ID) has recently captivated increasing interest, owing to its wide array of practical applications spanning surveillance, smart city solutions, and public safety measures. Nevertheless, video re-ID proves to be a formidable challenge, an ever-evolving domain fraught with a multitude of uncertainties like viewpoint variations, occlusions, pose changes, and unpredictable video sequences. Over the past few years, the realm of deep learning applied to video re-ID has consistently delivered remarkable outcomes on public datasets, showcasing a range of innovative strategies devised to tackle the array of issues encountered in video re-ID. In stark contrast to image-based re-ID, video re-ID stands out as significantly more intricate and demanding. In a bid to inspire forthcoming research endeavors and confronts emerging challenges, this paper presents a comprehensive overview of the latest advancements in deep learning methodologies tailored for video re-ID. It delves into three crucial facets; encompassing succinct explanations of video re-ID techniques along with their constraints, pivotal breakthroughs coupled with the technical hurdles faced, and the architectural framework underpinning these developments. The paper further furnishes a comparative analysis of performance across diverse datasets, offers insightful guidance on enhancing video re-ID strategies, and outlines compelling avenues for future research exploration.}, archive = {J_ACCESS}, author = {C. Selvan and H. Anwar Basha and K. Meenakshi and Soumyalatha Naveen}, doi = {10.1109/ACCESS.2025.3536478}, journal = {IEEE Access}, pages = {22133-22145}, shortjournal = {IEEE Access}, title = {A review on person re-identification techniques and its analysis}, volume = {13}, year = {2025}, }
• (2025). Robot differential drive navigation through probabilistic roadmap and pure pursuit. ACCESS, 13, 22118-22132. (www)

  @article{ , abstract = {This research addresses autonomous navigation for differential drive robots by integrating probabilistic roadmap (PRM) and pure pursuit algorithms. The proposed method innovatively tackles path planning challenges in complex environments by developing a novel framework that combines forward kinematics, binary occupancy mapping, and adaptive path generation. The approach demonstrates superior navigation performance through efficient path planning in constrained spaces, validated by a case study in Riyadh, Saudi Arabia. By using PRM’s probabilistic path generation and pure pursuit’s real-time control, the method outperforms traditional navigation techniques in generating feasible trajectories through intricate environments. Key contributions include a comprehensive framework for robot navigation that offers enhanced adaptability and robust path planning. While being limited to static environments, the research provides a foundational approach for developing more resilient autonomous robotic systems, setting the stage for future navigation algorithm advancements.}, archive = {J_ACCESS}, author = {Maram Ali and Saptarshi Das and Stuart Townley}, doi = {10.1109/ACCESS.2025.3536383}, journal = {IEEE Access}, pages = {22118-22132}, shortjournal = {IEEE Access}, title = {Robot differential drive navigation through probabilistic roadmap and pure pursuit}, volume = {13}, year = {2025}, }
• (2025). SACWOM: Synergistic adaptive congestion window optimization mechanism for self-clocked algorithm. ACCESS, 13, 22086-22117. (www)

  @article{ , abstract = {Congestion Control (CC) is essential in networked systems, especially in environments with strict delay and throughput requirements. While CC algorithms like Self-Clocked Rate Adaptation for Multimedia (SCReAM) and Bottleneck Bandwidth and Round-trip propagation time (BBR) have shown progress, each faces limitations: BBR encounters difficulty in balancing responsiveness and fairness when rapid fluctuations in network conditions arise, which can lead to inefficient performance in shared environments. On the other hand, although SCReAM is designed for multimedia traffic, it struggles to dynamically adapt the congestion window to unpredictable or highly congested networks, leading to inefficient resource utilization. This paper proposes SACWOM, a hybrid congestion window optimization mechanism that combines our novel method with SCReAM’s rate control and BBR’s bandwidth-delay estimation to enhance throughput stability, fairness, and adaptability by dynamically adjusting the congestion window. Extensive simulations demonstrate SACWOM’s significant improvements over SCReAM in managing congestion window and bytes in flight under diverse network conditions. In Phase I, SACWOM achieved up to 11.96-13.27% increase in congestion window and bytes in flight by maintaining higher data flow. Phase II analysis shows up to 20.76-22.64% improvements with optimized configurations. Finally, Phase III, comprising 100 experiments, reveals SACWOM’s dynamic adaptability, achieving up to 50-70% improvements. These results highlight SACWOM as a robust mechanism suitable for various applications across diverse network scenarios.}, archive = {J_ACCESS}, author = {Haider Dhia Zubaydi and Ahmed Samir Jagmagji and Sándor Molnár}, doi = {10.1109/ACCESS.2025.3536884}, journal = {IEEE Access}, pages = {22086-22117}, shortjournal = {IEEE Access}, title = {SACWOM: Synergistic adaptive congestion window optimization mechanism for self-clocked algorithm}, volume = {13}, year = {2025}, }
• (2025). Joint optimization in underwater image enhancement: A training framework integrating pixel-level and physical-channel techniques. ACCESS, 13, 22074-22085. (www)

  @article{ , abstract = {In recent years, with the increasing interest in marine research, the need to collect and process clear underwater optical images has become crucial. However, underwater images suffer from the absorption and scattering effects of the environment. In this paper, we propose Hybrid Underwater Image Enhancement Network (HUWIE-Net), a novel deep learning-based underwater image enhancement framework consisting of three distinct sections, which include an Image-to-Image Module, a Physics-Informed Module and a Fusion Module. The training methodology of HUWIE-Net is designed to jointly optimize both pixel-level-based and physical-channel-based enhancement modules. In this framework, while Image-to-Image Module is used for color correction in pixel level, Physics-Informed Module is used for dehazing by exploiting the underwater image formation model which defines the deformations in the underwater light propagation channel. We also propose to use the joint loss function for both Image-to-Image Module and Physics-Informed Module to enforce the joint optimization for better underwater image enhancement performance. The results of experiments conducted with real-world underwater images show that the proposed model achieves improved performance compared to state-of-the-art methods. The code for the newly developed HUWIE-Net is available at https://github.com/UIE-Lab/HUWIE-Net.}, archive = {J_ACCESS}, author = {Ozan Demir and Metin Aktas and Ender M. Eksioglu}, doi = {10.1109/ACCESS.2025.3536173}, journal = {IEEE Access}, pages = {22074-22085}, shortjournal = {IEEE Access}, title = {Joint optimization in underwater image enhancement: A training framework integrating pixel-level and physical-channel techniques}, volume = {13}, year = {2025}, }
• (2025). A real-time intelligent system based on machine-learning methods for improving communication in sign language. ACCESS, 13, 22055-22073. (www)

  @article{ , abstract = {In this article, we introduce a cost-effective and real-time intelligent system tailored to Pakistan sign language (PSL) recognition, aimed at facilitating communication for hearing-impaired individuals. The system utilizes a specialized glove equipped with flex sensors and an MPU-6050 device to capture finger movements and hand orientation in a three-dimensional space. A dataset comprising ten unique PSL signs, each performed by five participants for a total of 5000 samples, was used to train machine learning classifiers. These signs involve single-hand and single-movement gestures, optimizing the system for real-time PSL recognition. Machine learning classifiers, including decision trees, k-nearest neighbors, and support vector machines, achieved accuracy levels of 96%, 96.5%, and 97%, respectively. While direct quantitative comparisons with state-of-the-art systems are limited due to the uniqueness of PSL, we discuss our system in the context of recent advancements in sign language recognition. Real-time testing underscores the system’s practical applicability and portability, demonstrating its potential for deployment in resource-constrained settings as an accessible initial step toward more comprehensive PSL recognition solutions.}, archive = {J_ACCESS}, author = {Víctor Leiva and Muhammad Zia Ur Rahman and Muhammad Azeem Akbar and Cecilia Castro and Mauricio Huerta and Muhammad Tanveer Riaz}, doi = {10.1109/ACCESS.2025.3529025}, journal = {IEEE Access}, pages = {22055-22073}, shortjournal = {IEEE Access}, title = {A real-time intelligent system based on machine-learning methods for improving communication in sign language}, volume = {13}, year = {2025}, }
• (2025). A novel audio copy move forgery detection method with classification of graph-based representations. ACCESS, 13, 22029-22054. (www)

  @article{ , abstract = {The rapid advancement of digital environments has led to an increase in multimedia forgery, particularly in the realm of audio, which leads to significant threats to the reliability of digital evidence. This paper presents a novel method to detect audio copy-move forgery, a type of manipulation where segments of an audio file are duplicated and moved to different locations within the same file. The proposed method consists of two main stages. In the first stage, the frequency range containing the forged segments is identified by extracting high-resolution spectrograms from the audio and matching keypoints within the spectrogram images to detect duplicated segments. The frequency range of the sub-spectrogram images with the highest match density is considered the location of the repeated segments. A swarm-based optimization approach is used to adaptively determine this dense region. The audio is then a bandpass filtered using the identified frequency range, and the second stage begins. In this stage, the filtered audio is represented as a graph using the proposed spiral pattern information extraction method. Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. It provides a robust solution for verifying audio authenticity, even under various additional attack scenarios, and shows potential for generalization.}, archive = {J_ACCESS}, author = {Beste Ustubioglu and Gul Tahaoglu and Arda Ustubioglu and Guzin Ulutas and Muhammed Kilic}, doi = {10.1109/ACCESS.2025.3535840}, journal = {IEEE Access}, pages = {22029-22054}, shortjournal = {IEEE Access}, title = {A novel audio copy move forgery detection method with classification of graph-based representations}, volume = {13}, year = {2025}, }
• (2025). Autonomous real-time smoothness control for reliable DDQN-based UAV navigation using cellular networks. ACCESS, 13, 22011-22028. (www)

  @article{ , abstract = {Reliable Unmanned Aerial Vehicle (UAV) navigation in urban environments is a crucial prerequisite for major civilian and military applications. Many existing Global Positioning System (GPS)-based UAV navigation solutions do not meet the performance requirements given their unreliability in urban environments. In this paper, we present a smooth trajectory planning approach to generate reliable UAV trajectories with less chatter and sharp turns. We propose to utilize broadcast signals from existing cellular networks to practically navigate the UAV from a given source to a destination in urban environments independent of GPS or other transmissible signals. For this purpose, we formulate the smooth trajectory planning problem as an optimization problem to provide a probabilistic guarantee on the success of the UAV mission considering the UAV dynamic and kinematic constraints. We utilize proper optimization-based techniques to determine the optimal bound of the solution for benchmarking purposes. Next, we propose a machine learning based approach to provide a practical real-time solution to the formulated UAV navigation problem. Finally, we present an in-depth comparative analysis to evaluate the performance of the proposed double deep Q-network (DDQN)-based technique as compared to other solutions from the literature.}, archive = {J_ACCESS}, author = {Ghada Afifi and Yasser Gadallah}, doi = {10.1109/ACCESS.2025.3531931}, journal = {IEEE Access}, pages = {22011-22028}, shortjournal = {IEEE Access}, title = {Autonomous real-time smoothness control for reliable DDQN-based UAV navigation using cellular networks}, volume = {13}, year = {2025}, }
• (2025). A physics-based hyper parameter optimized federated multi-layered deep learning model for intrusion detection in IoT networks. ACCESS, 13, 21992-22010. (www)

  @article{ , abstract = {The Internet of Things (IoT) is reshaping our lives with its omnipresence. The sudden uptick in the ubiquitous nature of IoT devices ranging from fitness watches to aircraft has led to a surge of cyber-attacks. Artificial Intelligence powered Intrusion Detection Systems (IDS) are being used recently to combat this increasing surge of attacks in the IoT environment. However, existing solutions lack optimization for training in distributed decentralized environments. A popular solution for training a model in a decentralized environment is Federated Learning. Multiple client models collaboratively train a global model while keeping the individual client’s data decentralized and private. This, however, suffers from poor generalization of the individual client data. This work proposes a new Federated Multi-Layered Deep-Learning (Fed-MLDL) model that employs physics-based hyperparameter optimization (HPO) technique FedRIME in a distributed federated learning environment for intrusion detection on the CICIoT23, CICIoT22, ToN_IoT, Edge_IIoT and, IoT-23 datasets. FedRIME ensures good generalization for all clients’ data by finetuning the model’s hyperparameters according to each client. The experimental results indicate that the Fed-MLDL with Fed-RIME optimization exhibits the highest accuracy for independent and identically distributed datasets with the scores being 99.2% with CICIoT23, 98.1% with CICIoT22, 98.2% with ToN_IoT, 98.5% with Edge_IIoTset and, 98.6% with IoT-23 dataset respectively. Further, the proposed Fed-MLDL with Fed-RIME optimization has demonstrated a significant improvement in the speed of convergence, stability, and client specific customization in federated learning. The study provides a comprehensive comparison with the most recent physics based HPO techniques. This study observes that coupling a Deep-Learning model with HPO techniques results in a much faster convergence requiring only 10-15 communication rounds. The proposed Fed-MLDL with Fed-RIME optimization outperforms existing state-of-the-art models on the CIC-IoT23 dataset.}, archive = {J_ACCESS}, author = {Chirag Jitendra Chandnani and Vedik Agarwal and Shlok Chetan Kulkarni and Aditya Aren and D. Geraldine Bessie Amali and Kathiravan Srinivasan}, doi = {10.1109/ACCESS.2025.3535952}, journal = {IEEE Access}, pages = {21992-22010}, shortjournal = {IEEE Access}, title = {A physics-based hyper parameter optimized federated multi-layered deep learning model for intrusion detection in IoT networks}, volume = {13}, year = {2025}, }
• (2025). FedBFGCN: A graph federated learning framework based on balanced channel attention and cross-layer feature fusion convolution. ACCESS, 13, 21980-21991. (www)

  @article{ , abstract = {Graph Federated Learning (GFL) is an emerging distributed training paradigm that combines federated learning with graph data. Due to its ability to effectively handle complex and heterogeneous graph data while protecting user privacy, GFL has shown great potential in processing various types of graph structures and has been proven effective in a wide range of applications. However, existing methods normally assign equal attention to all nodes within a single graph, focusing too much on the information of neighboring nodes, even if some nodes are more important in the graph structure or task (such as high consumption users or popular products), which inevitably leads to inefficient node embedding. To address this issue, this paper proposes an innovative graph federated learning framework called FedBFGCN (Graph Federated Learning Based on Balanced Channel Attention and Cross-Layer Feature Fusion Convolution) to optimize the embedding and analysis efficiency of graph data. This proposed framework converts single graph data into node features and adjacency matrices for processing, and combines a customized Cross-Layer Feature Fusion Convolution(CLF) and an improved Attention Mechanism that is Balanced Channel Attention Mechanism (BCAM). The FedBFGCN improves the attention to important nodes by dynamically weighting and adjusting the weights of features through BCAM; Using CLF effectively integrates its own features with neighbor information, enhancing feature expression capability. Through the organic fusion of these two modules, the FedBFGCN achieves efficient, robust, and more comprehensive node embedding representation, demonstrating excellent performance in node classification and prediction tasks. In addition, this framework also uses homomorphic encryption methods to enhance privacy protection and improve data security. The FedBFGCN was evaluated on standard reference network datasets (Cora, Citeseer, Polblogs), and experimental results showed that it has lower losses and higher performance in multiple aspects. This framework is capable of addressing various challenges in graph federated learning, significantly improving learning effectiveness and application capabilities. This study not only provides new ideas for graph federated learning and GCN, but also demonstrates its enormous potential in practical applications.}, archive = {J_ACCESS}, author = {Hefei Wang and Ruichun Gu and Jingyu Wang and Xiaolin Zhang and Hui Wei}, doi = {10.1109/ACCESS.2025.3536001}, journal = {IEEE Access}, pages = {21980-21991}, shortjournal = {IEEE Access}, title = {FedBFGCN: A graph federated learning framework based on balanced channel attention and cross-layer feature fusion convolution}, volume = {13}, year = {2025}, }
• (2025). Chip design of PTP time synchronization for industrial IoT. ACCESS, 13, 21966-21979. (www)

  @article{ , abstract = {The world is currently in the midst of the fourth industrial revolution (also known as Industry 4.0). Its core concept is to apply technologies such as computer networking, big data, artificial intelligence and cloud services to manufacturing. Because information between each other must be communicated immediately to ensure that each device in the system performs work at an accurate time. Therefore, each device in the computer network needs a precise reference time, and high-precision time synchronization technology plays an indispensable role. After a detailed study of the IEEE 1588 protocol, this paper found that the main sources of time errors are frequency changes caused by the temperature characteristics and aging of the crystal oscillator, the uncertainty of network protocol stacking delay, and the queuing delay caused by packets. In order to improve time accuracy and reduce delays caused by network protocol stacking, this study designed the chip on the data link layer and optimized the original IEEE 1588 protocol. These optimization methods include compensating clock skew and removing run-trip delay Time is too large mechanism. Finally, this paper designed an IEEE 1588 protocol chip located on the data connection layer based on these methods, and achieved an operating frequency of 434MHz under the 45nm process. Experimental results show that the time synchronization value can be maintained within ±26 nanoseconds. This can not only effectively reduce the delay caused by network protocol stacking, but also meet the requirements for time synchronization accuracy of the modern industrial Internet of Things.}, archive = {J_ACCESS}, author = {Yee-Shao Chen and Yuan-Sun Chu and da-Wei Lo and Yen-Ting Chiang and Ting-Chao Hou}, doi = {10.1109/ACCESS.2025.3533384}, journal = {IEEE Access}, pages = {21966-21979}, shortjournal = {IEEE Access}, title = {Chip design of PTP time synchronization for industrial IoT}, volume = {13}, year = {2025}, }
• (2025). MLPR: YOLOv3 for real-time license plate recognition in moroccan video streams. ACCESS, 13, 21956-21965. (www)

  @article{ , abstract = {Automated License Plate Detection and Recognition (ALPR) is a technology that identifies and reads license plates in transportation and surveillance systems. However, ALPR is a challenging task due to several factors, including differences in vehicle license plates between countries, a lack of public datasets, environmental factors that can decrease the performance of recognition models, and challenging lighting conditions for vehicle license plates. In this work, we present an ALPR system for real-time applications specifically designed to detect and recognize Moroccan license plates. Our contribution is threefold. First, we employ the YOLOv3 algorithm to extract valuable features from input scenes and detect vehicles. Second, we track vehicles and assign a unique id to each detected vehicle. Finally, we detect and recognize the plates of tracked vehicles based on computer vision techniques and a voting system. The latter is a post-processing step that selects the most consistent content as the final prediction. Furthermore, we collected and annotated a set of videos that contain vehicles with Moroccan license plates in various conditions. The collected data is used to assess the performance of our system. The obtained results demonstrate that our system achieves an accuracy of 99.19% and 95.62% for detection and recognition, respectively.}, archive = {J_ACCESS}, author = {Hatim Derrouz and Hamza Alami and Reda Rabie}, doi = {10.1109/ACCESS.2025.3532538}, journal = {IEEE Access}, pages = {21956-21965}, shortjournal = {IEEE Access}, title = {MLPR: YOLOv3 for real-time license plate recognition in moroccan video streams}, volume = {13}, year = {2025}, }
• (2025). Quantized l₂-l∞ control for nonlinear discrete-time systems with DoS attacks. ACCESS, 13, 21946-21955. (www)

  @article{ , abstract = {The quantitative control problem of nonlinear discrete-time systems under DoS attacks is studied in this paper. The nonlinear part of the system is approximated linearly by T-S fuzzy model. Unknown DoS attacks are defined and modeled in terms of attack frequency and attack duration. By adding dynamic system, an intermediate observer is designed to estimate the system states. Dynamic quantizers and controllers are used to quantify and control system signals in different modes, a descriptor representation form of mode-dependent closed-loop control system can be constructed. By defining the mode-dependent Lyapunov function, the design conditions of quantizers and controllers can be obtained to satisfy the system mean-square exponential stability and the desired $l_{2}-l_{\infty }$ performance. Finally, a nonlinear mass-spring-damper mechanical system is used to verify the effectiveness of the method.}, archive = {J_ACCESS}, author = {Ying Tian and Ming-Yang Qiao}, doi = {10.1109/ACCESS.2025.3536620}, journal = {IEEE Access}, pages = {21946-21955}, shortjournal = {IEEE Access}, title = {Quantized l₂-l∞ control for nonlinear discrete-time systems with DoS attacks}, volume = {13}, year = {2025}, }
• (2025). Striking the balance: Evaluating content quality and reward dynamics in blockchain online social media. ACCESS, 13, 21927-21945. (www)

  @article{ , abstract = {Various forms of decentralized solutions for social media development have been proposed in the last years to address some of the open problems of centralized ones, which include security issues, micro-targeting, opinion polarization, and various forms of information pollution. Regarding the latter issue in particular, some decentralized social media have implemented reward systems based on the blockchain technology with the stated purpose of incentivizing users to generate quality content, which can be seen as a simple mechanism of bottom-up content moderation in decentralized environments. However, users can still cheat the logic of such reward systems, to maximize their economic gain, regardless of content quality. In this paper, we focus on this problem by studying the actual relationship between the quality of User-Generated Content disseminated in the context of blockchain-based decentralized social media platforms, and the rewards provided to users by their reward systems. To this end, we analyze distinct sets of features characterizing quality content in social media, in association with distinct classifiers to identify content actually worthy of reward. By exploiting Steemit, one of the most well-known decentralized social media platforms based on blockchain, we construct a labeled dataset to both train and evaluate distinct configurations of the different feature sets in association with the considered classifiers. The performed evaluation shows the effectiveness of some of these configurations in identifying reward-worthy content, demonstrates that current reward systems are indeed unable through their mechanisms to promote actual quality content, and suggests the need for the development of further reward strategies allowing for better reward distribution and, ultimately, help pursue the original purpose of reward systems.}, archive = {J_ACCESS}, author = {Davide Mancino and Barbara Guidi and Andrea Michienzi and Marco Viviani}, doi = {10.1109/ACCESS.2025.3536205}, journal = {IEEE Access}, pages = {21927-21945}, shortjournal = {IEEE Access}, title = {Striking the balance: Evaluating content quality and reward dynamics in blockchain online social media}, volume = {13}, year = {2025}, }
• (2025). Automatic MRI lymph node annotation from CT labels. ACCESS, 13, 21906-21926. (www)

  @article{ , abstract = {After annotating a medical imaging modality that is relatively straightforward to label, doctors often expect automatic annotations for images from other modalities of the same region, even though these modalities differ in contrast and structure. This study focuses on creating automatic lymph node annotation in MRI images using available CT annotations via deep-learning models. Training such models typically requires partial MRI labels for semi-supervision. However, annotating lymph nodes in MRI images is particularly challenging due to their small size and the high cost of MRI scans. These factors make it difficult to create labeled MRI datasets for deep learning model training. Moreover, existing cross-modal annotation methods primarily focus on large tumors and require large datasets, making them unsuitable for small lymph nodes with less training data. We address these challenges using cross-modal supervision through image registration. Our algorithm reduces the burden of manual annotation and the reliance on large labeled datasets and eliminates the need for any MRI ground truth. The algorithm has three steps: 1) unsupervised deformable image translation-based registration of MRI to CT image, producing registered MRI; 2) annotating lymph nodes in registered MRI with the available CT labels; and 3) deregistration of registered annotated MRI back to the original shape of MRI. The translation-based registration model for the algorithm’s first and third steps uses a discriminator-free StyleGAN2 translation network and a deformable image registration network with a U-Net-inspired architecture. This registration network includes local and global feature extraction modules, a local-global spatial correlation module, and a superresolution loss function. Our approach eliminates the need for MRI labels by registering MRI with CT images. Experiments show 2.19% and 4.08% MSE reductions, 5.40% and 3.28% SSIM improvements, 29.85% and 3.82% NCC increases for cross-modality and mono-modality registration, respectively, along with a 36.7% training speedup over state-of-the-art translation-based registration models. The lymph node annotation method achieves an average of 74.3% DSC in the region of interest. It also has broader applications in multimodality image segmentation. We open-source the code through a GitHub repository.INDEX TERMS Image registration, image annotation, magnetic resonance imaging, computed tomography, unsupervised learning, superresolution.}, archive = {J_ACCESS}, author = {Souraja Kundu and Yuji Iwahori and M. K. Bhuyan and Manish Bhatt and Boonserm Kijsirikul and Aili Wang and Akira Ouchi and Yasuhiro Shimizu}, doi = {10.1109/ACCESS.2025.3535219}, journal = {IEEE Access}, pages = {21906-21926}, shortjournal = {IEEE Access}, title = {Automatic MRI lymph node annotation from CT labels}, volume = {13}, year = {2025}, }
• (2025). Scalable and privacy-preserving inter-AS routing through machine-learning-based graph pruning. ACCESS, 13, 21891-21905. (www)

  @article{ , abstract = {The decentralized nature of traditional inter-domain routing protocols may lead to several issues, including convergence issues and proneness to misconfiguration. In response to these problems, alternative approaches that leverage the Software Defined Networking (SDN) paradigm to increase the control over routing operations have been recently proposed. In this context, Autonomous Systems (ASs) form a multi-domain network where routing tasks are delegated to an SDN controller. To perform inter-domain routing, each controller must learn how to reach any other node outside its domain. Thus, severe privacy concerns emerge, as the controllers need to access sensitive, business-critical information (e.g., links costs) of all the domains. Recently, protocols for computing the shortest path between a source and a destination (i.e., a common policy in routing tasks) in a privacy-preserving manner have been proposed. These protocols are based on Multi-Party Computation (MPC) schemes, which guarantee privacy at the cost of high computational and communication complexity, thus limiting scalability. In this paper, we exploit machine learning (ML) techniques to prune the network graph by removing the nodes with a low likelihood of being traversed by the shortest path. Privacy-preserving shortest path algorithms are then executed on the pruned graph, at a much lower complexity. Extensive experiments performed in multiple scenarios (varying topologies and number of nodes) indicate a major reduction of computational complexity (up to 75%) and communication complexity (up to 85%), at the expense of an acceptable increase in the average path cost (at most by 16%).}, archive = {J_ACCESS}, author = {Davide Andreoletti and Cristina Rottondi and Silvia Giordano and Andrea Bianco}, doi = {10.1109/ACCESS.2025.3536545}, journal = {IEEE Access}, pages = {21891-21905}, shortjournal = {IEEE Access}, title = {Scalable and privacy-preserving inter-AS routing through machine-learning-based graph pruning}, volume = {13}, year = {2025}, }
• (2025). Drawing-aware parkinson’s disease detection through hierarchical deep learning models. ACCESS, 13, 21880-21890. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a chronic neurological disorder that progresses slowly and shares symptoms with other diseases. Early detection and diagnosis are vital for appropriate treatment through medication and/or occupational therapy, ensuring patients can lead productive and healthy lives. Key symptoms of PD include tremors, muscle rigidity, slow movement, and balance issues, along with psychiatric ones. Handwriting (HW) dynamics have been a prominent tool for detecting and assessing PD-associated symptoms. Still, many handcrafted feature extraction techniques suffer from low accuracy, which is rather than optimal for diagnosing such a serious condition. To that end, various machine learning (ML) and deep learning (DL) approaches have been explored for early detection. Meanwhile, concerning the latter, large models that introduce complex and difficult-to-understand architectures reduce the system’s recognition transparency and efficiency in terms of complexity and reliability. To tackle the above problem, an efficient hierarchical scheme based on simpler DL models is proposed for early PD detection. This way, we deliver a more transparent and efficient solution for PD detection from HW records. At the same time, we conclude that a careful implementation of each component of the introduced hierarchical pipeline enhances recognition rates. A rigorous 5-fold cross-validation strategy is adopted for evaluation, indicating our system’s robust behavior under different testing scenarios. By directly comparing it against a similar end-to-end classifier, the benefits of our technique are clearly illustrated during experiments. Finally, its performance is compared against several state-of-the-art ML- and DL-based PD detection methods, demonstrating the method’s supremacy.}, archive = {J_ACCESS}, author = {Ioannis Kansizoglou and Konstantinos A. Tsintotas and Daniel Bratanov and Antonios Gasteratos}, doi = {10.1109/ACCESS.2025.3535232}, journal = {IEEE Access}, pages = {21880-21890}, shortjournal = {IEEE Access}, title = {Drawing-aware parkinson’s disease detection through hierarchical deep learning models}, volume = {13}, year = {2025}, }
• (2025). Dueling network architecture for GNN in the deep reinforcement learning for the automated ICT system design. ACCESS, 13, 21870-21879. (www)

  @article{ , abstract = {This paper presents an improved deep reinforcement learning-based (DRL) approach for end-to-end models using a Graph Neural Network(GNN). The proposed method aims to improve end-to-end deep Q learning with a GNN by decomposing the GNN-based Q-network structure into two sub-streams to separately estimate the global state value and the state-dependent action advantage instead. By doing that, our dueling GNN architecture can independently learn which states are valuable or not. This is achieved by utilizing the graph-dependent global-state value rather than relying on the effect of each action for each state. This approach provides a more accurate approximation of the Q-value. With better Q-value approximation, the network can deal with the problem of massive state space with sparse rewards and significantly achieve higher learning efficiency without imposing any change to the underlying reinforcement learning algorithm. The proposed method was introduced into an automated ICT system design model. The automated ICT system design model faces a fundamental challenge characterized by prolonged learning times, primarily attributable to the tendency to overestimate particular configurations owing to the scarcity of rewards despite the vast exploration space encompassing numerous possible combinations of ICT system components. The results reveal that the proposed architecture effectively improves the learning efficiency of the DRL model without imposing any changes to the underlying reinforcement learning algorithm.}, archive = {J_ACCESS}, author = {Tianchen Zhou and Yutaka Yakuwa and Natsuki Okamura and Hiroyuki Hochigai and Takayuki Kuroda and Ikuko Eguchi Yairi}, doi = {10.1109/ACCESS.2025.3534246}, journal = {IEEE Access}, pages = {21870-21879}, shortjournal = {IEEE Access}, title = {Dueling network architecture for GNN in the deep reinforcement learning for the automated ICT system design}, volume = {13}, year = {2025}, }
• (2025). UEF-HOCUrdu: Unified embeddings ensemble framework for hate and offensive text classification in urdu. ACCESS, 13, 21853-21869. (www)

  @article{ , abstract = {Hate speech and other forms of hostile communication on social media have several implications such as; fostering violence, promoting social divide, and negative psychological effects. Since such toxic language is becoming more and more common, it is imperative to have a proper way of identifying it, especially in low resource language like Urdu. To meet this challenge, this research proposed a new ensemble based multi-classification model and generated new dataset of 36,000 Urdu tweets categorized as ‘Hate’, ‘Offensive’ and ‘Neither’. This study sought to create a model that not only achieves a high classification accuracy but also overcome key challenges inherent in natural language processing, namely, high dimensionality, sparsity, overfitting, OOV words and dialectal variations. For this purpose, an extensive comparison of different learning algorithms were conducted. As a result, the most efficient models, namely FastText, XLM-RoBERTa, ULMFiT, and XGBoost were incorporated in the proposed ensemble approach to achieve the best results in both classification and mitigation of NLP issues. To further enhance the confidence in proposed model, a stratified 5-fold cross-validation technique has been utilized. The ensemble model performed the best and achieved macro F1 score of 0.94, complemented by comprehensive labeled dataset focusing on hate and offensive speech in Urdu. By addressing key research gaps, this research provides a valuable foundation for future work and benchmarking in Urdu hate speech multi-classification tasks.}, archive = {J_ACCESS}, author = {Kifayat Ullah and Muhammad Aslam and Muhammad Usman Ghani Khan and Faten S. Alamri and Amjad Rehman Khan}, doi = {10.1109/ACCESS.2025.3532611}, journal = {IEEE Access}, pages = {21853-21869}, shortjournal = {IEEE Access}, title = {UEF-HOCUrdu: Unified embeddings ensemble framework for hate and offensive text classification in urdu}, volume = {13}, year = {2025}, }
• (2025). Future research and trends in ultra-wideband indoor tag localization. ACCESS, 13, 21827-21852. (www)

  @article{ , abstract = {Ultra-wideband (UWB) signals are widely used for indoor tag localization because of their high accuracy, wide bandwidth, and low power consumption. The diversity and breadth of research in the field of UWB-indoor tag localization can be confusing for new researchers to determine how this research has progressed. The way to determine the trend and research opportunities in UWB indoor tag localization is to read recent papers. However, no one has reviewed the Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) methods. SMS is a quantitative mapping method for identifying, categorizing, and analyzing existing literature relevant to a certain research topic. SLR is a qualitative method for collecting and critically analyzing multiple research studies or papers through a systematic process. It aims to synthesize the findings of these studies and conclude the state of the art on the topic. This paper reviews the recent UWB indoor tag localization study using the SMS and SLR methods. The reviews include demographics of recent study, topic areas of indoor tag localization from 2017-2023, UWB indoor tag localization research trends, issues, metrics, and opportunities for future research. Each optimized metric related to different issues is represented in the form of a taxonomy for easy understanding and is explained in detail in this paper. Several promising future research opportunities are described in this paper to provide insights to other researchers in this field.}, archive = {J_ACCESS}, author = {Gita Indah Hapsari and Rendy Munadi and Bayu Erfianto and Indrarini Dyah Irawati}, doi = {10.1109/ACCESS.2024.3399476}, journal = {IEEE Access}, pages = {21827-21852}, shortjournal = {IEEE Access}, title = {Future research and trends in ultra-wideband indoor tag localization}, volume = {13}, year = {2025}, }
• (2025). Study on the effect of deadman state on blast furnace hearth erosion based on solidification and melting model. ACCESS, 13, 21811-21826. (www)

  @article{ , abstract = {Blast Furnace (BF) is the primary production equipment for modern ironmaking, and the hearth is a crucial part of the BF where high-temperature molten iron is periodically stored. The deadman, located within the hearth, poses a significant obstruction to the flow of molten iron. Changes in the state of the deadman directly affect the flow and discharge behavior of molten iron within the hearth, subsequently influencing the lifespan of the hearth. This study develops a three-dimensional Computational Fluid Dynamics (CFD) model that incorporates the flow of molten iron and the solidification phase transition within the blast furnace hearth. Using the skull’s thickness in the erosion area as an evaluation index, it investigates the effects of deadman parameters, such as floating height, maximum diameter, eccentricity, porosity, and bottom shape, on the hearth’s erosion area. Additionally, considering the iron flow velocity and furnace wall temperature, the study offers suggestions for the comprehensive protection of the furnace hearth. The findings revealed that maintaining a higher floating height of the deadman is beneficial for the comprehensive safety of the hearth. When the deadman is eccentric towards the erosion direction, its volume increases, or its porosity decreases, the thickness of the skull in the erosion area will decrease, posing potential safety hazards to the operation of the BF hearth. The deadman with a spherical bottom, in both its seated and floating states, results in a stable thickness of the skull in the erosion area, which is favorable for extending the service life of the hearth.}, archive = {J_ACCESS}, author = {Fei Yuan and Liangyu Chen and Lei Wang and Lei Zhao and Yan Zhong}, doi = {10.1109/ACCESS.2025.3532755}, journal = {IEEE Access}, pages = {21811-21826}, shortjournal = {IEEE Access}, title = {Study on the effect of deadman state on blast furnace hearth erosion based on solidification and melting model}, volume = {13}, year = {2025}, }
• (2025). VisGraphVar: A benchmark generator for assessing variability in graph analysis using large vision-language models. ACCESS, 13, 21788-21810. (www)

  @article{ , abstract = {The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structures, particularly graphs with their inherent flexibility and complexity, serve as an excellent benchmark for evaluating these models’ predictive capabilities. While human observers can readily identify subtle visual details and perform accurate analyses, our investigation reveals that state-of-the-art LVLMs exhibit consistent limitations in specific visual graph scenarios, especially when confronted with stylistic variations. In response to these challenges, we introduce VisGraphVar (Visual Graph Variability), a customizable benchmark generator able to produce graph images for seven distinct task categories (detection, classification, segmentation, pattern recognition, link prediction, reasoning, matching), designed to systematically evaluate the strengths and limitations of individual LVLMs. We use VisGraphVar to produce 990 graph images and evaluate six LVLMs, employing two distinct prompting strategies, namely zero-shot and chain-of-thought. The findings demonstrate that variations in visual attributes of images (e.g., node labeling and layout) and the deliberate inclusion of visual imperfections, such as overlapping nodes, significantly affect model performance. This research emphasizes the importance of a comprehensive evaluation across graph-related tasks, extending beyond reasoning alone. VisGraphVar offers valuable insights to guide the development of more reliable and robust systems capable of performing advanced visual graph analysis. The project URL is available at: [https://camilochs.github.io/visgraphvar-website].}, archive = {J_ACCESS}, author = {Camilo Chacón Sartori and Christian Blum and Filippo Bistaffa}, doi = {10.1109/ACCESS.2025.3535837}, journal = {IEEE Access}, pages = {21788-21810}, shortjournal = {IEEE Access}, title = {VisGraphVar: A benchmark generator for assessing variability in graph analysis using large vision-language models}, volume = {13}, year = {2025}, }
• (2025). Graphical smooth switching control method of four-switch buck-boost converter in fuel cell systems. ACCESS, 13, 21773-21787. (www)

  @article{ , abstract = {This paper proposes a graphical smooth switching control method for a four-switch Buck-Boost (FSBB) converter in fuel cell systems. Traditional FSBB converters can reduce average inductance current in multi-mode operations. However, maintaining high efficiency across the full load range remains challenging. Additionally, voltage gain dead spots during mode switching lead to poor dynamic performance and stability. To address these issues, this article first analyzes the four-mode working process of FSBB converters and explores the causes of voltage gain dead spots through graphical methods, then an innovative four-mode graphical control strategy is proposed, which effectively eliminates voltage gain dead spots and achieves smooth switching by introducing Buck-Boost Buck mode and Buck-Boost Boost mode. In addition, in order to accelerate the response speed during the system mode switching process and solve the problem of sudden duty cycle changes, this paper designs input voltage feedforward functions for each of the four working modes based on the voltage-current dual loop PI control, greatly enhance the controller’s response speed and effectively reducing the output voltage fluctuation during the mode switching process. The experimental results fully verify the effectiveness and progressiveness of the control strategy, and provide scientific basis and technical support for the efficient and stable operation of FSBB converter in the fuel cell systems.}, archive = {J_ACCESS}, author = {Hongwei Feng and Yuanyuan Liu and Chunping Guo and Ye Li and Tai Li}, doi = {10.1109/ACCESS.2025.3535562}, journal = {IEEE Access}, pages = {21773-21787}, shortjournal = {IEEE Access}, title = {Graphical smooth switching control method of four-switch buck-boost converter in fuel cell systems}, volume = {13}, year = {2025}, }
• (2025). Optimized prediction of weapon effectiveness in BVR air combat scenarios using enhanced regression models. ACCESS, 13, 21759-21772. (www)

  @article{ , abstract = {This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. Accurate WEZ predictions are crucial for decision-making in air combat, and high-performance solutions are essential for developing and deploying autonomous systems. To optimize the model training process, we introduce novel feature engineering and data augmentation strategies, achieving a 70% improvement in the Mean Absolute Error (MAE) of WEZ predictions. A comparison of various regression methods highlights the potential of polynomial-based alternatives when fully utilized. In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. This study also provides a new open dataset to facilitate further research and advancements in this field.}, archive = {J_ACCESS}, author = {Andre R. Kuroswiski and Annie S. Wu and Angelo Passaro}, doi = {10.1109/ACCESS.2025.3535555}, journal = {IEEE Access}, pages = {21759-21772}, shortjournal = {IEEE Access}, title = {Optimized prediction of weapon effectiveness in BVR air combat scenarios using enhanced regression models}, volume = {13}, year = {2025}, }
• (2025). A hybrid contextual embedding and hierarchical attention for improving the performance of word sense disambiguation. ACCESS, 13, 21744-21758. (www)

  @article{ , abstract = {Word Sense Disambiguation is determining the correct sense of an ambiguous word within context. It plays a crucial role in natural language applications such as machine translation, question-answering, chatbots, information retrieval, sentiment analysis, and overall language comprehension. Recent advancements in this area have focused on utilizing deep contextual models to address these challenges. However, despite this positive progress, semantical and syntactical ambiguity remains a challenge, especially when dealing with polysomy words, and it is considered an AI-complete problem. In this work, we propose an approach that integrates hierarchical attention mechanisms and BERT embeddings to enhance WSD performance. Our model, incorporating local and global attention, demonstrates significant improvements in accuracy, particularly in complex sentence structures. To the best of our knowledge, our model is the first to incorporate hierarchical attention mechanisms integrated with contextual embedding. We conducted experiment on publicly available datasets for English and Italian language. Experimental results show that our model achieves state-of-the-art results in WSD, surpassing baseline models up to 2.9% F1 accuracy on English WSD. Additionally, it demonstrates superior performance in Italian WSD, outperforming existing papers up to 0.7% F1 accuracy. We further adapted the model for Amharic word sense disambiguation. Despite the absence of a standard benchmark dataset for Amharic WSD, our model achieved an accuracy of 92.4% on a dataset we prepared ourselves. Our findings underscore the significance of linguistic features in contextual information capture for WSD. While Part-of-Speech (POS) tagging has a limited impact, word embeddings significantly influence performance. Local and global attention further improve results, particularly at the word level. Overall the results emphasize the importance of context in WSD, advancing context-aware natural language processing systems.}, archive = {J_ACCESS}, author = {Robbel Habtamu Yigzaw and Beakal Gizachew Assefa and Elefelious Getachew Belay}, doi = {10.1109/ACCESS.2025.3536300}, journal = {IEEE Access}, pages = {21744-21758}, shortjournal = {IEEE Access}, title = {A hybrid contextual embedding and hierarchical attention for improving the performance of word sense disambiguation}, volume = {13}, year = {2025}, }
• (2025). SaliencyMix+: Noise-minimized image mixing method with saliency map in data augmentation. ACCESS, 13, 21734-21743. (www)

  @article{ , abstract = {Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting the significance of blended regions, and SaliencyMix applies uniform patch generation across a batch, resulting in suboptimal augmentation. This paper introduces SaliencyMix+, a novel data augmentation technique that enhances the performance of deep-learning models using saliency maps for image mixing and label generation. It identifies critical patch coordinates in batch images and refines label generation based on target object proportions, reducing label noise. Experiments on CIFAR-100 and Oxford-IIIT Pet datasets show that SaliencyMix+ consistently outperforms CutMix and SaliencyMix, achieving the lowest Top-1 errors of 24.95% and 34.89%, and Top-5 errors of 7.00% and 12.13% on CIFAR-100 and Oxford-IIIT Pet, respectively. These findings highlight the effectiveness of SaliencyMix+ in boosting model accuracy and robustness across different models and datasets. The code is publicly available on GitHub: https://github.com/SS-hj/SaliencyMixPlus.git.}, archive = {J_ACCESS}, author = {Hajeong Lee and Zhixiong Jin and Jiyoung Woo and Byeongjoon Noh}, doi = {10.1109/ACCESS.2025.3537083}, journal = {IEEE Access}, pages = {21734-21743}, shortjournal = {IEEE Access}, title = {SaliencyMix+: Noise-minimized image mixing method with saliency map in data augmentation}, volume = {13}, year = {2025}, }
• (2025). Toward generality of a framework for quantifying biomechanical stability: Demonstration on the ankle. ACCESS, 13, 21723-21733. (www)

  @article{ , abstract = {This paper presents a mathematical description and experimental investigation of using an industrial robot to evaluate biomechanical stability in human cadaveric ankle specimens. We previously proposed a methodology and parameterizable task-space motion framework for biomechanical investigations of shoulders. The present study aimed to demonstrate the generality and application of our framework to replicate an ankle test protocol from the literature, and to experimentally evaluate the replicated test protocol. Stability tests in cadaveric studies can be performed by applying a known force or torque to the joint, and measuring the resulting linear or angular motion. The relative stability between joint states can be determined by comparing the magnitude of motion, for example, between the intact, injured, or surgically repaired states. Increased joint motion between states can be interpreted as decreased stability, whereas decreased joint motion can be interpreted as increased stability. Comparing the motion between different states can be used to investigate the effects of soft tissue or bony structures on stability. The replicated protocol was tested in a technical pilot study using a fresh frozen human cadaveric ankle specimen. The specimen was tested in the intact state and in three subsequent injury states. This pilot study demonstrated that the framework could successfully be parameterized to conduct tests on the ankle joint. The expected trend for subsequent injury states was progressive instability, and the observed trends from experimental testing aligned with this prior expectation. However, additional work on the framework is necessary to make it truly generalized.}, archive = {J_ACCESS}, author = {Aleksander Skrede and Andreas Fagerhaug Dalen and Alf Inge Hellevik and Øyvind Stavdahl and Robin T. Bye}, doi = {10.1109/ACCESS.2025.3535406}, journal = {IEEE Access}, pages = {21723-21733}, shortjournal = {IEEE Access}, title = {Toward generality of a framework for quantifying biomechanical stability: Demonstration on the ankle}, volume = {13}, year = {2025}, }
• (2025). Supporting efficient family joins for big data tables via multiple freedom family index. ACCESS, 13, 21707-21722. (www)

  @article{ , abstract = {The Hadoop/MapReduce framework has been widely utilized for processing big data. To overcome the limitations of existing work and meet the growing requirements of querying big data, this paper introduces novel join operations, called family joins, for HBase tables using their column families as join keys. Family joins possess the closure property that is demanded by many big data applications. This work explores four types of family joins according to different types of freedom in prefix matching for join comparisons. Two approaches to processing such family joins are discussed. The first is the direct method, which is inspired by the straightforward nested-loop strategy. The second is an index-based method, which utilizes a special index for HBase tables. Detailed definitions, practical applications, and processing strategies and algorithms for family joins are provided. Experimental results demonstrate that the index-based join method is quite promising in efficiently processing family joins.}, archive = {J_ACCESS}, author = {Qiang Zhu and Chao Zhu}, doi = {10.1109/ACCESS.2025.3535693}, journal = {IEEE Access}, pages = {21707-21722}, shortjournal = {IEEE Access}, title = {Supporting efficient family joins for big data tables via multiple freedom family index}, volume = {13}, year = {2025}, }
• (2025). Raw camera data object detectors: An optimisation for automotive video processing and transmission. ACCESS, 13, 21695-21706. (www)

  @article{ , abstract = {Whilst Deep Neural Networks (DNNs) have been developing swiftly, most of the research has been focused on videos based on RGB frames. RGB data has been traditionally optimised for human vision and is a highly re-elaborated and interpolated version of the collected raw data. In fact, the sensor collects the light intensity value per pixel, but an RGB frame contains 3 values, for red, green, and blue colour channels. This conversion to RGB requires computational resource, time, power, and increases by a factor of three the amount of output data. This work investigates DNN based detection using (for training and evaluation) Bayer frames, generated from a benchmarking automotive dataset (i.e. KITTI dataset). A Deep Neural Network (DNN) is deployed in an unmodified form, and also modified to accept only single channel frames, such as Bayer frames. Eleven different re-trained versions of the DNN are produced, and cross-evaluated across different data formats. The results demonstrate that the selected DNN has the same accuracy when evaluating RGB or Bayer data, without significant degradation in the perception (the variation of the Average Precision is <1%). Moreover, the colour filter array position and the colour correction matrix do not seem to contribute significantly to the DNN performance. This work demonstrates that Bayer data can be used for object detection in automotive without significant perception performance loss, allowing for more efficient sensing-perception systems.}, archive = {J_ACCESS}, author = {Pak Hung Chan and Chuheng Wei and Anthony Huggett and Valentina Donzella}, doi = {10.1109/ACCESS.2025.3529287}, journal = {IEEE Access}, pages = {21695-21706}, shortjournal = {IEEE Access}, title = {Raw camera data object detectors: An optimisation for automotive video processing and transmission}, volume = {13}, year = {2025}, }
• (2025). A novel approach to test-induced defect detection in semiconductor wafers, using graph-based semi-supervised learning (GSSL). ACCESS, 13, 21678-21694. (www)

  @article{ , abstract = {The semiconductor industry plays a vital role in modern technology, with semiconductor devices embedded in almost all electronic products. As these devices become increasingly complex, ensuring quality and reliability poses significant challenges. Electrical testing on semiconductor wafers for defects is crucial, but paradoxically, the testing process itself can introduce defects. These test-induced defects could remain undetected on the wafer, proceed through assembly, and may only be discovered later by customers, leading to returns and significant yield loss. This study proposes a novel graph-based semi-supervised learning (GSSL) algorithm to identify these test-induced hidden defects on the semiconductor wafer that escape conventional methods. The algorithm, which incorporates domain knowledge in creating a graph representation of wafer, and utilizing a weighted edge label propagation model, has demonstrated its effectiveness by achieving a 68% accuracy on a real-world dataset, offering a promising approach to enhance quality control in semiconductor manufacturing.}, archive = {J_ACCESS}, author = {Pedram Tabatabaeemoshiri and Narendra Kumar and Anis Salwa Mohd Khairuddin and Daniel Ting and Vivek Regeev}, doi = {10.1109/ACCESS.2025.3535103}, journal = {IEEE Access}, pages = {21678-21694}, shortjournal = {IEEE Access}, title = {A novel approach to test-induced defect detection in semiconductor wafers, using graph-based semi-supervised learning (GSSL)}, volume = {13}, year = {2025}, }
• (2025). Cyber threat inference focused on threat class sequence with threat class sequence system. ACCESS, 13, 21665-21677. (www)

  @article{ , abstract = {Cyber threats are becoming increasingly diverse, sophisticated, and intelligent, posing significant challenges in various domains. Among these threats, advanced persistent threat (APT) attacks are particularly concerning because of sustained and intelligent assault tactics. These attacks have a tendency to continuously expand the scale of the damage during the attack stage. This study developed novel attack-inference techniques for significantly minimizing cyberattack damage. By proactively identifying potential additional attacks, the impact of APT attacks is mitigated. Cyberattack points are anticipated, and attack types are discerned using the compositional patterns of pretrained threat payloads and the continuous correlations inherent in these attacks. The cyber-threat inference method minimizes damage through early detection and blocking of successive attacks using a threat class sequence system (TCSS). Experiments were conducted using 12,054,842,738,181 extensive real-world security device events collected by more than 420 companies. To assess the efficacy of TCSS in detecting connected attacks, a 39-month threat dataset comprising 27,832,130 instances manually analyzed by real-world security analysts was utilized. The analysis confirmed the necessity of TCSS, revealing it could identify 63.99% of successive attack patterns. Various experimental results demonstrate the superiority of TCSS over comparable methods, achieving an impressive 98.73% post-connection attack detection rate. TCSS outperformed other similar methods in terms of true positive rate by 15.62% to 3.37%. By preemptively blocking persistent cyber threats, TCSS is highly effective in minimizing cyberattack damage. In particular, TCSS shows promising potential for proactive defense of cyberattacks, including APT in security operations center goals.}, archive = {J_ACCESS}, author = {Hyoungjun Kim and Jae-Yeol Kim and Kyung-Ho Lee}, doi = {10.1109/ACCESS.2025.3536011}, journal = {IEEE Access}, pages = {21665-21677}, shortjournal = {IEEE Access}, title = {Cyber threat inference focused on threat class sequence with threat class sequence system}, volume = {13}, year = {2025}, }
• (2025). Static and dynamic routing, fiber, modulation format, and spectrum allocation in hybrid ULL fiber-SSMF elastic optical networks. ACCESS, 13, 21652-21664. (www)

  @article{ , abstract = {Traditional standard single-mode fibers (SSMFs) are unable to satisfy the future long-distance and high-speed optical channel transmission requirement due to their relatively large signal losses. To address this issue, the ultra-low loss and large effective area (ULL) fibers are successfully manufactured and expected to deployed in the existing optical networks. For such ULL fiber deployment, network operators prefer adding ULL fibers to each link rather than replace existing SSMFs, resulting in a scenario where both of SSMF and ULL fiber coexist on the same link. In this paper, we investigated the routing, fiber, modulation format, and spectrum allocation (RFMSA) problem in the context of an elastic optical network (EON) where ULL fiber and SSMF coexisting on each link under both the static and dynamic traffic demands. We formulated this RFMSA problem as a node-arc based Mixed Integer Linear Programming (MILP) model and developed Spectrum Window Plane (SWP)-based heuristic algorithms based on different fiber selection strategies, including spectrum usage based (SU), optical signal-to-noise ratio (OSNR) aware, ULL fiber first (UFF), and random strategies. Simulation results show that in the static traffic demand situation, the RFMSA algorithm based on the OSNR-aware (OA) strategy exhibits optimal performance, attaining a performance similar to that of the MILP model regarding the maximum number of frequency slots (FSs) used in the entire network. Moreover, in the dynamic traffic demand scenario, the SU strategy remarkably surpasses the other strategies in terms of the lightpath blocking probability.}, archive = {J_ACCESS}, author = {Kangao Ouyang and Fengxian Tang and Zhilin Yuan and Jun Li and Yongcheng Li}, doi = {10.1109/ACCESS.2025.3535532}, journal = {IEEE Access}, pages = {21652-21664}, shortjournal = {IEEE Access}, title = {Static and dynamic routing, fiber, modulation format, and spectrum allocation in hybrid ULL fiber-SSMF elastic optical networks}, volume = {13}, year = {2025}, }
• (2025). Urdu toxic comment classification with PURUTT corpus development. ACCESS, 13, 21635-21651. (www)

  @article{ , abstract = {This study addresses the critical gap in toxic comment classification in Urdu, a widely spoken language devoid of high-quality standard datasets. To address this gap, we employed an existing labeled Roman Urdu (RU) corpus, which was developed originally for Roman Urdu toxic comment classification, and supplemented that corpus by adding its Urdu equivalent transliterations. The motivation behind such an extension is twofold: firstly, to provide a large comprehensive dataset for the classification of toxic comments in Urdu; secondly, to facilitate bidirectional transliteration between Urdu and RU, however, transliteration is currently outside the scope of this study and is envisioned as a future research direction. We introduce the extended corpus as PURUTT (Parallel Urdu and Roman Urdu Corpus for Toxic Comments and Transliteration), boasting 72,771 labeled comments as parallel comments in both Urdu and Roman Urdu scripts. Specific to Urdu toxic comment classification, our methodology begins by training those classification models that were trained on the original Roman Urdu corpus. We leverage pre-trained Word2Vec and FastText Urdu word embeddings to evaluate model performance through transfer learning. Furthermore, we fine-tune five multilingual large language models capitalizing on their inherent multilingual capabilities. To further enhance the classification performance, this study proposes an ensemble approach that aggregates the strengths of multiple base models. Our extensive empirical validation demonstrates the superiority of the ensemble model, achieving a state-of-the-art F1-score of 91.65% on PURUTT, setting a benchmark F1-score on PURUTT corpus for Urdu toxic comment classification.}, archive = {J_ACCESS}, author = {Hafiz Hassaan Saeed and Tahir Khalil and Faisal Kamiran}, doi = {10.1109/ACCESS.2025.3535862}, journal = {IEEE Access}, pages = {21635-21651}, shortjournal = {IEEE Access}, title = {Urdu toxic comment classification with PURUTT corpus development}, volume = {13}, year = {2025}, }
• (2025). Edge-preserving probabilistic downsampling for reliable medical segmentation in resource-constrained environments. ACCESS, 13, 21620-21634. (www)

  @article{ , abstract = {Downsampling, often necessitated due to resource constraints such as limited memory, development timelines, or the need to accelerate learning from large volumes of unlabeled medical data, results in the loss of fine-grained details, including small objects and thin class boundaries. When applied to ground truth (GT) labels without proper care, downsampling can significantly impair the segmentation network’s ability to accurately interpret and predict detailed features. This, in turn, leads to a degraded performance compared to networks trained on higher-resolution labels. This situation exemplifies the trade-off between computational efficiency and segmentation accuracy, with higher downsampling factors further impairing performance. Preserving critical information during label downsampling is particularly crucial for medical image segmentation. This study introduces a novel approach, Edge-Preserving Probabilistic Downsampling (EPD), designed to retain critical details and bridge the performance gap between networks trained on original and downsampled resolutions. It leverages class uncertainty within a local window to produce soft labels, with the window size determining the downsampling factor. This approach enables segmentation networks to maintain high-quality predictions at lower resolutions. Beyond preserving edge details more effectively than conventional nearest-neighbor downsampling, the proposed algorithm, when adapted for images, exhibits notable performance improvements over conventional bilinear interpolation. Experimental results indicate EPD improves Intersection over Union (IoU) for a multi-class abdominal CT dataset by 2.54%, 10.66%, and 16.31% when downsampling to ½, ¼, and ⅛, respectively, compared to conventional interpolation methods.}, archive = {J_ACCESS}, author = {Shahzad Ali and Yu Rim Lee and Soo Young Park and Won Young Tak and Soon Ki Jung}, doi = {10.1109/ACCESS.2025.3536286}, journal = {IEEE Access}, pages = {21620-21634}, shortjournal = {IEEE Access}, title = {Edge-preserving probabilistic downsampling for reliable medical segmentation in resource-constrained environments}, volume = {13}, year = {2025}, }
• (2025). Nonsingular terminal sliding mode control of the yarn winding process based on a finite-time extended state observer. ACCESS, 13, 21610-21619. (www)

  @article{ , abstract = {Tension control in yarn winding is crucial for maintaining product quality and process continuity in the textile industry, particularly in the presence of varying rotational speeds, winding radii, and external disturbances. To address this issue, this paper proposes a yarn tension sliding mode control method based on a finite-time extended state observer for rapid and accurate tension regulation. Firstly, considering the dynamic characteristics of the tension control system, both internal and external disturbances are expanded into a total disturbance state, and a finite-time extended state observer is established to accurately estimate the system state and total disturbance. Secondly, based on the estimated state, a nonsingular terminal sliding mode control scheme is designed, with an appropriate exponential convergence law chosen to ensure that the system reaches the sliding surface within finite time. The finite-time stability of the estimation error and system tracking error is analyzed using the Lyapunov method. Finally, the simulation results demonstrate that the proposed control scheme effectively achieves finite-time stability of both estimation and tracking errors, and exhibits favorable dynamic characteristics.}, archive = {J_ACCESS}, author = {Jieshi Yang and Yinghao Ye and Yun Cheng and Liang Hua}, doi = {10.1109/ACCESS.2025.3535946}, journal = {IEEE Access}, pages = {21610-21619}, shortjournal = {IEEE Access}, title = {Nonsingular terminal sliding mode control of the yarn winding process based on a finite-time extended state observer}, volume = {13}, year = {2025}, }
• (2025). Process-dependent evolution of channel stress and stress-induced mobility gain in FinFET, normal GAAFET, and Si/SiGe hybrid channel GAAFET. ACCESS, 13, 21600-21609. (www)

  @article{ , abstract = {This study investigates the evolution of stress and its induced carrier mobility gain in FinFET, GAAFET, and Si/SiGe hybrid channel GAAFET throughout the process flow using technology computer-aided design (TCAD) tool, which has been calibrated with experimental data from the transmission electron microscopy (TEM) and nano-beam precession electron diffraction (PED) techniques. The stress evolution indicates that Fin recess, S/D epi growth and Gate removal are three crucial process steps that influence channel stress. For nFETs, FinFET achieves a mobility gain from channel stress of approximately 0.8%, whereas GAAFET exhibits a mobility gain of 7.5%, representing nearly a tenfold increase, mainly due to the differences in process flow starting from multi epitaxial layers. For pFETs, GAAFET achieves a 20% and 60% improvement in stress and stress-induced hole mobility gain compared to FinFET. Furthermore, Si/Si0.7Ge0.3 hybrid channel GAAFET shows a further improvement of 100% and 65% on stress level, and an improvement of 231% and 105% on hole mobility gain over FinFET and GAAFET, respectively. Moreover, it implies that the higher stress-induced mobility gain in p-type than n-type GAAFET is expected to mitigate the mobility imbalance between holes and electrons caused by the change of dominant surface orientation from {110} in FinFET to {001} in GAAFET. This work gives a comprehensive picture of the process-dependent evolution of channel stress and its mobility gain in different advanced device structures, and offers new insights into the mobility balance in GAAFET from the perspective of stress.}, archive = {J_ACCESS}, author = {Chiang Zhu and Xiaona Zhu and Shaofeng Yu and David Wei Zhang}, doi = {10.1109/ACCESS.2025.3534280}, journal = {IEEE Access}, pages = {21600-21609}, shortjournal = {IEEE Access}, title = {Process-dependent evolution of channel stress and stress-induced mobility gain in FinFET, normal GAAFET, and Si/SiGe hybrid channel GAAFET}, volume = {13}, year = {2025}, }
• (2025). Highly efficient dual-band rectifier based on symmetric impedance control network. ACCESS, 13, 21594-21599. (www)

  @article{ , abstract = {This study proposes a dual-band rectifier that utilizes a symmetric impedance control network (SICN) for highly efficient wireless power transfer. The proposed rectifier comprises a voltage doubler and an SICN connected to each diode of the voltage doubler. The SICN controls the input impedance seen by each diode at two fundamental frequencies and the second harmonic impedance at those frequencies at the diode terminals, thus controlling the voltage across each diode. This minimizes the overlap time when the two diodes are turned on and maximizes the voltage applied to them. Thus, the maximum power conversion efficiency (PCE) is achieved within a wide input power range at the two frequencies. The proposed rectifier is designed and fabricated with a compact size of $28.9\times 38.9$ mm2. Measurements show that the maximum PCE reaches 82.1 % and 75 % at 0.915 and 2.42 GHz, respectively. Moreover, the PCE remains above 50% over a wide input power range of 6.8–30.5 dBm at 0.915 GHz and 14.8–31 dBm at 2.42 GHz. The PCE exceeds 50% at bandwidths of 0.79–1.66 GHz and 2.3–2.53 GHz at an input power of 24.5 dBm.}, archive = {J_ACCESS}, author = {Eojin Kim and Yeonsoo Kim and Juntaek Oh}, doi = {10.1109/ACCESS.2025.3536003}, journal = {IEEE Access}, pages = {21594-21599}, shortjournal = {IEEE Access}, title = {Highly efficient dual-band rectifier based on symmetric impedance control network}, volume = {13}, year = {2025}, }
• (2025). Performance of RIS-secured short-packet NOMA systems with discrete phase-shifter to protect digital content and copyright against untrusted user. ACCESS, 13, 21580-21593. (www)

  @article{ , abstract = {Future wireless communications are expected to serve a wide range of emerging applications, such as Online Gaming, Extended Reality (XR), Metaverses, Healthcare or Telemetry, where communication from diverse connected Internet of Things (IoT) devices require not only stringent conditions such as ultra-reliability and low-latency communication (URLLC) together with high bandwidth but also concerns about content security as well as copyright protection. To deal with URLLC demands, Short-Packet Communication (SPC) has been recently considered a vital solution. Meanwhile, to meet high spectrum utilization, Non-Orthogonal Multiple Access (NOMA) has emerged as a potential technology in the last decade, for its ability to serve multi-user communication simultaneously by exploiting power-domain rather than time or frequency domains. Especially, incorporating Reconfigurable Intelligent Surfaces (RIS) with NOMA/SPC-based systems can further boost the system’s spectral efficiency as well as enhance communication coverage. However, NOMA-based systems hugely demand a reliable user-paring process, which imposes challenges in ensuring secure short-packet delivery for emerging IoT applications. Hence, this paper studies downlink RIS-assisted short-packet NOMA systems with the focus of improving the secure performance of the pairing process with untrusted users. Our primary innovation lies in developing an arbitrary user-pairing strategy for NOMA-based systems to secure short-packet delivery for IoT applications by jointly designing the power allocation policy and RIS’s phase shifter. In this strategy, untrustworthy users will be allocated higher power levels than trustworthy users, while the latter will be configured with sub-optimal phase shift criteria at RIS to maximize its cascaded channel gain. On this foundation, we derive closed-form expressions for the average block-error rate (BLER) to analyze the performance of trustworthy users as well as the average secure BLER to quantify the secure performance when untrustworthy users wiretap trustworthy users’ information using successive interference mechanisms. Moreover, we further develop asymptotic expressions for both cases to measure the diversity gain and induce key parameters. Subsequently, Monte Carlo simulations are provided as a benchmark to corroborate the theoretical findings. This demonstrates that our work can offer a groundbreaking solution for secure and efficient wireless communication, which can also be applied to digital content protection, such as gaming.}, archive = {J_ACCESS}, author = {Nguyen Quang Sang and Hye-Young Kim and Tan N. Nguyen and Minh Bui Vu and Phuong T. Tran and Tran Trung Duy and Byung-Seo Kim and Miroslav Voznak}, doi = {10.1109/ACCESS.2025.3535813}, journal = {IEEE Access}, pages = {21580-21593}, shortjournal = {IEEE Access}, title = {Performance of RIS-secured short-packet NOMA systems with discrete phase-shifter to protect digital content and copyright against untrusted user}, volume = {13}, year = {2025}, }
• (2025). Simulation of self-occlusion virtual dataset method for robust point matching algorithm, with applications to positioning of guide vanes. ACCESS, 13, 21569-21579. (www)

  @article{ , abstract = {The application of point cloud registration technology for workpiece positioning compensation using optical three-dimensional measurement methods has attracted widespread attention in the manufacturing industry, particularly point cloud registration methods integrated with deep learning are booming. Since the training of current deep learning registration methods is often based on public datasets, the performance of point cloud registration of guide vanes depends on the relevance, quality, and quantity of the training dataset, if the training is directly based on the current public dataset used for guide vanes, the accuracy of the registration criteria cannot meet the requirements. Additionally, in real industrial scenarios, manually obtaining the real dataset is time-consuming, labor-intensive, and error-prone. To address these issues, this paper proposes a virtual simulation method based on the CAD model of the workpiece to set up a virtual camera so that a large number of near-real datasets can be generated quickly. The method can simulate the incomplete vane point cloud obtained by real shooting due to self-occlusion by setting multi-angle virtual cameras on the hemispherical surface wrapped in the CAD model. The experimental results show that the combination of the deep learning registration method and the virtual dataset method in this paper can improve the accuracy, efficiency, and stability of the deep learning registration for workpiece positioning compensation, which has a good prospect for practical application.}, archive = {J_ACCESS}, author = {Fenglin Han and Hang Peng and Xian Wu and Haonan Ren and Yiwei Sun and Bin Su}, doi = {10.1109/ACCESS.2025.3533629}, journal = {IEEE Access}, pages = {21569-21579}, shortjournal = {IEEE Access}, title = {Simulation of self-occlusion virtual dataset method for robust point matching algorithm, with applications to positioning of guide vanes}, volume = {13}, year = {2025}, }
• (2025). Response time analysis with cause-effect chain considering DAG structure and high-load tasks. ACCESS, 13, 21557-21568. (www)

  @article{ , abstract = {Recent developments in autonomous driving systems highlight the significance of response time analysis. In autonomous driving systems, the complexities of response time analysis stem from different periods of tasks, tasks with high computational demands (high-load tasks), and Directed Acyclic Graph (DAG) structures. Although Cause-Effect Chain can consider tasks that are activated at different periods, there is no method that can handle high-load tasks. This paper proposes an allocation algorithm that assigns multiple processor cores to a single task and response time analysis using the Cause-Effect Chain in systems that include high-load tasks is realized. In addition, this paper extends a response time bound of Cause-Effect Chain, and proposes a response time bound of systems with DAG structures. The proposed algorithm and the proposed response time bound are evaluated by task sets generated from measured autonomous driving data. The evaluation results show that the proposed algorithm consistently indicates higher acceptance rates than existing algorithms. Furthermore, the proposed response time bound for the DAG can estimate response times that are shorter than existing bounds.}, archive = {J_ACCESS}, author = {Tomoya Kobayashi and Ryo Okamura and Takuya Azumi}, doi = {10.1109/ACCESS.2025.3536013}, journal = {IEEE Access}, pages = {21557-21568}, shortjournal = {IEEE Access}, title = {Response time analysis with cause-effect chain considering DAG structure and high-load tasks}, volume = {13}, year = {2025}, }
• (2025). BMNet: Enhancing deepfake detection through BiLSTM and multi-head self-attention mechanism. ACCESS, 13, 21547-21556. (www)

  @article{ , abstract = {When forgery techniques can generate highly realistic videos, traditional convolutional neural network (CNN)-based detection models often struggle to capture subtle forgery features and temporal dependencies. Most existing models focus on feature extraction from static frames, neglecting the temporal correlation in videos, which decreases accuracy in detecting dynamic forged videos. Furthermore, the ability to detect localized forgery features remains insufficient. To address these limitations, we propose a deep forgery detection framework named BiLSTM Multi-Head Self-Attention Network (BMNet). By leveraging the Bi-Directional Long Short-Term Memory Network (BiLSTM) for modeling temporal dependencies between video frames, and the Multi-Head Self-Attention Mechanism (MHSA) for capturing features from different regions of an image, BMNet more effectively identifies dynamic and local forgery features. In our experiments, we extract features from 68 facial landmarks of each video frame and evaluate detection performance on four datasets: UADFV, FF++, Celeb-DF and DFDC. The results show significant improvements over traditional methods. We further validate the necessity of each network component through ablation studies, demonstrating that BMNet achieves accuracies of 95.54%, 92.18%, 80.20% and 84.72% on the FF++, UADFV, Celeb-DF and DFDC datasets, respectively, indicating its superior performance in deep forgery detection.}, archive = {J_ACCESS}, author = {Demao Xiong and Zhan Wen and Cheng Zhang and Dehao Ren and Wenzao Li}, doi = {10.1109/ACCESS.2025.3533653}, journal = {IEEE Access}, pages = {21547-21556}, shortjournal = {IEEE Access}, title = {BMNet: Enhancing deepfake detection through BiLSTM and multi-head self-attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Progressive bitwidth assignment approaches for efficient capsule networks quantization. ACCESS, 13, 21533-21546. (www)

  @article{ , abstract = {Capsule Networks (CapsNets) are a class of neural network architectures that can be used to more accurately model hierarchical relationships due to their hierarchical structure and dynamic routing algorithms. However, their high accuracy comes at the cost of significant memory and computational resources, making them less feasible for deployment on resource-constrained devices. In this paper, progressive bitwidth assignment approaches are introduced to efficiently quantize the CapsNets. Initially, a comprehensive and detailed analysis of parameter quantization in CapsNets is performed exploring various granularities, such as block-wise quantization and dynamic routing quantization. Then, three quantization approaches are applied to progressively quantize the CapsNet, considering various insights into the susceptibility of layers to quantization. The proposed approaches include Post-Training Quantization (PTQ) strategies that minimize the dependence on floating-point operations and incorporates layer-specific integer bit-widths based on quantization error analysis. PTQ strategies employ Power-of-Two (PoT) scaling factors to simplify computations, effectively utilizing hardware shifts and significantly reducing the computational complexity. This technique not only reduces the memory footprint but also maintains accuracy by introducing a range clipping method tailored to the hardware’s capabilities, obviating the need for data preprocessing. Our experimental results on ShallowCaps and DeepCaps networks across multiple datasets (MNIST, Fashion-MNIST, CIFAR-10, and SVHN) demonstrate the efficiency of our approach. Specifically, on the CIFAR-10 dataset using the DeepCaps architecture, we achieved a substantial memory reduction ( $7.02\times $ for weights and $3.74\times $ for activations) with a minimal accuracy loss of only 0.09%. By using progressive bitwidth assignment and post-training quantization, this work optimizes CapsNets for efficient, real-time visual processing on resource-constrained edge devices, enabling applications in IoT, mobile platforms, and embedded systems.}, archive = {J_ACCESS}, author = {Mohsen Raji and Amir Ghazizadeh Ahsaei and Kimia Soroush and Behnam Ghavami}, doi = {10.1109/ACCESS.2025.3534434}, journal = {IEEE Access}, pages = {21533-21546}, shortjournal = {IEEE Access}, title = {Progressive bitwidth assignment approaches for efficient capsule networks quantization}, volume = {13}, year = {2025}, }
• (2025). Adaptive interference restriction among spatial streams based on difference in singular values for PAPR reduction in uplink eigenmode massive MIMO transmission. ACCESS, 13, 21520-21532. (www)

  @article{ , abstract = {In this paper, we propose two peak-to-average power ratio (PAPR) reduction methods that apply adaptive in-band interference restriction based on singular value differences between spatial channels in uplink eigenmode massive multiple-input multiple-output (MIMO) transmission. In uplink transmission utilizing high-frequency bands, reducing PAPR is crucial to suppress non-linear distortion and ensure sufficient transmission distance, given the stringent amplification requirements of power amplifiers. Therefore, the proposed methods direct the PAPR reduction signals caused by clipping and filtering (CF) only to spatial channels with relatively small singular values, thereby avoiding in-band interference in spatial channels with larger singular values, which are more susceptible to such interference compared to the case without PAPR reduction. After that, the two proposed methods restrict PAPR reduction signals for all or a subset of subcarriers based on the transmission quality requirements. Both interference restrictions allow for more effective PAPR reduction while tolerating interference in the data streams. Computer simulations demonstrate that the transmission quality of the proposed methods is improved by up to approximately 30% compared to the case without PAPR reduction. Moreover, a comparative evaluation of the two proposed methods demonstrates that the interference restriction across all subcarriers effectively mitigates in-band interference caused by the resulting PAPR reduction signals while reducing the PAPR.}, archive = {J_ACCESS}, author = {Yuta Abekura and Takanori Hara and Satoshi Suyama and Satoshi Nagata and Kenichi Higuchi}, doi = {10.1109/ACCESS.2025.3534783}, journal = {IEEE Access}, pages = {21520-21532}, shortjournal = {IEEE Access}, title = {Adaptive interference restriction among spatial streams based on difference in singular values for PAPR reduction in uplink eigenmode massive MIMO transmission}, volume = {13}, year = {2025}, }
• (2025). ECC-based authentication protocol for military internet of drone (IoD): A holistic security framework. ACCESS, 13, 21503-21519. (www)

  @article{ , abstract = {In modern military operations, network systems capable of maintaining real-time communication and high security while operating in resource-constrained environments are essential. Since military systems face significant risks when exposed to adversarial attacks, authentication of all participants is critical. However, stronger security through authentication often results in reduced usability and efficiency. This study acknowledges these challenges and proposes a protocol tailored to military environments, enabling participant authentication across all communication segments in the Internet of Drones environment. The proposed protocol introduces independently managed session keys among drones, soldiers, and command centers to prevent security breaches from spreading throughout the network. The employed Elliptic Curve Cryptography security technology provides robust encryption with minimal computational complexity, making it suitable for resource-constrained IoD environments. In soldier-to-drone communication, trust-based authentication is performed using a trust-anchor approach with the public key of the Certificate Authority. For drone-to-Certificate Authority communication, pre-shared group keys of all drones are used to establish each drone’s session key through the Elliptic Curve Diffie-Hellman key exchange. During this process, timestamps and hops are transmitted to enable position verification through Ultra-Wideband, ensuring secure communication even in environments where GPS signals are restricted. Drone-to-drone communication uses an Implicit Key-Based Authentication method with group keys and random nonces, providing lightweight authentication while preventing replay attacks. Furthermore, timestamp-based verification and group key management across all communication segments are implemented to prevent replay attacks and key reuse attacks. The proposed protocol demonstrates strong resistance to various military threats, including DoS attacks, replay attacks, and man-in-the-middle attacks. It satisfies the unique requirements of military networks and offers a secure and reliable military communication environment through enhanced authentication and session key management.}, archive = {J_ACCESS}, author = {Hyeon Choe and Dongsu Kang}, doi = {10.1109/ACCESS.2025.3536014}, journal = {IEEE Access}, pages = {21503-21519}, shortjournal = {IEEE Access}, title = {ECC-based authentication protocol for military internet of drone (IoD): A holistic security framework}, volume = {13}, year = {2025}, }
• (2025). Multimodal zero-shot shelf deformation detection based on MEMS sensors and images. ACCESS, 13, 21486-21502. (www)

  @article{ , abstract = {As the variety and quantity of goods in modern warehouse management continue to increase, optimizing space utilization and ensuring the safe and orderly storage of goods have become critical challenges. High-rise shelving systems are increasingly favored by enterprises, but long-term use, collisions with stacker cranes, and overloading can lead to structural deformation of the shelves. If these deformations are not detected and addressed in a timely manner, they may result in serious safety incidents and significant property damage. To address this issue, this study proposes a zero-shot shelf deformation detection method based on multimodal data fusion. The proposed approach integrates Micro-Electro-Mechanical Systems (MEMS) sensors and image data to establish a real-time monitoring and alert mechanism. Specifically, MEMS sensors are employed for real-time acquisition of shelf status, with threshold values set to trigger an initial alert mechanism. Simultaneously, cameras capture shelf images, and multiple You Only Look Once (YOLO) models are used to detect and classify critical components of the shelf, such as beams and columns. YOLOv11n is ultimately selected as the optimal model for detecting these structural elements. Based on the detected beams and columns, further feature extraction is performed, and the sensor data is fused with these features. A K-Means clustering algorithm is then applied to conduct the clustering analysis. To address the issue of a lack of negative samples in the dataset, the study employs oversampling techniques, including SMOTE, ADASYN, and Borderline-SMOTE, combined with machine learning models such as Random Forest and Gradient Boosting Decision Trees (GBDT). The experimental results demonstrate that both Random Forest and GBDT achieved precision, recall, and F1 scores exceeding 95%, confirming the effectiveness and accuracy of the proposed method in shelf deformation detection. The multimodal detection method proposed in this study not only improves the accuracy and real-time performance of shelf deformation detection but also provides strong technical support for the safety management of warehouse operations.}, archive = {J_ACCESS}, author = {Hong Yan and Jingjing Fan and Yajun Liu}, doi = {10.1109/ACCESS.2025.3534411}, journal = {IEEE Access}, pages = {21486-21502}, shortjournal = {IEEE Access}, title = {Multimodal zero-shot shelf deformation detection based on MEMS sensors and images}, volume = {13}, year = {2025}, }
• (2025). Applying the block-based programming language ALICE for developing programming competencies in university students. ACCESS, 13, 21471-21485. (www)

  @article{ , abstract = {The information society is part of modern life, and algorithmic thinking and programming are relevant to everybody, regardless of educational background. Today’s world needs professionals with computing competencies. Higher education programming classes usually use text-based programming languages to develop programming competencies, usually without success for all students. Traditional programming languages consider syntax barriers that complicate their adoption and usefulness for students. Block-based programming languages such as SCRATCH and ALICE permit the development of programming competencies without text coding and syntax restrictions to solve that issue. There are different research works on SCRATCH experiences, but not many experiences on how to use ALICE overall in higher education. This article aims to evaluate the effectiveness of the ALICE programming language in enhancing programming learning outcomes for university students and addressing the limited number of empirical studies of ALICE in higher education contexts. The research involved a literature review to provide a theoretical foundation, followed by an experimental phase that involved two groups: the control group, which followed the traditional programming instruction method with a text-based programming language, and the experimental group, which used the block-based ALICE environment. The experimental group achieved better results with an average score of 80, while the control group scored 65 points on a 100-point scale. The results obtained highlight the effectiveness of the ALICE environment in supporting the programming learning process for first-level Software Engineering university students.}, archive = {J_ACCESS}, author = {Mariuxi Vinueza-Morales and Jesennia Cárdenas-Cobo and Jéssica Cabezas-Quinto and Cristian Vidal-Silva}, doi = {10.1109/ACCESS.2025.3536279}, journal = {IEEE Access}, pages = {21471-21485}, shortjournal = {IEEE Access}, title = {Applying the block-based programming language ALICE for developing programming competencies in university students}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient resource allocation in mobile edge computing using NOMA and massive MIMO. ACCESS, 13, 21456-21470. (www)

  @article{ , abstract = {This work addresses the critical challenge of energy consumption in Mobile Edge Computing (MEC), a burgeoning field that extends cloud computing capabilities to the edge of cellular networks. Given the exponential growth of mobile devices and the resultant surge in energy demands, there is an urgent need for efficient energy management strategies to ensure sustainable development and operation of MEC infrastructures. This paper introduces a comprehensive framework for reducing energy consumption in MEC environments by leveraging advanced optimization techniques and energy-efficient resource allocation algorithms. We propose a novel approach that dynamically adjusts the computational resources based on the current network load and the type of services requested, thus minimizing unnecessary energy consumption. We derive and propose an optimized energy consumption for local processing. Then, we study the two network scenarios: Non-Orthogonal Multiple Access (NOMA) and Massive Multiple-Input Multiple-Output (mMIMO). We propose an optimized energy consumption algorithm in NOMA based on the derived processing resource requirements. Then, in mMIMO, we derive optimized power allocation algorithms. Simulations validate the effectiveness of our proposed framework, demonstrating significant energy savings.}, archive = {J_ACCESS}, author = {Qusay Alghazali and Husam Al-Amaireh and Tibor Cinkler}, doi = {10.1109/ACCESS.2025.3535233}, journal = {IEEE Access}, pages = {21456-21470}, shortjournal = {IEEE Access}, title = {Energy-efficient resource allocation in mobile edge computing using NOMA and massive MIMO}, volume = {13}, year = {2025}, }
• (2025). From queries to courses: SKYRAG’s revolution in learning path generation via keyword-based document retrieval. ACCESS, 13, 21434-21455. (www)

  @article{ , abstract = {Large Language Models (LLMs) hold immense potential for transforming education by automating the generation of personalized learning paths. However, traditional LLMs often suffer from hallucinations and content irrelevance. To address these challenges, we propose SKYRAG, a Separated Keyword Retrieval Augmentation Generation system that enhances the learning path generation process by integrating advanced retrieval mechanisms with LLMs. SKYRAG retrieves relevant course materials from Massive Open Online Course (MOOC) platforms, aligning them with individual learner profiles to provide personalized and coherent learning paths. Compared with Naïve RAG, SKYRAG demonstrates superior performance in terms of accuracy, relevance, and user satisfaction, as confirmed by human evaluations across four domains. By improving retrieval precision and addressing the limitations of traditional methods, SKYRAG represents a significant advancement in educational technology. This study contributes to the growing body of research on AI-driven learning systems and highlights SKYRAG’s potential for widespread adoption in dynamic educational environments.}, archive = {J_ACCESS}, author = {Yosua Setyawan Soekamto and Leonard Christopher Limanjaya and Yoshua Kaleb Purwanto and Dae-Ki Kang}, doi = {10.1109/ACCESS.2025.3535618}, journal = {IEEE Access}, pages = {21434-21455}, shortjournal = {IEEE Access}, title = {From queries to courses: SKYRAG’s revolution in learning path generation via keyword-based document retrieval}, volume = {13}, year = {2025}, }
• (2025). Unsupervised anomaly detection in hyperspectral imaging: Integrating tensor robust principal component analysis with autoencoding adversarial networks. ACCESS, 13, 21422-21433. (www)

  @article{ , abstract = {Hyperspectral (HS) image analysis has gained significant attention due to its ability to capture detailed spectral information across hundreds of bands, making it useful for environmental monitoring and mineral exploration applications. However, detecting anomalies in HS images, especially in complex scenes, remains challenging. This paper proposes a novel approach for robust anomaly detection by integrating tensor robust principal component analysis (TRPCA) with autoencoding adversarial networks (AEAN). Our method utilizes the AEAN model to learn a nonlinear low-dimensional representation of the spectral characteristics of background regions, which is then incorporated into the TRPCA framework. The TRPCA is further enhanced by incorporating prior knowledge of the sparsity of anomalous regions, enabling more accurate separation of background and anomaly components. This integration, achieved through a plug-and-play alternating direction method of multipliers (PnP-ADMM), significantly improves detection accuracy and robustness. Experimental results on benchmark datasets widely used for HS anomaly detection confirm that the proposed method consistently outperforms conventional techniques, achieving superior area-under-the-curve (AUC) scores across diverse and complex scenes. By leveraging both nonlinear modeling of background characteristics and sparsity-based anomaly separation, this research provides a more accurate and robust solution for HS anomaly detection, highlighting its potential for practical applications in remote sensing.}, archive = {J_ACCESS}, author = {Atsuya Emoto and Ryo Matsuoka}, doi = {10.1109/ACCESS.2025.3534981}, journal = {IEEE Access}, pages = {21422-21433}, shortjournal = {IEEE Access}, title = {Unsupervised anomaly detection in hyperspectral imaging: Integrating tensor robust principal component analysis with autoencoding adversarial networks}, volume = {13}, year = {2025}, }
• (2025). A CNN-based microwave imaging system for detecting watermelon ripeness. ACCESS, 13, 21413-21421. (www)

  @article{ , abstract = {The integration of a non-invasive microwave imaging system with a machine learning algorithm could improve food quality and food safety. In this paper, a S- and C-band microwave imaging system that utilizes DAS (Delay and Sum) beamforming with an automated high-frequency switching network is built to scan watermelons and determine their ripeness. A total of 288 images were collected from eight different watermelons varying the height and angle of capture. A convolutional neural network (CNN) was employed to assess the ripeness level, which was determined by analyzing the Brix sugar content. The results show 86% accuracy for ripeness classification in three fold cross validation. This novel approach demonstrates the potential of combining microwave imaging with machine learning for non-destructive food quality assessment, offering a scalable and reliable tool for real-time evaluation of fruit ripeness and quality.}, archive = {J_ACCESS}, author = {Zachary M. Choffin and Lingyan Kong and Yu Gan and Nathan Jeong}, doi = {10.1109/ACCESS.2025.3535804}, journal = {IEEE Access}, pages = {21413-21421}, shortjournal = {IEEE Access}, title = {A CNN-based microwave imaging system for detecting watermelon ripeness}, volume = {13}, year = {2025}, }
• (2025). Leveraging digital twin technology for battery management: A case study review. ACCESS, 13, 21382-21412. (www)

  @article{ , abstract = {The increasing complexity of battery management systems (BMS) has led to challenges processing the vast amounts of data required for accurate real-time monitoring and control. Existing BMS frameworks, which rely heavily on artificial intelligence (AI), often struggle with data limitations that impact the precision of state estimates, ultimately affecting battery performance and safety. The integration of digital twin (DT) technology has been proposed to address these challenges. DTs create virtual representations of physical battery systems, enabling enhanced monitoring, predictive maintenance, and optimized performance through advanced AI algorithms. This study presents a comprehensive exploration of DT technology for BMS. First, we review the fundamental concepts, including DTs’ definitions, roles, and high-level architecture in battery management. Second, we examine research and industry-based case studies to identify the necessary technologies and tools for developing robust battery DTs. We propose a detailed framework for integrating DTs with existing BMS infrastructure, focusing on scalability, cost-effectiveness, and practical implementation strategies. Finally, we discuss the open research challenges and future opportunities in the field, emphasizing the potential impact of DTs on the evolution of BMSs.}, archive = {J_ACCESS}, author = {Judith Nkechinyere Njoku and Ebuka Chinaechetam Nkoro and Robin Matthew Medina and Cosmas Ifeanyi Nwakanma and Jae-Min Lee and Dong-Seong Kim}, doi = {10.1109/ACCESS.2025.3531833}, journal = {IEEE Access}, pages = {21382-21412}, shortjournal = {IEEE Access}, title = {Leveraging digital twin technology for battery management: A case study review}, volume = {13}, year = {2025}, }
• (2025). The development of human-robot interaction design for optimal emotional expression in social robots used by older people: Design of robot facial expressions and gestures. ACCESS, 13, 21367-21381. (www)

  @article{ , abstract = {Showing facial expressions and using emotion-appropriate gestures are essential for social robots. As a robot’s behavior becomes more anthropomorphic, the intimacy and naturalness of human-robot interactions improve. This study aims to derive optimized facial expression and gesture designs for social robots interacting with elderly individuals, thereby enhancing emotional interactions. First, we utilized user-robot integrated scenarios to identify the emotional states required for robot interactions. Subsequently, we conducted surveys and user preference evaluations on commercially available robot faces. The results indicated that suitable components for robot faces include the eyes, eyebrows, mouth, and cheeks; geometric shapes were deemed the most appropriate. Accordingly, we collected and analyzed human facial expression images using the Facial Action Coding System to identify action unit combinations and facial landmarks. This analysis informed the design of robot faces capable of expressing humanlike emotions. Furthermore, we collected and evaluated human gesture videos representing various emotions to select the most suitable gestures, which were analyzed using motion capture technology. We utilized these data to design robot gestures. The designed robot facial expressions and gestures were validated and refined through emotion-based user preference evaluations. As a result of the study, we developed facial expression and gesture designs for six emotions (Loving, Joyful, Upbeat, Hopeful, Concerned, Grateful) in social robots interacting with elderly individuals. The results provide guidelines for designing human-friendly robot facial expressions and gestures, thus enabling social robots to form deep emotional bonds with users. By analyzing human facial expressions and gestures in relation to emotions and applying these findings to robots, we successfully developed natural and emotionally expressive robot behaviors. These findings contribute to the advancement of robots as reliable and comforting companions for humans.}, archive = {J_ACCESS}, author = {Sujin Jo and Seongsoo Hong}, doi = {10.1109/ACCESS.2025.3534845}, journal = {IEEE Access}, pages = {21367-21381}, shortjournal = {IEEE Access}, title = {The development of human-robot interaction design for optimal emotional expression in social robots used by older people: Design of robot facial expressions and gestures}, volume = {13}, year = {2025}, }
• (2025). Recent advances in sharding techniques for scalable blockchain networks: A review. ACCESS, 13, 21335-21366. (www)

  @article{ , abstract = {Blockchain is a technology that has gained popularity in recent years due to its immutability and decentralized features. Various industries have started integrating this technology into their business models, resulting in the development of various types of blockchain networks. This paper reviews the latest academic advancements in sharding techniques within the various types of sharded blockchain networks implemented in the past five years to identify their strengths and weaknesses. The increasing volume of transactions in blockchain networks has caused issues in terms of scalability, transaction efficiency, storage, and security, which obstruct the global adoption of blockchain technology. Sharding in blockchain is a solution that aims to address these issues through various types of sharding techniques, such as network, transaction, or state sharding, along with the various types of networks and systems. The general definition of sharding is the partitioning of the network into subsets known as shards, which allows them to validate transactions in parallel, resulting in performance and scalability improvements of the existing network. The existing sharding techniques (before 2020) and the latest sharding techniques (2020–2024), along with their characteristics, advantages, and disadvantages, are also discussed. An analysis based on the performance evaluation of recent sharding techniques was conducted to further understand the impact of these techniques. The result of the review provides critical insights into the advancements in sharding techniques and fosters innovation while facilitating the widespread adoption of sharding-based solutions in the blockchain ecosystem.}, archive = {J_ACCESS}, author = {Brandon Liew Yi Quan and Nur Haliza Abdul Wahab and Arafat Al-Dhaqm and Ahmad Alshammari and Ali Aqarni and Shukor Abd Razak and Koh Tieng Wei}, doi = {10.1109/ACCESS.2024.3523256}, journal = {IEEE Access}, pages = {21335-21366}, shortjournal = {IEEE Access}, title = {Recent advances in sharding techniques for scalable blockchain networks: A review}, volume = {13}, year = {2025}, }
• (2025). Digital twin’s anatomy: A cross-sector framework with healthcare validation. ACCESS, 13, 21306-21334. (www)

  @article{ , abstract = {Digital twins (DT) in manufacturing, healthcare, and across different industrial domains are often over-simplified as solely a virtual representation of a physical object or service. Such a definition constitutes a dilemma in distinguishing DTs from digital models, digital shadows, digital threads, and cyber-physical systems. In this article, we aim to elucidate the concept of digital twins and its definition. Therefore, we go through the connotation of digital twins, which has its roots in space exploration and product-life cycle management, and describe the four evolution stages of DT developments. This article employs an ontological approach to clearly and comprehensively define digital twins and related key concepts, including digital models, assets, prototypes, shadows, and threads. Additionally, it presents a structured framework detailing the meta-model and the reference-level ontology of digital twins. To evaluate the proposed structure, definitions, and its important entities, we have examined our framework against 73 peer-reviewed papers in the healthcare sector from 2018 until July 2024. The evaluation and classification criteria of the selected works were based on four research questions. These criteria are driven by the core definitions provided by the main cross-domain digital twin researchers and this article’s proposed anatomy of digital twins.}, archive = {J_ACCESS}, author = {Sina Namaki Araghi and Zhengyu Liu and Arkopaul Sarkar and Thierry Louge and Mohamed Hedi Karray}, doi = {10.1109/ACCESS.2025.3528736}, journal = {IEEE Access}, pages = {21306-21334}, shortjournal = {IEEE Access}, title = {Digital twin’s anatomy: A cross-sector framework with healthcare validation}, volume = {13}, year = {2025}, }
• (2025). Cₙ² modeling for free-space optical communications: A review. ACCESS, 13, 21279-21305. (www)

  @article{ , abstract = {Atmospheric turbulence influence on optical wave propagation, referred to as optical turbulence, has long been studied for astronomical applications and is now being addressed for free-space optical communication links between ground and satellites. While challenges overlap, models developed for astronomical applications are not fully transferable to optical communications. This paper provides a literature review of optical turbulence models, i.e., models giving vertical profiles of the refractive index structure parameter $C_{n}^{2}$ , highlighting differences between astronomical and optical communication sites. It presents different classifications of available $C_{n}^{2}$ models, based on the atmospheric layer they target and their necessary input parameters. Boundary layer $C_{n}^{2}$ models are also addressed, and recent machine learning approaches for $C_{n}^{2}$ modeling are discussed. Additionally, commonly used metrics for comparing $C_{n}^{2}$ profiles are introduced. Therefore, this work provides important insights into optical turbulence model selection, enabling accurate site characterization and informed optical terminal design.}, archive = {J_ACCESS}, author = {Florian Quatresooz and Claude Oestges}, doi = {10.1109/ACCESS.2025.3535093}, journal = {IEEE Access}, pages = {21279-21305}, shortjournal = {IEEE Access}, title = {Cₙ² modeling for free-space optical communications: A review}, volume = {13}, year = {2025}, }
• (2025). Integrating data mining, deep learning, and gene ontology analysis for gene expression-based disease diagnosis systems. ACCESS, 13, 21265-21278. (www)

  @article{ , abstract = {The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer’s disease using gene expression data derived from both DNA microarray experiments and mRNA sequencing. It outlines a conceptual framework and provides a block diagram of the stepwise procedure for analyzing gene expression data, aiming to enhance the accuracy and objectivity of disease diagnosis. The research methodology involves initial gene ontology analysis, followed by the application of the Self Organizing Tree Algorithm (SOTA) for clustering gene expression profiles, an ensemble algorithm for data biclustering, and CNN for sample classification. Bayesian optimization method was employed to determine the optimal hyperparameters for all models. The analysis of simulation results demonstrates the high efficacy of the proposed approach. Specifically, for Alzheimer’s data, the number of genes analyzed was reduced from 44,662 to 4,004. Subsequent cluster-bicluster analysis divided this data into two subsets containing 1,158 and 2,846 genes, respectively. Classification accuracy for samples within these subsets reached 89.8% and 91.8%. In cancer data analysis, the gene count was reduced from 60,660 to 10,422, with 3,955 and 6,467 genes in the first and second clusters, respectively. The classification accuracy for these subsets was 97.4% and 97.6%, respectively. To our mind, the implementation of this model promises to significantly improve the efficacy of early diagnosis systems for complex diseases.}, archive = {J_ACCESS}, author = {Sergii Babichev and Igor Liakh and Jiří Škvor}, doi = {10.1109/ACCESS.2025.3535999}, journal = {IEEE Access}, pages = {21265-21278}, shortjournal = {IEEE Access}, title = {Integrating data mining, deep learning, and gene ontology analysis for gene expression-based disease diagnosis systems}, volume = {13}, year = {2025}, }
• (2025). The relationship between emotional and other factors in information diffusion. ACCESS, 13, 21249-21264. (www)

  @article{ , abstract = {With the recent proliferation of social media, the spread of disinformation and misinformation has become a global risk. This study analyzes and characterizes the relationship between human emotions and other elements (social bots and echo chambers), which are major factors in the spread of information, including disinformation and misinformation. Specifically, we obtain from X (formerly Twitter) the case that was fact-checked as disinformation in Japan and analyze the relationship between each factor in a time sequence using existing tools and methods. After confirming the similar results to previous studies that emotional posts were more likely to be spread, our analysis showed the possibility that social bots were highly influential in amplifying negative sentiment in the early stages of spread. Regarding emotions and echo chambers, we observed that the increase in negative emotions and the formation of echo chambers followed a similar time sequence. In addition, we confirmed that the amplification of negative emotions precedes the formation of echo chambers. The results of this analysis suggested the importance of detecting social bot activity in the early stages of information diffusion and controlling the amplification of negative emotions.}, archive = {J_ACCESS}, author = {Shuhei Ippa and Takao Okubo and Masaki Hashimoto}, doi = {10.1109/ACCESS.2025.3535547}, journal = {IEEE Access}, pages = {21249-21264}, shortjournal = {IEEE Access}, title = {The relationship between emotional and other factors in information diffusion}, volume = {13}, year = {2025}, }
• (2025). A co-evolutionary genetic algorithm approach to optimizing deep learning for brain tumor classification. ACCESS, 13, 21229-21248. (www)

  @article{ , abstract = {Brain tumors are among the deadliest diseases, leading researchers to focus on improving the accuracy of tumor classification—a critical task for prompt diagnosis and effective treatment. Recent advancements in brain tumor diagnosis have significantly increased the use of deep learning techniques, particularly pre-trained models, for classification tasks. These models serve as feature extractors or can be fine-tuned for specific tasks, reducing both training time and data requirements. However, achieving high accuracy in multi-class brain tumor classification remains a major challenge, driving continued research in this area. Key obstacles include the need for expert interpretation of deep learning model outputs and the difficulty of developing highly accurate categorization systems. Optimizing the hyperparameters of Convolutional Neural Network (CNN) architectures, especially those based on pre-trained models, plays a crucial role in improving training efficiency. Manual hyperparameter adjustment is time-consuming and often results in suboptimal outcomes. To address these challenges, we propose an advanced approach that combines transfer learning with enhanced coevolutionary algorithms. Specifically, we utilize EfficientNetB3 and DenseNet121 pre-trained models in conjunction with the Co-Evolutionary Genetic Algorithm (CEGA) to classify brain tumors into four categories: gliomas, meningiomas, pituitary adenomas, and no tumors. CEGA optimizes the hyperparameters, improving both convergence speed and accuracy. Experiments conducted on a Kaggle dataset demonstrate that CEGA-EfficientNetB3 achieved the highest accuracy of 99.39%, while CEGA-DenseNet121 attained 99.01%, both without data augmentation. These results outperform cutting-edge methods, offering a rapid and reliable method for brain tumor classification. This approach has great potential to support radiologists and physicians in making timely and accurate diagnoses.}, archive = {J_ACCESS}, author = {Abdelmgeid A. Ali and Mohamed T. Hammad and Hassan S. Hassan}, doi = {10.1109/ACCESS.2025.3535844}, journal = {IEEE Access}, pages = {21229-21248}, shortjournal = {IEEE Access}, title = {A co-evolutionary genetic algorithm approach to optimizing deep learning for brain tumor classification}, volume = {13}, year = {2025}, }
• (2025). Green AI: Enhancing sustainability and energy efficiency in AI-integrated enterprise systems. ACCESS, 13, 21216-21228. (www)

  @article{ , abstract = {The rise of Artificial Intelligence (AI) in automating tasks and driving decision-making within enterprise systems has led to growing concerns over the significant energy consumption involved in model training and inference processes. This paper introduces an innovative framework focused on optimizing energy efficiency in AI models, all while preserving high performance. The system employs advanced optimization algorithms aimed at minimizing energy usage during both AI training and inference, ensuring minimal impact on model accuracy. A dynamic, multi-objective optimization approach is used to achieve an optimal balance between energy reduction and performance, identifying Pareto-optimal solutions tailored to various operational needs. Validated within large-scale enterprise settings, the system delivers a 30.6% decrease in overall energy consumption, with only a slight 0.7% reduction in model accuracy. Furthermore, scalability is demonstrated through a 5.0% improvement in task execution time and a 4.8% increase in system throughput. The findings highlight the practicality of this framework for promoting sustainable AI deployment, aiding both cost efficiency and environmental responsibility. The paper concludes by discussing limitations and outlining potential avenues for future research to further enhance scalability and broaden the framework’s application.}, archive = {J_ACCESS}, author = {Saumya Dash}, doi = {10.1109/ACCESS.2025.3532838}, journal = {IEEE Access}, pages = {21216-21228}, shortjournal = {IEEE Access}, title = {Green AI: Enhancing sustainability and energy efficiency in AI-integrated enterprise systems}, volume = {13}, year = {2025}, }
• (2025). Frequency-tunable tri-band metamaterial-inspired antenna. ACCESS, 13, 21210-21215. (www)

  @article{ , abstract = {A frequency-tunable tri-band metamaterial-inspired antenna is proposed. The antenna consists of patches, interdigital capacitors, and meander lines inductors, and loaded with a split ring resonator. The metamaterial-inspired antenna can provide three band operations at 1.575/2.1/2.6 GHz, which cover the GPS band1, LTE band1, and LTE band7 applications. The antenna has three different current flows, which create different resonances. The proposed antenna can adjust each resonant frequency independently by adjusting the antenna parameters, the length of SRR, meander lines, and ground branch line, which can affect each current path. The prototype antenna has been designed and fabricated using the FR4 substrate with a thickness of 1.6 mm and a dielectric constant of 4.3. The measured return loss and far-field radiation patterns of the fabricated antenna have good agreement with the simulated results. The fabricated antenna shows well-matched impedance characteristics with a reasonable bandwidth and has an omnidirectional radiation pattern at each operating band. The proposed antenna has the advantages of multi-band, frequency-tunable characteristics, and easy fabrication, and can be widely used in various wireless mobile communication systems.}, archive = {J_ACCESS}, author = {Kyung-Duk Jang and Jae-Hyun Lee}, doi = {10.1109/ACCESS.2025.3533023}, journal = {IEEE Access}, pages = {21210-21215}, shortjournal = {IEEE Access}, title = {Frequency-tunable tri-band metamaterial-inspired antenna}, volume = {13}, year = {2025}, }
• (2025). A 3D approach for the virtual commissioning of processing plants. ACCESS, 13, 21190-21209. (www)

  @article{ , abstract = {This paper presents a novel 3D approach for virtual commissioning (VC) in process engineering, utilizing a semi-automated model generation methodology based on 3D MCAD data. Current VC practices primarily benefit larger manufacturing systems due to the complexity and time-intensive nature of model generation, which poses challenges for broader adoption, especially in small and medium-sized enterprises. Our approach addresses this by proposing a 3D simulation model architecture that integrates physics-based simulation environments, enabling efficient and accurate generation of dynamic simulation models. By leveraging a physics engine, the proposed method automatically derives essential parameters and system topology, thereby significantly reducing the manual modeling effort. The simulation model is designed to depict complex fluid and material flows within a 3D environment, facilitating realistic, real-time interaction and improved visualization for VC. This paper further evaluates the method’s accuracy through a representative case study involving a liquid food processing setup, demonstrating high fidelity in dynamic behavior and alignment with physical processes. The results indicate that this approach offers substantial potential for improving virtual engineering and commissioning workflows, enhancing simulation model fidelity, and promoting interdisciplinary collaboration in process plant development. This development promises an adaptable, user-friendly tool that can advance the integration of VC in diverse engineering fields, particularly where hybrid production processes intersect.}, archive = {J_ACCESS}, author = {Nikolai Striffler and Tobias Voigt}, doi = {10.1109/ACCESS.2025.3536088}, journal = {IEEE Access}, pages = {21190-21209}, shortjournal = {IEEE Access}, title = {A 3D approach for the virtual commissioning of processing plants}, volume = {13}, year = {2025}, }
• (2025). Physics-based and data-driven modeling of degradation mechanisms for lithium-ion Batteries—A review. ACCESS, 13, 21164-21189. (www)

  @article{ , abstract = {Lithium-ion batteries (LIB) are widely used in various applications. The LIB degradation curve and, most significantly, the knee-point and End-of-life (EoL) point identification are critical factors for the selection of the appropriate application, such as electric vehicles and stationary energy storage systems, due to their effect on performance and lifespan, safety, and environmental footprint. Linear degradation models can be inaccurate in capturing the highly nonlinear behavior of LIB degradation caused by multiple simultaneous degradation mechanisms. Hence, this work first analyzes the main different mechanisms, their causes, and their interrelations. Secondly, the various single- and multi-mechanism physics-based (PB) and data-driven (DD) models for LIB degradation and knee-point identification are summarized and compared regarding their prediction performance on degradation and transition from stabilized to saturated aging. While single-mechanism PB models can be effective in the LIB first-life prediction, they can seriously undermine the knee-point and saturated aging. Moreover, the modeling of the different aging mechanisms can significantly increase the complexity of the multi-mechanism PB models. Finally, while DD models for LIB degradation have been developed, a DD model focused on knee-point identification and LIB second-life is still missing from the literature.}, archive = {J_ACCESS}, author = {Pedro Lozano Ruiz and Nikolaos Damianakis and Gautham Ram Chandra Mouli}, doi = {10.1109/ACCESS.2025.3535918}, journal = {IEEE Access}, pages = {21164-21189}, shortjournal = {IEEE Access}, title = {Physics-based and data-driven modeling of degradation mechanisms for lithium-ion Batteries—A review}, volume = {13}, year = {2025}, }
• (2025). An improved HM-SAC-CA algorithm for mobile robot path planning in unknown complex environments. ACCESS, 13, 21152-21163. (www)

  @article{ , abstract = {Path planning and its optimization is a critical and difficult task for a mobile robot in a complex and unknown environment. To tackle this problem, we propose an improved SAC (HM-SAC-CA) algorithm for path planning in unknown complex environments. First, based on the SAC maximum entropy framework, a deep reinforcement learning algorithm with clipped automatic entropy adjustment is proposed to improve the quality of policy learning by suppressing entropy evaluation. Second, an innovative hierarchical experience storage structure is constructed during experience replay, and the overfitting phenomenon caused by using good experiences is eliminated by a bias-free sampling strategy. Finally, a posture reward function and a staged incentive mechanism are proposed. The staged incentive mechanism uses both the sparse reward function and the posture reward function in stages to reduce the blindness of exploration during training and accelerate the training learning process. Experiments are conducted using a simulated Turtlebot3 and a real mobile robot and the results validate the performance of the proposed work.}, archive = {J_ACCESS}, author = {Ting Jiao and Conglin Hu and Lingxin Kong and Xihao Zhao and Zhongbao Wang}, doi = {10.1109/ACCESS.2025.3535728}, journal = {IEEE Access}, pages = {21152-21163}, shortjournal = {IEEE Access}, title = {An improved HM-SAC-CA algorithm for mobile robot path planning in unknown complex environments}, volume = {13}, year = {2025}, }
• (2025). Blockchain-based anonymous reputation system for performance appraisal. ACCESS, 13, 21139-21151. (www)

  @article{ , abstract = {Performance appraisal is crucial in human resource management to identify areas within organizations. Ensuring anonymity and confidentiality is important to obtain honest feedback and prevent retaliation. Although blockchain-based anonymous reputation systems have been discussed, permissioned blockchains are susceptible to Sybil attack vulnerabilities, while permissionless private blockchains do not provide full anonymity. We present the Anonymous Reputation System for Performance Appraisal (ARSPA), which uses a permissionless public blockchain. This system is designed for upward feedback in performance appraisals, employing cryptographic techniques such as non-interactive zero-knowledge proofs, public key encryption, and Merkle trees to ensure security. Our protocol addresses the risks of Sybil attacks, ensures review limitation and unforgeability. We validate the security of ARSPA through analysis and demonstrate its feasibility through proof-of-concept on Ethereum test networks. ARSPA provides a secure and efficient approach to improve the reliability and fairness of performance appraisal.}, archive = {J_ACCESS}, author = {Hye Jin Lee and Duc Anh Luong and Jong Hwan Park and Hyoseung Kim}, doi = {10.1109/ACCESS.2025.3532602}, journal = {IEEE Access}, pages = {21139-21151}, shortjournal = {IEEE Access}, title = {Blockchain-based anonymous reputation system for performance appraisal}, volume = {13}, year = {2025}, }
• (2025). Design of an integrated model combining CycleGAN, PPO, and vision transformer for adaptive scene rendering in the metaverse. ACCESS, 13, 21117-21138. (www)

  @article{ , abstract = {The emergence of the metaverse demands adaptive rendering systems that produce high-quality scenes, balancing the dimensions of visual fidelity with computational efficiency. Methods used for scene generation and rendering usually fail to incorporate user interactions in real time, leading to resource inefficiency and reduced user experience. This paper proposes a new, all-in-one model that integrates CycleGAN, Proximal Policy Optimization (PPO), Vision Transformer (ViT), Bootstrap Your Own Latent (BYOL), and Adaptive Feedback Learning (AFL-RT) in an attempt at dynamic optimization of the scene rendering based on user behavior and environmental data. The CycleGAN module ensures the production of the high-resolution scenes even from low-resolution inputs, whereas PPO provides for continuous adaptation of rendering parameters so as to reduce latency and computational load. ViT uses attention-based rendering, providing better visualization to user-centric areas, and BYOL does self-supervised scene understanding and compression, maintaining important information though reducing the bandwidth usage. AFL-RT is actually a closed-loop feedback system used to refine rendering parameters in real-time by actually reflecting the user’s preference and interests. Experimental results demonstrate that the proposed model reduces latency by as much as 30% in rendering, improves visual fidelity by up to 15%, and decreases energy consumption by 25% compared with the baseline methods. The user satisfaction also increases up to 25% regarding real-time adaptations by using this model, which only validates the practicality of such a model for dynamic, interactive metaverse environments. This work offers a comprehensive solution to the challenges of adaptive scene rendering, thus highly applicable to future metaverse applications in almost all industries and scenarios.}, archive = {J_ACCESS}, author = {Durga Prasad Kavadi and Kailash Chandra Mishra and Sai Babu Veesam and Mudimela Madhusudhan and Palacharla Ravi Kumar and Pannangi Naresh and Yuvaraju Chinnam}, doi = {10.1109/ACCESS.2025.3532327}, journal = {IEEE Access}, pages = {21117-21138}, shortjournal = {IEEE Access}, title = {Design of an integrated model combining CycleGAN, PPO, and vision transformer for adaptive scene rendering in the metaverse}, volume = {13}, year = {2025}, }
• (2025). Real-time classification of deep and non-deep sleep with comparative intervention experiments. ACCESS, 13, 21107-21116. (www)

  @article{ , abstract = {Sleep is an essential part of human life, and sleep quality is a critical indicator of overall health. This paper presents a system that utilizes a Brain-Computer Interface and a Deep Learning Network for the real-time classification of non-deep sleep and deep sleep. By collecting, uploading, preprocessing, and classifying sleep EEG data in real-time, the system quantitatively evaluates the subjects’ sleep quality. Additionally, we conducted sleep experiments on the same subjects under varying environments and interventions and compared the results. Using our deep learning model, we calculated the proportion of deep sleep in each experimental group and validated the effects of fatigue state and white noise environment on improving the proportion. Based on this system, we examined the specific effects of different interventions (fatigue state and white noise intervention) on sleep states, providing concrete data to determine whether these factors enhance sleep quality.}, archive = {J_ACCESS}, author = {Mo Xia and Hongxi Xue and Boning Li and Jianting Cao}, doi = {10.1109/ACCESS.2024.3514890}, journal = {IEEE Access}, pages = {21107-21116}, shortjournal = {IEEE Access}, title = {Real-time classification of deep and non-deep sleep with comparative intervention experiments}, volume = {13}, year = {2025}, }
• (2025). Networked air defense radar resource management under power and tracking constraints. ACCESS, 13, 21096-21106. (www)

  @article{ , abstract = {Air dominance plays a crucial role in today’s battlefield, and its importance has increased over time with the development of advanced platforms and commercial technologies that enable the proliferation of air threats. The importance of ground based air defense systems, which protect air space and critical infrastructure, is also increasing in line with the development of those air threats. It has come to a point where networking of those air defense systems is needed to further improve the overall effectiveness of air defense operations by sharing radar resources between them. Sharing resources is beneficial, especially in saturation constraints where a single radar’s tracking capacity is overwhelmed. Another important aspect is to lower the transmit power of the networked radars to decrease detectability from the adversary’s electronic warfare capability. This paper proposes a networked radar resource optimization methodology for fire control radars with limited tracking capacity and tries to minimize the transmit power of the networked radars while keeping the tracking quality needed for air defense engagement operations. Simulation results are presented for a sample scenario under saturation conditions. Numerical results show that with the proposed methodology, the targets are collaboratively tracked by the air defense radar network with the required tracking quality needed for engagement operations while minimizing the overall transmit power of the radar network.}, archive = {J_ACCESS}, author = {O. Tuncer and Hakan Ali Cirpan}, doi = {10.1109/ACCESS.2025.3535872}, journal = {IEEE Access}, pages = {21096-21106}, shortjournal = {IEEE Access}, title = {Networked air defense radar resource management under power and tracking constraints}, volume = {13}, year = {2025}, }
• (2025). Fixed-time sliding mode control for vehicle platoon with input dead-zone and prescribed performance. ACCESS, 13, 21085-21095. (www)

  @article{ , abstract = {This article studies the fixed-time sliding mode control (FTSMC) issue for vehicle platoon with prescribed performance, input dead-zone (IDZ), unknown nonlinearities, and external disturbances. Unlike the traditional funnel-shaped prescribed performance function, a new tunnel fixed-time prescribed performance function (TFTPPF) is proposed, which defines more compact tunnel-shaped performance boundaries that can accelerate convergence speed and reduce overshoot of tracking error. The convergence time of TFTPPF is predefined and is independent of control parameters, which enhances the design flexibility of performance function. Furthermore, Chebyshev neural network (CNN) is adopted to approximate unknown nonlinearities, and new adaptive mechanisms are designed to estimate IDZ slope and external disturbances. Utilizing the proposed TFTPPF, CNN, and adaptive mechanisms, a novel FTSMC strategy for vehicle platoon is proposed to ensure string stability and achieve predefined tracking performance within a fixed time. Finally, comparative numerical examples are employed to validate the validity and advantages of the proposed strategy.}, archive = {J_ACCESS}, author = {Yongqiang Jiang and Yiguang Wang and Xiaojie Li and Xiaoyan Zhan and Xubing Tang}, doi = {10.1109/ACCESS.2025.3535827}, journal = {IEEE Access}, pages = {21085-21095}, shortjournal = {IEEE Access}, title = {Fixed-time sliding mode control for vehicle platoon with input dead-zone and prescribed performance}, volume = {13}, year = {2025}, }
• (2025). Analyzing the influence of online test-driving reviews on the adoption of autonomous vehicles. ACCESS, 13, 21074-21084. (www)

  @article{ , abstract = {Autonomous vehicle (AV) technology has led to disruptive changes in transportation. Improving consumers’ trust and awareness of self-driving cars has become an urgent problem in market promotion and technological development. Besides the information provided by car makers and salespeople, consumers rely on information from online blogs and reviews, especially that provided by test drivers. This study explores the impact of test-driving reviews (TDRs) on the consumer decision-making process regarding the purchase and usage of AVs. We aim to understand whether and how different types of TDRs (professional, manufacturer, and amateur) affect consumers’ perceptions and attitudes toward AVs. Questionnaire surveys and structural equation modeling were used to test the hypotheses. The results indicate that both risk perception and perceived value significantly influence adoption intentions. For TDRs, positive professional reviews and manufacturer reviews reduced risk perception, leading to adoption intention. Both amateur reviews (AM) and manufacturer reviews (MR) positively influence perceived value, further increasing adoption intentions. Based on the analyses, manufacturers and policymakers were provided with practical implications for promoting AVs. Finally, this study has several limitations. First, it examines the influence of review channels on consumers’ purchase intentions of AVs, not actual purchasing behavior, warranting further exploration of this relationship. Additionally, the data were collected from China, so societal and cultural differences may affect the findings, highlighting the need to validate the model with buyers from other countries in future research.}, archive = {J_ACCESS}, author = {Chuan Hui Liao and Xuan Zheng Wang and Ren Gang Guo and Siqi Wang}, doi = {10.1109/ACCESS.2025.3527686}, journal = {IEEE Access}, pages = {21074-21084}, shortjournal = {IEEE Access}, title = {Analyzing the influence of online test-driving reviews on the adoption of autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Variation in photovoltaic energy rating and underlying drivers across modules and climates. ACCESS, 13, 21064-21073. (www)

  @article{ , abstract = {The performance of photovoltaic (PV) modules is determined by the interplay between their inherent characteristics and the prevailing weather conditions. Although the impacts of different characteristics (e.g., low-light behavior, spectral mismatch, temperature coefficient, etc) are known, they have not been quantified over large geographic regions. This study uses the Climate Specific Energy Rating (CSER) and specific yield metrics as criteria to determine how different PV modules perform across climates in the contiguous United States (CONUS) and identifies the underlying drivers behind the observed variations. The annual CSER and specific yield of various PV technologies vary by more than 10% and 30%, respectively, across the CONUS. As expected, temperature has the most significant impact on CSER, affecting CSER by up to 13.1%, while spectral effects account for up to 4.9% variation in the case of cadmium telluride modules. Additionally, minor differences in parameter estimation procedures are shown to result in CSER differences of up to 1.5% in some climates. Furthermore, the IEC 61853-4 reference climatic datasets are found to overestimate CSER by 2–4% relative to climatic data for locations of actual PV systems in the United States. A new set of reference locations that accurately represents CSER across CONUS is proposed as an alternative to the IEC 61853-4 reference datasets.}, archive = {J_ACCESS}, author = {Kevin S. Anderson and Joshua S. Stein and Marios Theristis}, doi = {10.1109/ACCESS.2025.3534678}, journal = {IEEE Access}, pages = {21064-21073}, shortjournal = {IEEE Access}, title = {Variation in photovoltaic energy rating and underlying drivers across modules and climates}, volume = {13}, year = {2025}, }
• (2025). VisualSAF-A novel framework for visual semantic analysis tasks. ACCESS, 13, 21052-21063. (www)

  @article{ , abstract = {We introduce VisualSAF, a novel Visual Semantic Analysis Framework designed to enhance the understanding of contextual characteristics in Visual Scene Analysis (VSA) tasks. The framework leverages semantic variables extracted using machine learning algorithms to provide additional high-level information, augmenting the capabilities of the primary task model. Comprising three main components – the General DL Model, Semantic Variables, and Output Branches – VisualSAF offers a modular and adaptable approach to addressing diverse VSA tasks. The General DL Model processes input images, extracting high-level features through a backbone network and detecting regions of interest. Semantic Variables are then extracted from these regions, incorporating a wide range of contextual information tailored to specific scenarios. Finally, the Output Branch integrates semantic variables and detections, generating high-level task information while allowing for flexible weighting of inputs to optimize task performance. The framework is demonstrated through experiments on the HOD Dataset, showcasing improvements in mean average precision and mean average recall compared to baseline models; the improvements are 0.05 in both mAP and 0.01 in mAR compared to the baseline. Future research directions include exploring multiple semantic variables, developing more complex output heads, and investigating the framework’s performance across context-shifting datasets.}, archive = {J_ACCESS}, author = {Antonio V. A. Lundgren and Byron L. D. Bezerra and Carmelo J. A. Bastos-Filho}, doi = {10.1109/ACCESS.2025.3535314}, journal = {IEEE Access}, pages = {21052-21063}, shortjournal = {IEEE Access}, title = {VisualSAF-A novel framework for visual semantic analysis tasks}, volume = {13}, year = {2025}, }
• (2025). Feature selection and classification optimization of transformer oil odor data with recursive feature elimination using grid search and cross-validation. ACCESS, 13, 21043-21051. (www)

  @article{ , abstract = {Power transformers are vital for transmitting, distributing, and using energy produced in electrical energy systems. The failure of a distribution transformer can cause significant financial losses to society and cause irreparable problems. Therefore, power transformers’ reliability, continuous monitoring, and fault-free operation are critical. In this study, an electronic nose system was developed to detect the duration of oil usage in power transformers based on its smell. In the system designed with eleven inexpensive gas sensors, a total of 200 transformer oil odors with four different usage periods were analyzed. Four features were selected from eighty-eight features using the Recursive Feature Elimination method with Grid Search and Cross-Validation, and they were classified with six different classifiers. With the Extra Trees algorithm, the most successful classifier, classification performance of 0.9810 CA, 0.9810 SE, 0.9937 SF was achieved without feature selection, and 0.9610 CA, 0.9610 SE, 0.9870 SF was achieved by selecting features. Dielectric breakdown voltage tests of the oils in the study were also performed, and the results supported the results of the electronic nose system. According to the results obtained, it can be concluded that transformer oil maintenance can be performed economically, practically, and reliably with the proposed system.}, archive = {J_ACCESS}, author = {Bilge Han Tozlu}, doi = {10.1109/ACCESS.2025.3536288}, journal = {IEEE Access}, pages = {21043-21051}, shortjournal = {IEEE Access}, title = {Feature selection and classification optimization of transformer oil odor data with recursive feature elimination using grid search and cross-validation}, volume = {13}, year = {2025}, }
• (2025). Steganography in game actions. ACCESS, 13, 21029-21042. (www)

  @article{ , abstract = {The exchange of messages has always carried with it the timeless challenge of secrecy. From whispers in shadows to the enigmatic notes written in the margins of history, humanity has long sought ways to convey thoughts that remain imperceptible to all but the chosen few. In the intricate patterns of imagery, the nuanced modulation of sound and the meticulous orchestration of language, the challenge of subliminal communication has been addressed in various forms of steganography. However, the field faces a fundamental paradox: as the art of concealment advances, so too does the science of revelation, leading to an ongoing evolutionary interplay. This study seeks to extend the boundaries of what is considered a viable steganographic medium. We explore a steganographic paradigm, in which hidden information is communicated through the episodes of multiple agents interacting with an environment. Each agent, acting as an encoder, learns a policy to disguise the very existence of hidden messages within actions seemingly directed toward innocent objectives. Meanwhile, an observer, serving as a decoder, learns to associate behavioural patterns with their respective agents despite their dynamic nature, thereby unveiling the hidden messages. The interactions of agents are governed by the framework of multi-agent reinforcement learning and shaped by feedback from the observer. This framework encapsulates a game-theoretic dilemma, wherein agents face decisions between cooperating to create distinguishable behavioural patterns or defecting to pursue individually optimal yet potentially overlapping episodic actions. As a proof of concept, we exemplify action steganography through the game of labyrinth, a navigation task where subliminal communication is concealed within the act of steering toward a destination. The stego-system has been systematically validated through experimental evaluations, assessing its distortion and capacity alongside its secrecy and robustness when subjected to simulated passive and active adversaries.}, archive = {J_ACCESS}, author = {Ching-Chun Chang and Isao Echizen}, doi = {10.1109/ACCESS.2025.3530961}, journal = {IEEE Access}, pages = {21029-21042}, shortjournal = {IEEE Access}, title = {Steganography in game actions}, volume = {13}, year = {2025}, }
• (2025). A multi-faceted approach to trending topic attack detection using semantic similarity and large-scale datasets. ACCESS, 13, 21005-21028. (www)

  @article{ , abstract = {Twitter’s widespread popularity has made it a prime target for malicious actors exploiting trending hashtags to disseminate harmful content. This study marks the first systematic exploration of semantic consistency in tweets to detect trending topic attacks. Unlike previous approaches, we emphasize the semantic aspect of tweets, leveraging advanced techniques such as semantic similarity estimation using WordNet and contextual understanding through Sentence-Transformers. To support this methodology, we curated large-scale, high-quality datasets comprising 7,000 Arabic and 28,000 English tweets, applying tailored preprocessing steps to ensure efficiency and accuracy. A novel data augmentation technique further enriched the quality and diversity of these datasets. We evaluated our approach using a comprehensive framework that assessed textual, image, and overall similarity. Five machine learning models—Random Forest, Decision Tree, K-Neighbors, Gradient Boosting, and XGBoost—were tested, with results benchmarked against nine baseline methods across different linguistic datasets and learning scenarios. Our approach demonstrated superior performance, achieving F1-scores of 96% for English and 97% for Arabic, with accuracy improvements ranging from 2% to 14% for English and 5% to 28% for Arabic. These results establish a new benchmark for detecting trending topic attacks across languages, highlighting the robustness and effectiveness of our method in combating malicious activities on social platforms.}, archive = {J_ACCESS}, author = {Insaf Kraidia and Afifa Ghenai and Samir Brahim Belhaouari}, doi = {10.1109/ACCESS.2025.3535996}, journal = {IEEE Access}, pages = {21005-21028}, shortjournal = {IEEE Access}, title = {A multi-faceted approach to trending topic attack detection using semantic similarity and large-scale datasets}, volume = {13}, year = {2025}, }
• (2025). Improving sleep disorder diagnosis through optimized machine learning approaches. ACCESS, 13, 20989-21004. (www)

  @article{ , abstract = {Classifying sleep disorders, such as obstructive sleep apnea and insomnia, is crucial for improving human quality of life due to their significant impact on health. The traditional expert-based classification of sleep stages, particularly through visual inspection, is challenging and prone to errors. This fact highlights the need for accurate machine learning algorithms (MLAs) for analyzing, monitoring, and diagnosing sleep disorders. This paper compares the MLAs for sleep disorder classification, specifically targeting None, Sleep Apnea, and Insomnia, using the Sleep Health and Lifestyle Dataset. We conducted two experiments. In the first one, we selected five key features from the feature spaces using the Gradient Boosting Regressor based on the Mean Decrease Impurity (MDI) technique. We chose two key features using the same methodology in the second experiment. We utilized 15 machine learning classifiers, and Gradient Boosting, Voting, Catboost, and Stacking Classifiers achieved an identical classification accuracy of 97.33%, with Precision, Recall, F1-score of 0.9733, and Specificity of 0.9569 in the original feature space. Among these, Gradient Boosting had the highest AUC of 0.9953 and was 3.36, 5.86, and 20.16 times faster than Voting, Catboost, and Stacking Classifiers, respectively. In the second experiment, the Decision Tree achieved the highest accuracy of 96% in the original and engineered feature spaces and was 149.33 times faster in the engineered feature space. Thus, this research proposes Gradient Boosting as the most effective method, outperforming all state-of-the-art techniques by achieving the highest accuracy, precision, recall, F1-score, and AUC, highlighting its superior classification performance and computational efficiency.}, archive = {J_ACCESS}, author = {Md. Atiqur Rahman and Israt Jahan and Maheen Islam and Taskeed Jabid and Md Sawkat Ali and Mohammad Rifat Ahmmad Rashid and Mohammad Manzurul Islam and Md. Hasanul Ferdaus and Md Mostofa Kamal Rasel and Mahmuda Rawnak Jahan and Shayla Sharmin and Tanzina Afroz Rimi and Atia Sanjida Talukder and Md. Mafiul Hasan Matin and M. Ameer Ali}, doi = {10.1109/ACCESS.2025.3535535}, journal = {IEEE Access}, pages = {20989-21004}, shortjournal = {IEEE Access}, title = {Improving sleep disorder diagnosis through optimized machine learning approaches}, volume = {13}, year = {2025}, }
• (2025). An adaptive finite resolution selection scheme for RIS assisted NOMA networks. ACCESS, 13, 20974-20988. (www)

  @article{ , abstract = {Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) have recently been recognized as innovative technologies for future beyond fifth generation (B5G) wireless networks due to their ability to improve the spectral efficiency and the wireless propagation properties, respectively. This paper presents an integration between RIS and NOMA to communicate with distant vehicular users. To alleviate the complexity of coherent phase shifting design at RIS, we propose a dynamic gain adaptation RIS selection scheme based on discrete phase shifting (i.e., termed as GADPS) to serve two vehicular users paired to perform NOMA. Regarding channel state information (CSI), the adaptation mechanism determines which user can be considered as a near user and the other as a far user with respect to RIS. To characterize the benefits of the GADPS scheme, the system outage performance is derived and compared with the cases of random discrete phase shifting (i.e., termed as GARPS) and conventional RIS-NOMA schemes; then the diversity order is also obtained. Furthermore, the impact of network parameters on the throughput and asymptotic average sum rate is discussed in both delay-limited and delay-tolerant transmission modes, respectively. Monte Carlo simulations emphasize the accuracy of the analytical work and investigate that: 1) The proposed GADPS scheme is more flexible than conventional RIS-NOMA scheme; 2) By joint optimization of RIS selection and phase shifting, the proposed GADPS scheme is capable of achieving superior performance over GARPS and conventional RIS-NOMA schemes with a superior diversity gain; 3) The throughput performance of the proposed GADPS scheme outperforms GARPS, conventional RIS-NOMA and other relay-assisted NOMA schemes, whereas full-duplex (FD) relay selection NOMA schemes suffer from throughput floor values; 4) By increasing the number of reflecting elements of RIS, the proposed GADPS scheme achieves the enhanced average sum rate performance over benchmark schemes in all scenarios of relative distance difference between users and the selected RIS.}, archive = {J_ACCESS}, author = {Shady M. Ibraheem and Mona Shokair and Mohamed E. Nasr and Nancy Alshaer}, doi = {10.1109/ACCESS.2025.3532852}, journal = {IEEE Access}, pages = {20974-20988}, shortjournal = {IEEE Access}, title = {An adaptive finite resolution selection scheme for RIS assisted NOMA networks}, volume = {13}, year = {2025}, }
• (2025). Indoor visible light 3D localization system based on black wing kite algorithm. ACCESS, 13, 20960-20973. (www)

  @article{ , abstract = {This study proposes an indoor visible light localization technique incorporating the black-winged kite algorithm to address problems such as limited indoor localization accuracy. The black-winged kite algorithm is an intelligent optimization algorithm inspired by the hunting behavior of black-winged kites in nature, and it is designed to solve the problem of global optimization. The indoor visible light localization problem can be transformed into a problem of solving the optimal coordinates of the receiver globally. Combining the black-winged kite algorithm and the indoor visible light problem can effectively localize the receiver’s position accurately in a complex indoor environment. The simulation results show that in the indoor environment of 5m $\times 5$ m $\times 6$ m, the average error of 1.29cm and the maximum error of 6.29cm are achieved after 100 iterations and 90.02% of the average localization error is less than 2.39cm, the horizontal error is less than 2.20cm. The vertical error is 0.39cm, which realizes high-precision localization. The three-dimensional positioning error distribution map visualizes the distribution of the space error in space, further demonstrating the scheme’s effectiveness. It provides a new method for visible light indoor positioning technology.}, archive = {J_ACCESS}, author = {Jiahui Wang and Xiangqing Wang and Chaochuan Jia and Cui Yang}, doi = {10.1109/ACCESS.2025.3535528}, journal = {IEEE Access}, pages = {20960-20973}, shortjournal = {IEEE Access}, title = {Indoor visible light 3D localization system based on black wing kite algorithm}, volume = {13}, year = {2025}, }
• (2025). Possibility-based sizing method for hybrid electric aircraft. ACCESS, 13, 20945-20959. (www)

  @article{ , abstract = {At the early stage of aircraft design, the sizing process plays a critical role in determining key parameters such as mass, geometry, and propulsion system characteristics based on design requirements. However, existing sizing methods, relying on historical data, assumptions, and low-fidelity models, often fail to address uncertainties, leading to costly design modifications, particularly for electric aircraft, where technological uncertainties pose significant challenges. It is crucial to account for uncertainties at the sizing stage to ensure reliable outcomes, highlighting the need for a method that efficiently integrates uncertainty into the process. We propose a possibility-based sizing approach, which accounts for design uncertainties into sizing process, yields possible feasible design outcomes and identifies critical parameters for refinement in later stages. We verified our approach with two case studies meeting CS23 certification. The results demonstrate design feasibility through trade-offs between conservative and optimistic designs, with possibility indices from 0.75 to 1, identifying maximum and stall speeds as key limiting factors. Sensitivity analysis shows that aerodynamic design, airframe, and propulsion efficiencies significantly impact maximum takeoff mass. Propulsion system integration achieves a relative mass reduction of 0.05, outperforming hydrogen fuel cells (0.025) and battery systems (0.009). The study highlights that power density is more critical than battery energy density in high-power segments, providing valuable insights for sizing and key parameters in future hybrid electric aircraft development.}, archive = {J_ACCESS}, author = {Zin Win Thu and Maxim Tyan and Yong-Hyeon Choi and Mohammad Irfan Alam and Jae-Woo Lee}, doi = {10.1109/ACCESS.2025.3531696}, journal = {IEEE Access}, pages = {20945-20959}, shortjournal = {IEEE Access}, title = {Possibility-based sizing method for hybrid electric aircraft}, volume = {13}, year = {2025}, }
• (2025). Robust forest sound classification using pareto-mordukhovich optimized MFCC in environmental monitoring. ACCESS, 13, 20923-20944. (www)

  @article{ , abstract = {As a complex ecosystem composed of flora and fauna, the forest has always been vulnerable to threats. Previous researchers utilized environmental audio collections, such as the ESC-50 and UrbanSound8k datasets, as proximate representatives of sounds potentially present in forests. This study focuses on the application of deep learning models for forest sound classification as an effort to establish an early threats detection system. The research evaluates the performance of several pre-trained deep learning models, including MobileNet, GoogleNet, and ResNet, on the limited FSC22 dataset, which consists of 2,025 forest sound recordings classified into 27 categories. To improve classification capabilities, the study introduces a hybrid model that combines neural network (CNN) with a Bidirectional Long-Short-Term Memory (BiLSTM) layer, designed to capture both spatial and temporal features of the sound data. The research also employs Pareto-Mordukhovich-optimized Mel Frequency Cepstral Coefficients (MFCC) for feature extraction, improving the representation of audio signals. Data augmentation and dimensionality reduction techniques were also explored to assess their impact on model performance. The results indicate that the proposed hybrid CNN-BiLSTM model significantly improved classification loss and accuracy scores compared to the standalone pre-trained models. GoogleNet, with an added BiLSTM layer and augmented data, achieved an average reduced loss score of 0.7209 and average accuracy of 0.7852, demonstrating its potential to classify forest sounds. Improvements in loss score and classification performance highlight the potential of hybrid models in environmental sound analysis, particularly in scenarios with limited data availability.}, archive = {J_ACCESS}, author = {Ahmad Qurthobi and Robertas Damaševičius and Vytautas Barzdaitis and Rytis Maskeliūnas}, doi = {10.1109/ACCESS.2025.3535796}, journal = {IEEE Access}, pages = {20923-20944}, shortjournal = {IEEE Access}, title = {Robust forest sound classification using pareto-mordukhovich optimized MFCC in environmental monitoring}, volume = {13}, year = {2025}, }
• (2025). Improving crack detection precision of concrete structures using U-net architecture and novel DBCE loss function. ACCESS, 13, 20903-20922. (www)

  @article{ , abstract = {Monitoring the health of infrastructure is critical to maintaining the integrity of concrete construction. Conventional crack detection methods that rely on visual inspection and image processing often produce inconsistent results. U-Net, an architecture often used in image processing, has limitations in crack segmentation, especially regarding the loss function. The main challenge is class imbalance, as cracks usually occupy a much smaller area compared to the background in concrete images. To address this, we developed an automated technique utilizing the U-Net framework with a novel loss function that we named DBCE Loss. Unlike the typical BCED we added the LogCoshDice function to get an improvement so that the resulting loss will be better and can improve the performance of the model. The performance of our method is rigorously evaluated by combining DeepCrack, GAPS, Crack500, and CrackForest datasets to illustrate that the model can detect cracks in various material conditions. DeepCrack represents cracks in concrete with minimal distress, while GAPS represents cracks in asphalt. Crack500 covers cracks with significant distress such as gravel, and CrackForest focuses on small cracks in concrete.The proposed U-Net model achieved Maximum accuracies between 98.58% and 99.22%, Maximum Dice coefficients from 88.27% to 98.03%, and Maximum F1-scores up to 97.11%. The proposed method is 7.69% ahead of the largest comparison method (DICE Loss) on Average IOU, while on Average Precission is leading 6.72% ahead of the largest comparison method (DICE). Then in terms of Recall our method is 8.84% superior to the largest comparison method (BCE). Sera in terms of Average F1-Score is 14.64% ahead of the largest comparison method (DICE). These results show that our method has surpassed conventional loss methods such as BCE, DICE and BCED. This research can facilitate a more reliable infrastructure health monitoring process and help with crack detection in the future.}, archive = {J_ACCESS}, author = {Andrew Prasetyo and I Ketut Eddy Purnama and Eko Mulyanto Yuniarno and Priyo Suprobo}, doi = {10.1109/ACCESS.2025.3534803}, journal = {IEEE Access}, pages = {20903-20922}, shortjournal = {IEEE Access}, title = {Improving crack detection precision of concrete structures using U-net architecture and novel DBCE loss function}, volume = {13}, year = {2025}, }
• (2025). Study on the thermal state of a transverse-flux motor. ACCESS, 13, 20893-20902. (www)

  @article{ , abstract = {Based on the analysis of transverse flux machine designs, it was established that they exhibit a relative simplicity of design and demonstrate high specific power indices. This paper seeks to explore the influence of design features on the heating of the stator coil, identified as the most temperature-sensitive element in the system. Additionally, the study aims to characterize the temperature distribution pattern within the stator. To achieve this goal, experiments were conducted using a 3D model of a low-speed transverse flux motor. Thermal analysis was carried out using modern software, enabling the determination of temperature patterns in the coil, cores, and stator body. Graphs illustrating the temperature rise over time for each motor component were generated. The obtained results include corresponding graphs and dependencies, revealing that the average coil temperature reached 92°C, deviating by 3.3% from the experimental value. A significant finding is that the stator coil in a transverse flux motor experiences non-uniform heating, with temperature variations in areas lacking circulated air. Introducing thermal paste in the region enclosed by the U-shaped cores, coil, and body was found to equalize and reduce the stator coil temperature by 10%. These modeling results were subsequently validated through experimentation on the operational prototype of the TFM-200/32 transverse flux motor.}, archive = {J_ACCESS}, author = {Andrii Yehorov and Oleksii Duniev and Andrii Masliennikov and Rupert Gouws and Oleksandr Dobzhanskyi and Mario Stamann}, doi = {10.1109/ACCESS.2025.3534284}, journal = {IEEE Access}, pages = {20893-20902}, shortjournal = {IEEE Access}, title = {Study on the thermal state of a transverse-flux motor}, volume = {13}, year = {2025}, }
• (2025). Hate speech detection using large language models: A comprehensive review. ACCESS, 13, 20871-20892. (www)

  @article{ , abstract = {The widespread use of social media and other online platforms has facilitated unprecedented communication and information exchange. However, it has also led to the spread of hate speech and poses serious challenges to societal harmony as well as individual well-being. Traditional methods for detecting hate speech, such as keyword matching, rule-based systems, and machine learning algorithms, often struggle to capture the subtle and context-dependent nature of hateful content. This paper provides a comprehensive review of the application of large language models (LLMs) like GPT-3, BERT, and their successors in hate speech detection. We analyze the evolution of LLMs in natural language processing and examine their strengths and limitations in identifying hate speech. Additionally, we address the significant challenges and explore how LLMs method can affect the accuracy and fairness of hate speech detection systems. By synthesizing recent research, this review aims to offer a holistic understanding of the current state-of-the-art methods in hate speech detection utilizing LLMs and to suggest directions for future research that could enhance the efficacy and equity of these systems.}, archive = {J_ACCESS}, author = {Aish Albladi and Minarul Islam and Amit Das and Maryam Bigonah and Zheng Zhang and Fatemeh Jamshidi and Mostafa Rahgouy and Nilanjana Raychawdhary and Daniela Marghitu and Cheryl Seals}, doi = {10.1109/ACCESS.2025.3532397}, journal = {IEEE Access}, pages = {20871-20892}, shortjournal = {IEEE Access}, title = {Hate speech detection using large language models: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). CMACGSA: Improved gravitational search algorithm based on cerebellar model articulation controller for optimization. ACCESS, 13, 20847-20870. (www)

  @article{ , abstract = {Metaheuristic algorithms have gained significant attention in recent years for addressing complex and challenging optimization problems, especially in engineering. These algorithms often take inspiration from natural phenomena, systems or biological behaviour to find optimal solutions. Recent advances in the field often involve hybrid methods that combine several algorithms to improve performance. This study introduces an improved Gravitational Search Algorithm, named CMACGSA, which incorporates the Cerebellar Model Articulation Controller (CMAC)-a neural network model-to enhance the performance of Gravitational Search Algorithm (GSA). By employing the CMAC neural network, CMACGSA dynamically learns the masses of particles/agents of GSA, enabling a learning-driven approach to mass computation. Additional enhancements include Lévy mutation, boundary control methods and an error handling mechanism, which together improve the robustness and adaptability of the algorithm. The effectiveness of CMACGSA is demonstrated through extensive testing on a set of 2D CEC 2014 benchmark functions, where it significantly outperforms the original GSA. Further evaluations on multidimensional CEC 2014 test problems, including 30-dimensional cases, reveal improved performance over widely used optimization algorithms and state-of-the-art (SOTA) algorithms. Furthermore, CMACGSA consistently achieves top-tier average performance metrics when benchmarked against four well-established GSA variants. The applicability of the algorithm is further validated by engineering design problems where it demonstrates outstanding performance, confirming its value in solving complex engineering challenges.}, archive = {J_ACCESS}, author = {Nazmiye Ebru Bulut and Emre Dandil and Ugur Yuzgec and Alpaslan Duysak}, doi = {10.1109/ACCESS.2025.3535667}, journal = {IEEE Access}, pages = {20847-20870}, shortjournal = {IEEE Access}, title = {CMACGSA: Improved gravitational search algorithm based on cerebellar model articulation controller for optimization}, volume = {13}, year = {2025}, }
• (2025). AI-based holistic framework for cyber threat intelligence management. ACCESS, 13, 20820-20846. (www)

  @article{ , abstract = {Cyber Threat Intelligence (CTI) is an important asset for organisations to facilitate the safeguarding of their systems against new and emerging cyber threats. CTI continuously provides up-to-date information which enables the design and implementation of better security measures and mitigation strategies. Organisations gather data from different sources either internal or external to the organisation, which are analysed, resulting in CTI. Nevertheless, the gathered data usually contain a large amount of content that is irrelevant to CTI or even to cybersecurity. Furthermore, most approaches concerning CTI management (e.g., gathering, analysis) involve simply gathering and storing the information without any enrichment such as classification or correlation. However, in order to obtain optimal results, organisations should be able to utilise all capabilities of CTI. Therefore, in this work, we propose ThreatWise AI, a novel framework that enables the gathering, analysis, enrichment, storage, and sharing of CTI in an efficient and secure manner. In particular, we have developed a novel pipeline in ThreatWise AI which incorporates different advanced tools, with distinct capabilities that interact with each other to provide a complete set of functionalities for the administration of the overall CTI lifecycle. The developed tools integrate various Python scripts and provide gathering and analysis functionalities of CTI. Furthermore, the proposed framework leverages the MISP platform for storing, enriching and sharing while also integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms for advanced data enrichment.}, archive = {J_ACCESS}, author = {Arnolnt Spyros and Ilias Koritsas and Angelos Papoutsis and Panos Panagiotou and Despoina Chatzakou and Dimitrios Kavallieros and Theodora Tsikrika and Stefanos Vrochidis and Ioannis Kompatsiaris}, doi = {10.1109/ACCESS.2025.3533084}, journal = {IEEE Access}, pages = {20820-20846}, shortjournal = {IEEE Access}, title = {AI-based holistic framework for cyber threat intelligence management}, volume = {13}, year = {2025}, }
• (2025). Digital rebirth of dongba pattern: An improved active contour model for pattern contour extraction. ACCESS, 13, 20810-20819. (www)

  @article{ , abstract = {In response to the time-consuming and laborious problem of manually drawing elements in existing poster designs, this paper proposes a method for extracting the contours of Dongba patterns based on improved active contour model. The improved active contour method adds discrete wavelet transform for energy minimization. Moreover, the minimization formula assigns greater weight to foreground pixels. In addition, to prevent evolutionary instability during the iteration process, we employed an optimization process that is specifically designed to maintain clear contours along the edges of the pattern. Based on the established Dongba pattern dataset, the qualitative and quantitative analyses are conducted on the proposed method. The results indicate that the proposed improved active contour model has advantages in contour extraction visual quality and evaluation indicators compared to existing methods, demonstrating the effectiveness of the proposed method. Finally, based on the proposed pattern contour extraction method, the design innovation practice of Dongba cultural posters is completed from two aspects: pattern selection and transformation, artistic processing and poster design. The code is available at: https://github.com/jsluen/IACM.}, archive = {J_ACCESS}, author = {Yuan Li and Tao Wu and Rongbing Fu and En Lu}, doi = {10.1109/ACCESS.2025.3534401}, journal = {IEEE Access}, pages = {20810-20819}, shortjournal = {IEEE Access}, title = {Digital rebirth of dongba pattern: An improved active contour model for pattern contour extraction}, volume = {13}, year = {2025}, }
• (2025). Enhancing accuracy and precision in omni-angle edge-based modulation transfer function measurements. ACCESS, 13, 20799-20809. (www)

  @article{ , abstract = {The modulation transfer function (MTF) quantitatively describes the ability of an optical or imaging system to reproduce contrast at various spatial frequencies. The ISO 12233 edge-based method adopted diagonal MTF measurement for sampled imaging systems by analyzing the gradient of a slanted edge captured by the system and estimating MTF as a function of spatial frequency perpendicular to the edge. Pixels in a region of interest (ROI) are projected into a quarter-pixel wide bin array on a horizontal or vertical axis, yielding a $4\times $ oversampled edge spread function. However, the accuracy and precision of MTF estimates can degrade considerably at high spatial frequencies for certain edge angles because of the clustering of the projection paths at or around specific locations on the bin array axis. Thus, a novel edge-based method, Omni-sine, was proposed to overcome this limitation by optimizing a noninteger oversampling ratio based on the edge angle. The accuracy and precision of the proposed method were demonstrated through computer simulations using synthesized edge images and through actual measurements with a machine vision camera. Implementing the Omni-sine method does not affect the real-time nature of the edge-based measurement and facilitates MTF anisotropy measurements using multiple nonrectangular ROIs with slanted starburst charts.}, archive = {J_ACCESS}, author = {Kenichiro Masaoka}, doi = {10.1109/ACCESS.2025.3532119}, journal = {IEEE Access}, pages = {20799-20809}, shortjournal = {IEEE Access}, title = {Enhancing accuracy and precision in omni-angle edge-based modulation transfer function measurements}, volume = {13}, year = {2025}, }
• (2025). Development of a zero-bias detector based on T-type structure. ACCESS, 13, 20792-20798. (www)

  @article{ , abstract = {The detector plays a crucial role in terahertz (THz) receiving systems, converting weak high-frequency signals into low-frequency or direct current signals that are easier to process. This paper proposes a zero-bias Schottky diode THz detector based on a T-shaped structure. The design is based on the InGaAs/InP material system and achieves efficient detection at 500 GHz. Experimental results show that the voltage responsivity of the detector reaches over 2000 V/W at 500 GHz, demonstrating excellent performance. This paper provides a detailed introduction to the design concept, working principle, and experimental results of the detector, which lays the device foundation for the development of terahertz focal plane imaging systems and provides support for future terahertz applications in higher frequency bands.}, archive = {J_ACCESS}, author = {Xiaobo Zhang and Jin Meng and Jingtao Zhou and Baijun Zhang and Zenghui Liu and Zexing Yuan}, doi = {10.1109/ACCESS.2025.3534292}, journal = {IEEE Access}, pages = {20792-20798}, shortjournal = {IEEE Access}, title = {Development of a zero-bias detector based on T-type structure}, volume = {13}, year = {2025}, }
• (2025). Scalable context-based facial emotion recognition using facial landmarks and attention mechanism. ACCESS, 13, 20778-20791. (www)

  @article{ , abstract = {Deciphering emotions from a person’s perspective is critical for meaningful human relationships. Enabling computers to interpret emotional cues similarly could significantly improve human-machine interaction. Accurate emotion recognition involves more than just analyzing facial expressions; it requires situational context and facial landmarks, which together reveal a broader range of emotional states. Existing emotion recognition frameworks primarily focus on facial imaging, often overlooking the contextual elements and the subtle significance of facial landmarks. This paper proposes a scalable approach to emotion recognition that combines situational context comprehension, accurate facial landmark detection, and facial feature analysis. Due to its scalability, our model can be applied across diverse computational platforms and operational circumstances while maintaining high performance. The model’s robustness and utility were validated against the EMOTIC benchmark, achieving an impressive overall accuracy of 84%. The findings underscore the importance of incorporating contextual information and facial landmarks to enhance emotion recognition accuracy. This advancement is expected to contribute substantially to fields such as augmented reality, medical imaging, and sophisticated human-computer interaction systems.}, archive = {J_ACCESS}, author = {Savina Jassica Colaco and Dong Seog Han}, doi = {10.1109/ACCESS.2025.3534328}, journal = {IEEE Access}, pages = {20778-20791}, shortjournal = {IEEE Access}, title = {Scalable context-based facial emotion recognition using facial landmarks and attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Federated digital twins: A scheduling approach based on temporal graph neural network and deep reinforcement learning. ACCESS, 13, 20763-20777. (www)

  @article{ , abstract = {The concept of digital twin (DT), which has supported the optimization of physical system across various industries, now extends into federated digital twin (fDT) to manage complex, interconnected systems. Recent research and standardization efforts in DT have expanded their focus from optimizing individual physical twins to coordinating DT functionalities to optimize the services provided by set of physical twins. In fDT-based DT coordination, scheduling techniques that determine when to utilize available DTs efficiently are critical. However, the variable processing speeds of DT functionalities, as traceable from digital twins, present significant challenges in deriving optimal schedules. Traditional scheduling algorithms, which assume static processing speeds for machines (corresponding to DTs), encounter limitations in generating optimal schedules. These limitations are further exacerbated in scenarios that demand the flexibility and scalability inherent to fDT. To address this challenge, this paper proposes the fDT-DT scheduling framework. This framework effectively represents relationships among DTs using temporal heterogeneous graphs and generates flexible and scalable schedules by incorporating temporal graph neural networks and deep reinforcement learning to account for variable processing speeds. Experimental results demonstrate that, in scenarios where multiple DTs exhibit diverse speed profiles, the proposed approach improves makespan by 16.9% and reduces tardiness by 76.2%, demonstrating both flexibility and superior performance. Furthermore, even in untrained scenarios with increasing numbers of fDTs and DTs, it outperforms baseline algorithms on both metrics, confirming its scalability.}, archive = {J_ACCESS}, author = {Young-Jin Kim and Hanjin Kim and Beomsu Ha and Won-Tae Kim}, doi = {10.1109/ACCESS.2025.3530558}, journal = {IEEE Access}, pages = {20763-20777}, shortjournal = {IEEE Access}, title = {Federated digital twins: A scheduling approach based on temporal graph neural network and deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Integrating explainable artificial intelligence with advanced deep learning model for crowd density estimation in real-world surveillance systems. ACCESS, 13, 20750-20762. (www)

  @article{ , abstract = {Crowd Density Detection in Smart Video Surveillance involves advanced computer vision (CV) techniques to improve the efficiency and accuracy of crowd monitoring. The system assists in detecting and analyzing crowd density in real-time by utilizing artificial intelligence and machine learning (ML) models on surveillance videos. It detects crowded areas, manages crowd flow, and combines automated analysis with human oversight for improved public safety and early intervention. Explainable Artificial Intelligence (XAI) improves the interpretability and transparency of crowd management methods. Incorporating XAI models provides clear, understandable insights into predictions, ensuring more actionable and reliable crowd management. This study proposes an Osprey Optimization Algorithm with Deep Learning Assisted Crowd Density Detection and Classification (OOADL-CDDC) technique for smart video surveillance systems. The aim of the OOADL-CDDC technique is to enable the automated and efficient detection of distinct kinds of crowd densities. To achieve this, the OOADL-CDDC technique primarily utilizes a bilateral filtering (BF) approach for noise removal process. The OOADL-CDDC technique utilizes an advanced DL method, employing the SE-DenseNet model for feature extraction, while the hyperparameter selection is performed by using the OOA model. Finally, the detection and classification of the crowd density is accomplished by using the attention bidirectional gated recurrent unit (ABiGRU) model. A series of experiments are performed to demonstrate the improved performance of the OOADL-CDDC method. The performance validation of the OOADL-CDDC technique portrayed a superior accuracy value of 98.30% over existing models in terms of distinct measures.}, archive = {J_ACCESS}, author = {Sultan Refa Alotaibi and Hanan Abdullah Mengash and Mohammed Maray and Faiz Abdullah Alotaibi and Abdulwhab Alkharashi and Ahmad A. Alzahrani and Moneerah Alotaibi and Mrim M. Alnfiai}, doi = {10.1109/ACCESS.2025.3529843}, journal = {IEEE Access}, pages = {20750-20762}, shortjournal = {IEEE Access}, title = {Integrating explainable artificial intelligence with advanced deep learning model for crowd density estimation in real-world surveillance systems}, volume = {13}, year = {2025}, }
• (2025). Automatic estimation of solar rooftops and power generation from publicly available satellite imagery through georeferencing and large-scale support. ACCESS, 13, 20740-20749. (www)

  @article{ , abstract = {Rooftop photovoltaic (PV) power systems constitute a viable alternative energy technology that can significantly reduce electricity costs. The rapid increase in installations has led to a mismatch between planned power generation and actual electricity demand, necessitating effective monitoring and impact assessment. This study proposes a novel approach for detecting solar rooftops using publicly available satellite imagery over large areas. We also introduce a technique for estimating solar panel size and potential energy production, with outputs formatted for GIS applications. Employing a modified U-Net architecture with pre- and post-processing techniques, our experiments achieved an Intersection over Union score of 0.7879 and a Dice score of 0.8808. Image tiling and mosaicking with georeferencing were used to support large-scale imagery. The detection results were post-processed through polygonization and smoothing using the Douglas-Peucker algorithm. Panel size and power generation were then calculated and attached as attributes. Through satellite image analysis, this study aims to accurately identify and evaluate solar rooftops nationwide, providing valuable insights for homeowners, businesses, and government authorities. This facilitates informed decision-making, cost reduction, and contributions to environmental goals.}, archive = {J_ACCESS}, author = {John Sullivan and Apichon Witayangkurn}, doi = {10.1109/ACCESS.2025.3535817}, journal = {IEEE Access}, pages = {20740-20749}, shortjournal = {IEEE Access}, title = {Automatic estimation of solar rooftops and power generation from publicly available satellite imagery through georeferencing and large-scale support}, volume = {13}, year = {2025}, }
• (2025). Integrating technology acceptance model with UTAUT to increase the explanatory power of the effect of HCI on students’ intention to use E-learning system and perceive success. ACCESS, 13, 20720-20739. (www)

  @article{ , abstract = {This study aimed to investigate the potential human-computer interaction factors (HCI) affecting students’ behavioural intentions (BI) to use the e-learning system and perceive success. This paper proposes a comprehensive model, integrating the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT). The data were collected via an online survey conducted on 232 students utilizing the Khas Learn system of Kadir has University in Turkey. The proposed hypotheses were tested by multi-linear regression. The results illustrated that the main predictors of students’ success (SS) are behaviour intention, ease of use, usefulness, visual design, and learner interface interactivity which explained 53.6% of perceived success in using the system. While, the main predictors of BI are facilitating condition, effort expectancy, ease of use, and usefulness which explained 71% of the variance in continuance intentions to use e-learning. Therefore, the empirical findings provide strong backing to the technological-social-psychological dimensions extended by HCI main factors, which showed a high explanatory power in accepting e-learning technology and leads to enhance the SS, where five of the model’s goodness-of-fit values meet five criteria of structural equation modeling (SEM).}, archive = {J_ACCESS}, author = {Fareed Al-Sayid and Gokhan Kirkil}, doi = {10.1109/ACCESS.2025.3534740}, journal = {IEEE Access}, pages = {20720-20739}, shortjournal = {IEEE Access}, title = {Integrating technology acceptance model with UTAUT to increase the explanatory power of the effect of HCI on students’ intention to use E-learning system and perceive success}, volume = {13}, year = {2025}, }
• (2025). Filter estimator based on the probability distribution. ACCESS, 13, 20713-20719. (www)

  @article{ , abstract = {In this paper, we propose a model of a parameter estimator filter for Black Box-type Stochastic Systems (BBSS), that is, only its inputs and outputs are known; considering its intrinsic properties observed in the second probability moment, in which the probability has the description of a specific distribution function relative to the random variables and the law of large numbers, allowing the stochastic system to be described with a Gaussian Distribution Function (GDF) and its linear transformation core in the Taylor series. Commonly, all estimates are assumed in the Second Moment of Probability (SMP) with equiprobable conditions, but in this case, a specific description of the Probability Distribution Function (PDF) is considered a priori, which is a contribution to the theory of digital filtering in estimation for signal reconstruction. The simulation of the parameter estimation is carried out for a given reference signal, maintaining the stability of the system in discrete time, and a comparison is made between the estimator based on the SMP and the PDF to observe the level of convergence, where the PDF approach has a better performance respect to the SMP one.}, archive = {J_ACCESS}, author = {Romeo Urbieta Parrazalez and Rosaura Palma Orozco and María Teresa Zagaceta Álvarez and José Luis Fernández Muñoz and Karen Alicia Aguilar Cruz}, doi = {10.1109/ACCESS.2025.3534180}, journal = {IEEE Access}, pages = {20713-20719}, shortjournal = {IEEE Access}, title = {Filter estimator based on the probability distribution}, volume = {13}, year = {2025}, }
• (2025). Business process modeling patterns for blockchain application development. ACCESS, 13, 20691-20712. (www)

  @article{ , abstract = {Despite the significant potential of blockchain technology to transform e-business through smart contracts, developing blockchain applications remains a challenging task. A critical gap exists in the software development process, where the analysis and design phases are not adequately integrated. This gap is exacerbated by the lack of a standardized approach to modeling blockchain application requirements, which hinders effective communication between requirement analysis and software design. To address this gap, this study proposes a comprehensive collection of data-driven patterns for business process modeling, comprising nine patterns that capture various blockchain-specific aspects, including token circulation, supply, and authorization, as well as smart contract security and authorization. Through a trend analysis of real-world blockchain applications, we identified recurring business processes across domains and validated our patterns by comparing them to existing literature and a proof-of-concept application. The results reveal statistically significant trends in blockchain business processes, providing a foundation for future research in this area. Specifically, the analysis of patterns in different domains (e.g., marketplace, game, finance, and media) shows some interesting trends: Transfer patterns are most common in marketplace and game applications; lifecycle patterns are most common in game applications; and accessibility patterns are most common in marketplace, finance, IT, and game applications. Our contribution provides a standardized framework for modeling blockchain application requirements, enabling software developers to better understand the complex needs of blockchain applications and design more effective solutions.}, archive = {J_ACCESS}, author = {Fouzia Alzhrani and Kawther Saeedi and Liping Zhao}, doi = {10.1109/ACCESS.2025.3535809}, journal = {IEEE Access}, pages = {20691-20712}, shortjournal = {IEEE Access}, title = {Business process modeling patterns for blockchain application development}, volume = {13}, year = {2025}, }
• (2025). A secure and robust machine learning model for intrusion detection in internet of vehicles. ACCESS, 13, 20678-20690. (www)

  @article{ , abstract = {The rapid advancement of communication is introducing a new era for the Internet of Vehicles (IoV) in the context of Smart Cities. Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). In this paper, we proposed a fine tree-based model to study the complex behavior of network traffic inside the IoV to detect and classify anomalies for securing the IoV. The proposed fine tree-based model can be validated by conducting extensive experiments with benchmark real-world datasets which can simulate emerging IoV scenarios. The proposed Fine Tree-based IDS model, along with other models, has been evaluated using metrics such as mean accuracy, precision, recall, F1-score, specificity and error rate. The proposed model outperformed the others across each metric, achieving near-perfect results with a mean accuracy, precision, recall, F1-score, and specificity of 0.99999. However, the other models achieved mean values ranging from 0.90 to 0.98 across these metrics. Additionally, the proposed model achieved an exceptionally low mean error rate of 0.00001, while the error rates of the other models ranged from 0.02 to 0.05. The experimental findings demonstrate the superior performance of the proposed model in detecting and classifying intrusions within IoV.}, archive = {J_ACCESS}, author = {Pradeep Kumar Tiwari and Shiv Prakash and Animesh Tripathi and Tiansheng Yang and Rajkumar Singh Rathore and Manish Aggarwal and Narendra Kumar Shukla}, doi = {10.1109/ACCESS.2025.3532716}, journal = {IEEE Access}, pages = {20678-20690}, shortjournal = {IEEE Access}, title = {A secure and robust machine learning model for intrusion detection in internet of vehicles}, volume = {13}, year = {2025}, }
• (2025). Generating deeply-engineered technical features for basketball video understanding. ACCESS, 13, 20667-20677. (www)

  @article{ , abstract = {Investigating video-guided basketball movement understanding is essential for enhancing sports coaching. Integrating basketball videos with human-computer interaction (HCI) algorithms significantly improves training efficiency. In this paper, we propose a novel method for basketball player motion recognition and prediction. We engineer the technical features of gameplay through video analysis and introduce a behavioral analysis method using a multi-layer learning architecture. Our main contributions include: 1) an LSTM-based deep learning architecture for player action recognition and prediction; 2) a clustering-based algorithm for basketball court and line detection; and 3) a keyframe selection technique for basketball videos based on spatial-temporal scoring. Experimental validation on a comprehensive basketball video dataset demonstrates the effectiveness of our method in accurately identifying player movements and analyzing behaviors.}, archive = {J_ACCESS}, author = {Shaohua Fang and Guifeng Wang and Yongbin Li and Yue Yu and Jun Li}, doi = {10.1109/ACCESS.2025.3535808}, journal = {IEEE Access}, pages = {20667-20677}, shortjournal = {IEEE Access}, title = {Generating deeply-engineered technical features for basketball video understanding}, volume = {13}, year = {2025}, }
• (2025). A CNN-based approach for classical music recognition and style emotion classification. ACCESS, 13, 20647-20666. (www)

  @article{ , abstract = {Music recognition refers to the process of automatically recognizing and classifying the musical content in audio signals using computer technology and algorithms. Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. In this study, The model based on convolutional neural networks (CNNs) is proposed, allowing people to recognize the classical music title, style, and emotions contained in a piece of music. The proposed model is particularly beneficial for individuals who are interested in classical music but lack extensive knowledge about it, as it provides essential information about the pieces. By extracting multidimensional features from classical music, the model can recognize the title, style, and emotions expressed. To improve the model’s recognition accuracy, various noises are introduced to the dataset. Meanwhile, in this study, a novel loss function has been devised to more effectively assess the model’s performance. For searching for optimal performance of the model, a novel optimization algorithm also be proposed to find optimal hyperparameters of loss function. The experiment results show average title recognition accuracy is 0.98, average style recognition accuracy is 0.89 and average emotion recognition accuracy is 0.93. These results adequately demonstrate that the proposal model significantly enhances the model’s ability to accurately recognize the titles, styles, and emotions of classical music, achieving high recognition rates even in noisy environments.}, archive = {J_ACCESS}, author = {Yawen Shi}, doi = {10.1109/ACCESS.2025.3535411}, journal = {IEEE Access}, pages = {20647-20666}, shortjournal = {IEEE Access}, title = {A CNN-based approach for classical music recognition and style emotion classification}, volume = {13}, year = {2025}, }
• (2025). Enhancing fraud detection in banking with deep learning: Graph neural networks and autoencoders for real-time credit card fraud prevention. ACCESS, 13, 20633-20646. (www)

  @article{ , abstract = {Under the umbrella of artificial intelligence (AI), deep learning enables systems to cluster data and provide incredibly accurate results. This study explores deep learning for fraud detection, utilizing Graph Neural Networks (GNNs) and Autoencoders to enhance business practices and reduce fraudulent activities in large organizations. For real-time fraud detection, we propose Graph neural network with lambda architecture while for credit card fraud detection, we use an autoencoder, validated through case studies from two banks. The findings demonstrate that these methods effectively detect fraud with balance of precision and recall, improving the efficiency of banking systems. Python is employed for analysis, emphasizing the ability of deep learning to manage and prevent fraud in real-time on dynamic datasets. In the end, this study concludes that by using deep learning algorithms, we can control online credit card fraud detection in banks, improve the efficiency of the banking system. We can manage fraudulent activity in real-time and on dynamic datasets by utilizing deep learning algorithms, which allows for ongoing improvement of the fraud detection and prevention system.}, archive = {J_ACCESS}, author = {Fawaz Khaled Alarfaj and Shabnam Shahzadi}, doi = {10.1109/ACCESS.2024.3466288}, journal = {IEEE Access}, pages = {20633-20646}, shortjournal = {IEEE Access}, title = {Enhancing fraud detection in banking with deep learning: Graph neural networks and autoencoders for real-time credit card fraud prevention}, volume = {13}, year = {2025}, }
• (2025). Beyond finance: Enhancing support for new technology start-up founders. ACCESS, 13, 20620-20632. (www)

  @article{ , abstract = {The article addresses a gap in the literature by examining the stressors and challenges specific to new technology start-up founders, an under-researched group of entrepreneurs operating at the intersection of innovation and business. The study’s novelty lies in applying the Job Demands-Resources (JD-R) model to the venture capitalist-founder relationship, highlighting the importance of balancing high job demands with adequate resources to enhance entrepreneurial well-being and success. Using a qualitative research design, the conducted study involved semi-structured interviews with 22 technology start-up founders across Europe to explore their unique challenges, demands, and support needs. The data were analyzed using template analysis, revealing three core challenges: managing the development of their technology-driven companies, maintaining mental and physical health, and achieving work-life balance. Based on these findings, the authors propose a novel, tailored support system for technology founders, incorporating mentorship, mental health resources, and strategies for maintaining work-life balance. This system is designed to meet the unique needs of technology entrepreneurs, with a focus on confidentiality, proven effectiveness, and flexibility. Venture capitalists are encouraged to implement such systems to alleviate the pressures on technology start-up founders and foster the sustainability and success of their ventures.}, archive = {J_ACCESS}, author = {Pawel Ziemianski and Katarzyna Stankiewicz and Jan Wyrwinski and Sylwiusz Retowski and Bartosz Lipnicki}, doi = {10.1109/ACCESS.2025.3534716}, journal = {IEEE Access}, pages = {20620-20632}, shortjournal = {IEEE Access}, title = {Beyond finance: Enhancing support for new technology start-up founders}, volume = {13}, year = {2025}, }
• (2025). Optimization of fault identification and location using adaptive neuro-fuzzy inference system and support vector machine for an AC microgrid. ACCESS, 13, 20599-20619. (www)

  @article{ , abstract = {Conventional methods for high-impedance faults, low fault current levels, and communication delays could not properly identify the fault identification and location of an AC Microgrid. Fault identification and locating are crucial when integrating renewable energy sources with AC Microgrids. In an AC Microgrid, high-impedance faults, low-fault current levels, and communication delays are incapable approaches for fault identification and fault location. Machine learning enables rapid fault identification and location. This paper develops an adaptive neuro-fuzzy inference system and a support vector machine approach. To address these issues, lower fault current levels, detect high-impedance faults and affect communication delays. The proposed method is tested and evaluated in IEEE12BUS system, both in islanded and grid-connected modes, with execution times of 0.00202s in islanded mode and 0.0022s in grid-connected mode. The proposed adaptive neuro-fuzzy inference system method recognizes the optimal fault type. At the same time, Support vector machine identifies fault location accurately, resulting in the shortest execution time and minimal error percentage. This approach is demonstrated by using the IEEE12BUS AC Microgrid system. Hence, this approach is well-suited for real-time applications in AC Microgrid systems.}, archive = {J_ACCESS}, author = {A. Kurmaiah and C. Vaithilingam}, doi = {10.1109/ACCESS.2025.3534147}, journal = {IEEE Access}, pages = {20599-20619}, shortjournal = {IEEE Access}, title = {Optimization of fault identification and location using adaptive neuro-fuzzy inference system and support vector machine for an AC microgrid}, volume = {13}, year = {2025}, }
• (2025). End-to-end security mechanism using blockchain for industrial internet of things. ACCESS, 13, 20584-20598. (www)

  @article{ , abstract = {The urgent penetration of the Industrial Internet of Things (IIoT) in industrial sectors has fostered unmatched benefits in terms of efficiency, productivity, and overall performance. Although current security frameworks already offer a sound level of protection, they do not adequately scale to the growing diversity and number of devices integrated into the Industrial Internet of Things. They rely on static, centralized network organization and management, which cannot ensure data integrity across a huge number of highly distributed devices. The proposed research provides insight into an innovative end-to-end security solution model, which caters to the needs of IIoT via the introduction of the blockchain. The current security solutions seem to fail in terms of security, scalability, data efficiency, integrity, and the actual needs of IIoT. As a result, the top chance of a cyberattack or similar threat remains high. The proposed work demonstrates that IIoTs may benefit from blockchain frameworks because blockchains reuse existing security mechanisms, enable transaction validation without central trust, and implement appropriate authorization. Additionally, the IIoT environment can benefit from implementing security frameworks that reflect their characteristics. In this paper, an overall solution that employed the decentralized setup of blockchain was proposed for authentication, authorization, and data integrity for all devices. Our approach is free from the need for any central authority. That mechanism is based on smart contracts that enforce security policies. Our framework allows for the switching of any access rule at any time, as well as it can immediately react to some suspicious behavior. In our paper, we proposed some lightweight cryptographic schemes, and Data management overhead which could fit any device. Moreover, our algorithm for the blockchain implementation is hybrid because this approach incorporates all the best features of the private and public implementation due to the individual blocks containing feasible information rather than the entire history.}, archive = {J_ACCESS}, author = {Muhammad Shahid Dildar and Adnan Shahid Khan and Irshad Ahmed Abbasi and Reema Shaheen Naseem Akhtar and Khalil Al Ruqaishi and Shakeel Ahmed Ghulam Sarwar}, doi = {10.1109/ACCESS.2025.3535821}, journal = {IEEE Access}, pages = {20584-20598}, shortjournal = {IEEE Access}, title = {End-to-end security mechanism using blockchain for industrial internet of things}, volume = {13}, year = {2025}, }
• (2025). Advancing trans-domain classification with knowledge distillation: Bridging LIDAR and image data. ACCESS, 13, 20574-20583. (www)

  @article{ , abstract = {Recent advancements in deep learning have significantly improved image classification models, yet extending these models to alternative data forms, such as point clouds from Light Detection and Ranging (LiDAR) sensors, presents considerable challenges. This paper explores applying knowledge distillation techniques as a solution, aiming to transfer the learned competencies from established image classification frameworks to point cloud classification tasks. Our methodology involves distilling complex model insights into more computationally efficient forms suitable for LIDAR data, thus enabling substantial resource savings without sacrificing performance. Experimental evaluations across various benchmark datasets demonstrate that our distilled models not only rival their original counterparts in accuracy but also surpass conventional point cloud classification methods in both efficiency and scalability. Additionally, we delve into the impact of varying distillation techniques on model adaptability and performance within the LIDAR domain. The findings underscore the utility of knowledge distillation in enhancing the trans-domain applicability of image classification models, potentially revolutionizing their deployment across diverse data types.}, archive = {J_ACCESS}, author = {Jesus Eduardo Ortíz and Werner Creixell}, doi = {10.1109/ACCESS.2025.3530445}, journal = {IEEE Access}, pages = {20574-20583}, shortjournal = {IEEE Access}, title = {Advancing trans-domain classification with knowledge distillation: Bridging LIDAR and image data}, volume = {13}, year = {2025}, }
• (2025). Single-view depth estimation: Advancing 3D scene interpretation with one lens. ACCESS, 13, 20562-20573. (www)

  @article{ , abstract = {This paper introduces an advanced technique for monocular 3D scene understanding, utilizing deep learning to estimate depth from a single image. Traditional methods, such as stereo vision systems or hardware-based approaches like LiDAR, require multiple viewpoints or specialized equipment to calculate depth, making them complex and costly. In contrast, this method leverages convolutional neural networks (CNNs) trained on large-scale datasets containing ground truth depth information to predict the relative depth of objects in a scene and visualize it in a 3D perspective. By recognizing visual cues such as perspective, shading, and occlusion, the model effectively captures spatial relationships from a single viewpoint, offering a streamlined solution for depth estimation. The approach is evaluated on benchmark datasets like KITTI and NYU Depth V2, which cover a wide range of environments, including urban streets and indoor settings. The results demonstrate that this single-lens depth estimation method achieves accuracy comparable to traditional stereo systems, without the need for additional hardware. Its versatility makes it particularly valuable for real-world applications like autonomous navigation, where accurate depth perception is essential for tasks such as obstacle avoidance and path planning, and augmented reality, where it enhances the interaction between virtual and physical objects. Furthermore, its computational efficiency allows for real-time depth estimation, making it suitable for time-critical applications in fields such as robotics and drones. Overall, this paper highlights the potential of single-lens depth perception as a scalable, cost-effective alternative to conventional 3D scene understanding techniques, with promising implications for industries relying on depth sensing technology.}, archive = {J_ACCESS}, author = {Kavitha Dhanushkodi and Akila Bala and Neelam Chaplot}, doi = {10.1109/ACCESS.2025.3534324}, journal = {IEEE Access}, pages = {20562-20573}, shortjournal = {IEEE Access}, title = {Single-view depth estimation: Advancing 3D scene interpretation with one lens}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Graphene-metamaterial based tunable broadband polarization insensitive absorber for terahertz antenna design”. ACCESS, 13, 20561. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Graphene-Metamaterial Based Tunable Broadband Polarization Insensitive Absorber for Terahertz Antenna Design”).}, archive = {J_ACCESS}, author = {Rummanur Rahad and Abu S. M. Mohsin and Mohammed Belal Hossain Bhuian and Md. Mosaddequr Rahman}, doi = {10.1109/ACCESS.2025.3534296}, journal = {IEEE Access}, pages = {20561}, shortjournal = {IEEE Access}, title = {Corrections to “Graphene-metamaterial based tunable broadband polarization insensitive absorber for terahertz antenna design”}, volume = {13}, year = {2025}, }
• (2025). DCWM-LSTM: A novel attack detection framework for robotic arms. ACCESS, 13, 20547-20560. (www)

  @article{ , abstract = {Robotic systems have become integrated components across various industries, offering improved operational efficiency and automation. However, this wide usage area makes robotic systems susceptible to cyberattacks and security vulnerabilities. Replay and subscriber flood attacks pose significant challenges to the integrity and security of robotic arms. In response to these challenges, this study proposes a new approach that leverages deep learning techniques for attack detection. We present a new framework, LSTM-based Dynamic Compound Weight Mechanism (DCWM), designed to identify cyberattacks targeting robotic arms effectively. In addition, we introduce a new feature reduction method in the preprocessing module. Our methodology is evaluated through ACC and MSE measurements. Compared to simple LSTM models, our proposed framework achieves a significant enhancement in accuracy, with performance gains observed across both replay and subscriber flood attack scenarios. Specifically, the DCWM-LSTM model exhibits an increase in accuracy from 88% to 92% for replay attacks and from 93% to 97% for subscriber flood attacks. These findings underscore the effectiveness and robustness of the DCWM-LSTM framework as a practical solution for detecting and mitigating cyber threats against robotic systems. By enhancing the security posture of robotic arms, our approach contributes to safeguarding critical infrastructure and ensuring the continued reliability and safety of automated processes in diverse industrial settings.}, archive = {J_ACCESS}, author = {Esra Nergis Yolaçan and Hande Çavşi Zaim}, doi = {10.1109/ACCESS.2025.3535225}, journal = {IEEE Access}, pages = {20547-20560}, shortjournal = {IEEE Access}, title = {DCWM-LSTM: A novel attack detection framework for robotic arms}, volume = {13}, year = {2025}, }
• (2025). Corrections to “The optimization of carbon emission prediction in low carbon energy economy under big data”. ACCESS, 13, 20546. (www)

  @article{ , archive = {J_ACCESS}, author = {Ji Luo and Wuyang Zhuo and Siyan Liu and Bingfei Xu}, doi = {10.1109/ACCESS.2025.3534998}, journal = {IEEE Access}, pages = {20546}, shortjournal = {IEEE Access}, title = {Corrections to “The optimization of carbon emission prediction in low carbon energy economy under big data”}, volume = {13}, year = {2025}, }
• (2025). Enhancing democratic processes: A survey of DRE, internet, and blockchain in electronic voting systems. ACCESS, 13, 20512-20545. (www)

  @article{ , abstract = {Electronic voting (e-voting) systems have significantly improved the traditional voting process by addressing key concerns such as security, public acceptability, and convenience. However, these systems often face unique challenges, such as ensuring voter privacy and verifiability, preventing coercion and double voting, and maintaining scalability while protecting participant confidentiality. This study critically analyses and compares various e-voting schemes and technologies, evaluating their security features, verifiability mechanisms, and potential vulnerabilities. This paper reviews Direct Recording Electronic (DRE) voting, internet voting, and blockchain-based e-voting systems. In so doing, we provide an understanding of cryptographic primitives employed in e-voting systems and how they address specific characteristics and challenges associated with each voting scheme. Furthermore, we examine the applications proposed by previous studies in the context of these voting systems, assessing their strengths, limitations, and impact on democratic procedures. The cryptographic primitives reviewed include techniques like homomorphic encryption, blind signatures, and zero-knowledge proofs, which can enhance voter privacy, verifiability, and resistance to coercion and double voting.}, archive = {J_ACCESS}, author = {Mosbah Alown and Mehmet Sabir Kiraz and Muhammed Ali Bingol}, doi = {10.1109/ACCESS.2025.3531349}, journal = {IEEE Access}, pages = {20512-20545}, shortjournal = {IEEE Access}, title = {Enhancing democratic processes: A survey of DRE, internet, and blockchain in electronic voting systems}, volume = {13}, year = {2025}, }
• (2025). Design of an iterative model for incremental enhancements in quantum image processing using reinforcement learning-based optimizations. ACCESS, 13, 20491-20511. (www)

  @article{ , abstract = {In this text we propose a collection of enhanced quantum methods that would drastically enhance the efficiency and accuracy of biomedical image processing. There are four key methodologies that have been proposed in this work: Quantum Support Vector Machines with Enhanced Feature Extraction, Quantum Generative Adversarial Networks for Data Augmentation, Quantum Entanglement and Correlation Analysis for Feature Detection, and Quantum-Enhanced Image Reconstruction for Noisy Data Samples. Q-SVM-EFE is a Quantum Support Vector Machine-based framework that incorporates advanced feature extraction to map classical image data into high-dimensional quantum states, which should capture intricate patterns and therefore be more accurate for classification. This approach scored more than 95 percent in terms of classification accuracy and tenfold increased the speed of training compared to classical SVMs. QGAN-DA generates high-quality synthetic images to alleviate the scarcity of labeled medical data. It trains a generator network through quantum circuits for the generation of new samples and a discriminator network for real image and sample classification, thus ensuring high diversity and quality in synthetic data with SSIM values above 0.90. QECA-FD applies quantum entanglement in the analysis of biomedical image correlations and is therefore very effective in detecting subtle features and anomalies. This resulted in over 95% sensitivity in anomaly detection and increased the speed of the correlation analysis by five times. QIR-ND enables enhanced image reconstruction from sullied or incomplete data through quantum noise reduction and reconstruction techniques. This new approach gave a PSNR greater than 40 dB and an eightfold improvement in reconstruction delays.}, archive = {J_ACCESS}, author = {Lalitha Kumari Pappala and Sai Babu Veesam and Kannaiah Chattu and Janapati Venkata Krishna and Jyostna Devi Bodapati and B. Tarakeswara Rao}, doi = {10.1109/ACCESS.2025.3531407}, journal = {IEEE Access}, pages = {20491-20511}, shortjournal = {IEEE Access}, title = {Design of an iterative model for incremental enhancements in quantum image processing using reinforcement learning-based optimizations}, volume = {13}, year = {2025}, }
• (2025). A lightweight mitigation technique against a modified version number attack in IoT networks. ACCESS, 13, 20472-20490. (www)

  @article{ , abstract = {The potential worldwide deployment of Internet of Things (IoT) networks for a broad scope of critical applications has led to significant security concerns. With no effective support for IoT security, this rising trend would expose IoT deployments to numerous security threats. Due to their inherent resource constraints, IoT networks are critically susceptible to a wide range of adverse attacks. In particular, internal routing attacks pose serious challenges to network stability and lifetime. This is evident even for an IETF-standardized routing protocol like the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). It lacks adequate provisioning of network security, making an internal routing attack such as the common Version Number (VN) attack easy to initiate and effective to target performance degradation and resource exhaustion. It is also possible for further variants of such an adverse attack to emerge and incur a different and significant impact on RPL networks. In this paper, a novel variant of the VN attack, referred to as the Decreased VN (DVN) attack, in experimentally investigated. It differently operates by decreasing the propagated VN value in DIO messages to cause disruptive subnet partitioning and topological re-establishment. Experimental evaluation results revealed the severe effects of DVN attacks on overall network performance. Network overhead and energy consumption were increased by more than 300% and 220%, respectively. Additionally, a reduction of over 28% and an increase of over 400% in PDR and latency, respectively, were experienced during the attack. To effectively mitigate the DVN attack, a novel lightweight solution called DVN-Secure RPL (DVN-SRPL) is introduced in this paper. It is based on an effective and simple distributed security scheme with limited modifications to certain operational aspects of RPL design. Experimental testing of DVN-SRPL demonstrated its efficiency in detecting and containing DVN attacks. It succeeded in mitigating the adverse effects of the attacks with considerable reductions of 179% and 142% in energy consumption and network overhead, respectively. Comparable QoS performance to the attack-free RPL was also maintained during the attack.}, archive = {J_ACCESS}, author = {Ibrahim S. Alsukayti and Mohammed Alreshoodi and Mehdi Rouissat}, doi = {10.1109/ACCESS.2025.3535166}, journal = {IEEE Access}, pages = {20472-20490}, shortjournal = {IEEE Access}, title = {A lightweight mitigation technique against a modified version number attack in IoT networks}, volume = {13}, year = {2025}, }
• (2025). Img2Vocab: Explore words tied to your life with LLMs and social media images. ACCESS, 13, 20456-20471. (www)

  @article{ , abstract = {Psychological studies highlight the importance of combining new knowledge with one’s prior experience. Hence personalization for a learner plays a key role for vocabulary acquisition. However, this faces two challenges: probing a learner’s experiences in their lives and crafting tailored material for every different individual. With the prevalence of visual social media, such as Instagram, people share their photos from favorite moments, providing rich contexts, and emerging generative AI would create learning material in an effortless fashion. We prototyped an online vocabulary exploration system, which displays a learner’s selected photos from their Instagram along with a generated sentence using image recognition and a language model, GPT-3. The system lets a learner find new words that are strongly tied to their daily life with the approximated context. We carried out our within-subject design evaluation of the system with 23 participants with three conditions: contexts grounded with learner’s Instagram photos, contexts grounded from general images, and text-only modality. From learners’ recall task accuracy, we found that having a context grounded with a learner’s social image allowed them to find difficult words to quickly learn than having context generated by someone’s image, or text only modality—although this finding is statistically insignificant. The Zipf frequency comparisons revealed that generally having image-based context allowed learners to extract difficult vocabulary than having text-only context. We also discuss quantitative and qualitative results regarding participants’ acceptance of the personalization system using their personal photos from social media. Generally, they reported positive impressions for our system such as high engagement. While our system prioritizes user privacy with opt-in data control and secure design, we explore additional ethical considerations. This paves the way for a future where personalized language learning, grounded in real-world experiences and generative AI, benefits learners.}, archive = {J_ACCESS}, author = {Kanta Yamaoka and Ko Watanabe and Koichi Kise and Andreas Dengel and Shoya Ishimaru}, doi = {10.1109/ACCESS.2025.3533076}, journal = {IEEE Access}, pages = {20456-20471}, shortjournal = {IEEE Access}, title = {Img2Vocab: Explore words tied to your life with LLMs and social media images}, volume = {13}, year = {2025}, }
• (2025). A space efficient circular patch MIMO antenna having six degrees of freedom for 5G-n79/WLAN applications. ACCESS, 13, 20448-20455. (www)

  @article{ , abstract = {In this article, a single circular patch radiator element of radius 25 mm $\approx ~0.417\lambda _{\mathrm {c}}$ is excited by six $50~\Omega $ co-axial probe feeds spaced equally by ${60}^{0}$ to generate six non-orthogonal polarized decoupled radiations with non-orthogonal pattern diversity to realize a multi-input multi-output (MIMO) antenna having six degrees of freedom. This is further extended to two element MIMO system with inter element spacing of 0.5 mm ( $\approx 0.008\lambda _{\mathrm {c}}$ ), where $\lambda _{\mathrm {c}}$ is the wavelength corresponding to centre carrier frequency 5 GHz. The total size of the proposed design is 101 mm $\times 50.5$ mm $\times 1.55$ mm ( $\approx ~1.683\lambda _{\mathrm {c}} \times 0.842\lambda _{\mathrm {c}} \times 0.026\lambda _{\mathrm {c}}$ ). An acceptable inter port and inter element isolation below -15.2 dB is achieved by loading a simple magneto-electric structure on the patches in the operating range 4.48 GHz to 5.53 GHz which covers 5G-n79, all channels of IEEE 802.11j and few channels of IEEE 802.11a/h/n/ac/ax/be WLAN bands. All the required diversity parameters such as envelope correlation coefficient, diversity gain and mean effective gain ratio are investigated in both uniform and non-uniform media and found within acceptable limit. In addition, channel capacity loss and total active reflection coefficient are studied for multiport MIMO antenna system and found within acceptable limit.}, archive = {J_ACCESS}, author = {Susanta Kumar Badi and Om Prakash Acharya and Bhargav Appasani and Nicu Bizon}, doi = {10.1109/ACCESS.2025.3530954}, journal = {IEEE Access}, pages = {20448-20455}, shortjournal = {IEEE Access}, title = {A space efficient circular patch MIMO antenna having six degrees of freedom for 5G-n79/WLAN applications}, volume = {13}, year = {2025}, }
• (2025). Optimal power management via dynamic power pools in DC MG clusters. ACCESS, 13, 20437-20447. (www)

  @article{ , abstract = {This paper introduces a methodology for managing and controlling microgrid (MG) clusters through the establishment of diverse power pools and optimizing resource allocation based on available generation. The proposed algorithm, incorporating equivalent droop control, facilitates integrated economic optimization by the MG cluster operator (MCO), considering market participation through generation and consumer bids. The methodology dynamically adapts to fluctuations in power generation and consumer demand, ensuring effective power management with reduced control complexity. Real-world feasibility is demonstrated through Hardware-in-the-Loop (HIL) experiments on an off-grid MG cluster, leveraging RT-LAB and Xilinx Kintex-7 FPGA.}, archive = {J_ACCESS}, author = {Makarand S. Ballal and Shivpal R. Verma and Sarvesh A. Wakode and Hiralal M. Suryawanshi}, doi = {10.1109/ACCESS.2025.3534059}, journal = {IEEE Access}, pages = {20437-20447}, shortjournal = {IEEE Access}, title = {Optimal power management via dynamic power pools in DC MG clusters}, volume = {13}, year = {2025}, }
• (2025). The DOA estimation algorithm based on virtual domain block matching for nested arrays in the presence of mutual coupling. ACCESS, 13, 20427-20436. (www)

  @article{ , abstract = {Mutual coupling severely impact Direction of Arrival (DOA) estimation, and the existence of mutual coupling can lead to significant errors in the received signal model of the array. Using algorithms to reduce it is an effective approach. Few algorithms are suitable for nested arrays, and due to incomplete elimination or the loss of some data, they performance deteriorates in the small number of snapshot and low signal-to-noise ratio environments. To address these issues, this paper proposes a Virtual Domain Block Matching DOA estimation algorithm for nested arrays in mutual coupling. The algorithm begins by partitioning the mutual coupling matrix of nested arrays, then applies a classical separation method used in Uniform Linear Array to obtain the separation parameters. Next, it reconstructs an equivalent single-snapshot received data vector in the virtual domain. A sparse reconstruction optimization model is then formulated by exploiting signal sparsity and virtual domain block matching. Solving this model provides a block representation of DOA estimation, where the $l_{2}$ norm of each block yields the DOA estimation for the actual incident signals. Extensive simulation results demonstrate that this algorithm fully utilizes the high degrees of freedom of the nested arrays to achieve underestimation under unknown mutual coupling and maintains high accuracy in small snapshot and low SNR. Additionally, the algorithm is not sensitive to the grid’s size. The experimental results show that this algorithm outperforms several existing methods.}, archive = {J_ACCESS}, author = {Shun He and Nan Sun and Zhiwei Yang and Le Qin}, doi = {10.1109/ACCESS.2025.3534841}, journal = {IEEE Access}, pages = {20427-20436}, shortjournal = {IEEE Access}, title = {The DOA estimation algorithm based on virtual domain block matching for nested arrays in the presence of mutual coupling}, volume = {13}, year = {2025}, }
• (2025). Generating explanations for autonomous robots: A systematic review. ACCESS, 13, 20413-20426. (www)

  @article{ , abstract = {Building trust between humans and robots has long interested the robotics community. Various studies have aimed to clarify the factors that influence the development of user trust. In Human-Robot Interaction (HRI) environments, a critical aspect of trust development is the robot’s ability to make its behavior understandable. The concept of an eXplainable Autonomous Robot (XAR) addresses this requirement. However, giving a robot self-explanatory abilities is a complex task. Robot behavior includes multiple skills and diverse subsystems. This complexity led to research into a wide range of methods for generating explanations about robot behavior. This paper presents a systematic literature review that analyzes existing strategies for generating explanations in robots and studies the current XAR trends. Results indicate promising advancements in explainability systems. However, these systems are still unable to fully cover the complex behavior of autonomous robots. Furthermore, we also identify a lack of consensus on the theoretical concept of explainability, and the need for a robust methodology to assess explainability methods and tools has been identified.}, archive = {J_ACCESS}, author = {David Sobrín-Hidalgo and Ángel Manuel Guerrero-Higueras and Vicente Matellán-Olivera}, doi = {10.1109/ACCESS.2025.3535097}, journal = {IEEE Access}, pages = {20413-20426}, shortjournal = {IEEE Access}, title = {Generating explanations for autonomous robots: A systematic review}, volume = {13}, year = {2025}, }
• (2025). MLHOps: Machine learning health operations. ACCESS, 13, 20374-20412. (www)

  @article{ , abstract = {Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations and describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment. We also include a checklist to ensure thorough verification of each step in the process.}, archive = {J_ACCESS}, author = {Faiza Khan Khattak and Vallijah Subasri and Amrit Krishnan and Chloe Pou-Prom and Sedef Akinli-Kocak and Elham Dolatabadi and Deval Pandya and Laleh Seyyed-Kalantari and Frank Rudzicz}, doi = {10.1109/ACCESS.2024.3521279}, journal = {IEEE Access}, pages = {20374-20412}, shortjournal = {IEEE Access}, title = {MLHOps: Machine learning health operations}, volume = {13}, year = {2025}, }
• (2025). Optimized ANFIS-based robust nonlinear control of a solar off-grid charging station for electric vehicles. ACCESS, 13, 20361-20373. (www)

  @article{ , abstract = {This paper attempts to improve the performance of an off-grid electric vehicle charging station powered by photovoltaic (PV) panels and batteries. To ensure optimal performance of the PV panels, maximum power point tracking (MPPT) is implemented using an adaptive neuro-fuzzy inference system. A robust control technique is designed for regulating the currents and voltages within the system. Conventional sliding mode controllers (SMCs) are prone to chattering, and while the inclusion of a super-twisting component mitigates this issue, it introduces windup problems. To address these challenges, we propose a conditioned-based super-twisting SMC (CST-SMC), which effectively resolves the windup issue. The controller gains are optimized using the grey wolf optimization algorithm, and global stability is demonstrated through Lyapunov stability analysis. The proposed system is simulated in MATLAB and experimentally validated using a Delfino F28379D-based hardware-in-the-loop setup. Numerical results show that the proposed CST-SMC has a faster rise time (0.0564 sec), less overshoot (0.00389), and a shorter settling time (0.4256 sec) compared to the conventional ST-SMC, which has a slower rise time (0.137 sec), greater overshoot (0.38095), and a longer settling time (3.8071 sec). Thus, the system’s dynamic response is improved. Furthermore, the proposed CST-SMC controller provides smoother regulation during the constant current and constant voltage stages, unlike the ST-SMC, which exhibits chattering. The improved performance makes the CST-SMC more reliable for EV charging.}, archive = {J_ACCESS}, author = {Bibi Tabassam Gul and Iftikhar Ahmad and Habibur Rehman and Ammar Hasan}, doi = {10.1109/ACCESS.2025.3535571}, journal = {IEEE Access}, pages = {20361-20373}, shortjournal = {IEEE Access}, title = {Optimized ANFIS-based robust nonlinear control of a solar off-grid charging station for electric vehicles}, volume = {13}, year = {2025}, }
• (2025). Audio-language datasets of scenes and events: A survey. ACCESS, 13, 20328-20360. (www)

  @article{ , abstract = {Audio-language models (ALMs) generate linguistic descriptions of sound-producing events and scenes. Advances in dataset creation and computational power have led to significant progress in this domain. This paper surveys 69 datasets used to train ALMs, covering research up to September 2024 (https://github.com/GLJS/audio-datasets). The survey provides a comprehensive analysis of dataset origins, audio and linguistic characteristics, and use cases. Key sources include YouTube-based datasets such as AudioSet, with over two million samples, and community platforms such as Freesound, with over one million samples. The survey evaluates acoustic and linguistic variability across datasets through principal component analysis of audio and text embeddings. The survey also analyzes data leakage through CLAP embeddings, and examines sound category distributions to identify imbalances. Finally, the survey identifies key challenges in developing large, diverse datasets to enhance ALM performance, including dataset overlap, biases, accessibility barriers, and the predominance of English-language content, while highlighting specific areas requiring attention: multilingual dataset development, specialized domain coverage and improved dataset accessibility.}, archive = {J_ACCESS}, author = {Gijs Wijngaard and Elia Formisano and Michele Esposito and Michel Dumontier}, doi = {10.1109/ACCESS.2025.3534621}, journal = {IEEE Access}, pages = {20328-20360}, shortjournal = {IEEE Access}, title = {Audio-language datasets of scenes and events: A survey}, volume = {13}, year = {2025}, }
• (2025). Evaluating large language models for optimized intent translation and contradiction detection using KNN in IBN. ACCESS, 13, 20316-20327. (www)

  @article{ , abstract = {Intent-Based Networking (IBN) simplifies network management by enabling users to express high-level intents in natural language, but existing approaches often fail to ensure alignment with network policies, leading to misconfigurations. Moreover, many methods lack robust validation mechanisms, reducing their reliability in dynamic environments. This research addresses these gaps by evaluating advanced Large Language Models (LLMs) such as BERT-base uncased (BERT-bu), GPT2, LLaMA3, Claude2 and small deep learning model BiLSTM with attention for translating intents and detecting contradictions. Using a curated dataset of 10,000 intent pairs, the proposed hybrid framework integrates a K-Nearest Neighbors (KNN) classifier to validate translations and recalibrate erroneous outputs. Experimental results demonstrate up to 5% higher accuracy (88%) and F1 scores compared to existing methods, ensuring precise intent translation and reliable network orchestration. This approach significantly enhances scalability and policy compliance in automated network environments.}, archive = {J_ACCESS}, author = {Muhammad Asif and Talha Ahmed Khan and Wang-Cheol Song}, doi = {10.1109/ACCESS.2025.3534880}, journal = {IEEE Access}, pages = {20316-20327}, shortjournal = {IEEE Access}, title = {Evaluating large language models for optimized intent translation and contradiction detection using KNN in IBN}, volume = {13}, year = {2025}, }
• (2025). A machine learning-based platform for monitoring and prediction of hazardous gases in rural and remote areas. ACCESS, 13, 20297-20315. (www)

  @article{ , abstract = {The emissions of pollutants and radioactive gases are the main causes of several environmental disasters that may cause premature deaths. The significant impact of these gases on public health is a major concern, especially in remote and rural regions. In these areas, access to security and health services is scarce, and real-time monitoring of citizens and the conditions in which they live is very difficult. Without means to monitor or predict, healthcare and government stakeholders typically act too late when indoor incidents occur. Hence, this paper presents a digital decision support system that uses Machine Learning (ML) for monitoring and prediction of incidents related with indoor hazardous gases. This system is implemented on top of an Internet of Things (IoT) ecosystem named RuraLTHINGS. This project, developed by the University of Beira Interior, Portugal, monitors the quality of air in remote and rural regions in real-time. The platform aims to predict and notify residents and other stakeholders about environmental conditions and prevent the risk of exposure to dangerous gases. The system uses ML techniques to analyze the collected data and provide future predictions using unidirectional Long Short-Term Memory (LSTM) layers overlaid on bidirectional LSTM layers, meaning layers stacked together, which was the model architecture that delivered the best results in this context. This paper presents the validation of the digital platform and the ML model using a real test bed environment. The model successfully predicted future data trends related to indoor monitoring of hazardous gases.}, archive = {J_ACCESS}, author = {Edgar F. Ladeira and Bruno M. C. Silva}, doi = {10.1109/ACCESS.2025.3535158}, journal = {IEEE Access}, pages = {20297-20315}, shortjournal = {IEEE Access}, title = {A machine learning-based platform for monitoring and prediction of hazardous gases in rural and remote areas}, volume = {13}, year = {2025}, }
• (2025). Smart GNSS integrity monitoring for road vehicles: An overview of AI methods. ACCESS, 13, 20278-20296. (www)

  @article{ , abstract = {Integrity monitoring is a key criterion for achieving robust and safe navigation systems. This work explores two integrity frameworks: the classical methods and their respective evolution towards the road vehicle urban scenario, and the artificial intelligence-based methods, where the monitoring process is accomplished by data analysis and learning techniques. In most cases, machine learning outperforms traditional models, which are often observed under controlled, non-real-time conditions, by employing simple algorithms that may have limited success in real-world applications. An overview is provided on how these algorithms have been used, including a comparison of their characteristics and performances, offering insights into how they can evolve and possible future directions to achieve more reliable solutions.}, archive = {J_ACCESS}, author = {Inês Viveiros and Hélder Silva and Yuri Andrade and Cristiano Pendão}, doi = {10.1109/ACCESS.2025.3534659}, journal = {IEEE Access}, pages = {20278-20296}, shortjournal = {IEEE Access}, title = {Smart GNSS integrity monitoring for road vehicles: An overview of AI methods}, volume = {13}, year = {2025}, }
• (2025). A novel and effective model for automatic modulation classification prediction based on multi-BIGRU, multi-encoder, and hyper-cross. ACCESS, 13, 20260-20277. (www)

  @article{ , abstract = {Automatic Modulation Classification (AMC) is a pivotal technology in various communication systems. In recent years, deep learning (DL) has been widely applied in AMC methods due to its powerful feature extraction capabilities. However, currently proposed AMC methods still have room for improvement in classification performance. To further enhance the prediction accuracy of AMC, we propose a new model called MMH-AMC: Automatic Modulation Classification with Multi-BIGRU, Multi-Encoder, and Hyber-Cross. Multi-BIGRU employs a multi-layer bidirectional GRU architecture to extract deep distribution patterns of AP data from both directions simultaneously. Multi-Encoder adopts a multi-layer encoder architecture with a core of Multi-Head Attention, aiming to extract deep-level IQ data distributions. To unify the feature distributions outputted by Multi-Encoder and Hyber-Cross modules, we designed and utilized the Hyber-Cross module. To validate the model’s performance, we compared six different deep learning models and achieved the best performance in various scenarios.}, archive = {J_ACCESS}, author = {Jianzheng Lin}, doi = {10.1109/ACCESS.2024.3514078}, journal = {IEEE Access}, pages = {20260-20277}, shortjournal = {IEEE Access}, title = {A novel and effective model for automatic modulation classification prediction based on multi-BIGRU, multi-encoder, and hyper-cross}, volume = {13}, year = {2025}, }
• (2025). UAV-NavS: Three-dimensional navigation system of multiple unmanned aerial vehicles using hybrid optimization algorithm. ACCESS, 13, 20247-20259. (www)

  @article{ , abstract = {This paper proposes a hybrid approach for multiple Unmanned Aerial Vehicle navigation. This is an NP-hard problem since the robots must find the optimal safe path without colliding with other robots and obstacles in a three-dimensional search space. The proposed approach enhances the exploration capabilities of the whale optimization algorithm. Then, it hybridises this improved whale optimization algorithm with the sine cosine algorithm to improve the overall exploitation capabilities. The efficiency of the proposed hybrid approach is compared with other meta-heuristic algorithms for multi-UAV navigation. Results obtained through simulation ensure the validity of the proposed approach.}, archive = {J_ACCESS}, author = {Monia Digra and Upma Jain and Ram Kishan Dewangan and Himanshu Suyal}, doi = {10.1109/ACCESS.2025.3534630}, journal = {IEEE Access}, pages = {20247-20259}, shortjournal = {IEEE Access}, title = {UAV-NavS: Three-dimensional navigation system of multiple unmanned aerial vehicles using hybrid optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Prioritizing DevOps implementation guidelines for sustainable software projects”. ACCESS, 13, 20246. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Prioritizing DevOps Implementation Guidelines for Sustainable Software Projects”).}, archive = {J_ACCESS}, author = {Muhammad Zohaib and Ahmed Alsanad and Areej Abdullah Alhogail}, doi = {10.1109/ACCESS.2025.3533237}, journal = {IEEE Access}, pages = {20246}, shortjournal = {IEEE Access}, title = {Corrections to “Prioritizing DevOps implementation guidelines for sustainable software projects”}, volume = {13}, year = {2025}, }
• (2025). Systematic literature review on organizational and financial resilience. ACCESS, 13, 20218-20245. (www)

  @article{ , abstract = {Organizational and financial resilience have emerged as crucial topics for the sustainability of SMEs, particularly in emerging economies where organizations face uncertain and volatile environments. This study presents a systematic literature review (SLR) to evaluate the current state of research on organizational and financial resilience in SMEs. Fifty-one primary studies published between 2000 and 2023 were reviewed, using strict inclusion and quality evaluation criteria to ensure the relevance and robustness of the findings. The results revealed that only 5.88% of the evaluated studies rigorously followed SLR guidelines, highlighting the scarcity of comprehensive reviews in this field. This review also underscores that organizational resilience, encompassing both planned and adaptive dimensions, is essential for SMEs to respond effectively to financial and operational crises. However, few studies have focused specifically on the Ecuadorian context, indicating a gap in the literature that should be addressed more thoroughly. This study concludes that future research should adopt more holistic approaches that incorporate both qualitative and quantitative methodologies to develop models that strengthen resilience among SMEs. Future studies should consider applying more rigorous SLR guidelines to improve research quality in this field.}, archive = {J_ACCESS}, author = {Juan Diego Ochoa Crespo and José Manuel Feria-Domínguez and Diego Marcelo Cordero Guzmán}, doi = {10.1109/ACCESS.2025.3534057}, journal = {IEEE Access}, pages = {20218-20245}, shortjournal = {IEEE Access}, title = {Systematic literature review on organizational and financial resilience}, volume = {13}, year = {2025}, }
• (2025). Consensus-based resilience assurance for system of systems. ACCESS, 13, 20203-20217. (www)

  @article{ , abstract = {The complexity of a System of Systems makes resilience one of its key attributes. Numerous studies have focused on the quantitative assessment of resilience by trailing indicators, yet discussions on resilience assurance through monitoring leading indicators remain scarce. Resilience assurance in SoS faces two major challenges: lack of structured argumentation work related to resilience and conflicts among multiple independent stakeholders. To address these challenges, this paper first introduces a resilience argumentation approach based on STAMP (Systems-Theoretic Accident Model and Processes), then employs cooperative consensus process model to seek consensus on resilience assurance. Additionally, under the requirements of the international standard IEC 62853 for open systems dependability, a consensus based resilience assurance framework is proposed. Within the framework, the resilient team can discuss the specific implementation details of failure response, accountability, and change accommodation based on stakeholder consensus. Finally, two SoS case studies, Microgrid and Mobility as a Service, are used to demonstrate the application of the proposed approach.}, archive = {J_ACCESS}, author = {Huanjun Zhang and Yutaka Matsubara}, doi = {10.1109/ACCESS.2025.3535519}, journal = {IEEE Access}, pages = {20203-20217}, shortjournal = {IEEE Access}, title = {Consensus-based resilience assurance for system of systems}, volume = {13}, year = {2025}, }
• (2025). Re-calibrating network by refining initial features through generative gradient regularization. ACCESS, 13, 20191-20202. (www)

  @article{ , abstract = {In the domain of Deep Neural Networks (DNNs), the deployment of regularization techniques is a common strategy for optimizing network performance. While these methods have been shown to be effective for optimization, they typically necessitate complete retraining of the network. We propose a training methodology that emphasizes on refining the features extracted from the initial layer of a DNN by regularizing the network with the help of a reference gradient. Our findings indicate that augmenting the gradients produced by the filters of the initial layer of a DNN, through the introduction of a reference gradient, leads to refined feature extraction and enhanced performance. We produce the reference gradient from the decoder of a generative network and subsequently encourage the target classifier network to adjust its weights to minimize discrepancies between the reference gradient and the gradient produced by the classifier network. The experiments show that implementing this method on a pre-trained network effectively re-calibrates the network and augments higher variance filters of the initial layer of the network, which helps produce refined features. Notably, this refinement in features translates to improved generalization and the proposed method also eliminates the necessity of total retraining of the target network. In empirical evaluation, we applied the proposed methodology to CIFAR, SVHN and ImageNet datasets, utilizing a range of network architectures. The results evidenced a performance gain of 1.66% for the CIFAR dataset using WideResNet, 1.22% for the SVHN dataset using PreResNet and 0.57% for the ImageNet dataset using ResNet.}, archive = {J_ACCESS}, author = {Naim Reza and Ho Yub Jung}, doi = {10.1109/ACCESS.2025.3534216}, journal = {IEEE Access}, pages = {20191-20202}, shortjournal = {IEEE Access}, title = {Re-calibrating network by refining initial features through generative gradient regularization}, volume = {13}, year = {2025}, }
• (2025). Assessment of rear-end collision risk based on a deep reinforcement learning technique: A break reaction assessment approach. ACCESS, 13, 20171-20190. (www)

  @article{ , abstract = {Rear-end crashes are a major type of traffic crash that occur more frequently on the road, leading to a large number of injuries and fatalities each year around the world. Examining the overtaking behaviors and predicting the collision risk probability are essential issues for preventing a rear-end collision risk and improving road safety. To this end, we proposed the rear-end collision model to examine the risks associated with the lagging vehicle (LAV) movements. Specifically, this research aims to develop a model for assessing rear-end collision risks by considering different braking reaction times (BRTs) of the LAV. Firstly, we introduce the deep neural network (DNN) to learn the movements of LAV. Then, a collision-free modeling based on the deep reinforcement model (DRM) is proposed to mitigate the collision risks associated with LAV movements to nearby vehicles thus improving traffic safety. Finally, we incorporate the generalized linear model (GLM)-based BR time with driver’s driving behavior, aiming to identify the driver’s distraction with different vehicle movements. Various performance metrics, such as modified time to collision (MTTC), deceleration rate to avoid collision (DRAC), and post-collision change in velocity (Delta-V), are used to identify rear-end conflicts and to demonstrate the effectiveness of the developed model. The simulation results indicated that the developed model could reduce rear-end collision risks with the leading vehicle (LEV) based on different LAV speeds. Furthermore, the North Carolina (NC) traffic real-time crash data is used to demonstrate the efficacy of the developed model. The results indicated that different traffic conditions, such as driver behaviors, and road and climate conditions influence the severity of rear-end crashes.}, archive = {J_ACCESS}, author = {Muhammad Sameer Sheikh and Yinqiao Peng}, doi = {10.1109/ACCESS.2025.3534787}, journal = {IEEE Access}, pages = {20171-20190}, shortjournal = {IEEE Access}, title = {Assessment of rear-end collision risk based on a deep reinforcement learning technique: A break reaction assessment approach}, volume = {13}, year = {2025}, }
• (2025). Understanding and optimizing oxygen plasma treatment for enhanced cu-cu bonding application. ACCESS, 13, 20160-20170. (www)

  @article{ , abstract = {This study investigates the optimization of O2 plasma treatment conditions to enhance Cu-Cu bonding. The O2 plasma treatment conditions were optimized using Design of Experiments (DOE), adjusting three parameters: O2 flow rate, plasma power, and treatment time, to minimize oxidation while maximizing surface energy. X-ray photoelectron spectroscopy (XPS) was employed to calculate the Cu atomic percentage (at%) at the surface and at a depth of 25 seconds of etching, while water contact angle (WCA) measurements assessed surface energy. The results indicated that decreasing the O2 flow rate reduced oxidation without significantly impacting surface energy. Plasma power and treatment time were optimized through a balanced approach. The identified optimal conditions were an O2 flow rate of 50 sccm, plasma power of 50 W, and a process time of 20 seconds. Subsequent SEM analysis confirmed a wavy bonding interface indicative of strong Cu diffusion bonding, resulting in approximately a 40% increase in shear strength. The findings suggest that controlled O2 plasma treatment effectively enhances bonding strength, providing direction for the optimization of O2 plasma for Cu bonding in advanced packaging technologies and hybrid bonding applications.}, archive = {J_ACCESS}, author = {Sangwoo Park and Sangmin Lee and Junyoung Choi and Sarah Eunkyung Kim}, doi = {10.1109/ACCESS.2025.3534169}, journal = {IEEE Access}, pages = {20160-20170}, shortjournal = {IEEE Access}, title = {Understanding and optimizing oxygen plasma treatment for enhanced cu-cu bonding application}, volume = {13}, year = {2025}, }
• (2025). Energy efficient tree-based routing algorithm for wireless sensor networks. ACCESS, 13, 20149-20159. (www)

  @article{ , abstract = {To address the issue of uneven sensor node distribution and unbalanced energy consumption leading to premature node death in wireless sensor networks, an energy efficient tree-based routing algorithm is proposed. The algorithm calculates the optimal number of branches that minimize network energy consumption by constructing a tree-based energy model. Based on the optimal number of branches, with the base station as the root node, a multi-layer tree routing is formed from near to far according to the distance between the node and the base station. During the formation of routing tree, the nodes whose residual energy of the nodes is less than the energy threshold can only become end nodes, thus avoiding premature death of the nodes due to excessive energy consumption of the nodes. Nodes transmit data to the base station along the routing tree. The routing tree is updated at dynamic intervals instead of every round to reduce energy consumption. Simulation results show that the algorithm has more balanced node energy consumption, lower network energy consumption, and longer network stability period and network lifespan than the other three protocols.}, archive = {J_ACCESS}, author = {Ni Chen and Rong Wen}, doi = {10.1109/ACCESS.2025.3535229}, journal = {IEEE Access}, pages = {20149-20159}, shortjournal = {IEEE Access}, title = {Energy efficient tree-based routing algorithm for wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Predicting individual systems thinking skills using bayesian network approach. ACCESS, 13, 20133-20148. (www)

  @article{ , abstract = {Emergence in complex systems is often compounded by diverse information and rapid technological acceleration. The problems and behaviors of increasingly complex systems continue confining practitioners’ systems engineering capabilities to maintain performance consistency. While systems engineering provides a process for integrating various engineering disciplines to deliver desired end results, systems thinking (ST) provides the mechanism for drawing broad perspectives on the configuration, patterns, and cycles of complex systems, with a view to analyzing and improving system performance. ST can, therefore, be construed as an essential skill for designing and managing complex systems that need to sustain desired specifications. Although several methods exist in the extant literature to appraise the ST skills of practitioners, none has been recommended for prediction and diagnostic purposes. To fill this void, this research study aims to develop and validate a Bayesian network tool that incorporates seven main factors and the corresponding underpinning sub-factors that influence individual ST skills, as identified by Jaradat and Keating. The study seeks to answer whether differences in systems thinking skills are evident between practitioners in two sectors, namely defense and industry/business. The results indicate that all the main ST factors are imperative to predicting overall individual ST skills for defense and industry/business practitioners. However, defense practitioners scored higher along six dimensions, resulting in a higher overall individual ST score than industry practitioners. Although industry practitioners scored higher than defense practitioners on the independence (autonomy) dimension, this dimension alone was insufficient to strengthen the overall ST skills above that of defense practitioners.}, archive = {J_ACCESS}, author = {Niamat Ullah Ibne Hossain and Raed Jaradat and Morteza Nagahi and Alex Gorod}, doi = {10.1109/ACCESS.2025.3534632}, journal = {IEEE Access}, pages = {20133-20148}, shortjournal = {IEEE Access}, title = {Predicting individual systems thinking skills using bayesian network approach}, volume = {13}, year = {2025}, }
• (2025). Generalizing the brady-yong algorithm: Efficient fast hough transform for arbitrary image sizes. ACCESS, 13, 20101-20132. (www)

  @article{ , abstract = {The Hough (discrete Radon) transform (HT/DRT) is a digital image processing tool that has become indispensable in many application areas, ranging from general image processing to neural networks and X-ray computed tomography. The utilization of the HT in applied problems demands its computational efficiency and increased accuracy. The de facto standard algorithm for the fast HT is the Brady-Yong algorithm. However, it is applicable only to images of a power-of-two width. The algorithm that generalizes the Brady-Yong algorithm for non-power-of-two image width with the same asymptotic complexity is known, but it has not been studied neither in terms of the constant in the asymptotics nor in accuracy. Thus, supported both by theory and experiments, generalization of the Brady-Yong algorithm for images of arbitrary size while maintaining asymptotic computational complexity and acceptable accuracy remains of paramount necessity. In this paper, we proposed 5 novel algorithms that incorporate the core idea of the Brady-Yong algorithm and are suitable for computing the fast HT for images of arbitrary size. We investigated the properties of 5 new algorithms, along with one previously proposed algorithm from the literature, through both theoretical analysis and experimental validation. As one of our major contributions, among the proposed algorithms, we singled out a $FHT2DT$ algorithm and proved that it provides a substantial compromise between accuracy and computational complexity. The $FHT2DT$ algorithm is significantly more accurate than the algorithm previously suggested in the literature and, hence, $FHT2DT$ can substitute it in practical applications. During the analysis of the properties of the proposed algorithms, we created a map that characterizes the algorithms based on their accuracy and speed parameters. Users can select the method that best suits their needs — whether prioritizing computational complexity or accuracy — by referring to our map.}, archive = {J_ACCESS}, author = {Danil D. Kazimirov and Ekaterina O. Rybakova and Vitalii V. Gulevskii and Arseniy P. Terekhin and Elena E. Limonova and Dmitry P. Nikolaev}, doi = {10.1109/ACCESS.2025.3534405}, journal = {IEEE Access}, pages = {20101-20132}, shortjournal = {IEEE Access}, title = {Generalizing the brady-yong algorithm: Efficient fast hough transform for arbitrary image sizes}, volume = {13}, year = {2025}, }
• (2025). Enhancing sentiment analysis and rating prediction using the review text granularity (RTG) model. ACCESS, 13, 20071-20100. (www)

  @article{ , abstract = {In the era of digital technology, when material created by users is prevalent on online platforms, considerable difficulty is faced in analyzing large volumes of text in order to comprehend user emotions and forecast product ratings. The rapid rise in online reviews and comments necessitates the use of advanced tools to assess this data and extract valuable insights. This is considered crucial for the effectiveness of recommendation systems in many industries. This paper introduces the Review Text Granularity (RTG) Model, a new way to use the complex information in review texts to improve sentiment analysis and rating prediction. The RTG Model uses an advanced approach to scoring sentiments. It measures the strength of sentiments and gives a continuous sentiment score instead of a simple positive or negative label. This makes it different from other binary sentiment analysis methods. Multiple predictive modeling techniques are used, which makes it possible for this comprehensive sentiment analysis to greatly improve the accuracy of rating predictions. It has been shown by research that the depth of textual reviews is better captured and measured by the RTG Model, providing a more detailed and precise picture of user opinions. The RTG Model works really well, making rating predictions in recommendation systems more accurate and useful. A detailed study using a real-world dataset of IMDb movie reviews demonstrated this. The study emphasizes the benefits of utilizing intricate sentiment scores in addition to conventional rating data. The potential applicability of the RTG Model in several fields, including entertainment, e-commerce, and social media, is demonstrated, leading to enhanced and tailored user experiences.}, archive = {J_ACCESS}, author = {Rajesh Garapati and Manomita Chakraborty}, doi = {10.1109/ACCESS.2025.3534261}, journal = {IEEE Access}, pages = {20071-20100}, shortjournal = {IEEE Access}, title = {Enhancing sentiment analysis and rating prediction using the review text granularity (RTG) model}, volume = {13}, year = {2025}, }
• (2025). Micro-expression recognition using convolutional variational attention transformer (ConVAT) with multihead attention mechanism. ACCESS, 13, 20054-20070. (www)

  @article{ , abstract = {Micro-Expression Recognition is crucial in various fields such as behavioral analysis, security, and psychological studies, offering valuable insights into subtle and often concealed emotional states. Despite significant advancements in deep learning models, challenges persist in accurately handling the nuanced and fleeting nature of micro-expressions, particularly when applied across diverse datasets with varied expressions. Existing models often struggle with precision and adaptability, leading to inconsistent recognition performance. To address these limitations, we propose the Convolutional Variational Attention Transformer (ConVAT), a novel model that leverages a multi-head attention mechanism integrated with convolutional networks, optimized specifically for detailed micro-expression analysis. Our methodology employs the Leave-One-Subject-Out (LOSO) cross-validation technique across three widely used datasets: SAMM, CASME II, and SMIC. The results demonstrate the effectiveness of ConVAT, achieving impressive performance with 98.73% accuracy on the SAMM dataset, 97.95% on the SMIC dataset, and 97.65% on CASME II. These outcomes not only surpass current state-of-the-art benchmarks but also highlight ConVAT’s robustness and reliability in capturing micro-expressions, marking a significant advancement toward developing sophisticated automated systems for real-world applications in micro-expression recognition.}, archive = {J_ACCESS}, author = {Hafiz Khizer Bin Talib and Kaiwei Xu and Yanlong Cao and Yuan Ping Xu and Zhijie Xu and Muhammad Zaman and Adnan Akhunzada}, doi = {10.1109/ACCESS.2025.3530114}, journal = {IEEE Access}, pages = {20054-20070}, shortjournal = {IEEE Access}, title = {Micro-expression recognition using convolutional variational attention transformer (ConVAT) with multihead attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Engineering scheme decision system integrating case reasoning and BIM technology. ACCESS, 13, 20040-20053. (www)

  @article{ , abstract = {To solve the problem of lack of science and poor reusability of experience in traditional engineering scheme decision-making, which leads to the increase of time and cost of pre-decision-making, the study first uses case-based reasoning and ontology to construct a solution library for engineering solution decision-making system, and standardizes the cases using methods such as eigenfrequency. In addition, a retrieval mechanism based on residual similarity is designed to achieve effective retrieval of similar cases. The experiment outcomes denoted that the resource utilization rate of the traditional scheme was 75% before implementation, but decreased to 72% after implementation, a decrease of 3%. The resource utilization rate of the decision-making system scheme was 75% before implementation, and increased to 80% after implementation, an increase of 5%. The results indicated that the decision system scheme designed by the research performed better in terms of resource utilization, could more efficiently utilize resources, and reduce waste. The average decision accuracy of integrating CBR and BIM systems was 92%, significantly higher than the 84% of traditional decision systems. The CBR technology improved the scientificity and reliability of decision-making through continuous updating and optimization of the case library.}, archive = {J_ACCESS}, author = {Qi Zhang}, doi = {10.1109/ACCESS.2025.3531899}, journal = {IEEE Access}, pages = {20040-20053}, shortjournal = {IEEE Access}, title = {Engineering scheme decision system integrating case reasoning and BIM technology}, volume = {13}, year = {2025}, }
• (2025). 3-DOF inertial piezoelectric actuator for angular positioning of the optical mirror. ACCESS, 13, 20025-20039. (www)

  @article{ , abstract = {Article introduces a novel 3-DOF piezoelectric actuator designed to achieve the high-precision angular motion of a spherical rotor and an optical mirror around three axes. The actuator consists of three interlinked piezoelectric bimorph plates arranged in a low-profile triangular structure. Each piezoelectric plate contains a cylindrical contact that is used to transfer the vibrations to induce the angular motion of the rotor. The actuator has a low profile and is mounted on a printed circuit board (PCB), enhancing its structural integrity. The actuator occupies a footprint of 986 mm2 and, including the rotor, weighs 35.5 g. The actuator operation is based on the inertial stick-slip principle, using the first and second out-of-plane bending modes of the bimorph plates. Simultaneous excitation of the second vibration mode of all three bimorph plates produces angular motion of the rotor around the vertical axis. In contrast, the excitation of the first vibration mode of the individual plate enables angular motion around one of the horizontal axes. Numerical analysis identified the vibration modes, resonant frequencies, and mechanical and electromechanical characteristics of the actuator, leading to design optimization of clamping beams and contact locations on the bimorph plates. Experimental measurements revealed that the maximum rotation speeds are 363.7 RPM around the vertical axis and 129.1 RPM around the horizontal axis. Maximum torques of 218.2 mN/m and 159.7 mN/m were achieved around the vertical and horizontal axes, respectively, when the actuator is driven at 200 V $_{\mathrm {p-p}}$ . The actuator demonstrated angular resolutions of 2.47 mrad and 1.033 mrad for the vertical and horizontal axes, respectively.}, archive = {J_ACCESS}, author = {Andrius Čeponis and Dalius Mažeika and Regimantas Bareikis}, doi = {10.1109/ACCESS.2025.3534847}, journal = {IEEE Access}, pages = {20025-20039}, shortjournal = {IEEE Access}, title = {3-DOF inertial piezoelectric actuator for angular positioning of the optical mirror}, volume = {13}, year = {2025}, }
• (2025). Research on gait recognition based on GaitSet and multimodal fusion. ACCESS, 13, 20017-20024. (www)

  @article{ , abstract = {With the continuous technological progress, especially the development in biometrics, gait recognition has shown broad application prospects in healthcare (e.g., health monitoring), security (e.g., assisted identity verification), and human-computer interaction. However, individual differences, such as changes in physical condition, and environmental variability, such as differences in lighting, can impact its accuracy. Based on the information derived from the gait contour sequence during walking (such as temporal and spatial information), this study proposes an improved gait recognition method based on the GaitSet model, which improves video-based gait recognition performance by combining gait energy images and silhouette images to form a multimodal representation. The experimental results showed a significant performance improvement compared with the original model, especially in subjects with bags. Large-sample training experiment results based on the CASIA-B database indicated that the recognition rates in the Normal (NM), Bag (BG), and Coat (CL) states were 95.8%, 89.3%, and 72.5%, respectively, and that in the CL state achieved a significant improvement of 3.3%.}, archive = {J_ACCESS}, author = {Xiling Shi and Wenqiang Zhao and Huandou Pei and Hongru Zhai and Yongxia Gao}, doi = {10.1109/ACCESS.2025.3533571}, journal = {IEEE Access}, pages = {20017-20024}, shortjournal = {IEEE Access}, title = {Research on gait recognition based on GaitSet and multimodal fusion}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Information security and artificial intelligence-assisted diagnosis in an internet of medical thing system (IoMTS)”. ACCESS, 13, 20016. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Information Security and Artificial Intelligence-Assisted Diagnosis in an Internet of Medical Thing System (IoMTS)”).}, archive = {J_ACCESS}, author = {Pi-Yun Chen and Yu-Cheng Cheng and Zi-Heng Zhong and Feng-Zhou Zhang and Neng-Sheng Pai and Chien-Ming Li and Chia-Hung Lin}, doi = {10.1109/ACCESS.2025.3534266}, journal = {IEEE Access}, pages = {20016}, shortjournal = {IEEE Access}, title = {Corrections to “Information security and artificial intelligence-assisted diagnosis in an internet of medical thing system (IoMTS)”}, volume = {13}, year = {2025}, }
• (2025). VRU-YOLO: A small object detection algorithm for vulnerable road users in complex scenes. ACCESS, 13, 19996-20015. (www)

  @article{ , abstract = {Accurate detection of vulnerable road users (VRUs) is critical for enhancing traffic safety and advancing autonomous driving systems. However, due to their small size and unpredictable movements, existing detection methods struggle to provide stable and accurate results under real-time conditions. To overcome these challenges, this paper proposes an improved VRU detection algorithm based on YOLOv8, named VRU-YOLO. First, we redesign the neck structure and construct a Detail Enhancement Feature Pyramid Network (DEFPN) to enhance the extraction and fusion capabilities of small target features. Second, the YOLOv8 network’s Spatial Pyramid Pooling Fast (SPPF) module is replaced with a novel Feature Pyramid Convolution Fast (FPCF) module based on dilated convolution, effectively mitigating feature loss in small target processing. Additionally, a lightweight Optimized Shared Detection Head (OSDH-Head) is introduced, reducing computational complexity while improving detection efficiency. Finally, to alleviate the deficiencies of traditional loss functions in shape matching and computational efficiency, we propose the Wise-Powerful Intersection over Union (WPIoU) loss function, which further optimizes the regression of target bounding boxes. Experimental results on a custom-built multi-source VRU dataset show that the proposed model enhances precision, recall, mAP50, and mAP50:95 by 1.3%, 3.4%, 3.3%, and 1.8%, respectively, in comparison to the baseline model. Moreover, in a generalization test conducted on the remote sensing small target dataset VisDrone2019, the VRU-YOLO model achieved an mAP50 of 31%. This study demonstrates that the improved model offers more efficient performance in small object detection scenarios, making it well-suited for VRU detection in complex road environments.}, archive = {J_ACCESS}, author = {Yunxiang Liu and Yuqing Shi}, doi = {10.1109/ACCESS.2025.3534321}, journal = {IEEE Access}, pages = {19996-20015}, shortjournal = {IEEE Access}, title = {VRU-YOLO: A small object detection algorithm for vulnerable road users in complex scenes}, volume = {13}, year = {2025}, }
• (2025). IAR 2.0: An algorithm for refining inconsistent annotations for time-series data using discriminative classifiers. ACCESS, 13, 19979-19995. (www)

  @article{ , abstract = {The performance of discriminative machine-learning classifiers, such as neural networks, is limited by training label inconsistencies. Even expert-based annotations can suffer from label inconsistencies, especially in the case of ambiguous phenomena-to-annotate. To address this, we propose a novel algorithm, iterative annotation refinement (IAR) 2.0, for refining inconsistent annotations for time-series data. IAR 2.0 uses a procedure that utilizes discriminative classifiers to iteratively combine original annotations with increasingly accurate posterior estimates of classes present in the data. Unlike most existing label refinement approaches, IAR 2.0 offers a simpler yet effective solution for resolving ambiguities in training labels, working with real label noise on time-series data instead of synthetic label noise on image data. We demonstrate the effectiveness of our algorithm through five distinct classification tasks on two highly distinct data modalities. As a result, we show that the labels produced by IAR 2.0 systematically improve classifier performance compared to using the original labels or a previous state-of-the-art method for label refinement. We also conduct a set of controlled simulations to systematically investigate when IAR 2.0 fails to improve on the original training labels. The simulation results demonstrate that IAR 2.0 improves performance in nearly all tested conditions. We also find that the decrease in performance when IAR 2.0 fails is small compared to the average performance gain when IAR 2.0 succeeds, encouraging the use of IAR 2.0 even when the nature of data is unknown. The code is freely available at https://github.com/SPEECHCOG/IAR_2.}, archive = {J_ACCESS}, author = {Einari Vaaras and Manu Airaksinen and Okko Räsänen}, doi = {10.1109/ACCESS.2025.3534637}, journal = {IEEE Access}, pages = {19979-19995}, shortjournal = {IEEE Access}, title = {IAR 2.0: An algorithm for refining inconsistent annotations for time-series data using discriminative classifiers}, volume = {13}, year = {2025}, }
• (2025). Comprehensive environmental monitoring system for industrial and mining enterprises using multimodal deep learning and CLIP model. ACCESS, 13, 19964-19978. (www)

  @article{ , abstract = {Addressing the challenges of limited accuracy in anomaly detection within comprehensive environmental monitoring of industrial and mining enterprises, and the constraints posed by singular data modalities, this study proposes an integration of a multimodal Long Short-Term Memory (LSTM) model with the Contrastive Language-Image Pretraining (CLIP) model. The initial phase employs ResNet within the CLIP model for extracting image features, and a Transformer for encoding text features. Subsequently, feature vectors obtained from monitoring images and text are fused using a rudimentary concatenation method to generate a joint embedding representation. Principal Component Analysis (PCA) is then applied to diminish the dimensionality of the amalgamated features derived from environmental monitoring images, descriptive texts, and sensor data collected by industrial and mining enterprises. Finally, a multimodal LSTM model is leveraged to detect anomalies in the monitoring data by capturing long-term dependencies within time series information. The model was trained and evaluated using real-time data from a coal mining enterprise’s environmental monitoring system spanning March to September 2023. Results reveal that the multimodal LSTM-CLIP model achieved an anomaly detection accuracy of 0.98 in environmental monitoring, marking a 0.10 improvement over the unimodal LSTM model, with a response time of merely 110.25 milliseconds. These findings underscore the efficacy of the multimodal LSTM-CLIP model in integrating multimodal information, thereby significantly enhancing the accuracy of anomaly detection and the speed of environmental anomaly warnings, ultimately ensuring the safety of industrial and mining enterprises.}, archive = {J_ACCESS}, author = {Shuqin Wang and Na Cheng and Yan Hu}, doi = {10.1109/ACCESS.2025.3533537}, journal = {IEEE Access}, pages = {19964-19978}, shortjournal = {IEEE Access}, title = {Comprehensive environmental monitoring system for industrial and mining enterprises using multimodal deep learning and CLIP model}, volume = {13}, year = {2025}, }
• (2025). Classification of speech emotion state based on feature map fusion of TCN and pretrained CNN model from korean speech emotion data. ACCESS, 13, 19947-19963. (www)

  @article{ , abstract = {In this paper, we propose a method for designing a classification model of speech emotional state based on the feature-map fusion of temporal convolutional network (TCN) and the pretrained convolutional neural networks (CNN) from Korean speech database. For this purpose, the proposed approach is comprised of four main stages. In the first stage, we extract Mel-frequency cepstral coefficient (MFCC) and gammatone cepstral coefficient features (GFCC) in the frequency domain as well as log-Mel spectrogram in the time-frequency domain. From these features, the second stage performs training process using TCN and the yet another audio Mobile Net network (YAMNet), respectively. In the third stage, we perform feature-map fusion using canonical correlation analysis (CCA), stationary wavelet transform (SWT), and fuzzy c-means-based principal component averaging (FCMPCA), respectively. From these steps, speech emotion recognition model is effectively designed through the fusion model of TCN and YAMNet as well as feature-map fusion methods. Finally, we evaluate the performance comparison from five databases: the AI-Hub speech emotion dataset built in Korea and Korean speech emotional state classification dataset built from Chosun University as well as Emo-DB, RAVDESS, and TESS datasets. The experimental results showed that the proposed model revealed good performance in comparison to other previous works in most datasets.}, archive = {J_ACCESS}, author = {A-Hyeon Jo and Keun-Chang Kwak}, doi = {10.1109/ACCESS.2025.3534176}, journal = {IEEE Access}, pages = {19947-19963}, shortjournal = {IEEE Access}, title = {Classification of speech emotion state based on feature map fusion of TCN and pretrained CNN model from korean speech emotion data}, volume = {13}, year = {2025}, }
• (2025). Corrections to “The attention mechanism performance analysis for football players using the internet of things and deep learning”. ACCESS, 13, 19946. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning”).}, archive = {J_ACCESS}, author = {Chuan Mou}, doi = {10.1109/ACCESS.2025.3534333}, journal = {IEEE Access}, pages = {19946}, shortjournal = {IEEE Access}, title = {Corrections to “The attention mechanism performance analysis for football players using the internet of things and deep learning”}, volume = {13}, year = {2025}, }
• (2025). YOLO-UP: A high-throughput pest detection model for dense cotton crops utilizing UAV-captured visible light imagery. ACCESS, 13, 19937-19945. (www)

  @article{ , abstract = {Accurate detection of pest species in cotton fields is vital for effective agricultural management and the development of pest-resistant crops. However, achieving high-throughput and precise pest detection in cotton fields remains a challenging task. Although unmanned aerial vehicle (UAV) enable the rapid acquisition of extensive crop images, detecting pests accurately from these images is difficult due to the small size of pests and background interference. This study introduces YOLO-PEST, a novel pest detection model based on YOLOv8n, utilizing a personal cotton field imagery dataset acquired by UAVs. YOLO-PEST incorporates a custom-designed SC3 module to enhance low-level feature extraction and employs the GeLU activation function to address the vanishing gradient issue. Additionally, the model optimizes the neck design to re-duce semantic discrepancies between feature layers, improving small-target detection, and integrates large-kernel separable convolutions to bolster high-level feature processing. Experimental results demonstrate that YOLO-PEST outperforms original YOLOv8n models, with a 3.46 percentage points increase in mAP50, a 5.16 percentage points increase in Precision, and a 7.81 percentage points increase in Recall. YOLO-PEST also shows superior Precision compared to DenseNet and FasterNet, with improvements of 1.14 and 6.13 percentage points, respectively. Overall, YOLO-PEST offers high accuracy and a compact parameter footprint, making it highly effective for pest detection in UAV-acquired crop images.}, archive = {J_ACCESS}, author = {Chenglei Sun and Afizan Bin Azman and Zaiyun Wang and Xiaoxiao Gao and Kai Ding}, doi = {10.1109/ACCESS.2025.3529873}, journal = {IEEE Access}, pages = {19937-19945}, shortjournal = {IEEE Access}, title = {YOLO-UP: A high-throughput pest detection model for dense cotton crops utilizing UAV-captured visible light imagery}, volume = {13}, year = {2025}, }
• (2025). Reliability analysis of baremetal and FreeRTOS applications on microchip PolarFire SoC RISC-V multiprocessors using high-energy protons. ACCESS, 13, 19922-19936. (www)

  @article{ , abstract = {Modern space applications are increasingly demanding reliable processing systems with high computational capabilities for missions with ambitious targets, tight budgets, and fast deployment. Hence, the space sector is accelerating the adoption of Commercial Off-The-Shelf (COTS) high-performance and reconfigurable processors. In this context, we propose the analysis and characterization of software applications’ reliability on a novel RISC-V-based processing system: Microchip’s PolarFire System-on-Chip (SoC). For that, we introduce reliability benchmarks using baremetal and FreeRTOS implementations and perform an experimental evaluation using high-energy protons, reporting Single-Event Effects (SEE) cross sections and the impact of Total Ionizing Dose (TID). Moreover, we discuss in detail the error propagation and masking, proposing a reliability metric to support the standardization of radiation effects reporting on complex processing systems. Finally, we evaluate the effectiveness of error mitigation and awareness in PolarFire SoC using built-in monitoring and test instrumentation with enhanced observability.}, archive = {J_ACCESS}, author = {André M. P. Mattos and Douglas A. Santos and Luigi Dilillo}, doi = {10.1109/ACCESS.2024.3524726}, journal = {IEEE Access}, pages = {19922-19936}, shortjournal = {IEEE Access}, title = {Reliability analysis of baremetal and FreeRTOS applications on microchip PolarFire SoC RISC-V multiprocessors using high-energy protons}, volume = {13}, year = {2025}, }
• (2025). An efficient frequency domain based attribution and detection network. ACCESS, 13, 19909-19921. (www)

  @article{ , abstract = {People nowadays can easily synthesize high fidelity fake images with different types of image content due to the rapid advances of deep learning technologies. Detecting such images and attributing them to their generative models (GMs) is crucial. Existing deep learning methods attempt to identify and classify GM-specific artifacts but often struggle with content-independence and generalizability. In this paper, we observe that while GMs leave unique artifacts in the frequency domain, they are coupled with the image content. Based on this observation, we propose a novel deep learning-based solution that learns input-adaptive masks to highlight GMs’ artifacts and achieve high accuracy on the synthesized image attribution task. In addition, we observed that GMs’ artifacts in the frequency domain remain intact in sub-images of the original image, and they are even retained when the images are distorted. To further improve the accuracy of the proposed solution, we leverage the characteristics of GMs artifacts in sub-images and distorted images to make our network perform more effectively. Our evaluation results show that our proposed solution outperforms other state-of-the-art methods on unseen image types, showing great generalizability.}, archive = {J_ACCESS}, author = {Junbin Zhang and Yixiao Wang and Hamid Reza Tohidypour and Panos Nasiopoulos}, doi = {10.1109/ACCESS.2025.3534829}, journal = {IEEE Access}, pages = {19909-19921}, shortjournal = {IEEE Access}, title = {An efficient frequency domain based attribution and detection network}, volume = {13}, year = {2025}, }
• (2025). DaLAMED: A clock-frequency and data-lifetime-aware methodology for energy-efficient memory design in edge devices. ACCESS, 13, 19898-19908. (www)

  @article{ , abstract = {Energy-efficient memory design has become increasingly critical with the proliferation of IoT devices. Although hybrid architectures, which combine multiple memory technologies, are widely used, we show that unified emerging Non-Volatile Memory (eNVM) systems can achieve superior efficiency when driven at their optimal frequencies. This paper describes Data Lifetime Aware Memory Energy-efficient Design (DaLAMED), a technology-agnostic methodology to optimize memory system energy efficiency by considering application-specific data lifetime patterns along with operating frequencies. DaLAMED analyzes memory access patterns to determine the most energy-efficient memory technology for a given clock frequency or the critical frequency points at which energy efficiency advantages change between technologies. Through a thorough analysis using the MiBench benchmark suite, we determined that unified eNVM architectures, when optimized with DaLAMED, can reduce energy consumption by 30-60% compared to hybrid memory designs featuring DRAM at frequencies below 30 MHz, and offer comparable benefits over hybrid memory structures containing SRAM at frequencies up to 125 MHz. These results contradict many of the prevailing assumptions about hybrid memory architectures and also provide a platform for optimization of memory systems with or without new emerging memory technologies.}, archive = {J_ACCESS}, author = {Belal Jahannia and Abdolah Amirany and Elham Heidari and Hamed Dalir}, doi = {10.1109/ACCESS.2025.3531338}, journal = {IEEE Access}, pages = {19898-19908}, shortjournal = {IEEE Access}, title = {DaLAMED: A clock-frequency and data-lifetime-aware methodology for energy-efficient memory design in edge devices}, volume = {13}, year = {2025}, }
• (2025). The design and development of an atraumatic soft robotic surgical retractor. ACCESS, 13, 19888-19897. (www)

  @article{ , abstract = {This paper introduces a novel soft robotic surgical retractor system, designed to overcome the limitations of traditional surgical retractors through the integration of soft robotics. Unlike conventional rigid retractors, this innovative system features a dual-component design combining a 3D-printed thermoplastic polyurethane actuator with a stainless-steel splint (SSS), enabling variable stiffness for more effective and less traumatic tissue retraction. The study presents a comprehensive evaluation, including bending angle, blocked force measurements, actuation time efficiency, and a comparative analysis with traditional stainless steel Langenbeck retractors. Results demonstrate the soft robotic retractor’s superior performance, especially when utilizing the tip for retraction, indicating a significant alignment with the requirements for abdominal organ retraction. Furthermore, the system exhibited reduced deformation to retracted soft objects, suggesting a potential for minimizing tissue trauma compared to conventional retractors. The study proposes directions for enhancing retraction strength, optimizing design for improved force output, and conducting comprehensive testing to ensure clinical viability. This work lays the groundwork for future advancements in soft robotic surgical retractors, promising to revolutionize surgical retraction practices by offering enhanced safety and efficiency.}, archive = {J_ACCESS}, author = {Gavril Yong En Tan and Shaun Mahesh Buxani and Benjamin Wee Keong Ang and Chao Yu Chen and Raye Chen Hua Yeow and Hung Leng Kaan}, doi = {10.1109/ACCESS.2025.3534019}, journal = {IEEE Access}, pages = {19888-19897}, shortjournal = {IEEE Access}, title = {The design and development of an atraumatic soft robotic surgical retractor}, volume = {13}, year = {2025}, }
• (2025). Knowledge graph-enhanced digital twin framework for optimized job shop scheduling in smart manufacturing. ACCESS, 13, 19863-19887. (www)

  @article{ , abstract = {The emergence of Digital Twin (DT) technology over the past decade has introduced a transformative perspective to various research areas, notably the Job Shop Scheduling Problem (JSSP) in smart manufacturing. This paper proposes a novel algorithm to address the JSSP in industrial contexts, operating under different modes to minimize makespan and calculate energy consumption. The approach is grounded in a DT framework that leverages a knowledge graph to represent the assets of the manufacturing system. Two algorithms are designed to balance the time required to complete a list of jobs with the energy consumed by the machines in the factory, all while considering the delivery deadline of the final product. The first algorithm is a genetic algorithm (GA) that minimizes the makespan required to complete the job list for each mode of operation followed by a grey wolf optimization algorithm (GWO) to verify and enhance the results of the GA. The second algorithm uses the results of the first algorithm to calculate the running time and energy consumption for each machine in each production line, considering that the production lines of the factory vary in capacity and power, then computes the total energy consumption and presents the results to the decision maker via a dashboard. The dashboard displays, for each mode of operation, the makespan, total energy consumption, and the deadline for all the jobs. To validate the efficiency and accuracy, a case study of a Smart Poultry Feed Planet (SPFP) has been conducted.}, archive = {J_ACCESS}, author = {Nehal Tarek and Abeer D. Algarni and Nancy A. El-Hefnawy and Hatem Abdel-Kader and Amira Abdelatey}, doi = {10.1109/ACCESS.2025.3532600}, journal = {IEEE Access}, pages = {19863-19887}, shortjournal = {IEEE Access}, title = {Knowledge graph-enhanced digital twin framework for optimized job shop scheduling in smart manufacturing}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A semi-supervised learning approach to quality-based web service classification”. ACCESS, 13, 19862. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Semi-Supervised Learning Approach to Quality-Based Web Service Classification”).}, archive = {J_ACCESS}, author = {Mehdi Nozad Bonab and Jafar Tanha and Mohammad Masdari}, doi = {10.1109/ACCESS.2025.3534337}, journal = {IEEE Access}, pages = {19862}, shortjournal = {IEEE Access}, title = {Corrections to “A semi-supervised learning approach to quality-based web service classification”}, volume = {13}, year = {2025}, }
• (2025). Remote epilepsy monitoring: Signal quality and reliability comparative analysis. ACCESS, 13, 19845-19861. (www)

  @article{ , abstract = {Epilepsy, a neurological condition affecting approximately 50 million individuals globally, is among the most common nervous system disorders. Electroencephalography (EEG) is vital for evaluating epilepsy, yet its intricate nature often restricts its application to specialized clinical environments. OptiEEG, a remote monitoring system incorporating OpenBCI’s EEG technology, addresses challenges by integrating a communication gateway and a mobile application for user-friendly operation. This study benchmarks OptiEEG’s performance against the clinically validated Natus NicoletOne EEG System through three routine EEG tests: Eye Open Close, Hyperventilation, and Photic Stimulation. Signal quality, component analysis, and reliability were evaluated using error metrics, time-frequency analysis, Bland-Altman plots, repeatability, Pearson Correlation Coefficient (PCC) and also EEG characteristics analysis of individual channels. OptiEEG demonstrated comparable signal quality to Natus, with average standard deviations for signal-to-noise ratio (Natus: 3.27 vs. OptiEEG: 2.95), peak signal-to-noise ratio (Natus: 2.76 vs. OptiEEG: 2.16), and mean squared error (Natus: 0.01 vs. OptiEEG: 0.04). Time-frequency analysis revealed less than 10% differences across alpha, theta, and delta bands. Reliability tests confirmed repeatability, with intra-system differences lower than inter-system differences, and Bland-Altman plots meeting 83% agreement criteria. PCC analysis highlighted moderate signal alignment, confirming similar EEG patterns across systems. Channel-specific analysis showed median differences as low as 0.80%, validating OptiEEG’s ability to capture critical EEG features. The results establish OptiEEG as a reliable alternative to traditional systems, combining clinically comparable performance with a portable design. These findings highlight its potential as a robust remote monitoring tool for epilepsy, enabling broader access to EEG diagnostics and management.}, archive = {J_ACCESS}, author = {Theeban Raj Shivaraja and Rabani Remli and Wan Asyraf Wan Zaidi and Kalaivani Chellappan}, doi = {10.1109/ACCESS.2025.3531130}, journal = {IEEE Access}, pages = {19845-19861}, shortjournal = {IEEE Access}, title = {Remote epilepsy monitoring: Signal quality and reliability comparative analysis}, volume = {13}, year = {2025}, }
• (2025). Enhanced performance of new scaling-free CORDIC for memory-based fast fourier transform architecture. ACCESS, 13, 19828-19844. (www)

  @article{ , abstract = {Coordinate rotation digital computer (CORDIC) algorithm is an iterative method and it performs the vector rotation operation by micro-rotation with scaling operation in each iteration. This study introduces a high-performance power-efficient new scaling-free coordinate rotation digital computer (NSF-CORDIC) algorithm to perform vector rotation operation in circular coordinates. The scaling operation required in the existing algorithm has been completely removed by the fourth-order approximation of Taylor series (TS). The number of iterations is reduced by an optimized shift value prediction technique for known and fixed angles, like the twiddle factor angle available in the fast Fourier transform (FFT) algorithm. The angle of convergence (AOC) of the algorithm is 57.1°, and it is extended to 180° using the pre-rotation operation and optimized shift value prediction technique. In addition to that, the new CORDIC cell architecture is designed to perform Taylor series-based iterations. Further, a memory-based FFT architecture is designed using a new CORDIC cell. In the FFT architecture, the CORDIC cell performs all the multiplication of twiddle factor. Therefore, the complex constant multiplier required to execute the twiddle factor multiplication is eliminated. The proposed architectures are implemented in the Zynq-7ZC706 FPGA board using the Vivado EDA tool. The scaling-free CORDIC cell architecture has 31% and 3% slice reduction, 72% and 45% slice delay product reduction, and 38% and 23% power reduction compared to the two different existing scaling-free CORDIC designs. The memory-based FFT architecture has 37.8% and 19.6% slice reduction, 38.4% and 27% slice delay product reduction, and 45% and 34% power reduction compared to the two different existing memory-based FFT architecture.}, archive = {J_ACCESS}, author = {C. Paramasivam and Sandeep Singh Chauhan and Veerpratap Meena and A. Sreejagathi and B. A. V. N. Hasini and K. L. K. Kishore and T. V. N. G. Vamsikrishna and M. Durga Ananta Sai and Abdessamad Didi}, doi = {10.1109/ACCESS.2025.3530684}, journal = {IEEE Access}, pages = {19828-19844}, shortjournal = {IEEE Access}, title = {Enhanced performance of new scaling-free CORDIC for memory-based fast fourier transform architecture}, volume = {13}, year = {2025}, }
• (2025). SVD-GoRank: Recommender system algorithm using SVD and gower’s ranking. ACCESS, 13, 19796-19827. (www)

  @article{ , abstract = {Recommender systems using ranking-oriented collaborative filtering are currently widely used. One widely used approach is a memory-based model with ranking orientation. Recently, a ranking algorithm that combines user rating values from SVD (Singular Value Decomposition) and user similarity values has been proposed. The problem is that this algorithm is limited to only the rating weights used. This results in an accuracy value that can still be improved. Therefore, this research proposes a new collaborative filtering-based algorithm that combines the matrix factorisation method using SVD and the ranking method by utilising Gower’s Coefficient similarity weight as an aggregation component known as the SVD-GoRank method. Experimental results using the MovieLens-100K, MovieLens-1M, Book-Crossing, Ciao, Epinions, Flixster, and MovieLens-10M datasets can provide the best accuracy results at the Top-N level, especially in the NDCG, MRR, Precision, Hit Rate, and Recall metrics, which are indicators important in recommendation systems that focus on the relevance of recommendations at the top of the list. Apart from that, the SVD-GoRank algorithm can also have efficient running time.}, archive = {J_ACCESS}, author = {Ilham Saifudin and Triyanna Widiyaningtyas and Ilham Ari Elbaith Zaeni and Afrig Aminuddin}, doi = {10.1109/ACCESS.2025.3533558}, journal = {IEEE Access}, pages = {19796-19827}, shortjournal = {IEEE Access}, title = {SVD-GoRank: Recommender system algorithm using SVD and gower’s ranking}, volume = {13}, year = {2025}, }
• (2025). Thyroid nodule ultrasound image segmentation based on improved swin transformer. ACCESS, 13, 19788-19795. (www)

  @article{ , abstract = {To address the issue of inaccurate segmentation caused by blurred edges and strong noise in thyroid nodule ultrasound images, an image segmentation method based on an improved Swin Transformer is proposed. First, depthwise convolutional layers are integrated into the encoder/decoder of the Swin Transformer to enhance global-local feature representations. Second, a multi-scale feature fusion module is introduced through skip connections between the encoder and decoder to improve information flow and feature integration. Additionally, a multi-level patch embedding convolution is designed to enable layer-by-layer feature extraction from coarse to fine levels. Experimental results show that the proposed method achieves superior segmentation accuracy compared to state-of-the-art methods such as Attention U-Net, with Dice scores of 82.26% and 78.64% and IoU values of 73.00% and 67.93% on the TN3K and DDTI datasets, respectively.}, archive = {J_ACCESS}, author = {Yue Wu and Lin Huang and Tiejun Yang}, doi = {10.1109/ACCESS.2025.3532264}, journal = {IEEE Access}, pages = {19788-19795}, shortjournal = {IEEE Access}, title = {Thyroid nodule ultrasound image segmentation based on improved swin transformer}, volume = {13}, year = {2025}, }
• (2025). Enhanced emotion-aware conversational agent: Analyzing user behavioral status for tailored reponses in chatbot interactions. ACCESS, 13, 19770-19787. (www)

  @article{ , abstract = {While chatbots are increasingly popular for communication, their effectiveness is limited by their difficulty in understanding users’ emotions. To address this, this study proposes a new hybrid chatbot model called “TEBC-Net” (Text Emotion Bert CNN Network), which combines text and video analysis to interpret user emotions and generate more empathetic responses. At the core of TEBC-Net is a multi-modal emotion analysis system. One component uses Bidirectional Encoder Representations from Transformers (BERT), a well-regarded model in natural language processing (NLP), achieving an 87.21% accuracy rate in detecting emotional cues from text inputs. The second component captures users’ facial expressions through webcam footage. It begins by detecting faces using a pre-trained classifier like Haarcascade. Then, to improve emotion recognition, it preprocesses the image through brightness adjustments and contrast enhancement with Automatic CLAHE and dual gamma correction. This processed image is analyzed by a Convolutional Neural Network (CNN) model trained specifically for emotion recognition, reaching 74.14% accuracy by assigning probabilities to different emotions. By integrating insights from both text and video analysis, TEBC-Net gains a comprehensive understanding of the user’s emotional state and intent. This combined data then informs the chatbot’s response generation module, enabling it to craft responses that are both empathetic and more directly aligned with the user’s emotional needs.}, archive = {J_ACCESS}, author = {S. Abinaya and K. S. Ashwin and A. Sherly Alphonse}, doi = {10.1109/ACCESS.2025.3534197}, journal = {IEEE Access}, pages = {19770-19787}, shortjournal = {IEEE Access}, title = {Enhanced emotion-aware conversational agent: Analyzing user behavioral status for tailored reponses in chatbot interactions}, volume = {13}, year = {2025}, }
• (2025). An automatic approach for the identification of offensive language in perso-arabic urdu language: Dataset creation and evaluation. ACCESS, 13, 19755-19769. (www)

  @article{ , abstract = {Offensive language is a type of unacceptable language that is impolite amongst individuals, specific community groups, and society as well. With the advent of various social media platforms, offensive language usage has been widely reported, thus developing a toxic online environment that has real-life endangers within society. Therefore, to foster a culture of respect and acceptance, a prompt response is needed to combat offensive content. On the other hand, the identification of offensive language has become a challenging task, specifically in low-resource languages such as Urdu. Urdu text poses challenges because of its unique features, complex script, and rich morphology. Applying methods directly that work in other languages is difficult. It also requires exploring new linguistic features and computational techniques on a relatively large dataset to ensure the results can be generalized effectively. Unfortunately, the Urdu language got very limited attention from the research community due to the scarcity of language resources and the non-availability of high-quality datasets and models. This study addresses those challenges, firstly by collecting and annotating a dataset of 12020 Urdu tweets using OLID taxonomy as a benchmark. Secondly, by extracting character-level and word-level features based on bag-of-words, n-grams and TFIDF representation. Finally, an extensive series of experiments were conducted on the extracted features using seven machine learning classifiers to identify the most effective features and classifiers. The experimental findings indicate that word unigrams, character trigrams, and word TFIDF are the most prominent ones. Similarly, among the classifiers, logistic regression and support vector machine attained the highest accuracy of 86% and F1-Score of 75%.}, archive = {J_ACCESS}, author = {Salah Ud Din and Shah Khusro and Farman Ali Khan and Munir Ahmad and Oualid Ali and Taher M. Ghazal}, doi = {10.1109/ACCESS.2025.3534662}, journal = {IEEE Access}, pages = {19755-19769}, shortjournal = {IEEE Access}, title = {An automatic approach for the identification of offensive language in perso-arabic urdu language: Dataset creation and evaluation}, volume = {13}, year = {2025}, }
• (2025). Research on image recognition and classification algorithms in cloud computing environment based on deep neural networks. ACCESS, 13, 19728-19754. (www)

  @article{ , abstract = {Image recognition provides an essential role in many areas of modern life, such as security, video analysis, healthcare, and autonomous vehicles. The present article analyzes the evolution of image recognition via the automation of feature extraction processes, leading to the achievement of very sophisticated outcomes. This is achieved by comparing conventional approaches with deep learning methodologies, particularly Convolutional Neural Networks (CNNs). The integration of CNNs with cloud computing has significantly enhanced the speed, scalability, and usability, enabling the real-time analysis of extremely extensive picture datasets. This article discusses significant algorithms, including CNN architectures such as AlexNet, VGG, and ResNet-based models. Transfer learning techniques and the impact of ensemble approaches are also discussed in these articles. Data privacy, security, and the impact of AI on cloud computing are expected to become more critical in the future.}, archive = {J_ACCESS}, author = {Zheshu Jia}, doi = {10.1109/ACCESS.2025.3530242}, journal = {IEEE Access}, pages = {19728-19754}, shortjournal = {IEEE Access}, title = {Research on image recognition and classification algorithms in cloud computing environment based on deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Hybrid subQUBO annealing with a correction process for multi-day intermodal trip planning. ACCESS, 13, 19716-19727. (www)

  @article{ , abstract = {The multi-day intermodal trip planning problem (MITPP) is an optimization problem that seeks to create the optimal route to visit Point-of-Interest (POI) and hotels over days. This problem involves coordinating intermodal transportation, such as walking, public transportation, to create a well-crafted travel itinerary. Quantum annealers have recently been explored as a powerful tool for solving combinatorial optimization problems by converting the problems into Quadratic Unconstrained Binary Optimization (QUBO). However, current quantum annealers have a small QUBO input size so that they cannot directly solve large-scale MITPPs. In this paper, we address this issue by extracting a subQUBO from the original large QUBO based on variable (spin) deviations and randomness. Then, we iteratively solve the subQUBOs by the quantum annealer and update the (quasi-)optimal solution. As the obtained (quasi-)optimal solution may violate constraints, we apply the correction processing till all constraints are satisfied. According to the experiment results using a real quantum annealer, our proposed method obtained high-quality solutions for large-scale MITPPs in the Tokyo area, and compared to the full QUBO method, we achieve a maximum spin reduction of 98.9%. Especially, compared to the method by a conventional computer and two conventional subQUBO methods, POI satisfaction is improved by 10.2%, and travel costs are improved by 23.2% respectively.}, archive = {J_ACCESS}, author = {Tatsuya Noguchi and Keisuke Fukada and Siya Bao and Nozomu Togawa}, doi = {10.1109/ACCESS.2025.3534529}, journal = {IEEE Access}, pages = {19716-19727}, shortjournal = {IEEE Access}, title = {Hybrid subQUBO annealing with a correction process for multi-day intermodal trip planning}, volume = {13}, year = {2025}, }
• (2025). Unified visual-aware representations for data analytics. ACCESS, 13, 19694-19715. (www)

  @article{ , abstract = {One of the characteristics of big data is its internal complexity and variety manifested in many types of datasets that are to be managed, searched, or analyzed. In their natural forms, some data entities are unstructured, such as texts or multimedia objects, while some are structured but too complex (e.g., high-dimensional tabular data). Due to the many different forms of data managed in many domain-specific problems, there are many different data representations used – tailored to a specific data form, domain and task. In this paper, we propose a framework for universal visual representations of complex data. The desired property of the visualizations is the ability to visually encode the semantic features of the original data. Hence, processing of visualizations (images) by generic deep learning models results in deep feature vectors that could be uniformly used in standard data retrieval/analytics tasks. Specifically, we develop a semi-automated transfer learning pipeline for transformation of input arbitrary tabular data into visual representations. The visual representations serve for data analytics tasks performed by human users as well as serve for universal data representations used in machine learning models for automated tasks. We show in large study that visual representations of complex data are effective in a number of domains while we also propose a recommender to help with the parameterization of the entire pipeline for certain domains and use cases. In summary, the proposed framework enables rapid prototyping of data representations (in an arbitrary domain) using a shared concept – visual representations applicable in data analytics using generic deep learning models.}, archive = {J_ACCESS}, author = {Ladislav Peška and Ivana Sixtová and David Hoksza and David Bernhauer and Jakub Lokoč and Tomáš Skopal}, doi = {10.1109/ACCESS.2025.3534330}, journal = {IEEE Access}, pages = {19694-19715}, shortjournal = {IEEE Access}, title = {Unified visual-aware representations for data analytics}, volume = {13}, year = {2025}, }
• (2025). A deep Q-learning algorithm with guaranteed convergence for distributed and uncoordinated operation of cognitive radios. ACCESS, 13, 19678-19693. (www)

  @article{ , abstract = {This paper studies a deep reinforcement learning technique for distributed resource allocation among cognitive radios operating under an underlay dynamic spectrum access paradigm which does not require coordination between agents during learning. The key challenge that is addressed in this work is that of a non-stationary reinforcement learning environment that arises from the uncoordinated and distributed radios operation over a shared wireless environment. This challenge is illustrated by presenting a simulation result where the use of a standard single-agent deep reinforcement learning approach does not achieve convergence because of being applied in the uncoordinated interacting multi-radio scenario. To address this challenge, this work presents the uncoordinated and distributed multi-agent DQL (UDMA-DQL) technique that combines a deep neural network with learning in exploration phases, and with the use of a Best Reply Process with Inertia for the gradual learning of the best policy. Key in the study of UDMA-DQL herein, it is shown by considering aspects specific to deep reinforcement learning, that under an arbitrarily long time the UDMA-DQL technique converges with probability one to equilibrium policies in the non-stationary environment resulting from the distributed and uncoordinated operation of cognitive radios. This analytical study is confirmed through simulation results showing that, in cases when an optimal policy can be identified, UDMA-DQL is able to find such policy in 99% of cases for a sufficiently long learning time. Importantly, further simulations show that the presented UDMA-DQL approach achieves a much faster learning performance compared to an equivalent table-based Q-learning implementation.}, archive = {J_ACCESS}, author = {Ankita Tondwalkar and Andres Kwasinski}, doi = {10.1109/ACCESS.2025.3534657}, journal = {IEEE Access}, pages = {19678-19693}, shortjournal = {IEEE Access}, title = {A deep Q-learning algorithm with guaranteed convergence for distributed and uncoordinated operation of cognitive radios}, volume = {13}, year = {2025}, }
• (2025). Tea disease recognition based on image segmentation and data augmentation. ACCESS, 13, 19664-19677. (www)

  @article{ , abstract = {Accurate identification of tea leaf diseases is crucial for intelligent tea cultivation and monitoring. However, the complex environment of tea plantations—affected by weather variations and uneven lighting—poses significant challenges for building effective disease recognition models using raw field-captured images. To address this, we propose a method that combines two-stage image segmentation with an improved conditional generative adversarial network (IC-GAN). The two-stage segmentation approach, integrating graph cuts and support vector machines (SVM), effectively isolates disease regions from complex backgrounds. The IC-GAN augments the dataset by generating high-quality synthetic disease images for model training. Finally, an Inception Embedded Pooling Convolutional Neural Network (IDCNN) is developed for disease recognition. Experimental results demonstrate that the segmentation method improves recognition accuracy from 53.36% to 75.63%, while the IC-GAN increases the dataset size. The IDCNN achieves 97.66% accuracy, 97.36% recall, and a 96.98% F1 score across three types of tea diseases. Comparative evaluations on two additional datasets further confirm the method’s robustness and accuracy, offering a practical solution to reduce tea production losses and improve quality.}, archive = {J_ACCESS}, author = {Ji Li and Chenyi Liao}, doi = {10.1109/ACCESS.2025.3534024}, journal = {IEEE Access}, pages = {19664-19677}, shortjournal = {IEEE Access}, title = {Tea disease recognition based on image segmentation and data augmentation}, volume = {13}, year = {2025}, }
• (2025). A new shunt capacitor-commutated converter for reducing commutation failure probability of HVDC transmission and converter transformer noise. ACCESS, 13, 19650-19663. (www)

  @article{ , abstract = {The HVDC transmission system with a line-commutated converter is prone to commutation failure when connected to a weak AC grid and has disadvantages such as a large footprint of AC filters and high noise from the converter transformer. This paper presents a shunt capacitor-commutated converter (SCCC) without AC filters, using a constant extinction voltage-time area (VTA) control to enhance the performance of the inverter station. First, the topology and the mathematical model of the SCCC are established, with its commutation and frequency characteristics analyzed. Second, the minimum extinction VTA criterion for preventing commutation failure is proposed, revealing how various parameters affect VTA and the mechanism by which the SCCC reduces commutation failure probability. Furthermore, a constant extinction VTA control strategy is designed for the SCCC-based inverter. Then, the reactive power and harmonic characteristics of the SCCC are analyzed. A selection method for the SC is proposed to meet the inverter’s requirements for unit power factor operation and filtering. Simulations in PSCAD/EMTDC and experimental results from a scaled-down rectifier prototype demonstrate that the SCCC and the proposed control strategy effectively reduce commutation failure probability and converter transformer noise.}, archive = {J_ACCESS}, author = {Hao Zhang and Sijia Hu and Longfu Luo and Jinjie Lin and Liang Zhao and Zhiwen Zhang}, doi = {10.1109/ACCESS.2025.3530108}, journal = {IEEE Access}, pages = {19650-19663}, shortjournal = {IEEE Access}, title = {A new shunt capacitor-commutated converter for reducing commutation failure probability of HVDC transmission and converter transformer noise}, volume = {13}, year = {2025}, }
• (2025). D2CBDAMAttUnet: Dual-decoder convolution block dual attention unet for accurate retinal vessel segmentation from fundus images. ACCESS, 13, 19635-19649. (www)

  @article{ , abstract = {Gathering detailed morphological information from retinal blood vessels is crucial in clinical diagnostics, enabling doctors to make precise assessments of patient conditions and to devise custom treatments. Traditional methods of segmenting these vessels from fundus images are not only tedious but require a high degree of specialized knowledge. In light of this, Deep Convolutional Neural Networks (DCNNs), particularly those based on the U-Net architecture, have been acknowledged for their effectiveness in capturing and utilizing contextual features within this context. However, these methods often grapple with challenges such as loss of vital information during pooling and insufficient handling of local context in skip connections, leading to less than optimal results. To address these limitations, this research propounds the novel Convolution Block Dual Attention Module (CBDAM), as well as two pioneering network architectures: The Convolution Block Dual Attention Module Unet (CBDAMUNet) and the Dual Decoder Convolution Block Attention Module with Attention U-Net (D2CBDAMAttUnet). These are built upon a fortified encoder-decoder structure aimed at providing an automated, streamlined detection mechanism from fundus imagery. Thoroughly tested against the DRIVE, CHASEDB1 and STARE datasets, recognized standards in retinal vessel segmentation, the proposed models not only redefine the accuracy benchmarks but also represent a significant stride in automated retinal vessel analysis. The introduction of these advanced networks is a milestone in ophthalmologic diagnostics and research, offering a potent asset for medical professionals and specialists in the field.}, archive = {J_ACCESS}, author = {Vo Trong Quang Huy and Chih-Min Lin}, doi = {10.1109/ACCESS.2025.3535239}, journal = {IEEE Access}, pages = {19635-19649}, shortjournal = {IEEE Access}, title = {D2CBDAMAttUnet: Dual-decoder convolution block dual attention unet for accurate retinal vessel segmentation from fundus images}, volume = {13}, year = {2025}, }
• (2025). Camera absolute pose estimation using hierarchical attention in multi-scene. ACCESS, 13, 19624-19634. (www)

  @article{ , abstract = {The multi-scene camera pose estimation approach aims to recover the camera pose from any given scene, catering to the demands of real-life mobile devices to perform tasks. Facing the challenge that it is difficult to extract efficient features in training multi-scene models, we present a modified model named Hierarchical Attention Absolute Pose Regression(H-AttnAPR) which can obtain different scales of feature dependencies. A Hierarchical Attention(HA) module is introduced prior to the scene classification module, where it captures both intra- and inter-correlations among image patches, utilizing both local and global key information from images to restore the absolute camera pose without the need for additional point cloud data. H-AttnAPR efficiently models global dependencies without compromising fine-grained feature information. Therefore, it overcomes the limitations that solely focus on long-range pixel-level feature dependencies within images while neglecting local patches of image feature dependencies. Our approach has been validated on the 7Scenes and Cambridge benchmark datasets. Compared to the baseline algorithm PoseNet, our algorithm has achieved a 41.1% reduction in translation error and a 61.1% decrease in rotation error, demonstrating superior performance in multi-scene absolute camera pose regression.}, archive = {J_ACCESS}, author = {Xinhua Lu and Jingui Miao and Qingji Xue and Hui Wan and Hao Zhang}, doi = {10.1109/ACCESS.2025.3531896}, journal = {IEEE Access}, pages = {19624-19634}, shortjournal = {IEEE Access}, title = {Camera absolute pose estimation using hierarchical attention in multi-scene}, volume = {13}, year = {2025}, }
• (2025). YOLOV9-CBM: An improved fire detection algorithm based on YOLOV9. ACCESS, 13, 19612-19623. (www)

  @article{ , abstract = {Regarding the current problems of false alarms and missed detections in fire detection, we propose a high-precision fire detection algorithm, YOLOV9-CBM (C3-SE, BiFPN, MPDIoU), by optimizing YOLOV9. Firstly, to tackle the shortage of both quality and quantity in the existing fire datasets, we collected 2,000 fire and smoke images to establish a dataset named CBM-Fire. Secondly, the RepNCSPELAN4 module of the YOLOv9 backbone was replaced with the C3 module containing SE Attention to improve detection efficiency while guaranteeing accuracy. Besides, we transformed the multi-scale fusion network PANet in the baseline algorithm into a bidirectional feature network pyramid BiFPN to facilitate the bidirectional flow of features, enabling the algorithm to fuse information at different scales more effectively. Finally, instead of CIoU losses, we adopted MPDIoU losses in bounding box regression, which improved the accuracy of model regression and classification. Experimental results indicate that compared with YOLOV9, the recall rate of YOLOV9-CBM has increased by 7.6% and the mAP has risen by 3.8%. The revised model demonstrates good generalization performance and robustness. Code and dataset are at https://github.com/GengHan-123/yolov9-cbm.git.}, archive = {J_ACCESS}, author = {Xin Geng and Xiao Han and Xianghong Cao and Yixuan Su and Dongxue Shu}, doi = {10.1109/ACCESS.2025.3534782}, journal = {IEEE Access}, pages = {19612-19623}, shortjournal = {IEEE Access}, title = {YOLOV9-CBM: An improved fire detection algorithm based on YOLOV9}, volume = {13}, year = {2025}, }
• (2025). Effective underground facilities management in virtual reality by applying a cybersickness reduction technique based on a four-degree-of-freedom motion simulator. ACCESS, 13, 19598-19611. (www)

  @article{ , abstract = {Managing underground utility tunnels (UUT) poses challenges such as safety risks, limited accessibility, and high training costs. To address these, we developed an effective VR-based UUT management training simulator, integrating digital twins with a multi-degree-of-freedom (DoF) motion platform to provide an immersive, cybersickness-reducing experience. Advances in actuator and computer graphics technology have increased the research and development of virtual reality (VR)-based training simulators that use multi-DoF motion platforms. In this study, the proposed system provides precisely synchronized visual feedback through a head-mounted display and kinematic feedback via four-DoF motor-based actuators, realistically simulating UUT conditions, including momentum shifts such as falls and micro-swaying. To mitigate cybersickness, visual and kinematic acceleration control techniques are employed, ensuring users can comfortably and safely experience the UUT without stability concerns. We used a LiDAR sensor to model the actual UUT for realistic visualization in the VR environment and created various scenario-based content, such as facility installation, inspection, and device repair, based on the physical characteristics inside the UUT. This integrated approach enables users to effectively manage and operate UUT facilities in a safe, controlled virtual setting, bridging the gap between traditional training and real-world operations.}, archive = {J_ACCESS}, author = {Namkyoo Kang and Sang-Woo Seo and Woo-Sug Jung and Jongchan Lee and Yong-Tae Lee}, doi = {10.1109/ACCESS.2025.3532597}, journal = {IEEE Access}, pages = {19598-19611}, shortjournal = {IEEE Access}, title = {Effective underground facilities management in virtual reality by applying a cybersickness reduction technique based on a four-degree-of-freedom motion simulator}, volume = {13}, year = {2025}, }
• (2025). Exploring generative adversarial networks: Comparative analysis of facial image synthesis and the extension of creative capacities in artificial intelligence. ACCESS, 13, 19588-19597. (www)

  @article{ , abstract = {Neural networks have become foundational in modern technology, driving advancements across diverse domains such as medicine, law enforcement, and information technology. By enabling algorithms to learn from data and perform tasks autonomously, they eliminate the need for explicit programming. A significant challenge in this field is replicating the uniquely human capacity for creativity—envisioning and realizing novel concepts and tangible creations. Generative Adversarial Networks (GANs), a leading approach in this effort, are especially notable for synthesizing realistic human facial images. Despite the success of GANs, comprehensive comparative studies of face-generating GAN methodologies are limited. This paper addresses this gap by analyzing the scope and capabilities of facial generation, detailing the principles of the original GAN framework, and reviewing prominent GAN variants specifically designed for facial synthesis. Through performance evaluations and fidelity analysis of generated images, this study contributes to a deeper understanding of GAN potential in advancing artificial intelligence creativity through performance evaluations and fidelity analysis of generated images.}, archive = {J_ACCESS}, author = {Tomas Eglynas and Dovydas Lizdenis and Aistis Raudys and Sergej Jakovlev and Miroslav Voznak}, doi = {10.1109/ACCESS.2025.3531726}, journal = {IEEE Access}, pages = {19588-19597}, shortjournal = {IEEE Access}, title = {Exploring generative adversarial networks: Comparative analysis of facial image synthesis and the extension of creative capacities in artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). DR_PVNet: An improved PVNet network for weakly textured object pose estimation in indoor complex environments. ACCESS, 13, 19576-19587. (www)

  @article{ , abstract = {6D pose estimation of objects is an essential task in computer vision. However, estimating the 6D pose of a weakly textured object from an image is challenging due to the occluded environment and the weakly textured appearance. PVNet locates the 2D keypoints by obtaining the segmentation results of the object and the prediction results of the dense unit vector field of the keypoints, followed by a voting strategy. This method shows some robustness to weakly textured object pose estimation. In order to enhance the precision of target object segmentation and the efficacy of the voting scheme, this paper builds upon the PVNet model to develop DR_PVNet, which is capable of performing pose estimation for weakly textured objects in complex environments. The first step is to add the RFB_a module between the encoder and decoder, enhancing the semantic segmentation of the network. Secondly, a distance filtering scheme is incorporated into the keypoint voting scheme to force pixels that are further away from the keypoint hypotheses to improve the vote quality. The DR_PVNet algorithm achieves a 2D projection metric of 99.38% and an ADD(-S) metric of 90.12 % on the weakly textured public dataset LINEMOD. Finally, the methodology of this paper is extended to the pose estimation of weakly textured objects in everyday life using a dataset production method rendered by BlenderProc. The 2D projection metric reached 94.95%, and the ADD(-S) metric reached 91.65 % for the self-made dataset.}, archive = {J_ACCESS}, author = {Mengru Niu and Changyong Li}, doi = {10.1109/ACCESS.2025.3534856}, journal = {IEEE Access}, pages = {19576-19587}, shortjournal = {IEEE Access}, title = {DR_PVNet: An improved PVNet network for weakly textured object pose estimation in indoor complex environments}, volume = {13}, year = {2025}, }
• (2025). Low profile planar inverted-L antenna (PILA) for UHF RFID tag on signal absorption and reflection surfaces. ACCESS, 13, 19560-19575. (www)

  @article{ , abstract = {In this study, we propose a novel design incorporating six Planar Inverted-L Antennas (PILAs) to miniaturize Ultra-High Frequency Radio Frequency Identification (UHF-RFID) tags, thereby enhancing tag efficiency when mounted on various conductive surfaces. The design enables effective tag antenna operation when attached to surfaces with varying signal absorption and reflection characteristics, obviating the need for a complete antenna redesign. By dominating the capacitive reactance between gaps or the inductive reactance of vias, the tag matches the proposed antenna and the microchip (with an impedance of 10.7–j $134.5~\Omega $ at 915 MHz). Both simulation and measurement results confirm the effectiveness of our approach. With this configuration of six adjacent coupled PILA antennas, the proposed tag minimizes electromagnetic field absorption from surfaces, such as containers filled with water or the human body, while enhancing radiation from reflective surfaces like metal. The tag antenna was fabricated on a single FR4 substrate, measuring $30\times 20\times 3.2$ mm3 ( $0.10~\lambda _{0} \times 0.08~\lambda _{0} \times 0.01~\lambda _{0}$ ), and successfully tested on various conductive materials. In a free-space medium, a reader antenna with an Effective Isotropic Radiated Power (EIRP) of 4.0 W achieved maximum reading distances of 7.8 m, 6.1 m, and 5.3 m as the tag antenna was positioned on a metal plane, a water-filled container, and on a human body, respectively.}, archive = {J_ACCESS}, author = {Minh-Tan Nguyen and Yi-Fang Lin and Chien-Hung Chen and Hoang-Son Luong and Hua-Ming Chen}, doi = {10.1109/ACCESS.2025.3533641}, journal = {IEEE Access}, pages = {19560-19575}, shortjournal = {IEEE Access}, title = {Low profile planar inverted-L antenna (PILA) for UHF RFID tag on signal absorption and reflection surfaces}, volume = {13}, year = {2025}, }
• (2025). Wireless sensor network optimization for constrained environments with limited a priori target information. ACCESS, 13, 19545-19559. (www)

  @article{ , abstract = {In target localization problems, positioning between sensor networks and targets plays a critical role in localization estimation. Because estimation performance is strongly dependent on sensor-target relative positions, many analytically optimal expressions for sensor positions have been constructed as a function of target locations under ideal circumstance. But as more fields have adopted wireless sensing networks, these analytically-optimal placements are not applicable under physical limitations, including placement constraints, network limitations, and sensor modalities. Similarly, these analytically optimal expressions assume the target position is known a priori, but is not a valid assumption for many practical applications. To address this limitation, this paper defines a procedure for optimally placing sensors in a heterogeneous wireless sensor network for localizing targets without an estimate of the target location a priori. We address this by defining a general region where the target may exist, and characterize the network’s sensing performance using the Fisher information matrix. We consider a heterogeneous network of arbitrary size under distance-dependent noise as we search for and continue to optimally monitor a set of unknown static target positions. We define this optimization problem to generally include physical constraints, which are much more practical for most localization applications. Extensive simulations are conducted under two different environmental monitoring example problems to corroborate our approach and demonstrate its range and flexibility. This work may find application in wireless sensor network planning and design for defense, environmental monitoring, surveillance, or autonomous system applications.}, archive = {J_ACCESS}, author = {Joseph Mockler and Sarah Wielgosz and Huan Xu}, doi = {10.1109/ACCESS.2025.3535155}, journal = {IEEE Access}, pages = {19545-19559}, shortjournal = {IEEE Access}, title = {Wireless sensor network optimization for constrained environments with limited a priori target information}, volume = {13}, year = {2025}, }
• (2025). Hierarchical feature attention learning network for detecting object and discriminative parts in fine-grained visual classification. ACCESS, 13, 19533-19544. (www)

  @article{ , abstract = {This paper proposes a novel hierarchical feature attention learning network for improved fine-grained visual classification (FGVC). Existing fine-grained classification methods rely heavily on attention mechanisms to differentiate minute details of similar objects. These mechanisms often assume that critical locations have a similar scale and are uniquely localizable, which is not always accurate. For instance, the size of a bird may vary across images, and the color of its beak might only be significant for species identification when its wing and tail colors are specific. This paper addresses this limitation by proposing a so-called hierarchical feature attention learning network, which initially focuses on the target object within the image, followed by multi-headed attention to identify key discriminative locations (patches). Especially, we develop a novel hierarchical attention approach that appropriately reduces misleading attentions by considering the object’s size for capturing correct attention parts. In addition, the proposed multi-headed attention allows for examining more complementary attention parts to identify the most discriminative features. Further, our framework is implemented as an architectural constraint, eliminating the need for object or part-level annotations in a weakly supervised detection manner. We conducted extensive and comparative experiments on three benchmark datasets: NABirds, CUB-200, and Oxford 102 Flower. The results demonstrate that our proposed hierarchical attention approach provides a robust and efficient solution for improved FGVC. Specifically, our method achieved a top-1 accuracy increase of approximately 93.0%, 92.7%, and 99.4% on the CUB-200-2011, NABirds, and Oxford 102 Flower benchmarks, respectively.}, archive = {J_ACCESS}, author = {A. Yeong Han and Kwang Moo Yi and Kyeong Tae Kim and Jae Young Choi}, doi = {10.1109/ACCESS.2025.3534444}, journal = {IEEE Access}, pages = {19533-19544}, shortjournal = {IEEE Access}, title = {Hierarchical feature attention learning network for detecting object and discriminative parts in fine-grained visual classification}, volume = {13}, year = {2025}, }
• (2025). Large-scale experimental validation of real-time monitoring in underground gas storage wells using distributed fiber optic sensing. ACCESS, 13, 19523-19532. (www)

  @article{ , abstract = {Underground gas storage (UGS) is essential for balancing the natural gas supply and demand, but faces multiple risk factors, including well deformation and gas leaks. Current well investigation methods often require suspending operations, resulting in infrequent inspections and delayed detection of potential issues. This study investigated the sensitivity of distributed fiber optic sensing (DFOS) that integrates distributed temperature (DTS) and strain sensing (DSS) for well integrity monitoring without disturbing the well operations. Due to the impracticality of introducing risks to operational wells, laboratory simulated wellbore tests were conducted to simulate gas leak events and pressure cycles. These tests included temperature variation, gas leak detection, and accelerated cyclic pressure testing to simulate various UGS well operation environments and signals associated with leak events. To measure temperature and strain using a single optical fiber cable for installation simplicity, a new downhole optical fiber cable was designed. The results showed that the DFOS system could identify thermal events from gas injection and withdrawal operations, and leaks at tubing in old boreholes. It also successfully detected gas leaks through the cement behind the casing for newly constructed borehole, with a minimum leak rate of 1.5 LPM (Liter Per Minute). During simulated pressure cycles, the DFOS system detected small strain changes and minor tubing deformations, highlighting its potential for early detection of tubing failure. Additionally, the optical fiber remained intact after 500 pressure cycles demonstrating the robustness of the proposed installation method. These findings validate a new methodology for proactive real-time UGS well integrity management.}, archive = {J_ACCESS}, author = {Linqing Luo and Diana Abdulhameed and Gang Tao and Tianchen Xu and Jiangnan Wang and David Xu and Kenichi Soga and Yuxin Wu}, doi = {10.1109/ACCESS.2025.3533389}, journal = {IEEE Access}, pages = {19523-19532}, shortjournal = {IEEE Access}, title = {Large-scale experimental validation of real-time monitoring in underground gas storage wells using distributed fiber optic sensing}, volume = {13}, year = {2025}, }
• (2025). Optimal index code with rate 4 over f₂. ACCESS, 13, 19484-19522. (www)

  @article{ , abstract = {High throughput broadcasting systems requires efficient broadcasting that can broadcast the information source symbols with optimal rate (i.e., maximizing the usage of broadcast channel which exploits the broadcast nature of the wireless medium through index coding). Index coding is the elegant and beautiful idea, which is used to transform the information source symbols for efficient broadcasting. Efficient broadcasting means minimizing the number of transmissions, which facilitate to achieve high throughput in broadcasting systems is the main goal for index coding. In index coding, determining the optimal rate of an index code for the class of index coding problem is the central open problem. In this paper, we present class $\mathbb{I V}$ index coding problems, a new class of index coding problems with the size of maximum acyclic induced subgraph is 4. The main goal of this work is to characterize the optimal rate of class $\mathbb{I V}$ index coding problems over finite field with 2 elements $\left(\mathbb{F}_2\right)$ . We present that optimal linear index code of the class $\mathbb{I V}$ index coding problem over $\mathbb{F}_2$ achieves the rate 4 on the broadcast rate. To present this new result, results of index coding network coding duality are used. We also present that there exists index coding problems with size of maximum acyclic induced subgraph is 4 and optimal broadcast rate greater than 4. We use the technique interference alignment using alignment graph and the conflict hyper graph to present this contrast.}, archive = {J_ACCESS}, author = {Rajesh Neelakandan and Ganesan Kaliyaperumal}, doi = {10.1109/ACCESS.2025.3529186}, journal = {IEEE Access}, pages = {19484-19522}, shortjournal = {IEEE Access}, title = {Optimal index code with rate 4 over f₂}, volume = {13}, year = {2025}, }
• (2025). Extrapolation of metal gate with high-K spacer in strained nanosystem channel QWB cylindrical FET for high-speed applications. ACCESS, 13, 19469-19483. (www)

  @article{ , abstract = {The development of novel strain-engineered channel Cylindrical Gate-All-Around (CGAA) quantum well-barrier (QWB) field-effect transistors (FETs) using high-k gate stacks and metallic gates with varying work functions is analyzed, offering enhanced performance to meet the 1 nm technology node of IRDS 2028. The devices incorporate a QWB system incorporating strain engineering in the ultrathin channel region flanked by high-k spacers surrounding the underlaps and metal gate with a stack high-k dielectric. Key electrostatic characteristics, including the Ion/Ioff ratio, leakage current, on-current, sub-threshold swing (SS), drain-induced barrier lowering (DIBL), and transconductance, were extrapolated and analyzed for the CGAA FETs developed in this study. The tungsten metal gate device provides a significantly improved Ion/Ioff ratio with a notable 98.18% decrease in the off-current and 22.5% increase in the ON current, in contrast to existing cylindrical GAA FET. In addition, the novel strain-engineered channel CGAA QWB FET (Device C), which has a higher metal gate work function, is endorsed for near-optimal SS with augmented transconductance. The output performance (ID-V $_{\mathrm {DS}}$ ) resolves a huge enhancement in contrast to the existing GAA and IRDS 2028 1 nm technology node criteria. Hence, the device (nanowire-strained channel QWB CGAA FET) with a tungsten gate is better suited for low-power, high-speed applications with minimal short-channel effects, and is the device of future connecting numerous RF and digital applications as well as faster switching speed.}, archive = {J_ACCESS}, author = {Rasmita Barik and Rudra Sankar Dhar and Kuleen Kumar and Yash Sharma and Amit Banerjee}, doi = {10.1109/ACCESS.2025.3534561}, journal = {IEEE Access}, pages = {19469-19483}, shortjournal = {IEEE Access}, title = {Extrapolation of metal gate with high-K spacer in strained nanosystem channel QWB cylindrical FET for high-speed applications}, volume = {13}, year = {2025}, }
• (2025). Leveraging cognitive machine reasoning and NLP for automated intent-based networking and e2e service orchestration. ACCESS, 13, 19456-19468. (www)

  @article{ , abstract = {Modern networks are increasingly complex, necessitating dynamic and automated solutions to connect user intents with network actions effectively. This study presents a new framework for automating Intent Based Networking (IBN) by combining cognitive Machine Reasoning (MR) with Natural Language Processing (NLP) and utilizing the RASA (Robust Automated Speech Assistant) architecture. RASA is a flexible open-source framework for building conversational AI, adapted for end-to-end (e2e) network orchestration. In contrast to traditional static methods, this innovative system empowers network operators to manage and optimize networks dynamically through intuitive voice commands or a Graphical User Interface (GUI). The system identifies user intents, converts them into actionable network policies, and ensures they align with real-time network states and Quality of Service (QoS) requirements via a feedback loop. Cognitive MR and AI-based optimization techniques are integrated to enhance system performance, enabling intelligent adaptation to network conditions and ensuring optimal resource allocation. A simulated testbed was created to assess the system’s performance using Containernet, a lightweight Container-Based Network Emulator, and Open Networking Operating System (ONOS) Software Defined Networking (SDN) controllers. The results of the testbed indicated a 25% reduction in latency, a 30% increase in throughput, and a 40% enhancement in real-time response times, demonstrating the system’s effectiveness in a controlled environment. These impressive results underscore the system’s potential to enhance network performance, efficiency, and responsiveness. By effectively addressing modern networks’ challenges, this solution proves its ability to confidently and seamlessly convert user intents into automated network actions without manual intervention, providing adaptability and scalability for today’s network environments.}, archive = {J_ACCESS}, author = {Muhammad Asif and Talha Ahmed Khan and Wang-Cheol Song}, doi = {10.1109/ACCESS.2025.3534282}, journal = {IEEE Access}, pages = {19456-19468}, shortjournal = {IEEE Access}, title = {Leveraging cognitive machine reasoning and NLP for automated intent-based networking and e2e service orchestration}, volume = {13}, year = {2025}, }
• (2025). Unveiling the physics secrets of bajiquan: A STEAM-integrated teaching approach. ACCESS, 13, 19438-19455. (www)

  @article{ , abstract = {A compelling vision is emerging in education - integrating the arts with STEM, forming a holistic approach known as STEAM. Leveraging recent advances in motion analysis technology, this study explores the potential of a STEAM-based pedagogy incorporating the Chinese martial art of Bajiquan to enhance college students’ understanding and application of physics principles. An empirical study was conducted with sophomore university students to investigate the feasibility of this approach. A three-phase intervention utilizing STEAM principles was implemented, focusing on Bajiquan movement practice and analysis, supplemented by instructional videos and images created using motion analysis tools to visualize the underlying physics concepts. These materials highlighted principles such as “Conservation of momentum,” “Linear dynamics,” and “Rotational dynamics” within the context of Bajiquan techniques. Learning interest questionnaires and physics knowledge tests were administered before and after the intervention to assess its impact. One-way repeated measures ANOVA and paired samples t-tests revealed significant improvements in learning interest and physics knowledge scores following the intervention. These findings suggest that integrating Bajiquan and motion analysis within a STEAM framework can promote a deeper understanding of physics concepts and enhance students’ overall engagement with the subject matter.}, archive = {J_ACCESS}, author = {Haidong Chen and Weilei Yang and Jinyong Feng and Jiasheng Lv and Ran Chen}, doi = {10.1109/ACCESS.2025.3535146}, journal = {IEEE Access}, pages = {19438-19455}, shortjournal = {IEEE Access}, title = {Unveiling the physics secrets of bajiquan: A STEAM-integrated teaching approach}, volume = {13}, year = {2025}, }
• (2025). Power controlled resource allocation and task offloading via optimized deep reinforcement learning in D2D assisted mobile edge computing. ACCESS, 13, 19420-19437. (www)

  @article{ , abstract = {Device-to-device (D2D) technology enables continuous communication between devices, effectively addressing the challenge of limited bandwidth resources in 5G communication systems. However, shared resources among multiple D2D user pairs can result in significant interference. In advanced 5G networks and beyond, mobile edge computing (MEC) has emerged as a promising technology for reducing power consumption in cloud data centers while ensuring reliability and real-time access for end devices. Nonetheless, the inherent complexity and variability of MEC networks present significant challenges in task offloading ( $T_{o}$ ) solutions. This study introduces an optimized deep reinforcement learning (DRL) approach for D2D-assisted task offloading ( $T_{o}$ ) and resource allocation ( $R_{a}$ ) to address these challenges. The process begins with the construction of the MEC network scenario, followed by the implementation of a human evolutionary optimization-aided DRL ( $HEOp-DRL$ ) model to handle task offloading and resource allocation tasks jointly. The HEOp method minimizes time and power consumption constraints while efficiently allocating resources across end devices. The proposed DRL model uses the Markov decision process (MDP) to facilitate collaborative task offloading between end devices and MEC servers, deriving an optimal policy for offloading and resource allocation. The system is simulated on the MATLAB platform, and key performance metrics such as latency, average energy consumption, and sum rate (SR) are analyzed and compared with those of existing methods. The results demonstrate enhanced robustness in addressing resource allocation and task offloading challenges in D2D-MEC systems, with a minimized overall latency, and less energy consumption.}, archive = {J_ACCESS}, author = {Sambi Reddy Gottam and Udit Narayana Kar}, doi = {10.1109/ACCESS.2025.3533034}, journal = {IEEE Access}, pages = {19420-19437}, shortjournal = {IEEE Access}, title = {Power controlled resource allocation and task offloading via optimized deep reinforcement learning in D2D assisted mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). Application of big data technology to assessments of female ovarian reserve dysfunction. ACCESS, 13, 19408-19419. (www)

  @article{ , abstract = {This paper evaluated the potential application of big data technology to assessments of diminished ovarian reserve (DOR). The study enrolled 162 patients who underwent ovarian reserve function assessment for the first time in the Department of Ultrasound, Jiangsu Province Hospital of Chinese Medicine from January 2023 to December 2023. Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models—multilayer perceptron, one-vs-rest, and random forest classifiers—to classify and analyse the ovarian reserve function dataset and evaluate the platform’s performance. In the big data platform, the random forest algorithm achieved the highest classification accuracy (89.47%), followed by the neural network (81.06%) and support vector machine (72.91%) methods. The random forest algorithm had the least time overhead for datasets smaller than 50 MB; for datasets exceeding 50 MB, the support vector machine algorithm had the least time overhead, followed by the random forest and neural network algorithms. The neural network algorithm’s speedup ratio was lower than that of the other two algorithms for small datasets, but with increasing dataset size, its speedup ratio significantly exceeded those of the other two algorithms. The random forest algorithm showed substantial growth for large datasets.}, archive = {J_ACCESS}, author = {Xia Ji’An and Ma YunFei and Wu YiYun and Zhao YouLin and Ni HaoRang and Liu XinYan}, doi = {10.1109/ACCESS.2025.3532799}, journal = {IEEE Access}, pages = {19408-19419}, shortjournal = {IEEE Access}, title = {Application of big data technology to assessments of female ovarian reserve dysfunction}, volume = {13}, year = {2025}, }
• (2025). Minimum description length and multi-criteria decision analysis in predictive modeling. ACCESS, 13, 19388-19407. (www)

  @article{ , abstract = {Accurate model selection is essential in predictive modelling across various domains, significantly impacting decision-making and resource allocation. Despite extensive research, the model selection process remains challenging. This work aims to integrate the Minimum Description Length principle with the Multi-Criteria Decision Analysis to enhance the selection of forecasting machine learning models. The proposed MDL-MCDA framework combines the MDL principle, which balances model complexity and data fit, with the MCDA, which incorporates multiple evaluation criteria to address conflicting error measurements. Four datasets from diverse domains, including software engineering (effort estimation), healthcare (glucose level prediction), finance (GDP prediction), and stock market prediction, were used to validate the framework. Various regression models and feed-forward neural networks were evaluated using criteria such as MAE, MAPE, RMSE, and Adjusted $R^{2}$ . We employed the Analytic Hierarchy Process (AHP) to determine the relative importance of these criteria. We conclude that the integration of MDL and MCDA significantly improved model selection across all datasets. The cubic polynomial regression model and the multi-layer perceptron models outperformed other models in terms of AHP score and MDL criterion. Specifically, the MDL-MCDA approach provided a more nuanced evaluation, ensuring the selected models effectively balanced complexity and predictive accuracy.}, archive = {J_ACCESS}, author = {Petr Silhavy and Kateřina Hlaváčková-Schindler and Radek Silhavy}, doi = {10.1109/ACCESS.2025.3532815}, journal = {IEEE Access}, pages = {19388-19407}, shortjournal = {IEEE Access}, title = {Minimum description length and multi-criteria decision analysis in predictive modeling}, volume = {13}, year = {2025}, }
• (2025). SViG: A similarity-thresholded approach for vision graph neural networks. ACCESS, 13, 19379-19387. (www)

  @article{ , abstract = {Image representation in computer vision is a long-standing problem that has a significant impact on any machine learning model performance. There have been multiple attempts to tackle this problem that were introduced in the literature, starting from traditional Convolutional Neural Networks (CNNs) to Vision Transformers and MLP-Mixers that were more recently introduced to represent images as sequences. Most recently, Vision Graph Neural Networks (ViG) have shown very promising performance through representing images as graphs. The performance of ViG models heavily depends on how the graph is constructed. The ViG model relies on k-nearest neighbors (k-nn) for graph construction, which while achieving very good performance on classical computer vision tasks, imposes a number of challenges, such as determining the optimal value for k, as well as using the same chosen value for all nodes in a graph, which in turns reduces the graph expressiveness and limits the power of the model. In this paper, we propose a new approach that relies on similarity score thresholding to create the graph edges and, subsequently, pick the neighboring nodes. Rather than the number of neighbors, we allow for the specification of the normalized similarity threshold as an input parameter for each layer, which is more intuitive. We also propose a decreasing threshold framework to select the input threshold for all layers. We show that our proposed method can achieve higher performance than the ViG model for image classification on the benchmark ImageNet-1K dataset, without increasing the complexity of the model. PyTorch code and checkpoints are available at https://github.com/IsmaelElsharkawi/SViG.}, archive = {J_ACCESS}, author = {Ismael Elsharkawi and Hossam Sharara and Ahmed Rafea}, doi = {10.1109/ACCESS.2025.3531691}, journal = {IEEE Access}, pages = {19379-19387}, shortjournal = {IEEE Access}, title = {SViG: A similarity-thresholded approach for vision graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Fully CMOS boost converter operating at 2.65 GHz for photovoltaic energy harvesting. ACCESS, 13, 19365-19378. (www)

  @article{ , abstract = {This research describes the design of a fully CMOS-integrated boost DC-DC converter to be supplied by an ultra-low power solar cell for energy harvesting applications. The monolithic system is based on the inductive boost converter, with both the energy storage spiral inductor and the output filter capacitor integrated together with an LC oscillator at 2.65 GHz, to drive both MOSFET switches, in a 110 nm CMOS process. The system delivers 1.2 V for a 10 k $\Omega $ load, with an output power of the order of $144~\mu $ W. Experimental results also show that the proposed inductor-based fully CMOS DC-DC boost converter has a record low silicon footprint (0.13 mm2) and highest power density (1.1 mW/mm2) when compared with other counterparts resorting to the same boost topology. The fully integrated solution can be used in the power management units of stand-alone or remote integrated systems, for several applications such as biomedical or IoT, usually characterised for ultra-low voltage photovoltaic applications.}, archive = {J_ACCESS}, author = {Pedro Mendonça Dos Santos and Ricardo Alexandre Marques Lameirinhas and Catarina P. Correia V. Bernardo and João Paulo Neto Torres and António Serralheiro and Rafael Vieira and Nuno Lourenço}, doi = {10.1109/ACCESS.2025.3533743}, journal = {IEEE Access}, pages = {19365-19378}, shortjournal = {IEEE Access}, title = {Fully CMOS boost converter operating at 2.65 GHz for photovoltaic energy harvesting}, volume = {13}, year = {2025}, }
• (2025). Enhanced DeepSORT and StrongSORT for multicattle tracking with optimized detection and re-identification. ACCESS, 13, 19353-19364. (www)

  @article{ , abstract = {Recently, labor shortages in the farming industry have increased the demand for automation. Object tracking technology has emerged as a critical tool for monitoring livestock through automated systems. This study focuses on tracking individual cattle using object detection and tracking algorithms. Data were collected noninvasively using cameras, and a tracking-by-detection (TBD) approach was adopted. The proposed framework introduces multiple enhancements optimized for cattle tracking. These enhancements include a comparison of five different bounding box regression losses to improve detection accuracy, modifications to the Kalman filter state vector for more accurate bounding box predictions, and adjustments to the feature vector distance metric in the re-identification algorithm. YOLOv9-t was used as the detector, whereas DeepSORT and StrongSORT served as trackers. Compared with the baseline, which uses DeepSORT, the proposed method achieved significant improvements in higher-order tracking accuracy (HOTA) by 4.1%, multiple object tracking accuracy (MOTA) by 1.08%, and identification F1 score (IDF1) by 5.12%, reaching values of 78.64%, 90.29%, and 91.41%, respectively, while reducing the number of ID switches (IDSW).}, archive = {J_ACCESS}, author = {Hyeon-Seok Sim and Hyun-Chong Cho}, doi = {10.1109/ACCESS.2025.3535092}, journal = {IEEE Access}, pages = {19353-19364}, shortjournal = {IEEE Access}, title = {Enhanced DeepSORT and StrongSORT for multicattle tracking with optimized detection and re-identification}, volume = {13}, year = {2025}, }
• (2025). Innovative grouting process monitoring: Design and implementation of a measurement device with datalogger function. ACCESS, 13, 19340-19352. (www)

  @article{ , abstract = {Recording data from processes and activities has become indispensable for quality assurance and process verification, serving as a cornerstone in achieving research objectives. This requirement is particularly significant in the construction industry, where modern grouting techniques are used for waterproofing during tunnel wall renovations to safeguard structures from capillary water ingress, which is vital for their durability. Detailed documentation of grouting processes is often necessary, as these records offer valuable insights into substrate conditions and grouting quality. This paper focuses on designing and implementing a device that captures physical data during the grouting process and stores it in a defined format on a microSD card. The paper covers problem analysis, key input variables identification, and suitable hardware components selection. The proposed device is versatile, enabling real-time monitoring of the grouting process, and can be adapted to measure the consumption of other materials, such as one- or two-component paints. The device underwent rigorous testing under laboratory conditions to ensure its functionality and reliability, and thus significantly contributes to the control and responsiveness of the grouting process by providing real-time data on critical operational parameters. In addition to the controlled testing, the device prototype was subjected to real-world testing during a major grouting project at the Bratislava-Lamač railway tunnel in the Slovak republic. This large-scale construction site provided a valuable opportunity to assess the device’s performance under operational conditions.}, archive = {J_ACCESS}, author = {Gabriel Gaspar and Roman Budjac and Stefan Sedivy and Maximilian Stremy and Denis Benka and Eduard Nemlaha and Rastislav Elias}, doi = {10.1109/ACCESS.2025.3534816}, journal = {IEEE Access}, pages = {19340-19352}, shortjournal = {IEEE Access}, title = {Innovative grouting process monitoring: Design and implementation of a measurement device with datalogger function}, volume = {13}, year = {2025}, }
• (2025). Aggregated time series features in a voxel-based network architecture. ACCESS, 13, 19329-19339. (www)

  @article{ , abstract = {Using point cloud sequences is a popular way to harness the additional information represented in the time domain in order to enhance the performance of 3D object detector neural networks. However, it is not trivial to decide which abstraction level should the additional information presented to the network, or what is the point in the architecture, where aggregating the additional information is most beneficial. In this article, the authors propose various voxel-based networks and analyze their performance in relation to the abstraction level of the time series data. During the evaluation, the authors examine the object detection performance of a popular voxel-based neural network with its original architecture and several variants where the time domain related features were propagated through the network and aggregated at different stages of processing. Based on the evaluation results, a conclusion is drawn regarding the abstraction level at which the time-series aggregation step is performed in order to improve the performance of the baseline voxel-based detector.}, archive = {J_ACCESS}, author = {Zsolt Vincze and András Rövid}, doi = {10.1109/ACCESS.2025.3535151}, journal = {IEEE Access}, pages = {19329-19339}, shortjournal = {IEEE Access}, title = {Aggregated time series features in a voxel-based network architecture}, volume = {13}, year = {2025}, }
• (2025). BERT-residual quantum language model inspired by ODE multi-step method. ACCESS, 13, 19320-19328. (www)

  @article{ , abstract = {Quantum-inspired language models model finer-grained semantic interactions in higher-order Hilbert spaces. However, previous methods usually capture semantic features based on context-free word vectors such as Word2Vec and GloVe. Building on natural language encoding, incorporating quantum-inspired density matrix modeling can capture more fine-grained semantic interactions. However, when applied to large pre-trained language models like BERT, using quantum density matrices often leads to issues such as gradient explosion or vanishing. Therefore, how to effectively integrate the quantum-inspired language model and the pre-trained model, and make them function under the fine-tuning paradigm of the pre-trained model has become a key issue for the further development of the quantum-inspired language model. Therefore, in this paper, we propose the BERT-Residual quantum language model inspired by the multi-step method of ordinary differential equations (ODE), using the density matrix to capture the semantic high-order interaction features missing in the BERT modeling process, and obtain the sentence representation, and perform the first step Residuals. Then quantum measurement is performed on the sentence representation, and the second step of residual connection is performed with the BERT layer. This residual connection method based on the multi-step method can more effectively combine the advantages of BERT representation and quantum density matrix representation to enhance representation learning. Experiments show that in text classification benchmarks, our proposed method generally surpasses baseline models.}, archive = {J_ACCESS}, author = {Shaohui Liang and Yingkui Wang and Shuxin Chen}, doi = {10.1109/ACCESS.2025.3533554}, journal = {IEEE Access}, pages = {19320-19328}, shortjournal = {IEEE Access}, title = {BERT-residual quantum language model inspired by ODE multi-step method}, volume = {13}, year = {2025}, }
• (2025). Advanced text summarization model incorporating NLP techniques and feature-based scoring. ACCESS, 13, 19302-19319. (www)

  @article{ , abstract = {The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study’s improved summarization methodology overcomes these limitations by combining the linguistic and semantic resources. Moreover, although it is more computationally complex, it efficiently combines higher quality with faster summarization. Specifically, a method relies on a weighted feature score scheme. For example, various textual features such as Named Entity Counts, Noun Counts, and Sentence Position contribute to the summarization quality appropriately. This study’s summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. The methodology was checked against traditional methods using ROUGE-1 and ROUGE-2, ROUGE-L and BERTScore. The last one, BERTScore, evaluates the semantic similarity of the generated summaries and the references. This study shows that the proposed methodology generates summaries that are not only informative but even semantically faithfully reproduce the original textual information; it achieves high scores in terms of F1-measure across different evaluations like BERTSCORE (0.8857) and ROUGE-1(0.6388), ROUGE-2(0.5662) and ROUGE-L (0.6421). It thus suggests that the approach is applicable in real-life situations and deserves further research.}, archive = {J_ACCESS}, author = {Estabraq Abdulreda Kadhim and Mohammad-Reza Feizi-Derakhshi and Hadi S. Aghdasi}, doi = {10.1109/ACCESS.2025.3528830}, journal = {IEEE Access}, pages = {19302-19319}, shortjournal = {IEEE Access}, title = {Advanced text summarization model incorporating NLP techniques and feature-based scoring}, volume = {13}, year = {2025}, }
• (2025). A joint sparse space-time adaptive processing method. ACCESS, 13, 19295-19301. (www)

  @article{ , abstract = {At present, most of the sparse space-time adaptive processing(STAP) methods focus on exploiting the clutter sparsity. In this paper, different from the present sparse STAP methods, both the clutter sparsity and the target sparsity in STAP are considered at the same time, and a novel joint sparse STAP method is proposed. The proposed method imposes a sparse regularization on the clutter and the target to the minimum Capon Spectrum criterion. The processing results of the measured data shows that the output SCNR of the proposed method is 3dB higher than the method proposed by Yang et al. and 2dB higher than the method proposed by Zhiqi et al.}, archive = {J_ACCESS}, author = {Jinfeng Hu and Yuyan Xia and Huiyong Li and Jing Liang and Tao Lin}, doi = {10.1109/ACCESS.2020.3001304}, journal = {IEEE Access}, pages = {19295-19301}, shortjournal = {IEEE Access}, title = {A joint sparse space-time adaptive processing method}, volume = {13}, year = {2025}, }
• (2025). Hybridize machine learning methods and optimization techniques to analyze and repair welding defects via digital twin of jidoka simulator. ACCESS, 13, 19266-19294. (www)

  @article{ , abstract = {Lean thinking is interested in identifying and resisting defects that affect business safety, like welding defects of the cooling pipe exposing the chilled foodstuffs parcels to spoilage, posing a danger to the land transportation investment. Four clusters are used to identify welding flaws’ severity to keep them from getting worse via mapping their finite element meshes (MFEM) and using the high-frequency current ring (HFCR) lean technique to identify their size, depth, growth direction ( $\boldsymbol {{R}_{s}}$ ), and shape deployment via prediction to assign the flaws as Susceptible (S), and preludes to transfer to a Quarantine cluster (Q) to take one decision whether rescued (R), Isolated (I) or Eliminated (E). We are creating a “digital Jidoka twin system” (SQ(R/I/E)) with a controller segment programmed with machine learning (ML) algorithms that use the MFEM’s huge and uneven data to sort defects and their causes. Hybridising the Random-Forest algorithm with Dingo optimisation and called Regulated Random Forest (RRF) to precisely identify defect clusters and then predict the welding defect growth rate ( $\boldsymbol {{R}_{s}}$ ) using the Cat-boost optimiser, which is enhanced by a beetle search mechanism called CatBAS. The RRF is superior to Apriori, ECLAT, and FP Growth by 23.98%, 7.44%, and 8.38%, respectively, while CatBAS is superior to XG-boost by 94.62% in response time with a 1.04175% error that activates the treating stage-V quickly. The SQ(R/I/E) increased parcel rescues by 38.2% and reduced financial losses. Protecting chilled foodstuffs transport from spoilage serves the SDG (2).}, archive = {J_ACCESS}, author = {Ahmed M. Abed and Tamer S. Gaafar}, doi = {10.1109/ACCESS.2025.3533377}, journal = {IEEE Access}, pages = {19266-19294}, shortjournal = {IEEE Access}, title = {Hybridize machine learning methods and optimization techniques to analyze and repair welding defects via digital twin of jidoka simulator}, volume = {13}, year = {2025}, }
• (2025). Assessing the antenna performance (Radiated power) and electromagnetic impact (SAR) on a user of a wearable RF communication device, involving variously structured models. ACCESS, 13, 19257-19265. (www)

  @article{ , abstract = {The investigations focused on the significance of the structural design of models of a wearable device equipped with a wireless radiofrequency communication module (W-RFCD) operating at 2.45 GHz. Computer modeling was used to determine antenna performance (radiated power and related battery operating time), as well as the associated electromagnetic impact (SAR) in the user’s head. The findings showed a potential reduction in battery life of 20-40% for body-worn devices when used near the head, compared to their performance in free space, while maintaining the same quality of wireless connection. In the exposure scenarios examined, results revealed that the 10g-SAR in the user’s head could increase significantly: 1) by up to 65% when the RF module’s battery was centered, versus when it was offset; and 2) by up to 240% with a 0 mm distance between the RF module and the battery, compared to a 2 mm distance. The use of a simplified model of W-RFCD may lead to incorrect conclusions about its performance and a substantial underestimation of the SAR values (10g-SAR values are up to double in the most complex exposure scenarios examined, compared to the most simplified). The antenna performance and SAR of W-RFCD developed using ready-made components can be optimized without altering antenna matching circuits or inserting additional physical structures.}, archive = {J_ACCESS}, author = {Patryk Zradziński and Jolanta Karpowicz and Krzysztof Gryz}, doi = {10.1109/ACCESS.2025.3528656}, journal = {IEEE Access}, pages = {19257-19265}, shortjournal = {IEEE Access}, title = {Assessing the antenna performance (Radiated power) and electromagnetic impact (SAR) on a user of a wearable RF communication device, involving variously structured models}, volume = {13}, year = {2025}, }
• (2025). Aircraft bearing fault diagnosis method based on LSTM-IDRSN. ACCESS, 13, 19248-19256. (www)

  @article{ , abstract = {A fault diagnosis model for aviation bearing is proposed to tackle the challenge of feature extraction from bearing vibration signals amidst noise. This model combines a long short-term memory (LSTM) network with an improved deep residual shrinkage network (IDRSN) based on semi-soft threshold optimization. The LSTM module first extracts temporal features from the original one-dimensional vibration signals, followed by deep feature extraction via the IDRSN. A fully connected layer with a SoftMax activation function is then used to classify faults in the training set. The model is then validated through different fault test sets. Experimental results show that the LSTM-IDRSN model outperforms the traditional deep residual shrinkage network (DRSN) model, achieving a 4.98% increase in classification accuracy, reaching 96.43%. Additionally, the model maintains high precision and stability even under noise interference, outperforming other methods.}, archive = {J_ACCESS}, author = {Lei Wang and Kun He and Haipeng Fu and Weixing Chen}, doi = {10.1109/ACCESS.2025.3533551}, journal = {IEEE Access}, pages = {19248-19256}, shortjournal = {IEEE Access}, title = {Aircraft bearing fault diagnosis method based on LSTM-IDRSN}, volume = {13}, year = {2025}, }
• (2025). Predictors of medical students’ adoption of emergency medicine virtual simulation platforms. ACCESS, 13, 19237-19247. (www)

  @article{ , abstract = {Emergency medicine virtual simulation platforms have transformed student training, allowing students to engage with a variety of realistic and replicable emergency scenarios. However, the completion and pass rates are low. The aim of this study is to explore the factors influencing medical students’ adoption of intelligent virtual simulation platforms in China via Technology Acceptance Model 3. A total of 819 medical students from southern Chinese universities participated in the study. Structural equation modeling is used to examine data from cross-sectional quantitative surveys. The results revealed that image, relevance, and result demonstrability significantly affected perceived usefulness, whereas computer self-efficacy, perceptions of external control, anxiety, perceived enjoyment, and playfulness shaped perceived ease of use. Subjective norms emerged as the strongest predictor of behavioral intention, despite not significantly influencing perceived usefulness. The combined influence of subjective norms, perceived usefulness, and perceived ease of use positively affected students’ behavioral intentions. These findings highlight the importance of establishing supportive environments and designing user-friendly virtual simulation tools to promote technology adoption in medical education. These insights can help educational institutions and policymakers integrate virtual simulations effectively into medical curricula, thereby providing better flexible and personalized training experiences, which enhance the quality of skills acquisition and accessibility of emergency medical training.}, archive = {J_ACCESS}, author = {Lingjiao Tang and Yu Ning and Hui Lv and Xuebin Li and Aihe Tang}, doi = {10.1109/ACCESS.2025.3531507}, journal = {IEEE Access}, pages = {19237-19247}, shortjournal = {IEEE Access}, title = {Predictors of medical students’ adoption of emergency medicine virtual simulation platforms}, volume = {13}, year = {2025}, }
• (2025). Design of an interleaved boost serial resonant converter with high transformation ratio for fuel cell battery. ACCESS, 13, 19219-19236. (www)

  @article{ , abstract = {In this study, an interleaved boost serial resonant converter with a high transformation ratio is proposed. The main architecture of this converter is a boost converter serial resonant converter, the first stage utilizes an interleaved boost converter to reduce input current ripple, In the second stage, a resonant converter is used, where power MOSFETs and rectifier diodes enable soft switching at an appropriate operating frequency. This feature improves efficiency by reducing losses caused by the hard switching of traditional circuits. Finally, this converter is designed with the rated power of 1.5kW and the digital signal processor TMS320F28335 is employed as the main system controller core to realize the wide output voltage range $40\sim 125$ V from fuel cell as an input power source which can output a 400V for output load or DC bus applications through the proposed converter. The experimental results show that the maximum conversion efficiency of this converter when operating at the low input voltage 40V is up to 92.69%, and at the high input voltage 125V is up to 93.57%.}, archive = {J_ACCESS}, author = {Sen-Tung Wu and Jun-Teng Hong and Ching-Chun Chuang}, doi = {10.1109/ACCESS.2025.3531981}, journal = {IEEE Access}, pages = {19219-19236}, shortjournal = {IEEE Access}, title = {Design of an interleaved boost serial resonant converter with high transformation ratio for fuel cell battery}, volume = {13}, year = {2025}, }
• (2025). Controlled service scheduling scheme for user-centric software-defined network- based internet of things. ACCESS, 13, 19198-19218. (www)

  @article{ , abstract = {Software Defined Networks (SDNs) support different applications’ data and control operations through operational plane differentiations. Such differentiations rely on the service providers’ user density and processing capacity. This article introduces a Controlled Service Scheduling Scheme (CS3) to ensure responsive user service support. This scheme exploits the SDN’s operation plane differentiation to confine immobile request stagnancies. The routed regression learning model decides the SDN plane selection. This learning is a modified version of linear learning where the scheduling rate is the plane differentiator. The process is un-iterated until the combination of device processing capacity and number of devices is less than the service population observed. In the scheduling process, the operation to data plane migrations is decided using the maximum routed threshold. The threshold is computed for the operation and data plane from which the rate of service response or capacity of service admittance is decided. The routed regression analyzes the change in the threshold factor to ensure flexible scheduling is achieved regardless of dense IoT requests. This scheme achieves a high scheduling rate for maximizing service distributions under controlled delay. The experimental findings show that compared to the current models, the suggested method improves the scheduling rate by 13.92%, increases the distribution of services by 8.31%, and decreases delays by 11.58%. Further evidence of the approach’s efficacy in managing heavy IoT traffic is its low distribution failure rate of 1.7%. These findings demonstrate that the scheme can enhance performance in ever-changing Internet of Things settings by optimizing the allocation of resources.}, archive = {J_ACCESS}, author = {Mohammed Albekairi}, doi = {10.1109/ACCESS.2025.3533310}, journal = {IEEE Access}, pages = {19198-19218}, shortjournal = {IEEE Access}, title = {Controlled service scheduling scheme for user-centric software-defined network- based internet of things}, volume = {13}, year = {2025}, }
• (2025). Empowering security operation center with artificial intelligence and machine Learning—A systematic literature review. ACCESS, 13, 19162-19197. (www)

  @article{ , abstract = {Organizational cybersecurity relies heavily on security operation centers (SOCs) to protect businesses and institutions from emerging cyber threats. In recent years, the complexity and sophistication of cyber threats have increased, pushing SOCs to their limits. As a result, SOCs struggle to address the evolving threat landscape due to their reliance on isolation technologies and reactive strategies. However, advanced technologies, such as artificial intelligence (AI) and machine learning (ML), have the potential to revolutionize SOCs by enhancing threat identification and response capabilities, as well as predicting and preempting risks. To address these challenges and highlight the full potential of SOC, this study provides a detailed overview through a comprehensive literature review that identifies gaps in existing research and examines the latest technologies used in the SOC environment to help address different operational and technical challenges and bring out their capabilities. Various methods, ranging from automated incident response and behavioral analytics to neural networks and deep learning, have been classified and compared. In addition, an in-depth reference architectural model, which is a blueprint for SOC integrating AI and ML into SOCs, is introduced. The proposed model provides a structured framework for implementation and offers insights into different SOC components and their interactions. Moreover, this systematic review emphasizes the benefits of these technologies for enhancing security operations. Finally, a case study is presented to describe the function of ML- and AI-powered SOC components to achieve optimum security. This paper concludes by discussing additional challenges and future research directions that may help advance the cybersecurity sector and provide insights into improving SOCs.}, archive = {J_ACCESS}, author = {Mohamad Khayat and Ezedin Barka and Mohamed Adel Serhani and Farag Sallabi and Khaled Shuaib and Heba M. Khater}, doi = {10.1109/ACCESS.2025.3532951}, journal = {IEEE Access}, pages = {19162-19197}, shortjournal = {IEEE Access}, title = {Empowering security operation center with artificial intelligence and machine Learning—A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Investigating low-frequency electromagnetic fluctuations on PCB surfaces: A near-field analysis of CBCPW transmission lines up to 15.2 MHz. ACCESS, 13, 19144-19161. (www)

  @article{ , abstract = {The paper employs statistical methods to model low-frequency electromagnetic field fluctuations over printed circuit boards (PCBs) using near-field measurements within the frequency domain along the Coplanar Broadband Capacitance Wave (CBCPW) transmission line. The study characterizes electromagnetic noise profiles up to 15.2 MHz, assesses the impact of transmission lines (TL) on PCB environments, and explores the effects of signal interference. Advanced statistical methods, including the Hurst exponent and Ljung-Box tests, are integrated with traditional near-field measurements to quantify electromagnetic noise persistence and independence across frequencies. Additionally, 2-D autocovariance analysis provides a precise evaluation of electromagnetic field characteristics over the PCB surface. This research further develops a model for electromagnetic field fluctuations by incorporating theoretical frameworks aligned with stochastic Maxwell’s equations and addressing challenges such as measurement uncertainty and mechanical stepping of the probe head over the PCB surface. The autocovariance function enhances data analysis robustness, which is advantageous for the sparse data typically associated with PCBs and integrated circuits. Measurements extend up to 328.27 MHz, with a focus on significant fluctuations up to 15.2MHz due to their dominance over the measured noise. This study proposes a comprehensive statistical and modelling approach tailored for applications involving CBCPW transmission lines, especially relevant to microscale detectors and sensitive electronic devices.}, archive = {J_ACCESS}, author = {Mariusz Zubert and Mariusz Jankowski and Zbigniew Kulesza}, doi = {10.1109/ACCESS.2025.3531783}, journal = {IEEE Access}, pages = {19144-19161}, shortjournal = {IEEE Access}, title = {Investigating low-frequency electromagnetic fluctuations on PCB surfaces: A near-field analysis of CBCPW transmission lines up to 15.2 MHz}, volume = {13}, year = {2025}, }
• (2025). MagicItem: Dynamic behavior design of virtual objects with large language models in a commercial metaverse platform. ACCESS, 13, 19132-19143. (www)

  @article{ , abstract = {To create rich experiences in virtual reality (VR) environments, it is essential to define the behavior of virtual objects through programming. However, programming in 3D spaces requires a wide range of background knowledge and programming skills. Although Large Language Models (LLMs) have provided programming support, they are still primarily aimed at programmers. In metaverse platforms, where many users inhabit VR spaces, most users are unfamiliar with programming, making it difficult for them to modify the behavior of objects in the VR environment easily. Existing LLM-based script generation methods for VR spaces require multiple lengthy iterations to implement the desired behaviors and are difficult to integrate into the operation of metaverse platforms. To address this issue, we propose a tool that generates behaviors for objects in VR spaces from natural language within Cluster, a metaverse platform with a large user base. By integrating LLMs with the Cluster Script provided by this platform, we enable users with limited programming experience to define object behaviors within the platform freely. We have also integrated our tool into a commercial metaverse platform and are conducting online experiments with 63 general users of the platform. The experiments show that even users with no programming background can successfully generate behaviors for objects in VR spaces, resulting in a highly satisfying system. Our research contributes to democratizing VR content creation by enabling non-programmers to design dynamic behaviors for virtual objects in metaverse platforms.}, archive = {J_ACCESS}, author = {Ryutaro Kurai and Takefumi Hiraki and Yuichi Hiroi and Yutaro Hirao and Monica Perusquía-Hernández and Hideaki Uchiyama and Kiyoshi Kiyokawa}, doi = {10.1109/ACCESS.2025.3530439}, journal = {IEEE Access}, pages = {19132-19143}, shortjournal = {IEEE Access}, title = {MagicItem: Dynamic behavior design of virtual objects with large language models in a commercial metaverse platform}, volume = {13}, year = {2025}, }
• (2025). High-fidelity depth map reconstruction system with RGB-guided super resolution CNN and cross-calibrated chaos LiDAR. ACCESS, 13, 19118-19131. (www)

  @article{ , abstract = {High-fidelity 3D models are essential for immersive virtual and augmented reality (VR/AR) applications. However, the performance of current 3D recording devices is limited in several scenarios, such as dim light environments, long-distance measurements, and large-scale objects. Therefore, their applicability to indoor scenes is hindered. In this work, we propose a depth map reconstruction system that integrates an RGB-guided depth map super-resolution convolutional neural network (CNN) into a stand-alone Chaos LiDAR depth sensor. This system provides highly accurate depth estimates in various scenarios, particularly for indoor scenes with dim lighting or long distances ranging from 4 m to 6 m. We address two design challenges to maximize the quality of the reconstructed depth map of the system. First, the misalignment across RGB-depth sensors is addressed using a two-stage calibration pipeline. Second, the lack of large-scale real-world LiDAR datasets is addressed by generating a large-scale synthetic dataset and adopting transfer learning. Experimental results show that our proposed system significantly outperforms the commercial RGB-D recording device RealSense D435i in terms of subjective visual perception, precision, and density of depth estimates, making it a promising solution for general indoor scene recording.}, archive = {J_ACCESS}, author = {Yu-Chun Ding and Chia-Yu Chang and Pei-Rong Li and Chao-Tsung Huang and Yung-Chen Lin and Tsung Chen and Wei-Lun Lin and Cheng-Ting Lee and Fan-Yi Lin and Yuan-Hao Huang}, doi = {10.1109/ACCESS.2025.3532621}, journal = {IEEE Access}, pages = {19118-19131}, shortjournal = {IEEE Access}, title = {High-fidelity depth map reconstruction system with RGB-guided super resolution CNN and cross-calibrated chaos LiDAR}, volume = {13}, year = {2025}, }
• (2025). A 3D-printed spherical dual-circular polarized luneburg lens antenna for ku-band. ACCESS, 13, 19110-19117. (www)

  @article{ , abstract = {In this paper, we propose a novel three-dimensional-printed Luneburg lens for converting linear polarization to dual-circular polarization and successfully apply it in a multibeam antenna. The discrete unit cell, used as the gradient-index material to approximate the required refractive index in the Luneburg lens, has its effective refractive index calculated by effective medium theory. To achieve the polarization conversion, the symmetry of the unit cell is disrupted. A 100 mm diameter lens is designed to operate at 12.6-17.1 GHz, fed by a low-profile dual-polarized magneto-electric dipole antenna. By analyzing the size and isolation of the antenna, a multibeam lens antenna with five feeds was designed, which features a compact antenna layout and low coupling between them. The multibeam Luneburg lens antenna enables beam scanning of ±60° in the horizontal plane. Measurement results show an overlapped bandwidth of 30% with reflection coefficients below -10 dB and axial ratios under 3 dB. At the center frequency of 15 GHz, the gain reaches 21 dBi, approximately 14 dB higher than the magneto-electric dipole antenna without the lens.}, archive = {J_ACCESS}, author = {Qing Wang and Xiaohai Han and Pu Yu and Jian-Qiang Hou and Zhen-Ya Lei}, doi = {10.1109/ACCESS.2025.3531956}, journal = {IEEE Access}, pages = {19110-19117}, shortjournal = {IEEE Access}, title = {A 3D-printed spherical dual-circular polarized luneburg lens antenna for ku-band}, volume = {13}, year = {2025}, }
• (2025). Linear active disturbance rejection control-based voltage controller for buck and boost DC/DC converters in DC distribution grids. ACCESS, 13, 19085-19109. (www)

  @article{ , abstract = {DC distribution grids have recently gained research attention for the efficient integration of converter-interfaced distributed energy resources (DER). This paper presents a voltage controller for the buck and boost DC/DC DER-interfacing converters that operate in voltage control mode, acting as DC grid-forming converters. A linear active disturbance rejection control (L-ADRC) model is proposed, consisting of an augmented Kalman filter for the state and disturbance estimation, an adaptive state reference trajectory generator and a linear quadratic regulator as feedback controller. This L-ADRC model is formulated according to the generalised ADRC concept, making the voltage controller applicable to converters of the non-minimum phase (NMP) class, like the boost DC/DC converters in voltage control mode, and suitable for matched and mismatched disturbances, opposite to the original ADRC, which exists mostly in literature of converter controllers. The formulation of the proposed L-ADRC model in the non-canonical form facilitates the employment of model-based estimation and feedback control methods, whose performance is determined through the design of several parameters. This provides more degrees-of-freedom in the design of the voltage controller, beyond the design of the common L-ADRC formulation based on the bandwidth of the linear extended state observer and the scaling factor of a proportional error feedback controller. In addition, the physical significance of the converter’s states allows the integration of additional control functions, relying on the electrical quantities of the converter, for the enhancement of the performance of the voltage controller. For this, a virtual impedance-based current limiter is integrated in the L-ADRC model, which is necessary for preventing high currents at the converter’s switches. Moreover, the formulation of the adaptive state reference trajectory of the L-ADRC model according to the estimated disturbance provides a smooth state reference to the state feedback controller and enhances the robustness of the voltage controller against disturbances. The L-ADRC is designed based on both frequency and time domain analyses, contrary to the design approaches of ADRC converter controllers in literature. It is validated in a hardware-in-the-loop implementation and the performance is analysed in simulation against a PID voltage controller.}, archive = {J_ACCESS}, author = {Asimenia Korompili and Oemer Ekin and Marija Stevic and Veit Hagenmeyer and Antonello Monti}, doi = {10.1109/ACCESS.2025.3533080}, journal = {IEEE Access}, pages = {19085-19109}, shortjournal = {IEEE Access}, title = {Linear active disturbance rejection control-based voltage controller for buck and boost DC/DC converters in DC distribution grids}, volume = {13}, year = {2025}, }
• (2025). Set-membership estimation for complex networks under FlexRay protocol based on the proportional-integral observer with probability constraint. ACCESS, 13, 19075-19084. (www)

  @article{ , abstract = {This paper explores the design of proportional-integral observer (PIO) for a type of time-varying stochastic nonlinear complex networks in which the network dynamics are corrupted by the unknown but bounded noises. With the aim to efficiently regulate the data exchange between network nodes, a transmitting scheme called FlexRay protocol (FRP) is adopted. It is the primary objective to provide a proportional-integral (PI) estimation approach with the capability of driving all estimation errors at individual nodes into a desired region with a predetermined probability. Sufficient conditions are presented, via certain optimization techniques, to enure the existence of desired PIO and corresponding observer parameters. Subsequently, within the established framework, we solve two sub-optimization problems to guarantee the locally optimal estimation performance. Finally, the effectiveness of the proposed paradigm is shown via a numerical simulation example.}, archive = {J_ACCESS}, author = {Chaochen Wang and Miaomiao Shi}, doi = {10.1109/ACCESS.2025.3528653}, journal = {IEEE Access}, pages = {19075-19084}, shortjournal = {IEEE Access}, title = {Set-membership estimation for complex networks under FlexRay protocol based on the proportional-integral observer with probability constraint}, volume = {13}, year = {2025}, }
• (2025). Rapid control prototyping and analysis of a fault diagnosis and amplifying algorithm for broken rotor bar induction motor drives. ACCESS, 13, 19064-19074. (www)

  @article{ , abstract = {This paper introduces a sensitivity-enhanced fault detection algorithm, termed the Fault Diagnosis and Amplifying (FDnA) method, designed to amplify fault signals in induction motors with broken rotor bars. The FDnA algorithm employs a cascaded state observer structure, integrating stator flux linkage and stator current observers based on an analytical model of an induction motor. By multiplying the output signals of these observers, the algorithm significantly enhances the sensitivity of fault detection. The FDnA method is robust against external environmental conditions and excels in accurately detecting broken rotor bar faults. Additionally, it offers a cost-effective and retrofittable solution by replacing traditional sensors and interfacing circuits with state observers, eliminating the need for additional hardware. Comprehensive validation through simulations and rapid control prototyping highlights the superior fault detection sensitivity of the FDnA method compared to conventional approaches, establishing it as a reliable and efficient advancement in fault detection for induction motors.}, archive = {J_ACCESS}, author = {Jina Choi and Se Hwan Kim and Woongkul Lee and Jae Suk Lee}, doi = {10.1109/ACCESS.2025.3532990}, journal = {IEEE Access}, pages = {19064-19074}, shortjournal = {IEEE Access}, title = {Rapid control prototyping and analysis of a fault diagnosis and amplifying algorithm for broken rotor bar induction motor drives}, volume = {13}, year = {2025}, }
• (2025). Fabrication and performance analysis of novel HTV silicon rubber nanocomposites for high voltage insulation in outdoor environment. ACCESS, 13, 19049-19063. (www)

  @article{ , abstract = {Silicon rubber incorporated with nano silica with a wide range of percentages has been investigated for the performance analysis of high voltage insulation. Electrical power systems need robust and long-term high-performance insulation. Polymeric insulators have revolutionized electrical power system insulations with their lightweight and high mechanical endurance. Adding silica as an inorganic filler has enhanced electrical, mechanical, and thermal properties with variations among the specific ranges. Fabrication of silicon rubber nanocomposites was accomplished by a highly accurate procedure that included extensive testing with rubber technology. The extraordinarily high resistive nature of silicon rubber nanocomposites displays the insulation levels attained by nano-filler addition to the base matrix. The most considerable band gap energy was found in Silicone Rubber with 1% silica nano-composite filling, followed by 3% and 7% nanocomposites with an improvement of 71% and 76%, respectively. 1% silica nanocomposites have the highest DC resistance value among all nanocomposites. The lowest DC resistance was noticed in 5% silica-filled nanocomposites with 134.5G $\Omega $ at 60°. At the same time, 3% silica-filled samples showed the highest stability under mechanical and thermal stresses. This research was performed at the High Voltage Laboratory, National University of Sciences & Technology, Pakistan. Performance analysis of nanocomposites has been personated with an extensive improvement in insulating regions and lower values of partial discharge behaviors when electric stress is applied. Mechanical and thermal characterization of nanocomposites shows that silica has strengthened the silicon rubber structure for resilient and long-term insulation behaviors in outdoor environments.}, archive = {J_ACCESS}, author = {Aqeel Ur Rehman and Muhammad Salman Khan and Abraiz Khattak and Yasin Khan and Khalid Hamad Alkhalid}, doi = {10.1109/ACCESS.2025.3533654}, journal = {IEEE Access}, pages = {19049-19063}, shortjournal = {IEEE Access}, title = {Fabrication and performance analysis of novel HTV silicon rubber nanocomposites for high voltage insulation in outdoor environment}, volume = {13}, year = {2025}, }
• (2025). A simple cable radiated emission evaluation method for table-top setups according to CISPR 11 and CISPR 32. ACCESS, 13, 19040-19048. (www)

  @article{ , abstract = {CISPR 11 and CISPR 32 define the radiated emission tests to be conducted in a semi-anechoic chamber equipped with controllable turntable and antenna mast, leading to an expensive and time-consuming test procedure. By noting the fact that table-top test setup usually involves long interconnecting cable bundle that radiates significantly, the work proposes a fast and simple pre-compliance radiated emission evaluation method based on cascaded Hertzian dipoles and its radiation theory. Several factors in the CISPR 11/32 test setups are integrated into this evaluation method to mimic a real test procedure. The accuracy of the proposed method is validated by an experimental test. A systematic analysis of the accuracy and applicability of the proposed method is also provided, showing that the method presents a simple, reliable and easy-to-be-implemented prediction tool for early-stage pre-compliance radiation evaluation, according to CISPR 11/32 testing.}, archive = {J_ACCESS}, author = {Xinglong Wu and Alessandro Grande and Xiaokang Liu and Flavia Grassi and Giordano Spadacini and Sergio A. Pignari and Fabio Vallarino and Luca Ghezzi}, doi = {10.1109/ACCESS.2025.3532460}, journal = {IEEE Access}, pages = {19040-19048}, shortjournal = {IEEE Access}, title = {A simple cable radiated emission evaluation method for table-top setups according to CISPR 11 and CISPR 32}, volume = {13}, year = {2025}, }
• (2025). Application of minimax optimization mechanism in chinese-english machine translation quality estimation. ACCESS, 13, 19026-19039. (www)

  @article{ , abstract = {Machine Translation Quality Estimation (MTQE) is pivotal in bridging the gap between machine-generated translations and human translation quality, especially in real-time applications where post-editing is not feasible. Despite advancements with Neural Machine Translation (NMT), challenges such as mistranslation, omissions, and over-translation persist. Traditional MTQE models often suffer from incoherent optimization goals due to their dual phase architecture, limiting their effectiveness. In this study, we introduce a novel approach that integrates minimax optimization to unify the prediction and estimation phases under a common optimization goal. Utilizing the Ranger optimizer, our model comprises a generator based on the T5 (Text-to-Text Transfer Transformer) and a discriminator leveraging Convolutional Neural Networks (ConvNet). Additionally, we incorporate a data reliability screening module to ensure the discriminator is trained on high-quality data. Experiments conducted on the Chinese-English corpus demonstrate that the superiority of proposed approach. Furthermore, the generator maintains comparable BLEU scores to baseline models, confirming that the minimax optimization mechanism does not compromise its performance. Ablation studies highlight the optimal settings for the optimizer, pooling strategies, and learning rates, underscoring the importance of data reliability screening in achieving stable training outcomes. Our findings indicate that minimax optimization is a viable strategy for enhancing MTQE models, offering a path toward more accurate and reliable translation quality assessments.}, archive = {J_ACCESS}, author = {Xiaomei Zhang}, doi = {10.1109/ACCESS.2025.3533656}, journal = {IEEE Access}, pages = {19026-19039}, shortjournal = {IEEE Access}, title = {Application of minimax optimization mechanism in chinese-english machine translation quality estimation}, volume = {13}, year = {2025}, }
• (2025). Predefined time anti-unwinding control of rigid spacecraft attitude tracking. ACCESS, 13, 19016-19025. (www)

  @article{ , abstract = {This paper addresses the predefined time anti-unwinding attitude tracking control of rigid spacecraft with inertial uncertainties and external disturbances. Firstly, a new predefined-time terminal sliding surface (FTS) is proposed with hybrid technique. Secondly, a simple predefined-time sliding mode control (PTAC) is exploited to solve the robust attitude tracking problem. It is proved that the proposed PTAC can ensure that the attitude tracking errors tend to an arbitrary small bound centered on equilibrium point within predefined time and then go to equilibrium point asymptotically. The features of the PTAC includes robust, anti-unwinding and predefined time. To verify the effectiveness of the developed scheme, the numerical simulations are demonstrated.}, archive = {J_ACCESS}, author = {Liangmou Hu and Zeng Wang and Hanghang Wang and Changrui Chen and Heng Yue and Jiale Wang}, doi = {10.1109/ACCESS.2025.3529035}, journal = {IEEE Access}, pages = {19016-19025}, shortjournal = {IEEE Access}, title = {Predefined time anti-unwinding control of rigid spacecraft attitude tracking}, volume = {13}, year = {2025}, }
• (2025). Using attention for improving defect detection in existing RC bridges. ACCESS, 13, 18994-19015. (www)

  @article{ , abstract = {In the constantly growing need for sustainable mobility and transportation, on-site inspections of existing reinforced concrete (RC) bridges are critical in ensuring the safety of such infrastructures. However, surveying RC bridges presents several challenges, such as the high costs and effort required by the surveyors, the subjectivity in assessing identified defects, and the possible lapses of attention when inspections are systematically repeated on different bridges. Hence, traditional methods of on-site inspection can be enhanced by leveraging digital innovations and by developing new instruments that support road management companies in ensuring the safety of the existing infrastructure. Among the new technologies, deep learning-based object detection systems provide promising and effective solutions. As such, this research proposes a new, simple, intuitive and efficient tool to support engineers and surveyors in assessing the health state of existing RC bridges. To this end, domain experts gathered and labelled a dataset of real images containing typical defects found in existing RC bridges. Consequently, an improved version of YOLO11, embedding attention mechanisms to allow the network to focus on the most relevant details in each image, was trained, tested, and validated on the provided dataset, showing an overall improvement of quantitative metrics such as precision and recall, while retaining enough computational efficiency to allow real-time implementation on constrained devices. Visual explanations achieved via the Eigen-CAM algorithm were also exploited to evaluate the reliability of the predictions. The model was finally embedded in an end-to-end tool offering a graphical user interface (GUI) to allow an effective interaction between the domain expert and the machine. Overall, the proposal revealed its potential to improve the effectiveness of the survey, lowering the burden on surveyors and engineers and providing a reliable method to improve the overall security in large RC bridges portfolios.}, archive = {J_ACCESS}, author = {Sergio Ruggieri and Angelo Cardellicchio and Andrea Nettis and Vito Renò and Giuseppina Uva}, doi = {10.1109/ACCESS.2025.3532832}, journal = {IEEE Access}, pages = {18994-19015}, shortjournal = {IEEE Access}, title = {Using attention for improving defect detection in existing RC bridges}, volume = {13}, year = {2025}, }
• (2025). The effect of magnetization on the production process of soft magnetic composite stators of axial flux motors. ACCESS, 13, 18983-18993. (www)

  @article{ , abstract = {Axial flux offer a promising and reliable solution for various applications. The use of soft magnetic composite (SMC) materials allows to resolve some of the challenges associated with these motors. Two axial flux motors with identical designs are devolved, with the only variation being in the preparation of the SMC stators. The effect of magnetization during the stator manufacturing process has been assessed. Various tests are conducted in order to compare the performance of the magnetized and non-magnetized stators. Distinct differences have been noted, showing some advantages for the prototype with aligned SMC material. These results are further explained through microstructural analyses.}, archive = {J_ACCESS}, author = {Emir Pošković and Fausto Franchini and Elisa Fracchia and Silvio Vaschetto and Luca Ferraris and Alberto Tenconi}, doi = {10.1109/ACCESS.2025.3532207}, journal = {IEEE Access}, pages = {18983-18993}, shortjournal = {IEEE Access}, title = {The effect of magnetization on the production process of soft magnetic composite stators of axial flux motors}, volume = {13}, year = {2025}, }
• (2025). Identifying the factors affecting student academic performance and engagement prediction in MOOC using deep learning: A systematic literature review. ACCESS, 13, 18952-18982. (www)

  @article{ , abstract = {The increasing reliance on Massive Open Online Courses (MOOCs) has transformed the landscape of education, particularly during the COVID-19 pandemic, where e-learning became essential. However, the effectiveness of MOOCs in enhancing student academic performance and engagement remains a key challenge, compounded by high dropout rates and low retention. This study presents a systematic literature review (SLR) conducted over a five-year period (2019–2024) to identify factors affecting student academic performance and engagement prediction in MOOCs, utilizing Deep Learning (DL) methods. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematically analyzing articles from five major academic databases: ScienceDirect, SpringerLink, Scopus, Taylor & Francis, and Wiley Online. A total of 70 articles were selected for in-depth analysis, focusing on key predictors of student performance and engagement, including demographic data, behavioral patterns, learning activities, and clickstream data. The review highlights the capabilities of DL techniques in predicting student outcomes, such as retention, dropout, and engagement, offering valuable insights for educators and policymakers aiming to improve MOOC-based learning environments. By conducting SLR using PRISMA model, we identified research findings and gaps by proposing a conceptual framework for developing future personalized and adaptive e-learning environment for the inclusive MOOC based hard of hearing and low vision learners. This paper concludes by discussing implications for future personalized and adaptive e-learning environments and the necessity of comprehensive teacher training programs to navigate these evolving educational technologies.}, archive = {J_ACCESS}, author = {Shahzad Rizwan and Chee Ken Nee and Salem Garfan}, doi = {10.1109/ACCESS.2025.3533915}, journal = {IEEE Access}, pages = {18952-18982}, shortjournal = {IEEE Access}, title = {Identifying the factors affecting student academic performance and engagement prediction in MOOC using deep learning: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Highly parallel regular expression matching using a real processing-in-memory system. ACCESS, 13, 18937-18951. (www)

  @article{ , abstract = {Processing-in-Memory (PIM) is an emerging computing paradigm exploiting a cutting-edge memory device (PIM device) that integrates hundreds to thousands of processing units with the memory modules. A data-intensive application running in a host system can offload a portion of its tasks to the processing units in the PIM device, not only to exploit their processing capabilities but also to mitigate the contention in host memory accesses. However, such task offloading has the intrinsic overhead of transferring data between host memory and PIM device, which frequently hinders obtaining performance gain by exploiting the device. In this paper, we present a framework for a PIM-enabled regular expression matching that offloads the pattern-matching (scanning) engine to the PIM device, taking care to minimize the overhead. We implement an application based on the framework that runs on an off-the-shelf PIM system that has recently emerged into the market, and investigate the feasibility of Processing-in-Memory by comparing its performance with its PIM-oblivious implementation. Experimental results on a real system show that our application reduces the overall execution time by up to 96% compared with the multithreaded PIM-oblivious application when the input data size is 1 GB.}, archive = {J_ACCESS}, author = {Jeonghyeon Joo and Hyojune Kim and Hyuck Han and Eul Gyu Im and Sooyong Kang}, doi = {10.1109/ACCESS.2025.3532944}, journal = {IEEE Access}, pages = {18937-18951}, shortjournal = {IEEE Access}, title = {Highly parallel regular expression matching using a real processing-in-memory system}, volume = {13}, year = {2025}, }
• (2025). Agentic AI: Autonomous intelligence for complex Goals—A comprehensive survey. ACCESS, 13, 18912-18936. (www)

  @article{ , abstract = {Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability. We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively.}, archive = {J_ACCESS}, author = {Deepak Bhaskar Acharya and Karthigeyan Kuppan and B. Divya}, doi = {10.1109/ACCESS.2025.3532853}, journal = {IEEE Access}, pages = {18912-18936}, shortjournal = {IEEE Access}, title = {Agentic AI: Autonomous intelligence for complex Goals—A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). Optimal active-reactive power dispatch for distribution network with carbon trading based on improved multi-objective equilibrium optimizer algorithm. ACCESS, 13, 18899-18911. (www)

  @article{ , abstract = {In distribution networks with a high proportion of renewable energy, the optimal active-reactive power dispatch problem (OARPD) is increasingly complex due to the volatility and randomness of wind and solar output, as well as variations in grid load. This paper proposes a new low-carbon scheduling model that incorporates a tiered carbon emission trading (CET) mechanism, minimizes operating costs, and reduces voltage deviations. Additionally, to address constraints on the number of discrete device actions within the scheduling period, a dynamic reactive power optimization strategy based on tiered operating costs is introduced. Then proposed an improved multi-objective equilibrium optimizer (IMOEO) algorithm for solving the OARPD problem with renewable generators. The IMOEO algorithm incorporates a Sobol sequence initialization method, dynamic adjustment factors, and particles that employ various evolutionary strategies based on the crowding distance. These elements are integrated to enhance population diversity and improve global exploration capabilities. Subsequently, the algorithm is applied to an modified IEEE-33 distribution network, followed by a thorough statistical analysis of the results. The findings indicate that IMOEO outperforms other methods in achieving a superior non-dominated Pareto front, effectively addresses the OARPD problem with renewable energy, and delivers high-quality solutions.}, archive = {J_ACCESS}, author = {Furong Tu and Sumei Zheng and Kuncan Chen}, doi = {10.1109/ACCESS.2025.3532750}, journal = {IEEE Access}, pages = {18899-18911}, shortjournal = {IEEE Access}, title = {Optimal active-reactive power dispatch for distribution network with carbon trading based on improved multi-objective equilibrium optimizer algorithm}, volume = {13}, year = {2025}, }
• (2025). Transient electromagnetic data noise suppression method based on MPA-VMD-SVD. ACCESS, 13, 18890-18898. (www)

  @article{ , abstract = {The transient electromagnetic method (TEM) is an efficient physical detection method widely used in underground space detection. However, electromagnetic noise interference poses significant challenges, as the TEM late signal is often submerged in noise, severely impacting the detection accuracy and depth. Therefore, this study proposes a TEM data noise suppression method based on the marine predators algorithm (MPA) to optimize variational mode decomposition (VMD) combined with singular value decomposition (SVD). Firstly, MPA is employed to select the main parameters of VMD. Secondly, the noisy data are decomposed into several intrinsic mode functions using the adaptive variational property of VMD. Finally, the mode containing signal information undergoes SVD to remove residual noise, after which the denoised TEM signal is reconstructed. This study simulates TEM signals with different noise levels for testing. The proposed method is compared to stacking-averaging, wavelet threshold denoising, SVD, empirical mode decomposition, and unoptimized VMD. The results showed that the model exhibits superior noise reduction performance. In addition, measured noise experiments are conducted to verify the practicability of the method. Simulation and field experiments indicated that MPA-VMD-WTD is an effective method for suppressing TEM data noise.}, archive = {J_ACCESS}, author = {Yuheng Li and Yang Zhang and Jiwei Shen and Xinze Wen and Jianmei Chen and Wanqiang Zhu}, doi = {10.1109/ACCESS.2025.3530447}, journal = {IEEE Access}, pages = {18890-18898}, shortjournal = {IEEE Access}, title = {Transient electromagnetic data noise suppression method based on MPA-VMD-SVD}, volume = {13}, year = {2025}, }
• (2025). Novel national high-precision time synchronization system for mobile communication. ACCESS, 13, 18878-18889. (www)

  @article{ , abstract = {Time synchronization networks are vital infrastructures for numerous applications. We introduce an innovative architecture addressing the high-precision requirements of national time networks. This scheme combines DCV (distributed common-view) time transfer in the backbone with TDM-based (time division multiplex-based) PTP in metro networks. It enables flexible designation of time servers as national primaries. All the time servers are interconnected via a centralized data center. Theoretical analysis and extensive experiments validate successful high-precision national synchronization over large-scale transport networks. Notably, we achieved end-to-end accuracy of 36 ns across 2150 km spanning 3 provinces/cities, marking a milestone. This solution paves the way for future networks and applications requiring ultra-high time synchronization precision.}, archive = {J_ACCESS}, author = {Liuyan Han and Rongduo Lu and Jiachen Li and Xinyu Chen and Hui Cao and Jia Shen and Dechao Zhang and Han Li}, doi = {10.1109/ACCESS.2025.3532088}, journal = {IEEE Access}, pages = {18878-18889}, shortjournal = {IEEE Access}, title = {Novel national high-precision time synchronization system for mobile communication}, volume = {13}, year = {2025}, }
• (2025). Non-fragile h∞ deconvolution filter design for uncertain two-dimensional markovian jump systems with state-varying delays. ACCESS, 13, 18863-18877. (www)

  @article{ , abstract = {This paper addresses the problem of non-fragile $H_{\infty }$ deconvolution filtering for two-dimensional (2-D) Markovian jump systems with state-varying delays and norm-bounded uncertain terms. First, 2-D Markovian jump systems are modeled by using Fornasini-Marchesini (FM) model. Based on this system, a 2-D non-fragile $H_{\infty }$ deconvolution filter is designed. Second, by utilizing 2-D Lyapunov stability theory, stability criteria are derived to ensure that the filtering error system remains stochastically stable and satisfies the $H_{\infty }$ performance level $\gamma $ . Furthermore, sufficient conditions for the mode and delay dependence of the non-fragile $H_{\infty }$ deconvolution filter, as well as mode-dependent filter parameters, are achieved by using the linear matrix inequality (LMI) methods. Finally, the feasibility and effectiveness of the proposed non-fragile $H_{\infty }$ deconvolution filtering scheme are further demonstrated by an image denoising experiment.}, archive = {J_ACCESS}, author = {Yakufu Kasimu and Gulijiamali Maimaitiaili}, doi = {10.1109/ACCESS.2025.3533025}, journal = {IEEE Access}, pages = {18863-18877}, shortjournal = {IEEE Access}, title = {Non-fragile h∞ deconvolution filter design for uncertain two-dimensional markovian jump systems with state-varying delays}, volume = {13}, year = {2025}, }
• (2025). Decentralized frequency regulation by using novel PID sliding mode structure in multi-area power systems with hydropower turbines. ACCESS, 13, 18850-18862. (www)

  @article{ , abstract = {This paper introduces a novel sliding mode control (SMC) design utilizing a Proportional-Integral-Derivative (PID) Sliding Surface (SS) for frequency regulation in multi-area electrical power systems (EPSs) with hydropower turbines, accounting for random load conditions, parameter variations, and matched uncertainties. The global system stability of this new approach is mathematically analyzed using Lyapunov theory alongside a novel Linear Matrix Inequality (LMI) technique. A robust strategy is employed through the reaching law method to ensure that frequency deviations converge to zero, even under varying load demands. Despite the presence of parameter variations and random load conditions, the control objectives remain achievable, highlighting the robustness of the proposed method. Moreover, this strategy results in lower overshoot, quicker response times, and reduced chattering effects compared to current traditional SMCs including Proportional-Integral (PI), double PI (DPI), and Proportional-Derivative (PD) sliding surfaces, demonstrating its advantages. Finally, the effectiveness of the suggested scheme is further verified based on three-area EPSs with matched uncertainties, indicating that the proposed scheme achieves stable and robust performance, further confirming its superiority through simulation results.}, archive = {J_ACCESS}, author = {Dao Trong Tran and Anh-Tuan Tran and van van Huynh and Ton Duc do}, doi = {10.1109/ACCESS.2025.3532516}, journal = {IEEE Access}, pages = {18850-18862}, shortjournal = {IEEE Access}, title = {Decentralized frequency regulation by using novel PID sliding mode structure in multi-area power systems with hydropower turbines}, volume = {13}, year = {2025}, }
• (2025). Social engineering analysis framework: A comprehensive playbook for human hacking. ACCESS, 13, 18827-18849. (www)

  @article{ , abstract = {Social Engineering attacks are among the most exploited methods in today’s cybersecurity threat landscape. Despite the impact and the volume of such incidents, there is still a surprising lack of comprehensive tools or frameworks offering an in-depth insight into Social Engineering attacks. The paper delivers a handy yet comprehensive framework for the analysis of the Social Engineering tactics, techniques, and procedures (TTPs), distinguishing six major phases of the Social Engineering process, together with detailed TTPs linked with each of them. In the long-term, it may lead to devising better and more effective defense mechanisms against such attacks by providing an in-depth insight into the process and methods behind them. The outcome presents that framework in the form of a legible, transparent, and ready-to-use matrix, similar to the MITRE ATT&CK matrix. The paper also contains a cross-comparison between the proposed framework and the MITRE ATT&CK to underline the added value of the proposed approach. In order to demonstrate the practical usefulness of the approach proposed in this paper, after formulating the entire framework, we apply it to decompose and analyze in detail some real-life Social Engineering scenarios.}, archive = {J_ACCESS}, author = {Wojciech Nowakowski}, doi = {10.1109/ACCESS.2025.3532999}, journal = {IEEE Access}, pages = {18827-18849}, shortjournal = {IEEE Access}, title = {Social engineering analysis framework: A comprehensive playbook for human hacking}, volume = {13}, year = {2025}, }
• (2025). Automated zebrafish spine scoring system based on instance segmentation. ACCESS, 13, 18814-18826. (www)

  @article{ , abstract = {In studying new medicines for osteoporosis, researchers use zebrafish as animal subjects to test drugs and observe the growth situation of their vertebrae in the spine to confirm the efficacy of new medicines. However, the current method for evaluating efficacy is time-consuming and labor-intensive, requiring manual observation. Taking advantage of advancements in deep learning technology, we propose an automatic method for detecting and recognizing zebrafish vertebrae of the images captured from image sensors to solve this problem. Our method was designed using Mask R-CNN as the instance segmentation backbone, enhanced with a mask enhancement module and a small object preprocessing approach to strengthen its detection abilities. Compared to the original Mask R-CNN architecture, our method improved the mean average precision (mAP) score for vertebra bounding box and mask detection by 7.1% to 97.7% and by 1.2% to 96.6%, respectively. Additionally, we developed a system using these detection algorithms to automatically calculate spinal vertebra growth scores, providing a valuable tool for researchers to assess drug efficacy.}, archive = {J_ACCESS}, author = {Wen-Hsin Chen and Tien-Ying Kuo and Yu-Jen Wei and Cheng-Jung Ho and Ming-der Lin and Huan Chen and Wen-Ying Lin}, doi = {10.1109/ACCESS.2025.3532680}, journal = {IEEE Access}, pages = {18814-18826}, shortjournal = {IEEE Access}, title = {Automated zebrafish spine scoring system based on instance segmentation}, volume = {13}, year = {2025}, }
• (2025). An enhanced end-to-end object detector for drone aerial imagery. ACCESS, 13, 18798-18813. (www)

  @article{ , abstract = {DETR-like detectors have gained increasing popularity in current practical applications. However, we observe that their pipeline still suffer from several challenges, including unbalanced distribution of positive and negative samples, low-quality initial prediction boxes, and unreasonable gradient structure in the decoding stage. These challenges hinder both the convergence speed and detection performance of the model. To address these issues, we propose an enhanced DETR-like model called EM-DETR. It combines three innovative methods, including Dynamic Groups Assignment, Mixed Query Re-Selection, and Look Forward Stage. Dynamic Groups Assignment employs adaptive parameters to balance the number of positive and negative samples, providing more effective supervision signals for ground-truth boxes. Mixed Query Re-Selection utilizes high-quality bounding boxes regressed by subnet to initialize decoder queries, offering superior prior information for the decoder. Look Forward Stage introduces a more rational gradient structure which eliminates inter-layer information bias between decoders. We conduct extensive experiments to evaluate the effectiveness of our proposed method. On VisDrone2021-DET, EM-DETR with ResNet50 achieved 23.9% AP after 12 epochs of training. Compared to the baseline, this represents an improvement of 4.7% AP. Moreover, the excellent performance of EM-DETR on AI-TOD and Crowdhuman proves the generalization capability of the proposed method.}, archive = {J_ACCESS}, author = {Quan Yu and Qiang Tong and Lin Miao and Lin Qi and Xiulei Liu}, doi = {10.1109/ACCESS.2025.3533037}, journal = {IEEE Access}, pages = {18798-18813}, shortjournal = {IEEE Access}, title = {An enhanced end-to-end object detector for drone aerial imagery}, volume = {13}, year = {2025}, }
• (2025). Physics informed neural networks for modeling large-scale wind driven ocean circulation. ACCESS, 13, 18773-18797. (www)

  @article{ , abstract = {This study investigates the application of the physics informed neural network as a meshfree collocation method for approximating solutions to large-scale wind driven ocean circulation models. By integrating the Stommel and Stommel-Munk models into the neural network framework, the neural network provides a viable alternative to traditional numerical methods for simulating ocean circulation. The architecture of the neural network was systematically optimized through hyperparameter tuning, including the selection of optimizers, activation functions, network configurations, and learning rate schedulers to ensure stable convergence and minimize fluctuations in training loss. The effects of different training point distributions, such as uniform, uniform-refined, random, and random-refined, were also examined. The results show that refining the distribution of training points near the western boundary layer can achieve similar accuracy and training performance even with fewer points. This approach highlights the potential of the physics informed neural network to address more complex oceanographic models, where conventional numerical methods may be constrained by data availability and computational cost.}, archive = {J_ACCESS}, author = {Boohyun An and Mohammad Z. Shanti and Chan Yeob Yeun and Ernesto Damiani and Sungmun Lee and Tae-Yeon Kim}, doi = {10.1109/ACCESS.2025.3532669}, journal = {IEEE Access}, pages = {18773-18797}, shortjournal = {IEEE Access}, title = {Physics informed neural networks for modeling large-scale wind driven ocean circulation}, volume = {13}, year = {2025}, }
• (2025). Designing a trust-aware reputation model for enhanced data quality in human-centric sensing systems. ACCESS, 13, 18760-18772. (www)

  @article{ , abstract = {Human-Centric Sensing (HCS) systems rely heavily on the participation of individuals using their mobile devices to gather data. Ensuring the quality of the data collected in such systems is critical, as poor or malicious data can significantly degrade system performance. To address this, we propose a trust-aware reputation model that evaluates the quality of user submissions and dynamically adjusts participant reputations. Firstly, our model distinguishes between intentional and unintentional low-quality contributions, applying stricter penalties for deliberate misbehavior while mitigating harsh consequences for accidental errors. Additionally, the model incorporates multi-source feedback, including witness reports, to provide a more accurate assessment of participant behavior. A forgiving mechanism is implemented, allowing participants who have previously misbehaved to regain their reputation through consistent high-quality contributions. Simulation results show that the proposed model effectively reduces the influence of malicious users and improves the overall trustworthiness of the system by promoting high-quality data submissions. Finally, we have compared our approach to the PRBTD method, and the results show that our model offers faster reputation growth for honest users, stronger penalties for malicious users, and better handling of unintentional contributions through adaptive penalties.}, archive = {J_ACCESS}, author = {Konstantina Banti and Malamati Louta}, doi = {10.1109/ACCESS.2025.3533306}, journal = {IEEE Access}, pages = {18760-18772}, shortjournal = {IEEE Access}, title = {Designing a trust-aware reputation model for enhanced data quality in human-centric sensing systems}, volume = {13}, year = {2025}, }
• (2025). Lighting spectrum optimization with deep learning for moss species classification. ACCESS, 13, 18749-18759. (www)

  @article{ , abstract = {Mosses, due to their sensitivity to environmental changes, are utilized in investigations related to air pollution, water quality, and carbon consumption and emissions. Methods modeling vegetation distribution, such as species distribution modeling (SDM), are useful for understanding climate change, but require precise sampling, which is a significant effort when large-scale periodic surveys are required. Remote sensing technology using satellite imagery and unmanned aerial vehicles (UAVs) reduces labor, but it is difficult to determine the species that are surrounded by a canopy in forests. Additionally, adequate lighting is crucial for detailed imaging in dark environments lacking ambient light. Hence, we propose a method for obtaining spectral information on moss in the forest using a deep learning model to train convolutional neural network models while optimizing a suitable light source for moss identification. For the image classification tasks of five species of Sphagnum, an optimized light source that combines 10 different spectral distributions within 400–800 nm, excluding spectral information at 600–700 nm from the RGB image’s red channel and emphasizing that at 700–800 nm, improved discrimination accuracy by 10% compared with that of images obtained with the D65 sunlight source. Imaging using lighting spectrum optimization is more cost-effective than multispectral imaging, and provides more accurate classification of plant species compared with RGB images. By using six or more optimal light sources, the classification accuracy was almost equivalent to that of a spectral bands selection model with spectral-wise self-attention from hyperspectral imaging (HSI) with 400–800 nm in 20 nm step (21 dimensions). The proposed method, which does not require expensive spectral cameras, has the potential to deliver recognition performance comparable to HSI, making it a practical and cost-effective solution for moss species identification.}, archive = {J_ACCESS}, author = {Kenichi Ito and Pauli Fält and Markku Hauta-Kasari and Shigeki Nakauchi}, doi = {10.1109/ACCESS.2025.3532760}, journal = {IEEE Access}, pages = {18749-18759}, shortjournal = {IEEE Access}, title = {Lighting spectrum optimization with deep learning for moss species classification}, volume = {13}, year = {2025}, }
• (2025). Dynamic connectivity hub: Multiple ISPs smart aggregation for optimized IoT connectivity. ACCESS, 13, 18734-18748. (www)

  @article{ , abstract = {The increasing complexity of Internet of Things (IoT) networks demands scalable and adaptive solutions to ensure reliable and efficient connectivity. As IoT ecosystems grow increasingly complex, multiple Internet Service Providers (ISPs) aggregation and dynamic traffic optimization have become critical for ensuring reliable, cost-effective, and high-performance network services. This paper proposes the Dynamic Connectivity Hub (DCH), a novel system that aggregates services from multiple ISPs - ranging from terrestrial (e.g., fiber optics, mobile 4G/5G), and satellite networks (e.g., LEO, GEO) - to dynamically distribute traffic to IoT devices. DCH uses an Artificial Neural Network (ANN) model to continuously optimize load balancing by assigning data rates to the most efficient ISPs in real time. By analyzing performance metrics such as latency, data rate, congestion, and cost, the ANN ensures that traffic is routed through the best-performing ISPs while discarding sub-optimal connections. The DCH is optimized for urban deployments, filling gaps in connectivity and providing backup coverage in areas with limited ISP infrastructure. While for rural deployments, DCH is optimized to serve as a primary connectivity provider. Extensive simulations demonstrate that the DCH outperforms traditional static ISP models in terms of latency reduction, cost efficiency, and resilience to outages. Validation through MATLAB simulations highlights the DCH’s ability to outperform baseline models by demonstrating a 50% reduction in latency, a 25% improvement in data rate allocation, and a 20% reduction in operational costs compared to traditional static models. This paper not only highlights the DCH’s potential as a flexible, scalable, and efficient solution but also presents it as a transformative solution for next-generation IoT networks, addressing connectivity challenges across diverse deployment scenarios.}, archive = {J_ACCESS}, author = {Hany Ali and Mohamed A. Abouelatta and Khaled Y. Youssef}, doi = {10.1109/ACCESS.2025.3529486}, journal = {IEEE Access}, pages = {18734-18748}, shortjournal = {IEEE Access}, title = {Dynamic connectivity hub: Multiple ISPs smart aggregation for optimized IoT connectivity}, volume = {13}, year = {2025}, }
• (2025). Contributions to the virtualization of the BNG. ACCESS, 13, 18713-18733. (www)

  @article{ , abstract = {The Border Network Gateway (BNG) assumes a pivotal role within the telecommunications network operator domain, particularly in broadband access services, by establishing and managing subscribers’ connections. It serves as a crucial component, being responsible for various essential services that allow subscribers’ connections and help optimizing infrastructure resources, including enforcing QoS policies and implementing Access Control Lists to safeguard against unauthorized access. Similarly to other elements within the operators’ infrastructure, the BNG has faced escalating challenges due to the surge in bandwidth demand, rapid subscriber growth, and the introduction of novel services with unique requirements. As a single point of failure for subscriber-operator access, it becomes crucial to enhance management flexibility, simplify the device, and improve overall resilience. Consequently, there has been a growing interest among operators in virtualizing the BNG, following a trajectory which is similar to the virtualization of other telecommunications networks devices, such as the residential gateway. This paper examines the case for virtualizing the BNG, starting with an analysis of the underlying motivations driving this virtualization effort. Next, it provides an overview of the State-of-Art, highlighting the current proposals and advances related with the Virtual Border Network Gateway (vBNG) concept. Furthermore, it proposes a reference vBNG architecture, also exploring the seamless integration with a virtual Customers’ Premise Equipment infrastructure. Finally, a practical Proof-of-Concept is presented and used to assess the feasibility of the vBNG concept.}, archive = {J_ACCESS}, author = {Jorge Proença and Ricardo Figueiredo and Vasco Pereira and Tiago Cruz and Paulo Simões}, doi = {10.1109/ACCESS.2025.3532548}, journal = {IEEE Access}, pages = {18713-18733}, shortjournal = {IEEE Access}, title = {Contributions to the virtualization of the BNG}, volume = {13}, year = {2025}, }
• (2025). An OFDM signal enhancement and demodulation method based on segmented asymmetric bistable stochastic resonance. ACCESS, 13, 18701-18712. (www)

  @article{ , abstract = {To address the output saturation issue in classical bistable stochastic resonance systems during the enhancement of weak orthogonal frequency division multiplexing (OFDM) signals, which results in low noise utilization efficiency, this study proposes an enhancement and demodulation technique based on a segmented asymmetric bistable stochastic resonance (SABSR) system. The SABSR model is developed by integrating a classical bistable SR system with a linear function and introducing an asymmetry factor. Using the adiabatic approximation theory, the Kramers escape rate and output signal-to-noise ratio (SNR) of the SABSR system are derived and analyzed. Additionally, transient response expressions for the left and right wells, as well as steady-state response expressions under OFDM signal input, are formulated, and the influence of the asymmetry factor on transient responses is thoroughly investigated. The SABSR system is then applied to OFDM signal enhancement and demodulation, with SNR gain used as the optimization metric. The quantum particle swarm optimization algorithm is employed to fine-tune system parameters. Simulation results demonstrate that, at an input SNR of 8 dB, the SABSR system achieves a bit error rate (BER) approximately 30% lower than that of the segmented symmetric system, significantly improving OFDM signal detection and demodulation performance.}, archive = {J_ACCESS}, author = {Gaohui Liu and Xiaqiang Chu}, doi = {10.1109/ACCESS.2025.3531924}, journal = {IEEE Access}, pages = {18701-18712}, shortjournal = {IEEE Access}, title = {An OFDM signal enhancement and demodulation method based on segmented asymmetric bistable stochastic resonance}, volume = {13}, year = {2025}, }
• (2025). A generalized zero-shot deep learning classifier for emotion recognition using facial expression images. ACCESS, 13, 18687-18700. (www)

  @article{ , abstract = {In the real world environment, various micro and nano facial expressions are generated according to the mental state of thought, which leads to a change in an emotional state. A generalizable automatic emotion recognition system is needed to catch these micro and nano facial emotions correctly. Unfortunately, the literature lacks a generalizable Automatic Emotion Recognitions (AER) system. There are various AER systems based on learning and non-learning techniques, but they find it difficult to novel unseen facial expressions, which means they lack generalization ability. This study proposes a generalized deep learning classifier, Generalized Zero-Shot Convolutional Neural Network (GZS-ConvNet), to solve the problem of generalization. The objective of the proposed approach is to enhance the classifier’s generalizability by employing sophisticated adaptation methodologies. The newly developed classifier is meticulously calibrated and optimized using facial expression datasets. Six different datasets of facial expressions were employed to test the effectiveness of the proposed method: FER2013, AffectNet, RAF-DB, CK+, KDEF, and JAFEE. Zero-shot classification is performed on different facial expression datasets to justify the generalizability of the proposed model. The results prove that the proposed model is highly generalizable.}, archive = {J_ACCESS}, author = {Vishal Singh Bhati and Namita Tiwari and Meenu Chawla}, doi = {10.1109/ACCESS.2025.3533580}, journal = {IEEE Access}, pages = {18687-18700}, shortjournal = {IEEE Access}, title = {A generalized zero-shot deep learning classifier for emotion recognition using facial expression images}, volume = {13}, year = {2025}, }
• (2025). A novel finite-control-set model predictive torque control for five-phase PMSM with improved harmonic current suppression. ACCESS, 13, 18677-18686. (www)

  @article{ , abstract = {There is the third harmonic current in five-phase permanent magnet synchronous motors (PMSMs), thus causing the distortion of stator currents and additional copper loss. Although the existing finite-control-set with virtual voltage vectors (FCS-V3s) can eliminate the harmonic voltage of harmonic subspace, it completely loses the ability to control the harmonic subspace. Moreover, the DC-link utilization is reduced due to the usage of V3s. To address these problems, a novel FCS model predictive torque control (FCS-MPTC) was proposed to be an alternative to the existing FCS-MPTC in this paper. First, a novel FCS was designed to achieve the double closed-loop control for both subspaces. Then, all the specific duty ratios were calculated and further limited to the normal range from the geometric point of view. Moreover, a wider amplitude range of available voltages was obtained in the novel FCS. Consequently, compared with the existing virtual-VVs-based FCS-MPTC (FCS-V3s-MPTC), the proposed method can effectively suppress the third harmonic current generated by the motor itself, improve the DC-link utilization while achieving the good steady-state and dynamic response performance. Finally, experimental results were presented to validate the effectiveness of proposed method.}, archive = {J_ACCESS}, author = {Weiguo Tao and Huawei Zhou and Chen Ye and Cheng Chen}, doi = {10.1109/ACCESS.2025.3532624}, journal = {IEEE Access}, pages = {18677-18686}, shortjournal = {IEEE Access}, title = {A novel finite-control-set model predictive torque control for five-phase PMSM with improved harmonic current suppression}, volume = {13}, year = {2025}, }
• (2025). Blockchain-enabled zero trust architecture for privacy-preserving cybersecurity in IoT environments. ACCESS, 13, 18660-18676. (www)

  @article{ , abstract = {This research introduced a new novel “Unified Quantum-Resilient Blockchain-Zero-Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF)” to upgrade the IoT environments with greater security. To enable privacy-preserving authentication, access control, and secure communication, the framework integrates blockchain technology with Zero Trust Architecture (ZTA) and post-quantum cryptography. A hybrid Reinforcement-Lattice Blockchain KeyGen for quantum-resilient key generation, Deep Q-Network Multi-Factor Secure Key (DQN-MFSK) for dynamic selection of keys, and Zero-Knowledge Proof for privacy-preserving signatures are employed to achieve secure IoT settings. This architecture entails data privacy and confidentiality, auditability and traceability, and withstanding evolving threats, including potential threats in terms of quantum attacks. It then uses blockchain technology for recording unalterable data of identity and access management while Zero-Knowledge Proofs (ZKP) ensures authentication and verification without revealing sensitive information. By decentralizing identity management and enabling multi-factor authentication, QBC-ZKPAF provides robust security and privacy solutions for IoT networks. The experimental results demonstrate the model’s effectiveness with 98% privacy preservation, 700 TPS throughput, 0.7 J energy consumption, 0.98 quantum resilience, and 96% access control effectiveness, making it highly suitable for modern IoT and blockchain applications.}, archive = {J_ACCESS}, author = {Mohammed A. Aleisa}, doi = {10.1109/ACCESS.2025.3529309}, journal = {IEEE Access}, pages = {18660-18676}, shortjournal = {IEEE Access}, title = {Blockchain-enabled zero trust architecture for privacy-preserving cybersecurity in IoT environments}, volume = {13}, year = {2025}, }
• (2025). USSD: Unsupervised sleep spindle detector. ACCESS, 13, 18644-18659. (www)

  @article{ , abstract = {Sleep spindles (SSs) appear in electroencephalogram (EEG) recordings during sleep stage N2, and they are usually detected through visual inspection by an expert. Labeling SSs in large datasets is time-consuming and depends on the expert criteria. In this work, we propose an unsupervised SS detector based on dictionary learning called the Unsupervised Sleep Spindle Detector (USSD). The proposed detector learns prototype SSs of different lengths (called atoms). An unsupervised adaptive threshold method based on the distribution of the automatically detected SS lengths is developed, which allows the adaptation of the USSD algorithm to different datasets in an unsupervised way. For each detection, the USSD estimates the probability of being an SS. The USSD performances on the labeled MASS-SS2 and INTA-UCH datasets yield F1-scores of $0.72 \pm 0.02$ and $0.72 \pm 0.04$ , respectively. The USSD outperforms the A7 and LUNA detectors, which are traditional unsupervised models. Next, we fine-tune the resulting USSD model with 20% of the labeled MASS-SS2 and INTA-UCH datasets, achieving F1 scores of $0.78 \pm 0.06$ and $0.75 \pm 0.05$ , respectively. In addition, the SSs detected by USSD on the unlabeled CAP dataset are used to pre-train a supervised deep learning method, which after fine-tuning with 20% of the MODA dataset, reaches an F1-score of $0.81 \pm 0.02$ .}, archive = {J_ACCESS}, author = {Edgardo Ramirez and Pablo A. Estevez and Martin D. Adams and Claudio A. Perez and Marcelo Garrido González and Patricio Peirano}, doi = {10.1109/ACCESS.2025.3532536}, journal = {IEEE Access}, pages = {18644-18659}, shortjournal = {IEEE Access}, title = {USSD: Unsupervised sleep spindle detector}, volume = {13}, year = {2025}, }
• (2025). An enhanced active disturbance rejection control scheme for DC voltage regulation in photovoltaic grid-connected four-leg inverter using a sliding mode observer. ACCESS, 13, 18623-18643. (www)

  @article{ , abstract = {The integration of photovoltaic (PV) systems with the grid connected four-leg voltage source inverters (4LVSI) offers more efficient power conversion and distribution. However, the complexity of these higher-order systems presents significant control challenges due to the sensitive nature of both DC bus voltage and 4LVSI output currents, which introduce complex dynamic interactions within the 4LVSI control system and limit its overall control performance, particularly under system uncertainties and disturbances such as variations in irradiance, PV cell temperature, and grid voltage. To address these challenges, an enhanced active disturbance rejection control (EADRC) scheme based on a super-twisting sliding mode observer (STSMO) is designed for the outer DC bus voltage control loop. The STSMO is incorporated in the ADRC method for its fast uncertainties and external disturbances estimations, high resilience against uncertainties, and good immunity to measurement noise. The proposed STSMO-based ADRC approach effectively estimates and compensates for system uncertainties and external disturbances, thereby enhancing DC bus voltage dynamics and stability, improving resilience against uncertainties, increasing immunity to measurement noise, ensuring better steady-state accuracy of the DC bus voltage, and increasing system reliability while reducing cost and size. The effectiveness and superiority of the proposed control method for the PV grid-connected 4LVSI system are validated through both simulation and real-time studies using the OPAL-RT simulator. The results demonstrate robust performance under parameter uncertainties and disturbances, including variations in irradiance, PV cell temperature, DC bus capacitor value, and grid voltage sags.}, archive = {J_ACCESS}, author = {Chebabhi Ali and Defdaf Mabrouk and Syphax Ihammouchen and Nicu Bizon and Benbouhenni Habib and Kessal Abdelhalim and Rekioua Toufik and Benkhoris Mohamed Fouad}, doi = {10.1109/ACCESS.2025.3533015}, journal = {IEEE Access}, pages = {18623-18643}, shortjournal = {IEEE Access}, title = {An enhanced active disturbance rejection control scheme for DC voltage regulation in photovoltaic grid-connected four-leg inverter using a sliding mode observer}, volume = {13}, year = {2025}, }
• (2025). A study of the interaction between implanted pacemakers and the power-frequency field produced by traction transformer in high-speed EMU. ACCESS, 13, 18608-18622. (www)

  @article{ , abstract = {The widespread integration of high-speed trains has significantly enhanced the quality of human life. However, the intricate electromagnetic environment within carriages has raised notable concerns, especially for passengers with pacemakers. This work assessed the electromagnetic exposure of three-chamber pacemaker wearers with standed and seated posture by a traction transformer and its cable system. The analysis included evaluating the distribution of induced electromagnetic fields within passengers’ pacemakers and heart tissue, calculating the induced voltage on electrodes to determine the potential health risks. The results indicated that the B $_{in~max}$ , E $_{in~max}$ of the passengers’ heart tissue are $7.53~\mu $ T and 47.626 mV/m. All the B $_{in~max}$ , E $_{in~max}$ well within the basic limits established for public electromagnetic exposure set by the International Commission on Non-Ionizing Radiation. The Ein on the pacemakers of both passengers mainly appeared at the metal parts, and the B $_{in~max}$ of passengers’ pacemakers is $20.754~\mu $ T, which achieves the minimum requirements for pacemakers to anti-electromagnetic interference as specified by the International Organization for Standardization. The $U_{in~max}$ at the unipolar and bipolar electrodes of passengers are 0.409 mV and 0.005 mV, which are smaller than the perception sensitivity. The above results illustrated that the electromagnetic fields generated by the traction transformer are within the recognized accepted limits for passengers with pacemaker, and we recommended that individuals with heart diseases should try to choose a pacemaker with better anti-interference ability in bipolar pacing mode and keep a distance from windows as possible when riding on high-speed trains.}, archive = {J_ACCESS}, author = {Rui Tian and Yu-Long Zhang and Mai Lu and Jian-Fang Deng}, doi = {10.1109/ACCESS.2025.3529939}, journal = {IEEE Access}, pages = {18608-18622}, shortjournal = {IEEE Access}, title = {A study of the interaction between implanted pacemakers and the power-frequency field produced by traction transformer in high-speed EMU}, volume = {13}, year = {2025}, }
• (2025). Efficient and fast light field compression via VAE-based spatial and angular disentanglement. ACCESS, 13, 18594-18607. (www)

  @article{ , abstract = {Light field (LF) imaging captures both spatial and angular information, which is essential for applications such as depth estimation, view synthesis, and post-capture refocusing. However, the efficient processing of this data, particularly in terms of compression and encoding/decoding time, presents challenges. We propose a Variational Autoencoder (VAE)-based framework to disentangle the spatial and angular features of light field images, focusing on fast and efficient compression. Our method uses two separate sub-encoders—one for spatial and one for angular features—to allow for independent processing in the latent space, which facilitates more streamlined compression workflows. Evaluations on standard light field datasets demonstrate that our approach reduces encoding and decoding time significantly, with a slight trade-off in Rate-Distortion (RD) performance, making it suitable for real-time applications.}, archive = {J_ACCESS}, author = {Soheib Takhtardeshir and Roger Olsson and Christine Guillemot and Mårten Sjöström}, doi = {10.1109/ACCESS.2025.3532608}, journal = {IEEE Access}, pages = {18594-18607}, shortjournal = {IEEE Access}, title = {Efficient and fast light field compression via VAE-based spatial and angular disentanglement}, volume = {13}, year = {2025}, }
• (2025). FWA-SVM network intrusion identification technology for network security. ACCESS, 13, 18579-18593. (www)

  @article{ , abstract = {In the digital age, the increasing demand for network security has driven research on efficient network intrusion detection systems. The effectiveness of traditional network intrusion is limited in the face of complex network attacks and constantly increasing data volume. Support vector machine has attracted much attention for its excellent classification ability, but it faces challenges in feature selection and parameter optimization when dealing with large-scale high-dimensional data. Therefore, the study introduces the fireworks algorithm to improve it and optimize parameter selection and feature subset selection. The study also proposes a discretized binary fireworks algorithm to further improve the efficiency and adaptability of support vector machines in feature selection. The experiment outcomes denote that on the feature dense Sonar dataset, the average number of features selected by the proposed method is 24.39, a decrease of 25.51% compared to the comparison algorithm, and a classification accuracy improvement of 2.99%. The average detection rate of the raised method is 96.43%, the false alarm rate is 0.91, and the average correlation coefficient is as high as 0.987, which is better than the other four comparative algorithms. The training and testing time are 26.12 seconds and 11.23 seconds, respectively. Consequently, the primary contribution of the research lies in solving key problems in network intrusion detection through the BFWA-SVM model. Introducing the discrete fireworks algorithm to optimize support vector machines has improved the ability to process large-scale high-dimensional data. This model significantly reduces false positive and false negative rates, enhances real-time security awareness, and provides new guidance for network security management.}, archive = {J_ACCESS}, author = {Yaohui Zhang}, doi = {10.1109/ACCESS.2025.3532619}, journal = {IEEE Access}, pages = {18579-18593}, shortjournal = {IEEE Access}, title = {FWA-SVM network intrusion identification technology for network security}, volume = {13}, year = {2025}, }
• (2025). A safety case for the use of bipolar ±60V DC for microgrids. ACCESS, 13, 18569-18578. (www)

  @article{ , abstract = {Low voltage dc microgrids are an increasingly viable option for low-volume distribution of electrical power in developing countries. 48V systems have proven popular, owing largely to familiarity from the automotive sector, but there remains to date no universal consensus on optimal system voltage. In this work we propose the use of ±60V bipolar dc distribution and explore the relative advantages and disadvantages of such an approach from the standpoint of electrical safety. Despite significant technical advantages over existing ac and unipolar 48V dc microgrids, two safety aspects must be considered: the detection and extinguishing of series arcs and control of touch safety voltages. We show that an arc suppression technique using load-side voltage monitoring can reliably detect and extinguish series arcs. This functionality has been incorporated into a practical system providing protection against series, line-line and line-earth arc faults. A comprehensive touch voltage assessment with reference to IEC 60479 confirms that the proposed system voltage does not present risk of ventricular fibrillation based on values of body impedance corresponding to the 5th percentile of the population.}, archive = {J_ACCESS}, author = {Stephen A. Jay and David Clark and Chris Stone and Renke Han and Daniel J. Rogers and Malcolm McCulloch}, doi = {10.1109/ACCESS.2025.3532096}, journal = {IEEE Access}, pages = {18569-18578}, shortjournal = {IEEE Access}, title = {A safety case for the use of bipolar ±60V DC for microgrids}, volume = {13}, year = {2025}, }
• (2025). On risk assessment for out-of-distribution detection. ACCESS, 13, 18546-18568. (www)

  @article{ , abstract = {This paper challenges the conventional approach treating of out-of-distribution (OOD) risk as uniform and aimed at reducing OOD risk on average. We argue that managing OOD risk on average fails to account for the potential impact of rare, high-consequence events, which can undermine trust in a model even with just a single OOD incident. First, we show that OOD performance depends on both the rate of outliers and the number of samples processed by a machine learning (ML) model. Second, we introduce a novel perspective that assesses OOD risk by considering the expected maximum risk within a limited sample size. Our theoretical findings clearly distinguish when OOD detection is essential and when it becomes redundant, allowing efforts to be directed towards improving ID performance once adequate OOD robustness is achieved. Finally, an analysis of popular computer vision benchmarks reveals that ID errors often dominate overall risk, highlighting the importance of strong ID performance as a foundation for effective OOD detection. Our framework offers both theoretical insights and practical guidelines for deploying ML models in high-stakes applications, where trust and reliability are paramount.}, archive = {J_ACCESS}, author = {Anton Vasiliuk}, doi = {10.1109/ACCESS.2025.3533201}, journal = {IEEE Access}, pages = {18546-18568}, shortjournal = {IEEE Access}, title = {On risk assessment for out-of-distribution detection}, volume = {13}, year = {2025}, }
• (2025). IMViT: Adjacency matrix-based lightweight plain vision transformer. ACCESS, 13, 18535-18545. (www)

  @article{ , abstract = {Transformers are becoming dominant deep learning backbones for both computer vision and natural language processing. While extensive experiments prove its outstanding ability for large models, transformers with small sizes are not comparable with convolutional neural networks in various downstream tasks due to its lack of inductive bias which can benefit image understanding. Hierarchical vision transformers are a big family for better efficiency in computer vision, but in order to obtain global dependencies, their design is often complex. This paper proposes a non-hierarchical transformer network which can capture both long-range and short-range as non-hierarchical transformers, and keep its performance on small-sized transformers. First, we discard the framework of progressively reduced feature maps but design two separate stages, i.e., multi-scale feature preparatory stages and multi-scale feature perception stages, where the first stage uses the lightweight multi-branch structure to extract multi-scale features, and the second stage leverages the non-hierarchical networks to learn semantic information for downstream tasks. Second, we design a multi-receptive attention and interaction mechanism to perceive global and local correlations of the images in every transformer block for effective feature learning for small-sized networks. Extensive experiments show that the proposed lightweight IMViT-B outperforms DeiTIII, this paper IMViT-B(300 epochs) achieves a top accuracy of $82.8~\%$ on ImageNet-1K with only 26M parameters, surpasses the DeiTIII-S(800 epochs) +1.4%, with a similar number of parameters and computation cost. Codes are available at https://github.com/LQchen1/IMViT.}, archive = {J_ACCESS}, author = {Qihao Chen and Yunfeng Yan and Xianbo Wang and Jishen Peng}, doi = {10.1109/ACCESS.2025.3532603}, journal = {IEEE Access}, pages = {18535-18545}, shortjournal = {IEEE Access}, title = {IMViT: Adjacency matrix-based lightweight plain vision transformer}, volume = {13}, year = {2025}, }
• (2025). Don’t cache, speculate!: Speculative address translation for flash-based storage systems. ACCESS, 13, 18524-18534. (www)

  @article{ , abstract = {Address translation using a logical-to-physical (L2P) mapping table is essential for the NAND Flash-based SSDs. Unfortunately, the L2P mapping table size increases as SSD capacity increases. The mapping table is basically stored in the NAND flash, and a small number of the table entries are cached in the DRAM, leading to performance degradation due to the overhead of loading the mapping table entries from the slow NAND flash. The performance overhead of the address translation is more severe in low-cost flash-based storage systems (e.g., DRAM-less SSD) because they do not employ the DRAM for caching the mapping table, and thus, every I/O request involves an additional read request to the flash to load an address mapping information. To tackle the address translation overhead in the SSDs, this paper proposes ASTRO framework that speculatively translates the logical addresses to physical ones by maintaining the contiguity in the address mappings as much as possible. ASTRO consists of three novel mechanisms: 1) Lazy Page Ordering (LPO) to rearrange the pages to maintain the contiguity in the address mappings for each region, 2) Speculative Read (SpecREAD) to convert logical addresses to physical addresses speculatively, and 3) Contiguity Checking (ContCHECK) to monitor the updates in the rearranged regions. These three mechanisms are implemented in the FTL software, and some functions are accelerated by adding simple hardware to the SSD controller. Experimental results demonstrate that ASTRO enhances SSD performance by an average of 80% and 34% for synthetic random read workloads and real-world workloads, respectively, while minimally impacting the write amplification factor.}, archive = {J_ACCESS}, author = {Hyungjin Kim and Seongwook Kim and Junhyeok Park and Gwangeun Byeon and Seokin Hong}, doi = {10.1109/ACCESS.2025.3530776}, journal = {IEEE Access}, pages = {18524-18534}, shortjournal = {IEEE Access}, title = {Don’t cache, speculate!: Speculative address translation for flash-based storage systems}, volume = {13}, year = {2025}, }
• (2025). Design of optical lens assembly method based on star point bonding and surface shape accuracy. ACCESS, 13, 18510-18523. (www)

  @article{ , abstract = {With the development of digital optical systems, the industry quality requirements for optical lenses are becoming higher. A design method for optical lens assembly based on star point bonding and a method for surface shape accuracy are proposed in response to the development of digital optical systems and the continuously improving quality requirements of the optical lens industry. The study adopts the three-point support principle to bond optical lenses, thereby optimizing the assembly process of optical lens components. Finite element analysis is conducted on the lens assembly process using computer-aided engineering software to ensure accuracy and reliability of the assembly. In addition, the matching technique is used to analyze the surface shape error of the lens, further improving the control technology of surface shape accuracy. Compared with traditional binding and ring pressing methods, the research method improved assembly accuracy by at least 30%, significantly reducing stress concentration caused by uneven tightening, and effectively avoiding deformation of optical lenses during processing and assembly. In addition, the surface shape accuracy analysis method was about 50% faster than traditional methods. At the same conditions, the calculation error was reduced by an order of magnitude. These improvements not only improve the assembly accuracy and work efficiency of optical lenses, but also provide strong technical support for the production of optical lenses.}, archive = {J_ACCESS}, author = {Qiuhan Ma and Xiaozeng Xie and Chunlei Yang and Tianshuo Zhang and Chuncheng Lang and Bo Tang}, doi = {10.1109/ACCESS.2025.3531397}, journal = {IEEE Access}, pages = {18510-18523}, shortjournal = {IEEE Access}, title = {Design of optical lens assembly method based on star point bonding and surface shape accuracy}, volume = {13}, year = {2025}, }
• (2025). Railway foreign object intrusion detection using UAV images and YOLO-UAT. ACCESS, 13, 18498-18509. (www)

  @article{ , abstract = {Rapidly detecting foreign objects in the railway’s natural environment is crucial for safe railway operation and passenger safety. Traditional detection methods are limited by technology and environment and are costly and inefficient for large-scale detection. To improve the efficiency of foreign object intrusion detection in railways, this study proposes a YOLO-UAT method to detect foreign objects in railways effectively. First, a railway foreign object intrusion image dataset is created. Then the EfficientNet network is employed to replace the backbone extraction network of YOLOv5s, achieving a lightweight model that enhances detection speed. Secondly, a C $3\_$ CBAM module is constructed to enhance feature extraction and enhance the model’s detection ability for small-scale targets. Concurrently, the K-means++ algorithm is introduced to cluster the a priori frames, which improves the accuracy and convergence speed of the a priori frame clustering. YOLO-UAT reduces the number of parameters by 36% compared to the original YOLOv5s and mAP increased by 6.1% to 91.5%. Implemented on a Jetson Nano, it achieves a detection rate of 26.4 FPS. The experimental results demonstrate that the enhanced YOLOv5s model effectively enhances the accuracy and speed of foreign object encroachment detection in railroads while ensuring lightweightness, facilitating deployment, and aligning with the operational needs of railroads.}, archive = {J_ACCESS}, author = {Yang Yang and Zhanhao Liu and Junming Chen and Haiming Gao and Tao Wang}, doi = {10.1109/ACCESS.2025.3533304}, journal = {IEEE Access}, pages = {18498-18509}, shortjournal = {IEEE Access}, title = {Railway foreign object intrusion detection using UAV images and YOLO-UAT}, volume = {13}, year = {2025}, }
• (2025). Advances in light field spatial super-resolution: A comprehensive literature survey. ACCESS, 13, 18470-18497. (www)

  @article{ , abstract = {Super-resolution reconstruction of light field images has recently become a central focus in the fields of computational photography and computer vision. We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public datasets. Integrating experimental results, we specifically analyze the performance of deep learning-based super-resolution algorithms at various magnification levels. Although these models have made significant progress at lower magnifications (e.g., $2\times $ and $4\times $ ), current methods exhibit clear limitations at higher magnifications (e.g., $8\times $ and $16\times $ ), particularly in maintaining structural integrity and disparity consistency. Our experimental findings indicate substantial differences in robustness and adaptability among methods: approaches such as DistgSSR and DPT perform exceptionally well at high magnifications, while others, like HLFSR, exhibit comparatively poorer performance in complex scenes. Additionally, the unique characteristics of light field images add complexity to the super-resolution task. Future research should focus on enhancing the robustness, generalization, and capability of algorithms to handle complex scenarios. This review offers valuable direction for future research on light field image super-resolution and provides a solid foundation for its applications in virtual reality, augmented reality, and autonomous driving.}, archive = {J_ACCESS}, author = {Wenqi Lyu and Hao Sheng and Wei Ke and Xiao Ma}, doi = {10.1109/ACCESS.2025.3532610}, journal = {IEEE Access}, pages = {18470-18497}, shortjournal = {IEEE Access}, title = {Advances in light field spatial super-resolution: A comprehensive literature survey}, volume = {13}, year = {2025}, }
• (2025). Fault diagnosis of magnetically controlled on-column circuit breaker based on small sample condition. ACCESS, 13, 18457-18469. (www)

  @article{ , abstract = {As the pivotal component of power grid supply protection, the fault diagnosis of magnetically controlled on-column circuit breakers is a crucial element in ensuring regional power supply reliability. Addressing challenges such as difficult fault signal acquisition, noise interference, and a limited number of fault samples, this paper introduces a fault diagnosis method for magnetically controlled on-column circuit breakers under conditions of small sample sizes, based on VAE-ACGAN-SDAE. Initially, a Variational Autoencoder (VAE) is employed to extract the latent distribution of genuine samples, which are then integrated with the Auxiliary Classifier Generative Adversarial Network (ACGAN) generator to learn the characteristics of real data. Subsequently, to address the problem of real-world operational data being susceptible to noise, a Stacked Denoising Autoencoder (SDAE) is utilized as the discriminator in the ACGAN framework. This approach not only enhances noise resistance and optimizes the feature centers but also synchronizes training with the VAE, thereby improving the quality of the generated samples and refining the weight bias parameters of the discriminator. The experimental outcomes demonstrate that the generated data can be systematically produced and categorized. Optimal sample classification is achieved when the expansion ratio is set to 3, and the method achieves the highest accuracy of 98.8% with a minimal number of fault samples. Compared to the VAE-GAN-CNN network, this network shows a 3.6% increase in accuracy.}, archive = {J_ACCESS}, author = {He Tian and Chao Liang and Wenpeng Ma and Tianchang Zhang}, doi = {10.1109/ACCESS.2025.3532469}, journal = {IEEE Access}, pages = {18457-18469}, shortjournal = {IEEE Access}, title = {Fault diagnosis of magnetically controlled on-column circuit breaker based on small sample condition}, volume = {13}, year = {2025}, }
• (2025). Convolutional neural network compression via dynamic parameter rank pruning. ACCESS, 13, 18441-18456. (www)

  @article{ , abstract = {While Convolutional Neural Networks (CNNs) excel at learning complex latent-space representations, their over-parameterization can lead to overfitting and reduced performance, particularly with limited data. This, alongside their high computational and memory demands, limits the applicability of CNNs for edge deployment and applications where computational resources are constrained. Low-rank matrix approximation has emerged as a promising approach to reduce CNN parameters, but existing methods often require pre-determined ranks or involve complex post-training adjustments, leading to challenges in rank selection, performance loss, and limited practicality in resource-constrained environments. This underscores the need for an adaptive compression method that integrates into the training process, dynamically adjusting model complexity based on data and task requirements. To address this, we propose an efficient training method for CNN compression via dynamic parameter rank pruning. Our approach integrates efficient matrix factorization and novel regularization techniques, forming a robust framework for dynamic rank pruning and model compression. By using Singular Value Decomposition (SVD) to model low-rank convolutional filters and dense weight matrices, and training the SVD factors with back-propagation in an end-to-end manner, we achieve model compression. We evaluate our method on modern CNNs, including ResNet-18, ResNet-20, and ResNet-32, using datasets like CIFAR-10, CIFAR-100, and ImageNet (2012). Our experiments demonstrate that the proposed method can reduce model parameters by up to 50% and improve classification accuracy by up to 2% over baseline models, making CNNs more feasible for practical applications.}, archive = {J_ACCESS}, author = {Manish Sharma and Jamison Heard and Eli Saber and Panagiotis Markopoulos}, doi = {10.1109/ACCESS.2025.3533419}, journal = {IEEE Access}, pages = {18441-18456}, shortjournal = {IEEE Access}, title = {Convolutional neural network compression via dynamic parameter rank pruning}, volume = {13}, year = {2025}, }
• (2025). Zero-BertXGB: An empirical technique for abstract classification in systematic reviews. ACCESS, 13, 18418-18440. (www)

  @article{ , abstract = {Abstract classification in systematic reviews (SRs) is a crucial step in evidence synthesis but is often time-consuming and labour-intensive. This study evaluates the effectiveness of various Machine Learning (ML) models and embedding techniques in automating this process. Five diverse datasets are utilized: Aceves-Martins (2021), comprising 1,258 excluded and 230 included abstracts on the utilization of animal models in depressive behaviour studies; Bannach-Brown (2016), with 896 excluded and 73 included abstracts focusing on the methodological rigour of environmental health systematic reviews; Meijboom (2021), containing 599 excluded and 32 included abstracts on the retransitioning of Etanercept in rheumatic disease patients; Menon (2022), with 896 excluded and 73 included abstracts on environmental health reviews; and a custom Clinical Review Paper Abstract (CRPA) dataset, featuring 500 excluded and 50 included abstracts. A significant research gap in abstract classification has been identified in previous literature, particularly in comparing Large Language Models (LLMs) with traditional ML and Natural Language Processing (NLP) techniques regarding scalability, adaptability, computational efficiency, and real-time application. Addressing this gap, this study employs GloVe for word embedding via matrix factorization, FastText for character n-gram representation, and Doc2Vec for capturing paragraph-level semantics. A novel Zero-BertXGB technique is introduced, integrating a transformer-based language model, zero-shot learning, and an ML classifier to enhance abstract screening and classification into “Include” or “Exclude” categories. This approach leverages contextual understanding and precision for efficient abstract processing. The Zero-BertXGB technique is compared against other prominent LLMs, including BERT, PaLM, LLaMA, GPT-3.5, and GPT-4, to validate its effectiveness. The Zero-BertXGB model achieved accuracy values of 99.3% for Aceves-Martins2021, 92.6% for Bannach-Brown2016, 85.7% for Meijboom2021, 94.1% for Menon2022, and 98.8% for CRPA. The findings indicate that the Zero-BertXGB model, alongside other LLMs, can deliver reliable results with minimal human intervention, enhancing abstract screening efficiency and potentially revolutionizing systematic review workflows.}, archive = {J_ACCESS}, author = {Mohammad Shariful Islam and Mohammad Abu Tareq Rony and Md Rasel Hossain and Samah Alshathri and Walid El-Shafai}, doi = {10.1109/ACCESS.2025.3531778}, journal = {IEEE Access}, pages = {18418-18440}, shortjournal = {IEEE Access}, title = {Zero-BertXGB: An empirical technique for abstract classification in systematic reviews}, volume = {13}, year = {2025}, }
• (2025). Error-mask-adaptive dynamic filtering for image inpainting. ACCESS, 13, 18403-18417. (www)

  @article{ , abstract = {A novel error-mask-adaptive dynamic filtering (EMDF) algorithm is proposed in this paper, which uses a continuous error mask to inpaint an image adaptively and faithfully. In an EMDF layer, we determine a spatially varying filter adaptively according to an error mask and perform separable dynamic filtering. Meanwhile, we update the error mask by modeling the error propagation during the filtering. Through several EMDF layers, we predict an inpainting result. Finally, we refine it to reconstruct a more faithful image. Experimental results on diverse datasets show that the proposed EMDF algorithm outperforms existing inpainting algorithms significantly. The source codes are available at https://github.com/keunsoo-ko/EMDF.}, archive = {J_ACCESS}, author = {Keunsoo Ko and Seunggyun Woo and Chang-Su Kim}, doi = {10.1109/ACCESS.2025.3531440}, journal = {IEEE Access}, pages = {18403-18417}, shortjournal = {IEEE Access}, title = {Error-mask-adaptive dynamic filtering for image inpainting}, volume = {13}, year = {2025}, }
• (2025). AI-based classification model for low-energy buildings: Promoting sustainable economic development of smart cities with spherical fuzzy decision algorithm. ACCESS, 13, 18386-18402. (www)

  @article{ , abstract = {AI-based classification models for low-energy buildings are essential in promoting the economic development of smart cities by optimizing energy use, reducing operational costs, and enhancing overall efficiency. These models classify buildings based on energy consumption patterns, predicting energy needs and identifying areas for improvement. The spherical fuzzy set (SFS) is a potent approach used to handle ambiguous information during decision analysis and integrate big expert’s opinions. This article evaluates intelligent AI-based models for low-energy buildings and economic development under some specific characteristics or key features. For this purpose, we modified the theory of criteria importance through intercriteria correlation (CRITIC) and combined compromise solution (COCOSO) methods to assess suitable optimal options for the multi-attribute group decision-making (MAGDM) problem. Moreover, we also propose a novel decision algorithm of the CRITIC-COCOSO method under consideration of spherical fuzzy situations. The CRITIC-COCOSO method has great capabilities to investigate weights of criterion and accurate ranking of preferences under specific linguistic scales. An experimental case study is established to investigate a reliable AI-based advanced technology and prove the superiority of diagnosed theories. Additionally, a robust comparison method is used to prove the supremacy of derived theories and pioneered mathematical terminologies. In the end, some key features and remarkable comments are also discussed.}, archive = {J_ACCESS}, author = {Lin Yang}, doi = {10.1109/ACCESS.2025.3530965}, journal = {IEEE Access}, pages = {18386-18402}, shortjournal = {IEEE Access}, title = {AI-based classification model for low-energy buildings: Promoting sustainable economic development of smart cities with spherical fuzzy decision algorithm}, volume = {13}, year = {2025}, }
• (2025). The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis. ACCESS, 13, 18366-18385. (www)

  @article{ , abstract = {This study investigates factors influencing the acceptance of artificial intelligence (AI) in education, focusing on attitudes and intentions toward its use. Drawing on the Technology Acceptance Model (TAM) and Social Support Theory (SST), the research examines perceived usefulness, ease of use, and support measures, including educational and emotional support. The study was conducted at King Saud University, where it adopted a quantitative approach, collecting data from 350 participants through a structured survey. A final sample of 308 valid responses was analyzed using Structural Equation Modeling (SEM). The findings revealed significant relationships between key constructs: perceived educational support positively influenced perceived usefulness ( $\beta = 0.160$ , p = 0.000) and attitudes toward AI ( $\beta = 0.140$ , p = 0.010). Perceived usefulness ( $\beta = 0.670$ , p = 0.000) and ease of use ( $\beta = 0.330$ , p = 0.000) were strong predictors of positive attitudes, which, in turn, significantly shaped intentions to use AI ( $\beta = 0.760$ , p = 0.000). Emotional support and cognitive support also had notable but mixed effects on perceived usefulness. However, social interaction support and perceived enjoyment did not demonstrate significant influence. These findings highlight the importance of designing AI systems that emphasise usability and provide robust educational and emotional support to enhance acceptance. The study contributes to theory by extending TAM with constructs from SST, offering new insights into AI adoption in academic contexts. Practically, the results guide policymakers and developers in fostering AI acceptance by addressing educational and emotional needs, ensuring effective implementation in higher education.}, archive = {J_ACCESS}, author = {Ahmed A. Aldraiweesh and Uthman Alturki}, doi = {10.1109/ACCESS.2025.3534099}, journal = {IEEE Access}, pages = {18366-18385}, shortjournal = {IEEE Access}, title = {The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis}, volume = {13}, year = {2025}, }
• (2025). AI applications for power quality issues in distribution systems: A systematic review. ACCESS, 13, 18346-18365. (www)

  @article{ , abstract = {The integration of distributed generation (DG), renewable energy sources (RES), and power electronic converters into distribution systems (DSs) has introduced significant power quality (PQ) challenges, such as voltage fluctuations, harmonic distortions, and transients. These issues can undermine the reliability and stability of power systems, making it essential to address them to ensure a consistent and resilient power supply, especially as RES adoption continues to grow. While previous reviews have explored artificial intelligence (AI) applications for PQ management, most have been limited to specific AI techniques or targeted PQ problems, such as harmonics. This review, however, offers a comprehensive synthesis of AI-based approaches across a wide range of PQ applications, encompassing detection, classification, and improvement, while also considering the specific PQ issues addressed in each case. By adopting an integrated approach, this review identifies key research gaps, particularly the limited focus on leveraging AI to control power converters in RESs for PQ improvement, as most existing studies emphasize devices like active power filters, compensators, and conditioners. The review also evaluates the effectiveness of these AI methods in terms of accuracy and the extent of total harmonic distortion (THD) reduction. In addition, it provides novel insights that can help guide researchers, engineers, and industry professionals toward developing more adaptive, scalable, and robust PQ solutions. Finally, future research directions are proposed to advance AI-based PQ management, facilitating the integration of AI into diverse and evolving power systems.}, archive = {J_ACCESS}, author = {Mitra Nabian Dehaghani and Tarmo Korõtko and Argo Rosin}, doi = {10.1109/ACCESS.2025.3533702}, journal = {IEEE Access}, pages = {18346-18365}, shortjournal = {IEEE Access}, title = {AI applications for power quality issues in distribution systems: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Modeling and control of a three-phase interleaved buck converter as a battery charger. ACCESS, 13, 18325-18345. (www)

  @article{ , abstract = {In this paper, a control design methodology is proposed for the implementation of a three-phase interleaved buck converter as a battery charger. The control strategy consists of a multiple-loop controller in cascade configuration to implement the constant-current constant-voltage (CC-CV) protocol for the fast charging of an electric vehicle (EV) battery. To compensate for the inherent asymmetric distribution of the current between the phases, the first control loop (inner loop) is dedicated to implement the democratic current sharing technique, while the two outer loops constitute a seamless controller, which allows a soft transition from CC mode to CV mode when charging the battery. The controllers are designed using the root locus method and conventional rules for cascade controllers. The design methodology is validated and tested by numerical simulations of the switched model system implemented in PSIM© software. The obtained results put in evidence a robust performance in front of input voltage and load variations, failure conditions and other parametric uncertainties. A 1.5 kW experimental prototype is implemented to charge a battery of 48 V from a 100 V DC input voltage and to validate the theoretical predictions and the simulation results. The proposal opens the way for subsequent research in ultrafast charging of batteries.}, archive = {J_ACCESS}, author = {El Nouha Mammeri and Oswaldo Lopez-Santos and Abdelali El Aroudi and Jure Domajnko and Natasa Prosen and Luis Martinez-Salamero}, doi = {10.1109/ACCESS.2025.3533032}, journal = {IEEE Access}, pages = {18325-18345}, shortjournal = {IEEE Access}, title = {Modeling and control of a three-phase interleaved buck converter as a battery charger}, volume = {13}, year = {2025}, }
• (2025). Agent-based adaptive dynamic round robin (AADRR) scheduling algorithm. ACCESS, 13, 18308-18324. (www)

  @article{ , abstract = {Scheduling techniques are essential to increase resource utilization and task execution within modern computing environments. Round Robin Scheduling (RR) ensures a fair distribution of processes needing attention but often leads to inefficiencies in systems with heterogeneous tasks or different priorities due to large latency or resource usage differences. To address such problems, this paper introduces the Agent-based Adaptive Dynamic Round Robin (AADRR) process scheduling technique, which enhances process scheduling by continuously adjusting the time quantum and criteria, combining CPU burst time and priority. In the proposed AADRR, all processes are ranked dynamically by a software agent based on user preferences and current system load. This agent operates independently by keeping an eye on system parameters and making the required adjustments in real-time without requiring human intervention. We place processes in the queue according to their order of importance. A dynamic time quantum policy is suitable whenever it meets the mean duration of each process in the queue. Every round has the time quantum adjusted based on this method average burst time. AADRR highlights that the short processes are managed properly and the long processes are completed within a few rounds to fairly complete and maintain all the processes in the system. The proposed AADRR is more suitable for periodic tasks that employ a dynamic scheduling system and adapt time quantum according to the system state and job features. Additionally, AADRR efficiently manages preemptable tasks, using dynamic scheduling policies to accommodate variations in process priority and CPU burst times, ensuring fair scheduling, efficient resource utilization, and dynamic adaptability. To validate the effectiveness of the AADRR algorithm, we performed a comparative performance analysis with twelve other algorithms, including five traditional CPU scheduling algorithms and seven advanced job scheduling techniques, demonstrating optimal performance results. In our experiments, synthetic workload traces were generated using the Monte Carlo probability distribution method, which is scientifically recommended for creating diverse workload traces. Small, medium, and large datasets were used, with the small workload traces obtained from published studies and the large traces produced by the Monte Carlo simulation. AADRR efficiently reduces average turnaround times and average waiting times for each workload and performs better in response time. AADRR may not always provide the most favorable measures in all scenarios. Still, it performs better than other scheduling techniques in system performance, while being more efficient and flexible for different workloads.}, archive = {J_ACCESS}, author = {Zafar Iqbal Khan and Muzafar Khan and Syed Nasir Mehmood Shah}, doi = {10.1109/ACCESS.2025.3534031}, journal = {IEEE Access}, pages = {18308-18324}, shortjournal = {IEEE Access}, title = {Agent-based adaptive dynamic round robin (AADRR) scheduling algorithm}, volume = {13}, year = {2025}, }
• (2025). SliceScore: A network function sharing aware and slice-oriented DDoS filtering approach. ACCESS, 13, 18294-18307. (www)

  @article{ , abstract = {Network slicing presents new opportunities for cost-effective implementations of next-generation communication networks that target diverse service requirements. Deploying vertical use cases on shared resources is the main motivation for network slicing. Recently, network function (NF) sharing has been proposed in the literature in order to further exploit unused resources and lower the costs for network operators. However, such optimizations through NF sharing expose new security threats and need appropriate defense mechanisms. In this paper, we describe new attack vectors that arise due to NF sharing and list some of the possible defense strategies and security precautions. Furthermore, we propose an NF sharing-aware and network slice-oriented distributed denial of service (DDoS) filtering mechanism, SliceScore, that successfully mitigates the threat of resource exhaustion. We prove by simulations that SliceScore outperforms previous filtering mechanisms, which did not focus on differences among network slices. In this regard, we encourage researchers to investigate slice-oriented approaches to improve defense mechanisms in next-generation communication networks.}, archive = {J_ACCESS}, author = {Ömer Zekvan Yilmaz and Kubra Kalkan and Fatih Alagöz}, doi = {10.1109/ACCESS.2025.3532543}, journal = {IEEE Access}, pages = {18294-18307}, shortjournal = {IEEE Access}, title = {SliceScore: A network function sharing aware and slice-oriented DDoS filtering approach}, volume = {13}, year = {2025}, }
• (2025). Evaluating physical education quality in higher education using a picture fuzzy decision framework with muirhead mean operator and MULTIMOORA method. ACCESS, 13, 18277-18293. (www)

  @article{ , abstract = {The quality assessment of physical education programs in higher education is essential for fostering student development and institutional excellence. However, several uncertain and ambiguous factors cause the complexity of the quality assessment of physical education. This study aims to evaluate the quality of physical education, considering the uncertain factors using a well-known framework known as picture fuzzy set (PFS). This study introduces an innovative decision-making model by integrating the Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form (MULTIMOORA) method and the Muirhead Mean (MM) operator. The PFS enables the model of the expert’s opinion using membership degree (MD), degrees of neutral membership (DONM), and non-membership degree (ND) and consequently effectively addresses uncertainty and ambiguity in multi-criteria decision-making problems. The MM operator enhances the accuracy by capturing interdependencies among evaluation criteria, ensuring more precise and comprehensive analyses. The MULTIMOORA method ensures robust analysis in the developed decision model for assessing the physical education quality because of the components of the Ratio System (RS), Reference Point Approach (RPA), and Full Multiplicative Form (FMF). The practical implications of this work are significant, as it equips stakeholders with actionable insights for curriculum development, resource optimization, and policy-making in physical education. A numerical example demonstrates the method’s utility in real-world scenarios, showcasing its effectiveness in addressing challenges inherent in higher education quality assessments. This study advances decision science by providing a scientifically rigorous and practically impactful tool for evaluating and improving physical education programs.}, archive = {J_ACCESS}, author = {Rui Xue}, doi = {10.1109/ACCESS.2025.3532949}, journal = {IEEE Access}, pages = {18277-18293}, shortjournal = {IEEE Access}, title = {Evaluating physical education quality in higher education using a picture fuzzy decision framework with muirhead mean operator and MULTIMOORA method}, volume = {13}, year = {2025}, }
• (2025). Exploring LLMs applications in law: A literature review on current legal NLP approaches. ACCESS, 13, 18253-18276. (www)

  @article{ , abstract = {Artificial Intelligence (AI) is reshaping the legal landscape, with software tools now impacting various aspects of legal work. The intersection of Natural Language Processing (NLP) and law holds potential to transform how legal professionals, including lawyers and judges, operate, resolve disputes, and retrieve case information to formulate their decisions. To identify the current state of the applications of Transformers (also known as Large Language Models or LLMs) in the legal domain, we analysed the existing literature from 2017 to 2023 through a database search and snowballing method. From 61 selected publications, we identified key application categories such as legal document analysis, case prediction, and contract review, along with their main characteristics. We observed a discernible upsurge in the volume of scholarly publications, a diversification of tasks undertaken (e.g., legal research, contract analysis, and regulatory compliance), and an increased range of languages considered. There has been a notable enhancement in the methodological sophistication employed by researchers in practical applications. The performance of models grounded in the Generative Pre-trained Transformer (GPT) architecture has consistently improved across various legal domains, including contract review, legal document summarization, and case outcome prediction. This paper makes several significant contributions to the field. Firstly, it identifies emerging trends in the application of LLMs within the legal domain, highlighting the growing interest and investment in this area. Secondly, it pinpoints methodological gaps in current research, suggesting areas where further development and refinement are needed. Lastly, it discusses the broader implications of these advancements for real-world legal tasks, offering insights into how LLM-based AI can enhance legal practice while addressing the associated challenges.}, archive = {J_ACCESS}, author = {Marco Siino and Mariana Falco and Daniele Croce and Paolo Rosso}, doi = {10.1109/ACCESS.2025.3533217}, journal = {IEEE Access}, pages = {18253-18276}, shortjournal = {IEEE Access}, title = {Exploring LLMs applications in law: A literature review on current legal NLP approaches}, volume = {13}, year = {2025}, }
• (2025). EMSFomer: Efficient multi-scale transformer for real-time semantic segmentation. ACCESS, 13, 18239-18252. (www)

  @article{ , abstract = {Transformer-based models have achieved impressive performance in semantic segmentation in recent years. However, the multi-head self-attention mechanism in Transformers incurs significant computational overhead and becomes impractical for real-time applications due to its high complexity and large latency. Numerous attention variants have been proposed to address this issue, yet the overall performance and inference speed still have limitations. In this paper, we propose an efficient multi-scale Transformer (EMSFormer) that employs learnable keys and values based on the single-head attention mechanism and a dual-resolution structure for real-time semantic segmentation. Specifically, we propose multi-scale single-head attention (MS-SHA) to effectively learn multi-scale attention and improve feature representation capability. In addition, we introduce cross-resolution single-head attention (CR-SHA) to efficiently combine the global context-rich features in the low-resolution branch to the features in the high-resolution branch. Experimental results show that our proposed method can achieve state-of-the-art performance with real-time inference speed on ADE20K, Cityscapes, and CamVid datasets.}, archive = {J_ACCESS}, author = {Zhengyu Xia and Joohee Kim}, doi = {10.1109/ACCESS.2025.3534117}, journal = {IEEE Access}, pages = {18239-18252}, shortjournal = {IEEE Access}, title = {EMSFomer: Efficient multi-scale transformer for real-time semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Optimization configuration model for intelligent measurement multi-core modules considering “Cloud-edge-end-core” collaboration. ACCESS, 13, 18228-18238. (www)

  @article{ , abstract = {The new power system with new energy as the main body gives the low-voltage distribution network (LVDN) a richer connotation, requiring intelligent measurement equipment to have good scalability and collaborative ability. To address these requirements, an optimization configuration model for intelligent measurement multi-core modules considering “cloud-edge-end-core” collaboration is proposed in this paper. Initially, a technical framework for “cloud-edge-end-core” collaboration in intelligent measurement is designed, expanding the conventional “cloud-edge-end” vertical cooperation architecture with multi-core module collaboration and horizontal synergy at the same hierarchical level to enhance the flexibility of data interaction. Then, an optimization configuration model for multi-core modules is formulated, to minimize both the chip configuration costs and the data transmission costs associated with “cloud-edge-end-core” collaboration. The decision variables include the core chip level of the intelligent measurement terminal, the management core chip level for smart meters, and the placement of application configurations. Finally, the effectiveness of the proposed model is verified in the LVDN with 300 users. The performance of the proposed optimization configuration model in different scenarios is compared and the influence of model parameters on the optimization results is analyzed. The results show that the proposed multi-core module optimization configuration model can optimize the selection of intelligent measurement terminal core and smart meter management core according to different application data requirements. It meets the application requirements while minimizing the multi-core module optimization configuration cost and the “cloud-edge-end-core” collaboration cost.}, archive = {J_ACCESS}, author = {Lai Zhou and Qinhao Li and Guoying Lin}, doi = {10.1109/ACCESS.2025.3530953}, journal = {IEEE Access}, pages = {18228-18238}, shortjournal = {IEEE Access}, title = {Optimization configuration model for intelligent measurement multi-core modules considering “Cloud-edge-end-core” collaboration}, volume = {13}, year = {2025}, }
• (2025). Efficient method for robust backdoor detection and removal in feature space using clean data. ACCESS, 13, 18215-18227. (www)

  @article{ , abstract = {The steady increase of proposed backdoor attacks on deep neural networks highlights the need for robust defense methods for their detection and removal. A backdoor attack is a type of attack where hidden triggers are added to the input data during training, with the goal of changing the behavior of the model during inference. These attacks pose a significant security threat in critical applications, such as street sign or pedestrian recognition for autonomous vehicles, biometric authentication, image retrieval, semantic labeling, etc. To combat these threats, many defense mechanisms have been proposed. These methods target different areas, such as computer vision (CV), natural language processing (NLP), and thus utilize different assumptions about the nature of the input data and the type of backdoor trigger used in the attack. However, the attacker can exploit these assumptions, which reduces their successfulness in real-world scenarios. Thus, a robust method for backdoor detection needs to have broad and simple assumptions. Furthermore, detection methods that rely on the input data suffer from the fact that they are constrained to the modality of the input and cannot apply to different modalities. In this work, a novel method for backdoor detection and removal for classification tasks using features extracted by the attacked model called FEAT-IN is proposed. This method can detect and reconstruct the feature representation of the possible triggers used in attacking the neural network. Using these reconstructed trigger features, the method can be used to efficiently mitigate the effects of an attack. Extensive experiments on multiple datasets and attack methods demonstrate that, when compared to state-of-the-art methods such as Neural Cleanse, Neural Attention Distillation, I-BAU, BTI-DBF etc. the FEAT-IN method provides several benefits. It can more consistently detect and mitigate backdoor attacks than similar trigger inversion defense methods that conduct the defense in the input space instead of feature space (where, on average, it achieves approx. 10% higher decrease in attack success rate during mitigation compared to the second-best method). Secondly, it reduces the memory footprint and the computation time by at least an order of magnitude compared to other methods, which allows FEAT-IN to be used practically in real-world scenarios. Finally, it is not constrained to only computer vision tasks, as this assumption holds for feature spaces of different problems, which is demonstrated by applying it without any change to semantic analysis on the SST-2 dataset.}, archive = {J_ACCESS}, author = {Donik Vršnak and Marko Subašić and Sven Lončarić}, doi = {10.1109/ACCESS.2025.3531716}, journal = {IEEE Access}, pages = {18215-18227}, shortjournal = {IEEE Access}, title = {Efficient method for robust backdoor detection and removal in feature space using clean data}, volume = {13}, year = {2025}, }
• (2025). Knowledge distillation in object detection for resource-constrained edge computing. ACCESS, 13, 18200-18214. (www)

  @article{ , abstract = {Edge computing, a distributed computing paradigm that places small yet capable computing devices near data sources and IoT sensors, is gaining widespread adoption in various real-world applications, such as real-time intelligent drones, autonomous vehicles, and robotics. Object detection (OD) is an essential task in computer vision. Although state-of-the-art deep learning-based OD methods achieve high detection rates, their large model size and high computational demands often hinder deployment on resource-constrained edge devices. Given their limited memory and computational power, edge devices like the Jetson Nano (J. Nano), Jetson Orin Nano (Orin Nano), and Raspberry Pi 4B (Raspi4B) require model optimization and compression techniques in order to deploy large OD models such as YOLO. YOLOv4 is a widely used OD model with a backbone for image feature extraction and a prediction layer. Originally, YOLOv4 was designed to use CSPDarkNet53 as its backbone, which requires significant computational power. In this paper, we propose replacing its backbone with a smaller model, such as MobileNetV2 and RepViT. In order to ensure the strong backbone performance, we perform knowledge distillation (KD), using CSPDarknet53 as the teacher and the smaller model as the student. We compare various KD algorithms to identify the technique that produces a smaller model with the modest accuracy drop. According to our experiments, Contrastive Representation Distillation (CRD) yields MobileNetV2 and RepViT with an acceptable accuracy drop. We consider both accuracy drop and model size to choose either MobileNetV2 or RepViT model to replace CSPDarknet53 in the modified YOLOv4 named M-YOLO-CRD and RV-YOLO-CRD. Our evaluation results demonstrate that RV-YOLO-CRD reduces 30% of model size and achieves better mean average precision (mAP) than M-YOLO-CRD. Our experiments show that the M-YOLO-CRD significantly reduces model size (from 245.5 MB to 35.76 MB) and inference time ( $6\times $ faster on CPU, $4\times $ faster on J. Nano, and $2.5\times $ faster on Orin Nano. While the precision decreased slightly (less than 4%), the model still performs well on edge devices. The M-YOLO-CRD achieved latency per frame at around 37 ms on Orin Nano, 168 ms on J. Nano, and 1310 ms on Raspi4B.}, archive = {J_ACCESS}, author = {Arief Setyanto and Theopilus Bayu Sasongko and Muhammad Ainul Fikri and Dhani Ariatmanto and I. Made Artha Agastya and Rakandhiya Daanii Rachmanto and Affan Ardana and In Kee Kim}, doi = {10.1109/ACCESS.2025.3534020}, journal = {IEEE Access}, pages = {18200-18214}, shortjournal = {IEEE Access}, title = {Knowledge distillation in object detection for resource-constrained edge computing}, volume = {13}, year = {2025}, }
• (2025). Edge-of-chaos and chaotic dynamics in resistor-inductor-diode-based reservoir computing. ACCESS, 13, 18191-18199. (www)

  @article{ , abstract = {Series Resistor-Inductor-Diode (RLD) circuits are known to exhibit rich nonlinear dynamical behaviour that opens up intriguing opportunities for design of novel computational systems. In this paper, we suggest and theoretically validate a technically simple RLD circuit that implements a reservoir computing (RC) architecture optimized to predict the future evolution of highly nonlinear and chaotic time series. We demonstrate that the exploitation of the proposed RLD circuit in a periodic operating regime with a particular tuning on an edge-of-chaos mode enables the RLD-based reservoir computer to achieve high prediction accuracy, quantified as a normalized mean square error (NMSE) of approximately $10^{-4}$ . We also evaluate the performance of RLD-RC tuned to operate in a chaotic regime under forced initial conditions, revealing the ability of the so-designed computer to accurately forecast complex time series and highlighting the potential of RLD circuits to serve as a backbone of efficient and versatile hardware RC systems.}, archive = {J_ACCESS}, author = {A. H. Abbas and Hend Abdel-Ghani and Ivan S. Maksymov}, doi = {10.1109/ACCESS.2025.3529815}, journal = {IEEE Access}, pages = {18191-18199}, shortjournal = {IEEE Access}, title = {Edge-of-chaos and chaotic dynamics in resistor-inductor-diode-based reservoir computing}, volume = {13}, year = {2025}, }
• (2025). State of the art review of wearable devices for respiratory monitoring. ACCESS, 13, 18178-18190. (www)

  @article{ , abstract = {Respiratory frequency and volume are essential physiological signals for diagnosing and managing respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Wearable devices have emerged as a transformative tool for continuous, non-invasive respiratory monitoring and data portals, providing massive real-time data critical for both clinical and home settings. This state-of-the-art review delves into the advancements and applications of wearable respiratory monitoring devices, specifically focusing on inertial measurement units (IMUs), piezoresistive sensors, and optical fibre sensors. Detailed analyses of sensor designs, sensing methods, and clinical applications are presented, highlighting key studies such as the development of low-cost IMU devices for breathing frequency monitoring and the integration of piezoresistive sensors for real-time respiratory rate detection. This review identifies major challenges, including power efficiency, ergonomic design, data accuracy, and data privacy concerns. It also introduces innovative solutions proposed in recent research. Future research directions are suggested to address these challenges and further enhance the capabilities and reliability of wearable respiratory monitoring devices. This review underscores the potential of wearable technology to improve patient outcomes and reduce resource cost through enabling early diagnosis, facilitating continuous health monitoring, and supporting artificial intelligence applications through data generation.}, archive = {J_ACCESS}, author = {Zhiyu Jia and Hannah Huth and Wen Qi Teoh and Sheng Xu and Bradford Wood and Zion Tsz Ho Tse}, doi = {10.1109/ACCESS.2025.3529437}, journal = {IEEE Access}, pages = {18178-18190}, shortjournal = {IEEE Access}, title = {State of the art review of wearable devices for respiratory monitoring}, volume = {13}, year = {2025}, }
• (2025). Study on single event transients in amplifier for switched-capacitor circuits in CMOS technology. ACCESS, 13, 18164-18177. (www)

  @article{ , abstract = {This article presents a comprehensive analysis of the sensitivity of different switched-capacitor amplifier circuits to Single Event Transients (SETs). SETs are temporary variations in circuit output voltage or current caused by the interaction of heavy ions or high-energy protons with sensitive device nodes. The study focuses on three types of amplifier circuits commonly used in Multiplying Digital-to-Analog Converter (MDAC) stages of state-of-the-art pipelined ADCs: operational amplifier (Op-Amp) MDACs, comparator-based switched-capacitor (CBSC) MDACs, and ring amplifier (RAMP) based MDACs. By employing a TCAD-calibrated double exponential transient current pulse model, we simulate the SET responses resulting from heavy ion strike experiments on sensitive nodes of each MDAC type. Our analysis and simulations reveal the amplitude and recovery time of the output results for each MDAC type when subjected to SETs. Notably, we propose a novel radiation hardening solution: the parallel-auxiliary ring amplifier (PA-RAMP) structure, which demonstrates significantly better tolerance to SETs compared to other designs. This research not only contributes to the understanding of SET effects on analog switched-capacitor amplifier circuits but also introduces a cost-effective approach to mitigate these effects. As technology nodes scale down and circuits become more susceptible to SETs, the PA-RAMP structure offers a promising solution for radiation-hardened applications, enabling the use of scaling-friendly RAMPs with enhanced tolerance to SETs.}, archive = {J_ACCESS}, author = {Ming Yan and Jaime Cardenas Chavez and Adriana Noguera Cundar and Kamal El-Sankary and Li Chen}, doi = {10.1109/ACCESS.2025.3533965}, journal = {IEEE Access}, pages = {18164-18177}, shortjournal = {IEEE Access}, title = {Study on single event transients in amplifier for switched-capacitor circuits in CMOS technology}, volume = {13}, year = {2025}, }
• (2025). A mm-wave 5G double slot array antenna based on gap waveguide technology and glide symmetries. ACCESS, 13, 18152-18163. (www)

  @article{ , abstract = {We propose a double slot array antenna based on Gap Waveguide Technology for 5G mm-Waves wireless systems. The antenna is formed by assembling three metal layers, the so-called feed, cavity, and radiation layers. A double slot array at the radiation layer is backed by the cavity layer which is fed by a ridge gap waveguide corporate feeding network. The double slot configuration provides better control of the impedance bandwidth at the cost of increasing sidelobes. In addition, a glide-symmetry metasurface is integrated into the intermediate and upper layers. This eliminates energy leakages that may occur if any gap exists between the two layers. The realization of metasurface via holey glide symmetry allows for larger periodicity and much-reduced milling depth in comparison with using periodic metal pins. Hence, the fabrication cost of the cavity layer is considerably lower. A prototype of a $4\times 4$ double slot configuration is designed, fabricated, and measured. The measurement results show an 11.4% achieved impedance bandwidth (S $_{11} \lt $ -10 dB), covering the 37.20 GHz - 41.58 GHz frequency range. The measured normalized radiation patterns show that the level of lateral lobes is below -9 dB and -10 dB at $\varphi = 0^{\mathrm {o}}$ and $\varphi = 90^{\mathrm {o}}$ respectively over the operating frequency range, while the measured peak gain ranges from 16.56 dBi to 18.75 dBi, and the aperture efficiency is up to 75%. Measurements are in good agreement with the simulations.}, archive = {J_ACCESS}, author = {Panagiotis Petroutsos and Stavros Koulouridis}, doi = {10.1109/ACCESS.2025.3532021}, journal = {IEEE Access}, pages = {18152-18163}, shortjournal = {IEEE Access}, title = {A mm-wave 5G double slot array antenna based on gap waveguide technology and glide symmetries}, volume = {13}, year = {2025}, }
• (2025). An improved stochastic configuration networks with compact structure and parameter adaptation. ACCESS, 13, 18141-18151. (www)

  @article{ , abstract = {Stochastic Configuration Networks (SCNs) perform well in machine learning and data mining tasks in complex data environments. However, traditional SCNs have limitations in network size and computation time. To address these issues, this paper proposes an improved version of SCNs. There are two key improvements: First, the stopping condition for generating neurons is optimized to improve the effectiveness of new neurons. Second, the regularization parameter r is adjusted dynamically to speed up the learning process. These improvements are trying to increase the efficiency of SCN construction, reduce the number of redundant neurons, and shorten the overall computation time. Experiments comparing this method with existing ones show that the proposed approach not only reduces network complexity but also effectively decreases training time. In addition, experimental results using baseline datasets and UCI databases show that the number of nodes required for oscn is reduced by approximately 50% and the computation time is reduced by approximately 40% compared to traditional algorithms.}, archive = {J_ACCESS}, author = {Sanyi Li and Hongyu Guan and Peng Liu and Weichao Yue and Qian Wang}, doi = {10.1109/ACCESS.2025.3533555}, journal = {IEEE Access}, pages = {18141-18151}, shortjournal = {IEEE Access}, title = {An improved stochastic configuration networks with compact structure and parameter adaptation}, volume = {13}, year = {2025}, }
• (2025). An adversarial attack via penalty method. ACCESS, 13, 18123-18140. (www)

  @article{ , abstract = {Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. For example, adversarial examples can fool deep learning systems easily by perturbing inputs with small, imperceptible noises. There has been extensive research regarding the generation of and defense against adversarial examples in computer vision tasks, and existing attacking methods based on optimization fall into two categories: maximizing the loss and minimizing the perturbation size. To solve the optimization problem for generating adversarial examples, the latter approach incorporates a misclassifying constraint into the objective using a Lagrangian multiplier or penalty parameter, usually determined by binary search. However, this is relatively inefficient because the parameter varies for each input. To address this inefficiency, based on the penalty method, also called the sequential unconstrained minimization technique, we propose PenaltyAttack. Unlike traditional methods, it generates white-box $\ell _{2}$ and $\ell _{1}$ adversarial examples by progressively increasing the penalty parameter instead of employing binary search. Extensive experiments on three test benches (MNIST, CIFAR10, and ImageNet) demonstrate that compared with existing methods, our attack can generate adversarial examples with minor perturbations at a higher success rate. The implementation and experimental code are publicly available at https://github.com/sjysjy1/PenaltyMethodAttack}, archive = {J_ACCESS}, author = {Jiyuan Sun and Haibo Yu and Jianjun Zhao}, doi = {10.1109/ACCESS.2025.3529217}, journal = {IEEE Access}, pages = {18123-18140}, shortjournal = {IEEE Access}, title = {An adversarial attack via penalty method}, volume = {13}, year = {2025}, }
• (2025). SVDD: SAR vehicle dataset construction and detection. ACCESS, 13, 18107-18122. (www)

  @article{ , abstract = {With the advent of high-quality SAR images and the rapid development of computing technology, the object detection algorithms based on convolution neural network have attracted a lot of attention in the field of SAR object detection. At present, the main dataset for SAR target detection in China focus on ships, there is a lack of SAR vehicle detect datasets, and complex ground scenes can affect vehicle detection performance. To solve these problems, we proposed a lightweight SAR vehicle detection algorithm, aiming to improve the vehicle detection accuracy and simplify the model complexity. First, we constructed a multi-band SAR vehicle detection dataset (SVDD) with annotations as the training dataset of the object detection model. Then, we introduced dual conv into the RT-DETR model. Dual conv uses group convolution technology to filter the convolutional network to reduce model parameters, so we can achieve a lightweight and real-time end-to-end detection. Finally, we used the mmdetection framework as a benchmark and test the robust performance under different conditions. Experimental results show that the AP50 of we proposed method reaches 98.5%, achieving excellent detection performance.}, archive = {J_ACCESS}, author = {Dan Gao and Xiaofang Wu and Zhijin Wen and Yue Xu and Zhengchao Chen}, doi = {10.1109/ACCESS.2025.3532294}, journal = {IEEE Access}, pages = {18107-18122}, shortjournal = {IEEE Access}, title = {SVDD: SAR vehicle dataset construction and detection}, volume = {13}, year = {2025}, }
• (2025). Music generation using deep learning and generative AI: A systematic review. ACCESS, 13, 18079-18106. (www)

  @article{ , abstract = {This paper presents a systematic review of recent advances in music generation using deep learning techniques, categorizing the latest research in the field and identifying key contributions from various approaches. The study examines common data representations in music generation, including raw waveforms, spectrograms, and MIDI, alongside the most prominent deep learning architectures like Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Transformer-based models. Through a comparative analysis, the paper highlights the strengths and limitations of these approaches. The findings suggest that GANs with spectrograms and RNNs with MIDI data are particularly effective for generating multi-track music, while autoregressive models like MusicGen and transformer models demonstrate superior performance in capturing long-term dependencies in music generation. Additionally, the paper underscores the emergence of diffusion models, which are gaining popularity for generating high-quality, complex music outputs. The major contribution of this review is the identification of the best-performing models for various music generation tasks and the provision of comprehensive insights into data representation methods, evaluation metrics, and future research directions.}, archive = {J_ACCESS}, author = {Rohan Mitra and Imran Zualkernan}, doi = {10.1109/ACCESS.2025.3531798}, journal = {IEEE Access}, pages = {18079-18106}, shortjournal = {IEEE Access}, title = {Music generation using deep learning and generative AI: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Optimizing supply chain resilience using advanced analytics and computational intelligence techniques. ACCESS, 13, 18063-18078. (www)

  @article{ , abstract = {This paper presents a novel resilient supply chain management (SCM) structure leveraging advanced artificial intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) networks and Particle Swarm Optimization (PSO). The primary objective is to enhance supply chain efficiency and robustness by integrating these AI methods to address common challenges such as demand forecasting, resource allocation, and cost reduction. The proposed methodology combines LSTM for accurate demand forecasting with PSO for optimizing resource allocation and cost management. LSTM’s strength in capturing complex temporal patterns is utilized to predict demand with high precision, while PSO is employed to optimize various supply chain components, including inventory management, transportation, and production planning. The system’s effectiveness is evaluated through extensive experimentation and case studies, focusing on metrics such as forecasting accuracy, cost reduction, and resource utilization. Key findings indicate that the integrated LSTM-PSO system significantly outperforms traditional SCM methods. It achieves a 12% reduction in overall SCM costs, improves demand forecasting accuracy with reduced mean absolute error (MAE) and root mean squared error (RMSE), and enhances resource utilization efficiency by up to 20%. Additionally, the system demonstrates notable improvements in operational efficiency, with increased system uptime and reduced order error rates. The implications of this research are substantial; it provides a comprehensive framework that combines predictive and optimization capabilities, offering a robust solution to contemporary SCM challenges. By integrating LSTM and PSO, the research advances the field toward achieving resilient and efficient supply chain operations, with practical implications for enhancing overall performance and reducing costs in complex supply chain environments.}, archive = {J_ACCESS}, author = {Jie Xu and Lixing Bo}, doi = {10.1109/ACCESS.2024.3523470}, journal = {IEEE Access}, pages = {18063-18078}, shortjournal = {IEEE Access}, title = {Optimizing supply chain resilience using advanced analytics and computational intelligence techniques}, volume = {13}, year = {2025}, }
• (2025). A miniaturized and high optically transparent frequency selective surface for RF shielding using double-glazed glass windows for green building applications. ACCESS, 13, 18053-18062. (www)

  @article{ , abstract = {This research presents a miniaturized and high optically transparent (OT) frequency selective surface (FSS) for achieving RF shielding through glass window panels. The proposed FSS consists of a single-layered copper pattern sandwiched between two ordinary glass substrates to suppress the dual bands of sub-6 fifth generation (5G). In particular, the design effectively shields n65-downlink (2.1 GHz) and a portion of n78-band (3.5 GHz). The unit cell (UC) design consists of square and butterfly rings with a maximum copper width of 0.1 mm. The dimensions of FSS unit cell (UC) are optimized to $0.0714\lambda _{0}\times 0.07146\lambda _{0}$ , where $\lambda _{0}$ is the wavelength at 2.1 GHz resonant frequency. Full-wave electromagnetic (EM) simulations, equivalent circuit modeling (ECM), and experimental testing are performed to validate the FSS performance. The design miniaturization and 0.1 mm copper trace width offered a maximum OT of 91.6 % and angular stability up to ${85}^{^{\circ }}$ for both transverse electric (TE) and transverse magnetic (TM) polarized waves.}, archive = {J_ACCESS}, author = {Muhammad Nasir and Slawomir Koziel and Adnan Iftikhar}, doi = {10.1109/ACCESS.2025.3532787}, journal = {IEEE Access}, pages = {18053-18062}, shortjournal = {IEEE Access}, title = {A miniaturized and high optically transparent frequency selective surface for RF shielding using double-glazed glass windows for green building applications}, volume = {13}, year = {2025}, }
• (2025). Dual-channel deepfake audio detection: Leveraging direct and reverberant waveforms. ACCESS, 13, 18040-18052. (www)

  @article{ , abstract = {Deepfake content-including audio, video, images, and text-synthesized or modified using artificial intelligence is designed to convincingly mimic real content. As deepfake generation technology advances, detecting deepfake content presents significant challenges. While recent progress has been made in detection techniques, identifying deepfake audio remains particularly challenging. Previous approaches have attempted to capture deepfake features by combining video and audio content; however, these methods are ineffective when video and audio are mismatched due to occlusion. To address this, we propose a novel dual-channel deepfake audio detection model that leverages the direct and reverberant components extracted from raw audio signals, focusing exclusively on audio-based detection without reliance on video content. Across various datasets, including ASVspoof2019, FakeAVCeleb, and sport press conference datasets collected by our group, the proposed dual-channel model demonstrates significant improvements in quantitative metrics such as equal error rate and area under the curve. The implementation is available at https://github.com/gunwoo5034/Dual-Channel-Audio-Deepfake-Detection.}, archive = {J_ACCESS}, author = {Gunwoo Lee and Jungmin Lee and Minkyo Jung and Joseph Lee and Kihun Hong and Souhwan Jung and Yoseob Han}, doi = {10.1109/ACCESS.2025.3532775}, journal = {IEEE Access}, pages = {18040-18052}, shortjournal = {IEEE Access}, title = {Dual-channel deepfake audio detection: Leveraging direct and reverberant waveforms}, volume = {13}, year = {2025}, }
• (2025). Improving quality evaluation for control systems of machining line product processing with smart algorithmic techniques in intuitionistic fuzzy multiple-attribute group decision-making. ACCESS, 13, 18024-18039. (www)

  @article{ , abstract = {The evaluation of a machining line product quality monitoring system assesses its effectiveness in ensuring consistent product quality during manufacturing. It involves analyzing the system’s ability to detect defects, monitor key process parameters, and provide real-time feedback for corrective actions. Key evaluation criteria include accuracy, reliability, response speed, and integration with other production systems. By identifying strengths and areas for improvement, the evaluation ensures the system meets quality standards, enhances operational efficiency, and reduces waste. This process is crucial for maintaining high-quality output in automated or semi-automated machining lines. The quality evaluation control systems for machining line product processing fall under the category of multi-attribute group decision-making (MAGDM). To address this, the TODIM and MABAC approaches were applied, leveraging their strengths in solving MAGDM problems. Intuitionistic Fuzzy Sets (IFSs) were utilized as a decision-making tool to handle uncertain and imprecise data during the evaluation process. In this study, an integrated intuitionistic fuzzy TODIM-MABAC (IF-TODIM-MABAC) approach in light with intuitionistic fuzzy Euclidean distance (IFED) and intuitionistic fuzzy logarithmic distance (IFLD) is proposed to tackle MAGDM problems under the framework of IFSs. This approach combines the advantages of the TODIM and MABAC methods, enhanced with IFSs, to better manage uncertainty in decision-making. A numerical study is conducted to demonstrate the application of the proposed approach for the quality evaluation control systems of machining line product processing, validating its effectiveness and practicality. The main contributions of this study are outlined: 1) TODIM-MABAC approach in light with IFED and IFLD is extended and enhanced with IFSs to handle uncertainty in MAGDM. 2) The entropy method is employed to objectively determine weight values under the IFS framework. 3) IF-TODIM-MABAC approach is developed to solve MAGDM problems effectively. 4) A numerical example, along with comparative analyses, is provided to validate the practicality and reliability of the proposed approach.}, archive = {J_ACCESS}, author = {Chunjiang He and Jian Jiang}, doi = {10.1109/ACCESS.2025.3532961}, journal = {IEEE Access}, pages = {18024-18039}, shortjournal = {IEEE Access}, title = {Improving quality evaluation for control systems of machining line product processing with smart algorithmic techniques in intuitionistic fuzzy multiple-attribute group decision-making}, volume = {13}, year = {2025}, }
• (2025). MIMO control architectures for secondary voltage regulation in electrically coupled transmission grids: Design and dynamic performance. ACCESS, 13, 18005-18023. (www)

  @article{ , abstract = {The progressive shift towards renewable energy sources in electric power production requires a revolution in transmission system control. In the latter, progressive reduction of high-power conventional power plants in favor of small distributed ones based on renewable energies (which present different dynamic performance), as well as the integration of new elements (regulating HVDC links, STATCOMs, etc.), makes the present transmission system structure and behavior significantly different compared to those taken as bases for the design of voltage control systems. Moreover, limitations due to technology obsolescence of the existing architecture and the greatly increased capabilities of modern Phase Measurement Units (PMU) contribute to requiring a revision of the voltage control architecture, which should also provide robustness to parameter variation and be adaptive in nature. To this end, in this work the applicability of different multiple input multiple output control methods to the transmission system secondary voltage regulation is investigated. Specifically, the Decoupling control method and the Linear Quadratic Regulator with Integral action control method are applied to the secondary voltage regulation of the Italian transmission system. Three different control system architectures, resulting from the application of these methods to the peculiarities of Italian transmission system voltage control architecture, are proposed in the paper. Their dynamic performance is tested on the model of a portion of the Italian transmission network in different scenarios, and compared with current Italian voltage control. The results suggest one of the proposed architectures as a promising solution to address the power system’s evolution.}, archive = {J_ACCESS}, author = {Andrea Vicenzutti and Fabio Marzolla and Massimiliano Chiandone and Salvatore Tessitore and Cosimo Pisani and Giorgio M. Giannuzzi and Giorgio Sulligoi}, doi = {10.1109/ACCESS.2025.3532356}, journal = {IEEE Access}, pages = {18005-18023}, shortjournal = {IEEE Access}, title = {MIMO control architectures for secondary voltage regulation in electrically coupled transmission grids: Design and dynamic performance}, volume = {13}, year = {2025}, }
• (2025). 0.5 v digitally-tunable filters for biomedical applications exploiting segmented duty-cycled resistors. ACCESS, 13, 17996-18004. (www)

  @article{ , abstract = {In this paper, we present the design and the experimental validation of a biquad filter for biomedical applications that takes advantage of segmented duty-cycled resistors to allow for digital tuning over a wide range of the cutoff frequency ( $f_{0}$ ), quality factor (Q), and gain (K). Additionally, we report on the experimental findings for an 8th-order band pass filter that was created to extract the epilepsy biomarker, related to the fast ripple band (250 Hz–500 Hz). The results of measurements on a prototype chip, implemented in a commercial 130 nm CMOS technology with a supply voltage of 0.5 V and a power consumption of 120 nW, confirm that the $f_{0}$ of the proposed biquad filter can be digitally tuned from 15 Hz up to 517 Hz, whereas the Q can be tuned from 0.7 up to 13. A comparison against the state of the art has shown that the proposed biquad filter exhibits the lowest area, with the highest Q, and can be tuned at the lowest $f_{0}$ . It also exhibits the unique feature to allow for independent tunability of $f_{0}$ , Q, and K by varying the duty-cycle of digital control signals. The 8th-order filter, implemented with 4 cascaded instances of the proposed biquad filter, guarantees an attenuation higher than 30 dB at $f_{0}$ /2 and 2f0 with a power consumption of 480 nW, and an area footprint of 0.152 mm2, thus satisfying the requirements to extract the epilepsy biomarker in closed-loop deep brain stimulation systems.}, archive = {J_ACCESS}, author = {Alessandro Fava and Francesco Centurelli and Giuseppe Scotti}, doi = {10.1109/ACCESS.2025.3532824}, journal = {IEEE Access}, pages = {17996-18004}, shortjournal = {IEEE Access}, title = {0.5 v digitally-tunable filters for biomedical applications exploiting segmented duty-cycled resistors}, volume = {13}, year = {2025}, }
• (2025). Hypergraph user embeddings and session contrastive learning for POI recommendation. ACCESS, 13, 17983-17995. (www)

  @article{ , abstract = {Internet technologies have enabled location-based social networks (LBSNs) to provide users with a variety of services. In this context, next Point-of-Interest (POI) recommendation has become a key task. The goal of this task is to mine users’ travel behavior preferences based on their historical check-in session sequences and recommend the next POI. However, existing methods are insufficient in capturing the interactions between users and POIs, fail to fully mine personalized user preferences, and do not adequately reveal the complex, high-order collaborative signals between users, which makes it difficult to effectively alleviate the problem of data sparsity. Moreover, existing approaches also struggle to distinguish the different travel behavior patterns of users. To address these issues, we propose a novel method called Hypergraph User Embedding and Session Contrastive Learning (HUE-SCL) for next POI recommendation. We model users as hyperedges and the POIs they visit as nodes within these hyperedges, constructing a hypergraph that reveals the interaction relationships between users and POIs. Based on this hypergraph and POI embeddings, we perform personalized embedding of users to better capture their travel preferences and fully exploit the high-order collaborative signals between users, thus alleviating the data sparsity problem. Additionally, to better distinguish behavioral pattern differences between users, we introduce session enhancement and contrastive learning techniques to more accurately capture users’ travel preferences. Extensive experiments on two real-world datasets show that HUE-SCL outperforms existing state-of-the-art baseline methods.}, archive = {J_ACCESS}, author = {Yan Zhang and Bin Wang and Qian Zhang and Sulei Zhu and Yan Ma}, doi = {10.1109/ACCESS.2025.3531394}, journal = {IEEE Access}, pages = {17983-17995}, shortjournal = {IEEE Access}, title = {Hypergraph user embeddings and session contrastive learning for POI recommendation}, volume = {13}, year = {2025}, }
• (2025). Low-cost LoRa-based system for continuous GHGs monitoring in cattle: Enhancing agricultural sustainability. ACCESS, 13, 17963-17982. (www)

  @article{ , abstract = {Rising concern over livestock emissions and their contribution to climate change demands innovative and cost-effective monitoring solutions. This study introduces and validates a low-cost, LoRa based system for the continuous monitoring of methane (CH4) and carbon dioxide (CO2) emissions in cattle. Unlike previous fixed installation systems, our design features a movable cattle collar equipped with gas sensors, enabling real-time data collection directly from the emission source. The system combines advanced sensor technology with long-range LoRa communication, thereby providing a scalable solution for remote agricultural environments. The experimental results demonstrate that the cattle collar system achieves higher accuracy in detecting CO2 emissions owing to its proximity to the emission source, outperforming fixed systems. The closed feeding slot design showed a comparable accuracy for CH4 detection. Both systems proved capable of reliable data transmission over distances of up to 400 m, making them ideal for large-scale deployments. In terms of cost, our system is significantly more affordable than other commercially available systems. The collar’s portability, low power consumption, and long battery life make it well-suited for continuous monitoring in both indoor and outdoor settings. This study highlights the potential of a movable, low-cost solution for monitoring livestock emissions, contributing to agricultural sustainability and climate change mitigation by providing accurate, real-time data for effective emission management strategies.}, archive = {J_ACCESS}, author = {Boonyarit Kumkhet and Nisit Pukrongta and Noraset Wichaipanich and Pubet Sangmahamad and Tanaporn Pechrkool and Virote Pirajnanchai and Patchadaporn Sangpet and Utthapon Issara and Attaphongse Taparugssanagorn}, doi = {10.1109/ACCESS.2025.3532471}, journal = {IEEE Access}, pages = {17963-17982}, shortjournal = {IEEE Access}, title = {Low-cost LoRa-based system for continuous GHGs monitoring in cattle: Enhancing agricultural sustainability}, volume = {13}, year = {2025}, }
• (2025). EMSPAN: Efficient multi-scale pyramid attention network for object counting under size heterogeneity and dense scenarios. ACCESS, 13, 17945-17962. (www)

  @article{ , abstract = {Computer vision is becoming an increasingly vital field, offering significant opportunities for real-world applications. Object counting is one of its core aspects, with increasing utilization across scientific fields involving objects of varying sizes. Traditional counting methods, however, face challenges in dense scenarios, as they are often ineffective in handling objects of different sizes. To address these challenges, this paper proposes the Efficient Multi-Scale Pyramid Attention Network (EMSPAN) model, which is designed to tackle both dense and size-heterogeneous object counting tasks. Additionally, a novel ground truth density map generation method using size-adaptive Gaussian kernels is introduced, which dynamically adjusts kernel size based on object dimensions. This approach preserves spatial information more effectively and produces more accurate density maps, even in complex scenes. The EMSPAN model utilizes advanced attention mechanisms to capture the multi-scale spatial distribution and size variations of objects. Experiments on the shrimp larvae and crowd datasets, characterized by significant size diversity of individual objects, have demonstrated the superior performance of the proposed method in handling object counting tasks in dense and size-heterogeneous environments.}, archive = {J_ACCESS}, author = {Phu Nguyen Phan Hai and Bao Bui Quoc and Trang Hoang}, doi = {10.1109/ACCESS.2025.3532962}, journal = {IEEE Access}, pages = {17945-17962}, shortjournal = {IEEE Access}, title = {EMSPAN: Efficient multi-scale pyramid attention network for object counting under size heterogeneity and dense scenarios}, volume = {13}, year = {2025}, }
• (2025). Sybil-resistant self-sovereign identity utilizing attested execution secure processors and zero-knowledge membership proofs. ACCESS, 13, 17919-17944. (www)

  @article{ , abstract = {Increasing attention to digital identity and self-sovereign identity (SSI) is gaining momentum. SSI brings various benefits to natural persons, such as owning controls; conversely, digital identity systems in the real world require Sybil-resistance to comply with anti-money laundering (AML) and other needs. CanDID by Maram et al. proposed that decentralized digital identity systems may achieve Sybil-resistance and preserve privacy by utilizing multi-party computation (MPC), assuming a distributed committee of trusted nodes. Pass et al. proposed the formal abstraction of attested execution secure processors (AESPs) while equipping hardware-assisted security in mobile devices has become the norm. We first describe our proposal to utilize AESPs for building secure Sybil-resistant SSI systems, the architecture with a set of system protocols $\Pi ^{{\mathcal {G}}_{\mathtt {att}}}$ , which brings drastic flexibility and efficiency compared to existing systems. In addition, we propose a novel scheme that enables users (holders) to request verifiers to verify their credentials without AESPs, and it further achieves unlinkability among credentials created for public verification. Our scheme introduces a simplified format for computed claims and commitment-based anonymous identifiers. We also describe a technique to utilize zero-knowledge membership proofs, in particular, “One-Out-of-Many Proofs” $\Sigma $ -protocol by Groth and Kohlweiss, which can prove the existence of an expected credential without identifying it. Along with other techniques, such as utilizing the BBS+ signature scheme, we demonstrate how our scheme can achieve its goals with the extended anonymous and Sybil-resistant SSI system protocols $\Pi ^{{\mathcal {G}}_{\mathtt {att}}+}$ . Entitling unlinkability among derived credentials in the anonymous Sybil-resistant SSI results in proper privacy preservation.}, archive = {J_ACCESS}, author = {Koichi Moriyama and Akira Otsuka}, doi = {10.1109/ACCESS.2025.3533877}, journal = {IEEE Access}, pages = {17919-17944}, shortjournal = {IEEE Access}, title = {Sybil-resistant self-sovereign identity utilizing attested execution secure processors and zero-knowledge membership proofs}, volume = {13}, year = {2025}, }
• (2025). Complementarity-oriented feature fusion for face-phone trajectory matching. ACCESS, 13, 17911-17918. (www)

  @article{ , abstract = {CCTVs and telecom base stations act as sensors, and collect massive face and phone related data. When used for person localization and trajectory characterization, they each present quite different spatiotemporal characteristics: CCTV is associated with slowly sampled face ID trajectories with spatial resolution of approximately 20 meters, while telecom readings provide fast sampled phone ID trajectories with spatial uncertainty of a few hundred meters. The face or phone trajectory can be seen as an observation of the real trajectory of a moving pedestrian. It is useful to identify the correspondence between face and phone trajectories to reconstruct the trajectory of moving persons. To this end, we propose a complementarity-oriented feature fusion mechanism (COFFM) to model and utilize the common embedding and complementarity of these two measurement modalities. Specifically, a Cycle Heterogeneous Trajectory Translation Network (CCTTN) is proposed to realize a TFE (Trajectory Feature Extractor) which captures the latent transforming relationships between the face and phone modalities. The latent features from both transforming directions are concatenated in the Feature Unifying (FU) module and fed into a binary face-phone trajectory matching discriminator (FPTPMD) to infer whether a face-phone trajectory pair corresponds to the same underlying motion trajectory. We evaluated our method on a large real-world face-phone trajectory dataset and showed promising results with the accuracy of 97.1% which exceeds the comparable similarity-based methods. The developed principle and framework generalize well to other multi-modality trajectory matching tasks.}, archive = {J_ACCESS}, author = {Changfeng Cao and Wenchuan Zhang and Hua Yang and Dan Ruan}, doi = {10.1109/ACCESS.2025.3531106}, journal = {IEEE Access}, pages = {17911-17918}, shortjournal = {IEEE Access}, title = {Complementarity-oriented feature fusion for face-phone trajectory matching}, volume = {13}, year = {2025}, }
• (2025). The application of artificial intelligence in engineering education: A systematic review. ACCESS, 13, 17895-17910. (www)

  @article{ , abstract = {Artificial Intelligence (AI) is increasingly impacting the environment, pedagogy, and management practices in engineering education. However, literature reviews that provide a systematic review and analysis of AI in engineering education remain limited. To address this research gap and gain a comprehensive understanding of AI applications in engineering education, this study conducts a systematic review based on 161 studies on AI applications in engineering education. Using VOSviewer, this paper analyzes the research hotspots of AI in engineering education, as well as the top ten organizations, countries, cited sources, and cited authors. Furthermore, the study categorizes the application of AI technologies in engineering education into seven distinct categories: Virtual Experiment Environments, Learning Prediction, Learning Analytics, Engineering Education Robots, Intelligent Tutoring Systems, Automatic Evaluation, and Assisted Learning. The study reveals that these technologies have already been widely adopted. Moreover, this paper summarizes the influence of AI technologies on engineering education, along with the implications and challenges, providing a foundation for further exploration of the integration of AI technologies into educational systems.}, archive = {J_ACCESS}, author = {Cong Liu and Guang-Chao Wang and Hong-Feng Wang}, doi = {10.1109/ACCESS.2025.3532595}, journal = {IEEE Access}, pages = {17895-17910}, shortjournal = {IEEE Access}, title = {The application of artificial intelligence in engineering education: A systematic review}, volume = {13}, year = {2025}, }
• (2025). An improved control strategy for managing reactive power and reducing capacity of interlinking converters by participating of electric vehicles in hybrid AC/DC microgrids. ACCESS, 13, 17878-17894. (www)

  @article{ , abstract = {Nowadays, interface converters based hybrid AC/DC microgrids have gained great interests in smart grids. The interface converters can perform tasks such as accurate power sharing, power quality enhancement, bidirectional power transmission, and so on by using advanced and accurate control strategies. In this work, a dc voltage droop based hierarchical control is introduced for bidirectional power transfer by an interface converter in a hybrid AC/DC microgrid. This proposed approach can exchange active power between AC or DC subgrids according to the requirement of each subgrid with slight DC voltage deviation around its nominal value. Moreover, a reactive power control algorithm is presented which can compensate the reactive power of the AC subgrid based on the interface converter’s maximum rated capacity. Also, the impact of the participation of electric vehicles in the DC subgrid on the improvement of reactive power management and the capacity reduction of the interface converter is investigated. Another droop control is considered in the voltage control loop of the bidirectional boost converter in the electric vehicle to determine the participation rate of the electric vehicle in reactive power management through the charging and discharging of its battery storage system. The simulation results in Simulink MATLAB software illustrate that the compensation of reactive power by the interface converter improves significantly by employing the proposed control method and the maximum participation of electric vehicles, leading to a decrease in the rated capacity of the interface converter.}, archive = {J_ACCESS}, author = {Abbas Safari and Hesam Rahbarimagham}, doi = {10.1109/ACCESS.2025.3533110}, journal = {IEEE Access}, pages = {17878-17894}, shortjournal = {IEEE Access}, title = {An improved control strategy for managing reactive power and reducing capacity of interlinking converters by participating of electric vehicles in hybrid AC/DC microgrids}, volume = {13}, year = {2025}, }
• (2025). CenterNet-elite: A small object detection model for driving scenario. ACCESS, 13, 17868-17877. (www)

  @article{ , abstract = {With the rapid development of deep learning networks, the accuracy of generic object detection has consistently improved. Nonetheless, small object detection tasks still face a range of challenges. On one hand, the limited pixel size of small objects severely constrains their visual features in images, making them susceptible to distortion and difficult to distinguish from background noise. On the other hand, small objects often appear in complex scenarios with severe occlusion and dense arrangement, further increasing the complexity and difficulty of small object detection tasks. In this context, this study proposes a new model, CenterNet-Elite, to overcome these challenges. To address the issue of feature information loss resulting from multiple downsamplings during feature extraction for small objects, we introduce the spatial and channel reconstruction convolution (SCConv) into the bottleneck to reduce spatial and channel redundancy and enhance feature representation. In the meantime, we construct multiple short connections to integrate feature maps of the same scale during the downsampling and upsampling stages, thereby retaining critical shallow spatial information. We introduce a multi-scale pooling module, SPPCSPC, to address the challenge of significant variations in object scale. This module obtains receptive fields of different sizes through max-pooling layers of diverse sizes, adapting to changes in object scale on the feature maps. Furthermore, we introduce the content-aware reassembly of features (CARAFE) to replace deconvolution, refining the upsampling process to enhance the quality of feature maps. A series of comparative experiments and ablation studies demonstrate the effectiveness of our method in small object detection. The CenterNet-Elite achieves a 2.3% increase in the average precision on the large-scale small object detection dataset (SODA-D).}, archive = {J_ACCESS}, author = {Lingling Wang and Xiang Li and Xiaoyan Chen and Bin Zhou}, doi = {10.1109/ACCESS.2025.3532786}, journal = {IEEE Access}, pages = {17868-17877}, shortjournal = {IEEE Access}, title = {CenterNet-elite: A small object detection model for driving scenario}, volume = {13}, year = {2025}, }
• (2025). Advancing underwater vision: A survey of deep learning models for underwater object recognition and tracking. ACCESS, 13, 17830-17867. (www)

  @article{ , abstract = {Underwater computer vision plays a vital role in ocean research, enabling autonomous navigation, infrastructure inspections, and marine life monitoring. However, the underwater environment presents unique challenges, including color distortion, limited visibility, and dynamic light conditions, which hinder the performance of traditional image processing methods. Recent advancements in deep learning (DL) have demonstrated remarkable success in overcoming these challenges by enabling robust feature extraction, image enhancement, and object recognition. This review provides a comprehensive analysis of cutting-edge deep learning architectures designed for underwater object detection, segmentation, and tracking. State-of-the-art (SOTA) models, including AGW-YOLOv8, Feature-Adaptive FPN, and Dual-SAM, have shown substantial improvements in addressing occlusions, camouflaging, and small underwater object detection. For tracking tasks, transformer-based models like SiamFCA and FishTrack leverage hierarchical attention mechanisms and convolutional neural networks (CNNs) to achieve high accuracy and robustness in dynamic underwater environments. Beyond optical imaging, this review explores alternative modalities such as sonar, hyperspectral imaging, and event-based vision, which provide complementary data to enhance underwater vision systems. These approaches improve performance under challenging conditions, enabling richer and more informative scene interpretation. Promising future directions are also discussed, emphasizing the need for domain adaptation techniques to improve generalizability, lightweight architectures for real-time performance, and multi-modal data fusion to enhance interpretability and robustness. By critically evaluating current methodologies and highlighting gaps, this review provides insights for advancing underwater computer vision systems to support ocean exploration, ecological conservation, and disaster management.}, archive = {J_ACCESS}, author = {Mahmoud Elmezain and Lyes Saad Saoud and Atif Sultan and Mohamed Heshmat and Lakmal Seneviratne and Irfan Hussain}, doi = {10.1109/ACCESS.2025.3534098}, journal = {IEEE Access}, pages = {17830-17867}, shortjournal = {IEEE Access}, title = {Advancing underwater vision: A survey of deep learning models for underwater object recognition and tracking}, volume = {13}, year = {2025}, }
• (2025). Electromagnetic signaling outperforms quorum sensing in bacterial biofilms: A communication channel capacity perspective. ACCESS, 13, 17820-17829. (www)

  @article{ , abstract = {There is a growing body of theoretical and experimental evidence that in addition to the biochemical signaling using auto-inducers and ion channels, biological cells and more specifically certain bacterial biofilms can transmit and receive electromagnetic (EM) waves as a means of communication. It was recently hypothesized that certain cellular structures and biofilms may be equipped with simple radios whose operation is based on mechanical antennas. For instance, biofilms produced by bacterial cells are composed of elastic helical fibers known as amyloid fibrils, which possess permanent electric dipoles. Through theoretical analysis, it was recently proposed that cells in such biofilms are capable of transmitting EM signal to their surrounding environment through mechanical vibration of these charged fibrils. This theory has been also validated experimentally by carrying careful measurements of radio emission from staphylococcus aureus biofilms. In this paper, we explore the potential biological advantages of electromagnetic (EM) signaling over chemical signaling in terms of data rate and communication range, to illustrate why such an EM signaling mechanism might evolve in certain biological cells. Basically by using communication channel modeling, channel capacities for the biochemical communication method known as quorum sensing (QS) and that for EM-based communication are evaluated and shown that EM-based communication provides much higher data rate and over longer intercellular distances.}, archive = {J_ACCESS}, author = {Navid Barani and Kamal Sarabandi}, doi = {10.1109/ACCESS.2025.3532461}, journal = {IEEE Access}, pages = {17820-17829}, shortjournal = {IEEE Access}, title = {Electromagnetic signaling outperforms quorum sensing in bacterial biofilms: A communication channel capacity perspective}, volume = {13}, year = {2025}, }
• (2025). QTrustNet virtual private network (VPN): Enhancing security in the quantum era. ACCESS, 13, 17807-17819. (www)

  @article{ , abstract = {This paper introduces qTrustNet VPN, a next-generation Virtual Private Network (VPN) designed to enhance security in the quantum era. As traditional encryption methods face obsolescence due to the advent of quantum computing, there is an urgent need for more robust security solutions. qTrustNet VPN addresses this need by ensuring the confidentiality and integrity of data transmission through the WireGuard protocol, known for its simplicity and high performance. At the core of qTrustNet VPN’s enhanced security is the integration of a Post-Quantum Cryptography (PQC) algorithm, developed by the National Institute of Standards and Technology (NIST). This PQC algorithm is designed to withstand the decryption capabilities of quantum computers, thus providing long-term security. To further bolster security, the encryption key of the PQC algorithm is protected using Physically Unclonable Function (PUF) technology. PUFs offer a hardware-based security mechanism that is resistant to cloning and tampering, ensuring that the encryption keys remain secure against both classical and quantum attacks. This innovative combination of WireGuard protocol, PQC algorithms, and PUF technology creates a VPN system with significantly improved security features. To validate its effectiveness, the performance of the proposed qTrustNet VPN was rigorously evaluated in a 10GbE NIC network environment. The results demonstrated a remarkable throughput of up to 8.5 Gbps, ensuring high-speed data transmission capabilities. Additionally, the latency was measured at 1ms or less, highlighting the system’s efficiency in maintaining low-latency communication. In summary, qTrustNet VPN represents a significant advancement in VPN technology, offering robust security features tailored for the quantum era while maintaining high performance and low latency. This paper details the design, implementation, and performance evaluation of qTrustNet VPN, showcasing its potential to secure data transmission against emerging quantum threats.}, archive = {J_ACCESS}, author = {Hyoungsub Shim and Bongho Kang and Haejung Im and Duhyun Jeon and Seok-Min Kim}, doi = {10.1109/ACCESS.2025.3530985}, journal = {IEEE Access}, pages = {17807-17819}, shortjournal = {IEEE Access}, title = {QTrustNet virtual private network (VPN): Enhancing security in the quantum era}, volume = {13}, year = {2025}, }
• (2025). A systematic review of access control models: Background, existing research, and challenges. ACCESS, 13, 17777-17806. (www)

  @article{ , abstract = {Data security has become paramount, especially with the exponential growth of data, the rise of cyber threats, and the increasing prevalence of remote work. Data confidentiality, integrity, and availability are crucial, particularly in sensitive domains like healthcare and cloud computing. While robust access control (AC) is essential, it must be balanced with maintaining operational efficiency. In access control models, there is a general lack of a comprehensive cross-domain overview, insufficient focus on evolving access control requirements, and inadequate analysis of challenges and issues. This paper comprehensively analyzes access control models (ACMs) through a two-pronged approach. First, we conduct a Narrative Literature Review(NLR) to classify traditional ACMs, evaluating their security strengths and weaknesses and compatible security protocols. Second, we perform a Systematic Literature Review (SLR) of emerging ACMs developed over the past decade across various domains. This systematic review has three primary objectives: (1) to introduce and analyze these emerging ACMs, (2) to present their current technological status, and (3) to identify key challenges and promising research directions. By combining these approaches, this paper offers a comprehensive overviewofACMapplications and techniques, identifies existing challenges, and explores future research directions to guide advancements in access control.}, archive = {J_ACCESS}, author = {Nastaran Farhadighalati and Luis A. Estrada-Jimenez and Sanaz Nikghadam-Hojjati and Jose Barata}, doi = {10.1109/ACCESS.2025.3533145}, journal = {IEEE Access}, pages = {17777-17806}, shortjournal = {IEEE Access}, title = {A systematic review of access control models: Background, existing research, and challenges}, volume = {13}, year = {2025}, }
• (2025). Near-field hierarchical localization for integrated sensing and communication. ACCESS, 13, 17766-17776. (www)

  @article{ , abstract = {Localization is expected to play a significant role in future wireless networks, as positioning and situational awareness, navigation and tracking, are integral parts of 6G usage scenarios. Nevertheless, in many cases localization requires extra equipment, which interferes with communications systems, while also requiring additional resources. On the other hand, high frequency and highly directional communications offer a new framework of improved resolution capabilities in the angular and range domains. The implementation of integrated sensing and communications is being explored to unify the sensing and communications systems and promote a communicate-to-sense approach. To this end, a localization algorithm is presented that utilizes beam-forming and the emerging beam-focusing technique, to estimate the location of the receiver. The algorithm can be implemented with large antenna arrays, and large intelligent surfaces. The performance of the algorithm for static and mobile users is evaluated through Monte-Carlo simulations. The results are presented with the empirical CDF for both static and mobile users, and the probability of successful estimation for static users.}, archive = {J_ACCESS}, author = {Giorgos Stratidakis and Sotiris Droulias and Angeliki Alexiou}, doi = {10.1109/ACCESS.2025.3528857}, journal = {IEEE Access}, pages = {17766-17776}, shortjournal = {IEEE Access}, title = {Near-field hierarchical localization for integrated sensing and communication}, volume = {13}, year = {2025}, }
• (2025). Heavy and lightweight deep learning models for semantic segmentation: A survey. ACCESS, 13, 17745-17765. (www)

  @article{ , abstract = {Semantic segmentation is an important computer vision task due to its numerous real-world applications such as autonomous driving, video surveillance, medical image analysis, robotics, augmented reality, among others, and its popularity increased with the development of deep learning approaches. We provide a detailed review comprising the most significant methods for both heavy and lightweight two-dimensional (2D) semantic segmentation, starting with the introduction of convolutional neural networks until the use of Transformer architecture, the latter being a widely adopted model with state-of-the-art results in several artificial intelligence fields. The methods involved are described from the architectural design perspective, including encoder-decoder architectures, multi-resolution branches approaches, two-pathway encoder architectures, attention-based models, and pyramid-based models. Additionally, some of the most popular datasets and performance metrics are presented. Further, we investigate the limitations of these methods, compare their performance on Pascal VOC 2012, Cityscapes, and ADE20K datasets, and finally indicate future research directions.}, archive = {J_ACCESS}, author = {Cristina Cărunta and Alina Cărunta and Călin-Adrian Popa}, doi = {10.1109/ACCESS.2025.3529812}, journal = {IEEE Access}, pages = {17745-17765}, shortjournal = {IEEE Access}, title = {Heavy and lightweight deep learning models for semantic segmentation: A survey}, volume = {13}, year = {2025}, }
• (2025). Charge diagnostics and state estimation of battery energy storage systems through transformer models. ACCESS, 13, 17733-17744. (www)

  @article{ , abstract = {With the continuous development of Artificial Intelligence (AI), designing accurate algorithms that provide diagnostics and maintenance of energy technologies is a challenging task in the energy transition domain. This research work focuses on the implementation of Transformer models for charge diagnostics and algorithm design of Battery Energy Storage Systems (BESSs). Experimentally, two Lithium-ion (Li-ion) battery cells were tested using a programmable DC electronic load to evaluate charge indicators, and 20 battery tests were performed for each cell. Filter, Wrapper, and Embedded methods are the techniques implemented to achieve Feature Selection and illustrate Key Performance Indicators (KPIs) in battery testing. Time series and state estimation are the Supervised Learning techniques executed for charge diagnostics and State of Charge (SOC) predictions. The results show remarkable performance metrics of the Transformer models, achieving over 94% accuracy in Model evaluation compared to traditional Deep Learning algorithms.}, archive = {J_ACCESS}, author = {Rolando Antonio Gilbert Zequera and Anton Rassõlkin and Toomas Vaimann and Ants Kallaste}, doi = {10.1109/ACCESS.2025.3532858}, journal = {IEEE Access}, pages = {17733-17744}, shortjournal = {IEEE Access}, title = {Charge diagnostics and state estimation of battery energy storage systems through transformer models}, volume = {13}, year = {2025}, }
• (2025). A dual-controller of quadrotor UAV elastic hanging model based on event-triggered mechanism and its control design. ACCESS, 13, 17724-17732. (www)

  @article{ , abstract = {In this paper, the dynamic model of the quadrotor with an elastic hangded payload is analysised, and a new control strategy based on the event triggering mechanism, sliding mode control and energy analysis method is designed for the dynamic model. The energy based control law is employed for the accurate positioning task near the equilibrium point, and sliding mode control law is used to avoid the swing of the hanging payload during the transporting process. The former is designed based on the robust sliding mode control, and the latter is designed based on the energy analysis method, and the stability analysis of the designed system and the control law is carried out using the Lyapunov method and the Russell’s invariant theorem. A group of simulation results is presented to illustrate the effectiveness of the algorithms.}, archive = {J_ACCESS}, author = {Sen Yang and Hang Guo and Jigang Tong and Tian Xie and Jingyi Yan}, doi = {10.1109/ACCESS.2025.3533028}, journal = {IEEE Access}, pages = {17724-17732}, shortjournal = {IEEE Access}, title = {A dual-controller of quadrotor UAV elastic hanging model based on event-triggered mechanism and its control design}, volume = {13}, year = {2025}, }
• (2025). Computer vision-based framework for data extraction from heterogeneous financial tables: A comprehensive approach to unlocking financial insights. ACCESS, 13, 17706-17723. (www)

  @article{ , abstract = {Information extraction from financial document images is crucial in computer vision and NLP, as financial data often exists in image or PDF format, enabling organizations to analyze and make informed business decisions using OCR advancements. The table contents of financial document images are one of the prominent structures to confine important portions of data of the document and many Deep learning-based methods have been proposed to detect Table regions inside document images. The shortcomings of the current approach are that it is bounded within the detection of the table region and struggles in cases such as handling different layouts and preserving the relation among the different attributes of the table. Therefore, in this work, we proposed an end-to-end architecture to extract information from Financial table images while preserving the column row structures of the attributes within the table. We divided the task into four modules and generated synthesized data with different augmentation techniques to overcome data scarcity challenges and boost the performance of the pipeline modules. In terms of information extraction, the proposed method acquired 85% accuracy in the target invoice dataset.}, archive = {J_ACCESS}, author = {Iftakhar Ali Khandokar and Priya Deshpande}, doi = {10.1109/ACCESS.2024.3522141}, journal = {IEEE Access}, pages = {17706-17723}, shortjournal = {IEEE Access}, title = {Computer vision-based framework for data extraction from heterogeneous financial tables: A comprehensive approach to unlocking financial insights}, volume = {13}, year = {2025}, }
• (2025). How destination image building tourists’ immersive experience for online tour and stimulate the intention to visit in person. ACCESS, 13, 17695-17705. (www)

  @article{ , abstract = {This study investigates the impact of destination image in online tourism on tourists’ intention to travel in person, as well as the role that immersive experience plays in this relationship. Using data collected from 644 tourists, the findings suggested that the three dimensions of destination image in online museum visits are all significantly positively related to the immersive experience, and it worked the same way to tourist satisfaction. Both the immersive experience and satisfaction during online museum visits are significantly positively related to tourists’ intention to visit the site in person. Among the three dimensions of destination image, cognitive image and overall image are significantly positively related to tourists’ intention to visit the actual site, while the emotional image has no significant correlation with the intention to visit in person.}, archive = {J_ACCESS}, author = {Wei Sun and Liwei Li and Yidong Liu and Fengyuan Sun and Ya Zhu}, doi = {10.1109/ACCESS.2025.3532457}, journal = {IEEE Access}, pages = {17695-17705}, shortjournal = {IEEE Access}, title = {How destination image building tourists’ immersive experience for online tour and stimulate the intention to visit in person}, volume = {13}, year = {2025}, }
• (2025). A multi-signature scheme for defending malleability attack on DeFi. ACCESS, 13, 17683-17694. (www)

  @article{ , abstract = {Signatures are crucial in blockchain-based Decentralised Finance (DeFi) protocols because they ensure the security and integrity of transactions and smart contracts. Due to the weakness in the signature scheme, it is possible to carry out a malleability attack (MA) by changing the transaction ID (TxId) without affecting the transaction’s actual content or validity. Currently, signature systems can only resist partial malleability attacks from multiple attack paths. This paper proposes an advanced multi-signature scheme (MSS) as a supplementary signature that integrates unmalleable transaction implementations. In MSS, the owners and block producers themselves generate the signature. MSS improves transaction efficiency by allowing many signers to establish a joint signature, which has piqued interest. Despite the method’s complexity and time-consuming nature, this research has adapted MSS to the blockchain to safeguard against malleability attacks. By integrating it with several other optimisations, such as executing intermediate transactions using a hash function, MSS ensures complete resistance against malleability attacks. In comparison to baseline approaches, testbed simulations demonstrate scalability and 15% higher resistance against malleability attack success.}, archive = {J_ACCESS}, author = {Chen Zhang and Wenyu Liao and Xiaobin Liu and Huanrong Wu and Mohammed J. F. Alenazi}, doi = {10.1109/ACCESS.2025.3530696}, journal = {IEEE Access}, pages = {17683-17694}, shortjournal = {IEEE Access}, title = {A multi-signature scheme for defending malleability attack on DeFi}, volume = {13}, year = {2025}, }
• (2025). LASSO-mCGA: Machine learning and modified compact genetic algorithm-based biomarker selection for breast cancer subtype classification. ACCESS, 13, 17673-17682. (www)

  @article{ , abstract = {Breast cancer is the most common cancer type among females and is one of the leading causes of death worldwide. Being a heterogeneous disease, subtyping breast cancer plays a vital role in its treatment. In this regard, gene expression plays an important role. Thus, in this work gene expression data is used to identify the most significant gene biomarkers. The identified biomarkers are highly associated with each breast cancer subtype such as Luminal A, Luminal B, HER2-Enriched and Basal-Like. To identify such biomarkers, initially LASSO in association with four machine learning models such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Naive Bayes (NB) are applied on the dataset to find the initial reduced set of genes as well as the best learning model based on classification accuracy; SVM in this case. Thereafter, Modified Compact Genetic Algorithm (mCGA) is performed to identify the final set of genes as biomarkers for each specific subtype. Experimental results suggest that our proposed method assesses AUC-ROC values of 0.9878 and 0.97311 for LumA and LumB and 1 for Basal and HER2 subtypes. To validate the biological significance of the identified biomarkers, KEGG pathway and GO enrichment analysis are carried out.}, archive = {J_ACCESS}, author = {Nimisha Ghosh and Sankar Kumar Mridha and Rourab Paul}, doi = {10.1109/ACCESS.2025.3532361}, journal = {IEEE Access}, pages = {17673-17682}, shortjournal = {IEEE Access}, title = {LASSO-mCGA: Machine learning and modified compact genetic algorithm-based biomarker selection for breast cancer subtype classification}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based normal white blood cell multi-classification optimization. ACCESS, 13, 17662-17672. (www)

  @article{ , abstract = {Clinically, the proportion and classification of white blood cell (WBC)s are currently established using manual methods, which rely on subjective judgment. Therefore, many studies are being conducted to automate the classification of WBC types. Several studies have employed deep learning (DL) or machine learning (ML) methods; no significant difference in performance between the two methods has been demonstrated. However, if the feature extraction and selection processes are optimized when using ML for WBC classification, its performance is improved. Therefore, in this study, we proposed an ML-based optimization system for five normal WBC classifications. Open datasets, Raabin-WBC, and private data were used. WBCs were segmented into nucleus, cytoplasm, and cell regions using U-Net, a DL model. The nucleus showed high segmentation performance with an average accuracy of 98.58% and a Dice coefficient of 0.9233, whereas the cells achieved an average accuracy of 99.47% and a Dice coefficient of 0.9324. Among the five multiple classifiers, the support vector machine achieved the highest accuracy of 97.36% and was chosen for WBC classification. Features used for WBC classification were determined through three experiments, resulting in a final selection of 108 features combining intensity histogram, hue saturation value, and CIE La $^{\ast }$ b $^{\ast }$ features. When using the selected features, the classification accuracy was 98.22%, indicating a high performance. Segmentation and classification of WBCs were performed using a graphical user interface and required approximately 137 s. The proposed system in this study is expected to enhance the efficiency of the existing PBS tests.}, archive = {J_ACCESS}, author = {Taeyeon Gil and Sukjun Lee and Onseok Lee}, doi = {10.1109/ACCESS.2025.3532719}, journal = {IEEE Access}, pages = {17662-17672}, shortjournal = {IEEE Access}, title = {Machine learning-based normal white blood cell multi-classification optimization}, volume = {13}, year = {2025}, }
• (2025). Floods’ impact on cities’ critical infrastructure: A case study on dubai. ACCESS, 13, 17652-17661. (www)

  @article{ , abstract = {Critical infrastructure is defined as the core health, safety, and security facilities that maintain key social functions at the time of an emergency. With the weather instability driven by the effects of climate change, many normally dry lands are facing an increase in rainfall-induced floods. The expected lack of preparedness in these cities to handle floods raises concerns regarding the possible impacts on regular civilian’s lives. This problem requires a thorough analysis of cities’ ability to keep critical infrastructure safe, accessible, and easy to reach during disrupting events such as floods. In this study, we propose a new methodology to analyze the impact of floods on arid cities’ critical infrastructure using open data. We showcase our methodology on the city of Dubai which was hit by a thunderstorm earlier this year causing nation-wide floods. Our quantitative analysis was capable of determining that the Al Quoz area suffered the most from floods due to issues related to the city’s connectivity and critical infrastructure distribution. The main road connecting both sides of the area was completely blocked due to the floods with distant alternative connections while critical infrastructure was mainly concentrated on one of the two sides only. This resulted in a high increase in travel time delay and lower reachability scores. On the other hand, the Airport and Trade Center areas show a high reachability and low travel time delays as per the results obtained.}, archive = {J_ACCESS}, author = {Diaa Addeen Abuhani and Raghad Aldamani and Imran Zualkernan and Reem Alali and Himanshu Grover}, doi = {10.1109/ACCESS.2025.3527554}, journal = {IEEE Access}, pages = {17652-17661}, shortjournal = {IEEE Access}, title = {Floods’ impact on cities’ critical infrastructure: A case study on dubai}, volume = {13}, year = {2025}, }
• (2025). A general framework for closed loop negative feedback multivariable physiological control systems: Existence, uniqueness, and stability of homeostatic equilibrium points. ACCESS, 13, 17630-17651. (www)

  @article{ , abstract = {The study of homeostatic equilibrium is a key concern in several fields, from physiology and biology to medicine and biomedical engineering. Control theory approaches can provide effective strategies to model physiological control systems, helping in understanding the dynamics of bio- and physio-logical regulation processes. However, the intrinsic complexity of living systems makes it difficult to identify unified biomodels that can represent a wide variety of physiological systems. In this context, the present work proposes a general framework to model the dynamics and describe the behavior of a wide class of multivariable physiological control systems, from the molecular to the whole-organ scale. The framework adopts a structure based on a closed-loop topology taking into account multiple inputs and outputs and with the negative feedback action intrinsically embedded within the model. The development of such a general model has at least three important repercussions: the first concerns the possibility of better understanding the basic mechanisms common to many physiological systems; the second is to develop a common theoretical framework to enable effective approaches to the analysis and design of synthetic biological control systems; finally, the investigation of the structural properties of the model in a general context, allows a guided and simplified application to specific cases. To this regard, in this paper, the existence, possible uniqueness and stability properties of the homeostatic equilibrium points of the general model are investigated; the theoretical framework is then illustrated through two real-world case-studies: (i) the PI3K/AKT/mTOR pathway nonlinear dynamics, a critical regulator of cellular growth, proliferation, and survival; (ii) the control mechanism of the neuromuscular stretch reflex, among the prime triggers implicated in postural control. Results proved the capability of the proposed framework to capture the intricate dynamics of multivariable physiological systems at different scales, highlighting the existence of asymptotically stable homeostatic equilibrium and allowing the study of the impact of transmission delays on the system’s stability. At the best of authors’ knowledge, following the paper Ponsiglione et al. (2023) where monovariable systems where dealt with, the proposed methodology is the first attempt to represent and investigate homeostasis from the molecular up to systemic level by exploiting a unified multivariable biomodeling architecture, which makes it a novel approach to understanding homeostatic control from a broader perspective.}, archive = {J_ACCESS}, author = {Anna Procopio and Annarita Tedesco and Fabrizio Lo Regio and Giuseppe Cesarelli and Leandro Donisi and Carlo Ricciardi and Alessio Merola and Alfonso Maria Ponsiglione and Maria Romano and Francesco Montefusco and Carlo Cosentino and Francesco Amato}, doi = {10.1109/ACCESS.2025.3532846}, journal = {IEEE Access}, pages = {17630-17651}, shortjournal = {IEEE Access}, title = {A general framework for closed loop negative feedback multivariable physiological control systems: Existence, uniqueness, and stability of homeostatic equilibrium points}, volume = {13}, year = {2025}, }
• (2025). Systematic review of fake news, propaganda, and disinformation: Examining authors, content, and social impact through machine learning. ACCESS, 13, 17583-17629. (www)

  @article{ , abstract = {In recent years, the world has witnessed a global outbreak of fake news, propaganda and disinformation (FNPD) flows on online social networks (OSN). In the context of information warfare and the capabilities of generative AI, FNPDs have proliferated. They have become a powerful and quite effective tool for influencing people’s social identities, attitudes, opinions and even behavior. Ad hoc malicious social media accounts and organized networks of trolls and bots target countries, societies, social groups, political campaigns and individuals. As a result, conspiracy theories, echo chambers, filter bubbles and other processes of fragmentation and marginalization are polarizing, radicalizing, and disintegrating society in terms of coherent politics, governance, and social networks of trust and cooperation. This systematic review aims to explore advances in using machine and deep learning to detect FNPD in OSNs effectively. We present the results of a combined PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) review in three analysis domains: 1) propagators (authors, trolls, and bots), 2) textual content, 3) social impact. This systemic research framework integrates meta-analyses of three research domains, providing an overview of the wider research field and revealing important relationships between these research domains. It not only addresses the most promising ML/DL research methodologies and hybrid approaches in each domain, but also provides perspectives and insights on future research directions.}, archive = {J_ACCESS}, author = {Darius Plikynas and Ieva Rizgelienė and Gražina Korvel}, doi = {10.1109/ACCESS.2025.3530688}, journal = {IEEE Access}, pages = {17583-17629}, shortjournal = {IEEE Access}, title = {Systematic review of fake news, propaganda, and disinformation: Examining authors, content, and social impact through machine learning}, volume = {13}, year = {2025}, }
• (2025). Quantum computing for advanced driver assistance systems and autonomous vehicles: A review. ACCESS, 13, 17554-17582. (www)

  @article{ , abstract = {Advanced Driver Assistance System (ADAS) has become an essential feature in vehicles, and it is leading to the evolution of autonomous vehicles. But the technologies to implement ADAS suffer from certain inherent limitations, such as latency rate, computational speed, accuracy of the algorithm, security, and privacy, which are also the important factors for realizing full autonomous vehicles. With respect to these hindrances, an in-depth analysis of the existing research has shown that quantum machine learning (QML) can hold a powerful and alternate solution for the development of autonomous vehicles. The perks of quantum computation (QC) over classical systems are apparent with respect to security, privacy, and an exponentially high computation rate. The current review study underlines the benefits of quantum computation and asks for more QML research to improve real-time decision-making in autonomous vehicles, ultimately improving their safety and efficiency. The promise of quantum computing to handle the massive data and computational complexity that classical methods struggle with necessitates new studies in quantum machine learning (QML) for autonomous vehicles.}, archive = {J_ACCESS}, author = {Avantika Rattan and Abhishek Rudra Pal and Muralimohan Gurusamy}, doi = {10.1109/ACCESS.2025.3532958}, journal = {IEEE Access}, pages = {17554-17582}, shortjournal = {IEEE Access}, title = {Quantum computing for advanced driver assistance systems and autonomous vehicles: A review}, volume = {13}, year = {2025}, }
• (2025). Expedited colorectal cancer detection through a dexterous hybrid CADx system with enhanced image processing and augmented polyp visualization. ACCESS, 13, 17524-17553. (www)

  @article{ , abstract = {The complexity and variability of medical imaging continue to impair the feasibility of early detection of colorectal cancer, despite its critical role in improving patient outcomes. This research presents a new multistage ensemble method that combines the strengths of three advanced deep learning models: vision transformers, RDV-22 (a mix of ResNet-50, DenseNet-201, and VGG-16), and Bidirectional Long Short-Term Memory (BiLSTM) networks. It improves the accuracy and robustness of Colorectal Cancer (CRC) detection. For suitable evaluation of our methodology, we adopted two benchmark datasets: CVC Clinic DB for binary classification and Kvasir dataset for multiclass classifications. To decrease the sizes of data significantly along with the generalizability of the model, we have used various data augmentation techniques, including magnification, inversion, and rotation. The best performing model was RDV-22+ BiLSTM + Vision Transformers, which recorded a 95.0% test accuracy and an area of 0.96 under the curve with the CVC Clinic DB dataset. The model showed a high performance in wide classes of samples such as esophagitis, polyps, and ulcerative colitis, with an accuracy rate of 92.5% and Area Under Curve (AUC) values 1.00 on the Kvasir dataset. We applied Local Interpretable Model-Agnostic Explanations (LIME) visualizations to both datasets, which proved to be an insightful identification of regions that the model relied on during the polyp detection process. Based on the LIME explanation, this analysis found that the visualizations generated by the Kvasir dataset were more informative and even clearer when complex cases, including ulcerative colitis and polyps are concerned. This indicates the model’s ability to handle challenging medical images effectively. Because of the complexity and diversity of the datasets, these results constitute a significant improvement on the current methodology. There is expectation that the proposed ensemble method could significantly improve the dependability and precision of diagnosing colorectal cancer. More accurate diagnoses and faster diagnoses in clinical practice might improve patient outcomes and reduce death rates if this is successful. While the ensemble model presents highly improved performance, this computationally and dataset-specific performance requirement suggests further optimization and validation for larger clinical applicability.}, archive = {J_ACCESS}, author = {Akella Subrahmanya Narasimha Raju and K. Venkatesh and Ranjith Kumar Gatla and Marwa M. Eid and Aymen Flah and Zdenek Slanina and Ramy N. R. Ghaly}, doi = {10.1109/ACCESS.2025.3532807}, journal = {IEEE Access}, pages = {17524-17553}, shortjournal = {IEEE Access}, title = {Expedited colorectal cancer detection through a dexterous hybrid CADx system with enhanced image processing and augmented polyp visualization}, volume = {13}, year = {2025}, }
• (2025). Performance analysis of DAG-based blockchain systems based on markovian arrival process. ACCESS, 13, 17506-17523. (www)

  @article{ , abstract = {The Direct Acyclic Graph (DAG)-based blockchain offers impressive scalability and finds applications across various sectors, notably the Internet of Things (IoT) industry. Within this domain, IOTA has been purpose-built for IoT. This article analyzes the performance of IOTA’s data structure Tangle based on the Markovian arrival process (MAP). Meanwhile, we have considered this situation in a newly issued transaction and selected multiple tips for validation. Firstly, we show that this Markov process is a level-dependent quasi-birth-death (QBD) process. Then, we prove that the QBD process is irreducible, positive, and recurrent. Furthermore, smooth probability vectors for the QBD process are given, and the performance of the DAG-based blockchain system is analyzed. Moreover, we verify the validity of our theoretical results with numerical examples, pointing out how some key system parameters affect the performance metrics of this system. Therefore, we hope that the methodology and results presented in this paper will shed some light on DAG-based blockchain systems so that a series of promising research projects can be developed.}, archive = {J_ACCESS}, author = {Xing-Shuo Song and Shi-Yong Li}, doi = {10.1109/ACCESS.2025.3529739}, journal = {IEEE Access}, pages = {17506-17523}, shortjournal = {IEEE Access}, title = {Performance analysis of DAG-based blockchain systems based on markovian arrival process}, volume = {13}, year = {2025}, }
• (2025). Two approaches to constructing certified dominating sets in social networks. ACCESS, 13, 17495-17505. (www)

  @article{ , abstract = {Social networks are an important part of our community. In this context, certified dominating sets help to find in networks a group of people, referring as officials, such that 1) for each civilian, there is an official that can serve the civilian, and 2) no official is adjacent to exactly one civilian, to prevent potential abuses. To delve deeper into this topic, this study considers two approaches to the problem of finding certified dominating sets of small cardinality. One approach is to transform an ordinary dominating set, which is subject only to condition 1) given above, into a certified dominating set. The second approach involves constructing such a set from scratch. To compare the two methods, we first studied the computational complexity of the decision problem of whether, for a given graph, the domination number, which is the minimum size of a dominating set, is equal to the certified domination number, that is, the minimum size of a certified dominating set. Next, we constructed and compared the performance of two approximate algorithms that find certified dominating sets, one for each of the two approaches. The effectiveness and efficiency of the proposed algorithms were validated through experiments that compare their results with each other and with a previous study: the linear-time algorithm, which determines the certified domination number for trees and with known results for the domination number in social networks.}, archive = {J_ACCESS}, author = {Joanna Raczek and Mateusz Miotk}, doi = {10.1109/ACCESS.2025.3532392}, journal = {IEEE Access}, pages = {17495-17505}, shortjournal = {IEEE Access}, title = {Two approaches to constructing certified dominating sets in social networks}, volume = {13}, year = {2025}, }
• (2025). Microgrids for extraterrestrial habitats: A review of technologies, needs, and challenges. ACCESS, 13, 17473-17494. (www)

  @article{ , abstract = {The National Aeronautics and Space Administration (NASA) and other space organizations are preparing for new space exploration and habitation initiatives. These include human return to the Moon through the Artemis mission, human exploration of Mars and deeper space habitats, and the establishment of permanent human bases on the Moon, Mars, and beyond. Such installations demand a highly reliable, resilient, safe, and autonomous power system to support the habitats’ different functions, systems, and electrical loads. This paper presents a review of the different power sources, energy storage options, microgrid architectures, in addition to challenges and needs for future extraterrestrial habitat microgrids. In addition, this paper reviews the economic challenges and life cycle costs of future extraterrestrial microgrids. Of special focus in this paper are the degradation causes and mechanisms of space microgrid components due to harsh environments in which such microgrids will operate. With the information presented in this paper, which accurately compares different technologies’ performance for the same function, researchers in the space community will be able to make better decisions regarding the requirements and technology development of an extraterrestrial power system.}, archive = {J_ACCESS}, author = {Leila Chebbo and Muhammed Ali Gultekin and Ali Bazzi and Sean A. Q. Young and Joseph P. Kozak}, doi = {10.1109/ACCESS.2025.3532163}, journal = {IEEE Access}, pages = {17473-17494}, shortjournal = {IEEE Access}, title = {Microgrids for extraterrestrial habitats: A review of technologies, needs, and challenges}, volume = {13}, year = {2025}, }
• (2025). Design of an early prediction model for parkinson’s disease using machine learning. ACCESS, 13, 17457-17472. (www)

  @article{ , abstract = {Parkinson’s Disease (PD) is a chronic and progressive neurological disorder that impairs the body’s nervous system pathways. This disruption results in multiple complications related to movement and control, manifesting as symptoms such as tremors, rigidity, and impaired coordination. In the initial phases of PD, individuals have trouble with speech and exhibit a slow rate of verbal expression. Dysphonia is seventy to ninety percent of persons with Parkinson’s disease report a speech impairment or modification in speech, and it serves as a preliminary indicator of the disease. Consequently, speech can be a crucial modality in the initial phase of Parkinson’s disease prediction. In literature, diverse Machine Learning models are employed for Parkinson’s disease diagnosis utilizing voice analysis. Challenges such as class imbalance, feature selection, and interpretable predictive analysis still need to be addressed. Furthermore, the precision and reliability of the predictive outcomes are crucial to enhance healthcare services. Consequently, we propose an Explainable balanced Recursive Feature Importance with Logistic Regression (XRFILR) model to address the abovementioned issues. The proposed model extracts the pertinent features using an RFE with a Logistic Regression classifier and evaluates the feature significance in Parkinson’s disease prediction using eXplainable Artificial Intelligence. We employed the seven machine learning classifiers the model offers to diagnose Parkinson’s disease using significant speech data. Among these ML models, the proposed model achieved an accuracy of 96.46%, surpassing existing machine learning techniques.}, archive = {J_ACCESS}, author = {K. Velu and N. Jaisankar}, doi = {10.1109/ACCESS.2025.3533703}, journal = {IEEE Access}, pages = {17457-17472}, shortjournal = {IEEE Access}, title = {Design of an early prediction model for parkinson’s disease using machine learning}, volume = {13}, year = {2025}, }
• (2025). Enhanced vector quantization for embedded machine learning: A post-training approach with incremental clustering. ACCESS, 13, 17440-17456. (www)

  @article{ , abstract = {TinyML enables the deployment of Machine Learning (ML) models on resource-constrained devices, addressing a growing need for efficient, low-power AI solutions. However, significant challenges remain due to strict memory, processing, and energy limitations. This study introduces a novel method to optimize Post-Training Quantization (PTQ), a widely used technique for reducing model size, by integrating Vector Quantization (VQ) with incremental clustering. While VQ is a technique that reduces model size by grouping similar parameters, incremental clustering, implemented via the AutoCloud K-Fixed algorithm, preserves accuracy during compression. This combined approach was validated on an automotive dataset predicting CO2 emissions from vehicle sensor measurements such as mass air flow, intake pressure, temperature, and speed. The model was quantized and deployed on Macchina A0 hardware, demonstrating over 90% compression with negligible accuracy loss. Results show improved performance and deployment efficiency, showcasing the potential of this combined technique for real-world embedded applications.}, archive = {J_ACCESS}, author = {Thommas K. S. Flores and Morsinaldo Medeiros and Marianne Silva and Daniel G. Costa and Ivanovitch Silva}, doi = {10.1109/ACCESS.2025.3532849}, journal = {IEEE Access}, pages = {17440-17456}, shortjournal = {IEEE Access}, title = {Enhanced vector quantization for embedded machine learning: A post-training approach with incremental clustering}, volume = {13}, year = {2025}, }
• (2025). Zero-shot classification of art with large language models. ACCESS, 13, 17426-17439. (www)

  @article{ , abstract = {Art has become an important new investment vehicle. Thus, interest is growing in art price prediction as a tool for assessing the returns and risks of art investments. Both traditional statistical methods and machine learning methods have been used to predict art prices. However, both methods incur substantial human costs for data preprocessing for the construction of prediction models, necessitating a reduction in the workload. In this study, we propose the zero-shot classification method to perform automatic annotation in data processing for art price prediction by leveraging large language models (LLMs). The proposed method can perform annotation without new training data. Thus, it minimizes human costs. Our experiments demonstrated that the 4-bit quantized Llama-3 70B model, which can run on a local server, achieved the most accurate (over 0.9) automatic annotation of different art forms using LLMs, performing slightly better than the GPT-4o model from OpenAI. These results are practical for data preprocessing and comparable with the results of previous machine learning methods.}, archive = {J_ACCESS}, author = {Tatsuya Tojima and Mitsuo Yoshida}, doi = {10.1109/ACCESS.2025.3532995}, journal = {IEEE Access}, pages = {17426-17439}, shortjournal = {IEEE Access}, title = {Zero-shot classification of art with large language models}, volume = {13}, year = {2025}, }
• (2025). Hazard analysis of weather conditions on distribution transformer failures via cox proportional hazard model. ACCESS, 13, 17414-17425. (www)

  @article{ , abstract = {As the operational years of power facilities increase, the importance of accurately assessing the remaining lifespan or risk of failures of distribution transformers for the stable power supply has also grown. However, unlike the typical transformer failures, it is not quantified that the failures are also influenced by weather conditions such as temperature, humidity, precipitation, etc. In this regard, we propose the method for investigating the impact of the weather conditions on the failures of distribution transformers to improve the asset management. By employing survival analysis approach, frameworks are employed to quantify the spatial influence of weather conditions on distribution transformer failures, and the weather risk factors are integrated into lifespan indicators to improve maintenance strategies and reliability assessments. Distribution transformer failure cases of over 11 years in South Korea and the corresponding historical weather conditions are configured to enable the survival analysis and derive the meaningful real-world analysis results. Specifically, weather data are quantified and interpolated to configure the spatial data. The notable results underline quantifying significant impact of high summer temperature and humidity in term of hazard ratios. These findings indicate that incorporating historical weather data into asset management indices can significantly enhance the accuracy of transformer lifespan predictions and inform maintenance planning. This study represents the first long-term survival analysis correlating historical weather conditions with transformer failures and provides a guideline for improving power-facility management in the context of environmental changes.}, archive = {J_ACCESS}, author = {Cheonkyu Park and Chongeun Cho and Do-In Kim}, doi = {10.1109/ACCESS.2025.3532507}, journal = {IEEE Access}, pages = {17414-17425}, shortjournal = {IEEE Access}, title = {Hazard analysis of weather conditions on distribution transformer failures via cox proportional hazard model}, volume = {13}, year = {2025}, }
• (2025). Quantum key distribution applicability to smart grid cybersecurity systems. ACCESS, 13, 17398-17413. (www)

  @article{ , abstract = {To meet the increasing demand for electricity and to have a more reliable and resilient electric grid against conventional and extreme events, grid modernization is more crucial now than ever before. This will require the development and deployment of devices that provide advanced communication capabilities. The overall efficiency, reliability, and resilience of the smart grid will be inextricably linked to the exchange of information between these devices. Unfortunately, the increased information flow will increase the potential attack surface and introduce new vulnerabilities. While a smarter grid will depend critically on information flow, these benefits will be accrued only if that information can be protected. Nowadays, information is secured in smart grids primarily through cryptography. However, with the increasing number of sophisticated attacks as well as the increasing computational power, the security of the “classical” cryptographic algorithms is threatened. Quantum information science offers solutions to this problem, specifically quantum key distribution (QKD), which provides a means for the generation and secure distribution of symmetric cryptographic keys. The security of QKD stems ultimately from the very nature of quantum physics. In this paper, we investigate the applicability of QKD to the various smart grid sectors and specific use cases. We have identified 18 smart grid use cases of interest for QKD suitability together with 7 QKD factors used for the assessment of the various use cases. For each use case, the impact to security of the loss of confidentiality, integrity, and/or availability is specified. In addition, the suitability of QKD is assessed for each use case with respect to multiple factors.}, archive = {J_ACCESS}, author = {Warren Grice and Mohammed Olama and Annabelle Lee and Philip G. Evans}, doi = {10.1109/ACCESS.2025.3533942}, journal = {IEEE Access}, pages = {17398-17413}, shortjournal = {IEEE Access}, title = {Quantum key distribution applicability to smart grid cybersecurity systems}, volume = {13}, year = {2025}, }
• (2025). From data to decisions: The power of machine learning in business recommendations. ACCESS, 13, 17354-17397. (www)

  @article{ , abstract = {This research aims to explore the impact of machine learning (ML) on the evolution and efficacy of recommendation systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of recommendation engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These REs not only streamline information discovery and enhance collaboration, but also accelerate knowledge acquisition, which is vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with the individual needs of the customer. The research identifies the growing expectations of users for a seamless and intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research includes exploring advances in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and using ML in RS for researchers and practitioners to tap into the full potential of personalized recommendation in commercial business prospects.}, archive = {J_ACCESS}, author = {Kapilya Gangadharan and Anoop Purandaran and K. Malathi and Barathi Subramanian and Rathinaraja Jeyaraj and Soon Ki Jung}, doi = {10.1109/ACCESS.2025.3532697}, journal = {IEEE Access}, pages = {17354-17397}, shortjournal = {IEEE Access}, title = {From data to decisions: The power of machine learning in business recommendations}, volume = {13}, year = {2025}, }
• (2025). Papillary thyroid carcinoma semantic segmentation using multi-scale adaptive convolutional network with dual decoders. ACCESS, 13, 17340-17353. (www)

  @article{ , abstract = {Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Currently, pathologists diagnose the PTC by interpreting their nuclei. However, the existing diagnosis method is difficult to interpret, especially for the cases falling in the borderline zones. According to the advances in artificial intelligence (AI) technology, semantic segmentation is used to support medical personnel. This study proposes Multi-scale Adaptive Convolutional Network with DBUNet (MSAC-DBUNet) and Multi-scale Adaptive Convolutional Network with Dual Decoders (MSAC-DD), which are more accurate and faster than the traditional networks. The experimental result shows that MSAC-DBUNet achieves good PTC segmentation outcome, and MSAC-DD provides a comparable score while reducing computation time and GPU usage compared to MSAC-DBUNet. It can be used for further studies to support the pathology practice.}, archive = {J_ACCESS}, author = {Thanat Payatsuporn and Pittipol Kantavat and Nichthida Tangnuntachai and Nopporn Tipparawong and Waratchanok Techapapa and Boonserm Kijsirikul and Somboon Keelawat}, doi = {10.1109/ACCESS.2025.3532505}, journal = {IEEE Access}, pages = {17340-17353}, shortjournal = {IEEE Access}, title = {Papillary thyroid carcinoma semantic segmentation using multi-scale adaptive convolutional network with dual decoders}, volume = {13}, year = {2025}, }
• (2025). Enhanced IoT-based face mask detection framework using optimized deep learning models: A hybrid approach with adaptive algorithms. ACCESS, 13, 17325-17339. (www)

  @article{ , abstract = {The COVID-19 pandemic has made face mask detection into a big thing because it is essential in public health monitoring. Meanwhile, the growing number of things that can be connected to the internet and the increasing integration of this technology mean that edge devices are now in demand for effective real-time face mask detection models. Often, existing methods require some kind of pre-installed equipment or difficult-to-manipulate environmental conditions, and computational resource constraints essentially put an end to them. In the present study, a hybrid Flame-Sailfish Optimization (HFSO)-based deep learning framework is proposed. It combines the feature extraction capabilities of ResNet50 with the efficiency of MobileNetV2. The HFSO algorithm optimizes crucial parameters such as detection thresholds and learning rates. So that the model can take full advantage of computing capacity and still operate in real time on devices with limited resources. The model was tested on three data sets—Kaggle Face Mask Detection dataset, Public Places dataset, and Public Videos dataset—achieving up to 97.5% accuracy. It outperformed the previous leader in all cases. The results prove that this framework is reliable and easily applicable for identifying people wearing masks under different conditions. However, where there is great occlusion of the face or video feed quality is bad, the model’s performance will drop somewhat. Future work should focus on increasing difficulty in detections, broadening the application of this method to other health monitoring systems based on the Internet of Things, and ensuring that its robustness remains unaltered.}, archive = {J_ACCESS}, author = {Parul Dubey and Pushkar Dubey and Celestine Iwendi and Cresantus N. Biamba and Deepak Dasaratha Rao}, doi = {10.1109/ACCESS.2025.3532764}, journal = {IEEE Access}, pages = {17325-17339}, shortjournal = {IEEE Access}, title = {Enhanced IoT-based face mask detection framework using optimized deep learning models: A hybrid approach with adaptive algorithms}, volume = {13}, year = {2025}, }
• (2025). Performance enhancement of NavIC with low-cost MEMS-IMU, barometer, and magnetometer. ACCESS, 13, 17303-17324. (www)

  @article{ , abstract = {Integration of the global navigation satellite systems with the Inertial Navigation System (INS) is a sensor fusion stratagem for continuous and reliable navigation. Micro-electromechanical system (MEMS) based INS meets the high demand for low-cost autonomous navigation of land vehicles economically. However, their navigational performance degrades quickly, and their attitude estimation is poor. Therefore, other auxiliary sensors such as barometer, odometer and magnetometer have been used with integrated GNSS/INS to improve the accuracy of position, velocity and attitude estimates. When the MEMS-IMUs are integrated with the newly developed NavIC (Navigation with Indian Constellation) system, comprising seven satellites with a fair geometric dilution of precision, it may be challenging to provide the required navigation performance for land vehicles. Therefore, this paper examines the navigation performance of land vehicles using loosely coupled NavIC and MEMS-IMU augmented by a barometer and a three-axis magnetometer. The magnetometer is calibrated for accurate heading estimation, and bad measurements are identified by their excessive innovation and rejected. The performance of loosely coupled GPS/INS/Barometer/Magnetometer and NavIC+GPS/INS/Barometer/Magnetometer are compared. When aided by a barometer and a magnetometer, the achieved horizontal and vertical position accuracies ( $1\sigma $ ) for NavIC/INS are 1.96 m and 0.24 m; for GPS/INS, 0.53 m and 0.24 m; and NavIC+GPS/INS, 0.43 m and 0.23 m, respectively. The heading accuracy for all three systems is ~0.73°. This last ensemble of devices meets the required accuracy for identification of the lane for autonomous navigation of land vehicles in Indian urban areas, where the lane span is 3.75 m. However, further improvement in the accuracy is recommended.}, archive = {J_ACCESS}, author = {Saraswathi Sirikonda and Laxminarayana Parayitam and Hari B. Hablani}, doi = {10.1109/ACCESS.2025.3533572}, journal = {IEEE Access}, pages = {17303-17324}, shortjournal = {IEEE Access}, title = {Performance enhancement of NavIC with low-cost MEMS-IMU, barometer, and magnetometer}, volume = {13}, year = {2025}, }
• (2025). Enhancing reliability in embedded systems hardware: A literature survey. ACCESS, 13, 17285-17302. (www)

  @article{ , abstract = {Embedded Systems are used in extreme conditions, often for long lifespans; as such, ensuring hardware reliability is essential. Additionally, the applications of embedded systems can be safety-critical or costly in the event of a failure. Applications and environments such as these demand high reliability. This literature survey explores the challenges of achieving hardware reliability in embedded systems. It examines critical works using different methodologies and viewpoints to summarize hardware reliability comprehensively. The paper discusses the main failure modes identified in embedded systems hardware, evaluates various mitigation strategies, and identifies emerging trends influencing the future of embedded system design. By critically analyzing existing literature, this survey is a resource for future research efforts focused on growing the reliability of embedded systems. Finally, this paper outlines the motivation and first methods for a systems-level approach to embedded systems reliability.}, archive = {J_ACCESS}, author = {Ryan Aalund and Vincent Philip Paglioni}, doi = {10.1109/ACCESS.2025.3534138}, journal = {IEEE Access}, pages = {17285-17302}, shortjournal = {IEEE Access}, title = {Enhancing reliability in embedded systems hardware: A literature survey}, volume = {13}, year = {2025}, }
• (2025). A joint network of 3D-2D CNN feature hierarchy and pyramidal residual model for hyperspectral image classification. ACCESS, 13, 17273-17284. (www)

  @article{ , abstract = {Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by the complexity of hyperspectral pictures. The typical 2D-CNN method is unable to capture the discriminant spectral-spatial features due to the massive dimensionality of HSI data as well as potential noise and redundancy, while the 3D-CNN method significantly increases the computational burden of the model. Moreover, the gradient problem of the deeper CNN architecture impedes the convergence performance of the neural network, leading to poor classification accuracy. To mitigate these problems, this paper proposes a joint network of Hybrid 3D-2D CNN and pyramid residual (J-NHPR) to realize the classification of HSI. The combined deep pyramid residual network can significantly enhance the performance of the suggested network with the HSI data by progressively improving the diversity of high-level spectral-spatial properties across layers. Our tests, which included eight distinct approaches for classification and three well-known HSI data sets, revealed that our recently built model, J-NHPR, can provide comparative advantage (in terms of classification accuracy) over other HSI classification methods. The source code of the J-NHPR model is available at https://github.com/Dreamvai/J-NHPR.}, archive = {J_ACCESS}, author = {Hongwei Wei and Yufan Wang and Yu Sun and Jianfeng Zheng and Xiaodong Yu}, doi = {10.1109/ACCESS.2025.3532016}, journal = {IEEE Access}, pages = {17273-17284}, shortjournal = {IEEE Access}, title = {A joint network of 3D-2D CNN feature hierarchy and pyramidal residual model for hyperspectral image classification}, volume = {13}, year = {2025}, }
• (2025). EMT model validation throughout the life cycle of wind power plants based on type IV turbines: Experiences and recommendations. ACCESS, 13, 17261-17272. (www)

  @article{ , abstract = {The validation of Electromagnetic Transients (EMT) models is essential for ensuring the accuracy of grid compliance and stability studies. However, there is still room for improvement in understanding the processes and a lack of standards for EMT model validation, especially after the commissioning of a wind power plant. This paper addresses this gap and presents experiences and recommendations for EMT model validation from an Original Equipment Manufacturer (OEM) perspective throughout the entire life cycle of offshore type IV wind turbines and wind power plants. The paper showcases different perspectives on model validation, including methodologies, requirements, and accuracy. It illustrates the process of wind turbine level model validation during design or pre-commissioning with practical examples and also demonstrates the wind power plant level model validation for two operational plants. In conclusion, the paper addresses various challenges and recommendations for model validation related to measurement location placement, data acquisition, aggregation methods, preprocessing, turbine connection status, and operating conditions at the time of the fault.}, archive = {J_ACCESS}, author = {Gabriel Miguel Gomes Guerreiro and Ranjan Sharma and Pankaj Kumar and Sulav Ghimire and Frank Martin and Pramod Ghimire and Guangya Yang}, doi = {10.1109/ACCESS.2025.3531796}, journal = {IEEE Access}, pages = {17261-17272}, shortjournal = {IEEE Access}, title = {EMT model validation throughout the life cycle of wind power plants based on type IV turbines: Experiences and recommendations}, volume = {13}, year = {2025}, }
• (2025). From text to vision: Examining how emotional expression and information consistency affect perceived helpfulness. ACCESS, 13, 17246-17260. (www)

  @article{ , abstract = {This study used natural language processing tools and econometric analysis methods to examine the impact of emotional expressions on the helpfulness of online reviews. This study conducts a sentiment analysis of 71,850 reviews collected by Yelp.com in Los Angeles, California, United States, and divides them into positive and negative according to the valence of the text content. It examines the direct impact of reviews with different valences on the reviews’ perceived helpfulness and clarifies how the information consistency among review cues, that is, the inconsistency between a single review rating and the average merchant rating, and the consistency between the review content and rating moderate this effect. Results revealed that rating inconsistency significantly amplifies the positive effect of negative reviews and the negative effect of positive reviews on helpfulness. Greater consistency in review content and ratings reduced the negative effect of positive reviews on perceived helpfulness but did not significantly moderate the perceived helpfulness of negative reviews. Additionally, research has examined the moderating role of visual information in enhancing the helpfulness of reviews, indicating that visual elements such as pictures significantly affect the perceived helpfulness of reviews. Accordingly, this study enriches our understanding of online review dynamics and identifies practical strategies for online review platforms and social media to leverage user-generated content to optimize consumer experience and satisfaction.}, archive = {J_ACCESS}, author = {Yuhao Zhang and Jinzhe Yan and Qianru Li}, doi = {10.1109/ACCESS.2024.3524467}, journal = {IEEE Access}, pages = {17246-17260}, shortjournal = {IEEE Access}, title = {From text to vision: Examining how emotional expression and information consistency affect perceived helpfulness}, volume = {13}, year = {2025}, }
• (2025). A 28-GHz CMOS-65 nm dual mode class-J doherty power amplifier with integrated class-G driver and impedance-modulated- quadrature-transformer achieving 21% back-off efficiency. ACCESS, 13, 17236-17245. (www)

  @article{ , abstract = {In this paper, a CMOS Class-J Doherty power amplifier (PA) integrating a Class-G driver and an Impedance-Modulated-Quadrature-Transformer (IMQT) to enhance back-off efficiency for multimode operations is presented. The Class-J Doherty PA utilizes a second harmonic termination to improve its efficiency. To further augment efficiency at deep back-off output power, a single-supply-rail Class-G driver operates with the IMQT, modulating the interstage impedance between the driver and the Doherty stages. The IMQT features a stacked transformer architecture and dynamically adjusts its impedance by switching a floating, intermediate coil between the transformer’s primary and secondary windings. A comparator regulates the IMQT based on the Class-G driver’s operation. The IMQT-DPA, fabricated using a 65nm CMOS process, operates at a center frequency of 28 GHz with a supply headroom of 1.8V. The PA achieves an average output power of 14 dBm with a back-off efficiency of 21%, representing a 1.4-fold efficiency enhancement compared to conventional Class-B operation. Under low-power mode (VDD/2) with a Class-G driver operation, the average output power is 11.3 dBm with a 1.3-fold efficiency improvement.}, archive = {J_ACCESS}, author = {Selvakumar Mariappan and Jagadheswaran Rajendran and Norhamizah Idros and Sofiyah Sal Hamid and Asrulnizam Bin Abd Manaf and Narendra Kumar and Abdullah Alghaihab and Arokia Nathan and Binboga Siddik Yarman}, doi = {10.1109/ACCESS.2025.3530958}, journal = {IEEE Access}, pages = {17236-17245}, shortjournal = {IEEE Access}, title = {A 28-GHz CMOS-65 nm dual mode class-J doherty power amplifier with integrated class-G driver and impedance-modulated- quadrature-transformer achieving 21% back-off efficiency}, volume = {13}, year = {2025}, }
• (2025). Bringing intelligence to SAR missions: A comprehensive dataset and evaluation of YOLO for human detection in TIR images. ACCESS, 13, 17208-17235. (www)

  @article{ , abstract = {Effective search and rescue (SAR) missions are critical for locating and assisting injured or missing individuals while optimizing resource allocation and minimizing costs. This work aims to enhance the efficiency of these missions by exploring advanced deep learning techniques for precise and efficient human detection in thermal images. The primary focus of this work is on YOLOv8, the latest version of the You Only Look Once (YOLO) object detection method. The paper also evaluates YOLOv7-Tiny, which is the most streamlined variant derived from YOLOv7. To support the investigation, a novel dataset comprising 17,148 thermal images with 90,882 instances of human subjects representing various conditions and scenarios has been carefully curated. This dataset is used for training and evaluating different variants of YOLOv8 and YOLOv7. The evaluation of the trained models reveals the efficacy of YOLOv8 in detecting humans in thermal images, achieving an average precision rate of 95% with the largest model, YOLOv8x, and an average precision rate of 91% with the smallest model, YOLOv8n. The evaluation of YOLOv7-Tiny shows that it achieves an average precision similar to YOLOv8n, which is 48% lighter in size and more practical choice for real-world deployment. Also, the trained models are deployed on graphical processing units. The tiniest trained model, YOLOv8n, achieves an inference rate of 273.6 frames per second (FPS) while the largest model, YOLOv8, achieves an inference rate of 100.29 FPS. The achieved inference rates along with the achieved detection performances meet with the requirement of fast detection of humans in SAR missions.}, archive = {J_ACCESS}, author = {Mostafa Rizk and Israa Bayad}, doi = {10.1109/ACCESS.2025.3529484}, journal = {IEEE Access}, pages = {17208-17235}, shortjournal = {IEEE Access}, title = {Bringing intelligence to SAR missions: A comprehensive dataset and evaluation of YOLO for human detection in TIR images}, volume = {13}, year = {2025}, }
• (2025). Japanese short answer grading for japanese language learners using the contextual representation of BERT. ACCESS, 13, 17195-17207. (www)

  @article{ , abstract = {The automatization of grading short answers in examinations aims to help teachers grade more efficiently and fairly. The Japanese SIMPLE-O attempts to grade Japanese language learners’ short answers using a dataset from a real examination. Bidirectional encoder representations from transformer (BERT), which has shown potential for natural language processing (NLP) tasks, is implemented to grade answers without fine-tuning due to the small amount of data. Two experiments are conducted in this study. The first experiment attempts to grade based on similarities, while the second classifies the answers as either correct or incorrect. Five BERT models are tested in the system, and two additional sentence BERT (SBERT) and RoBERTa models are tested for the similarity problem. The best Pearson’s correlation for grading with similarities is obtained with the Tohoku BERT Base. The use of hiragana-kanji conversion improves the correlation to 0.615 for BERT and 0.593 for SBERT but does not show much improvement for RoBERTa. In the binary classification experiments, all models have an accuracy above 90%, with Tohoku BERT Large having the best performance. Even without fine-tuning, BERT can be used as an embedding method to perform binary classification with high accuracy.}, archive = {J_ACCESS}, author = {Dyah Lalita Luhurkinanti and Prima Dewi Purnamasari and Takashi Tsunakawa and Anak Agung Putri Ratna}, doi = {10.1109/ACCESS.2025.3532659}, journal = {IEEE Access}, pages = {17195-17207}, shortjournal = {IEEE Access}, title = {Japanese short answer grading for japanese language learners using the contextual representation of BERT}, volume = {13}, year = {2025}, }
• (2025). Carbon emission evaluation and low carbon economy optimization scheduling of rural integrated energy system based on LCA method. ACCESS, 13, 17182-17194. (www)

  @article{ , abstract = {Rural energy has the characteristics of diversity, dispersion, and regionalism. The construction and operation of its comprehensive energy system are essential for promoting the development of a low-carbon economy in rural areas and reducing carbon emissions. In response, the structure of the rural integrated energy system is initially analyzed and the equipment is planned. Subsequently, the carbon emission in each link of the power generation system is estimated by using the life cycle assessment method. Based on the evaluation results, a suitable low-carbon economy optimal scheduling model is established. The generator set and energy storage equipment in the integrated energy system are designed to enhance the efficiency of energy utilization. Then, the life cycle assessment method is used to calculate the carbon emissions of each stage. Next, the optimal scheduling model is established by taking the minimum carbon emissions and system operating costs as the objective function. In addition, the study introduced a water method and a kinetic accelerator to capture carbon dioxide and feed it into a high-pressure pump for storage. This will enable carbon capture and net zero emissions. The findings revealed that Model 1 exhibited minimal carbon emissions, amounting to 48,530.5 kg, and a comparatively low total operating cost of 30,625.48. After being applied to practical systems, the carbon emissions have been significantly reduced. The research model can effectively guide the low-carbon transformation of rural integrated energy systems and provide important technical support for achieving sustainable development goals in rural areas.}, archive = {J_ACCESS}, author = {Chunxiao Wu}, doi = {10.1109/ACCESS.2025.3533099}, journal = {IEEE Access}, pages = {17182-17194}, shortjournal = {IEEE Access}, title = {Carbon emission evaluation and low carbon economy optimization scheduling of rural integrated energy system based on LCA method}, volume = {13}, year = {2025}, }
• (2025). Enhancing renewable energy hosting capacity in unbalanced microgrids via empowering smart inverters. ACCESS, 13, 17161-17181. (www)

  @article{ , abstract = {This article presents a coordinated planning strategy for renewable energy sources (RESs) and energy storage systems (ESSs) in unbalanced microgrids. The approach aims to mitigate voltage unbalance, reduce power losses, alleviate feeder congestion, and maximize the hosting capacity (HC) of RESs in grid-connected unbalanced microgrids. By employing smart inverter control for photovoltaic (PV) and ESS inverters, the strategy enhances the integration of additional RESs while minimizing power exchange between operational zones and the utility grid (UG). To achieve such an ambitious goal, smart inverter control functions are employed, including combined mode, volt-var (VV), volt-watt (VW) for Photovoltaic (PV) inverters, and VW for ESS inverters. The IEEE 123-bus test system, divided into six operational zones, is used as a case study, incorporating plug-in electric vehicle (PEV) demand and wind-based distributed generation (DG). A metaheuristic algorithm is developed for optimal DG and ESS deployment using MATLAB and OpenDSS. The results demonstrate significant improvements, including a 16% reduction in feeder congestion, a 150% increase in PV penetration, a 13% reduction in power losses, and decreased reliance on the UG, ensuring enhanced power quality and system reliability.}, archive = {J_ACCESS}, author = {Hossam H. H. Mousa and Karar Mahmoud and Matti Lehtonen}, doi = {10.1109/ACCESS.2025.3533043}, journal = {IEEE Access}, pages = {17161-17181}, shortjournal = {IEEE Access}, title = {Enhancing renewable energy hosting capacity in unbalanced microgrids via empowering smart inverters}, volume = {13}, year = {2025}, }
• (2025). Impact of serious games on teacher training: An analysis of competence, immersion, and positive emotions. ACCESS, 13, 17150-17160. (www)

  @article{ , abstract = {In recent times, the use of serious games has significantly advanced in science education, with notable contributions in the design and implementation of game mechanics in immersive environments. This research aims to assess the usability and experience of the serious game ABCCI, identifying both the positive and negative effects generated by its interaction from the perspective of pre-service teachers, considering levels of competence, immersion, tension, flow, and challenge. To evaluate usability, the System Usability Scale (SUS) was adapted, and the Game Experience Questionnaire (GEQ) was used to assess the overall experience. The results indicate a moderate positive correlation between the experience and usability of the serious game. Most pre-service teachers expressed positive views about the frequent use of the ABCCI serious game in science education, which successfully created an engaging and positive experience characterized by a sense of competence, immersion, and positive emotions, with an appropriate level of challenge and low levels of negative affect. Further studies are needed to develop a framework for designing games in science education. ABCCI proves to be a promising tool for managing learning.}, archive = {J_ACCESS}, author = {Rina Machaca Viza and Fabian Hugo Rucano Paucar and Fabiola Talavera-Mendoza}, doi = {10.1109/ACCESS.2025.3526416}, journal = {IEEE Access}, pages = {17150-17160}, shortjournal = {IEEE Access}, title = {Impact of serious games on teacher training: An analysis of competence, immersion, and positive emotions}, volume = {13}, year = {2025}, }
• (2025). An ultrasound-based non-invasive blood pressure estimation method based on optimal vascular wall tracking position. ACCESS, 13, 17132-17149. (www)

  @article{ , abstract = {This study presents, for the first time, an ultrasound method combining focused and single plane wave transmissions to achieve single calibration, non-invasive, cuffless blood pressure monitoring. High-quality echo signals obtained from focused transmissions were first used to identify the optimal segment of the radial artery wall. Then, single plane wave signals were utilized to track this segment frame-by-frame and calculate local pulse wave velocity (PWV). Finally, the M-K equation was applied to estimate systolic and diastolic blood pressures. To validate the effectiveness of the proposed algorithm, 25 volunteers participated in the study. Data were collected using both an oscillometric cuff-based blood pressure monitor and our ultrasound system in four states: morning, evening, pre-exercise, and post-exercise. A total of 300 sets of comparative data were obtained. The overall mean deviation for systolic blood pressure was $2.2~\pm ~2.1$ mmHg, and for diastolic blood pressure, it was $2.1~\pm ~2.2$ mmHg. To our knowledge, this is the first study to achieve real-time blood pressure calculation within seconds using a single-array ultrasound transducer. Compared to existing non-invasive continuous blood pressure monitoring methods, this approach is faster and provides more accurate vascular wall information. Significance: Combined with affordable hardware, this method holds significant potential for the development of wearable devices for real-time cardiovascular health monitoring.}, archive = {J_ACCESS}, author = {Liyuan Liu and Xingguang Geng and Fei Yao and Yitao Zhang and Haiying Zhang and Yunfeng Wang and Zhaoying Zheng}, doi = {10.1109/ACCESS.2024.3524614}, journal = {IEEE Access}, pages = {17132-17149}, shortjournal = {IEEE Access}, title = {An ultrasound-based non-invasive blood pressure estimation method based on optimal vascular wall tracking position}, volume = {13}, year = {2025}, }
• (2025). Reliable quantized fuzzy controller design for nonlinear singularly perturbed systems with markovian jumps and dynamic event triggered mechanism. ACCESS, 13, 17120-17131. (www)

  @article{ , abstract = {This paper proposes a novel approach to handling random actuator failures in control systems through robust and reliable control techniques. The investigation seeks to establish a framework for assessing the $ l_{2}-l_{\infty } $ stability characteristics of nonlinear Markov jump singularly perturbed systems (MJSPSs) using an output Takagi-Sugeno (TS) fuzzy-based controller. The study focuses on implementing a component-based dynamic event-triggered mechanism (CBDETM), including quantization, to investigate the transmission of the outputs across networked communication channels. The proposed approach is based on the following key attributes: 1) An event-triggered mechanism that establishes a suitable condition for independent signal transmission from each sensor node to the controller; 2) A stochastic failure model that describes actuator failures; 3) A non-stationary Markov chain that reflects the asynchronous relationship between the system and controller modes; 4) A mode-dependent Lyapunov function is used to establish sufficient conditions to show that the resultant closed-loop system is stochastically mean-square stable with a $\gamma $ level of $ l_{2}-l_{\infty } $ performance index; 5) The theoretical findings are validated by a real numerical example of the Van Der Pol circuit, confirming the effectiveness of the proposed strategy.}, archive = {J_ACCESS}, author = {Rabeh Abbassi and Mourad Kchaou and Hamdi Gassara and Houssem Jerbi}, doi = {10.1109/ACCESS.2025.3532546}, journal = {IEEE Access}, pages = {17120-17131}, shortjournal = {IEEE Access}, title = {Reliable quantized fuzzy controller design for nonlinear singularly perturbed systems with markovian jumps and dynamic event triggered mechanism}, volume = {13}, year = {2025}, }
• (2025). Optimization of jamming type selection for countering multifunction radar based on generative adversarial imitation learning. ACCESS, 13, 17110-17119. (www)

  @article{ , abstract = {In recent years, deep reinforcement learning (DRL) has made some progress in jamming type selection (JTS). However, during the training process of the agent, exploration of the action space is necessary, which leads to poor jamming effects in the early stages of training, posing a significant threat to the aircraft’s survivability. Therefore, this paper proposes a method of JTS based on generative adversarial imitation learning (GAIL). Firstly, the agent learns from expert strategies to achieve high reward returns, avoiding wasted time from unguided exploration, thereby ensuring that the agent maintains a good jamming effect throughout its application process. Secondly, based on generative adversarial theory, the discriminator measures the difference between the generated and expert strategies. This difference is used as an internal reward to assist in updating the neural network parameters, effectively reducing the complexity of reward function design. Finally, through case analysis, it can be observed that using the GAIL algorithm can achieve rewards close to those of the expert strategy. When applied online, it does not rely on accurate predictions or precise modeling of the external environment, allowing for quick real-time decision-making. Additionally, its performance surpasses that of traditional JTS strategies.}, archive = {J_ACCESS}, author = {Tianjian Yang and You Chen and Siyi Cheng and Xing Wang and Xi Zhang}, doi = {10.1109/ACCESS.2025.3531016}, journal = {IEEE Access}, pages = {17110-17119}, shortjournal = {IEEE Access}, title = {Optimization of jamming type selection for countering multifunction radar based on generative adversarial imitation learning}, volume = {13}, year = {2025}, }
• (2025). Testbed configuration for minimizing PAPR in GFDM systems through a novel companding approach. ACCESS, 13, 17099-17109. (www)

  @article{ , abstract = {Generalized frequency division multiplexing (GFDM) has emerged as a quintessential waveform candidate poised to meet the stringent requirements of next-generation wireless communication frameworks, addressing pivotal 5G physical layer challenges through its flexible, adaptive structure and spectral efficiency. Despite its numerous advantages, GFDM, akin to other multi-carrier schemes, is afflicted by an inherent high peak-to-average power ratio (PAPR), which can degrade power efficiency and impair system performance under high-power amplifier constraints. In this paper, we rigorously derive the complementary cumulative distribution function (CCDF) for GFDM systems, elucidating its intricate dependence on the design and characteristics of pulse-shaping filters, which are instrumental in managing spectral and temporal properties. Furthermore, we introduce an optimized non-linear companding scheme, specifically calibrated to enhance PAPR performance in GFDM systems by tailoring key parameters to achieve a balance between signal integrity and power efficiency. Extensive simulations demonstrate that the proposed approach, in conjunction with refined pulse-shaping filter configurations, yields substantial enhancements in PAPR reduction, outperforming conventional methods within the GFDM framework. To substantiate the theoretical and simulated outcomes, we have developed an experimental testbed leveraging the universal software radio peripheral (USRP) and LabVIEW environment, providing empirical validation and underscoring the real-world applicability of our proposed technique.}, archive = {J_ACCESS}, author = {Sowbhagya Appalla and Siva Prasad Valluri and K. N. G. B. Yaswanth and Kishore Vejandla and Chakravarthy Gunturu and Gundla Jagapathi and Nittala Noel Anurag Prashanth and A. V. Prabu}, doi = {10.1109/ACCESS.2025.3526691}, journal = {IEEE Access}, pages = {17099-17109}, shortjournal = {IEEE Access}, title = {Testbed configuration for minimizing PAPR in GFDM systems through a novel companding approach}, volume = {13}, year = {2025}, }
• (2025). A novel approach to cognitive load measurement in N-back tasks using wearable sensors, empirical mode decomposition with machine learning, and explainable AI for feature importance. ACCESS, 13, 17083-17098. (www)

  @article{ , abstract = {This study introduces a novel approach for detecting mental workload and stress, utilizing ECG and Fingertip-PPG data from the MAUS dataset. The dataset includes physiological recordings such as ECG, Fingertip-PPG, Wrist-PPG, and GSR signals from 22 participants exposed to varying levels of mental workload and stress conditions, serving as a valuable resource for developing mental workload assessment systems. Focusing on ECG and Fingertip-PPG signals, we evaluated their effectiveness in detecting mental workload and stress. Early detection of mental workload and stress is vital for preventing adverse health outcomes, emphasizing the importance of timely intervention. In this study, by applying Empirical Mode Decomposition (EMD) for feature extraction and machine learning classifiers, we achieved high performance with accuracy rates of 88.64% for ECG and 80.68% for Fingertip-PPG Signal. Combining both data types (ECG and Fingertip-PPG data) further improved recall to 94.87% and boosted overall accuracy to 87.50%, marking an improvement of approximately 13%. SHAP analysis identified key features contributing to this performance, revealing key features such as the waveform length of the first Intrinsic Mode Function (IMF) for the ECG signal, the mean of the second IMF for fingertip-PPG, and the waveform length of the second IMF for combined ECG and fingertip-PPG signals. These results highlight the potential of combining physiological signals and EMD with machine learning for detecting mental workload and stress.}, archive = {J_ACCESS}, author = {Aakanksha Dayal and Ziaullah Khan and Kiramat Ullah and Hee-Cheol Kim}, doi = {10.1109/ACCESS.2024.3523491}, journal = {IEEE Access}, pages = {17083-17098}, shortjournal = {IEEE Access}, title = {A novel approach to cognitive load measurement in N-back tasks using wearable sensors, empirical mode decomposition with machine learning, and explainable AI for feature importance}, volume = {13}, year = {2025}, }
• (2025). Premier dynamic bandwidth management and tensile wavelength selection ensuring QoS for NG-EPONs. ACCESS, 13, 17068-17082. (www)

  @article{ , abstract = {Next Generation Ethernet Passive Optical Networks (NG-EPONs) have emerged as a leading choice for global network connectivity due to their cost-effectiveness, enhanced security, and energy efficiency. As data demands continue to surge with technological advancements, the need for efficient bandwidth management and wavelength selection in NG-EPONs becomes paramount. This paper presents two innovative algorithms—Tensile Wavelength and Dynamic Bandwidth Allocation (TW-DBA) and Premier Dynamic Bandwidth Allocation (PDBA)—designed to optimize bandwidth allocation and maintain Quality of Service (QoS) under varying network conditions. The TW-DBA algorithm achieves a remarkable throughput of 2.34 Gbps, driven by its dynamic wavelength selection mechanism that accounts for factors such as ONU-OLT distance, power availability, and bandwidth demand. Comparative analysis reveals that TW-DBA outperforms existing algorithms like Flexible Wavelength (FW), First-Fit, and Water Filled, both in computational efficiency and resource allocation. Furthermore, the PDBA algorithm demonstrates a minimum blockage probability of 0.0025 for limited ONU scenarios and 0.005 in unlimited scenarios, ensuring uniform bandwidth distribution. Simulation results confirm the superior performance and effectiveness of the proposed models, positioning them as robust solutions for the evolving demands of NG-EPONs.}, archive = {J_ACCESS}, author = {Purushottam Singh and Pushpendra Kumar and Harshita Patel and Kanojia Sindhuben Babulal and Gayathri Ananthakrishnan}, doi = {10.1109/ACCESS.2025.3532496}, journal = {IEEE Access}, pages = {17068-17082}, shortjournal = {IEEE Access}, title = {Premier dynamic bandwidth management and tensile wavelength selection ensuring QoS for NG-EPONs}, volume = {13}, year = {2025}, }
• (2025). Robotization of miniature-scale radio-controlled excavator: A new medium for construction- specific DNN data generation. ACCESS, 13, 17054-17067. (www)

  @article{ , abstract = {Digital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. Moreover, the absence of deployable robot agents hinders reinforcement learning, impeding task-oriented AI development. In response, we introduce an innovative approach utilizing a miniature-scale, radio-controlled excavator (RC-excavator). This presents a cost-effective method for automated data collection and labeling, as well as interactive reinforcement learning. The RC-excavator’s electric circuit was modified, its motion dynamics were modeled, and it was fully robotized for precise computer-directed motion control. Statistical validation of its motions achieved a Normalized Range Adjusted Accuracy (NRAA) of 99.14% for the bucket, 97.97% for the main arm, and 98.63% for the cabin. This confirms its adequacy for image labeling and task-oriented automation research.}, archive = {J_ACCESS}, author = {Seyedeh Fatemeh Saffari and Daeho Kim and Byungjoo Choi}, doi = {10.1109/ACCESS.2025.3532203}, journal = {IEEE Access}, pages = {17054-17067}, shortjournal = {IEEE Access}, title = {Robotization of miniature-scale radio-controlled excavator: A new medium for construction- specific DNN data generation}, volume = {13}, year = {2025}, }
• (2025). Leaf classification for sustainable agriculture and in-depth species analysis. ACCESS, 13, 17043-17053. (www)

  @article{ , abstract = {Effective leaf classification is crucial for ecological research and agricultural management, though handling diverse leaf shapes and environmental variations can be difficult. This study introduces an innovative approach that combines advanced feature extraction techniques with optimization and classification strategies. We employ Scale Invariant feature transform (SIFT), wavelet transform, and particle gradient motion for feature extraction, followed by bee colony optimization to enhance the process. A Gaussian distribution-based classifier is subsequently utilized, achieving an accuracy of 92%, while a Random forest classifier applied to the Grapevine Leave Dataset resulted in an accuracy of 84.63%. This approach highlights the potential for improved plant identification and disease management, providing a reliable solution for both small and large-scale agricultural applications. The resilience of this framework guarantees adaptability across various datasets, making it a valuable asset for improving crop quality and sustainability.}, archive = {J_ACCESS}, author = {Sara Mumtaz and Shabbab Algamdi and Haifa F. Alhasson and Dina Abdulaziz Alhammadi and Ahmad Jalal and Hui Liu}, doi = {10.1109/ACCESS.2025.3526888}, journal = {IEEE Access}, pages = {17043-17053}, shortjournal = {IEEE Access}, title = {Leaf classification for sustainable agriculture and in-depth species analysis}, volume = {13}, year = {2025}, }
• (2025). Design of approximate adder with reconfigurable accuracy. ACCESS, 13, 17030-17042. (www)

  @article{ , abstract = {Arithmetic circuits such as adders are fundamental components in implementing image processing applications. Since these applications are error-tolerant, the adders can be approximated to improve their PDP (Power-Delay-Product) metric while maintaining accuracy within tolerance limits. This paper proposes a segmentation technique to design ternary approximate adders, where the entire adder chain is split into segments, with the intent to reduce the delay. Next, for applications that require tunable accuracy, we incorporate a reconfiguration technique in the segmented approximate adders, using a divide-and-conquer methodology that dynamically optimizes the approximate adder’s accuracy, leading to efficient computation. Also, an algorithm was proposed to compute accuracy and hardware complexity in an N-trit accuracy reconfigurable adder. The proposed methodology was validated using an approximate 6-trit adder on its power consumption and delay performance metrics. Compared with the best design in literature, the proposed approximate 6-trit adder exhibits 63% lesser power consumption and 69% lesser delay. The proposed approximate 6-trit adder accuracy was progressively enhanced through the reconfigurable method. After a few reconfiguration stages, the proposed adder matched the accuracy of an exact 6-trit adder while achieving an improved power-delay product (PDP). Finally, the proposed 6-trit adders were validated by using them in the image-blending application.}, archive = {J_ACCESS}, author = {Aalelai Vendhan and Syed Ershad Ahmed and S. Gurunarayanan}, doi = {10.1109/ACCESS.2025.3531943}, journal = {IEEE Access}, pages = {17030-17042}, shortjournal = {IEEE Access}, title = {Design of approximate adder with reconfigurable accuracy}, volume = {13}, year = {2025}, }
• (2025). Clean-room air conditioning control based on improved min-max robust model predictive control algorithm. ACCESS, 13, 17016-17029. (www)

  @article{ , abstract = {With the increasing demand for indoor environmental comfort, air conditioning has become an essential electrical device for modern life. To improve the comfort of indoor environment in clean-room and to reduce the operating cost of the equipment, the study proposes an improved Min-Max robust model predictive control algorithm for clean-room air conditioning control strategy. Firstly, for the comfort of indoor air conditioning, the MPC algorithm is investigated to regulate the air conditioning air supply based on indoor temperature and humidity ratio in order to control the indoor comfort of the clean-room. Subsequently, an improved Min Max robust model predictive control algorithm was proposed to control the parameters of the model, and compared and analyzed with the original algorithm. The simulation results indicated that the PMV index of the model predictive control algorithm was 0.66 at the lowest and 0.68 at the highest. The Min-Max robust model predictive control algorithm was 0.73 at the lowest and 4.3 at the highest. The improved Min-Max robust model predictive control algorithm was 0.45 at the lowest and 0.47 at the highest. The experimental data indicate that the improved Min-Max robust model predictive control algorithm has the best performance and has more obvious effect on the comfort control of clean room environment.}, archive = {J_ACCESS}, author = {Qiang Luo and Ting Wang}, doi = {10.1109/ACCESS.2025.3532629}, journal = {IEEE Access}, pages = {17016-17029}, shortjournal = {IEEE Access}, title = {Clean-room air conditioning control based on improved min-max robust model predictive control algorithm}, volume = {13}, year = {2025}, }
• (2025). Exploring the effectiveness of machine learning and deep learning techniques for EEG signal classification in neurological disorders. ACCESS, 13, 17002-17015. (www)

  @article{ , abstract = {Neurological disorders are among the leading causes of both physical and cognitive disabilities worldwide, affecting approximately 15% of the global population. This study explores the use of machine learning (ML) and deep learning (DL) techniques in processing Electroencephalography (EEG) signals to detect various neurological disorders, including Epilepsy, Autism Spectrum Disorder (ASD), and Alzheimer’s disease. We present a detailed workflow that begins with EEG data acquisition using a headset, followed by data preprocessing with Finite Impulse Response (FIR) filters and Independent Component Analysis (ICA) to eliminate noise and artifacts. Furthermore, the data is segmented, allowing the extraction of key features such as Bandpower and Shannon entropy, which improve classification accuracy. These features are stored in an offline database for easy access during analysis, to be then applied for both ML and DL models, systematically testing their performance and comparing the results to prior studies. Hence, our findings show impressive accuracy, with the random forest model achieving 99.85% accuracy in classifying autism vs. healthy subjects and 100% accuracy in distinguishing healthy individuals from those with dementia using Support Vector Machines (SVM). Moreover, deep learning models, including Convolutional Neural Networks (CNN) and ChronoNet, demonstrated accuracy rates ranging from 92.5% to 100%. In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.}, archive = {J_ACCESS}, author = {Souhaila Khalfallah and William Puech and Mehdi Tlija and Kais Bouallegue}, doi = {10.1109/ACCESS.2025.3532515}, journal = {IEEE Access}, pages = {17002-17015}, shortjournal = {IEEE Access}, title = {Exploring the effectiveness of machine learning and deep learning techniques for EEG signal classification in neurological disorders}, volume = {13}, year = {2025}, }
• (2025). Protecting medical images using a zero-watermarking approach based on fractional racah moments. ACCESS, 13, 16978-17001. (www)

  @article{ , abstract = {This article introduces a new family of orthogonal moments, the fractional Racah moments (FrROMs), developed to meet the demands of copyright protection for medical images, a field requiring robust and secure methods that preserve the integrity of the original image. The FrROMs are derived from a matrix of fractional Racah orthogonal polynomials (FrROPs), which in turn are obtained from the matrix of classical Racah polynomials (ROPs) using the spectral decomposition theorem. To compute the ROPs, we enhanced a method for calculating the initial terms of these polynomials to avoid instability issues associated with higher-order ROPs. Subsequently, the FrROMs are used as descriptors for embedding and extracting a zero-watermark. Experimental results demonstrate that the proposed zero-watermarking scheme efficiently leverages the FrROMs, surpassing classical ROMs in terms of robustness and reconstruction capability. This scheme provides strong protection against various image processing attacks while maintaining the original content intact. Compared to existing methods, it stands out for its enhanced robustness and reliability, making a significant contribution to the secure protection of medical images.}, archive = {J_ACCESS}, author = {Karim El-Khanchouli and Hanaa Mansouri and Nawal El Ghouate and Hicham Karmouni and Nour-Eddine Joudar and Mhamed Sayyouri and S. S. Askar and Mohamed Abouhawwash}, doi = {10.1109/ACCESS.2025.3532747}, journal = {IEEE Access}, pages = {16978-17001}, shortjournal = {IEEE Access}, title = {Protecting medical images using a zero-watermarking approach based on fractional racah moments}, volume = {13}, year = {2025}, }
• (2025). Federated learning based on kernel local differential privacy and low gradient sampling. ACCESS, 13, 16959-16977. (www)

  @article{ , abstract = {Federated learning that is an approach to addressing the “data silo” problem in a collaborative fashion may face the risk of data leakage in real-world contexts. To solve this problem, we introduce the random Fourier feature mapping (RFFM) together with kernel local differential privacy (KLDP) and develop a new privacy protection mechanism, called the RFFM-KLDP mechanism, for high-dimensional context data. Theoretical properties show that the proposed privacy-preserving mechanism has the properties of $\epsilon $ -LDP and $\epsilon $ -distance-LDP in the federated learning framework. To guarantee the effectiveness of federated learning in the presence of contaminated data, we develop a modified low-gradient sampling technique to sample representative subset of uncontaminated data by incorporating large gradients and unbalanced information. By combining RFFM-KLDP and modified low-gradient sampling technique, we develop a novel and robust federated learning method for classification in the presence of the noisy text data, which can preserve data privacy and largely improve the accuracy of classification algorithm compared to the existing classifiers in terms of the area under curve and classification accuracy. Simulation studies and a context example are used to illustrate the proposed methodologies.}, archive = {J_ACCESS}, author = {Yi Chen and Dan Chen and Niansheng Tang}, doi = {10.1109/ACCESS.2025.3532683}, journal = {IEEE Access}, pages = {16959-16977}, shortjournal = {IEEE Access}, title = {Federated learning based on kernel local differential privacy and low gradient sampling}, volume = {13}, year = {2025}, }
• (2025). Optimizing security in IoT ecosystems using hybrid artificial intelligence and blockchain models: A scalable and efficient approach for threat detection. ACCESS, 13, 16933-16958. (www)

  @article{ , abstract = {The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks.}, archive = {J_ACCESS}, author = {William Villegas-Ch and Jaime Govea and Rommel Gutierrez and Aracely Mera-Navarrete}, doi = {10.1109/ACCESS.2025.3532800}, journal = {IEEE Access}, pages = {16933-16958}, shortjournal = {IEEE Access}, title = {Optimizing security in IoT ecosystems using hybrid artificial intelligence and blockchain models: A scalable and efficient approach for threat detection}, volume = {13}, year = {2025}, }
• (2025). Advances in biomedical missing data imputation: A survey. ACCESS, 13, 16918-16932. (www)

  @article{ , abstract = {Ensuring data quality in biomedical sciences is crucial for reliable research outcomes, particularly as precision medicine continues to gain prominence. Missing values compromise data quality and can make it difficult to perform data-based studies. The origins of missing values in biomedical datasets are diverse, including experimental errors, equipment malfunctions, and variations in data collection protocols tailored to individual patient conditions. To address the complex nature of missing values and the unique characteristics of biomedical data, a diverse spectrum of computational imputation techniques has emerged. These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. We outline the fundamental principles underlying each imputation technique and present a detailed analysis of their advantages and disadvantages, categorized by missing data patterns. To aid practitioners in method selection, we offer practical recommendations based on critical factors such as dataset size, data type, and missingness rate. By synthesizing insights from existing literature, we provide a holistic perspective on the effectiveness of various imputation methods under different biomedical contexts, thereby facilitating informed decision-making for researchers and practitioners in applying imputation techniques to biomedical data processing.}, archive = {J_ACCESS}, author = {Míriam Barrabés and Maria Perera and Víctor Novelle Moriano and Xavier Giró-I-Nieto and Daniel Mas Montserrat and Alexander G. Ioannidis}, doi = {10.1109/ACCESS.2024.3516506}, journal = {IEEE Access}, pages = {16918-16932}, shortjournal = {IEEE Access}, title = {Advances in biomedical missing data imputation: A survey}, volume = {13}, year = {2025}, }
• (2025). Current trends in class imbalance learning for software defect prediction. ACCESS, 13, 16896-16917. (www)

  @article{ , abstract = {Software defect prediction is of high importance to manage the software development efforts by focusing the testing efforts on the fault-prone modules. Imbalanced defect data causes detrimental impact on the performance of software defect predictors. Researchers deployed a diverse range of learning methods to tackle the class-imbalance issues resulting into the remarkable achievements improving the performance of defect prediction models. An analysis on the current trends addressing the Class Imbalanced Learning methods is essential in domain of SDP. This article presents a review of the contributions made over the period from year 2019 to 2024 towards handling class imbalance while applying learning machines for SDP. This study will provide current market trends to future researchers to handle class imbalance. This review has uncovered that – The most pronounced datasets are Promise and NASA which are highly imbalanced in nature. Hybridization of data-sampling techniques with ensembles is effective to handle the class imbalance issue. Among the available evaluation metrics, Area under the Curve is the most used one as it is insulated from the impact of imbalanced datasets. Deep learning models have potential and prospects to be explored for class imbalance handling in SDP in a full-fledged capacity.}, archive = {J_ACCESS}, author = {Somya R. Goyal}, doi = {10.1109/ACCESS.2025.3532250}, journal = {IEEE Access}, pages = {16896-16917}, shortjournal = {IEEE Access}, title = {Current trends in class imbalance learning for software defect prediction}, volume = {13}, year = {2025}, }
• (2025). Exploring factors influencing crash occurrence involving autonomous vehicle: Random parameters model with heterogeneity in means and variances approach. ACCESS, 13, 16879-16895. (www)

  @article{ , abstract = {Autonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-involved crashes is crucial, especially as human drivers may struggle to comprehend the behavior of AVs during interactions. This study addresses this gap by employing a random parameter probit model with heterogeneity in means and variances. Dataset comprises AV crash records obtained from the California Department of Motor Vehicles from 2018 to the first quarter of 2024. Crashes on roadway segments and intersection are modeled separately. Modeling results reveal that factors such as poor lighting conditions, braking maneuver of AVs, proceeding straight movement of HDVs, involvement of bikes/scooters, residential land-use significantly contribute to AV-involved crash occurrence on segments and at intersections. On segments, first quarter of the year, the retail/entertainment land use, sideswipe collision, dangerous maneuver of HDVs and proceeding straight moment of AVs affect the likelihood of AV-involved crashes. Meanwhile, at intersection, rear-end collision, raining/snowing, unusual road conditions, four-leg intersection, lack of pedestrian island/intersection control significantly increases the probability of AV-involved crashes while angle collision and large skew angle decreases it. The findings highlight the need for more targeted goals to improve AV’s safety, such as enhancing AV sensor perception capabilities, incorporating scenario-based tests by categorization of crash location, and developing mass education initiatives to facilitate the broader acceptance and understanding of AV technologies.}, archive = {J_ACCESS}, author = {Haocheng Li and Amjad Pervez and Jaeyoung Jay Lee}, doi = {10.1109/ACCESS.2025.3532937}, journal = {IEEE Access}, pages = {16879-16895}, shortjournal = {IEEE Access}, title = {Exploring factors influencing crash occurrence involving autonomous vehicle: Random parameters model with heterogeneity in means and variances approach}, volume = {13}, year = {2025}, }
• (2025). Repetitive control of robotic joint with variable impedance utilizing agonist-antagonist muscle pair structure based on virtual trajectories. ACCESS, 13, 16866-16878. (www)

  @article{ , abstract = {Realizing accurate trajectory-tracking control for flexible robotic joints using a model-free approach is challenging. In this study, the repetitive control based on virtual trajectory, which is a model-free control method proposed for flexible human multi-joints, is extended and applied to a real robotic joint with a variable mechanical impedance mechanism. The virtual trajectory is modeled by a time series of the equilibrium position of the spring. In conventional methods, the virtual trajectory is corrected according to the trajectory error resulting from repetitive control. To achieve trajectory tracking even with a soft joint, this study proposes a strategy for starting repetitive control with a high joint impedance and switching to a lower joint impedance after convergence to the target trajectory, thereby successively correcting the estimated impedance change ratio by repetitive control. We developed a robotic joint with an agonist-antagonist muscle pair structure that can easily change its mechanical impedance using a position-controlled motor combined with a grommet, which changes its stiffness nonlinearly with respect to elongation, in addition to a wire traction mechanism as the control target. The results of simulations and real robot experiments demonstrated that the proposed method can achieve trajectory-tracking control even when the joint impedance is so low that the trajectory diverges from that of the conventional method. These results show that the proposed method is capable of trajectory tracking control while maintaining joint softness even when the dynamics of the control target and the impedance values before and after the change are unknown, that is, model-free.}, archive = {J_ACCESS}, author = {Kaho Umeda and Yinlai Jiang and Hiroshi Yokoi and Shunta Togo}, doi = {10.1109/ACCESS.2025.3532633}, journal = {IEEE Access}, pages = {16866-16878}, shortjournal = {IEEE Access}, title = {Repetitive control of robotic joint with variable impedance utilizing agonist-antagonist muscle pair structure based on virtual trajectories}, volume = {13}, year = {2025}, }
• (2025). Convergence of blockchain, IoT, and AI for enhanced traceability systems: A comprehensive review. ACCESS, 13, 16838-16865. (www)

  @article{ , abstract = {The need for sophisticated traceability systems has become essential in increasingly complex and globalized supply chains. The convergence of Blockchain (BC), Internet of Things (IoT), and Artificial Intelligence (AI) technologies offers promising solutions to enhance traceability systems across various sectors, particularly supply chain management (SCM). This paper presents a bibliometric and systematic literature review (SLR) to examine trends, research patterns, and methodologies in integrating BC IoT and AI into traceability systems. Bibliometric analysis of 530 documents from SCOPUS (2014–2024) identified key trends, while the SLR, conducted across multiple databases following PRISMA guidelines, refined the dataset to 43 peer-reviewed studies based on inclusion criteria. Recent research output has notably increased, focusing on agricultural supply chains and SCM, with India and China leading in publications. The analysis shows a predominance of experimental and hybrid methodologies, using Ethereum and Hyperledger Fabric as key platforms. Key trends include AI-driven analytics, real-time IoT data collection, and the need for secure, tamper-proof data by BC. However, interoperability, scalability, and standardization challenges hinder adoption. The paper proposes a four-layer framework for integrating BC, IoT, and AI to improve transparency, security, and efficiency and highlights the need for more empirical studies, industry-specific frameworks, and standardization to overcome existing limitations.}, archive = {J_ACCESS}, author = {Yahaya Saidu and Shuhaida Mohamed Shuhidan and Dahiru Adamu Aliyu and Izzatdin Abdul Aziz and Shamsuddeen Adamu}, doi = {10.1109/ACCESS.2025.3528035}, journal = {IEEE Access}, pages = {16838-16865}, shortjournal = {IEEE Access}, title = {Convergence of blockchain, IoT, and AI for enhanced traceability systems: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). Context-aware fine-grained product recognition on grocery shelves. ACCESS, 13, 16824-16837. (www)

  @article{ , abstract = {Product recognition is a fine-grained image retrieval problem because grocery stores can contain several thousand products on their shelves, some of which have minimal visual differences. Given that the products on the store’s shelves change frequently, it would be practical to build a system using a single reference image or a few per product. This task is very challenging in computer vision, whereas humans solve it more effortlessly by relying on contextual information. Our work incorporates semantic and spatial context into a novel product recognition method. We take advantage of stores being organized into different sections and aisles, where similar products are placed nearby. First, we propose the Hierarchical Auxiliary Loss (HAL) for learning an organized feature space in which products from the same category, usually placed in the same store section, are close to each other. Second, we propose the Context-Aware Query Expansion (CAQE) module for the inference phase, in which each feature vector of a product on a shelf is expanded with the feature vectors of neighboring products. The amount of information exchanged between the two products depends on the similarity of their feature vectors and spatial distance on the store shelves. To demonstrate the effectiveness of our contributions, we conducted detailed experiments on publicly available grocery product datasets and showed that our method achieves state-of-the-art results.}, archive = {J_ACCESS}, author = {Lovre Antonio Budimir and Zoran Kalafatić and Marko Subašić and Sven Lončarić}, doi = {10.1109/ACCESS.2025.3532745}, journal = {IEEE Access}, pages = {16824-16837}, shortjournal = {IEEE Access}, title = {Context-aware fine-grained product recognition on grocery shelves}, volume = {13}, year = {2025}, }
• (2025). Research on density peak clustering color-separation algorithm based on SOM neural network. ACCESS, 13, 16811-16823. (www)

  @article{ , abstract = {Pattern images input to The knitting CAD system have a large number of different colors, and it is necessary to reduce the number of colors in the image by merging similar colors through color-separation algorithms. However, current pattern images usually have the problem of image degradation, which seriously affects the accuracy of color-separation algorithms. In addition, the traditional color-separation algorithm needs to rely on the manual setting of clustering parameters, which is very time-consuming and laborious. To solve these problems, In this paper, we propose a density peak clustering color-separation algorithm based on self-organizing mapping (SOM) neural network, which firstly uses enhanced super-resolution generative adversarial network (Real-ESRGAN) blind super-resolution reconstruction network to clarify the degraded image and obtain a high-resolution image with clear boundaries; secondly, we carry out the initial clustering through the SOM neural network to simplify the image information; and then we use an improved density peak clustering (DPC) algorithm to calculate clustering centers under the conditions of conforming to the perception of human eyes, and carry out secondary clustering on the image; finally, carry out image post-processing through the variegated spots merging algorithm based on the connected component analysis. The experimental results show that the algorithm proposed in this study can effectively deal with degraded pattern images, and the clustering effectiveness evaluation indices perform well.}, archive = {J_ACCESS}, author = {Xin Ru and Yanhao Wang and Ran Chen and Zhifa Zeng and Laihu Peng and Wei Tang and Weimin Shi and Hui Wang}, doi = {10.1109/ACCESS.2025.3526233}, journal = {IEEE Access}, pages = {16811-16823}, shortjournal = {IEEE Access}, title = {Research on density peak clustering color-separation algorithm based on SOM neural network}, volume = {13}, year = {2025}, }
• (2025). CNN accelerator performance dependence on loop tiling and the optimum resource-constrained loop tiling. ACCESS, 13, 16800-16810. (www)

  @article{ , abstract = {This paper analyzes the dependence of the convolutional neural network (CNN) accelerator performance on loop tiling. More specifically, based on the closed-form expression of the CNN accelerator performance, the dependence on the tile sizes is characterized by the derivative, the asymptote and the switching point between the computation-limited condition and the communication-limited condition. The analysis provides a useful insight into how to determine the tile sizes to achieve the required performance while avoiding an unnecessary static random access memory (SRAM) size increase. The paper also deals with the optimum resource-constrained loop tiling for CNN accelerators. Given the constraint on either the on-chip buffer size or the multiply-accumulate (MAC) array size, tile sizes are optimized to maximize the performance. The closed-form expressions of the optimum tile sizes provide useful insights into how to allocate the available hardware resources for maximum performance. From performance evaluation, the proposed tile sizes achieve almost the maximum performance, which enables the optimization of tile sizes without relying on exhaustive search, speeding up design space exploration.}, archive = {J_ACCESS}, author = {Chester Sungchung Park and Sungkyung Park}, doi = {10.1109/ACCESS.2025.3532790}, journal = {IEEE Access}, pages = {16800-16810}, shortjournal = {IEEE Access}, title = {CNN accelerator performance dependence on loop tiling and the optimum resource-constrained loop tiling}, volume = {13}, year = {2025}, }
• (2025). Graph convolution for large-scale graph node classification task based on spatial and frequency domain fusion. ACCESS, 13, 16787-16799. (www)

  @article{ , abstract = {In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating local and global information, enhancing robustness to noise, and overcoming the rigidity of graph structures. To address these issues, we propose a new GNN algorithm, LEGNN (Local and Global Enhanced Graph Neural Network), which introduces several key improvements over traditional GNN models such as GraphSAGE and GCN:Firstly, LEGNN combines a hybrid convolution method of spatial and spectral convolutions, enabling it to simultaneously capture local neighborhood relationships and global topological structures. This fusion mechanism effectively enhances the model’s ability to learn node representations, especially in complex graph structures, with accuracy improvements of 1%-2% compared to GCN and GraphSAGE. Secondly, LEGNN incorporates a noise prediction mechanism that injects controlled perturbations into the node representations, improving the model’s robustness, reducing overfitting, and enhancing generalization to unseen data. Finally, LEGNN introduces an adaptive graph structure adjustment mechanism, allowing the model to dynamically adjust the graph topology based on input data, enabling it to flexibly handle evolving data scenarios. In experiments on OGB datasets, LEGNN reduces training time by 55%-88% compared to GCN and GraphSAGE, and demonstrates higher stability when handling large-scale graph data. We validated the effectiveness of LEGNN through experiments on two OGB datasets, and the results show that LEGNN outperforms traditional GNN models in graph learning tasks, highlighting its potential in advancing the field.}, archive = {J_ACCESS}, author = {Junwen Lu and Lingrui Zheng and Xianmei Hua and Yankun Wang}, doi = {10.1109/ACCESS.2025.3532806}, journal = {IEEE Access}, pages = {16787-16799}, shortjournal = {IEEE Access}, title = {Graph convolution for large-scale graph node classification task based on spatial and frequency domain fusion}, volume = {13}, year = {2025}, }
• (2025). A joint forgetting factor-based adaptive extended kalman filtering approach to predict the state-of-charge and model parameter of lithium-ion battery. ACCESS, 13, 16770-16786. (www)

  @article{ , abstract = {The accuracy of model-based State of Charge (SOC) estimators often degrades due to parametric uncertainty, measurement errors, and variations in operating temperature. Many Kalman filter-driven SOC estimators in the literature overlook these uncertainties, leading to imprecise SOC estimation. To address these challenges, this study proposes a joint forgetting factor-based adaptive extended Kalman filter (JFFAEKF). The JFFAEKF approach evaluates uncertainties in the battery model and incorporates them into the SOC estimation process under dynamic operating conditions. By augmenting both the SOC and battery model parameters into a single state vector, the estimator concurrently updates these variables. An adaptive correction mechanism for process and measurement noise covariance matrices is introduced, leveraging the innovation and residual errors of estimated terminal voltage. These covariance updates enable the computation of an appropriate filter gain to mitigate the adverse effects of model and measurement uncertainties. Additionally, a forgetting factor is integrated into the design to enhance computational efficiency and convergence rate. The practical applicability of the proposed JFFAEKF is validated using real-world current profiles from the LA92, UDDS, and US06 drive cycles at various operating temperatures. The accuracy of the SOC estimation is demonstrated by comparing the root mean square error ( $E_{RMS}$ ) and maximum absolute error ( $Max_{AE}$ ) with other Kalman filter-based estimators. Furthermore, the estimator’s robustness is tested under adverse conditions, including offset current, sensor bias voltage, and parametric uncertainties in the battery model and state estimator. Results from diverse dynamic operating conditions confirm the superior performance of the JFFAEKF in SOC estimation compared to existing methods.}, archive = {J_ACCESS}, author = {Satyaprakash Rout and Satyajit Das}, doi = {10.1109/ACCESS.2025.3533137}, journal = {IEEE Access}, pages = {16770-16786}, shortjournal = {IEEE Access}, title = {A joint forgetting factor-based adaptive extended kalman filtering approach to predict the state-of-charge and model parameter of lithium-ion battery}, volume = {13}, year = {2025}, }
• (2025). Novel speech-based emotion climate recognition in peers’ conversations incorporating affect dynamics and temporal convolutional neural networks. ACCESS, 13, 16752-16769. (www)

  @article{ , abstract = {Peers’ conversation provides a domain of rich emotional information. The latter, apart from facial and gestural expressions, it is also naturally conveyed via peers’ speech, contributing to the establishment of a dynamic emotion climate (EC) during their conversational interaction. Recognition of EC could provide an additional source in understating peers’ social interaction and behavior on top of peers’ actual conversational content.We propose a novel approach for speech-based EC recognition, termed AffECt, which combines peers’ complex affect dynamics (AD) with deep features extracted from speech signals using Temporal Convolutional Neural Networks (TCNNs). AffECt was tested and cross-validated on data drawn from there open datasets, i.e., K-EmoCon, IEMOCAP, and SEWA, in terms of EC arousal/valence level classification. The experimental results have shown that AffECt achieves EC classification accuracy up to 83.3% and 80.2% for arousal and valence, respectively, clearly surpassing the results reported in the literature, exhibiting robust performance across different languages. Moreover, there is a distinct improvement when the AD are combined with the TCNN, compared to the baseline deep learning approaches. These results demonstrate the effectiveness of AffECt in speech-based EC recognition, paving the way for many applications, e.g., in patients’ group therapy, negotiations, and emotion-aware mobile applications.}, archive = {J_ACCESS}, author = {Ghada Alhussein and Mohanad Alkhodari and Ahsan H. Khandoker and Leontios J. Hadjileontiadis}, doi = {10.1109/ACCESS.2025.3529125}, journal = {IEEE Access}, pages = {16752-16769}, shortjournal = {IEEE Access}, title = {Novel speech-based emotion climate recognition in peers’ conversations incorporating affect dynamics and temporal convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Modeling burst error processes in ITU-T g.hnem-based PLC systems: Noisy indoor environment analysis using a generative semi-hidden markov model. ACCESS, 13, 16733-16751. (www)

  @article{ , abstract = {Power line communication (PLC) systems are essential for modern telecommunications, providing a cost-effective solution for data transmission over existing electrical wiring. However, indoor environments pose significant challenges for these systems, primarily due to burst errors caused by impulsive noise, narrowband interference, and signal attenuation. This study seeks to address these challenges by utilizing three-state Semi-Hidden Markov Models (SHMMs) to analyze and characterize burst error processes in narrowband PLC (NB-PLC) systems. We developed a software-defined multi-state quadrature amplitude modulation (M-QAM) orthogonal frequency division multiplexing (OFDM)-based NB-PLC testbed that adheres to the International Telecommunication Union - Telecommunication Standardization Sector (ITU-T) G.hnem standard for home networking over existing wiring, such as power lines, phone lines, and coaxial cables. The system incorporates 4-QAM-OFDM, 8-QAM-OFDM, and 16-QAM-OFDM modulation schemes, utilizing Universal Software Radio Peripheral (USRP) hardware for real-time transmission and reception. Using this experimental setup, we generated and analyzed a dataset of 200,000 transmitted bits, capturing the characteristics of burst errors under realistic indoor noise conditions. A comparative analysis of reference error sequences and SHMM simulations demonstrates the model’s ability to accurately replicate NB-PLC-specific error dynamics, including error clustering and state transitions. This research makes four key contributions: (1) the development of a robust experimental testbed for real-time burst error analysis in NB-PLC systems, (2) the creation of a comprehensive dataset that characterizes noise-induced disruptions in real-world indoor scenarios, (3) a theoretical exploration and practical application of SHMMs for modeling error sequences in NB-PLC channels, and (4) the validation of SHMMs and a modified Baum-Welch algorithm (BWA) for efficient modeling and parameter estimation of burst error processes. Together, these contributions enhance the understanding of burst error dynamics and provide a foundation for improving the resilience and efficiency of NB-PLC networks in real-world indoor environments.}, archive = {J_ACCESS}, author = {Akintunde Oluremi Iyiola and Theo G. Swart and Ayokunle Damilola Familua and Thokozani Shongwe}, doi = {10.1109/ACCESS.2025.3531817}, journal = {IEEE Access}, pages = {16733-16751}, shortjournal = {IEEE Access}, title = {Modeling burst error processes in ITU-T g.hnem-based PLC systems: Noisy indoor environment analysis using a generative semi-hidden markov model}, volume = {13}, year = {2025}, }
• (2025). Study on artifacts elimination for three-dimensional interaction virtual reality corridors. ACCESS, 13, 16716-16732. (www)

  @article{ , abstract = {Our team had proposed a Ground Control Points-free three-dimensional interactive virtual reality (3DIVR) for building scene models. However, during the actual scene model construction, we found that the blinking-artifacts and shadow-artifacts generated by light reflection and occlusion have potential effects on corner point detection and also affect the accuracy of 3DIVR. Therefore, this paper proposes a generalized artifact elimination method for eliminating blinking-artifacts and shadow-artifacts based on previous studies. The method first identifies blinking-artifacts by detecting blinking-pixels and calculating the difference values during blinking. Then, it compares these difference values with the blinking threshold. Based on the blinking threshold, it distributes compensation values to the blinking-artifacts. Finally, it smooths the deviated data after blinking-artifact elimination. Additionally, the method eliminates shadow-artifacts by improving the local brightness balance across the three color channels. Since the most important effect of light on the image is on the brightness layer, we used a set of brightness images for a detailed implementation of the principle. And then, we used the three sets of colors to verify the effectiveness of the method proposed in this paper, comparing them with the adjusted brightness method and the adjusted background method. Compared with another method, the results obtained by the method proposed in this paper exhibit two key advantages. On the one hand, they exhibit better visual quality. On the other hand, the mean value and entropy value of our results are superior to those of the other method. Currently, our team has incorporated this method into our self-developed 3DIVR system, which, in the future, will provide better assistance to related researchers.}, archive = {J_ACCESS}, author = {Xiaofan Liu and Jinye Wang and Wenjun Zheng and Ying Chen and Er Wu}, doi = {10.1109/ACCESS.2025.3527492}, journal = {IEEE Access}, pages = {16716-16732}, shortjournal = {IEEE Access}, title = {Study on artifacts elimination for three-dimensional interaction virtual reality corridors}, volume = {13}, year = {2025}, }
• (2025). Automated generation of multiple-choice questions for computer science education using conditional generative adversarial networks. ACCESS, 13, 16697-16715. (www)

  @article{ , abstract = {This work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs), our model provides a versatile approach to addressing the need for diversity and context in relevant MCQ generation across proficiency levels, topic areas. Resulting MCQs inspire implementations within a variety of educational environments - from classrooms, to online courses, and finally exams - equipping teachers with an instrument that could be easily adapted based on the specific needs o students. The model is trained on a carefully constructed dataset that includes material from more than 20 subareas in computer science, consisting of materials such as textbooks, online encyclopedias and Q&A websites. Through rigorous evaluation using comprehensive performance metrics, including Question Relevance Score (QRS), Diversity Index (DI), and Difficulty Alignment Accuracy (DAA), we demonstrate the efficacy and robustness of our framework in generating high-quality MCQs. Moreover, we address ethical considerations inherent in AI-driven educational assessment, ensuring fairness, transparency, and accountability in the MCQ generation process. The cGAN architecture facilitates the generation of contextually relevant MCQs across various proficiency levels and subject domains, enhancing the educational assessment process. The comprehensive dataset developed for this study encompasses diverse computer science topics curated from authoritative textbooks, online resources, question banks, and instructor-generated content. Additionally, a user-friendly QT application has been developed, enabling seamless integration of the cGAN model into educational environments. Through rigorous evaluation and ethical considerations, this framework demonstrates its efficacy, ensuring fairness, transparency, and accountability in MCQ generation. This interdisciplinary work represents a significant advancement in computer science education, providing educators with a powerful tool to enhance student engagement and learning outcomes.}, archive = {J_ACCESS}, author = {Muhammad Shoaib and Ghassan Husnain and Nasir Sayed and Yazeed Yasin Ghadi and Masoud Alajmi and Ayman Qahmash}, doi = {10.1109/ACCESS.2025.3530474}, journal = {IEEE Access}, pages = {16697-16715}, shortjournal = {IEEE Access}, title = {Automated generation of multiple-choice questions for computer science education using conditional generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). WiFi indoor positioning based on EGA-PF and fernet algorithm. ACCESS, 13, 16683-16696. (www)

  @article{ , abstract = {With the massive popularity of WiFi in public places, using WiFi for indoor positioning has become a viable and popular technique. In this article, a method for WiFi indoor positioning utilizing EGA-PF and Fernet is proposed. Firstly, in response to the challenge of balancing positioning time and accuracy, the extreme learning machine (ELM) does not require iterative adjustment of the weights of the hidden layer, allowing it to achieve high positioning accuracy in a short time, but the difficulty in adjusting the parameters restricts the development of the ELM, with each partial parameter of the ELM as an individual, the genetic algorithm (GA) uses operations such as crossover, compilation and selection to optimize until the system performance is met continuously. Secondly, the above algorithms reflect the problem of large system variance and unstable results when dealing with too much noise or incomplete data. In this regard, the random sampling and resampling techniques of the particle filtering (PF) algorithm are utilized for secondary optimization of the selection of the GA. Finally, to ensure the offline fingerprint database’s security, encrypting fingerprint database using the Fernet algorithm, and database is decrypted when the positioning request is received in the online phase. The suggested method is validated using UJIIndoorLoc dataset, and the research findings indicate that the average positioning error of the system is 0.95 m, 90% of the positioning errors are below 2 m, the variance is 0.0031, and the positioning time is 13.253 s.}, archive = {J_ACCESS}, author = {Yanchun Wang and Shaoye Sun and Fengjuan Miao and Ying Xia}, doi = {10.1109/ACCESS.2025.3532331}, journal = {IEEE Access}, pages = {16683-16696}, shortjournal = {IEEE Access}, title = {WiFi indoor positioning based on EGA-PF and fernet algorithm}, volume = {13}, year = {2025}, }
• (2025). Implementation and performance of lightweight authentication encryption ASCON on IoT devices. ACCESS, 13, 16671-16682. (www)

  @article{ , abstract = {The Internet of Things (IoT) is growing rapidly, enabling interconnected devices to communicate and share data seamlessly. While this expansion drives significant advancements across industries, it also introduces critical challenges in ensuring secure device communication. IoT devices often operate under strict constraints in processing power, memory, and energy consumption, complicating the implementation of robust security measures. Lightweight cryptographic algorithms have emerged to address these challenges, with ASCON gaining recognition for its robustness and efficiency. Selected as the winner of the National Institute of Standards and Technology (NIST) competition for lightweight encryption algorithms, ASCON ensures confidentiality and integrity through authenticated encryption with associated data (AEAD) tailored for resource-constrained environments. This paper presents the implementation and performance analysis of ASCON on IoT devices. Using the CupCarbon simulator framework, optimized for modeling smart cities and wireless sensor networks, we evaluate its effectiveness under resource-limited conditions. Performance metrics are analyzed, including latency, response times, and resource utilization. Additionally, ASCON is implemented on Raspberry Pi devices to validate its feasibility in real-world scenarios. Results demonstrate that ASCON enables efficient and secure communications while minimizing computational overhead, making it a viable solution for IoT environments with constrained resources. These findings highlight ASCON’s potential to address critical security needs in the rapidly evolving IoT landscape.}, archive = {J_ACCESS}, author = {Gabriela Cagua and Valérie Gauthier-Umaña and Carlos Lozano-Garzon}, doi = {10.1109/ACCESS.2025.3529757}, journal = {IEEE Access}, pages = {16671-16682}, shortjournal = {IEEE Access}, title = {Implementation and performance of lightweight authentication encryption ASCON on IoT devices}, volume = {13}, year = {2025}, }
• (2025). Evolutionary algorithms and quantum computing: Recent advances, opportunities, and challenges. ACCESS, 13, 16649-16670. (www)

  @article{ , abstract = {Quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.}, archive = {J_ACCESS}, author = {Junaid Ur Rehman and Muhammad Shohibul Ulum and Abdurrahman Wachid Shaffar and Amirul Adlil Hakim and Mujirin and Zaid Abdullah and Hayder Al-Hraishawi and Symeon Chatzinotas and Hyundong Shin}, doi = {10.1109/ACCESS.2025.3530952}, journal = {IEEE Access}, pages = {16649-16670}, shortjournal = {IEEE Access}, title = {Evolutionary algorithms and quantum computing: Recent advances, opportunities, and challenges}, volume = {13}, year = {2025}, }
• (2025). Integrating pull request comment analysis and developer profiles for expertise-based recommendations in global software development. ACCESS, 13, 16637-16648. (www)

  @article{ , abstract = {Determining a suitable software developer to match project needs within the Global software development (GSD) context requires detailed information. The complexity of this problem arises from the required combination of the developer’s level of technical expertise, domain knowledge, and the extent to which they possess the collaborative skills necessary for a successful project. Typical developer recommendation systems do not consider the dynamics of expertise and cooperative nature of the tasks for assessing their correctness, often restricting themselves to extracting review comments only to measure their usefulness and suggest reviewers. This research intends to create a recommendation system using pull request review comments and selected data from developers’ profiles to recommend better experts based on their dynamic expertise. Using advanced algorithm techniques, the proposed model Global Developer Expertise Recommendation System (GDERS) aims to improve the quality of captured data and substantially increase the accuracy of developer recommendations. Impressively, the proposed model significantly outperformed all other text-based classifiers TextCNN, TextRCNN, and Bilstm in this study, showing an accuracy of 91.85%. This research provides a significant achievement of recommendation systems in the global software development context that support more effective collaboration and increase the probability of project completion on time by allowing project managers to find easily accessible developers in the field with the right expertise.}, archive = {J_ACCESS}, author = {Sara Zamir and Abdul Rehman and Hufsa Mohsin and Elif Zamir and Assad Abbas and Fuad A. M. Al-Yarimi}, doi = {10.1109/ACCESS.2025.3532386}, journal = {IEEE Access}, pages = {16637-16648}, shortjournal = {IEEE Access}, title = {Integrating pull request comment analysis and developer profiles for expertise-based recommendations in global software development}, volume = {13}, year = {2025}, }
• (2025). Precision sensing-aided multi-target beamforming prediction in high-mobility ISAC systems based on OTFS. ACCESS, 13, 16623-16636. (www)

  @article{ , abstract = {The integration of orthogonal time frequency space signals into integrated sensing and communication systems has emerged as a highly promising approach for constructing intelligent transportation systems. Among these advancements, beamforming prediction techniques that incorporate multiple-input multiple-output technology have gained widespread attention. However, challenges remain in acquiring initial high-precision state parameters of vehicles and establishing continuous, reliable communication links in high-mobility scenarios. In this paper, we propose a novel MIMO-OTFS beamforming prediction scheme, leveraging a continuous-delay-and-Doppler-shift channel to facilitate information exchange between vehicles and the roadside unit. Furthermore, we develop a three-dimensional parameter estimation algorithm named orthogonal matching pursuit based on maximum likelihood dictionary correction, which offers high accuracy and low complexity. Utilizing this algorithm, we achieve precise multi-target beamforming prediction without approximation. Simulation results demonstrate that our approach significantly outperforms the traditional unscented Kalman filter method based on matched filtering in terms of multi-target beamforming prediction in the ISAC system.}, archive = {J_ACCESS}, author = {Chunyue Wang and Yong Wang and Huiming Zheng and Yunhao Chai and Ying Dong}, doi = {10.1109/ACCESS.2025.3532827}, journal = {IEEE Access}, pages = {16623-16636}, shortjournal = {IEEE Access}, title = {Precision sensing-aided multi-target beamforming prediction in high-mobility ISAC systems based on OTFS}, volume = {13}, year = {2025}, }
• (2025). A novel multi-attribute decision-making method for supplier selection in the health care industry using cosine similarity measures of single-valued neutrosophic cubic hypersoft sets. ACCESS, 13, 16603-16622. (www)

  @article{ , abstract = {Cosine similarity measures are essential in situations that assess the similarities and differences between two potential outcomes. For different extensions of fuzzy sets, soft sets, and hypersoft sets, a wide range of similarity metrics have been examined in the literature. On the other hand, decision-making problems like sustainable supply chain management, in a single-valued neutrosophic cubic hypersoft set (svNCHSS) scenario have not been addressed so far previously using similarity metrics. Improved cosine similarity measures of svNCHSSs based on the cosine function are proposed by combining the cosine similarity measures of simplified neutrosophic sets in vector space that are currently available. It also looks into their properties and discusses their problems. In the svNCHSS environment, we developed a multi-attribute strategy for evaluating sustainable supplier selection (SuSS) in the healthcare sector using cosine and weighted cosine similarity measures. To construct the ranking order of these values and opt for the most appropriate supplier, the method entails calculating the similarity measure values between each assessed supplier and the ideal supplier. Four options are ranked based on thirteen sustainability-related sub-criteria that are connected to each of the three primary criteria like economic, social, and environmental that take into account all aspects of sustainability in a case study of a SuSS procedure for the healthcare sector. The results of the ranking technique are strongly influenced by the weights given to the attributes. For a given circumstance, no ranking method can ensure trustworthy selection. By contrasting their results with current similarity measures, the suggested decision-making problems involving many criteria and their sub-criteria are verified.}, archive = {J_ACCESS}, author = {Muhammad Sajid and Khuram Ali Khan and Jaroslav Frnda and Atiqe Ur Rahman}, doi = {10.1109/ACCESS.2025.3532453}, journal = {IEEE Access}, pages = {16603-16622}, shortjournal = {IEEE Access}, title = {A novel multi-attribute decision-making method for supplier selection in the health care industry using cosine similarity measures of single-valued neutrosophic cubic hypersoft sets}, volume = {13}, year = {2025}, }
• (2025). Synthetic data generation and evaluation techniques for classifiers in data starved medical applications. ACCESS, 13, 16584-16602. (www)

  @article{ , abstract = {With their ability to find solutions among complex relationships of variables, machine learning (ML) techniques are becoming more applicable to various fields, including health risk prediction. However, prediction models are sensitive to the size and distribution of the data they are trained on. ML algorithms rely heavily on vast quantities of training data to make accurate predictions. Ideally, the dataset should have an equal number of samples for each label to encourage the model to make predictions based on the input data rather than the distribution of the training data. In medical applications, class imbalance is a common issue because the occurrence of a disease or risk episode is often rare. This leads to a training dataset where healthy cases outnumber unhealthy ones, resulting in biased prediction models that struggle to detect the minority, unhealthy cases effectively. This paper addresses the problem of class imbalance, given the scarcity of training datasets by improving the quality of generated data. We propose an incremental synthetic data generation system that improves data quality over iterations by gradually adjusting to the data distribution and thus avoids overfitting in classifiers. Through extensive experimental assessments on real asthma patients’ datasets, we demonstrate the efficiency and applicability of our proposed system for individual-based health risk prediction models. Incremental SMOTE methods were compared to the original SMOTE variants as well as various architectures of autoencoders. Our incremental data generation system enhances selected state-of-the-art SMOTE methods, resulting in sensitivity improvements for deep transfer learning (TL) classifiers ranging from 4.01% to 7.79%. Compared with the performance of TL without oversampling, the improvement achieved by the incremental SMOTE methods ranged from 27.18% to 40.97%. These results highlight the effectiveness of our technique in predicting asthma risk and their applicability to imbalanced, data-starved medical contexts.}, archive = {J_ACCESS}, author = {Wan D. Bae and Shayma Alkobaisi and Matthew Horak and Siddheshwari Bankar and Sartaj Bhuvaji and Sungroul Kim and Choon-Sik Park}, doi = {10.1109/ACCESS.2025.3532222}, journal = {IEEE Access}, pages = {16584-16602}, shortjournal = {IEEE Access}, title = {Synthetic data generation and evaluation techniques for classifiers in data starved medical applications}, volume = {13}, year = {2025}, }
• (2025). A novel air-to-ground communication scheme for advanced big data collection in smart farming using UAVs. ACCESS, 13, 16564-16583. (www)

  @article{ , abstract = {The evolution of Flying Ad Hoc Networks (FANETs) demands the development of advanced routing protocols that can address the unique challenges associated with Unmanned Aerial Vehicle (UAV) missions. This paper proposes a novel Air-to-ground, Energy-awaRe, mission-Oriented protocol, leveraging Fuzzy Logic (AERO-FL), to enhance UAV cooperation and optimize network performance in deterministic scanning operations. The study focuses on a Smart Farming (SF) scenario for extensive crop surveillance, which traditionally relies on a single UAV. This conventional approach is often inefficient because of its inability to enable real-time data transmission and its vulnerability to operational failures. The proposed system replaces the single UAV approach with a cooperative FANET comprising multiple UAVs, significantly reducing mission completion time while facilitating real-time data transmission through the cooperation of aerial and ground nodes. The simulation results demonstrate that AERO-FL outperforms established routing protocols in key performance metrics. These findings underscore the potential of AERO-FL in a variety of scanning applications, including traffic monitoring, environmental observation, disaster response, and military operations. By enabling UAV cooperation in a mission-oriented routing framework, this study provides a foundation for future research aimed at achieving full FANET capabilities.}, archive = {J_ACCESS}, author = {Georgios A. Kakamoukas and Thomas D. Lagkas and Vasileios Argyriou and Sotirios K. Goudos and Panagiotis Radoglou-Grammatikis and Stamatia Bibi and Panagiotis G. Sarigiannidis}, doi = {10.1109/ACCESS.2025.3532393}, journal = {IEEE Access}, pages = {16564-16583}, shortjournal = {IEEE Access}, title = {A novel air-to-ground communication scheme for advanced big data collection in smart farming using UAVs}, volume = {13}, year = {2025}, }
• (2025). Enhancing intelligent control strategies for UAVs: A comparative analysis of fuzzy logic, fuzzy PID, and GA-optimized fuzzy PID controllers. ACCESS, 13, 16548-16563. (www)

  @article{ , abstract = {This paper presents advanced control strategies to enhance the stability and trajectory tracking performance of quadrotor systems. The study investigates three control methodologies: the Fuzzy Logic Controller (Fuzzy), the Fuzzy Proportional-Integral-Derivative (FPID) controller, and the Genetic Algorithm (GA)-optimized Fuzzy PID controller (GAFPID). The Fuzzy controller leverages heuristic rules for adaptive control, while the FPID controller integrates conventional PID dynamics with fuzzy logic to improve precision and robustness. The GAFPID controller employs evolutionary computation through a genetic algorithm to optimize parameter tuning, offering superior control performance. Comparative simulations are conducted under diverse operating conditions, including external disturbances and parameter variation scenarios, with performance evaluated using the Integral of Time-weighted Absolute Error (ITAE) metric. Results demonstrate that the GAFPID controller outperforms the other approaches in terms of precision, adaptability, and robustness, establishing it as a promising solution for complex quadrotor applications.}, archive = {J_ACCESS}, author = {Nigatu Wanore Madebo}, doi = {10.1109/ACCESS.2025.3532743}, journal = {IEEE Access}, pages = {16548-16563}, shortjournal = {IEEE Access}, title = {Enhancing intelligent control strategies for UAVs: A comparative analysis of fuzzy logic, fuzzy PID, and GA-optimized fuzzy PID controllers}, volume = {13}, year = {2025}, }
• (2025). A prototype gate-drive circuit for high-voltage inverter adapting simultaneously inductive wireless transfer for both control signal and power. ACCESS, 13, 16539-16547. (www)

  @article{ , abstract = {When driving an IGBT in a high-voltage inverter, the gate drive circuit requires high insulation for both the control signal and the power supply circuit. Non-integrated photo couplers are often used to isolate control signal circuits, and custom-made transformers for DC-DC converters are used to isolate power supply circuits. Because the floating gate drive circuit is located on the high-voltage side, the system with non-integrated photo couplers and custom-made transformers has low reliability and complexity, therefore, there is a high demand for robust and simple isolation methods. In this paper, a highly reliable and simple isolation method that utilizes simultaneous inductive wireless transfer for both the control signal and power is proposed, and the gate drive circuit is moved to the low-voltage side for high-voltage inverters used in large industrial motors and DC power transmission converters. Using the proposed method, it is easy to access the gate drive circuit and the insulation configuration becomes robust and simple compared with conventional systems. The effectiveness of this method is verified using a 40-V prototype three-phase inverter. A three-phase motor is successfully driven using the proposed prototype.}, archive = {J_ACCESS}, author = {Ning Li and Haruto Yamamoto and Yuki Okada and Takeshi Shinkai}, doi = {10.1109/ACCESS.2025.3530455}, journal = {IEEE Access}, pages = {16539-16547}, shortjournal = {IEEE Access}, title = {A prototype gate-drive circuit for high-voltage inverter adapting simultaneously inductive wireless transfer for both control signal and power}, volume = {13}, year = {2025}, }
• (2025). Network digital twin toward networking, telecommunications, and traffic engineering: A survey. ACCESS, 13, 16489-16538. (www)

  @article{ , abstract = {Network Digital Twin (NDT) is an evolving technology that provides a framework through which a network administrator can have a virtual representation of a computer network. As a result, analysis, monitoring, testing, running new protocols, and more can be performed using the NDT before the final deployment of the developed approach. In this way, the consequences of direct deployment and the negative impact on network operations can be avoided. Telecommunications, along with traffic engineering as one of its critical components, play a prominent role across various networking domains, including Internet service providers, data centers, cellular networks, intelligent transportation systems, and smart cities. In this context, NDT has the potential to serve as a key enabler for optimizing these domains by providing a digital framework, which can facilitate the evaluation and enhancement of different scenarios. Accordingly, this paper presents a comprehensive survey on how NDT can facilitate advancements in network traffic engineering across a wide range of networking domains. First, we start with an in-depth analysis of the evolution of the network digital twin technology and provide a comparison with simulation tools. Next, we examine the role of NDT in various networking and telecommunication domains. We also explore the applicability of NDT technology from a traffic engineering perspective across different network types. Subsequently, we highlight key open research questions and potential future directions that warrant further investigation. Finally, we conclude by outlining the promising future trajectory of NDT within the aforementioned domains.}, archive = {J_ACCESS}, author = {Reza Poorzare and Dimitris N. Kanellopoulos and Varun Kumar Sharma and Poulami Dalapati and Oliver P. Waldhorst}, doi = {10.1109/ACCESS.2025.3531947}, journal = {IEEE Access}, pages = {16489-16538}, shortjournal = {IEEE Access}, title = {Network digital twin toward networking, telecommunications, and traffic engineering: A survey}, volume = {13}, year = {2025}, }
• (2025). A novel multi-fidelity support vector classification method for boundary prediction in engineering applications. ACCESS, 13, 16466-16488. (www)

  @article{ , abstract = {The accurate prediction of failure boundaries in engineering applications is essential for ensuring safety and reliability. Traditional methods often rely heavily on high-fidelity physical experiments or numerical simulations, which are prohibitively expensive and time-consuming. In response to this challenge, our research proposes an innovative multi-fidelity support vector classification approach that leverages an abundant supply of low-fidelity data alongside a limited amount of high-fidelity data. This combination significantly reduces modeling costs while maintaining or even enhancing predictive accuracy. The key points of the proposed method include the design of a reasonable kernel function to effectively describe the relationship between the input and output of multiple fidelities, and the determination of the optimal hyperparameters. In addition, in practical engineering problems, real data often exhibit data imbalance, leading to poor performance of the trained models. Our novel method addresses this limitation by integrating a strategy for managing the data imbalance. By effectively treating data imbalance, our approach significantly improves the classification and boundary prediction capabilities of the model. To validate our method, we applied it to three distinct engineering problems: predicting the failure boundary of a zero Poisson ratio structure, analyzing surge and choke boundaries in an axial flow compressor rotor, and tackling a 31-dimensional simulation failure boundary prediction problem within the computational fluid dynamics context of the same rotor. The results demonstrate that our multi-fidelity support vector classification method not only effectively predicts boundaries in these practical scenarios but also outperforms alternative methods, showing its potential as a powerful tool for engineers.}, archive = {J_ACCESS}, author = {Jinliang Luo and Lingzhi Liu and Youwei He and Kuan Tan}, doi = {10.1109/ACCESS.2025.3530417}, journal = {IEEE Access}, pages = {16466-16488}, shortjournal = {IEEE Access}, title = {A novel multi-fidelity support vector classification method for boundary prediction in engineering applications}, volume = {13}, year = {2025}, }
• (2025). Answer distillation network with bi-text-image attention for medical visual question answering. ACCESS, 13, 16455-16465. (www)

  @article{ , abstract = {Medical Visual Question Answering (Med-VQA) is a multimodal task that aims to obtain the correct answers based on medical images and questions. Med-VQA, as a classification task, is typically more challenging for algorithms to predict answers to open-ended questions than to closed-ended questions due to the larger number of answer categories for the former. Consequently, the accuracy of predictions for open-ended questions is generally lower than that for closed-ended questions. In this study, we design answer distillation network with bi-text-image attention (BTIA-AD Net) to solve the above problem. We present an answer distillation network to refine the answers and convert an open-ended question into a multiple-choice question with a selection of candidate answers. To fully utilize the candidate answer information from answer distillation network, we propose a bi-text-image attention fusion module composed of self-attention and guided attention to automatically fuse image features, question representations, and candidate answer information and achieve intra-modal and inter-modal semantic interaction. Extensive experiments validate the effectiveness of BTIA-AD Net. Results prove that our model can efficiently compress the answer space of open-ended tasks, improve the answer accuracy, and provide new state-of-the-art performance on the VQA-RAD dataset.}, archive = {J_ACCESS}, author = {Hongfang Gong and Li Li}, doi = {10.1109/ACCESS.2025.3532308}, journal = {IEEE Access}, pages = {16455-16465}, shortjournal = {IEEE Access}, title = {Answer distillation network with bi-text-image attention for medical visual question answering}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based fully automatic segmentation of the paranasal sinuses in chronic rhinosinusitis patients using computed tomographic images. ACCESS, 13, 16444-16454. (www)

  @article{ , abstract = {Chronic rhinosinusitis with nasal polyps (CRSwNP), as one of the most common chronic nasal inflammations, has been a major research focus in the field of rhinology due to its complex and varied pathophysiological features and suboptimal clinical treatment outcomes. However, existing diagnostic methods still face many challenges, especially shortcomings in accurate typing and the development of individualized treatment plans. Currently, sinus CT is an essential non-invasive tool for preoperative assessment of CRSwNP endotypes, and formulation of personalized and precise treatment strategies. It provides otolaryngologists with a valuable means to successfully diagnose and treat CRSwNP patients. This paper introduces a 3D semantic segmentation technology to achieve fully automatic 3D segmentation of sinus lesion regions, allowing physicians to observe the anatomical structures and lesions in the sinuses more clearly, thereby improving the diagnostic accuracy for CRSwNP. The study involved 242 Computed Tomography (CT) images of patients with CRSwNP, constructing a high-quality professional CRSwNP database for the training, validation, and testing of neural networks. We chose a custom 3D nnU-Net v2 network model because of its excellent performance in the field of 3D medical image segmentation, especially in automated training and accurate segmentation of complex structures. The model can accurately segment the sinus cavity by deeply learning the microstructure and deep features of 3D sinus CT images. Testing results demonstrated that the model accurately identified the segmentation areas, achieving a Dice Similarity Coefficient of 92.8%, Intersection over Union of 86.64%, accuracy of 99.69%, precision of 92.63%, and recall of 93.22%. This deep learning-based fully automatic CRSwNP sinus segmentation model exhibits excellent segmentation performance, aiding clinicians in further diagnosing CRSwNP endotypes and contributing to the advancement of clinical application deployment.}, archive = {J_ACCESS}, author = {Yuhang Wang and Xiaolei Zhang and Weidong Du and Na Dai and Yi Lyv and Keying Wu and Yiyang Tian and Yuxin Jie and Yu Lin and Weipiao Kang}, doi = {10.1109/ACCESS.2025.3531396}, journal = {IEEE Access}, pages = {16444-16454}, shortjournal = {IEEE Access}, title = {Deep learning-based fully automatic segmentation of the paranasal sinuses in chronic rhinosinusitis patients using computed tomographic images}, volume = {13}, year = {2025}, }
• (2025). Modern RFID reader antennas: A review of the design, state-of-the-art, and research challenges. ACCESS, 13, 16427-16443. (www)

  @article{ , abstract = {The internet of things (IoT) paradigm aims to make the world smarter by reducing human intervention and promoting automated interactions. A radio frequency identification (RFID) reader is a necessary component for automated object identification and data transfer in an IoT network. Its performance is primarily determined by the characteristics of the integrated or externally connected antennas. This article provides a review of the state-of-the-art reader antenna designs, as well as research challenges. The RFID reader antennas are investigated based on their operating frequency bands, gain, and polarization. Generalized procedures for designing specific antenna configurations, which are useful and widely used by readers, are illustrated and explained. Several design challenges have been investigated and analyzed, including narrow bandwidth, bidirectional radiation pattern, low gain, linear polarization, and a larger footprint. Subsequent solutions have also been provided. Cross-dipole configurations are one method for achieving a compact footprint and circular polarization characteristics without using complicated feeding circuitry. Low antenna gain has a direct impact on the read range of the reader. Therefore, increasing the gain of the reader antennas is an essential requirement. One of the gain-enhancing techniques is to use an artificial magnetic conductor (AMC) surface beneath the antenna. This reduces backward radiation of the antenna-AMC integrated configuration, increasing the antenna peak gain. The AMC also helps in the achievement of unidirectional radiation.}, archive = {J_ACCESS}, author = {Chandni Bajaj and Sachin Kumar and Dharmendra Kumar Upadhyay and Binod Kumar Kanaujia and Dhiraj Gupta and Tanweer Ali}, doi = {10.1109/ACCESS.2024.3524387}, journal = {IEEE Access}, pages = {16427-16443}, shortjournal = {IEEE Access}, title = {Modern RFID reader antennas: A review of the design, state-of-the-art, and research challenges}, volume = {13}, year = {2025}, }
• (2025). Intelligent models and architectures for global learning-oriented cooperation. ACCESS, 13, 16413-16426. (www)

  @article{ , abstract = {This paper presents the design of intelligent models and architectures for global learning-oriented cooperation. The research work brings our current investigations towards the definition of a general development methodology for global intelligent systems. We use a basic top-down approach to analyze global cooperation, adding more natural intelligence into our models. Then we design, integrate and adapt software architectures to be able to extract the computational requirements of our systems at an appropriate level of abstraction. Our architectural designs serve to dynamically generate cooperative knowledge and behaviour using hybrid registries and intelligent engines. The registries store knowledge and behaviour descriptions. The functional engines control knowledge delivery processes and behaviour executions involving single and composite actions. All these elements are evaluated using a designing case study in current Sustainable Development Goals for education. This last part of the paper also discusses how our intelligent models and architectures can be further developed adding implementation details based on recent technologies.}, archive = {J_ACCESS}, author = {Ana M. González de Miguel and Antonio Sarasa-Cabezuelo}, doi = {10.1109/ACCESS.2025.3532328}, journal = {IEEE Access}, pages = {16413-16426}, shortjournal = {IEEE Access}, title = {Intelligent models and architectures for global learning-oriented cooperation}, volume = {13}, year = {2025}, }
• (2025). Graph-based approach for industrial market segmentation using financial transaction network structure and firm’s attribute. ACCESS, 13, 16394-16412. (www)

  @article{ , abstract = {Market segmentation is a challenging and necessary task for improving marketing strategies and allocating resources effectively in a complex scenario of industrial or business-to-business (B2B) markets. Most of the traditional methods have not provided comprehensive and detailed guidelines for processing massive, complex, and complicated data from financial transactions in industrial markets. Furthermore, they often overlook the overlapping nature of segments, where a firm might belong to multiple market segments. Financial transaction networks (FTNs) can be depicted as graphs, where nodes and edges represent firms and transactions, respectively. A limited number of network analysis studies have been employed for industrial market analysis, but those efforts failed to capture the important features of financial transactions, such as the number of transactions, transaction volume, direction of transactions, and firm-specific attributes. In this study, we propose a novel method for industrial market segmentation that utilizes both financial multiple transactions and an attribute of firms. By integrating a graph-based generative probabilistic model, we reveal and understand the complex and overlapping relationships within financial transaction data. Subsequently, we apply the proposed method to a real-world dataset. The results show that the proposed method outperforms established methods in network analysis, such as clustering and community detection in transactional industrial market (B2B) networks.}, archive = {J_ACCESS}, author = {Aparajita Bose and Byunghoon Kim}, doi = {10.1109/ACCESS.2025.3529865}, journal = {IEEE Access}, pages = {16394-16412}, shortjournal = {IEEE Access}, title = {Graph-based approach for industrial market segmentation using financial transaction network structure and firm’s attribute}, volume = {13}, year = {2025}, }
• (2025). Frequency and texture aware multi-domain feature fusion for remote sensing scene classification. ACCESS, 13, 16380-16393. (www)

  @article{ , abstract = {Remote Sensing (RS) scene classification, a pivotal task in Earth observation, involves categorizing satellite or aerial imagery into distinct land-use and land-cover classes. Major challenges in this task include high intraclass variability and low interclass distinctions. Historically, state-of-the-art methods in this field have struggled to achieve satisfactory results without a significant trade-off in computational efficiency. These methods often require substantial computational resources to process the complex data characteristics of RS imagery, leading to inefficiencies that limit their practical application in real-time or on resource-constrained platforms. Delving into these complexities, the Efficient Spectral Inception Former (ESIF) architecture is proposed, which introduces a novel paradigm to RS scene classification by integrating multi-domain feature fusion of the spatial, texture, and spectral (frequency) domains. The proposed approach leverages the strengths of convolutional neural networks (CNNs) for spatial information extraction, a novel texture feature alignment block (TFAB) for nuanced texture differentiation, an efficient spectro-former block (ESFB) that uses spectral analysis for enhanced pattern recognition, a cross-domain fusion block (CDFB) and finally, an inception transformer block (iFB) that balances high and low-frequency information. Furthermore, we construct a new remote scene dataset named ISL-RS50, which is significantly more challenging than the existing ones. The proposed method yield the best results when trained from scratch, in all seven tested datasets:: ISL-RS50 (60%), Optimal-31 (86.55%), UC-Merced (94.52%), RSSCN7 (94.1%), SIRI-WHU (95%), WHU-RS19 (94.52%), AID (93.5%). Finally, ESIF exemplifies an optimal accuracy-efficiency trade-off, supporting its suitability for deployment in real-world applications.}, archive = {J_ACCESS}, author = {Russo Ashraf and Kang-Hyun Jo}, doi = {10.1109/ACCESS.2024.3522899}, journal = {IEEE Access}, pages = {16380-16393}, shortjournal = {IEEE Access}, title = {Frequency and texture aware multi-domain feature fusion for remote sensing scene classification}, volume = {13}, year = {2025}, }
• (2025). Robust and sparse kernel-free quadratic surface LSR via l2,p-norm with feature selection for multi-class image classification. ACCESS, 13, 16362-16379. (www)

  @article{ , abstract = {Least Squares Regression (LSR) is a powerful machine learning method for image classification and feature selection. In this study, a framework approach is introduced for the multi-classification problem based on the $L_{2,p}$ -norm, utilizing more general loss functions and regularization terms, which is a robust sparse kernel-free quadratic surface least squares regression (RSQSLSR). The nonlinear relationship between features is addressed using a quadratic kernel-free technique combined with $\epsilon $ -dragging technology and manifold regularization to learn soft labels, which can achieve the goal of feature selection and classification, simultaneously. This model utilizes K quadratic surfaces mapping samples from the input space to the label space, preserving the local structure of the samples. To enhance practical applications, such as image classification, a simplified version of the method is proposed. An iterative algorithm for RSQSLSR is designed and its convergence is proved theoretically. The salient features and theoretical analysis of our proposed method are comprehensively discussed in this paper. Extensive experiments on synthetic and real datasets validate the effectiveness of our method, surpassing other state-of-the-art methods in terms of classification accuracy and feature selection performance.}, archive = {J_ACCESS}, author = {Yongqi Zhu and Zhixia Yang and Junyou Ye and Yongxing Hu}, doi = {10.1109/ACCESS.2025.3532483}, journal = {IEEE Access}, pages = {16362-16379}, shortjournal = {IEEE Access}, title = {Robust and sparse kernel-free quadratic surface LSR via l2,p-norm with feature selection for multi-class image classification}, volume = {13}, year = {2025}, }
• (2025). Mitigation of diffraction effect in GNSS positioning considering azimuth and PDOP in canyon environment. ACCESS, 13, 16352-16361. (www)

  @article{ , abstract = {Global navigation satellite system (GNSS) monitoring stations established in canyon environments inevitably face challenges such as multipath effects, diffracted signals, and non-line-of-sight (NLOS) reception due to obstructions. To address this problem, existing methods employ a stochastic model based on GNSS receiver signal-to-noise ratio (SNR) to detect NLOS reception. However, the method only accounts for NLOS reception concerning elevation angles of satellites, neglecting NLOS reception from obstructions at side edges. To overcome this limitation, this paper proposes a threshold of azimuth angle. When the threshold is considered, it does not only improve NLOS detection, but also increases the position dilution of precision (PDOP) value, potentially affecting positioning precision. Thus, we introduced a PDOP stochastic model and proposed a method that integrates both the azimuth angle threshold and PDOP considerations. Experimental results show that the precision of the stochastic model based on equivalent elevation angles improves by approximately 24.25% after considering the threshold of azimuth angle. Furthermore, the precision increases by an additional 8.69% when the PDOP stochastic model is also taken into account.}, archive = {J_ACCESS}, author = {Qingfeng Wu and Xiaoya Wang and Houxiang Zhou}, doi = {10.1109/ACCESS.2025.3531395}, journal = {IEEE Access}, pages = {16352-16361}, shortjournal = {IEEE Access}, title = {Mitigation of diffraction effect in GNSS positioning considering azimuth and PDOP in canyon environment}, volume = {13}, year = {2025}, }
• (2025). Construction of prompt verbalizer based on dynamic search tree for text classification. ACCESS, 13, 16338-16351. (www)

  @article{ , abstract = {Prompt tuning has shown impressive performance in the domain of few-shot text classification tasks, yet the coverage of its crucial module, i.e., the verbalizer, has a considerable effect on the results. Existing methods have not addressed breadth and depth in constructing the verbalizer. Specifically, breadth refers to the cross-granularity issue of label words, while depth refers to the number of elements within a granularity that make a positive contribution to classification. This study proposes a dynamic search tree (DST) method to enhance the coverage of the verbalizer further. The core idea is to utilize the hierarchical relationships within the tree to automatically unearth concealed high-quality words, thereby ensuring that the constructed verbalizer possesses both higher breadth and depth. DST involves amalgamating knowledgeable prompt tuning (KPT) by leveraging the breadth of the KPT’s label word space, which encompasses characteristics at various granularities and from various perspectives, thereby addressing the problem of the verbalizer’s breadth. Subsequently, a method that is capable of measuring the interrelation between words on a designated feature is proposed by analyzing the word vector, which successfully eradicates the noise introduced by irrelevant dimensions during the process of extending the verbalizer and effectively enhances the quality of the verbalizer in terms of depth. Extensive experiments were conducted on zero- and few-shot text classification tasks to demonstrate the effectiveness of our method. Our source code is publicly available at https://github.com/XianliangXia/VerbalizerConstrucionByDynamicSearchTree.}, archive = {J_ACCESS}, author = {Jinfeng Gao and Xianliang Xia and Ruxian Yao and Junming Zhang and Yu Zhang}, doi = {10.1109/ACCESS.2025.3532458}, journal = {IEEE Access}, pages = {16338-16351}, shortjournal = {IEEE Access}, title = {Construction of prompt verbalizer based on dynamic search tree for text classification}, volume = {13}, year = {2025}, }
• (2025). Multimodal autism spectrum disorder method using GCN with dual transformers. ACCESS, 13, 16324-16337. (www)

  @article{ , abstract = {Autism spectrum disorder (ASD) presents diagnostic challenges due to its heterogeneous nature, necessitating advanced methods for accurate identification. This study proposes a novel diagnostic approach that integrates Graph convolutional networks (GCN) with dual transformer architectures, optimized through a Co-training strategy. The first Transformer is dedicated to extracting intricate temporal features from fMRI data, which are crucial for understanding brain activity over time. The second Transformer is employed to enhance the fusion of these temporal features with spatial features learned by the GCN, effectively combining both dimensions of the neuroimaging data. Co-training is introduced to simultaneously harness both functional magnetic resonance imaging and structural magnetic resonance imaging data, improving the model’s capacity to generalize across different datasets. This comprehensive method was rigorously evaluated using the ABIDE-I and ABIDE-II datasets, with performance assessed through nested ten-fold cross-validation and leave-one-out cross-validation. The experimental results reveal that our approach significantly outperforms existing baseline and state-of-the-art models. The method achieves 79.47% of accuracy, 78.97% precision, 82.11% recall, and 0.85 of AUC metrics. These findings highlight the robustness of combining dual transformer models with GCNs and Co-training, providing a powerful framework for ASD classification.}, archive = {J_ACCESS}, author = {Tianming Song and Zhe Ren and Jian Zhang and Yawei Qu and Yingying Cui and Zhengda Liang}, doi = {10.1109/ACCESS.2025.3532302}, journal = {IEEE Access}, pages = {16324-16337}, shortjournal = {IEEE Access}, title = {Multimodal autism spectrum disorder method using GCN with dual transformers}, volume = {13}, year = {2025}, }
• (2025). Adversarial attack detection in smart grids using deep learning architectures. ACCESS, 13, 16314-16323. (www)

  @article{ , abstract = {Smart grids themselves have emerged as vital structures of the up-to-date practical power systems or electricity networks that incorporate high technologies and information handling. Yet, they are more susceptible to an adversarial attack that can interfere with the critical functions like energy distribution and faults detection. This paper therefore proposes a new alternative to developing a DL and ML framework for identifying adversarial attacks on smart grids. After analyses of the performances of Logistic Regression, Perceptron, Gaussian Naive Bayes and Multi-Layer Perceptron, LSTM network has better results with an accuracy of 99.81%. The suggested framework strengthens smart grid immunity to cyber threats such as DoS attacks, back door injections, and adversarial perturbations while increasing energy distribution stability and security. For enhancing smart grid security, our results emphasize the importance of integration of ML and DL techniques and provide such an understanding of threat environment for future research and development on threat identification.}, archive = {J_ACCESS}, author = {Stephanie Ness}, doi = {10.1109/ACCESS.2024.3523409}, journal = {IEEE Access}, pages = {16314-16323}, shortjournal = {IEEE Access}, title = {Adversarial attack detection in smart grids using deep learning architectures}, volume = {13}, year = {2025}, }
• (2025). A pre-activation residual convolutional network with attention modules for high-resolution segmented EEG emotion recognition. ACCESS, 13, 16303-16313. (www)

  @article{ , abstract = {Emotion recognition based on electroencephalography (EEG) signals has attracted considerable research interest over the past few years and several potential applications have been proposed such as enhancing human-computer interaction, improving mental health diagnosis, and fine-tuning the customer experience at the marketing level. This paper introduces a novel model, called Pre-Activation Residual Convolutional Network with Attention Modules (PRCN-AM), designed to enhance the accuracy and robustness of emotion recognition based on EEG signals. PRCN-AM combines residual convolutional layers with pre-activation and attention modules to effectively capture and analyze the complex spatial-temporal patterns inherent in EEG signals. Two experimental procedures, namely, subject-dependent and subject-independent, were conducted and different time segmentations on the preprocessing stage were tested. The suggested exploitation of the temporal dynamics of the EEG signals in emotion recognition turns out to be useful, as classification accuracies of up to 99.51% and 97.51% on SEED and SEED-IV datasets have been achieved, respectively, thus, outperforming the current state-of-the-art models.}, archive = {J_ACCESS}, author = {Ioannis Charalampous and Christos Mavrokefalidis and Kostas Berberidis}, doi = {10.1109/ACCESS.2025.3530567}, journal = {IEEE Access}, pages = {16303-16313}, shortjournal = {IEEE Access}, title = {A pre-activation residual convolutional network with attention modules for high-resolution segmented EEG emotion recognition}, volume = {13}, year = {2025}, }
• (2025). An open-source multi-robot framework system for collaborative environments based on ROS2. ACCESS, 13, 16288-16302. (www)

  @article{ , abstract = {Despite the rise of robotics and automation in industrial applications, the widespread adoption of collaborative robotics still needs to be improved due to the lack of interoperability between robots and the low adaptability of existing systems. Solving this problem would mean a significant advance in robotics and industrial automation. Under this context, an open-source MultiRobot Framework based on ROS2 was developed in the present research to effectively communicate and coordinate robotics agents and sensors in closed collaborative environments. A simulation-based control and software design was performed using the GAZEBO tool. A centralized architecture was obtained with an autonomous navigation module for the planning and robot routes monitoring, a computer vision module for the location and management of uncertainties, and a task controller module to assign mobilization mission objects. In conclusion, using ROS2 to communicate and coordinate various mechatronic systems effectively results in a robust, flexible, and scalable solution critical to industrial processes.}, archive = {J_ACCESS}, author = {Francisco Yumbla and Marcelo Fajardo-Pruna and Anthonny Piguave and Diego Ronquillo and Ricardo Ortiz and Jongseong Brad Choi and Gabriel Díaz and Xabiel G. Pañeda and Hyungpil Moon}, doi = {10.1109/ACCESS.2025.3530391}, journal = {IEEE Access}, pages = {16288-16302}, shortjournal = {IEEE Access}, title = {An open-source multi-robot framework system for collaborative environments based on ROS2}, volume = {13}, year = {2025}, }
• (2025). Performance of quantum annealing machine learning classification models on ADMET datasets. ACCESS, 13, 16263-16287. (www)

  @article{ , abstract = {The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interest, particularly in quantum machine learning. However, the application of quantum annealing in machine learning remains limited due to the lack of conclusive performance evaluations on real-world datasets. There is still no clear consensus on the efficacy of these models in practical scenarios. This work focuses on experimentally evaluating quantum annealing machine learning (QAML) classification methods, specifically Quantum Support Vector Machines (QSVM) and QBoost, on ADMET datasets—one of the most important datasets in the drug discovery domain. We compare QAML with classical machine learning to evaluate their relative performance. This study seeks to address this gap by rigorously comparing the performance of QBoost and QSVM models using ADMET datasets, employing D-Wave’s Quantum Annealer. This work provides a comprehensive analysis of the potential and limitations of quantum annealers in quantum machine learning, with a focus on their application to real-world data in the ADMET domain. The findings offer critical insights into the nuanced advantages and challenges of quantum annealers in advancing machine learning methodologies.}, archive = {J_ACCESS}, author = {Hadi Salloum and Kamil Sabbagh and Vladislav Savchuk and Ruslan Lukin and Osama Orabi and Marat Isangulov and Manuel Mazzara}, doi = {10.1109/ACCESS.2025.3531391}, journal = {IEEE Access}, pages = {16263-16287}, shortjournal = {IEEE Access}, title = {Performance of quantum annealing machine learning classification models on ADMET datasets}, volume = {13}, year = {2025}, }
• (2025). VoxelSky-3D: A weather radar visualization prototype for air traffic control. ACCESS, 13, 16252-16262. (www)

  @article{ , abstract = {This paper presents VoxelSky-3D, a new 3D weather radar visualization prototype for civil aviation air traffic control. While previous research has explored text-based, image-based, and some three-dimensional meteorological displays, none have fully integrated voxelization techniques to deliver real-time, comprehensive 3D weather visualizations specifically tailored for Air Traffic Control Officers (ATCOs). The lack of traditional 2D weather radar displays vertical atmospheric structures for ATCOs, which are critically important for safety and efficiency during flight. VoxelSky-3D is an auxiliary system that uses voxelization to transform weather radar data into 3D voxel-based representations. The methods used in this study include the conversion of coordinates, voxelizing of the geographic coordinate system, and integration with Geographic Information Systems for rendering via the Web Graphics Library. The experimental results verified that VoxelSky-3D is an effective application for visualizing weather radar data in 3D, which directly provides ATCOs with intuitive views of the vertical atmospheric conditions. The performance test results revealed that the system could build real-time complex 3D weather scenes and provide high frame rates under relatively high volumes of incoming data. The VoxelSky-3D prototype is a considerable step forward in the field of meteorological data visualization for air traffic control, which improves the situational awareness and decision-making capabilities of ATCOs.}, archive = {J_ACCESS}, author = {Zhenhao Zhan and Tek-Yong Lim and Kannan Ramakrishnan}, doi = {10.1109/ACCESS.2025.3531711}, journal = {IEEE Access}, pages = {16252-16262}, shortjournal = {IEEE Access}, title = {VoxelSky-3D: A weather radar visualization prototype for air traffic control}, volume = {13}, year = {2025}, }
• (2025). Looking for peer circles: Graph-mining-based educational assessment and refinement toward physical instructors. ACCESS, 13, 16238-16251. (www)

  @article{ , abstract = {Exploring the identification of peer circles (or communities) with shared attributes in educational settings provides valuable insights for categorizing instructors’ teaching styles and methodologies. This paper introduces a framework for organizing a large number of physical education instructors into distinct “circles” or clusters based on similar instructional traits and practices. It addresses two main challenges: 1) creating a flexible framework that adapts to diverse instructional characteristics across various educational environments, and 2) handling data insufficiency, as some instructors have limited documented teaching activities. To overcome these challenges, we propose a geometry-based feature selection technique to identify high-quality features that best represent each instructor’s teaching style. An advanced probabilistic model is then applied to represent each instructor’s attributes as a distribution in the latent space. This approach enables the creation of a graph capturing instructional similarities among instructors. By applying an optimized algorithm to detect densely connected subgraphs, we group instructors into circles with shared instructional features. Using these peer circles, we develop a scoring system to evaluate teaching performance. Our experiments, conducted on a dataset of numerous physical education instructors, demonstrate the effectiveness of this approach in identifying distinct teaching styles. Additionally, the framework enhances the assessment and improvement of instructional methods.}, archive = {J_ACCESS}, author = {Yuhe Zhu}, doi = {10.1109/ACCESS.2025.3532746}, journal = {IEEE Access}, pages = {16238-16251}, shortjournal = {IEEE Access}, title = {Looking for peer circles: Graph-mining-based educational assessment and refinement toward physical instructors}, volume = {13}, year = {2025}, }
• (2025). AlgaeClass_Net: Optimizing few-shot marine microalgae classification with multi-scale feature enhancement network. ACCESS, 13, 16223-16237. (www)

  @article{ , abstract = {As the eutrophication of the water body becomes more and more serious, the algae in the water body grow in large quantities and eventually form harmful algal blooms, causing great harm to the marine ecosystem. Therefore, how to quickly and accurately identify algae and make precautions becomes the key to solving this problem. Currently, more than tens of thousands of microalgae species are known around the world, but publicly available data are sparse, many of the species are characterized similarly to each other, and it is currently challenging to train an effective classification model with limited data. Existing few-shot learning classification algorithms that utilize meta-learning for metrics can be a good solution to this problem. In this paper, an AlgaeClass_Net algorithm that combines an improved multi-scale feature fusion with a feature enhancement module is proposed for the fine-grained features of microalgae. Furthermore, it utilizes a metric learning approach to classify few-shot microalgae by calculating the distances between the feature vectors of samples in the query set and the feature vectors of samples in the support set. The experimental results showed that the method achieves 78.55% and 91.20% classification accuracies under different tasks of 5-way 1-shot and 5-way 5-shot, respectively, with an improvement of 3.51% and 4.97% on the suboptimal model, respectively. It provides new research ideas for the identification of marine microalgae and the development and utilization of marine renewable energy.}, archive = {J_ACCESS}, author = {Dan Liu and Guihong Yuan and Huachao Tan and Yanbo Jiang and Hai Bi and Yuan Cheng}, doi = {10.1109/ACCESS.2024.3436838}, journal = {IEEE Access}, pages = {16223-16237}, shortjournal = {IEEE Access}, title = {AlgaeClass_Net: Optimizing few-shot marine microalgae classification with multi-scale feature enhancement network}, volume = {13}, year = {2025}, }
• (2025). Deep joint demosaicking and super-resolution for spectral filter array images. ACCESS, 13, 16208-16222. (www)

  @article{ , abstract = {In spectral imaging, the constraints imposed by hardware often lead to a limited spatial resolution within spectral filter array images. On the other hand, the process of demosaicking is challenging due to intricate filter patterns and a strong spectral cross correlation. Moreover, demosaicking and super resolution are usually approached independently, overlooking the potential advantages of a joint solution. To this end, we use a two-branch framework, namely a pseudo-panchromatic image network and a pre-demosaicking sub-branch coupled with a novel deep residual demosaicking and super resolution module. This holistic approach ensures a more coherent and optimized restoration process, mitigating the risk of error accumulation and preserving image quality throughout the reconstruction pipeline. Our experimental results underscore the efficacy of the proposed network, showcasing an improvement of performance both qualitatively and quantitatively when compared to the sequential combination of state-of-the-art demosaicking and super resolution. With our proposed method, we obtained on the ARAD-1K dataset an average PSNR of 48.02 (dB) for domosaicking only, equivalent to the best method of the state-of-the-art. Moreover, for joint demosaicking and super resolution our model averages 35.26 (dB) and 26.29 (dB), respectively for $\times 2$ and $\times 4$ upscale, outperforming state-of-the-art sequential approach.The codes and datasets are available at https://github.com/HamidFsian/DRDmSR.}, archive = {J_ACCESS}, author = {Abdelhamid N. Fsian and Jean-Baptiste Thomas and Jon Y. Hardeberg and Pierre Gouton}, doi = {10.1109/ACCESS.2025.3528753}, journal = {IEEE Access}, pages = {16208-16222}, shortjournal = {IEEE Access}, title = {Deep joint demosaicking and super-resolution for spectral filter array images}, volume = {13}, year = {2025}, }
• (2025). A mobile image-driven PM2.5 estimation framework using deep learning techniques. ACCESS, 13, 16196-16207. (www)

  @article{ , abstract = {Particulate pollution (PM2.5) is an important concern in Asian countries owing to its health hazards. When planning outdoor activities, understanding the PM2.5 concentration measurement is essential. Because of the lower number of government-run Air Quality Monitoring Stations, other options for obtaining location-specific PM2.5 concentration values are sought. This paper proposes using photo image processing to estimate the PM2.5 concentration. This research aims to improve the efficacy and reduce the computational complexity of the PM2.5 concentration estimation process. The proposed Efficient PM2.5 estimation framework uses EfficientNet-B1 and BiLSTM to estimate PM2.5 concentrations. Met-EfficientNet-B1-BiLSTM was designed and implemented to incorporate the meteorological features - temperature, wind speed, and humidity to further improve the estimation accuracy. The EfficientNet-B1 neural network is applied in the image feature vector extraction process. EfficientNet-B1, with a resolution of $240\times 240$ pixels, was determined to be the optimal variant of EfficientNet for a small dataset of images needed for the estimation of the PM2.5 concentration value. The BiLSTM was used for the regression of these image features with PM2.5 concentration values to obtain the estimated PM2.5 concentration. A dataset comprising HDR and non-HDR images was explicitly created for this study to compare the types of images that improve the accuracy of PM2.5 concentration estimation and the feature extraction process. The proposed Efficient PM2.5 estimation framework reduces computational complexity and outperforms ResNet-18-LSTM by improving the efficacy by 5.75% in MAE and 11.43% in SMAPE metrics. The proposed Efficient PM2.5 estimation framework demonstrates that the mobile image can be efficiently used for PM2.5 concentration estimation.}, archive = {J_ACCESS}, author = {Anupam Kamble and Somrawee Aramkul and Paskorn Champrasert}, doi = {10.1109/ACCESS.2024.3521966}, journal = {IEEE Access}, pages = {16196-16207}, shortjournal = {IEEE Access}, title = {A mobile image-driven PM2.5 estimation framework using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). MSTD: A multi-scale transformer-based method to diagnose benign and malignant lung nodules. ACCESS, 13, 16182-16195. (www)

  @article{ , abstract = {The identification of benign and malignant lung nodules is crucial for timely treatment to reduce the risk of the progression and metastasis of diseases. However, the varied sizes, diverse morphologies, non-fixed positions, and dynamic growth of lung nodules in computed tomography (CT) images make their accurate identification challenging. To address these issues, we propose a multi-scale transformer-based diagnosis (MSTD) method for benign and malignant lung nodules. To handle significant variations in the shapes and sizes of the lung nodules, we first design a multi-scale module based on parallel branches to extract multi-scale features. To make full use of these features, we then introduce a multi-scale transformer fusion (MSTF) module to integrate the information obtained at different scales. Unlike conventional vision transformers, our MSTF can simultaneously extract attention-based features from the spatial dimensions at different scales to enhance the accuracy of classification of lung nodules. We conducted extensive ablation experiments on multi-scale structures and transformer-based methods of fusion to explore the impact of features obtained at different scales on the accuracy of classification of lung nodules. The results of verification on the LUNA16 dataset showed that the average F1Score, Specificity, and Sensitivity of the proposed MSTD exceeded 90% (94.5%, 96.5%, and 91.1%, respectively), where this shows that it can accurately identify both benign and malignant lung nodules. Its average performance was superior to the state-of-the-art method by about 1%, 3.4%, and 3.6% in terms of the area under the curve (AUC), Accuracy, and F1Score, respectively.}, archive = {J_ACCESS}, author = {Xiaoyu Zhao and Jiao Li and Man Qi and Xuxin Chen and Wei Chen and Yongqun Li and Qi Liu and Jiajia Tang and Zhihai Han and Chunyang Zhang}, doi = {10.1109/ACCESS.2025.3531001}, journal = {IEEE Access}, pages = {16182-16195}, shortjournal = {IEEE Access}, title = {MSTD: A multi-scale transformer-based method to diagnose benign and malignant lung nodules}, volume = {13}, year = {2025}, }
• (2025). Evaluating the relationship between relative permittivity and infill density in 3D printed dielectric slabs. ACCESS, 13, 16171-16181. (www)

  @article{ , abstract = {This paper investigates the relationship between relative permittivity and infill density in 3D printed dielectric slabs. As the infill density of a 3D printed substrate is varied, its relative permittivity also varies which affects the performance of radio frequency devices. Hence, it is essential to characterize the relationship between relative permittivity and infill density in 3D printed substrates. To explore this, dielectric slabs with varying sizes and infill densities were additively manufactured using Fused Deposition Modeling and measured for permittivity using reflection/transmission methods in air-filled waveguides. The measured relative permittivity at specific infill density is coined as effective relative permittivity throughout the paper. The relationship between effective relative permittivity and infill density, found to be linear in our preceding simulation study was validated for higher infill densities through empirical measurements. Additionally, the paper presents a novel standardized method using the Weiner bounds to quantify this relationship. The findings indicate that the relationship is linear for the infill layers, however, the inclusion of solid layers impacts the linear relationship. Therefore, an equation is introduced to estimate the permittivity of a 3D printed substrate when solid layers are incorporated. Finally, the practical applicability of this relationship is demonstrated by fabricating a 3D printed patch antenna with a 50% infill density substrate, establishing infill density as a crucial parameter in radio frequency design when using additive manufacturing methods.}, archive = {J_ACCESS}, author = {Bibek Kattel and Winn Elliott Hutchcraft and Richard K. Gordon}, doi = {10.1109/ACCESS.2025.3528877}, journal = {IEEE Access}, pages = {16171-16181}, shortjournal = {IEEE Access}, title = {Evaluating the relationship between relative permittivity and infill density in 3D printed dielectric slabs}, volume = {13}, year = {2025}, }
• (2025). A review on frequency compensation and transient enhancement schemes of flipped voltage follower LDO regulators for SoC applications. ACCESS, 13, 16150-16170. (www)

  @article{ , abstract = {The low dropout (LDO) regulator is an essential circuit in the wireless power module of any portable device. The power management integrated circuits demand low power, high performance LDO regulator. Designing the LDO regulators for the desired performance parameters is challenging since it has design trade-offs. The power reduction is one main design challenge in the LDO to make it suitable for low-power, high-performance applications. The output capacitor-less LDOs are attractive for system-on-chip (SoC) applications, however the removal of the external capacitor degrades its stability and transient responses. The flipped voltage follower (FVF) output capacitor-less LDO regulators gained attention due to its superior transient performance with less power, as this structure demands lesser quiescent current to achieve the same transient response compared to the conventional LDOs. To our knowledge, the FVF LDOs in terms of transient enhancement schemes and stability techniques are not yet reviewed to understand the challenges in these LDO designs to suit for SoC applications. This paper provides a comprehensive study of different types of FVF low dropout regulators, the compensation circuits used in these LDOs for the stability of feedback loop and different transient enhancement schemes to improve the settling time and to reduce the output voltage spikes. A capacitor-less FVF LDO regulator with improved settling time and PSR is proposed in this paper. The transient performance of the FVF LDOs are measured based on the figure of merits and stability in terms of the phase margin. The proposed FVF LDO achieves a settling time of $100 \ ns$ and PSR of $-31\ dB\text{@}1\ MHz$ . The design challenges of FVF LDOs to achieve the required specifications to use for wireless power module of battery-operated devices are also discussed.}, archive = {J_ACCESS}, author = {P. Manikandan and B. Bindu}, doi = {10.1109/ACCESS.2025.3532761}, journal = {IEEE Access}, pages = {16150-16170}, shortjournal = {IEEE Access}, title = {A review on frequency compensation and transient enhancement schemes of flipped voltage follower LDO regulators for SoC applications}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection in network traffic using advanced machine learning techniques. ACCESS, 13, 16133-16149. (www)

  @article{ , abstract = {Anomaly detection in network traffic is a critical aspect of network security, particularly in defending against the increasing sophistication of cyber threats. This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. The models assessed include Isolation Forest, Naive Bayes, XGBoost, LightGBM, and SVM classification. Through comprehensive evaluation, this research explores both supervised and unsupervised approaches, comparing their performance across key metrics like accuracy, F1-score, and recall. The results reveal that while models like XGBoost and LightGBM exhibit impressive performance, with LightGBM achieving near-perfect training accuracy (1.0) and solid test accuracy (0.85), others like Isolation Forest show limitations with low accuracy. The study highlights the strengths and weaknesses of each model, providing valuable insights into their practical application for network anomaly detection. By comparing different algorithms, this research contributes to advancing the application of machine learning in network security, offering guidance on model selection and optimization for improved detection of cyber threats.}, archive = {J_ACCESS}, author = {Stephanie Ness and Vishwanath Eswarakrishnan and Harish Sridharan and Varun Shinde and Naga Venkata Prasad Janapareddy and Vineet Dhanawat}, doi = {10.1109/ACCESS.2025.3526988}, journal = {IEEE Access}, pages = {16133-16149}, shortjournal = {IEEE Access}, title = {Anomaly detection in network traffic using advanced machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). On the adversarial robustness of decision trees and a symmetry defense. ACCESS, 13, 16120-16132. (www)

  @article{ , abstract = {Gradient-boosting decision tree classifiers (GBDTs) are susceptible to adversarial perturbation attacks that change inputs slightly to cause misclassification. GBDTs are customarily used on non-image datasets that lack inherent symmetries, which might be why data symmetry in the context of GBDT classifiers has not received much attention. In this paper, we show that GBDTs can classify symmetric samples differently, which means that GBDTs lack invariance with respect to symmetry. Based on this, we defend GBDTs against adversarial perturbation attacks using symmetric adversarial samples in order to obtain correct classification. We apply and evaluate the symmetry defense against six adversarial perturbation attacks on the GBDT classifiers of nine datasets with a threat model that ranges from zero-knowledge to perfect-knowledge adversaries. Against zero-knowledge adversaries, we use the feature inversion symmetry and exceed the accuracies of default and robust classifiers by up to 100% points. Against perfect-knowledge adversaries for the GBDT classifier of the F-MNIST dataset, we use the feature inversion and horizontal flip symmetries and exceed the accuracies of default and robust classifiers by up to 96% points. Finally, we show that the current definition of adversarial robustness based on the minimum perturbation values of misclassifying adversarial samples might be inadequate for two reasons. First, this definition assumes that attacks mostly succeed, failing to consider the case when attacks are unable to construct misclassifying adversarial samples against a classifier. Second, GBDT adversarial robustness as currently defined can decrease by training with additional samples, even training samples, which counters the common wisdom that more training samples should increase robustness. With the current definition of GBDT adversarial robustness, we can make GBDTs more adversarially robust by training them with fewer samples! The code is publicly available at https://github.com/blertal/xgboost-symmetry-defense.}, archive = {J_ACCESS}, author = {Blerta Lindqvist}, doi = {10.1109/ACCESS.2025.3530695}, journal = {IEEE Access}, pages = {16120-16132}, shortjournal = {IEEE Access}, title = {On the adversarial robustness of decision trees and a symmetry defense}, volume = {13}, year = {2025}, }
• (2025). QUERY2BERT: Combining knowledge graph and language model for reasoning on logical queries. ACCESS, 13, 16103-16119. (www)

  @article{ , abstract = {Answering logical questions with a knowledge graph has been a critical research focus because this needs to reason and synthesize information. Previous studies have mainly dealt with logical operations using graph embedding techniques, such as conjunctions, disjunctions, and negation. However, these studies have neither effectively organized the data to retrieve multi-hop reasoning quickly nor combined text description to enhance logical operations’ semantics. Our study introduces a model called QUERY2BERT, which solves two of the above limitations. Specifically, QUERY2BERT first combined the node2vec and the BERT models to embed a knowledge graph with description information of every entity. Then, embedded nodes were indexed with a K-D tree structure. Finally, we used nearest neighbor search on K-D tree to retrieve neighbor-embedded nodes and implemented logical operations like projection, intersection, union, and negation to find answers to complex questions. We tested our model on three benchmark knowledge graph datasets and showed that QUERY2BERT significantly improved accuracy and speed compared to other state-of-the-art models.}, archive = {J_ACCESS}, author = {Truong H. V. Phan and Phuc do}, doi = {10.1109/ACCESS.2025.3528097}, journal = {IEEE Access}, pages = {16103-16119}, shortjournal = {IEEE Access}, title = {QUERY2BERT: Combining knowledge graph and language model for reasoning on logical queries}, volume = {13}, year = {2025}, }
• (2025). Diagnosing grass seed infestation: Convolutional neural network-based terahertz imaging. ACCESS, 13, 16094-16102. (www)

  @article{ , abstract = {Grass seed infestation is a significant issue in the Australian sheep industry. Detecting the seeds when they are in wool or on the surface of the skin could assist with prevention of the grass seed infestation. Terahertz imaging provides a viable option for detecting seeds due to its short wavelength, non-ionizing feature, and penetration ability through wool. Here we demonstrate that accuracy of seeds detection can be improved utilising a Convolutional Neural Network even when the seeds are not visually distinguishable in terahertz images. Our studies reveal accuracies of greater than 95% and 67% can be achieved in identification of seed hidden underneath 1 cm and 2 cm thick wool under normal incidence. Moreover, our analysis finds that terahertz frequencies in the 0.3–0.4 THz range have better overall classification accuracy compared to other frequency bands. The combination of machine learning and terahertz imaging has the potential to be widely implemented in rapid and on-site detection of grass seed infestation with high efficiency.}, archive = {J_ACCESS}, author = {Qigejian Wang and Amus Chee Yuen Goay and Deepak Mishra and Ewa M. Goldys and Shaghik Atakaramians}, doi = {10.1109/ACCESS.2025.3530900}, journal = {IEEE Access}, pages = {16094-16102}, shortjournal = {IEEE Access}, title = {Diagnosing grass seed infestation: Convolutional neural network-based terahertz imaging}, volume = {13}, year = {2025}, }
• (2025). Classifying IoT botnet attacks with kolmogorov-arnold networks: A comparative analysis of architectural variations. ACCESS, 13, 16072-16093. (www)

  @article{ , abstract = {The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). The evaluation was performed on three IoT botnet datasets: N-BaIoT, IoT23, and IoT-BotNet, using metrics such as accuracy, precision, recall, F1-score, training time, and model complexity. KAN variants consistently demonstrated robust performance, often exceeding traditional ML and DL models in accuracy and stability across all datasets. The Original-KAN variant, in particular, excelled in capturing complex, non-linear patterns inherent in IoT botnet traffic, achieving higher accuracy and faster convergence rates. Variations such as Fast-KAN and Deep-KAN offered favorable trade-offs between computational efficiency and modeling capacity, making them suitable for real-time and resource-constrained IoT environments. Kolmogorov-Arnold Networks prove to be highly effective for IoT botnet classification, outperforming conventional models and offering significant advantages in adaptability and accuracy. The integration of KAN-based models into existing cybersecurity frameworks can enhance the detection and mitigation of sophisticated botnet threats, thus contributing to more resilient and secure IoT ecosystems.}, archive = {J_ACCESS}, author = {Phuc Hao do and Tran Duc Le and Truong Duy Dinh and van Dai Pham}, doi = {10.1109/ACCESS.2025.3528940}, journal = {IEEE Access}, pages = {16072-16093}, shortjournal = {IEEE Access}, title = {Classifying IoT botnet attacks with kolmogorov-arnold networks: A comparative analysis of architectural variations}, volume = {13}, year = {2025}, }
• (2025). C-IVAL: A longitudinal study of knowledge retention and technology acceptance in collaborative virtual reality-based medical education. ACCESS, 13, 16055-16071. (www)

  @article{ , abstract = {Anatomy education faces challenges in providing engaging, interactive, and collaborative learning experiences, particularly in understanding complex three-dimensional structures and maintaining long-term knowledge retention. Although virtual reality (VR) has shown promise in addressing spatial comprehension challenges, questions remain regarding its effectiveness in supporting collaborative learning and sustained knowledge retention. This longitudinal study examined Collaborative Immersive Virtual Anatomy Laboratory (C-IVAL), an innovative VR platform designed to enhance traditional anatomy learning through integrated collaborative features, immersive technology, and serious game elements. We conducted an experimental study with 65 medical students to evaluate their knowledge acquisition and technology acceptance compared to its non-collaborative predecessor, the Immersive Virtual Anatomy Laboratory (IVAL). Our evaluation framework combined quantitative assessments (knowledge tests, comprising pre-test, immediate post-test, and delayed post-test) with Technology Acceptance Model (TAM) analysis. Knowledge assessment revealed significant cognitive improvements, with mean knowledge scores increasing from 2.48 to 3.94 in the immediate post-tests, while long-term retention of anatomy knowledge showed sustained engagement for over two months. Importantly, C-IVAL demonstrated significantly higher scores across all TAM dimensions than the non-collaborative IVAL system, particularly for perceived usefulness and intention to use. Post-session feedback analysis showed 73.8% positive responses, highlighting enhanced social presence, immersive engagement, and effective collaboration, with 26.2% of constructive feedback focusing on system refinement and feature enhancement. This study contributes to the understanding of the effectiveness of collaborative features in virtual reality education by offering insights into designing and implementing virtual learning environments that enhance both knowledge retention and user acceptance.}, archive = {J_ACCESS}, author = {Mohammed Kadri and Fatima-Ezzahra Boubakri and Gwo-Jen Hwang and Fatima Zahra Kaghat and Ahmed Azough and Khalid Alaoui Zidani}, doi = {10.1109/ACCESS.2024.3523860}, journal = {IEEE Access}, pages = {16055-16071}, shortjournal = {IEEE Access}, title = {C-IVAL: A longitudinal study of knowledge retention and technology acceptance in collaborative virtual reality-based medical education}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in science and society: The vision of USERN. ACCESS, 13, 15993-16054. (www)

  @article{ , abstract = {The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI’s state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.}, archive = {J_ACCESS}, author = {Tommaso Dorigo and Gary D. Brown and Carlo Casonato and Artemi Cerdà and Joseph Ciarrochi and Mauro da Lio and Nicole D’Souza and Nicolas R. Gauger and Steven C. Hayes and Stefan G. Hofmann and Robert Johansson and Marcus Liwicki and Fabien Lotte and Juan J. Nieto and Giulia Olivato and Peter Parnes and George Perry and Alice Plebe and Idupulapati M. Rao and Nima Rezaei and Fredrik Sandin and Andrey Ustyuzhanin and Giorgio Vallortigara and Pietro Vischia and Niloufar Yazdanpanah}, doi = {10.1109/ACCESS.2025.3529357}, journal = {IEEE Access}, pages = {15993-16054}, shortjournal = {IEEE Access}, title = {Artificial intelligence in science and society: The vision of USERN}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Sandpiper optimization algorithm with region growing based robust retinal blood vessel segmentation approach”. ACCESS, 13, 15992. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Sandpiper Optimization Algorithm With Region Growing Based Robust Retinal Blood Vessel Segmentation Approach”).}, archive = {J_ACCESS}, author = {Ibrahim Almohimeed and Mohamed Yacin Sikkandar and A. Mohanarathinam and Velmurugan Subbiah Parvathy and Mohamad Khairi Ishak and Faten Khalid Karim and Samih M. Mostafa}, doi = {10.1109/ACCESS.2024.3524860}, journal = {IEEE Access}, pages = {15992}, shortjournal = {IEEE Access}, title = {Corrections to “Sandpiper optimization algorithm with region growing based robust retinal blood vessel segmentation approach”}, volume = {13}, year = {2025}, }
• (2025). A gray-box equivalent neural network circuit small-signal modeling applied to GaN transistors. ACCESS, 13, 15981-15991. (www)

  @article{ , abstract = {Efficient transistor modeling is an essential step toward improved fabrication processes and reliable circuit design. This places more pressure on the model developer to consider the physical relevance, accuracy, and simulation convergence rate. This paper proposes an improved gray-box modeling technique that combines the accuracy of the neural network (NN) technique with the physical insight of the equivalent circuit (EC) approach. The extrinsic part of the device was characterized by an extended electrical EC to accurately characterize distributed parasitic effects, especially in the mm-wave frequency range. A hybrid technique of EC and NN is used to simulate the intrinsic transistor. Instead of employing a black box, a gray box of interconnected NN models of the intrinsic branches’ admittances was utilized. This approach addresses the challenges of extracting reliable values for the intrinsic capacitances, conductances, and resistances, particularly in the presence of higher measurement uncertainty. Additionally, the NN model provides accurate simulations of the bias and frequency dependence in the active region of the device. The proposed approach was applied to GaN High Electron Mobility Transistors (HEMTs) of various sizes on SiC and Diamond substrates under different bias conditions and across a wide range of frequencies. The simulation results exhibited an excellent agreement with the measurements and verified the validity of the proposed approach for small-signal modeling and linear circuit design. This work could also be extended to develop large-signal models for nonlinear circuit design applications.}, archive = {J_ACCESS}, author = {Anwar Jarndal}, doi = {10.1109/ACCESS.2025.3531423}, journal = {IEEE Access}, pages = {15981-15991}, shortjournal = {IEEE Access}, title = {A gray-box equivalent neural network circuit small-signal modeling applied to GaN transistors}, volume = {13}, year = {2025}, }
• (2025). Leveraging RRT*: Probabilistically interpreted mechanisms enhanced with P-HOPE and FLEX-OPT for complex path planning. ACCESS, 13, 15965-15980. (www)

  @article{ , abstract = {Path planning is a great challenge in the autonomous navigation of mobile robots. The Rapidly-exploring Random Tree $^{\ast }$ (RRT $^{\ast } $ ) algorithm is widely used for its probabilistic completeness. In the literature, improved RRT $^{\ast }$ -based algorithms usually enhance search efficiency through different target bias strategies. However, these algorithms often fall into obstacle traps in complex environments with narrow passages or high obstacle densities due to the loLcal minima problem in the optimization process. In addition, the existing algorithms also exhibit inefficient sampling and slow convergence in large-scale maps. To tackle with these problems, we propose an improved algorithm, called the RRT $^{\ast }$ -PRIME (Probabilistically Interpreted Mechanisms Enhanced RRT $^{\ast } $ ) algorithm, in this paper. First, a powerful strategy, called the P-HOPE (Probability-Driven Heuristic Optimization for Path Exploration) strategy, that integrates multidimensional influencing factors is designed in the proposed RRT $^{\ast }$ -PRIME algorithm to optimize target sampling direction by considering angle, direction consistency, and obstacle distribution. Second, a flexible mechanism FLEX-OPT is developed to adaptively and dynamically adjust the search strategy through real-time feedback and monitoring of the cost function to tackle the above-mentioned local minima problem, which significantly improves the convergence speed and path quality of the algorithm. The experimental results suggest that the proposed RRT $^{\ast }$ -PRIME algorithm can reduce the initial solution search time by 76.32%, reduce the number of search nodes by about 80.67%, and improve the search path quality compared with the RRT $^{\ast }$ algorithm. In both narrow complex and large-scale map environments, the RRT $^{\ast }$ -PRIME algorithm significantly outperforms the RRT $^{\ast }$ , Informed-RRT $^{\ast }$ , h-RRT $^{\ast }$ , and PF-RRT $^{\ast }$ algorithms in terms of reliability and efficiency. These results demonstrate the effectiveness of the RRT $^{\ast }$ -PRIME algorithm as a robust and efficient solution for path planning in complex and large-scale environments.}, archive = {J_ACCESS}, author = {Yujie Miao and Haiyang Liu and Ziqiang Zhang and Yanju Liang}, doi = {10.1109/ACCESS.2025.3526195}, journal = {IEEE Access}, pages = {15965-15980}, shortjournal = {IEEE Access}, title = {Leveraging RRT*: Probabilistically interpreted mechanisms enhanced with P-HOPE and FLEX-OPT for complex path planning}, volume = {13}, year = {2025}, }
• (2025). Factors affecting student academic performance: A combined factor analysis of mixed data and multiple linear regression analysis. ACCESS, 13, 15946-15964. (www)

  @article{ , abstract = {Understanding student academic performance is a cornerstone for developing sustainable educational practices that benefit students, teachers, policymakers, and society. This analysis directly impacts students’ ability to engage in and promote sustainable practices, thereby shaping their future academic success. While many studies focus on predicting student performance based on a set of features, our study takes an approach by reducing these features into factors and analyzing their impact. We aim to identify the factors influencing student performance within the middle school education system using a combined approach of Factor Analysis for Mixed Data (FAMD) and Multiple Linear Regression (MLR). Our analysis is based on a robust and reliable large dataset of 1,073,450 observations, encompassing qualitative and quantitative features. FAMD analysis identified four underlying factors: prior academic performance, academic delay, socioeconomic status, and class environment; all these factors have good to excellent reliability, with Cronbach’s Alpha values ranging from 0.809 to 0.930. Feeding these factors into the MLR produces a robust model that explains 88.53% of the variance in the CGPA, indicating a strong fit. Prior Academic Performance factor emerges as the most powerful predictor, accounting for 76.6% of the explained variance. Academic Delay follows, explaining 14.34% of the variance. Socioeconomic Status contributes 6.02%, and Class Environment adds 3.03%, reflecting smaller but meaningful impacts. All predictors are statistically significant (p <0.001), confirming their critical roles in influencing student performance (CGPA). The insights gained from this study are critically important in the field of education. They enable teachers and educational leaders to identify at-risk students early and develop targeted interventions that address the factors influencing their performance. This approach aims to enhance learning outcomes, improve educational practices, and promote sustainable education.}, archive = {J_ACCESS}, author = {Mohamed El Jihaoui and Oum El Kheir Abra and Khalifa Mansouri}, doi = {10.1109/ACCESS.2025.3532099}, journal = {IEEE Access}, pages = {15946-15964}, shortjournal = {IEEE Access}, title = {Factors affecting student academic performance: A combined factor analysis of mixed data and multiple linear regression analysis}, volume = {13}, year = {2025}, }
• (2025). SumGPT: A multimodal framework for radiology report summarization to improve clinical performance. ACCESS, 13, 15929-15945. (www)

  @article{ , abstract = {Radiology report summarization plays a critical role in medical imaging, addressing the growing need for concise and accessible interpretation of complex radiology findings. However, existing models often fail to fully leverage the potential of multimodal data integration. In this study, we propose a novel model, SumGPT, which integrates T5 with a Vision Transformer to harness the power of transformer-based architectures for enhanced radiology report summarization. The dataset used in this study comprises 1,952 radiology images with detailed textual reports for training and 488 images with reports for testing. The SumGPT technique was evaluated against several baseline models, including BERT + EfficientNet, XLM-RoBERTa + ViT, T5+ CLIP, VisualGPT (GPT-2+ ViT), and others, using a dataset explicitly designed for this task. The experimental results indicate that SumGPT outperformed all baseline models, achieving the highest performance across all metrics. Specifically, it attained a ROUGE-1 score of 0.8514, ROUGE-2 of 0.8471, ROUGE-L of 0.8514, and a BLEU score of 0.8470. The results demonstrate that SumGPT effectively produces clear and accurate summaries of radiology reports. Combining a Vision Transformer(ViT) with a language model enhances its ability to capture detailed information. The study also shows that SumGPT performs well with different types of reports and could be beneficial in other areas, such as pathology and cardiology. In the future, this approach could pave the way for applications in other medical domains while further optimizing the model for real-time clinical use.}, archive = {J_ACCESS}, author = {Tipu Sultan and Mohammad Abu Tareq Rony and Mohammad Shariful Islam and Samah Alshathri and Walid El-Shafai}, doi = {10.1109/ACCESS.2025.3528335}, journal = {IEEE Access}, pages = {15929-15945}, shortjournal = {IEEE Access}, title = {SumGPT: A multimodal framework for radiology report summarization to improve clinical performance}, volume = {13}, year = {2025}, }
• (2025). Predicting traffic accident risk in seoul metropolitan city: A dataset construction approach. ACCESS, 13, 15917-15928. (www)

  @article{ , abstract = {In contemporary society, the rapid progression of urbanization and technological advancements has led to a substantial increase in the number of vehicles, consequently elevating the rate of traffic accidents. To minimize the human and economic losses resulting from these accidents, extensive research has been conducted over the past decades. Recent studies utilizing Grid Maps and time-series methods have shown promising results in identifying factors related to traffic accidents and predicting accident occurrence rates. However, most existing research employs data without thoroughly analyzing its definitive correlation with traffic accidents, focusing solely on an overarching integration process or exclusively considering road conditions, while neglecting environmental factors surrounding the roads. Therefore, this paper focuses on a detailed analysis of factors contributing to traffic accidents in Seoul, the capital city of South Korea, limiting the study period from January 2020 to December 2021. The data encompasses various aspects such as traffic accidents, weather conditions, standard node links, traffic speed, Points of Interest (POI), solar altitude and azimuth, speed bumps, and traffic surveillance cameras. Most of this data is available from national public institutions, with some being computed through specific formulas. Since weather and POI data possess a wide range of features, a Pearson correlation analysis is conducted to extract features relevant to traffic accidents. The extracted features are then compiled and normalized to facilitate the deep learning model’s training, thereby constructing a comprehensive dataset.}, archive = {J_ACCESS}, author = {Ji-Woong Yang and Hyeon-Jin Jung and Tae-Wook Kim and Han-Jin Lee and Ellen J. Hong}, doi = {10.1109/ACCESS.2024.3493138}, journal = {IEEE Access}, pages = {15917-15928}, shortjournal = {IEEE Access}, title = {Predicting traffic accident risk in seoul metropolitan city: A dataset construction approach}, volume = {13}, year = {2025}, }
• (2025). Impact of communication device installation rate on vehicle behavior stability in V2X-based freeway merging assistance systems. ACCESS, 13, 15905-15916. (www)

  @article{ , abstract = {This paper evaluates the effectiveness of merging assistance systems in reducing highway congestion. One of the key causes of congestion on highways is the inability of vehicles to merge smoothly. In recent years, research has advanced on merging assistance systems that utilize wireless communication technologies to enable real-time monitoring of traffic conditions and achieve more precise and adaptive control. However, previous studies have often assumed that all vehicles are equipped with communication devices and have not adequately addressed the impact of the varying installation rates of these devices on vehicle behavior. This paper investigates the influence of communication device installation rates on vehicle behavior during merging through simulations of merging assistance systems based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Specifically, it focuses on typical highway merging scenarios and performs a statistical analysis of acceleration patterns in merging vehicles resulting from the implementation of merging assistance systems.}, archive = {J_ACCESS}, author = {Noriyasu Kikuchi}, doi = {10.1109/ACCESS.2025.3532485}, journal = {IEEE Access}, pages = {15905-15916}, shortjournal = {IEEE Access}, title = {Impact of communication device installation rate on vehicle behavior stability in V2X-based freeway merging assistance systems}, volume = {13}, year = {2025}, }
• (2025). Parameter identification for primary-side control of inductive wireless power transfer systems: A review. ACCESS, 13, 15885-15904. (www)

  @article{ , abstract = {Wireless power transfer (WPT) is an innovative technology that allows for seamless delivery of electrical energy from a power source to an electrical load without any physical connectors. Primary-side parameter identification, an aspect of primary-side control in WPT systems, enables the monitoring of system parameters without the conventional need for any secondary-side feedback signal. By accurately identifying key system parameters such as coupling or load conditions, the primary-side controller mechanisms can then adjust the operating conditions to maintain optimal power transfer efficiency and ensure reliable operation across varying conditions and energy coupler misalignments. This approach reduces complexity and minimizes the need for additional communication links between the primary-side and the secondary-side hence reduces cost of the system. In literature, numerous techniques for parameter identification along with primary-side control methods have been proposed. However, based on our gathered information, a review work regarding primary-side control techniques applying parameter identification for anti-misalignment and load variation has not been published yet. Therefore, this paper presents a comprehensive review regarding existing techniques in detail to provide an understanding of their operation and classification, investigating their pros and cons contributions to the WPT system.}, archive = {J_ACCESS}, author = {Mwangi Andrew Ngini and Chanh-Tin Truong and Sung-Jin Choi}, doi = {10.1109/ACCESS.2025.3532661}, journal = {IEEE Access}, pages = {15885-15904}, shortjournal = {IEEE Access}, title = {Parameter identification for primary-side control of inductive wireless power transfer systems: A review}, volume = {13}, year = {2025}, }
• (2025). AdaEcoFusion: Adaptive ecological feature fusion for enhancing postgraduate training work in the context of “Ecological civilization”. ACCESS, 13, 15872-15884. (www)

  @article{ , abstract = {In the realm of postgraduate training within the framework of ecological civilization, accurately assessing and enhancing training programs is essential. This paper introduces AdaEcoFusion, a framework aimed at improving postgraduate education quality through hypergraph learning techniques and an adaptive ecological feature fusion model. The model categorizes and evaluates training methods by identifying peer groups of educators and mentors who share similar ecological and educational attributes. The framework addresses challenges such as the variability in training approaches across different environments and the availability of diverse ecological data. To address these challenges, AdaEcoFusion uses a hypergraph-based feature fusion that identifies high-quality ecological and educational features, reflecting each mentor’s teaching style and ecological impact. With a probabilistic model, the framework represents each mentor’s attributes in a latent space, offering a nuanced understanding of their contributions. Then we construct a large-scale graph to map relationships and similarities among educators, identifying dense subgraphs or “circles” of mentors with shared attributes. By mining these mentor circles, AdaEcoFusion enhances postgraduate training quality and provides adaptive fusion of ecological features, optimizing the training process in line with the goals of ecological civilization. Experiments conducted on a dataset of postgraduate mentors from 29 prominent universities demonstrate the model’s effectiveness in improving training programs. This underscores its capability to align postgraduate training with the objectives of ecological civilization, fostering a sustainable and adaptive educational system.}, archive = {J_ACCESS}, author = {Jue Lu}, doi = {10.1109/ACCESS.2025.3532774}, journal = {IEEE Access}, pages = {15872-15884}, shortjournal = {IEEE Access}, title = {AdaEcoFusion: Adaptive ecological feature fusion for enhancing postgraduate training work in the context of “Ecological civilization”}, volume = {13}, year = {2025}, }
• (2025). Robust yaw stability control of commercial vehicles. ACCESS, 13, 15856-15871. (www)

  @article{ , abstract = {This paper describes a robust yaw stability control for commercial vehicles. In the case of commercial vehicles, parameter variations are relatively large compared to passenger vehicles due to significant variations in load conditions. The parameter variations in commercial vehicles can cause performance deterioration of a yaw stability controller designed with nominal parameters. So, it is necessary to design a robust yaw stability controller to guarantee its performance against parameter variations. In this study, the range of changes in significant parameters affecting the cornering behavior of the vehicle was analyzed, and a stochastic distribution was assumed for the changes. The methods of stochastic root locus and parameter sensitivity reduction are adopted to design a robust yaw stability controller. Computer simulations have been conducted to evaluate the stability and robust performance of the proposed controller. The simulations show that the proposed robust yaw stability controller is effective against parameter variations in commercial vehicles.}, archive = {J_ACCESS}, author = {Jae-Hoon Jeong and Baek-Soon Kwon}, doi = {10.1109/ACCESS.2025.3532319}, journal = {IEEE Access}, pages = {15856-15871}, shortjournal = {IEEE Access}, title = {Robust yaw stability control of commercial vehicles}, volume = {13}, year = {2025}, }
• (2025). Air cargo network resilience: Concepts, influencing factors, and enhancement paths. ACCESS, 13, 15840-15855. (www)

  @article{ , abstract = {Air cargo networks play a crucial role in global economic growth, but are often hit by unpredictable events such as wars and natural disasters. The aim of this study is to explore the factors affecting the resilience of air cargo networks and to propose paths to enhance their resilience. In this paper, the impact of key factors such as network connectivity, redundancy, market demand, information sharing and collaboration, emergency response capability, and natural disasters on the resilience of the air cargo networks is thoroughly investigated, mainly from the three parts of concept definition, analysis of influencing factors, and exploration of enhancement paths. It is found that a single factor cannot adequately explain air cargo network resilience, while the combination of redundancy, information sharing and collaboration, and emergency response capability can significantly enhance network resilience. In addition, it was found that improving network connectivity and the ability to adapt to natural disasters also have a significant impact on network resilience. This study utilizes the fuzzy set qualitative comparative analysis (fsQCA) methodology and a questionnaire survey of air cargo networks stakeholders to construct a comprehensive framework that influences air cargo network resilience, and provide strategic recommendations for the government, air cargo industry, and enterprises to enhance network resilience. This study’s results have significant theoretical research value. They also offer important practical insights for optimizing air cargo network design and management, which can boost economic growth and stability.}, archive = {J_ACCESS}, author = {Xu Xu and Xuting Jiang and Chan He and Jie Wang and Qianru An}, doi = {10.1109/ACCESS.2025.3529617}, journal = {IEEE Access}, pages = {15840-15855}, shortjournal = {IEEE Access}, title = {Air cargo network resilience: Concepts, influencing factors, and enhancement paths}, volume = {13}, year = {2025}, }
• (2025). Virtual agents-based attack-resilient distributed control for islanded AC microgrid. ACCESS, 13, 15825-15839. (www)

  @article{ , abstract = {Due to its dependence on a communication network, distributed secondary control of microgrids is susceptible to denial-of-service (DoS) attacks in channel shutdown mode, which may negatively impact the network connectivity and thus deteriorate the coordination and power sharing among distributed generators (DGs). Honeypot is a common method for cyber deception by introducing fake targets. However, in the context of microgrid, the misleading information spread by honeypots will also impact the system performance. This paper proposes an attack-resilient distributed control for AC microgrids utilizing virtual agents (VAs) to counteract both DoS edge and node attacks. The VAs are designed to not impact the system’s steady state during normal operation but to share information among neighboring real agents and serve as dummy targets for DoS attacks. The control with VAs is implemented by a primal-dual gradient-based distributed algorithm to efficiently obtain a practical solution for voltage/frequency regulation and power sharing. The simulation results on a 4-DG test system and a modified IEEE 34-bus system show that 1) VAs do not impact the normal functionality of the test system, and 2) deploying VAs can enhance the resilience of the microgrid control against DoS edge and node attacks.}, archive = {J_ACCESS}, author = {Jun Zhang and Sheik M. Mohiuddin and Junjian Qi}, doi = {10.1109/ACCESS.2025.3532172}, journal = {IEEE Access}, pages = {15825-15839}, shortjournal = {IEEE Access}, title = {Virtual agents-based attack-resilient distributed control for islanded AC microgrid}, volume = {13}, year = {2025}, }
• (2025). Spatial-temporal fusion graph neural networks with mixed adjacency for weather forecasting. ACCESS, 13, 15812-15824. (www)

  @article{ , abstract = {Rapidly accumulating, large-scale and long-term meteorological data provide unprecedented opportunities for data-driven meteorological models and fine-grained numerical weather prediction. Many existing approaches based on deep learning models, e.g., recurrent neutral networks and graph neural networks, have been proposed for weather forecasting. However, the subtle spatial correlations hidden in the vast amount of historical meteorological data have not been fully explored, such as dynamic spatial correlation. In this paper, we propose STGAMAM, which integrates Spatial-Temporal fusion Graph neural networks with a novel Adjacency Matrix and self-Attention Mechanisms to capture both long-term temporal periodicity and short-term spatial-temporal dependencies based on mixed adjacency via graph attention networks and then makes fine-grained prediction on concatenated features which combines diverse correlations. Our approach is validated by extensive experiment on two real-world datasets, which demonstrates the superiority of the proposed method over existing methods.}, archive = {J_ACCESS}, author = {Ang Guo and Yanghe Liu and Shiyu Shao and Xiaowei Shi and Zhenni Feng}, doi = {10.1109/ACCESS.2025.3532473}, journal = {IEEE Access}, pages = {15812-15824}, shortjournal = {IEEE Access}, title = {Spatial-temporal fusion graph neural networks with mixed adjacency for weather forecasting}, volume = {13}, year = {2025}, }
• (2025). Comprehensive review of privacy, utility, and fairness offered by synthetic data. ACCESS, 13, 15795-15811. (www)

  @article{ , abstract = {Automation is the core transformation strategy that every industry wants to get on its roadmap today. Artificial Intelligence (AI) and Machine Learning (ML) are the key components of automation. It is increasingly used in both data analysis and building predictive models from the data. Growing privacy concerns, data confidentiality, and disclosure risks have posed a challenge to the accessibility of right and meaningful data. Several privacy-preserving and disclosure-limiting techniques have come up through research. One such disclosure limiting technique is Synthetic Data. Early research efforts have shown that synthetic data is an effective substitute for real data which can be effectively used to train AI and ML models. However, this needs a comprehensive evaluation before the data user can be confident enough that it is indeed a good substitute for real data. In this paper, we look at three main parameters of synthetic data which should provide a holistic assessment of the quality of synthetic data. First and foremost, how well synthetic data can preserve privacy and control disclosure, second is how good is its utility, and third, are they able to give fair results without any bias when used in machine learning. We review the existing literature to understand various disclosure control limiting methods, synthetic data generators, and then the validation methodologies and evaluation techniques. We understand how data privacy, utility and the fairness of synthetic data intervene with each other and identify the areas for future work.}, archive = {J_ACCESS}, author = {A. Kiran and P. Rubini and S. Saravana Kumar}, doi = {10.1109/ACCESS.2025.3532128}, journal = {IEEE Access}, pages = {15795-15811}, shortjournal = {IEEE Access}, title = {Comprehensive review of privacy, utility, and fairness offered by synthetic data}, volume = {13}, year = {2025}, }
• (2025). Some novel concepts of bipolar picture fuzzy influence graphs models with application toward MADM. ACCESS, 13, 15773-15794. (www)

  @article{ , abstract = {The main objective of this study is to introduce the concepts of some connectivity parameters and dominations in the frame of bipolar picture fuzzy influence graphs and explore their application in multi-attribute decision-making. Due to its inherent prosperities, bipolar picture fuzzy influence graphs is a powerful mathematical tool for addressing uncertain problems where fuzzy influence graphs, bipolar fuzzy influence graph and picture fuzzy influence graph may fail to yield precise results. Initially, some useful terms related to connectivity parameters like bipolar picture fuzzy influence path and its strength, bipolar picture fuzzy influence pair, weak bipolar picture fuzzy influence pair, strongest bipolar picture fuzzy influence pair, strong bipolar picture fuzzy influence pair etc are introduced. Following this, several characteristics of these terms in the domain of bipolar picture fuzzy influence graphs are explored. The concepts of complete-BPPFIGs, size of BPPFIG, minimum strong bipolar picture fuzzy influence pair domination numbers are also investigated. To demonstrate effectiveness of this study, we present a bipolar picture fuzzy influence graphs model to identify the brand’s store with the best shopping variety. Comparative study establishes the fact that our presented model based on BPPFIGs is more efficient and flexible than the other existing models in the literature.}, archive = {J_ACCESS}, author = {Waheed Ahmad Khan and Mahnoor Bushra and Trung Tuan Nguyen and Minh Hoan Pham and Hai van Pham}, doi = {10.1109/ACCESS.2025.3530009}, journal = {IEEE Access}, pages = {15773-15794}, shortjournal = {IEEE Access}, title = {Some novel concepts of bipolar picture fuzzy influence graphs models with application toward MADM}, volume = {13}, year = {2025}, }
• (2025). Fan blade crack detection algorithm based on multi-scale feature fusion. ACCESS, 13, 15762-15772. (www)

  @article{ , abstract = {With the rapid development of social economy, energy consumption is growing tremendously so green energy such as wind energy has become widely used, thus promoting the construction of wind turbines. Due to the long-term use of the electro-mechanical unit, the traditional maintenance cost is too high. In order to quickly and accurately detect and maintain the fan blades, based on the intelligent big data from the environment, we propose the convolutional neural network model to solve the problem of low recognition rate due to the lack of feature extraction in the fan blade crack image, and the long short-term memory network (Long Short-Term Memory, LSTM) convolutional neural network model, and the dimensionality reduction of the captured image data, which is beneficial to improve the recognition rate of the picture and reduce the loss rate of the picture through the detection model’s suitable recognition of complex background problems such as target occlusion and overlap. Using LSTM to extract the global context module can effectively improve the target detection accuracy. When this part is added, the detection accuracy will increase by about 3% to 7%. The image position can be accurately captured and the recognition rate is greatly improved through the optimized convolutional neural network, which can provide a reference for future research in other fields.}, archive = {J_ACCESS}, author = {Yongjun Qi and Hailin Tang and Altangerel Khuder}, doi = {10.1109/ACCESS.2025.3532077}, journal = {IEEE Access}, pages = {15762-15772}, shortjournal = {IEEE Access}, title = {Fan blade crack detection algorithm based on multi-scale feature fusion}, volume = {13}, year = {2025}, }
• (2025). Enhancing microgrid protection with impedance-based blocking: An embedded validation on a dual-layer architecture. ACCESS, 13, 15751-15761. (www)

  @article{ , abstract = {Integrating microgrids has posed many challenges for distribution systems. Due to bidirectional power flow and intermittence of some renewable sources in microgrids, conventional protection methods are prone to failure. Aiming at improving them, this paper proposes a new blocking strategy to enhance dual-layer protection of the microgrid. The first layer contains the conventional overcurrent protection and the internal protection of the distributed energy resources (DERs). The second layer uses undervoltage logic and operates in the event of a possible failure or delayed operation of first-layer devices. As the undervoltage used in the second layer may present problems with selectivity losses, an impedance-based blocking strategy was proposed to mitigate it. Intelligent electronic devices close to the DERs receive voltage and current phasors, allowing the estimation of the impedance value used in the blocking strategy. The proposed protection scheme was firstly validated in a simulation environment using more than 10,000 simulated fault cases. Subsequently, an analysis was carried out on conventional and dual-layer protection with and without impedance blocking in a real-time hardware-in-the-loop test, where the proposed protection was embedded in hardware. The proposed approach not only outperformed conventional strategies in both analysis but also enhanced microgrid protection. Overall, the proposed dual-layer protection with impedance blocking reduced both the maximum clearing time and the occurrences of selectivity losses, while increasing the accuracy.}, archive = {J_ACCESS}, author = {Thiago S. Menezes and Ricardo A. S. Fernandes and Denis V. Coury}, doi = {10.1109/ACCESS.2025.3531762}, journal = {IEEE Access}, pages = {15751-15761}, shortjournal = {IEEE Access}, title = {Enhancing microgrid protection with impedance-based blocking: An embedded validation on a dual-layer architecture}, volume = {13}, year = {2025}, }
• (2025). A quantum-based machine learning approach for autism detection using common spatial patterns of EEG signals. ACCESS, 13, 15739-15750. (www)

  @article{ , abstract = {Autism Spectrum Disorder (ASD) significantly impacts social communication, interaction, and behavior. Early diagnosis and timely intervention can improve outcomes by enabling tailored therapeutic strategies. Electroencephalography (EEG) has emerged as a non-invasive tool to capture brain activity and facilitate the early detection of ASD using machine learning techniques. However, attaining high accuracy with minimal EEG channels remains a challenge. This study analyzed EEG data from 10 children with ASD and 10 Typically Developed (TD) children using three electrode combinations: C3-C4, C3-Cz, and C4-Cz. EEG signals were spatially filtered using a wavelet-based regularized filter bank common spatial pattern. Key features, including peak-to-peak amplitude, were extracted, and correlation-based feature selection identified the most informative features. Classification with Support Vector Machine (SVM) identified the C4-Cz pair as the most effective, achieving the highest accuracy. Further analysis applied Neural Networks (NN), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNN) to classify data from the C4-Cz pair. QSVM with amplitude embedding feature map outperformed others, achieving an accuracy of 94.7%. Performance was further improved by incorporating an enhanced feature set comprising peak frequency, Stockwell transform coefficients, and peak-to-peak amplitude. The proposed system, leveraging these refined features and QSVM, achieved an exceptional accuracy of 98.9%. To our knowledge, this is the first study utilizing an enhanced feature set derived from reduced brain lobes and quantum machine learning for ASD classification, offering a novel and highly accurate diagnostic approach.}, archive = {J_ACCESS}, author = {S. Saranya and R. Menaka}, doi = {10.1109/ACCESS.2025.3531979}, journal = {IEEE Access}, pages = {15739-15750}, shortjournal = {IEEE Access}, title = {A quantum-based machine learning approach for autism detection using common spatial patterns of EEG signals}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral anomaly detection based on intrinsic image decomposition and background subtraction. ACCESS, 13, 15723-15738. (www)

  @article{ , abstract = {Hyperspectral anomaly detection is a detection of abnormal targets in a region based on spectral and spatial information under the premise of no prior knowledge of the target, which is a very important research topic in the field of remote sensing. In the anomaly detection of hyperspectral images, the salient feature map and spatial-spectral features are not effectively used, which greatly limits the detection performance. To solve the above problems, this paper proposes a hyperspectral anomaly detection method based on intrinsic image decomposition and background subtraction. Firstly, the optimal clustering framework is used to select the appropriate bands as the subsequent input images. Secondly, the hyperspectral visual attention model is applied to extract the salient feature map of the image, and the initial anomaly detection map can be obtained by morphological filtering and background subtraction. Then, the pure spectral information in the reflection component of the hyperspectral image is obtained by the intrinsic image decomposition, and the adaptive weight map is calculated on the reflectance image by the spectral angle distance. Finally, the weight map is fused with the initial anomaly detection map to obtain the final anomaly detection result. In the experimental, the proposed method is compared with eleven anomaly detection methods, which including GRXD, RPCA-RX, LSMAD, LRASR, GTVLRR, PTA, HAD-LEBSR, TPCA, PCA-TLRSR, VABS, and GNLTR. The results demonstrate that the proposed method can better enhance the separability of background and anomaly target, so it improves the accuracy of anomaly detection.}, archive = {J_ACCESS}, author = {Jiao Jiao and Longlong Xiao and Chonglei Wang}, doi = {10.1109/ACCESS.2025.3530437}, journal = {IEEE Access}, pages = {15723-15738}, shortjournal = {IEEE Access}, title = {Hyperspectral anomaly detection based on intrinsic image decomposition and background subtraction}, volume = {13}, year = {2025}, }
• (2025). Mosaic-mixed attention-based unexpected traffic scene classification. ACCESS, 13, 15712-15722. (www)

  @article{ , abstract = {With the rapid advancements in artificial intelligence and smart mobility technologies, traffic monitoring systems are evolving quickly. Among these systems, in-vehicle monitoring systems using Object Detection (OD) algorithms are gaining attention for identifying traffic participants in distress. However, current OD algorithms often underperform in complex or unexpected traffic scenarios, such as accidents. In this study, we propose a novel scene classification network that integrates OD with a cross-attention mechanism By leveraging spatial mosaic and mixed attention mechanisms, the network emphasizes spatial relationships and inter-channel correlations, significantly enhancing accuracy in identifying critical traffic events. The detailed evaluation demonstrates improved efficiency and accuracy, underscoring the potential of the system for future traffic incident classification.}, archive = {J_ACCESS}, author = {Sang-Hyun Lee and Soomok Lee and Ilsoo Yun}, doi = {10.1109/ACCESS.2025.3531121}, journal = {IEEE Access}, pages = {15712-15722}, shortjournal = {IEEE Access}, title = {Mosaic-mixed attention-based unexpected traffic scene classification}, volume = {13}, year = {2025}, }
• (2025). Mathematical model and algorithm for accurate main content extraction from news websites. ACCESS, 13, 15694-15711. (www)

  @article{ , abstract = {Irrelevant elements like ads, menus, and footers in web pages hinder data extraction and reduce the performance of Retrieval-Augmented Generation (RAG) systems in Large Language Models (LLMs). This paper tackles the challenge of accurately identifying and extracting the main content from web pages to enhance the efficiency of these systems. We present a novel mathematical model and algorithm that leverages the Document Object Model (DOM) structure, effectively isolating relevant content with high accuracy. Our approach is language-neutral and performs well across diverse languages, including those with complex tokenization, such as Arabic. To validate the model, we created a dataset from 500 websites, allowing for comprehensive evaluation and benchmarking. The algorithm’s practical application demonstrates a reduction in token usage for LLM tasks, contributing to cost-effectiveness. This work introduces a robust, open-source tool for the academic and commercial communities, fostering further innovation in web content extraction and information retrieval.}, archive = {J_ACCESS}, author = {Hamza Salem and Hadi Salloum and Manuel Mazzara}, doi = {10.1109/ACCESS.2024.3524656}, journal = {IEEE Access}, pages = {15694-15711}, shortjournal = {IEEE Access}, title = {Mathematical model and algorithm for accurate main content extraction from news websites}, volume = {13}, year = {2025}, }
• (2025). A novel brushless wound rotor synchronous machine utilizing inherent sub-harmonic component. ACCESS, 13, 15683-15693. (www)

  @article{ , abstract = {Permanent magnet synchronous machines (PMSMs) contain a simple structure, high efficiency, and simple control. However, their cost has increased significantly due to the increase in the price of permanent magnets. Wound rotor synchronous machines (WRSMs) can be used as an alternative to PMSMs. In WRSMs, there is no need for permanent magnets as the rotors of these machines are electromagnets. However, there is the sparking issue which consequently requires regular maintenance of slip rings and carbon brushes. The research focus in recent times is to eliminate the brushes and slip rings in the conventional WRSMs. This manuscript presents a novel brushless wound rotor synchronous machine (BL-WRSM) utilizing an inherent subharmonic component (SHC). The proposed design achieves a brushless function by utilizing the inherent SHC of the stator magnetomotive force (MMF) inherently available in the air gap. As compared to the existing distributed winding BL-WRSM, the proposed BL-WRSM does not involve any extra circuitry at the stator side, additional inverter, or special stator winding arrangements. Utilizing SHC provides benefits such as reduced torque ripple and symmetrical flux distribution as compared to existing brushless machines. There are two distinct windings present on the rotor in the suggested BL-WRSM. These are field winding (FW) and harmonic winding (HW). A rectifier connects these windings in parallel with one another. The SHC of stator MMF generates an AC voltage in HW. This AC voltage is converted to DC by a rectifier, which powers the rotor FW. Validation of brushless operation of the suggested 8-pole, 12-slot machine is conducted through a 2-dimensional finite element analysis with the help of JMAG software. The suggested topology results in desired torque at rated speed with low torque ripples.}, archive = {J_ACCESS}, author = {Muhammad Ahsan Ul Haq and Haroon Farooq and Asif Hussain and Syed Abdul Rahman Kashif}, doi = {10.1109/ACCESS.2025.3531229}, journal = {IEEE Access}, pages = {15683-15693}, shortjournal = {IEEE Access}, title = {A novel brushless wound rotor synchronous machine utilizing inherent sub-harmonic component}, volume = {13}, year = {2025}, }
• (2025). Crisis monitoring in financial sectors using CAMEL partial triadic analysis model. ACCESS, 13, 15650-15682. (www)

  @article{ , abstract = {This study presents the CAMEL Partial Triadic Analysis (CPTA) model, which combines CAMEL methodology with Partial Triadic Analysis (PTA) to evaluate financial indicators on a quarterly basis and delineate risk levels. By analyzing symmetries in data matrices and quantifying vector correlations, the model provides detailed insight into financial trends during recessionary periods, which is useful for financial regulators, public policy makers, and private banking institutions. The CPTA incorporates the commitment matrix, which synthesizes indicator values and provides stability in data matrices in adverse environments. Furthermore, the Fibonacci retracements technique was utilized to categorize variables, identify upward and downward trends, and estimate the solvency of each bank in relation to its respective segment. These results are contrasted using the HJ-Biplot and the dynamic HJ-Biplot, which allow for the evaluation of banking scenarios in the context of financial turbulence. When applied to the Ecuadorian banking sector, the model identified significant fluctuations in three key periods: pre-pandemic, pandemic, and the territorial crisis between Russia and Ukraine. Future research could extend the model by integrating all balance sheet variables against the base CPTA model through the application of Hidden Markov models.}, archive = {J_ACCESS}, author = {Rody Guzmán-Garzón and Purificación Galindo-Villardón and Purificación Vicente-Galindo}, doi = {10.1109/ACCESS.2025.3528412}, journal = {IEEE Access}, pages = {15650-15682}, shortjournal = {IEEE Access}, title = {Crisis monitoring in financial sectors using CAMEL partial triadic analysis model}, volume = {13}, year = {2025}, }
• (2025). Image quality assessments by leveraging diverse visual tasks. ACCESS, 13, 15639-15649. (www)

  @article{ , abstract = {Image quality assessment (IQA) is a fundamental task in computer vision with the goal of accurately predicting the mean opinion score of humans for assessing the quality of images. While recent advances in deep neural networks (DNNs) have sparked much research on IQA, with the hope for IQA to mimic humans effectively, there has been a lack of systematic and analytical research on understanding what factors humans prioritize during IQA. This paper aims to identify human priorities in image evaluation by leveraging the DNN models for diverse computer vision tasks and proposes simple, but effective IQA metrics through our comprehensive analyses on those models. Then, these analyses led us to propose a novel vision-ensemble IQA (VE-IQA) method that demonstrated superior performance as compared to prior arts in IQA on popular IQA benchmarks such as LIVE, CSIQ, and TID2013.}, archive = {J_ACCESS}, author = {Joonhee Lee and Dongwon Park and Se Young Chun}, doi = {10.1109/ACCESS.2025.3531500}, journal = {IEEE Access}, pages = {15639-15649}, shortjournal = {IEEE Access}, title = {Image quality assessments by leveraging diverse visual tasks}, volume = {13}, year = {2025}, }
• (2025). A novel mobility concept for terrestrial wheel-legged lunar rover. ACCESS, 13, 15618-15638. (www)

  @article{ , abstract = {The mobility system of lunar rovers is crucial for the success of space exploration missions. Existing mobility systems face significant challenges in navigating rough and complex terrains. In this paper, we present a novel mobility system for a wheel-legged lunar rover that utilizes a single motor along with an assembly of bevel gears, electromagnetic clutches, and worm gears to control the articulation of four legs, achieving a total of 15 degrees of freedom (DoF). This mobility concept offers the advantage of a self-locking function, which secures the leg positions even in the event of component failure - an improvement over systems with individual motors for each leg. A prototype space rover, the Khalifa University Space Rover (KUSR), was built based on the proposed system and tested in both simulations and real-world experiments. A series of tests were conducted to compare the performance of KUSR with that of the Rashid rover and the Japan Aerospace Exploration Agency’s MMX rover. The results demonstrate that KUSR can effectively overcome obstacles greater than 20 cm in height and steep slopes greater than 40°. Performance was evaluated based on the successful completion of each task. KUSR achieved an average success rate of 82.08%, compared to 80.42% for the MMX rover and 52.08% for the Rashid rover. When failure was introduced in the motors, KUSR achieved a success rate of 52.6% due to the self-locking capability of the system, compared to 12.6% for the MMX rover. A supplementary video is available at https://youtu.be/-TbM14IcnRE.}, archive = {J_ACCESS}, author = {Mubarak Yakubu and Yahya Zweiri and Laith Abuassi and Rana Azzam and Ahmad Abubakar and Amna Busoud and Lakmal Seneviratne}, doi = {10.1109/ACCESS.2025.3529262}, journal = {IEEE Access}, pages = {15618-15638}, shortjournal = {IEEE Access}, title = {A novel mobility concept for terrestrial wheel-legged lunar rover}, volume = {13}, year = {2025}, }
• (2025). A speed-invariant template-based approach for estimating running temporal parameters using inertial sensors. ACCESS, 13, 15604-15617. (www)

  @article{ , abstract = {Segmentation of running data into gait cycles and stance/swing phases is crucial for evaluating running biomechanics. The benefit of magneto-inertial sensors is their ability to capture data in outdoor conditions. However, state-of-the-art inertial-based methods for estimating running temporal parameters are limited to a restricted range of running speeds and, thus, not able to analyze running at variable speeds. This limitation prevents their use for real-world analysis for a wide range of runners and for sports disciplines where athletes vary their running speed. This study evaluated the speed-dependance of eight relevant foot-mounted inertial-based methods from previous research and proposed a novel method that could be robust to speed changes. The proposed method applied, for the first time, a template-matching algorithm based on dynamic time warping to running analysis and compared it to existing methods. All the implemented methods were tested on 30 runners at different speeds ranging from jogging to sprinting (8 km/h, 10 km/h, 14 km/h, 19-30 km/h) on both treadmill and overground. The most speed-robust performance was achieved by the proposed template-based method, providing estimation errors below 0.1% in stride, between 7%-19% in stance, and between 3%-6% in swing across running speeds. Conversely, all the tested methods from the literature were significantly speed-dependent. Thus, this study suggested that template-based approach is a valid solution for the inertial-based estimation of temporal parameters during running from slow jogging to fast sprinting. MATLAB codes and templates have been made available online.}, archive = {J_ACCESS}, author = {Rachele Rossanigo and Marco Caruso and Elena Dipalma and Cristine Agresta and Lucia Ventura and Franca Deriu and Andrea Manca and Taian M. Vieira and Valentina Camomilla and Andrea Cereatti}, doi = {10.1109/ACCESS.2025.3530687}, journal = {IEEE Access}, pages = {15604-15617}, shortjournal = {IEEE Access}, title = {A speed-invariant template-based approach for estimating running temporal parameters using inertial sensors}, volume = {13}, year = {2025}, }
• (2025). Scalable system to support a digital twin of a stone factory production line. ACCESS, 13, 15585-15603. (www)

  @article{ , abstract = {The industrial sector has suffered changes over time until it came to the fourth phase, Industry 4.0. Despite developments in this area, some companies are failing to keep up, which makes them less competitive. Thus, the present work proposes a system that can support a Digital Twin of Stone Factory, a sector that has not been evolving technologically so gradually. Although the system was designed for this sector, it can be adapted for other industrial sectors. This system aims to help companies gain more control and awareness of their production lines. This is achieved through real-time data and historical logs, presented on a web platform. This data is retrieved from industrial machines equipped with servers that provide information from the OPC Unified Architecture (OPC UA) protocol, which is a protocol very used by industries. Moreover, the system was designed to respond to changes in factory machinery. To evaluate the solution usability tests were developed and executed with participants, with and without industry experience. In addition, the solution was validated in laboratory environment. The proposed system showed that it can be used by operators on the factory floor to monitor the machines in the production line at a distance and in real-time.}, archive = {J_ACCESS}, author = {Diogo Francisco and Roberto Ribeiro and Nuno Rodrigues and Alexandrino Gonçalves and Anabela Marto and Irene Ferreira}, doi = {10.1109/ACCESS.2025.3531940}, journal = {IEEE Access}, pages = {15585-15603}, shortjournal = {IEEE Access}, title = {Scalable system to support a digital twin of a stone factory production line}, volume = {13}, year = {2025}, }
• (2025). Novel multiple criteria group decision-making method based on hesitant fuzzy clustering algorithm. ACCESS, 13, 15572-15584. (www)

  @article{ , abstract = {The aim of this paper is to investigate the multiple criteria group decision-making problem in which the evaluation values provided by experts construct some hesitant fuzzy numbers. We first analyze the characteristics of hesitant fuzzy number (HFN) and define the concept of feature vector of HFN. Then, applying the feature vectors of HFNs, some new similarity measures of HFNs are presented, which do not need to add elements to the HFN with fewer elements in the calculation process. Therefore, fuzzy similarity matrix is constructed, which is used to obtain a fuzzy equivalent matrix by using transitive closure method. By applying the fuzzy equivalent matrix, a novel hesitant fuzzy clustering algorithm is given. Furthermore, a new multiple criteria group decision making (MCGDM) algorithm is developed on the basis of the hesitant fuzzy clustering algorithm and the idea of ideal solution in multiple criteria decision making theory. To illustrate the effectiveness and feasibility of the developed MCGDM method, a numerical example is given and analyzed in detail. The results illustrate that the proposed method can provide more reasonable and credible rankings comparing with existing methods owing to keeping original data during computation.}, archive = {J_ACCESS}, author = {Hongya Bian and Deqing Li and Yuang Liu and Rong Ma and Wenyi Zeng and Zeshui Xu}, doi = {10.1109/ACCESS.2024.3486370}, journal = {IEEE Access}, pages = {15572-15584}, shortjournal = {IEEE Access}, title = {Novel multiple criteria group decision-making method based on hesitant fuzzy clustering algorithm}, volume = {13}, year = {2025}, }
• (2025). Super-resolution reconstruction of motor long-wave infrared images based on improved USR-net. ACCESS, 13, 15556-15571. (www)

  @article{ , abstract = {Temperature analysis of long-wave infrared images serves as an effective approach for motor fault detection. However, current long-wave infrared cameras suffer from relatively low imaging resolution. To overcome this limitation, this paper proposes a super-resolution reconstruction method that emphasizes the restoration of complex edge structures in motor images. The method first applies adaptive Wiener filtering to eliminate environmental noise and subsequently incorporates a spatial attention mechanism (SA) within the residual blocks of the USR-Net to enhance edge feature extraction. An improved adaptive channel attention mechanism (Simple-Se) is introduced after the decoder to further refine the reconstruction of complex edges. The reconstruction performance is further optimized by combining perceptual loss and mean squared error loss. Experimental results indicate that, for 2x degraded images, the proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) value exceeding 41 dB, outperforming other methods, with the Structural Similarity Index (SSIM) reaching 0.9872. Furthermore, the Learned Perceptual Image Patch Similarity (LPIPS) value for 2x degraded images is below 0.095. Overall, the proposed method demonstrates outstanding performance in image reconstruction quality, detail preservation, and visual effects, making it highly suitable for high-precision image restoration in practical applications requiring superior image quality.}, archive = {J_ACCESS}, author = {Darong Zhu and Ziyan Sun and Fangbin Wang}, doi = {10.1109/ACCESS.2025.3530759}, journal = {IEEE Access}, pages = {15556-15571}, shortjournal = {IEEE Access}, title = {Super-resolution reconstruction of motor long-wave infrared images based on improved USR-net}, volume = {13}, year = {2025}, }
• (2025). Robust tracking control of piezoelectric actuators using super-twisting sliding mode with bouc-wen hysteresis model-based enhanced jaya algorithm. ACCESS, 13, 15547-15555. (www)

  @article{ , abstract = {This paper introduces a Super-Twisting Sliding Mode Control (STSMC) strategy integrated with a Bouc-Wen hysteresis model for tracking control of the piezoelectric (PZT) actuator. The Bouc-Wen model, which captures the nonlinear hysteretic behavior of the PZT actuator, is estimated using a modified Jaya algorithm enhanced with Lévy flight distribution (called LeJaya). This modification improves the update mechanism by using Lévy flight distribution for better global search capabilities and employing an adaptive population scheme to optimize both exploration and exploitation. The STSMC with Bouc-Wen hysteresis is designed to overcome the chattering phenomenon and guarantee global asymptotic stability. The stability control of the PZT system is verified by Lyapunov’s Law. Simulation results demonstrate the robustness and effectiveness of the proposed method in comparison to classical sliding mode control (SMC), neural sliding mode control (RBFSMC), and fuzzy sliding mode control (FuzzySMC), highlighting improved tracking performance and removed chattering phenomena. The proposed method offers a promising solution for precise and stable control of the PZT actuator in the presence of hysteresis.}, archive = {J_ACCESS}, author = {Nguyen Thi Thu Hang and Nguyen Ngoc Son}, doi = {10.1109/ACCESS.2025.3530477}, journal = {IEEE Access}, pages = {15547-15555}, shortjournal = {IEEE Access}, title = {Robust tracking control of piezoelectric actuators using super-twisting sliding mode with bouc-wen hysteresis model-based enhanced jaya algorithm}, volume = {13}, year = {2025}, }
• (2025). Ship formation and route optimization design based on improved PSO and D-P algorithm. ACCESS, 13, 15529-15546. (www)

  @article{ , abstract = {The rapid development of the global shipping industry and the changes in complex marine environments have put forward higher requirements for ship formation and route optimization. The purpose of the research is to improve the efficiency and accuracy of ship formation and route planning through improved algorithms. Based on this, a ship formation model combining improved particle swarm optimization algorithm and a generative route optimization method based on improved Douglas-Peucker algorithm are proposed. The particle swarm algorithm introduces dynamic adaptive parameter adjustment and the cross mutation strategy of genetic algorithm, while the Douglas-Peucker algorithm integrates density-based noise application spatial clustering algorithm to improve model performance. The test results show that the total navigation distance of the allocation path generated by the ship formation model is 605.3 meters, the calculation time is 31.8 seconds, and all ships can be accurately allocated to the target point. When the number of iterations is 1000, the route optimization model has a route coverage rate of 95.9% on the training set, an average error of 30.5 meters, and a computation time of 45.9 seconds, achieving zero collisions. The experimental results show that the improved algorithm outperforms traditional methods in accuracy and stability of formation target allocation and route planning, especially under complex sea conditions, and can significantly reduce computation time and errors. The research provides a new technological means for optimizing ship formations and routes, which has certain application potential and practical value.}, archive = {J_ACCESS}, author = {Peilong Xu and Dan Lan and Haolin Yang and Shengtian Zhang and Hyeonseok Kim and Incheol Shin}, doi = {10.1109/ACCESS.2025.3525591}, journal = {IEEE Access}, pages = {15529-15546}, shortjournal = {IEEE Access}, title = {Ship formation and route optimization design based on improved PSO and D-P algorithm}, volume = {13}, year = {2025}, }
• (2025). Wearable regionally trained AI-enabled bruxism-detection system. ACCESS, 13, 15503-15528. (www)

  @article{ , abstract = {Sleep Bruxism (SB) and Awake Bruxism (AB) can cause severe discomfort, exhaustion, and problems with day-to-day functioning, including poor sleep and bad performance at work. This emphasizes the significance of early identification and treatment of bruxism. To date, some tools like mouthpieces have been designed for teeth protection. However, they are not user-friendly due to their internal placement in the mouth. Bruxers require a gadget that not only identifies and continually monitors their bruxism activity, but also alerts them. In this study, a wearable EMG-based device has been designed to monitor and detect jaw clenching in the supine position using EMG of the two facial muscles, Temporalis and Masseter. This study purposely found which muscle varies most with bruxism activity. The EMG signals’ data of 30 regional subjects, with 5 trials each, have been acquired and pre-processed using filters and three data oversampling techniques, SMOTE, SMOTE-ENN, and ADSYN. The augmented data has been trained, validated, and tested on six machine-learning classifiers and three deep-learning models. The Recurrent Neural Network provided the highest accuracy 0.99 and a recall value 0.98 for the temporalis muscle dataset. The other eight classifiers have provided accuracies in descending order such as Convolutional Neural Network, Long Short-Term Memory, k-Nearest Neighbors, and Decision Tree 0.98; Logistic Regression 0.96; Support Vector Machine 0.97, and Naïve Bayes 0.89, respectively. The module has been tested on several participants, and bruxism is identified when they do jaw clenching or teeth grinding. In the future, the size of the gadget could be miniaturized to ensure the users’ comfort level.}, archive = {J_ACCESS}, author = {Anusha Ishtiaq and Jahanzeb Gul and Zia Mohy Ud Din and Azhar Imran and Khalil El Hindi}, doi = {10.1109/ACCESS.2025.3532360}, journal = {IEEE Access}, pages = {15503-15528}, shortjournal = {IEEE Access}, title = {Wearable regionally trained AI-enabled bruxism-detection system}, volume = {13}, year = {2025}, }
• (2025). Exploring the integration of blockchain and distributed DevOps for secure, transparent, and traceable software development. ACCESS, 13, 15489-15502. (www)

  @article{ , abstract = {Distributed DevOps is a software development methodology that aims to integrate the work of development and operations teams without being bound by geographical constraints. This methodology excels in enhancing collaboration and speeding software development. However, it does suffer from a lack of security, transparency, and traceability, which can result in project delays, a lack of trust between stakeholders, and even project failure. This paper addresses these issues of Distributed DevOps by implementing Blockchain technology. In this paper, we propose a novel framework that leverages blockchain technology to address the challenges faced by Distributed DevOps. Through performance analysis, we demonstrate the effectiveness of our framework in a real-world scenario, highlighting its ability to improve transparency, traceability, and the security of the DevOps pipeline. Our findings underscore the potential of blockchain-empowered solutions in revolutionizing DevOps practices. Furthermore, this research offers a practical framework for organizations seeking to optimize their development processes by integrating blockchain technology.}, archive = {J_ACCESS}, author = {Junaid Nasir Qureshi and Muhammad Shoaib Farooq and Usman Ali and Adel Khelifi and Zabihullah Atal}, doi = {10.1109/ACCESS.2024.3509036}, journal = {IEEE Access}, pages = {15489-15502}, shortjournal = {IEEE Access}, title = {Exploring the integration of blockchain and distributed DevOps for secure, transparent, and traceable software development}, volume = {13}, year = {2025}, }
• (2025). CFO-CR: Carrier frequency offset methodology for high-rate common randomness generation. ACCESS, 13, 15469-15488. (www)

  @article{ , abstract = {Common Randomness (CR) provides sequences of random variables at two physically separated locations. Ideally, the random variables at the two locations should be identical, i.e., have low probability of discrepancy, and should have high entropy. Previous CR research has focused on CR for physical layer security (mainly physical layer secret key generation), where a low CR generation rate, i.e., a low rate of CR bits per second is sufficient. However, emerging semantic communication paradigms, e.g., identification via channels, require high CR rates. We develop and evaluate a Carrier Frequency Offset (CFO) based methodology for high-rate CR generation from reciprocal observations of a common wireless channel between two distinct wireless terminals. The proposed CFO-CR methodology proceeds in several stages, including channel probing, random parameter extraction, noise reduction, quantization, information reconciliation, and randomization. Our evaluations with single-carrier software-defined radios, for which we make measurement traces publicly available, indicate that high-rate CR generation should observe (probe) the CFO and employ a Savitzky Golay low-pass filter with a low cut-off frequency for noise reduction in conjunction with multi-bit quantization, Gray code encoding, and a shuffling based randomization. We provide insights into the tradeoffs between the reconciliation cost for correcting bit discrepancies and the CR generation parameters. Our proposed CFO-CR methodology can generate 2048 bits of CR at a comparatively low reconciliation cost of 72 bytes while only making 256 channel observations and passing all common randomness tests. For generating 2048 bits of CR, other state-of-the-art approaches either require more channel observations ( $\ge 2048$ ) or incur a higher reconciliation cost ( $\ge 450$ bytes).}, archive = {J_ACCESS}, author = {Prashanth Kumar Herooru Sheshagiri and Martin Reisslein and Juan A. Cabrera and Frank H. P. Fitzek}, doi = {10.1109/ACCESS.2025.3529665}, journal = {IEEE Access}, pages = {15469-15488}, shortjournal = {IEEE Access}, title = {CFO-CR: Carrier frequency offset methodology for high-rate common randomness generation}, volume = {13}, year = {2025}, }
• (2025). Disentangled contrastive learning from synthetic matching pairs for targeted chest X-ray generation. ACCESS, 13, 15453-15468. (www)

  @article{ , abstract = {Disentangled generation enables the synthesis of images with explicit control over disentangled attributes. However, traditional generative models often struggle to independently disentangle these attributes while maintaining the ability to generate entirely new, fully randomized, and diverse synthetic data. In this study, we propose a novel framework for disentangled Chest X-ray (CXR) generation that enables explicit control over person-specific and disease-specific attributes. This framework synthesizes CXR images that preserve the same patient identity—either real or randomly generated—while selectively varying the presence or absence of specific diseases. These synthesized matching-paired CXRs not only augment training datasets but also aid in identifying lesions more effectively by comparing attribute-specific differences between paired images. The proposed method leverages contrastive learning to disentangle latent spaces for patient and disease attributes, modeling these spaces with multivariate Gaussians for precise and exclusive attribute sampling. This disentangled representation enables the training of a controllable generative model capable of manipulating disease attributes in CXR images. Experimental results demonstrate the fidelity and diversity of the generated images through qualitative assessments and quantitative comparisons, outperforming state-of-the-art class-conditional generative adversarial networks on two public CXR datasets. Further experiments on clinical efficacy demonstrate that our method improves disease classification and detection tasks by leveraging data augmentation and employing the difference maps generated from paired images as effective attention maps for lesion localization. These findings underscore the potential of our framework to improve medical imaging analysis and facilitate novel clinical applications.}, archive = {J_ACCESS}, author = {Euyoung Kim and Soochahn Lee and Kyoung Mu Lee}, doi = {10.1109/ACCESS.2025.3531366}, journal = {IEEE Access}, pages = {15453-15468}, shortjournal = {IEEE Access}, title = {Disentangled contrastive learning from synthetic matching pairs for targeted chest X-ray generation}, volume = {13}, year = {2025}, }
• (2025). Human strategy learning-based multi-agent deep reinforcement learning for online team sports game. ACCESS, 13, 15437-15452. (www)

  @article{ , abstract = {In 3 vs. 3 online basketball games, finite state machine (FSM)-based Game artificial intelligence (AI) has traditionally been employed. However, limitations such as repetitive behavior patterns and challenges in maintaining systems during redesigns have led to increased research into reinforcement learning-based AI. This shift aims to address the shortcomings of FSM-based approaches. Nevertheless, applying multi-agent reinforcement learning-based AI in commercial online basketball games presents significant challenges, particularly in ensuring real-time processing, which requires efficient methods for managing observational data. In addition, the stochastic nature of action selection in reinforcement learning complicates the accurate learning of behaviors through explicit decision data. Moreover, reinforcement learning, which self-optimizes through exploration and develops its own rules, struggles to mimic human-like behavior patterns that follow predefined strategies in Sports Game. This study introduces a human strategy-based reinforcement learning method designed to address these challenges and replicate human gameplay that adheres to human-defined strategies. The learning of human strategies is enhanced using Ray for the real-time processing of observational data and a multi-phase reward system that distinctly defines rewards based on specific objectives. Furthermore, the proposed method enables real-time, strategy-based action guidance through a Human Strategy AI trained on human-defined strategies. Experimental results demonstrate that in a stochastic basketball game environment, this approach enabled the determination of precise actions and achieved human-like gameplay through the Human Strategy AI.}, archive = {J_ACCESS}, author = {Seongbeen Lee and Gyuhyuk Lee and Wongyeom Kim and Junoh Kim and Jisun Park and Kyungeun Cho}, doi = {10.1109/ACCESS.2025.3531435}, journal = {IEEE Access}, pages = {15437-15452}, shortjournal = {IEEE Access}, title = {Human strategy learning-based multi-agent deep reinforcement learning for online team sports game}, volume = {13}, year = {2025}, }
• (2025). Doppler effect estimation and compensation for satellite systems in geolocation problems. ACCESS, 13, 15430-15436. (www)

  @article{ , abstract = {The framework of the article is a theoretical investigation of the Doppler effect on electromagnetic signals. The target application is relevant to satellite missions with resource-constrained systems based on low-cost hardware platforms and the innovation of the paper is on the error compensation strategy and on the proposal of an approximate polynomial model. In particular, the scenario of interest considers a non-cooperative emitter of electromagnetic waves on the surface of the Earth and a receiver able to perform Angle of Arrival measurements embarked on a satellite in Low Earth Orbit. Assuming the problem of geolocation is solved, and starting from the kinematics of the satellite and its relative motion with respect to the emitter, the correction on the frequency is derived with respect to the orbital parameters, which is a common representation of data for satellite systems. The purpose of the paper is not only to provide a reference model to predict the necessary correction on the frequency of the signal due to the Doppler effect, but also to present a real-time strategy for compensation in the pipeline of signal processing after its geolocation. The main goal of the paper is to give real-time estimation and compensation strategy for the Doppler factor considering only the current instantaneous kinematic information, without the information on the history of the signal over time. Furthermore, data fitted from simulations show that a first degree polynomial model has a maximum error prediction on the order of less than 0.01 percent around Nadir pointing and can be appealing for fast computation of this correction with low-cost hardware based on Field Programmable Gate Array.}, archive = {J_ACCESS}, author = {Marcello Asciolla and Angela Cratere and Francesco Dell’Olio}, doi = {10.1109/ACCESS.2025.3531329}, journal = {IEEE Access}, pages = {15430-15436}, shortjournal = {IEEE Access}, title = {Doppler effect estimation and compensation for satellite systems in geolocation problems}, volume = {13}, year = {2025}, }
• (2025). An in-depth study of personalized anesthesia management models in gastrointestinal endoscopy based on multimodal deep learning. ACCESS, 13, 15410-15429. (www)

  @article{ , abstract = {In response to the annual occurrence of over 10 million gastrointestinal endoscopic examinations in China, this study proposes a personalized anesthesia management model based on multimodal deep learning. This model was designed to enhance anesthesia management efficiency and disease detection rates. In collaboration with the Department of Anesthesiology at Renji Hospital, which is affiliated with the Shanghai Jiao Tong University School of Medicine, data pertaining to anesthesia were collected from 398 patients, who were undergoing gastrointestinal endoscopy. This yielded a total of 327 valid samples. Analysis of the patients’ basic information and physiological parameters during surgery revealed that body mass index (BMI) and age significantly impacted anesthesia management. Based on these findings, a multimodal deep learning model was developed that integrates Long Short-Term Memory (LSTM) networks, hyperparameter geometric manifold optimization (GMO) methods and data-driven sparse matrix classifiers. The model is capable of dynamically adjusting its parameters based on the specific needs of each individual patient, utilizing real-time physiological data to predict vital signs and anesthesia states with a 10-second lead time. In the experimental evaluations, the model demonstrated superior performance in drug usage prediction tasks. Compared with LSTM networks integrated with convolutional neural networks (CNN) and support vector machines (SVM), the LSTM model combined with GMO and sparse matrix classifiers, along with personalized physiological data, achieved a recall rate of 83% and an F1-score of 0.711 in drug usage prediction. The total computation time was maintained within 2.99 seconds, thereby satisfying the requisite for real-time applications. This model significantly improves prediction accuracy and stability over traditional methods, thereby enhancing operational efficiency in complex surgical environments. It is anticipated that the outcomes of this study will promote widespread adoption of gastrointestinal endoscopy, thereby improving the early diagnosis and treatment rates of gastrointestinal diseases.}, archive = {J_ACCESS}, author = {Hanqi Shi and Hongyu Wang and Xibing Ding and Zheng Dang}, doi = {10.1109/ACCESS.2025.3526229}, journal = {IEEE Access}, pages = {15410-15429}, shortjournal = {IEEE Access}, title = {An in-depth study of personalized anesthesia management models in gastrointestinal endoscopy based on multimodal deep learning}, volume = {13}, year = {2025}, }
• (2025). Synergistic hierarchical bi-level optimization of demand response programs and smart inverter Volt/Var control for maximizing hosting capacity of power systems with renewable-based distributed generators. ACCESS, 13, 15390-15409. (www)

  @article{ , abstract = {Renewable-based distributed generation (DG) offers economic and environmental benefits, but integrating it into the distribution network (DN) poses technical challenges. Hosting capacity (HC) defines the maximum amount of DG that can be safely integrated into the DN without violating technical constraints, with various methods available to enhance the DN and increase HC. This article proposes an optimization-based demand response (DR) program using dynamic pricing to enhance the HC. A hierarchical bi-level optimization framework is developed for the DR program, where the upper level optimizes the hosting capacity depending on the optimal load profiles optimized in the lower level. Additionally, DR is combined with smart inverters’ (SIs) optimal Volt/VAR control to further improve the HC. The pelican optimization algorithm (POA) is employed to solve the optimization problem and is compared with four other optimization techniques, demonstrating its good performance. The proposed planning scheme is implemented on the IEEE 33-bus test system and a real DN consisting of 59 buses in Cairo, Egypt. The results show that applying DR alone can increase the average maximum HC value by 29.4%, while combining DR with optimal Volt/VAR control can achieve a 39.6% increase.}, archive = {J_ACCESS}, author = {Zenhom M. Zenhom and Shady H. E. Abdel Aleem and Essam Aboul Zahab and Ziad M. Ali and Abdulaziz Almalaq and Tarek A. Boghdady}, doi = {10.1109/ACCESS.2025.3530557}, journal = {IEEE Access}, pages = {15390-15409}, shortjournal = {IEEE Access}, title = {Synergistic hierarchical bi-level optimization of demand response programs and smart inverter Volt/Var control for maximizing hosting capacity of power systems with renewable-based distributed generators}, volume = {13}, year = {2025}, }
• (2025). A huge innovation in diagnosis of obstructive sleep apnea syndrome: With an artificial intelligence-based algorithm, obstructive sleep apnea syndrome can now be diagnosed with pulmonary function test. ACCESS, 13, 15376-15389. (www)

  @article{ , abstract = {Obstructive sleep apnea syndrome (OSAS) is a life-threatening disease characterized by upper airway narrowing or obstruction. The diagnostic process is difficult, costly, and time-consuming. Many individuals with OSAS do not apply for a diagnosis or are unaware of their disease. This study aimed to develop a practical, fast, and reliable diagnostic system for early diagnosis and treatment of OSAS. For the first time, features were extracted from flow-volume curves obtained using a Pulmonary Function Test (PFT), and an Artificial Intelligence (AI)-based algorithm was developed to diagnose OSAS. Spearman correlation coefficients determined the degree of influence of the features in determining OSAS. Several models were created using different features and AI methods according to their effect levels. The models obtained by hyperparameter optimization and cross-validation were tested with unseen data, and their performance was evaluated using seven different criteria. Using only five features extracted from the flow-volume curve (TLC/PIF, PIF/PEF, TLC/FIF50, TLC/FIF25, and FIF25/FEF25), OSAS was diagnosed with 97.1% accuracy using the Neural Network (NN) algorithm. The results showed that OSAS can be diagnosed quickly and reliably using PFT available at every hospital. The features extracted from the flow-volume curve could be used as biomarkers for diagnosing OSAS. The proposed method can be adapted to PC-based spirometry devices without additional hardware developments. This is a significant innovation in both literature and practice. This method will enable early diagnosis for patients and many people unaware of their disease. This will shed light on several future studies.}, archive = {J_ACCESS}, author = {Seval Bulut Eris and Mehmet Recep Bozkurt and Omer Eris and Cahit Bilgin}, doi = {10.1109/ACCESS.2025.3531501}, journal = {IEEE Access}, pages = {15376-15389}, shortjournal = {IEEE Access}, title = {A huge innovation in diagnosis of obstructive sleep apnea syndrome: With an artificial intelligence-based algorithm, obstructive sleep apnea syndrome can now be diagnosed with pulmonary function test}, volume = {13}, year = {2025}, }
• (2025). Violence detection from industrial surveillance videos using deep learning. ACCESS, 13, 15363-15375. (www)

  @article{ , abstract = {The integration of Internet of Things (IoT) technology in industrial surveillance and the proliferation of surveillance cameras in smart cities has empowered the development of real-time activity recognition and violence detection systems, respectively. These systems are crucial in enhancing safety measures, improving operational efficiency, reducing accident risks, and providing automatic monitoring in dynamic environments. In this paper, we propose a three-stage deep learning-based end-to-end framework for violence detection. The lightweight convolutional neural network (CNN) model initially identifies individuals in the video stream to minimize the processing of irrelevant frames. Subsequently, a sequence of 50 frames with identified persons is directed to a 3D-CNN model, where the spatiotemporal features of these sequences are extracted and passed to the classifier. Unlike traditional methods that process all frames indiscriminately, this targeted filtering mechanism allows computational resources to be allocated more effectively. Next, SoftMax classifier processes the extracted features to categorize frame sequences as violent or non-violent. The classifier’s predictions trigger real-time alerts, enabling rapid intervention. The modularity of this stage supports adaptability to new datasets, as it can leverage transfer learning to generalize across diverse surveillance contexts. Unlike traditional systems constrained by hand-crafted features, this design dynamically learns from data, reducing reliance on prior domain knowledge and improving generalizability. We conducted experiments on violence detection across four datasets, comparing the performance of our model with convolutional CNN models. A computation time analysis revealed that our lightweight model requires significantly less computation time, demonstrating its efficiency. We also conducted cross-data experiments to assess the model’s capacity to perform consistently across various datasets. Experiments show that our proposed model outperforms the methods mentioned in the existing literature. These experiments demonstrate that the model’s adaptability and robustness need to be improved.}, archive = {J_ACCESS}, author = {Hamza Khan and Xiaohong Yuan and Letu Qingge and Kaushik Roy}, doi = {10.1109/ACCESS.2025.3531213}, journal = {IEEE Access}, pages = {15363-15375}, shortjournal = {IEEE Access}, title = {Violence detection from industrial surveillance videos using deep learning}, volume = {13}, year = {2025}, }
• (2025). Reinforcement learning-based generative security framework for host intrusion detection. ACCESS, 13, 15346-15362. (www)

  @article{ , abstract = {Protecting users’ systems from evolving cybercrime is becoming increasingly challenging. Attackers create more complicated attack patterns and configure attack behavior to resemble normal behavior to evade detection by defenders. Thus, it is indispensable to configure a security system that accurately detects attacks on each user’s system. Since the attack does not occur only at a specific point in the network, there is a limitation in identifying computer intrusion simply using network packets. A Host-based Intrusion Detection System (HIDS) is a highly effective tool for monitoring computer systems and detecting unusual or unauthorized activities. HIDS can quickly identify potential security threats by closely monitoring and analyzing system logs, configurations, file integrity, and events specific to a host machine. It helps maintain the security and integrity of individual systems by detecting unauthorized activities or policy violations. With its advanced capabilities and reliable performance, HIDS is essential to any comprehensive host-based security strategy. Although HIDS can detect insider intrusions, the known HIDS detection methods are limited to specific attacks and may be ineffective against new attack patterns. Recently, researchers applied Natural Language Processing (NLP) in HIDS to scrutinize complex attack patterns, but they could have more effectively provided useful outputs for detecting intrusions based on these patterns. In this paper, we use reinforcement learning methodology, Actor-Critic, and NLP to extract keywords that occur on each anomaly system call log and propose a rule generation framework to prevent future intrusion detection using the extracted words. We analyze the anomaly log using NLP and extract the characteristics of each attack log as the ‘keyword.’ Based on the unique keywords of each attack log, we utilize reinforcement learning to establish a set of rules to protect against attacks. We extracted keywords based on textrank from the system call log sequence and simultaneously provided ground truth data using the extracted keywords. Based on the extracted keywords, the pre-trained Seq2Seq model generate rules according to the reward calculation method in reinforcement learning. When calculating the reward in reinforcement learning, we used the comparison value with the pre-trained Seq2Seq model, the malware log sequence detected by the rule set based on reinforcement learning, and the false positive value generated by the normal data to create its own rule set. We verified the proposed framework using the system call log datasets: ADFA-LD, LID-DS 2021 dataset. The proposed framework demonstrated a high accuracy rate of 96.5% average when faced with different attacks. We compared the accuracy based on the proposed framework detection, textrank, and Seq2Seq model-based keyword extraction methods. As a result, the proposed framework showed relatively high accuracy against various attack logs.}, archive = {J_ACCESS}, author = {Yongsik Kim and Su-Youn Hong and Sungjin Park and Huy Kang Kim}, doi = {10.1109/ACCESS.2025.3532353}, journal = {IEEE Access}, pages = {15346-15362}, shortjournal = {IEEE Access}, title = {Reinforcement learning-based generative security framework for host intrusion detection}, volume = {13}, year = {2025}, }
• (2025). More on codes over finite quotients of polynomial rings. ACCESS, 13, 15339-15345. (www)

  @article{ , abstract = {Let $q=p^{r}$ be a prime power, ${\mathbb {F}}_{q}$ be the finite field of order q and $f(x)$ be a monic polynomial in ${\mathbb {F}}_{q}[x]$ . Set ${\mathbb {A}}:={\mathbb {F}}_{q}[x]/{\left \lt {{ f(x) }}\right \gt }$ . In this paper we continue the study (started by T. P. Berger and N. El Amrani) of $\mathbb {A}$ -codes of length l over $\mathbb {A}$ , i.e. $\mathbb {A}$ -submodules of ${\mathbb {A}}^{l}$ . We introduce two types of unique generating sets, called type I and type II basis of divisors, for an $\mathbb {A}$ -code. Using this, we present a building-up construction so that one can obtain all distinct $\mathbb {A}$ -codes of length l, with their basis of divisors. We complete the classification for the special case $l=2$ and enumerate all the $\mathbb {A}$ -codes of length 2. As an example, we list all binary index-2 quasi-cyclic codes of lengths 16 and 32, and all ternary index-2 quasi-cyclic codes of lengths 6 and 18, which are best-known codes.}, archive = {J_ACCESS}, author = {Emad Kadhim Al-Lami and Reza Sobhani and Alireza Abdollahi and Javad Bagherian}, doi = {10.1109/ACCESS.2025.3531644}, journal = {IEEE Access}, pages = {15339-15345}, shortjournal = {IEEE Access}, title = {More on codes over finite quotients of polynomial rings}, volume = {13}, year = {2025}, }
• (2025). Blockchain-assisted generation rescheduling in imperfect market environments. ACCESS, 13, 15324-15338. (www)

  @article{ , abstract = {In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.}, archive = {J_ACCESS}, author = {Salman Tariq and Mohamed Shaaban and Hazlie Mokhlis and Nurulafiqah N. Mansor and Hasmaini Mohamad}, doi = {10.1109/ACCESS.2025.3530469}, journal = {IEEE Access}, pages = {15324-15338}, shortjournal = {IEEE Access}, title = {Blockchain-assisted generation rescheduling in imperfect market environments}, volume = {13}, year = {2025}, }
• (2025). PolyRes-net: A polyhierarchical residual network for decoding anatomical complexity in medical image segmentation. ACCESS, 13, 15312-15323. (www)

  @article{ , abstract = {Medical image segmentation entails assigning each pixel in an image to its corresponding class label, a challenging task given the considerable anatomical variations in different cases. The encoder-decoder approach, exemplified by architectures such as U-Net, has emerged as the predominant framework for medical imaging segmentation tasks. In recent years, diverse modifications to the U-Net architecture have been explored, giving rise to distinct models that showcase noteworthy results in comparison to the conventional U-Net design. In this paper, we introduce a novel architectural framework, which we refer to as the Polyhierarchical Residual Network (PolyRes-Net). Each encoder step comprises a Multi-Level Residual Block (MLR-block) designed to extract local and global feature maps. Furthermore, each decoder step is preceded by an attention gate, aiding in the extraction of the most salient features from the preceding layer, while skip connections correspond to the respective encoder steps. Lastly, the multi-scale feature aggregation (MSFA) block consolidates features from various decoder steps. Four benchamar datasets are used for evaluating our model: Krusir-SEG, CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset based on two metrics: the Mean Dice Similarity Coefficient (mDSC) and the Mean Intersection Over Union (mIOU). The results of the proposed PolyRes-Net outperformed the state-of-the-art segmentation methods. Specifically, PolyRes-Net achieves the highest mDSC scores of 91.02%, 91.80%, and 89.25% on CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset, respectively. Additionally, the highest mIOU scores are 85.60%, 85.32%, and 82.14% for the same datasets, further underscoring the efficacy of the proposed model.}, archive = {J_ACCESS}, author = {Amr Magdy and Khalid N. Ismail and Marghny H. Mohamed and Mahmoud Hassaballah and Haitham Mahmoud and Mohammed M. Abdelsamea}, doi = {10.1109/ACCESS.2024.3475877}, journal = {IEEE Access}, pages = {15312-15323}, shortjournal = {IEEE Access}, title = {PolyRes-net: A polyhierarchical residual network for decoding anatomical complexity in medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). Fault estimation method for nonlinear systems with perturbations based on augmented fault observer. ACCESS, 13, 15301-15311. (www)

  @article{ , abstract = {This paper proposes a robust fault estimation method for nonlinear systems with both actuator and sensor faults. Specifically, a singular system is constructed by combining the state vector of the system with the fault vector of the sensor, and an augmented fault observer is designed by combining the fault estimation algorithm of the adaptive actuator. First, an augmented fault observer for linear systems with perturbations is designed, and the sufficient conditions for the asymptotic stability of the observation error system are ascertained so that the state of the singular system can be observed. Second, considering the nonlinear characteristics of the actual control system, sufficient conditions for the existence of the augmented fault observer are determined. Finally, the design method of augmented fault observer for nonlinear systems is extended to nonlinear systems with both input interference and sensor disturbance, and sufficient conditions for the existence of the observer are found. In the design of the observer, the influence of disturbance on the system fault estimation, convergence speed of fault estimation error, and convenience of observer design are considered. Moreover, an estimation method is devised for the fault diagnosis of actuators and sensors in the control system. Finally, a numerical example that tests the design method of augmented fault observer and effect of system fault estimation is presented. The superiority and effectiveness of the method are thus verified.}, archive = {J_ACCESS}, author = {Yanxiu Sun and Ying Du and Yuping Zhang}, doi = {10.1109/ACCESS.2025.3531559}, journal = {IEEE Access}, pages = {15301-15311}, shortjournal = {IEEE Access}, title = {Fault estimation method for nonlinear systems with perturbations based on augmented fault observer}, volume = {13}, year = {2025}, }
• (2025). Skeleton-based data augmentation for sign language recognition using adversarial learning. ACCESS, 13, 15290-15300. (www)

  @article{ , abstract = {In recent years, visual-based sign language recognition (SLR) has become an active research area with the advancement of deep learning. However, it is difficult to collect sign language data, and many datasets suffer from data lack and imbalance, leading to overfitting and reduced accuracy in machine learning. In general, data augmentation is used as a solution to the problems but model training and data augmentation are performed independently, and it is difficult to adjust the parameters for data augmentation. Therefore, we focus on visual-based SLR using skeletal data and propose an adversarial learning SLR model called Adversarial Vulnerability-Seeking Networks (AVSN), which jointly trains two independent processes, data augmentation, and machine learning. The AVSN is particularly applicable in scenarios where diverse and extensive sign language datasets are not available. For example, when developing SLR systems for lesser-known sign languages or specialized vocabularies used in specific professional contexts, AVSN can improve model performance by generating high-quality, diverse training data. The generator produces hard adversarial data intended to mislead the machine learning model, which acts as a discriminator. On the other hand, the discriminator learns from both raw and adversarial data. In other words, the generator exposes the vulnerabilities of the discriminator, and the discriminator improves its performance by learning from both types of data. We evaluated the proposed method from two aspects, the performance of the model trained by data augmentation and the quality of the data generated by data augmentation. First, we evaluated the performance of the model trained using data augmentation using common evaluation metrics such as accuracy and F-score. The proposed method achieved an improvement of 0.58% in accuracy and 0.017 in F-score compared to the model without data augmentation. Next, we quantitatively evaluated the quality of the augmented data by data augmentation using evaluation metrics such as FID, variety, multimodality, affinity, and diversity. The proposed method was successful in enhancing the novelty of the data, which contributed to the generalization improvement of the model.}, archive = {J_ACCESS}, author = {Yuriya Nakamura and Lei Jing}, doi = {10.1109/ACCESS.2024.3481254}, journal = {IEEE Access}, pages = {15290-15300}, shortjournal = {IEEE Access}, title = {Skeleton-based data augmentation for sign language recognition using adversarial learning}, volume = {13}, year = {2025}, }
• (2025). Temporal convolutional network approach to secure open charge point protocol (OCPP) in electric vehicle charging. ACCESS, 13, 15272-15289. (www)

  @article{ , abstract = {Securing data transactions across computer networks presents significant challenges, particularly concerning data privacy and cybersecurity threats. These issues are particularly critical in electric vehicle charging stations (EVCSs) due to the sensitive data involved, such as data breaches, unauthorized access, and privacy concerns. The primary challenge within EVCS architecture lies in defending against various cyberattacks. Several machine learning models, including convolutional neural networks, recurrent neural networks, and long short-term memory, have been employed to enhance EVCS security. However, these models often struggle to effectively handle the temporal dependencies, complexity, and scalability required for real-time threat detection. In this study, we propose a novel temporal convolutional network (TCN) to enhance EVCS security by rapidly detecting Open Charge Point Protocol (OCPP) attacks. Our model was trained and tested on the latest CICEVSE2024 dataset, which encompasses attacks targeting EVCS communication protocols, including OCPP and ISO15118, during charging and idle states. The proposed model achieved 100% accuracy in detecting binary labels and 5-class scenarios, and 93% accuracy in identifying 17-class attack types, surpassing existing approaches. Additionally, we conducted a comparative analysis with a hybrid model, demonstrating that the proposed TCN delivers superior performance with lower computational complexity, fewer parameters, a smaller model size, and shorter computation times. These features make the TCN model highly suitable for real-time EVCS applications. This work establishes a new benchmark for EVCS security, showcasing the potential of TCNs to deliver scalable and efficient solutions for safeguarding critical charging infrastructure against cyber threats.}, archive = {J_ACCESS}, author = {Ikram Benfarhat and Vik Tor Goh and Chun Lim Siow and Muhammad Sheraz and Teong Chee Chuah}, doi = {10.1109/ACCESS.2025.3529526}, journal = {IEEE Access}, pages = {15272-15289}, shortjournal = {IEEE Access}, title = {Temporal convolutional network approach to secure open charge point protocol (OCPP) in electric vehicle charging}, volume = {13}, year = {2025}, }
• (2025). ChatGPT versus modest large language models: An extensive study on benefits and drawbacks for conversational search. ACCESS, 13, 15253-15271. (www)

  @article{ , abstract = {Large Language Models (LLMs) are effective in modeling text syntactic and semantic content, making them a strong choice to perform conversational query rewriting. While previous approaches proposed NLP-based custom models, requiring significant engineering effort, our approach is straightforward and conceptually simpler. Not only do we improve effectiveness over the current state-of-the-art, but we also curate the cost and efficiency aspects. We explore the use of pre-trained LLMs fine-tuned to generate quality user query rewrites, aiming to reduce computational costs while maintaining or improving retrieval effectiveness. As a first contribution, we study various prompting approaches — including zero, one, and few-shot methods — with ChatGPT (e.g., gpt-3.5-turbo). We observe an increase in the quality of rewrites leading to improved retrieval. We then fine-tuned smaller open LLMs on the query rewriting task. Our results demonstrate that our fine-tuned models, including the smallest with 780 million parameters, achieve better performance during the retrieval phase than gpt-3.5-turbo. To fine-tune the selected models, we used the QReCC dataset, which is specifically designed for query rewriting tasks. For evaluation, we used the TREC CAsT datasets to assess the retrieval effectiveness of the rewrites of both gpt-3.5-turbo and our fine-tuned models. Our findings show that fine-tuning LLMs on conversational query rewriting datasets can be more effective than relying on generic instruction-tuned models or traditional query reformulation techniques.}, archive = {J_ACCESS}, author = {Guido Rocchietti and Cosimo Rulli and Franco Maria Nardini and Cristina Ioana Muntean and Raffaele Perego and Ophir Frieder}, doi = {10.1109/ACCESS.2025.3529741}, journal = {IEEE Access}, pages = {15253-15271}, shortjournal = {IEEE Access}, title = {ChatGPT versus modest large language models: An extensive study on benefits and drawbacks for conversational search}, volume = {13}, year = {2025}, }
• (2025). UBICU: A gamified respiratory incentive system for pulmonary re-expansion. ACCESS, 13, 15244-15252. (www)

  @article{ , abstract = {Pulmonary complications can arise due to trauma, postoperative conditions, or as sequelae of diseases such as COVID-19. To help patients restore lung functionality, most undergo lung re-expansion physiotherapy using a Respiratory Incentive Spirometer. However, currently available Respiratory Incentive Spirometers do not allow for quantitative tracking of performance during inspiration exercises, require physiotherapist supervision to ensure exercise quality, and show low treatment adherence. To address these limitations, we developed UBICU, an assistive product for lung re-expansion physiotherapy. UBICU includes a flow measurement device, an app with a video game interface to motivate exercise performance, and cloud-based software for the remote prescription and evaluation of physiotherapy. We conducted a quasi-experimental study with a sample of 30 healthy individuals, aged 18 to 65 years and balanced by age and sex. We assessed pulmonary ventilation distribution before, during, and after using two flow respiratory incentive spirometers, UBICU and TriFlo, with electrical impedance tomography. Our findings indicate that UBICU provides accurate and reliable measurements of pulmonary ventilation distribution, showing statistically significant improvements after use (p < 0.01), validating its effectiveness as a respiratory incentive spirometer. To evaluate the usability of the video game component, we conducted a perception survey, achieving an acceptance rating above 5 on a scale of 0 to 6. Notably, UBICU was shown to favor lung re-expansion more effectively than the traditional TriFlo mechanical system.}, archive = {J_ACCESS}, author = {Jaime Aguilar-Zambrano and Esther C. Wilches-Luna and Helberg Asencio-Santofimio and Manuel Valencia and Diana Riveros and Andrés Navarro and Juan Carlos Martínez and Eugenio Tamura and José Ángel Loaiza and Julián Hernández and Diana Sánchez and Juan David García and Leonardo Arzayus-Patiño and Valeria Pérez-Hortua and Elizabeth Moncada and Melissa Ramírez}, doi = {10.1109/ACCESS.2025.3528754}, journal = {IEEE Access}, pages = {15244-15252}, shortjournal = {IEEE Access}, title = {UBICU: A gamified respiratory incentive system for pulmonary re-expansion}, volume = {13}, year = {2025}, }
• (2025). Enhancing medical image classification with context modulated attention and multi-scale feature fusion. ACCESS, 13, 15226-15243. (www)

  @article{ , abstract = {This research proposes a multi-stage feature fusion network (MSFF) for medical image classification. In view of the problems existing in medical images, such as noise, diversity, and similarity among different classes, MSFF enhances the global context perception in the window partitioning framework through Context Modulation Attention (CMA). Meanwhile, it extracts fine-grained local information via the multi-stage Contextual Information Refinement (CIR) module and gradually fuses multi-stage local and global features to generate richer semantic representations. The experimental results demonstrate that MSFF significantly outperforms existing methods in multiple performance metrics (including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Kappa coefficient, Area Under the Curve (AUC), balanced accuracy, and geometric mean) on four datasets (Endoscopic Bladder Tissue, Kvasir, SARS-COV-2 Ct-Scan, and Thyroid Nodule), showing its excellent performance in the task of medical image classification.}, archive = {J_ACCESS}, author = {Renhan Zhang and Xuegang Luo and Junrui Lv and Junyang Cao and Yangping Zhu and Juan Wang and Bochuan Zheng}, doi = {10.1109/ACCESS.2025.3532354}, journal = {IEEE Access}, pages = {15226-15243}, shortjournal = {IEEE Access}, title = {Enhancing medical image classification with context modulated attention and multi-scale feature fusion}, volume = {13}, year = {2025}, }
• (2025). A hybrid efficient U-net framework for detection of anterior belly of the digastric muscle on ultrasonography. ACCESS, 13, 15215-15225. (www)

  @article{ , abstract = {The digastric muscle is an important muscle involved in functions such as chewing and swallowing. Ultrasonography is the preferred method for imaging the soft tissues of the head and neck but is highly operator-dependent. Artificial intelligence, particularly deep learning-based segmentation models, has the potential to improve the accuracy and precision of ultrasound images. In this study, a MultiResUNet-Fusion model including residual blocks, multiscale feature fusion, and SE blocks was developed for segmentation of the anterior belly of the digastric muscle. The model was trained on 198 ultrasound images from 99 participants. Combo Loss (a combination of Binary Cross-Entropy and Dice Loss) was used to train the model and segmentation metrics such as F1-score, Intersection over Union (IoU) and Dice Co-efficient were used to evaluate performance. The proposed MultiResUNet-Fusion model provided high accuracy and reliability for the segmentation of the anterior belly of the digastric muscle. The proposed MultiResUNet-Fusion model demonstrated high performance by achieving F1 score (95.38%) and IoU (91.17%). The visual results showed that the segmentation masks of the MultiResUNet-Fusion models provided predictions close to the real labels, and all models generally localized the region of interest accurately. The MultiResUNet-Fusion model provides high accuracy in low-contrast ultrasound images, making it suitable for clinical applications. The model can contribute to clinical diagnostic processes with its ability to accurately detect small and large structures. Future studies can increase the generalization capacity of the model by testing it in different modalities.}, archive = {J_ACCESS}, author = {Sule Erdem and Suheda Erdem and Muammer Turkoglu and Abdulkadir Sengur and Nebras M. Sobahi}, doi = {10.1109/ACCESS.2025.3532090}, journal = {IEEE Access}, pages = {15215-15225}, shortjournal = {IEEE Access}, title = {A hybrid efficient U-net framework for detection of anterior belly of the digastric muscle on ultrasonography}, volume = {13}, year = {2025}, }
• (2025). Intelligent acting digital twins (IADT). ACCESS, 13, 15201-15214. (www)

  @article{ , abstract = {Digital twin technology has become a key enabler in modern system design and operation, allowing for real-time monitoring, simulation, and optimization. In this article, we present the Intelligent Acting Digital Twin (IADT), a concept that addresses a critical limitation of conventional digital twins, which are primarily passive tools for observation and simulation. The IADT introduces an active dimension by enabling digital twins to autonomously and adaptively control the physical counterpart. This is achieved through a reengineered system architecture that makes the physical system dynamically reprogrammable by the digital twin, ensuring seamless coordination and operational efficiency. The main contribution of the IADT lies in its ability to bridge the gap between virtual and physical systems, allowing for real-time decision-making and control in complex environments. For example, in a scenario involving a drone intercepting a virtual aircraft, the IADT functions as the central control unit, where real-time data and predictive models are used to autonomously guide the physical system. As proposed to traditional approaches, this bidirectional communication enhances adaptability, responsiveness, and overall system intelligence. The concept’s feasibility is validated through practical implementations using the CupCarbon simulator. These implementations demonstrate how the IADT integrates virtual and physical components to create a unified and effective framework, offering a significant advancement in the application of digital twin technology across various domains.}, archive = {J_ACCESS}, author = {Ahcene Bounceur and Mostefa Kara}, doi = {10.1109/ACCESS.2025.3532545}, journal = {IEEE Access}, pages = {15201-15214}, shortjournal = {IEEE Access}, title = {Intelligent acting digital twins (IADT)}, volume = {13}, year = {2025}, }
• (2025). Leveraging partial labels for cervical lesion classification via a multilabel approach. ACCESS, 13, 15188-15200. (www)

  @article{ , abstract = {Early detection and diagnosis of cervical lesions is crucial for mitigating cervical cancer. Because of limited access to adequate annotations, existing studies on cervical lesion classification have primarily focused on generalized classification. This study introduces a multilabel approach to cervical lesion classification through a data-centric framework. We first enhanced data quality via a comprehensive preprocessing pipeline that reduces the dataset to usable cervigrams. This refined dataset is then fed to a Vision Transformer, which performs per-class feature extraction and incorporates a part selection module that emphasizes critical areas within the cervigram, while integrating inter-class information within both early and late stages of the model. These procedures are conducted under the assumption of potential partial labeling. Upon evaluation, our proposed method outperformed existing models, achieving the highest ROC-AUC scores across all lesion grades. These findings suggest that the improved attention mechanisms led to enhanced localization of lesions, enabling focus on the fine details of each lesion. Overall, this study explores the potential of multilabel classification for advancing cervical lesion detection.}, archive = {J_ACCESS}, author = {Margaret Dy Manalo and Kota Aoki and Shuqiong Wu and Mariko Shindo and Yutaka Ueda and Yasushi Yagi}, doi = {10.1109/ACCESS.2025.3530504}, journal = {IEEE Access}, pages = {15188-15200}, shortjournal = {IEEE Access}, title = {Leveraging partial labels for cervical lesion classification via a multilabel approach}, volume = {13}, year = {2025}, }
• (2025). High step-up ZVT converter based on active switched coupled inductors. ACCESS, 13, 15178-15187. (www)

  @article{ , abstract = {To meet the requirements of high voltage boosting and high efficiency, a novel high step-up zero voltage transition (ZVT) DC/DC converter based on active switched-inductor (ASL) is proposed. This converter combines ASL and coupled inductor structure, thus can reduce the voltage and current stress of power switch, and the active clamping technology provides zero voltage transition (ZVT) for all switches. The coupled inductors can realize relatively high voltage gain with appropriate turns ratio while all magnetic components can be integrated in one core. In addition, thanks to the leakage inductance, the reverse recovery problem of diodes is resolved. The working principle of the proposed converter is analyzed in detail, also the characteristics including voltage gain, the condition of ZVT is discussed. Then, a family of derived converters are listed and compared to each other. According to the proposed converter, a 500W prototype with 100kHz switching frequency is established in the lab, and the experimental results are given to verify the analysis.}, archive = {J_ACCESS}, author = {Yu Tang and Haisheng Tong and Raheel Afzal and Yingjun Guo}, doi = {10.1109/ACCESS.2020.3041004}, journal = {IEEE Access}, pages = {15178-15187}, shortjournal = {IEEE Access}, title = {High step-up ZVT converter based on active switched coupled inductors}, volume = {13}, year = {2025}, }
• (2025). Evaluating the influence of execution speed on real-time simulation accuracy: A buck converter case study. ACCESS, 13, 15168-15177. (www)

  @article{ , abstract = {The relevance of power electronics converters is undoubted and is growing in many modern applications. Real-Time Simulation (RTS) is commonly used to test converters before deployment, assessing what-if scenarios that may pose risks or add costs. RTS can also run alongside the real system for online assessment through direct comparison. Most of the research in this area focuses on accelerating the execution of RTS to comply with shorter sampling times. As a result, Field-Programmable Gate Arrays (FPGAs) are typically preferred, while microcontrollers have been mostly overlooked under the premise of their inferior throughput. However, it has not been assessed how fast a RTS must actually be, beyond the usual suggestion of always accelerating its execution. Therefore, this study was intended to find a fair balance between computational requirements and RTS error, aiming to identify operating conditions applicable to digital systems. A case study on a commercial buck converter with a RTS in a microcontroller tested the modeling Integral Squared Error (ISE). The RTS was run at various execution frequencies while the input PWM frequency varied between 2 and 100 times faster; 6,000 randomized duty cycles (0.2 to 0.8) were tested and their ISEs were statistically analyzed. The results show that smaller RTS time steps do not necessarily reduce the error. The minimum ISE occurred when the RTS execution frequency was about 35 to 45 times higher than the PWM frequency, regardless of the RTS frequency. This study challenges the idea that faster RTS always means lower error and demonstrates the potential of low-end digital devices for RTS.}, archive = {J_ACCESS}, author = {Jesús Camacho and Luis Ibarra and Pedro Ponce}, doi = {10.1109/ACCESS.2024.3452048}, journal = {IEEE Access}, pages = {15168-15177}, shortjournal = {IEEE Access}, title = {Evaluating the influence of execution speed on real-time simulation accuracy: A buck converter case study}, volume = {13}, year = {2025}, }
• (2025). Halal or not: Knowledge graph completion for predicting cultural appropriateness of daily products. ACCESS, 13, 15158-15167. (www)

  @article{ , abstract = {The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on analyzing the discrete and specific ingredients within separate cosmetics, which ignore the high-order and complex relations between cosmetics and ingredients. To address this problem, we propose a halal cosmetic recommendation framework, namely HaCKG, that leverages a knowledge graph of cosmetics and their ingredients to explicitly model and capture the relationships between cosmetics and their components. By representing cosmetics and ingredients as entities within the knowledge graph, HaCKG effectively learns the high-order and complex relations between entities, offering a robust method for predicting halal status. Specifically, we first construct a cosmetic knowledge graph representing the relations between various cosmetics, ingredients, and their properties. We then propose a pre-trained relational graph attention network model with residual connections to learn the structural relation between entities in the knowledge graph. The pre-trained model is then fine-tuned on downstream cosmetic data to predict halal status. Extensive experiments on the cosmetic dataset over halal prediction tasks demonstrate the superiority of our model over state-of-the-art baselines.}, archive = {J_ACCESS}, author = {Van Thuy Hoang and Tien-Bach-Thanh do and Jinho Seo and Seung Charlie Kim and Luong Vuong Nguyen and Duong Nguyen Minh Huy and Hyeon-Ju Jeon and O-Joun Lee}, doi = {10.1109/ACCESS.2025.3528488}, journal = {IEEE Access}, pages = {15158-15167}, shortjournal = {IEEE Access}, title = {Halal or not: Knowledge graph completion for predicting cultural appropriateness of daily products}, volume = {13}, year = {2025}, }
• (2025). A novel approach to faster convergence and improved accuracy in deep learning-based electrical energy consumption forecast models for large consumer groups. ACCESS, 13, 15090-15157. (www)

  @article{ , abstract = {Deep learning-based models are ideally suited for accurately predicting electrical load in a smart grid. However, the computational overhead in training models and identifying optimal training hyperparameters are challenging problems. Two important challenges are addressed in this study. Firstly, a novel technique is proposed to accelerate the model training process by selecting optimal mini-batch size. The second challenge encountered is maintaining and operationalizing individual consumption forecasting models as they are not easily scalable. A methodology is proposed to generate deep learning-based models capable of forecasting energy consumption simultaneously for large user groups. The proposed methodology was empirically validated on data from 4710 individual users in ISSDA CER Dataset (approximately 25,632 datapoints/user) and 11 Feeder dataset data from NYISO (approximately 106163 datapoints/feeder). Eight deep learning algorithms trained using eleven mini-batch combinations, for two forecasting horizons resulted in 176 workflows. Training on 167 sample CER ISSDA user data resulted in 24,288 models and 75,360 individual models for the optimal mini batch. Similarly, 1,936 models were generated for the 11 NYISO Feeder datasets. A clustering approach is employed to group users with similar consumption patterns resulting in 107 models. Effectiveness of models is validated with eight deep learning algorithms trained for two forecasting horizons. Results demonstrate a 93.74% improvement on an average in convergence compared to conventional mini-batch sizes, achieving one among top three accuracy across various prediction horizons and user categories. This underscores the importance of tailored mini-batch selection methods for effective energy forecasting and management in Smart Grid systems.}, archive = {J_ACCESS}, author = {A. Jayanth Balaji and Binoy B. Nair and D. S. Harish Ram and Kuruvachan Kalluvelil George}, doi = {10.1109/ACCESS.2025.3527863}, journal = {IEEE Access}, pages = {15090-15157}, shortjournal = {IEEE Access}, title = {A novel approach to faster convergence and improved accuracy in deep learning-based electrical energy consumption forecast models for large consumer groups}, volume = {13}, year = {2025}, }
• (2025). Pencil beam planar ka-band antenna array using waveguide displaced radiators sections for millimeter wave applications. ACCESS, 13, 15079-15089. (www)

  @article{ , abstract = {This study presents the design and analysis of a wideband planar Ka-band antenna array for millimeter-wave 5G wireless systems, radar, and satellite communication systems. The proposed antenna array utilizes waveguide sections with radiating elements (grooves) positioned on the outer side and a feeder at a specific distance from the planar input surface. The radiating elements in each waveguide section are symmetrically at a distance from the planar input surface, determined mathematically to ensure in-phase excitation. The design simplifies construction by eliminating the need for a separate mirror component, thereby increasing the gain coefficient by reducing shading and scattering effects. The methodology involves arranging waveguide sections in a single plane, with radiating elements positioned to create an excitation field with specific amplitude and phase distributions. The theoretical framework is based on using waveguide structures with periodically placed irregularities to produce a pencil beam radiation pattern. The proposed antenna has been carefully studied using CST microwave studio software. Key results of the proposed design include a main lobe directivity of 29.9 dBi, a side lobe level (SSL) of -14.8 dB, width of radiation pattern of 3.6° at 37.5 GHz, and an impedance bandwidth of $S_{11} \lt -10$ dB from 28 to 34.4 GHz and 34.9 to 37.9 GHz, validating the antenna’s effectiveness.}, archive = {J_ACCESS}, author = {Hany S. Hussein and Mohamed Ismeil and M. S. Yasseen and Yuri E. Sedelnikov and Mohamed N. Shaaban}, doi = {10.1109/ACCESS.2024.3517475}, journal = {IEEE Access}, pages = {15079-15089}, shortjournal = {IEEE Access}, title = {Pencil beam planar ka-band antenna array using waveguide displaced radiators sections for millimeter wave applications}, volume = {13}, year = {2025}, }
• (2025). Smart city traffic flow and signal optimization using STGCN-LSTM and PPO algorithms. ACCESS, 13, 15062-15078. (www)

  @article{ , abstract = {Urban traffic congestion remains a critical challenge for smart city development, necessitating innovative approaches to improve traffic flow and reduce delays. This study presents a novel framework that integrates the Spatiotemporal Graph Convolutional Network-Long Short-Term Memory (STGCN-LSTM) model for traffic flow prediction with the Proximal Policy Optimization (PPO) algorithm for dynamic traffic signal control. The STGCN-LSTM model captures complex spatiotemporal dependencies, achieving an R2 of 0.904 on the METR-LA dataset. Extensive experiments and ablation studies highlight the complementary strengths of STGCN and LSTM, with the hybrid model outperforming standalone variants. The PPO algorithm dynamically adjusts signal timings, reducing vehicle waiting times by 30% and increasing traffic throughput by 15%. Incorporating external factors, such as weather and holidays, enhances the framework’s robustness in dynamic conditions, including adverse weather and traffic surges. GPU acceleration ensures scalability, enabling deployment in large-scale urban networks efficiently. This framework demonstrates significant potential to address urban congestion, reduce carbon emissions by 12%, and support sustainable urban development. Future research will explore edge computing, multi-agent reinforcement learning, and real-time data integration to further enhance scalability and adaptability.}, archive = {J_ACCESS}, author = {Tuxiang Lin and Rongliang Lin}, doi = {10.1109/ACCESS.2024.3519512}, journal = {IEEE Access}, pages = {15062-15078}, shortjournal = {IEEE Access}, title = {Smart city traffic flow and signal optimization using STGCN-LSTM and PPO algorithms}, volume = {13}, year = {2025}, }
• (2025). Advancing environmental sustainability: A comprehensive review of waste-based composite materials for efficient electromagnetic shielding and absorption. ACCESS, 13, 15028-15061. (www)

  @article{ , abstract = {The surge in electronic devices and wireless communication technologies has increased the demand for effective electromagnetic interference (EMI) shielding and microwave-absorbing materials. Traditional EMI shielding materials, which predominantly utilize metals and polymer composites, pose significant environmental challenges due to their non-biodegradable nature. Recently, a range of novel electromagnetic shielding materials have been developed, with composite materials derived from waste sources emerging as promising alternatives for next-generation shielding solutions. This comprehensive review explores sustainable materials derived from various sources of recycled waste for EMI shielding applications. It begins by examining the fundamental principles and mechanisms of EMI shielding and absorption techniques necessary for cost-effective performance. The review further investigates a variety of sustainable materials, including agricultural residues, industrial by-products, animal waste, and electronic waste, evaluating their potential for EMI shielding. Additionally, it explores the structural characteristics, and electromagnetic properties, of composite materials created from different combinations of raw materials. The review further highlights the processing techniques and integration strategies used to develop effective shielding materials, as well as the potential applications of these materials and the challenges associated with their synthesis. The innovative use of solid waste materials from diverse origins in creating these shielding materials not only enhances their efficiency but also contributes to effective waste management and environmental sustainability.}, archive = {J_ACCESS}, author = {Mohamed Ouda and Ala A. Abu Sanad and Aparna Krishna and Munther Issa Kandah and Jamal Kurdi}, doi = {10.1109/ACCESS.2025.3529280}, journal = {IEEE Access}, pages = {15028-15061}, shortjournal = {IEEE Access}, title = {Advancing environmental sustainability: A comprehensive review of waste-based composite materials for efficient electromagnetic shielding and absorption}, volume = {13}, year = {2025}, }
• (2025). Benchmarking vision-based object tracking for USVs in complex maritime environments. ACCESS, 13, 15014-15027. (www)

  @article{ , abstract = {Vision-based target tracking is crucial for unmanned surface vehicles (USVs) to perform tasks such as inspection, monitoring, and surveillance. However, real-time tracking in complex maritime environments is challenging due to dynamic camera movement, low visibility, and scale variation. Typically, object detection methods combined with filtering techniques are commonly used for tracking, but they often lack robustness, particularly in the presence of camera motion and missed detections. Although advanced tracking methods have been proposed recently, their application in maritime scenarios is limited. To address this gap, this study proposes a vision-guided object tracking framework for USVs, integrating state-of-the-art tracking algorithms with low-level control systems to enable precise tracking in dynamic maritime environments. We benchmarked the performance of seven distinct trackers, developed using advanced deep learning techniques such as Siamese Networks and Transformers, by evaluating them on both simulated and real-world maritime datasets. In addition, we evaluated the robustness of various control algorithms in conjunction with these tracking systems. The proposed framework was validated through simulations and real-world sea experiments, demonstrating its effectiveness in handling dynamic maritime conditions. The results show that SeqTrack, a Transformer-based tracker, performed best in adverse conditions, such as dust storms. Among the control algorithms evaluated, the linear quadratic regulator controller (LQR) demonstrated the most robust and smooth control, allowing for stable tracking of the USV. Videos and code can be found here: https://muhayyuddin.github.io/tracking/.}, archive = {J_ACCESS}, author = {Muhayy Ud Din and Ahsan Baidar Bakht and Waseem Akram and Yihao Dong and Lakmal Seneviratne and Irfan Hussain}, doi = {10.1109/ACCESS.2025.3532299}, journal = {IEEE Access}, pages = {15014-15027}, shortjournal = {IEEE Access}, title = {Benchmarking vision-based object tracking for USVs in complex maritime environments}, volume = {13}, year = {2025}, }
• (2025). Comprehensive study of E-bike braking dynamics: Modeling, simulation, and experimental validation. ACCESS, 13, 14998-15013. (www)

  @article{ , abstract = {The use of e-bikes has been on a rapid rise in the last few years. However, there are safety concerns associated with the use of these products that will limit their acceptance and the growth rate of their use. The objective of this work is to develop a physics-based model of bike braking dynamics that describes the braking performance under critical safety events (CSE) conditions. The CSE includes faults in braking system components. The early detection of these faults will facilitate sending a proactive alert to undertake proactive maintenance of the braking system are crucial to ensure safety and prevent injuries. In this paper, a model of the bike braking system during straight line maneuver is developed and validated. This model will be used for detection and isolation of braking system components faults as well as prediction of their failure. The model includes bike and passive rider dynamics, wheel dynamics, the mechanical brake linkages and the tire-road friction interaction including tire slip. The behavior of the mechanical brake linkages that relates the brake lever force, lever displacement and the brake pad normal force on the brake disk is modeled based on experimental tests. The model developed is tested through simulation and experimental acceleration test, where the bike is driven to achieve a desired speed and the brakes are applied at front and back levers with different intensities.}, archive = {J_ACCESS}, author = {Mutasim Salman and Shivam Chaturvedi and Wencong Su}, doi = {10.1109/ACCESS.2025.3532296}, journal = {IEEE Access}, pages = {14998-15013}, shortjournal = {IEEE Access}, title = {Comprehensive study of E-bike braking dynamics: Modeling, simulation, and experimental validation}, volume = {13}, year = {2025}, }
• (2025). Hyper-heuristics and scheduling problems: Strategies, application areas, and performance metrics. ACCESS, 13, 14983-14997. (www)

  @article{ , abstract = {Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed publications over the last decade. We categorize and examine the prevailing strategies and configurations of hyper-heuristics, mainly focusing on their application across diverse scheduling scenarios such as job shop, flow shop, timetabling, and project scheduling. Our findings reveal a strong inclination towards selection and perturbative hyper-heuristics, with evolutionary computation emerging as the most commonly employed technique in this context, particularly in job shop and timetabling problems. Despite the robust development in hyper-heuristic methodologies, our analysis indicates an under-representation of multi-objective optimization and a limited use of performance metrics beyond makespan and tardiness. We also identify potential areas for future research, such as expanding hyper-heuristic applications to underexplored industries and exploring less conventional performance metrics. By providing a comprehensive overview of the current landscape and outlining future research directions, we aim to guide and inspire ongoing innovations in scheduling problem research.}, archive = {J_ACCESS}, author = {Alonso Vela and Gerardo Humberto Valencia-Rivera and Jorge M. Cruz-Duarte and José Carlos Ortiz-Bayliss and Ivan Amaya}, doi = {10.1109/ACCESS.2025.3532201}, journal = {IEEE Access}, pages = {14983-14997}, shortjournal = {IEEE Access}, title = {Hyper-heuristics and scheduling problems: Strategies, application areas, and performance metrics}, volume = {13}, year = {2025}, }
• (2025). Recent developments of directional overcurrent relay coordination in ring distribution network based on hybrid optimization techniques. ACCESS, 13, 14961-14982. (www)

  @article{ , abstract = {As power distribution systems evolve from conventional radial structures to more complex ring networks, driven by the widespread integration of distributed generation (DG), the challenge of ensuring reliable and effective protection becomes increasingly critical. Directional Overcurrent Relays (DOCRs) are pivotal in this context, protect the network through precise coordination between primary and backup protection schemes. However, optimizing these settings is a daunting task due to the nonlinear nature of the problem and the myriad constraints involved. This paper embarks on a comprehensive exploration of cutting-edge optimization techniques deployed to tackle DOCR coordination challenges. The paper delves into Nature-Inspired Algorithms (NIAs), metaheuristic approaches, mathematical formulations, and artificial intelligence methods, critically assessing their capabilities in navigating the complex landscape of relay coordination. While NIAs excel in exploring vast search spaces, their slower convergence can be a drawback. Metaheuristics offer robust solutions but at the cost of high computational demand, and traditional mathematical methods, though precise, may fall short in dynamic environments. Pushing the boundaries of current methodologies, the paper investigates hybrid optimization techniques that fuse the strengths of multiple approaches. These hybrids show promise in transcending the limitations of individual methods, offering innovative solutions for optimizing relay coordination in increasingly intricate network configurations. Moreover, this paper sheds light on the profound impact of emerging energy technologies on existing protection frameworks, emphasizing the urgent need for adaptive strategies to ensure stable and reliable power delivery. Through this investigation, actionable insights and forward-thinking solutions are proposed to bolster the safety and resilience of modern distribution networks, positioning them to meet the demands of a rapidly transforming energy landscape.}, archive = {J_ACCESS}, author = {Annas Alamshah and Suzana Pil Ramli and Nurulafiqah Nadzirah Mansor and Muhamad Usama and Hazlie Mokhlis and Hadi Suyono and Rini Nur Hasanah}, doi = {10.1109/ACCESS.2024.3524589}, journal = {IEEE Access}, pages = {14961-14982}, shortjournal = {IEEE Access}, title = {Recent developments of directional overcurrent relay coordination in ring distribution network based on hybrid optimization techniques}, volume = {13}, year = {2025}, }
• (2025). Volt-PF control mode for distribution feeder voltage management under high penetration of distributed energy resources. ACCESS, 13, 14949-14960. (www)

  @article{ , abstract = {Voltage-power factor (Volt-PF) control is introduced and compared with the popular voltage-reactive power (Volt-VAr) control for feeder voltage management. An inherent limitation of the Volt-VAr control is that the reactive power (Q) demanded from the distributed energy resources (DERs) is only a function of the terminal voltage and not the DER active power (P). This leads to an unfair allocation of the burden of Q support among the DERs, in the sense that those DERs generating lower P, and hence contributing less to overvoltage issues, may be required to provide more than their share of Q support, and operate at a very low power factor (PF). The proposed volt-PF scheme, where the Q support is inherently a function of both the voltage and P of the DERs, ensures fair allocation of the Q support burden and ensures that all the DERs operate at a high PF (0.9 to 1) The performance of the proposed scheme is validated through extensive static and dynamic simulations on a real, large (8000+ nodes) feeder with very high penetration (>200%) of DERs. The implementation of the proposed scheme in new and existing commercial hardware inverters is described.}, archive = {J_ACCESS}, author = {Madhura Sondharangalla and Dan Moldovan and Raja Ayyanar}, doi = {10.1109/ACCESS.2025.3531492}, journal = {IEEE Access}, pages = {14949-14960}, shortjournal = {IEEE Access}, title = {Volt-PF control mode for distribution feeder voltage management under high penetration of distributed energy resources}, volume = {13}, year = {2025}, }
• (2025). Robust classification of smartphone captured handwritten document images using deep learning. ACCESS, 13, 14922-14948. (www)

  @article{ , abstract = {Document classification is a challenging research problem in Document image analysis, majorly the presence of degradations like low illumination, blur, and shadows. In this paper, a deep learning-based two-level hierarchical approach is proposed for classifying smartphone-captured handwritten document images into three classes. The model is devised based on the pre-trained weights transferred from XceptionNet and the additional convolutional layers for feature extraction focused on high-frequency details from document images. A set of synthetically generated datasets concerning all three classes with two to three levels of degradation severity is considered for training the proposed deep learning model along with the real images labelled based on reference datasets. The dataset comprises blur and low illumination images with three levels of severity, and shadow images with two levels of severity. About 2,841 images from classes with 783 for blur, 828 for low illumination, and 1,230 for shadow were collected. The proposed model is compared with nine state-of-the-art deep-learning classification models. The proposed model showcases robust performance under varying levels of degradation severity levels by achieving 100% sensitivity towards shadow documents, while maintaining a high F1 score of 98.79% for Level 1. In case of low illumination conditions, again the model produces 100% precision, sensitivity, and specificity at Level 3 towards testsets. The results emphasize the model’s robustness to perform reliably, particularly under shadow and low illumination conditions.}, archive = {J_ACCESS}, author = {K. S. Koushik and B. J. Bipin Nair and N. Shobha Rani and Mohammed Javed}, doi = {10.1109/ACCESS.2024.3520327}, journal = {IEEE Access}, pages = {14922-14948}, shortjournal = {IEEE Access}, title = {Robust classification of smartphone captured handwritten document images using deep learning}, volume = {13}, year = {2025}, }
• (2025). Automated string art creation: Integrated advanced computational techniques and precision art designing. ACCESS, 13, 14908-14921. (www)

  @article{ , abstract = {The Thread Art Machine project automates the traditional, labour-intensive process of string art creation by integrating advanced computational and manufacturing techniques. Utilizing CAD models in Fusion 360, CNC machining, 3D printing, and Arduino programming, the machine precisely sets threads and nails to form intricate string art patterns. Key components of the machine include adaptive algorithms for real-time thread tension adjustments and transposed convolution layers for image processing, which enhance both the accuracy and aesthetic quality of the final artwork. The project faced challenges such as material selection, CAD design, and hardware-software interfacing, all of which were addressed through iterative design and validation processes. A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. The machine’ s calibration system, involving LDR sensors and laser alignment, ensures precision in thread placement. The resulting string art pieces, derived from famous images, demonstrate the machine’ s capability to capture key features through varying string densities, offering a blend of mathematical precision and artistic abstraction. Validation through training and testing revealed consistent performance, with minimal overfitting, as indicated by flat training and validation loss and accuracy curves.}, archive = {J_ACCESS}, author = {Spoorthi Singh and Navya T. Hegde and Mohammad Zuber and Yuvraj Singh Nain and Vishnu G. Nair}, doi = {10.1109/ACCESS.2025.3530970}, journal = {IEEE Access}, pages = {14908-14921}, shortjournal = {IEEE Access}, title = {Automated string art creation: Integrated advanced computational techniques and precision art designing}, volume = {13}, year = {2025}, }
• (2025). The use of data mining in public budgeting: A systematic literature mapping. ACCESS, 13, 14891-14907. (www)

  @article{ , abstract = {Planning and allocating public resources is essential because resources are always limited and must be sufficient to meet a country’s needs. Therefore, it is necessary to define how resources are distributed based on the amount collected, directly affecting society in the most diverse areas, such as education and health. Owing to advances in artificial intelligence in recent years, studies have been conducted to explore and propose intelligent solutions that enable the most diverse analyses in this critical area. Among these, data mining has emerged as a viable solution. Generally, data mining consists of three major steps: pre-processing, pattern extraction, and post-processing. Thus, to understand how data mining has been used in the most diverse subjects related to public planning and budgeting, this study presents systematic literature mapping. The aims were (i) to provide an overview of the aspects related to the data mining steps in the presented context and, (ii) to identify gaps that can be addressed and/or explored. The results are presented and discussed throughout this paper based on 30 papers selected over 10 years (from 2014 to 2023), with the potential to significantly impact future research and practice in public planning and data mining.}, archive = {J_ACCESS}, author = {José Cláudio Guedes Das Neves and Veronica Oliveira De Carvalho}, doi = {10.1109/ACCESS.2025.3531834}, journal = {IEEE Access}, pages = {14891-14907}, shortjournal = {IEEE Access}, title = {The use of data mining in public budgeting: A systematic literature mapping}, volume = {13}, year = {2025}, }
• (2025). Adaptive parameter approaching law-based sliding mode control for wheeled robots. ACCESS, 13, 14881-14890. (www)

  @article{ , abstract = {A method combining adaptive parameter approaching law with Radial Basis Function (RBF) neural network hierarchical sliding mode control is proposed to address the issues of low accuracy and significant oscillations during the trajectory tracking process of wheeled inverted pendulum robots (WIPR) with nonlinear underactuation characteristics. Compared to the traditional approach of combining approaching law with neural network sliding mode control, the innovation lies in the adaptive adjustment of the velocity of the motion point based on its distance to the sliding mode surface. By integrating the advantages of hierarchical sliding mode control and neural networks, the method effectively tracks the target trajectory. For the control system of multivariable complex robots, it is decomposed into second-order underactuated subsystems and first-order fully actuated subsystems, and the stability of the designed system is verified using Lyapunov functions. Simulations and comparative experiments are conducted using Matlab/Simulink. The results indicate that, compared to the method combining exponential approaching law, saturation approaching law, and RBF neural network hierarchical sliding mode control, this method exhibits better tracking performance and effectively suppresses oscillations generated during the motion process, demonstrating improved stability when the system is subjected to disturbances.}, archive = {J_ACCESS}, author = {Ming Hou and Limeng Jia and Zhengqin Wang}, doi = {10.1109/ACCESS.2024.3474301}, journal = {IEEE Access}, pages = {14881-14890}, shortjournal = {IEEE Access}, title = {Adaptive parameter approaching law-based sliding mode control for wheeled robots}, volume = {13}, year = {2025}, }
• (2025). Improved K-means algorithm for nearby target localization. ACCESS, 13, 14872-14880. (www)

  @article{ , abstract = {In a multi-source localization system, direction of arrival (DOA) estimation of angles always suffers from errors due to noise interference, sensor position inaccuracies, and other factors. When the distance between target sources is much smaller than the distance between sensors and target sources, the accuracy of traditional localization algorithms based on direction finding and cross-fixing deteriorates. In this paper, we propose a localization algorithm based on K-means clustering. To tackle the problem of unknown initial positions of target sources, we employ a grid density peak clustering(DPC) method for initial localization. In the K-means algorithm, we integrate a quartile range anomaly detection algorithm to address interference signal issues. Finally, we propose an invalid compensation algorithm to filter out invalid signals, thereby compensating for the estimation errors in angles. Through the collection of real-world data, we compare the performance of the traditional direction finding and cross-fixing algorithms with the proposed algorithm in the localization of nearby target points. Experimental results demonstrate that the proposed algorithm significantly improves localization accuracy.}, archive = {J_ACCESS}, author = {Zongwen Yuan and Xingdi Wang and Fuyang Chen and Xicheng Ma}, doi = {10.1109/ACCESS.2024.3479091}, journal = {IEEE Access}, pages = {14872-14880}, shortjournal = {IEEE Access}, title = {Improved K-means algorithm for nearby target localization}, volume = {13}, year = {2025}, }
• (2025). Development of a multi-sensor mobile device for urban air quality monitoring at the street corner: The SMILE project. ACCESS, 13, 14857-14871. (www)

  @article{ , abstract = {Air pollution is a critical contributor to the global climate change crisis and poses severe threats to human health worldwide. In this context, this paper introduces an innovative multi-sensor air quality monitoring (AQM) device designed to address the critical challenge of atmospheric pollution in urban environments. The AQM device features an optimized geometry specifically suited for mobility. It enables precise air quality (AQ) monitoring by measuring concentrations of NO2, O3, and CO, along with particulate matter (PM) across three size categories: PM1, PM2.5, and PM10. The AQM device ensures reliable operation across temperature variations from 10°C to 40°C and humidity levels ranging from 10% to 70%. Outdoor experiments were conducted in the city of Marseille to validate the efficiency of the AQM device and assess its ability to provide street-level air quality data. The AQM device was validated by comparing its measurements with data from four reference instruments (i.e., government-operated fixed stations) located within an 8 km radius of the center of Marseille. Real-time data is made available through a dedicated software server platform, offering geo-localized air quality information and detailed reports. The developed device offers excellent autonomy, with a full battery discharge lasting up to 28 hours, allowing it to be used throughout an entire day without the need for recharging. The proposed multi-sensor device was developed in the scope of the SMILE project (Self-calibrating air pollution Multi-sensors and ICDT platform to Leverage citizen’s Empowerment). The project aims to empower end users with the responsibility of carrying, activating, and monitoring their own sensor devices while utilizing specialized apps on their mobile phones.}, archive = {J_ACCESS}, author = {Jonas Pellegrino and Hassen Aziza and Mathieu Guerin and Pascal Taranto and Wenceslas Rahajandraibe and Blaise Ravelo}, doi = {10.1109/ACCESS.2025.3530145}, journal = {IEEE Access}, pages = {14857-14871}, shortjournal = {IEEE Access}, title = {Development of a multi-sensor mobile device for urban air quality monitoring at the street corner: The SMILE project}, volume = {13}, year = {2025}, }
• (2025). PaySwitch: Smart contract-based payment switch for off-chain payment channel networks. ACCESS, 13, 14837-14856. (www)

  @article{ , abstract = {Payment channel networks (PCNs) are a viable solution to the issue of blockchain scalability by offering off-chain transactions between two untrusted peers, without committing each transaction to the blockchain. The payment channel cannot be used when its deposit is depleted, which hinders the payment completion, such as it is overly utilized in one direction. However, they cannot migrate the deposit from one channel to another to facilitate payment completion when a depletion in the channel deposit arises. This limitation arises from the basic design assumption of payment channels, i.e., each channel is independent from others. To overcome this limitation, this paper proposes PaySwitch, inspired by the traditional network switch, to enable connecting multiple payment channels through one PaySwitch smart contract. PaySwitch is designed to enable any peer in the PaySwitch to dynamically reallocate the deposits in their payment channels while maintaining the independence of each other to ensure the trustworthiness of the channel. This paper presents the security definition of PaySwitch and formally proves its security using the UC-framework. To demonstrate the feasibility of PaySwitch, this paper applies it to several routing algorithms. The experimental results show that PaySwitch is capable of improving the transaction success rate and throughput compared with the traditional payment channel based approaches.}, archive = {J_ACCESS}, author = {Anan Jin and Yuhang Ye and Brian Lee and Xiang Li and Yuansong Qiao}, doi = {10.1109/ACCESS.2025.3527746}, journal = {IEEE Access}, pages = {14837-14856}, shortjournal = {IEEE Access}, title = {PaySwitch: Smart contract-based payment switch for off-chain payment channel networks}, volume = {13}, year = {2025}, }
• (2025). Metabolomics biomarkers in prediction of sudden infant death syndrome: The role of short chain fatty acids. ACCESS, 13, 14820-14836. (www)

  @article{ , abstract = {Sudden Infant Death Syndrome (SIDS) presents a significant challenge, necessitating ongoing research and preventive measures. The intricate landscape of lipid metabolism plays a crucial role in SIDS, with disruptions in key lipid components like Short Chain Fatty Acids (SCFA), alongside other lipids such as triglycerides (TG) and phospholipids (PL), being significant. In this context, SCFA are essential products of the fermentation process by gut microbiota, hold particular interest. SCFA are integral to energy regulation and metabolism, influencing overall well-being. Their unique characteristics, such as chain length and saturation level, provide insights into their potential effects. Alterations in SCFA metabolism can disrupt energy balance, adding to the complexity of SIDS. Leveraging machine learning (ML) presents a promising avenue for unraveling the intricate profiles of SCFA and decoding patterns indicative of heightened SIDS risk. Ensuring interpretability in healthcare is essential for building trust and developing effective prevention strategies. This research delves into understanding SIDS, with a specific focus on SCFA and their role in metabolic health. The application of ML, particularly the Artificial Neural Network (ANN) and Stacking model, demonstrated exceptional accuracy of 94% and 96.15% with a recall of 100% and 92.31%, respectively. The models also demonstrated strong classification capabilities, as indicated by a high True Positive Rate (TPR) in the AUC, a low Root Mean Square Error (RMSE) of 0.20, Mean Absolute Error (MAE) of 0.04 and Standard deviation (SD) of 0.10, emphasizing the robustness and precision of the approach. These results underscore the potential of ML in the early assessment of SIDS risk, highlighting the critical role of SCFA and advancing the prospects for preventative healthcare.}, archive = {J_ACCESS}, author = {Maria Aslam and Omer Riaz and Jawaria Aslam and Dost Muhammad Khan and Mustafa Hameed and Muhammad Suleman and Rizwan Shahid and Turke Althobaiti and Naeem Ramzan}, doi = {10.1109/ACCESS.2024.3521116}, journal = {IEEE Access}, pages = {14820-14836}, shortjournal = {IEEE Access}, title = {Metabolomics biomarkers in prediction of sudden infant death syndrome: The role of short chain fatty acids}, volume = {13}, year = {2025}, }
• (2025). Grid-scale battery systems for VAR support in con edison distribution substations via bi-directional smart inverters. ACCESS, 13, 14801-14819. (www)

  @article{ , abstract = {Battery Energy Storage Systems (BESSs) play a pivotal role in enhancing the grid’s reliability by integrating Distributed Energy Resources (DERs) and offering a range of services, notably including reactive power (VAR) support through the use of bidirectional smart inverters. According to the literature review carried out and presented in this paper, it is evident that reactive power control for DER applications and grid have been examined in various studies. However, there is still a need to perform a thorough and explicit investigation on the VAR support impact on battery lifetime– we attempt to fill this gap with this study. This work aims to explore the effects of reactive power compensation provided by BESS smart inverters on the system’s lifespan, and to identify strategies for mitigating any potential negative impacts. For this purpose, a simulation was conducted on a 7.5MW/30MWh BESS connected to the grid at a Con Edison substation. The analysis revealed the significance of filter design on the DC side in mitigating battery current flow, thereby influencing the State of Charge (SoC) and extending the battery’s life. Implementing adequately sized inductive filters on both AC and DC sides proved effective in controlling battery current fluctuations while keeping the total harmonic distortion (THD) within acceptable ranges. Various scenarios were examined to ascertain the optimal inductive filter size that balances the BESS’s real and reactive power outputs. Additionally, simulations incorporating real-world grid elements at the substation were performed, including an evaluation of the combined effects of BESS VAR support with substation capacitor banks and shunt reactors. The findings suggest that inductive filters sized at 1mH and 5mH for both AC and DC interfaces can facilitate VAR support with minimal detrimental effects on BESS longevity. Moreover, a load flow analysis, integrating the BESS with Con Edison’s existing substation infrastructure, demonstrated the potential for bus voltage regulation and improvement by adjusting the BESS inverter’s reactive power settings.}, archive = {J_ACCESS}, author = {Mohamed K. Kamaludeen and Yusef Esa and Kirn Zafar and Elihu Nyemah and Ahmed Ali A. Mohamed and Tamer Ibrahim and Simon Odie}, doi = {10.1109/ACCESS.2025.3528931}, journal = {IEEE Access}, pages = {14801-14819}, shortjournal = {IEEE Access}, title = {Grid-scale battery systems for VAR support in con edison distribution substations via bi-directional smart inverters}, volume = {13}, year = {2025}, }
• (2025). Online parameter estimation in digital twins for real-time condition monitoring. ACCESS, 13, 14789-14800. (www)

  @article{ , abstract = {This paper introduces innovative online parameter estimation algorithms that employ both deterministic and stochastic methodologies in digital twins for real-time condition monitoring. The deterministic approach utilizes an exponential forgetting factor adaptive observer, while the stochastic approach involves an adaptive Kalman filter. In contrast to conventional methods, these online algorithms demonstrate robustness against variations in initial conditions and measurement noise. Notably, the algorithms exhibit the capability to manage multiple parameters and directly estimate them from sensor measurements. The effectiveness of the proposed algorithms is demonstrated through experiments focused on parameter estimation in DC motors and marine surface vessels. The results highlight the algorithms’ accuracy in estimating parameters under diverse conditions. This research contributes to the advancement of online parameter estimation techniques for condition monitoring, showcasing their applicability and reliability in real-world scenarios involving complex systems.}, archive = {J_ACCESS}, author = {Agus Hasan}, doi = {10.1109/ACCESS.2025.3531983}, journal = {IEEE Access}, pages = {14789-14800}, shortjournal = {IEEE Access}, title = {Online parameter estimation in digital twins for real-time condition monitoring}, volume = {13}, year = {2025}, }
• (2025). Transforming 5G mega-constellation communications: A self-organized network architecture perspective. ACCESS, 13, 14770-14788. (www)

  @article{ , abstract = {With the widespread adoption of 5G as a communication standard, satellite mega-constellations have emerged as viable alternatives and complement terrestrial networks, offering extensive and reliable communication services across a broad spectrum of users and applications. These constellations are already equipped with inter-satellite links and adaptable payloads capable of supporting Radio Access Network (RAN) and core network functionalities, forming complex space-based networks characterized by overlapping layers of multi-orbit, grid-like topologies that undergo continuous, yet predictable, changes—peculiarities not currently addressed within the 5G standards framework. To cope with this technology gap, this paper introduces a novel architecture for 5G services relying on satellite mega-constellations, which adhere to the principles of self-organized networks. This architecture is designed to align seamlessly with 5G service requirements, while also accommodating the unique topological and infrastructural constraints of mega-constellations. In more detail, the paper first outlines the fundamental principles of self-organizing networks that facilitate real-time system adaptation to internal topological shifts and external fluctuations in service demand. Then, we detail a 5G network architecture incorporating these principles, which includes 1) dynamic placement and migration of radio and core network control plane functions, 2) the strategic positioning of the data path, service, and AI decision functionalities to improve end-to-end service quality and reliability, and 3) the integration of dynamically established multi-connectivity options to increase the overall service dependability. These innovations aim for a seamless integration of space-based networks with terrestrial counterparts, creating a robust, cost-effective convergent telecommunication system.}, archive = {J_ACCESS}, author = {Marius Corici and Màrius Caus and Xavier Artiga and Alessandro Guidotti and Benjamin Barth and Tomaso de Cola and Justin Tallon and Hemant Zope and Daniele Tarchi and Fanny Parzysz and David Naseh and Swapnil Sadashiv Shinde}, doi = {10.1109/ACCESS.2025.3530930}, journal = {IEEE Access}, pages = {14770-14788}, shortjournal = {IEEE Access}, title = {Transforming 5G mega-constellation communications: A self-organized network architecture perspective}, volume = {13}, year = {2025}, }
• (2025). Skin lesion image segmentation algorithm based on MC-UNet. ACCESS, 13, 14760-14769. (www)

  @article{ , abstract = {Aiming at the situation of dermatoscopic images with fuzzy lesion boundaries, variable morphology and high similarity to background, this paper proposes a skin lesion segmentation algorithm that achieves higher segmentation accuracy by combining existing convolutional neural network methods. The algorithm begins by using a Multiscale Residual Block (MRB) with different-sized convolutional kernels to enlarge the receptive field and extract multi-scale features of dermatoscopic images. Secondly, the skip connections are enhanced with a Bidirectional Information Fusion Module (BFM) to refine features by bidirectionally fusing semantic information from high-level feature maps and spatial information from low-level feature maps. Finally, the network’s segmentation accuracy is improved through the use of a new loss function called MixLoss, which combines BceLoss and DiceLoss. Specifically, it achieves a Dice coefficient of 92.37% and an accuracy of 95.32% with a sensitivity of 93.41% on the ISIC2016 dataset. On the ISIC2017 dataset, it achieves a Dice coefficient of 89.43%, an accuracy of 94.81%, and a sensitivity of 90.41%. The experimental results show that the proposed algorithm outperforms other mainstream algorithms and exhibits superior performance in skin lesion segmentation.}, archive = {J_ACCESS}, author = {Guihua Yang and Bingxing Pan}, doi = {10.1109/ACCESS.2025.3531508}, journal = {IEEE Access}, pages = {14760-14769}, shortjournal = {IEEE Access}, title = {Skin lesion image segmentation algorithm based on MC-UNet}, volume = {13}, year = {2025}, }
• (2025). Performance optimization techniques on point absorber and oscillating water column wave energy converter: A comprehensive review. ACCESS, 13, 14743-14759. (www)

  @article{ , abstract = {In recent years, harnessing renewable energy from different sources of energies readily available to us in different forms such as solar, wind, wave, hydro, tidal, biomass and hydrogen has gained popularity among researchers in different fields to counter the emissions released into the atmosphere by utilization of fossil fuels. Amongst these, wave energy is driving researchers to develop efficient systems due to its remarkably high potential providing a reliable and sustainable energy source both onshore and offshore. This paper provides a comprehensive review of the developments, various design innovations, and performance optimization techniques in the recent years of the two most common wave energy converters installed across the globe i.e., point absorber wave energy converter and oscillating water column wave energy converter. The research papers on numerical modeling, experimental analysis on laboratory scaled models, and hydrodynamic performance simulations using various softwares on both the types of wave energy converters are tabulated in a year wise format. Moreover, a list of all the installed point absorber wave energy converters and oscillating water column wave energy converters are tabulated in a year wise format along with their installed capacities, location of deployment and the country of deployment. Lastly the key advantages of both the wave energy converters are outlined below.}, archive = {J_ACCESS}, author = {Aryan Manan Jariwala and Santanu Kumar Dash and Umesh Kumar Sahu and Harin M. Mohan}, doi = {10.1109/ACCESS.2025.3531298}, journal = {IEEE Access}, pages = {14743-14759}, shortjournal = {IEEE Access}, title = {Performance optimization techniques on point absorber and oscillating water column wave energy converter: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). IoT-based multisensors fusion for activity recognition via key features and hybrid transfer learning. ACCESS, 13, 14727-14742. (www)

  @article{ , abstract = {Human activity recognition (HAR) has attracted significant attention in various fields, including healthcare, smart homes, and human-computer interaction. Accurate HAR can enhance user experience, provide critical health insights, and enable sophisticated context-aware applications. This paper presents a comprehensive system for HAR utilizing both RGB videos and inertial measurement unit (IMU) sensor data. The system employs a multi-stage processing pipeline involving preprocessing, segmentation, feature extraction, and classification to achieve high accuracy in activity recognition. In the preprocessing stage, frames are extracted from RGB videos, and IMU sensor data undergoes denoising. The segmentation phase applies Naive Bayes segmentation for video frames and Hamming windows for sensor data to prepare them for feature extraction. Key features are extracted using techniques such as ORB (Oriented FAST and Rotated BRIEF), MSER (Maximally Stable Extremal Regions), DFT (Discrete Fourier Transform), and KAZE for image data, and LPCC (Linear Predictive Cepstral Coefficients), PSD (Power Spectral Density), AR Coefficient, and entropy for sensor data. Feature fusion is performed using Linear Discriminant Analysis (LDA) to create a unified feature set, which is then classified using ResNet50 (Residual Neural Network) to recognize activities such as using a smartphone, cooking, and reading a newspaper. The system was evaluated using the LARa and HWU-USP datasets, achieving classification accuracies of 92% and 93%, respectively. These results demonstrate the robustness and effectiveness of the proposed HAR system in diverse scenarios.}, archive = {J_ACCESS}, author = {Ahmad Jalal and Danyal Khan and Touseef Sadiq and Moneerah Alotaibi and Sultan Refa Alotaibi and Hanan Aljuaid and Hameedur Rahman}, doi = {10.1109/ACCESS.2024.3524431}, journal = {IEEE Access}, pages = {14727-14742}, shortjournal = {IEEE Access}, title = {IoT-based multisensors fusion for activity recognition via key features and hybrid transfer learning}, volume = {13}, year = {2025}, }
• (2025). Challenges and opportunities in fog computing scheduling: A literature review. ACCESS, 13, 14702-14726. (www)

  @article{ , abstract = {The integration of the Internet of Things (IoT) has significantly transformed the way physical objects communicate, facilitating a wide range of applications. Nonetheless, both IoT and Artificial Intelligence (AI)-based applications face obstacles concerning the availability of computing resources, security, and constraints on energy consumption. While cloud computing provides certain resolutions, it also poses additional challenges, including heightened communication expenses and security vulnerabilities due to its physical disconnection from endpoint devices. As a countermeasure, fog computing emerges as a viable alternative by relocating computational resources nearer to the data origin at the edge of the network, yet it is not without its own set of issues such as the diversity of nodes and resource scarcity. This survey conducts an exhaustive analysis of the literature on scheduling within fog computing. It meticulously compares and contrasts various scheduling methodologies, taking into account critical aspects like the characteristics of fog computing, case studies, performance metrics, and simulation tools for evaluation, in addition to outlining their advantages and limitations. Moreover, it elaborates on a detailed taxonomy, assesses performance metrics, and examines performance parameters such as resource utilization, execution time, energy consumption, etc. Additionally, it identifies numerous prevailing challenges and issues. This survey paper aims to support the research community by highlighting potential avenues for future studies and key considerations in the development of scheduling strategies that leverage diverse contextual data.}, archive = {J_ACCESS}, author = {Wu Chia Chuan and Shams Ul Arfeen Laghari and Selvakumar Manickam and Erum Ashraf and Shankar Karuppayah}, doi = {10.1109/ACCESS.2024.3525261}, journal = {IEEE Access}, pages = {14702-14726}, shortjournal = {IEEE Access}, title = {Challenges and opportunities in fog computing scheduling: A literature review}, volume = {13}, year = {2025}, }
• (2025). Research on UAV jamming signal generation based on intelligent jamming. ACCESS, 13, 14686-14701. (www)

  @article{ , abstract = {With the widespread application of UAV technology, various countries are facing new threats in both military and civilian domains. To effectively counter these threats, UAV communication jamming has become a reliable countermeasure. Traditional communication jamming methods face several challenges. These include low efficiency, difficulties in managing dynamic and complex electromagnetic environments, and a heavy reliance on prior knowledge about the signals. This study presents intelligent jamming techniques for UAV signal processing to overcome the limitations found in current communication jamming technologies. The aim is to achieve precise jamming of individual target signals using intelligent jamming methods. This paper introduces an enhanced jamming signal generation algorithm built on the traditional convolutional autoencoder. Without relying on prior signal knowledge, the algorithm introduces complex convolutional networks and residual modules. The model leverages advanced convolutional networks to integrate the real and imaginary components of the signal for feature extraction, while the residual modules further refine signal processing and enhance feature learning. The model generates jamming signals by altering hidden layer features. These signals closely resemble the original communication waveforms. Simulation results show that, across different communication systems, the generated jamming signal waveforms exhibit strong similarity to the original signal waveforms. Moreover, under various signal-to-interference ratios (SIR), the jamming performance of the generated signals surpasses that of conventional jamming signals such as Gaussian noise and amplitude modulation noise.}, archive = {J_ACCESS}, author = {Haonan Xue and Zhihai Zhuo and Weihao Yan and Yuexia Zhang}, doi = {10.1109/ACCESS.2025.3530987}, journal = {IEEE Access}, pages = {14686-14701}, shortjournal = {IEEE Access}, title = {Research on UAV jamming signal generation based on intelligent jamming}, volume = {13}, year = {2025}, }
• (2025). Controlled non-dominated sorting genetic algorithms for multi-objective optimal design of standalone and grid-connected renewable energy systems in integrated energy sectors. ACCESS, 13, 14658-14685. (www)

  @article{ , abstract = {Non-dominated sorting genetic algorithms are recognized for their robustness and flexibility in optimizing renewable energy systems, surpassing traditional methods by handling multiple objectives and generating diverse Pareto-optimal solutions. However, inefficiencies due to random initial populations and mutations can impact processing times and error rates. This study introduces the controlled non-dominated sorting genetic algorithm, which enhances optimization with controlled population initialization and mutation mechanisms. Compared to the conventional non-dominated sorting genetic algorithms, the controlled version shows superior performance, achieving a 2.4% error reduction, a 117% lower task violation rate, and a 157% faster processing time at high energy demands. A case study in Ifrane, Morocco—a tourism village with significant seasonal energy demand—illustrates the application of the algorithm. Results show optimal scenarios for standalone and grid-connected systems, considering potential grid export opportunities. Standalone configurations generate 271 MWh surplus energy annually, with 15 MWh unmet demand, requiring 125 kW power converters. Real scenarios synchronize lower rated power with grid imports, reducing net present costs by 18% and levelized costs by 24%. Hypothetical scenarios demonstrate potential revenue generation with negative net present and levelized costs if export prices match import costs. Grid-connected and thermal energy storage systems are more cost-effective despite higher emissions.}, archive = {J_ACCESS}, author = {Hamza El Hafdaoui and Ahmed Khallaayoun and Salah Al-Majeed}, doi = {10.1109/ACCESS.2025.3530084}, journal = {IEEE Access}, pages = {14658-14685}, shortjournal = {IEEE Access}, title = {Controlled non-dominated sorting genetic algorithms for multi-objective optimal design of standalone and grid-connected renewable energy systems in integrated energy sectors}, volume = {13}, year = {2025}, }
• (2025). Neuromorphic vision data coding: Classifying and reviewing the literature. ACCESS, 13, 14626-14657. (www)

  @article{ , abstract = {In recent years, visual sensors have been quickly improving towards mimicking the visual information acquisition process of human brain by responding to illumination changes as they occur in time rather than at fixed time intervals. In this context, the so-called neuromorphic vision sensors depart from the conventional frame-based image sensors by adopting a paradigm shift in the way visual information is acquired. This new way of visual information acquisition enables faster and asynchronous per-pixel responses/recordings driven by the scene dynamics with a very high dynamic range and low power consumption. However, depending on the application scenario, the emerging neuromorphic vision sensors may generate a large volume of data, thus critically demanding highly efficient coding solutions in order applications may take full advantage of these new, attractive sensors’ capabilities. For this reason, considerable research efforts have been invested in recent years towards developing increasingly efficient neuromorphic vision data coding (NVDC) solutions. In this context, the main objective of this paper is to provide a comprehensive overview of NVDC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this emerging field. In this way, more solid conclusions can be drawn about the current NVDC status quo, thus allowing to better drive future research and standardization developments in this emerging technical area.}, archive = {J_ACCESS}, author = {Catarina Brites and João Ascenso}, doi = {10.1109/ACCESS.2025.3528375}, journal = {IEEE Access}, pages = {14626-14657}, shortjournal = {IEEE Access}, title = {Neuromorphic vision data coding: Classifying and reviewing the literature}, volume = {13}, year = {2025}, }
• (2025). Implementation of CSMA/CA-based MAC protocol for optical camera communication. ACCESS, 13, 14611-14625. (www)

  @article{ , abstract = {Optical camera communication (OCC), a branch of optical wireless communication (OWC), enables data transmission using LEDs and reception via cameras. This technology offers benefits such as a broad spectrum, low cost, high efficiency, and resistance to electromagnetic interference. However, OCC faces challenges in medium access management, which necessitates the development of effective medium access control (MAC) protocols. The IEEE 802.15.7 standard proposes MAC protocols tailored for various OCC modes and simplifies frame structures. In this study, we develop and test a MAC protocol based on carrier sense multiple access with collision avoidance (CSMA/CA) and integrate a novel transform alignment technique aimed at optimizing the bit error rate (BER) performance. By addressing the inherent medium access challenges in OCC, such as signal interference and access contention, the new protocol significantly boosts data transmission efficiency and enhances communication reliability.}, archive = {J_ACCESS}, author = {Muhammad Miftah Faridh and Herfandi Herfandi and Ones Sanjerico Sitanggang and Muhammad Rangga Aziz Nasution and Huy Nguyen and Yeong Min Jang}, doi = {10.1109/ACCESS.2025.3531198}, journal = {IEEE Access}, pages = {14611-14625}, shortjournal = {IEEE Access}, title = {Implementation of CSMA/CA-based MAC protocol for optical camera communication}, volume = {13}, year = {2025}, }
• (2025). Optimizing multimodal transportation: A novel decision-making approach with fuzzy risk assessment. ACCESS, 13, 14584-14610. (www)

  @article{ , abstract = {In today’s complex transportation networks, optimizing multimodal transportation decisions is critical for enhancing efficiency and sustainability. On improving multimodal transportation, there is still a complexity, ambiguity and risk occur during route selection from one place to another. To handle such scenarios efficiently, this study proposes a novel approach that integrates Fuzzy Risk Assessment with three advanced multi-criteria decision-making (MCDM) methods: the Best-Worst Method (BWM), Superiority-Inferiority Ranking (SIR) method, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The BWM is employed to determine the precise weights of the criteria, ensuring that decision-making reflects the relative importance of each factor. The SIR method is utilized to rank the alternatives based on their superiority and inferiority, providing a clear hierarchy of options. Finally, TOPSIS is used as an aggregate operator in the SIR method to synthesize the results, offering a comprehensive ranking that accounts for the proximity of each alternative to an ideal solution. The integration of these methods within a fuzzy risk assessment framework allows for a robust handling of uncertainties and subjective judgments that are inherent in multimodal transportation planning. The proposed approach is tested on a case study involving the optimization of a multimodal transportation network, demonstrating its effectiveness in providing a clear, rational, and reliable ranking of transportation alternatives. The results underscore the potential of this hybrid methodology to enhance decision-making in complex, real-world transportation scenarios. The advantage of using dual-faceted model incorporates diverse transportation methods and fuzzy logic for risk evaluation, thus enabling balanced, adaptive decision-making. This system can analyze scenarios that can help select optimal transportation modes with economic and environmental difficulties.}, archive = {J_ACCESS}, author = {R. Muthunandhini and K. Palanivel}, doi = {10.1109/ACCESS.2025.3528037}, journal = {IEEE Access}, pages = {14584-14610}, shortjournal = {IEEE Access}, title = {Optimizing multimodal transportation: A novel decision-making approach with fuzzy risk assessment}, volume = {13}, year = {2025}, }
• (2025). A dynamic interval auto-scaling optimization method based on informer time series prediction. ACCESS, 13, 14572-14583. (www)

  @article{ , abstract = {With the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly adjust the allocation of cloud resources according to the resource requirements of applications. Currently, widely used responsive auto-scaling methods, such as Kubernetes HPA, exhibit certain lags due to the startup time costs of containers and Pods. This lag makes it difficult to guarantee the service quality of applications when there is a sudden increase in online application load. This paper proposes a dynamic interval auto-scaling optimization method based on Informer time series prediction. By predicting online application load and dynamically determining the auto-scaling interval, sufficient resources are allocated to the application in advance. In the experiments conducted on the official World Cup forum load and Alibaba cluster CPU load, the Informer time series prediction algorithm demonstrated better long-sequence time series prediction capabilities compared to algorithms such as LSTM and RNN. In elastic scaling experiments, compared to Kubernetes HPA, the method proposed in this paper reduces the average application response time from 0.821 seconds to 0.692 seconds, and the SLA violation rate decreases from 18.277% to 9.157%. This indicates a significant improvement in the service quality metrics of online applications. Furthermore, the proposed method effectively maintains a balance between high CPU resource utilization and low application response time and SLA violation rate, which is something RNN-based elastic scaling method cannot achieve, as it can only reduce application response time and SLA violation rate by sacrificing CPU resource utilization.}, archive = {J_ACCESS}, author = {Yu Ding and Chenhao Li and Zhengong Cai and Xinghao Wang and Bowei Yang}, doi = {10.1109/ACCESS.2024.3513564}, journal = {IEEE Access}, pages = {14572-14583}, shortjournal = {IEEE Access}, title = {A dynamic interval auto-scaling optimization method based on informer time series prediction}, volume = {13}, year = {2025}, }
• (2025). A new hybrid MFO-WO algorithm and its application to design of FOPID/PID controller for IoT applications. ACCESS, 13, 14557-14571. (www)

  @article{ , abstract = {The article presents a new optimization algorithm called the hybrid MFO-WOA algorithm, which is designed to tackle global optimization problems and optimize FOPID/PID controller designs. The integration of IoT applications for experimental validation is also discussed. Compared to the existing MFO and WOA algorithms, the proposed algorithm demonstrates faster convergence. To evaluate its efficiency, the algorithm is tested on 23 benchmark functions, and statistical parameters are used to describe its performance qualitatively. The results indicate that the proposed algorithm is competitive and can be considered state-of-the-art. The study also includes a comparison of the efficiency of the hybrid MFO-WOA controller with other controller designs for tuning controller parameters. The results of the FOPID/PID controller design are analyzed and compared. The next step involves implementing the PID controller on the ESP32 microcontroller module using the proposed algorithm. An Android-based application using the Blynk framework is developed for monitoring and controlling plants via IoT. The ESP32 microcontroller serves as the IoT gateway, and the controller and interface software are developed using the Arduino IDE. Based on the comparative findings of various case studies, it is evident that the proposed hybrid MFO-WOA algorithm-based controller design outperforms other approaches in terms of performance.}, archive = {J_ACCESS}, author = {Jailsingh Bhookya}, doi = {10.1109/ACCESS.2024.3476322}, journal = {IEEE Access}, pages = {14557-14571}, shortjournal = {IEEE Access}, title = {A new hybrid MFO-WO algorithm and its application to design of FOPID/PID controller for IoT applications}, volume = {13}, year = {2025}, }
• (2025). Application of artificial intelligence virtual image technology in photography art creation under deep learning. ACCESS, 13, 14542-14556. (www)

  @article{ , abstract = {With the continuous advancement of artificial intelligence (AI) and deep learning technologies, virtual image generation exhibits significant potential for application in photographic art creation. The primary objective of this study is to investigate the use of AI virtual image technology in photography, particularly focusing on achieving creative expression and artistic style transfer through deep learning models. Consequently, this study proposes a novel model that integrates conditional generative adversarial networks (cGANs) with variational autoencoders (VAEs). This model aims to effectively address the challenges associated with image generation and style conversion in photographic art by leveraging the realistic generation capabilities of cGANs alongside the diversity maintenance features of VAEs. In the experimental section, the proposed cGANs + VAEs model is systematically compared with traditional Deep Convolutional GANs (DCGAN) and Pix2Pix models through empirical analysis. The experimental results indicate that the cGANs + VAEs model significantly outperforms traditional models in terms of image quality, artistic expression, and user satisfaction. Expert reviews further confirm the model’s superiority in artistic style imitation and creative generation. Additionally, user surveys reveal that most participants are highly satisfied with the images generated by the model, particularly regarding artistic perception and visual effects. Moreover, the cGANs + VAEs model demonstrates strong performance in Frechet Inception Distance (FID) and Inception Score (IS) across multiple datasets, yielding FID values of 13.67, 9.45, and 11.90 on the COCO, CelebA, and WikiArt datasets, respectively. In summary, the proposed cGANs + VAEs model not only achieves remarkable advancements in the technical performance of image generation but also exhibits considerable potential for practical applications in photographic art creation.}, archive = {J_ACCESS}, author = {Qiong Yao}, doi = {10.1109/ACCESS.2025.3529521}, journal = {IEEE Access}, pages = {14542-14556}, shortjournal = {IEEE Access}, title = {Application of artificial intelligence virtual image technology in photography art creation under deep learning}, volume = {13}, year = {2025}, }
• (2025). Multi-functional reconfigurable intelligent surface assisted security communication systems under imperfect CSI of eavesdroppers. ACCESS, 13, 14531-14541. (www)

  @article{ , abstract = {In order to overcome the “multiplicative fading” loss and half-space coverage limitations faced by reconfigurable intelligent surfaces (RIS) in secure communications, a more practical multi-functional reconfigurable intelligent surface (MF-RIS) with coupled phase shifts is introduced to assist in enhancing the security performance of multiple-input single-output (MISO) systems. In addition, the paper especially focuses on the case where the eavesdropper’s channel state information (CSI) is imperfect. Based on the bounded CSI error model, this paper strictly limits the upper bound of information leakage to potential eavesdroppers and seeks to maximize the system sum rate by jointly optimizing the beamforming at the transmitter side and the coupled phase shifts of the MF-RIS. For this nonconvex optimization problem, we combine the penalty dual decomposition method, weighted minimum mean square error (WMMSE) method, S-Procedure Lemma, and general sign-definiteness to design an efficient solution. Simulation results show that: 1) All MF-RIS schemes achieve better secrecy performance gains than other existing RIS designs; and 2) Compared to the optimal independent phase shifts, coupled phase shifts of MF-RIS only lead to a slight performance loss.}, archive = {J_ACCESS}, author = {Rui-Bao Zhang and Dian-Wu Yue and Yu Sun}, doi = {10.1109/ACCESS.2025.3529932}, journal = {IEEE Access}, pages = {14531-14541}, shortjournal = {IEEE Access}, title = {Multi-functional reconfigurable intelligent surface assisted security communication systems under imperfect CSI of eavesdroppers}, volume = {13}, year = {2025}, }
• (2025). FCDNet: A lightweight network for real-time wildfire core detection in drone thermal imaging. ACCESS, 13, 14516-14530. (www)

  @article{ , abstract = {The increasing number of wildfires damage nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model’s size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection. This paper introduces a novel infrared wildfire detection network named FCDNet to tackle this issue. It includes an Efficient Processing (EP) module based on the novel Partial Depthwise Convolution (PDWConv) and the lightweight feature-sharing decoupled detection head (Fast Head), achieving low-size and low-computation wildfire detection. An Adaptive Sample Attention (ASA) Loss is introduced to enhance the detection accuracy of wildfire cores in combination with Normalized Wasserstein Distance (NWD) Loss. The experiment shows that the model size of FCDNet is only 4.0MB, representing 63.5% of the baseline YOLOv8n network, with 63.3% of its parameters. It operates at just 5 Giga Floating Point Operations Per Second (GFLOPs), 38.3% lower, and achieves a 77.5% mAP (@50-95 IOU), a 1% increase, with a $460\times 460$ input image size. Compared to the state-of-the-art YOLOv11n, FCDNet reduces parameters, computation, and model size by 26.9%, 20.6%, and 27.3%, respectively. The thermal dataset and training codes used in this study are made publicly available at: https://github.com/WangLF1996/FCDNet-Dataset-and-Algorithm}, archive = {J_ACCESS}, author = {Linfeng Wang and Oualid Doukhi and Deok Jin Lee}, doi = {10.1109/ACCESS.2025.3526974}, journal = {IEEE Access}, pages = {14516-14530}, shortjournal = {IEEE Access}, title = {FCDNet: A lightweight network for real-time wildfire core detection in drone thermal imaging}, volume = {13}, year = {2025}, }
• (2025). Fast task scheduling with model predictive control integrating a priority-based heuristic. ACCESS, 13, 14499-14515. (www)

  @article{ , abstract = {This paper presents a scalable Model Predictive Control (MPC) algorithm for task scheduling and real time re-scheduling. The use case motivating the work is given by the problem of managing the integration activities involved in the final assembly of the Vega rocket at the European space center in Kourou, French Guiana. There are two main objectives. The algorithm shall suggest to the planning operators an optimized scheduling of the activities, i.e., one which minimizes the total completion time (the makespan), while satisfying all the applicable constraints. In addition, the algorithm shall provide in real time an update of the planning, in case some unforeseen events require a re-scheduling of the activities. While a standard application of mixed-integer optimization would not be feasible in practice due to the combinatorial complexity of the problem, the scalable MPC algorithm proposed in this paper retains all the flexibility and modelling power of optimization-based techniques, and is almost as fast as the state of the art scheduling heuristics, which in real scenarios can provide a sub-optimal solution in few seconds, or less. Extensive simulations on randomly generated realistic scenarios are carried out to validate the proposed approach. On average, the proposed MPC algorithm decreased by nearly 2% the makespan, compared to a state of the art scheduling heuristic, while having a comparable solving time, in the order of milliseconds, and while retaining (contrary to heuristics) all the flexibility and modelling power of the optimization based approaches (which took several hours to run on the test scenarios).}, archive = {J_ACCESS}, author = {Francesco Liberati and Manuel Donsante and Chiara Maria Francesca Cirino and Andrea Tortorelli}, doi = {10.1109/ACCESS.2025.3529909}, journal = {IEEE Access}, pages = {14499-14515}, shortjournal = {IEEE Access}, title = {Fast task scheduling with model predictive control integrating a priority-based heuristic}, volume = {13}, year = {2025}, }
• (2025). From ChatGPT to sora: Analyzing public opinions and attitudes on generative artificial intelligence in social media. ACCESS, 13, 14485-14498. (www)

  @article{ , abstract = {This study examines public opinions, emotional tendencies, and psychological linguistic characteristics associated with the launch of OpenAI’s ChatGPT and the advanced video generation model, Sora, by analyzing discussions on the Chinese social media platform Weibo. A total of 24,727 valid user-generated texts (1,762,296 words) were collected and analyzed using Python and its associated APIs. Word co-occurrence network analysis, topic modeling based on Latent Dirichlet Allocation (LDA), and emotional characteristics based on the DLUT Emotion Ontology and psycholinguistic analyses based on the Linguistic Inquiry and Word Count (LIWC) dictionary were employed to explore public views on these generative AI technologies. The findings reveal a shift in public focus over time, from initial excitement about technological advancements to growing interest in commercialization, labor, education, ethics, and global competition. The public’s emotional responses to AI were a mix of excitement and apprehension. The study identifies seven distinct emotional types, providing a nuanced understanding of public psychological reactions, which contrasts with previous binary classifications. This research contributes valuable insights for policymakers, businesses, and researchers, highlighting the public’s evolving acceptance of generative AI technologies.}, archive = {J_ACCESS}, author = {Wenyan Feng and Yuhang Li and Chunhao Ma and Lisai Yu}, doi = {10.1109/ACCESS.2025.3530683}, journal = {IEEE Access}, pages = {14485-14498}, shortjournal = {IEEE Access}, title = {From ChatGPT to sora: Analyzing public opinions and attitudes on generative artificial intelligence in social media}, volume = {13}, year = {2025}, }
• (2025). Loads mismatch and network voltage behavior for future planning of demand response with customer satisfaction. ACCESS, 13, 14471-14484. (www)

  @article{ , abstract = {Thermostatically controlled loads (TCLs) aggregated into a generalized virtual battery (VB) offer a systematic approach to optimally manage such devices. However, challenges arise when operational conditions are oversimplified or overlooked, leading to a mismatch between expected and actual outcomes. This paper delves into the demand response mismatch (DRM) challenge within the context of peak load management, exploring the implications of power reductions stemming from external management and aggregator control. A customer satisfaction index has been introduced to assess the impact of such reductions on customer comfort. A comprehensive VB model is employed to govern the aggregator, ensuring adherence to all operational constraints. The DRM percentage is evaluated under both standard operating conditions and peak shaving strategies. The study also investigates the different types of customer discomfort according to device operation and cost. A predictive analysis of the percentages of load DRM serves as a valuable tool for future peak shaving planning. This research further analyzes the impacts of voltage variation on a distribution network that ends with customer loads.}, archive = {J_ACCESS}, author = {Ismail Arafat and Eduardo Castillo-Guerra and Julian Meng}, doi = {10.1109/ACCESS.2025.3531300}, journal = {IEEE Access}, pages = {14471-14484}, shortjournal = {IEEE Access}, title = {Loads mismatch and network voltage behavior for future planning of demand response with customer satisfaction}, volume = {13}, year = {2025}, }
• (2025). Simplified physical stability assessment of chilean mine waste storage facilities using GIS and AI: Application in the antofagasta region. ACCESS, 13, 14453-14470. (www)

  @article{ , abstract = {Chile’s mining industry, a global leader in copper production, faces challenges due to increasing volumes of mining waste, particularly Waste Rock Dumps (WRD) and Leaching Waste Dumps (LWD). The National Service of Geology and Mining (SERNAGEOMIN) requires assessment of the physical stability (PS) of these facilities, but current methods are hindered by data scarcity and resource constraints. This study proposes a simplified evaluation methodology using first-order parameters from open-access data. By integrating Geographic Information Systems (GIS) and Artificial Intelligence (AI)—utilizing models like YOLOv11 and convolutional neural networks—we automate the detection and characterization of WRD and LWD from satellite imagery, extracting critical parameters for PS assessment. This approach reduces analysis time and minimizes human error. Validated in the Antofagasta Region, Chile’s primary mining area, we identified and evaluated 70 WRD and 54 LWD. The results demonstrate the effectiveness of prioritizing deposits based on potential risk, enhancing SERNAGEOMIN’s capacity for supervision. The successful application suggests scalability to other mining regions and adaptability to different facility types, including tailings storage facilities. This work offers a practical tool to improve safety and risk management in the mining industry, addressing critical challenges in PS evaluation under current regulatory constraints.}, archive = {J_ACCESS}, author = {Gabriel Hermosilla and Gabriel Villavicencio and Giovanni Cocca-Guardia and Vicente Aprigliano and Manuel Silva and Juan Carlos Quezada and Pierre Breul and Vinicius Minatogawa and Jaime Morales}, doi = {10.1109/ACCESS.2025.3530856}, journal = {IEEE Access}, pages = {14453-14470}, shortjournal = {IEEE Access}, title = {Simplified physical stability assessment of chilean mine waste storage facilities using GIS and AI: Application in the antofagasta region}, volume = {13}, year = {2025}, }
• (2025). Experimental study on pressure-deformation-contact resistance characteristics of converter transformer valve side casing louver contacts. ACCESS, 13, 14444-14452. (www)

  @article{ , abstract = {The performance degradation of oil-immersed louver contacts are the main reason for the DC resistance exceeding the standard on the valve side of UHV converter transformers. In this paper, a louver contact performance test platform is designed and built according to the actual assembly structure of the louver contact. The relationships between pressure, deformation and contact resistance of the louver contacts are measured and obtained by utilizing the louver contact test platform. The characteristics between the parameters of contact resistance, pressure and load current are investigated based on the test platform. The results show that the contact resistance decreases exponentially with the increase of the pressure on the louver contacts, and the contact resistance decreases with the increase of load current applied on the louver contacts. At the same time, based on the test results, the change characteristics are analysised of the surface shrinkage resistance and film layer resistance of the contact piece. The obtained conclusions can provide a practical basis for the design of electric contact components of power transformer bushings and fault analysis research.}, archive = {J_ACCESS}, author = {Shihong Hu and Zhicheng Huang and Jiahui Chen and Chi Yuan and Bozhang Feng and Tao Zhao and Yunpeng Liu}, doi = {10.1109/ACCESS.2025.3529869}, journal = {IEEE Access}, pages = {14444-14452}, shortjournal = {IEEE Access}, title = {Experimental study on pressure-deformation-contact resistance characteristics of converter transformer valve side casing louver contacts}, volume = {13}, year = {2025}, }
• (2025). Empowering diabetes management through blockchain and edge computing: A systematic review of healthcare innovations and challenges. ACCESS, 13, 14426-14443. (www)

  @article{ , abstract = {In the evolving landscape of health information management, the application of blockchain and edge computing technologies to chronic disease management remains underexplored, despite the urgent need for scalable, secure, and real-time solutions. This systematic review examines the integration of these technologies in healthcare, with a specific focus on diabetes management. We analyzed 52 studies, categorizing findings into three key areas: enhanced data security and privacy through decentralized frameworks and tamper-proof storage; real-time data processing, enabled by edge computing for immediate analytics and alerts; and scalability, achieved via hybrid architectures that optimize data handling. Our review identifies critical gaps in the existing literature, particularly the lack of tailored approaches for chronic disease contexts like diabetes, and outlines future directions for developing robust, secure, scalable, and real-time data solutions. This study provides a foundation for innovation in healthcare technology that can significantly impact diabetes care and chronic disease management.}, archive = {J_ACCESS}, author = {K. Tlemçani and K. Azbeg and E. Saoudi and L. Fetjah and O. Ouchetto and S. Jai Andaloussi}, doi = {10.1109/ACCESS.2025.3531350}, journal = {IEEE Access}, pages = {14426-14443}, shortjournal = {IEEE Access}, title = {Empowering diabetes management through blockchain and edge computing: A systematic review of healthcare innovations and challenges}, volume = {13}, year = {2025}, }
• (2025). A hybrid energy harvester based on bowtie periodic antenna surface. ACCESS, 13, 14417-14425. (www)

  @article{ , abstract = {An innovative method is introduced for energy harvesting by combining a bowtie frequency selective rectenna surface with a traditional photovoltaic solar panel. The design of the rectenna antenna surface aims to optimize the geometry of the bowtie antennas for enhanced ambient RF energy absorption and rectification efficiency. Several integration techniques are explored to efficiently merge the RF and solar energy harvesting components into a unified system, considering factors such as spatial arrangement and DC electrical connections in both series and parallel. Tested efficiency analysis under various loads demonstrates the feasibility and effectiveness of the hybrid system as it captures the RF and solar energy and converts it into usable electrical energy. The RF-solar hybrid energy harvesting system can be employed in environment settings where energy sources exist intermittently.}, archive = {J_ACCESS}, author = {Mahmoud Abushab and Thamer S. Almoneef and Muhammad Amin and Omar F. Siddiqui}, doi = {10.1109/ACCESS.2025.3529936}, journal = {IEEE Access}, pages = {14417-14425}, shortjournal = {IEEE Access}, title = {A hybrid energy harvester based on bowtie periodic antenna surface}, volume = {13}, year = {2025}, }
• (2025). Robot simulation on agri-field point cloud with centimeter resolution. ACCESS, 13, 14404-14416. (www)

  @article{ , abstract = {The need for robotic agricultural automation has been driven by global population growth and climate change.To efficiently evaluate and develop agricultural robots not limited to the growing season, we developed a dynamics simulator that works fast on 3D point-cloud models of agricultural fields.The point-cloud models have been widely used in recent agricultural research thanks to advances in aerial photography technology. Therefore, the simulator can be easily applied to many agricultural fields.To speed up the dynamics calculation on the dense point-cloud model, we developed a method to quickly detect collision points using a grid table, and a method to calculate collision forces between the points and robot meshes.The performance of the simulator was evaluated on an agri-field model ( $31 \times 14$ m2) represented by $1.7 \times 10^{6}$ points. The computation time of the simulation was 8.8 times faster than real time, and the simulation accuracy compared to actual robot movements was ~1 cm in Root Mean Square Error (RMSE). The simulator in this study enables fast computation and accurate prediction of robot movements on centimeter-resolution agri-field point-cloud models, supporting research on agricultural robots not limited to the growing season.}, archive = {J_ACCESS}, author = {Shintaro Noda and Masayuki Kogoshi and Wataru Iijima}, doi = {10.1109/ACCESS.2025.3530967}, journal = {IEEE Access}, pages = {14404-14416}, shortjournal = {IEEE Access}, title = {Robot simulation on agri-field point cloud with centimeter resolution}, volume = {13}, year = {2025}, }
• (2025). Experimental implementation of an algorithm for real time compensation of phase current sensors DC offset in field oriented controlled PMSM drives. ACCESS, 13, 14392-14403. (www)

  @article{ , abstract = {DCoffset faults of phase current sensors in AC Permanent Magnet Synchronous Machines (PMSMs) are widely studied in the literature since they can lead to heavy torque oscillations and thermal instability of the drive. This paper presents the experimental implementation of an algorithm for the real time estimation and compensation of these faults in PMSM drives controlled by Field Oriented Control (FOC). The method uses equations derived from the analytically closed form solution of the steady-state d-q axes model of PMSM, taking into account the full FOC dynamics and DC offset faults of the phase current sensors. The algorithm allows for detecting, isolating, and estimating the fault conditions by identifying the phase current offsets, both when using three sensors and two current sensors. The real time compensation is based on the developed model, does not require additional hardware, and uses only a digital filter of the measured phase currents for compensating online the offset fault. Experimental results and sensitivity analysis to the motor parameters variations are presented to validate the proposed algorithm.}, archive = {J_ACCESS}, author = {Ciro Attaianese and Matilde D'Arpino and Mauro Di Monaco and Luigi Pio Di Noia}, doi = {10.1109/ACCESS.2025.3532220}, journal = {IEEE Access}, pages = {14392-14403}, shortjournal = {IEEE Access}, title = {Experimental implementation of an algorithm for real time compensation of phase current sensors DC offset in field oriented controlled PMSM drives}, volume = {13}, year = {2025}, }
• (2025). AReLU: Agile rectified linear unit for improving lightweight convolutional neural networks. ACCESS, 13, 14379-14391. (www)

  @article{ , abstract = {Dynamic activation functions usually gain remarkable improvements for neural networks. Dynamic activation functions depending on input features show better performance than the input-independents. But the improvements are achieved with extra memory and computational cost, which is non-negligible for lightweight convolutional networks. To address this issue, a lightweight input-dependent dynamic activation function is proposed, namely, Agile Rectified Linear Unit (AReLU). And Parallel Local Cross-Feature Interaction (PLCFI) is proposed as the lightweight feature extractor of AReLU. Considering the multi-channel characteristic of input features, Channel-Exclusive AReLU (CE-AReLU) and Channel-Shared AReLU (CS-AReLU) are designed based on PLCFI. CE-AReLU learns exclusive structure of activation function for each channel, while the functional structure of CS-AReLU is shared by channels. Furthermore, a specific initialization method for neurons in PLCFI is proposed to facilitate model convergence, called imitative initialization. Extensive experiments on different datasets and lightweight convolutional neural network architectures demonstrate the superiority and generality of AReLU. It shows that CE-AReLU gains remarkable performance in common visual classification benchmarks. Moreover, CS-AReLU shows its capacity of capturing dependencies among different samples in a batch, achieving higher recognition accuracy on fine-grained classification task.}, archive = {J_ACCESS}, author = {Fu Chen and Yepeng Guan}, doi = {10.1109/ACCESS.2025.3530424}, journal = {IEEE Access}, pages = {14379-14391}, shortjournal = {IEEE Access}, title = {AReLU: Agile rectified linear unit for improving lightweight convolutional neural networks}, volume = {13}, year = {2025}, }
• (2025). Securing brain-to-brain communication channels using adversarial training on SSVEP EEG. ACCESS, 13, 14358-14378. (www)

  @article{ , abstract = {In this study, we investigate the effects of Adversarial Neural Network Training (ANNT) on the robustness and effectiveness of Brain-to-Brain Communication (B2B-C) systems using Steady-State Visually Evoked Potentials (SSVEP) EEG data. We utilized a combined Convolutional Neural Network-Temporal Convolutional Network (CNN-TCN) architecture to classify the data and assessed the system’s resistance to various adversarial strategies, including Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Basic Iterative Method (BIM), Carlini & Wagner (C&W), and Momentum Iterative Method (MIM). By analyzing publicly accessible datasets, specifically Lee2019_SSVEP and Nakanishi2015, we observed significant enhancements in both accuracy and AUC metrics when ANNT was applied. Specifically, the Lee2019_SSVEP dataset exhibited a 24% increase in accuracy and a 0.23-point improvement in AUC, while the Nakanishi2015 dataset demonstrated improvements of 9% and 0.07 points, respectively. Our results indicate that PGD posed the greatest challenge to the model, significantly reducing accuracy and AUC across various scenarios, whereas FGSM was the least impactful. These findings highlight ANNT’s potential in fortifying the security and stability of B2B-C systems against diverse adversarial conditions.}, archive = {J_ACCESS}, author = {Hossein Ahmadi and Ali Kuhestani and Mohammadreza Keshavarzi and Luca Mesin}, doi = {10.1109/ACCESS.2025.3528770}, journal = {IEEE Access}, pages = {14358-14378}, shortjournal = {IEEE Access}, title = {Securing brain-to-brain communication channels using adversarial training on SSVEP EEG}, volume = {13}, year = {2025}, }
• (2025). Day-ahead and intraday economic optimization models of the stackelberg game considering source-load uncertainty. ACCESS, 13, 14344-14357. (www)

  @article{ , abstract = {In order to enhance the demand response rate on the demand side and the wind power accommodation rate on the supply side, as well as to curtail the operational cost effectively, this paper formulates a day-ahead and intraday economic dispatch model predicated on Stackelberg game theory. Within this model, specific attention is given to the influence of source-load uncertainties on energy prices, and the concept of the net load is incorporated during the intraday phase to meticulously calibrate intraday prices. For the purpose of resolving the devised model, an ameliorated lighting attachment procedure optimization algorithm is employed. Via simulation and comparative analysis, the energy pricing strategy proposed herein markedly bolsters the users’ engagement in demand response and the wind power accommodation rate, concomitantly reducing the operational cost. Furthermore, by leveraging the improved Lightning Attachment Procedure Optimization algorithm to address this model, the system cost is decreased by 9.6%, and the iteration speed is increased by 18.75%. This research provides robust support for overcoming the dual conundrums of demand response and wind power accommodation in contemporary energy systems.}, archive = {J_ACCESS}, author = {Ning Li and Weibo Liu and Xiaodong Wan and Xiu Ren and Haining Yu}, doi = {10.1109/ACCESS.2025.3530153}, journal = {IEEE Access}, pages = {14344-14357}, shortjournal = {IEEE Access}, title = {Day-ahead and intraday economic optimization models of the stackelberg game considering source-load uncertainty}, volume = {13}, year = {2025}, }
• (2025). From technical prerequisites to improved care: Distributed edge AI for tomographic imaging. ACCESS, 13, 14317-14343. (www)

  @article{ , abstract = {Recent years have seen a surge in AI-driven medical image processing, leading to significant improvements in diagnostic performance. However, medical imaging technologies tend to create staggering volumes of medical data, necessitating high-performance computing. Cloud systems with robust GPUs and resource capacity are optimal choices for DL-based medical image processing. However, transferring data to the cloud for processing strains communication links, introduces high communication latency, and raises privacy and security concerns. Consequently, despite the undisputed benefits of cloud computing, dedicated standalone local computers are still used for image reconstruction in today’s systems. This localized strategy uses expensive hardware inefficiently and falls short of scalability and maintainability. Edge computing emerges as an innovative concept by bringing cloud processing capabilities closer to data sources. A continuum of computing including local, edge, and cloud tiers would offer a promising solution for medical image processing. According to literature survey, there are no significant works on utilizing edge cloud continuum for CBCT imaging. To fill this gap, we introduce novel 3-TECC architectural concept, specifically designed for CBCT data reconstruction in medical imaging. This article explores the evolving synergy among medical imaging, distributed AI, containerized solutions, and edge-cloud continuum technologies, highlighting their clinical implications and illuminating the potential for transformative patient care. We uncover challenges and opportunities this convergence provides with the CBCT image reconstruction use case, while aligning with regulatory compliance. The proposed 3-TECC architecture advocates a decentralized data processing paradigm, reducing reliance on the centralized approach and emphasizing the role of local-edge computing.}, archive = {J_ACCESS}, author = {Bilgehan Akdemir and Hafiz Faheem Shahid and Mikael A. K. Brix and Juho Lääkkölä and Johirul Islam and Tanesh Kumar and Jarmo Reponen and Miika T. Nieminen and Erkki Harjula}, doi = {10.1109/ACCESS.2025.3530297}, journal = {IEEE Access}, pages = {14317-14343}, shortjournal = {IEEE Access}, title = {From technical prerequisites to improved care: Distributed edge AI for tomographic imaging}, volume = {13}, year = {2025}, }
• (2025). Leveraging simplex gradient variance and bias reduction for black-box optimization of noisy and costly functions. ACCESS, 13, 14304-14316. (www)

  @article{ , abstract = {Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We investigate the reduction in both bias and variance errors attributed to the simplex gradient estimated from noisy function measurements, in favor of the finite-differences gradient, when both are used for black-box optimization methods. Regardless of the simplex orientation, while reducing the gradient bias error owned to several factors such as truncation, numerical or measurement noise, we claim and verify that, under relaxed assumptions about the underlying function’s differentiability, the estimated gradient by the simplex method has at most half the variance of the finite-difference gradient. The findings are validated with two comprehensive and representative case studies, one related to the minimization of a nonlinear feedback control system cost function and the second related to a deep machine learning classification problem whose hyperparameters are tuned. We conclude that in up to medium-size practical black-box optimization problems with unknown variable domains and where the noisy function measurements are expensive, a simplex gradient-based search is an attractive option.}, archive = {J_ACCESS}, author = {Mircea-Bogdan Radac and Titus Nicolae}, doi = {10.1109/ACCESS.2025.3529915}, journal = {IEEE Access}, pages = {14304-14316}, shortjournal = {IEEE Access}, title = {Leveraging simplex gradient variance and bias reduction for black-box optimization of noisy and costly functions}, volume = {13}, year = {2025}, }
• (2025). Comprehensive analysis of masking techniques in molecular graph representation learning. ACCESS, 13, 14290-14303. (www)

  @article{ , abstract = {Molecule representation learning is a primary area of focus in drug discovery and molecular property prediction. In previous studies, molecules have been modeled as graphs, enabling graph neural networks (GNNs) to capture essential structural information. Recent approaches have enhanced molecular representations by introducing advanced masking strategies, such as extending granularity from nodes to subgraphs, shifting masking locations, and applying masking during downstream tasks. However, comprehensive analyses of these strategies remain limited. In this study, we systematically evaluate masking techniques across various phases, granularities, locations, feature types, and ratios. Our findings reveal that node feature masking during pre-training achieves high performance, while rich features may reduce gains, and the commonly used 25% masking ratio is not universally optimal, with alternative ratios performing better depending on the dataset. Our study provides deeper insights into the benefits of masking techniques in molecular graphs and highlights their potential to improve semantic understanding and predictive accuracy in graph-based learning.}, archive = {J_ACCESS}, author = {Bonyou Koo and Sunyoung Kwon}, doi = {10.1109/ACCESS.2025.3531302}, journal = {IEEE Access}, pages = {14290-14303}, shortjournal = {IEEE Access}, title = {Comprehensive analysis of masking techniques in molecular graph representation learning}, volume = {13}, year = {2025}, }
• (2025). ϵ-confidence approximately correct (ϵ-CoAC) learnability and hyperparameter selection in linear regression modeling. ACCESS, 13, 14273-14289. (www)

  @article{ , abstract = {In a data based learning process, training data set is utilized to provide a hypothesis that can be generalized to explain all data points from a domain set. The hypothesis is chosen from classes with potentially different complexities. Linear regression modeling is an important category of learning algorithms. The practical uncertainty of the label samples in the training data set has a major effect in the generalization ability of the learned model. Failing to choose a proper model or hypothesis class can lead to serious issues such as underfitting or overfitting. These issues have been addressed mostly by alternating modeling cost functions or by utilizing cross-validation methods. Drawbacks of these methods include introducing new hyperparameters with their own new challenges and uncertainties, potential increase of the computational complexity or requiring large set of training data sets. On the other hand, the theory of probably approximately correct (PAC) aims at defining learnability based on probabilistic settings. Despite its theoretical value, PAC bounds can’t be utilized in practical regression learning applications with only available training data sets. This work is motivated by practical issues in regression learning generalization and is inspired by the foundations of the theory of statistical learning. The proposed approach, denoted by $\epsilon $ -Confidence Approximately Correct ( $\epsilon $ -CoAC), utilizes the conventional Kullback-Leibler divergence (relative entropy) and defines new related typical sets to develop a unique method of probabilistic statistical learning for practical regression learning and generalization. $\epsilon $ -CoAC learnability is able to validate the learning process as a function of training data sample size, as well as a function of the hypothesis class complexity order. Consequently, it enables the learner to automatically compare hypothesis classes of different complexity orders and to choose among them the optimum class with the minimum $\epsilon $ in the $\epsilon $ -CoAC framework. The $\epsilon $ -CoAC learnability overcomes the issues of overfitting and underfitting. In addition, it shows advantages over the well-known cross-validation method in the sense of accuracy and data length requirements for convergence. Simulation results, for both synthetic and real data, confirm not only strength and capability of $\epsilon $ -CoAC in providing learning measurements as a function of data length and/or hypothesis complexity, but also superiority of the method over the existing approaches in hypothesis complexity and model selection.}, archive = {J_ACCESS}, author = {Soosan Beheshti and Mahdi Shamsi}, doi = {10.1109/ACCESS.2025.3529622}, journal = {IEEE Access}, pages = {14273-14289}, shortjournal = {IEEE Access}, title = {ϵ-confidence approximately correct (ϵ-CoAC) learnability and hyperparameter selection in linear regression modeling}, volume = {13}, year = {2025}, }
• (2025). Enhancing chronic disease prediction in IoMT-enabled healthcare 5.0 using deep machine learning: Alzheimer’s disease as a case study. ACCESS, 13, 14252-14272. (www)

  @article{ , abstract = {Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. The main objective of this work is to present a deep machine learning-based approach that provides better results in terms of accuracy. These findings have significance for tailored healthcare 5.0, enabling healthcare professionals to predict chronic disease more efficiently. A comparative examination of the most recent methods has been provided in our work reveals that it might be more advantageous to use the proposed model in which segmentation of the MRI is performed using U-net architecture and then classification is done using transfer learning for chronic disease prediction. Our proposed model provides 96.06% accuracy, it advances our understanding of deep machine learning’s potential for chronic disease prediction and emphasizes the need to tailor model selection to specific disease types using data from IoMT enabled devices. In order to make advanced improvement in the field of healthcare 5.0, future studies should focus on refining these models and investigating how well they work with a wider range of datasets.}, archive = {J_ACCESS}, author = {Rabia Javed and Tahir Abbas and Tariq Shahzad and Khadija Kanwal and Sadaqat Ali Ramay and Muhammad Adnan Khan and Khmaies Ouahada}, doi = {10.1109/ACCESS.2025.3525514}, journal = {IEEE Access}, pages = {14252-14272}, shortjournal = {IEEE Access}, title = {Enhancing chronic disease prediction in IoMT-enabled healthcare 5.0 using deep machine learning: Alzheimer’s disease as a case study}, volume = {13}, year = {2025}, }
• (2025). Enhancing credit risk decision-making in supply chain finance with interpretable machine learning model. ACCESS, 13, 14239-14251. (www)

  @article{ , abstract = {The increasing complexity of supply chain finance poses significant challenges to effective credit risk assessment. Traditional black-box models often fail to provide insights into the factors driving credit risk, which is essential for stakeholders when making informed decisions. By conducting analysis of interpretable machine learning models, the study evaluated their performance in assessing credit risks. Specifically, we applied Extreme Gradient Boosting (XGBoost), Random Forest (RF), Least Squares Support Vector Machine (LSSVM) and Convolutional Neural Network (CNN) models for risk assessment. Our methodology included an ablation experiment along with utilizing Shapley Additive Explanation (SHAP) to elucidate the contribution and significance of specific risk factors. The results indicated that the asset-liability ratio, cash ratio, and quick ratio notably influence credit risk. This study clarified the applicability and limitations of various models, highlighting the superior performance and interpretability of XGBoost through the SHAP algorithm. Ultimately, the insights from this study provided valuable guidance for companies and financial institutions, fostering more sustainable allocation of financial resources.}, archive = {J_ACCESS}, author = {Guanglan Zhou and Shiru Wang}, doi = {10.1109/ACCESS.2025.3530433}, journal = {IEEE Access}, pages = {14239-14251}, shortjournal = {IEEE Access}, title = {Enhancing credit risk decision-making in supply chain finance with interpretable machine learning model}, volume = {13}, year = {2025}, }
• (2025). A state-supervised model and novel anomaly index for gas turbines blade fault detection under multi-operating conditions. ACCESS, 13, 14225-14238. (www)

  @article{ , abstract = {To meet industrial demands, gas turbines typically operate under multiple conditions, presenting unique challenges for fault diagnosis. This paper proposes a novel blade fault detection framework designed for such environments. First, a State-Supervised Variational Autoencoder (SS-VAE) model is introduced, which integrates the learning process of turbine operational states into the VAE bypass, enabling it to capture variations in vibration signal data across different operating conditions. Through the fusion decoding of operating states and vibration signals, the foundation of the SS-VAE model and its loss function is established. Secondly, a new State Mapping Error (SME) index is introduced to further address the issue of reconstruction error variability across multiple conditions. The proposed method is validated through a blade fault test rig and applied in an industrial gas turbine blade fault case. Results demonstrate that the SS-VAE model and SME index effectively capture vibration signal changes due to blade faults across multi-operating conditions, achieving accurate monitoring and diagnosis. Compared to traditional methods, the proposed approach shows higher effectiveness and robustness.}, archive = {J_ACCESS}, author = {Yuan Xiao and Kun Feng and Dongyan Miao and Peng Zhang and Jiaxin Yang}, doi = {10.1109/ACCESS.2025.3529899}, journal = {IEEE Access}, pages = {14225-14238}, shortjournal = {IEEE Access}, title = {A state-supervised model and novel anomaly index for gas turbines blade fault detection under multi-operating conditions}, volume = {13}, year = {2025}, }
• (2025). Improved CLIP-ILP model for detecting illegal passenger transport in freight trucks. ACCESS, 13, 14214-14224. (www)

  @article{ , abstract = {In recent years, illegal passenger transport in freight trucks has become a critical concern for traffic safety and law enforcement. This study proposes an automated system for detecting illegal passenger transport using an improved CLIP-ILP (Illegal Passenger Detection) model. The proposed model incorporates multi-scale feature fusion, a cross-modal self-attention mechanism, and a more powerful text encoder to enhance detection performance. To evaluate the model, we constructed a comprehensive dataset consisting of tens of thousands of truck images, categorized into two types of trucks (four-wheeled and three-wheeled) and further classified into two subcategories: “illegal passenger transport” and “non-illegal passenger transport.” The model was trained using this dataset, with an emphasis on leveraging the CLIP framework’s ability to understand and integrate visual and textual data. Experimental results demonstrate that the proposed CLIP-ILP model achieves superior accuracy and robustness in detecting illegal passenger transport under various conditions. This research not only highlights the potential of deep learning technologies in enhancing traffic safety but also provides a novel and efficient approach for law enforcement agencies to monitor and address this growing issue effectively. You can access the code for our proposed method at https://github.com/wu-xuan-git/CLIP-ILP.}, archive = {J_ACCESS}, author = {Xuan Wu and Wenlin Pan}, doi = {10.1109/ACCESS.2025.3528006}, journal = {IEEE Access}, pages = {14214-14224}, shortjournal = {IEEE Access}, title = {Improved CLIP-ILP model for detecting illegal passenger transport in freight trucks}, volume = {13}, year = {2025}, }
• (2025). Gaze assistance for efficient segmentation correction of medical images. ACCESS, 13, 14199-14213. (www)

  @article{ , abstract = {The segmentation of medical images is an important step in various diagnostic applications, including abnormality detection and radiotherapy planning. Recent developments in Artificial Intelligence (AI) have significantly advanced the field of segmentation automation. However, expert-level accuracy has not been achieved for most segmentation tasks, which significantly hampers the adoption of fully-automated medical image segmentation. This paper investigates the idea of efficient correction of medical image segmentation by using not manual controller commands, which can be time-consuming, but gaze movements. We propose a lightweight fine-tuning approach of the Segment Anything Model in medical images, known as MedSAM, to interactively adjust segmentation masks based on gaze point prompts. Our model is specifically trained for the abdominal CT imaging task using the publicly available WORD database. While surpassing state-of-the-art segmentation models, comprehensive studies with medical experts demonstrated that our gaze-assisted interactive approach led to significant improvements in segmentation quality. Specifically, the gaze-assisted corrections increased the average segmentation performance by nearly 62% for difficult medical cases, compared to traditional segmentation methods based on bounding boxes. The main findings of our proposed work include: 1) the substantial improvement in segmentation quality through gaze interaction, 2) the development of an efficient correction mechanism leveraging eye movements, and 3) the demonstration of gaze-assisted segmentation’s superior performance in abdominal imaging tasks. Our innovative approach shows promise for interactive segmentation of medical images and opens the door for further advancements in human-AI interaction in medicine using eye-tracking technology.}, archive = {J_ACCESS}, author = {Leila Khaertdinova and Tatyana Shmykova and Ilya Pershin and Andrey Laryukov and Albert Khanov and Damir Zidikhanov and Bulat Ibragimov}, doi = {10.1109/ACCESS.2025.3530701}, journal = {IEEE Access}, pages = {14199-14213}, shortjournal = {IEEE Access}, title = {Gaze assistance for efficient segmentation correction of medical images}, volume = {13}, year = {2025}, }
• (2025). An observer-based event triggered mechanism for the detection and mitigation of FDI attacks in deep brain stimulation systems. ACCESS, 13, 14186-14198. (www)

  @article{ , abstract = {Deep brain stimulation (DBS) is recognized as one of the prominent solutions to treat Parkinson’s disease. In the DBS, an accelerometer sensor is often adopted to measure the value of the tremor and then wirelessly transmit the measured signals to the programming device using a communication network. False data injection (FDI) attacks are among the most prevalent attacks in DBS, where the attacker injects false data into the communication infrastructure. In this paper, an event triggered adaptive defense mechanism is designed to mitigate the effect of FDI attacks in the DBS system. To the best of our knowledge, this is the first work focusing on the design of defense mechanisms against cybersecurity threats directed toward deep brain stimulation systems. To this end, a fractional order extended state observer (FOESO) is adopted to predict the tremor measurement signals of the accelerometer sensor. To achieve accurate prediction, Heuristic Adaptive Dynamic Programming (HADP) is utilized to adjust the coefficients embedded in the FOESO structure. Using a neural networks-based approach, the HADP agent regulates the parameters of the FOESO by maximizing a reward (reinforcement) signal according to the desired system requirements. An event-triggered mechanism is also utilized to mitigate manipulated signals measured by sensors and this strategy can address challenges such as bandwidth limitations and high network traffic. The comprehensive simulations of DBS under various levels of FDI threats reveal the feasibility of the suggested defense mechanism against cyber threats. Moreover, comparative dynamic responses and real-time examination of tremors demonstrate that by adopting the suggested adaptive defense, a superior performance than other state-of-the-art schemes can be achieved.}, archive = {J_ACCESS}, author = {Ping Yu and Ding Yang and Khalid A. Alattas and Ardashir Mohammadzadeh and Afef Fekih}, doi = {10.1109/ACCESS.2025.3529477}, journal = {IEEE Access}, pages = {14186-14198}, shortjournal = {IEEE Access}, title = {An observer-based event triggered mechanism for the detection and mitigation of FDI attacks in deep brain stimulation systems}, volume = {13}, year = {2025}, }
• (2025). Manufacturing reliability assessment of fine ag alloy wire during wire loop formation process. ACCESS, 13, 14177-14185. (www)

  @article{ , abstract = {In present study, the thermo-mechanical responses of Ag alloy wire during wire bonding manufacture procedure is analyzed by the combined experimental and simulation approach. A micro-tensile test method is adopted to investigate the temperature and strain rate dependence mechanical properties of Ag alloy wire. Extracted stress-strain curves are further utilized to demonstrate the coupled thermo-mechanical response via the utilization of Johnson-Cook (J-C) constitutive model. Aforementioned constitutive model is imported into transient dynamic simulation to estimate the characteristics on wire loop formation profile and wire stress distribution. Under the different consideration of wire loop formation speed, it is found that the strain rate has significant influence on plastic strain and corresponding stress at kink region, which is interpolated by the J-C model. The critical stress/strain location and loop height of concerned wire can be effectively estimated by the present approach, and provided the design guidelines of fine Ag alloy wire in application of electronic packaging.}, archive = {J_ACCESS}, author = {Deshin Liu and Tingsheng Chang and Zhenwei Zhuang and Yungching Chao and Sungting Chung and Peichen Huang}, doi = {10.1109/ACCESS.2025.3530509}, journal = {IEEE Access}, pages = {14177-14185}, shortjournal = {IEEE Access}, title = {Manufacturing reliability assessment of fine ag alloy wire during wire loop formation process}, volume = {13}, year = {2025}, }
• (2025). Model-based variation-aware optimization for offset calibration and pre-sensing in DRAM sense amplifiers. ACCESS, 13, 14165-14176. (www)

  @article{ , abstract = {This article proposes a new mathematical model that accurately predicts statistical margin characteristics of bit-line sense amplifiers (BLSAs) with offset calibration (OC) and pre-sensing (PS), while providing techniques to improve sensing margins. In particular, threshold voltage mismatch caused by reduced transistor sizes introduces sensing offsets, further degrading the already limited sensing margins under low-voltage conditions. While various BLSAs incorporating OC and PS techniques have been proposed to address these challenges, and studies have been conducted on models predicting statistical offset, previous research has not adequately considered OC timing ( $t_{OC}$ ) and transistor size effects. We independently model the OC, charge sharing (CS), and PS operations of DRAM BLSAs to accurately predict both deterministic and stochastic offsets resulting from various operation combinations. Notably, our model incorporates $t_{OC}$ dependency, which was not considered in previous models, and accurately analyzes transistor size effects through integration with Pelgrom’s equation. The primary advantage of the proposed model lies in its design optimization efficiency. While HSPICE simulations combining Monte Carlo (MC) and binary search methods require numerous iterations for single design point verification, our model significantly reduces design time by effectively narrowing the region of interest through pre-optimization using statistical characteristics. Furthermore, the model’s general form demonstrates high practicality through easy application to various BLSA types based on OC scheme types and PS operation presence. In conclusion, this article presents optimal design guidelines by accurately predicting deterministic and stochastic offset characteristics according to $t_{OC}$ and transistor size ratios, showing high correlation with HSPICE MC simulation results.}, archive = {J_ACCESS}, author = {Dongyeong Kim and Geon Kim and Suyeon Kim and Jewon Park and Sinwook Kim and Hyeona Seo and Chaehyuk Lim and Sowon Kim and Juwon Lee and Jeonghyeon Yun and Hyerin Lee and Jinseok Park and Yongbok Lee and Seungchan Lee and Myoungjin Lee}, doi = {10.1109/ACCESS.2025.3530412}, journal = {IEEE Access}, pages = {14165-14176}, shortjournal = {IEEE Access}, title = {Model-based variation-aware optimization for offset calibration and pre-sensing in DRAM sense amplifiers}, volume = {13}, year = {2025}, }
• (2025). Low-cost driver monitoring system using deep learning. ACCESS, 13, 14151-14164. (www)

  @article{ , abstract = {Driver monitoring systems are becoming an essential part of Advanced Driver Assistance Systems (ADAS) safety features in modern vehicles. The U.S. National Highway Traffic Safety Administration reports that drowsy/fatigued driving results in almost 100,000 road accidents per year. Driver’s fatigue can have different causes, such as lack of sleep, long journeys, restlessness, mental pressure and alcohol consumption. Early monitoring systems relied on data from vehicle sensors, and modern systems commonly use driver’s eye tracking. Recently, there has been growing interest in utilizing machine vision and deep learning for driver monitoring. Using machine vision can create more advanced driver monitoring systems capable of detecting driver attention state as well as other features like smartphone usage while driving and seat belts. Machine vision systems usually require extensive processing power, which raises the cost of such systems. In this paper, we present a low-cost driver monitoring system using a 15 Raspberry Pi Zero 2 W board and deep learning CNN to deliver a system capable of monitoring and identifying different states of the driver like safe driving, distracted, drowsy, and smartphone usage, the system achieves an inference rate for 10 Frames Per Second (FPS) and above 90% accuracy with the testing dataset. In addition to the deep learning CNN which runs on Raspberry Pi CPU, we utilize the Raspberry Pi GPU to run a head pose estimation algorithm to boost the system’s accuracy.}, archive = {J_ACCESS}, author = {Hady A. Khalil and Sherif A. Hammad and Hossam E. Abd El Munim and Shady A. Maged}, doi = {10.1109/ACCESS.2025.3530296}, journal = {IEEE Access}, pages = {14151-14164}, shortjournal = {IEEE Access}, title = {Low-cost driver monitoring system using deep learning}, volume = {13}, year = {2025}, }
• (2025). Refactoring android source code smells from android applications. ACCESS, 13, 14122-14150. (www)

  @article{ , abstract = {As technology advances and new features emerge, the demand for Android applications continues to grow, leading to rapid release schedules. These accelerated development timelines often push developers to make rushed changes, often resulting in suboptimal design practices, commonly known as code smells. These issues can degrade application quality, drive up maintenance costs, lead to unexpected behaviors, and complicate evolution and re-engineering efforts. While substantial research has focused on identifying Android-specific and object-oriented code smells, comparatively less attention has been devoted to their systematic refactoring and evaluation. This study introduces a web-based technique, validated through a tool specifically developed to detect 20 Android-specific code smells and automatically refactor 10 of them. Our approach surpasses traditional desktop and plugin solutions by providing easy accessibility, cross-platform compatibility, and eliminating setup requirements. When applied to six open-source and two industrial Android applications and evaluated against the ISO/IEC 25010 quality standard, our tool demonstrated considerable improvements: reducing CPU utilization by 15.39%, lowering memory consumption by 12.85%, and enhancing battery efficiency by up to 5.78%. The tool’s accuracy, validated through precision, recall, and F-measure metrics, achieved averages of 91.81% precision, 97.77% recall, and a 94.67% F-measure. This study enhances the Android application development lifecycle by offering developers a feasible solution for optimizing CPU efficiency, reducing memory use, and minimizing battery consumption.}, archive = {J_ACCESS}, author = {Muhammad Fawad and Ghulam Rasool and Muhammad Bilal Riaz}, doi = {10.1109/ACCESS.2025.3529687}, journal = {IEEE Access}, pages = {14122-14150}, shortjournal = {IEEE Access}, title = {Refactoring android source code smells from android applications}, volume = {13}, year = {2025}, }
• (2025). Multi-physical intelligent analysis of the thermal-structural-optical coupling for mirror based on surrogate model. ACCESS, 13, 14113-14121. (www)

  @article{ , abstract = {The thermal-structural-optical multi-physical coupling analysis (TSO-MCA) of infrared optical systems and their mirrors during the establishment of a cryogenic environment is a complex multi-physical coupling problem involving mechanics, thermal, optics and other factors. Currently, the finite element method’s workflow is complex and cumbersome, and it cannot simulate the continuous process of establishing a cryogenic environment. Therefore, a surrogate modeling approach based on a neural network for the TSO-MCA of mirrors has been proposed to analyze the surface shape of optical system mirrors under the action of multi-physical fields. Firstly, a thermal-structural-optical multi-physical integrated chain(TSO-IC) is established by integrating various disciplinary simulation and analysis tools, through which computational data necessary for the surrogate model are acquired. Secondly, a hybrid network combining Transformer and Long Short-Term Memory (LSTM) networks is proposed. The high-precision TSO-MCA surrogate model is obtained through training and K-fold cross validation. Compared with traditional methods, the hybrid network has the smallest symmetric mean absolute percentage error (SMAPE) of 2.674% on the same test set, which indicates that the hybrid network obtains a higher prediction accuracy of the surrogate model. Finally, through the thermal structure-optical coupling analysis of a mirror of an optical system, it is shown that the high precision mirror TSO-MCA based on the surrogate model is 3 orders of magnitude faster than the traditional finite element analysis in terms of computation time, indicating that the surrogate model is more efficient and convenient.}, archive = {J_ACCESS}, author = {Wenxiong Li and Junli Shen and Zhenyu Lu and Shudong Men and Qingwen Wu}, doi = {10.1109/ACCESS.2025.3530978}, journal = {IEEE Access}, pages = {14113-14121}, shortjournal = {IEEE Access}, title = {Multi-physical intelligent analysis of the thermal-structural-optical coupling for mirror based on surrogate model}, volume = {13}, year = {2025}, }
• (2025). A novel pattern recognition method for self-powered TENG sensor embedded to the robotic hand. ACCESS, 13, 14101-14112. (www)

  @article{ , abstract = {This paper presents the development and implementation of a human-like robotic hand integrated with advanced triboelectric nanogenerator (TENG) based tactile sensors for shape and material recognition. Meanwhile, traditional piezo sensors’ effectiveness is limited, sensitive to the temperature, and the manufacturing cost is high. TENG sensors offer a self-powered alternative with simplified circuitry, cost-effective fabrication, and enhanced durability. To capitalize on these benefits, we propose a novel machine learning approach that represents time-series data as two-dimensional images processed using a two-dimensional convolutional neural network (2D CNN). This method is compared against the traditional one-dimensional convolutional neural network (1D CNN) method. The research methodology encompasses TENG sensor preparation, noise cancellation, robotic hand design, and control electronics. Experimental results demonstrate that the proposed 2D CNN method significantly improves shape and material recognition accuracy, achieving 98% and 99%, respectively, compared to 94% and 98% with the 1D CNN method. Real-time evaluation further validates the robustness and adaptability of the proposed model in unstructured environments. These findings underscore the potential of integrating TENG sensors with advanced neural network architectures for autonomous dexterous manipulation in various industrial applications, paving the way for future advancements in robotic tactile sensing.}, archive = {J_ACCESS}, author = {Azat Balapan and Rauan Yeralkhan and Alikhan Aryslanov and Gulnur Kalimuldina and Azamat Yeshmukhametov}, doi = {10.1109/ACCESS.2025.3530465}, journal = {IEEE Access}, pages = {14101-14112}, shortjournal = {IEEE Access}, title = {A novel pattern recognition method for self-powered TENG sensor embedded to the robotic hand}, volume = {13}, year = {2025}, }
• (2025). Queue formation and obstacle avoidance navigation strategy for multi-robot systems based on deep reinforcement learning. ACCESS, 13, 14083-14100. (www)

  @article{ , abstract = {In contemporary society, the widespread application of robotics across various domains emphasizes the critical role of robotic systems in performing tasks that are too dangerous or complex for humans. However, individual robots often struggle with these intricate tasks, necessitating the collaboration of multi-robot systems. This study proposes a novel deep reinforcement learning (DRL)-based method for achieving queue formation, obstacle avoidance, and navigation in multi-robot systems. The approach includes a specialized state representation and reward mechanism tailored to queue formation, where the state design incorporates the relative positions and actions of robots in the queue to ensure they maintain formation and avoid obstacles. The reward mechanism encourages robots to adjust their speed and position, preserving the queue structure even in complex environments. To address the challenges associated with initial DRL training, we integrate curriculum learning, which facilitates smoother and faster convergence by gradually increasing task difficulty. This approach enables the system to efficiently complete the training process and enhance performance. Upon successful formation training, a speed control mechanism is introduced to improve the stability of the queue during navigation, ensuring robots maintain uniform speed and formation while avoiding obstacles. Simulation results demonstrate the effectiveness of the proposed method in managing queue formations across various robot numbers and environmental complexities. The results highlight robust performance with up to 10 robots, maintaining effectiveness in both static and dynamic environments. However, performance limitations are observed with larger fleets, indicating the need for further advancements in collision avoidance and speed control.}, archive = {J_ACCESS}, author = {Tianyi Gao and Zhanlan Li and Zhixin Xiong and Ling Wen and Kai Tian and Kewei Cai}, doi = {10.1109/ACCESS.2025.3527640}, journal = {IEEE Access}, pages = {14083-14100}, shortjournal = {IEEE Access}, title = {Queue formation and obstacle avoidance navigation strategy for multi-robot systems based on deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). An efficient detector for automatic tomato classification systems. ACCESS, 13, 14073-14082. (www)

  @article{ , abstract = {Nowadays, artificial intelligence and robotics have been deployed in almost all areas of human life. Especially in agriculture, it has helped people free up labor, speed up production, and ensure product quality. This research aims to develop a vision-based tomato detector to support robots and automatic tomato classification systems. In this idea, the original convolution blocks in the Backbone and Neck modules of the YOLOv8n architecture are replaced by a new version, called Receptive Field Attention Convolution (RFAConv). The model was trained and evaluated using four benchmarks, including the Laboro Tomato, Tomato Plantfactory, Sai Gon University, and CubeAI datasets. It achieved the best performance at 89.8% of mAP@0.5 and 65.2% of mAP@0.5:0.95 on the Tomato Plantfactory dataset. These results show that the proposed network is superior to others under a fixed set of experimental conditions. The video demonstration, datasets, weights, and models are available at https://bit.ly/3XdLrLM.}, archive = {J_ACCESS}, author = {Duy-Linh Nguyen and Xuan-Thuy Vo and Adri Priadana and Jehwan Choi and Kang-Hyun Jo}, doi = {10.1109/ACCESS.2024.3522349}, journal = {IEEE Access}, pages = {14073-14082}, shortjournal = {IEEE Access}, title = {An efficient detector for automatic tomato classification systems}, volume = {13}, year = {2025}, }
• (2025). Power transmission for millimeter-wave Indoor/Outdoor wearable IoT devices using grounded coplanar waveguide-fed on-body antenna. ACCESS, 13, 14063-14072. (www)

  @article{ , abstract = {This paper presents for the first-time evaluation of wireless power transmission (WPT) for sustainable low-powered Internet of Things (IoT) devices in realistic indoor/outdoor scenarios using empirical propagation models at 28 GHz. The used empirical propagation models have shown that using an on-body $9\times 9$ mm-wave rectenna array based on a proposed mm-wave antenna is able to charge IoT devices at a distance of 57 m for line-of-sight (LOS) indoor temporal environment, and at a distance of 10 m for LOS outdoor tropical propagation model using a base station with 53 dBm transmission power. Furthermore, the mm-wave on-body $9\times 9$ rectenna array occupies an area equal to that of a single UHF rectenna, while collecting 17-fold more power. In addition, the article discusses the design and experimental results of a single-element on-body mm-wave antenna used to design the $9\times 9$ rectenna array. The proposed mm-wave antenna is a single-layer low-profile structure. Furthermore, the antenna has a stable gain of over 9.5 dBi and a wide beamwidth. The on-body antenna structure consists of rectangular multi-slot patch fed by a $50~\Omega $ grounded coplanar waveguide (GCPW) line. Employing the multi-slot configuration results in a wearable antenna’s impedance bandwidth of 3.73 GHz. The peak measured gain of the antenna is 10.5 for chest/arm-mounted case in the operating 28 GHz N257 5G band. The antenna’s radiation pattern forms a wide off-body forward direction beam. A prototype of the proposed antenna is fabricated and validated experimentally for both cases on a human volunteer arm/chest and in the free space. The size of the proposed structure is small and can collect power with high efficiency due to the short wavelength of millimeter wave (mm-wave) in contrast to UHF antennas.}, archive = {J_ACCESS}, author = {Fuad Erman and Slawomir Koziel and Alhareth Zyoud and Leifur Leifsson and Ubaid Ullah and Shaker Alkaraki}, doi = {10.1109/ACCESS.2025.3528871}, journal = {IEEE Access}, pages = {14063-14072}, shortjournal = {IEEE Access}, title = {Power transmission for millimeter-wave Indoor/Outdoor wearable IoT devices using grounded coplanar waveguide-fed on-body antenna}, volume = {13}, year = {2025}, }
• (2025). Lightweight small target detection algorithm based on YOLOv8 network improvement. ACCESS, 13, 14051-14062. (www)

  @article{ , abstract = {The primary objective of this paper is to address the shortcomings of existing algorithms in the context of UAV-based object detection. The paper introduces SFD-YOLOv8, a lightweight algorithm based on YOLOv8n, with the aim of enhancing detection performance while maintaining a streamlined architecture. The innovation of SFD-YOLOv8 is characterised by its incorporation of several pioneering modules, including the Dilation-wise Residual (DWR) attention module and the FasterBlock module. The modules have been designed to optimise feature extraction and improve model efficiency. The paper also discusses the challenges associated with low accuracy in small target detection and high model complexity in UAV applications. It emphasises the necessity for efficient models capable of accurately identifying small targets with limited computational resources, a balance that existing algorithms frequently fail to achieve. The paper’s contributions can be summarised as follows. Firstly, it proposes SFD-YOLOv8, a novel lightweight algorithm tailored for UAV applications. Secondly, it introduces the Dilation-wise Residual (DWR) attention module and FasterBlock module to optimise feature extraction and improve model efficiency. Thirdly, it presents the FocalEloU-Loss function, which significantly enhances detection accuracy by refining bounding box predictions. Finally, the Std detection layer is integrated into YOLOv8n, thereby enhancing the model’s ability to accurately detect small targets. Experimental results demonstrate that SFD-YOLOv8 reduces parameters by 16.95% compared to YOLOv8n. On the VisDrone2019 dataset, it achieves 2.50% improvement in mAP@0.5 and a 1.40% increase in mAP@0.5-0.95.SFD-YOLOv8 demonstrates superior accuracy in comparison to other leading detection models, making it well-suited for real-time detection requirements.}, archive = {J_ACCESS}, author = {Xiaoyi Hao and Ting Li}, doi = {10.1109/ACCESS.2025.3529835}, journal = {IEEE Access}, pages = {14051-14062}, shortjournal = {IEEE Access}, title = {Lightweight small target detection algorithm based on YOLOv8 network improvement}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of AI’s current impact and future prospects in cybersecurity. ACCESS, 13, 14029-14050. (www)

  @article{ , abstract = {The use of artificial intelligence (AI) technology signifies a significant milestone in the swiftly evolving domain of cybersecurity. This study offers a comprehensive literature review on the role, effect, and future prospects of AI across five critical areas of cybersecurity: threat detection, endpoint security, phishing and fraud detection, network security, and adaptive authentication. The study examines contemporary developments in AI for cybersecurity, highlighting the use of these technologies to enhance security protocols. We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. Essential comparisons of AI models are presented for each area, outlining their main applications, advantages, and drawbacks. The article examines the assessment criteria and performance outcomes of AI-driven cybersecurity solutions. This report synthesizes previous research while identifying gaps and future prospects, including the integration of emerging AI approaches, the enhancement of real-time threat detection capabilities, and the addressing of changing attack vectors. By providing a holistic view of the current state and future potential of AI in cybersecurity, this paper aims to serve as a foundational reference for researchers and practitioners seeking to leverage AI for robust and adaptive security solutions.}, archive = {J_ACCESS}, author = {Abdullah Al Siam and Moutaz Alazab and Albara Awajan and Nuruzzaman Faruqui}, doi = {10.1109/ACCESS.2025.3528114}, journal = {IEEE Access}, pages = {14029-14050}, shortjournal = {IEEE Access}, title = {A comprehensive review of AI’s current impact and future prospects in cybersecurity}, volume = {13}, year = {2025}, }
• (2025). Algorithm selection for allocating pods within robotic mobile fulfillment systems: A hyper-heuristic approach. ACCESS, 13, 14010-14028. (www)

  @article{ , abstract = {Robotic Mobile Fulfillment Systems (RMFS) are an example of warehouse automation. Nonetheless, the complexity of RMFS is such that tackling the entire problem at once is unfeasible. So, this work focuses on a component known as the Pod Allocation Problem (PAP). We analyze the performance of 20 hyper-heuristic models over 60 instances and compare them against a baseline of six low-level heuristics. Our data revealed three key insights. First, sequence-based hyper-heuristics outperformed low-level heuristics in 22% of the models we tested. Second, we noted that, under certain conditions, even the worst-performing heuristics can lead to successful hyper-heuristics. For example, when simulating for 24 hours, the best hyper-heuristic uses the worst heuristic for 80% of the time, and yields a solution with a throughput time 1.4% better than that of the best heuristic. Finally, the resulting model is affected by simulation time, sequence ordering, and heuristic subset.}, archive = {J_ACCESS}, author = {Maria Torcoroma Benavides-Robles and Jorge M. Cruz-Duarte and José Carlos Ortiz-Bayliss and Ivan Amaya}, doi = {10.1109/ACCESS.2025.3530842}, journal = {IEEE Access}, pages = {14010-14028}, shortjournal = {IEEE Access}, title = {Algorithm selection for allocating pods within robotic mobile fulfillment systems: A hyper-heuristic approach}, volume = {13}, year = {2025}, }
• (2025). Quantum algorithms for the physical layer: Potential applications to physical layer security. ACCESS, 13, 13988-14009. (www)

  @article{ , abstract = {The field of quantum technologies has garnered considerable interest and witnessed noteworthy progress in recent years. It is also anticipated that these technologies will continue to flourish and exert a considerable influence on society, i.e., a plethora of real-world problems that cannot be solved by classical algorithms are believed to benefit from the implementation of quantum algorithms. Meanwhile, as an alternative to cryptographic methods, physical layer security (PLS) has been extensively studied as a means to realize secure wireless communication that is resistant to attacks by both classical and quantum computers, i.e., quantum-safe. While the prevailing approach to PLS has been based on classical algorithms, this could potentially be accelerated by the application of quantum algorithms. This paper examines the potential applications of various quantum algorithms, including quantum annealing, hybrid quantum-classical algorithms, and Grover-based algorithms, to the the PLS problems. In particular, we begin with a concise overview of their applications to physical layer techniques and then proceed to discuss their use in addressing the challenges of secret message transmission and secret key generation from wireless channels.}, archive = {J_ACCESS}, author = {Toshiki Matsumine and Hideki Ochiai and Junji Shikata}, doi = {10.1109/ACCESS.2025.3528443}, journal = {IEEE Access}, pages = {13988-14009}, shortjournal = {IEEE Access}, title = {Quantum algorithms for the physical layer: Potential applications to physical layer security}, volume = {13}, year = {2025}, }
• (2025). Machine learning-integrated usability evaluation and monitoring of human activities for individuals with special needs during hajj and umrah. ACCESS, 13, 13972-13987. (www)

  @article{ , abstract = {Human activity recognition (HAR) is an important aspect of the safety and accessibility of individuals with disabilities. This is especially essential during large-scale events like Hajj and Umrah, which attract millions of participants each year. These gatherings pose significant challenges for people with disabilities and affect both their mobility and security. To address these issues, this study introduces a new approach to improve usability and tracking for disabled pilgrims performing the two holy pilgrimages: Hajj and Umrah, which are performed annually by millions of Muslims. These acts of worship are still very challenging for the mobility, security, and accessibility of people with special needs. Using clustering, anomaly detection, and predictive modeling, it was intended to enhance the safety and security of sensitive participants. Using the K-Means algorithm and the Elbow Method as the initial indicators to classify the clusters, we reveal four clusters that relate to different human activities that are further visualized through principal component analysis (PCA). Activities are clustered based on the behavioral patterns observed among participants, followed by the performance of anomaly detection. The analysis reveals that the string ‘WALKING_DOWNSTAIRS’ represents a sensitive and rare event in the training data set with a count of 36, which indicates possible walking disabilities that human beings may experience. The proposed study used two machine learning models, i.e., random forest and sequential neural networks, both with 93% accuracy. The performance was fairly accurate, with RF scoring 1 for ‘LAYING’ and SNN scoring 0.99 for ‘WALKING_UPSTAIRS’. The usefulness of this study is to improve the safety of the disabled participants in performing Hajj and Umrah. Besides extending the knowledge base and technical development of HAR and machine learning, this work has implications for specific real-life problems of accessibility and security in large-scale religious gatherings.}, archive = {J_ACCESS}, author = {Ghadah Naif Alwakid}, doi = {10.1109/ACCESS.2025.3532385}, journal = {IEEE Access}, pages = {13972-13987}, shortjournal = {IEEE Access}, title = {Machine learning-integrated usability evaluation and monitoring of human activities for individuals with special needs during hajj and umrah}, volume = {13}, year = {2025}, }
• (2025). Transformer-based model for monocular visual odometry: A video understanding approach. ACCESS, 13, 13959-13971. (www)

  @article{ , abstract = {Estimating the camera’s pose given images from a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and often relies on geometric approaches that require considerable engineering effort for a specific scenario. Deep learning methods have been shown to be generalizable after proper training and with a large amount of available data. Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6 degrees of freedom of a camera’s pose. We contribute by presenting the TSformer-VO model based on spatio-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset, outperforming the DeepVO implementation highly accepted in the visual odometry community. The code is publicly available at https://github.com/aofrancani/TSformer-VO.}, archive = {J_ACCESS}, author = {André O. Françani and Marcos R. O. A. Maximo}, doi = {10.1109/ACCESS.2025.3531667}, journal = {IEEE Access}, pages = {13959-13971}, shortjournal = {IEEE Access}, title = {Transformer-based model for monocular visual odometry: A video understanding approach}, volume = {13}, year = {2025}, }
• (2025). K-SMPC: Koopman operator-based stochastic model predictive control for enhanced lateral control of autonomous vehicles. ACCESS, 13, 13944-13958. (www)

  @article{ , abstract = {This paper proposes Koopman operator-based Stochastic Model Predictive Control (K-SMPC) for enhanced lateral control of autonomous vehicles. The Koopman operator is a linear map representing the nonlinear dynamics in an infinite-dimensional space. Thus, we use the Koopman operator to represent the nonlinear dynamics of a vehicle in dynamic lane-keeping situations. The Extended Dynamic Mode Decomposition (EDMD) method is adopted to approximate the Koopman operator in a finite-dimensional space for practical implementation. We consider the modeling error of the approximated Koopman operator in the EDMD method. Then, we design K-SMPC to tackle the Koopman modeling error, where the error is handled as a probabilistic signal. The recursive feasibility of the proposed method is investigated with an explicit first-step state constraint by computing the robust control invariant set. A high-fidelity vehicle simulator, i.e., CarSim, is used to validate the proposed method with a comparative study. From the results, it is confirmed that the proposed method outperforms other methods in tracking performance. Furthermore, it is observed that the proposed method satisfies the given constraints and is recursively feasible.}, archive = {J_ACCESS}, author = {Jin Sung Kim and Ying Shuai Quan and Chung Choo Chung and Woo Young Choi}, doi = {10.1109/ACCESS.2025.3530984}, journal = {IEEE Access}, pages = {13944-13958}, shortjournal = {IEEE Access}, title = {K-SMPC: Koopman operator-based stochastic model predictive control for enhanced lateral control of autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). Advancing passwordless authentication: A systematic review of methods, challenges, and future directions for secure user identity. ACCESS, 13, 13919-13943. (www)

  @article{ , abstract = {As reliance on digital services grows, traditional password-based authentication methods have been increasingly scrutinized due to their susceptibility to cyber-attacks, including phishing and brute force attacks. This has led to a shift toward passwordless authentication, an approach that promises enhanced security and user experience. This paper provides a comprehensive review of passwordless authentication techniques, analysing their application in user identity verification across multiple platforms. Various methods such as biometrics, security keys, and token-based systems are explored for their efficacy in mitigating security vulnerabilities. The review highlights the advantages of passwordless authentication, including improved security, reduced user friction, and compatibility with modern identity management frameworks like FIDO2. Challenges such as usability issues, cost of deployment, and scalability are also discussed. Moreover, the paper identifies future research areas aimed at overcoming these challenges and facilitating the broad adoption of passwordless authentication in critical industries such as healthcare, finance, and public services. Future opportunities are outlined, emphasizing the need for real-world implementation, enhanced scalability, integration of AI-driven adaptive mechanisms, and innovative designs to improve user accessibility and system resilience. By consolidating existing knowledge and identifying gaps in current solutions, this study provides valuable insights for researchers and industry stakeholders seeking to enhance security through passwordless systems.}, archive = {J_ACCESS}, author = {Mohd Imran Md Yusop and Nazhatul Hafizah Kamarudin and Nur Hanis Sabrina Suhaimi and Mohammad Kamrul Hasan}, doi = {10.1109/ACCESS.2025.3528960}, journal = {IEEE Access}, pages = {13919-13943}, shortjournal = {IEEE Access}, title = {Advancing passwordless authentication: A systematic review of methods, challenges, and future directions for secure user identity}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning-based speed predictor for distributionally robust eco-driving. ACCESS, 13, 13904-13918. (www)

  @article{ , abstract = {This paper proposes an eco-driving technique for an ego vehicle operating behind a non-communicating leading Heavy-Duty Vehicle (HDV), aimed at minimizing energy consumption while ensuring inter-vehicle distance. A novel data-driven approach based on Deep Reinforcement Learning (DRL) is developed to predict the future speed trajectory of the leading HDV using simulated speed profiles and road slope information. The DQN-based speed predictor achieves a prediction accuracy of 95.4% and 93.2% in Driving Cycles 1 and 2, respectively. This predicted speed is then used to optimize the ego vehicle’s speed plan through a distributionally robust Model Predictive Controller (MPC), which accounts for uncertainties in the prediction, ensuring operational safety. The proposed method demonstrates energy savings of 12.5% in Driving Cycle 1 and 8.6% in Driving Cycle 2, compared to traditional leading vehicle speed prediction methods. Validated through case studies across simulated and real-world driving cycles, the solution is scalable, computationally efficient, and suitable for real-time applications in Intelligent Transportation Systems (ITS), making it a viable approach for enhancing sustainability in non-communicating vehicle environments.}, archive = {J_ACCESS}, author = {Rajan Chaudhary and Nalin Kumar Sharma and Rahul Kala and Sri Niwas Singh}, doi = {10.1109/ACCESS.2025.3530087}, journal = {IEEE Access}, pages = {13904-13918}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning-based speed predictor for distributionally robust eco-driving}, volume = {13}, year = {2025}, }
• (2025). Virtual cities: From digital twins to autonomous AI societies. ACCESS, 13, 13866-13903. (www)

  @article{ , abstract = {Virtual Cities (VCs) transcend simple digital replicas of real-world systems, emerging as complex socio-technical ecosystems where autonomous AI entities function as citizens. Agentic AI systems are on track to engage in cultural, economic, and political activities, effectively forming societal structure within VC. This paper proposes an integrated simulation framework that combines physical, structural, behavioral, cognitive, and data fidelity layers, allowing multi-scale simulation from microscopic interactions to macro-urban dynamics. A composite fidelity metric ( $F_{0}$ ) provides systematic approach to evaluate accuracy variations across applications in VCs. We also discuss autonomy of AI entities and classify them according to their capacity to modify goals—ranging from “tools” with fixed objectives to “entities” capable of redefining their very purpose. We also outline the requirements to define a coefficient to evaluate the degree of autonomy for AI beings. Our results demonstrate that such virtual environments can support the emergence of AI-driven societies, where governance mechanisms like Decentralized Autonomous Organizations (DAOs) and an Artificial Collective Consciousness (ACC) provide ethical and regulatory oversight. By blending horizon scanning with systems engineering method for defining novel AI governance models, this study reveals how VCs can catalyze breakthroughs in urban innovation while driving socially beneficial AI development - consequently opening a new frontier for exploring human-AI coexistence.}, archive = {J_ACCESS}, author = {Andrey Nechesov and Ivan Dorokhov and Janne Ruponen}, doi = {10.1109/ACCESS.2025.3531222}, journal = {IEEE Access}, pages = {13866-13903}, shortjournal = {IEEE Access}, title = {Virtual cities: From digital twins to autonomous AI societies}, volume = {13}, year = {2025}, }
• (2025). XAI-enhanced machine learning for obesity risk classification: A stacking approach with LIME explanations. ACCESS, 13, 13847-13865. (www)

  @article{ , abstract = {Obesity remains a critical global health challenge, necessitating early risk assessment to guide preventive measures and mitigate potential complications. While various research endeavors have explored obesity classification, many existing approaches lack reliability due to limited integration with explainable artificial intelligence (XAI) methodologies. In this study, we propose a robust machine learning framework that incorporates Explainable AI (XAI) principles to accurately estimate obesity levels and provide insights into the factors influencing the predictions. We utilize the publicly available dataset from Palechor and Manotas available in the UCI ML repository which contains relevant information on individuals’ physical characteristics and behaviors. Our proposed model employs an ensemble approach, specifically a stacking algorithm, where the base estimators include the Light Gradient Boosting Machine (LGBM) classifier, the Logistic Regression (LR) classifier, and the Random Forest (RF) Classifier, and the Stochastic Gradient Descent (SGD) classifier is selected as the final estimator. To enhance model interpretability and reliability, we integrate a widely accepted XAI method, Local Interpretable Model-agnostic Explanations (LIME). Our proposed framework achieves a peak accuracy of 98.82%, surpassing most existing techniques. By incorporating LIME, we not only enhance model trustworthiness but also provide deeper insights into the factors driving obesity risk. Overall, our approach contributes to advancing personalized interventions and bridging the gap between model complexity and human understanding.}, archive = {J_ACCESS}, author = {Mohammad Azad and Md Faraz Kabir Khan and Sameh Abd El-Ghany}, doi = {10.1109/ACCESS.2025.3530840}, journal = {IEEE Access}, pages = {13847-13865}, shortjournal = {IEEE Access}, title = {XAI-enhanced machine learning for obesity risk classification: A stacking approach with LIME explanations}, volume = {13}, year = {2025}, }
• (2025). Geographical origin identification of panax notoginseng using a modified K-nearest neighbors model with near-infrared spectroscopy. ACCESS, 13, 13832-13846. (www)

  @article{ , abstract = {This study introduces a novel method for identifying the geographical origins of Panax notoginseng using near-infrared spectroscopy and a modified K-nearest neighbors algorithm. The proposed model integrates distance and cosine similarity metrics to enhance classification performance, particularly for imbalanced datasets. Principal component analysis is employed to reduce dimensionality, significantly improving computational efficiency without sacrificing accuracy. Comparative analyses reveal that the modified K-nearest neighbors surpasses classic K-nearest neighbors and other models, achieving up to 96.90% accuracy on balanced datasets and 95.79% on imbalanced datasets. These results demonstrate the potential of combining spectral data processing with advanced machine learning techniques for efficient and accurate geographical origin identification, providing a robust tool for quality assurance and traceability in traditional Chinese medicine.}, archive = {J_ACCESS}, author = {Xuefeng Cheng and Min Liao and Juan Liu}, doi = {10.1109/ACCESS.2025.3530766}, journal = {IEEE Access}, pages = {13832-13846}, shortjournal = {IEEE Access}, title = {Geographical origin identification of panax notoginseng using a modified K-nearest neighbors model with near-infrared spectroscopy}, volume = {13}, year = {2025}, }
• (2025). Clustering large-scale biomedical data to model dynamic accumulation processes in disease progression and anti-microbial resistance evolution. ACCESS, 13, 13816-13831. (www)

  @article{ , abstract = {Accumulation modelling uses machine learning to discover the dynamics by which systems acquire discrete features over time. Many systems of biomedical interest show such dynamics: from bacteria acquiring resistances to sets of drugs, to patients acquiring symptoms during the course of progressive disease. Existing approaches for accumulation modelling are typically limited either in the number of features they consider or their ability to characterise interactions between these features – a limitation for the large-scale genetic and/or phenotypic datasets often found in modern biomedical applications. Here, we demonstrate how clustering can make such large-scale datasets tractable for powerful accumulation modelling approaches. Clustering resolves issues of sparsity and high dimensionality in datasets, but complicates the intepretation of the inferred dynamics, especially if observations are not independent. Focussing on hypercubic hidden Markov models (HyperHMM), we introduce several approaches for interpreting, estimating, and bounding the results of the dynamics in these cases and discuss how biomedical insight could be gained from such analyses. We demonstrate this ‘Cluster-based HyperHMM’ (CHyperHMM) pipeline for synthetic data, clinical data on disease progression in severe malaria, and genomic data for anti-microbial resistance evolution in Klebsiella pneumoniae, reflecting two global health threats.}, archive = {J_ACCESS}, author = {Kazeem A. Dauda and Olav N. L. Aga and Iain G. Johnston}, doi = {10.1109/ACCESS.2025.3527715}, journal = {IEEE Access}, pages = {13816-13831}, shortjournal = {IEEE Access}, title = {Clustering large-scale biomedical data to model dynamic accumulation processes in disease progression and anti-microbial resistance evolution}, volume = {13}, year = {2025}, }
• (2025). DAG-based swarm learning approach in healthcare: A survey. ACCESS, 13, 13796-13815. (www)

  @article{ , abstract = {Healthcare systems are advancing at a rapid pace as a result of new technologies to address several issues in the sector such as shortage of skilled health workers and to deal with new diseases like COVID-19. Incorporating technologies like blockchain, federated learning, swarm learning, and Directed Acyclic Graphs is transforming healthcare. This article thoroughly examines recent progress and uses at the intersection of these technologies within the healthcare field. Blockchain’s innovative consensus mechanisms and secure data flow systems offer encouraging solutions to crucial issues in healthcare data management and security. Also, federated learning has been deployed in various ways to tackle healthcare challenges enabling collaborative data analysis while upholding patient confidentiality. Swarm learning algorithms have been notably effective in healthcare, enriching medical diagnostics, disease prognosis, and precision medicine. Solutions based on Directed Acyclic Graphs present scalable and effective alternative to traditional blockchain frameworks, providing improved consensus speed and decreased bottlenecks in transaction processing. These advancements signify a shift in direction towards fully decentralised and secure healthcare systems. This paper highlights the transformative impact of these technologies on medical diagnostics, disease prediction, and precision medicine.}, archive = {J_ACCESS}, author = {David Gana and Faisal Jamil}, doi = {10.1109/ACCESS.2025.3531216}, journal = {IEEE Access}, pages = {13796-13815}, shortjournal = {IEEE Access}, title = {DAG-based swarm learning approach in healthcare: A survey}, volume = {13}, year = {2025}, }
• (2025). Experimental verification of the field robotic system for pipeline maintenance. ACCESS, 13, 13782-13795. (www)

  @article{ , abstract = {In the domain of field robotic system, it must deal with several challenges during both design and development durations. This study aims to investigate the whole design of robotic system for the internal inspection of tube. Firstly, the robot configuration is launched to identify the physical dimensions. Owing to the size of tube and the curved trajectory, the estimated parameters and shape of robot could be known. Also, some thresholds are obtained in this design which allows the range of dimensions. Secondly, the kinematic robot is analyzed to compute the flexible motion. Through theoretical computations, its locomotion could be evaluated directly. To realize our design, thirdly the detailed selection of servo motor is introduced in order to provide the powerful drive. Hence, the result of driving computations is shown due to the practical specifications of motor. Correspondingly, the spring which plays a role as damper, is measured to suppress the required torques. This spring ensures the continuous contact between leg of robot and internal wall of tube. As a result of these evaluations, both computer-based model and real-world model have been experimented. To guarantee the effectiveness and feasibility of our approach, three levels of validations are presented. In the numerical simulation, the virtual environment is established to substantiate the robotic locomotion. For the laboratory validation, the hardware robot is confirmed the working abilities as well as some trial results. Lastly, this robot must be exemplified in the real scene such as factory or site construction. From these achievements, it could be obviously seen that our approach is proper and feasible for many applications.}, archive = {J_ACCESS}, author = {Thanh Phuong Nguyen and Hung Nguyen and Nhat Minh Ngo and Ha Quang Thinh Ngo}, doi = {10.1109/ACCESS.2025.3529131}, journal = {IEEE Access}, pages = {13782-13795}, shortjournal = {IEEE Access}, title = {Experimental verification of the field robotic system for pipeline maintenance}, volume = {13}, year = {2025}, }
• (2025). Rethinking exploration and experience exploitation in value-based multi-agent reinforcement learning. ACCESS, 13, 13770-13781. (www)

  @article{ , abstract = {Cooperative Multi-Agent Reinforcement Learning (MARL) focuses on developing strategies to effectively train multiple agents to learn and adapt policies collaboratively. Despite being a relatively new area of research, most MARL methods are based on well-established approaches used in single-agent deep learning tasks due to their proven effectiveness. In this paper, we focus on the exploration problem inherent in many MARL algorithms. These algorithms often introduce new hyperparameters and incorporate auxiliary components, such as additional models, which complicate the adaptation process of the underlying RL algorithm to better fit multi-agent environments. We aim to optimize a deep MARL algorithm with minimal modifications to the well-known QMIX approach. Our investigation of the exploitation-exploration dilemma shows that the performance of state-of-the-art MARL algorithms can be matched by a simple modification of the $\epsilon $ -greedy policy. This modification depends on the ratio of available joint actions to the number of agents. We also improve the training aspect of the replay buffer to decorrelate experiences based on recurrent rollouts rather than episodes. The improved algorithm is not only easy to implement, but also aligns with state-of-the-art methods without adding significant complexity. Our approach outperforms existing algorithms in four of seven scenarios across three distinct environments while remaining competitive in the other three.}, archive = {J_ACCESS}, author = {Anatolii Borzilov and Alexey Skrynnik and Aleksandr Panov}, doi = {10.1109/ACCESS.2025.3530974}, journal = {IEEE Access}, pages = {13770-13781}, shortjournal = {IEEE Access}, title = {Rethinking exploration and experience exploitation in value-based multi-agent reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Enhancing indoor mmWave communication with ML-based propagation models. ACCESS, 13, 13748-13769. (www)

  @article{ , abstract = {With the advancement of 5G and emerging wireless communication technologies, accurate modeling of wave propagation in indoor environments has become increasingly crucial. This study focuses on demonstrating how machine learning (ML) techniques can be applied to predict path loss within the millimeter wave (mmWave) spectrum in a specific indoor environment. We address high-frequency challenges like path loss and complex building layouts that impact signal propagation. We employ various ML models, including Artificial Neural Networks (ANNs), hybrid models integrating linear regression, ANNs, and Gaussian Processes, and Extreme Gradient Boosting (XGBoost), to predict and analyze the propagation loss in a controlled indoor setting. The models were trained and validated using data collected from a comprehensive measurement campaign at 28 GHz, which involved high precision radio equipment in a complex indoor environment. Our results demonstrate that while traditional models provide a baseline for understanding path loss, advanced ML models, particularly hybrid approaches, significantly enhance prediction accuracy and provide a deeper understanding of indoor propagation dynamics within this specific environment. The study highlights the potential of ML in overcoming the limitations of empirical models and showcases methodologies that can be adapted for similar indoor scenarios. This research advances our understanding of mmWave propagation indoors and sets a framework for utilizing ML in telecommunication system design and optimization in specific environments.}, archive = {J_ACCESS}, author = {Gustavo Adulfo López-Ramírez and Alejandro Aragón-Zavala}, doi = {10.1109/ACCESS.2025.3527500}, journal = {IEEE Access}, pages = {13748-13769}, shortjournal = {IEEE Access}, title = {Enhancing indoor mmWave communication with ML-based propagation models}, volume = {13}, year = {2025}, }
• (2025). Implementation of a real-time force estimation system based on sEMG signals and gaussian process regression: Human–Robot interaction in rehabilitation. ACCESS, 13, 13731-13747. (www)

  @article{ , abstract = {Human force estimation has numerous applications, including biomedical models, rehabilitation, biomechanical system control, and human-machine interfaces. To enable such applications, it is necessary and challenging to develop methods for efficiently estimating force. In this work, we propose a system for muscle force estimation using eight-channel surface electromyography (sEMG) signals. The main contribution of this work is the implementation of a real-time force estimation system in MATLAB/Simulink to support real-time testing and usability. The regression models (i.e., Gaussian process regression (GPR), neural networks (NN), linear regression (LR), and support vector machines (SVM)) are applied to estimate the forearm muscle forces exerted by different elbow placement patterns for rehabilitation applications. The GPR model with exponential kernel function is chosen as the best model and is used to calculate forces at various elbow placement scenarios. Experimental results show that the GPR model can estimate forces on the X and Y axes concurrently in four movement directions: X+, X-, Y+, and Y-. The average root mean square errors (RMSEs) indicating estimation performance for offline and online testing of different elbow placement patterns are 1.09 to 1.99 N and 3.58 to 4.16 N, respectively.}, archive = {J_ACCESS}, author = {Thantip Sittiruk and Kiattisak Sengchuai and Apidet Booranawong and Pornchai Phukpattaranont}, doi = {10.1109/ACCESS.2025.3529986}, journal = {IEEE Access}, pages = {13731-13747}, shortjournal = {IEEE Access}, title = {Implementation of a real-time force estimation system based on sEMG signals and gaussian process regression: Human–Robot interaction in rehabilitation}, volume = {13}, year = {2025}, }
• (2025). Intrusion detection for wireless sensor network using particle swarm optimization based explainable ensemble machine learning approach. ACCESS, 13, 13711-13730. (www)

  @article{ , abstract = {Wireless Sensor Networks (WSN) play a pivotal role in various domains, including monitoring, security, and data transmission. However, their susceptibility to intrusions poses a significant challenge. This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. The system addresses key challenges such as the imbalanced nature of datasets and the evolving complexity of network attacks. By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. Extensive experimentation on WSN-DS dataset demonstrates the system’s efficacy, achieving an accuracy of 99.73%, with precision, recall, and F1 score values of 99.72% each, outperforming existing approaches. This work offers a robust, scalable solution for securing WSNs, contributing to both academic research and practical applications.}, archive = {J_ACCESS}, author = {Shaikh Afnan Birahim and Avijit Paul and Fahmida Rahman and Yamina Islam and Tonmoy Roy and Mohammad Asif Hasan and Fariha Haque and Muhammad E. H. Chowdhury}, doi = {10.1109/ACCESS.2025.3528341}, journal = {IEEE Access}, pages = {13711-13730}, shortjournal = {IEEE Access}, title = {Intrusion detection for wireless sensor network using particle swarm optimization based explainable ensemble machine learning approach}, volume = {13}, year = {2025}, }
• (2025). Targeted discrepancy attacks: Crafting selective adversarial examples in graph neural networks. ACCESS, 13, 13700-13710. (www)

  @article{ , abstract = {In this study, we present a novel approach to adversarial attacks for graph neural networks (GNNs), specifically addressing the unique challenges posed by graphical data. Unlike traditional adversarial attacks, which aim to perturb the input data to induce misclassifications in the target model, our approach strategically crafts adversarial examples to exploit discrepancies in model behavior. We introduce the concept of selective adversarial examples, which are instances that are correctly classified by a “friendly” model but misclassified by an “adversary” model. To achieve this, we propose a novel loss function formulation that simultaneously maximizes the probability of correct classification using a friendly model and minimizes the probability of correct classification using an adversary model. This approach facilitates the generation of adversarial examples that are both subtle and effective, necessitating minimal perturbations in the input graph. We systematically explain the principles and structure of our method and evaluate its performance through experiments conducted on a GNN using the Reddit, ogbn-product, and Citeseer datasets. Our results demonstrate the effectiveness of the proposed approach in generating selective adversarial examples, highlighting its potential applications in military environments, where the ability to selectively target adversary models is crucial. In addition, we provide visualizations of graph adversarial examples to aid in understanding the nature of the attacks. Overall, our contributions are threefold: First, we pioneer the concept of selective adversarial examples within the graph domain. Second, we provide comprehensive insights into the systematic generation and evaluation of these examples. Third, we furnish empirical evidence demonstrating their effectiveness in compromising the robustness of models.}, archive = {J_ACCESS}, author = {Hyun Kwon and Jang-Woon Baek}, doi = {10.1109/ACCESS.2024.3456728}, journal = {IEEE Access}, pages = {13700-13710}, shortjournal = {IEEE Access}, title = {Targeted discrepancy attacks: Crafting selective adversarial examples in graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Imbalanced data problem in machine learning: A review. ACCESS, 13, 13686-13699. (www)

  @article{ , abstract = {One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates achieving accurate model classifications. This survey delves into various machine learning techniques developed to address the difficulties posed by imbalanced data. It discusses data-level methods such as oversampling and undersampling, algorithm-level solutions including ensemble learning and specific algorithm adjustments, cost-sensitive algorithms, and hybrid strategies that combine multiple approaches. Moreover, this paper emphasizes the crucial role of evaluation methods like Precision, F1 Score, Recall, G-mean, and AUC in measuring the effectiveness of these strategies under imbalanced conditions. A detailed review of recent research articles helps pinpoint persistent gaps in generalizability, scalability, and robustness across these methods, underscoring the necessity for ongoing improvements. The survey seeks to offer an extensive overview of current approaches that improve the efficiency and effectiveness of machine learning models dealing with imbalanced datasets, thus equipping researchers with the insights needed to develop robust and effective models ready for real-world application.}, archive = {J_ACCESS}, author = {Manahel Altalhan and Abdulmohsen Algarni and Monia Turki-Hadj Alouane}, doi = {10.1109/ACCESS.2025.3531662}, journal = {IEEE Access}, pages = {13686-13699}, shortjournal = {IEEE Access}, title = {Imbalanced data problem in machine learning: A review}, volume = {13}, year = {2025}, }
• (2025). A covert α-stable noise-based extended random communication by incorporating multiple inverse systems. ACCESS, 13, 13675-13685. (www)

  @article{ , abstract = {Covert communication involves sending information in a manner that hides the presence of communication from recipients. This study introduces a strategy for ensuring covert communication by incorporating inverse systems to design an Extended Random Communication System (ERCS), where the carrier conveying the information is skewed alpha-stable random noise. In contrast to the approach that utilizes a single inverse system to provide covertness through a Random Communication System, this new technique integrates several m th-order inverse systems to transform skewed $\alpha $ -stable random signals on the transmitting end and the corresponding inverse systems on the receiving end to invert the signals received from the Additive White Gaussian Noise (AWGN) channel. The retrieval of the binary message stream from the inverted signals is performed by the ERCS receiver by performing an estimation using the maximum extreme-value method. By spreading the tasks of encoding and decoding across various inverse systems, the suggested approach raises the difficulty level for eavesdroppers in retrieving the hidden stream of a binary message without the knowledge of ERCS parameters that are pre-shared solely between the intended receiver and transmitter. The results from Monte Carlo simulations show that deploying the proposed ERCS not only leads to better Bit Error Rate (BER) performance but also provides increased covertness values, which proves the design’s ability to enhance performance and security compared to earlier models. Simulation analysis also suggests that the utilization of multiple inverse systems in the proposed ERCS can enhance the physical-layer security of next-generation communication devices.}, archive = {J_ACCESS}, author = {Areeb Ahmed and Zoran Bosnić}, doi = {10.1109/ACCESS.2025.3529712}, journal = {IEEE Access}, pages = {13675-13685}, shortjournal = {IEEE Access}, title = {A covert α-stable noise-based extended random communication by incorporating multiple inverse systems}, volume = {13}, year = {2025}, }
• (2025). Modulation and multiple access for beyond 6G electromagnetic nanonetworks in the terahertz band. ACCESS, 13, 13655-13674. (www)

  @article{ , abstract = {The advent of groundbreaking nanomaterials like graphene, renowned for their exceptional electrical properties, has ignited significant interest in nanocommunication. The potential to develop graphene-based plasmonic nanoantenna arrays and transceivers, combined with the capability of transmitting nanocommunication pulses at terahertz (THz) frequencies—bridging the THz gap in the electromagnetic spectrum—has been particularly compelling. Over the past decade, research in nanocommunication has led to remarkable advancements, particularly in the physical and link layers. These developments have paved the way for integrating fully operational nanonetworks into beyond 6G (B6G) wireless systems. Given the anticipated data rates in the terabits per second range for nanonetwork applications, continuous efforts are being made to develop new modulation and multiple access (MA) schemes. This survey paper explores the most advanced modulation and MA strategies tailored for B6G nanosystems operating in the THz band. Unlike previous surveys, our focus is on the physical layer, emphasizing rate-division and time-hopping strategies, including pulse position and pulse amplitude modulation techniques. We begin with an introduction to the foundational aspects of nanocommunication as an integral part of B6G nanosystems, followed by a detailed examination of various modulation schemes and their applicability to MA in nanonetworks. The paper concludes with a comparative analysis of these approaches regarding link capacity and a discussion of key open issues and future research directions for the design of electromagnetic nanocommunication systems.}, archive = {J_ACCESS}, author = {Pankaj Singh and Sung-Yoon Jung}, doi = {10.1109/ACCESS.2025.3529948}, journal = {IEEE Access}, pages = {13655-13674}, shortjournal = {IEEE Access}, title = {Modulation and multiple access for beyond 6G electromagnetic nanonetworks in the terahertz band}, volume = {13}, year = {2025}, }
• (2025). Pac-man-shaped patch-driven broadband circularly polarized metasurface antenna with CMA-based quadruple-mode excitation. ACCESS, 13, 13644-13654. (www)

  @article{ , abstract = {This paper introduces a novel quadruple-mode excited low-profile metasurface (MTS)-based broadband circularly polarized (CP) patch antenna. For a quadruple-mode excitation, the antenna uses a unique pac-man-shaped patch (PSP) as the primary CP radiator and above it a $4\times 4$ sized rectangular MTS, which acts as the secondary radiator. Characteristic mode analysis (CMA)-based mode manipulation approach facilitates effortless separation of two orthogonal modes (OMs) of PSP, creating a 90° phase difference for circular polarization. Further, to increase the design’s axial ratio bandwidth (ARBW), two additional OMs of the MTS layer are cascaded with the PSP modes. Consequently, the four OMs exhibit quadruple-mode excitation, and the design achieves broadband CP radiation. A prototype of the proposed antenna is fabricated using an FR-4 substrate, and the overall dimension of the antenna is $0.55\lambda \times 0.7\lambda \times 0.06\lambda $ at 6.46 GHz center operating frequency. The 10 dB impedance bandwidth of the antenna is 55.22% (4.68–8.25 GHz), and the 3 dB ARBW is 35.48% (5.1–7.3 GHz). It exhibits a peak gain of 6.2 dBi. The measurement results agree well with the simulation results.}, archive = {J_ACCESS}, author = {Deepak K. Naik and Dhruba Charan Panda and Rajanikanta Swain and Arjuna Muduli and Sambhudutta Nanda}, doi = {10.1109/ACCESS.2025.3526682}, journal = {IEEE Access}, pages = {13644-13654}, shortjournal = {IEEE Access}, title = {Pac-man-shaped patch-driven broadband circularly polarized metasurface antenna with CMA-based quadruple-mode excitation}, volume = {13}, year = {2025}, }
• (2025). Emphasizing the early phases of the software development process before deploying smart contracts. ACCESS, 13, 13628-13643. (www)

  @article{ , abstract = {Immutability is one of the main characteristics of Blockchain. However, most software development is not static. This dilemma, among others, has caused a new branch of blockchain-oriented software engineering. This paper emphasizes the importance of the early phases of software development before deploying blockchain-based software. It follows case-based research to illustrate the implications of smart contracts designed in the early phases without including all requirements. The paper presents a digital identity case designed within a microservice architecture. We show two stages: an initial design and an upgrading requirement, which causes considerable changes in the architecture. The case is analyzed from three different perspectives: 1) Economic, finding that re-deploying smart contracts does not implicate considerable cost; 2) Computational perspective, finding that it generates various implications: smart contract purpose duplication, storage wastage, failure to recognize the original smart contract, cascade dependency repercussion, and migration problems; and 3) Interconnected effect, a simple change, required for upgrading smart contracts, generates broad collateral repercussions in both on-chain (within the blockchain) and off-chain.}, archive = {J_ACCESS}, author = {Juan-Carlos López-Pimentel and Carolina del-Valle-Soto and Leonardo J. Valdivia and Raúl Monroy}, doi = {10.1109/ACCESS.2025.3530269}, journal = {IEEE Access}, pages = {13628-13643}, shortjournal = {IEEE Access}, title = {Emphasizing the early phases of the software development process before deploying smart contracts}, volume = {13}, year = {2025}, }
• (2025). Hydra radio access network (H-RAN): Multi-functional communications and sensing networks, initial access implementation, and task-2 approach. ACCESS, 13, 13606-13627. (www)

  @article{ , abstract = {Hydra radio access networks (H-RANs) are poised to revolutionize the way data is gathered, transmitted, and analyzed, ushering in an era of unprecedented connectivity, efficiency, and intelligence. By proactively managing changes and addressing potential disruptions, H-RAN networks aim to provide consistent service quality, even under varied operating conditions. This means that networks can automatically detect issues, assess their impact, implement corrective measures, and modify operating parameters in response to variations in user behavior or environmental factors. However, conventional RANs typically implement a uniform strategy when dealing with diverse communication scenarios. This one-size-fits-all methodology limits their ability to autonomously adjust to the unique requirements of the millimeter and terahertz wave (MMW/THz) communications environment, which is characterized by high sensitivity to environmental factors. As a result, these systems face significant challenges in maintaining a reliable and efficient communication link under varying conditions. Furthermore, conventional RANs typically require considerable manual tuning and configuration, making them time-consuming and costly. Indeed, while the potential of H-RAN networks encompasses numerous improvements and innovations across various aspects of next-generation technologies, this paper addresses the initial access implementation and data transmission approaches under the non-line-of-sight (NLoS) of the $\text {level}_{1}$ case study. Our solution contains several novelties that enhance both overhead and model accuracy. To this end, we have developed a novel adaptive environment-aware blockage beamforming codebook structure. This codebook consists of edge-line-of-sight (ELoS) vectors and reconfigurable intelligent surface (RIS) vectors, RIS-LoS (SLoS). These elements are integral to optimizing beamforming in scenarios where direct line-of-sight (LoS) is obstructed by $\text {level}_{1}$ blockage scenario. Furthermore, we employed a multi-sparse input and multi-task learning (SMTL) framework in the Hydra distributed unit (H-DU) artificial intelligence and machine learning (AI/ML) (AI/ML D-engine), where each task is tailored to be executed in a particular environment based on online feedback. The SMTL model employs an approach to handling sparse and heterogeneous input data while performing multiple tasks based on the current network condition. More specifically, a novel $\text {task}_{2}$ is developed in this study to generate a list of recommended beamforming vectors and patterns designed to address the challenges arising from blockage scenarios of $\text {level}_{1}$ in MMW/THz environments.}, archive = {J_ACCESS}, author = {Rafid I. Abd and Daniel J. Findley and Kwang Soon Kim}, doi = {10.1109/ACCESS.2025.3531909}, journal = {IEEE Access}, pages = {13606-13627}, shortjournal = {IEEE Access}, title = {Hydra radio access network (H-RAN): Multi-functional communications and sensing networks, initial access implementation, and task-2 approach}, volume = {13}, year = {2025}, }
• (2025). Matching sensing to actuation and dynamics in distributed sensorimotor architectures. ACCESS, 13, 13584-13605. (www)

  @article{ , abstract = {In this article we explore the benefits of matching sensing characteristics to actuation and dynamics in the context of spatially distributed sensorimotor architectures, motivated by recently discovered connections in blowfly flight physics and visual physiology. Within the proposed framework, we present novel semidefinite programs with linear matrix inequality constraints which yield directions encoded in the sensory output that maximize the smallest unstable Hankel singular value of the system. This is a coordinate-invariant metric that minimizes the control energy required to stabilize an unstable system and maximizes the achievable robustness to unstructured additive uncertainty over all possible controllers. We also reformulate the problem to achieve a prescribed speed of response, which can be applied to stable and unstable systems. We adapt a maximally robust controller synthesis method from previous work which provides a tool for validation. We additionally present an $H_{\infty }$ controller formulation which allows for a trade-off between minimization of actuator effort and robustness versus disturbance rejection and tracking capability, providing design flexibility over the maximally robust controller.}, archive = {J_ACCESS}, author = {Zoe Turin and Graham K. Taylor and Holger G. Krapp and Emily Jensen and J. Sean Humbert}, doi = {10.1109/ACCESS.2025.3528343}, journal = {IEEE Access}, pages = {13584-13605}, shortjournal = {IEEE Access}, title = {Matching sensing to actuation and dynamics in distributed sensorimotor architectures}, volume = {13}, year = {2025}, }
• (2025). Unraveling power consumption pattern using multidimensional scaling and clustering analysis. ACCESS, 13, 13573-13583. (www)

  @article{ , abstract = {Understanding regional and seasonal patterns in electricity consumption is essential for ensuring grid reliability, resiliency, and sustainable infrastructure management. With the rapid growth in electricity demand data and the integration of distributed energy resources, traditional methods struggle to capture zonal load patterns effectively. This study presents a dimensionality reduction approach combined with clustering to analyze seasonal energy load dynamics across zones managed by the New York Independent System Operator (NYISO). By applying Multidimensional Scaling (MDS), we condense complex high-dimensional data into a form that reveals nuanced relationships among zones. This transformation allows for effective K-means clustering of load profiles, uncovering four distinct seasonal clusters representing shared load characteristics among NYISO zones. Our analysis, covering eleven zones across four seasons, provides visually compelling two- and three-dimensional scatter plots that highlight both converging and diverging demand patterns. This approach unveils similarities in electricity usage and significant seasonal variations, offering deeper insights into the regional and temporal complexity of electricity demand across NYISO’s operational landscape. The Python script developed for this work is available on GitHub and the signalsciencelab.com website.}, archive = {J_ACCESS}, author = {Utkarsh Misra and Gyana Ranjan Sahoo and Shikhar Prakash and Madhur Srivastava}, doi = {10.1109/ACCESS.2025.3529470}, journal = {IEEE Access}, pages = {13573-13583}, shortjournal = {IEEE Access}, title = {Unraveling power consumption pattern using multidimensional scaling and clustering analysis}, volume = {13}, year = {2025}, }
• (2025). Hybrid harmony search algorithm integrating differential evolution and lévy flight for engineering optimization. ACCESS, 13, 13534-13572. (www)

  @article{ , abstract = {Harmony search algorithm (HSA) is extensively utilized in engineering optimization. Nevertheless, it encounters problems of slow convergence and reduced accuracy, which hinder its capability to escape local optima. This paper proposes HSA-DELF, a novel hybrid algorithm that combines differential evolution (DE) and Lévy flight (LF) techniques to enhance the performance of HSA. HSA-DELF leverages multi-mutation strategies of DE and LF random walk combined with weighted individuals to improve exploration and exploitation based on population fitness standard deviation comparison, and adopts pairwise iterative updates of the population to achieve faster convergence and higher solution quality. Extensive experiments were conducted to validate performance on 23 classic benchmark functions and 12 CEC 2022 benchmark functions, followed by comprehensive testing on 7 engineering problems, demonstrating the superiority of HSA-DELF. Comparative analysis with 5 well-known algorithms (HSA, DE, CSA, GA, and PSO) and 4 HSA variants (IHS, MHSA, IHSDE, and IMGHSA) confirmed the robustness of HSA-DELF. Statistical results, including best, mean, standard deviation, and worst values, consistently highlight the superior performance of HSA-DELF in terms of convergence speed, solution quality, and robustness. The Wilcoxon signed-rank test further corroborated these significant advantages. HSA-DELF showed notable improvements in 6 out of 7 engineering problems, achieving an accuracy of 85.71%. This study establishes HSA-DELF as an effective and reliable method for solving complex engineering optimization problems.}, archive = {J_ACCESS}, author = {Feng Qin and Azlan Mohd Zain and Kai-Qing Zhou and Norfadzlan Bin Yusup and Didik Dwi Prasetya and Rozita Abdul Jalil and Zaheera Zainal Abidin and Mahadi Bahari and Yusri Kamin and Mazlina Abdul Majid}, doi = {10.1109/ACCESS.2025.3529714}, journal = {IEEE Access}, pages = {13534-13572}, shortjournal = {IEEE Access}, title = {Hybrid harmony search algorithm integrating differential evolution and lévy flight for engineering optimization}, volume = {13}, year = {2025}, }
• (2025). Multi-band torsional vibration amplitude demodulation for planetary gearbox fault diagnosis under time-varying speed conditions. ACCESS, 13, 13522-13533. (www)

  @article{ , abstract = {Torsional vibration signal analysis is promising for diagnosing gear faults in planetary gearboxes. However, early-stage fault signatures are relatively-weak and may be ignored or wrongly identified in conventional demodulation analysis, especially under time-varying speed conditions. Two main contributions are made in this paper. Firstly, an analytical model of planetary gearbox torsional vibration signal is established, by jointly considering the gear mesh vibration, torsional resonance, and other time-invariant components. The gear fault characteristics under time-varying speeds are accordingly revealed explicitly. Furthermore, inspired by the unique time-frequency structure of the torsional vibration signal, the multi-band torsional vibration amplitude demodulation method is proposed for gear fault feature extraction. The proposed method combines the gear fault features in multiple characteristic frequency bands in torsional vibration signal, so that the influences from adjacent carrier frequencies, independent frequency harmonics and random noises can be eliminated in amplitude demodulation. As a result, the obtained amplitude demodulated order spectrum outperforms conventional envelope order spectrum and narrow-band amplitude demodulated order spectrum in revealing relatively weak gear fault features. Both numerical simulation and laboratory experiments are conducted, to demonstrate the correctness of the established analytical model and the advantages of the proposed multi-band torsional vibration amplitude demodulation method. Localized fault on the sun, planet and ring gears are successfully diagnosed.}, archive = {J_ACCESS}, author = {Xiaowang Chen and Yubo Sun and Ying Zhang and Zhipeng Feng}, doi = {10.1109/ACCESS.2024.3513359}, journal = {IEEE Access}, pages = {13522-13533}, shortjournal = {IEEE Access}, title = {Multi-band torsional vibration amplitude demodulation for planetary gearbox fault diagnosis under time-varying speed conditions}, volume = {13}, year = {2025}, }
• (2025). A framework for privacy-preserving in IoV using federated learning with differential privacy. ACCESS, 13, 13507-13521. (www)

  @article{ , abstract = {Vehicles become more advanced and smarter due to advancements in technology in the modern world. Every person now a days, demand a smart vehicle due to their automobility and smart controls. This is all possible through advancements in VANET (Vehicular Adhoc Network) and the Internet of Vehicles (IoV). Vehicles in the VANET are highly connected to each other and this thing can cause security, safety, and privacy risks for the asset itself and driver also. It can become a reason of major threat. And these threats can occur due to tracing the location of the vehicle. Existing techniques like group-based shadowing schemes, obfuscation, silent periods, and mix-zone have preserved privacy of location somehow, but don’t have a good QoS and optimized efficient security. To overcome these issues, we introduced a new privacy framework, which is an improvement of the existing shadowing scheme. We proposed a computationally efficient group leader selection process based on centeredness, rule obeyed, and OBU resources, reducing overhead by 20%, integrating FL with DP to preserve data privacy without sacrificing utility, and achieving a 15% improvement in location accuracy under privacy constraints, validating the scalability and robustness of the framework through extensive simulations involving up to 300 vehicles. Group Leader is used as an optimization of the overall framework including efficiency and implementation of the scheme. This scheme increases privacy if the number of vehicles also increases, and this thing makes our scheme more scalable. This scheme overcomes the many drawbacks of existing techniques like a higher tracing ratio in shadowing schemes, totally depending on the group leader, and reduced utility of all schemes based on distances. The most important thing, the single point of failure in the group leader base shadowing scheme is overcome by using local federated learning with differential privacy. Validation results of our proposed scheme showed that it outperformed the current schemes mainly based on group leader.}, archive = {J_ACCESS}, author = {Muhammad Adnan and Madiha Haider Syed and Adeel Anjum and Semeen Rehman}, doi = {10.1109/ACCESS.2025.3526934}, journal = {IEEE Access}, pages = {13507-13521}, shortjournal = {IEEE Access}, title = {A framework for privacy-preserving in IoV using federated learning with differential privacy}, volume = {13}, year = {2025}, }
• (2025). Synergy effect in platform ecosystem: A quantitative analysis of smart home products. ACCESS, 13, 13482-13506. (www)

  @article{ , abstract = {With the platform ecosystem’s development, synergy effects’ importance has grown. This study aims to elucidate synergy effect characteristics within the platform ecosystem and their impact on consumers’ value perception. We collected 405 smart home products from Amazon.co.jp as samples. Synergy effect is categorized into synergy with platform and complements. Firstly, text mining summarizes the characteristics of synergy effect between complements and platform (System-to-system interoperability, Coordinated interconnection integration, User interaction coordination, Remote coordination flexibility) and synergy effect between complements and complements (Dynamic interaction coordination, Multi-scenario coordination, Spatial Controllability, Personalized setting coordination). Then, two models, purchase intention and user experience, explore these characteristics’ impact on consumers’ value perception. The results show that interactions between system-to-system interoperability and multi-scenario coordination, and between coordinated interconnection integration and spatial controllability, positively affect purchase intention. However, interactions between user interaction coordination and spatial controllability, and between user interaction coordination and personalized setting coordination, negatively affect purchase intention. Interactions between user interaction coordination and multi-scenario coordination, between remote coordination flexibility and dynamic interaction coordination, and between remote coordination flexibility and spatial controllability, positively affect user experience. This research provides new perspectives on synergy effect within the platform ecosystem and implications for product development.}, archive = {J_ACCESS}, author = {Ruiyang Tang and Yuki Inoue and Masaharu Tsujimoto}, doi = {10.1109/ACCESS.2025.3531993}, journal = {IEEE Access}, pages = {13482-13506}, shortjournal = {IEEE Access}, title = {Synergy effect in platform ecosystem: A quantitative analysis of smart home products}, volume = {13}, year = {2025}, }
• (2025). Lithium-ion battery state of health degradation prediction using deep learning approaches. ACCESS, 13, 13464-13481. (www)

  @article{ , abstract = {Timely prediction of the State of Health (SoH) of lithium-ion batteries is important for battery management and longevity. Traditional centralized deep learning models have shown promising results, but they raise concerns related to data privacy, as data needed to be collected and trained on a single node. This study addresses this challenge by utilizing both centralized (i.e., deep learning) and decentralized (i.e., federated learning) approaches for SoH prediction. The NASA battery dataset, containing charging and discharging cycles, is used for model training and evaluation. Three deep learning architectures 1D Convolutional Neural Networks (CNN), CNN plus Long Short-Term Memory (LSTM), and CNN plus Gated Recurrent Units (GRU) are used in the centralized approach. The 1D CNN model outperforms, demonstrating strong predictive capabilities, thus for decentralized learning (i.e., federated learning), the 1D CNN model is utilized with federated averaging technique across five clients, allowing for local training without sharing raw data. Obtained results shows that the highest testing RMSE (0.666) and MAPE (0.980) are observed during decentralized learning, while the centralized approach shows varying performance across different batteries. The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.}, archive = {J_ACCESS}, author = {Talal Alharbi and Muhammad Umair and Abdulelah Alharbi}, doi = {10.1109/ACCESS.2025.3530351}, journal = {IEEE Access}, pages = {13464-13481}, shortjournal = {IEEE Access}, title = {Lithium-ion battery state of health degradation prediction using deep learning approaches}, volume = {13}, year = {2025}, }
• (2025). EMG-based continuous estimation of index finger movements with varying interjoint coordination patterns by modeling musculoskeletal dynamics. ACCESS, 13, 13454-13463. (www)

  @article{ , abstract = {Surface electromyography (EMG) is widely used for predicting kinematics of intended finger movements in applications including teleoperation. Generally, ‘black-box’ models with high complexity such as neural networks (NN) were used to improve prediction accuracy, which may not reproduce other important movement characteristics such as smoothness or kinematic similarity. The goal of this study is to develop a novel EMG-based approach that models impact of finger intersegmental dynamics to reproduce ‘physiologic’ characteristics of coordinated finger movements. Performance of the proposed dynamic model was compared with a polynomial model with the same level of complexity (no dynamics considered) and NN models, based on (A) simulation data from four musculoskeletal systems with varying parameters; and (B) experimental data from 10 subjects performing finger movements with four distinct coordination patterns. Performance of the proposed dynamic model in predicting simulated movements was significantly better than the polynomial model with the same level of complexity (18 parameters). In predicting experimental data, performance of the dynamic model was significantly better than that of the NN model of lower complexity (480 parameters), and similar to that of the NN model of higher complexity (2976 parameters). Furthermore, movement quality produced by the dynamic model, quantified by jerk (smoothness) and kinematic similarity, and its computational efficiency were significantly better than other models. The proposed technique, which captures the impact of musculoskeletal dynamics in a compact form, can accurately reproduce physiologic finger movements with a higher computational efficiency than existing models, thus could serve as a robust tool for teleoperation.}, archive = {J_ACCESS}, author = {Trenton A. Gilstrap and Mada M. Alghamdi and Thanh Phan and Sang Wook Lee}, doi = {10.1109/ACCESS.2025.3528902}, journal = {IEEE Access}, pages = {13454-13463}, shortjournal = {IEEE Access}, title = {EMG-based continuous estimation of index finger movements with varying interjoint coordination patterns by modeling musculoskeletal dynamics}, volume = {13}, year = {2025}, }
• (2025). Combining region-guided attention and attribute prediction for thangka image captioning method. ACCESS, 13, 13440-13453. (www)

  @article{ , abstract = {To enhance the understanding of the core regions in Thangka images and improve the richness of generated content during decoding, we propose a Thangka image captioning method based on Region-Guided Feature Enhancement and Attribute Prediction (RGFEAP). The image feature enhancement encoder, guided by regions, introduces a region-guided module with a distance-weighted strategy to enhance the feature representation of the central sacred elements in Thangka images. Additionally, we designed a Thangka feature enhancement encoder to further refine the regional feature vectors, which are then fused with global features extracted by CLIP through multi-scale convolutional fusion, injecting richer object-related information into the Thangka image features.Furthermore, to enhance the detailed representation capability for generating long-sequence captions of Thangka images, we designed an attribute predictor. This predictor leverages feature maps from four different convolutional blocks within the region-guided module to incorporate more detailed information into the model. Experimental results on the Thangka dataset demonstrate that RGFEAP achieves significant improvements compared to the baseline model ClipCap, with BLEU-1, BLEU-4, CIDEr, and METEOR scores increasing by 14.0%, 17.7%, 185.7%, and 11.5%, respectively. On the COCO dataset in the natural domain, RGFEAP achieves performance comparable to other state-of-the-art models, showcasing its strong adaptability.}, archive = {J_ACCESS}, author = {Fujun Zhang and Wendong Kang and Wenjin Hu}, doi = {10.1109/ACCESS.2025.3526954}, journal = {IEEE Access}, pages = {13440-13453}, shortjournal = {IEEE Access}, title = {Combining region-guided attention and attribute prediction for thangka image captioning method}, volume = {13}, year = {2025}, }
• (2025). Multitenancy in single instance of data persistence service for supporting low-code platforms. ACCESS, 13, 13427-13439. (www)

  @article{ , abstract = {Corporate digitalization, especially among Small and Medium Enterprises, has led to a significant increase in the demand for scarce professionals with expertise in the IT area, especially in Web Information Systems area. On the other hand, concerns about high levels of resource idleness in the cloud are constant. In this scenario, Low-Code Platforms have gained traction in the software industry, whose most commonly found service is the automation of code generation to perform data persistence tasks driven by data models. This approach, however, implies a mode of operation that is not the most mature among cloud computing providers, implying for each model a service instance running on the provider side. Here, we propose a data persistence service for these platforms that avoids code generation, since it interprets data models at runtime and operates in multi-tenant mode with a single service instance. This approach improves resource sharing, mitigating resource idleness within the platform. In addition, we present experiments to support the technical feasibility of the proposed approach. The proposed solution offers an alternative to code generation methods, with the potential to optimize resource utilization while preserving the flexibility to adapt to changes in data models as business needs evolve.}, archive = {J_ACCESS}, author = {Júlio Gustavo Costa and Luiz Marcos Garcia Gonçalves and Samuel Xavier-de-Souza}, doi = {10.1109/ACCESS.2025.3525639}, journal = {IEEE Access}, pages = {13427-13439}, shortjournal = {IEEE Access}, title = {Multitenancy in single instance of data persistence service for supporting low-code platforms}, volume = {13}, year = {2025}, }
• (2025). Model-oriented training of coordinators of the decentralized control system of technological facilities with resource interaction. ACCESS, 13, 13414-13426. (www)

  @article{ , abstract = {The control process of technological facilities with resource interaction in a decentralized system requires coordination of local systems for control of the state of objects. For the implementation of coordination methods, learning systems have an advantage since they can flexibly adapt to the specifics of each facility control. However, the coordinators’ training process is complicated by the lack of labelled datasets for technological facilities. In decentralized control systems, the problem is complicated by the need to train all coordinators, with the outcome depending on the coordinator’s position within the structure of the distributed control system. This article explores the prospects of model-based learning for solving the problem of missing datasets used for coordinators’ training. An approach to determining the optimal statistics of the training dataset for the coordination control of nonlinear technological facilities with resource interaction is proposed. A combined three-stage process of coordinator training for the decentralized system is proposed. In the first stage, one coordinator is trained on the basis of a distributed system simulation. In the second stage, the settings of the trained coordinator are applied to other coordinators, which are retrained in parallel on the basis of simulation models of local control systems of the relevant parts of the technological facilities. In the third stage, coordinators are fine-tuned to real conditions using Bayesian random search. Conducted experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, showed greater effectiveness of Collaborative Federated Learning compared to independent training of coordinators or direct transfer of learning outcomes between coordinators.}, archive = {J_ACCESS}, author = {Volodymyr M. Dubovoi and Maria S. Yukhimchuk and Viacheslav V. Kovtun and Krzysztof R. Grochla}, doi = {10.1109/ACCESS.2025.3528828}, journal = {IEEE Access}, pages = {13414-13426}, shortjournal = {IEEE Access}, title = {Model-oriented training of coordinators of the decentralized control system of technological facilities with resource interaction}, volume = {13}, year = {2025}, }
• (2025). Joint optimization design of UAV-active RIS system based on a quadratic transformation. ACCESS, 13, 13405-13413. (www)

  @article{ , abstract = {Aiming at the problem of maximizing the average achievable rate (AAR) of users in the scenario of unmanned aerial vehicle (UAV) carrying an active reconfigurable intelligent surface (RIS) in the downlink communication system, a method based on quadratic transformation combined with semidefinite relaxation (SDR) and successive convex approximation (SCA) is proposed. Due to the characteristics of active RIS, its thermal noise cannot be ignored. The additive noise in the objective function makes its fractional form highly complex. To solve the nonconvex fractional programming problem with highly coupled multiple variables, the objective function is split into three sub-problems: the transmit power allocation of the base station, the phase shift of active RIS, and the trajectory optimization of the UAV. The quadratic transformation method is used to incorporate the influence of thermal noise into the optimization framework. By introducing auxiliary variables, the multi-fractional problem can be effectively dealt with. At the same time, the nonconvex quadratically constrained quadratic problem (QCQP) is transformed into a convex optimization problem with multiple single variables by combining the semidefinite relaxation method and the successive convex approximation method. Finally, the alternating iteration method is used to obtain the suboptimal solution of the original problem. The simulation results show that compared with the Taylor scheme, the traditional passive RIS scheme, and the random phase shift scheme, the optimized scheme improves the AAR by 10%-20%, 20%-40%, and 80%-240%, respectively.}, archive = {J_ACCESS}, author = {Yi Peng and Haolin Li and Qingqing Yang}, doi = {10.1109/ACCESS.2025.3530994}, journal = {IEEE Access}, pages = {13405-13413}, shortjournal = {IEEE Access}, title = {Joint optimization design of UAV-active RIS system based on a quadratic transformation}, volume = {13}, year = {2025}, }
• (2025). Evaluation of selected modulation techniques in underwater multipath channel. ACCESS, 13, 13395-13404. (www)

  @article{ , abstract = {There is a growing interest in the use of Autonomous Underwater Vehicles (AUVs) and Underwater Sensor Networks (USNs) for scientific, military, and industrial applications, leading to the development of wireless underwater communications. However, the channel for Underwater Acoustic (UWA) communications is challenging compared to the radio wave propagation in the air. Especially in shallow waters, where the channel has strong multipath effects. The difficulties are even greater when the communicating objects are in motion. Nevertheless, it is worth to analyze if solutions known from radio communication systems are applicable for underwater hydroacoustic purposes. In this paper, a comparison is made between two well-known modulation techniques used in radio communication: the widely used BPSK (Binary Phase Shift Keying) modulation and CSS (Chirp Spread Spectrum) technique used in LoRa (Long Range communication) system. The modulation techniques mentioned are compared with MBFSK (Multiple Binary Frequency Shift Keying) modulation used in underwater wireless communication systems, which is said to be very resistant to the multipath effect. In MBFSK, the value of the bit determines the value of the frequency on which the power is transmitted. The paper presents simulation results as well as results of measurements in the towing tank of the CTO (Maritime Advanced Research Centre). The comparison of simulation and real measurements leads to worrying conclusions, as they differ considerably.}, archive = {J_ACCESS}, author = {Agnieszka Czapiewska and Andrzej Łuksza and Ryszard Studański and Łukasz Wojewódka and Andrzej Żak}, doi = {10.1109/ACCESS.2025.3530948}, journal = {IEEE Access}, pages = {13395-13404}, shortjournal = {IEEE Access}, title = {Evaluation of selected modulation techniques in underwater multipath channel}, volume = {13}, year = {2025}, }
• (2025). Effect of intelligent reflecting surface on WSN communication with access points configuration. ACCESS, 13, 13380-13394. (www)

  @article{ , abstract = {As signal quality and communication channel reliability is critical for wireless communication, and to overcome some of the negative effects affecting the transmitted signals and data, due to environment and line-of-sight problems, and to enable internet based, on-line, and IoT applications, the use of Intelligent Reflecting Surface devices (IRS) become critical. Thus, this work objective is to simulate, model, and analyze parameters that are critical to the quality of transmitted signal over a wireless network and a WSN sensor nodes arrangement. The successful simulation, mathematical modeling, and analysis used Signal-to-Noise Ratio (SNR) as a calibrating parameter to enable design and optimization of a network consisting of WSN sensor nodes, access points (APs), and IRS using two arrangements for the purpose of comparison between direct communication (Nodes-APs), and indirect communication (Nodes-IRS-APs). This enabled direct and indirect WSN sensor node communication and provided a comparison of how effective an IRS can be on improving the Quality of links and communication channel in a wireless network. The work looked at three distinct, but related cases of SNR covering IRS elements, access point numbers, and IRS location. Several simulation runs are carried out, in order to show effect of geometrical distribution of WSN sensor nodes, access points, and IRS, with averaging at the end to obtain an overall comparison between direct and indirect communication paths, and to enable mathematical modeling, as random distribution is introduced in the simulation. The simulation and subsequent analysis showed effectiveness of increasing IRS elements, and access points on improving SNR, and proved that better SNR or optimum SNR can be obtained when the IRS is at the center point within the application area. Exponential and incrementing function is found describing both effect of IRS elements and access point numbers in relation to SNR. Also, higher SNR is found when the IRS is at the center or middle lines compared to the corners. Mathematical expressions are developed, and relation between access point number and number of IRS elements is established. This simulation is critical in terms of design, as it shows the spread of the SNR values in relation to both WSN sensor nodes and access points graphically and analytically.}, archive = {J_ACCESS}, author = {Mahmoud Z. Iskandarani}, doi = {10.1109/ACCESS.2025.3531637}, journal = {IEEE Access}, pages = {13380-13394}, shortjournal = {IEEE Access}, title = {Effect of intelligent reflecting surface on WSN communication with access points configuration}, volume = {13}, year = {2025}, }
• (2025). Path planning for fully autonomous UAVs-A taxonomic review and future perspectives. ACCESS, 13, 13356-13379. (www)

  @article{ , abstract = {Autonomous Unmanned Aerial Vehicles (UAVs) rely on advanced path planning to operate independently, especially in unfamiliar settings without human intervention. The process typically involves localization, mapping, optimal path selection, motion planning, and control. Achieving autonomous navigation from one point to another requires balancing various factors, such as energy efficiency, speed, cost, path length, and computation time. In this study, a novel taxonomy that categorizes UAV path planning into four distinct classifications is introduced. A systematic review of the technological advancements in path planning methodologies, tracking the evolution from classical techniques to cutting-edge solutions, with a particular focus on dynamic environments is also represented. This review includes a comparative analysis of various methods based on factors including approach utilized, testing platform, environment type, time efficiency, etc. Various relevant parameters and benchmark datasets that are crucial for UAV path planning are also explored. Despite widespread use, current methodologies still face significant challenges, such as handling unknown threats in dynamic environments, effective obstacle avoidance, limited payload capacity, real-time responsiveness, and energy consumption. These issues limit their overall usefulness. This study aims to highlight these challenges and suggest potential directions for future research, ultimately contributing to the advancement of real-time UAV path planning.}, archive = {J_ACCESS}, author = {Geeta Sharma and Sanjeev Jain and Radhe Shyam Sharma}, doi = {10.1109/ACCESS.2025.3529754}, journal = {IEEE Access}, pages = {13356-13379}, shortjournal = {IEEE Access}, title = {Path planning for fully autonomous UAVs-A taxonomic review and future perspectives}, volume = {13}, year = {2025}, }
• (2025). Unveiling the core design elements of bamboo furniture. ACCESS, 13, 13341-13355. (www)

  @article{ , abstract = {Bamboo furniture, as an emerging low-carbon industry, has become a significant component of the furniture sector’s sustainable development. However, unclear design elements and challenges in quantifying user needs hinder its competitiveness and product quality. To address these issues, this study employs an integrated approach combining the SECI knowledge creation model, text mining, and the AHP-QFD framework. The SECI model establishes the research process framework, while BERTopic analyzes online reviews to extract key user needs. AHP is then applied to prioritize these needs, and QFD transforms them into specific design elements and technical attributes with corresponding weights and rankings. The findings reveal that safety, comfort, and ease of use are the most critical factors, with safety identified as the core element throughout the product lifecycle. Enhancing connector performance, selecting durable materials, and optimizing ergonomic design are key strategies for improving safety and extending product lifespan. This study provides valuable insights into design elements to prioritize in bamboo furniture product development. This contributes to improved design quality and promoting sustainable growth of the industry.}, archive = {J_ACCESS}, author = {Wei Liu and Yi-Nan Fei and Chang-Long Yu and Zi-Yan Hu and Jia-Qi Chen}, doi = {10.1109/ACCESS.2025.3529779}, journal = {IEEE Access}, pages = {13341-13355}, shortjournal = {IEEE Access}, title = {Unveiling the core design elements of bamboo furniture}, volume = {13}, year = {2025}, }
• (2025). Mathematical modeling of cyberattack defense mechanism using hybrid transfer learning with snow ablation optimization algorithm in critical infrastructures. ACCESS, 13, 13329-13340. (www)

  @article{ , abstract = {Cybersecurity is a significant topic that has turned into an efficient one at present owing to the increasing dependency on interconnected methods and technology. As digitalization upsurges, the requirement for cybersecurity measures becomes even more vital for numerous networks. To certify the security of critical infrastructures, numerous cyber security solutions must be taken together and the essential infrastructure must be developed. Industrial control methods are one of the most vital aspects of the cybersecurity of critical infrastructures. It is possible to entirely stop these physically located devices’ operations and do substantial destruction with a cyberattack. Whereas AI solutions are being utilized in numerous fields, cyber security has started to become one of the concentrated fields of artificial intelligence (AI) domain. Consequently, there are many studies on identifying cyberattacks by utilizing AI methods. It is probable to utilize AI to help and support cybersecurity solutions to develop cybersecurity of significant infrastructures. This study develops a Cyberattack Defense Mechanism using Hybrid Transfer Learning with Snow Ablation Optimization Algorithm (CDMHTL-SAOA) technique in Critical infrastructures. The main cause of the CDMHTL-SAOA model is to improve the cyber security maturity level of critical infrastructures and inspect both traditional cyberattack and AI approaches. Primarily, the data normalization process can be implemented to scale the raw data into a uniform format. In addition, the snow ablation optimization (SAO) algorithm can be exploited for the optimum choice of feature subsets. For the cybersecurity classification process, the presented CDMHTL-SAOA technique applies the hybrid of convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) method. Eventually, the parameter choice of the CNN-BiLSTM technique has been implemented by the design of the hippopotamus optimization algorithm (HOA). To represent the better solution of the CDMHTL-SAOA classifier, a simulation validation can be tested on a benchmark database and the solutions are measured for various aspects. The simulation outcomes certified the improved execution of the CDMHTL-SAOA method over other techniques.}, archive = {J_ACCESS}, author = {Mohamad Khairi Ishak}, doi = {10.1109/ACCESS.2025.3530931}, journal = {IEEE Access}, pages = {13329-13340}, shortjournal = {IEEE Access}, title = {Mathematical modeling of cyberattack defense mechanism using hybrid transfer learning with snow ablation optimization algorithm in critical infrastructures}, volume = {13}, year = {2025}, }
• (2025). Machine reading comprehension for the tamil language with translated SQuAD. ACCESS, 13, 13312-13328. (www)

  @article{ , abstract = {Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP), crucial for automated customer support, enabling chatbots and virtual assistants to accurately understand and respond to queries. It also enhances question-answering systems, benefiting educational tools, search engines, and help desks. The introduction of attention-based transformer models has significantly boosted MRC performance, especially for well-resourced languages such as English. However, MRC for low-resourced languages (LRL) remains an ongoing research area. Although Large Language Models show exceptional NLP performance, they are less effective for LRL and are expensive to train and deploy. Consequently, simpler language models that are targeted at specific tasks remain viable for these languages. This research examines high-performing language models on the Tamil MRC task, detailing the preparation of a Tamil-translated and processed SQuAD dataset to establish a standard dataset for Tamil MRC. The study analyzes the performance of multilingual transformer models on the Tamil MRC task, including Multilingual DistilBERT, Multilingual BERT, XLM-RoBERTa, MuRIL, and RemBERT. Additionally, it explores improving these models’ performance by fine-tuning them with English SQuAD, Tamil SQuAD, and a newly developed Tamil Short Story (TSS) dataset for MRC. Tamil’s agglutinative nature, which expresses grammatical information through suffixation, results in a high degree of word inflexion. Given this characteristic, the BERT score was chosen as the evaluation metric for MRC performance. The analysis shows that the XLM-RoBERTa model outperformed the others for Tamil MRC, achieving a BERT score of 86.29% on the TSS MRC test set. This superior performance is attributed to the model’s cross-lingual learning capability and the increased number of data records used for fine-tuning. The research underscores the necessity of language-specific fine-tuning of multilingual models to enhance NLP task performance, for low-resourced languages.}, archive = {J_ACCESS}, author = {Anton Vijeevaraj Ann Sinthusha and Eugene Y. A. Charles and Ruvan Weerasinghe}, doi = {10.1109/ACCESS.2025.3530949}, journal = {IEEE Access}, pages = {13312-13328}, shortjournal = {IEEE Access}, title = {Machine reading comprehension for the tamil language with translated SQuAD}, volume = {13}, year = {2025}, }
• (2025). Benchmarking standing stability for bipedal robots. ACCESS, 13, 13300-13311. (www)

  @article{ , abstract = {Developing robust benchmarking methods is crucial to evaluate the standing stability of bipedal systems, including humanoid robots and exoskeletons. This paper presents a standardized benchmarking procedure based on the Linear Inverted Pendulum Model and the Capture Point concept to normalize the maximum angular momentum before falling. Normalizing these variables establishes absolute and relative benchmarks achieving comprehensive comparisons across different bipedal systems. Simulations were conducted on two humanoid robots, COMAN and WALK-MAN, to validate the approach, demonstrating its applicability to various sizes and configurations of robots. Furthermore, the same benchmarking method was applied to the therapeutic exoskeleton H3, illustrating its potential to optimize mechanical design and therapeutic performance. The results indicate that this standardized procedure provides a valuable tool for assessing and improving the stability of anthropomorphic robotic systems, providing insights into both hardware capabilities and control strategies.}, archive = {J_ACCESS}, author = {Juan A. JUANCASTANO and Eugenio Manuel Espuela and Jaime Ramos Rojas and Enrico Mingo Hoffman and Chengxu Zhou}, doi = {10.1109/ACCESS.2025.3529191}, journal = {IEEE Access}, pages = {13300-13311}, shortjournal = {IEEE Access}, title = {Benchmarking standing stability for bipedal robots}, volume = {13}, year = {2025}, }
• (2025). A cloud API personalized recommendation method based on multiple attribute features and mashup requirement attention. ACCESS, 13, 13285-13299. (www)

  @article{ , abstract = {In current mashup-oriented cloud API recommendation systems, insufficient attention to personalized development requirements remains a common issue, particularly regarding developers’ needs for attributes such as functionality similarity and complementarity. This paper proposes a novel approach for personalized cloud API feature representation and recommendation. We construct a graph of the cloud API ecosystem with rich side information and design metapaths to capture and characterize various API features. To fully leverage information from intermediate nodes in the metapaths and emphasize the significance of different instances, we employ a translational distance model and graph neural network techniques to aggregate cloud API feature information. Furthermore, we introduce mashup requirement attention, a mechanism that customizes recommendations based on the specific needs of each mashup project, thereby enhancing the accuracy and personalization of API recommendations. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed method.}, archive = {J_ACCESS}, author = {Limin Shen and Yuying Wang and Chengyu Li and Zhen Chen}, doi = {10.1109/ACCESS.2024.3505943}, journal = {IEEE Access}, pages = {13285-13299}, shortjournal = {IEEE Access}, title = {A cloud API personalized recommendation method based on multiple attribute features and mashup requirement attention}, volume = {13}, year = {2025}, }
• (2025). Transforming education through blockchain: A systematic review of applications, projects, and challenges. ACCESS, 13, 13264-13284. (www)

  @article{ , abstract = {Blockchain technology has revolutionized several fields, including finance, healthcare, and supply chain management. The unique features of blockchain, notably decentralization, trustworthiness, and security, have attracted considerable attention from researchers investigating its application in education. Within the education sphere, blockchain applications protect academic records, ensure their trustworthiness, reduce fraud, and improve the value of educational assets. Blockchain projects in education play a crucial role in establishing a more dependable and transparent system for verifying academic credentials and achievements, benefiting students and institutions alike. Many previous studies have provided limited insight into the practical implementation of blockchain technology’s role in education. This systematic study follows Okoli and Schabram guidelines to analyze the 64 articles published between 2016 and 2023. This review encompasses the latest blockchain applications, projects, benefits, and adoption challenges in education. In this study, four main themes are discussed: 1) what are the potential applications of blockchain in education 2) What Blockchain projects are currently implemented in education 3) What benefits does blockchain offer to the education sector 4) What challenges are associated with the adoption of blockchain in education. Each theme includes a detailed discussion and prospective evidence-based findings in this study. This study draws the researcher’s attention to leveraging blockchain technology in future research and encourages educational institutions to develop blockchain applications, transforming from traditional systems to digital platforms. This study also identifies future research directions and acknowledges limitations.}, archive = {J_ACCESS}, author = {Xianxian Wang and Muhammad Younas and Yicun Jiang and Muhammad Imran and Norah Almusharraf}, doi = {10.1109/ACCESS.2024.3519350}, journal = {IEEE Access}, pages = {13264-13284}, shortjournal = {IEEE Access}, title = {Transforming education through blockchain: A systematic review of applications, projects, and challenges}, volume = {13}, year = {2025}, }
• (2025). A second harmonic current suppression strategy based on a digital band-stop filter with center frequency tracking grid frequency. ACCESS, 13, 13250-13263. (www)

  @article{ , abstract = {In a single-phase grid-tie system, the presence of a second harmonic current (SHC) on the DC side reduces the conversion efficiency and lifetime of the DC source. For a system whose front-end is a boost type converter, a digital band-stop filter (BSF) with a center frequency of 120 Hz could be introduced in the voltage sampling loop to suppress the SHC. However, in a grid that relatively lacks inertial support, the frequency will fluctuate over a broader range, resulting in a corresponding variation in the pulsation frequency of the SHC. Based on this, the digital BSF is combined with a phase-locked loop, and the center frequency is further designed to track the acquired grid frequency in real-time. The parameters of the BSF are updated in each interrupt program to provide optimal SHC suppression performance for arbitrary frequencies. An experimental prototype is built and tested. In the case of the grid frequency fluctuating continuously, the proposed method can effectively suppress SHC with a percentage of about 1% at all frequencies, which proves its validity and stability.}, archive = {J_ACCESS}, author = {Jun-Hyeong Kwon and Guangxu Zhou and Chun-Sung Kim and Sung-Jun Park and Yipei Wang}, doi = {10.1109/ACCESS.2025.3529886}, journal = {IEEE Access}, pages = {13250-13263}, shortjournal = {IEEE Access}, title = {A second harmonic current suppression strategy based on a digital band-stop filter with center frequency tracking grid frequency}, volume = {13}, year = {2025}, }
• (2025). Multi-tier blockchain-based land registry using fog computing. ACCESS, 13, 13238-13249. (www)

  @article{ , abstract = {The conventional method of selling and purchasing land has several drawbacks because it requires a lot of time, a lengthy verification procedure, and prolonged final processing. Various blockchain-based solutions have been implemented to tackle the problems of the existing land record. The government of India is currently developing its blockchain-based network to store and verify land records. The solutions cannot handle large amounts of hard-copy documents and rely on a single-tier block network, which affects integrity and decentralization. In this paper, we address the existing problem by introducing a blockchain network at the ground level for monitored lands. Each country will have an associated device as part of the blockchain network to carry out the verification process, thereby distributing the control and workload across two tiers. The devices will interact using the concept of fog computing over the decentralized blockchain network, forming a peer-to-peer network. The proposed solution is compared with an existing solution in terms of cost, computational time complexity, and overall computation cost.}, archive = {J_ACCESS}, author = {Ashank Priyadarshi and Thankaraja Raja Sree}, doi = {10.1109/ACCESS.2025.3528457}, journal = {IEEE Access}, pages = {13238-13249}, shortjournal = {IEEE Access}, title = {Multi-tier blockchain-based land registry using fog computing}, volume = {13}, year = {2025}, }
• (2025). Deep learning for traffic scene understanding: A review. ACCESS, 13, 13187-13237. (www)

  @article{ , abstract = {This review paper presents an in-depth analysis of deep learning (DL) models applied to traffic scene understanding, a key aspect of modern intelligent transportation systems. It examines fundamental techniques such as classification, object detection, and segmentation, and extends to more advanced applications like action recognition, object tracking, path prediction, scene generation and retrieval, anomaly detection, Image-to-Image Translation (I2IT), and person re-identification (Person Re-ID). The paper synthesizes insights from a broad range of studies, tracing the evolution from traditional image processing methods to sophisticated DL techniques, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The review also explores three primary categories of domain adaptation (DA) methods: clustering-based, discrepancy-based, and adversarial-based, highlighting their significance in traffic scene understanding. The significance of Hyperparameter Optimization (HPO) is also discussed, emphasizing its critical role in enhancing model performance and efficiency, particularly in adapting DL models for practical, real-world use. Special focus is given to the integration of these models in real-world applications, including autonomous driving, traffic management, and pedestrian safety. The review also addresses key challenges in traffic scene understanding, such as occlusions, the dynamic nature of urban traffic, and environmental complexities like varying weather and lighting conditions. By critically analyzing current technologies, the paper identifies limitations in existing research and proposes areas for future exploration. It underscores the need for improved interpretability, real-time processing, and the integration of multi-modal data. This review serves as a valuable resource for researchers and practitioners aiming to apply or advance DL techniques in traffic scene understanding.}, archive = {J_ACCESS}, author = {Parya Dolatyabi and Jacob Regan and Mahdi Khodayar}, doi = {10.1109/ACCESS.2025.3529289}, journal = {IEEE Access}, pages = {13187-13237}, shortjournal = {IEEE Access}, title = {Deep learning for traffic scene understanding: A review}, volume = {13}, year = {2025}, }
• (2025). Interrupted sampling repeater jamming recognition method based on wavelet packet transform. ACCESS, 13, 13175-13186. (www)

  @article{ , abstract = {The radar jammers operating in an interrupted sampling repeater jamming (ISRJ) mode can intercept transmitted signal, modulate the intercepted signal slices, and retransmit them back to the radar receiver to create coherent interference. This interference can generate false target groups at the receiving end, significantly disrupting the detection and recognition of targets. To address this issue, we propose a method for interference and target recognition based on the difference in the coefficient of variation obtained from wavelet packet transforms of jamming and target echoes, which can effectively eliminate the impact of ISRJ attacks. In the proposed method, after performing target detection on the received signals to obtain distance information of potential targets and interference, the received signals are up-converted to an intermediate frequency, and wavelet packet transforms are applied to the up-converted signals at locations of potential targets and interference to obtain the corresponding wavelet packet transform coefficients’ coefficient of variation (WPTCCV). Since the WPTCCV for targets is smaller than that for interference, the interference can be identified by comparing the obtained WPTCCV with a defined threshold. Simulation results demonstrate that the proposed method exhibits good recognition performance for the false targets generated by interrupted sampling repeater jamming.}, archive = {J_ACCESS}, author = {Zhong Qi and Fan Shoutao and He Zhiyi and Liu Jianxin}, doi = {10.1109/ACCESS.2025.3530249}, journal = {IEEE Access}, pages = {13175-13186}, shortjournal = {IEEE Access}, title = {Interrupted sampling repeater jamming recognition method based on wavelet packet transform}, volume = {13}, year = {2025}, }
• (2025). Selection and resource allocation strategies for chatbot technologies in higher education: An optimization model approach. ACCESS, 13, 13156-13174. (www)

  @article{ , abstract = {Chatbot technologies are increasingly integral to higher education, offering significant potential to enhance teaching effectiveness and student engagement. This study explores the strategic integration of chatbot technologies, focusing on optimal selection and resource allocation to maximize educational efficacy. We developed a mathematical optimization model and introduced the “Relative Net Utility Differential” (RNUD) as a pivotal metric to facilitate the decision-making process for chatbot technology selection and resource distribution. Our findings reveal that when RNUD values for all chatbots fall within a specified range, the model efficiently determines the optimal resource allocations, providing clear analytical solutions for each technology. Additionally, we devised an algorithm for scenarios where RNUD values vary significantly, effectively isolating optimal solutions by excluding suboptimal choices. Numerical analysis confirms that this algorithm maintains robust performance and high computational efficiency, particularly when the chatbot count does not exceed 20. This efficiency persists across various distributions of chatbots’ technical attributes, and increasing the number of candidate chatbots generally enhances overall educational utility. This suggests that a diverse technological environment can lead to more tailored and effective educational interventions. Moreover, our study highlights a competitive interaction among chatbots: those with superior technical attributes tend to receive more resources. However, when the technical attributes of one chatbot improve, the resources allocated to others decrease, and vice versa. These insights underscore the importance of not only providing more sophisticated chatbots but also carefully managing their interactions to maximize overall educational utility.}, archive = {J_ACCESS}, author = {Suhan Wu and Min Luo}, doi = {10.1109/ACCESS.2025.3530413}, journal = {IEEE Access}, pages = {13156-13174}, shortjournal = {IEEE Access}, title = {Selection and resource allocation strategies for chatbot technologies in higher education: An optimization model approach}, volume = {13}, year = {2025}, }
• (2025). Joint channel and nonlinearity estimation for memoryless nonlinear systems. ACCESS, 13, 13143-13155. (www)

  @article{ , abstract = {System nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, these receivers require knowledge of the system nonlinearity which is usually unknown in practical systems. Bussgang decomposition and its general form denoted Generalized Bussgang decomposition (GBD), have been commonly used to model system nonlinearity. In GBD the nonlinearity output is decomposed as the sum of uncorrelated terms of increased orders and provides spectral characteristics of the useful and distortion terms. In this paper we consider nonlinearity at the transmitter side and model it with GBD. We aim to estimate the scalar weights in the GBD to later use them at the BNC receiver. However, knowledge of the channel is required to make a reliable estimate of the NL parameters. On the other hand the pilots for channel estimation are affected by the system nonlinearity, which can preclude reliable channel estimation. Therefore, in this paper we propose a joint channel and NL parameter estimation technique by designing appropriate training signals for each estimation phase (i.e. channel estimation and NL parameter estimation). We also derive a closed form expression for the average power of residual distortion in GBD with estimated parameters to see how well this model can characterize the nonlinearity. The results show that the proposed estimation technique has good accuracy and enables quasi-ideal performance for a BNC receiver.}, archive = {J_ACCESS}, author = {Zahra Mokhtari and Rui Dinis and Sha Hu and Dzevdan Kapetanovic}, doi = {10.1109/ACCESS.2025.3530817}, journal = {IEEE Access}, pages = {13143-13155}, shortjournal = {IEEE Access}, title = {Joint channel and nonlinearity estimation for memoryless nonlinear systems}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based channel estimation with 1D CNN for OFDM systems under high-speed railway environments. ACCESS, 13, 13128-13142. (www)

  @article{ , abstract = {In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity.}, archive = {J_ACCESS}, author = {Aphitchaya Siriwanitpong and Kosuke Sanada and Hiroyuki Hatano and Kazuo Mori and Pisit Boonsrimuang}, doi = {10.1109/ACCESS.2025.3531009}, journal = {IEEE Access}, pages = {13128-13142}, shortjournal = {IEEE Access}, title = {Deep learning-based channel estimation with 1D CNN for OFDM systems under high-speed railway environments}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid model for credit risk assessment of supply chain finance based on topological data analysis and graph neural network. ACCESS, 13, 13101-13127. (www)

  @article{ , abstract = {Supply Chain Finance (SCF) in the energy sector has emerged as a critical area of focus due to the need for sustainable and efficient financial solutions to manage the complex interactions between various stakeholders, including suppliers, financial institutions, and energy companies. This study proposes a novel hybrid Topological Data Analysis (TDA) and Graph Neural Network (GNN) to optimize credit risk assessment in SCF. By leveraging BallMapper (BM) topological data analysis model and network-based features, the proposed model provides deeper insights into credit risk factors, enhancing the accuracy and dependability of credit risk evaluation for SMEs. Results demonstrate that the proposed BallMapper- Graph Neural Network (BM-GNN) model achieves higher accuracy and F1-scores, outperforming traditional machine learning approaches. Notably, incorporating network-based features alongside financial ratios yields the most favorable results in credit risk assessment. The SHapley Additive exPlanations (SHAP) model highlights the pivotal role of certain features in predicting bankruptcy, offering valuable insights for risk mitigation strategies. These results contribute to the growing body of evidence supporting the efficacy of TDA and GNN in financial applications, particularly in credit risk evaluation for SMEs in supply chain finance. Using network-based models opens up new avenues for improving accuracy and reliability in risk assessment, ultimately empowering financial institutions and stakeholders to make more informed decisions.}, archive = {J_ACCESS}, author = {Kosar Farajpour Mojdehi and Babak Amiri and Amirali Haddadi}, doi = {10.1109/ACCESS.2025.3528373}, journal = {IEEE Access}, pages = {13101-13127}, shortjournal = {IEEE Access}, title = {A novel hybrid model for credit risk assessment of supply chain finance based on topological data analysis and graph neural network}, volume = {13}, year = {2025}, }
• (2025). A testing framework for joint communication and sensing in synthetic aperture radars. ACCESS, 13, 13088-13100. (www)

  @article{ , abstract = {Nowadays the modern progress in wireless communication technologies is motivating the mutual integration of heterogeneous services, in particular enabling resource and hardware-saving in platform deployment. In this perspective, Integrated Sensing and Communications (ISAC) is emerging as one of the most promising areas of research, especially for sensing technologies that are not originally designed for communication purposes, such as radar and Synthetic Aperture Radar (SAR). Current approaches leverage code modulation techniques through specific ground targets, which enable communication integration within SARs without requiring hardware modification. Besides, code transparency reduces the contribution of the target with low additive interference into the final SAR images. However, ISAC testing in real SAR systems poses numerous challenges. This paper introduces a novel testing framework that enables comprehensive evaluation of ISAC functionalities without the operational constraints of real-time tests. The framework provides flexibility across SAR platforms, operational parameters, and acquisition modes, ensuring practical, scalable, and cost-effective validation of communication embedding in SAR systems. To validate the proposed testing framework, real raw data from the ESA Sentinel-1 constellation have been employed to emulate ISAC target behaviour, demonstrating the effectiveness of the framework.}, archive = {J_ACCESS}, author = {Alex Piccioni and Roberto Alesii and Fortunato Santucci and Fabio Graziosi}, doi = {10.1109/ACCESS.2025.3531328}, journal = {IEEE Access}, pages = {13088-13100}, shortjournal = {IEEE Access}, title = {A testing framework for joint communication and sensing in synthetic aperture radars}, volume = {13}, year = {2025}, }
• (2025). Smart control models used for nutrient management in hydroponic crops: A systematic review. ACCESS, 13, 13070-13087. (www)

  @article{ , abstract = {In recent years, agriculture has significantly evolved with the integration of technology, enabling the development of new cultivation techniques that respond to the growing demand for food and the need to conserve natural resources. In this context, we conducted a comprehensive review of models of intelligent control for managing nutrients in hydroponic systems by analyzing studies from the last five years. The selection of articles was based on the guidelines of PRISMA and research questions, focusing on control techniques based on fuzzy Logic, Artificial Intelligence and artificial Vision. These models are essential to automatically adjust the concentrations of nutrients, adapting to the needs of the plants at each stage of their growth. The review results highlight essential advances but also identify significant challenges, such as the need for precise sensors, the management of large volumes of data, and adapting the models to different crops and conditions. Despite these challenges, the benefits include a more efficient use of nutrients, a reduction in the consumption of water, and increased crop yields. Continuous research in this field is essential to improve the sustainability and productivity of hydroponic systems, offering new opportunities for agriculture in the future. The findings of this review provide a solid basis for evaluating the effectiveness of the control models and their application in real agricultural scenarios.}, archive = {J_ACCESS}, author = {Pablo Catota-Ocapana and Cesar Minaya-Andino and Paul Astudillo and Diego Pichoasamin}, doi = {10.1109/ACCESS.2025.3526171}, journal = {IEEE Access}, pages = {13070-13087}, shortjournal = {IEEE Access}, title = {Smart control models used for nutrient management in hydroponic crops: A systematic review}, volume = {13}, year = {2025}, }
• (2025). An interpretable prediction method for tobacco drying process based on CGTNN. ACCESS, 13, 13052-13069. (www)

  @article{ , abstract = {The moisture content of tobacco is a critical quality indicator in the tobacco processing, it is essential to control the moisture content of the tobacco within an appropriate range through the drying process. However, production often faces large delays in moisture measurement, leading to the difficulty of anomaly detection and moisture control. This paper proposes a novel approach combining Correlation Graph and multi-scale Temporal Neural Networks(CGTNN) for real-time moisture prediction. The model transforms the raw data into graph data based on correlation coefficients, extracting the features in the production data from both spatial and temporal perspectives according to the strength of the relationships between different production processes. In addition, to address the risks posed by the uninterpretability of deep learning models,shapley values are used for interpretability analysis, aligning predictions with production experience. Experiments with production data show that the model accurately predicts moisture content with a Mean Absolute Error (MAE) of 0.016%, Root Mean Squared Error (RMSE) of 0.024%, and an Explainable variance (R2) of 0.987, outperforming other models. This approach significantly reduces delays and errors in moisture monitoring, enhancing accuracy and reliability in practical applications.}, archive = {J_ACCESS}, author = {Wencai Wang and Chen Yang and Wenwei Niu and Sidi Lin and Qiang Gao and Zhe Cao and Jianning Chen and Jianzhong Li and Zhengkui Li}, doi = {10.1109/ACCESS.2025.3529992}, journal = {IEEE Access}, pages = {13052-13069}, shortjournal = {IEEE Access}, title = {An interpretable prediction method for tobacco drying process based on CGTNN}, volume = {13}, year = {2025}, }
• (2025). Pulse width modulation control of mecanum-wheeled mobile robot with random noise. ACCESS, 13, 13044-13051. (www)

  @article{ , abstract = {Pulse width modulation (PWM) control is investigated for the Mecanum-wheeled mobile robot (MWMR) driven by servo motors with random disturbance. As a preliminary, PWM strategy of random nonlinear systems is obtained by the aid of the functional analysis tool. A smooth voltage controller is presented by the backstepping technique for MWMR. Based on the boundedness of input voltage caused by direct-current motors, the optimal bounded controller is obtained by the quadratic programming method. Therefore, the optimal duty ratio is generated by the positive part and the negative part of the optimal control, the digital controller is synthesized in turns. Finally, a simulation example is given to demonstrate the efficiency of the proposed PWM scheme.}, archive = {J_ACCESS}, author = {Ruijie Yin and Zhaojing Wu and Likang Feng}, doi = {10.1109/ACCESS.2025.3530405}, journal = {IEEE Access}, pages = {13044-13051}, shortjournal = {IEEE Access}, title = {Pulse width modulation control of mecanum-wheeled mobile robot with random noise}, volume = {13}, year = {2025}, }
• (2025). Object placement across robotics, image composition, and XR: A mixed reality framework proposal. ACCESS, 13, 13030-13043. (www)

  @article{ , abstract = {Object placement, a critical task involving the optimal positioning, scaling, and orientation of objects within a given environment, is vital across multiple domains, including robotics, computer vision, and virtual/augmented/mixed reality (VR/AR/MR). Despite its broad applications, no similar study was found that explores object placement across such diverse domains. This paper provides a comprehensive review of existing research on object placement, identifying key stages such as object and environment representation, placement algorithms, and evaluation metrics. A framework for object placement in MR environments is proposed, which integrates geometric and semantic representations, a human-in-the-loop approach, and real-time interaction features. Unlike existing solutions, the framework combines automation with user control, allowing users to adjust placements dynamically while leveraging spatial data for more efficient initialization. The framework was evaluated using both objective and subjective measures. Objective results revealed that the experimental group completed placement tasks 25% faster than the control group, indicating that the framework is effective in reducing task time while maintaining spatial accuracy. Subjective evaluations, indicated a moderate cognitive load, with users reporting significantly lower mental demand and effort and reflected participants’ sense of mastery and control successfully. This study highlights the interdisciplinary nature of object placement research and presents a significant step forward in developing more efficient and adaptable placement solutions. Future studies should explore the integration of multiple technologies and the need for extensive real-world validation.}, archive = {J_ACCESS}, author = {Jalal Safari Bazargani and Abolghasem Sadeghi-Niaraki and Soo-Mi Choi}, doi = {10.1109/ACCESS.2025.3528422}, journal = {IEEE Access}, pages = {13030-13043}, shortjournal = {IEEE Access}, title = {Object placement across robotics, image composition, and XR: A mixed reality framework proposal}, volume = {13}, year = {2025}, }
• (2025). PBVit: A patch-based vision transformer for enhanced brain tumor detection. ACCESS, 13, 13015-13029. (www)

  @article{ , abstract = {Brain Tumor holds a significant holds in human health, classified into three primary types: glioma, meningioma, and pituitary tumors. Early detection and accurate classification are vital for effective diagnosis and lowering healthcare costs. In PBvit we presents a novel brain tumor detection framework, the Patch Base Vision Transformer (PBVit). PBVit adopts a patch-based approach where input tumor images are divided into fixed-size patches, with each patch treated as a token. These image patches are linearly projected into lower-dimensional token embeddings, and positional encodings are added to help the model understand spatial relationships within the image. PBVit enhances the detection of intricate patterns and anomalies in brain scans, improving diagnostic accuracy. We trained PBVit using the Figshare brain tumor dataset and observed notable performance improvements compared to traditional CNN-based models. The PBVit reached an accuracy of 95.8%, a precision of 95.3%, a recall of 93.2%, and an F1-score of 92%, indicating its robustness in identifying brain tumors. The promising results demonstrate that PBVit can play a important role in facilitating early-stage diagnosis, reducing unnecessary biopsies, and ultimately enhancing patient care, while also showcasing the potential of transformer-based architectures in medical imaging.}, archive = {J_ACCESS}, author = {Pratikkumar Chauhan and Munindra Lunagaria and Deepak Kumar Verma and Krunal Vaghela and Ghanshyam G. Tejani and Sunil Kumar Sharma and Ahmad Raza Khan}, doi = {10.1109/ACCESS.2024.3521002}, journal = {IEEE Access}, pages = {13015-13029}, shortjournal = {IEEE Access}, title = {PBVit: A patch-based vision transformer for enhanced brain tumor detection}, volume = {13}, year = {2025}, }
• (2025). Control characteristics analysis of multi-section morphing wing based on centroid self-trimming. ACCESS, 13, 12996-13014. (www)

  @article{ , abstract = {Based on the research on the sweep angle change of the elbow and hand wing sections during the flight of large birds, this paper proposes a multi-section variable-sweep wing structure suitable for large-scale UAVs. The characteristics of multi-section variable-sweep wing are explored from the perspective of morphing controller design. This research selects stable roll and pitching motion to investigate the feasibility and effectiveness of using the asymmetric change of sweep angle as the control input and using the reverse collaborative morphing of the inner and outer wing sections to achieve centroid self-trimming. The computational analysis results show: 1) The asymmetry morphing of the sweep angle can significantly improve the maneuvering control performance of the UAV with a large lift coefficient. 2) The overall control efficiency of the UAV can be effectively improved by increasing the deflection rate of the sweep angle and the system parameters of the main controller and supervisory controller. 3) Increasing the feedback value of the integral term of the PI compensator in the inner loop can reduce the oscillation degree of the inner loop system in tracking the target signal when the Mach number is large, and improve the tracking accuracy of the inner loop system. 4) Increasing the wingspan ratio of the inner wing section can reduce the elevator deflection angle required to track the target pitch signal during the morphing process. This research provides a reference for the practical application of multi-section variable-sweep wings.}, archive = {J_ACCESS}, author = {Hang Ma and Zhongbin Zhou and Dingshan Zhang}, doi = {10.1109/ACCESS.2024.3519800}, journal = {IEEE Access}, pages = {12996-13014}, shortjournal = {IEEE Access}, title = {Control characteristics analysis of multi-section morphing wing based on centroid self-trimming}, volume = {13}, year = {2025}, }
• (2025). Position determination for dynamic scenes with unsynchronized image sequences. ACCESS, 13, 12981-12995. (www)

  @article{ , abstract = {Solving the stereo camera synchronization problem or estimating the temporal offset for dynamic scenes in triangulation is crucial in computer vision and photogrammetry. However, for cheap cameras like webcams, or cameras with long distance, synchronization is costly and complex. This paper proposed a novel method to determine 3D position for dynamic scenes with unsynchronized image sequences. For the proposed method, synchronized cameras are not essential, also, the temporal offset is not essential to be known. Additionally, it can be used for linear and curve motion, which provides a wider range of applications. In linear motion, the traditional triangulation is abandoned in this paper. Two planes can be determined with unsynchronized image sequences captured by two cameras and the intersection of the two planes is the motion trajectory. Also, one plane can be determined with unsynchronized image sequences captured by one camera, one ray can be determined with 2D projection captured by another camera, and the intersection of the plane and ray represents the current position. In curve motion, the correspondence points can be determined with epipolar line and 2D trajectory determined by unsynchronized image sequences, then the current 3D position is estimated by triangulation. This means using two completely independent cameras to capture dynamic scenes, our method can achieve determination under only ‘see’ and has nothing to do with other conditions (e.g., synchronization, frame rate). Compared to several SOTA (state-of-the-art) solvers, the experimental results show our proposed method performs robustly and high precisely.}, archive = {J_ACCESS}, author = {Kai Guo and Rui Cao and Chenyang Yue and Xin Zhou and Boyu Liu}, doi = {10.1109/ACCESS.2025.3531660}, journal = {IEEE Access}, pages = {12981-12995}, shortjournal = {IEEE Access}, title = {Position determination for dynamic scenes with unsynchronized image sequences}, volume = {13}, year = {2025}, }
• (2025). Smell-ML: A machine learning framework for detecting rarely studied code smells. ACCESS, 13, 12966-12980. (www)

  @article{ , abstract = {Code smells are design flaws that reduce the software quality and maintainability. Machine learning classification models have been used to detect different code smells. However, such studies targeted code smells in depth, while leaving other under-explored smells; even so, such smells have a significant impact on source code quality. Recent surveys have highlighted a group of code smells that has rarely been studied by researchers. Furthermore, some machine learning classification models were evaluated on a subset of the source code features while ignoring significant features during classification. This paper proposes a novel approach, called Smell-ML, for detecting five rarely studied code smells: Middle Man (MM), Class Data Should Be Private (CDSBP), Inappropriate Intimacy (II), Refused Bequest (RB), and Speculative Generality (SG). The novelty of this approach stems from the improvement in both the data preparation and classification phases. During data preparation, Smell-ML relies on data balancing and an extended source code feature list to improve accuracy. In the classification phase, different classifiers were assessed, including traditional, ensemble, and multi-level classifiers. We evaluated Smell-ML on a dataset composed of 13 open source Java projects with 125 versions per project. The results show that Smell-ML’s detection F1-score values surpass those of previous studies with significant improvements across various code smells. The F1-score measure of the 11 machine learning classifiers improved after using the extended feature list. Data balancing and multi-level classification notably boosted accuracy.}, archive = {J_ACCESS}, author = {Esraa Hamouda and Abeer El-Korany and Soha Makady}, doi = {10.1109/ACCESS.2025.3530927}, journal = {IEEE Access}, pages = {12966-12980}, shortjournal = {IEEE Access}, title = {Smell-ML: A machine learning framework for detecting rarely studied code smells}, volume = {13}, year = {2025}, }
• (2025). Hybrid henry gas-harris hawks comprehensive-opposition algorithm for task scheduling in cloud computing. ACCESS, 13, 12956-12965. (www)

  @article{ , abstract = {Users can use online data computing services and computational resources from a distance in cloud computing environments. Task scheduling is a crucial part of cloud computing since it necessitates the creation of dependable and effective techniques for allocating tasks to resources. To achieve optimal performance, it requires accurate task allocation to resources. By optimizing task scheduling, cloud computing solutions can decrease processing times, boost efficiency, and improve overall system performance. To address these challenges, this paper proposes an improved version of Henry gas solubility optimization, which is presented as the Henry Gas-Harris Hawks-Comprehensive Opposition (HGHHC) method. This method is based on two elements: comprehensive opposition-based learning (COBL) and Harris Hawks Optimization (HHO). The HHO algorithm was employed as a local search strategy in this suggested algorithm to improve the quality of authorized solutions. Through meticulous analysis of their opposites and selecting an efficient option, COBL improves the less effective options. This method made it easier to improve insufficient solutions, which increased the overall effectiveness of the chosen strategies. The suggested technique was tested using CloudSim on the NASA, HPC2N, and Synthetic datasets. For makespan (MKS), it achieved performance of 34.30, 72.95, and 28.67, respectively. Regarding resource utilization (RU), the corresponding values were 16.92, 28.72, and 25.58. Therefore, the simulated makespan and resource usage of the proposed HGHHC algorithm were better than those of previous approaches. This highlights the effectiveness of hybrid meta-heuristic algorithms in achieving a balance between exploration and exploitation, preventing them from getting stuck in local optima.}, archive = {J_ACCESS}, author = {Nora Omran Alkaam and Abu Bakar Md Sultan and Masnida B. Hussin and Khaironi Yatim Sharif}, doi = {10.1109/ACCESS.2025.3530860}, journal = {IEEE Access}, pages = {12956-12965}, shortjournal = {IEEE Access}, title = {Hybrid henry gas-harris hawks comprehensive-opposition algorithm for task scheduling in cloud computing}, volume = {13}, year = {2025}, }
• (2025). Pavement potholes quantification: A study based on 3D point cloud analysis. ACCESS, 13, 12945-12955. (www)

  @article{ , abstract = {Currently, the detection technology for road surface potholes, primarily focuses on the identification and segmentation, lacking the ability to quantitatively analyze the damage inflicted by road potholes. Therefore, this pa per proposes a method based on three-dimensional point clouds for the identification, segmentation, and reconstruction of road potholes, ultimately leading to the quantification of the damage volume. An RGB-D depth sensor is employed to collect point cloud data of road potholes. Voxel filtering and voxelization downsampling are used for denoising, filtering, and enhancing data processing efficiency. Surface segmentation is achieved through RANSAC (Random Sample Consensus) and Euclidean clustering, while the Alpha Shapes algorithm is utilized for three-dimensional volume reconstruction, facilitating the volumetric quantification of potholes. For evaluation, comparative experiments were conducted under different lighting conditions and shooting distances. The experimental results demonstrate that the proposed algorithm achieves an accuracy of 96.4% in volumetric damage measurement of road potholes, accurately determining the damage volume of pothole.}, archive = {J_ACCESS}, author = {Qingzhen Sun and Lei Qiao and Yibo Shen}, doi = {10.1109/ACCESS.2025.3531766}, journal = {IEEE Access}, pages = {12945-12955}, shortjournal = {IEEE Access}, title = {Pavement potholes quantification: A study based on 3D point cloud analysis}, volume = {13}, year = {2025}, }
• (2025). Design of coil patterns for an axial flux permanent magnet synchronous motor with PCB stator. ACCESS, 13, 12936-12944. (www)

  @article{ , abstract = {Recently, the demand for miniaturization in various applications within the automotive and industrial sectors has been steadily increasing. Consequently, there is a growing need for research and development of the Axial Flux Permanent Magnet Synchronous Motor (AFPMSM), which is advantageous in thin structures. However, in conventional AFPMSMs, the variation in tooth thickness from the inner diameter to the outer diameter of the stator poses significant manufacturing challenges. To address this, a Coreless AFPMSM, which does not use a stator core, can be employed. However, this design introduces its own challenges, including significantly increased magnetic reluctance due to the absence of teeth and the need for robust support structures for the stator windings. This paper proposes a solution to these issues by utilizing a Printed Circuit Board (PCB) substrate as the stator in a PCB Stator Motor. It describes the design process for the coil pattern of the PCB stator. Using conventional winding methods, the coil pattern layout of the PCB stator is designed, effective conductors are arranged and connected, and the PCB stator is completed. The PCB stator is then applied to an AFPMSM, with Halbach permanent magnets used to improve power density. The design is carried out using 3D finite element analysis (FEA), and the proposed model is validated by comparing the back electromotive force (EMF) under no-load conditions with that of the manufactured model.}, archive = {J_ACCESS}, author = {Dong-Woo Nam and Min-Ki Hong and Na-Rim Jo and Dong-Hoon Jung and Won-Ho Kim}, doi = {10.1109/ACCESS.2025.3526928}, journal = {IEEE Access}, pages = {12936-12944}, shortjournal = {IEEE Access}, title = {Design of coil patterns for an axial flux permanent magnet synchronous motor with PCB stator}, volume = {13}, year = {2025}, }
• (2025). Experimental analysis using IoT-based smart power quality analyzer system with remote data access and GSM alerting mechanism. ACCESS, 13, 12921-12935. (www)

  @article{ , abstract = {Power Quality Analyzers (PQA) play an important role in monitoring and controlling health of the electrical systems. They can report the fluctuations in the field measurements with different power quality issues as well as due to load variations. Internet of Things (IoT) is a potential technology to design Smart PQAs for remote monitoring and easy integration of the field information on the cloud platform using gateway units. This paper focuses on the development of a Smart PQA system using low-cost IoT hardware and software design solutions. The hardware development is completed using Arduino Mega 2560 microcontroller in combination with ESP32 Wroom Wi-Fi gateway and SIM900A GSM gateway. The real-time field data is gathered at the ThingSpeak platform for future analysis, while GSM-based design ensures timely alerts to the end users for any major fluctuation in the power supply. The performance of the proposed low-cost system has been compared with the readings obtained from FPGA-based conventional PQA and standard Fluke Meter when connected to different loads. The proposed system output values are tabulated and compared using graphs. The proposed system is expected to be useful for the critical assessment, monitoring, and control of power quality parameters in various commercial and residential premises.}, archive = {J_ACCESS}, author = {Balasubbareddy Mallala and Mohammad Faridun Naim Tajuddin and Sudhakar Babu Thanikanti and Rajasekhar Reddy Manyam}, doi = {10.1109/ACCESS.2025.3529685}, journal = {IEEE Access}, pages = {12921-12935}, shortjournal = {IEEE Access}, title = {Experimental analysis using IoT-based smart power quality analyzer system with remote data access and GSM alerting mechanism}, volume = {13}, year = {2025}, }
• (2025). Optimizing secure AI lifecycle model management with innovative generative AI strategies. ACCESS, 13, 12889-12920. (www)

  @article{ , abstract = {Generative AI (GAI) is one of the significant components that can efficiently improve and augment the AI cycle model’s robustness when it comes to different threats, weaknesses, and abnormalities detection. When applied in this field, GAI is very useful in emulating the various forms of security violations in actual adversarial settings. These scenarios are important when different aspects of an AI system are tested on how robust they are and thus permit the developers to amend any vulnerability that may be induced before the time it could be utilized in practice. Data and model manipulation, data theft, and adversarial attacks as well as model inference threats which we do a systematic analysis to disrupt the integrity, confidentiality as well as availability of AI models. Considering the current weaknesses and threats related to GAI we provide a systematic approach to how safety concerns that are currently relevant can be integrated with every stage of Artificial Intelligence (AI) lifecycle management: from continuous monitoring to the application of cybersecurity trends and practices, etc. In our approach, the emphasis is placed on the multi-level security management strategy that incorporates the improvement of coding practices, validation and testing, and the implementation of advanced intrusion detection systems. Before proceeding to further analysis and discussion of the given topic, it is also critical to mention the aspect of regulation and ethical concern as the major drivers of GAI usage. Additionally, organizations can involve GAI in the lifecycle to address security needs, during the development, acquisition, deployment, updating, maintenance, and decommissioning of the AI system, making them reliable, safe, and secure all through their lifecycle. Toward these ends, the goal of this work is to present a set of canonical recommendations for the many scientists, engineers, managers, technologists, and policymakers who will play a key role in constructing a sound and secure AI future.}, archive = {J_ACCESS}, author = {Alaa Omran Almagrabi and Rafiq Ahmad Khan}, doi = {10.1109/ACCESS.2024.3491373}, journal = {IEEE Access}, pages = {12889-12920}, shortjournal = {IEEE Access}, title = {Optimizing secure AI lifecycle model management with innovative generative AI strategies}, volume = {13}, year = {2025}, }
• (2025). Big data governance challenges arising from data generated by intelligent systems technologies: A systematic literature review. ACCESS, 13, 12859-12888. (www)

  @article{ , abstract = {The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.}, archive = {J_ACCESS}, author = {Yunusa Adamu Bena and Roliana Ibrahim and Jamilah Mahmood and Arafat Al-Dhaqm and Ahmad Alshammari and Maged Nasser and Muhammed Nura Yusuf and Matthew O. Ayemowa}, doi = {10.1109/ACCESS.2025.3528941}, journal = {IEEE Access}, pages = {12859-12888}, shortjournal = {IEEE Access}, title = {Big data governance challenges arising from data generated by intelligent systems technologies: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Addressing technical challenges in large language model-driven educational software system. ACCESS, 13, 12846-12858. (www)

  @article{ , abstract = {The integration of large language models (LLMs) into educational systems poses significant challenges across several key attributes, including integration, explainability, testability, and scalability. These challenges arise from the complexity of coordinating system components, difficulty interpreting LLM decision-making processes, and the need for reliable, consistent model outputs in varied educational scenarios. Additionally, ensuring scalability requires robust autoscaling mechanisms and suitable architecture design to handle fluctuating workloads. This paper tackles these challenges by proposing tactics to improve system integration, enhance explainability through metadata and an algorithm process, ensure response consistency via regression testing, and facilitate efficient autoscaling through an event-driven microservice architecture. The evaluation results highlight the effectiveness of these tactics, confirming both functional consistency and robust system performance under varying loads.}, archive = {J_ACCESS}, author = {Nacha Chondamrongkul and Georgi Hristov and Punnarumol Temdee}, doi = {10.1109/ACCESS.2025.3531380}, journal = {IEEE Access}, pages = {12846-12858}, shortjournal = {IEEE Access}, title = {Addressing technical challenges in large language model-driven educational software system}, volume = {13}, year = {2025}, }
• (2025). Clustering and investigation of human driving behavior using autoencoder and risk assessment. ACCESS, 13, 12832-12845. (www)

  @article{ , abstract = {This paper introduces a novel methodology for evaluating human driving behavior influenced by shoe type and its impact on collision risk. While human factors, such as footwear, are recognized to affect driving safety, studies quantitively assessing the effects of shoe types on safety has been limited. To address this, we utilize an autoencoder and human-centered risk assessment algorithms to investigate human driving behavior and collision risk. Experiments were conducted in various real-world driving scenarios, involving two distinct types of shoes. The autoencoder extracts features from the driving data and enables us to analyze the effects of shoe type on driving behavior. Additionally, collision risk analysis is used to verify the validity and impact of the feature extraction results on safe driving. This study contributes to enhancing our understanding of how footwear influences driver behavior and safety. Furthermore, this methodology establishes a groundwork for future research on applying quantitative evaluations to other human factors that influence driving behavior.}, archive = {J_ACCESS}, author = {Donghoon Shin and Jinhee Myoung and Woongsun Jeon and Kang-Moon Park}, doi = {10.1109/ACCESS.2025.3529883}, journal = {IEEE Access}, pages = {12832-12845}, shortjournal = {IEEE Access}, title = {Clustering and investigation of human driving behavior using autoencoder and risk assessment}, volume = {13}, year = {2025}, }
• (2025). 2DUMAP: Two-dimensional uniform manifold approximation and projection for fault diagnosis. ACCESS, 13, 12819-12831. (www)

  @article{ , abstract = {With the continuous development of industry, the operational data of mechanical equipment has grown exponentially. High-dimensional fault data often contain a significant amount of noise signals and redundant information, severely impacting the performance of fault diagnosis methods. The currently popular manifold learning dimensionality reduction algorithm, Uniform Manifold Approximation and Projection (UMAP), effectively extract complex nonlinear relationships in fault diagnosis data while preserving both local and global structures. However, the UMAP has Out-of-Sample Problem, making it unable to directly map new samples to the learned low-dimensional space. To address this problem, a mapping matrix is learned to directly extend Out-of-Sample data, but this approach still faces the issue of the mapping matrix being too large. To tackle this challenge, the Two-Dimensional UMAP method is proposed, which transforms one-dimensional vector inputs into two-dimensional matrix representations. Subsequently, considering discriminant information, a Supervised Two-dimensional UMAP algorithm (2DSUMAP) is proposed. This study conducts fault diagnosis experiments on 7 datasets of bearings and gears to comprehensively evaluate the proposed methods. The experiments demonstrate that these methods solve the inherent Out-of-Sample Problem of the UMAP, effectively reduce algorithm complexity, and have superior classification performance and strong robustness in practical fault diagnosis applications.}, archive = {J_ACCESS}, author = {Benchao Li and Yuanyuan Zheng and Ruisheng Ran}, doi = {10.1109/ACCESS.2025.3531712}, journal = {IEEE Access}, pages = {12819-12831}, shortjournal = {IEEE Access}, title = {2DUMAP: Two-dimensional uniform manifold approximation and projection for fault diagnosis}, volume = {13}, year = {2025}, }
• (2025). Depression detection in social media: A comprehensive review of machine learning and deep learning techniques. ACCESS, 13, 12789-12818. (www)

  @article{ , abstract = {Depression is a widespread mental health disorder that may remain undiagnosed by conventional clinical methods. The rapidly growing world of social media sites such as Twitter, Reddit, Facebook, Instagram, and Weibo has provided new avenues for depression detection using Machine Learning (ML) as well as Deep Learning (DL), which analyze user behavior patterns and linguistic cues for more accurate detection of depression. Many techniques have been developed for this aim over the years. Identifying relevant publications on this topic using current academic search systems is challenging due to the rapid growth of research publications, unclear or limited search terms, and the complexity of citation networks. Several review papers have been published to ease this task by summarizing the methodologies, key findings, and recommendations for future research. However, most current reviews often do not provide a clear overview of the evolution, latest techniques, and challenges. This paper aims to address that gap by providing a comprehensive review of ML and DL methodologies for detecting depression on social media. We propose a generic architecture for these systems and present a detailed analysis of methodologies and datasets used for evaluation in this field. In addition, we highlight key open research areas, providing a useful starting point for further research and development. By narrowing our focus to social media, this review contributes to advancing the understanding and application of cutting-edge methods for depression detection. While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.}, archive = {J_ACCESS}, author = {Waleed Bin Tahir and Shah Khalid and Sulaiman Almutairi and Mohammed Abohashrh and Sufyan Ali Memon and Jawad Khan}, doi = {10.1109/ACCESS.2025.3530862}, journal = {IEEE Access}, pages = {12789-12818}, shortjournal = {IEEE Access}, title = {Depression detection in social media: A comprehensive review of machine learning and deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Improve fine-grained feature learning in fine-grained DataSet GAI. ACCESS, 13, 12777-12788. (www)

  @article{ , abstract = {This article starts from the perspective of breaking the integrity of the feature matrix, dividing it into retained and sacrificed parts, and using the sacrificed parts to strengthen the retained parts. We propose SpiltAtt and ShuSpilt modules to sacrifice some features to enhance the backbone without introducing any parameters. To ensure that this enhancement is effective, we also propose STloss function based on a specific structure. During training, only a slight increase in computation is required, and the SpiltAtt structure is deleted after the training is completed. This article selected three datasets from GAI for experiments, using Macro-F1 score as the evaluation index. Through a series of comparative experiments, the effectiveness of these methods was demonstrated. Due to the fact that the method proposed in this article does not increase the number of parameters, the added computational cost can be ignored. Therefore, it has certain advantages compared to other methods, and the construction ideas of these methods have certain reference significance when doing other tasks. This approach of sacrificing some features to enhance retained features elucidates the essence of neural networks from a new perspective.}, archive = {J_ACCESS}, author = {Hai Peng Wang and Zhi Qing Geng}, doi = {10.1109/ACCESS.2024.3520503}, journal = {IEEE Access}, pages = {12777-12788}, shortjournal = {IEEE Access}, title = {Improve fine-grained feature learning in fine-grained DataSet GAI}, volume = {13}, year = {2025}, }
• (2025). Joint resource allocation for V2X sensing and communication based on MADDPG. ACCESS, 13, 12764-12776. (www)

  @article{ , abstract = {Vehicle-to-Everything (V2X) communication is expected to play a critical role in enabling Intelligent Transportation Systems (ITS) within sixth-generation (6G) networks. Integrated Sensing and Communication (ISAC) technology is essential for enhancing spectrum efficiency and reducing resource overhead. However, this also demands a more intelligent and efficient resource allocation framework for next-generation vehicular networks. In this paper, we propose a joint resource allocation method for V2X communication and sensing, aiming to optimize both communication rate and sensing performance. We consider both communication and sensing, using the communication rate as the measure of communication and the Cramér-Rao Lower Bound (CRLB) as the measure of sensing accuracy. In addition, a reward function is designed based on the characteristics of the scenario. The power allocation is used as a continuous action space, and we employ the MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve this optimization problem to address the challenge of dynamic resource allocation. Simulation results show that the proposed method achieves joint optimization of communication and sensing resources across various scenarios, significantly improving the overall system performance. Compared with the PPO algorithm, the proposed algorithm can improve the communication rate by 60% and achieve the trade-off between communication and sensing performance.}, archive = {J_ACCESS}, author = {Zhiyong Zhong and Zhangyou Peng}, doi = {10.1109/ACCESS.2025.3527049}, journal = {IEEE Access}, pages = {12764-12776}, shortjournal = {IEEE Access}, title = {Joint resource allocation for V2X sensing and communication based on MADDPG}, volume = {13}, year = {2025}, }
• (2025). Ad click fraud detection using machine learning and deep learning algorithms. ACCESS, 13, 12746-12763. (www)

  @article{ , abstract = {In online advertising, click fraud poses a significant challenge, draining budgets and threatening the industry’s integrity by redirecting funds away from legitimate advertisers. Despite ongoing efforts to combat these fraudulent practices, recent data emphasizes their widespread and persistent nature. Toward detecting click fraud effectively, this study employed a comprehensive feature engineering and extraction approach to identify subtle differences in click behavior that could be used to distinguish fraudulent from legitimate clicks. Subsequently, a thorough evaluation was conducted involving nine diverse machine learning (ML) and Deep Learning (DL) models. After Recursive Feature Elimination (RFE), the ML models consistently demonstrated robust performance. DT and RF surpassed 98.99% accuracy, while GB, LightGBM, and XGBoost achieved 98.90% or higher. Precision scores, measuring accurate identification of fraudulent clicks, exceeded 98% for models like ANN. In parallel, deep learning (DL) models, including Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Recurrent Neural Network (RNN), showcased strong performance. RNN, in particular, achieved 97.34% accuracy, emphasizing its efficacy. The study underscores the prowess of tree-based methods and advanced algorithms in detecting click fraud, as evidenced by high accuracy, precision, and recall scores. These findings contribute valuable insights to combat click fraud and establish the groundwork for the strategic development of anti-fraud measures in online advertising.}, archive = {J_ACCESS}, author = {Reem A. Alzahrani and Malak Aljabri and Rami A. Mustafa Mohammad}, doi = {10.1109/ACCESS.2025.3532200}, journal = {IEEE Access}, pages = {12746-12763}, shortjournal = {IEEE Access}, title = {Ad click fraud detection using machine learning and deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). CausMatch: Causal matching learning with counterfactual preference framework for cross-modal retrieval. ACCESS, 13, 12734-12745. (www)

  @article{ , abstract = {Cross-modal retrieval exhibits significant promise within the realm of multimedia analysis. Numerous sophisticated techniques have gained widespread adoption for harnessing attention mechanisms to facilitate cross-modal correspondence in matching tasks. However, most existing methods learn cross-modal attention based on conventional likelihood, leading to the fine-gained matching from regions and words containing numerous invalid local relationships and false global connections. This phenomenon will bring negative effects on alignment. Different from these methods, we propose a novel Causal Matching Learning (CausMatch) with counterfactual preference framework for cross-modal retrieval in this paper. This work seeks to ascertain the matching relation by incorporating a counterfactual causality preference, enhancing the quality of attention and providing a robust supervisory signal for the learning process are pivotal objectives. This study specifically investigates the influence of acquired visual and textual attention on network predictions by employing counterfactual intervention as a method of scrutiny. This approach aims to discern and analyze the consequential effects on the learning process. Our approach involves maximizing the positive influence to incentivize the network to assimilate more pertinent relationships and connections conducive to fine-grained cross-modal retrieval. The proposed CausMatch model’s effectiveness is systematically substantiated through a comprehensive series of experiments conducted on two widely recognized benchmark datasets, namely MS-COCO and Flickr30K. The results unequivocally demonstrate its superiority over existing state-of-the-art methods, underscoring its robust performance in cross-modal retrieval tasks.}, archive = {J_ACCESS}, author = {Chen Chen and Dan Wang}, doi = {10.1109/ACCESS.2025.3529942}, journal = {IEEE Access}, pages = {12734-12745}, shortjournal = {IEEE Access}, title = {CausMatch: Causal matching learning with counterfactual preference framework for cross-modal retrieval}, volume = {13}, year = {2025}, }
• (2025). TCN-based DDoS detection and mitigation in 5G healthcare-IoT: A frequency monitoring and dynamic threshold approach. ACCESS, 13, 12709-12733. (www)

  @article{ , abstract = {The Internet of Things (IoT) revolutionizes precision healthcare by enhancing patient care and reducing costs. However, this technology poses challenges in securing Healthcare-IoT (H-IoT) devices, as low-latency packet transmission increases vulnerability to attacks (e.g., Distributed Denial of Service (DDoS)) in 5G networks. This research develops a monitoring frequency-based detection and dynamic threshold mitigation method using Temporal Convolutional Networks (TCNs) in 5G H-IoT environments. A monitoring frequency-based detection method calculates each H-IoT node’s message count over the past five seconds as a percentage of total traffic. A dynamic threshold strategy also provides adaptive security by averaging detected malicious behavior messages across all nodes to determine the base threshold. This approach enhances classification accuracy, effectively mitigates actual DDoS attack nodes, and blacklists malicious nodes without false positives in H-IoT environments. Moreover, this research creates DDoS attack models, monitoring parameters using two simulators (Cooja and ns-3) on H-IoT devices. Data is collected over the Message Queuing Telemetry Transport (MQTT) and User Datagram Protocol (UDP) in a realistic 5G-based H-IoT environment, creating two datasets with malicious and benign data. The proposed TCN-based DDoS prediction and mitigation method achieves 99.98% and 95% accuracy on the MQTT dataset. This model also attains a slightly higher prediction accuracy of 99.99% and mitigation of 80% on the UDP dataset. This research evaluates the proposed model against prior methods, including Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), demonstrating improved accuracy. Thus, the proposed TCN model is a versatile and resilient security solution for H-IoT environments.}, archive = {J_ACCESS}, author = {Mirza Akhi and Ciarán Eising and Lubna Luxmi Dhirani}, doi = {10.1109/ACCESS.2025.3531659}, journal = {IEEE Access}, pages = {12709-12733}, shortjournal = {IEEE Access}, title = {TCN-based DDoS detection and mitigation in 5G healthcare-IoT: A frequency monitoring and dynamic threshold approach}, volume = {13}, year = {2025}, }
• (2025). Advancing anaesthetist rostering quality: A practical approach toward fairness and efficiency. ACCESS, 13, 12692-12708. (www)

  @article{ , abstract = {The Anaesthetist Rostering Problem (ARP) presents significant challenges in healthcare management due to complex constraints and regulations. Existing models for the ARP often fail to address the complexities of real-world hospital environments, particularly in integrating monthly and weekly schedules across multiple locations. This study addresses the key question: How can we develop anaesthetist rosters that improve both staff fairness and operational efficiency while meeting complex hospital requirements? To address this challenge, we propose a novel mixed-integer linear programming model with updated constraints, parameters, and an enhanced evaluation function. We implemented the model at Hospital Canselor Tuanku Muhriz (HCTM), Malaysia, to handle various shift types across multiple locations for both monthly and weekly rosters. The proposed model optimises the roster by satisfying mandatory constraints first (such as legal requirements), and then minimising soft constraint violations, such as employee preferences, through iterative refinement. The evaluation function assesses roster quality by minimising penalties for soft constraint violations. We modified the evaluation function and constraints to accommodate HCTM’s specific shift patterns and rest requirements, enhancing fairness, flexibility, and workload distribution. The model reduced penalties by 69.57% for monthly rosters and 64.37% for weekly rosters compared to manual scheduling. Statistical analysis proved significant enhancement in monthly rosters and weekly rosters. The model improves workload fairness and scheduling efficiency, bridging theoretical models and practical applications. It contributes to healthcare workforce management by offering better resource allocation and increased staff satisfaction.}, archive = {J_ACCESS}, author = {Norizal Abdullah and Masri Ayob and Meng Chun Lam and Nasser R. Sabar and Graham Kendall and Liu Chian Yong}, doi = {10.1109/ACCESS.2025.3526842}, journal = {IEEE Access}, pages = {12692-12708}, shortjournal = {IEEE Access}, title = {Advancing anaesthetist rostering quality: A practical approach toward fairness and efficiency}, volume = {13}, year = {2025}, }
• (2025). PDE-based physics guided neural network for SAR image segmentation. ACCESS, 13, 12682-12691. (www)

  @article{ , abstract = {Academicians and researchers have been keen on climatic and maritime monitoring. Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean. Sea-ice classification using Sentinel-1, a type of SAR, has gained popularity over the years. Automatic image segmentation is a fundamental task in SAR image analysis. This paper proposes a novel, innovative approach to SAR image segmentation by integrating physics-based knowledge into a neural network framework. The implementation of a PDE for SAR image segmentation offers a promising avenue for advancing the field of remote sensing. PDEs provide the required physics for the model. This integration not only enhances the network’s capability to learn meaningful features but also enables it to generate more interpretable and physically meaningful segmentation maps. The Partial Differential Equations (PDE) based Physics-Guided Neural Network (PGNN) model can achieve an accuracy of about 96%, which is greater than all existing state-of-art techniques. By harnessing the synergy between deep learning and physics-based knowledge, this work not only improves segmentation accuracy but also facilitates a deeper understanding of SAR data, paving the way for more reliable and insightful remote sensing applications.}, archive = {J_ACCESS}, author = {Rachana Rao and B. Roja Reddy and M. Uttara Kumari}, doi = {10.1109/ACCESS.2025.3531208}, journal = {IEEE Access}, pages = {12682-12691}, shortjournal = {IEEE Access}, title = {PDE-based physics guided neural network for SAR image segmentation}, volume = {13}, year = {2025}, }
• (2025). Effect of clip characteristics on thermal performance of Dr.MOS packages. ACCESS, 13, 12676-12681. (www)

  @article{ , abstract = {This study explores the impact of copper clip design on the thermal performance of driver MOSFET (Dr.MOS) packages with the goal of optimizing heat dissipation efficiency in microelectronic components. The study examines four key variables: clip thickness, material, attachment material, and plating material. The experimental results show that increasing the clip thickness from 0.15mm to 0.2mm leads to a 2.1% reduction in thermal resistance (from 3.3667°C/W to 3.2958°C/W). Furthermore, using copper C1100, which has higher thermal conductivity (391 W/m-K), further reduces thermal resistance, improving overall heat dissipation. Additionally, employing high-conductivity adhesives like SAC305 improves the thermal interface between the clip and wafer, while coatings such as graphene enhance thermal stability and corrosion resistance. Under high-voltage conditions (12V and 19V), the package with thicker copper clips and better attachment materials exhibited a significant reduction in surface temperature, dropping from 93.3°C to 71.7°C at 12V. These findings offer valuable insights for enhancing the thermal management efficiency of Dr.MOS packages, ensuring better reliability and longevity in high-power applications.}, archive = {J_ACCESS}, author = {Hao-Lin Yen and Fang-I Lai}, doi = {10.1109/ACCESS.2025.3530997}, journal = {IEEE Access}, pages = {12676-12681}, shortjournal = {IEEE Access}, title = {Effect of clip characteristics on thermal performance of Dr.MOS packages}, volume = {13}, year = {2025}, }
• (2025). Amplitude death in extended time-delay coupled oscillator networks. ACCESS, 13, 12666-12675. (www)

  @article{ , abstract = {The present paper applies the concept of extended delayed feedback control, a commonly used method for controlling chaos, to a quenching phenomenon (i.e., amplitude death) in time-delay coupled oscillator networks. We show that a simple characteristic function governs the stability of amplitude death. The simple function enables us to explicitly obtain the stability ranges of the delay time. These ranges depend on the number of oscillators and the network topology. Thus, for situations where the number of oscillators and/or network topology are unknown, one cannot determine the delay time that induces amplitude death. We propose a simple procedure for designing a delay time for which amplitude death is locally stable for any number of oscillators and any network topology. The theoretical results are confirmed by numerical examples. In addition, it is found that the stability ranges of the delay time are much wider than those for conventional delay-coupled oscillator networks.}, archive = {J_ACCESS}, author = {Shinsuke Mizukami and Keiji Konishi and Koki Yoshida and Yoshiki Sugitani and Naoyuki Hara}, doi = {10.1109/ACCESS.2025.3529885}, journal = {IEEE Access}, pages = {12666-12675}, shortjournal = {IEEE Access}, title = {Amplitude death in extended time-delay coupled oscillator networks}, volume = {13}, year = {2025}, }
• (2025). An efficient malware detection approach based on machine learning feature influence techniques for resource-constrained devices. ACCESS, 13, 12647-12665. (www)

  @article{ , abstract = {The growing use of computer resources in modern society makes it extremely vulnerable to several cyber-attacks, including unauthorized access to equipment and computer systems’ manipulation or utter breakdown. Malicious attacks in the form of malware cause significant harm to systems with limited resources. Hence, detecting these attacks and promptly implementing a computationally efficient technique is imperative. Utilizing a machine learning (ML) based model is a superior option for promptly identifying malware. This study develops fourteen machine learning models using a five-fold cross-validation technique on the dataset it obtained for research. We compute the execution time and memory used for each of the fourteen ML model developments, considering both all features and the reduced features after applying the data preprocessing methods. We utilized the Extra Tree classifier (ETC) to identify the top ten significant contributing features based on Gini impurity scores, which led to improved accuracy and reduced processing time. After that, we compared the experimental results and found that the Random Forest (RF) classification model on the reduced features set had a prediction accuracy of 99.39% and ROC-AUC values of 0.99. The ETC model prediction yields comparable results, confirming the viability of the suggested model. The proposed model is very resilient, exhibiting an extremely small standard deviation. It is also highly responsive, with reduced execution time and memory utilization.}, archive = {J_ACCESS}, author = {Subir Panja and Subhash Mondal and Amitava Nag and Jyoti Prakash Singh and Manob Jyoti Saikia and Anup Kumar Barman}, doi = {10.1109/ACCESS.2025.3526878}, journal = {IEEE Access}, pages = {12647-12665}, shortjournal = {IEEE Access}, title = {An efficient malware detection approach based on machine learning feature influence techniques for resource-constrained devices}, volume = {13}, year = {2025}, }
• (2025). A federated learning framework for enhanced data security and cyber intrusion detection in distributed network of underwater drones. ACCESS, 13, 12634-12646. (www)

  @article{ , abstract = {Underwater drones are vital for scientific research, environmental monitoring, and maritime operations, allowing data collection in challenging environments. However, their deployment faces issues such as low bandwidth, high latency, signal attenuation, and intermittent connectivity due to mobility and water currents. Traditional centralized data processing approaches are inefficient under these conditions as they require transmitting large volumes of raw data to a central location. To address these challenges, this study proposes a Federated Learning (FL) framework specifically tailored for underwater networks. Unlike centralized approaches, FL enables underwater drones to collaboratively train a global intrusion detection model by processing data locally and sharing only model updates with the central server. This approach significantly improves data security by ensuring that sensitive information never leaves the local devices, reducing the risk of interception or compromise during transmission. Furthermore, FL’s decentralized architectures inherently aligns with the dynamic and distributed nature of underwater drone networks. The proposed framework improves cyber intrusion detection by leveraging localized insights from individual drones to detect threats, including zero-day attacks, without directly exposing sensitive data. By preserving privacy and enabling collaborative anomaly detection, FL addresses key cybersecurity challenges in the Internet of Underwater Things (IoUT).}, archive = {J_ACCESS}, author = {Mansahaj Singh Popli and Rudra Pratap Singh and Navneet Kaur Popli and Mohammad Mamun}, doi = {10.1109/ACCESS.2025.3530499}, journal = {IEEE Access}, pages = {12634-12646}, shortjournal = {IEEE Access}, title = {A federated learning framework for enhanced data security and cyber intrusion detection in distributed network of underwater drones}, volume = {13}, year = {2025}, }
• (2025). TLB coalescing with range compressed page table for embedded I/O devices. ACCESS, 13, 12623-12633. (www)

  @article{ , abstract = {Modern embedded system on a chip (SoC) usually accommodates input/output (I/O) memory management units to support virtual memory. When a translation look-aside buffer (TLB) is miss, a page-table walk occurs and frequent page-table walks can significantly degrade the memory system performance. To improve TLB utilization, a number of TLB coalescing schemes have recently been reported. The TLB coalescing schemes exploit the contiguous memory allocation and can efficiently coalesce the contiguous virtual-to-physical page mappings into a single TLB entry. Accordingly, TLB coalescing schemes efficiently utilize TLB entries, reduce page-table walks, and can improve the memory system performance. However, the conventional page table is still organized in the page level and contains redundant page information. Subsequently, it is difficult for operating system to effectively represent the block-level contiguity in the page table and it is difficult for TLB coalescing hardware to exploit the contiguity. In this work, to improve the memory system performance, we propose a range compression technique in a page table. In the presented page-table entry, the redundant page attributes are removed and multiple block mappings are added. Considering high-bandwidth memory intensive mobile workloads, we conduct the performance experiments. As a result, the presented scheme can significantly perform better than the traditional scheme and the reference TLB coalescing schemes.}, archive = {J_ACCESS}, author = {Tran Dai Duong and Young Seung Kim and Jae Young Hur}, doi = {10.1109/ACCESS.2025.3528945}, journal = {IEEE Access}, pages = {12623-12633}, shortjournal = {IEEE Access}, title = {TLB coalescing with range compressed page table for embedded I/O devices}, volume = {13}, year = {2025}, }
• (2025). Multi-modal social media analytics: A sentiment perception-driven framework in nanjing districts. ACCESS, 13, 12603-12622. (www)

  @article{ , abstract = {This study investigates the complex urban-rural dynamics of Nanjing through a novel multi-modal analysis of 76,288 social media posts, addressing the critical gap in understanding intra-city variations in perceived urban characteristics across rapidly developing Chinese cities. By integrating computer vision techniques with natural language processing, we develop a comprehensive framework for analyzing public sentiment and attention patterns across eleven districts. Our findings reveal distinct spatial gradients in urban perception that challenge traditional urban-rural dichotomies. While central districts exhibit higher positive sentiments toward built environment (0.65-0.71) and economic factors, peripheral areas show stronger positive associations with environmental quality (0.42) and community cohesion (0.47). Correlation analysis demonstrates significant relationships between socioeconomic indicators and digital engagement patterns, with education levels strongly correlating with cultural heritage attention (r=0.76) and income levels with economic discourse (r=0.94). SHAP analysis further reveals non-linear interaction effects between urban characteristics and public sentiment, particularly in transitional zones. These findings contribute to theories of post-reform Chinese urbanization while offering practical insights for targeted urban planning strategies. The study’s methodological framework provides a replicable approach for analyzing intra-city variations in urban perceptions across rapidly urbanizing regions.}, archive = {J_ACCESS}, author = {Meng Xia and Zhengyang Lu and Feng Wang}, doi = {10.1109/ACCESS.2025.3531769}, journal = {IEEE Access}, pages = {12603-12622}, shortjournal = {IEEE Access}, title = {Multi-modal social media analytics: A sentiment perception-driven framework in nanjing districts}, volume = {13}, year = {2025}, }
• (2025). Magnetization estimation technique of permanent magnets loaded on rotor of IPMSM using actual measurement value of leakage flux. ACCESS, 13, 12596-12602. (www)

  @article{ , abstract = {The production of mass-produced permanent magnet synchronous motors (PMSMs) includes a post-assembly magnetization process. Given that the performance of PMSMs is contingent upon the magnetization quality of the permanent magnets (PMs), the estimation of PM magnetization emerges as a crucial procedure. While various magnetization estimation methods for pre-assembly magnetization have been proposed, no method has been proposed for post-assembly magnetization to estimate the magnetization distribution of PMs loaded onto the rotor core of PMSMs. This study introduces the magnetization estimation technique using the leakage flux derived from the magnetic circuit (MELF). MELF stands out because it can estimate the magnetization distribution of PMs without the need for prior magnetization data. This innovative approach employs a gradient-based optimization method, incorporated with input data composed of accurate measurement values, facilitating fast and detailed estimations. The performance of the proposed method (MELF) is verified using the actual rotor of IEEJ D-model, which serves as one of the benchmark models of interior permanent magnet synchronous motors (IPMSMs).}, archive = {J_ACCESS}, author = {Narichika Nakamura and Masahide Shioyama and Yoshifumi Okamoto}, doi = {10.1109/ACCESS.2025.3530996}, journal = {IEEE Access}, pages = {12596-12602}, shortjournal = {IEEE Access}, title = {Magnetization estimation technique of permanent magnets loaded on rotor of IPMSM using actual measurement value of leakage flux}, volume = {13}, year = {2025}, }
• (2025). Lithium battery life prediction for electric vehicles using enhanced TCN and SVN quantile regression. ACCESS, 13, 12581-12595. (www)

  @article{ , abstract = {The need for lithium battery life forecast algorithms has increased due to the global acceptance of electric vehicles. To further enhance the forecast’s precision and resilience about lithium batteries’ remaining life, this study implements quantile regression with in support vector networks to evaluate battery health conditions. Furthermore, it proceeds to integrate self-coding neural networks with temporal convolutional networks for the purpose of processing and extracting battery life data, and finally proposes a novel prediction model. The outcomes of the experiment demonstrate that when the width parameter is 0.75 and the penalty coefficient is 1, the battery health prediction accuracy of this new model is up to 88%, the remaining life prediction accuracy is up to 95.41%, and the number of battery capacity degradation times is up to 340, which is up to 45 times more than the number of the same type of model. In addition, the minimum difference in temperature prediction of battery charging under this model is close to 0.2°C, the minimum difference in temperature prediction during discharge is 0.3°C, and the battery capacity fidelity test findings’ average value is 91.08%. It is evident that the study’s suggested model offers a considerable advantage in estimating lithium battery lifespan for electric vehicles. Additionally, the study’s findings provide a quicker, more precise, and more flexible reference for estimating the lithium batteries’ condition and remaining life.}, archive = {J_ACCESS}, author = {Xinyue Li and Jiangwei Chu}, doi = {10.1109/ACCESS.2025.3528314}, journal = {IEEE Access}, pages = {12581-12595}, shortjournal = {IEEE Access}, title = {Lithium battery life prediction for electric vehicles using enhanced TCN and SVN quantile regression}, volume = {13}, year = {2025}, }
• (2025). Battery state of charge and state of health estimation using a new hybrid deep neural network approach. ACCESS, 13, 12566-12580. (www)

  @article{ , abstract = {The increasing adoption of Battery Electric Vehicles (BEVs) is driving advancements in battery management systems (BMS) to address challenges like cost and range anxiety, both tied to battery performance. This paper investigates various state of charge (SOC) and state of health (SOH) estimation methods, presenting a novel hybrid neural network that combines Gate Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models. Our proposed method demonstrates significant improvements in SOH and SOC estimation accuracy, with minimal training data required. Key contributions include (1) a hybrid GRU-LSTM model improving SOC/SOH accuracy, (2) self-optimization capabilities, (3) effective handling of temperature variations without OCV-SOC lookup tables, and (4) its application to various lithium battery types. Experimental results show the method achieves an RMSE of 2% and MAE of 1.7% for SOC, and an RMSE of 0.65% and MAE of 0.85% for SOH across a temperature range of −10°C to 40°C. These results indicate a reliable and cost-effective approach for BEV battery management, contributing to the wider adoption of sustainable transportation.}, archive = {J_ACCESS}, author = {Saeid Jorkesh and Ryan Ahmed and Saeid Habibi and Reza Hosseininejad and Siyuan Xu}, doi = {10.1109/ACCESS.2024.3488596}, journal = {IEEE Access}, pages = {12566-12580}, shortjournal = {IEEE Access}, title = {Battery state of charge and state of health estimation using a new hybrid deep neural network approach}, volume = {13}, year = {2025}, }
• (2025). Dust storm attenuation prediction using a hybrid machine learning model based on measurements in sudan. ACCESS, 13, 12554-12565. (www)

  @article{ , abstract = {Sand and dust storms significantly challenge microwave and millimeter-wave communications, particularly in arid and semi-arid regions. Various models have been developed to predict attenuation caused by these storms theoretically and empirically based on two meteorological parameters, namely visibility and humidity. However, these models are found unable to predict most of the attenuation measurements. This study presents a hybrid Machine Learning (ML) model that predicts dust storm attenuation for 22 GHz terrestrial links using meteorological data. The received signal levels were measured for a 22 GHz link over a month in Khartoum, Sudan. The visibility, humidity, atmospheric pressure, temperature and wind speed were also monitored simultaneously by Automatic Weather Station (AWS). The proposed model incorporates XGBoost for feature selection and combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers to capture both short-term and long-term dependencies in meteorological data. The results demonstrate a strong correlation between meteorological parameters and dust storm attenuation. The model’s performance is validated against the measured data at 22 GHz, outperforming existing empirical and theoretical models. The RMSE for the proposed model is 0.07, while all existing theoretical and empirical models are higher than 0.25. Furthermore, the proposed model demonstrates significant enhancements over the available ML model for dust attenuation prediction. This hybrid ML approach offers a more accurate and robust solution for predicting microwave and millimetre wave attenuation during dust storms, enhancing the reliability of communication systems in affected regions.}, archive = {J_ACCESS}, author = {Elfatih A. A. Elsheikh and E. I. Eltahir and Abdulkadir Tasdelen and Mosab Hamdan and Md Rafiqul Islam and Mohamed Hadi Habaebi and Aisha H. Abdullah Hashim}, doi = {10.1109/ACCESS.2025.3530261}, journal = {IEEE Access}, pages = {12554-12565}, shortjournal = {IEEE Access}, title = {Dust storm attenuation prediction using a hybrid machine learning model based on measurements in sudan}, volume = {13}, year = {2025}, }
• (2025). Approximate CNN hardware accelerators for resource constrained devices. ACCESS, 13, 12542-12553. (www)

  @article{ , abstract = {Implementation of Convolutional Neural Networks (CNNs) on edge devices require reduction in computational complexity. Leveraging optimization techniques or approximate computing techniques can reduce the overhead associated with hardware implementation. In this paper, we propose a modular pipelined Feedforward CNN Hardware Accelerator (FHA) and a novel Approximate Feedforward CNN Hardware Accelerator (AFHA). The AFHA design is achieved through the incorporation of hardware pruning and Approximate Multiply Accumulate (AMAC) units. Our proposed architectures are validated for functionality through an image classification application, utilising the popular MNIST dataset for 8-bit, 16-bit and 32-bit operational word size. Performance analysis of our proposed architectures show that the 32-bit FHA consumes 307.04pJ of energy while achieving acceleration of 76.91x. AFHA attains an acceleration of 120.69x with an energy consumption of 295.85pJ. Similarly, the 16-bit and 8-bit architectures demonstrate substantial acceleration while significantly reducing power consumption. The performance of our proposed architectures demonstrates significant acceleration and reduced power consumption compared to popular edge machine learning framework - TinyML TensorFlow Lite. FHA achieves a significant accuracy improvement of 6.2% along with a speedup of 1.07x and AFHA achieves accuracy enhancement of 4.3% and an impressive speedup of 1.42x.}, archive = {J_ACCESS}, author = {P Thejaswini and Gautham Suresh and V. Chiraag and Sukumar Nandi}, doi = {10.1109/ACCESS.2025.3529668}, journal = {IEEE Access}, pages = {12542-12553}, shortjournal = {IEEE Access}, title = {Approximate CNN hardware accelerators for resource constrained devices}, volume = {13}, year = {2025}, }
• (2025). RFMVDA: An enhanced deep learning approach for customer behavior classification in E-commerce environments. ACCESS, 13, 12527-12541. (www)

  @article{ , abstract = {Customer Relationship Management (CRM) systems, widely used in enterprises, have evolved into Software-as-a-Service (SaaS) platforms. With the advent of Customer Data Platforms (CDP), these systems continuously store customer behavior data for purposes such as creating single customer profiles, analyzing, tracking, and managing customer interactions from various perspectives. With the global expansion of the e-commerce market, research on customer analysis and classification optimized for the e-commerce environment has been actively conducted. The RFM (Recency, Frequency, Monetary) model is a straightforward method for classifying customers and is applied across various industries. However, in the e-commerce environment, where customers can access services at any time, there are limitations in collecting, storing, and reflecting customer behavior data for classification. To resolve these limitations, this paper proposes the RFMVDA (Recency, Frequency, Monetary, Visits, Durations, Actions) model. This model is designed to capture customer data, sessions, and behavior units suitable for the e-commerce environment. By utilizing the RFMVDA model for customer behavior-based segmentation and classification, we constructed a Deep Neural Network (DNN) to predict customer behavior-based classifications. As a result, the proposed model demonstrated a segmentation prediction accuracy of 92.98% for customers in the e-commerce environment.}, archive = {J_ACCESS}, author = {Kwanhee Kim and Mingyu Jo and Ilkyeun Ra and Sangoh Park}, doi = {10.1109/ACCESS.2025.3529023}, journal = {IEEE Access}, pages = {12527-12541}, shortjournal = {IEEE Access}, title = {RFMVDA: An enhanced deep learning approach for customer behavior classification in E-commerce environments}, volume = {13}, year = {2025}, }
• (2025). Advanced circuit design and implementation of a highly digitalized third-order VCO-based sigma-delta modulator with enhanced noise transfer function. ACCESS, 13, 12515-12526. (www)

  @article{ , abstract = {This study proposes an advanced circuit design to implement a highly digitalized current input third-order sigma-delta modulator (SDM) with enhanced noise transfer function (NTF). The proposed current input SDM can directly quantize current-type signals without any trans-impedance amplifier. Noise shaping with the proposed third-order SDM is realized via a passive integrator and two-stage voltage-controlled oscillator (VCO) phase integrators, which make the circuit structure highly digitalized and benefit from evolutionary fabrication techniques. The second VCO phase integrator can be easily realized by a current-steering digital-to-analog converter (I-DAC) because of the digital output from the previous phase integrator. This I-DAC is also used as the feedback path to develop current subtraction. A zero is implemented in the transfer function to establish the Chebyshev transfer function and further increase the resolution of the proposed SDM by the enhanced NTF. Measurement results reveal that the proposed highly digitalized SDM has a signal-to-noise and distortion ratio (SNDR) of 80.35 dB in the 10 kHz bandwidth and consumes $67.34~\mu $ W under a supply voltage of 1.2 V, achieving a peak Schreier figure of merit (FoM) of 164.7 dB. The experimental setup of the proposed analog-front-end (AFE) architecture is also presented to showcase the AFE functionality.}, archive = {J_ACCESS}, author = {Shuenn-Yuh Lee and Yi-Ting Hsieh and Ju-Yi Chen}, doi = {10.1109/ACCESS.2025.3530686}, journal = {IEEE Access}, pages = {12515-12526}, shortjournal = {IEEE Access}, title = {Advanced circuit design and implementation of a highly digitalized third-order VCO-based sigma-delta modulator with enhanced noise transfer function}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based detection of anomalies, intrusions, and threats in industrial control systems. ACCESS, 13, 12502-12514. (www)

  @article{ , abstract = {Industrial Control Systems (ICS) are critical to the efficient operation of essential sectors such as manufacturing, energy, and water management. However, their increasing integration with IT systems exposes them to sophisticated cyberattacks, particularly lateral attacks targeting Programmable Logic Controllers (PLCs). Advanced preventive measures are necessary because, despite their significance, many ICS continue to rely on outdated technologies with few security features. This paper proposes a machine learning (ML)-based approach to anomaly detection in ICS communication networks, focusing on techniques such as 1D Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVMs), and Isolation Forest (iForest) algorithms. We generated a dataset by capturing both normal and manipulated ICS communication patterns, including TCP/IP traffic. Simulated lateral attacks provided realistic data for training and testing the ML models. The results demonstrate that the 1D CNN model achieved the highest accuracy (0.92) and F1 score (0.91) with minimal processing time, making it ideal for real-time intrusion detection. This research highlights the potential of ML techniques to fortify ICS cybersecurity and lays the groundwork for future advancements in critical infrastructure resilience.}, archive = {J_ACCESS}, author = {Denis Benka and Dušan Horváth and Lukáš Špendla and Gabriel Gašpar and Maximilián Strémy}, doi = {10.1109/ACCESS.2025.3530902}, journal = {IEEE Access}, pages = {12502-12514}, shortjournal = {IEEE Access}, title = {Machine learning-based detection of anomalies, intrusions, and threats in industrial control systems}, volume = {13}, year = {2025}, }
• (2025). Enhancing essay scoring: An analytical and holistic approach with few-shot transformer-based models. ACCESS, 13, 12483-12501. (www)

  @article{ , abstract = {In the field of automated essay scoring (AES), the task of evaluating written compositions has been a persistent challenge. Despite the impressive capabilities of generalized transformer models in various natural language processing (NLP) domains, their application to essay scoring has often fallen short of expectations. In response to this ongoing challenge, this research delves into the intricate nuances of holistic and analytical essay assessment. This work presents an innovative approach centered on Few-Shot transformer-based models, capitalizing on the strengths of pretrained language models while enabling fine-tuning with limited essay-specific data, often called ‘Few-Shot.’ The outcomes of this study are highly promising, with significant improvements in essay scoring accuracy that surpass the performance benchmarks established by conventional methods. The proposed methodology demonstrates remarkable enhancements in the Quadratic Weighted Kappa (QWK) score, indicating its potential. This represents a significant stride towards automating sophisticated essay evaluation, addressing a long-standing issue in the field.}, archive = {J_ACCESS}, author = {Tahira Amin and Zahoor-Ur-Rehman Tanoli and Farhan Aadil and Khalid Mahmood Awan and Sangsoon Lim}, doi = {10.1109/ACCESS.2025.3530272}, journal = {IEEE Access}, pages = {12483-12501}, shortjournal = {IEEE Access}, title = {Enhancing essay scoring: An analytical and holistic approach with few-shot transformer-based models}, volume = {13}, year = {2025}, }
• (2025). Analyzing the efficacy of computer-aided detection in cerebral aneurysm diagnosis using MRI modality: A review. ACCESS, 13, 12468-12482. (www)

  @article{ , abstract = {Computer-aided detection (CAD) models play a critical role in the clinical diagnosis of cerebral aneurysms, significantly contributing to the reduction of mortality rates associated with this condition. This article provides a comprehensive overview of the evolution of CAD models for aneurysm detection, with a particular focus on MRI modalities. It explores the motivations behind CAD systems, the methodologies employed, and their respective advantages and limitations, offering valuable insights into the current state-of-the-art (SOTA) CAD systems. The research papers selected for this review focus on research utilizing TOF MRA as the imaging modality and emphasize computer-aided detection through both traditional and deep learning techniques, with a particular emphasis on Convolutional Neural Networks (CNNs). CNNs have proven to be a crucial component in improving the accuracy and efficiency of aneurysm detection by automatically learning features from raw imaging data, bypassing the need for manual feature extraction. The article also presents a detailed experimental analysis of deep learning models, benchmarked using TOF MRA datasets. Key research gaps are identified, including the need for large training samples, challenges in Maximum Intensity Projection (MIP) imaging, limitations of 2D architectures, and issues related to overfitting and computational complexity. The review also observes that shallow networks and pretrained models are effective in addressing these challenges. In addition to identifying these gaps, the review outlines future directions for the development of CAD systems, aiming to further advance CAD models for aneurysm detection.}, archive = {J_ACCESS}, author = {Keerthi A. S. Pillai and Preena K. P. and Madhu S. Nair}, doi = {10.1109/ACCESS.2025.3530932}, journal = {IEEE Access}, pages = {12468-12482}, shortjournal = {IEEE Access}, title = {Analyzing the efficacy of computer-aided detection in cerebral aneurysm diagnosis using MRI modality: A review}, volume = {13}, year = {2025}, }
• (2025). INTELTRADE: Intelligent peer-to-peer energy trading system with dynamic pricing and coalition formation. ACCESS, 13, 12458-12467. (www)

  @article{ , abstract = {The decentralization of energy generation and consumption has become increasingly vital, with peer-to-peer (P2P) energy trading promoting resilience, and sustainability. This paper introduces INTELTRADE, a system addressing the energy sustainability and pricing challenges in P2P trading. INTELTRADE integrates a sophisticated coalition formation mechanism and an optimal pricing policy. Initially, the Approximate INTELTRADE (AINTELTRADE) mechanism uses matching theory to quickly determine the initial buyer-seller matches by ignoring prosumers’ externalities. This output feeds the Accurate INTELTRADE (AccINTELTRADE) mechanism, which refines the coalition formation using game theory while considering the prosumers’ externalities. Additionally, a non-cooperative game among energy-selling prosumers is formulated, deriving a unique Nash Equilibrium to maximize the sellers’ profits. INTELTRADE’s efficacy and scalability are validated through simulations with real-world data. A detailed comparative evaluation demonstrates INTELTRADE’s superiority in meeting the buyers’ energy demands and optimizing the sellers’ profits.}, archive = {J_ACCESS}, author = {Nicholas Kemp and Md Sadman Siraj and Eirini Eleni Tsiropoulou and Symeon Papavassiliou}, doi = {10.1109/ACCESS.2025.3530428}, journal = {IEEE Access}, pages = {12458-12467}, shortjournal = {IEEE Access}, title = {INTELTRADE: Intelligent peer-to-peer energy trading system with dynamic pricing and coalition formation}, volume = {13}, year = {2025}, }
• (2025). 3D LiDAR multi-object tracking using multi positive contrastive learning and deep reinforcement learning. ACCESS, 13, 12447-12457. (www)

  @article{ , abstract = {Due to its precise distance measurement capabilities, 3D LiDAR is a critical sensor in autonomous systems, including autonomous vehicles and self-driving robots. It plays a key role in Multi-Object Tracking (MOT). Current MOT methods typically employ a Tracking-by-Detection(TbD) approach, where objects are detected in each frame and matched across frames. However, 3D LiDAR-based tracking faces challenges such as sparsity and occlusion, often leading to ID-switching errors where object identities are incorrectly swapped due to incomplete data. This paper presents a novel 3D LiDAR-based MOT method to address these challenges and enhance tracking accuracy. We propose refining object similarity using contrastive learning, leveraging the distinct shapes of detected objects at varying distances. Additionally, we tackle occlusion issues through reinforcement learning, modeling occlusion dynamics to ensure that re-detected objects retain their original IDs thus improving tracking consistency. Our method is evaluated using the KITTI MOT dataset, demonstrating improved higher-order tracking Accuracy (HOTA) and reduced ID-switching compared to existing 3D LiDAR and camera-LiDAR fusion methods. These findings underscore the effectiveness of our approach across diverse road environments.}, archive = {J_ACCESS}, author = {Minho Cho and Euntai Kim}, doi = {10.1109/ACCESS.2024.3521334}, journal = {IEEE Access}, pages = {12447-12457}, shortjournal = {IEEE Access}, title = {3D LiDAR multi-object tracking using multi positive contrastive learning and deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). A novel AI-based integrated cybersecurity risk assessment framework and resilience of national critical infrastructure. ACCESS, 13, 12427-12446. (www)

  @article{ , abstract = {The modern digital world is experiencing growing security risks, with cyber threats happening more often and becoming more complicated. These threats affect both businesses and individuals. Machine learning (ML) and deep learning (DL) have emerged as vital tools in cybersecurity, enabling the analysis of extensive datasets to identify potential cyber threats. This study proposes a novel technique utilizing ML and DL algorithms for threat detection. We began with data preprocessing, which included cleansing the data, addressing missing values through Multiple Imputation by Chained Equations (MICE), and applying transformations such as encoding and standard scaling. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score, alongside loss graphs and confusion matrices. The highest accuracy at 99% was attained by the LSTM model, while the CNN demonstrated superior precision (98%), recall (97%), and F1 score (97%). Additionally, the CNN, RNN, and SVM models achieved an accuracy of 98%. These results illustrate the effectiveness of ML and DL in identifying cybersecurity threats, highlighting their potential to enhance defenses against emerging cyber risks.}, archive = {J_ACCESS}, author = {Sardar Muhammad Ali and Abdul Razzaque and Muhammad Yousaf and Sardar Sadaqat Ali}, doi = {10.1109/ACCESS.2024.3524884}, journal = {IEEE Access}, pages = {12427-12446}, shortjournal = {IEEE Access}, title = {A novel AI-based integrated cybersecurity risk assessment framework and resilience of national critical infrastructure}, volume = {13}, year = {2025}, }
• (2025). Detection of false data injection attacks (FDIA) on power dynamical systems with a state prediction method. ACCESS, 13, 12411-12426. (www)

  @article{ , abstract = {With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disrupt the system’s stability like frequency stability, thereby leading to catastrophic failures. Therefore, an FDIA detection method would be valuable to protect power systems. FDIAs typically induce a discrepancy between the desired and the effective behavior of the power system dynamics. A suitable detection method can leverage power dynamics predictions to identify whether such a discrepancy was induced by an FDIA. This work investigates the efficacy of temporal and spatio-temporal state prediction models, such as Long Short-Term Memory (LSTM) and a combination of Graph Neural Networks (GNN) with LSTM, for predicting frequency dynamics in the absence of an FDIA but with noisy measurements, and thereby identify FDIA events. For demonstration purposes, the IEEE 39 New England Kron-reduced model simulated with a swing equation is considered. It is shown that the proposed state prediction models can be used as a building block for developing an effective FDIA detection method that can maintain high detection accuracy across various attack and deployment settings. It also shows how the FDIA detection should be deployed to limit its exposure to detection inaccuracies and mitigate its computational burden.}, archive = {J_ACCESS}, author = {Abhijeet Sahu and Truc Nguyen and Kejun Chen and Xiangyu Zhang and Malik Hassanaly}, doi = {10.1109/ACCESS.2024.3524942}, journal = {IEEE Access}, pages = {12411-12426}, shortjournal = {IEEE Access}, title = {Detection of false data injection attacks (FDIA) on power dynamical systems with a state prediction method}, volume = {13}, year = {2025}, }
• (2025). Automatic MRI image classification using attention and residual CNNs with enhanced image denoising filters. ACCESS, 13, 12399-12410. (www)

  @article{ , abstract = {In clinical diagnosis, magnetic resonance imaging (MRI) plays a vital role in analyzing soft tissues. However, the images are affected by noise that is random in nature. The noise affects the quality of the image, which impacts the accuracy of diagnosis. To address this, in this paper, four enhanced versions of filters: enhanced Lee filter (ELF), enhanced Frost filter (EFF), enhanced Kuan filter (EKF) and enhanced Boxcar filter (EBCF) are used. The enhanced image features are used for classification with three different CNN classification models: Convolutional Neural Network (CNN), CNN with attention module (CNN-AM) and CNN with a residual module (CNN-RM). The enhanced filters help in improving the features. In the proposed architecture, the spatial attention module achieves these benefits by applying operations such as average pooling, max pooling, and sigmoid activation to selectively highlight key spatial regions in MRI images, leading to improved classification accuracy and overall performance. The proposed methods are compared with the state-of-the-art methods, and they ensure better image enhancement with high PSNR values of 32.1, 36.7 and 39.1, respectively. The classification models ensure high accuracy of 94%, 95% and 98%.}, archive = {J_ACCESS}, author = {Suganya Athisayamani and A. Robert Singh and Faten Khalid Karim and Samih M. Mostafa}, doi = {10.1109/ACCESS.2024.3523516}, journal = {IEEE Access}, pages = {12399-12410}, shortjournal = {IEEE Access}, title = {Automatic MRI image classification using attention and residual CNNs with enhanced image denoising filters}, volume = {13}, year = {2025}, }
• (2025). Classification based on the support vector machine for determining operational targets for controlling electricity usage with conventional meters: A case study of industrial and business tariff customers from PT PLN (Persero) indonesia. ACCESS, 13, 12388-12398. (www)

  @article{ , abstract = {Electricity theft remains a significant challenge for PT PLN (Persero), Indonesia’s primary electricity provider, serving over 89 million customers as of 2023. The study focuses on industrial and business tariff customers, using a dataset from 2019 to 2023, which includes monthly consumption data from PLN’s postpaid customers across thirty operational units with the highest Electricity Use Control (P2TL) levels, covering customers with a maximum power of 6,600 VA. This approach differs from previous studies that rely on open or smart meter data, as this study uses conventional meters for data collection. In the dataset used for this research, losses from confirmed electricity theft amounted to approximately IDR 19 billion. This research aims to improve the detection of electricity theft through a machine learning-based model utilizing the Support Vector Machine (SVM) classification technique. The goal is to enhance the P2TL mechanism by accurately identifying potential targets for field verification. Various SVM kernels were tested, including Radial Basis Function (RBF), Linear, Polynomial (Poly), and Sigmoid, alongside classifiers such as SVM, Logistic Regression, Decision Tree, and Naïve Bayes. Results show that the SVM model, particularly with the RBF kernel, achieves optimal performance, with balanced precision and recall, especially with 30 months of historical data. This optimized model contributes to improving PLN’s operational efficiency, offering more accurate identification of electricity theft cases, leading to substantial financial savings by reducing losses from unpaid consumption. The findings offer practical benefits for reducing electricity theft and improving PLN’s monitoring system, especially in industrial and business sectors.}, archive = {J_ACCESS}, author = {Galih Arisona and Alief Pascal Taruna and Dwi Irwanto and Arif Bijak Bestari and Wildan Juniawan}, doi = {10.1109/ACCESS.2025.3529295}, journal = {IEEE Access}, pages = {12388-12398}, shortjournal = {IEEE Access}, title = {Classification based on the support vector machine for determining operational targets for controlling electricity usage with conventional meters: A case study of industrial and business tariff customers from PT PLN (Persero) indonesia}, volume = {13}, year = {2025}, }
• (2025). SL-GCNN: A graph convolutional neural network for granular human motion recognition. ACCESS, 13, 12373-12387. (www)

  @article{ , abstract = {Human motion recognition has significantly advanced, with applications in human-computer interaction, virtual reality, intelligent video surveillance, and athletic training. This paper presents SL-GCNN, a novel Graph Convolutional Neural Network framework specifically designed for granular skeletal motion recognition. SL-GCNN incorporates Space-Time Feature Harmonization (STFH) and Linkage Isolation (LI) blocks to enhance classification accuracy for similar motion categories. By leveraging contrastive learning, the model refines hidden layer features and isolates output layer features, effectively addressing the challenges posed by subtle differences in granular motions. Experimental results on the two different datasets demonstrate that SL-GCNN outperforms existing state-of-the-art methods, achieving accuracies of 92.73% and 97.47% on the X-Sub and X-View benchmarks of NTU_RGB_plus_D, and 89.19% and 90.56% on the X-Sub and X-View benchmarks of NTU_RGB_plus_D_120, respectively. The findings highlight the model’s robust performance in distinguishing complex granular motion categories, validating its efficacy in real-world applications.}, archive = {J_ACCESS}, author = {Yang Li and Jingyu Zhang}, doi = {10.1109/ACCESS.2024.3514082}, journal = {IEEE Access}, pages = {12373-12387}, shortjournal = {IEEE Access}, title = {SL-GCNN: A graph convolutional neural network for granular human motion recognition}, volume = {13}, year = {2025}, }
• (2025). IncSAR: A dual fusion incremental learning framework for SAR target recognition. ACCESS, 13, 12358-12372. (www)

  @article{ , abstract = {Deep learning techniques have achieved significant success in Synthetic Aperture Radar (SAR) target recognition using predefined datasets in static scenarios. However, real-world applications demand that models incrementally learn new information without forgetting previously acquired knowledge. The challenge of catastrophic forgetting, where models lose past knowledge when adapting to new tasks, remains a critical issue. In this paper, we introduce IncSAR, an incremental learning framework designed to tackle catastrophic forgetting in SAR target recognition. IncSAR combines the power of a Vision Transformer (ViT) and a custom-designed Convolutional Neural Network (CNN) in a dual-branch architecture, integrated via a late-fusion strategy. Additionally, we explore the use of TinyViT to reduce computational complexity and propose an attention mechanism to dynamically enhance feature representation. To mitigate the speckle noise inherent in SAR images, we employ a denoising module based on a neural network approximation of Robust Principal Component Analysis (RPCA), leveraging a simple neural network for efficient noise reduction in SAR imagery. Moreover, a random projection layer improves the linear separability of features, and a variant of Linear Discriminant Analysis (LDA) decorrelates extracted class prototypes for better generalization. Extensive experiments on the MSTAR, SAR-AIRcraft-1.0, and OpenSARShip benchmark datasets demonstrate that IncSAR significantly outperforms state-of-the-art approaches, achieving a 99.63% average accuracy and a 0.33% performance drop, representing an 89% improvement in retention compared to existing techniques. The source code is available at https://github.com/geokarant/IncSAR.}, archive = {J_ACCESS}, author = {George Karantaidis and Athanasios Pantsios and Ioannis Kompatsiaris and Symeon Papadopoulos}, doi = {10.1109/ACCESS.2025.3528633}, journal = {IEEE Access}, pages = {12358-12372}, shortjournal = {IEEE Access}, title = {IncSAR: A dual fusion incremental learning framework for SAR target recognition}, volume = {13}, year = {2025}, }
• (2025). The impact of metacognitive capabilities on product and process innovations in supply chain relationships. ACCESS, 13, 12343-12357. (www)

  @article{ , abstract = {This study explores how organizations can capture and create knowledge in supply chain relationships. In particular, this study investigated the impact of metacognitive capabilities on knowledge creation in supply chain networks. The research model was empirically verified using sample data from 151 U.S. corporations. Empirical evidence showed that organizations with strong metacognitive capabilities tend to more effectively externalize tacit knowledge gained from supply chain activities into explicit knowledge outcomes such as product and process innovations. These results suggest that organizations can better handle the complex challenges of product and process innovation when leveraging metacognitive capabilities. The practical and theoretical implications of the findings and future research agendas are discussed in depth.}, archive = {J_ACCESS}, author = {Young Sik Cho and Kevin W. Linderman}, doi = {10.1109/ACCESS.2024.3523929}, journal = {IEEE Access}, pages = {12343-12357}, shortjournal = {IEEE Access}, title = {The impact of metacognitive capabilities on product and process innovations in supply chain relationships}, volume = {13}, year = {2025}, }
• (2025). Vehicle-to-vehicle communication channel estimator based on gate recurrent unit. ACCESS, 13, 12332-12342. (www)

  @article{ , abstract = {With the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. However, these methods typically perform poorly in high-speed mobility scenarios or have excessively high computational complexity. To alleviate such issues, this study proposes a channel estimation method by combining the sparrow search algorithm (SSA) and gated recurrent unit (GRU). In addition, this paper adds the attention mechanism to GRU to improve the robustness of the model. The computer simulation results confirm that, compared to traditional schemes, the proposed estimator can achieve a lower bit error rate (BER) and normalized mean squared error (NMSE). At the same time, the computational complexity of the algorithm has been reduced to some extent, allowing the estimator to complete the channel estimation faster. This study provides a useful reference for optimizing neural networks and thus improving the performance of channel estimators.}, archive = {J_ACCESS}, author = {Jun-Han Wang and He He and Kosuke Tamura and Shun Kojima and Jaesang Cha and Chang-Jun Ahn}, doi = {10.1109/ACCESS.2025.3529768}, journal = {IEEE Access}, pages = {12332-12342}, shortjournal = {IEEE Access}, title = {Vehicle-to-vehicle communication channel estimator based on gate recurrent unit}, volume = {13}, year = {2025}, }
• (2025). Hybrid intersection over union loss for a robust small object detection in low-light conditions. ACCESS, 13, 12321-12331. (www)

  @article{ , abstract = {In computer vision, most existing works about object detection focus on detecting objects in the good lighting conditions instead of low-light conditions. Even the few existing works that are centered on object detection in the low-light conditions, predominantly focus on the general object detection rather than the detection of small objects. The main challenges affecting small object detection accuracy in low-light conditions are occlusion caused by the low light, shadows, and darkness that adversely affect the surrounding context leading to poor object classification and the insufficient spatial information that negatively affect object localization resulting in poor small object detection. To address the challenge of poor small object detection in low-light conditions we propose the Hybrid Intersection over Union (HIoU) localization loss to enhance the detection accuracy of small objects in these conditions. This loss utilizes the top-bottom distances of the targeted and predicted bounding boxes and the manhattan distance of the boxes’ centres to deal with the issue of misalignment that negatively affect the small object detection accuracy. Also, it jointly works with the classification loss to offer a joint optimization that facilitates a network to learn features that are important for both localization and classification. Experimental results show that the proposed loss enhances the detection accuracy of small objects in low-light conditions.}, archive = {J_ACCESS}, author = {Twahir Kiobya and Junfeng Zhou and Baraka Maiseli and Maqbool Khan}, doi = {10.1109/ACCESS.2025.3530089}, journal = {IEEE Access}, pages = {12321-12331}, shortjournal = {IEEE Access}, title = {Hybrid intersection over union loss for a robust small object detection in low-light conditions}, volume = {13}, year = {2025}, }
• (2025). A few-shot knowledge graph completion model with neighbor filter and affine attention. ACCESS, 13, 12308-12320. (www)

  @article{ , abstract = {In recent times, extensive scholarly focus has been directed towards the knowledge graph completion (KGC) due to the large number of triples that perform well in training tasks. However, the relations of realistic knowledge graphs (KGs) usually have long-tailed distributions, posing a great challenge in inferring new triples of task relationships from a limited number of triples. To tackle this challenge, methodologies for few-shot knowledge graph completion (FKGC) have been devised. These approaches employ a limited set of reference triples to forecast novel triples for various relations. However, existing FKGC approaches suffer from the drawbacks of not fully utilizing the structural information in KGs and ignoring the fine-grained information of interactions between entity pairs. In this paper, a FKGC model with neighbor filter and affine attention (NFAA) is proposed. The NFAA model filters 2-hop neighbors into a neighborhood scope for an entity aggregator and constructs a relation generator utilizing the affine attention mechanism to efficiently infer new triples for the few-shot relation task. Evaluations are performed using two publicly available benchmark datasets: NELL-one and Wiki-one. Experimental results validate the superiority of the NFAA model relative to several state-of-the-art approaches.}, archive = {J_ACCESS}, author = {Hongfang Gong and Yingjing Ding and Minyi Ma}, doi = {10.1109/ACCESS.2025.3529528}, journal = {IEEE Access}, pages = {12308-12320}, shortjournal = {IEEE Access}, title = {A few-shot knowledge graph completion model with neighbor filter and affine attention}, volume = {13}, year = {2025}, }
• (2025). Energy-efficient discrete cosine transform architecture using reversible logic for IoT-enabled consumer electronics. ACCESS, 13, 12292-12307. (www)

  @article{ , abstract = {As IoT applications proliferate, the demand for energy-efficient data processing becomes increasingly vital, particularly for devices operating under power constraints. This paper introduces a novel Discrete Cosine Transform (DCT) architecture implemented using reversible logic to meet the requirements for effective data compression in IoT systems, such as smart surveillance and wearable health monitors. The proposed 8-point fully parallel DCT leverages a customized datapath based on a standard cell approach, integrating pass transistor logic and a full custom layout in UMC 90 nm CMOS technology. Comprehensive validation through functional simulations and design rule checks demonstrates the architecture’s reliability. With a compact core area of 0.806 mm2, the DCT operates at a low power consumption of just 1.12 mW, achieving a clock frequency of 257 MHz. This low-power design facilitates significant energy savings while maintaining high performance and low complexity, making it well-suited for energy-constrained IoT applications.}, archive = {J_ACCESS}, author = {Muhammad Awais and Wilayat Khan and Tallha Akram and Yunyoung Nam}, doi = {10.1109/ACCESS.2025.3528261}, journal = {IEEE Access}, pages = {12292-12307}, shortjournal = {IEEE Access}, title = {Energy-efficient discrete cosine transform architecture using reversible logic for IoT-enabled consumer electronics}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimization techniques in cloud task scheduling: A systematic literature review. ACCESS, 13, 12255-12291. (www)

  @article{ , abstract = {Task scheduling in cloud computing environment aims to identify alternative methods for effectively allocating competing cloud tasks to constrained resources, optimizing one or more objectives. This systematic literature review (SLR) examines advancements in multi-objective optimization techniques for cloud task scheduling from year 2010 to October 2024, providing an up-to-date analysis of the field. Cloud task scheduling, critical for optimizing performance, cost, and resource use, increasingly relies on multi-objective approaches to address complex and competing scheduling goals. This comprehensive review presents a detailed taxonomy and classification of multi-objective optimization methods, highlighting trends and developments across various approaches. Additionally, we conduct a comparative analysis of key scheduling objectives, testing environments, statistical evaluation methods, and datasets employed in recent studies, offering insights into current practices and best-fit approaches for different scenarios. The findings of this SLR aim to guide researchers and practitioners in selecting appropriate techniques, metrics, and datasets, supporting effective decision-making and advancing the design of cloud task scheduling systems.}, archive = {J_ACCESS}, author = {Olanrewaju L. Abraham and Md Asri Bin Ngadi and Johan Bin Mohamad Sharif and Mohd Kufaisal Mohd Sidik}, doi = {10.1109/ACCESS.2025.3529839}, journal = {IEEE Access}, pages = {12255-12291}, shortjournal = {IEEE Access}, title = {Multi-objective optimization techniques in cloud task scheduling: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Link prediction in complex hyper-networks leveraging HyperCentrality. ACCESS, 13, 12239-12254. (www)

  @article{ , abstract = {In complex networks, predicting the formation of new connections, or links, within complex networks has been a central challenge, traditionally addressed using graph-based models. These models, however, are limited in their ability to capture higher-order interactions that exist in many real-world networks, such as social, biological, and technological systems. To account for these multi-node interactions, hyper-networks have emerged as a more flexible framework, where hyperedges can connect multiple nodes simultaneously. Traditional link prediction methods often treat all common neighbors equally, overlooking the fact that not all nodes contribute uniformly to the formation of future links. Each node within a network holds a distinct level of importance, which can influence the likelihood of link formation among its neighbors. To address this, we introduce a link prediction approach leveraging hypercentrality measures adapted from traditional centrality metrics such as degree, clustering coefficient, betweenness, and closeness to capture node significance and improve link prediction in hyper-networks. We propose the Link Prediction Based on HyperCentrality in hyper-networks (LPHC) model, which enhances traditional common neighbor and jaccard coefficient of hyper-network frameworks by incorporating centrality scores to account for node importance. Our approach is evaluated across multiple real-world hyper-networks datasets, demonstrating its superiority over traditional link prediction methods. The results show that link prediction in hypercentrality-based models, particularly those utilizing hyperdegree and hyperclustering coefficients for common neighbor and jaccard coefficent approaches in hyper-networks, consistently outperform existing methods in terms of both F1-score and Area Under the Precision-Recall Curve (AUPR), offering a more precise understanding of potential link formations in hyper-networks. The proposed LPHC model consistently outperforms the existing HCN and HJC models across all datasets, achieving an overall improvement of 69% compared to HCN and 68% compared to HJC.}, archive = {J_ACCESS}, author = {Y. V. Nandini and T. Jaya Lakshmi and Murali Krishna Enduri and Mohd Zairul Mazwan Jilani}, doi = {10.1109/ACCESS.2025.3530245}, journal = {IEEE Access}, pages = {12239-12254}, shortjournal = {IEEE Access}, title = {Link prediction in complex hyper-networks leveraging HyperCentrality}, volume = {13}, year = {2025}, }
• (2025). A two-point association tracking system incorporated with YOLOv11 for real-time visual tracking of laparoscopic surgical instruments. ACCESS, 13, 12225-12238. (www)

  @article{ , abstract = {The application of real-time visual tracking in laparoscopic surgery has gained significant attention in recent years, driven by the growing demand for precise and automated surgical assistance. Instrument tracking, in particular, is critical for enhancing the safety and efficacy of minimally invasive surgery, where direct visibility is often limited. Real-time tracking of surgical instruments allows for more accurate maneuvering, reduces the risk of accidental tissue damage, and enables the development of advanced computer-assisted surgical systems. In this context, advancements in deep learning, particularly through detection models and modern tracking algorithms, have opened new avenues for addressing the challenges posed by real-time laparoscopic instrument tracking. However, according to the preliminary results, the existing combination of the detection model and tracking algorithm could not often handle the remaining challenges, including fast-motion speed, occlusion, overlapping, and close proximity of surgical instruments. This paper proposes a novel two-point association approach for surgical instrument tracking using a combination of YOLOv11 for object detection and refined ByteTrack for tracking. The proposed system is evaluated on a comprehensive dataset of surgical videos. The experimental results demonstrate superior performance in terms of segmentation accuracy (via F1-score), tracking robustness (via MOTA and HOTA), and real-time processing speed (via FPS). In order to validate the effectiveness of this research, real-time surgical instrument tracking is performed with the streaming of laparoscopic gynecologic surgery on a donated soft-tissue cadaver. The results indicate that the proposed system significantly improves the segmentation and tracking of surgical instruments, offering a reliable tool for enhancing intraoperative navigation and reducing the risk of surgical errors. This work contributes to the advancement of intelligent surgical systems, providing a foundation for further integration of machine learning techniques in the operating room.}, archive = {J_ACCESS}, author = {Nyi Nyi Myo and Apiwat Boonkong and Kovit Khampitak and Daranee Hormdee}, doi = {10.1109/ACCESS.2025.3529710}, journal = {IEEE Access}, pages = {12225-12238}, shortjournal = {IEEE Access}, title = {A two-point association tracking system incorporated with YOLOv11 for real-time visual tracking of laparoscopic surgical instruments}, volume = {13}, year = {2025}, }
• (2025). Anomaly usage behavior detection based on multi-source water and electricity consumption information. ACCESS, 13, 12215-12224. (www)

  @article{ , abstract = {The construction of smart cities contributes to promoting residents’ life convenience and sustainable energy development. Despite these advancements, the challenge of fully analyzing and understanding residents’ energy usage behaviors leads to inefficient energy use and potential economic losses. Current resident anomaly detection technologies rely on single-source energy data, lacking detailed behavior pattern analysis. Hence, this paper proposes a method to detect abnormal residential water and electricity usage by incorporating multi-source information. Specifically, the correlation between water and electricity usage of residential customers is analyzed based on real metering data and the use of the Copula distribution function, followed by the integration of two innovative data mining techniques to form an anomaly detection framework. The distance correlation coefficient algorithm is used to measure the relevance of users’ water and electricity usage data. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is utilized to cluster the distance correlation coefficient for users and detect abnormal users whose distance correlation coefficient curves deviate from the normal user clusters. This multi-source approach avoids single-source bias by improving the data accuracy over one-dimensional methods. Experiments are implemented in a real low-voltage transformer area to prove the validity of the proposed method.}, archive = {J_ACCESS}, author = {Wenqing Zhou and Chaoqiang Chen and Qin Yan and Bin Li and Kang Liu and Yingjun Zheng and Hongming Yang and Hui Xiao and Sheng Su}, doi = {10.1109/ACCESS.2025.3525726}, journal = {IEEE Access}, pages = {12215-12224}, shortjournal = {IEEE Access}, title = {Anomaly usage behavior detection based on multi-source water and electricity consumption information}, volume = {13}, year = {2025}, }
• (2025). Residential energy management method based on the proposed A3C-FER. ACCESS, 13, 12203-12214. (www)

  @article{ , abstract = {Deep reinforcement learning has been widely applied in the field of residential energy management, showcasing considerable promise in enhancing energy efficiency and reducing energy consumption. However, it is observed that some methodologies still suffer from inadequate data exploitation, which results in suboptimal policy performance. In this study, focusing on the residential energy management system, an innovative reinforcement learning method is proposed. This novel method fuses the asynchronous advantage actor-critic architecture with a familiarity-based experience replay mechanism, with the ambition of markedly improving learning efficiency and control performance. Numerical comparisons were made to justify the effectiveness of the method. Experimental results across diverse cases confirm that the proposed algorithm can effectively achieve optimal energy scheduling for residential sectors. Furthermore, the proposed methodology has demonstrated a notable reduction in grid interaction expenses, achieving a decrease of 27.03% and 16.38% relative to the other two scenarios. In comparison with the Proximal Policy Optimization (PPO) and Deep Q-Network (DQN) algorithms, the novel approach not only improves the average reward value post-convergence by 38.48% and 47.17%, respectively, but also significantly reduces the training duration by 81.19% within a multi-threaded computational environment.}, archive = {J_ACCESS}, author = {Jinjiang Zhang and Qiang Lin and Lu Wang and Orefo Victor Arinze and Zihan Hu and Yantai Huang}, doi = {10.1109/ACCESS.2025.3529872}, journal = {IEEE Access}, pages = {12203-12214}, shortjournal = {IEEE Access}, title = {Residential energy management method based on the proposed A3C-FER}, volume = {13}, year = {2025}, }
• (2025). Fusion ConvLSTM-net: Using spatiotemporal features to increase residential load forecast horizon. ACCESS, 13, 12190-12202. (www)

  @article{ , abstract = {Power systems are undergoing a significant transition towards renewable energy technologies. To make the most of these energy sources, optimizing the generation, storage, and distribution of energy can be enhanced with accurate forecasts of future energy consumption. Forecasting the load of individual residents plays a key role in load balancing, but it remains challenging due to the irregular nature of individual consumption patterns. Moreover, the current literature is limited to forecasting residential load to only a few hours in the future. In this paper, we propose Fusion ConvLSTM-Net, a novel fusion encoder-decoder architecture that combines both spatial and temporal features to extend the load forecast to a full 24 hour period. We evaluated the model against several benchmark neural network models by: 1) testing different forecast window sizes ranging from 1.5 to 24 hours, 2) assessing model performance across multiple households, and 3) performing large-scale forecasting by aggregating predictions from 100 households. Additionally, we analyzed the model’s forecasts to identify potential degradation. Our extensive experiments demonstrate that the Fusion ConvLSTM-Net not only extends the forecast window to 24 hours but also reduces the prediction error rate by approximately 47% compared to the next best model, improves the accuracy of aggregate load forecasts, and prevents model degradation.}, archive = {J_ACCESS}, author = {Abhishu Oza and Dhaval K. Patel and Bryan J. Ranger}, doi = {10.1109/ACCESS.2025.3528072}, journal = {IEEE Access}, pages = {12190-12202}, shortjournal = {IEEE Access}, title = {Fusion ConvLSTM-net: Using spatiotemporal features to increase residential load forecast horizon}, volume = {13}, year = {2025}, }
• (2025). Blinder oaxaca and wilk neutrosophic fuzzy set-based IoT sensor communication for remote healthcare analysis. ACCESS, 13, 12178-12189. (www)

  @article{ , abstract = {In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions. Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals. As fuzzy systems have reformed several areas of work, health has also made the most of it. In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption. In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed. Data collection is performed with the WESAD dataset. Duplicated data are eliminated by Blinder Oaxaca Linear Regression-based Preprocessing model. With the application of the Blinder Oaxaca function, energy efficiency is enhanced. Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis. The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution. The result is conducted by using PYTHON application to analyze stress detection with the WESAD dataset. The proposed BO-SWNF method achieved an overall accurate data analysis of 12 % with minimum time ensuring 56 % improvement and minimizing energy consumption by 54 %.}, archive = {J_ACCESS}, author = {Osamah Ibrahim Khalaf and Rajesh Natarajan and Natesh Mahadev and Prasanna Ranjith Christodoss and Thangarasu Nainan and Carlos Andrés Tavera Romero and Ghaida Muttashar Abdulsahib}, doi = {10.1109/ACCESS.2022.3207751}, journal = {IEEE Access}, pages = {12178-12189}, shortjournal = {IEEE Access}, title = {Blinder oaxaca and wilk neutrosophic fuzzy set-based IoT sensor communication for remote healthcare analysis}, volume = {13}, year = {2025}, }
• (2025). Improving image quality and controllability in speed + angular velocity to image generation through synthetic data for driving simulator generation. ACCESS, 13, 12168-12177. (www)

  @article{ , abstract = {With advancements in cross-modal techniques, methods for generating images and videos from text or speech have become increasingly practical. However, research on video generation from modalities other than text or speech remains limited. One major reason for this shortage is the lack of large-scale datasets, which leads to skewed data distributions and causes issues such as unresponsiveness to untrained patterns, image collapse, and scene skipping. To address these challenges, this study focuses on a driving simulation generation model, which produces driving scenarios from speed and steering angle information. We propose a synthetic dataset for speed + angular velocity to image (SAV2IMG) generation. Specifically, we create a half-synthetic dataset, in which only the query (control inputs) is synthetic and the response (images) is derived from real data, as well as a fully synthetic dataset, in which both the query and response are synthetic. By doing so, we construct a training environment that enables the model to handle diverse driving patterns and previously unseen control conditions. We conducted experiments comparing three training conditions for SAV2IMG: using real data only, using real plus half-synthetic data, and using real plus both half- and fully synthetic data. The results demonstrate improved image quality as measured by FID, enhanced controllability, and flexible adaptability to unknown control conditions. Moreover, employing fully synthetic data generated from 3D city models allowed for stable responses to unfamiliar scenarios. At the same time, we found that simple physical models failed to fully reproduce complex control patterns. These findings are not only valuable for improving SAV2IMG but also hold broader implications for vehicle-related generative tasks and cross-modal generation models in general. They provide a meaningful foundation for future model development and data augmentation strategies.}, archive = {J_ACCESS}, author = {Yuto Imai and Tomoya Senda and Yusuke Kajiwara}, doi = {10.1109/ACCESS.2025.3525714}, journal = {IEEE Access}, pages = {12168-12177}, shortjournal = {IEEE Access}, title = {Improving image quality and controllability in speed + angular velocity to image generation through synthetic data for driving simulator generation}, volume = {13}, year = {2025}, }
• (2025). A hybrid transformer architecture for multiclass mental illness prediction using social media text. ACCESS, 13, 12148-12167. (www)

  @article{ , abstract = {Mental illness prediction through text involves employing natural language processing (NLP) techniques and deep learning algorithms to analyze textual data for the identification of mental disorders. Therefore, machine learning and deep learning algorithms have been utilized in the existing literature for the detection of mental illness. However, current systems exhibit suboptimal performance primarily due to their reliance on traditional embedding techniques and generic language models to generate text embeddings. To address this limitation, there is a requirement for domain-specific pretrained language models that comprehensively understand the context found in posts of person with a psychiatric disability patients. Posts from individuals with mental illness often contain metaphorical expressions, posing a challenge for existing models in understanding such figurative language. In this study, we propose a hybrid transformer architecture, comprising MentalBERT and MelBERT pretrained language models, cascaded with CNN models to generate and concatenate deep features. MentalBERT is pretrained on an extensive corpus of text data specifically related to the mental health domain, while MelBERT is trained on a large corpus of metaphorical data for improved understanding of metaphorical expressions. The results reveal outstanding performance of the proposed architecture with an overall accuracy of 92% and an F1-score of 92%, surpassing state-of-the-art models in comparison. This study underscores the necessity for further research in this field and illustrates the potential of advanced technologies to address mental health issues in contemporary society.}, archive = {J_ACCESS}, author = {Adnan Karamat and Muhammad Imran and Muhammad Usman Yaseen and Rasool Bukhsh and Sheraz Aslam and Nouman Ashraf}, doi = {10.1109/ACCESS.2024.3519308}, journal = {IEEE Access}, pages = {12148-12167}, shortjournal = {IEEE Access}, title = {A hybrid transformer architecture for multiclass mental illness prediction using social media text}, volume = {13}, year = {2025}, }
• (2025). Improved low-complexity demodulation: Integrating minimum-mean-square-error and maximum-likelihood detection for image-sensor-based visible light communication. ACCESS, 13, 12138-12147. (www)

  @article{ , abstract = {This study proposes an improved low-complexity signal demodulation method for a visible light communication (VLC) system with an image sensor as a receiver and light-emitting diode (LED) transmitters. Signal demodulation by VLC systems is challenging when image sensors are used as receivers because of defocusing. The traditional maximum likelihood detection (MLD)-based demodulation, although effective, incurs high computational costs, restricting its use in real-time applications. To address these limitations, this study introduces an improved error signal candidate selection algorithm that focuses on residual signals and channel matrix characteristics to refine the signal estimation result. Through iterative refinement using low-complexity detectors such as those based on zero-forcing (ZF), and minimum-mean-square error (MMSE), our method substantially narrows the search space for the MLD, thus striking a balance between demodulation performance and computational efficiency. The proposed method was validated through laboratory experiments to demonstrate substantial performance gains over existing techniques. Thus, this method advances the field of VLC demodulation, particularly for systems with image sensors.}, archive = {J_ACCESS}, author = {Yuki Ohira and Shintaro Arai and Kengo Fujii and Tomohiro Yendo}, doi = {10.1109/ACCESS.2025.3529498}, journal = {IEEE Access}, pages = {12138-12147}, shortjournal = {IEEE Access}, title = {Improved low-complexity demodulation: Integrating minimum-mean-square-error and maximum-likelihood detection for image-sensor-based visible light communication}, volume = {13}, year = {2025}, }
• (2025). Enhanced cognitive load detection in air traffic control operators using EEG and a hybrid deep learning approach. ACCESS, 13, 12127-12137. (www)

  @article{ , abstract = {The automatic and effective detection of cognitive load for air traffic control (ATC) operators through electroencephalography (EEG) signals provides a covert and objective method for enhancing ATC safety. Nevertheless, the extant paradigm is limited to simple cognitive tasks and lacks real-world scenarios. In this study, a cognitive load-elicited experiment was therefore designed to record the EEG data of eight ATC operators under four distinct simulation scenarios, ascertaining whether they experienced varying degrees of workload. Subsequently, the collected EEG signal was preprocessed. We then used one hybrid deep learning model based on the convolutional layers and a self-attention mechanism to extract the pertinent EEG features. In conjunction with multi-layer perceptron, we decoded cognitive load state into low, high, overload, and special. The experimental results demonstrated that EEG could serve as a reliable measure for predicting ATC load, with an average accuracy of 88.76% and a peak accuracy of 99% at the single-subject level. Additionally, it highlighted the critical role of the frontal regions in decoding cognitive load. This study serves to enhance the efficacy of personalized EEG decoding for ATC operators, furnishing evidence for the feasibility of developing an intelligent load-detecting system.}, archive = {J_ACCESS}, author = {Yueying Zhou and Junji Jiang and Lijun Wang and Shanshan Liang and Hao Liu}, doi = {10.1109/ACCESS.2025.3530091}, journal = {IEEE Access}, pages = {12127-12137}, shortjournal = {IEEE Access}, title = {Enhanced cognitive load detection in air traffic control operators using EEG and a hybrid deep learning approach}, volume = {13}, year = {2025}, }
• (2025). Tailored channel pruning: Achieve targeted model complexity through adaptive sparsity regularization. ACCESS, 13, 12113-12126. (www)

  @article{ , abstract = {In deep learning, the size and complexity of neural networks have been rapidly increased to achieve higher performance. However, this poses a challenge when utilized in resource-limited environments, such as mobile devices, particularly when trying to preserve the network’s performance. To address this problem, structured pruning has been widely studied as it effectively reduces the network with little impact on performance. To enhance a model’s performance with limited resources, it is crucial to 1) utilize all available resources and 2) maximize performance within these limitations. However, existing pruning methods often require iterations of training and pruning or many experiments to find hyperparameters that satisfy a given budget or forcibly truncate parameters with a given budget, resulting in performance loss. To solve this problem, we propose a novel channel pruning method called Tailored Channel Pruning. Given a target budget (e.g., FLOPs and parameters), our method outputs a tailored network that automatically takes the budget into account during training and satisfies the target budget. During the integrated training and pruning process, our method adaptively controls sparsity regularization and selects important weights that can help maximize the accuracy within the target budget. Through various experiments on the CIFAR-10 and ImageNet datasets, we demonstrate the effectiveness of the proposed method and achieve state-of-the-art accuracy after pruning.}, archive = {J_ACCESS}, author = {Suwoong Lee and Yunho Jeon and Seungjae Lee and Junmo Kim}, doi = {10.1109/ACCESS.2025.3529465}, journal = {IEEE Access}, pages = {12113-12126}, shortjournal = {IEEE Access}, title = {Tailored channel pruning: Achieve targeted model complexity through adaptive sparsity regularization}, volume = {13}, year = {2025}, }
• (2025). A distribution agnostic rank-based measure for proximity search. ACCESS, 13, 12103-12112. (www)

  @article{ , abstract = {Proximity search is extensively used in modern machine learning algorithms across various applications. Proximity search aims at finding data points which are close to the data point of interest. Extant algorithms depend on distance-based metrics to find the closest data points. However, these metrics are limited by their dependency on the distribution of data along different dimensions, making them sensitive to scaling and translation. The performance also suffers as the number of dimensions increase. Furthermore, proximity estimation between any two data points in extant metrics does not factor in the relative position of the rest of the data. In this paper, we aim to provide an alternative to these metrics by proposing Rank Adjacency Measure (RAM) which is agnostic to the distribution of the data. RAM estimates the probability of proximity between points by extending the concept of ordering in one dimension. We provide a detailed mathematical construction of RAM. We illustrate the effectiveness of the proposed methodology using five datasets in three application areas - Outlier Detection, Nearest Neighbor Search, and Text Similarity. While our proposed methodology outperforms existing algorithms in outlier detection by 50%, it performs at par with existing metrics for other two applications. We conclude the paper with discussion on its limitations and research directions for improving RAM.}, archive = {J_ACCESS}, author = {Mayur Garg and Ashutosh Nayak and Rajasekhara Reddy Duvvuru Muni}, doi = {10.1109/ACCESS.2024.3522669}, journal = {IEEE Access}, pages = {12103-12112}, shortjournal = {IEEE Access}, title = {A distribution agnostic rank-based measure for proximity search}, volume = {13}, year = {2025}, }
• (2025). Cooperative behaviors and multienergy coupling through distributed energy storage in the peer-to-peer market mechanism. ACCESS, 13, 12081-12102. (www)

  @article{ , abstract = {The integration of distributed energy storage systems into multienergy systems has garnered significant attention due to the increased use of renewable energy sources and the demand for improved energy management. This study explores the concept of multienergy coupling by facilitating energy storage through a peer-to-peer marketplace. An innovative peer-to-peer market structure is proposed, supported by advanced algorithms, to enable direct energy trading among prosumers. Key challenges are addressed through a systematic methodology, encompassing user registration, multienergy systems involving electricity, heat, and gas, storage cost optimization, multienergy trading, and blockchain-based transactions. A consortium blockchain-based framework ensures secure and private registration. A peer-to-peer network facilitates multienergy trading, enabling efficient energy generation and storage. A storage virtualization model is developed to minimize storage costs, and a nonlinear programming multiobjective model optimizes battery energy storage. For multienergy trading, Nash bargaining under uncertainty is introduced, allowing participants to negotiate based on their contributions. The proposed model’s performance is benchmarked against conventional peer-to-peer energy trading and the Q-learning algorithm. Transactions are secured using blockchain-based payment systems. The implementation is carried out using Matlab-R2023b Network Simulator, and the results are evaluated using various performance metrics.}, archive = {J_ACCESS}, author = {Zeynep Tanis and Ali Durusu}, doi = {10.1109/ACCESS.2025.3529205}, journal = {IEEE Access}, pages = {12081-12102}, shortjournal = {IEEE Access}, title = {Cooperative behaviors and multienergy coupling through distributed energy storage in the peer-to-peer market mechanism}, volume = {13}, year = {2025}, }
• (2025). New SEPIC derived semi-bridgeless PFC converter for battery charging application. ACCESS, 13, 12068-12080. (www)

  @article{ , abstract = {This paper introduces an AC-DC semi-bridgeless dual-switch SEPIC converter, specifically designed for battery charging applications. It focuses on improving power factor (PF) by substantially reducing total harmonic distortion (THD) in the AC input current through a modified converter structure. Additionally, the converter achieves significant reductions in the sizes of its inductors as it operates in discontinuous conduction mode (DCM) to achieve the low current THD. This converter topology employs two power switches to realize the power factor correction (PFC). Primary novelty of the proposed converter lies in designing and selecting a circuit structure which ensures low THD and unity power factor by the energy balance principle of inductors and capacitors. Additionally, the incorporation blocking diodes in the proposed converter effectively eliminates circulating current through input inductor which in turn makes the converter more efficient. This novel circuit structure also eliminates the requirement of additional closed loop control algorithm for PFC. To validate the proposed concept, a prototype converter of 100W/53V is developed and tested. This converter yields a current THD of 2.1%, unity PF and efficiency of 92.4% at rated condition during hardware testing. The paper also conducts a comparative analysis with similar other converters to assess the performance of the proposed solution thoroughly. This comprehensive study underscores the effectiveness of the new converter for PFC and reducing input current THD for battery charging applications.}, archive = {J_ACCESS}, author = {Manish Kumar Dwivedi and R. Jayapragash}, doi = {10.1109/ACCESS.2025.3529351}, journal = {IEEE Access}, pages = {12068-12080}, shortjournal = {IEEE Access}, title = {New SEPIC derived semi-bridgeless PFC converter for battery charging application}, volume = {13}, year = {2025}, }
• (2025). Predefined-time h ∞ cooperative control for multi-robot systems based on adjustable prescribed performance control and adaptive command filter. ACCESS, 13, 12055-12067. (www)

  @article{ , abstract = {A predefined-time $H_{\infty } $ coordinated formation controller with an adjustable prescribed performance function (PPF) and an adaptive command filter is proposed for multi-robot systems in this work. First, an adjustable prescribed performance function is developed to limit the angular error and adaptively adjust the state convergence performance subject to actuator saturation, which effectively avoids the singularity problem. Second, the “explosion of complexity” issue is solved by proposing a predefined-time adaptive command filter and accelerate the convergence time and improve filter precision. Third, the predefined-time $H_{\infty } $ control theory is developed to guarantee that the nonlinear system has global predefined-time stabilization and that the $L_{2}$ gain is less than $\gamma $ . Fourth, the predefined-time $H_{\infty } $ coordinated formation controller for multi-robot systems (MRSs) is designed to achieve strong robustness to various disturbances. Finally, all the signals in the control system are bounded and converge within the predefined time, and the results of the virtual simulation experiments verify the validity and performance of the MRSs.}, archive = {J_ACCESS}, author = {Haitao Liu and Weichen Li and Xin Huang and Xuehong Tian and Qingqun Mai}, doi = {10.1109/ACCESS.2025.3529130}, journal = {IEEE Access}, pages = {12055-12067}, shortjournal = {IEEE Access}, title = {Predefined-time h ∞ cooperative control for multi-robot systems based on adjustable prescribed performance control and adaptive command filter}, volume = {13}, year = {2025}, }
• (2025). Study on the motion patterns of nested test cabin and its shock response spectrum analysis. ACCESS, 13, 12044-12054. (www)

  @article{ , abstract = {This study investigates the motion patterns of the nested test cabin in a gunpowder gas overload test device. Multiple factors during the overload impact process were explored. Under the conditions of keeping the gunpowder combustion model, the friction coefficient between the inner and outer cabins, and the mass of the cabins unchanged, the special acceleration curve and its frequency spectrum and the impact response spectrum of the pseudo-velocity are analyzed. Numerical simulations and experimental studies revealed that there is compound motion between the inner and outer cabins in the gunpowder gas overload test device, resulting in small oscillations (referred to as oscillation wavelets) in the measurement results of the test system within the inner cabin. These oscillation wavelets occur when the critical acceleration of the test cabin reaches approximately 4700g. Increasing the initial velocity of the test cabin leads to a larger amplitude of the oscillation wavelets in the overall acceleration curve. In the frequency domain, secondary spectra appear under high overload conditions, and the bandwidth of the secondary spectra increases with the overload. The PVSRS trends for all overloads are roughly the same, with the variation in peak pseudo velocity being only on the order of $10^{-3}$ . Theoretical analysis and experimental results show good consistency, which helps to better understand the motion process of the test cabin in the gunpowder gas overload test device and provides support for the improvement and design of the overall device.}, archive = {J_ACCESS}, author = {Wei Wu and Wei Luo and Xing Liu and Jia Cui and Pengyu Zhang}, doi = {10.1109/ACCESS.2025.3529874}, journal = {IEEE Access}, pages = {12044-12054}, shortjournal = {IEEE Access}, title = {Study on the motion patterns of nested test cabin and its shock response spectrum analysis}, volume = {13}, year = {2025}, }
• (2025). SpaceCAM: A 16 nm FinFET low-power soft-error tolerant TCAM design for space communication applications. ACCESS, 13, 12032-12043. (www)

  @article{ , abstract = {The Ternary Content Addressable Memory (TCAM) is a crucial component of satellite communication systems. Space-oriented TCAMs face unique challenges, as they must operate within a very limited energy budget and are susceptible to high Soft Error Rates (SER) due to ionizing particle radiation. The Dual Interlocked Storage Cell (DICE) based memory is capable of withstanding soft errors. However, its reliability diminishes in presence of multiple node upsets. Moreover, recent studies indicate that DICE resilience to even single-node upsets degrades in advanced technology nodes. This issue is further exacerbated by the scaling of the supply voltage to reduce power consumption. In this paper, we propose SpaceCAM, a DICE-based TCAM that overcomes the above limitations and enables aggressive voltage scaling while withstanding multiple node upsets in each memory row. SpaceCAM enables soft error tolerance by applying an approximate rather than an exact search. It tolerates up to 5 soft errors per 144-bit row, provided the minimum Hamming distance between stored data patterns (such as the Active Control List (ACL) rules) is 26. When designed using a 16nm FinFET commercial process, SpaceCAM $144\times 512$ -bit memory core operates at a supply voltage of as low as 350mV, consuming 2mW while running at 500MHz.}, archive = {J_ACCESS}, author = {Itay Merlin and Benjamin Zambrano and Marco Lanuzza and Alexander Fish and Avner Haran and Leonid Yavits}, doi = {10.1109/ACCESS.2025.3528745}, journal = {IEEE Access}, pages = {12032-12043}, shortjournal = {IEEE Access}, title = {SpaceCAM: A 16 nm FinFET low-power soft-error tolerant TCAM design for space communication applications}, volume = {13}, year = {2025}, }
• (2025). Bridging precision and complexity: A novel machine learning approach for ambiguity detection in software requirements. ACCESS, 13, 12014-12031. (www)

  @article{ , abstract = {Ambiguity in software requirements is a significant challenge as it often leads to misunderstandings, implementation errors, and costly project delays. This research proposes a hybrid framework that combines rule-based techniques with machine learning to identify ambiguity in software requirements with precision and efficiency. The framework begins with a rule-based model that systematically detects ambiguities using a carefully prepared list of ambiguous phrases. The analysis utilizes a dataset of 1,553 software requirements drawn from diverse project domains. To capture more intricate ambiguities that traditional rule-based systems might miss, the framework integrates TF-IDF vectorization and a Random Forest classifier, enhancing the precision and coverage of classification. In addition, clustering analysis identifies patterns to provide deeper insights into ambiguous requirements, while sentiment analysis explores the relationship between ambiguity and the emotional tone of requirements. Together, these analyses offer a broader understanding of ambiguity trends and stakeholder perceptions. The framework’s performance is validated using standard evaluation metrics, achieving an accuracy of 97%, precision of 97%, recall of 89%, and an F1-score of 92%, significantly surpassing traditional rule-based methodologies. This research advances automatic ambiguity detection by delivering a flexible and interpretable solution. The proposed approach enhances the clarity and quality of software requirements, strengthening requirements engineering practices and supporting more effective software development.}, archive = {J_ACCESS}, author = {Rahat Izhar and Shahid N. Bhatti and Sultan A. Alharthi}, doi = {10.1109/ACCESS.2025.3529943}, journal = {IEEE Access}, pages = {12014-12031}, shortjournal = {IEEE Access}, title = {Bridging precision and complexity: A novel machine learning approach for ambiguity detection in software requirements}, volume = {13}, year = {2025}, }
• (2025). Physics-informed denoising model for dynamic data recovery of power systems. ACCESS, 13, 12002-12013. (www)

  @article{ , abstract = {For a variety of applications within power systems, the precision of data acquisition is of paramount importance. However, the actual data may be corrupted by noise in the process of measurement or transmission, and the accuracy of dynamic security assessment (DSA) will be affected. In light of the poor interpretability exhibited by traditional machine learning (ML) methods in denoising, a physics-informed denoising model (PIDM) for dynamic data recovery is proposed. The differential equations of physical models in power systems are employed to guide the training of PIDM. They are transformed into physical constraints and subsequently incorporated into the loss function of stacked denoising autoencoder (SDAE) to cleanse noisy data. By integrating the powerful learning capabilities of ML with the rigorous constraints of physical laws, the noisy data recovered by PIDM can better satisfy the dynamic equations. Consequently, a more pronounced denoising effect can be achieved. The improvement of the PIDM over common ML-based models is explored when dealing with the noisy data with varying degrees of interference or those of unexpected faults. The effectiveness is validated through simulation results in IEEE 39-bus system and the East China power grid. The results show that this method can reduce the total mean square error (MSE) of the recovery of noisy data to at least 65.27% of that of the traditional methods under the same conditions. In addition to demonstrating superior denoising performance, the generalization capability under diverse noise conditions is also deemed excellent.}, archive = {J_ACCESS}, author = {Jian Li and Guoqiang Lu and Yongbin Li and Dongning Zhao and Huaiyuan Wang and Yucheng Ouyang}, doi = {10.1109/ACCESS.2025.3529853}, journal = {IEEE Access}, pages = {12002-12013}, shortjournal = {IEEE Access}, title = {Physics-informed denoising model for dynamic data recovery of power systems}, volume = {13}, year = {2025}, }
• (2025). Hybrid deep learning for gas price prediction using multi-factor and temporal features. ACCESS, 13, 11989-12001. (www)

  @article{ , abstract = {Natural gas prices are a vital indicator of a country’s economic conditions. Accurately forecasting natural gas prices is challenging due to the complex interaction of various factors. Traditional methods often consider linear factors or the impact of historical natural gas prices in isolation, failing to fully capture the intrinsic connections between these factors. In this paper, we innovatively apply k-means clustering to analyze the correlations of multiple factors affecting natural gas prices and design a hybrid deep learning model that integrates both multi-factor and time series features. Through experimental validation on three public datasets, our proposed model achieves industry-leading predictive performance with a mean squared absolute error of 2.27, which is approximately a 1/3 improvement over the current state-of-the-art methods.}, archive = {J_ACCESS}, author = {Shuliang Zhang and Hao Wu and Jin Wang and Longsheng Du}, doi = {10.1109/ACCESS.2024.3525128}, journal = {IEEE Access}, pages = {11989-12001}, shortjournal = {IEEE Access}, title = {Hybrid deep learning for gas price prediction using multi-factor and temporal features}, volume = {13}, year = {2025}, }
• (2025). A verifiable and secure industrial IoT data deduplication scheme with real-time data integrity checking in fog-assisted cloud environments. ACCESS, 13, 11969-11988. (www)

  @article{ , abstract = {In the Industrial Internet of Things (IIoT), the vast amount of data generated by interconnected devices presents significant challenges in storage management. Existing deduplication methods often fail to account for data correlation across devices, resulting in inefficient encrypted data handling, excessive computational overhead on fog nodes, and insufficient support for lightweight authenticity checks. Additionally, most current approaches lack mechanisms for real-time data integrity verification. In this paper, we proposed VERDUP, a verifiable and secure deduplication scheme with the support of real-time data integrity checking and fine-grained access control specifically designed for IIoT environments. VERDUP utilizes a two-stage deduplication approach based on a dynamic tree model in a fog-assisted cloud architecture, integrated with blockchain technology to enhance deduplication efficiency and data integrity verification. Our tree-based model enables the grouping of IIoT devices under the same branch, facilitating efficient search and management of indexing in both the cloud and blockchain. Additionally, we introduced a temporal data removal algorithm to manage outdated IoT data effectively, significantly reducing storage costs in fog computing environments. A lightweight PUF-based authentication mechanism is also presented, where challenge-response pairs from Physical Unclonable Functions (PUFs) are discarded after each successful authentication. This is achieved using an incremental value configured at the fog node, allowing authentication requests to be processed without generating new challenges for each transaction. For real-time data integrity, we propose an integrity verification algorithm that cross-references hash values stored in the cloud and blockchain, enabling fast and lightweight integrity checks. Finally, extensive experiments and comparative evaluations demonstrate that VERDUP significantly improves deduplication performance compared to existing methods.}, archive = {J_ACCESS}, author = {Jakkarin Lapmoon and Somchart Fugkeaw}, doi = {10.1109/ACCESS.2025.3529765}, journal = {IEEE Access}, pages = {11969-11988}, shortjournal = {IEEE Access}, title = {A verifiable and secure industrial IoT data deduplication scheme with real-time data integrity checking in fog-assisted cloud environments}, volume = {13}, year = {2025}, }
• (2025). Multi-objective optimal tuning framework of the entire PMSM-driven servo system considering high-frequency disturbance suppression. ACCESS, 13, 11957-11968. (www)

  @article{ , abstract = {A universal PMSM-driven servo system comprises cascaded PI controllers with a low-pass speed filter for high-frequency disturbance suppression. However, the speed filter and the current loop have received limited attention during the tuning of the speed loop, potentially leading to high-frequency fluctuations or significant resonances in the entire servo system under disturbances. This paper proposes a novel framework of multi-objective optimal tuning for the entire PMSM-driven servo system, focusing on suppressing high-frequency disturbances. The controller parameters to be tuned include the PI gains of the speed loop, the filter time constant and the equivalent time constant of the current loop. The closed-loop cut-off frequency of disturbances is established as a constraint to optimize the speed filter, and the current loop time constant is selected as an objective to improve control smoothness. Experimental results demonstrate that the proposed method can reduce the high-frequency fluctuations of speed and current without causing system resonances.}, archive = {J_ACCESS}, author = {Zihan Tang and Liping Wang and Jihui Han and Dong Wang and Xuekun Li}, doi = {10.1109/ACCESS.2025.3529983}, journal = {IEEE Access}, pages = {11957-11968}, shortjournal = {IEEE Access}, title = {Multi-objective optimal tuning framework of the entire PMSM-driven servo system considering high-frequency disturbance suppression}, volume = {13}, year = {2025}, }
• (2025). Active suspension based on a motor-driving compacted double-gas-chamber hydro-pneumatic strut. ACCESS, 13, 11944-11956. (www)

  @article{ , abstract = {Vehicle attitude is one of the most important factors affecting ride comfort, safety, and handling, and it is influenced by road conditions, driver’s maneuvers, and other factors. It is usually achieved by pumping methods (air suspension, hydro-pneumatic suspension, and so on). However, the nonlinearities, hysteresis, and uncertainties in the adjusting process caused by the gas property and the valve characteristic will increase the difficulty of controller design. And frequent use of the pump for continuous adjustment also affects the life span. This paper develops an active suspension based on Motor-driving compacted Double-gas-chamber Hydro-Pneumatic Strut (MDHPS) and verifies the possibility of attitude control with a simple PID controller. The controller is built based on the quarter-car model with the MDHPS model and the optimal values are identified through the Ziegler-Nichols method and Genetic Algorithm (GA). Finally, to investigate the attitude control effect of MDHPS, the full-car model is established based on the co-simulation of Carsim and Matlab/Simulink. The anti-roll and anti-pitch properties based on the proposed height adjustment method are evaluated under different working conditions. Through the comparison between the active and passive methods, it has been shown that the proposed method can greatly improves vehicle performance. The improvement rate of the Root Mean Square (RMS) for both roll and pitch angles exceeds 50.46% under different working conditions.}, archive = {J_ACCESS}, author = {Ruihong Li and Bing Ma and Fan Yang and Dezhao Lin and Weiqiang Zhang}, doi = {10.1109/ACCESS.2025.3525760}, journal = {IEEE Access}, pages = {11944-11956}, shortjournal = {IEEE Access}, title = {Active suspension based on a motor-driving compacted double-gas-chamber hydro-pneumatic strut}, volume = {13}, year = {2025}, }
• (2025). Automatic generation of simulators for processors enhanced for security in virtualization. ACCESS, 13, 11930-11943. (www)

  @article{ , abstract = {All new computer architectures need to be performance evaluated for acceptance and simulation is the most widely used method for evaluation of new processor designs. Sharing resources with virtualization raised many security concerns, leading to the development of processors that are enhanced for security in virtualization. The simulators for processors enhanced for security in virtualization need to perform simulation of hypervisor instructions, simulation of security in virtualization, and simulation of new instructions. However, a simulator with all of these three features is not found in the literature. Hence, this paper proposes an approach for the simulation of processors enhanced for security in virtualization that provides all these three features. For user convenience, the simulators are generated automatically from the target processor specifications using a simulator generator. The paper also proposes an approach for simulating a new pipeline with a designer-specified number of stages with automatic detection of pipeline hazards and automatic stalling or flushing of the pipeline on detection of hazards. To demonstrate the use of the simulator generator and the generated simulator, three case studies are considered - simulation of RISC-V with HyperWall, simulation of RISC-V with bit-serial dot-product unit, and simulation of RISC-V with Galois Field arithmetic extension. The paper concludes that the proposed approaches help in accurately simulating the overhead due to security in virtualization and also in providing flexibility to the designer to simulate the desired processor configurations.}, archive = {J_ACCESS}, author = {Swapneel C. Mhatre and Priya Chandran}, doi = {10.1109/ACCESS.2025.3529667}, journal = {IEEE Access}, pages = {11930-11943}, shortjournal = {IEEE Access}, title = {Automatic generation of simulators for processors enhanced for security in virtualization}, volume = {13}, year = {2025}, }
• (2025). TAS scheduling with grouping flows. ACCESS, 13, 11918-11929. (www)

  @article{ , abstract = {IEEE802.1Qbv (Time-aware Shaper: TAS) in IEEE802.1-Time-Sensitive Networking (TSN) has recently been applied within user networks for realizing factory automation and remote control. As mass customization matures, TAS is expected to extend its coverage to not only user networks but also wide-area networks to realize ultra-low-latency (ULL) communication between remote locations. TAS is TSN technology expected to be used for long-distance communications because it can guarantee maximum latency limits. However, designing a TAS schedule is an NP-hard problem, which becomes more difficult as the amount of delay guarantee flow increases. This paper proposes a method to reduce the computational complexity of TAS schedules for the problem that the computation time increases exponentially as the amount of scheduled traffic (ST) flows increases. This problem has serious impacts on TAS scheduling in wide-area networks. In the proposed method, flows with the same characteristics are grouped to reduce the number of flows to be input into the scheduling calculation. This suppresses computation infeasibility in the NP-hard problem and improves the schedulability of ST flows. This paper contributes to realizing TAS over wide-area networks, which are currently underutilized.}, archive = {J_ACCESS}, author = {Hironao Abe and Yuhei Kawakami and Hideo Kawata and Takashi Nakanishi and Tomoaki Yoshida}, doi = {10.1109/ACCESS.2025.3528396}, journal = {IEEE Access}, pages = {11918-11929}, shortjournal = {IEEE Access}, title = {TAS scheduling with grouping flows}, volume = {13}, year = {2025}, }
• (2025). A review of causal methods for high-dimensional data. ACCESS, 13, 11892-11917. (www)

  @article{ , abstract = {Causal learning from observational data is an important scientific endeavor, but the statistical and computational challenges posed by the high-dimensionality of many modern datasets are substantial. Peculiarities such as spurious correlations, endogeneity, noise accumulation, and deflated empirical covariance estimation complicate analysis. These issues may lead to confounding bias, which can be misleading when attempting to learn the true causal relationships and causal effects between variables. In this survey, we provide a comprehensive review of causal analysis and the theory behind high-dimensionality. We discuss the effects of high-dimensionality on causal estimation methods and their corresponding solutions. Finally, we present evaluation metrics and software tools for both causal effect estimation and causal discovery.}, archive = {J_ACCESS}, author = {Zewude A. Berkessa and Esa Läärä and Patrik Waldmann}, doi = {10.1109/ACCESS.2024.3524261}, journal = {IEEE Access}, pages = {11892-11917}, shortjournal = {IEEE Access}, title = {A review of causal methods for high-dimensional data}, volume = {13}, year = {2025}, }
• (2025). OptiBench: An optical distance measurement test bench using adaptive algorithm with sensor fusion and environmental shielding. ACCESS, 13, 11876-11891. (www)

  @article{ , abstract = {Optical Distance Measurement (ODM) systems have a wide range of applications in many sectors, including industrial, aerospace, and telecommunication. It is essential in systems where accuracy and reliability are critical. Environmental factors such as temperature fluctuations, humidity, and light intensity randomly affect the performance of ODM systems. The uncertainty of these natural factors makes it challenging to maintain the accuracy and reliability of ODM systems. This paper presents an innovative displacement test bench, OptiBench, that combines Adaptive Environmental Compensation (AEnC) algorithms, Multi-Sensor Fusion (MSF) techniques, and dynamic environmental shielding that improves ODM systems’ accuracy, precision, and robustness. The effectiveness of the OptiBench has been evaluated under a wide range of environmental conditions. The experimental result shows that it achieves a Mean Absolute Error (MAE) of 0.10 mm to 0.19 mm and precision values between 0.07 mm and 0.13 mm. The AEnC algorithm reduced temperature-induced errors from 0.25 mm to as low as 0.05 mm, and the MSF technique reduced the measurement variance from a range of 0.18 mm–0.47 mm2 (before fusion) to 0.12 mm–0.30 mm2 (after fusion), demonstrating its effectiveness in enhancing reliability and precision under varying environmental conditions. The Environmental Shielded Optical (ESO) system improved signal strength by up to 9 dB. Overall, the OptiBench system represents a significant advancement in optical distance measurement, providing a reliable, adaptable solution for applications where precision is paramount.}, archive = {J_ACCESS}, author = {Kanchana Chathurangi Ahangama and Hemali Pasqual and Chinthaka Premachandra and H. Waruna Haripriya Premachandra}, doi = {10.1109/ACCESS.2025.3529632}, journal = {IEEE Access}, pages = {11876-11891}, shortjournal = {IEEE Access}, title = {OptiBench: An optical distance measurement test bench using adaptive algorithm with sensor fusion and environmental shielding}, volume = {13}, year = {2025}, }
• (2025). Adaptive kalman filtering: Measurement and process noise covariance estimation using kalman smoothing. ACCESS, 13, 11863-11875. (www)

  @article{ , abstract = {The Kalman filter is one of the best-known and most frequently used methods for dynamic state estimation. In addition to a measurement and state transition model, the Kalman filter requires knowledge about the covariance of the measurement and process noise. However, the noise covariances are mostly unknown and may vary during the application. Adaptive Kalman filters solve this problem by estimating the noise covariances online to improve the state estimation. Existing methods are often limited in their application because they are designed to adapt only the measurement noise or the process noise covariance and tend to diverge when both are unknown. Moreover, most methods provide no or only local convergence results, which implies that a poor initialization can adversely affect the estimation of the noise covariances, leading to a deteriorated state estimation. This paper introduces a novel adaptive Kalman filter based on additional Kalman smoothing and analytically derived covariance estimators. Firstly, the unbiased measurement and process noise covariance estimators are derived from the maximum a posteriori formulation of the Kalman smoother. Then, based on these estimators, which depend on the system formulation and the state estimates of the Kalman smoother, the adaptive Kalman filter algorithm is presented. The convergence of the derived estimators can be shown for time-invariant systems for one-dimensional measurement and process noise. For higher-dimensional problems, the convergence can be tested simulatively for the specific dynamical system. A detailed evaluation of various simulation scenarios is presented, demonstrating the accuracy and robustness of the proposed method.}, archive = {J_ACCESS}, author = {Theresa Kruse and Thomas Griebel and Knut Graichen}, doi = {10.1109/ACCESS.2025.3528348}, journal = {IEEE Access}, pages = {11863-11875}, shortjournal = {IEEE Access}, title = {Adaptive kalman filtering: Measurement and process noise covariance estimation using kalman smoothing}, volume = {13}, year = {2025}, }
• (2025). Integrating human learning factors and bayesian analysis into software reliability growth models for optimal release strategies. ACCESS, 13, 11846-11862. (www)

  @article{ , abstract = {This study presents a Software Reliability Growth Model (SRGM) that incorporates imperfect debugging and employs Bayesian analysis to optimize the timing of software releases. The primary objective is to reduce software testing costs while enhancing the model’s practical applicability. A significant limitation of traditional estimation techniques, such as MLE and LSE, is their challenge in accurately estimating model parameters when historical data is limited. To overcome this issue, the proposed Bayesian approach utilizes prior knowledge from domain experts and integrates available software testing data to predict both the software’s reliability and associated costs. This method facilitates both prior and posterior analyses, making it effective even in scenarios with limited data. The model also considers the efficiency of the debugging process, which can be influenced by factors such as the testing team’s learning curve and human error. By integrating these human elements and the intrinsic characteristics of the debugging process, the model becomes more comprehensive and realistic. This results in parameter estimates that more accurately represent real-world scenarios, making the model more intuitive for experts to apply. Additionally, the study incorporates numerical examples and sensitivity analyses that provide essential insights for management. These examples offer strategic guidance for software release decisions, assisting stakeholders in balancing the trade-offs between testing costs, reliability, and release timing. To further enhance decision-making, a computerized application system is proposed to help determine the optimal software release point. This tool streamlines the process, ensuring a more efficient approach to addressing this critical challenge in software development.}, archive = {J_ACCESS}, author = {Chih-Chiang Fang and Liping Ma and Wenfeng Kuo}, doi = {10.1109/ACCESS.2025.3529775}, journal = {IEEE Access}, pages = {11846-11862}, shortjournal = {IEEE Access}, title = {Integrating human learning factors and bayesian analysis into software reliability growth models for optimal release strategies}, volume = {13}, year = {2025}, }
• (2025). Classification of cigarette types using computer vision: An analysis of smoke aggregation features. ACCESS, 13, 11836-11845. (www)

  @article{ , abstract = {The aggregation of cigarette smoke is one of the key indicators when consumers taste cigarettes. The purpose of this study is to explore the characteristics of smoke images through computer vision technology, and distinguish different brands of cigarettes by using the aggregation characteristics of smoke, hoping to provide preliminary support for automatic cigarette identification. Firstly, we constructed a 3D model experimental platform based on the human upper respiratory tract to create the cigarette smoke image dataset SmogAg. This data set contains sequence image data of smoke movement patterns of 51 types of cigarettes, including thick, medium and thin cigarettes. Based on this data set, we applied the traditional vision algorithm to extract the diffusion width, sedimentation concentration and diffusion distance of smoke, and used the KNN model for training, and initially reached an accuracy of 69.8%. To further improve performance, we have adopted a new network architecture. We first adopted SmokeSeqNet model for experiments, which combined residual CNN as a feature extractor and captured the dynamic sequence characteristics of smoke through the LSTM layer. This method, combined with time series analysis, not only improved the accuracy, but also gave us insight into the time-dependent behavior of the smoke, ultimately achieving an accuracy of 81.6%. In order to further improve the performance, we build SmokeSeqNetV2 model to continue the experiment, which introduces Swin-Transformer feature extraction module and attention mechanism, and the final accuracy rate reaches 88.9%. Through computer vision technology, we began to explore the image characteristics of smoke, hoping to provide a new idea for distinguishing different brands of cigarettes by the aggregation characteristics of smoke.}, archive = {J_ACCESS}, author = {Shishuan Guan and Lei Jiao and Zengyu Wang and Xiaofei Ji and Hongwei Zheng and Cunfeng Yu and Hongtao Li and Liwen Zheng and Shuaishuai Sun and Qiang Sun and Jun Li and Guangwei Jiang and Kezhi Wu and Erge Lin and Xinlong Zhang}, doi = {10.1109/ACCESS.2024.3508748}, journal = {IEEE Access}, pages = {11836-11845}, shortjournal = {IEEE Access}, title = {Classification of cigarette types using computer vision: An analysis of smoke aggregation features}, volume = {13}, year = {2025}, }
• (2025). Optimized application of CGA-SVM in tight reservoir horizontal well production prediction. ACCESS, 13, 11823-11835. (www)

  @article{ , abstract = {Horizontal well production prediction is crucial for the efficient development of tight reservoirs. However, owing to the complexity of the parameters affecting horizontal well production, improving the prediction accuracy has always been the unremitting goal pursued by the oil and gas industry. Limited by the number of parameters, the traditional linear fitting method has low computational efficiency and a large error, which brings difficulties to horizontal well production prediction. In this paper, chaotic genetic algorithm is used to optimize the traditional support vector machine, and the problems of slow convergence and local convergence are solved by chaotic genetic algorithm, and an improved support vector machine horizontal well production prediction method is established. At the same time, on the basis of the previous data processing, the fuzzy set classification method is used to build the model, and the learning model is more close to different types of well production, which enhances the applicability of the model in the field practice. Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. The results show that the improved support vector machine method has high accuracy in the prediction of small sample data, and the method can be widely used in the production prediction of horizontal Wells in tight reservoirs, providing a reference for the efficient development of tight reservoirs.}, archive = {J_ACCESS}, author = {Chao Wang and Ruogu Wang and Yuhan Lin and Jiafei Zhang and Xiaofei Xie and Zidan Zhao and Yunlin Xu}, doi = {10.1109/ACCESS.2025.3527465}, journal = {IEEE Access}, pages = {11823-11835}, shortjournal = {IEEE Access}, title = {Optimized application of CGA-SVM in tight reservoir horizontal well production prediction}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based intrusion detection system for detecting IoT botnet attacks: A review. ACCESS, 13, 11792-11822. (www)

  @article{ , abstract = {The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats.}, archive = {J_ACCESS}, author = {Tamara Al-Shurbaji and Mohammed Anbar and Selvakumar Manickam and Iznan H Hasbullah and Nadia Alfriehat and Basim Ahmad Alabsi and Ahmad Reda Alzighaibi and Hasan Hashim}, doi = {10.1109/ACCESS.2025.3526711}, journal = {IEEE Access}, pages = {11792-11822}, shortjournal = {IEEE Access}, title = {Deep learning-based intrusion detection system for detecting IoT botnet attacks: A review}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based approach for two-phase flow pattern classification using void fraction time series analysis. ACCESS, 13, 11778-11791. (www)

  @article{ , abstract = {Flow regime classification is essential for analyzing and modeling two-phase flows, as it demarcates the flow behavior and influences the selection of appropriate predictive models. Machine learning-based approaches have gained relevance in flow regime classification research in the last few years. However, they are still solidly based on the construction and careful definition of hand-crafted features. Deep learning approaches, on the other hand, can provide more robust and end-to-end solutions. However, they are underexplored and have not evaluated the generalization of the models to other data or acquisition systems. Hence, this work proposes using end-to-end state-of-the-art (SOTA) time-series classification methods (ResNet, LSTM-FCN, and TSTPlus) for two-phase flow patterns (churn, bubbly, and slug). We also present the generalization analysis of the models with cross-dataset experiments, training the model with one dataset and testing it with another dataset collected in another system for two datasets: HZDR (from the Helmholtz-Zentrum Dresden-Rossendorf research laboratory) and TUD (from Technische Universität Dresden). The results demonstrate that the approach chosen here presents superior classification metrics in all cases evaluated, particularly in cross-dataset experiments. With our proposed SOTA methods, all the evaluated metrics (accuracy and F1-Score) consistently surpass 85% in all cases, while the baseline method can decrease the performance under 75%. This demonstrates the relevance of the analysis proposed here for flow regime classification literature and opens up a new set of possibilities for research in this area, aiming at robust solutions that are viable for practical use. Codes are available at https://github.com/ambrosioj/two-phase-time-series-deep-learning.}, archive = {J_ACCESS}, author = {Jefferson Dos Santos Ambrosio and Marco Jose da Silva and André Eugênio Lazzaretti}, doi = {10.1109/ACCESS.2025.3529472}, journal = {IEEE Access}, pages = {11778-11791}, shortjournal = {IEEE Access}, title = {A deep learning-based approach for two-phase flow pattern classification using void fraction time series analysis}, volume = {13}, year = {2025}, }
• (2025). Cooperative identification of prolonged motor movement from EEG for BCI without feedback. ACCESS, 13, 11765-11777. (www)

  @article{ , abstract = {This paper presents a novel approach for recognition of prolonged motor movements from a subject’s electroencephalogram (EEG) using orthogonal functions to model a sequence of sub-gestures. In this approach, an individual’s EEG signals corresponding to physical (or imagery) continuous movement for different gestures are divided into segments associated with their related sub-gestures. Then, a diffusion adaptation approach is introduced to model the interface between the brain neural activity and the corresponding gesture dynamics. In such a formulation, orthogonal Bessel functions are utilized to represent different gestures and used as the target for the adaptation algorithm. This method aims at detecting and evaluating the prolonged motor movements as well as identifying highly complex sub-gestures. This technique can perform satisfactory classification even in the presence of small data sizes while, unlike many regressors, maintaining a low computational cost. The method has been validated using two different publicly available EEG datasets. An inter-subject average validation accuracy of 70% after performing a leave-one-subject-out k-fold cross-validation is obtained for the classification of the smallest dataset when ten sub-gestures are considered.}, archive = {J_ACCESS}, author = {Alicia Falcon-Caro and João Filipe Ferreira and Saeid Sanei}, doi = {10.1109/ACCESS.2025.3528539}, journal = {IEEE Access}, pages = {11765-11777}, shortjournal = {IEEE Access}, title = {Cooperative identification of prolonged motor movement from EEG for BCI without feedback}, volume = {13}, year = {2025}, }
• (2025). A multi-agent approach to modeling task-oriented dialog policy learning. ACCESS, 13, 11754-11764. (www)

  @article{ , abstract = {Dialogue policy is a critical research area in human-computer interaction, vital for guiding dialogue generation and improving controllability and interpretability. Multi-agent dialogue policy learning demonstrates superior learning speed and exploration capabilities, positioning it as a promising approach for developing more effective and adaptive dialogue agents. However, many studies neglect to holistically model collaboration between agents, which limits the effectiveness of policy learning. Therefore, this paper proposes a new multi-agent group collaboration mechanism for dialogue policy learning, named GMPL. Concretely, we employ an Actor-Critic network to implement the proposed model, alternately updating individual dialogue agents to optimize policy selection. In each update, we utilize the maximum action value function to determine the appropriate dialogue action, while the maximum state value function serves to guide the policy learning process. This integrated approach ensures that both decision-making and learning phases are effectively aligned, thereby enhancing the overall performance of the dialogue agents. Furthermore, we conduct a theoretical analysis of the convergence properties of the proposed model. Experiments were conducted on two distinct task-oriented dialogue datasets, revealing that the proposed multi-agent model exhibits a significantly faster learning speed and a higher dialogue success rate compared to baseline approaches.}, archive = {J_ACCESS}, author = {Songfeng Liang and Kai Xu and Zhurong Dong}, doi = {10.1109/ACCESS.2025.3529469}, journal = {IEEE Access}, pages = {11754-11764}, shortjournal = {IEEE Access}, title = {A multi-agent approach to modeling task-oriented dialog policy learning}, volume = {13}, year = {2025}, }
• (2025). Automatic identification of narratives: Evaluation framework, annotation methodology, and dataset creation. ACCESS, 13, 11734-11753. (www)

  @article{ , abstract = {One of the fundamental components of understanding online discourse in social networks is the identification of narratives. For example, the analysis of disinformation campaigns requires some inference about their communication goals that, in turn, requires the identification of the narratives that they promote. The research in this task involves a number of challenges such as the limited availability of labelled datasets, the subjectivity of the annotators and the time cost of annotation. This article present a definition of the Narrative Identification task, proposes an evaluation framework for Narrative Identification, and a methodology for the creation and annotation of Narrative Identification datasets taking into account the subjectivity of the task. Keeping in mind the goal of comparing systems performance, we explore how to reduce the annotation time while maintaining the reliability of the evaluation. Following this methodology, a set of eight tasks for narrative identification in the political domain has been developed in Spanish and English. Finally, we validated the evaluation framework by analysing its application to DIPROMATS 2024 shared task, together with the performance analysis of baseline and participant systems.}, archive = {J_ACCESS}, author = {Jesús M. Fraile-Hernández and Anselmo Peńas and Pablo Moral}, doi = {10.1109/ACCESS.2024.3475579}, journal = {IEEE Access}, pages = {11734-11753}, shortjournal = {IEEE Access}, title = {Automatic identification of narratives: Evaluation framework, annotation methodology, and dataset creation}, volume = {13}, year = {2025}, }
• (2025). SLG-net: Small-large-global feature-based multilevel feature extraction network for ultrasound image segmentation. ACCESS, 13, 11720-11733. (www)

  @article{ , abstract = {Automatic ultrasound image segmentation improves the efficiency of clinical diagnosis and decreases the workload of doctors. Many ultrasound image segmentation methods only focus on capturing local details and global dependencies, whereas ignoring large-scale context information. However, it is essential to extract large-scale context features for large targets in images. To enhance the capability of feature extraction of the model for targets with various sizes and improve segmentation performance, we propose an effective multilevel feature extraction network (SLG-Net) which can extract features from local small details, large-scale context to global dependencies. The SLG-Net is parallel dual-encoder architecture which consists of a CNN encoder and a transformer encoder. Specifically, the CNN encoder improves the representation and interaction of fine feature and large-scale context feature for targets of different sizes by large-small kernel attention (LSKA) modules. The LSKA module firstly extracts features by parallel small kernel module and large-scale feature selection (LSFS) module. The extracted features from above modules are added for further information interaction through a following multi-scale feature interaction module. To fully leverage the feature extraction capability of large kernel convolutions and decrease the number of parameters, we design the large kernel decomposition module (LKDM) to extract large-scale context features in LSFS module. The transformer encoder is used to capture global features for compensating the limitations of CNN encoder. To merge multilevel features, a multi-scale feature fusion module is introduced after the dual-encoder. In addition, at the skip connection, a multi-scale attention module is integrated to retain significant shallow features for subsequent fusion of deep and shallow features. Experiments on three public ultrasound datasets indicate that the proposed network accomplishes the prominent performance for ultrasound image segmentation. It shows the potential of our study to promote intelligence in clinical medicine.}, archive = {J_ACCESS}, author = {Xinya Fan and Jianwen Hu and Kai Hu}, doi = {10.1109/ACCESS.2025.3528380}, journal = {IEEE Access}, pages = {11720-11733}, shortjournal = {IEEE Access}, title = {SLG-net: Small-large-global feature-based multilevel feature extraction network for ultrasound image segmentation}, volume = {13}, year = {2025}, }
• (2025). Modeling and solving multi-objective path planning problem for cooperative cable-suspended load transportation considering the time variable risk. ACCESS, 13, 11704-11719. (www)

  @article{ , abstract = {Cooperative load transportation has the potential to revolutionize the urban logistics industry and can also be utilized in rescue operations following natural disasters. Utilizing cooperative cable-suspended load transportation in such applications involves addressing three problems: static path planning, dynamic path planning, and control. In this article, we aim to tackle the first problem, i.e., static path planning, which is largely separate from the other two tasks. In this regard, we propose a model for the offline path planning of the cooperative cable-suspended load transportation system, including practical constraints. Using the proposed model, we can limit parameters such as the speed and acceleration of UAVs, flight altitude, task completion time, and safety distances between UAVs and obstacles, while minimizing the path length and risk. In addition, we propose an improved hybrid fuzzy particle swarm optimization (PSO) and whale optimization algorithm (WOA) to solve the introduced path planning problem efficiently. Furthermore, a chaotic mutation operator prevents premature convergence in the PSO algorithm. To demonstrate the efficiency of the proposed model and algorithm, three case studies inspired by a fire incident in a forest park have been conducted, and the results have been compared with those of seven other well-known algorithms.}, archive = {J_ACCESS}, author = {Amir Arslan Haghrah and Sehraneh Ghaemi and Mohammad Ali Badamchizadeh}, doi = {10.1109/ACCESS.2025.3529618}, journal = {IEEE Access}, pages = {11704-11719}, shortjournal = {IEEE Access}, title = {Modeling and solving multi-objective path planning problem for cooperative cable-suspended load transportation considering the time variable risk}, volume = {13}, year = {2025}, }
• (2025). Tongue-LiteSAM: A lightweight model for tongue image segmentation with zero-shot. ACCESS, 13, 11689-11703. (www)

  @article{ , abstract = {Objective: Tongue image segmentation is a crucial step in the intelligent recognition of tongue diagnosis in Traditional Chinese Medicine (TCM). Existing deep learning-based tongue image segmentation models face issues such as poor versatility and insufficient expressiveness in zero-shot tasks. This study aims to construct an efficient model with zero-shot suitable for tongue image segmentation in TCM. Methods: We developed the Tongue-LiteSAM model by improving the SAM (Segment Anything Model) framework to suit tongue segmentation. Based on the basic SAM model, the improvement involved modifying the image encoder by integrating two lightweight ViT-Tiny image encoders, effectively reducing the model’s parameter count. Additionally, data perturbation techniques were employed to enhance the zero-shot segmentation capability of the model and ensure robust performance across different data sources. Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. Conclusion: The Tongue-LiteSAM model provides a more objective and consistent solution for tongue diagnosis, and has better zero-shot segmentation capabilities. By optimizing the model structure and data processing strategies, the accuracy and practicality of tongue diagnosis models are effectively improved, offering new technical support for the modernization and precision of TCM tongue diagnosis.}, archive = {J_ACCESS}, author = {Daiqing Tan and Hao Zang and Xinyue Zhang and Han Gao and Ji Wang and Zaijian Wang and Xing Zhai and Huixia Li and Yan Tang and Aiqing Han}, doi = {10.1109/ACCESS.2025.3528658}, journal = {IEEE Access}, pages = {11689-11703}, shortjournal = {IEEE Access}, title = {Tongue-LiteSAM: A lightweight model for tongue image segmentation with zero-shot}, volume = {13}, year = {2025}, }
• (2025). Using peer-learning and game-based instruction for achieving long-lasting knowledge of cybersecurity in primary schools. ACCESS, 13, 11679-11688. (www)

  @article{ , abstract = {As the world becomes increasingly dependent on technology, cybersecurity education has become more important than ever before. However, teaching cybersecurity to primary school students faces several challenges, including limited resources, lack of specialized teachers, and age-appropriate content. In this paper, we propose a new and innovative approach for teaching cybersecurity to primary school students, which addresses these challenges. The proposed approach, implemented with 339 students in five primary schools in North Macedonia, combines peer learning and a game-based approach to develop students’ communication, collaboration, and critical thinking skills, and create engaging learning environments. Upper-grade students deliver lectures and create educational games for their lower-grade peers, fostering learning communities where students share their knowledge and experiences, and learn from one another’s diverse perspectives. The game-based approach is used to create interactive and engaging learning environments where students design or play games to develop their critical thinking skills and empower their cybersecurity knowledge. Initial evaluation results after one year of implementing this cybersecurity learning concept, carried out with 124 students from three participating schools, indicate that students exposed to peer learning and a game-based approach could retain their knowledge, with their knowledge levels outperforming those of students who received traditional classroom instruction. Moreover, students have positive attitudes toward implemented approach and believe that it has effectively enhanced their communication, collaboration, and critical thinking skills while creating exciting and engaging learning environments. These findings suggest that this new and innovative approach has the potential to effectively teach cybersecurity to primary school students, fostering long-lasting knowledge and creating engaging learning environments. Its implementation can be easily adopted in different educational contents and surroundings.}, archive = {J_ACCESS}, author = {Maja Videnovik and Vladimir Trajkovik and Tone Vold and Linda Vibeke Kiønig and Ana Madevska Bogdanova and Sonja Filiposka}, doi = {10.1109/ACCESS.2024.3479921}, journal = {IEEE Access}, pages = {11679-11688}, shortjournal = {IEEE Access}, title = {Using peer-learning and game-based instruction for achieving long-lasting knowledge of cybersecurity in primary schools}, volume = {13}, year = {2025}, }
• (2025). Literature review of machine learning and threat intelligence in cloud security. ACCESS, 13, 11663-11678. (www)

  @article{ , abstract = {Cloud computing has transformed IT services by making them more scalable and cost-effective. However, this shift has also introduced new security challenges that traditional methods are finding hard to tackle. This review paper looks at how combining machine learning (ML) with threat intelligence can improve cloud security — an approach that hasn’t been widely explored yet. By reviewing recent studies, we show that ML and threat intelligence does more than detect known threats. They can also adapt to new and evolving ones, making cloud systems more secure against cyberattacks. Our analysis highlights how this combined approach provides better protection and flexibility. We also identify some important gaps in the current research and suggest areas for future study to make these security systems even more effective. This review aims to provide useful insights for researchers, helping to build more proactive cloud security strategies.}, archive = {J_ACCESS}, author = {Rrezearta Thaqi and Bujar Krasniqi and Artan Mazrekaj and Blerim Rexha}, doi = {10.1109/ACCESS.2025.3529636}, journal = {IEEE Access}, pages = {11663-11678}, shortjournal = {IEEE Access}, title = {Literature review of machine learning and threat intelligence in cloud security}, volume = {13}, year = {2025}, }
• (2025). Student engagement recognition: Comprehensive analysis through EEG and verification by image traits using deep learning techniques. ACCESS, 13, 11639-11662. (www)

  @article{ , abstract = {With the advancement in educational technologies and strategies, hybrid and online teaching methods are gaining significant popularity. Student engagement is an important factor that influences the quality of any teaching and learning process. Tracking student attention can be achieved from facial and body posture analysis. Neurophysiological signals also play a substantial role in measuring students’ academic states in the learning environment. EEG signals are more sensitive to cognitive states, providing constructive perceptions of students’ emotional and cognitive understandings. In this paper, we propose an engagement recognition system that detects student engagement using EEG signals by integrating levels of valence and arousal with the Russel 2D circumplex model using deep learning algorithm. The public DEAP dataset was used for training the model to predict valence and arousal values. Our system achieved an accuracy of 84.75% for detecting valence and arousal values in the four quadrants (HVHA, HVLA, LVHA, LVLA) for the DEAP dataset. For the engagement classification into four categories, ‘highly engaged’, ‘confused’, ‘boredom’, and ‘sleepy’, our system attained an accuracy of 84%. The experimental results of the proposed system were verified by analyzing the image traits of the students using the engagement indicator algorithm. Both systems were further validated through a quiz conducted at the end of the session.}, archive = {J_ACCESS}, author = {Ajitha Sukumaran and Arun Manoharan}, doi = {10.1109/ACCESS.2025.3526187}, journal = {IEEE Access}, pages = {11639-11662}, shortjournal = {IEEE Access}, title = {Student engagement recognition: Comprehensive analysis through EEG and verification by image traits using deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Deep transfer learning for lip reading based on NASNetMobile pretrained model in wild dataset. ACCESS, 13, 11623-11638. (www)

  @article{ , abstract = {Lip reading is extensively used in visual speech recognition to classify and recognize words by analyzing mouth movements in silent videos. Transfer learning models are often employed to improve performance in this area. However, the effectiveness of these models can be significantly affected by the quality of the data, especially when dealing with unstructured wild data. This includes scenarios where subjects are not directly facing the camera, words are not clearly articulated, or image cropping techniques are not used. In this study, we present a novel framework for visual speech recognition utilizing the NASNetMobile architecture. We evaluate the performance of this model on the GLips 2022 wild dataset, which is known for its unstructured and challenging nature. This lightweight model, with its low parameter count, is particularly well-suited for this task. The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. We empirically demonstrate that NASNetMobile outperforms VGG19, DenseNet121, and MobileNetV3Large, which are used in existing models. The performance of NASNetMobile is evaluated using several key metrics. The model contains 4,680,739 parameters. It achieves an accuracy of 0.4840, an F1 score of 0.4850, and a weighted F1 score of 0.4850. Our findings show that NASNetMobile is more effective, increasing accuracy and F1 scores while reducing model complexity.}, archive = {J_ACCESS}, author = {Ashwaq Waleed Abdul Ameer and Pedram Salehpour and Mohammad Asadpour}, doi = {10.1109/ACCESS.2024.3470521}, journal = {IEEE Access}, pages = {11623-11638}, shortjournal = {IEEE Access}, title = {Deep transfer learning for lip reading based on NASNetMobile pretrained model in wild dataset}, volume = {13}, year = {2025}, }
• (2025). Hybrid movie recommendation system with user partitioning and log likelihood content comparison. ACCESS, 13, 11609-11622. (www)

  @article{ , abstract = {fIn the domain of recommendation systems, matrix decomposition is an effective strategy for mitigating issues related to sparsity and low space utilization. The Alternating Least Squares (ALS) method, in particular, stands out for its ability to process data in parallel, thereby enhancing computational efficiency. However, when dealing with an original rating matrix, the ALS method may inadvertently sacrifice some information, leading to increased error rates. To address these challenges, this paper proposes a novel hybrid model that integrates matrix factorization with additional features. Furthermore, it leverages weighted similarity measures and employs advanced log-likelihood text mining techniques. These innovations are designed to tackle cold-start problems and sparsity issues while compensating for information loss to mitigate errors. Under the premise that our model employs consistent evaluation metrics and datasets, comparative analysis against existing models from related literature demonstrates superior performance. Specifically, our model achieves a lower Root Mean Square Error (RMSE) of 0.82 and 0.88, alongside a higher F1 score of 0.94 and 0.92 in two datasets. Our proposed hybrid approach effectively addresses sparsity and mitigates information loss in matrix factorization, as demonstrated by these results.}, archive = {J_ACCESS}, author = {Yongmao Yang and Kampol Woradit and Kenneth Cosh}, doi = {10.1109/ACCESS.2025.3529515}, journal = {IEEE Access}, pages = {11609-11622}, shortjournal = {IEEE Access}, title = {Hybrid movie recommendation system with user partitioning and log likelihood content comparison}, volume = {13}, year = {2025}, }
• (2025). Plant leaf identification using feature fusion of wavelet scattering network and CNN with PCA classifier. ACCESS, 13, 11594-11608. (www)

  @article{ , abstract = {Deep learning models, particularly Convolutional Neural Networks (CNNs), are pivotal in enabling botanists to efficiently identify plant species, which is essential for applications in medicine, agriculture, and the food industry. Unlike traditional machine learning methods that often struggle to capture the intricate features of leaves, CNNs are well-suited for this task. However, their reliance on large datasets and substantial computational resources poses a significant challenge. To overcome these challenges, we present a new approach that combines features from Wavelet Scattering Networks (WSNs) and MobileNetV2. WSNs are particularly effective in capturing texture patterns using fixed filters that do not require a learning process, making them effective even with smaller datasets. Conversely, MobileNetV2 deep layer features complement this by capturing more complex, high-level features like shapes and edges, which are essential for distinguishing between different plant species. The extracted features are classified using a PCA-based classifier, which reduces redundancy and enhances accuracy. We tested our approach on the Flavia and Folio datasets, achieving impressive accuracies of 98.75% and 98.7%, respectively. Additionally, we used the Cope dataset to assess the scalability of our model across different classes and the UK Leaf dataset to evaluate its performance under varying background and noise conditions. This approach delivers good accuracy while minimizing computational demands, providing a practical and efficient solution for automated leaf classification, particularly in resource-constrained environments.}, archive = {J_ACCESS}, author = {S. Gowthaman and Abhishek Das}, doi = {10.1109/ACCESS.2025.3528992}, journal = {IEEE Access}, pages = {11594-11608}, shortjournal = {IEEE Access}, title = {Plant leaf identification using feature fusion of wavelet scattering network and CNN with PCA classifier}, volume = {13}, year = {2025}, }
• (2025). A comprehensive analysis of cryptographic algorithms: Evaluating security, efficiency, and future challenges. ACCESS, 13, 11576-11593. (www)

  @article{ , abstract = {Cryptographic techniques are reviewed in this literature review, with particular attention paid to their applicability, importance, contributions, and field strengths. These algorithms include DES, 3DES, AES, blowfish, two-fish, RC4, one-time pad, RSA, ECC, Diffie-Hellman, MD5, and SHA-256. To analyze the efficiency of these algorithms, a comparative study was conducted using crucial factors, including security, secrecy, and integrity. The assessment also examines known flaws and prospective attacks for each algorithm and highlights current methods and future research gaps. This study provides valuable information on the advantages and disadvantages of different algorithms using a detailed diagram that summarizes the performance outcomes based on secrecy, integrity, and security. In addition, each algorithm’s issues are covered in the review, giving readers a clear grasp of their limitations and possible areas for future research.}, archive = {J_ACCESS}, author = {Donagani Ramakrishna and Mohammed Ali Shaik}, doi = {10.1109/ACCESS.2024.3518533}, journal = {IEEE Access}, pages = {11576-11593}, shortjournal = {IEEE Access}, title = {A comprehensive analysis of cryptographic algorithms: Evaluating security, efficiency, and future challenges}, volume = {13}, year = {2025}, }
• (2025). A cloud-based optimized ensemble model for risk prediction of diabetic Progression—An azure machine learning perspective. ACCESS, 13, 11560-11575. (www)

  @article{ , abstract = {The application of Machine Learning for predictive analysis in healthcare, particularly for diseases like diabetes, has proven highly beneficial. This study introduces an optimized Light Gradient-Boosting Machine (Light GBM) and K-Nearest Neighbour (KNN) based ensemble algorithm for predicting diabetic progression of Type 2 Diabetes, classifying it as high or low risk, using patient health parameters and serum measurements. Our model uses LightGBM, a rapid and efficient gradient boosting framework, coupled with KNN, which uses proximity to classify data points. The proposed model uses various optimization techniques, such as 10 fold cross validation, grid search method etc. to get the best results out of the ensemble model. As the model combines optimized version of LightGBM and KNN through a voting classifier which uses soft voting technique to find the final class, it utilizes the predictive capabilities of both the methods in an effective manner. The experiment is performed and implemented in Microsoft’s Azure cloud, using Azure Machine Learning service, that leverages the advantages of cloud computing with respect to scalability, security and its potential integration possibilities into IoT-based smart healthcare systems.This aspect highlights its versatility and impact with respect to remote monitoring of patients as well. The ensemble achieves an 83.2% Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) score, indicating good classification efficiency. It produced 75% accuracy as well. The proposed model is compared with other classification and ensemble models, showcasing its superiority against other models.The ensemble is also tested with some meta heuristic optimization methods, which produced comparable scores. The method’s effectiveness is validated against another risk prediction dataset, proving its reliability. The model’s accurate predictions can aid individuals in understanding disease progression risks and guide medical professionals in intervention strategies.}, archive = {J_ACCESS}, author = {V. K. Daliya and T. K. Ramesh}, doi = {10.1109/ACCESS.2025.3528033}, journal = {IEEE Access}, pages = {11560-11575}, shortjournal = {IEEE Access}, title = {A cloud-based optimized ensemble model for risk prediction of diabetic Progression—An azure machine learning perspective}, volume = {13}, year = {2025}, }
• (2025). Oculomotor plant mathematical model in kalman filter form with peak velocity-based neural pulse for continuous gaze prediction. ACCESS, 13, 11544-11559. (www)

  @article{ , abstract = {An oculomotor plant mathematical model (OPMM) employs physical and neurological characteristics of human visual system to define its dynamics. One of its most prominent applications in modern eye-tracking pipelines was hypothesized to be latency reduction via the means of eye movement prediction. However, this use case was only explored with OPMMs originally designed for saccade simulation. Such models typically relied on the neural pulse control being estimated from intended saccade amplitude - a property that becomes fully observed only after a saccade already ended, which greatly limits the model’s prediction capabilities. We present the first OPMM designed with the prediction task in mind. We draw our inspiration from a “peak velocity - amplitude” main sequence relationship and propose to use saccade’s peak velocity for neural pulse estimation. We additionally extend the prior work by evaluating the proposed model on the largest to date pool of 322 subjects against the naive zero displacement baseline and a long short-term memory (LSTM) neural network.}, archive = {J_ACCESS}, author = {Dmytro Katrychuk and Dillon J. Lohr and Oleg V. Komogortsev}, doi = {10.1109/ACCESS.2025.3528104}, journal = {IEEE Access}, pages = {11544-11559}, shortjournal = {IEEE Access}, title = {Oculomotor plant mathematical model in kalman filter form with peak velocity-based neural pulse for continuous gaze prediction}, volume = {13}, year = {2025}, }
• (2025). Exploring urban crime through the lens of an adaptive region partitioning technique and built environment features. ACCESS, 13, 11532-11543. (www)

  @article{ , abstract = {In the realm of urban studies, the choice of regional partitioning schemes can significantly influence analytical outcomes, thereby affecting the precision and dependability of research findings. This paper proposes a new region partitioning method with multi-density distribution, namely the Adaptive Region Partitioning (ARP) method, and explores the impact of built environment features under different region partitioning methods on crime analysis. Compared with the traditional partitioning method, the research unit under the ARP method exhibits improved intra-regional homogeneity and inter-regional heterogeneity characteristics, more accurately maps the spatial distribution characteristics of the urban data, effectively mitigates the risk of multicollinearity, improves the accuracy of the regression model, and provides a new and reliable regional partitioning scheme for crime research. Finally, by applying the SHAP (SHapley Additive exPlanations) method, this study further reveals the extent to which different built environment features influence crime, providing important data support for urban planning and the formulation of crime prevention strategies.}, archive = {J_ACCESS}, author = {Yuchen Yan and Hua Wang and Wei Quan and Yuxin Wang}, doi = {10.1109/ACCESS.2025.3525660}, journal = {IEEE Access}, pages = {11532-11543}, shortjournal = {IEEE Access}, title = {Exploring urban crime through the lens of an adaptive region partitioning technique and built environment features}, volume = {13}, year = {2025}, }
• (2025). Virtual inertia support for renewable energy integration: A review. ACCESS, 13, 11517-11531. (www)

  @article{ , abstract = {Inertia problems in power networks with significant RES penetration are the primary focus of this review. An increasing number of distributed generation (DG) units that are based on renewable energy resources are being integrated into the power system. At the same time, the power system is experiencing low inertia and damping, which results in significant problems with the power system’s stability and performance. The decoupling of applications from sources, facilitated by power electronic inverters, leads to a decrease in the inertia of power systems The frequency stability of the system is significantly impacted by the quick changes in frequency and fluctuations in frequency that occur due to power imbalances.This study reviews various modelling and control algorithms and provides extensive parameter descriptions to fill the gap. This study serves as a reference for future research on VSG models and control algorithms in microgrid applications.}, archive = {J_ACCESS}, author = {M. Chethan and K. Ravi}, doi = {10.1109/ACCESS.2024.3416694}, journal = {IEEE Access}, pages = {11517-11531}, shortjournal = {IEEE Access}, title = {Virtual inertia support for renewable energy integration: A review}, volume = {13}, year = {2025}, }
• (2025). Emotion-aware ensemble learning (EAEL): Revolutionizing mental health diagnosis of corporate professionals via intelligent integration of multi-modal data sources and ensemble techniques. ACCESS, 13, 11494-11516. (www)

  @article{ , abstract = {In this contemporary landscape of corporate environments, the increasing prevalence of mental health challenges necessitates the development of innovative diagnostic methodologies. This research introduces the Emotion-Aware Ensemble Learning (EAEL) framework, a cutting-edge approach designed to revolutionize early mental health diagnosis among corporate professionals. EAEL integrates machine learning and deep learning paradigms to process multimodal data, including facial expression analysis and typing pattern recognition, offering a holistic evaluation of emotional well-being. Our investigation methodically trains base classifiers, such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Random Forests (RF), on distinct and combined datasets derived from facial expressions and typing patterns. The EAEL framework demonstrates robust performance, achieving an accuracy of 0.95, precision of 0.96, recall of 0.94, and F1-Score of 0.95 when applied to the integrated dataset. These findings underscore EAEL’s transformative potential as a proactive tool for mental health interventions in corporate settings. Future iterations could enhance the framework by incorporating physiological signals, such as heart rate variability and EEG data, further improving diagnostic accuracy. EAEL’s ability to seamlessly integrate diverse data modalities not only sets a new standard for technology-driven mental health assessments but also promises substantial benefits for employee welfare and organizational effectiveness, with the potential for adaptation in clinical environments as well.}, archive = {J_ACCESS}, author = {Gaurav Yadav and Mohammad Ubaidullah Bokhari and Saleh I. Alzahrani and Shadab Alam and Mohammed Shuaib}, doi = {10.1109/ACCESS.2025.3529032}, journal = {IEEE Access}, pages = {11494-11516}, shortjournal = {IEEE Access}, title = {Emotion-aware ensemble learning (EAEL): Revolutionizing mental health diagnosis of corporate professionals via intelligent integration of multi-modal data sources and ensemble techniques}, volume = {13}, year = {2025}, }
• (2025). Adaptive high-performance optimization tool for real-time operation of renewable-based virtual power plants. ACCESS, 13, 11479-11493. (www)

  @article{ , abstract = {This paper presents an adaptive high-performance optimization tool for the real-time operation of Renewable-based Virtual Power Plants (RVPPs). The increasing integration of Renewable Energy Sources (RESs) into power systems introduces challenges due to their intermittent nature. Aggregating RES units into RVPPs creates a more controllable and competitive entity for energy market participation. However, real-time operation, particularly for ancillary services like Frequency Containment Reserve and automatic Frequency Restoration Reserve, remains challenging since the offered delivery of such ancillary services needs to be guaranteed at all times. In this paper, the proposed Adaptive High-performance Optimal Real-time operation Algorithm (AHORA) addresses these challenges by executing re-dispatches every four seconds, mitigating internal and external disturbances while ensuring compliance with System Operator requirements. The framework supports both event-driven and periodic activation strategies, enabling dynamic adaptation to system changes. Real-time implementation of the operation framework is carried out using OPAL-RT real-time simulator, verifying the RVPP’s ability to meet the demands of real-time applications effectively. Test results demonstrate that AHORA achieves a minimum of 85% of the required regulation in worst case disturbances and maintains service provision within a stringent 4-second window. The findings demonstrate AHORA’s practical applicability in enhancing the reliability and efficiency of renewable energy integration into modern power grids, providing a robust solution for managing the complexities of RVPP real-time operation.}, archive = {J_ACCESS}, author = {Oluwaseun Oladimeji and Álvaro Ortega and Lukas Sigrist and Bogdan Marinescu and Vinu Thomas}, doi = {10.1109/ACCESS.2025.3528123}, journal = {IEEE Access}, pages = {11479-11493}, shortjournal = {IEEE Access}, title = {Adaptive high-performance optimization tool for real-time operation of renewable-based virtual power plants}, volume = {13}, year = {2025}, }
• (2025). Integrating multilayer perceptron and support vector regression for enhanced state of health estimation in lithium-ion batteries. ACCESS, 13, 11463-11478. (www)

  @article{ , abstract = {Accurately evaluating the State of Health (SOH) of batteries is crucial for guaranteeing the secure and dependable functioning of Electric Vehicles (EVs). This paper presents a novel strategy for tackling the difficulties associated with intricate preprocessing and the demand for extensive data in conventional approaches to SOH measurement. Using sophisticated machine learning algorithms, we suggest an all-encompassing methodology for predicting the SOH. Our approach includes meticulous data preparation, which includes analyzing crucial operating elements such as voltage, current, and temperature. We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. The models were assessed using evaluation metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared $R^{2}$ . In order to improve the accuracy of our predictions, we combined these models into a stacked ensemble using a Random Forest (RF) meta-model. This resulted in an $R^{2}$ value of 0.987, MAE of 0.02559, MSE of 0.0013, and RMSE of 0.00624. The results indicate that the ensemble outperforms individual models in predicting SOH. This research highlights the capacity of ensemble learning in predictive maintenance and battery management.}, archive = {J_ACCESS}, author = {Sadiqa Jafari and Jisoo Kim and Wonil Choi and Yung-Cheol Byun}, doi = {10.1109/ACCESS.2024.3497656}, journal = {IEEE Access}, pages = {11463-11478}, shortjournal = {IEEE Access}, title = {Integrating multilayer perceptron and support vector regression for enhanced state of health estimation in lithium-ion batteries}, volume = {13}, year = {2025}, }
• (2025). VSRDiff: Learning inter-frame temporal coherence in diffusion model for video super-resolution. ACCESS, 13, 11447-11462. (www)

  @article{ , abstract = {Video Super-Resolution (VSR) aims to reconstruct high-quality high-resolution (HR) videos from low-resolution (LR) inputs. Recent studies have explored diffusion models (DMs) for VSR by exploiting their generative priors to produce realistic details. However, the inherent randomness of diffusion models presents significant challenges for controlling content. In particular, current DM-based VSR methods often neglect inter-frame temporal coherence and reconstruction-oriented objectives, leading to visual distortion and temporal inconsistency. In this paper, we introduce VSRDiff, a DM-based framework for VSR that emphasizes inter-frame temporal coherence and adopts a novel reconstruction perspective. Specifically, the Inter-Frame Aggregation Guidance (IFAG) module is developed to learn contextual inter-frame aggregation guidance, alleviating visual distortion caused by the randomness of diffusion models. Furthermore, the Progressive Reconstruction Sampling (PRS) approach is employed to generate reconstruction-oriented latents, balancing fidelity and detail richness. Additionally, temporal consistency is enhanced through second-order bidirectional latent propagation using the Flow-guided Latent Correction (FLC) module. Extensive experiments on the REDS4 and Vid4 datasets demonstrate that VSRDiff achieves highly competitive VSR performance with more realistic details, surpassing existing state-of-the-art methods in both visual fidelity and temporal consistency. Specifically, VSRDiff achieves the best scores on the REDS4 dataset in LPIPS, DISTS, and NIQE, with values of 0.1137, 0.0445, and 2.970, respectively. The result will be released at https://github.com/aigcvsr/VSRDiff.}, archive = {J_ACCESS}, author = {Linlin Liu and Lele Niu and Jun Tang and Yong Ding}, doi = {10.1109/ACCESS.2025.3529758}, journal = {IEEE Access}, pages = {11447-11462}, shortjournal = {IEEE Access}, title = {VSRDiff: Learning inter-frame temporal coherence in diffusion model for video super-resolution}, volume = {13}, year = {2025}, }
• (2025). MSCPNet: A multi-scale convolutional pooling network for maize disease classification. ACCESS, 13, 11423-11446. (www)

  @article{ , abstract = {Maize (Zea mays) is a critical crop for global food security and economic stability. However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. These models also suffer from high computational complexity when deeper layers are introduced to handle fine-grained details. Transformer-based models, on the other hand, provide long-range dependencies but come with significant computational overhead, limiting their use in real-time agricultural applications. To overcome these challenges, we propose MSCPNet, a novel architecture that combines a truncated MobileNetV2 backbone with a Multi-Scale Convolutional PoolFormer block. The truncated backbone ensures that only essential layers for general feature extraction are retained, enhancing the model’s adaptability across domains. The Multi-Scale Convolutional PoolFormer block captures both local and global dependencies through parallel convolutional branches of varying kernel sizes, while the PoolFormer module efficiently handles feature aggregation without the heavy computational cost associated with traditional attention mechanisms. This design allows the model to balance computational efficiency and high accuracy, making it highly suitable for real-time maize disease detection. Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. Furthermore, the model was evaluated on the PlantVillage dataset for tomato leaf disease classification, where it achieved an accuracy of 99.32%, precision of 99.32%, recall of 99.33%, F1-score of 99.32%, and an MCC of 0.9925. These results demonstrate the effectiveness and efficiency of MSCPNet in disease classification across different domains.}, archive = {J_ACCESS}, author = {Mehdhar S. A. M. Al-Gaashani and Reem Alkanhel and Muthana Ali Salem Ali and Mohammed Saleh Ali Muthanna and Ahmed Aziz and Ammar Muthanna}, doi = {10.1109/ACCESS.2024.3524729}, journal = {IEEE Access}, pages = {11423-11446}, shortjournal = {IEEE Access}, title = {MSCPNet: A multi-scale convolutional pooling network for maize disease classification}, volume = {13}, year = {2025}, }
• (2025). Formal verification of ERC-based smart contracts: A systematic literature review. ACCESS, 13, 11396-11422. (www)

  @article{ , abstract = {Defined as an agreement between multiple parties and systematically executed by a computer code, smart contracts enable trust-less execution without a third party. Despite the trusted implementations that smart contracts offer, including those based on standards, different security problems and vulnerabilities arise during their development and execution. To address these issues, multiple studies have proposed potential solutions, focusing particularly on the verification of smart contracts and considering the standard-based ones using formal verification techniques. However, the sheer amount of research makes it difficult to accurately articulate the state-of-the-art. To tackle this challenge, we propose a systematic literature review that deals with formal verification of ERC-based smart contracts. ERC (Ethereum Request for Comments) standards enable a range of functionalities, such as the creation and management of tokens. Thus, our review provides an overview of ERC standards and examines their related potential issues. Furthermore, we investigate existing solutions presented in 19 relevant studies published between 2019 and July 2023. We analyze and classify approaches to formal modeling, properties’ specification and techniques used in the verification of smart contracts. Finally, we discuss the research challenges and suggest some promising future directions to stir research efforts into this area.}, archive = {J_ACCESS}, author = {Rim Ben Fekih and Mariam Lahami and Salma Bradai and Mohamed Jmaiel}, doi = {10.1109/ACCESS.2025.3527158}, journal = {IEEE Access}, pages = {11396-11422}, shortjournal = {IEEE Access}, title = {Formal verification of ERC-based smart contracts: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Fuzzy logic recommender model for housing. ACCESS, 13, 11380-11395. (www)

  @article{ , abstract = {Recommending suitable housing implies significant challenges owing to the continuous increase in demand and the need to meet habitability standards. This document presents an innovative approach with which to address these challenges through the use of a housing recommendation method based on distances to key spatial points and the latent characteristics of the properties. The proposed method employs objective distances from the properties to points of interest, such as educational centers, medical centers, pharmacies, shops, entertainment, 911 security cameras and public transport stations. These distances are calculated on the basis of the area in which the property is located, thus providing an accurate assessment of the environment. Moreover, housing features are grouped into three correlated latent factors: Size and Value, Environment and Comfort, and Age and Safety. The recommendation system relies on fuzzy control to manage user preferences and select appropriate input data with which to test the model. A content-based filtering approach is used initially, as housing ratings are unavailable. The model predicts a percentage of membership in each cluster, which makes it possible to handle uncertainty by offering properties from different groups in a proportional manner. Euclidean distance is employed in order to measure the similarity between user preferences and housing characteristics, after which the search time is optimized by utilizing metaheuristic methods, of which the bat algorithm provides the best performance in terms of time. This algorithm selects the properties displayed to the user on the basis of natural features extracted from real estate platforms by means of web scraping techniques. The system is built with a Model-View-Controller architecture using Python, Flask, and SQLite. Personal customer data is also recorded in order to create clusters and calculate distances for new customers, thus allowing properties with high ratings to be recommended. This approach combines collaborative and content-based filtering, creating a hybrid system that improves recommendation accuracy and relevance. This analysis shows that the new recommendation method is an effective and accessible solution with which to select suitable housing.}, archive = {J_ACCESS}, author = {Emanuel G. Muñoz and Jaime Meza and Sebastian Ventura}, doi = {10.1109/ACCESS.2025.3527924}, journal = {IEEE Access}, pages = {11380-11395}, shortjournal = {IEEE Access}, title = {Fuzzy logic recommender model for housing}, volume = {13}, year = {2025}, }
• (2025). Political bias in large language models: A comparative analysis of ChatGPT-4, perplexity, google gemini, and claude. ACCESS, 13, 11341-11379. (www)

  @article{ , abstract = {Artificial Intelligence large language models have rapidly gained widespread adoption, sparking discussions on their societal and political impact, especially for political bias and its far-reaching consequences on society and citizens. This study explores the political bias in large language models by conducting a comparative analysis across four popular AI models—ChatGPT-4, Perplexity, Google Gemini, and Claude. This research systematically evaluates their responses to politically charged prompts and questions from the Pew Research Center’s Political Typology Quiz, Political Compass Quiz, and ISideWith Quiz. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances based on their training data sets. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate any of these biases. The most significant question surrounding political bias in AI is its consequences, particularly its influence on public discourse, policy-making, and democratic processes. The results of this study advocate for ethical implications for the development of AI models and the need for transparency to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex AI bias issue.}, archive = {J_ACCESS}, author = {Tavishi Choudhary}, doi = {10.1109/ACCESS.2024.3523764}, journal = {IEEE Access}, pages = {11341-11379}, shortjournal = {IEEE Access}, title = {Political bias in large language models: A comparative analysis of ChatGPT-4, perplexity, google gemini, and claude}, volume = {13}, year = {2025}, }
• (2025). In-depth partial discharge inception voltage analysis in laminated busbar by means of experimentations and simulations. ACCESS, 13, 11332-11340. (www)

  @article{ , abstract = {Laminated Busbars (LBBs) have emerged as the most effective solution for high power electronic devices, offering low parasitic inductance and remarkable current capabilities. However, the design of LBB presents challenges related to criteria such as the busbar’s parasitic inductance and Partial Discharge Inception Voltage (PDIV). As power converter devices evolve, the balance between these two factors becomes increasingly complex, as increasing PDIV through thicker insulation alone also leads to higher parasitic inductance. This paper aims to address this issue by introducing a comprehensive model that correlates the PDIV of the LBBs with various parameters, including geometric aspects and dielectric material properties. A probabilistic approach is also used in order to study the Partial Discharge (PD) occurrence. The model is incorporated into an efficient software package, empowering LBB manufacturers to achieve more streamlined designs that meet PDIV requirements while adhering to other constraints. Moreover, practical tests have been carried out to validate the model with varying temperatures and pressures. It was found that the experimental PDIV values for the LBB samples drop by 19% between 30°C and 200°C at ground level and by 51.4% between 1 bar and 0.145 bar at 30°C. It was also found that the model based on Paschen’s criteria is capable of accurately predicting PDIV in LBB samples as a function of both temperature as well as pressure. It was only off by around 5.9%.}, archive = {J_ACCESS}, author = {Oussama Abdel Illah Rahmouni and Sonia Ait-Amar and Stephane Duchesne and Jean-François Wecxsteen}, doi = {10.1109/ACCESS.2024.3413964}, journal = {IEEE Access}, pages = {11332-11340}, shortjournal = {IEEE Access}, title = {In-depth partial discharge inception voltage analysis in laminated busbar by means of experimentations and simulations}, volume = {13}, year = {2025}, }
• (2025). A fusion method incorporating dual-attention mechanism and transfer learning into UNet++ for remote sensing image coastline extraction. ACCESS, 13, 11320-11331. (www)

  @article{ , abstract = {The segmentation of land and sea in remote sensing imagery is of great significance for coastline extraction and dynamic monitoring. Traditional coastline recognition and extraction methods based on spectral features and image processing can only generate limited image feature results when facing the complex textures and spatial distributions of high-spatial resolution remote sensing images, leading to low accuracy in segmentation outcomes. This paper applies a deep convolutional neural network to the problem of sea-land segmentation in high-spatial resolution remote sensing images and innovates upon the classic encoder-decoder architecture. Firstly, to enhance the network’s ability to distinguish coastlines, a dual attention mechanism is introduced into the UNet++ network to improve the learning capacity of coastline features while suppressing the learning of non-coastline features. Secondly, an improved joint loss function is adopted to enhance training effectiveness, thereby significantly improving the accuracy of semantic segmentation. Lastly, transfer learning is utilized to strengthen the detailed features of coastlines and enhance the network’s ability to identify them. Experimental results using the GID dataset for coastline segmentation demonstrate that compared to the latest algorithms such as PSPNet, CS-Deeplab v3+, and UNet++, the improved UNet++ network achieves lower boundary blurriness and more accurate segmentation results for coastlines, with fewer missed and false detections. Amidst the proliferation of high-spatial resolution remote sensing image data, the utilization of the enhanced UNet++ model for coastline extraction has demonstrated remarkable abilities in preserving boundary information and achieving superior semantic segmentation performance. This advancement enables a more refined extraction of spatial distributions, textures, and spectral features from these images, ultimately contributing to an improvement in classification accuracy.}, archive = {J_ACCESS}, author = {Yanru Song and Bai Xue and Yueyue Meng and Xiang Qin and Yixiao Li and Qi Liu}, doi = {10.1109/ACCESS.2024.3467998}, journal = {IEEE Access}, pages = {11320-11331}, shortjournal = {IEEE Access}, title = {A fusion method incorporating dual-attention mechanism and transfer learning into UNet++ for remote sensing image coastline extraction}, volume = {13}, year = {2025}, }
• (2025). CUSE-TD algorithm for precision detection of coherent and uncorrelated signals. ACCESS, 13, 11307-11319. (www)

  @article{ , abstract = {This paper presents a novel algorithm, Coherent and Uncorrelated Sources Estimation using Tensor Decomposition (CUSE-TD), designed to enhance the accuracy of Direction of Arrival (DOA) estimation in Multiple-Input Multiple-Output (MIMO) systems. The CUSE-TD algorithm addresses the complex challenge of estimating both coherent and uncorrelated sources by leveraging advanced sensor-driven tensor decomposition techniques, with an emphasis on Tucker decomposition. By meticulously analyzing factor matrices derived from sensor data, the algorithm effectively decodes intricate spatiotemporal patterns within the received tensor, offering precise source estimation even in scenarios where the number and nature of the sources are unknown. The paper provides an in-depth assessment of the algorithm’s performance, highlighting its computational efficiency and ability to handle diverse sensor-driven scenarios. Extensive simulations in intricate sensing environments demonstrate the algorithm’s robustness, affirming its potential to significantly improve DOA estimation accuracy in MIMO systems across a wide range of operational conditions. The usefulness of the CUSE-TD algorithm lies in its applicability CUSE -TD to real-world MIMO systems, particularly in complex environments such as radar, wireless communications, and surveillance systems, where accurate source detection is critical for operational effectiveness. Its adaptability to varying sensor configurations and source conditions makes it a powerful tool for enhancing overall system reliability and performance.}, archive = {J_ACCESS}, author = {Rohini Dakulagi and Ravi Raushan and Soham Dutta and Mohammed Theeb Alosaimi and Miguel Villagomez-Galindo}, doi = {10.1109/ACCESS.2024.3525360}, journal = {IEEE Access}, pages = {11307-11319}, shortjournal = {IEEE Access}, title = {CUSE-TD algorithm for precision detection of coherent and uncorrelated signals}, volume = {13}, year = {2025}, }
• (2025). Model predictive stator flux control of permanent magnet synchronous motor based on vector duty ratio modulation. ACCESS, 13, 11295-11306. (www)

  @article{ , abstract = {To address the issues of extensive computation and laborious weight coefficient selection in model predictive torque control (MPTC) for permanent magnet synchronous motors (PMSM), an optimized model predictive flux control (MPFC) is proposed based on three-phase voltage vector duty ratio modulation. First, we established a prediction model of PMSM based on stator flux linkage, constructed a prediction error cost function of stator flux linkage without weight coefficient. Meanwhile, the duty ratio of neighboring voltage vectors in each sector and the stator flux reference value were computed using the stator flux deadbeat control concept, and the stator flux reference value is computed. Finally, we proposed an optimal stator flux predictive control method based on three-phase voltage vector duty ratio modulation. Its effectiveness and reliability were verified by simulation and experiment. This method had excellent steady-state performance in the voltage vector output range, requires fewer predictions than the classic model predictive torque control method, and greatly reduces both the stator flux and the electromagnetic torque ripple.}, archive = {J_ACCESS}, author = {Jing He and Zongyu Li and Gongping Wu and Runzhong Tang}, doi = {10.1109/ACCESS.2024.3433508}, journal = {IEEE Access}, pages = {11295-11306}, shortjournal = {IEEE Access}, title = {Model predictive stator flux control of permanent magnet synchronous motor based on vector duty ratio modulation}, volume = {13}, year = {2025}, }
• (2025). Boosting cyberattack detection using binary metaheuristics with deep learning on cyber-physical system environment. ACCESS, 13, 11280-11294. (www)

  @article{ , abstract = {The swift advancement of cyber-physical systems (CPSs) across sectors such as healthcare, transportation, critical infrastructure, and energy enhances the crucial requirement for robust cybersecurity measures to protect these systems from cyberattacks. The cyber-physical method is a hybrid of cyber and physical components, and a safety breach in the element is central to catastrophic consequences. Cyberattack recognition and mitigation techniques in CPSs include using numerous models like intrusion detection systems (IDSs), access control mechanisms, encryption, and firewalls. Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. By leveraging the integral ability of DL methods to learn complex representations, this technique enhances the accuracy and efficiency of detecting diverse and growing cyber-attacks. Thus, the study proposes an automated Cyberattack Detection using Binary Metaheuristics with Deep Learning (ACAD-BMDL) method in a CPS environment. The ACAD-BMDL method mainly focuses on enhancing security in the CPS environment via the cyberattack detection process. The ACAD-BMDL method uses Z-score normalization to scale the input dataset. In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. Moreover, the Enhanced Elman Spike Neural Network (EESNN) model detects cyber-attacks. Furthermore, the Archimedes Optimization Algorithm (AOA) model is employed to select the optimum hyperparameter for the EESNN model. The empirical analysis of the ACAD-BMDL technique is performed on a benchmark dataset. The experimental validation of the ACAD-BMDL technique portrayed a superior accuracy value of 99.12% and 99.36% under NSLKDD2015 and CICIDS2017 datasets in the CPS environment.}, archive = {J_ACCESS}, author = {Alanoud Al Mazroa and Fahad R. Albogamy and Mohamad Khairi Ishak and Samih M. Mostafa}, doi = {10.1109/ACCESS.2025.3526258}, journal = {IEEE Access}, pages = {11280-11294}, shortjournal = {IEEE Access}, title = {Boosting cyberattack detection using binary metaheuristics with deep learning on cyber-physical system environment}, volume = {13}, year = {2025}, }
• (2025). FBATCNet: A temporal convolutional network with frequency band attention for decoding motor imagery EEG. ACCESS, 13, 11265-11279. (www)

  @article{ , abstract = {Motor imagery-based brain-computer interfaces (MI-BCIs) hold significant promise for upper limb rehabilitation in stroke patients. However, traditional MI paradigm primarily involves various limbs and fails to effectively address unilateral upper limb rehabilitation needs. In addition, compared to decoding MI-EEG signals from different limbs, decoding MI-EEG signals from same limb faces more challenges. We introduced a novel tri-class fine motor imagery (FMI) paradigm and collected electroencephalogram (EEG) data from 20 healthy subjects for decoding research. Furthermore, we proposed a frequency band attention-based temporal convolutional network (FBATCNet) for MI-EEG decoding. First, an innovative use of the channel attention mechanism adaptively assigned weights to segmented EEG frequency bands, improving the frequency resolution of MI-EEG signals. Subsequently, convolutional block further integrated frequency-domain features and extracted spatial features. Finally, a temporal convolution block was utilized to capture advanced temporal features. The proposed model achieved accuracy of 84.73% on BCI Competition IV-2a (Dataset 1) and 66.06% on the private FMI dataset (Dataset 2). In the classification of subject dependent, the FBATCNet is better than the baseline methods mentioned in this paper. These results confirm that the FBATCNet is feasible and offer fresh insights for designing and applying FMI-BCI.}, archive = {J_ACCESS}, author = {Shuaishuai Ma and Jidong Lv and Wenjie Li and Yan Liu and Ling Zou and Yakang Dai}, doi = {10.1109/ACCESS.2025.3525528}, journal = {IEEE Access}, pages = {11265-11279}, shortjournal = {IEEE Access}, title = {FBATCNet: A temporal convolutional network with frequency band attention for decoding motor imagery EEG}, volume = {13}, year = {2025}, }
• (2025). Transferability evaluation in wi-fi intrusion detection systems through machine learning and deep learning approaches. ACCESS, 13, 11248-11264. (www)

  @article{ , abstract = {Intrusion Detection System (IDS) plays a pivotal role in safeguarding network security. The efficacy of these systems is rigorously assessed through established metrics including precision, recall, F1 score, and AUC score. When subjected to rigorous testing on well-known datasets like AWID and AWID3, individual IDS models consistently deliver exceptional performances, boasting F1 scores ranging from 0.98 to 1 and AUC scores spanning 0.97 to 0.99. However, the true challenge surfaces when the objective is to extend the transferability of these high-performing models to entirely novel, unseen datasets. This endeavor unravels a diverse performance landscape, demonstrating that the outstanding performance observed on a particular dataset doesn’t guarantee the transferability of features across dissimilar datasets nestled within different network environments. In order to evaluate the feature transferability, we turn to AWID and AWID3 datasets as the main distinction between AWID (potentially referring to AWID2) and AWID3 lies in their specific focuses and contexts within the field of Wi-Fi intrusion detection. Although both datasets are centered on the general goal of detecting Wi-Fi intrusions, AWID3 has been carefully designed to meet the specific needs of corporate Wi-Fi applications. A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.}, archive = {J_ACCESS}, author = {Saud Yonbawi and Adil Afzal and Muhammad Yasir and Muhammad Rizwan and Natalia Kryvinska}, doi = {10.1109/ACCESS.2025.3528214}, journal = {IEEE Access}, pages = {11248-11264}, shortjournal = {IEEE Access}, title = {Transferability evaluation in wi-fi intrusion detection systems through machine learning and deep learning approaches}, volume = {13}, year = {2025}, }
• (2025). Pricing strategy of electric vehicle aggregators based on locational marginal price to minimize photovoltaic (PV) curtailment. ACCESS, 13, 11232-11247. (www)

  @article{ , abstract = {The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.}, archive = {J_ACCESS}, author = {Hyunwoo Song and Gab-Su Seo and Dongjun Won}, doi = {10.1109/ACCESS.2025.3528626}, journal = {IEEE Access}, pages = {11232-11247}, shortjournal = {IEEE Access}, title = {Pricing strategy of electric vehicle aggregators based on locational marginal price to minimize photovoltaic (PV) curtailment}, volume = {13}, year = {2025}, }
• (2025). Textile defect detection algorithm based on the improved YOLOv8. ACCESS, 13, 11217-11231. (www)

  @article{ , abstract = {Automatic detection of textile defects is a crucial factor in improving textile quality. Fast and accurate detection of these defects is key to achieving automation in the textile industry. However, the detection of textile defects faces challenges such as small defect targets, low contrast between defects and the background, and significant variations in the aspect ratio of defects. To address these issues, this study proposes a new method for textile defect detection based on an improved version of You Only Look Once Version 8(YOLOv8) called DA-YOLOv8s. Deep & Cross Network(DCNv2) is introduced into the Backbone Network to replace the C2F module, enhancing the extraction of network features; an self-attention mechanism, Polarized Self-Attention(PSA), is adopted to increase feature fusion capability and reduce feature loss in both channel and spatial dimensions; finally, a Small Object Detection Head (SOHead) is added to improve the feature extraction ability for small targets. Experimental results show that the improved YOLOv8 algorithm achieves has achieved mAP@0.5 and mAP of 44.6% and 48.6% respectively, which is an improvement of 4.2% and 3.8% over the original algorithm, and also outperforms the Optimal YOLOv9s model and the latest YOLOv11s model in these two metrics. The speed of textile defect detection has reached 257.38 frames per second (FPS) and the floating-point operation speed is 36.6 GFLOPS, ensuring the accuracy and speed of textile defect detection, with practical engineering application value.}, archive = {J_ACCESS}, author = {Wenfei Song and Du Lang and Jiahui Zhang and Meilian Zheng and Xiaoming Li}, doi = {10.1109/ACCESS.2025.3528771}, journal = {IEEE Access}, pages = {11217-11231}, shortjournal = {IEEE Access}, title = {Textile defect detection algorithm based on the improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Automated fog node audit and certification scheme with multiple attestation certificate authorities. ACCESS, 13, 11204-11216. (www)

  @article{ , abstract = {Fog computing is a complementary paradigm to cloud computing. It aims to improve the performance of Internet of Things (IoT) applications by positioning fog computing nodes closer to IoT networks. Establishing trust between fog nodes and cloud service providers is challenging because fog providers are typically third parties. The automated Fog Node Audit and Certification (FoNAC) scheme is a solution for establishing cloud trust in fog nodes. A key component for fog node trust establishment in the scheme is the attestation key certificate issued by an attestation Certificate Authority (CA). The FoNAC scheme assumes that the attestation CA certification service is readily available. However, this is a strong assumption that requires further study. Therefore, this paper aims to investigate the service availability of the attestation CA in the FoNAC scheme. This study examines the CA service availability in the FoNAC scheme, revealing that a single centralized attestation CA leads to a single point of failure that disables FoNAC and causes service bottlenecks. This paper proposes a distributed multiple attestation CA system to overcome these limitations. The certification service availability of the system is investigated as a finite-queue queuing system. Results demonstrate that increasing the number of CAs significantly increases service availability, achieving 93% availability with three CAs versus 42% with one CA at 500 requests per second. The distributed nature of the proposed system overcomes the limitation of a single point of failure, and the results show that multiple CAs increase the availability of the certification service, thereby reducing bottlenecks.}, archive = {J_ACCESS}, author = {Shuaib Anath and Tahmid Quazi and Bashan Naidoo}, doi = {10.1109/ACCESS.2025.3529702}, journal = {IEEE Access}, pages = {11204-11216}, shortjournal = {IEEE Access}, title = {Automated fog node audit and certification scheme with multiple attestation certificate authorities}, volume = {13}, year = {2025}, }
• (2025). Mirror target YOLO: An improved YOLOv8 method with indirect vision for heritage buildings fire detection. ACCESS, 13, 11195-11203. (www)

  @article{ , abstract = {Fires can cause severe damage to heritage buildings, making timely fire detection essential. Traditional dense cabling and drilling can harm these structures, so reducing the number of cameras to minimize such impact is challenging. Additionally, avoiding false alarms due to noise sensitivity and preserving the expertise of managers in fire-prone areas is crucial. To address these needs, we propose a fire detection method based on indirect vision, called Mirror Target YOLO (MITA-YOLO). MITA-YOLO integrates indirect vision deployment and an enhanced detection module. It uses mirror angles to achieve indirect views, solving issues with limited visibility in irregular spaces and aligning each indirect view with the target monitoring area. The Target-Mask module is designed to automatically identify and isolate the indirect vision areas in each image, filtering out non-target areas. This enables the model to inherit managers’ expertise in assessing fire-risk zones, improving focus and resistance to interference in fire detection. In our experiments, we created an 800-image fire dataset with indirect vision. Results show that MITA-YOLO significantly reduces camera requirements while achieving superior detection performance compared to other mainstream models.}, archive = {J_ACCESS}, author = {Jian Liang and Junsheng Cheng}, doi = {10.1109/ACCESS.2025.3528046}, journal = {IEEE Access}, pages = {11195-11203}, shortjournal = {IEEE Access}, title = {Mirror target YOLO: An improved YOLOv8 method with indirect vision for heritage buildings fire detection}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of NewSQL databases in a distributed architecture. ACCESS, 13, 11185-11194. (www)

  @article{ , abstract = {In the last decade, application architectures have evolved drastically, moving from monolithic architectures to distributed architectures where deployment has shifted from dedicated on-premises servers to the cloud. Distributed architectures and cloud computing has enabled businesses to scale their application components across different geographical locations. While it is easy to scale the application layer, scaling its database layer that relies on traditional SQL databases is challenging and often is a common source of bottlenecks when it comes to application performance. This paper evaluates the performance characteristics between two NewSQL databases solutions, MySQL NDB Cluster vs. TIBCO ActiveSpaces IMDG. Serving as an application layer, a simulation of banking microservices, handling loyalty rewards, is developed to interface with these databases to derive additional insights and comparison. In the context of a geographically distributed architecture, our experiment results show that MySQL NDB Cluster is more suited for critical business transactions requiring ACID properties while TIBCO ActiveSpaces is better suited for use cases that require big data ingestion, such as IoT platforms.}, archive = {J_ACCESS}, author = {Zhiyao Zhang and Alan Megargel and Lingxiao Jiang}, doi = {10.1109/ACCESS.2025.3529740}, journal = {IEEE Access}, pages = {11185-11194}, shortjournal = {IEEE Access}, title = {Performance evaluation of NewSQL databases in a distributed architecture}, volume = {13}, year = {2025}, }
• (2025). Tennis assistance technology based on dynamic time warping algorithm. ACCESS, 13, 11170-11184. (www)

  @article{ , abstract = {With the improvement of economic level, people’s demand for sports activities is increasing, especially for on-net opposability sports such as tennis. However, learning tennis techniques is often difficult for beginners and requires a lot of repeated practice to master. Traditional teaching methods are inefficient and difficult to quantify the correctness of actions. In view of this research, a tennis sports assistance technology based on dynamic time warping algorithm is developed. By collecting athletes’ motion data and using dynamic time warping algorithm for motion similarity analysis, personalized technical improvement suggestions are provided for athletes. This technology combines components such as normalization, support vector machine, joint detection, sparse matrix, and second-order stepping mode to improve algorithm performance and reduce computational complexity. The experiment outcomes indicate that this method can validly raise the training effect of tennis players, with an accuracy rate of 95.66%, a calculation time of 0.32 seconds, a variance of 0.88, and an average absolute error of 4.22. Compared with the experimental group that does not use normalization, support vector mechanism node detection, sparse matrix, and second-order stepping mode, there is a significant improvement in performance. Therefore, technology significantly improves the scientific and targeted nature of tennis training through advanced algorithms and data processing techniques. This technology not only provides real-time and accurate feedback to help athletes improve their technical movements, but also enhances training productivity and precision, which is important for promoting the popularization and development of tennis.}, archive = {J_ACCESS}, author = {Penggang Wang and Pengpeng Zhang and Guanxi Fan}, doi = {10.1109/ACCESS.2025.3528880}, journal = {IEEE Access}, pages = {11170-11184}, shortjournal = {IEEE Access}, title = {Tennis assistance technology based on dynamic time warping algorithm}, volume = {13}, year = {2025}, }
• (2025). Defending deep neural networks against backdoor attack by using de-trigger autoencoder. ACCESS, 13, 11159-11169. (www)

  @article{ , abstract = {A backdoor attack is a method that causes misrecognition in a deep neural network by training it on additional data that have a specific trigger. The network will correctly recognize normal samples (which lack the specific trigger) as their proper classes but will misrecognize backdoor samples (which contain the trigger) as target classes. In this paper, I propose a method of defense against backdoor attacks that uses a de-trigger autoencoder. In the proposed scheme, the trigger in the backdoor sample is removed using the de-trigger autoencoder, and the backdoor sample is detected from the change in the classification result. Experiments were conducted using MNIST, Fashion-MNIST, and CIFAR-10 as the experimental datasets and TensorFlow as the machine learning library. For MNIST, Fashion-MNIST, and CIFAR-10, respectively, the proposed method detected 91.5%, 82.3%, and 90.9% of the backdoor samples and had 96.1%, 89.6%, and 91.2% accuracy on legitimate samples.}, archive = {J_ACCESS}, author = {Hyun Kwon}, doi = {10.1109/ACCESS.2021.3086529}, journal = {IEEE Access}, pages = {11159-11169}, shortjournal = {IEEE Access}, title = {Defending deep neural networks against backdoor attack by using de-trigger autoencoder}, volume = {13}, year = {2025}, }
• (2025). Understanding well-being in urban context: A survey. ACCESS, 13, 11136-11158. (www)

  @article{ , abstract = {This survey explores the landscape of well-being research in an urban context with a focus on data. First, we conduct a review of well-being in general and efforts to measure it. Then, we perform an analysis of relevant research papers, reports, surveys, and standards published between January 2018 and September 2023 for the identification of reported factors contributing to urban well-being. These indicators were placed into distinct categories, creating a taxonomy of current efforts toward capturing urban well-being. This taxonomy, presented in a table format, details each indicator alongside its measurement method, data source, and other relevant information. Therefore, we can gain a deeper understanding of how urban well-being is assessed, what the challenges are, the current research focus, and the gaps. Our analysis reveals that urban well-being is a multifaceted construct influenced by a combination of factors. The taxonomy highlights the interconnectedness of these indicators, emphasizing that well-being results from their integration rather than isolated factors. This underscores the complexity of urban life and the need for holistic assessment methods. By providing this taxonomy, we aim to facilitate the development of more holistic approaches to measure and enhance urban well-being. This taxonomy could serve as a valuable tool for practitioners, researchers, and policymakers in the field, providing a comprehensive framework to understand and assess various aspects of capturing well-being in an urban context.}, archive = {J_ACCESS}, author = {Amirhossein Ghaffari and Susanna Pirttikangas and Ekaterina Gilman}, doi = {10.1109/ACCESS.2024.3506935}, journal = {IEEE Access}, pages = {11136-11158}, shortjournal = {IEEE Access}, title = {Understanding well-being in urban context: A survey}, volume = {13}, year = {2025}, }
• (2025). Monitoring bone healing: Integrating RF sensing with AI. ACCESS, 13, 11114-11135. (www)

  @article{ , abstract = {This study presents the development of an advanced machine learning model based on a two-dimensional (2D) Radio Frequency (RF) sensing framework for refined monitoring of femoral bone fractures. Utilising MATLAB simulations, we created a comprehensive dataset enhanced with variations in bone diameter, muscle thickness, fat thickness, and hematoma size, augmented with multiple sensor configurations (two, four, six, and eight sensors). The model aims to provide a frequent, non-invasive assessment of the fracture healing process compared to conventional imaging methods. Our approach leverages data from six RF sensors, achieving a high overall accuracy of 99.2% in classifying different fracture stages, including “no fracture” and varying degrees of hematoma sizes. The findings indicate that increasing the number of sensors up to six significantly enhances detection accuracy and sensitivity across all fracture stages. However, the marginal improvement from six to eight sensors was not statistically significant, suggesting that a six-sensor configuration offers an optimal balance between performance and system complexity. The results demonstrate significant potential for this technology to revolutionise orthopaedic treatment and recovery management by offering continuous, real-time monitoring without radiation exposure. The proposed system enhances personalised patient care by integrating RF sensing with artificial intelligence, enabling timely interventions and more informed, data-driven treatment strategies. This research lays a robust foundation for future advancements, including three-dimensional modelling and clinical validations, toward the practical implementation of non-invasive fracture monitoring systems.}, archive = {J_ACCESS}, author = {Ahmad Aldelemy and Ebenezer Adjei and Prince O. Siaw and Ali Al-Dulaimi and Viktor Doychinov and Nazar T. Ali and Rami Qahwaji and John G. Buckley and Pete Twigg and Raed A. Abd-Alhameed}, doi = {10.1109/ACCESS.2024.3524178}, journal = {IEEE Access}, pages = {11114-11135}, shortjournal = {IEEE Access}, title = {Monitoring bone healing: Integrating RF sensing with AI}, volume = {13}, year = {2025}, }
• (2025). 3D web application of wind prediction under typhoon on coastal cities based on ensemble k-NN and OpenFOAM. ACCESS, 13, 11095-11113. (www)

  @article{ , abstract = {Coastal cities are vulnerable areas to the impacts of strong wind under typhoon events. To anticipate the impact, this study proposes a novel approach to integrate the typhoon track predictions generated by an ensemble k-Nearest Neighbors (k-NN) as the reference locations of the typhoon center. The global weather forecast data corrected using an ensemble k-NN-based typhoon track was transferred to the OpenFOAM model simulation to obtain the urban-scale wind with multiple obstacles. The Gwangan Bridge, which faces the East Sea and is one of the sites in Busan City used in this study, is used for evaluating the approach with three typhoon events, i.e. Nari in 2007, Chaba in 2016, and Cimaron in 2018. The simulation results are disseminated in a 3D web application framework using Node JS for the web framework, PostgreSQL and PostGIS for database management, and CesiumJS for the main JavaScript library of 3D visualization. The study results show that this approach can performed well when compared with observed data from the wind station on the bridge, with correlation coefficient, mean absolute error (MAE), and root mean square error (RMSE) of 0.902, 1.505, and 1.927, respectively. The adjustment of grid positions through an ensemble k-NN-based typhoon prediction model on the global weather forecast data for OpenFOAM input significantly contributes to the accuracy of urban-scale wind prediction. This study also produces a more sophisticated web-based virtual globe and 3D city model to demonstrate a current typhoon prediction and past typhoons, online simulation results of ensemble k-NN algorithm, a volumetric profile of typhoons, wind flow, streamlines, wind pressure around buildings and a bridge, warning status, and time series of predicted wind velocity for supporting the disaster early warning system.}, archive = {J_ACCESS}, author = {Mamad Tamamadin and Seong-Hoon Kee and Jurng-Jae Yee}, doi = {10.1109/ACCESS.2024.3525361}, journal = {IEEE Access}, pages = {11095-11113}, shortjournal = {IEEE Access}, title = {3D web application of wind prediction under typhoon on coastal cities based on ensemble k-NN and OpenFOAM}, volume = {13}, year = {2025}, }
• (2025). Visibility enhancement of lesion regions in chest X-ray images with image fidelity preservation. ACCESS, 13, 11080-11094. (www)

  @article{ , abstract = {X-ray image enhancement can aid a physician’s diagnosis by improving lesion visibility. This study proposes a chest X-ray image enhancement framework for enhancing lesion visibility while preserving image features. Our framework assesses the background signals, whereas conventional methods focus on the visibility of the global image. The proposed method predicts the image processing parameters that enhance the lesion signals via the inference neural network. The framework consists of an X-ray image enhancer and an enhanced model predictor for reference. The enhancer regressively estimates the processing parameters for enhancing the lesions using the inference network and processes the input X-ray image. As the inference network requires training, the model predictor computes the reference parameters that maximize the visibility of the lesions within a tolerable loss of fidelity using image pairs—with and without lesions. We created a synthesized dataset, with and without lesions, from healthy chest and phantom lesion X-ray images. The experiments show that after the proposed method was trained on 2000 images, it improved lesion visibility with an acceptable fidelity loss. We also performed pairwise comparisons and confirmed that trade-offs between fidelity loss and visibility gain were attained. A technique for improving lesion visibility while maintaining the fidelity of X-ray images was developed. This method enabled the enhancement of specific signals in the background. Various image processing methods that require parameters could be incorporated into this framework for many different applications.}, archive = {J_ACCESS}, author = {Ryoichi Ishikawa and Tomohisa Yuzawa and Taiki Fukiage and Masataka Kagesawa and Toru Watsuji and Takeshi Oishi}, doi = {10.1109/ACCESS.2025.3528489}, journal = {IEEE Access}, pages = {11080-11094}, shortjournal = {IEEE Access}, title = {Visibility enhancement of lesion regions in chest X-ray images with image fidelity preservation}, volume = {13}, year = {2025}, }
• (2025). Evaluating the impact of face anonymization methods on computer vision tasks: A trade-off between privacy and utility. ACCESS, 13, 11070-11079. (www)

  @article{ , abstract = {Data anonymization is an essential prerequisite that enables data sharing in a privacy-preserving manner. However, anonymization affects the quality of the data and thus might affect the performance of later conducted data analysis. In this work, we investigate the influence of different image-based subject anonymization methods on the performance of the three common computer vision (CV) tasks: keypoint detection, instance segmentation, and face detection. We compare the anonymization methods on how much they affect the performance of the CV tasks, as well as their degree of anonymization. Furthermore, a re-identification attack scenario is applied. The study findings indicate a clear trade-off between increased privacy and decreased CV performance. Latent diffusion (called L-Diff in the following) shows the best results with minimal performance decrease on all CV tasks with a low recognition rate. However, the retrieval rate of our attack scenario, which measures the re-identification rate, remains high for all anonymization methods, which contrasts with the respective low level of recognition.}, archive = {J_ACCESS}, author = {Roland Stenger and Steffen Busse and Jonas Sander and Thomas Eisenbarth and Sebastian Fudickar}, doi = {10.1109/ACCESS.2024.3519441}, journal = {IEEE Access}, pages = {11070-11079}, shortjournal = {IEEE Access}, title = {Evaluating the impact of face anonymization methods on computer vision tasks: A trade-off between privacy and utility}, volume = {13}, year = {2025}, }
• (2025). Understanding ChatGPT: Impact analysis and path forward for teaching computer science and engineering. ACCESS, 13, 11049-11069. (www)

  @article{ , abstract = {Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.}, archive = {J_ACCESS}, author = {P. Banerjee and Anurag K. Srivastava and Donald A. Adjeroh and Ramana Reddy and Nima Karimian}, doi = {10.1109/ACCESS.2024.3524102}, journal = {IEEE Access}, pages = {11049-11069}, shortjournal = {IEEE Access}, title = {Understanding ChatGPT: Impact analysis and path forward for teaching computer science and engineering}, volume = {13}, year = {2025}, }
• (2025). GuardianMPC: Backdoor-resilient neural network computation. ACCESS, 13, 11029-11048. (www)

  @article{ , abstract = {The rapid growth of deep learning (DL) has raised serious concerns about users’ data and neural network (NN) models’ security and privacy, particularly the risk of backdoor insertion when outsourcing the training or employing pre-trained models. To ensure resilience against such backdoor attacks, this work presents GuardianMPC, a novel framework leveraging secure multiparty computation (MPC). GuardianMPC is built upon garbled circuits (GC) within the LEGO protocol framework to accelerate oblivious inference on FPGAs in the presence of malicious adversaries that can manipulate the model weights and/or insert a backdoor in the architecture of a pre-trained model. In this regard, GuardianMPC is the first to offer private function evaluation in the LEGO family. GuardianMPC also supports private training to effectively counter backdoor attacks targeting NN model architectures and parameters. With optimized pre-processing, GuardianMPC significantly accelerates the online phase, achieving up to $13.44\times $ faster computation than its software counterparts. Our experimental results for multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) assess GuardianMPC’s time complexity and scalability across diverse NN model architectures. Interestingly, GuardianMPC does not adversely affect the training accuracy, as opposed to many existing private training frameworks. These results confirm GuardianMPC as a high-performance, model-agnostic solution for secure NN computation with robust security and privacy guarantees.}, archive = {J_ACCESS}, author = {Mohammad Hashemi and Domenic Forte and Fatemeh Ganji}, doi = {10.1109/ACCESS.2025.3528304}, journal = {IEEE Access}, pages = {11029-11048}, shortjournal = {IEEE Access}, title = {GuardianMPC: Backdoor-resilient neural network computation}, volume = {13}, year = {2025}, }
• (2025). Realization of arithmetic logic units using electro-optic microring resonators in photonic circuits. ACCESS, 13, 11021-11028. (www)

  @article{ , abstract = {This work presents a new approach to designing Arithmetic Logic Units (ALUs) using electro-optic microring resonators. The methodology covers the demonstration of full and half addition and subtraction, parity checking, and dynamic logic gates functionalities at the data speed of 10 Gbps. In this work, integrated electro-optic circuits have been meticulously designed to have minimum footprints using microring resonators (MRRs) with low losses, thus, ensuring efficient and scalable optical computing system. A unique design for electro-optic full adder/subtractor and three input odd-even parity checkers were implemented and validated successfully within the Lumerical simulation environment. Simulation results demonstrate the successful alignment of optical outputs with their respective truth tables, confirming the reliability and accuracy of the designed circuits. These advancements represent significant progress in the field of optical computing, offering notable improvements in computational speed, efficiency, and scalability. This research contributes to the ongoing exploration and development in optical computing, with the potential to revolutionize computational efficiency and performance in various applications.}, archive = {J_ACCESS}, author = {Assylkhan Nurgali and Bikash Nakarmi and Carlo Molardi and Ikechi Augustine Ukaegbu}, doi = {10.1109/ACCESS.2025.3528437}, journal = {IEEE Access}, pages = {11021-11028}, shortjournal = {IEEE Access}, title = {Realization of arithmetic logic units using electro-optic microring resonators in photonic circuits}, volume = {13}, year = {2025}, }
• (2025). Disturbance observer-based super-twisting SMC for variable speed wind energy conversion system under parametric uncertainties. ACCESS, 13, 11003-11020. (www)

  @article{ , abstract = {Effective control of the generator’s shaft speed will ensure maximum power is captured from the wind turbines. However, the parameters of the wind energy conversion system (WECS)-based generators, including stator resistance and inductance, could change over time due to power loss, winding degradation, or core saturation. These parametric uncertainties affect the performance of the designed controllers. Although the sliding mode controllers (SMCs) are robust to matched uncertainties, the unmatched parametric uncertainties were not effectively compensated for by the SMC. This study investigated the performance of the SMC and super-twisting SMC (ST-SMC) under unmatched uncertainties using variable wind speed. Initially, the controllers were designed with the nominal parameters of the WECS-based permanent magnet synchronous generator (PMSG). Then, the values of the stator resistance and inductance were changed without changing the control design to test the robustness of the controllers to unmatched uncertainties. Finally, the uncertainties were estimated by the disturbance observer and incorporated into the controllers as a compensation mechanism. The controllers were designed using the synthetic wind profile and validated with the real-wind data. The transient and overall response of the controllers were analyzed using mean-absolute error (MAE) and root-mean-square error (RMSE) of the shaft speed tracking. The results demonstrated that the uncertainty compensation-based SMC/ST-SMC approach provides satisfactory shaft speed tracking performance even under parametric uncertainties.}, archive = {J_ACCESS}, author = {Ahmad Bala Alhassan and Muhammad Auwal Shehu and Vijayakumar Gali and Ton Duc do}, doi = {10.1109/ACCESS.2025.3528772}, journal = {IEEE Access}, pages = {11003-11020}, shortjournal = {IEEE Access}, title = {Disturbance observer-based super-twisting SMC for variable speed wind energy conversion system under parametric uncertainties}, volume = {13}, year = {2025}, }
• (2025). A data-driven approach to engineering instruction: Exploring learning styles, study habits, and machine learning. ACCESS, 13, 10978-11002. (www)

  @article{ , abstract = {This study examined the impact of learning style and study habit alignment on the academic success of engineering students. Over a 16-week semester, 72 students from Process Engineering and Electronic Engineering programs at the Universidad de Los Llanos participated in this study. They completed the Learning Styles Index questionnaire on the first day of class, and each week, teaching methods and class activities were aligned with one of the four learning dimensions of the Felder-Silverman Learning Styles Model. Lesson 1 focused on one side of a learning dimension, lesson 2 on the opposite side, and the tutorial session incorporated both. Quizzes and engagement surveys assessed short-term academic performance, whereas midterm and final exam results measured long-term performance. Paired t-tests, Cohen’s effect size, and two-way ANOVA showed that aligning teaching methods with learning styles improved students’short-term exam scores and engagement. However, multiple regression analysis indicated that study habits (specifically time spent studying, frequency, and scores on a custom-developed study quality survey) were much stronger predictors of midterm and final exam performance. Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. Voting Ensemble was found to be the strongest model, explaining 83% of the variance in final exam scores, with a mean absolute error of 3.18. Our findings suggest that, while learning style alignment improves short-term engagement and comprehension, effective study habits and time management play a more important role in long-term academic success.}, archive = {J_ACCESS}, author = {Lauren Genith Isaza Domínguez and Antonio Robles-Gómez and Rafael Pastor-Vargas}, doi = {10.1109/ACCESS.2025.3528263}, journal = {IEEE Access}, pages = {10978-11002}, shortjournal = {IEEE Access}, title = {A data-driven approach to engineering instruction: Exploring learning styles, study habits, and machine learning}, volume = {13}, year = {2025}, }
• (2025). Securing urban landscape: Cybersecurity mechanisms for resilient smart cities. ACCESS, 13, 10966-10977. (www)

  @article{ , abstract = {This article explores a novel approach to enhancing cybersecurity in smart cities by integrating Convolutional Neural Networks (CNNs) with Genetic Algorithms (GAs). The primary objective is to develop a robust cybersecurity framework capable of effectively detecting and responding to a wide range of cyber threats in interconnected urban infrastructures. The proposed CNN-GA framework, resembles a well-manicured garden, where leverages the advanced threat detection capabilities of CNNs as the precision-trimmed hedges and the optimization power of GAs as the strategically placed pathways to create a synergistic solution that improves detection accuracy, reduces response times, and enhances overall system resilience. The study demonstrates that the CNN-GA framework significantly outperforms traditional cybersecurity methods. Key findings include a detection rate of 92% for various threat types and an average response time of 1.2 seconds, compared to 80% and 3.5 seconds, respectively, for traditional methods. Additionally, the framework achieves a 45% improvement in system resilience during attacks and optimizes defense strategies to reduce deployment costs while increasing overall effectiveness. The implications of this approach are profound for securing urban infrastructures in smart cities. By combining deep learning with evolutionary algorithms, the CNN-GA framework offers a dynamic and adaptive solution to address the complex and evolving nature of cyber threats. This integration not only enhances the security posture of smart cities but also provides a foundation for future advancements in urban cybersecurity. The results advocate for the broader adoption of such integrated approaches to ensure the resilience and safety of smart city infrastructures (sustainable landscaping, green roof, etc) in the face of increasing cyber risks.}, archive = {J_ACCESS}, author = {Qiang Lyu and Sujuan Liu and Zhouyuan Shang}, doi = {10.1109/ACCESS.2024.3522078}, journal = {IEEE Access}, pages = {10966-10977}, shortjournal = {IEEE Access}, title = {Securing urban landscape: Cybersecurity mechanisms for resilient smart cities}, volume = {13}, year = {2025}, }
• (2025). A hybrid retrieval method for an Ω-class bianisotropic metamaterial using scattering parameter method. ACCESS, 13, 10954-10965. (www)

  @article{ , abstract = {In this study, a hybrid retrieval method is proposed to retrieve all terms in the electric and magnetic tensors (along with coupling tensors) of $\Omega $ -class bianisotropic metamaterial (MM) slabs using scattering (S-) parameters of both normal incidence TE2 mode and oblique incidence TM2 and TE modes. Compared with other extraction methods in the literature, our retrieval method fully characterizes the MM slab (extracts all constitutive parameters) without necessitating any numerical technique (non-iterative) using fewer wave configurations (two modes) and sample configurations (one configuration). Contrary to two sample configurations required in the application of previous retrieval methods in the literature, one sample configuration (with and without its rotation in the transverse plane) significantly decreases the effect of fabrication tolerances on the accuracy of extraction. The proposed extraction method is first validated by a bulk MM slab synthesized by the Lorentz and Drude models. Then, it is applied for extracting the electromagnetic properties of two bianisotropic MM slabs in the form of square split-ring-resonators and $\Omega $ -shape using their S-parameters simulated by a full wave simulation program. Finally, its accuracy is examined by comparing simulated S-parameters with those reconstructed by using extracted electromagnetic parameters. It is observed that our proposed method determines the constitutive parameters with a relatively higher accuracy in a relatively short amount of time.}, archive = {J_ACCESS}, author = {Gokhan Ozturk and Ugur Cem Hasar and Yunus Kaya and Huseyin Korkmaz and Ivaylo Stoyanov}, doi = {10.1109/ACCESS.2025.3529193}, journal = {IEEE Access}, pages = {10954-10965}, shortjournal = {IEEE Access}, title = {A hybrid retrieval method for an Ω-class bianisotropic metamaterial using scattering parameter method}, volume = {13}, year = {2025}, }
• (2025). FArSS: Fast and efficient semantic question similarity in arabic. ACCESS, 13, 10944-10953. (www)

  @article{ , abstract = {This paper addresses the challenge of efficient semantic question similarity in Arabic by leveraging fastText embeddings and a simple neural network architecture. Our model (FArSS) avoids the complexities of recurrent connections and attention mechanisms, resulting in a streamlined and efficient approach. With strategic data augmentation, our model achieves an F1-score of 0.928, closely competing with state-of-the-art models that rely on advanced architectures employing self-attention mechanisms. Additionally, our model outperforms both GPT-4o and GPT-4 in semantic question similarity in Arabic, underscoring the potential of specialized, efficient models to surpass large language models in specific tasks. This work demonstrates that our method not only maintains high performance but also ensures fast training and inference times. The practical advantages of our approach make it especially suitable for real-time applications, contributing to the development of more effective and efficient natural language processing systems. Our findings highlight the continued importance of efficient tailored models in addressing specific natural language processing challenges.}, archive = {J_ACCESS}, author = {Mohamed Alkaoud}, doi = {10.1109/ACCESS.2025.3529527}, journal = {IEEE Access}, pages = {10944-10953}, shortjournal = {IEEE Access}, title = {FArSS: Fast and efficient semantic question similarity in arabic}, volume = {13}, year = {2025}, }
• (2025). Graph knowledge structure for attentional knowledge tracing with self-supervised learning. ACCESS, 13, 10933-10943. (www)

  @article{ , abstract = {As intelligent education advances and online learning becomes more prevalent, Knowledge Tracing (KT) has become increasingly important. KT assesses students’ learning progress by analysing their historical performance in related exercises. Despite significant advances in the field, there are still shortcomings in two aspects: first, a lack of effective integration between exercises and knowledge points; second, an overemphasis on nodal information, neglecting deep semantic relationships. To address these, we propose a self-supervised learning approach that uses an enhanced heterogeneous graph attention network to represent and analyse complex relationships between exercises and knowledge points. We introduce an innovative surrogate view generation method to optimise the integration of local structural information and global semantics within the graph, addressing relational inductive bias. In addition, we incorporate the improved representation algorithm into the loss function to handle data sparsity, thereby improving prediction accuracy. Experiments on three real-world datasets show that our model outperforms baseline models.}, archive = {J_ACCESS}, author = {Zhaohui Liu and Sainan Liu and Weifeng Gu}, doi = {10.1109/ACCESS.2024.3521883}, journal = {IEEE Access}, pages = {10933-10943}, shortjournal = {IEEE Access}, title = {Graph knowledge structure for attentional knowledge tracing with self-supervised learning}, volume = {13}, year = {2025}, }
• (2025). Abstractive summarization of historical documents: A new dataset and novel method using a domain-specific pretrained model. ACCESS, 13, 10918-10932. (www)

  @article{ , abstract = {Automatic Text Summarization (ATS) systems aim to generate concise summaries of documents while preserving their essential aspects using either extractive or abstractive approaches. Transformer-based ATS methods have achieved success in various domains; however, there is a lack of research in the historical domain. In this paper, we introduce HistBERTSum-Abs, a novel method for abstractive historical single-document summarization. A major challenge in this task is the lack of annotated datasets for historical text summarization. To address this issue, we create a new dataset using archived documents obtained from the Centre Virtuel de la Connaissance sur l’Europe group at the University of Luxembourg. Furthermore, we leverage the potential of HistBERT, a domain-specific bidirectional language model trained on the balanced Corpus of Historical American English, (https://www.english-corpora.org/coha/) to capture the semantics of the input documents. Specifically, our method adopts an encoder-decoder architecture, combining the pre-trained HistBERT encoder with a randomly initialized Transformer decoder. To address the mismatch between the pre-trained encoder and the non-pre-trained decoder, we employ a novel fine-tuning schedule that uses different optimizers for each component. Experimental results on our constructed dataset demonstrate that our HistBERTSum-Abs method outperforms recent state-of-the-art deep learning-based methods and achieves results comparable to state-of-the-art LLMs in zero-shot settings in terms of ROUGE-1, ROUGE-2, and ROUGE-L F1 scores. To the best of our knowledge, this is the first work on abstractive historical text summarization.}, archive = {J_ACCESS}, author = {Keerthana Murugaraj and Salima Lamsiyah and Christoph Schommer}, doi = {10.1109/ACCESS.2025.3528733}, journal = {IEEE Access}, pages = {10918-10932}, shortjournal = {IEEE Access}, title = {Abstractive summarization of historical documents: A new dataset and novel method using a domain-specific pretrained model}, volume = {13}, year = {2025}, }
• (2025). Satellite-based forest stand detection using artificial intelligence. ACCESS, 13, 10898-10917. (www)

  @article{ , abstract = {The forest constitutes an essential and irreplaceable component of life for all organisms, with its primary significance lying in its role in creating a breathable atmosphere on Earth. Forests are vital for human health and well-being and hold significant ecological and economic value for humanity. This study aims to propose a method for identifying forest stands using artificial intelligence techniques. A custom dataset was developed, comprising high-quality satellite images that capture various structures such as forests, fields, roads, buildings, and lakes. This dataset was employed to train models from the category of convolutional neural networks that operate on the principle of instance segmentation. Several models, including YOLOv8, YOLOv5 and Mask R-CNN, were tested and compared. An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. The selected model was then evaluated using images not included in the original training dataset to simulate real-world deployment scenarios. The final accuracy of best model achieved 91.67%. This model can detect the presence of forest stands in satellite images, as well as other features such as roads, buildings etc. The proposed method offers potential benefits for forest technicians, who can integrate it with other methods to monitor forest cover effectively.}, archive = {J_ACCESS}, author = {Patrik Kovačovič and Rastislav Pirník and Júlia Kafková and Mário Michálik and Alžbeta Kanáliková and Pavol Kuchár}, doi = {10.1109/ACCESS.2025.3528215}, journal = {IEEE Access}, pages = {10898-10917}, shortjournal = {IEEE Access}, title = {Satellite-based forest stand detection using artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Searching optimum route with variable travel time. ACCESS, 13, 10884-10897. (www)

  @article{ , abstract = {In the near future, in a society where connected and autonomous vehicles (connected automated vehicles) enable the transmission and sharing of traffic information, the demand for navigation systems is expected to increase; based on this background, this study proposes a shortest route search method using a time-expansion network. Assuming that the travel time to the destination can be accurately predicted, the proposed Method 1 finds a route that can reach the destination in the shortest possible travel time. This results in more realistic and accurate travel times than those provided by the current method. The proposed Method 2 facilitates vehicle dispersion by allowing drivers to set arrival times at their destinations in advance. We believe that this will alleviate the total level of congestion on the road by distributing routes according to the arrival time established by the drivers.We are convinced that if more vehicles were to use our proposed methods, energy consumption would be reduced in society as a whole.}, archive = {J_ACCESS}, author = {Yamauchi Shuki and Tokunaga Junpei and Ebara Hiroyuki and Ueda Naonori}, doi = {10.1109/ACCESS.2025.3529027}, journal = {IEEE Access}, pages = {10884-10897}, shortjournal = {IEEE Access}, title = {Searching optimum route with variable travel time}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Toward privacy preservation using clustering based anonymization: Recent advances and future research outlook”. ACCESS, 13, 10883. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook”.}, archive = {J_ACCESS}, author = {Abdul Majeed and Safiullah Khan and Seong Oun Hwang}, doi = {10.1109/ACCESS.2024.3523116}, journal = {IEEE Access}, pages = {10883}, shortjournal = {IEEE Access}, title = {Corrections to “Toward privacy preservation using clustering based anonymization: Recent advances and future research outlook”}, volume = {13}, year = {2025}, }
• (2025). Formation producing control of multi-quadcopter systems under the cloud access. ACCESS, 13, 10870-10882. (www)

  @article{ , abstract = {The use of cloud-connected UAV swarm plays an important role in the future of mobility. One of the challenges to address before realising this technology is achieving a formation producing control of quadcopter swarms under the cloud access. For this purpose, the problem of cloud-based formation control for a nonlinear 6-DOF under-actuated multi-quadcopter system is studied in this paper. This is different compared to the existing literature, studying the rendezvous problem for a second-order multi-agent system. As the first step, a hierarchical control structure is provided to derive the control laws and conditions for the stability of the nonlinear under-actuated multi-quadcopter system to guarantee the asymptotic consensus of the quads’ positions to the biased average of the initial positions. Then, the control laws are extended and derived under the cloud access condition and the stability proofs are analysed for the quadcopters’ dynamics. The results guarantee the practical consensus of the quadcopter system to the biased position of their initial values. Toward this, a scheduling rule for the access to the cloud is designed and it is shown that the rule avoids the Zeno behavior.For this purpose, upper bounds on the control laws of the cloud-connected neighboring agents between two consecutive connections of each agent are considered. The numerical results verify the efficacy of the proposed method.}, archive = {J_ACCESS}, author = {Nargess Sadeghzadeh-Nokhodberiz and Mohammad-Reza Ghahramani-Tabrizi and Allahyar Montazeri}, doi = {10.1109/ACCESS.2025.3527932}, journal = {IEEE Access}, pages = {10870-10882}, shortjournal = {IEEE Access}, title = {Formation producing control of multi-quadcopter systems under the cloud access}, volume = {13}, year = {2025}, }
• (2025). Waveguide-based all-metal near-field metasurfaces for linearly and circularly polarized beam steering antennas. ACCESS, 13, 10857-10869. (www)

  @article{ , abstract = {This article presents an efficient near-field meta-steering antenna design using metasurfaces composed of waveguide-type, all-metal cells to achieve beam steering in both azimuth and elevation planes. The inherent rigidity of metallic metasurfaces at high temperatures ensures robust performance while maintaining the mechanical stability offered by the waveguide-based framework. The use of shielded cells in the metasurface design helps mitigate pattern variations caused by mutual coupling among cells. The Jerusalem cross slots used in the cells are polarization-insensitive and utilise a phase delay technique to tune the cell phase response. Experimental results demonstrate a steering range of ±34° in the elevation plane and a complete 360-degree coverage in the azimuth plane. The antenna exhibits a planar profile with metasurfaces placed in the near field directly over a waveguide adapter and has an overall height of 75 mm $(3\lambda _{o})$ at 12 GHz design frequency. Regardless of the rotation of the metasurfaces, the antenna is matched from 11.3 to 12.8 GHz, whereas the 3 dB gain bandwidth is 700 MHz (11.9-12.6 GHz).}, archive = {J_ACCESS}, author = {Maira I. Nabeel and Muhammad U. Afzal and Khushboo Singh and Dushmantha N. Thalakotuna and Karu P. Esselle}, doi = {10.1109/ACCESS.2025.3525590}, journal = {IEEE Access}, pages = {10857-10869}, shortjournal = {IEEE Access}, title = {Waveguide-based all-metal near-field metasurfaces for linearly and circularly polarized beam steering antennas}, volume = {13}, year = {2025}, }
• (2025). UAV-enabled inspection system with no-fly zones: DRL-based joint mobile nest scheduling and UAV trajectory design. ACCESS, 13, 10844-10856. (www)

  @article{ , abstract = {Unmanned aerial vehicle (UAV)-enabled inspection is regarded as a promising technology in the electricity system. This paper investigates a UAV-enabled inspection system in an urban environment with no-fly zones (NFZs), where the UAV flies to inspection points to capture images while constrained by limited onboard energy. The aim of this paper is to minimize the whole inspection time via joint optimization of the mobile nest’s scheduling and UAV trajectory while satisfying constraints related to energy maximization and avoiding NFZs. The formulated problem is a multivariate mixed non-convex optimization problem (due to the presence of NFZs), which makes it challenging to address with conventional techniques such as successive convex approximation and graph theory. To solve these issues, we first model the proposed problem as a Markov decision process (MDP). Then we propose an efficient two-step optimization method that involves optimizing the UAV trajectory with a modified multi-step dueling DDQN (MSD-DDQN) algorithm and planning the mobile nest’s path using the A* algorithm. Finally, the effectiveness of the proposed algorithm is verified by simulation experiments. In particular, the proposed method not only accelerates algorithm convergence but also reduces inspection time by 35 % compared to baseline methods.}, archive = {J_ACCESS}, author = {Jin Dai and Yunfei Gao and Chao Cai and Wei Xiong and Manjia Liu}, doi = {10.1109/ACCESS.2025.3529085}, journal = {IEEE Access}, pages = {10844-10856}, shortjournal = {IEEE Access}, title = {UAV-enabled inspection system with no-fly zones: DRL-based joint mobile nest scheduling and UAV trajectory design}, volume = {13}, year = {2025}, }
• (2025). State-of-the-art review of emerging trends in renewable energy generation technologies. ACCESS, 13, 10820-10843. (www)

  @article{ , abstract = {Renewable energy generation sector has grown rapidly over the past decade, driven by increasing investments, heightened political and public support, and advancements in technology. This review highlights recent technological developments in the sector and analyzes the state-of-the-art to uncover major overarching trends. The findings suggest that the main maturation directions for emerging renewable technologies are towards harnessing new untapped sources of renewables and combining energy generation with additional value creation for more versatile and effective land, infrastructure, or labor utilization. The overarching concept is clear: generating greater economic or environmental value with minimal investment. The key emerging technological trends were identified as the development of means for: distributed small-scale generation, large-scale land versatility, and accessing marine renewables. Notable examples of maturing technologies with significant deployment include agrivoltaics, floating photovoltaics, and floating wind, all of which are on track to becoming industrial standard practices. Looking ahead, the deployment of renewable energy technologies is expected to accelerate over the next decade, driven by rising demand, improved access to new energy resources, trends toward grid decentralization, expanding utility-scale energy storage solutions, and the economic benefits of co-located, multi-purpose installations.}, archive = {J_ACCESS}, author = {Hans Tiismus and Vahur Maask and Victor Astapov and Tarmo Korõtko and Argo Rosin}, doi = {10.1109/ACCESS.2025.3528640}, journal = {IEEE Access}, pages = {10820-10843}, shortjournal = {IEEE Access}, title = {State-of-the-art review of emerging trends in renewable energy generation technologies}, volume = {13}, year = {2025}, }
• (2025). Quantifying uncertainty in state estimation: The MoK-FoBS method via interval analysis. ACCESS, 13, 10805-10819. (www)

  @article{ , abstract = {The Bayesian framework is conventionally adopted in power system static state estimation (SSE) to quantify uncertainty via probability density functions (PDFs). However, the reliability of such PDFs is frequently undermined by the complex nature of noise in measurement systems, potentially leading to significant estimation inaccuracies. To address this problem, this paper proposes a modified Krawczyk-forward-backward synthesis (MoK-FoBS) method to quantify uncertainty in SSE through interval analysis. The proposed MoK-FoBS method combines the strengths of the modified forward-backward propagation (FBP) method with the modified Krawczyk method to mitigate the overestimation problem. Employing simulation data derived from IEEE testing systems, it is verified through the Monte Carlo method that the MoK-FoBS method can estimate hard boundaries that invariably contain the true state values. In contrast, the true state values may lie outside the uncertainty boundaries estimated by the weighted least squares approach. A comparative analysis reveals that the MoK-FoBS method can achieve narrower state boundaries than the FBP method, thereby improving estimation precision.}, archive = {J_ACCESS}, author = {Yuting Chen and Ning Zhou and Ziang Zhang}, doi = {10.1109/ACCESS.2024.3524563}, journal = {IEEE Access}, pages = {10805-10819}, shortjournal = {IEEE Access}, title = {Quantifying uncertainty in state estimation: The MoK-FoBS method via interval analysis}, volume = {13}, year = {2025}, }
• (2025). EduCardia: A modern technology-powered methodology for the assessment and improvement of social and emotional competencies of students in k12 schools. ACCESS, 13, 10786-10804. (www)

  @article{ , abstract = {The application of Social and Emotional Learning (SEL) methodologies within classrooms has been widely adopted and examined, highlighting its positive impact on the academic, emotional, and social sectors at both the individual and classroom levels. Through this wide applicability, a need is identified to focus more on the development of SEL approaches that support the integration of the various parts of a SEL methodology (e.g., planning, implementation, monitoring and assessment). Such approaches should not underestimate the value of the SEL assessment. It is necessary to engage both the developers and the practitioners of SEL methodologies and provide tools to assist teachers to better interpret the collected data and results and plan targeted SEL activities. Motivated by such needs, we detail the EduCardia SEL methodology, which focuses on the provision of a set of guidelines and tools to assess and improve the social and emotional competencies of students in primary and secondary educational levels, taking advantage of Information and Communication Technologies (ICT). The EduCardia SEL methodology considers the integration of the various parts of a SEL methodological approach and supports continuous assessment processes while offering user-friendly interfaces to interpret the produced results. Evaluation results are provided based on the application of the EduCardia SEL methodology in K12 schools in Greece for a short period of time, considering three different age groups. The targeted competencies are shown to be improved, while teachers provide insights on the applicability and efficiency of the proposed approach.}, archive = {J_ACCESS}, author = {Eleni Fotopoulou and Èlia López Cassà and Lydia Mavraidi and Júlia Peris and Anastasios Zafeiropoulos and Adina Colomeischi and Christina Papasolomontos and Symeon Papavassiliou}, doi = {10.1109/ACCESS.2025.3529120}, journal = {IEEE Access}, pages = {10786-10804}, shortjournal = {IEEE Access}, title = {EduCardia: A modern technology-powered methodology for the assessment and improvement of social and emotional competencies of students in k12 schools}, volume = {13}, year = {2025}, }
• (2025). The role of big data analytics in revolutionizing diabetes management and healthcare decision-making. ACCESS, 13, 10767-10785. (www)

  @article{ , abstract = {The evolving healthcare domain necessitates an upgrade through digitization, integrating patient data, and advanced medical results. In the last couple of decades, advances in information and storage technologies in healthcare have produced vast amounts of data. The remarkable increases in data volumes, along with the enticing prospects and potential inherent in data analysis, have contributed to the concept of Big Data. There is a pressing need within the research community to analyze these large volumes of Big Data. To address this challenge, Big Data Analytics (BDA), the systematic process of examining large and complex datasets to uncover hidden patterns, correlations, and insights for informed decision-making, has emerged. It employs various methodologies and techniques to enable informed decision-making. This study delves into using Machine Learning (ML) in big data environments, explicitly utilizing the MLib library in Apache Spark to derive meaningful insights from diabetic healthcare dataset. The CDC’s Behavioral Risk Factor Surveillance System (BRFSS) was used to empirically demonstrate the advantages of integrating BDA with ML for medical decision-making in Big Data environments. The research finding highlighted the superior performance of Logistic Regression (LR) models compared to other models like Naive Bayes (NB), providing valuable insights for healthcare applications.}, archive = {J_ACCESS}, author = {Muhammad Nauman and Ahmad S. Almadhor and Mohammed Albekairi and Ali R. Ansari and Muhammad A. B. Fayyaz and Raheel Nawaz}, doi = {10.1109/ACCESS.2025.3526456}, journal = {IEEE Access}, pages = {10767-10785}, shortjournal = {IEEE Access}, title = {The role of big data analytics in revolutionizing diabetes management and healthcare decision-making}, volume = {13}, year = {2025}, }
• (2025). Design of single feed, dual-band circularly polarized quad-mode slot antenna using coupled mode theory. ACCESS, 13, 10758-10766. (www)

  @article{ , abstract = {In this article, a dual-band, dual-sense circularly polarized (CP) rectangular slot antenna with folded slots, fed by a microstrip line, is presented for GPS applications at 1575.4 MHz (L1) and 1227.5 MHz (L2). The excitation of the rectangular slot antenna maintains a small frequency ratio by generating two modes at closely spaced frequencies through characteristic mode analysis (CMA) and coupling mode theory (CMT). Dual circular polarization is then achieved by creating two orthogonal modes with respect to the fundamental modes by inserting two folded slots at angles of −45° and 45° relative to the tilted arm of the rectangular slot antenna. The proposed antenna has $100\times 100\times 1.6$ mm3 dimensions and radiates right-hand CP (RHCP) in the L1 band and left-hand CP (LHCP) in the L2 band. The measured impedance bandwidth is 3.6 % (1615-1675 MHz) for L1 and 26.7 % (1055-1380 MHz) for L2. The axial ratio bandwidth (ARBW) is observed as 3.2 % (1565-1616 MHz) for L1 and 4.04 % (1212-1262 MHz) for L2, with a maximum gain of 4.1 dBic and 3.8 dBic, respectively.}, archive = {J_ACCESS}, author = {Mekaladevi Ⅴ and Natarajamani S}, doi = {10.1109/ACCESS.2025.3529458}, journal = {IEEE Access}, pages = {10758-10766}, shortjournal = {IEEE Access}, title = {Design of single feed, dual-band circularly polarized quad-mode slot antenna using coupled mode theory}, volume = {13}, year = {2025}, }
• (2025). Exploration degree bias: The hidden influence of node degree in graph neural network-based reinforcement learning. ACCESS, 13, 10746-10757. (www)

  @article{ , abstract = {Graph Neural Networks (GNNs) have demonstrated remarkable performance in tasks involving graph-structured data, but they also exhibit biases linked to node degrees. This paper explores a specific manifestation of such bias, termed Exploration Degree Bias (EDB), in the context of Reinforcement Learning (RL). We show that EDB arises from the inherent design of GNNs, where nodes with high or low degrees disproportionately influence output logits used for decision-making. This phenomenon impacts exploration in RL, skewing it away from mid-degree nodes, potentially hindering the discovery of optimal policies. We provide a systematic investigation of EDB across widely used GNN architectures—GCN, GraphSAGE, GAT, and GIN—by quantifying correlations between node degrees and logits. Our findings reveal that EDB varies by architecture and graph configuration, with GCN and GIN exhibiting the strongest biases. Moreover, analysis of DQN and PPO RL agents illustrates how EDB can distort exploration patterns, with DQN exhibiting EDB under low exploration rates and PPO showing a partial ability to counteract these effects through its probabilistic sampling mechanism. Our contributions include defining and quantifying EDB, providing experimental insights into its existence and variability, and analyzing its implications for RL. These findings underscore the need to address degree-related biases in GNNs to enhance RL performance on graph-based tasks.}, archive = {J_ACCESS}, author = {Peter Tarábek and Dávid Matis}, doi = {10.1109/ACCESS.2025.3528878}, journal = {IEEE Access}, pages = {10746-10757}, shortjournal = {IEEE Access}, title = {Exploration degree bias: The hidden influence of node degree in graph neural network-based reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Swarm-based trajectory generation and optimization for stress-aligned 3D printing. ACCESS, 13, 10735-10745. (www)

  @article{ , abstract = {In this study, we present a novel swarm-based approach for generating optimized stress-aligned trajectories for 3D printing applications. The method utilizes swarming dynamics to simulate the motion of virtual agents along the stress field of a part under loading conditions. The resulting agent trajectories are then used as print trajectories. With this approach, the complex global trajectory generation problem is subdivided into a set of sequential and computationally efficient quadratic programs. Through comprehensive evaluations in both simulation and experiments, we compare our method to state-of-the-art approaches. Our results demonstrate a remarkable improvement in computational efficiency, achieving a $115\times $ faster computation speed than existing methods. This efficiency, combined with the possibility to tune the trajectory spacing to match the deposition process constraints, makes the potential integration of our approach into existing 3D printing processes seamless. Additionally, the open-hole tensile specimen produced on a conventional fused filament fabrication setup with our algorithm achieves a notable $\approx 10\%$ improvement in specific modulus compared to existing trajectory optimization methods.}, archive = {J_ACCESS}, author = {Xavier Guidetti and Efe C. Balta and and John Lygeros}, doi = {10.1109/ACCESS.2025.3528531}, journal = {IEEE Access}, pages = {10735-10745}, shortjournal = {IEEE Access}, title = {Swarm-based trajectory generation and optimization for stress-aligned 3D printing}, volume = {13}, year = {2025}, }
• (2025). An improved YOLOv7-tiny-based algorithm for wafer surface defect detection. ACCESS, 13, 10724-10734. (www)

  @article{ , abstract = {Wafer surface defect detection is a critical component in the chip manufacturing process. To address the shortcomings of manual inspection and the limitations of existing machine learning methods, this paper proposes a wafer defect detection algorithm based on an improved YOLOv7-tiny. First, a coordinate attention (CA) module is incorporated into the feature extraction network to enhance the network’s ability to learn features at defect locations. Next, a lightweight convolutional module, ghost shuffle convolution (GSConv), is introduced into the feature fusion network to reduce the network’s parameter count while maintaining a certain level of detection accuracy. Finally, the loss function is optimized by adopting IoU with minimum points distance (MPDIoU) to address issues such as small sizes and dense distributions. Experiments conducted on a self-constructed dataset show that the improved algorithm achieved a mean Average Precision (mAP) of 90.1%, representing a 3.2% increase over the original algorithm. The model size is only 5.85MB and the detection speed has been effectively enhanced, providing valuable insights for research in industrial real-time detection applications.}, archive = {J_ACCESS}, author = {Mengyun Li and Xueying Wang and Hongtao Zhang and Xiaofeng Hu}, doi = {10.1109/ACCESS.2025.3528242}, journal = {IEEE Access}, pages = {10724-10734}, shortjournal = {IEEE Access}, title = {An improved YOLOv7-tiny-based algorithm for wafer surface defect detection}, volume = {13}, year = {2025}, }
• (2025). Latent graph induction networks and dependency graph networks for events detection. ACCESS, 13, 10713-10723. (www)

  @article{ , abstract = {The goal of event detection is to identify instances of various event types within text. In real-world scenarios, multiple events often coexist within the same sentence, making the extraction of these events more challenging than extracting a single event. While graph neural networks operating over dependency parsing trees have shown some capability in handling multi-event scenarios and improving event detection effectiveness, their improvement is limited. This limitation arises because dependency trees cannot automatically establish connections between trigger words and other key words, which are crucial for recognizing and classifying trigger words. Additionally, syntactic-based methods typically focus on the closest neighbors in the dependency graphs to aggregate information for the trigger candidate word, even though relevant words are often multi-hop away. In this paper, we combine the word dependency graphs with our automatically induced latent graph structure for event detection and multiple events detection. Furthermore, we propose two regularizers to enhance the representation of the dependency graphs and the induced latent graph structure. Experimental results demonstrate the effectiveness of our model for events detection.}, archive = {J_ACCESS}, author = {Jing Yang and Hu Gao and Depeng Dang}, doi = {10.1109/ACCESS.2024.3523895}, journal = {IEEE Access}, pages = {10713-10723}, shortjournal = {IEEE Access}, title = {Latent graph induction networks and dependency graph networks for events detection}, volume = {13}, year = {2025}, }
• (2025). Automatic calculation of cardiometric coefficients on chest X-ray images. ACCESS, 13, 10702-10712. (www)

  @article{ , abstract = {Chest radiography is an indispensable diagnostic method for detecting a variety of medical conditions, such as infections, tumors, injuries, etc. Millions of chest X-ray examinations are conducted annually, providing crucial information about the functioning of the respiratory and circulatory systems. The conventional approach to quantifying cardiothoracic indices, such as the Lupi and Moore indices and the Cardiothoracic Index (CTI), requires considerable time and effort from radiologists. Consequently, it calls for the exploration of computational methods for improvement through deep learning. In this study, we addressed the challenge of automating the calculation of these cardiometric indices. We engaged four experienced radiologists to manually label 800 chest X-ray images each. Using these labeled images, we trained a deep learning model that achieved the level of performance comparable to that of a professional radiologist. Additionally, we have replaced the central points of the indices with landmarks based on the vertebrae, improving the accuracy. The use of AI led to improved accuracy of correct predictions, increasing it from 85.94% to 87.34% for the MOORE coefficient and from 87.55% to 90.67% for the LUPI coefficient.}, archive = {J_ACCESS}, author = {Alexey Kornaev and Dmitry Lvov and Ilya Pershin and Semen Kiselev and Danil Afonchikov and Iskander Bariev and Bulat Ibragimov}, doi = {10.1109/ACCESS.2024.3524116}, journal = {IEEE Access}, pages = {10702-10712}, shortjournal = {IEEE Access}, title = {Automatic calculation of cardiometric coefficients on chest X-ray images}, volume = {13}, year = {2025}, }
• (2025). Dynamic identification of critical transmission lines in power systems with wind power integration based on maximum influence theory. ACCESS, 13, 10689-10701. (www)

  @article{ , abstract = {With the integration of large-scale renewable energy sources, the risk of power system operation has increased significantly. To effectively prevent large-scale blackout incidents, it is essential to identify the critical transmission lines within the system accurately. However, most of the existing identification methods are based on complex network theory and use static graphs to model the propagation of cascading faults, which cannot consider the impact of wind power output fluctuations. To address this issue, this paper introduces and improves the maximum influence model of the community network to identify the critical lines. By developing the time-sequence cascading faults graph, the proposed method can comprehensively consider the impact of wind power output fluctuations on the propagation of cascading faults. Based on the influence maximization model in time-sequence cascading faults graph (IMTG), the line fault influence calculation algorithm (LFIC) and improved critical line identification algorithm (ILIT) are proposed to identify the critical transmission lines. In order to verify the effectiveness of the proposed method, case studies are carried out on the IEEE 39-bus system and the IEEE 118-bus system. The results show that the critical lines identified by the proposed method can effectively cope with the fluctuation of wind power output in the future, and can provide timely feedback for power system operators.}, archive = {J_ACCESS}, author = {Gang Ruan and Changzheng Shao and Hao Wang and Yingxu Jin and Lingzi Zhu and Dongxu Chang}, doi = {10.1109/ACCESS.2025.3526660}, journal = {IEEE Access}, pages = {10689-10701}, shortjournal = {IEEE Access}, title = {Dynamic identification of critical transmission lines in power systems with wind power integration based on maximum influence theory}, volume = {13}, year = {2025}, }
• (2025). Hyperspectral image classification based on attentional residual networks. ACCESS, 13, 10678-10688. (www)

  @article{ , abstract = {Aiming at the problem that the features extracted by the existing convolutional neural Network model are insufficient and the more important information lacks key Attention in the process of hyperspectral image classification, this paper designs an Attention Residual Network (ARN). Firstly, the residual network is used to extract the features of hyperspectral images, and the quality of feature extraction is effectively improved by solving the problem of gradient disappearance and gradient explosion in deep neural network training. Then, the Attention Module (AM) is introduced to optimize the feature extraction process, so that the model can focus on the important regions in the image. This method was tested on the self-collected Herbage (HB) dataset and the public datasets Indian Pines (IP) and Pavia University (PU), and compared with four classical deep learning classification methods. The experimental results show that the proposed method performs best in hyperspectral image classification and greatly improves the classification accuracy.}, archive = {J_ACCESS}, author = {Ning Wang and Xin Pan and Xiaoling Luo and Xiaojing Gao}, doi = {10.1109/ACCESS.2024.3519789}, journal = {IEEE Access}, pages = {10678-10688}, shortjournal = {IEEE Access}, title = {Hyperspectral image classification based on attentional residual networks}, volume = {13}, year = {2025}, }
• (2025). Cyclic training of dual deep neural networks for discovering user and item latent traits in recommendation systems. ACCESS, 13, 10663-10677. (www)

  @article{ , abstract = {Recommendation systems face the complex challenge of modeling high-dimensional interactions between users and items to deliver personalized recommendations. This paper introduces Cyclic Dual Latent Discovery (CDLD), a novel method that employs dual deep neural networks (DNNs) in a cyclic training process to discover the latent traits of users and items based on their interactions. CDLD operates on the principle that interactions between users and items inherently possess information about their latent traits. By cyclically refining these traits through two interconnected DNNs, CDLD effectively captures the non-linear relationships between them. When evaluated with the MovieLens 100K dataset, CDLD demonstrated superior performance, achieving a root mean square error (RMSE) that was 2.86% lower than that of matrix factorization and 2.51% lower than that of neural collaborative filtering, thus showing strong generalization capabilities. Additionally, CDLD addresses the cold-start problem effectively by adjusting only the new entity’s latent traits during training, while maintaining fixed model weights, even with limited data samples. The proposed method not only enhances the accuracy of recommendations but also improves the scalability and robustness of the model. Through its cyclical training mechanism, CDLD enables the ongoing refinement of latent traits, paving the way for more adaptive and efficient recommendation systems with diverse applications.}, archive = {J_ACCESS}, author = {Dohyoung Rim and Sirojiddin Nuriev and Younggi Hong}, doi = {10.1109/ACCESS.2025.3526270}, journal = {IEEE Access}, pages = {10663-10677}, shortjournal = {IEEE Access}, title = {Cyclic training of dual deep neural networks for discovering user and item latent traits in recommendation systems}, volume = {13}, year = {2025}, }
• (2025). Automatic identification model for landslide disaster using remote sensing images based on improved multiresunet. ACCESS, 13, 10653-10662. (www)

  @article{ , abstract = {The current imagery of landslides presents multiple challenges, including considerable scale variations among landslides, similarities in spectral characteristics to those of bare ground surfaces, and irregular edges. Despite significant progress in semantic segmentation achieved by convolutional neural networks (CNNs), the local sensory field of CNNs poses difficulties in differentiating between landslides and bare surfaces. As a result, efficient and accurate landslide extraction remains a challenging issue within the global research community. To address this problem, a novel automatic landslide hazard remote sensing image identification model, MultiResUNet-BFDC, is proposed. Initially, null convolutions with various null rates are introduced to replace some standard convolutions, thereby expanding the sensory field without increasing the parameter count and making the model more suitable for multi-scale landslide segmentation. Additionally, the Canny operator is employed to design a lightweight boundary-focused attention (BFA) mechanism, enhancing the model’s ability to emphasize landslide edge features. Furthermore, a new hybrid loss function, adaptive focal and Dice loss (AFD loss), is introduced through the adaptive AdaLoss algorithm by combining focal loss and Dice loss, improving the model’s ability to handle unbalanced samples. The experimental results indicate that the MultiResUNet-BFDC model demonstrates enhanced performance in landslide edge detail segmentation, with fewer misidentifications and omissions and superior functionality of the BFA attentional mechanism compared with other attentional mechanisms.}, archive = {J_ACCESS}, author = {Zhenyu Zhao and Shucheng Tan and Qinghua Zhang and Hui Chen}, doi = {10.1109/ACCESS.2024.3525067}, journal = {IEEE Access}, pages = {10653-10662}, shortjournal = {IEEE Access}, title = {Automatic identification model for landslide disaster using remote sensing images based on improved multiresunet}, volume = {13}, year = {2025}, }
• (2025). Machine learning-driven optimization for solution space reduction in the quadratic multiple knapsack problem. ACCESS, 13, 10638-10652. (www)

  @article{ , abstract = {The quadratic multiple knapsack problem (QMKP) is a well-studied problem in operations research. This problem involves selecting a subset of items that maximizes the linear and quadratic profit without exceeding a set of capacities for each knapsack. While its solution using metaheuristics has been explored, exact approaches have recently been investigated. One way to improve the performance of these exact approaches is by reducing the solution space in different instances, considering the properties of the items in the context of QMKP. In this paper, machine learning (ML) models are employed to support an exact optimization solver by predicting the inclusion of items with a certain level of confidence and classifying them. This approach reduces the solution space for exact solvers, allowing them to tackle more manageable problems. The methodological process is detailed, in which ML models are generated and the best one is selected to be used as a preprocessing approach. Finally, we conduct comparison experiments, demonstrating that using a ML model is highly beneficial for reducing computing times and achieving rapid convergence.}, archive = {J_ACCESS}, author = {Diego Yáñez-Oyarce and Carlos Contreras-Bolton and Fredy Troncoso-Espinosa and Carlos Rey}, doi = {10.1109/ACCESS.2025.3529317}, journal = {IEEE Access}, pages = {10638-10652}, shortjournal = {IEEE Access}, title = {Machine learning-driven optimization for solution space reduction in the quadratic multiple knapsack problem}, volume = {13}, year = {2025}, }
• (2025). A pedaling torque observation approach for sensorless electric bicycles. ACCESS, 13, 10619-10637. (www)

  @article{ , abstract = {This study proposes an innovative unknown input observation approach based on Kalman filtering to estimate the cycling torque and provide assistance in electrically powered bicycles. Specifically, a constant and a sinusoidal pedaling torque model are compared, underlining the need for an enhanced mathematical description to improve system performance. Using a nonlinear model of the bicycle longitudinal dynamics, the cycling torque is reconstructed with an Extended Kalman Filter. Also, an online low-computational effort road slope estimation method based on Kalman filtering, that accounts for cornering effect errors, is proposed. The considered approaches, that utilize wheel speed, inertial, and motor current measurements, are tested in an outdoor setting with variable slopes and curves. Differently from the current state-of-the-art, the estimation performances are not only expressed in terms of pedaling torque estimation error minimization. This work presents a novel pedaling power and delivered energy analysis to evaluate the provided electrical assistance and the consequent pedaling effort decrease. The experimental results show that a cycling endeavor reduction, similar to what can be achieved when electrical assistance is provided employing a torque sensor, is possible, especially when relying on improved pedaling modeling.}, archive = {J_ACCESS}, author = {Riccardo Mandriota and Niklas König and Emanuele Grasso and Matthias Nienhaus}, doi = {10.1109/ACCESS.2025.3529307}, journal = {IEEE Access}, pages = {10619-10637}, shortjournal = {IEEE Access}, title = {A pedaling torque observation approach for sensorless electric bicycles}, volume = {13}, year = {2025}, }
• (2025). Enhancing building evacuation efficiency in case of fire breakout by integrating evacuee categorization into an agent-based simulation. ACCESS, 13, 10595-10618. (www)

  @article{ , abstract = {Evacuating occupants from a burning building presents significant challenges owing to the complex interplay between environmental factors and evacuee behavior. Numerous models have been proposed in recent years for building evacuation during fire emergencies. However, many of these models fail to adequately account for variations in individuals’ environmental knowledge and health statuses, both of which can critically affect the evacuation process. To address this gap, this study introduces an agent-based model for building evacuation that differentiates between individuals familiar with the building layout and those who are not and considers the health status of the occupants. The resulting model assigns specific evacuation strategies tailored to each group within the building. Simulations carried out in a hospital setting demonstrated that incorporating factors such as familiarity with a building’s configuration and the health status of the evacuees results in more realistic and effective evacuation outcomes. To evaluate the robustness of the proposed model and highlight the significance of our contribution, we conducted multiple simulations in various high-risk settings and presented a benchmarking study that compared our model with several existing models from the literature. The proposed model is sufficiently generalized to be applicable to various types of hospital buildings, with minimal modifications.}, archive = {J_ACCESS}, author = {Selain K. Kasereka and Noella E. Kalondji and Godwill W. K. Ilunga and Ruffin-Benoît M. Ngoie and Kyandoghere Kyamakya and Nathanaël M. Kasoro}, doi = {10.1109/ACCESS.2025.3525480}, journal = {IEEE Access}, pages = {10595-10618}, shortjournal = {IEEE Access}, title = {Enhancing building evacuation efficiency in case of fire breakout by integrating evacuee categorization into an agent-based simulation}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv8 object detection model for construction worker safety using image transformations. ACCESS, 13, 10582-10594. (www)

  @article{ , abstract = {The rapid growth of Deep Learning techniques plays a vital role in automation of manual work in various areas. One such area for application of new technology is that of Construction Worker Safety. It has thus become imperative to improve existing systems with the new capabilities of technology. This paper discusses a methodology of improving the performance of an existing approach of object detection, YOLOv8. The proposed work comprises of improved training of model and detection of helmet in worker images, using Test Time Augmentation (TTA) based approach. Image Transformations such as Histogram Equalization, Gamma Correction, Gaussian Blurring and Contrast Stretching are applied to augment the dataset by creating more versions of the existing data. This has shown to improve the performance of the model and also generalize better by preventing overfitting. A Test Time Augmentation-based Confidence Thresholding formula (TTACT) is also proposed, to improve the performance of helmet detection.}, archive = {J_ACCESS}, author = {Yash Seth and M. Sivagami}, doi = {10.1109/ACCESS.2025.3527511}, journal = {IEEE Access}, pages = {10582-10594}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv8 object detection model for construction worker safety using image transformations}, volume = {13}, year = {2025}, }
• (2025). A novel stacked model for classification of vocal cord paralysis over imbalanced vocal data. ACCESS, 13, 10559-10581. (www)

  @article{ , abstract = {Over time, many classification systems have been developed for voice-related disorders using machine learning methods and limited usage of deep learning techniques. These systems were evaluated across accuracy, F1-score, precision, and recall using the Mel-Frequency Cepstral Coefficient (MFCC), time-domain features, etc. The aforementioned gave adequate results over the dataset of voice recordings of the vowel that either have a balanced dataset across all the classes or multiple voice pathologies are selected to bring the balance in the dataset equal to healthy subjects. In real-world scenarios, anticipating imbalance and a small amount of data is often associated with voice disorders. Vocal Cord Paralysis is one such voice pathology with limited data. In this paper, the proposed stacked ensemble model, InceptionV3-EfficientNetB0-ViT-B/16, is employed to classify Vocal Cord Paralysis (VCP) and healthy subjects over an imbalanced dataset in hand using spectrograms as a feature. Voice samples from the Saarbruecken Voice Database (SVD) for healthy and VCP are selected of the vowels /a/, /i/, and /u/ over neutral, high, low, and low-high-low pitch conditions and the phrase. Further, using the Short-time Fourier Transform (STFT), the voice samples are preprocessed, and each sample is augmented at various frequencies. The results from the experiments express that the proposed stacked model achieved an excellent accuracy of 94.11% for the vowel /a/ at normal and low-high-low pitch conditions using an imbalanced dataset. In addition, the proposed model’s robustness and trustworthiness are proven by the False Discovery Rate of 0.07142, Cohen Kappa of 0.82105, Mathew’s Correlation coefficient (MCC) of 0.83452, and F1-score 0.91005. The vowels /i/ and /u/, were also evaluated over the proposed model, and 88.23% accuracy is procured over most pitch conditions for the vowels and 90% for the phrase. Overall, the proposed method exhibited a powerful and successful capability for diagnosis throughout an unbalanced dataset without overtly favoring the majority class of healthy individuals and maintained an adequate balance in precisely recognizing the minority class VCP.}, archive = {J_ACCESS}, author = {K. Jayashree Hegde and K. Manjula Shenoy and K. Devaraja}, doi = {10.1109/ACCESS.2025.3525721}, journal = {IEEE Access}, pages = {10559-10581}, shortjournal = {IEEE Access}, title = {A novel stacked model for classification of vocal cord paralysis over imbalanced vocal data}, volume = {13}, year = {2025}, }
• (2025). Degrade or super-resolve to recognize? bridging the domain gap for cross-resolution face recognition. ACCESS, 13, 10542-10558. (www)

  @article{ , abstract = {In this work, we address the problem of cross-resolution face recognition, where a low-resolution probe face is compared against high-resolution gallery faces. To address this challenging problem, we investigate two approaches for bridging the quality gap between low-quality probe faces and high-quality gallery faces. The first approach focuses on degrading the quality of high-resolution gallery images to bring them closer to the quality of the probe images. The second approach involves enhancing the resolution of the probe images using face hallucination. Our experiments on the SCFace and DroneSURF datasets reveal that the success of face hallucination is highly dependent on the quality of the original images, since poor image quality can severely limit the effectiveness of the hallucination technique. Therefore, the selection of the appropriate face recognition method should consider the quality of the images. Additionally, our experiments also suggest that combining gallery degradation and face hallucination in a hybrid recognition scheme provides the best overall results for cross-resolution face recognition with relatively high-quality probe images, while the degradation process on its own is the more suitable option for low-quality probe images. Our results show that the combination of standard computer vision approaches such as degradation, super-resolution, feature fusion, and score fusion can be used to substantially improve performance on the task of low resolution face recognition using off-the-shelf face recognition models without re-training on the target domain.}, archive = {J_ACCESS}, author = {Klemen Grm and Berk Kemal Özata and Alperen Kantarci and Vitomir Štruc and Hazim Kemal Ekenel}, doi = {10.1109/ACCESS.2025.3527236}, journal = {IEEE Access}, pages = {10542-10558}, shortjournal = {IEEE Access}, title = {Degrade or super-resolve to recognize? bridging the domain gap for cross-resolution face recognition}, volume = {13}, year = {2025}, }
• (2025). High-efficiency continuous wave 16 w–7.5 GHz power amplifier with a GaN transistor in plastic packaging. ACCESS, 13, 10535-10541. (www)

  @article{ , abstract = {This paper presents an alternative solution for higher frequency power amplifier design. Using a plastic-packaged GaN transistor and high-efficiency design techniques, a power amplifier for the space-to-earth satellite was designed and fabricated. The proposed design approach relies on determining an appropriate design space for the matching networks, defined by identifying optimal power and efficiency contours derived from source/load-pull techniques. The fabricated amplifier achieved in continuous wave (CW) a saturated output power of 41.7–42.4 dBm in the entire frequency band of 7.25–7.85 GHz while maintaining a drain efficiency of 35–39 %. Thermal simulations and measurements demonstrate stable and safe operating conditions in CW operation.}, archive = {J_ACCESS}, author = {Alireza Sadeghi-Fard and Sayyed-Hossein Javid-Hosseini and Vahid Nayyeri and Paolo Colantonio}, doi = {10.1109/ACCESS.2025.3528430}, journal = {IEEE Access}, pages = {10535-10541}, shortjournal = {IEEE Access}, title = {High-efficiency continuous wave 16 w–7.5 GHz power amplifier with a GaN transistor in plastic packaging}, volume = {13}, year = {2025}, }
• (2025). DM-KD: Decoupling mixed-images for efficient knowledge distillation. ACCESS, 13, 10527-10534. (www)

  @article{ , abstract = {Knowledge distillation (KD) is a method of model compression. It involves extracting valuable knowledge from a high-performance and high-capacity teacher model and transferring this knowledge to a target student model having relatively small capacity. However, we discover that naively applying mixed-augmentation to KD impacts the student model’s learning through knowledge distillation negatively. We analyze this side-effect of mix-augmentation when using knowledge distillation and propose a new method that addresses this problem. That is, we found that mixed images tend to make the teacher generate unstable and poor-quality logits which hinder transferring the knowledge. To solve this problem, we decouple an input mixed image into two original images and feed them into the teacher model individually. After then we interpolate the two individual logits to generate a logit for KD. For the student, the mixed image is still used as input of the student. This decoupling strategy allows the stability of logit distributions of the teacher, thus resulting in higher KD performance with mixed augmentation. To verify the effectiveness of the proposed method, we experiment on various datasets and mixed augmentation methods, demonstrating that the proposed method showed 0.31%-0.69% improvement in top-1 accuracy compared to the original KD method on theImageNet dataset.}, archive = {J_ACCESS}, author = {Jongkyung Im and Younho Jang and Junpyo Lim and Taegoo Kang and Chaoning Zhang and Sung-Ho Bae}, doi = {10.1109/ACCESS.2024.3524734}, journal = {IEEE Access}, pages = {10527-10534}, shortjournal = {IEEE Access}, title = {DM-KD: Decoupling mixed-images for efficient knowledge distillation}, volume = {13}, year = {2025}, }
• (2025). Accelerating multilingual cryptocurrency forensics: An NLP-driven approach for efficient mnemonic identification. ACCESS, 13, 10513-10526. (www)

  @article{ , abstract = {The increasing use of cryptocurrencies in criminal activities presents significant challenges to society and the judicial system, particularly in tracking and seizing illicit digital assets. Among all relevant digital evidence, mnemonic phrases, which are critical for accessing cryptocurrency wallets, are crucial digital evidence for confiscating criminal proceeds and conducting investigations. However, traditional digital forensics tools, such as the Mnemonic Library Matching Method, lack flexibility and efficiency when handling cryptocurrency-related data. This study introduces an innovative Natural Language Processing (NLP) and deep learning approach for rapid mnemonic identification across 11 languages, including English, Spanish, and Japanese. We trained and compared four NLP deep learning models: RNN, LSTM, BiLSTM, and TextCNN, on a large-scale, real-world dataset. Our analysis reveals that the Text Convolutional Neural Network (TextCNN) model exhibits superior performance, achieving a 99.9993% accuracy rate, nearly matching the 100% accuracy of the Mnemonic Library Matching Method. Crucially, our TextCNN-driven approach processes data 40.47 times faster than the traditional method, significantly enhancing efficiency in time-sensitive forensic environments. This NLP-driven method not only maintains high accuracy while dramatically reducing processing time but also offers greater adaptability for diverse forensic needs compared to traditional techniques. By enabling more effective tracking and seizure of criminal assets, this approach aims to address the broader societal and judicial challenges posed by cryptocurrency-related criminal activities. Our research showcases the potential of NLP and deep learning in digital forensics, providing law enforcement with advanced tools for investigating cryptocurrency-related crimes and curbing the misuse of cryptocurrencies in illicit activities.}, archive = {J_ACCESS}, author = {Hsin-Hsiung Kao}, doi = {10.1109/ACCESS.2025.3528829}, journal = {IEEE Access}, pages = {10513-10526}, shortjournal = {IEEE Access}, title = {Accelerating multilingual cryptocurrency forensics: An NLP-driven approach for efficient mnemonic identification}, volume = {13}, year = {2025}, }
• (2025). An innovative solution to design problems: Applying the chain-of-thought technique to integrate LLM-based agents with concept generation methods. ACCESS, 13, 10499-10512. (www)

  @article{ , abstract = {To enhance the application capabilities of large language models (LLMs) in conceptual design, this study explores how to achieve deep integration between LLM-based agents and concept generation methods using the chain-of-thought (CoT) technique and evaluates its feasibility. Using GPT-4 as a case study, we designed two agents: IntelliStorm (based on the unstructured brainstorming method) and EvoluTRIZ (based on the structured TRIZ method). Thirty participants were recruited, and through two experimental phases spaced one month apart, a comparative analysis of the effects of collaboration groups (human-agent vs. human-human) and concept generation methods (brainstorming vs. TRIZ) on participants’ physiological activation and creative thinking performance were conducted. The results show that the involvement of LLM-based agents can effectively reduce participants’ electrodermal activity(EDA) response levels, indicating a reduction in cognitive load. Moreover, participants maintained their distinct physiological patterns and performance advantages across different concept generation methods. For example, IntelliStorm, like brainstorming, evokes stronger responses to information stimuli, demonstrating superior thinking fluency; EvoluTRIZ, like the TRIZ, exhibits a higher frequency of information responses, showcasing enhanced thinking elaboration. However, originality tends to favor human-human collaboration. The findings confirm that integrating LLMs with traditional concept generation methods is an effective strategy made possible by combining CoT and retrieval-augmented generation (RAG) technologies. In the future, LLM-based agents are expected to achieve broader application in the design field by incorporating additional concept generation methods.}, archive = {J_ACCESS}, author = {Shijun Ge and Yuanbo Sun and Yin Cui and Dapeng Wei}, doi = {10.1109/ACCESS.2024.3494054}, journal = {IEEE Access}, pages = {10499-10512}, shortjournal = {IEEE Access}, title = {An innovative solution to design problems: Applying the chain-of-thought technique to integrate LLM-based agents with concept generation methods}, volume = {13}, year = {2025}, }
• (2025). Accelerated reconstruction of scenes using CUDA-based parallel computing. ACCESS, 13, 10489-10498. (www)

  @article{ , abstract = {In recent years, perceiving the environment has been the most important part of unmanned aerial vehicles (UAVs) inspection tasks on the battlefield scene. This article aims to study how to quickly reconstruct unknown battlefield scenes, such as buildings, streets, etc. using UAVs equipped with binocular cameras and stereo-matching technology. We use a binocular module to reduce the cost of drone-borne equipment and lighten the load of the UAVs. Since the drone needs to use an embedded onboard computer and stereo matching algorithms require massive computing resources, this paper studies the optimization of CUDA-based stereo matching algorithms on embedded devices to improve the reconstruction effect of scenes and achieve rapid reconstruction, thereby reducing time costs, and enhancing drone efficiency.}, archive = {J_ACCESS}, author = {Gui Zou and Jin Jiang and Qi Chen}, doi = {10.1109/ACCESS.2024.3523099}, journal = {IEEE Access}, pages = {10489-10498}, shortjournal = {IEEE Access}, title = {Accelerated reconstruction of scenes using CUDA-based parallel computing}, volume = {13}, year = {2025}, }
• (2025). Short-term power load prediction method based on VMD and EDE-BiLSTM. ACCESS, 13, 10481-10488. (www)

  @article{ , abstract = {Aiming at the diversity and flexibility of electric loads and their inherent nonlinearity and temporality in the context of the new era. A forecasting method is proposed combining Variational Modal Decomposition (VMD) and EDE-BiLSTM. Initially, the load data are decomposed using VMD to obtain several modal components. These components are then integrated with temperature, wind speed, precipitation, electricity price, and holiday characteristics to capture a comprehensive set of influencing factors. Subsequently, the modal components undergo training and learning through the EDE-BiLSTM model, aiming to minimize the test set’s root mean square error (RMSE). Each modal component is modelled individually, and the final prediction is derived by summing the predicted values of all components. The method’s effectiveness is validated using load datasets from Singapore and Australia. The model in this paper outperforms the comparison model with root mean square error (RMSE), mean absolute percentage error (MAPE),and R-squared (R2) for the two datasets respectively. The experimental results show that the proposed method performs better than other methods and can improve the accuracy of short-term power load forecasting.}, archive = {J_ACCESS}, author = {Yibo Lai and Qifeng Wang and Gang Chen and Yu Bai and Peiyu Zhao and Xiaojing Liao and Shuang Wu and Changyou Men and Quan Sun}, doi = {10.1109/ACCESS.2024.3522656}, journal = {IEEE Access}, pages = {10481-10488}, shortjournal = {IEEE Access}, title = {Short-term power load prediction method based on VMD and EDE-BiLSTM}, volume = {13}, year = {2025}, }
• (2025). Time-sensitive smart cities: Addressing smart city challenges with TSN. ACCESS, 13, 10459-10480. (www)

  @article{ , abstract = {As cities transition into interconnected hubs of technology, the need for efficient time management across diverse interconnected devices becomes a requirement for supporting critical services for emergency, traffic, and other use cases. Time-sensitive network (TSN) emerges as a promising solution, offering standards and mechanisms to synchronize and regulate time-critical traffic in smart city infrastructures. This study researches the main use cases, key standards and mechanisms of TSN, evaluating their feasibility and adaptability to the heterogeneous equipment present in an urban environment of a smart city infrastructure. Through experimental assessments, this article demonstrates the efficacy of TSN-based solutions and also discusses hardware enhancements that complement TSN protocols, improving precision and resource utilization to ensure criticality in urban connectivity, enabling precise control over traffic conditions and service delivery towards the future smart city, as an environment of interconnected things. Moreover, we propose a classification of nine traffic categories to encompass the various traffic flows in a smart city deployment, and validate the application of time-aware and credit-based shaping of all nodes in the network according to the type of service. Results show that TSN is a valid choice to manage mixed-criticality of services in a smart city.}, archive = {J_ACCESS}, author = {Rui Eduardo Lopes and Duarte Raposo and Rodrigo Martins and Susana Sargento}, doi = {10.1109/ACCESS.2025.3528257}, journal = {IEEE Access}, pages = {10459-10480}, shortjournal = {IEEE Access}, title = {Time-sensitive smart cities: Addressing smart city challenges with TSN}, volume = {13}, year = {2025}, }
• (2025). Cross-supervised LiDAR-camera fusion for 3D object detection. ACCESS, 13, 10447-10458. (www)

  @article{ , abstract = {Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. Due to inherent differences between different modalities, seeking an efficient and accurate fusion method is of great importance. Recently, significant progress has been made in 3D object detection methods based on lift-splat-shot (LSS-based) approaches. However, inaccurate depth estimation and substantial semantic information loss remain significant factors limiting the accuracy of 3D detection. In this paper, we propose a cross-fusion framework under a dual spatial representation, by integrating information in different spatial representations, namely bird’s-eye view (BEV) and camera view, and establishing soft links to fully utilize the information carried by different modalities. It consists of two important components, gated LiDAR supervised BEV (GLS-BEV) and multi-attention cross fusion (MACF) modules. The former achieves accurate depth estimation by supervising the transformation of LiDAR data with clear depth into the image space, constructing point cloud features in vehicle’s perspective. The latter utilizes three sub-attention modules with different roles to achieve cross-modal interaction within the same space, effectively reducing semantic loss. On the nuScenes benchmark, our proposed method achieves outstanding 3D object detection results with 71.8 mAP and 74.2 NDS. The code is available at https://github.com/zcj223311/CSDSFusion.}, archive = {J_ACCESS}, author = {Chao Jie Zuo and Cao Yu Gu and Yi Kun Guo and Xiao Dong Miao}, doi = {10.1109/ACCESS.2024.3518564}, journal = {IEEE Access}, pages = {10447-10458}, shortjournal = {IEEE Access}, title = {Cross-supervised LiDAR-camera fusion for 3D object detection}, volume = {13}, year = {2025}, }
• (2025). Rotation-invariant convolution with point sort and curvature radius for point cloud classification and segmentation. ACCESS, 13, 10432-10446. (www)

  @article{ , abstract = {Recently, the distance-based and angle-based geometric descriptors and local reference axes have been used widely to explore the rotation invariance of point clouds. However, they tend to encounter with two challenges. (i) Similar distances and angles among different points would lead to ambiguous descriptions of local regions. (ii) Establishing a local reference axis may reduce the number of neighbor points, resulting in information loss in local regions. To this end, a Rotation-invariant Convolution with Point Sorting and Curvature Radius $\text {(RCPC)}$ is proposed. Firstly, to solve the challenge (i), a neighbor point sorting module $\text {(NPS)}$ is introduced. Neighbor points on the local tangent disk are sorted according to the local reference axis at the first step. When neighbor points occlude each other along the local reference axis direction, NPS calculates the Euclidean distances from the sampling point to each neighbor point. With these distances, neighbor points in the local region are reorganized to establish multiple triangles to retain as much information. To solve the challenge (ii), a curvature-based geometric descriptor $\text {(CGD)}$ is developed. It calculates the Euclidean distance and angle between the points within established triangles. Further, the CGD constructs a curvature circle for each triangle and calculate the curvature radius which is highly sensitive to small local shape changes. Even Euclidean distances and angles are similar, the CGD can maintain high uniqueness for local regions. Experiments on ModelNet40, ScanObjectNN, and ShapeNet have proved that the proposed approach outperforms other state-of-the-art methods.}, archive = {J_ACCESS}, author = {Zhao Shen and Xin Jia and Jinglei Zhang}, doi = {10.1109/ACCESS.2025.3528435}, journal = {IEEE Access}, pages = {10432-10446}, shortjournal = {IEEE Access}, title = {Rotation-invariant convolution with point sort and curvature radius for point cloud classification and segmentation}, volume = {13}, year = {2025}, }
• (2025). Sparse representation-based LDCT image quality assessment using the JND model. ACCESS, 13, 10422-10431. (www)

  @article{ , abstract = {In clinical environments, the quality of low-dose computerized tomography (LDCT) images is inevitably degraded by artifacts and noise. Accurate and clinically relevant image quality assessment (IQA) for LDCT images is crucial to provide appropriate medical care. However, due to the absence of reference images and inadequate understanding of the human visual system (HVS), there is currently a lack of effective IQA methods. To address this problem, this paper proposes a no-reference IQA method that emulates the perceptual characteristics of the HVS. The method is based on the use of sparse representation in conjunction with a just noticeable distortion (JND) model. Specifically, for each tested image, a patch-level JND map is first calculated to indicate the noticeable level of distortion in different regions. Subsequently, noticeable patches of the image are encoded via sparse representation, followed by max pooling to obtain sparse features. Finally, these sparse features, along with the sorted JND values, are combined as overall features into a regression model to predict an objective quality score. By combining two types of features, our method can measure the quality from the perspectives of both local spatial structures and visual distortion sensitivity. Our method is evaluated on the LDCTIQAC2023 database, and the experimental results demonstrate the effectiveness of our method, which correlates reasonably well with the radiologists’ subjective scores.}, archive = {J_ACCESS}, author = {Mo Shen and Rongrong Sun and Wen Ye}, doi = {10.1109/ACCESS.2025.3528882}, journal = {IEEE Access}, pages = {10422-10431}, shortjournal = {IEEE Access}, title = {Sparse representation-based LDCT image quality assessment using the JND model}, volume = {13}, year = {2025}, }
• (2025). A low-duty-cycle and congestion-robust design of adaptive wi-fi 7 multi-link operation control over user side. ACCESS, 13, 10412-10421. (www)

  @article{ , abstract = {Wi-Fi 7 has been developed recently to support new applications such as 4K videos and AR/VR, which demand higher network throughput and lower latency. One of its key features, Multi-link Operation (MLO), enables Wi-Fi 7 to utilize multiple carrier frequencies, enhancing throughput and reducing latency. Recent research has extensively covered MAC-layer architecture and multi-link channel access algorithm design for MLO implementation, but relatively few studies address the power efficiency problem when MLO is implemented in user devices under dynamic traffic conditions. For user devices, power efficiency is critical due to stricter power budgets, and hence, a minimal radio activation time (i.e., duty cycle) is needed. We have devised an adaptive algorithm that dynamically adjusts MLO configurations based on periodic measurements at the MAC/IP layers. Our simulations using the NS-3 platform demonstrate that our approach maintains MLO’s congestion-resistant throughput advantages while significantly reducing the radio’s duty cycle compared to a static MLO mode. This adaptation makes MLO more feasible for power-sensitive user devices.}, archive = {J_ACCESS}, author = {Yirong Cheng and Abhishek Sehgal and Kyeong Jin Kim and Neha Dawar and Yuming Zhu}, doi = {10.1109/ACCESS.2024.3524999}, journal = {IEEE Access}, pages = {10412-10421}, shortjournal = {IEEE Access}, title = {A low-duty-cycle and congestion-robust design of adaptive wi-fi 7 multi-link operation control over user side}, volume = {13}, year = {2025}, }
• (2025). Drug efficacy recommendation system of glioblastoma (GBM) using deep learning. ACCESS, 13, 10398-10411. (www)

  @article{ , abstract = {Glioblastoma (GBM), a common cancer of the central nervous system (CNS), is considered incurable worldwide. The treatment of GBM varies from patient to patient, as conventional medical treatments do not apply to all patients with similar symptoms. Therefore, drug efficacy recommendation systems are very useful in the treatment of various types of cancers. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used as the primary data source, containing 135,242 cell line-drug interactions, inhibitory concentration of cancer drug for more than 800 cancer cell lines. Each cell line provides gene expression values, which are further normalized through Z-transformation for each gene. However, we utilized only 47 genes in our research due to limitations in computer processing speed and memory. A drug efficacy recommendation system was constructed using a deep learning method that combines gene expression, drug Simplified Molecular Input Line Entry System (SMILES), and inhibitory concentration features. A panel of 47 genes associated with GBM was processed using two deep learning models: Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). This approach addresses the challenge of personalized treatment for GBM, offering the potential for improved therapeutic outcomes. The results of the recommendation system are calculated based on Half Maximal Inhibitory Concentration (IC50) values, which represent the therapeutic effectiveness in inhibiting the growth of GBM cells. CNN outperformed ANN with a significant margin, achieving a Root Mean Square Error (RMSE) of 0.9822 compared to 1.2127. These results are also consistent with other metrics, including Pearson correlation, Spearman correlation, and Mean Absolute Error (MAE). According to the study, the system can accurately predict the effectiveness of drugs on GBM cancer genes. This study has the potential to predict drug efficacy during medical procedures.}, archive = {J_ACCESS}, author = {Sajid Naveed and Mujtaba Husnain and Ali Samad and Amna Ikram and Hina Afreen and Ghulam Gilanie and Najah Alsubaie}, doi = {10.1109/ACCESS.2024.3514912}, journal = {IEEE Access}, pages = {10398-10411}, shortjournal = {IEEE Access}, title = {Drug efficacy recommendation system of glioblastoma (GBM) using deep learning}, volume = {13}, year = {2025}, }
• (2025). Event-based slip estimation framework for space rovers traversing soft terrains. ACCESS, 13, 10386-10397. (www)

  @article{ , abstract = {Estimating terrain-induced longitudinal slip poses a significant challenge in space rover navigation, particularly when traversing soft terrains in low-light conditions. Precise estimation of this slip is important for rover navigation algorithms, as it helps to prevent rovers from traversing areas with excessive slippage, thereby avoiding entrapment —an outcome that could lead to mission failure. This paper presents a novel event-based longitudinal slip estimation framework designed to operate in low-light conditions. Existing vision-based slip estimation techniques suffer from low temporal resolution sensors and a high sensitivity to motion blur. These limitations prevent the immediate detection of slip events, which can occur in microseconds, making such methods unsuitable for highly variable terrain environments such as lunar surfaces. Our proposed framework addresses these challenges, offering a robust solution for precise slip estimation in microseconds with limited energy consumption. Specifically, we generate the data directly from real-time wheel traces at varied light conditions and motion patterns and then propose a framework to estimate the slip. This framework consists of a denoising filter and a Hough transformer. Experiments conducted on a single-wheel test rig demonstrate the effectiveness of the proposed event-based framework in estimating slip ratios that range from 2% to 90% with an estimated accuracy of above 95% in well-lit conditions and above 94% in low-light conditions. A supplementary video is available at https://youtu.be/eac6Q2a8A9g.}, archive = {J_ACCESS}, author = {Ruqayya Alhammadi and Yahya Zweiri and Ahmad Abubakar and Mubarak Yakubu and Laith Abuassi and Lakmal Seneviratne}, doi = {10.1109/ACCESS.2024.3434719}, journal = {IEEE Access}, pages = {10386-10397}, shortjournal = {IEEE Access}, title = {Event-based slip estimation framework for space rovers traversing soft terrains}, volume = {13}, year = {2025}, }
• (2025). Parametric study and experimental validation of acoustic leaky wave antenna in spatial localization. ACCESS, 13, 10371-10385. (www)

  @article{ , abstract = {Acoustic signals, which have been utilized for decades in the spatial localization of objects, have found applications in fields as diverse as sonar for underwater navigation, communication, and object detection. Traditional methods often rely on arrays of transducers, which necessitate the use of expensive hardware and processing algorithms. An emerging alternative is the Acoustic Leaky Wave Antenna (ALWA), which is inspired by electromagnetic leaky wave antennas. ALWA technology employs a single transducer to emit directional beams that scan angular space by frequency manipulation. Conventional arrays offer cost-effectiveness and simplicity of design, but ALWAs have the advantage of operating on the principle of energy leakage, which is achieved by various mechanisms, such as uniform apertures or slits periodic along the waveguide. This technology, applicable to underwater and airborne communications, offers compact and energy-efficient solutions, which facilitate the development of the “Underwater Internet of Things” and autonomous communication systems for underwater vehicles. This work presents a parametric study of this type of antennas with axisymmetric geometry by means of a numerical solution based on the Finite Element Method. Together with analytical studies, the physical phenomenology underlying this technology will be described, including directivity, transmission and reflection parameters, beam scanning and dispersion curve. Finally, the design is validated through experiments.}, archive = {J_ACCESS}, author = {Alejandro Fernández-Garrido and María Campo-Valera and Elena Abdo-Sánchez and Rubén Picó and Antonio-Javier Garcia-Sanchez and Rafael Asorey-Cacheda}, doi = {10.1109/ACCESS.2025.3528081}, journal = {IEEE Access}, pages = {10371-10385}, shortjournal = {IEEE Access}, title = {Parametric study and experimental validation of acoustic leaky wave antenna in spatial localization}, volume = {13}, year = {2025}, }
• (2025). Advanced fraud detection: Leveraging K-SMOTEENN and stacking ensemble to tackle data imbalance and extract insights. ACCESS, 13, 10356-10370. (www)

  @article{ , abstract = {This study proposes an innovative solution for credit card fraud detection, utilizing a stacking ensemble of machine learning classifiers enhanced with sophisticated data resampling techniques. The model demonstrates exceptional performance, achieving an F1-score of 0.92, precision of 0.95, recall of 0.88, an AUPRC of 0.96, and a perfect ROC-AUC of 1.00. These results significantly outperform standalone models like XGBoost and Decision Tree, showcasing the strength of the proposed approach. The study addresses two critical challenges, class imbalance and overfitting, by employing K-means SMOTEENN to balance minority class representation while reducing synthetic data noise, thus lowering the risk of overfitting. Additionally, the stacking ensemble’s integration of diverse classifiers produces a generalized decision boundary, enhancing model robustness in real-world scenarios. This ensures a precise fraud detection mechanism without compromising sensitivity. Furthermore, the proposed model incorporates Explainable AI (XAI) techniques to enhance interpretability and trust. The study identifies key features driving model predictions by leveraging Local Interpretable Model-Agnostic Explanations (LIME), illustrating how base learners and the meta-learner contribute to the final decision. This transparency bolsters stakeholder confidence and provides actionable insights for financial institutions. Overall, the proposed approach marks a notable step in protecting financial transactions from fraud.}, archive = {J_ACCESS}, author = {Nurafni Damanik and Chuan-Ming Liu}, doi = {10.1109/ACCESS.2025.3528079}, journal = {IEEE Access}, pages = {10356-10370}, shortjournal = {IEEE Access}, title = {Advanced fraud detection: Leveraging K-SMOTEENN and stacking ensemble to tackle data imbalance and extract insights}, volume = {13}, year = {2025}, }
• (2025). Accelerated convergence method for flow field based on DMD-POD combined reduced-order optimization model. ACCESS, 13, 10340-10355. (www)

  @article{ , abstract = {This work presents a novel acceleration method that achieves more efficient convergence of steady-state flow fields. This method involves conducting dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) model reduction on the field snapshots. Subsequently, the residual of the reduced-order model is optimized in the POD modal space to obtain a more accurate solution. This optimized solution is then used as the initial field, and the solver continues iterating until the residual converges. Taking full advantage of both DMD and POD, the proposed approach removes the interference of high-frequency oscillatory flow components and concentrates on the main energy components. This effectively overcomes the problems of slow convergence and residual jumps caused by system stiffness, thereby accelerating the convergence process. The results show that for linear equations, the proposed method achieves a significant acceleration, with a convergence speed five times faster than traditional numerical methods. For the nonlinear Burgers equation, the proposed method also reduces the number of convergence steps by nearly 70%. Additionally, the performance of the proposed accelerated convergence method was further validated through the complex flow around a high-dimensional dual ellipsoid.}, archive = {J_ACCESS}, author = {Jianhui Li and Jun Huang and Yahui Sun and Guoqiang Li}, doi = {10.1109/ACCESS.2025.3527630}, journal = {IEEE Access}, pages = {10340-10355}, shortjournal = {IEEE Access}, title = {Accelerated convergence method for flow field based on DMD-POD combined reduced-order optimization model}, volume = {13}, year = {2025}, }
• (2025). Semantic image synthesis via class-adaptive cross-attention. ACCESS, 13, 10326-10339. (www)

  @article{ , abstract = {In semantic image synthesis the state of the art is dominated by methods that use customized variants of the SPatially-Adaptive DE-normalization (SPADE) layers, which allow for good visual generation quality and editing versatility. By design, such layers learn pixel-wise modulation parameters to de-normalize the generator activations based on the semantic class each pixel belongs to. Thus, they tend to overlook global image statistics, ultimately leading to unconvincing local style editing and causing global inconsistencies such as color or illumination distribution shifts. Also, SPADE layers require the semantic segmentation mask for mapping styles in the generator, preventing shape manipulations without manual intervention. In response, we designed a novel architecture where cross-attention layers are used in place of SPADE for learning shape-style correlations and so conditioning the image generation process. Our model inherits the versatility of SPADE, at the same time obtaining state-of-the-art generation quality improving FID score by 5.6%, 1.4% and 3.4% on CelebMask-HQ, Ade20k and DeepFashion datasets respectively, as well as improved global and local style transfer. Code and models available at https://github.com/TFonta/CA2SIS.}, archive = {J_ACCESS}, author = {Tomaso Fontanini and Claudio Ferrari and Giuseppe Lisanti and Massimo Bertozzi and Andrea Prati}, doi = {10.1109/ACCESS.2025.3529216}, journal = {IEEE Access}, pages = {10326-10339}, shortjournal = {IEEE Access}, title = {Semantic image synthesis via class-adaptive cross-attention}, volume = {13}, year = {2025}, }
• (2025). Securing maritime autonomous surface ships: Cyber threat scenarios and testbed validation. ACCESS, 13, 10311-10325. (www)

  @article{ , abstract = {The maritime industry is undergoing rapid digital transformation, with Maritime Autonomous Surface Ships (MASS) at the forefront, introducing unprecedented cybersecurity challenges. This study investigates the unique characteristics and cybersecurity requirements of MASS, presenting a tailored approach to address these vulnerabilities effectively. To validate the proposed solution, a testbed environment replicating real-world shipboard networks was developed, incorporating eight attack scenarios designed using the MITRE ATT&CK ICS framework. Among these, the most critical scenario was systematically evaluated, demonstrating the solution’s robustness and practical applicability. By bridging the gap between theoretical models and real-world implementations, this research provides a replicable methodology and foundational tools to enhance the cybersecurity of autonomous maritime systems, supporting the safe and reliable adoption of MASS in the global maritime industry.}, archive = {J_ACCESS}, author = {Seongmin Lee and Yonghun No and Juwon Cho and Guckhyeon Jin and Jeongho Kim and Jeongmin Lee and Sanghyun Jeon and Faisal Abdulaziz Alfouzan and Kyounggon Kim}, doi = {10.1109/ACCESS.2025.3527132}, journal = {IEEE Access}, pages = {10311-10325}, shortjournal = {IEEE Access}, title = {Securing maritime autonomous surface ships: Cyber threat scenarios and testbed validation}, volume = {13}, year = {2025}, }
• (2025). Optimization of truck platoon size in freeway diverging areas based on comprehensive performance evaluation. ACCESS, 13, 10299-10310. (www)

  @article{ , abstract = {Truck platoons can enhance traffic throughput, achieve better fuel economy, and yield environmental benefits. However, in freeway diverging areas, excessively long truck platoons can cause a blocking effect on small vehicles exiting the freeway, thereby causing congestion and impacting the traffic flow in the section. Therefore, to balance the benefits between truck platoons and small vehicles, a comprehensive evaluation model was established, considering traffic efficiency, safety, and fuel economy. The analytic hierarchy process (AHP) method was employed to determine the importance of each indicator, resulting in a composite score. Using the Simulation of Urban Mobility (SUMO) platform, this paper examines the effects of truck platoon size (ranging from 2 to 15 trucks) on traffic efficiency, safety, and fuel consumption under varying conditions. The analysis considers low, medium, and high small vehicle traffic volumes of 750, 1200, and 1650 pcu/h/lane, as well as off-ramp probabilities for small vehicles of 10%, 20%, 30%, and 40%. Simulation results indicate that traffic efficiency initially increases and then decreases as the truck platoon size increases. An increase in the number of truck platoon members leads to a decrease in section safety, particularly noticeable under medium and high flow conditions. In three flow scenarios, truck platoon size of more than 5 trucks can achieve higher fuel economy. Taking an off-ramp probability of 10% for small vehicles as an example, the optimal truck platoon size ranges from 3 to 8 vehicles under low flow conditions, 2 to 7 vehicles under medium flow conditions, and 2 to 6 vehicles under high flow conditions. Hence, traffic managers in freeway diverging areas can utilize the findings of this study to select suitable truck-platoon size, enabling them to implement preemptive adjustment strategies for achieving optimal comprehensive performance.}, archive = {J_ACCESS}, author = {Zhifa Yang and Zongyao Li and Zhuo Yu and Wencai Sun and Jingjing Tian}, doi = {10.1109/ACCESS.2025.3528460}, journal = {IEEE Access}, pages = {10299-10310}, shortjournal = {IEEE Access}, title = {Optimization of truck platoon size in freeway diverging areas based on comprehensive performance evaluation}, volume = {13}, year = {2025}, }
• (2025). IRSnet: An implicit residual solver and its unfolding neural network with 0.003M parameters for total variation models. ACCESS, 13, 10289-10298. (www)

  @article{ , abstract = {Solving total variation problems is fundamentally important for many computer vision tasks, such as image smoothing, optical flow estimation and 3D surface reconstruction. However, the traditional iterative solvers require a large number of iterations to converge, while deep learning solvers have a huge number of parameters, hampering their practical deployment. To address these issues, this paper first introduces a novel iterative algorithm that is 6 ~ 75 times faster than previous iterative methods. The proposed iterative method converges and converges to the optimal solution. These two facts are theoretically guaranteed and numerically confirmed, respectively. Then, we generalize this algorithm to a compact implicit neural network that has only 0.003M parameters. The network is shown to be more effective and efficient. Thanks to the small number of parameters, the proposed network can be applied in a wide range of applications where total variation is imposed. The source code for the iterative solver and the neural network is publicly available at https://github.com/gyh8/IRS.}, archive = {J_ACCESS}, author = {Yuanhao Gong}, doi = {10.1109/ACCESS.2025.3528637}, journal = {IEEE Access}, pages = {10289-10298}, shortjournal = {IEEE Access}, title = {IRSnet: An implicit residual solver and its unfolding neural network with 0.003M parameters for total variation models}, volume = {13}, year = {2025}, }
• (2025). Hypergraph-based organizers’ behavior scheduling optimization: High-order relationships discovery. ACCESS, 13, 10277-10288. (www)

  @article{ , abstract = {In the context of organizers’ behavior scheduling optimization, accurately assessing and optimizing scheduling programs is essential. This paper introduces HyperFusion, a framework designed to enhance scheduling efficiency through hypergraph learning techniques and an adaptive feature fusion model tailored to organizers’ behavioral patterns. The model categorizes and evaluates scheduling methods by identifying peer groups of organizers who share similar behavioral and operational attributes. Addressing challenges such as variability in scheduling approaches across different environments and the availability of diverse behavioral data, HyperFusion utilizes hypergraph-based feature fusion to identify high-quality scheduling and behavioral features that reflect each organizer’s unique scheduling style and operational impact. Employing a probabilistic model, the framework represents each organizer’s attributes in a latent space, enabling a nuanced understanding of their contributions. A large-scale hypergraph is constructed to map relationships and similarities among organizers, identifying dense subgraphs or “circles” of organizers with shared attributes. By mining these high-order relationships within these “organizer circles”, HyperFusion enhances scheduling quality and provides adaptive fusion of behavioral features, optimizing the scheduling process to meet the objectives of high-order relationship discovery. Experiments conducted on a dataset of organizers from 36 prominent institutions (our complied data set) demonstrate the model’s effectiveness in improving scheduling programs, underscoring its capability to align scheduling practices with adaptive, data-driven optimization and fostering a responsive and efficient scheduling system.}, archive = {J_ACCESS}, author = {Ziqi Xu and Huiqi Zhang}, doi = {10.1109/ACCESS.2025.3528651}, journal = {IEEE Access}, pages = {10277-10288}, shortjournal = {IEEE Access}, title = {Hypergraph-based organizers’ behavior scheduling optimization: High-order relationships discovery}, volume = {13}, year = {2025}, }
• (2025). New auxiliary vector synthesis for expanding linear modulation range with the ability to eliminate CMV spikes in the SVM three-phase inverters. ACCESS, 13, 10265-10276. (www)

  @article{ , abstract = {Some strategies are used to reduce the high output common-mode voltage (CMV) in three-phase inverters with the space-vector modulation (SVM). However, these reduced CMV (RCMV) strategies may still lead to high spikes in some cases. The traditional auxiliary vectors are used to eliminate the CMV spikes in some RCMV strategies, which have the lower total harmonic distortion (THD) in RCMV SVMs. This paper proposes a new auxiliary vector synthesis SVM (NAVSSVM) strategy. In the NAVSSVM, the new auxiliary vectors are a set of vectors with variable angles. Compared to the use of traditional auxiliary vectors, the new auxiliary vector synthesis expands the linear modulation range of the inverter to the maximum when the modulation index $M_{a}~\gt 1$ . Through optimization, the optimal NAVSSVM is found to expand the modulation region without the CMV spikes in the low $M_{a}$ . In addition, the NAVSSVM strategy maintains the low THD characteristic of the auxiliary vector synthesis method. The feasibility and effectiveness of the NAVSSVM are verified by the simulations and experiments in a three-phase inverter.}, archive = {J_ACCESS}, author = {Jin Huang and Kaicheng Li}, doi = {10.1109/ACCESS.2025.3528514}, journal = {IEEE Access}, pages = {10265-10276}, shortjournal = {IEEE Access}, title = {New auxiliary vector synthesis for expanding linear modulation range with the ability to eliminate CMV spikes in the SVM three-phase inverters}, volume = {13}, year = {2025}, }
• (2025). Temporal forecasting of distributed temperature sensing in a thermal hydraulic system with machine learning and statistical models. ACCESS, 13, 10252-10264. (www)

  @article{ , abstract = {We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.}, archive = {J_ACCESS}, author = {Stella Pantopoulou and Matthew Weathered and Darius Lisowski and Lefteri H. Tsoukalas and Alexander Heifetz}, doi = {10.1109/ACCESS.2025.3526438}, journal = {IEEE Access}, pages = {10252-10264}, shortjournal = {IEEE Access}, title = {Temporal forecasting of distributed temperature sensing in a thermal hydraulic system with machine learning and statistical models}, volume = {13}, year = {2025}, }
• (2025). An attention-based residual U-net for tumour segmentation using multi-modal MRI brain images. ACCESS, 13, 10240-10251. (www)

  @article{ , abstract = {Detecting brain tumours is challenging due to the complex brain anatomy and wide range of tumour sizes, shapes, and locations. A crucial stage in diagnosing and treating brain tumours is automatically segmenting the tumour area from brain MRI. It involves the precise delineation of tumour boundaries within MRI scans, which helps to understand the tumour’s extent, monitor its growth, plan treatment strategies, and assess treatment response over time. Hence, this research proposes a novel automated deep-learning approach based on U-Net for segmenting Glioma tumours. The basic U-Net model is enhanced with several components to improve its performance in the proposed model. The U-Net’s encoder has an improved MCA (Multi-scale Context Attention) module designed to extract and collect rich spatial contextual information from the input image. The proposed U-Net’s decoder uses a Squeeze and Excitation module and residual blocks. The residual blocks help reduce network degradation and gradient disappearance, enabling the model to retain important information during decoding. The Squeeze and Excitation module allows the model to retrieve high-level semantic properties and a high level of spatial context, which have been collected from the encoder module and IMCA-Block. The performance of proposed model is evaluated on two datasets BraTS 2020 and BraTS 2018. The experiments on both datasets demonstrate that the proposed framework enhances multi-modal MRI brain tumour segmentation performance on all metrics evaluated. For BraTS 2020 it achieved Dice Coefficient of 0.9978, 0.9378 and 0.9478 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively and for BraTS 2018 it achieved Dice Coefficient 98.32, 93.32 and 92.32 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively.}, archive = {J_ACCESS}, author = {Najme Zehra Naqvi and K. R. Seeja}, doi = {10.1109/ACCESS.2025.3528654}, journal = {IEEE Access}, pages = {10240-10251}, shortjournal = {IEEE Access}, title = {An attention-based residual U-net for tumour segmentation using multi-modal MRI brain images}, volume = {13}, year = {2025}, }
• (2025). End-to-end stroke imaging analysis using effective connectivity and interpretable artificial intelligence. ACCESS, 13, 10227-10239. (www)

  @article{ , abstract = {In this paper, we propose a reservoir computing-based and directed graph analysis pipeline. The goal of this pipeline is to define an efficient brain representation for connectivity in stroke data derived from magnetic resonance imaging. Ultimately, this representation is used within a directed graph convolutional architecture and investigated with explainable artificial intelligence (AI) tools, offering a more detailed understanding of how stroke alters communication within the brain. Stroke is one of the leading causes of mortality and morbidity worldwide, and it demands precise diagnostic tools for timely intervention and improved patient outcomes. Neuroimaging data, with their rich structural and functional information, provide a fertile ground for biomarker discovery. However, the complexity and variability of information flow in the brain require advanced analysis, especially if we consider the case of disrupted networks as those given by the brain connectome of stroke patients. To address the needs given by this complex scenario we proposed an end-to-end pipeline. This pipeline begins with defining the effective connectivity of the brain. This allows directed graph network representations that have not been fully investigated so far by graph convolutional network classifiers. To have a complete overview, the analysis with reservoir computing-based causality is compared to other two effective connectivity approaches: one linear (Granger causality) and one non-linear method (transfer entropy). Then, the pipeline subsequently incorporates a classification module to categorize the effective connectivity (directed graphs) of brain networks of patients versus matched healthy control. The graph convolutional architecture is also compared to legacy methods such as random forest and support vector machine providing a complete benchmark. While the pipeline includes a classification module for distinguishing between stroke patients and healthy controls, the focus is on the interpretation of these directed graphs, which reveal critical disruptions in connectivity. Indeed, the classification led to an area under the curve of 0.69 by using graph convolutional networks, 0.72 by using local topological profiling random forest, and 0.71 by using support vector machine with the given heterogeneous dataset. More importantly, thanks to explainable tools, an interpretation of disrupted networks across the brain networks was possible. This elucidates the effective connectivity biomarker’s contribution to stroke classification, fostering insights into disease mechanisms and treatment responses. This transparent analytical framework not only enhances clinical interpretability but also instills confidence in decision-making processes, crucial for translating research findings into clinical practice. Our proposed machine learning pipeline showcases the potential of reservoir computing to define causality and therefore directed graph networks, which can in turn be used in a directed graph classifier and explainable analysis of neuroimaging data. This method prioritizes uncovering miscommunication in brain networks, with the potential to improve our understanding of stroke and other brain diseases.INDEX TERMS Effective connectivity, explainable AI, reservoir computing, stroke, graph convolutional networks, GCN, GNN.}, archive = {J_ACCESS}, author = {Wojciech Ciezobka and Joan Falcó-Roget and Cemal Koba and Alessandro Crimi}, doi = {10.1109/ACCESS.2025.3529179}, journal = {IEEE Access}, pages = {10227-10239}, shortjournal = {IEEE Access}, title = {End-to-end stroke imaging analysis using effective connectivity and interpretable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Application of bayesian optimization in gripper design for effective grasping. ACCESS, 13, 10215-10226. (www)

  @article{ , abstract = {Despite many recent technological advancements, grasping remains a challenging open problem in robotic manipulation. In contrast with most research which focuses equipping grippers with varying degree of intelligence, we approach grasping from a gripper design perspective, aiming to find the best tool for grasping a specific set of objects. Building on our previous work, this paper reviews a suitable parametrization for the geometry of two common families of industrial grippers and presents a grasp score beneficial for gripper design. We then formally cast the problem of finding the best gripper parametrization within a probabilistic framework, addressing it using Bayesian Optimization tools. Numerical results on a set of industrial objects demonstrate the effectiveness of the approach showing up to $\approx 300 \%$ improvement compared to the performance obtained using a fixed set of grippers.}, archive = {J_ACCESS}, author = {Marco Todescato and Dominik T. Matt and Andrea Giusti}, doi = {10.1109/ACCESS.2025.3528643}, journal = {IEEE Access}, pages = {10215-10226}, shortjournal = {IEEE Access}, title = {Application of bayesian optimization in gripper design for effective grasping}, volume = {13}, year = {2025}, }
• (2025). Design a robust DDoS attack detection and mitigation scheme in SDN-edge-IoT by leveraging machine learning. ACCESS, 13, 10194-10214. (www)

  @article{ , abstract = {The Internet of Things (IoT) has rapidly expanded, providing significant benefits across various fields. However, the complexity of IoT networks, with their resource-constrained devices, presents substantial security challenges, particularly Distributed Denial of Service (DDoS) attacks. Integrating Software Defined Networking (SDN) with IoT has emerged as a promising solution to enhance security. Despite this, DDoS attacks through IoT botnets remain a significant threat. Existing studies on DDoS detection in SDN-IoT networks often suffer from inefficient detection accuracy due to poor algorithm design and latency issues arising from deploying models in the control plane. This study aims to improve DDoS detection accuracy by training a robust Machine Learning (ML) model using effective hyper-parameter tuning and Cross-Validation (CV) techniques. To mitigate latency issues, we deploy the model at the edge of the SDN-IoT network, enforcing mitigation rules through the SDN controller. We evaluated four popular classifiers (K-Nearest Neighbor (K-NN), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and FeedForward Neural Network (FFNN)) on benchmark datasets CICIDS2017 and Edge-IIoTset, conducting both binary and multi-class classifications. Our implementation using the Mininet-WiFi emulation tool revealed that XGBoost outperformed others in binary DDoS detection, achieving accuracy, precision, recall, and F1-score all above 99.997%, with a testing time of 3.559 seconds on the Edge-IIoTset dataset. Compared to recent studies, the proposed approach demonstrates XGBoost’s clear superiority. Consequently, XGBoost was deployed at the edge of the SDN-IoT for live traffic classification, showing improved performance by classifying live traffic within 3.946 ms and using only 8.80% of memory with a 0.5-second window size.}, archive = {J_ACCESS}, author = {Habtamu Molla Belachew and Mulatu Yirga Beyene and Abinet Bizuayehu Desta and Behaylu Tadele Alemu and Salahadin Seid Musa and Alemu Jorgi Muhammed}, doi = {10.1109/ACCESS.2025.3526692}, journal = {IEEE Access}, pages = {10194-10214}, shortjournal = {IEEE Access}, title = {Design a robust DDoS attack detection and mitigation scheme in SDN-edge-IoT by leveraging machine learning}, volume = {13}, year = {2025}, }
• (2025). Diagnosis of coronary heart disease through deep learning-based segmentation and localization in computed tomography angiography. ACCESS, 13, 10177-10193. (www)

  @article{ , abstract = {Coronary heart disease (CHD), a leading cause of global mortality, requires precise and early diagnosis for effective intervention. Coronary computed tomography angiography (CCTA) has emerged as a non-invasive modality for detailed coronary artery visualization; however, automatic and accurate segmentation of coronary structures from CCTA images remains challenging. Conventional convolutional neural networks (CNNs), despite their success in medical imaging, face limitations in capturing the complex, long-range dependencies in coronary artery images due to their localized receptive fields. Vision transformers, with their self-attention mechanisms, offer a global perspective, yet demand extensive data and computational resources, making them less adaptable for the often limited medical imaging datasets. This research addresses these challenges by proposing TransCHD, a hybrid CNN-Transformer architecture developed for coronary artery segmentation in CCTA. TransCHD incorporates a Contextual Representation Learning (CRL) module and a Spatially-Aware Feature (SAF) module, enabling both local feature extraction and global contextual awareness within a unified architecture. The CRL module mitigates spatial continuity disruptions caused by standard patch-based transformers, while the SAF module enhances spatial locality and preserves fine-grained anatomical details essential for accurate segmentation. The segmentation outcomes are clinically significant as they provide quantitative assessments of arterial stenosis, plaque characterization, and ischemia-prone regions, supporting risk assessment and treatment planning. Trained and evaluated on the CorArtTS2020 dataset, TransCHD achieved superior performance compared to state-of-the-art CNN- and transformer-based models, with a Dice score of 0.81 and an Intersection over Union (IoU) of 0.65. Results show that our proposed TransCHD is effective in CCTA segmentation.}, archive = {J_ACCESS}, author = {Bo Zhao and Jianjun Peng and Ce Chen and Yongyan Fan and Kai Zhang and Yang Zhang}, doi = {10.1109/ACCESS.2025.3528638}, journal = {IEEE Access}, pages = {10177-10193}, shortjournal = {IEEE Access}, title = {Diagnosis of coronary heart disease through deep learning-based segmentation and localization in computed tomography angiography}, volume = {13}, year = {2025}, }
• (2025). A survey of post-quantum cryptography migration in vehicles. ACCESS, 13, 10160-10176. (www)

  @article{ , abstract = {The advent of quantum computers makes asymmetric cryptographic algorithms insecure and vulnerable. A promising solution to ensure the continued security attributes of confidentiality, integrity, and availability is the use of Post-Quantum Cryptography (PQC) algorithms. The adoption of quantum-safe algorithms takes place at a vastly different pace in different industries. In some industries, adoption processes are slow due to the required adaptation of regulations and standards. At the same time, long-lived products with non-PQC algorithms are more likely to become insecure during their lifetime. Vehicles are a prime example of long-lived products where the adoption of PQC proceeds slowly. When quantum computers are able to break relevant key lengths of asymmetric cryptography and, thus, render current vehicle systems insecure, vehicles that are developed and produced today will most likely remain in use. In this work, we provide a structured and comprehensive overview of the current migration state of PQC algorithms in the automotive area. We address use cases involving asymmetric cryptography in the automotive context that face the challenge of adapting PQC, such as internal vehicle networks, manufacturer-specific communication, and vehicle-to-everything communication. In addition, we review the standards concerning vehicle security and their relevance to PQC. Finally, we identify and discuss open research challenges regarding the adoption of PQC in the automotive domain and further steps towards quantum-safe vehicles.}, archive = {J_ACCESS}, author = {Nils Lohmiller and Sabrina Kaniewski and Michael Menth and Tobias Heer}, doi = {10.1109/ACCESS.2025.3528562}, journal = {IEEE Access}, pages = {10160-10176}, shortjournal = {IEEE Access}, title = {A survey of post-quantum cryptography migration in vehicles}, volume = {13}, year = {2025}, }
• (2025). Conformal and flexible antennas in ultra-high frequencies: Prospects and challenges for partial discharge diagnostics. ACCESS, 13, 10139-10159. (www)

  @article{ , abstract = {With the exponential increase in smart device usage across various domains, flexible and conformal antenna technologies have emerged as transformative solutions, enabling wireless systems in numerous smart applications. However, selecting a suitable flexible and conformal substrate remains challenging because of the intricate characteristics and performance considerations of each material. Additionally, most applications of these substrates are confined to specific areas, such as body-worn devices, biomedical uses, and health services. In contrast, the use of ultra-high frequency (UHF, 0.3–3 GHz) antennas is largely concentrated in radio frequency identification (RFID) and telecommunications, with limited exploration in other areas. Recently, UHF antennas have gained attention for unconventional applications, including high-voltage (HV) defect detection, particularly partial discharge (PD) diagnosis. However, practical challenges arise due to the rigidity of commercial substrate materials. This paper provides a comprehensive survey on flexible and conformal UHF antennas’ applicability for PD diagnostics, a field that remains underexplored. We systematically assess the electrical, mechanical, and thermal properties of various flexible and conformal substrates relevant to UHF antenna development. In this investigation, we thoroughly analyse five substrate materials, namely polyimide (PI), polydimethylsiloxane (PDMS), Rogers laminates, polytetrafluoroethylene (PTFE), and polyethylene terephthalate (PET), and several types of UHF antennas including planar monopole, spiral antenna, Hilbert antenna, biconical antenna, and so on. The synthesis of this research delivers a complete roadmap, addressing existing limitations and proposing future directions for PD diagnosis by conformal and flexible antennas.}, archive = {J_ACCESS}, author = {M. A. Taher and Mohamadariff Othman and Hazlee Azil Illias and Tarik Abdul Latef and Tengku Faiz Tengku Mohmed Noor Izam and S. M. Kayser Azam and Muhammad Ubaid Ullah and Mohamed Alkhatib and Mousa I. Hussein}, doi = {10.1109/ACCESS.2025.3529128}, journal = {IEEE Access}, pages = {10139-10159}, shortjournal = {IEEE Access}, title = {Conformal and flexible antennas in ultra-high frequencies: Prospects and challenges for partial discharge diagnostics}, volume = {13}, year = {2025}, }
• (2025). Evaluating explainable AI methods in deep learning models for early detection of cerebral palsy. ACCESS, 13, 10126-10138. (www)

  @article{ , abstract = {Early detection of Cerebral Palsy (CP) is crucial for effective intervention and monitoring. This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. Specifically, we use XAI evaluation metrics — namely faithfulness and stability — to quantitatively assess the reliability of Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) in this specific medical application. We utilize a unique dataset of infant movements and apply skeleton data perturbations without distorting the original dynamics of the infant movements. Our CP prediction model utilizes an ensemble approach, so we evaluate the XAI metrics performances for both the overall ensemble and the individual models. Our findings indicate that both XAI methods effectively identify key body points influencing CP predictions and that the explanations are robust against minor data perturbations. Grad-CAM significantly outperforms CAM in the Relative Input Stability velocity (RISv) metric, which measures stability in terms of velocity. In contrast, CAM performs better in the Relative Input Stability bone (RISb) metric, which relates to bone stability, and the Relative Representation Stability (RRS) metric, which assesses internal representation robustness. Individual models within the ensemble show varied results, and neither CAM nor Grad-CAM consistently outperform the other, with the ensemble approach providing a representation of outcomes from its constituent models. Both CAM and Grad-CAM also perform significantly better than random attribution, supporting the robustness of these XAI methods. Our work demonstrates that XAI methods can offer reliable and stable explanations for CP prediction models. Future studies should further investigate how the explanations can enhance our understanding of specific movement patterns characterizing healthy and pathological development.}, archive = {J_ACCESS}, author = {Kimji N. Pellano and Inga Strümke and Daniel Groos and Lars Adde and Espen F. Alexander Ihlen}, doi = {10.1109/ACCESS.2025.3525571}, journal = {IEEE Access}, pages = {10126-10138}, shortjournal = {IEEE Access}, title = {Evaluating explainable AI methods in deep learning models for early detection of cerebral palsy}, volume = {13}, year = {2025}, }
• (2025). FedDrip: Federated learning with diffusion-generated synthetic image. ACCESS, 13, 10111-10125. (www)

  @article{ , abstract = {In the realm of machine learning in healthcare, federated learning (FL) is often recognized as a practical solution for addressing issues related to data privacy and data distribution. However, many real-world datasets are not identically and independently distributed (non-IID). That is, the data characteristics differ from one institute to another. Non-IID data poses challenges to the convergence of FL models, such as client drifting, where the model weights drift towards local optima instead of the global optimum. As a solution, leveraging synthetic data not only addresses data distribution issues but also helps overcome data scarcity. In this work, we propose a novel framework called “FedDrip (Federated Learning with Diffusion Reinforcement at Pseudo-site)” that utilizes diffusion-generated synthetic data to alleviate these challenges in non-IID environments. In addition to traditional federated learning, we introduce a pseudo-site concept, where an additional model—the pseudo-site student model—is trained using synthetic data to extract knowledge from real clients acting as teacher models. By leveraging this strategy, the pseudo-site improves model generalization across diverse datasets while preserving data privacy. Moreover, we demonstrate that this virtual-client strategy can be integrated into any federated learning framework, including foundational algorithms such as FedAvg and advanced algorithms such as FedDyn and FedProx. Experiments conducted on the NIH ChestX-ray14 dataset show that our method enhances the performance of the state-of-the-art and foundational methods by 2.15%, 0.95%, and 1.96% for FedAvg, FedDyn, and FedProx, respectively, using the AUC metric. We also performed empirical experiments investigating the effects of prompting style, prompt correctness, and data availability on the inference of diffusion models, using the Fréchet Inception Distance (FID) metric for generative models.}, archive = {J_ACCESS}, author = {Karin Huangsuwan and Timothy Liu and Simon See and Aik Beng Ng and Peerapon Vateekul}, doi = {10.1109/ACCESS.2025.3525806}, journal = {IEEE Access}, pages = {10111-10125}, shortjournal = {IEEE Access}, title = {FedDrip: Federated learning with diffusion-generated synthetic image}, volume = {13}, year = {2025}, }
• (2025). Balancing privacy and utility in split learning: An adversarial channel pruning-based approach. ACCESS, 13, 10094-10110. (www)

  @article{ , abstract = {Machine Learning (ML) has been exploited across diverse fields with significant success. However, the deployment of ML models on resource-constrained devices, such as edge devices, has remained challenging due to the limited computing resources. Moreover, training such models using private data is prone to serious privacy risks resulting from inadvertent disclosure of sensitive information. Split Learning (SL) has emerged as a promising technique to mitigate these risks through partitioning neural networks into the client and the server subnets. One should note that although only the extracted features are transmitted to the server, sensitive information can still be unwittingly revealed. Existing approaches addressing this privacy concern in SL struggle to maintain a balance of privacy and utility. This research introduces a novel privacy-preserving split learning approach that integrates: 1) Adversarial learning and 2) Network channel pruning. Specifically, adversarial learning aims to minimize the risk of sensitive data leakage while maximizing the performance of the target prediction task. Furthermore, the channel pruning performed jointly with the adversarial training allows the model to dynamically adjust and reactivate the pruned channels. The association of these two techniques makes the intermediate representations (features) exchanged between the client and the server models less informative and more robust against data reconstruction attacks. Accordingly, the proposed approach enhances data privacy without ceding the model’s performance in achieving the intended utility task. The contributions of this research were validated and assessed using benchmark datasets. The experiments demonstrated the superior defense ability, against data reconstruction attacks, of the proposed approach in comparison with relevant state-of-the-art approaches. In particular, the SSIM between the original data and the data reconstructed by the attacker, achieved by our approach, decreased significantly by 57%. In summary, the obtained quantitative and qualitative results proved the efficiency of the proposed approach in balancing privacy and utility for typical split learning frameworks.}, archive = {J_ACCESS}, author = {Afnan Alhindi and Saad Al-Ahmadi and Mohamed Maher Ben Ismail}, doi = {10.1109/ACCESS.2025.3528575}, journal = {IEEE Access}, pages = {10094-10110}, shortjournal = {IEEE Access}, title = {Balancing privacy and utility in split learning: An adversarial channel pruning-based approach}, volume = {13}, year = {2025}, }
• (2025). Quantile regression for probabilistic electricity price forecasting in the U.K. electricity market. ACCESS, 13, 10083-10093. (www)

  @article{ , abstract = {The volatility and uncertainty of electricity prices due to renewable energy sources create challenges for electricity trading, necessitating reliable probabilistic electricity-price forecasting (EPF) methods. This study introduces an EPF approach using quantile regression (QR) with general predictors, focusing on the UK market. Unlike market-specific models, this method ensures adaptability and reduces complexity. Using 1,132 days of training data, including electricity prices, demand forecasts, and generation forecasts obtained from UK electricity companies, results show that the proposed model achieved a mean absolute error of 18.27 [(£/MWh] for predicting volatile short-term spot market prices. The QR model achieved high predictive accuracy and stability, with only a 4–25% average pinball loss increases when the previous day’s prices ( $P_{t-1}$ ) were excluded due to bidding deadlines. These findings demonstrate the model’s robustness and its potential to enhance market efficiency by providing reliable and simplified probabilistic forecasts, aiding stakeholders in mitigating risks and optimizing strategies.}, archive = {J_ACCESS}, author = {Yuki Osone and Daisuke Kodaira}, doi = {10.1109/ACCESS.2025.3528450}, journal = {IEEE Access}, pages = {10083-10093}, shortjournal = {IEEE Access}, title = {Quantile regression for probabilistic electricity price forecasting in the U.K. electricity market}, volume = {13}, year = {2025}, }
• (2025). Achieving faster and smarter chest X-ray classification with optimized CNNs. ACCESS, 13, 10070-10082. (www)

  @article{ , abstract = {X-ray imaging is essential in medical diagnostics, particularly for identifying anomalies like respiratory diseases. However, building accurate and efficient deep learning models for X-ray image classification remains challenging, requiring both optimized architectures and low computational complexity. In this paper, we present a three-stage framework to enhance X-ray image classification using Neural Architecture Search (NAS), Transfer Learning, and Model Compression via filter pruning, specifically targeting the ChestX-Ray14 dataset. First, NAS is employed to automatically discover the optimal convolutional neural network (CNN) architecture tailored to the ChestX-Ray14 dataset, reducing the need for extensive manual tuning. Subsequently, we leverage transfer learning by incorporating pre-trained models, which enhances the model’s generalizability and reduces dependency on large volumes of labeled X-ray data. Finally, model compression through filter pruning, driven by evolutionary algorithms, trims redundant parameters to improve computational efficiency while preserving model accuracy. Experimental results demonstrate that this approach not only boosts classification accuracy on the ChestX-Ray14 dataset but also significantly reduces model size, making it suitable for deployment in resource-constrained environments, such as mobile and edge devices. This framework provides a practical, scalable solution to improve both the accuracy and efficiency of medical image classification.}, archive = {J_ACCESS}, author = {Hassen Louati and Ali Louati and Khalid Mansour and Elham Kariri}, doi = {10.1109/ACCESS.2025.3529206}, journal = {IEEE Access}, pages = {10070-10082}, shortjournal = {IEEE Access}, title = {Achieving faster and smarter chest X-ray classification with optimized CNNs}, volume = {13}, year = {2025}, }
• (2025). Improved identification method for equivalent network parameters of transformer windings based on driving point admittance. ACCESS, 13, 10062-10069. (www)

  @article{ , abstract = {To accurately and efficiently determine the operating state of transformers, based on the driving point admittance method, a trapezoidal equivalent network model for dual-windings transformers was established. Based on Kirchhoff’s law, the node voltages and branch currents of the equivalent network model are obtained, and then the state space equations describing the network model parameters and driving point admittance data are obtained. The state space equation of the equivalent network model was constructed. Then, an improved whale optimization algorithm was proposed for the identification of parameters in the transformer equivalent network model. Random populations were generated using chaotic mapping, and the performance of the algorithm was improved by changing nonlinear control and adding adaptive weight coefficients. An objective function that simultaneously includes amplitude and phase frequency information at resonance points was established. Based on the improved whale optimization algorithm, the parameters of the equivalent network model were inverted. Finally, a comparative simulation test was conducted between the proposed method and existing main methods such as GA and PSO. The results indicate that the minimum objective value for parameter identification using the IWOA is merely 0.71, indicating that the IWOA possesses excellent parameter identification capabilities.}, archive = {J_ACCESS}, author = {Yi Liu and Xiaobo Pei}, doi = {10.1109/ACCESS.2025.3528886}, journal = {IEEE Access}, pages = {10062-10069}, shortjournal = {IEEE Access}, title = {Improved identification method for equivalent network parameters of transformer windings based on driving point admittance}, volume = {13}, year = {2025}, }
• (2025). A traffic management system by identifying pollution hotspots among sensitive points in a smart city. ACCESS, 13, 10043-10061. (www)

  @article{ , abstract = {Vehicular pollution becomes a crucial issue within the travel planning of a smart city. Especially, the pollution level at Sensitive Points (SP) like Schools and Hospitals should be kept within a threshold level while a routing solution is offered. In the existing works, the attempt to consider environmental pollution within traffic planning is minimal. In this attempt, we have proposed a framework for offering a routing strategy maintaining the desired pollution level at Sensitive Points. However, the most crucial challenge is to generate an estimation model for measuring pollution at Sensitive Points in an accurate way. In the proposed estimation model, we have attempted to accommodate the meteorological and other essential factors to make it more accurate. The pollution measures as computed by the model within SPs are analyzed for identifying the hot-spots, i.e., the alarming points where the pollution measure is supposed to be higher than the pre-defined threshold. Finally, the rerouting is executed on the affected road segments to maintain the desired level of pollution measured at the hot spots. Moreover, the re-routing has been done (if needed) so that the average remaining travel time of the vehicles will be minimal. Thus, the solution not only focuses on the environmental issues but also addresses the users’ satisfaction in terms of travel time. In the experiment phase, the traffic network is simulated by SUMO, and the entire proposal is implemented to compare with the notable existing comparable works. The proposed approach performs better in terms of the identified metrics, achieving a reduction in Average Vehicle Rerouting (AVR) to 17.26% compared to 20.10% in OPFTCaAP and maintaining a minimal Average Travel Time (ATT) increase for buses (-0.06%).}, archive = {J_ACCESS}, author = {Pratik Dutta and Soumyadeep Sur and Sankhayan Choudhury and Sunirmal Khatua}, doi = {10.1109/ACCESS.2025.3528987}, journal = {IEEE Access}, pages = {10043-10061}, shortjournal = {IEEE Access}, title = {A traffic management system by identifying pollution hotspots among sensitive points in a smart city}, volume = {13}, year = {2025}, }
• (2025). Inducing and assessing acute mental stress in controlled conditions: Topical review and guidelines for effective experimental protocols. ACCESS, 13, 10022-10042. (www)

  @article{ , abstract = {Mental health is influenced by the fast-paced nature of life. In this scenario, stressful events play an important role. Extensive research has been carried out to develop non-invasive devices for stress detection, which primarily use physiological data and, more recently, artificial intelligence algorithms. When developing either a new device or algorithm, tests in controlled environments are preferred, because of the supervision of possible confounding factors while running the experiments. However, because of the extremely subjective perception of stress, the characteristics of the investigated samples, and the conditions under which the experiment is conducted, the data may not be representative of a perceived stressful condition, leading to biases. Given the importance of reliable experimental protocols for stress induction, especially if cortisol level is not monitored, this work aims to present approaches for inducing and assessing acute mental stress in controlled conditions, analyzing the problem from engineering and psychological perspectives. All the phases of the experimental protocol are discussed, examining both the factors that could induce stress and the assessment tools, like questionnaires and physiological signals. The analysis of the latter will be focused on the exogenous factors that may compromise the measures, providing solutions for their mitigation. With this work, researchers with different backgrounds can improve the efficacy of their studies, limiting biases and misleading results.}, archive = {J_ACCESS}, author = {Matteo Raggi and Lee J. Moore and Luca Mesin}, doi = {10.1109/ACCESS.2025.3528518}, journal = {IEEE Access}, pages = {10022-10042}, shortjournal = {IEEE Access}, title = {Inducing and assessing acute mental stress in controlled conditions: Topical review and guidelines for effective experimental protocols}, volume = {13}, year = {2025}, }
• (2025). Arranging a pool of functional test sequences for variable in-field test periods. ACCESS, 13, 10009-10021. (www)

  @article{ , abstract = {High workloads applied to a system cause chips to be more susceptible to aging effects that may eventually result in hardware defects. The detection of the defects requires tests for delay faults to be applied in-field. Both scan-based tests and functional test sequences are important to apply. In-field test periods vary in length. Therefore, test sets of both types should be arranged such that every test period would be utilized for targeting the most likely to occur faults. This is preferred over the alternative where each test period is used for achieving the highest possible fault coverage since the highest possible fault coverage may be achieved without detecting the most likely to occur faults. This article considers the problem of arranging a pool of functional test sequences to match different in-field test periods when the goal is to ensure that the most likely to occur faults are detected in every test period. The procedure described in this article produces a series of solutions with subsets of increasing lengths of the pool (subpools) to detect subsets of transition faults of increasing sizes. The increase in the length of every subpool in the series is minimum or close-to-minimum relative to the length of the previous subpool. The procedure is implemented in an academic simulation environment and applied to benchmark circuits to demonstrate its effectiveness.}, archive = {J_ACCESS}, author = {Irith Pomeranz}, doi = {10.1109/ACCESS.2025.3528741}, journal = {IEEE Access}, pages = {10009-10021}, shortjournal = {IEEE Access}, title = {Arranging a pool of functional test sequences for variable in-field test periods}, volume = {13}, year = {2025}, }
• (2025). The effects of multipath on the performance of high rate digital QAM and OFDM wireless link operating in tunnels. ACCESS, 13, 9995-10008. (www)

  @article{ , abstract = {Wide-band wireless communication in tunnels and long corridors faces challenges due to multipath interferences and delay spread. Wall reflections induce dispersion, causing amplitude and phase distortions in the received signal, with the extent of distortion determined by multiple factors. Accurate modeling of space-frequency effects is essential for designing reliable indoor wireless links. This study presents an experimentally validated, precise, and versatile model for the propagation of modulated wireless signals in tunnels. Quasi-optical, multi-ray model is employed to derive the transfer function, describing the indoor propagation in the frequency domain. The model enables analysis of the impact of changes among other in modulations, frequencies, and different bandwidths. The results include a comprehensive comparison of various cases for Bit Error Rate (BER), Error Vector Magnitude (EVM), constellations, and facilitating a thorough examination of the tunnel effects in each scenario. Several representative results are presented in the paper. Notably, a significant correlation is observed between the bandwidth used and an increase in BER. Furthermore, the comparison between Orthogonal Frequency Division Multiplexing (OFDM) and Quadrature Amplitude Modulation (QAM) reveals a distinct preference for OFDM due to its ability to overcome the frequency-selective fading nature of the tunnel.}, archive = {J_ACCESS}, author = {Yehuda Taragin and Niv Elkayam and Gad A. Pinhasi and Yosef Pinhasi}, doi = {10.1109/ACCESS.2025.3528645}, journal = {IEEE Access}, pages = {9995-10008}, shortjournal = {IEEE Access}, title = {The effects of multipath on the performance of high rate digital QAM and OFDM wireless link operating in tunnels}, volume = {13}, year = {2025}, }
• (2025). Combined oriented data augmentation method for brain MRI images. ACCESS, 13, 9981-9994. (www)

  @article{ , abstract = {In recent years, deep learning’s use in medical imaging has grown exponentially. However, one of the biggest problems with training deep learning models is the unavailability of large amounts of data, which leads to overfitting. Collecting large quantities of labelled medical images is expensive, time-consuming, and depends on specialists’ availability. In this paper, we proposed a novel method namely Oriented Combination MRI (OCMRI) for augmenting brain MRI dataset. The proposed method helps CNN models overcome overfitting and address class imbalance issues by combining Brain MRI images to generate new images. The image fusion is performed by selecting two images of the same tumor class if the Mean Squared Error (MSE) between these two images is greater than threshold 1 and lower than threshold 2. Both thresholds are adjustable, initially set by the user and automatically fine-tuned by the algorithm to control the number of images produced for each class, thus helping to address the data imbalance problem. The proposed approach was evaluated by training and testing the PRCnet model on four publicly available datasets before and after applying the proposed method to the datasets. Where the classification accuracy without data augmentation was 85.19% for dataset A, 90.12% for dataset B, 94.77% for dataset C, and 90% for dataset D respectively. After adding the synthetic data; the accuracy improved to 92.7% for dataset A, 95.37% for dataset B, 96.51% for dataset C and 98% for dataset D respectively.}, archive = {J_ACCESS}, author = {Ahmeed Suliman Farhan and Muhammad Khalid and Umar Manzoor}, doi = {10.1109/ACCESS.2025.3526684}, journal = {IEEE Access}, pages = {9981-9994}, shortjournal = {IEEE Access}, title = {Combined oriented data augmentation method for brain MRI images}, volume = {13}, year = {2025}, }
• (2025). Location information-based routing protocol for energy harvesting cognitive radio wireless sensor network. ACCESS, 13, 9965-9980. (www)

  @article{ , abstract = {This paper explores the integration of Location Information-based Routing (LIBRO) protocol into Energy Harvesting-based Cognitive Radio Wireless Sensor Networks (EH-CWSNs) to address energy consumption challenges and ensure sustainable operation. LIBRO has been recently proposed as an efficient routing algorithm for traditional WSN networks. In this paper, we extend its application to EH-CWSNs, in which the WSN nodes act as Secondary Users (SUs). By proposing LIBRO-based EH-CWSN, we aim to eliminate energy constraints on the nodes and reduce the need for frequent battery replacements in EH-CWSN, thus enabling deployment in challenging environments. Additionally, LIBRO’s unique feature of minimizing nodes’ cooperation during routing offers advantages for protecting Primary Users (PUs) against interference caused by secondary transmissions. The paper’s primary goal is to adapt LIBRO to the CWSN functionalities, especially the spectrum sensing operation. Then, EH-CWSN network parameters are optimized to ensure lower energy consumption compared to the harvested energy from EH units, establishing a sustainable network while respecting PU collision limits. Through a multi-objective optimization approach, we seek to strike a balance between collision probability with PUs, energy consumption, packet delivery latency, and network throughput, creating a self-powered network that meets various constraints. Numerical findings demonstrate the effectiveness of LIBRO in enhancing energy and spectral efficiencies in EH-CWSNs compared to state-of-the-art routing protocols.}, archive = {J_ACCESS}, author = {Mohamad-Rida Mortada and Abbass Nasser and Alaaeddine Ramadan and Koffi-Clément Yao and Ali Mansour}, doi = {10.1109/ACCESS.2024.3517741}, journal = {IEEE Access}, pages = {9965-9980}, shortjournal = {IEEE Access}, title = {Location information-based routing protocol for energy harvesting cognitive radio wireless sensor network}, volume = {13}, year = {2025}, }
• (2025). Asymmetric phase MPCC interleaving method for boost PFC converter with enhanced input current harmonic characteristic. ACCESS, 13, 9955-9964. (www)

  @article{ , abstract = {This paper proposes an asymmetric phase model predictive current control (MPCC) interleaving method to improve power density, power factor (PF), and total harmonic distortion (THD) of power factor correction (PFC) converters. The MPCC method operates without a modulator, making it difficult to apply the conventional interleave method. Furthermore, the conventional interleaved method increases dead volume under light load conditions. To solve this problem, this paper proposes to implement the interleaving effect without a modulator by adjusting the timing of the discrete-time model prediction by setting the sampling time of the main phase and the auxiliary phase differently, and to reduce the dead volume by setting the inductance value of the auxiliary phase smaller than the main phase. The sampling period for the auxiliary phase is selected based on the inductance values of the main and auxiliary phases, as well as the main phase’s sampling period, to achieve similar ripple characteristics with a smaller inductor. The proposed method uses a smaller auxiliary inductance to maintain similar input current ripple as the conventional method, and moreover, improves PF and THD by increasing power density and reducing cusp distortion. The performance of the proposed method is verified through experiments on a boosted PFC converter using an Imperix module with 3.3 kW load. The verification results showed that the proposed method achieved improved PF under all load conditions, and improved THD at high load conditions, meeting the IEC-61000-3-2 CLASS A standard.}, archive = {J_ACCESS}, author = {Hee-Jeong Seon and Hyun-Gyu Koh and Yeong-Jun Choi}, doi = {10.1109/ACCESS.2025.3528639}, journal = {IEEE Access}, pages = {9955-9964}, shortjournal = {IEEE Access}, title = {Asymmetric phase MPCC interleaving method for boost PFC converter with enhanced input current harmonic characteristic}, volume = {13}, year = {2025}, }
• (2025). A novel algorithm for aspect ratio estimation in SRAM design to achieve high SNM, high speed, and low leakage power. ACCESS, 13, 9942-9954. (www)

  @article{ , abstract = {Performance optimization is a crucial aspect of enhancing the efficiency of electronic systems, and scaling is a primary method for achieving optimal performance while maintaining the integrity of system architecture. This paper introduces a novel algorithm for optimizing transistor sizing in static random-access memory (SRAM) to enhance speed, improve Static Noise Margin (SNM), and reduce leakage power consumption. The SRAM is designed using 45 nm technology and operates at a supply voltage of 1.2 V. To validate the algorithm’s effectiveness, Monte Carlo simulations were conducted under varying process, voltage, and temperature conditions. The results demonstrate read access times of 11.17 ps (HIGH) and 9.97 ps (LOW), and write access times of 12.00 ps (HIGH) and 17.00 ps (LOW). The measured SNM values for the read, write, and hold states were 328.2 mV, 453.7 mV, and 452.3 mV, respectively. The inclusion of precharge and write driver circuits allows for a compact SRAM layout, occupying $9.79~\mu $ m2, with the SRAM cell itself occupying $4.1~\mu $ m2. Furthermore, the proposed SRAM design exhibits low leakage power consumption of 1.64 pW, demonstrating the efficiency and performance benefits of the optimized transistor sizing approach.}, archive = {J_ACCESS}, author = {Sanket M. Mantrashetti and Arunkumar P Chavan and Prakash Pawar and H. V. Ravish Aradhya and Omkar S. Powar}, doi = {10.1109/ACCESS.2025.3527333}, journal = {IEEE Access}, pages = {9942-9954}, shortjournal = {IEEE Access}, title = {A novel algorithm for aspect ratio estimation in SRAM design to achieve high SNM, high speed, and low leakage power}, volume = {13}, year = {2025}, }
• (2025). An enhanced vehicle-to-vehicle wireless power transfer system for electric vehicle applications using a reconfigurable coil approach. ACCESS, 13, 9931-9941. (www)

  @article{ , abstract = {The growth of Electric Vehicle (EV) technologies necessitates adequate charging infrastructure and energy storage systems for reliable operation. Conversely, vehicle-to-grid, vehicle-to-home, and vehicle-to-vehicle (V2V) technologies are being researched to enhance EV usage. V2V technology can assist EV users with convenient power sharing during emergencies. However, the conventional plug-in approach limits safety and human intervention aspects. Recent advancements in wireless power transfer (WPT) offer both convenience and reliable power exchange, making them the most suitable approach for V2V technology. WPT is a loosely coupled system, and the air gap increases leakage inductance, which weakens the coupling factor (K) and affects power transfer efficiency (PTE). In this article, the enhancement of the coupling factor is achieved by employing a reconfigurable coil between the Transmitter (Tx) coil and the Receiver (Rx) coil. This reconfigurable coil functions as a resonator, enhancing the flux generated by the Tx coil and improving PTE. Furthermore, the proposed system facilitates bidirectional power flow between two EVs. The phase shift control technique regulates the power flow between the two EVs. Improved efficient WPT conserves energy and reduces the reliance on energy storage devices. The proposed WPT system is validated with a 500W prototype model and realized efficiency of 92.6 % in aligned condition and 86.6% at 40% lateral misaligned condition.}, archive = {J_ACCESS}, author = {Venkatesan Ramakrishnan and Dominic Savio A and Mohammad Shorfuzzaman and Waleed Mohammed Abdelfattah}, doi = {10.1109/ACCESS.2025.3527513}, journal = {IEEE Access}, pages = {9931-9941}, shortjournal = {IEEE Access}, title = {An enhanced vehicle-to-vehicle wireless power transfer system for electric vehicle applications using a reconfigurable coil approach}, volume = {13}, year = {2025}, }
• (2025). Memory state dynamics in BEOL FeFETs: Impact of area ratio on analog write mechanisms and charging. ACCESS, 13, 9923-9930. (www)

  @article{ , abstract = {This work presents dynamic state writing by combining ferroelectric (FE) polarization together with charge injection (CI) on Si-based ferroelectric MOSFETs as a novel approach for non-volatile memory design. FE capacitors are non-destructively integrated in the Back-End-of-Line (BEOL) with Si MOSFETs to create FE-Metal-FETs (FeMFETs). We explore the FE/MOS area ratio (AR) as a critical design parameter, particularly in the context of dynamic writing processes, where various voltage pulse trains are applied for analog potentiation and depression of the memory state. AR significantly influences both the electric field distribution over the FE and the extent of CI from the top electrode. Constant-pulse writing schemes enable analog threshold voltage modulation by considering the AR, with reduced voltages and faster operation for smaller ARs. Retention of intermittent states written by FE polarization combined with CI is demonstrated, illustrating the stability and effectiveness of FeMFET devices and AR optimization for memory applications.}, archive = {J_ACCESS}, author = {Hannes Dahlberg and Oscar Kaatranen and Karl-Magnus Persson and Arto Rantala and Jacek Flak and Lars-Erik Wernersson}, doi = {10.1109/ACCESS.2025.3527628}, journal = {IEEE Access}, pages = {9923-9930}, shortjournal = {IEEE Access}, title = {Memory state dynamics in BEOL FeFETs: Impact of area ratio on analog write mechanisms and charging}, volume = {13}, year = {2025}, }
• (2025). Signal processing-free intelligent model for power quality disturbances identification. ACCESS, 13, 9910-9922. (www)

  @article{ , abstract = {Integrating different types of renewable energy sources in the power system substantially challenges the power quality (PQ), directly affecting the system’s stability and service life span. The rise of power quality disturbances (PQD) generates irregularities in voltage and current waveforms, harming smart grid networks and linked devices. Traditional methods for PQD classification use complicated feature extraction techniques, which can be computationally expensive and lack scalability. This research proposes applying basic convolutional neural network (CNN) models for automated PQD detection and categorization as a prospective solution to these issues. By directly examining PQD images generated from signal data, these models reduce the requirement for human-crafted features. The study analyzes alternative CNN setups, training datasets, and disturbance types to measure model performance. The results demonstrate that these simple CNN models maintain stable accuracy values in normal and noisy environments, even with increasing classes and noise, the models managed to maintain a high-performance level with up to 99.39% accuracy for 17 classes when the Adam optimizer was used instead of RMSprop. The models could deal with noise-related disturbances, still achieving accuracy as high as 96.42% when trained by just 50% of the dataset under 30dB SNR (Signal to Noise Ratio) conditions. Moreover, comparing the two frequencies on 50Hz and 60Hz performance does not show the equivalent models’ robustness over different operating levels. This study highlights the potential of CNNs in boosting power quality disturbance categorization and presents paths for further inquiry in model refining and optimization. The study focuses on CNN-based models applied in power quality disturbance detection and classification research.}, archive = {J_ACCESS}, author = {Mohammed F. Al-Mashdali and Asif Islam and Abdulbasit Hassan and Md Shafiullah and Mujahed Al-Dhaifallah and Khalid Al Fuwail}, doi = {10.1109/ACCESS.2025.3528296}, journal = {IEEE Access}, pages = {9910-9922}, shortjournal = {IEEE Access}, title = {Signal processing-free intelligent model for power quality disturbances identification}, volume = {13}, year = {2025}, }
• (2025). Majority or minority: Data imbalance learning method for named entity recognition. ACCESS, 13, 9902-9909. (www)

  @article{ , abstract = {Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a single majority class (i.e., $\mathcal {O}$ -class). This imbalance leads to misclassifications of the entity classes as the $\mathcal {O}$ -class. To tackle this issue, we propose a simple and effective learning method named majority or minority (MoM) learning. MoM learning incorporates the loss computed only for samples whose ground truth is the majority class into the loss of the conventional ML model. Evaluation experiments on four NER datasets (Japanese and English) showed that MoM learning improves the prediction performance of the minority classes without sacrificing the performance of the majority class and is more effective than widely known and state-of-the-art methods. We also evaluated MoM learning using frameworks such as sequential labeling and machine reading comprehension, which are commonly used in NER. Furthermore, MoM learning has achieved consistent performance improvements regardless of language, model, framework, or data size.}, archive = {J_ACCESS}, author = {Sota Nemoto and Shunsuke Kitada and Hitoshi Iyatomi}, doi = {10.1109/ACCESS.2024.3522972}, journal = {IEEE Access}, pages = {9902-9909}, shortjournal = {IEEE Access}, title = {Majority or minority: Data imbalance learning method for named entity recognition}, volume = {13}, year = {2025}, }
• (2025). SOA services identification and design methods from business models: A systematic literature review. ACCESS, 13, 9879-9901. (www)

  @article{ , abstract = {Modern organizations are process-oriented. To remain competitive in the digital transformation era, these organizations design and implement Information Systems (IS) to support their business processes. The emergence of the Service-Oriented Architecture (SOA) style made it possible to design IS architectural models that meet software quality criteria and are aligned with the organizations’ business models. However, designing such SOA services is a complex task that requires extensive knowledge and skills in software architecture and the business domain. Researchers and practitioners have proposed several methods to derive SOA services from business models during the last two decades. However, SOA design initiatives from business models still fail. Existing methods and processes to design process-aware IS have limitations related to their usability and implementation complexity. These limitations triggered the necessity for a survey that extracts more information about how to design SOA architectural models from business models. This work proposes a systematic literature review (SLR) to establish the state of the knowledge about the existing methods that derive service models from business models. This SLR provides practitioners, such as solutions architects, business architects, and application architects, a comprehensive overview of available methods to derive SOA architectural models from business models, helping them design SOA services and build effective software solutions. We selected forty-one primary studies published between 2006 and 2023. We compared selected methods according to seven specific criteria, namely: design life cycle coverage, detailed specification support, SoaML support, service granularity, the use of patterns, automation, and tool support. The results confirm that SOA is an established architectural style for building effective ISs that support organizations’ business processes. As far as we observed from comparing selected methods according to the selected criteria, our findings explain the need for a new method that provides organizations with an easy-to-use and comprehensive process to derive quality SOA models from business models.}, archive = {J_ACCESS}, author = {Redouane Blal and Abderrahmane Leshob and Hafedh Mili and Imen Benzarti and Pierre Hadaya and Raqeebir Rab}, doi = {10.1109/ACCESS.2025.3528297}, journal = {IEEE Access}, pages = {9879-9901}, shortjournal = {IEEE Access}, title = {SOA services identification and design methods from business models: A systematic literature review}, volume = {13}, year = {2025}, }
• (2025). Enhancing cervical cancer classification: Through a hybrid deep learning approach integrating DenseNet201 and InceptionV3. ACCESS, 13, 9868-9878. (www)

  @article{ , abstract = {This paper proposes a hybrid deep learning model integrating DenseNet201 and InceptionV3 to address the challenges in achieving accurate and reliable cervical cancer classification. Current models often exhibit limitations in balancing precision and recall, which are critical for dependable clinical applications. The hybrid model leverages DenseNet201’s efficient feature reuse and InceptionV3’s capacity for handling multi-scale and hierarchical features through fine-tuning and feature fusion techniques. The methodology involves rigorous data preprocessing, including normalization, augmentation, and dataset splitting, to ensure robust training and validation. Feature extraction and dimensionality optimization are employed to identify the most critical and discriminative features for classification. The experimental setup utilizes Python, TensorFlow, and Keras within a GPU-enabled environment to handle computational demands effectively. Comprehensive evaluation metrics, including accuracy, precision, recall, and F1-score, indicate that the proposed model achieves an accuracy of 96.54%, 95.91% Presicion, 96.44% Recall and 96.17% F1 Score surpassing state-of-the-art models such as ResNet-50, DenseNet-201, InceptionV3, and Xception. Visualization tools, including high-resolution confusion matrices and ROC curves, further demonstrate the hybrid model’s capability to differentiate between cervical cancer cell classes accurately. Comparative analyses validate the model’s superior performance and its potential as a dependable tool for clinical implementation. This study presents a robust and efficient classification system that addresses the limitations of existing models. Future research will focus on further improving the system’s performance and investigating its applicability to other medical imaging tasks. The proposed model is expected to contribute significantly to early and accurate cervical cancer diagnosis, enhancing patient outcomes and supporting healthcare professionals in clinical decision-making.}, archive = {J_ACCESS}, author = {Abhiram Sharma and R. Parvathi}, doi = {10.1109/ACCESS.2025.3527677}, journal = {IEEE Access}, pages = {9868-9878}, shortjournal = {IEEE Access}, title = {Enhancing cervical cancer classification: Through a hybrid deep learning approach integrating DenseNet201 and InceptionV3}, volume = {13}, year = {2025}, }
• (2025). Differential evolution-based end-fire realized gain optimization of active and parasitic arrays. ACCESS, 13, 9857-9867. (www)

  @article{ , abstract = {We propose a novel approach for boosting the realized gain of arrays with enhanced directivity, utilizing both active and parasitic dipoles. The optimization process first maximizes the end-fire gain in the active array by selecting the optimal current excitation vector. For the parasitic arrays, the dipoles are reactively loaded based on the input impedances of the active dipoles, after which the optimization focuses on the inter-element distance to achieve a balance between the gain and the reflection efficiency. This multi-objective optimization, underpinned by the differential evolution (DE) algorithm, uses a simple wire dipole as the unit element. Full-wave simulations validate our theoretical results, showing that our two- and three-element parasitic arrays achieve realized gain comparable to state-of-the-art designs without relying on intricate unit elements or resource-intensive simulations, while our four- and five-element parasitic arrays yield the highest realized gain values reported in the literature. The simplicity of our approach allows optimizations to run significantly faster than full-wave simulations, whilst the sensitivity analysis showcases the robustness of the design under small deviations in loads and element positioning. Compact and power-efficient, the proposed parasitic arrays are well-suited for base stations, aligning with modern communication system requirements while minimizing hardware complexity.}, archive = {J_ACCESS}, author = {Rozita Konstantinou and Ihsan Kanbaz and Okan Yurduseven and Michail Matthaiou}, doi = {10.1109/ACCESS.2025.3526501}, journal = {IEEE Access}, pages = {9857-9867}, shortjournal = {IEEE Access}, title = {Differential evolution-based end-fire realized gain optimization of active and parasitic arrays}, volume = {13}, year = {2025}, }
• (2025). Face detection using hybrid SNN-ANN to process neuromorphic event stream. ACCESS, 13, 9844-9856. (www)

  @article{ , abstract = {This paper tackles the challenges of face detection, a vital computer vision task with wide-ranging applications, particularly in driver monitoring systems, where both accuracy and computational efficiency are crucial. Traditional frame-based methods often suffer from high computational complexity and under-sampling issues. In turn this limits their effectiveness in real-time applications and increase energy budget. To overcome these limitations, this paper explores the benefits of neuromorphic event cameras, which capture asynchronous pixel-level changes, offering lower data processing demands and reduced latency. The paper proposes a hybrid architecture combining Spiking Neural Networks (SNNs) and Artificial Neural Networks (ANNs). This approach leverages the energy efficiency and low-latency of SNNs while maintaining the high accuracy of ANNs, resulting in a highly efficient and accurate face detection system. The proposed Spiking-Face approach demonstrates a 57.76% improvement in mean average precision (mAP) over state-of-the-art methods. Additionally, the paper provides a comprehensive analysis of the system’s performance across different temporal resolutions, showing that the system performs robustly and adapts effectively to varying conditions. This hybrid SNN-ANN architecture achieves up to three times higher computational efficiency compared to equivalent traditional ANN methods, significantly reducing computational complexity while maintaining accuracy. These findings underscore the potential of this hybrid architecture for real-time, energy-constrained applications.}, archive = {J_ACCESS}, author = {Waseem Shariff and Paul Kielty and Joe Lemley and Peter Corcoran}, doi = {10.1109/ACCESS.2025.3527160}, journal = {IEEE Access}, pages = {9844-9856}, shortjournal = {IEEE Access}, title = {Face detection using hybrid SNN-ANN to process neuromorphic event stream}, volume = {13}, year = {2025}, }
• (2025). Envisioning the potential of 6G use Cases—A dual perspective of innovation and business. ACCESS, 13, 9831-9843. (www)

  @article{ , abstract = {Currently, 6G technology is at a pivotal stage for research and standardization, with the next three to five years being crucial for achieving technological breakthroughs and fostering industrial development. 6G is anticipated to support a broader spectrum of application scenarios, lead us into a more intelligent and interconnected future. The applications and use cases of 6G serve as a vital link between technological innovation and user demands, laying the groundwork for key network performance indicators, functional design, and service capability planning, and paving the way for its future commercialization. The forward-looking exploration and identification of potential 6G use cases, particularly those with high development prospects, can offer strategic guidance for technological research and development. This study introduces an innovative 6G Use Case Potential Matrix, designed from dual perspectives of innovation and business viability. It assesses application innovation through the volume and trends of invention patents and evaluates business potential using market outlook analyses. Based on these insights, typical 6G use cases are categorized into four groups: leading use cases, use cases to be broken through, use cases to be excavated, and use cases to be cultivated. We select 23 typical 6G use cases with consensus for empirical analysis, and recommendations for the development and layout of 6G use cases are given. The findings of this research aim to offer fresh perspectives for the systematic planning of 6G applications development pathways and to support decision-making on aligning technology research directions with network requirements definitions.}, archive = {J_ACCESS}, author = {Muhua Wei and Jun Wu and Huili Liu and Lin Lin and Zheng Zhang and Mayuan Sun}, doi = {10.1109/ACCESS.2025.3527577}, journal = {IEEE Access}, pages = {9831-9843}, shortjournal = {IEEE Access}, title = {Envisioning the potential of 6G use Cases—A dual perspective of innovation and business}, volume = {13}, year = {2025}, }
• (2025). Output feedback-based fuzzy event-triggered synchronization control for nonlinear multiagent systems under DoS attacks. ACCESS, 13, 9821-9830. (www)

  @article{ , abstract = {This article studies the fuzzy event-triggered synchronization control problem for nonlinear multiagent systems (NMASs) under denial-of-service (DoS) attacks and unmeasured states. First, to estimate the leader’s system matrix and state under DoS attacks, a novel distributed secure estimator is constructed. Second, fuzzy logic systems (FLSs) are applied to approximate unknown dynamics, and a fuzzy state observer is proposed to estimate unknown states of NMASs. To save the data transmission and communication resources, an event-triggered mechanism (ETM) is employed to the controller design. Then, based on the developed distributed secure estimator and fuzzy state observer, an output feedback based fuzzy event-triggered synchronization control approach is proposed by utilizing backstepping technology. It is proven that all the signals are bounded and synchronization tracking is achieved. At last, a simulation example is provided to demonstrate the results achieved.}, archive = {J_ACCESS}, author = {Xiaoru Lan and Limin Hou}, doi = {10.1109/ACCESS.2025.3526375}, journal = {IEEE Access}, pages = {9821-9830}, shortjournal = {IEEE Access}, title = {Output feedback-based fuzzy event-triggered synchronization control for nonlinear multiagent systems under DoS attacks}, volume = {13}, year = {2025}, }
• (2025). Volatile organic compounds for the prediction of lung cancer by using ensembled machine learning model and feature selection. ACCESS, 13, 9809-9820. (www)

  @article{ , abstract = {The advancement of biomarkers is critically important at present, as lung cancer is a leading cause of death. In the present study, volatile organic compounds (VOCs) are considered as biomarkers to predict lung cancer. VOCs from seven different sources including breath, blood, urine, cell line, plerual fluid, cancer tissue and lung tissue are targeted to enhance the prediction reliability. Feature selection and models fusion have been focused on during this study. Five in-built and one proposed ensemble machine learning model have been utilised to investigate the different types of VOCs. The idea behind designing one ensemble model is to combine multiple individual models for better performance by using optimal feature sets. This reasoning led to the design of an ensemble model to predict breath VOCs. The AvNNet model has superior performance in predicting blood VOCs, cancer tissue VOCs, cell line VOCs, and urine VOCs compared to four other models, achieving accuracies of 70%, 80%, 70%, and 90% accordingly on the validation dataset. The Blackboost model achieved 90% accuracy on the validation dataset in its prediction of lung tissue VOCs. With 90% accuracy on a validation dataset, the random forest model predicts pleural fluid volatile organic compounds efficiently. When compared to individual models, the proposed ensemble model predicts breath VOCs more effectively and achieves 100% accuracy on the validation dataset.}, archive = {J_ACCESS}, author = {Divya Khanna and Arun Kumar and Shahid Ahmad Bhat}, doi = {10.1109/ACCESS.2025.3527027}, journal = {IEEE Access}, pages = {9809-9820}, shortjournal = {IEEE Access}, title = {Volatile organic compounds for the prediction of lung cancer by using ensembled machine learning model and feature selection}, volume = {13}, year = {2025}, }
• (2025). Evaluating wireless power transfer technologies for electric vehicles: Efficiency and practical implementation of inductive, capacitive, and hybrid systems. ACCESS, 13, 9792-9808. (www)

  @article{ , abstract = {This study evaluated wireless power transfer (WPT) technologies for electric vehicles (EVs), focusing on inductive (IPT), capacitive (CPT), and hybrid (HPT) systems. IPT utilizes resonant magnetic fields, CPT employs resonant electric fields, and HPT combines both methods to optimize the use of electromagnetic fields and electronic components. Pilot experiments were conducted using WPT standards and the relevant literature to investigate the efficiency and practical implementation of these WPT technologies. The evaluation included measuring system efficiencies with multimeters, assessing input and output waveform smoothness using oscilloscopes, detecting power losses through thermal scans, and monitoring electromagnetic field (EMF) exposure with EMF detectors. The results demonstrated that IPT achieved higher efficiency, smoother waveforms, and lower EMF exposure than CPT and HPT at lower frequencies. Moreover, IPT has a more straightforward circuit design owing to the lack of high-frequency components, further enhancing its practicality. The study also examined the effects of ground clearance and misalignment on WPT performance, and addressed safety concerns and potential solutions for all three types of WPT systems.}, archive = {J_ACCESS}, author = {Thamvarit Singhavilai and Jarurote Tippayachai and Kamon Jirasereeamornkul and Chainarin Ekkaravarodome and Taweesak Samanchuen}, doi = {10.1109/ACCESS.2025.3527122}, journal = {IEEE Access}, pages = {9792-9808}, shortjournal = {IEEE Access}, title = {Evaluating wireless power transfer technologies for electric vehicles: Efficiency and practical implementation of inductive, capacitive, and hybrid systems}, volume = {13}, year = {2025}, }
• (2025). Multimedia tasks-oriented edge computing offloading scheme based on graph neural network in vehicular networks. ACCESS, 13, 9780-9791. (www)

  @article{ , abstract = {With the advancement of vehicular networking technologies, in-vehicle devices are increasingly involved in complex computational tasks, posing new challenges to the vehicles’ computational capabilities and energy consumption. This study addresses the complexities associated with task deployment by proposing a novel integrated framework that synergizes the strengths of deep reinforcement learning (DRL) and graph neural networks (GNNs). The proposed framework leverages the relational capabilities of GNNs to capture inter-task dependencies while utilizing the adaptive learning capabilities of DRL to optimize task offloading decisions in real time. First, we propose a GNN-based task offloading scheme that utilizes graph structures to represent task dependencies and optimizes task deployment through graph neural networks. Second, deep Reinforcement Learning (DRL) is introduced to learn optimal task deployment policies, enhancing the efficiency and accuracy of task deployment. Finally, the performance of the proposed scheme is validated through simulation experiments, including metrics such as model stability, subtask deployment error rate, expected energy consumption, and algorithm solution time. The proposed hybrid approach not only enhances the efficiency of resource allocation but also minimizes processing delays, ultimately contributing to improved performance in vehicular networks.}, archive = {J_ACCESS}, author = {Yong Huang}, doi = {10.1109/ACCESS.2025.3526627}, journal = {IEEE Access}, pages = {9780-9791}, shortjournal = {IEEE Access}, title = {Multimedia tasks-oriented edge computing offloading scheme based on graph neural network in vehicular networks}, volume = {13}, year = {2025}, }
• (2025). Feature-centered first order structure tensor scale-space in 2D and 3D. ACCESS, 13, 9766-9779. (www)

  @article{ , abstract = {The structure tensor method is often used for 2D and 3D analysis of imaged structures, but its results are in many cases very dependent on the user’s choice of method parameters. We simplify this parameter choice in first order structure tensor scale-space by directly connecting the width of the derivative filter to the size of image features. By introducing a ring-filter step, we substitute the Gaussian integration/smoothing with a method that more accurately shifts the derivative filter response from feature edges to their center. We further demonstrate how extracted structural measures can be used to correct known inaccuracies in the scale map, resulting in a reliable representation of the feature sizes both in 2D and 3D. Compared to the traditional first order structure tensor, or previous structure tensor scale-space approaches, our solution is much more accurate and can serve as an out-of-the-box method for extracting a wide range of structural parameters with minimal user input.}, archive = {J_ACCESS}, author = {Pawel Tomasz Pieta and Anders Bjorholm Dahl and Jeppe Revall Frisvad and Siavash Arjomand Bigdeli and Anders Nymark Christensen}, doi = {10.1109/ACCESS.2025.3527227}, journal = {IEEE Access}, pages = {9766-9779}, shortjournal = {IEEE Access}, title = {Feature-centered first order structure tensor scale-space in 2D and 3D}, volume = {13}, year = {2025}, }
• (2025). Application of machine learning for radiowave propagation modeling below 6 GHz. ACCESS, 13, 9755-9765. (www)

  @article{ , abstract = {This paper presents the application of supervised learning and use of fully connected neural network (FCNN) for the development of a path specific propagation model for frequencies below 6 GHz. The model has been trained and tested against an extensive measurement dataset capturing several areas and the diverse topography of the UK. In addition to the measurement data, propagation environment related features that are provided as an input to the neural network have been extracted by employing quantised radio path profiles based on detailed topographic datasets (such as terrain, surface and land use). The results provide an insight in the choice of appropriate features that describe the propagation environment, trade-offs between prediction accuracy, extent of considered key features and associated complexity. The performance comparison between varying number of input features highlights that whilst an excellent prediction accuracy can be achieved when the training data include all test scenarios, it also reveals the limitations of pure data driven prediction methods and their inability to generalise. Noting the multifaceted nature and impact of propagation mechanisms and environments in conjunction with the limited availability of empirical measurements covering a wide range of spectrum bands, alternative hybrid approaches which may include physical modeling of propagation mechanisms are expected to further enhance the performance of neural networks based radiowave prediction methods. We present a simple hybrid approach in this paper that resolves the generalisation issues and provide far superior performance compared to pure physical propagation models or data driven models based on FCNN.}, archive = {J_ACCESS}, author = {Mohammud Z. Bocus and Afzal Lodhi}, doi = {10.1109/ACCESS.2025.3526329}, journal = {IEEE Access}, pages = {9755-9765}, shortjournal = {IEEE Access}, title = {Application of machine learning for radiowave propagation modeling below 6 GHz}, volume = {13}, year = {2025}, }
• (2025). Can electric vehicle charging stations be carbon neutral with solar renewables?. ACCESS, 13, 9739-9754. (www)

  @article{ , abstract = {In line with sustainable energy and environmental targets, the share of electric vehicles in the automobile market is expected to reach 80%. On the other hand, unplanned electric vehicle charging station installation for EV charging demands and high dependency rates on the distribution grid may adversely affect grid reliability, energy costs, and environmental targets. This article investigates whether electric vehicle charging stations can achieve carbon neutrality through strategic techno-economic integration with solar renewables. Furthermore, an exhaustive analysis investigated achieving carbon neutrality via integrating energy storage systems with photovoltaics, factoring in investment costs and carbon taxes. The findings indicate a significant potential for reducing grid dependency by up to 54.3%. Implementing a more stringent carbon tax has facilitated a notable enhancement in energy storage system capacity, increasing the self-consumption rate by 72% and declining carbon emissions by 25.55%. Self-consumption decreases by 30% during high charging demand in the morning and evening hours, leading to increased dependency on the grid and emphasizing the critical requirement for improved strategies to reduce carbon emissions. Despite the significant investment required for energy storage systems, a gradual increase in carbon taxes has effectively reduced the grid’s dependency by up to 34.7%. Moreover, decreases in storage costs can increase the decline in grid dependency by an additional 18%. Despite the integration of energy storage systems, the ambitious zero carbon target remains unattainable due to the existing installation area constraints of EV charging stations. This study can help policies that align with global efforts to mitigate climate change, enhance energy security, optimize costs, drive technological innovation, and meet increasing demands for sustainable policies.}, archive = {J_ACCESS}, author = {Alpaslan Demirci and Zafer Ozturk and Musa Terkes and Said Mirza Tercan and Recep Yumurtaci and Umit Cali}, doi = {10.1109/ACCESS.2025.3526736}, journal = {IEEE Access}, pages = {9739-9754}, shortjournal = {IEEE Access}, title = {Can electric vehicle charging stations be carbon neutral with solar renewables?}, volume = {13}, year = {2025}, }
• (2025). Geographical peer matching for P2P energy sharing. ACCESS, 13, 9718-9738. (www)

  @article{ , abstract = {Significant cost reductions attract ever more households to invest in small-scale renewable electricity generation and storage. Such distributed resources are not used in the most effective way when only used individually, as sharing them provides even greater cost savings. Energy Peer-to-Peer (P2P) systems have thus been shown to be beneficial for prosumers and consumers through reductions in energy cost while also being attractive to grid or service providers. However, many practical challenges have to be overcome before all players could gain in having efficient and automated local energy communities; such challenges include the inherent complexity of matching together geographically distributed peers and the significant computations required to calculate the local efficient matching options. We define and analyze in this work a precise mathematical modeling of the geographical peer matching problem, and demonstrate the inherent intractability of the problem, highlighting its high computational cost and underscoring the critical need for scalable approaches that effectively balance performance trade-offs as system size grows. Furthermore, we propose and study analytically and empirically a spectrum of approaches to address it and perform a cost-efficient matching of peers in a computationally efficient fashion. Our experimental study, based on real-world energy data, demonstrates that our proposed solutions are efficient both in terms of cost savings achieved by the peers and in terms of communication and computing requirements. Our scalable algorithms thus provide one core building block for practical and data-efficient peer-to-peer energy sharing communities within large-scale optimization systems.}, archive = {J_ACCESS}, author = {Romaric Duvignau and Vincenzo Gulisano and Marina Papatriantafilou and Ralf Klasing}, doi = {10.1109/ACCESS.2024.3524091}, journal = {IEEE Access}, pages = {9718-9738}, shortjournal = {IEEE Access}, title = {Geographical peer matching for P2P energy sharing}, volume = {13}, year = {2025}, }
• (2025). Wafer defect classification algorithm with label embedding using contrastive learning. ACCESS, 13, 9708-9717. (www)

  @article{ , abstract = {Classifying wafer defects in the wafer manufacturing process is increasingly critical for ensuring high-quality production, optimizing processes, and reducing costs. Most existing methods for wafer map defect classification primarily rely on images alone for model training and prediction. However, these approaches often lack interpretability, which can hinder process improvement and problem-solving efforts. In other words, existing methods only calculate the probability of a specific image belonging to each class, making it difficult to visually judge why the image belongs to a particular class. Additionally, these methods make it challenging to assess the distance of new images from each class. Furthermore, it is difficult to obtain representative images of each class. To address these limitations, we propose a novel approach for wafer defect classification using contrastive learning with label embedding. The proposed method aims to map label information and wafer defect images into a shared latent space through contrastive learning using label embedding. This not only facilitates defect class prediction from images but also enhances interpretability by visualizing relationships between images and defects (labels) and providing representative defect images. Moreover, compared to previous methods, our approach demonstrates better classification performance and computational efficiency, even in situations with imbalanced labels. This method also shows significant potential in identifying unseen defects not defined in the original classification tasks. Consequently, the proposed approach extends its applicability beyond wafer map defect patterns, showing promising potential for use in various domains.}, archive = {J_ACCESS}, author = {Jeongjoon Hwang and Somi Ha and Dohyun Kim}, doi = {10.1109/ACCESS.2025.3527491}, journal = {IEEE Access}, pages = {9708-9717}, shortjournal = {IEEE Access}, title = {Wafer defect classification algorithm with label embedding using contrastive learning}, volume = {13}, year = {2025}, }
• (2025). Context is all you need: A hybrid attention-based method for detecting code design patterns. ACCESS, 13, 9689-9707. (www)

  @article{ , abstract = {Software reverse engineering plays a crucial role in identifying design patterns and reconstructing software architectures by analyzing system implementations and producing abstract representations across multiple layers. This research introduces a novel feature engineering approach that integrates both behavioral and structural analysis of code, resulting in a feature-rich sequential representation. This transformation enables the effective use of transformers and attention mechanisms to detect design patterns in source code. Our results emphasize the importance of context in distinguishing between various design patterns, demonstrating that the proposed sequence format, with its sensitivity to token order, significantly improves the model’s capacity to differentiate between similar patterns. By leveraging the power of attention mechanisms, our approach efficiently discards irrelevant code elements, focusing on the most critical features for accurate patterns detection. Additionally, we show that this sequential code representation can be utilized to augment training data, leading to enhanced model accuracy. Trained on a diverse set of code samples representing all 23 GoF design patterns, sourced from repositories such as GitHub and Bitbucket, our methodology achieved an accuracy of 92%. Evaluation metrics further validate the robustness of the approach. This study underscores the potential of context-driven, feature-engineered representations in advancing design patterns detection and contributes a comprehensive new dataset that supports behavioral code analysis, setting the stage for future research in this area.}, archive = {J_ACCESS}, author = {Tarik Houichime and Younes El Amrani}, doi = {10.1109/ACCESS.2025.3525849}, journal = {IEEE Access}, pages = {9689-9707}, shortjournal = {IEEE Access}, title = {Context is all you need: A hybrid attention-based method for detecting code design patterns}, volume = {13}, year = {2025}, }
• (2025). Power quality assessment and optimization in FUZZY-driven healthcare devices. ACCESS, 13, 9679-9688. (www)

  @article{ , abstract = {The advent of FUZZY technology has revolutionized healthcare, empowering smarter medical devices and equipment. However, the successful operation of these FUZZY-driven systems is contingent on high power quality. This paper introduces an innovative FUZZY-driven energy management system that combines convolutional neural networks (CNNs) for real-time power quality event detection, long short-term memory (LSTM) networks for predictive analytics, and reinforcement learning for optimized control. Through extensive simulations on an IEEE 13-bus test feeder, we demonstrate the system’s superior performance in detecting and mitigating power quality disturbances. The CNN-based detection achieves 97% accuracy in classifying events, while the LSTM enables 95% accurate prediction of emerging issues. The reinforcement learning controller achieves 50% faster voltage sag restoration, 20% greater harmonic reduction, and 30% faster critical load recovery during outages compared to conventional methods. Key challenges, including data quality concerns, cybersecurity risks, and integration with legacy infrastructure, are discussed. This work represents a significant advancement in applying FUZZY technology to healthcare power quality management, offering a comprehensive solution that balances efficiency, reliability, and patient safety. The proposed system provides a scalable framework for modernizing power quality monitoring and control in healthcare facilities.}, archive = {J_ACCESS}, author = {Dinesh Kumar Nishad and Saifullah Khalid and Rashmi Singh}, doi = {10.1109/ACCESS.2025.3526001}, journal = {IEEE Access}, pages = {9679-9688}, shortjournal = {IEEE Access}, title = {Power quality assessment and optimization in FUZZY-driven healthcare devices}, volume = {13}, year = {2025}, }
• (2025). Intelligent network selection mechanisms in the internet of everything system. ACCESS, 13, 9666-9678. (www)

  @article{ , abstract = {In the Internet of Everything (IoE) ecosystem, it is necessary for connected devices to move between different access points seamlessly. As IoE is deployed in diverse environments, such as smart cities, industrial settings, and vehicular networks, ensuring reliable connectivity and uninterrupted services across different networks becomes important. As part of the IoE framework, this research paper focuses on developing and evaluating intelligent handoff decision mechanisms. So, in this work, the optimal handoff decision technique is proposed, which transmits the information in a fast manner. The Fuzzy Analytical Hierarchical Process (FAHP) and Analytical Hierarchical Process (AHP) two-user preference calculation algorithms are used. Then, the user preference is included in the prospect algorithm and the optimal network is selected. After that, the result of the prospect algorithm is compared with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Rational Analysis (GRA), and Multiplicative Exponent Weighting (MEW) algorithms in terms of network scores and the number of handoffs. The advantage of network selection using a prospect algorithm is that it will decrease the failure rate and increase the data transfer and user experience. These advantages deliver seamless connectivity for a diverse range of applications and devices using an IoE infrastructure that is more robust and reliable. The network selected by combining different techniques is the optimal network because it selects the most efficient or effective network.}, archive = {J_ACCESS}, author = {Indrasen Singh and Meenakshi Munjal}, doi = {10.1109/ACCESS.2025.3528099}, journal = {IEEE Access}, pages = {9666-9678}, shortjournal = {IEEE Access}, title = {Intelligent network selection mechanisms in the internet of everything system}, volume = {13}, year = {2025}, }
• (2025). Load invariant CC and CV modes for Static/Dynamic wireless charging system with half-bridge multi-leg converter topology. ACCESS, 13, 9654-9665. (www)

  @article{ , abstract = {Resonant Inductive Power Transmission (RIPT) is an advanced Wireless Power Transfer (WPT) technology, emerging as a secure and efficient solution for Electric Vehicle (EV) charging. Although Dynamic Wireless Charging Systems (DWCS) reduce the need for large batteries compared to static charging, they require higher initial investments. This study introduces a novel approach utilizing LCC-S compensation to achieve load-invariant Constant Current (CC) and Constant Voltage (CV) outputs at two distinct Zero Phase Angle (ZPA) frequencies in DWCS, leveraging a half-bridge-based multi-legged inverter configuration. The article presents a new method with fewer iterations to determine the optimal compensation parameters, ensuring that both the CC and CV outputs with ZPA comply with the SAE J2954 frequencies standards. The half-bridge-based DWCS with LCC-S compensation is designed to operate in either CC or CV mode, depending on whether the application involves static or dynamic charging. A 1-kW LCC-S compensated DWCS prototype was developed and built to verify and support the theoretical findings and analysis. This study offers valuable insights for optimizing RIPT technology, making EV charging more efficient and cost-effective.}, archive = {J_ACCESS}, author = {Aganti Mahesh and Bharatiraja Chokkalingam and Rajesh Verma and Lucian Mihet-Popa}, doi = {10.1109/ACCESS.2025.3526654}, journal = {IEEE Access}, pages = {9654-9665}, shortjournal = {IEEE Access}, title = {Load invariant CC and CV modes for Static/Dynamic wireless charging system with half-bridge multi-leg converter topology}, volume = {13}, year = {2025}, }
• (2025). Consistency regularization for semi-supervised semantic segmentation of flood regions from SAR images. ACCESS, 13, 9642-9653. (www)

  @article{ , abstract = {As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster response and mitigation efforts. Accurately defining the extent of floods is a problem for traditional flood mapping approaches, which emphasizes the vital need for modern technologies such as Synthetic Aperture Radar (SAR) imaging. Additionally, there is a need to develop computer-aided tools specifically designed for automatically identifying areas that are vulnerable to flooding using SAR data. Nonetheless, the lack of consistent large datasets presents a barrier that prevents these algorithms from progressing and being used in real-world scenarios. For this reason, the present study aims to develop a semi-supervised semantic segmentation algorithm for accurate flood region delineation in SAR data. In particular, the paper proposes labeling unannotated instances of data using a pseudo-label generation strategy. In order to accomplish this, the study suggests using a self-supervised trained teacher model to generate pseudo-labels and speed up the training procedure. The teacher model is then trained with a student model to efficiently extract features from the labeled data. Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. A comprehensive assessment conducted on publicly available datasets produces promising results. These results confirm the usefulness and possible relevance of the suggested methodology in enhancing efforts related to flood zone identification and management.}, archive = {J_ACCESS}, author = {G. Savitha and S. Girisha and Pundarika Sughosh and Dasharathraj K. Shetty and Jayaraj Mymbilly Balakrishnan and Rahul Paul and Nithesh Naik}, doi = {10.1109/ACCESS.2025.3526244}, journal = {IEEE Access}, pages = {9642-9653}, shortjournal = {IEEE Access}, title = {Consistency regularization for semi-supervised semantic segmentation of flood regions from SAR images}, volume = {13}, year = {2025}, }
• (2025). Machine learning approach to predict the DC bias for adaptive OFDM transmission in indoor li-fi applications. ACCESS, 13, 9627-9641. (www)

  @article{ , abstract = {Multilevel quadrature amplitude modulation (M-QAM) combined with DC-bias in optical orthogonal frequency division multiplexing (DCO-OFDM) offers a spectrally efficient solution and adaptive transmission rates for indoor light-fidelity (Li-Fi) systems. However, a significant challenge posed by the DCO-OFDM scheme is the additional power of the DC bias required to ensure that the amplitudes of the transmitted signals are nonnegative. These biased signals are clipped according to optical power constraints, imposing clipping noise that affects the transmission bit error rate (BER). This performance degradation is conditioned by the adjustments made to the DC bias, which requires continuous modification to support adaptive transmission. Therefore, simultaneously addressing DC bias optimization and clipping mitigation is essential to provide reliable and power-efficient transmissions. This paper proposes a machine learning (ML) approach to predict the optimum DC bias based on the statistical properties of the OFDM signal and system characteristics. A robust ML regressor selection process using LazyPredict algorithm (LPA) was employed to identify the optimal regressors for developing the predictive model. The model demonstrated significant prediction accuracy for DC bias across a wide range of transmission settings. In particular, the models built on variants of gradient boosting regressor (GBR) and support vector regressor (SVR) demonstrated superior performance, with R-squared evaluation scores of 0.9792 and 0.9225, respectively, for two different sets of features. Furthermore, the BER performance of our adaptive DC bias approach was compared to a fixed DC bias in adaptive DCO-OFDM transmission, demonstrating the superiority of our approach in effectively mitigating clipping noise at high transmission rates while maintaining power efficiency at lower rates. These results provide a promising solution for the future practical deployment of Li-Fi systems in indoor applications.}, archive = {J_ACCESS}, author = {Marwah T. Salman and David R. Siddle and Amadi G. Udu}, doi = {10.1109/ACCESS.2025.3527205}, journal = {IEEE Access}, pages = {9627-9641}, shortjournal = {IEEE Access}, title = {Machine learning approach to predict the DC bias for adaptive OFDM transmission in indoor li-fi applications}, volume = {13}, year = {2025}, }
• (2025). Vibration analysis and optimal control of hydraulic turbine based on sliding mode control strategy. ACCESS, 13, 9615-9626. (www)

  @article{ , abstract = {The turbine generator unit plays a very important role in maintaining the stability of the power system and improving the quality of power. However, hydropower units will bring the risk of low-frequency oscillation or ultra-low frequency oscillation to the power system, threatening the stable operation of the power grid. Therefore, it is necessary to deeply study the mechanism of low-frequency oscillation of hydropower units, analyze the adverse factors to the system and adjust and control them. In this paper, a speed change method based on exponential rules is proposed on the basis of the traditional axial flow turbine model, and a coupling model of fuzzy sliding mode control of the turbine is constructed. A control strategy combining fuzzy logic and sliding mode is adopted to adjust the convergence speed according to the change of working conditions and working points by using mechanical power. By incorporating the angle and voltage variables into the sliding mode function design, the influence of controller output on system damping is improved, and the stability of working angle output is improved. The simulation results under various working conditions show that the control law can suppress the low-frequency oscillation of the system when the system is subjected to power disturbance, effectively improve the power angle oscillation behavior of the hydropower unit, significantly improve the stability of the system and the robustness of the controller, and verify the effectiveness and feasibility of the designed controller’s power angle oscillation suppression ability. It provides theoretical support and a new solution for suppressing low frequency oscillation of hydro-generator set.}, archive = {J_ACCESS}, author = {Xiaoping Huang and Yaqiong Zhang and Wenzhe Huang}, doi = {10.1109/ACCESS.2025.3526182}, journal = {IEEE Access}, pages = {9615-9626}, shortjournal = {IEEE Access}, title = {Vibration analysis and optimal control of hydraulic turbine based on sliding mode control strategy}, volume = {13}, year = {2025}, }
• (2025). Study on finger gesture interface using one-channel EMG. ACCESS, 13, 9606-9614. (www)

  @article{ , abstract = {Electromyography (EMG) is used to recognize user finger gestures for applications in real-time interfaces. Finger movements are classified by preprocessing to extract the features from the collected EMG data, which are then used for machine learning. The data were extracted using the overlapped segmentation method to ensure sufficient training data. The preprocessing of EMG data uses standard formulae, such as integrated EMG (IEMG) and mean absolute value (MAV). Furthermore, preprocessing involves using original data, simple moving average (SMA), and Fast Fourier transform (FFT) for feature extraction. Subsequently, these preprocessed data sets are used to train machine learning models, facilitating a comparative analysis. Four machine learning models were used: eXtreme Gradient Boost, Random Forest, k-Nearest Neighbors, and Logistic Regression. The experimental results revealed the best accuracy from preprocessing using a simple moving average followed by a Fourier transform, but classification was not possible using all nine finger movements. On the other hand, it showed more than 90% accuracy because the model learned by reducing it to a specific finger gesture. Rest movements, index finger taps, and force-taps movements achieved the highest accuracy, approximately 95%.}, archive = {J_ACCESS}, author = {Hee-Yeong Yang and Young-Shin Han and Choon-Sung Nam}, doi = {10.1109/ACCESS.2025.3527684}, journal = {IEEE Access}, pages = {9606-9614}, shortjournal = {IEEE Access}, title = {Study on finger gesture interface using one-channel EMG}, volume = {13}, year = {2025}, }
• (2025). Asynchronous distributed model predictive control for multi-agent systems. ACCESS, 13, 9592-9605. (www)

  @article{ , abstract = {This paper primarily focuses on the asynchronous cooperation control problem of multi-agent systems with external disturbances. Here, all agents take control actions, such as sampling measurements, computing control inputs, and exchanging information based on their individual clocks rather than a global synchronized clock. Owing to the asynchronous nature, the information of each agent received by all its neighbors at the same time instant may be different and deviate from what this agent predicts, potentially resulting in poor decision-making and even instability. To ensure stability, dynamic compatibility constraints related to all the uncertain deviations are established and incorporated into the optimization problem. Additionally, the constraint restriction method is employed to satisfy physical constraints and optimize performance in the presence of external perturbations. Then, an asynchronous distributed model predictive control with a dual-mode strategy is developed and sufficient conditions on the design parameters are constructed to ensure recursive feasibility and system stability. Finally, simulation experiments on a multiple-vehicle system with asynchronous control actions are carried out to validate the theoretical results.}, archive = {J_ACCESS}, author = {Xiaoxiao Mi and Yuanjiang Liao and Hongzheng Zeng}, doi = {10.1109/ACCESS.2025.3525487}, journal = {IEEE Access}, pages = {9592-9605}, shortjournal = {IEEE Access}, title = {Asynchronous distributed model predictive control for multi-agent systems}, volume = {13}, year = {2025}, }
• (2025). FECI-RTDETR a lightweight unmanned aerial vehicle infrared small target detector algorithm based on RT-DETR. ACCESS, 13, 9578-9591. (www)

  @article{ , abstract = {Addressing the challenges of small target detection in aerial infrared images from a drone’s perspective, such as diverse target scales, complex backgrounds, the clustering of small targets, and limited computational resources of the drone platform. This paper proposes a lightweight UAV infrared small target detection algorithm, FECI-RTDETR. Initially, we introduce a lightweight RFConv-Block module that enhances spatial feature extraction capabilities while reducing computational redundancy. Subsequently, we combine the Efficient Additive feature selection mechanism with an intra-scale feature interaction module to form the EA-AIFI module, which strengthens the model’s focus on dense targets and reduces computational burden. Moreover, we introduce the CHS-FPN structure as a cross-scale feature fusion structure, utilizing the coordinate attention mechanism combined with a hierarchical scale-based feature pyramid network. This allows the model to better understand the contextual semantics of targets and improves detection accuracy. Finally, the original GIoU loss is replaced with Inner-GIoU loss, using a scaling factor to control the auxiliary enclosing box, which accelerates convergence speed and enhances detection accuracy for small targets. Experimental results indicate that compared to RT-DETR, the FECI-RTDETR model reduces the number of parameters by 24.56% and floating-point operations by 19.12% on the HIT-UAV dataset. The mAP50 and mAP50:95 metrics improved by 4.2% and 2.9%, respectively, with the mAP50 reaching 84.2%. This algorithmic model achieves a balance between resource reduction and accuracy enhancement while maintaining lightweight characteristics.}, archive = {J_ACCESS}, author = {Renzheng Xue and Shijie Hua and Haiqiang Xu}, doi = {10.1109/ACCESS.2025.3528237}, journal = {IEEE Access}, pages = {9578-9591}, shortjournal = {IEEE Access}, title = {FECI-RTDETR a lightweight unmanned aerial vehicle infrared small target detector algorithm based on RT-DETR}, volume = {13}, year = {2025}, }
• (2025). Cyber attacks on commercial drones: A review. ACCESS, 13, 9566-9577. (www)

  @article{ , abstract = {s, also known as drones, are increasingly used in several applications and a variety of cyber attacks can be performed on them using several tools. Some examples of these attacks include breaking the connection between the drone and the controller with deauthentication attacks, discovering a password or cryptographic key used in a communication protocol, gaining control of the drone through command/code injection, and attacks. This paper covers drone attacks by analyzing different components of the drone, including the remote controller and communication protocols. The main purpose is to provide an overview of the possible ways in which cyber attacks can be executed. This analysis concludes that drones, designed for diverse purposes, are vulnerable to a range of cyber attacks. The paper also reviews existing penetration testing methodologies for UAVs, which provide a logical framework for their realization. This review covers the different cyber attack methods and tools used against a UAV, intending to improve defense mechanisms.}, archive = {J_ACCESS}, author = {Bruno Branco and José Silvestre Serra Silva and Miguel Correia}, doi = {10.1109/ACCESS.2025.3527698}, journal = {IEEE Access}, pages = {9566-9577}, shortjournal = {IEEE Access}, title = {Cyber attacks on commercial drones: A review}, volume = {13}, year = {2025}, }
• (2025). A process model for the implementation of blockchain-based systems. ACCESS, 13, 9549-9565. (www)

  @article{ , abstract = {Although blockchain technology has immense potential to revolutionize various industries, its practical adoption faces significant challenges. This study tackles these hurdles by introducing a process model for the implementation of a blockchain-based system (PM-IBS), a comprehensive framework that guides organizations through the complexities of the adoption of blockchain systems. The model offers a structured approach that includes decision trees for suitability evaluation, platform selection guidance, high-level architectural design strategies, cost estimation techniques, and an understanding of best practices and design principles. Furthermore, a real-world use case, “Blockchain-Based Property Registration and Succession System,” exemplifies the model’s applicability in tackling practical challenges. Finally, a comprehensive feedback survey evaluated the PM-IBS model in various dimensions, providing valuable information on its strengths and potential improvements. This dual contribution of a novel process model, functional prototype, real-world use case, and user feedback significantly enriches the knowledge of blockchain adoption strategies. The PM-IBS model empowers organizations to overcome adoption barriers and leverage the transformative power of blockchain technology.}, archive = {J_ACCESS}, author = {Muhammad Nasim Bahar and Michal Ries and Kristián Košťál}, doi = {10.1109/ACCESS.2024.3521361}, journal = {IEEE Access}, pages = {9549-9565}, shortjournal = {IEEE Access}, title = {A process model for the implementation of blockchain-based systems}, volume = {13}, year = {2025}, }
• (2025). Hybrid graph representation learning for carotid artery stenosis detection based on multimodal retinal OCTA images. ACCESS, 13, 9538-9548. (www)

  @article{ , abstract = {Carotid artery stenosis (CAS) is one of the major causes of cerebral ischemic stroke. Rapid and precise detection of CAS is crucial for early intervention and reducing ischemic stroke incidence. Neuroimaging techniques, as the gold standard for evaluating cerebral abnormalities in CAS, suffer from limitations including expensive and time-consuming, hindering their use in large-scale screening. The ophthalmic artery is a branch of the internal carotid artery, several studies suggest that the biomarkers on retinal optical coherence tomography angiopraphy (OCTA) images are associated with CAS. Thus, retinal OCTA as a non-invasive and high-resolution imaging technique has potential as a suitable approach for identifying CAS patients. In this work, we developed a hybrid graph-based deep learning model to detect CAS from OCTA images. Given the differential impact of CAS on arteries and veins, we explicitly leverage the artery and vein information within the retinal region to enhance the sensitivity of the model to the change in microvasculature. We construct a hybrid graph representation by combining arterial and venous features, with the aim of improving the model’s ability to extract and integrate diverse anatomical information for more accurate CAS detection. For evaluation, we enrolled 182 CAS and 239 control subjects in this study. The experimental results demonstrated our retinal image analysis-based AI model, received promising results in distinguishing CAS and control subjects, with AUC of 0.7765 and an accuracy of 0.7750.}, archive = {J_ACCESS}, author = {Wenting Lan and Jinkui Hao and Shengjun Zhou and Jingfeng Zhang and Shaodong Ma and Yitian Zhao}, doi = {10.1109/ACCESS.2024.3412961}, journal = {IEEE Access}, pages = {9538-9548}, shortjournal = {IEEE Access}, title = {Hybrid graph representation learning for carotid artery stenosis detection based on multimodal retinal OCTA images}, volume = {13}, year = {2025}, }
• (2025). Multi-scale channel distillation network for image compressive sensing. ACCESS, 13, 9524-9537. (www)

  @article{ , abstract = {Recently, convolutional neural networks (CNNs) have demonstrated striking success in computer vision tasks. Methods based on CNNs for image compressive sensing (CS) have also gained prominence. However, existing methods tend to increase the depth of the network in feature space for better reconstruction quality, neglecting the hierarchical representation of intermediate features in pixel space. In order to coordinate the feature space and pixel space to complete the deep reconstruction of images, and further improve the reconstruction performance of current CS methods, we propose a multi-scale channel distillation network (MSCDN). This network first obtains images of multiple scales using a scale-space image decomposition method at the sampling stage, followed by sampling these decomposed images through a convolutional operation. In this way, multi-scale information in the compressed domain is aggregated. During the reconstruction phase, a low-frequency information recovery network generates a preliminary image, whereas a high-frequency feature aggregation network refines the image further. Specifically, we design a dual-branch deep reconstruction architecture with channel distillation residual block (CDRB) as the core component. One branch extracts features gradually by cascading multiple CDRB modules, thereby supplementing the initial reconstructed image with a large amount of high-frequency content in feature space. The other branch takes the initial reconstructed image as input and sequentially fuses the intermediate feature outputs by CDRBs to increase the local details of the image in pixel space. Combining outputs from both branches, we achieve an optimal reconstructed image. Extensive experimental results on four benchmark datasets demonstrate that MSCDN surpasses state-of-the-art CS methods not only in reconstruction accuracy but also in perceptual visual quality.}, archive = {J_ACCESS}, author = {Tianyu Zhang and Kuntao Ye and Yue Zhang and Rui Lu}, doi = {10.1109/ACCESS.2025.3527756}, journal = {IEEE Access}, pages = {9524-9537}, shortjournal = {IEEE Access}, title = {Multi-scale channel distillation network for image compressive sensing}, volume = {13}, year = {2025}, }
• (2025). Strategy-switch: From all-reduce to parameter server for faster efficient training. ACCESS, 13, 9510-9523. (www)

  @article{ , abstract = {Deep learning plays a pivotal role in numerous big data applications by enhancing the accuracy of models. However, the abundance of available data presents a challenge when training neural networks on a single node. Consequently, various distributed training methods have emerged. Among these, two prevalent approaches are All-Reduce and Parameter Server. All-Reduce, operating synchronously, faces synchronization-related bottlenecks, while the Parameter Server, often used asynchronously, can potentially compromise the model’s performance. To harness the strengths of both setups, we introduce Strategy-Switch, a hybrid approach that offers the best of both worlds, combining speed with efficiency and high-quality results. This method initiates training under the All-Reduce system and, guided by an empirical rule, transitions to asynchronous Parameter Server training once the model stabilizes. Our experimental analysis demonstrates that we can achieve comparable accuracy to All-Reduce training but with significantly accelerated training.}, archive = {J_ACCESS}, author = {Nikodimos Provatas and Iasonas Chalas and Ioannis Konstantinou and Nectarios Koziris}, doi = {10.1109/ACCESS.2025.3528248}, journal = {IEEE Access}, pages = {9510-9523}, shortjournal = {IEEE Access}, title = {Strategy-switch: From all-reduce to parameter server for faster efficient training}, volume = {13}, year = {2025}, }
• (2025). Designing a data-driven energy management service: A case study of south korea’s national industrial complex. ACCESS, 13, 9498-9509. (www)

  @article{ , abstract = {Big data plays a crucial role in energy management services by providing valuable insights to customers. However, data analytics alone cannot create value, and it is essential to effectively transform and integrate these data into energy-management services. Although numerous studies have focused on developing data-driven architectures or platforms to manage energy efficiency, few have addressed the design process of data-driven energy management services. To fill this research gap, we conducted a case study to design a novel service concept aimed at improving the energy efficiency of companies within a national industrial complex based on an analysis of electricity consumption data. Finally, our findings, supported by interviews with a small company within the complex, suggest that the proposed information and service concept can be both effective and beneficial for businesses. This study contributes to the integration of data analytics with service design, thereby improving the effectiveness and adoption of such services.}, archive = {J_ACCESS}, author = {Tagir Salakhov and Jaejoon Choi and Minjun Kim}, doi = {10.1109/ACCESS.2024.3525194}, journal = {IEEE Access}, pages = {9498-9509}, shortjournal = {IEEE Access}, title = {Designing a data-driven energy management service: A case study of south korea’s national industrial complex}, volume = {13}, year = {2025}, }
• (2025). A novel active learning technique for fetal health classification based on XGBoost classifier. ACCESS, 13, 9485-9497. (www)

  @article{ , abstract = {Ensuring safe pregnancy and reducing maternal and infant mortality rates require early prediction of fetal health. The application of machine learning algorithms in monitoring fetal health helps to improve the chances of timely intervention and better outcomes in the event of any possible health issues in fetuses. Existing studies offered to help this issue, typically by training models using a significant portion of the dataset, ranging mainly above 70%. The only existing active learning method in this field employs around 41% training samples to achieve 98% accuracy. This work presents a novel active learning technique to identify the most informative data samples to train a model, leading to high accuracy with a limited number of training samples. It employs a novel query function built upon uncertainty and diversity criteria which are derived based on properties of XGBoost classifier and distance from each other. For deriving uncertainty criterion the soft probabilities obtained for the unlabeled samples are used, while the distance among the uncertain samples in feature space is utilized for deriving diversity criterion. The proposed approach shows superior performance compared to all state-of-the-art methods. Through analysis and experimentation, the proposed solution achieves an accuracy greater than 99% using less than 20% of the dataset for training. This shows its efficacy and potential in the monitoring of fetal health. The code and dataset are available on the GitHub repository https://github.com/niyg7/fetal-health-dataset.}, archive = {J_ACCESS}, author = {Kaushal Bhardwaj and Niyati Goyal and Bhavika Mittal and Vandna Sharma and Shiv Naresh Shivhare}, doi = {10.1109/ACCESS.2025.3527151}, journal = {IEEE Access}, pages = {9485-9497}, shortjournal = {IEEE Access}, title = {A novel active learning technique for fetal health classification based on XGBoost classifier}, volume = {13}, year = {2025}, }
• (2025). Visual question answering in robotic surgery: A comprehensive review. ACCESS, 13, 9473-9484. (www)

  @article{ , abstract = {Visual Question Answering (VQA) in robotic surgery is rapidly becoming a pivotal technology in medical AI, addressing the complex challenge of interpreting multimodal surgical data to support real-time decision-making. This comprehensive review synthesizes key advancements in Surgical VQA, highlighting the integration of large language models (LLMs), multimodal fusion techniques, and visual grounding methods. By reviewing 62 key studies selected through a systematic search of major scientific databases, including IEEE Xplore, Google Scholar, SpringerLink, and PubMed, we trace the evolution of VQA systems and their application in surgical environments. Current limitations, including dataset scarcity, multimodal alignment challenges, and issues of interpretability, are critically examined. This survey aims to not only provide a structured overview of the field but also identify critical research gaps and propose future directions to enhance VQA systems for robotic surgery, with the ultimate goal of improving intraoperative performance and patient outcomes.}, archive = {J_ACCESS}, author = {Di Ding and Tianliang Yao and Rong Luo and Xusen Sun}, doi = {10.1109/ACCESS.2024.3525145}, journal = {IEEE Access}, pages = {9473-9484}, shortjournal = {IEEE Access}, title = {Visual question answering in robotic surgery: A comprehensive review}, volume = {13}, year = {2025}, }
• (2025). MFFTNet: A novel 3D point cloud segmentation network based on multi-scale feature fusion and transformer architecture. ACCESS, 13, 9462-9472. (www)

  @article{ , abstract = {Intelligent analysis of 3D point clouds has become a frontier in emerging fields such as autonomous driving, digital twins, and the metaverse. Precise segmentation of 3D point clouds is particularly important within these domains; however, it faces several challenges: (1) point cloud data inherently lacks structured topological information; (2) point cloud shapes are complex and highly variable, making it difficult to utilize semantic priors; and (3) the sampling process of point clouds may result in sparse and uneven data. To address these issues, this paper proposes a novel Point Cloud Segmentation Network based on multi-scale feature fusion and Transformer architecture (MFFTNet). MFFTNet enhances the performance of existing segmentation methods by globally modeling the overall point cloud shape and embedding local point cloud details. Specifically, MFFTNet divides the segmentation task into encoding and decoding stages. The encoder is designed as a hierarchical pyramid structure that extracts relatively sparse local center points and fuses local features during progressive downsampling. It also utilizes a Transformer for global feature modeling to establish multi-scale topological and semantic information of the point cloud. Subsequently, multi-scale feature fusion further enhances the network’s perception of local features and global structure. The decoder progressively upsamples to restore the original point cloud and injects multi-scale feature information to achieve precise segmentation. Based on the aforementioned encoding-decoding structure and multi-scale feature fusion, MFFTNet outperforms existing methods on the point cloud semantic segmentation datasets ShapeNetPart and S3DIS.}, archive = {J_ACCESS}, author = {Hao Bai and Xiongwei Li and Qing Meng and Shulong Zhuo and Lili Yan}, doi = {10.1109/ACCESS.2025.3528245}, journal = {IEEE Access}, pages = {9462-9472}, shortjournal = {IEEE Access}, title = {MFFTNet: A novel 3D point cloud segmentation network based on multi-scale feature fusion and transformer architecture}, volume = {13}, year = {2025}, }
• (2025). Performance comparison of loop source and grounded-wire source for shallow refined TEM detection. ACCESS, 13, 9450-9461. (www)

  @article{ , abstract = {Loop-source transient electromagnetic method (TEM) and grounded-wire-source TEM are two of the most popular methods in geological electromagnetic detection. Advances in power electronics have made it possible to achieve high magnetic moments by transmitting large current pulses through small excitation coils, enabling the use of independent loop sources as magnetic dipoles in active detection techniques. The independent loop source, by overcoming the attenuation of the magnetic field caused by distance, becomes an efficient and low-cost tool for shallow detection. Both the independent loop source and the grounded-wire source share similarities in detection principles and scanning modes. This paper discusses the merits and shortcomings of the loop source and grounded-wire source for shallow, multiscale refined detection. The three-dimensional model is established and the electromagnetic response characteristics are compared by COMSOL finite element simulation software. Additionally, the influence of anomalous burial depth, offset distance, and measurement height on the detection results of these two near-source methods are thoroughly discussed. Finally, it is explained by the field measurement results. The comparison of simulation and experimental results shows that the loop-source TEM is more suitable for shallow refined detection in terms of response quality and construction efficiency.}, archive = {J_ACCESS}, author = {Zhengyu Xu and Guofeng Zhao and Jiangying Peng and Xiao Ma and Wei Liu}, doi = {10.1109/ACCESS.2025.3527870}, journal = {IEEE Access}, pages = {9450-9461}, shortjournal = {IEEE Access}, title = {Performance comparison of loop source and grounded-wire source for shallow refined TEM detection}, volume = {13}, year = {2025}, }
• (2025). Assessing ECG interpretation expertise in medical practitioners through eye movement data and neuromorphic models. ACCESS, 13, 9430-9449. (www)

  @article{ , abstract = {This study introduces an innovative method for assessing ECG interpretation abilities in medical professionals via eye-tracking data. We examine eye movement patterns from five separate groups of cardiology practitioners utilizing a combination of neuromorphic computing models, including Spiking Neural Networks (SNN), Spiking Convolutional Neural Networks (SCNN), Recurrent Spiking Neural Networks (RSNN), and Spiking Convolutional Long Short-Term Memory (SCLSTM). Utilizing eye movement data, we analyze the skill levels of practitioners in diverse medical positions, including consultants, nurses, and technicians, during ECG evaluations. Our proposed work combines spiking neuron activations with convolutional and recurrent architectures to analyze spatial and temporal gaze patterns that reflect clinical expertise. The suggested SNN, SCNN, RSNN, and SCLSTM models attained accuracies of 84.35%, 93.04%, 94.68%, 99.76% respectively, exceeding standard machine learning approaches in both precision and recall for identifying expertise levels based on visual attention patterns. This paradigm has the potential to construct skill evaluation tools in medical education, specifically for ECG interpretation training, thereby addressing prevalent difficulties related to inconsistent ECG diagnosis methods.}, archive = {J_ACCESS}, author = {Syed Mohsin Bokhari and Muhammad Shafi and Fazal Noor and Sarmad Sohaib and Saad Alqahtany and Mark Donnelly}, doi = {10.1109/ACCESS.2025.3528253}, journal = {IEEE Access}, pages = {9430-9449}, shortjournal = {IEEE Access}, title = {Assessing ECG interpretation expertise in medical practitioners through eye movement data and neuromorphic models}, volume = {13}, year = {2025}, }
• (2025). A customized local energy trading approach using a comprehensive set of performance indices. ACCESS, 13, 9413-9429. (www)

  @article{ , abstract = {This paper presents a customized local energy trading approach using a comprehensive set of performance indices (i.e. Community Savings, Self-Consumption, Self-Sufficiency, Fairness Index, Individual Household Savings) for evaluation of the trading practices. The simulations performed on historical data sets compared dispatch mechanisms based on Mixed Integer Linear Programming (MILP) and techniques originating from Auction-Based approaches. The intent was to use performance metrics in building Local Energy Market (LEM) trading solutions based on a given set of limited inputs and use them to optimize pricing or trading dispatch to get the best consumer returns. Modeling results indicate that the Auction-Based Model performed better than the MILP methods in terms of all performance indices and had potential to provide participants with higher levels of savings.}, archive = {J_ACCESS}, author = {Smitali Patnaik and Ümit Cali and Maciej A. Noras}, doi = {10.1109/ACCESS.2025.3525616}, journal = {IEEE Access}, pages = {9413-9429}, shortjournal = {IEEE Access}, title = {A customized local energy trading approach using a comprehensive set of performance indices}, volume = {13}, year = {2025}, }
• (2025). Optimization configuration method of energy storage considering photovoltaic power consumption and source-load uncertainty. ACCESS, 13, 9401-9412. (www)

  @article{ , abstract = {The proposal of a “double carbon” target has resulted in a gradual and continuous increase in the proportion of photovoltaic (PV) access to the distribution network area. To enhance the capability of PV consumption and mitigate the voltage overrun issue stemming from the substantial PV access proportion, this paper presents a multi-objective energy storage optimization allocation methodology. Firstly, a PV and load uncertainty model is established based on Beta and Normal distributions, and the Monte Carlo Method (MC) is used to simulate the annual output. The representative scenarios are obtained after the combination is cut down by the K-means clustering algorithm. The poor operation scenarios are identified by using the variation of the maximum total node voltage deviation. Finally, an energy storage optimization allocation is proposed. Subsequently, the objective function, which seeks to minimize the total daily operating cost of the energy storage system and the PV abandonment rate, is constructed using the evaluation-based function method. The constraints, including node voltage and energy storage device, are considered, and the model is solved using the improved grey wolf optimization algorithm. Finally, a distribution network station system is employed to compare and analyze the capacity allocation results of poor operation scenarios under different schemes, the results showed that after energy storage optimization, the voltage of each scheme decreased from 0.4869kV to 0.42kV, and the curtailment rate decreased by 13.09%, 21.33%, and 23.69% respectively compared to before optimization,which verifies the effectiveness of the models and methods proposed in this paper.}, archive = {J_ACCESS}, author = {Junjie Qiao and Xiaofang Meng and Weigang Zheng and Pengxue Huang and Tiefeng Xu and Yupeng Xu}, doi = {10.1109/ACCESS.2025.3528050}, journal = {IEEE Access}, pages = {9401-9412}, shortjournal = {IEEE Access}, title = {Optimization configuration method of energy storage considering photovoltaic power consumption and source-load uncertainty}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Decentralized asynchronous formation planning of multirotor aerial vehicles in dynamic environments using flexible formation graphs and tight trajectory hulls”. ACCESS, 13, 9399-9400. (www)

  @article{ , abstract = {Presents corrections to the paper, Corrections to “Decentralized Asynchronous Formation Planning of Multirotor Aerial Vehicles in Dynamic Environments Using Flexible Formation Graphs and Tight Trajectory Hulls”.}, archive = {J_ACCESS}, author = {Fahad Tanveer and Muhammad Bilal Kadri}, doi = {10.1109/ACCESS.2025.3527322}, journal = {IEEE Access}, pages = {9399-9400}, shortjournal = {IEEE Access}, title = {Corrections to “Decentralized asynchronous formation planning of multirotor aerial vehicles in dynamic environments using flexible formation graphs and tight trajectory hulls”}, volume = {13}, year = {2025}, }
• (2025). Quarter-wavelength two-section coaxial stepped impedance resonators for harmonics suppression in tx bandpass filters. ACCESS, 13, 9391-9398. (www)

  @article{ , abstract = {This paper presents the implementation of two-section coaxial stepped impedance resonators (SIRs) solution to control the attenuated band of Tx bandpass filters. This type of resonator has two degrees of freedom, in the transverse and longitudinal dimensions. At a fixed fundamental frequency, this allows the dimensions of the resonators to be modulated to control both the location of the harmonics and the power handling for space applications. It is therefore possible to design a filter for which the harmonic frequencies of the resonators are not the same, leading to destructive recombinations in the attenuated band. This is illustrated through the design of a 6th order narrowband L-band bandpass filter with controlled attenuation up to $6.6\times $ F0 and a multipactor threshold of around 30 W. This filter was fabricated in aluminum and tested. The experimental results are presented and discussed. Finally, a comparison is made with the state-of-the-art.}, archive = {J_ACCESS}, author = {Alexandre Bonizec and Jessica Benedicto and Jean-François Favennec and Eric Rius and Christophe Goujon}, doi = {10.1109/ACCESS.2025.3528259}, journal = {IEEE Access}, pages = {9391-9398}, shortjournal = {IEEE Access}, title = {Quarter-wavelength two-section coaxial stepped impedance resonators for harmonics suppression in tx bandpass filters}, volume = {13}, year = {2025}, }
• (2025). A novel remote sensing recognition using modified GMM segmentation and DenseNet. ACCESS, 13, 9372-9390. (www)

  @article{ , abstract = {The accurate classification of aerial images is a crucial task in remote sensing, with applications ranging from land cover mapping and urban planning to disaster response and environmental monitoring. However, challenges such as limited labeled data, inherent data complexity, and high computational demands often hinder the performance of traditional methods. To address these challenges, we present an innovative framework that combines advanced segmentation techniques, diverse feature extraction methods, optimization algorithms, and deep learning. Our approach begins with novel Graph-cut Optimized Fuzzy GMM Segmentation (GC-GMM), ensuring precise object identification and boundary delineation. We employ Azimuthal Average Feature Extraction, Haar Wavelet Transform, and Maximally Stable Extremal Regions (MSER) to capture a rich set of features encompassing texture, frequency, and shape information. These features are fused and refined using Particle Swarm Optimization (PSO) to create a robust and informative representation. Leveraging the power of deep learning, a DenseNet architecture achieves superior classification accuracy based on the optimized feature set. This framework effectively tackles the limitations of previous methods by combining the strengths of diverse feature extraction techniques with deep learning capabilities. The use of optimization algorithms further enhances the discriminative power of the features, leading to improved classification accuracy.}, archive = {J_ACCESS}, author = {Muhammad Waqas Ahmed and Moneerah Alotaibi and Sultan Refa Alotaibi and Dina Abdulaziz Alhammadi and Asaad Algarni and Ahmad Jalal and Jeongho Cho}, doi = {10.1109/ACCESS.2025.3526476}, journal = {IEEE Access}, pages = {9372-9390}, shortjournal = {IEEE Access}, title = {A novel remote sensing recognition using modified GMM segmentation and DenseNet}, volume = {13}, year = {2025}, }
• (2025). Evaluating visual satisfaction in urban sculpture spaces: A multi-criteria decision making approach. ACCESS, 13, 9354-9371. (www)

  @article{ , abstract = {The assessment of visual satisfaction in urban sculpture spaces has emerged as a topic of paramount interest due to the increased emphasis given to aesthetic quality and the general quality of urban environments. Such an evaluation usually entails making qualitative observations on items such as public art installations and sculptures, meaning findings need to comprise multiple perspectives and choices. To this end, a clear and comprehensive decision support model is required that adequately weighs the concerns of style in relationship to the environment and culture. Hence, this paper proposes the disc interval-valued fermatean fuzzy set (D-IVFFS) based decision-making framework for evaluating the MCDM problems associated with uncertainties in urban sculptures. D-IVFFS improves the decision making flexibility due to better quantification of uncertainties and impreciseness in comparison with conventional fuzzy systems. Due to the combination of interval-valued fuzzy logic with disc geometry, D-IVFFS offers a form of more flexible, efficient, and accurate approach for managing uncertainty than F-sets. It is integrated with the Complex Proportional Assessment (COPRAS) method for increasing the effectiveness of evaluation and ranking of options in benefit-cost context. The proposed framework can provide stable and reliable decision results even if the decision makers are in different levels of uncertainty. The real-life problems are illustrated in the twelve case studies, which prove the cogency and versatility of the D-IVFFS-COPRAS method as a certain, useful solution to various decision-making issues.}, archive = {J_ACCESS}, author = {Dianwei Zou and Liping Pan}, doi = {10.1109/ACCESS.2025.3528258}, journal = {IEEE Access}, pages = {9354-9371}, shortjournal = {IEEE Access}, title = {Evaluating visual satisfaction in urban sculpture spaces: A multi-criteria decision making approach}, volume = {13}, year = {2025}, }
• (2025). Projection-type holographic display with observation area expanded using time-division reproduced light generation system. ACCESS, 13, 9343-9353. (www)

  @article{ , abstract = {Projection-type holographic displays are three-dimensional (3D) displays that utilize computer holography. These displays consist of a projection lens and a transparent holographic optical element (HOE) with off-axis concave mirror phase distribution and allow the user to observe a large 3D image by designing the magnification of the projection lens and the light-collecting function of the HOE screen independently. However, the observation area of the 3D image is limited. In this paper, we propose a method of expanding the observation area using time-division projection of multiple reproduced lights. With the proposed system developed to synchronize illumination lights and holograms, the proposed method can expand the observation area by a factor of 1.7 while maintaining the image size twofold that obtained by the conventional system. In addition, we succeeded in generating reproducing 3D video at 20 frames per second.}, archive = {J_ACCESS}, author = {Tomoki Kanno and Yasuyuki Ichihashi and Takashi Kakue and Koki Wakunami and Ryutaro Oi and Tomoyoshi Shimobaba and Tomoyoshi Ito}, doi = {10.1109/ACCESS.2025.3527576}, journal = {IEEE Access}, pages = {9343-9353}, shortjournal = {IEEE Access}, title = {Projection-type holographic display with observation area expanded using time-division reproduced light generation system}, volume = {13}, year = {2025}, }
• (2025). The decomposition pathways of C₄F₇N/CO₂ mixtures in the presence of organic insulator vapors. ACCESS, 13, 9335-9342. (www)

  @article{ , abstract = {As a potential substitute for SF6, C4F7N/CO2 mixture gas has a broad application prospect in gas insulated substation (GIS) busbars and high-voltage circuit breakers. Under high temperature conditions, Polytetrafluoroethylene (PTFE) vapor may have a significant effect on the decomposition path of C4F7N/CO2 mixture, which has far-reaching significance for the study of arc extinction characteristics. The decomposition mechanism of C4F7N/CO2 mixture in PTFE vapor environment was investigated by using density functional theory (DFT) for quantum chemical calculation. In this study, the structure and vibration frequency of the optimized intermediates were calculated accurately, and the energy of the reactants and intermediates was evaluated by G4 theory method. The decomposition path of C4F7N/CO2 mixture under the influence of PTFE vapor was revealed by analyzing the decomposition path potential energy. This paper not only provides a theoretical basis for the interaction between PTFE vapor and C4F7N/CO2 mixture, but also provides a reference for the study of arc extinguishing characteristics of environmentally friendly gases in high voltage circuit breakers.}, archive = {J_ACCESS}, author = {Sijie Liu and Xiaolong Li and Wen Wang and Zhenxin Geng and Ying Lin}, doi = {10.1109/ACCESS.2025.3526273}, journal = {IEEE Access}, pages = {9335-9342}, shortjournal = {IEEE Access}, title = {The decomposition pathways of C₄F₇N/CO₂ mixtures in the presence of organic insulator vapors}, volume = {13}, year = {2025}, }
• (2025). Low-complexity data-driven communication neural receivers. ACCESS, 13, 9325-9334. (www)

  @article{ , abstract = {Communication receivers based on deep learning have become a research hotspot in recent years. However, the excessive computational complexity and storage complexity prevent it from being deployed on communication hardware with limited resources. In order to reduce the computational complexity and required storage resources of communication neural receivers based on deep learning, we propose to use candecomp parafac (CP), Tucker, tensor-train (TT) and tensor-ring (TR) decomposition respectively to compress the data-driven deep learning based communication neural receiver. Through compression, the storage resources required by the communication neural receiver are reduced by about half with bit error rate (BER) performance degradation of only 0.75dB to 1.3dB.}, archive = {J_ACCESS}, author = {Qingle Wu and Yuanhui Liang and Benjamin K. Ng and Chan-Tong Lam and and Yan Ma}, doi = {10.1109/ACCESS.2024.3524571}, journal = {IEEE Access}, pages = {9325-9334}, shortjournal = {IEEE Access}, title = {Low-complexity data-driven communication neural receivers}, volume = {13}, year = {2025}, }
• (2025). A TSF control strategy with zero voltage modulation in interval segmented for permanent magnet-assisted switched reluctance motor. ACCESS, 13, 9313-9324. (www)

  @article{ , abstract = {Aiming at the problems of large torque ripple in permanent magnet-assisted switched reluctance motor (PMa-SRM) under conventional voltage pulse width modulation (PWM) method. A torque sharing function (TSF) control strategy with interval segmented zero-voltage modulation is proposed in this paper. Firstly, the zero voltage is decentralized by optimizing the PWM modulation method. Secondly, the control interval is divided and a segmented TSF based on hyperbolic tangent function transformation is proposed tailored to the torque characteristics. Then, the PWM variable duty cycle control is carried out according to the inductance characteristics. In the single-phase conduction region (SpC), the optimal duty cycle is predicted by using the deadbeat predictive control (DPC); in the two-phase exchange region (TpE), the duty cycle function is designed for each interval, and the duty cycles of the incoming and outgoing phases are obtained online through the torque error. Finally, simulations and experimental verification of the 6/20 PMa-SRM prototype confirm that the proposed control strategy to have good performance in torque ripple suppression.}, archive = {J_ACCESS}, author = {Chaozhi Huang and Renquan Xiao and Yong Xiao and Chengyi Gong}, doi = {10.1109/ACCESS.2025.3527460}, journal = {IEEE Access}, pages = {9313-9324}, shortjournal = {IEEE Access}, title = {A TSF control strategy with zero voltage modulation in interval segmented for permanent magnet-assisted switched reluctance motor}, volume = {13}, year = {2025}, }
• (2025). A modified WASPAS method for the evaluation of E-commerce websites based on pythagorean fuzzy information. ACCESS, 13, 9303-9312. (www)

  @article{ , abstract = {Web and business e-commerce site assessment is the act of comparing e-commerce sites based on their functionality, efficiency, or ability to meet the needs of a business or its clients. They are essential to keep up with the market changes and shifts to provide better compliance with security requirements and to optimize the interface to hold onto user attention. The modified versions of the WASPAS method hybrid with Pythagorean fuzzy sets are exploited to evaluate the e-commerce websites like the Amazon review dataset, Chnsenti Corp., and IMDB dataset using various performance indicators. The WASPAS model with balancing factor $\beta =\exp \,(j)$ demonstrates an average accuracy of 94.01% for the Amazon review dataset, 98.19% for the Chnsenti Corp. dataset, and 98.83% in the case of the IMBD dataset with a mean execution time of 806.60 sec, 787.10 sec and 809.19 sec, respectively. The proposed model is compared with reported state of art results methods SVM, SVM hybrid with unigram, SVM hybrid with unigram, SVM hybrid with unigram & grid search techniques, XGBoost and ERF-XGB methods with their accuracy of 50.40%, 80.81%, 80.81%, 90.1 and 98.2, respectively. The global fitness value and mean execution time from 50 independent runs are calculated for these datasets, and statistical indicators are used to see the stability of the study. The proposed approach significantly enhanced performance and reduced computational complexity, making it a promising tool for real-time detection and prediction of the efficiency of employees in various organizations and a tool for growth.}, archive = {J_ACCESS}, author = {Xiufang Ou and Bingbin Chen}, doi = {10.1109/ACCESS.2025.3527212}, journal = {IEEE Access}, pages = {9303-9312}, shortjournal = {IEEE Access}, title = {A modified WASPAS method for the evaluation of E-commerce websites based on pythagorean fuzzy information}, volume = {13}, year = {2025}, }
• (2025). Suppressing the endogenous negative influence through node intervention in social networks. ACCESS, 13, 9290-9302. (www)

  @article{ , abstract = {Viral marketing, a marketing strategy that utilizes word-of-mouth (WOM), is effective in increasing brand awareness and acquiring new customers, as WOM allows information to reach a large audience in social networks. In the past two decades, for efficient viral marketing, many studies on maximizing advertising reach, known as influence maximization, have been conducted in the field of data mining. However, most of them ignore the possibility of the emergence of negative opinions in the information diffusion process. In general, negative opinions are more contagious than positive ones, and ignoring them may even lead undesirable outcomes, such as a decline in brand image and a decrease in purchases. To address this issue, we consider the problem of suppressing the negative influence that emerges endogenously on social networks through preemptive node interventions, such as persuasion, nudging, or warnings. Namely, given a limited budget for interventions, who should be targeted to efficiently suppress the spread of negative opinions in the social network? We formulate this problem as a combinatorial optimization problem on graphs. We prove that this problem is NP-hard and propose approximation algorithms to identify optimal intervention nodes that minimize the negative influence. Through numerical experiments, we demonstrate that our algorithms effectively suppress the negative influence regardless of the type of social network or experimental setting.}, archive = {J_ACCESS}, author = {Satoshi Furutani and Tatsuhiro Aoshima and Toshiki Shibahara and Mitsuaki Akiyama and Masaki Aida}, doi = {10.1109/ACCESS.2024.3523036}, journal = {IEEE Access}, pages = {9290-9302}, shortjournal = {IEEE Access}, title = {Suppressing the endogenous negative influence through node intervention in social networks}, volume = {13}, year = {2025}, }
• (2025). Detection and classification method for early-stage colorectal cancer using dyadic wavelet packet transform. ACCESS, 13, 9276-9289. (www)

  @article{ , abstract = {Incorporating deep learning into computer-aided medical diagnosis has led to significant advancements. However, a major challenge remains in interpreting deep learning models, especially in identifying the features critical for diagnosis. This study proposes a method for the early detection of colorectal cancer. The method uses the dyadic wavelet packet transform, root mean square, and zero count metrics to extract meaningful features from endoscopic images. These features effectively capture variations in pixel values and delineate contours and patterns. A standout strength of the proposed method lies in its interpretability. The direct feature extraction process enhances both the method’s transparency and the classification results’ comprehensibility. Furthermore, by analyzing the mean absolute values of principal component scores, we provide insightful explanations for misclassification cases, offering a deeper understanding of the classification mechanics. We experimentally evaluated the performance of the proposed method against five deep learning models on three diverse datasets. The results showed that our method achieved a validation accuracy of 99.2% on the Saga University Hospital dataset, 97.4% on the Kvasir-SEG dataset, and 97.8% on the CVC-ClinicDB dataset. These scores surpassed all other models tested, with precision, recall, and F1-measure values consistently higher across datasets, highlighting the method’s robustness and potential for reliable early colorectal cancer detection.}, archive = {J_ACCESS}, author = {Daigo Takano and Hajime Omura and Teruya Minamoto}, doi = {10.1109/ACCESS.2025.3526786}, journal = {IEEE Access}, pages = {9276-9289}, shortjournal = {IEEE Access}, title = {Detection and classification method for early-stage colorectal cancer using dyadic wavelet packet transform}, volume = {13}, year = {2025}, }
• (2025). Multiview multimodal feature fusion for breast cancer classification using deep learning. ACCESS, 13, 9265-9275. (www)

  @article{ , abstract = {The increasing incidence and mortality of breast cancer pose significant global challenges for women. Deep learning (DL) has shown superior diagnostic performance in breast cancer classification compared to human experts. However, most DL methods have relied on unimodal features, which may limit the performance of diagnostic models. Recent studies focus on multimodal data along with multiple views of mammograms, typically two: Cranio-Caudal (CC) and Medio-Lateral-Oblique (MLO). Combining multimodal data has shown improvements in classification effectiveness over single-modal systems. In this study, we compiled a multimodal dataset comprising imaging and textual data (combination of clinical and radiological features). We propose a DL-based multiview multimodal feature fusion (MMFF) strategy for breast cancer classification that utilizes images (four views of mammograms) and tabular data (extracted from radiological reports) from our newly developed in-house dataset. Various augmentation techniques are applied to both imaging and textual data to expand the training dataset size. Imaging features were extracted using a Squeeze-and-Excitation (SE) network-based ResNet50 model, while textual features were extracted using an artificial neural network (ANN). Afterwards, extracted features from both modalities were fused using a late feature fusion strategy. Finally, fused features were fed into the ANN for the final classification of breast cancer. In our study, we compared the performance of our proposed MMFF model with single-modal models (image only) and models built on textual data. The performance was evaluated using accuracy, precision, sensitivity, F1 score and area under the receiver operating characteristic curve (AUC) metrics. Our model MMFF achieved an AUC of 0.965 for benign vs malignant classification as compared to image-only (ResNet $50=0.545$ ), text-only (ANN =0.688, SVM =0.842) and other multimodal approaches (ResNet50+ANN =0.748, EfficientNetb7+ANN =0.874).}, archive = {J_ACCESS}, author = {Sadam Hussain and Mansoor Ali Teevno and Usman Naseem and Daly Betzabeth Avendaño Avalos and Servando Cardona-Huerta and Jose Gerardo Tamez-Peña}, doi = {10.1109/ACCESS.2024.3524203}, journal = {IEEE Access}, pages = {9265-9275}, shortjournal = {IEEE Access}, title = {Multiview multimodal feature fusion for breast cancer classification using deep learning}, volume = {13}, year = {2025}, }
• (2025). Design and implementation of multiple ring oscillator-based TRNG architecture by using ADPLL. ACCESS, 13, 9252-9264. (www)

  @article{ , abstract = {A new technique for generating true random numbers by using the ADPLL (All Digital Phase Locked Loop)-based multiple ring oscillator TRNG (MURO-TRNG) is discussed in this paper. The proposed ADPLL-based MURO-TRNG contains 10 ring oscillators, 1 conventional ADPLL, 11 sampling DFFs, 1 XOR gate, and an XOR corrector-based post-processing circuit. Ring oscillators are the entropy sources for the proposed MURO-TRNG architecture, and they are constructed by ADPLL with different frequencies. A new DCO(Digital Controlled Oscillator) constructed by using 9 NOR gates and 1 DFF is designed for constructing ADPLL-based ring oscillator circuits. Conventional ADPLL operates at 3 different reference frequencies to sample the raw random bits and to provide a clock for the post-processing circuit. The proposed MURO-TRNG architecture is designed using VHDL, implemented on the Artix 7, Kintex-7, and Zynq7000 FPGAs, and simulated by the Xilinx Vivado 2015.2 tool. The designed and implemented MURO-TRNG architectures consume 2-4 LUTS and 2-4 FFs. Energy consumption per bit of the generated bitstream is in the range of 4.22 nJ/bit-5.85 nJ/bit, and throughput values are in the range of 206.82 Mbps-260.07 Mbps. The NIST SP 800-22 test is conducted to validate the randomness of the generated bit stream outputs from the post-processing circuit.}, archive = {J_ACCESS}, author = {Huirem Bharat Meitei and Manoj Kumar}, doi = {10.1109/ACCESS.2025.3527507}, journal = {IEEE Access}, pages = {9252-9264}, shortjournal = {IEEE Access}, title = {Design and implementation of multiple ring oscillator-based TRNG architecture by using ADPLL}, volume = {13}, year = {2025}, }
• (2025). Minimizing power consumption and interference mitigation of downlink NOMA HetNets by IRS-supported aerial base stations. ACCESS, 13, 9230-9251. (www)

  @article{ , abstract = {Intelligent reflection surface (IRS) is an advanced technology that effectively transforms the wireless propagation environment to next-generation wireless networks. In this paper, we employ the IRS-supported multi-aerial base station (ABS) in a downlink non-orthogonal multiple access (NOMA) heterogeneous network (HetNets), where the transmission signals from multiple ABSs to ground users (GUs) are enhanced using the IRS. Our goal is to maximize the system sum rate (SSR) by mitigating inter-cluster and intra-cluster interference and to maximize system energy efficiency (SEE) by minimizing ABS power transmission while preserving the required minimum data rate. The formulated optimization problem is non-convex due to the joint optimization of ABS power transmission, ABS three-dimensional (3D) positions, IRS reflection angles (RAs), IRS reflection coefficients (RCs), and decoding order (DEO) among GUs. To tackle this problem, a meta-heuristic algorithm based on the modified gray wolf optimization (MGWO) and an alternation max/min technique based on the developed Block Coordinate Descent (DBCD) are proposed. In particular, the original optimization problem is divided into four sub-problems; the first three sub-problems (RS RAs, IRS RCs, and ABS 3D positions) are alternately addressed using the MGWO technique; and the fourth sub-problem (ABS power transmission) is addressed using the DBCD technique. Finally, a dynamic DEO strategy is utilized to address the NOMA DEO among GUs. The numerical results demonstrated that the proposed schemes outperformed the conventional schemes, achieving up to a 43.289% improvement in SSR and a 71.536% enhancement in SEE, while minimizing the total power consumption. Moreover, the results demonstrate that integrating IRS into multi-ABS HetNets significantly improves the total performance by enhancing the quality of the channel between ABSs and their corresponding GUs while minimizing inter-ABS interference. Finally, by strategically optimizing ABS 3D positions and utilizing DEO among GUs, the SSR and SEE gain achieved by NOMA is shown to be more significant compared to conventional methods.}, archive = {J_ACCESS}, author = {Osamah Thamer Hassan Alzubaidi and Mhd Nour Hindia and Kaharudin Dimyati and Kamarul Ariffin Noordin and Faizan Qamar and Atef Abdrabou}, doi = {10.1109/ACCESS.2025.3525758}, journal = {IEEE Access}, pages = {9230-9251}, shortjournal = {IEEE Access}, title = {Minimizing power consumption and interference mitigation of downlink NOMA HetNets by IRS-supported aerial base stations}, volume = {13}, year = {2025}, }
• (2025). A cross-modal tactile reproduction utilizing tactile and visual information generated by conditional generative adversarial networks. ACCESS, 13, 9223-9229. (www)

  @article{ , abstract = {Tactile reproduction technology represents a promising advancement within the rapidly expanding field of virtual/augmented reality, necessitating the development of innovative methods specifically tailored to correspond with tactile sensory labels. Since human tactile perception is known to be influenced by visual information, this study has developed a cross-modal tactile sensory display using Conditional Generative Adversarial Networks, CGANs, to generate both mechanical and visual information. Initially, sensory evaluation experiments were conducted with 32 participants using twelve metal plate samples to collect tactile information. Subsequently, we prepared 320 images of variety of materials and conducted sensory evaluation experiments with 30 participants per image to gather tactile information evoked by viewing the images. Utilizing the collected tactile information, used as labels, and images as a dataset, we developed four types of visual information generation models using CGAN, each trained with weighted concatenated data of images and labels, in which image elements are amplified by factors of 1, 1,000, 5,000, and 10,000, respectively. Each of these four models was then used to generate twelve images corresponding to the sensory evaluation result of twelve different metal plate samples. We performed a cross-modal tactile reproduction experiment using the previously developed tactile information generation model to input signals to a tactile display, alongside the images generated by the visual information generation model. In this experiment, 20 subjects conducted sensory evaluations where tactile sensations were displayed concurrently with the visual display of the images. The results confirmed that the concurrent display of mechanical and visual information significantly reduced the mean absolute error between the displayed tactile information and that of the metal plate samples from 2.2 to 1.6 out of a 7-digit scale in sensory evaluation. These findings underscore the effectiveness of the visual information generation model and highlight the potential of integrating tactile and visual information for enhanced tactile reproduction systems.}, archive = {J_ACCESS}, author = {Koki Hatori and Takashi Morikura and Akira Funahashi and Kenjiro Takemura}, doi = {10.1109/ACCESS.2025.3527946}, journal = {IEEE Access}, pages = {9223-9229}, shortjournal = {IEEE Access}, title = {A cross-modal tactile reproduction utilizing tactile and visual information generated by conditional generative adversarial networks}, volume = {13}, year = {2025}, }
• (2025). A study on the classification of atopic dermatitis by spectral features of hyperspectral imaging. ACCESS, 13, 9209-9222. (www)

  @article{ , abstract = {Atopic dermatitis (AD) is a chronic skin disease typically evaluated using the Invesrigator’s Global Assessment (IGA), SCORing Aoptic Dermatitis (SCORAD), and Eczema Area and Severity Index (EASI) (SCORAD and EASI; S/E). However, the subjectivity of the diagnostic process remains limited. Hyperspectral cameras can detect optical signals to simultaneously generate morphological information and collect spatial information. The depth of skin penetration of caries can be determined according to the wavelength of light and its structural feature. Thus, after dividing the colors and information into six datasets, classified and analyzed their features and severities using representative diagnostic evaluation tools. First, the expansion of blood vessels and structural changes in the lesion area were confirmed between 550 and 650 nm, and information analysis of the lesion features was confirmed through the difference in the rate of change between the visible and near-infrared (NIR) areas. Subsequently, severity classification of the datasets for each wavelength of AD was performed. IGA-NIR-DenseNet121 showed the best classification performance, with 88% accuracy and S/E-G-ResNet18 showed 97% accuracy. Thus, IGA is sensitive to NIR, whereas SCORAD and EASI are sensitive to color. Although the overall accuracy was lower than existing RGB images, the t-distributed stochastic neighbor embedding results showed that the severity groups were well distinguished. The newly constructed dataset and severity classification study by wavelength suggested generalizations and methods for diagnosing and evaluating AD. Additionally, it shows the possibility of expanding research by identifying features that appear in the deep skin layers.}, archive = {J_ACCESS}, author = {Eun Bin Kim and Yoo Sang Baek and Onseok Lee}, doi = {10.1109/ACCESS.2024.3482557}, journal = {IEEE Access}, pages = {9209-9222}, shortjournal = {IEEE Access}, title = {A study on the classification of atopic dermatitis by spectral features of hyperspectral imaging}, volume = {13}, year = {2025}, }
• (2025). Local-global and multi-scale (LG-MS) mixer architecture for long-term time series forecasting. ACCESS, 13, 9199-9208. (www)

  @article{ , abstract = {Although deep learning models dominate time series forecasting, they still struggle with long-sequence processing due to the challenges of extracting dynamic fluctuations and pattern features as input length increases. To address this challenge, we propose a framework – LG-MSMixer—to enhance long-term time series forecasting through three key steps: multi-scale dual decomposition, local-global information extraction, and fusion prediction. Specifically, we first conduct multi-scale dual decomposition of the long input sequence to derive a seasonal-trend component combination. To capture a more comprehensive effective information within the components, we then utilize a customized patch-based triple attention local-global information extractor that models both temporal feature information and variable dependencies, alongside an MLP-based feature interaction iterator facilitating interactions among multi-scale information to guide macro-level predictions. Finally, we integrate the predictions from the multi-scale sequences to leverage their complementary advantages. In our experiments, we demonstrate the effectiveness of LG-MSMixer across various real-world long-term forecasting tasks, significantly outperforming previous baselines.}, archive = {J_ACCESS}, author = {Zhennan Peng and Boyong Gao and Ziqi Xia and Jie Liu}, doi = {10.1109/ACCESS.2024.3524499}, journal = {IEEE Access}, pages = {9199-9208}, shortjournal = {IEEE Access}, title = {Local-global and multi-scale (LG-MS) mixer architecture for long-term time series forecasting}, volume = {13}, year = {2025}, }
• (2025). Code-switching ASR for low-resource indic languages: A hindi-marathi case study. ACCESS, 13, 9171-9198. (www)

  @article{ , abstract = {This work examines the development of Automatic Speech Recognition (ASR) systems for low-resource languages, focusing on Hindi and Marathi, particularly in multilingual and code-switching environments. ASR systems, which convert spoken language into text, face significant challenges when applied to low-resource languages with limited data for training models. These challenges are exacerbated in multilingual settings, particularly during code-switching, where speakers alternate between languages within a conversation. This paper underscores the current state of ASR for Indic languages, highlighting linguistic complexities such as diverse sentence structures, phonetic variety, and frequent code-switching. Code-switching introduces additional challenges, as ASR systems must rapidly identify language boundaries and adapt to linguistic shifts. Present systems struggle to perform adequately with code-switched data due to the complexity of phonetic structures and the lack of comprehensive, annotated speech corpora. This work critically evaluates current methods and proposes improvements using modern deep-learning techniques to address the primary challenges in developing efficient ASR models for Hindi and Marathi. Moreover, performance comparisons of monolingual, bilingual, and multilingual ASR systems indicate that multilingual approaches are more effective in managing linguistic diversity. The efficacy of these systems can be evaluated using performance metrics such as the Phoneme Error Rate (PER) and the Word Error Rate (WER), which assess word recognition accuracy.}, archive = {J_ACCESS}, author = {Hemant Palivela and Meera Narvekar and David Asirvatham and Shashi Bhushan and Vinay Rishiwal and Udit Agarwal}, doi = {10.1109/ACCESS.2025.3527745}, journal = {IEEE Access}, pages = {9171-9198}, shortjournal = {IEEE Access}, title = {Code-switching ASR for low-resource indic languages: A hindi-marathi case study}, volume = {13}, year = {2025}, }
• (2025). A generic framework for mobile crowdsensing: A comprehensive survey. ACCESS, 13, 9134-9170. (www)

  @article{ , abstract = {Mobile Crowdsensing (MCS) has emerged as a powerful paradigm for aggregating sensory data through the collaborative efforts of various mobile devices. Despite the innovative solutions inherent in this paradigm, it also introduces new challenges. The MCS literature has proposed various solutions, but many problems remain. Existing studies have addressed different aspects and processes of MCS and are proposing various solutions, each with a specific framework. Consequently, the diversity of frameworks complicates the integration and comparison of different works in this field. In response, our work presents a structured framework for MCS, consolidating its operational processes into a cohesive system. Our framework integrates key steps, including the registration process, anterior data processing, incentivization process, task allocation, task execution, and posterior data processing. By providing a unified framework, we aim to offer a comprehensive and structured approach to Mobile Crowdsensing (MCS), breaking it down into multiple subprocesses. This allows each work to fit into the framework more easily, facilitating the comparison and integration of various contributions in the field. This structured framework serves as a foundation for researchers and practitioners in the field, encouraging progress and innovation in the ongoing development of MCS applications.}, archive = {J_ACCESS}, author = {Abderrafi Abdeddine and Loubna Mekouar and Youssef Iraqi}, doi = {10.1109/ACCESS.2025.3526739}, journal = {IEEE Access}, pages = {9134-9170}, shortjournal = {IEEE Access}, title = {A generic framework for mobile crowdsensing: A comprehensive survey}, volume = {13}, year = {2025}, }
• (2025). A novel telerehabilitation system for physical exercise monitoring in elderly healthcare. ACCESS, 13, 9120-9133. (www)

  @article{ , abstract = {The increasing demand for remote healthcare solutions has driven the need for effective telerehabilitation systems to support elderly individuals recovering from chronic conditions or post-operative impairments. Existing rehabilitation methods face limitations such as restricted access to specialized care, overburdened healthcare providers, and the need for consistent, real-time monitoring. To address these challenges, we propose a novel telerehabilitation system that processes depth video frames using a multi-stage methodology. The pipeline begins with noise and floor removal, followed by 3D connected component labeling (CCL) to identify the human subject and extract the human silhouette. Next, skeleton joint points are estimated, and features are extracted from both the joints and silhouette. These multimodal features are fused and input into a deep learning model for classification and correctness assessment. Advanced feature extraction techniques, including Synchrosqueezing Transform (SST) and Hilbert-Huang Transform (HHT), are employed to capture dynamic time-frequency characteristics of human actions. The proposed system classifies nine distinct exercises and assesses the correctness of movements. Experimental evaluation on the IRDS dataset demonstrates a classification accuracy of 91% for exercise recognition and 82% for movement correctness assessment. These results highlight the system’s potential to deliver scalable, cost-effective, real-time rehabilitation, reducing the need for in-person clinical visits and supporting healthcare services for elderly populations.}, archive = {J_ACCESS}, author = {Muhammad Abrar Ashraf and Shaheryar Najam and Touseef Sadiq and Shabbab Algamdi and Hanan Aljuaid and Hameedur Rahman and Ahmad Jalal}, doi = {10.1109/ACCESS.2025.3526710}, journal = {IEEE Access}, pages = {9120-9133}, shortjournal = {IEEE Access}, title = {A novel telerehabilitation system for physical exercise monitoring in elderly healthcare}, volume = {13}, year = {2025}, }
• (2025). Global perspectives on laptop features: A sentiment analysis of user preferences in developed and developing countries. ACCESS, 13, 9102-9119. (www)

  @article{ , abstract = {This study examines laptop user sentiments across developed and developing countries using aspect-based sentiment analysis (ABSA) on social media data. Employing a mixed-methods approach, the research combines large-scale sentiment analysis of 1,467,535 tweets with expert insights gathered through a Delphi panel of ten industry professionals. The study analyzes user opinions on 16 key laptop features, revealing distinct preferences and pain points between users in developed and developing nations. Findings show that consumers in developing countries express higher overall satisfaction, while those in developed countries demonstrate more critical views, particularly regarding performance-related features. Universal concerns include battery life and cooling efficiency. The research identifies key priorities for each market: high performance and advanced features for developed countries, and affordability, durability, and reliability for developing nations. Based on these insights, the study proposes tailored strategies for laptop manufacturers, including differentiated product lines, universal charging solutions, and market-specific innovations in cooling systems and warranties. The methodology incorporates data preprocessing, ABSA implementation using the PyABSA framework, and a three-phase Delphi process for expert analysis. This comprehensive analysis provides valuable guidance for product development and marketing strategies in the global laptop industry, emphasizing the importance of market-specific approaches to enhance user satisfaction and maintain competitiveness. The study also acknowledges limitations and suggests directions for future research, including multi-source data analysis and longitudinal studies.}, archive = {J_ACCESS}, author = {Mehrdad Maghsoudi and Mohammadreza Bakhtiari and Hamidreza Bakhtiari}, doi = {10.1109/ACCESS.2025.3528324}, journal = {IEEE Access}, pages = {9102-9119}, shortjournal = {IEEE Access}, title = {Global perspectives on laptop features: A sentiment analysis of user preferences in developed and developing countries}, volume = {13}, year = {2025}, }
• (2025). EfficientNet-b0-based 3D quantification algorithm for rectangular defects in pipelines. ACCESS, 13, 9089-9101. (www)

  @article{ , abstract = {Quantitative analysis of the magnetic leakage signal is crucial for evaluating the magnitude of pipeline damage after fault detection. To address these issues, it is necessary to establish a correlation between the leakage signal and the magnitude of the defect to make accurate predictions through regression analysis. This research introduces EffiTriDimNet (ETDN), a multi-task convolutional neural network that combines one-dimensional pipeline defect leakage detection data into a unified feature map while simultaneously measuring the three-dimensional characteristics of the defects. This multi-task learning model utilizes a shared feature extraction layer and numerous branching networks to create individual predictions for each task: length, width, and depth. The network uses the EfficientNet structure for feature extraction, while the training and validation process utilizes the PyTorch Lightning framework. Experimental results demonstrate that when compared to other algorithms, ETDN outperforms them on the identical test set, exhibiting average absolute errors of 0.1920, 0.1809, and 0.2904 for length, width, and depth, respectively.}, archive = {J_ACCESS}, author = {Di Wu and Yong Hong and Jie Wang and Shaojun Wu and Zhihao Zhang and Yizhang Liu}, doi = {10.1109/ACCESS.2025.3525540}, journal = {IEEE Access}, pages = {9089-9101}, shortjournal = {IEEE Access}, title = {EfficientNet-b0-based 3D quantification algorithm for rectangular defects in pipelines}, volume = {13}, year = {2025}, }
• (2025). Consistency algorithm-based SOC balancing scheme of retired non-equal capacity lithium battery in virtual synchronous generator controlled microgrids. ACCESS, 13, 9073-9088. (www)

  @article{ , abstract = {Retired electric vehicle lithium batteries can be used in microgrids to fully utilize the remaining capacity of lithium batteries. Due to differences in service life and usage environment, the state of charge (SOC) and capacity of retired lithium battery packs in microgrids are difficult to be completely consistent. SOC balancing is an important means to extend the lifespan of retired lithium battery (RLB). However, the traditional virtual synchronous generator (VSG) control scheme and the existing VSG-based SOC balancing scheme fail to achieve the SOC balancing of non-equal capacity RLB in VSG-controlled microgrids. Therefore, a VSG control-based SOC balancing scheme for non-equal capacity RLB is proposed. This paper analyzes the influence mechanism of traditional VSG control and RLB capacity difference in SOC balancing. The influence of capacity on SOC balancing can be eliminated by correcting the traditional VSG control parameters and line impedance. Then, a SOC balancing factor is added to the VSG to regulate the output active power of inverter and achieve SOC balancing. The consistency algorithm is introduced in the process of calculating the SOC balancing factor, which realizes the SOC balancing of the non-equal capacity RLB under the premise of fewer communications and guaranteed frequency quality. In addition, a small signal model for VSG is established to analyze the influence of control parameters on system stability. Finally, the simulation and experimental results verify the effectiveness of the proposed scheme. Compared with the traditional SOC balancing scheme based on VSG, the proposed SOC balancing difference is only 0.0023, the frequency deviation is only 0.02Hz, and the energy loss is reduced by 2.13%, which reflects the advancement of the proposed scheme.}, archive = {J_ACCESS}, author = {Qingfeng Wu and Xiaolin Chu and Yamin Fan and Liqun Liu and Xiaofeng Sun}, doi = {10.1109/ACCESS.2025.3527462}, journal = {IEEE Access}, pages = {9073-9088}, shortjournal = {IEEE Access}, title = {Consistency algorithm-based SOC balancing scheme of retired non-equal capacity lithium battery in virtual synchronous generator controlled microgrids}, volume = {13}, year = {2025}, }
• (2025). CCL-YOLO: Catenary components location based on YOLO and gather-distribute mechanism. ACCESS, 13, 9064-9072. (www)

  @article{ , abstract = {The catenary is an important part of the railway power supply system, and the good health of its key components is an important condition for the operation of high-speed trains. Due to the complex composition of catenary components and the large size difference between components, it is very difficult to implement the locating and detection of all components. It severely limits the subsequent failure analysis of catenary components. Aiming at the problem of the difficult locating of multiple components, this paper proposes a catenary complex component locating method based on the improved YOLO algorithm named CCL-YOLO. Firstly, based on the YOLO baseline method, a framework for locating complex components of railway catenary is proposed, which achieves the accurate locating of 40 catenary components and paves the way for subsequent defect detection. Furthermore, a novel “Gather-and-Distribute” mechanism is introduced, which improves the original feature fusion method and its global feature fusion capability and effectively improves the locating accuracy of complex catenary components. In addition, the Wasserstein distance loss is also applied to improve the detection capability of small objects. Finally, the comprehensive experimental results show that the proposed method for locating complex components of railway catenary can achieve a mean average precision of 94.1% for 40 components of different sizes. Meanwhile, it reduces the number of parameters and the complexity of the algorithm to a certain extent, thus paving the way for subsequent fault detection, which is of important application value.}, archive = {J_ACCESS}, author = {Shuai Xu and Jiyou Fei and Geng Zhao and Xiaodong Liu and Hua Li}, doi = {10.1109/ACCESS.2024.3403716}, journal = {IEEE Access}, pages = {9064-9072}, shortjournal = {IEEE Access}, title = {CCL-YOLO: Catenary components location based on YOLO and gather-distribute mechanism}, volume = {13}, year = {2025}, }
• (2025). Enhancing image-based JPEG compression: ML-driven quantization via DCT feature clustering. ACCESS, 13, 9047-9063. (www)

  @article{ , abstract = {JPEG compression is a widely used technique for reducing the file size of digital images, but it often compromises visual quality. The purpose of this research is to explore a novel approach that combines machine learning, discrete cosine transform (DCT) feature clustering, and genetic algorithms to customize image compression methods. The goal is to enhance visual quality while maintaining an appropriate bit-rate. In this study, an auto-encoder neural network is utilized to extract DCT features from images. These features are then clustered, and optimized quantization tables for each cluster center are generated using a genetic algorithm. The resulting tables are assigned to their respective clusters, enabling the preservation of visual quality during compression. Experimental evaluations were conducted on 1800 random images using this machine learning-based approach. The results demonstrate superior visual quality compared to traditional JPEG compression, while maintaining comparable bit-rates. The research shows significant improvements in peak signal-to-noise ratio (PSNR) by 2.34 dB and structural similarity index (SSIM) by 1.26%, indicating enhanced image quality. The findings of this research highlight the potential of combining machine learning, DCT feature clustering, and genetic algorithms to customize image compression techniques. The proposed approach enables effective image compression with improved visual quality preservation and maintained bit-rates. This research contributes to the advancement of image-based methods in achieving optimized image compression.}, archive = {J_ACCESS}, author = {Shahrzad Sabzavi and Reza Ghaderi}, doi = {10.1109/ACCESS.2024.3465353}, journal = {IEEE Access}, pages = {9047-9063}, shortjournal = {IEEE Access}, title = {Enhancing image-based JPEG compression: ML-driven quantization via DCT feature clustering}, volume = {13}, year = {2025}, }
• (2025). Hybrid-hierarchical synchronization for resilient large-scale SDN architectures. ACCESS, 13, 9032-9046. (www)

  @article{ , abstract = {Interest in hierarchical Software-Defined Networking (SDN) controllers is growing recently due to their ability to address the challenges associated with the SDN paradigm, such as responsiveness and scalability. This design enables efficient domain control separation, which uses different child instances to manage large-scale networks. Parent controller computational resources can be dedicated to cross-domain decision making, exploiting network views provided by its children. In this context, the correctness of the process fully relies on the network view synchronization mechanism, which should be fast and resilient. This paper presents a hybrid synchronization model combining a hierarchical design with established resilient cluster mechanisms. In this way, high-level control over large-scale networks can be guaranteed even with failures affecting every level of the management plane. Specifically, two applications are developed for the ONOS controller to share topology events using low-latency channels from child clusters to parent clusters. The performance of both applications is measured under different cluster configurations, topology sizes and number of generated topology updates. The results show that the proposed approach offers high performance while being fully compliant with the platform for which it is designed. This makes the solution easily extendable to heterogeneous child controllers. In fact, events are propagated from children to parents using gRPC, achieving end-to-end latency of less than 10ms under normal conditions and 40-60ms under high-rate event conditions. Consistency of network views is also guaranteed by strong event ordering and delivery mechanisms. Failures are handled seamlessly at both cluster levels (i.e. parent and child controllers) with a maximum synchronization delay of 2 seconds, which is quickly recovered.}, archive = {J_ACCESS}, author = {Alessandro Pacini and Davide Scano and Andrea Sgambelluri and Luca Valcarenghi and Alessio Giorgetti}, doi = {10.1109/ACCESS.2025.3527224}, journal = {IEEE Access}, pages = {9032-9046}, shortjournal = {IEEE Access}, title = {Hybrid-hierarchical synchronization for resilient large-scale SDN architectures}, volume = {13}, year = {2025}, }
• (2025). Secure user authentication with information theoretic security using secret sharing-based secure computation. ACCESS, 13, 9015-9031. (www)

  @article{ , abstract = {When using an insecure communication channel, the initial step involves authenticating the user (verifying the other party) to ensure the legitimacy of the communication partner, followed by an encrypted communication. Public key encryption-based digital signatures are widely used for user authentication; however, with the development of quantum computers they are highly likely to be deciphered. Studies are also ongoing on post-quantum cryptography, although they require significant computational resources and are challenging to implement in Internet of Things (IoT) devices. Therefore, this study suggests the implementation of user authentication and secure communication that guarantees information-theoretic security through the use of secure computation based on a computationally lightweight $(k,n)$ -threshold secret sharing scheme. In this study, a user authentication system is proposed with information-theoretical security that utilizes constantly changing information. Subsequently, it is demonstrated that secure communication with information-theoretic security can be achieved without the need to distribute a substantial number of true random numbers by employing secure computation based on $(k,n)$ -threshold secret sharing. The proposed methods are suitable for implementation in IoT environments because they require minimal processing overhead. The practical application of the proposed methods are also demonstrated through an implementation using C++. For example, the average execution time of the claimant was less than 0.1 [ms], proving that the proposed methods are very efficient.}, archive = {J_ACCESS}, author = {Keiichi Iwamura and Ahmad Akmal Aminuddin Mohd Kamal}, doi = {10.1109/ACCESS.2025.3526632}, journal = {IEEE Access}, pages = {9015-9031}, shortjournal = {IEEE Access}, title = {Secure user authentication with information theoretic security using secret sharing-based secure computation}, volume = {13}, year = {2025}, }
• (2025). Integral sliding mode and integral supertwisting sliding mode control for ship course-keeping. ACCESS, 13, 9006-9014. (www)

  @article{ , abstract = {This paper proposes variable structure-based nonlinear control algorithms, including synergetic, terminal synergetic, integral sliding mode, and integral supertwisting sliding mode controllers, for ship course-keeping. The global asymptotic stability of the system is ensured using Lyapunov stability theory. Simulations are conducted using the Simulink toolbox in the MATLAB environment. The smoothness, energy consumption, and output response performances of the proposed controllers are evaluated to verify their effectiveness. Finally, a comparative analysis is presented to demonstrate the efficiency of the proposed controllers relative to each other and to existing controllers from the literature for ship course-keeping control.}, archive = {J_ACCESS}, author = {Muhammad Munir Butt and Azhar Iqbal Kashif Butt and Iftikhar Ahmad}, doi = {10.1109/ACCESS.2025.3528126}, journal = {IEEE Access}, pages = {9006-9014}, shortjournal = {IEEE Access}, title = {Integral sliding mode and integral supertwisting sliding mode control for ship course-keeping}, volume = {13}, year = {2025}, }
• (2025). Detection of diabetic retinopathy using a multi-decision inception-ResNet-blended hybrid model. ACCESS, 13, 8988-9005. (www)

  @article{ , abstract = {Diabetic retinopathy (DR) is a severe complication of diabetes that affects the retinal structures and can lead to significant visual impairment or even blindness. Early diagnosis is crucial for reducing and preventing the progression of this condition. However, detecting DR’s early stages remains challenging due to subtle symptoms that are difficult to recognize independently. Our proposed model leverages 172 weighted layers to analyze both sequential and non-sequential fundus images for effective DR detection. By incorporating a multi-layered transfer learning approach, 86 layers are used for processing color fundus images, while the remaining 86 layers focus on grayscale images. The model undergoes thorough pre-processing and testing phases, utilizing eight layers of convolutions at each stage to handle various data matrices and integrate global and specialized features. The chi-square testing mechanism refines the evaluation of test cases, contributing to the model’s overall performance. Using multi-decision hybrid techniques, the model achieves a detection accuracy of 98.1%, outperforming other existing models.}, archive = {J_ACCESS}, author = {Santosh Kumar Henge and Nikhil Reddy Viraati and Musaed Alhussein and Ajay Shriram Kushwaha and Khursheed Aurangzeb and Ravleen Singh}, doi = {10.1109/ACCESS.2024.3525154}, journal = {IEEE Access}, pages = {8988-9005}, shortjournal = {IEEE Access}, title = {Detection of diabetic retinopathy using a multi-decision inception-ResNet-blended hybrid model}, volume = {13}, year = {2025}, }
• (2025). Deepfakes detection by iris analysis. ACCESS, 13, 8977-8987. (www)

  @article{ , abstract = {Deepfake is an advanced technology that creates extremely realistic facial images and videos. This new technique operates under specific conditions and has a wide range of applications. For example, it can be used in the entertainment industry to create impressive visual effects or to insert actors into scenes convincingly. Similarly, in the film industry, deepfakes can help make movies by faithfully reproducing the appearance of actors who are not physically present. It is also useful for creating realistic digital avatars of people, which can be used in virtual environments, video games, or augmented reality applications. Recently, the emergence of new content generation models capable of creating impressively realistic images has been gaining momentum. Despite their advantages, they also cause significant issues when used maliciously, such as for identity theft, misinformation, and obscene depictions of well-known individuals. Therefore, it is crucial to implement effective methods to expose this generated content and thus reduce crime associated with deepfakes. This article presents a novel method for detecting fake content based on an in-depth analysis of the characteristics of eye irises. By applying a gradient map to the iris, it is possible to visualize the biological characteristics specific to eye irises, such as the round shape, identical reflections in the two irises of the same face, the size of the iris, etc. The gradient map highlights all the contours of the objects present in the iris; thus, the reflected light present in the corneas is represented by brighter pixels comparable to heat. We show that two irises of the same face are almost identical in shape, reflection, and size. Our experimental results on the Flickr-Faces-HQ (FFHQ) dataset and images obtained from StyleGAN2 demonstrate that our algorithm achieves a remarkable detection accuracy of 0.979 and 0.921 sensitivity. Furthermore, the method has a specificity of 0.937 and a precision of 0.960, thereby proving the effectiveness of the gradient map associated with the shape of the pupil in detecting Generative adversarial network (GAN) generated faces.}, archive = {J_ACCESS}, author = {Elisabeth Tchaptchet and Elie Fute Tagne and Jaime Acosta and Danda B. Rawat and Charles Kamhoua}, doi = {10.1109/ACCESS.2025.3527868}, journal = {IEEE Access}, pages = {8977-8987}, shortjournal = {IEEE Access}, title = {Deepfakes detection by iris analysis}, volume = {13}, year = {2025}, }
• (2025). GNN-EADD: Graph neural network-based E-commerce anomaly detection via dual-stage learning. ACCESS, 13, 8963-8976. (www)

  @article{ , abstract = {E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity. This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems. Our key contributions include: 1) A heterogeneous graph representation incorporating products, sellers, and buyers as nodes with their relationships as edges; 2) A novel dual-stage learning framework combining unsupervised graph embedding with semi-supervised fine-tuning; and 3) An attention mechanism that effectively captures complex patterns within network structures. Extensive experiments on a large-scale Amazon dataset demonstrate that GNN-EADD significantly outperforms state-of-the-art baselines in terms of anomaly detection accuracy, precision, and recall, while showing robustness to various types of anomalies and scalability to large networks.}, archive = {J_ACCESS}, author = {Zhouhang Shao and Xuran Wang and Enkai Ji and Shiyang Chen and Jin Wang}, doi = {10.1109/ACCESS.2025.3526239}, journal = {IEEE Access}, pages = {8963-8976}, shortjournal = {IEEE Access}, title = {GNN-EADD: Graph neural network-based E-commerce anomaly detection via dual-stage learning}, volume = {13}, year = {2025}, }
• (2025). Classification of epileptic seizures by simple machine learning techniques: Application to animals’ electroencephalography signals. ACCESS, 13, 8951-8962. (www)

  @article{ , abstract = {Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth’s parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90% and a specificity of 74% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components’ values to 10% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episode detection.}, archive = {J_ACCESS}, author = {Illia Pidvalnyi and Anna Kostenko and Oleksandr Sudakov and Dmytro Isaev and Oleksandr Maximyuk and Oleg Krishtal and Olena Iegorova and Ievgen Kabin and Zoya Dyka and Steffen Ortmann and Peter Langendörfer}, doi = {10.1109/ACCESS.2025.3527866}, journal = {IEEE Access}, pages = {8951-8962}, shortjournal = {IEEE Access}, title = {Classification of epileptic seizures by simple machine learning techniques: Application to animals’ electroencephalography signals}, volume = {13}, year = {2025}, }
• (2025). TM-chain: TCB measurement management using cloud blockchain for IoT devices. ACCESS, 13, 8941-8950. (www)

  @article{ , abstract = {Remote attestation (RA) is a process for checking whether a device’s underlying Trusted Computing Base (TCB) and sensing data from the TCB are trustworthy. In the IoT era, RA is an important security feature because verifiers make critical decisions based on the RA result. However, a massive and diverse collection of measurements for IoT devices makes managing the devices’ TCB measurements difficult and complex. The cloud blockchain is a possible candidate for addressing the problem, but the tailored approach is essential to support scalable and secure management. We present a cloud blockchain-based management of the TCB measurements for IoT devices called TM-Chain (TCB Measurement-Chain). TM-Chain presents the TM-Chain transactions, commands, and protocols to manage the TCB measurements using the cloud blockchain. Based on TM-Chain, the verifiers launch remote attestation against the target devices using the TCB measurements verified by the cloud blockchain. We implemented a proof-of-concept (PoC) on a device to evaluate the performance overhead and a PoC on a public cloud to assess the feasibility.}, archive = {J_ACCESS}, author = {Jaemin Park and Sungjin Park}, doi = {10.1109/ACCESS.2025.3525807}, journal = {IEEE Access}, pages = {8941-8950}, shortjournal = {IEEE Access}, title = {TM-chain: TCB measurement management using cloud blockchain for IoT devices}, volume = {13}, year = {2025}, }
• (2025). Telecommand rejection probability in CCSDS-compliant LDPC-coded space transmissions with tail sequence. ACCESS, 13, 8924-8940. (www)

  @article{ , abstract = {According to the Consultative Committee for Space Data Systems (CCSDS) recommendation for Telecommand (TC) synchronization and coding, the Communications Link Transmission Unit (CLTU) consists of a start sequence, followed by coded data, and a tail sequence, which might be optional depending on the employed coding scheme. With regard to the latter, these transmissions traditionally use a modified Bose-Chaudhuri–Hocquenghem (BCH) code, to which two state-of-the-art Low-Density Parity-Check (LDPC) codes were later added. A low-complexity approach classically used to detect CLTU termination is to choose a non-correctable string as the tail sequence, and then exploit the decoder failure on that sequence as termination detection. This works very well with the BCH code, for which bounded-distance decoders are employed. Instead, when the same approach is employed with LDPC codes and probabilistic belief propagation iterative decoders, the scenario becomes more challenging. In this paper, we study CCSDS-compliant space communications in which LDPC codes are employed, and analyze the TC rejection probability both theoretically and through intensive numerical simulations. Such a performance figure, being the rate at which CLTUs are discarded, should clearly be minimized. Our numerical analysis considers many different choices of the system parameters (e.g., length of the CLTU, decoding algorithm, maximum number of decoding iterations). Particular attention is devoted to the probability of not-acknowledged termination, i.e., the probability that the tail sequence is not recognized.}, archive = {J_ACCESS}, author = {Rebecca Giuliani and Massimo Battaglioni and Marco Baldi and Franco Chiaraluce and Nicola Maturo}, doi = {10.1109/ACCESS.2025.3527057}, journal = {IEEE Access}, pages = {8924-8940}, shortjournal = {IEEE Access}, title = {Telecommand rejection probability in CCSDS-compliant LDPC-coded space transmissions with tail sequence}, volume = {13}, year = {2025}, }
• (2025). MaDis-stereo: Enhanced stereo matching via distilled masked image modeling. ACCESS, 13, 8912-8923. (www)

  @article{ , abstract = {In stereo matching, Convolutional Neural Networks (CNNs), a class of deep learning models designed to process grid-like data such as images, have traditionally served as the predominant architectures. Although Transformer-based stereo models have been studied recently, their performance still lags behind CNN-based stereo models due to the inherent data scarcity issue in the stereo matching task. In this paper, we propose Masked Image Modeling Distilled Stereo matching model, termed MaDis-Stereo, that enhances locality inductive bias by leveraging Masked Image Modeling (MIM), a technique that improves model performance by randomly masking parts of the input image in training Transformer-based stereo model. Given randomly masked stereo images as inputs, our method attempts to conduct both image reconstruction and depth prediction tasks. While this strategy is beneficial to resolving the data scarcity issue, the dual tasks of reconstructing masked tokens and subsequently performing stereo matching pose significant challenges, particularly in terms of training stability. To address this, we propose to use an auxiliary network (teacher) that is updated via Exponential Moving Average (EMA), a method for updating model weights by averaging previous weights with a decaying factor. Specifically, the teacher knowledge is distilled and transferred to the original stereo model (student) by providing pseudo supervisory signals, enhancing training stability and overall model performance. Experimentally, our approach achieves state-of-the-art results on the ETH3D benchmark and competitive performance on the KITTI 2015 benchmark. Our findings highlight the potential for extending this approach to other vision tasks, such as object detection and semantic segmentation, that require sufficient locality inductive bias in Transformer-based architectures. Code is available at: https://github.com/ja053199/madis/}, archive = {J_ACCESS}, author = {Jihye Ahn and Hyesong Choi and Soomin Kim and Dongbo Min}, doi = {10.1109/ACCESS.2025.3528022}, journal = {IEEE Access}, pages = {8912-8923}, shortjournal = {IEEE Access}, title = {MaDis-stereo: Enhanced stereo matching via distilled masked image modeling}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review of key cyber-physical systems, and assessment of their education challenges. ACCESS, 13, 8898-8911. (www)

  @article{ , abstract = {The importance of cyber-physical systems (CPS) continues to grow today, with AI applications in the CPS design principles that formulate smart cyber-physical systems (S-CPS). These called Extended Cyber-Physical Systems (E-CPS) or Next Generation Cyber-Physical Systems (NG-CPS) are expanding to address more complex problems such as the impact of intellectualization on human roles and the social embedding of heterogeneous systems. The industrial metaverse for smart manufacturing and its novel characteristics require new types of advanced cyber-physical coordinated situational awareness and active defense against cyber-attacks. These new challenges raise the profile of engineering education at the university level, while the addition of new skills requires rigorous preparatory work and the development of tailored requirements for effective knowledge transfer methods that meet the educational needs of the current environment. Students need to be prepared to manage information and influence decision-makers effectively. This article presents a pedagogical approach to teaching CPS as an advanced undergraduate subject by integrating elements of competency and personal development into daily classroom activities. This reflects the circumstances and demands of everyday engineering work, focusing on soft skills such as time management, precision, accuracy, focus, communication, teamwork, and also applying the methods of continuous accountability and reward. The specific tasks and assessment techniques simulate real-life situations in the industry, and R&D activities, where the students became efficient engineers, researchers and later on could fulfil the position of reliable BOSSs. The conclusions are based on individual and group interviews conducted with students in Hungarian and international mechatronics undergraduate programs.}, archive = {J_ACCESS}, author = {Imre Kocsis and Boglárka Burján-Mosoni and István Balajti}, doi = {10.1109/ACCESS.2025.3527714}, journal = {IEEE Access}, pages = {8898-8911}, shortjournal = {IEEE Access}, title = {A comprehensive review of key cyber-physical systems, and assessment of their education challenges}, volume = {13}, year = {2025}, }
• (2025). A new website fingerprinting method for tor hidden service. ACCESS, 13, 8886-8897. (www)

  @article{ , abstract = {Although anonymous communication systems protect user privacy, they also facilitate evasion of network censorship. Currently, evaders use anonymous hidden services to carry out various illegal activities, which pose a serious threat to network management. To address this problem, a new website fingerprinting method that combines the Bidirectional Encoder Representations from Transformers (BERT) model and a Long Short-Term Memory (LSTM) network was proposed to improve the accuracy of website fingerprinting for Tor hidden services. The proposed method uses the BERT model to extract the semantic features of webpage content, and the LSTM model is combined to capture the long-term dependencies to achieve efficient recognition of website fingerprints. This method not only deals with the homepage, but also considers the features of sub-pages in depth, which can effectively improve the performance of the model in closed-world and open-world scenarios through deep textual and time-series feature learning. The experimental results show that compared with existing state-of-the-art techniques, the proposed method exhibits better performance in terms of key performance metrics.}, archive = {J_ACCESS}, author = {Zihang Hui and Jiangtao Zhai and Shengxian Wang and Weijie Ji}, doi = {10.1109/ACCESS.2024.3523925}, journal = {IEEE Access}, pages = {8886-8897}, shortjournal = {IEEE Access}, title = {A new website fingerprinting method for tor hidden service}, volume = {13}, year = {2025}, }
• (2025). Increasing safety of vulnerable road users in scenarios with occlusion: A collaborative approach for smart infrastructures and automated vehicles. ACCESS, 13, 8851-8885. (www)

  @article{ , abstract = {The impact of Automated Vehicles (AVs) on road traffic safety has become the focus of discussions among governmental organizations, academia, stakeholders, and OEMs. Questions about how safe the automated driving features should be and how the road infrastructure should be improved for the arrival of this new technology must be clarified to enable full acceptance by the customers and society and prepare the mobility of future cities. The fundamental architecture of automated vehicles comprises perception, planning, decision, and actuation. The operation of the perception system, which is responsible for understanding the environment in which the vehicle is inserted, relies mainly on the onboard sensors. However, the available ranging and vision sensors, e.g., LiDAR, radar, and camera, have several limitations. Scenarios with occlusion present a real challenge for state-of-the-art perception systems. The occlusion, caused by obstructing the sensors’ detection field, limits the vehicle’s perception ability and inhibits the detection of other road users in the surroundings, especially Vulnerable Road Users (VRUs). Infrastructure composed of Roadside Units (RSUs) equipped with infrastructure-based sensors can overcome the perception limitations of a system based solely on onboard sensors by monitoring the road environment with a larger field of view and reduced sensitivity to occlusion. This paper presents a collaborative approach for smart infrastructures and automated vehicles for vulnerable road users’ collision avoidance. The proposed extended perception system comprises four main modules: traffic monitoring, long-term motion prediction, collision risk assessment, and trajectory planning. In the event of a safety-critical scenario, the infrastructure generates a safe and comfortable evasive maneuver to avoid a possible collision. Hence, the proposed approach provides a complete solution to overcome scenarios with occluded VRUs. It allows AVs to react to a critical situation with a longer time-to-collision than other systems relying only on onboard sensors, increasing the chance of successful avoidance even when implementing smoother maneuvers. This contributes considerably to the safe and comfortable operation of automated vehicles.}, archive = {J_ACCESS}, author = {Thiago de Borba and Ondřej Vaculín and Hormoz Marzbani and Reza Nakhaie Jazar}, doi = {10.1109/ACCESS.2025.3527865}, journal = {IEEE Access}, pages = {8851-8885}, shortjournal = {IEEE Access}, title = {Increasing safety of vulnerable road users in scenarios with occlusion: A collaborative approach for smart infrastructures and automated vehicles}, volume = {13}, year = {2025}, }
• (2025). Exploring the side-information fusion for sequential recommendation. ACCESS, 13, 8839-8850. (www)

  @article{ , abstract = {Side information fusion for sequential recommendation aims to mitigate the data sparsity problems by leveraging the additional knowledge besides item ID. While most state-of-the-art methods devised elaborate fusion methods to incorporate side-information, they overlooked that there are distinct characteristics of the side-information, which can be grouped into two types: item attribute (e.g., category and brand) and user behavior (e.g., position and rating). In this paper, we argue that attribute information and behavior information are fundamentally different in relation to the item. The former is inherent to the item, whereas the latter is not. Based on this intuition, we systematically analyzed the previous fusion approach and introduced a comprehensive framework for two types of side information. Finally, we devise self-supervised objectives fitting for each type of side-information in a multi-task training scheme. To validate the effectiveness of our proposed method, we conduct experiments across various domains.}, archive = {J_ACCESS}, author = {Seunghwan Choi and Donghoon Lee and Hyeoungguk Kang and Hyunsouk Cho}, doi = {10.1109/ACCESS.2025.3525812}, journal = {IEEE Access}, pages = {8839-8850}, shortjournal = {IEEE Access}, title = {Exploring the side-information fusion for sequential recommendation}, volume = {13}, year = {2025}, }
• (2025). Hybrid method for point cloud classification. ACCESS, 13, 8825-8838. (www)

  @article{ , abstract = {In this study, we introduce a novel hybrid ResPOL method that integrates a bidirectional architecture with a newly developed Patch-Offset Lambda (POL) mechanism for feature extraction from point cloud data. This hybrid approach effectively combines the Residual Machine Learning Perceptron (ResMLP) with the POL mechanism to capture both local and global features of 3D point clouds. The ResMLP component hierarchically extracts high-frequency local features from point cloud patches, while the POL mechanism is specifically designed to capture low-frequency global features. This dual extraction process ensures minimal loss of both high and low-frequency features. The POL mechanism employs Lambda layers within a linear framework, significantly enhancing the classification speed and accuracy compared to traditional attention mechanisms that suffer from quadratic complexity and non-linear structures. By processing local and global features in parallel, the Hybrid ResPOL method combines these features and feeds them into the classification head, optimizing performance. Experimental results indicate that the Hybrid ResPOL method achieves an overall accuracy (OA) of 94.3% and a mean accuracy (mAcc) of 91.3% on the ModelNet40 dataset. Additionally, it demonstrates robust performance on the challenging ScanObjectNN dataset, with accuracies of 92% OA and 91.2% mAcc. The method processes data at rates of 205.1 samples per second during training and 493.6 samples per second during testing, outperforming the PointMLP method by a factor of 4.3. The superior performance of Hybrid ResPOL in both accuracy and speed highlights its effectiveness over existing attention-based methods.}, archive = {J_ACCESS}, author = {Abdurrahman Hazer and Remzi Yildirim}, doi = {10.1109/ACCESS.2025.3525739}, journal = {IEEE Access}, pages = {8825-8838}, shortjournal = {IEEE Access}, title = {Hybrid method for point cloud classification}, volume = {13}, year = {2025}, }
• (2025). Transformer-based multi-player tracking and skill recognition framework for volleyball analytics. ACCESS, 13, 8806-8824. (www)

  @article{ , abstract = {Volleyball is a dynamic sport requiring high technical skills and tactical awareness, demanding effective training methods for performance improvement. Traditional training approaches often rely heavily on subjective analysis by coaches, leading to inconsistencies in skill development. The integration of advanced computing technologies has opened new avenues in sports analytics, enabling data-driven methods for performance monitoring and strategy development. Despite advancements in sports like baseball, soccer, and basketball, volleyball has not yet been thoroughly explored for computer-assisted analysis, particularly in player tracking and skill recognition. This study proposes a novel Volleyball Player Tracking and Skill Analytics (VPTSA) framework designed specifically for comprehensive volleyball match analysis. The framework integrates multi-player tracking and action recognition systems, leveraging YOLOv7 for player detection and a memory transformer to maintain spatiotemporal information for accurate tracking. Additionally, a transformer-based action recognition module identifies volleyball maneuvers, providing detailed analytics on player performance and behavior. The proposed methodology effectively overcomes challenges such as occlusions, similar appearances among players, and complex motion patterns, which are prevalent in high-intensity sports like volleyball. Results demonstrate that our model achieves superior performance, with an IDF1 score of 72.6, MOTA of 94.8, and HOTA of 73.7, outperforming state-of-the-art models such as TransTrack and ByteTrack on the SportsMOT dataset. In terms of action recognition, our Volleyball Skill Analytics Network (VSAN) model outperforms existing methods, achieving an individual action mAP of 85.5% and a group action mAP of 90.7% on the Volleyball dataset, demonstrating its efficacy in accurately identifying and classifying volleyball maneuvers.}, archive = {J_ACCESS}, author = {Lei Jiang and Zhihong Yang and Lei Gang}, doi = {10.1109/ACCESS.2025.3526775}, journal = {IEEE Access}, pages = {8806-8824}, shortjournal = {IEEE Access}, title = {Transformer-based multi-player tracking and skill recognition framework for volleyball analytics}, volume = {13}, year = {2025}, }
• (2025). Aggregating contextual information for multi-criteria online music recommendations. ACCESS, 13, 8790-8805. (www)

  @article{ , abstract = {This paper introduces CAMCMusic, a novel context-aware multi-criteria music recommendation system designed to address these limitations without relying on user-specific attributes, music features, or explicit user ratings. CAMCMusic integrates contextual information into a multi-criteria decision-making framework to align music genre recommendations with the user’s contextual situation for generating Top-N music recommendations. CAMCMusic begins by assessing the relevance of contextual information based on the relationship between three key elements: the user, the music genre, and the user’s context. Subsequently, we employ an aggregation technique to reveal the connection between context and overall ratings. On the MusiClef dataset, CAMCMusic achieves a precision@5 of 0.61, a recall@5 of 0.77, and F1@5 of 0.68, surpassing the next best performer, SVD++, by 52.5%, 11.6%, and 36%, respectively. On the CAL500 dataset, CAMCMusic outperforms the FM algorithm by 11.9% in precision, 16.4% in recall, and 20% in F1-Score. CAMCMusic significantly enhances recommendation outcomes by effectively capturing and utilizing the relationships between user contexts and music genres, thus providing a robust solution to the cold start problem while maintaining high recommendation quality and user satisfaction. The findings underscore the potential of combining context-awareness with multi-criteria decision-making (MCDM) to advance the state of the art in music recommendation systems.}, archive = {J_ACCESS}, author = {Jieqi Liu}, doi = {10.1109/ACCESS.2025.3527512}, journal = {IEEE Access}, pages = {8790-8805}, shortjournal = {IEEE Access}, title = {Aggregating contextual information for multi-criteria online music recommendations}, volume = {13}, year = {2025}, }
• (2025). Joint Active/Passive beamforming optimization via RWMMSE and gradient projection for downlink IRS-aided MU-MISO systems. ACCESS, 13, 8779-8789. (www)

  @article{ , abstract = {Metamaterials are significantly advancing the way we wirelessly transmit electromagnetic signals between communicating devices. Specifically, intelligent reflecting surfaces (IRS), a metamaterial-derived technology with multiple passive reconfigurable reflecting elements, facilitate a wide array of novel opportunities. The proper reconfiguration of its elements, which is the primary task towards achieving these prospects, has been previously explored by many researchers, where most proposals are characterized by high computational complexity. This study builds upon previous research on downlink IRS-assisted multi-user multiple-input single-output (MISO) systems by introducing innovative methods to accelerate their operations. Specifically, we introduce a weighted sum rate problem and utilize the reduced weighted minimum mean square error (RWMMSE) algorithm to determine active precoders, alongside the employment of gradient projection (GP) method to enhance passive beamforming techniques. These two techniques undergo a novel restructuring process, where the RWMMSE framework, originally designed for MIMO systems, is transformed to work with a MISO system. The GP framework is enhanced with a Barzilai-Borwein method for the step size selection that ensures a fast convergence of the proposed algorithm, resulting in lower complexity. Ultimately, the numerical findings confirm our assertion, demonstrating the efficacy of our suggested algorithm in achieving performance similar to benchmark methods while significantly reducing complexity.}, archive = {J_ACCESS}, author = {Seraphin F. Kimaryo and Eduard E. Bahingayi and Kyungchun Lee}, doi = {10.1109/ACCESS.2025.3527579}, journal = {IEEE Access}, pages = {8779-8789}, shortjournal = {IEEE Access}, title = {Joint Active/Passive beamforming optimization via RWMMSE and gradient projection for downlink IRS-aided MU-MISO systems}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of a parallel elastic shoulder joint for humanoid robotics application. ACCESS, 13, 8761-8778. (www)

  @article{ , abstract = {This paper presents an innovative hybrid cable-driven shoulder joint for humanoid robotics application. A blend of a flexible central limb and three rigid lateral limbs, form a 2-degree-of-freedom (2 DOF) mechanism that connects the mobile platform to the fixed base. This design leverages both good mechanical stability and the integration of an elastic element, which mitigates vibrations up to 40% and allows the storage and release of elastic potential energy up to $1.9 \,J$ . Initially, three alternative and incremental shoulder joint designs are presented and evaluated across two distinct experiments: 1) Energy storage analysis of the parallel mechanism during single cable motion and 2) Dynamic response and vibration damping. Following these experiments, a detailed analysis is performed on the stiffness properties of the proposed prototype, as it outperforms the other two designs. The dimension of the central elastic limb, made out of Thermo-Plastic Polyurethane (TPU), is optimized using Finite Element Analysis (FEA). The kinematic behavior of the proposed mechanism is approximated as a combination of linkages equipped with two universal joints, and its motion evaluated through numerical simulations and real experiments.}, archive = {J_ACCESS}, author = {Sharafatdin Yessirkepov and Timur Umurzakov and Michele Folgheraiter}, doi = {10.1109/ACCESS.2025.3527873}, journal = {IEEE Access}, pages = {8761-8778}, shortjournal = {IEEE Access}, title = {Design and analysis of a parallel elastic shoulder joint for humanoid robotics application}, volume = {13}, year = {2025}, }
• (2025). A comparative study of DC and BLDC actuation for kinesthetic haptics. ACCESS, 13, 8753-8760. (www)

  @article{ , abstract = {Haptic simulation systems enable users to manipulate virtual environments through their interaction with haptic devices. Stable high fidelity interaction with virtual environments for many applications requires actuators with both high torque density and low parasitic forces. Brushless direct-current (BLDC) motors offer both high torque density and low friction, making them a prime candidate for this application. This paper evaluates the use of a BLDC motor for use as a direct drive haptic actuator, in comparison with the brushed direct-current (DC) motor and capstan mechanism commonly used in indirect drive actuation of haptic devices. The comparison metrics include data sheet parameters, peak torque determined using a thermal simulation, current and position step responses, uncoupled stability regions, and dynamic rendering areas. It is concluded that a direct drive BLDC motor can supply higher peak torque, while projecting lower inertia and friction and thus offering higher acceleration. These results indicate that BLDC motors are a promising technology for effective direct drive haptic actuation.}, archive = {J_ACCESS}, author = {Arjun Ivimey and Majid Pahlevani and Keyvan Hashtrudi-Zaad}, doi = {10.1109/ACCESS.2025.3526052}, journal = {IEEE Access}, pages = {8753-8760}, shortjournal = {IEEE Access}, title = {A comparative study of DC and BLDC actuation for kinesthetic haptics}, volume = {13}, year = {2025}, }
• (2025). Reconfigurable mmWave planar phased array featuring wide elevation and full azimuth spatial coverage for 5G vehicular application. ACCESS, 13, 8740-8752. (www)

  @article{ , abstract = {A wide-spatial coverage, low-profile, pattern reconfigurable, millimeter-wave antenna array designed for Vehicle-to-Everything (V2X) communication systems, crucial for fifth-generation wireless networks. The array features a hybrid structure and beam-scanning capabilities, ensuring extensive spatial coverage in the n257 band. Utilizing PIN diode-controlled strips, the antenna enables vertical beam tilting up to ±40° on a car with less than 1 dB gain variation. Furthermore, the subarray achieves beam scanning at angles of ±32° while maintaining a gain exceeding 8 dBi. The overall circular antenna array, incorporating director and reflector elements alongside the antenna on an additive substrate, measures $74.2 \, \text {mm} \times 74.2 \, \text {mm} \times 2.2 \, \text {mm}$ , highlighting its compact design. During simulation, the array demonstrates isolation levels surpassing 20 dB and exhibits a broad $-10 \, \text {dB}$ impedance bandwidth spanning $6570 \, \text {MHz}~(24.36$ – $30.93 \, \text {GHz}$ ). The antenna array can operate in three distinct modes—(a) endfire −z, (b) endfire, and (c) endfire +z—depending on the ON/OFF state of the diodes. In each mode, the radiation performances is verified with and without the car model. To validate the simulated results, an antenna array is fabricated, and measurements encompassing beam scanning, S-parameters, and communication are conducted. The obtained results from the physical implementation align with the simulation, confirming the effectiveness of the proposed V2X antenna array for 5G wireless communication systems.}, archive = {J_ACCESS}, author = {Saiful Islam and Hanvit Kim and Tuyen Dinh Nguyen and Sunwoo Kim and Hyoungsuk Yoo}, doi = {10.1109/ACCESS.2025.3526804}, journal = {IEEE Access}, pages = {8740-8752}, shortjournal = {IEEE Access}, title = {Reconfigurable mmWave planar phased array featuring wide elevation and full azimuth spatial coverage for 5G vehicular application}, volume = {13}, year = {2025}, }
• (2025). Fusing emotion and art communication: A disc interval-valued fermatean fuzzy decision-making approach. ACCESS, 13, 8723-8739. (www)

  @article{ , abstract = {In clinical therapy, enhancing emotional engagement and experience is held to be pivotal for achieving better treatment outcomes. The traditional application techniques of integrating emotional feedback with interventions are often imprecise in capturing real-time variations in emotions and translating these into effective decision-making processes. This paper bridges this gap by proposing an innovative Disc Interval-Valued Fermatean Fuzzy structure to be presented in order to improve the accuracy of the process of decision-making while dealing with some imprecise emotional data. We propose some basic aggregation operation techniques based on Ordered Weighted Averaging and Ordered Weighted Geometric, which are able to capture real-time emotions. The operations used are applied to a case study of interactive digital art that is meant to enrich clients’ emotional experiences and overall effectiveness of clinical therapy. Then, the new format is compared with the technique of CoCoSo that presents an improved flexibility and accuracy hence producing better therapy results.}, archive = {J_ACCESS}, author = {Liu Peng}, doi = {10.1109/ACCESS.2025.3527580}, journal = {IEEE Access}, pages = {8723-8739}, shortjournal = {IEEE Access}, title = {Fusing emotion and art communication: A disc interval-valued fermatean fuzzy decision-making approach}, volume = {13}, year = {2025}, }
• (2025). On modeling agent behavior change through multi-typed information diffusion in online social networks. ACCESS, 13, 8709-8722. (www)

  @article{ , abstract = {The rapid spread of information on online social networks (OSNs) has had both beneficial and detrimental societal impacts. Notably, the propagation of misinformation during events such as the COVID-19 pandemic has driven collective behavioral responses, sometimes exacerbating crises such as panic buying and bank runs. The toilet paper shortage in Japan in 2020 caused by people’s selfish behavior owing to the diffusion of information to correct misinformation, which is considered a general phenomenon based on three sociological characteristics: a collective action problem, a self-fulfilling prophecy, and pluralistic ignorance. Understanding what kind and how information can inhibit selfish behavior to mitigate the problem under the phenomenon is critical for developing strategies to mitigate it. However, existing models addressing misinformation diffusion often lack the capacity to represent the complex behavioral phenomena driven by multiple information types and do not fully capture the subjective decision-making processes of individuals influenced by information. Therefore, this study introduces a computational model that combines subjective logic to represent opinion changes of individuals using multi-typed information and cumulative prospect theory to simulate individuals’ decision-making. This model aims to depict the effectiveness of information in inhibiting selfish behavior (behavior-guiding information) in the phenomenon. The simulation results demonstrate that behavior-guiding information can either inhibit or unintentionally promote selfish behavior, depending on network parameters and information characteristics. These findings suggest that the strategies of behavior-guiding information for this phenomenon should be carefully designed, and that our model could help in these design.}, archive = {J_ACCESS}, author = {Masaaki Miyashita and Norihiko Shinomiya and Daisuke Kasamatsu and Genya Ishigaki}, doi = {10.1109/ACCESS.2025.3528129}, journal = {IEEE Access}, pages = {8709-8722}, shortjournal = {IEEE Access}, title = {On modeling agent behavior change through multi-typed information diffusion in online social networks}, volume = {13}, year = {2025}, }
• (2025). Enhanced YOLOv10 framework featuring DPAM and DALSM for real-time underwater object detection. ACCESS, 13, 8691-8708. (www)

  @article{ , abstract = {Recently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid networks that also deliver anchor-free detection. In delivering YOLOv10, we enhance it with dual partial attention mechanism (DPAM) and dual adaptive label assignment with sun glint removal module (DALSM) along with marine fusion loss (MFL). With DPAM, the latest refinement processes for conservation focus on feature extraction to account for key highlights and in the scene, include temporal context, both critical for interpretation of the dynamic realm below the ocean. The prefix with DALSM involves adaptive dual label assignment and techniques for sun glint removal. The marine fusion loss (MFL) provides an object detection prediction that combines both binary cross-entropy loss and complete intersection over union (CIoU) loss to enhance bounding box localization while also including spatial context to incorporate important underwater features. With the experiments, we attenuate enhancements with device gradient clipping, model checkpointing, and advanced augmentation processes to gain 3.04% improvement in mean Average Precision (mAP). These enhancements mitigate perceived challenges of underwater detection while enhancing knowledge of understanding marine life.}, archive = {J_ACCESS}, author = {Suthir Sriram and Aburvan P. and Arun Kaarthic T. P. and Nivethitha Vijayaraj and Thangavel Murugan}, doi = {10.1109/ACCESS.2025.3527315}, journal = {IEEE Access}, pages = {8691-8708}, shortjournal = {IEEE Access}, title = {Enhanced YOLOv10 framework featuring DPAM and DALSM for real-time underwater object detection}, volume = {13}, year = {2025}, }
• (2025). UPL-SLAM: Unconstrained RGB-D SLAM with accurate point-line features for visual perception. ACCESS, 13, 8676-8690. (www)

  @article{ , abstract = {In mainstream simultaneous localization and mapping (SLAM) algorithms, feature points are commonly utilized to represent image features. However, the quantity and quality of these feature points are contingent upon the environmental texture, lighting conditions, and motion speed. Although existing algorithms enhance adaptability by extracting point-line features simultaneously, the presence of trivial short lines resulting from environmental noise and object occlusion can adversely affect system robustness. Therefore, in this study, we propose a line feature fusion strategy along with a model incorporating an adaptive length suppression parameter for line features. A new line feature residual model is defined, and the mathematical analytical form of line feature Jacobian matrix is derived in detail. Additionally, the point features are organized into a lattice structure and utilized to construct a global pointcloud map in a dedicated thread, aiming to enhance the semantic comprehension of environmental information. Finally, our algorithm is compared against state-of-the-art algorithms on the publicly available datasets TUM RGB-D and ICL-NUIM. Through quantitative trajectory error analysis and qualitative trajectory effect and mapping quality analysis, the final results indicate that the algorithm proposed in this paper achieves superior positioning accuracy and mapping quality, enabling robust 3D reconstruction of indoor scenes.}, archive = {J_ACCESS}, author = {Xianshuai Sun and Yuming Zhao and Yabiao Wang and Zhigang Li and Zhen He and Xiaohui Wang}, doi = {10.1109/ACCESS.2024.3524465}, journal = {IEEE Access}, pages = {8676-8690}, shortjournal = {IEEE Access}, title = {UPL-SLAM: Unconstrained RGB-D SLAM with accurate point-line features for visual perception}, volume = {13}, year = {2025}, }
• (2025). K-band waveguide resonator bandpass filter structure with adjustable transmission zeros for producing various symmetric and asymmetric responses. ACCESS, 13, 8664-8675. (www)

  @article{ , abstract = {This work is to present a versatile K-band waveguide resonator bandpass filter structure capable of producing various responses with a pair of transmission zeros. One of the significant features of the presented filter structure is its ability to produce very dissimilar responses through fine adjustment of tuning screws without changing the geometries of the resonators and coupling structures. The versatility of the filter includes the ability to adjust the proximity of a pair of transmission zeros to the passband. In addition, its asymmetric responses can be laterally inverted by simply changing the resonant frequencies of the resonators without altering the coupling values of the coupling structures. The versatility of the filter can be attributed to the waveguide filter structure using the same type of coupling structures, inductive irises, and avoiding both negative couplings and cross couplings. For filter designs, this work presents a direct synthesis approach to finding the normalized coupling values from a prescribed canonical power transfer function containing a pair of transmission zeros without using numerical methods. One interesting feature is that there is an infinite number of synthesis results corresponding to a predefined response. The filter synthesis approach is demonstrated using examples of third- and fourth-order filters, each featuring a pair of transmission zeros. The versatility of the presented filter is illustrated by a fabricated fourth-order waveguide filter, tuned to produce, for example, three symmetric and four asymmetric responses.}, archive = {J_ACCESS}, author = {Sanggu Lee and Jongheun Lee and Boyoung Lee and Jaehwan Lim and Jaesub Han and Juseop Lee}, doi = {10.1109/ACCESS.2025.3527545}, journal = {IEEE Access}, pages = {8664-8675}, shortjournal = {IEEE Access}, title = {K-band waveguide resonator bandpass filter structure with adjustable transmission zeros for producing various symmetric and asymmetric responses}, volume = {13}, year = {2025}, }
• (2025). High-performance multi-object tracking for autonomous driving in urban scenarios with heterogeneous embedded boards. ACCESS, 13, 8649-8663. (www)

  @article{ , abstract = {Autonomous Driving is emerging as a paradigm shift in the way we conceive people and goods transportation. It promises to improve road safety, reduce traffic congestion, and increase overall transportation efficiency. It is made possible by a plethora of modern technologies, such as AI, low-power hardware, and complex computing. Common stages of Autonomous Driving systems are the identification of objects in the scene (Object Detection), and the ability to predict the evolution of the tracked objects’ states – usually, positions and velocities (Multi-Object Tracking). In this paper, we address the issues behind high-performance Multi-Object Tracking (MOT) algorithms for a real-world urban transportation scenario. The main objective is to exploit the high-performance capabilities of NVIDIA heterogeneous embedded platforms, which are not automatically compatible with the peculiar features of these algorithms. We propose and discuss some non-trivial software design choices and implementation strategies that are needed to match the specific computational and memory access needs of MOT strategies, and the architectural opportunities of NVIDIA embedded GPGPU systems. Our code is made available as open-source. We compare our solution against a highly optimized multi-core version and show that we are able to trigger significant performance speedups (up to $7.19\times $ ) in all power-modes, sometimes even surpassing the CPU reference with the GPU in a lower-power operating mode.}, archive = {J_ACCESS}, author = {Alessio Medaglini and Biagio Peccerillo and Sandro Bartolini}, doi = {10.1109/ACCESS.2024.3484129}, journal = {IEEE Access}, pages = {8649-8663}, shortjournal = {IEEE Access}, title = {High-performance multi-object tracking for autonomous driving in urban scenarios with heterogeneous embedded boards}, volume = {13}, year = {2025}, }
• (2025). ZeST: A zero-resourced speech-to-speech translation approach for unknown, unpaired, and untranscribed languages. ACCESS, 13, 8638-8648. (www)

  @article{ , abstract = {Speech-to-speech translation (S2ST) has emerged as a practical solution for overcoming linguistic barriers, enabling direct translation between spoken languages without relying on intermediate text representations. However, existing S2ST systems face significant challenges, including the requirement for extensive parallel speech data and the limitations of known written languages. This paper proposes ZeST, a novel zero-resourced approach to speech-to-speech translation that addresses the challenges of processing unknown, unpaired, and untranscribed languages. ZeST consists of two main phases: (1) Discovering semantically related speech pairs from unpaired data by leveraging self-supervised visually grounded speech (VGS) models and (2) Achieving textless speech-to-speech translation for untranscribed languages using discrete speech representations and sequence-to-sequence modeling. Experimental evaluations using three different data scenarios demonstrate that the ZeST system effectively performs direct speech-to-speech translation without relying on transcribed data or parallel corpora. The experimental results highlight the potential of ZeST in contributing to the field of zero-resourced speech processing and improving communication in multilingual societies.}, archive = {J_ACCESS}, author = {Luan Thanh Nguyen and Sakriani Sakti}, doi = {10.1109/ACCESS.2025.3527012}, journal = {IEEE Access}, pages = {8638-8648}, shortjournal = {IEEE Access}, title = {ZeST: A zero-resourced speech-to-speech translation approach for unknown, unpaired, and untranscribed languages}, volume = {13}, year = {2025}, }
• (2025). NeuralACT: Accounting analytics using neural network for real-time decision making from big data. ACCESS, 13, 8621-8637. (www)

  @article{ , abstract = {The accounting division plays a crucial role in providing accurate financial data and insights, which are instrumental to informed decision-making and strategic planning. Organizational financial transparency and effective support for resource management depend on accounting. This paper presents a deep neural network (DNN)-based accounting analytics, NeuralACT, to support the decision-making process using real-time predictions made from big data. It uses a Bidirectional Long Short-Term Memory (BiLSTM) network trained with Visegrad Group Companies (VGC) dataset customized and improved for accounting features prediction. The incorporation of bootstrapping while ensuring proper data variance significantly improved the performance of the system. Experimental results demonstrate the outstanding performance of NeuralACT, with an RMSE of 0.0181, MAE of 0.0145, MAPE of 5.14%, and an average $R^{2}$ of 0.99, significantly outperforming existing methods. The experimental setup involves 10 categories of data from 2000 sources, sending data at a rate of 512 Mbps to 720 Mbps. The NeuralACT framework demonstrates consistent performance for big data, maintaining an average throughput of 0.1338 Gbps and latency of 0.039 s. In a nutshell, NeuralACT is a high-performing, innovative, and real-time accounting analytics framework capable of handling big data with exceptional performance.}, archive = {J_ACCESS}, author = {Leonidas Theodorakopoulos and Alexandra Theodoropoulou and Georgios Kampiotis and Ioanna Kalliampakou}, doi = {10.1109/ACCESS.2025.3526706}, journal = {IEEE Access}, pages = {8621-8637}, shortjournal = {IEEE Access}, title = {NeuralACT: Accounting analytics using neural network for real-time decision making from big data}, volume = {13}, year = {2025}, }
• (2025). A case study on strategic multi-objective optimization of influenza vaccine allocation: Age-specific approaches in saudi arabia. ACCESS, 13, 8604-8620. (www)

  @article{ , abstract = {Acute lower respiratory tract infections pose a significant health challenge, affecting over 15% of Saudi Arabia’s population annually. Research over the past three decades highlights that viruses are the primary cause of these infections. The epidemiology of respiratory viruses in Saudi Arabia is influenced by the movement of foreign workers and the mass gatherings of Muslims in Mecca during the Hajj and Umrah seasons. This study exploring the impact of different vaccination rates across various age groups during the 2019/2020 influenza season in Saudi Arabia. We used a two-phased multi-objective optimization approach with a Controlled Elitism Non-Dominated Sorting Genetic Algorithm applied to an age-structured SEIR model (Susceptible-Exposed–Infectious-Recovered) to design vaccination policies tailored to age-specific infection rates. The research divides the population into five age categories—infants and children, adolescents, young adults, middle-aged adults, and seniors—across five regions: Eastern, Central, Northern, Western, and Southern Saudi Arabia. The study compares three vaccination strategies: no vaccination, national vaccination, and a two-phased multi-objective optimization approach under various pandemic scenarios. It evaluates the outcomes regarding infection rates, mortality, and morbidity. The findings highlight how demographic factors significantly influence disease transmission and vaccine distribution. The optimized vaccination strategy outperformed the national vaccination approach, leading to more significant reductions in infection rates (by 0.01% to 0.45%), morbidity (by 0.01% to 0.04%), and mortality (by 0.001% to 0.013%).}, archive = {J_ACCESS}, author = {Asma K. Alkhamis and Manar I. Hosny}, doi = {10.1109/ACCESS.2025.3526146}, journal = {IEEE Access}, pages = {8604-8620}, shortjournal = {IEEE Access}, title = {A case study on strategic multi-objective optimization of influenza vaccine allocation: Age-specific approaches in saudi arabia}, volume = {13}, year = {2025}, }
• (2025). Event-triggered asynchronous dissipative control for markov jump systems with unknown probabilities. ACCESS, 13, 8594-8603. (www)

  @article{ , abstract = {This paper investigates the issue of the event-triggered asynchronous dissipative control for Markov jump systems with unknown probabilities and packet losses. A hidden Markov model is used to describe the asynchronous problem. A Bernoulli model and an event-triggered scheme are adopted to address the packet loss and reduce the transmission rate of sampled signals, respectively. Furthermore, there exists unknown probability information of the controller. Then, Lyapunov functions are constructed to obtain sufficient conditions for the stochastic stability and dissipative performance of the closed-loop system. Moreover, a design method for controller parameters and event-triggered matrices is proposed. Finally, the effectiveness of the proposed approach is verified through a three-modal numerical example.}, archive = {J_ACCESS}, author = {Huiying Chen and Mingshuai Jiang and Renwei Liu and Zuxin Li and Yanfeng Wang}, doi = {10.1109/ACCESS.2025.3527506}, journal = {IEEE Access}, pages = {8594-8603}, shortjournal = {IEEE Access}, title = {Event-triggered asynchronous dissipative control for markov jump systems with unknown probabilities}, volume = {13}, year = {2025}, }
• (2025). Vehicle turn pattern counting and short term forecasting using deep learning for urban traffic management system. ACCESS, 13, 8585-8593. (www)

  @article{ , abstract = {Urban traffic management has been facing increasing challenges due to the surge in number of vehicles and traffic congestion. As cities expand and population grows, efficient and accurate monitoring of vehicle counts is crucial for better traffic management. Hence, as part of the Bengaluru Mobility Challenge 2024, organized by Bengaluru Traffic Police in collaboration with The Indian Institute of Science, we propose a solution to address the issue by developing a predictive model to estimate vehicle counts by turning pattern from traffic video footage. The dataset consists of traffic video footage of 23 different junctions around Bengaluru, on which 7 vehicle classes had to be detected, namely, Car, Truck, Bus, Two-Wheeler, Three-Wheeler, Light Commercial Vehicle and Bicycle. The proposed work focuses on two key objectives: counting vehicle turns over 30-minute clips and forecasting future vehicle turn counts by class for the next 30 minutes. A You Only Look Once (YOLOv8) and Auto-ARIMA based pipeline was deployed to address the challenge, which demonstrated robust detection capabilities, with an overall precision of 92.59% for vehicle detection. Building on this, we designed a custom vehicle counting algorithm that integrated the BoT-SORT tracker with dynamic counting boxes, accurately capturing vehicle movements and turn patterns in real-time and this integrated approach attained a best deviation of 20.79%. for turn pattern counting and 28.41% for forecasting. Furthermore, the system is scalable to accommodate any number of cameras and is capable of forecasting traffic over extended time frames, allowing it to be applied to a variety of urban traffic monitoring scenarios. These results highlight the effectiveness of our custom designed framework in real-world scenarios as a reliable model for applications needing high-precision detection and predictive analytics.}, archive = {J_ACCESS}, author = {Sundarakrishnan Narayanan and Sohan Varier and Tarun Bhupathi and Manaswini Simhadri Kavali and Mohana and P. Ramakanth Kumar and K. Sreelakshmi}, doi = {10.1109/ACCESS.2025.3526880}, journal = {IEEE Access}, pages = {8585-8593}, shortjournal = {IEEE Access}, title = {Vehicle turn pattern counting and short term forecasting using deep learning for urban traffic management system}, volume = {13}, year = {2025}, }
• (2025). ML-based self-optimization handover technique for beyond 5G mobile network. ACCESS, 13, 8568-8584. (www)

  @article{ , abstract = {The Fifth Generation (5G) and Beyond (B5G) mobile systems employ advanced technologies, such as millimeter Wave (mmWave) and Ultra-Dense Networks (UDNs), to meet future networks’ requirements. However, implementing these technologies may pose several challenges to the B5G network. One key challenge is the need for efficient Handover (HO) optimization processes. HO aims to ensure seamless connectivity and uninterrupted services for users while moving from one cell to another within the coverage area. Thus, this study introduces a new, intelligent, and robust self-optimization HO technique designed to work efficiently with the B5G networks. The technique utilizes Machine Learning (ML), particularly leveraging the Regression Tree (RT) model. In this study, the proposed technique is referred to as the ML-based Self-Optimization Handover Technique (ML-SOHOT). The technique is evaluated and validated using different major HO metrics, including Handover Probability (HOP), Handover Failure (HOF), and Handover Ping-Pong (HOPP) across various mobility patterns in B5G, specifically considering an urban environment. The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. Furthermore, ML-SOHOT achieves an average HO performance improvement of up to 96% compared to competitive algorithms from the literature. Consequently, the technique could enhance the overall B5G system performance and user experience.}, archive = {J_ACCESS}, author = {Saddam Alraih and Rosdiadee Nordin and Asma Abu-Samah and Ibraheem Shayea and Nor Fadzilah Abdullah}, doi = {10.1109/ACCESS.2025.3528357}, journal = {IEEE Access}, pages = {8568-8584}, shortjournal = {IEEE Access}, title = {ML-based self-optimization handover technique for beyond 5G mobile network}, volume = {13}, year = {2025}, }
• (2025). A hybrid approach to model additive manufacturing factory flow for the aerospace sector. ACCESS, 13, 8552-8567. (www)

  @article{ , abstract = {Modular manufacturing is highly desirable due to its ability to quickly adapt to changing client demands and therefore its propensity to create high-margin products. However, this flexibility comes with the challenge of rapidly redesigning facility layouts when product demand or specifications change. Traditional Discrete Event Simulations (DES) are often time-consuming and computationally expensive, leading to delays in decision-making and potential impacts on revenue. To address this challenge, this paper proposes a hybrid approach that combines Linear Programming (LP) with DES for efficient and accurate facility design. A case study focusing on the adoption of Additive Manufacturing (AM) in the aerospace industry is presented to demonstrate the effectiveness of the proposed LP/DES approach. The specifications of the factory represent realistic requirements for a facility that’s responsible for a sizeable market share of global aeroengine component production. The LP/DES methodology was employed to optimize factory flow using LP first, with two optimization techniques applied for comparison. The LP results were then fed directly into a DES framework to test the transient behaviour of the network when disruption was introduced. The results highlight the robustness and efficiency of the hybrid approach in optimizing factory operations and managing disruptions.}, archive = {J_ACCESS}, author = {Mostafa K. A. Salem and Fabrizio Scarpa and Ashutosh Tiwari}, doi = {10.1109/ACCESS.2024.3523236}, journal = {IEEE Access}, pages = {8552-8567}, shortjournal = {IEEE Access}, title = {A hybrid approach to model additive manufacturing factory flow for the aerospace sector}, volume = {13}, year = {2025}, }
• (2025). A review of digital pixel sensors. ACCESS, 13, 8533-8551. (www)

  @article{ , abstract = {Digital pixel sensor (DPS) has evolved as a pivotal component in modern imaging systems and has the potential to revolutionize various fields such as medical imaging, astronomy, surveillance, IoT devices, etc. Compared to analog pixel sensors, the DPS offers high speed and good image quality. However, the introduced intrinsic complexity within each pixel, primarily attributed to the accommodation of the ADC circuit, engenders a substantial increase in the pixel pitch. Unfortunately, such a pronounced escalation in pixel pitch drastically undermines the feasibility of achieving high-density integration, which is an obstacle that significantly narrows down the field of potential applications. Nonetheless, designing compact conversion circuits along with strategic integration of 3D architectural paradigms can be a potential remedy to the prevailing situation. This review article presents a comprehensive overview of the vast area of DPS technology. The operating principles, advantages, and challenges of different types of DPS circuits have been analyzed. We categorize the schemes into several categories based on ADC operation. A comparative study based on different performance metrics has also been showcased for a well-rounded understanding.}, archive = {J_ACCESS}, author = {Md Rahatul Islam Udoy and Shamiul Alam and Md Mazharul Islam and Akhilesh Jaiswal and Ahmedullah Aziz}, doi = {10.1109/ACCESS.2025.3526879}, journal = {IEEE Access}, pages = {8533-8551}, shortjournal = {IEEE Access}, title = {A review of digital pixel sensors}, volume = {13}, year = {2025}, }
• (2025). Metasurface-cladded waveguide filled with magnetized plasma. ACCESS, 13, 8523-8532. (www)

  @article{ , abstract = {This paper analyses electromagnetic (EM) wave propagation within a metasurface-cladded waveguide filled with magnetized plasma. First, an analytical model is developed to understand the governing dynamics of the system. Second, a realistic numerical design is presented to assess the feasibility of the proposed concept. This technology targets microwave-driven plasma production, with the cladding metasurface enabling control over the cut-off frequency of the waveguide. Results suggest that EM propagation can be triggered regardless of the value of the static magnetic induction field. This represents a significant advance over standard implementations, promoting system miniaturization and facilitating the integration of waveguides with plasma sources.}, archive = {J_ACCESS}, author = {Mirko Magarotto and Luca Schenato and Marco Santagiustina and Andrea Galtarossa and Antonio-Daniele Capobianco}, doi = {10.1109/ACCESS.2025.3528084}, journal = {IEEE Access}, pages = {8523-8532}, shortjournal = {IEEE Access}, title = {Metasurface-cladded waveguide filled with magnetized plasma}, volume = {13}, year = {2025}, }
• (2025). Attention-guided shared hybrid network for enhanced land cover segmentation. ACCESS, 13, 8508-8522. (www)

  @article{ , abstract = {Accurate land cover segmentation is crucial for numerous environmental and urban planning applications. However, irregular land types and varying illumination conditions can adversely affect segmentation results. Most existing remote sensing image segmentation models prioritize lightweight design, which often leads to difficulties in identifying small objects and edge information, as well as insufficient multi-scale capabilities. To address these issues, we propose an innovative Attention-Guided Shared Hybrid Network (AGSHN) aimed at enhancing the precision and robustness of land cover segmentation. Our network integrates attention mechanisms with shared hybrid architectures to effectively capture spatial dependencies and contextual information. During the network fusion stage, our Dual Feature Complementary Modules (DFCM) selectively emphasizes informative features while suppressing irrelevant data. Additionally, during the decoding stage, we introduce a Multi-Scale Dual Representation Alignment Filter(MDAF) module to mitigate semantic ambiguity between shallow and deep features. Finally, a specialized auxiliary segmentation method is employed to reinforce the network’s boundary representation. Extensive experiments conducted on benchmark land cover datasets, including WHDLD, GID-15, and L8SPARCS, demonstrate that AGSHN significantly outperforms existing state-of-the-art methods in segmentation accuracy.}, archive = {J_ACCESS}, author = {Yinbing Jiang and Linfeng Shi and Xinyu Fan}, doi = {10.1109/ACCESS.2024.3524454}, journal = {IEEE Access}, pages = {8508-8522}, shortjournal = {IEEE Access}, title = {Attention-guided shared hybrid network for enhanced land cover segmentation}, volume = {13}, year = {2025}, }
• (2025). Identification of fake comments in E-commerce based on triplet convolutional twin network and CatBoost model. ACCESS, 13, 8495-8507. (www)

  @article{ , abstract = {The continuous progress of Internet technology has promoted the maturity of online shopping market. The false comments accompanying online shopping not only infringe the rights and interests of consumers, but also pose a threat to the healthy development of e-commerce. To promote the development of e-commerce, a false comment recognition model based on triplet convolutional twin network and CatBoost model is proposed. Key semantic features are refined from the perspective of semantic similarity through triplet convolutional twin networks. Finally, integrating the advantages of the CatBoost model and combining text and behavioral data aims to reduce over-fitting and enhance recognition accuracy. It provides an efficient and accurate identification tool for e-commerce platforms. The benchmark experimental results show that the proposed TriCNN-CatBoost model significantly outperforms traditional Naive Bayes, Support Vector Machines, and Random Forest models in terms of accuracy, recall, and F1 score, demonstrating stronger false comment recognition ability and generalization performance. In addition, the proposed model not only converged quickly, but also had the lowest loss values on two different datasets. The F1 scores on the four key features were 0.8136, 0.8267, 0.8046, and 0.7966, all of which were superior to other comparison models. At the same time, the accuracy of the four features was 0.8931, 0.9012, 0.8846, and 0.8961, respectively, which verified the excellent predictive recognition performance. Compared with the other two comparison algorithms, the detection time required by the proposed model was the shortest at 115us and consumed the least amount of resources. Overall, the model has improved the recognition performance, proving its effectiveness in providing consumers with a more accurate and reliable shopping experience, while also providing platform managers with a scientific and efficient regulatory tool.}, archive = {J_ACCESS}, author = {Juanjuan Peng}, doi = {10.1109/ACCESS.2024.3520717}, journal = {IEEE Access}, pages = {8495-8507}, shortjournal = {IEEE Access}, title = {Identification of fake comments in E-commerce based on triplet convolutional twin network and CatBoost model}, volume = {13}, year = {2025}, }
• (2025). Multi-view prototypical transport for unsupervised domain adaptation. ACCESS, 13, 8482-8494. (www)

  @article{ , abstract = {Unsupervised Domain Adaptation (UDA) methods struggle to bridge the gap between a labeled source domain and an unlabeled target domain, particularly due to the rigidity of deep feature representations derived from the penultimate layer of backbone feature extractors. These deeper representations, while discriminative, often fail to generalize under distributional shifts due to their specificity. To overcome these limitations, we introduce a novel representation learning framework, Multi-view Prototypical Transport (MPT), which leverages a multi-view hypothesis model to integrate and utilize the general information present in shallower layers. This approach facilitates a more comprehensive understanding of the relationships among intermediate features. Additionally, our framework incorporates a novel multi-view prototypical learning strategy that not only transfers domain-general representations, but also significantly enhances robustness against target domain outliers. Extensive experimental evaluations on various benchmark datasets demonstrate that our method outperforms existing state-of-the-art UDA approaches, confirming the effectiveness of our strategy in adapting to complex domain shifts.}, archive = {J_ACCESS}, author = {Sunhyeok Lee and Dae-Shik Kim}, doi = {10.1109/ACCESS.2025.3528054}, journal = {IEEE Access}, pages = {8482-8494}, shortjournal = {IEEE Access}, title = {Multi-view prototypical transport for unsupervised domain adaptation}, volume = {13}, year = {2025}, }
• (2025). Real-time multi-task deep learning model for polyp detection, characterization, and size estimation. ACCESS, 13, 8469-8481. (www)

  @article{ , abstract = {While performing a colonoscopy, there are many tasks to be done: finding polyps, classifying them, and deciding the next procedure for the polyps, whether to incise them or not. Such tasks are challenging for fellow doctors. All these three tasks can have an intrapersonal error, which varies among endoscopists. A proven method for enhancing performance is computer-aided detection and a diagnosis system for endoscopists, which tends to be a real-time system. In this work, we present a modified convolutional neural network (CNN) based deep learning (DL) model to perform these tasks in real-time, utilizing existing object detection models: YOLOv5 and YOLOv8. For the various tasks, the models are trained using datasets with incomplete labels, leading to a comparison of different training strategies. Our model, YOLOv8, achieved an F1-score of 95.96% for the polyp detection task, 85.24% F1-score for the polyp classification task, and 78.41% macro F1-score for the polyp size estimation task. Such results, when compared with fellow doctors’ findings proved superior in both accuracy and macro F1-score, maintaining a real-time inference speed.}, archive = {J_ACCESS}, author = {Phanukorn Sunthornwetchapong and Kasichon Hombubpha and Kasenee Tiankanon and Satimai Aniwan and Pasit Jakkrawankul and Natawut Nupairoj and Peerapon Vateekul and Rungsun Rerknimitr}, doi = {10.1109/ACCESS.2025.3527720}, journal = {IEEE Access}, pages = {8469-8481}, shortjournal = {IEEE Access}, title = {Real-time multi-task deep learning model for polyp detection, characterization, and size estimation}, volume = {13}, year = {2025}, }
• (2025). Optimizing supplier selection: A comparative study of fuzzy VIKOR and fuzzy moora for performance-based decision making. ACCESS, 13, 8456-8468. (www)

  @article{ , abstract = {Supplier selection is crucial in supply chain management, directly impacting operational efficiency and production continuity. This study compares two Multi-Criteria Decision Making (MCDM) techniques—Fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) and Fuzzy Multi-Objective Optimization based on Ratio Analysis (MOORA)—to optimize supplier selection for a manufacturing company in Indonesia facing single supplier dependency. Fuzzy logic is employed to reduce subjectivity and ambiguity in the selection process. The Fuzzy MOORA method achieved a sensitivity score of 16.7% compared to 0.00% for Fuzzy VIKOR, indicating higher responsiveness to changes in criteria weights. Additionally, Fuzzy MOORA reduced the supplier selection process time by 28.5% compared to the current system, demonstrating its efficiency in practical applications. In general, the Fuzzy MOORA is superior in terms of the performance level with a rate of 3 out of 4 factors including the computational times, simplicity, and mathematical calculation. This study contributes to enhancing supplier selection methodologies by providing a comparative analysis of these techniques in a real-world context.}, archive = {J_ACCESS}, author = {Johan Krisnanto Runtuk and Steven Christanto and Poh Kiat Ng}, doi = {10.1109/ACCESS.2024.3525362}, journal = {IEEE Access}, pages = {8456-8468}, shortjournal = {IEEE Access}, title = {Optimizing supplier selection: A comparative study of fuzzy VIKOR and fuzzy moora for performance-based decision making}, volume = {13}, year = {2025}, }
• (2025). A secure framework for privacy-preserving analytics in healthcare records using zero-knowledge proofs and blockchain in multi-tenant cloud environments. ACCESS, 13, 8439-8455. (www)

  @article{ , abstract = {In the realm of healthcare analytics, preserving the privacy of sensitive data while enabling valuable insights poses a significant challenge, particularly given the increasing prevalence of data breaches and the sensitivity of personal health information. This paper presents a secure framework that addresses these concerns by integrating privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud environment. Through advanced cryptographic techniques, specifically zk-SNARKs, the framework ensures that healthcare records remain protected during analytics computations, without exposing raw data. The privacy-preserving analytics engine utilizes anonymized healthcare records and generates zk-SNARKs to validate computations. These proofs, integrated into a blockchain network, create a tamper-proof, transparent ledger that ensures secure healthcare transactions. This approach is critical in scenarios such as telemedicine, where secure data sharing and computation are paramount. By demonstrating its application in a telemedicine app, the framework highlights its practical significance in balancing data utility and privacy in healthcare analytics, providing a scalable and secure solution to a pressing problem.}, archive = {J_ACCESS}, author = {S. Bharath Babu and K. R. Jothi}, doi = {10.1109/ACCESS.2024.3509457}, journal = {IEEE Access}, pages = {8439-8455}, shortjournal = {IEEE Access}, title = {A secure framework for privacy-preserving analytics in healthcare records using zero-knowledge proofs and blockchain in multi-tenant cloud environments}, volume = {13}, year = {2025}, }
• (2025). Development of CNN-based semantic segmentation algorithm for crop classification of korean major upland crops using NIA AI HUB. ACCESS, 13, 8425-8438. (www)

  @article{ , abstract = {Accurately estimating crop cultivation areas is critical for predicting yields and managing overproduction, particularly for staple crops grown in regions like Jeju Island, South Korea, where reporting cultivation areas is mandatory. This study developed a modified U-Net architecture for semantic segmentation, utilizing UAV-based high-resolution imagery in the open-source NIA AI HUB dataset. The dataset includes labeled RGB images of six winter crops—white radish, cabbage, onion, garlic, broccoli, and carrot—grown on Jeju Island, a key agricultural hub. The proposed model incorporates a ResNet-34 backbone, Attention Gates, and Residual Modules, achieving a mean F1 score of 85.4% and an intersection over union (IoU) of 74.6%, outperforming the original U-Net. This advancement significantly reduces misclassifications among visually similar crops, such as garlic and onion. Application to three unknown fields demonstrated a mean prediction accuracy of 90.2%, effectively estimating cultivation areas with high precision. By leveraging public datasets and innovative AI techniques, this study highlights the scalability and practicality of the proposed model in enhancing precision agriculture. These findings demonstrate the model’s potential to improve crop yield prediction, optimize resource allocation, and support sustainable farming practices in diverse agricultural environments.}, archive = {J_ACCESS}, author = {Dong-Wook Kim and Gyujin Jang and Hak-Jin Kim}, doi = {10.1109/ACCESS.2025.3527502}, journal = {IEEE Access}, pages = {8425-8438}, shortjournal = {IEEE Access}, title = {Development of CNN-based semantic segmentation algorithm for crop classification of korean major upland crops using NIA AI HUB}, volume = {13}, year = {2025}, }
• (2025). Intelligent prediction of flood disaster risk levels based on knowledge graph and graph neural networks. ACCESS, 13, 8416-8424. (www)

  @article{ , abstract = {Flash flood disasters pose a significant threat to human life and property, making accurate prediction of risk levels crucial for disaster prevention and mitigation. This study introduces an innovative artificial intelligence approach based on knowledge graphs and graph neural networks. The method integrates multi-source data to construct a knowledge graph, which is then modeled using graph neural networks. We evaluate the model’s performance using metrics such as accuracy, precision, recall, F1 score, and AUC. Under five-fold cross-validation, the AUC reached 0.84, with all performance indicators showing good results, indicating significant performance improvement. Experimental results demonstrate high prediction accuracy when tested on a dataset containing 9000 records. Compared with the three classical models in traditional machine learning, such as RF, SVM and ANN, the performance of this model is improved, and it is better than the traditional model. Through case analysis, risk levels in multiple regions were accurately predicted. Additionally, statistical analysis of flood disaster warning levels and flash flood risk zoning across cities in Jiangxi Province provides a visual representation of flood risk distribution and risk level proportions in different cities, offering strong reference for flood prevention, disaster mitigation, and urban planning. This method provides important scientific support for precise flash flood disaster prediction and risk management.}, archive = {J_ACCESS}, author = {Peisheng Yang and Xiaohua Xu and Meilan Shao and Yewei Liu}, doi = {10.1109/ACCESS.2025.3525757}, journal = {IEEE Access}, pages = {8416-8424}, shortjournal = {IEEE Access}, title = {Intelligent prediction of flood disaster risk levels based on knowledge graph and graph neural networks}, volume = {13}, year = {2025}, }
• (2025). Survey of distributed ledger technology (DLT) for secure and scalable computing. ACCESS, 13, 8393-8415. (www)

  @article{ , abstract = {Distributed Ledger Technology (DLT), encompassing blockchain and similar decentralized systems, is revolutionizing secure and scalable computing. This paper comprehensively surveys DLT, highlighting its core principles, cloud integration and enhanced data security/scalability. It examines various DLT systems, advanced consensus mechanisms and Layer 2 solutions to address scalability. It explores DLT’s innovative use cases in finance, healthcare, supply chains and IoT, emphasizing advantages and challenges. It also delves into DLT’s regulatory landscape, addressing compliance, data privacy and governance, while assessing the potential for regulatory clarity and international cooperation. The paper aims to guide researchers and practitioners navigating DLT’s evolving landscape, underscoring its transformative potential for secure and scalable computing in the digital age.}, archive = {J_ACCESS}, author = {Shreya Girish Savadatti and Shruthi Krishnamoorthy and Radhakrishnan Delhibabu}, doi = {10.1109/ACCESS.2025.3528211}, journal = {IEEE Access}, pages = {8393-8415}, shortjournal = {IEEE Access}, title = {Survey of distributed ledger technology (DLT) for secure and scalable computing}, volume = {13}, year = {2025}, }
• (2025). Cutting-edge intrusion detection in IoT networks: A focus on ensemble models. ACCESS, 13, 8375-8392. (www)

  @article{ , abstract = {As the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance comparison of various machine learning classifiers, including K-Nearest Neighbors, Gradient Boosting, XGBoost, Support Vector Machines, Random Forests, Decision Trees, and Extremely Randomized Trees, for intrusion detection in IoT networks. Comparative analysis shows that although all models did very well, the ensemble methods—GB, XGBoost, RF, and ERT—constantly performed better than others in F1-Score, recall, accuracy, and precision. Among them, ERT is turned out to be the most effective model for real-time attack detection on IoT devices, with an accuracy of 99.7% besides excellent precision and recall. XGBoost and RF also turn out to have high reliability and accuracy with F1-Scores of 0.95. These findings further underscore that ensemble methods outperform in intrusion detection for IoT networks and, thus, offer important insights to improve security within networks and protect critical IoT-based infrastructures from a variety of threats.}, archive = {J_ACCESS}, author = {Najm Us Sama and Saeed Ullah and S. M. Ahsan Kazmi and Manuel Mazzara}, doi = {10.1109/ACCESS.2024.3491831}, journal = {IEEE Access}, pages = {8375-8392}, shortjournal = {IEEE Access}, title = {Cutting-edge intrusion detection in IoT networks: A focus on ensemble models}, volume = {13}, year = {2025}, }
• (2025). Personalized decision support enhanced by multiple expected solution points in the characteristic objects method. ACCESS, 13, 8355-8374. (www)

  @article{ , abstract = {This paper introduces a novel decision-making framework designed to enhance personalized support in Decision Support Systems (DSS). Existing methods often rely on single-dimensional modeling, which can introduce bias and uncertainty in criteria weighting. To address this, the study introduces a new approach for integrating subjective preferences with the multiple Expected Solution Points (ESPs) approach within the Characteristic Objects Method (COMET). This dual-dimensional approach tailors decision-making to align more closely with individual preferences and desired outcomes. Applied to the housing location selection problem, the proposed approach captures diverse user needs, delivering highly personalized and relevant recommendations. A comparative analysis with selected subjective weighting methods validates the stability and reliability of the multiple ESP-COMET model. By advancing personalization in decision support systems, this research offers a robust framework for more accurate and user-centered decision-making across complex scenarios.}, archive = {J_ACCESS}, author = {Jakub WięCkowski and Andrii Shekhovtsov and Bogdan Franczyk and Jarosław Watróbski and Wojciech Sałabun}, doi = {10.1109/ACCESS.2024.3524161}, journal = {IEEE Access}, pages = {8355-8374}, shortjournal = {IEEE Access}, title = {Personalized decision support enhanced by multiple expected solution points in the characteristic objects method}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of re-identification methods of multi-criteria decision analysis models. ACCESS, 13, 8338-8354. (www)

  @article{ , abstract = {One of the major hurdles in Multi-Criteria Decision Analysis (MCDA) is the re-identification of pre-existing decision models. Due to factors like limited access to domain experts, some models become impractical, leading to the need for methods that aid in their re-identification. Addressing the challenge of re-identifying decision models brings up issues related to updating the last version of models to preserve their effectiveness, particularly in nonlinear decision scenarios. Common MCDA methods, which are based on linearity assumptions, encounter difficulties when dealing with nonlinearity, making it necessary to investigate practical methods for re-identifying nonlinear models. In this paper, we present innovative methods for re-identifying MCDA models utilizing optimization and machine learning techniques. Firstly, we introduce Support Vector Regression - Characteristic Objects Method (SVR-COMET), which combines the SVR and COMET methods. Secondly, we developed several extensions of the Stochastic Identification of Weights (SITW) algorithm. These methods were evaluated against a benchmark comprising four selected MCDA techniques. Comparisons were performed using Spearman’s weighted correlation coefficient ( $r_{w}$ ). The findings from the study indicate that the proposed methods for re-identifying MCDA models are capable of mapping both linear and non-linear decision-making models.}, archive = {J_ACCESS}, author = {Bartłomiej Kizielewicz and Jakub Więckowski and Bogdan Franczyk and Jarosław Wątróbski and Wojciech Sałabun}, doi = {10.1109/ACCESS.2024.3524672}, journal = {IEEE Access}, pages = {8338-8354}, shortjournal = {IEEE Access}, title = {Comparative analysis of re-identification methods of multi-criteria decision analysis models}, volume = {13}, year = {2025}, }
• (2025). Optimal magnetizing inductance design method for a two-switch forward converter. ACCESS, 13, 8327-8337. (www)

  @article{ , abstract = {This paper proposes an optimal design method for the transformer magnetizing inductance of a Two-Switch Forward Converter (TSFC) to achieve its maximum efficiency. The selection of a magnetizing inductance significantly affects the associated major losses, making it challenging for designers to choose the appropriate value. It is crucial to design the magnetizing inductance that minimizes the total losses by mathematically analyzing the losses related to the TSFC elements. This paper analytically investigates the impact of the transformer magnetizing inductance on the losses of TSFC components. A simple mathematical loss modeling approach is used to propose a method for selecting the optimal magnetizing inductance. To verify the proposed optimal magnetizing inductance selection method, a 500 W TSFC prototype was designed. The prototype was used to experimentally compare transformers with different magnetizing inductances, validating the effectiveness of the proposed method. The experimental results demonstrate that the proposed method can achieve the maximum efficiency of TSFC by selecting the optimal magnetizing inductance.}, archive = {J_ACCESS}, author = {Sanghyuk Ji and Eurak Jung and Yohan Jang and Sungwoo Bae}, doi = {10.1109/ACCESS.2025.3527060}, journal = {IEEE Access}, pages = {8327-8337}, shortjournal = {IEEE Access}, title = {Optimal magnetizing inductance design method for a two-switch forward converter}, volume = {13}, year = {2025}, }
• (2025). Technical analysis of comfort and energy consumption in smart buildings with three levels of automation: Scheduling, smart sensors, and IoT. ACCESS, 13, 8310-8326. (www)

  @article{ , abstract = {Energy consumption and efficiency, along with occupant well-being, are key global challenges across various sectors, especially in the industrial and residential domains. Building automation, which exists at multiple levels, plays a crucial role in addressing these challenges. This study evaluates three distinct levels of automation—scheduling-based, sensor-based, and IoT-enabled control—allowing a clear comparison of each system’s effectiveness in managing energy consumption and enhancing occupant comfort. This approach simplifies the decision-making process by identifying the level of control that optimally balances energy efficiency with thermal comfort requirements, particularly in residential contexts. The integration of the Internet of Things (IoT) into building control systems, a hallmark of the Fourth Industrial Revolution, further enhances these capabilities. Building automation and control systems can be categorized into standard and advanced systems. Conventional control methods fall short of meeting the increasingly complex demands of smart buildings. However, leveraging IoT in smart building design can significantly enhance quality of life by making smart systems more efficient, optimizing resource allocation, and improving management capabilities and services. A key application of IoT in smart buildings is the efficient management of HVAC (heating, ventilation, and air conditioning) systems, which enhances occupant comfort while reducing energy consumption. This paper presents a simulation of a residential building in Ilam, where energy consumption and occupant comfort levels are analyzed across various scenarios. The study identifies optimal conditions for both energy efficiency and resident well-being. By evaluating a residential building, this research compares energy consumption and comfort across basic scheduling, sensor-based control, and advanced IoT-enabled automation levels. Optimal configurations that improve energy savings and occupant comfort are identified, highlighting the effectiveness of IoT integration in HVAC management. The results demonstrate that advanced automation, particularly IoT-based control, offers significant advantages for energy management and comfort optimization in residential buildings.}, archive = {J_ACCESS}, author = {Rahmat Aazami and Mohammad Moradi and Mohammadamin Shirkhani and Ambe Harrison and Saad F. Al-Gahtani and Z. M. S. Elbarbary}, doi = {10.1109/ACCESS.2025.3526858}, journal = {IEEE Access}, pages = {8310-8326}, shortjournal = {IEEE Access}, title = {Technical analysis of comfort and energy consumption in smart buildings with three levels of automation: Scheduling, smart sensors, and IoT}, volume = {13}, year = {2025}, }
• (2025). How big data has changed technology roadmapping: A review on data-driven roadmapping. ACCESS, 13, 8297-8309. (www)

  @article{ , abstract = {With the rise of data mining and predictive technologies, systematic data analytics has become popular in business practices. As data analysis can contribute to technology planning in various ways, previous studies have attempted to integrate data analysis and technology planning tools. This is also the case for the technology roadmap, which is a prominent and promising technology-planning tool. Many studies have discussed data-driven technology roadmaps using various approaches. However, studies on the dynamic trends in data-driven approaches are lacking. In response, this study collected data-driven roadmap literature and conducted various analyses to identify publication patterns and changes in methodological characteristics. Keywords, networks, topics, and methodology analyses were conducted to provide in-depth implications for data-driven roadmapping. Results indicated that patent analysis still occupies a big seat in data-driven roadmapping. In addition, data-driven roadmapping has changed from business and market analyses to intelligent frameworks for future trend prediction, together with recent deep learning techniques. Apart from simple trend analysis to support decision-making, it has evolved to generate the technology roadmap using generative AI techniques such as generative adversarial network (GAN).}, archive = {J_ACCESS}, author = {Jinhong Kim and Gamunnarbi Park and Myoungkyun Woo and Youngjung Geum}, doi = {10.1109/ACCESS.2025.3526173}, journal = {IEEE Access}, pages = {8297-8309}, shortjournal = {IEEE Access}, title = {How big data has changed technology roadmapping: A review on data-driven roadmapping}, volume = {13}, year = {2025}, }
• (2025). Mask-UnMask regions (MUMR) framework for classifying AMD grades using inter-regional interaction analysis. ACCESS, 13, 8286-8296. (www)

  @article{ , abstract = {Early diagnosis and effective treatment of age-related macular degeneration (AMD), a leading cause of vision impairment, are critically dependent on accurate grading. This paper presents a novel framework, named Mask-UnMask Regions (MUMR), designed to differentiate between normal retina, intermediate AMD, geographic atrophy (GA), and wet AMD using standardized retinal fundus images with an input resolution of $1024 \times 1024$ pixels. The framework initiates with the downscaling of images to a quarter of their original size via a Preserving High-Frequency Information (PHFI) module, which retains key details essential for further analysis. Additionally, we developed a simple, lightweight, yet efficient ResNet-like network for feature extraction and introduced a Region Interaction (RI) module. This module incorporates Adaptive Mask and UnMask Sub-Modules, identifying significant regions while reconstructing less relevant areas using a direction-constrained self-attention mechanism to ensure the learning of global structural cues critical for AMD grade classification. The proposed method was evaluated on a dataset comprising 864 retinal fundus images. Our model consistently outperforms state-of-the-art approaches, achieving mean accuracy, mean F1-score, and mean Cohen’s Kappa of 92.55%, 92.59%, and 89.97%, respectively. In the binary classification task of distinguishing between Non-AMD and AMD cases, the proposed approach also surpasses competing models, achieving mean accuracy, mean F1-score, and mean Cohen’s Kappa of 97.11%, 97.03%, and 94.06%, respectively. Furthermore, statistical analysis of these metrics confirms that the improvements are statistically significant, demonstrating the robustness and improved performance of our proposed framework in AMD grading.}, archive = {J_ACCESS}, author = {Ibrahim Abdelhalim and Namuunaa Nadmid and Mohamed Elsharkawy and Mohammed Ghazal and Ali H. Mahmoud and Ayman El-Baz}, doi = {10.1109/ACCESS.2025.3526948}, journal = {IEEE Access}, pages = {8286-8296}, shortjournal = {IEEE Access}, title = {Mask-UnMask regions (MUMR) framework for classifying AMD grades using inter-regional interaction analysis}, volume = {13}, year = {2025}, }
• (2025). Efficient path planning and formation control in multi-robot systems: A neural fields and auto-switching mechanism approach. ACCESS, 13, 8270-8285. (www)

  @article{ , abstract = {Current implementations of leader-follower systems in multi-robot environments typically separate path planning and formation control, often leading to inefficiencies in execution speed, path quality, and system stability. These systems rely on complex control strategies that struggle to adapt to dynamic and challenging environments, resulting in sub-optimal performance and potential collisions. This paper presents a novel approach to enhancing the path planning and formation control of multi-robot leader-follower systems by integrating neural fields and novel potential field modeling. The primary focus is on developing a unified platform that addresses the challenges of maintaining formation integrity, optimizing path quality, and ensuring real-time responsiveness. Neural Fields, which has gained significant traction in the past few years, utilizes fully connected neural networks to effectively encode continuous signals across various dimensions and resolutions. This growing interest in neural fields has highlighted their potential to provide solutions that are not only more precise and high-fidelity but also highly expressive and efficient in terms of memory usage. Thus, this work proposes leveraging neural fields to achieve high-quality path planning in significantly reduced time. By utilizing the expressiveness and memory efficiency of neural fields, the suggested approach aims to generate optimized paths quickly, making it well-suited for real-time applications where both accuracy and speed are critical. On the other hand, the follower robots are equipped with an auto-switching potential method, which intelligently toggles between attractive forces guiding the followers toward the leader and repulsive forces preventing collisions based on a mathematical model. The effectiveness of this approach is validated through experiments, demonstrating significant improvements in execution speed, path smoothness, and overall system stability compared to competitive methods, including well-known techniques such as A*, Probabilistic RoadMap (PRM), Rapidly-exploring Random Tree Star (RRT*), and also against recent optimization techniques including Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA).}, archive = {J_ACCESS}, author = {Raouf Fareh and Mohammed Baziyad and Tamer Farouk Rabie and Sofiane Khadraoui and Mohammad Habibur Rahman}, doi = {10.1109/ACCESS.2024.3521969}, journal = {IEEE Access}, pages = {8270-8285}, shortjournal = {IEEE Access}, title = {Efficient path planning and formation control in multi-robot systems: A neural fields and auto-switching mechanism approach}, volume = {13}, year = {2025}, }
• (2025). Onboard person retrieval system with model compression: A case study on nvidia jetson orin AGX. ACCESS, 13, 8257-8269. (www)

  @article{ , abstract = {A person retrieval system (PRS) in video surveillance identifies an individual based on descriptive attributes, a task that employs several computationally intensive deep learning models. We implement and analyse a PRS for pre-recorded videos on a graphics processing unit (GPU) and Nvidia Jetson Orin AGX. This paper presents a new Person Attribute Recognition (PAR) architecture, CorPAR, using three backbone networks, ConvNext, ResNet-50, and EfficientNet-B0. It enhances the F1-score by 4.1% with ConvNeXT-Base, 1.63% with the ResNet, and by 8.07% with EfficientNet-B0, surpassing the performance of the state-of-the-art Weighted-PAR method. The proposed method uses model compression techniques like quantisation and pruning with L1 regularisation to assess their impact on person retrieval. The study reveals that the PRS utilising EfficientNet-B0, with 32-bit quantisation, achieves the best performance, delivering a throughput of 22 frames per second and a True Positive Rate of 71% on Nvidia Jetson Orin AGX matching the performance of a model implemented using GPU.}, archive = {J_ACCESS}, author = {Jay N. Chaudhari and Hiren Galiyawala and Paawan Sharma and Pancham Shukla and Mehul S. Raval}, doi = {10.1109/ACCESS.2025.3527134}, journal = {IEEE Access}, pages = {8257-8269}, shortjournal = {IEEE Access}, title = {Onboard person retrieval system with model compression: A case study on nvidia jetson orin AGX}, volume = {13}, year = {2025}, }
• (2025). DStream: An online-based dynamic operator-level query mapping scheme on discrete CPU-GPU architectures. ACCESS, 13, 8239-8256. (www)

  @article{ , abstract = {In streaming systems with a discrete CPU-GPU architecture, leveraging the strengths of both processing units can significantly improve query performance. Existing studies assign entire queries to either the CPU or GPU to reduce data transfer overhead and boost throughput. However, this coarse-grained approach can limit performance for two main reasons. Firstly, PCIe transfer overhead is minimal for small data sizes, and the device preference of each operator within a query may change with variations in data size. Secondly, it neglects performance fluctuations based on the placement location of consecutive operators within the device. To address these issues, we propose dSTREAM, a distributed stream processing system that dynamically maps queries at the operator level on discrete CPU-GPU architectures. dSTREAM adapts to runtime conditions by selecting the optimal device for each operator dynamically. Through dynamic operator-level query mapping without prior knowledge, dSTREAM consistently achieves high performance. Extensive evaluation has confirmed that dSTREAM enhances average throughput by up to 45% and reduces average latency by up to 42.5% across various types of stream SQL queries, regardless of traffic types.}, archive = {J_ACCESS}, author = {Gyeonghwan Jung and Yeonwoo Jeong and Kyuli Park and Dongjae Lee and Hongsu Byun and Suyeon Lee and Sungyong Park}, doi = {10.1109/ACCESS.2024.3510885}, journal = {IEEE Access}, pages = {8239-8256}, shortjournal = {IEEE Access}, title = {DStream: An online-based dynamic operator-level query mapping scheme on discrete CPU-GPU architectures}, volume = {13}, year = {2025}, }
• (2025). Exploring the impact of large language models on disease diagnosis. ACCESS, 13, 8225-8238. (www)

  @article{ , abstract = {The emergence of large language models (LLMs) has revolutionized various fields, including education, finance, marketing, healthcare, and medicine. In this review, we aim to explore the application of LLMs in the healthcare sector, with a specific focus on disease diagnostics. The review highlighted the widespread use of LLMs, such as GPT-4, ChatGPT, GPT-3.5, and LLaMA, with GPT-4 being the most frequently used in disease diagnostics due to its diverse applications, improved accuracy, and efficiency. This review shows that LLMs have utilized a variety of medical data sources, including general medical databases, specialized documents, medical images, and genomic data. Moreover, the focus of these LLMs spans a broad spectrum of healthcare fields, addressing chronic conditions, respiratory diseases, cancer, and rare diseases. The performance evaluation of LLMs involves both qualitative and quantitative measures assessing their diagnostic accuracy. The findings highlight the evolving nature of LLMs in improving diagnostic accuracy.}, archive = {J_ACCESS}, author = {Ibrahim Almubark}, doi = {10.1109/ACCESS.2025.3527025}, journal = {IEEE Access}, pages = {8225-8238}, shortjournal = {IEEE Access}, title = {Exploring the impact of large language models on disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Design of an ultra compact 180-degree ring coupler with harmonic suppression using composite lines for modern RF systems. ACCESS, 13, 8214-8224. (www)

  @article{ , abstract = {This paper presents lumped reactive capacitors and inductors to create a compact filtering hybrid ring coupler (HRC) with a broad suppression band. The traditional 180-degree ring coupler has six long $\lambda $ /4 branches. In the proposed coupler, six compact proposed branches are applied instead of these six long $\lambda $ /4 branches, resulting in size reduction and harmonic suppression. The designed HRC works at 800 MHz and provide 220 MHz operating bandwidth from 690 MHz from 910MHz, which is equal to 27.5% FBW. The designed HRC has acceptable performance in operating frequency and higher frequencies. The designed 180-degree ring hybrid coupler (HRC) achieves a broad stop-band ranging from 1.7 GHz to 5 GHz, effectively rejecting $2^{\mathrm {nd}}$ to $6^{\mathrm {th}}$ harmonic. The proposed HRC is designed, simulated and fabricated, which the measured results validate the simulations. In the pass band, the proposed hybrid coupler exhibits less than 0.2 dB insertion loss, over 26 dB return loss, and 49 dB isolation measured at 800 MHz. Moreover, the conventional 800 MHz HRC occupied $0.5~\lambda \times 0.25~\lambda $ (140.7 mm $\times 70.8$ mm), while size of the proposed HRC is 10.7 mm $\times 6.7$ mm ( $0.038~\lambda \times 0.024~\lambda $ ), which provides 99% size reduction.}, archive = {J_ACCESS}, author = {Saeed Roshani and Farid Zubir and Mohsen Karimi and Muhammad Akmal Chaudhary and Maher Assaad and Fawwaz Hazzazi and Noorlindawaty Md Jizat and Sobhan Roshani}, doi = {10.1109/ACCESS.2025.3527743}, journal = {IEEE Access}, pages = {8214-8224}, shortjournal = {IEEE Access}, title = {Design of an ultra compact 180-degree ring coupler with harmonic suppression using composite lines for modern RF systems}, volume = {13}, year = {2025}, }
• (2025). Tapered CSRR-based sensor for relative permittivity measurement with application to biomedical microfluidic sensing. ACCESS, 13, 8201-8213. (www)

  @article{ , abstract = {This paper proposes miniaturized, lightweight and ultrasensitive planar metamaterial sensor for relative permittivity measurement of nondispersive materials. The proposed sensor is designed using a thin-substrate microstrip line loaded with tapered sectorial Complementary Split Ring Resonator (CSRR). Compared to similar state-of-the-art sensors, the proposed one is at least (25)% more sensitive with a wide dynamic range of the sensing related frequency. Moreover, unlike previously proposed sensors, the relative permittivity of a dielectric sample can be estimated using the variation of the minimum transmission frequency as well as the variation of the 10-dB sensor’s bandwidth which increases the integrity and accuracy of the obtained results. The minimum transmission frequency of the proposed sensor shifts by almost 7.6 GHz with a percentage change of 61% when the relative permittivity of the material under test (MUT) changes from 1 to 10. In addition, the 10-dB bandwidth is reduced by almost 7.7 GHz for the same MUT relative permittivity changes. Experimental measurements are in good agreement with the numerical findings. The paper includes a comprehensive sensitivity analysis that investigates the effect of resonator’s split length as well as its path width on the sensitivity and dynamic range of CSRR based sensors. Finally, the proposed sensor was used for microfluidic sensing to further demonstrate its practicality using different samples with different electrical properties. The sensor was able to provide distinct features for three different eye drops. The proposed sensor can be utilized as an effective permittivity sensor for various sensing applications such as displacement, nondestructive and biomedical sensing.}, archive = {J_ACCESS}, author = {Salem A. Alotaibi and Yepu Cui and Manos M. Tentzeris}, doi = {10.1109/ACCESS.2024.3521431}, journal = {IEEE Access}, pages = {8201-8213}, shortjournal = {IEEE Access}, title = {Tapered CSRR-based sensor for relative permittivity measurement with application to biomedical microfluidic sensing}, volume = {13}, year = {2025}, }
• (2025). EMO-TS: An enhanced multi-objective optimization algorithm for energy-efficient task scheduling in cloud data centers. ACCESS, 13, 8187-8200. (www)

  @article{ , abstract = {The rapid expansion of cloud data centers, driven by the increasing demand for diverse user services, has escalated energy consumption and greenhouse gas emissions, posed severe environmental risks, and increased operational costs. Addressing these challenges requires innovative solutions for optimizing resource allocation without compromising service quality. This paper presents the Enhanced Multi-Objective Optimization Algorithm for Task Scheduling (EMO-TS). This novel approach integrates Deep Reinforcement Learning (DRL) and Enhanced Electric Fish Optimization (EEFO) to create an adaptive, dynamic, and energy-efficient scheduling framework. The primary objective of EMO-TS is to significantly reduce the energy consumption of cloud data centers while maintaining high levels of resource utilization, time efficiency, and service quality. Through the hybrid methodology of DRL and EEFO, EMO-TS dynamically adjusts task scheduling based on real-time workloads and operational conditions, effectively minimizing power consumption without sacrificing system performance. Additionally, EMO-TS introduces improvements in makespan and task execution, ensuring timely completion and optimal resource use. A comprehensive set of experiments and simulations demonstrates the practical implications of EMO-TS’s results. EMO-TS outperforms traditional scheduling approaches, reducing energy consumption by up to 25% and decreasing makespan by 15%. These results underscore the algorithm’s potential to address cloud service providers’ economic and environmental concerns, offering a practical solution for green cloud computing initiatives. Furthermore, the integration of renewable energy sources within the EMO-TS framework shows potential for further reducing the carbon footprint of cloud operations, aligning with global sustainability goals.}, archive = {J_ACCESS}, author = {S. Nambi and P. Thanapal}, doi = {10.1109/ACCESS.2025.3527031}, journal = {IEEE Access}, pages = {8187-8200}, shortjournal = {IEEE Access}, title = {EMO-TS: An enhanced multi-objective optimization algorithm for energy-efficient task scheduling in cloud data centers}, volume = {13}, year = {2025}, }
• (2025). Few-shot object detection in remote sensing: Mitigating label inconsistencies and navigating category variations. ACCESS, 13, 8169-8186. (www)

  @article{ , abstract = {Over recent years, the increasing expansion of remote sensing image (RSI) datasets has made annotation tasks more challenging and labor-intensive, drawing considerable attention toward few-shot object detection (FSOD). Nevertheless, current mainstream FSOD models are primarily designed for natural images and encounter two substantial challenges when applied to RSIs. 1) Inconsistent label assignment for novel instances between pre-training and fine-tuning confuses detectors, leading to diminished generalization performance. 2) Complex scenes within RSIs result in significant category variations, comprising high inter-class similarity and large intra-class variance, which impairs classification accuracy. Against the aforementioned challenges, we propose a novel FSOD approach in RSIs, termed EC-FSOD. Specifically, our approach introduces two key modules: Ensemble Class-free RPN (ECF-RPN) and Contrastive Prototype ETF Classifier (CPEC). The preceding module, ECF-RPN, generates proposals by integrating multiple dissimilar yet cooperative Class-free RPNs that perceive the shape and location of target objects, mitigating the confusion caused by label inconsistencies. Furthermore, the subsequent CPEC module combines two submodules, namely Contrastive Prototype Learning Network (CPLN) and Simplex ETF Classifier (SEC), to obtain a set of representative class prototypes and robust discriminative feature representations, which are employed to overcome the category variations and enhance the generalization performance of novel instances. Extensive experiments have revealed that our approach achieves top-2 results on the DIOR dataset and optimal performance on the NWPU VHR-10.v2 dataset.}, archive = {J_ACCESS}, author = {Tiancheng Si and Shenyu Kong}, doi = {10.1109/ACCESS.2025.3527881}, journal = {IEEE Access}, pages = {8169-8186}, shortjournal = {IEEE Access}, title = {Few-shot object detection in remote sensing: Mitigating label inconsistencies and navigating category variations}, volume = {13}, year = {2025}, }
• (2025). Engaging students in audiovisual coding through interactive MATLAB GUIs. ACCESS, 13, 8158-8168. (www)

  @article{ , abstract = {Traditional educational methods in higher education, such as lectures and hands-on exercises, often struggle to fully engage students or address the complexities inherent in audiovisual signal coding. This gap underscores the need for innovative educational tools that not only foster deeper understanding but also enhance student engagement within the university setting. Effective evaluation of these tools requires comprehensive assessment of both learning outcomes and user experience. This study introduces three novel graphical user interface applications designed to strengthen foundational knowledge in audio, image, and video signal coding. These interactive tools, specifically developed for use in university courses, allow students to experiment with various coding techniques, providing hands-on experience that bridges theoretical concepts and practical application. Furthermore, we propose a robust methodology to evaluate the effectiveness of these tools in improving learning outcomes. The tools, developed using MATLAB, were integrated into a computer vision course and assessed through a combination of pre- and post-tests, the System Usability Scale, and the Evaluation Tool for Learning Quality. Results indicate a significant improvement in student knowledge and positive feedback regarding the usability and educational value of the tools. These findings suggest that the interactive nature of the tools not only enhances knowledge retention but also boosts student engagement, offering a valuable complement to traditional educational methods in higher education.}, archive = {J_ACCESS}, author = {Carlos Cuevas and Carlos Cortés and Narciso García}, doi = {10.1109/ACCESS.2024.3521511}, journal = {IEEE Access}, pages = {8158-8168}, shortjournal = {IEEE Access}, title = {Engaging students in audiovisual coding through interactive MATLAB GUIs}, volume = {13}, year = {2025}, }
• (2025). Filter for traffic congestion prediction: Leveraging traffic control signal actions for dynamic state estimation. ACCESS, 13, 8140-8157. (www)

  @article{ , abstract = {The field of intelligent transportation systems is rapidly evolving, with increasing focus on addressing traffic congestion, a pervasive problem in urban environments. This study contributes to this domain by enhancing traffic prediction models. Traditional traffic models often fall short in accurately predicting traffic flow, particularly in dynamic urban settings. This limitation necessitates the development of more adaptive and accurate predictive models to manage traffic congestion effectively. In urban environments, traffic estimation at multi-intersection nodes and connecting roadis approached using statistical filters (Kalman or particle filters). The challenge lies in accurately predicting the vehicular traffic state at intersections, considering the dynamic nature of urban traffic and potential communication failures. This study introduces a novel approach in traffic modeling using statistical filters. The method involves the use of dynamically interchangeable state transition matrices, aligned with specific traffic signal control actions. This allows for more precise predictions under varying traffic conditions and control scenarios. The effectiveness of the proposed models is validated across multiple intersections in urban settings. The evaluation focuses on the models’ scalability, adaptability to complex traffic networks, and robustness in communication failure scenarios. Our proposed Kalman filter showcased superior performance in traffic prediction with an average RMSE of 12.15975, demonstrating a significant improvement over the average measurement RMSE of 13.996875 and over traditional smooth Kalman filter and traditional particle filter.}, archive = {J_ACCESS}, author = {Younus Hasan Taher and Jit Singh Mandeep and Mohammad Tariqul Islam and Omer Tareq Abdulhae and Ahmed Thair Shakir and Md. Shabiul Islam and Mohamed S. Soliman}, doi = {10.1109/ACCESS.2024.3525119}, journal = {IEEE Access}, pages = {8140-8157}, shortjournal = {IEEE Access}, title = {Filter for traffic congestion prediction: Leveraging traffic control signal actions for dynamic state estimation}, volume = {13}, year = {2025}, }
• (2025). Efficient chlorophyll prediction and sampling in the sea: A real-time approach with UCB-based path planning. ACCESS, 13, 8127-8139. (www)

  @article{ , abstract = {This study focuses on predicting chlorophyll concentration in the sea, which is a key factor influencing fish populations, oxygen production, and carbon balance in marine ecosystems. Traditional methods for measuring chlorophyll involve time-consuming and costly sample collection and laboratory analysis, making real-time monitoring a challenging task. To address these challenges, the research utilizes real-time measurable parameters, such as temperature and salinity, to predict chlorophyll levels. A feature selection method is employed to identify relevant factors, such as wind speed and conductivity, ensuring accurate predictions with minimal uncertainty. In the second phase of the study, an exhaustive search algorithm is combined with reward functions like Upper Confidence Bound (UCB), entropy, and variance reduction. This combination allows for balancing exploration (sampling across the area) and exploitation (focusing on high-chlorophyll regions). The results show that UCB initially sampled from high-chlorophyll areas but gradually shifted towards broader exploration, achieving a balance between exploration and exploitation. Furthermore, the performance of UCB was found to closely match that of entropy and variance reduction in reducing uncertainty.}, archive = {J_ACCESS}, author = {Perihan Karaköse and Cafer Bal}, doi = {10.1109/ACCESS.2024.3524917}, journal = {IEEE Access}, pages = {8127-8139}, shortjournal = {IEEE Access}, title = {Efficient chlorophyll prediction and sampling in the sea: A real-time approach with UCB-based path planning}, volume = {13}, year = {2025}, }
• (2025). Uniform physics informed neural network framework for microgrid and its application in voltage stability analysis. ACCESS, 13, 8110-8126. (www)

  @article{ , abstract = {This paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately extracting the parameters of these models is essential for effectively controlling and optimizing the overall stability of Chongqing power system (CPS). Despite numerous algorithms proposed to tackle this issue, accurately and reliably extracting the parameters of these remains a significant challenge. This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. The PINN difficulty is overcome in UPINN by configuring four strategies: feedback operator, GRU gating mechanisms, transfer operator with historic population, and modification factor with PPO aided reinforcement learning. UPINN models are trained iteratively to maximize parameters and reduce RMSE. UPINN accurately extracts parameters and describes the behavior of PV, wind, and energy storage equipment models as it converges towards optimal solutions through parameter adjustments and RMSE evaluations. The UPINN was implemented for real-time voltage stability monitoring of CPS. The results show that UPINN performs better than other neural network models in respect of accuracy and stability, demonstrating the effectiveness of improved strategies. Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.}, archive = {J_ACCESS}, author = {Renhai Feng and Khan Wajid and Muhammad Faheem and Jiang Wang and Fazal E. Subhan and Muhammad Shoaib Bhutta}, doi = {10.1109/ACCESS.2025.3527047}, journal = {IEEE Access}, pages = {8110-8126}, shortjournal = {IEEE Access}, title = {Uniform physics informed neural network framework for microgrid and its application in voltage stability analysis}, volume = {13}, year = {2025}, }
• (2025). A novel Hierarchical/Decentralized AGC scheme for power systems integrated with large-scale solar power plants. ACCESS, 13, 8092-8109. (www)

  @article{ , abstract = {As the penetration level of large-scale solar power plants (LSSPPs) in transmission systems increases, their contribution to the stability of networks cannot be overlooked. Theoretically, such resources can be considered akin to traditional power plants in preserving network stability. Moreover, diverse frequency regulation resources exert varying levels of system complexity, capacity, and response speed, thereby posing challenges to appropriate performance automatic generation control (AGC). As a remedy, a new hybrid (hierarchical/decentralized) scheme is proposed to improve the performance of traditional AGC mechanisms in the presence of LSSPPs and utilize maximum potential capability to ensure network stability. First, a new method is employed to calculate the spinning reserve for LSSPPs considering the performance of AGC for traditional power plants, the dynamics of the DC-link voltage in LSSPPs, the critical operating point related to the most severe disturbance, and the load model. Following this, the decentralized AGC system works hierarchically and in parallel with the centralized algorithm to regulate the frequency and tie lines exchange power. Furthermore, a simple and accurate index ( $\Delta P_{IPS\_x}$ ) is provided to estimate the amount of active power changes after the disturbance in an interconnected power system (IPS). The simulation results are conducted in IEEE 39-bus and PST-16 test systems using DIgSILENT PowerFactory software. The simulation results verify the efficacy and performance of our proposed scheme to improve the AGC system performance and system stability.}, archive = {J_ACCESS}, author = {Siavash Yari and Masood Mottaghizadeh and Innocent Kamwa and Dmitry Rimorov}, doi = {10.1109/ACCESS.2025.3527920}, journal = {IEEE Access}, pages = {8092-8109}, shortjournal = {IEEE Access}, title = {A novel Hierarchical/Decentralized AGC scheme for power systems integrated with large-scale solar power plants}, volume = {13}, year = {2025}, }
• (2025). Gaussian mixture model-based vector approach to real-time three-dimensional path planning in cluttered environment. ACCESS, 13, 8077-8091. (www)

  @article{ , abstract = {This work presents an obstacle-free three-dimensional (3D) path planning algorithm for unmanned aerial vehicles (UAV) navigating in cluttered environments. Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an obstacle by an ellipsoidal surface defined by its eigenvectors and eigenvalues. The eigenstructure specifies a region occupied by the obstacle so that the path planner re-routes the path whenever the UAV comes closer to this region. The proposed path planner is verified to bypass the challenges and limitations of computationally lightweight path planners such as artificial potential fields. The path planner is also compared with rapidly exploring random tree and shows better performance in optimal path and low computational time. The performance of the proposed path planner is validated in a simulated environment filled with obstacles of various sizes, shapes, and orientations.}, archive = {J_ACCESS}, author = {Abera Tullu and Yunsang Cho and Sangho Ko}, doi = {10.1109/ACCESS.2025.3527123}, journal = {IEEE Access}, pages = {8077-8091}, shortjournal = {IEEE Access}, title = {Gaussian mixture model-based vector approach to real-time three-dimensional path planning in cluttered environment}, volume = {13}, year = {2025}, }
• (2025). Drone-view haze removal via regional saturation-value translation and soft segmentation. ACCESS, 13, 8060-8076. (www)

  @article{ , abstract = {This paper proposes an innovative single image dehazing framework, termed Regional Saturation-Value Translation (RSVT), to address the color distortion problems commonly encountered in bright regions by conventional dehazing approaches. The proposed RSVT framework is developed based on two key insights derived from the HSV color space: first, the hue component shows negligible variation between corresponding hazy and haze-free points; and second, in the 2D saturation-value coordinate system, the majority of lines connecting hazy-clean point pairs tend to converge near the atmospheric light coordinates. Consequently, haze removal can be achieved through appropriate translations within the saturation-value coordinates. Additionally, a robust soft segmentation method that employs a morphological min-max channel is integrated into the framework. By combining the soft segmentation mask with the RSVT prior, a comprehensive single image dehazing framework is established. Experimental evaluations across various datasets demonstrate that the proposed approach effectively mitigates color distortion and successfully restores visually appealing images. Moreover, a case study involving actual flight test demonstrates the feasibility and effectiveness of the proposed approach in real-world scenarios. The code is available at https://github.com/tranleanh/rsvt.}, archive = {J_ACCESS}, author = {Truong-Dong do and Le-Anh Tran and Seokyong Moon and Jio Chung and Ngoc-Phi Nguyen and Sung Kyung Hong}, doi = {10.1109/ACCESS.2024.3524316}, journal = {IEEE Access}, pages = {8060-8076}, shortjournal = {IEEE Access}, title = {Drone-view haze removal via regional saturation-value translation and soft segmentation}, volume = {13}, year = {2025}, }
• (2025). Optimal network reconfiguration and scheduling with hardware-in-the-loop validation for improved microgrid resilience. ACCESS, 13, 8042-8059. (www)

  @article{ , abstract = {With the increased occurrence of various major extreme weather events, power outages and prompt power system restorations have recently drawn more attention to the resilience and recovery of power systems. From the perspective of a more resilient power delivery at the distribution grid, system restoration using network topology reconfiguration together with optimal scheduling of distributed energy resources are adopted in this paper. The proposed optimization model aims at minimizing the total load shedding cost and other operational costs, in which linearized topological constraints borrowed from graph theory and linearized DistFlow models are respectively used to maintain the radial network topology and power flow balance after system contingencies. To demonstrate the applicability of the proposed strategy, a real-world case study of a networked three-microgrid system in Adjuntas, Puerto Rico, is used with the consideration of different independent/interconnected microgrid scenarios, contingencies, and fairness settings. Furthermore, hardware-in-the-loop testing is conducted for the same three-microgrid network, where the closely matched results with the simulated ones have validated the effectiveness of the proposed restoration strategy, which is now ready to move one step forward towards field deployment. Finally, to test the proposed restoration strategy in a larger networked system, the modified IEEE-33 bus test distribution system is considered, and the results show a more resilient power delivery for critical loads under three and four line outages.}, archive = {J_ACCESS}, author = {Yang Chen and Mohammed Olama and Maximiliano F. Ferrari and Guodong Liu and Qingxin Shi and Aditya Sundararajan and Byungkwon Park and Arturo A. Massol-Deya and Thomas B. Ollis}, doi = {10.1109/ACCESS.2025.3527329}, journal = {IEEE Access}, pages = {8042-8059}, shortjournal = {IEEE Access}, title = {Optimal network reconfiguration and scheduling with hardware-in-the-loop validation for improved microgrid resilience}, volume = {13}, year = {2025}, }
• (2025). Wide voltage gain range enhanced cubic boost converter with reduced components voltage stress. ACCESS, 13, 8032-8041. (www)

  @article{ , abstract = {In conventional boost and quadratic boost converters, achieving the required output voltage necessitates a higher duty ratio. Additionally, these converters require switch and load diode ratings equal to the output voltage, which increases switching losses and reduces converter efficiency. This paper introduces an enhanced cubic boost converter (ECBC) to address these drawbacks. The ECBC can achieve a wide voltage gain range, allowing the converter to operate at a lower duty ratio and reducing voltage stress across the components. Consequently, it is possible to select components with lower ratings, thus improving the performance of the ECBC. This paper details the steady-state operation of both continuous and discontinuous conduction modes (CCM and DCM) and examines the effects of parasitic elements on voltage gain and efficiency. The performance of the ECBC is compared with the characteristics of conventional converters. Using the state space averaging technique, the small signal model of ECBC is developed, and the PI controller is designed to improve the dynamic response. An experimental model with an output voltage of 400 V was built to validate the operation and confirm the theoretical analysis with experimental results.}, archive = {J_ACCESS}, author = {Vemparala Seshagiri Rao and M. S. Bhaskar and Mahmoud F. Elmorshedy and Dhafer Almakhles}, doi = {10.1109/ACCESS.2025.3526780}, journal = {IEEE Access}, pages = {8032-8041}, shortjournal = {IEEE Access}, title = {Wide voltage gain range enhanced cubic boost converter with reduced components voltage stress}, volume = {13}, year = {2025}, }
• (2025). Research on the evolution mechanism of partial discharge signals for arcing faults in transformers. ACCESS, 13, 8023-8031. (www)

  @article{ , abstract = {Effective monitoring and identification of partial discharge (PD) signals caused by arcing faults is essential for the prevention of transformer explosions. However, there is still a deficiency in the comprehension of the evolution of PD signals resulting from arcing faults. This study investigated the characteristics of PD signals, including electricity, sound, and pressure, during arc discharge through transformer arc discharge experiments. During the experiment, the ultra-high frequency (UHF) electromagnetic PD signal excited by arc discharge demonstrated good continuity, yet the amplitude of the pulse signal was low. The high-frequency current PD signal merely generates a pulse signal at the instant of arc initiation. The mediate-frequency current PD signal emerges in the form of periodic pulse signals. The amplitude of the low-frequency current PD signal is the highest and shows a continuous periodic pattern. Additionally, arc discharge leads to rapid pressure variations accompanied by audible and ultrasonic signals. The experimental results bridge the gap in the study of the PD signal characteristics during arc discharge in transformers, offering data support for the identification and early warning of transformer arcing faults.}, archive = {J_ACCESS}, author = {Chenlei Han and Keli Gao and Wenzhi Chang and Bo Qi and Jiangang Bi and Shuai Yuan and Meng Huang and Tingyu Gao}, doi = {10.1109/ACCESS.2025.3525953}, journal = {IEEE Access}, pages = {8023-8031}, shortjournal = {IEEE Access}, title = {Research on the evolution mechanism of partial discharge signals for arcing faults in transformers}, volume = {13}, year = {2025}, }
• (2025). Heart disease classification from echocardiogram images using deep learning. ACCESS, 13, 8011-8022. (www)

  @article{ , abstract = {Heart disease remains one of the leading causes of death globally. Echocardiography is a commonly used method to diagnose cardiovascular conditions. However, accurately interpreting echocardiogram images requires specialized cardiovascular knowledge, making it difficult for those who are not expert in this field. This study aimed to address this challenge by developing advanced deep learning models capable of automatically classifying heart disease based on echocardiogram data. A deep learning model, inspired by VGG16, is specifically designed for this task. Following the development phase, the model underwent rigorous evaluation using various metrics such as classification measures, confusion matrices, and confidence tests to assess their performance. Upon analyzing the experimental results, it is observed that the proposed model demonstrated superior performance compared to existing deep learning models, achieving the highest scores in terms of both F1 score and accuracy. The experimental results suggests that the proposed method can be used in classification of cardiovascular diseases, potentially improving the early detection and prognostic.}, archive = {J_ACCESS}, author = {Muhammad Raoof and Muhammad Mahtab and Sohail Masood Bhatti and Muhammad Rashid and Arfan Jaffar}, doi = {10.1109/ACCESS.2024.3524732}, journal = {IEEE Access}, pages = {8011-8022}, shortjournal = {IEEE Access}, title = {Heart disease classification from echocardiogram images using deep learning}, volume = {13}, year = {2025}, }
• (2025). Attention-driven hybrid ensemble approach with bayesian optimization for accurate energy forecasting in jeju island’s renewable energy system. ACCESS, 13, 7986-8010. (www)

  @article{ , abstract = {The rapid integration of renewable energy sources into power grids has created an urgent need for accurate energy demand and supply forecasting models capable of managing the inherent variability of renewable energy generation. The combination of fluctuating consumer demand patterns and high variability across different energy sources presents significant challenges in maintaining a reliable balance between supply and demand. To address these challenges, we propose a Attention-driven Bayesian-Optimized Hybrid Ensemble Framework (ABHEF), evaluated on Jeju Island’s energy mix data. ABHEF integrates state-of-the-art models—ConvBiLSTM (Convolutional Bidirectional Long Short-Term Memory), ETCN (Enhanced Temporal Convolutional Network), TFT (Temporal Fusion Transformer), and DAT (Dual Attention Transformer)—to capture both short-term fluctuations and long-term trends in energy data. The proposed framework is evaluated on actual energy demand and supply data from Jeju Island, along with key weather attributes, thereby enhancing the model’s real-world applicability and accuracy. Bayesian optimization was applied to each model to determine optimal hyperparameters, to ensure the peak predictive performance. The output of the base models was stacked, and four meta-models (Gradient Boosting, LGBM, Ridge, and CatBoost) were applied. Among these, CatBoost demonstrated the best performance and was selected as the final meta-model. For hourly supply prediction, our selected meta-model achieved a 52% reduction in MAE and a 50% reduction in RMSE compared to BiLSTM, the best-performing standalone time-series model, validated through a consistent evaluation of accuracy metrics across all models on the same dataset. For hourly demand predictions, it achieved a 43% reduction in MAE and a 34% reduction in RMSE. For daily supply predictions, it demonstrated a 76% reduction in MAE and a 77% reduction in RMSE, while for daily demand predictions, the reductions were 70% in MAE and 69% in RMSE. These results highlight the superior accuracy of the proposed framework, offering significant benefits for energy management and resource planning in renewable energy systems.}, archive = {J_ACCESS}, author = {Muhammad Ali Iqbal and Joon-Min Gil and Soo Kyun Kim}, doi = {10.1109/ACCESS.2025.3526943}, journal = {IEEE Access}, pages = {7986-8010}, shortjournal = {IEEE Access}, title = {Attention-driven hybrid ensemble approach with bayesian optimization for accurate energy forecasting in jeju island’s renewable energy system}, volume = {13}, year = {2025}, }
• (2025). Improving identification by removing obstructive minutiae from latent fingerprints. ACCESS, 13, 7968-7985. (www)

  @article{ , abstract = {Latent fingerprint identification is essential in forensic science for linking suspects to crime scenes or, conversely, confirming a person’s innocence. However, latent fingerprints often are partial prints with undesirable characteristics such as noise or distortion. Due to these characteristics, identifying the physical details of a latent fingerprint, known as minutiae, is a complex task. Recent publications found that there are minutiae in latent fingerprints that, when removed, increase the matching score. We have defined this type of minutia as obstructive. The importance of obstructive minutiae lies in their ability to increase the identification rate when they are identified and removed. In this work, we propose a new set of features to describe obstructive minutiae in latent fingerprints. Using this set of features, we have built datasets that describe latent fingerprints from which a subset of minutiae has been removed. Additionally, we have evaluated a set of multi-class classifiers trained with our datasets to predict if there are obstructive minutiae in a latent fingerprint. Finally, we developed two novel algorithms to identify and remove, in latent fingerprints, the obstructive minutia that generates the maximum increase in the matching score according to our set of classifiers. We employed Cumulative Match Characteristic (CMC) curves to compare the relative change of identifying an initial latent fingerprint versus a latent fingerprint with the removed obstructive minutia with the maximum increase in the matching score. We achieved an identification rate of 88.2% at Rank 1 using the XGBoost classifier on the NIST SD27 database. Similarly, the Random Forest classifier achieved an identification rate of 73.5% at Rank 1 on the NIST SD302 database.}, archive = {J_ACCESS}, author = {Marcos Daniel Calderón-Calderón and Miguel Angel Medina-Pérez and Raúl Monroy}, doi = {10.1109/ACCESS.2025.3527071}, journal = {IEEE Access}, pages = {7968-7985}, shortjournal = {IEEE Access}, title = {Improving identification by removing obstructive minutiae from latent fingerprints}, volume = {13}, year = {2025}, }
• (2025). Analytical estimation of induced voltage in rotating electrical machines. ACCESS, 13, 7961-7967. (www)

  @article{ , abstract = {Whether for the estimation of circulating current in the parallel circuits of the stator winding, damper current in the rotor of a synchronous machine, or voltage oscillation in the stator winding, an exact calculation of induced voltage is always inevitable. However, even when using FEM software, this voltage estimation is a post-process calculation based on pre-calculated flux densities or vector potentials. Since the air gap flux density is the primer parameter used in analytical estimations, it is therefore desirable to have a general and programmable method for calculating induced voltages regardless of their origin based on the air gap flux density. The rotating Tensor and its projection function offer a suitable alternative for these kinds of estimations. In this paper, the projection function is used to present a general method of estimating induced voltage in rotating machines with different winding topologies. This has been done for integer and fractional multi-layer windings in different machines, as well as the damper bars in the rotor. The results are compared to FEM calculation.}, archive = {J_ACCESS}, author = {Marius Meiswinkel and Amir Ebrahimi and Anjali Biju}, doi = {10.1109/ACCESS.2025.3526371}, journal = {IEEE Access}, pages = {7961-7967}, shortjournal = {IEEE Access}, title = {Analytical estimation of induced voltage in rotating electrical machines}, volume = {13}, year = {2025}, }
• (2025). Investigating the formation of trace metal contamination in insulating oil: From electrical erosion and chemical corrosion perspectives. ACCESS, 13, 7951-7960. (www)

  @article{ , abstract = {The presence of trace metal in transformer oil significantly affects its insulating properties, posing potential risks to the safe operation of transformers. Therefore, understanding the intrinsic mechanisms behind the formation of the trace copper within transformers is of critical importance. In this study, we investigated the two primary forms of trace copper in transformer oil—suspended particulate and soluble ionic states—and conducted detailed experimental analyses to elucidate their formation mechanisms. Through electrical arcing experiments, we examined the behavior of metal contacts in insulating oil, identifying the transfer of surface material during arc interruption as a crucial pathway for the generation of suspended metallic particulates. Additionally, we explored the impact of electrical arcing on the damage mechanisms of the contacts and the resulting microstructural characteristics of the ablated surfaces. Corrosion tests were employed to study the formation mechanisms of metal ions, with both macroscopic and microscopic analyses performed on different corrosion morphologies. Our findings suggest the presence of copper corrosion products and their soluble forms, specifically identifying copper-oleate as a major component, which demonstrates high solubility and diffusion capacity in the oil.}, archive = {J_ACCESS}, author = {Feng Wang and Huimin Zhang and Haocheng Wang and Dilixiati Hayireding and Canguan Gao and Guoliang Zhang and Kaibin Liang and Heng Yi and Zeping Huang and Changhao Hu}, doi = {10.1109/ACCESS.2025.3526970}, journal = {IEEE Access}, pages = {7951-7960}, shortjournal = {IEEE Access}, title = {Investigating the formation of trace metal contamination in insulating oil: From electrical erosion and chemical corrosion perspectives}, volume = {13}, year = {2025}, }
• (2025). Mix-spectrum for generalization in visual reinforcement learning. ACCESS, 13, 7939-7950. (www)

  @article{ , abstract = {Visual Reinforcement Learning (RL) trains agents on policies using images showing the potential for real-world applications. However, the limited diversity in the training environment often results in overfitting with agents underperforming in unseen environments. To address this issue, image augmentation is utilized in visual RL to increase data diversity, but the effectiveness is limited due to the potential to alter the semantic information of the image. Therefore, we introduce Mix-Spectrum, a straightforward yet highly effective frequency-based augmentation method that maintains the semantic consistency of data and enhances the agent’s focus on semantic information. The proposed method combines two existing methods: mixing amplitudes of original and reference images, and Random Convolution. Through this synergistic combination of established methods, our approach not only maintains the advantages of each method but also introduces a novel characteristic that enhances performance. Furthermore, the proposed method stands out for adaptability when integrated with any visual RL algorithm, whether off-policy or on-policy. Through extensive experiments on the DMControl Generalization Benchmark (DMControl-GB) and Procgen, our method demonstrates superior performance compared to existing frequency-based, normalization-based, and image augmentation methods in zero-shot generalization. In DMControl-GB, our method improved by 35.5% over the baseline and 15.2% over the second-best. In Procgen, it achieved 15.2% and 10.1% improvements, respectively.}, archive = {J_ACCESS}, author = {Jeong Woon Lee and Hyoseok Hwang}, doi = {10.1109/ACCESS.2025.3526959}, journal = {IEEE Access}, pages = {7939-7950}, shortjournal = {IEEE Access}, title = {Mix-spectrum for generalization in visual reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Empirical study of far-field radio frequency wireless power transfer testbed for underground mines based on 1.8 GHz. ACCESS, 13, 7923-7938. (www)

  @article{ , abstract = {A novel 1.8 GHz far-field wireless power transfer (WPT) testbed was designed to achieve long-distance energy harvesting in underground mine (UM). Prior experimental studies confirm that underground mines experience 50 dB radio frequency (RF) signal attenuation compared to a typical wireless environment. Due to this severe signal attenuation, the currently proposed radio frequency energy harvesting (RF-EH) testbed cannot be deployed in underground mines. In this study, we proposed, designed and performed an empirical study with a military-grade class A radio frequency wireless power transfer (RF-WPT) testbed. The various digital modulation schemes experimented with included: BPSK, DBPSK, QPSK, OQPSK, Pi4DQPSK, 8PSK, D8PSK,16PSK, QAM16, QAM 64, QAM 256, QAM 1024, 2ASK, 2FSK, 4FSK, 8FSK, 16FSK, and OFDM. With the proposed testbed, we successfully performed long-distance WPT in an underground mine at 20 meters by harvesting 4.2 volts DC with a Powercast P21XXCSR-EVB RF-EH receiver. We are specifically interested in understanding the optimal digital modulation scheme from the perspective of WPT efficiency in real-world deployment scenarios. Based on the outcome of this study, the optimal modulation scheme/s for WPT and wireless information transfer (WIT) in a simultaneous wireless information and power transfer (SWIPT) technology is established and amongst the various digital modulation schemes experimented with, 2ASK, 2FSK, 4FSK, 8FSK, 16 FSK, and Pi4DQPSK exhibited high performance based on the maximum power transfer distance. Therefore, we recommend them as good candidates for WPT in digital wireless communications. The research also compared the impact of using Yagi and omnidirectional antennas. It was observed that the omnidirectional antenna suffers a 5-meter performance loss compared to the Yagi antenna. Additionally, we mimicked a multipath wireless channel by introducing an N directional 15 dB coupler at the transmitter side, it was observed that there is an absence of multipath effect in UM, and hence, antenna diversity and MIMO technology will perform poorly in UM.}, archive = {J_ACCESS}, author = {Anabi Hilary Kelechi and Frimpong Samuel}, doi = {10.1109/ACCESS.2024.3525068}, journal = {IEEE Access}, pages = {7923-7938}, shortjournal = {IEEE Access}, title = {Empirical study of far-field radio frequency wireless power transfer testbed for underground mines based on 1.8 GHz}, volume = {13}, year = {2025}, }
• (2025). From information overload to lucidity: A survey on leveraging GPTs for systematic summarization of medical and biomedical artifacts. ACCESS, 13, 7902-7922. (www)

  @article{ , abstract = {In medical research, the rapid proliferation of condition-specific studies has led to an information overload, making it challenging for researchers and practitioners to stay abreast of the latest findings. This paper presents a comprehensive survey on leveraging Generative Pretrained Transformers (GPTs) to summarize medical and biomedical artifacts systematically. We delve into the current applications of GPTs in this domain, discussing their role in understanding and summarizing research papers, medical dialogues, and medical records. Through a comparative analysis of recent studies and methodologies, we highlight the effectiveness of GPTs in distilling complex medical information into concise, understandable summaries. Our survey underscores the potential of GPTs as a tool for navigating the information overload in medical research and bringing clarity to healthcare professionals. This transformation will enhance patient care and outcomes, such as improving the accessibility and comprehensibility of medical research, assisting in rapid information retrieval, and facilitating the summarization of complex medical studies for broader audiences.}, archive = {J_ACCESS}, author = {Balamurugan Palanisamy and Arjab Chakrabarti and Anushka Singh and Vikas Hassija and G. S. S. Chalapathi and Amit Singh}, doi = {10.1109/ACCESS.2024.3521596}, journal = {IEEE Access}, pages = {7902-7922}, shortjournal = {IEEE Access}, title = {From information overload to lucidity: A survey on leveraging GPTs for systematic summarization of medical and biomedical artifacts}, volume = {13}, year = {2025}, }
• (2025). Special vehicle classification algorithm-based system for dedicated parking zone violation detection in south korea. ACCESS, 13, 7883-7901. (www)

  @article{ , abstract = {To address the problem of managing dedicated parking zones arising from the increasing number of electric vehicles and vehicles for the physically challenged, this paper proposes a license plate recognition (LPR)-based parking control system that combines the YOLO and MobileNet algorithms. These two algorithms are designed for real-time object detection and efficient preprocessing, respectively, and can operate in real time in resource-constrained edge-device environments. In tests using data from more than 51,000 vehicles, the system achieved an accuracy rate of 95.76% in classifying electric vehicles and 97.18% in classifying vehicles for the physically challenged. The average CPU and RAM utilizations of the system were 34.54% and 45.04%, respectively. In addition, the processing time per image was recorded as approximately 1.04 s, demonstrating its potential to run reliably on edge devices. These results are expected to facilitate the efficient resolution of parking management problems in smart cities and effective operation of parking zones reserved for electric vehicles and vehicles for the physically challenged.}, archive = {J_ACCESS}, author = {Hyunseong Park and Kapyeol Kim and Incheol Jeong and Jungil Jung and Jinsoo Cho}, doi = {10.1109/ACCESS.2025.3526862}, journal = {IEEE Access}, pages = {7883-7901}, shortjournal = {IEEE Access}, title = {Special vehicle classification algorithm-based system for dedicated parking zone violation detection in south korea}, volume = {13}, year = {2025}, }
• (2025). Effect of the controllers with control delay on vehicle-guideway coupling vibration for maglev train at standing still. ACCESS, 13, 7872-7882. (www)

  @article{ , abstract = {The commercial operation of maglev trains is significantly affected by vibrations resulting from the coupling between the vehicle and track. Previous research has primarily focused on single electromagnet suspension systems, neglecting the dynamic disparities between these systems and vehicle suspension systems. Furthermore, there is a lack of comprehensive analysis regarding instability mechanisms in the suspension of maglev trains on flexible track beams. To address these gaps, a vertical dynamics model for vehicle-guideway coupling was developed with three levitation frames. Initially, this study examined the impact of controller delays on the divergence rate between the vehicle and track beam when employing a double-loop PID control algorithm. Additionally, it analyzed how implementing a redundant control mode at levitation points influences vibration responses caused by these delays. The findings indicate that control delays have a more pronounced impact on the track beam compared to the vehicle system, making it more susceptible to initial vibration divergence. For instance, instability in the suspension system occurs when the time delay of the single-point suspension controller reaches about 2.146 ms. Moreover, utilizing a redundant control method for levitation points can partially alleviate coupling vibrations resulting from controller delays. For example, the instability of the suspension system can be caused by a controller delay exceeding about 7ms. Importantly, when the first-order vibration frequency of the track beam falls within a specific range and reaches a critical threshold for controller delay, energy supplied to the track beam by the levitation system can surpass its damping dissipation capacity, leading to sustained coupling vibrations.}, archive = {J_ACCESS}, author = {Keren Wang and Weihua Ma and Xiaohao Chen and Ruiming Zou}, doi = {10.1109/ACCESS.2024.3524411}, journal = {IEEE Access}, pages = {7872-7882}, shortjournal = {IEEE Access}, title = {Effect of the controllers with control delay on vehicle-guideway coupling vibration for maglev train at standing still}, volume = {13}, year = {2025}, }
• (2025). Multi-strategy enhanced slime mould algorithm for optimization problems. ACCESS, 13, 7850-7871. (www)

  @article{ , abstract = {The slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and exploitation of the solution space. While SMA is simple and flexible, it faces challenges such as slow convergence and a tendency to become trapped in local optima. To address these limitations, this paper introduces an enhanced algorithm that integrates bloch sphere-based Elite Population Initialization with an adaptive search operator strategy based on cauchy inverse cumulative distribution(QCMSMA). The proposed algorithm employs a Bloch sphere-based elite population initialization strategy, which utilizes quantum state mapping to enhance diversity and incorporates elite selection to guarantee high-quality initial solutions, ultimately improving optimization performance. An adaptive search operator leveraging the Cauchy inverse cumulative distribution is employed to dynamically adjust step sizes, improving exploration and efficiency. Additionally, a local Gaussian perturbation mutation strategy is incorporated to mitigate the risk of premature convergence to local optima. The QCMSMA algorithm was rigorously evaluated using 23 benchmark functions and the CEC2017 test suite. Comparative analysis against several well-known optimization algorithms was performed, accompanied by statistical assessments using the Wilcoxon rank-sum test and Friedman ranking analysis. Specifically, QCMSMA ranked first in overall performance compared to the other algorithms. Experimental results indicate that QCMSMA consistently outperforms its counterparts in terms of optimization efficiency, convergence speed, and stability. Finally, the algorithm was applied to a real-world unmanned aerial vehicle (UAV) path planning problem, where QCMSMA achieved the best optimization performance compared to other algorithms, ranking first in terms of solution quality. This result demonstrates its practical engineering applicability and effectiveness in solving complex optimization tasks.}, archive = {J_ACCESS}, author = {Zaixin Duan and Xuezhong Qian and Wei Song}, doi = {10.1109/ACCESS.2025.3527509}, journal = {IEEE Access}, pages = {7850-7871}, shortjournal = {IEEE Access}, title = {Multi-strategy enhanced slime mould algorithm for optimization problems}, volume = {13}, year = {2025}, }
• (2025). ESO-based direct model-free adaptive predictive compensation control for permanent magnet synchronous motors. ACCESS, 13, 7837-7849. (www)

  @article{ , abstract = {For a class of permanent magnet synchronous motors (PMSM) characterized by strong coupling, significant nonlinearity, load uncertainties, and external disturbances, this paper investigates an extended state observer-based direct model-free adaptive predictive compensation control (ESO-dMFAPCC) method for speed regulation. Initially, a dynamic linearization model of a PMSM system with unknown external disturbances is developed. Subsequently, a novel direct model-free adaptive predictive compensation controller, theoretically equivalent to an ideal controller, is proposed. An extended state observer (ESO) is designed to estimate the state variable, capturing the characteristics of the external disturbance and system uncertainty. On this basis, control gains with online adaptive capabilities are designed to achieve adaptive speed control for the PMSM system. The primary advantage of the proposed control method lies in its novel framework for directly constructing a predictive compensation controller, which simplifies the controller structure design process by reducing it to a parameter-optimization problem. Moreover, utilizing only the PMSM system’s input/output (I/O) data, the proposed ESO-dMFAPCC is purely data-driven and exhibits strong robustness against external disturbances. Furthermore, the stability of the closed-loop PMSM system is rigorously analyzed. Finally, the simulation results demonstrate that, compared to PI control, sliding mode control (SMC), and model-free adaptive predictive control (MFAPC), the proposed method offers superior dynamic performance while significantly enhancing the anti-disturbance capabilities of the PMSM system.}, archive = {J_ACCESS}, author = {Yang Liu and Guangxu Zhou and Lei Guo and Zibo Sun}, doi = {10.1109/ACCESS.2025.3526282}, journal = {IEEE Access}, pages = {7837-7849}, shortjournal = {IEEE Access}, title = {ESO-based direct model-free adaptive predictive compensation control for permanent magnet synchronous motors}, volume = {13}, year = {2025}, }
• (2025). A unified definition of hosting capacity, applications, and review. ACCESS, 13, 7819-7836. (www)

  @article{ , abstract = {Over the past decade, the landscape of distribution network hosting capacity has been extensively explored through numerous studies. However, a notable gap remains in the absence of a standardised and universally accepted generic definition. Bridging this gap, this paper provides a comprehensive and unified definition of the hosting capacity concept for distributed energy resources (DERs). To illustrate the presented definition, a practical demonstration using a basic illustrative example is provided. Additionally, this paper aligns the most impactful papers in the field, as well as select literature articles, with the proposed definition. This comparative analysis showcases the versatility and applicability of the definition across a spectrum of research contributions. This paper seeks to establish a shared foundation for the determination of hosting capacity that can guide future research.}, archive = {J_ACCESS}, author = {Amina Benzerga and Alireza Bahmanyar and Guillaume Derval and Damien Ernst}, doi = {10.1109/ACCESS.2024.3521223}, journal = {IEEE Access}, pages = {7819-7836}, shortjournal = {IEEE Access}, title = {A unified definition of hosting capacity, applications, and review}, volume = {13}, year = {2025}, }
• (2025). A deep learning for optimization and visualization of expressway toll lane management. ACCESS, 13, 7801-7818. (www)

  @article{ , abstract = {Managing the integration of Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) systems is complex, especially during peak hours due to high traffic volumes. While ETC is more efficient, an inappropriate proportion of ETC lanes can increase congestion, making optimal ETC lane selection crucial. This paper proposes a novel framework combining deep learning and multi-objective optimization to improve toll plaza efficiency. A Gated Recurrent Unit (GRU) model with Optuna hyperparameter tuning predicts average queue lengths for various ETC lane proportions. The predictions guide the identification of the ideal ETC lane configuration to minimize queue lengths and balance traffic flow between MTC and ETC lanes. The framework dynamically adjusts ETC lane proportions based on real-time traffic data to ensure optimal lane allocations during peak hours. A multi-objective optimization function is applied to balance key objectives such as minimizing queue length, reducing queue time, lowering operational costs, and optimizing ETC utilization. Simulations with real-world data from high-traffic toll plazas demonstrate the framework’s effectiveness, reducing queue lengths by up to 95.03% during peak hours, decreasing operational costs by 28.72%, and improving overall toll plaza performance. The results are visualized with graphs showing predicted average queue lengths across different ETC lane proportions, aiding stakeholders in understanding the relationship between ETC adoption and congestion. The framework provides insights into operational costs and resource allocation, enhancing financial and operational planning. Validated through extensive simulations with real-time data, this research advances empirical studies with thorough model validation and introduces innovative visualization techniques for toll plaza management.}, archive = {J_ACCESS}, author = {Pattarapon Klaykul and Wilaiporn Lee and Kanabadee Srisomboon and Luepol Pipanmekaporn and Akara Prayote}, doi = {10.1109/ACCESS.2024.3525017}, journal = {IEEE Access}, pages = {7801-7818}, shortjournal = {IEEE Access}, title = {A deep learning for optimization and visualization of expressway toll lane management}, volume = {13}, year = {2025}, }
• (2025). Reconstruction of fan beam X-ray fluorescence computed tomography based on parallel hole collimator via total variation and ordered subsets. ACCESS, 13, 7793-7800. (www)

  @article{ , abstract = {In this paper, an Ordered Subsets Expectation Maximization (OSEM) reconstruction algorithm based on Total Variation (TV) constraint was applied for sparse reconstruction of X-ray fluorescence CT. First, the Geant4 Monte Carlo code was used to simulate the imaging process of fan beam X-ray fluorescence CT imaging system based on parallel hole collimator. Then, the reconstructed image quality of the proposed algorithm with varying numbers of projections was evaluated using RMSE and CNR. Finally, the relationship between the number of subsets of the algorithm and the quality of the reconstructed image and the reconstruction time was explored. The results demonstrated that, compared with the conventional OSEM algorithm, the proposed OSEM algorithm based on Total Variation constraint has higher quality of reconstructed images at different projection numbers, and the reconstruction time of the algorithm decreases with the increase of subset, which achieves the purpose of improving the quality of the reconstructed image and reducing the reconstruction time when sparse reconstruction.}, archive = {J_ACCESS}, author = {Shanghai Jiang and Le Chen and Jie Zhong and Li Ai and Hua Yang and Hong Lu}, doi = {10.1109/ACCESS.2025.3526693}, journal = {IEEE Access}, pages = {7793-7800}, shortjournal = {IEEE Access}, title = {Reconstruction of fan beam X-ray fluorescence computed tomography based on parallel hole collimator via total variation and ordered subsets}, volume = {13}, year = {2025}, }
• (2025). Novel considerations in the ML/AI modeling of large-scale learning loss. ACCESS, 13, 7780-7792. (www)

  @article{ , abstract = {This study is a path forward for the large-scale, data-driven quantitative analysis of noisy open-source data resources. The goal is to support qualitative findings of smaller studies with extensive open-source data-driven analytics in a new way. The study presented in this research focuses on learning interventions. It uses nine publicly accessible datasets to understand and mitigate factors contributing to learning loss and the practical learning recovery measures in Texas public school districts after the recent school closures. The data came from the Census Bureau 2010, USAFACTS, Texas Department of State Health Services (DSHS), the National Center for Education Statistics (CCD), the US Bureau of Labor Statistics (LAUS), and three sources from the Texas Education Agency (STAAR, TEA, ADA, ESSER). We demonstrate a novel data-driven approach to discover insights from an extensive collection of heterogeneous public data sources. For the pandemic school closure period, the mode of instruction and prior score emerged as the primary resilience factors in the learning recovery intervention method. Grade level and census community income level are the most influential factors in predicting learning loss for both Math and Reading. We demonstrate that data-driven unbiased data analysis at a larger scale can offer policymakers an actionable understanding of how to identify learning-loss tendencies and prevent them in public schools.}, archive = {J_ACCESS}, author = {Mirna Elizondo and June Yu and Daniel Payan and LI Feng and Jelena Tešić}, doi = {10.1109/ACCESS.2025.3526412}, journal = {IEEE Access}, pages = {7780-7792}, shortjournal = {IEEE Access}, title = {Novel considerations in the ML/AI modeling of large-scale learning loss}, volume = {13}, year = {2025}, }
• (2025). Advancing aspect-based sentiment analysis in course evaluation: A multi-task learning framework with selective paraphrasing. ACCESS, 13, 7764-7779. (www)

  @article{ , abstract = {Aspect-based sentiment analysis (ABSA) is essential for extracting valuable perspectives from textual data, particularly within educational contexts where understanding student feedback is vital for course evaluation. Multi-task Learning (MTL) models, which concurrently learn several related tasks, have shown promise in enhancing the performance of ABSA by leveraging shared representations. This research explores using MTL with three prominent pre-trained language models (PLMs): MTL-BERT, MTL-RoBERTa, and MTL-XLNet. To further enhance ABSA performance, we integrate a data augmentation method—selective paraphrasing—with the MTL-based PLMs, including SP-MTL-BERT, SP-MTL-RoBERTa, and SP-MTL-XLNet, aimed at enriching the training dataset without compromising the integrity of aspect terms. Additionally, a nuance control mechanism is integrated into the selective paraphrasing process to preserve sentiment intensity and polarity, ensuring semantic consistency and minimizing unintended sentiment drift in the augmented data. However, the scarcity of diverse and comprehensive training data can hinder the effectiveness of these models. For this study, we develop a custom academic dataset by collecting student feedback data on course evaluations comprising more than 11,000 comments from a public university. We conducted experiments on MTL-based models for the two sub-tasks of ABSA, aspect extraction, and sentiment classification, both with and without the integration of selective paraphrasing. The experimental findings indicate that our approach substantially improves performance across all models. Specifically, the SP-MTL-BERT model achieved the highest performance, showing an improvement of +11.0 in aspect recall, +10.8 in aspect F1-score, and +3.7 in sentiment precision, establishing it as the best-performing model in our study. Moreover, comparative analysis with baseline approaches and other data augmentation techniques, such as back translation and easy data augmentation, demonstrates the superior performance of our proposed approach, particularly in improving aspect classification recall and F1 scores.}, archive = {J_ACCESS}, author = {Shahla Gul and Muhammad Asif and Fazal-E- Amin and Kashif Saleem and Muhammad Imran}, doi = {10.1109/ACCESS.2025.3527367}, journal = {IEEE Access}, pages = {7764-7779}, shortjournal = {IEEE Access}, title = {Advancing aspect-based sentiment analysis in course evaluation: A multi-task learning framework with selective paraphrasing}, volume = {13}, year = {2025}, }
• (2025). Effect of number of probes on spherical near-field measurement system. ACCESS, 13, 7757-7763. (www)

  @article{ , abstract = {Multi-probe spherical near-field measurement systems offer significant advantages in antenna characterization but are constrained by a fixed number of probes. Determining the optimal probe count for such systems is challenging, as it directly impacts measurement accuracy, system complexity, and cost-effectiveness. This work explores using a robotic arm to replicate a multi-probe spherical near-field measurement system and demonstrates a method for determining the ideal probe count during system design.}, archive = {J_ACCESS}, author = {Dongming Wei and Junwei Dong and Ravi Kumar Arya and Yida Fan and Shiyuan Kong and Lijuan Li}, doi = {10.1109/ACCESS.2024.3523182}, journal = {IEEE Access}, pages = {7757-7763}, shortjournal = {IEEE Access}, title = {Effect of number of probes on spherical near-field measurement system}, volume = {13}, year = {2025}, }
• (2025). A bayesian inference-based method for uncertainty analysis in raman spectroscopy. ACCESS, 13, 7746-7756. (www)

  @article{ , abstract = {Raman spectroscopy is an important analytical technique with advantages in non-destructive and rapid analysis, and it is widely used in fields such as chemical analysis, materials science, and biomedical research. The integration of Raman spectroscopy with deep learning methods has been shown to produce excellent chemical analysis results. However, challenges arise when neural networks are faced with issues such as environmental noise and the analysis of samples outside the training dataset, where the reliability and interpretability of the prediction results become critical. Bayesian inference allows for the updating of the hypothesis probability of the neural network’s predictions, thereby incorporating additional evidence and information. To address the issue of uncertainty analysis in Raman spectroscopy predictions, this paper proposes a novel uncertainty analysis method based on the combination of neural networks and Bayesian inference, referred to as BayesianVGG. This method achieves accurate sample classification while quantifying the confidence of the model’s output results through Bayesian inference, significantly enhancing the robustness of Raman spectroscopy analysis in complex environments. In experiments on an animal blood Raman spectroscopy dataset (both reflection and transmission modes), the proposed method achieves classification accuracies of 95.36% and 94.83%, respectively, showing slight improvements over other classical machine learning and neural network methods. Furthermore, by generating prediction confidence heatmaps, BayesianVGG effectively addresses the uncertainty analysis of unknown samples, thereby improving the interpretability of the prediction results.}, archive = {J_ACCESS}, author = {Hanxuan Zhou}, doi = {10.1109/ACCESS.2024.3510927}, journal = {IEEE Access}, pages = {7746-7756}, shortjournal = {IEEE Access}, title = {A bayesian inference-based method for uncertainty analysis in raman spectroscopy}, volume = {13}, year = {2025}, }
• (2025). Joint source-channel coding for multi-channel vibrotactile sensors. ACCESS, 13, 7736-7745. (www)

  @article{ , abstract = {Wireless transmission of vibrotactile information holds great promise for immersive Extended Reality (XR) applications, particularly in enabling fine-grained remote manipulation through multi-channel vibrotactile signals. Existing vibrotactile coding solutions rely on conventional source and channel coding techniques to transmit high-quality vibrotactile data over band-limited and error-prone wireless channels. However, these methods often suffer from severe quality degradation due to bit errors and the unrecoverable source coding distortion. This study presents a novel vibrotactile communication scheme based on joint source-channel coding (JSCC) principles. The key advancement of this approach is the replacement of traditional source and channel coding with a joint source-channel vibrotactile coder and transceiver. The proposed coder compresses vibrotactile data using a two-dimensional discrete cosine transform (2D-DCT) without binarization, and the transmitter maps the DCT coefficients directly to transmission symbols. Optimized power allocation is used to avoid the catastrophic quality degradation and to maintain high vibrotactile quality under various wireless channel conditions. Experimental results using up to 30 vibrotactile sensors and 13 gestures show that the proposed scheme consistently outperforms state-of-the-art vibrotactile coding schemes, delivering high-quality vibrotactile signals over a range of wireless channels, from low to high channel signal-to-noise ratios (SNRs). In addition, the proposed scheme achieved approximately 80 % traffic reduction compared to the existing schemes under the same vibrotactile signal quality.}, archive = {J_ACCESS}, author = {Tom Ogura and Shogo Kitamura and Takuya Fujihashi and Shunsuke Saruwatari and Takashi Watanabe}, doi = {10.1109/ACCESS.2025.3526664}, journal = {IEEE Access}, pages = {7736-7745}, shortjournal = {IEEE Access}, title = {Joint source-channel coding for multi-channel vibrotactile sensors}, volume = {13}, year = {2025}, }
• (2025). RMHA-net: Robust optic disc and optic cup segmentation based on residual multiscale feature extraction with hybrid attention networks. ACCESS, 13, 7715-7735. (www)

  @article{ , abstract = {Glaucoma is a chronic eye disease that damages the optic nerve, often leading to permanent vision loss. Early screening with automated technology is crucial to assist ophthalmologists in making accurate diagnoses. One of the key technologies for automated diagnosis is the segmentation of the optic disc (OD) and optic cup (OC). In this paper, RMHA-Net is developed using a residual multiscale feature extractor with a hybrid attention mechanism, which is introduced purposely for automated OD and OC segmentation. This network’s encoder is designed based on advanced convolutional neural network (CNN) blocks that combine dilated convolution, which allows field-of-view expansion by capturing larger-scale features. In addition, the encoder also embeds residual connections to improve the model capacity in extracting low-level features. This design accurately separates the OD and OC from surrounding retinal tissues, handling complex environmental and anatomical changes. The proposed network is further improved by integrating two modules to enhance the segmentation performance: 1) a multiscale feature extractor module to provide various scales contextual information, and 2) dual attention mechanisms through channel-wise and spatial-wise mechanisms so that irrelevant information or noise can be excluded by assigning lesser weights to irrelevant features. To validate RMHA-Net’s effectiveness, extensive experiments were conducted using five public datasets: Drishti-GS, ORIGA, PAPILA, Chaksu, and REFUGE, and one private dataset, Ibn Al-Haitham. The proposed model outperformed seven cutting-edge segmentation models for OD and OC segmentation. The results demonstrate that the network extracts detailed features, offering an efficient framework for future studies.}, archive = {J_ACCESS}, author = {Mohammad J. M. Zedan and Siti Raihanah Abdani and Jaesung Lee and Mohd Asyraf Zulkifley}, doi = {10.1109/ACCESS.2025.3525813}, journal = {IEEE Access}, pages = {7715-7735}, shortjournal = {IEEE Access}, title = {RMHA-net: Robust optic disc and optic cup segmentation based on residual multiscale feature extraction with hybrid attention networks}, volume = {13}, year = {2025}, }
• (2025). Donor impurity related linear and nonlinear optical properties of symmetric and asymmetric double triangular quantum dots. ACCESS, 13, 7705-7714. (www)

  @article{ , abstract = {Linear and third-order nonlinear optical absorption coefficients and relative refractive index changes in symmetric and asymmetric double triangular quantum dots are explored theoretically. The dependence of these optical properties on the presence and position of the impurity is explored. After the computation of energies and wave functions in the framework of the effective mass and parabolic band approaches, analytical formulas for linear and nonlinear optical properties are obtained using the compact density matrix technique and the iterative method. Numerical computations for a typical GaAs/AlGaAs material are presented. The results demonstrate that the donor binding energy substantially depends on the position of the impurity and the geometry of the quantum dot and the presence and position of the donor atom has various impacts on the $\pi $ and $\sigma ^{\pm }$ transitions. In addition, the estimated findings clearly reveal that the resonant frequency and the nonlinear contribution to the optical characteristics are different in symmetric and asymmetric systems. It can be inferred that the hydrogenic donor impurity plays a crucial and important function in the electrical and optical properties of the system and can be used to modify the inter-subband transitions and change the corresponding optical sensitivities.}, archive = {J_ACCESS}, author = {Emre Bahadir Al and Norshamsuri Ali and Syed Alwee Aljunid Syed Junid and Rosdisham Bin Endut and Nor Roshidah Yusof}, doi = {10.1109/ACCESS.2024.3422370}, journal = {IEEE Access}, pages = {7705-7714}, shortjournal = {IEEE Access}, title = {Donor impurity related linear and nonlinear optical properties of symmetric and asymmetric double triangular quantum dots}, volume = {13}, year = {2025}, }
• (2025). Analysis of standards-based counterfeit microelectronics detection methods. ACCESS, 13, 7691-7704. (www)

  @article{ , abstract = {Counterfeiting of microelectronic parts is an ever-growing threat to the reliability and security of electronic systems. A study was conducted to investigate techniques for the detection of counterfeit microelectronic parts. Authentic, remarked, and cloned parts were included in the study. The data presented in this paper is the first collection of quantitative data on counterfeit testing, including the performance of individual test methods. Testing was performed by a test laboratory based on SAE AS6171 methods, and the reported results included qualitative observations and observed counterfeit defects. The study demonstrated that standards-based methods effectively detect remarked and cloned microelectronic parts. Cloned parts are of particular concern as more sophisticated versions contain fewer defects and are at risk of not being detected. Two of the cloned part numbers included in the study were found to have few counterfeit defects, all of which could be corrected by a counterfeiter to avoid detection. Insights into the performance of individual test methods based on the type of counterfeit are presented. These results contribute to the ongoing efforts to mitigate the risks from counterfeit microelectronics by continually improving testing standards and practices, such as the possible need to perform additional testing to ensure cloned parts are detected.}, archive = {J_ACCESS}, author = {Devon R. Richman and Michael H. Azarian and Diganta Das and Michael Pecht}, doi = {10.1109/ACCESS.2025.3526518}, journal = {IEEE Access}, pages = {7691-7704}, shortjournal = {IEEE Access}, title = {Analysis of standards-based counterfeit microelectronics detection methods}, volume = {13}, year = {2025}, }
• (2025). Research on detecting AI-generated forged handwritten signatures via data-efficient image transformers. ACCESS, 13, 7683-7690. (www)

  @article{ , abstract = {Handwritten signature verification, a behavioral physiological biometric, is widely accepted in legal and social practices. However, the advent of artificial intelligence (AI) and its application in handwriting synthesis, known as AI-generated content (AIGC), has introduced a new security threat: AI-generated forged handwritten signatures. To address this challenge, a series of AI-generated forged handwritten signatures was constructed using various font-creation software. A model based on a pre-trained Data-Efficient Image Transformer (DeiT) was proposed to identify forged handwritten signatures. The results show that for AI-generated forged handwritten signatures constructed using Huawei and Baidu font software, the DeiT model achieved a high identification accuracy of nearly 100%. Signatures generated by the Shouji font software also showed a high recognition rate (97.76%). The overall accuracy across all the datasets was 98.46%. Hence, in our experimental states, the DeiT model shows better performance in forged signature identification than other models, including the Vision Transformer (ViT), VGG16, ResNet, Convolutional Neural Networks (CNN), and XGBoost. The DeiT model demonstrated superior performance with AUC values of 100% for the Huawei and Baidu datasets, 99.99% for the combined Huawei and Baidu datasets, 100% for the AI & Handwriting dataset, and 99.72% for the Shouji dataset. These results strongly prove the status of the DeiT model as a state-of-the-art (SOTA) solution for the identification of AI-generated handwritten signature verification.}, archive = {J_ACCESS}, author = {Yanli Hao and Zuyang Zheng}, doi = {10.1109/ACCESS.2025.3525808}, journal = {IEEE Access}, pages = {7683-7690}, shortjournal = {IEEE Access}, title = {Research on detecting AI-generated forged handwritten signatures via data-efficient image transformers}, volume = {13}, year = {2025}, }
• (2025). Ultrathin variable aperture mechanism utilizing a micro flat motor with a multipole ring magnet. ACCESS, 13, 7672-7682. (www)

  @article{ , abstract = {Recent advancements in smartphone camera technology have led to the integration of variable aperture mechanisms to enhance the image quality. This study introduces a 0.8 mm thick variable aperture mechanism driven by a micro flat motor. The motor incorporates an 80 poles ring magnet, magnetized using a laser-assisted local heating method, with an outer diameter of 9.6 mm, inner diameter of 7 mm, and thickness of 0.3 mm, and magnetization width of 0.3 mm. The stator features a two-phase planar micro meander coil with a mechanical angle shift of 2.25°, enabling rotational angle control using Hall sensors and a phase-locked loop. The experimental tests demonstrated that the motor achieved a step response with a target angle of 9° and a settling time of 0.18 s. The aperture mechanism, comprising six polyacetal polymer blades, successfully demonstrated opening and closing operations with the opening diameter changed from 6.1 to 1.4 mm by the motor rotation.}, archive = {J_ACCESS}, author = {Keita Nagai and Riku Fukazawa and Yu Okawara and Haruhiro Komura and Tadahiko Shinshi}, doi = {10.1109/ACCESS.2025.3527146}, journal = {IEEE Access}, pages = {7672-7682}, shortjournal = {IEEE Access}, title = {Ultrathin variable aperture mechanism utilizing a micro flat motor with a multipole ring magnet}, volume = {13}, year = {2025}, }
• (2025). Innovation of urban circular economy growth path based on neural network. ACCESS, 13, 7663-7671. (www)

  @article{ , abstract = {With the aggravation of global ecological and environmental problems, coordinating the development of the relationship between natural resources and seeking sustainable social and economic development has become an important topic of concern to the scientific community today. Economic forecasting serves for economic decision-making, which is to improve the scientific level of economic management, reduce the blindness of economic decision-making and improve the correctness of decision-making. This article constructs the prediction model of urban circular economy development based on artificial neural network (ANN), and puts forward the innovation strategy of urban circular economy development path. Simulation results show that the response speed of this method is 21.55% higher than that of the comparison method, and the accuracy rate reaches 97.78%. Moreover, it has obvious advantages over the traditional algorithm in terms of error and recall rate. Compared with the actual economic data, the economic data predicted by the model is quite consistent, and the prediction of future data by the model basically accords with the development goal of the regional master plan. Therefore, it is feasible to use this method to analyze the development path of urban circular economy.}, archive = {J_ACCESS}, author = {Weifeng Qiu and Yi Yang}, doi = {10.1109/ACCESS.2023.3348415}, journal = {IEEE Access}, pages = {7663-7671}, shortjournal = {IEEE Access}, title = {Innovation of urban circular economy growth path based on neural network}, volume = {13}, year = {2025}, }
• (2025). Always clear days: Degradation type and severity aware all-in-one adverse weather removal. ACCESS, 13, 7650-7662. (www)

  @article{ , abstract = {All-in-one adverse weather removal is an emerging topic on image restoration, which aims to restore multiple weather degradations in a unified model, and the challenges are twofold. First, discover and handle the properties of the multi-domain in the target distribution formed by multiple weather conditions. Second, design efficient and effective operations for different degradations. To resolve this problem, most prior works focus on the multi-domain caused by different weather types. Inspired by inter&intra-domain adaptation literature, we observe that not only weather type but also weather severity introduce multi-domain within each weather type domain, which is ignored by previous methods and further limits their performance. To this end, we propose a degradation type and severity aware model, called UtilityIR, for blind all-in-one bad weather image restoration. To extract weather information from a single image, we propose a novel Marginal Quality Ranking Loss (MQRL) and utilize Contrastive Loss (CL) to guide weather severity and type extraction, and leverage a bag of novel techniques such as Multi-Head Cross Attention (MHCA) and Local-Global Adaptive Instance Normalization (LG-AdaIN) to efficiently restore spatial varying weather degradation. The proposed method can outperform the state-of-the-art methods subjectively and objectively on different weather removal tasks with a large margin, and enjoy fewer model parameters. The proposed method can even restore previously unseen combined multiple degradation images, and modulate restoration level. Implementation code and pre-trained weights will be available at https://github.com/fordevoted/UtilityIR.}, archive = {J_ACCESS}, author = {Yu-Wei Chen and Soo-Chang Pei}, doi = {10.1109/ACCESS.2025.3526168}, journal = {IEEE Access}, pages = {7650-7662}, shortjournal = {IEEE Access}, title = {Always clear days: Degradation type and severity aware all-in-one adverse weather removal}, volume = {13}, year = {2025}, }
• (2025). Hybrid prophet-NAR model for short-term electricity load forecasting. ACCESS, 13, 7637-7649. (www)

  @article{ , abstract = {Electricity load forecasting is crucial for effective energy management, particularly in minimizing energy production and distribution costs. Traditional models like SARIMA and Singular Spectrum Analysis (SSA) have been widely used but often need to capture complex nonlinear patterns and deal with data uncertainties. This study aims to develop and evaluate a hybrid forecasting model that combines the Prophet model with a Neural Network Autoregressive (Prophet-NAR) model, referred to as PropNAR. The objective is to enhance the accuracy of hourly electricity load forecasting in Malaysia by addressing the limitations of existing models. The proposed PropNAR integrated the strengths of the Prophet model in capturing deterministic structures, such as trends, seasonality, and holiday effects with the NAR model’s ability to handle nonlinear stochastic relationships. Additionally, SSA-based bagging is employed to manage data uncertainties, and ensemble techniques are applied to further refine the forecasting accuracy. The hybrid PropNAR model demonstrated significant improvements in forecasting accuracy. Specifically, it reduced Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by approximately 21%-86% compared to the standalone Prophet model. On the Malaysian electricity load datasets, the PropNAR model achieved MAE values ranging from 527.26 to 1023.78, RMSE values from 752.70 to 1498.54, and MAPE values from 1.12% to 2.04%. These results indicate a substantial enhancement over SARIMA and SSA-NAR in handling outliers and data variability. The proposed hybrid PropNAR model offers a robust solution for Malaysian short-term electricity load forecasting, outperforming conventional models in accuracy and reliability.}, archive = {J_ACCESS}, author = {Winita Sulandari and Yudho Yudhanto and Etik Zukhronah and Isnandar Slamet and Hilman Ferdinandus Pardede and Paulo Canas Rodrigues and Muhammad Hisyam Lee}, doi = {10.1109/ACCESS.2025.3526735}, journal = {IEEE Access}, pages = {7637-7649}, shortjournal = {IEEE Access}, title = {Hybrid prophet-NAR model for short-term electricity load forecasting}, volume = {13}, year = {2025}, }
• (2025). Advanced microgrid protection utilizing zero sequence components with hard-ware-in-the-loop testing. ACCESS, 13, 7623-7636. (www)

  @article{ , abstract = {Microgrid protection and ground fault management are critical aspects of modern power distribution systems, especially with the increasing integration of Distributed Generators (DGs) such as renewable energy sources. Effective protection schemes are essential to ensure the reliability, safety, and resilience of microgrids under various fault conditions. This study addresses a new advancement in microgrid protection and ground fault management. Firstly, the research integrates zero sequence components into the time-inverse characteristics of phase Overcurrent Relays (OCR) and creates a dynamic scheme between two group settings for phase and ground faults. This enhancement improves ground fault detection and provides robust backup for ground OCR, thereby enhancing the overall reliability of microgrid protection schemes. Secondly, the study demonstrates the use of Configurable Function Blocks (CFCs) in digital relays to dynamically adjust relay settings based on zero sequence current detection. This functionality optimizes relay performance under varying fault conditions, addressing mis-coordination issues in low-value ground fault scenarios at traditional OCR scheme and improving fault detection and clearance times. The proposed strategy is extensively validated through Hardware-in-the-Loop (HIL) testing, ensuring its feasibility and effectiveness in real-world scenarios. HIL testing confirms the practical applicability and robustness of the proposed protection scheme, enhancing its reliability. Finally, the study provides a comprehensive framework for the implementation of the proposed protection strategy in real-case protective relays. It includes a detailed methodology and validation process, offering practical guidance for operators to implement and optimize microgrid protection systems.}, archive = {J_ACCESS}, author = {Zahra Ghanem and Feras Alasali and Naser El-Naily and Hassen Loukil and Haytham Y. Mustafa and Saad M. Saad and Abdelaziz Salah Saidi and William Holderbaum}, doi = {10.1109/ACCESS.2025.3527023}, journal = {IEEE Access}, pages = {7623-7636}, shortjournal = {IEEE Access}, title = {Advanced microgrid protection utilizing zero sequence components with hard-ware-in-the-loop testing}, volume = {13}, year = {2025}, }
• (2025). Selectivity and efficacy of herbicides applied in pre-emergence in cassava crop. ACCESS, 13, 7614-7622. (www)

  @article{ , abstract = {The presence of weeds at the beginning of cultivation can negatively affect cassava productivity because of the slow initial development of the crop. To minimize weed interference, pre-emergence herbicides can be used, provided they are selective to crops. In this study, we evaluated the selectivity and efficacy of herbicides applied in pre-emergence of cassava crop in weed control. Two experiments were conducted in the field during the years 2017, 2018 and 2019. Each year, ten different herbicides were evaluated, along and untreated controls. In the 2017 efficacy experiment, the mixture of atrazine + s-metolachlor provided the highest rate of weed control at 42 days after application (DAA) (76%). The herbicides sulfentrazone, flumioxazin, clomazone, s-metolachlor and metribuzin caused the lowest poisonings in 2017, with rates below 20% in all evaluation periods. Diuron and metribuzin reduced cassava yield. In the second experiment, only the combinations of ametryn + clomazone and ametryn + flumioxazin maintained control levels greater than 85% in all evaluations. Cassava plants experienced less than 25% poisoning for all herbicides tested. However, the use of linuron and the application of paraquat alone did not result in yield reductions. Regarding the relative productivity of the roots, in 2017, only ametryn + clomazone did not result in reductions, while in 2018, the combination s-metolachlor + clomazone was the only one that did not reduce productivity. The results indicate selective and effective herbicides can help reduce production costs and increase yield in cassava crops.}, archive = {J_ACCESS}, author = {Antonio Gideilson Correia da Silva and Alex Lima Monteiro and Matheus de Freitas Souza and Jesley Nogueira Bandeira and Ester Dos Santos Coelho and Bianca Pietra Santos Diniz and Aurélio Paes Barros and Daniel Valadão Silva}, doi = {10.1109/ACCESS.2025.3526699}, journal = {IEEE Access}, pages = {7614-7622}, shortjournal = {IEEE Access}, title = {Selectivity and efficacy of herbicides applied in pre-emergence in cassava crop}, volume = {13}, year = {2025}, }
• (2025). Optimal containment control for unknown active heterogeneous MASs via model-free recursive reinforcement learning. ACCESS, 13, 7603-7613. (www)

  @article{ , abstract = {The distributed optimal output containment control problem for multi-agent systems (MASs) involves coordinating a group of autonomous agents to drive the outputs of all followers into the convex hull spanned by the outputs of the leaders while optimizing system performance, which has numerous applications. In this paper, a fully distributed optimal containment tracking control protocol is established for unknown active heterogeneous MASs with external disturbances. Firstly, a fully distributed observer is designed to ensure its trajectory stays within the convex hull established by active leaders without requiring global network topology information. Subsequently, an augmented system is constructed using the dynamics of the followers and the observers to design $H_{\infty }$ optimal containment control protocol. Then, a model-free recursive reinforcement learning (RRL) algorithm is devised to learn the optimal control protocol, which demonstrates that the weight iteration error asymptotically converges to zero, and the algorithm exhibits favorable convergence speed. Finally, the effectiveness of the proposed improved algorithm is validated using a heterogeneous nonlinear multi-agent model.}, archive = {J_ACCESS}, author = {Lina Xia and Qing Li and Ruizhuo Song and Gaofu Yang}, doi = {10.1109/ACCESS.2025.3526871}, journal = {IEEE Access}, pages = {7603-7613}, shortjournal = {IEEE Access}, title = {Optimal containment control for unknown active heterogeneous MASs via model-free recursive reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Discrete-time markovian jump linear robust filtering for visual and GPS aided magneto-inertial navigation. ACCESS, 13, 7590-7602. (www)

  @article{ , abstract = {Sensor fusion is a major field in autonomous mobile robots navigation research. These methods integrate information obtained from accelerometers, rate gyros and monocular cameras to provide pose and orientation of the robot, which are known in the literature as Visual-Inertial Simultaneous Localization and Mapping systems. For outdoor navigation, sensor fusion algorithms may also use magnetometers and GPS modules, since in indoor environments and certain urban areas they may suffer from measurements corruption in the presence of ferromagnetic materials and signal occlusion, respectively. To avoid combining corrupted or noisy data, we propose a Robust Kalman Filter (RKF) for Discrete-time Markovian Jump Linear Systems which estimates the position and orientation of the platform considering the best Markovian mode at each instant. The proposed RKF approach reduces the degradation of the filter performance due to parametric uncertainties present in the system models. We present experimental results in comparison with standard and state-of-the-art sensor fusion techniques to show that our system is robust even in challenging conditions.}, archive = {J_ACCESS}, author = {Kenny A. Q. Caldas and Roberto S. Inoue and Marco H. Terra and Vitor Guizilini and Fabio Ramos}, doi = {10.1109/ACCESS.2025.3527449}, journal = {IEEE Access}, pages = {7590-7602}, shortjournal = {IEEE Access}, title = {Discrete-time markovian jump linear robust filtering for visual and GPS aided magneto-inertial navigation}, volume = {13}, year = {2025}, }
• (2025). Transformer-driven inverse learning for AI-powered ceramic material innovation with advanced data preprocessing. ACCESS, 13, 7574-7589. (www)

  @article{ , abstract = {In the advanced landscape of materials science, particularly in the development of ceramic materials, artificial intelligence (AI) emerged as a transformative tool for accelerating innovation. This study proposed a comprehensive analysis of the Transformer-based Inverse Learning model to optimize component and process recommendations. K-Nearest Neighbors (KNN) imputation was first applied, improving data accuracy and completeness to address data gaps. Subsequently, Variational Autoencoders (VAE) were used for data augmentation, enriching the dataset’s diversity. The Transformer model, leveraging this enhanced data, demonstrated strong predictive performance, achieving an R2 score of 0.966 for component analysis and an outstanding R2 score of 0.982 for process analysis in Barium Titanate (BaTiO3) material data. These results show the effectiveness of combining imputation, augmentation, and advanced AI modeling in capturing complex material properties. The study highlights the potential of AI-driven methodologies to significantly improve prediction accuracy in material discovery, offering valuable insights for developing future ceramic materials.}, archive = {J_ACCESS}, author = {Murad Ali Khan and Syed Shehryar Ali Naqvi and Muhammad Faseeh and Do-Hyeun Kim}, doi = {10.1109/ACCESS.2024.3519390}, journal = {IEEE Access}, pages = {7574-7589}, shortjournal = {IEEE Access}, title = {Transformer-driven inverse learning for AI-powered ceramic material innovation with advanced data preprocessing}, volume = {13}, year = {2025}, }
• (2025). Machine learning in money laundering detection over blockchain technology. ACCESS, 13, 7555-7573. (www)

  @article{ , abstract = {Layering through cryptocurrency transactions represents a sophisticated mechanism for laundering money within cybercrime circles. This process methodically merges illegal funds into the legitimate financial system. Blockchain technology plays a crucial role in this integration by facilitating the quick and automated dispersal of assets across various digital wallets and exchanges. Machine learning emerges as a powerful tool for analyzing and identifying illicit transactions within Blockchain networks; however, a significant challenge remains in the form of a gap in advanced pattern recognition algorithms. This paper introduces a novel machine learning-based approach called Value-driven-Transactional tracking Analytics for Crypto compliance (VTAC) for the detection of illegal crypto transactions via Blockchain. The approach combines machine learning algorithms with a pre-training process, normalization, model training, and a de-anonymization process to analyze and identify illicit transactions effectively. Experimental evaluations show VTAC’s capability to detect illegal transactions with a 97.5% accuracy using the XG Boost model, outperforming existing methods with an accuracy of up to 95.9%. Key performance metrics, including precision, recall, and F1-score, consistently exceeded 95%, highlighting VTAC’s enhanced precision and reliability. The proposed solution will serve as an advisory framework to help financial crime investigators enhance the detection and reporting of suspicious cryptocurrency transactions in cyberspace.}, archive = {J_ACCESS}, author = {Algimantas Venčkauskas and Šarūnas Grigaliūnas and Linas Pocius and Rasa Brūzgienė and Andrejs Romanovs}, doi = {10.1109/ACCESS.2024.3452003}, journal = {IEEE Access}, pages = {7555-7573}, shortjournal = {IEEE Access}, title = {Machine learning in money laundering detection over blockchain technology}, volume = {13}, year = {2025}, }
• (2025). Leveraging multilingual transformer for multiclass sentiment analysis in code-mixed data of low-resource languages. ACCESS, 13, 7538-7554. (www)

  @article{ , abstract = {The widespread use of online social media has enabled users to express their thoughts, feelings, opinions, and sentiments in their preferred languages. These diverse perspectives offer valuable insights for data-driven decision-making. While extensive sentiment analysis approaches have been developed for resource-rich languages like English and Chinese, low-resource languages such as Roman Urdu and Roman Punjabi, especially in code-mixed contexts, have been largely neglected due to the lack of datasets and limited research on their unique morphological structures and grammatical complexities. This study aims to present a novel approach for multiclass sentiment analysis of low-resource, code-mixed datasets using multilingual transformers. Specifically, a dataset comprising Roman Urdu, Roman Punjabi, and English comments was collected. After applying traditional natural language preprocessing techniques, transformer-based libraries were used for tokenization and embedding. Subsequently, the Multilingual Bidirectional Encoder Representations from Transformers (mBERT) model was optimized and trained for multiclass sentiment analysis on the code-mixed data. The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. These findings highlight the potential of the proposed approach for sentiment analysis in low-resource, code-mixed languages.}, archive = {J_ACCESS}, author = {Muhammad Kashif Nazir and Cm Nadeem Faisal and Muhammad Asif Habib and Haseeb Ahmad}, doi = {10.1109/ACCESS.2025.3527710}, journal = {IEEE Access}, pages = {7538-7554}, shortjournal = {IEEE Access}, title = {Leveraging multilingual transformer for multiclass sentiment analysis in code-mixed data of low-resource languages}, volume = {13}, year = {2025}, }
• (2025). Advancing sequential manga colorization for AR through data synthesis. ACCESS, 13, 7526-7537. (www)

  @article{ , abstract = {Manga colorization in augmented reality (AR) environments presents unique challenges, particularly when colorizing manga pages captured in photos under various real-world conditions. Testing models in AR settings for manga colorization has been a significant challenge, primarily because of the absence of suitable datasets tailored for this task. To address this, we propose a benchmark for evaluating existing colorization models. We first collected a relatively small dataset of manga book photos taken in settings suitable for AR applications. Then, we developed a method that leverages a pretrained diffusion model to generate synthetic photos from scans of manga pages. Using large datasets of manga scans, we created an extensive synthetic dataset. Combining both real and synthetic data, we established a comprehensive benchmark for manga colorization in AR scenarios. We tested existing models for natural image and manga colorization on this benchmark. As a result, our evaluation showed that current models are not well-suited for AR-based colorization tasks, indicating a need for further improvement.}, archive = {J_ACCESS}, author = {Maksim Golyadkin and Sergey Saraev and Ilya Makarov}, doi = {10.1109/ACCESS.2025.3526882}, journal = {IEEE Access}, pages = {7526-7537}, shortjournal = {IEEE Access}, title = {Advancing sequential manga colorization for AR through data synthesis}, volume = {13}, year = {2025}, }
• (2025). Simulation of crowd evacuation in asymmetrical exit layout based on improved dynamic parameters model. ACCESS, 13, 7512-7525. (www)

  @article{ , abstract = {Evacuation systems are crucial for minimizing casualties and property losses during emergencies. Understanding evacuee behavior in overcrowded situations is essential for developing effective evacuation strategies. However, evacuating large crowds from buildings with multiple exits is a challenging problem, especially when the exits are asymmetrical and the crowds are dense. This paper proposes a novel two-dimensional cellular automaton model for multi-exit evacuation, which simplifies evacuee decision-making in an asymmetrical exit layout within dense crowds. The model introduces the dynamic counting area technique, which dynamically adjusts the size and shape of the counting area around each exit based on the evacuee density level. This technique plays a crucial role in preventing the creation of overlapping counting areas between exits, which often leads to overestimated average evacuation time, unit evacuation time, and travel distance. Comparative analysis with previous dynamic parameter models (DPM) reveals notable results: the model achieved an average evacuation time of 201.20 time steps, a unit evacuation time of 0.50 time steps, and a travel distance of 28204 steps. These findings demonstrate the effectiveness of the improved model in addressing evacuation imbalances caused by asymmetrical exit layouts or evacuee distributions. Moreover, the study highlights the pivotal role of evacuee density around exits in determining exit choices during densely crowded emergency situations. The improved model can be applied to various scenarios and settings where multi-exit evacuation is required, such as stadiums, airports, or shopping malls.}, archive = {J_ACCESS}, author = {Omar Alidmat and Hamza Abu Owida and Umi Kalsom Yusof and Ahmed Almaghthawi and Askar Altalidi and Rami S. Alkhawaldeh and Suhaila Abuowaida and Nawaf Alshdaifat and Jamil AlShaqsi}, doi = {10.1109/ACCESS.2024.3420068}, journal = {IEEE Access}, pages = {7512-7525}, shortjournal = {IEEE Access}, title = {Simulation of crowd evacuation in asymmetrical exit layout based on improved dynamic parameters model}, volume = {13}, year = {2025}, }
• (2025). A fused multiscale pictorial sequence learning mechanism applied to pavement detection. ACCESS, 13, 7502-7511. (www)

  @article{ , abstract = {Considering that most pavement anomaly detection algorithms are difficult to play a stable role in data related to different distributions of pavement anomalies, this paper proposes a pavement anomaly detection algorithm based on multi-scale fusion time series information. The algorithm iteratively extracts autoregressive latent variables at different time series scales to quantify dynamic regularity information. Then iterative feature set is embedded into the pictographic subsequence learning module to rapidly extract most representative subsequence features through deep learning. The static statistical evaluation value is retained as one of the specific data to assist in identifying the sample, and the two angle data complement each other to maximize the feature information entropy. Using the SVM classifier with the global sequence alignment algorithm as the main kernel function, the data classification module detects anomalies, so that the algorithm can mine valuable feature attributes from multiple angles as much as possible while stably exerting its classification function. The experimental results show that the multi-scale fusion features capture more complete information about the temporal trajectory than the single latent variable feature information, suggesting that the proposed algorithm exhibits better comprehensive performance and generalization ability than unimproved machine learning algorithms and typical deep learning classifiers.}, archive = {J_ACCESS}, author = {Zhang Jiazhen and Wang Xiulai and Zhao Shuxu}, doi = {10.1109/ACCESS.2025.3525988}, journal = {IEEE Access}, pages = {7502-7511}, shortjournal = {IEEE Access}, title = {A fused multiscale pictorial sequence learning mechanism applied to pavement detection}, volume = {13}, year = {2025}, }
• (2025). An auto-tuning goal-oriented dynamic feature selection for non-stationary message stream classification using a multi-agent approach. ACCESS, 13, 7483-7501. (www)

  @article{ , abstract = {In an organization’s digital messaging environment, information is exchanged both internally and externally, resulting in a continuous evolution of the message stream at both the conceptual and feature levels. Feature-level changes occur due to the introduction of new attributes or the evolving meaning and relevance of existing features over time. Nevertheless, the prevailing approaches to addressing this issue frequently entail the initialization of a fixed set of parameters, which can be both intricate and expensive. Furthermore, maintaining these parameters in a fixed state over time may result in solutions becoming less adaptable, efficient, or effective as the message stream evolves. This paper introduces a goal-oriented, self-tuning solution for dynamic feature selection, based on approaches from the literature. The proposed solution is applied to the classification of messages within a multi-agent system. The approach employs the iStar modeling language to integrate proactive strategies and adaptive tuning plans, which are then operationalized through the multi-agent system. The effectiveness of the solution is evaluated through test cases and an experimental studies, comparing the results of the dynamic feature selection method from the literature with the variants introduced in this work. In spam detection, the proposed solution demonstrates improvements in several of the quality measures assessed. In news classification, the proposed solution was assessed, a variant was proposed and it demonstrated improved adaptability and effectiveness. This highlights the proposed solution potential for enhanced adaptability in dynamic messaging environments.}, archive = {J_ACCESS}, author = {Wenny Hojas-Mazo and Mailyn Moreno-Espino and Francisco Maciá-Pérez and José Vicente Berná-Martínez and Cornelio Yáñez-Márquez}, doi = {10.1109/ACCESS.2025.3527314}, journal = {IEEE Access}, pages = {7483-7501}, shortjournal = {IEEE Access}, title = {An auto-tuning goal-oriented dynamic feature selection for non-stationary message stream classification using a multi-agent approach}, volume = {13}, year = {2025}, }
• (2025). A novel generic autonomous synchronization method for microgrids. ACCESS, 13, 7462-7482. (www)

  @article{ , abstract = {This research paper proposes a novel generic synchronization method for microgrids that addresses the key limitations of existing approaches. The method enables seamless synchronization and reconnection across multiple microgrid operating scenarios without relying on communication infrastructure or phase-locked loops (PLLs). It can synchronize islanded microgrids with grid-connected systems, interconnect multiple islanded microgrids, and facilitate smooth transitions between operating modes. Notably, the approach maintains system stability and works effectively under both balanced and unbalanced conditions, including short circuits. The proposed technique is implemented at circuit breaker terminals and requires no modifications to the underlying converter control loops. This preserves system stability while enabling plug-and-play capability. The method also provides inherent protection features, automatically disconnecting during faults and re-synchronizing after fault clearance. Comprehensive simulations in PSCAD/EMTDC validate the approach across multiple test systems and operating scenarios. Results demonstrate the method’s effectiveness for synchronization, re-synchronization, and seamless mode transitions in various microgrid configurations including synchronizing multipoles microgrids with the IEEE 14-bus benchmark. The proposed generic synchronization technique represents a significant advancement in microgrid control, enhancing reliability and resiliency without compromising stability or requiring complex communication systems.}, archive = {J_ACCESS}, author = {Hasan K. Alrajhi}, doi = {10.1109/ACCESS.2025.3526638}, journal = {IEEE Access}, pages = {7462-7482}, shortjournal = {IEEE Access}, title = {A novel generic autonomous synchronization method for microgrids}, volume = {13}, year = {2025}, }
• (2025). Residential electrical load forecasting based on a real-time evidential time series prediction method. ACCESS, 13, 7448-7461. (www)

  @article{ , abstract = {Load forecasting is essential for efficient microgrid management, providing key advantages in operational efficiency, cost control, and grid reliability. As microgrids become increasingly critical in the global transition toward decentralized renewable energy systems, accurately predicting load demand is vital for optimizing performance and ensuring a stable, resilient, and sustainable power supply. This study introduces a novel short-term load forecasting approach based on Belief Functions Theory (BFT). The proposed method employs information fusion techniques to combine multiple predictors, each with its own forecasting mechanism. Using lagged power values and weather data, the predictors generate estimated power values along with corresponding uncertainty or error levels. A mass function is assigned to each predictor, taking into account both prediction and error data, even when some information is missing. These mass functions are then merged to produce a final, reliable prediction. Application of this method to publicly available load datasets demonstrates its effectiveness, achieving a 12% reduction in forecasting error compared to state-of-the-art methods and delivering substantial improvements in computational efficiency.}, archive = {J_ACCESS}, author = {M. Mroueh and M. Doumiati and C. Francis and M. Machmoum}, doi = {10.1109/ACCESS.2025.3526578}, journal = {IEEE Access}, pages = {7448-7461}, shortjournal = {IEEE Access}, title = {Residential electrical load forecasting based on a real-time evidential time series prediction method}, volume = {13}, year = {2025}, }
• (2025). A novel LQI-based speed control of switched reluctance motors for high performance applications. ACCESS, 13, 7437-7447. (www)

  @article{ , abstract = {The increasing use of Switched Reluctance Machines (SRMs) across various applications has been driven by their notable advantages and advancements in power electronics. However, controlling SRMs remains inherently complex due to their nonlinear characteristics and parameter variations at different operating points. To address this challenge, this paper proposes the implementation of a Linear-Quadratic-Integral Regulator (LQI) for precise speed control of SRMs. The LQI controller, renowned for its optimal control capabilities, offers several benefits, such as favorable dynamic response, and robustness against disturbances and changing parameters. Unlike traditional controllers, the absence of an inner current control loop results in reduced torque ripple at the phase commutation frequency, simplifying design and implementation. By using PWM for voltage command, torque ripple at the switching frequency is reduced, and noise due to magnetostriction is minimized. A comprehensive design procedure for the proposed LQI speed control scheme is outlined, followed by a comparative analysis with established SRM controllers based on Proportional-Integral (PI) and Hysteresis (HC) controllers. Experimental validation of the proposed controller is provided, illustrating its superior performance across various operating points. The results demonstrate improved dynamic response, enhanced disturbance robustness, reduced torque ripple, a superior switching frequency spectrum, and minimal computational burden for embedded implementation.}, archive = {J_ACCESS}, author = {Paulo Robson M. Costa and Marcelo Vinícius De Paula and Pedro J. Dos Santos Neto and Tárcio André Dos Santos Barros}, doi = {10.1109/ACCESS.2025.3525553}, journal = {IEEE Access}, pages = {7437-7447}, shortjournal = {IEEE Access}, title = {A novel LQI-based speed control of switched reluctance motors for high performance applications}, volume = {13}, year = {2025}, }
• (2025). Sensitivity analysis of biosensor-based SiGe source dual gate tunnel FET having negative capacitance. ACCESS, 13, 7426-7436. (www)

  @article{ , abstract = {This work proposes a unique design of charge plasma based junctionless SiGe source TFET with dual cavity and ferroelectric gate dielectric. The biosensor works on the principle of dielectric modulation for label-free detection, where the cavity lies under the source and gate metal just around the tunneling junction of the dual gate TFET. Nanocavity above the source and gate regions act as reservoir of biomolecules (to be detected) around the tunneling junction, letting the drain current modulate. Cavity dimensions and its placement have been optimized to achieve better current sensitivity. Dual gate architecture with laterally split dielectric is used to overcome the short channel impact and to enhance the current ratio. The biosensor can recognize neutral, positive, and negative charges with the highest drain current sensitivity and ION/IOFF ratio for biomolecule gelatin. It engages the principle of negative capacitance for better subthreshold swing and ON current for identification of biomolecules such as gelatin (k = 12), keratin (k = 8), streptavidin (k = 2.1), bacteriophage T7 (k = 6.3), and APTES (k = 3.57) at low voltage biasing. Finally, RF analyzes are carried out to explore the benefits of using negative capacitance where the biosensor acts as an intrinsic voltage amplifier exhibiting superior gain-bandwidth product (GBP) and cut-off frequency (fT), indicating its potential for high-speed operation and real-time sensing applications. The research yields repeatable outcomes for several calculated analyses.}, archive = {J_ACCESS}, author = {Dipshika Das and Rudra Sankar Dhar and Pradip Kumar Ghosh and Yash Sharma and Amit Banerjee}, doi = {10.1109/ACCESS.2025.3526892}, journal = {IEEE Access}, pages = {7426-7436}, shortjournal = {IEEE Access}, title = {Sensitivity analysis of biosensor-based SiGe source dual gate tunnel FET having negative capacitance}, volume = {13}, year = {2025}, }
• (2025). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. ACCESS, 13, 7407-7425. (www)

  @article{ , abstract = {Non-small cell lung cancer (NSCLC) remains the major contributor to global deaths annually. A slow and long screening and diagnostic process may lower 5-year survival rates. In cancer precision medicine, radiomics and machine learning are emerging as paradigm shifts in complementing cancer experts. However, these solutions usually suffer from small sample sizes, high dimensional radiomics features, and class imbalance issues inherently. As a unified approach to address all the above problems, sparse regularized Autoencoders with Kullback-Leibler (GSRA-KL) divergence capable of augmenting radiomics data alongside inherent dimension reduction are proposed. It incorporates an additional Kullback-Leibler (KL) divergence term to the cost function of generic sparse regularized Autoencoders (GSRA) for preserving input data distribution at the output layer. Experiments for data augmentation improved the sample size to 430 using an 18F-FDG metastasis radiomics dataset of 43 NSCLC patients with known EGFR mutations. In empirical quality evaluation using resemblance and utility dimension metrics, the GSRA-KL-based approach surpassed GSRA while exhibiting competitive performance against the state-of-the-art deep learning approaches. In downstream EGFR gene mutation prediction enhancement, two multilayer perceptron (MLP) models (Model1 and Model2) with the same control setup were trained using augmented data from GSRA and GSRA-KL, respectively. The testing performance difference between Model2 and Model1 was observed as (Accuracy-18%, area under the receiver operating characteristics curve (AUC)-23%, Precision-30%, Recall-5%, and F1-score-17%). Given the ablation-like characteristics of GSRA-KL compared to GSRA, this performance improvement was probably caused directly by the higher-quality augmented data of GSRA-KL. Additionally, GSRA-KL demonstrated competitive performance in gene mutation prediction enhancement compared to deep learning techniques, confirming its efficacy. We expect to effectively identify NSCLC mutations in scenarios like small samples, imbalanced class distributions, and large dimensions for precision medicine treatments and 5-year survival improvements.}, archive = {J_ACCESS}, author = {Muhammad Asif Munir and Reehan Ali Shah and Munwar Ali and Asif Ali Laghari and Ahmad Almadhor and Thippa Reddy Gadekallu}, doi = {10.1109/ACCESS.2024.3523330}, journal = {IEEE Access}, pages = {7407-7425}, shortjournal = {IEEE Access}, title = {Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis}, volume = {13}, year = {2025}, }
• (2025). Cloud and cloud shadow detection for multi-modal imagery with gap-filling applications. ACCESS, 13, 7396-7406. (www)

  @article{ , abstract = {Cloud and cloud shadow (CCS) detection algorithms play a crucial role in the preprocessing of remote sensing data and directly affect the accuracy of subsequent analyses, making them an essential step in most analytical processes. Recent techniques for detecting CCS often employ deep learning methods, which are effective but typically require extensive training data specific to each type of satellite imagery. This study presents a new methodology that applies a model trained on the preconstructed KOMPSAT-3/3A CCS dataset to Landsat-8 and Sentinel-2 satellite imagery. The experimental results demonstrated that the CCS detection model based on KOMPSAT-3/3A achieved a mean F1 score of 0.846 on the test dataset. When applied to Landsat-8 SPARCS and Sentinel-2 CloudSEN12 test datasets, it also showed high performance, with mean F1 scores of 0.741 and 0.8, respectively, effectively indicating that multi-modal CCS detection can be successfully implemented. Applying this model to different sensor imagery confirmed its effectiveness in gap filling, which can be utilized to enhance time-series analyses where continuous monitoring is required. In conclusion, this approach not only proves beneficial for time-series analysis but also significantly reduces the time and effort required to build datasets in deep learning-based CCS detection.}, archive = {J_ACCESS}, author = {Keunhoo Cho and Seongwook Park and Boram Seong and Seongwhan Lee and Jae-Pil Park}, doi = {10.1109/ACCESS.2025.3526956}, journal = {IEEE Access}, pages = {7396-7406}, shortjournal = {IEEE Access}, title = {Cloud and cloud shadow detection for multi-modal imagery with gap-filling applications}, volume = {13}, year = {2025}, }
• (2025). Surveying you only look once (YOLO) multispectral object detection advancements, applications, and challenges. ACCESS, 13, 7366-7395. (www)

  @article{ , abstract = {Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of you only look once (YOLO). Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is red-green-blue (RGB) with long-wave infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. Future research needs to focus on: 1) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications; 2) exploring methods to generate large synthetic multispectral datasets; 3) advancing multispectral YOLO transfer learning techniques to address dataset scarcity; and 4) innovating fusion research with other sensor types beyond RGB and LWIR.}, archive = {J_ACCESS}, author = {James E. Gallagher and Edward J. Oughton}, doi = {10.1109/ACCESS.2025.3526458}, journal = {IEEE Access}, pages = {7366-7395}, shortjournal = {IEEE Access}, title = {Surveying you only look once (YOLO) multispectral object detection advancements, applications, and challenges}, volume = {13}, year = {2025}, }
• (2025). Intelligent vehicle path tracking and stability control method based on extension coordinated control of AFS and DYC. ACCESS, 13, 7353-7365. (www)

  @article{ , abstract = {To enhance vehicle handling stability and address the limitations of active front steering (AFS) in maintaining stability when the tire lateral force becomes saturated, this paper designed a phase-plane-based extension coordinated control system by using direct yaw moment control (DYC). The vehicle driving state is divided into the classical domain, extension domain and non-domain in the extension set, and the correlation function is solved using the extension theory. The value of the correlation function determines the range of the vehicle domain and the control mode. When the vehicle is in the classical domain, the AFS control method alone improves the path-tracking accuracy. When the vehicle is in the extended domain, the AFS and DYC coordinated control method is adopted. When the vehicle is in the non-domain, DYC is used alone to improve stability. The coordination weights between controllers are determined by the correlation function. The simulation results show that the maximum tracking error under double lane change and lane changing conditions is reduced by 27.59% and 55.56%, respectively.}, archive = {J_ACCESS}, author = {Jiankang Lu and Renrui Wang and Changdong Wan}, doi = {10.1109/ACCESS.2025.3527207}, journal = {IEEE Access}, pages = {7353-7365}, shortjournal = {IEEE Access}, title = {Intelligent vehicle path tracking and stability control method based on extension coordinated control of AFS and DYC}, volume = {13}, year = {2025}, }
• (2025). Optimally located subconductors and phases to achieve transmission lines with high natural power and narrow corridor width. ACCESS, 13, 7338-7352. (www)

  @article{ , abstract = {The number and location of subconductors in an overhead transmission line affect its inductance and capacitance, leading to a change in its natural power. Traditionally, for conventional transmission lines around above 300 kV, more than one conductor has been used as bundles for each phase. The subconductors in these bundles have been placed in a symmetrical arrangement in space. Conventional high-natural power lines are designed to have a greater number of subconductors per bundle and a greater bundle radius than a conventional line at the same voltage level. In this paper, new configurations with various numbers of subconductors for unconventional high natural power line designs are studied. The term “unconventional” denotes that the subconductors are not arranged in space in the traditional symmetrical configurations used in the conventional lines and conventional high natural power lines mentioned above. Instead, their optimized locations are found so that their natural power can be further increased. The aim is to enhance the natural power of these new line designs via optimally located subconductor configurations to achieve narrower corridor width as well. Moreover, for these optimally designed transmission lines, 1) corona loss, 2) audible noise, 3) radio and television interferences, 4) location of shield wires, and 5) electric and magnetic fields under these lines are calculated and compared with them for conventional lines. It has been shown that for our newly designed unconventional line with $N=8$ , the SIL achieved is 1414.70 MW, which is a 43% increase compared to that of the conventional line; and the reduced line width of 9.7 m is only 40% of that of the conventional line. Consequently, this leads to the mentioned unconventional line having a natural power density of 3.38 times that of the conventional line.}, archive = {J_ACCESS}, author = {Mushfiqul Abedin Khan and Easir Arafat and Saikat Chowdhury and Mona Ghassemi}, doi = {10.1109/ACCESS.2025.3526890}, journal = {IEEE Access}, pages = {7338-7352}, shortjournal = {IEEE Access}, title = {Optimally located subconductors and phases to achieve transmission lines with high natural power and narrow corridor width}, volume = {13}, year = {2025}, }
• (2025). Full range analysis and application of frozen leg operation for 3-phase DAB converters with open-circuit failure. ACCESS, 13, 7323-7337. (www)

  @article{ , abstract = {The three-phase dual-active-bridge (3p-DAB) converter possesses inherent fault-tolerant capability to address open-circuit failures (OCFs). The frozen leg method tackles OCFs without additional hardware by disabling the two switches in the faulty leg, allowing the 3p-DAB to transfer reduced power, generally by maintaining the original phase shift angle. However, prior literature on the frozen leg method focused only on operation with phase shift angles from 0 to $\pi/3$ . The power transfer characteristics beyond this range, specifically from $\pi/3$ to $\pi/2$ , have not been explored in the literature. To address this gap, this paper derives novel equations to describe the transferred power of a 3p-DAB operating with frozen leg control over the phase shift range of $\pi/3$ to $\pi/2$ . These equations show for the first time that the theoretical maximum power transfer during frozen leg operation is 75.6% of the normal operation maximum power transfer. Further, based on the derived curves, this paper proposes a method to increase power transfer of the frozen leg method by dynamically adjusting the phase shift angle, which would not be possible without the full power transfer curve from 0 to $\pi/2$ . The soft-switching analysis for the proposed method is also presented. The theoretical analysis and proposed method are validated through extensive experimental testing.}, archive = {J_ACCESS}, author = {Cun Wang and Jennifer Bauman}, doi = {10.1109/ACCESS.2025.3527201}, journal = {IEEE Access}, pages = {7323-7337}, shortjournal = {IEEE Access}, title = {Full range analysis and application of frozen leg operation for 3-phase DAB converters with open-circuit failure}, volume = {13}, year = {2025}, }
• (2025). A hybrid optimization approach for improving sensor configuration to enhance nasogastric tube (NGT) localization during insertion and withdrawal. ACCESS, 13, 7310-7322. (www)

  @article{ , abstract = {The increasing usage of passive magnetic field-based localization systems offers real-time, non-invasive tracking solutions for medical devices such as nasogastric tubes (NGTs) within the human body. These systems rely on external magnetic sensors to detect the magnetic field produced by a small permanent magnet attached to the NGT, allowing for accurate, real-time tracking. However, achieving great precision usually necessitates many sensors, increasing computational burden and system complexity. To overcome this issue, we propose a novel approach that reduces the number of sensors needed while retaining accurate localization. By incorporating mathematical models of magnetic dipoles into a Genetic Algorithm (GA), our technology dynamically optimizes the sensor setup, lowering the system’s processing demands while maintaining accuracy. Experiments show that this hybrid approach can help identify optimal sensor combinations using around 33% fewer sensors, while keeping localization accuracy within a 5% range. These results indicate the potential for clinical applications, as fewer sensors reduce system complexity and costs while maintaining good localization performance.}, archive = {J_ACCESS}, author = {M. Souganttika and Shaohui Foong}, doi = {10.1109/ACCESS.2025.3525773}, journal = {IEEE Access}, pages = {7310-7322}, shortjournal = {IEEE Access}, title = {A hybrid optimization approach for improving sensor configuration to enhance nasogastric tube (NGT) localization during insertion and withdrawal}, volume = {13}, year = {2025}, }
• (2025). Artificial flora algorithm-based feature selection with support vector machine for cardiovascular disease classification. ACCESS, 13, 7293-7309. (www)

  @article{ , abstract = {Accurately categorizing medical information is crucial for determining effective cardiac treatment options, especially as the volume of data grows and feature selection becomes increasingly challenging. This work proposes a model to identify the presence of Cardiovascular Disease based on various patient features, aiming to enhance prediction accuracy through a powerful feature selection method. This approach utilizes the Cleveland dataset by combining the Artificial Flora Optimization algorithm with the Support Vector Machine. The proposed algorithm functions as a meticulous gardener, selectively identifying the most significant features for heart disease prediction through an objective function. The model demonstrates impressive performance, achieving an accuracy of 96.63%, specificity of 95.73%, sensitivity of 97.74%, precision of 94.89%, and an F1-score of 96.29%. The model promises high-accuracy heart disease predictions by optimizing feature selection, potentially transforming clinical practice, and advancing research. The novel combination of the proposed technique holds significant potential for improving medical categorization and patient outcomes.}, archive = {J_ACCESS}, author = {M. M. Asha and G. Ramya}, doi = {10.1109/ACCESS.2024.3524577}, journal = {IEEE Access}, pages = {7293-7309}, shortjournal = {IEEE Access}, title = {Artificial flora algorithm-based feature selection with support vector machine for cardiovascular disease classification}, volume = {13}, year = {2025}, }
• (2025). Gas leak real-time detection and volume flow quantification based on infrared imaging and advanced algorithms. ACCESS, 13, 7284-7292. (www)

  @article{ , abstract = {Due to the semi-transparent and irregular nature of gases, it is still a highly challenging task to effectively detect and quantify gas leaks especially those with small flow rates by only utilizing economical equipments. In this paper, we present a strategy for automating real-time identification and quantification of gases in the mid-infrared band by combining an infrared camera combined with a series optimized algorithms. A basic network DeepLabV3+ is first modified by replacing its Xception backbone with MobileNetv2 for real-time gas detection and segmentation. Then special attention mechanisms tailored to the characteristics of the gas are added into the network to enhance the perception and recognition of the gas edges. The optimized Kmeans clustering algorithm is integrated to identify the Region of Interest (ROI) in the image containing the target gas. The quantification of the volume flow rate within the ROI is realized by integrating the radiation transfer model with the optical flow method. The experimental results indicate that the quantification limit of the gas flow rate can reach 0.01 L/min, which is comparable to that obtained by the methods with complicated instruments. Our detection and quantification strategy can find vast applications in hazardous gas monitoring field.}, archive = {J_ACCESS}, author = {Man Yan and Zhou Li and Zheng Dong and Yiming Liun and Liyun Chen and Xiaosong Wu and Lijun Wu}, doi = {10.1109/ACCESS.2025.3525764}, journal = {IEEE Access}, pages = {7284-7292}, shortjournal = {IEEE Access}, title = {Gas leak real-time detection and volume flow quantification based on infrared imaging and advanced algorithms}, volume = {13}, year = {2025}, }
• (2025). Knowledge graph as pre-training corpus for structural reasoning via multi-hop linearization. ACCESS, 13, 7273-7283. (www)

  @article{ , abstract = {Large language models have demonstrated exceptional performance across various natural language processing tasks. However, their reliance on unstructured text corpora for pre-training limits their effectiveness in tasks requiring structured reasoning such as multi-hop question-answering. Knowledge Graphs provide a rich, structured source of relational data, offering an opportunity to enhance the reasoning capabilities of Large language models. In this paper, we propose a novel framework, Knowledge Graph as Pre-training Corpus (KGPC), which transforms knowledge graphs into text using a multi-hop linearization process. Unlike existing approaches that linearize singular triples, our method captures the interconnected nature of knowledge graphs by linking multiple triples across multiple hops, preserving their relational structure during the pre-training phase. This structured knowledge injection improves language models to perform complex reasoning tasks. We evaluate our approach on multi-hop reasoning benchmarks, demonstrating significant performance gains over existing models, particularly in question-answering tasks. Our results highlight the potential of multi-hop linearization in enhancing the structural reasoning capacity of language models, reducing error propagation, and improving the integration of structured knowledge into language models.}, archive = {J_ACCESS}, author = {Wooyoung Kim and Haemin Jung and Wooju Kim}, doi = {10.1109/ACCESS.2024.3523579}, journal = {IEEE Access}, pages = {7273-7283}, shortjournal = {IEEE Access}, title = {Knowledge graph as pre-training corpus for structural reasoning via multi-hop linearization}, volume = {13}, year = {2025}, }
• (2025). GGLA-NeXtE2NET: A dual-branch ensemble network with gated global-local attention for enhanced brain tumor recognition. ACCESS, 13, 7234-7257. (www)

  @article{ , abstract = {Due to the limited availability of training data, the diverse shapes of brain tumors among different patients, inter-class similarity, and intra-class variation, achieving high recognition accuracy and speed in deep learning-based brain tumor recognition remains challenging. To address these issues, we propose a Dual-Branch Ensemble and Gated Global-Local Attention network based on EfficientNetV2S and ConvNeXt (GGLA-NeXtE2NET) to improve identification accuracy and model interpretability. For inter-class and intra-class problems, we designed a Gated Global-Local Attention (GGLA) mechanism that captures dependency information of query points in both horizontal and vertical directions, thereby obtaining global information indirectly. Simultaneously, local information is captured through multiple convolutions with a gating layer. The gating mechanism within the GGLA dynamically balances the contributions of global and local information, enabling the model to adaptively focus on the most relevant features for accurate classification. Furthermore, we introduce a dual-branch ensemble network to address the issue of image variety. This network uses two branches to extract image features at different resolutions for fusion, thereby expanding the network receptive field. Additionally, we utilized an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to generate images that balance MRI data and implemented multiple preprocessing techniques to tackle inherent noise in MRI images. These techniques enhance the clarity of MRI images while preserving essential details. This results in a clear improvement in the identification of tumor boundaries, crucial for accurate surgical planning and treatment strategies. We evaluated GGLA-NeXtE2NET on 3-class and 4-class brain tumor datasets and achieved 99.06%, and 99.62% overall accuracy on both datasets respectively.}, archive = {J_ACCESS}, author = {Adnan Saeed and Khurram Shehzad and Shahzad Sarwar Bhatti and Saim Ahmed and Ahmad Taher Azar}, doi = {10.1109/ACCESS.2025.3525518}, journal = {IEEE Access}, pages = {7234-7257}, shortjournal = {IEEE Access}, title = {GGLA-NeXtE2NET: A dual-branch ensemble network with gated global-local attention for enhanced brain tumor recognition}, volume = {13}, year = {2025}, }
• (2025). Detailed analysis of a regenerative active clamping snubber for a phase-shifted converter. ACCESS, 13, 7223-7233. (www)

  @article{ , abstract = {The phase-shifted converter (PSFB) is one of the most used converters for applications where high power and galvanic isolation are required. To achieve zero voltage switching (ZVS) on the primary side these converters use the leakage inductance of the main transformer and the parasitic capacitances of the primary bridge MOSFET transistors. The higher this inductance is, the lower the output power that ensures ZVS. However, the resonant inductor, together with the parasitic capacitances of the secondary bridge and the transformer’s secondary winding, induce ringing and high voltage spikes at the secondary bridge output. To mitigate this problem snubber circuits are used. These converters’ maximum power depends on the energy stored in leakage inductances and parasitic capacitances, which relate to the choice of the snubber circuits used. The present paper analyzes in detail the behavior of an active clamp circuit. It determines the conditions for zero voltage turn ON of the snubber transistor thus improving the snubber’s efficiency and of the entire phase-shifted converter. All the results are validated through simulation and experimental models.}, archive = {J_ACCESS}, author = {Dorin Petreus and Toma Patarau}, doi = {10.1109/ACCESS.2025.3526210}, journal = {IEEE Access}, pages = {7223-7233}, shortjournal = {IEEE Access}, title = {Detailed analysis of a regenerative active clamping snubber for a phase-shifted converter}, volume = {13}, year = {2025}, }
• (2025). Variable subarray beamforming in low earth orbit satellite feeder link MIMO. ACCESS, 13, 7208-7222. (www)

  @article{ , abstract = {In the future low earth orbit (LEO) satellite constellation system in the 6G era, it is essential to increase the feeder link capacity. MIMO (Multiple-Input Multiple-Output) transmission is attracting attention as a method to increase the capacity. In the case of MIMO transmission in LEO satellite feeder links, the channel transmission capacity degradation due to satellite movement and the degradation of transmission quality due to an increase in the peak-to-average power ratio (PAPR) during beamforming at the satellite side are issues that need to be resolved. This paper proposes variable subarray beamforming (VS-BF) for MIMO transmission in LEO satellite feeder links, which reduces transmission quality degradation due to PAPR and improves capacity. This paper shows that the proposed method improves channel capacity by a factor of 2 compared to the conventional method with fixed subarrays and reduces the required output backoff by 2 dB achieving BER $= 10^{-4}$ compared to full array beamforming by computer simulations.}, archive = {J_ACCESS}, author = {Koyo Tategami and Daisuke Goto and Kiyohiko Itokawa and Fumihiro Yamashita}, doi = {10.1109/ACCESS.2025.3526964}, journal = {IEEE Access}, pages = {7208-7222}, shortjournal = {IEEE Access}, title = {Variable subarray beamforming in low earth orbit satellite feeder link MIMO}, volume = {13}, year = {2025}, }
• (2025). CDGFD: Cross-domain generalization in ethnic fashion design using LLMs and GANs: A symbolic and geometric approach. ACCESS, 13, 7192-7207. (www)

  @article{ , abstract = {In this paper, we propose a novel framework that leverages Large Language Models (LLMs) and Generative Adversarial Networks (GANs) to address the challenges of cross-domain generalization in ethnic fashion design. By introducing the concept of “Digital Cousins,” our approach generates culturally rich fashion designs that maintain both the symbolic integrity and geometric consistency of traditional ethnic garments. Specifically, we explore how LLMs can effectively encode the complex semantic relationships in cultural symbols and how GANs can transform these embeddings into visually coherent and geometrically plausible designs. Unlike traditional digital twin models, which require exact replication, our method allows for symbolic and geometric variations while preserving core cultural values. Our contributions are threefold: First, we propose a mathematical framework for mapping cultural symbols to a multi-dimensional semantic space using LLMs, ensuring the symbolic accuracy of generated designs. Second, we present a method to generate “Digital Cousins” of ethnic garments by employing GANs to introduce geometric transformations, enabling cross-cultural and cross-contextual adaptation. Finally, we demonstrate the robustness of our method in a series of experiments, including real-to-sim and sim-to-real tests, where we evaluate the generalization capabilities of the generated designs across different cultural contexts. Our results show that the proposed approach achieves higher cross-domain performance compared to traditional methods, while maintaining cultural authenticity. Experimental results show that our approach achieves a 15% improvement in cultural fidelity and a 20% enhancement in geometric adaptability compared to traditional methods. These findings suggest significant potential for AI-driven innovation in ethnic fashion design, with applications in both cultural preservation and modern fashion industries.}, archive = {J_ACCESS}, author = {Meizhen Deng and Ling Chen}, doi = {10.1109/ACCESS.2024.3524444}, journal = {IEEE Access}, pages = {7192-7207}, shortjournal = {IEEE Access}, title = {CDGFD: Cross-domain generalization in ethnic fashion design using LLMs and GANs: A symbolic and geometric approach}, volume = {13}, year = {2025}, }
• (2025). Electricity theft detection using machine learning in traditional meter postpaid residential customers: A case study on state electricity company (PLN) indonesia. ACCESS, 13, 7167-7191. (www)

  @article{ , abstract = {Electricity theft is a major challenge for PT PLN (Persero), particularly in managing 27 million postpaid customers, most of whom still use traditional meters. Detecting and addressing electricity theft has become increasingly complex, requiring more efficient approaches. Unlike smart meters, traditional meters lack communication capabilities, making detection reliant on manual processes. This research develops a machine learning model to optimize the Target Operation (TO) process. TO is a list of customers targeted for on-site verification due to suspected electricity theft. This study focuses on optimizing the formation of TO by analyzing monthly electricity usage, particularly in the 450 VA household segment receiving government subsidies. The model aims to reduce reliance on subjective manual observations while ensuring proper subsidy allocation. Various classification models, including Decision Tree, Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, and Deep Neural Network, were evaluated, with Random Forest achieving the best performance across simulations. A sequential evaluation method is introduced to enhance accuracy through layered filtering, where detection results from the three-theft model are further filtered using the two-theft and one-theft models, resulting in a more precise TO. The combination of Random Forest and K-Nearest Neighbors achieved the highest performance, with an accuracy of 0.89, precision of 0.83, recall of 0.98, F1-Score of 0.90, and AUC of 0.89. These findings demonstrate the model’s effectiveness in delivering reliable TO recommendations, supporting PLN’s operational strategies, and offering practical benefits through a more objective, standardized TO process that minimizes human error and improves efficiency.}, archive = {J_ACCESS}, author = {Alief Pascal Taruna and Galih Arisona and Dwi Irwanto and Arif Bijak Bestari and Wildan Juniawan}, doi = {10.1109/ACCESS.2025.3526764}, journal = {IEEE Access}, pages = {7167-7191}, shortjournal = {IEEE Access}, title = {Electricity theft detection using machine learning in traditional meter postpaid residential customers: A case study on state electricity company (PLN) indonesia}, volume = {13}, year = {2025}, }
• (2025). On the stability of the kubernetes horizontal autoscaler control loop. ACCESS, 13, 7160-7166. (www)

  @article{ , abstract = {Kubernetes is a widely used platform for deploying and managing containerized applications due to its efficient elastic capabilities. The Horizontal Pod Autoscaler (HPA) in Kubernetes independently adjusts the number of pods for each service, yet these services often operate in an interconnected manner. This study aims to understand the effects of autoscaling events on a graph of interconnected services. To achieve this, we apply control theory to model the HPA’s behavior. We analyze the stability of this model, perform numerical simulations, and deploy a real testbed to evaluate the performance. Our findings demonstrate that the control theory-based model accurately predicts the HPA’s behavior, ensuring system stability with CPU utilization meeting desired thresholds and no traffic loss after a transitional period. The model provides insights into optimizing resource scheduling and improving application performance in Kubernetes environments. Additionally, we extend our model to the whole service graph to understand how individual scaling decisions influence the complex graphs of cloud applications.}, archive = {J_ACCESS}, author = {Berta Serracanta and Andor Lukács and Alberto Rodriguez-Natal and Albert Cabellos and Gábor Rétvári}, doi = {10.1109/ACCESS.2025.3526751}, journal = {IEEE Access}, pages = {7160-7166}, shortjournal = {IEEE Access}, title = {On the stability of the kubernetes horizontal autoscaler control loop}, volume = {13}, year = {2025}, }
• (2025). Fuzzy enhanced kidney tumor detection: Integrating machine learning operations for a fusion of twin transferable network and weighted ensemble machine learning classifier. ACCESS, 13, 7135-7159. (www)

  @article{ , abstract = {Kidney tumors, often asymptomatic, can lead to serious health problems if left undiagnosed. This study tackles the crucial issue of kidney tumor detection using CT scans. The proposed approach leverages the power of image enhancement using fuzzy systems, deep learning, and machine learning for automated kidney tumor detection in CT images. The study proposes a fuzzy inference system to enhance kidney CT image contrast. This system analyzes image data and uses fuzzy logic to adjust pixel intensities, aiming to improve the distinction between features in the image without creating over-enhancement. Two pre-trained deep convolutional neural networks (PT-DCNNs), DenseNet121 and ResNet101, are used to extract features from the enhanced CT images. These features capture essential characteristics that differentiate between normal and tumor-containing scans. Combining features from twin PT-DCNNs (ensemble approach) creates a richer representation of the image content. The informative features are fed into a combined classifier where Support Vector Machines and Random Forests are combined using a weighted average to achieve the final and potentially more robust classification of kidney tumors. To improve training, we amplified the original dataset by creating variations with added noise and artificial modifications to simulate real-world image imperfections. The integration of Machine Learning Operations practices ensures the scalability, reproducibility, and clinical deployment of the system. The model achieved an impressive accuracy of 99.2% on high-quality images and 98.5% on noisy images, surpassing traditional methods. This automated approach can assist urologists in confirming the presence of kidney tumors, minimizing human error during physical inspection and potentially leading to improved patient outcomes.}, archive = {J_ACCESS}, author = {Ananya Ghosh and Jyotismita Chaki}, doi = {10.1109/ACCESS.2025.3526272}, journal = {IEEE Access}, pages = {7135-7159}, shortjournal = {IEEE Access}, title = {Fuzzy enhanced kidney tumor detection: Integrating machine learning operations for a fusion of twin transferable network and weighted ensemble machine learning classifier}, volume = {13}, year = {2025}, }
• (2025). Artificial intelligence in dyslexia research and education: A scoping review. ACCESS, 13, 7123-7134. (www)

  @article{ , abstract = {Advancements in artificial intelligence have enabled its integration into research and educational settings, particularly in supporting students with dyslexia. This scoping review explores the transformative role of artificial intelligence in dyslexia research and education, outlining its applications, opportunities, and challenges. A search across Google Scholar, Scopus, Web of Science, PsycINFO, and ERIC yielded 683 unique articles, of which 84 met the inclusion criteria. The findings reveal four primary applications of AI in dyslexia education and research: early detection and diagnosis, personalised learning and interventions, speech and language processing as well as brain imaging and neuroimaging analysis. Additionally, five significant challenges were identified: data quality and quantity, accessibility of artificial intelligence-powered support, bias and validity in assessments, balancing artificial intelligence assistance with human expertise, and transparency in artificial intelligence decision-making. Actionable steps are recommended to enhance dyslexia research and education to address these challenges. Key approaches include improving artificial intelligence tools for early detection, ensuring inclusivity in affordable systems, addressing ethical concerns, fostering interdisciplinary collaboration, training educators and developing culturally relevant tools. Future research should focus on the creation of tools for non-English-speaking and multicultural contexts, advancing research in neuroimaging and personalised learning, longitudinal studies on artificial intelligence interventions, and the establishment of ethical frameworks for the responsible deployment of artificial intelligence in dyslexia education. By navigating these complexities of artificial intelligence integration, we can unlock its full potential to revolutionise dyslexia education and create more inclusive, effective, and ethical learning environments for all.}, archive = {J_ACCESS}, author = {Jia Rong Yap and Thirishankari Aruthanan and Mellisa Chin}, doi = {10.1109/ACCESS.2025.3526189}, journal = {IEEE Access}, pages = {7123-7134}, shortjournal = {IEEE Access}, title = {Artificial intelligence in dyslexia research and education: A scoping review}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of two-port self-decoupled highly efficient MIMO antenna for 5G new radio mid-band (n1/n2/n3) wireless communication. ACCESS, 13, 7110-7122. (www)

  @article{ , abstract = {In this communication, dual port, self-decoupled, highly efficient multiple-input- multiple-output (MIMO) antenna for 5G new radio mid-band wireless communication is reported. The proposed MIMO antenna have a total foot print area of $0.82\lambda _{0}$ mm $_{\mathrm {x}}~0.41\lambda _{0}$ mm x $0.0057\lambda _{0}$ mm, where $\lambda _{0}$ is free space wavelength considering 2.05GHz frequency. In this antenna configuration, single antenna unit is made up of optimized L-shape rectangular patch and ground loaded with defective ground structure (DGS) slot, printed on Rogers 5880 double-sided copper coated substrate with relative permittivity of 2.2. Antenna design is validated using characteristic mode analysis. To achieve antenna diversity, single unit is replicated and converted into two-port MIMO. Further, to show the effectiveness of antenna orientation on inter- element isolation two ports are placed in 0°, 90° and 180° orientation. The proposed structure provides 98.8% average radiation efficiency along with 4.1dBi peak gain in the direction of broadside radiation over the operating frequency. Mutual coupling between the antenna elements is discussed using surface current distribution and surface wave propagation. The maximum isolation is -41.9dB between ports, <0.008 envelope coefficient correlation, 9.99dB diversity gain, <0.4bits/s/Hz channel capacity loss, less than -1dB multiplexing efficiency, -31.2dB TARC and zero mean effective gain (MEG1/MEG2) ratio are some of the salient features of the proposed design. In order to verify the simulated results, the prototype is fabricated and the measurements are carried out using vector network analyzer and anechoic chamber. Good coherence between experimental and simulation results were found.}, archive = {J_ACCESS}, author = {Durai Pandurangan and Naveen Mishra}, doi = {10.1109/ACCESS.2025.3526276}, journal = {IEEE Access}, pages = {7110-7122}, shortjournal = {IEEE Access}, title = {Design and analysis of two-port self-decoupled highly efficient MIMO antenna for 5G new radio mid-band (n1/n2/n3) wireless communication}, volume = {13}, year = {2025}, }
• (2025). Performance evaluation of a novel sequence-based directional detection strategy for protection of active distribution networks. ACCESS, 13, 7094-7109. (www)

  @article{ , abstract = {Directional elements are relied on to achieve selectivity in fault detection in power systems. Although such elements have been deployed successfully for many years, there is an increased need for novel methods to deal with the unique challenges of directional protection in modern distribution networks. This article analyzes the impact of inverter-based resources (IBRs) on existing directional protection methods in distribution systems. It identifies parts of such elements that pose a risk of misoperation when IBRs are used in distribution networks. The authors have developed a new directional detection method for unbalanced faults in such networks using superimposed symmetrical sequence quantities. The phase angle of the superimposed negative sequence admittance is used to determine fault direction. The paper also presents a real-time co-simulation platform between a simulated distribution system and physical protection relay, using OPAL-RT. An SEL-411L relay is used to program the detection algorithm. This hardware-in-the-loop (HIL) setup is used to verify the performance of the method and the results are compared with existing directional methods}, archive = {J_ACCESS}, author = {Kwasi Opoku and Aleksandar Dimitrovski and Maximiliano Ferrari}, doi = {10.1109/ACCESS.2024.3525057}, journal = {IEEE Access}, pages = {7094-7109}, shortjournal = {IEEE Access}, title = {Performance evaluation of a novel sequence-based directional detection strategy for protection of active distribution networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing social media user’s trust: A comprehensive framework for detecting malicious profiles using multi-dimensional analytics. ACCESS, 13, 7071-7093. (www)

  @article{ , abstract = {Information transparency, user privacy, and digital security are significantly vulnerable to the proliferation of counterfeit bot accounts on OSN. Traditional methods for distinguishing these accounts archetypally depend on an inadequate set of features, which hampers their effectiveness in contrast to the progressively sophisticated maneuvers employed by malicious users. A state-of-the-art methodology for Fake Bot Account Detection (FAD) that assimilates sophisticated deep learning techniques to scrutinize multimodal data, such as visual content, temporal activity patterns, and network interactions, to incredulous this challenge Visual features are analyzed using sophisticated methods, including deconstructing into smaller segments and extracting high-level patterns using encoder models. Specialized convolutions are employed to identify dependencies in user behavior over time from sequential data. By aggregating features from connected nodes and considering numerous forms of relationships, network analysis is accompanied by the social graph to learn node representations. A unified representation is created by merging these multimodal features. This representation is then transmitted through a completely associated layer and an activation function to predispose whether a bot account is genuine or counterfeit. The detection accuracy of false bot accounts is improved by integrating these diverse data modalities, which addresses the limitations of single-modality approaches. Compared with conventional methods, the FAD method is validated using the Cresci 2017 dataset, demonstrating substantial enhancements in momentous performance metrics. The consequences suggest that the proposed methodology effectively captures the multifaceted character of fake bot accounts, providing a robust tool for enhancing the security of OSNs.}, archive = {J_ACCESS}, author = {Sailaja Terumalasetti and S. R. Reeja}, doi = {10.1109/ACCESS.2024.3521951}, journal = {IEEE Access}, pages = {7071-7093}, shortjournal = {IEEE Access}, title = {Enhancing social media user’s trust: A comprehensive framework for detecting malicious profiles using multi-dimensional analytics}, volume = {13}, year = {2025}, }
• (2025). Flatness-based motion planning and control strategy of a 3D overhead crane. ACCESS, 13, 7053-7070. (www)

  @article{ , abstract = {This paper investigates an advanced control strategy for a three-dimensional overhead crane (3DOC) to address the limitations of traditional control methods in trajectory optimization, disturbance rejection, and robustness. Conventional approaches often fail to provide optimal motion planning that handles all the dynamic constraints of the 3DOC and cannot simultaneously address system uncertainty and external disturbances. Moreover, these methods cannot guarantee the convergence time of the observer and controllers. To fill this gap, we developed a time-optimal motion planning algorithm based on differential flatness theory incorporating dynamic constraints and Control limits. Additionally, a Fixed-Time Extended State Observer (FxTESO) is implemented to estimate states and disturbances in fixed time, and a Terminal Sliding Mode Control (TSMC) ensures robust trajectory tracking. Simulation studies and lab-scale experiments on the 3DOC demonstrate the method’s improvements in trajectory optimization, disturbance rejection, and overall performance. Compared to existing control strategies, the proposed framework provides enhanced trajectory accuracy, robustness, and fixed-time disturbance rejection, confirming its effectiveness for 3DOC applications.}, archive = {J_ACCESS}, author = {Van Chung Nguyen and Hue Luu Thi and Hoa Bui Thi Khanh and Danh Huy Nguyen and Minh Nhat Vu and Tung Lam Nguyen}, doi = {10.1109/ACCESS.2024.3524404}, journal = {IEEE Access}, pages = {7053-7070}, shortjournal = {IEEE Access}, title = {Flatness-based motion planning and control strategy of a 3D overhead crane}, volume = {13}, year = {2025}, }
• (2025). Operation of a grid-forming converter controlled by the flux vector. ACCESS, 13, 7040-7052. (www)

  @article{ , abstract = {The electric power system is undergoing a significant transition. Renewable energy generation capacity has increased, leading to the displacement of synchronous generators (SG) and a growing concern about grid stability due to the decrease of rotating inertial energy. Grid-forming (GFM) converters have become a technological solution to this challenge, as they are capable of operating in low strength system conditions and helping to stabilize the voltage and frequency of the grid. In this paper, a novel control scheme for GFM converters is validated in a commercial converter according to the National Grid ESO, called National Energy System Operator (NESO) since October 2024. The control scheme uses the flux vector as a simple and effective method for limiting the active and reactive currents without internal current loops. For this purpose, a hardware test bed has been implemented consisting of two DC sources emulating a photovoltaic (PV) plant which feed the DC bus a voltage source converter (VSC), a grid emulator which allows to generate the desired disturbances in the grid to evaluate the converter’s response and a three-phase load. The results obtained have validated that these GFM converters based on the flux vector are capable of synchronizing with the grid, responding against frequency disturbances like phase jumps or faults in the grid and operating in islanded mode. In addition, the innovative method used to limit the active and reactive current has also been tested, comparing its performance with that of a conventional GFM scheme, showing an improvement in the response stability.}, archive = {J_ACCESS}, author = {Juan Dolado Fernández and Eduardo Rausell Navarro and José Luis Rodríguez Amenedo and Joaquín Eloy-García and Santiago Arnaltes Gómez}, doi = {10.1109/ACCESS.2025.3526752}, journal = {IEEE Access}, pages = {7040-7052}, shortjournal = {IEEE Access}, title = {Operation of a grid-forming converter controlled by the flux vector}, volume = {13}, year = {2025}, }
• (2025). A novel network for choroidal segmentation based on enhanced boundary information. ACCESS, 13, 7027-7039. (www)

  @article{ , abstract = {Due to the inherent imaging characteristics of Optical Coherence Tomography (OCT), the contrast between the choroid and sclera is low, presenting significant challenges in choroidal segmentation, such as subchoroidal boundary blurring and difficulty in accurate boundary delineation. To address these issues, this paper proposes an automatic choroid segmentation network, termed Boundary Enhancement Net (BENet), which enhances boundary information to facilitate precise recognition and achieve end-to-end automatic segmentation. BENet is constructed on the TransUnet backbone and integrates a boundary enhancement module, leveraging a multi-branch architecture and dilated convolutions to capture features across multiple scales. The attention mechanism is incorporated to dynamically highlight critical features, thus improving the model’s capacity to represent boundary details. Furthermore, the channel enhancement mechanism further refines the expression of salient channel futures, while the adaptive activation function improves the sensitivity of the network to boundary futures. A custom loss function, specifically designed to replace the conventional Mean Squared Error (MSE) loss, is utilized to optimize network parameters, leading to further improvements in segmentation performance. The BENet architecture achieved a Dice coefficient of 95.24%, a Hausdorff distance of 2.68, and an accuracy of 99.12%. Compared to the original TransUnet, BENet demonstrated an increase of nearly 5% in the Dice coefficient and 0.85% in accuracy. Ablation studies and results from multiple medical image datasets validate that BENet consistently delivers precise segmentation outcomes. The boundary enhancement module effectively improves the accuracy of choroidal segmentation, while the custom loss function optimizes network performance, increasing both stability and generalization capabilities. Moreover, the modular design of the boundary enhancement module ensures its portability across different segmentation tasks, making it a versatile component for integration into existing frameworks.}, archive = {J_ACCESS}, author = {Wenbo Huang and Chaofan Qu and Yang Yan}, doi = {10.1109/ACCESS.2025.3526657}, journal = {IEEE Access}, pages = {7027-7039}, shortjournal = {IEEE Access}, title = {A novel network for choroidal segmentation based on enhanced boundary information}, volume = {13}, year = {2025}, }
• (2025). MVR: Synergizing large and vision transformer for multimodal natural language-driven vehicle retrieval. ACCESS, 13, 7014-7026. (www)

  @article{ , abstract = {In recent years, intelligent transportation systems have played a pivotal role in the development of smart cities, with vehicle retrieval becoming a critical component of traffic management and surveillance. Traditional vehicle retrieval systems rely heavily on image-based matching techniques derived from vehicle re-identification (VReID) tasks. However, these approaches are limited by their dependency on image queries, which may not always be available in real-world scenarios. Natural language (NL)-based vehicle retrieval systems offer a more flexible and accessible alternative by enabling users to query vehicles using textual descriptions. Despite progress in NL-based retrieval, existing methods face challenges in fully capturing multi-granularity information and aligning heterogeneous visual and linguistic inputs. This paper addresses these limitations by proposing a robust Multimodal Vehicle Retrieval (MVR) model that integrates both visual and textual data through a dual-stream architecture. Our model captures complementary local features, alongside global information including motion and environmental context. We utilize InfoNCE and instance losses to align the visual and textual modalities within a shared feature space, while post-processing modules, including Granular Vehicle Feature Refinement and Spatial Relationship Modeling, further enhance retrieval performance by refining vehicle attributes and contextual relationships. Our experiments, conducted on the CityFlow-NL dataset, demonstrate that our model achieves a 35.6% improvement in Mean Reciprocal Rank (MRR), a 41.3% increase in recall at 5 (R@5), and a 22.9% improvement in recall at 10 (R@10) compared to the baseline, and overcomes the inherent challenges of cross-modal retrieval in improving real-world VReID.}, archive = {J_ACCESS}, author = {Tareq Mahmod AlZubi and Umar Raza Mukhtar}, doi = {10.1109/ACCESS.2024.3524392}, journal = {IEEE Access}, pages = {7014-7026}, shortjournal = {IEEE Access}, title = {MVR: Synergizing large and vision transformer for multimodal natural language-driven vehicle retrieval}, volume = {13}, year = {2025}, }
• (2025). L2D2: A novel LSTM model for multi-class intrusion detection systems in the era of IoMT. ACCESS, 13, 7002-7013. (www)

  @article{ , abstract = {The rapid growth of IoT has significantly changed modern technology by allowing devices, systems, and services to connect easily across different areas. Due to the growing popularity of Internet of Things (IoT) devices, attackers focus more and more on finding new methods, ways, and vulnerabilities to penetrate IoT networks. Although IoT devices are utilized across a wide range of domains, the Internet of Medical Things (IoMT) holds particular significance due to the sensitive and critical nature of medical information. Consequently, the security of these devices must be treated as a paramount concern within the IoT landscape. In this paper, we propose a novel approach for detecting various intrusion attacks targeting Internet of Medical Things (IoMT) devices, utilizing an enhanced version of the LSTM deep learning algorithm. To evaluate and compare the proposed algorithm with other methods, we used the CICIoMT2024 dataset, which encompasses various types of equipment and corresponding attacks. The results demonstrate that the proposed novel approach achieved an accuracy of 98% for 19 classes, which is remarkably high for classifications and presents a significant and promising outcome for IoMT environments.}, archive = {J_ACCESS}, author = {Gökhan Akar and Shaaban Sahmoud and Mustafa Onat and Ünal Cavusoglu and Emmanuel Malondo}, doi = {10.1109/ACCESS.2025.3526883}, journal = {IEEE Access}, pages = {7002-7013}, shortjournal = {IEEE Access}, title = {L2D2: A novel LSTM model for multi-class intrusion detection systems in the era of IoMT}, volume = {13}, year = {2025}, }
• (2025). Deep learning landscape evaluation system integrating poetic emotion and visual features. ACCESS, 13, 6988-7001. (www)

  @article{ , abstract = {This study integrates classical Chinese poetry’s textual and visual elements to quantitatively assess emotions in landscape designs and develop a corresponding evaluation system. A meticulously curated poetry dataset encompassing diverse emotional types and dynasties was selected. Utilizing Chinese-Contrastive Language-Image Pretraining (CN-CLIP) models, the visual features of the poetry were extracted, and a neural network model was trained to perform emotional analysis on landscape designs. The developed evaluation system combines a self-trained neural network with the Contrastive Language-Image Pretraining (CLIP) model, leveraging the emotions conveyed in poetry to assess landscape designs. An attention mechanism generates heatmaps that highlight the regions the model focuses on, enabling targeted design optimization. Experimental results demonstrate that the proposed “Emotion-Visual Features” model achieves an accuracy of 88.09% in emotion classification, significantly outperforming traditional methods such as Support Vector Machine (SVM) (64.51%) and Random Forest (RF) (81.47%), representing increases of approximately 36.5% and 8.1%, respectively. Additionally, the system’s evaluation module shows high consistency with expert manual evaluations, with system scoring improvements of 35.15% compared to 26.81% by experts, resulting in a minimal difference of 8.34%. These findings indicate that the deep learning-based evaluation system not only provides objective and quantitative emotional assessments but also effectively guides the optimization process in landscape design, validating the system’s reliability and effectiveness.}, archive = {J_ACCESS}, author = {Chuanyuan Li}, doi = {10.1109/ACCESS.2024.3523069}, journal = {IEEE Access}, pages = {6988-7001}, shortjournal = {IEEE Access}, title = {Deep learning landscape evaluation system integrating poetic emotion and visual features}, volume = {13}, year = {2025}, }
• (2025). Mechanical/Electrical hybrid two-dimensional beam scanning cylindrical dielectric lens antenna fed by a phased array primary radiator. ACCESS, 13, 6977-6987. (www)

  @article{ , abstract = {A hybrid two-dimensional (2D) beam-scanning lens antenna through the combination of a mechanical lens slide and a phased array primary radiator is proposed. The proposed antenna allowed for mechanical scanning of the main beam in one dimension by utilizing a piezo motor to slide the lens, while also enabling electrical scanning of the beam through a phased array of primary radiators arranged orthogonal to the mechanical beam scanning direction. The effectiveness of the proposed antenna was demonstrated through electromagnetic simulations and fabricated antenna measurements. We have shown the capability of the proposed antenna to perform 2D beam scanning with a scan loss of approximately 3 dB over a spherical angular range of ±10° from the front direction of the antenna.}, archive = {J_ACCESS}, author = {Yoshiki Sugimoto and Kunio Sakakibara and Tuan Hung Nguyen and Takanori Narita and Nobuyoshi Kikuma}, doi = {10.1109/ACCESS.2024.3525265}, journal = {IEEE Access}, pages = {6977-6987}, shortjournal = {IEEE Access}, title = {Mechanical/Electrical hybrid two-dimensional beam scanning cylindrical dielectric lens antenna fed by a phased array primary radiator}, volume = {13}, year = {2025}, }
• (2025). Specialized scalar and SIMD instructions for error correction codes decoding on RISC-V processors. ACCESS, 13, 6964-6976. (www)

  @article{ , abstract = {The rapid deployment of Internet-of-Things (IoT) devices for a few years has been impressive, and the progressive deployment of 5G will accelerate things even further. Indeed, this standard opens the door to a new generation of standards aimed at a convergence of networks and communication protocols (WiFi, LTE, 4G etc.). This results in the need for flexible implementations of different families of codes, such as, LPDC, NB-LDPC, turbo codes and polar codes. In this context, the work presented in this article proposes the design of a flexible instruction set processor for an IoT context. The objective is to improve the performance level of low-complexity processor cores through instruction set extensions for Error Correction Code (ECC) decoding. The approach discussed is supported by experimental results obtained based on a RISC-V architecture to which specific instruction sets have been added. The results demonstrate a reduction in the required processing clock cycles up to 44.1% for polar codes, 39.2% for LDPC codes, 21.8% for NB-LDPC codes, and 24.3% for turbo codes (4G LTE) codes with a classical Single Instruction Single Data (SISD) approach. Moreover, Single Instruction Multiple Data (SIMD) parallelization strategy enables execution time savings that are far more impressive. The number of clock cycles required to decode a data bit is reduced by 65.6% to 76.9%, with a limited hardware over-cost from 0.6% to 34% (depending on the error correction code family and the targeted RISC-V core).}, archive = {J_ACCESS}, author = {Mael Tourres and Cyrille Chavet and Bertrand Le Gal and Philippe Coussy}, doi = {10.1109/ACCESS.2025.3527028}, journal = {IEEE Access}, pages = {6964-6976}, shortjournal = {IEEE Access}, title = {Specialized scalar and SIMD instructions for error correction codes decoding on RISC-V processors}, volume = {13}, year = {2025}, }
• (2025). Digital twins and enabling technology applications in mining: Research trends, opportunities, and challenges. ACCESS, 13, 6945-6963. (www)

  @article{ , abstract = {Industry 4.0 is making a positive impact on the world’s industries in areas such as productivity, efficiency, reliability, and human safety. Although the mining industry has not undergone revolutionary digital change, numerous technologies have been applied and improved during the past two decades. Through a systematic literature survey, this study presents research trends, opportunities, and challenges in the context of digital twinning in the mining industry. The research team initially set the objectives of the research and formulated the search criteria to extract the most relevant and manageable number of scientific peer-reviewed publications. The gathered publications were then filtered using a web-based text reading and analysis environment based on defined criteria. The set of papers that were filtered during the first step was further reduced by manually reading through the abstracts, introductions, and conclusions while classifying relevant publications based on aspects such as the country of origin, technologies, and application areas. The set of papers that were retained following the second step was subjected to detailed manual reading to further explore the technologies and applications to capture the research trends, opportunities, and challenges. The research indicates that China, the USA, Australia, Russia, and Canada are the leading countries in terms of applied research and development of digital twins and enabling technologies for the mining industry. Using virtual reality, a digital twin-enabling technology, to create safer mining environments is a popular research area. In the last twenty years, academic institutions and scholars have led research efforts compared to the industry. Over the past five years, both have significantly increased their research contributions, presenting various opportunities and challenges to inspire future studies.}, archive = {J_ACCESS}, author = {Mihiran Galagedarage Don and Thumeera R. Wanasinghe and Raymond G. Gosine and Peter J. Warrian}, doi = {10.1109/ACCESS.2025.3526881}, journal = {IEEE Access}, pages = {6945-6963}, shortjournal = {IEEE Access}, title = {Digital twins and enabling technology applications in mining: Research trends, opportunities, and challenges}, volume = {13}, year = {2025}, }
• (2025). Enhancing gas distribution network resilience utilizing a mixed social network analysis-simulation approach: Application of artificial intelligence. ACCESS, 13, 6924-6944. (www)

  @article{ , abstract = {This study introduces a resilience-oriented framework to enhance the performance of energy distribution networks under partial high workload conditions with a particular emphasis on natural gas systems. Given the critical role of gas delivery in economic stability and societal well-being, ensuring the reliability and robustness of its network design is imperative. This research adopts a Social Network Analysis (SNA) methodology, augmented by an artificial intelligence-based approach, to appraise the dynamics and interdependencies of gas distribution networks under varying operational conditions. The proposed framework integrates SNA with simulation-based network analysis to enhance resilience by identifying critical nodes and edges susceptible to disruption, especially during demand fluctuations periods. A key advantage of this approach lies in its applicability as a consumer vulnerability mitigation strategy during high-demand scenarios, thereby reinforcing network robustness. The findings offer significant insights and methodological contributions applicable across gas networks, ultimately promoting smarter and more resilient energy solutions. Overall, this research provides a comprehensive strategy for strengthening network resilience, supporting the broader objectives of energy security and sustainability.}, archive = {J_ACCESS}, author = {Mehrdad Agha Mohammad Ali Kermani and Negar Mohammadi and Hasan Ghasemi and Hadi Sahebi and Hani Gilani}, doi = {10.1109/ACCESS.2024.3520216}, journal = {IEEE Access}, pages = {6924-6944}, shortjournal = {IEEE Access}, title = {Enhancing gas distribution network resilience utilizing a mixed social network analysis-simulation approach: Application of artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). A novel attention-guided enhanced U-net with hybrid edge-preserving structural loss for low-dose CT image denoising. ACCESS, 13, 6909-6923. (www)

  @article{ , abstract = {Computed Tomography (CT) scan, pivotal for medical diagnostics, involves exposure to electromagnetic radiation, potentially elevating the risk of leukemia and cancer. Low-dose CT (LDCT) imaging has emerged to mitigate these risks, extensively reducing radiation exposure by up to 86%. However, it significantly reduces the quality of LDCT images and introduces noise and artifacts, degrading the diagnostic accuracy of the Computer Aided Diagnostic (CAD) system. This study presents a novel U-Net architecture, featuring several key enhancements. The model integrates residual blocks to improve feature representation and employs a custom hybrid loss function that combines structural loss with gradient regularization using the Euclidean norm, promoting superior CT image quality retention. Additionally, incorporating Attention Gates in the up-sampling layers of a proposed model optimizes the extraction of critical features, ensuring more precise denoising of CT images. The proposed model undergoes iterative training, using a custom loss function to refine its parameters and improve CT image denoising progressively. Its performance is rigorously evaluated both qualitatively and quantitatively on the ‘2016 Low-dose CT AAPM Grand Challenge dataset’. The results, assessed through the metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Square Error (RMSE), demonstrated promising improvements compared to state-of-the-art techniques. The model effectively reduces noise while preserving critical fine details, establishing itself as a highly efficient solution for LDCT image denoising.}, archive = {J_ACCESS}, author = {Muhammad Zubair and Helmi Md Rais and Talal Alazemi}, doi = {10.1109/ACCESS.2025.3526619}, journal = {IEEE Access}, pages = {6909-6923}, shortjournal = {IEEE Access}, title = {A novel attention-guided enhanced U-net with hybrid edge-preserving structural loss for low-dose CT image denoising}, volume = {13}, year = {2025}, }
• (2025). Hardware and deep learning-based authentication through enhanced RF fingerprints of 3D-printed chaotic antenna arrays. ACCESS, 13, 6893-6908. (www)

  @article{ , abstract = {Radio frequency (RF) fingerprinting is a hardware-based authentication technique utilizing the distinct distortions in the received signal due to the unique hardware differences in the transmitting device. Existing RF fingerprinting methods only utilize the naturally occurring hardware imperfections during fabrication; hence their authentication accuracy is limited in practical settings even when state-of-the-art deep learning classifiers are used. In this work, we propose a Chaotic Antenna Array (CAA) system for significantly enhanced RF fingerprints and a deep learning-based device authentication method for CAA. We provide a mathematical model for CAA, explain how it can be cost-effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM), and comprehensively analyze the authentication performance of several deep learning classifiers for CAA. Our results show that the enhanced RF signatures of CAA enable highly accurate authentication of hundreds of devices under practical settings.}, archive = {J_ACCESS}, author = {Justin O. McMillen and Fawaz Abdul Razak and Gokhan Mumcu and Yasin Yilmaz}, doi = {10.1109/ACCESS.2025.3526583}, journal = {IEEE Access}, pages = {6893-6908}, shortjournal = {IEEE Access}, title = {Hardware and deep learning-based authentication through enhanced RF fingerprints of 3D-printed chaotic antenna arrays}, volume = {13}, year = {2025}, }
• (2025). A review of applying large language models in healthcare. ACCESS, 13, 6878-6892. (www)

  @article{ , abstract = {In response to the growing demand for healthcare and the increasing importance people place on medical services, efficiently meeting these needs within the constraints of limited healthcare resources is of great social and economic benefit. Therefore, research into applying Large Language Models (LLMs) in the healthcare sector holds significant importance. This paper provides a review of the research progress on the application of LLMs in the healthcare field. First, the basic framework of LLMs is summarized, and the training process of LLMs in healthcare is systematically reviewed. Next, six specific application areas of LLMs in healthcare are reviewed: disease diagnosis and decision support, dissemination of medical knowledge, medical assistance, medical image analysis, biomedicine, and medical education. Then, several representative healthcare-specific LLMs are discussed, along with their performance analysis. Following this, the challenges faced by LLMs in healthcare are summarized, and relevant suggestions are provided. The future development trends of LLMs in healthcare are also explored. Finally, a bibliometric analysis is performed. Through the literature review, we found: 1) After pretraining, LLMs are widely adaptable to downstream tasks, significantly enhancing processing performance and efficiency; 2) LLMs in healthcare possess multiple capabilities and can handle multimodal data; 3) Bibliometric analysis shows that researchers are paying increasing attention to the application of LLMs in healthcare; 4) Further research is needed in optimizing, improving reliability, and expanding practical applications of large healthcare models.}, archive = {J_ACCESS}, author = {Qiming Liu and Ruirong Yang and Qin Gao and Tengxiao Liang and Xiuyuan Wang and Shiju Li and Bingyin Lei and Kaiye Gao}, doi = {10.1109/ACCESS.2024.3524588}, journal = {IEEE Access}, pages = {6878-6892}, shortjournal = {IEEE Access}, title = {A review of applying large language models in healthcare}, volume = {13}, year = {2025}, }
• (2025). Research on weld identification and defect localization based on an improved watershed algorithm. ACCESS, 13, 6869-6877. (www)

  @article{ , abstract = {The quality of weld joints is a pivotal factor influencing the strength and structural reliability for mechanical parts. Difficulties in identification caused by weld joint adhesion and defects, such as missing weld joints, this paper designs an efficient welded joint detection system, which utilizes Otsu threshold segmentation and morphological processing methods to achieve the initial segmentation of the welded joint region. Furthermore, the contour of the weld joint is extracted with greater accuracy by random incremental algorithm, which contributes to accelerating the following the detection speed only considering the weld joint region. Subsequently, watershed algorithm based on distance transformation is adopted to segment each weld joint precisely. Considering defect localization, the edge features of the missing weld region are identified and separated using the difference calculation method. The results demonstrate that the system could accurately segment adhesive weld joints and identify missing weld joint locations with the variation of the workpiece positions and angles, fully satisfying the real-time detection requirement during the weld quality identification.}, archive = {J_ACCESS}, author = {Lingjiang Guo and Yong Yan and Junjie Cui and Zhongsi Xu}, doi = {10.1109/ACCESS.2025.3526728}, journal = {IEEE Access}, pages = {6869-6877}, shortjournal = {IEEE Access}, title = {Research on weld identification and defect localization based on an improved watershed algorithm}, volume = {13}, year = {2025}, }
• (2025). Predicting the birth delivery mode: Uncovering the link between EHG topological features and BMI. ACCESS, 13, 6855-6868. (www)

  @article{ , abstract = {The appropriateness of selected features, contingent upon anthropometric factors, influences the efficacy of algorithm classification in pregnancy monitoring and obstetric outcome forecasting. Obstetricians often lack the technology to determine whether a cesarean delivery is necessary based on antepartum circumstances. The objective of this study is to develop an automated system for an effective detection of mode of birth delivery using EHG and analyze the data points shape structures across different BMI groups. Topological features (Area, Perimeter, Circularity, Variance, Bending Energy) were extracted from the boundaries of convex and concave hulls which applied to the whole signal, including the quiescent periods in this study. Pregnant women who underwent cesarean sections and those who delivered spontaneously were categorized into four BMI groups: unhealthy obese, healthy obese, overweight, and normal weight. EHG signals from the 31st week of gestation were examined. Topological characteristics derived from the border shape of the concave hull with a tolerance of 1.7 exhibits a marked statistically significant difference (p<0.005) between cesarean and spontaneous EHG signals. All of the features reached an accuracy above 90% in the classification using a random forest classifier between cesarean and vaginal deliveries across all BMI groups. On the other hand, it was found that aside from variance, women who delivered spontaneously exhibited higher topological feature values compared to those who underwent cesarean sections. The obtained results assist obstetricians in selecting the optimal birth delivery mode, hence minimizing unnecessary cesarean sections, complications, and emergency medical interventions.}, archive = {J_ACCESS}, author = {Rubana Hoque Chowdhury and Quazi Delwar Hossain and Mohiuddin Ahmad}, doi = {10.1109/ACCESS.2024.3525358}, journal = {IEEE Access}, pages = {6855-6868}, shortjournal = {IEEE Access}, title = {Predicting the birth delivery mode: Uncovering the link between EHG topological features and BMI}, volume = {13}, year = {2025}, }
• (2025). SPICE-level demonstration of unsupervised learning with spintronic synapses in spiking neural networks. ACCESS, 13, 6845-6854. (www)

  @article{ , abstract = {Spiking Neural Networks (SNNs) are Artificial Neural Networks which promise to mimic the biological brain processing with unsupervised online learning capability for various cognitive tasks. However, SNN hardware implementation with online learning support is not trivial and might prove highly inefficient. This paper proposes an energy-efficient hardware implementation for SNN synapses. The implementation is based on parallel-connected Magnetic Tunnel Junction (MTJ) devices and exploits their inherent stochasticity. In addition, it uses a dedicated unsupervised learning rule based on optimized Spike-Timing-Dependent Plasticity (STDP). To facilitate the design of the SNN, its training and evaluation, an open-source Python-based platform is developed; it takes as input the SNN parameters and discrete circuit components, and it automatically generates the associated full netlist in SPICE then launches the simulation; moreover, it extracts the simulation results and makes them available in python for evaluation and manipulation. Unlike conventional neuromorphic hardware that relies on simple weight mapping post-off-line training, our approach emphasizes continuous, unsupervised learning, ensuring an energy efficiency of 11.2nW per synaptic update during training and as low as 109fJ/spike during inference.}, archive = {J_ACCESS}, author = {Salah Daddinounou and Anteneh Gebregiorgis and Said Hamdioui and Elena-Ioana Vatajelu}, doi = {10.1109/ACCESS.2024.3411519}, journal = {IEEE Access}, pages = {6845-6854}, shortjournal = {IEEE Access}, title = {SPICE-level demonstration of unsupervised learning with spintronic synapses in spiking neural networks}, volume = {13}, year = {2025}, }
• (2025). Identification of maize diseases based on dynamic convolution and tri-attention mechanism. ACCESS, 13, 6834-6844. (www)

  @article{ , abstract = {Accurate, non-destructive classification of maize diseases is crucial for efficiently managing agricultural losses. While existing methods perform well in controlled environment dataset like PlantVillage, their accuracy often declines in real-world scenarios. In this work, ResNet50 is enhanced by integrating a dynamic convolution module and triplet attention modules. This method adaptively recalibrates the convolution kernel weights, establishing dependencies across spatial and channel dimensions through tensor rotation and residual transformations. The proposed method surpasses state-of-the-art alternatives, reaching 98.79% validation accuracy on the PlantVillage maize dataset and 97.47% on the Corn Leaf Disease Dataset through cross-validation. Even with complex backgrounds, it attains an average accuracy of 88.33% for classifying six types of maize diseases. Experimental results confirm its effectiveness in maize disease detection.}, archive = {J_ACCESS}, author = {Feilong Tang and Rosalyn R. Porle and Hoe Tung Yew and Farrah Wong}, doi = {10.1109/ACCESS.2025.3525661}, journal = {IEEE Access}, pages = {6834-6844}, shortjournal = {IEEE Access}, title = {Identification of maize diseases based on dynamic convolution and tri-attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Integrating two-tier optimization algorithm with convolutional bi-LSTM model for robust anomaly detection in autonomous vehicles. ACCESS, 13, 6820-6833. (www)

  @article{ , abstract = {Industrial development has changed vehicles of traditional into autonomous vehicles (AVs). AVs play a significant role since they are measured as a vital module of smart cities. The AV is an advanced automobile efficient in preserving secure driving by evading collisions formed by drivers. In contrast with traditional vehicles, which are fully coordinated and functioned by humans, AVs gather information regarding the exterior environment utilizing sensors to guarantee secure navigation. AVs decrease environmental effects since they regularly employ electricity to function rather than fossil fuel, thus diminishing greenhouse gasses. However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. ML and DL-based anomaly detection scholars have focused on improving accuracy as a typical classification task without aiming at mischievous information. This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. The major aim of the IWOAFS-XAIAD technique is an endwise XAI structure to construe and imagine the anomaly recognition classifications prepared by AI models securing autonomous driving systems. Initially, the IWOAFS-XAIAD technique utilizes the Z-score data normalization method to convert input data into a compatible layout. Besides, the IWOAFS-XAIAD technique employs an improved whale optimization algorithm (IWOA)-based feature subset selection to pick an optimum set of features. An attention mechanism with the CNN-BiLSTM (CNN-BiLSTM-A) model is employed for anomaly detection and classification. Moreover, the catch-fish optimization algorithm (CFOA) selects the hyperparameters connected to the CNN-BiLSTM-A model. Finally, utilizing the SHAP XAI method, the IWOAFS-XAIAD technique performs local and global descriptions for the black-box AI model. To demonstrate the optimum classification outcome of the IWOAFS-XAIAD technique, a wide range of experiments is performed on a VeReMi dataset. The experimental validation of the IWOAFS-XAIAD technique portrayed a superior accuracy value of 98.52% over the existing methods.}, archive = {J_ACCESS}, author = {Moneerah Alotaibi and Manal Abdullah Alohali and Khalid Mahmood and Asma A. Alhashmi and Jehad Saad Alqurni and Sultan Refa Alotaibi and Ahmad A. Alzahrani and Imène Issaoui}, doi = {10.1109/ACCESS.2024.3523539}, journal = {IEEE Access}, pages = {6820-6833}, shortjournal = {IEEE Access}, title = {Integrating two-tier optimization algorithm with convolutional bi-LSTM model for robust anomaly detection in autonomous vehicles}, volume = {13}, year = {2025}, }
• (2025). A single-inductor bipolar-output DC-DC converter with tunable asymmetric power distribution control (APDC) for AMOLED applications. ACCESS, 13, 6810-6819. (www)

  @article{ , abstract = {This paper presents a single-inductor bipolar output (SIBO) converter with tunable asymmetric power distribution control (APDC) for active-matrix organic light-emitting diode (AMOLED) display applications. This power stage architecture requires only a single external inductor to provide the bipolar voltages needed for the AMOLED pixel circuit. In AMOLED applications, the specifications for positive voltage are more stringent than for negative voltage. This work implements the APDC technique to deliberately sacrifice the transient performance of the negative voltage within acceptable limits to depress the over/undershoot voltage magnitude of the positive voltage. For design completion, a skipping mode is introduced to improve power conversion efficiency by 14% to 17%. The proposed SIBO DC-DC converter, fabricated using TSMC’s 0.18um 1.8V/3.3V 1P6M Mixed Signal process, is suitable for load ranges from 20mA to 200mA. With the APDC technique, the positive transient overshoot voltage is improved from 4.66% to 0.82%, and the undershoot voltage from 5.32% to 1.08%. The results validate the effectiveness of the proposed APDC technique.}, archive = {J_ACCESS}, author = {Wei-Ting Yeh and Meng-Xun Cai and Chien-Wu Tsai and Chien-Hung Tsai}, doi = {10.1109/ACCESS.2024.3483568}, journal = {IEEE Access}, pages = {6810-6819}, shortjournal = {IEEE Access}, title = {A single-inductor bipolar-output DC-DC converter with tunable asymmetric power distribution control (APDC) for AMOLED applications}, volume = {13}, year = {2025}, }
• (2025). The impact of YOLO algorithms within fall detection application: A review. ACCESS, 13, 6793-6809. (www)

  @article{ , abstract = {The phenomenon of human falls is a highly significant health concern, particularly for elderly individuals and disabled individuals who reside alone. The global elderly population is steadily growing. Consequently, human fall detection is emerging as a highly efficient method for enhancing the quality of life for individuals in need of assistance. Deep learning (DL) and computer vision have been extensively employed for assistive living purposes. This review article focuses on advanced DL model known as YOLO algorithm for non-intrusive fall detection using vision-based methods. Furthermore, we describe the evaluation architecture of YOLO algorithms (YOLOv1 - YOLOv8). Additionally, we provide a survey on benchmark datasets for fall detection. To enhance comprehension, we will provide a concise overview of various metrics employed to assess the efficacy of fall detection systems. This article also provides insight into future advancements in YOLO algorithms for detecting human falls.}, archive = {J_ACCESS}, author = {Ahlam R. Khekan and Hadi S. Aghdasi and Pedram Salehpour}, doi = {10.1109/ACCESS.2024.3496823}, journal = {IEEE Access}, pages = {6793-6809}, shortjournal = {IEEE Access}, title = {The impact of YOLO algorithms within fall detection application: A review}, volume = {13}, year = {2025}, }
• (2025). Robust control design for the higher harmonic vibration reduction of a nonlinear aeroservoelastic rotor. ACCESS, 13, 6775-6792. (www)

  @article{ , abstract = {In this study, we present an improved linear quadratic closed-loop rotor vibration controller that uses a steady-state Kalman filter and an augmented integral controller to improve the traditional T-matrix-based rotor vibration controller. The proposed linearized vibration controller features closed-loop stability more rigorously than the conventional algebraic T-matrix-based controller. A relative multi-loop stability margin analysis was performed and examined using a nonlinear aeroservoelastic rotor software-in-the-loop simulation. The rotor aeroservoelastic simulation was performed using the multi-body dynamic analysis software DYMORE, equipped with a new improved inflow model employing multiple trailer time-marching free-vortex wakes. A linear time periodic representation of the rotor was identified from the exponentially modulated periodic response obtained using a recursive Fourier series filter. We found that the proposed linear controller showed improved numerical phase-margin prediction accuracy compared with the T-matrix-based method. Finally, the trade-off between the rotor vibratory load reduction performance and closed-loop stability was evaluated and discussed. We found that the proposed controller asymptotically reduced the N/rev vibratory loads that were included in the Kalman filter, and the amount of load reduction was similar to that predicted from the LTP rotor harmonic system.}, archive = {J_ACCESS}, author = {Byeonguk Im and Kunhyuk Kong and Wonjong Eun and Sangjoon Shin}, doi = {10.1109/ACCESS.2025.3526769}, journal = {IEEE Access}, pages = {6775-6792}, shortjournal = {IEEE Access}, title = {Robust control design for the higher harmonic vibration reduction of a nonlinear aeroservoelastic rotor}, volume = {13}, year = {2025}, }
• (2025). Inversion-based face swapping with diffusion model. ACCESS, 13, 6764-6774. (www)

  @article{ , abstract = {Face swapping involves replacing a face in an image with another face and ensuring the seamless integration of the source face into the target image. Previous studies have primarily utilized generative adversarial network-based models for face swapping. This paper introduces inversion-based face swapping (InFS), a novel framework employing diffusion inversion. The key contributions of our work include: 1) a facial attribute encoder that consolidates attribute information into a single embedding vector, utilizing the architecture of the pSp encoder, and 2) an enhanced face swapping pipeline that overcomes pose limitations through reenactment preprocessing addressing the challenge of incorrect face swapping at extreme angles. To preserve the target image’s attribute information that may be lost during the diffusion inversion process, we incorporate the extracted information from the facial attribute encoder. This embedding vector serves as a crucial condition in the diffusion inversion process, facilitating the prediction of noisy images. Subsequently, the predicted noisy image undergoes processing using a pretrained ID conditional DDPM for face swapping. Our experimental results show that InFS outperforms state-of-the-art methods in preserving identity, expression, and shape characteristics of target images. Furthermore, the proposed InFS achieved effective face swapping results without requiring additional guidance and reduces inference time by approximately 6.96 seconds compared to previous diffusion-based approaches.}, archive = {J_ACCESS}, author = {Daehyun Yoo and Hongchul Lee and Jiho Kim}, doi = {10.1109/ACCESS.2024.3519163}, journal = {IEEE Access}, pages = {6764-6774}, shortjournal = {IEEE Access}, title = {Inversion-based face swapping with diffusion model}, volume = {13}, year = {2025}, }
• (2025). Brain age prediction using a lightweight convolutional neural network. ACCESS, 13, 6750-6763. (www)

  @article{ , abstract = {Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesting using brain age prediction models for early diagnosis of neurodegenerative disorders, such as Parkinson’s, Schizophrenia, and Alzheimer’s disease. However, data availability, acquisition protocols diversity and models’ computational complexity remain limiting factors for clinical adoption. This study proposes a low-complexity convolutional neural network (CNN) model that tackles these challenges, focusing on three main aspects: performance accuracy, computational complexity, and adaptability to new, external datasets. We developed a brain-age prediction system using a minimally preprocessed T1-weighted MRI images with a multi-site dataset of healthy individuals covering the whole human lifespan (2251 subjects, age range 6-90 years). We proposed a lighter version of the Simple Fully Convolutional Network (SFCN) that contain only 1.2 million parameters. Computational load was further reduced by cropping the brain images. Finally, we employed transfer learning approach to achieve domain adaptation to external, unseen sites. We demonstrated that leveraging the cropped brain images reduced the computational time for training by 50%, maintaining a comparable accuracy to using the entire brain. The model achieved a Mean Absolute Error (MAE) of 3.557 for the full brain and 4.139 for the cropped images with a Pearson correlation $ r = 0.988 $ between the full and cropped brain predictions when evaluated on the same test set. Domain adaptation of our model to new external data showed a significant improvement in the prediction performance, reducing MAE from 7.219 to 4.750 for full brain images and from 12.107 to 5.770 for the cropped images. This study is the first to demonstrate comparable prediction accuracy using only a small segment of a 3D full brain MRI scan. Our results show that it is feasible to build lightweight CNN models trained on small-scale, heterogeneous datasets and fine-tuned to new external clinical data, making significant steps toward practical clinical application.}, archive = {J_ACCESS}, author = {Fatma Eltashani and Mario Parreno-Centeno and James H. Cole and João Paulo Papa and Fumie Costen}, doi = {10.1109/ACCESS.2025.3526520}, journal = {IEEE Access}, pages = {6750-6763}, shortjournal = {IEEE Access}, title = {Brain age prediction using a lightweight convolutional neural network}, volume = {13}, year = {2025}, }
• (2025). Enhanced fetal arrhythmia classification by non-invasive ECG using cross domain feature and spatial differences windows information. ACCESS, 13, 6729-6749. (www)

  @article{ , abstract = {Fetal arrhythmia refers to an abnormal heart rhythm in a fetus, characterized by irregular, too fast (tachycardia), or too slow (bradycardia) heartbeats. Diagnosis is typically made using ultrasound or non-invasive fetal electrocardiography (NI-FECG), which monitors the electrical activity of the fetal heart. Accurate detection and management are crucial, as severe arrhythmias may lead to complications that affect the fetus’s health during pregnancy and delivery. This study aims to develop an effective screening method for detecting arrhythmia (ARR) to aid physicians in diagnosing potential heart disease in fetal during pregnancy. This study presents a new cross-domain feature extraction method that incorporates temporal relationships between consecutive windows, improving feature representation by examining the correlation between neighboring windows. First, the original waveform signals from six sensors were transformed into a multi-level decomposition using the HAAR wavelet. Subsequently, a sample expansion was applied using a various-sized window sliding approach to each ARR and normal signal. Second, feature selection was implemented to reduce data dimensionality by selecting features highly relevant to the class labels. Finally, we applied oversampling techniques to address the issue of imbalanced data. Based on the results of an in-depth experimental analysis, it was found that the application of window sampling for data expansion produced favorable outcomes, particularly at a window size of 500. The combination of dimensionality reduction using Mutual Information and oversampling with the Radius-SMOTE method achieved the highest performance, yielding an accuracy of 96.5%, precision of 95.2%, recall of 96.3%, and an F1-measure of 96.2%, while reducing the feature dimensionality to 100. Moreover, the proposed method demonstrated improved performance compared to state-of-the-art approaches. With further development, this method is expected to serve as a foundational model for the advancement of diagnostic tools for physicians.}, archive = {J_ACCESS}, author = {Gede Angga Pradipta and Putu Desiana Wulaning Ayu and Made Liandana and Dandy Pramana Hostiadi}, doi = {10.1109/ACCESS.2025.3526586}, journal = {IEEE Access}, pages = {6729-6749}, shortjournal = {IEEE Access}, title = {Enhanced fetal arrhythmia classification by non-invasive ECG using cross domain feature and spatial differences windows information}, volume = {13}, year = {2025}, }
• (2025). Manifold energy projection twin support vector machine for semi-supervised classification. ACCESS, 13, 6704-6728. (www)

  @article{ , abstract = {The Projection Twin Support Vector Machine (PTSVM) and its variant, the Least Squares PTSVM (LSPTSVM), have demonstrated significant effectiveness in supervised classification tasks due to their strong generalization capabilities. However, their reliance on fully labeled data limits their applicability in real-world scenarios, where obtaining complete labeled datasets is often challenging and expensive. To overcome this limitation, we propose a novel Manifold Energy Projection Twin SVM (MEPTSVM) model for semi-supervised learning, which extends PTSVMs by integrating manifold regularization and energy-based margins. Our proposed MEPTSVM offers the following advancements over traditional PTSVMs: Firstly, MEPTSVM incorporates manifold regularization to leverage unlabeled data, capturing the underlying geometric structure of the dataset. By integrating both labeled and unlabeled samples, the model derives a more generalizable classification boundary that accounts for the global data distribution. Secondly, instead of employing a fixed distance margin between classes, MEPTSVM introduces an energy-based margin for each projection. This adaptive approach better captures the discriminative characteristics of different classes, enhancing classification performance and improving generalization. Finally, the effectiveness and practicality of MEPTSVM are demonstrated through extensive experiments on both synthetic and real-world datasets. The results validate its superiority in leveraging unlabeled data to improve classification accuracy and robustness.}, archive = {J_ACCESS}, author = {Jia-Nan Zhou and Zhi-Lin Feng}, doi = {10.1109/ACCESS.2025.3526828}, journal = {IEEE Access}, pages = {6704-6728}, shortjournal = {IEEE Access}, title = {Manifold energy projection twin support vector machine for semi-supervised classification}, volume = {13}, year = {2025}, }
• (2025). A novel method for news recommendation on websites using the clustered-vectors optimization algorithm. ACCESS, 13, 6685-6703. (www)

  @article{ , abstract = {Recommendation systems have been extensively implemented across various sectors, including e-commerce, social media, and banking. Numerous studies have employed established machine learning techniques to enhance the performance of recommendation models to a certain degree. This research introduces a recommendation model for news content utilizing a novel machine learning technique known as the “Clustered-Vectors Optimization (CVO)” algorithm. The proposed algorithm aims to optimize the clustering of news titles to improve the performance of news recommendations. Once the news titles are effectively clustered, a recommendation list is generated for website visitors based on their experiences. The recommendation list for each visitor is selected from news titles within the same cluster that previous visitors have accessed. This study utilized two sources of news datasets. The primary dataset, collected from a back-end website, was provided by a private digital television company in Thailand, while additional datasets were sourced from publicly available data on Kaggle. Experimental results indicated that the proposed CVO algorithm outperformed five well-known algorithms, which were TF-IDF, Word2Vec, Doc2Vec, Bag-of-Words (BoW), and Transformer, in terms of predictive performance. For instance, on the Thai news dataset, the CVO algorithm achieved an accuracy of 97.56%, a precision of 94.59%, a recall of 97.36%, and an F1-score of 97.46%. Similarly, the English language dataset, such as Indian news, also demonstrated high performance, with the CVO algorithm achieving an accuracy of 99.90%, a precision of 99.41%, a recall of 99.62%, and an F1-score of 99.53%.}, archive = {J_ACCESS}, author = {Piyanuch Chaipornkaew and Thepparit Banditwattanawong}, doi = {10.1109/ACCESS.2025.3526885}, journal = {IEEE Access}, pages = {6685-6703}, shortjournal = {IEEE Access}, title = {A novel method for news recommendation on websites using the clustered-vectors optimization algorithm}, volume = {13}, year = {2025}, }
• (2025). SafeTrax: Smart collision prediction and alert system using IoT for sustainable traffic safety. ACCESS, 13, 6667-6684. (www)

  @article{ , abstract = {With the rise in traffic accidents worldwide, it has become very important to have a more effective way to ensure road safety. To address this issue, we have made a system called SafeTrax. SafeTrax uses deep learning to predict traffic accidents and car crashes. Additionally, it has been implemented with the Internet of Things (IoT) to gather real-time data from sensors, and AWS Greengrass to process that data quickly and give drivers timely warnings. Unlike old safety systems, SafeTrax can predict if an accident might happen. It warns drivers beforehand to make roads safer for cars, people, and other animals. SafeTrax uses video frames from camera sensors in cars to look for dangers. It then uses a special deep-learning program to figure out if a crash might happen. One important part of SafeTrax is the integration of AWS IoT Greengrass. It helps the system work faster by using cloud services on devices like the Raspberry Pi. This means the system can quickly check the data and send warnings in time. SafeTrax can ensure safer roads, and it can help save lives.}, archive = {J_ACCESS}, author = {Md. Fahim Bin Amin and Israt Jahan Khan and Faria Akter and Adib Ahmed and Md. Motaharul Islam}, doi = {10.1109/ACCESS.2024.3524676}, journal = {IEEE Access}, pages = {6667-6684}, shortjournal = {IEEE Access}, title = {SafeTrax: Smart collision prediction and alert system using IoT for sustainable traffic safety}, volume = {13}, year = {2025}, }
• (2025). Coverage analysis for RIS-assisted two-tier HetNets with repulsion. ACCESS, 13, 6656-6666. (www)

  @article{ , abstract = {The performance analysis for the reconfigurable intelligent surface (RIS)-aided large-scale heterogeneous networks (HetNets) with spatial repulsion among base stations (BSs) in real networks has yet to be fully explored. To fill the gap, the user performance in the RIS-aided two-tier HetNet with repulsion is analyzed in this paper. Specifically, the approximate closed-form expressions of the coverage probability (CP) of the typical user (TU) for the RIS-assisted two-tier HetNet, in which the spatial locations of the macrocell BSs are modeled as Beta Ginibre Point Process ( $\beta $ -GPP), under different operation models of RIS are derived. Moreover, for some special situations, simple formulas can be derived from these closed-form expressions, which numerically reveal the effectiveness of RIS on enhancing the CP in HetNet over the case without RIS. In addition, the extension of our analysis to the case where RISs are randomly scattered in the HetNets is discussed in this paper. Finally, we find that the CP of the TU in the real RIS-aided HetNet can be well predicted with high accuracy by our analysis in the numerical results, in which the performance comparison under different cases for different operation models of RIS is also conducted to provide a guidance for the effective utilization of RIS in practical HetNet.}, archive = {J_ACCESS}, author = {Changshan Chen}, doi = {10.1109/ACCESS.2025.3527228}, journal = {IEEE Access}, pages = {6656-6666}, shortjournal = {IEEE Access}, title = {Coverage analysis for RIS-assisted two-tier HetNets with repulsion}, volume = {13}, year = {2025}, }
• (2025). Generating user privacy-controllable synthetic data for recommendation systems. ACCESS, 13, 6643-6655. (www)

  @article{ , abstract = {Recommender systems are widely used in e-commerce, news, and advertising, providing personalized recommendations by analyzing user interaction history. However, during large-scale data analysis and sharing, user privacy faces the risk of exposure, especially for users who wish to remain anonymous. While existing synthetic data methods perform well in privacy protection, there is still room for improvement in balancing personalized privacy protection and data utility, particularly in sparse data environments. To address this issue, this paper proposes a user-privacy-controllable data augmentation model that generates synthetic substitutes to replace original data, ensuring anonymity. The model mitigates the data sparsity problem by introducing a global average user-item vector and an attention mechanism. The global vector captures the overall user interest trend, reducing noise and outliers, which allows for a more accurate representation of user interests and a precise selection of items to replace, even in sparse data environments. Additionally, the model dynamically generates synthetic substitutes that align with user interests based on privacy preferences, prioritizing replacing low-interest items, thus achieving a good balance between privacy protection and data utility. Experimental results on three public datasets show that the model effectively protects privacy while maintaining high data utility, outperforming existing mainstream methods in personalized privacy protection.}, archive = {J_ACCESS}, author = {Zhenxiang He and Ke Chen and Zhenyu Zhao}, doi = {10.1109/ACCESS.2025.3525513}, journal = {IEEE Access}, pages = {6643-6655}, shortjournal = {IEEE Access}, title = {Generating user privacy-controllable synthetic data for recommendation systems}, volume = {13}, year = {2025}, }
• (2025). A timer and mixed integer linear programming load shedding scheme for resilient DC microgrids. ACCESS, 13, 6632-6642. (www)

  @article{ , abstract = {Islanded DC microgrids are vulnerable to voltage instability caused by excessive power demand, which can adversely impact downstream consumers and disrupt overall microgrid operation. Existing load-shedding techniques face limitations such as over-shedding due to fixed voltage thresholds and time delays, predetermined load-shedding actions that fail to account for disturbance magnitude, and delayed stabilization caused by sequential load-shedding steps. To address these challenges, this paper proposes a novel load-shedding strategy for islanded DC microgrids that integrates a short-timer mechanism with Mixed Integer Linear Programming (MILP) optimization. The proposed approach reduces reliance on communication systems and achieves optimal load-shedding decisions using local voltage measurements. Simulation results on a DC microgrid model adapted from the IEEE 37-bus network demonstrate the effectiveness of the proposed scheme. The scheme results in a 20% reduction in unnecessary load shedding, an 18% improvement in voltage stabilization (measured as the final voltage after a disturbance), and a 25% decrease in response time compared to conventional methods. The results show that the proposed strategy ensures that the DC bus voltage remains above the critical threshold of 720 V, enhancing system reliability by minimizing voltage transients, reducing regulation time, and maintaining power balance. These improvements highlight the potential of the proposed scheme to support robust, secure, and resilient DC microgrid operation.}, archive = {J_ACCESS}, author = {Abdulrahman Babagana and Isah A. Jimoh and Yljon Seferi and Graeme Burt}, doi = {10.1109/ACCESS.2025.3525975}, journal = {IEEE Access}, pages = {6632-6642}, shortjournal = {IEEE Access}, title = {A timer and mixed integer linear programming load shedding scheme for resilient DC microgrids}, volume = {13}, year = {2025}, }
• (2025). Automatic detection of hard exudate in color retinal fundus image and diabetic maculopathy grading. ACCESS, 13, 6618-6631. (www)

  @article{ , abstract = {Diabetic macular degeneration (DM) is often characterized by the early accumulation of lipid deposits on the retina, referred to as hard exudates (HEs), which originate from damaged retinal blood vessels. Timely detection of HEs is crucial to prevent vision loss and ensure prompt treatment for DM patients. This study introduces a novel approach leveraging mathematical morphology for the automated detection of HEs and grading of diabetic maculopathy using color fundus images. The identification and grading of diabetic maculopathy are based on two primary factors: precise delineation of the macular region, including the fovea and its center, using the optic disc’s location and diameter, and accurate detection of hard exudates. The severity of diabetic maculopathy is determined by the presence of HEs within the macula. When tested on the publicly available MESSIDOR database, the proposed method demonstrated an impressive 92% accuracy in detecting diabetic maculopathy and identifying hard exudates within the macular region.}, archive = {J_ACCESS}, author = {Rajesh I. S. and Bharati M. Reshmi and Bharathi Malakreddy A and Shavantrevva Bilakeri}, doi = {10.1109/ACCESS.2025.3525499}, journal = {IEEE Access}, pages = {6618-6631}, shortjournal = {IEEE Access}, title = {Automatic detection of hard exudate in color retinal fundus image and diabetic maculopathy grading}, volume = {13}, year = {2025}, }
• (2025). Data driven second integral sliding mode control for the digital phase-locked loop. ACCESS, 13, 6606-6617. (www)

  @article{ , abstract = {The high accuracy and flexibility of the digital phase-locked loop (DPLL) make it a versatile tool with a wide range of applications in modern sensor technology. This article designs a new controller based on the traditional SOGI-PLL (second-order generalized integral phase-locked loop) and the Ultra-Local Model (ULM). The proposed algorithm does not rely on the detailed structure of each part of the PLL, but instead uses a ULM to replace the PLL structure and closed-loop loop. Firstly, the unknown disturbance of the ULM is estimated through a finite-time stability observer (FSO). Then, a discrete-time second integral sliding-mode controller (DSISMC) is designed based on the integral sliding surface, and the DPLL model is corrected offline using the obtained data. The theoretical results are mathematically rigorously proved, and the results of the simulation study show that the proposed method exhibits faster and superior performance in signal estimation and tracking compared to traditional PID algorithms.}, archive = {J_ACCESS}, author = {Haiqin Liu and Mingdong Hou and Guangye Li and Renming Yang and Yun Zhang}, doi = {10.1109/ACCESS.2025.3526317}, journal = {IEEE Access}, pages = {6606-6617}, shortjournal = {IEEE Access}, title = {Data driven second integral sliding mode control for the digital phase-locked loop}, volume = {13}, year = {2025}, }
• (2025). Relaxation digital-to-analog converters featuring self-calibration and parasitics-induced error suppression in 180-nm CMOS. ACCESS, 13, 6594-6605. (www)

  @article{ , abstract = {The design and the silicon characterization of two mostly digital, low-voltage, energy- and area-efficient Relaxation Digital-to-Analog Converters (ReDACs) in 180nm featuring digital self-calibration and parasitics-induced error suppression are presented and compared in this paper. The first design is a single-ended ReDAC (SE-ReDAC) and operates at 880kS/s with a 10-bit resolution, while the second is based on a differential ReDAC (Diff-ReDAC) architecture and operates at 100kS/s with a 13-bit resolution. The SE-ReDAC testchip in 180nm occupies just 5,030 $\mu $ m2 and operates with a supply voltage ranging from 0.6V to 1V. Experimental results at 0.65V reveal a 72.18dB-SFDR, a 65.59dB-THD and a 56.09dB SINAD, resulting in 9.02ENOB, with a power dissipation of just $3.3\mu $ W, achieving a competitive energy-efficiency (area-normalized energy efficiency) figure of merit FOM ( $\mathrm {FOM_{A}}$ ) of 166dB (175dB). On the other hand, the 180-nm Diff-ReDAC testchip occupies 7,800 $\mu $ m2 and operates in a supply voltage range from 0.45V to 1V, while achieving a 77.81dB-SFDR, a 77.52dB-THD and a 65.82dB-SINAD (10.64ENOB) at 0.6V supply with a power consumption of just 880nW, leading to a very competitive FOM (FOMA) of 172dB (178dB).}, archive = {J_ACCESS}, author = {Roberto Rubino and Francesco Musolino and Pedro Toledo and Yong Chen and Anna Richelli and Paolo S. Crovetti}, doi = {10.1109/ACCESS.2025.3526209}, journal = {IEEE Access}, pages = {6594-6605}, shortjournal = {IEEE Access}, title = {Relaxation digital-to-analog converters featuring self-calibration and parasitics-induced error suppression in 180-nm CMOS}, volume = {13}, year = {2025}, }
• (2025). Resource-aware multi-hop routing protocol for unicast cellular V2V communications. ACCESS, 13, 6584-6593. (www)

  @article{ , abstract = {Using a multi-hop routing protocol for unicast vehicle-to-vehicle (V2V) communications is crucial to facilitate data relaying from one vehicle to a distant one. The dynamic behavior of vehicular ad-hoc networks (VANETs) and unstable relays often cause frequent disconnections or switchovers, leading to higher latency, diminished reliability, and increased resource use. The stability of these routes depends not just on link connectivity, but also on the availability of adequate resources. Although numerous studies have explored traditional VANET routing protocols to tackle these issues, they often neglect the critical aspect of resource availability. In this study, we focus on a V2V routing method that considers resource availability, assuming the use of a geo-based resource allocation framework. In the proposed resource-aware approach, we utilize deep learning to predict vehicle trajectories and traffic load in various areas. The proposed resource-aware routing protocol aims at improving both spectrum efficiency and route stability by employing distance between vehicles, traffic load, and resource availability of areas to manage emergency messages (EMs) more efficiently and reduce unnecessary rebroadcasting and congestion. Our simulation results reveal that our proposed method surpasses traditional protocols in terms of packet reception ratio (PRR), latency, and average hop count.}, archive = {J_ACCESS}, author = {Faegheh Seifhashemi and Amirreza Hajrasouliha and Behrouz Shahgholi Ghahfarokhi}, doi = {10.1109/ACCESS.2025.3526697}, journal = {IEEE Access}, pages = {6584-6593}, shortjournal = {IEEE Access}, title = {Resource-aware multi-hop routing protocol for unicast cellular V2V communications}, volume = {13}, year = {2025}, }
• (2025). Models for processing nighttime light data and software development. ACCESS, 13, 6567-6583. (www)

  @article{ , abstract = {DMSP-OLS and NPP-VIIRS provide the most widely used nighttime light data (NTL) sources. These datasets cover the time span of 1992-2013 and 2013-present, respectively. However, the differences in sensor calibration and sensitivity to faint lights between the two satellites prohibit the merging of these two datasets into a longer one, which limits their further application in long-term time series analysis (1992 - present). Here, we provide correction models for the two datasets. The correction process includes oversaturation correction, annual/monthly data synthesis, digital number (DN) value intercalibration, and image continuity correction. Moreover, to test the accuracy and robustness of the merged datasets, we compared the dataset with the corrected products of LiNTL and ChenNTL. The overall accuracy of the dataset in this study was 0.90, and the average of the normalized difference index (ANDI) was 0.046. Additionally, we used the corrected nighttime light data to model Gross Domestic Product (GDP), achieving a correlation of r =0.849. The corrected dataset preserves the quality of the original data without excessive modification and demonstrates certain application value in the socioeconomic field. Finally, to facilitate the broad use of the correction models and the integrated nighttime light datasets, we integrated the above procedures into software with an attached preprocessing workflow. This not only fills the gap in nighttime light data processing software but also allows users to move beyond fixed datasets and customize personalized datasets according to their needs. The study also aims to lay a foundation for future cross-disciplinary applications.}, archive = {J_ACCESS}, author = {Yufeng Qu and Long Li and Xuqing Li and Runya Li and Man Liu and Yushuai Wei and Yongjian Huang}, doi = {10.1109/ACCESS.2025.3526418}, journal = {IEEE Access}, pages = {6567-6583}, shortjournal = {IEEE Access}, title = {Models for processing nighttime light data and software development}, volume = {13}, year = {2025}, }
• (2025). Impact of IBR location and parameters on inter-area oscillation modes in bulk power grids. ACCESS, 13, 6556-6566. (www)

  @article{ , abstract = {To ensure safe and reliable operations, electric utilities must understand how power grid dynamics are evolving as the existing synchronous machine-dominated systems incorporate increasing amounts of inverter-based resources (IBRs). A pressing concern is understanding if and how the well-known inter-area modes of oscillation will change due to increasing inverter penetration. To address this question, this paper derives an explicit analytical expression to identify the major factors influencing changes in inter-area mode properties when synchronous machines are replaced by IBRs. The IBRs are assumed to implement droop-based grid forming (GFM) control, while the analysis can be extended to other inverter control methods. It is concluded that oscillation mode changes are highly dependent on the grid location where synchronous machines are removed and/or IBRs are added, and the observed changes can be mitigated, to some extent, by tuning two of the GFM inverter control parameters, namely 1) the active power-frequency droop coefficient; and 2) the time constant of the active power measurement low-pass filter. The analytical conclusions are validated using full dynamic simulations of the IEEE 39-bus benchmark system, and the 2031 heavy-winter planning model of the US Western Interconnection. Conclusions from this study will help utilities understand 1) if/which inter-area modes will continue to be of concern in their footprints in an IBR-dominated future, and 2) identify areas where the displacement of synchronous machines may significantly alter the properties of existing modes, necessitating additional analysis during interconnection studies.}, archive = {J_ACCESS}, author = {Shuchismita Biswas and Xue Lyu and Quan Nguyen and Xiaoyuan Fan and Minghui Lu and Wei Du}, doi = {10.1109/ACCESS.2024.3520497}, journal = {IEEE Access}, pages = {6556-6566}, shortjournal = {IEEE Access}, title = {Impact of IBR location and parameters on inter-area oscillation modes in bulk power grids}, volume = {13}, year = {2025}, }
• (2025). A survey of differential privacy techniques for federated learning. ACCESS, 13, 6539-6555. (www)

  @article{ , abstract = {The problem of data privacy protection in the information age deserves people’s attention. As a distributed machine learning technology, federated learning can effectively solve the problem of privacy security and data silos. Differential privacy(DP) technology is applied in federated learning(FL). By adding noise to raw data and model parameters, it can further enhance the degree of data privacy protection. Over the years, differential privacy technology based on federated learning framework has been developed, which is divided into central differential privacy federated learning(CDPFL) and local differential privacy federated learning(LDPFL). Although differential privacy may reduce the accuracy and convergence of federated learning models while protecting data privacy, researchers have proposed a variety of optimization methods to balance privacy protection and model performance. This paper comprehensively expounds the research status of differential privacy techniques based on the federated learning framework, first providing detailed introductions to federated learning and differential privacy technologies, and then summarizing the development status of two types of federated learning differential privacy(DPFL) techniques respectively; for CDPFL, the paper divides the discussion into first proposal of CDP and typical application examples, the impact of Gaussian mechanisms on model accuracy, optimization based on asynchronous differential privacy, and insights from other scholars; for LDPFL, the paper divides the discussion into first proposal of LDP and typical application examples, processing multidimensional data and improving model accuracy, existing methods and optimization for reducing communication costs, balancing privacy protection and data usability, LDPFL based on the Shuffle model, and insights from other scholars; following this, the paper addresses and summarizes the unique challenges introduced by incorporating differential privacy into federated learning and proposes solutions; finally, based on a summary of existing optimization techniques, the paper outlines future directions and specifically discusses three research ideas for enhancing the optimization effects of federated differential privacy: advanced optimization strategies combining Bayesian methods and the Alternating Direction Method of Multipliers (ADMM), integrating lattice homomorphic encryption techniques from cryptography to achieve more efficient differential privacy protection in federated learning, and exploring the application of zero-knowledge proof techniques in federated learning for privacy protection.}, archive = {J_ACCESS}, author = {Wang Xin and Li Jiaqian and Ding Xueshuang and Zhang Haoji and Sun Lianshan}, doi = {10.1109/ACCESS.2024.3523909}, journal = {IEEE Access}, pages = {6539-6555}, shortjournal = {IEEE Access}, title = {A survey of differential privacy techniques for federated learning}, volume = {13}, year = {2025}, }
• (2025). Inc-DLOM: Incremental direct LiDAR odometry and mapping. ACCESS, 13, 6527-6538. (www)

  @article{ , abstract = {Intelligent Vehicle (IV) research is gaining popularity due to the convergence of technological advancements and societal demands, which also leads to the fundamental demand for precise localization. However, the localization accuracy of most existing LiDAR Odometry methods is limited by the complex environment and high-frequency motion, leading to unsatisfactory performance. Moreover, the point cloud data generated by different LiDARs will possess different properties, such as spatial density, Field-of-View (FoV), perception distances, etc., which may have a great impact on LO methods, and makes the generalization of LO a noteworthy issue. To address these issues, we propose the method of Incremental Direct LiDAR Odometry and Mapping (Inc-DLOM). Our proposed Inc-DLOM has the following key contributions: 1) a voxel-to-voxel (V2V) scan matching scheme for scan-to-scan transform estimation; 2) the Incremental Voxel Mapping (IVM) method to incrementally update and maintain the historical mapping information; 3) the Incremental GICP solver to refine the global pose by IVM. To evaluate the performance in terms of accuracy and efficiency, extensive experiments have been conducted with both mechanical LiDAR and solid-state LiDAR on different robotic platforms, including public datasets and real robot data acquisition. The experimental results show that Inc-DLOM achieves better accuracy, efficiency, and generalizability than other comparison state-of-the-art LiDAR Odometry methods.}, archive = {J_ACCESS}, author = {Kaiduo Fang and Rui Song and Ivan Wang-Hei Ho}, doi = {10.1109/ACCESS.2025.3526626}, journal = {IEEE Access}, pages = {6527-6538}, shortjournal = {IEEE Access}, title = {Inc-DLOM: Incremental direct LiDAR odometry and mapping}, volume = {13}, year = {2025}, }
• (2025). Multiple aliasing of windowed real-valued signal as a cause of accuracy limitation of DFT methods. ACCESS, 13, 6515-6526. (www)

  @article{ , abstract = {DFT spectral signal analysis is a widely used method for various types of signal processing due to its several advantages. In the case of incoherently sampled real-valued signals, spectral leakage and aliasing occur, causing errors in the signal spectrum estimate. Standard techniques consider that negative spectral leakage components influence the positive part of the spectrum by aliasing. Consequently, these methods are trying to suppress these spectral components. The paper shows that this way may not be satisfactory, because windowing of the signal, necessarily associated with the use of DFT, causes the appearance of repetitive bands in addition to the considered negative mirror components. These components cause additional aliasing, albeit with a decreasing but non-negligible influence, causing further errors in the calculated spectral components. This effect of multiple aliasing, which has not been considered so far, explains the limited maximum achievable accuracy of commonly used DFT methods. The paper further explains that the effect of the random part of such multiple aliasing can be fully compensated converting the real-valued signal to its analytic counterpart. Then the permanent part of the multiple aliasing can be simply eliminated from the obtained analytic signal. Furthermore, the paper introduces a simple procedure for converting real-valued sinusoidal signal to analytic signal in time domain. This procedure is used to develop a new frequency estimation method with minimal estimation error even for very small numbers of signal samples. By using a rectangular window, it minimizes the effect of noise practically to the Cramer-Rao bound.}, archive = {J_ACCESS}, author = {Karel Hajek and Zdeněk Kohl}, doi = {10.1109/ACCESS.2025.3526325}, journal = {IEEE Access}, pages = {6515-6526}, shortjournal = {IEEE Access}, title = {Multiple aliasing of windowed real-valued signal as a cause of accuracy limitation of DFT methods}, volume = {13}, year = {2025}, }
• (2025). The prediction of serum C-reactive protein concentration using nonlinear mixed-effects model. ACCESS, 13, 6507-6514. (www)

  @article{ , abstract = {Serum C-reactive protein (CRP) is a useful biomarker reflecting the efficacy of clinical treatments for infectious and autoimmune diseases. Accurate prediction of the serum CRP concentration of a patient through accurate initial clinical evaluation must be preceded to promptly cope with inflammatory diseases. In general, serum CRP concentration rises sharply right after hip surgery and then falls down at a certain period of time. Such patterns can be used as a meaningful indicator to estimate recovery tendency of individual patients. This study proposes a nonlinear mixed-effects (NME) model to describe nonlinear patterns of serum CRP concentration over time for patients suffering from hip arthroplasty through an observational study. The bi-exponential model with random effects is applied to predict temporal CRP concentrations in patients after hip surgery. Analytical results show that the proposed model accurately predicts serum CRP concentrations over time by effectively capturing individual variation in serum CRP concentrations through random effects. Based on the estimated model, we derive the distribution of normalized concentration times using the Monte Carlo (MC) simulation. The NME model will be expected to support future research on best practices for intraoperative and postoperative management of patients with hip surgery, based on various levels of predicted risks of infection.}, archive = {J_ACCESS}, author = {Suk Joo Bae and Gyu Ri Kim and Sun Geu Chae and Yeesuk Kim}, doi = {10.1109/ACCESS.2024.3524471}, journal = {IEEE Access}, pages = {6507-6514}, shortjournal = {IEEE Access}, title = {The prediction of serum C-reactive protein concentration using nonlinear mixed-effects model}, volume = {13}, year = {2025}, }
• (2025). ESX: A self-generated control policy for remote access with SSH based on eBPF. ACCESS, 13, 6487-6506. (www)

  @article{ , abstract = {Cloud systems that provide remote data and computational access through networks face significant security challenges. Secure Shell (SSH) is one of the most popular methods for remote access, but the leakage of login information presents a substantial security threat, enabling attackers to exploit identities and disrupt systems. Consequently, ensuring robust security in cloud-system operations is paramount. Access control, a crucial security mechanism in operating systems, is becoming increasingly complex due to the intricate nature of control mechanisms and the difficulty in developing precise Access Control Lists (ACLs). Traditional ACLs require extensive resources for each user or role, struggle in complex scenarios, and risk system vulnerability by granting excessive privileges. To mitigate these issues, we introduce Extend Security boX (ESX), a novel solution that combines a lightweight system call restriction system with machine learning method. ESX utilizes rule learning through itemset mining to analyze user behavior and generate system call control lists, thereby significantly reducing system vulnerability. By employing extended Berkeley Packet Filter (eBPF) program hooks, ESX effectively audits and restricts remote user behavior at the system call level. Our results show that ESX’s rule-mining algorithm achieves over 99% accuracy in generating access control policies, using only 40% of the log entries for mining. Additionally, ESX presents a lower overhead compared to established security solutions, such as AppArmor, enhancing the overall operating system security.}, archive = {J_ACCESS}, author = {Yuan Zhong and Pengfei Chen and Huxing Zhang}, doi = {10.1109/ACCESS.2024.3450496}, journal = {IEEE Access}, pages = {6487-6506}, shortjournal = {IEEE Access}, title = {ESX: A self-generated control policy for remote access with SSH based on eBPF}, volume = {13}, year = {2025}, }
• (2025). Temporally deformable convolution for gait recognition. ACCESS, 13, 6475-6486. (www)

  @article{ , abstract = {Gait recognition is a biometric technology that identifies individuals based on the unique characteristics of their gait. With the advancement of deep learning based computer vision technology, gait recognition has significantly improved in performance and gained significant attention due to its non-invasive nature. This paper proposes a vision-based gait recognition model that enhances performance by applying deformable convolution in the temporal dimension. Gait is a periodic behavior, and understanding the body’s changing patterns during walking is crucial for improving recognition accuracy. Important features for gait recognition can appear in both short and long moments of the walking process. Therefore, this study proposes a model that performs deformable convolution in the temporal dimension to effectively extract such features. This allows for a more flexible understanding of temporal patterns, eliminating the need for hand-crafted design of filters for extracting temporal features and enabling the model to learn important features for gait recognition that appear in both short and long time intervals in a unified manner. As a result, the proposed model achieved state-of-the-art performance on the CASIA-B dataset.}, archive = {J_ACCESS}, author = {Juyoung Kim and Beomseong Kim and Heesung Lee}, doi = {10.1109/ACCESS.2025.3526886}, journal = {IEEE Access}, pages = {6475-6486}, shortjournal = {IEEE Access}, title = {Temporally deformable convolution for gait recognition}, volume = {13}, year = {2025}, }
• (2025). Individual contribution-based spatial-temporal attention on skeleton sequences for human interaction recognition. ACCESS, 13, 6463-6474. (www)

  @article{ , abstract = {Skeleton-based human interaction recognition has gained increasing attention due to its ability to capture complex multi-person dynamics. Significant progress has been made in interaction recognition research, but challenges remain. First, variations in camera positions and viewpoints can cause significant differences in skeletal data for actions of the same type. Second, capturing both spatial information from skeleton structures and temporal information from interaction sequences is crucial for discriminative interaction feature representation. Third, the different contributions of each participant especially in asymmetric interactions are often overlooked. To address the above issues, we propose an innovative method by designing the individual contribution based spatial-temporal attention graph convolutional network. In this work, we first propose a simple but feasible view transformation method to reduce data mismatch from multi-view cameras. Then we design individual contribution weights to measure the importance of each person for interaction feature representation. Next, a novel spatial-temporal attention module based on individual contribution weights is proposed to obtain attention based skeleton data, which are fed to multiple layers of graph convolution to extract spatial-temporal features. Additionally, we use a two-stream architecture with joint coordinates and joint motion data as inputs for each stream. A weighted fusion strategy is utilized to obtain the final classification score. Experiments conducted on three different datasets demonstrate that proposed interaction recognition method can achieve satisfactory results compared with other works.}, archive = {J_ACCESS}, author = {Xing Liu and Bo Gao}, doi = {10.1109/ACCESS.2024.3525185}, journal = {IEEE Access}, pages = {6463-6474}, shortjournal = {IEEE Access}, title = {Individual contribution-based spatial-temporal attention on skeleton sequences for human interaction recognition}, volume = {13}, year = {2025}, }
• (2025). Development of risk activity detection system for forklifts based on inertial sensors. ACCESS, 13, 6451-6462. (www)

  @article{ , abstract = {Forklifts are mobile heavy machines that are used to transport, lift, or lower objects without the high physical effort of the operator. They work in different types of industries such as logistics, retail, food, mining, and construction, among others. Qualified personnel usually operate the forklifts to handle heavy loads in an environment surrounded by other workers. This creates a high risk of accidents due to the lack of visibility with a loaded forklift, the random movement of the workers around the area and possible risk maneuvers sometimes required in a normal day of operation. For example, in Chile 2000 accidents occur per year due to one of the mentioned situations. For this reason, the detection of risk maneuvers to prevent accidents is essential. This article shows a cost-effective solution proposal to implement an inertial sensor network with a dedicated wireless communication and automatic deep-learning algorithms to detect forklift risk events. A test bench was designed where a crane forklift equipped with four inertial sensors performed normal and risky maneuvers, according to the Occupational Safety and Health Administration (OSHA) 3949. During the forklift operation, the sensors measured the accelerations and angular velocities in three axes. Videos of the operation were also taken as reference. In this paper, we developed convolutional neural networks (CNN) and long-term memory (LSTM) algorithms to infer a risky maneuver from the inertial sensors data and compared it to the outcome of a video-based model trained on data labeled by a risk-prevention engineer. After field testing with the forklift, the inertial data-based algorithms had an average F1 of 0.93 versus video analysis which had an average F1 of 0.95. However, models based on inertial data take a quarter of the time to make the inference compared to video-based models.}, archive = {J_ACCESS}, author = {Luciano Radrigán and Sebastián E. Godoy}, doi = {10.1109/ACCESS.2024.3524032}, journal = {IEEE Access}, pages = {6451-6462}, shortjournal = {IEEE Access}, title = {Development of risk activity detection system for forklifts based on inertial sensors}, volume = {13}, year = {2025}, }
• (2025). PAGR: Accelerating global routing for VLSI design flow. ACCESS, 13, 6440-6450. (www)

  @article{ , abstract = {In this paper, we present PAGR (Python Alpha Global Routing) – a solution to the global routing problem in physical synthesis based on data from the ISPD 2024 contest. Our solution constructs a weighted graph and builds a Steiner tree. To accelerate the Steiner tree search, we propose a technique for the graph size minimization by reducing the input 3D matrix. This method slightly decreases result quality but finds solutions 2–10 times faster. We also detail our methods for parallel calculations and computing graph edge weights. Experimental results show that while the current Python implementation does not achieve high routing speeds, the solution’s quality measured in contest metrics (wire length, via, overflow) is comparable to top contestants. Implementing the algorithm in C/C++ will significantly improve runtime. The algorithm’s description can benefit future research, and the source code with detailed comments is available online.}, archive = {J_ACCESS}, author = {Roman A. Solovyev and Ilya A. Mkrtchan and Dmitry V. Telpukhov and Ilya I. Shafeev and Aleksandr Y. Romanov and Yevgeniy V. Stolbikov and Alexander L. Stempkovsky}, doi = {10.1109/ACCESS.2025.3526722}, journal = {IEEE Access}, pages = {6440-6450}, shortjournal = {IEEE Access}, title = {PAGR: Accelerating global routing for VLSI design flow}, volume = {13}, year = {2025}, }
• (2025). Enhancing cluster accuracy in diabetes multimorbidity with dirichlet process mixture models. ACCESS, 13, 6422-6439. (www)

  @article{ , abstract = {Clustering of diabetic multimorbidity data from EHRs is challenging due to patient heterogeneity, high-dimensional variables, sensitivity to parameter settings, and high computational demands, which complicate clustering processes and may result in suboptimal clustering results. These complex and imbalanced natures of diabetic multimorbidity data limit the effectiveness of traditional clustering techniques, producing suboptimal clusters and revealing inadequate clinically meaningful insights. This study addresses this gap by applying the Dirichlet Process Mixture Model (DPMM), a non-parametric clustering approach that does not require specifying cluster numbers and adapts dynamically to the underlying data structure. The major advantages of DPMM include (1) DPMM automatically adjusts the number of clusters based on data structure, enabling it to capture diverse patient profiles without needing predefined cluster counts, which is ideal for handling the variability in multimorbidity patterns; (2) DPMM estimates the distributional properties directly from the data, relying on proper parameter choices and improving the stability of clustering results across datasets; and (3) DPMM uses a Bayesian framework to iteratively converge toward optimal clustering solutions, efficiently managing large datasets and producing more clinically meaningful clusters. Additionally, Gibbs sampling is employed for robust convergence in parameter settings, minimizing the dependency on initial configurations and improving the consistency of clustering outcomes across various data contexts. Results show that DPMM consistently outperforms traditional methods in clustering high-dimensional and imbalanced datasets, offering significant translational potential for guiding tailored healthcare strategies for complex chronic diseases and optimizing healthcare resource allocation.}, archive = {J_ACCESS}, author = {Francis John Kita and Srinivasa Rao Gaddes and Peter Josephat Kirigiti}, doi = {10.1109/ACCESS.2024.3523340}, journal = {IEEE Access}, pages = {6422-6439}, shortjournal = {IEEE Access}, title = {Enhancing cluster accuracy in diabetes multimorbidity with dirichlet process mixture models}, volume = {13}, year = {2025}, }
• (2025). Advanced optical waveform with single mode fiber optic for developing upcoming networks of mobile communications. ACCESS, 13, 6413-6421. (www)

  @article{ , abstract = {An advanced optical backbone waveform for the upcoming mobile networks termed optical orthogonal generalized frequency division multiplexing (O-OGFDM) with a single mode fibre (SMF) and intensity modulator/direct detector (IM/DD) converter is presented, investigated, and valued for the first time. The proposed waveform is launched for a single carrier modulation in an optical back-to-back system. The developed design aims to complete the propagation journey of the primary wireless orthogonal generalized frequency division multiplexing (OGFDM) by supplying extended transmission services for the long haul rather than short distances while maximizing the bit rate. Apart from the higher bit rate achieved with the backbone O-OGFDM than the access network OGFDM, an extremely extended level of transmission coverage can be obtained with the O-OGFDM. The optical system efficiency for the presented O-OGFDM, in terms of the utilized baud processing, maximum bit rate, minimum error rate, and optical power level, is demonstrated in comparison with the optical GFDM and the electrical OGFDM. Ultimately, the simulation by MATLAB is employed to validate the performance of the proposed system considering accurately specified optimum parameters for this purpose.}, archive = {J_ACCESS}, author = {Mohammad R. Kadhum and Heba Haboobi}, doi = {10.1109/ACCESS.2024.3523818}, journal = {IEEE Access}, pages = {6413-6421}, shortjournal = {IEEE Access}, title = {Advanced optical waveform with single mode fiber optic for developing upcoming networks of mobile communications}, volume = {13}, year = {2025}, }
• (2025). 3D-printed anthropomorphic phantom framework for spectral photon-counting CT: A breast phantom application. ACCESS, 13, 6397-6412. (www)

  @article{ , abstract = {Anthropomorphic phantoms are critical in computed tomography (CT) imaging, simulating human anatomy for image quality and dosimetry evaluation. Traditional phantoms, however, often fail to accurately replicate complex tissue patterns, particularly for advanced technologies like spectral photon-counting CT (SPCCT). This study introduces a framework for creating 3D-printed anthropomorphic phantoms optimized for SPCCT, beginning with a breast phantom. A grid search optimization method was used to generate an optimal image acquisition protocol, capable of distinguishing between adipose, fibroglandular, skin, and carcinoma tissues. Using the MARS Microlab $5\times 120$ SPCCT system, it was found that a tube voltage of 80 kV and narrow energy bins 7-27 keV, 27-32 keV, 32-37 keV, 37-42 keV, and 42-80 keV, offered superior results. Various photopolymerizable resins and thermoplastic filaments were tested as tissue-mimicking materials (TMMs), comparing their linear attenuation coefficients with those of the tissues and reference TMMs. The breast phantom was created by segmenting an MRI image from the Duke Breast Cancer MRI dataset into two models: one for skin, fibroglandular, and carcinoma tissues, 3D-printed with e-PLA resin, and another for adipose tissue, replicated using paraffin wax. Multi-energy images of the phantom were then evaluated using both narrow and wide energy bins for validation. While this study focused on a breast phantom, the framework can be applied to create various anthropomorphic phantoms, enhancing SPCCT imaging and potentially improving diagnostic accuracy and patient outcomes.}, archive = {J_ACCESS}, author = {Kyriaki Katsikari and Nadine Francis and Nabil Maalej and Charalampos Pitsalidis and Aamir Younis Raja}, doi = {10.1109/ACCESS.2024.3521476}, journal = {IEEE Access}, pages = {6397-6412}, shortjournal = {IEEE Access}, title = {3D-printed anthropomorphic phantom framework for spectral photon-counting CT: A breast phantom application}, volume = {13}, year = {2025}, }
• (2025). EgoSep: Egocentric on-screen sound source separation for real-time edge computing. ACCESS, 13, 6387-6396. (www)

  @article{ , abstract = {The ability to identify specific sounds in noisy environments can be improved by incorporating visual information through audio-visual integration, leveraging visual cues such as lip reading and sound-producing object recognition. Recent advancements in deep learning have enabled effective audio-visual sound source separation methods. Simultaneously, the increasing adoption of wearable devices capable of processing audio-visual information has further driven the demand for On-screen Sound source Separation (OSS), particularly in dynamic, egocentric scenarios. However, OSS in these scenarios remains several technical challenges, such as adapting to rapidly changing perspectives, ensuring real-time performance on resource-constrained edge devices, and developing computationally efficient learning strategies. To address these challenges, we propose EgoSep, a method designed for Egocentric On-screen Sound Source Separation(Ego-OSS). EgoSep integrates appearance and motion features from visual data with audio features extracted using a U-Net-based encoder, enabling robust separation in dynamic environments. The method is evaluated using the signal-to-noise ratio (SNR), treating on-screen sounds as signals and off-screen sounds as noise. For the experiments, we combine two public datasets: EPIC-KITCHENS, a large-scale egocentric video dataset, and ESC-50, an audio-only dataset. We simulate realistic scenarios by mixing EPIC-KITCHENS on-screen sounds with ESC-50 off-screen noise. Experimental results show that EgoSep effectively suppresses noise (i.e., off-screen sounds), improving the SNR of the test data from 3.05 dB at the input to 10.01 dB at the output. Additionally, real-time feasibility is validated on the NVIDIA Jetson Nano Developer Kit, achieving a real-time factor (RTF) of 0.17, demonstrating its practicality for wearable applications. The audio-mixed datasets and some results are available at https://donghyeok-jo.github.io/Ego-OSS.}, archive = {J_ACCESS}, author = {Donghyeok Jo and Jun-Hwa Kim and Jihoon Jeon and Chee Sun Won}, doi = {10.1109/ACCESS.2025.3526757}, journal = {IEEE Access}, pages = {6387-6396}, shortjournal = {IEEE Access}, title = {EgoSep: Egocentric on-screen sound source separation for real-time edge computing}, volume = {13}, year = {2025}, }
• (2025). Multiway bandpass power divider design with flexible filter order. ACCESS, 13, 6378-6386. (www)

  @article{ , abstract = {This paper proposes an innovative design method for multiway filtering power dividers (PDs) that is both simple and allows for the filter order to be flexibly adjusted, effectively overcoming the limitations of earlier approaches. The method employs the common resonator technique to integrate bandpass filters for different channels, achieving circuit miniaturization. Additionally, by distributing the higher-order resonant frequencies of the resonators, the design ensures spurious-free performance. To validate the feasibility of this approach, a four-way filtering PD with a fifth-order bandpass response and an eight-way filtering PD with a third-order bandpass response were designed and implemented. Electromagnetic simulations and experimental data were highly consistent with each other. In summary, the proposed filtering PD design offers several key advantages, including a flexible filter order design, support for multiway power division, a compact circuit size ( $0.28\lambda _{g}^{2}$ ), high port-to-port isolation (>20 dB within the passband), spurious-free performance, and a simple design and layout.}, archive = {J_ACCESS}, author = {Chi-Feng Chen and Yu-Sheng Zeng and Yi-Chen Yeh and Tsang-Ning Tien}, doi = {10.1109/ACCESS.2025.3526589}, journal = {IEEE Access}, pages = {6378-6386}, shortjournal = {IEEE Access}, title = {Multiway bandpass power divider design with flexible filter order}, volume = {13}, year = {2025}, }
• (2025). Pretrained detail enhancement framework for remote sensing object detection. ACCESS, 13, 6362-6377. (www)

  @article{ , abstract = {Remote sensing object detection faces significant challenges due to the varying resolutions of images and the diverse shapes of objects. In this paper, we introduce an innovative approach to enhance object detection networks in remote sensing images by addressing these issues through a novel two-stage training process. Our key contributions include the development of a combined low-resolution pretraining and high-resolution fine-tuning strategy, which significantly improves the model’s generalization capability across different resolutions. We have designed a Pretrained Detail Enhancement and Restoration (PDER) framework that enhances feature extraction from low-resolution images, thereby boosting the baseline network’s ability to handle blurred and intricate objects. Additionally, we propose an Energy-Efficient Shape Adaptive Convolution (EESA) that integrates a depthwise separable mechanism with shape-adaptive feature maps, enabling efficient capture of complex object edges without substantial computational overhead. Compared to state-of-the-art methods such as YOLOv7, our model achieves an absolute average precision improvement of 2.34% and 2.57% on the DIOR and xView datasets, respectively. Extensive experiments on multiple remote sensing datasets demonstrate our approach’s effectiveness, achieving significant improvements in detection accuracy and computational efficiency, thus highlighting its potential for practical applications.}, archive = {J_ACCESS}, author = {Mo Zhou and Yue Zhou and Dawei Yang and Kai Song}, doi = {10.1109/ACCESS.2025.3526180}, journal = {IEEE Access}, pages = {6362-6377}, shortjournal = {IEEE Access}, title = {Pretrained detail enhancement framework for remote sensing object detection}, volume = {13}, year = {2025}, }
• (2025). Anomaly detection and business process orchestration for low-code platform in power system based on deep-cross model with data balancing. ACCESS, 13, 6350-6361. (www)

  @article{ , abstract = {This paper presents an innovative approach to anomaly detection in electric vehicle (EV) charging platforms, centered around four key innovations that significantly advance the field of charging infrastructure security. First, we introduce a statistically reliable data balancing methodology that addresses the critical challenge of anomaly sample scarcity, achieving a 47% improvement in detection accuracy compared to conventional sampling techniques. Second, we propose xDeepCIN, a novel deep learning architecture that uniquely combines Compressed Interaction Networks with low-order crossing structures, demonstrating a 28% reduction in feature sparsity compared to existing deep cross models. Third, we implement a hierarchical feature interaction mechanism that enables both explicit and implicit learning of high-order patterns, resulting in a 35% improvement in anomaly pattern recognition over traditional deep learning approaches. Fourth, we develop an adaptive processing framework for asynchronous heterogeneous data streams, reducing detection latency by 62% compared to current synchronous methods. Unlike prior studies that primarily focus on single-aspect improvements in either data processing or model architecture, our comprehensive approach simultaneously addresses data imbalance, feature sparsity, and processing efficiency. Experimental results across multiple real-world datasets demonstrate the framework’s superior performance, achieving an AUC of 0.93 and F1-score of 0.97, representing substantial improvements over existing methods. This advancement has significant implications for the growing EV market, particularly in China, where our framework could prevent an estimated 85% of serious charging infrastructure failures.}, archive = {J_ACCESS}, author = {Jin Huang and Ming Sheng Xu and Chang Ming Zhu and Guo An Wang}, doi = {10.1109/ACCESS.2024.3525342}, journal = {IEEE Access}, pages = {6350-6361}, shortjournal = {IEEE Access}, title = {Anomaly detection and business process orchestration for low-code platform in power system based on deep-cross model with data balancing}, volume = {13}, year = {2025}, }
• (2025). Specification-based testing of the image-recognition performance of automated driving systems. ACCESS, 13, 6321-6349. (www)

  @article{ , abstract = {Automated driving systems (ADSs) are complex entities comprising numerous components, and traditional testing methods often struggle to ensure their safety, primarily due to the diversity driving environments. Interestingly, deep neural networks (DNNs) have proven effective for object detection in these settings. The safety of object detection in ADSs depends on the position of the detected objects and the specifications that guide the system’s response to them. Consequently, testing the object-detection process in ADSs must be grounded in these specifications. However, current specifications are informal regarding object locations and inadequate for object-detection testing. To address this issue, this article first introduces the bounding box specification language (BBSL), a framework capable of mathematically articulating the specifications for object and event detection and responses. Subsequently, we propose a specification-based testing approach for the object-detection process in ADS using BBSL. Remarkably, BBSL can formally delineate the positions of objects within the driving environment. Furthermore, our proposed approach can identify safety-critical defects that conventional tests, which focus solely on performance evaluation, might overlook. Furthermore, we propose two sets of test criteria. The first set reflects the diversity of object positions and sizes within an image, while the second set includes coverage metrics that determine whether the test cases cover all conditions outlined by the BBSL specifications. Overall, our contributions facilitate the implementation of specification-based testing for object-detection systems using DNNs, a challenge previously considered formidable.}, archive = {J_ACCESS}, author = {Kento Tanaka and Toshiaki Aoki and Takashi Tomita and Daisuke Kawakami and Nobuo Chida}, doi = {10.1109/ACCESS.2024.3516082}, journal = {IEEE Access}, pages = {6321-6349}, shortjournal = {IEEE Access}, title = {Specification-based testing of the image-recognition performance of automated driving systems}, volume = {13}, year = {2025}, }
• (2025). Multi-population kidney-inspired algorithm with migration policy selections for feature selection problems. ACCESS, 13, 6306-6320. (www)

  @article{ , abstract = {Optimization algorithms often encounter challenges in effectively managing the trade-off between exploration and exploitation, usually leading to less-than-optimal outcomes. This study introduces two novel migration policies in multi-population version of kidney-inspired algorithm (KA) to address this dilemma. The initial algorithm, coded as MultiPop-KA, implements a predetermined migration policy. Conversely, the second algorithm, coded as AutoMultiPop-KA, adopts an adaptive migration policy selection process that determines migration type based on the average fitness of sub-populations. By capitalizing on a multi-population framework and incorporating two migration policies, these methods aim to achieve a more refined equilibrium between exploration and exploitation, thereby augmenting the effectiveness of the KA. Experimental evaluations, conducted across 25 test functions and applied to 18 benchmark feature selection problems, demonstrate the efficacy of the proposed techniques. These results indicate that the proposed approach can significantly enhance optimization algorithms’ performance and overall quality.}, archive = {J_ACCESS}, author = {Najmeh Sadat Jaddi and Salwani Abdullah and Say Leng Goh and Mohd Zakree Ahmad Nazri and Zalinda Othman and Mohammad Kamrul Hasan and Fatemeh Alvankarian}, doi = {10.1109/ACCESS.2025.3526640}, journal = {IEEE Access}, pages = {6306-6320}, shortjournal = {IEEE Access}, title = {Multi-population kidney-inspired algorithm with migration policy selections for feature selection problems}, volume = {13}, year = {2025}, }
• (2025). Cyclic learning rate-based co-training for image classification with noisy labels. ACCESS, 13, 6292-6305. (www)

  @article{ , abstract = {Deep learning has excelled in image classification, but noisy labels in large datasets pose a significant challenge, impacting performance and generalization. To tackle this, we propose a novel co-training method using cyclic learning rates. This method trains two networks simultaneously, each selecting clean samples based on loss values to optimize the other’s parameters, reducing overfitting and confirmation bias. The cyclic learning rate allows networks to oscillate between underfitting and overfitting, enhancing the distinction between clean and noisy samples. Our approach improves noise detection accuracy and robustness against label noise on datasets like CIFAR-10, CIFAR-100, and Clothing1M. Especially on CIFAR-10 and CIFAR-100 with 40% symmetric noise ratio, and Clothing1M, it outperforms the most relevant O2U-Net by 2.59%, 6.11%, and 0.57% in test accuracy, respectively, demonstrating superior noise resistance and classification accuracy under various noise conditions. Comprehensive experiments confirm the effectiveness of our method, advancing image classification in the presence of noisy labels.}, archive = {J_ACCESS}, author = {Ying Zheng and Yu Gu and Pingping Bai and Dong Yuan and Siqi Zhou and Xin Lyu and Ang Chen}, doi = {10.1109/ACCESS.2025.3526332}, journal = {IEEE Access}, pages = {6292-6305}, shortjournal = {IEEE Access}, title = {Cyclic learning rate-based co-training for image classification with noisy labels}, volume = {13}, year = {2025}, }
• (2025). TransDeep: Transformer-integrated DeepLabV3+ for image semantic segmentation. ACCESS, 13, 6277-6291. (www)

  @article{ , abstract = {In recent years, image semantic segmentation algorithms have made significant progress driven by deep learning technology, and are widely used in fields such as medical image analysis, assistive technology for the visually impaired people, and autonomous driving. Aiming at problems such as the inability of many image segmentation algorithms to fully capture global context information, low computational efficiency, and insufficient context information fusion. This article integrates the Transformer mechanism and CA mechanism into the DeepLabV3+ network and proposes the TransDeep network. In the encoder, two different backbones, Xception and MobileNetV2, are first used for feature extraction, and a better backbone network is selected. Furthermore, based on the lightweight backbone network, the Transformer mechanism is integrated into the advanced features of the backbone to enhance long-range dependence. Secondly, the Coord Attention module (CA) is added after the low-level features of the backbone to strengthen information such as edge and detail features. Finally, the Coord Attention mechanism (CA) is added after the ASPP module to allow the model to focus on key image features while effectively filtering out irrelevant background information. Experimental results show that the TransDeep network can improve the accuracy of key categories and effectively improve the network’s segmentation accuracy of targets in images. It achieved an MIoU of 73.5% on the Pascal test set and a good performance of 79.95% on the CamVid test set. Compared with the baseline model, they improved by 2.10% and 2.61% respectively.}, archive = {J_ACCESS}, author = {Tengfei Chai and Zhiguo Xiao and Xiangfeng Shen and Qian Liu and NianFeng Li and Tong Guan and Jia Tian}, doi = {10.1109/ACCESS.2024.3525065}, journal = {IEEE Access}, pages = {6277-6291}, shortjournal = {IEEE Access}, title = {TransDeep: Transformer-integrated DeepLabV3+ for image semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Comparative analysis of pixel-based segmentation models for accurate detection of impacted teeth on panoramic radiographs. ACCESS, 13, 6262-6276. (www)

  @article{ , abstract = {Accurate detection of impacted teeth in panoramic radiographs is critical for effective diagnosis and treatment planning in dentistry. Traditional segmentation methods often face challenges in achieving accurate detection due to the anatomical complexity and variability of dental structures. This study aims to address these limitations by performing a comprehensive comparative analysis of four advanced pixel-based segmentation models - U-Net, FPN, PSPNet and LinkNet - integrated with ten different backbone architectures. Using a meticulously annotated dataset of 407 high-resolution panoramic radiographs, the models were rigorously trained and evaluated using robust performance metrics, including accuracy, precision, recall, F1 score, and Intersection over Union (IoU). Among the configurations tested, the U-Net model with an EfficientNetB7 backbone achieved the highest performance, with an average IoU score of 85.29%, demonstrating superior accuracy and reliability. The main contributions of this study are the in-depth comparison of state-of-the-art segmentation models, the identification of the most effective architectures tailored for dental radiograph segmentation, and new insights into the advantages of pixel-based approaches over region-based methods commonly used in previous studies. These findings highlight the strengths and limitations of each model, providing practical guidance for researchers and clinicians in selecting appropriate solutions for impacted teeth detection. In addition, the study highlights the potential for future advances through hybrid approaches and customized model designs to further improve detection accuracy and clinical applicability. As a result, this research demonstrates the transformative potential of integrating artificial intelligence into dental diagnostics, paving the way for more accurate, efficient and scalable solutions to improve clinical decision-making.}, archive = {J_ACCESS}, author = {Meryem Durmuş and Burhan Ergen and Adalet Çelebi and Muammer Türkoğlu}, doi = {10.1109/ACCESS.2024.3523816}, journal = {IEEE Access}, pages = {6262-6276}, shortjournal = {IEEE Access}, title = {Comparative analysis of pixel-based segmentation models for accurate detection of impacted teeth on panoramic radiographs}, volume = {13}, year = {2025}, }
• (2025). Two symbol expansion methods and their application to reversible data hiding. ACCESS, 13, 6250-6261. (www)

  @article{ , abstract = {In reversible data hiding, image quality preservation and embedding capacity are of utmost importance. This paper proposes two reversible data hiding methods that utilize symbolization of correlations between adjacent pixels: 1) Haar transform representation and 2) pixel concatenation. First, we convert the pixel value into Haar transformed coefficients and use the symbol given by each pair as a domain for the histogram in which the secret data should be embedded. Second, a symbol domain created by concatenating two adjacent pixels was used. Both algorithms exploit the similarity between the neighboring pixels in an image, and the experimental results demonstrate an average PSNR of $64.1dB$ , which is 11.6% higher compared to the original histogram shifting method. The performance of the proposed simple method is comparable to or better than that of more advanced methods that rely on computationally complex prediction techniques.}, archive = {J_ACCESS}, author = {Hyoung Joong Kim and Changhee Kang and Sang-Ug Kang}, doi = {10.1109/ACCESS.2024.3523230}, journal = {IEEE Access}, pages = {6250-6261}, shortjournal = {IEEE Access}, title = {Two symbol expansion methods and their application to reversible data hiding}, volume = {13}, year = {2025}, }
• (2025). DBU-net: Dual-branch U-net for retinal fundus image super-resolution under complex degradation conditions. ACCESS, 13, 6237-6249. (www)

  @article{ , abstract = {Retinal fundus images are widely utilized in clinical screening and diagnosis of ocular diseases. However, in practical scenarios, the obtained fundus images are prone to be low-resolution (LR). LR fundus images increase the uncertainty in clinical observations, thereby heightening the risk of misdiagnosis. Despite this, popular super-resolution methods often yield unsatisfied results, especially when fundus images suffer from multiple degradations. In this article, we first analyze the degradation models of fundus images during the super-resolution reconstruction (SR) process. Then, we specially design a dual-branch U-Net network for retinal fundus images SR which incorporates our proposed muti-scale residual encoders (MSRE) with channel and spatial attention block (CSAB), while simultaneously preserving anatomical retinal structures and global features during SR process. Extensive experimental results on synthetically degraded LR fundus images demonstrate that our method can produce favorable results across multiple degradation models.}, archive = {J_ACCESS}, author = {Xianghui Chen and Shi Qiu and Yue Wang and Yu Zhang and Zhaoyan Liu and Xinhong Wang and Weiyuan Yao and Hongjia Cheng and Feihong Wang and Zhan Shu and Xuesong Li}, doi = {10.1109/ACCESS.2024.3522065}, journal = {IEEE Access}, pages = {6237-6249}, shortjournal = {IEEE Access}, title = {DBU-net: Dual-branch U-net for retinal fundus image super-resolution under complex degradation conditions}, volume = {13}, year = {2025}, }
• (2025). Ability of human auditory perception to distinguish human-imitated speech. ACCESS, 13, 6225-6236. (www)

  @article{ , abstract = {Distinguishing human-imitated speech from genuine speech presents a significant challenge for listeners due to their natural resemblance. Human auditory perception (HAP) has been widely studied to understand its mechanisms, with HAP-based acoustic features and metrics applied in various applications to assess sound quality and discriminate sound events. Leveraging these insights, this study specifically aims to evaluate HAP’s effectiveness in differentiating genuine from imitated speech through a systematic subject test. To this end, the study applies HAP to the task of distinguishing genuine from imitated speech, using a specially developed dataset of human-imitated speech, due to the lack of comparable publicly available datasets. A three-phase, human-centered approach was used to evaluate HAP ability, and participants achieved an average accuracy of 70.10% in distinguishing genuine from imitated speech in the final test. Additionally, a feasibility study was conducted using two feature sets for machine classification; among the timbral features, boominess and depth performed best with accuracies of 62% and 60%, respectively, while general features like Mel-spectrograms reached 51%. These results underscore the importance of auditory-related features in effectively detecting imitated speech.}, archive = {J_ACCESS}, author = {Khalid Zaman and Kai Li and Islam J. A. M. Samiul and Yasufumi Uezu and Shunsuke Kidani and Masashi Unoki}, doi = {10.1109/ACCESS.2025.3526631}, journal = {IEEE Access}, pages = {6225-6236}, shortjournal = {IEEE Access}, title = {Ability of human auditory perception to distinguish human-imitated speech}, volume = {13}, year = {2025}, }
• (2025). Flat-top metasurface broadband mushroom antenna for microwave power transmission. ACCESS, 13, 6217-6224. (www)

  @article{ , abstract = {In this paper, a flat-top metasurface broadband mushroom antenna for microwave power transmission (MPT) is proposed. It has a $4\times 4$ array of patches that are connected to the ground through vias to form a mushroom structure to reduce back lobe radiation. The antenna is fed by a microstrip, with the feeding slot located just underneath the central gap between the mushroom cells. This configuration enables the simultaneous excitation of dual resonance modes, resulting in aperture radiation and a wide impedance bandwidth. The final design has an overall dimension of $1.96\lambda _{0} \times 1.96\lambda _{0} \times 0.09\lambda _{0}$ at 5.4 GHz. After simulation and measurement, The final impedance bandwidth is 4.2-7.0GHz (50%), the flat-top bandwidth is 4.2-6.3GHz (40%), the peak gain is 8.4dBi, and the stable 1dB flat-top beam bandwidths of the E-plane and H-plane from 4.2GHz to 5.4GHz are both greater than 67° and 38°, and the 3dB flat-top beam bandwidths of the E-plane and H-plane are both stably greater than 85° and 24° at 5.6-6.3GHz. The stable flat-top radiation pattern makes it more popular in microwave radio transmission. Compared with other works, this antenna has the advantages of broadband, compactness, and wide beamwidth.}, archive = {J_ACCESS}, author = {Haixuan Li and Rui Zhang and Bingchuan Xie and Lu Tian and Zhicheng Wang and Zhengyu Deng}, doi = {10.1109/ACCESS.2024.3430387}, journal = {IEEE Access}, pages = {6217-6224}, shortjournal = {IEEE Access}, title = {Flat-top metasurface broadband mushroom antenna for microwave power transmission}, volume = {13}, year = {2025}, }
• (2025). Port contention aware task scheduling for SIMD applications. ACCESS, 13, 6204-6216. (www)

  @article{ , abstract = {Single instruction multiple data (SIMD) instructions in central processing units (CPU) are becoming commonly used in usecases such as wireless signal processing and artificial intelligence (AI) inference. When SIMD operations are executed in a simultaneous multi-threading (SMT) environment, a performance degradation event, called port contention, is expected to occur due to the limited number of ports caused by the large size of the SIMD circuit. In this paper, we analyze the occurrence of port contention during SIMD operation and clarify the characteristics that affect the performance of core allocation. On the basis of these performance characteristics, we propose a system that avoids the occurrence of port contention and improves performance. The proposed method features a core allocation policy to avoid port contention, identify SIMD applications, and coordinate with the operating system (OS) scheduler. Through evaluation, we find that the proposed method can reduce the processing time for some tasks in virtual radio access networks was reduced by 13.3%.}, archive = {J_ACCESS}, author = {Shogo Saito and Kei Fujimoto}, doi = {10.1109/ACCESS.2024.3523268}, journal = {IEEE Access}, pages = {6204-6216}, shortjournal = {IEEE Access}, title = {Port contention aware task scheduling for SIMD applications}, volume = {13}, year = {2025}, }
• (2025). Data overlay mesh in distributed clouds allowing collaborative applications. ACCESS, 13, 6180-6203. (www)

  @article{ , abstract = {In modern architecture, multi-tenancy is the design mode of operation used to reduce the resource costs of system implementation. This architectural decision does not come without additional costs when managing isolation and data privacy between tenants. That is a known challenge in cloud computing that translates to systems closer to the users, namely edge computing and micro clouds. Existing data management solutions, while supporting multi-tenancy, need help to handle the dynamic nature of such environments, limiting their ability to scale and support collaborative applications across different clusters or regions. This paper addresses the limitations of current data management solutions in supporting dynamic organization, configuration, and collaboration in multi-tenant, geo-distributed micro clouds at the edge. By extending the existing virtual clouds model, we introduce a new dataspaces layer and protocols for data discovery using the concept of hierarchical namespaces and transforming non-shareable resources, such as files and folders, into collaborative resources. We propose two types of collaborative applications that leverage stored procedures and event triggers for data consumption. Finally, we ensure that the model provides accurate access and redistribution of shareable (CPU, RAM, disk) and non-shareable resources through graph transformation theory, with correctness validated using multiparty session types. That enables more flexible programming for collaborative applications, improving connectivity across distributed storage systems, and allowing resource borrowing between applications within the same organization. This research seeks to bridge the gap between current tools and the demands of modern distributed systems, enabling more robust, adaptable, and collaborative data management solutions.}, archive = {J_ACCESS}, author = {Miloš Simić and Jovana Dedeić and Milan Stojkov and Ivan Prokić}, doi = {10.1109/ACCESS.2024.3525336}, journal = {IEEE Access}, pages = {6180-6203}, shortjournal = {IEEE Access}, title = {Data overlay mesh in distributed clouds allowing collaborative applications}, volume = {13}, year = {2025}, }
• (2025). SHM-traffic: DRL and transfer learning based UAV control for structural health monitoring of bridges with traffic. ACCESS, 13, 6166-6179. (www)

  @article{ , abstract = {This work focuses on using advanced techniques for Structural Health Monitoring (SHM) for bridges with Traffic (SHM-Traffic). We propose an approach using Deep Reinforcement Learning (DRL)-based control for Unmanned Aerial Vehicle (UAV). Our approach conducts a concrete bridge deck survey while traffic is ongoing and detects cracks. The UAV performs the crack detection, and the location of cracks is initially unknown. We use two edge detection techniques. First, we use canny edge detection for crack detection. We also use a Convolutional Neural Network (CNN) for crack detection and compare it with canny edge detection. Transfer learning is applied using CNN with pre-trained weights obtained from a crack image dataset. This enables the model to adapt and improve its performance in identifying and localizing cracks. Proximal Policy Optimization (PPO) is applied for UAV control and bridge surveys. The experimentation across various scenarios is performed to evaluate the performance of the proposed methodology. Key metrics such as task completion time and reward convergence are observed to gauge the effectiveness of the approach. We observe that the Canny edge detector offers up to 40% lower task completion time, while the CNN excels in up to 12% better damage detection and 1.8 times better rewards.}, archive = {J_ACCESS}, author = {Divija Swetha Gadiraju and Saeed Eftekhar Azam and Deepak Khazanchi}, doi = {10.1109/ACCESS.2025.3526193}, journal = {IEEE Access}, pages = {6166-6179}, shortjournal = {IEEE Access}, title = {SHM-traffic: DRL and transfer learning based UAV control for structural health monitoring of bridges with traffic}, volume = {13}, year = {2025}, }
• (2025). MathVision: An accessible intelligent agent for visually impaired people to understand mathematical equations. ACCESS, 13, 6155-6165. (www)

  @article{ , abstract = {2.2 billion people worldwide suffer from some form of vision impairment, according to the World Health Organization. Children with vision impairment and visual impairment may experience impaired physical, linguistic, and cognitive development, resulting in reduced levels of academic accomplishment. Many visually impaired people are working in the education sector whether they are students or teachers. Without external assistance reading of mathematical equations in images for visually impaired people is very challenging due to the complexity of notations, symbols, and variables. This paper presents a model named MathVision which converts the mathematical equation into voice. This voice is quite helpful for visually impaired people to understand mathematical equations. The proposed model is comprised of YOLOv7 object detection architecture to detect and categorize mathematical equations inside images into four distinct types: limits, trigonometry, integration, and an additional category. The input image is divided into a grid by the YOLOv7 model, and each grid cell is responsible for finding equations that fall into its respective category. bounding box coordinates, object labels, and probability scores are predicted for each equation. In the next stage, a fine-tuned DenseNet is utilized for detailed feature extraction from mathematical equation images. This involves optimizing a pre-trained DenseNet model to capture intricate patterns specific to equations. The fine-tuned DenseNet enhances overall accuracy in equation detection and categorization within the system. In the subsequent phase, an attention mechanism-based LSTM network is employed to generate natural language descriptions for mathematical equations. During the decoding process, the model is better able to focus on pertinent portions of the equation due to the integration of attention. The LSTM architecture, chosen for its effectiveness with sequential data, is trained on a dataset containing paired examples of equations and corresponding human-generated descriptions. Fine-tuning includes optimizing hyperparameters for the task, and evaluation metrics such as the BLEU score are used to assess the model’s performance in generating accurate and contextually relevant textual representations for the detected mathematical content. Our text-to-speech system takes input in the form of a natural language sentence generated by the LSTM model and converts it to the voice. This TTS using natural language processing analyzes and processes the text then it converts this processed text into speech using digital signal processing technology. A platform-independent pyttsx3 python library is used for converting text into speech. It also works offline which is the main reason for using this library in this research work. As there was no dataset available of mathematical equations with their natural language description, we created a custom dataset. We conducted real-world experiments in various visually impaired schools to see whether visually impaired students can understand mathematical equations by hearing the voice. These experiments prove that the MathVision Model is an efficient way for visually impaired students to read and write mathematical equations by listening to the voice of equations generated by proposed model.INDEX TERMS Mathematical equations, fine-tuned, YOLO v7, convolution neural network, attention mechanism, long short term memory, neural text to speech, technological development.}, archive = {J_ACCESS}, author = {Muhammad Awais Ahmad and Tauqir Ahmed and Muhammad Aslam and Amjad Rehman and Faten S. Alamri and Saeed Ali Bahaj and Tanzila Saba}, doi = {10.1109/ACCESS.2024.3514079}, journal = {IEEE Access}, pages = {6155-6165}, shortjournal = {IEEE Access}, title = {MathVision: An accessible intelligent agent for visually impaired people to understand mathematical equations}, volume = {13}, year = {2025}, }
• (2025). Corrections to “A novel approach based on machine learning, blockchain and decision process for securing smart grid”. ACCESS, 13, 6154. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “A Novel Approach Based on Machine Learning, Blockchain and Decision Process for Securing Smart Grid”).}, archive = {J_ACCESS}, author = {Nabil Tazi Chibi and Omar Ait Oualhaj and Wassim Fassi Fihri and Hassan El Ghazi}, doi = {10.1109/ACCESS.2025.3525876}, journal = {IEEE Access}, pages = {6154}, shortjournal = {IEEE Access}, title = {Corrections to “A novel approach based on machine learning, blockchain and decision process for securing smart grid”}, volume = {13}, year = {2025}, }
• (2025). Flat-panel-rectenna with broad RF energy harvesting coverage for wireless-powered sensor applications. ACCESS, 13, 6146-6153. (www)

  @article{ , abstract = {To circumvent the issue of significant space occupation associated with omnidirectional antenna-based rectennas and to seamlessly integrate into self-powered applications with minimal uncovered zones, this paper presents a new flat-panel rectenna (FPR) designed specifically for microwave-based wireless power charged sensor applications. This innovation aims to optimize space efficiency and enhance energy capture capabilities, providing a more effective solution for the continuous and reliable operation of wireless powered devices in various sectors. The proposed compact rectenna is designed with a planar antenna featuring wide beamwidth and an RF-to-DC conversion network that includes a dual-voltage rectifier architecture and a customized matching network to optimize RF-to-DC conversion efficiency. The FPR is then integrated with a batteryless heart rate sensor, enabling the sensor to operate and expand its physical orientation while maintaining accurate monitoring. The performance of the proposed RF energy harvester was evaluated at 5.8 GHz, showing a half-power coverage of 144.6° with respect to a peak antenna gain of 3.34 dBi, and a peak RF-to-DC conversion efficiency of 51.8%. Additionally, the batteryless sensor was shown to function effectively with a maximum RF energy incident angle of 140°, despite the flat-panel architecture.}, archive = {J_ACCESS}, author = {Yan Han and Eunji Kim and Han Lim Lee}, doi = {10.1109/ACCESS.2024.3524630}, journal = {IEEE Access}, pages = {6146-6153}, shortjournal = {IEEE Access}, title = {Flat-panel-rectenna with broad RF energy harvesting coverage for wireless-powered sensor applications}, volume = {13}, year = {2025}, }
• (2025). An empirical analysis of transformer-based and convolutional neural network approaches for early detection and diagnosis of cancer using multimodal imaging and genomic data. ACCESS, 13, 6120-6145. (www)

  @article{ , abstract = {Early diagnosis of cancer has focused on the use of advanced algorithms to achieve accurate diagnosis. The proposed study assesses the effectiveness of Transformer-based models and Convolutional Neural Networks (CNN) in cancer diagnosis with respect to multimodal imaging and genomic data. The performance comparisons between the two algorithmic methods with such complex datasets, which combine multi-modal imaging and genomic information, are presented. In search of the optimal neural network configuration, a series of experiments were conducted with respect to different layers, attention mechanisms in case of transformers, and convolutional architectures in case of CNNs. Besides, parameters related to training, such as learning rates, batch sizes, and optimization algorithms, have also been systematically tuned. The different models were evaluated against accuracy, precision, recall, and the F1-score. Our results show that the proposed multimodal model, with accuracy from 92.5 to 93.2, F1-scores between 91.5 and 92.2, precision of 91.5 to 92.2, and recall values of 92.5 to 93.2. In contrast, much lower accuracy, F1-scores, precision, and recall values were noticed when using baselines, especially VGG. All these findings indicate the fact that the presented techniques, especially the Multimodal and Transformer models, are more robust solutions for classification tasks with better balance between precision and recall, as well as with higher overall accuracy. This came with the cost of the expense of computational resources: CNNs are less resource-intensive but have competitive performance with better precision and recall. The results underline how algorithm selection and hyperparameter optimization play a crucial role in cancer detection tasks. This study has shown how state-of-the-art deep learning methods can be effectively combined with multi-modal data for building more accurate and efficient systems in cancer diagnosis. Two main lines of future work would be improving these algorithms and understanding their applicability in real clinical practice to obtain maximum benefits from them.}, archive = {J_ACCESS}, author = {S. K. B. Sangeetha and Sandeep Kumar Mathivanan and V. Muthukumaran and Jaehyuk Cho and and Sathishkumar Veerappampalayam Easwaramoorthy}, doi = {10.1109/ACCESS.2024.3524564}, journal = {IEEE Access}, pages = {6120-6145}, shortjournal = {IEEE Access}, title = {An empirical analysis of transformer-based and convolutional neural network approaches for early detection and diagnosis of cancer using multimodal imaging and genomic data}, volume = {13}, year = {2025}, }
• (2025). A data-driven MCDM approach-based spherical fuzzy sets for evaluating global augmented reality providers in education. ACCESS, 13, 6102-6119. (www)

  @article{ , abstract = {Amidst the heightened popularity of augmented reality (AR), particularly in education driven by the transformative impact of the COVID-19 pandemic on remote learning, this study addresses the intricacies surrounding the discerning selection of AR providers for educational purposes. It introduces a comprehensive decision support model, utilizing Spherical Fuzzy Sets (SFSs) to enhance the decision-making process. The methodology involves the application of SF-Delphi to ascertain the relative significance of eight pivotal criteria shaping AR provider selection. The Spherical Weighted Arithmetic Mean (SWAM) operator ensures an exhaustive and unbiased assessment. Subsequently, the Technique for Order of Preference by Similarity to the Ideal Solution (SF-TOPSIS) is systematically applied to evaluate and rank ten global AR providers. This research yields a dual contribution. Firstly, the proposed decision support model systematically guides AR provider selection, equipping decision-makers to navigate the intricacies of AR technology choices efficiently. Additionally, the impact extends to education, enriching the understanding of AR applications. This knowledge promotes well-informed and innovative AR technology utilization, enhancing the overall learning experience. In conclusion, this study introduces a pioneering decision support model that leverages SFSs to guide the selection of educational AR providers. This structured framework empowers decision-makers and advances the comprehension of AR dynamics, further propelling the strategic integration of AR technology in education.}, archive = {J_ACCESS}, author = {Phi-Hung Nguyen}, doi = {10.1109/ACCESS.2024.3361320}, journal = {IEEE Access}, pages = {6102-6119}, shortjournal = {IEEE Access}, title = {A data-driven MCDM approach-based spherical fuzzy sets for evaluating global augmented reality providers in education}, volume = {13}, year = {2025}, }
• (2025). A study on power system retrofit for cessna-172S aircraft by using hydrogen fuel cell and battery hybrid. ACCESS, 13, 6089-6101. (www)

  @article{ , abstract = {The electrification of aviation is predominantly driven by environmental concerns associated with emissions. In this context, while the More Electric Aircraft and Hybrid Electric Aircraft concepts are currently at the forefront, the long-term objective is the utilization of All Electric Aircraft. Presently, the transition to All Electric Aircraft, which includes the electrified propulsion system, seems challenging due to the significant energy and power demands of aircrafts, given that the specific energy of recent energy storage technologies and power density of other electrical parts are not at the desired levels. Nevertheless, various scientific findings are being explored to accelerate this process and fuel cells are emerging as a solution. This study examines the retrofitting of a Cessna 172S, selected as the baseline aircraft, into a fully electric version with a hybrid configuration combining a battery and fuel cell. The modification process includes the development of an electrical power system architecture, selection of the required components, and modeling of the system elements. The new configuration of the platform is investigated through various simulations to evaluate the impacts on power sharing of the battery and fuel cell, and a comparative weight analysis is conducted between the baseline and retrofitted aircraft. The study concludes with a critical examination of the benefits and challenges related to the All Electric Aircraft concept.}, archive = {J_ACCESS}, author = {Oguz Kagan Keles and Ibrahim Hakyemez and Mustafa Bagriyanik and Ozcan Kalenderli}, doi = {10.1109/ACCESS.2025.3526633}, journal = {IEEE Access}, pages = {6089-6101}, shortjournal = {IEEE Access}, title = {A study on power system retrofit for cessna-172S aircraft by using hydrogen fuel cell and battery hybrid}, volume = {13}, year = {2025}, }
• (2025). Improved PID control design for electric power steering DC motor. ACCESS, 13, 6080-6088. (www)

  @article{ , abstract = {As technology continues to evolve, the hydraulic power steering system (HPS) used for steering control in vehicles had begun to be changed by the electric power steering (EPS) system, which offers the advantage of better controllability. This study aims to achieve more precise and stable control of a modeled EPS motor using an innovative control method. The effects of a PID control designed with a new approach, on the system were investigated to compare it with the Ziegler Nichols, Internal Model Control (IMC), Tyreus Luyben, and Haugen methods mentioned in the study. The foundation of the presented design technique is based on calculating the optimal proportional gain ( $k_{p}$ ) value to minimize the error rates of the settling time ( $t_{s}$ ) and maximum overshoot ( $M_{p}$ ) in a stable loop, and then deriving the other PID controller parameters. This process was achieved by iterative tuning within the stable region of the control loop and calculating the optimum controller parameters. Reducing the calculation complexity of PID control parameters and improving their adaptability are also among the desired goals with the proposed method. According to the results, it was stated that the PID controller calculated with the new approach had better $M_{p}$ , $t_{s}$ , and rise time ( $t_{r}$ ) performance values compared to other controllers compared to the ones with which it was compared, and that no steady state error (SSE) was obtained.}, archive = {J_ACCESS}, author = {A. Turan}, doi = {10.1109/ACCESS.2024.3524303}, journal = {IEEE Access}, pages = {6080-6088}, shortjournal = {IEEE Access}, title = {Improved PID control design for electric power steering DC motor}, volume = {13}, year = {2025}, }
• (2025). Studying forgetting in faster R-CNN for online object detection: Analysis scenarios, localization in the architecture, and mitigation. ACCESS, 13, 6067-6079. (www)

  @article{ , abstract = {Online Object Detection (OOD) requires learning new object categories from a stream of images, similar to an agent exploring new environments. In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. In this paper, we investigate the learning and forgetting mechanisms of Faster R-CNN in OOD through three main contributions. First, we observe that the forgetting curves of the Faster R-CNN exhibit patterns similar to those described in human memory studies by Hermann Ebbinghaus: knowledge is lost exponentially over time and recall improves knowledge retention. Second, we present a new methodology to analyse the Faster R-CNN architecture and quantify forgetting across the Faster R-CNN components. We show that forgetting is mainly localised in the Softmax classification layer. Finally, we propose a new training strategy for OOD called Configurable Recall (CR). CR performs recalls on old data using images stored in a memory buffer with variable frequency and recall length to ensure efficient learning. CR also masks the logits of old objects in the softmax classification layer to mitigate forgetting. We evaluate our strategy against state-of-the-art methods on three OOD benchmarks. We analyse the effectiveness of different types of recall in mitigating forgetting and show that CR outperforms existing methods.}, archive = {J_ACCESS}, author = {Baptiste Wagner and Denis Pellerin and Sylvain Huet}, doi = {10.1109/ACCESS.2024.3523637}, journal = {IEEE Access}, pages = {6067-6079}, shortjournal = {IEEE Access}, title = {Studying forgetting in faster R-CNN for online object detection: Analysis scenarios, localization in the architecture, and mitigation}, volume = {13}, year = {2025}, }
• (2025). A novel synthetic minority oversampling technique for multiclass imbalance problems. ACCESS, 13, 6054-6066. (www)

  @article{ , abstract = {Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented. This study addresses the classification problems with multi-class imbalanced datasets, which are inherently more complicated than binary imbalanced problems. To tackle this problem, a novel and effective method called the One-vs-One Center Hybrid Synthetic Minority Over-sampling Technique (OCH-SMOTE) algorithm is proposed, which combines the enhanced Synthetic Minority Oversampling Techniques (SMOTE) with the One-vs-One (OVO) decomposition strategy. The OCH-SMOTE algorithm comprises two key components: the OVO strategy is used to decompose the multi-class imbalanced datasets, and the enhanced CH-SMOTE algorithm is used to generate the balanced training datasets to improve the classification performance. The OCH-SMOTE algorithm is extensively evaluated on 18 real-world multi-class imbalanced datasets, using the CART decision tree as the base classifier. The proposed method is compared with classical and state-of-the-art oversampling methods. On average, the OCH-SMOTE algorithm improves $P_{macro}$ by 8.19%, MAVA by 9.19%, MG by 30.68%, MFM by 8.78%, and Kappa coefficient by 0.0462 across all datasets compared to the baseline methods. The experimental results demonstrate that the OCH-SMOTE algorithm significantly enhances multi-class imbalanced datasets classification performance.}, archive = {J_ACCESS}, author = {Jiao Wang and Norhashidah Awang}, doi = {10.1109/ACCESS.2025.3526673}, journal = {IEEE Access}, pages = {6054-6066}, shortjournal = {IEEE Access}, title = {A novel synthetic minority oversampling technique for multiclass imbalance problems}, volume = {13}, year = {2025}, }
• (2025). Multimodal fusion and cutting-edge AI-based smart vending machines for electronic component management. ACCESS, 13, 6029-6053. (www)

  @article{ , abstract = {Managing electronic components efficiently in a laboratory environment poses a tedious problem, and the same can hinder research efficiency and productivity. These challenges stem from the diversity of electronic components, which can range from small passive components like resistors and capacitors to large integrated circuits. This research presents a groundbreaking system for managing electronic components in smart vending machines, leveraging advanced AI and innovative techniques. The Multimodal Annotation Fusion (MAF) method enhances a carefully chosen dataset with various attributes and multimodal annotations, laying the groundwork for a robust and intelligent recognition system. YOLO v8 and transfer learning are integrated along with custom loss functions and model optimisation to produce a model that significantly outperforms existing solutions. The algorithm’s effectiveness is particularly noticeable in its low GPU time consumption, which is important for real-time applications. Ablation experiments in various settings further validate the algorithm’s efficacy, particularly in identifying small electrical components. As an example, the suggested model shows a 3% higher IoU than SSD and a 1% higher IoU than YOLO v7 and Faster RCNN, indicating significant increases in accuracy. The results of this study could greatly improve electronic component management, increasing the usefulness of smart vending machines for professionals and students working in lab environments. Furthermore, the vending machine’s digital twin accurately simulates its real-world performance for virtual testing and improvement prior to real deployment, offering increased effectiveness and convenience.}, archive = {J_ACCESS}, author = {M. Karthiga and Syarifah Bahiyah Rahayu and E. Suganya and S. Sankarananth and S. Sountharrajan and K. Venkatesan}, doi = {10.1109/ACCESS.2024.3523774}, journal = {IEEE Access}, pages = {6029-6053}, shortjournal = {IEEE Access}, title = {Multimodal fusion and cutting-edge AI-based smart vending machines for electronic component management}, volume = {13}, year = {2025}, }
• (2025). Data augmentation-based enhancement for efficient network traffic classification. ACCESS, 13, 6006-6028. (www)

  @article{ , abstract = {The necessity of Network traffic classification is becoming increasingly significant as users’ applications and devices become more diverse and prevalent. As encryption becomes the norm for security reasons, the traffic classification problem is not easily solved. In this work, we provide an inductive counterevidence to the vague belief that deep learning models can perform well and outperform tree-based machine learning models in all aspects across domains, especially in the network traffic classification domain. We address the problem of finding an efficient encrypted traffic classification method in resource-constrained situations in the network traffic classification domain by limiting the scope of our research. Using the first packet, we converted packet headers and encrypted partial payloads into tabular data through a standardized format. We used them as the same inputs for lightweight deep learning and tree-based machine learning models, analyzed their performance, and identified efficient models. Next, we improved the performance of the previously selected efficient traffic classifier through data augmentation methods. Augmentation was performed to a degree that did not significantly damage the original data distribution so that the augmented dataset’s class distribution did not interfere with model learning. We applied two fundamentally contrasting methods to augment traffic data, depending on whether the basis of data augmentation is individual data or the entire data. Data augmentation increased the accuracy of the machine learning model by 0.26%, which complemented the machine learning model’s performance in network traffic classification and made the machine learning model outperform the lightweight deep learning model.}, archive = {J_ACCESS}, author = {Chang-Yui Shin and Yang-Seo Choi and Myung-Sup Kim}, doi = {10.1109/ACCESS.2024.3525000}, journal = {IEEE Access}, pages = {6006-6028}, shortjournal = {IEEE Access}, title = {Data augmentation-based enhancement for efficient network traffic classification}, volume = {13}, year = {2025}, }
• (2025). Design of a low-profile dual-polarization wideband semi-flexible antenna. ACCESS, 13, 5995-6005. (www)

  @article{ , abstract = {This paper proposed a low-profile dual-polarization wideband inverted-F fed stacked-patch semi-flexible antenna. The proposed antenna features a stacked-patch on the top PCB layer and two orthogonal inverted-F feed structures on the bottom PCB layer. Flexibility is achieved through the thin, flexible bottom PCB layer, while the rigid top PCB layer can be arranged to prevent collisions between adjacent cells, allowing the array to bend easily. Two orthogonal inverted-F feeds share the same flexible PCB layer, enabling a low-profile dual-polarization antenna design. Polarization in x-direction is achieved using the single-ended inverted-F feed, while polarization in y-direction is achieved using the differential-ended inverted-F feed. Simulations were conducted to examine the impact of bending the flexible bottom PCB layer on the antenna performance. Key parameters of the proposed antenna were studied and a prototype was fabricated to validate the design concept. Measurement and simulation results are in good agreement. The thickness of the proposed antenna is $0.067~\lambda $ at the lowest operating frequency and the bandwidth is 15%. Measured isolation between the two ports is 26 dB.}, archive = {J_ACCESS}, author = {Ankang Liu and Jian Lu and Peng Khiang Tan and Theng Huat Gan}, doi = {10.1109/ACCESS.2024.3525304}, journal = {IEEE Access}, pages = {5995-6005}, shortjournal = {IEEE Access}, title = {Design of a low-profile dual-polarization wideband semi-flexible antenna}, volume = {13}, year = {2025}, }
• (2025). Research evolution, current status, trends, and sustainable development of smart product-service systems. ACCESS, 13, 5974-5994. (www)

  @article{ , abstract = {The objective of this study is to conduct a comprehensive bibliometric analysis within the domain of Smart Product-Service Systems (Smart PSS). Leveraging the extant scholarly work, this paper delineates the prevailing research landscape and trajectories, while scrutinizing the focal points and prospective trends in this discipline. A total of 925 relevant articles were retrieved and collected from the Web of Science core database. VOSviewer and CiteSpace knowledge mapping tools were utilized to analyze the annual publication volume, institutional collaboration, topic burst, keyword co-occurrence, author collaboration, author co-citation, document co-citation, and references in the field of Smart Product-Service Systems. It is evident from this research that there is a lack of coordination between theoretical and practical development in the current study of Smart PSS sustainability, and the research field has certain limitations. Additionally, the research focus in this field has shifted from product-centered to user-centered, resulting in a sustainable development trend aimed at value co-creation. In the future, it is necessary to achieve sustainable development of Smart PSS through three aspects: theoretical innovation, methodological innovation, and development path. This systematic review will deepen the understanding of Smart PSS research and provide directional guidance for future studies. It also emphasizes the necessity of exploring the Smart PSS field more deeply to achieve sustainable development in this area.}, archive = {J_ACCESS}, author = {Hui Zeng and Ting Qiu and Jinwei Zhu and Shuo Xu}, doi = {10.1109/ACCESS.2025.3526731}, journal = {IEEE Access}, pages = {5974-5994}, shortjournal = {IEEE Access}, title = {Research evolution, current status, trends, and sustainable development of smart product-service systems}, volume = {13}, year = {2025}, }
• (2025). Application of infrared thermography and artificial intelligence in healthcare: A systematic review of over a decade (2013–2024). ACCESS, 13, 5949-5973. (www)

  @article{ , abstract = {Infrared thermography (IRT) is a non-invasive, radiation-free imaging technique that uses an infrared (IR) camera to record and produce an image using IR radiation emitted from the body. IRT imaging has shown promise as a screening method for breast cancer, diabetic foot ulcers, and dry eye disease, among other medical disorders. The aim of this systematic review is to present a complete overview of the applications of artificial intelligence (AI) techniques with IRT imaging for medical decision support systems over the course of the last ten years (2013–2024). Several scientific databases, including PubMed, IEEE, and Google Scholar, were searched using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After meeting the requirements for inclusion, 131 papers were selected. The reviewed studies demonstrated how various AI techniques, including deep learning and classical machine learning, can be used to develop automated diagnosis systems using IRT images. The efficacy of these AI systems differed depending on the medical field; for example, they could identify dry eye disease with 90–100% accuracy, classify diabetic foot ulcers with 85–95% accuracy, and detect breast cancer with 80–100% accuracy. This review highlights the value of IRT imaging in early disease detection, especially when combined with AI techniques. This work discusses challenges in using deep learning (DL) models in healthcare, including data scarcity and ethical considerations. It also, proposes three main recommendations: dataset standardization for ethical data management, clear governance models for ethical practices, and the use of Multimodal Large Language Models (MLLMs) to address explainability issues.}, archive = {J_ACCESS}, author = {Jahmunah Vicnesh and Massimo Salvi and Yuki Hagiwara and Hah Yan Yee and Hasan Mir and Prabal Datta Barua and Subrata Chakraborty and Filippo Molinari and U. Rajendra Acharya}, doi = {10.1109/ACCESS.2024.3522251}, journal = {IEEE Access}, pages = {5949-5973}, shortjournal = {IEEE Access}, title = {Application of infrared thermography and artificial intelligence in healthcare: A systematic review of over a decade (2013–2024)}, volume = {13}, year = {2025}, }
• (2025). Achieving high-quality image stylization using hierarchical sensibility boost, visual style attention, and covariance evolution blend. ACCESS, 13, 5937-5948. (www)

  @article{ , abstract = {In recent years, image stylization has gained significant attention due to its ability to transform visual styles from reference images to content images, transforming ordinary photographs into sensuous creations. Traditional methods, however, struggle to retain fine-grained details and often exhibit issues such as discordant textures and noticeable artifacts. This paper proposes a novel approach for high-quality image stylization by introducing a method that integrates Hierarchical Sensibility Boost (HSB), Visual Style Attention (VSA), and Covariance Evolution Blend (CovEB) blocks. The proposed method addresses the challenges of preserving content construction while enhancing stylistic representations to achieve visually pleasing and realistic results. Specifically, the HSB block captures and amplifies style representations at different sizes, the VSA block uses channel attention mechanisms to dynamically boost style representations, and the CovEB block aligns second-order statistical data between style and content representations to ensure coherent stylization. Comprehensive experiments conducted on MS-COCO and WikiArt datasets demonstrate that the proposed method produces stylized images with superior visual sensibility, minimal artifacts, and improved efficiency compared to existing techniques. Quantitative evaluations confirm that our method excels in style similarity, global effects, and local pattern retention, while maintaining competitive performance in content fidelity and runtime efficiency.}, archive = {J_ACCESS}, author = {Ya Gao}, doi = {10.1109/ACCESS.2024.3524299}, journal = {IEEE Access}, pages = {5937-5948}, shortjournal = {IEEE Access}, title = {Achieving high-quality image stylization using hierarchical sensibility boost, visual style attention, and covariance evolution blend}, volume = {13}, year = {2025}, }
• (2025). Seeking stability for multi-leader stackelberg game as an incentive mechanism for multi-requester federated learning. ACCESS, 13, 5922-5936. (www)

  @article{ , abstract = {Federated learning (FL) is an emerging distributed-learning technology that allows a requester to hire a set of independent FL workers for a model training task. The fact that FL workers are not obliged to do the training task calls for an incentive mechanism to motivate workers. However, existing incentive mechanisms for FL mainly were designed for single requester. A few studies considered multiple requesters but constrained a worker’s potential to train multiple models simultaneously. In this paper, we remove the constraint to investigate the interplays among requesters in addition to inherent interplays among workers and between requesters and workers. Two crucial issues in this setting are whether the interplays lead to a stable (i.e., attainable) outcome and how to find it. We model the problem as seeking an equilibrium in a multi-leader multi-follower Stackelberg game and propose an exact method based on the derivation and backward induction to identify the Stackelberg equilibrium. We show how to adapt the proposed method to scenarios with capacity/budget constraints and privacy concerns. We have done a series of analyses to study the interplays of workers and requesters in Stackelberg equilibria. The results confirm the first-mover advantage of the requesters and reveal the possibility of mutual benefits between the requesters and workers.}, archive = {J_ACCESS}, author = {Min-Chun Cho and Li-Hsing Yen and Jing-Xuan Wang}, doi = {10.1109/ACCESS.2025.3526426}, journal = {IEEE Access}, pages = {5922-5936}, shortjournal = {IEEE Access}, title = {Seeking stability for multi-leader stackelberg game as an incentive mechanism for multi-requester federated learning}, volume = {13}, year = {2025}, }
• (2025). Autonomous mental development at the individual and collective levels: Concept and challenges. ACCESS, 13, 5907-5921. (www)

  @article{ , abstract = {The increasing complexity and unpredictability of many ICT scenarios let us envision that future systems will have to dynamically learn how to act and adapt to face evolving situations with little or no a priori knowledge, both at the level of individual components and at the collective level. In other words, such systems should become able to autonomously (i.e., self-) develop mental models of themselves and of their environment. Autonomous mental development includes: learning models of own capabilities; learning how to act purposefully towards the achievement of specific goals; and learning how to act in the presence of others, i.e., at the collective level. In this paper, we introduce a conceptual framework for autonomous mental development in ICT systems – at both the individual and collective levels – by framing its key concepts and illustrating suitable application domains. Then, we overview the many research areas that are contributing or can potentially contribute to the realization of the framework, and identify some key research challenges.}, archive = {J_ACCESS}, author = {Marco Lippi and Stefano Mariani and Matteo Martinelli and Franco Zambonelli}, doi = {10.1109/ACCESS.2024.3522362}, journal = {IEEE Access}, pages = {5907-5921}, shortjournal = {IEEE Access}, title = {Autonomous mental development at the individual and collective levels: Concept and challenges}, volume = {13}, year = {2025}, }
• (2025). Energy management in microgrids using model-free deep reinforcement learning approach. ACCESS, 13, 5871-5891. (www)

  @article{ , abstract = {Electric power systems are undergoing rapid modernization driven by advancements in smart-grid technologies, and microgrids (MGs) play a crucial role in integrating renewable energy sources (RESs), such as wind and solar energy, into existing grids. MGs offer a flexible and efficient framework for accommodating dispersed energy resources. However, the intermittent nature of renewable sources, coupled with the rising demand for Electric Vehicles (EVs) and fast charging stations (FCSs), poses significant challenges to the stability and efficiency of microgrid (MG) operations. These challenges stem from the uncertainties in both energy generation and fluctuating demand patterns, making efficient energy management in MG a complex task. This study introduces a novel model-free strategy for real-time energy management in MG aimed at addressing uncertainties without the need for traditional uncertainty modeling techniques. Unlike conventional methods, the proposed approach enhances MG performance by minimizing power losses and operational costs. The problem is formulated as a Markov Decision Process (MDP) with well-defined objectives. To optimize decision-making, an actor-critic-based Deep Deterministic Policy Gradient (DDPG) algorithm is developed, leveraging reinforcement learning (RL) to adapt dynamically to changing system conditions. Comprehensive numerical simulations demonstrated the effectiveness of the proposed strategy. The results show a total cost of 51.8770 €ct/kWh, representing a reduction of 3.19% compared to the Dueling Deep Q Network (Dueling DQN) and 4% compared to the Deep Q Network (DQN). This highlights the robustness and scalability of the proposed model-free approach for modern MG energy management.}, archive = {J_ACCESS}, author = {Odia A. Talab and Isa Avci}, doi = {10.1109/ACCESS.2025.3525843}, journal = {IEEE Access}, pages = {5871-5891}, shortjournal = {IEEE Access}, title = {Energy management in microgrids using model-free deep reinforcement learning approach}, volume = {13}, year = {2025}, }
• (2025). Digital health: The cybersecurity for AI-based healthcare communication. ACCESS, 13, 5858-5870. (www)

  @article{ , abstract = {The implementation of AI-based healthcare communication systems presents a significant challenge for the development of cost-effective medical environments. Despite numerous approaches introduced internationally, the field continues to grapple with real-world issues, demands, and a rising number of unforeseen diseases stemming from vulnerabilities, such as zero-day attacks. Purpose: The healthcare system, including communication between medical devices, must evolve alongside new technologies, such as AI-based proactive devices. Future healthcare hubs (future proactive devices), which will be deployed in various remote locations, utilize their own cybersecurity solutions to ensure secure AI-based healthcare communication services. Addressing these challenges requires managing potential issues arising in current medical scenarios. For instance, sudden security issues like ransomware attacks disrupting medical activities in London hospitals in May 2024 highlight the urgency of developing effective solutions that can be applied globally. Method: The AI-based approach offers a potential solution to these problems, although its implementation in medical security management presents challenges for the healthcare profession. While cybersecurity solutions are necessary, they can be developed through various management strategies. Anticipated Results: Implementing management solutions will require updating medical systems with AI-based policies. Efficient AI algorithms, derived from advanced mathematical or scientific techniques, are essential. The effectiveness of these solutions will ultimately determine the outcomes. Conclusion: Healthcare applications should be designed to be affordable while accommodating the essential facilities for human life.}, archive = {J_ACCESS}, author = {Abdullah M. Algarni and Vijey Thayananthan}, doi = {10.1109/ACCESS.2025.3526666}, journal = {IEEE Access}, pages = {5858-5870}, shortjournal = {IEEE Access}, title = {Digital health: The cybersecurity for AI-based healthcare communication}, volume = {13}, year = {2025}, }
• (2025). Optimization of depth map intra coding algorithms based on machine learning. ACCESS, 13, 5846-5857. (www)

  @article{ , abstract = {As a cutting-edge field of global research, 3D video technology faces the dual challenges of large data volumes and high processing complexity. Although the most recent video coding standard VVC, surpasses HEVC in coding efficiency, dedicated research on 3D video coding remains relatively scarce. Building on existing research, this study aims to develop a 3D video coding algorithm based on VVC that lowers the complexity of the encoding procedure. We focus specifically on the depth maps in 3D video content and introduce an extreme forest model from machine learning to optimize intra-frame coding. This paper proposes a novel CU partitioning strategy implemented through a two-stage extreme forest model. First, the initial model predicts the CU partitioning type, including no partition, QuadTree partitioning, Multi-type tree horizontal partitioning, and Multi-type tree vertical partitioning. For the latter two cases, a second model further refines the partitioning into binary or ternary trees. Through this two-stage prediction mechanism, we effectively bypass CU partitioning types with low probability, significantly reducing the coding complexity. The experimental results demonstrate that the proposed algorithm saves 47.46% in encoding time while maintaining coding quality, with only a 0.26% increase in Bjontegaard Delta Bitrate. This achievement provides an effective low-complexity solution for the 3D video coding field.}, archive = {J_ACCESS}, author = {Wenjun Song and Jiaying Yang and Qiuwen Zhang}, doi = {10.1109/ACCESS.2025.3525703}, journal = {IEEE Access}, pages = {5846-5857}, shortjournal = {IEEE Access}, title = {Optimization of depth map intra coding algorithms based on machine learning}, volume = {13}, year = {2025}, }
• (2025). Analysis of the virtual reality user experience improvement through human vision adjustment using diopter dial. ACCESS, 13, 5832-5845. (www)

  @article{ , abstract = {The head-mounted display (HMD) form factor is an essential element that influences the various emotions users experience during their virtual reality (VR) sessions. In particular, it has been announced that the VR experience highly depends on the user’s field of vision because it blocks the user’s view of the real world and transmits information exclusively in the virtual world. Users with eye diseases wore vision aids to experience VR environments or used them without sufficient vision. Furthermore, this problem led to factors that reduced the user’s sense of immersion and ease of use. In this paper, we analyzed how providing users with adjusted human vision via diopter dial affects the improvement of VR user experiences. The experimental procedure consisted of 2D video and 360-degree video as audiovisual content, as well as sitting mode games and room-scale mode games as playable content. We measured the immersion and sickness of the experimental environment using each verified questionnaire on the 5-point Likert scale and the discomfort score for each experimental content to measure the degree of discomfort. The experimental environment is Phase 1, where users experienced VR while wearing vision aids, and Phase 2, where users experience VR with human vision adjustment using a diopter dial without vision aids. The difference between the evaluation results of both experimental environments was 1.56 for immersion, 2.35 for sickness, and 3.85 for discomfort. Additionally, user feedback was gathered through in-depth interviews to enhance the reliability of the statistical analysis. Through these results, we confirmed that human vision adjustment using the diopter dial provides users with an improved form factor, reduces discomfort and sickness, and enhances the immersive feeling of the VR environment through improved display visibility.}, archive = {J_ACCESS}, author = {Sungeun Park and Wooksung Kim and Jihyung Kim}, doi = {10.1109/ACCESS.2024.3521918}, journal = {IEEE Access}, pages = {5832-5845}, shortjournal = {IEEE Access}, title = {Analysis of the virtual reality user experience improvement through human vision adjustment using diopter dial}, volume = {13}, year = {2025}, }
• (2025). Knowledge graph completion with pattern-based methods. ACCESS, 13, 5815-5831. (www)

  @article{ , abstract = {Knowledge graphs (KGs) are popularly used to develop several intelligent applications. Revealing valuable knowledge hidden in these graphs opened up a branch of research, known as KG reasoning, aiming at predicting the missing links. Some methods take advantage of external information such as entity description but at the cost of more computational complexity. Besides, most of the current techniques focus solely on local information in the KG. However, the learning process can utilise valuable global information in the entire graph. In this paper, we propose a Pattern-based Knowledge Graph Completion (PKGC) method that consists of three phases. The first phase utilizes multi-source information and expands the KG using entity description as external information with efficient Natural Language Processing (NLP) techniques. In the second phase, we mine frequent patterns from the expanded KG, extract connections between them and assign entities to the patterns that construct the abstraction layer. Based on the extracted patterns, connections, and entity assignments, a flow network is constructed on the abstraction layer in the third phase. We use global internal information, namely patterns, by adapting the minimum-cost circulation problem to the flow network. This way the links in a larger neighborhood are involved in the inference. We conducted experiments on the link prediction task and evaluated the training time on two benchmark datasets, WordNet and Freebase. Experiments have demonstrated that the proposed method is superior to the state-of-the-art methods and that pattern extraction is effective for knowledge graph completion tasks.}, archive = {J_ACCESS}, author = {Maryam Sabet and Mohammadreza Pajoohan and Mohammad Reza Moosavi}, doi = {10.1109/ACCESS.2025.3525586}, journal = {IEEE Access}, pages = {5815-5831}, shortjournal = {IEEE Access}, title = {Knowledge graph completion with pattern-based methods}, volume = {13}, year = {2025}, }
• (2025). Workflow for conflict and reinforcement identification based on STPA and STRIDE. ACCESS, 13, 5785-5814. (www)

  @article{ , abstract = {Critical Cyber-Physical Systems (CPSs) are becoming more complex and characterized by increasing automation, computational power, communication, and integration. CPSs involve processing and storage units, mechanical and electrical components, sensors, actuators, and communication networks, having software as the dominant part of this integration. CPS development includes safety and cybersecurity analyses to identify constraints and requirements to improve the system’s operation. Mechanisms and countermeasures are identified and employed to meet requirements. Mechanisms and countermeasures consist of a variety of hardware, software, and protocols. Identifying requirements’ inconsistencies and conflicts is challenging due to two reasons. First, requirements identification is generally made using methods of specific concern, e.g. safety, cybersecurity, data privacy, and business, and it does not consider concerns jointly. Second, mechanisms and countermeasures are specified to meet specific requirements and not all the requirements. For instance, a cybersecurity mechanism might interfere with the satisfaction of a safety requirement. It prompts us to seek methods that jointly allow us to analyze requirements. We are particularly interested in studying the interplay of safety mechanisms and cybersecurity countermeasures to address the requirements of CPSs. We also consider the business needs that drive the CPS projects. We analyze the mechanisms and countermeasures from a specific perspective - performance - to identify conflicts and reinforcements. In this perspective, conflict occurs when mechanisms compete for resources or require incompatible resources representing an issue in the design. Reinforcement occurs when two different mechanisms can meet the two requirements that originated them, and any mechanism of them can be employed. We then propose a workflow to systematically assess performance resource utilization to identify potential conflicts and reinforcements of mechanisms and countermeasures driven by safety, security, and business requirements. To provide a common basis for identifying requirements, we consider that safety requirements are identified by the STPA (System-Theoretic Process Analysis) method and cybersecurity requirements are identified using an STPA extended with STRIDE method. We present a use case of a drone delivery system to evaluate the workflow. The results demonstrate the feasibility of our workflow.}, archive = {J_ACCESS}, author = {Andrei Carniel and Juliana de Melo Bezerra and Celso Massaki Hirata}, doi = {10.1109/ACCESS.2024.3525002}, journal = {IEEE Access}, pages = {5785-5814}, shortjournal = {IEEE Access}, title = {Workflow for conflict and reinforcement identification based on STPA and STRIDE}, volume = {13}, year = {2025}, }
• (2025). Research on optimization of large-scale heterogeneous combat network based on graph embedding. ACCESS, 13, 5773-5784. (www)

  @article{ , abstract = {In the era of information warfare, the combat system-of-systems consists of interconnected entities that can be abstracted as heterogeneous combat network (HCN). Developing HCNs with exceptional performance is crucial to building effective combat system-of-systems. Currently, large-scale research on HCN suffers from significant time costs and poor convergence results. To address these problems, a graph embedding-based Heterogeneous Combat Network Optimization Framework (HCNOF) is proposed, which optimizes HCN features and reduces time overhead. In this paper, we design crossover operators and mutation operators for HCNs to iteratively search for the optimal HCN. Additionally, we developed a heterogeneous graph embedding method for HCNs to transform the high-dimensional HCN representation matrix into a low-dimensional embedding matrix. Finally, we designed experiments to optimize the functional robustness of HCN, verifying the effectiveness of HCNOF. The results demonstrate that, in the graph embedding task, the MAP value of HCNOF is higher than several conventional graph embedding methods, indicating better preservation of network structure. In the optimization task, HCNOF demonstrates superior performance, particularly in terms of convergence speed, which is a measure of optimization efficiency. It has shown a 17.23% improvement in this aspect compared to HCNOF0 and a 6.89% enhancement when compared to the GE-SU-EANet algorithm, underscoring its advantages in both optimization performance and cost reduction. Overall, our research findings motivate the establishment of stronger combat systems and provide an innovative approach for large-scale HCN optimization.}, archive = {J_ACCESS}, author = {Xianzheng Meng and Changrong Xie and Hui Li and Guangjun Zeng and Kebin Chen}, doi = {10.1109/ACCESS.2025.3526650}, journal = {IEEE Access}, pages = {5773-5784}, shortjournal = {IEEE Access}, title = {Research on optimization of large-scale heterogeneous combat network based on graph embedding}, volume = {13}, year = {2025}, }
• (2025). A reproducible analysis of sequential recommender systems. ACCESS, 13, 5762-5772. (www)

  @article{ , abstract = {Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in user behaviour, significantly improving recommendation accuracy and relevance. Ensuring the reproducibility of these models is paramount for advancing research and facilitating comparisons between them. Existing works exhibit shortcomings in reproducibility and replicability of results, leading to inconsistent statements across papers. Our work fills these gaps by standardising data pre-processing and model implementations, providing a comprehensive code resource, including a framework for developing SRSs and establishing a foundation for consistent and reproducible experimentation. We conduct extensive experiments on several benchmark datasets, comparing various SRSs implemented in our resource. We challenge prevailing performance benchmarks, offering new insights into the SR domain. For instance, SASRec does not consistently outperform GRU4Rec. On the contrary, when the number of model parameters becomes substantial, SASRec starts to clearly dominate all the other SRSs. This discrepancy underscores the significant impact that experimental configuration has on the outcomes and the importance of setting it up to ensure precise and comprehensive results. Failure to do so can lead to significantly flawed conclusions, highlighting the need for rigorous experimental design and analysis in SRS research. Our code is available at https://github.com/federicosiciliano/easy_lightning.}, archive = {J_ACCESS}, author = {Filippo Betello and Antonio Purificato and Federico Siciliano and Giovanni Trappolini and Andrea Bacciu and Nicola Tonellotto and Fabrizio Silvestri}, doi = {10.1109/ACCESS.2024.3522049}, journal = {IEEE Access}, pages = {5762-5772}, shortjournal = {IEEE Access}, title = {A reproducible analysis of sequential recommender systems}, volume = {13}, year = {2025}, }
• (2025). Interval-aware scheduling of surveillance drones: Exact and heuristic approaches. ACCESS, 13, 5749-5761. (www)

  @article{ , abstract = {In recent years, the patrol security of facilities has faced various challenges, including labor shortages and rising labor costs. Drones, with their high mobility and advanced features such as high-resolution cameras, offer a promising alternative to human patrols. Moreover, establishing an optimal patrol schedule for drones is crucial for efficient security operations. This paper proposes four methods for determining the optimal patrol schedule for drones: the ILP-based method, the Greedy method, the Simulated Annealing method, and the Multi-stage method. In experiments, we evaluate each method by comparing total flight time and algorithm runtime. Compared to the ILP-based method, the Multi-stage method exhibited a maximum of 13.4% degradation in total flight time but significantly outperformed it in terms of algorithm runtime. In comparison to the Greedy method, the Multi-stage method achieved an average improvement of 37.8% in total flight time, while its algorithm runtime deteriorated by a maximum of 1.26 seconds. Compared to the Simulated Annealing method, the Multi-stage method achieved an average improvement of 37.8% in total flight time and consistently outperformed the Simulated Annealing method in solving all problems. Conversely, when operating with a single drone, the number of unsolved problems increased significantly when the number of patrol points exceeded 17. Subsequently, experiments were conducted by increasing the maximum number of drones to two and three. The total flight time was compared using the Multi-stage method. By expanding the drone fleet to three, the percentage of solvable problems increased by 87.9%, without compromising total flight time. As a case study, the Multi-stage method was applied to determine the optimal patrol schedule for drones at the Osaka-Ibaraki Campus of Ritsumeikan University.}, archive = {J_ACCESS}, author = {Kaito Mori and Hiroki Nishikawa and Hiroyuki Tomiyama}, doi = {10.1109/ACCESS.2025.3526771}, journal = {IEEE Access}, pages = {5749-5761}, shortjournal = {IEEE Access}, title = {Interval-aware scheduling of surveillance drones: Exact and heuristic approaches}, volume = {13}, year = {2025}, }
• (2025). Combination hand-crafted features and semi-supervised features selection from deep features for atrial fibrillation detection. ACCESS, 13, 5739-5748. (www)

  @article{ , abstract = {Atrial fibrillation (AF) is the one of most prevalent type of heart disorder, and early diagnosis plays a crucial role in reducing the risk of cardiac failure and stroke. However, detecting AF is often problematic as it is frequently asymptomatic or presents occasional symptoms. Currently, there are hand-held Electroencephalogram (ECG) devices, such as smartwatches, that could be helpful in detecting AF. Single-lead ECG devices are being widely recognized for their potential in monitoring patients’ heart health globally. Studies have shown that regular monitoring of standardized ECG signals can considerably reduce mortality rates associated with severe cardiovascular diseases. The researchers aimed to utilize a machine learning approach for detecting AF from short ECG signals. However, the current automated detection method for AF has scope for enhancement. This study proposed a novel feature set that combines deep features extracted from cyclic correlation of ECG signals with manually designed features for predicting ECG rhythms. The effectiveness of this method was evaluated using the PhysioNet Challenge 2017 dataset, which consists of numerous ECG recordings. The performance of the proposed method was superior to state-of- art techniques employed on the same dataset and assessed using the same metric. The proposed method classified ECG recordings into four different classes and obtained an average F-score of 90.21% based on 10fold cross-validations.}, archive = {J_ACCESS}, author = {Sara Mihandoost}, doi = {10.1109/ACCESS.2024.3524764}, journal = {IEEE Access}, pages = {5739-5748}, shortjournal = {IEEE Access}, title = {Combination hand-crafted features and semi-supervised features selection from deep features for atrial fibrillation detection}, volume = {13}, year = {2025}, }
• (2025). Mode selection of a relativistic cherenkov generator based on coaxial combined periodic double-corrugation slow-wave structure. ACCESS, 13, 5731-5738. (www)

  @article{ , abstract = {The mode selection of a low guiding magnetic field relativistic Cherenkov generator based on coaxial combined periodic double-corrugation slow-wave structure (CPD-SWS) is studied. Owing to the flexible combination of different double-corrugation periods of the CPD-SWS, the electric field distribution can be modified and the coupling impedance of the specific mode can be enhanced, and thus the expected mode can be selected. The research results indicate that the quasi-TEM mode can be selected by adjusting the depth of the outer and inner corrugations of CPD-SWS. Moreover, the TM01 mode can be selected by adjusting the length of the corrugation and the depth of the outer corrugations of CPD-SWS. Furthermore, the TM02 mode can be selected by adjusting the depth of the outer corrugations and increasing the blade thickness. In addition, the particle in cell (PIC) simulation results show that the output microwave spectrum is pure for each case, which demonstrates that the quasi-TEM mode, TM01 mode and TM02 mode was selected successfully by using the CPD-SWS, respectively.}, archive = {J_ACCESS}, author = {Ying Li and Jinchuan Ju and Ting Shu and Wei Zhang and Yunxiao Zhou and Hongtao Yao}, doi = {10.1109/ACCESS.2025.3525667}, journal = {IEEE Access}, pages = {5731-5738}, shortjournal = {IEEE Access}, title = {Mode selection of a relativistic cherenkov generator based on coaxial combined periodic double-corrugation slow-wave structure}, volume = {13}, year = {2025}, }
• (2025). SACU-net: Shape-aware U-net for biomedical image segmentation with attention mechanism and context extraction. ACCESS, 13, 5719-5730. (www)

  @article{ , abstract = {With the rapid development of convolutional neural networks in image processing, deep learning has been widely applied to medical image segmentation tasks, including liver, retinal vessels, nuclei, and COVID-19 lesion segmentation. However, the accuracy and interpretability of these methods still need further improvement. This paper proposes a novel Shape-aware U-Net architecture with Attention Mechanism and Context Extraction (SACU-Net) to address the aforementioned issues regarding segmentation performance and shape identification. Our model introduces three major modifications to the standard U-Net: 1) a new context extraction module block (MRC) to capture high-level context features, 2) redesigned skip pathways with spatial and channel attention mechanisms to reduce the semantic gap between the feature maps of encoder and decoder sub-networks, and 3) a novel loss function to enhance segmentation accuracy. We have evaluated SACU-Net against various U-Net variants on four different medical image segmentation tasks: liver segmentation in abdominal CT scans, retinal vessel segmentation, nuclei segmentation, and COVID-19 lesion segmentation. The results demonstrate relative improvements in performance of 7.30%, 5.5%, and 8.40% compared to state-of-the-art U-Net variants.}, archive = {J_ACCESS}, author = {Yinuo Cao and Yong Cheng}, doi = {10.1109/ACCESS.2025.3526602}, journal = {IEEE Access}, pages = {5719-5730}, shortjournal = {IEEE Access}, title = {SACU-net: Shape-aware U-net for biomedical image segmentation with attention mechanism and context extraction}, volume = {13}, year = {2025}, }
• (2025). Adaptable meta-signal tracking for next-generation satellite navigation systems and LEO PNT. ACCESS, 13, 5706-5718. (www)

  @article{ , abstract = {In this study, the concept of multichannel signal tracking, previously discussed in the Global Navigation Satellite System literature, is expanded towards next-generation satellite navigation paradigms by leveraging multiple narrowband signals that are synchronously transmitted from the same satellite. This approach achieves a performance comparable to that of signals transmitted over larger bandwidths while effectively reallocating frequency resources to enhance the capacity of a wideband communication channel. This methodology can be applied to orthogonal signals, independently from their respective modulations and regardless of whether they are transmitted over different carrier frequencies or occupy the same bandwidth. The resulting architecture is tested in modern positioning, navigation and timing (PNT) scenarios, addressing in particular, the forthcoming low earth orbit (LEO) PNT paradigm to broad the technique’s applicability also towards potential piggybacked services hosted by broadband megaconstellations (e.g. Kuiper, Starlink). The applicability domain of the signal tracking architecture is explored through a parametric analysis, assessing the performance under different combined conditions such as transmission bands, multichannel combination modes, Doppler profiles, and propagation conditions. This technique has shown large performance improvements with respect to conventional single channel processing, yielding code estimation error reductions above 90% in almost all the tested conditions.}, archive = {J_ACCESS}, author = {Andrea Nardin and Fabio Dovis and Francesca Zanier and Floor Melman}, doi = {10.1109/ACCESS.2025.3526334}, journal = {IEEE Access}, pages = {5706-5718}, shortjournal = {IEEE Access}, title = {Adaptable meta-signal tracking for next-generation satellite navigation systems and LEO PNT}, volume = {13}, year = {2025}, }
• (2025). Review of lower-limb (Quasi-)Passive exoskeletons for human augmentation. ACCESS, 13, 5694-5705. (www)

  @article{ , abstract = {Researchers have been trying to develop lower-limb exoskeletons to reduce the metabolic cost for human augmentation. The research on (quasi-)passive exoskeletons has gained more widespread attention since the first passive ankle exoskeleton was demonstrated to reduce the metabolic cost of human walking in 2015. Here, we reviewed studies on lower-limb (quasi-)passive exoskeletons for human performance augmentation in the past decade and highlighted key innovations and techniques to enable some of these exoskeletons to achieve the goal of reducing metabolic cost. We reviewed the (quasi-)passive exoskeleton research from three aspects including biological fundamentals for exoskeleton design, assistive principle and mechanical design of exoskeleton, which are the primary considerations of designing and evaluating (quasi-)passive exoskeletons. Lastly, we underlined some practical challenges and emerging trends of (quasi-)passive exoskeleton technology for further enhancing human mobility performance in the future.}, archive = {J_ACCESS}, author = {Tiancheng Zhou and Tianyun Li and Hongkuan Zhou and Wei Zheng}, doi = {10.1109/ACCESS.2024.3521052}, journal = {IEEE Access}, pages = {5694-5705}, shortjournal = {IEEE Access}, title = {Review of lower-limb (Quasi-)Passive exoskeletons for human augmentation}, volume = {13}, year = {2025}, }
• (2025). Leveraging social networks for cyber threat intelligence: Analyzing attack trends and TTPs in the arab world. ACCESS, 13, 5679-5693. (www)

  @article{ , abstract = {The primary objective of this study is to investigate cybercrime threat intelligence in the context of attack trends and tactics, techniques, and procedures (TTPs) employed by cyber attack groups targeting the Arab world. Numerous scholarly papers have explored methods for collecting and extracting data from social networks, shedding light on the importance of cyber threat intelligence. In this paper, data were collected from X (former ‘Twitter’) using a script based on specific keywords. Our research underscores the widespread nature of social networks, where a wealth of information is disseminated, and a substantial number of threat information. Given that a considerable portion of these cybercrime threats are shared through social networks, making them a priority becomes crucial. The findings of our research highlight the importance of effectively using social networks in understanding and responding to cyber threats. The study contributes to improving cybersecurity in the Arab region by providing information on the dynamics of cyber threats and the method of operation of attack groups. It emphasizes the critical role of social networks in contemporary cyber-attack landscapes, offering practical implications for cybersecurity practitioners, law enforcement agencies, and policymakers committed to securing the digital environment in Arab nations.}, archive = {J_ACCESS}, author = {Seokhee Lee and Aisha Abdu Hassan Mujammami and Kyounggon Kim}, doi = {10.1109/ACCESS.2024.3508025}, journal = {IEEE Access}, pages = {5679-5693}, shortjournal = {IEEE Access}, title = {Leveraging social networks for cyber threat intelligence: Analyzing attack trends and TTPs in the arab world}, volume = {13}, year = {2025}, }
• (2025). Network-based video recommendation using viewing patterns and modularity analysis: An integrated framework. ACCESS, 13, 5660-5678. (www)

  @article{ , abstract = {The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with social network analysis to enhance personalization in VOD platforms. The methodology constructs user-item interaction graphs based on viewing patterns and applies centrality measures (degree, closeness, and betweenness) to identify important videos. Modularity-based clustering groups related content, enabling personalized recommendations. The system was evaluated on a documentary-focused VOD platform with 328 users over four months. Results showed significant improvements: a 63% increase in click-through rate (CTR), a 24% increase in view completion rate, and a 17% improvement in user satisfaction. The approach outperformed traditional methods like Naive Bayes and SVM. Future research should explore advanced techniques, such as matrix factorization models, graph neural networks, and hybrid approaches combining content-based and collaborative filtering. Additionally, incorporating temporal models and addressing scalability challenges for large-scale platforms are essential next steps. This study contributes to the state of the art by introducing modularity-based clustering and ego-centric ranking methods to enhance personalization in video recommendations. The findings suggest that integrating network-based features and implicit feedback can significantly improve user engagement, offering a cost-effective solution for VOD platforms to enhance recommendation quality.}, archive = {J_ACCESS}, author = {Mehrdad Maghsoudi and Mohammad Hossein Valikhani and Mohammad Hossein Zohdi}, doi = {10.1109/ACCESS.2025.3526876}, journal = {IEEE Access}, pages = {5660-5678}, shortjournal = {IEEE Access}, title = {Network-based video recommendation using viewing patterns and modularity analysis: An integrated framework}, volume = {13}, year = {2025}, }
• (2025). On the development of vehicle dynamics active systems: The handling stability ratio as a strategic indicator for integrating multiple actuators. ACCESS, 13, 5634-5647. (www)

  @article{ , abstract = {Mechatronic systems for enhancing vehicle dynamics are a widely studied topic, especially in relation to high-performance vehicles, which must be characterized by high levels of handling capability and stability, as well as “natural” driving behavior. Despite the broad availability of research on the integration and control of multiple active systems, there is a lack of discussion on how they can effectively extend the range of achievable vehicle performance. To address the gap, this paper introduces and analyzes a novel performance indicator, the Handling Stability Ratio (HSR). HSR is the ratio of the maximum lateral grip level of the rear axle to that of the front one, and can be computed along the ISO open-loop steady-state circular tests. By means of HSR, it is possible to quantify the relative axle force reserve that is available for exploitation by an active system. The study considers how different chassis control actuations, namely Front Active Camber (FAC), Rear Active Camber (RAC), Rear Wheel Steering (RWS), and Rear Torque Vectoring (RTV), can virtually vary HSR, thereby increasing the cornering or stability performance in limit handling conditions, compared to the corresponding passive vehicle. In the initial vehicle setup phase, HSR can be adopted for obtaining the vehicle-level performance limit, and, when coupled to the actuator limits, provides the feasibility range of the considered chassis actuation suite.}, archive = {J_ACCESS}, author = {Roberto Aratri and Stefano de Pinto and Giuseppe Guastadisegni and Antonio Gonzalez and Aldo Sorniotti and Francesco Bottiglione and Giacomo Mantriota}, doi = {10.1109/ACCESS.2024.3521588}, journal = {IEEE Access}, pages = {5634-5647}, shortjournal = {IEEE Access}, title = {On the development of vehicle dynamics active systems: The handling stability ratio as a strategic indicator for integrating multiple actuators}, volume = {13}, year = {2025}, }
• (2025). An explainable artificial intelligence model for the classification of breast cancer. ACCESS, 13, 5618-5633. (www)

  @article{ , abstract = {Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The best-performing machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area’s worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease’s onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems.}, archive = {J_ACCESS}, author = {Tarek Khater and Abir Hussain and Riyad Bendardaf and Iman M. Talaat and Hissam Tawfik and Sam Ansari and Soliman Mahmoud}, doi = {10.1109/ACCESS.2023.3308446}, journal = {IEEE Access}, pages = {5618-5633}, shortjournal = {IEEE Access}, title = {An explainable artificial intelligence model for the classification of breast cancer}, volume = {13}, year = {2025}, }
• (2025). A study of cryptographic algorithms on special linear group over ring of integers modulo m. ACCESS, 13, 5606-5617. (www)

  @article{ , abstract = {Despite the development of numerous cryptographic algorithms over the last decade to ensure greater security for the users, the cyberattack forms are still advancing, which creates a challenge for the research community to build more efficient infrastructure. It motivates us to design more advanced algorithms based on a set of matrices that form a group under matrix multiplication, meaning they satisfy the group axioms of closure, associativity, identity, and irreversibility. Specifically, this study explores the application of three cryptographic algorithms on a special linear group of matrices over a ring of integers modulo m. The first algorithm relies on the RSA key exchange algorithm, which we modify to a special linear group. The second is an asymmetric RSA variant of a cryptographic algorithm defined on the co-adjoint orbit of element K belonging to a special linear group. All matrices from the orbit of matrix K have the same order and can be easily encrypted and decrypted by a pair of secret asymmetric cryptographic keys. We apply the Kronecker product of non-Abelian groups to construct an energy-efficient cryptographic algorithm with possible application on sensor networks. Due to the multiplicative property of the Kronecker product, both the encryption and decryption can be performed in parallel over the matrices of low degree. The third one is another RSA variant of a symmetric cryptographic algorithm, which can be encoded with a special linear group to significantly increase its complexity at the stage of non-negative matrix factorization. To encrypt a given set of numbers, we write it in a matrix form with a secret sequence of generators. As a result, the decryption relies on solving a simple system of nonlinear equations. Security of the algorithm lies in its complexity, aimed at building a correct sequence of generators necessary for encryption.}, archive = {J_ACCESS}, author = {Zbigniew Lipiński and Jolanta Mizera-Pietraszko and Jolanta Tańcula}, doi = {10.1109/ACCESS.2024.3515452}, journal = {IEEE Access}, pages = {5606-5617}, shortjournal = {IEEE Access}, title = {A study of cryptographic algorithms on special linear group over ring of integers modulo m}, volume = {13}, year = {2025}, }
• (2025). Gesture recognition system based on time-frequency point density of sEMG. ACCESS, 13, 5595-5605. (www)

  @article{ , abstract = {Gesture recognition technology based on surface electromyography signal (sEMG) has important application value in human-computer interaction, medical rehabilitation, and other fields. It is usually realized by extracting the characteristics of different finger movements and then using machine learning or deep learning algorithms to classify and recognize them. This process involves complicated calculations, and few studies have achieved this purpose by combining different fingers’ flexion or relaxation. In this study, we designed a wearable acquisition system to collect sEMG with five fingers flexed/relaxed, and then combined with the movement status of the five fingers to identify different gestures. And in the process of detecting the movement status of the finger, focusing on the deficiency of traditional methods in using empirical thresholds to detect the sEMG active segment, we proposed an adaptive recognition method based on time-frequency point density. This method innovatively uses the time-frequency point density (TFPD) as the characteristic parameter of the sEMG, and then adaptively normalizes the feature extraction results in the interval [−1,1]. Finally, it uses a binary judgment method based on sliding windows to identify whether the active segment of the sEMG starts or ends, thus judging the flexion/relaxation state of the fingers. A large number of experimental results show that this method can realize quasi-real-time recognition within 0.5s, and the accuracy rate is nearly 100%. The influence of individual differences can be weakened through normalized positive and non-positive values. Therefore, it has strong self-adaptability. In addition, this method is very practical in the gesture recognition system.}, archive = {J_ACCESS}, author = {Qiang Wang and Yao Chen and Chunhua Sheng and Shuaidi Song}, doi = {10.1109/ACCESS.2024.3514317}, journal = {IEEE Access}, pages = {5595-5605}, shortjournal = {IEEE Access}, title = {Gesture recognition system based on time-frequency point density of sEMG}, volume = {13}, year = {2025}, }
• (2025). SynC2S: An efficient method for synthesizing tabular data with a learnable pre-processing. ACCESS, 13, 5575-5594. (www)

  @article{ , abstract = {There has been a growing demand to access large public datasets to extract valuable insights or enhance their services. However, this also involves risks, such as privacy breaches and unauthorized data exposure. Data synthesis has emerged as a popular technique to address privacy preservation and data usability simultaneously. Recently, numerous methods based on deep learning have been developed, while a clear understanding of their effectiveness is still insufficient, and the necessity for more efficient frameworks persists. In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality synthetic tabular data. First, we introduce a novel technique called C2Smap–a learnable pre-processing method that automatically transforms continuous distributions into simpler and easily generatable forms. We then develop a conditional generative model with a hierarchical structure and its corresponding learning framework, called HCIWAE, to successfully capture imbalanced categorical distributions. Combining these two components, we coin our method Synthetic data generation with C2Smap (SynC2S). Through comprehensive experimental analyses, we demonstrate the superiority and efficiency of SynC2S in generating synthetic data compared to other recent competitors. Furthermore, as a by-product, we claim that SynC2S could be a favorable option to solve over-sampling tasks, constructing high-performance prediction models by generating synthetic data for the minority class.}, archive = {J_ACCESS}, author = {Jiwoo Kim and Seri Park and Junsung Koh and Dongha Kim}, doi = {10.1109/ACCESS.2024.3472706}, journal = {IEEE Access}, pages = {5575-5594}, shortjournal = {IEEE Access}, title = {SynC2S: An efficient method for synthesizing tabular data with a learnable pre-processing}, volume = {13}, year = {2025}, }
• (2025). Laser stripe extraction for 3D reconstruction of complex internal surfaces. ACCESS, 13, 5562-5574. (www)

  @article{ , abstract = {Extracting the laser stripe center is crucial for 3D reconstruction using laser triangulation. However, automatically extracting laser stripes from complex surfaces is a challenging task. In this work, we have improved upon the Steger method and introduced a new laser stripe identification technique called the Breakdown Discharging Method. This method transforms the laser stripe extraction process into an electrical breakdown problem, where the path of discharge across an insulating medium’s surface is tracked to extract the laser stripe centerline. The primary advantage of the Breakdown Discharging Method lies in its ability to optimize the extraction of laser stripe centerlines, even on complex surfaces where the stripe may be unevenly illuminated or discontinuous—tasks that existing methods struggle to accomplish. Results from various complex surfaces demonstrate that the Breakdown Discharging Method is highly robust, effectively adapting to laser stripes on intricate shapes. This novel extraction method will advance the application and development of 3D reconstruction for objects with complex shapes.}, archive = {J_ACCESS}, author = {Xiaowei Zhao and Daguo Yu and Hongyan Shi}, doi = {10.1109/ACCESS.2025.3526162}, journal = {IEEE Access}, pages = {5562-5574}, shortjournal = {IEEE Access}, title = {Laser stripe extraction for 3D reconstruction of complex internal surfaces}, volume = {13}, year = {2025}, }
• (2025). Efficient encoding and decoding of voxelized models for machine learning-based applications. ACCESS, 13, 5551-5561. (www)

  @article{ , abstract = {Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.}, archive = {J_ACCESS}, author = {Damjan Strnad and Štefan Kohek and Borut Žalik and Libor Váša and Andrej Nerat}, doi = {10.1109/ACCESS.2025.3526202}, journal = {IEEE Access}, pages = {5551-5561}, shortjournal = {IEEE Access}, title = {Efficient encoding and decoding of voxelized models for machine learning-based applications}, volume = {13}, year = {2025}, }
• (2025). Climate change impact on cereal production in northern africa: A comprehensive modeling and control approach. ACCESS, 13, 5534-5550. (www)

  @article{ , abstract = {The Earth’s climate is undergoing unprecedented and rapid changes primarily due to human activities, especially greenhouse gas emissions, leading to a temperature rise of 1.1°C to 1.2°C in the last fifty years. In spite of global efforts like the 2015 Paris Agreement which aims to limit the increase in global temperature to 1.5°C above pre-industrial levels, projections indicate that this threshold will likely be exceeded, causing severe climatic disruptions, that will affect vulnerable regions and populations, altering weather patterns and compromising food security, water resources, and economic stability. This study integrates a data-driven downscaling system within an emission control framework aimed at controlling temperature anomalies by reducing CO2 emissions and evaluating its impact on cereal production in North Africa. The approach employs a simplified autoregressive model with exogenous input (ARX) for downscaling. During the validation phase, the ARX models show strong agreement with data, effectively reproducing global temperatures (correlation close to 1 and a normalized mean absolute error around 0.02) and local temperature (correlation of 0.75 and a normalized mean absolute error of 0.2). Moreover, an ARX model is also used to establish links between cereal production, global and regional temperature anomalies and CO2 emissions. This model demonstrates reasonably good agreement with the data, achieving a correlation of 0.72 and a normalized mean absolute error of 0.18. Once validated, the models are integrated into a decision support system based on model predictive control aimed to balance global and regional climate objectives with economic considerations. North Africa is an ideal case study since it is highly susceptible to the impacts of climate change, despite contributing only 1.76% of global carbon emissions in 2022. The region faces significant risks from increased aridity and heat, threatening agriculture, ecosystems, and socio-economic stability. The results of this research highlight the urgent need for environmental and climate-resilient measures to achieve sustainable development and enhance resilience against climate impacts.}, archive = {J_ACCESS}, author = {Sabrina De Nardi and Claudio Carnevale and Sara Raccagni and Lucia Sangiorgi}, doi = {10.1109/ACCESS.2024.3525146}, journal = {IEEE Access}, pages = {5534-5550}, shortjournal = {IEEE Access}, title = {Climate change impact on cereal production in northern africa: A comprehensive modeling and control approach}, volume = {13}, year = {2025}, }
• (2025). Designing and analyzing virtual avatar based on rigid-body tracking in immersive virtual environments. ACCESS, 13, 5522-5533. (www)

  @article{ , abstract = {This study proposes a novel method for virtual avatar applications based on rigid-body tracking in immersive virtual environments (IVEs). This method aims to design a method to efficiently and accurately estimate motions defined with few conditions. For this purpose, we design a virtual avatar motion process that proceeds from rigid-body tracking to motion estimation using Final IK (inverse kinematics). This study uses optical motion capture equipment based on markers for rigid-body tracking and calculating the movement of a defined virtual avatar based on the transformation information of the precisely tracked rigid-body object. To analyze the effectiveness of the proposed method, a comparative analysis of equipment and environment with existing research on animation fidelity in self-avatars is performed. In addition, two upper body-centered actions (simultaneous two-hand motion and separate two-hand motion) are presented to analyze latent factors of virtual avatar embodiment. A comparative survey experiment using the virtual embodiment questionnaire (VEQ) is conducted based on full-body tracking and the proposed rigid-body tracking-based method. Furthermore, to compare and analyze users’ realism in an environment where they coexist with virtual avatars in IVE, a survey experiment using a presence questionnaire is conducted. Therefore, we confirmed that similar avatar motion generation is possible compared to full-body tracking even with fewer tracking targets (e.g. type of tracking device or number of markers), inputs, and processing steps. Additionally, it is possible to design a virtual avatar that provides satisfactory realism in IVE.}, archive = {J_ACCESS}, author = {Myeongseok Park and Jiwon Lee and Hyeonyong Yang and Jinmo Kim}, doi = {10.1109/ACCESS.2025.3525630}, journal = {IEEE Access}, pages = {5522-5533}, shortjournal = {IEEE Access}, title = {Designing and analyzing virtual avatar based on rigid-body tracking in immersive virtual environments}, volume = {13}, year = {2025}, }
• (2025). Dense-ShuffleGCANet: An attention-driven deep learning approach for diabetic foot ulcer classification using refined spatio-dimensional features. ACCESS, 13, 5507-5521. (www)

  @article{ , abstract = {Diabetic foot ulcers (DFU) are a common and serious complication of diabetes, often leading to severe health implications like limb amputation if left untreated. Timely intervention and treatment are crucial in mitigating the impact of DFU on patients’ well-being. However, manual identification of DFUs remains a challenge due to their heterogeneous visual characteristics, leading to several undiagnosed cases and avoidable complications. Deep learning provides an efficient solution to the challenge by automating the process of DFU detection, thereby alleviating the burden on the healthcare industry. This research introduces a novel model, Dense-ShuffleGCANet, for DFU detection by leveraging DenseNet-169, Channel-Centric Depth-wise Group Shuffle (CCDGS) block, and triplet attention. The architecture of DenseNet-169 is modified to integrate the CCDGS block and triplet attention, enabling the model to efficiently capture long-range dependencies and utilise cross-channel features. These improvements enable the effective extraction of both channel-wise and spatial features. The proposed Dense-ShuffleGCANet model achieved an accuracy of 86.09% and an F1-score of 85.77% on the DFUC2021 dataset, outperforming state-of-the-art architectures.}, archive = {J_ACCESS}, author = {Armaano Ajay and Akshaj Singh Bisht and R. Karthik}, doi = {10.1109/ACCESS.2024.3524549}, journal = {IEEE Access}, pages = {5507-5521}, shortjournal = {IEEE Access}, title = {Dense-ShuffleGCANet: An attention-driven deep learning approach for diabetic foot ulcer classification using refined spatio-dimensional features}, volume = {13}, year = {2025}, }
• (2025). Continuous sign language recognition with multi-scale spatial-temporal feature enhancement. ACCESS, 13, 5491-5506. (www)

  @article{ , abstract = {Continuous Sign Language Recognition (CSLR) seeks to interpret the gestures used by people who are hard of hearing-mute individuals and translate them into natural language, thereby enhancing communication and interaction. A successful CSLR method relies on the continuous tracking of the presenter’s gestures and facial movements. Existing CSLR methods struggle with fully leveraging fine-grained continuous frame information and often overlook the importance of multi-scale feature integration during decoding. To solve the above-mentioned issues, in this paper, we propose a spatial-temporal feature-enhanced network, called STNet for CSLR task. Firstly, for better continuous frame information exploration, based on the optimal transport algorithm, we first propose a spatial resonance module, which is used to extract the global common spatial features of two adjacent frames along the frame sequence. Secondly, we design a frame-wise loss to preserve and enhance the specific features of each frame. Lastly, to emphasize the multi-scale feature fusion, on the decoder side, we design a multi-temporal perception module, to allow each frame to focus on a larger range of other frames and enhance information interaction from different scales. Extensive experiments on three benchmark datasets including PHOENIX14, PHOENIX14-T, and CSL-Daily demonstrate that STNet consistently outperforms state-of-the-art methods, with a notable improvement of 2.9% in CSLR, showcasing its effectiveness and generalizability. Our approach provides a robust foundation for real-world applications such as sign language education and communication tools, while ablation and case studies highlight the impact of each module, paving the way for future research in CSLR.}, archive = {J_ACCESS}, author = {Zhen Wang and Dongyuan Li and Renhe Jiang and Manabu Okumura}, doi = {10.1109/ACCESS.2025.3526330}, journal = {IEEE Access}, pages = {5491-5506}, shortjournal = {IEEE Access}, title = {Continuous sign language recognition with multi-scale spatial-temporal feature enhancement}, volume = {13}, year = {2025}, }
• (2025). Transfer learning-empowered physical layer security in aerial reconfigurable intelligent surfaces-based mobile networks. ACCESS, 13, 5471-5490. (www)

  @article{ , abstract = {This paper investigates the enhancement of physical layer security (PHY security) in Reconfigurable Intelligent Surfaces (RIS)-aided terrestrial and non-terrestrial networks (TN/NTN), focusing on the challenges posed by node mobility. In the context of next-generation mobile networks, ensuring secure communication is critical, especially under varying channel conditions caused by mobility. We explore different mobility models, including random walk, Gauss-Markov, and reference point group mobility, to assess their impact on key security metrics such as secrecy capacity and average secrecy rate. To address these challenges, we develop robust algorithms for optimizing the phase-shift configurations of RIS. Additionally, we employ Artificial Intelligence (AI) and Machine Learning (ML) techniques, specifically Deep Neural Networks (DNN), for performance prediction of PHY security metrics. We also leverage transfer learning to enhance model robustness across different mobility scenarios through domain adaptation. Our results demonstrate the effectiveness of our proposed methods in maintaining high levels of PHY security despite the dynamic nature of the channel conditions and the mobility of nodes. The proposed phase-shift configuration algorithms and ML-based solutions ensure secure and resilient communication in aerial RIS-aided TN/NTN, contributing to the advancement of secure mobile networks.}, archive = {J_ACCESS}, author = {Yosefine Triwidyastuti and Tri Nhu Do and Ridho Hendra Yoga Perdana and Kyusung Shim and Beongku An}, doi = {10.1109/ACCESS.2025.3526178}, journal = {IEEE Access}, pages = {5471-5490}, shortjournal = {IEEE Access}, title = {Transfer learning-empowered physical layer security in aerial reconfigurable intelligent surfaces-based mobile networks}, volume = {13}, year = {2025}, }
• (2025). Application of blockchain technology in carbon trading market: A systematic review. ACCESS, 13, 5446-5470. (www)

  @article{ , abstract = {The carbon trading system is a mechanism that employs carbon caps (limits) and taxation to regulate the rate of carbon dioxide emissions produced by human activities. To further strengthen the effectiveness of this mechanism, the United Nations (UN) initiated the Kyoto Protocol in 1997, enabling the trading of carbon emission rights in exchange for financial compensation. As a result, the market value rose to 10.9 billion in 2005, with an annual growth rate of approximately 8%, reaching 95 billion in 2023. Despite this expansion, the system’s management has remained centralised since its inception, leading to issues such as a lack of transparency and openness in the trading process, inefficiencies in the trading mechanisms, and inaccurate or dishonest centralised data recording. The rapid expansion of the market and the accompanying challenges underscore the need for blockchain technology (BCT), which offers a decentralised, secure, and tamper-proof system. Although several research publications have demonstrated the potential enhancements that BCT could contribute to the carbon market, no systematic review has yet examined the optimal implementation of BCT within the context of carbon trading and taxation. This study, therefore, undertakes a systematic review of peer-reviewed research articles published between 2015 and 2023 using the PRISMA methodological framework. Our analysis establishes the feasibility and viability of a blockchain-powered solution for carbon trading, alongside the development of a comprehensive framework for its effective implementation, designed to stand the test of time in the fast-evolving technological landscape. Furthermore, this comprehensive review identifies research gaps, offering future researchers direction in exploring blockchain applications within the Emissions Trading System (ETS) domain. Overall, this review fosters a digitalised and incentivised shift towards renewable energy sources.}, archive = {J_ACCESS}, author = {Timileyin P. Abiodun and Nnamdi I. Nwulu and Peter O. Olukanmi}, doi = {10.1109/ACCESS.2024.3523672}, journal = {IEEE Access}, pages = {5446-5470}, shortjournal = {IEEE Access}, title = {Application of blockchain technology in carbon trading market: A systematic review}, volume = {13}, year = {2025}, }
• (2025). Combining multi-scale U-net with transformer for welding defect detection of Oil/Gas pipeline. ACCESS, 13, 5437-5445. (www)

  @article{ , abstract = {Accurate welding defect detection (WDD) of Oil/Gas pipelines (OGP) is an active and challenging task in the reliability engineering of OGPs. To solve the problems that U-Net cannot effectively extract multi-scale global context details of the image by simple skip connection, and small defects cannot be accurately detected, a WDD method of OGP by combining residual-dilated-Inception U-Net (RDIU-Net) and Transformer (RDIUTrans) is proposed. In the model, RDIU-Net is used to extract the multi-scale local features, and Transformer is utilized to model multi-scale global contextual relationships and spatial dependency. Compared with U-Net and its variants, RDIUTrans can extract the global feature and local detail features for WDD. The results on the welding defect image dataset show that RDIUTrans is effective for WDIS with the segmentation accuracy of 95.34%. It is suitable for WDD scenes with various welding defects.}, archive = {J_ACCESS}, author = {Shanwen Zhang and Xuqi Wang and Ting Zhang}, doi = {10.1109/ACCESS.2024.3521220}, journal = {IEEE Access}, pages = {5437-5445}, shortjournal = {IEEE Access}, title = {Combining multi-scale U-net with transformer for welding defect detection of Oil/Gas pipeline}, volume = {13}, year = {2025}, }
• (2025). Corrections to “The recent technologies to curb the second-wave of COVID-19 pandemic”. ACCESS, 13, 5436. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”).}, archive = {J_ACCESS}, author = {M. Poongodi and Mohit Malviya and Mounir Hamdi and Hafiz Tayyab Rauf and Seifedine Kadry and Orawit Thinnukool}, doi = {10.1109/ACCESS.2024.3518212}, journal = {IEEE Access}, pages = {5436}, shortjournal = {IEEE Access}, title = {Corrections to “The recent technologies to curb the second-wave of COVID-19 pandemic”}, volume = {13}, year = {2025}, }
• (2025). An improved correlation filtering method for tracking maritime small targets of GF-4 staring satellite sequence images. ACCESS, 13, 5424-5435. (www)

  @article{ , abstract = {Continuous, accurate and real-time earth observation plays an increasingly important role in battlefield situation awareness, environmental monitoring and many other fields. In this paper, GF-4 staring satellite’s sequence images are taken as the research objects, and its features such as “short imaging interval, long image sequence and high resolution” are used to solve the tracking problem of large and medium ships at sea. Using the target information, the dark-channel background modeling method and multi-scale Retinex illumination algorithm are improved to achieve target enhancement. The multi-scale features of the target based on time and space information are extracted, and the target tracking is realized by improved kernel correlation filtering algorithm. By comprehensively utilizing unscented Kalman filter model and target significance features, tracking results are optimized to improve tracking accuracy. The experimental results show that the proposed algorithm can track dim and small targets in the staring satellite sequence images effectively, and it has reference significance for the application of similar high-precision satellites.}, archive = {J_ACCESS}, author = {Deyang Zhang and Yao Xu and Haimin Hu}, doi = {10.1109/ACCESS.2025.3525548}, journal = {IEEE Access}, pages = {5424-5435}, shortjournal = {IEEE Access}, title = {An improved correlation filtering method for tracking maritime small targets of GF-4 staring satellite sequence images}, volume = {13}, year = {2025}, }
• (2025). A spatial transformation based next frame predictor. ACCESS, 13, 5406-5423. (www)

  @article{ , abstract = {In recent years, the automobile industry has achieved astonishing success in making autonomous cars safer, more affordable, and more reliable. However, current autonomous driving technology is mainly based on reactive controllers that attempt to respond to the various events the car encounters. Yet, achieving a truly safe and reliable autonomous system necessitates anticipating such events and planning the correct actions in advance to avoid undesirable behavior. Recent advances in deep learning have shown remarkable performance in predicting future frames from video sequences. However, most of these approaches can only handle a few moving elements in the scene and perform poorly when the camera is in motion. This is mainly due to the difficulty of disentangling camera intrinsic motion from object-dependent motion. In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. Static elements of the scene are then used to estimate camera intrinsic motion, which is applied to the background to predict how it will be viewed in the next frame. Notably, our approach significantly reduces the complexity typically associated with such models by requiring the estimation of only 14 parameters per moving object, independent of image resolution. We have validated the generalization capabilities of our model through training on simulated datasets and testing on real-world datasets. The results indicate that our model not only outperforms existing models trained solely on real data but also exhibits superior resilience to occlusions and incomplete data in the input sequences. These findings underscore the potential of our model to significantly improve the predictive analytics capabilities of autonomous driving systems, thereby enhancing their safety and reliability in dynamic environments.}, archive = {J_ACCESS}, author = {Saad Mokssit and Daniel Bonilla Licea and Bassma Guermah and Mounir Ghogho}, doi = {10.1109/ACCESS.2024.3524997}, journal = {IEEE Access}, pages = {5406-5423}, shortjournal = {IEEE Access}, title = {A spatial transformation based next frame predictor}, volume = {13}, year = {2025}, }
• (2025). A comparative analysis of middle-of-line contact architectures for complementary FETs. ACCESS, 13, 5396-5405. (www)

  @article{ , abstract = {In this paper, we have investigated various middle-of-line contact architectures applied to monolithic complementary FET inverters and have performed a comparative analysis to assess their respective advantages and limitations. For each scheme, we carried out segmentation analysis of resistance and capacitance, and evaluated the DC performance as well as the power-performance characteristics alongside enhancement strategies. The middle VIA scheme features the lowest capacitance but exhibits inferior AC performance to the conventional structure due to the high resistance of the increased contact region parts and the highly doped silicon. The wrap-around-contact (WAC) structure, and the top metal source/drain (TMS) structure which is formed by increasing the contact depth of the top-placed NMOS, have in common that the external resistance is greatly reduced by enlarging the contact area and shortening the length of the high-resistance power VIA. Despite the trade-off with higher capacitance, AC performances of WAC and TMS are improved by 9.0% and 6.5%, respectively, for the same dynamic power. A sensitivity analysis was also performed to clarify the impact of MOL resistance and capacitance on AC performance. The performance gain when applied only to the drain side is less than 1%, highlighting the importance of minimizing the resistance on the source side. In addition, the segmentation analysis of resistance and capacitance shows that while WAC offers the best inverter performance, TMS provides higher DC performance and lower capacitance for NMOS. A hybrid approach using TMS for NMOS and WAC for PMOS combined with further optimization to reduce capacitance on the drain side, would result in an 11.1% speed improvement or a 22.7% reduction in dynamic power consumption.}, archive = {J_ACCESS}, author = {Seung Kyu Kim and Johyeon Kim and Gunhee Choi and Kee-Won Kwon and Jongwook Jeon}, doi = {10.1109/ACCESS.2024.3523434}, journal = {IEEE Access}, pages = {5396-5405}, shortjournal = {IEEE Access}, title = {A comparative analysis of middle-of-line contact architectures for complementary FETs}, volume = {13}, year = {2025}, }
• (2025). A generalized distributed energy dispatch and congestion management approach applied to german and japanese grid systems. ACCESS, 13, 5380-5395. (www)

  @article{ , abstract = {The growing penetration of renewable energy sources and the increasing number of electrical consumers at the low-voltage level present significant challenges for controlling the low-voltage grid. This paper addresses these challenges by proposing a decentralized approach to energy dispatch and grid congestion management. While the global energy transition is driven by the urgent need to address the climate crisis, much of the current research remains focused on national solutions. Furthermore, centralized approaches to energy transition and congestion management pose risks such as single points of failure and limited scalability. To overcome these limitations, this paper introduces a decentralized method applicable to both German and Japanese systems. The proposed method is validated using the Comprehensive Analysis Tool for Distribution System and Distributed Generation for power flow analysis, combined with a road traffic simulation to model Japanese charging behavior patterns. Simulation results demonstrate the approach’s generalizability across various systems, highlighting the distinct country-specific characteristics of Germany and Japan. Additionally, the decentralized approach’s applicability to both countries is demonstrated and discussed. Furthermore, the decentralized control strategy is tested in a laboratory setting, integrating DC loads and sources, back-to-back inverters, and three-phase energy distribution. The results confirm that the decentralized control method effectively transmits control signals to real systems and that the underlying mathematical models accurately reflect real-world scenarios.}, archive = {J_ACCESS}, author = {Maximilian Kilthau and Toko Mannari and Tomo Tadokoro and Hiroyuki Hatta and Alexander Fay and Felix Gehlhoff}, doi = {10.1109/ACCESS.2025.3525845}, journal = {IEEE Access}, pages = {5380-5395}, shortjournal = {IEEE Access}, title = {A generalized distributed energy dispatch and congestion management approach applied to german and japanese grid systems}, volume = {13}, year = {2025}, }
• (2025). FFDL: Feature fusion-based deep learning method utilizing federated learning for forged face detection. ACCESS, 13, 5366-5379. (www)

  @article{ , abstract = {The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for detecting forgery, commonly referred to as the classical forgery method, lacks the capability to accurately identify such fraudulent activities. This limitation arises because these algorithms are trained on publicly available centralized datasets and do not prioritize privacy and security considerations. Consequently, they adversely affect the ability to detect counterfeit content. As a potential solution to this problem, we employed a highly effective deep learning methodology rooted in federated learning. We introduced a novel deep learning approach that combines features to assess the authenticity of photographs and videos shared on social media platforms. The proposed model was trained using three widely recognized forensic datasets: FaceForensics++, Deepforensic-1.0, and WildDeepfake. Visual features were extracted using two widely recognized deep learning approaches, namelyInception and Xception. These features were then combined into a feature vector using Canonical Correlation Analysis, and Convolutional Neural Networks were trained on these features to identify manipulated images and videos. The experiments were carried out with publicly available datasets and involved changing several parameters. Finally, the proposed model’s performance was compared with other deep learning models within federated learning environments to identify forgeries. Our proposed approach demonstrated exceptional performance, achieving an accuracy rate of 98.99% when evaluated on the merged dataset.}, archive = {J_ACCESS}, author = {Vinay Gautam and Gaganpreet Kaur and Meena Malik and Ankush Pawar and Akansha Singh and Krishna Kant Singh and S. S. Askar and Mohamed Abouhawwash}, doi = {10.1109/ACCESS.2024.3523257}, journal = {IEEE Access}, pages = {5366-5379}, shortjournal = {IEEE Access}, title = {FFDL: Feature fusion-based deep learning method utilizing federated learning for forged face detection}, volume = {13}, year = {2025}, }
• (2025). A novel high-efficiency S-band conical axial vircator. ACCESS, 13, 5359-5365. (www)

  @article{ , abstract = {A VIRtual CAThode oscillatOR (Vircator) is a High-Power Microwave (HPM) vacuum electron device (VED) with applications in the field of health, energy, space, environment, and defense. In this work, a novel approach to increase S-band Vircator efficiency by a conical waveguide for counter Ummanned Aerial Vehicle (UAV) is presented. At first, starting from the geometrical and physical parameter values chosen, an axial vircator is designed. Then, to enhance its efficiency, a conical structure is added into the cylindric waveguide, altering the electron beam dynamic and optimizing the interaction with the virtual cathode. The Particle-in-Cell simulation carried out by CST Studio suite 2024, shows an average output power of 50 MW and an efficiency up to 60% than the basic vircator configuration ones, with an applied voltage of 520 kV and a cathode current of 20 kA at 2.55 GHz.}, archive = {J_ACCESS}, author = {P. Livreri}, doi = {10.1109/ACCESS.2024.3443108}, journal = {IEEE Access}, pages = {5359-5365}, shortjournal = {IEEE Access}, title = {A novel high-efficiency S-band conical axial vircator}, volume = {13}, year = {2025}, }
• (2025). A simple approach for mitigating a new flooding attack in RPL-based IoT networks. ACCESS, 13, 5342-5358. (www)

  @article{ , abstract = {The growing deployment of Internet of Things (IoT) devices in diverse daily-life smart applications makes the security of IoT networks a critical requirement. However, efficient support of network security remains challenging even for a standardized IoT network protocol such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). It incorporates limited protection from external security attacks but stays considerably vulnerable to internal routing attacks. The inherent design of RPL, particularly its topology establishment and maintenance mechanism, makes it easy to initiate more damaging attacks such as flooding attacks. Given the constrained characteristics of IoT devices, flooding IoT networks can easily lead to resource exhaustion and network performance degradation. This paper introduces a new variant of the flooding attack namely the Destination Advertisement Object Flooding (DAOF) attack. It is based on using routing communications, particularly the DAO messages, to disseminate unnecessary routing traffic which instigate excessive transmissions of control messages across the network. As demonstrated by the experimental results of this study, the DAOF attack can increase network overhead by more than 65% even in a relatively small-scale setup. Additionally, it can notably lead to high energy consumption with an average increase of 36% and low QoS performance with an average latency increase of 150%. For effective mitigation of the DAOF attack, a new lightweight solution based on a simple collaboration among RPL nodes is presented in this paper. It is referred to as DAOF-Secure RPL (DAOF-SRPL). It is based on simple in-protocol modifications to provide RPL with effective security support against the DAOF attack. In contrast to RPL in the attack scenarios, DAOF-SRPL achieved a reduction of over 80% in the total transmission rates of control messages. Meanwhile, it was able to maintain energy consumption and latency at minimal levels while preserving the same PDR results.}, archive = {J_ACCESS}, author = {Mehdi Rouissat and Ibrahim S. Alsukayti and Mohammed Belkheir and Mohammed Alreshoodi and Allel Mokaddem and Djamila Ziani}, doi = {10.1109/ACCESS.2025.3525798}, journal = {IEEE Access}, pages = {5342-5358}, shortjournal = {IEEE Access}, title = {A simple approach for mitigating a new flooding attack in RPL-based IoT networks}, volume = {13}, year = {2025}, }
• (2025). Radio- and power-over-fiber integration for 6G networks: Challenges and future prospects. ACCESS, 13, 5321-5341. (www)

  @article{ , abstract = {The rapid evolution of wireless communication networks has fueled the demand for high-speed data transmission and low-latency connectivity. As the world looks toward the development of sixth-generation (6G) Networks, the need for innovative power distribution solutions becomes critical. Power-over-fiber (PoF) technology has emerged as a promising candidate for addressing these challenges by enabling the delivery of power through optical fibers. This review paper focuses on the future prospects of PoF technology for 6G Networks, with a particular emphasis on the integration of the radio-over-fiber (RoF) technique. In this context, we explore the application scenarios for radio- and power-over-fiber (RPoF) systems, enabling simultaneous data and power transmission over optical fibers. Furthermore, we discuss the underlying principles of PoF systems, including operation and key components, e.g., high-power laser diode (HPLD), optical fiber, and photovoltaic power converter (PPC), highlighting their key features, performance metrics, and design considerations. A comprehensive analysis of the current state of PoF technology is also presented, focusing on its potential applications in the context of 6G Networks. Furthermore, the main challenges and limitations of RPoF systems are identified, including power transmission efficiency (PTE), optical fiber design and deployment, scalability, capacity, safety, and reliability. Finally, we discuss the potential research directions and technological advancements needed to fully exploit the capabilities of RPoF in the 6G era.}, archive = {J_ACCESS}, author = {Letícia Carneiro de Souza and Victoria Dala Pegorara Souto and Arismar Cerqueira Sodré}, doi = {10.1109/ACCESS.2024.3523413}, journal = {IEEE Access}, pages = {5321-5341}, shortjournal = {IEEE Access}, title = {Radio- and power-over-fiber integration for 6G networks: Challenges and future prospects}, volume = {13}, year = {2025}, }
• (2025). A distributed consensus scenario approach to optimization and control with uncertainties. ACCESS, 13, 5308-5320. (www)

  @article{ , abstract = {This paper addresses the problem of distributed optimization with uncertainties for multiagent systems. We propose a new distributed consensus scenario approach, handling uncertainties through a scenario program method. An approximate solution is developed, followed by a distributed estimation algorithm to manage large data volumes without a centralized approach. Our design inherently offers robustness against adversarial attacks and preserves data privacy. We provide rigorous convergence analysis and demonstrate the effectiveness of our approach through application examples in regression and robust internal model control. This work presents a significant step towards holistic solutions for optimization in safety-critical multiagent systems.}, archive = {J_ACCESS}, author = {Jing Wang and Steven Drager}, doi = {10.1109/ACCESS.2025.3525963}, journal = {IEEE Access}, pages = {5308-5320}, shortjournal = {IEEE Access}, title = {A distributed consensus scenario approach to optimization and control with uncertainties}, volume = {13}, year = {2025}, }
• (2025). FEN-MRMGCN: A frontend-enhanced network based on multi-relational modeling GCN for bus arrival time prediction. ACCESS, 13, 5296-5307. (www)

  @article{ , abstract = {Accurate bus arrival time prediction is crucial for enhancing passenger experience and optimizing smart city transit systems. Existing methods, typically based on single-route, sparse stop data, struggle with the complex spatiotemporal interactions present in dense stop areas and multi-route networks, resulting in lower prediction accuracy. In this paper, we propose a frontend-enhanced time-series prediction network, in which the Multi-Relational Modeling Graph Convolution (MRMGCN) as the frontend-enhanced module, called FEN-MRMGCN. The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. The network then uses a conventional time-series model to capture temporal dynamics. Our approach effectively combines external factors, such as traffic congestion and weather conditions, particularly in dense bus route areas, thereby significantly enhancing bus arrival time prediction accuracy. Moreover, we compile and analyze a comprehensive dataset comprising passenger flow, traffic conditions, weather information, and arrival times for both densely populated bus stop areas and regular areas in Macao. Experimental results demonstrate that our frontend-enhanced network achieves a reduction in the Mean Absolute Percentage Error (MAPE) by 15.36%, 13.44%, and 19.07% compared to traditional time series forecasting models like CNN, LSTM, and Transformer, respectively. Future research will focus on leveraging additional data sources and exploring advanced graph convolutional architectures to further elevate prediction accuracy.}, archive = {J_ACCESS}, author = {Ting Qiu and Chan-Tong Lam and Bowie Liu and Benjamin K. Ng and Xiaochen Yuan and Sio Kei Im}, doi = {10.1109/ACCESS.2024.3525357}, journal = {IEEE Access}, pages = {5296-5307}, shortjournal = {IEEE Access}, title = {FEN-MRMGCN: A frontend-enhanced network based on multi-relational modeling GCN for bus arrival time prediction}, volume = {13}, year = {2025}, }
• (2025). Secure biometric verification in the presence of malicious adversaries. ACCESS, 13, 5284-5295. (www)

  @article{ , abstract = {In a secure biometric verification system, users authenticate themselves by submitting their encrypted biometric data to the application server. Such systems must be able to defend against 1) malicious clients that try to gain unauthorized access to the system; and 2) malicious servers that aim to identify the users’ plaintext biometric data. To this end, our work introduces an efficient biometric verification protocol that is provably secure against both a malicious client and a malicious server. We formally prove the security of our scheme in the random oracle model and also present results from a proof-of-concept implementation. Our results demonstrate that the protocol is very efficient, incurring just 880 ms of computational overhead and 99 KB of communication cost.}, archive = {J_ACCESS}, author = {Kamela Al-Mannai and Elmahdi Bentafat and Spiridon Bakiras and Jens Schneider}, doi = {10.1109/ACCESS.2024.3522859}, journal = {IEEE Access}, pages = {5284-5295}, shortjournal = {IEEE Access}, title = {Secure biometric verification in the presence of malicious adversaries}, volume = {13}, year = {2025}, }
• (2025). Fast and high-precision human fall detection using improved YOLOv8 model. ACCESS, 13, 5271-5283. (www)

  @article{ , abstract = {Human falls refer to a sudden, accidental, unintentional, involuntary, and traumatizing action of a person losing stability and ending up lying down (often on the ground). These incidents of collapsing, tripping, crashing, tumbling, falling, etc. cause major harm and injuries to the elderly in our society. According to WHO, it is the second-leading cause of death worldwide. Therefore, having a fast and high-precision fall detection system operating in real time is crucial. It will ensure quick assistance and early treatment are provided to the impacted elderly individuals. Recently, Deep Learning based systems have shown promising results for detecting changes in postures and promptly responding to them in real-time. The You Only Look Once (YOLO) models have been widely used previously for designing and implementing human fall detection systems. This research presents an improved YOLOv8 model for fast and highly accurate human fall detection. The improvements include reducing the number of layers in the backbone of YOLOv8 and incorporating the attention mechanism in the head of the network. For training and evaluation, the CAUCAFall dataset is used. The original YOLOv8 and improved YOLOv8 are evaluated on the same dataset, achieving mAP of 0.995. Also, the training time of the improved YOLOv8 is 0.457 hours, faster than other YOLO models. Therefore, the presented model is a practical approach for human fall detection and can serve in public places for safeguarding the elderly of our society against potential injuries.}, archive = {J_ACCESS}, author = {Ahlam R. Khekan and Hadi S. Aghdasi and Pedram Salehpour}, doi = {10.1109/ACCESS.2024.3470319}, journal = {IEEE Access}, pages = {5271-5283}, shortjournal = {IEEE Access}, title = {Fast and high-precision human fall detection using improved YOLOv8 model}, volume = {13}, year = {2025}, }
• (2025). Detection of false data injection attack in smart grid based on extended kalman and smooth variable structure filter. ACCESS, 13, 5257-5270. (www)

  @article{ , abstract = {False Data Injection Attack (FDIA) is a prevalent cyber threat in modern smart grids, capable of altering measurement data and circumventing Bad Data Detection (BDD) mechanisms, which results in inaccurate state estimations. This paper proposes a combined filtering algorithm, the extended Kalman filter and smooth variable structure filter (EK-SVSF), to address state estimation issues under FDIA and to overcome the limitations of traditional bad data detection (BDD) algorithms in detecting sophisticated FDIA. The EKF introduces a smooth variable structure to force the estimates to stay within the region of the true state trajectory by switching the gains, keeping the state estimates close to the true values. This approach enhances the robustness and stability of the estimation, effectively addressing the accuracy challenges of EKF based estimation under FDIA. Second, a state estimation-based FDIA detection method is proposed by leveraging the different characteristics presented by static state estimation Weighted Least Squares (WLS) and dynamic state estimation EK-SVSF under attack conditions. The Euclidean distance between the dynamic state estimates from EK-SVSF and the static estimates from WLS is calculated and compared to a predefined detection threshold to identify FDIA effectively. Finally, simulations conducted on IEEE 14-bus and IEEE 30-bus systems demonstrate that the proposed method effectively detects FDIA.}, archive = {J_ACCESS}, author = {Wenlong Jiang and Wengen Gao and Wenjing Wang and Yunfei Li and Yaru Li and Guoqing Zhang}, doi = {10.1109/ACCESS.2024.3524558}, journal = {IEEE Access}, pages = {5257-5270}, shortjournal = {IEEE Access}, title = {Detection of false data injection attack in smart grid based on extended kalman and smooth variable structure filter}, volume = {13}, year = {2025}, }
• (2025). Ultra-low static power circuits addressing the fan-out problem of analog neuron circuits in spiking neural networks. ACCESS, 13, 5248-5256. (www)

  @article{ , abstract = {In hardware-based spiking neural network (SNN) systems, neuron circuits face significant fan-out issues owing to their massively parallel connections to post-synaptic arrays, leading to distortion in the output spikes. This not only affects spike generation rates but also causes read voltage drops in post-synaptic arrays, ultimately degrading the system’s accuracy. Previous solutions to the fan-out problem, including the incorporation of delay-capacitors, inverter chains, or operational amplifiers, require substantial area and consume considerable static power, thereby impeding low-power computing for event-driven operations in SNN. To address this issue, we propose the integration of spike-based voltage buffers (S-VBs) and pull-down pulse width modulation (PD-PWM) circuits. The proposed circuits operate in a fully event-driven manner, resulting in a very low static power consumption. Remarkably, the PD-PWM circuit consumes less than 1 % of the energy required by the conventional delay-capacitor method for spike-width modulation. When simulated with a $512\times 512$ scale post-synaptic resistive random-access memory array, the neuron circuit with proposed fan-out circuits demonstrated an energy consumption of 447 pJ/spike, including the power to drive the synaptic arrays. In addition, the proposed fan-out circuit enables low-power operation, even at low firing rates, owing to its ultra-low static power consumption. Furthermore, system-level simulations indicate that the proposed circuit maintains high accuracy, with a drop in accuracy of less than 0.1 %p, even with a 10-%p fluctuation in the driving voltage.}, archive = {J_ACCESS}, author = {Jonghyuk Park and Woo Young Choi}, doi = {10.1109/ACCESS.2025.3525583}, journal = {IEEE Access}, pages = {5248-5256}, shortjournal = {IEEE Access}, title = {Ultra-low static power circuits addressing the fan-out problem of analog neuron circuits in spiking neural networks}, volume = {13}, year = {2025}, }
• (2025). A self-adaptive frequency decomposition approach for denoising to enhance data-driven learning of cyclic time series in medical signal estimation. ACCESS, 13, 5236-5247. (www)

  @article{ , abstract = {In recent advancements within the data-driven world, cyclic time series have become indispensable in industries such as healthcare, manufacturing, and energy. Deep learning methodologies have significantly improved predictions for cyclic time series across these domains. This study investigates the impact of various filtering techniques on the accuracy of deep learning models in medical signal estimation, specifically using photoplethysmography (PPG) signals as input. We argue that conventional filtering techniques, when applied as preprocessing to noisy signals, often distort the waveform and hinder the representation learning capabilities of deep learning models. Additionally, diverse and dynamic noise sources in clinical environments remain significant challenges to overcome. To address these challenges, we propose a self-adaptive frequency decomposition approach with regularization weighting policy. Through experimentation with different combinations of filtering methods and sequence-to-sequence deep learning models, our results demonstrate a 45% improvement in MSE over the Butterworth filter, 56% over the FIR filter, and 18% over the wavelet-based filter, confirming that self-adaptive decomposition filters offer superior performance for noisy cyclic time series in deep learning applications.}, archive = {J_ACCESS}, author = {Shang-Wei Chao and Feng-Li Lian}, doi = {10.1109/ACCESS.2024.3525152}, journal = {IEEE Access}, pages = {5236-5247}, shortjournal = {IEEE Access}, title = {A self-adaptive frequency decomposition approach for denoising to enhance data-driven learning of cyclic time series in medical signal estimation}, volume = {13}, year = {2025}, }
• (2025). Cyber deception against battery drain DoS attacks in wireless sensor networks using signaling game. ACCESS, 13, 5219-5235. (www)

  @article{ , abstract = {Wireless sensor networks (WSNs) are essential in various domains such as environmental monitoring, industrial automation, healthcare, and military applications. Their ability to collect real-time data has transformed operations and decision-making. However, the limited energy resources of WSNs make them vulnerable to energy-draining attacks, particularly drain denial of service (DoS) attacks, which aim to exhaust their energy supply. To mitigate this issue, we propose a lightweight defense mechanism against energy-draining DoS attacks using signaling theory and game theory to model the interaction between an attacker and a regular node. In this model, the regular node sends signals to mask true information, deceiving the attacker. By applying a perfect Bayesian Nash equilibrium, we derive optimal strategies for both players, analyzing how deceptive signaling affects the attacker’s behavior and reduces their potential gain. This approach allows us to identify energy-efficient scenarios and improve network security by distorting the attacker’s perception of the target. Ultimately, a node with low energy capacity is able to protect its energy by emitting distorted signals.}, archive = {J_ACCESS}, author = {Ines Carole Kombou Sihomnou and Abderrahim Benslimane and Ahmed H. Anwar and Gabriel Deugoue and Charles A. Kamhoua}, doi = {10.1109/ACCESS.2024.3525064}, journal = {IEEE Access}, pages = {5219-5235}, shortjournal = {IEEE Access}, title = {Cyber deception against battery drain DoS attacks in wireless sensor networks using signaling game}, volume = {13}, year = {2025}, }
• (2025). Optimization techniques for physician scheduling problem: A systematic review of recent advancements and future directions. ACCESS, 13, 5203-5218. (www)

  @article{ , abstract = {The Physician Scheduling Problem (PSP) has emerged as a critical challenge in healthcare management, directly relevant to Sustainable Development Goal 3 (SDG 3) - Good Health and Well-being. Driven by physician shortages, rising operational costs, and the need for efficient workforce planning, PSP affects the quality of patient care, staff satisfaction, and the overall efficiency of the healthcare system. While previous reviews have addressed PSP, they are lacking in a comprehensive analysis of recent optimization methodologies and their effectiveness. This work aims to bridge this gap by analyzing 60 research studies which addressed PSP, published between January 2014 and June 2024. Our study also extends the problem definition, constraints, evaluation functions, and the variants of PSP. We examine a wide range of optimization methodologies, including mathematical programming, heuristics, matheuristics, and machine learning, highlighting their strengths and limitations in addressing the multifaceted nature of PSP. This review also analyzes the datasets used in PSP research, noting the lack of standardized benchmarks. Key findings reveal the prevalence of mathematical optimization methods, the growing importance of multi-objective optimization and robustness, as well as the potential of machine learning and data-driven approaches. Future research directions are outlined, emphasizing the need for more scalable algorithms, real-time scheduling capabilities, improved user interfaces, and comprehensive validation studies. This review contributes to the advancement of PSP optimization, aiming to enhance healthcare workforce management, improve patient care, and ultimately address the pressing challenges faced by healthcare systems worldwide, thus supporting the achievement of SDG 3 and promoting universal health coverage.}, archive = {J_ACCESS}, author = {Norizal Abdullah and Masri Ayob and Meng Chun Lam and Nasser R. Sabar and Graham Kendall and Mohamad Khairulamirin Md Razali}, doi = {10.1109/ACCESS.2024.3524599}, journal = {IEEE Access}, pages = {5203-5218}, shortjournal = {IEEE Access}, title = {Optimization techniques for physician scheduling problem: A systematic review of recent advancements and future directions}, volume = {13}, year = {2025}, }
• (2025). ILN-YOLOv8: A lightweight image recognition model for crimped wire connectors. ACCESS, 13, 5193-5202. (www)

  @article{ , abstract = {With the rapid growth of China’s new energy vehicle industry, the quality of crimped wire connectors directly impacts the performance of wiring harnesses, which are critical to the overall vehicle quality. At present, reliable methods for inspecting crimped wire connectors are still primarily based on image recognition evaluations. To address this, we propose an Improved Lightweight Network based on YOLOv8 (ILN-YOLOv8) to achieve high-precision and high-efficiency detection of crimped wire connectors. Taking the original YOLOv8 model as a baseline, the new model enhances the ability to extract shallow features from small targets by increasing the P2 detection layer and improving the Feature Pyramid Network(FPN) and Path Aggregation Network(PAN) structures. Next, the improved Selective Boundary Aggregation(SBA) module replaces the Concat module in the Neck, enhancing the fusion of deep and shallow features. Additionally, the Efficient Local Attention(ELA) attention mechanism is introduced into the Cryptographic Service Provider(CSP) bottleneck with 2 convolutions(C2F) module in the Backbone, improving feature localization accuracy without increasing network complexity. The Minimum Point Distance based IoU(MPDIoU) loss function is used to calculate localization loss, improving detection accuracy while preventing gradient explosion. Finally, lightweighting of the ILN-YOLOv8 model is achieved using the slim-neck network, the backbone with Depthwise Separable Convolution (DWConv), and Lightweight Convolution (LightConv) modules. After pruning and knowledge distillation, the model’s complexity and computational load significantly decreased while accuracy improved, meeting the industry’s requirements for crimped wire connectors detection and achieves superior performance. Experimental results show that, compared to the original YOLOv8 model, the proposed method achieved 96.2% accuracy on a real-world crimped wire connectors dataset, with mAP@0.5 and mAP@.5:.95 improving by 6.1% and 7.4%, respectively, while Parameters and FLOPs decreased by 66.7% and 34.6%.}, archive = {J_ACCESS}, author = {Xiaojian Zhou and Jicheng Kan and Nur Fatin Liyana Mohd Rosely and Xu Duan and Jiajing Cai and Zihan Zhou}, doi = {10.1109/ACCESS.2025.3525564}, journal = {IEEE Access}, pages = {5193-5202}, shortjournal = {IEEE Access}, title = {ILN-YOLOv8: A lightweight image recognition model for crimped wire connectors}, volume = {13}, year = {2025}, }
• (2025). MEAT-SAM: More efficient automated tongue segmentation model. ACCESS, 13, 5175-5192. (www)

  @article{ , abstract = {In Traditional Chinese Medicine (TCM) diagnostics, the appearance of the tongue is a crucial indicator of health. TCM practitioners traditionally assess the tongue’s shape, color, texture, and other features to aid diagnosis. With advancements in technology, digitizing and analyzing tongue images using computer has become possible, making tongue image segmentation an important step in realizing automated tongue diagnosis. While existing network models have shown good results, they often struggle with tongue images against complex backgrounds, particularly on resource-limited edge devices. To tackle this challenge, this paper introduces a novel, more efficient automated tongue image segmentation model (MEAT-SAM). MEAT-SAM leverages a lightweight large model, a first in the field of tongue image segmentation. Compared to previous models, MEAT-SAM reduces the overall model parameters, improves segmentation speed, and enables operation on more resource-constrained edge devices. Despite its efficiency, MEAT-SAM maintains performance close to the state-of-the-art (SOTA) even with complex tongue image backgrounds. Tested on three different datasets (TongueDataset01, TongueDataset02, TongueDataset03), MEAT-SAM achieved IoU of 96.63%, 95.58%, and 95.07%, demonstrating excellent generalization and robustness against various complex background conditions in tongue images. Furthermore, MEAT-SAM can run effectively on the computationally limited Jetson Nano single-board computer, achieving similar segmentation effects as in experimental testing.}, archive = {J_ACCESS}, author = {Fudong Zhong and Chuanbo Qin and Yue Feng and Junying Zeng and Xudong Jia and Fuguang Zhong and Jun Luo and Min Yang}, doi = {10.1109/ACCESS.2024.3522961}, journal = {IEEE Access}, pages = {5175-5192}, shortjournal = {IEEE Access}, title = {MEAT-SAM: More efficient automated tongue segmentation model}, volume = {13}, year = {2025}, }
• (2025). Explainability of network intrusion detection using transformers: A packet-level approach. ACCESS, 13, 5154-5174. (www)

  @article{ , abstract = {Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. The primary objective of this research is to generate tags and descriptions for the packets that are difficult to classify by the NIDS. Most NIDS datasets that are publicly available have focused on flow data, offering aggregated information about network connections, and have played a crucial role in enabling researchers and network security professionals to design and develop flow-based NIDS solutions. While flow records provide valuable information for detecting network-level anomalies and attacks, they do not consider packet-level information and payload content. In this research, we propose a packet-level approach for NIDS that leverages the flow information with the packet header fields and payload. To facilitate this research, we have curated a comprehensive Packet-level dataset constructed by extracting the network packet captures from two widely used flow-level datasets, namely CIC-IDS2017 and UNSW-NB15. Recent advancements in natural language processing have demonstrated the effectiveness of Transformer-based models in handling sequence data with tasks such as token classification and text generation. We have adapted this technology to NIDS to extract key features and characteristics of the header and payload in the context of various attacks. Unlike traditional classification methods that assign predefined labels to network packets, this method focuses on generating tags based on the packet signature that explains the packet content and potential risks through high variability and explainability based on cosine semantic similarity scores of embeddings. The tags and descriptions offer network security professionals a tool to comprehend suspicious packets with an unfamiliar or potentially malicious signature, assess their nature, and help make informed decisions promptly. For the purposes of open science, all the code related to this paper is shared publicly.}, archive = {J_ACCESS}, author = {Pahavalan Rajkumardheivanayahi and Ryan Berry and Nicholas U. Costagliola and Lance Fiondella and Nathaniel D. Bastian and Gökhan Kul}, doi = {10.1109/ACCESS.2024.3525008}, journal = {IEEE Access}, pages = {5154-5174}, shortjournal = {IEEE Access}, title = {Explainability of network intrusion detection using transformers: A packet-level approach}, volume = {13}, year = {2025}, }
• (2025). Subset-selection weight post-training quantization method for learned image compression task. ACCESS, 13, 5145-5153. (www)

  @article{ , abstract = {Post-training quantization(PTQ) has been widely studied in recent years because it does not require retraining the network or the entire training dataset. However, naively applying the PTQ method to quantize low-level image tasks such as learned image compression(LIC) usually incurs significant accuracy degradation. Existing solutions often aim to optimize the calibration process or minimize quantization loss in the context of uniform quantization, which makes it difficult to further reduce the quantization loss. This work achieves accurate weight quantization using a non-uniform quantization method called subset-selection within the PTQ method. Subset-selection method uses a clustering algorithm to select a proper batch of quantization points from a large uniformly constructed quantization points pool that matches the current layer’s weight distribution correctly. Compared to the state-of-the-art LIC PTQ method, experimental results show that our proposed method achieves an average 0.8% better BD-rate on several LIC models. Meanwhile, we extended our method to image classification models and achieved an average 0.64% better accuracy, further proving the generalization of our method.}, archive = {J_ACCESS}, author = {Jinru Yang and Xiaoqin Wang and Qiang Li and Shushan Qiao and Yumei Zhou}, doi = {10.1109/ACCESS.2024.3524553}, journal = {IEEE Access}, pages = {5145-5153}, shortjournal = {IEEE Access}, title = {Subset-selection weight post-training quantization method for learned image compression task}, volume = {13}, year = {2025}, }
• (2025). Convergence-driven adaptive many-objective particle swarm optimization. ACCESS, 13, 5129-5144. (www)

  @article{ , abstract = {In recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications.}, archive = {J_ACCESS}, author = {Yunfei Yi and ZhiYong Wang and Yunying Shi and Zhengzhuo Song and Binbin Zhao}, doi = {10.1109/ACCESS.2025.3525850}, journal = {IEEE Access}, pages = {5129-5144}, shortjournal = {IEEE Access}, title = {Convergence-driven adaptive many-objective particle swarm optimization}, volume = {13}, year = {2025}, }
• (2025). Research on the rapid detection of formaldehyde emission from wood-based panels based on the AMSHKELM. ACCESS, 13, 5112-5128. (www)

  @article{ , abstract = {To enable quick and accurate detection of formaldehyde emissions from wood-based panels and to address the complexities of the full-scale chamber method and the inaccuracy of sensor-based methods, a modified formaldehyde emission model based on AdaBoost-MSBKA-SVMD-HKELM (AMSHKELM) is proposed. This model utilizes sensor data, which provides ease of use and rapid detection, as input and formaldehyde emission data measured by the full-scale chamber method as output. Initially, multiple strategies are employed to address the inherent limitations of the black-winged kite algorithm, which struggles to effectively find optimal solutions and is susceptible to getting trapped in local optima. The multi-strategy improved black-winged kite algorithm then optimizes key parameters of the successive variational mode decomposition (SVMD) and hybrid kernel extreme learning machine (HKELM). In addition, adaptive boosting is introduced to further improve the accuracy and robustness of the model. Then, the AMSHKELM formaldehyde emission modified deviation model is constructed to fit the decomposed subsequence. Moreover, an adaptive bandwidth kernel density estimation combined with the AMSHKELM is developed to construct an interval prediction model. Experimental results indicate that the AMSHKELM model achieves the coefficient of determination of up to 0.9767 and the root mean square error of 2.7141e-03, demonstrating higher fitting accuracy and stronger robustness compared to various other models. Additionally, the interval prediction model performs superiorly. This model can combine interval prediction information to effectively and comprehensively assess the pass rate of test samples, providing a fast and reliable solution for formaldehyde emission testing of wood-based panels.}, archive = {J_ACCESS}, author = {Yinuo Wang and Huanqi Zheng and Hua Wang and Yucheng Zhou}, doi = {10.1109/ACCESS.2024.3453970}, journal = {IEEE Access}, pages = {5112-5128}, shortjournal = {IEEE Access}, title = {Research on the rapid detection of formaldehyde emission from wood-based panels based on the AMSHKELM}, volume = {13}, year = {2025}, }
• (2025). An invariant geometric feature for inter-subject lumbar curve alignment to detect spondylolisthesis. ACCESS, 13, 5092-5111. (www)

  @article{ , abstract = {Spondylolisthesis classification commonly relies on the shift distance (SD) feature. This feature measures the displacement between vertebrae but is sensitive to variations in size and rotational alignment of the vertebrae. This study introduces a new diagonal ratio (DR) feature to overcome these limitations. The DR feature, defined as the ratio of the difference in diagonal lengths to the width of the lower vertebra, is robust against variations in vertebral size and effectively detects displacement in curved lumbar spines. The Bhattacharyya coefficients for the DR feature are significantly lower than those for the SD feature, highlighting its superior ability to distinguish between spondylolisthesis classes. Additionally, single-feature classification using Naive Bayes, based on corner points detected by ResNet, achieved its highest accuracy of 83.56% for the anterior-posterior (AP) view and 67.09% for the lateral (LA) view with the proposed DR feature. Factor analysis revealed that the DR feature contributed most significantly, with an average factor loading of 32.1% for the AP view, where the second highest factor loading was 22.4% by ASD, and 42.3% for the LA view, where the second highest factor loading was 24.0% by SDR, across three classifiers—Support Vector Classifier (SVC), Random Forest Classifier (RF), and Gradient Boosting Classifier (GB). These findings underscore our DR feature’s potential to overcome size variation and rotational alignment challenges.}, archive = {J_ACCESS}, author = {Podchara Klinwichit and Watcharaphong Yookwan and Athita Onuean and Paramate Horkaew and Sornsupha Limchareon and Jun-Su Jang and Suwanna Rasmequan and Krisana Chinnasarn}, doi = {10.1109/ACCESS.2024.3522970}, journal = {IEEE Access}, pages = {5092-5111}, shortjournal = {IEEE Access}, title = {An invariant geometric feature for inter-subject lumbar curve alignment to detect spondylolisthesis}, volume = {13}, year = {2025}, }
• (2025). Real-time stitching algorithm of vehicle side view image based on multi-region fast phase correlation. ACCESS, 13, 5076-5091. (www)

  @article{ , abstract = {Aiming at the problem that the camera field of view is too small to obtain the complete side image of the vehicle in the traffic management scene, a real-time stitching algorithm of vehicle side view image based on Multi-Region Fast Phase Correlation (MFPC) is proposed. Firstly, the background subtraction method based on Gaussian mixture model is used to obtain vehicle foreground image sequence, and the image is downsampled by Gaussian pyramid to reduce the running time of the program. Subsequently, multi-region phase correlation and registration check based on normalized cross-correlation are used to improve the accuracy of registration, and a local inverse discrete Fourier transform method is proposed to improve the computational efficiency. To mitigate background interference in registration, a peak filtering algorithm is proposed, combined with a sub-pixel refinement algorithm to enhance accuracy. Experimental results indicate that the proposed algorithm has better registration performance than the traditional phase correlation and methods based on feature point detection. It requires only 26% of the time taken by the traditional phase correlation, with an average processing time per frame of 3.81 ms, which meets the real-time requirements. In practical applications, the stitching accuracy reached 99.33%, demonstrating high precision and robustness.}, archive = {J_ACCESS}, author = {Mubin Xu and Xiaoyong Liu and Chuanhai Wan}, doi = {10.1109/ACCESS.2024.3525181}, journal = {IEEE Access}, pages = {5076-5091}, shortjournal = {IEEE Access}, title = {Real-time stitching algorithm of vehicle side view image based on multi-region fast phase correlation}, volume = {13}, year = {2025}, }
• (2025). Real time fractional robust adaptive scheme for frequency regulation in double area hybrid power system with renewables. ACCESS, 13, 5056-5075. (www)

  @article{ , abstract = {Load frequency regulation in hybrid grid is a very crucial issue. This work introduces robust-adaptive control methodology to provide accurate response against any disturbances. Control scheme of two main parts has been introduced. The first one is a novel fractional order model reference adaptive controller where fractional calculus merits and e-modification robust algorithm have been merged with the adaptive skills creating the fractional order model reference robust-adaptive controller. This robust-adaptive controller has utilized output feedback approach that requires only the output state without detailed system model information. The fractional orders have been optimized using a nature-inspired algorithm named Artificial Rabbits. The second part of the control scheme is a disturbance rejection observer that is able to estimate and eliminate external and internal disturbances. For fair comparison, results of the proposed scheme have been compared to that of the most efficient controllers obtained from the literature named fractional order proportional integral derivative controller and integral controller. Furthermore, the integer version of the proposed controller has been tested and compared with the proposed controller to prove the robustness of the fractional modification. To validate the superiority of the proposed controller, five challenging scenarios have been considered, encompassing load fluctuations, integration of renewable energy sources, changes in system parameters and time delay attacks. The proposed controller effectively minimizes area control error achieving stability and showing cost function enhancement of 15% to 152% compared to other tested controllers in all scenarios. Thus, it is strongly recommended for load frequency regulation in multi-area power systems.}, archive = {J_ACCESS}, author = {Ahmed Ragab and Dalia Allam and Ahmed F. Zobaa and Hazem Ali Attia}, doi = {10.1109/ACCESS.2025.3525999}, journal = {IEEE Access}, pages = {5056-5075}, shortjournal = {IEEE Access}, title = {Real time fractional robust adaptive scheme for frequency regulation in double area hybrid power system with renewables}, volume = {13}, year = {2025}, }
• (2025). Proof of inherent intelligence consensus mechanism empowering blockchain-enabled transactive energy. ACCESS, 13, 5042-5055. (www)

  @article{ , abstract = {An evolving energy system with a dispersed infrastructure may not be compatible with traditional centralized optimization and management techniques. Blockchain, a peer-to-peer immutable distributed ledger technology, has the potential to significantly contribute to the management of emerging trends of decentralized power networks. However, complex optimization problems associated with the decentralized power grid are poorly integrated into the existing blockchain applications. Here, we suggest Proof of Inherent Intelligence (PoII), a novel prosumer-centric consensus mechanism designed to assist multi-interest party optimization challenges of the distributed power grid. We demonstrate PoII’s operation and performance with comprehensive mathematical modeling of energy pool-market trading and scheduling optimization problems. The efficiency of the proposed framework is evaluated against the existing blockchain applications for peer-to-peer energy transactions in terms of latency, throughput, tolerance against adversaries, vulnerability, and optimization capabilities. A thorough case study of the power grid that includes thermal, wind, and intermittent generation sources is presented to assess the effectiveness of the proposed consensus mechanism. Power demand, reserves, trading, and scheduling scenarios in both the day-ahead and balancing markets are among the peer-to-peer energy transactional elements that are assessed to support the efficacy of the suggested consensus approach.}, archive = {J_ACCESS}, author = {Imran Hussain and Hafiz Ashiq Hussain and Nasim Ullah and Stanislav Misak}, doi = {10.1109/ACCESS.2024.3525416}, journal = {IEEE Access}, pages = {5042-5055}, shortjournal = {IEEE Access}, title = {Proof of inherent intelligence consensus mechanism empowering blockchain-enabled transactive energy}, volume = {13}, year = {2025}, }
• (2025). A study on lower limb movement intention recognition based on multi-source information fusion. ACCESS, 13, 5032-5041. (www)

  @article{ , abstract = {In order to enhance the suppleness of a lower limb rehabilitation medical robot during the re-habilitation process, this study proposes a multi-source information fusion lower limb motion intention recognition method based on surface electromyographic signals (sEMG) and lower limb joint angles. To solve the problem of data traffic surge during the collection process, a multi-source current limiting sliding time window algorithm (MLS) is proposed. The MLS algorithm controls the data flow through a flow limiting and sliding time window mechanism to ensure the efficiency and stability of the system in handling large data volumes. On this basis, the study combines the Back Propagation Generalized Algorithm Neural-network (BPGN) to construct a prediction model for lower limb joint angles. The experimental results show that under the same conditions of the algorithm, the fusion of multi-source information reduces the average error of knee joint angle prediction by 10.8° and the average error of ankle joint angle prediction by 7.2° compared with the method using a single lower limb joint angle signal. Under the same condition of input signal, the multivariate flow-limiting sliding time-window BPGN reduced the average knee joint error by 13.6° and the average ankle joint angle error by 8.5° compared to the BPGN intent recognition. The multivariate flow-limited sliding time window BPGN reduced the mean knee error by 11.2° and the mean ankle angle error by 7.4° compared to Radial Basis Function (RBF) Neural-network intent recognition. By integrating the sEMG signal and lower limb joint angle information, the system can more accurately capture the patient’s movement intention and realize more precise lower limb rehabilitation training.}, archive = {J_ACCESS}, author = {Siyu Zong and Wei Li and Dawen Sun and Xiaojie Wei and Junjie Chen and Zhengwei Yue and Daxue Sun}, doi = {10.1109/ACCESS.2024.3521510}, journal = {IEEE Access}, pages = {5032-5041}, shortjournal = {IEEE Access}, title = {A study on lower limb movement intention recognition based on multi-source information fusion}, volume = {13}, year = {2025}, }
• (2025). Simulation study on single-event burnout reliability of 900V 4H-SiC quasi vertical double diffused MOSFET. ACCESS, 13, 5023-5031. (www)

  @article{ , abstract = {In this work, the single-event burnout (SEB) performance and reasons of the proposed 900V SiC quasi-vertical double diffusion MOSFET with deepened drain (T-QVDMOSFET) are analyzed from the spatial distribution of physical quantities such as power density, lattice temperature and total current density by 2-D numerical simulation, and a SEB-hardened structure (TB-QVDMOSFET) with buried oxygen layer (BOX) and heavily doped N-type current expansion layer (CSL) inside the device is proposed. Simulation results indicate that when heavy-ion with linear energy transfer (LET) of 0.5pC/ $\mu $ m strikes the device, the primary cause of SEB in the SiC T-QVDMOSFET is the high transient current density and electric field at the trench gate corner. This phenomenon leads to increased power dissipation, resulting in excessive temperatures that ultimately cause thermal failure. The BOX and a heavily doped N-type CSL added in the SEB-hardened structure change the current flow path, and the transient current concentrated in the region is dispersed. This modification reduces the high current density and power dissipation at the trench corner, thereby significantly enhancing the device’s resistance to SEB. Compared to the original device, the SEB threshold voltage is increased from 270V to 478V, marking a 77% improvement.}, archive = {J_ACCESS}, author = {Jin-Ke Shi and Ying Wang and Xin-Xing Fei and Biao Sun and Yan-Xing Song and Yu-Qian Liu and Wei Zhang}, doi = {10.1109/ACCESS.2024.3524391}, journal = {IEEE Access}, pages = {5023-5031}, shortjournal = {IEEE Access}, title = {Simulation study on single-event burnout reliability of 900V 4H-SiC quasi vertical double diffused MOSFET}, volume = {13}, year = {2025}, }
• (2025). Numerical weather data-driven sensor data generation for PV digital twins: A hybrid model approach. ACCESS, 13, 5009-5022. (www)

  @article{ , abstract = {With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.}, archive = {J_ACCESS}, author = {Jooseung Lee and Jimyung Kang and Sangwoo Son and Hui-Myoung Oh}, doi = {10.1109/ACCESS.2025.3525659}, journal = {IEEE Access}, pages = {5009-5022}, shortjournal = {IEEE Access}, title = {Numerical weather data-driven sensor data generation for PV digital twins: A hybrid model approach}, volume = {13}, year = {2025}, }
• (2025). Investigation of factors that influence human presence and robot anthropomorphism in telepresence robot. ACCESS, 13, 4996-5008. (www)

  @article{ , abstract = {These days, we see different types of telepresence robots, and there has been a tremendous amount of research and development on these robots. Some telepresence robots have monitors to show the faces of remote operators and mobility to move around, but some do not have a monitor and instead have a robot face, and also they have robotic arms to do motions and perform functions. Many people feel that artificial intelligence (AI) in robots depends on their appearance, which is anthropomorphism, and telepresence robots without arm or body motions can also have a human presence. It is important to identify and configure how these robots give people a sense of presence and anthropomorphism by including human-like and robot-like faces and arm motions. We carried out web-based experiments and used videos of a telepresence robot ( $2\times 2$ between-participant study; face factor: human face, robot face; motion factor: moving, static) to investigate which factors significantly give users the sense of human presence and anthropomorphism in a robot. Our results show that participants felt that the robot had more anthropomorphism when its face was replaced with a human’s face and it did not make any motion. In addition, the robot’s motion invoked a feeling of human presence regardless of whether the face was human-like or robot-like. Our novel findings provide a guide for designing telepresence robots, revealing that motion enhances presence, and displaying the operator’s face increases anthropomorphism. These innovative insights offer a new approach to optimizing the design of telepresence robots depending on the desired user experience. When designing these robots, focusing on the sense of presence involves considering both stationary and moving robots to evoke a feeling of human presence. Conversely, to emphasize anthropomorphism, it is crucial to display the remote operator’s face.}, archive = {J_ACCESS}, author = {Nungduk Yun and Seiji Yamada}, doi = {10.1109/ACCESS.2024.3516782}, journal = {IEEE Access}, pages = {4996-5008}, shortjournal = {IEEE Access}, title = {Investigation of factors that influence human presence and robot anthropomorphism in telepresence robot}, volume = {13}, year = {2025}, }
• (2025). Design and study of LoRa-based IIoT network for underground coal mine environment. ACCESS, 13, 4984-4995. (www)

  @article{ , abstract = {The paper presents a novel LoRa-based IIoT system for monitoring the underground coal mine environment. Additionally, a new adaptive data rate algorithm for mesh network topology is proposed to optimize the data rate and maximize the coverage distance. Maximum coverage distance is achieved with a minimum number of repeaters. However, unlike previous works, the current work takes into account the power constraints in the underground coal mine environment, and most low-power consumption system is presented with an intelligent design approach. The implementation of the proposed adaptive data rate algorithm achieves 1.46 Kbps data rate at spreading factor 9, bandwidth 125 KHz, and transmitted power 11 dBm and extended the battery life of the deployed end nodes. The deployed end nodes achieved a maximum of 180 m under diffused line-of-sight condition at the roadway region of the operational mine. The relevant parameters are measured from the operational mine. Finally, the hardware is fabricated and the proposed system is validated in an operational underground coal mine.}, archive = {J_ACCESS}, author = {Ankita Ray Chowdhury and Saikat Chandra Bakshi and Ankita Pramanik and Gopal Chandra Roy}, doi = {10.1109/ACCESS.2024.3470120}, journal = {IEEE Access}, pages = {4984-4995}, shortjournal = {IEEE Access}, title = {Design and study of LoRa-based IIoT network for underground coal mine environment}, volume = {13}, year = {2025}, }
• (2025). A survey of open-source tools for transmission and distribution systems research. ACCESS, 13, 4966-4983. (www)

  @article{ , abstract = {This work presents a review of open-source electric power transmission and distribution systems analysis tools suitable for use by industry professionals and academic researchers. Due to the high complexity of the electric grid, there exist numerous tools and extensive research pertaining to nearly every aspect of the design, operation, and control of transmission and distribution networks. In addition to the commercial tools, a wide range of free, open-source tools, models, and data usable by the scientific community for related research have been developed by different organizations, including both international and US universities and national laboratories. However, due to the absence of a catalog of available tools, models and data, researchers often lack a knowledge of existing capabilities and may develop duplicative software and tools. Increasing awareness of these available resources seeks to accelerate their broader use, leading to more efficient and standardized grid analysis. This review paper (which is part of a larger survey effort that studied over 400 tools in the transmission, distribution, buildings, and electric vehicles space) outlines selected open-source resources that have been developed in power transmission and distribution systems research. It is anticipated that this work can serve as a guide for industry and academic researchers alike, ensuring that research efforts are well-channeled.}, archive = {J_ACCESS}, author = {Jennifer Appiah-Kubi and Alexander A. Anderson and Hayden M. Reeve}, doi = {10.1109/ACCESS.2024.3520878}, journal = {IEEE Access}, pages = {4966-4983}, shortjournal = {IEEE Access}, title = {A survey of open-source tools for transmission and distribution systems research}, volume = {13}, year = {2025}, }
• (2025). PRISM-med: Parameter-efficient robust interdomain specialty model for medical language tasks. ACCESS, 13, 4957-4965. (www)

  @article{ , abstract = {Language Models (LMs) have shown remarkable potential in healthcare applications, yet their widespread adoption faces challenges in achieving consistent performance across diverse medical specialties while maintaining parameter efficiency. Current approaches to fine-tuning language models for medical tasks often require extensive computational resources and struggle with managing specialized medical knowledge across different domains. To address these challenges, we present PRISM-Med (Parameter-efficient Robust Interdomain Specialty Model), a novel framework that enhances domain-specific performance through supervised domain classification and specialized adaptation. Our framework introduces three key innovations: (1) a domain detection model that accurately classifies medical text into specific medical domains using supervised learning, (2) a domain-specific Low-Rank Adaptation (LoRA) strategy that enables efficient parameter utilization while preserving specialized knowledge, and (3) a neural domain detector that dynamically selects the most relevant domain-specific models during inference. Through comprehensive empirical evaluation across multiple medical benchmarks (MedProb, MedNER, MedQuAD), we demonstrate that PRISM-Med achieves consistent performance improvements, with gains of up to 10.1% in medical QA tasks and 2.7% in medical knowledge evaluation compared to traditional fine-tuning baselines. Notably, our framework achieves these improvements while using only 0.1% to 0.4% of the parameters required for traditional fine-tuning approaches. PRISM-Med represents a significant advancement in developing efficient and robust medical language models, providing a practical solution for specialized medical applications where both performance and computational efficiency are crucial.}, archive = {J_ACCESS}, author = {Jieui Kang and Hyungon Ryu and Jaehyeong Sim}, doi = {10.1109/ACCESS.2024.3525041}, journal = {IEEE Access}, pages = {4957-4965}, shortjournal = {IEEE Access}, title = {PRISM-med: Parameter-efficient robust interdomain specialty model for medical language tasks}, volume = {13}, year = {2025}, }
• (2025). Exploring the impact of eHMI display location and vehicle type on pedestrian perceptions: A VR user study. ACCESS, 13, 4947-4956. (www)

  @article{ , abstract = {External human-machine interfaces (eHMIs) are expected to improve pedestrian interactions with automated vehicles (AVs) and foster greater social acceptance. While earlier research has primarily focused on refining eHMI designs by examining different modalities and color preferences, determining the most effective eHMI location on vehicles remains a significant challenge. Although some studies have started investigating various eHMI display locations, a comprehensive analysis of factors related to vehicle type and eHMI location that affect pedestrian preferences remains insufficient. To address this gap, we conducted a Virtual Reality user study to evaluate pedestrian responses to different AVs equipped with eHMIs positioned in various locations. Our results indicate that pedestrians perceive larger vehicles as less safe, yet the increased visibility provided by longer light bands on these vehicles influences their crossing decisions. Furthermore, the location and height of the eHMI in relation to eye level are critical, as improper positioning can reduce its visibility, leading to a negative impression among participants.}, archive = {J_ACCESS}, author = {Nianzhao Zheng and Jialong Li and Yan Zhang and Kenji Tei}, doi = {10.1109/ACCESS.2025.3526172}, journal = {IEEE Access}, pages = {4947-4956}, shortjournal = {IEEE Access}, title = {Exploring the impact of eHMI display location and vehicle type on pedestrian perceptions: A VR user study}, volume = {13}, year = {2025}, }
• (2025). Microscopic defect detection on aircraft engine blades via improved YOLOv8. ACCESS, 13, 4932-4946. (www)

  @article{ , abstract = {To fully bring into play the functions of aircraft engine blades, it is indispensable to perform regular inspections of engine blades, which currently rely on inefficient manual visual assessments. While artificial intelligence technology can be utilized, benchmark datasets are not available yet. To tackle these issues, in this work, we first construct two datasets that are collected from real blade defect images at different microscopic magnifications under an electron microscope and a metallographic microscope. Subsequently, we propose an efficient lightweight YOLOv8 framework, incorporating a hierarchical feature fusion module MS-Block for better multi-scale integration, as well as an Efficient Multi-Scale Attention (EMA) and Dilation-wise Residual (DWR) modules to enhance the detection of small targets and replace the loss function with Inner-IoU. The improved YOLOv8 demonstrates a noteworthy increase in mean average precision (mAP), achieving an enhancement of 1.5% on the Electron Microscope Taken (EMT) dataset and 1.8% on the Metallographic Microscope Taken (MMT) dataset compared to the original model. Our approach significantly surpasses the performance of contemporary target detection algorithms, thereby offering a robust solution for microscopic defect detection in aeroengines. This advancement not only streamlines the inspection process but also contributes to the overall safety and reliability of aircraft operations.}, archive = {J_ACCESS}, author = {Yujie Jiang and Ping Li and Bing Lin and Yingying Wang and Tian Li and Hui Xue and Weidong Liu and Zaihu Han and Dan Wang and Junlei Tang}, doi = {10.1109/ACCESS.2024.3522240}, journal = {IEEE Access}, pages = {4932-4946}, shortjournal = {IEEE Access}, title = {Microscopic defect detection on aircraft engine blades via improved YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Prediction of high-pressure physical properties of crude oil using explainable machine learning models. ACCESS, 13, 4912-4931. (www)

  @article{ , abstract = {High-pressure physical property parameters of formation crude oil are crucial for oilfield exploration, development, and production. Various prediction models have been developed using PVT experimental methods, empirical formulas, regression analysis, and machine learning techniques. However, these models often lack sufficient transparency and clarity in their computational processes, limiting their effectiveness in oil and gas exploration and development. This paper employs the Extreme Gradient Boosting (XGBoost) machine learning algorithm, utilizing experimental data such as formation pressure, formation temperature, surface crude oil density, natural gas relative density, gas-oil ratio, saturation pressure, volume factor, formation crude oil density, and formation crude oil viscosity as the dataset. The machine learning model is trained with these parameters to develop the XGBoost prediction model. Optimal parameters, including gas-oil ratio, saturation pressure, volume factor, formation crude oil density, and formation crude oil viscosity, are selected, and the Particle Swarm Optimization (PSO) algorithm is applied to further optimize the model, resulting in the PSO-XGBoost prediction model. To enhance the understanding of the model’s operation and the influencing factors, Shapley Additive Explanations (SHAP) and Explainable Boosting Machine (EBM) techniques are employed for both local and global interpretations of the machine learning model. This study identifies key factors and their weights in predicting high-pressure physical property parameters of formation crude oil, providing valuable insights for predicting shale oil reservoir parameters and enhancing exploration and development efficiency.}, archive = {J_ACCESS}, author = {Yawen He and Jianwen Chen and Zhiyu Wu and Yaxin Dun and Guichao Du and Mengsen Feng and Yifan Lu and Wei Dang}, doi = {10.1109/ACCESS.2024.3522595}, journal = {IEEE Access}, pages = {4912-4931}, shortjournal = {IEEE Access}, title = {Prediction of high-pressure physical properties of crude oil using explainable machine learning models}, volume = {13}, year = {2025}, }
• (2025). A novel electrical load forecasting model for extreme weather events based on improved gated spiking neural p systems and frequency enhanced channel attention mechanism. ACCESS, 13, 4884-4911. (www)

  @article{ , abstract = {Accurate short-term load forecasting (LF) under extreme weather is vital for the sustainable development of energy systems. This paper proposes a basic framework for future load forecasting researches of sustainable energy systems under extreme weather events and provides new direction for membrane computing model in terms of load forecasting. Inspired by nonlinear spiking mechanisms in nonlinear spiking neural P systems, the gated spiking neural P (GSNP) model is a new recurrent-like network. In this study, we develop an innovative membrane computing model, termed frequency attention temporal convolutional network-load forecasting-frequency attention gated spiking neural P (FATCN-LF-FAGSNP) model. Frequency enhanced channel attention mechanism (FECAM) is utilized to enhance the features extraction ability of temporal convolutional network (TCN) and improve prediction ability of GSNP systems. FATCN fully extracts the temporal relationship of features, the features of each channel interact with each frequency component to learn more temporal information effectively and comprehensively in frequency domain. Moreover, adding FECAM to extract features from the data fully reveals the relationship between influencing factors and the load series, which improves the quality of data features and the forecasting accuracy of the FAGSNP model. Then inspired by the interaction mechanism of impulses between biological neuronal cells, FAGSNP is able to consider the load variability and effectively predict load trends. In addition, to address load prediction challenges posed by extreme weather and promote the sustainable development of power systems, the proposed model integrates many models to solve this problem. First, optimized variational mode decomposition (VMD) is used to decompose the load series and the sub-sequences are combined with relevant features, to form the different input sequences of the prediction model. Then, FATCN-LF-FAGSNP model is developed to accurately forecast each high frequency component. Subsequently inverted Transformer model and Informer model are utilized to predict low frequency components and residual component, respectively. Finally all predicted components are reconstructed to get the final predicted results. We conducted extensive comparative experiments with ten baseline models on three real-world datasets, compared with GSNP model and TCN-GSNP model, the coefficient of determination (R2) of the FATCN-LF-FAGSNP model increases and mean absolute percentage error (MAPE), mean absolute error (MAE) and relative absolute error (RAE) reduce, the LF accuracy (measured by R2) of the proposed hybrid model gets 99.7% in seasonal LF task. In addition, the proposed hybrid model gets the best in MAPE, MAE, R2 and RAE metrics in all cases, which demonstrates the effectiveness of the proposed model in LF tasks under both extreme weather scenarios and seasonal prediction scenarios.}, archive = {J_ACCESS}, author = {Yuanshuo Guo and Jun Wang and Yan Zhong and Tao Wang and Zeyuan Sui}, doi = {10.1109/ACCESS.2025.3525479}, journal = {IEEE Access}, pages = {4884-4911}, shortjournal = {IEEE Access}, title = {A novel electrical load forecasting model for extreme weather events based on improved gated spiking neural p systems and frequency enhanced channel attention mechanism}, volume = {13}, year = {2025}, }
• (2025). Diagnosis of open and short circuit intermittent connection faults for DeviceNet based on multilayer information fusion and circuit network analysis. ACCESS, 13, 4869-4883. (www)

  @article{ , abstract = {DeviceNet plays an important role in manufacturing automation systems, hence the reliability of DeviceNet networks determines system performance and even operation safety. Intermittent connections (ICs) are randomly recurring cable transient misconnection faults, which may degrade network reliability and, in severe cases, even lead to system-wide failures. Typical ICs are intermittent open circuit (IOC) and intermittent short circuit (ISC). However, there is no diagnostic method for mixed IC faults where IOC and ISC coexist. This paper proposes a novel mixed IC fault diagnosis method for DeviceNet based on multilayer information fusion and circuit network analysis to identify the types and locations of IOC and ISC faults. First, the error patterns of different IC fault types are obtained for fault identification through a hybrid analysis of logic and analog signals. Then, the alarms are defined by analyzing the voltage drop behavior of the circuit network upon faults to target the fault scope. Next, by mapping the fault scope to exact cable links using an undirected graph-based topology representation, the possibilities of faults occurring on every link are derived based on the mapping relationships and corresponding alarms. Finally, a search-based locating algorithm is developed using the possibility order and observed network states to pinpoint IOC and ISC faults separately. The testbed is constructed and case studies are conducted to demonstrate the effectiveness of the proposed method in various fault scenarios and network scales. Experiment results show that the types and locations of mixed IC faults identified by the proposed method agree well with the experiment setups.}, archive = {J_ACCESS}, author = {Longkai Wang and Yong Lei}, doi = {10.1109/ACCESS.2024.3524331}, journal = {IEEE Access}, pages = {4869-4883}, shortjournal = {IEEE Access}, title = {Diagnosis of open and short circuit intermittent connection faults for DeviceNet based on multilayer information fusion and circuit network analysis}, volume = {13}, year = {2025}, }
• (2025). Inverse kinematics of a 7-degree-of-freedom robotic arm based on deep reinforcement learning and damped least squares. ACCESS, 13, 4857-4868. (www)

  @article{ , abstract = {As we advance towards the future of the smart manufacturing industry, our research focuses on enhancing manipulator technology. Inverse kinematics is a key component of robotic arm control, yet many existing methods struggle to achieve high performance when dealing with high-precision target points and highly redundant robotic arms. In this paper, we propose a novel solution to the inverse kinematics problem by combining Proximal Policy Optimization (PPO) with the Damped Least Squares (DLS) method, forming the Multistep PPO-DLS Inverse Kinematics (MPDIK) algorithm. The algorithm was trained and tested in the PyBullet virtual environment, using random seven-dimensional position and pose target points. The MPDIK algorithm demonstrated outstanding performance, with the end effector achieving a distance error of less than 0.1 mm and an orientation error of less than 0.001°. Additionally, it exhibited excellent stability and fast convergence, with a post-training task completion success rate of 98.37% and an average of 20.68 time steps per task. This represents a significant improvement over existing methods, such as PPO and DLS, and demonstrates universal applicability. Our experiments also revealed that this method holds great potential for improving both the accuracy and real-time application capabilities of robotic systems.}, archive = {J_ACCESS}, author = {Shusheng Yu and Gongquan Tan}, doi = {10.1109/ACCESS.2024.3521539}, journal = {IEEE Access}, pages = {4857-4868}, shortjournal = {IEEE Access}, title = {Inverse kinematics of a 7-degree-of-freedom robotic arm based on deep reinforcement learning and damped least squares}, volume = {13}, year = {2025}, }
• (2025). Vessel trajectory data mining: A review. ACCESS, 13, 4827-4856. (www)

  @article{ , abstract = {Recent advancements in sensor and tracking technologies have facilitated the real-time tracking of marine vessels as they traverse the oceans. As a result, there is an increasing demand to analyze these datasets to derive insights into vessel movement patterns and to investigate activities occurring within specific spatial and temporal contexts. This survey offers a comprehensive review of contemporary research in trajectory data mining, with a particular focus on maritime applications. The article collects and evaluates state-of-the-art algorithmic approaches and key techniques pertinent to various use case scenarios within this domain. Furthermore, this study provides an in-depth analysis of recent developments in trajectory data mining as applied to the maritime sector, identifying available data sources and conducting a detailed examination of significant applications, including trajectory forecasting, activity recognition, and trajectory clustering.}, archive = {J_ACCESS}, author = {Alexandros Troupiotis-Kapeliaris and Christos Kastrisios and Dimitris Zissis}, doi = {10.1109/ACCESS.2025.3525952}, journal = {IEEE Access}, pages = {4827-4856}, shortjournal = {IEEE Access}, title = {Vessel trajectory data mining: A review}, volume = {13}, year = {2025}, }
• (2025). Optimizing breast cancer mammogram classification through a dual approach: A deep learning framework combining ResNet50, SMOTE, and fully connected layers for balanced and imbalanced data. ACCESS, 13, 4815-4826. (www)

  @article{ , abstract = {Breast cancer is a global health concern where early and accurate diagnosis is crucial. Mammogram scans provide detailed imaging but require expert interpretation, which is time-consuming. While deep learning shows promise in medical image analysis, the prevalence of imbalanced datasets in medical diagnosis hinders the development of accurate and reliable classification models. We propose a novel deep-learning framework for breast cancer classification from Mammogram scans. The framework addresses imbalanced data through a unique two-module pipeline incorporating the Synthetic Minority Over-sampling Technique (SMOTE). One module employs SMOTE on the entire dataset to balance class distribution. At the same time, the second separates a portion (20%) of the original imbalanced data for evaluation and applies SMOTE to the remaining 80%. The framework incorporates a blockwise Convolutional Neural Network (CNN), utilizing VGG16 preprocessing for input standardization and ResNet50 for feature extraction. A fully connected classification model, consisting of multiple dense layers with batch normalization and dropout for regularization, was developed to assess the extracted features. The model architecture was iteratively refined to combat overfitting, with the final version incorporating three dense layers (128, 256, and 128 neurons) with dropout rates of 0.5. Our model achieved 99% accuracy on a balanced dataset and 90% on an imbalanced portion. The framework includes an interpretable visualization technique for randomly selected predictions across all classes. Our approach significantly improves diagnostic accuracy in breast cancer classification from Mammogram scans, effectively addressing the challenge of imbalanced data in medical image analysis. This work contributes to medical image analysis and computer-aided diagnosis. The proposed techniques for handling imbalanced data and providing interpretable results can be extended to improve diagnostic accuracy across various medical conditions.}, archive = {J_ACCESS}, author = {Abdullah Fahad A. Alshamrani and Faisal Saleh Zuhair Alshomrani}, doi = {10.1109/ACCESS.2024.3524633}, journal = {IEEE Access}, pages = {4815-4826}, shortjournal = {IEEE Access}, title = {Optimizing breast cancer mammogram classification through a dual approach: A deep learning framework combining ResNet50, SMOTE, and fully connected layers for balanced and imbalanced data}, volume = {13}, year = {2025}, }
• (2025). An improved bare bones particle swarm optimization algorithm based on sequential update mechanism and a modified structure. ACCESS, 13, 4789-4814. (www)

  @article{ , abstract = {The past three decades have witnessed the rapid development of nature-inspired algorithms. Among these, population-based optimization algorithms have gained significant popularity due to their effectiveness in solving a wide range of problems. Particle Swarm Optimization (PSO) stands out as a pioneering algorithm in this domain. Bare-Bones Particle Swarm Optimization (BBPSO) is a simplified variant of PSO that eliminates the velocity term and additional parameters. This study introduces a novel sequential update rule for BBPSO, along with a modification to the standard algorithm. The proposed methods were evaluated on a comprehensive benchmark suite, including 36 benchmark problems from the literature, 30 benchmark problems from CEC2021, consisting of 10 basic and 20 transformed variants and 5 engineering optimization problems. Comparative analysis with standard BBPSO and other simplified PSO variants demonstrated the effectiveness of our proposed approach.}, archive = {J_ACCESS}, author = {Ali Solak and Altan Onat and Onur Kilinç}, doi = {10.1109/ACCESS.2025.3525603}, journal = {IEEE Access}, pages = {4789-4814}, shortjournal = {IEEE Access}, title = {An improved bare bones particle swarm optimization algorithm based on sequential update mechanism and a modified structure}, volume = {13}, year = {2025}, }
• (2025). A new robust adaptive fading unscented kalman filter-based vector tracking loop for mitigating GNSS NLOS reception. ACCESS, 13, 4774-4788. (www)

  @article{ , abstract = {The positioning accuracy of global navigation satellite system (GNSS) in urban areas can be significantly affected by non-line-of-sight (NLOS) receptions. Vector tracking loop (VTL) which utilizes information from multiple satellites has been proposed for mitigating the NLOS detection. This paper develops a robust adaptive fading unscented Kalman filter (AF-UKF-) based navigation filter for NLOS mitigation in VTL-based systems. It integrates the AF-KF approach previously proposed for adaptive Bayesian KF to the UKF-based navigation filter to update the scale of the state and measurement noise covariances so that system noise and measurement uncertainties can be mitigated. A robust extension of the AF-UKF algorithm based on robust statistics is proposed to effectively detect and correct NLOS reception in each KF update. A robust initialization method is also proposed to provide a reliable warm start to the robust AF-UKF so that the latter can reliably start-up even under NLOS reception and limited initial position information. The proposed method was evaluated and compared with conventional algorithms using real GPS signal datasets and simulated data. Results showed that the proposed navigation solution yields more accurate and reliable positioning performance than conventional methods under NLOS reception. The advantage and stability of the AF-KF and AF-UKF are also analyzed.}, archive = {J_ACCESS}, author = {Mengxia He and Shing-Chow Chan and Bo Chai and Zhong Liu}, doi = {10.1109/ACCESS.2024.3522578}, journal = {IEEE Access}, pages = {4774-4788}, shortjournal = {IEEE Access}, title = {A new robust adaptive fading unscented kalman filter-based vector tracking loop for mitigating GNSS NLOS reception}, volume = {13}, year = {2025}, }
• (2025). A fast parallel processing algorithm for triangle collision detection based on AABB and octree space slicing in Unity3D. ACCESS, 13, 4759-4773. (www)

  @article{ , abstract = {This research develops an effective and precise collision detection (CD) algorithm for real-time simulation in virtual environments such as computer graphics, realistic and immersive virtual reality (VR), augmented reality (AR) and physical-based simulation within an enhanced algorithm for object collision detection in 3D geometry. We describe an improved algorithm through a comparison in the application of a central processing unit (CPU) and graphics processing units (GPU). Although leveraging CPU for computational speed improvements has gained significant recognition in recent years, this study distinguishes by tracking 3D geometry bounding volume hierarchy (BVH) constructed in a spatial decomposition structure with a focus on Octree-based Axis-Aligned Bounding Box (AABB) structure in 3D scene to compute collision detection to swiftly reject disjoint objects and minimize the number of triangle primitives that need to be processed and then the Möller method is utilized to compute precise triangle primitives, further enhancing the efficiency and precision of the collision detection process. This approach is also designed to implement computation with GPU which utilizes the high-level shader language (HLSL) programming language on the compute shader Unity3D. AABB is structured as the maximum and minimum hexahedron enclosing an object that is parallel to the coordinate axis. Otherwise, GPU computational technique is a crucial method for further enhancing the object’s performance. The proposed method utilizes Octree AABB-based GPU parallel processing to reduce the computational load of real-time collision detection simulations and to handle multiple computations simultaneously. Comparative performance evaluations demonstrate that our GPU-accelerated framework consistently reaches the fastest collision detection times from 1.01 to 45.62 times, respectively.}, archive = {J_ACCESS}, author = {Kunthroza Hor and Nak-Jun Sung and Jun Ma and Min-Hyung Choi and Min Hong}, doi = {10.1109/ACCESS.2024.3525025}, journal = {IEEE Access}, pages = {4759-4773}, shortjournal = {IEEE Access}, title = {A fast parallel processing algorithm for triangle collision detection based on AABB and octree space slicing in Unity3D}, volume = {13}, year = {2025}, }
• (2025). Automatically constructing multi-dimensional resource space by extracting class trees from texts for operating and analyzing texts from multiple abstraction dimensions. ACCESS, 13, 4737-4758. (www)

  @article{ , abstract = {Abstraction is a key part of understanding and representation. Discovering different abstraction dimensions on a large set of texts can help understand the texts from multiple dimensions therefore support multi-dimensional operations required by advanced applications. This paper proposes a low-cost approach to automatically discovering abstraction dimensions represented as class trees on texts. The approach consists of three steps: 1) extract subclass relations from input texts based on modifier pattern and syntactic pattern; 2) construct class trees based on the extracted subclass relations; and 3) select independent class trees with high coverage on texts as abstraction dimensions. The correctness and feasibility of the approach are validated on seven data sets of different types. The average precision, recall and F1-score of the extracted subclass relations of the proposed approach are all greater than 85%. The application of the proposed approach to managing GitHub projects demonstrates that searching on the class trees ensures strong relevance between query and return, can quickly reduce search space and support effective management of projects. The proposed approach not only greatly extends the pattern-based approach to finding abstraction relation from texts with a high coverage but also verifies the feasibility of automatically extracting abstraction dimensions from texts. It can be applied to efficiently manage large-scale text resources from different dimensions to support advanced applications.}, archive = {J_ACCESS}, author = {Jian Zhou and Jiazheng Li and Sirui Zhuge and Hai Zhuge}, doi = {10.1109/ACCESS.2024.3516872}, journal = {IEEE Access}, pages = {4737-4758}, shortjournal = {IEEE Access}, title = {Automatically constructing multi-dimensional resource space by extracting class trees from texts for operating and analyzing texts from multiple abstraction dimensions}, volume = {13}, year = {2025}, }
• (2025). D-wave’s nonlinear-program hybrid solver: Description and performance analysis. ACCESS, 13, 4724-4736. (www)

  @article{ , abstract = {The development of advanced quantum-classical algorithms is among the most prominent strategies in quantum computing. Numerous hybrid solvers have been introduced recently. Many of these methods are created ad hoc to address specific use cases. However, several well-established schemes are frequently utilized to address optimization problems. In this context, D-Wave launched the Hybrid Solver Service in 2020, offering a portfolio of methods designed to accelerate time-to-solution for users aiming to optimize performance and operational processes. Recently, a new technique has been added to this portfolio: the Nonlinear-Program Hybrid Solver. This paper describes this solver and evaluates its performance through a benchmark of 45 instances across three combinatorial optimization problems: the Traveling Salesman Problem, the Knapsack Problem, and the Maximum Cut Problem. To facilitate the use of this relatively unexplored solver, we provide details of the implementation used to solve these three optimization problems.}, archive = {J_ACCESS}, author = {Eneko Osaba and Pablo Miranda-Rodriguez}, doi = {10.1109/ACCESS.2025.3525620}, journal = {IEEE Access}, pages = {4724-4736}, shortjournal = {IEEE Access}, title = {D-wave’s nonlinear-program hybrid solver: Description and performance analysis}, volume = {13}, year = {2025}, }
• (2025). CGDINet: A deep learning-based salient object detection algorithm. ACCESS, 13, 4697-4723. (www)

  @article{ , abstract = {Salient object detection (SOD) is a key preprocessing step in computer vision, widely used in object tracking, action recognition, and image retrieval, among other fields. However, traditional SOD algorithms often face issues such as rough object boundaries, incomplete extraction of global image features, and insufficient attention to key areas. To address these problems, an improved significance object detection network—CGDINet (Coordinate Attention-Group Consensus Aggregation Module-Depth Auxiliary Module-Inverse Saliency Pyramid Reconstruction Network)—is proposed. CGDINet introduces the Group Consensus Aggregation Module (GCAM) embedded with the Coordinate Attention (CA) mechanism, named CAGM (Coordinate Attention-Group Consensus Aggregation Module), to enhance global feature capture capabilities and improve the processing of directional features. Additionally, the Depth Auxiliary Module (DAM) is incorporated to enhance the focus on important regions. Experiments were conducted on five public datasets (DUT-OMRON, ECSSD, PASCAL-S, DUTS-TE, and HKU-IS). The results show that CGDINet outperforms other mainstream significance object detection models in evaluation metrics such as ${\mathrm {maxF}}_{\mathrm {\beta }}$ , $\mathrm {S}_{\mathrm {\alpha }}$ , and MAE, with almost no increase in computational cost (FLOPs) and parameters. The experimental results validate that CGDINet can effectively address the issues of incomplete global feature extraction and insufficient attention to key areas, thereby significantly enhancing the performance of significance object detection.}, archive = {J_ACCESS}, author = {Chengyu Hu and Jianxin Guo and Hanfei Xie and Qing Zhu and Baoxi Yuan and Yujie Gao and Xiangyang Ma and Jialu Chen and Juan Tian}, doi = {10.1109/ACCESS.2024.3525303}, journal = {IEEE Access}, pages = {4697-4723}, shortjournal = {IEEE Access}, title = {CGDINet: A deep learning-based salient object detection algorithm}, volume = {13}, year = {2025}, }
• (2025). Real-time voltage stability assessment with a novel bus index considering impact of connection to distribution networks. ACCESS, 13, 4683-4696. (www)

  @article{ , abstract = {Many real-world power systems operate near voltage stability limits due to rising power demands, limited generation sources, and delays in development projects. Therefore, conducting online voltage stability assessments of power systems is crucial to ensure stable system operation. Various indices have been proposed in recent years to assess voltage stability in this context. One of the main challenges of these indexes for implementation in real-world power systems is their formulation with two-bus and single-line systems and their generalization to multi-bus and multi-line systems, which makes them non-rigorous. In formulating the existing indexes that use the concept of voltage sensitivity to active power, the effect of the lines from which the bus receives active power can be seen separately in evaluating bus voltage stability. At the same time, for accurate evaluation, the set of lines and the received power of all lines should be considered together in formulating the index. To meet this challenge, a new real-time index for voltage stability assessment is introduced in this paper. Furthermore, the effect of distribution systems in voltage stability is investigated in this study using simultaneous modeling transmission and distribution systems. The test results of the proposed index on the power system of the 3-bus system and the IEEE 14, 30, and 118 bus systems show its superior performance compared to the existing index. Specifically, to increase the apparent power loading in the 118-bus system, the maximum error of the proposed index is 3.37%. Meanwhile, for index P and index L, the maximum error is 29.6% and 30.75%, respectively. Finally, the results show that modeling the distribution system in the 3 and 118 bus systems reduces the maximum active power loading by 35.21% and 59.27%, respectively.}, archive = {J_ACCESS}, author = {Sasan Azad and Mohammad Taghi Ameli and Mohammad Mehdi Amiri and Hossein Ameli and Mohammad Reza Shadi and Goran Strbac}, doi = {10.1109/ACCESS.2024.3523503}, journal = {IEEE Access}, pages = {4683-4696}, shortjournal = {IEEE Access}, title = {Real-time voltage stability assessment with a novel bus index considering impact of connection to distribution networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing physical layer security in WPCNs with IRS and controlled jamming for 6G IoT applications. ACCESS, 13, 4670-4682. (www)

  @article{ , abstract = {This paper investigates the integration of an intelligent reflecting surface (IRS) and a jammer controlled interference to enhance the physical layer security (PLS) of wireless powered communication networks (WPCNs). In 6G networks, where low-power Internet of Things (IoT) devices are prevalent, security and energy efficiency are critical. While existing studies primarily focus on IRS-assisted methods to improve PLS by manipulating signal reflections, such approaches may not fully degrade the reception of eavesdroppers. We address this limitation by proposing an approach that combines IRS-assisted signal reflection with controlled jamming to further degrade the reception of eavesdroppers. Specifically, we jointly optimize the downlink/uplink time allocation, IRS phase shift matrices, and jamming interference to maximize the sum secrecy throughput. To address the non-convex nature of the problem, we develop an alternating optimization algorithm that decouples the problem into subproblems, ensuring efficient convergence to a locally optimal solution. Through detailed performance analysis, we demonstrate the superiority of our proposed scheme in terms of secrecy throughput and energy efficiency. Numerical results reveal that, compared to traditional IRS-assisted or non-jamming schemes, our approach yields substantial performance gains. This highlights the potential of integrating IRS and jamming to provide more secure and energy-efficient communication solutions for WPCNs, particularly in the context of 6G IoT applications.}, archive = {J_ACCESS}, author = {Iqra Hameed and Abid Afridi and Myung-Sun Baek and Hyoung-Kyu Song}, doi = {10.1109/ACCESS.2024.3525036}, journal = {IEEE Access}, pages = {4670-4682}, shortjournal = {IEEE Access}, title = {Enhancing physical layer security in WPCNs with IRS and controlled jamming for 6G IoT applications}, volume = {13}, year = {2025}, }
• (2025). Detecting subtle signs of school attendance issues using smartphone-based sensing. ACCESS, 13, 4652-4669. (www)

  @article{ , abstract = {In recent years, school attendance issues among university students have been increasing, which can lead to repeating courses, dropping out of school, or even social withdrawal. Despite the existence of counseling services, students often delay help-seeking, which can cause symptoms to worsen and make support more difficult. Thus, it is essential to identify at-risk students early and encourage them to seek help. A realistic approach must minimize the burden on students, rely only on devices they already own, and operate correctly even for students who are less engaged or prone to social withdrawal. While several techniques have been proposed to estimate individual indicators, they fail to address one of these requirements due to requiring additional devices or requiring user attention and interaction. In this paper, we propose an unobtrusive screening method for detecting subtle signs of school attendance issues in university students. We develop a smartphone app to collect sensor data and collect ground truth information using questionnaires for 1) sleep problems; and 2) decreased student engagement. We collect data from 58 university students for about 10 months, and build estimation models for the above indicators. Our evaluation shows that the estimation models are sufficiently accurate in flagging problematic cases. The indicators can then be used to notify at-risk students and medical practitioners, enabling timely intervention. This screening is not intended to replace traditional face-to-face medical examinations, but rather to selectively flag at-risk students and connect them with medical experts.}, archive = {J_ACCESS}, author = {Viktor Erdélyi and Teruhiro Mizumoto and Yuichiro Kitai and Daiki Ishimaru and Hiroyoshi Adachi and Teruo Higashino and Manabu Ikeda}, doi = {10.1109/ACCESS.2024.3523108}, journal = {IEEE Access}, pages = {4652-4669}, shortjournal = {IEEE Access}, title = {Detecting subtle signs of school attendance issues using smartphone-based sensing}, volume = {13}, year = {2025}, }
• (2025). Application of phi (Φ), the golden ratio, in computing: A systematic review. ACCESS, 13, 4621-4651. (www)

  @article{ , abstract = {The Golden Ratio ( $\phi $ ) has intrigued researchers for centuries due to its widespread presence in both nature and science. This article systematically examines $\phi $ ’s diverse applications in computing, focusing on its role in enhancing visual appeal, optimizing algorithms, system design, and influencing fields such as biometrics, machine intelligence, and engineering. Through a systematic review of recent studies, this work aims to identify how $\phi $ can improve computational efficiency across various computing domains. The methodology involves a comprehensive analysis of cutting-edge research sourced from 12 reputable digital platforms to explore $\phi $ ’s potential. This study highlights the versatility of Golden Ratio, as its applications and relationships were found to account for 51% and 49%, respectively, across a total of 51 selected studies from 27 countries spanning six continents. The review also categorizes findings by their specific applications and contexts, providing valuable insights into $\phi $ ’s impact on mathematics, cryptography, search algorithms, machine learning, artificial intelligence, photonics, natural sciences, system design, power engineering, robotics, and practical human life. This study opens promising avenues for future research on the applications of $\phi $ in diverse fields of science, engineering, and computing.}, archive = {J_ACCESS}, author = {M. Akhtaruzzaman and Jamal Uddin Tanvin and Amir Akramin Shafie and Fahim Shahryer and Sachitra Halder}, doi = {10.1109/ACCESS.2024.3522206}, journal = {IEEE Access}, pages = {4621-4651}, shortjournal = {IEEE Access}, title = {Application of phi (Φ), the golden ratio, in computing: A systematic review}, volume = {13}, year = {2025}, }
• (2025). On the design of a high band-ratio mid-section crossed dual-band branch-line coupler with arbitrary power division ratio using perturbation factor. ACCESS, 13, 4609-4620. (www)

  @article{ , abstract = {This article presents a Mid-section crossed dual-band Branch-Line Coupler (MBLC) with port extensions, along with a new design methodology for both complex and conventional structured branch-Line couplers. The proposed MBLC structure features a conventional branch-line coupler with a crossed line at the center, as well as an L-section impedance matching network at all four ports, which facilitates analysis and design. A comprehensive theoretical analysis is performed to derive closed-form design equations for determining the design parameters of the coupler using a perturbation factor. The analysis demonstrates that the proposed MBLC can support a wide band ratio of up to 11, indicating a broad operating frequency range. To evaluate the performance of the proposed MBLC, two prototypes were fabricated on a 62 mil Rogers 4003C substrate, operating at 1 GHz and 2 GHz for equal and unequal power division cases. The simulated and measured results for both prototypes showed an excellent match, with a magnitude imbalance of less than 4% and a phase imbalance of less than 2%. Overall, the proposed MBLC with port extensions in conjunction with the new design methodology demonstrated promising results for the development of efficient and effective dual-band branch-line couplers.}, archive = {J_ACCESS}, author = {Ahammad and Asif Iftekhar Omi and Mohammad H. Maktoomi and Mohammad A. Maktoomi and Praveen K. Sekhar}, doi = {10.1109/ACCESS.2024.3524594}, journal = {IEEE Access}, pages = {4609-4620}, shortjournal = {IEEE Access}, title = {On the design of a high band-ratio mid-section crossed dual-band branch-line coupler with arbitrary power division ratio using perturbation factor}, volume = {13}, year = {2025}, }
• (2025). Machine and deep learning models for stress detection using multimodal physiological data. ACCESS, 13, 4597-4608. (www)

  @article{ , abstract = {Stress can disrupt daily activities and harm health if prolonged or severe. Early detection of mental stress, indicated by changes in bio-signals like thermal, electrical, and acoustic signals, can prevent related health issues. This study employs machine learning and deep learning techniques on multimodal dataset from wearable sensors, focusing on processed metrics for the three-axis acceleration (ACC), electrocardiogram (ECG), blood volume pulse (BVP), body temperature (TEMP), respiration (RESP), electromyogram (EMG), and electrodermal activity (EDA) from the 15 subjects in the WESAD dataset to effectively classify four different states – baseline, stress, amusement, and meditation. Seven traditional machine learning algorithms – Logistic Regression, Gaussian Naïve Bayes Classifier, AdaBoost Classifier, XGB Classifier, Decision Trees Classifier, Extra Trees Classifier, and Random Forest Classifier and three widely used deep learning algorithms – Deep Neural Network, Convolutional Neural Network and Recurrent Neural Network were trained and tested on the dataset on two phases to predict the state of different subject at any given time. Our findings indicate that Recurrent Neural Networks achieved an F1 score of 93% when trained on a selected set of subjects and tested on the data from different subjects. Traditional machine learning algorithms, including Random Forest, Extra Trees, and XGB Classifiers, on the other hand, each achieved an F1 score of 99% when trained and tested on the data for the same set of subjects. Additionally, models performed better on chest data when trained and tested on the same subjects, while they perform better on wrist data when trained on a random group of subjects and tested on the remaining ones.}, archive = {J_ACCESS}, author = {Eman Abdelfattah and Shreehar Joshi and Shreekar Tiwari}, doi = {10.1109/ACCESS.2024.3525459}, journal = {IEEE Access}, pages = {4597-4608}, shortjournal = {IEEE Access}, title = {Machine and deep learning models for stress detection using multimodal physiological data}, volume = {13}, year = {2025}, }
• (2025). Arbitrary fixed-time sliding mode control for buck converter with matched and mismatched disturbances based on fixed-time observer. ACCESS, 13, 4582-4596. (www)

  @article{ , abstract = {This article introduces an arbitrary fixed-time sliding mode controller based on the fixed-time disturbance observer (FxDO-FxSMC) for a buck converter with disturbance. FxDO-FxSMC is proposed to address the issues of finite time convergence and large initial conditions present in existing sliding mode controllers. FxDO-FxSMC uses an arbitrary sliding surface to stabilize the buck converter in the presence of both matched and mismatched disturbances while ensuring arbitrary fixed-time convergence. First, the buck converter’s modified state-space model, which contains both matched and mismatched disturbances, is defined. Secondly, an arbitrary fixed-time disturbance observer is designed to predict disturbances. Finally, an arbitrary FXSMC is constructed by defining the sliding surface with an arbitrary term and estimated mismatched disturbance. The FxDO-FxSMC method 1) tackles an arbitrary fixed time sliding surface for the buck converter with mismatched disturbance, a feature not achievable with the current finite/fixed time sliding mode control; 2) allows the user to adjust the convergence time of the proposed method, regardless of the initial conditions; and 3) concurrently mitigates chattering issues. Additionally, Lyapunov stability is carried out. To highlight the advantages of the proposed combined structure, it is compared with the classical SMC, integral SMC (ISMC), disturbance observer-based SMC (DO-SMC), and finite-time disturbance observer-based non-singular terminal SMC (FnDO-NTSMC). As a result of the simulation tests, the chattering rate value (CRV) has been enhanced by 36.19% according to FnDO-NTSMC, which provided the best response compared to other traditional techniques. The simulation results demonstrate that the proposed controller effectively reduces overshoot, steady state error, and response time compared to traditional and other controllers. We evaluated system performance using dynamic response MSE, RMSE, and MAE for fair comparison. DO-SMC outperformed existing approaches in RMSE and MAE; however, the proposed controller improved 174.27% and 99.26%. The simulation results prove that the proposed controller design exhibits more rapid convergence and dynamic response to disturbances, regardless of initial conditions.}, archive = {J_ACCESS}, author = {Orhan Kaplan and Ferhat Bodur and Mustafa Bahadir Ozdemir}, doi = {10.1109/ACCESS.2024.3525092}, journal = {IEEE Access}, pages = {4582-4596}, shortjournal = {IEEE Access}, title = {Arbitrary fixed-time sliding mode control for buck converter with matched and mismatched disturbances based on fixed-time observer}, volume = {13}, year = {2025}, }
• (2025). Corrections to “Effect of material changes on the total harmonic distortions caused by aluminum electrolytic capacitors”. ACCESS, 13, 4581. (www)

  @article{ , abstract = {Presents corrections to the paper, (Corrections to “Effect of Material Changes on the Total Harmonic Distortions Caused by Aluminum Electrolytic Capacitors”).}, archive = {J_ACCESS}, author = {René Kalbitz}, doi = {10.1109/ACCESS.2024.3521142}, journal = {IEEE Access}, pages = {4581}, shortjournal = {IEEE Access}, title = {Corrections to “Effect of material changes on the total harmonic distortions caused by aluminum electrolytic capacitors”}, volume = {13}, year = {2025}, }
• (2025). Methods for improving point cloud authenticity in LiDAR simulation for autonomous driving: A review. ACCESS, 13, 4562-4580. (www)

  @article{ , abstract = {Collecting LiDAR data for autonomous driving using real vehicles is costly, scenario-limited, and challenging to annotate. Simulated LiDAR point clouds offer flexible configurations, reduced costs, and readily available labels but often lack the realism of real-world data. This study provides a comprehensive review of methods to enhance the authenticity of simulated LiDAR data, focusing on simulation scenarios, environmental conditions, and point cloud features. Additionally, we discuss verification techniques, including direct and indirect methods, to assess authenticity improvements. Experimental results demonstrate the effectiveness of these techniques in enhancing perception algorithm performance. The paper identifies challenges in simulating LiDAR data, such as accuracy discrepancies, brand adaptability, and the need for comprehensive evaluation metrics. It also proposes future directions to bridge the gap between simulated and real-world data, aiming to optimize hybrid training models for improved autonomous driving applications.}, archive = {J_ACCESS}, author = {Yanzhao Yang and Jian Wang and Xinyu Guo and Xinyu Yang and Wei Qin}, doi = {10.1109/ACCESS.2025.3525805}, journal = {IEEE Access}, pages = {4562-4580}, shortjournal = {IEEE Access}, title = {Methods for improving point cloud authenticity in LiDAR simulation for autonomous driving: A review}, volume = {13}, year = {2025}, }
• (2025). User plane function (UPF) allocation for C-V2X network using deep reinforcement learning. ACCESS, 13, 4547-4561. (www)

  @article{ , abstract = {In this paper, we proposed an online learning method for predicting an allocation of User Plane Function (UPF) in Cellular Vehicle-to-Everything (C-V2X) networks integrated with Multi-Access Edge Computing (MEC). Our study employed Deep Reinforcement Learning (DRL) techniques, specifically Deep Q-Network (DQN) and Actor-Critic (AC) algorithms. The DQN and AC algorithms were implemented to decide the optimal location of UPFs subject to vehicle positions and speed data of the vehicles. Our objective was to reduce the latency of communications between UPF and vehicles by placing the UPF(s) in optimal way. The simulation results showed that both DQN and AC algorithms reduced the latency significantly. We compared our proposed methods with the existing approaches which are K-mean Greedy Average and Greedy Average algorithms. The proposed AC algorithm achieved up to 40% reduction of average latency compared with the baseline methods when the placement of multiple UPFs are considered.}, archive = {J_ACCESS}, author = {Pruk Sasithong and Tachporn Sanguanpuak and Pisit Vanichchanunt and Lunchakorn Wuttisittikulkij}, doi = {10.1109/ACCESS.2024.3524886}, journal = {IEEE Access}, pages = {4547-4561}, shortjournal = {IEEE Access}, title = {User plane function (UPF) allocation for C-V2X network using deep reinforcement learning}, volume = {13}, year = {2025}, }
• (2025). Multi-slot secure offloading and resource management in VEC networks: A deep reinforcement learning-based method. ACCESS, 13, 4533-4546. (www)

  @article{ , abstract = {Vehicular Edge Computing (VEC) networks have emerged as an efficient paradigm to support a range of computation-intensive applications. However, potential eavesdropping attacks pose significant threats to massive confidential information. Furthermore, the rapid growth of Intelligent Connected Vehicles (ICVs) intensifies the scarcity of communication and computational resources, generating an urgent need for improving resource utilization. Since both security concerns and resource limitation influence decisions on offloading strategies and transmission rate, it is necessary to investigate a joint optimization scheme. In this paper, we employ Physical Layer Security (PLS) and design a workflow to address the Joint Secure Offloading and Resource Allocation (JSORA) problem in VEC networks. This workflow models the interaction patterns of multiple ICVs with resource cluster on edge servers, and accurately reflects the occupancy and release of each resource unit. Given the dynamic nature and high complexity of the JSORA problem, we propose a Filtered Deep Reinforcement Learning-based Secure Offloading and Allocation (FDRL-SOA) algorithm to control the offloading and resource allocation within the cluster. Finally, our simulation results demonstrate significant improvements over benchmark methods, with energy consumption reduced by 5.16%, latency decreased by 1.4%, and system cost was minimized by 3.3%.}, archive = {J_ACCESS}, author = {Zhen Li and Jialong Gong and Xiong Xiong and Dong Wang}, doi = {10.1109/ACCESS.2024.3524636}, journal = {IEEE Access}, pages = {4533-4546}, shortjournal = {IEEE Access}, title = {Multi-slot secure offloading and resource management in VEC networks: A deep reinforcement learning-based method}, volume = {13}, year = {2025}, }
• (2025). Vietnamese sentence fact checking using the incremental knowledge graph, deep learning, and inference rules on online platforms. ACCESS, 13, 4517-4532. (www)

  @article{ , abstract = {In the digital era, the accuracy and reliability of information are paramount, and fact-checking is essential to achieving this objective. Researching this area presents numerous challenges, particularly for the Vietnamese language, due to the current scarcity of tools and data for Vietnamese fact-checking. To address these challenges and advance fact-checking research both broadly and within the Vietnamese context, this paper introduces a fact-checking model tailored for Vietnamese, named ViKGFC. ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. Its notable capability is the extraction of triples from complex Vietnamese sentences, which significantly enhances information extraction in the Vietnamese language. Additionally, ViKGFC utilizes inference rules to improve the KG’s accuracy and employs matching techniques for rapid verification, thereby aiding in the swift prevention of misinformation spread on media and online platforms. Evaluated on a dataset of 130,190 Vietnamese samples sourced from Wikipedia, ViKGFC attains an outstanding accuracy level, reaching 95%. This proposed method offers an optimistic solution for verifying facts in Vietnamese and could potentially assist in creating fact-checking tools and techniques for other languages. In summary, this study significantly advances data science by introducing a reliable and accurate approach to Vietnamese fact-checking.}, archive = {J_ACCESS}, author = {Huong To Duong and Van Hai Ho and Phuc do}, doi = {10.1109/ACCESS.2024.3523416}, journal = {IEEE Access}, pages = {4517-4532}, shortjournal = {IEEE Access}, title = {Vietnamese sentence fact checking using the incremental knowledge graph, deep learning, and inference rules on online platforms}, volume = {13}, year = {2025}, }
• (2025). A multispectral automated transfer technique (MATT) for machine-driven image labeling utilizing the segment anything model (SAM). ACCESS, 13, 4499-4516. (www)

  @article{ , abstract = {Segment Anything Model (SAM) is drastically accelerating the speed and accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB) imagery datasets. However, SAM is unable to segment and label images outside of the visible light spectrum, for example, for multispectral or hyperspectral imagery. Therefore, this paper outlines a method we call the Multispectral Automated Transfer Technique (MATT). By transposing SAM segmentation masks from RGB images we can automatically segment and label multispectral imagery with high precision and efficiency. For example, the results demonstrate that segmenting and labeling a 2,400-image dataset utilizing MATT achieves a time reduction of 87.8% in developing a trained model, reducing roughly 20 hours of manual labeling, to only 2.4 hours. This efficiency gain is associated with only a 6.7% decrease in overall mean average precision (mAP) when training multispectral models via MATT, compared to a manually labeled dataset. We consider this an acceptable level of precision loss when considering the time saved during training, especially for rapidly prototyping experimental modeling methods. This research greatly contributes to the study of multispectral object detection by providing a novel and open-source method to rapidly segment, label, and train multispectral object detection models with minimal human interaction. Future research needs to focus on applying these methods to 1) space-based multispectral; and 2) drone-based hyperspectral imagery.}, archive = {J_ACCESS}, author = {James E. Gallagher and Aryav Gogia and Edward J. Oughton}, doi = {10.1109/ACCESS.2024.3522236}, journal = {IEEE Access}, pages = {4499-4516}, shortjournal = {IEEE Access}, title = {A multispectral automated transfer technique (MATT) for machine-driven image labeling utilizing the segment anything model (SAM)}, volume = {13}, year = {2025}, }
• (2025). An effective deep neural network architecture for EEG-based recognition of emotions. ACCESS, 13, 4487-4498. (www)

  @article{ , abstract = {Emotions are caused by a human brain reaction to objective events. The purpose of this study is to investigate emotion identification by machine learning using electroencephalography (EEG) data. Current research in EEG-based emotion recognition faces significant challenges due to the high-dimensionality and variability of EEG signals, which complicate accurate classification. Traditional methods often struggle to extract relevant features from noisy and high-dimensional data, and they typically fail to capture the complex temporal dependencies within EEG signals. Recent progress in machine learning by deep neural networks has opened up opportunities to develop methods highly efficient and practicable as to serve useful real-world applications. The purpose of this study is to investigate a novel end-to-end deep learning method of emotion recognition using EEG data, which prefaces a combination of two-dimensional (2D) convolutional network (CNN) and Long short-term memory network (LSTM) by an autoencoder. The autoencoder layers seek a lower dimensionality encoding for optimal input signal reconstruction, and the 2D CNN/LSTM combination layers capture both spatial and temporal features that best describe the emotion classes present in the data. Experiments in four-category classification of emotions, using the public and freely available DEAP dataset, revealed that the method reached superior performance: 90.04% for the “arousal” category, 89.97% for “valence”, 87.73% for “dominance,” and 90.84% for liking”, as measured by the accuracy metric.}, archive = {J_ACCESS}, author = {Khadidja Henni and Neila Mezghani and Amar Mitiche and Lina Abou-Abbas and Amel Benazza-Ben Yahia}, doi = {10.1109/ACCESS.2025.3525996}, journal = {IEEE Access}, pages = {4487-4498}, shortjournal = {IEEE Access}, title = {An effective deep neural network architecture for EEG-based recognition of emotions}, volume = {13}, year = {2025}, }
• (2025). Phish fighter: Self updating machine learning shield against phishing kits based on HTML code analysis. ACCESS, 13, 4460-4486. (www)

  @article{ , abstract = {Phishing attacks are a growing threat that has evolved along with technological advancements. Existing detection methods struggle with constantly evolving tactics and “zero-day” attacks that exploit unknown vulnerabilities. This paper proposes a novel approach for identifying phishing web pages built on phishing kits and, consequently, detecting such attack attempts. Our method, Phish Fighter, analyzes the HTML code structure, focusing on recurring blocks across different phishing pages derived from the same source. These features are then fed to clustering and classification components to detect common structural patterns without relying on textual or visual content. This approach overcomes the limitations of existing solutions, and is robust against attacks that target various brands. Furthermore, we implemented a dedicated module for continuous data updates for the Phish Fighter. This module effectively recognizes even “zero-day” phishing attempts by analyzing only three pages associated with a new phishing kit. In addition, we successfully identified the phishing pages created by cloning the original source code of legitimate entities without requiring prior knowledge to distinguish such clones. The results support the efficiency and accuracy of this approach: weighted precision, recall and F1 score are all greater than 90%, and the respective micro-averaged metrics are above 95%.}, archive = {J_ACCESS}, author = {Gabriela Brezeanu and Alexandru Archip and Codrut-Georgian Artene}, doi = {10.1109/ACCESS.2025.3525998}, journal = {IEEE Access}, pages = {4460-4486}, shortjournal = {IEEE Access}, title = {Phish fighter: Self updating machine learning shield against phishing kits based on HTML code analysis}, volume = {13}, year = {2025}, }
• (2025). An automated compliance framework for critical infrastructure security through artificial intelligence. ACCESS, 13, 4436-4459. (www)

  @article{ , abstract = {Current data on cybercrime shows a rising influence of online threats, leading to significant financial impacts across vital industries like finance, healthcare, and energy. These impacts encompass the theft of confidential information, service interruptions, and expenses tied to breach remediation, underscoring the urgent necessity for strengthened cybersecurity strategies. Machine learning (ML) is highly effective in signifying cybersecurity standards, leveraging large-scale data analysis, pattern recognition, and adaptability to emerging threats. Unlike other traditional methods such as rule-based that relay on predefined criteria. This study presents an automated ML framework to recommend cybersecurity standards, audits, and compliance measures, enhancing and monitoring cybersecurity infrastructure systematically. Nine cybersecurity standards, including seven international (e.g., ISO/IEC 27001:2022, NIST, CIS) and two national standards (UAE, KSA), were analyzed using data from official sources. Data preprocessing addressed duplicates and missing values, with validation via Pearson Correlation and Chi-square tests (Chi-square = 55.79, p = 0.0017). Attributes were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) technique and refined through Recursive Feature Elimination (RFE). A content-based filtering (CBF) recommender system, aligned with organizational maturity levels, was developed and enhanced with a feedback loop for user insights. The recommendation model was validated across three organizational growth scenarios: ad-hoc, managed, and adaptive. The UAE standard recommended 158 controls for Scenario-1, while KSA led in Scenario-2. We evaluate the performance of the model using 3-ML classifiers including Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Model performance was assessed using the F1 score and ROC AUC score. Testing in ad-hoc, managed, and adaptive scenarios showed the UAE standard recommended 158 controls for Scenario-1, while KSA led Scenario-2. The RF classifier achieved 81% accuracy and an ROC AUC score of 0.98. This framework supports alignment with global standards, enhances cybersecurity governance, and enables ongoing cybersecurity maturity tracking, aiding resilience and improving GCI rankings.}, archive = {J_ACCESS}, author = {Sardar Muhammad Ali and Abdul Razzaque and Muhammad Yousaf and Rafi Us Shan}, doi = {10.1109/ACCESS.2024.3524496}, journal = {IEEE Access}, pages = {4436-4459}, shortjournal = {IEEE Access}, title = {An automated compliance framework for critical infrastructure security through artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Optimal system, reductions, and conservation laws of a nonlinear damped klein-gordon- fock equation. ACCESS, 13, 4424-4435. (www)

  @article{ , abstract = {A detailed Lie symmetry analysis of the nonlinear damped Klein-Gordon Fock equation: $u_{tt}\,+\alpha (u)\,u_{t}\,=\,u_{xx}\,+\,f(u)$ is addressed in this paper. Applying the Lie symmetry method, a comprehensive Lie group classification is performed for the arbitrary smooth functions $\alpha (u)$ and $f(u)$ present in the equation, leading to two distinct cases. Additionally, for each case an optimal system of one-dimensional subalgebras is derived, which is a minimal set of all the linearly independent symmetry generators without redundant symmetries. Using the similarity transformation method, the above-mentioned partial differential equation is reduced into a set of ordinary differential equations. In certain cases, several exact invariant solutions encompassing the travelling wave solutions and soliton waves are obtained. The graphs of the soliton solutions and traveling wave solutions are also presented. Finally, the conservation laws are identified via the partial Noether approach, leading to distinct cases with several subcases. The derived conservation laws provide valuable tools for examining the dynamics and stability of physical systems, making this research suitable to a range of scientific studies.}, archive = {J_ACCESS}, author = {Faiza Arif and F. M. Mahomed and F. D. Zaman and M. T. Mustafa}, doi = {10.1109/ACCESS.2025.3525503}, journal = {IEEE Access}, pages = {4424-4435}, shortjournal = {IEEE Access}, title = {Optimal system, reductions, and conservation laws of a nonlinear damped klein-gordon- fock equation}, volume = {13}, year = {2025}, }
• (2025). A uniform robust exact differentiator based neuro-fuzzy fractional order sliding mode control for optimal standalone solar photovoltaic system. ACCESS, 13, 4411-4423. (www)

  @article{ , abstract = {This study introduces an innovative neurofuzzy fractional-order sliding mode control approach for standalone photovoltaic systems, designed to mitigate uncertainties and disturbances caused by fluctuating environmental conditions. The method combines a fuzzy logic neural network, uniform robust exact differentiator, and fractional-order sliding mode control. The neural network accurately predicts nonlinear reference voltage trajectories, whereas the differentiator estimates unmeasurable states and external disturbances. The inclusion of fractional-order control improved the adaptability and robustness of the system. The stability of the proposed approach is rigorously validated using the Lyapunov theory. MATLAB simulations and experimental results significantly improve tracking accuracy and overall system performance, providing a robust and efficient solution to optimize energy extraction in standalone PV systems.}, archive = {J_ACCESS}, author = {Safeer Ullah and Ahmed S. Alsafran and Ambe Harrison and Ghulam Hafeez and Abouobaida Hassan and Baheej Alghamdi and Habib Kraiem}, doi = {10.1109/ACCESS.2024.3524887}, journal = {IEEE Access}, pages = {4411-4423}, shortjournal = {IEEE Access}, title = {A uniform robust exact differentiator based neuro-fuzzy fractional order sliding mode control for optimal standalone solar photovoltaic system}, volume = {13}, year = {2025}, }
• (2025). Automatic comparative chest radiography using deep neural networks. ACCESS, 13, 4398-4410. (www)

  @article{ , abstract = {Comparative medical radiography is a scientific identification method that involves directly comparing antemortem (AM) radiographs (e.g., X-rays) to postmortem (PM) radiographs taken of a deceased individual. Forensic anthropologists use chest radiographs for comparative radiography due to their availability through health screening. In this paper, we leverage the power of deep neural networks and expert domain knowledge to create a radiographic identification system based on AM and PM chest radiographs. We compiled a dataset of 5,165 anonymized chest radiographs representing 760 individuals from two databases: NIH Chest X-Ray Database and case files from the Michigan State University Forensic Anthropology Laboratory (MSUFAL). We first manually annotated 3 different regions of interest (ROIs) on the radiographs based on expert domain knowledge, viz., thoracic vertebrae from T1-T5; clavicles; and complete vertebral column. We then explored three families of deep neural networks, each selected for its unique strengths in addressing specific challenges in deep learning for processing these ROIs as well as the entire radiograph. Our experiments reveal several compelling findings: (a) the thoracic vertebrae from T1-T5 results in better recognition performance compared to other regions; (b) Efficient Nets result in better recognition accuracy compared to ResNets and DenseNets; and (c) an ensemble of models based on different networks and different ROIs further improves recognition accuracy. We release the expert annotated MSUFAL dataset and our codebase to advance research in comparative medical radiography for deceased identification. This research marks a significant step forward in forensic radiography, being the first to systematically assess the impact of ROIs for robust human identification.}, archive = {J_ACCESS}, author = {Redwan Sony and Carolyn V. Isaac and Alexis Vanbaarle and Clara J. Devota and Todd Fenton and Joseph T. Hefner and Arun Ross}, doi = {10.1109/ACCESS.2025.3525579}, journal = {IEEE Access}, pages = {4398-4410}, shortjournal = {IEEE Access}, title = {Automatic comparative chest radiography using deep neural networks}, volume = {13}, year = {2025}, }
• (2025). Short-term wind speed predicting based on legendre multiwavelet transform and GRU-ENN. ACCESS, 13, 4381-4397. (www)

  @article{ , abstract = {Wind energy has become a vital component of the power system. Due to the stochastic and intermittent characteristics of wind speed, thus enhancing accuracy and stability in short-term wind speed prediction is imperative and important for effectively harnessing wind energy. This paper proposes a novel hybrid model combing Legendre multiwavelet transform, Gated recurrent unit and Elman neural network (LMWT-GRU-ENN) for short-term wind speed prediction. More precisely, the rich properties, especial various regularities of LMW bases are utilized to effectively match the non-linearity and larger non-stationary features of short-term wind speed corresponding to multi-resolution level and multi-wavelet bases. GRU model is used to predict the low frequency components, and ENN model is implemented to predict the high frequency components obtained by LMWT, which can effectively improve the prediction performance by thoroughly making use of their advantages. Finally, massive experiments are conducted on two short-term wind speed datasets, and the experimental results demonstrate the proposed method attains the excellent performance of in both accuracy and stability compared with other state-of-the-art methods.}, archive = {J_ACCESS}, author = {Xiaoyang Zheng and Xiaoheng Luo and Dezhi Liu}, doi = {10.1109/ACCESS.2024.3435674}, journal = {IEEE Access}, pages = {4381-4397}, shortjournal = {IEEE Access}, title = {Short-term wind speed predicting based on legendre multiwavelet transform and GRU-ENN}, volume = {13}, year = {2025}, }
• (2025). A toolchain for assisting migration of software executables towards post-quantum cryptography. ACCESS, 13, 4368-4380. (www)

  @article{ , abstract = {Quantum computing poses a significant global threat to modern security mechanisms. As such, security experts and public sectors have issued guidelines to help organizations transition their software to post-quantum cryptography (PQC). However, there is a lack of (semi-)automatic tools to support this transition, particularly for software deployed as binary executables. To address this gap, in this work, we first propose a set of requirements necessary for this type of tool to detect quantum-vulnerable software executables. Following these requirements, we introduce $\mathsf {QED}$ : a toolchain for Quantum-vulnerable Executable Detection. $\mathsf {QED}$ uses a three-phase approach to identify quantum-vulnerable dependencies in a given set of executables, from file-level to API-level, and finally, precise identification of a static trace that triggers a quantum-vulnerable API. The key benefit of this design is that it provides efficiency without compromising accuracy, as it incorporates fast initial analyses to filter out executables unlikely to be quantum-vulnerable that in turn allows the more resource-intensive analysis to be performed on a smaller subset of executables. To demonstrate this claim, we evaluate $\mathsf {QED}$ on both a synthetic dataset with four cryptography libraries and a real-world dataset with over 200 software executables. The results show that: 1) $\mathsf {QED}$ discerns quantum-vulnerable from quantum-safe executables with 100% accuracy in the synthetic dataset; 2) $\mathsf {QED}$ is practical and scalable, completing analyses on average in less than 4 seconds per real-world executable; and 3) $\mathsf {QED}$ reduces the manual workload required by analysts to identify quantum-vulnerable executables in the real-world dataset by more than 90%.}, archive = {J_ACCESS}, author = {Norrathep Rattanavipanon and Jakapan Suaboot and Warodom Werapun}, doi = {10.1109/ACCESS.2024.3524309}, journal = {IEEE Access}, pages = {4368-4380}, shortjournal = {IEEE Access}, title = {A toolchain for assisting migration of software executables towards post-quantum cryptography}, volume = {13}, year = {2025}, }
• (2025). ASAD: A meta learning-based auto-selective approach and tool for anomaly detection. ACCESS, 13, 4341-4367. (www)

  @article{ , abstract = {Anomaly detection, crucial for identifying issues such as financial fraud or medical malfunctions, has advanced significantly with machine learning (ML) and deep learning (DL). However, a major problem in the field is that no single model works best with diverse datasets and problem domains. To address this, we propose an innovative auto-selective approach and software tool based on meta-learning, called ASAD (Auto-Selective Anomaly Detection), to dynamically select the most appropriate model based on the unique features of a given dataset or problem domain. ASAD trains an ML model to predict the best candidate from a large pool of models by considering the specific characteristics and requirements of the dataset. It is trained using 139 datasets built upon 60 base datasets from 11 diverse domains (finance, healthcare, network security) and 80 ML and DL models composed of 22 base anomaly detection algorithms. It uses meta-features and correlation functions to evaluate 300 features. It selects the best-performing model, evaluates models based on 7 metrics, and delivers significantly better performance than the cutting-edge in this area. ASAD addresses the critical challenge of model generalization and adaptability, aiming to enhance the efficiency and accuracy of anomaly detection across varied application domains. By automating the selection process, the method aims to reduce the reliance on trial-and-error methods, streamline the anomaly detection workflow, and lead to more robust, adaptable, and efficient anomaly detection systems. We provide the methodology, implementation, and evaluation of our approach, offering insights into its potential to revolutionize anomaly detection in this era characterized by vast and complex datasets.}, archive = {J_ACCESS}, author = {Nadia Rashid and Rashid Mehmood and Fahad Alqurashi and Saad Alqahtany and Juan M. Corchado}, doi = {10.1109/ACCESS.2024.3524908}, journal = {IEEE Access}, pages = {4341-4367}, shortjournal = {IEEE Access}, title = {ASAD: A meta learning-based auto-selective approach and tool for anomaly detection}, volume = {13}, year = {2025}, }
• (2025). A nonintrusive nonlinear model reduction method for thermal cycling-induced viscoplastic deformation problems based on segmented gaussian process regression machine learning. ACCESS, 13, 4326-4340. (www)

  @article{ , abstract = {Numerical simulation of thermal cycling-induced viscoplastic deformation is important to design a reliable underfilled flip chip package, where the time histories of viscoplastic strains are applied to predict the fatigue life of solder joints. However, the high-fidelity full model simulations of a flip chip package under thermal cycling are time intensive tasks. Nonintrusive model order reduction combined with machine learning is increasingly being used to create efficient surrogate models for problems with high computational costs. In this paper, a segmented Gaussian process regression machine learning method combined with proper orthogonal decomposition is proposed to build the reduced order models of underfilled flip chip packages for thermal cycling-induced viscoplastic displacements and strains, respectively. Simulation results show the derived reduced order models can effectively predict the viscoplastic displacements and strains induced by thermal cycling across the entire solution domain and time history for different material properties of underfills and provide better accuracy than those generated by the non-segmented Gaussian process regression method.}, archive = {J_ACCESS}, author = {Jiunn-Horng Lee and Chia-Hsiu Ou and Chih-Min Yao and Ming-Hsiao Lee}, doi = {10.1109/ACCESS.2025.3525851}, journal = {IEEE Access}, pages = {4326-4340}, shortjournal = {IEEE Access}, title = {A nonintrusive nonlinear model reduction method for thermal cycling-induced viscoplastic deformation problems based on segmented gaussian process regression machine learning}, volume = {13}, year = {2025}, }
• (2025). Sum rate maximization for active RIS MISO systems based on DRL. ACCESS, 13, 4315-4325. (www)

  @article{ , abstract = {The reconfigurable intelligent surface (RIS) has garnered significant attention in recent years due to its cost-effectiveness and ability to reconfigure the wireless environment in a controlled manner. However, signals reflected by the RIS need to traverse two paths—from the base station (BS) to the RIS and from the RIS to the user, which results in the multiplicative fading effect and limits the capacity gains achievable by the passive RIS. In this paper, we investigate the active RIS to overcome the limitation. We consider a multiple input single output system utilizing active RIS, and based on recent advancements in deep reinforcement learning (DRL), we integrate the joint design of the BS transmit beamforming matrix and the RIS phase shift matrix into a unified framework for sum rate maximization. We propose a DRL-based algorithm that obtains the joint design through trial-and-error interactions with the environment and is capable of identifying an appropriate power allocation scheme to balance the power consumption between the BS and the active RIS. Simulation results demonstrate that the proposed active RIS-assisted system outperforms traditional passive RIS-assisted systems, particularly when there are constraints on the number of reflective elements. Additionally, it is observed that proper parameter settings significantly enhance the performance of the proposed algorithm. Finally, the algorithm can allocate suitable power to the active RIS while maintaining a constant total power, thereby optimizing the system performance.}, archive = {J_ACCESS}, author = {Zhipeng Xi and Jianbo Ji}, doi = {10.1109/ACCESS.2025.3525562}, journal = {IEEE Access}, pages = {4315-4325}, shortjournal = {IEEE Access}, title = {Sum rate maximization for active RIS MISO systems based on DRL}, volume = {13}, year = {2025}, }
• (2025). Implicit euler discretization for enhanced sliding mode observer in sensorless permanent magnet synchronous motor control. ACCESS, 13, 4304-4314. (www)

  @article{ , abstract = {This paper presents a discrete-time sliding mode observer designed to estimate the rotor position of PMSMs for sensorless control strategies. Without compromising the sliding feature, this study employs a discretization method known as implicit Euler discretization, converting the set-valued function to a projection over a limited interval, simplifying the coding compared to other ordinary implementations. As a result, output chatter is significantly reduced without additional filters. The observer’s performance is evaluated under sudden changes in load and parameter variations and then compared with other approaches. The proposed SMO is also tested and validated on an experimental setup of a 600W PMSM coupled with a DC generator for loading. The results show the robustness of the proposed observer, with a reduction in execution time by approximately 20% compared to conventional implementations like the sigmoid-based sliding mode observer.}, archive = {J_ACCESS}, author = {Nehal Baiomy and Mohamed Mahmoud Abdelhafiz and Abdelmomen Mahgoub}, doi = {10.1109/ACCESS.2024.3524159}, journal = {IEEE Access}, pages = {4304-4314}, shortjournal = {IEEE Access}, title = {Implicit euler discretization for enhanced sliding mode observer in sensorless permanent magnet synchronous motor control}, volume = {13}, year = {2025}, }
• (2025). Supervised convolutional encoder-decoder with gated linear units for detecting fetal R-peaks. ACCESS, 13, 4290-4303. (www)

  @article{ , abstract = {Noninvasive fetal electrocardiography (ECG) is prevalently used for monitoring fetal heartbeats during pregnancy due to its affordability, ease of use, and constant monitoring capability. A crucial aspect of noninvasive fetal ECG is detecting the R-peak series from the abdominal electrode signal, which is a fundamental baseline for determining fetal heart rate. This paper explores the direct detection of R-peaks by assigning categorical labels to each member of the observed values in the ECG sequence and proposes a convolutional encoder-decoder network and training strategy for processing the sequence annotation task. Specifically, the encoder is a stacked convolutional layer equipped with a gating linear unit (GLU), and the decoder is a recurrent neural network. The GLU convolutional layer can effectively extract and aggregate the features to improve the generalization ability. To address the issue of unbalanced sequence labels, we adopt and fine-tune the focal loss function, promoting superior prediction and faster convergence. The experimental results suggest that the proposed method can achieve promising performance on two benchmark datasets. The versatility of our approach is validated through tests of different label encoding strategies, demonstrating its potential for other complex fetal ECG labeling tasks.}, archive = {J_ACCESS}, author = {Yao Chen and Jian Wang and Jing Zhang and Junkun Zhang and Zhentao Qin and Xinran Liu}, doi = {10.1109/ACCESS.2025.3525555}, journal = {IEEE Access}, pages = {4290-4303}, shortjournal = {IEEE Access}, title = {Supervised convolutional encoder-decoder with gated linear units for detecting fetal R-peaks}, volume = {13}, year = {2025}, }
• (2025). Ischemic heart disease prognosis: A hybrid residual attention-enhanced LSTM model. ACCESS, 13, 4281-4289. (www)

  @article{ , abstract = {Well-timed prediction and an accurate diagnosis of Ischemic Heart disease (IHD) can reduce the risk of death, whereas an inaccurate diagnosis can prove fatal. So, there is a need to develop an optimal heart disease prediction model to avoid inaccurate ischemic heart disease diagnosis and further treatment. Recently, researchers have developed several deep learning techniques that take input from medical practitioners, automatically find hidden patterns in enormous volumes of data, and predict heart diseases without human intervention. Further, the deep learning model can help doctors to classify the severity of heart disease and choose appropriate treatment accordingly. These deep learning models can be improved to achieve greater accuracy and stability. Creating a hybrid model that combines attention residual learning with a Long Short-Term Memory (LSTM) is one method to prove it. Our suggested Hybrid Residual Attention-Enhanced LSTM (HRAE-LSTM) approach improves accuracy and stability by combining attention residual learning with an LSTM. For evaluating the effectiveness of the proposed HRAE-LSTM model, realistic datasets of 303 instances from the heart disease dataset (UCI), were used. The proposed HRAE-LSTM outperforms existing cardiac disease prediction systems by 97.7 % with the UCI dataset, respectively.}, archive = {J_ACCESS}, author = {D. Cenitta and R. Vijaya Arjunan and Ganesh Paramasivam and N. Arul and Anisha Palkar and Krishnaraj Chadaga}, doi = {10.1109/ACCESS.2024.3524604}, journal = {IEEE Access}, pages = {4281-4289}, shortjournal = {IEEE Access}, title = {Ischemic heart disease prognosis: A hybrid residual attention-enhanced LSTM model}, volume = {13}, year = {2025}, }
• (2025). Multifunctional reconfigurable millimeter-wave array antenna. ACCESS, 13, 4267-4280. (www)

  @article{ , abstract = {This paper presents the design and implementation of an innovative 4-element multifunctional reconfigurability array antenna (MRAA) operating at 27.5 GHz, leveraging PIN diodes for enhanced functionality. By strategically controlling the alignment of PIN diodes, the antenna utilizes only two coplanar strip line biasing networks, streamlining its design and implementation. The proposed array demonstrates exceptional versatility by easily generating scanning beams at angles up to ±25° across three distinct polarizations: right-hand and left-hand circular polarizations and linear polarization. Furthermore, the system achieves a reconfigurable frequency interface between circular and linear polarizations, enhancing its adaptability to diverse communication scenarios. Through comprehensive analysis, the proposed MRAA exhibits commendable array gain, surpassing 10 dBic, and very good radiation efficiency exceeding 74%. This work marks the pioneering effort in designing a multifunctional reconfigurable array antenna for millimeter-wave (mmWave) applications. Additionally, the proposed array offers simplicity in design and outperforms existing counterparts operating at lower frequencies. A prototype has been fabricated, and measurement results demonstrate close agreement with simulation, validating the efficacy of the proposed design.}, archive = {J_ACCESS}, author = {Rawad W. Asfour and Salam K. Khamas and Edward A. Ball}, doi = {10.1109/ACCESS.2024.3523136}, journal = {IEEE Access}, pages = {4267-4280}, shortjournal = {IEEE Access}, title = {Multifunctional reconfigurable millimeter-wave array antenna}, volume = {13}, year = {2025}, }
• (2025). Machine learning applications in energy harvesting internet of things networks: A review. ACCESS, 13, 4235-4266. (www)

  @article{ , abstract = {The growth of Internet of Things (IoT) devices continues to experience an exponential rise due to their vast applications in various industries. However, sustaining their operational lifetime is a major challenge due to critical factors, such as the need for frequent recharging of energy buffers. The recent convergence of green energy harvesting (EH) and IoT technologies has proven to be a potential solution to this challenge. However, the intermittent characteristics of green energy sources and wireless fading channels pose another challenge, as the quality of service in IoT networks revolves around the available energy and channel conditions. The traditional optimization strategies based on non-causal knowledge about these random quantities are deemed unsuitable for realizing an autonomous IoT network operation. To combat this challenge, various algorithms from the field of machine learning (ML) have been proposed as potential solutions. Therefore, in this article, we aim to investigate the applications of ML algorithms in EH IoT networks. To achieve this, first, we provide an overview of ML categories commonly adopted in IoT networks. Secondly, due to the peculiarity of EH IoT networks, we provide an extensive description of the ML categories widely explored in this domain. This includes reinforcement learning, deep learning, and deep reinforcement learning. Thirdly, we present a review of studies where the applications of the aforementioned ML algorithms are demonstrated. Further, we identify challenges that are likely to impact the implementation of these algorithms. In conclusion, we highlight unexplored and emerging areas for potential future research.}, archive = {J_ACCESS}, author = {Olumide Alamu and Thomas O. Olwal and Emmanuel M. Migabo}, doi = {10.1109/ACCESS.2024.3525263}, journal = {IEEE Access}, pages = {4235-4266}, shortjournal = {IEEE Access}, title = {Machine learning applications in energy harvesting internet of things networks: A review}, volume = {13}, year = {2025}, }
• (2025). Effective partitioning method with predictable hardness for CircuitSAT. ACCESS, 13, 4218-4234. (www)

  @article{ , abstract = {Many industrial verification problems are solved via reduction to CircuitSAT (curcuit satisfibiliaty). It is often the case that the resulting SAT instances are very hard and require the use of parallel computing to be solved in reasonable time. The particularly relevant problem in this context is how to best plan the use of the computing resources, because SAT solvers’ runtime is well known to be hard to predict. In the present paper we propose two methods that employ the knowledge about a circuit’s structure to partition a CircuitSAT instance into a specific number of simpler subproblems. A distinctive feature of the proposed partitioning methods is that they make it possible to estimate the hardness (e.g. the total runtime of a SAT solver on all subproblems) of a partitioning via the Monte Carlo method. In the experimental evaluation we apply these methods to hard CircuitSAT instances and compare their performance with the well known Cube and Conquer approach. The proposed partitioning methods not only often outperform Cube and Conquer, but also show remarkably small variance in the runtime of a SAT solver on subproblems from a partitioning, thus making it possible to construct accurate estimations of time required to process all subproblems, using random samples of small size. As a consequence, we have the efficient stochastic estimation procedure which provides an additional opportunity to employ hyperparameter tuning methods to further increase the SAT solver performance on (partitioned) hard SAT instances. We demonstrate the effectiveness of the proposed constructions by applying them to some problems associated with CircuitSAT, in particular, Logical Equivalence Checking benchmarks, Automated Test Pattern Generation benchmarks and the inversion problems of some cryptographic functions.}, archive = {J_ACCESS}, author = {Konstantin Chukharev and Irina Gribanova and Dmitry Ivanov and Stepan Kochemazov and Victor Kondratiev and Alexander Semenov}, doi = {10.1109/ACCESS.2024.3525122}, journal = {IEEE Access}, pages = {4218-4234}, shortjournal = {IEEE Access}, title = {Effective partitioning method with predictable hardness for CircuitSAT}, volume = {13}, year = {2025}, }
• (2025). An adaptive intrusion detection system for evolving IoT threats: An autoencoder-FNN fusion. ACCESS, 13, 4201-4217. (www)

  @article{ , abstract = {The increasing sophistication of attacks targeting the Internet of Things environment underscores the critical need for robust intrusion detection systems. This study introduces a specialized IDS classification approach to address IoT networks’ evolving and diverse threat landscape. The proposed model integrates an Autoencoder for feature extraction with a Feedforward Neural Network for classification, creating a fusion that enhances the precision and efficiency of intrusion detection. The Autoencoder captures and reduces the dimensionality of high-dimensional IoT network traffic data, and the FNN distinguishes between normal network behaviour and various intrusion patterns by leveraging its ability to model nonlinear relationships. This dual-stage architecture offers adaptability to dynamic network conditions, accurately identifying attack instances while minimizing false positives. Rigorous evaluations demonstrate the model’s robust performance, achieving a remarkable accuracy of 99.55% in binary classification and 90.91% in multiclass classification. Key metrics such as precision, recall, F1-score, and high ROC-AUC values validate its effectiveness in detecting diverse intrusion patterns and handling various attack scenarios. Additionally, the study employs a two-stage data balancing strategy that significantly enhances the detection of minority class attacks. By addressing critical challenges in IoT network security, including imbalanced datasets and evolving threats, this research contributes a reliable and efficient IDS model optimized for real-world applications. The AE-FNN model’s high accuracy, robustness, and scalability position it as a valuable tool in securing interconnected IoT ecosystems, making it an important advancement in network security.}, archive = {J_ACCESS}, author = {J. Jasmine Shirley and M. Priya}, doi = {10.1109/ACCESS.2024.3525074}, journal = {IEEE Access}, pages = {4201-4217}, shortjournal = {IEEE Access}, title = {An adaptive intrusion detection system for evolving IoT threats: An autoencoder-FNN fusion}, volume = {13}, year = {2025}, }
• (2025). Improving the performance of answers ranking in Q&A communities: A long-text matching technique and pre-trained model. ACCESS, 13, 4188-4200. (www)

  @article{ , abstract = {This paper introduces TR-BERT, a novel method to answer ranking in Question & Answer (Q&A) communities, designed to tackle the widespread challenges of irrelevant popular answers and the neglect of new questions. TR-BERT integrates a long-text matching technique with a pre-trained language model. This ranking method effectively filters the noise and extracts textual features of questions and answers in QA communities. The experimental results on the Zhihu Q&A community dataset and the SemEval-2017 dataset showed the effectiveness and superiority of the TR-BERT. The contributions are as follows: Designing a new framework to process long-text data by filtering the noise and developing the TR-BERT to optimize the issue of answer ranking in the Q&A community. The experiment also showed that the TR-BERT model has the advantages of faster speed and requires less computational resources, which makes the TR-BERT valuable for practical applications. Meanwhile, TR-BERT offers an insight: By removing noise from the input text to shorten the length of the input sequence, we can decrease the time and computational resources required for model training and computation. This leads to the potential for smaller models, faster speeds, reduced computational resource demands, and improved efficiency.}, archive = {J_ACCESS}, author = {Siyu Sun and Yiming Wang and Jiale Cheng and Zhiying Xiao and Daqing Zheng and Xiaoling Hao}, doi = {10.1109/ACCESS.2024.3521999}, journal = {IEEE Access}, pages = {4188-4200}, shortjournal = {IEEE Access}, title = {Improving the performance of answers ranking in Q&A communities: A long-text matching technique and pre-trained model}, volume = {13}, year = {2025}, }
• (2025). Temporal-logic-based testing tool for programs using the message passing interface (MPI) and open multi-processing (OpenMP) programming models. ACCESS, 13, 4171-4187. (www)

  @article{ , abstract = {The recent surge in high-performance computing (HPC) demands, particularly with the advent of Exascale supercomputers, has highlighted the need for robust parallel systems. Achieving such systems involves not only advancements in hardware but also the integration of multiple programming models to maximize computational efficiency. The dual programming model that combines the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) is a common approach to enhance parallelism. However, the reliability of these concurrent systems remains a critical concern due to runtime errors, such as deadlocks and race conditions, that are often undetectable by compilers. This paper introduces a novel testing tool that leverages linear temporal logic (LTL) properties to detect runtime errors in hybrid MPI and OpenMP applications. Our tool dynamically tests applications, running in parallel with application threads to ensure portability across different architectures while maintaining an execution overhead of less than 36.94%. By focusing on runtime errors resulting from the integration of MPI and OpenMP, our tool effectively detects deadlocks and race conditions, enhancing system reliability. We provide an implementation and an initial evaluation of the tool, which demonstrates its capability to uncover runtime errors specific to hybrid MPI and OpenMP applications.}, archive = {J_ACCESS}, author = {Salwa Saad and Etimad Fadel and Ohoud Alzamzami and Fathy Eassa and Ahmed Mohammed Alghamdi}, doi = {10.1109/ACCESS.2025.3525578}, journal = {IEEE Access}, pages = {4171-4187}, shortjournal = {IEEE Access}, title = {Temporal-logic-based testing tool for programs using the message passing interface (MPI) and open multi-processing (OpenMP) programming models}, volume = {13}, year = {2025}, }
• (2025). Feature point matching method of weak texture environment in farmland based on improved GMS-PROSAC fusion algorithm. ACCESS, 13, 4158-4170. (www)

  @article{ , abstract = {Visual SLAM technology has been widely used in all aspects of life, but has been applied relatively little in the field of weakly textured environments in agricultural fields. To make visual SLAM technology can be better applied in farmland environment, aiming at the problem of low accuracy of direct image recognition and matching in weak texture environment of farmland, A GMS-PROSAC feature point matching algorithm based on ORB feature point extraction is applied. This algorithm integrates the screening principle of the PROSAC algorithm with the smoothness constraint and multi-region generalization construction principle of the GMS algorithm, embedding a polar geometric constraint model that incorporates projection error fusion. It effectively overcomes the impact of light sensitivity and noise interference on matching accuracy. Due to its excessive plant repeatability, a judgment method is proposed to eliminate the wrong matching pairs under correct matching, so that the intelligent farm machine can correctly recognize the complex road conditions in different farmlands for reasonable harvesting. Through experimental verification, the highest matching accuracy can reach 86.0% in the weak texture environment of the farmland, which can be more effective for feature point extraction and matching, that provide new directions for subsequent unmanned agricultural machines that can harvest intelligently in different kinds of fields.}, archive = {J_ACCESS}, author = {Zhenlin Li and Di Wu and Weiping Xu and Qiaoqiao Wu and Guoqiang Yang and Dakun Zhou}, doi = {10.1109/ACCESS.2025.3525471}, journal = {IEEE Access}, pages = {4158-4170}, shortjournal = {IEEE Access}, title = {Feature point matching method of weak texture environment in farmland based on improved GMS-PROSAC fusion algorithm}, volume = {13}, year = {2025}, }
• (2025). Automated defect detection in solar cell images using deep learning algorithms. ACCESS, 13, 4136-4157. (www)

  @article{ , abstract = {This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. This study is interesting since it does a thorough assessment of a wide variety of models and concentrates on high-performance architectures and lightweight models that may be used in contexts with limited resources. The research paper performed our studies using a balanced and well-curated dataset of 3,102 images of solar cells with a range of common faults. MobileNetV2 and Xception demonstrated excellent performance in defect identification, with accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. This study demonstrates the potential of efficient models such as MobileNetV2 for real-world use in solar energy generation. It also provides a detailed comparison of several DL models. The results suggest that the inclusion of these models might significantly enhance quality control systems, offering a reliable and efficient method for detecting flaws in solar cells.}, archive = {J_ACCESS}, author = {Montaser Abdelsattar and Ahmed Abdelmoety and Mohamed A. Ismeil and Ahmed Emad-Eldeen}, doi = {10.1109/ACCESS.2024.3525183}, journal = {IEEE Access}, pages = {4136-4157}, shortjournal = {IEEE Access}, title = {Automated defect detection in solar cell images using deep learning algorithms}, volume = {13}, year = {2025}, }
• (2025). Maximal information coefficient-based undersampling method for highly-imbalanced learning. ACCESS, 13, 4126-4135. (www)

  @article{ , abstract = {Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is an alternative kind of methods to deal with imbalanced learning. In this paper, we propose a new undersampling method based on maximal information coefficient (including two algorithms MICU-1 and MICU-2) to rebalance the datasets. In order to evaluate the effectiveness of the method, 20 highly- imbalanced datasets are used for the benchmarks. Results show that compared with other undersampling methods, maximal information coefficient-based undersampling method are competitive in terms of G-mean and F-measure.}, archive = {J_ACCESS}, author = {Haiou Qin}, doi = {10.1109/ACCESS.2025.3525475}, journal = {IEEE Access}, pages = {4126-4135}, shortjournal = {IEEE Access}, title = {Maximal information coefficient-based undersampling method for highly-imbalanced learning}, volume = {13}, year = {2025}, }
• (2025). An ensemble for automatic time series forecasting with K-nearest neighbors. ACCESS, 13, 4117-4125. (www)

  @article{ , abstract = {In this paper a novel approach for automatically configuring a k-nearest neighbors regressor for univariate time series forecasting is presented. The approach uses an ensemble consisting of several k-nearest neighbors models with different configurations for their hyperparameters and model selection choices. One advantage of this scheme is that the uncertainty associated with choosing a wrong configuration for the model is reduced. This approach is compared with the classical way of selecting a configuration by doing a grid search among several configurations of hyperparameters and model selection choices and choosing the one that performs best on a validation set. The experimental results, using datasets from time series forecasting competitions, show that, in line with previous works, the use of an ensemble produces a robust model, outperforming the approach that uses a grid search for obtaining the best configuration on a validation set and almost any specific configuration. The forecast accuracy of the ensemble is similar to state-of-the-art models. Furthermore, this paper also tests the effectiveness of some recent approaches for dealing with trending time series when using the k-nearest neighbors algorithm.}, archive = {J_ACCESS}, author = {María P. Frías and Francisco Martínez}, doi = {10.1109/ACCESS.2025.3525561}, journal = {IEEE Access}, pages = {4117-4125}, shortjournal = {IEEE Access}, title = {An ensemble for automatic time series forecasting with K-nearest neighbors}, volume = {13}, year = {2025}, }
• (2025). POMA-C: A framework for solving the problem of precise anesthesia control under incomplete observation environment in low-income areas. ACCESS, 13, 4098-4116. (www)

  @article{ , abstract = {This paper introduces the POMA-C (Partial Observable Model for Anesthesia Control) framework, developed to address the challenge of anesthesia management in environments with incomplete physiological monitoring, such as low-resource settings where critical indicators like the Bispectral Index (BIS) are often unavailable. Unlike traditional methods that rely on fully observable data, POMA-C frames the problem of anesthesia control under incomplete observability within a Partially Observable Markov Decision Process (POMDP), enabling the precise control of anesthesia despite missing data. By establishing a formal correspondence between the anesthesia control process and POMDP, this framework provides a theoretical foundation for modeling anesthesia control in uncertain environments. The framework employs the POMCPOW (Partially Observable Monte Carlo Planning with Observation Weighting) algorithm, which integrates Monte Carlo Tree Search (MCTS) and particle filtering to estimate the patient’s true physiological state and guide optimal anesthetic decisions. Through comprehensive ablation experiments—where key observation dimensions are systematically reduced to simulate missing data—POMA-C demonstrates significantly higher decision accuracy and cumulative reward optimization compared to methods like Q-learning and human expertise, even in data-constrained environments. This work not only provides a robust solution for anesthesia control under incomplete observability but also bridges the gap between MDP and POMDP models, offering a foundation for future research in automated anesthesia management.}, archive = {J_ACCESS}, author = {Yide Yu and Huijie Li and Dennis Wong and Anmin Hu and Jian Huo and Yan Ma and Yue Liu}, doi = {10.1109/ACCESS.2024.3524262}, journal = {IEEE Access}, pages = {4098-4116}, shortjournal = {IEEE Access}, title = {POMA-C: A framework for solving the problem of precise anesthesia control under incomplete observation environment in low-income areas}, volume = {13}, year = {2025}, }
• (2025). Development of tributary mapping system using multiple unmanned aerial vehicles with LiDAR. ACCESS, 13, 4081-4097. (www)

  @article{ , abstract = {This paper proposes a hybrid system comprising multiple unmanned aerial vehicles (UAVs) to monitor nature. The proposed tributary mapping system consists of perception and control systems. A perception system is established for tributary mapping using three-dimensional light detection and ranging (LiDAR) semantic segmentation for surface water recognition. The system defines a water point cloud and segments the surface water area using singular value decomposition, applying the water-absorbing property of LiDAR. A path is generated through random sample consensus by calculating the center point from the segmented area. Each UAV is modeled using a continuous-time dynamic-based low-level model and a discrete event-time dynamic-based high-level model to control the multiple UAVs system. The desired plant behavior is designed considering the control objectives of the plant, and a supervisory controller is developed accordingly. A physics-based robot simulator is employed to verify the proposed tributary mapping system in perception and path generation and the performance of the hybrid system-based supervisory controller.}, archive = {J_ACCESS}, author = {Jaehwi Seol and Jeonghyeon Pak and Hyoung Il Son}, doi = {10.1109/ACCESS.2024.3524586}, journal = {IEEE Access}, pages = {4081-4097}, shortjournal = {IEEE Access}, title = {Development of tributary mapping system using multiple unmanned aerial vehicles with LiDAR}, volume = {13}, year = {2025}, }
• (2025). A marked point process approach for continuous valence estimation using respiration activity. ACCESS, 13, 4067-4080. (www)

  @article{ , abstract = {In this study, we present a method for continuously estimating emotional valence levels using a marked point process representation of features extracted from respiration amplitude signals. The amplitude of the breath, time of inhalation, and inhalation rate are used to label individuals breaths as potential pleasant or unpleasant valence events using an unsupervised k-means clustering algorithm. We generate two marked point processes consisting of both location and magnitude of inferred valence events corresponding to pleasant and unpleasant (high and low) changes in valence. A state-space model is then used to model high and low valence states based on the occurrence of events indicative of either state in each marked point process. The resulting high valence and low valence states are combined to yield a single estimate of valence level. The algorithm is tested on a dataset containing 23 participants viewing emotion-eliciting video clips. The estimation results for high and low periods, as identified by self-reported ratings, are then compared using a Wilcoxon signed rank test, showing that the method is capable of distinguishing high and low valence periods. The estimated valence level is generally able to capture the trends of the self-reported ratings for most subjects, but fails to fully capture rapid and drastic changes in valence. Continuously estimating valence levels can have applications in the monitoring of patients with mental disorders, such as clinical depression, or multimedia recommendation to identify trends and better develop control strategies to regulate emotions.}, archive = {J_ACCESS}, author = {Revanth Reddy and Rose T. Faghih}, doi = {10.1109/ACCESS.2024.3521339}, journal = {IEEE Access}, pages = {4067-4080}, shortjournal = {IEEE Access}, title = {A marked point process approach for continuous valence estimation using respiration activity}, volume = {13}, year = {2025}, }
• (2025). Multi-level foreground prompt for incremental object detection. ACCESS, 13, 4048-4066. (www)

  @article{ , abstract = {In the study of incremental object detection, knowledge distillation and data replay are effective methods to mitigate catastrophic forgetting. However, current research on single-stage detectors is limited, single-stage detector outputs often contain excessive negative sample information, and direct application of knowledge distillation to them is ineffective. To address this, this paper proposes a multi-level foreground prompt incremental learning algorithm for single-stage detectors like YOLO, including foreground prompts at the image level, feature map level, and knowledge level. First, to obtain fewer but more representative replay samples, representative images with a high number of old class foregrounds are selected by maximizing sample structure distance, providing direct foreground information at the image level. Second, the feature map output by the teacher model is used as a feature-level prompt, with a feature distillation loss guiding the student model to encode new class foreground information in less significant channels of the old feature map, reducing interference. Lastly, the teacher model’s inference output serves as a knowledge-level prompt, and the adaptive select object method is introduced to avoid foreground conflict in traditional knowledge distillation, enhancing the model’s plasticity by selectively merging foreground information. Extensive experiments on PASCAL VOC and MS COCO datasets demonstrate that this approach significantly improves model plasticity while maintaining stability.}, archive = {J_ACCESS}, author = {Jianwen Mo and Ronghua Zou and Hua Yuan}, doi = {10.1109/ACCESS.2024.3524539}, journal = {IEEE Access}, pages = {4048-4066}, shortjournal = {IEEE Access}, title = {Multi-level foreground prompt for incremental object detection}, volume = {13}, year = {2025}, }
• (2025). LATrack: Limited attention for visual object tracking. ACCESS, 13, 4034-4047. (www)

  @article{ , abstract = {The use of temporal information is becoming increasingly important in mainstream visual object trackers. Mainstream trackers typically interact trajectory information with image features. However, existing methods of interaction cannot effectively utilize trajectory information, and the significance of the interaction remains unclear. To address these issues, we propose a Limited Attention module (LA module). The LA module more effectively utilizes image features by masking certain image features based on historical trajectory information or prediction information. Based on the LA module, we propose Limited Attention Track (LATrack), which can make more effective use of trajectory information. LATrack can continuously approach the target object by utilizing the predicted coordinates of the historical trajectory, thereby obtaining the object’s position in the current frame. Our model excels in handling challenges such as motion blur, distraction from similar objects, and occlusion. LATrack demonstrates excellent performance across multiple datasets, notably achieving 76.7% AO and 53.8% AUC on the GOT-10k and $\mathrm {LaSOT_{ext}}$ datasets, respectively.}, archive = {J_ACCESS}, author = {Jian Shi and Zheng Chang and Yang Yu and Junze Shi and Haibo Luo}, doi = {10.1109/ACCESS.2024.3525016}, journal = {IEEE Access}, pages = {4034-4047}, shortjournal = {IEEE Access}, title = {LATrack: Limited attention for visual object tracking}, volume = {13}, year = {2025}, }
• (2025). Integrated detection of respiration and heartbeat with communication capabilities using OFDM-LFM-MP signals. ACCESS, 13, 4021-4033. (www)

  @article{ , abstract = {To address the evolving demands of intelligent networks, integrated sensing and communication (ISAC) has emerged as a focal area of research. This encompasses innovative signal design, radar-based and communication hardware integration, and other vital directions. In particular, Linear Frequency Modulated (LFM) signals have shown considerable promise in contactless health monitoring applications. This paper presents an integrated radar system design leveraging Orthogonal Frequency Division Multiplexing (OFDM) LFM signals, emphasizing the dual capabilities of communication and sensing. Compared to traditional radar signals, the proposed design enhances information transmission without sacrificing sensing performance. Initially, we developed an OFDM-LFM-MP signal by utilizing M-sequences with strong auto-correlation properties for coding communication data. Subsequently, we conduct MATLAB simulations to evaluate the sensing efficacy of the OFDM-LFM-MP signal in detecting heartbeats and respiration alongside its communication performance. Theoretical analyses and simulation outcomes demonstrate that the proposed radar-based integration design significantly improves the detection of heartbeat and respiration while effectively transmitting high-rate information at a signal-to-noise ratio (SNR) exceeding $20~dB$ .}, archive = {J_ACCESS}, author = {Zhaoshen Dong and Yan Liu and Yukai Ouyang}, doi = {10.1109/ACCESS.2024.3525058}, journal = {IEEE Access}, pages = {4021-4033}, shortjournal = {IEEE Access}, title = {Integrated detection of respiration and heartbeat with communication capabilities using OFDM-LFM-MP signals}, volume = {13}, year = {2025}, }
• (2025). Common-mode impedance stabilizing device applied to the output of railroad traction inverters in the evaluation of conductive emissions. ACCESS, 13, 4008-4020. (www)

  @article{ , abstract = {We present a new device called a common-mode impedance stabilizing device (CMISD), which is applied to the output of inverters installed in railroad vehicles. The proposed device is used in the evaluation of the disturbance voltage in conductive emissions; its purpose is to stabilize the common-mode (CM) impedance ( $150~\pm ~20~\Omega $ ) in the 100 kHz – 10 MHz frequency range, which corresponds to the operating frequencies of railroad signaling devices called “balises.” The balises are installed on railroad tracks and used for communication between trains and wayside systems, and electromagnetic compatibility between rolling stock and the balises are severe problem. In the design of the CMISD, we considered two factors: the ferrite core size of the CM inductor and the heat dissipation in the resistors. In the CM inductor, we assumed realistic conditions to reduce its core mass to ~3.4 kg. Regarding heat dissipation, considering that the negative terminal of a railroad vehicle with DC feeding voltage must be grounded, we assumed that square-wave peak voltage is applied directly to the CM circuit of the CMISD. The experimental results showed that the power dissipation in the CR series circuit in the CMISD was proportional to the carrier frequency, capacitance, and the square of the line voltage. Eventually, we fabricated a prototype CMISD, which can withstand the heat generated when applying a 750-V DC input voltage at a 3-kHz carrier frequency for five minutes.}, archive = {J_ACCESS}, author = {Satoru Hatsukade and Keiji Wada}, doi = {10.1109/ACCESS.2025.3525472}, journal = {IEEE Access}, pages = {4008-4020}, shortjournal = {IEEE Access}, title = {Common-mode impedance stabilizing device applied to the output of railroad traction inverters in the evaluation of conductive emissions}, volume = {13}, year = {2025}, }
• (2025). A survey of deep learning approaches for pedestrian detection in autonomous systems. ACCESS, 13, 3994-4007. (www)

  @article{ , abstract = {This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.}, archive = {J_ACCESS}, author = {Majdi Sukkar and Rajendrasinh Jadeja and Madhu Shukla and Rajesh Mahadeva}, doi = {10.1109/ACCESS.2024.3524501}, journal = {IEEE Access}, pages = {3994-4007}, shortjournal = {IEEE Access}, title = {A survey of deep learning approaches for pedestrian detection in autonomous systems}, volume = {13}, year = {2025}, }
• (2025). Characterizing extra credit accumulation by undergraduate students. ACCESS, 13, 3970-3993. (www)

  @article{ , abstract = {Most degree-seeking undergraduate students at US universities graduate with a higher number of credit hours than is required for graduation, thereby accumulating extra credits. Although existing literature is rich with studies on extra credit accumulation, most of them are limited to comparing overall credit numbers and thus do not consider the usability of individual credits towards the degree program requirements. This limitation can be attributed to the use of inflexible and/or opaque commercial degree audit tools, which curtails the possible scope of analytics on degree audit data. This work uses a custom-built audit tool that determines an optimal match between individual classes and requirements and then decomposes student credits in the following categories: unusable credits that do not match any degree requirement, excess credits that can satisfy at least one requirement but can be removed without changing overall requirement satisfaction, and applied credits that contribute to requirement satisfaction without excess. Thus, extra credits are decomposed into unusable and excess credits. This paper uses this decomposition to investigate (and quantify) numerous possible reasons for extra credit accumulation including transfer credit loss, program change, financial aid retention, hidden requirements, repeated classes, remedial classes, filler classes, and program requirement characteristics. These investigations compare Transfer and Non-Transfer students, compare extra credit accumulation by college, and explore the term-wise accumulation of extra credits. Surprisingly, this analysis suggests that transfer credit loss (i.e., transfer credits not being accepted or not applying to the chosen major) is not a major contributor to extra credits. It also shows that extra credit accumulation by Transfer and Non-Transfer students is very similar, suggesting that the transfer process may have little influence on extra credit accumulation. Finally, it suggests that extra credit accumulation is likely due to multiple factors without a single dominating factor.}, archive = {J_ACCESS}, author = {Tushar Ojha and Don R. Hush}, doi = {10.1109/ACCESS.2025.3525775}, journal = {IEEE Access}, pages = {3970-3993}, shortjournal = {IEEE Access}, title = {Characterizing extra credit accumulation by undergraduate students}, volume = {13}, year = {2025}, }
• (2025). Studies on underwater image processing using artificial intelligence technologies. ACCESS, 13, 3929-3969. (www)

  @article{ , abstract = {Underwater image processing is a dynamic field that has garnered increasing interest due to its critical applications in marine biology, geological explorations and military reconnaissance. This paper presents a comprehensive survey of the methodologies employed in the enhancement and restoration of underwater imaging with a specific focus on the challenges posed by aquatic medium such as light absorption, scattering and color distortion. The survey reviews a range of techniques from traditional histogram equalization and white balancing methods to cutting edge AI approaches, including CNNs and GANs. Through examination of various datasets and quality metrics, we assess the performance of these methodologies in overcoming the inherent challenges of undersea imaging. Our study highlights the significant advances in AI driven underwater image processing technologies with a need for more resilient and flexible algorithms that can manage the intricacies of an undersea environment. The findings of this survey suggest promising directions for future research, particularly in the development of more sophisticated deep learning models that can further improve image quality and contribute to the underwater exploration and monitoring system.}, archive = {J_ACCESS}, author = {Sugunapriya A and Markkandan S}, doi = {10.1109/ACCESS.2024.3524593}, journal = {IEEE Access}, pages = {3929-3969}, shortjournal = {IEEE Access}, title = {Studies on underwater image processing using artificial intelligence technologies}, volume = {13}, year = {2025}, }
• (2025). Compact workspace decomposition based on a bottom-up approach. ACCESS, 13, 3917-3928. (www)

  @article{ , abstract = {In this paper, we present an algorithm that addresses the challenge of dividing a workspace among multiple UAVs. The workspace can be any convex or non-convex polygon and may contain holes of various shapes that represent no-fly zones. The UAVs can be heterogeneous, with different levels of autonomy, speed, and range. The goal of the workspace division is to obtain areas whose sizes are best matched to the capabilities of the UAVs while maximizing compactness. The algorithm decomposes the polygon representing the workspace into a triangular grid, followed by an iterative process of accumulating adjacent triangles while maximizing the compactness of the resulting regions. The performance of the algorithm and the quality of the partitions generated by the algorithm are compared to existing methods. Results show that this approach outperforms others in several metrics, achieving a 5% to 10% improvement in compactness, while maintaining reasonable performance, with a time overhead of up to approximately two seconds when splitting a polygon into ten parts.}, archive = {J_ACCESS}, author = {Georgy Skorobogatov and Toni Calvo and Cristina Barrado and Esther Salamí}, doi = {10.1109/ACCESS.2025.3525800}, journal = {IEEE Access}, pages = {3917-3928}, shortjournal = {IEEE Access}, title = {Compact workspace decomposition based on a bottom-up approach}, volume = {13}, year = {2025}, }
• (2025). Cloud-integrated meteorological parameter prediction by leveraging multivariate statistical time series and GANs. ACCESS, 13, 3900-3916. (www)

  @article{ , abstract = {Meteorological parameters are increasingly influenced by unsustainable development and environmental pollution, contributing to the rising frequency of natural calamities. Accurate prediction of these parameters is crucial for prevention. This study uses historical data to predict six key meteorological parameters, such as dew point, cloud cover, precipitation, temperature, wind speed, and wind direction of Bengaluru and Delhi city of India. Using cloud-based services ensures scalability and computational efficiency for data storage, processing, and model deployment in weather parameters. To address data gaps, generative adversarial networks (GANs) are employed for data imputation within those parameters. Hence, the present work predicts six important meteorological parameters using the vector autoregression model (VAR), vector moving average model (VMA), vector autoregression moving average model (VARMA), and cointegrated vector autoregression model (CVAR). The comparative analysis demonstrates the superior performance of the CVAR model over other models, as measured by the normalized mean square error (nMSE) and normalized root mean square error (nRMSE) for a single parameter (2.12, 0.3) and (1.21, 0.24) for both the cities, in forecasting all the specified meteorological parameters.}, archive = {J_ACCESS}, author = {Archana Rout and Biswa Ranjan Senapati and Debahuti Mishra}, doi = {10.1109/ACCESS.2024.3524608}, journal = {IEEE Access}, pages = {3900-3916}, shortjournal = {IEEE Access}, title = {Cloud-integrated meteorological parameter prediction by leveraging multivariate statistical time series and GANs}, volume = {13}, year = {2025}, }
• (2025). A cross-layer FL-based clustering protocol to support multicast routing in IoT-enabled MANETs with CF-mMIMO. ACCESS, 13, 3881-3899. (www)

  @article{ , abstract = {This paper proposes a novel cross-layer federated learning (FL)-based clustering (CFLC) protocol to support multicast routing in internet of things (IoT)-enabled mobile ad hoc networks (MANETs) with cell-free massive multiple-input multiple-output (CF-mMIMO). The proposed CFLC protocol leverages cross-layer and FL approaches to enhance network stability and connectivity by optimizing cluster head (CH) selection and cluster formation. The cross-layer design integrates physical layer information such as mobility (speed and direction), position, channel capacity, and remaining energy, with network layer information (connectivity) to maximize the cost function value for cluster formation. We design the FL model to improve the clustering performance and satisfy future mobile network requirements. Specifically, during the CH selection step, FL can decide which nodes should be elected as CHs or cluster members (CMs) by using classification. In the cluster formation step, FL addresses a regression problem by optimizing the cost function weights for parameters such as mobility similarity, link quality, remaining energy, and channel capacity to decide which CH each node should follow. The simulation results show that the proposed CFLC protocol outperforms the benchmark protocols in terms of connectivity, scalability, and control overhead. Additionally, the results indicate that the CFLC protocol performs particularly well when using the reference point group mobility (RPGM) model, highlighting its advantage over the random waypoint (RWP) mobility model in maintaining network stability and connectivity.}, archive = {J_ACCESS}, author = {Amalia Amalia and Yushintia Pramitarini and Ridho Hendra Yoga Perdana and Kyusung Shim and Beongku An}, doi = {10.1109/ACCESS.2025.3525636}, journal = {IEEE Access}, pages = {3881-3899}, shortjournal = {IEEE Access}, title = {A cross-layer FL-based clustering protocol to support multicast routing in IoT-enabled MANETs with CF-mMIMO}, volume = {13}, year = {2025}, }
• (2025). LAMARS: Large language model-based anticipation mechanism acceleration in real-time robotic systems. ACCESS, 13, 3864-3880. (www)

  @article{ , abstract = {Large language models (LLMs) have assumed an increasingly crucial role in robotic systems because of their ability to leverage the extensive knowledge they possess in robotic inference and task handling. Although LLMs offer significant potential, their integration into robotic systems poses substantial challenges, particularly with regard to computational efficiency and latency. To address this challenge, this study presents LAMARS, an LLM-based anticipation mechanism designed to accelerate real-time robotic systems. LAMARS leverages the predictive power and zero-shot capabilities of LLMs combined with an anticipation mechanism and vision-language processing to position a robot in advance for upcoming tasks. This reduces latency and optimizes path planning without requiring expensive training data. Our evaluations in a realistic simulation environment and with a variation of the RLBench dataset demonstrated that LAMARS achieved an average success rate of 0.79 and improves efficiency by up to 52.4% compared to existing methods, significantly lowering path planning costs. These results indicate that LAMARS effectively accelerates directive execution, making it a promising solution to minimize delays in real-time robotic systems.}, archive = {J_ACCESS}, author = {Yifang Gao and Wei Luo and Xuye Wang and Shunshun Zhang and Patrick Goh}, doi = {10.1109/ACCESS.2024.3524906}, journal = {IEEE Access}, pages = {3864-3880}, shortjournal = {IEEE Access}, title = {LAMARS: Large language model-based anticipation mechanism acceleration in real-time robotic systems}, volume = {13}, year = {2025}, }
• (2025). Short blocklength nonbinary raptor-like LDPC coding systems design and simulation. ACCESS, 13, 3849-3863. (www)

  @article{ , abstract = {This paper explores an efficient rate-adaptive error correction coding scheme with a nonbinary (NB) $\mathrm {GF}(2^{q})$ extension of Raptor-like (RL) quasi-cyclic (QC) subclass of Low-Density Parity-Check (LDPC) codes. In the NB RL scheme, the high-rate (HR) code vectors are supplemented by the incremental redundancy (IR) symbols, generated by the parity check equations over $\mathrm {GF}(2^{q})$ . We provide a procedure for constructing short NB QC-RL-LDPC codes with low encoding complexity and optimized graph structures. The proposed method is based on the graph optimization of the HR code and Monte Carlo simulation-supported placement of additional NB parity checks in the IR graph. Numerical simulations validate the approach, demonstrating the effectiveness of the constructed short NB QC-RL-LDPC codes, compared to the 5G standard binary coding with IR. Further exploration shows that, when the NB QC-RL-LDPC coding over Galois Field $\mathrm {GF}(2^{q})$ is combined with adaptive $2^{X}$ -ary digital modulations, which provides flexibility in diversifying modulation orders by mixing higher order modulations for initial HR transmission with a lower order for IR, it can improve performance in the low Signal to Noise Ratio regime. In conclusion, this investigation shows that the Raptor-Like nonbinary coding, when the code and the transmission scheme are designed with proper optimizations, exhibits a performance improvement over the counterpart binary coding.}, archive = {J_ACCESS}, author = {Jakub Hyla and Wojciech Sułek}, doi = {10.1109/ACCESS.2024.3517171}, journal = {IEEE Access}, pages = {3849-3863}, shortjournal = {IEEE Access}, title = {Short blocklength nonbinary raptor-like LDPC coding systems design and simulation}, volume = {13}, year = {2025}, }
• (2025). Friend-guard textfooler attack on text classification system. ACCESS, 13, 3841-3848. (www)

  @article{ , abstract = {Deep neural networks provide good performance for image classification, text classification, speech classification, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a sample created by adding a little noise to the original sample data and that, although presenting no change identifiable to human perception, will be misclassified by a deep neural network. Most studies on adversarial examples have focused on images, but research is expanding to include the field of text. Textual adversarial examples can be useful in certain situations, such as when models of both friend and enemy coexist, as in a military scenario. Here, a specific message may be generated as an adversarial example such that no grammatical or semantic problems are apparent to human perception and it will be correctly classified by the friend model but incorrectly classified by the enemy model. In this paper, I propose a “friend-guard” textual adversarial example for a text classification system. Unlike the existing methods for generating image adversarial examples, the proposed method creates adversarial examples designed to be misclassified by an enemy model and correctly classified by a friend model while retaining the meaning and grammar of the original sentence by replacing words of importance with substitutions. Experiments were conducted using a movie review dataset and the TensorFlow library. The experimental results show that the proposed method can generate an adversarial example that will be correctly classified with 88.2% accuracy by the friend model and 26.1% accuracy by the enemy model.}, archive = {J_ACCESS}, author = {Hyun Kwon}, doi = {10.1109/ACCESS.2021.3080680}, journal = {IEEE Access}, pages = {3841-3848}, shortjournal = {IEEE Access}, title = {Friend-guard textfooler attack on text classification system}, volume = {13}, year = {2025}, }
• (2025). High-level design and implementation of a configurable cryptosystem with a novel chaos-enhanced function. ACCESS, 13, 3823-3840. (www)

  @article{ , abstract = {The widespread availability of networks has significantly increased the exposure of data to security threats, necessitating robust encryption methods. While existing symmetric block ciphers offer varying levels of security, they are susceptible to advancements in cryptanalysis. From this perspective, in this article, we present an efficient approach to enhancing the security of these ciphers through the integration of chaos theory. The key to this enhancement is a newly developed adaptive chaotic map based on sine map and the dynamic interplay of two key parameters (rn,k $_{\mathrm {n}}$ ). Of course, the efficiency of the proposed chaotic map is demonstrated by the time evolution, the Bifurcation diagram, Permutation Entropy (PE), Fuzzy Correlation Dimension (FCD), and Lyapunov Exponent (LE) analyses and comparison with the existing reported maps. Thereafter, by applying chaos theory to address the limitations of default cipher parameters, and operating modes and to control S-boxes, permutation functions, key schedules, data masking, we introduce nonlinearity and diffusion, making the block ciphers more resistant to attacks. Through extensive simulations and evaluations, we demonstrate the effectiveness of our approach in strengthening the security of the enhanced ciphers against pattern, statistical, differential, key sensitivity, Linear, and Padding Oracle attacks. Additionally, we present a high-level design and implementation of a configurable cryptosystem using SystemC. The configurable design allows for flexibility in integrating different ciphers and adapting to evolving security needs.}, archive = {J_ACCESS}, author = {Medien Zeghid and Hassan Yousif Ahmed and Akhtar Nawaz Khan}, doi = {10.1109/ACCESS.2025.3525729}, journal = {IEEE Access}, pages = {3823-3840}, shortjournal = {IEEE Access}, title = {High-level design and implementation of a configurable cryptosystem with a novel chaos-enhanced function}, volume = {13}, year = {2025}, }
• (2025). Source code error understanding using BERT for multi-label classification. ACCESS, 13, 3802-3822. (www)

  @article{ , abstract = {Programming is an essential skill in computer science and across a wide range of engineering disciplines. However, errors, often referred to as ‘bugs’ in code, can be challenging to identify and rectify for both students learning to program and experienced professionals. Understanding, identifying, and effectively addressing these errors are critical aspects of programming education and software development. To aid in understanding and classifying these errors, we propose a multi-label error classification approach for source code using fine-tuned BERT models (BERT_Uncased and BERT_Cased). The models achieved average classification accuracies of 90.58% and 90.80%, exact match accuracies of 48.28% and 49.13%, and weighted F1 scores of 0.796 and 0.799, respectively. Precision, Recall, Hamming Loss, and ROC-AUC metrics further evaluate the effectiveness of our models. Additionally, we employed several combinations of large language models (CodeT5, CodeBERT) with machine learning classifiers (Decision Tree, Random Forest, Ensemble Learning, ML-KNN), demonstrating the superiority of our proposed approach. These findings highlight the potential of multi-label error classification to advance programming education, software engineering, and related research fields.}, archive = {J_ACCESS}, author = {Md Faizul Ibne Amin and Yutaka Watanobe and Md Mostafizer Rahman and Atsushi Shirafuji}, doi = {10.1109/ACCESS.2024.3525061}, journal = {IEEE Access}, pages = {3802-3822}, shortjournal = {IEEE Access}, title = {Source code error understanding using BERT for multi-label classification}, volume = {13}, year = {2025}, }
• (2025). Cyber-physical system security for manufacturing industry 4.0 using LSTM-CNN parallel orchestration. ACCESS, 13, 3788-3801. (www)

  @article{ , abstract = {Interoperability among different machines, systems, and humans connected via the Internet of Things (IoT) has blessed Industry 4.0 with numerous advantages over the years. However, these benefits have unleashed risks of cyber attacks on internet-connected manufacturing units such as autonomous intelligent computer-controlled cutting (ICNC) machines. These are used in different manufacturing industries to ensure high precision and faster production. Over the Internet these machines receive product designs and instructions of how to produce them. Intrusions through malicious code embedded in the design can hamper precision and cause production delays, resulting in significant revenue loss. This paper presents an innovative cyber-physical system (CPS) security mechanism, using a long short-term memory (LSTM) network and a convolutional neural network (CNN) coordinated by a parallel orchestration (PLO) algorithm. It detects intrusions from both image and text data with 90.85% and 91.66% accuracy, respectively. Applying the proposed methodology in a simulated manufacturing industry shows an average yearly successful intrusion reduction from 184 to 15, saving an average of 30,474 in revenue. Its innovative concept, the distinctive mechanism of the PLO algorithm, and applying it in a simulated manufacturing industry make the proposed security system superior to comparable approaches.}, archive = {J_ACCESS}, author = {Salman Saeidlou and Nikdokht Ghadiminia and Kwadwo Oti-Sarpong}, doi = {10.1109/ACCESS.2025.3525520}, journal = {IEEE Access}, pages = {3788-3801}, shortjournal = {IEEE Access}, title = {Cyber-physical system security for manufacturing industry 4.0 using LSTM-CNN parallel orchestration}, volume = {13}, year = {2025}, }
• (2025). Embedded vehicle routing problem: A modeling and optimization framework for real-world multi-purpose truck network. ACCESS, 13, 3768-3787. (www)

  @article{ , abstract = {This paper introduces original contributions to the field of vehicle routing problems, specifically addressing the optimization of multi-purpose truck routing within the agricultural third-party logistics domain. The study presents a novel approach aimed at optimizing the routing of a 3PL provider’s truck fleet, considering efficient commodity delivery and hygienic practices for transporting agricultural products. To tackle the complexities associated with multi-purpose truck routing, we propose the concept of the Embedded Vehicle Routing Problem (EMBDD-VRP). The EMBDD-VRP adopts a two-phase framework, embedding multiple local Vehicle Routing Problems (VRPs) into a global VRP structure. By leveraging existing VRP solutions in each phase, the EMBDD-VRP retains the desirable characteristics of classical VRP models while effectively addressing the unique challenges faced by 3PL providers. Case studies and practical implementations conducted within the Thai agriculture sector demonstrate the applicability and viability of the proposed methodology. Compared to the exact VRP method, our analysis shows that the proposed EMBDD-VRP reduces the search space size by approximately 100-fold for medium-sized problems. The computational results indicate that the solution gap between EMBDD-VRP and the exact VRP method (using Gurobi) ranges from 0% to 6.2% in the simplified problem, where the exact VRP is applicable. While the exact method becomes infeasible for more complex problems, EMBDD-VRP can efficiently solve much larger instances, including real-world cases. This research not only fills gaps in the literature but also offers a promising avenue for optimizing multi-purpose truck routing within agricultural third-party logistics operations.}, archive = {J_ACCESS}, author = {Tipaluck Krityakierne and Chanon Boonkangwan and Chanoknun Phunnasorn and Wasakorn Laesanklang}, doi = {10.1109/ACCESS.2024.3524395}, journal = {IEEE Access}, pages = {3768-3787}, shortjournal = {IEEE Access}, title = {Embedded vehicle routing problem: A modeling and optimization framework for real-world multi-purpose truck network}, volume = {13}, year = {2025}, }
• (2025). Recent advancements in planning and reliability aspects of large-scale deep sea offshore wind power plants: A review. ACCESS, 13, 3738-3767. (www)

  @article{ , abstract = {The transition towards renewable energy sources has propelled the rapid expansion of offshore wind power, with large-scale deep offshore wind power plants (OWPPs) emerging as a promising solution to meet escalating energy demands while mitigating climate change. This comprehensive review delves into recent advancements in the planning and reliability aspects of large-scale deep offshore wind power plants, crucial determinants of project success, economic viability, and long-term sustainability. The review synthesizes current research findings, technological innovations, and industry practices, and the evaluation elucidates state-of-the-art methodologies, tools, and strategies employed in developing and operating these offshore renewable energy assets. Key focus areas include site selection and resource assessment, technological innovations in turbine design and foundation systems, risk assessment and mitigation strategies, grid integration, and socio-economic and environmental considerations. The review identifies emerging trends, future research directions, and policy implications by critically analyzing these dimensions, offering valuable insights for researchers, practitioners, policymakers, and investors engaged in global sustainable offshore wind energy deployment.}, archive = {J_ACCESS}, author = {Pawan Kumar and Santanu Paul and Akshay Kumar Saha and Omkar Yadav}, doi = {10.1109/ACCESS.2024.3524446}, journal = {IEEE Access}, pages = {3738-3767}, shortjournal = {IEEE Access}, title = {Recent advancements in planning and reliability aspects of large-scale deep sea offshore wind power plants: A review}, volume = {13}, year = {2025}, }
• (2025). Secure satellite downlink with hybrid RIS and AI-based optimization. ACCESS, 13, 3726-3737. (www)

  @article{ , abstract = {In this paper, we explore a secure multiuser multiple-input single-output (MISO) satellite downlink communication system, enhanced by the integration of a hybrid reconfigurable intelligent surface (RIS). The study formulates a robust joint design for satellite and RIS beamforming, aimed at maximizing the secrecy rate of the overall system. Both the active and passive elements of the RIS are optimized, taking into account practical models that reflect real-world constraints, such as outdated channel state information (CSI) and the power consumption of the system. To address the highly complex, dynamic, and multidimensional nature of the beamforming design problem, deep reinforcement learning (DRL) techniques are employed. Simulation results demonstrate the effectiveness of the proposed beamforming strategy, highlighting significant performance improvements when utilizing hybrid-RIS compared to traditional passive RIS solutions in wireless communication systems.}, archive = {J_ACCESS}, author = {Mian Muhammad Kamal and Syed Zain Ul Abideen and Syed Sikandar Shah and Nabila Sehito and Salahuddin Khan and Bal S. Virdee and Mohammad Alibakhshikenari and Patrizia Livreri}, doi = {10.1109/ACCESS.2024.3520796}, journal = {IEEE Access}, pages = {3726-3737}, shortjournal = {IEEE Access}, title = {Secure satellite downlink with hybrid RIS and AI-based optimization}, volume = {13}, year = {2025}, }
• (2025). Leveraging ChatGPT for enhancing arabic NLP: Application for semantic role labeling and cross-lingual annotation projection. ACCESS, 13, 3707-3725. (www)

  @article{ , abstract = {Semantic role labeling involves assigning semantic roles to sentence arguments, providing rich information for various NLP tasks and applications. Annotated corpora with semantic roles are a critical factor in improving the performance of semantic-based models. Besides, Arabic as a low resourced language, have to pay more attention to alternative methods to build such annotated corpora. To this end, two traditional methods have been intensively used, namely, manual annotation and crowed-resourced annotation. The former is highly precise but it demands substantial training and extensive resources, while, the latter, reduce human effort but often results in lower-quality annotations. Recently, Large language model (LLM) based conversational systems like ChatGPT have emerged as a promising tool for text annotation across various NLP tasks. In this paper, we leverage ChatGPT for two main sub-tasks in Arabic language processing. (1) Creating an Arabic annotated resource with emotional semantic roles from an English corpus, using cross-lingual annotation projection approach. (2) Annotating the Arabic corpus of emotional sentences with emotion categories and semantic roles. Furthermore, we evaluate ChatGPT’s potential for translating English sentences into Arabic. From the perspective of generalization, we test the performance of open-LLMs, specifically, mBERT, and mBART for the same tasks. The evaluation process includes assessing the impact of sentence complexity on the performance of ChatGPT, and open-LLMs in semantic role labeling, and cross-lingual annotation projection. We compared the obtained zero-shot annotation accuracy with that of human base annotations, where the GPT results achieved an accuracy of 0.94 for cross-lingual projection and 0.76 in semantic role labelling, While the open-LLMs achieved notable accuracies of 0.72, and 0.38 respectively.}, archive = {J_ACCESS}, author = {Ferial Senator and Abdelaziz Lakhfif and Imene Zenbout and Hanane Boutouta and Chahrazed Mediani}, doi = {10.1109/ACCESS.2025.3525493}, journal = {IEEE Access}, pages = {3707-3725}, shortjournal = {IEEE Access}, title = {Leveraging ChatGPT for enhancing arabic NLP: Application for semantic role labeling and cross-lingual annotation projection}, volume = {13}, year = {2025}, }
• (2025). State of charge prediction for electric loader battery based on extreme learning machine. ACCESS, 13, 3696-3706. (www)

  @article{ , abstract = {In order to accurately predict the state of charge (SOC) of electric loader battery, a multi-strategy improved dung beetle optimization algorithm (MIDBO) is proposed to optimize the SOC prediction method of the extreme learning machine (ELM). Firstly, principal component analysis (PCA) is used to screen the input features and reduce dimensionality. Secondly, logistic chaos mapping is introduced in the population initialization stage to improve the diversity and quality of the population. The dynamic spiral search strategy is utilized in the breeding stage of the dung beetle algorithm, and the Lévy flight mechanism is incorporated in the foraging stage to escape local optima. Finally, adaptive t-distribution variation and a dynamic selection strategy are employed to update the position of the dung beetle, enhancing the convergence speed of the algorithm. This approach enables accurate prediction of the electric loader battery SOC. Experiments using real vehicle data from electric loaders show that the accuracy of the MIDBO-ELM model reaches 99.985%, with an error of less than 5‰. The robustness of the model is verified by adding different types of noise to simulate measurement errors. Additionally, the accuracy of the MIDBO algorithm is validated through test functions, and the generalization capability of the algorithm for SOC prediction is confirmed using a public data set.}, archive = {J_ACCESS}, author = {Wei Ding and Fumin Zou and Jishun Liu and Cheng Zhang}, doi = {10.1109/ACCESS.2024.3517708}, journal = {IEEE Access}, pages = {3696-3706}, shortjournal = {IEEE Access}, title = {State of charge prediction for electric loader battery based on extreme learning machine}, volume = {13}, year = {2025}, }
• (2025). RBMDC-net: Effective jaw cyst segmentation network using residual bottleneck and multiscale dilated convolution. ACCESS, 13, 3683-3695. (www)

  @article{ , abstract = {Automatic segmentation of jaw cysts in cone-beam computed tomography (CBCT) scans plays a crucial role in clinical and treatment planning, and it provides an efficient alternative to labor-intensive manual procedures. However, cysts on pathological sections often present with irregular and complex morphology, as well as extremely similar to surrounding tissue, which poses a great challenge in achieving their accurate segmentation. To overcome these limitations, this paper proposes a U-shaped image segmentation network based on residual bottleneck and multiscale dilated convolution, called RBMDC-Net. In our approach, the standard convolutional module in the U-Net encoding path is replaced with residual bottleneck module (RBM) that enables the network to extract features at various scales using multiple kernel sizes and residual connection. Next, we process each feature extracted from the encoder by passing it through a series of multiscale dilated convolution modules (MDCM), which capture more diverse and reliable features by increasing the acceptance field. In addition, we introduce the multi-level feature guidance module (MFGM) to address the issue of semantic dilution that often occurs during the progressive up-sampling. Finally, to evaluate the effectiveness of RBMDC-Net, we developed two image datasets: the first is the original images of jaw cyst collected by Quzhou People’s Hospital, and the second is its augmented dataset. These datasets encompass a wide range of jaw cyst characteristics, including size, shape, and location, as well as varying environmental conditions such as lighting and imaging quality. The experimental results demonstrate that our proposed RBMDC-Net significantly outperforms other comparable methods in the task of image segmentation. Specifically, when applied to the original jaw cyst dataset, RBMDC-Net achieved F1 of 93.13%, Mcc of 93.08% and Jaccard of 87.17%. These results represent improvements of 5.75%, 5.36%, and 9.31% over the performance of the widely used U-Net architecture. Furthermore, when tested on the augmented jaw cyst dataset, the RBMDC-Net continued to show superior performance with F1 of 91.92%, Mcc of 91.85% and Jaccard of 85.14%. These metrics were 3.03%, 2.84%, and 4.88% higher than the corresponding values obtained using U-Net. Besides, ablation experiments also indicate the effectiveness of residual bottleneck module, multiscale dilated convolution module and multi-level feature guidance module.}, archive = {J_ACCESS}, author = {Huixia Zheng and Xiaoliang Jiang and Xu Xu and Zhenfei Yuan}, doi = {10.1109/ACCESS.2024.3525408}, journal = {IEEE Access}, pages = {3683-3695}, shortjournal = {IEEE Access}, title = {RBMDC-net: Effective jaw cyst segmentation network using residual bottleneck and multiscale dilated convolution}, volume = {13}, year = {2025}, }
• (2025). Unsupervised visual-to-geometric feature reconstruction for vision-based industrial anomaly detection. ACCESS, 13, 3667-3682. (www)

  @article{ , abstract = {Industrial anomaly detection involves identifying abnormal regions in products and plays a crucial role in quality inspection. While 2D image-based anomaly detection has been extensively explored, combining two-dimensional (2D) images with three-dimensional (3D) point clouds remains less studied. Existing multimodal methods often combine features from different modalities, leading to feature interference and degraded performance. To overcome this, we propose a novel framework for unsupervised industrial anomaly detection that leverages both visual and geometric information. Specifically, we use pre-trained 2D and 3D models to extract visual features from color images and geometric features from 3D point clouds. Instead of directly fusing these features, we propose a geometric feature reconstruction network that predicts 3D geometric features from the 2D visual features. During training, we minimize the difference between the predicted geometric features and the extracted geometric features, enabling the model to learn how 2D appearance correlates with 3D structure in anomaly-free images. During inference, this learned relationship allows the model to detect anomalies: significant discrepancies between the reconstructed and actual geometric features indicate abnormal regions. Evaluated on the MVTec 3D-AD dataset, our method achieves state-of-the-art performance with an average image-level AUROC score of 0.968, surpassing previous approaches. Additionally, it provides fast inference at 8.2 frames per second with a memory footprint of only 1045 MB, making it highly efficient for industrial applications.}, archive = {J_ACCESS}, author = {Dinh-Cuong Hoang and Phan Xuan Tan and Anh-Nhat Nguyen and Duc-Thanh Tran and Van-Hiep Duong and Anh-Truong Mai and Duc-Long Pham and Khanh-Toan Phan and Minh-Quang Do and Ta Huu Anh Duong and Tuan-Minh Huynh and Son-Anh Bui and Duc-Manh Nguyen and Viet-Anh Trinh and Khanh-Duong Tran and Thu-Uyen Nguyen}, doi = {10.1109/ACCESS.2025.3525567}, journal = {IEEE Access}, pages = {3667-3682}, shortjournal = {IEEE Access}, title = {Unsupervised visual-to-geometric feature reconstruction for vision-based industrial anomaly detection}, volume = {13}, year = {2025}, }
• (2025). End-to-end architecture for english reading and writing content assessment based on prompt learning. ACCESS, 13, 3653-3666. (www)

  @article{ , abstract = {With the acceleration of globalization, the use of English as an international language has become increasingly widespread, making the assessment of English reading and writing skills a key issue in the field of language education. However, traditional methods of English reading and writing content assessment rely on manually designed features, which struggle to effectively handle complex and diverse language structures. To address this issue, this study proposes an English reading and writing content assessment algorithm based on Prompt Learning, utilizing an end-to-end architecture enhanced with multi-scale attention mechanisms. The algorithm preprocesses the input English texts through the prompt learning framework and uses multi-scale attention mechanisms to improve the model’s ability to capture features at different linguistic levels. Within an end-to-end architecture, the entire assessment process is automated, from text input to output of assessment results, eliminating the need for manually designed feature extraction steps. Experimental results show that the algorithm performs excellently on multiple English reading and writing assessment datasets, significantly enhancing the accuracy and efficiency of assessments and offering an effective solution for English reading and writing evaluations.}, archive = {J_ACCESS}, author = {Su-Qin Wu and Ming-Yong Pang and Xue-Mei Sun and Xin-Jian Wang and Yun-Peng Ji}, doi = {10.1109/ACCESS.2024.3509990}, journal = {IEEE Access}, pages = {3653-3666}, shortjournal = {IEEE Access}, title = {End-to-end architecture for english reading and writing content assessment based on prompt learning}, volume = {13}, year = {2025}, }
• (2025). Advanced detection of AI-generated images through vision transformers. ACCESS, 13, 3644-3652. (www)

  @article{ , abstract = {The rapid advancement of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has been a great success in the field of image synthesis and creation. Artificially generated GAN-based images are widely spread over the Internet along with the development in generation of natural and photorealistic images. While this could lead to better digital media and content, it also poses a risk to security, legitimacy, and authenticity. The advancement of AI-generated images, particularly those that are produced by Generative Adversarial Networks (GANs), has created a rising concern about the potential misuse of these images in spreading misinformation and creating deepfakes. Detecting such fake or AI-generated images has become an important challenge in maintaining the integrity of digital media. In this research, we have explored the application of the Vision Transformer (ViTs) model for detecting AI-generated images, leveraging the Kaggle dataset - a balanced collection of real and AI-generated images. The Vision Transformer is recognized for its innovative method of treating images as sequences of patches and excels at identifying long-range dependencies and complex patterns within images. That makes it exceptionally well-suited for this task of detecting fake images. We have fine-tuned the ViT model on the dataset, performing data augmentation techniques on it and leveraging pretrained weights to boost the model’s performance. The findings thus obtained demonstrate that the ViT model attains a high level of accuracy in differentiating between real and AI-generated images, outperforming traditional CNN-based approaches. Beyond performance evaluation, we also conducted an ablation study to examine the impact of various components of the ViT model, including the number of attention heads, patch size, the impact of data augmentation, and the depth of layers. The results obtained in this study indicate that the ViT model not only excels in accuracy but also provides a robust framework for detecting AI-generated images across diverse scenarios. Our study shows the strength of transformer based models in addressing the increasing challenge of AI-generated image detection, laying a foundation for future research in this critical area. This experiment highlights that when the ViT model is fine tuned with optimal data augmentation techniques, it gains state of the art performance in AI-generated image detection, emphasizing its potential for real-world applications.}, archive = {J_ACCESS}, author = {Darshan Lamichhane}, doi = {10.1109/ACCESS.2024.3522759}, journal = {IEEE Access}, pages = {3644-3652}, shortjournal = {IEEE Access}, title = {Advanced detection of AI-generated images through vision transformers}, volume = {13}, year = {2025}, }
• (2025). Enhancing security with hybrid active- passive RIS: A DRL approach against eavesdropping and jamming. ACCESS, 13, 3632-3643. (www)

  @article{ , abstract = {The security of wireless communications is increasingly threatened by eavesdropping and jamming attacks. This paper proposes a novel framework for enhancing secure transmission using hybrid active-passive Reconfigurable Intelligent Surfaces (RIS). By combining active and passive elements, the hybrid RIS can dynamically adjust the amplitude and phase of reflected signals, providing a robust defense against eavesdropping and jamming. The key challenge lies in optimizing the beamforming at the base station (BS) and the hybrid RIS configuration in real time. To tackle this, we employ a Deep Reinforcement Learning (DRL) approach using the Deep Deterministic Policy Gradient (DDPG) algorithm, enabling efficient and dynamic optimization. Simulation results demonstrate that the proposed DRL-based method significantly improves the secrecy rate compared to conventional passive RIS and benchmark methods. Our results indicate that the system can achieve substantial security gains even with a limited number of active RIS elements, making it a viable solution for next-generation wireless networks.}, archive = {J_ACCESS}, author = {Abdul Wahid and Syed Zain Ul Abideen and Nouman Imtiaz and Mian Muhammad Kamal and Abdullah Alharbi and Amr Tolba and M. A. Al-Khasawneh and Inam Ullah}, doi = {10.1109/ACCESS.2024.3520151}, journal = {IEEE Access}, pages = {3632-3643}, shortjournal = {IEEE Access}, title = {Enhancing security with hybrid active- passive RIS: A DRL approach against eavesdropping and jamming}, volume = {13}, year = {2025}, }
• (2025). Levenberg-marquardt algorithm-based neural network smart control strategy for a low-input current ripple and high-voltage gain power converter in fuel-cells energy systems. ACCESS, 13, 3613-3631. (www)

  @article{ , abstract = {A crucial aspect of DC-DC converters employed in renewable energy sources such as fuel cells is their ability to exhibit substantial increases in DC voltage and maintain an efficient structure while minimizing input current ripple. These factors play a pivotal role in enhancing the longevity of these energy sources and ensuring their compatibility with high-voltage AC and DC grids. This study introduces a high-gain DC-DC step-up converter that incorporates a continuous input current cell and a switched capacitor voltage-boosting output cell to address these requirements. The control process of this proposed converter is executed using an artificial neural network based on the Levenberg-Marquardt learning algorithm. The primary difference in this research lies in obtaining the artificial neural network-based controller directly from the circuit’s characteristic equations, rather than generating it through another controller. A feedback control strategy has been formulated, where the artificial neural network consistently produces duty increment values based on the reference voltage. Additionally, the network’s input includes not only the reference signal but also the circuit input voltage and output current value. As a result, the stability of the circuit’s output voltage is maintained against variations in input voltage and load changes. A laboratory-designed workbench underwent testing, and the experimental results substantiated the theoretical inquiries and simulation outcomes.}, archive = {J_ACCESS}, author = {Mustafa Özden and Davut Ertekin and Pierluigi Siano}, doi = {10.1109/ACCESS.2024.3524378}, journal = {IEEE Access}, pages = {3613-3631}, shortjournal = {IEEE Access}, title = {Levenberg-marquardt algorithm-based neural network smart control strategy for a low-input current ripple and high-voltage gain power converter in fuel-cells energy systems}, volume = {13}, year = {2025}, }
• (2025). Edge-version of fault-tolerant resolvability in networks. ACCESS, 13, 3601-3612. (www)

  @article{ , abstract = {Fault tolerance refers to a system’s capacity to continue functioning as intended, even when one of its components fails. Such a system is known as a fault-tolerant, self-stable system. The idea of fault-tolerant resolving sets (FTRS) arises from the concept that removing any vertex from a resolving set (RS) still results in another RS, hence designated as an FTRS. The minimum size of this set is called the fault-tolerant metric dimension (FTMD). This paper extends the concept to edges by introducing the edge version of the fault-tolerant resolving set (EVFTRS) and its corresponding edge version of the fault-tolerant metric dimension (EVFTMD), which is based on edge distances in the network analysis. We calculate the EVFTMD values for n-sunlet and cycle with chord networks, demonstrating that these values remain constant. These findings illustrate the reliability of these network topologies in environments prone to edge failures, offering valuable insights for designing resilient communication systems such as optical networks and smart grids. By adopting an edge-based perspective, this study advances fault tolerance analysis in graph theory and practical network design.}, archive = {J_ACCESS}, author = {Muhammad Faheem and Muhammad Ahmad and Zohaib Zahid and Muhammad Javaid and Mamo Abebe Ashebo}, doi = {10.1109/ACCESS.2024.3524408}, journal = {IEEE Access}, pages = {3601-3612}, shortjournal = {IEEE Access}, title = {Edge-version of fault-tolerant resolvability in networks}, volume = {13}, year = {2025}, }
• (2025). Design of dual polarized FMCW radar system with high gain lens antenna for through-the-wall human detection. ACCESS, 13, 3590-3600. (www)

  @article{ , abstract = {This paper presents the design of a dual-polarized Frequency Modulated Continuous Wave (FMCW) radar system equipped with a high-gain lens antenna for the detection of human targets behind walls. The radar system parameters were designed for through-the-wall human detection, and based on these parameters, both the antenna and radar system were developed accordingly. The proposed radar system harnesses the benefits of dual polarization and a compact lens antenna to enhance the signal-to-noise ratio (SNR), a critical parameter for accurate through-the-wall detection. The lens antenna is engineered to provide wideband and high-gain performance in the X-band while maintaining a compact form factor, effectively addressing the conventional trade-offs between antenna size and performance. Furthermore, the application of polarization synthesis markedly enhances the SNR of the received signal, enabling more reliable detection of multiple human targets through the wall. Experimental validation demonstrates the system’s efficacy in detecting human targets through a wall. The dual-polarization synthesis technique yields substantial improvements in both SNR and detection accuracy in through-the-wall scenarios.}, archive = {J_ACCESS}, author = {Chanhee Lee and Kyung Hwan Park and Hyungju Kim and Jayol Lee and Hyunseok Kim and Kyeong-Han Shin and Jeong-Wook Kim}, doi = {10.1109/ACCESS.2024.3521387}, journal = {IEEE Access}, pages = {3590-3600}, shortjournal = {IEEE Access}, title = {Design of dual polarized FMCW radar system with high gain lens antenna for through-the-wall human detection}, volume = {13}, year = {2025}, }
• (2025). Dynamic peak threshold estimation method for noise mitigation in hybrid PLC/VLC systems. ACCESS, 13, 3583-3589. (www)

  @article{ , abstract = {Most communication systems suffer from signal distortions as a result of noise interference. These distortions have multiple effects on communication channels, including higher bit error rate (BER), reduced signal-to-noise ratio (SNR), and restricted channel capacity. It has been determined that impulsive noise is one of the most detrimental noise types in hybrid power line communication-visible light communication (PLC/VLC) systems. Several techniques to combat impulsive noise in telecommunication systems have been discussed in scholarly research. Most impulsive mitigation methods discussed in the literature require knowledge of the noise parameters in order to function properly. Their principle of operation is based on prior knowledge of impulsive noise characteristics such as probability of occurrence of impulsive noise and noise power at the receiver, to ascertain the optimum blanking threshold. However, it is important to acknowledge that it is extremely difficult to determine such parameters in real-life applications due to the dynamic nature of impulsive noise. Thus, in this paper, we exploit the dynamic peak threshold estimation (DPTE) method in asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) based hybrid PLC/VLC system. This is essential for determining the ideal blanking threshold without prior knowledge concerning the distinctive characteristics of impulsive noise. The acquired results demonstrate that the proposed method does not only eliminates the need to know impulsive noise parameters, but also enhances the receiver’s output SNR. Moreover, the results showed a clear correlation between the optimal blanking threshold and the peak-to-average power value of the ACO-OFDM symbol which we leveraged to ascertain the optimal blanking threshold.}, archive = {J_ACCESS}, author = {Irvine Mapfumo and Thokozani Shongwe}, doi = {10.1109/ACCESS.2024.3525042}, journal = {IEEE Access}, pages = {3583-3589}, shortjournal = {IEEE Access}, title = {Dynamic peak threshold estimation method for noise mitigation in hybrid PLC/VLC systems}, volume = {13}, year = {2025}, }
• (2025). DRCO: Dense-label refinement and cross optimization for semi-supervised object detection. ACCESS, 13, 3572-3582. (www)

  @article{ , abstract = {In semi-supervised object detection (SSOD), the methods based on dense pseudo-labeling bypass complex post-processing while maintaining competitive performance compared to the methods based on sparse pseudo-labeling. However, there are still relatively few researches focused on the dense pseudo-labeling paradigm. In this work, we first experimentally point out the shortcomings of current dense pseudo-labeling methods: 1) Low-quality sampling: the fixed threshold strategies can result in numerous false negatives and false positives. 2) Inconsistency between classification scores and localization quality: classification scores cannot represent localization quality, resulting in poor quality of predicted bounding boxes for sampled positive samples. 3) Suboptimal training approach: current training methods only utilize its knowledge from the perspective of final dense pseudo-labels, failing to fully exploit the teacher model. To address these issues, we propose a method called Dense-Label Refinement and Cross Optimization (DRCO) based on dense pseudo-labels. Specifically, to tackle the issue of low-quality sampling, we introduce the Adaptive Sampling Approach (ASA), which achieves high-quality sampling at the image level and dynamic sampling ratios without introducing any additional hyperparameters. For the inconsistency between classification scores and localization quality, Comprehensive FRS (cFRS) is proposed to jointly optimize the classification and localization branches more efficiently, thereby obtaining a more comprehensive score. Finally, for the suboptimal training approach, we introduce Cross Prediction Optimization (CPO). CPO efficiently leverages the knowledge of the teacher model through a Cross-Head operation, thereby achieving more effective teacher-student interaction. DRCO achieves 27.31% mAP with only 1% COCO labeled data, which is approximately 1.24% mAP higher than the previous state-of-the-art. Based on MS COCO and PASCAL VOC benchmarks, further comprehensive experiments demonstrate that our method alleviates the aforementioned shortcomings and achieves competitive performance.}, archive = {J_ACCESS}, author = {Yunlong Qin and Yanjun Li and Feifan Ji and Yan Liu and Yu Wang and Ji Xiang}, doi = {10.1109/ACCESS.2024.3524029}, journal = {IEEE Access}, pages = {3572-3582}, shortjournal = {IEEE Access}, title = {DRCO: Dense-label refinement and cross optimization for semi-supervised object detection}, volume = {13}, year = {2025}, }
• (2025). Stability analysis of load frequency control systems with electric vehicle considering time-varying delay. ACCESS, 13, 3562-3571. (www)

  @article{ , abstract = {The stability analysis problem is being studied for load frequency control (LFC) systems with electric vehicle (EV) integration and time delays. This research proposes new power system models that take into account time-varying delay and state of charge. Using a novel Lyapunov-Krasovskii functional and an innovative inequality approach, two stability criteria are derived to achieve less conservative results. The study also examines the relationships between the proportional-integral (PI) parameter, maximum allowed upper bounds, and the effect of the state of charge through a case study. Finally, simulations are performed on single-area LFC systems to demonstrate the effectiveness and benefits of the proposed approach.}, archive = {J_ACCESS}, author = {Wei Wang and Rui-Ke Xie and Liming Ding}, doi = {10.1109/ACCESS.2024.3519343}, journal = {IEEE Access}, pages = {3562-3571}, shortjournal = {IEEE Access}, title = {Stability analysis of load frequency control systems with electric vehicle considering time-varying delay}, volume = {13}, year = {2025}, }
• (2025). Development of view optimization techniques for structural monitoring systems using post-processing of structural analysis and image processing. ACCESS, 13, 3546-3561. (www)

  @article{ , abstract = {This study aimed to enhance the efficiency of monitoring systems for structures undergoing continuous deformation by integrating structural analysis and image processing techniques. It addresses the challenge of optimizing real-time structural monitoring for precise deformation visualization, critical for engineering applications. The proposed framework for view optimization utilized the results from structural analysis performed in ABAQUS to extract initial camera positions and direction vectors, which served as inputs for an optimization algorithm. The optimization process applied image processing methods to detect deformation areas, while performance was evaluated at each iteration and saved when improvements were found. Camera parameters were iteratively updated using gradient descent and adjusted learning rates to ensure effective visualization of deformation. Experimental results confirm that this approach not only visualizes structural analysis outcomes effectively but also optimizes camera views for tailored monitoring systems. This integrated approach significantly enhances real-time monitoring, making it suitable for infrastructure health monitoring, civil engineering, and mechanical systems analysis. Overall, this work demonstrates the feasibility of combining structural analysis with imaging techniques for more accurate and efficient monitoring solutions.}, archive = {J_ACCESS}, author = {Yonghui Park}, doi = {10.1109/ACCESS.2024.3524786}, journal = {IEEE Access}, pages = {3546-3561}, shortjournal = {IEEE Access}, title = {Development of view optimization techniques for structural monitoring systems using post-processing of structural analysis and image processing}, volume = {13}, year = {2025}, }
• (2025). Quantum safe multi-factor user authentication protocol for cloud-assisted medical IoT. ACCESS, 13, 3532-3545. (www)

  @article{ , abstract = {The Medical Internet of Things (MIoT) plays a pivotal role in modern healthcare by integrating wireless communication and cloud computing to enhance medical practices. However, this framework presents security vulnerabilities, such as the risk of eavesdroppers exploiting public channels to intercept sensitive patient data or impersonate legitimate devices and services. Consequently, authentication between wearable devices and servers is essential before transmitting confidential information through open channels. While several authentication techniques offer protection against quantum attacks, they often incur high communication and computation costs, underscoring the need for further advancement. This paper introduces a multifactor authentication protocol designed to strengthen the integrity of cloud-assisted MIoT systems by incorporating post-quantum security. The proposed protocol utilizes a post-quantum fuzzy commitment (PQFC) scheme to enhance security and is rigorously analyzed under the random oracle model and ProVerif tool. Its functionality and security are thoroughly assessed, demonstrating adherence to key requirements such as memoryless operation, user anonymity, mutual authentication, and resistance to various threats, including biometric tampering, stolen-verifier attacks, and insider attacks. Additionally, a comprehensive comparison with existing schemes highlights a balanced trade-off between security and efficiency.}, archive = {J_ACCESS}, author = {Arman Ahmad and S. Jagatheswari}, doi = {10.1109/ACCESS.2024.3523530}, journal = {IEEE Access}, pages = {3532-3545}, shortjournal = {IEEE Access}, title = {Quantum safe multi-factor user authentication protocol for cloud-assisted medical IoT}, volume = {13}, year = {2025}, }
• (2025). Hierarchical reputation consensus model for VANET. ACCESS, 13, 3521-3531. (www)

  @article{ , abstract = {The vehicular ad hoc network (VANET) enables vehicles to disseminate messages to improve traffic safety and efficiency. However, the task of enabling consensus through synchronous communication among them remains a challenging problem due to the high mobility of vehicles. This prompts the need for a consensus model suitable for VANETs. Thus, A hierarchical reputation consensus model for VANET has been proposed. The model dynamically partitions consensus groups based on vehicles’ global reputation, which restricts malicious nodes from participating in consensus to improve consensus efficiency. Secondly, an improved event reliability assessment mechanism is introduced into the reputation model to detect the authenticity of the events and mitigate the fabrication of false events. Finally, a hierarchical directed gossip consensus model based on directed acyclic graph (DAG) called as HDGgraph has been proposed. It is aimed at addressing the challenge of achieving consensus when vehicle communication synchronization cannot be guaranteed. In HDGgraph, vehicles can construct a DAG locally and implement virtual voting to achieve local consensus, which can reduce system communication overhead and significantly improve consensus efficiency. Experimental results demonstrate that our proposed approach outperforms some state-of-the-art methods in terms of performance.}, archive = {J_ACCESS}, author = {Ruilong Chen and Yepeng Guan}, doi = {10.1109/ACCESS.2024.3525188}, journal = {IEEE Access}, pages = {3521-3531}, shortjournal = {IEEE Access}, title = {Hierarchical reputation consensus model for VANET}, volume = {13}, year = {2025}, }
• (2025). Internet of things assisted plant disease detection and crop management using deep learning for sustainable agriculture. ACCESS, 13, 3512-3520. (www)

  @article{ , abstract = {The integration of Internet of Things (IoT) technology with deep learning (DL) algorithms has revolutionized plant disease detection and crop management and paved the way for sustainable agricultural practices. Real-time information on soil moisture, plant health, and environmental conditions can be collected by deploying a network of connected devices and sensors in agricultural fields. DL algorithms, specifically convolutional neural networks (CNN), analyze this massive dataset, facilitating timely and accurate recognition of plant diseases. This early detection allows farmers to implement targeted interventions, like adjustment to irrigation or precision application of pesticides, maximizing crop yield, and minimizing resource wastage. Therefore, this article develops an automated Plant Disease Detection and Crop Management using a spotted hyena optimizer with deep learning (APDDCM-SHODL) technique for Sustainable Agriculture. The APDDCM-SHODL approach aims to detect the existence of plant diseases and improve crop productivity in the IoT infrastructure. To achieve this, the APDDCM-SHODL method primarily employs the Vector Median Filter (VMF) technique. In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. Furthermore, the classification algorithm is implemented by using the recurrent spiking neural network (RSNN) model. A brief set of experiments has been made to determine the experimental validation of the APDDCM-SHODL model. The comprehensive results inferred that the APDDCM-SHODL method reaches remarkable performance over other existing methods with the highest accuracy of 98.60%.}, archive = {J_ACCESS}, author = {Eman A. Al-Shahari and Ghadah Aldehim and Mohammed Aljebreen and Jehad Saad Alqurni and Ahmed S. Salama and Sitelbanat Abdelbagi}, doi = {10.1109/ACCESS.2024.3397619}, journal = {IEEE Access}, pages = {3512-3520}, shortjournal = {IEEE Access}, title = {Internet of things assisted plant disease detection and crop management using deep learning for sustainable agriculture}, volume = {13}, year = {2025}, }
• (2025). Robust spike sorting using dual tree complex wavelet transform: Overcoming traditional limitations. ACCESS, 13, 3497-3511. (www)

  @article{ , abstract = {Accurate spike sorting is vital for understanding the neural network dynamics of the brain through extracellular neural recordings. Traditional feature extraction methods like the Haar wavelet and principal component analysis (PCA) face limitations in high-noise environments and when dealing with similar spike waveforms. This study proposes the use of the dual-tree complex wavelet transform (DT-CWT), which is an enhanced version of the traditional discrete wavelet transform, as a feature extractor in spike sorting. The proposed approach aims to leverage the approximate shift invariance and superior time-frequency localization capabilities of the DT-CWT in spike discrimination. A simulated dataset with varying noise levels and spike waveform similarities was used for validation. The performance of DT-CWT was compared with Haar wavelet and PCA using both original and modified superparamagnetic clustering approaches. Results demonstrated that the DT-CWT-based feature extraction achieved significantly lower average error rates across all subsets, especially in challenging conditions with high noise and similar waveforms. Moreover, the DT-CWT’s computational efficiency and suitability for real-time, on-chip implementations in next-generation wireless brain computer interface devices highlight its practical advantages. By providing robust and discriminative features under stringent resource constraints, the DT-CWT-based approach outperforms traditional methods, establishing itself as a preferred choice for scalable and efficient spike sorting in demanding scenarios.}, archive = {J_ACCESS}, author = {Gorkem Serbes}, doi = {10.1109/ACCESS.2024.3524763}, journal = {IEEE Access}, pages = {3497-3511}, shortjournal = {IEEE Access}, title = {Robust spike sorting using dual tree complex wavelet transform: Overcoming traditional limitations}, volume = {13}, year = {2025}, }
• (2025). Exploring the preprocessing of a time-series healthcare administrative dataset on deep learning to improve prediction. ACCESS, 13, 3485-3496. (www)

  @article{ , abstract = {Preprocessing methods are important in enhancing prediction performance for time-series administrative data. This study underscores the importance of preprocessing methods by comparing two data representation techniques, that can be used with sliding window techniques for time-series administrative healthcare data, for use with gated recurrent unit (GRU) networks. The first method uses a sequence event representation and the second employs a sequence matrix representation. The evaluation was conducted through a retrospective administrative healthcare data case study to predict multiple outcomes. The target outcomes were cases where a person’s First Healthcare Encounter indicated they were experiencing Homelessness (FHE-H) or involved Police (FHE-P), as recorded in the healthcare data. Results reveal that the GRU combined with sequence matrix representation and sliding window outperformed the sequence of events with the sliding window by over 20% in the area under the curve (AUC) and sensitivity for both outcomes. Given the data used in this analysis, sequence matrix representation was superior to sequence event representation while using GRU models. The results remained consistent across the evaluation in real-world clinical frameworks using two trigger methods for prediction time, such as the sliding window and clinical demand window structures.}, archive = {J_ACCESS}, author = {Faezehsadat Shahidi and M. Ethan Macdonald and Dallas Seitz and Rebecca Barry and Geoffrey Messier}, doi = {10.1109/ACCESS.2024.3520425}, journal = {IEEE Access}, pages = {3485-3496}, shortjournal = {IEEE Access}, title = {Exploring the preprocessing of a time-series healthcare administrative dataset on deep learning to improve prediction}, volume = {13}, year = {2025}, }
• (2025). Computerized adaptive testing framework based on excitation block and gumbel-softmax. ACCESS, 13, 3475-3484. (www)

  @article{ , abstract = {Computerized Adaptive Testing (CAT) is a personalized assessment method that adaptively selects the most suitable questions for students of different abilities based on their response data. Its primary goal is to effectively measure students’ proficiency in a specific subject in a shorter period. The selection algorithm is pivotal in CAT. The current algorithms inadequately consider the impact of knowledge concept weights in question and student potential factors (e.g., memory) on question selection. In addition, most algorithms primarily focus on accurately predicting students’ abilities, neglecting critical factors such as concept diversity and question exposure rate, which are essential for model effectiveness. Therefore, this paper introduces a new framework for CAT, GECAT. It proposes a selection algorithm based on an excitation block to learn the weights of each knowledge concept in the questions and analyze the impact of student potential factors on their answering performance, thereby selecting more suitable questions for students. Additionally, it views CAT as reinforcement learning, introducing Gumbel-Softmax to provide students with diverse, non-repetitive, and valuable test questions. The experimental results on three real-world datasets demonstrate that the proposed CAT framework improves ACC and AUC by 0.71% and 0.86%, respectively, while reducing question exposure rate and overlap rate by 1.33% and 1.59%, respectively.}, archive = {J_ACCESS}, author = {Chengsong Liu and Yan Wei}, doi = {10.1109/ACCESS.2024.3524009}, journal = {IEEE Access}, pages = {3475-3484}, shortjournal = {IEEE Access}, title = {Computerized adaptive testing framework based on excitation block and gumbel-softmax}, volume = {13}, year = {2025}, }
• (2025). Enhancing aquaculture net pen inspection: A benchmark study on detection and semantic segmentation. ACCESS, 13, 3453-3474. (www)

  @article{ , abstract = {The aquaculture industry is critical in global food production, with net pens being a vital component in fish farming operations. Regular inspection of these net pens is essential to ensure their structural integrity, prevent fish escapes, and monitor biofouling. However, manual inspections are time-consuming, labor-intensive, and subject to human error, driving the need for automated solutions. Detection and segmentation are computer vision techniques that offer a promising approach to automating these inspections by enabling precise identification and classification of various components within underwater images. This paper presents a novel dataset designed specifically for detection and semantic segmentation in the context of aquaculture net-pen inspections. The dataset comprises diverse high-resolution underwater images and annotated with multiple classes, including net holes, biofouling, and vegetation. We also provide a benchmark evaluation of state-of-the-art detection and semantic segmentation models using standard performance metrics. We evaluate their benefits both qualitatively and quantitatively in aquaculture inspection. As a result, we recommend using the YOLOV8 model for the detection and segmentation task, as it offers an optimal balance between performance and computational efficiency, making it well-suited for real-time inspection. The dataset and the detection pipeline provide promising opportunities for further research in aquaculture net-pen inspection tasks.}, archive = {J_ACCESS}, author = {Waseem Akram and Ahsan Baidar Bakht and Muhayy Ud Din and Lakmal Seneviratne and Irfan Hussain}, doi = {10.1109/ACCESS.2024.3524635}, journal = {IEEE Access}, pages = {3453-3474}, shortjournal = {IEEE Access}, title = {Enhancing aquaculture net pen inspection: A benchmark study on detection and semantic segmentation}, volume = {13}, year = {2025}, }
• (2025). Fail-safe logic design strategies within modern FPGA architectures. ACCESS, 13, 3434-3452. (www)

  @article{ , abstract = {Fail-safe computing refers to computing systems that revert to a non-operational safe state when a fault occurs. In this paper, we investigate a circuit level technique as mitigation for single event upsets (SEUs) and fault injection attacks on field programmable gate arrays (FPGAs), and analyze the effectiveness of the technique as a fail-safe monitor for an encryption algorithm. The propagation of fault effects through FPGA primitives including lookup tables (LUTs) and programmable interconnect points (PIPs) is assessed within an FPGA architecture created using an open source tool, and validated using fault injection experiments on an FPGA. The analysis reveals additional vulnerabilities exist within reconfigurable architectures over those in equivalent fail-safe application specific integrated circuit (ASIC), thus requiring a more elaborate network of redundant circuits and checking logic. The configuration memory bits (CMBs), which configure routing and designate logic functions within the LUTs of the FPGA, add complexity to fail-safe design strategies by introducing additional fault conditions and fault propagation paths. A resource-efficient fail-safe circuit design technique called DEsign for Fail-safe in reCONfigurable systems (DEFCON) is proposed. The benefits and limitations associated with DEFCON are described in the context of fault injection experiments carried out as simulations and in FPGA hardware.}, archive = {J_ACCESS}, author = {Priya A. Bhakta and Jim Plusquellic and Andrew Suchanek and Tom J. Mannos}, doi = {10.1109/ACCESS.2024.3520877}, journal = {IEEE Access}, pages = {3434-3452}, shortjournal = {IEEE Access}, title = {Fail-safe logic design strategies within modern FPGA architectures}, volume = {13}, year = {2025}, }
• (2025). Adaptive hierarchical multi-headed convolutional neural network with modified convolutional block attention for aerial forest fire detection. ACCESS, 13, 3412-3433. (www)

  @article{ , abstract = {Effective detection and classification of forest fire imagery are critical for timely and efficient wildfire management. Convolutional Neural Networks (CNNs) have demonstrated potential in this domain but encounter limitations when addressing varying scales, resolutions, and complex spatial dependencies inherent in wildfire datasets. Building upon our prior work on the Unmanned Aerial Vehicle-based Forest Fire Database (UAVs-FFDB) and the multi-headed CNN (MHCNN), this study introduces a novel architecture, namely, the Adaptive Hierarchical Multi-Headed Convolutional Neural Network with Modified Convolutional Block Attention Module (AHMHCNN-mCBAM). This enhanced framework addresses prior challenges by integrating adaptive pooling, concatenated convolutions for multi-scale feature extraction, and an improved attention mechanism incorporating shared fully connected layers, Glorot initialization, rectified linear units (ReLU), layer normalization, and attention-gating. AHMHCNN-mCBAM incorporates Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal context modeling to further refine classification accuracy. Experiments conducted on the UAVs-FFDB dataset achieved outstanding results, including 100% accuracy, a 100% Cohen’s kappa coefficient (cKappa), and compact model parameter sizes of 1.49 million (M), 0.25 M, and 0.12 M. On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. Compared to the baseline hierarchical MHCNN with CBAM (HMHCNN-CBAM), AHMHCNN-mCBAM demonstrated significant performance gains, including a 6.80% and 6.59% increase in accuracy, a 9.26% and 14.11% improvement in cKappa, and a 13.87% and 13.76% reduction in parameter size on the UAVs-FFDB and FLAME datasets, respectively. Additionally, AHMHCNN-mCBAM outperformed HMHCNN-CBAM in recall (25% improvement), precision (21.95%), F1-score (14.94%), and fire detection rate (FDR) reduction (25.01%), while achieving a 100% reduction in error warning rate (EWR). Leveraging Explainable Artificial Intelligence (XAI) techniques, the model provides interpretable insights into decision-making processes.}, archive = {J_ACCESS}, author = {Md. Najmul Mowla and Davood Asadi and Shamsul Masum and Khaled Rabie}, doi = {10.1109/ACCESS.2024.3524320}, journal = {IEEE Access}, pages = {3412-3433}, shortjournal = {IEEE Access}, title = {Adaptive hierarchical multi-headed convolutional neural network with modified convolutional block attention for aerial forest fire detection}, volume = {13}, year = {2025}, }
• (2025). Sequence control mechanism for the handover of distribution networks under disaster recovery. ACCESS, 13, 3403-3411. (www)

  @article{ , abstract = {In the distribution network under the background of disaster, the distribution terminal embodies the characteristics of disorder. Therefore, when the distribution terminal node accesses the backup network, there will be congestion, delay and other phenomena. In order to solve this problem, this paper proposes a distribution terminal access mechanism based on sequence control. Then, a family of control sequences is constructed based on the trace function in the finite field. The control sequences can realize the rapid access of distribution terminal node users and the safe transmission without interference. Finally, the program algorithm for the process of distribution terminal node accessing and exiting the backup network based upon the control sequences is given. The fast and stable information transmission of distribution terminal node can be realized by means of the sequence control mechanism.}, archive = {J_ACCESS}, author = {Cheng Long and Jian Zhang and Hua Zhang and Xing Liu and Yiwen Gao and Xueneng Su and Shilong Li}, doi = {10.1109/ACCESS.2024.3524680}, journal = {IEEE Access}, pages = {3403-3411}, shortjournal = {IEEE Access}, title = {Sequence control mechanism for the handover of distribution networks under disaster recovery}, volume = {13}, year = {2025}, }
• (2025). Research on high buck DC-DC converters based on resonant switched capacitor and LLC circuits. ACCESS, 13, 3390-3402. (www)

  @article{ , abstract = {As the power consumption of data center continues to increase, the topology of their power supply has become a significant area of concern. This paper investigates the performance of diverse circuit topologies in pre-step-down scenarios (48V to 8V-6V) based on the 48V bus mechanism proposed by Google. This paper employs the resonant switched capacitor (RSC) converter and LLC resonant converter as case studies, elucidating their operational principles and optimizing them for the pre-buckling scenario. Subsequently, test prototypes of the RSC converter and LLC converter, with a power level of 200W, were constructed, resulting in a peak efficiency of 95.5% and a power density of 544.15W/in3 for the RSC converter; and a peak efficiency of 96.6% and a power density of 93.8W/in3 for the LLC converter. Ultimately, this paper compares RSC, LLC and MASC in terms of working efficiency, power density and manufacturing cost, based on experimental data and the high-quality topologies proposed by relevant scholars in recent years. The aim is to provide a reference for selecting the pre-step-down topology for data center power supply.}, archive = {J_ACCESS}, author = {Zhuxuan Tian and Yuan Wang and Haijun Han and Qi Zhang and Chen Zhang and Yuqing Bu and Yueshi Guan}, doi = {10.1109/ACCESS.2024.3519886}, journal = {IEEE Access}, pages = {3390-3402}, shortjournal = {IEEE Access}, title = {Research on high buck DC-DC converters based on resonant switched capacitor and LLC circuits}, volume = {13}, year = {2025}, }
• (2025). Parametric digital twins for preserving historic buildings: A case study at löfstad castle in Östergötland, sweden. ACCESS, 13, 3371-3389. (www)

  @article{ , abstract = {This study showcases the digitalization of Löstad Castle in Sweden to contribute to preserving its heritage values. The castle and its collections are deteriorating due to an inappropriate indoor climate. To address this, thirteen cloud-connected sensor boxes, equipped with 84 sensors, were installed throughout the main building, from the basement to the attic, to continuously monitor various indoor environmental parameters. The collected extensive multi-parametric data form the basis for creating a parametric digital twin of the building. The digital twin and detailed data analytics offer a deeper understanding of indoor climate and guide the adoption of appropriate heating and ventilation strategies. The results revealed the need to address high humidity problems in the basement and on the ground floor, such as installing vapor barriers. Opportunities for adopting energy-efficient heating and ventilation strategies on the upper floors were also highlighted. The digitalization solution and findings are not only applicable to Löfstad Castle but also provide valuable guidance for the conservation of other historic buildings facing similar challenges.}, archive = {J_ACCESS}, author = {Zhongjun Ni and Jelrik Hupkes and Petra Eriksson and Gustaf Leijonhufvud and Magnus Karlsson and Shaofang Gong}, doi = {10.1109/ACCESS.2024.3525442}, journal = {IEEE Access}, pages = {3371-3389}, shortjournal = {IEEE Access}, title = {Parametric digital twins for preserving historic buildings: A case study at löfstad castle in Östergötland, sweden}, volume = {13}, year = {2025}, }
• (2025). R-CNN based vehicle object detection via segmentation capabilities in road scenes. ACCESS, 13, 3355-3370. (www)

  @article{ , abstract = {In the realm of intelligent transportation systems, vehicle detection and classification stand as pivotal tasks. An effective traffic monitoring system should be capable of detecting, counting, and categorizing moving vehicles. Vehicle classification is a crucial task that can offer insights into road users and help make decisions to reduce congestion, for instance. This paper delves into advanced methodologies for detecting and classifying vehicles on roadways, addressing the limitations of traditional techniques that are often computationally intensive and data acquisition-sensitive. We propose a novel approach that leverages state-of-the-art machine learning and deep learning algorithms to enhance accuracy and efficiency. The proposed model comprises five stages. Initially, all images undergo preprocessing to reduce noise and enhance brightness. In the second stage, the foreground elements are extracted using segmentation techniques. The YOLOv8 algorithm is then used to process these segmented images to identify the vehicles inside them. Next, in the feature extraction phase, the detected vehicles are analyzed using Maximally Stable Estimated Features (MSER), Geometric features and Binary Robust Invariant Scalable Keypoints (BRISK) features. We employ the Recurrent Convolutional Neural Network (R-CNN) classifier for classification. Experimental results from two datasets demonstrate superior performance, with the model achieving an accuracy of 0.94% on the BITVehicle dataset and 0.98% on the Vehicle-OpenImage dataset. Additionally, a comparative analysis was conducted, showcasing the model’s performance against the latest techniques in the field.}, archive = {J_ACCESS}, author = {Bisma Riaz Chughtai and Haifa F. Alhasson and Mohammed Alnusayri and Mohammed Alatiyyah and Hanan Aljuaid and Ahmad Jalal and Jeongmin Park}, doi = {10.1109/ACCESS.2024.3524453}, journal = {IEEE Access}, pages = {3355-3370}, shortjournal = {IEEE Access}, title = {R-CNN based vehicle object detection via segmentation capabilities in road scenes}, volume = {13}, year = {2025}, }
• (2025). Augmenting energy sustainability of static nodes using hybrid KGNN-AHP driven approach for IoT-based heterogeneous WSN. ACCESS, 13, 3320-3354. (www)

  @article{ , abstract = {As the nodes used in Internet of Things (IoT)- based wireless sensor network (WSN) are constrained by the limited source of energy, contemporary applications incorporate the heterogeneous energy model WSN. Although the node energies are heterogeneous in application, further study is required to improve the potential of heterogeneous WSN and not much is explored on Graph Neural Network (GNN) clustering and Routing methods, which impels this study on novel optimal clustering of nodes with efficient cluster head selection and routing for heterogeneous WSN. In the proposed work, a novel clustering model with K-nearest neighbour (KNN) and GNN clustering, followed by the Analytic Hierarchical Process (AHP) weight-based cluster head (CH) selection method to find the optimal and eligible CH with classified fitness functions. The chosen parameters for the fitness functions are the base station to node distance, lifetime of the nodes in the network, average number of neighbouring nodes in the cluster, peak power transmission by the node in the network, and the average lifetime of nodes in the cluster. Additionally, the proposed algorithm ensures the eligibility of the optimal relay nodes using the GNNSage based routing and steady-state of data transmission from source nodes to the base station. As the model is designed for static nodes deployment for monitoring environmental changes in realtime application. The simulation results exhibit the enhancement of network lifetime and data transmission by following the proposed algorithm for static network.}, archive = {J_ACCESS}, author = {R. Blessina Preethi and M. Saranya Nair}, doi = {10.1109/ACCESS.2024.3523401}, journal = {IEEE Access}, pages = {3320-3354}, shortjournal = {IEEE Access}, title = {Augmenting energy sustainability of static nodes using hybrid KGNN-AHP driven approach for IoT-based heterogeneous WSN}, volume = {13}, year = {2025}, }
• (2025). Continuous speech-based fatigue detection and transition state prediction for air traffic controllers. ACCESS, 13, 3298-3319. (www)

  @article{ , abstract = {Air traffic controllers (ATC) play a critical role in ensuring aviation safety, but their demanding workload can lead to fatigue, potentially compromising their performance. This paper presents a study that investigates speech features responsible for detecting ATC fatigue and proposes an approach to predict the timestamp at which an ATC transitions into a fatigue state from a continuous speech sample. The main contributions of this work are the creation of a continuous speech ATC dataset and the identification of a lightweight optimum feature set for fatigue classification from ATC speech. For the initial task, the classification of raw speech signals into fatigue and non-fatigue categories was performed using the top-10 best features selected from the openSMILE feature set. The evaluation was carried out using various learning algorithms such as XGBoost, Adaboost, Random Forest, HistogramGB, and 1D-CNN. The ensemble algorithms demonstrated the best performance, achieving a maximum accuracy of 100% on the XGBoost test set. Further, interpretability was analyzed using the SHAP tool, which identified the prominent features for the task. The second task involved creating a continuous speech dataset comprising approximately 18,900 samples from the ATC corpus, with an average duration of 63-65 seconds per sample. The continuous speech samples were prepared by the randomized concatenation of fatigue and non-fatigue chunks, each with a duration of approximately 15 seconds. Automated sequence labeling was performed on uniformly segmented continuous speech samples. MFCC and statistical features were extracted from the labelled continuous speech and input into various Recurrent Neural Networks, such as bi-LSTM and Bi-GRU, for fatigue state prediction tasks. A combination of these features using bi-LSTM modeling achieved a maximum precision, recall, F-score, and average accuracy of 99% each. Finally, sample-wise timestamp prediction was performed using the labels: fatigue, non-fatigue, and ambiguous (transition). To the best of the authors’ knowledge, this research is the first of its kind to address continuous speech-based fatigue state prediction for ATCs. All tasks were conducted using the Civil Aviation Administration of China ATC corpus.}, archive = {J_ACCESS}, author = {Susmitha Vekkot and Surya Teja Chavali and Charan Tej Kandavalli and Rama Sai Abhishek Podila and Deepa Gupta and Mohammed Zakariah and Yousef Ajami Alotaibi}, doi = {10.1109/ACCESS.2024.3524452}, journal = {IEEE Access}, pages = {3298-3319}, shortjournal = {IEEE Access}, title = {Continuous speech-based fatigue detection and transition state prediction for air traffic controllers}, volume = {13}, year = {2025}, }
• (2025). Downsizing of electric motors by utilizing differential gear for traction and hydraulic pump of heavy mobile machine. ACCESS, 13, 3284-3297. (www)

  @article{ , abstract = {Heavy mobile machines should be productive and energy efficient. Electrification drives and enables the design of new solutions that can outperform older technologies. While components of heavy mobile machines should be as small, inexpensive, and reliable as possible, all components should also be utilized extensively to maximize space usage. This is achieved through proper sizing and multifunctional use of components. In this paper, a novel utilization of a differential gear mechanism is proposed to couple two electric motors and a hydraulic pump. The analysis focuses on the simultaneous operation of a traction drive and working hydraulics in a wheel loader application. Simulations utilizing a recorded driving cycle are used to analyze the torque distribution of the electric motors. The performance of the coupling system is compared to a reference system where the electric motors are separated and working independently. The results indicate that the differential dual-motor system can easily reduce the torque demand of the traction motor by 20 percent in an uphill section, and also significantly reduce the torque demand during simultaneous operation compared to the reference system. However, the energy efficiency of the proposed system decreased by 4 percent in simultaneous operation compared to the reference system.}, archive = {J_ACCESS}, author = {Topias Tyni and Jari Vepsäläinen}, doi = {10.1109/ACCESS.2024.3521500}, journal = {IEEE Access}, pages = {3284-3297}, shortjournal = {IEEE Access}, title = {Downsizing of electric motors by utilizing differential gear for traction and hydraulic pump of heavy mobile machine}, volume = {13}, year = {2025}, }
• (2025). Real-time adaptive tractor ride comfort adjustment system based on machine learning method. ACCESS, 13, 3274-3283. (www)

  @article{ , abstract = {Tractors are among the most widely used machinery in agriculture. However, low-frequency vibrations during tractor operations pose significant health risks to drivers, such as musculoskeletal disorders, and negatively impact ride comfort. Current approaches rely on offline comfort prediction models, which lack real-time feedback, making them unsuitable for practical field applications. To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. Our low-cost IoT-enabled solution is compatible with existing tractors, requiring no expensive intelligent upgrades. Using the XGBoost model for ride comfort prediction, we achieved superior performance (R $^{2} =0.96$ , RMSE =0.015) compared to other ML models. Additionally, the Particle Swarm Optimization (PSO) algorithm is employed to recommend optimal operational parameters, reducing the ride comfort value (OVV) by 6.67% in real-time experiments. This study highlights a scalable, data-driven approach for improving tractor comfort and offers a reference for intelligent control strategies in Agriculture 4.0.}, archive = {J_ACCESS}, author = {Hu Tian and Anping Ji}, doi = {10.1109/ACCESS.2024.3522959}, journal = {IEEE Access}, pages = {3274-3283}, shortjournal = {IEEE Access}, title = {Real-time adaptive tractor ride comfort adjustment system based on machine learning method}, volume = {13}, year = {2025}, }
• (2025). A multi-dimensional resource allocation scheme: In beam hopping technique enhanced MF-TDMA satellite systems. ACCESS, 13, 3261-3273. (www)

  @article{ , abstract = {Against the backdrop of the widespread deployment of 5G systems and humanity’s entry into a new era of sixth-generation mobile communication, countries are now turning their attention to exploring uncharted territories through satellite communication. Multi-beam satellite communication enables the provision of services to ground user terminals via multiple spot beams, thereby increasing the power spectral density and achieving ground terminal miniaturization. While both MF-TDMA technology and beam hopping technology can significantly enhance the flexibility of multi-beam satellite communication systems, there are certain limitations associated with using either technology in isolation. In this study, we focus on the downlink transmission scenario for an MF-TDMA satellite system enhanced by beam hopping technology. We propose a joint optimization approach that encompasses subcarrier power allocation, subcarrier bandwidth allocation, beam hopping pattern selection, and subcarrier-user assignment to minimize the matching error between the satellite capacity and traffic demand of users with different priorities. To address this problem effectively, we present a two-stage optimization strategy. Simulation results demonstrate that our proposed two-stage resource allocation scheme outperforms other benchmark algorithms in minimizing the total priority-weighted matching error by at least 14% across various system settings.}, archive = {J_ACCESS}, author = {Yifeng Jin and Zhong Zheng and Hanyu Zheng and Minglong Pu and Junjie Zeng and Zihe Gao}, doi = {10.1109/ACCESS.2024.3524396}, journal = {IEEE Access}, pages = {3261-3273}, shortjournal = {IEEE Access}, title = {A multi-dimensional resource allocation scheme: In beam hopping technique enhanced MF-TDMA satellite systems}, volume = {13}, year = {2025}, }
• (2025). Research on BHAST test method for plastic encapsulated microcircuit. ACCESS, 13, 3251-3260. (www)

  @article{ , abstract = {Compared to metal packaging and ceramic packaging, plastic encapsulated microcircuits (PEMs) have the advantages of light weight and low cost. However, due to their non-airtightness, they are prone to delamination, corrosion, and other failures in humid and hot environments. In engineering practice, the unbiased highly accelerated stress test (UHAST) without bias voltage is commonly used to evaluate the moisture and heat resistance of PEMs, but it cannot fully excite the potential defects of PEMs. Therefore, it is necessary to consider the biased highly accelerated stress test (BHAST) with bias voltage applied. In this paper, the BHAST test method for PEM was studied, and four different bias voltage application methods were set up. Four consecutive HAST tests were conducted, and failure analysis was conducted on the samples. It can be confirmed that the BHAST test mainly stimulated the corrosion failure of the aluminum solder pads in the bonding area of the PEMs. Comparing the failure situation of the samples, it was found that under the condition of voltage application method 4, the samples failed the fastest and the number of corroded bonding areas appeared the most. It can be concluded that a reasonable bias voltage application method can be summarized as follows: after supplying power to the power pins, apply bias voltage alternately to the IO pins as much as possible.}, archive = {J_ACCESS}, author = {Yuege Zhou and Bo Wan and Qingsheng Liu and Jingyang Zhong and Shupeng Li and Tengfei Ma and Wei Guo}, doi = {10.1109/ACCESS.2024.3525123}, journal = {IEEE Access}, pages = {3251-3260}, shortjournal = {IEEE Access}, title = {Research on BHAST test method for plastic encapsulated microcircuit}, volume = {13}, year = {2025}, }
• (2025). Bio-inspired polarization compass for solar azimuth prediction under clear and cloudy sky conditions. ACCESS, 13, 3242-3250. (www)

  @article{ , abstract = {Sunlight becomes partially polarized due to Rayleigh scattering while passing through the atmosphere. Many insects such as ants and beetles utilize the polarization information of skylight for navigation. Unlike magnetic compass and GPS, this scheme is free from interference and unlike inertial schemes, its error does not accumulate with time. In recent years this navigation scheme has received a lot of attention for navigation of aerial and terrestrial vehicles. Development of a polarization compass that can provide accurate heading information in different weather conditions will benefit a wide range of applications. Here we report the application of linear regression for implementation of a polarization compass. The model is trained with real sky images and then used to predict the solar azimuth in both clear and cloudy sky conditions. The root mean square errors for both conditions were less than 1°. We also compared the performance of the proposed scheme with that of Hough transform and support vector machine, which have been successfully utilized for the same application in the past. Linear regression outperformed Hough transform for all sky conditions considered, and its performance was comparable with support vector machine. However, unlike the other two methods considered, the accuracy of linear regression will increase significantly when trained with a large set of sky images. Therefore, with sufficient training, linear regression can be a promising option for implementation of polarization compass.}, archive = {J_ACCESS}, author = {Devyansh Agarwal and Ben Potter and Jawad Yaseen Siddiqui and Yahia M. M. Antar and Muhammad Zulfiker Alam}, doi = {10.1109/ACCESS.2024.3523249}, journal = {IEEE Access}, pages = {3242-3250}, shortjournal = {IEEE Access}, title = {Bio-inspired polarization compass for solar azimuth prediction under clear and cloudy sky conditions}, volume = {13}, year = {2025}, }
• (2025). On the optimum detection of MIMO-SVD signals with strong nonlinear distortion effects at the transmitter. ACCESS, 13, 3230-3241. (www)

  @article{ , abstract = {Multiple-Input Multiple-Output (MIMO) architectures are now widely adopted in wireless systems, providing substantial capacity benefits by harnessing spatial diversity and spatial multiplexing. Nonetheless, the large Peak-to-Average Power Ratio (PAPR) associated with common pre-processing techniques, like Singular Value Decomposition (SVD), increases the system’s susceptibility to nonlinear distortion. Conventional receiver designs that mitigate this distortion often neglect the fact that it has useful information on the transmitted data. Maximum Likelihood (ML) detection offers the capability to take advantage of the nonlinear distortion, but its inherent complexity is prohibitively high. This paper introduces a new MIMO receiver design aimed at exploiting the diversity introduced by the transmitter nonlinearities. It also provides an approximate bound on the achievable ML Bit Error Rate (BER) performance. Our results indicate that the proposed receiver can have a performance close to the ML receiver with just a few iterations.}, archive = {J_ACCESS}, author = {João Gonçalves and M. Teresa Nogueira and Daniel Dinis and João Guerreiro and Rui Dinis}, doi = {10.1109/ACCESS.2024.3521513}, journal = {IEEE Access}, pages = {3230-3241}, shortjournal = {IEEE Access}, title = {On the optimum detection of MIMO-SVD signals with strong nonlinear distortion effects at the transmitter}, volume = {13}, year = {2025}, }
• (2025). Cypress copilot: Development of an AI assistant for boosting productivity and transforming web application testing. ACCESS, 13, 3215-3229. (www)

  @article{ , abstract = {In today’s fast-paced software development environment, Agile methodologies demand rapid delivery and continuous improvement, making automated testing essential for maintaining quality and accelerating feedback loops. Our study addresses the challenges of developing and maintaining automation code for web-based application testing. In this paper, we propose a novel approach that leverages large language models (LLMs) and a novel prompt technique, few-shot chain, to automate code generation for web application testing. We chose the Behavior-Driven Development (BDD) methodology owing to its advantages and selected the Cypress tool for automating web application testing, as it is one of the most popular and rapidly growing frameworks in this domain. We comprehensively evaluated various OpenAI models, including GPT-4-Turbo, GPT-4o, and GitHub Copilot, using zero-shot and few-shot chain prompt techniques. Furthermore, we extensively validated with a vast set of test cases to identify the optimal approach. Our results indicate that the Cypress automation code generated by GPT-4o using a few-shot chained prompt approach excels in generating complete code for each test case, with fewer empty methods and improved syntactical accuracy and maintainability. Based on these findings, we developed a novel open-source Visual Studio Code (IDE) extension, “Cypress Copilot” utilizing GPT-4o and a few-shot chain prompt technique, which has shown promising results. Finally, we validate the Cypress Copilot tool by generating automation code for end-to-end web tests, demonstrating its effectiveness in testing various web applications and its ability to streamline development processes. More importantly, we are releasing this tool to the open-source community, as it has the potential to be a promising partner in enhancing productivity in web application automation testing.}, archive = {J_ACCESS}, author = {Suresh Babu Nettur and Shanthi Karpurapu and Unnati Nettur and Likhit Sagar Gajja}, doi = {10.1109/ACCESS.2024.3521407}, journal = {IEEE Access}, pages = {3215-3229}, shortjournal = {IEEE Access}, title = {Cypress copilot: Development of an AI assistant for boosting productivity and transforming web application testing}, volume = {13}, year = {2025}, }
• (2025). Safely imitating predictive control policies for real-time human-aware manipulator motion planning: A dataset aggregation approach. ACCESS, 13, 3204-3214. (www)

  @article{ , abstract = {This paper proposes a dataset-aggregation approach for imitating a nonlinear model predictive control law via deep neural networks, to safely allow a robot manipulator to share its workspace with a human operator. As the robot approaches the human, its speed is gradually reduced using the “speed and separation monitoring” framework. Specific time-varying upper bounds are explicitly imposed on the control input generated by the deep neural network through a “safety filter” based on real-time numerical optimization. The proposed method is experimentally tested on a UR5 manipulator, comparing the performance of different neural network structures and types of training. As a result, it is shown that the dataset-aggregation approach provides better performance with respect to a “naive” approach to training, and that the presence of the safety filter is indeed needed to avoid the violation of the speed-and-separation-monitoring constraints.}, archive = {J_ACCESS}, author = {Aigerim Nurbayeva and Matteo Rubagotti}, doi = {10.1109/ACCESS.2024.3524946}, journal = {IEEE Access}, pages = {3204-3214}, shortjournal = {IEEE Access}, title = {Safely imitating predictive control policies for real-time human-aware manipulator motion planning: A dataset aggregation approach}, volume = {13}, year = {2025}, }
• (2025). Advanced GPU techniques for dynamic remeshing and self-collision handling in real-time cloth tearing. ACCESS, 13, 3188-3203. (www)

  @article{ , abstract = {In this paper, we propose a GPU-based framework to accelerate dynamic remeshing and self-collision handling required for tearing thin-shell materials such as cloth: 1) Dynamic remeshing involves updating the shape and connectivity information at the element/node level, making parallel computing challenging and computationally costly. In this paper, we propose a new data structure that enables GPU-friendly calculations. Additionally, 2) to efficiently handle the computationally intensive self-collision handling in tearing cloth, we propose a GPU-based BVH (Bounding Volume Hierarchy) tree construction, update, and traversal method, along with an optimized R-Triangle (Representative-Triangle) technique. We also present a method to optimize kernels based on a complete binary tree in arbitrary triangular meshes, improving performance. Thin-shell materials are utilized in various fields including simulation/animation based on physics, games, surgical simulation, and virtual reality. Tearing cloth requires dynamically updating its geometry and connectivity, which is complex and computationally intensive. This process needs to be performed quickly, especially when dealing with interactive content. Most existing approaches perform remeshing through low-resolution simulations to maintain real-time performance or use pre-divided patterns, which cannot be considered dynamic remeshing and result in low-quality torn surface patterns. In this study, we propose a GPU-optimized dynamic remeshing algorithm that enables real-time processing of high-resolution cloth tearing. Additionally, we introduce a method to accelerate the computationally intensive self-collision handling in tearing cloth simulations using GPUs. To achieve this, we propose a GPU-based complete binary tree method to efficiently construct, update, and traverse a BVH (Bounding Volume Hierarchy) tree and optimize the R-Triangle (Representative-Triangle) technique on triangular meshes using GPUs to minimize primitive collision checks. The proposed method performs dynamic remeshing rather than using predefined split mesh structures, making it suitable for interactive physics-based modeling in applications such as virtual reality surgery simulation, real-time gaming, and virtual environments.}, archive = {J_ACCESS}, author = {Jong-Hyun Kim and Jung Lee}, doi = {10.1109/ACCESS.2024.3524318}, journal = {IEEE Access}, pages = {3188-3203}, shortjournal = {IEEE Access}, title = {Advanced GPU techniques for dynamic remeshing and self-collision handling in real-time cloth tearing}, volume = {13}, year = {2025}, }
• (2025). PSTR: A test case reuse method based on path similarity. ACCESS, 13, 3175-3187. (www)

  @article{ , abstract = {Software testing plays a critical role throughout the software development lifecycle. In modern development practices, frequent and incremental updates to code are common, and each update typically necessitates the generation of new test cases. While the generation of test cases can be automated, the creation and adjustment of test oracles (expected outputs or behaviors) still require significant manual effort. This process is time-consuming and labor-intensive, particularly when code changes are minor, making repeated oracle creation inefficient. Meanwhile, test cases and their associated oracles from previous versions, having been refined and validated through multiple iterations, are highly reliable and valuable. However, due to the changes in execution paths introduced by code updates, it is unclear which old test cases remain applicable to the new version, leaving these valuable resources underutilized. Consequently, the ability to effectively identify and reuse applicable test cases and their oracles from previous versions is of paramount importance. Currently, research on test case reuse in unit testing is relatively sparse. Existing approaches often focus on software requirements and pay limited attention to code-level changes. Even methods that consider code changes fail to ensure precision in selecting test cases or maintain high coverage in the reused test set. To address these challenges, this paper proposes a novel approach, Path Similarity-based Test case Reuse (PSTR), tailored for the characteristics of unit testing. PSTR significantly enhances testing efficiency and reduces costs by accurately identifying reusable test cases through path similarity analysis. The proposed PSTR method consists of three core modules: static analysis, dynamic analysis, and reuse. First, static analysis is performed on both the old and new code versions to generate their respective sets of static paths. Next, the test cases from the old version are executed on its code, establishing a mapping between each test case and its covered execution paths. Finally, the path similarity algorithm compares paths between the old and new versions, allowing test cases associated with the most similar paths to be reused for the new version. For cases where direct reuse is not possible, the method provides clear guidance to assist testers in completing subsequent tasks more efficiently. For evaluation, a custom dataset derived from LeetCode problem solutions was used, and the PSTR method was compared with the classical ATR (All Test case Reuse) method. The results demonstrated the superiority of PSTR, achieving a precision rate of 95.64%, which is 14.87% higher than ATR. Additionally, while PSTR reused an average of 77.81% of test cases, it maintained a path coverage rate comparable to ATR, which reused 100% of test cases. Metrics such as F1-score, recall, and misuse rate further highlighted PSTR’s advantages in accurate and effective test case reuse. Specifically, PSTR achieved comparable coverage with fewer reused test cases while significantly reducing the misuse rate. In summary, as an innovative test case reuse method, PSTR has demonstrated remarkable effectiveness and practicality. It reduces resource consumption, improves testing efficiency, and lowers testing costs while maintaining high coverage standards. The findings underscore PSTR’s potential to make software testing more efficient and cost-effective.}, archive = {J_ACCESS}, author = {Xinyue Xu and Sinong Chen and Zhonghao Guo and Xiangxian Chen}, doi = {10.1109/ACCESS.2024.3524596}, journal = {IEEE Access}, pages = {3175-3187}, shortjournal = {IEEE Access}, title = {PSTR: A test case reuse method based on path similarity}, volume = {13}, year = {2025}, }
• (2025). Finite-time adaptive control of nonstrict-feedback nonlinear systems with time-varying full state constraints. ACCESS, 13, 3165-3174. (www)

  @article{ , abstract = {This paper proposes a finite-time adaptive tracking fuzzy control approach for nonlinear nonstrict-feedback systems with unknown disturbances and time-varying full state constraints. The fuzzy logic system is used to approximate the unknown nonlinear function, and the tan-type barrier technique is used to keep all states inside a predetermined region. The adaptive fuzzy controller is formulated utilizing the barrier Lyapunov functions (BLFs) and the backstepping approach. This guarantees that all the signals in the closed loop are bounded, and the system reaches stability in a finite amount of time while complying with the time-varying full state constraints, and the tracking error varies within the predefined compact range around zero. Finally, the efficacy of the proposed method is exemplified via a simulation example.}, archive = {J_ACCESS}, author = {Jiahuan Lv and Rui Zhang and Junmin Li and Jianmin Jiao and Yuan Cheng and Xuejie Yang}, doi = {10.1109/ACCESS.2024.3517425}, journal = {IEEE Access}, pages = {3165-3174}, shortjournal = {IEEE Access}, title = {Finite-time adaptive control of nonstrict-feedback nonlinear systems with time-varying full state constraints}, volume = {13}, year = {2025}, }
• (2025). Adaptive balancing of series-connected SiC MOSFETs based on active clamping and comparison circuitry. ACCESS, 13, 3154-3164. (www)

  @article{ , abstract = {This paper proposes a control method for balancing the voltages of series-connected SiC MOSFETs based on active clamping and comparison circuitry. Each SiC MOSFET is paired with an active clamping module (ACM), which comprises an auxiliary switch and a capacitor. Equal voltage distribution across the SiC MOSFETs is achieved by maintaining balance among the voltages of the ACM capacitors. This voltage balance is achieved through a local comparison circuit with the threshold set at the blocking voltage of the SiC MOSFET in its OFF state. When the ACM capacitor voltage exceeds this threshold, the auxiliary switch is triggered on during the dead time, and the excess energy of this capacitor is recycled back to the DC bus. The proposed adaptive balancing process maintains all ACM capacitor voltages at the threshold without requiring system-level coordination or communication between ACMs, thus simplifying the control circuits. The method does not affect the SiC MOSFETs’ switching speed and enables zero-voltage switching on for the auxiliary switches. Comprehensive operating principles and key parameter designs are provided for both DC transformer (DCT) and Voltage Source Converter (VSC) applications. A high-voltage power module, incorporating six SiC MOSFETs in series, was constructed to demonstrate the effectiveness of the proposed approach. Tests on a DCT and a VSC incorporating this high-power module validated the efficacy of the proposed voltage balancing method.}, archive = {J_ACCESS}, author = {Shuai Shao and Wentao Cui and Yibo Jiang and Xingyu Pei and Junming Zhang}, doi = {10.1109/ACCESS.2024.3523357}, journal = {IEEE Access}, pages = {3154-3164}, shortjournal = {IEEE Access}, title = {Adaptive balancing of series-connected SiC MOSFETs based on active clamping and comparison circuitry}, volume = {13}, year = {2025}, }
• (2025). Blending static and dynamic analysis for web application vulnerability detection: Methodology and case study. ACCESS, 13, 3139-3153. (www)

  @article{ , abstract = {Static Analysis (SA) and Dynamic Analysis (DA) are complementary techniques for searching web application vulnerabilities. Typically, SA detects more vulnerabilities but reports a higher number of false positives, whereas DA finds less but with better precision. In this paper, we blend SA and DA to simultaneously improve the detection and decrease the false alarms. Our approach starts with SA to identify an initial set of potential vulnerabilities. Then, the target application is executed to obtain specific runtime information. These data are used to automatically configure the DA, improving its ability to confirm if the vulnerabilities reported by the SA are indeed exploitable. We evaluated the proposed approach using 49 WordPress plugins with more than 450 SQLi vulnerabilities. Our approach was able to confirm either as a vulnerability or a false alarm 76.7% of the results reported by the SA, decreasing tremendously the usual need for manual work, which is a huge improvement for security practitioners.}, archive = {J_ACCESS}, author = {Paulo Nunes and José Fonseca and Marco Vieira}, doi = {10.1109/ACCESS.2024.3522094}, journal = {IEEE Access}, pages = {3139-3153}, shortjournal = {IEEE Access}, title = {Blending static and dynamic analysis for web application vulnerability detection: Methodology and case study}, volume = {13}, year = {2025}, }
• (2025). Average achievable rate maximization for RIS-assisted UAV communication systems. ACCESS, 13, 3130-3138. (www)

  @article{ , abstract = {Unmanned aerial vehicle (UAV) communication can effectively improve the quality of signal transmission in wireless communication due to its advantages of easy installation, high flexibility, and easy access to line-of-sight (LoS) links. However, in a complex communication environment, it is not easy to obtain the LoS link between the UAV and a ground user. As a promising technology, the reconfigurable intelligent surface (RIS) can provide a virtual LoS link to boost the communication quality when the LoS link is blocked. In the paper, we consider a RIS-assisted UAV communication systems which consist of a UAV and a RIS as well as a ground user. In order to maximize the average achievable rate of the ground user, this paper studies a design of joint RIS phase shift, UAV time slot power allocation and UAV trajectory in the considered systems. But the constructed joint optimization problem is a non-convex function, it is difficult to solve directly. We propose to divide the original problem into three sub-problems: power allocation optimization, phase shift optimization and UAV trajectory optimization, and propose an alternating iterative optimization algorithm to obtain a suboptimal solution. Compared with the other benchmark schemes, the algorithm we consider can significantly enhance the average achievable rate of ground user.}, archive = {J_ACCESS}, author = {Yuandong Liu and Jianbo Ji and Juan Yang}, doi = {10.1109/ACCESS.2024.3522988}, journal = {IEEE Access}, pages = {3130-3138}, shortjournal = {IEEE Access}, title = {Average achievable rate maximization for RIS-assisted UAV communication systems}, volume = {13}, year = {2025}, }
• (2025). Exploring aggregated wav2vec 2.0 features and dual-stream TDNN for efficient spoken dialect identification. ACCESS, 13, 3115-3129. (www)

  @article{ , abstract = {Dialect identification (DID) is a challenging task due to high inter-class similarities between the dialects. Efficiency of a DID system depends on how well the input features encode the DID-specific contents in the speech that is spread across the utterance. In this paper, we explore different representations for efficient DID, which are motivated by the recent advancements in related areas. Firstly, we propose to learn a representation by aggregating the layers-wise features from wav2vec 2.0. We propose multiple approaches to combine the layer-wise features. Since different layers of wav2vec 2.0 are known to capture different acoustic-linguistic characteristics, such aggregated representation encode DID-specific contents in a better way. Followed by this, we explore the usage of recently proposed global-aware filter (GAF) layer based dual-stream time delay neural network (DS-TDNN) for DID. The GAF layer employs a set of learnable transform-domain filters between a 1D discrete Fourier transform and its inverse transform to capture global context along with dynamic filtering and sparse regularization. DS-TDNN has two separate input branches, one for capturing global context and the other for local context which are combined in a parallel pattern. Results obtained on dialects of Kannada, Tamil, Konkani and Marathi, four low-resource languages of India show that aggregated wav2vec 2.0 features perform better compared to DS-TDNN approach.}, archive = {J_ACCESS}, author = {Ananya Angra and H. Muralikrishna and Dileep Aroor Dinesh and Veena Thenkanidiyoor}, doi = {10.1109/ACCESS.2024.3523951}, journal = {IEEE Access}, pages = {3115-3129}, shortjournal = {IEEE Access}, title = {Exploring aggregated wav2vec 2.0 features and dual-stream TDNN for efficient spoken dialect identification}, volume = {13}, year = {2025}, }
• (2025). Mapping the landscape of AI-driven human resource management: A social network analysis of research collaboration. ACCESS, 13, 3090-3114. (www)

  @article{ , abstract = {As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies. These findings provide valuable insights for researchers exploring collaboration opportunities, practitioners implementing AI solutions, and policymakers developing strategic frameworks for AI adoption in HRM. This research contributes to understanding the evolving landscape of AI-HRM research and offers practical guidelines for leveraging AI in HR practices.}, archive = {J_ACCESS}, author = {Mehrdad Maghsoudi and Motahareh Kamrani Shahri and Mehrdad Agha Mohammad Ali Kermani and Rahim Khanizad}, doi = {10.1109/ACCESS.2024.3523437}, journal = {IEEE Access}, pages = {3090-3114}, shortjournal = {IEEE Access}, title = {Mapping the landscape of AI-driven human resource management: A social network analysis of research collaboration}, volume = {13}, year = {2025}, }
• (2025). Reliability test on vienna rectifier for wide bandgap devices in EV charging systems. ACCESS, 13, 3072-3089. (www)

  @article{ , abstract = {This study examines the reliability of several electronic components in a Vienna Rectifier configuration, which is a critical topology for power conversion systems. Component selection has become increasingly important over the past few years since the power electronics of today demand more efficiency, power density, and operational reliability. Extreme reliability testing such as temperature cycling, electrical overload, and long duration of high-frequency operation was a part of the study. GaN MOSFETs had an edge over Si and SiC MOSFETs in several aspects, such as decreased conduction and switching losses, better thermal management, and more consistent performance with time. While GaN MOSFETs performed better in general and especially at high frequencies and temperatures, SiC MOSFETs showed some improvements over the conventional Si devices. Capacitors, diodes, MOSFETs, and inductors are put to test for reliability under different stress conditions. The combination of diodes and GaN MOSFETs showed a synergistic effect in improving system dependability and reducing temperature-induced degradation. This is a significant result. The combined effects allowed active and passive parts to last longer and function more reliably. These results open insights into selecting components for systems in the automotive and aerospace industries, which mostly rely on reliability.}, archive = {J_ACCESS}, author = {Bharaneedharan Balasundaram and P. Suresh and Parvathy Rajendran and It Ee Lee and C. Ahamed Saleel}, doi = {10.1109/ACCESS.2024.3523528}, journal = {IEEE Access}, pages = {3072-3089}, shortjournal = {IEEE Access}, title = {Reliability test on vienna rectifier for wide bandgap devices in EV charging systems}, volume = {13}, year = {2025}, }
• (2025). Advancements and challenges in active magnetic bearings: A comprehensive review of performance, control, and future prospects. ACCESS, 13, 3051-3071. (www)

  @article{ , abstract = {This paper provides an in-depth assessment of Active Magnetic Bearings (AMBs), which are electromagnetic bearings powered by power electronic components for high-speed equipment. The paper highlights the advantages of AMBs as frictionless bearings that utilize magnetic force to support the rotor, resulting in reduced wear, lower losses, and extended lifespan compared to classical ball bearings. AMBs are particularly well-suited for high-speed applications and demonstrate compatibility with both clean environments, such as renewable energy sectors, and challenging conditions like extreme pressures, temperatures, and harsh environments. However, controlling AMBs presents difficulties due to their nonlinear and inherently unstable nature. The paper focuses on examining the benefits and drawbacks of AMBs, including fault analyses. It further emphasizes recent advancements in power electronics supervision, including quick-switching power components, power conversion devices, and advanced control techniques, which have significantly expanded the potential applications of AMBs across various industries. Additionally, the paper explores the electromagnetic setups, electrical converters, sensors, and optimization techniques employed in AMBs, providing valuable insights into their construction, fault-finding methods, control strategies, and ongoing and future research trends.}, archive = {J_ACCESS}, author = {Debarghya Dutta and Pabitra Kumar Biswas and Sukanta Debnath and Furkan Ahmad}, doi = {10.1109/ACCESS.2024.3523205}, journal = {IEEE Access}, pages = {3051-3071}, shortjournal = {IEEE Access}, title = {Advancements and challenges in active magnetic bearings: A comprehensive review of performance, control, and future prospects}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based methods for lithology classification and identification in remote sensing images. ACCESS, 13, 3038-3050. (www)

  @article{ , abstract = {This study presents a deep learning model that integrates Vision Transformers (ViT) with Fourier spectral filtering for remote sensing lithology classification. The model automates the process of identifying and classifying various rock types in remote sensing images, addressing a multi-class classification challenge. It utilizes ViT for feature extraction, enhanced by pretrained weights for improved efficiency and accuracy in recognizing geographical features. Fourier spectral filtering further augments the model by leveraging frequency domain information for accurate classification. The model preprocesses images, extracts spatial features, applies spectral filtering, and employs a classification head to predict rock types. Optimization of parameters through backpropagation and gradient descent methods, coupled with regularization strategies, aims to prevent overfitting and ensure generalizability. This approach combines deep learning’s capability for feature extraction with the analytical power of signal processing, offering a significant advancement for automatic rock type classification in remote sensing.}, archive = {J_ACCESS}, author = {Zhijun Zhang and Ming Wang and Yueji Qi and Xiaoqin Su and Di Kong}, doi = {10.1109/ACCESS.2024.3469228}, journal = {IEEE Access}, pages = {3038-3050}, shortjournal = {IEEE Access}, title = {Deep learning-based methods for lithology classification and identification in remote sensing images}, volume = {13}, year = {2025}, }
• (2025). RED-net: A neural network for 3D thyroid segmentation in chest CT using residual and dilated convolutions for measuring thyroid volume. ACCESS, 13, 3026-3037. (www)

  @article{ , abstract = {Unlike the lungs or the heart, the thyroid gland is not a primary target in chest computed tomography (CT) scans and is relatively small; hence, it is difficult for radiologists to always clinically delineate it in chest CT to incidentally detect a goiter. We designed a residual and dilated convolution neural network (RED-Net), which automatically measures thyroid volume by segmenting the thyroid gland in contrast-enhanced chest CT scans. Its fundamental structure comprises a residual downsampling and upsampling pathway, complemented by a parallel dilated convolution module. This combination allows the model to extract features at multiple scales and capture contextual information to effectively segment even tiny thyroid glands in the complex anatomical structures observed in chest CT scans. Additionally, we constructed training and validation sets comprising CT scans of 1,150 adults (aged $\ge 19$ years) who underwent chest CT scans at the National Cancer Center and included data of those without a history of thyroid nodules, C73 diagnosis, or thyroid surgery before scanning procedure. We evaluated the performance of our method on a test dataset (600 patients) comprising chest CT scans of individuals collected at Chungbuk National University Hospital using the same criteria. The results showed that it achieved state-of-the-art performance with a Dice similarity coefficient of 0.8901.}, archive = {J_ACCESS}, author = {Min-Ji Kim and Jin-A Kim and Naae Kim and Yul Hwangbo and Hyun Jeong Jeon and Dong-Hwa Lee and Ji Eun Oh}, doi = {10.1109/ACCESS.2024.3523766}, journal = {IEEE Access}, pages = {3026-3037}, shortjournal = {IEEE Access}, title = {RED-net: A neural network for 3D thyroid segmentation in chest CT using residual and dilated convolutions for measuring thyroid volume}, volume = {13}, year = {2025}, }
• (2025). Remote sensing retrieval method based on few-shot learning: A case study of surface dissolved organic carbon in jiangsu coastal waters, china. ACCESS, 13, 3014-3025. (www)

  @article{ , abstract = {The ocean, Earth’s largest carbon sink, plays a critical role in studying the carbon cycle in estuarine and coastal environments, making the retrieval of dissolved organic carbon (DOC) content highly significant. Machine learning retrieval techniques outperform traditional band ratio methods in handling high-dimensional and nonlinear features. However, the scarcity of in-situ observation data, a result of the challenges in acquiring marine environmental data, restricts the application of machine learning retrieval methods. To tackle this challenge, we propose a high-precision retrieval method for ocean surface DOC, combining spatial interpolation with deep neural networks (DNN), utilizing a small yet evenly distributed in-situ observation dataset. The same spatial interpolation technique was applied to both in-situ observation data and reflectance data of multiple bands at corresponding locations, expanding the original data distribution, thus increasing the sample size and diversity to create a virtual dataset. Subsequently, the machine learning model learned the complex relationships between target parameters and multidimensional features using the virtual dataset, which was then applied to real remote sensing reflectance images to obtain retrieval results. The validation results demonstrate that this method produces highly accurate DOC retrieval results (RMSE =0.08 mg/L, MAPE =4.13%, RPD =3.28). This method fully leverages the limited in-situ observation data, offering a novel approach to quantitatively retrieving DOC concentrations via machine learning in the context of sparse observational data. This method holds potential for applications in large-scale ocean areas and for the retrieval of other dissolved substances.}, archive = {J_ACCESS}, author = {Jiahao Zhang and Huan Li and Yiyang Miao and Zeng Zhou and Huihua Lyu and Zheng Gong}, doi = {10.1109/ACCESS.2024.3524257}, journal = {IEEE Access}, pages = {3014-3025}, shortjournal = {IEEE Access}, title = {Remote sensing retrieval method based on few-shot learning: A case study of surface dissolved organic carbon in jiangsu coastal waters, china}, volume = {13}, year = {2025}, }
• (2025). SThing: A novel configurable ring oscillator based PUF for hardware-assisted security and recycled IC detection. ACCESS, 13, 2994-3013. (www)

  @article{ , abstract = {The ring oscillator (RO) is widely used to address different hardware security issues. For example, the RO-based physical unclonable function (PUF) generates a secure and reliable key for the cryptographic application, and the RO-based aging sensor is used for the efficient detection of recycled ICs. In this paper, a CMOS inverter with two voltage control signals is used to design a configurable RO (CRO). With its control signals, the proposed CRO can both accelerate and lower the impact of aging on the oscillation frequency. This vital feature of the proposed CRO makes it suitable for use in PUFs and RO-based sensors. The performance of both the proposed modified architecture, i.e., CRO PUF and CRO sensor, is evaluated in 90 nm CMOS technology. The aging tolerant feature of the proposed CRO enhances the reliability of CRO PUF. Similarly, the aging acceleration property of CRO improves the rate of detection of recycled ICs. Finally, both the proposed architectures are area and power-efficient compared to standard architectures.}, archive = {J_ACCESS}, author = {Saswat Kumar Ram and Sauvagya Ranjan Sahoo and Banee Bandana Das and Kamalakanta Mahapatra and Saraju P. Mohanty}, doi = {10.1109/ACCESS.2024.3523918}, journal = {IEEE Access}, pages = {2994-3013}, shortjournal = {IEEE Access}, title = {SThing: A novel configurable ring oscillator based PUF for hardware-assisted security and recycled IC detection}, volume = {13}, year = {2025}, }
• (2025). Motion classification with embroidery bend sensors using multiple zigzag-stitch for loose-fitting garments. ACCESS, 13, 2982-2993. (www)

  @article{ , abstract = {This study proposes a wearable motion classification system by employing commercially available conductive threads and everyday garments. The unique feature of the proposed system is an embroidery bending sensor that does not necessitate tight-fitting with the body, which contrasts with traditional motion sensing systems. Therefore, integration can be simplified by allowing motion classification onto loose-fitting everyday garments. The sensor that exhibits a change in resistance to bending deformation is realized by applying multiple zigzag stitch to the fabric. In addition, a significant resistance change is realized during the phase transition of motions (e.g., stance and swing phase). Therefore, motion can be categorized without making joint angle measurements. We fabricated a prototype by attaching the sensors to a commercially available work jacket and pants by sewing them onto the fabric. Ten participants were requested to perform ten various activities (e.g., walking, jogging, ascending and descending stairs). The findings demonstrated that the sensor can measure the degree of joint flexion, the flexion cycle, and the timing of flexion during the wearer’s activities. Moreover, motion classification was performed by training a one-dimensional convolutional neural network (1D-CNN) model with the sensor signals. The model successfully learned the differences in signal amplitude and frequency as distinguishing features of each activity, resulting in an average classification accuracy of 99.02% across the ten types of activities.}, archive = {J_ACCESS}, author = {Kaisei Minami and Yasuhiro Akiyama and Takuya Umedachi}, doi = {10.1109/ACCESS.2024.3523292}, journal = {IEEE Access}, pages = {2982-2993}, shortjournal = {IEEE Access}, title = {Motion classification with embroidery bend sensors using multiple zigzag-stitch for loose-fitting garments}, volume = {13}, year = {2025}, }
• (2025). Cooperative static and dynamic correlation-aware learning for vehicle maintenance demand prediction. ACCESS, 13, 2970-2981. (www)

  @article{ , abstract = {Accurate prediction of vehicle maintenance demands is crucial for enhancing service longevity and minimizing costs. However, current methods are limited to predicting maintenance demands for individual vehicle components. They fail to offer a comprehensive prediction that encompasses diverse maintenance demands. Additionally, vehicle maintenance demand prediction must consider the interrelationships among various maintenance projects and maintenance project records. To address these issues, we propose a vehicle maintenance demand prediction method that employs a collaborative approach. This method utilizes both static and dynamic correlation-aware learning techniques. We design a static correlation-aware method for maintenance project representation learning by leveraging prior statistical data from various maintenance projects. To effectively capture the temporal correlations inherent in different maintenance project records, we propose an attention-based dynamic correlation-aware technique. Experiments conducted on real-world datasets demonstrate that the proposed model outperforms existing methods.}, archive = {J_ACCESS}, author = {Fanghua Chen and Deguang Shang and Gang Zhou and Muhao Xu and Ke Ye and Fujie Ren and Guofang Wu}, doi = {10.1109/ACCESS.2024.3524433}, journal = {IEEE Access}, pages = {2970-2981}, shortjournal = {IEEE Access}, title = {Cooperative static and dynamic correlation-aware learning for vehicle maintenance demand prediction}, volume = {13}, year = {2025}, }
• (2025). Predicting the compressive strength of recycled concrete using ensemble learning model. ACCESS, 13, 2958-2969. (www)

  @article{ , abstract = {This research proposes a stacking machine learning method to accurately predict the compressive strength of recycled concrete. The model integrates eXtreme Gradient Boosting (XGBoost), Extra Trees (ET), Decision Tree (DT), and Linear Regression (LR) models, aiming to maximize the prediction accuracy of concrete compressive strength. The model was evaluated using a combination of 63 self-made recycled concrete datasets and 1030 concrete datasets from the UCI Machine Learning Repository. Through optimization based on SHAP values, the new model achieved statistical metrics of RMSE = 1.969, MAE = 1.113, and R2 = 0.987. The comparison and analysis with the existing work show that this method has excellent performance. Additionally, the feature importance analysis based on SHAP values identified the key input variables affecting concrete compressive strength and improved the model’s prediction performance.}, archive = {J_ACCESS}, author = {Binghao Pan and Wensheng Liu and Pan Zhou and Dapeng Oliver Wu}, doi = {10.1109/ACCESS.2024.3519669}, journal = {IEEE Access}, pages = {2958-2969}, shortjournal = {IEEE Access}, title = {Predicting the compressive strength of recycled concrete using ensemble learning model}, volume = {13}, year = {2025}, }
• (2025). Customer behavior analysis and predictive modeling in supermarket retail: A comprehensive data mining approach. ACCESS, 13, 2945-2957. (www)

  @article{ , abstract = {In the dynamic landscape of supermarket retail, understanding customer behaviour is paramount for optimising business strategies and enhancing profitability. This paper presents a comprehensive data mining approach to analyse customer behaviour and build predictive models within the supermarket retail domain. Leveraging advanced data analytics techniques, our methodology encompasses data preprocessing, exploratory data analysis, feature engineering, model selection, and evaluation. This paper presents a comprehensive approach to customer behaviour analysis and predictive modelling within the context of supermarket retail. We delve into the intricacies of data mining methodologies, exploring how retailers can leverage diverse datasets to uncover valuable insights and build predictive models that drive business growth and customer satisfaction. From data preprocessing to model evaluation, each step in the process is meticulously examined, highlighting best practices and key considerations for effective implementation.}, archive = {J_ACCESS}, author = {Kavitha Dhanushkodi and Akila Bala and Nithin Kodipyaka and V. Shreyas}, doi = {10.1109/ACCESS.2024.3407151}, journal = {IEEE Access}, pages = {2945-2957}, shortjournal = {IEEE Access}, title = {Customer behavior analysis and predictive modeling in supermarket retail: A comprehensive data mining approach}, volume = {13}, year = {2025}, }
• (2025). Addressing data imbalance in crash data: Evaluating generative adversarial network’s efficacy against conventional methods. ACCESS, 13, 2929-2944. (www)

  @article{ , abstract = {In the realm of traffic safety analysis, the inherent imbalance in crash datasets, particularly in terms of injury severity, poses a significant challenge for machine learning-based classification models. This study delves into the efficacy of Generative Adversarial Networks (GANs), with a specific focus on Conditional Tabular GAN (CTGAN), for synthesizing minority crash data to address this imbalance. Utilizing traffic crash data from Chicago spanning 2020 to 2022, the research evaluates the capabilities of CTGAN against three traditional data resampling methods, as well as an additional cost-sensitive learning approach. These methods are evaluated across various injury severity classification scenarios (2-class, 3-class, and 4-class) using five commonly applied injury severity classification models. The study’s dual evaluation approach encompasses both the quality of synthetic data and the enhancement of classification model performance. The pivotal findings reveal that: 1) CTGAN markedly outperforms other data resampling techniques in generating superior quality synthetic data, particularly for the least represented injury severity category; 2) While CTGAN demonstrates substantial improvements over traditional data resampling methods in classification model performance, this advantage diminishes as the number of injury categories increases; 3) Surprisingly, CTGAN’s superior data quality does not result in better classification performance compared to cost-sensitive learning, especially in more complex classification scenarios. Cost-sensitive learning combined with LightGBM achieves the best classification performance across all scenarios. Given the significantly lower computational resources required by cost-sensitive learning, this approach is recommended for handling imbalanced injury severity data.}, archive = {J_ACCESS}, author = {Bei Zhou and Qianxi Zhou and Zongzhi Li}, doi = {10.1109/ACCESS.2024.3524620}, journal = {IEEE Access}, pages = {2929-2944}, shortjournal = {IEEE Access}, title = {Addressing data imbalance in crash data: Evaluating generative adversarial network’s efficacy against conventional methods}, volume = {13}, year = {2025}, }
• (2025). Early diagnosis of autism: A review of video-based motion analysis and deep learning techniques. ACCESS, 13, 2903-2928. (www)

  @article{ , abstract = {The growing incidence of ASD has made the development of early diagnostic techniques increasingly crucial. The paper provides a systematic review of video-based motion analysis and deep learning (DL) techniques for early diagnosis of ASD. It evaluates related articles published between 2018 and June 2024. We categorize the leading research according to the body parts involved: head pose, hand and arm pose, and trunk pose. And conduct an in-depth analysis of the procedures, feature extraction techniques, and classification models used in these studies. The paper delves into the general processes and methods proposed in the field, offering a detailed analysis. It presents a timeline of the evolution of ASD diagnostic methods and summarizes publicly available datasets, categorized into behavioral and multimodal datasets. The paper also reports the successes and challenges of applying AI in motion analysis for ASD detection. It provides a complete and systematic overview for researchers in the field of ASD.}, archive = {J_ACCESS}, author = {Ziqian Yang and Yuyao Zhang and Jiachuan Ning and Xin Wang and Zhihui Wu}, doi = {10.1109/ACCESS.2024.3523872}, journal = {IEEE Access}, pages = {2903-2928}, shortjournal = {IEEE Access}, title = {Early diagnosis of autism: A review of video-based motion analysis and deep learning techniques}, volume = {13}, year = {2025}, }
• (2025). Magnetic and thermal characterization of Fe2.9wt.%Si for magnetic shielding applications. ACCESS, 13, 2890-2902. (www)

  @article{ , abstract = {This article provides an in-depth analysis of the magnetic shielding and thermal properties of additively manufactured materials, focusing on the Fe2.9wt.%Si alloy produced via Laser Powder Bed Fusion (LPBF). The study compares these materials’ performance with conventional counterparts, particularly in the context of their application in magnetic shielding of a relay switch. The research integrates experimental findings from thermal imaging techniques to evaluate the efficiency of these materials under operational conditions, offering valuable insights into the future of additive manufacturing in high-performance magnetic shielding.}, archive = {J_ACCESS}, author = {Michele Quercio and Luca Santoro and Emir Poskovic and Raffaella Sesana and Aldo Canova}, doi = {10.1109/ACCESS.2024.3524238}, journal = {IEEE Access}, pages = {2890-2902}, shortjournal = {IEEE Access}, title = {Magnetic and thermal characterization of Fe2.9wt.%Si for magnetic shielding applications}, volume = {13}, year = {2025}, }
• (2025). High data-rate hilbert-curved-shaped MIMO antenna with improved bandwidth and circular polarization for wireless capsule endoscopy. ACCESS, 13, 2876-2889. (www)

  @article{ , abstract = {The growing demand for wireless communication in biomedical engineering has opened new research frontiers. This paper introduces an innovative miniaturized ( $4\times 9$ mm2) multiple-input multiple-output (MIMO) circularly polarized (CP) implantable antenna for a wireless capsule endoscopy (WCE) system. The presented device features two Hilbert curve-shaped meandered resonators. The space-filling characteristic inherent in the Hilbert curve contributes to miniaturization and bandwidth enhancement of up to 250 MHz to cover possible detuning between diverse organs. Simultaneously, employing the curve’s inherent natural symmetry and adopting an orthogonal port arrangement led to a noteworthy improvement in isolation up to 27 dB with an enhanced gain of −27.9 dBi. Comprehensive simulation analysis of antenna-integrated WCE systems in homogeneous and hetrogenous environments is conducted in the stomach, small intestine, and large intestine. Without the need for an external decoupling structure, the system demonstrated good MIMO characteristics with isolation of 27 dB. A fabricated prototype has been tested in a saline solution and minced pork. A good match between the measured and simulated results has been observed. At both ports, the axial ratio (<3 dB) covers part of the ISM band in the homogeneous stomach, small intestine, and large intestine phantoms. Additionally, a safety assessment, employing a realistic human model, aligns findings with established safety guidelines, revealing average specific absorption rates of 543 W/kg (1 g) and 58.6 W/kg (10 g) at 2.45 GHz. Moreover, link budget analysis confirms reliable wireless data transmission up to 1.8 m at 78 Mbps. The proposed MIMO antenna, with its superb dominant characteristics, outperforms leading state-of-the-art endoscopic antennas, offering promising advancements for capsule endoscopy systems.}, archive = {J_ACCESS}, author = {Syed Misbah Un Noor and Syed Ahson Ali Shah and Shahid Khan and Izaz Ali Shah and Jamal Nasir and Salahuddin Khan and Slawomir Koziel}, doi = {10.1109/ACCESS.2024.3511617}, journal = {IEEE Access}, pages = {2876-2889}, shortjournal = {IEEE Access}, title = {High data-rate hilbert-curved-shaped MIMO antenna with improved bandwidth and circular polarization for wireless capsule endoscopy}, volume = {13}, year = {2025}, }
• (2025). Analog computing for nonlinear shock tube PDE models: Test and measurement of CMOS chip. ACCESS, 13, 2862-2875. (www)

  @article{ , abstract = {Long left ignored by the digital computing industry since its heyday in 1940’s, analog computing is today making a comeback as Moore’s Law slows down. Analog CMOS has power efficiency advantages over digital CMOS for low-precision applications in edge computing, scientific computing, and artificial intelligence/machine learning (AI/ML) verticals. Driven by observed non-trivial improvements in performance over digital processors while solving linear partial differential equations (PDEs), this paper presents experimental results and analysis from a single-chip CMOS analog computer for solving nonlinear PDEs. The chip integrates a 15-point fully-parallel spatially-discrete time-continuous (SDTC) finite difference time-domain (FDTD) solver for acoustic shock wave equations with radiation boundary conditions. The design was realized in TSMC 180 nm CMOS technology. It has an active area of 7.38 mm $\times 4.64$ mm and consumes 936 mW while delivering an equivalent FDTD temporal update rate of 80 MHz and an analog bandwidth of 2 MHz. The paper discusses the challenges and associated design trade-offs in realizing such high-performance CMOS analog computers, including sensitivity to process, voltage, and temperature (PVT) variations, sensitivity to bias and voltage regulation, errors associated with noise, difficulties with calibration; it also outlines possible approaches for mitigating these challenges.}, archive = {J_ACCESS}, author = {Hasantha Malavipathirana and Soumyajit Mandal and Nilan Udayanga and Yingying Wang and S. I. Hariharan and Arjuna Madanayake}, doi = {10.1109/ACCESS.2024.3524500}, journal = {IEEE Access}, pages = {2862-2875}, shortjournal = {IEEE Access}, title = {Analog computing for nonlinear shock tube PDE models: Test and measurement of CMOS chip}, volume = {13}, year = {2025}, }
• (2025). Deep reinforcement learning-based controller for field-oriented control of SynRM. ACCESS, 13, 2855-2861. (www)

  @article{ , abstract = {Synchronous reluctance motors offer several advantages that make them suitable for use in electric vehicle traction systems. Motor-drive systems constitute the most significant share of the energy consumption of electric vehicles. Controller performance is essential for achieving accurate, stable, efficient, and safe motor control. Conventional controllers, such as PID controllers, remain popular owing to their simplicity, ease of implementation, and effectiveness in many control applications. However, advanced control techniques may offer better performance and robustness for more complex or challenging control tasks. Successful studies employing deep reinforcement learning have been conducted across various control applications, including motor control. Because deep reinforcement learning is a relatively recent approach to control, its utilization in motor control remains a subject of ongoing research and development. This paper presents the application of deep reinforcement learning as a speed-control strategy for synchronous reluctance motors. The simulation results are presented to demonstrate the effectiveness of the proposed deep reinforcement learning control strategy. These results highlight the improved robustness of the system to speed changes and load disturbances and demonstrate the superior performance achieved in synchronous reluctance motor speed control compared with the conventional PI control method.}, archive = {J_ACCESS}, author = {Erdal Kiliç}, doi = {10.1109/ACCESS.2024.3524156}, journal = {IEEE Access}, pages = {2855-2861}, shortjournal = {IEEE Access}, title = {Deep reinforcement learning-based controller for field-oriented control of SynRM}, volume = {13}, year = {2025}, }
• (2025). Integrating AutoGluon for real-time monitoring and classification of dental equipment performance. ACCESS, 13, 2844-2854. (www)

  @article{ , abstract = {This study aims to introduce AutoGluon, an automated machine learning (AutoML) framework that monitors and classifies the performance of dental equipment in real time. The intent is to enable predictive maintenance through data processing automation, model selection and performance classification. Using sensor data, such as temperature readings, vibration readings, and pressure readings from different dental tools, we trained machine learning models using AutoGluon. Data preprocessing consisted of label encoding and normalization. The system evaluates multiple performance metrics: accuracy, balanced accuracy and Matthews correlation coefficient (MCC). We compared models to identify the best approach for real-time monitoring. The Weighted Ensemble model achieved a 100% accuracy, balanced accuracy and MCC score of 1.0 (indicating perfect reliability in classifying equipment states as ’Optimal,’ ’Warning,’ and ’Failure’). The system was computationally efficient, generalizable across classes, and showed robust generalization, making it suitable for real-time deployment. Temperature and vibration were the most influential features in predicting equipment states based on the SHAP analysis. By taking advantage of AutoGluon, the proposed system significantly increases the reliability of dental equipment monitoring while also performing real-time machine classification and prediction maintenance. This scalable solution ensures quality patient care, optimizes maintenance scheduling and reduces downtime. The system will be enlarged with additional sensors, used to develop the system further in future work, and deployed to live clinical environments.}, archive = {J_ACCESS}, author = {Muxiu Yang and Fengzhou Li and Wenfeng Qiu}, doi = {10.1109/ACCESS.2024.3523519}, journal = {IEEE Access}, pages = {2844-2854}, shortjournal = {IEEE Access}, title = {Integrating AutoGluon for real-time monitoring and classification of dental equipment performance}, volume = {13}, year = {2025}, }
• (2025). Active element arrangement for deep learning-based CSI prediction in IRS-assisted systems. ACCESS, 13, 2829-2843. (www)

  @article{ , abstract = {Recently, intelligent reflecting surface (IRS) components have garnered attention as a technology for advancing next-generation wireless communications. For effective IRS control with minimized overhead, a scheme is needed for estimating partial channels using active elements. Given that the number of active elements affects production costs and energy consumption, various methods have been proposed for predicting channels with the fewest elements. However, despite the significant impact of active element arrangement on prediction accuracy, as corroborated in this study, there has been limited discussion on this aspect, especially in deep learning (DL)-based prediction methods. In this study, we conducted experimental investigations to explore the conditions under which an active element arrangement resulting in accurate DL based predictions. Subsequently, we developed a novel active element arrangement based on three policies extracted from the simulation results. Through numerical experiments, we demonstrated that the proposed arrangement facilitates accurate prediction regardless of the number of active elements.}, archive = {J_ACCESS}, author = {Yoshihiko Tsuchiya and Norisato Suga and Kazunori Uruma and Masaya Fujisawa}, doi = {10.1109/ACCESS.2024.3524218}, journal = {IEEE Access}, pages = {2829-2843}, shortjournal = {IEEE Access}, title = {Active element arrangement for deep learning-based CSI prediction in IRS-assisted systems}, volume = {13}, year = {2025}, }
• (2025). Atomic energy optimization: A novel meta-heuristic inspired by energy dynamics and dissipation. ACCESS, 13, 2801-2828. (www)

  @article{ , abstract = {In this paper, we present Atomic Energy Optimization (AEO), a novel meta-heuristic optimization technique inspired by atomic energy dynamics and the process of static electricity dissipation. AEO models optimization by mimicking the energy accumulation, transfer, and dissipation behaviors observed in atoms, particularly during processes involving electrostatic charge and discharge. Each solution in AEO is represented as an atom with electrons, where its energy state corresponds to solution quality. Solutions interact, exchanging and dissipating energy in a manner analogous to static electricity buildup and release—such as when a plastic rod is rubbed to accumulate charge, then gradually neutralized upon contact with another surface. This energy-driven model allows AEO to balance exploration and exploitation dynamically, enhancing its ability to navigate com-plex, multimodal landscapes. The paper delves into key AEO mechanisms, including energy transfer, dissipation, and parameter sensitivity, exploring their impact on the algorithm’s performance across challenging benchmarks. Notable applications of AEO to problems like the Rastrigin function and the Traveling Salesman Problem (TSP) showcase its effectiveness, with experimental results demonstrating superior convergence and robustness compared to traditional methods like Genetic Algorithms (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Through extensive experimentation, AEO achieved up to a 20% faster convergence rate and a 15% improvement in solution quality over peer methods, demonstrating its superiority in solving benchmark optimization problems.}, archive = {J_ACCESS}, author = {Mohammed Omari and Mohammed Kaddi and Khouloud Salameh and Ali Alnoman and Mohammed Benhadji}, doi = {10.1109/ACCESS.2024.3524322}, journal = {IEEE Access}, pages = {2801-2828}, shortjournal = {IEEE Access}, title = {Atomic energy optimization: A novel meta-heuristic inspired by energy dynamics and dissipation}, volume = {13}, year = {2025}, }
• (2025). Predicting patients’ revisit intention based on satisfaction scores: Combination of penalized regression and neural networks. ACCESS, 13, 2783-2800. (www)

  @article{ , abstract = {A company’s survival in the current competitive market hinges on its ability to not only meet but exceed customer expectations, as customers are invaluable assets. Patient satisfaction is crucial in the healthcare sector, directly influencing whether patients will return to a hospital or recommend it to others. This study uses advanced data mining techniques to accurately estimate and predict patients’ likelihood of returning for future appointments by assessing their satisfaction levels. In addition to feature selection models such as Random Forest, Genetic Algorithm, and Lasso Regression, the study employs various methods, including Neural Networks, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Rule-based systems, and Naive Bayes algorithms. The analysis of the results indicates that while the Neural Network model shows superior prediction accuracy, the Lasso Regression method is efficient in identifying relevant features. By integrating AI approaches and thoroughly examining satisfaction ratings in the Iranian healthcare industry, this research makes a significant contribution. Moreover, the findings demonstrate that the Artificial Neural Network model best fits the predictive model and offers the highest reliability. This study aims to forecast patient satisfaction in the healthcare industry and develop a strategic roadmap for hospitals, thereby expanding the knowledge of machine learning methods for predicting customer satisfaction.}, archive = {J_ACCESS}, author = {Farshid Abdi and Shaghayegh Abolmakarem and Amir Karbassi Yazdi and Paul Leger and Yong Tan and Giuliani Coluccio}, doi = {10.1109/ACCESS.2024.3522767}, journal = {IEEE Access}, pages = {2783-2800}, shortjournal = {IEEE Access}, title = {Predicting patients’ revisit intention based on satisfaction scores: Combination of penalized regression and neural networks}, volume = {13}, year = {2025}, }
• (2025). Improving monocular depth estimation through knowledge distillation: Better visual quality and efficiency. ACCESS, 13, 2763-2782. (www)

  @article{ , abstract = {This paper introduces a novel knowledge distillation (KD) framework for monocular depth estimation (MDE), incorporating dynamic weight adaptation to address critical challenges. The proposed approach effectively mitigates visual limitations, including blurred object boundaries and discontinuous artifacts, while preserving quantitative performance. Beyond addressing these visual challenges, the innovative KD framework reduces the complexity of depth information, thereby significantly enhancing computational efficiency. To validate the effectiveness of the proposed framework, extensive comparative evaluations were performed using state-of-the-art models, including AdaBins, LocalBins, BinsFormer, PixelFormer, and ZoeDepth. These evaluations were conducted on benchmark datasets, including NYU Depth V2 and SUN RGB-D for indoor environments and KITTI for outdoor scenarios, to ensure a rigorous and comprehensive assessment of robustness and generalization capabilities. The results demonstrate that the proposed KD framework outperforms existing methods in visual quality across all datasets while achieving notable computational benefits, including a 15.45% reduction in Floating Point Operations (FLOPs) for the LocalBins model and 7.72% for the ZoeDepth model.}, archive = {J_ACCESS}, author = {Chang Yeop Lee and Dong Ju Kim and Young Joo Suh and Do Kyung Hwang}, doi = {10.1109/ACCESS.2024.3523912}, journal = {IEEE Access}, pages = {2763-2782}, shortjournal = {IEEE Access}, title = {Improving monocular depth estimation through knowledge distillation: Better visual quality and efficiency}, volume = {13}, year = {2025}, }
• (2025). Data-driven modeling of weakly nonlinear circuits via generalized transfer function approximation. ACCESS, 13, 2746-2762. (www)

  @article{ , abstract = {This paper presents an extension of the Vector Fitting algorithm with the purpose of constructing compact behavioral models of weakly nonlinear circuits starting from frequency-domain input-output data. Using the concept of generalized transfer function provided by Volterra series theory for nonlinear systems, the algorithm approximates a given dataset of generalized transfer function samples with a black-box multivariate rational model. The fitted model can then be recast into a bilinear state-space form for time-domain analysis. Practical extraction of the required data samples can be carried out by measurement or harmonic balance analysis available in commercial solvers. Examples demonstrating the accuracy and efficiency of the behavioral models include a Low-Dropout Regulator and a Low Noise Amplifier.}, archive = {J_ACCESS}, author = {Antonio Carlucci and Ion Victor Gosea and Stefano Grivet-Talocia}, doi = {10.1109/ACCESS.2024.3520388}, journal = {IEEE Access}, pages = {2746-2762}, shortjournal = {IEEE Access}, title = {Data-driven modeling of weakly nonlinear circuits via generalized transfer function approximation}, volume = {13}, year = {2025}, }
• (2025). Water pipeline leak detection and localization with an integrated AI technique. ACCESS, 13, 2736-2745. (www)

  @article{ , abstract = {A pipeline leak detection and localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. The main aim of this approach is to propose a standalone architecture for leak detection and localization using a single sensor. The sensor used in this approach is an Acousto-optic vibration sensor, which is highly sensitive to capture the vibrations caused by the pipeline leak. The proposed standalone architecture contains two steps: 1) Feature extraction and 2) leak detection and localization. This approach uses a one-dimensional convolutional neural network (1DCNN) for feature extraction. This paper tunes the AdaBoost to have support vector machines (SVM), Decision Trees (DT), and multi-layer perceptron (MLP) instead of the inbuilt weak estimators to give improved performance. The modified AdaBoost detects and localizes the leak by classifying the leak locations. The proposed 1DCNN-modified AdaBoost’s performance is cross-verified with nine models and cross-correlation. All the models are tested with 200000 and 300000 Pascal pressure to check the stability. The proposed 1DCNN-modified AdaBoost outperforms all the other methods implemented in this research. In the future, this research can be extended with different leak sizes and pipeline materials and real-time pipeline environments with longer distances.}, archive = {J_ACCESS}, author = {Uma Rajasekaran and Mohanaprasad Kothandaraman and Chang Hong Pua}, doi = {10.1109/ACCESS.2024.3524669}, journal = {IEEE Access}, pages = {2736-2745}, shortjournal = {IEEE Access}, title = {Water pipeline leak detection and localization with an integrated AI technique}, volume = {13}, year = {2025}, }
• (2025). A conceptual model for claim validation based on cryptographically signed data. ACCESS, 13, 2723-2735. (www)

  @article{ , abstract = {In this paper, we propose a conceptual model for claim validation based on signed data while clarifying the gap between validation and verification. As various activities are conducted via the Internet and the Web, verifying information and its originators is essential for countermeasures against threats, such as spoofing or falsifying an achievement. These discussions utilize cryptographically signed data, which includes a digital signature that allows a recipient can confirm the authenticity of the source using cryptographic techniques. In applications that utilize signed data to represent a claim, the recipient judges whether the claim is valid to avoid misidentification of the claim. However, the standards for digital certificates, such as Verifiable Credentials, do not cover the validation of the claim represented by the certificate, including the truth or falsity of the claim. Considering that verifying a signature does not directly indicate the validity of the claim, the gap between verification and validation should be clarified to cover the validation of the claim. We then hypothesize that the gap is the presence or absence of the validator’s criteria, and review the definitions in two standard documents to confirm our hypothesis. We then propose a conceptual model for claim validation that utilizes signed data. In our model, a validator, who is a recipient of the claim, defines a validation policy to represent the validator’s criteria. We also discuss a communication model between the claimant, who is a originator of the claim, and the validator. For claims that the validator cannot directly validate, we introduce an additional model that involves a certifier that the validator trusts. We analyze the applicability of our model through three use cases, and conclude that our models are applicable. Based on our model, we anticipate exploring several scenarios to validate diverse claims using signed data.}, archive = {J_ACCESS}, author = {Ryosuke Abe and Shigeya Suzuki and Osamu Nakamura}, doi = {10.1109/ACCESS.2024.3524509}, journal = {IEEE Access}, pages = {2723-2735}, shortjournal = {IEEE Access}, title = {A conceptual model for claim validation based on cryptographically signed data}, volume = {13}, year = {2025}, }
• (2025). Detection of adversarial attacks using deep learning and features extracted from interpretability methods in industrial scenarios. ACCESS, 13, 2705-2722. (www)

  @article{ , abstract = {The adversarial training technique has been shown to improve the robustness of Machine Learning and Deep Learning models to adversarial attacks in the Computer Vision field. However, the effectiveness of this approach needs to be proven in the field of Anomaly Detection on industrial environments, where adversarial training has critical limitations. First, the time to train the Anomaly Detection system is higher since adversarial samples needs to be generated in each epoch. Second, the adversarial training blurs the decision border and, therefore, the performance of the Anomaly Detection system decreases. To solve these limitations, we propose a novel framework that can be deployed in constrained devices typically used in industrial scenarios. The framework relies on features extracted after applying interpretability methods to time-series data. These features will be used to train a new Deep Learning model that discriminates between adversarial and non-adversarial samples. We validated two configurations of the framework using two different industrial scenarios: the Tennessee Eastman Process (TEP) and Secure Water Treatment (SWaT). Next, we compared the results between our proposal and the approach using traditional adversarial training. Our proposal took significantly less time to be trained (around 42.5% and 90% less time for TEP and SWaT, respectively). Besides, after applying the adversarial training, we observed a downgrade of the F1-score of the Anomaly Detection system from 0.929 to 0.707 in the TEP scenario and from 0.972 to 0.923 for SWaT scenario. Finally, we observed that our approach was slower than adversarial training in terms of evaluation time due to the intensive computation of features from interpretability methods. However, this did not prevent our approach from being used in real time to detect adversarial samples.}, archive = {J_ACCESS}, author = {Ángel Luis Perales Gómez and Lorenzo Fernández Maimó and Alberto Huertas Celdrán and Félix J. García Clemente}, doi = {10.1109/ACCESS.2024.3524996}, journal = {IEEE Access}, pages = {2705-2722}, shortjournal = {IEEE Access}, title = {Detection of adversarial attacks using deep learning and features extracted from interpretability methods in industrial scenarios}, volume = {13}, year = {2025}, }
• (2025). Gradient-based algorithms with intermediate observations in static and differential games. ACCESS, 13, 2694-2704. (www)

  @article{ , abstract = {In two-player static and differential games, strategic players often use available or delayed information about the other player’s decisions and solve an optimization or optimal control problem to determine their strategic choices. Without this information, the player’s ability to determine its optimal decisions becomes problematic. In this paper, we propose an approach in which each player implements an iterative discrete-time gradient-based algorithm that relies only on intermediate either current or prior observations about the other player’s actions. We explore the implementation of such gradient play algorithms in the case of non-zero-sum static games and in the more complex case of differential games. We discuss the properties of these algorithms with heterogeneous stepsizes and derive explicit necessary and sufficient conditions on the game parameters in the objective functions and stepsizes that guarantee convergence to the Nash equilibrium in static games with quadratic objective functions. Examples in both static and differential games are presented to illustrate the results.}, archive = {J_ACCESS}, author = {Mohammad Safayet Hossain and Marwan A. Simaan and Zhihua Qu}, doi = {10.1109/ACCESS.2024.3523258}, journal = {IEEE Access}, pages = {2694-2704}, shortjournal = {IEEE Access}, title = {Gradient-based algorithms with intermediate observations in static and differential games}, volume = {13}, year = {2025}, }
• (2025). Real-time long-range object tracking based on ensembled model. ACCESS, 13, 2679-2693. (www)

  @article{ , abstract = {Accurate Object distance estimation with recognition is essential for various computer vision (CV) applications, including autonomous vehicles and many military operations. While significant advancements have been achieved in supervised and self-supervised techniques for short-range and real-time recognition, existing methods often focus on monocular depth estimation. They are constrained by the limitations of supervised deep learning (DL) models. Taking advantage of temporal information from sequential frames through attention mechanisms to address these challenges offers a promising avenue for enhancing recognition quality. For a real-time military object recognition system, this study integrates RGB images along Depth maps from the KITTI Dataset for short-range measurements. Due to the limitation of the KITTI dataset, a synthetic dataset is generated for long-range object recognition. YOLOv8 is trained for real-time object detection, utilizing the KITTI and a synthetic dataset for long-range analysis. Our method achieves an RMSE of 1.24 meters and an RMSE(log) of 0.18 for depth estimation, outperforming existing approaches. Furthermore, the system accurately detects objects at distances of up to 250 meters, with an average inference time of 15 ms per frame for short-range detection and 18 ms per frame for long-range detection. Comparative evaluations against state-of-the-art methods demonstrate our approach’s superior accuracy and efficiency.}, archive = {J_ACCESS}, author = {Muhammad Faseeh and Misbah Bibi and Murad Ali Khan and do-Hyeun Kim}, doi = {10.1109/ACCESS.2024.3517711}, journal = {IEEE Access}, pages = {2679-2693}, shortjournal = {IEEE Access}, title = {Real-time long-range object tracking based on ensembled model}, volume = {13}, year = {2025}, }
• (2025). Damage detection method for high-strength aramid conveyor belts. ACCESS, 13, 2671-2678. (www)

  @article{ , abstract = {Mining conveyor belts play a crucial role in the mining transportation system, and the detection of damage caused to them can affect productivity and safety. Since the traditional methods of detection perform poorly in efficiency and accuracy, a new method of damage detection is proposed based on the FCSA - YOLOv10 model in this paper for high-strength aramid conveyor belts. This method is used to capture the image of aramid conveyor belts by irradiating the conveyor belt with X-rays, and the FCSA-YOLOv10 model is applied to damage detection. By introducing the Faster structure, the C2f structure is improved. Also, the Convolutional Block Attention Module (CBAM) is used to replace the Multi-head Self-Attention (MHSA) in the PSA structure, while the SimSPPFCSPC module is used to replace the original SPPF structure. Meanwhile, the Adaptive Threshold Focal Loss Function (ATFL) is applied to address the imbalance between the background and the target in images of the aramid conveyor belt. Experimental results show that the FCSA-YOLOv10 model significantly improves the accuracy of detection, recall rate, and precision on the dataset of mining aramid conveyor belt. Specifically, they reach 98.5%, 95.1%, and 96.7%, respectively. To sum up, this research contributes an effective solution to the efficient detection of damage caused to high-strength aramid conveyor belts.}, archive = {J_ACCESS}, author = {Ling Yang and Yimin Wang and Di Miao and Xiaoxian Duan}, doi = {10.1109/ACCESS.2024.3520204}, journal = {IEEE Access}, pages = {2671-2678}, shortjournal = {IEEE Access}, title = {Damage detection method for high-strength aramid conveyor belts}, volume = {13}, year = {2025}, }
• (2025). Dynamic service placement in edge computing: A comparative evaluation of nature-inspired algorithms. ACCESS, 13, 2653-2670. (www)

  @article{ , abstract = {Edge computing has emerged as a promising solution for delivering services that demand low latency, high bandwidth, and stringent privacy requirements in numerous data- and compute-intensive applications, such as those in Smart Cities. Heterogeneity in edge computing resources and diverse application requirements demand adaptive optimization techniques, such as service placement, to conform to changing conditions. A service placement model must optimize the selection of edge nodes for deploying and executing services, thereby improving application QoS and maximizing resource utilization. Numerous optimization techniques for adaptive service placement problem have been proposed in the recent past. However, in most cases, the results have been evaluated in limited scenarios. This paper presents a comprehensive comparative study evaluating representative optimization algorithms applied to the problem of dynamic service placement across various application scenarios. The study covers nature-inspired approaches, including both meta-heuristics and reinforcement learning. Our experimental findings offer valuable insights into the strengths and weaknesses of the selected nature-inspired algorithms for service placement optimization, evaluated for applications with different QoS requirements. In our analysis, the Genetic Algorithm shows superior performance in achieving lower average distance and the average number of servers selected. Particle Swarm Optimization excels in minimizing average waiting time and placement decision time. The Artificial Bee Colony maintains low average latency, whereas the RL Proximal Policy Optimization demonstrates superior performance in terms of balancing the utilization of network resources.}, archive = {J_ACCESS}, author = {Aqeel H. Kazmi and Alessandro Staffolani and Tianhao Zhang and Christian Cabrera and Siobhán Clarke}, doi = {10.1109/ACCESS.2024.3520701}, journal = {IEEE Access}, pages = {2653-2670}, shortjournal = {IEEE Access}, title = {Dynamic service placement in edge computing: A comparative evaluation of nature-inspired algorithms}, volume = {13}, year = {2025}, }
• (2025). Enhancing HEVC efficiency: A novel approach to intra-mode estimation through hadamard cost analysis. ACCESS, 13, 2638-2652. (www)

  @article{ , abstract = {In the realm of video coding, the quest for enhanced efficiency is unending. This study introduces a novel optimization in High Efficiency Video Coding (HEVC) by refining intra-mode estimation through Hadamard cost analysis. With the rapid evolution of codecs, real-time applications demand more than ever from the complexity of intra-mode selection. Here we show how by employing a refined approach to the rate distortion optimization (RDO) module, we achieve a 5.8% reduction in HEVC complexity and a minor yet notable −0.02% improvement in Bjontegaard Delta Bit Rate (BD-BR). Our method stands out by not only reducing computational load but also by improving bit-rate, a rare feat in the literature. By integrating this approach, future versions of HEVC could witness a significant leap in performance. We believe our findings contribute to the broader context of video compression technologies, inviting further exploration and development. Code is available at https://github.com/drjunaidtariq/ImprovedHEVC/.}, archive = {J_ACCESS}, author = {Junaid Tariq and Imran Javed and Touseef Sadiq and Hameedur Rahman and Ashfaq Hussain Farooqi and Amir Ijaz}, doi = {10.1109/ACCESS.2024.3521578}, journal = {IEEE Access}, pages = {2638-2652}, shortjournal = {IEEE Access}, title = {Enhancing HEVC efficiency: A novel approach to intra-mode estimation through hadamard cost analysis}, volume = {13}, year = {2025}, }
• (2025). Modeling behaviors for a new compliant mechanism by recursive hierarchy of random forest models. ACCESS, 13, 2628-2637. (www)

  @article{ , abstract = {Compliant mechanisms have been widely employed for precision engineering. Due to a kinematic coupling between rigid kinematics and flexible kinematics of compliant mechanisms, modeling behavior of two degrees of freedom (2-DOF) compliant mechanism is a challenging task. Therefore, the goal of this paper is to model complicated behaviors of the 2-DOF compliant mechanism. Random Forest is one of the popular and best machine learning models for tabular data. However, most of them have aggregated the component predictions based on the average or the voting process. The objective of this paper is to explore a more reasonable rule for aggregate. In the new technique, several Random Forest regressors are utilized as the weak learners, and their predictions are considered predictors again. The aggregated rule is then effectively learned by a primary Random Forest. The new method is subsequently applied to modeling the behaviors of a two-degrees of freedom compliant mechanism, a research area to which applying machine learning methods is limited. The numerical results demonstrate that the proposed hierarchical manner can improve the performance of component Random Forest models tuned with different values of number of trees and max-depth. Particularly, the results on the test sets of the two case studies have shown that the proposed method can reduce the MAE by 0.02 to 0.04 (about 10%) compared to the best method among the component RFs. Those results verify the applicability of the proposed method to compliant mechanism data.}, archive = {J_ACCESS}, author = {Hieu Giang Le and Thanh-Phong Dao and Minh Phung Dang and Thao Nguyen-Trang}, doi = {10.1109/ACCESS.2024.3518574}, journal = {IEEE Access}, pages = {2628-2637}, shortjournal = {IEEE Access}, title = {Modeling behaviors for a new compliant mechanism by recursive hierarchy of random forest models}, volume = {13}, year = {2025}, }
• (2025). A survey on machine learning techniques in smart grids based on wireless sensor networks. ACCESS, 13, 2604-2627. (www)

  @article{ , abstract = {Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable.}, archive = {J_ACCESS}, author = {Ashraf M. Etman and Mohamed S. Abdalzaher and Ahmed A. Emran and Ahmed Yahya and Mostafa Shaaban}, doi = {10.1109/ACCESS.2024.3524097}, journal = {IEEE Access}, pages = {2604-2627}, shortjournal = {IEEE Access}, title = {A survey on machine learning techniques in smart grids based on wireless sensor networks}, volume = {13}, year = {2025}, }
• (2025). Coordinated design of power system stabilizer and virtual inertia control using modified harris hawk optimization for improving power system stability. ACCESS, 13, 2581-2603. (www)

  @article{ , abstract = {In the current era, power system stability faces typical problems due to the Renewable Energy Sources (RES) integration trend. This trend makes the coordination between power system controllers crucial to maintain stability across a wide-range of operating behaviors. To address this problem, this paper proposes the coordinated design of Power System Stabilizer and Virtual Inertia Control (PSS-VIC) to improve the stability of the power system integrated with RES. The proposed method uses the modified version of Harris Hawk Optimization with Memory Saving Strategy (HHO-MSS) to find the equilibrium point of global parameters of PSS-VIC through various simulations to ensure scalability. In this proposed method, PSS is focused on increasing the power system stability from the traditional generator sides with diesel engines, thermal, and hydro turbines. Meanwhile, the modified VIC design is proposed to increase the power system stability from the RES sides using virtual inertia emulation with the integration of wind generators, solar photovoltaic units, and energy storage systems. The global parameters of PSS-VIC are determined by calculating the optimal damping ratio which is permitted by grid codes alongside various stability criteria validation. Based on the obtained results, HHO-MSS is 1.44% to 9.28% more accurate and 34.63% to 53.94% more consistent than Electric Eel Foraging Optimization (EEFO), and Puma Optimizer (PO), Evolutionary Mating Algorithm (EMA). With the optimal damping ratios of 9.94% to 9.96% achieved by HHO-MSS, the overall power system stability improvements, including both local and interarea responses across 38 simulations involving sudden load changes, varying inertia, and different RES levels, are as follows: 41.17% to 70.89% frequency nadir improvement, 25.9% to 67.38% power angle deviation improvement, 84.83% to 85.26% settling time reduction, and 51.57% to 89.73% average error reduction calculated with performance indices. The proposed coordinated PSS-VIC design offers excellent scalability and can effectively improve power system stability across a wide-range of operating conditions.}, archive = {J_ACCESS}, author = {Mohamad Almas Prakasa and Imam Robandi and Alberto Borghetti and Muhammad Ruswandi Djalal and Waseda Himawari}, doi = {10.1109/ACCESS.2024.3522291}, journal = {IEEE Access}, pages = {2581-2603}, shortjournal = {IEEE Access}, title = {Coordinated design of power system stabilizer and virtual inertia control using modified harris hawk optimization for improving power system stability}, volume = {13}, year = {2025}, }
• (2025). Characteristics and control of high current vacuum arc deflection under the impact of lateral magnetic field. ACCESS, 13, 2571-2580. (www)

  @article{ , abstract = {In high-voltage switchgear, the external busbars and conductive rods of a handcart-type vacuum circuit breaker form a “U” shaped loop. During the interruption of short-circuit currents, an asymmetric external lateral magnetic field is generated in the arc region of the arc chamber, leading to arc deflection and electrode erosion. These results in localized high temperatures on the electrode surface, ultimately reducing the circuit breaker’s breaking capacity and service life. This study establishes a high-current vacuum arc magnetohydrodynamic model considering the structure of the arc chamber, and investigates the lateral magnetic field distribution in the arc chamber and its impact on plasma transport characteristics. The distribution results show that, under the influence of the lateral magnetic field generated by the external busbars, the lateral magnetic field in the arc region is severely asymmetric. This asymmetry leads to a shift in the distribution of typical plasma parameters of high-current vacuum arcs, resulting in localized increases in anode temperature, which is detrimental to the interruption of vacuum circuit breakers. To eliminate the impact of the external lateral magnetic field and considering the stainless steel shielding hood structure of the actual vacuum arc zone, this study constructs shielding hoods with intermediate layers made of iron and silicon steel respectively to shield the external lateral magnetic field. Results indicate that both types of intermediate layer shielding hoods have a certain shielding effect on the lateral magnetic field. The findings of this study provide important reference for eliminating external lateral magnetic fields in the arc region.}, archive = {J_ACCESS}, author = {Jingjing Li and Xinyu Zhu and Yiping Jiang and Xu Peng and Qihan Li and Xiaolong Huang}, doi = {10.1109/ACCESS.2024.3523635}, journal = {IEEE Access}, pages = {2571-2580}, shortjournal = {IEEE Access}, title = {Characteristics and control of high current vacuum arc deflection under the impact of lateral magnetic field}, volume = {13}, year = {2025}, }
• (2025). An optimized hybrid PSO-ELM for parkinson’s disease diagnosis. ACCESS, 13, 2546-2570. (www)

  @article{ , abstract = {Parkinson’s disease (PD) is a neurodegenerative disease that gradually causes movement impairment and various symptoms. It is difficult to precisely diagnose PD, especially in its early stages, because the signs and symptoms can resemble those of other medical conditions or normal age-related changes. This paper proposes a hybrid model combining Particle Swarm Optimization with Extreme Learning Machine (PSO-ELM) for PD diagnosis. This paper employs three feature ranking algorithms, namely ReliefF, minimum Redundancy Maximum relevance (mRMR), and Fisher, on six publicly available PD datasets. Various top-ranked feature subsets are created to identify the most discriminative features and enhance the performance of the proposed hybrid PSO-ELM model for all datasets. Furthermore, the efficiency of the proposed model is compared with basic models, namely ELM, SVM, RF, and the previous works. The results show that the proposed PSO-ELM model achieved the highest average accuracy, recall, precision, and F1-score of 100% each over the 3-fold cross-validation with a minimum number of features for all the six datasets. Therefore, the PSO-ELM model may be used as a highly accurate and efficient tool for PD diagnosis.}, archive = {J_ACCESS}, author = {Gunakala Archana and Afzal Hussain Shahid}, doi = {10.1109/ACCESS.2024.3520482}, journal = {IEEE Access}, pages = {2546-2570}, shortjournal = {IEEE Access}, title = {An optimized hybrid PSO-ELM for parkinson’s disease diagnosis}, volume = {13}, year = {2025}, }
• (2025). Characterizations of effective parameters and circuit modeling of U-coupled hybrid ring resonator band pass filter. ACCESS, 13, 2529-2545. (www)

  @article{ , abstract = {A two-port symmetrical, reciprocal, U-shaped mutually coupled hybrid ring resonator for ISM, L-band, and S-band applications is presented. The designed resonator is novel with a hybrid ring which comprises a square ring, a circular ring, and two U-shaped couplers for effective mutual coupling between rings and microstrip feeds. The proposed resonator serves as a bandpass filter with a passband ranging from 1.31 GHz to 2.68 GHz. This design offers superior performance compared to conventional ring resonators. The equivalent circuit model of the hybrid ring resonator validated the behavior of the filtering action. The resonator is validated using the measurement of reflection and transmission coefficients. The comparisons of measured values of the prototyped resonator model with CST-designed and HFSS-designed simulated values are found in good agreement in terms of design frequency 2.45 GHz, reflection and transmission coefficient values, and −10 dB bandwidth values. The equivalent circuit model is validated using ADS software. The designed circuit parameter values are found to be in excellent match with manually evaluated circuit parameters with tolerances under ±20%. Selectivity of the ring resonator is investigated for applicability of proposed resonator filter for practical applications. The hybrid resonator is good for the measurement of dielectric permittivity and loss tangent estimations at any frequency without the need for a calculator. The effective parameters are evaluated for characterizations of dielectric properties and their behavior as metamaterials. These attributes make the proposed hybrid ring resonator a highly versatile and efficient solution for next generation communication devices and applications.}, archive = {J_ACCESS}, author = {Atul Varshney and Duygu Nazan Gençoğlan and Issa Elfergani and Jonathan Rodriguez and Chemseddine Zebiri and T. Mary Neebha}, doi = {10.1109/ACCESS.2024.3523440}, journal = {IEEE Access}, pages = {2529-2545}, shortjournal = {IEEE Access}, title = {Characterizations of effective parameters and circuit modeling of U-coupled hybrid ring resonator band pass filter}, volume = {13}, year = {2025}, }
• (2025). Cramér-rao bound analysis of spatial effects in 2D TOA localization with directive antennas. ACCESS, 13, 2518-2528. (www)

  @article{ , abstract = {Although Time-of-Arrival (TOA) localization is widely studied in the literature, most analyses assume omnidirectional antennas, which oversimplifies system performance and leaves the impact of antenna directivity underexplored. This paper introduces a new, more realistic and comprehensive model that accounts for directive antennas, offering a significant improvement over conventional TOA localization theory. Expressions are derived for the Fisher Information Matrix (FIM) terms corresponding to the location coordinates of a passive target, explicitly incorporating arbitrary antenna radiation patterns. Our analysis reveals that the FIM singularity conditions depend on the derivatives of the anchor antenna radiation pattern, which introduces new geometrical insights into observability. While our analysis is applicable for any TOA setup, we focus on scenarios that extend conventional TOA theory and have not been previously reported. TOA studies often conclude that 2D localization is only possible with at least two anchors, and that collinear anchor-target layouts leads to insufficient observability. We prove that a single anchor with a directive antenna can suffice for localization using multiple range measurements, and that the FIM can be inverted even in the collinear anchor-target geometry. In both of these cases, observability emerges from the directional noise introduced by non-isotropic antenna radiation patterns. The results are validated via Maximum Likelihood Estimator (MLE) simulations for both single anchor and collinear anchor cases. In regions where the FIM is near-singular, the MLE bias is significant and the Cramér-Rao Bound (CRB) is not a lower bound due to insufficient information. Conversely, in non-singular regions, the MLE is unbiased and achieves the CRB.}, archive = {J_ACCESS}, author = {Imran Moez Khan and Xuezhi Wang and William Moran and Wayne S. T. Rowe}, doi = {10.1109/ACCESS.2024.3524308}, journal = {IEEE Access}, pages = {2518-2528}, shortjournal = {IEEE Access}, title = {Cramér-rao bound analysis of spatial effects in 2D TOA localization with directive antennas}, volume = {13}, year = {2025}, }
• (2025). An optimal approach for assessing weibull parameters and wind power potential for six coastal cities in pakistan. ACCESS, 13, 2503-2517. (www)

  @article{ , abstract = {Assessing and effectively optimizing wind power potential for a wind site demands accurate calculation of Weibull parameters. In this research, we have ameliorated the performance of the recently-introduced novel energy pattern factor method (NEPFM) via a direct search algorithm, i.e., simplex search algorithm (SSA). We designate the resulting algorithm as NEPFM-SSA as it took NEPFM’s Weibull distribution parameters as an initial guess and retuned them with the help of the simplex search algorithm to get updated Weibull distribution parameters, which ensure better fitting characteristics. When applied to wind speed data collected from six coastal cities in Pakistan: Gwadar, Jiwani, Karachi, Ormara, Pasni, and Sonmiani Bay, NEPFM exhibited poor fitting characteristics to the observed wind data. Four statistical indicators, namely root mean square error, mean absolute error, coefficient of determination, and coefficient of efficiency, are taken into consideration to measure the accuracy offered by NEPFM-SSA and NEPFM. An enormous reduction in wind power density-based percentage error (for example, 15.3531% than 51.7205% for Gwadar at 10 m height) was observed in NEPFM-SSA compared to NEPFM. The numerical results reveal that NEPFM-SSA outshined NEPFM for all the cases under consideration, authenticating its workability. This increased accuracy results in improving the reliability of assessments of wind energy and the wider operation of wind energy installations. The MATLAB environment was used to achieve numerical results. The proposed NEPFM-SSA approach may be treated as the first choice to determine Weibull distribution parameters and wind power density for the considered wind sites rather than NEPFM.}, archive = {J_ACCESS}, author = {Ghulam Abbas and Arshad Ali and Faizan Rashid and Naveed Ashraf and Zohaib Mushtaq and Muhammad Zubair}, doi = {10.1109/ACCESS.2024.3524445}, journal = {IEEE Access}, pages = {2503-2517}, shortjournal = {IEEE Access}, title = {An optimal approach for assessing weibull parameters and wind power potential for six coastal cities in pakistan}, volume = {13}, year = {2025}, }
• (2025). Integrated spatiotemporal hybrid solar PV generation forecast between countries on different continents using transfer learning method. ACCESS, 13, 2486-2502. (www)

  @article{ , abstract = {Solar photovoltaic (PV) generation is a cornerstone of sustainable energy production, but predicting its capacity across countries remains challenging due to factors like climate, terrain, and population density. To address this, a recent study proposed a novel approach using transfer learning, which is particularly valuable when historical data for newly established PV plants is limited. The study evaluated four PV plants in South Korea and Germany, selected for their diverse geographical and climatic conditions. The proposed CL-Transformer model outperformed established machine learning models such as LSTM, CNN-LSTM, and Transformer, consistently demonstrating superior predictive capabilities. Notably, when trained on Korean data and applied to both South Korea and Germany, the model achieved an average R $^{2}_{\mathrm {adj}}$ improvement of 23.5 %. When trained on German data, the improvement was even more pronounced at 67.3 %. Additionally, transfer learning experiments revealed up to a 50.6 % enhancement in R $^{2}_{\mathrm {adj}}$ across different plant scales. By integrating external weather variables and satellite data, this hybrid model provides valuable insights for accurate capacity prediction and strategic planning in deploying new PV plants, contributing to greater stability and efficiency in the power industry.}, archive = {J_ACCESS}, author = {Bowoo Kim and Kaouther Belkilani and Gerd Heilscher and Marc-Oliver Otto and Jeung-Soo Huh and Dongjun Suh}, doi = {10.1109/ACCESS.2024.3514098}, journal = {IEEE Access}, pages = {2486-2502}, shortjournal = {IEEE Access}, title = {Integrated spatiotemporal hybrid solar PV generation forecast between countries on different continents using transfer learning method}, volume = {13}, year = {2025}, }
• (2025). Fault diagnosis of wind turbines based on improved dynamic network marker. ACCESS, 13, 2474-2485. (www)

  @article{ , abstract = {With the rapid increase in the installed capacity of wind power in China, early warning and identification of defects in wind turbine units has become crucial for the healthy development of the wind power industry. This paper proposes a comprehensive fault warning method for the entire wind turbine unit that is distinct from most current methods that focus solely on partial components. It addresses the complex coupling between various monitoring data from the supervisory control and data acquisition system and the internal defects of the unit. Utilizing a sample covariance matrix and the dynamic network marker theory, the method integrates diverse data inputs to predict potential failures. Case validation reveals that this approach maintains robustness across different operating conditions, issues warnings approximately 6 hours and 40 minutes before faults occur, and effectively identifies fault types. This capability is beneficial for initiative maintenance and rational planning of maintenance schedules in wind turbines.}, archive = {J_ACCESS}, author = {Zesheng Pan and Ruiming Fang and Tingyu Wei and Rongyan Shang and Changqing Peng}, doi = {10.1109/ACCESS.2024.3504543}, journal = {IEEE Access}, pages = {2474-2485}, shortjournal = {IEEE Access}, title = {Fault diagnosis of wind turbines based on improved dynamic network marker}, volume = {13}, year = {2025}, }
• (2025). Enhancing power grid reliability with machine learning and auxiliary classifier generative adversarial networks: A study on fault detection using the georgia electric system load dataset. ACCESS, 13, 2463-2473. (www)

  @article{ , abstract = {Power networks are vital to society, yet service outages and faults can have devastating consequences. This study introduces a novel integration of machine learning and data augmentation techniques for fault detection and classification, addressing gaps in data diversity and imbalance. Unlike traditional approaches, the research utilizes an Auxiliary Classifier Generative Adversarial Network (ACGAN) to generate synthetic data representative of underrepresented fault types, enhancing model training and performance. By extracting both spectral and statistical features from the Grid Event Signature Library (GESL) dataset, a comprehensive representation of power system signals is achieved. A comparative evaluation of models including Decision Trees (DT), Random Forest (RF), Extra Tree Classifier (ETC), Gradient Boosting Classifier (GBC), and K-Nearest Neighbors (KNN) revealed the Extra Tree Classifier achieved the highest testing accuracy of 93.85%. The methodologies demonstrated scalability by using a dataset augmented to 9,000 samples and validated robustness through 10-fold cross-validation with a standard deviation of 0.00659. These results highlight the proposed framework’s potential for real-world implementation in modern power grids, offering enhanced fault prediction and resilience. This research establishes a pathway for integrating advanced data augmentation and machine learning techniques into operational power grid systems, ensuring stability and reliability.}, archive = {J_ACCESS}, author = {Hafeez Ur Rehman Siddiqui and Robert Brown and Adil Ali Saleem and Muhammad Amjad Raza and Sandra Dudley}, doi = {10.1109/ACCESS.2024.3524061}, journal = {IEEE Access}, pages = {2463-2473}, shortjournal = {IEEE Access}, title = {Enhancing power grid reliability with machine learning and auxiliary classifier generative adversarial networks: A study on fault detection using the georgia electric system load dataset}, volume = {13}, year = {2025}, }
• (2025). Next-generation active-clamp forward-flyback converter with integrated planar transformer for 4 kW LDC. ACCESS, 13, 2449-2462. (www)

  @article{ , abstract = {This paper proposes a single-module Active-Clamp Forward-Flyback (ACFF) converter for the next-generation low-voltage DC-DC converter (LDC) ensuring 4kW output power. The proposed converter employs an integrated planar transformer for applications requiring wide input voltage and high output current. Previously, to meet the 4kW output specification, two 2kW LDC single modules were connected in series-parallel, resulting in a total of four cores functioning as transformers and output inductors. The core of the proposed converter consists of one core functioning as a transformer and two cores functioning as output inductors, resulting in higher power density and cost competitiveness compared to the conventional 4kW LDC modules. Additionally, integrating the cores from four to three offers the advantage of reduced magnetic core losses. In conclusion, the proposed converter is suitable for high-power LDC applications exceeding 3kW.}, archive = {J_ACCESS}, author = {Gyeong-Hyun Kwon and Dong-In Lee and Seong-Wook Jeong and Han-Shin Youn}, doi = {10.1109/ACCESS.2024.3511495}, journal = {IEEE Access}, pages = {2449-2462}, shortjournal = {IEEE Access}, title = {Next-generation active-clamp forward-flyback converter with integrated planar transformer for 4 kW LDC}, volume = {13}, year = {2025}, }
• (2025). Prompt conditioned batik pattern generation using LoRA weighted diffusion model with classifier-free guidance. ACCESS, 13, 2436-2448. (www)

  @article{ , abstract = {Batik, a significant element of Indonesian cultural heritage, is renowned for its intricate patterns and profound philosophical meanings. While preserving traditional batik is crucial, the creation of modern patterns is equally encouraged to keep the art form vibrant and evolving. Current research primarily focuses on batik classification, leaving a gap in the exploration of generative models for batik pattern creation. This paper investigates the application of text-to-image (T2I) generative models to synthesize batik motifs, leveraging latent diffusion models (LDM), Low-Rank Adaptation (LoRA), and classifier-free guidance. Our methodology employed a dataset of 20,000 batik images. Multimodal models such as LLaVA and BLIP were utilized to generate detailed captions for these images. A pretrained LDM was subsequently fine-tuned on its denoising U-Net part, either by naively fine-tuned the entire layer or by employing using LoRA. The fine-tuning process was critical in enhancing the model’s capability to generate high-quality and user-specific batik patterns. The results demonstrated that the LDM fine-tuned on the entire denoising U-Net with LLaVA-captioned images outperformed other models, achieving the lowest Fréchet Inception Distance (FID) and highest Inception Score (IS). The thoroughness of LLaVA captions proved superior to those generated by BLIP, emphasizing the significance of detailed image descriptions in generative tasks. Notably, the model not only replicated existing batik patterns but also innovatively combined multiple motifs and even able to create entirely new designs, as verified by batik expert. This research contributes to the field of computer-assisted batik pattern generation, providing significant advantages for batik artists, manufacturers, and users by accelerating the pattern creation process and expanding the possibilities of batik art.}, archive = {J_ACCESS}, author = {Rahmatulloh Daffa Izzuddin Wahid and Novanto Yudistira and Candra Dewi and Irawati Nurmala Sari and Dyanningrum Pradhikta and Fatmawati}, doi = {10.1109/ACCESS.2024.3523494}, journal = {IEEE Access}, pages = {2436-2448}, shortjournal = {IEEE Access}, title = {Prompt conditioned batik pattern generation using LoRA weighted diffusion model with classifier-free guidance}, volume = {13}, year = {2025}, }
• (2025). Evaluation of vision-language transformers for multimodal news authenticity and integrity in journalism: A multi-criteria decision-making approach. ACCESS, 13, 2424-2435. (www)

  @article{ , abstract = {In today’s digital age, inaccurate and invalid news spread may spread faster than fire. Evaluating textual and visual data is very important to stop this from happening. So, in Journalism, the Vision-Language Transformer (VLTs) are required to improve the verification of news integrity and authenticity throughout the Multimedia formats. The VLTs give us the unique benefit of recognizing false or altered information as they can process and examine data like texts and images at the same time. This approach involves accuracy, robustness, and situational symmetry to make the VLT model suitable for journalism. To assess VLT models’ suitability for journalism, this study looks at contextual alignment, accuracy, and robustness. The results highlight how VLTs may support trustworthy journalism by giving media outlets cutting-edge resources to uphold public confidence. This paper proposes a multi-attribute decision-making (MADM) framework and examines the VLT models on accuracy, flexibility, comprehensiveness, and reliability. The results in creating a potential VLT model to enhance the media integrity and authenticity.}, archive = {J_ACCESS}, author = {Juan Liu}, doi = {10.1109/ACCESS.2024.3523704}, journal = {IEEE Access}, pages = {2424-2435}, shortjournal = {IEEE Access}, title = {Evaluation of vision-language transformers for multimodal news authenticity and integrity in journalism: A multi-criteria decision-making approach}, volume = {13}, year = {2025}, }
• (2025). Design and analysis of enhanced phase-locked loop: Methods of lyapunov and natural gradient. ACCESS, 13, 2409-2423. (www)

  @article{ , abstract = {The phase-locked loop (PLL) plays a crucial role in modern power systems, primarily for estimating line voltage parameters and tracking variations needed to synchronize and control grid-connected power converters. The enhanced phase-locked loop (EPLL) builds upon the standard PLL by tracking sinusoidal signal amplitude. While EPLL has been extensively explored in experimental applications, the theoretical modeling of a modified EPLL has received limited attention. Originally introduced by M. Karimi-Ghartemani et al. in IEEE Trans. Instrum. Meas., 61(4):930–940, 2012, the modified EPLL has not been fully explored in certain areas. In this work, we first address the limitations in existing gradient- and Hessian-based EPLL designs by examining stationary points in their autonomous forms. We then introduce two new derivations of the modified EPLL for single-phase power systems, which incorporate synthesized quadrature components of the input signal. These derivations are based on Lyapunov stability theory and natural gradient optimization. We comprehensively analyze convergence and stability by employing averaging theory and Poincaré maps to establish stability limits for the filter’s proportional and integral gains. Additionally, we show that the design and tuning of the EPLL can be simplified by managing all three core equations with a single control parameter. Simulation results confirm that, within the derived gain limits, the EPLL effectively tracks sudden changes in amplitude, phase, and frequency without inducing double-frequency effects.}, archive = {J_ACCESS}, author = {Shafayat Abrar and Muhammad Mubeen Siddiqui and Azzedine Zerguine}, doi = {10.1109/ACCESS.2024.3510614}, journal = {IEEE Access}, pages = {2409-2423}, shortjournal = {IEEE Access}, title = {Design and analysis of enhanced phase-locked loop: Methods of lyapunov and natural gradient}, volume = {13}, year = {2025}, }
• (2025). Evaluation of machine learning algorithms for NB-IoT module energy consumption estimation based on radio channel quality. ACCESS, 13, 2389-2408. (www)

  @article{ , abstract = {In this study, we propose a method to estimate energy consumption in battery-powered Narrowband Internet of Things (NB-IoT) devices using the statistical data available from the NB-IoT modem, thereby circumventing the need for additional circuitry to measure battery voltage or current consumption. A custom edge node with an NB-IoT module and onboard current measurement circuit was developed and utilized to generate a labeled dataset. Each data point, generated upon UDP packet transmission, includes metadata such as radio channel quality parameters, temporal parameters (TX and RX time), transmission and reception power, and coverage extension mode. Feature selection through variance and correlation analysis revealed that coverage extension mode and temporal parameters significantly correlate to the energy consumption. Using these features, we tested 11 machine learning models for energy consumption estimation, assessing their performance and memory footprint, both of which are critical factors for resource-constrained embedded systems. Our best models achieved up to 93.8% of fit with measured values, with memory footprints below 100 KB, some as low as 3 KB. This approach offers a practical solution for the energy consumption estimation in NB-IoT devices without hardware modifications, thereby enabling energy-aware device management.}, archive = {J_ACCESS}, author = {Dusan Bortnik and Vladimir Nikic and Srdjan Sobot and Dejan Vukobratovic and Ivan Mezei and Milan Lukic}, doi = {10.1109/ACCESS.2024.3523864}, journal = {IEEE Access}, pages = {2389-2408}, shortjournal = {IEEE Access}, title = {Evaluation of machine learning algorithms for NB-IoT module energy consumption estimation based on radio channel quality}, volume = {13}, year = {2025}, }
• (2025). MARNet: An efficient two-stage intrusion detection model based on deep learning. ACCESS, 13, 2377-2388. (www)

  @article{ , abstract = {Network intrusion detection is a vital component in contemporary cybersecurity defense frameworks. As network traffic data experiences exponential growth, deep learning-based intrusion detection algorithms have become a hot research topic. However, existing intrusion detection methods commonly exhibit low accuracy, high false positive rates, and poor performance in detecting minority class attacks. To address these challenges, this paper presents an efficient two-stage intrusion detection model, MARNet. MARNet consists of two stages: the first stage is multi-scale feature-classification extraction, where numerical and categorical features in the dataset are processed separately and then fused. The second stage is the classification phase, which employs residual networks and channel attention to automatically and effectively extract features from network traffic, while identifying crucial features. Furthermore, we implemented EQL v2 loss to tackle the problem of class imbalance within the dataset. Experiments were conducted using the UNSW-NB15 and NSL-KDD datasets, After a thorough and comprehensive evaluation on two widely used datasets, the model achieved a multi-class accuracy of 90.55% and a detection rate of 99.51% with a false positive rate of 0.61% on the UNSW-NB15 dataset. On the NSL-KDD dataset, the model’s multi-class accuracy was 99.96%, detection rate was 99.96%, and the false positive rate was 0.01%. The experimental results show that the model surpasses existing ones in accuracy, detection rate, false alarm rate, and detection of minority class attacks, effectively meeting the requirements of modern large-scale intrusion detection systems.}, archive = {J_ACCESS}, author = {Jiang Wu and Qiang Fu and Liang Wang}, doi = {10.1109/ACCESS.2024.3523938}, journal = {IEEE Access}, pages = {2377-2388}, shortjournal = {IEEE Access}, title = {MARNet: An efficient two-stage intrusion detection model based on deep learning}, volume = {13}, year = {2025}, }
• (2025). A highly compact split ring resonator-based rectangular dielectric resonator antenna with multiband characterization. ACCESS, 13, 2360-2376. (www)

  @article{ , abstract = {This work encompasses a novel highly compact Split Ring Resonator (SRR) based quad-band Dielectric Resonator Antenna (DRA) for 4G and 5G applications. The proposed antenna comprises of an extended rectangular feedline and a rectangular DR in combination with two SRRs. The DRA uses the extended rectangular microstrip feedline for providing better impedance matching and enhanced performance. Remarkably, the introduction of SRRs on either side of the feedline enables the DRA to resonate at four distinct frequency bands, i.e., 1.62 GHz, 3.03 GHz and 4.7 GHz and 6.56 GHz respectively. The two lower operating bands with narrow band response are due to SRRs while two upper bands with wide band characteristics are due to rectangular DR. The simulated performance of the rectangular DRA is validated through measurements, performed on a fabricated prototype. At the corresponding resonance frequencies, the suggested design exhibits −10 dB impedance bandwidths of 9.2%, 5%, 8.3%, and 5.6%. With a notable peak gain of 2.3 dBi and an efficiency of 93%, the suggested design has excellent radiation pattern stability. To the best of authors knowledge, this is the first design where SRRs are incorporated with rectangular DR to achieve multiband operations. The simulated and measured findings show a strong agreement, highlighting the SRR-based DRA appropriateness for multiband wireless applications (such as satellite television, radio astronomy, Bluetooth, WiFi, and 6G), while demonstrating its significant contribution to the field.}, archive = {J_ACCESS}, author = {Shahid Khan and Owais Khan and Kiran Raheel and Syed Ahson Ali Shah and Bilal Tariq Malik and Salahuddin Khan and Neelam Gohar and Slawomir Koziel}, doi = {10.1109/ACCESS.2024.3512201}, journal = {IEEE Access}, pages = {2360-2376}, shortjournal = {IEEE Access}, title = {A highly compact split ring resonator-based rectangular dielectric resonator antenna with multiband characterization}, volume = {13}, year = {2025}, }
• (2025). Research on multi-channel spectral prediction model for printed matter based on HMSSA-BP neural network. ACCESS, 13, 2340-2359. (www)

  @article{ , abstract = {Accurate color reproduction requires completely faithful to the original and improve prints quality. To achieve this, the pre-press color prediction is particularly critical. However, traditional Chromaticity-based prediction fades in metamerism problem inevitably. Moreover, there is no single quality index that significantly outperforms others or provides the best performance in all situations. To overcome it, this paper proposes a multi-channel spectral prediction model for printed matter and the adaptive evaluation method based on multi-index fusion. First, BPNN prediction model utilizes multi-light sources multi-spectral imaging technology, mapping the dot area ratio of C, M, Y, K to the multi-channel spectral images. Second, Hybrid Multi-strategy Sparrow Search Algorithm (HMSSA) is constructed to optimize BPNN, which combines Tent mapping, step size phased control, and chaotic cosine transform factor. Third, multi-channel spectral images synthesis method which introduces adjusting factor Q, obtains the better predicted color image. Then, adaptive evaluation model of multi-index fusion is built to evaluate the predicted image quality, including SSIM, Spearman’s coefficient, Bhattacharyya distance, and PSNR. Several experiments are performed to verify the significance of the proposed method under different scenarios. Compared with the existing methods, the proposed multi-channel spectral prediction model exhibits the superiority in improving the accuracy of predicting the actual printing image.}, archive = {J_ACCESS}, author = {Dandan Fan and Hongwu Zhan and Fang Xu and Yifei Zou and Yankang Zhang}, doi = {10.1109/ACCESS.2024.3523471}, journal = {IEEE Access}, pages = {2340-2359}, shortjournal = {IEEE Access}, title = {Research on multi-channel spectral prediction model for printed matter based on HMSSA-BP neural network}, volume = {13}, year = {2025}, }
• (2025). Quantification of interference constraint for small cells in low SINR regime with steepest ascent method. ACCESS, 13, 2328-2339. (www)

  @article{ , abstract = {The ubiquitous proliferation of small cells (SCs) is for boosting system capacity, coverage, and quality of services (QoS) for smart applications. However, interference from the adjacent SCs is inevitable due to their dense deployments — satisfying the low signal-to-interference-plus-noise ratio (SINR) regime. In this paper, we quantify the minimum interference constraint (IC) for SCs, which is critical for the multiplexing gain in the low SINR regime. The iterative steepest ascent (SA) method is used to quantify ICs for SCs. With the proof of convergence and the comparative analysis of the ideal and real cases, supported by the simulated results, we show that the expected quantization error of SCs is the best IC to be considered for SC systems.}, archive = {J_ACCESS}, author = {Sanjeeb Shrestha and Xiaojing Huang and Kashif Saleem and Savitri Bevinakoppa and Tony Jan}, doi = {10.1109/ACCESS.2024.3524671}, journal = {IEEE Access}, pages = {2328-2339}, shortjournal = {IEEE Access}, title = {Quantification of interference constraint for small cells in low SINR regime with steepest ascent method}, volume = {13}, year = {2025}, }
• (2025). SambaMixer: State of health prediction of li-ion batteries using mamba state space models. ACCESS, 13, 2313-2327. (www)

  @article{ , abstract = {The state of health (SOH) of a Li-ion battery is determined by complex interactions among its internal components and external factors. Approaches leveraging deep learning architectures have been proposed to predict the SOH using convolutional networks, recurrent networks, and transformers. Recently, Mamba selective state space models have emerged as a new sequence model that combines fast parallel training with data efficiency and fast sampling. In this paper, we propose SambaMixer, a Mamba-based model for predicting the SOH of Li-ion batteries using multivariate time signals measured during the battery’s discharge cycle. Our model is designed to handle analog signals with irregular sampling rates and recuperation effects of Li-ion batteries. We introduce a novel anchor-based resampling method as an augmentation technique. Additionally, we improve performance and learn recuperation effects by conditioning the prediction on the sample time and cycle time difference using positional encodings. We evaluate our model on the NASA battery discharge dataset, reporting MAE, RMSE, and MAPE. Our model outperforms previous methods based on CNNs and recurrent networks, reducing MAE by 52%, RMSE by 43%, and MAPE by 7%.}, archive = {J_ACCESS}, author = {José Ignacio Olalde-Verano and Sascha Kirch and Clara Pérez-Molina and Sergio Martín}, doi = {10.1109/ACCESS.2024.3524321}, journal = {IEEE Access}, pages = {2313-2327}, shortjournal = {IEEE Access}, title = {SambaMixer: State of health prediction of li-ion batteries using mamba state space models}, volume = {13}, year = {2025}, }
• (2025). FF-YOLO: Fashion fabric detection algorithm based on YOLOv8. ACCESS, 13, 2298-2312. (www)

  @article{ , abstract = {In monitoring fashion trend and managing inventory, promptly detecting and identifying popular fashion fabric material is crucial for manufacturers and sellers. However, most traditional fabric detection are working on raw material(e.g. a block of raw fabric material with a solid background), which is not robust enough to handle complex fashion images(e.g. a human model and complex backgrounds). To address this problem, this paper proposes a lightweight and efficient fashion fabric detection algorithm named FF-YOLO, based on the YOLOv8 architecture. First, we introduce a Simple Attetion Module (SimAM) in the backbone to improve the feature extraction capability of the model. Furthermore, we propose a Lightweight Multi-Level Asymmetry Detector Head (LADH) to replace the head, improving the computational efficiency of the model’s inference process. Last, we replace the original loss function with Wise-IoU to improve the localization ability of the detection box. The experimental results show that FF-YOLO achives an average accuracy of 75.5% and a frames per second (FPS) of 105 for fashion fabric detection. Compared to the original YOLOv8 model, the mAP is improved by 2.9% and the FPS is improved by 6 frames. Meanwhile, the floating point operations per second (FLOPS) computational complexity is reduced by 5%. The results show our method proves to be effective and lightweight in fashion fabric detection tasks.}, archive = {J_ACCESS}, author = {Caixia Chen}, doi = {10.1109/ACCESS.2024.3524618}, journal = {IEEE Access}, pages = {2298-2312}, shortjournal = {IEEE Access}, title = {FF-YOLO: Fashion fabric detection algorithm based on YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Time-efficient contact consistent whole-body control framework via reduced-dimension dynamics construction. ACCESS, 13, 2286-2297. (www)

  @article{ , abstract = {Whole-body control frameworks for highly redundant, floating-base robots like humanoids are challenging due to the increased computational complexity arising from their high degrees of freedom. In this paper, the concept of reduced-dimension dynamics previously applied to whole-body control frameworks is extended by integrating it into the operational space formulation. The reduced-dimension dynamics simplifies the dimension of the dynamic model of humanoid robots by partitioning the kinematic chain into constrained and unconstrained chains. The unconstrained chain is reduced using centroidal dynamics, enabling efficient computational modeling in the operational space. By applying the reduced-dimension dynamics to operational space whole-body control, the computational burden caused by the high degrees of freedom in the whole-body dynamics is reduced, leading to shorter computation times. Unlike existing methods that utilize full-dimensional whole-body dynamics, the proposed method constructs a more efficient framework by performing optimization on reduced-dimensional dynamics, combined with operational space formulation. Comparative simulation results demonstrate a significant reduction in computation time, specifically a 41% decrease compared to existing methods, which provides a new approach to contact-consistent control in humanoid robots.}, archive = {J_ACCESS}, author = {Junewhee Ahn and Jaesug Jung and Hokyun Lee and Jaeheung Park}, doi = {10.1109/ACCESS.2024.3522967}, journal = {IEEE Access}, pages = {2286-2297}, shortjournal = {IEEE Access}, title = {Time-efficient contact consistent whole-body control framework via reduced-dimension dynamics construction}, volume = {13}, year = {2025}, }
• (2025). A comprehensive review on sensor fusion techniques for localization of a dynamic target in GPS-denied environments. ACCESS, 13, 2252-2285. (www)

  @article{ , abstract = {Although Global Navigation Satellite Systems (GNSS) typically provide sufficient accuracy for outdoor positioning, positioning systems face significant challenges in complex application scenarios, particularly in GPS-denied environments (GDE). As application demands increase, single-sensor positioning methods are increasingly inadequate to meet the requirements for high accuracy and robustness, making multi-sensor fusion techniques a growing research focus. In recent years, while many studies have aimed to achieve indoor positioning stability and reliability through Simultaneous Localization and Mapping (SLAM), Ultra-Wideband (UWB), Odometry, and Wi-Fi fusion technologies, these efforts have often been conducted in isolation, lacking a systematic synthesis of these fusion methods. Existing reviews predominantly focus on specific aspects of sensor fusion, without providing a comprehensive perspective on the integration of multiple sensor fusion approaches. Notably, there is a particular gap in the comprehensive analysis of Dynamic Target Localization (DTL). DTL involves real-time tracking of moving targets such as pedestrians, robots, vehicles, and Unmanned Aerial Vehicles (UAVs), which not only requires maintaining high accuracy in complex and dynamic environments but also demands the capability to handle the challenges posed by rapidly moving targets. This paper addresses this research gap by systematically reviewing the existing sensor fusion techniques, providing a detailed analysis of the application of SLAM fusion, UWB fusion, odometry fusion, and Wi-Fi fusion in DTL, and thoroughly evaluating their effectiveness and performance. Additionally, this paper summarizes the application of these technologies across different types of moving targets, discusses future development trends and potential challenges, and offers valuable insights for advancing the widespread application of multi-sensor fusion techniques in DTL. Finally, this paper discusses the challenges facing future work in this field, emphasizes what sets this review apart from existing studies, highlights the systematic analysis of multi-sensor fusion methods, and includes specific research findings to more clearly demonstrate the paper’s contributions.}, archive = {J_ACCESS}, author = {Shoude Wang and Nur Syazreen Ahmad}, doi = {10.1109/ACCESS.2024.3519874}, journal = {IEEE Access}, pages = {2252-2285}, shortjournal = {IEEE Access}, title = {A comprehensive review on sensor fusion techniques for localization of a dynamic target in GPS-denied environments}, volume = {13}, year = {2025}, }
• (2025). Observer-based asynchronous guaranteed cost control of 2-D markov jump roesser systems with nonideal transition probabilities. ACCESS, 13, 2239-2251. (www)

  @article{ , abstract = {This paper mainly investigates the asynchronous observer-based guaranteed cost control problem for two-dimensional Markov jump Roesser systems with nonideal transition probabilities. First, a hidden Markov model is developed to describe the asynchronous phenomenon caused by the mode mismatch between the controlled system and the controller. Second, an asynchronous observer-based feedback control law based on the hidden Markov model is designed against the inaccessibility of the system states. Third, since accurate transition probabilities of the Markov chain and the hidden Markov process are difficult to obtain, nonideal transition probabilities are simultaneously considered in both mode transition and mode observation processes. By using the quadratic Lyapunov function techniques and linear matrix inequality methods, some sufficient criteria are developed such that the resultant closed-loop system is asymptotically stable with/without nonideal transition probabilities and satisfies a guaranteed cost performance under three different boundary conditions. Finally, an asynchronous observer-based optimal guaranteed cost controller design algorithm is proposed and two numerical examples are considered to demonstrate its effectiveness.}, archive = {J_ACCESS}, author = {Jiazheng Liu and Yang Yang and Yaru Song}, doi = {10.1109/ACCESS.2024.3523911}, journal = {IEEE Access}, pages = {2239-2251}, shortjournal = {IEEE Access}, title = {Observer-based asynchronous guaranteed cost control of 2-D markov jump roesser systems with nonideal transition probabilities}, volume = {13}, year = {2025}, }
• (2025). Far-field antenna pattern reconstruction through front and back planar near-field scanning. ACCESS, 13, 2229-2238. (www)

  @article{ , abstract = {This paper introduces a method for obtaining the far-field radiation pattern for all elevation angles through planar near-field measurements of both the front and back sides of an antenna under test (AUT). To obtain near-field measurement data for both the front and back sides of the AUT, sequential measurements are performed for each side, and relative phase compensation is applied to correct the phase error between the two measurements. The phase compensated measurement data from both sides can be modeled as an infinitesimal dipole radiating the same near-field, allowing the far-field radiation pattern to be obtained. As a result of validating the proposed method, the radiation pattern showed sensitivity to noise due to the lower signal magnitude on the back side compared to the front, but the directivity demonstrated high accuracy.}, archive = {J_ACCESS}, author = {Jeong Yun Han and Dal-Jae Yun and Young Dam Kim}, doi = {10.1109/ACCESS.2024.3523946}, journal = {IEEE Access}, pages = {2229-2238}, shortjournal = {IEEE Access}, title = {Far-field antenna pattern reconstruction through front and back planar near-field scanning}, volume = {13}, year = {2025}, }
• (2025). Federal deep learning approach of intrusion detection system for in-vehicle communication network security. ACCESS, 13, 2215-2228. (www)

  @article{ , abstract = {In order to stay current with technology, in-vehicle communication has been enhanced daily. Control area network (CAN) is widely adopted due to the exceptional efficiency and reliability. CAN is susceptible to various network-level attacks due to the shortfall in security protocols. This article presents a new approach in addressing this challenge by introducing a unique intrusion detection system. It utilizes a federal learning framework which employs Convolutional Neural Networks as well as Long-Term Short Memory. The goal of this hybrid methodology is to improve intrusion detection efficacy within the framework of federal learning for IoT while overcoming the drawbacks of current techniques. This approach is commonly employed for spatial extraction of features, permitted the model to detect patterns that may indicate possible attacks. Additionally, it is capable of arresting temporal dependencies over time in the information. This work implements a zero-trust security approach for storing information in local device edges and only sharing the weights that were learned with the centralised federal network. This network collects information from different sources for enhancing the correctness of the universal learning approach. Experimental results show that the proposed technique can effectively detect attacks in real-time on the bus, with a much higher detection ratio. Based on the analysis, it can be discovered that the HCRL SA dataset, when used with the proposed hybrid model, with an impressive accuracy rate, and precision of 99.94% and 99.86% respectively. It is also found that the recall and area of the curve perform better by implementing the proposed technique. It is recorded to be 99.10% and 99.21% respectively.}, archive = {J_ACCESS}, author = {In-Seop Na and Anandakumar Haldorai and Nithesh Naik}, doi = {10.1109/ACCESS.2024.3521661}, journal = {IEEE Access}, pages = {2215-2228}, shortjournal = {IEEE Access}, title = {Federal deep learning approach of intrusion detection system for in-vehicle communication network security}, volume = {13}, year = {2025}, }
• (2025). Generalized centered binomial distribution for bimodal lattice signatures. ACCESS, 13, 2203-2214. (www)

  @article{ , abstract = {In CRYPTO 2013, Ducas et al. introduced a bimodal discrete Gaussian distribution into the Fiat-Shamir with abort paradigm, proposing a signature scheme called BLISS, which significantly improved rejection sampling efficiency and reduced the number of required abort steps compared to all previously proposed (uni-modal) lattice-based signature schemes. Although the theoretical structure of the bimodal distribution is elegant, the BLISS implementation suffers from several side-channel vulnerabilities. The primary issue arises from the fact that the probability mass function of the bimodal discrete Gaussian distribution is a transcendental function, which makes it difficult to implement solely with integers in constant-time. To address this issue, we introduce a new distribution called ‘Generalized Centered Binomial Distribution (GCBD)’ that can replace the (bimodal) discrete Gaussian distribution. As a generalized form of the centered binomial distribution (CBD), GCBD is simple and efficient in sampling integers from distributions with large standard deviations. GCBD sampling consists of sampling uniformly random bit-strings and integer addition/subtraction operations, allowing for efficient constant-time implementation. Furthermore, bimodal rejection sampling using GCBD can be implemented only with integer operations in constant-time, with the help of the Newton-Raphson method. By incorporating the bimodal GCBD rejection sampling into BLISS, we present a new variant of BLISS that can be implemented in constant-time, thereby eliminating the side-channel vulnerabilities associated with timing attacks. Finally, we compare the performance of our variant to GALACTICS, a previous constant-time implementation of BLISS. While the signing algorithm of our variant is about 3.3 times slower than that of GALACTICS, GCBD can serve as an alternative to the discrete Gaussian distribution for designing bimodal lattice signatures.}, archive = {J_ACCESS}, author = {Seungwoo Lee and Joo Woo and Jonghyun Kim and Jong Hwan Park}, doi = {10.1109/ACCESS.2024.3523521}, journal = {IEEE Access}, pages = {2203-2214}, shortjournal = {IEEE Access}, title = {Generalized centered binomial distribution for bimodal lattice signatures}, volume = {13}, year = {2025}, }
• (2025). Automated welder safety assurance: A YOLOv3-based approach for real-time detection of welding helmet availability. ACCESS, 13, 2187-2202. (www)

  @article{ , abstract = {This paper presents the development of a novel real-time monitoring and detection system designed to identify the presence of welding helmets on workers’ faces during welding activities. The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained and validated using a diverse dataset that includes images with varying levels of blur, grayscale images, and drone-captured photos. The model’s effectiveness is evaluated using five key performance metrics: accuracy, precision, recall, F1 score, and the AUC-ROC curve. Additionally, the study investigates the impact of various input image sizes, batch sizes, activation functions, and the incorporation of additional convolutional layers on model performance. The results indicate that the Swish activation function, combined with a batch size of 128, an input image size of $256\times 256$ , and the addition of one convolutional layer, yielded superior performance. The model achieved outstanding values of 98% for precision, recall, and F1 score, along with an AUC of 0.98, underscoring its accuracy and reliability in detecting welding helmets.}, archive = {J_ACCESS}, author = {Mohammad Z. Shanti and Chan Yeob Yeun and Chung-Suk Cho and Ernesto Damiani and Tae-Yeon Kim}, doi = {10.1109/ACCESS.2024.3523936}, journal = {IEEE Access}, pages = {2187-2202}, shortjournal = {IEEE Access}, title = {Automated welder safety assurance: A YOLOv3-based approach for real-time detection of welding helmet availability}, volume = {13}, year = {2025}, }
• (2025). Quantum-inspired multi-scale object detection in UAV imagery: Advancing ultra-small object accuracy and efficiency for real-time applications. ACCESS, 13, 2173-2186. (www)

  @article{ , abstract = {Ultra-small object detection in UAV imagery presents significant challenges due to scale variation, environmental complexity, and computational constraints. This study introduces a quantum-inspired multi-scale object detection model designed to address these issues effectively. By integrating quantum-inspired sub-pixel convolution, adversarial training, and self-supervised learning, the model enhances detection accuracy, robustness, and computational efficiency. These advancements are particularly critical for UAV applications such as surveillance, precision agriculture, disaster response, and environmental monitoring. The proposed model was evaluated on the VisDrone2019 dataset and benchmarked against state-of-the-art methods, including YOLOv4, YOLO11, RT-DETR, and EfficientDet. It achieved 65.3% precision, 52.4% recall, and a mean Average Precision (mAP) of 34.5%, outperforming conventional models in detecting ultra-small objects. Efficiency optimizations, including structured pruning and quantization, reduced computational load to 30 GFLOPS with an inference time of 8.1 milliseconds, ensuring suitability for real-time UAV applications on resource-constrained platforms. This research offers a practical and robust solution for UAV-based object detection tasks, combining state-of-the-art accuracy with operational efficiency. It also establishes a foundation for future advancements, including scalability to diverse datasets, integration with edge computing platforms, and the exploration of quantum computing techniques. These contributions pave the way for enhanced capabilities in computer vision and autonomous aerial systems.}, archive = {J_ACCESS}, author = {Muhammad Muzammul and Muhammad Assam and Ayman Qahmash}, doi = {10.1109/ACCESS.2024.3523405}, journal = {IEEE Access}, pages = {2173-2186}, shortjournal = {IEEE Access}, title = {Quantum-inspired multi-scale object detection in UAV imagery: Advancing ultra-small object accuracy and efficiency for real-time applications}, volume = {13}, year = {2025}, }
• (2025). A robust VMD-based modified multivariable output error statespace method for detection of low-frequency oscillations from noisy signals of WAMS. ACCESS, 13, 2152-2172. (www)

  @article{ , abstract = {A poorly damped set of power oscillations interconnected power systems may lead to system-wide breakups or considerably reduce the power transfer over critical channels and corridors. As a result, identifying and suppressing these oscillatory modes is a critical challenge in modern power systems. Advances in wide area monitoring systems in modern power system facilitates the numerous data for the analysis of the system characteristics. Wide Area Monitoring Systems (WAMS) have enabled the collection of large amounts of data, aiding in system analysis. Phasor measurement units may experience noisy signals during measurement which may lead to poor identification of low frequency oscillatory modes or introduce extraneous modes during analysis using state of the art methods. This research examines the performance of the Robust Variable Mode Decomposition (VMD) technique for analyzing signals contaminated with noise. The study employs particle swarm optimization to determine the appropriate quantity of Intrinsic Mode Functions (IMFs). Additionally, it applies Anderson-Darling (AD) and Kolmogorov-Smirnov (KD) statistical distance measures to identify the effective number of IMFs, enabling the differentiation between signal IMFs and noise IMFs. Wavelet de-noising technique is employed for the de-noising the sum of chosen IMF signals. To validate the selection of adequate IMFs a heuristic approach is used to monitor the Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for effectively seperating noisy contents from actual content of the signal. The proposed system incorporates the wavelet based filter in conventional VMD which not only helps in fetching the frequency but also helps in filtering the noise with appropriate selection of the mother wavelet, i.e. it utilizes the fusion of VMD and Wavelet Transform. The approach leverages the adaptability of VMD’s decomposition alongside the precise denoising capabilities of WT, resulting in high effectiveness for signals that contain noise. This paper introduces MMOESP, a modified Multivariable Output Error State Space method, for identifying low-frequency oscillations. Additionally, data-driven participation factors are discussed to identify the dominant generator and locate the damping controller. Case studies were performed on the IEEE 10-machine, 39-bus system. The results are found to be in consistent with the eigenvalue-based approach. The limitations of these approaches were analyzed for measured signals with noise. Modifications have been proposed to improve the identification accuracy in noisy environments.}, archive = {J_ACCESS}, author = {Dinesh Shetty and Nagesh Prabhu}, doi = {10.1109/ACCESS.2024.3519113}, journal = {IEEE Access}, pages = {2152-2172}, shortjournal = {IEEE Access}, title = {A robust VMD-based modified multivariable output error statespace method for detection of low-frequency oscillations from noisy signals of WAMS}, volume = {13}, year = {2025}, }
• (2025). A spatiotemporal feature extraction technique using superlet-CNN fusion for improved motor imagery classification. ACCESS, 13, 2141-2151. (www)

  @article{ , abstract = {In the realm of Brain-Computer Interface (BCI) research, the precise decoding of motor imagery electroencephalogram (MI-EEG) signals is pivotal for the realization of systems that can be seamlessly integrated into practical applications, enhancing the autonomy of individuals with mobility impairments. This study presents an enhanced method for the precise recognition of MI tasks using EEG data, to facilitate more intuitive interactions between individuals with mobility challenges and their environment. The core challenge addressed herein is the development of robust algorithms that enable the accurate identification of MI tasks, thereby empowering individuals with mobility impairments to control devices and interfaces through cognitive commands. Although there are many different methods for analyzing MI-EEG signals, research into deep learning and transfer learning approaches for MI-EEG analysis remains scarce. This research leverages the superlet transform (SLT) to transform EEG signals into a two-dimensional (2-D) high-resolution spectral representation. This 2-D representation of segmented MI-EEG signals is then processed through an adapted pretrained residual network, which classifies the MI-EEG signals. The effectiveness of the suggested technique is evident as the achieved classification accuracy is 99.9% for binary tasks and 96.4% for multi-class tasks, representing a significant advancement over existing methods. Through an intensive comparison with present algorithms assessed in variety of performance evaluating metrics the present study emphasize the exceptional ability of proposed approach to accurately classify the different MI categories from the EEG signals and which is a great contribution to the field of BCI research field.}, archive = {J_ACCESS}, author = {Neha Sharma and Manoj Sharma and Amit Singhal and Nuzhat Fatema and Vinay Kumar Jadoun and Hasmat Malik and Asyraf Afthanorhan}, doi = {10.1109/ACCESS.2024.3517639}, journal = {IEEE Access}, pages = {2141-2151}, shortjournal = {IEEE Access}, title = {A spatiotemporal feature extraction technique using superlet-CNN fusion for improved motor imagery classification}, volume = {13}, year = {2025}, }
• (2025). Uniform quantization for multi-antenna Amplify–Quantize–Forward relay. ACCESS, 13, 2127-2140. (www)

  @article{ , abstract = {In this paper, we investigate a multi-antenna amplify-quantize–forward (AQF) relay system, where a half-duplex relay quantizes both the real and imaginary components of amplified received signals, storing them in memory before forwarding them to a destination. Our focus lies on uniform quantization at the relay in the multiple-input multiple-output (MIMO) AQF relay channel. We start with theoretical analyses to determine quantization step sizes by deriving the power and mean square error of the quantized signals as functions of these step sizes. Subsequently, we introduce neural network-based deep learning methods to mitigate computational complexity. Specifically, we present supervised learning (SL) and unsupervised learning (USL) methodologies, with the latter employing a novel loss function designed to avoid the need for extensive training data collection. Furthermore, we propose a USL method with Monte Carlo (USL-MC), leveraging an approximated loss function to address the heightened complexity observed in multi-antenna systems. Given the challenge of finding optimal step sizes for the MIMO AQF relay, comparing outcomes across different determination algorithms proves to be a significant obstacle. To address this, we evaluate error performance at the destination for the entire AQF relay communication system by introducing a linear detection method with significantly reduced complexity in the MIMO AQF relay channel. The numerical results demonstrate that the quantization step sizes determined through deep learning methods exhibit excellent performance with small computational burden during implementation, particularly when compared to the theoretical approach. Notably, the USL-MC method stands out for its significantly reduced complexity, both in practical implementation and training, rendering half-duplex AQF relays viable for practical resource-constrained systems.}, archive = {J_ACCESS}, author = {Gangsan Jeong and Xianglan Jin}, doi = {10.1109/ACCESS.2024.3494752}, journal = {IEEE Access}, pages = {2127-2140}, shortjournal = {IEEE Access}, title = {Uniform quantization for multi-antenna Amplify–Quantize–Forward relay}, volume = {13}, year = {2025}, }
• (2025). Leveraging lightweight hybrid ensemble distillation (HED) for suspect identification with face recognition. ACCESS, 13, 2112-2126. (www)

  @article{ , abstract = {Face recognition biometric systems focus on identifying individuals by extracting their facial characteristics. However, these systems often fail or are misclassified because of external factors, obstructions, and varying environmental conditions. Traditional models cannot effectively handle these variations, leading to inaccuracies. Moreover, the complexity and computational demands of advanced models can hinder their real-time application. In this study, the Hybrid Ensemble Distillation (HED) model addresses these issues by leveraging both knowledge distillation and an ensemble of pre-trained models (VGG16, ResNet50, and DenseNet121) to enhance the precision and proficiency of categorization. The model combines the strengths of these architectures while utilizing data augmentation techniques such as GANs to enhance the training dataset. The proposed model demonstrated high efficiency and accuracy, with the teacher model achieving 98.42% accuracy and the student model reaching 96.78% validation accuracy, thereby highlighting the efficacy of knowledge distillation. It also showed progressive improvements in the validation accuracy and loss reduction over 350 epochs, emphasizing the robustness of the training process. This lightweight method helps identify suspects or individuals because the model was trained using 360-degree images in the dataset, ensuring comprehensive feature extraction from multiple angles. The reduced computational requirements and high accuracy make this approach suitable for real-time applications, thereby enhancing its practicality for various human identification tasks.}, archive = {J_ACCESS}, author = {Vaishnavi Munusamy and Sudha Senthilkumar}, doi = {10.1109/ACCESS.2024.3523101}, journal = {IEEE Access}, pages = {2112-2126}, shortjournal = {IEEE Access}, title = {Leveraging lightweight hybrid ensemble distillation (HED) for suspect identification with face recognition}, volume = {13}, year = {2025}, }
• (2025). A FSTCN-based leak detection method for large-scale pipeline transportation systems. ACCESS, 13, 2101-2111. (www)

  @article{ , abstract = {As one of the five major transportation systems, pipeline plays an important role in the energy transportation systems. In large-scale pipeline transportation systems, security issues such as leaks and explosions are prevalent, thus early detection of leaks is important to reduce security hazards in pipeline systems. Time series-based studies are widely used for leak detection in large-scale pipeline transportation systems, but single time-domain information, which ignores the spatial distribution of pressure sensors and does not consider periodic features, may not be sufficient for the detection accuracy of complex systems. To address it, a leak detection method based on frequency spatial-temporal convolution network (FSTCN) is proposed in this paper. Next, a spatial-encoder module for leak detection is proposed, which considers the spatial correlation of pressure sensors in pipeline systems. Second, a frequency-enhanced attention layer is proposed, which enables the feature extraction module to capture the periodic features of the pressure data. Meanwhile, a network self-updating mechanism is proposed which considers the changes in detection accuracy and data distribution to adapt to the continuously changing conditions of the pipeline systems. Finally, experiments are used to validate the proposed method, and nine time series classification models are chosen for comparison. The comprehensive results demonstrate that the effectiveness and superiority of the proposed leak detection method for large-scale pipeline systems.}, archive = {J_ACCESS}, author = {Du Zhang and Chang-Su Kim and Chul-Hyun Hwang and Tae-Jun Lee and Hoe-Kyung Jung}, doi = {10.1109/ACCESS.2024.3522303}, journal = {IEEE Access}, pages = {2101-2111}, shortjournal = {IEEE Access}, title = {A FSTCN-based leak detection method for large-scale pipeline transportation systems}, volume = {13}, year = {2025}, }
• (2025). RAPID: Redundancy analysis with parallelized and intelligent distribution. ACCESS, 13, 2089-2100. (www)

  @article{ , abstract = {The continuous progress in semiconductor technology, particularly in nanotechnology, has led to smaller memory cells and increased fault frequency due to their proximity. These faults reduce memory yield and raise production costs. Redundancy Analysis (RA) offers an effective solution by allocating spare cells within memory to repair faulty lines. The main goal of RA is to optimally place spare cells rapidly to achieve the highest repair rate, resulting in the development of various RA methods. In modern memory architectures, complex spare structures are essential for improving repair rates. However, traditional RA methods can be too time-consuming for such structures, limiting the repair of multiple memories. This paper proposes a method called RAPID, which leverages GPU technology for the parallel repair of multiple memories. RAPID generates repair cases by segmenting areas based on available spare types, ensuring efficient GPU memory use and enabling simultaneous repairs. Repair cases are applied immediately upon fault detection during testing, significantly reducing repair time. Experimental results show that RAPID can repair more memory units in less repair time than previous GPU-based RA methods under the same GPU and CPU memory specifications.}, archive = {J_ACCESS}, author = {Younwoo Yoo and Hayoung Lee and Seung Ho Shin and Sungho Kang}, doi = {10.1109/ACCESS.2024.3523940}, journal = {IEEE Access}, pages = {2089-2100}, shortjournal = {IEEE Access}, title = {RAPID: Redundancy analysis with parallelized and intelligent distribution}, volume = {13}, year = {2025}, }
• (2025). Improved sliding mode control for performance enhancement of PMD battery charger using fuzzy logic control. ACCESS, 13, 2080-2088. (www)

  @article{ , abstract = {This paper presents an improved sliding mode control (SMC) for performance enhancement of personal mobility device (PMD) battery charger using fuzzy logic control. The PMD industry has recently been activated to reduce environmental pollution and accordingly, research on PMD battery chargers is being actively conducted. Typically, proportional-integral (PI) control or SMC methods are used to control the output voltage and inductor current of PMD battery charger. However, increasing the gain of PI controllers and SMC method to improve performance results in overshoot in output voltage and ripple in inductor current due to chattering phenomena. Therefore, to overcome the disadvantage, this paper presents improved sliding mode control for performance enhancement of PMD battery charger using fuzzy logic control (FLC). The proposed control method determines the gain used in the SMC by using FLC, and determines the gain of the controller through the membership function and the output intensity, thereby reducing ripple and improving responsiveness. The validity of the proposed control method is verified by the simulation and experimental results.}, archive = {J_ACCESS}, author = {Gyuri Kim and Yeongsu Bak}, doi = {10.1109/ACCESS.2024.3523473}, journal = {IEEE Access}, pages = {2080-2088}, shortjournal = {IEEE Access}, title = {Improved sliding mode control for performance enhancement of PMD battery charger using fuzzy logic control}, volume = {13}, year = {2025}, }
• (2025). Metaheuristics and large language models join forces: Toward an integrated optimization approach. ACCESS, 13, 2058-2079. (www)

  @article{ , abstract = {Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algorithms. This paper introduces a novel approach that leverages LLMs as pattern recognition tools to improve MHs. The resulting hybrid method, tested in the context of a social network-based combinatorial optimization problem, outperforms existing state-of-the-art approaches that combine machine learning with MHs regarding the obtained solution quality. By carefully designing prompts, we demonstrate that the output obtained from LLMs can be used as problem knowledge, leading to improved results. Lastly, we acknowledge LLMs’ potential drawbacks and limitations and consider it essential to examine them to advance this type of research further. Our method can be reproduced using a tool available at: https://github.com/camilochs/optipattern.}, archive = {J_ACCESS}, author = {Camilo Chacón Sartori and Christian Blum and Filippo Bistaffa and Guillem Rodríguez Corominas}, doi = {10.1109/ACCESS.2024.3524176}, journal = {IEEE Access}, pages = {2058-2079}, shortjournal = {IEEE Access}, title = {Metaheuristics and large language models join forces: Toward an integrated optimization approach}, volume = {13}, year = {2025}, }
• (2025). Machine learning-based retinal neovascularization localization in en face optical coherence tomography angiography images using vessel features. ACCESS, 13, 2045-2057. (www)

  @article{ , abstract = {Retinal neovascularization (RNV) is a pathological ocular disorder marked by the development of abnormal blood vessels within the retinal tissue, leading to potential complications such as hemorrhage and vision loss. It can be visualized using Optical Coherence Tomography Angiography (OCTA), a specialized imaging tool that allows for the detailed analysis of retinal blood vessels. However, identifying RNVs within OCTA images poses challenges including variability in patterns and sizes, overlapping vascular networks, complex morphology, noise and artifacts, low contrast, depth variability, and interpatient variability. This study addressed this challenge by extracting vessel density, bifurcation points, and vessel thinness features to pinpoint regions of interest. Our systematic approach led to selecting potential regions using a support vector machine (SVM). Notably, our method achieved a localization precision of 90.33% in two datasets comprising 69 en face OCTA images, demonstrating a superior F1-score than the state-of-the-art methods, which used VNet and the weighted combination of feature maps by 44.19% and 57.46%, respectively.}, archive = {J_ACCESS}, author = {Yar Zar Tun and Pakinee Aimmanee}, doi = {10.1109/ACCESS.2024.3522324}, journal = {IEEE Access}, pages = {2045-2057}, shortjournal = {IEEE Access}, title = {Machine learning-based retinal neovascularization localization in en face optical coherence tomography angiography images using vessel features}, volume = {13}, year = {2025}, }
• (2025). Joint stochastic computation offloading and trajectory optimization for unmanned-aerial-vehicle-assisted mobile edge computing. ACCESS, 13, 2034-2044. (www)

  @article{ , abstract = {In Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAVs) equipped with communication and computation capabilities can be used as an edge node, which can not only satisfy the user’s demand for high computation power and low latency, but also extend the range of computation services and enhance the mission quality in environments with limited communication facilities. In this study, we investigate a UAV-assisted MEC system with stochastic computing tasks. The system seeks to minimize overall energy consumption by optimizing computation offloading, resource allocation, and the UAV’s trajectory scheduling. This objective corresponds to a stochastic optimization problem. Given the problem’s non-convex nature and the temporal coupling of variables, the Lyapunov optimization method is employed to analyze the task queue, breaking down the original optimization problem into three independent and more manageable subproblems. A joint optimization algorithm with iterative solving is proposed for solving the three sub-problems and then obtaining the stochastic computation offloading, resource allocation, and trajectory scheduling strategy. The simulation results show that the proposed strategy is able to achieve an effective compromise between the system’s energy consumption and queue stability by adjusting the Lyapunov parameters, and significantly reduce energy consumption compared to the baseline strategies.}, archive = {J_ACCESS}, author = {Yi Zhou}, doi = {10.1109/ACCESS.2024.3519598}, journal = {IEEE Access}, pages = {2034-2044}, shortjournal = {IEEE Access}, title = {Joint stochastic computation offloading and trajectory optimization for unmanned-aerial-vehicle-assisted mobile edge computing}, volume = {13}, year = {2025}, }
• (2025). TAD: A large-scale benchmark for traffic accidents detection from video surveillance. ACCESS, 13, 2018-2033. (www)

  @article{ , abstract = {Automatic traffic accident detection has attracted the attention of the machine vision community for the rapid development of autonomous intelligent transportation systems (ITS). However, previous studies in this domain have been constrained by small-scale datasets with limited scope, impeding their effectiveness and applicability. Specifically, highway traffic accidents, often resulting in severe consequences due to higher speeds, require a more comprehensive approach to detection. The use of video surveillance provides a unique perspective, capturing the entire accident sequence. Unfortunately, existing traffic accident datasets are either not sourced from surveillance cameras, not publicly available, or not tailored for highway scenarios. An open-sourced traffic accident dataset with various scenes from surveillance cameras is in great need and of practical importance. To fulfill the above urgent need, we endeavor to collect abundant video data of real traffic accidents and propose a large-scale traffic accidents dataset, named TAD. Various experiments on image classification, video classification, and object detection tasks, using public mainstream vision algorithms or frameworks are conducted in this work to demonstrate the performance of different methods. The proposed dataset together with the experimental results are presented as a new benchmark to improve computer vision research, especially in ITS. The dataset is publicly available at https://github.com/UnicomAI/UnicomBenchmark/tree/main/TADBench.}, archive = {J_ACCESS}, author = {Yajun Xu and Huan Hu and Chuwen Huang and Yibing Nan and Yuyao Liu and Kai Wang and Zhaoxiang Liu and Shiguo Lian}, doi = {10.1109/ACCESS.2024.3522384}, journal = {IEEE Access}, pages = {2018-2033}, shortjournal = {IEEE Access}, title = {TAD: A large-scale benchmark for traffic accidents detection from video surveillance}, volume = {13}, year = {2025}, }
• (2025). RHLS: A robust hybrid level set model using global-local signed energy-based pressure force for medical image segmentation. ACCESS, 13, 2004-2017. (www)

  @article{ , abstract = {Medical image segmentation often encounters significant challenges due to noise and intensity inhomogeneity. While Level Set Models (LSMs) are widely used for segmentation, their effectiveness in these scenarios remains limited. This paper proposes a novel Robust Hybrid Level Set (RHLS) model specifically designed to address these limitations in medical image segmentation. The RHLS model tackles noise and intensity inhomogeneity by incorporating both global and local information into its energy function through a Signed Pressure Force (SPF) formulation. It leverages three distinct energy terms: Global Region-Based Term (GRT) which enhances robustness against various noise types using the Huber loss function, Global-Local Region-Based Term (GLRT) which promotes accurate contour propagation while mitigating noise influence, and Local Region-Based Term (LRT) which improves detection of faint edges and mitigates intensity inhomogeneity. These terms, combined with a regularization term, enable the RHLS model to exploit the strengths of both region-based and edge-based approaches. This leads to superior handling of noise and intensity variations compared to existing methods. Extensive evaluation using synthetic and real medical images demonstrates the effectiveness of the RHLS model. It achieves an average accuracy of 97.2% and 95.6% on synthetic and real medical data, respectively, using the Dice similarity measure. These results validate the model’s ability to handle various noise types and intensity variations, showcasing its potential for improved medical image segmentation tasks.}, archive = {J_ACCESS}, author = {M. Almasganj and E. Fatemizadeh}, doi = {10.1109/ACCESS.2024.3522113}, journal = {IEEE Access}, pages = {2004-2017}, shortjournal = {IEEE Access}, title = {RHLS: A robust hybrid level set model using global-local signed energy-based pressure force for medical image segmentation}, volume = {13}, year = {2025}, }
• (2025). ImproveYourVideos: Architectural improvements for text-to-video generation pipeline. ACCESS, 13, 1986-2003. (www)

  @article{ , abstract = {In recent years, text-to-video models have made impressive strides toward producing high-quality results. However, the architectural aspects of these models remain largely unexplored, as prior studies have used well-known approaches that offer minimal variation between models. This study aims to address this gap by systematically exploring alternative options for text-to-video architecture building blocks, specifically focusing on the temporal layer, frame interpolation model, and autoencoder. Firstly, we propose and compare various alternative temporal consistency modules, referred to as temporal blocks, which demonstrate improvements in generation quality metrics and human evaluation. Secondly, we introduce a novel frame interpolation architecture that offers an inference speed boost over three times that of the standard Masked Frame interpolation approach. Finally, we evaluate different approaches for extending the MoVQ image autoencoder to the video domain. The experimental results show that the model with the proposed modifications strongly outperforms the model with classical blocks quantitatively and qualitatively. Our final model is available here: https://github.com/ai-forever/KandinskyVideo.}, archive = {J_ACCESS}, author = {Vladimir Arkhipkin and Zein Shaheen and Viacheslav Vasilev and Elizaveta Dakhova and Konstantin Sobolev and Andrey Kuznetsov and Denis Dimitrov}, doi = {10.1109/ACCESS.2024.3522510}, journal = {IEEE Access}, pages = {1986-2003}, shortjournal = {IEEE Access}, title = {ImproveYourVideos: Architectural improvements for text-to-video generation pipeline}, volume = {13}, year = {2025}, }
• (2025). Destination-aware time-constrained task allocation in mobile crowdsensing for orienteering application. ACCESS, 13, 1974-1985. (www)

  @article{ , abstract = {Orienteering, a sport that combines navigation with running, requires efficient route planning and real-time decision-making, which can benefit significantly from mobile crowdsensing (MCS) technologies. MCS enables the platform to provide data-based service by incentivizing mobile users to perform sensing tasks and gathering sensing data from them. An extremely important problem in MCS is how to achieve effective task allocation in the context of participatory sensing, where mobile users are selectively recruited to perform tasks. However, most current work in this aspect does not consider the condition where multiple time constraints coexist, but models the problem from a single time constraint. Moreover, only a very small number of studies have paid attention to the characteristics of user destinations. Therefore, according to the shortcomings of the above research, we propose a multi-time-constrained task allocation based on origin-destination users (TAOD) problem in MCS for orienteering application, which is proved to be NP-hard. In order to solve the TAOD problem, we firstly design a path reconnection algorithm based on double-ended priority queue (PR-DEPQ) for the single-user scenario. Then, we extend the PR-DEPQ to multi-user task allocation scenario and present a multi-user path reconnection algorithm based on sensing value priority (MUPR). Extensive simulation results show the higher performance of our proposed MUPR as compared with the existing baseline algorithms.}, archive = {J_ACCESS}, author = {Xia Hua and Lei Han}, doi = {10.1109/ACCESS.2024.3523948}, journal = {IEEE Access}, pages = {1974-1985}, shortjournal = {IEEE Access}, title = {Destination-aware time-constrained task allocation in mobile crowdsensing for orienteering application}, volume = {13}, year = {2025}, }
• (2025). Square duct cleaning machine using a multistage planetary gear mechanism for removing grease. ACCESS, 13, 1962-1973. (www)

  @article{ , abstract = {This study proposes a cleaning machine using a multistage planetary gear mechanism to remove accumulated grease from kitchen ducts with various geometries, including circular and square configurations. The contributions of this study encompass the design of the cleaning machine, that can scrub off grease accumulated at the four corners and near the central regions of the inner wall of square ducts at once—a task that has been difficult in the past. Furthermore, velocity simulations estimated the relative velocity of the brushes to the duct wall. Additionally, the results of the grease cleaning experiment conducted using the proposed machine were discussed. The aim was to clean accumulated grease within ducts using a single robot to prevent duct fires. It is difficult for conventional duct cleaning robots to clean grease because they do not consider the distinct properties inherent to grease, characterized by its elevated viscosity and tenacious adhesion. This study described the design and experiments of the cleaning machine, which enabled the machine to scrub the entire inner wall of a duct at a relative velocity of the brushes of 60 mm/s or higher, required to scrub off grease from the duct. The cleaning rate of the grease by the proposed machine was up to 80 % which is 2.3 times higher than in a previous study, and the proposed machine is expected to remove most of the grease accumulated in ducts. The results are expected to expand the cleaning area in cleaning the inner walls of square ducts.}, archive = {J_ACCESS}, author = {Yosuke Momma and Takehiro Hitomi and Fumio Ito and Taro Nakamura}, doi = {10.1109/ACCESS.2024.3520349}, journal = {IEEE Access}, pages = {1962-1973}, shortjournal = {IEEE Access}, title = {Square duct cleaning machine using a multistage planetary gear mechanism for removing grease}, volume = {13}, year = {2025}, }
• (2025). Visual deepfake detection: Review of techniques, tools, limitations, and future prospects. ACCESS, 13, 1923-1961. (www)

  @article{ , abstract = {In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. Though technology has primarily been utilized for beneficial purposes, such as education and entertainment, it is also used for malicious or unethical tasks to spread disinformation or ruin someone’s dignity, even if it encompasses harassing and blackmailing victims. Deepfakes refer to high-quality and realistic multimedia-manipulated content that has been digitally modified or synthetically generated. We conducted a systematic literature review of deepfake detection to offer an updated overview of existing research work that initially describes the widely accessible deepfake generation tools, classifications, and detection process. We highlighted recent techniques in visual deepfake detection based on the feature representations, grouped into four domains: spatial, temporal, frequency, and spatio-temporal, including their key features and limitations by providing details of existing datasets, together with the potentials of deepfake and its future directions. This study tried to add an updated repository of technological change in deepfake, which could help researchers to develop robust deepfake models.}, archive = {J_ACCESS}, author = {Naveed Ur Rehman Ahmed and Afzal Badshah and Hanan Adeel and Ayesha Tajammul and Ali Daud and Tariq Alsahfi}, doi = {10.1109/ACCESS.2024.3523288}, journal = {IEEE Access}, pages = {1923-1961}, shortjournal = {IEEE Access}, title = {Visual deepfake detection: Review of techniques, tools, limitations, and future prospects}, volume = {13}, year = {2025}, }
• (2025). Analysis of intergenerational trauma transmission in artificial human families: A metaverse-based study of complex psychological phenomena. ACCESS, 13, 1908-1922. (www)

  @article{ , abstract = {This study investigated the occurrence of intergenerational trauma transmission within artificial human families in metaverse. Using an artificial human generation platform, we created 24 replicated artificial human families consisting of parents with Posttraumatic Stress Disorder (PTSD) and depression and psychologically normal children, along with 24 emotionally stable control families for the experiment. The results confirmed that intergenerational trauma transmission, similar to that in real human societies, can occur in artificial human societies as well. Children of parents with PTSD showed significantly higher levels of depression, anxiety, and PTSD symptoms compared to the control group, and negative effects were also observed in language use patterns and emotional expression. These findings demonstrate that complex psychological phenomena analogous to humans can occur in artificial human societies, providing important implications for the design and management of artificial humans in metaverse. Furthermore, this research presents a new methodology that can overcome the ethical and practical constraints of various psychological and psychiatric studies involving real humans.}, archive = {J_ACCESS}, author = {Seung Hoon Kang and Jae Young Lee and Teressa Talluri and Amarnathvarma Angani and Kyoo Jae Shin}, doi = {10.1109/ACCESS.2024.3523398}, journal = {IEEE Access}, pages = {1908-1922}, shortjournal = {IEEE Access}, title = {Analysis of intergenerational trauma transmission in artificial human families: A metaverse-based study of complex psychological phenomena}, volume = {13}, year = {2025}, }
• (2025). Pixel-level non-local method-based depth image inpainting. ACCESS, 13, 1896-1907. (www)

  @article{ , abstract = {Each pixel intensity value in a depth image represents the depth information in the scene. However, during the generation of depth images, some important information may be lost in the form of pieces, which can seriously impact subsequent applications, such as computer vision. Existing methods attempt to apply traditional image restoration methods for filling the lost areas in depth images have proven ineffective in inpainting the structural information in the scene. In this paper, we propose a depth image inpainting method based on pixel-level non-local method. In order to effectively inpaint the depth images, we add 0.7 density salt-and-pepper noise to the original depth image in advance, we then implement image block-matching on the noise-added depth image to obtain some similar image block groups, and then scan each image block to form a column vector. We further stack all vectors to construct a two-dimensional matrix. Finally, we implement row-matching on the constructed matrix to obtain many similar pixel groups. The separable Haar transform is implemented on each similar pixel groups, hard-thresholding on transformed coefficients can effectively remove the added noise and bring the original neighbor pixels in the depth image into the information lost area, thus, the lost information can be effectively inpainted. Inverse Haar transform, row-aggregation and block-aggregation are successfully implemented to complete the depth image inpainting. Experimental results show that the proposed method can achieve higher PSNR values than existing methods on public datasets, and the subjective visual quality of the inpainted images is also satisfactory.}, archive = {J_ACCESS}, author = {Xiaoya Dai and Yingkun Hou and Shuqi Zhang and Hao Hou and Bin Feng and Tao Lin and Mengyu Liu}, doi = {10.1109/ACCESS.2024.3523504}, journal = {IEEE Access}, pages = {1896-1907}, shortjournal = {IEEE Access}, title = {Pixel-level non-local method-based depth image inpainting}, volume = {13}, year = {2025}, }
• (2025). Some remarks on practical stabilization via CLF-based control under measurement noise. ACCESS, 13, 1882-1895. (www)

  @article{ , abstract = {Practical stabilization of input-affine systems in the presence of measurement errors and input constraints is considered in this brief note. Assuming that a Lyapunov function and a stabilizing control exist for an input-affine system, the required measurement accuracy at each point of the state space is computed. This is done via the Lyapunov function-based decay condition, which describes along with the input constraints a set of admissible controls. Afterwards, the measurement time points are computed based on the system dynamics. It is shown that between these self-triggered measurement time points, the system evolves and converges into the so-called target ball, i.e. a vicinity of the origin, where it remains. Furthermore, it is shown that the approach ensures the existence of a control law, which is admissible for all possible states and it introduces a connection between measurement time points, measurement accuracy, target ball, and decay. The results of the approach are shown in two examples.}, archive = {J_ACCESS}, author = {Patrick Schmidt and Pavel Osinenko and Stefan Streif}, doi = {10.1109/ACCESS.2024.3521048}, journal = {IEEE Access}, pages = {1882-1895}, shortjournal = {IEEE Access}, title = {Some remarks on practical stabilization via CLF-based control under measurement noise}, volume = {13}, year = {2025}, }
• (2025). Improved motion correction in dynamic contrast-enhanced MRI using low rank with soft weighting. ACCESS, 13, 1867-1881. (www)

  @article{ , abstract = {This paper introduces a motion-corrected, free-breathing dynamic contrast-enhanced (DCE) MRI reconstruction method, termed low-rank plus sparse (L+S) with soft weighting. We designed a soft weighting matrix that smoothly transitions spokes between the target and other motion states to suppress motion blurring. The optimized fast iterative shrinkage-thresholding algorithm (FISTA) was employed to solve the L+S optimization problem, enabling faster convergence and better image quality. A DCE-MRI computer simulation framework, based on a modified Shepp-Logan model, was used as ground truth to quantify the motion suppression errors. Both simulation and clinical datasets demonstrate that the proposed method provides superior motion correction, higher temporal resolution, and over 5 times faster reconstruction speed than existing motion-corrected GRASP frameworks.}, archive = {J_ACCESS}, author = {Jichang Zhang and Faisal Najeeb and Yulin Wang and Xinpei Wang and Pengfei Xu and Hammad Omer and Jianjun Zheng and Jingfeng Zhang and Sue Francis and Paul Glover and Richard Bowtell and Thomas Meersmann and Chengbo Wang}, doi = {10.1109/ACCESS.2024.3522204}, journal = {IEEE Access}, pages = {1867-1881}, shortjournal = {IEEE Access}, title = {Improved motion correction in dynamic contrast-enhanced MRI using low rank with soft weighting}, volume = {13}, year = {2025}, }
• (2025). Multi-UE 5G RAN measurements: A gamut of architectural options. ACCESS, 13, 1846-1866. (www)

  @article{ , abstract = {The Radio Access Network (RAN) of Fifth Generation (5G) communication systems critically influences the overall performance and cost of a 5G system. 5G RANs allow for a gamut of implementations ranging from closed (carrier-grade) implementations by traditional vendors that are expensive and configured by the vendor to RANs that are compliant with the specifications of the O-RAN ALLIANCE, i.e., to so-called O-RANs. O-RANs are available in closed-source and open-source implementations; O-RANs can be configured by the operator and are cheaper than the closed RANs. Understanding the packet-level performance characteristics of these different 5G RAN implementations is a prerequisite for informed network planning and deployment, particularly for Non-Public Networks (NPNs) for specific use cases. Existing measurement studies have mainly considered a single User Equipment (UE); however, practical networks typically operate with multiple UEs. We conduct packet-level measurements for concurrent over-the-air transmissions to (downstream) and from (upstream) multiple UEs for a wide range of packet transmission rates. We consider current representative implementations of three RAN architectural options: closed RAN (based on Nokia system), closed-source O-RAN (based on Airspan system), and open-source O-RAN (based on srsRAN Project system). We find that the one-way packet delays and the packet loss probabilities can vary sharply among the multiple concurrently operating UEs (which are identical in model and configuration) in the closed RAN and open-source O-RAN; whereas, the packet-level performance tends to be more consistent among the UEs in the closed-source O-RAN. Our measurement results provide a baseline for future research on refining RAN operations for multiple concurrent packet flows.}, archive = {J_ACCESS}, author = {Andreas Ingo Grohmann and Mauri Seidel and Sebastian A. W. Itting and Ray-Guang Cheng and Martin Reisslein and Frank H. P. Fitzek}, doi = {10.1109/ACCESS.2024.3523467}, journal = {IEEE Access}, pages = {1846-1866}, shortjournal = {IEEE Access}, title = {Multi-UE 5G RAN measurements: A gamut of architectural options}, volume = {13}, year = {2025}, }
• (2025). Deep learning-based channel estimation to mitigate channel aging in massive MIMO with pilot contamination. ACCESS, 13, 1834-1845. (www)

  @article{ , abstract = {In time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) between the base station (BS) and user terminal (UT) is crucial for efficient signal processing, including received signal separation and transmission precoding. However, due to the time-varying nature of wireless channels and the limited coherence time, the pilot signals must be short, and the number of orthogonal pilot sequences is finite. Consequently, pilot signal reuse across neighboring cells leads to pilot contamination, significantly degrading channel estimation accuracy. Traditional methods like minimum mean square error (MMSE) estimation require prior knowledge of the channel covariance matrix, which is often unavailable. This paper proposes a novel deep learning-based channel estimation approach that effectively mitigates the dual challenges of channel aging and pilot contamination. The method leverages two distinct convolutional neural networks (CNNs): one for processing pilot signals to suppress inter-cell interference and another for data signals to address both inter-cell and intra-cell interference. To further enhance estimation accuracy, a smoothing filter is employed to minimize local distortions caused by incorrect symbol detection in the time domain. Simulation results demonstrate the effectiveness of the proposed method, particularly at normalized Doppler frequencies above 0.01, where it significantly outperforms conventional techniques and interpolation-based approaches. The results highlight the proposed method’s robustness in maintaining high channel estimation accuracy in dynamic and interference-prone environments.}, archive = {J_ACCESS}, author = {Hiroki Hirose and Siyuan Yang and Tomoaki Ohtsuki and Mondher Bouazizi}, doi = {10.1109/ACCESS.2024.3518763}, journal = {IEEE Access}, pages = {1834-1845}, shortjournal = {IEEE Access}, title = {Deep learning-based channel estimation to mitigate channel aging in massive MIMO with pilot contamination}, volume = {13}, year = {2025}, }
• (2025). Machine learning and deep learning optimization algorithms for unconstrained convex optimization problem. ACCESS, 13, 1817-1833. (www)

  @article{ , abstract = {This paper conducts a thorough comparative analysis of optimization algorithms for an unconstrained convex optimization problem. It contrasts traditional methods like Gradient Descent (GD) and Nesterov Accelerated Gradient (NAG) with modern techniques such as Adaptive Moment Estimation (Adam), Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP). Through empirical experiments, convergence speed, solution accuracy and robustness, is evaluated providing insights to aid algorithm selection. The convergence dynamics of convex optimization, is explored analyzing classical algorithms and contemporary neural network (NN) methodologies. The study concludes with a comparative assessment of these algorithms performance metrics and their respective strengths and weaknesses.}, archive = {J_ACCESS}, author = {Kainat Naeem and Amal Bukhari and Ali Daud and Tariq Alsahfi and Bader Alshemaimri and Mousa Alhajlah}, doi = {10.1109/ACCESS.2024.3522361}, journal = {IEEE Access}, pages = {1817-1833}, shortjournal = {IEEE Access}, title = {Machine learning and deep learning optimization algorithms for unconstrained convex optimization problem}, volume = {13}, year = {2025}, }
• (2025). YOLORM: An advanced key point detection method for accurate and efficient rotameter reading in low flow environments. ACCESS, 13, 1804-1816. (www)

  @article{ , abstract = {Automatic reading of rotameters in low flow and challenging environments poses substantial accuracy and efficiency challenges. To address these issues, this study introduces YOLORM, an advanced key point detection method for rotameters, built upon the YOLOv8n model. We de-veloped a comprehensive YOLO-RM dataset comprising 57,500 images to facilitate model training and evaluation. Several innovations were introduced to the YOLOv8n framework: (1) An ALKBlock-based C2f module incorporating DACov structure to enhance spatial perception; (2) An EffQA-FPN feature pyramid network inspired by QARepVGG to mitigate precision loss during quantization and reparameterization; (3) A DynamicHead attention mechanism with multi dimensional perception capabilities; and (4) EIoU and Adaptive Wing Loss functions to optimize bounding box and key point regression. Experimental results demonstrate that YOLORM achieved exceptional performance on the YOLO-RM dataset, with 99.67% precision, 98.83% recall, 99.48% mAP50, and 98.58% mAP50-95. Compared to the baseline YOLOv8n model, YOLORM achieved a 7.43% increase in detection accuracy and a 15.21% reduction in computational complexity. Moreover, YOLORM exhibited significant reductions in parameter count and computational cost while maintaining or enhancing detection performance relative to state-of-the-art algorithms. Notably, compared to Hourglass, HRNet, and HigherHRNet, YOLORM reduced parameters by 99.3%, 92.9%, and 96.8%, and computational cost by 98.8%, 90.2%, and 95.3%, respectively, while concurrently improving precision and recall. This study presents an efficient and reliable auto-matic reading solution for rotameters in industrial automation, demonstrating robust perfor-mance in complex environments. The proposed YOLORM model has the potential to significantly enhance safety and efficiency in industrial production processes.}, archive = {J_ACCESS}, author = {Huang Yong and Xia Xing and Xiao Shengwang}, doi = {10.1109/ACCESS.2024.3523954}, journal = {IEEE Access}, pages = {1804-1816}, shortjournal = {IEEE Access}, title = {YOLORM: An advanced key point detection method for accurate and efficient rotameter reading in low flow environments}, volume = {13}, year = {2025}, }
• (2025). A real-time vision transformers-based system for enhanced driver drowsiness detection and vehicle safety. ACCESS, 13, 1790-1803. (www)

  @article{ , abstract = {Drowsy driving is a leading cause of fatal traffic accidents worldwide. Drowsy driving has emerged from modern societal trends such as long working hours, heavy reliance on vehicles, and insufficient sleep. Despite considerable efforts by researchers to develop efficient driver drowsiness detection systems, none so far has been widely adopted due to their high cost, intrusive nature, and ineffectiveness in challenging real-life situations. This paper presents a novel, real-time, non-intrusive, and cost-effective driver drowsiness detection system leveraging vision transformers (ViT). Our approach detects the driver’s face from each video frame and classifies the driver’s state as either ‘drowsy’ or ‘alert’ based on the entire facial image, as opposed to previous systems that rely on analyzing specific facial features. We demonstrate that the proposed Vision Transformers-based Driver Drowsiness Detection (ViT-DDD) system surpasses existing state-of-the-art methods, particularly in challenging scenarios such as drivers wearing glasses or sunglasses, or in different lighting conditions. The model was trained and evaluated on two widely used public drowsiness detection datasets, achieving classification accuracies of 98.89% on the NTHU-DDD dataset and 99.4% on the UTA-RLDD dataset. Furthermore, the system was successfully deployed on a Raspberry Pi microcomputer, integrated with an infrared camera, a GSM/GPS module, and a buzzer to alert the driver and report the drowsiness condition to the vehicle owner. Testing the prototype yielded highly promising results, with the system’s strong performance attributed to the ViT-DDD system and advanced hardware. The promising test results suggest the potential of this system in significantly reducing accidents caused by drowsy driving, with future work aiming to expand its capabilities and integration into broader vehicular systems.}, archive = {J_ACCESS}, author = {Anwar Jarndal and Hissam Tawfik and Ali I. Siam and Imad Alsyouf and Ali Cheaitou}, doi = {10.1109/ACCESS.2024.3522111}, journal = {IEEE Access}, pages = {1790-1803}, shortjournal = {IEEE Access}, title = {A real-time vision transformers-based system for enhanced driver drowsiness detection and vehicle safety}, volume = {13}, year = {2025}, }
• (2025). Automatic sleep staging method using EEG based on STFT and residual network. ACCESS, 13, 1778-1789. (www)

  @article{ , abstract = {Sleep is a vital physiological process that affects both physical and mental health, with sleep disorders linked to various conditions such as mental illnesses and cardiovascular diseases. Accurate sleep stage classification is crucial for assessing sleep quality and diagnosing sleep disorders; however, traditional manual sleep staging methods are time-consuming and prone to human bias. This study introduces an enhanced method for automatic sleep staging that addresses the challenges of low training efficiency and reliance on extensive labeled datasets. Initially, high-dimensional time-frequency features of EEG signals are extracted using a short-time Fourier transform (STFT), transforming unidimensional signal data into multidimensional image datasets. Subsequently, the deep-seated temporal and spectral features of sleep stages are unearthed and learned through an improved ResNet-18 residual network, facilitating the automated classification of sleep stages. The proposed method demonstrated a classification accuracy of 85.71% on seven selected full-night recordings from the Sleep-EDF dataset, with a macro-average F1-score of 85.05%. Comparisons with other methods confirm the effectiveness of the proposed method in improving sleep quality evaluation and diagnosis. The results indicate the potential for using this approach in real-time clinical applications, offering a reliable and efficient solution for sleep disorder diagnosis. This work lays a foundation for further exploration of automated systems in sleep medicine, contributing to more accurate and accessible sleep health assessments.}, archive = {J_ACCESS}, author = {Ran Zhang and Rui Jiang and Haowei Hu and Ying Gao and Wei Xia and Boming Song}, doi = {10.1109/ACCESS.2024.3524267}, journal = {IEEE Access}, pages = {1778-1789}, shortjournal = {IEEE Access}, title = {Automatic sleep staging method using EEG based on STFT and residual network}, volume = {13}, year = {2025}, }
• (2025). Feedback-driven pattern matching in time series data. ACCESS, 13, 1764-1777. (www)

  @article{ , abstract = {While motif discovery methods have come a long way over the years, they generally match occurrences based on the similar shape of the whole subsequence. As patterns in production network monitoring environments, which monitors and manages entire infrastructures of millions of device metrics over time, frequently exhibit more complex characteristics such as differences in temporal size or expected noise, these methods often remain insufficient for accurately tracking important behavioral patterns such as backup cycles or transcode sessions. This paper therefore proposes a feedback framework that allows a user to select additional motif ranges to be included or excluded from the model. The method uses a distance matrix of subsequences to extract common patterns from feedback samples and defines temporal rules on how these patterns are allowed to occur. The technique was tested on synthetic data as well as production network monitoring data and a publicly available human motion primitives dataset. The tests show that the recall score can be significantly improved with the proposed feedback system, increasing from 37% to 95% while also maintaining a perfect precision score. This is achieved by providing only one to three feedback samples as input. While the scope of this paper is limited to shape based features, the proposed technique can also be used for less exact patterns such as changepoints in noise.}, archive = {J_ACCESS}, author = {M. Van Onsem and V. Ledoux and W. Mélange and D. Dreesen and S. Van Hoecke}, doi = {10.1109/ACCESS.2024.3520337}, journal = {IEEE Access}, pages = {1764-1777}, shortjournal = {IEEE Access}, title = {Feedback-driven pattern matching in time series data}, volume = {13}, year = {2025}, }
• (2025). A three-vector fast model predictive control method for steady state performance improvement. ACCESS, 13, 1751-1763. (www)

  @article{ , abstract = {A fast three-vector model predictive control method based on an improved H8 inverter is proposed for the Permanent Magnet Synchronous Motor (PMSM) driven by a two-level three-phase voltage source inverter. The method aims to reduce torque ripple, lower total current harmonic distortion (THD), and common-mode voltage, thus improving motor steady-state performance. The proposed method first expands the candidate voltage vectors of the improved H8 inverter by introducing the zero vector into the candidate set, thereby reducing current harmonic content. Then, the method selects the optimal voltage vector online from six effective voltage vectors based on current prediction, computes the error vector angle between the predicted current and reference current, and chooses a second voltage vector to minimize the current error. A zero vector is used to adjust the output voltage amplitude, and a new method for calculating the duty cycle of the voltage vectors is designed, considering the impact of dead-time on common-mode voltage. The voltage vector switching sequence is centralized to avoid equivalent zero vectors caused by dead-time, improving system performance. Finally, simulations compare the proposed method with traditional two-vector model predictive control, three-vector model predictive control, and model predictive control with common-mode voltage suppression. Results show that the proposed method effectively prevents common-mode voltage spikes caused by dead-time, reduces torque and flux ripples, and minimizes current harmonic content while lowering computational complexity.}, archive = {J_ACCESS}, author = {Yongzhen Zhang and Pu Cheng and Kai Shen and Weiming He and Di Nian and Jun Pan}, doi = {10.1109/ACCESS.2024.3519619}, journal = {IEEE Access}, pages = {1751-1763}, shortjournal = {IEEE Access}, title = {A three-vector fast model predictive control method for steady state performance improvement}, volume = {13}, year = {2025}, }
• (2025). Fine-tunned segment anything model (SAM) for reservoir extractions compared with popular CNNs: An experiment for space-borne synthetic-aperture radar images. ACCESS, 13, 1727-1750. (www)

  @article{ , abstract = {The freshwater resource is invaluable and indispensable for any nation like the Republic of Korea. Recently, deep learning (DL), AI models have become more popular and applied frequently for surface water studies. The Segment Anything Model (SAM) has been developing sharply and takes an adaptable approach with the ability to perform zero-shot inference. Although, pre-trained SAM was trained with millions of images (a billion masks), applying it to remote sensing data reveals limitations of inaccurate results and unlabeled classes, particularly for the more complex and noise data from Synthetic-aperture Radar Images. Hence, we fine-tune the SAM model and other popular CNN models of YOLOv8, U-net(ResNet50), and DeepLab(ResNet50, EfficientNet) for lake semantic segmentation using multi-SAR RS datasets of Kompsat-5, ALOS-2, Sentinel-1, and a combination of the three datasets (data link) for model result comparisons. This study’s accuracy assessment showed the SAM was the most precise model (accuracy overall $\approx ~0.95$ ) followed by the DeepLab(ResNet50), YOLOv8, U-net(ResNet50), and DeepLab(EfficientNet) model. Almost all models segmented highly accurate lake areas fitted well with ground-truth masks, nevertheless, the SAM (code link) presented the most well-performance model. The YOLO deals well with larger datasets and requires deeper trains to gain higher accuracy outputs. In addition, this research investigated the responses of each DL model to the SAR dataset proving a need for fine-tuning model results on SAR RS for better lake segmentations.}, archive = {J_ACCESS}, author = {Nguyen Hong Quang and Hanna Lee and Eui-Myoung Kim and Gihong Kim}, doi = {10.1109/ACCESS.2024.3516519}, journal = {IEEE Access}, pages = {1727-1750}, shortjournal = {IEEE Access}, title = {Fine-tunned segment anything model (SAM) for reservoir extractions compared with popular CNNs: An experiment for space-borne synthetic-aperture radar images}, volume = {13}, year = {2025}, }
• (2025). Saber with hybrid striding toom cook-based multiplier: Implementation using open-source tool flow and industry standard chip design tools. ACCESS, 13, 1714-1726. (www)

  @article{ , abstract = {Quantum computers are a significant threat to the existing cryptography algorithms. The Saber is a lattice-based post-quantum cryptographic or quantum-safe algorithm designed as a quantum computing attack-resistant protocol. The hardness of SABER is based on the Rounding with Learning (LWR) problem. This work presents a hardware implementation of Striding Toom Cook multiplier-based Saber. SABER is selected algorithms of the NIST (National Institute of Standards and Technology) round 3 criteria. After evaluation in the striding school book multiplier, a technique is also introduced to efficiently load the weighted polynomials into registers and multipliers. The implemented design is realized utilizing Cadence digital-flow (industry-standard) and open-source tool flow (OFRS) with CMOS 180nm TSMC and CMOS (130nm/180nm SKY-WATER) process, respectively. The realized chip for the proposed Saber implementation resulted in an area of 12 mm2 and 43.59 mm2/16 mm2 for TSMC CMOS 180nm and SKY-WATER CMOS 180nm/130nm process, respectively. This implementation provided roughly equal latency for Key encryption, decryption, and Key generation when compared to a state-of-the-art design which utilized different techniques for polynomial multiplication whereas this design utilized 2X, 2X, and 2.5X lesser clock cycles required for Key generation, Key encryption, and decryption, respectively, compared to Saber implementation utilizing Striding Toom cook based multiplier for polynomial multiplication.}, archive = {J_ACCESS}, author = {Muhammad Naveed Abbasi and Abdul Rehman Aslam and Muhammad Awais Bin Altaf and Wala Saadeh}, doi = {10.1109/ACCESS.2024.3521288}, journal = {IEEE Access}, pages = {1714-1726}, shortjournal = {IEEE Access}, title = {Saber with hybrid striding toom cook-based multiplier: Implementation using open-source tool flow and industry standard chip design tools}, volume = {13}, year = {2025}, }
• (2025). Automated morphology detection of nail-fold capillaries through enhanced object detection network. ACCESS, 13, 1697-1713. (www)

  @article{ , abstract = {The analysis of nail-fold anatomy can effectively evaluate microcirculation and diagnose vascular-related diseases. Early detection of these conditions is crucial due to the risk of severe complications if intervention is delayed. Extensive research supports the notion that nail-fold capillary morphology serves as a critical biomarker for various disease processes, with the degree of capillary structural damage potentially reflecting the involvement of internal organs. This study proposes a non-invasive methodology for detecting nail-fold capillary morphology by integrating an object detection model for improvement within a deep learning framework. We conducted an ablation study to enhance YOLOv8’s performance in detecting nail-fold capillaries and classifying their morphology. Our enhancements included adding a detection layer to improve the detection of various-sized objects, implementing Efficient Channel Attention (ECA) mechanisms, and incorporating data augmentation techniques and hyper-parameter tuning. These modifications yielded a notable improvement in mean Average Precision at IoU 0.50 (mAP@50), with increases of 3.7% in mAP, 3.6% in precision, and 2.5% in recall compared to the baseline YOLOv8 model. This culminated in a mAP@50 score of 79.9%. We also utilized Slicing-Aided Hyperinference (SAHI) to enhance inference performance on untrained multi-scale images and smaller capillaries, demonstrating significant effectiveness in real-time testing scenarios. The results from this research are promising for advancing early-stage diabetes detection using nail-fold image analysis and could potentially enable real-time applications in clinical environments.}, archive = {J_ACCESS}, author = {Hang Thi Phuong Nguyen and Hieyong Jeong}, doi = {10.1109/ACCESS.2024.3521937}, journal = {IEEE Access}, pages = {1697-1713}, shortjournal = {IEEE Access}, title = {Automated morphology detection of nail-fold capillaries through enhanced object detection network}, volume = {13}, year = {2025}, }
• (2025). Customizable restoration in multi-degradation scenarios: Joint deraining and low-light image enhancement via perceptual decoupling. ACCESS, 13, 1688-1696. (www)

  @article{ , abstract = {In real-world nighttime photography, images often suffer from both low-light conditions and rain-induced degradations simultaneously. Traditional methods typically address these issues separately, which limits their effectiveness when both types of degradation are present. To address this challenge, we propose the Perceptual Decoupling-based Learning Network (PDLN), designed to separate these degradations by leveraging their distinct physical properties. PDLN models rain and low-light priors and integrates them through the Decoupled Perception Transformer (DPformer), enabling a robust representation of these degradations. This approach harnesses interactions in both the spatial and frequency domains, resulting in state-of-the-art performance and allowing for personalized enhancement styles. The code and dataset will be available after receipt in https://github.com/lajiaolajiaolajiao/PDLN.}, archive = {J_ACCESS}, author = {Zhiyong He}, doi = {10.1109/ACCESS.2024.3523419}, journal = {IEEE Access}, pages = {1688-1696}, shortjournal = {IEEE Access}, title = {Customizable restoration in multi-degradation scenarios: Joint deraining and low-light image enhancement via perceptual decoupling}, volume = {13}, year = {2025}, }
• (2025). 1-ohm impedance criterion for diagnosis of single-phase ground fault of transmission line. ACCESS, 13, 1680-1687. (www)

  @article{ , abstract = {The transition resistance during single-phase grounding faults in transmission lines is closely related to the fault cause. Quickly determining the cause of the fault through the transition resistance value and taking targeted measures to restore the power supply quickly is of great significance to improving the stability of the power system. Starting from the basic circuit principle, this paper analyzes the composition of the transition resistance for two typical grounding situations: the line-to-tower discharge and the line-to-ground discharge, and derives the equivalent impedance of the tower-wire grounding system. Based on analyzing the grounding wire parameters, the tower grounding resistance, and the span size, the 1-ohm impedance criterion was proposed, that is, the transition resistance during line-to-tower discharges should be less than 1 ohm. Then, the equation for calculating the transition resistance using the double-terminal fault recording was derived and simplified for engineering calculation. Finally, combined with 56 real fault cases of 220 kV~1000 kV transmission lines, each case’s transition resistance value and fault cause are analyzed to verify the effectiveness of the proposed 1-ohm criterion.}, archive = {J_ACCESS}, author = {Xueming Zhou and Xiankang Wang and Yao Yao and Zhiqiang Feng and Zixuan Hu and Jianjin Fu and Zijian Li}, doi = {10.1109/ACCESS.2024.3523266}, journal = {IEEE Access}, pages = {1680-1687}, shortjournal = {IEEE Access}, title = {1-ohm impedance criterion for diagnosis of single-phase ground fault of transmission line}, volume = {13}, year = {2025}, }
• (2025). Compressive SAR imaging based on modified low-rank and sparse decomposition. ACCESS, 13, 1663-1679. (www)

  @article{ , abstract = {In this paper, we propose a novel imaging method for synthetic aperture radar (SAR) systems with compressive sensing (CS) by modifying the existing low-rank and sparse decomposition (LRSD) scheme. The proposed CS-based modified LRSD (CS-MLRSD) method uses 2D random sampling for SAR raw data compression and then employs the dual-tree complex wavelet transform (DCWT) instead of singular value decomposition to more accurately reconstruct directional information in the low-rank background image. For the CS-MLRSD scheme, an iterative thresholding algorithm is derived to separately reconstruct the low-rank and sparse components. A bivariate shrinkage function is employed to threshold the wavelet coefficients for sparse representation of the low-rank part, while soft thresholding is used to sparsely represent the dominant objects in the image space. The proposed CS-MLRSD scheme is applicable to arbitrary SAR geometries in which the SAR imaging and inverse SAR imaging functions are defined. Numerical simulations using SAR modeling data validate the convergence of the proposed method and compare its computational complexity with baseline CS-SAR imaging schemes. Moreover, using both SAR modeling data and real SAR measurement data, the proposed scheme demonstrates significant improvements in reconstructed image quality compared to existing CS-SAR techniques.}, archive = {J_ACCESS}, author = {Jeong-Il Byeon and Wookyung Lee and Jihoon Choi}, doi = {10.1109/ACCESS.2024.3524084}, journal = {IEEE Access}, pages = {1663-1679}, shortjournal = {IEEE Access}, title = {Compressive SAR imaging based on modified low-rank and sparse decomposition}, volume = {13}, year = {2025}, }
• (2025). M_AutNet–A framework for personalized multimodal emotion recognition in autistic children. ACCESS, 13, 1651-1662. (www)

  @article{ , abstract = {Challenges associated with autism spectrum disorder (ASD) include deficits in interpersonal communication, social interaction skills, and behavior. Autistic children experience difficulties in recognizing emotions and expressing emotions, along with intense emotional upheavals called meltdowns. These outbreaks lead to immense physical and emotional distress in children with autism. Generalized emotion recognition classifiers cannot handle the variations in the prototypical display of affect experienced by ASD children. This paper looks at developing a personalized multimodal neural framework, m_AutNet, that can effectively identify the emotions of autistic children by combining data from their facial and vocal expression modalities. The proposed network includes a personalized facial feature extraction module (that incorporates a distance metric to cluster embeddings with similar labels together and marginalizes dissimilar embeddings), and an audio modality CNN feature extractor that works on speech expression samples of autistic children. Domain adaptation of the multimodal features is achieved through a generative adversarial network tuned with the Wasserstein metric to form a domain-invariant distribution alignment of the feature vectors. A classifier performs emotion classification on this domain space following adaptation. The proposed algorithm shows higher performance than state-of-the-art affect recognition classifiers for autistic children, with an accuracy of 88.25%.}, archive = {J_ACCESS}, author = {Asha Kurian and Shikha Tripathi}, doi = {10.1109/ACCESS.2024.3403087}, journal = {IEEE Access}, pages = {1651-1662}, shortjournal = {IEEE Access}, title = {M_AutNet–A framework for personalized multimodal emotion recognition in autistic children}, volume = {13}, year = {2025}, }
• (2025). Accelerated multi-key homomorphic encryption via automorphism-based circuit bootstrapping. ACCESS, 13, 1636-1650. (www)

  @article{ , abstract = {In FHEW-like bootstrapping, blind rotation is based on the CMUX Gate (The CMux gate is a controlled selector gate that uses a control input to choose between two data inputs for the output), and blind rotation based on automorphism (A bijective transformation that maps a mathematical structure onto itself while preserving its original structure) supports arbitrary key distributions, uses smaller keys, and, compared to the CMUX gate-based blind rotation, has smaller noise growth without considering key distribution. It is noteworthy that the feature of supporting arbitrary key distributions makes it applicable to multi-key homomorphic encryption schemes. Multi-key homomorphic encryption (MKHE) allows computations to be performed on ciphertexts encrypted with different keys, thus expanding the application scenarios of homomorphic encryption to more fields. We improved the automorphism-based blind rotation by using the hybrid product, making it suitable for the multi-key schemes. However, previous multi-key schemes were limited to gate bootstrapping after a single NAND gate computation. Our scheme implements a multi-key circuit bootstrapping method based on automorphism-based blind rotation. In leveled homomorphic encryption mode, the ciphertext needs to go through computational circuits that include leveled operations. Due to the asymmetric nature of the input and output ciphertext types required for the computational circuit (with input being RGSW ciphertext and output being RLWE ciphertext), the circuit bootstrapping input and output ciphertext types are also asymmetric, with its output being low-noise RGSW ciphertext. To the best of our knowledge, this is the first attempt in the literature to implement multi-key circuit bootstrapping.}, archive = {J_ACCESS}, author = {Kangwei Xu and Ruwei Huang}, doi = {10.1109/ACCESS.2024.3520718}, journal = {IEEE Access}, pages = {1636-1650}, shortjournal = {IEEE Access}, title = {Accelerated multi-key homomorphic encryption via automorphism-based circuit bootstrapping}, volume = {13}, year = {2025}, }
• (2025). A 3D dual encoder mirror difference ResU-net for multimodal brain tumor segmentation. ACCESS, 13, 1621-1635. (www)

  @article{ , abstract = {Brain tumors are characterized by their relatively high incidence and mortality rates, highlighting the utmost importance of precise automatic segmentation for subsequent diagnosis and treatment. Although deep learning has significantly advanced the field of accurate and efficient automatic brain tumor segmentation, several challenges still persist. In this paper, we introduce a novel architecture called the Dual Encoder Mirror Difference Residual U-Net (DEMD-ResUNet). This approach incorporates dual encoders that process both the original and horizontally flipped images. Additionally, residual blocks are employed to substitute the original convolutional blocks in the encoder section of the U-Net structure. This modification not only streamlines network training but also mitigates issues related to network degradation and the loss of detailed information. To further enhance feature representation, we propose a Multimodal Difference Feature Augmentation (MDFA) module, which effectively highlights abnormal regions in both the original and mirrored brain tumor images to facilitate better feature discrimination. Moreover, a Mirror Difference Feature Fusion (MDFF) module is integrated between the dual encoders and the decoder. This module efficiently transfers features from both the original and mirrored images to the decoder, leveraging the symmetrical information inherent in the images and subsequently boosting the segmentation performance of the model. Ablation experiments conducted on the DEMD-ResUNet model demonstrate the efficacy of its various modules and hyperparameter settings. When evaluated on the BraTS 2018 and BraTS 2019 datasets, our model achieves impressive Dice similarity coefficient (DSC) values of 0.862, 0.925, and 0.905 for Enhanced tumor (ET), Whole tumor (WT), and Tumor core (TC) in the former, and 0.869, 0.922, and 0.916 in the latter, respectively.}, archive = {J_ACCESS}, author = {Qiwei Xing and Zhihua Li and Yongxia Jing and Xiaolin Chen}, doi = {10.1109/ACCESS.2024.3522682}, journal = {IEEE Access}, pages = {1621-1635}, shortjournal = {IEEE Access}, title = {A 3D dual encoder mirror difference ResU-net for multimodal brain tumor segmentation}, volume = {13}, year = {2025}, }
• (2025). Enhanced heart disease classification using dual attention mechanisms and 3D-echo fusion algorithm in echocardiogram videos. ACCESS, 13, 1609-1620. (www)

  @article{ , abstract = {Heart disease remains a leading cause of mortality worldwide, making early detection and diagnosis crucial for preventing severe outcomes. Echocardiogram based classification of heart valve conditions is vital for effectively diagnosing and managing heart disease. In this paper, we present a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with recurrent neural networks (RNNs) alongside a 3D-Echo Fusion approach and a Dual Attention Model for heart valve disease classification using echocardiogram videos. Our method employs a CNN-based encoder to extract spatial features from individual video frames, which are then enhanced through a 3D-Echo Fusion technique to capture comprehensive volumetric information. An RNN models the temporal dynamics of heart valve motion, while the Dual Attention Model refines the classification process by enabling the model to focus on the most relevant features. This integration results in a classification accuracy of 98.7% and an AUC score of 0.85, demonstrating the model’s effectiveness in heart valve disease classification. The high accuracy and interpretability of the model provide potential clinical benefits by supporting decision-making and improving patient care. Furthermore, our proposed method addresses challenges related to the variability and complexity of cardiac data, aligning with current research that advocates for automated learning models in interpreting echocardiographic imagery. By leveraging advanced deep learning techniques, including 3D-Echo Fusion, our approach promises high classification accuracy while reducing reliance on manual annotations, thus enhancing scalability in cardiac diagnostics.}, archive = {J_ACCESS}, author = {S Deepika and N. Jaisankar}, doi = {10.1109/ACCESS.2024.3522996}, journal = {IEEE Access}, pages = {1609-1620}, shortjournal = {IEEE Access}, title = {Enhanced heart disease classification using dual attention mechanisms and 3D-echo fusion algorithm in echocardiogram videos}, volume = {13}, year = {2025}, }
• (2025). Particle swarm optimization-based content delivery protocol for UAV VAENTs. ACCESS, 13, 1594-1608. (www)

  @article{ , abstract = {As the capabilities of mobile devices and mobile communication techniques expand, vehicular ad hoc networks (VANETs) play an increasingly significant role in transportation and traffic management, particularly in Intelligent Transportation Systems (ITS). However, the rapid topology changes, high vehicle mobility, and frequent link disconnections make routing in VANETs a challenging task, necessitating the development of efficient routing protocols. Moreover, as VANET services provided in our daily lives become more extensive and situations in which they are provided simultaneously to an increasing number of users become more frequent, numerous data delivery optimization algorithms have been researched to address these issues. Alongside this, the research related to Unnamed Aerial Vehicles (UAVs) has also been utilized as a solution to solve the physical data link disconnection problem of VANETs. In this paper, we propose a routing protocol called PSUV (Particle Swarm UAV-VANET). First, we have solved the software-related issues of VANETs by measuring data transmission availability of vehicles existing in the topology and conducting topology optimization by creating a vehicle routing table, utilizing a mathematical value calculation formula that combines Particle Swarm Optimization (PSO) from Swarm Intelligence (SI) and the directional vector information of the vehicle. Furthermore, by utilizing UAVs, we address the physical disconnectivity issues that arise during data transmission in VANETs, thereby minimizing the delay and maximizing the total data rate under realistic conditions. Finally, extensive simulations are conducted to validate the superiority of the proposed protocol in various circumstances in an NS-3 simulation. Simulation results indicate that the proposed protocol solves the challenging issues in realistic VANET with significant improvement.}, archive = {J_ACCESS}, author = {Yongje Shin and Hyun-Seok Choi and Youngju Nam and Euisin Lee}, doi = {10.1109/ACCESS.2024.3519702}, journal = {IEEE Access}, pages = {1594-1608}, shortjournal = {IEEE Access}, title = {Particle swarm optimization-based content delivery protocol for UAV VAENTs}, volume = {13}, year = {2025}, }
• (2025). Constrained bayesian optimization: A review. ACCESS, 13, 1581-1593. (www)

  @article{ , abstract = {Bayesian optimization is a sequential optimization method that is particularly well suited for problems with limited computational budgets involving expensive and non-convex black-box functions. Though it has been widely used to solve various optimization tasks, most of the literature has focused on unconstrained settings, while many real-world problems are characterized by constraints. This paper reviews the current literature on single-objective constrained Bayesian optimization, classifying it according to three main algorithmic aspects: (i) the metamodel, (ii) the acquisition function, and (iii) the identification procedure. We discuss the current methods in each of these categories and conclude by a discussion of real-world applications and highlighting the main shortcomings in the literature, providing some promising directions for future research.}, archive = {J_ACCESS}, author = {Sasan Amini and Inneke Vannieuwenhuyse and Alejandro Morales-Hernández}, doi = {10.1109/ACCESS.2024.3522876}, journal = {IEEE Access}, pages = {1581-1593}, shortjournal = {IEEE Access}, title = {Constrained bayesian optimization: A review}, volume = {13}, year = {2025}, }
• (2025). Enhancing secure medical data communication through integration of LSB and DCT for robust analysis in image steganography. ACCESS, 13, 1566-1580. (www)

  @article{ , abstract = {Steganography enhances data security by embedding information seamlessly within digital channels. While hospital systems focus on text and visuals in medical images, audio integration remains an overlooked area with untapped potential. This research addresses the security gap by exploring the embedding of audio data in medical images to enhance patient record protection and information integrity. The Proposed work introduced an innovative Steganography approach for medical image data by embedding Audio files aiming to addresses this research gap. This method integrate audio data into medical images, specifically DICOM-standard images without compromising their visual integrity. It also preserves the integrity of DICOM metadata, ensuring compliance with medical imaging standards. The Least Significant Bit (LSB) and Discrete Cosine Transform (DCT) steganography techniques are used to embed audio file data into medical images. The study illustrates the effectiveness of LSB and DCT techniques on a computed tomography(CT) Images. The LSB method is renowned for its simplicity and its ability to extract data without introducing errors. Additionally, the method offers efficient insertion and extraction time during the watermark embedding and retrieval process, making it highly practical for real-time applications. An algorithm employs DCT technique for Audio integration within DICOM images are more robust and imperceptible. Both techniques have been proven to be highly precise and supported by empirical evidence such as PSNR, SSIM, and MSE analysis. Robustness analysis was conducted on various image quality variation scenarios that includes compression level, resolution and noise tolerance. The proposed DCT approach achieves a PSNR of 57.89 dB, SSIM of 0.9997, and MSE of 0.013, demonstrating minimal distortion and superior structural integrity. Robustness analysis shows that DCT outperforms the LSB method, consistently maintaining PSNR above 47 dB, MSE approximately zero, and SSIM at 0.99 across varying compression levels, image resolutions, and noise conditions. After conducting an experiment, the DCT technique is the most suitable approach for secure communication of medical data with capacity of 2.03 bits per pixel (bpp). The overall analysis of proposed work significantly improves result in terms of the security and robustness under various attacks.}, archive = {J_ACCESS}, author = {Ramyashree and P. S. Venugopala and S. Raghavendra and Vijay S. Kubihal}, doi = {10.1109/ACCESS.2024.3522957}, journal = {IEEE Access}, pages = {1566-1580}, shortjournal = {IEEE Access}, title = {Enhancing secure medical data communication through integration of LSB and DCT for robust analysis in image steganography}, volume = {13}, year = {2025}, }
• (2025). Balancing explainability and privacy in bank failure prediction: A differentially private glass-box approach. ACCESS, 13, 1546-1565. (www)

  @article{ , abstract = {Predicting bank failures is a critical task requiring balancing the need for model explainability with the necessity of preserving data privacy. Traditional machine learning models often lack transparency, which poses challenges for stakeholders who need to understand the factors leading to predictions. In this study, we employ differentially private glass-box models, namely Explainable Boosting Machine (EBM) and Neural Additive Models (NAM), to address these issues. We analyzed data from 21,243 American banks spanning from 1969 to 2021, focusing on key financial ratios. By applying Differential Privacy (DP) to these models, we aimed to protect sensitive financial data while evaluating the trade-offs between privacy, accuracy, and explainability. Our main findings are as follows: 1) In the absence of privacy constraints, the models consistently identified Asset Turnover, Total Debt / Invested Capital, and ROE ratios as the most influential factors in predicting bank failure, in that order; 2) When the privacy budget $\epsilon \leq 1$ , only EBM maintained significant performance; 3) The reduction in explainability due to privacy protection was more pronounced for variables with initially lower explanatory power, while Asset Turnover retained its explanatory power even at $\epsilon = 0.01$ . These findings provide valuable insights for banks, policymakers, and investors, suggesting that glass-box models can offer a promising solution for reliable and explainable bank failure prediction under privacy constraints.}, archive = {J_ACCESS}, author = {Junyoung Byun and Jaewook Lee and Hyeongyeong Lee and Bumho Son}, doi = {10.1109/ACCESS.2024.3523967}, journal = {IEEE Access}, pages = {1546-1565}, shortjournal = {IEEE Access}, title = {Balancing explainability and privacy in bank failure prediction: A differentially private glass-box approach}, volume = {13}, year = {2025}, }
• (2025). Recognition of UAVs in infrared images based on YOLOv8. ACCESS, 13, 1534-1545. (www)

  @article{ , abstract = {With the advancement of Unmanned Aerial Vehicle (UAV) technology, there has been a notable increase in the utilization of UAVs by criminals for engaging in illegal activities, which poses significant threats to critical infrastructure and national security. Previous studies have predominantly relied on visible image recognition, which is vulnerable to variations in lighting conditions. Additionally, the performance of detection models is often hindered by the poor quality of images capturing UAV small targets. In contrast to visible light images, infrared images can capture the heat emitted by UAVs, rendering them less susceptible to fluctuations in lighting. To address the necessity for micro UAV detection in complex environments, this paper proposes an infrared image UAV detection method that employs super-resolution reconstruction through the Super-Resolution Convolutional Neural Network (SRCNN) and YOLOv8. Initially, a convolutional neural network is utilized to enhance the resolution and clarity of the infrared images. Subsequently, the YOLOv8 target detection model is applied to identify and detect UAVs. The results indicate that the average accuracy in detecting UAVs is 93.1%, which proves that it has excellent detection performance.}, archive = {J_ACCESS}, author = {Gang Zhou and Xiuqi Liu and Hongliang Bi}, doi = {10.1109/ACCESS.2024.3500583}, journal = {IEEE Access}, pages = {1534-1545}, shortjournal = {IEEE Access}, title = {Recognition of UAVs in infrared images based on YOLOv8}, volume = {13}, year = {2025}, }
• (2025). Improving performance of real-time object detection in edge device through concurrent multi-frame processing. ACCESS, 13, 1522-1533. (www)

  @article{ , abstract = {As the performance and accuracy of machine learning and AI algorithms improve, the demand for adopting computer vision techniques to solve various problems, such as autonomous driving and AI robots, increases. To meet such demand, IoT and edge devices, which are small enough to be adopted in various environments while having sufficient computing capabilities, are being widely adopted. However, as devices are utilized in IoT and edge environments, which have harsh restrictions compared to traditional server environments, they are often limited by low computational and memory resources, in addition to the limited electrical power supply. This necessitates a unique approach for small IoT devices that are required to run complex tasks. In this paper, we propose a concurrent multi-frame processing scheme for real-time object detection algorithms. To do this, we first divide the video into individual frames and group the frames according to the number of cores in the device. Then, we allocate a group of frames per core to perform the object detection, resulting in parallel detection of multiple frames. We implement our scheme in YOLO (You Only Look Once), one of the most popular real-time object detection algorithms, on a state-of-the-art, resource-constrained IoT edge device, Nvidia Jetson Orin Nano, using real-world video and image datasets, including MS-COCO, ImageNet, PascalVOC, DOTA, animal videos, and car-traffic videos. Our evaluation results show that our proposed scheme can improve the diverse aspect of edge performance and improve the runtime, memory consumption, and power usage by up to 445%, 69%, and 73%, respectively. Additionally, it demonstrates improvements of $2.10\times $ over state-of-the-art model optimization.}, archive = {J_ACCESS}, author = {Seunghwan Kim and Changjong Kim and Sunggon Kim}, doi = {10.1109/ACCESS.2024.3520240}, journal = {IEEE Access}, pages = {1522-1533}, shortjournal = {IEEE Access}, title = {Improving performance of real-time object detection in edge device through concurrent multi-frame processing}, volume = {13}, year = {2025}, }
• (2025). Using trust and reputation for detecting groups of colluded agents in social networks. ACCESS, 13, 1511-1521. (www)

  @article{ , abstract = {One of the most common types of malicious behavior in social networks is represented by collusion, which consists of a secret cooperation between two or more agents providing mutual, highly positive feedback to each other. This collusion creates misleading advantages for the involved agents, deceiving others and distorting the actual reputation perception of the colluding members. Although the well-known EigenTrust algorithm can be fruitfully used to detect colluded agents, two important issues arise which limit its effectiveness: 1) it requires input information about which agents can be a-priori considered particularly trustworthy; and 2) it is not designed to handle situations in which we have several, different groups of colluded agents. These problems lead EigenTrust, to produce a significant number of false positives in some real situations. In this paper, we address the aforementioned issues. We introduce an automatic procedure to provide EigenTrust with the necessary inputs, and we propose an appropriate algorithm that combines EigenTrust with a clustering process. This procedure groups agents based on their reputation scores to tackle the presence of different groups of colluded agents. Through experiments, we demonstrate that our method, while maintaining the same effectiveness as EigenTrust in detecting malicious agents, is significantly more capable of avoiding the generation of false positives.}, archive = {J_ACCESS}, author = {Mariantonia Cotronei and Sofia Giuffrè and Attilio Marcianò and Domenico Rosaci and Giuseppe M. L. Sarnè}, doi = {10.1109/ACCESS.2024.3522560}, journal = {IEEE Access}, pages = {1511-1521}, shortjournal = {IEEE Access}, title = {Using trust and reputation for detecting groups of colluded agents in social networks}, volume = {13}, year = {2025}, }
• (2025). Enhancing public safety through dynamic switching between unicast and broadcast transmission modes in cellular networks. ACCESS, 13, 1496-1510. (www)

  @article{ , abstract = {This article proposes new directions to enhance spectrum resource allocation in dense cellular networks, especially during massive dissemination of public safety alerts. We are conducting a comparative study to identify the most efficient transmission mode among the following options: conventional Unicast (UC) mode, UC mode combined with beamforming, Point-To-Multipoint (PTM) single-cell broadcast mode, and multi-cell broadcast mode via a Single-Frequency Network (SFN). In the absence of standardized norms for determining the optimum mode, we are developing a model to establish a threshold for the number of users per base station, at which point each broadcast mode between SFN and PTM becomes more spectrally efficient than conventional UC and UC with beamforming. Our contribution includes an in-depth simulation study to assess the sensitivity of this threshold to the variability of system parameters under different scenarios and conditions. This analysis will support informed decisions on the optimal transmission mode, thereby improving public safety in critical circumstances.}, archive = {J_ACCESS}, author = {Mohamad Younes and Yves Louët}, doi = {10.1109/ACCESS.2024.3520407}, journal = {IEEE Access}, pages = {1496-1510}, shortjournal = {IEEE Access}, title = {Enhancing public safety through dynamic switching between unicast and broadcast transmission modes in cellular networks}, volume = {13}, year = {2025}, }
• (2025). A QoE and availability-aware framework for network slice placement and resource allocation. ACCESS, 13, 1481-1495. (www)

  @article{ , abstract = {In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.}, archive = {J_ACCESS}, author = {Gergely Dobreff and Attila Báder and Alija Pašić}, doi = {10.1109/ACCESS.2024.3523900}, journal = {IEEE Access}, pages = {1481-1495}, shortjournal = {IEEE Access}, title = {A QoE and availability-aware framework for network slice placement and resource allocation}, volume = {13}, year = {2025}, }
• (2025). INRNet: Neighborhood re-ranking-based method for pedestrian text-image retrieval. ACCESS, 13, 1470-1480. (www)

  @article{ , abstract = {The Pedestrian Text-Image Retrieval task aims to retrieve the target pedestrian image based on textual description. The primary challenge of this task lies in mapping two heteromodal data (visual and textual descriptions) into a unified feature space. Previous approaches have focused on global or local matching methods. However, global matching methods are susceptible to result in weak alignment, while local matching methods may lead to ambiguous matching phenomenon. In order to address the issues arising from the above methods, we introduce Implicit Neighbourhood Reranking Network (INRNet) which utilizes a bilateral feature extractor to learn global image-text matching knowledge and leverages nearest neighbors as prior knowledge to mine positive samples. Specifically, our proposed approach involves using the bilateral feature extractor to extract features from both texts and pedestrian images and employs a Similarity Distribution Matching (SDM) method to establish preliminary global text-image alignment. Subsequently we establish a Neighborhood Data Construction Mechanism (NDCM), restructuring the data for re-ranking tasks. Finally, we input the restructured data into our Implicit Neighborhood Inference (INI) module, utilizing nearest neighbor intersection to optimize retrieval performance. Through extensive experimentation, our proposed method demonstrates superior performance across three public datasets.}, archive = {J_ACCESS}, author = {Kehao Wang and Yuhui Wang and Lian Xue and Qifeng Li}, doi = {10.1109/ACCESS.2024.3518535}, journal = {IEEE Access}, pages = {1470-1480}, shortjournal = {IEEE Access}, title = {INRNet: Neighborhood re-ranking-based method for pedestrian text-image retrieval}, volume = {13}, year = {2025}, }
• (2025). TS-PAID: A two-stage PUF-based lightweight authentication protocol for internet of drones. ACCESS, 13, 1458-1469. (www)

  @article{ , abstract = {The proliferation of Internet of Drones (IoDs)-based use cases has greatly transformed the cyberspace into pervasive and ubiquitous world. The IoD applications could range beyond military, civilian and industrial domains. However, these drones work in hostile environment, and face many challenges related to communication security. Owing to energy, storage and computational resource-constraints, the complex and heavy cryptographic solutions could not be directly deployed in IoD environment. Several authentication protocols for IoD system can be witnessed however with limitations. In this work, we have demonstrated a two-stage PUF-enabled lightweight authentication protocol (TS-PAID) for IoD setting. In comparison with related protocols, the TS-PAID bring more computational and communicational efficiencies along with resilience against known security threats including physical capture of drone. The security properties of TS-PAID are proven under formal analysis based on Real-or-Random (RoR) random oracle model. The performance findings exhibit practicality and effectiveness for the proposed scheme against the comparative studies.}, archive = {J_ACCESS}, author = {Shehzad Ashraf Chaudhry and Azeem Irshad and Bander A. Alzahrani and Ahmad Alhindi and Mohd Shariq and Ashok Kumar Das}, doi = {10.1109/ACCESS.2024.3523352}, journal = {IEEE Access}, pages = {1458-1469}, shortjournal = {IEEE Access}, title = {TS-PAID: A two-stage PUF-based lightweight authentication protocol for internet of drones}, volume = {13}, year = {2025}, }
• (2025). Industrial monitoring of residue deposition in semiconductor process exhaust pipelines using electrical capacitance measurements. ACCESS, 13, 1445-1457. (www)

  @article{ , abstract = {In semiconductor manufacturing, the accumulation of byproducts in exhaust pipelines under inadequate temperature control poses significant safety and operational risks. This study introduces an innovative approach employing an electrical capacitance measurement sensor system combined with an artificial neural network (ANN) to monitor residue buildup. The proposed method estimates the free volume index within industrial process exhaust pipes, enabling precise evaluation of residue deposition and gas phase fractions. Numerical simulations and field studies in semiconductor environments validate the model’s effectiveness, demonstrating accurate residue quantification and enhancing safety and operational efficiency. In numerical simulations, the error between true values and estimated values was within 1%, while the values estimated from experimental data showed an error within 5%. These findings underscore the robustness of the model in both controlled and real-world settings. This advancement offers a practical and reliable solution to mitigate hazards and optimize maintenance processes in semiconductor manufacturing.}, archive = {J_ACCESS}, author = {Minho Jeon and Anil Kumar Khambampati and Jong Hyun Song and Keun Joong Yoon and Kyung Youn Kim}, doi = {10.1109/ACCESS.2024.3523596}, journal = {IEEE Access}, pages = {1445-1457}, shortjournal = {IEEE Access}, title = {Industrial monitoring of residue deposition in semiconductor process exhaust pipelines using electrical capacitance measurements}, volume = {13}, year = {2025}, }
• (2025). Interference mitigation and collision avoidance of dynamic UAVBSs network via mobility control: A game theoretic approach. ACCESS, 13, 1422-1444. (www)

  @article{ , abstract = {Mobility control of Unmanned Aerial Vehicle Base Stations (UAVBSs) can avoid collision and improve the power efficiency and coverage of the wireless network. In this work, UAVBS mobility control is formulated as an exact potential game. Three algorithms are proposed to solve this problem under different connectivity and complexity scenarios. In the first scenario on board computation and power may be limited due to other functions. Under this scenario, the UAVBSs-Better Direction Control (UAVBSs-BDC) algorithm works iteratively based only on the UAV utility function with linear time to directly optimize the action selection based on the UAVBS’s utility. The Utility-Driven Partial Synchronous Learning (UDPSL) algorithm speeds up convergence by using a learning algorithm. This algorithm is seen to increase the incidence of collision when UAVBSs are located close together and require an additional collision avoidance mechanism. The Neighbor Responsive Adaptive-Partial Synchronous Learning (NRA-PSL) algorithm controls the UAVBS’s trajectory via conditioned response to its neighbor UAVBSs to select the action that guides the UAVBS towards a better direction. This algorithm requires additional information about the interference posed by neighbor UAVBS and their location in the cell, which allows it to design a better trajectory which converges faster to the optimal placement of UAVBSs in the cell.}, archive = {J_ACCESS}, author = {Omar Ali Thabet and Didem Kivanc Türeli and Ufuk Türeli}, doi = {10.1109/ACCESS.2024.3515006}, journal = {IEEE Access}, pages = {1422-1444}, shortjournal = {IEEE Access}, title = {Interference mitigation and collision avoidance of dynamic UAVBSs network via mobility control: A game theoretic approach}, volume = {13}, year = {2025}, }
• (2025). 6G: The intelligent network of everything. ACCESS, 13, 1319-1421. (www)

  @article{ , abstract = {The global 6G vision has taken its shape after years of international research and development efforts. This work culminated in ITU-R Recommendation on “IMT-2030 Framework”. While the definition phase of technological requirements is currently ongoing, 3GPP standardization process on 6G networks is expected to start in 2025 and worldwide commercialization around 2029–2030. It is timely to present an up-to-date overview of the entire 6G research field. This article serves as a comprehensive guide to 6G by providing an overall vision, a contemporary survey of the main literature, and an informative tutorial-type presentation style. In our vision, 6G will be based on three fundamental elements: wireless, artificial intelligence, and Internet of Everything. Consequently, 6G can ultimately become the Intelligent Network of Everything while serving as an enabling platform for the next major disruption in mobile communication, called mobile intelligence. The potential of mobile intelligence is that anything can be made connected, intelligent, and aware of its environment. This will revolutionize the way how devices, systems, and applications are designed; how they operate and interact with humans and each other; and how they can be used for the benefit of people, society, and the world in general. After high-level visioning, the main details of 6G are discussed, including fundamental elements, disruptive applications, key use cases, main performance requirements, potential technologies, and defining features. A special focus is given to a comprehensive set of potential 6G technologies, each of which is introduced in a tutorial manner, with a discussion on the vision, introduction, past, present, opportunities, challenges, literature, and future research directions. Finally, we speculate on what comes after 6G and sketch the first high-level vision of 7G. All in all, the objective of this article is to provide a thorough guide to 6G in order to serve as a source of knowledge and inspiration for further research and development work in academia, industry, and standardization bodies.}, archive = {J_ACCESS}, author = {Harri Pennanen and Tuomo Hänninen and Oskari Tervo and Antti Tölli and Matti Latva-Aho}, doi = {10.1109/ACCESS.2024.3521579}, journal = {IEEE Access}, pages = {1319-1421}, shortjournal = {IEEE Access}, title = {6G: The intelligent network of everything}, volume = {13}, year = {2025}, }
• (2025). Developing a transparent anaemia prediction model empowered with explainable artificial intelligence. ACCESS, 13, 1307-1318. (www)

  @article{ , abstract = {The worldwide health epidemic of anaemia which is a condition with low levels of red blood cells or haemoglobin requires accurate prediction models to act promptly and improve patient outcomes because it is widespread and has different causes. The effective management of anaemia is piled with obstructions, which may include the variability of diagnostic criteria, the resource limitations of healthcare, and the multifactorial nature of the disease including nutritional deficiencies, chronic disease, and genetic factors. Conventional anaemia prediction models, that predominantly rely on statistics and are trained on clinical risk scores, are frequently incapable of providing practical solutions and meaningful insights into anaemia diagnosis. There is a growing interest in focusing on Artificial Intelligence (AI) use for anaemia prediction, however, traditional AI models (black boxes) lack transparency, which causes doctors not to pick them up for practical usage. Actionable insights that are enabled by transparent AI models (white boxes) based on the explainable AI methodologies reveal the rationales of the prediction, clarify the features that are responsible for them, and help clinicians and healthcare providers. In this research work, a transparent anaemia prediction model (white box) empowered by explainable AI techniques is proposed to address the limitations of black boxes in terms of transparency. The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. With the integration of explainable AI techniques like SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME), the proposed model offers insights into the underlying factors influencing anaemia predictions. The proposed model, significantly, represents exceptional growth in the healthcare sector and helps in bridging the gap between predictive performance and clinical interpretability, thus improving patient care and disease management strategies. The model simulation results are showing promising results in terms of the accuracy (98.13%) and the miss-rate (1.87%) which are the superior performance compared to the previous published approaches.}, archive = {J_ACCESS}, author = {Muhammad Sajid Farooq and Muhammad Hassan Ghulam Muhammad and Oualid Ali and Zahid Zeeshan and Muhammad Saleem and Munir Ahmad and Sagheer Abbas and Muhammad Adnan Khan and Taher M. Ghazal}, doi = {10.1109/ACCESS.2024.3522080}, journal = {IEEE Access}, pages = {1307-1318}, shortjournal = {IEEE Access}, title = {Developing a transparent anaemia prediction model empowered with explainable artificial intelligence}, volume = {13}, year = {2025}, }
• (2025). Hybrid LLC and full bridge paralleled converter with variable-frequency plus phase-shift control for wide voltage gain range application. ACCESS, 13, 1295-1306. (www)

  @article{ , abstract = {LLC converters are attractive with their ability of enable to achieve soft switching, but there is a trade-off between voltage gain and efficiency in wide voltage regulation range applications. In this paper, a novel hybrid LLC and full-bridge (FB) converter is proposed for wide voltage gain applications. The converter consists of a half bridge (HB) LLC resonant converter and a switch-off current-clamp FB converter connected in parallel. The turn-off current of the LLC converter over the entire range is significantly reduced through the clamping operation of the FB converter. Consequently, all primary-side switches of LLC converter achieve zero-voltage switching-on (ZVS) and low-current switching-off (LCS), while secondary-side diodes achieve zero-current switching-off (ZCS), leading to a substantial reduction in switching losses. Furthermore, a smooth transition variable-frequency plus phase-shift control strategy is proposed to achieve wide-range of voltage regulation. The proposed control has three operation modes: variable-frequency with a shift angle $\varphi $ is 0 mode, phase-shift mode, and variable-frequency with $\varphi $ of $\pi $ . These modes cover the low, medium, and high voltage gain ranges, respectively. A 7.5 kW-rated experimental prototype is designed to convert a 400 V input to a 150-750 V wide output. The prototype demonstrates a peak efficiency of 98.04% and maintains an efficiency of over 93.8% from 30% load to full load across the 150-750 V output range.}, archive = {J_ACCESS}, author = {Fangfang Chen and Wenguang Chen and Bo Hu}, doi = {10.1109/ACCESS.2024.3521990}, journal = {IEEE Access}, pages = {1295-1306}, shortjournal = {IEEE Access}, title = {Hybrid LLC and full bridge paralleled converter with variable-frequency plus phase-shift control for wide voltage gain range application}, volume = {13}, year = {2025}, }
• (2025). Intelligent electronic device-in-the-loop: A real-time simulation as modern educational aid. ACCESS, 13, 1285-1294. (www)

  @article{ , abstract = {Intelligent Electronic Devices (IEDs) have a crucial role in modern power systems. These IEDs can enhance power systems by providing up-to-date information to operators and ensuring proper operations of power system components. However, incorrect configuration settings in IEDs can trigger malfunctions, such as unintended circuit breaker trips, leading to cascading failures, widespread outages, and reduced power system reliability. Therefore, before deploying IEDs in an actual power system, it is essential to validate their settings to prevent potential catastrophic incidents. To ensure an effective testing environment for modern IEDs, it demands not only technical proficiency but also a thorough understanding of the underlying principles governing their operation. Accordingly, many power engineers have to undergo specialised training provided by companies and vendors to acquire the requisite skills and knowledge for their roles. However, in educational institutions, equipping future power engineers with the necessary knowledge and skills for testing IEDs has remained challenges. These difficulties stem from the need for specialised, often costly equipment, along with a flexible academic environment that accommodates both in-person and remote learning. With the support of real-time simulation and hardware-in-the-loop technologies, these challenges can be addressed by utilising virtual and realistic IED devices, making it easier for learners to gain hands-on experiences in a controlled and safe environment without disrupting actual power supplies or damaging expensive equipment. For this reason, this article introduces the concept of “IED-in-the-loop” as an educational aid to enhance the existing electrical power engineering curriculum. This concept helps students gain a basic understanding of real-world devices through laboratory experiments, paving the way for future power engineers. Furthermore, universities, research institutes, and industry companies can use this aid as an additional training course for their students and employees.}, archive = {J_ACCESS}, author = {Francisco Gonzalez-Longatt and LE Nam Hai Pham}, doi = {10.1109/ACCESS.2024.3512779}, journal = {IEEE Access}, pages = {1285-1294}, shortjournal = {IEEE Access}, title = {Intelligent electronic device-in-the-loop: A real-time simulation as modern educational aid}, volume = {13}, year = {2025}, }
• (2025). Devanagari character recognition: A comprehensive literature review. ACCESS, 13, 1249-1284. (www)

  @article{ , abstract = {The Devanagari script originated from the ancient Brahmi script and is a widely used Indic script for writing different languages, like Sanskrit, Hindi, Marathi, Nepali, and Konkani. Recognizing handwritten Devanagari characters poses significant challenges due to their complexity and handwriting variability. This literature review examines the evolution of handwritten Devanagari character recognition (HDCR), exploring early template matching and feature extraction methods that struggled with the script’s intricacy. Advances introduced structural and statistical techniques, improving accuracy by analyzing geometric properties and patterns. The advent of machine learning, particularly deep learning, revolutionized HDCR with convolutional neural networks (CNNs) and recurrent neural networks (RNNs), significantly enhancing performance. Hybrid approaches that combine multiple techniques have shown promising results, balancing accuracy and computational complexity. Challenges remain, including handwriting variability, noise, and the need for real-time performance. The lack of large, diverse datasets for training and evaluation is a significant hurdle. This review highlights efforts to create annotated datasets and benchmarks, providing a comprehensive overview of HDCR methodologies, strengths, limitations, and future research directions. These insights aim to advance HDCR, contributing to more accurate and efficient recognition systems and enhancing digital text processing for linguistic, educational, and archival purposes.}, archive = {J_ACCESS}, author = {Sandhya Arora and Latesh Malik and Sonakshi Goyal and Debotosh Bhattacharjee and Mita Nasipuri and Ondrej Krejcar}, doi = {10.1109/ACCESS.2024.3520248}, journal = {IEEE Access}, pages = {1249-1284}, shortjournal = {IEEE Access}, title = {Devanagari character recognition: A comprehensive literature review}, volume = {13}, year = {2025}, }
• (2025). Design and evaluation of a cooperative-schedule priority transmission protocol for image transmission in WISNs. ACCESS, 13, 1236-1248. (www)

  @article{ , abstract = {Wireless image sensor networks equipped with tiny cameras face significant challenges in achieving reliable, timely, and energy-efficient image transmission due to the bursty nature of image data and the limitations of sensor nodes. This research introduces a cooperative-schedule priority transmission protocol (CSPTP), designed to overcome these challenges. CSPTP leverages a multi-resolution discrete wavelet transform to compress captured images, decomposing them into sub-bands with varying levels of importance. The protocol establishes a data path consisting of path nodes and cooperative nodes to efficiently manage the bursty nature of image packets. Priority levels are assigned to nodes based on their roles in image transmission and to packets based on their importance in image reconstruction. Nodes collaborate through overhearing, which enhances transmission reliability and accelerates packet delivery. The proposed protocol was rigorously evaluated through comparative analyses and experimental validation. Analytical results demonstrated that CSPTP significantly reduces image transmission time and balances energy consumption, outperforming existing protocols. Experimental validation further confirmed substantial improvements in packet delivery reliability, achieving success rates of 99.5% for important packets and 98.3% for other packets when the source multimedia node is at level five, and 99% and 96%, respectively, at level eight.}, archive = {J_ACCESS}, author = {van Khoe Ta and Ic-Pyo Hong}, doi = {10.1109/ACCESS.2024.3523481}, journal = {IEEE Access}, pages = {1236-1248}, shortjournal = {IEEE Access}, title = {Design and evaluation of a cooperative-schedule priority transmission protocol for image transmission in WISNs}, volume = {13}, year = {2025}, }
• (2025). Evaluating mathematical problem-solving abilities of generative AI models: Performance analysis of o1-preview and gpt-4o using the korean college scholastic ability test. ACCESS, 13, 1227-1235. (www)

  @article{ , abstract = {This study utilized the Korean College Scholastic Ability Test questions to evaluate the mathematical problem-solving abilities of the latest Generative AI models, o1-preview and gpt-4o. The performance of the AI models was analyzed using 92 questions from the mathematics sections of the 2023 and 2024 tests and compared with the performance of human learners. The results showed that the o1-preview model achieved an average accuracy rate of 81.52%, performing at a level comparable to top-tier human learners. The gpt-4o model demonstrated mid to lower-tier performance with an average accuracy rate of 49.46%. When analyzing different problem types, this study found that both models did better on multiple-choice questions, but their accuracy decreased as the problems got harder. AI uses reasoning processes similar to those of humans when solving mathematical problems. This study is significant because it offers new insights into AI’s mathematical abilities and shows the potential for using AI in education.}, archive = {J_ACCESS}, author = {Sejun Oh}, doi = {10.1109/ACCESS.2024.3523703}, journal = {IEEE Access}, pages = {1227-1235}, shortjournal = {IEEE Access}, title = {Evaluating mathematical problem-solving abilities of generative AI models: Performance analysis of o1-preview and gpt-4o using the korean college scholastic ability test}, volume = {13}, year = {2025}, }
• (2025). State estimation in power systems under random data attack using correlation matching, semidefinite relaxation, and truncated eigenvalue decomposition. ACCESS, 13, 1208-1226. (www)

  @article{ , abstract = {This work considers a state estimation problem in modern power systems from the perspective of smart grids. Due to the use of digital technology, smart grids often encounter malicious data that is deliberately injected to attack their grid operations. Such kind of perturbation could be targeted at any domain of a smart grid from household customers to bulk generation, leading to network failures and power disruption. To monitor the operational health of the smart grids, power system state estimation is a crucial task and becomes challenging when a false data injection occurs. In this work, three computation methods are proposed for estimating the state vector from contaminated measurement results that are typically available in the power transmission domain of a smart grid. This work is the first that proposes the correlation matching criterion to the power state estimation. As a non-convex function, the correlation matching loss is convexified using semidefinite relaxation. Furthermore, to preserve the rank-one condition inherent in the outer product of the power state vector, the best rank-one approximation based on the eigenvalue decomposition is employed. Numerical simulation is conducted to demonstrate the usability of the proposed algorithms and to illustrate their performance. Signal-plus-attack-to-noise ratio (SANR) and phase-to-attack ratio are examples of the situational quality that can exist in the power transmission systems. Both are chosen herein as the investigational aspects for comparing the performance of the proposed methods and corresponding low-rank approaches. Numerical results reveal that for a high region of the SANR, the new methods can provide significantly less root-mean-squared error and normalized bias norm than the previous approaches. Regarding the complexity, our proposed techniques consume more computational time than two former works due to iterative computation style, yet lower than a previous sophisticated work.}, archive = {J_ACCESS}, author = {Bamrung Tausiesakul and Krissada Asavaskulkiet and Chuttchaval Jeraputra and Ittiphong Leevongwat and Thamvarit Singhavilai and Supun Tiptipakorn}, doi = {10.1109/ACCESS.2024.3519388}, journal = {IEEE Access}, pages = {1208-1226}, shortjournal = {IEEE Access}, title = {State estimation in power systems under random data attack using correlation matching, semidefinite relaxation, and truncated eigenvalue decomposition}, volume = {13}, year = {2025}, }
• (2025). Ternary toward binary: Circuit-level implementation of ternary logic using depletion-mode and conventional MOSFETs. ACCESS, 13, 1193-1207. (www)

  @article{ , abstract = {The application of artificial intelligence (AI) requires advanced computation to address complex problems. However, the improvement of binary computing systems supporting these applications is approaching their limits due to atomic-level scaling. Regarding this challenging situation, ternary computing is gaining more attention due to its better data saving/computing/moving capability. Thus, ternary logic based on various devices was proposed, but these circuits are still encountering issues of high-power consumption, low operating speed, and challenges in manufacturing compared to silicon-based circuits. Therefore, this paper presents a methodology for designing ternary logic based on Depletion-mode metal-oxide-semiconductor field-effect transistor (DEPFET) and multi-threshold voltage complementary metal-oxide–semiconductor (MTCMOS). Our silicon-based devices are easier to manufacture and support high-speed/low-power operations through our complementary ternary logic. Our balanced ternary full adder (BTFA) is $9.70\times $ better energy efficiency than the latestcarbon nanotube field-effect transistor (CNTFET) based BTFA. We also propose the first methodology to design a ternary cell layout in multi-height standard cell design. We propose an algorithm for the best ternary cell layout and a concept of integrated layout that reduces area when required cells are close to each other.}, archive = {J_ACCESS}, author = {Hyundong Lee and Seonghoon Kim and Jongbeom Kim and Jaehoon Jeong and Jeonggyu Yang and Taigon Song}, doi = {10.1109/ACCESS.2024.3523344}, journal = {IEEE Access}, pages = {1193-1207}, shortjournal = {IEEE Access}, title = {Ternary toward binary: Circuit-level implementation of ternary logic using depletion-mode and conventional MOSFETs}, volume = {13}, year = {2025}, }
• (2025). EdgeSecureDP: Strengthening IoHTs differential privacy through graphvariate skellam. ACCESS, 13, 1179-1192. (www)

  @article{ , abstract = {The Internet of Health Things (IoHTs) has transformed healthcare systems, facilitating remote patient monitoring and personalized treatment. Federated Learning (FL) has emerged as a promising solution, enabling decentralized devices to collaboratively train machine learning models while ensuring privacy and security in healthcare applications. Differential Privacy (DP) is used to enhance privacy in FL frameworks by injecting controlled noise into data or model updates, preventing attackers from extracting specific information. However, existing DP mechanisms, such as the Gaussian mechanism and Univariate Skellam struggle to balance privacy-utility trade-off for graph-based data like drug-drug interactions. These mechanisms treat data points independently, failing to account for the complex interconnections between nodes (drugs) and edges (interactions), leaves the network vulnerable to structural attacks that can reverse-engineer relationships, thus limiting the security of collaborative drug discovery. To address these limitations, this work proposes Graphvariate Skellam, a novel DP approach that leverages graph structure information in FL settings, referred to as EdgeSecureDP. By exploiting the structural information encoded in graph edges, this method offers enhanced privacy protection. Experimental results and theoretical analysis demonstrate that Graphvariate Skellam effectively preserves privacy ( $15 \lt \epsilon \leq 20$ ) while achieving 78% accuracy in IoHT environments, making it a robust solution for privacy-preserving healthcare applications.}, archive = {J_ACCESS}, author = {Mohamed Amjath and Shagufta Henna}, doi = {10.1109/ACCESS.2024.3523749}, journal = {IEEE Access}, pages = {1179-1192}, shortjournal = {IEEE Access}, title = {EdgeSecureDP: Strengthening IoHTs differential privacy through graphvariate skellam}, volume = {13}, year = {2025}, }
• (2025). Multi-feature fusion for enhanced feature representation in automatic modulation recognition. ACCESS, 13, 1164-1178. (www)

  @article{ , abstract = {Modulation recognition plays a crucial role in the efficient management of spectrum resources. However, traditional methods have long posed challenges for researchers due to their excessive reliance on manual effort. With the advancement of deep learning, automatic modulation recognition has emerged as a promising solution. Nonetheless, most existing deep learning-based modulation recognition studies consider only single-domain feature information of the signal, such as time-domain or frequency-domain features. To further enhance recognition performance, this paper proposes a novel multi-feature input fusion network. By utilizing different representations and processing methods of the signal, the proposed approach designs distinct feature extraction networks tailored to specific processed signals, leveraging the characteristics of convolution kernels with varying sizes and receptive fields. Additionally, the attention mechanism module is improved to enhance network performance while minimizing the increase in parameter count. Experimental results on the publicly available RML2016.10a dataset demonstrate that the proposed model achieves highly efficient recognition above 2 dB, with accuracy approaching 100%. Testing with real-world signals collected under realistic channel conditions using USRP devices further confirms the model’s superior performance, achieving an effective recognition rate of 99.27% when the two USRP devices are in close proximity. These results validate the model’s high efficiency and robustness in both simulated and real-world environments.}, archive = {J_ACCESS}, author = {Jiuxiao Cao and Rui Zhu and Lingfeng Wu and Jun Wang and Guohao Shi and Peng Chu and Kang Zhao}, doi = {10.1109/ACCESS.2024.3517634}, journal = {IEEE Access}, pages = {1164-1178}, shortjournal = {IEEE Access}, title = {Multi-feature fusion for enhanced feature representation in automatic modulation recognition}, volume = {13}, year = {2025}, }
• (2025). Pseudo-random identification and efficient privacy-preserving V2X communication for IoV networks. ACCESS, 13, 1147-1163. (www)

  @article{ , abstract = {The advancement of Internet of Vehicles (IoV) technologies has significantly enhanced road safety and transportation efficiency through smart traffic management and precise control systems. With the advent of 5G and beyond, vehicles within the IoV ecosystem can seamlessly communicate with various smart entities (X) using V2X (Vehicle-to-Entity) communications. However, the openness of IoV networks and the exponential growth of V2X links have expanded potential attack surfaces, increasing the risk of security and privacy breaches. In response to these challenges, this article proposes a privacy-preserving and secure communication framework for IoV networks, addressing critical security challenges in V2X communication. By leveraging lightweight cryptographic mechanisms such as hash functions, quadratic residuosity, and Legendre symbols, the proposed scheme ensures secure authentication, group key sharing, and pseudonym management within IoV networks. The proposed scheme’s security and privacy features, along with its correctness, have been rigorously validated against various security threats where other state-of-the-art schemes fail. Comprehensive performance analysis demonstrates that our scheme completes authentication in a fraction of a millisecond, significantly outperforming existing approaches. The design simplicity and efficiency of the proposed authentication structure make it highly suitable for real-world IoV applications.}, archive = {J_ACCESS}, author = {Sujash Naskar and Gerhard Hancke and Tingting Zhang and Mikael Gidlund}, doi = {10.1109/ACCESS.2024.3523358}, journal = {IEEE Access}, pages = {1147-1163}, shortjournal = {IEEE Access}, title = {Pseudo-random identification and efficient privacy-preserving V2X communication for IoV networks}, volume = {13}, year = {2025}, }
• (2025). Bolt loosening detection method based on improved YOLOv8 and image matching. ACCESS, 13, 1133-1146. (www)

  @article{ , abstract = {Bolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved YOLOv8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. The experiments focused on a rectangular steel plate featuring four M6 standard bolts. The results demonstrate that the bolt target detection model can accurately locate and crop bolt positions and identify loosening angles under various shooting angles, distances, and lighting conditions. At specific shooting angles and appropriate distances, the detection error threshold was less than 2°. Subsequently, experiments conducted in real-world scenarios confirmed the accuracy and feasibility of the proposed algorithm.}, archive = {J_ACCESS}, author = {Peihe Jiang and Yuhang Geng and Zhongqi Sang and Lifeng Lin}, doi = {10.1109/ACCESS.2024.3521652}, journal = {IEEE Access}, pages = {1133-1146}, shortjournal = {IEEE Access}, title = {Bolt loosening detection method based on improved YOLOv8 and image matching}, volume = {13}, year = {2025}, }
• (2025). Engineering end-to-end remote labs using IoT-based retrofitting. ACCESS, 13, 1106-1132. (www)

  @article{ , abstract = {Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.}, archive = {J_ACCESS}, author = {Savitha Viswanadh Kandala and Akshit Gureja and Nagesh Walchatwar and Rishabh Agrawal and Shiven Sinha and Sachin Chaudhari and Karthik Vaidhyanathan and Venkatesh Choppella and Prabhakar Bhimalapuram and Harikumar Kandath and Aftab Hussain}, doi = {10.1109/ACCESS.2024.3523066}, journal = {IEEE Access}, pages = {1106-1132}, shortjournal = {IEEE Access}, title = {Engineering end-to-end remote labs using IoT-based retrofitting}, volume = {13}, year = {2025}, }
• (2025). Transformer-based amharic-to-english machine translation with character embedding and combined regularization techniques. ACCESS, 13, 1090-1105. (www)

  @article{ , abstract = {Amharic is the working language of Ethiopia and, owing to its Semitic characteristics, the language is known for its complex morphology. It is also an under-resourced language, presenting significant challenges for natural language processing tasks like machine translation. The primary challenges include the scarcity of parallel data, which increases the risk of overfitting and limits the model’s ability to generalize effectively, and the complex morphology of Amharic, which further complicates learning patterns in translation tasks. This study proposes a Transformer-based Amharic-to-English neural machine translation model that leverages character-level embeddings and integrates advanced regularization techniques, including dropout, L1, L2, and Elastic Net. By focusing on character-level embeddings, the model captures the intricate morphological patterns of Amharic and effectively handles out-of-vocabulary words. Our model significantly improves upon the previous state-of-the-art results on the Amharic-to-English neural machine translation benchmark, achieving a BLEU score of 40.59, which is 7% higher than the previous state-of-the-art result. Among the regularization techniques tested, the integration of L2 regularization with dropout applied to the pointwise feed-forward network yielded the best translation performance. Additionally, the proposed model significantly reduces the parameter count from 75 million to just 5.4 million, demonstrating substantial computational efficiency while maintaining high accuracy. Extensive experiments demonstrated improvements in test accuracy, loss reduction, and translation fidelity compared to word-level embedding models. This research provides valuable insights into addressing the challenges of low-resource and morphologically complex languages, while also offering promising directions for future work, including the exploration of multilingual models, attention mechanism optimization, and the broader application of hybrid regularization techniques in the Transformer model architecture.}, archive = {J_ACCESS}, author = {Surafiel Habib Asefa and Yaregal Assabie}, doi = {10.1109/ACCESS.2024.3521985}, journal = {IEEE Access}, pages = {1090-1105}, shortjournal = {IEEE Access}, title = {Transformer-based amharic-to-english machine translation with character embedding and combined regularization techniques}, volume = {13}, year = {2025}, }
• (2025). State estimation in power systems under false data injection attack using total least squares. ACCESS, 13, 1070-1089. (www)

  @article{ , abstract = {This work considers a state estimation problem in modern power systems, e.g., smart grids. Due to the use of digital technology, the smart grids often encounter malicious data that is deliberately injected to attack their network operations. This kind of perturbation affects the electricity stability to household users and eventually can lead to smart grid network failures. To monitor the operational health of the smart grids, the power system state estimation is a crucial task and becomes challenging when a false data injection occurs. In this work, two non-iterative computation methods based on total least squares are proposed for estimating the state vector from the measurement results contaminated by the additive noise and the malicious attack that can arise in the power transmission systems. To demonstrate the usability of the proposed algorithms and to illustrate their performance, numerical simulation is conducted taking into account two IEEE power system standards, such as IEEE 9-bus and 14-bus models. Signal-to-noise ratio (SNR), signal-and-attack-to-noise ratio (SANR), and phase-to-attack ratio are examples of the situational quality that can exist in the power distribution systems. These ratios are considered as the investigational aspects for comparing the performance of the proposed methods and corresponding low-rank approaches. Numerical results reveal that our proposed techniques consume much less computational time than two former works for all ranges of the SNR and the SANR. Regarding the estimation accuracy, for moderate and high regions of the SNR and the SANR, the two new methods provide significantly less root-mean-squared error and normalized bias norm than the two previous approaches. For the problem size solvability point of view, the traditional low-rank methods suffer from the trivial solution caused by too many unknown convex optimization variables due to a large number of time samples, whereas the proposed TLS-based algorithms can handle a large power distribution system with a large number of time samples.}, archive = {J_ACCESS}, author = {Bamrung Tausiesakul and Krissada Asavaskulkiet and Chuttchaval Jeraputra and Ittiphong Leevongwat and Thamvarit Singhavilai and Supun Tiptipakorn}, doi = {10.1109/ACCESS.2024.3519328}, journal = {IEEE Access}, pages = {1070-1089}, shortjournal = {IEEE Access}, title = {State estimation in power systems under false data injection attack using total least squares}, volume = {13}, year = {2025}, }
• (2025). Investigating the acceptance of voice user interfaces for users with disabilities. ACCESS, 13, 1055-1069. (www)

  @article{ , abstract = {It is known that the voice user interface (VUI) is very useful for people with disabilities. However, previous studies related to VUIs for people with disabilities have usually focused on developments and evaluations of new systems, with acceptance of commercially available VUI systems receiving little attention. The purpose of this study is to investigate acceptance of commercially available VUI systems for users with disabilities and the major factors influencing acceptance by people with different disabilities and their reasons for using, or not using, VUI systems. A survey was conducted of people with disabilities who had already used one or more VUI systems. The collected data were analyzed statistically and qualitatively. The results showed the acceptance of VUI systems and the relationships among the acceptance factors given, revealing some differences between users with different types of disabilities. The results of this study also provided insights into the usability and user experience of VUI systems. Although there are some limitations, this study could contribute to the related research fields by investigating acceptance of VUI systems for users with disabilities from their own perspectives.}, archive = {J_ACCESS}, author = {Dae Hwan Jin and Yong Min Kim and Yushin Lee}, doi = {10.1109/ACCESS.2024.3520149}, journal = {IEEE Access}, pages = {1055-1069}, shortjournal = {IEEE Access}, title = {Investigating the acceptance of voice user interfaces for users with disabilities}, volume = {13}, year = {2025}, }
• (2025). Tokenizers for african languages. ACCESS, 13, 1046-1054. (www)

  @article{ , abstract = {Despite incredible development in the field of natural language processing (NLP), there has been a huge gap in the performance of NLP tasks between high-resource languages (HRLs) and low-resource languages (LRLs). African languages belong mainly to the LRLs, and one of the major contributing factors to the performance gap is tokenization, which plays a crucial role in NLP performance in general. Many recent studies on African languages often rely on multilingual tokenizers or general-purpose tokenizers, which are not optimized for African languages. This may lead to suboptimal performance in downstream NLP tasks. In this paper, we systematically analyze the performance of language-specific tokenizers for three African languages: Swahili, Hausa, and Yoruba. By experimental results on two classification tasks (i.e. sentiment classification and news classification), we found that the language-specific tokenizers for African languages consistently outperformed other monolingual tokenizers, with performance gaps of up to 5.43% in sentiment classification and 4.58% in news classification. We also found that multilingual tokenizers generally work well if they are trained in many African languages rather than global HRLs. For instance, African multilingual tokenizers outperformed global multilingual tokenizers by an average of 1.70% in sentiment classification and 1.41% in news classification. The largest observed improvement was 2.61% in news classification using Logistic Regression (LR). Based on the results, we suggest a method for choosing tokenizers when analyzing data or developing models for African languages.}, archive = {J_ACCESS}, author = {Goodwill Erasmo Ndomba and Medard Edmund Mswahili and Young-Seob Jeong}, doi = {10.1109/ACCESS.2024.3522285}, journal = {IEEE Access}, pages = {1046-1054}, shortjournal = {IEEE Access}, title = {Tokenizers for african languages}, volume = {13}, year = {2025}, }
• (2025). Enhanced fault diagnosis in rotating machinery using a hybrid CWT-LeNet-5-LSTM model: Performance across various load conditions. ACCESS, 13, 1026-1045. (www)

  @article{ , abstract = {The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings.}, archive = {J_ACCESS}, author = {Muhammad Ahsan and Muhammad Waqar Hassan and Jose Rodriguez and Mohamed Abdelrahem}, doi = {10.1109/ACCESS.2024.3522948}, journal = {IEEE Access}, pages = {1026-1045}, shortjournal = {IEEE Access}, title = {Enhanced fault diagnosis in rotating machinery using a hybrid CWT-LeNet-5-LSTM model: Performance across various load conditions}, volume = {13}, year = {2025}, }
• (2025). An innovative strategy based on dynamic programming to solve power flow problems in radial electric distribution systems. ACCESS, 13, 1012-1025. (www)

  @article{ , abstract = {In this work, a new strategy based on dynamic programming is proposed to solve the power flow (PF) problem in radial distribution systems using the backward/forward sweep method (BFSM). The strategy consists of a dynamic tree of varied children to represent the radial configuration of the system, allowing a new way of storing and accessing the system information for the PF calculation. This strategy avoids both the bus renumbering procedure for branch ordering used in different BFSMs (due to their branch-oriented characteristic) and the matrix inversion procedure, also used in different BFSMs. The proposed strategy is innovative, since PF problems have been solved in the literature by methods based on static programming. Tests with 14-bus, 33-bus, 84-bus, 136-bus, and 417-bus systems were carried to evaluate both the performance of the construction procedure of the dynamic tree in comparison to the classical branch ordering procedure and the performance of the PF calculation procedure using the proposed dynamic tree in contrast to traditional BFSMs. Promising results were obtained in both evaluated aspects. Therefore, this work contributes to the opening of new studies for a dynamic approach aimed at calculating the PF in radial distribution systems.}, archive = {J_ACCESS}, author = {Max R. Marinho and Eliane S. Souza and Lucas K. Sperotto and Rubén Romero}, doi = {10.1109/ACCESS.2024.3522845}, journal = {IEEE Access}, pages = {1012-1025}, shortjournal = {IEEE Access}, title = {An innovative strategy based on dynamic programming to solve power flow problems in radial electric distribution systems}, volume = {13}, year = {2025}, }
• (2025). The impact of multitasking on transport mode choice in autonomous vehicle age. ACCESS, 13, 995-1011. (www)

  @article{ , abstract = {Travelers on board of transport modes conduct active and passive activities to mitigate the negative impact of traveling. Multitasking on board of conventional transport modes is studied by several researchers, but limited efforts are focusing on multitasking in case of autonomous vehicles (AVs) and shared autonomous vehicles (SAVs). In this paper, the effects of multitasking on behavior of travelers on board of conventional cars (cars), AVs, and SAVs are analyzed. Furthermore, finding the impact of several factors on the travel time, the acceptability of SAVs, and conducting onboard activities are assessed. The study considers solely main trip purposes within urban areas. A stated preference (SP) survey is distributed in Budapest, Hungary, and 276 participants are collected. An SP includes discrete choice experiment (DCE) that is designed to mimic the realistic situation when AV is on the market. The DCE considers the attributes and attributes levels of the alternatives where rationality is maintained in the design. A transport mode choice model, which includes several variables influencing the choice of a transport mode, is developed. In addition, an SP includes Likert scale and sociodemographic sections. Likert scale and exploratory factor analysis (EPA) are used to understand the impact of some factors on the travel time, the acceptability of SAVs as transport modes, and conducting onboard activities. The multinomial logit (MNL) model is applied, where a transport mode choice model of cars, AVs, and SAVs is developed. The results of the developed model show that travelers are willing to choose AVs over cars, and SAVs over AVs. Moreover, travelers with high income are more willing to use AVs over SAVs and more likely to use cars than SAVs. Besides, people from the older age group prefer using SAVs more than other age groups. The results demonstrate the probability of selecting a transport mode with active activities on board is larger than the probability of choosing a transport mode with passive activities. Besides, the findings of EFA and Likert scale analyses demonstrate that the waiting time has the largest negative effect on the travel time, seat availability affects the conduction of onboard activities, and the internal design of SAVs influences the use of SAVs as transport modes. The results of current research can be beneficial to transport planners, transport operators, and vehicle manufacturers.}, archive = {J_ACCESS}, author = {Jamil Hamadneh and Domokos Esztergár-Kiss}, doi = {10.1109/ACCESS.2024.3412393}, journal = {IEEE Access}, pages = {995-1011}, shortjournal = {IEEE Access}, title = {The impact of multitasking on transport mode choice in autonomous vehicle age}, volume = {13}, year = {2025}, }
• (2025). An iterative heuristic optimization method for the optimum sizing of battery energy storage system to augment the dispatchability of wind generators. ACCESS, 13, 980-994. (www)

  @article{ , abstract = {This research aims to devise a methodology for optimizing the size of a Battery Energy Storage System (BESS) supporting Wind Energy Systems (WES) to enhance power commitment flexibility in the energy market. The methodology involves three essential steps: (i) estimating rated kW, (ii) initializing rated kWh of the BESSs, and (iii) iteratively adjusting the BESS size based on heuristic rules to prevent State of Charge (SoC) limit violations following the load cycle. Three realistic load cycles for the BESSs out of which one load cycle is generated based on maximum error values and the other load cycles are generated based on the mean and $1\sigma $ of the Normal Distribution Curve (NDC) of forecast errors of WES located at Agasthianpalli, Tamil Nadu, India. Two simple yet effective heuristic rules have been proposed to optimize the BESS size, ensuring maximum SoC at the start of each day and maintaining SoC within limits throughout the day. This leads to two scenarios considering a single set of the BESS to serve the load cycle and two sets of the BESS operating alternatively, reaching maximum SoC on the subsequent day by charging from the grid. Cost analysis indicates that scenario 1 is more favorable in terms of both cost and BESS size, surpassing scenario 2 by 8.89% and 9.95%, respectively. This analysis results in shorter payback period for scenario 1. Validation using Genetic Algorithm (GA) is done by comparing the costs of BESSs, emphasizing the suitability of the proposed technique.}, archive = {J_ACCESS}, author = {Shubham Kashyap and Tirthadip Ghose}, doi = {10.1109/ACCESS.2024.3373056}, journal = {IEEE Access}, pages = {980-994}, shortjournal = {IEEE Access}, title = {An iterative heuristic optimization method for the optimum sizing of battery energy storage system to augment the dispatchability of wind generators}, volume = {13}, year = {2025}, }
• (2025). Identifying top-performing students via VKontakte social media communities using advanced NLP techniques. ACCESS, 13, 962-979. (www)

  @article{ , abstract = {Identifying potentially high-performing students is crucial for universities aiming to enhance educational outcomes, for companies seeking to recruit top talents early, and for advertising platforms looking to optimize targeted marketing. This paper introduces an algorithm designed to identify students with exceptional academic performance by analyzing their subscriptions to communities on the social network VKontakte. The study examines a sample of 4445 students from Tomsk State University with publicly accessible VK profiles. The research methodology involves generating vector representations for each community based on embeddings, topic modeling, sentiment and emotion analysis, as well as text complexity metrics. To generate the embeddings, a separate model was trained and made publicly available on HuggingFace. The integration of diverse features was achieved using attention mechanisms, allowing the model to dynamically weigh their importance and capture intricate interrelations. These representations are then used to construct a digital user profile, capturing the students’ interests as reflected in their community subscriptions. Additionally, the machine learning pipeline incorporated stacking to combine predictions from multiple models, enhancing robustness and classification performance. Through a series of experiments, we developed a machine learning algorithm that effectively distinguishes between high- and low-performing students based on these profiles. This approach also enabled the identification and interpretation of key factors differentiating high-performing students from their lower-performing peers. Additionally, we investigated the factors positively and negatively associated with academic performance.}, archive = {J_ACCESS}, author = {Sergei S. Gorshkov and Dmitry I. Ignatov and Anastasia Yu. Chernysheva and Vyacheslav L. Goiko and Vitaliy V. Kashpur}, doi = {10.1109/ACCESS.2024.3521857}, journal = {IEEE Access}, pages = {962-979}, shortjournal = {IEEE Access}, title = {Identifying top-performing students via VKontakte social media communities using advanced NLP techniques}, volume = {13}, year = {2025}, }
• (2025). Toward 6G: An overview of the next generation of intelligent network connectivity. ACCESS, 13, 925-961. (www)

  @article{ , abstract = {The development of 6G networks represents a groundbreaking leap in mobile communication, targeting the creation of an Intelligent Network of Everything that seamlessly integrates advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Everything (IoE). This next-generation network is projected to begin standardization in 2025, with commercial deployment anticipated by 2030. Building on the foundation of its predecessors, 6G aims to deliver ultra-reliable, low-latency, and high-speed communication essential for applications like autonomous systems, immersive virtual environments, and smart cities. This article provides a comprehensive overview of 6G, articulating its vision and identifying the core technologies that will enable its transformative capabilities. The key contributions of this work include a detailed exploration of prospective 6G applications and use cases, such as autonomous systems and integrated communication-sensing scenarios. Additionally, it identifies major challenges—such as advanced coding schemes, energy management, and security—and presents a roadmap for future research in these critical areas. The study also reviews potential coding and modulation techniques, such as non-binary codes and lattice codes, assessing their suitability for 6G’s stringent performance requirements. Security and privacy considerations are also covered, proposing a robust architecture across all network layers to address emerging threats. Furthermore, the article compiles a literature review on 6G visions and surveys, providing valuable insights into the current state of research, highlighting gaps, and guiding future studies. Through the exploration of spectrum advancements, antenna technologies, network intelligence, and AI/ML integration, this work establishes a clear vision for how 6G can reshape global communication infrastructure and drive innovation in a hyper-connected world.}, archive = {J_ACCESS}, author = {Sivarama Prasad Tera and Ravikumar Chinthaginjala and Giovanni Pau and Tae Hoon Kim}, doi = {10.1109/ACCESS.2024.3523327}, journal = {IEEE Access}, pages = {925-961}, shortjournal = {IEEE Access}, title = {Toward 6G: An overview of the next generation of intelligent network connectivity}, volume = {13}, year = {2025}, }
• (2025). CRE-YOLO: Efficient jaboticaba tree detection on UAV platforms. ACCESS, 13, 916-924. (www)

  @article{ , abstract = {This study focuses on the detection of Jaboticaba trees in an orchard located in Nanxiong City, Guangdong Province, utilizing UAV platforms to enhance precision agriculture practices. The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. The proposed CRE-YOLO model integrates a Cross-Scale Feature Fusion Module (CCFM), the RepDWBlock, and an Efficient Channel Attention (ECA) mechanism, reducing model parameters by 54%, effectively decreasing model complexity while improving detection precision. CRE-YOLO achieves a mean average precision (mAP) of 97.1% at IoU 0.5 and 60.3% at IoU 0.95, with a processing speed of 387 frames per second. Field experiments conducted in the research area identified over 13,000 Jaboticaba trees, demonstrating the model’s potential for practical UAV-based orchard management. The study contributes to the modernization of agricultural practices by providing an efficient, scalable solution for rapid and accurate tree detection. Future work will extend the model’s application to other crops and focus on enhancing its generalization capabilities.}, archive = {J_ACCESS}, author = {Junyu Huang and Renbo Luo and Yuna Tan and Zhuowen Wu}, doi = {10.1109/ACCESS.2024.3520115}, journal = {IEEE Access}, pages = {916-924}, shortjournal = {IEEE Access}, title = {CRE-YOLO: Efficient jaboticaba tree detection on UAV platforms}, volume = {13}, year = {2025}, }
• (2025). Enhanced CLIP-GPT framework for cross-lingual remote sensing image captioning. ACCESS, 13, 904-915. (www)

  @article{ , abstract = {Remote Sensing Image Captioning (RSIC) aims to generate precise and informative descriptive text for remote sensing images using computational algorithms. Traditional “encoder-decoder” approaches face limitations due to their high training costs and heavy reliance on large-scale annotated datasets, hindering their practical applications. To address these challenges, we propose a lightweight solution based on an enhanced CLIP-GPT framework. Our approach utilizes CLIP for zero-shot multimodal feature extraction of remote sensing images, followed by the design and optimization of a mapping network based on an improved Transformer with adaptive multi-head attention to align these features with the text space of GPT-2, facilitating the generation of high-quality descriptive text. Experimental results on the Sydney-captions, UCM-captions, and RSICD datasets demonstrate that the proposed mapping network outperforms existing methods in leveraging CLIP-extracted multimodal features, leading to more accurate and stylistically appropriate text generated by the GPT language model. Furthermore, our method achieves comparable or superior performance to traditional “encoder-decoder” baselines in terms of BLEU, CIDEr, and METEOR metrics, while requiring only one-fifth of the training time. Experiments conducted on an additional Chinese-English bilingual RSIC dataset underscore the distinct advantages of our CLIP-GPT framework, which leverages extensive multimodal pre-training to effectively demonstrate the robust potential of this approach in cross-lingual RSIC tasks.}, archive = {J_ACCESS}, author = {Rui Song and Beigeng Zhao and Lizhi Yu}, doi = {10.1109/ACCESS.2024.3522585}, journal = {IEEE Access}, pages = {904-915}, shortjournal = {IEEE Access}, title = {Enhanced CLIP-GPT framework for cross-lingual remote sensing image captioning}, volume = {13}, year = {2025}, }
• (2025). Comprehensive bibliographic survey and forward-looking recommendations for software defect prediction: Datasets, validation methodologies, prediction approaches, and tools. ACCESS, 13, 866-903. (www)

  @article{ , abstract = {The development of reliable software depends heavily on the effective collaboration between teams responsible for development and testing. Despite ongoing efforts, many software programs still contain bugs that can lead to financial losses and business risks. Therefore, detecting and fixing software defects after release is crucial. While binary classification methods have been commonly used for this purpose, recent Artificial Intelligence (AI) advancements offer new opportunities for software teams to create more robust software. To address challenges in Software Defect Prediction (SDP), we conducted a thorough bibliographic survey of 79 research articles from the year 2011 to 2023 that examined previous models, datasets, data validation techniques, defect detection, prediction methods, and SDP tools. The survey revealed that previous research often lacked appropriate datasets with the necessary characteristics and data validation methods. Additionally, many standard datasets suffer from a lack of labels, which hinders effective defect detection. Systematic literature reviews on SDP are scarce, further emphasizing the importance of this study. Based on the findings, we provide crucial recommendations for designing effective SDP models and tools. The proposed survey outlines an architecture for constructing SDP datasets with the appropriate characteristics, as well as multi-label classification and data validation methodologies for software defects. This approach aims to enhance SDP research and contribute to the development of high-quality software products by improving defect prediction accuracy.}, archive = {J_ACCESS}, author = {Mohd Mustaqeem and Mahfooz Alam and Suhel Mustajab and Faisal Alshanketi and Shadab Alam and Mohammed Shuaib}, doi = {10.1109/ACCESS.2024.3517419}, journal = {IEEE Access}, pages = {866-903}, shortjournal = {IEEE Access}, title = {Comprehensive bibliographic survey and forward-looking recommendations for software defect prediction: Datasets, validation methodologies, prediction approaches, and tools}, volume = {13}, year = {2025}, }
• (2025). TDR-model: Tomato disease recognition based on image dehazing and improved MobileNetV3 model. ACCESS, 13, 852-865. (www)

  @article{ , abstract = {Currently, deep learning applications in tomato disease recognition are becoming widespread. However, in greenhouse tomato cultivation environments, many techniques neglect the foggy image caused by wet conditions, adversely affecting the performance of disease recognition models, which still remains unresolved. To alleviate adverse environmental impacts, such as fog, and enhance the precision and robustness of the model, this study introduces an improved dark channel prior (DCP) dehazing algorithm and proposes the TDR-Model based on the MobileNetV3. Employing the quad-tree spatial hierarchy search strategy, guided filtering, tolerance mechansim, and multi-scale color restoration (MSRCR) algorithm, an innovative improved version of DCP dehazing algorithm is realized. Image preprocessing with the improved DCP dehazing algorithm obtains more authentic dehazed images. Moreover, MobileNetV3 is improved by the incorporation of the convolutional block attention module (CBAM) and Omni-Dimensional Dynamic Convolution(ODC), improving the accuracy of disease feature recognition. The experiment results demonstrate that, with the parameter size remaining nearly unchanged (a slight reduction of 0.04M), an increase of 16.88% is achieved in accuracy compared to the original version for foggy images. Additionally, we provide extensive ablation studies to evaluate effectiveness of the proposed framework.}, archive = {J_ACCESS}, author = {Zhixiang Zhang and Tong Liu and Jinyu Gao and Meng Yang and Wenjun Luo and Fanqiang Lin}, doi = {10.1109/ACCESS.2024.3522101}, journal = {IEEE Access}, pages = {852-865}, shortjournal = {IEEE Access}, title = {TDR-model: Tomato disease recognition based on image dehazing and improved MobileNetV3 model}, volume = {13}, year = {2025}, }
• (2025). Deep image synthesis, analysis and indexing using integrated CNN architectures. ACCESS, 13, 834-851. (www)

  @article{ , abstract = {The excessive use of Internet technology is leading to a massive increase in multimedia content. Fast and effective image retrieval over a wide range of databases is a difficult task in this modern research era. Various content-based image retrieval (CBIR) systems have been developed to store and retrieve related images to meet the needs of these systems. However, the existing systems lack high accuracy due to problems in foreground and background objects distinction and high semantic gap. The proposed model presents a three-phase approach including image analysis, synthesis, and indexing to improve image retrieval efficiency and accuracy by integrating deep features of CNN models. Initially, color images are converted to grayscale images and the analysis phase accelerates the feature extraction process by applying intensity functions, outer boundary detection, thresholding, connected component labeling, and intensity inversion techniques to efficiently process grayscale images. These features are further refined through synthesis phase containing comprehensive steps, such as the use of multi-scale detection, enumeration, local binarization, invariance and covariance computations to improve the precision of the extracted data. The deep features of CNN models such as VGG19, InceptionV3 and AlexNet are combined with hand-crafted feature vectors to overcome semantic gap and improve image content analysis. This fusion provides a significant increase in image retrieval precision. Finally, integrating bag-of-words (BOW) model in indexing phase significantly improves the accuracy of image retrieval. The model is evaluated on Cifar-10, Cifar-100, and Caltech-101 datasets. The results are evaluated in terms of precision, recall, average retrieval precision (ARP), average retrieval recall (ARR), mean average precision (MAP), and mean average recall (MAR). The proposed model achieves MAP of 95% using VGG19, and 92% for both AlexNet and InceptionV3 for Cifar-10 dataset. The results show that the proposed model achieves high precision compared to state-of-the-art methods presented in the literature.}, archive = {J_ACCESS}, author = {Muhammad Arslan and Muhammad Asad and Ali Haider Khan and Sajid Iqbal and Muhammad Nabeel Asghar and Abdullah Abdulrhman Alaulamie}, doi = {10.1109/ACCESS.2024.3515455}, journal = {IEEE Access}, pages = {834-851}, shortjournal = {IEEE Access}, title = {Deep image synthesis, analysis and indexing using integrated CNN architectures}, volume = {13}, year = {2025}, }
• (2025). Mined block withholding and imposed fork by using mining pool alliance Strategic—A case study in bitcoin system. ACCESS, 13, 817-833. (www)

  @article{ , abstract = {In the proof-of-work (PoW) blockchain consensus mechanism, miners perform numerous hash calculations to obtain a value below the difficulty threshold to generate a new block and obtain rewards. As the difficulty of mining increases, pools may develop competition strategies to reduce time and increase the rewards for mining blocks. This study investigates these strategies by examining whether mining pools in an alliance can perform selective broadcasting, hold mined blocks, and impose fork attacks. We developed algorithms to conduct simulation solutions based on the consensus mechanism of the Bitcoin operation, except for the hash function execution. The calculation results are replaced with two types of balls that represent hash values that satisfy the threshold of the mined block. The simulation algorithms were validated by comparing them with the expected average mining times. The results revealed a bias of less than 4%. Twelve simulation cases with various holding thresholds, holding periods, and forks for strategic alliance mining pools were simulated and evaluated. The results show that the mining rates of alliance mining pools increase, which means that the reward increases. The effects of these strategies on the winning rate reached 21.77% when the top three mining pools were constructed as alliances with withholding and imposing fork strategies. The effect of the average mining time is evaluated for mining pools with the same computing power distribution as practical mining pools for the Bitcoin system.}, archive = {J_ACCESS}, author = {Yean-Fu Wen and Ming-Fang Chen}, doi = {10.1109/ACCESS.2024.3522962}, journal = {IEEE Access}, pages = {817-833}, shortjournal = {IEEE Access}, title = {Mined block withholding and imposed fork by using mining pool alliance Strategic—A case study in bitcoin system}, volume = {13}, year = {2025}, }
• (2025). DiffRSS: A diffusion-guided multi-scale features remote sensing image segmentation method. ACCESS, 13, 802-816. (www)

  @article{ , abstract = {Semantic segmentation in remote sensing is a fundamental task with crucial applications across various domains. Traditional approaches primarily utilize bottom-up discriminative methods, where network architectures learn image features to generate segmentation masks. However, the complexity of remote sensing images, characterized by diverse ground object types and intricate scenes, often results in information redundancy and confusion during feature extraction, impacting segmentation accuracy. To address these challenges, we introduce a novel segmentation framework, DiffRSS, based on the denoising model paradigm. This top-down generative approach learns the data distribution of sample labels and uses image features as guiding priors for generating segmentation masks. We conceptualize the semantic segmentation of remote sensing images as a conditional generation task and design a Multiscale Cyclic Denoising Module (MSCDM), which effectively leverages multiscale features of remote sensing images, leading to superior segmentation outcomes. Inspired by diffusion models, our denoising structure, MSCDM, can be reused multiple times during inference, enhancing the quality of segmentation masks. This method allows for more precise capture and utilization of image features, resulting in finer and more accurate segmentation masks. Extensive testing on three public remote sensing datasets the ISPRS Vaihingen, ISPRS Potsdam, and GID Fine Land Cover Classification datasets demonstrates that our method achieves competitive results.}, archive = {J_ACCESS}, author = {Honghao Liu and Ruixia Yang and Yue Xu and Zhengchao Chen and Yuyang Zheng}, doi = {10.1109/ACCESS.2024.3522286}, journal = {IEEE Access}, pages = {802-816}, shortjournal = {IEEE Access}, title = {DiffRSS: A diffusion-guided multi-scale features remote sensing image segmentation method}, volume = {13}, year = {2025}, }
• (2025). Noise suppression method with low-complexity noise estimation model and heuristic noise-masking algorithm for real-time processing of robot vacuum cleaners. ACCESS, 13, 789-801. (www)

  @article{ , abstract = {Noise suppression in a high-level noise environment using a low-complexity method is challenging. This study proposes a low-complexity noise suppression algorithm for robot vacuum cleaner processors. We collected working noise from a robot vacuum cleaner along with speech signals and developed a method to extract the desired speech signal while estimating the noise. Our approach estimates the noise in the existing signal and converts it into the desired signal. In addition, we designed a low-complexity neural network capable of operating on mobile processors. The evaluation results demonstrate that our method achieves a performance comparable to that of highly computational methods. Notably, our method maintains superior performance when the intensity of the desired signal is low, and its performance is less degraded than that of other methods. It exhibits less degradation than existing methods, and in contrast to other neural networks, it avoids generating incorrect signals. Furthermore, we simplified the neural network architecture reducing its size by approximately 25% with minimal performance loss.}, archive = {J_ACCESS}, author = {Seunghyeon Shin and Minhan Kim and Inkoo Jeon and Ju-Man Song and Yongjin Park and Jungkwan Son and Seokjin Lee}, doi = {10.1109/ACCESS.2024.3522937}, journal = {IEEE Access}, pages = {789-801}, shortjournal = {IEEE Access}, title = {Noise suppression method with low-complexity noise estimation model and heuristic noise-masking algorithm for real-time processing of robot vacuum cleaners}, volume = {13}, year = {2025}, }
• (2025). Hyperparameter optimization in generative adversarial networks (GANs) using gaussian AHP. ACCESS, 13, 770-788. (www)

  @article{ , abstract = {This study explores optimizing hyperparameters in Generative Adversarial Networks (GANs) using the Gaussian Analytical Hierarchy Process (Gaussian AHP). By integrating machine learning techniques and multi-criteria decision methods, the aim is to enhance the performance and efficiency of GAN models. It trains GAN models using the Fashion MNIST dataset. It applies Gaussian AHP to optimize hyperparameters based on multiple performance criteria, such as the quality of generated images, training stability, and training time. Iterative experiments validate the methodology by automatically adjusting hyperparameters based on the obtained scores, thereby maximizing the model’s efficiency and quality. Results indicate significant improvements in image generation quality and computational efficiency. The study highlights the effectiveness of combining Gaussian AHP with GANs for systematic hyperparameter optimization, providing insights into achieving higher performance in image generation tasks. Future research could extend this approach to other neural network architectures and diverse datasets, further demonstrating the versatility of this optimization technique. This method’s potential applications extend across various domains, including data augmentation and anomaly detection, indicating its broad applicability and impact.}, archive = {J_ACCESS}, author = {Thiago Serafim Rodrigues and Plácido Rogério Pinheiro}, doi = {10.1109/ACCESS.2024.3518979}, journal = {IEEE Access}, pages = {770-788}, shortjournal = {IEEE Access}, title = {Hyperparameter optimization in generative adversarial networks (GANs) using gaussian AHP}, volume = {13}, year = {2025}, }
• (2025). Comprehensive fault location on transmission lines considering variation in line parameters and saturation in current transformers. ACCESS, 13, 734-769. (www)

  @article{ , abstract = {Precise and rapid fault location on transmission lines is a cornerstone for enhancing the reliability and cost-efficiency of power systems. This paper proposes an innovative approach to a fault location by solving an optimization problem. The process relies on transmission line parameters, which can vary significantly under various operating conditions. Inaccurate parameters can severely affect fault location results. To address this issue, the paper proposes a parameter estimation process. Additionally, when faults occur near transmission line terminals, high fault currents can cause current transformer (CT) saturation and secondary current distortion, further impacting fault location accuracy. Instead of using currents distorted by CT saturation, this paper proposes to estimate the currents for inputting into the fault location procedure. To achieve accurate parameter and current estimation at both terminals of the transmission line, advanced artificial bee colony (ABC) algorithms such as Chaos ABC algorithm and Chaos particle swarm optimization (PSO)-ABC algorithm are employed. These are enhanced variants designed to improve exploitation ability, leading to better convergence values and reduced iteration requirements. The proposal is applied for locating three-line (L-L-L), three-line to-ground (L-L-L-G), two-line (L-L), two-line to-ground (L-L-G), and line-to-ground (L-G) faults on the transmission line with various locations. More specifically, the transmission line parameter variations and the CT saturation are considered to enhance the practicality of the fault location problem. Numerical results and comparisons confirm the proposal’s effectiveness, being better than the previous traditional fault location technique such as the impedance-based (IB) technique, as well as other meta-heuristic algorithms such as a grey wolf optimization (GWO) algorithm, a gravitational search (GS) algorithm, and an inclined planes optimization (IPO) algorithm. The error percentages of the fault location results are always less than 1.16%, 1.19%, 1.18%, 1.16%, and 1.04% in the L-L-L, L-L-L-G, L-L, L-L-G, and L-G fault location problems, respectively by using the Chaos PSO-ABC algorithm.}, archive = {J_ACCESS}, author = {Duy C. Huynh and Loc D. Ho}, doi = {10.1109/ACCESS.2024.3522252}, journal = {IEEE Access}, pages = {734-769}, shortjournal = {IEEE Access}, title = {Comprehensive fault location on transmission lines considering variation in line parameters and saturation in current transformers}, volume = {13}, year = {2025}, }
• (2025). Technoeconomic assessment of electric vehicle battery disassembly-challenges and opportunities from a robotics perspective. ACCESS, 13, 716-733. (www)

  @article{ , abstract = {The rapid shift towards electric vehicles (EVs) demands effective end-of-life strategies for lithium-ion batteries (LIBs), necessitating examining recycling methodologies, particularly the disassembly process. This study presents a technoeconomic analysis of EV battery disassembly, focusing on incorporating robotics to address challenges and capitalize on opportunities. Based on the case study of the Mitsubishi Outlander PHEV battery pack, we identify the most labor and cost-intensive components and introduce a structured approach to evaluate automating disassembly tasks. Classifying tasks based on their automation potential, we find 57% of pack-to-module (P2M) tasks readily automatable, with an additional 24% requiring minimal human intervention. Incorporating this into an analysis of task feasibility, level of automation and robot time efficiency, we establish a roadmap towards robotizing the disassembly process based on human-robot colllaboration, demonstrating a 12.85% increase in net annual revenue and 43.75% reduction in disassembly cost with current level of task feasibility. Our findings indicate a long-term roadmap towards increasing level of automation, while surmounting feasibility of complex disassembly tasks remains an unsolved challenge.}, archive = {J_ACCESS}, author = {Jamie Hathaway and Cesar Alan Contreras and Mohammed Eesa Asif and Rustam Stolkin and Alireza Rastegarpanah}, doi = {10.1109/ACCESS.2024.3520414}, journal = {IEEE Access}, pages = {716-733}, shortjournal = {IEEE Access}, title = {Technoeconomic assessment of electric vehicle battery disassembly-challenges and opportunities from a robotics perspective}, volume = {13}, year = {2025}, }
• (2025). RadiomixNet: Integrating radiomics and feature extraction for advanced pneumonia diagnosis. ACCESS, 13, 695-715. (www)

  @article{ , abstract = {The research outlines a workflow for medical image analysis and disease diagnosis through radiomics feature extraction. The dataset, consisting of chest X-ray images, was pre-processed using techniques such as denoising, resizing, and enhancement to ensure uniformity and high image quality. Advanced texture analysis methods, including Gray Level Size Zone Matrix, Gray Level Co-Occurrence Matrix, Gray Level Dependence Matrix and, Gray Level Run Length Matrix were employed to extract radiomics features. Power Spectral Density analysis using Burg, Yule Walker, and Welch techniques further enriched the understanding of frequency characteristics within the radiomics feature matrices, uncovering patterns vital for precise predictions. Diverse classifiers, including Bernoulli Naïve Bayes, Random Subspace Boost, Quadratic Discriminant, and Gradient Boosting, were employed to classify test X-rays. The performance of these classifiers was evaluated using metrics such as Cohen’s Kappa, Accuracy, Matthews Correlation Coefficient, Sensitivity, Youden’s Index, Specificity, Log Loss, and Brier Score. Among these, Gradient Boosting demonstrated superior performance across all feature sets, achieving a Cohen’s Kappa of 0.93, MCC of 0.88, Youden’s Index of 0.82, and a Log Loss of 0.27.}, archive = {J_ACCESS}, author = {Rahul Gowtham Poola and Siva Sankar Yellampalli}, doi = {10.1109/ACCESS.2024.3522908}, journal = {IEEE Access}, pages = {695-715}, shortjournal = {IEEE Access}, title = {RadiomixNet: Integrating radiomics and feature extraction for advanced pneumonia diagnosis}, volume = {13}, year = {2025}, }
• (2025). A multi-target threat assessment method based on objective three-way decision. ACCESS, 13, 681-694. (www)

  @article{ , abstract = {Real-world applications of threat assessment (TA) require enhanced timeliness, which can be achieved by reducing reliance on subject matter experts. However, subjective procedures remain prevalent in the literature, particularly within decision methods and three-way decision (3WD) approaches. Traditional 3WD often utilizes a subjectively defined, fixed risk avoidance coefficient that reflects human perception of battlefield information. This study addresses these challenges by proposing a dynamic multi-target TA method within an intuitionistic fuzzy environment, eliminating subjective parameter settings in the technique for order of preference by similarity to ideal solution (TOPSIS), and incorporating an objective 3WD (O3WD) approach. The O3WD quantifies uncertainty through cosine entropy, modeling its relationship with risk avoidance coefficients non-linearly. Given the complex interplay of factors—such as sensor accuracy and environmental conditions—that contribute unpredictably to uncertainty, this concept better captures the dynamics inherent in TA. Two case studies were conducted to evaluate the O3WD’s effectiveness in target categorization across a range of $\gamma $ values, a parameter that controls the curvature of the risk function. The first case showed that the proposed method excelled in recent TA methods in threat ranking. An objective analysis of attribute uncertainties revealed that the compared methods applied overly high-risk avoidance coefficients, leading to inaccurate decisions, a finding also supported by the second case. The proposed method enables precise categorization based on attribute uncertainty without parameter adjustments, highlighting its effectiveness and suitability for multi-target TA in real-world scenarios.}, archive = {J_ACCESS}, author = {Humberto Baldessarini Pires and Lamartine Nogueira Frutuoso Guimarães and Sergio Rebouças}, doi = {10.1109/ACCESS.2024.3523817}, journal = {IEEE Access}, pages = {681-694}, shortjournal = {IEEE Access}, title = {A multi-target threat assessment method based on objective three-way decision}, volume = {13}, year = {2025}, }
• (2025). Toward semi-autonomous robotic arm manipulation operator intention detection from force data. ACCESS, 13, 664-680. (www)

  @article{ , abstract = {In hazardous environments like nuclear facilities, robotic systems are essential for executing tasks that would otherwise expose humans to dangerous radiation levels, which pose severe health risks and can be fatal. However, many operations in the nuclear environment require teleoperating robots, resulting in a significant cognitive load on operators as well as physical strain over extended periods of time. To address this challenge, we propose enhancing the teleoperation system with an assistive model capable of predicting operator intentions and dynamically adapting to their needs. The machine learning model processes robotic arm force data, analyzing spatiotemporal patterns to accurately detect the ongoing task before its completion. To support this approach, we collected a diverse dataset from teleoperation experiments involving glovebox tasks in nuclear applications. This dataset encompasses heterogeneous spatiotemporal data captured from the teleoperation system. We employ a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model to learn and forecast operator intentions based on the spatiotemporal data. By accurately predicting these intentions, the robot can execute tasks more efficiently and effectively, requiring minimal input from the operator. Our experiments validated the model using the dataset, focusing on tasks such as radiation surveys and object grasping. The proposed approach demonstrated an F1-score of 89% for task classification and an F1-score of 86% classification forecasted operator intentions over a 5-second window. These results highlight the potential of our method to improve the safety, precision, and efficiency of robotic operations in hazardous environments, thereby significantly reducing human radiation exposure.}, archive = {J_ACCESS}, author = {Abdullah S. Alharthi and Ozan Tokatli and Erwin Lopez and Guido Herrmann}, doi = {10.1109/ACCESS.2024.3523325}, journal = {IEEE Access}, pages = {664-680}, shortjournal = {IEEE Access}, title = {Toward semi-autonomous robotic arm manipulation operator intention detection from force data}, volume = {13}, year = {2025}, }
• (2025). Widening frequency envelope of an antenna-amplifier system with coupling structure. ACCESS, 13, 656-663. (www)

  @article{ , abstract = {Although large coupling is traditionally avoided in antenna arrays, we show that coupling might offer an advantage. We present a coupling circuit enabling the study of coupling effects in a dipole antenna system without modifying amplifiers or radiating structures. Our experiments demonstrate that controlled coupling can significantly increase, even by 36%, the operation frequency envelope of the radio system. The finding suggests that mutual coupling between elements of an array might be beneficial, provided that weighted element feeding can be used for active impedance and beam reconfiguration.}, archive = {J_ACCESS}, author = {Jaanus Kalde and Anu Lehtovuori and Alvo Aabloo and Ville Viikari}, doi = {10.1109/ACCESS.2024.3520225}, journal = {IEEE Access}, pages = {656-663}, shortjournal = {IEEE Access}, title = {Widening frequency envelope of an antenna-amplifier system with coupling structure}, volume = {13}, year = {2025}, }
• (2025). Exploring score-level and decision-level fusion of inertial and video data for intake gesture detection. ACCESS, 13, 643-655. (www)

  @article{ , abstract = {Recent research has employed deep learning to detect intake gestures from inertial sensor and video camera data. However, the fusion of these modalities has not been attempted. The present research explores the potential of fusing the outputs of two individual deep learning inertial and video intake gesture detection models (i.e., score-level and decision-level fusion) using the test sets from two publicly available multimodal datasets: (1) OREBA-DIS recorded from 100 participants while consuming food served in discrete portions and (2) OREBA-SHA recorded from 102 participants while consuming a communal dish. We first assess the potential of fusion by contrasting the performance of the individual models in intake gesture detection. The assessment shows that fusing the outputs of individual models is more promising on the OREBA-DIS dataset. Subsequently, we conduct experiments using different score-level and decision-level fusion approaches. Our results from fusion show that the score-level fusion approach of max score model performs best of all considered fusion approaches. On the OREBA-DIS dataset, the max score fusion approach ( $F_{1} =0.871$ ) outperforms both individual video ( $F_{1} =0.855$ ) and inertial ( $F_{1} =0.806$ ) models. However, on the OREBA-SHA dataset, the max score fusion approach ( $F_{1} =0.873$ ) fails to outperform the individual inertial model ( $F_{1} =0.895$ ). Pairwise comparisons using bootstrapped samples confirm the statistical significance of these differences in model performance ( $p \lt $ .001).}, archive = {J_ACCESS}, author = {Hamid Heydarian and Marc T. P. Adam and Tracy L. Burrows and Megan E. Rollo}, doi = {10.1109/ACCESS.2021.3119253}, journal = {IEEE Access}, pages = {643-655}, shortjournal = {IEEE Access}, title = {Exploring score-level and decision-level fusion of inertial and video data for intake gesture detection}, volume = {13}, year = {2025}, }
• (2025). The optimal acoustic measurement point selection for UHV reactors in substations based on equivalent impedance and information entropy theory. ACCESS, 13, 633-642. (www)

  @article{ , abstract = {The operation of an Ultra-High Voltage (UHV) shunt reactor generates significant acoustic signals containing crucial information. To accurately assess the various acoustic parameters of ultra-high voltage shunt reactors, it is crucial to establish an accurate acoustic field model and find the optimal acoustic measurement point for their voiceprint monitoring. This article first collects the surface acoustic signals of the reactor oil tank to form the equivalent sound source of the reactor. Considering the influence of the firewall on the radiated sound field, the complex model of the firewall is simplified using equivalent transfer impedance as the boundary condition. The boundary element method is employed to establish the model, and the optimal acoustic measurement point position in space is obtained by calculating the information entropy of the sound source and external measurement points. Finally, the model accurately reconstructed and simulated the near-field of the parallel reactor, and achieved quantitative evaluation of the optimal voiceprint detection position. The results indicate the high accuracy and reliability of the sound field reconstruction model, significantly improving computational efficiency, and the measurement point can fully characterize the acoustic properties of the reactor. This study provides a reliable method for the deployment, comprehensive evaluation, and optimization of acoustic performance of ultra-high voltage parallel reactors for online monitoring of reactor voiceprint.}, archive = {J_ACCESS}, author = {Yingwen Li and Zhen Wei and Huilian Liao and Lin Li and Peizhong Zhang and Jingyun Zhao}, doi = {10.1109/ACCESS.2024.3519742}, journal = {IEEE Access}, pages = {633-642}, shortjournal = {IEEE Access}, title = {The optimal acoustic measurement point selection for UHV reactors in substations based on equivalent impedance and information entropy theory}, volume = {13}, year = {2025}, }
• (2025). Sparse linear discriminant analysis with constant between-class distance for feature selection. ACCESS, 13, 618-632. (www)

  @article{ , abstract = {Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA) has become the foundation for many feature selection algorithms. As is known, TR-LDA is a challenging problem to solve because of its trace-ratio form, and it also faces the scale invariance problem. These two drawbacks of TR-LDA significantly reduce the performance of feature selection algorithms based on it. To overcome these drawbacks, this paper proposes the sparse LDA with constant between-class distance (SLDA-CBD) model to select relavant features. This model first transforms TR-LDA into a non-trace ratio problem with a constant between-class distance constraint, and then imposes row constraints on the projection matrix to implement feature selection. Since the SLDA-CBD model is rooted in TR-LDA, it ensures the discriminative performance of the selected features. The constant between-class distance constraint successfully avoids the scale invariance problem. Additionally, due to the non-trace ratio form of the SLDA-CBD model, it is easily solvable. The experimental results show that the proposed method has better performance compared to the baseline and six state-of-the-art relative methods, with improvements of over 1% on image datasets and over 2% on video datasets in most cases, while also demonstrating high stability, proving its effectiveness and advantage in practical applications.}, archive = {J_ACCESS}, author = {Shuangle Guo and Yongxia Li and Jianguang Zhang and Yue Liu and Tian Tian and Mengchen Guo}, doi = {10.1109/ACCESS.2024.3514612}, journal = {IEEE Access}, pages = {618-632}, shortjournal = {IEEE Access}, title = {Sparse linear discriminant analysis with constant between-class distance for feature selection}, volume = {13}, year = {2025}, }
• (2025). A novel hybrid cascaded and stacked coupled inductor boost converter with active clamping zero-voltage switching. ACCESS, 13, 608-617. (www)

  @article{ , abstract = {The application of a traditional boost converter requires a wide duty cycle for high boost voltage gain. The wide duty cycle has the problem of a high current peak due to a large input current. The Coupled inductor based boost converter achieves a high step-up voltage ratio and reduce the problem of wide duty cycle. However, the basic coupled inductor converters have some disadvantages. Such as high voltage spike on the switch caused by a leakage inductance and the problem of large input current still cannot effectively improve. This paper aims to address these problems. The proposed topology used a coupled inductor to achieve high step-up voltage ratio. The designed voltage stabilizing capacitor loop reduces the current peak value of the main coupling inductor. Active clamping is used to suppress voltage spikes and implement zero-voltage switching (ZVS) of the switch to improve efficiency. This study describes the working principle and time domain analysis of this topology. Detailed design of components and propose the best design process. A prototype converter with an output power of 400 W was constructed. Conducted at a DC input voltage of 40 VDC and a DC output voltage of 400 VDC. Experimental results show that the maximum conversion efficiency is 94.6%.}, archive = {J_ACCESS}, author = {Jing-Yuan Lin and Chuan-Ting Chen and Yi-Feng Lin}, doi = {10.1109/ACCESS.2024.3514711}, journal = {IEEE Access}, pages = {608-617}, shortjournal = {IEEE Access}, title = {A novel hybrid cascaded and stacked coupled inductor boost converter with active clamping zero-voltage switching}, volume = {13}, year = {2025}, }
• (2025). Application of deep dictionary learning and predefined filters for classification of retinal optical coherence tomography images. ACCESS, 13, 596-607. (www)

  @article{ , abstract = {In recent years, deep learning methods have excelled in Optical Coherence Tomography (OCT) image classification but demand high computational resources and extensive training data. We propose two effective methods for OCT image classification, combining the strength of deep learning with sparse representation of significant image features for improved detection of retinal abnormalities. The first method, Simplified Deep Dictionary Learning (S-DDL), is based on deep dictionary learning, where the loss function enforces a sparse feature representation of input image patches before classification. The second method uses the Wavelet Scattering Transform (WST), which employs predefined filters in network layers. We compare its performance with the S-DDL method. WST is a convolutional network with predefined wavelet filters that do not need to be learned, requiring lower processing time and complexity. Both methods can be directly applied to raw image data without time-consuming pre-processing, as the first layers act like denoising filters to achieve sparse significant image structures. We assess the methods on the Optical Coherence Tomography Image Database (OCTID), consisting of 572 spectral domain OCT volumetric scans in five categories. Our proposed S-DDL and WST-based methods achieved 97.2% accuracy in diagnosing AMD, MH, and Normal categories of OCT images. Additionally, WST achieved 100% accuracy in diagnosing DR or AMD from Normal images. These results are comparable to state-of-the-art methods. Specifically, our proposed method outperforms other methods in detecting one abnormality.}, archive = {J_ACCESS}, author = {Fariba Shaker and Zahra Baharlouei and Gerlind Plonka and Hossein Rabbani}, doi = {10.1109/ACCESS.2024.3522122}, journal = {IEEE Access}, pages = {596-607}, shortjournal = {IEEE Access}, title = {Application of deep dictionary learning and predefined filters for classification of retinal optical coherence tomography images}, volume = {13}, year = {2025}, }
• (2025). Dual attention dual-resolution networks for real-time semantic segmentation of street scenes. ACCESS, 13, 588-595. (www)

  @article{ , abstract = {Semantic segmentation is a crucial technology for autonomous vehicles to acquire information about their surrounding environment. To ensure that semantic segmentation has practical application value in autonomous driving and robotics, it must achieve corresponding real-time inference speeds. However, existing models either improve accuracy at the cost of high computational expense and long inference times or enhance inference speed by sacrificing resolution and multi-level detailed information, resulting in a significant drop in accuracy. In this paper, we propose a new architecture based on a bilateral segmentation network, called DADNet. We have designed a new attention mechanism to optimize feature maps and a feature fusion module with an attention mechanism to efficiently merge different features, effectively expanding the receptive field. Our method demonstrates an excellent balance between segmentation accuracy and speed on the Cityscapes and CamVid datasets. Specifically, DADNet achieves a mIoU of 78.2% at 90.5 FPS on the Cityscapes validation set using a single 2080Ti GPU. On the CamVid test set, it achieves a mIoU of 75.8% at 136.7 FPS. Our approach outperforms most state-of-the-art models while requiring less computational power.}, archive = {J_ACCESS}, author = {Baofeng Ye and Renzheng Xue}, doi = {10.1109/ACCESS.2024.3521958}, journal = {IEEE Access}, pages = {588-595}, shortjournal = {IEEE Access}, title = {Dual attention dual-resolution networks for real-time semantic segmentation of street scenes}, volume = {13}, year = {2025}, }
• (2025). A multi-granularity features representation and dimensionality reduction network for website fingerprinting. ACCESS, 13, 574-587. (www)

  @article{ , abstract = {Website fingerprinting (WF) refers to the identification of target websites accessed by users in anonymous communication scenarios, playing a critical role in cybercrime investigation and forensics. In recent years, deep learning-based WF has become a research focus in this field. Although these methods can achieve high accuracy, their performance tends to decline over time. This degradation results from the evolution of network protocol versions and the ongoing development of obfuscation techniques, a phenomenon known as concept drift. To address the problem of concept drift, this paper presents a multi-granularity features representation and dimensionality reduction network for Website Fingerprinting, referred to as LRCT. The LRCT network effectively leverages the temporal learning advantages of Local Recurrent Networks (Local RNN) and the spatial learning strengths of Convolutional Neural Network (CNN) by designing the local feature extraction block (denoted as LRC Block), which extracts fine-grained local features from 2000-dimensional original sequences and reduces the dimensionality to 125. The network then uses a Transformer Encoder to capture more robust global features from the low-dimensional data. Experimental results comparing LRCT with state-of-the-art WF methods demonstrate that the proposed LRCT achieves 99.34% accuracy in a closed-world scenario, outperforming other benchmark models. Compared to the benchmark WF-Transformer method, the LRCT network reduces the parameter count by half and decreases the average training time per epoch to approximately 20% of the benchmark. Even under concept drift conditions, LRCT maintains an accuracy of 89.9%. In open-world scenarios, LRCT also has better precision and recall.}, archive = {J_ACCESS}, author = {Yaojun Ding and Bingxuan Hu}, doi = {10.1109/ACCESS.2024.3522892}, journal = {IEEE Access}, pages = {574-587}, shortjournal = {IEEE Access}, title = {A multi-granularity features representation and dimensionality reduction network for website fingerprinting}, volume = {13}, year = {2025}, }
• (2025). Protection and security method for multiple energy power plant-based microgrids using dual filtering algorithm. ACCESS, 13, 562-573. (www)

  @article{ , abstract = {The multiple energy power plant-based microgrids (MEPPBM) gradually incorporates multiple energy sources such as solar, wind, and battery energy storage, ensuring reliable security & protection has become a paramount challenge. Conventional fault detection methods often fail to address the unique dynamics of these MEPPBM, leading to delays in fault detection and classification. The need for cutting-edge protection schemes that can operate efficiently in such environments is paramount to maintaining system stability and avoiding potential damage. The main objective of this research is to design such protection and security schemes which detect, classify, and locate faults with high accuracy and rapidly with very low computational burden. Therefore, the paper presents a robust security & protection method for modern MEPPBM, employing a hybrid methodology using the Unscented Kalman Filter (UKF) and Particle Filter (PF) algorithms. The UKF is employed for accurate state estimation of the current & voltage signal from faulty bus. While the PF is employed to calculate fault detection & classification indices named PF based residuals (PFBR) and PF-based Harmonic distortion (PFBHD) from UKF-estimated current signal. Then, the Fault section identification index named PF-computed reactive power (PFCRP) is generated from UKF estimated current & voltage signals. Extensive simulations are performed IEC 61850 microgrid test bed using MATLAB/Simulink 2023b software. The presented scheme effectively detects both high-impedance faults (HIF) and solid faults with a remarkable 99.9% accuracy in under 4 milliseconds. Furthermore, the scheme offers low computational burden, making it highly appropriate & efficient for real-time applications in modern MEPPBM.}, archive = {J_ACCESS}, author = {Danni Liu and Shengda Wang and Weijia Su and Xiaojuan Zhang and Shichun Hui}, doi = {10.1109/ACCESS.2024.3522556}, journal = {IEEE Access}, pages = {562-573}, shortjournal = {IEEE Access}, title = {Protection and security method for multiple energy power plant-based microgrids using dual filtering algorithm}, volume = {13}, year = {2025}, }
• (2025). An environmentally adaptive CRO-SL algorithm based on dynamic agents for the channel assignment problem in wireless networks. ACCESS, 13, 541-561. (www)

  @article{ , abstract = {In recent decades, metaheuristic algorithms have emerged as indispensable tools for addressing complex optimization challenges, particularly in several engineering fields, where NP-hard problems are prevalent. A common NP-hard problem in communication engineering is the Channel Assignment Problem (CAP) for wireless access points (APs), with a determined number of stations (STAs) connected to them. The performance of the complete network depends on the interference and noise among the different clusters of devices and the obstacles or elements placed in the physical transmission space. To address the CAP, a new environmentally adaptive approach is proposed for the Coral Reefs Optimization with Substrate Layers (CRO-SL) algorithm, introducing new environmental agents: algae (representing tabu positions) and ocean water acidification (lowering fitness thresholds). The Environmentally Adaptive CRO-SL (EnvAdapt-CRO-SL) implementation aims to improve solution exploration, enhancing computational efficacy in generating new candidate solutions within the coral reef population. An exhaustive comparative analysis of four configurations of the proposed EnvAdapt-CRO-SL variant assesses the impact of each environmental agent on the algorithm’s performance. Additionally, external benchmarks against four different metaheuristics, along with an analysis of the influence of pseudorandom number generators on initialization and search operators, and a robust optimization case study, provide deeper insights. The results show that incorporating the new environmental agents into the EnvAdapt-CRO-SL workflow significantly boosts throughput while reducing the computational time required to obtain optimal solutions.}, archive = {J_ACCESS}, author = {Antonio J. Romero-Barrera and Luis Cruz-Piris and Marino Tejedor-Romero and Jose Manuel Gimenez-Guzman and Ivan Marsa-Maestre}, doi = {10.1109/ACCESS.2024.3523464}, journal = {IEEE Access}, pages = {541-561}, shortjournal = {IEEE Access}, title = {An environmentally adaptive CRO-SL algorithm based on dynamic agents for the channel assignment problem in wireless networks}, volume = {13}, year = {2025}, }
• (2025). An effective pipeline for training variational autoencoders for synthesizable and optimized molecular design. ACCESS, 13, 529-540. (www)

  @article{ , abstract = {Variational auto-encoders (VAE) for molecular design and optimization have gained popularity due to their efficiency in exploring high-dimensional molecular space to identify novel molecules with various properties of interest. For example, when applied to drug discovery, one may want to optimize small molecules for higher affinity against a specific target, improved bioavailability, and solubility. However, many such existing models face challenges in producing molecules that not only possess the desired properties but can be synthesized in practice. On the other hand, there exist synthetic route decoding models that can decode a molecule’s synthetic route using known reactants and reaction templates and translate it into a synthesizable version of that molecule. However, these models may not be suitable for generating molecules with diverse optimized properties. In this paper, we aim to combine the strengths of the generative and synthetic route decoding models to overcome the shortcomings of the respective models when used separately. Specifically, we propose a practical framework for enhancing the synthesizability of property-optimized molecules suggested by VAE models by retraining the original baseline model with a constrained dataset curated using a synthetic route decoder. To demonstrate its efficacy, we apply the framework to a hierarchical molecular generation model built on a VAE and show that the enhanced model can effectively produce diverse molecules with significantly improved synthesizability.}, archive = {J_ACCESS}, author = {Fardeen H. Mozumder and Byung-Jun Yoon}, doi = {10.1109/ACCESS.2024.3523531}, journal = {IEEE Access}, pages = {529-540}, shortjournal = {IEEE Access}, title = {An effective pipeline for training variational autoencoders for synthesizable and optimized molecular design}, volume = {13}, year = {2025}, }
• (2025). PermGuard: A scalable framework for android malware detection using permission-to-exploitation mapping. ACCESS, 13, 507-528. (www)

  @article{ , abstract = {Android, the world’s most widely used mobile operating system, is increasingly targeted by malware due to its open-source nature, high customizability, and integration with Google services. The increasing reliance on mobile devices significantly raises the risk of malware attacks, especially for non-technical users who often grant permissions without thorough evaluation, leading to potentially devastating effects. This paper introduces PermGuard, a scalable framework for Android malware detection that maps permissions into exploitation techniques and employs incremental learning to detect malicious apps. It presents a novel technique for constructing the PermGuard dataset by mapping Android permissions to exploitation techniques, providing a comprehensive understanding of how permissions can be misused by malware. The dataset consists of 55,911 benign and 55,911 malware apps, providing a balanced and comprehensive foundation for analysis. Additionally, a new strategy using similarity-based selective training reduces the amount of data required for the training of an incremental learning-based model, focusing on the most relevant data to improve efficiency. To ensure robustness and accuracy, the model adopts a test-then-train approach, initially testing on application data to identify weaknesses and refine the training process. The framework’s resilience is tested against adversarial attacks, demonstrating its ability to withstand attempts to bypass or deceive detection mechanisms and enhance overall security. Designed for scalability, PermGuard can handle large and continuously growing datasets, making it suitable for real-world applications. Empirical results indicate that the model achieved an accuracy of 0.9933 on real datasets and 0.9828 on synthetic datasets, demonstrating strong resilience against both real and adversarial attacks.}, archive = {J_ACCESS}, author = {Arvind Prasad and Shalini Chandra and Mueen Uddin and Taher Al-Shehari and Nasser A. Alsadhan and Syed Sajid Ullah}, doi = {10.1109/ACCESS.2024.3523629}, journal = {IEEE Access}, pages = {507-528}, shortjournal = {IEEE Access}, title = {PermGuard: A scalable framework for android malware detection using permission-to-exploitation mapping}, volume = {13}, year = {2025}, }
• (2025). Predicting subsurface layer thickness and seismic wave velocity using deep learning: Knowledge distillation approach. ACCESS, 13, 495-506. (www)

  @article{ , abstract = {Seismic interpretation is a crucial task in geophysics, requiring accurate prediction of subsurface layer thickness and seismic wave velocity. Traditional methods are computationally intensive and often hindered by noise in seismic data. Deep learning offers a promising solution to analyze complex geographical structures, but its computational complexity can be a barrier for deployment. This study introduces a deep learning-based approach enhanced by knowledge distillation (KD) to predict subsurface layer thickness and seismic wave velocity. By leveraging pre-trained convolutional neural networks (CNNs) and Transformers as teacher models, we transfer knowledge to smaller, more efficient student models through multiple knowledge distillation strategies, including cross-architecture, multi-teacher, and self-distillation. Implementing knowledge distillation from a Vision Transformer (ViT) teacher to CNN student models enhances the performance of student models in depth and velocity predictions compared to baseline CNN models without knowledge distillation. Aggregating knowledge from multiple teacher models improves model generalization and reduces overfitting, as evidenced by a decreased gap between training and validation losses. Self-distillation significantly enhanced the performance of simpler architectures like VGG19, achieving over 95% accuracy in several depth and velocity prediction tasks. Our approach was validated using simulated velocity model data, demonstrating that distilling knowledge from deeper, complex models into smaller ones maintains high prediction accuracy while reducing computational requirements. These findings emphasize the efficiency of knowledge distillation in deploying deep learning models in resource-constrained environments. Integrating deep learning with knowledge distillation techniques not only enhances the accuracy of seismic interpretation models but also makes them more feasible for practical applications, offering a powerful tool for seismic exploration and potentially transforming how seismic data is processed for subsurface characterization and accelerating decision-making in exploration and production.}, archive = {J_ACCESS}, author = {Amir Moslemi and Anna Briskina and Jason Li and Zubeka Dang and Peyman Moghaddam}, doi = {10.1109/ACCESS.2024.3521895}, journal = {IEEE Access}, pages = {495-506}, shortjournal = {IEEE Access}, title = {Predicting subsurface layer thickness and seismic wave velocity using deep learning: Knowledge distillation approach}, volume = {13}, year = {2025}, }
• (2025). Enhancing software distribution and collaboration in manufacturing: Developing the IMOS platform. ACCESS, 13, 480-494. (www)

  @article{ , abstract = {In today’s fast-evolving tech landscape, small and medium-sized businesses often struggle to keep pace with innovation due to financial constraints and the complexity of integrating new solutions. As the industrial world transitions from Industry 4.0 to Industry 5.0, both manufacturers and software providers are embracing a modular approach to software procurement and deployment. This strategy not only boosts flexibility and affordability but also prioritizes sustainability and the value of collaborative efforts. The Industry Modular Operating System (IMOS) is presented as a platform supporting software accessibility, execution, and integration, while encouraging partnerships between software developers and industrial site managers. IMOS is designed to address recent industry challenges, with a focus on building a solid framework that highlights modularity, facilitates distribution, and promotes cooperation. This system facilitates the distribution of industry-oriented tools by offering containerized software solutions through a unified marketplace. The platform enables industry professionals to efficiently manage diverse applications and services, both on-site and in the cloud, granting access to tailored data and resources. IMOS uniquely extends the concept of Industrial Symbiosis to digital assets, proposing the development of a collaborative forum that values co-creation, facilitates resource sharing, and fosters symbiotic relations. By creating this flexible ecosystem, IMOS closes the gap between development and production, fostering collaboration, innovation, and shared expertise within an active community of industry leaders.}, archive = {J_ACCESS}, author = {António Monte Pegado and Andre Dionisio Rocha and Jose Barata}, doi = {10.1109/ACCESS.2024.3521867}, journal = {IEEE Access}, pages = {480-494}, shortjournal = {IEEE Access}, title = {Enhancing software distribution and collaboration in manufacturing: Developing the IMOS platform}, volume = {13}, year = {2025}, }
• (2025). Real-time duct prediction and propagation analysis using genetic algorithm-based RFC and parabolic equation method. ACCESS, 13, 471-479. (www)

  @article{ , abstract = {This paper proposes a real-time prediction of the duct using a refractivity from clutter (RFC) method based on genetic algorithm (GA) and the parabolic equation (PE) method. Three dates with duct formation are selected, and the refractivity calculated using atmospheric measurements is compared with the refractivity obtained through the GA-based RFC method. Additionally, using the PE method, the electromagnetic wave propagation for each refractivity is analyzed and compared. The atmospheric refractivity and propagation prediction results derived from the proposed process are validated to be similar to those of the measurements. The proposed method enables real-time duct prediction and propagation trend analysis at low altitudes using clutter signals.}, archive = {J_ACCESS}, author = {Jun Heo and Doyoung Jang and Hosung Choo and Dong-Yeop Na and Yong Bae Park}, doi = {10.1109/ACCESS.2024.3522376}, journal = {IEEE Access}, pages = {471-479}, shortjournal = {IEEE Access}, title = {Real-time duct prediction and propagation analysis using genetic algorithm-based RFC and parabolic equation method}, volume = {13}, year = {2025}, }
• (2025). Optimizing athlete training and injury mitigation using fuzzy information-based skeletal motion analysis. ACCESS, 13, 460-470. (www)

  @article{ , abstract = {In modern sports, enhancing athletic performance and minimizing the risk of injuries is crucial to optimize athlete training and injury mitigation. An athlete’s efficiency, endurance, and recovery times can be improved using targeted training programs. This study aims to develop a new strategy to study different training programs for injury mitigation based on an intuitionistic fuzzy rough set (IFRS). An IFRS is vital to reduce uncertainty during data collection and analysis. IFRS bridges two significant frameworks used to reduce uncertainty in information, i.e., the intuitionistic fuzzy set (IFS) and the rough set (RS). First, we develop new similarity measures based on IFRS to enhance accuracy. Then, we use the developed similarity measures to choose the most suitable training program for injury mitigation of athletes according to the demands of modern sports. To do so, different training programs are assessed based on several crucial factors using the proposed measures. The developed measures enhance the accuracy of data collection concerning various uncertain factors crucial in injury mitigation. An example is solved to show the application of the developed measures in injury mitigation. The main contribution of the proposed study is to enhance accuracy when human opinion is modelled.}, archive = {J_ACCESS}, author = {Zaiyong Shou and Ying Hou}, doi = {10.1109/ACCESS.2024.3512416}, journal = {IEEE Access}, pages = {460-470}, shortjournal = {IEEE Access}, title = {Optimizing athlete training and injury mitigation using fuzzy information-based skeletal motion analysis}, volume = {13}, year = {2025}, }
• (2025). A survey on adversarial attacks for malware analysis. ACCESS, 13, 428-459. (www)

  @article{ , abstract = {Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats. However, minor perturbations or ineffectual byte insertions can easily ‘fool’ these trained ML classifiers, making them ineffective against these crafted and smart malicious software. This survey aims to provide an encyclopedic overview of adversarial evasion attacks specifically targeting malware detection and classification systems, standing apart from previous surveys by focusing exclusively and comprehensively on this unique application domain. While significant strides have been made in adversarial research in other fields, the specific challenges of adversarial malware remain under-explored due to the intricate nature and constraints of the malware domain. Our survey addresses this gap by analyzing literature on adversarial evasion attacks published between 2013 and 2024, making it one of the first to systematically focus on malware-specific adversarial attacks in a detailed, self-contained manner. The paper will begin by introducing various machine-learning techniques used to generate adversarial malware samples, including the structural nuances of target files, which influence adversarial vulnerabilities. The work presents an in-depth threat model specific to adversarial malware evasion attacks, describing the unique attack surfaces of malware detectors and outlining adversarial goals tailored to the malware domain. We systematically analyze adversarial generation algorithms from broader domains adapted to malware evasion attacks, proposing a taxonomy of adversarial evasion attacks within malware detection based on target domains(Windows, Android and PDF). The survey highlights real-world adversarial evasion attacks on machine learning-based anti-malware engines under each taxonomical heading, demonstrating the evolution and refinement of these attack strategies over time. Our survey outlines current limitations and practical challenges in executing adversarial attacks against malware detectors in real-world environments. We identify open problems and propose future research directions for developing more practical, robust, efficient, and generalized adversarial attacks on ML-based malware classifiers.}, archive = {J_ACCESS}, author = {Kshitiz Aryal and Maanak Gupta and Mahmoud Abdelsalam and Pradip Kunwar and Bhavani Thuraisingham}, doi = {10.1109/ACCESS.2024.3519524}, journal = {IEEE Access}, pages = {428-459}, shortjournal = {IEEE Access}, title = {A survey on adversarial attacks for malware analysis}, volume = {13}, year = {2025}, }
• (2025). Influence of shunt branch on the current limiting and recovery performance of variable resistance SFCL. ACCESS, 13, 418-427. (www)

  @article{ , abstract = {With the continuous development of power grid, the high short-circuit threatens the stability of the power system. The short-circuit exceeds the disconnection capacity of the circuit breaker and has a negative impact on the integrity and stability of the power grid. Therefore, the research and application of the fault current limiter (FCL) has become particularly important, especially the superconducting fault current limiter (SFCL), which uses the zero resistance characteristics in the superconducting state and shows obvious advantages in the power system. In this paper, the current limiting and recovery performance of a variable resistance SFCL (VR-SFCL) is investigated. A test model is built with a 2 kV/20 kA LC source. The effects of the shunt branch parameters on the current-limiting resistance and recovery time are investigated. And further analysis of a 10 kV/630 A system is made through simulation based on the experimental results. From the test and simulation results, the parallel branch parameters can be designed from the current limiting demand, and the minimum tape length can be calculated from the fitted equation with recovery in 300 ms, which can be used to design VR-SFCL with appropriate tape length.}, archive = {J_ACCESS}, author = {Duanjiao Li and Bin Xiang and Congwei Yao and Qi Zhang and Xiaofeng Pang and Wenxing Sun and Haiyun Tang and Xue Zhang and Zhongbin Yuan and Linglong Cai}, doi = {10.1109/ACCESS.2024.3517558}, journal = {IEEE Access}, pages = {418-427}, shortjournal = {IEEE Access}, title = {Influence of shunt branch on the current limiting and recovery performance of variable resistance SFCL}, volume = {13}, year = {2025}, }
• (2025). Proposal and simulation analysis of anchor-less cooperative positioning method using multiple tags and tag-mounted mobile robot. ACCESS, 13, 407-417. (www)

  @article{ , abstract = {Ultra-wideband (UWB) positioning technology positions objects in environments where Global Navigation Satellite System (GNSS) signals are unavailable and is expected to be applied to a wide range of services. However, the challenges associated with anchor installation, such as the need for highly accurate manual positioning and the installation of many anchors, increase the effort and cost of system implementation. Therefore, this paper proposes an anchor-less tag positioning method based on the cooperative use of a mobile robot and multiple tags. This method uses a particle filter to integrate multiple distance measurements obtained via UWB and tag inertial information from Inertial Measurement Units (IMUs). The combination of tag self-calibration by the mobile robot and tag cooperative positioning using the distance between tags enables the omission of anchors. Simulation results show that the proposed method achieves positioning accuracy comparable to conventional anchor-based positioning systems.}, archive = {J_ACCESS}, author = {Mirei Yoshizawa and Taku Shimizu and Sousuke Nakamura}, doi = {10.1109/ACCESS.2024.3522500}, journal = {IEEE Access}, pages = {407-417}, shortjournal = {IEEE Access}, title = {Proposal and simulation analysis of anchor-less cooperative positioning method using multiple tags and tag-mounted mobile robot}, volume = {13}, year = {2025}, }
• (2025). Sliding mode disturbance observer-based control of a laboratory twin rotor multi input-multi output system. ACCESS, 13, 394-406. (www)

  @article{ , abstract = {This paper deals with the development, modeling, identification, and robust control of a twin-rotor MIMO system (TRMS). The TRMS is a two rotational degree-of-freedom unstable structure with significant channel-coupling, nonlinear characteristics, and external disturbances, which resembles a real helicopter’s characteristics and can be used as an aerodynamic testing setup. The coupled nonlinear state-space representation of the developed TRMS is obtained, and model parameters are identified. The identified model is validated using different experimental test scenarios. To estimate the lumped uncertainty including cross-coupling effects and external disturbances, a novel finite-time sliding mode disturbance observer is proposed. Based on the presented disturbance observer, a finite-time sliding mode controller is designed for the coupled identified TRMS. Performance evaluation of the presented observer-based controller is demonstrated through a series of experiments and simulations. The robustness of the proposed scheme is shown. Moreover, an average improvement of approximately 20% in system performance, as measured by the reduction in the Integrated Squared Error (ISE), is observed compared to the PID controller.}, archive = {J_ACCESS}, author = {Seyyed Sajjad Moosapour and Habib Mehdipour and Mehrdad Keramatzadeh}, doi = {10.1109/ACCESS.2024.3523850}, journal = {IEEE Access}, pages = {394-406}, shortjournal = {IEEE Access}, title = {Sliding mode disturbance observer-based control of a laboratory twin rotor multi input-multi output system}, volume = {13}, year = {2025}, }
• (2025). A hybrid machine learning model for efficient XML parsing. ACCESS, 13, 382-393. (www)

  @article{ , abstract = {The Extensible Markup Language (XML) files are extensively used for representing structured data on the web for file configuration, exchanging data between distinct applications, web development, and many other applications. Consequently, effective parsing techniques are necessary for XML files to enhance the performance of applications. The existing parsing techniques have their strengths and weaknesses affecting the performance of applications. Researchers point out that the selection of an efficient and appropriate parser is the most challenging issue regarding a particular condition. This paper proposes a framework XML Parsing Optimization using Hybrid Machine Learning (XPOHML) that makes use of Artificial Neural Network (ANN) and Support Vector Machine (SVM) machine learning techniques for efficient XML parsing. The newly developed framework performs analysis and prediction of different XML parsers using profiling, classification, performance evaluation, and finally generates code for efficient parsing. The XML profiling phase of the XPOHML framework generates a dataset by evaluating the performance of PXTG, SAX, StAX, DOM, and JDOM parsing models on separate cores by applying numerous file sizes. The Classification phase produces the classification model by applying ANN and SVM techniques to identify the appropriate parsing model. The performance evaluation phase of XPOHML assesses the performance of both parsing models through classification metrics (accuracy). Additionally, based on evaluation outcomes, the code generation phase produces an efficient parsing model of XML files. The newly designed and developed XPOHML framework has shown a meaningful improvement in the performance of parsing XML files.}, archive = {J_ACCESS}, author = {Muhammad Ali and Minhaj Ahmad Khan and Raihan Ur Rasool}, doi = {10.1109/ACCESS.2024.3520706}, journal = {IEEE Access}, pages = {382-393}, shortjournal = {IEEE Access}, title = {A hybrid machine learning model for efficient XML parsing}, volume = {13}, year = {2025}, }
• (2025). A deep learning-based approach to strawberry grasping using a telescopic-link differential drive mobile robot in ROS-gazebo for greenhouse digital twin environments. ACCESS, 13, 361-381. (www)

  @article{ , abstract = {The primary goal of this research is to develop a deep learning-powered robotic solution to address labor shortages and optimize harvesting processes in strawberry greenhouse farms. By incorporating this system into the development process, the aim is to provide continuous, 24/7 operational efficiency for strawberry harvesting in greenhouse environments. This study is grounded in a comprehensive literature review of simulated environments, such as ROS-Gazebo, deep learning detection models, and mobile robot platforms, with a focus on developing innovative robotic solutions for strawberry detection and grasping in a simulated digital twin greenhouse environment. The YOLOv9-GLEAN deep learning model, with super-resolution capabilities, is introduced to enhance strawberry detection accuracy by generating high-resolution image features. We developed a digital twin model of the SILAL strawberry greenhouse farm in Abu Dhabi, UAE, within the ROS-Gazebo environment, to validate our algorithm and test the MARTA (Mobile Autonomous Robot with Telescopic Arm) robot. The dataset used to improve model performance includes both real strawberry images from greenhouse farm and synthetic CAD-generated images. ROS-MoveIt was employed to implement visual servoing, allowing the robot to generate precise motion trajectories to approach and grasp identified strawberries, with visual feedback enhancing accuracy. Empirical results show that our proposed detection model outperforms other existing models, achieving a precision of 0.996 and a recall of 0.991. The model’s adaptability to varied datasets, including real and synthetic images, is notable, and it performs exceptionally well in the simulated digital twin model of the greenhouse farm. The model is uniquely trained on both real and synthetic strawberry images to ensure robust detection performance. It is compared to state-of-the-art models and deployed on a telescopic arm-based robotic platform, which is simpler to control than an articulated arm for strawberry harvesting and grasping tasks.}, archive = {J_ACCESS}, author = {Rajmeet Singh and Lakmal Seneviratne and Irfan Hussain}, doi = {10.1109/ACCESS.2024.3520233}, journal = {IEEE Access}, pages = {361-381}, shortjournal = {IEEE Access}, title = {A deep learning-based approach to strawberry grasping using a telescopic-link differential drive mobile robot in ROS-gazebo for greenhouse digital twin environments}, volume = {13}, year = {2025}, }
• (2025). Explainable AI-based approach for age-related macular degeneration (AMD) detection via fundus imaging. ACCESS, 13, 341-360. (www)

  @article{ , abstract = {Age-related macular degeneration (AMD) is a leading cause of vision loss in older people and is characterized by subtle retinal changes that make early identification difficult. Previous studies have demonstrated the efficacy of Vision Transformers (ViTs) in classifying medical images by successfully detecting retinal disorders such as AMD. This paper addresses multiple shortcomings in conventional AMD diagnostic techniques by exploring the detection and explanation of various AMD subtypes from numerical features extracted with a ViT model from fundus images through cascaded artificial intelligence (AI) models using transformers, convolutional neural networks (CNNs), and multilayer perceptrons (MLPs). The data were preprocessed to recognize intricate disease-related patterns. The best test results using the cascade method for each model type show that the MLP model achieved an accuracy of 91.86% (with a sensitivity of 92.22% and a specificity of 95.74%). The Transformer model achieved its highest accuracy of 83.72% (with a sensitivity of 83.86% and a specificity of 89.74%). The CNN model demonstrated the best performance, with an accuracy of 94.19% (with a sensitivity of 93.84% and a specificity of 96.00%). This work helps clinicians interpret AMD cases and supports decision-making revealing hidden features of AMD that are not visible to the human eye. Future research will focus on improving these systems by expanding the databases in aggregate and incorporating multimodal data.}, archive = {J_ACCESS}, author = {Ainhoa Osa-Sanchez and Hossam Magdy Balaha and Ali Mahmoud and Ashraf Sewelam and Mohammed Ghazal and Begonya Garcia-Zapirain and Ayman El-Baz}, doi = {10.1109/ACCESS.2024.3522862}, journal = {IEEE Access}, pages = {341-360}, shortjournal = {IEEE Access}, title = {Explainable AI-based approach for age-related macular degeneration (AMD) detection via fundus imaging}, volume = {13}, year = {2025}, }
• (2025). A novel procedure for real-time SOH estimation of EV battery packs based on time series extrinsic regression. ACCESS, 13, 326-340. (www)

  @article{ , abstract = {One of the critical challenges posed by the spread of Lithium-ion Batteries (LIBs) within Electric Vehicles (EVs) is the real-time estimation of their State-of-Health (SOH), commonly regarded as the leading indicator of EV aging. However, SOH estimation is still challenging due to the electrochemical complexity of LIBs. This work proposes a novel, computationally-inexpensive, and chemically agnostic Machine Learning (ML) procedure for onboard real-time SOH estimation. The proposed methodology requires a narrow time window of voltage, current, and State-of-Charge battery data, collected while driving the vehicle. We defined a Simulink-based EV simulator, modeling a specific real-world EV, and we utilized it to generate a synthetic dataset by simulating multiple driving sessions of the EV to compensate for the lack of large-scale publicly available EV monitoring data. Then, we examined three feature extraction methods and three ML regression models, estimating the battery pack’s SOH. We conducted a thorough comparison of the proposed feature extraction methods and ML models, training the ML model with processed synthetic data and inferring over real driving session monitoring data from the corresponding real-world EV model. The best synthetic-trained ML model achieves an MAE of 0.27% and 5.08%, and an RMSE of 0.37% and 5.92% over synthetic and real test data, respectively. Finally, we implemented transfer learning over the ML models, employing a portion of the available real data, reaching the lowest MAE of 1.97%, and an RMSE of 2.56% over the remaining real test set.}, archive = {J_ACCESS}, author = {Raimondo Gallo and Tommaso Monopoli and Marco Zampolli and Rémi Jacques Philibert Jaboeuf and Paolo Tosco and Edoardo Patti and Alessandro Aliberti}, doi = {10.1109/ACCESS.2024.3516215}, journal = {IEEE Access}, pages = {326-340}, shortjournal = {IEEE Access}, title = {A novel procedure for real-time SOH estimation of EV battery packs based on time series extrinsic regression}, volume = {13}, year = {2025}, }
• (2025). Exploring the influencing factors of carbon footprint tracking application usage intention: A combined UTAUT and NAM approach. ACCESS, 13, 316-325. (www)

  @article{ , abstract = {The rising concentration of greenhouse gases has created various environmental challenges. Carbon footprint tracking applications are designed to encourage individual-level energy conservation and emission reduction. These applications have significant potential for widespread adoption. This study combines the Unified Theory of Acceptance and Use of Technology with the Norm Activation Model to form a comprehensive research framework. Partial Least Squares Structural Equation Modeling is used to analyze key factors. These factors include performance expectancy, effort expectancy, social influence, awareness of consequences, ascription of responsibility, and personal norms. The aim is to explore their impact on the intention to use carbon footprint tracking applications. The results show that performance expectancy, effort expectancy, social influence, and personal norms significantly affect usage intention. Performance expectancy is the strongest predictor. Social influence and personal norms emphasize the role of external social factors and moral obligations in shaping user behavior. Additionally, ascription of responsibility has a stronger mediating effect on usage intention than awareness of consequences. This study offers insights into how factors from the Unified Theory of Acceptance and Use of Technology and the Norm Activation Model influence user intentions. It also provides practical recommendations for businesses and policymakers to promote the use of carbon footprint tracking applications.}, archive = {J_ACCESS}, author = {Shichuan Li and Xiang Liu}, doi = {10.1109/ACCESS.2024.3523102}, journal = {IEEE Access}, pages = {316-325}, shortjournal = {IEEE Access}, title = {Exploring the influencing factors of carbon footprint tracking application usage intention: A combined UTAUT and NAM approach}, volume = {13}, year = {2025}, }
• (2025). Modeling and analysis of quasi-3D equivalent magnetic network of direct-drive inner boost permanent magnet motor. ACCESS, 13, 304-315. (www)

  @article{ , abstract = {To address the problems of low space utilization and weak overload capacity of traditional dual-stator permanent magnet motors with equal-length cores, a kind of inner boost permanent magnet motor (IBPMM) with unequal-length cores is proposed. First, the topological structure and inner stator start-stop control method of the IBPMM are introduced. Then, aiming at the problem that finite element model (FEM) is difficult to analyze the variation law of the outer stator torque when the rotor yoke is saturated caused by the high inner stator current, a new quasi-3D equivalent magnetic network (EMN) model with invariable air-gap permeance and accounting for saturation and flux leakage is proposed by using superposition method. The model is used to analyze air-gap flux density, flux linkage and no-load EMF of the IBPMM under different working conditions, and further separates the inner stator torque and outer stator torque, so as to analyze the change of the outer stator torque under different high-density inner stator currents. According to the quasi-3D EMN results, the proposed IBPMM has 3 times short-term overload capacity, and the outer stator torque decreases by only approximately 0.5% at the maximum inner boost current. Finally, the performance of the proposed IBPMM and the accuracy of the quasi-3D EMN are verified by 3D FEM and prototype experiments.}, archive = {J_ACCESS}, author = {Jiacheng Wu and Shuai Wang and Yan Hu and Bingyi Zhang and Xuefeng Xu}, doi = {10.1109/ACCESS.2024.3522279}, journal = {IEEE Access}, pages = {304-315}, shortjournal = {IEEE Access}, title = {Modeling and analysis of quasi-3D equivalent magnetic network of direct-drive inner boost permanent magnet motor}, volume = {13}, year = {2025}, }
• (2025). Information extraction based on line chart for research paper in chemical science. ACCESS, 13, 297-303. (www)

  @article{ , abstract = {In chemical science research progress, reading other researcher research paper in same type area, will speed up the generation of their own research method. Researchers want to automate the extraction of information from charts in paper, because there are detail of what element involved and how experiment environment changed in the charts. This paper designs and implements a line chart information extraction algorithm using neural networks and Hough Transform. First, a large dataset of line charts was collected and annotated to provide a foundation for neural network training. Secondly, detect the line charts in the literature and save them as separate images. Then, the Hough transform line detection algorithm was used to detect the axes, and the line charts were segmented. For each segmented part, different recognition algorithms were designed to identify various elements in the line charts, including axes, line regions, and legends. To validate the effectiveness of the algorithm, experimental tests were conducted in the field of inorganic catalysis, automatically extracting information from line charts in the literature. The experimental results show that the designed algorithm can accurately recognize various elements in line charts and effectively extract experimental data. Compared with traditional manual methods, automated extraction not only saves a considerable amount of time but also improves the accuracy and consistency of data extraction on paper fast reading. In summary, this method provides researchers with an efficient tool that accelerates the acquisition and comparison of experimental data, thereby advancing the progress of related research electronic document.}, archive = {J_ACCESS}, author = {Hairong Yan and Shaohan Yang}, doi = {10.1109/ACCESS.2024.3520222}, journal = {IEEE Access}, pages = {297-303}, shortjournal = {IEEE Access}, title = {Information extraction based on line chart for research paper in chemical science}, volume = {13}, year = {2025}, }
• (2025). Are polymer-based smart materials unlocking the path to sustainable manufacturing for a net-zero economy? current trends and potential applications. ACCESS, 13, 284-296. (www)

  @article{ , abstract = {The drive toward a net-zero economy (NZE) has pressured the manufacturing sector to adopt sustainable practices to minimize negative environmental impacts while ensuring resource efficiency. In this context, polymer-based smart materials have emerged as a promising solution, offering unique properties that respond to external stimuli, enabling more adaptive and efficient manufacturing processes. These materials not only reduce waste and energy consumption but also enhance product lifespans through innovations such as self-healing, recyclability, and degradation control (techniques to manage the breakdown of materials for extended usability and reduced waste). Despite the growing interest, combining polymer-based smart materials into manufacturing processes to achieve sustainability is still underexplored. Limited studies have comprehensively reviewed their potential in supporting sustainable manufacturing, especially in the context of net-zero emissions. This study aims to fill this gap by employing a systematic literature review (SLR) and bibliometric analysis to enhance advancements and challenges in polymer-based smart materials for sustainable manufacturing (SM). This study examines how these materials reduce energy consumption, waste, and environmental impact across various manufacturing sectors through a comprehensive analysis of current advancements, challenges, and industrial applications. The outcomes provide actionable insights for researchers and industry practitioners to accelerate the integration of smart materials into SM processes. This study identifies key factors and future research directions, offering insights for industry practitioners and researchers on the role of these materials in achieving a NZE.}, archive = {J_ACCESS}, author = {Karishma M. Qureshi and Alok Yadav and Rajiv Kumar Garg and Anish Sachdeva and Amal Abdulrahman and Saleh Yahya Alghamdi and Mohamed Rafik Noor Mohamed Qureshi}, doi = {10.1109/ACCESS.2024.3521944}, journal = {IEEE Access}, pages = {284-296}, shortjournal = {IEEE Access}, title = {Are polymer-based smart materials unlocking the path to sustainable manufacturing for a net-zero economy? current trends and potential applications}, volume = {13}, year = {2025}, }
• (2025). A dual-polarized stacked ceramic antenna with high isolation. ACCESS, 13, 273-283. (www)

  @article{ , abstract = {In this work, two identical ceramic plates formed a stacked dense dielectric patch antenna is introduced for implementing a dual-polarized antenna. The proposed stacked ceramic structure generates highly pure adjacent modes, achieving wide impedance bandwidth, high isolation, and stable radiation performance across the entire operating bandwidth. The antenna combines with the advantages of the dense dielectric patch antenna and dielectric resonator antenna to produce symmetrical field distributions without spurious waves on the antenna body, resulting in high purities of radiation and polarization. A prototype of the proposed antenna is fabricated and tested. Its measured and simulated results are in good agreement. The 42% operating bandwidth covers from 3.3 GHz to 5.15 GHz for the reflection coefficient $\le $ -10 dB and with high isolation across the entire operating bandwidth. With the merits of low profile, wide bandwidth, high isolation and stable radiation performance, the proposed dual-polarized stacked ceramic antenna is promising for 5G/B5G wireless communication system.}, archive = {J_ACCESS}, author = {Liyuan Cao and Wenjian Sun and Hang Wong}, doi = {10.1109/ACCESS.2024.3522348}, journal = {IEEE Access}, pages = {273-283}, shortjournal = {IEEE Access}, title = {A dual-polarized stacked ceramic antenna with high isolation}, volume = {13}, year = {2025}, }
• (2025). On channel transforms to enhance reciprocity and quantization in physical-layer secret key generation. ACCESS, 13, 256-272. (www)

  @article{ , abstract = {Ensuring reliable and secure communications in the increasingly pervasive beyond $5{^{\text {th}}}$ generation (B5G) wireless networks is one of the key research challenges. Within the physical layer security (PLS) paradigm, the secret key generation (SKG) technique exploits the wireless channel’s randomness to generate symmetric secret keys for message encryption/decryption by legitimate communication nodes. The SKG procedure involves various steps such as wireless channel probing, quantization, information reconciliation, and privacy amplification to render effective symmetric secret key bits. This work proposes a novel multi-level channel quantization scheme, which for the given distribution of the channel envelopes’ gain, ensures an identical likelihood of the envelope samples falling into each quantization interval. Inspired by the classical companding transform (CT), the proposed probability integral transform (PIT)–based quantization scheme works in three sequential steps. First, average contiguous duration (ACD), a second-order fading statistics metric, is considered to select and bias the channel samples that are more likely to fall in the same quantization interval. Subsequently, by using an invertible transform the envelope values are transformed into samples drawn from a uniform distribution, which are then processed through a uniform quantizer. The proposed PIT-based transformation and quantization scheme aims to enhance the trade-offs between the SKG performance metrics namely the key generation rate (KGR), key disagreement rate (KDR), and the key randomness properties. A comprehensive performance analysis of the proposed PIT-based quantization scheme for generalized gamma (GG) fading channels is conducted and its performance is evaluated in relation to several channel and system parameters. Moreover, a comparative analysis of the proposed scheme with its counterparts in the literature is also provided to demonstrate its relative performance gains, where our proposed scheme is observed to outperform the existing schemes. Notably, when the correlation of the reciprocal channel is set to $\rho =0.8$ , the samples excursion qualification threshold is set to $L=3$ , and GG fading conditions set as defined by $\alpha =2$ , $\xi =1$ , and $\upsilon =1$ , the proposed algorithm, which employs sample biasing and transformation, offers an average improvement of 0.02% in KGR and 0.15% in KDR performance compared to the conventional UQ scheme. Additionally, the algorithm demonstrates superior performance in all considered eight the National Institute of Standards and Technology (NIST) randomness tests, exemplified by an average P-value of 0.78 in the frequency monobit test. Furthermore, for the considered system and channel parameters the proposed algorithm provides an average improvement of 0.06% in KDR compared to cumulative distribution function (CDF)-based non-uniform quantization (CDF-NUQ).}, archive = {J_ACCESS}, author = {Ghalib Hussain and Syed Junaid Nawaz and Shurjeel Wyne and Mohammad N. Patwary}, doi = {10.1109/ACCESS.2024.3523105}, journal = {IEEE Access}, pages = {256-272}, shortjournal = {IEEE Access}, title = {On channel transforms to enhance reciprocity and quantization in physical-layer secret key generation}, volume = {13}, year = {2025}, }
• (2025). High-resolution mapping of concentrated solar power site suitability in ghardaïa, algeria: A GIS-based fuzzy logic and multi-criteria decision analysis. ACCESS, 13, 231-255. (www)

  @article{ , abstract = {Algeria primarily relies on fossil fuels to meet its growing electricity demand, significantly contributing to climate change and the depletion of non-renewable resources. In response, the Algerian government has initiated ambitious plans to integrate renewable energy into the national grid. While photovoltaic solar energy has received substantial attention over the past decade, the potential for Concentrated Solar Power (CSP) remains largely untapped, despite its advantages for thermal energy storage and sustainable electricity production. This study assesses the feasibility of establishing CSP plants in Ghardaïa, a city in southern Algeria, using high-resolution $30\times 30$ -meter Geographic Information Systems (GIS) data. By integrating GIS with Fuzzy Logic and Multi-Criteria Decision-Making (MCDM) techniques, and utilizing a variety of spatial maps, this study identifies areas suitable for high- and medium-scale CSP plants. The analysis reveals that 10% of the region’s land is suitable for CSP installations, with 1% categorized as extremely suitable for large-scale projects. Furthermore, 33% of the land is suitable for small-scale CSP plants, of which 3% is deemed extremely suitable. These findings underscore the significant potential for expanding CSP infrastructure in Ghardaïa, aligning with Algeria’s renewable energy objectives.}, archive = {J_ACCESS}, author = {Abdelfetah Belaid and Mawloud Guermoui and Abdelkader Riche and Toufik Arrif and Hamdani Maamar and Cherier Mohamed Kamel and Abdelaziz Rabehi and Mohamad Mahmoud Al Rahhal}, doi = {10.1109/ACCESS.2024.3522572}, journal = {IEEE Access}, pages = {231-255}, shortjournal = {IEEE Access}, title = {High-resolution mapping of concentrated solar power site suitability in ghardaïa, algeria: A GIS-based fuzzy logic and multi-criteria decision analysis}, volume = {13}, year = {2025}, }
• (2025). SpikeMOT: Event-based multi-object tracking with sparse motion features. ACCESS, 13, 214-230. (www)

  @article{ , abstract = {In comparison to conventional RGB cameras, the exceptional temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking. Yet in practice, despite their theoretical advantages, the body of work on event-based multi-object tracking (MOT) remains in its infancy, especially in real-world environments where events from complex background and camera motion can easily obscure the true target motion. To address these limitations, we introduce SpikeMOT, an innovative event-based MOT framework employing spiking neural networks (SNNs) within a Siamese architecture. SpikeMOT extracts and associates sparse spatiotemporal features from event streams, enabling high-frequency object motion inference while preserving object identities. Additionally, a simultaneous object detector provides updated spatial information of these objects at an equivalent frame rate. To evaluate the efficacy of SpikeMOT, we present DSEC-MOT, a meticulously constructed, real-world event-based MOT benchmark. This dataset features manually corrected annotations for objects experiencing severe occlusions, frequent intersections, and out-of-view scenarios commonly encountered in real-world applications. Extensive experiments on the DSEC-MOT and the FE240hz dataset demonstrate SpikeMOT’s superior tracking accuracy under demanding conditions, advancing the state-of-the-art in event-based multi-object tracking.}, archive = {J_ACCESS}, author = {Song Wang and Zhu Wang and Can Li and Xiaojuan Qi and Hayden Kwok-Hay So}, doi = {10.1109/ACCESS.2024.3523411}, journal = {IEEE Access}, pages = {214-230}, shortjournal = {IEEE Access}, title = {SpikeMOT: Event-based multi-object tracking with sparse motion features}, volume = {13}, year = {2025}, }
• (2025). GANCE: Generative adversarial network assisted channel estimation for unmanned aerial vehicles empowered 5G and beyond wireless networks. ACCESS, 13, 198-213. (www)

  @article{ , abstract = {Unmanned aerial vehicles (UAVs) have discovered a plethora of societal applications such as remote sensing, disaster management, medical emergency, security and surveillance, etc. UAVs require fast and reliable communication lines, and selecting the appropriate channel estimation (CE) technique is pivotal in reliable communication. Orthogonal time frequency space (OTFS) is an innovative modulation technique designed to support reliable communication in rapid mobility environments for 5G and beyond applications, effectively addressing challenges posed by Doppler shifts and multipath propagation. Current OTFS receivers utilize threshold methods such as least squares (LS) and minimum mean square error estimators for CE. To further enhance the accuracy and robustness of the CE process, this paper proposes a generative adversarial network (GAN) for learning channel parameters and performing CE in OTFS-based communication systems for high-speed UAVs (100-500 km/h). Firstly, a system model considering the Doppler effect has been modeled mathematically, and then the solution to the CE problem is presented for UAVs-assisted networks using GAN. The proposed GAN architectures comprise a U-Net-based generator and a PatchGAN discriminator for adversarial training of the model. The proposed model is compared with the baseline approaches in terms of bit error rate (BER), outage probability (OP), and normalized mean squared error (NMSE) for different velocities and modulation schemes. The proposed model has given an improvement of 70%, 55%, and 45% in BER performance and 40%, 30%, and 20% in OP compared to the conventional LS estimator, machine learning-based estimator, and deep learning-based estimator, respectively. The proposed model has also demonstrated robustness against the Doppler effect caused by the high mobility of UAVs, with only a minimal decrease in NMSE performance of 2-3 dB for every 50 km/h increase in speed. Additionally, the time complexity and processing time of the proposed model have been studied to test its suitability for UAV applications.}, archive = {J_ACCESS}, author = {Chirag Gupta and Ramani Kumar Das and Rabindra K. Barik and Shahazad Niwazi Qurashi and Diptendu Sinha Roy and Satyendra Singh Yadav}, doi = {10.1109/ACCESS.2024.3522847}, journal = {IEEE Access}, pages = {198-213}, shortjournal = {IEEE Access}, title = {GANCE: Generative adversarial network assisted channel estimation for unmanned aerial vehicles empowered 5G and beyond wireless networks}, volume = {13}, year = {2025}, }
• (2025). AI-enhanced robotic process automation: A review of intelligent automation innovations. ACCESS, 13, 173-197. (www)

  @article{ , abstract = {The rapid technological growth in recent decades due to the integration of robust technologies and automation have led to the rise of digital services and the emergence of Industry 4.0. This paper explores the concept and potential of AI-powered intelligent automation based on the synergistic use of Robotic Process Automation (RPA) and Artificial Intelligence (AI) to enhance organizational and business processes across various sectors. RPA automates routine, rules-based tasks, thereby allowing human workers to engage in more innovative activities. When integrated with AI, RPA systems gain the capacity to analyze data, identify patterns, classify information and forecast which leads to significant improvement in accuracy and productivity. This literature review investigates the current state of RPA and AI integration while highlighting its applications in different sectors such as manufacturing, agriculture, healthcare, finance, and retail. Along with discussing the drawbacks and restrictions, such as technological issues and moral dilemmas, this paper also discusses the advantages of this integration, which include decreased costs, increased output, and simplified operations. By leveraging AI techniques such as classification, text mining of neural network, RPA technologies optimize business operations and advance Industry 4.0. This study also illustrates the challenges and limitations of this integration such as technical difficulties and ethical considerations. The aim of this review is to provide a comprehensive understanding of the synergistic potential of RPA and AI while offering insights into their contribution in shaping the future of intelligent automation.}, archive = {J_ACCESS}, author = {Sadia Afrin and Shobnom Roksana and Riad Akram}, doi = {10.1109/ACCESS.2024.3513279}, journal = {IEEE Access}, pages = {173-197}, shortjournal = {IEEE Access}, title = {AI-enhanced robotic process automation: A review of intelligent automation innovations}, volume = {13}, year = {2025}, }
• (2025). Low-complexity architecture of orthogonal N-continuous precoding for sidelobe suppression of OFDM signals and its analysis. ACCESS, 13, 160-172. (www)

  @article{ , abstract = {High sidelobes of orthogonal frequency-division multiplexing (OFDM) signals must be resolved for future wireless communications. In this study, we propose and analyze an efficient orthogonally linear precoding to suppress the sidelobes in OFDM systems. The proposed scheme uses a decomposed precoder on the real domain for a successive N-continuous technique. Comprehensive theoretical analyses justify its performance and have mathematical representations parallel with the counterparts of related precoding schemes. In conclusion, the proposed scheme drastically reduces the conventional precoding’s complexity to a level matching that of a fast Fourier transform for OFDM and provides 99 % storage overhead reduction. All its inherent advantages are maintained such as fast, smooth, and symmetrical spectral decay of band-edges, and ideal characteristics of error rate and peak power.}, archive = {J_ACCESS}, author = {Hikaru Kawasaki and Fumihide Kojima and Takeshi Matsumura}, doi = {10.1109/ACCESS.2024.3523280}, journal = {IEEE Access}, pages = {160-172}, shortjournal = {IEEE Access}, title = {Low-complexity architecture of orthogonal N-continuous precoding for sidelobe suppression of OFDM signals and its analysis}, volume = {13}, year = {2025}, }
• (2025). Adaptive multi-sensor fusion for SLAM: A scan context-driven approach. ACCESS, 13, 149-159. (www)

  @article{ , abstract = {This paper proposes a novel multi-sensor fusion SLAM algorithm, named SC-LVI-SAM, based on scanning context, to address the issues of decreased positioning accuracy caused by missing feature points and prolonged motion in complex large-scale scenes in multi-sensor fusion SLAM algorithms. Firstly, the scanning context method is used to preprocess LIDAR point cloud data, generating a descriptor of the environment. This descriptor, along with data from the IMU and vision sensors, is then used for state estimation, yielding initial pose estimates and motion information. Then, the scan context module uses the descriptor for environment recognition and loop closure detection, providing more accurate feature description and context information for fast loop closure matching. It avoids ignoring the spatial relationship and order between features due to the local feature description of DBoW2, and improves the accuracy and robustness of loop closure detection. Finally, global optimization is performed to correct accumulated errors in the entire trajectory and map. In KAIST02 and Riverside01 sequences of MulRan dataset, the root mean square error of the absolute pose error of the proposed method is reduced by 85.17% and 91.30% compared with LVI-SAM, respectively. Experimental results on multiple public benchmark datasets demonstrate that in the case of almost the same computational efficiency, the proposed algorithm effectively enhances the accuracy of positioning, the robustness of the algorithm and accuracy of mapping, improving the global consistency of the generated map.}, archive = {J_ACCESS}, author = {Yijing Zhang and Jia Liu and Runxi Cao and Yunxi Zhang}, doi = {10.1109/ACCESS.2024.3523129}, journal = {IEEE Access}, pages = {149-159}, shortjournal = {IEEE Access}, title = {Adaptive multi-sensor fusion for SLAM: A scan context-driven approach}, volume = {13}, year = {2025}, }
• (2025). Ball point game: Playing or learning agile project management?. ACCESS, 13, 135-148. (www)

  @article{ , abstract = {Agile project management has become essential in modern enterprises, particularly in the software industry, due to its flexibility and adaptability. This paper presents an empirical evaluation of the Ball Point Game (BPG), a non-digital serious game designed to teach Agile project management principles. The evaluation is based on three case studies conducted with undergraduate students at the University of São Paulo, aiming to explore the following aspects: ability to compare traditional methods and Agile methods; educational effectiveness, particularly regarding player experience and perceived learning; and skills development or improvement. The results indicate that the BPG effectively allows students to properly understand the pros and cons of Agile methodologies, fosters essential project management skills, and provides a positive and engaging learning experience. The BPG proved to be an effective tool for experiential learning by allowing students to go through all stages of the experiential learning cycle. This approach not only made learning more engaging, but also helped students develop essential skills for Agile project management. The study highlights the potential of educational games to bridge the gap between theoretical knowledge and hands-on activities, making them valuable tools in Agile management education. Our work has as novel element the integration of both game-based learning and experience-based learning approaches to teach Agile project management.}, archive = {J_ACCESS}, author = {Maria Lydia Fioravanti and Gustavo M. N. Avellar and Bruna Oliveira Romeiro and Bruna Gonçalves Rezende and Ellen Francine Barbosa and Ana M. Moreno}, doi = {10.1109/ACCESS.2024.3521237}, journal = {IEEE Access}, pages = {135-148}, shortjournal = {IEEE Access}, title = {Ball point game: Playing or learning agile project management?}, volume = {13}, year = {2025}, }
• (2025). Smart cognitive HMI with automated knowledge extraction for machine tool. ACCESS, 13, 120-134. (www)

  @article{ , abstract = {In the field of machine tools, it is general to use facilities from various vendors with different interfaces. However, manuals that vary from machine to machine and are less portable and usable, are adding the burden. In this case, the digital intelligent assistant can be used as an easy and unified interface to communicate with users and increase work efficiency. However, applying a digital assistant requires a large amount of dataset and effort separately, and there is a limitation that it only plays a role in a specific situation designed. In this paper, we propose a smart and cognitive chatbot with a dataset-constructing pipeline to solve the problems. The proposed pipeline aims to build a dataset and update source data for the chatbot automatically. It provides relevant manual context, machine status, current operation, and operation sequence, depending on the users’ intention. It utilizes various AI technologies including optical character recognition, reinforcement learning, and natural language preprocessing technologies to minimize human intervention in the dataset construction. Also, it can be applied to various heterogeneous facilities with different interfaces. Thanks to the proposed data extraction pipeline, the proposed chatbot interacts with users intuitively using conversational language, replacing cumbersome manipulation and unusable documentation. In the paper, one of the equipment, commonly used in the real world, is chosen to be applied the proposed system to the chosen facility, dealing with possible problems during the operation.}, archive = {J_ACCESS}, author = {Jongsu Park and Jinho Son and Seongwoo Cho and Jumyung Um}, doi = {10.1109/ACCESS.2024.3519607}, journal = {IEEE Access}, pages = {120-134}, shortjournal = {IEEE Access}, title = {Smart cognitive HMI with automated knowledge extraction for machine tool}, volume = {13}, year = {2025}, }
• (2025). A bibliometric analysis of agent-based systems in cybersecurity and broader security domains: Trends and insights. ACCESS, 13, 90-119. (www)

  @article{ , abstract = {Bibliometric analysis, a quantitative tool, assesses research trends, impacts, and interrelationships within a given field. Using data from the Web of Science, this research provides a thorough bibliometric examination of agent-based cybersecurity systems. We explore the motivation and significance of studying agent-based systems in cybersecurity, along with an overview of the existing literature. Using tools such as VOSviewer and Biblioshiny, we examine the growth and focus areas of publications over time, identify top contributors, and analyze collaboration patterns. The study also looks at the evolution of citation dynamics, their impact on field development, and thematic clusters of research topics. We identify major themes and their linkages, visually connecting frequently used phrases to create a hierarchical representation of the research landscape. Significant contributions at the intersection of agent-based systems and cybersecurity are emphasized. Our findings highlight growing trends, major research areas, and collaboration networks in the field. Key statistical findings show that publications on agent-based systems and cybersecurity have steadily increased since 2013, culminating in 2023 with over 1,200. Citations also peaked in 2023, at over 30,000. The top category, Computer Science Information Systems (1,424 publications), emphasizes the expanding importance of information security and artificial intelligence. Research articles are the most common document type (6,441). China is the largest contributor, followed by the US and India. Collaboration is prevalent, with important writers such as Zhang Y and Wang J driving cross-disciplinary research in fields like reinforcement learning and blockchain. To the best of our knowledge, this is the first work to conduct a comprehensive bibliometric analysis entirely focused on the intersection of agent-based systems and cybersecurity.}, archive = {J_ACCESS}, author = {Shreya Girish Savadatti and Kathiravan Srinivasan and Yuh-Chung Hu}, doi = {10.1109/ACCESS.2024.3520583}, journal = {IEEE Access}, pages = {90-119}, shortjournal = {IEEE Access}, title = {A bibliometric analysis of agent-based systems in cybersecurity and broader security domains: Trends and insights}, volume = {13}, year = {2025}, }
• (2025). A triple-band slotted patch antenna with metasurface loading for UHF-RFID, satellite communication, and 5G applications. ACCESS, 13, 80-89. (www)

  @article{ , abstract = {A triple-band, coaxial-fed, slotted microstrip patch antenna with metasurface loading is presented in this article. The first frequency band covers the 865-868MHz European UHF-RFID band, the second frequency band partially covers the 3300-4200MHz n77 and 3300-3800MHz n78 5G communication bands. Finally, the third frequency band partially covers the 3.7-4.2GHz satellite communication band. The S $_{11} \le -10$ dB regions of the antenna-metasurface system lie between 833-885MHz, 3400-3550MHz and 4059-4164MHz. The system exhibits right circular polarization within the first and third frequency bands with a 3dB axial ratio bandwidth of 41MHz (5%) within the first band and 111MHz (2.7%) within the third band. The 3dB axial ratio beamwidth is $\pm 60^{0}$ within the RFID band and $\pm 30^{0}$ within the satellite communication band. Moreover, the system also exhibits a dual-linear polarization within the second band. The radiation efficiency remains within the 75-85% region for all the three bands. The dimensions of the system are $0.28\lambda _{\mathrm {0}}$ x $0.28\lambda _{\mathrm {0}}$ x $0.023\lambda _{0}$ at 865MHz. The proposed antenna-metasurface system is therefore very versatile, finding use as an UHF-RFID reader as well as for 5G and satellite communication.}, archive = {J_ACCESS}, author = {Sayan Sarkar and Bhaskar Gupta}, doi = {10.1109/ACCESS.2024.3522916}, journal = {IEEE Access}, pages = {80-89}, shortjournal = {IEEE Access}, title = {A triple-band slotted patch antenna with metasurface loading for UHF-RFID, satellite communication, and 5G applications}, volume = {13}, year = {2025}, }
• (2025). Anticancer peptides classification using long-short-term memory with novel feature representation. ACCESS, 13, 67-79. (www)

  @article{ , abstract = {Cancer treatment is a challenging endeavor because of the intricacy, heterogeneity, and diversity of cancer causes. Comprehensive therapeutic approaches are crucial for cancer treatment. Anticancer peptides (ACPs) present a potentially effective therapeutic option. However, the extensive identification and synthesis of these peptides present a persistent difficulty that calls for the creation of effective prediction techniques. Existing techniques either suffer from low accuracy or employ high-dimensional feature sets, frequently producing sparse features and leading to ineffective model designs. This work presents a novel set of features and a long-short-term-memory (LSTM)-based classification strategy to create an efficient model. The suggested feature set includes three new and two modern feature extraction methods. The binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet are part of the modern feature set. The combination of the composition of the K-spaced side chain pairs (CKSSCP), the composition of the K-spaced electrically charged side chain pairs (CKSECSCP), and the combination of [pk(CO2H)] + [pk(NH2)] + [pk(R)] + [isoelectric point] is used to derive the novel features. The suggested LSTM model is trained using the combined feature set. The trials are carried out with a k-fold cross-validation method on benchmark datasets. The results indicate that the proposed model outperforms alternative ACP classification techniques in terms of Mathew’s correlation coefficient (MCC) and accuracy. The ACP740 dataset with 5-folds yields an MCC score of 75%, which is 12%, 11%, 3%, and 8% greater than those of the ACP-DL, ACP-DA, ACP-MHCNN, and ACP-KSRC approaches, respectively. For the ACP344 dataset with 10-folds, the proposed method achieves an MCC score of 85.14%, which is 23% and 2% higher than the MCC scores of ACP-DL and SAP methods, respectively. Better classification performance offered by the proposed approach could help identify new ACPs and better understand their structural and chemical characteristics. The source code and the datasets are available on the author’s GitHub page (https://github.com/Shujaat123/ACP-LSTM-NFR).}, archive = {J_ACCESS}, author = {Nazer Al Tahifah and Muhammad Sohail Ibrahim and Erum Rehman and Naveed Ahmed and Abdul Wahab and Shujaat Khan}, doi = {10.1109/ACCESS.2024.3523068}, journal = {IEEE Access}, pages = {67-79}, shortjournal = {IEEE Access}, title = {Anticancer peptides classification using long-short-term memory with novel feature representation}, volume = {13}, year = {2025}, }
• (2025). Semi-supervised learning with wafer-specific augmentations for wafer defect classification. ACCESS, 13, 56-66. (www)

  @article{ , abstract = {Semi-supervised learning (SSL) models, which leverage both labeled and unlabeled datasets, have been increasingly applied to classify wafer bin map patterns in semiconductor manufacturing. These models typically outperform supervised learning models in scenarios where labeled data are scarce. However, the challenges posed by wafer bin map data in applying conventional image augmentation methods, particularly within SSL frameworks such as FixMatch, have not yet been fully addressed despite their significant role. Recognizing the importance of thoughtful implementation of weak and strong augmentations within FixMatch, we propose a method that incorporates saliency map information into cutout augmentation. This approach preserves essential regions crucial for wafer defect pattern classification and thus improving model performance. Our approach achieves a macro F1-score of 0.841 with only 5% labeled data, surpassing state-of-the-art methods by 6.2% compared to WaPIRL and 7.5% compared to Manivannan’s method. Similarly, with 10%, 25%, and 50% labeled data, our method achieves F1-scores of 0.856, 0.874, and 0.891, respectively, showing improvements of 3.5%, 1.7%, and 1.2% over WaPIRL and 5.0%, 6.6%, and 11.9% over Manivannan’s method in each case. Experimental results indicate significant improvements in defect pattern classification by avoiding cutting important regions in cutout augmentation. The proposed method achieves new state-of-the-art performance in wafer bin map defect classification, demonstrating the potential of our tailored augmentation techniques and the effectiveness of incorporating saliency map information reflecting the characteristics of wafer bin maps.}, archive = {J_ACCESS}, author = {Uk Jo and Seoung Bum Kim}, doi = {10.1109/ACCESS.2024.3522180}, journal = {IEEE Access}, pages = {56-66}, shortjournal = {IEEE Access}, title = {Semi-supervised learning with wafer-specific augmentations for wafer defect classification}, volume = {13}, year = {2025}, }
• (2025). Transmission failure prediction using AI and structural modeling informed by distribution outages. ACCESS, 13, 42-55. (www)

  @article{ , abstract = {Understanding and quantifying the impact of severe weather events on the electric transmission and distribution system is crucial for ensuring its resilience in the context of the increasing frequency and intensity of extreme weather events caused by climate change. While weather impact models for the distribution system have been widely developed during the past decade, transmission system impact models lagged behind because of the scarcity of data. This study demonstrates a weather impact model for predicting the probability of failure of transmission lines. It builds upon a recently developed model and focuses on reducing model bias, through multi-model integration, feature engineering, and the development of a storm index that leverages distribution system data to aid the prediction of transmission risk. We explored three methods for integrating machine learning with mechanistic models. They consist of: (a) creating a linear combination of the outputs of the two modeling approaches, (b) including fragility curves as additional inputs to machine learning models, and (c) developing a new machine learning model that uses the outputs of the weather-based machine learning model, fragility curve estimates, and wind data to make new predictions. Moreover, due to the limited number of historical failures in transmission networks, a storm index was developed leveraging a dataset of distribution outages to learn about storm behavior to improve model skills. In the current version of the model, we substantially reduced the overestimation in the sum of predicted values of transmission line probability of failure that was present in the previously published model by a factor of 10. This has led to a reduction of model bias from 3352% to 14.46–15.43%. The model with the integrated approach and storm index demonstrates substantial improvements in the estimation of the probability of failure of transmission lines and their ranking by risk level. The improved model is able to capture 60% of the failures within the top 22.5% of the ranked power lines, compared to a value of 34.9% for the previous model. With an estimate of the probability of failure of transmission lines ahead of storms, power system planning and maintenance engineers will have critical information to make informed decisions, to create better mitigation plans and minimize power disruptions. Long term, this model can assist with resilience investments as it highlights areas of the system more susceptible to damage.}, archive = {J_ACCESS}, author = {Sita Nyame and William O. Taylor and William Hughes and Mingguo Hong and Marika Koukoula and Feifei Yang and Aaron Spaulding and Xiaochuan Luo and Slava Maslennikov and Diego Cerrai}, doi = {10.1109/ACCESS.2024.3523415}, journal = {IEEE Access}, pages = {42-55}, shortjournal = {IEEE Access}, title = {Transmission failure prediction using AI and structural modeling informed by distribution outages}, volume = {13}, year = {2025}, }
• (2025). NeRF view synthesis: Subjective quality assessment and objective metrics evaluation. ACCESS, 13, 26-41. (www)

  @article{ , abstract = {Neural radiance fields (NeRF) are a groundbreaking computer vision technology that enables the generation of high-quality, immersive visual content from multiple viewpoints. This capability has significant advantages for applications such as virtual/augmented reality, 3D modelling, and content creation for the film and entertainment industry. However, the evaluation of NeRF methods poses several challenges, including a lack of comprehensive datasets, reliable assessment methodologies, and objective quality metrics. This paper addresses the problem of NeRF view synthesis (NVS) quality assessment thoroughly, by conducting a rigorous subjective quality assessment test that considers several scene classes and recently proposed NVS methods. Additionally, the performance of a wide range of state-of-the-art conventional and learning-based full-reference 2D image and video quality assessment metrics is evaluated against the subjective scores of the subjective study. This study found that errors in camera pose estimation can result in spatial misalignments between synthesized and reference images, which need to be corrected before applying an objective quality metric. The experimental results are analyzed in depth, providing a comparative evaluation of several NVS methods and objective quality metrics, across different classes of visual scenes, including real and synthetic content for front-face and 360° camera trajectories.}, archive = {J_ACCESS}, author = {Pedro Martin and António Rodrigues and João Ascenso and Maria Paula Queluz}, doi = {10.1109/ACCESS.2024.3522768}, journal = {IEEE Access}, pages = {26-41}, shortjournal = {IEEE Access}, title = {NeRF view synthesis: Subjective quality assessment and objective metrics evaluation}, volume = {13}, year = {2025}, }
• (2025). SafeRespirator: Comprehensive database for n95 filtering facepiece respirator leakage detection including infrared, RGB videos, and quantitative fit testing. ACCESS, 13, 1-12. (www)

  @article{ , abstract = {The COVID-19 pandemic underscored the challenges of performing mandatory Quantitative Fit Tests (QNFT) for healthcare professionals and the limitations of self-administered fit checks. To address this, it is crucial to develop faster and more efficient methods for detecting, locating, and quantifying Filtering Facepiece Respirators (FFRs) leakage, providing wearers with immediate feedback on their safety. Infrared (IR) technology, which relies on temperature variation analysis around the face seal, has proven effective for locating leakage but has not yet achieved automated quantification. This paper introduces a validated protocol for creating a comprehensive database to advance automatic leakage detection. The database includes synchronized and calibrated IR and RGB video data, along with QNFT results, collected from 62 participants wearing four different N95 FFR models in four distinct positions. High-performance IR and RGB cameras were used to precisely capture temperature variations, while a PortaCount® instrument served as the reference for fit quantification. Preliminary results using the MediaPipe approach with synchronized and calibrated RGB and IR videos demonstrate that precise tracking of the human face is achievable even with an FFR. The normalized cross-correlation methods further highlight the capability of IR imaging to accurately monitor and detect leakage. This breakthrough paves the way for real-time, automated detection of N95 FFR leakage, potentially deployable at operator workstations. This large, high-quality, open-access database is available to the scientific community to drive innovation in respiratory protection research and beyond.}, archive = {J_ACCESS}, author = {Geoffrey Marchais and Mohamed Arbane and Barthelemy Topilko and Jean Brousseau and Clothilde Brochot and Yacine Yaddaden and Ali Bahloul and Xavier Maldague}, doi = {10.1109/ACCESS.2024.3522562}, journal = {IEEE Access}, pages = {1-12}, shortjournal = {IEEE Access}, title = {SafeRespirator: Comprehensive database for n95 filtering facepiece respirator leakage detection including infrared, RGB videos, and quantitative fit testing}, volume = {13}, year = {2025}, }
• (2025). Optimized identification of sentence-level multiclass events on urdu-language-text using machine learning techniques. ACCESS, 13, 1-25. (www)

  @article{ , abstract = {In today’s digital world, social media platforms generate a plethora of unstructured information. However, for low-resource languages like Urdu, there is a scarcity of well-structured data for specific tasks such as event classification. Urdu, a language prominent in South Asia, has boasted a complex morphological structure with unique features but has lacked standard linguistic resources like datasets. Long-text classification has demanded more effort than short-text classification due to its expansive vocabulary, information redundancy, and noise. Text processing has been the latest trend in research, with many machine learning and deep learning techniques widely used for it. Multiclass classification has been utilized to classify different languages for various purposes. In this research, a multiclass classification for the Urdu language was performed using a text dataset taken from five different social media platforms including Geo News, Samaa News, Dawn News, Express News, and Urdu Blogs totaling 103,771 sentences. We used sentence-level classification to categorize sentences including terrorist attacks, national news, sports, entertainment, politics, safety, earthquakes, fraud and corruption, sexual assault, weather, accidents, forces, inflation, murder and death, education, and international news. Deep learning, transformer-based and machine learning classifiers are used for event classification. The SMFCNN classifier achieved the greatest accuracy of 88.29%. We incorporated transformer-based models, with the proposed XLM-R+ model demonstrating superior performance with an accuracy of 89.8%. Our results were compared to previously reported techniques that used traditional models, highlighting the significant improvements offered by our approaches. The novelty of this research lies in the inclusion of 16 event categories to broaden coverage and the implementation of the SMFCNN and transformer-based algorithms. This study highlights the potential of deep learning and transformer-based models in enhancing the accuracy and generalizability of multiclass classification in low-resource languages Urdu.}, archive = {J_ACCESS}, author = {Somia Ali and Uzma Jamil and Muhammad Younas and Bushra Zafar and Muhammad Kashif Hanif}, doi = {10.1109/ACCESS.2024.3522992}, journal = {IEEE Access}, pages = {1-25}, shortjournal = {IEEE Access}, title = {Optimized identification of sentence-level multiclass events on urdu-language-text using machine learning techniques}, volume = {13}, year = {2025}, }
• (2025). A 3D clinical face phenotype space of genetic syndromes using a triplet-based singular geometric autoencoder. ACCESS, 13, 1-15. (www)

  @article{ , abstract = {Clinical diagnosis of syndromes benefits strongly from objective facial phenotyping. This study introduces a novel approach to enhance clinical diagnosis through the development and exploration of a low-dimensional metric space referred to as the clinical face phenotypic space (CFPS). As a facial matching tool for clinical genetics, such CFPS can enhance clinical diagnosis. It helps to interpret facial dysmorphisms of a subject by placing them within the space of known dysmorphisms. In this paper, a triplet loss-based autoencoder developed by geometric deep learning (GDL) is trained using multi-task learning, which combines supervised and unsupervised learning approaches. Experiments are designed to illustrate the following properties of CFPSs that can aid clinicians in narrowing down their search space: a CFPS can 1) classify syndromes accurately, 2) generalize to novel syndromes, and 3) preserve the relatedness of genetic diseases, meaning that clusters of phenotypically similar disorders reflect functional relationships between genes. The proposed model consists of three main components: an encoder based on GDL optimizing distances between groups of individuals in the CFPS, a decoder enhancing classification by reconstructing faces, and a singular value decomposition layer maintaining orthogonality and optimal variance distribution across dimensions. This allows for the selection of an optimal number of CFPS dimensions as well as improving the classification capacity of the CFPS, which outperforms the linear metric learning baseline in both syndrome classification and generalization to novel syndromes. We further proved the usefulness of each component of the proposed framework, highlighting their individual impact. From a clinical perspective, the unique combination of these properties in a single CFPS results in a powerful tool that can be incorporated into current clinical practices to assess facial dysmorphism.INDEX TERMS 3D shape analysis, clinical genetics, computer-aided diagnosis, deep phenotyping, geometric deep learning, precision public health.}, archive = {J_ACCESS}, author = {Soha S. Mahdi and Eduarda Caldeira and Harold Matthews and Michiel Vanneste and Nele Nauwelaers and Meng Yuan and Giorgos Bouritsas and Gareth S. Baynam and Peter Hammond and Richard Spritz and Ophir D. Klein and Michael Bronstein and Benedikt Hallgrimsson and Hilde Peeters and Peter Claes}, doi = {10.1109/ACCESS.2024.3524428}, journal = {IEEE Access}, pages = {1-15}, shortjournal = {IEEE Access}, title = {A 3D clinical face phenotype space of genetic syndromes using a triplet-based singular geometric autoencoder}, volume = {13}, year = {2025}, }
• (2025). Balancing profit and cultural heritage: Multi-objective dynamic pricing for hanfu using deep deterministic policy gradient. ACCESS, 13, 1-15. (www)

  @article{ , abstract = {Dynamic pricing is a critical strategy in e-commerce, enabling merchants to optimize sales profit while adapting to varying market conditions. However, existing approaches often fall short in balancing commercial objectives with the preservation of cultural heritage, particularly in niche markets like Hanfu apparel. To address this challenge, we developed a dynamic pricing simulation environment based on a Markov Decision Process (MDP) and introduced a novel multi-objective hybrid particle swarm optimization algorithm combined with Deep Deterministic Policy Gradient (DDPG), referred to as MOHPSO-DDPG. By applying principal component analysis (PCA) to consumer preference data and constructing utility functions and Logit choice models, we accurately simulated consumer behavior. MOHPSO-DDPG, Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Hybrid Particle Swarm Optimization (MOHPSO) were each deployed to interact with the environment to explore the Pareto front of pricing decisions. Experimental results demonstrate that MOHPSO-DDPG significantly outperforms other algorithms in terms of solution diversity and convergence efficiency. After 3,000 iterations, its Generation Distance (GD) reached 0.023 and the Diversity Metric $\Delta $ was 0.594, whereas GD and Diversity Metric $\Delta $ values remained larger for MOPSO and MOHPSO. Moreover, MOHPSO-DDPG continued to maintain a leading position in later iterations, underscoring its superiority in identifying comprehensive and near-optimal Pareto fronts. These findings validate that MOHPSO-DDPG provides an efficient multi-objective dynamic pricing decision-making framework for the Hanfu market, effectively balancing profit maximization with the demands of cultural heritage preservation.}, archive = {J_ACCESS}, author = {Qingcong Zhao and Guanghui Mao and Shen Wang}, doi = {10.1109/ACCESS.2025.3526685}, journal = {IEEE Access}, pages = {1-15}, shortjournal = {IEEE Access}, title = {Balancing profit and cultural heritage: Multi-objective dynamic pricing for hanfu using deep deterministic policy gradient}, volume = {13}, year = {2025}, }
• (2025). Dual-band absorptive bandpass filters using a dual-behavior matching section. ACCESS, 13, 1-6. (www)

  @article{ , abstract = {This study introduces a dual-band absorptive bandpass filter that employs a dual-behavior matching section. The proposed design utilizes a filter methodology based on a low-pass filter prototype, enabling the development of higher-order distributed dual-band absorptive bandpass filters with the control of two center frequencies and bandwidths. The proposed filter requires fewer resonators than the existing dual-band absorptive filters owing to the dual-band matching section design based on an image parameter method. The proposed configuration is implemented to design and fabricate two filter prototypes with center frequencies of 1.3 GHz and 1.6 GHz on a substrate characterized by a relative permittivity $\varepsilon _{\mathrm {r}}=3.42$ and a thickness $h=30$ mil. The first filter (two-pole configuration) demonstrated a measured return loss exceeding 11.3 dB at 0.8–2.2 GHz, and the second filter with three-pole exhibited a measured return loss exceeding 10.2 dB at 0.8–2.2 GHz. These dual-band absorptive bandpass filters are suitable for application in cognitive radio and carrier aggregation systems.}, archive = {J_ACCESS}, author = {Young-Ho Cho and Cheolsoo Park}, doi = {10.1109/ACCESS.2025.3561755}, journal = {IEEE Access}, pages = {1-6}, shortjournal = {IEEE Access}, title = {Dual-band absorptive bandpass filters using a dual-behavior matching section}, volume = {13}, year = {2025}, }
• (2025). MVS-GS: High-quality 3D gaussian splatting mapping via online multi-view stereo. ACCESS, 13, 1-13. (www)

  @article{ , abstract = {This study addresses the challenge of online 3D model generation for neural rendering using an RGB image stream. Previous research has tackled this issue by incorporating Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS) as scene representations within dense SLAM methods. However, most studies focus primarily on estimating coarse 3D scenes rather than achieving detailed reconstructions. Moreover, depth estimation based solely on images is often ambiguous, resulting in low-quality 3D models that lead to inaccurate renderings. To overcome these limitations, we propose a novel framework for high-quality 3DGS modeling that leverages an online multi-view stereo (MVS) approach. Our method estimates MVS depth using sequential frames from a local time window and applies comprehensive depth refinement techniques to filter out outliers. The refinement method produces temporally consistent depths by checking sequential geometric consistency, enabling accurate initialization of Gaussians in 3DGS. Furthermore, we introduce a parallelized backend module that optimizes the 3DGS model efficiently, ensuring timely updates with each new keyframe. Experimental results demonstrate that our method outperforms state-of-the-art dense SLAM methods, achieving an average PSNR improvement of approximately 2 dB on indoor scenes. Moreover, our method reliably produces consistent 3D models in complex outdoor scenes, where existing methods often fail due to tracking errors and depth noise. It also reconstructs large-scale aerial scenes effectively, achieving an average PSNR gain of about 10.28 dB over existing methods.}, archive = {J_ACCESS}, author = {Byeonggwon Lee and Junkyu Park and Khang Truong Giang and Sungho Jo and Soohwan Song}, doi = {10.1109/ACCESS.2025.3583156}, journal = {IEEE Access}, pages = {1-13}, shortjournal = {IEEE Access}, title = {MVS-GS: High-quality 3D gaussian splatting mapping via online multi-view stereo}, volume = {13}, year = {2025}, }
• (2025). Retentive time series: A scalable machine learning model for traffic prediction in elastic optical networks. ACCESS, 13, 1-17. (www)

  @article{ , abstract = {Accurate traffic prediction is crucial for elastic optical networks (EONs) as it improves network scalability, performance, and operational efficiency. High accuracy is fundamental for performing long-term traffic management and network planning, while time-sensitive predictions allow efficient real-time adaptability. In this study, we propose a new RNN-based model, retentive time series (Ret-TS), which overcomes some of the limitations of traditional RNNs in EONs scenarios. Compared to RNNs, which successively consider data and always suffer from problematic vanishing gradients, Ret-TS can handle long-term dependencies efficiently through parallel computation; hence, it is more suitable for large-scale and real-time applications. In contrast to the Transformer models, Ret-TS is computationally more efficient, with a lower time and memory complexity of $O(L)$ , making it more suitable for resource-limited devices. This research demonstrates that Ret-TS is robust for both short- and long-term traffic forecasting with better prediction accuracy and computational efficiency than traditional models based on RNNs and Transformers. Extensive simulations performed on traffic datasets have shown that Ret-TS reduces prediction errors and network blocking probabilities under different traffic loads in three network topologies: NSFNET, Janos-US, and US100. The results confirm Ret-TS’s robustness and scalability, making it an effective solution for real-world applications in modern optical networks, including both telecommunications and data center networks.}, archive = {J_ACCESS}, author = {Faranak Khosravi and Mehdi Shadaram}, doi = {10.1109/ACCESS.2025.3585076}, journal = {IEEE Access}, pages = {1-17}, shortjournal = {IEEE Access}, title = {Retentive time series: A scalable machine learning model for traffic prediction in elastic optical networks}, volume = {13}, year = {2025}, }